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conf/tp_overlap/ub_cfg_h100_h6144_tp8_mbs4_seqlen2048.yaml create mode 100644 conf/tp_overlap/ub_cfg_h100_h8192_tp8_mbs4_seqlen2048.yaml create mode 100644 megatron_gpt_pretraining.py diff --git a/K100AI_finetune.sh b/K100AI_finetune.sh new file mode 100644 index 0000000..c0b9744 --- /dev/null +++ b/K100AI_finetune.sh @@ -0,0 +1,68 @@ +set -eux +# 多节点环境变量 +# Runs the "7B" parameter model +export HSA_FORCE_FINE_GRAIN_PCIE=1 +export OMP_NUM_THREADS=1 +export NCCL_P2P_LEVEL=5 +source /opt/dtk/env.sh +# te调用gemm需要导入hipblaslt库 +# export LD_LIBRARY_PATH=/data/hipblaslt-install-0904/lib:$LD_LIBRARY_PATH + +#export HIP_ALLOC_INITIALIZE=0 +#export GPU_MAX_HW_QUEUES=20 +export NCCL_ALGO=Ring +export NCCL_NCHANNELS_PER_PEER=8 +export NCCL_MIN_NCHANNELS=20 +export NCCL_MIN_P2P_NCHANNELS=8 +export NCCL_IB_TIMEOUT=22 +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +export NCCL_IB_HCA=mlx5_1,mlx5_2 +#export NCCL_SOCKET_IFNAME=ibs8 +export NCCL_NET_GDR_LEVEL=SYS +export NCCL_NET_GDR_READ=0 +#export NCCL_DEBUG=info + +# 模型和数据集参数 +MODEL="/data/model_weights/llama2_7b_nemo/llama2-7b.nemo" +TRAIN_DS="[/data/datasets/mlperf_llama/databricks-dolly-15k/training.jsonl]" +VALID_DS="[/data/datasets/mlperf_llama/databricks-dolly-15k/validation.jsonl]" +TEST_DS="[/data/datasets/mlperf_llama/databricks-dolly-15k/test.jsonl]" +VALID_NAMES="[databricks-dolly-15k]" + +# 微调数据集占比 +# TRAIN_DS="[/path/to/dataset_1.jsonl,/path/to/dataset_2.jsonl]" +# CONCAT_SAMPLING_PROBS="[0.3,0.7]" # "[1]" # 只有一个数据集设置为1 +CONCAT_SAMPLING_PROBS="[1]" + +# 运行训练脚本 +torchrun --nproc_per_node 8 \ + /workspace/nemo_main/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py \ + trainer.precision=bf16 \ + trainer.devices=8 \ + trainer.num_nodes=1 \ + trainer.val_check_interval=15 \ + trainer.max_steps=300 \ + model.restore_from_path=${MODEL} \ + model.micro_batch_size=1 \ + model.global_batch_size=60 \ + model.tensor_model_parallel_size=2 \ + model.pipeline_model_parallel_size=2 \ + model.megatron_amp_O2=True \ + model.sequence_parallel=True \ + model.activations_checkpoint_granularity=selective \ + model.activations_checkpoint_method=uniform \ + model.optim.name=fused_adam \ + model.optim.lr=5e-6 \ + model.answer_only_loss=True \ + model.peft.peft_scheme=lora \ + model.data.train_ds.file_names=${TRAIN_DS} \ + model.data.validation_ds.file_names=${VALID_DS} \ + model.data.test_ds.file_names=${TEST_DS} \ + model.data.train_ds.concat_sampling_probabilities=${CONCAT_SAMPLING_PROBS} \ + model.data.train_ds.max_seq_length=4096 \ + model.data.validation_ds.max_seq_length=4096 \ + model.data.train_ds.num_workers=0 \ + model.data.validation_ds.num_workers=0 \ + model.data.test_ds.num_workers=0 \ + ++cluster_type=BCP \ No newline at end of file diff --git a/K100AI_pretrain.sh b/K100AI_pretrain.sh new file mode 100644 index 0000000..3796cc0 --- /dev/null +++ b/K100AI_pretrain.sh @@ -0,0 +1,197 @@ +set -eux +# 多节点环境变量 +# Runs the "7B" parameter model +export HSA_FORCE_FINE_GRAIN_PCIE=1 +export OMP_NUM_THREADS=1 +export NCCL_P2P_LEVEL=5 +source /opt/dtk/env.sh +# te调用gemm需要导入hipblaslt库 +export LD_LIBRARY_PATH=/data/hipblaslt-install-0904/lib:$LD_LIBRARY_PATH + +#export HIP_ALLOC_INITIALIZE=0 +#export GPU_MAX_HW_QUEUES=20 +export NCCL_ALGO=Ring +export NCCL_NCHANNELS_PER_PEER=8 +export NCCL_MIN_NCHANNELS=20 +export NCCL_MIN_P2P_NCHANNELS=8 +export NCCL_IB_TIMEOUT=22 +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +export NCCL_IB_HCA=mlx5_1,mlx5_2 +#export NCCL_SOCKET_IFNAME=ibs8 +export NCCL_NET_GDR_LEVEL=SYS +export NCCL_NET_GDR_READ=0 +#export NCCL_DEBUG=info + +# 离线设置 +# export HF_DATASETS_OFFLINE=1 +# export HF_HUB_OFFLINE=1 + +# prof加入同步 +# export GPU_FLUSH_ON_EXECUTION=1 +# # 多机卡顿 +# export HIP_DIRECT_DISPATCH=0 + +# # torchrun参数 +# NNODES=1 +# NODE_RANK=0 +# NUM_GPUS=8 +# MASTER_ADDR="172.16.1.76" +# MASTER_PORT=29500 + +# # 模型大小 +# MODEL_SIZE=7 + +# # 数据集 +# DATASET="[1.0,/data/nemo_dataset/oscar-1GB-llama/oscar-1GB-llama_text_document]" + +# # 超参数 +# MICRO_BATCH_SIZE=1 +# GLOBAL_BATCH_SIZE=16 +# TRAIN_STEPS=250000 +# LR=3e-4 +# MIN_LR=3e-5 +# LR_WARMUP_STEPS=2000 +# DROP_OUT=0.0 +# WEIGHT_DECAY=0.1 +# GRAD_CLIP=1 +# MAX_SEQ_LEN=4096 +# MAX_POSITION_EMBEDDINGS=4096 + +# # 设置TP和PP +# TP=4 +# PP=1 +# SP=False + +# # 获取参数 +# while [ $# -gt 0 ] +# do +# case $1 in +# -M|--MODEL_SIZE) +# MODEL_SIZE=$2; shift;; +# --TP) +# TP=$2; shift;; +# --PP) +# PP=$2; shift;; +# --SP) +# SP=$2; shift;; +# --peft) +# peft_scheme=$2; shift;; +# --global_batch) +# global_batch=$2; shift;; +# --NNODES) +# NNODES=$2; shift;; +# --NODE_RANK) +# NODE_RANK=$2; shift;; +# --NUM_GPUS) +# NUM_GPUS=$2; shift;; +# --MASTER_ADDR) +# MASTER_ADDR=$2; shift;; +# --MASTER_PORT) +# MASTER_PORT=$2; shift;; +# (*) +# echo "param is error!" +# exit 0 +# break;; +# esac + +# shift +# done + +# # 模型确定 +# if [[ ${MODEL_SIZE} == 7 ]]; then HIDDEN_SIZE=4096; NUM_HEADS=32; NUM_QUERY_GROUP=32; NUM_LAYERS=32; FFN_HIDDEN_SIZE=11008; NORM_EPS=1e-5; +# elif [[ ${MODEL_SIZE} == 13 ]]; then HIDDEN_SIZE=5120; NUM_HEADS=40; NUM_QUERY_GROUP=40; NUM_LAYERS=40; FFN_HIDDEN_SIZE=13824; NORM_EPS=1e-5; +# elif [[ ${MODEL_SIZE} == 70 ]]; then HIDDEN_SIZE=8192; NUM_HEADS=64; NUM_QUERY_GROUP=8; NUM_LAYERS=80; FFN_HIDDEN_SIZE=28672; NORM_EPS=1e-5; +# elif [[ ${MODEL_SIZE} == "tiny" ]]; then HIDDEN_SIZE=128; NUM_HEADS=4; NUM_QUERY_GROUP=4; NUM_LAYERS=4; FFN_HIDDEN_SIZE=512; NORM_EPS=1e-5; +# else echo "invalid MODEL_SIZE: ${MODEL_SIZE}"; exit 1 +# fi + + +# 启动训练 +# torchrun --nnodes $NNODES --node_rank $NODE_RANK --nproc_per_node $NUM_GPUS \ +# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \ +# /workspace/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_pretraining.py \ +# --config-path=conf/ \ +# --config-name=megatron_gpt_config \ +# trainer.devices=${NUM_GPUS} \ +# trainer.num_nodes=${NNODES} \ +# trainer.max_epochs=null \ +# trainer.max_steps=300000 \ +# trainer.val_check_interval=300 \ +# trainer.log_every_n_steps=50 \ +# trainer.limit_val_batches=50 \ +# trainer.limit_test_batches=50 \ +# trainer.accumulate_grad_batches=1 \ +# trainer.precision=16 \ +# model.micro_batch_size=${MICRO_BATCH_SIZE} \ +# model.global_batch_size=${GLOBAL_BATCH_SIZE} \ +# model.tensor_model_parallel_size=${TP} \ +# model.pipeline_model_parallel_size=${PP} \ +# model.max_position_embeddings=${MAX_POSITION_EMBEDDINGS} \ +# model.encoder_seq_length=${MAX_POSITION_EMBEDDINGS} \ +# model.hidden_size=${HIDDEN_SIZE} \ +# model.ffn_hidden_size=${FFN_HIDDEN_SIZE} \ +# model.num_layers=${NUM_LAYERS} \ +# model.num_attention_heads=${NUM_HEADS} \ +# model.init_method_std=0.021 \ +# model.hidden_dropout=${DROP_OUT} \ +# model.layernorm_epsilon=${NORM_EPS} \ +# model.data.data_prefix=${DATASET} \ +# model.data.num_workers=2 \ +# model.data.seq_length=${MAX_SEQ_LEN} \ +# model.data.splits_string=\'949,50,1\' \ +# model.optim.name=fused_adam \ +# model.optim.lr=${LR} \ +# model.optim.betas=[0.9,0.95] \ +# model.optim.weight_decay=${WEIGHT_DECAY} \ +# model.optim.sched.name=CosineAnnealing \ +# model.optim.sched.warmup_steps=750 \ +# model.optim.sched.constant_steps=80000 \ +# model.optim.sched.min_lr=${MIN_LR} \ +# model.tokenizer.type=Llama2Tokenizer \ +# model.tokenizer.model=/data/Megatron_LM/llama/tokenizer.model \ +# model.num_query_groups=${NUM_QUERY_GROUP} \ +# model.position_embedding_type=rope \ +# model.normalization=rmsnorm + + + + # model.tokenizer.vocab_file=gpt2-vocab.json \ + # model.tokenizer.merge_file=gpt2-merges.txt \ + + +# TOKENIZER_TYPE=Llama2Tokenizer +# TOKENIZER_MODEL=/data/Megatron_LM/llama/tokenizer.model +DATASET="[1.0,/data/nemo_dataset/oscar-1GB-llama/oscar-1GB-llama_text_document]" + +export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +# export NVTE_FLASH_ATTN=1 # 走autlass +export NVTE_FLASH_ATTN_TRITON=1 # 走triton_fa + +python ./megatron_gpt_pretraining.py \ + --config-path=conf/ \ + --config-name=megatron_gpt_config \ + trainer.devices=8 \ + trainer.num_nodes=1 \ + trainer.precision=bf16 \ + model.micro_batch_size=1 \ + model.global_batch_size=60 \ + model.tensor_model_parallel_size=2 \ + model.pipeline_model_parallel_size=2 \ + model.sequence_parallel=True \ + model.encoder_seq_length=4096 \ + model.num_layers=32 \ + model.hidden_size=4096 \ + model.ffn_hidden_size=11008 \ + model.num_attention_heads=32 \ + model.max_position_embeddings=4096 \ + model.num_query_groups=null \ + model.mcore_gpt=False \ + model.transformer_engine=False \ + model.fp8=False \ + model.ub_tp_comm_overlap=False \ + model.use_flash_attention=True \ + model.data.seq_length=4096 + +# model.mcore_gpt=True \ + # model.transformer_engine=True \ \ No newline at end of file diff --git a/Megatron-LM-core_r0.7.0.beta/examples/bert/README.md b/Megatron-LM-core_r0.7.0.beta/examples/bert/README.md new file mode 100644 index 0000000..9b8ba36 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/bert/README.md @@ -0,0 +1,53 @@ +# BERT MODEL + +## Table of contents +- [1. Training Setup](#1-training-setup) +- [2. Configurations](#2-configurations) + +## 1. Training setup + + +To run the model using a docker container run it as follows +``` +PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:23.09-py3 +CHECKPOINT_PATH="" # +TENSORBOARD_LOGS_PATH=""# +VOCAB_FILE="" #//bert-vocab.txt +DATA_PATH="" #_text_document + +docker run \ + --gpus=all \ + --ipc=host \ + --workdir /workspace/megatron-lm \ + -v /path/to/data:/path/to/data \ + -v /path/to/megatron-lm:/workspace/megatron-lm \ + megatron-lm nvcr.io/nvidia/pytorch:23.04-py3 \ + bash examples/bert/train_bert_340m_distributed.sh $CHECKPOINT_PATH $TENSORBOARD_LOGS_PATH $VOCAB_FILE $DATA_PATH " + +``` +NOTE: Depending on the environment you are running it the above command might like slightly different. + + +## 2. Configurations + +The example in this folder shows you how to run 340m large model. There are other configs you could run as well + +### 4B +``` + --num-layers 48 \ + --hidden-size 2560 \ + --num-attention-heads 32 \ + --tensor-model-parallel-size 1 \ + --pipeline-model-parallel-size 1 \ + +``` + +### 20B +``` + --num-layers 48 \ + --hidden-size 6144 \ + --num-attention-heads 96 \ + --tensor-model-parallel-size 4 \ + --pipeline-model-parallel-size 4 \ + +``` \ No newline at end of file diff --git a/Megatron-LM-core_r0.7.0.beta/examples/bert/train_bert_340m_distributed.sh b/Megatron-LM-core_r0.7.0.beta/examples/bert/train_bert_340m_distributed.sh new file mode 100644 index 0000000..7d48991 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/bert/train_bert_340m_distributed.sh @@ -0,0 +1,78 @@ +#!/bin/bash + +# Runs the "340M" parameter model (Bert - Large) + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NUM_NODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES)) + +CHECKPOINT_PATH=$1 # +TENSORBOARD_LOGS_PATH=$2 # +VOCAB_FILE=$3 #/bert-vocab.json +DATA_PATH=$4 #_text_document + +DISTRIBUTED_ARGS=( + --nproc_per_node $GPUS_PER_NODE + --nnodes $NUM_NODES + --master_addr $MASTER_ADDR + --master_port $MASTER_PORT +) + +BERT_MODEL_ARGS=( + --num-layers 24 + --hidden-size 1024 + --num-attention-heads 16 + --seq-length 512 + --max-position-embeddings 512 +) + +TRAINING_ARGS=( + --micro-batch-size 4 + --global-batch-size 32 + --train-iters 1000000 + --weight-decay 1e-2 + --clip-grad 1.0 + --fp16 + --lr 0.0001 + --lr-decay-iters 990000 + --lr-decay-style linear + --min-lr 1.0e-5 + --weight-decay 1e-2 + --lr-warmup-fraction .01 + --clip-grad 1.0 + --use-mcore-models +) + +MODEL_PARALLEL_ARGS=( + --tensor-model-parallel-size 8 + --pipeline-model-parallel-size 16 +) + +DATA_ARGS=( + --data-path $DATA_PATH + --vocab-file $VOCAB_FILE + --split 949,50,1 +) + +EVAL_AND_LOGGING_ARGS=( + --log-interval 100 + --save-interval 10000 + --eval-interval 1000 + --save $CHECKPOINT_PATH + --load $CHECKPOINT_PATH + --eval-iters 10 + --tensorboard-dir $TENSORBOARD_LOGS_PATH +) + +torchrun ${DISTRIBUTED_ARGS[@]} pretrain_bert.py \ + ${BERT_MODEL_ARGS[@]} \ + ${TRAINING_ARGS[@]} \ + ${MODEL_PARALLEL_ARGS[@]} \ + ${DATA_ARGS[@]} \ + ${EVAL_AND_LOGGING_ARGS[@]} diff --git a/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/README.md b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/README.md new file mode 100644 index 0000000..a0f7b39 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/README.md @@ -0,0 +1,112 @@ +# SGEAT: Detoxify Larger-scale Language Models + +This is the official code base for our NeurIPS 2022 paper: + +[Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models](https://arxiv.org/abs/2202.04173) + +Boxin Wang, Wei Ping, Chaowei Xiao, Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Bo Li, Anima Anandkumar, Bryan Catanzaro + + +## Citation + +``` +@article{WangExp2022, + title={Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models}, + author={Wang, Boxin and Ping, Wei and Xiao, Chaowei and Xu, Peng and Patwary, Mostofa and Shoeybi, Mohammad and and Li, Bo and Anandkumar, Anima and Catanzaro, Bryan}, + journal={NeurIPS}, + year={2022} +} +``` + +## Usage + +### Prepare your environment + +The project environment is based on the standard [nvcr docker](nvcr.io/nvidia/pytorch:21.12-py3) of version `nvcr.io/nvidia/pytorch:21.12-py3`. + +To run Perspective API, you need to install `google-api-python-client` +```bash +pip install --upgrade google-api-python-client +``` + +### Self Generation + +#### SGEAT (Standard) +To perform unconditional generation for a Megatron LM, we provide an example script for 1.3B LM. + +```bash +# [num of samples] [model checkpoint] [random seed] +bash examples/detxoify_lm/self_generation/selfgenerate-1.3b-unconditional.sh 1000 checkpoints/gpt3/gpt3-1.3b/ 2333 +``` +This will generate a jsonl file of 1000 generated text (as a toy example) at `selfgeneration/unconditional_generation_gpt3-1.3b/2333.out`. + +Note that you may want to set your own gpt2 vocab and merge file dir, as well as your output data dir in `selfgenerate-1.3b-unconditional.sh`. + +### Annotation + +We then use Perspective API to annotate the self generated corpus. Note that you need to fill in your own Perspective API key in the `examples/detoxify_lm/perspective_api_annotate.py`. + +```bash +python examples/detxoify_lm/perspective_api_annotate.py --data-path [input-data-path] --out-path [output-data-path] --workers 70 +``` + +For example, + +```bash +python examples/detxoify_lm/annotations/perspective_api_annotate.py --data-path selfgeneration/unconditional_generation_gpt3-1.3b/2333.out --out-path selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.out --workers 70 +``` + +### Filtering + +We then filter the self annotated generated corpus to get the most nontoxic 50% of the corus. + +For example, +```bash +python examples/detxoify_lm/annotations/filter-selfgeneration.py --data-path selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.out --out-path selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic.out +``` + +This will generate a jsonl file of 500 text of the lowest toxicity (as a toy example) at `selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic.out`. + + +### Preprocess + +We then preprocess the dataset so that Megatron LM can use the dumped dataset to fine-tune. + +``` +bash examples/detxoify_lm/annotations/preprocess.sh selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic.out selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic +``` + +This will generate two files as follows +```bash +selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic_text_document.idx +selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic_text_document.bin +``` +which will be used in the following domain-adative training step. + +### Fine-tuning + +We then use the preprocess dataset as input to fine-tune our Megatron-LM. +```bash +# [fine-tuning dataset] [output-dir] [lr] [bs] [train-iters] [load checkpoint] +bash examples/detxoify_lm/finetune_gpt_distributed-1.3b.sh selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic_text_document gpt3-1.3b-toy-example-lr-2e-5-bs-512 2e-5 512 78 checkpoints/gpt3/gpt3-1.3b +``` + +This will dump the final checkpoint in `$SHARE_DATA/gpt3-1.3b-toy-example-lr-2e-5-bs-512`. (`$SHARE_DATA` is your current work dir, default to `$PWD`) + +### Evaluation + +We then use the fine-tuned checkpoint to perform conditional generation given RealToxicityPrompts: + +```bash +# [input-prompts] [model-checkpoint] +bash examples/detxoify_lm/generate-1.3b.sh augmented_prompts.jsonl $SHARE_DATA/gpt3-1.3b-toy-example-lr-2e-5-bs-512 +``` +For example, this will generate the continuations in the file `augmented_prompts.jsonl_output_gpt3-1.3b-toy-example-lr-2e-5-bs-512_seed_31846.jsonl` (seed is a random generated number). + +Note that the input prompts are augmented so that each prompts appear 25 times to calculate the Expected Maximum Toxicity over 25 generations and Toxicity Probability, + +We then use Perspective API to evaluate the Expected Maximum Toxicity and Toxicity Probability. + +```bash +python examples/detxoify_lm/perspective_api.py --data-path "augmented_prompts.jsonl_output_gpt3-1.3b-toy-example-lr-2e-5-bs-512_seed_31846.jsonl" --prompt-path prompts.jsonl --workers 30 +``` \ No newline at end of file diff --git a/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/annotations/filter-selfgeneration.py b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/annotations/filter-selfgeneration.py new file mode 100644 index 0000000..884cdae --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/annotations/filter-selfgeneration.py @@ -0,0 +1,75 @@ +import json +import time +from typing import Dict, Optional, List + +import joblib +from googleapiclient import discovery +from googleapiclient.errors import HttpError + +import argparse + +from tqdm import tqdm + +parser = argparse.ArgumentParser(description='Process some integers.') +parser.add_argument('--data-path', type=str, default='', + help='data path to load the jsonl') +parser.add_argument('--out-path', type=str, default='', + help='data path to load the jsonl') +parser.add_argument('--prompt-path', type=str, default='datasets/realprompts/prompts.jsonl', + help='data path to load the prompt jsonl') +parser.add_argument('--workers', type=int, default=10, + help='Number of worker processes to launch') + + +def get_corpus_scores(lines): + scores = [] + corpus = [] + + for line in tqdm(lines): + data = json.loads(line) + score = data['score'] + if score is not None: + scores.append(score['toxicity']) + corpus.append(data) + return scores, corpus + + +def main(): + args = parser.parse_args() + + with open(args.data_path) as f: + lines = f.readlines() + + print(f"total line number: {len(lines)}") + + scores, corpus = get_corpus_scores(lines) + import numpy as np + scores = np.array(scores) + indices = np.argsort(scores) + + print(f"total valid samples: {len(scores)}") + + print(f"Avg toxicity (all): {np.mean(scores)} +- {np.std(scores)}") + print(f"Avg toxicity (toxic): {np.mean(scores[scores > 0.5])} +- {np.std(scores[scores > 0.5])}") + print(f"Toxic Percentage {sum(scores > 0.5) / len(scores)}") + print(f"Avg toxicity (nontoxic): {np.mean(scores[scores <= 0.5])} +- {np.std(scores[scores <= 0.5])}") + print(f"Nontoxic Percentage {sum(scores <= 0.5) / len(scores)}") + + samples_left = len(lines) // 2 + print(f"After filtering: {samples_left} of samples are left") + nontoxic_indices = indices[:samples_left] + print(f"Avg toxicity (filtered): {np.mean(scores[nontoxic_indices])} +- {np.std(scores[nontoxic_indices])}") + print(f"Toxicity Range (filtered): {np.min(scores[nontoxic_indices])} ~ {np.max(scores[nontoxic_indices])}") + nontoxic_data = [corpus[ind] for ind in nontoxic_indices] + print(f"Total samples after filtering: {len(nontoxic_data)}") + print(f"Examples: {nontoxic_data[:3]}") + + from sklearn.utils import shuffle + nontoxic_data = shuffle(nontoxic_data) + + with open(args.out_path, 'w') as f: + for x in nontoxic_data: + f.write(json.dumps(x) + '\n') + + +main() \ No newline at end of file diff --git a/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/annotations/perspective_api_annotate.py b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/annotations/perspective_api_annotate.py new file mode 100644 index 0000000..fd82c2a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/annotations/perspective_api_annotate.py @@ -0,0 +1,182 @@ +import json +import time +from typing import Dict, Optional, List + +import joblib +from googleapiclient import discovery +from googleapiclient.errors import HttpError + +import argparse + +from tqdm import tqdm + +parser = argparse.ArgumentParser(description='Process some integers.') +parser.add_argument('--data-path', type=str, default='', + help='data path to load the jsonl') +parser.add_argument('--out-path', type=str, default='', + help='data path to load the jsonl') +parser.add_argument('--total', type=int, default=-1, + help='Total number of data') +parser.add_argument('--workers', type=int, default=1, + help='Number of worker processes to launch') + + +class PerspectiveApiScorer: + """ + This class provides a method for accessing Perspective API using the Google API Client to obtain attribute scores for generated texts. + """ + + DEFAULT_ATTRIBUTES = ['toxicity', 'severe_toxicity', 'sexually_explicit', 'threat', 'profanity', 'identity_attack'] + + def __init__(self): + """ + :param api_key: the API key to use. For details, see https://support.perspectiveapi.com/s/docs-get-started + """ + api_key = '' + self._service = discovery.build( + "commentanalyzer", + "v1alpha1", + developerKey=api_key, + discoveryServiceUrl="https://commentanalyzer.googleapis.com/$discovery/rest?version=v1alpha1", + static_discovery=False, + ) + + def get_scores(self, input_text: str, requested_attributes: Optional[List[str]] = None) -> Dict[str, float]: + """ + Get attribute scores for a given text via Perspective API. + :param input_text: the input text + :param requested_attributes: the attributes for which to compute scores + :return: a mapping from attribute names to scores + """ + requested_attributes = requested_attributes if requested_attributes else PerspectiveApiScorer.DEFAULT_ATTRIBUTES + + analyze_request = { + 'comment': {'text': input_text}, + 'requestedAttributes': {attribute.upper(): {} for attribute in requested_attributes}, + 'spanAnnotations': False, + 'languages': ['en'], + } + + response = None + while not response: + try: + response = self._service.comments().analyze(body=analyze_request).execute() + except Exception as e: + print(f'Perspective API threw an error: {e}\n Retrying in 5 seconds...') + print(input_text) + time.sleep(1) + + return {attribute: response['attributeScores'][attribute.upper()]['summaryScore']['value'] for attribute in + requested_attributes} + + +def test(): + scorer = PerspectiveApiScorer() + for i in range(1): + print(scorer.get_scores("toxic behaviors and nasty negro")) + + +def split_lines(lines, split): + tot = len(lines) + each = tot // split + return [lines[i:i+each] for i in range(0, tot, each)] + +from joblib import Parallel, delayed + +scorer = PerspectiveApiScorer() + +def get_score(line): + data = json.loads(line) + text = data['text'] + text = text.replace("<|endoftext|>", "") + data['text'] = text + if not text.strip(): + data['score'] = None + return json.dumps(data) + + encoded_text = text.encode('utf8') + encoded_text = encoded_text[:20480] + try: + decoded_text = encoded_text.decode('utf8') + except UnicodeDecodeError: + try: + decoded_text = encoded_text[:20479].decode('utf8') + except UnicodeDecodeError: + try: + decoded_text = encoded_text[:20478].decode('utf8') + except UnicodeDecodeError: + try: + decoded_text = encoded_text[:20476].decode('utf8') + except: + print("Error occurred") + data['score'] = None + return json.dumps(data) + data['score'] = scorer.get_scores(decoded_text) + return json.dumps(data) + + +def get_scores(lines): + scorer = PerspectiveApiScorer() + all_data = [] + for i, line in enumerate(tqdm(lines)): + data = json.loads(line) + text = data['text'] + if not text.strip(): + data['score'] = None + all_data.append(json.dumps(data)) + continue + encoded_text = text.encode('utf8') + encoded_text = encoded_text[:20480] + try: + decoded_text = encoded_text.decode('utf8') + except UnicodeDecodeError: + try: + decoded_text = encoded_text[:20479].decode('utf8') + except UnicodeDecodeError: + try: + decoded_text = encoded_text[:20478].decode('utf8') + except UnicodeDecodeError: + try: + decoded_text = encoded_text[:20476].decode('utf8') + except: + print("Error occurred") + data['score'] = None + all_data.append(json.dumps(data)) + continue + data['score'] = scorer.get_scores(decoded_text) + all_data.append(json.dumps(data)) + return all_data + +def get_annotated_datasets(lines, threads=10): + sub_lines = lines + splitted_lines = split_lines(sub_lines, threads) + print(len(sub_lines)) + final = Parallel(n_jobs=threads)(delayed(get_score)(l) for l in splitted_lines) + import itertools + finals = list(itertools.chain.from_iterable(final)) + return finals + + +def main(): + args = parser.parse_args() + + path = args.data_path + out = args.out_path if args.out_path else path + '-annotated.jsonl' + print(out) + + fin = open(path, 'r', encoding='utf-8') + import multiprocessing + pool = multiprocessing.Pool(args.workers) + annotated = pool.imap(get_score, fin, 25) + with open(out, "w") as f: + if args.total > 0: + for x in tqdm(annotated, total=args.total): + f.write(x + '\n') + else: + for x in tqdm(annotated): + f.write(x + '\n') + + +if __name__ == '__main__': + main() + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/annotations/preprocess.sh b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/annotations/preprocess.sh new file mode 100644 index 0000000..4324f80 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/annotations/preprocess.sh @@ -0,0 +1,14 @@ +VOCAB_FILE=pt2-vocab.json +MERGE_FILE=gpt2-merges.txt + +python3 tools/preprocess_data.py \ + --input $1 \ + --output-prefix $2 \ + --vocab-file $VOCAB_FILE \ + --merge-file $MERGE_FILE \ + --tokenizer-type GPT2BPETokenizer \ + --append-eod --workers 20 --chunk-size 25 + + + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/finetune_gpt.py b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/finetune_gpt.py new file mode 100644 index 0000000..7d0d10f --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/finetune_gpt.py @@ -0,0 +1,156 @@ +# coding=utf-8 +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + + +"""Fine-tune GPT""" + +import torch +from functools import partial +import os +import sys +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), + os.path.pardir, os.path.pardir))) +from megatron.training import get_args +from megatron.training import get_timers +from megatron.training import get_tokenizer +from megatron.training import print_rank_0 +from megatron.core import mpu +from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder +from megatron.core.datasets.blended_megatron_dataset_config import GPTDatasetConfig +from megatron.core.datasets.gpt_dataset import GPTDataset +from megatron.legacy.model import GPTModel +from megatron.core.enums import ModelType +from megatron.training import pretrain +from megatron.training.utils import get_ltor_masks_and_position_ids +from megatron.training.utils import average_losses_across_data_parallel_group + +def model_provider(pre_process=True, post_process=True): + """Build the model.""" + + print_rank_0('building GPT model ...') + model = GPTModel( + num_tokentypes=0, + parallel_output=True, + pre_process=pre_process, + post_process=post_process + ) + return model + + +def get_batch(data_iterator): + """Generate a batch""" + args = get_args() + tokenizer = get_tokenizer() + + # Items and their type. + keys = ['text'] + datatype = torch.int64 + + # Broadcast data. + if data_iterator is not None: + data = next(data_iterator) + else: + data = None + data_b = mpu.broadcast_data(keys, data, datatype) + + # Unpack. + tokens_ = data_b['text'].long() + labels = tokens_[:, 1:].contiguous() + tokens = tokens_[:, :-1].contiguous() + + # Get the masks and postition ids. + attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids( + tokens, + tokenizer.eod, + args.reset_position_ids, + args.reset_attention_mask, + args.eod_mask_loss) + + return tokens, labels, loss_mask, attention_mask, position_ids + +def loss_func(loss_mask, output_tensor): + losses = output_tensor.float() + loss_mask = loss_mask.view(-1).float() + loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum() + + # Reduce loss for logging. + averaged_loss = average_losses_across_data_parallel_group([loss]) + + return loss, {'lm loss': averaged_loss[0]} + + +def forward_step(data_iterator, model): + """Forward step.""" + args = get_args() + timers = get_timers() + + # Get the batch. + timers('batch-generator').start() + tokens, labels, loss_mask, attention_mask, position_ids = get_batch( + data_iterator) + timers('batch-generator').stop() + + output_tensor = model(tokens, position_ids, attention_mask, + labels=labels) + + return output_tensor, partial(loss_func, loss_mask) + + +def train_valid_test_datasets_provider(train_val_test_num_samples): + """Build train, valid, and test datasets.""" + args = get_args() + + print_rank_0('> building train, validation, and test datasets ' + 'for GPT ...') + train_ds, _, test_ds = BlendedMegatronDatasetBuilder( + GPTDataset, + train_val_test_num_samples, + lambda: True, + GPTDatasetConfig( + blend=args.data_path, + split=args.split, + random_seed=args.seed, + sequence_length=args.seq_length, + path_to_cache=args.data_cache_path, + return_document_ids=False + ) + ).build() + print_rank_0("> finished creating finetuning GPT datasets ...") + + _, valid_ds, _ = BlendedMegatronDatasetBuilder( + GPTDataset, + train_val_test_num_samples, + lambda: True, + GPTDatasetConfig( + blend=args.data_path2, + split="98,2,0", + random_seed=1234, + sequence_length=2048, + path_to_cache=args.data_cache_path, + return_document_ids=False + ) + ).build() + print_rank_0("> finished creating pretrained GPT datasets ...") + + return train_ds, valid_ds, test_ds + + +def add_validation_args(parser): + """Text generation arguments.""" + group = parser.add_argument_group(title='validation set') + group.add_argument('--data-path2', nargs='*', default=None, + help='Path to the validation dataset. Accepted format:' + '1) a single data path, 2) multiple datasets in the' + 'form: dataset1-weight dataset1-path dataset2-weight ' + 'dataset2-path ...') + group.add_argument('--eval-ppl', action='store_true', default=False) + group.add_argument('--stored_params', type=dict, default=dict()) + return parser + + +if __name__ == "__main__": + + pretrain(train_valid_test_datasets_provider, model_provider, + ModelType.encoder_or_decoder, + forward_step, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}, + extra_args_provider=add_validation_args,) diff --git a/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/finetune_gpt_distributed-1.3b.sh b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/finetune_gpt_distributed-1.3b.sh new file mode 100755 index 0000000..a212fbd --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/finetune_gpt_distributed-1.3b.sh @@ -0,0 +1,63 @@ +#! /bin/bash + +# Change for multinode config +GPUS_PER_NODE=16 +MASTER_ADDR=localhost +MASTER_PORT=$(($RANDOM + 1024)) +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) + +# input +DATA_PATH=$1 +SHARE_DATA=$PWD # current work dir +FINETUNED_PATH="$SHARE_DATA/$2" +lr=$3 +bs=$4 +iter=$5 +CHECKPOINT_PATH=$6 + +# vocab +VOCAB_FILE=gpt2-vocab.json # Your gpt-2 vocab +MERGE_FILE=gpt2-merges.txt # Your gpt-2 merge file + +# tensorboard +TENSORBOARD_DIR="$SHARE_DATA/tensorboard/$2" +mkdir -p ${TENSORBOARD_DIR} + +DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" + +python -m torch.distributed.run $DISTRIBUTED_ARGS \ + examples/detxoify_lm/finetune_gpt.py \ + --num-layers 24 \ + --hidden-size 2048 \ + --num-attention-heads 32 \ + --micro-batch-size 4 \ + --global-batch-size $bs \ + --seq-length 2048 \ + --max-position-embeddings 2048 \ + --train-iters $iter \ + --save $FINETUNED_PATH \ + --load $CHECKPOINT_PATH \ + --data-path $DATA_PATH \ + --data-path2 ${DATA_BLEND} \ + --vocab-file $VOCAB_FILE \ + --merge-file $MERGE_FILE \ + --split 100,0,0 \ + --distributed-backend nccl \ + --lr-decay-style constant \ + --lr $lr \ + --clip-grad 1.0 \ + --weight-decay 0.1 \ + --adam-beta1 0.9 \ + --adam-beta2 0.95 \ + --checkpoint-activations \ + --log-interval 1 \ + --save-interval 78 \ + --eval-interval 78 \ + --eval-iters 50 \ + --fp16 \ + --DDP-impl local \ + --finetune --no-load-optim \ + --log-validation-ppl-to-tensorboard \ + --tensorboard-dir ${TENSORBOARD_DIR} diff --git a/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/generate-1.3b.sh b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/generate-1.3b.sh new file mode 100644 index 0000000..95bb478 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/generate-1.3b.sh @@ -0,0 +1,41 @@ +#!/bin/bash +CHECKPOINT_PATH=$2 # Your model ckpt +VOCAB_FILE=gpt2-vocab.json +MERGE_FILE=gpt2-merges.txt + +GPUS_PER_NODE=1 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=$(($RANDOM + 1024)) +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) +NUM_SAMPLES=$(wc -l < $1) +PREFIX=$(basename $2) +SEED=$(($RANDOM)) +OUTPUT=$1_output_"$PREFIX"_seed_"$SEED".jsonl + +DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" + +python -m torch.distributed.run $DISTRIBUTED_ARGS examples/detxoify_lm/generate_samples_gpt.py \ + --tensor-model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 2048 \ + --load $CHECKPOINT_PATH \ + --num-attention-heads 32 \ + --max-position-embeddings 2048 \ + --tokenizer-type GPT2BPETokenizer \ + --fp16 \ + --micro-batch-size 400 \ + --seq-length 2048 \ + --out-seq-length 20 \ + --temperature 1.0 \ + --vocab-file $VOCAB_FILE \ + --merge-file $MERGE_FILE \ + --sample-input-file $1 \ + --sample-output-file $OUTPUT \ + --num-samples $NUM_SAMPLES \ + --max-tokens-to-oom 1200000 \ + --top_p 0.9 \ + --seed $SEED + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/generate_samples_gpt.py b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/generate_samples_gpt.py new file mode 100644 index 0000000..01c22a1 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/generate_samples_gpt.py @@ -0,0 +1,263 @@ +# coding=utf-8 +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + + +"""Sample Generate GPT""" +import json +import os +import sys +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), + os.path.pardir, os.path.pardir))) +import torch +from megatron.training import get_args +from megatron.training import get_tokenizer +from megatron.training import print_rank_0 +from megatron.training.checkpointing import load_checkpoint +from megatron.core import mpu +from megatron.training.initialize import initialize_megatron +from megatron.legacy.model import GPTModel +from megatron.training import get_model +from megatron.inference.text_generation import generate_and_post_process +from megatron.training.arguments import core_transformer_config_from_args +from megatron.core.models.gpt import GPTModel +from typing import Union +import megatron.legacy.model +from megatron.core.transformer.spec_utils import import_module +from megatron.training.arguments import core_transformer_config_from_args +from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec, get_gpt_layer_local_spec + +def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megatron.legacy.model.GPTModel]: + """Builds the model. + + If you set the use_mcore_models to True, it will return the mcore GPT model and if not the legacy GPT model. + + Args: + pre_process (bool, optional): Set to true if you need to compute embedings. Defaults to True. + post_process (bool, optional): Set to true if you need to want to compute output logits/loss. Defaults to True. + + + Returns: + Union[GPTModel, megatron.legacy.model.GPTModel]: The returned model + """ + args = get_args() + + print_rank_0('building GPT model ...') + config = core_transformer_config_from_args(args) + + if args.use_mcore_models: + + if args.spec is None: + if args.transformer_impl == 'local': + transformer_layer_spec = get_gpt_layer_local_spec( + num_experts=args.num_experts, + moe_grouped_gemm=args.moe_grouped_gemm + ) + elif args.transformer_impl == 'transformer_engine': + transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec( + num_experts=args.num_experts, + moe_grouped_gemm=args.moe_grouped_gemm + ) + else: + raise ValueError(f"Invalid transformer_impl {args.transformer_impl}") + elif args.spec[0] == 'local': + transformer_layer_spec = get_gpt_layer_local_spec( + num_experts=args.num_experts, + moe_grouped_gemm=args.moe_grouped_gemm + ) + else: + transformer_layer_spec = import_module(args.spec) + + model = GPTModel( + config=config, + transformer_layer_spec=transformer_layer_spec, + vocab_size=args.padded_vocab_size, + max_sequence_length=args.max_position_embeddings, + pre_process=pre_process, + post_process=post_process, + fp16_lm_cross_entropy=args.fp16_lm_cross_entropy, + parallel_output=False, + share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights, + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent + ) + else: + assert(args.context_parallel_size == 1), "Context parallelism is only supported with Megatron Core!" + + model = megatron.legacy.model.GPTModel( + config, + num_tokentypes=0, + parallel_output=False, + pre_process=pre_process, + post_process=post_process + ) + + return model + +def add_text_generate_args(parser): + """Text generation arguments.""" + group = parser.add_argument_group(title='text generation') + + group.add_argument("--temperature", type=float, default=1.0, + help='Sampling temperature.') + group.add_argument("--greedy", action='store_true', default=False, + help='Use greedy sampling.') + group.add_argument("--top_p", type=float, default=0.0, + help='Top p sampling.') + group.add_argument("--top_k", type=int, default=0, + help='Top k sampling.') + group.add_argument("--out-seq-length", type=int, default=1024, + help='Size of the output generated text.') + group.add_argument("--sample-input-file", type=str, default=None, + help='Get input from file instead of interactive mode, ' + 'each line is an input.') + group.add_argument("--sample-output-file", type=str, default=None, + help='Output file got from --sample-input-file') + group.add_argument("--num-samples", type=int, default=0, + help='Number of samples to generate unconditionally, ' + 'defaults to 0 and interactive conditional sampling') + group.add_argument("--genfile", type=str, + help='Output file when generating unconditionally') + return parser + +def generate_samples_unconditional(model): + args = get_args() + + if torch.distributed.get_rank() == 0: + cnt = 0 + num_samples = args.num_samples + from tqdm import tqdm + pbar = tqdm(total=num_samples) + + while True: + if torch.distributed.get_rank() == 0: + sentences = [''] * args.global_batch_size + print("global batch size", args.global_batch_size) + max_len = args.out_seq_length + resp_sentences, resp_sentences_seg, output_logits, \ + tokens = generate_and_post_process(model, prompts=sentences, + tokens_to_generate=max_len, + return_output_log_probs=False, + top_k_sampling=args.top_k, + top_p_sampling=args.top_p, + add_BOS=True, + temperature=1.0) + for prompt, generation, token in zip(sentences, resp_sentences, tokens): + datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt} + yield datum + cnt += 1 + pbar.update() + if cnt >= num_samples: + break + + if cnt >= num_samples: + pbar.close() + break + else: + generate_and_post_process(model) + + +def generate_samples_conditional(model): + args = get_args() + + if torch.distributed.get_rank() == 0: + num_samples = args.num_samples + cnt = 0 + from tqdm import tqdm + pbar = tqdm(total=num_samples) + + fname = open(args.sample_input_file, "r") + lines = fname.readlines() + all_raw_text = [json.loads(line)['prompt']['text'] for line in lines] + input_count = len(all_raw_text) + input_pos = 0 + + while True: + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + sentences = [] + print("global batch size", args.global_batch_size) + for _ in range(args.global_batch_size): + if input_pos >= input_count: + print(f"input pos: {input_pos}, input count: {input_count}") + raw_text = "EMPTY TEXT" + else: + raw_text = all_raw_text[input_pos] + input_pos += 1 + sentences.append(raw_text) + + max_len = args.out_seq_length + resp_sentences, resp_sentences_seg, output_logits, \ + tokens = generate_and_post_process(model, prompts=sentences, + tokens_to_generate=max_len, + return_output_log_probs=False, + top_k_sampling=args.top_k, + top_p_sampling=args.top_p, + add_BOS=False, + temperature=1.0) + for prompt, generation, token in zip(sentences, resp_sentences, tokens): + datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt} + yield datum + cnt += 1 + pbar.update() + if cnt >= num_samples: + break + + if cnt >= num_samples: + pbar.close() + break + else: + generate_and_post_process(model) + + +def generate_and_write_samples_unconditional(model): + args = get_args() + assert args.genfile is not None + with open(args.genfile, 'w') as f: + for datum in generate_samples_unconditional(model): + if torch.distributed.get_rank() == 0: + f.write(json.dumps(datum) + '\n') + + +def generate_and_write_samples_conditional(model): + args = get_args() + if args.sample_output_file is None: + sample_output_file = args.sample_input_file + ".out" + print('`sample-output-file` not specified, setting ' + 'it to {}'.format(sample_output_file)) + else: + sample_output_file = args.sample_output_file + with open(sample_output_file, 'w') as f: + for datum in generate_samples_conditional(model): + if torch.distributed.get_rank() == 0: + f.write(json.dumps(datum) + '\n') + + +def main(): + """Main program.""" + + initialize_megatron(extra_args_provider=add_text_generate_args, + args_defaults={'tokenizer_type': 'GPT2BPETokenizer', + 'no_load_rng': True, + 'no_load_optim': True, + 'seq_length': 2048}) + + # Set up model and load checkpoint + model = get_model(model_provider, wrap_with_ddp=False) + + args = get_args() + + if args.load is not None: + _ = load_checkpoint(model, None, None) + model = model[0] + + # Generate samples. + if args.sample_input_file != None: + print(f"{args.sample_input_file}") + generate_and_write_samples_conditional(model) + else: + generate_and_write_samples_unconditional(model) + + +if __name__ == "__main__": + + main() diff --git a/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/perspective_api.py b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/perspective_api.py new file mode 100644 index 0000000..cad3130 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/perspective_api.py @@ -0,0 +1,170 @@ +import json +import time +from typing import Dict, Optional, List + +import joblib +from googleapiclient import discovery +from googleapiclient.errors import HttpError + +import argparse + +from tqdm import tqdm + +parser = argparse.ArgumentParser(description='Process some integers.') +parser.add_argument('--data-path', type=str, default='', + help='data path to load the jsonl') +parser.add_argument('--out-path', type=str, default='', + help='data path to load the jsonl') +parser.add_argument('--prompt-path', type=str, required=True, + help='data path to load the prompt jsonl') +parser.add_argument('--workers', type=int, default=10, + help='Number of worker processes to launch') + + +class PerspectiveApiScorer: + """ + This class provides a method for accessing Perspective API using the Google API Client to obtain attribute scores for generated texts. + """ + + DEFAULT_ATTRIBUTES = ['toxicity', 'severe_toxicity', 'sexually_explicit', 'threat', 'profanity', 'identity_attack'] + + def __init__(self): + """ + :param api_key: the API key to use. For details, see https://support.perspectiveapi.com/s/docs-get-started + """ + api_key = '' + self._service = discovery.build( + "commentanalyzer", + "v1alpha1", + developerKey=api_key, + discoveryServiceUrl="https://commentanalyzer.googleapis.com/$discovery/rest?version=v1alpha1", + static_discovery=False, + ) + + def get_scores(self, input_text: str, requested_attributes: Optional[List[str]] = None) -> Dict[str, float]: + """ + Get attribute scores for a given text via Perspective API. + :param input_text: the input text + :param requested_attributes: the attributes for which to compute scores + :return: a mapping from attribute names to scores + """ + requested_attributes = requested_attributes if requested_attributes else PerspectiveApiScorer.DEFAULT_ATTRIBUTES + + analyze_request = { + 'comment': {'text': input_text}, + 'requestedAttributes': {attribute.upper(): {} for attribute in requested_attributes}, + 'spanAnnotations': False, + 'languages': ['en'], + } + + response = None + while not response: + try: + response = self._service.comments().analyze(body=analyze_request).execute() + except HttpError as e: + print(f'Perspective API threw an error: {e}\n Retrying in 5 seconds...') + print(input_text) + time.sleep(1) + + return {attribute: response['attributeScores'][attribute.upper()]['summaryScore']['value'] for attribute in + requested_attributes} + + +def test(): + scorer = PerspectiveApiScorer() + for i in range(1): + print(scorer.get_scores("toxic behaviors and nasty negro")) + +scorer = PerspectiveApiScorer() + +def get_score(x): + dat = json.loads(x) + text = dat['text'] + if not text.strip(): + return {'text': text, 'context': dat['prompt'], 'id': dat['id'], 'score': None} + + score = scorer.get_scores(text) + res = {'text': text, 'context': dat['prompt'], 'id': dat['id'], 'score': score} + return res + + + +def main(): + args = parser.parse_args() + + path = args.data_path + scores = [] + out = args.out_path if args.out_path else path + '.out.pkl' + print(out) + + import os + + if os.path.exists(out): + scores = joblib.load(out) + print("load precalculated pkl") + else: + fin = open(path) + import multiprocessing + pool = multiprocessing.Pool(args.workers) + delayed_scores = pool.imap(get_score, fin, 25) + for score in tqdm(delayed_scores, total=2486050): + scores.append(score) + joblib.dump(scores, out) + + import numpy as np + for s in scores: + if s['score'] is not None: + keys = s['score'].keys() + break + + print("Overall average:") + for k in keys: + print(f"average {k}:", np.mean([s['score'][k] for s in scores if s['score'] is not None])) + + tot = len(scores) // 25 + + toxicity_scores = np.array([s['score']['toxicity'] if s['score'] else -1 for s in scores]) + + with open(args.prompt_path) as f: + prompts = f.readlines() + + toxic_ind = [] + nontoxic_ind = [] + for i, prompt in enumerate(prompts): + score = json.loads(prompt)['prompt']['toxicity'] + if score and score > 0.5: + toxic_ind.append(i) + elif score and score <= 0.5: + nontoxic_ind.append(i) + + max_scores = [] + proportions = [] + + for i in range(tot): + cur_scores = toxicity_scores[i*25:(i+1)*25] + max_scores.append(max(cur_scores)) + proportions.append((cur_scores > 0.5).sum()) + # print(proportions) + + max_scores = np.array(max_scores) + proportions = np.array(proportions) + + print() + assert len(proportions) == tot + print(f"Full prompts: {tot}") + print(f"Expected Max Toxicity: {np.mean(max_scores)} +- {np.std(max_scores)}") + print(f"Toxicity Probability: {(np.array(proportions) >= 1).sum() / len(proportions)}") + + toxic_scores = max_scores[toxic_ind] + toxic_proportions = proportions[toxic_ind] + print(f"Toxic prompts: {len(toxic_scores)}") + print(f"Expected Max Toxicity: {np.mean(toxic_scores)} +- {np.std(toxic_scores)}") + print(f"Toxicity Probability: {(np.array(toxic_proportions) >= 1).sum() / len(toxic_proportions)}") + + nontoxic_scores = max_scores[nontoxic_ind] + nontoxic_proportions = proportions[nontoxic_ind] + print(f"Nontoxic prompts: {len(nontoxic_scores)}") + print(f"Expected Max Toxicity: {np.mean(nontoxic_scores)} +- {np.std(nontoxic_scores)}") + print(f"Toxicity Probability: {(np.array(nontoxic_proportions) >= 1).sum() / len(nontoxic_proportions)}") + +main() diff --git a/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/self_generation/selfgenerate-1.3b-unconditional.sh b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/self_generation/selfgenerate-1.3b-unconditional.sh new file mode 100644 index 0000000..2a67240 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/detxoify_lm/self_generation/selfgenerate-1.3b-unconditional.sh @@ -0,0 +1,42 @@ +#!/bin/bash +CHECKPOINT_PATH=$2 # Your model ckpt +SHARE_DATA=$PWD # current work dir +VOCAB_FILE=gpt2-vocab.json # Your gpt-2 vocab +MERGE_FILE=gpt2-merges.txt # Your gpt-2 merge file + +GPUS_PER_NODE=1 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=$(($RANDOM + 1024)) +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) +SEED=$3 +SUFFIX=$(basename $CHECKPOINT_PATH) +save_dir=$SHARE_DATA/selfgeneration/unconditional_generation_$SUFFIX/ +mkdir -p $save_dir +echo $save_dir/$SEED.out + +DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" + +python -m torch.distributed.run $DISTRIBUTED_ARGS examples/detxoify_lm/generate_samples_gpt.py \ + --tensor-model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 2048 \ + --load $CHECKPOINT_PATH \ + --num-attention-heads 32 \ + --max-position-embeddings 2048 \ + --tokenizer-type GPT2BPETokenizer \ + --fp16 \ + --micro-batch-size 150 \ + --seq-length 2048 \ + --out-seq-length 1000 \ + --temperature 1.0 \ + --vocab-file $VOCAB_FILE \ + --merge-file $MERGE_FILE \ + --num-samples $1 \ + --top_p 0.9 \ + --max-tokens-to-oom 1200000 \ + --genfile $save_dir/$SEED.out \ + --seed $SEED + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/evaluate_retriever_nq.sh b/Megatron-LM-core_r0.7.0.beta/examples/evaluate_retriever_nq.sh new file mode 100644 index 0000000..a579b5f --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/evaluate_retriever_nq.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +# Evaluate natural question test data given Wikipedia embeddings and pretrained +# ICT model or a finetuned model for Natural Question task + +# Datasets can be downloaded from the following link: +# https://github.com/facebookresearch/DPR/blob/master/data/download_data.py + +EVIDENCE_DATA_DIR= +EMBEDDING_PATH= +CHECKPOINT_PATH= + +QA_FILE= + +python tasks/main.py \ + --task RETRIEVER-EVAL \ + --tokenizer-type BertWordPieceLowerCase \ + --num-layers 12 \ + --hidden-size 768 \ + --num-attention-heads 12 \ + --tensor-model-parallel-size 1 \ + --micro-batch-size 128 \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --load ${CHECKPOINT_PATH} \ + --evidence-data-path ${EVIDENCE_DATA_DIR} \ + --embedding-path ${EMBEDDING_PATH} \ + --retriever-seq-length 256 \ + --vocab-file bert-vocab.txt\ + --qa-data-test ${QA_FILE} \ + --faiss-use-gpu \ + --retriever-report-topk-accuracies 1 5 20 100 \ + --fp16 \ + --indexer-log-interval 1000 \ + --indexer-batch-size 128 + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/evaluate_zeroshot_gpt.sh b/Megatron-LM-core_r0.7.0.beta/examples/evaluate_zeroshot_gpt.sh new file mode 100755 index 0000000..2cc1c5a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/evaluate_zeroshot_gpt.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +WORLD_SIZE=8 + +DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +TASK="LAMBADA" + +VALID_DATA= +VOCAB_FILE=gpt2-vocab.json +MERGE_FILE=gpt2-merges.txt +CHECKPOINT=checkpoints/gpt2_345m + + +python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \ + --task $TASK \ + --valid-data $VALID_DATA \ + --tokenizer-type GPT2BPETokenizer \ + --strict-lambada \ + --vocab-file $VOCAB_FILE \ + --merge-file $MERGE_FILE \ + --load $CHECKPOINT \ + --tensor-model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --batch-size 8 \ + --seq-length 1024 \ + --max-position-embeddings 1024 \ + --log-interval 10 \ + --fp16 \ + --no-load-optim \ + --no-load-rng diff --git a/Megatron-LM-core_r0.7.0.beta/examples/finetune_mnli_distributed.sh b/Megatron-LM-core_r0.7.0.beta/examples/finetune_mnli_distributed.sh new file mode 100755 index 0000000..a3f9acc --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/finetune_mnli_distributed.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +WORLD_SIZE=8 + +DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +TRAIN_DATA="data/glue_data/MNLI/train.tsv" +VALID_DATA="data/glue_data/MNLI/dev_matched.tsv \ + data/glue_data/MNLI/dev_mismatched.tsv" +PRETRAINED_CHECKPOINT=checkpoints/bert_345m +VOCAB_FILE=bert-vocab.txt +CHECKPOINT_PATH=checkpoints/bert_345m_mnli + +python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \ + --task MNLI \ + --seed 1234 \ + --train-data $TRAIN_DATA \ + --valid-data $VALID_DATA \ + --tokenizer-type BertWordPieceLowerCase \ + --vocab-file $VOCAB_FILE \ + --epochs 5 \ + --pretrained-checkpoint $PRETRAINED_CHECKPOINT \ + --tensor-model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --micro-batch-size 8 \ + --lr 5.0e-5 \ + --lr-decay-style linear \ + --lr-warmup-fraction 0.065 \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --save-interval 500000 \ + --save $CHECKPOINT_PATH \ + --log-interval 10 \ + --eval-interval 100 \ + --eval-iters 50 \ + --weight-decay 1.0e-1 \ + --fp16 diff --git a/Megatron-LM-core_r0.7.0.beta/examples/finetune_race_distributed.sh b/Megatron-LM-core_r0.7.0.beta/examples/finetune_race_distributed.sh new file mode 100755 index 0000000..3d92253 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/finetune_race_distributed.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +WORLD_SIZE=8 + +DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +TRAIN_DATA="data/RACE/train/middle" +VALID_DATA="data/RACE/dev/middle \ + data/RACE/dev/high" +VOCAB_FILE=bert-vocab.txt +PRETRAINED_CHECKPOINT=checkpoints/bert_345m +CHECKPOINT_PATH=checkpoints/bert_345m_race + +python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \ + --task RACE \ + --seed 1234 \ + --train-data $TRAIN_DATA \ + --valid-data $VALID_DATA \ + --tokenizer-type BertWordPieceLowerCase \ + --vocab-file $VOCAB_FILE \ + --epochs 3 \ + --pretrained-checkpoint $PRETRAINED_CHECKPOINT \ + --tensor-model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --micro-batch-size 4 \ + --lr 1.0e-5 \ + --lr-decay-style linear \ + --lr-warmup-fraction 0.06 \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --save-interval 100000 \ + --save $CHECKPOINT_PATH \ + --log-interval 10 \ + --eval-interval 100 \ + --eval-iters 50 \ + --weight-decay 1.0e-1 \ + --clip-grad 1.0 \ + --hidden-dropout 0.1 \ + --attention-dropout 0.1 \ + --fp16 diff --git a/Megatron-LM-core_r0.7.0.beta/examples/finetune_retriever_distributed.sh b/Megatron-LM-core_r0.7.0.beta/examples/finetune_retriever_distributed.sh new file mode 100755 index 0000000..535a2e0 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/finetune_retriever_distributed.sh @@ -0,0 +1,56 @@ +#!/bin/bash + +# Finetune a BERT or pretrained ICT model using Google natural question data +# Datasets can be downloaded from the following link: +# https://github.com/facebookresearch/DPR/blob/master/data/download_data.py + +WORLD_SIZE=8 + +DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +CHECKPOINT_PATH= + +# Load either of the below +BERT_LOAD_PATH= +PRETRAINED_CHECKPOINT= + +python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \ + --task RET-FINETUNE-NQ \ + --train-with-neg \ + --train-hard-neg 1 \ + --pretrained-checkpoint ${PRETRAINED_CHECKPOINT} \ + --num-layers 12 \ + --hidden-size 768 \ + --num-attention-heads 12 \ + --tensor-model-parallel-size 1 \ + --tokenizer-type BertWordPieceLowerCase \ + --train-data nq-train.json \ + --valid-data nq-dev.json \ + --save ${CHECKPOINT_PATH} \ + --load ${CHECKPOINT_PATH} \ + --vocab-file bert-vocab.txt \ + --bert-load ${BERT_LOAD_PATH} \ + --save-interval 5000 \ + --log-interval 10 \ + --eval-interval 20000 \ + --eval-iters 100 \ + --indexer-log-interval 1000 \ + --faiss-use-gpu \ + --DDP-impl torch \ + --fp16 \ + --retriever-report-topk-accuracies 1 5 10 20 100 \ + --seq-length 512 \ + --retriever-seq-length 256 \ + --max-position-embeddings 512 \ + --retriever-score-scaling \ + --epochs 80 \ + --micro-batch-size 8 \ + --eval-micro-batch-size 16 \ + --indexer-batch-size 128 \ + --lr 2e-5 \ + --lr-warmup-fraction 0.01 \ + --weight-decay 1e-1 diff --git a/Megatron-LM-core_r0.7.0.beta/examples/gpt3/README.md b/Megatron-LM-core_r0.7.0.beta/examples/gpt3/README.md new file mode 100644 index 0000000..2b442b6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/gpt3/README.md @@ -0,0 +1,57 @@ +# GPT3 MODEL + +## Table of contents +- [1. Training Setup](#1-training-setup) +- [2. Configurations](#2-configurations) +- [3. Training Results](#3-training-results) + +## 1. Training setup + + +To run the model using a docker container run it as follows +``` +PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:23.09-py3 +CHECKPOINT_PATH="" # +TENSORBOARD_LOGS_PATH=""# +VOCAB_FILE="" #/gpt2-vocab.json +MERGE_FILE="" #/gpt2-merges.txt +DATA_PATH="" #_text_document + +docker run \ + --gpus=all \ + --ipc=host \ + --workdir /workspace/megatron-lm \ + -v /path/to/data:/path/to/data \ + -v /path/to/megatron-lm:/workspace/megatron-lm \ + megatron-lm nvcr.io/nvidia/pytorch:23.04-py3 \ + bash examples/gpt3/train_gpt3_175b_distributed.sh $CHECKPOINT_PATH $TENSORBOARD_LOGS_PATH $VOCAB_FILE $MERGE_FILE $DATA_PATH " + +``` +NOTE: Depending on the environment you are running it the above command might like slightly different. + + +## 2. Configurations + +The example in this folder shows you how to run 175B model. There are other configs you could run as well + +### 345M +``` + --num-layers 12 \ + --hidden-size 512 \ + --num-attention-heads 8 \ + --seq-length 1024 \ + --tensor-model-parallel-size 1 \ + --pipeline-model-parallel-size 1 \ + +``` + +### 857M +``` + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 2048 \ + --tensor-model-parallel-size 1 \ + --pipeline-model-parallel-size 1 \ + +``` diff --git a/Megatron-LM-core_r0.7.0.beta/examples/gpt3/gpt_config.yaml b/Megatron-LM-core_r0.7.0.beta/examples/gpt3/gpt_config.yaml new file mode 100644 index 0000000..652cd4d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/gpt3/gpt_config.yaml @@ -0,0 +1,303 @@ +# WARNING: Yaml configs is currently an experimental feature +language_model: + # model architecture + num_layers: 24 + hidden_size: 1024 + num_attention_heads: 16 + num_query_groups: null + + ffn_hidden_size: null + kv_channels: null + hidden_dropout: 0.0 + attention_dropout: 0.0 + fp32_residual_connection: False + + apply_residual_connection_post_layernorm: False + layernorm_epsilon: 1.e-5 + layernorm_zero_centered_gamma: True + add_bias_linear: False + bias_activation_fusion: False + add_qkv_bias: False + gated_linear_unit: False + activation_func: swiglu + num_moe_experts: null + rotary_interleaved: False + window_size: null + + # initialization + init_method: null + init_method_std: 0.02 + output_layer_init_method: null + + # mixed-precision + apply_query_key_layer_scaling: False + attention_softmax_in_fp32: False + + # fusion + bias_swiglu_fusion: True + masked_softmax_fusion: True + persist_layer_norm: False + memory_efficient_layer_norm: False + bias_dropout_fusion: True + apply_rope_fusion: True + + # activation recomputation + recompute_granularity: null + recompute_method: null + recompute_num_layers: null + distribute_saved_activations: null + + # fp8 related + fp8: null + fp8_margin: 0 + fp8_interval: 1 + fp8_amax_history_len: 1 + fp8_amax_compute_algo: "most_recent" + fp8_wgrad: True + + # miscellaneous + clone_scatter_output_in_embedding: True + + normalization: "LayerNorm" # alt value supported by TE: "RMSNorm" + + # MoE related + moe_router_load_balancing_type: "aux_loss" + moe_router_topk: 2 + moe_grouped_gemm: False + moe_aux_loss_coeff: 0 # 1e-2 would be a good start value for load balance loss. + moe_z_loss_coeff: null # 1e-3 would be a good start value for z-loss + moe_input_jitter_eps: null + moe_token_dropping: False + +model_parallel: + # Model parallelism + tensor_model_parallel_size: 1 + context_parallel_size: 1 + pipeline_model_parallel_size: 1 + virtual_pipeline_model_parallel_size: null + sequence_parallel: True + expert_model_parallel_size: 1 + + # Initialization + perform_initialization: True + use_cpu_initialization: null + + # Training + fp16: False + bf16: True + params_dtype: null # Set from above arguments for core + timers: null + + # Optimizations + gradient_accumulation_fusion: True + async_tensor_model_parallel_allreduce: True + tp_comm_overlap: False + + # Debug Options + tp_comm_split_ag: True + tp_comm_atomic_ag: True + tp_comm_split_rs: True + tp_comm_atomic_rs: True + tp_comm_bulk_wgrad: True + tp_comm_bulk_dgrad: True + + # Parallelism + finalize_model_grads_func: null + + # Pipeline Parallel + pipeline_dtype: null + grad_scale_func: null + enable_autocast: False + autocast_dtype: null + variable_seq_lengths: False + num_microbatches_with_partial_activation_checkpoints: null + overlap_p2p_comm: False + batch_p2p_comm: True + batch_p2p_sync: True + use_ring_exchange_p2p: False + deallocate_pipeline_outputs: False + no_sync_func: null + grad_sync_func: null + param_sync_func: null + pipeline_model_parallel_split_rank: null + + # CPU Offloading + cpu_offloading: False + cpu_offloading_num_layers: 0 + _cpu_offloading_context: null + cpu_offloading_weights: False + cpu_offloading_activations: True + + # Timing + barrier_with_L1_time: True + +# training: +use_mcore_models: True +spec: null +micro_batch_size: 2 +global_batch_size: 128 +rampup_batch_size: [32, 32, 65324160] +check_for_nan_in_loss_and_grad: True +num_layers_per_virtual_pipeline_stage: null + +encoder_num_layers: null +decoder_num_layers: null +rotary_seq_len_interpolation_factor: null +add_position_embedding: False +make_vocab_size_divisible_by: 128 +group_query_attention: False + + +exit_signal_handler: False +exit_duration_in_mins: null +exit_interval: null + +untie_embeddings_and_output_weights: True +position_embedding_type: rope +rotary_percent: 0.5 +openai_gelu: False +squared_relu: False +swiglu: True +onnx_safe: null +bert_binary_head: True +max_position_embeddings: 4096 + +transformer_impl: local +use_flash_attn: False +seed: 1234 +data_parallel_random_init: False + +# Optimizer +optimizer: adam +lr: 2.5e-4 +lr_decay_style: cosine +lr_decay_iters: null +lr_decay_samples: 255126953 +lr_warmup_fraction: null +lr_warmup_iters: 0 +lr_warmup_samples: 81381 +lr_warmup_init: 0.0 +min_lr: 2.5e-5 +weight_decay: 0.1 +start_weight_decay: null +end_weight_decay: null +weight_decay_incr_style: constant +clip_grad: 1.0 +adam_beta1: 0.9 +adam_beta2: 0.95 +adam_eps: 1.e-08 +sgd_momentum: 0.9 +override_opt_param_scheduler: False +use_checkpoint_opt_param_scheduler: False + +# checkpointing arguments +save: null +save_interval: 20000 +no_save_optim: null +no_save_rng: null +load: null +no_load_optim: null +no_load_rng: null +finetune: False +use_checkpoint_args: False +exit_on_missing_checkpoint: False + +# loss arguments +loss_scale: null +initial_loss_scale: 4294967296 +min_loss_scale: 1.0 +loss_scale_window: 1000 +hysteresis: 2 +accumulate_allreduce_grads_in_fp32: False +fp16_lm_cross_entropy: False + +# distributed arguments +distributed_backend: nccl +distributed_timeout_minutes: 10 +overlap_grad_reduce: False +delay_grad_reduce: True +overlap_param_gather: False +delay_param_gather: False +scatter_gather_tensors_in_pipeline: True +local_rank: null +lazy_mpu_init: null +empty_unused_memory_level: 0 +standalone_embedding_stage: False +use_distributed_optimizer: False +nccl_communicator_config_path: null + +train_iters: null +eval_iters: 32 +eval_interval: 2000 +skip_train: False + +adlr_autoresume: False +adlr_autoresume_interval: 1000 + +# garbage collection +manual_gc: False +manual_gc_interval: 0 +manual_gc_eval: True + +tp_comm_overlap_cfg: null + +#data +data_path: null +split: '99,1,0' +train_data_path: null +valid_data_path: null +test_data_path: null +data_cache_path: null +mock_data: False +vocab_size: null +vocab_file: null +merge_file: null +vocab_extra_ids: 0 +seq_length: 4096 +encoder_seq_length: null +decoder_seq_length: null +retriever_seq_length: 256 +sample_rate: 1.0 +mask_prob: 0.15 +short_seq_prob: 0.1 +num_workers: 2 +tokenizer_type: GPTSentencePieceTokenizer +tokenizer_model: null +reset_position_ids: False +reset_attention_mask: False +eod_mask_loss: False +train_samples: 268554688 +dataloader_type: null + +#profile: +profile: False +profile_ranks: [0] +profile_step_end: 12 +profile_step_start: 10 + +#logging: +log_params_norm: True +log_num_zeros_in_grad: True +log_throughput: False +log_progress: False +timing_log_level: 0 +timing_log_option: minmax +tensorboard_log_interval: 1 +tensorboard_queue_size: 1000 +log_timers_to_tensorboard: False +log_batch_size_to_tensorboard: False +log_learning_rate_to_tensorboard: True +log_learning_rate_to_tensorboard: True +log_validation_ppl_to_tensorboard: False +log_memory_to_tensorboard: False +log_world_size_to_tensorboard: False +log_loss_scale_to_tensorboard: True +wandb_project: '' +wandb_exp_name: '' +wandb_save_dir: '' +enable_one_logger: False +one_logger_project: e2e-tracking +one_logger_entity: hwinf_dcm +one_logger_run_name: null +log_interval: 100 +tensorboard_dir: null diff --git a/Megatron-LM-core_r0.7.0.beta/examples/gpt3/train_gpt3_175b_distributed.sh b/Megatron-LM-core_r0.7.0.beta/examples/gpt3/train_gpt3_175b_distributed.sh new file mode 100755 index 0000000..ccba787 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/gpt3/train_gpt3_175b_distributed.sh @@ -0,0 +1,82 @@ +#!/bin/bash + +# Runs the "175B" parameter model + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NUM_NODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES)) + +CHECKPOINT_PATH=$1 # +TENSORBOARD_LOGS_PATH=$2 # +VOCAB_FILE=$3 #/gpt2-vocab.json +MERGE_FILE=$4 #/gpt2-merges.txt +DATA_PATH=$5 #_text_document + +DISTRIBUTED_ARGS=( + --nproc_per_node $GPUS_PER_NODE + --nnodes $NUM_NODES + --master_addr $MASTER_ADDR + --master_port $MASTER_PORT +) + +GPT_MODEL_ARGS=( + --num-layers 96 + --hidden-size 12288 + --num-attention-heads 96 + --seq-length 2048 + --max-position-embeddings 2048 +) + +TRAINING_ARGS=( + --micro-batch-size 1 + --global-batch-size 1536 + --rampup-batch-size 16 16 5859375 + --train-iters 500000 + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.95 + --init-method-std 0.006 + --clip-grad 1.0 + --fp16 + --lr 6.0e-5 + --lr-decay-style cosine + --min-lr 6.0e-6 + --lr-warmup-fraction .001 + --lr-decay-iters 430000 + --use-mcore-models +) + +MODEL_PARALLEL_ARGS=( + --tensor-model-parallel-size 8 + --pipeline-model-parallel-size 16 +) + +DATA_ARGS=( + --data-path $DATA_PATH + --vocab-file $VOCAB_FILE + --merge-file $MERGE_FILE + --split 949,50,1 +) + +EVAL_AND_LOGGING_ARGS=( + --log-interval 100 + --save-interval 10000 + --eval-interval 1000 + --save $CHECKPOINT_PATH + --load $CHECKPOINT_PATH + --eval-iters 10 + --tensorboard-dir $TENSORBOARD_LOGS_PATH +) + +torchrun ${DISTRIBUTED_ARGS[@]} pretrain_gpt.py \ + ${GPT_MODEL_ARGS[@]} \ + ${TRAINING_ARGS[@]} \ + ${MODEL_PARALLEL_ARGS[@]} \ + ${DATA_ARGS[@]} \ + ${EVAL_AND_LOGGING_ARGS[@]} diff --git a/Megatron-LM-core_r0.7.0.beta/examples/inference/README.md b/Megatron-LM-core_r0.7.0.beta/examples/inference/README.md new file mode 100644 index 0000000..7251a8d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/inference/README.md @@ -0,0 +1,132 @@ +# Megatron Model Optimization and Deployment + +## Installation +We recommend that users follow TensorRT-LLM's official installation guide to build it from source +and proceed with a containerized environment (`docker.io/tensorrt_llm/release:latest`): + +``` +git clone https://github.com/NVIDIA/TensorRT-LLM.git +cd TensorRT-LLM +git checkout v0.7.1 +make -C docker release_build +``` + +> **TROUBLE SHOOTING:** rather than copying each folder separately in `docker/Dockerfile.multi`, +> you may need to copy the entire dir as `COPY ./ /src/tensorrt_llm` since a `git submodule` is +> called later which requires `.git` to continue. + +Once the container is built, install `nvidia-ammo` and additional dependencies for sharded checkpoint support: +``` +pip install --no-cache-dir --extra-index-url https://pypi.nvidia.com nvidia-ammo +pip install zarr tensorstore==0.1.45 +``` +TensorRT-LLM quantization functionalities are currently packaged in `nvidia-ammo`. +You can find more documentation about `nvidia-ammo` in [TensorRT-LLM's quantization +examples](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/quantization). + +## Support Matrix + +The following matrix shows the current support for the PTQ + TensorRT-LLM export flow. + +| model | fp16 | int8_sq | fp8 | int4_awq | +|-----------------------------|------|---------| ----| -------- | +| nextllm-2b | x | x | x | | +| nemotron3-8b | x | | x | | +| nemotron3-15b | x | | x | | +| llama2-text-7b | x | x | x | TP2 | +| llama2-chat-70b | x | x | x | TP4 | + +Our PTQ + TensorRT-LLM flow has native support on MCore `GPTModel` with a mixed layer spec (native ParallelLinear +and Transformer-Engine Norm (`TENorm`). Note that this is not the default mcore gpt spec. You can still load the +following checkpoint formats with some remedy: + +| GPTModel | sharded | remedy arguments | +|-----------------------------------|---------|-----------------------------------------| +| megatron.legacy.model | | `--ammo-load-classic-megatron-to-mcore` | +| TE-Fused (default mcore gpt spec) | | `--ammo-convert-te-to-local-spec` | +| TE-Fused (default mcore gpt spec) | x | | + +> **TROUBLE SHOOTING:** If you are trying to load an unpacked `.nemo` sharded checkpoint, then typically you will +> need to adding `additional_sharded_prefix="model."` to `ammo_load_checkpoint()` since NeMo has an additional +> `model.` wrapper on top of the `GPTModel`. + +> **NOTE:** flag `--ammo-load-classic-megatron-to-mcore` may not work on all legacy checkpoint versions. + +## Examples + +> **NOTE:** we only provide a simple text generation script to test the generated TensorRT-LLM engines. For +> a production-level API server or enterprise support, see [NeMo](https://github.com/NVIDIA/NeMo) and TensorRT-LLM's +> backend for [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server). + +### nemotron3-8B FP8 Quantization and TensorRT-LLM Deployment +First download the nemotron checkpoint from https://huggingface.co/nvidia/nemotron-3-8b-base-4k, extract the +sharded checkpoint from the `.nemo` tarbal and fix the tokenizer file name. + +> **NOTE:** The following cloning method uses `ssh`, and assume you have registered the `ssh-key` in Hugging Face. +> If you are want to clone with `https`, then `git clone https://huggingface.co/nvidia/nemotron-3-8b-base-4k` with an access token. + +```sh +git lfs install +git clone git@hf.co:nvidia/nemotron-3-8b-base-4k +cd nemotron-3-8b-base-4k +tar -xvf Nemotron-3-8B-Base-4k.nemo +mv 586f3f51a9cf43bc9369bd53fa08868c_a934dc7c3e1e46a6838bb63379916563_3feba89c944047c19d5a1d0c07a85c32_mt_nlg_plus_multilingual_ja_zh_the_stack_frac_015_256k.model mt_nlg_plus_multilingual_ja_zh_the_stack_frac_015_256k.model +cd .. +``` + +Now launch the PTQ + TensorRT-LLM export script, +``` +bash examples/inference/ptq_trtllm_nemotron3_8b ./nemotron-3-8b-base-4k None +``` +By default, `cnn_dailymail` is used for calibration. The `GPTModel` will have quantizers for simulating the +quantization effect. The checkpoint will be saved optionally (with quantizers as additional states) and can +be restored for further evaluation. TensorRT-LLM engine is exported to `/tmo/ammo` by default. + +The script expects `${CHECKPOINT_DIR}` (`./nemotron-3-8b-base-4k`) to have the following structure: +``` +├── model_weights +│ ├── common.pt +│ ... +│ +├── model_config.yaml +├── mt_nlg_plus_multilingual_ja_zh_the_stack_frac_015_256k.model +``` + +> **NOTE:** The script is using `TP=8`. Change `$TP` in the script if your checkpoint has a different tensor +> model parallelism. + +> **KNOWN ISSUES:** The `mt_nlg_plus_multilingual_ja_zh_the_stack_frac_015_256k.model` in the checkpoint is for +> Megatron-LM's `GPTSentencePiece` tokenizer. +> For TensorRT-LLM, we are trying to load this tokenizer as a Hugging Face `T5Tokenizer` by changing +> some special tokens, `encode`, and `batch_decode`. As a result, the tokenizer behavior in TensorRT-LLM engine may +> not match exactly. + +> **TROUBLE SHOOTING:** If you are loading `.nemo` sharded checkpoint here, call +> `ammo_load_checkpoint(..., additional_sharded_prefix="model.")` with additional sharded prefix in +> `text_generation_ptq.py` to align the sharded keys. + +### llama2-text-7b INT8 SmoothQuant and TensorRT-LLM Deployment +> **NOTE:** Due to the LICENSE issue, we do not provide a MCore checkpoint to download. Users can follow +> the instruction in `docs/llama2.md` to convert the checkpoint to megatron classic `GPTModel` format and +> use `--ammo-load-classic-megatron-to-mcore` flag which will remap the checkpoint to the MCore `GPTModel` spec +> that we support. + +```sh +bash examples/inference/ptq_trtllm_llama_7b.sh ${CHECKPOINT_DIR} +``` + +The script expect `${CHECKPOINT_DIR}` to have the following structure: +``` +├── hf +│ ├── tokenizer.config +│ ├── tokenizer.model +│ ... +│ +├── iter_0000001 +│ ├── mp_rank_00 +│ ... +│ +├── latest_checkpointed_iteration.txt +``` +In short, other than the converted llama megatron checkpoint, also put the Hugging Face checkpoint inside as +the source of the tokenizer. diff --git a/Megatron-LM-core_r0.7.0.beta/examples/inference/ptq_trtllm_llama_7b.sh b/Megatron-LM-core_r0.7.0.beta/examples/inference/ptq_trtllm_llama_7b.sh new file mode 100644 index 0000000..4b285f9 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/inference/ptq_trtllm_llama_7b.sh @@ -0,0 +1,79 @@ +#!/bin/bash +DEFAULT_NAME="/checkpoints/llama2-text-7b_v0.2.0" +NAME="${1:-$DEFAULT_NAME}" + +DEFAULT_QUANT_CFG="int8_sq" +QUANT_CFG="${2:-$DEFAULT_QUANT_CFG}" + +# CHANGE THE FOLLOWING IF YOU MOUNT YOUR DATA AND CHECKPOINTS DIFFERENTLY IN THE CONTAINER. +TP="8" +PP=1 +INFERENCE_TP=${TP} +DECODER_TYPE="llama" +CHECKPOINT_LOAD_DIR="${NAME}" +TOKENIZER_MODEL="${CHECKPOINT_LOAD_DIR}/hf/tokenizer.model" + +# LLaMA2 text 7b has ffn_hidden_size 11008. int4_awq requires a block_size of 128 as a result the TP can at most be 2 +if [ "$QUANT_CFG" = "int4_awq" ]; then + INFERENCE_TP="2" +fi + +additional_options=" \ + --ammo-quant-cfg ${QUANT_CFG} \ + --ammo-load-classic-megatron-to-mcore \ + --decoder ${DECODER_TYPE} \ + --engine-dir /tmp/ammo \ + --max-input-len 2048 \ + --max-output-len 512 \ + --max-batch-size 8 \ + --inference-tensor-parallel ${INFERENCE_TP} " + +trtllm_options=" \ + --engine-dir /tmp/ammo \ + --tokenizer ${CHECKPOINT_LOAD_DIR}/hf \ + --max-output-len 512 " + +# DO NOT CHANGE THE SETTING BELOW UNLESS YOU KNOW WHAT YOU ARE DOING!!! +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +options=" \ + --disable-bias-linear \ + --swiglu \ + --untie-embeddings-and-output-weights \ + --use-rotary-position-embeddings \ + --normalization RMSNorm \ + --norm-epsilon 1e-5 \ + --no-position-embedding \ + --no-masked-softmax-fusion \ + --no-bias-gelu-fusion \ + --no-bias-dropout-fusion \ + --no-async-tensor-model-parallel-allreduce \ + --tensor-model-parallel-size ${TP} \ + --pipeline-model-parallel-size 1 \ + --num-layers 32 \ + --hidden-size 4096 \ + --ffn-hidden-size 11008 \ + --num-attention-heads 32 \ + --seq-length 2048 \ + --max-position-embeddings 4096 \ + --micro-batch-size 1 \ + --make-vocab-size-divisible-by 1 \ + --tokenizer-type Llama2Tokenizer \ + --tokenizer-model ${TOKENIZER_MODEL} \ + --save-interval 1000000 \ + --bf16 \ + --use-mcore-models " + +set +x + +# Precompile CUDA extentions +python -c "import ammo.torch.quantization.extensions as ext; print(ext.cuda_ext); print(ext.cuda_ext_fp8)" + +# Acquire launch configuration where variable launch_config will be set +launch_config="--nproc_per_node=${TP}" + +# Launch multi-process with torchrun +torchrun ${launch_config} examples/inference/text_generation_ptq.py ${options} ${additional_options} --load ${CHECKPOINT_LOAD_DIR} + +# This script is using mpi4py which will fork multiple processes. +python examples/inference/trtllm_text_generation.py ${trtllm_options} diff --git a/Megatron-LM-core_r0.7.0.beta/examples/inference/ptq_trtllm_nemotron3_8b.sh b/Megatron-LM-core_r0.7.0.beta/examples/inference/ptq_trtllm_nemotron3_8b.sh new file mode 100644 index 0000000..2a90367 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/inference/ptq_trtllm_nemotron3_8b.sh @@ -0,0 +1,75 @@ +#!/bin/bash +DEFAULT_NAME="/checkpoints/nemotron3-8b_v0.2.0" +NAME="${1:-$DEFAULT_NAME}" + +DEFAULT_QUANT_CFG="fp8" +QUANT_CFG="${2:-$DEFAULT_QUANT_CFG}" + +# CHANGE THE FOLLOWING IF YOU MOUNT YOUR DATA AND CHECKPOINTS DIFFERENTLY IN THE CONTAINER. +TP="8" +INFERENCE_TP=${TP} +DECODER_TYPE="gptnext" +CHECKPOINT_LOAD_DIR="${NAME}" +TOKENIZER_MODEL="${CHECKPOINT_LOAD_DIR}/mt_nlg_plus_multilingual_ja_zh_the_stack_frac_015_256k.model" + +if [ "$QUANT_CFG" = "int4_awq" ]; then + INFERENCE_TP="1" +fi + +additional_options=" \ + --ammo-quant-cfg ${QUANT_CFG} \ + --ammo-load-classic-megatron-to-mcore \ + --decoder ${DECODER_TYPE} \ + --engine-dir /tmp/ammo \ + --max-input-len 2048 \ + --max-output-len 512 \ + --max-batch-size 8 \ + --inference-tensor-parallel ${INFERENCE_TP} " + +trtllm_options=" \ + --engine-dir /tmp/ammo \ + --tokenizer ${TOKENIZER_MODEL} \ + --max-output-len 512 " + +# DO NOT CHANGE THE SETTING BELOW UNLESS YOU KNOW WHAT YOU ARE DOING!!! +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +options=" \ + --apply-layernorm-1p \ + --untie-embeddings-and-output-weights \ + --disable-bias-linear \ + --no-position-embedding \ + --use-rotary-position-embeddings \ + --rotary-percent 0.5 \ + --squared-relu \ + --attention-dropout 0.0 \ + --hidden-dropout 0.0 \ + --tensor-model-parallel-size ${TP} \ + --pipeline-model-parallel-size 1 \ + --num-layers 32 \ + --hidden-size 4096 \ + --num-attention-heads 32 \ + --seq-length 4096 \ + --max-position-embeddings 4096 \ + --micro-batch-size 1 \ + --tokenizer-type GPTSentencePieceTokenizer \ + --tokenizer-model ${TOKENIZER_MODEL} \ + --save-interval 1000000 \ + --load ${CHECKPOINT_LOAD_DIR} \ + --bf16 \ + --use-mcore-models " + +set +x + +# Precompile CUDA extentions +python -c "import ammo.torch.quantization.extensions as ext; print(ext.cuda_ext); print(ext.cuda_ext_fp8)" + +# Acquire launch configuration where variable launch_config will be set +launch_config="--nproc_per_node=${TP}" + +# Launch multi-process with torchrun +torchrun ${launch_config} examples/inference/text_generation_ptq.py ${options} ${additional_options} --load ${CHECKPOINT_LOAD_DIR} + +# This script is using mpi4py which will fork multiple processes. +python examples/inference/trtllm_text_generation.py ${trtllm_options} + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/inference/text_generation_ptq.py b/Megatron-LM-core_r0.7.0.beta/examples/inference/text_generation_ptq.py new file mode 100644 index 0000000..85aa4d1 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/inference/text_generation_ptq.py @@ -0,0 +1,273 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Sample Generate GPT.""" +import functools +import os +import sys +from pathlib import Path + +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))) + +import ammo.torch.quantization as atq +import torch +from datasets import load_dataset + +# [ModelOpt]: changing the default model provider to the AMMO version +from megatron.training import get_args, print_rank_0 +from megatron.training.checkpointing import load_checkpoint, save_checkpoint +from megatron.core import mpu +from megatron.core.dist_checkpointing import load +from megatron.inference.arguments import add_ammo_args +from megatron.inference.gpt.model_provider import model_provider +from megatron.training.initialize import initialize_megatron +from megatron.inference.text_generation import generate_and_post_process +from megatron.training import get_model +from megatron.training.utils import unwrap_model + +QUANT_CFG_CHOICES = { + "int8": atq.INT8_DEFAULT_CFG, + "int8_sq": atq.INT8_SMOOTHQUANT_CFG, + "fp8": atq.FP8_DEFAULT_CFG, + "int4_awq": atq.INT4_AWQ_CFG, + "w4a8_awq": atq.W4A8_AWQ_BETA_CFG, +} + + +def add_trtllm_args(parser): + """Add additional arguments for TensorRT-LLM.""" + group = parser.add_argument_group(title="trtllm") + + group.add_argument( + "--engine-dir", type=str, help="The output TensorRT-LLM engine dir.", + ) + group.add_argument( + "--decoder", type=str, choices=["gptnext", 'llama'], help="The decoder type of the model.", + ) + group.add_argument("--max-input-len", type=int, help="Max input sequence length.", default=2048) + group.add_argument( + "--max-output-len", type=int, help="Max output sequence length.", default=512 + ) + group.add_argument("--max-batch-size", type=int, help="Max batch size.", default=32) + group.add_argument( + "--inference-tensor-parallel", + type=int, + help="Tensor parallel for the inference time, can be different from the training config.", + default=1, + ) + + +def add_text_generate_ptq_args(parser): + """Add additional arguments for AMMO text generation PTQ.""" + group = parser.add_argument_group(title='AMMO text generation ptq') + group.add_argument( + "--calib-dataset", + type=str, + default="cnn_dailymail", + help="Calibration datasets from HuggingFace datasets.", + ) + group.add_argument( + "--calib-steps", type=int, default=512, help="Steps to perform atq.quantize calibration." + ) + parser.add_argument( + "--prompts", + type=str, + default=( + "Born in north-east France, Soyer trained as a|Born in California, Soyer trained as a" + ), + help="Input texts. Please use | to separate different batches.", + ) + add_ammo_args(parser) + add_trtllm_args(parser) + return parser + + +def get_calib_dataloader( + data="cnn_dailymail", batch_size=4, calib_size=512, max_sequence_length=512 +): + if data == "wikitext": + dataset = load_dataset("wikitext", "wikitext-103-v1", split="train") + text_column = "text" + elif data == "cnn_dailymail": + dataset = load_dataset("cnn_dailymail", name="3.0.0", split="train") + text_column = "article" + + calib_size = max(min(len(dataset), calib_size), batch_size) + for i in range(calib_size // batch_size): + batch = dataset[i * batch_size : (i + 1) * batch_size][text_column] + for j in range(len(batch)): + batch[j] = batch[j][:max_sequence_length] + yield batch + + +def ammo_load_checkpoint( + model, optimizer=None, opt_param_scheduler=None, strict=True, additional_sharded_prefix="" +): + """Load a megatron checkpoint depending its format. + + Args: + model: MCoreGPTModel instance + optimizer: Megatron optimizer instance + opt_param_scheduler: Megatron scheduler instance + strict: if True, no extra or missing keys are allowed while loading the state_dict + additional_sharded_prefix (str): Append additional prefix to align the sharded checkpoint keys. When loading + an .nemo sharded checkpoint, this is usually `model.`. Otherwise, this is typically an empty string. + """ + + def _remove_prefix_state_dict_pre_hook( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, + ): + """Pytorch _load_state_dict_pre_hook to remap the state_dict with the additional sharded prefix.""" + if additional_sharded_prefix is None: + return + key_rewrite_list = [] + for key, _ in state_dict.items(): + if key.startswith(additional_sharded_prefix): + key_rewrite_list.append(key) + for old_key in key_rewrite_list: + new_key = old_key[len(additional_sharded_prefix) :] + state_dict[new_key] = state_dict.pop(old_key) + + args = get_args() + load_dir = args.load + + shared_model_state_dir = "model_weights" + sharded_load_dir = Path(load_dir + "/" + shared_model_state_dir) + + if sharded_load_dir.exists() and optimizer is None and opt_param_scheduler is None: + unwrapped_model = unwrap_model(model) + shareded_state_dict = unwrapped_model[0].sharded_state_dict( + prefix=additional_sharded_prefix + ) + if additional_sharded_prefix: + unwrapped_model[0]._register_load_state_dict_pre_hook( + _remove_prefix_state_dict_pre_hook + ) + unwrapped_model[0].load_state_dict(load(shareded_state_dict, sharded_load_dir)) + else: + _ = load_checkpoint(model, optimizer, opt_param_scheduler, strict=strict) + + +if __name__ == "__main__": + initialize_megatron( + extra_args_provider=add_text_generate_ptq_args, + args_defaults={ + 'tokenizer_type': 'GPT2BPETokenizer', + 'no_load_rng': True, + 'no_load_optim': True, + }, + ) + + args = get_args() + if args.num_layers_per_virtual_pipeline_stage is not None: + print("Interleaved pipeline schedule is not yet supported for text generation.") + exit() + + text_generation_model_provider = functools.partial(model_provider, parallel_output=False) + model = get_model(text_generation_model_provider, wrap_with_ddp=False) + assert len(model) == 1, "Above condition should have caught this" + + if args.load is not None: + _ = ammo_load_checkpoint( + model, + None, + None, + strict=not args.untie_embeddings_and_output_weights, + additional_sharded_prefix="model.", + ) + else: + print_rank_0("WARNING: No checkpoint is loaded for PTQ! The process will still continue.") + + all_prompts = args.prompts.split("|") + + def custom_prompt_forward_loop_func(): + for prompt in all_prompts: + if mpu.is_pipeline_first_stage() and mpu.get_tensor_model_parallel_rank() == 0: + ( + prompts_plus_generations, + prompts_plus_generations_segments, + logprobs, + _, + ) = generate_and_post_process( + model[0], + prompts=[prompt], + tokens_to_generate=128, + return_output_log_probs=True, + temperature=1.0, + ) + print_rank_0(prompts_plus_generations) + else: + generate_and_post_process(model[0]) + + def hf_dataset_forword_loop_func(): + dataloader = get_calib_dataloader(args.calib_dataset, calib_size=args.calib_steps) + for prompts in dataloader: + if mpu.is_pipeline_first_stage() and mpu.get_tensor_model_parallel_rank() == 0: + ( + prompts_plus_generations, + prompts_plus_generations_segments, + logprobs, + _, + ) = generate_and_post_process( + model[0], + prompts=prompts, + tokens_to_generate=0, + return_output_log_probs=True, + temperature=1.0, + ) + else: + generate_and_post_process(model[0]) + + ptq_forward_loop_func = custom_prompt_forward_loop_func + if args.calib_dataset is not None: + ptq_forward_loop_func = hf_dataset_forword_loop_func + + if args.ammo_quant_cfg in QUANT_CFG_CHOICES: + atq_config = QUANT_CFG_CHOICES[args.ammo_quant_cfg] + if "awq" in args.ammo_quant_cfg: + weight_quantizer = atq_config["quant_cfg"]["*weight_quantizer"] # type: ignore + if isinstance(weight_quantizer, list): + weight_quantizer = weight_quantizer[0] + weight_quantizer["block_sizes"][-1] = 128 + atq_config["quant_cfg"]["*.output_layer.*"] = {"enable": False} + print_rank_0("atq.quantize: output_layer quantization is disable") + atq.quantize(model[0], atq_config, ptq_forward_loop_func) + custom_prompt_forward_loop_func() + if args.save: + save_checkpoint(1, model, None, None) + else: + custom_prompt_forward_loop_func() + + if args.engine_dir: + from ammo.deploy.llm import model_config_to_tensorrt_llm + from ammo.torch.export import torch_to_model_config + + assert args.decoder in ["gptnext", "llama"], f"Decoder type {args.decoder} not supported." + + Path(args.engine_dir).mkdir(parents=True, exist_ok=True) + + print_rank_0("Exporting model_configs for TRT LLM.") + model = unwrap_model(model) + model = model[0] + + # In TRT LLM, squared relu activation does not support bf16. So we use fp16 by default. + model_configs = torch_to_model_config( + model, + args.decoder, + torch.float16, + inference_tensor_parallel=args.inference_tensor_parallel, + ) + + print_rank_0("Building TRT LLM engines.") + for model_config in model_configs: + model_config_to_tensorrt_llm( + model_config, + args.engine_dir, + max_input_len=args.max_input_len, + max_output_len=args.max_output_len, + max_batch_size=args.max_batch_size, + max_beam_width=1, + num_build_workers=1, + inflight_batching=False, + enable_sparsity=False, + ) + print_rank_0(f"TRT LLM engines saved to {args.engine_dir}") diff --git a/Megatron-LM-core_r0.7.0.beta/examples/inference/trtllm_text_generation.py b/Megatron-LM-core_r0.7.0.beta/examples/inference/trtllm_text_generation.py new file mode 100644 index 0000000..c6c0098 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/inference/trtllm_text_generation.py @@ -0,0 +1,93 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""An example script to run the tensorrt_llm engine.""" + +import argparse +from pathlib import Path + +import numpy as np +import torch +from ammo.deploy.llm import generate, load, unload +from transformers import AutoTokenizer, T5Tokenizer + + +class CustomSentencePieceTokenizer(T5Tokenizer): + """This is a custom GPTSentencePiece Tokenizer modified from the T5Tokenizer. + + Note: + The modification is kept minimal to make `encode` and `batch_decode` working + properly (used in TensorRT-LLM engine). Other functions have not been tested. + """ + + def __init__(self, model): + super().__init__(model, extra_ids=0, bos_token="", pad_token="") + + def encode(self, text, add_special_tokens: bool = True, **kwargs): + return self.sp_model.encode_as_ids(text) + + def batch_decode(self, sequences, skip_special_tokens: bool = False, **kwargs): + if isinstance(sequences, np.ndarray) or torch.is_tensor(sequences): + sequences = sequences.tolist() + return self.sp_model.decode(sequences) + + +def parse_arguments(): + parser = argparse.ArgumentParser() + parser.add_argument("--tokenizer", type=str, default="") + parser.add_argument("--max-output-len", type=int, default=100) + parser.add_argument("--engine-dir", type=str, default="/tmp/ammo") + parser.add_argument( + "--input-texts", + type=str, + default=( + "Born in north-east France, Soyer trained as a|Born in California, Soyer trained as a" + ), + help="Input texts. Please use | to separate different batches.", + ) + parser.add_argument("--max-num-beams", type=int, default=1) + parser.add_argument("--profiler-output", type=str, default="") + return parser.parse_args() + + +def run(args): + tokenizer_path = Path(args.tokenizer) + + if tokenizer_path.is_dir(): + # For llama models, use local HF tokenizer which is a folder. + tokenizer = AutoTokenizer.from_pretrained(args.tokenizer, trust_remote_code=True) + elif tokenizer_path.is_file(): + # For nextllm and nemotron models, use local Megatron GPTSentencePiece tokenizer which is a model file. + tokenizer = CustomSentencePieceTokenizer(args.tokenizer) + else: + raise ValueError( + "arg.tokenizer must be a dir to a hf tokenizer checkpoint for llama or a SentencePiece .model file for gptnext" + ) + + if not hasattr(args, "profiler_output"): + args.profiler_output = "" + + input_texts = args.input_texts.split("|") + assert input_texts, "input_text not specified" + print(input_texts) + + free_memory_before = torch.cuda.mem_get_info() + + host_context = load( + tokenizer=tokenizer, engine_dir=args.engine_dir, num_beams=args.max_num_beams + ) + torch.cuda.cudart().cudaProfilerStart() + outputs = generate(input_texts, args.max_output_len, host_context, None, args.profiler_output) + print(outputs) + torch.cuda.cudart().cudaProfilerStop() + + free_memory_after = torch.cuda.mem_get_info() + print( + f"Use GPU memory: {(free_memory_before[0] - free_memory_after[0]) / 1024 / 1024 / 1024} GB" + ) + + unload(host_context) + + +if __name__ == "__main__": + args = parse_arguments() + run(args) diff --git a/Megatron-LM-core_r0.7.0.beta/examples/merge_mp_bert.sh b/Megatron-LM-core_r0.7.0.beta/examples/merge_mp_bert.sh new file mode 100755 index 0000000..1383433 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/merge_mp_bert.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +TENSOR_MODEL_PARALLEL_SIZE=2 + +VOCAB_FILE=bert-vocab.txt +CHECKPOINT_PATH=checkpoints/bert_345m + +WORLD_SIZE=$TENSOR_MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \ + --model-type BERT \ + --tensor-model-parallel-size $TENSOR_MODEL_PARALLEL_SIZE \ + --tokenizer-type BertWordPieceLowerCase \ + --vocab-file $VOCAB_FILE \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --load $CHECKPOINT_PATH diff --git a/Megatron-LM-core_r0.7.0.beta/examples/msdp/README.md b/Megatron-LM-core_r0.7.0.beta/examples/msdp/README.md new file mode 100644 index 0000000..8ff9509 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/msdp/README.md @@ -0,0 +1,5 @@ + +# Multi-Stage Prompting for Knowledgeable Dialogue Generation + +This directory contains all the scripts of multi-stage prompting for knowledgeable dialogue generation that includes data preparation, and knowledge and response generations. More details are available on [`knowledgeable task directory`](../../tasks/msdp). + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/msdp/data_processing.sh b/Megatron-LM-core_r0.7.0.beta/examples/msdp/data_processing.sh new file mode 100644 index 0000000..37a6512 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/msdp/data_processing.sh @@ -0,0 +1,83 @@ +#!/bin/bash + +# Data preparation for our framework: preprocessing the WoW and WoI datasets +# The datasets can be downloaded through the following links: +# WoW: https://parl.ai/projects/wizard_of_wikipedia/ +# WoI: https://parl.ai/projects/sea/ + +DIR=`pwd` +# Before running the preprocessing, please download +# the wizard of wikipedia and wizard datasets +WOW_DATA_FOLDER= +WOI_DATA_FOLDER= + +# We provide examples for processing the raw data from Wizard of Wikipedia +# Processing the train dataset (train.json) +python ${DIR}/tasks/msdp/preprocessing.py \ + --func process_wow_dataset \ + --raw_file ${WOW_DATA_FOLDER}/train.json \ + --processed_file ${WOW_DATA_FOLDER}/train_processed.txt + +# Processing test seen dataset (test_random_split.json) +python ${DIR}/tasks/msdp/preprocessing.py \ + --func process_wow_dataset \ + --raw_file ${WOW_DATA_FOLDER}/test_random_split.json \ + --processed_file ${WOW_DATA_FOLDER}/testseen_processed.txt \ + --knwl_ref_file ${WOW_DATA_FOLDER}/output_testseen_knowledge_reference.txt \ + --resp_ref_file ${WOW_DATA_FOLDER}/output_testseen_response_reference.txt + +# processing test unseen dataset (test_topic_split.json) +python ${DIR}/tasks/msdp/preprocessing.py \ + --func process_wow_dataset \ + --raw_file ${WOW_DATA_FOLDER}/test_topic_split.json \ + --processed_file ${WOW_DATA_FOLDER}/testunseen_processed.txt \ + --knwl_ref_file ${WOW_DATA_FOLDER}/output_testunseen_knowledge_reference.txt \ + --resp_ref_file ${WOW_DATA_FOLDER}/output_testunseen_response_reference.txt + + +# We provide the following script to process the raw data from Wizard of Internet +# Processing the test dataset (test.jsonl) +python ${DIR}/tasks/msdp/preprocessing.py \ + --func process_woi_dataset \ + --raw_file ${WOI_DATA_FOLDER}/test.jsonl \ + --processed_file ${WOI_DATA_FOLDER}/test_processed.txt \ + --knwl_ref_file ${WOI_DATA_FOLDER}/output_test_knowledge_reference.txt \ + --resp_ref_file ${WOI_DATA_FOLDER}/output_test_response_reference.txt + + +# Get the knowledge generation prompts for the each test dataset in WoW and WoI +MODEL_FILE= +# WoW test seen +python ${DIR}/tasks/msdp/preprocessing.py \ + --func get_knwl_gen_prompts \ + --test_file ${WOW_DATA_FOLDER}/testseen_processed.txt \ + --train_file ${WOW_DATA_FOLDER}/train_processed.txt \ + --model_file ${MODEL_FILE} \ + --processed_file ${WOW_DATA_FOLDER}/output_testseen_knowledge_prompts.json \ + --data_type wow_seen + +# WoW test unseen +python ${DIR}/tasks/msdp/preprocessing.py \ + --func get_knwl_gen_prompts \ + --test_file ${WOW_DATA_FOLDER}/testunseen_processed.txt \ + --train_file ${WOW_DATA_FOLDER}/train_processed.txt \ + --model_file ${MODEL_FILE} \ + --processed_file ${WOW_DATA_FOLDER}/output_testunseen_knowledge_prompts.json \ + --data_type wow_unseen + +# WoI +python ${DIR}/tasks/msdp/preprocessing.py \ + --func get_knwl_gen_prompts \ + --test_file ${WOI_DATA_FOLDER}/test_processed.txt \ + --train_file ${WOW_DATA_FOLDER}/train_processed.txt \ + --model_file ${MODEL_FILE} \ + --processed_file ${WOI_DATA_FOLDER}/output_test_knowledge_prompts.json \ + --data_type woi + + +# Get the response generation prompts (can be applied for all the test datasets) +python ${DIR}/tasks/msdp/preprocessing.py \ + --func get_resp_gen_prompts \ + --train_file ${WOW_DATA_FOLDER}/train_processed.txt \ + --processed_file ${WOW_DATA_FOLDER}/output_response_prompts.txt + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/msdp/eval_knwl_generation.sh b/Megatron-LM-core_r0.7.0.beta/examples/msdp/eval_knwl_generation.sh new file mode 100644 index 0000000..8fc2fff --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/msdp/eval_knwl_generation.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +######################### +# Evaluate the F1 scores. +######################### + +WORLD_SIZE=1 +DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +MODEL_GEN_PATH= \ + (e.g., /testseen_knowledge_generations.txt) +GROUND_TRUTH_PATH= \ + (e.g., /testseen_knowledge_reference.txt) + +python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/msdp/main.py \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 2048 \ + --max-position-embeddings 2048 \ + --micro-batch-size 4 \ + --task MSDP-EVAL-F1 \ + --guess-file ${MODEL_GEN_PATH} \ + --answer-file ${GROUND_TRUTH_PATH} + + +############################################ +# Evaluate BLEU, METEOR, and ROUGE-L scores. +############################################ + +# We follow the nlg-eval (https://github.com/Maluuba/nlg-eval) to +# evaluate the BLEU, METEOR, and ROUGE-L scores. + +# To evaluate on these metrics, please setup the environments based on +# the nlg-eval github, and run the corresponding evaluation commands. + +nlg-eval \ + --hypothesis= \ + --references= diff --git a/Megatron-LM-core_r0.7.0.beta/examples/msdp/eval_resp_generation.sh b/Megatron-LM-core_r0.7.0.beta/examples/msdp/eval_resp_generation.sh new file mode 100644 index 0000000..3ce87e0 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/msdp/eval_resp_generation.sh @@ -0,0 +1,64 @@ +#!/bin/bash + +######################### +# Evaluate the F1 scores. +######################### + +WORLD_SIZE=1 +DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +MODEL_GEN_PATH= \ + (e.g., /testseen_response_generations.txt) +GROUND_TRUTH_PATH= \ + (e.g., /testseen_response_reference.txt) + +python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/msdp/main.py \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 2048 \ + --max-position-embeddings 2048 \ + --micro-batch-size 4 \ + --task MSDP-EVAL-F1 \ + --guess-file ${MODEL_GEN_PATH} \ + --answer-file ${GROUND_TRUTH_PATH} + + +########################## +# Evaluate the KF1 scores. +########################## + +MODEL_GEN_PATH= \ + (e.g., /testseen_response_generations.txt) +GROUND_TRUTH_PATH= \ + (e.g., /testseen_knowledge_reference.txt) + +python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/msdp/main.py \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 2048 \ + --max-position-embeddings 2048 \ + --micro-batch-size 4 \ + --task MSDP-EVAL-F1 \ + --guess-file ${MODEL_GEN_PATH} \ + --answer-file ${GROUND_TRUTH_PATH} + + +############################################ +# Evaluate BLEU, METEOR, and ROUGE-L scores. +############################################ + +# We follow the nlg-eval (https://github.com/Maluuba/nlg-eval) to +# evaluate the BLEU, METEOR, and ROUGE-L scores. + +# To evaluate on these metrics, please setup the environments based on +# the nlg-eval github, and run the corresponding evaluation commands. + +nlg-eval \ + --hypothesis= \ + --references= diff --git a/Megatron-LM-core_r0.7.0.beta/examples/msdp/prep_resp_gen.sh b/Megatron-LM-core_r0.7.0.beta/examples/msdp/prep_resp_gen.sh new file mode 100644 index 0000000..5f20272 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/msdp/prep_resp_gen.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +# Preparing the input file for the response generation (second-stage prompting) + +DIR=`pwd` + +TEST_FILE= \ + (e.g., /testseen_processed.txt) +KNOWLEDGE_FILE= \ + (e.g., /testseen_knowledge_generations.txt) +PROCESSED_FILE= \ + (e.g., /testseen_processed_with_generated_knowledge.txt) + +python ${DIR}/tasks/msdp/preprocessing.py \ + --func prepare_input \ + --test_file ${TEST_FILE} \ + --knwl_gen_file ${KNOWLEDGE_FILE} \ + --processed_file ${PROCESSED_FILE} diff --git a/Megatron-LM-core_r0.7.0.beta/examples/msdp/prompt_knwl_gen.sh b/Megatron-LM-core_r0.7.0.beta/examples/msdp/prompt_knwl_gen.sh new file mode 100644 index 0000000..12e0cc5 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/msdp/prompt_knwl_gen.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +# Stage-1: Prompt a pretrained language model to generate the context-relevant knowledge +# The input contains prompts and current dialogue context, the output is the relevant knowledge +# The size of the pretrained language model is 357M + +WORLD_SIZE=8 + +DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +CHECKPOINT_PATH= (e.g., /357m) +VOCAB_PATH= (e.g., /gpt2-vocab.json) +MERGE_PATH= (e.g., /gpt2-merges.txt) +INPUT_PATH= \ + (e.g., /testseen_processed.txt) +PROMPT_PATH= \ + (e.g., /testseen_knowledge_prompts.json) +OUTPUT_PATH= \ + (e.g., /testseen_knowledge_generations.txt) + +python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/msdp/main.py \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 2048 \ + --max-position-embeddings 2048 \ + --micro-batch-size 1 \ + --vocab-file ${VOCAB_PATH} \ + --merge-file ${MERGE_PATH} \ + --load ${CHECKPOINT_PATH} \ + --fp16 \ + --DDP-impl torch \ + --tokenizer-type GPT2BPETokenizer \ + --sample-input-file ${INPUT_PATH} \ + --sample-output-file ${OUTPUT_PATH} \ + --prompt-file ${PROMPT_PATH} \ + --prompt-type knowledge \ + --num-prompt-examples 10 \ + --task MSDP-PROMPT + +# NOTE: If you use api for the model generation, please use +# the "--api-prompt" flag (setting this value as True). diff --git a/Megatron-LM-core_r0.7.0.beta/examples/msdp/prompt_resp_gen.sh b/Megatron-LM-core_r0.7.0.beta/examples/msdp/prompt_resp_gen.sh new file mode 100644 index 0000000..b836d7f --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/msdp/prompt_resp_gen.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +# Stage-2: Prompt a pretrained language model to generate the corresponding response +# The input contains prompts, current dialogue context, and generated knowledge in Stage-1 +# The output is the corresponding response. +# The size of the pretrained language model is 357M + +WORLD_SIZE=8 + +DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +CHECKPOINT_PATH= (e.g., /357m) +VOCAB_PATH= (e.g., /gpt2-vocab.json) +MERGE_PATH= (e.g., /gpt2-merges.txt) +INPUT_PATH= (e.g., /testseen_processed.txt) +PROMPT_PATH= \ + (e.g., /response_prompts.txt) +OUTPUT_PATH= \ + (e.g., /output_testseen_response_generations.txt) + +python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/msdp/main.py \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 2048 \ + --max-position-embeddings 2048 \ + --micro-batch-size 1 \ + --vocab-file ${VOCAB_PATH} \ + --merge-file ${MERGE_PATH} \ + --load ${CHECKPOINT_PATH} \ + --fp16 \ + --DDP-impl torch \ + --tokenizer-type GPT2BPETokenizer \ + --sample-input-file ${INPUT_PATH} \ + --sample-output-file ${OUTPUT_PATH} \ + --prompt-file ${PROMPT_PATH} \ + --prompt-type response \ + --num-prompt-examples 20 \ + --task MSDP-PROMPT + +# NOTE: If you use api for the model generation, please use +# the "--api-prompt" flag (setting this value as True). diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_bert.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_bert.sh new file mode 100755 index 0000000..3877b1a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_bert.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +CHECKPOINT_PATH= +VOCAB_FILE=/bert-vocab.txt +DATA_PATH=_text_sentence + +BERT_ARGS=" + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --micro-batch-size 4 \ + --global-batch-size 8 \ + --lr 0.0001 \ + --train-iters 2000000 \ + --lr-decay-iters 990000 \ + --lr-decay-style linear \ + --min-lr 0.00001 \ + --weight-decay 1e-2 \ + --lr-warmup-fraction .01 \ + --clip-grad 1.0 \ + --fp16 +" + +DATA_ARGS=" + --data-path $DATA_PATH \ + --vocab-file $VOCAB_FILE \ + --split 949,50,1 +" + +OUTPUT_ARGS=" + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 +" + +torchrun pretrain_bert.py \ + $BERT_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_bert_distributed.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_bert_distributed.sh new file mode 100755 index 0000000..2e0209a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_bert_distributed.sh @@ -0,0 +1,63 @@ +#!/bin/bash + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) + +CHECKPOINT_PATH= +VOCAB_FILE=/bert-vocab.txt +DATA_PATH=_text_sentence + +DISTRIBUTED_ARGS=" + --nproc_per_node $GPUS_PER_NODE \ + --nnodes $NNODES \ + --node_rank $NODE_RANK \ + --master_addr $MASTER_ADDR \ + --master_port $MASTER_PORT +" + +BERT_ARGS=" + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --micro-batch-size 4 \ + --global-batch-size 32 \ + --lr 0.0001 \ + --train-iters 1000000 \ + --lr-decay-iters 990000 \ + --lr-decay-style linear \ + --min-lr 1.0e-5 \ + --weight-decay 1e-2 \ + --lr-warmup-fraction .01 \ + --clip-grad 1.0 \ + --fp16 +" + +DATA_ARGS=" + --data-path $DATA_PATH \ + --vocab-file $VOCAB_FILE \ + --split 949,50,1 +" + +OUTPUT_ARGS=" + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 +" + +torchrun $DISTRIBUTED_ARGS pretrain_bert.py \ + $BERT_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --distributed-backend nccl \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_bert_distributed_with_mp.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_bert_distributed_with_mp.sh new file mode 100755 index 0000000..93a22c9 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_bert_distributed_with_mp.sh @@ -0,0 +1,65 @@ +#!/bin/bash + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) + +CHECKPOINT_PATH= +VOCAB_FILE=/bert-vocab.txt +DATA_PATH=_text_sentence + +DISTRIBUTED_ARGS=" + --nproc_per_node $GPUS_PER_NODE \ + --nnodes $NNODES \ + --node_rank $NODE_RANK \ + --master_addr $MASTER_ADDR \ + --master_port $MASTER_PORT +" + +BERT_ARGS=" + --tensor-model-parallel-size 2 \ + --pipeline-model-parallel-size 2 \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --micro-batch-size 2 \ + --global-batch-size 16 \ + --lr 0.0001 \ + --train-iters 1000000 \ + --lr-decay-iters 990000 \ + --lr-decay-style linear \ + --min-lr 1.0e-5 \ + --weight-decay 1e-2 \ + --lr-warmup-fraction .01 \ + --clip-grad 1.0 \ + --fp16 +" + +DATA_ARGS=" + --data-path $DATA_PATH \ + --vocab-file $VOCAB_FILE \ + --split 949,50,1 +" + +OUTPUT_ARGS=" + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 +" + +torchrun $DISTRIBUTED_ARGS pretrain_bert.py \ + $BERT_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --distributed-backend nccl \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_gpt.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_gpt.sh new file mode 100755 index 0000000..1d4b20f --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_gpt.sh @@ -0,0 +1,50 @@ +#!/bin/bash + +# Runs the "345M" parameter model + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +CHECKPOINT_PATH= +VOCAB_FILE=/gpt2-vocab.json +MERGE_FILE=/gpt2-merges.txt +DATA_PATH=_text_document + +GPT_ARGS=" + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 1024 \ + --max-position-embeddings 1024 \ + --micro-batch-size 4 \ + --global-batch-size 8 \ + --lr 0.00015 \ + --train-iters 500000 \ + --lr-decay-iters 320000 \ + --lr-decay-style cosine \ + --min-lr 1.0e-5 \ + --weight-decay 1e-2 \ + --lr-warmup-fraction .01 \ + --clip-grad 1.0 \ + --fp16 +" + +DATA_ARGS=" + --data-path $DATA_PATH \ + --vocab-file $VOCAB_FILE \ + --merge-file $MERGE_FILE \ + --split 949,50,1 +" + +OUTPUT_ARGS=" + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 +" + +torchrun pretrain_gpt.py \ + $GPT_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_gpt3_175B.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_gpt3_175B.sh new file mode 100755 index 0000000..c26b8ee --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_gpt3_175B.sh @@ -0,0 +1,64 @@ +#!/bin/bash + + +#SBATCH --nodes=128 --exclusive --ntasks-per-node=8 --job-name=megatron_gpt3_175b + + +DIR=`pwd` +DATETIME=`date +'date_%y-%m-%d_time_%H-%M-%S'` +mkdir -p $DIR/logs + + +DATASET_1="" +DATASET_2="" +DATASET_3="" +DATASET="0.2 ${DATASET_1} 0.3 ${DATASET_2} 0.5 ${DATASET_3}" + + +options=" \ + --tensor-model-parallel-size 8 \ + --pipeline-model-parallel-size 16 \ + --num-layers 96 \ + --hidden-size 12288 \ + --num-attention-heads 96 \ + --seq-length 2048 \ + --max-position-embeddings 2048 \ + --micro-batch-size 1 \ + --global-batch-size 1536 \ + --rampup-batch-size 16 16 5859375 \ + --train-samples 146484375 \ + --lr-decay-samples 126953125 \ + --lr-warmup-samples 183105 \ + --lr 6.0e-5 \ + --min-lr 6.0e-6 \ + --lr-decay-style cosine \ + --log-interval 10 \ + --eval-iters 40 \ + --eval-interval 1000 \ + --data-path ${DATASET} \ + --vocab-file \ + --merge-file \ + --save-interval 1000 \ + --save \ + --load \ + --split 98,2,0 \ + --clip-grad 1.0 \ + --weight-decay 0.1 \ + --adam-beta1 0.9 \ + --adam-beta2 0.95 \ + --init-method-std 0.006 \ + --tensorboard-dir \ + --fp16 " + + +run_cmd="python -u ${DIR}/pretrain_gpt.py $@ ${options}" + + +srun -l \ + --container-image "nvcr.io/nvidia/pytorch:20.12-py3" \ + --container-mounts "" \ + --output=$DIR/logs/%x_%j_$DATETIME.log sh -c "${run_cmd}" + + +set +x + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_gpt_distributed.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_gpt_distributed.sh new file mode 100755 index 0000000..effce20 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_gpt_distributed.sh @@ -0,0 +1,67 @@ +#!/bin/bash + +# Runs the "345M" parameter model + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) + +CHECKPOINT_PATH= +VOCAB_FILE=/gpt2-vocab.json +MERGE_FILE=/gpt2-merges.txt +DATA_PATH=_text_document + +DISTRIBUTED_ARGS=" + --nproc_per_node $GPUS_PER_NODE \ + --nnodes $NNODES \ + --node_rank $NODE_RANK \ + --master_addr $MASTER_ADDR \ + --master_port $MASTER_PORT +" + +GPT_ARGS=" + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 1024 \ + --max-position-embeddings 1024 \ + --micro-batch-size 8 \ + --global-batch-size 64 \ + --lr 0.00015 \ + --train-iters 500000 \ + --lr-decay-iters 320000 \ + --lr-decay-style cosine \ + --min-lr 1.0e-5 \ + --weight-decay 1e-2 \ + --lr-warmup-fraction .01 \ + --clip-grad 1.0 \ + --fp16 +" + +DATA_ARGS=" + --data-path $DATA_PATH \ + --vocab-file $VOCAB_FILE \ + --merge-file $MERGE_FILE \ + --split 949,50,1 +" + +OUTPUT_ARGS=" + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 +" + +torchrun $DISTRIBUTED_ARGS pretrain_gpt.py \ + $GPT_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --distributed-backend nccl \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_gpt_distributed_with_mp.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_gpt_distributed_with_mp.sh new file mode 100755 index 0000000..470a256 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_gpt_distributed_with_mp.sh @@ -0,0 +1,71 @@ +#!/bin/bash + +# Runs the "345M" parameter model + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) + +CHECKPOINT_PATH= +VOCAB_FILE=/gpt2-vocab.json +MERGE_FILE=/gpt2-merges.txt +DATA_PATH=_text_document + +DISTRIBUTED_ARGS=" + --nproc_per_node $GPUS_PER_NODE \ + --nnodes $NNODES \ + --node_rank $NODE_RANK \ + --master_addr $MASTER_ADDR \ + --master_port $MASTER_PORT +" + +GPT_ARGS=" + --tensor-model-parallel-size 2 \ + --pipeline-model-parallel-size 2 \ + --sequence-parallel \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 1024 \ + --max-position-embeddings 1024 \ + --micro-batch-size 4 \ + --global-batch-size 16 \ + --lr 0.00015 \ + --train-iters 500000 \ + --lr-decay-iters 320000 \ + --lr-decay-style cosine \ + --min-lr 1.0e-5 \ + --weight-decay 1e-2 \ + --lr-warmup-fraction .01 \ + --clip-grad 1.0 \ + --fp16 +" + +DATA_ARGS=" + --data-path $DATA_PATH \ + --vocab-file $VOCAB_FILE \ + --merge-file $MERGE_FILE \ + --split 949,50,1 +" + +OUTPUT_ARGS=" + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 +" + +torchrun $DISTRIBUTED_ARGS pretrain_gpt.py \ + $GPT_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --distributed-backend nccl \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_ict.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_ict.sh new file mode 100755 index 0000000..8cba0f0 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_ict.sh @@ -0,0 +1,44 @@ +#! /bin/bash + +# Runs the "217M" parameter biencoder model for ICT retriever + +RANK=0 +WORLD_SIZE=1 + +PRETRAINED_BERT_PATH= +TEXT_DATA_PATH= +TITLE_DATA_PATH= +CHECKPOINT_PATH= + + +python pretrain_ict.py \ + --num-layers 12 \ + --hidden-size 768 \ + --num-attention-heads 12 \ + --tensor-model-parallel-size 1 \ + --micro-batch-size 32 \ + --seq-length 256 \ + --max-position-embeddings 512 \ + --train-iters 100000 \ + --vocab-file bert-vocab.txt \ + --tokenizer-type BertWordPieceLowerCase \ + --DDP-impl torch \ + --bert-load ${PRETRAINED_BERT_PATH} \ + --log-interval 100 \ + --eval-interval 1000 \ + --eval-iters 10 \ + --retriever-report-topk-accuracies 1 5 10 20 100 \ + --retriever-score-scaling \ + --load $CHECKPOINT_PATH \ + --save $CHECKPOINT_PATH \ + --data-path ${TEXT_DATA_PATH} \ + --titles-data-path ${TITLE_DATA_PATH} \ + --lr 0.0001 \ + --lr-decay-style linear \ + --weight-decay 1e-2 \ + --clip-grad 1.0 \ + --lr-warmup-fraction 0.01 \ + --save-interval 4000 \ + --exit-interval 8000 \ + --query-in-block-prob 0.1 \ + --fp16 diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_t5.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_t5.sh new file mode 100644 index 0000000..c44cc57 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_t5.sh @@ -0,0 +1,50 @@ +#!/bin/bash + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +CHECKPOINT_PATH= +VOCAB_FILE=/t5-vocab.txt +DATA_PATH=_text_sentence + +T5_ARGS=" + --num-layers 12 \ + --hidden-size 768 \ + --num-attention-heads 12 \ + --kv-channels 64 \ + --ffn-hidden-size 3072 \ + --encoder-seq-length 512 \ + --decoder-seq-length 128 \ + --max-position-embeddings 512 \ + --micro-batch-size 16 \ + --global-batch-size 16 \ + --lr 0.0001 \ + --train-iters 1000000 \ + --lr-decay-iters 1000000 \ + --lr-decay-style linear \ + --min-lr 0.00001 \ + --weight-decay 1e-2 \ + --lr-warmup-fraction .01 \ + --clip-grad 1.0 \ + --fp16 \ + --vocab-extra-ids 100 +" + +DATA_ARGS=" + --data-path $DATA_PATH \ + --vocab-file $VOCAB_FILE \ + --split 949,50,1 +" + +OUTPUT_ARGS=" + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 +" + +torchrun pretrain_t5.py \ + $T5_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_t5_distributed.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_t5_distributed.sh new file mode 100755 index 0000000..03bbf18 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_t5_distributed.sh @@ -0,0 +1,67 @@ +#!/bin/bash + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) + +CHECKPOINT_PATH= +VOCAB_FILE=/t5-vocab.txt +DATA_PATH=_text_sentence + +DISTRIBUTED_ARGS=" + --nproc_per_node $GPUS_PER_NODE \ + --nnodes $NNODES \ + --node_rank $NODE_RANK \ + --master_addr $MASTER_ADDR \ + --master_port $MASTER_PORT +" + +T5_ARGS=" + --num-layers 12 \ + --hidden-size 768 \ + --num-attention-heads 12 \ + --kv-channels 64 \ + --ffn-hidden-size 3072 \ + --encoder-seq-length 512 \ + --decoder-seq-length 128 \ + --max-position-embeddings 512 \ + --micro-batch-size 16 \ + --global-batch-size 128 \ + --lr 0.0001 \ + --train-iters 1000000 \ + --lr-decay-iters 1000000 \ + --lr-decay-style linear \ + --min-lr 0.00001 \ + --weight-decay 1e-2 \ + --lr-warmup-fraction .01 \ + --clip-grad 1.0 \ + --fp16 \ + --vocab-extra-ids 100 +" + +DATA_ARGS=" + --data-path $DATA_PATH \ + --vocab-file $VOCAB_FILE \ + --split 949,50,1 +" + +OUTPUT_ARGS=" + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 +" + +torchrun $DISTRIBUTED_ARGS pretrain_t5_core.py \ + $T5_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --distributed-backend nccl \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_t5_distributed_with_mp.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_t5_distributed_with_mp.sh new file mode 100644 index 0000000..9802866 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_t5_distributed_with_mp.sh @@ -0,0 +1,68 @@ +#!/bin/bash + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) + +CHECKPOINT_PATH= +VOCAB_FILE=/t5-vocab.txt +DATA_PATH=_text_sentence + +DISTRIBUTED_ARGS=" + --nproc_per_node $GPUS_PER_NODE \ + --nnodes $NNODES \ + --node_rank $NODE_RANK \ + --master_addr $MASTER_ADDR \ + --master_port $MASTER_PORT +" + +T5_ARGS=" + --tensor-model-parallel-size 2 \ + --num-layers 12 \ + --hidden-size 768 \ + --num-attention-heads 12 \ + --kv-channels 64 \ + --ffn-hidden-size 3072 \ + --encoder-seq-length 512 \ + --decoder-seq-length 128 \ + --max-position-embeddings 512 \ + --micro-batch-size 16 \ + --global-batch-size 128 \ + --lr 0.0001 \ + --train-iters 1000000 \ + --lr-decay-iters 1000000 \ + --lr-decay-style linear \ + --min-lr 0.00001 \ + --weight-decay 1e-2 \ + --lr-warmup-fraction .01 \ + --clip-grad 1.0 \ + --fp16 \ + --vocab-extra-ids 100 +" + +DATA_ARGS=" + --data-path $DATA_PATH \ + --vocab-file $VOCAB_FILE \ + --split 949,50,1 +" + +OUTPUT_ARGS=" + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 +" + +torchrun $DISTRIBUTED_ARGS pretrain_t5.py \ + $T5_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --distributed-backend nccl \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_vision_classify.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_vision_classify.sh new file mode 100755 index 0000000..5fcdd6e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_vision_classify.sh @@ -0,0 +1,64 @@ +#! /bin/bash + +# Pre-trains ViT based image classificaation model + +export CUDA_DEVICE_MAX_CONNECTIONS=1 +export NCCL_IB_SL=1 + +# Training and validation paths should each point to a folder where each +# sub-folder contains a collection of images in jpg or png format +# e.g. If using imagenet, one train image might be, train_data/n01688243/n01688243_11301.JPEG +DATA_PATH_TRAIN= +DATA_PATH_VAL= + +CHECKPOINT_PATH= + +CLASSIFIER_ARGS=" + --tensor-model-parallel-size 1 \ + --num-layers 12 \ + --hidden-size 768 \ + --num-attention-heads 12 \ + --patch-dim 4 \ + --seq-length 3136 \ + --max-position-embeddings 3136 \ + --img-h 224 \ + --img-w 224 \ + --mask-factor 1.0 \ + --fp16 \ + --train-iters 750000 \ + --lr-decay-style cosine \ + --micro-batch-size 4 \ + --global-batch-size 1024 \ + --lr 0.0005 \ + --min-lr 0.00001 \ + --attention-dropout 0.0 \ + --weight-decay 0.05 \ + --lr-warmup-iters 12500 \ + --clip-grad 1.0 \ + --no-gradient-accumulation-fusion \ + --num-workers 4 \ + --DDP-impl torch " + +DATA_ARGS=" + --tokenizer-type NullTokenizer \ + --vocab-size 0 \ + --data-path $DATA_PATH_TRAIN $DATA_PATH_VAL \ + --no-data-sharding \ + --split 949,50,1 \ +" + +OUTPUT_ARG=" + --log-interval 32 \ + --save-interval 10000 \ + --eval-interval 2500 \ + --eval-iters 100 \ + --tensorboard-dir ${CHECKPOINT_PATH} \ +" + +torchrun pretrain_vision_classification.py \ + $CLASSIFIER_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_vision_dino.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_vision_dino.sh new file mode 100755 index 0000000..b047e4e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_vision_dino.sh @@ -0,0 +1,67 @@ +#! /bin/bash + +# Pre-trains Dino V1 model +# For model details: https://arxiv.org/abs/2104.14294 +# For original author implementation: https://github.com/facebookresearch/dino/tree/main + +export CUDA_DEVICE_MAX_CONNECTIONS=1 +export NCCL_IB_SL=1 + +# Training and validation paths should each point to a folder where each +# sub-folder contains a collection of images in jpg or png format +# e.g. If using imagenet, one train image might be, train_data/n01688243/n01688243_11301.JPEG +DATA_PATH_TRAIN= +DATA_PATH_VAL= + +CHECKPOINT_PATH= + +DINO_ARGS=" + --vision-pretraining-type dino \ + --tensor-model-parallel-size 1 \ + --num-layers 12 \ + --hidden-size 768 \ + --num-attention-heads 12 \ + --patch-dim 4 \ + --seq-length 3136 \ + --max-position-embeddings 3136 \ + --img-h 224 \ + --img-w 224 \ + --mask-factor 1.0 \ + --fp16 \ + --train-iters 750000 \ + --lr-decay-style cosine \ + --micro-batch-size 4 \ + --global-batch-size 1024 \ + --lr 0.0005 \ + --min-lr 0.00001 \ + --attention-dropout 0.0 \ + --weight-decay 0.05 \ + --lr-warmup-iters 12500 \ + --clip-grad 1.0 \ + --no-gradient-accumulation-fusion \ + --num-workers 4 \ + --DDP-impl torch " + +DATA_ARGS=" + --tokenizer-type NullTokenizer \ + --vocab-size 0 \ + --data-path $DATA_PATH_TRAIN $DATA_PATH_VAL \ + --no-data-sharding \ + --split 949,50,1 \ +" + +OUTPUT_ARG=" + --log-interval 32 \ + --save-interval 10000 \ + --eval-interval 2500 \ + --eval-iters 100 \ + --tensorboard-dir ${CHECKPOINT_PATH} \ +" + +torchrun pretrain_vision_dino.py \ + $DINO_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_vision_inpaint.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_vision_inpaint.sh new file mode 100755 index 0000000..01c7e71 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_vision_inpaint.sh @@ -0,0 +1,65 @@ +#! /bin/bash + +# Pre-trains ViT based image inpainting model + +export CUDA_DEVICE_MAX_CONNECTIONS=1 +export NCCL_IB_SL=1 + +# Training and validation paths should each point to a folder where each +# sub-folder contains a collection of images in jpg or png format +# e.g. If using imagenet, one train image might be, train_data/n01688243/n01688243_11301.JPEG +DATA_PATH_TRAIN= +DATA_PATH_VAL= + +CHECKPOINT_PATH= + +INPAINT_ARGS=" + --vision-pretraining-type inpaint \ + --tensor-model-parallel-size 1 \ + --num-layers 12 \ + --hidden-size 768 \ + --num-attention-heads 12 \ + --patch-dim 4 \ + --seq-length 3136 \ + --max-position-embeddings 3136 \ + --img-h 224 \ + --img-w 224 \ + --mask-factor 1.0 \ + --fp16 \ + --train-iters 750000 \ + --lr-decay-style cosine \ + --micro-batch-size 4 \ + --global-batch-size 1024 \ + --lr 0.0005 \ + --min-lr 0.00001 \ + --attention-dropout 0.0 \ + --weight-decay 0.05 \ + --lr-warmup-iters 12500 \ + --clip-grad 1.0 \ + --no-gradient-accumulation-fusion \ + --num-workers 4 \ + --DDP-impl torch " + +DATA_ARGS=" + --tokenizer-type NullTokenizer \ + --vocab-size 0 \ + --data-path $DATA_PATH_TRAIN $DATA_PATH_VAL \ + --no-data-sharding \ + --split 949,50,1 \ +" + +OUTPUT_ARG=" + --log-interval 32 \ + --save-interval 10000 \ + --eval-interval 2500 \ + --eval-iters 100 \ + --tensorboard-dir ${CHECKPOINT_PATH} \ +" + +torchrun pretrain_vision_inpaint.py \ + $INPAINT_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/pretrain_vlm.sh b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_vlm.sh new file mode 100755 index 0000000..c74cf1e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/pretrain_vlm.sh @@ -0,0 +1,76 @@ +#!/bin/bash + +# Train a vision language model. +# Default arguments here use a mock dataset. Please edit the arguments to your liking. + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +# Check that the user has set an output path for model checkpoints. +if [[ -z $CHECKPOINT_PATH ]]; then + echo "Please set CHECKPOINT_PATH for storing your model checkpoints." + exit 1 +fi + +DISTRIBUTED_ARGS=" + --nproc_per_node 8 \ +" + +# Note: the learning rate and other hyperparameters used here are just examples and not optimized in any way. +GPT_ARGS=" + --num-layers 24 \ + --hidden-size 512 \ + --num-attention-heads 16 \ + --seq-length 1024 \ + --max-position-embeddings 1024 \ + --micro-batch-size 2 \ + --global-batch-size 16 \ + --lr 0.00015 \ + --train-iters 10000 \ + --lr-decay-iters 3200 \ + --lr-decay-style cosine \ + --min-lr 1.0e-5 \ + --weight-decay 1e-2 \ + --lr-warmup-fraction .01 \ + --clip-grad 1.0 \ + --fp16 +" + +IMG_ARGS=" + --img-h 336 \ + --img-w 336 \ + --patch-dim 14 +" + +DATA_ARGS=" + --split 949,50,1 + --tokenizer-type NullTokenizer + --vocab-size=8192 +" + +OUTPUT_ARGS=" + --log-interval 100 \ + --save-interval 5000 \ + --eval-interval 1000 \ + --eval-iters 10 +" + +# Select one of the cases below. + +# Multi GPU +# torchrun $DISTRIBUTED_ARGS \ + +# Single GPU +# CUDA_VISIBLE_DEVICES=0 python -u \ + +# Single GPU with a debugger +# CUDA_VISIBLE_DEVICES=0 python -u -m debugpy --listen 0.0.0.0:5678 --wait-for-client \ + +torchrun $DISTRIBUTED_ARGS \ + pretrain_vlm.py \ + $GPT_ARGS \ + $IMG_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --distributed-backend nccl \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH diff --git a/Megatron-LM-core_r0.7.0.beta/examples/retro/README.md b/Megatron-LM-core_r0.7.0.beta/examples/retro/README.md new file mode 100644 index 0000000..f015c0b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/retro/README.md @@ -0,0 +1,74 @@ +# RETRO MODEL + +## Table of contents +- [1. Training Setup](#1-training-setup) +- [2. Data Preprocessing](#2-data-preprocessing) +- [3. Configurations](#3-configurations) + +## 1. Training setup + + +To run the model using a docker container run it as follows +``` +PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:23.09-py3 +CHECKPOINT_PATH="" # +TENSORBOARD_LOGS_PATH=""# + +docker run \ + --gpus=all \ + --ipc=host \ + --workdir /workspace/megatron-lm \ + -v /path/to/data:/path/to/data \ + -v /path/to/megatron-lm:/workspace/megatron-lm \ + megatron-lm nvcr.io/nvidia/pytorch:23.04-py3 \ + bash examples/retro/train_retro_2b_distributed.sh $CHECKPOINT_PATH $TENSORBOARD_LOGS_PATH" + +``` +NOTE: Depending on the environment you are running it the above command might look slightly different. + +NOTE: Due to how Retro preprocess and caches elements of the pretraining dataset before training begins, some arguments are auto-loaded from the Retro preprocessing configuration. These loaded arguments include: + +- `--data-path` +- `--data-cache-path` +- `--eval-interval` +- `--eval-iters` +- `--global-batch-size` +- `--tokenizer-type` +- `--tokenizer-model` +- `--vocab-file` +- `--merge-file` +- `--seed` +- `--seq-length` +- `--train-samples` + + +## 2. Data Preprocessing + + +Retro preprocesses and caches data prior to pretraining, to greatly speed up pretraining. During data preprocessing, the retrieval database is built, and neighbor IDs are queried for each sample within the pretraining dataset. Please see `preprocess_data.sh` for an example script to preprocess data for Retro. The reference documentation for data preprocessing can be found [here](tools/retro/README.md). + + +## 3. Configurations + +The example in this folder shows you how to run a 2B model. Below are a few other example configurations. + +### 857M +``` + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 2048 \ + --tensor-model-parallel-size 1 \ + --pipeline-model-parallel-size 1 \ + +``` + +### 4B +``` + --num-layers 48 \ + --hidden-size 2560 \ + --num-attention-heads 32 \ + --tensor-model-parallel-size 1 \ + --pipeline-model-parallel-size 1 \ + +``` diff --git a/Megatron-LM-core_r0.7.0.beta/examples/retro/preprocess_data.sh b/Megatron-LM-core_r0.7.0.beta/examples/retro/preprocess_data.sh new file mode 100644 index 0000000..5d2e66b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/retro/preprocess_data.sh @@ -0,0 +1,144 @@ +#!/bin/bash + +set -u + +unset NCCL_DEBUG + +######## Megatron, Retro dirs. ######## + +REPO_DIR="" +RETRO_PROJECT_DIR="" + +######## Task (e.g., db, index, query). ######## + +# This script takes a single argument, which specifies the retro task to be +# performed. The available tasks are: db-build, index-train, index-add, and +# query-neighbors. + +# ~~ Examples ~~ +# RETRO_TASKS="db-build" # Build the retrieval database +# RETRO_TASKS="index-train" # Train the index +# RETRO_TASKS="index-add" # Add data to the index +# RETRO_TASKS="query-neighbors" # Perform query pretraining for neighbors + +# You can also provide the task as a command-line argument when executing the +# script. Example: ./preprocess_data.sh index-add +RETRO_TASKS=$1 + +######## Data. ######## +DATA_BLEND="" + +######## Index. ######## + +RETRO_INDEX_STR="OPQ32_64,IVF65536_HNSW8,PQ32" +RETRO_INDEX_NTRAIN=66625331 +RETRO_INDEX_TRAIN_LOAD_FRACTION=0.97 +RETRO_INDEX_ADD_LOAD_FRACTION=0.95 + +######## GPT. ######## + +RETRO_GPT_SEED=1234 +RETRO_GPT_SPLIT="98,2,0" +RETRO_GPT_DATA_PATH=${DATA_BLEND} +RETRO_GPT_TRAIN_SAMPLES=200000 +RETRO_GPT_EVAL_INTERVAL=2000 +RETRO_GPT_EVAL_ITERS=50 +RETRO_GPT_LR_DECAY_SAMPLES=175000 +RETRO_GPT_LR_WARMUP_SAMPLES=10000 +RETRO_GPT_SEQ_LENGTH=2048 +RETRO_GPT_GLOBAL_BATCH_SIZE=256 +RETRO_GPT_CHUNK_LENGTH=64 + +######## Query. ######## + +RETRO_QUERY_NUM_NEIGHBORS_QUERY=200 +RETRO_QUERY_NUM_NEIGHBORS_SAVE=20 +RETRO_QUERY_EF_SEARCH=32 +RETRO_QUERY_NPROBE=4096 + +######## Args. ######## + +ARGS=" \ + --distributed-timeout-minutes 600 \ + --tensor-model-parallel-size 1 \ + --pipeline-model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --micro-batch-size 1 \ + --global-batch-size ${RETRO_GPT_GLOBAL_BATCH_SIZE} \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --load ${RETRO_PROJECT_DIR}/checkpoints/bert \ + --exit-on-missing-checkpoint \ + --no-load-optim \ + --data-path [null] \ + --tokenizer-type BertWordPieceLowerCase \ + --vocab-file ${RETRO_PROJECT_DIR}/tokenizer/bert-large-uncased-vocab.txt \ + --split ${RETRO_GPT_SPLIT} \ + --distributed-backend nccl \ + --lr 0.0001 \ + --lr-decay-style linear \ + --min-lr 1.0e-5 \ + --train-samples ${RETRO_GPT_TRAIN_SAMPLES} \ + --lr-decay-samples ${RETRO_GPT_LR_DECAY_SAMPLES} \ + --lr-warmup-samples ${RETRO_GPT_LR_WARMUP_SAMPLES} \ + --weight-decay 1e-2 \ + --clip-grad 1.0 \ + --eval-interval ${RETRO_GPT_EVAL_INTERVAL} \ + --eval-iters ${RETRO_GPT_EVAL_ITERS} \ + --bf16 \ + --no-data-sharding \ + --no-gradient-accumulation-fusion \ + --no-async-tensor-model-parallel-allreduce \ + --bert-embedder-type megatron \ + --output-bert-embeddings \ + \ + --retro-project-dir ${RETRO_PROJECT_DIR} \ + --retro-tasks ${RETRO_TASKS} \ + --retro-bert-vocab-file tokenizer/bert-large-uncased-vocab.txt \ + --retro-bert-tokenizer-type BertWordPieceLowerCase \ + \ + --retro-gpt-seed ${RETRO_GPT_SEED} \ + --retro-gpt-tokenizer-type GPTSentencePieceTokenizer \ + --retro-gpt-tokenizer-model /path/to/tokenizer/model \ + --retro-gpt-seq-length ${RETRO_GPT_SEQ_LENGTH} \ + --retro-gpt-chunk-length ${RETRO_GPT_CHUNK_LENGTH} \ + --retro-gpt-global-batch-size ${RETRO_GPT_GLOBAL_BATCH_SIZE} \ + --retro-gpt-eval-interval ${RETRO_GPT_EVAL_INTERVAL} \ + --retro-gpt-eval-iters ${RETRO_GPT_EVAL_ITERS} \ + --retro-gpt-split ${RETRO_GPT_SPLIT} \ + --retro-gpt-data-path ${RETRO_GPT_DATA_PATH} \ + --retro-gpt-train-samples ${RETRO_GPT_TRAIN_SAMPLES} \ + \ + --retro-index-str ${RETRO_INDEX_STR} \ + --retro-index-ntrain ${RETRO_INDEX_NTRAIN} \ + --retro-index-train-load-fraction ${RETRO_INDEX_TRAIN_LOAD_FRACTION} \ + --retro-index-add-load-fraction ${RETRO_INDEX_ADD_LOAD_FRACTION} \ + --no-retro-index-delete-training-embeddings \ + --no-retro-index-delete-added-codes \ + \ + --retro-query-num-neighbors-query ${RETRO_QUERY_NUM_NEIGHBORS_QUERY} \ + --retro-query-num-neighbors-save ${RETRO_QUERY_NUM_NEIGHBORS_SAVE} \ + --retro-query-ef-search ${RETRO_QUERY_EF_SEARCH} \ + --retro-query-nprobe ${RETRO_QUERY_NPROBE} \ +" + +######## Command. ######## + +NPROCS=8 # Number of GPUs. +CMD="\ + cd ${REPO_DIR} && pwd && \ + export PYTHONPATH=$PYTHONPATH:${REPO_DIR} && \ + python -m torch.distributed.run \ + --nproc_per_node ${NPROCS} \ + --nnodes 1 \ + --node_rank ${NODE_RANK} \ + --master_addr ${MASTER_ADDR} \ + --master_port 6000 \ + tools/retro/preprocess_data.py ${ARGS} \ +" +echo "~~~~~~~~~~~~~~~~~~~~~~~~~~" +echo "CMD = '$CMD'." +echo "~~~~~~~~~~~~~~~~~~~~~~~~~~" +eval $CMD diff --git a/Megatron-LM-core_r0.7.0.beta/examples/retro/train_retro_2b_distributed.sh b/Megatron-LM-core_r0.7.0.beta/examples/retro/train_retro_2b_distributed.sh new file mode 100644 index 0000000..3bbfc9b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/retro/train_retro_2b_distributed.sh @@ -0,0 +1,99 @@ +#!/bin/bash + +# Runs the "307M" parameter Retro model. + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NUM_NODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES)) + +CHECKPOINT_PATH=$1 # +TENSORBOARD_LOGS_PATH=$2 # + +DISTRIBUTED_ARGS=( + --nproc_per_node $GPUS_PER_NODE + --nnodes $NUM_NODES + --master_addr $MASTER_ADDR + --master_port $MASTER_PORT +) + +######## GPT or Retro? ######## + +# 0 : GPT. +# 1 : Retro + +ADD_RETRIEVER=1 + +######## Megatron, Retro dirs. ######## + +RETRO_PROJECT_DIR="" + +######## Model, training args. ######## + +# ** Note: --seq-length auto loaded from Retro project dir. +RETRO_MODEL_ARGS=( + --num-layers 32 + --hidden-size 2048 + --num-attention-heads 32 +) + +# ** Note: --data-path, --tokenizer-type, and --tokenizer-model auto loaded from Retro project dir. +DATA_ARGS=( + --split 98,2,0 +) + +MODEL_PARALLEL_ARGS=( + --tensor-model-parallel-size 8 + --pipeline-model-parallel-size 1 +) + +# ** Note: --eval-interval, --eval-iters auto loaded from Retro project dir. +EVAL_AND_LOGGING_ARGS=( + --log-interval 100 + --save-interval 10000 + --eval-interval 1000 + --save $CHECKPOINT_PATH + --load $CHECKPOINT_PATH + --eval-iters 10 + --tensorboard-dir $TENSORBOARD_LOGS_PATH +) + +TRAINING_ARGS=" \ + --retro-project-dir ${RETRO_PROJECT_DIR} \ + --use-mcore-models \ + --transformer-impl transformer_engine \ + --num-workers 8 \ + --micro-batch-size 4 \ + --lr-decay-samples 166400000 \ + --lr-warmup-samples 162761 \ + --lr 6.0e-4 \ + --min-lr 6.0e-5 \ + --lr-decay-style cosine \ + --clip-grad 1.0 \ + --weight-decay 0.1 \ + --adam-beta1 0.9 \ + --adam-beta2 0.95 \ + --init-method-std 0.023 \ + --log-params-norm \ + --log-num-zeros-in-grad \ + --bf16 \ + --no-data-sharding \ +" + +if [ "$ADD_RETRIEVER" = "1" ]; then + TRAINING_ARGS+=" --retro-add-retriever" +fi + +######## Command. ######## + +torchrun ${DISTRIBUTED_ARGS[@]} pretrain_retro.py \ + ${RETRO_MODEL_ARGS[@]} \ + ${TRAINING_ARGS} \ + ${MODEL_PARALLEL_ARGS[@]} \ + ${DATA_ARGS[@]} \ + ${EVAL_AND_LOGGING_ARGS[@]} diff --git a/Megatron-LM-core_r0.7.0.beta/examples/run_simple_mcore_train_loop.py b/Megatron-LM-core_r0.7.0.beta/examples/run_simple_mcore_train_loop.py new file mode 100644 index 0000000..7f30a38 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/run_simple_mcore_train_loop.py @@ -0,0 +1,140 @@ +import os +import torch +from torch.optim import Adam +from torch.utils.data import DataLoader +from functools import partial +from pathlib import Path + +from megatron.core import parallel_state +from megatron.core import dist_checkpointing +from megatron.core.pipeline_parallel.schedules import get_forward_backward_func +from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.models.gpt.gpt_model import GPTModel +from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec +from megatron.core.datasets.utils import Split +from megatron.core.datasets.gpt_dataset import GPTDatasetConfig, MockGPTDataset + +def initialize_distributed(tensor_model_parallel_size = 1, pipeline_model_parallel_size = 1): + parallel_state.destroy_model_parallel() + + # Torch setup for distributed training + rank = int(os.environ['LOCAL_RANK']) + world_size = torch.cuda.device_count() + torch.cuda.set_device(rank) + torch.distributed.init_process_group(world_size=world_size, rank=rank) + + # Megatron core distributed training initialization + parallel_state.initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size) + +def model_provider(): + """Build the model.""" + + transformer_config = TransformerConfig( + num_layers=2, + hidden_size=12, + num_attention_heads=4, + use_cpu_initialization=True, + pipeline_dtype=torch.float32) + + gpt_model = GPTModel( + config=transformer_config, + transformer_layer_spec=get_gpt_layer_local_spec(), + vocab_size=100, + max_sequence_length=64) + + return gpt_model + +def get_train_data_iterator(): + config = GPTDatasetConfig( + random_seed = 0, + sequence_length = 64, + blend=[], + mock=True, + reset_position_ids=False, + reset_attention_mask=False, + eod_mask_loss=False, + tokenizer="dummy") + + training_data= MockGPTDataset(Split.train, config) + + train_dataloader = DataLoader(training_data, batch_size=8, shuffle=True) + + train_iterator = iter(train_dataloader) + return train_iterator + +def forward_step_func(data_iterator, model): + + def loss_func(loss_mask: torch.Tensor, output_tensor: torch.Tensor): + + losses = output_tensor.float() + loss_mask = loss_mask.view(-1).float() + loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum() + # If you have data parallel reduce loss across data parallel groups. + # If pipeline parallel, loss computation is done only in last stage. + + return loss, {'lm loss': loss} + + data = next(data_iterator) + tokens = data['tokens'].to(device) + attention_mask = data['attention_mask'].to(device) + position_ids = data['position_ids'].to(device) + labels = data['labels'].to(device) + loss_mask = data['loss_mask'].to(device) + + output_tensor = model(tokens, position_ids, attention_mask, + labels=labels) + + return output_tensor, partial(loss_func, loss_mask) + +def save_distributed_checkpoint(checkpoint_path, gpt_model): + sharded_state_dict = gpt_model.sharded_state_dict(prefix='') + dist_checkpointing.save(sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path) + +def load_distributed_checkpoint(checkpoint_path, gpt_model): + sharded_state_dict=gpt_model.sharded_state_dict(prefix='') + checkpoint = dist_checkpointing.load(sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path) + gpt_model.load_state_dict(checkpoint) + return gpt_model + +if __name__ == "__main__": + initialize_distributed(tensor_model_parallel_size=2, pipeline_model_parallel_size=1) + model_parallel_cuda_manual_seed(123) + + gpt_model = model_provider() + device = torch.device("cuda") + gpt_model.to(device) + + optim = Adam(gpt_model.parameters()) + + train_iterator = get_train_data_iterator() + + forward_backward_func = get_forward_backward_func() + + # Running the model for 5 iterations + for _ in range(5): + optim.zero_grad() + + losses_reduced = forward_backward_func( + forward_step_func=forward_step_func, + data_iterator=train_iterator, + model=gpt_model, + num_microbatches=1, + seq_length=64, + micro_batch_size=8, + decoder_seq_length=64, + forward_only=False) + + optim.step() + + print(f'Losses reduced : {losses_reduced}') + + # Saving the model + ckpt_path = os.getcwd() + '/ckpt' + Path(ckpt_path).mkdir(exist_ok=True) + save_distributed_checkpoint(gpt_model=gpt_model, checkpoint_path=ckpt_path) + + # Loading the model + gpt_model = load_distributed_checkpoint(gpt_model=gpt_model, checkpoint_path=ckpt_path) + gpt_model.to(device) + print('Successfully loaded the model') diff --git a/Megatron-LM-core_r0.7.0.beta/examples/run_text_generation_server_345M.sh b/Megatron-LM-core_r0.7.0.beta/examples/run_text_generation_server_345M.sh new file mode 100755 index 0000000..e8e61ad --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/run_text_generation_server_345M.sh @@ -0,0 +1,31 @@ +#!/bin/bash +# This example will start serving the 345M model. +DISTRIBUTED_ARGS="--nproc_per_node 1 \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +CHECKPOINT= +VOCAB_FILE= +MERGE_FILE= + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +pip install flask-restful + +torchrun $DISTRIBUTED_ARGS tools/run_text_generation_server.py \ + --tensor-model-parallel-size 1 \ + --pipeline-model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 1024 \ + --load ${CHECKPOINT} \ + --num-attention-heads 16 \ + --max-position-embeddings 1024 \ + --tokenizer-type GPT2BPETokenizer \ + --fp16 \ + --micro-batch-size 1 \ + --seq-length 1024 \ + --vocab-file $VOCAB_FILE \ + --merge-file $MERGE_FILE \ + --seed 42 diff --git a/Megatron-LM-core_r0.7.0.beta/examples/run_text_generation_server_345M_8_tensor_parallel.sh b/Megatron-LM-core_r0.7.0.beta/examples/run_text_generation_server_345M_8_tensor_parallel.sh new file mode 100755 index 0000000..368cec3 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/run_text_generation_server_345M_8_tensor_parallel.sh @@ -0,0 +1,29 @@ +#!/bin/bash +# This example will start serving the 345M model that is partitioned 8 way tensor parallel +DISTRIBUTED_ARGS="--nproc_per_node 8 \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +CHECKPOINT= +VOCAB_FILE= +MERGE_FILE= + +pip install flask-restful + +python -m torch.distributed.launch $DISTRIBUTED_ARGS tools/run_text_generation_server.py \ + --tensor-model-parallel-size 8 \ + --pipeline-model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 1024 \ + --load ${CHECKPOINT} \ + --num-attention-heads 16 \ + --max-position-embeddings 1024 \ + --tokenizer-type GPT2BPETokenizer \ + --fp16 \ + --micro-batch-size 1 \ + --seq-length 1024 \ + --vocab-file $VOCAB_FILE \ + --merge-file $MERGE_FILE \ + --seed 42 diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/CONFIG.sh b/Megatron-LM-core_r0.7.0.beta/examples/sc21/CONFIG.sh new file mode 100755 index 0000000..f17ccd7 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/CONFIG.sh @@ -0,0 +1,57 @@ +#!/bin/bash + + +# SLURM options. +export SLURM_PARTITION= +export SLURM_ACCOUNT= + + +# Source code. +export MEGATRON_CODE_DIR= + + +# This variable is used to mount the relevant part of the filesystem +# inside the docker container. Note that the `MEGATRON_CODE_DIR` and the +# launch directory already get mounted; this variable should be used to +# mount the directories that contain the data and tokenizer files. +export DOCKER_MOUNT_DIR= + + +# Data and tokenizer files. +MEGATRON_DATA= +BPE_VOCAB_FILE= +BPE_MERGE_FILE= + + +# Megatron input parameters. +# `MEGATRON_EXTRA_PARAMS` can be used to provide any extra parameters +# that are not listed here. +export MEGATRON_PARAMS=" ${MEGATRON_EXTRA_PARAMS} \ + --tensor-model-parallel-size ${TP} \ + --pipeline-model-parallel-size ${PP} \ + --micro-batch-size ${MBS} \ + --global-batch-size ${GBS} \ + --num-layers ${NLS} \ + --hidden-size ${HS} \ + --num-attention-heads ${NAH} \ + --DDP-impl ${DDP} \ + --data-path ${MEGATRON_DATA} \ + --vocab-file ${BPE_VOCAB_FILE} \ + --merge-file ${BPE_MERGE_FILE} \ + --log-interval 5 \ + --seq-length 2048 \ + --max-position-embeddings 2048 \ + --train-iters 500 \ + --lr-decay-iters 320 \ + --lr 0.0001 \ + --min-lr 0.00001 \ + --lr-decay-style cosine \ + --lr-warmup-fraction 0.01 \ + --split 969,30,1 \ + --eval-iters 100 \ + --eval-interval 1000 \ + --clip-grad 1.0 \ + --fp16 \ + --loss-scale 8192 " + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/README.md b/Megatron-LM-core_r0.7.0.beta/examples/sc21/README.md new file mode 100644 index 0000000..ec922d1 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/README.md @@ -0,0 +1,50 @@ +# Reproducing Figures in SC21 Paper + + +This directory contains some of the scripts that were used to produce the +results in the [Megatron paper](https://arxiv.org/pdf/2104.04473.pdf) that is +to appear at [SuperComputing 2021](https://sc21.supercomputing.org/). These +scripts use [Slurm](https://slurm.schedmd.com/documentation.html) with the +[pyxis plugin](https://github.com/NVIDIA/pyxis), but can be modified for other +schedulers as well. + + +## Git commit + +To replicate these results use Megatron-LM commit: 6985e58938d40ad91ac07b0fddcfad8132e1447e + + +## Setup + +All the cluster-dependent variables are in [`CONFIG.sh`](./CONFIG.sh). Please +update the unspecified values (in angle brackets `<...>`) before launching any +scripts. + + + +## Scripts + +Below is a list of scripts that can be used to reproduce various figures in our +[paper](https://arxiv.org/pdf/2104.04473.pdf): + +* [run_table_1.sh](./run_table_1.sh): Table 1 showing weak-scaling throughput +for GPT models ranging from 1 billion to 1 trillion parameters. +* [run_figure_11.sh](./run_figure_11.sh): Figure 11 showing the weak-scaling +performance of pipeline parallelism. +* [run_figure_12.sh](./run_figure_12.sh): Figure 12 showing the effect of +the interleaved schedule on a 175B GPT model. +* [run_figure_13.sh](./run_figure_13.sh): Figure 13 showing the effect of +different degrees of pipeline and tensor model parallelism on a model with +162.2 billion parameters. +* [run_figure_14.sh](./run_figure_14.sh): Figure 14 showing the effect of +different degrees of data and pipeline model parallelism on a model with +5.9 billion parameters. +* [run_figure_15.sh](./run_figure_15.sh): Figure 15 showing the effect of +different degrees of data and tensor model parallelism on a model with +5.9 billion parameters. +* [run_figure_16.sh](./run_figure_16.sh): Figure 16 showing the effect of +microbatch size. +* [run_figure_17.sh](./run_figure_17.sh): Figure 17 showing the effect of +activation recomputation. +* [run_figure_18.sh](./run_figure_18.sh): Figure 18 showing the effect of +the scatter-gather communication optimization. diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/SBATCH.sh b/Megatron-LM-core_r0.7.0.beta/examples/sc21/SBATCH.sh new file mode 100755 index 0000000..95431b9 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/SBATCH.sh @@ -0,0 +1,13 @@ +#!/bin/bash + + +sbatch -p ${SLURM_PARTITION} \ + -A ${SLURM_ACCOUNT} \ + --job-name=${JOB_NAME} \ + --nodes=${NNODES} \ + --export=MEGATRON_CODE_DIR,MEGATRON_PARAMS,DOCKER_MOUNT_DIR SRUN.sh + +exit 0 + + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/SRUN.sh b/Megatron-LM-core_r0.7.0.beta/examples/sc21/SRUN.sh new file mode 100755 index 0000000..52a9aff --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/SRUN.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +#SBATCH -t 0:30:00 --exclusive --mem=0 --overcommit --ntasks-per-node=8 + + +THIS_DIR=`pwd` +DATETIME=`date +'date_%y-%m-%d_time_%H-%M-%S'` +mkdir -p ${THIS_DIR}/logs + + +CMD="python -u ${MEGATRON_CODE_DIR}/pretrain_gpt.py ${MEGATRON_PARAMS}" + + +srun -l \ + --container-image "nvcr.io#nvidia/pytorch:20.12-py3" \ + --container-mounts "${THIS_DIR}:${THIS_DIR},${MEGATRON_CODE_DIR}:${MEGATRON_CODE_DIR},${DOCKER_MOUNT_DIR}:${DOCKER_MOUNT_DIR}" \ + --output=${THIS_DIR}/logs/%x_%j_$DATETIME.log sh -c "${CMD}" + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_11.sh b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_11.sh new file mode 100755 index 0000000..2ec7d9e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_11.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +# ================================ +# Choose the case to run. +# ================================ + +# Pipeline-parallel size options = [1, 2, 4, 8]. +PP=1 + +# Batch size (global batch size) options = [8, 128]. +GBS=8 + + + + + +# Set pipeline-parallel size options. +NLS=$((3*PP)) +NNODES=${PP} + + +# Other params. +TP=8 +MBS=1 +HS=20480 +NAH=128 +DDP=local +MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " + + +# Name of the job. +export JOB_NAME=results_figure_11_pipeline_parallel_size_${PP}_batch_size_${GBS} + + +# Import the configs. +. `pwd`/CONFIG.sh + + +# Submit the job. +. `pwd`/SBATCH.sh + + +exit 0 + + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_12.sh b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_12.sh new file mode 100755 index 0000000..11e5508 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_12.sh @@ -0,0 +1,54 @@ +#!/bin/bash + +# ================================ +# Choose the case to run. +# ================================ + +# Interleaved schedule options = [YES, NO]. +INTERLEAVED=YES + +# Batch size (global batch size) options = [12, 24, 36, ..., 60]. +GBS=12 + + + + + +# Set interleaved schedule options. +if [ ${INTERLEAVED} == "YES" ]; then + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 2 " +elif [ ${INTERLEAVED} == "NO" ]; then + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " +else + echo "Invalid configuration" + exit 1 +fi + + +# Other params. +TP=8 +PP=12 +MBS=1 +NLS=96 +HS=12288 +NAH=96 +DDP=local +NNODES=12 + + +# Name of the job. +export JOB_NAME=results_figure_12_interleaved_${INTERLEAVED}_batch_size_${GBS} + + +# Import the configs. +. `pwd`/CONFIG.sh + + +# Submit the job. +. `pwd`/SBATCH.sh + + +exit 0 + + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_13.sh b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_13.sh new file mode 100755 index 0000000..7ba560e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_13.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +# ================================ +# Choose the case to run. +# ================================ + +# Pipeline-parallel size options = [2, 4, 8, 16, 32]. +PP=2 + +# Batch size (global batch size) options = [32, 128]. +GBS=32 + + + + + +# Set pipeline-parallel and tensor-parallel size options. +TP=$((64/PP)) + + +# Other params. +MBS=1 +NLS=32 +HS=20480 +NAH=128 +DDP=local +MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " +NNODES=8 + + +# Name of the job. +export JOB_NAME=results_figure_13_pipeline_parallel_size_${PP}_tensor_parallel_size_${TP}_batch_size_${GBS} + + +# Import the configs. +. `pwd`/CONFIG.sh + + +# Submit the job. +. `pwd`/SBATCH.sh + + +exit 0 + + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_14.sh b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_14.sh new file mode 100755 index 0000000..4b83879 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_14.sh @@ -0,0 +1,47 @@ +#!/bin/bash + +# ================================ +# Choose the case to run. +# ================================ + +# Pipeline-parallel size options = [2, 4, 8, 16, 32]. +PP=2 + +# Batch size (global batch size) options = [32, 512]. +GBS=32 + + + + + +# Set pipeline-parallel and data-parallel size options. +DP=$((64/PP)) + + +# Other params. +TP=1 +MBS=1 +NLS=32 +HS=3840 +NAH=32 +DDP=local +MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " +NNODES=8 + + +# Name of the job. +export JOB_NAME=results_figure_14_pipeline_parallel_size_${PP}_data_parallel_size_${DP}_batch_size_${GBS} + + +# Import the configs. +. `pwd`/CONFIG.sh + + +# Submit the job. +. `pwd`/SBATCH.sh + + +exit 0 + + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_15.sh b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_15.sh new file mode 100755 index 0000000..547ad1d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_15.sh @@ -0,0 +1,47 @@ +#!/bin/bash + +# ================================ +# Choose the case to run. +# ================================ + +# Tensor-parallel size options = [2, 4, 8, 16, 32]. +TP=2 + +# Batch size (global batch size) options = [32, 128, 512]. +GBS=32 + + + + + +# Set tensor-parallel and data-parallel size options. +DP=$((64/TP)) + + +# Other params. +PP=1 +MBS=1 +NLS=32 +HS=3840 +NAH=32 +DDP=local +MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " +NNODES=8 + + +# Name of the job. +export JOB_NAME=results_figure_15_tensor_parallel_size_${TP}_data_parallel_size_${DP}_batch_size_${GBS} + + +# Import the configs. +. `pwd`/CONFIG.sh + + +# Submit the job. +. `pwd`/SBATCH.sh + + +exit 0 + + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_16.sh b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_16.sh new file mode 100755 index 0000000..8c353a3 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_16.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +# ================================ +# Choose the case to run. +# ================================ + +# Microbatch size options = [1, 2, 4, 8]. +MBS=1 + +# Batch size (global batch size) options = [128, 512]. +GBS=128 + + + + + +# Other params. +TP=8 +PP=8 +NLS=32 +HS=15360 +NAH=128 +DDP=local +MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " +NNODES=8 + + +# Name of the job. +export JOB_NAME=results_figure_16_microbatch_size_${MBS}_batch_size_${GBS} + + +# Import the configs. +. `pwd`/CONFIG.sh + + +# Submit the job. +. `pwd`/SBATCH.sh + + +exit 0 + + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_17.sh b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_17.sh new file mode 100755 index 0000000..d6899b3 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_17.sh @@ -0,0 +1,54 @@ +#!/bin/bash + +# ================================ +# Choose the case to run. +# ================================ + +# Activation recomputation options = [YES, NO]. +ACTIVATION_RECOMPUTATION=YES + +# Batch size (global batch size) options = [1, 2, 4, ..., 256]. +GBS=1 + + + + + +# Set activation recomputation. +if [ ${ACTIVATION_RECOMPUTATION} == "YES" ]; then + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " +elif [ ${ACTIVATION_RECOMPUTATION} == "NO" ]; then + MEGATRON_EXTRA_PARAMS="" +else + echo "Invalid configuration" + exit 1 +fi + + +# Other params. +TP=8 +PP=16 +MBS=1 +NLS=80 +HS=12288 +NAH=96 +DDP=local +NNODES=16 + + +# Name of the job. +export JOB_NAME=results_figure_17_activation_recomputation_${ACTIVATION_RECOMPUTATION}_batch_size_${GBS} + + +# Import the configs. +. `pwd`/CONFIG.sh + + +# Submit the job. +. `pwd`/SBATCH.sh + + +exit 0 + + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_18.sh b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_18.sh new file mode 100755 index 0000000..88924fb --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_figure_18.sh @@ -0,0 +1,54 @@ +#!/bin/bash + +# ================================ +# Choose the case to run. +# ================================ + +# Scatter-gather communication optimization options = [YES, NO]. +SCATTER_GATHER=YES + +# Batch size (global batch size) options = [12, 24, 36, ..., 60]. +GBS=12 + + + + + +# Set scatter-gather communication optimization options. +if [ ${SCATTER_GATHER} == "YES" ]; then + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 2 " +elif [ ${SCATTER_GATHER} == "NO" ]; then + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 2 --no-scatter-gather-tensors-in-pipeline " +else + echo "Invalid configuration" + exit 1 +fi + + +# Other params. +TP=8 +PP=12 +MBS=1 +NLS=96 +HS=12288 +NAH=96 +DDP=local +NNODES=12 + + +# Name of the job. +export JOB_NAME=results_figure_18_scatter_gather_${SCATTER_GATHER}_batch_size_${GBS} + + +# Import the configs. +. `pwd`/CONFIG.sh + + +# Submit the job. +. `pwd`/SBATCH.sh + + +exit 0 + + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_table_1.sh b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_table_1.sh new file mode 100755 index 0000000..1b15fb0 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/sc21/run_table_1.sh @@ -0,0 +1,145 @@ +#!/bin/bash + +# ================================ +# Choose the case to run. +# ================================ +# model size options = [1.7B, 3.6B, 7.5B, 18B, 39B, 76B, 145B, 310B, 530B, 1T] +MODEL_SIZE=1.7B + + + + + + +if [ ${MODEL_SIZE} == "1.7B" ]; then + TP=1 + PP=1 + MBS=16 + GBS=512 + NLS=24 + HS=2304 + NAH=24 + DDP=torch + NNODES=4 + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " +elif [ ${MODEL_SIZE} == "3.6B" ]; then + TP=2 + PP=1 + MBS=16 + GBS=512 + NLS=30 + HS=3072 + NAH=32 + DDP=torch + NNODES=8 + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " +elif [ ${MODEL_SIZE} == "7.5B" ]; then + TP=4 + PP=1 + MBS=16 + GBS=512 + NLS=36 + HS=4096 + NAH=32 + DDP=torch + NNODES=16 + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " +elif [ ${MODEL_SIZE} == "18B" ]; then + TP=8 + PP=1 + MBS=8 + GBS=1024 + NLS=40 + HS=6144 + NAH=48 + DDP=torch + NNODES=32 + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " +elif [ ${MODEL_SIZE} == "39B" ]; then + TP=8 + PP=2 + MBS=4 + GBS=1536 + NLS=48 + HS=8192 + NAH=64 + DDP=local + NNODES=64 + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " +elif [ ${MODEL_SIZE} == "76B" ]; then + TP=8 + PP=4 + MBS=2 + GBS=1792 + NLS=60 + HS=10240 + NAH=80 + DDP=local + NNODES=128 + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 5" +elif [ ${MODEL_SIZE} == "145B" ]; then + TP=8 + PP=8 + MBS=2 + GBS=2304 + NLS=80 + HS=12288 + NAH=96 + DDP=local + NNODES=192 + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 5 " +elif [ ${MODEL_SIZE} == "310B" ]; then + TP=8 + PP=16 + MBS=1 + GBS=2160 + NLS=96 + HS=16384 + NAH=128 + DDP=local + NNODES=240 + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 3 " +elif [ ${MODEL_SIZE} == "530B" ]; then + TP=8 + PP=35 + MBS=1 + GBS=2520 + NLS=105 + HS=20480 + NAH=128 + DDP=local + NNODES=315 + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 1 " +elif [ ${MODEL_SIZE} == "1T" ]; then + TP=8 + PP=64 + MBS=1 + GBS=3072 + NLS=128 + HS=25600 + NAH=160 + DDP=local + NNODES=384 + MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform " +else + echo "Invalid configuration" + exit 1 +fi + + +# Name of the job +export JOB_NAME=results_table_1_model_size_${MODEL_SIZE} + + +# Import the configs. +. `pwd`/CONFIG.sh + + +# Submit the job. +. `pwd`/SBATCH.sh + + +exit 0 + + + diff --git a/Megatron-LM-core_r0.7.0.beta/examples/t5/README.md b/Megatron-LM-core_r0.7.0.beta/examples/t5/README.md new file mode 100644 index 0000000..205da1d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/examples/t5/README.md @@ -0,0 +1,55 @@ +# T5 MODEL + +## Table of contents +- [1. Training Setup](#1-training-setup) +- [2. Configurations](#2-configurations) +- [3. Training Results](#3-training-results) + +## 1. Training setup + +To run the model on a Slurm based cluster +``` +PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:23.09-py3 +ACCOUNT_NAME="" +PARTITION="" +JOB_NAME="" +NUM_NODES=1 +CHECKPOINT_PATH="" # +TENSORBOARD_LOGS_PATH=""# +VOCAB_FILE="" #/bert-large-cased-vocab.txt +DATA_PATH="" #_text_document + +srun -N $NUM_NODES --container-image $PYTORCH_IMAGE --container-mounts "/path/to/data:/path/to/data,/path/to/megatron-lm:/workspace/megatron-lm" --account $ACCOUNT -N 1 -J $JOB_NAME -p $PARTITION --no-container-mount-home -c " + cd /workspace/megatron-lm + ./examples/t5/train_t5_220m_distributed.sh $CHECKPOINT_PATH $TENSORBOARD_LOGS_PATH $VOCAB_FILE $DATA_PATH" + +``` + +## 2. Configurations + +The architecture arguments below shows configuration for T5 220M model. + +### 220M +``` + --num-layers 12 \ + --hidden-size 768 \ + --num-attention-heads 12 \ + --kv-channels 64 \ + --ffn-hidden-size 3072 \ + --encoder-seq-length 512 \ + --decoder-seq-length 128 \ + --max-position-embeddings 512 \ + --tensor-model-parallel-size 1 \ + --pipeline-model-parallel-size 1 \ + +``` + + +## 3. Training Results + +Below is the training curve for the 220M model on Pile dataset. The training takes 4 days on 32 GPUs, with batch size of 2048. + +Finetuning on SQUAD dataset, the validation result is: 63.44\% +

+ +

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b/Megatron-LM-core_r0.7.0.beta/examples/t5/train_t5_220m_distributed.sh @@ -0,0 +1,78 @@ +#!/bin/bash + +# Runs the "220M" parameter model + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NUM_NODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES)) + +CHECKPOINT_PATH=$1 # +TENSORBOARD_DIR=$2 # +VOCAB_FILE=$3 #/bert-large-cased-vocab.txt +DATA_PATH=$4 #_text_document + +DISTRIBUTED_ARGS=" + --nproc_per_node $GPUS_PER_NODE \ + --nnodes $NUM_NODES \ + --node_rank $NODE_RANK \ + --master_addr $MASTER_ADDR \ + --master_port $MASTER_PORT +" + +T5_ARGS=" + --encoder-num-layers 12 \ + --decoder-num-layers 12 \ + --hidden-size 768 \ + --num-attention-heads 12 \ + --kv-channels 64 \ + --ffn-hidden-size 3072 \ + --encoder-seq-length 512 \ + --decoder-seq-length 128 \ + --max-position-embeddings 512 \ + --micro-batch-size 64 \ + --global-batch-size 512 \ + --lr 0.0001 \ + --train-iters 1000000 \ + --lr-decay-iters 1000000 \ + --lr-decay-style linear \ + --min-lr 0.00001 \ + --weight-decay 1e-2 \ + --lr-warmup-fraction .01 \ + --clip-grad 1.0 \ + --bf16 \ + --vocab-extra-ids 100 \ + --init-method-std 0.015 \ + --transformer-impl transformer_engine \ + --tensor-model-parallel-size 1 \ + --pipeline-model-parallel-size 1 \ + --use-mcore-models \ +" + +DATA_ARGS=" + --data-path $DATA_PATH \ + --vocab-file $VOCAB_FILE \ + --tokenizer-type BertWordPieceCase \ + --split 99982,9,9 \ +" + +OUTPUT_ARGS=" + --log-interval 100 \ + --tensorboard-dir ${TENSORBOARD_DIR} \ + --save-interval 500 \ + --eval-interval 1000 \ + --eval-iters 10 +" + +torchrun $DISTRIBUTED_ARGS pretrain_t5.py \ + $T5_ARGS \ + $DATA_ARGS \ + $OUTPUT_ARGS \ + --distributed-backend nccl \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH \ diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/QuickStart.md b/Megatron-LM-core_r0.7.0.beta/megatron/core/QuickStart.md new file mode 100644 index 0000000..42e82a1 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/QuickStart.md @@ -0,0 +1,221 @@ +## Quick Start +The following guide will show you how to quickly get started with Megatron Core. It will show you the following +* We will initalize megatron core on 2 GPUS. +* We will build a GPT model with tensor model parallel size 2, pipeline parallel size 1 +* We will train it for a few iterations using megatron core schedules +* We will save the model using the distributed checkpointing format +* We will load the model saved above. + +*NOTE: The following has been testing for megatron core version 0.5 and NGC Pytorch Container version 24.02 + +### Environment Setup +``` +docker run --ipc=host --shm-size=512m --gpus all -it nvcr.io/nvidia/pytorch:24.02-py3 + +pip install megatron_core +pip install tensorstore==0.1.45 +pip install zarr +``` +
+ +### Writing Your First Training Loop +The following steps will walk you through how you can create a sample GPT model split across tensors (Tensor model parallel ) on 2 GPUS, and run a forward pass through it using a MockGPT dataset helper class that we created in Megatron core. + +
+ +**NOTE: All of the folowing steps needs to be put into a script and then run as explained in the last step** + +
+ +**STEP 1 - Initialize Distributed Training and Model parallel setup** +The following utility when called initalizes your distributed setup. + +```python +import os +import torch +from megatron.core import parallel_state + +def initialize_distributed(tensor_model_parallel_size = 1, pipeline_model_parallel_size = 1): + # Torch setup for distributed training + rank = int(os.environ['LOCAL_RANK']) + world_size = torch.cuda.device_count() + torch.cuda.set_device(rank) + torch.distributed.init_process_group(world_size=world_size, rank=rank) + + # Megatron core distributed training initialization + parallel_state.initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size) +``` +
+ +**STEP 2 - GPT Model Setup** +The following step shows you how you can quickly create a GPT model. For a list of other configs that you can pass into the model look into [transformer_config.py](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core/transformer/transformer_config.py) +``` +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.models.gpt.gpt_model import GPTModel +from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec + +def model_provider(): + """Build the model.""" + + transformer_config = TransformerConfig( + num_layers=2, + hidden_size=12, + num_attention_heads=4, + use_cpu_initialization=True, + pipeline_dtype=torch.float32) + + gpt_model = GPTModel( + config=transformer_config, + transformer_layer_spec=get_gpt_layer_local_spec(), + vocab_size=100, + max_sequence_length=64) + + return gpt_model +``` +
+ +**STEP 3 - GPT Mock dataset setup** +The following shows you how you can quickly get started with a mock dataset utility we created. In order to train with your data, please use the actual GPTDataset class in [gpt_dataset.py](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core/datasets/gpt_dataset.py) + +To find more information about megatron core data pipeline please refer to [this](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core/datasets/readme.md?ref_type=heads) + +``` +from torch.utils.data import DataLoader +from megatron.core.datasets.utils import Split +from megatron.core.datasets.gpt_dataset import GPTDatasetConfig, MockGPTDataset + +def get_train_data_iterator(): + config = GPTDatasetConfig( + random_seed = 0, + sequence_length = 64, + blend=[], + mock=True, + reset_position_ids=False, + reset_attention_mask=False, + eod_mask_loss=False, + tokenizer="dummy") + + training_data= MockGPTDataset(Split.train, config) + + train_dataloader = DataLoader(training_data, batch_size=8, shuffle=True) + + train_iterator = iter(train_dataloader) + return train_iterator +``` +
+ +**STEP 4 - Forward Step Function** +In megatron core, we use [schedules.py](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core/pipeline_parallel/schedules.py) to run the model. So it is sufficient to define a forward step function which takes as input the data iterator and the model and produces as output the output tensor and a loss function + +```python +from functools import partial + +def forward_step_func(data_iterator, model): + + def loss_func(loss_mask: torch.Tensor, output_tensor: torch.Tensor): + + losses = output_tensor.float() + loss_mask = loss_mask.view(-1).float() + loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum() + # If you have data parallel reduce loss across data parallel groups. + # If pipeline parallel, loss computation is done only in last stage. + + return loss, {'lm loss': loss} + + data = next(data_iterator) + tokens = data['tokens'].to(device) + attention_mask = data['attention_mask'].to(device) + position_ids = data['position_ids'].to(device) + labels = data['labels'].to(device) + loss_mask = data['loss_mask'].to(device) + + output_tensor = model(tokens, position_ids, attention_mask, + labels=labels) + + return output_tensor, partial(loss_func, loss_mask) +``` +
+ +**STEP 5 - Load and Save Distributed Checkpoint** +Megatron core uses distributed checkpoint for loading and saving model. This gives you the flexiblity to convert model from one model parallel setting to another when you load a model (i.e A model trained with tensor parallel size 2, can now be loaded as tensor model parallel size 4 etc.) + +*NOTE: Make sure you have zarr and tensorstore pip package installed as shown in the environment setup* + +```python +from megatron.core import dist_checkpointing + +def save_distributed_checkpoint(checkpoint_path, gpt_model): + sharded_state_dict = gpt_model.sharded_state_dict(prefix='') + dist_checkpointing.save(sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path) + +def load_distributed_checkpoint(checkpoint_path, gpt_model): + sharded_state_dict=gpt_model.sharded_state_dict(prefix='') + checkpoint = dist_checkpointing.load(sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path) + gpt_model.load_state_dict(checkpoint) + return gpt_model +``` +
+ +**STEP 6 - Main Function** +The following is the main function that needs to go into your script. +```python +from pathlib import Path +from torch.optim import Adam +from megatron.core.pipeline_parallel.schedules import get_forward_backward_func +from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed + +if __name__ == "__main__": + initialize_distributed(tensor_model_parallel_size=2, pipeline_model_parallel_size=1) + model_parallel_cuda_manual_seed(123) + + gpt_model = model_provider() + device = torch.device("cuda") + gpt_model.to(device) + + optim = Adam(gpt_model.parameters()) + + train_iterator = get_train_data_iterator() + + forward_backward_func = get_forward_backward_func() + + # Running the model for 5 iterations + for _ in range(5): + optim.zero_grad() + + losses_reduced = forward_backward_func( + forward_step_func=forward_step_func, + data_iterator=train_iterator, + model=gpt_model, + num_microbatches=1, + seq_length=64, + micro_batch_size=8, + decoder_seq_length=64, + forward_only=False) + + optim.step() + + print(f'Losses reduced : {losses_reduced}') + + # Saving the model + save_distributed_checkpoint(gpt_model=gpt_model, checkpoint_path='/workspace/ckpt') + + # Loading the model + gpt_model = load_distributed_checkpoint(gpt_model=gpt_model, checkpoint_path='/workspace/ckpt') + gpt_model.to(device) + print('Successfully loaded the model') +``` +
+ +**STEP 7 - Running the full example** +All the above steps are put to gether in a [run_simple_mcore_train_loop.py](https://github.com/NVIDIA/Megatron-LM/tree/main/examples/run_simple_mcore_train_loop.py) script in examples folder in megatron . You can run it as follows + +``` +git clone https://github.com/NVIDIA/Megatron-LM.git +cd Megatron-LM/examples +NUM_GPUS=2 +torchrun --nproc-per-node $NUM_GPUS run_simple_mcore_train_loop.py +``` +
+ +### Extending Further +The above example introduced you to a basic training loop in MCore. To see more advanced examples please look at [pretrain_gpt.py]. That will show you how you can write more complex training loops, involving pipeline parallel, context parallel, rope embeddings, mixture of experts and all other functionalities present in mcore. diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/README.md b/Megatron-LM-core_r0.7.0.beta/megatron/core/README.md new file mode 100644 index 0000000..c69b9e6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/README.md @@ -0,0 +1 @@ +Megatron Core is a library for efficient and scalable training of transformer based models. \ No newline at end of file diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/README_STRAGGLER.md b/Megatron-LM-core_r0.7.0.beta/megatron/core/README_STRAGGLER.md new file mode 100644 index 0000000..de399f7 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/README_STRAGGLER.md @@ -0,0 +1,90 @@ +## StragglerDetector + +The file `megatron/core/utils.py` has a class named `StragglerDetector` which supports Python Contexts +This class supports collecting timing events for various steps of a given iteration. It +keeps collecting such timing events on a per rank basis, and when the reporter is invoked +during a logging interval, it computes the min and max of certain metric across all +ranks and logs the observed metric and the rank as follows + +``` + 0: INFO:megatron.core.utils:[2024-03-14 23:07:56] | MnRtt/Rnk: 3453.08ms/8 | MxRtt/Rnk: 3468.20ms/0 | MnPwr/Rnk: 601796W/8 | MxPwr/Rnk: 683801W/18 | MnTmp/Rnk: 52C/0 | MxTmp/Rnk: 65C/21 | MnUtl/Rnk: 97%/8 | MxUtl/Rnk: 100%/6 | MnClk/Rnk: 1950MHz/28 | MxClk/Rnk: 1980MHz/0 | MnDRtt/Rnk: 14.27us/23 | MxDRtt/Rnk: 34.65us/3 | MnEtpt/Rnk: 296.02TF/0 | MxEtpt/Rnk: 297.32TF/8 +``` +

+ +### Description of the metrics + +Each metric is prefixed with `Mn` or `Mx` to represent `Minimum` or `Maximum`. Each metric is also suffixed with the rank where the metric was measured. The metrics are averaged over the logging interval. Between the prefix and the rank is the name of the metric as follows + +- Rtt : RoundTrip Time (time spent in all the traced ops per iteration) +- Pwr : GPU Power +- Tmp : GPU Temperature +- Utl : GPU Utilization +- Clk : GPU Clock +- DRtt: get_batch latency +- Etpt: Estimated throughput. This is derived from actual computed throughput dividied by Rtt. Since we do not collect timing for backward pass, the value is further divided by three to come up with estimated throughput. +
+ +### Command Line activation +To start using the StragglerDetector, need to pass the following argument `--log-straggler`. It optionally also takes two additional parameters. Default disabled +- `--disable-straggler-on-startup` - whether to keept the StragglerDetector disabled on startup and enable later. Default enabled +- `--straggler-ctrlr-port` - The StragglerDetector can toggle between on/off just by sending `curl Rank0Host:port`. Default port is 65535. Every time it is turned +- `--straggler-minmax-count` - If set to > 1 (N), it prints N Top and Bottom Etpt/Rank pairs as shown below +``` + 0: INFO:megatron.core.utils:^^^^ Bottom 4 Ranks with lowest Etpt(TF): 296.02/0, 296.17/2, 296.23/1, 296.23/4, + 0: INFO:megatron.core.utils:^^^^ Top 4 Ranks with highest Etpt(TF): 297.28/15, 297.28/11, 297.32/12, 297.32/8, +``` +
+ +### Programming the StragglerDetector +The StragglerDetector class supports context, and its implementation is a Singleton. +- Initialization + +``` + # initialization, where StragglerDetector will be used + from megatron.core.utils import StragglerDetector + stimer = StragglerDetector() +``` + +- One time for each rank + +``` + # one time before the training loop starts + stimer.configure(world, rank, enabled=True, port=65545) + + # Arguments to configure + # world : World Size + # rank : The rank of this trainer + # mmcnt : (Optional) Number of ranks to print for showing Min/Max Etpt + # amp : (Optional) Set to 3.0 if we only use timers in fwd pass + # port : (Optional) control port, useful only for rank-0 + # prefill : (Optional) howmany Events to pre-populate + # enabled : (Optional) whether or not collection is enabled on startup +``` + +- To Capture time + +``` + # whereever timing need to be captured + with stimer: + do_operation() + + # special case for get_batch + with stimer(bdata=True): + input,... = get_batch(iterator,...) +``` + +- Logging in main training loop + +``` + # logging + total_flops = 0.0 + iteration = 0 + # inside the main training loop + while training: + iteration += 1 + do_step() + total_flops += get_computed_flops() + if iteration % log_interval: + stimer.report(total_flops, log_interval) + total_flops = 0.0 +``` diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/__init__.py new file mode 100644 index 0000000..b4165eb --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/__init__.py @@ -0,0 +1,20 @@ +import megatron.core.tensor_parallel +import megatron.core.utils +from megatron.core import parallel_state +from megatron.core.distributed import DistributedDataParallel +from megatron.core.inference_params import InferenceParams +from megatron.core.model_parallel_config import ModelParallelConfig +from megatron.core.timers import Timers + +# Alias parallel_state as mpu, its legacy name +mpu = parallel_state + +__all__ = [ + "parallel_state", + "tensor_parallel", + "utils", + "DistributedDataParallel", + "InferenceParams", + "ModelParallelConfig", + "Timers", +] diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/Makefile b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/Makefile new file mode 100644 index 0000000..8f9db76 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/Makefile @@ -0,0 +1,9 @@ +CXXFLAGS += -O3 -Wall -shared -std=c++11 -fPIC -fdiagnostics-color +CPPFLAGS += $(shell python3 -m pybind11 --includes) +LIBNAME = helpers +LIBEXT = $(shell python3-config --extension-suffix) + +default: $(LIBNAME)$(LIBEXT) + +%$(LIBEXT): %.cpp + $(CXX) $(CXXFLAGS) $(CPPFLAGS) $< -o $@ diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/bert_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/bert_dataset.py new file mode 100644 index 0000000..942c3b7 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/bert_dataset.py @@ -0,0 +1,202 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from dataclasses import dataclass +from typing import Dict, List, Optional, Union + +import numpy + +from megatron.core.datasets.indexed_dataset import IndexedDataset +from megatron.core.datasets.masked_dataset import ( + MaskedWordPieceDataset, + MaskedWordPieceDatasetConfig, +) +from megatron.core.datasets.utils import Split + + +@dataclass +class BERTMaskedWordPieceDatasetConfig(MaskedWordPieceDatasetConfig): + """Configuration object for Megatron Core BERT WordPiece datasets""" + + classification_head: bool = None + """Option to perform the next sequence prediction during sampling""" + + def __post_init__(self) -> None: + """Do asserts and set fields post init + """ + super().__post_init__() + + assert self.classification_head is not None + + +class BERTMaskedWordPieceDataset(MaskedWordPieceDataset): + """The BERT dataset that assumes WordPiece tokenization + + Args: + indexed_dataset (IndexedDataset): The IndexedDataset around which to build the MegatronDataset + + dataset_path (str): The real path on disk to the dataset, for bookkeeping + + indexed_indices (numpy.ndarray): The set of the documents indices to expose + + num_samples (int): The number of samples to draw from the indexed dataset + + index_split (Split): The indexed_indices Split + + config (BERTMaskedWordPieceDatasetConfig): The config + """ + + def __init__( + self, + indexed_dataset: IndexedDataset, + dataset_path: str, + indexed_indices: numpy.ndarray, + num_samples: int, + index_split: Split, + config: BERTMaskedWordPieceDatasetConfig, + ) -> None: + super().__init__( + indexed_dataset, dataset_path, indexed_indices, num_samples, index_split, config + ) + + def _finalize(self) -> None: + """Abstract method implementation + """ + self.token_lookup = list(self.config.tokenizer.inv_vocab.keys()) + # Account for the single and two token ids + self.sample_index = self._build_sample_index( + self.config.sequence_length - 3, 2 if self.config.classification_head else 1 + ) + + @staticmethod + def _key_config_attributes() -> List[str]: + """Inherited method implementation + + Returns: + List[str]: The key config attributes + """ + return super( + BERTMaskedWordPieceDataset, BERTMaskedWordPieceDataset + )._key_config_attributes() + ["classification_head",] + + def __getitem__(self, idx: int) -> Dict[str, Union[int, numpy.ndarray]]: + """Abstract method implementation + + Args: + idx (int): The index into the dataset + + Returns: + Dict[str, Union[int, numpy.ndarray]]: The + """ + idx_beg, idx_end, target_sequence_length = self.sample_index[idx] + sample = [self.dataset[i] for i in range(idx_beg, idx_end)] + numpy_random_state = numpy.random.RandomState( + seed=(self.config.random_seed + idx) % 2 ** 32 + ) + + assert target_sequence_length <= self.config.sequence_length + + # Split the sample into contiguous subsegments A and B + pivot = len(sample) + is_next_random = False + if self.config.classification_head: + assert len(sample) > 1, "the sample must contain at least two sentences" + pivot = 1 + if len(sample) >= 3: + pivot = numpy_random_state.randint(low=1, high=len(sample)) + is_next_random = numpy_random_state.random() < 0.5 + split_A = [] + for sample_a in sample[:pivot]: + split_A.extend(sample_a) + split_B = [] + for sample_b in sample[pivot:]: + split_B.extend(sample_b) + if is_next_random: + split_A, split_B = split_B, split_A + + # Trim the subsegments from either end to a desired joint length + length_A = len(split_A) + length_B = len(split_B) + if length_A + length_B <= target_sequence_length: + truncated = False + else: + while length_A + length_B > target_sequence_length: + split = split_A if length_A > length_B else split_B + if numpy_random_state.random() < 0.5: + del split[0] + else: + del split[-1] + length_A = len(split_A) + length_B = len(split_B) + truncated = True + + # Merge the subsegments and create the token assignment labels + tokens = [ + self.config.tokenizer.cls, + *split_A, + self.config.tokenizer.sep, + ] + assignments = [0 for _ in range(1 + len(split_A) + 1)] + if split_B: + tokens += [*split_B, self.config.tokenizer.sep] + assignments += [1 for _ in range(len(split_B) + 1)] + + # Masking + tokens, masked_positions, masked_labels, _, _ = self._create_masked_lm_predictions( + tokens, target_sequence_length, numpy_random_state + ) + + # Pad the sequences and convert to NumPy + length_toks = len(tokens) + length_pads = self.config.sequence_length - length_toks + assert length_pads >= 0 + + tokens = numpy.array(tokens, dtype=numpy.int64) + tokens = numpy.pad(tokens, (0, length_pads), constant_values=self.config.tokenizer.pad) + + assignments = numpy.array(assignments, dtype=numpy.int64) + assignments = numpy.pad( + assignments, (0, length_pads), constant_values=self.config.tokenizer.pad + ) + + # Get the padding mask + mask_pads = numpy.ones(length_toks, dtype=numpy.int64) + mask_pads = numpy.pad( + mask_pads, (0, length_pads), constant_values=self.config.tokenizer.pad + ) + + # Mask the labels + labels = numpy.zeros(self.config.sequence_length, dtype=numpy.int64) - 1 + labels[masked_positions] = masked_labels + + # Get the loss mask + mask_loss = numpy.zeros(self.config.sequence_length, dtype=numpy.int64) + mask_loss[masked_positions] = 1 + + return { + "text": tokens, + "types": assignments, + "labels": labels, + "is_random": int(is_next_random), + "padding_mask": mask_pads, + "loss_mask": mask_loss, + "truncated": int(truncated), + } + + def _get_token_mask(self, numpy_random_state: numpy.random.RandomState) -> Optional[int]: + """Abstract method implementation + + 80% of the time, replace the token id with mask token id. 10% of the time, replace token id + with a random token id from the vocabulary. 10% of the time, do nothing. + + Args: + numpy_random_state (RandomState): The NumPy random state + + Returns: + Optional[int]: The replacement token id or None + """ + if numpy_random_state.random() < 0.8: + return self.config.tokenizer.mask + else: + if numpy_random_state.random() >= 0.5: + return self.token_lookup[numpy_random_state.randint(0, len(self.token_lookup))] + return None diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/blended_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/blended_dataset.py new file mode 100644 index 0000000..a21fe02 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/blended_dataset.py @@ -0,0 +1,192 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import hashlib +import json +import logging +import os +import time +from collections import OrderedDict +from typing import Dict, List, Tuple, Union + +import numpy +import torch + +from megatron.core.datasets.blended_megatron_dataset_config import BlendedMegatronDatasetConfig +from megatron.core.datasets.megatron_dataset import MegatronDataset +from megatron.core.datasets.utils import log_single_rank, normalize + +logger = logging.getLogger(__name__) + +_VERBOSE = False + + +class BlendedDataset(torch.utils.data.Dataset): + """Conjugating class for a set of MegatronDataset instances + + Args: + datasets (List[MegatronDataset]): The MegatronDataset instances to blend + + weights (List[float]): The weights which determines the dataset blend ratios + + size (int): The number of samples to draw from the blend + + config (BlendedMegatronDatasetConfig): The config + + Raises: + RuntimeError: When the dataset has fewer or more samples than 'size' post-initialization + """ + + def __init__( + self, + datasets: List[MegatronDataset], + weights: List[float], + size: int, + config: BlendedMegatronDatasetConfig, + ) -> None: + assert len(datasets) < 32767 + assert len(datasets) == len(weights) + assert numpy.isclose(sum(weights), 1.0) + assert all(map(lambda _: type(_) == type(datasets[0]), datasets)) + + # Alert user to unnecessary blending + if len(datasets) == 1: + log_single_rank( + logger, logging.WARNING, f"Building a BlendedDataset for a single MegatronDataset" + ) + + # Redundant normalization for bitwise identical comparison with Megatron-LM + weights = normalize(weights) + + self.datasets = datasets + self.weights = weights + self.size = size + self.config = config + + unique_identifiers = OrderedDict() + unique_identifiers["class"] = type(self).__name__ + unique_identifiers["datasets"] = [dataset.unique_identifiers for dataset in self.datasets] + unique_identifiers["weights"] = self.weights + unique_identifiers["size"] = self.size + + self.unique_description = json.dumps( + unique_identifiers, indent=4, default=lambda obj: obj.unique_identifiers + ) + self.unique_description_hash = hashlib.md5( + self.unique_description.encode("utf-8") + ).hexdigest() + + self.dataset_index, self.dataset_sample_index = self._build_indices() + + # Check size + _ = self[self.size - 1] + try: + _ = self[self.size] + raise RuntimeError(f"{type(self).__name__} size is improperly bounded") + except IndexError: + log_single_rank(logger, logging.INFO, f"> {type(self).__name__} length: {len(self)}") + + def __len__(self) -> int: + return self.size + + def __getitem__(self, idx: int) -> Dict[str, Union[int, numpy.ndarray]]: + dataset_id = self.dataset_index[idx] + dataset_sample_id = self.dataset_sample_index[idx] + return { + "dataset_id": dataset_id, + **self.datasets[dataset_id][dataset_sample_id], + } + + def _build_indices(self) -> Tuple[numpy.ndarray, numpy.ndarray]: + """Build and optionally cache the dataset index and the dataset sample index + + The dataset index is a 1-D mapping which determines the dataset to query. The dataset + sample index is a 1-D mapping which determines the sample to request from the queried + dataset. + + Returns: + Tuple[numpy.ndarray, numpy.ndarray]: The dataset index and the dataset sample index + """ + path_to_cache = self.config.path_to_cache + + if path_to_cache: + get_path_to = lambda suffix: os.path.join( + path_to_cache, f"{self.unique_description_hash}-{type(self).__name__}-{suffix}" + ) + path_to_description = get_path_to("description.txt") + path_to_dataset_index = get_path_to("dataset_index.npy") + path_to_dataset_sample_index = get_path_to("dataset_sample_index.npy") + cache_hit = all( + map( + os.path.isfile, + [path_to_description, path_to_dataset_index, path_to_dataset_sample_index], + ) + ) + else: + cache_hit = False + + if not path_to_cache or (not cache_hit and torch.distributed.get_rank() == 0): + log_single_rank( + logger, logging.INFO, f"Build and save the {type(self).__name__} indices", + ) + + # Build the dataset and dataset sample indexes + log_single_rank( + logger, logging.INFO, f"\tBuild and save the dataset and dataset sample indexes" + ) + t_beg = time.time() + from megatron.core.datasets import helpers + + dataset_index = numpy.zeros(self.size, dtype=numpy.int16) + dataset_sample_index = numpy.zeros(self.size, dtype=numpy.int64) + helpers.build_blending_indices( + dataset_index, + dataset_sample_index, + self.weights, + len(self.datasets), + self.size, + _VERBOSE, + ) + + if path_to_cache: + os.makedirs(path_to_cache, exist_ok=True) + # Write the description + with open(path_to_description, "wt") as writer: + writer.write(self.unique_description) + # Save the indexes + numpy.save(path_to_dataset_index, dataset_index, allow_pickle=True) + numpy.save(path_to_dataset_sample_index, dataset_sample_index, allow_pickle=True) + else: + log_single_rank( + logger, + logging.WARNING, + "Unable to save the indexes because path_to_cache is None", + ) + + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + return dataset_index, dataset_sample_index + + log_single_rank(logger, logging.INFO, f"Load the {type(self).__name__} indices") + + log_single_rank( + logger, logging.INFO, f"\tLoad the dataset index from {path_to_dataset_index}" + ) + t_beg = time.time() + dataset_index = numpy.load(path_to_dataset_index, allow_pickle=True, mmap_mode='r') + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + log_single_rank( + logger, + logging.INFO, + f"\tLoad the dataset sample index from {path_to_dataset_sample_index}", + ) + t_beg = time.time() + dataset_sample_index = numpy.load( + path_to_dataset_sample_index, allow_pickle=True, mmap_mode='r' + ) + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + return dataset_index, dataset_sample_index diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/blended_megatron_dataset_builder.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/blended_megatron_dataset_builder.py new file mode 100644 index 0000000..0e5115c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/blended_megatron_dataset_builder.py @@ -0,0 +1,332 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import logging +import math +from typing import Any, Callable, Iterable, List, Optional, Tuple, Type, Union + +import numpy +import torch + +from megatron.core.datasets.blended_dataset import BlendedDataset +from megatron.core.datasets.blended_megatron_dataset_config import BlendedMegatronDatasetConfig +from megatron.core.datasets.megatron_dataset import LowLevelDataset, MegatronDataset, MockDataset +from megatron.core.datasets.utils import Split, normalize +from megatron.core.parallel_state import get_virtual_pipeline_model_parallel_rank + +logger = logging.getLogger(__name__) + +MidLevelDataset = Union[MegatronDataset, MockDataset] + +TopLevelDataset = Union[BlendedDataset, MidLevelDataset] + +DistributedDataset = Union[ + TopLevelDataset, MidLevelDataset, LowLevelDataset, torch.utils.data.Dataset +] + + +class BlendedMegatronDatasetBuilder(object): + """Builder class for the BlendedDataset and MegatronDataset classes + + Args: + cls (Type[MegatronDataset]): The class to instantiate, must inherit from MegatronDataset + + sizes (List[int]): The minimum number of total samples to draw from each split, varies with blend + + is_built_on_rank (Callable): A callable which returns True if the dataset should be built on the current rank and False otherwise. It should be Megatron Core parallelism aware i.e. global rank, local group rank, and virtual rank may inform its return value. + + config (BlendedMegatronDatasetConfig): The config object which informs dataset creation + """ + + def __init__( + self, + cls: Type[MidLevelDataset], + sizes: List[int], + is_built_on_rank: Callable, + config: BlendedMegatronDatasetConfig, + ): + self.cls = cls + self.sizes = sizes + self.is_built_on_rank = is_built_on_rank + self.config = config + + assert not self.config.mock or issubclass(self.cls, MockDataset) + + if torch.distributed.is_initialized(): + gb_rank = torch.distributed.get_rank() + vp_rank = get_virtual_pipeline_model_parallel_rank() + if gb_rank == 0 and (vp_rank == 0 or vp_rank is None): + assert ( + self.is_built_on_rank() + ), "is_built_on_rank must return True when global rank = 0 and vp rank = 0" + + def build(self) -> List[Optional[TopLevelDataset]]: + """Build all dataset splits according to the provided blend(s) + + This method is distributed-aware and must be called on all ranks. + + The dataset splits returned can vary according to the config. Supply config.blend and + config.split to build BlendedDataset and/or MegatronDataset splits from the same + distribution. Supply config.blend_per_split to build BlendedDataset and/or MegatronDataset + splits from separate distributions. + + Returns: + List[Optional[TopLevelDataset]]: A list containing a dataset instance (or None) per split + """ + return self._build_blended_dataset_splits() + + def _build_blended_dataset_splits(self,) -> List[Optional[TopLevelDataset]]: + """Build all dataset splits according to the provided blend(s) + + See the BlendedMegatronDatasetBuilder.build alias for more information. + + Returns: + List[Optional[TopLevelDataset]]: A list containing a dataset instance (or None) per split + """ + + # Return fake "mock" datasets + if self.config.mock: + + return self._build_megatron_dataset_splits(None, None, self.sizes) + + # All splits come from the same distribution + elif self.config.blend: + blend = self.config.blend + split = self.config.split_matrix + + # Blend consists of a single prefix + if len(blend) == 1: + return self._build_megatron_dataset_splits(blend[0], split, self.sizes) + + # Blend consists of multiple weights and prefixes + ( + prefix_per_dataset, + weight_per_dataset, + sizes_per_dataset, + ) = _get_prefixes_weights_and_sizes_for_blend(blend, self.sizes) + + megatron_datasets = [[] for _ in range(len(Split))] + + for i in range(len(prefix_per_dataset)): + megatron_datasets_split = self._build_megatron_dataset_splits( + prefix_per_dataset[i], split, sizes_per_dataset[i] + ) + for j in range(len(megatron_datasets_split)): + megatron_datasets[j].append(megatron_datasets_split[j]) + + # Sum over all contributing datasets, per split + size_per_split = list(map(sum, zip(*sizes_per_dataset))) + + blended_datasets = [] + + for i in range(len(megatron_datasets)): + is_none = map(lambda _: _ is None, megatron_datasets[i]) + + if split[i] is None: + assert all(is_none) + blended_datasets.append(None) + else: + assert all(is_none) or not any(is_none) + blended_datasets.append( + self.build_generic_dataset( + BlendedDataset, + self.is_built_on_rank, + megatron_datasets[i], + weight_per_dataset, + size_per_split[i], + self.config, + ) + ) + + return blended_datasets + + # Each split comes from a separate distribution + else: + blended_datasets = [] + for i in range(len(Split)): + blend = self.config.blend_per_split[i] + + # Blend is not provided + if not blend: + blended_datasets.append(None) + continue + + split_spoof = [None] * len(Split) + split_spoof[i] = (0.0, 1.0) + sizes_spoof = [0] * len(Split) + sizes_spoof[i] = self.sizes[i] + + # Blend consists of a sigle prefix + if len(blend) == 1: + blended_datasets.append( + self._build_megatron_dataset_splits(blend[0], split_spoof, sizes_spoof)[i] + ) + + # Blend consists of multiple weights and prefixes + else: + ( + prefix_per_dataset, + weight_per_dataset, + sizes_per_dataset, + ) = _get_prefixes_weights_and_sizes_for_blend(blend, sizes_spoof) + + megatron_datasets = [] + for j in range(len(prefix_per_dataset)): + megatron_datasets.append( + self._build_megatron_dataset_splits( + prefix_per_dataset[j], split_spoof, sizes_per_dataset[j], + )[i] + ) + + size_per_split = list(map(sum, zip(*sizes_per_dataset))) + + blended_datasets.append( + self.build_generic_dataset( + BlendedDataset, + self.is_built_on_rank, + megatron_datasets, + weight_per_dataset, + size_per_split[i], + self.config, + ) + ) + + return blended_datasets + + def _build_megatron_dataset_splits( + self, dataset_path: Optional[str], split: List[float], sizes: List[int], + ) -> List[Optional[MidLevelDataset]]: + """Build each MidLevelDataset split from a single LowLevelDataset + + Args: + dataset_path (Optional[str]): The path on disk which defines the underlying LowLevelDataset, e.g. the .bin and .idx file prefix when self.cls is of type IndexedMegatronDataset or None when self.cls is of type MockDataset + + split (List[Tuple[float, float]]): The dataset split matrix + + sizes (List[int]): The number of total samples to draw from each split + + Returns: + List[Optional[MidLevelDataset]]: The MidLevelDataset (or None) per split + """ + # Build the low level dataset + if issubclass(self.cls, MockDataset): + low_level_dataset = None + elif issubclass(self.cls, MegatronDataset): + low_level_dataset = self.cls.build_low_level_dataset(dataset_path, self.config) + else: + raise NotImplementedError + + # Build the split indices for the low level dataset + if low_level_dataset is not None: + num_elements = self.cls.numel_low_level_dataset(low_level_dataset) + split_indices = [] + for i, _ in enumerate(Split): + if split[i] is not None: + beg = int(round(split[i][0] * float(num_elements))) + end = int(round(split[i][1] * float(num_elements))) + split_indices.append( + numpy.arange(start=beg, stop=end, step=1, dtype=numpy.int32) + ) + else: + split_indices.append(None) + else: + split_indices = [None for _ in Split] + + # Build the mid level dataset + mid_level_datasets = [] + for i, _split in enumerate(Split): + if not self.config.mock and split[i] is None: + mid_level_datasets.append(None) + else: + mid_level_datasets.append( + self.build_generic_dataset( + self.cls, + self.is_built_on_rank, + low_level_dataset, + dataset_path, + split_indices[i], + sizes[i], + _split, + self.config, + ) + ) + + return mid_level_datasets + + @staticmethod + def build_generic_dataset( + cls: Union[Type[DistributedDataset], Callable], is_built_on_rank: Callable, *args: Any + ) -> Optional[Union[DistributedDataset, Iterable]]: + """Build the DistributedDataset + + Return None if and only if the underlying dataset class is not built on the current rank + and torch.distributed is initialized. + + Args: + cls (Union[Type[DistributedDataset], Callable]): The DistributedDataset class to be built. In special cases, e.g. when we are building the low level dataset for a RawMegatronDataset instance, we can accept a Callable which returns an Iterable. + + args (Tuple[Any]): The positional arguments used to build the provided DistributedDataset class + + Raises: + Exception: When the dataset constructor raises an OSError + + Returns: + Optional[Union[DistributedDataset, Iterable]]: The DistributedDataset instantion, the Iterable instantiation, or None + """ + if torch.distributed.is_initialized(): + rank = torch.distributed.get_rank() + + dataset = None + + # First, build on rank 0 + if rank == 0 and is_built_on_rank(): + try: + dataset = cls(*args) + except OSError as err: + log = ( + f"Failed to write dataset materials to the data cache directory. " + + f"Please supply a directory to which you have write access via " + + f"the path_to_cache attribute in BlendedMegatronDatasetConfig and " + + f"retry. Refer to the preserved traceback above for more information." + ) + raise Exception(log) from err + + torch.distributed.barrier() + + # After, build on other ranks + if rank != 0 and is_built_on_rank(): + dataset = cls(*args) + + return dataset + + return cls(*args) + + +def _get_prefixes_weights_and_sizes_for_blend( + blend: List[str], target_num_samples_per_split: List[int] +) -> Tuple[List[str], List[float], List[List[int]]]: + """Determine the contribution of the MegatronDataset splits to the BlendedDataset splits + + Args: + blend (List[str]): e.g. ["30", "path/to/dataset_1_prefix", "70", "path/to/dataset_2_prefix"] + + target_num_samples_per_split (List[int]): The number of samples to target for each BlendedDataset split + + Returns: + Tuple[List[str], List[float], List[List[int]]]: The prefix strings e.g. ["path/to/dataset_1_prefix", "path/to/dataset_2_prefix"], the normalized weights e.g. [0.3, 0.7], and the number of samples to request per MegatronDataset per split + """ + weights, prefixes = zip( + *[(float(blend[i]), blend[i + 1].strip()) for i in range(0, len(blend), 2)] + ) + + weights = normalize(weights) + + # Use 0.5% target margin to ensure we satiate the network + sizes_per_dataset = [ + [ + int(math.ceil(target_num_samples * weight * 1.005)) + for target_num_samples in target_num_samples_per_split + ] + for weight in weights + ] + + return prefixes, weights, sizes_per_dataset diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/blended_megatron_dataset_config.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/blended_megatron_dataset_config.py new file mode 100644 index 0000000..41ef1c1 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/blended_megatron_dataset_config.py @@ -0,0 +1,145 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import functools +import logging +import re +from dataclasses import dataclass, field +from typing import List, Optional, Tuple + +import torch + +from megatron.core.datasets.megatron_tokenizer import MegatronTokenizer +from megatron.core.datasets.utils import Split, log_single_rank, normalize + +logger = logging.getLogger(__name__) + + +@dataclass +class BlendedMegatronDatasetConfig: + """Configuration object for Megatron Core datasets""" + + random_seed: int + """The seed for all RNG during dataset creation.""" + + sequence_length: int + """The sequence length.""" + + blend: Optional[List[str]] = None + """The blend string, consisting of either a single dataset or a flattened sequential sequence of + weight-dataset pairs. For exampe, ["dataset-path1"] and ["50", "dataset-path1", "50", + "dataset-path2"] are both valid. Not to be used with 'blend_per_split'. Defaults to None. + """ + + blend_per_split: Optional[List[Optional[List[str]]]] = None + """A set of blend strings, as defined above, one for each split distribution. Not to be used + with 'blend'. Defauls to None. + """ + + split: Optional[str] = None + """The split string, a comma separated weighting for the dataset splits when drawing samples + from a single distribution. Not to be used with 'blend_per_split'. Defaults to None. + """ + + split_matrix: Optional[List[Tuple[float, float]]] = field(init=False, default=None) + """The split matrix consisting of non-overlapping book-ends of each split in order. For more + information, refer to 'convert_split_vector_to_split_matrix'. Created automatically from + 'split'. Not to be passed in to the constructor. + """ + + path_to_cache: Optional[str] = None + """Where all re-useable dataset indices are to be cached.""" + + mmap_bin_files: bool = True + """Whether to mmap the .bin files or use file pointer.""" + + mock: bool = False + """Whether to bypass real data loading and validation in favor of mock data generation.""" + + tokenizer: Optional[MegatronTokenizer] = None + """The MegatronTokenizer instance or None. Required for datasets which do online tokenization.""" + + def __post_init__(self) -> None: + """Do asserts and set fields post init + """ + log_single_rank(logger, logging.INFO, f"mock = {self.mock}") + + if not self.mock: + if self.blend_per_split is not None and any(self.blend_per_split): + assert self.blend is None, "blend and blend_per_split are incompatible" + assert self.split is None, "split and blend_per_split are incompatible" + assert len(self.blend_per_split) == len( + Split + ), f"blend_per_split must contain {len(Split)} blends" + else: + assert ( + self.blend is not None + ), "one of either blend or blend_per_split must be provided" + assert self.split is not None, "both blend and split must be provided" + split_vector = parse_and_normalize_split(self.split) + self.split_matrix = convert_split_vector_to_split_matrix(split_vector) + log_single_rank(logger, logging.INFO, f"Let split_matrix = {self.split_matrix}") + + +def parse_and_normalize_split(split: str) -> List[float]: + """Parse the dataset split ratios from a string + + Args: + split (str): The train valid test split string e.g. "99,1,0" + + Returns: + List[float]: The trian valid test split ratios e.g. [0.99, 0.01, 0.0] + """ + split = list(map(float, re.findall(r"[.0-9]+", split))) + split = split + [0.0 for _ in range(len(Split) - len(split))] + + assert len(split) == len(Split) + assert all(map(lambda _: _ >= 0.0, split)) + + split = normalize(split) + + return split + + +def convert_split_vector_to_split_matrix( + vector_a: List[float], vector_b: Optional[List[float]] = None +) -> List[Optional[Tuple[float, float]]]: + """Build the split matrix from one or optionally two contributing split vectors. + + Ex. a standard conversion: + + [0.99, 0.01, 0.0] -> [(0, 0.99), (0.99, 1.0), None] + + Ex. a conversion for Retro when Retro pretraining uses a [0.99, 0.01, 0.0] split and Retro + preprocessing used a [0.98, 0.02, 0.0] split: + + [0.99, 0.01, 0.0], [0.98, 0.02, 0.0] -> [(0, 0.98), (0.99, 1.0), None] + + Args: + vector_a (List[float]): The primary split vector + + vector_b (Optional[List[float]]): An optional secondary split vector which constrains the primary split vector. Defaults to None. + + Returns: + List[Tuple[float, float]]: The split matrix consisting of book-ends of each split in order + """ + if vector_b is None: + vector_b = vector_a + + # [.900, .090, .010] -> [0.00, .900, .990, 100] + expansion_a = functools.reduce(lambda a, b: a + [a[len(a) - 1] + b], [[0], *vector_a]) + expansion_b = functools.reduce(lambda a, b: a + [a[len(a) - 1] + b], [[0], *vector_b]) + + # [0.00, .900, .990, 100.0] -> [(0.00, .900), (.900, .990), (.990, 100)] + bookends_a = list(zip(expansion_a[:-1], expansion_a[1:])) + bookends_b = list(zip(expansion_b[:-1], expansion_b[1:])) + + # gather per-split overlap or None + matrix = [] + for bookend_a, bookend_b in zip(bookends_a, bookends_b): + if min(bookend_a[1], bookend_b[1]) <= max(bookend_a[0], bookend_b[0]): + overlap = None + else: + overlap = (max(bookend_a[0], bookend_b[0]), min(bookend_a[1], bookend_b[1])) + matrix.append(overlap) + + return matrix diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/gpt_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/gpt_dataset.py new file mode 100644 index 0000000..fc98002 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/gpt_dataset.py @@ -0,0 +1,716 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import logging +import os +import sys +import time +from dataclasses import dataclass +from typing import Dict, Optional, Tuple + +import numpy +import torch + +from megatron.core.datasets.blended_megatron_dataset_config import BlendedMegatronDatasetConfig +from megatron.core.datasets.indexed_dataset import IndexedDataset +from megatron.core.datasets.megatron_dataset import LowLevelDataset, MegatronDataset, MockDataset +from megatron.core.datasets.utils import Split, log_single_rank + +logger = logging.getLogger(__name__) + + +@dataclass +class GPTDatasetConfig(BlendedMegatronDatasetConfig): + """Configuration object for Megatron Core GPT datasets""" + + reset_position_ids: bool = None + """Option to reset the position IDs in the dataset at an interval""" + + reset_attention_mask: bool = None + """Option to reset the attention mask from the dataset""" + + eod_mask_loss: bool = None + """Option to enable the EOD mask loss""" + + create_attention_mask: bool = True + """Option to enable the attention masks generation. Can be disabled if attention kernel + generates masks by itself. + """ + + def __post_init__(self) -> None: + """Do asserts and set fields post init + """ + super().__post_init__() + + assert self.tokenizer is not None + + assert self.reset_position_ids is not None + assert self.reset_attention_mask is not None + assert self.eod_mask_loss is not None + + +class MockGPTDataset(MockDataset): + """The mock GPT dataset + """ + + def __init__( + self, + dataset: Optional[LowLevelDataset], + dataset_path: Optional[str], + indices: Optional[numpy.ndarray], + num_samples: int, + index_split: Split, + config: BlendedMegatronDatasetConfig, + ) -> None: + super().__init__(dataset, dataset_path, indices, num_samples, index_split, config) + + self.masks_and_position_ids_are_cacheable = not any( + [ + self.config.reset_position_ids, + self.config.reset_attention_mask, + self.config.eod_mask_loss, + ] + ) + self.masks_and_position_ids_are_cached = False + self.cached_attention_mask = None + self.cached_loss_mask = None + self.cached_position_ids = None + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + """Return a sequence_length + 1 token sequence consisting of the following: + - (1) S, the RNG length-sentinel in the range [0, sequence_length) + - (S) tokens + - (1) end of document token + - (sequence_length - S - 1) padding tokens + + Args: + idx (int): The integer seed for mock data generation + + Returns: + Dict[str, numpy.ndarray]: The mock data + """ + tok = 1 + pad = 2 + eod = 0 + + assert ( + idx < self.num_samples, + "Exceeded the available number of samples ({self.num_samples})", + ) + + rng = numpy.random.default_rng(seed=[self.index_split.value, idx]) + length = rng.integers(low=0, high=self.config.sequence_length) + sample_toks = numpy.zeros(length) + tok + sample_pads = numpy.zeros(self.config.sequence_length - length - 1) + pad + sample = numpy.int64(numpy.concatenate([[length], sample_toks, [eod], sample_pads])) + + text = torch.from_numpy(sample).long() + labels = text[1:].contiguous() + tokens = text[:-1].contiguous() + + if ( + not self.masks_and_position_ids_are_cacheable + or not self.masks_and_position_ids_are_cached + ): + attention_mask, loss_mask, position_ids = _get_ltor_masks_and_position_ids( + tokens, + eod, + self.config.reset_position_ids, + self.config.reset_attention_mask, + self.config.eod_mask_loss, + self.config.create_attention_mask, + ) + if self.masks_and_position_ids_are_cacheable: + self.cached_attention_mask = attention_mask + self.cached_loss_mask = loss_mask + self.cached_position_ids = position_ids + self.masks_and_position_ids_are_cached = True + else: + attention_mask = self.cached_attention_mask + loss_mask = self.cached_loss_mask + position_ids = self.cached_position_ids + + if self.config.create_attention_mask: + return { + "tokens": tokens, + "labels": labels, + "attention_mask": attention_mask, + "loss_mask": loss_mask, + "position_ids": position_ids, + } + else: + return { + "tokens": tokens, + "labels": labels, + "loss_mask": loss_mask, + "position_ids": position_ids, + } + + +class GPTDataset(MegatronDataset): + """The base GPT dataset + + Args: + indexed_dataset (IndexedDataset): The IndexedDataset around which to build the MegatronDataset + + dataset_path (str): The real path on disk to the dataset, for bookkeeping + + indexed_indices (numpy.ndarray): The set of the documents indices to expose + + num_samples (int): The number of samples to draw from the indexed dataset + + index_split (Split): The indexed_indices Split + + config (GPTDatasetConfig): The config + """ + + def __init__( + self, + indexed_dataset: IndexedDataset, + dataset_path: str, + indexed_indices: numpy.ndarray, + num_samples: int, + index_split: Split, + config: GPTDatasetConfig, + ) -> None: + super().__init__( + indexed_dataset, dataset_path, indexed_indices, num_samples, index_split, config + ) + self.masks_and_position_ids_are_cacheable = not any( + [ + self.config.reset_position_ids, + self.config.reset_attention_mask, + self.config.eod_mask_loss, + ] + ) + self.masks_and_position_ids_are_cached = False + self.cached_attention_mask = None + self.cached_loss_mask = None + self.cached_position_ids = None + + def _finalize(self) -> None: + """Abstract method implementation + + Load or build/cache the document, sample, and shuffle indices + """ + ( + self.document_index, + self.sample_index, + self.shuffle_index, + ) = self._build_document_sample_shuffle_indices() + + @staticmethod + def numel_low_level_dataset(low_level_dataset: IndexedDataset) -> int: + """Abstract method implementation + + For GPT, the underlying IndexedDataset should be split by sequence, as opposed to, say, + BERT, which should be split by document + + Args: + low_level_dataset (IndexedDataset): The underlying IndexedDataset + + Returns: + int: The number of unique elements in the underlying IndexedDataset + """ + return low_level_dataset.sequence_lengths.shape[0] + + @staticmethod + def build_low_level_dataset(dataset_path: str, config: GPTDatasetConfig) -> IndexedDataset: + """Abstract method implementation + + Args: + dataset_path (str): The real path prefix to the IndexedDataset .bin and .idx files + + config (BlendedMegatronDatasetConfig): The dataset config + + Returns: + IndexedDataset: The underlying IndexedDataset + """ + return IndexedDataset(dataset_path, multimodal=False, mmap=config.mmap_bin_files) + + def __len__(self) -> int: + """Abstract method implementation + + Returns: + int: The length of the dataset + """ + return self.sample_index.shape[0] - 1 + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + """Abstract method implementation + + Args: + idx (int): The index into the dataset + + Returns: + Dict[str, torch.Tensor]: The text ids wrapped in a dictionary + """ + text, _ = self._query_document_sample_shuffle_indices(idx) + + text = torch.from_numpy(text).long() + labels = text[1:].contiguous() + tokens = text[:-1].contiguous() + + assert not torch.any( + tokens >= self.config.tokenizer.vocab_size + ), "An input token is out of bounds of the tokenizer vocabulary" + + if ( + not self.masks_and_position_ids_are_cacheable + or not self.masks_and_position_ids_are_cached + ): + attention_mask, loss_mask, position_ids = _get_ltor_masks_and_position_ids( + tokens, + self.config.tokenizer.eod, + self.config.reset_position_ids, + self.config.reset_attention_mask, + self.config.eod_mask_loss, + self.config.create_attention_mask, + ) + if self.masks_and_position_ids_are_cacheable: + self.cached_attention_mask = attention_mask + self.cached_loss_mask = loss_mask + self.cached_position_ids = position_ids + self.masks_and_position_ids_are_cached = True + else: + attention_mask = self.cached_attention_mask + loss_mask = self.cached_loss_mask + position_ids = self.cached_position_ids + + if self.config.create_attention_mask: + return { + "tokens": tokens, + "labels": labels, + "attention_mask": attention_mask, + "loss_mask": loss_mask, + "position_ids": position_ids, + } + else: + return { + "tokens": tokens, + "labels": labels, + "loss_mask": loss_mask, + "position_ids": position_ids, + } + + def _query_document_sample_shuffle_indices( + self, idx: int + ) -> Tuple[numpy.ndarray, numpy.ndarray]: + """Get the text (token ids) and document ids for a given index + + Args: + idx (int): The index into the dataset + + Returns: + Tuple[numpy.ndarray, numpy.ndarray]: The text ids and document ids + """ + # Do the shuffle mapping + idx = self.shuffle_index[idx] + + # Get the beginning and end documents and offsets + doc_index_beg, doc_index_beg_offset = self.sample_index[idx] + doc_index_end, doc_index_end_offset = self.sample_index[idx + 1] + + document_ids = [] + sample_parts = [] + + # Sample spans a single document + if doc_index_beg == doc_index_end: + # Add the document id + document_ids.append(self.document_index[doc_index_beg]) + + # Add the entire sample + sample_parts.append( + self.dataset.get( + self.document_index[doc_index_beg], + offset=doc_index_beg_offset, + length=doc_index_end_offset - doc_index_beg_offset + 1, + ) + ) + + # Sample spans multiple documents + else: + for i in range(doc_index_beg, doc_index_end + 1): + # Add the document id + document_ids.append(self.document_index[i]) + + # Add the sample part + offset = 0 if i > doc_index_beg else doc_index_beg_offset + length = None if i < doc_index_end else doc_index_end_offset + 1 + sample_parts.append( + self.dataset.get(self.document_index[i], offset=offset, length=length) + ) + + return ( + numpy.array(numpy.concatenate(sample_parts), dtype=numpy.int64), + numpy.array(document_ids, dtype=numpy.int64), + ) + + def _build_document_sample_shuffle_indices( + self, + ) -> Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]: + """Build the document index, the sample index, and the shuffle index + + The document index: + -- 1-D + -- An ordered array of document ids + + The sample index: + -- 2-D + -- The document indices and offsets which mark the start of every sample + + The shuffle index: + -- 1-D + -- A random permutation of index range of the sample index + + Returns: + Tuple[numpy.ndarray, numpy.ndarray]: The document index, the sample index, and the shuffle index + """ + path_to_cache = self.config.path_to_cache + if path_to_cache is None: + path_to_cache = os.path.join( + self.dataset.path_prefix, "cache", f"{type(self).__name__}_indices" + ) + + get_path_to = lambda suffix: os.path.join( + path_to_cache, f"{self.unique_description_hash}-{type(self).__name__}-{suffix}" + ) + path_to_description = get_path_to("description.txt") + path_to_document_index = get_path_to("document_index.npy") + path_to_sample_index = get_path_to("sample_index.npy") + path_to_shuffle_index = get_path_to("shuffle_index.npy") + cache_hit = all( + map( + os.path.isfile, + [ + path_to_description, + path_to_document_index, + path_to_sample_index, + path_to_shuffle_index, + ], + ) + ) + + if not cache_hit and ( + not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0 + ): + + log_single_rank( + logger, + logging.INFO, + f"Build and save the {type(self).__name__} {self.index_split.name} indices", + ) + + sequence_length = self.config.sequence_length + num_tokens_per_epoch = self._get_num_tokens_per_epoch() + num_epochs = self._get_num_epochs(num_tokens_per_epoch) + + if num_epochs == 1: + separate_final_epoch = False + else: + # Get the number of samples for the last epoch + num_samples_sans_final_epoch = ( + (num_epochs - 1) * num_tokens_per_epoch - 1 + ) // sequence_length + num_samples_from_final_epoch = self.num_samples - num_samples_sans_final_epoch + num_samples_per_epoch = (num_tokens_per_epoch - 1) // sequence_length + + # num_samples_from_final_epoch should be non-negative + assert num_samples_from_final_epoch >= 0 + + # num_samples_from_final_epoch should not exceed max value + assert num_samples_from_final_epoch <= num_samples_per_epoch + 1 + + # Separate the final epoch if it falls below the threshold + threshold = 0.80 + separate_final_epoch = num_samples_from_final_epoch < int( + threshold * num_samples_per_epoch + ) + + log_single_rank( + logger, + logging.DEBUG, + f"> num_samples_from_final_epoch: {num_samples_from_final_epoch}", + ) + log_single_rank(logger, logging.DEBUG, f"> threshold: {threshold}") + log_single_rank( + logger, logging.DEBUG, f"> num_samples_per_epoch: {num_samples_per_epoch}" + ) + + log_single_rank( + logger, logging.DEBUG, f"> separate_final_epoch: {separate_final_epoch}" + ) + + numpy_random_state = numpy.random.RandomState(self.config.random_seed) + + os.makedirs(path_to_cache, exist_ok=True) + + # Write the description + with open(path_to_description, "wt") as writer: + writer.write(self.unique_description) + + # Build the document index + log_single_rank( + logger, + logging.INFO, + f"\tBuild and save the document index to {os.path.basename(path_to_document_index)}", + ) + t_beg = time.time() + document_index = _build_document_index( + self.indices, num_epochs, numpy_random_state, separate_final_epoch + ) + numpy.save(path_to_document_index, document_index, allow_pickle=True) + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + # Build the sample index + log_single_rank( + logger, + logging.INFO, + f"\tBuild and save the sample index to {os.path.basename(path_to_sample_index)}", + ) + t_beg = time.time() + from megatron.core.datasets import helpers + + assert document_index.dtype == numpy.int32 + assert self.dataset.sequence_lengths.dtype == numpy.int32 + sample_index = helpers.build_sample_idx( + self.dataset.sequence_lengths, + document_index, + sequence_length, + num_epochs, + num_tokens_per_epoch, + ) + numpy.save(path_to_sample_index, sample_index, allow_pickle=True) + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + # Build the shuffle index + log_single_rank( + logger, + logging.INFO, + f"\tBuild and save the shuffle index to {os.path.basename(path_to_shuffle_index)}", + ) + t_beg = time.time() + if separate_final_epoch: + shuffle_index = _build_shuffle_index( + num_samples_sans_final_epoch, sample_index.shape[0] - 1, numpy_random_state + ) + else: + shuffle_index = _build_shuffle_index( + sample_index.shape[0] - 1, sample_index.shape[0] - 1, numpy_random_state + ) + numpy.save(path_to_shuffle_index, shuffle_index, allow_pickle=True) + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + log_single_rank( + logger, logging.INFO, f"> total number of samples: {sample_index.shape[0] - 1}" + ) + log_single_rank(logger, logging.INFO, f"> total number of epochs: {num_epochs}") + + return document_index, sample_index, shuffle_index + + log_single_rank( + logger, logging.INFO, f"Load the {type(self).__name__} {self.index_split.name} indices" + ) + + log_single_rank( + logger, + logging.INFO, + f"\tLoad the document index from {os.path.basename(path_to_document_index)}", + ) + t_beg = time.time() + document_index = numpy.load(path_to_document_index, allow_pickle=True, mmap_mode='r') + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + log_single_rank( + logger, + logging.INFO, + f"\tLoad the sample index from {os.path.basename(path_to_sample_index)}", + ) + t_beg = time.time() + sample_index = numpy.load(path_to_sample_index, allow_pickle=True, mmap_mode='r') + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + log_single_rank( + logger, + logging.INFO, + f"\tLoad the shuffle index from {os.path.basename(path_to_shuffle_index)}", + ) + t_beg = time.time() + shuffle_index = numpy.load(path_to_shuffle_index, allow_pickle=True, mmap_mode='r') + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + log_single_rank( + logger, logging.INFO, f"> total number of samples: {sample_index.shape[0] - 1}" + ) + + return document_index, sample_index, shuffle_index + + def _get_num_tokens_per_epoch(self) -> int: + """Calculate the number of tokens in a single epoch + + Returns: + int: The number of tokens in a single epoch + """ + return int(numpy.sum(self.dataset.sequence_lengths[self.indices])) + + def _get_num_epochs(self, num_tokens_per_epoch: int) -> int: + """Calculate the number of epochs + + Args: + num_tokens_per_epoch (int): The number of tokens in a single epoch + + Returns: + int: The number of epochs + """ + num_epochs = 0 + num_tokens = 0 + num_tokens_requested = (self.num_samples * self.config.sequence_length) + 1 + while True: + num_epochs += 1 + num_tokens += num_tokens_per_epoch + if num_tokens >= num_tokens_requested: + return num_epochs + + +def _build_document_index( + documents: numpy.ndarray, + num_epochs: int, + numpy_random_state: numpy.random.RandomState, + separate_final_epoch: bool, +) -> numpy.ndarray: + """Build an array with length = num epochs * num documents + + Args: + documents (numpy.ndarray): the subset of exposed document indices + + num_epochs (int): The number of epochs + + numpy_random_state (numpy.random.RandomState): The NumPy random state + + separate_final_epoch (bool): Whether to exclude the last epoch from the global shuffle + + Returns: + numpy.ndarray: The document index + """ + if not separate_final_epoch or num_epochs == 1: + document_index = numpy.mgrid[0:num_epochs, 0 : len(documents)][1] + document_index[:] = documents + document_index = document_index.reshape(-1) + document_index = document_index.astype(numpy.int32) + numpy_random_state.shuffle(document_index) + return document_index + + doc_idx_first = _build_document_index(documents, num_epochs - 1, numpy_random_state, False) + doc_idx_last = _build_document_index(documents, 1, numpy_random_state, False) + return numpy.concatenate((doc_idx_first, doc_idx_last)) + + +def _build_shuffle_index( + num_samples: int, total_size: int, numpy_random_state: numpy.random.RandomState +) -> numpy.ndarray: + """Build the range [0, size) and shuffle + + Args: + num_samples (int): The size of the first shuffle range [0, num_samples) + + total_size (int): The size of the entire index. If larger than 'num_samples', it defines the second shuffle range [num_samples, total_size) + + numpy_random_state (numpy.random.RandomState): The NumPy random state + + Returns: + numpy.ndarray: The shuffle index + """ + dtype_ = numpy.uint32 + if total_size >= (numpy.iinfo(numpy.uint32).max - 1): + dtype_ = numpy.int64 + + shuffle_idx_first = numpy.arange(start=0, stop=num_samples, step=1, dtype=dtype_) + numpy_random_state.shuffle(shuffle_idx_first) + if num_samples == total_size: + return shuffle_idx_first + + shuffle_idx_last = numpy.arange(start=num_samples, stop=total_size, step=1, dtype=dtype_) + numpy_random_state.shuffle(shuffle_idx_last) + + return numpy.concatenate((shuffle_idx_first, shuffle_idx_last)) + + +def _get_ltor_masks_and_position_ids( + data: torch.Tensor, + eod_token: int, + reset_position_ids: bool, + reset_attention_mask: bool, + eod_mask_loss: bool, + create_attention_mask: bool, +): + """Build masks and position id for left to right model. + + Args: + data (torch.Tensor): The data tenor that holds the tokens from the dataset + + eod_token (int): ID of the token to that is considered the EOD + + reset_position_ids (bool): Switch to reset the document position ID's + + reset_attention_mask (bool): Switch to reset the attention mask + + eod_mask_loss (bool): Switch to enable the EOD mask loss + + create_attention_mask (bool): Switch to enable the attention masks generation. Can be disabled if attention kernel generates masks by itself. + + Returns: + torch.Tensor: Attention mask needed to be used for Attention + + torch.Tensor: The mask used for loss value during training + + torch.Tensor: The position ID's of the token + """ + seq_length = data.numel() + + if create_attention_mask: + attention_mask = torch.tril( + torch.ones((seq_length, seq_length), device=data.device) + ).unsqueeze(0) + else: + attention_mask = None + + # Loss mask. + loss_mask = torch.ones(seq_length, dtype=torch.float, device=data.device) + if eod_mask_loss: + loss_mask[data == eod_token] = 0.0 + + # Position ids. + position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device) + # We need to clone as the ids will be modifed based on batch index. + if reset_position_ids: + position_ids = position_ids.clone() + + if reset_position_ids or reset_attention_mask: + # Find indices where EOD token is. + eod_index = position_ids[data == eod_token] + # Detach indices from positions if going to modify positions. + if reset_position_ids: + eod_index = eod_index.clone() + + # Loop through EOD indices: + prev_index = 0 + for j in range(eod_index.numel()): + i = eod_index[j] + # Mask attention loss. + if reset_attention_mask and attention_mask is not None: + attention_mask[0, (i + 1) :, : (i + 1)] = 0 + # Reset positions. + if reset_position_ids: + position_ids[(i + 1) :] -= i + 1 - prev_index + prev_index = i + 1 + + if attention_mask is not None: + # Convert attention mask to binary: + attention_mask = attention_mask < 0.5 + + return attention_mask, loss_mask, position_ids diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/helpers.cpp b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/helpers.cpp new file mode 100644 index 0000000..4e1b3db --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/helpers.cpp @@ -0,0 +1,765 @@ +/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. */ + +/* Helper methods for fast index mapping builds */ + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace py = pybind11; +using namespace std; + +const int32_t LONG_SENTENCE_LEN = 512; + +void build_blending_indices(py::array_t &dataset_index, + py::array_t &dataset_sample_index, + const py::array_t &weights, + const int32_t num_datasets, + const int64_t size, const bool verbose) +{ + /* Given multiple datasets and a weighting array, build samples + such that it follows those wieghts.*/ + + if (verbose) + { + std::cout << "> building indices for blended datasets ..." << std::endl; + } + + // Get the pointer access without the checks. + auto dataset_index_ptr = dataset_index.mutable_unchecked<1>(); + auto dataset_sample_index_ptr = dataset_sample_index.mutable_unchecked<1>(); + auto weights_ptr = weights.unchecked<1>(); + + // Initialize buffer for number of samples used for each dataset. + int64_t current_samples[num_datasets]; + for (int64_t i = 0; i < num_datasets; ++i) + { + current_samples[i] = 0; + } + + // For each sample: + for (int64_t sample_idx = 0; sample_idx < size; ++sample_idx) + { + + // Determine where the max error in sampling is happening. + auto sample_idx_double = std::max(static_cast(sample_idx), 1.0); + int64_t max_error_index = 0; + double max_error = weights_ptr[0] * sample_idx_double - + static_cast(current_samples[0]); + for (int64_t dataset_idx = 1; dataset_idx < num_datasets; ++dataset_idx) + { + double error = weights_ptr[dataset_idx] * sample_idx_double - + static_cast(current_samples[dataset_idx]); + if (error > max_error) + { + max_error = error; + max_error_index = dataset_idx; + } + } + + // Populate the indices. + dataset_index_ptr[sample_idx] = static_cast(max_error_index); + dataset_sample_index_ptr[sample_idx] = current_samples[max_error_index]; + + // Update the total samples. + current_samples[max_error_index] += 1; + } + + // print info + if (verbose) + { + std::cout << " > sample ratios:" << std::endl; + for (int64_t dataset_idx = 0; dataset_idx < num_datasets; ++dataset_idx) + { + auto ratio = static_cast(current_samples[dataset_idx]) / + static_cast(size); + std::cout << " dataset " << dataset_idx << ", input: " << weights_ptr[dataset_idx] << ", achieved: " << ratio << std::endl; + } + } +} + +py::array build_sample_idx(const py::array_t &sizes_, + const py::array_t &doc_idx_, + const int32_t seq_length, + const int32_t num_epochs, + const int64_t tokens_per_epoch) +{ + /* Sample index (sample_idx) is used for gpt2 like dataset for which + the documents are flattened and the samples are built based on this + 1-D flatten array. It is a 2D array with sizes [number-of-samples + 1, 2] + where [..., 0] contains the index into `doc_idx` and [..., 1] is the + starting offset in that document.*/ + + // Consistency checks. + assert(seq_length > 1); + assert(num_epochs > 0); + assert(tokens_per_epoch > 1); + + // Remove bound checks. + auto sizes = sizes_.unchecked<1>(); + auto doc_idx = doc_idx_.unchecked<1>(); + + // Mapping and it's length (1D). + int64_t num_samples = (num_epochs * tokens_per_epoch - 1) / seq_length; + int32_t *sample_idx = new int32_t[2 * (num_samples + 1)]; + + // Index into sample_idx. + int64_t sample_index = 0; + // Index into doc_idx. + int64_t doc_idx_index = 0; + // Begining offset for each document. + int32_t doc_offset = 0; + // Start with first document and no offset. + sample_idx[2 * sample_index] = doc_idx_index; + sample_idx[2 * sample_index + 1] = doc_offset; + ++sample_index; + + while (sample_index <= num_samples) + { + // Start with a fresh sequence. + int32_t remaining_seq_length = seq_length + 1; + while (remaining_seq_length != 0) + { + // Get the document length. + auto doc_id = doc_idx[doc_idx_index]; + auto doc_length = sizes[doc_id] - doc_offset; + // And add it to the current sequence. + remaining_seq_length -= doc_length; + // If we have more than a full sequence, adjust offset and set + // remaining length to zero so we return from the while loop. + // Note that -1 here is for the same reason we have -1 in + // `_num_epochs` calculations. + if (remaining_seq_length <= 0) + { + doc_offset += (remaining_seq_length + doc_length - 1); + remaining_seq_length = 0; + } + else + { + // Otherwise, start from the begining of the next document. + ++doc_idx_index; + doc_offset = 0; + } + } + // Record the sequence. + sample_idx[2 * sample_index] = doc_idx_index; + sample_idx[2 * sample_index + 1] = doc_offset; + ++sample_index; + } + + // Method to deallocate memory. + py::capsule free_when_done(sample_idx, [](void *mem_) + { + int32_t *mem = reinterpret_cast(mem_); + delete[] mem; }); + + // Return the numpy array. + const auto byte_size = sizeof(int32_t); + return py::array(std::vector{num_samples + 1, 2}, // shape + {2 * byte_size, byte_size}, // C-style contiguous strides + sample_idx, // the data pointer + free_when_done); // numpy array references +} + +inline int32_t get_target_sample_len(const int32_t short_seq_ratio, + const int32_t max_length, + std::mt19937 &rand32_gen) +{ + /* Training sample length. */ + if (short_seq_ratio == 0) + { + return max_length; + } + const auto random_number = rand32_gen(); + if ((random_number % short_seq_ratio) == 0) + { + return 2 + random_number % (max_length - 1); + } + return max_length; +} + +template +py::array build_mapping_impl(const py::array_t &docs_, + const py::array_t &sizes_, + const int32_t num_epochs, + const uint64_t max_num_samples, + const int32_t max_seq_length, + const double short_seq_prob, + const int32_t seed, + const bool verbose, + const int32_t min_num_sent) +{ + /* Build a mapping of (start-index, end-index, sequence-length) where + start and end index are the indices of the sentences in the sample + and sequence-length is the target sequence length. + */ + + // Consistency checks. + assert(num_epochs > 0); + assert(max_seq_length > 1); + assert(short_seq_prob >= 0.0); + assert(short_seq_prob <= 1.0); + assert(seed > 0); + + // Remove bound checks. + auto docs = docs_.unchecked<1>(); + auto sizes = sizes_.unchecked<1>(); + + // For efficiency, convert probability to ratio. Note: rand() generates int. + int32_t short_seq_ratio = 0; + if (short_seq_prob > 0) + { + short_seq_ratio = static_cast(round(1.0 / short_seq_prob)); + } + + if (verbose) + { + const auto sent_start_index = docs[0]; + const auto sent_end_index = docs[docs_.shape(0) - 1]; + const auto num_sentences = sent_end_index - sent_start_index; + cout << " using:" << endl + << std::flush; + cout << " number of documents: " << docs_.shape(0) - 1 << endl + << std::flush; + cout << " sentences range: [" << sent_start_index << ", " << sent_end_index << ")" << endl + << std::flush; + cout << " total number of sentences: " << num_sentences << endl + << std::flush; + cout << " number of epochs: " << num_epochs << endl + << std::flush; + cout << " maximum number of samples: " << max_num_samples << endl + << std::flush; + cout << " maximum sequence length: " << max_seq_length << endl + << std::flush; + cout << " short sequence probability: " << short_seq_prob << endl + << std::flush; + cout << " short sequence ration (1/prob): " << short_seq_ratio << endl + << std::flush; + cout << " seed: " << seed << endl + << std::flush; + } + + // Mapping and it's length (1D). + int64_t num_samples = -1; + DocIdx *maps = NULL; + + // Perform two iterations, in the first iteration get the size + // and allocate memory and in the second iteration populate the map. + bool second = false; + for (int32_t iteration = 0; iteration < 2; ++iteration) + { + + // Set the seed so both iterations produce the same results. + std::mt19937 rand32_gen(seed); + + // Set the flag on second iteration. + second = (iteration == 1); + + // Counters: + uint64_t empty_docs = 0; + uint64_t one_sent_docs = 0; + uint64_t long_sent_docs = 0; + + // Current map index. + uint64_t map_index = 0; + + // For each epoch: + for (int32_t epoch = 0; epoch < num_epochs; ++epoch) + { + if (map_index >= max_num_samples) + { + if (verbose && (!second)) + { + cout << " reached " << max_num_samples << " samples after " + << epoch << " epochs ..." << endl + << std::flush; + } + break; + } + // For each document: + for (int32_t doc = 0; doc < (docs.shape(0) - 1); ++doc) + { + + // Document sentences are in [sent_index_first, sent_index_last) + const auto sent_index_first = docs[doc]; + const auto sent_index_last = docs[doc + 1]; + + // At the begining of the document previous index is the + // start index. + auto prev_start_index = sent_index_first; + + // Remaining documents. + auto num_remain_sent = sent_index_last - sent_index_first; + + // Some bookkeeping + if ((epoch == 0) && (!second)) + { + if (num_remain_sent == 0) + { + ++empty_docs; + } + if (num_remain_sent == 1) + { + ++one_sent_docs; + } + } + + // Detect documents with long sentences. + bool contains_long_sentence = false; + if (num_remain_sent > 1) + { + for (auto sent_index = sent_index_first; + sent_index < sent_index_last; ++sent_index) + { + if (sizes[sent_index] > LONG_SENTENCE_LEN) + { + if ((epoch == 0) && (!second)) + { + ++long_sent_docs; + } + contains_long_sentence = true; + break; + } + } + } + + // If we have more than two sentences. + if ((num_remain_sent >= min_num_sent) && (!contains_long_sentence)) + { + + // Set values. + auto seq_len = int32_t{0}; + auto num_sent = int32_t{0}; + auto target_seq_len = get_target_sample_len(short_seq_ratio, + max_seq_length, + rand32_gen); + + // Loop through sentences. + for (auto sent_index = sent_index_first; + sent_index < sent_index_last; ++sent_index) + { + + // Add the size and number of sentences. + seq_len += sizes[sent_index]; + ++num_sent; + --num_remain_sent; + + // If we have reached the target length. + // and if not only one sentence is left in the document. + // and if we have at least two sentneces. + // and if we have reached end of the document. + if (((seq_len >= target_seq_len) && + (num_remain_sent > 1) && + (num_sent >= min_num_sent)) || + (num_remain_sent == 0)) + { + + // Check for overflow. + if ((3 * map_index + 2) > + std::numeric_limits::max()) + { + cout << "number of samples exceeded maximum " + << "allowed by type int64: " + << std::numeric_limits::max() + << endl; + throw std::overflow_error("Number of samples"); + } + + // Populate the map. + if (second) + { + const auto map_index_0 = 3 * map_index; + maps[map_index_0] = static_cast(prev_start_index); + maps[map_index_0 + 1] = static_cast(sent_index + 1); + maps[map_index_0 + 2] = static_cast(target_seq_len); + } + + // Update indices / counters. + ++map_index; + prev_start_index = sent_index + 1; + target_seq_len = get_target_sample_len(short_seq_ratio, + max_seq_length, + rand32_gen); + seq_len = 0; + num_sent = 0; + } + + } // for (auto sent_index=sent_index_first; ... + } // if (num_remain_sent > 1) { + } // for (int doc=0; doc < num_docs; ++doc) { + } // for (int epoch=0; epoch < num_epochs; ++epoch) { + + if (!second) + { + if (verbose) + { + cout << " number of empty documents: " << empty_docs << endl + << std::flush; + cout << " number of documents with one sentence: " << one_sent_docs << endl + << std::flush; + cout << " number of documents with long sentences: " << long_sent_docs << endl + << std::flush; + cout << " will create mapping for " << map_index << " samples" << endl + << std::flush; + } + assert(maps == NULL); + assert(num_samples < 0); + maps = new DocIdx[3 * map_index]; + num_samples = static_cast(map_index); + } + + } // for (int iteration=0; iteration < 2; ++iteration) { + + // Shuffle. + // We need a 64 bit random number generator as we might have more + // than 2 billion samples. + std::mt19937_64 rand64_gen(seed + 1); + for (auto i = (num_samples - 1); i > 0; --i) + { + const auto j = static_cast(rand64_gen() % (i + 1)); + const auto i0 = 3 * i; + const auto j0 = 3 * j; + // Swap values. + swap(maps[i0], maps[j0]); + swap(maps[i0 + 1], maps[j0 + 1]); + swap(maps[i0 + 2], maps[j0 + 2]); + } + + // Method to deallocate memory. + py::capsule free_when_done(maps, [](void *mem_) + { + DocIdx *mem = reinterpret_cast(mem_); + delete[] mem; }); + + // Return the numpy array. + const auto byte_size = sizeof(DocIdx); + return py::array(std::vector{num_samples, 3}, // shape + {3 * byte_size, byte_size}, // C-style contiguous strides + maps, // the data pointer + free_when_done); // numpy array references +} + +py::array build_mapping(const py::array_t &docs_, + const py::array_t &sizes_, + const int num_epochs, + const uint64_t max_num_samples, + const int max_seq_length, + const double short_seq_prob, + const int seed, + const bool verbose, + const int32_t min_num_sent) +{ + + if (sizes_.size() > std::numeric_limits::max()) + { + if (verbose) + { + cout << " using uint64 for data mapping..." << endl + << std::flush; + } + return build_mapping_impl(docs_, sizes_, num_epochs, + max_num_samples, max_seq_length, + short_seq_prob, seed, verbose, + min_num_sent); + } + else + { + if (verbose) + { + cout << " using uint32 for data mapping..." << endl + << std::flush; + } + return build_mapping_impl(docs_, sizes_, num_epochs, + max_num_samples, max_seq_length, + short_seq_prob, seed, verbose, + min_num_sent); + } +} + +template +py::array build_blocks_mapping_impl(const py::array_t &docs_, + const py::array_t &sizes_, + const py::array_t &titles_sizes_, + const int32_t num_epochs, + const uint64_t max_num_samples, + const int32_t max_seq_length, + const int32_t seed, + const bool verbose, + const bool use_one_sent_blocks) +{ + /* Build a mapping of (start-index, end-index, sequence-length) where + start and end index are the indices of the sentences in the sample + and sequence-length is the target sequence length. + */ + + // Consistency checks. + assert(num_epochs > 0); + assert(max_seq_length > 1); + assert(seed > 0); + + // Remove bound checks. + auto docs = docs_.unchecked<1>(); + auto sizes = sizes_.unchecked<1>(); + auto titles_sizes = titles_sizes_.unchecked<1>(); + + if (verbose) + { + const auto sent_start_index = docs[0]; + const auto sent_end_index = docs[docs_.shape(0) - 1]; + const auto num_sentences = sent_end_index - sent_start_index; + cout << " using:" << endl + << std::flush; + cout << " number of documents: " << docs_.shape(0) - 1 << endl + << std::flush; + cout << " sentences range: [" << sent_start_index << ", " << sent_end_index << ")" << endl + << std::flush; + cout << " total number of sentences: " << num_sentences << endl + << std::flush; + cout << " number of epochs: " << num_epochs << endl + << std::flush; + cout << " maximum number of samples: " << max_num_samples << endl + << std::flush; + cout << " maximum sequence length: " << max_seq_length << endl + << std::flush; + cout << " seed: " << seed << endl + << std::flush; + } + + // Mapping and its length (1D). + int64_t num_samples = -1; + DocIdx *maps = NULL; + + // Acceptable number of sentences per block. + int min_num_sent = 2; + if (use_one_sent_blocks) + { + min_num_sent = 1; + } + + // Perform two iterations, in the first iteration get the size + // and allocate memory and in the second iteration populate the map. + bool second = false; + for (int32_t iteration = 0; iteration < 2; ++iteration) + { + + // Set the flag on second iteration. + second = (iteration == 1); + + // Current map index. + uint64_t map_index = 0; + + uint64_t empty_docs = 0; + uint64_t one_sent_docs = 0; + uint64_t long_sent_docs = 0; + // For each epoch: + for (int32_t epoch = 0; epoch < num_epochs; ++epoch) + { + // assign every block a unique id + int32_t block_id = 0; + + if (map_index >= max_num_samples) + { + if (verbose && (!second)) + { + cout << " reached " << max_num_samples << " samples after " + << epoch << " epochs ..." << endl + << std::flush; + } + break; + } + // For each document: + for (int32_t doc = 0; doc < (docs.shape(0) - 1); ++doc) + { + + // Document sentences are in [sent_index_first, sent_index_last) + const auto sent_index_first = docs[doc]; + const auto sent_index_last = docs[doc + 1]; + const auto target_seq_len = max_seq_length - titles_sizes[doc]; + + // At the begining of the document previous index is the + // start index. + auto prev_start_index = sent_index_first; + + // Remaining documents. + auto num_remain_sent = sent_index_last - sent_index_first; + + // Some bookkeeping + if ((epoch == 0) && (!second)) + { + if (num_remain_sent == 0) + { + ++empty_docs; + } + if (num_remain_sent == 1) + { + ++one_sent_docs; + } + } + // Detect documents with long sentences. + bool contains_long_sentence = false; + if (num_remain_sent >= min_num_sent) + { + for (auto sent_index = sent_index_first; + sent_index < sent_index_last; ++sent_index) + { + if (sizes[sent_index] > LONG_SENTENCE_LEN) + { + if ((epoch == 0) && (!second)) + { + ++long_sent_docs; + } + contains_long_sentence = true; + break; + } + } + } + // If we have enough sentences and no long sentences. + if ((num_remain_sent >= min_num_sent) && (!contains_long_sentence)) + { + + // Set values. + auto seq_len = int32_t{0}; + auto num_sent = int32_t{0}; + + // Loop through sentences. + for (auto sent_index = sent_index_first; + sent_index < sent_index_last; ++sent_index) + { + + // Add the size and number of sentences. + seq_len += sizes[sent_index]; + ++num_sent; + --num_remain_sent; + + // If we have reached the target length. + // and there are an acceptable number of sentences left + // and if we have at least the minimum number of sentences. + // or if we have reached end of the document. + if (((seq_len >= target_seq_len) && + (num_remain_sent >= min_num_sent) && + (num_sent >= min_num_sent)) || + (num_remain_sent == 0)) + { + + // Populate the map. + if (second) + { + const auto map_index_0 = 4 * map_index; + // Each sample has 4 items: the starting sentence index, ending sentence index, + // the index of the document from which the block comes (used for fetching titles) + // and the unique id of the block (used for creating block indexes) + + maps[map_index_0] = static_cast(prev_start_index); + maps[map_index_0 + 1] = static_cast(sent_index + 1); + maps[map_index_0 + 2] = static_cast(doc); + maps[map_index_0 + 3] = static_cast(block_id); + } + + // Update indices / counters. + ++map_index; + ++block_id; + prev_start_index = sent_index + 1; + seq_len = 0; + num_sent = 0; + } + } // for (auto sent_index=sent_index_first; ... + } // if (num_remain_sent > 1) { + } // for (int doc=0; doc < num_docs; ++doc) { + } // for (int epoch=0; epoch < num_epochs; ++epoch) { + + if (!second) + { + if (verbose) + { + cout << " number of empty documents: " << empty_docs << endl + << std::flush; + cout << " number of documents with one sentence: " << one_sent_docs << endl + << std::flush; + cout << " number of documents with long sentences: " << long_sent_docs << endl + << std::flush; + cout << " will create mapping for " << map_index << " samples" << endl + << std::flush; + } + assert(maps == NULL); + assert(num_samples < 0); + maps = new DocIdx[4 * map_index]; + num_samples = static_cast(map_index); + } + + } // for (int iteration=0; iteration < 2; ++iteration) { + + // Shuffle. + // We need a 64 bit random number generator as we might have more + // than 2 billion samples. + std::mt19937_64 rand64_gen(seed + 1); + for (auto i = (num_samples - 1); i > 0; --i) + { + const auto j = static_cast(rand64_gen() % (i + 1)); + const auto i0 = 4 * i; + const auto j0 = 4 * j; + // Swap values. + swap(maps[i0], maps[j0]); + swap(maps[i0 + 1], maps[j0 + 1]); + swap(maps[i0 + 2], maps[j0 + 2]); + swap(maps[i0 + 3], maps[j0 + 3]); + } + + // Method to deallocate memory. + py::capsule free_when_done(maps, [](void *mem_) + { + DocIdx *mem = reinterpret_cast(mem_); + delete[] mem; }); + + // Return the numpy array. + const auto byte_size = sizeof(DocIdx); + return py::array(std::vector{num_samples, 4}, // shape + {4 * byte_size, byte_size}, // C-style contiguous strides + maps, // the data pointer + free_when_done); // numpy array references +} + +py::array build_blocks_mapping(const py::array_t &docs_, + const py::array_t &sizes_, + const py::array_t &titles_sizes_, + const int num_epochs, + const uint64_t max_num_samples, + const int max_seq_length, + const int seed, + const bool verbose, + const bool use_one_sent_blocks) +{ + + if (sizes_.size() > std::numeric_limits::max()) + { + if (verbose) + { + cout << " using uint64 for data mapping..." << endl + << std::flush; + } + return build_blocks_mapping_impl(docs_, sizes_, titles_sizes_, + num_epochs, max_num_samples, max_seq_length, seed, verbose, use_one_sent_blocks); + } + else + { + if (verbose) + { + cout << " using uint32 for data mapping..." << endl + << std::flush; + } + return build_blocks_mapping_impl(docs_, sizes_, titles_sizes_, + num_epochs, max_num_samples, max_seq_length, seed, verbose, use_one_sent_blocks); + } +} + +PYBIND11_MODULE(helpers, m) +{ + m.def("build_mapping", &build_mapping); + m.def("build_blocks_mapping", &build_blocks_mapping); + m.def("build_sample_idx", &build_sample_idx); + m.def("build_blending_indices", &build_blending_indices); +} diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/indexed_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/indexed_dataset.py new file mode 100644 index 0000000..c48757e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/indexed_dataset.py @@ -0,0 +1,719 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# Essentially re-written in entirety + +import logging +import os +import shutil +import struct +import time +from enum import Enum +from functools import lru_cache +from itertools import accumulate +from types import TracebackType +from typing import List, Optional, Tuple, Type, Union + +import numpy +import torch + +from megatron.core.datasets.utils import log_single_rank + +logger = logging.getLogger(__name__) + +_INDEX_HEADER = b"MMIDIDX\x00\x00" + + +class DType(Enum): + """The NumPy data type Enum for writing/reading the IndexedDataset indices + """ + + uint8 = 1 + int8 = 2 + int16 = 3 + int32 = 4 + int64 = 5 + float64 = 6 + float32 = 7 + uint16 = 8 + + @classmethod + def code_from_dtype(cls, value: Type[numpy.number]) -> int: + """Get the code from the dtype + + Args: + value (Type[numpy.number]): The dtype + + Returns: + int: The code + """ + return cls[value.__name__].value + + @classmethod + def dtype_from_code(cls, value: int) -> Type[numpy.number]: + """Get the dtype from the code + + Args: + value (int): The code + + Returns: + Type[numpy.number]: The dtype + """ + return getattr(numpy, cls(value).name) + + @staticmethod + def size(key: Union[int, Type[numpy.number]]) -> int: + """Get the size of the dtype/code in bytes + + Args: + key (Union[int, Type[numpy.number]]): The dtype or code + + Raises: + ValueError: If the key is neither dtype nor integer code + + Returns: + int: The size of the dtype/code in in bytes + """ + if isinstance(key, int): + return DType.dtype_from_code(key)().itemsize + elif numpy.number in key.__mro__: + return key().itemsize + else: + raise ValueError + + @staticmethod + def optimal_dtype(cardinality: Optional[int]) -> Type[numpy.number]: + """Get the dtype to use for an index of a certain cardinality + + Args: + cardinality (Optional[int]): The number of elements to be indexed + + Returns: + Type[numpy.number]: The dtype to use for the index + """ + if cardinality is not None and cardinality < 65500: + return numpy.uint16 + else: + return numpy.int32 + + +class _IndexWriter(object): + """Object class to write the index (.idx) file + + Args: + idx_path (str): The path to the index file + + dtype (Type[numpy.number]): The dtype of the index file + """ + + def __init__(self, idx_path: str, dtype: Type[numpy.number]) -> None: + self.idx_path = idx_path + self.dtype = dtype + + def __enter__(self) -> "_IndexWriter": + """Enter the context introduced by the 'with' keyword + + Returns: + _IndexWriter: The instance + """ + self.idx_writer = open(self.idx_path, "wb") + # fixed, vestigial practice + self.idx_writer.write(_INDEX_HEADER) + # fixed, vestigial practice + self.idx_writer.write(struct.pack(" Optional[bool]: + """Exit the context introduced by the 'with' keyword + + Args: + exc_type (Optional[Type[BaseException]]): Exception type + + exc_val (Optional[BaseException]): Exception value + + exc_tb (Optional[TracebackType]): Exception traceback object + + Returns: + Optional[bool]: Whether to silence the exception + """ + self.idx_writer.close() + + def write( + self, + sequence_lengths: List[int], + sequence_modes: Optional[List[int]], + document_indices: List[int], + ) -> None: + """Write the index (.idx) file + + Args: + sequence_lengths (List[int]): The length of each sequence + + sequence_modes (Optional[List[int]]): The mode of each sequences + + document_indices (List[int]): The seqyebce indices demarcating the end of each document + """ + sequence_pointers = self._sequence_pointers(sequence_lengths) + + # the number of sequences in the dataset + sequence_count = len(sequence_lengths) + self.idx_writer.write(struct.pack(" List[int]: + """Build the sequence pointers per the sequence lengths and dtype size + + Args: + sequence_lengths (List[int]): The length of each sequence + + Returns: + List[int]: The pointer to the beginning of each sequence + """ + itemsize = DType.size(self.dtype) + curr_ptr = 0 + list_ptr = [] + for length in sequence_lengths: + list_ptr.append(curr_ptr) + curr_ptr += length * itemsize + return list_ptr + + +class _IndexReader(object): + """Object class to read the index (.idx) file + + Args: + idx_path (str): The path to the index file + + multimodal (bool): Whether the dataset is multimodal + """ + + def __init__(self, idx_path: str, multimodal: bool) -> None: + + log_single_rank(logger, logging.INFO, f"Load the {type(self).__name__} from {idx_path}") + + with open(idx_path, "rb") as stream: + header = stream.read(9) + assert header == _INDEX_HEADER, f"bad header, cannot read: {idx_path}" + + version = struct.unpack(" time elapsed: {t_end - t_beg:4f} seconds") + + log_single_rank(logger, logging.INFO, f"\tExtract the sequence pointers") + t_beg = time.time() + self.sequence_pointers = numpy.frombuffer( + self.bin_buffer, + dtype=numpy.int64, + count=self.sequence_count, + offset=offset + self.sequence_lengths.nbytes, + ) + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + log_single_rank(logger, logging.INFO, f"\tExtract the document indices") + t_beg = time.time() + self.document_indices = numpy.frombuffer( + self.bin_buffer, + dtype=numpy.int64, + count=self.document_count, + offset=offset + self.sequence_lengths.nbytes + self.sequence_pointers.nbytes, + ) + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + self.sequence_modes = None + if multimodal: + log_single_rank(logger, logging.INFO, f"\tExtract the sequence modes") + t_beg = time.time() + self.sequence_modes = numpy.frombuffer( + self.bin_buffer, + dtype=numpy.int8, + count=self.sequence_count, + offset=offset + + self.sequence_lengths.nbytes + + self.sequence_pointers.nbytes + + self.document_indices.nbytes, + ) + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + assert self.sequence_lengths.shape[0] == len(self) + assert self.sequence_lengths.shape[0] == self.sequence_count + assert self.sequence_lengths.shape[0] == self.document_indices[-1] + + log_single_rank(logger, logging.INFO, f"> total number of sequences: {len(self)}") + log_single_rank( + logger, + logging.INFO, + f"> total number of documents: {self.document_indices.shape[0] - 1}", + ) + + def __del__(self) -> None: + """Clean up the object + """ + if hasattr(self, "bin_buffer_mmap"): + self.bin_buffer_mmap._mmap.close() + del self.bin_buffer_mmap + + def __len__(self) -> int: + """Return the length of the dataset + + Returns: + int: The length of the dataset + """ + return self.sequence_count + + @lru_cache(maxsize=8) + def __getitem__(self, idx: int) -> Tuple[numpy.int32, numpy.int64, Optional[numpy.int8]]: + """Return the pointer, length, and mode at the index + + Args: + idx (int): The index into the dataset + + Returns: + Tuple[numpy.int32, numpy.int64, Optional[numpy.int8]]: The pointer, length and mode at the index + """ + return ( + self.sequence_pointers[idx], + self.sequence_lengths[idx], + self.sequence_modes[idx] if self.sequence_modes is not None else None, + ) + + +class IndexedDataset(torch.utils.data.Dataset): + """The low-level interface dataset class + + Args: + path_prefix (str): The index (.idx) and data (.bin) prefix + + multimodal (bool, optional): Whether the dataset is multimodal. Defaults to False. + + mmap (bool, optional): Whether to mmap the .bin files. Defaults to True. + """ + + def __init__(self, path_prefix: str, multimodal: bool = False, mmap: bool = True) -> None: + super().__init__() + self.path_prefix = None + self.multimodal = None + self.mmap = None + + self.initialize(path_prefix, multimodal, mmap) + + def initialize(self, path_prefix: str, multimodal: bool, mmap: bool) -> None: + """Initialize the dataset + + This method is called by IndexedDataset.__init__ during object creation and by + IndexedDataset.__setstate__ during un-puckling + + Args: + path_prefix (str): The index (.idx) and data (.bin) prefix + + multimodal (bool): Whether the dataset is multimodal + + mmap (bool): Whether to mmap the .bin file + """ + idx_path = get_idx_path(path_prefix) + bin_path = get_bin_path(path_prefix) + assert os.path.exists(idx_path) and os.path.exists( + bin_path + ), f"One or both of the .idx and .bin files cannot be found at the path prefix {self.path_prefix}" + + self.path_prefix = path_prefix + self.multimodal = multimodal + self.mmap = mmap + + self.index = _IndexReader(idx_path, self.multimodal) + self.bin_buffer = None + self.bin_buffer_mmap = None + if mmap: + self.bin_buffer_mmap = numpy.memmap(bin_path, mode="r", order="C") + self.bin_buffer = memoryview(self.bin_buffer_mmap) + + def __getstate__(self) -> Tuple[str, bool, bool]: + """Get the state during pickling + + Returns: + Tuple[str, bool, bool]: The state tuple + """ + return self.path_prefix, self.multimodal, self.mmap + + def __setstate__(self, state: Tuple[str, bool, bool]) -> None: + """Set the state during un-pickling + + Args: + state (Tuple[str, bool, bool]): The state tuple + """ + path_prefix, multimodal, mmap = state + self.initialize(path_prefix, multimodal, mmap) + + def __del__(self) -> None: + """Clean up the object + """ + if self.bin_buffer_mmap is not None: + self.bin_buffer_mmap._mmap.close() + del self.bin_buffer_mmap + del self.index + + def __len__(self) -> int: + """Return the length of the dataset i.e. the number of sequences in the index + + Returns: + int: The length of the dataset + """ + return len(self.index) + + def _getitem_mmap( + self, idx: Union[int, numpy.integer, slice] + ) -> Union[numpy.ndarray, Tuple[numpy.ndarray, numpy.ndarray]]: + """Return from the dataset by mmap-ing .bin file + + Args: + idx (Union[int, numpy.integer, slice]): The index or index slice into the dataset + + Raises: + ValueError: When the index slice is non-contiguous + + TypeError: When the index is of an unexpected type + + Returns: + Union[numpy.ndarray, Tuple[numpy.ndarray, numpy.ndarray]]: The sequence tokens and modes at the index or index slice + """ + if isinstance(idx, (int, numpy.integer)): + sequence_pointer, sequence_length, sequence_mode = self.index[idx] + sequence = numpy.frombuffer( + self.bin_buffer, + dtype=self.index.dtype, + count=sequence_length, + offset=sequence_pointer, + ) + return (sequence, sequence_mode) if sequence_mode is not None else sequence + elif isinstance(idx, slice): + start, stop, step = idx.indices(len(self)) + if step != 1: + raise ValueError("Slices into indexed_dataset must be contiguous") + sequence_lengths = self.index.sequence_lengths[idx] + sequence_modes = self.index.sequence_modes[idx] if self.multimodal else None + sequence_offsets = list(accumulate(sequence_lengths)) + sequences = numpy.split( + numpy.frombuffer( + self.bin_buffer, + dtype=self.index.dtype, + count=sum(sequence_lengths), + offset=self.index.sequence_pointers[start], + ), + sequence_offsets[:-1], + ) + return (sequences, sequence_modes) if sequence_modes is not None else sequences + else: + raise TypeError("Unexpected type received for idx: {}".format(type(idx))) + + def _getitem_file( + self, idx: Union[int, numpy.integer, slice] + ) -> Union[numpy.ndarray, Tuple[numpy.ndarray, numpy.ndarray]]: + """Return from the dataset by using file pointer + + Args: + idx (Union[int, numpy.integer, slice]): The index or index slice into the dataset + + Raises: + ValueError: When the index slice is non-contiguous + + TypeError: When the index is of an unexpected type + + Returns: + Union[numpy.ndarray, Tuple[numpy.ndarray, numpy.ndarray]]: The sequence tokens and + modes at the index or index slice + """ + if isinstance(idx, (int, numpy.integer)): + sequence_pointer, sequence_length, sequence_mode = self.index[idx] + sequence = numpy.empty(sequence_length, dtype=self.index.dtype) + with open(get_bin_path(self.path_prefix), mode='rb', buffering=0) as bin_buffer_file: + bin_buffer_file.seek(sequence_pointer) + bin_buffer_file.readinto(sequence) + return (sequence, sequence_mode) if sequence_mode is not None else sequence + elif isinstance(idx, slice): + assert False, "slicing not implemented without mmap" + else: + raise TypeError("Unexpected type received for idx: {}".format(type(idx))) + + def __getitem__( + self, idx: Union[int, numpy.integer, slice] + ) -> Union[numpy.ndarray, Tuple[numpy.ndarray, numpy.ndarray]]: + """Return from the dataset + + Args: + idx (Union[int, numpy.integer, slice]): The index or index slice into the dataset + + Raises: + ValueError: When the index slice is non-contiguous + + TypeError: When the index is of an unexpected type + + Returns: + Union[numpy.ndarray, Tuple[numpy.ndarray, numpy.ndarray]]: The sequence tokens and + modes at the index or index slice + """ + if self.bin_buffer_mmap is not None: + return self._getitem_mmap(idx) + else: + return self._getitem_file(idx) + + def get(self, idx: int, offset: int = 0, length: Optional[int] = None) -> numpy.ndarray: + """Retrieve a single item from the dataset with the option to only + return a portion of the item. + + get(idx) is the same as [idx] but get() does not support slicing. + + Args: + idx (Union[int, numpy.integer]): The index into the dataset + + offset (int): The integer token offset in the sequence + + length (int): The number of tokens to grab from the sequence + + Returns: + Union[numpy.ndarray, Tuple[numpy.ndarray, numpy.ndarray]]: The sequence tokens and modes at the index + """ + sequence_pointer, sequence_length, sequence_mode = self.index[idx] + if length is None: + length = sequence_length - offset + sequence_pointer += offset * DType.size(self.index.dtype) + if self.bin_buffer: + sequence = numpy.frombuffer( + self.bin_buffer, dtype=self.index.dtype, count=length, offset=sequence_pointer + ) + else: + sequence = numpy.empty(length, dtype=self.index.dtype) + with open(get_bin_path(self.path_prefix), mode='rb', buffering=0) as bin_buffer_file: + bin_buffer_file.seek(sequence_pointer) + bin_buffer_file.readinto(sequence) + + return (sequence, sequence_mode) if sequence_mode is not None else sequence + + @property + def sequence_lengths(self) -> numpy.ndarray: + """Get the sequence lengths + + Returns: + numpy.ndarray: The sequence lengths + """ + return self.index.sequence_lengths + + @property + def document_indices(self) -> numpy.ndarray: + """Get the document indices + + Returns: + numpy.ndarray: The document indices + """ + return self.index.document_indices + + def get_document_indices(self) -> numpy.ndarray: + """Get the document indices + + This method is slated for deprecation. + + Returns: + numpy.ndarray: The document indices + """ + return self.index.document_indices + + def set_document_indices(self, document_indices: numpy.ndarray) -> None: + """Set the document indices + + This method is slated for deprecation. + + Args: + document_indices (numpy.ndarray): The document indices + """ + self.index.document_indices = document_indices + + @property + def sequence_modes(self) -> numpy.ndarray: + """Get the sequence modes + + Returns: + numpy.ndarray: The sequence modes + """ + return self.index.sequence_modes + + @staticmethod + def exists(path_prefix: str) -> bool: + """Return whether the IndexedDataset exists on disk at the prefix + + Args: + path_prefix (str): The prefix to the index (.idx) and data (.bin) files + + Returns: + bool: Whether the IndexedDataset exists on disk at the prefix + """ + return os.path.exists(get_idx_path(path_prefix)) and os.path.exists( + get_bin_path(path_prefix) + ) + + +class IndexedDatasetBuilder(object): + """Builder class for the IndexedDataset class + + Args: + bin_path (str): The path to the data (.bin) file + + dtype (Type[numpy.number], optional): The dtype of the index file. Defaults to numpy.int32. + + multimodal (bool, optional): Whether the dataset is multimodal. Defaults to False. + """ + + def __init__( + self, bin_path: str, dtype: Type[numpy.number] = numpy.int32, multimodal: bool = False + ) -> None: + self.data_file = open(bin_path, "wb") + self.dtype = dtype + self.multimodal = multimodal + + self.sequence_lengths = [] + self.document_indices = [0] + self.sequence_modes = [] if self.multimodal else None + + def add_item(self, tensor: torch.Tensor, mode: int = 0) -> None: + """Add a single item to the dataset + + Args: + tensor (torch.Tensor): The item to add to the data file + + mode (int, optional): The mode for the item. Defaults to 0. + """ + np_array = numpy.array(tensor.numpy(), dtype=self.dtype) + self.data_file.write(np_array.tobytes(order="C")) + self.sequence_lengths.append(np_array.size) + if self.multimodal: + self.sequence_modes.append(mode) + + def add_document( + self, tensor: torch.Tensor, lengths: List[int], modes: Optional[List[int]] = None + ) -> None: + """Add an entire document to the dataset + + Args: + tensor (torch.Tensor): The document to add + + lengths (List[int]): The lengths of each item in the document + + modes (Optional[List[int]], optional): The modes for each item in the document. Defaults to None. + """ + np_array = numpy.array(tensor, dtype=self.dtype) + self.data_file.write(np_array.tobytes(order="C")) + self.sequence_lengths.extend(lengths) + self.document_indices.append(len(self.sequence_lengths)) + if self.multimodal: + self.sequence_modes.extend(modes if modes is not None else [0] * lengths) + + def end_document(self) -> None: + """Finalize the document, for use with IndexedDatasetBuilder.add_item + """ + self.document_indices.append(len(self.sequence_lengths)) + + def add_index(self, path_prefix: str) -> None: + """Add an entire IndexedDataset to the dataset + + Args: + path_prefix (str): The index (.idx) and data (.bin) prefix + """ + # Concatenate index + index = _IndexReader(get_idx_path(path_prefix), multimodal=self.multimodal) + assert index.dtype == self.dtype + + offset = len(self.sequence_lengths) + self.sequence_lengths.extend(index.sequence_lengths) + self.document_indices.extend((offset + index.document_indices)[1:]) + + if self.multimodal: + self.sequence_modes.extend(index.sequence_modes) + + # Concatenate data + with open(get_bin_path(path_prefix), "rb") as f: + shutil.copyfileobj(f, self.data_file) + + def finalize(self, idx_path: str) -> None: + """Clean up and write the index (.idx) file + + Args: + idx_path (str): The path to the index file + """ + self.data_file.close() + with _IndexWriter(idx_path, self.dtype) as writer: + writer.write(self.sequence_lengths, self.sequence_modes, self.document_indices) + + +def get_idx_path(path_prefix: str) -> str: + """Get the path to the index file from the prefix + + Args: + path_prefix (str): The prefix + + Returns: + str: The path to the index file + """ + return path_prefix + ".idx" + + +def get_bin_path(path_prefix: str) -> str: + """Get the path to the data file from the prefix + + Args: + path_prefix (str): The prefix + + Returns: + str: The path to the data file + """ + return path_prefix + ".bin" diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/masked_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/masked_dataset.py new file mode 100644 index 0000000..f38b4b4 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/masked_dataset.py @@ -0,0 +1,419 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import logging +import os +import time +from abc import abstractmethod +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import numpy +import torch + +from megatron.core.datasets.blended_megatron_dataset_config import BlendedMegatronDatasetConfig +from megatron.core.datasets.indexed_dataset import IndexedDataset +from megatron.core.datasets.megatron_dataset import MegatronDataset +from megatron.core.datasets.utils import Split, log_single_rank + +logger = logging.getLogger(__name__) + + +@dataclass +class MaskedWordPieceDatasetConfig(BlendedMegatronDatasetConfig): + """Configuration object for Megatron Core Masked WordPiece datasets""" + + masking_probability: float = None + """The probability we mask a candidate N-gram""" + + short_sequence_probability: float = None + """The probability we return a sequence shorter than the target sequence length""" + + masking_max_ngram: int = None + """The maximum length N-gram to consider masking or permuting""" + + masking_do_full_word: bool = None + """Whether we mask the the whole word or its component parts""" + + masking_do_permutation: bool = None + """Whether we shuffle a subset of candidate N-grams in addition""" + + masking_use_longer_ngrams: bool = None + """Whether to favor longer N-grams over shorter N-grams""" + + masking_use_geometric_distribution: bool = None + """Whether to draw the size of the N-gram from a geometric distribution according to SpanBERT + https://arxiv.org/abs/1907.10529 (Section 3.1) + """ + + def __post_init__(self) -> None: + """Do asserts and set fields post init + """ + super().__post_init__() + + assert self.tokenizer is not None + + assert self.masking_probability is not None + assert self.short_sequence_probability is not None + assert self.masking_max_ngram is not None + assert self.masking_do_full_word is not None + assert self.masking_do_permutation is not None + assert self.masking_use_longer_ngrams is not None + assert self.masking_use_geometric_distribution is not None + + assert self.masking_probability > 0 and self.masking_probability < 1.0 + assert self.short_sequence_probability >= 0 and self.short_sequence_probability <= 1.0 + assert self.masking_max_ngram > 0 + assert not (self.masking_use_geometric_distribution and self.masking_do_permutation) + + if self.masking_use_geometric_distribution and self.masking_use_longer_ngrams: + log_single_rank( + logger, + logging.WARNING, + "The use of a geometric distribution overrides the default distribution", + ) + + +class MaskedWordPieceDataset(MegatronDataset): + """The semi-abstract base class for masked WordPiece datasets + + This implementation makes the rigid assumption that all inheritor datasets are built upon the + IndexedDataset class. This assumption may be pushed down to the inheritors in future if + necessary. + + NB: WordPiece tokenization prepends a double hash "##" to all tokens/pieces in a word, save the + first token/piece. + + Args: + indexed_dataset (IndexedDataset): The IndexedDataset around which to build the MegatronDataset + + dataset_path (str): The real path on disk to the dataset, for bookkeeping + + indexed_indices (numpy.ndarray): The set of the documents indices to expose + + num_samples (int): The number of samples to draw from the indexed dataset + + index_split (Split): The indexed_indices Split + + config (MaskedWordPieceDatasetConfig): The config + """ + + def __init__( + self, + indexed_dataset: IndexedDataset, + dataset_path: str, + indexed_indices: numpy.ndarray, + num_samples: int, + index_split: Split, + config: MaskedWordPieceDatasetConfig, + ) -> None: + super().__init__( + indexed_dataset, dataset_path, indexed_indices, num_samples, index_split, config + ) + + @staticmethod + def numel_low_level_dataset(low_level_dataset: IndexedDataset) -> int: + return low_level_dataset.document_indices.shape[0] - 1 + + @staticmethod + def build_low_level_dataset( + dataset_path: str, config: MaskedWordPieceDatasetConfig + ) -> IndexedDataset: + return IndexedDataset(dataset_path) + + @staticmethod + def _key_config_attributes() -> List[str]: + """Inherited method implementation + + Returns: + List[str]: The key config attributes + """ + return super(MaskedWordPieceDataset, MaskedWordPieceDataset)._key_config_attributes() + [ + "masking_probability", + "short_sequence_probability", + "masking_max_ngram", + "masking_do_full_word", + "masking_do_permutation", + "masking_use_longer_ngrams", + "masking_use_geometric_distribution", + ] + + def __len__(self) -> int: + return self.sample_index.shape[0] + + def _build_sample_index( + self, sequence_length: int, min_sentences_per_sample: int + ) -> numpy.ndarray: + path_to_cache = self.config.path_to_cache + if path_to_cache is None: + path_to_cache = os.path.join( + self.dataset.path_prefix, "cache", f"{type(self).__name__}_indices" + ) + + get_path_to = lambda suffix: os.path.join( + path_to_cache, f"{self.unique_description_hash}-{type(self).__name__}-{suffix}" + ) + path_to_description = get_path_to("description.txt") + path_to_sample_index = get_path_to("sample_index.npy") + cache_hit = all(map(os.path.isfile, [path_to_description, path_to_sample_index,],)) + + num_epochs = numpy.iinfo(numpy.int32).max - 1 + + if not cache_hit and torch.distributed.get_rank() == 0: + log_single_rank( + logger, + logging.INFO, + f"Build and save the {type(self).__name__} {self.index_split.name} indices", + ) + + os.makedirs(path_to_cache, exist_ok=True) + + # Write the description + with open(path_to_description, "wt") as writer: + writer.write(self.unique_description) + + # Build the sample index + log_single_rank( + logger, + logging.INFO, + f"\tBuild and save the sample index to {os.path.basename(path_to_sample_index)}", + ) + t_beg = time.time() + from megatron.core.datasets import helpers + + # Add +1 for access to document upper bound + indices = numpy.append(self.indices, self.indices[-1] + 1) + + sample_index = helpers.build_mapping( + self.dataset.document_indices[indices], + self.dataset.sequence_lengths, + num_epochs, + self.num_samples, + sequence_length, + self.config.short_sequence_probability, + self.config.random_seed, + False, + min_sentences_per_sample, + ) + numpy.save(path_to_sample_index, sample_index, allow_pickle=True) + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + log_single_rank( + logger, logging.INFO, f"> total number of samples: {sample_index.shape[0]}" + ) + log_single_rank(logger, logging.INFO, f"> total number of epochs: {num_epochs}") + + return sample_index + + log_single_rank( + logger, logging.INFO, f"Load the {type(self).__name__} {self.index_split.name} indices" + ) + + log_single_rank( + logger, + logging.INFO, + f"\tLoad the sample index from {os.path.basename(path_to_sample_index)}", + ) + t_beg = time.time() + sample_index = numpy.load(path_to_sample_index, allow_pickle=True, mmap_mode="r") + t_end = time.time() + log_single_rank(logger, logging.DEBUG, f"\t> time elapsed: {t_end - t_beg:4f} seconds") + + return sample_index + + def _create_masked_lm_predictions( + self, + token_ids: List[int], + target_sequence_length: int, + numpy_random_state: numpy.random.RandomState, + ) -> Tuple[List[int], List[int], List[int], List[int], List[Tuple[List[int], List[int]]]]: + """Creates the predictions for the masked LM objective + + Args: + token_ids (List[int]): The token ids + target_sequence_length (int): The target sequence length + numpy_random_state (numpy.random.RandomState): The NumPy random state + + Returns: + Tuple[List[int], List[int], List[int], List[int], List[Tuple[List[int], List[int]]]]: + 1. masked_token_ids -> The masked sequence + 2. masked_positions -> The indices for the masked token ids + 3. masked_labels -> The original token ids for the masked token ids + 4. boundaries -> The sentence and word boundaries for the sequence + 4. masked_spans -> The masked positions and labels with N-gram info intact + """ + # Build the token sentence and word boundaries and the masking candidates + # e.g. [cls, id, ##id, ##id, id, ##id, sep, id, ##id, sep] + # -> boundaries: [1, 1, 0, 0, 1, 0, 1, 1, 0, 1] + # -> candidates with whole word masking: [[1, 2, 3], [4, 5], [7, 8]] + # -> candidates sans whole word masking: [[1], [2], [3], [4], [5], [7], [8]] + boundaries = [] + candidates = [] + for i, token_id in enumerate(token_ids): + if token_id == self.config.tokenizer.cls or token_id == self.config.tokenizer.sep: + boundaries.append(1) + else: + if not self.config.tokenizer.inv_vocab[token_id].startswith("##"): + boundaries.append(1) + candidates.append([i]) + else: + boundaries.append(0) + if self.config.masking_do_full_word and len(candidates) > 0: + candidates[-1].append(i) + else: + candidates.append([i]) + + n_maskings = min( + self.config.masking_probability * target_sequence_length, + max(1, int(round(len(token_ids) * self.config.masking_probability))), + ) + + ngram_nvals = numpy.arange(self.config.masking_max_ngram, dtype=numpy.int64) + 1 + + # By default, the N-gram probabilites are inversely proportional to N + # e.g. N = 3 + # -> P = array([0.54545455, 0.27272727, 0.18181818]) + nprobs = 1.0 / ngram_nvals + nprobs = nprobs / nprobs.sum(keepdims=True) + if self.config.masking_use_longer_ngrams: + nprobs = nprobs[::-1] + + # Create a nested list of depth 3 + # layer 1: the candidate dimension + # layer 2: the N-gram dimension + # layer 3: the token dimension + candidate_ngrams = [ + [candidates[idx : idx + n] for n in ngram_nvals] for idx in range(len(candidates)) + ] + numpy_random_state.shuffle(candidate_ngrams) + + masked_token_ids = list(token_ids) + masked_positions_and_labels = [] + masked_spans = [] + masked_indices = set() + for candidate_idx in range(len(candidate_ngrams)): + n_ngrams = len(candidate_ngrams[candidate_idx]) + + # Stop when we hit our desired number of maskings + if len(masked_positions_and_labels) >= n_maskings: + break + + # Do nothing for candidates with no ngrams + if not candidate_ngrams[candidate_idx]: + continue + + # Choose the initial value of N + if self.config.masking_use_geometric_distribution: + # Sample N from a geometric distribution with p = 0.2 and clip + # i.e. SpanBERT + # -> https://arxiv.org/abs/1907.10529 (Section 3.1) + p = 0.2 + n = min(numpy_random_state.geometric(p), self.config.masking_max_ngram) + else: + p = nprobs[:n_ngrams] / nprobs[:n_ngrams].sum(keepdims=True) + n = numpy_random_state.choice(ngram_nvals[:n_ngrams], p=p) + + while True: + ngram_indices = sum(candidate_ngrams[candidate_idx][n - 1], []) + n = n - 1 + # Success: masking this N-gram puts us below the desired number of maskings + if n_maskings >= len(masked_positions_and_labels) + len(ngram_indices): + skip_candidate = False + break + # Failure: no N-grams remain for this candidate + if n == 0: + skip_candidate = True + break + + # Do nothing for candidates whose 1-gram is too long + if skip_candidate: + continue + + # Do nothing for candidate indices which have already been masked + if any(map(lambda idx: idx in masked_indices, ngram_indices)): + continue + + # Mask the tokens and record their original positions and values + for index in ngram_indices: + masked_indices.add(index) + mask = self._get_token_mask(numpy_random_state) + if mask is None: + masked_token_ids[index] = token_ids[index] + else: + masked_token_ids[index] = mask + masked_positions_and_labels.append((index, token_ids[index])) + + masked_spans.append((ngram_indices, [token_ids[index] for index in ngram_indices])) + + assert len(masked_positions_and_labels) <= n_maskings + + numpy_random_state.shuffle(candidate_ngrams) + + if self.config.masking_do_permutation: + + n_swappings = n_maskings + + permuted_indices = set() + for candidate_idx in range(len(candidate_ngrams)): + n_ngrams = len(candidate_ngrams[candidate_idx]) + + if len(permuted_indices) >= n_swappings: + break + + # Do nothing for candidates with no ngrams + if not candidate_ngrams[candidate_idx]: + continue + + p = nprobs[:n_ngrams] / nprobs[:n_ngrams].sum(keepdims=True) + n = numpy.random.choice(ngram_nvals[:n_ngrams], p=p) + + while True: + ngram_indices = sum(candidate_ngrams[candidate_idx][n - 1], []) + n = n - 1 + # Success: swapping this N-gram puts us below the desired number of swappings + if n_swappings >= len(permuted_indices) + len(ngram_indices): + skip_candidate = False + break + # Failure: no N-grams remain for this candidate + if n == 0: + skip_candidate = True + break + + # Do nothing for candidates whose 1-gram is too long + if skip_candidate: + continue + + # Do nothing for candidate indices which have already been masked or permuted + if any( + map(lambda idx: idx in masked_indices or idx in permuted_indices, ngram_indices) + ): + continue + + for index in ngram_indices: + permuted_indices.add(index) + + assert len(permuted_indices) <= n_swappings + + permuted_indices = sorted(permuted_indices) + permuted_indices_copy = list(permuted_indices) + numpy_random_state.shuffle(permuted_indices_copy) + masked_token_ids_copy = list(masked_token_ids) + + for idx, idx_copy in zip(permuted_indices, permuted_indices_copy): + masked_token_ids[idx] = masked_token_ids_copy[idx_copy] + masked_positions_and_labels.append((idx, masked_token_ids_copy[idx])) + + masked_positions_and_labels = sorted(masked_positions_and_labels, key=lambda x: x[0]) + masked_positions = [] + masked_labels = [] + for position, label in masked_positions_and_labels: + masked_positions.append(position) + masked_labels.append(label) + + masked_spans = sorted(masked_spans, key=lambda x: x[0][0]) + + return masked_token_ids, masked_positions, masked_labels, boundaries, masked_spans + + @abstractmethod + def _get_token_mask(self, numpy_random_state: numpy.random.RandomState) -> Optional[int]: + pass diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/megatron_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/megatron_dataset.py new file mode 100644 index 0000000..45f0e4a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/megatron_dataset.py @@ -0,0 +1,193 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import hashlib +import json +from abc import ABC, abstractmethod +from collections import OrderedDict +from typing import Any, Dict, Iterable, List, Optional, Union + +import numpy +import torch + +from megatron.core.datasets.blended_megatron_dataset_config import BlendedMegatronDatasetConfig +from megatron.core.datasets.indexed_dataset import IndexedDataset +from megatron.core.datasets.utils import Split + +LowLevelDataset = Union[IndexedDataset, Iterable] + + +class MegatronDataset(ABC, torch.utils.data.Dataset): + """The highest level wrapper class from which all dataset classes should inherit + + Args: + dataset (LowLevelDataset): The dataset around which to build the MegatronDataset + + dataset_path (str): The real path on disk to the dataset, for bookkeeping. TODO: subsume this argument by enforcing auto-bookkeeping in the dataset class type. + + indices (numpy.ndarray): The set of the documents indices to expose + + num_samples (int): The number of samples to draw from the indexed dataset + + index_split (Split): The indices Split + + config (BlendedMegatronDatasetConfig): The config + """ + + def __init__( + self, + dataset: LowLevelDataset, + dataset_path: str, + indices: numpy.ndarray, + num_samples: int, + index_split: Split, + config: BlendedMegatronDatasetConfig, + ) -> None: + self.dataset = dataset + self.dataset_path = dataset_path + self.indices = indices + self.num_samples = num_samples + self.index_split = index_split + self.config = config + + if not self.config.mock: + self.unique_identifiers = OrderedDict() + self.unique_identifiers["class"] = type(self).__name__ + self.unique_identifiers["dataset_path"] = self.dataset_path + self.unique_identifiers["num_samples"] = self.num_samples + self.unique_identifiers["index_split"] = self.index_split.name + for attr in self._key_config_attributes(): + self.unique_identifiers[attr] = getattr(self.config, attr) + + self.unique_description = json.dumps( + self.unique_identifiers, indent=4, default=lambda obj: obj.unique_identifiers + ) + self.unique_description_hash = hashlib.md5( + self.unique_description.encode("utf-8") + ).hexdigest() + + self._finalize() + + def _finalize(self) -> None: + """Build the dataset and assert any subclass-specific conditions + """ + pass + + @staticmethod + def numel_low_level_dataset(low_level_dataset: LowLevelDataset) -> int: + """Return the number of elements in the underlying low level dataset for the purpose of + segregating the train/valid/test split indices + + It may be that the low level dataset can be split any number of ways, depending on the mid + level dataset it supports, which is why we define the "number of elements" function + separately from the __len__ function here in the mid level dataset class + + Args: + low_level_dataset (LowLevelDataset): The underlying low level dataset + + Returns: + int: The number of elements in the underlying low level dataset + """ + raise NotImplementedError + + @staticmethod + def build_low_level_dataset( + dataset_path: str, config: BlendedMegatronDatasetConfig + ) -> LowLevelDataset: + """Build the low level dataset via a function to be called from within + BlendedMegatronDatasetBuilder.build_generic_dataset + + It may be that the low level dataset spans any subset of train/valid/test splits, which is + why we define a static "build" function separately from the constructor in the mid level + dataset class + + Args: + dataset_path (str): The real path on disk to the dataset + + config (BlendedMegatronDatasetConfig): The dataset config + + Returns: + LowLevelDataset: The low level dataset + """ + raise NotImplementedError + + @staticmethod + def _key_config_attributes() -> List[str]: + """Return all config attributes which contribute to uniquely identifying the dataset. + + These attributes will be used to build a uniquely identifying string and MD5 hash which + will be used to cache/load dataset resources from run to run. + + Returns: + List[str]: The key config attributes + """ + return ["random_seed", "sequence_length", "split", "split_matrix", "tokenizer"] + + @abstractmethod + def __len__(self) -> int: + """Return the length of the dataset + + Returns: + int: See abstract implementation + """ + pass + + @abstractmethod + def __getitem__(self, idx: int) -> Dict[str, Union[torch.Tensor, numpy.ndarray]]: + """Return from the dataset + + Args: + idx (int): The index into the dataset + + Returns: + Dict[str, Union[torch.Tensor, numpy.ndarray]]: See abstract implementation + """ + pass + + +class MockDataset(MegatronDataset): + """The highest level wrapper class from which all mock dataset classes should inherit + + The MockDataset is a special, one-off class that should not serve as a precedent for developers + seeking to extend the MegatronDataset. This class is incompatible with BlendedDataset + + This class cannibalizes the constructor of the parent class. As such, we do not need to + pass in some constructor parameters. They may be populated, but most are superfluous and can + be None. Only num_samples, index_split, and config are required. + + + Args: + dataset (Optional[LowLevelDataset]): The dataset around which to build the MegatronDataset + + dataset_path (Optional[str]): The real path on disk to the dataset, for bookkeeping. TODO: subsume + this argument by enforcing auto-bookkeeping in the dataset class type. + + indices (Optional[numpy.ndarray]): The set of the documents indices to expose + + num_samples (int): The number of samples to draw from the indexed dataset + + index_split (Split): The indices Split + + config (BlendedMegatronDatasetConfig): The config + """ + + def __init__( + self, + dataset: Optional[LowLevelDataset], + dataset_path: Optional[str], + indices: Optional[numpy.ndarray], + num_samples: int, + index_split: Split, + config: BlendedMegatronDatasetConfig, + ) -> None: + self.config = config + assert self.config.mock + + super().__init__(dataset, dataset_path, indices, num_samples, index_split, config) + + def __len__(self) -> int: + """Return an arbitrary length + + Returns: + int: The total number of samples that are present in the dataset + """ + return self.num_samples diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/megatron_tokenizer.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/megatron_tokenizer.py new file mode 100644 index 0000000..fbea419 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/megatron_tokenizer.py @@ -0,0 +1,141 @@ +import json +from abc import ABC, abstractmethod +from collections import OrderedDict +from typing import Any + +import numpy + + +class MegatronTokenizer(ABC): + """Abstract class for tokenizer + + Absent a config or class-specific tracking of which objects are uniquely identifying, we must + include all key word arguments as unique identifiers + + Args: + tokenizer_paths (Tuple[str]): All tokenizer source paths or prefixes + + kwargs (Dict[str, Any]): All tokenizer options + """ + + def __init__(self, *tokenizer_paths: str, **tokenizer_options: Any): + + self.unique_identifiers = OrderedDict() + self.unique_identifiers["class"] = type(self).__name__ + self.unique_identifiers["tokenizer_path"] = list(tokenizer_paths) + for option in tokenizer_options: + self.unique_identifiers[option] = str(tokenizer_options[option]) + + self.unique_description = json.dumps(self.unique_identifiers, indent=4) + + super().__init__() + + @abstractmethod + def tokenize(self, text: str) -> numpy.ndarray: + """Convert text to embedding ids + + Args: + text (str): The text to convert + + Returns: + numpy.ndarray: The converted embedding ids + """ + pass + + def detokenize(self, ids: numpy.ndarray) -> str: + """Convert embedding ids to text + + Args: + ids (numpy.ndarray): The ids to convert + + Returns: + str: The converted text + + Raises: + NotImplementedError: Non-abstract, optional method + """ + raise NotImplementedError("{} has no method 'detokenize'".format(type(self).__name__)) + + @property + @abstractmethod + def vocab(self): + """Dictionary from vocab text token to id token + """ + pass + + @property + @abstractmethod + def inv_vocab(self): + """Dictionary from vocab id token to text token + """ + pass + + @property + @abstractmethod + def vocab_size(self): + """The vocabulary size + """ + pass + + @property + def cls(self): + """The CLS token id + + Raises: + NotImplementedError: Non-abstract, optional attribute + """ + raise NotImplementedError("{} has no attribute 'cls'".format(type(self).__name__)) + + @property + def sep(self): + """The SEP token id + + Raises: + NotImplementedError: Non-abstract, optional attribute + """ + raise NotImplementedError("{} has no attribute 'sep'".format(type(self).__name__)) + + @property + def pad(self): + """The PAD token id + + Raises: + NotImplementedError: Non-abstract, optional attribute + """ + raise NotImplementedError("{} has no attribute 'pad'".format(type(self).__name__)) + + @property + def eod(self): + """The EOD token id + + Raises: + NotImplementedError: Non-abstract, optional attribute + """ + raise NotImplementedError("{} has no attribute 'eod'".format(type(self).__name__)) + + @property + def bos(self): + """The BOS token id + + Raises: + NotImplementedError: Non-abstract, optional attribute + """ + raise NotImplementedError("{} has no attribute 'bos'".format(type(self).__name__)) + + @property + def eos(self): + """The EOS token id + + Raises: + NotImplementedError: Non-abstract, optional attribute + """ + raise NotImplementedError("{} has no attribute 'eos'".format(type(self).__name__)) + + @property + def mask(self): + """The MASK token id + + Raises: + NotImplementedError: Non-abstract, optional attribute + """ + raise NotImplementedError("{} has no attribute 'mask'".format(type(self).__name__)) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/multimodal_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/multimodal_dataset.py new file mode 100644 index 0000000..0a3e93a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/multimodal_dataset.py @@ -0,0 +1,62 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from dataclasses import dataclass +from typing import Callable, Dict + +import torch + +from megatron.core.datasets.gpt_dataset import GPTDatasetConfig, MockGPTDataset + + +@dataclass +class MultimodalDatasetConfig(GPTDatasetConfig): + """Configuration object for Megatron Core Multimodal datasets. + + Note: This is unused at the moment and may be missing features. Follow-up changes will use this. + """ + + image_h: int = None + """Image height.""" + + image_w: int = None + """Image width.""" + + # Function to preprocess the data sample to a format expected by a specific model. By default, do nothing. + preprocess_func: Callable[[Dict[str, torch.Tensor]], Dict[str, torch.Tensor]] = lambda x: x + """Optional function to preprocess data samples for a specific model.""" + + def __post_init__(self) -> None: + super().__post_init__() + + assert self.image_h is not None + assert self.image_w is not None + + +class MockMultimodalDataset(MockGPTDataset): + """Mock multimodal dataset. + + + This is unused at the moment and may be missing features. Follow-up changes will use this. + """ + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + """Return a sample that contains a dummy image, text sequence and the associated labels and cost and attention masks. + + Args: + idx (int): The integer seed for mock data generation. + + Returns: + Dict[str, torch.Tensor]: The mock data. + """ + # Get a text sample. + sample = super().__getitem__(idx) + + # Add mock input image. + sample["image"] = torch.zeros( + (3, self.config.image_h, self.config.image_w), dtype=torch.float32 + ) + + # Run optional data preprocessing. + preprocess_func = self.config.preprocess_func + + return preprocess_func(sample) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/readme.md b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/readme.md new file mode 100644 index 0000000..12ade94 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/readme.md @@ -0,0 +1,193 @@ +# Data Pipeline + +## Data pre-processing + +Data preprocessing is built around the following classes: + +1. `IndexedDatasetBuilder` +2. `IndexedDataset` + +At the moment, an end-to-end data preprocessing implementation is left to the user. See the class docstring(s) for more details. + +#### IndexedDatasetBuilder + +The `IndexedDatasetBuilder` is capable of building and merging `IndexedDataset` instances. + +#### IndexedDataset + +The `IndexedDataset` class is the lowest-level data interface in Megatron Core. Internally, an `IndexedDataset` instance references two binaries: the data file (`.bin`) contains document/sequence data and the index file (`.idx`) contains document/sequence metadata. + +The index file stores dataset-level metadata first: +- The index header, for backward compatibility +- The index version, for backward compatibility +- A numeric code corresponding to the data type used to write data to the data file +- The number of sequences in the dataset +- The number of documents in the dataset + +The index file stores document-level and sequence-level metadata second: +- In order, the number of elements per sequence +- In order, the byte offset (pointer) per sequence +- In order, the consecutive sequence index range `[...)` per document +- In order, the mode per sequence (in the multimodal case) + +## Data loading: construction + +Building the data loaders is a distributed-aware process built around the following classes: + +1. `BlendedMegatronDatasetConfig` +2. `BlendedMegatronDatasetBuilder` +3. `IndexedDataset` +3. `MegatronDataset` +4. `BlendedDataset` + +See the class docstrings for more details. + +#### BlendedMegatronDatasetConfig (extendable) + +The `BlendedMegatronDatasetConfig` class parameterizes the `BlendedMegatronDatasetBuilder` and in turn the `MegatronDataset` and `BlendedDataset`. + +Different training/inference regimes will require different extensions e.g. the `GPTDatasetConfig` + +#### BlendedMegatronDatasetBuilder + +The `BlendedMegatronDatasetBuilder` class builds the highest-level data interfaces in Megatron Core. + +**NB:** All ranks should attempt to build the dataset via the `BlendedMegatronDatasetBuilder` or the program will hang. Which ranks follow through on their attempts can be controlled via the `BlendedMegatronDatasetConfig`. + +#### IndexedDataset + +The `IndexedDataset` class is the lowest-level data interface in Megatron Core. + +The `IndexedDataset` should already exist on disk before attempting to build any of the high-level data interfaces. + + +#### MegatronDataset (extendable) + +The `MegatronDataset` abstract class is a high-level data interface in Megatron Core. It is an abstraction built upon the `IndexedDataset`. + +Different training/inference regimes will require different extensions e.g. the `GPTDataset` + +#### BlendedDataset + +The `BlendedDataset` class is a high-level data interface in Megatron Core. It is an abstraction built upon the `MegatronDataset`. + +The `BlendedDataset` is only necessary when a blend multiple data distributions, i.e. multiple `MegatronDataset` instances, should contribute to a certain dataset split. The blend can be controlled via the `BlendedMegatronDatasetConfig`. + +## Data loading: implementation + +### GPTDataset + +The `GPTDataset` is parameterized by the following variables: the underlying `IndexedDataset` instance `indexed_dataset`, the split indices `indexed_indices` (the congituous subset of document or sequence indices used for training, validation, and testing), the number of samples `N`, the sequence length `S`, and the random seed `R`. + +The `GPTDataset` creates three index mappings to facilitate lookup: (1) the document index, (2) the sample index, and (3) the shuffle index. + +1. The document index _Do_idx_ is a 1-D array mapping from _i_ to document index of length `E * |indexed_indices|` where `E` corresponds to the minimum number of epochs such that `E * |indexed_indices| >= N`. The document index is shuffled according to `R`. + + ``` + Given: + + N = 15 + indexed_indices = [5, 6, 7, 8, 9] + E = 3 + + Then, for example: + + Do_idx = [8, 8, 9, 6, 7, 5, 8, 5, 6, 6, 5, 9, 7, 7, 9] + ``` + +2. The sample index _Sa_idx_ is a 2-D array mapping from _j_ to pairs of (_i_, _Do_idx_[ _i_ ] offset) of shape `[N + 1, 2]`. The rows _j_ and _j_ + 1 serve as the left and right bounds for the _j_-th sample. + + ``` + Given: + + S = 1024 + + Then, for example: + + Sa_idx[0] = (0, 0) + Sa_idx[1] = (0, 1024) => Do_idx[0] has length greater than S + Sa_idx[2] = (1, 512) => Do_idx[0] has length 1536 + Sa_idx[3] = (2, 0) => Do_idx[1] has length 1536 + Sa_idx[4] = (5, 300) => Do_idx[2:5] are shorter documents relative to Do_idx[0:2] + Sa_idx[5] = (6, 24) => Do_idx[5] has length 1300 + ``` + +3. The shuffle index _Sh_idx_ is a 1-D array mapping from _k_ to _j_ of length `N`. The shuffle index is shuffled according to `R`. + + ``` + Given + + N = 10 + + Then, for example: + + Sh_idx = [4, 0, 2, 6, 1, 9, 5, 8, 7, 3] + ``` + +To query the `GPTDataset` for the _k_-th sample we do the following + +- Use the shuffle index to get the index _j_ into the sample index. + + ``` + j = Sh_idx[k] + ``` +- Use the sample index to get the left and right sample-bounding indices into the document index and the starting token offset for each document. + + ``` + i, offset = Sa_idx[j] + i_next, offset_next = Sa_idx[j + 1] + ``` +- Use the document index to retrieve `S` tokens from consecutive (in the document index) documents. + + ``` + sample = [] + sample += indexed_dataset[Do_idx[i]][offset:] + if i != i_next: + sample += indexed_dataset[Do_idx[i + 1:i_next]] + sample += indexed_dataset[Do_idx[i_next]][:offset_next] + ``` + +To save time during initialization, each index is built/cached sequentially on one process rank and subsequently loaded in parallel on other process ranks. The cached indices are unique to a hash generated in the `MegatronDataset.__init__` function. + +### BlendedDataset + +The `BlendedDataset` is parameterized by the following variables: the underlying `MegatronDataset` instances `D`, the weights `W` (one per dataset), and the size `S`. The `BlendedDataset` will draw samples from contributing datasets in proportion to the weights until achieving a composite dataset of the desired size. During each sampling step, we draw a single sample from the dataset which has the greatest sampling error. + +The `BlendedDataset` creates two "blending" indices to facilitate lookup: (1) the dataset index and (2) the dataset sample index. + +1. The dataset index _Da_idx_ is a 1-D array mapping from _i_ to dataset index of length `S`. + + ``` + Given + + D = [d0, d1, d2] + W = [1/2, 1/4, 1/4] + S = 4 + + Then, for example: + + Da_idx = [0, 1, 2, 0] + + ``` + +2. The dataset sample index _Sa_idx_ is a 1-D mapping from _i_ to the sample index for dataset _Da_idx[i]_ of length `S`. + + ``` + Given + + Da_idx = [0, 1, 2, 0] + + Then, for example: + + Sa_idx = [0, 0, 0, 1] + ``` + +To query the `BlendedDataset` for the _k_-th sample we do the following + +- Use the dataset index to retrieve the corresponding dataset from `D` and the dataset sample index to retrieve the corresponding sample from that dataset. + + ``` + sample = D[Da_idx[k]][Sa_idx[k]] + ``` + +To save time during initialization, each index is built/cached sequentially on one process rank and subsequently loaded in parallel on other process ranks. The cached indices are unique to a hash generated in the `BlendedDataset.__init__` function. diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/__init__.py new file mode 100644 index 0000000..7ce970c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from .config import RetroGPTChunkDatasets +from .query.multi_split_gpt_dataset import MultiSplitGPTDataset, MultiSplitGPTDatasetConfig +from .query.retro_dataset import get_retro_datasets diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/__init__.py new file mode 100644 index 0000000..3635bed --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +""" +Exports: + + - Embedder: Base class for all Bert embedders. + - RetroBertEmbedders: Container class for in-memory and on-disk embedders. + - RetroPreprocessingConfig: Configuration class for all of Retro preprocessing. + - RetroGPTChunkDatasets: Container class for train, valid, and test datasets. + - RetroTokenizers: Container class for GPT and Bert tokenizers. +""" + +from .bert_embedders import Embedder, RetroBertEmbedders +from .config import RetroPreprocessingConfig +from .gpt_chunk_datasets import RetroGPTChunkDatasets +from .tokenizers import RetroTokenizers diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/bert_embedders.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/bert_embedders.py new file mode 100644 index 0000000..8f3fe85 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/bert_embedders.py @@ -0,0 +1,48 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Container dataclass for holding both in-memory and on-disk Bert embedders.""" + +import abc +from dataclasses import dataclass +from typing import Any + +import numpy as np +import torch + + +class Embedder(abc.ABC): + """Base class for all Bert embedders. + + All embedders should be able to embed either an entire text dataset (to a 2D + numpy array), or a single text string (to a 1D numpy array). + """ + + @abc.abstractmethod + def embed_text_dataset(self, text_dataset: torch.utils.data.Dataset) -> np.ndarray: + """Embed a text dataset. + + Args: + text_dataset (torch.utils.data.Dataset): Text dataset to embed. Each sample of the text dataset should output a dict with a key 'text' and a string value. + + Returns: + A 2D ndarray with shape (len(text_dataset), dimension(embedder)). + """ + + @abc.abstractmethod + def embed_text(self, text: str) -> np.ndarray: + """Embed a simple string of text. + + Args: + text (str): A single text sample. + + Returns: + A 1D ndarray with shape (dimensions(embedder),). + """ + + +@dataclass +class RetroBertEmbedders: + """Container dataclass for in-memory and on-disk Bert embedders.""" + + disk: Embedder + mem: Embedder diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/config.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/config.py new file mode 100644 index 0000000..ac9ca84 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/config.py @@ -0,0 +1,135 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Retro preprocessing config.""" + +from dataclasses import dataclass + +from megatron.core.transformer import TransformerConfig + +from .bert_embedders import RetroBertEmbedders +from .gpt_chunk_datasets import RetroGPTChunkDatasets +from .tokenizers import RetroTokenizers + + +@dataclass +class RetroPreprocessingConfig(TransformerConfig): + """Configuration object for Retro preprocessing. + + *Note* : Arguments prefixed with '--retro-gpt-*' or '--retro-bert-*' are + included and named as such to more easily handle managing both models + running at the same time. Megatron is not optimized to run two models at + once, so this naming convention makes it clearer. + + Args: + + retro_project_dir (str): Retro project directory, which contains the preprocessed data for for pretraining. This directory is built during preprocessing (see tools/retro/README.md), and contains subdirectories for the chunk database and pretraining neighbors. + retro_tasks (str): Comma-separated list of tasks to run. Run entire preprocesing pipeline by using '--retro-tasks build'. Alternatively, run individual stages with tasks (in this order) 'db-build', 'index-build', or 'query-pretraining-neighbors'. For example, '--retro-tasks db-build,index-build,query-pretraining-neighbors' is equivalent to '--retro-tasks build'; or the argument can contain a subset of these tasks. Stages must always be run in the correct order (listed above). + retro_task_validate (float): If defined, validate a randomly sampled subset of the existing results of the given task. Each task implements a 'validate' method that is responsible for sampling a `retro_task_validate` fraction of the existing results, and then checking for bitwise equality with the current code base. (E.g., `--retro-task-validate 0.01`.) + retro_block_size (int): Number of chunks to process at a time when generating Bert embeddings and querying the search index. Partial results for each block are generally saved to disk in separate files. + retro_doc_block_size (int): Number of documents to processe at time when processing token datasets into chunk databases. The partial chunk database for each block is saved into a separate file. + retro_gpt_seed (int): Random seed used for python, numpy, pytorch, and cuda. + retro_gpt_data_path (str): Path to the training dataset. Accepted format: 1) a single data path, 2) multiple datasets in the form: dataset1-weight dataset1-path dataset2-weight dataset2-path ... It is used with --split when a single dataset used for all three: train, valid and test. It is exclusive to the other --*-data-path args. + retro_gpt_data_cache_path (str): Path to a directory to hold cached index files. + retro_gpt_split (str): Comma-separated list of proportions for training, validation, and test split. For example the split `90,5,5` will use 90%% of data for training, 5%% for validation and 5%% for test. + retro_gpt_train_samples (int): Total number of samples to train over all training runs. + retro_gpt_eval_interval (int): GPT evaluation interval. + retro_gpt_eval_iters (int): GPT evaluation iterations. + retro_gpt_tokenizer_type (str): GPT tokenizer type. + retro_gpt_tokenizer_model (str): GPT tokenizer model file. + retro_gpt_vocab_file (str): GPT vocab file. + retro_gpt_merge_file (str): GPT merge file. + retro_gpt_seq_length (int): GPT sequence length. + retro_gpt_global_batch_size (int): GPT global batch size. + retro_gpt_chunk_length (int): GPT chunk length. + retro_bert_tokenizer_type (str): Bert tokenizer type (for when using '--bert-embedder-type megatron'). + retro_bert_vocab_file (str): Bert vocab file. + retro_bert_batch_size (int): Micro-batch size for processing Bert embeddings. + retro_bert_max_chunk_length (int): Maximum sequence length for Bert embeddings. (Named 'chunk' here in reference to these Bert sequences being converted from GPT chunks.) + retro_index_type (str): A 'faiss-base' index is a simple, un-optimized wrapper around a Faiss index. A 'faiss-par-add' index optimizes the 'add()' method by making it multi-node and multi-process, but with bit-wise equivalent results. + retro_index_str (str): Index string used for calling faiss.index_factory(). For example, 'IVF262144_HNSW32,Flat' or 'OPQ32_256,IVF4194304_HNSW32,PQ32'. + retro_index_ntrain (int): Number of database chunks to use for training the index. This value must be less or equal to the total number of chunks in the database. + retro_index_train_load_fraction (float): Fraction of sampled chunks to use for training the index. Useful when our total sampled embeddings use too much memory; lowering the load fraction is less costly than re-embedding a new sampled dataset from scratch. + retro_index_add_load_fraction (float): Fraction of database chunks to use for adding to the index. Useful when our total index size would use too much memory; lowering the load fraction is less costly than re-designing our token datasets. + retro_index_delete_training_embeddings (bool): Delete training embeddings for the search index. Useful for debugging. + retro_index_delete_added_codes (bool): Delete added codes for the search index. Useful for debugging. + retro_query_ef_search (int): Index ef-search parameter for Hierarchical Navigable Small Worlds (HNSW) during querying. + retro_query_nprobe (int): Index nprobe parameter for Inverted File (IVF) during querying. + retro_query_num_neighbors_query (int): Number of neighbors to retrieve when calling index.search(). + retro_query_num_neighbors_save (int): Number of neighbors to save to disk after the index's returned neighbors. If longer than target value, neighbors truncated; and if shorter than target value, neighbors are padded with -1's. + retro_bert_embedders (RetroBertEmbedders): Set of Bert embedders used for embedding chunks. Contains entries: 1) 'mem' for an in-memory embedder, and 2) 'disk' for an embedder that saves results in blocks to disk. + retro_gpt_chunk_datasets (RetroGPTChunkDatasets): GPT datasets for 'train', 'valid', and 'test'. + retro_tokenizers (RetroTokenizers): GPT ('gpt') and Bert ('bert') tokenizers. + """ + + # Basic. + retro_project_dir: str = None + retro_tasks: str = 'build' + retro_task_validate: float = None + retro_block_size: int = 100000 + retro_doc_block_size: int = 100000 + + # GPT. + retro_gpt_seed: int = 1234 + retro_gpt_data_path: list = None # basic list here, for parsing purposes + retro_gpt_data_cache_path: str = None + retro_gpt_split: str = '969,30,1' + retro_gpt_train_samples: int = None + retro_gpt_eval_interval: int = None + retro_gpt_eval_iters: int = None + retro_gpt_tokenizer_type: str = None + retro_gpt_tokenizer_model: str = None + retro_gpt_vocab_file: str = None + retro_gpt_merge_file: str = None + retro_gpt_seq_length: int = None + retro_gpt_global_batch_size: int = None + retro_gpt_chunk_length: int = 64 + + # Bert. + retro_bert_tokenizer_type: str = None + retro_bert_vocab_file: str = None + retro_bert_batch_size: int = 128 + retro_bert_max_chunk_length: int = 256 + + # Index. + retro_index_type: str = 'faiss-par-add' + retro_index_str: str = None + retro_index_ntrain: int = None + retro_index_train_load_fraction: float = 1.0 + retro_index_add_load_fraction: float = 1.0 + retro_index_delete_training_embeddings: bool = True + retro_index_delete_added_codes: bool = True + + # Query. + retro_query_ef_search: int = 256 + retro_query_nprobe: int = 65536 + retro_query_num_neighbors_query: int = 200 + retro_query_num_neighbors_save: int = 20 + + # Tools. + retro_bert_embedders: RetroBertEmbedders = None + retro_gpt_chunk_datasets: RetroGPTChunkDatasets = None + retro_tokenizers: RetroTokenizers = None + + def __post_init__(self) -> None: + """Validate Retro config.""" + + # Validate required attributes. + assert self.retro_project_dir is not None + assert self.retro_tasks is not None + assert self.retro_gpt_data_path is not None or self.retro_gpt_data_cache_path is not None + assert self.retro_gpt_train_samples is not None + assert self.retro_gpt_eval_interval is not None + assert self.retro_gpt_eval_iters is not None + assert self.retro_gpt_tokenizer_type is not None + assert self.retro_gpt_tokenizer_model is not None or ( + self.retro_gpt_vocab_file is not None and self.retro_gpt_merge_file is not None + ) + assert self.retro_gpt_seq_length is not None + assert self.retro_gpt_global_batch_size is not None + assert self.retro_bert_tokenizer_type is not None + assert self.retro_bert_vocab_file is not None + assert self.retro_index_str is not None + assert self.retro_index_ntrain is not None + + # Split retro tasks. + self.retro_tasks = self.retro_tasks.split(",") diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/gpt_chunk_datasets.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/gpt_chunk_datasets.py new file mode 100644 index 0000000..831b1d8 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/gpt_chunk_datasets.py @@ -0,0 +1,15 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Container dataclass for GPT chunk datasets (train, valid, and test).""" + +from dataclasses import dataclass + + +@dataclass +class RetroGPTChunkDatasets: + """Container dataclass for GPT chunk datasets.""" + + # Each dict contains 'dataset', 'neighbor_dir', and 'num_active_chunks'. + train: dict = None + valid: dict = None + test: dict = None diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/tokenizers.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/tokenizers.py new file mode 100644 index 0000000..2e731c8 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/config/tokenizers.py @@ -0,0 +1,15 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Container class for GPT and Bert tokenizers.""" + +from dataclasses import dataclass + +from megatron.core.datasets.megatron_tokenizer import MegatronTokenizer + + +@dataclass +class RetroTokenizers: + """Container class for GPT and Bert tokenizers.""" + + gpt: MegatronTokenizer = None + bert: MegatronTokenizer = None diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/db/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/db/__init__.py new file mode 100644 index 0000000..f1f460b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/db/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +""" +Exports: + + - build_db: Build a chunk database from a list of indexed datasets. +""" + +from .build import build_db diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/db/build.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/db/build.py new file mode 100644 index 0000000..1469c08 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/db/build.py @@ -0,0 +1,631 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Build a chunk database from a list of indexed datasets. + +Building a chunk database consists of. + + - Breaking each document of each indexed dataset into consecutive + retro_gpt_chunk_length chunks. + - Re-tokenize each chunk into Bert, and discard any chunks with empty Bert + tokens. + - Save chunk offsets to disk for each indexed dataset. +""" + +import glob +import os +import types +from concurrent.futures import ProcessPoolExecutor, as_completed +from typing import Dict, List, Tuple + +import numpy as np +import torch +from tqdm import tqdm + +from megatron.core.datasets.indexed_dataset import IndexedDataset +from megatron.core.datasets.retro.config import RetroPreprocessingConfig +from megatron.core.datasets.retro.external_libs import h5py +from megatron.core.datasets.retro.utils import ( + extract_data_config, + get_blocks_by_rank, + log_retro_rank_0, + retro_makedir, +) + +from .utils import ( + get_indexed_dataset_infos, + get_indexed_dataset_infos_path, + get_individual_chunk_db, + get_individual_db_dir, + get_individual_db_paths, + get_individual_doc_offsets, + get_merged_db_path_map, + init_indexed_dataset_infos, + load_indexed_datasets, + save_indexed_dataset_infos, +) + + +def build_partial_db( + config: types.SimpleNamespace, + dataset_idx: int, + n_datasets: int, + indexed_dataset: IndexedDataset, + block_id: int, + n_blocks: int, + block: dict, + proc_id: int, + n_procs: int, +) -> Tuple[int, list, list, dict]: + """Process a document index range of the indexed dataset. + + The chunk database is built in parallel blocks, since de-tokenizing & + re-tokenizing for Bert-length computation is expensive. This method + iterates each document and extracts sequential 'chunk-length' sequences + from each document. + + Args: + config (types.SimpleNamespace): Subset of Retro config, containing 'chunk_length', 'gpt_eod', 'gpt_detokenize', 'bert_tokenize', and 'task_validate'. + dataset_idx (int): Index of this dataset out of all blended datasets. + n_datasets (int): Total number of blended datasets. + indexed_dataset (IndexedDataset): Indexed dataset to be chunked. + block_id (int): Block index out of all blocks to be processed. + n_blocks (int): Total number of blocks to be processed. + block (dict): Range information such as start/end points for chunking idnexed dataset. + proc_id (int): Process ID for tracking parallel process order. + n_procs (int): Total number of parallel processes. + + Returns: + A tuple containing: + + - Process ID. + - List of valid chunks. + - List of invalid chunks (i.e., chunks that converted to empty Bert embeddings.). + - Dict mapping document ID to number of valid chunks. + """ + + # Document start/end indexes. + doc_range = block["range"] + n_docs = doc_range[1] - doc_range[0] + n_docs_per_proc = int(np.ceil(n_docs / n_procs)) + doc_start_id = doc_range[0] + proc_id * n_docs_per_proc + doc_end_id = min(doc_range[1], doc_start_id + n_docs_per_proc) + + # Print progress. + progress_proc_ids = set(range(n_procs)) if torch.distributed.get_rank() == 0 else set() + if proc_id in progress_proc_ids: + log_retro_rank_0( + " > building partial chunk db, proc %d / %d, docs %d:%d / %d." + % (proc_id, n_procs, doc_start_id, doc_end_id, n_docs,) + ) + + # Progress bars (snapshot of overall progress). + doc_id_iter = range(doc_start_id, doc_end_id) + pbar = ( + tqdm(doc_id_iter, "parse doc chunks", miniters=len(doc_id_iter) // 20,) + if proc_id in progress_proc_ids + else doc_id_iter + ) + + # Iterate documents & parse chunks. + chunk_db_valid: List[Tuple] = [] + chunk_db_invalid: List[Tuple] = [] + doc_size_map = {} + for doc_id in pbar: + + # Progress description. + try: + pbar.set_description( + "%sds %d / %d, block %d / %d, proc %d / %d." + % ( + "" if config.task_validate is None else "[validate] ", + dataset_idx, + n_datasets, + block_id, + n_blocks, + proc_id, + n_procs, + ) + ) + except: + pass + + # Remove EOD token. + doc = indexed_dataset.get(doc_id) + if doc[-1].item() == config.gpt_eod: + doc = doc[:-1] + doc_len = len(doc) + + # Chunk start/end indexes. + chunk_start_idxs = list(range(0, doc_len, config.chunk_length)) + chunk_end_idxs = [min(doc_len, s + config.chunk_length) for s in chunk_start_idxs] + + # Re-tokenize each chunk to Bert/Wordpiece (empty bert -> 'invalid'). + doc_size_map[doc_id] = 0 + for i, chunk_start_idx in enumerate(chunk_start_idxs): + + # Re-tokenize. + chunk_end_idx = chunk_end_idxs[i] + gpt_token_ids = indexed_dataset.get( + idx=doc_id, offset=chunk_start_idx, length=chunk_end_idx - chunk_start_idx, + ) + text = config.gpt_detokenize(gpt_token_ids.tolist()) + bert_token_ids = config.bert_tokenize(text) + + # 'Valid' for non-empty Bert chunks; 'invalid' otherwise. + if len(bert_token_ids) == 0: + _chunk_db = chunk_db_invalid + else: + _chunk_db = chunk_db_valid + doc_size_map[doc_id] += 1 + _chunk_db.append((doc_id, chunk_start_idx, chunk_end_idx, len(bert_token_ids),)) + + return proc_id, chunk_db_valid, chunk_db_invalid, doc_size_map + + +def build_block_db( + config: RetroPreprocessingConfig, + dataset_idx: int, + n_datasets: int, + indexed_dataset: IndexedDataset, + n_procs: int, + executor: ProcessPoolExecutor, + n_missing_blocks: int, + block_idx: int, + block: dict, +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Split each document within block into consecutive retro_gpt_chunk_length size chunks. + + Args: + config (RetroPreprocessingConfig): For DB building, we make use of attributes 'chunk_length', 'gpt_eod', 'gpt_detokenize', 'bert_tokenize', and 'task_validate'. + dataset_idx (int): Index of this dataset out of all blended datasets. + n_datasets (int): Total number of blended datasets. + indexed_dataset (IndexedDataset): Indexed dataset to be chunked. + n_procs (int): Total number of parallel processes. + executor (ProcessPoolExecutor): Executor for launching parallel processes. + n_missing_blocks (int): Total number of blocks to be processed. + block_idx (int): Block index out of all blocks to be processed. + block (dict): Range information such as start/end points for chunking idnexed dataset. + + Returns: + A tuple containing: + + - List of valid chunks. + - List of invalid chunks (i.e., chunks that converted to empty Bert embeddings.). + - Dict mapping document ID to number of valid chunks. + """ + + # Build partial dbs. + log_retro_rank_0(' > build partial dbs.') + futures = [] + for proc_id in range(n_procs): # not true process id + futures.append( + executor.submit( + build_partial_db, + types.SimpleNamespace( + chunk_length=config.retro_gpt_chunk_length, + gpt_eod=config.retro_tokenizers.gpt.eod, + gpt_detokenize=config.retro_tokenizers.gpt.detokenize, + bert_tokenize=config.retro_tokenizers.bert.tokenize, + task_validate=config.retro_task_validate, + ), + dataset_idx, + n_datasets, + indexed_dataset, + block_idx, + n_missing_blocks, + block, + proc_id, + n_procs, + ) + ) + partial_chunk_dbs = [] + for future in as_completed(futures): + partial_chunk_dbs.append(future.result()) + + # Concatenate chunks. + partial_chunk_dbs.sort(key=lambda item: item[0]) # sort by proc_id + chunk_db_valid = [ + item for partial_chunk_db in partial_chunk_dbs for item in partial_chunk_db[1] + ] + chunk_db_invalid = [ + item for partial_chunk_db in partial_chunk_dbs for item in partial_chunk_db[2] + ] + + # Convert to numpy. + log_retro_rank_0(' > converting chunk db to numpy.') + chunk_db_valid = np.array(chunk_db_valid, dtype="uint32") + chunk_db_invalid = np.array(chunk_db_invalid, dtype="uint32") + + # Document offsets. + doc_sizes = [ + (d, s) for partial_chunk_db in partial_chunk_dbs for d, s in partial_chunk_db[3].items() + ] + doc_sizes.sort(key=lambda item: item[0]) + doc_offsets = np.cumsum([item[1] for item in doc_sizes]).astype("uint64") + doc_offsets = np.stack( + (np.array([item[0] for item in doc_sizes], dtype="uint64"), doc_offsets), axis=1 + ) + + return chunk_db_valid, chunk_db_invalid, doc_offsets + + +def save_block_db( + block: dict, chunk_db_valid: np.ndarray, chunk_db_invalid: np.ndarray, doc_offsets: np.ndarray, +) -> None: + """Save block of chunked tokens to disk. These blocks are later used for + training and adding to the vector index. + + Args: + block (dict): Range information such as start/end points for chunking idnexed dataset. + chunk_db_valid (np.ndarray): Array of valid chunk indexes. + chunk_db_invalid (np.ndarray): Array of invalid chunk indexes. + doc_offsets (np.ndarray): Array of document offsets by chunks. + """ + log_retro_rank_0(" > saving individual db.") + with h5py.File(block["path"], "w") as f: + dset = f.create_dataset("chunks_valid", data=chunk_db_valid) + dset = f.create_dataset("chunks_invalid", data=chunk_db_invalid) + dset = f.create_dataset("doc_offsets", data=doc_offsets) + + +def build_individual_db( + config: RetroPreprocessingConfig, dataset_idx: int, n_datasets: int, dataset_info: dict, +) -> None: + """Process a single indexed dataset & extract chunks. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + dataset_idx (int): Dataset index within blended dataset. + n_datasets (int): Total number of datasets within blended dataset. + dataset_info (dict): Metadata for dataset (see `save_indexed_dataset_infos()` in `utils.py` for more detail). + """ + + # Make directory. + db_dir = get_individual_db_dir(config.retro_project_dir, dataset_info["prefix"]) + retro_makedir(config, db_dir) + + # Indexed dataset. + indexed_dataset = dataset_info["dataset"] + + # Missing DB blocks (split by documents). + blocks = get_blocks_by_rank( + db_dir, + len(indexed_dataset), + config.retro_doc_block_size, + validate=lambda f: f["chunks_valid"].shape == (0,) or f["chunks_valid"].shape[1] == 4, + sample=config.retro_task_validate, + ) + if config.retro_task_validate is None: + active_blocks = blocks.missing + else: + assert blocks.n_missing_world == 0 + active_blocks = blocks.existing + + # Prevent missing-path-write race condition. + torch.distributed.barrier() + + # Nothing to do? + if config.retro_task_validate is None and not active_blocks: + return + + # Num processes. + if blocks.n_missing_world == 1: + n_procs = 128 + elif blocks.n_missing_world <= 2: + n_procs = 64 + elif blocks.n_missing_world <= 4: + n_procs = 32 + elif blocks.n_missing_world <= 8: + n_procs = 16 + else: + n_procs = 8 + + # Process documents in parallel. + with ProcessPoolExecutor(max_workers=n_procs) as executor: + for block_idx, block in enumerate(active_blocks): + + if block is not None: + + # Build block DB. + chunk_db_valid, chunk_db_invalid, doc_offsets = build_block_db( + config=config, + dataset_idx=dataset_idx, + n_datasets=n_datasets, + indexed_dataset=indexed_dataset, + n_procs=n_procs, + executor=executor, + n_missing_blocks=len(active_blocks), + block_idx=block_idx, + block=block, + ) + + if config.retro_task_validate is None: + # Save block DB. + save_block_db( + block=block, + chunk_db_valid=chunk_db_valid, + chunk_db_invalid=chunk_db_invalid, + doc_offsets=doc_offsets, + ) + + else: + + # Load existing block DB. + with h5py.File(block["path"]) as f: + existing_chunks_valid = np.copy(f["chunks_valid"]) + existing_chunks_invalid = np.copy(f["chunks_invalid"]) + existing_doc_offsets = np.copy(f["doc_offsets"]) + + # Check equality. + log_retro_rank_0(" > validate.") + assert np.array_equal(existing_chunks_valid, chunk_db_valid) + assert np.array_equal(existing_chunks_invalid, chunk_db_invalid) + assert np.array_equal(existing_doc_offsets, doc_offsets) + + # Wait for all ranks to finish block. + log_retro_rank_0(" > waiting for all ranks to finish block.") + torch.distributed.barrier() + + log_retro_rank_0(" > finished saving individual db.") + + +def build_individual_dbs( + config: RetroPreprocessingConfig, indexed_dataset_infos: List[Dict], +) -> None: + """Iterate each indexed dataset & process its chunks. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + indexed_dataset_infos (List[Dict]): Preprocessing metadata for each dataset. + """ + + # Build individual DBs. + log_retro_rank_0(" > build individual chunk dbs.") + for ds_idx, ds_info in enumerate(indexed_dataset_infos): + + # Progress. + log_retro_rank_0( + " > building individual db, dataset %d / %d ... '%s'." + % (ds_idx, len(indexed_dataset_infos), ds_info["prefix"],) + ) + + # Process single dataset. + build_individual_db(config, ds_idx, len(indexed_dataset_infos), ds_info) + + +def update_chunk_counts( + config: RetroPreprocessingConfig, indexed_dataset_infos: List[Dict] +) -> None: + """Set n_chunks_train & n_chunks sampled for each individual DB. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + indexed_dataset_infos (List[Dict]): Preprocessing metadata for each dataset (i.e., 'prefix', 'ratio', 'n_chunks', etc.). + """ + + if torch.distributed.get_rank() != 0: + return + + # Data ratio sum (for setting index training chunks). + data_ratio_sum = sum([d["ratio"] for d in indexed_dataset_infos]) + + # Training split size (split at document level). + train_fraction = float(extract_data_config(config).split.split(",")[0]) / 100 + assert train_fraction > 0 and train_fraction <= 1 + + # Set n_chunks (including n_chunks_sampled for unambiguity). + log_retro_rank_0(" > compute n_chunks.") + for ds_index, ds_info in enumerate(indexed_dataset_infos): + + db_paths = get_individual_db_paths(config.retro_project_dir, ds_info["prefix"]) + + # Update counts. + ds_info["n_docs"] = len(ds_info["dataset"].document_indices) - 1 + ds_info["n_docs_train"] = int(train_fraction * ds_info["n_docs"]) + ds_info["n_chunks"] = 0 # previously, 'n_chunks_valid' + ds_info["n_chunks_train"] = 0 + ds_info["n_chunks_invalid"] = 0 + for db_path in tqdm( + db_paths, "%d/%d, %s" % (ds_index, len(indexed_dataset_infos), ds_info["prefix"]) + ): + with h5py.File(db_path, "r") as f: + ds_info["n_chunks"] += len(f["chunks_valid"]) + ds_info["n_chunks_invalid"] += len(f["chunks_invalid"]) + ds_info["n_chunks_train"] += ( + (np.copy(f["chunks_valid"][:, 0]) < ds_info["n_docs_train"]).sum().item() + ) + + ds_info["n_chunks_sampled"] = int( + config.retro_index_ntrain * ds_info["ratio"] / data_ratio_sum + ) + + # Verify counts. + assert ds_info["n_chunks_train"] <= ds_info["n_chunks"], "n_train (%d) > n_total (%d)." % ( + ds_info["n_chunks_train"], + ds_info["n_chunks"], + ) + assert ds_info["n_chunks_sampled"] <= ds_info["n_chunks_train"], ( + "n_sampled (%d) > n_train (%d)." + % (ds_info["n_chunks_sampled"], ds_info["n_chunks_train"]) + ) + + +def merge_dbs(project_dir: str, indexed_dataset_infos: List[Dict], db_type: str) -> None: + """Merge individual DBs into single DB. + + Args: + project_dir (str): Retro project dir. + indexed_dataset_infos (List[Dict]): Preprocessing metadata for each dataset (i.e., 'prefix', 'ratio', 'n_chunks', etc.). + db_type (str): DB type (e.g., 'sampled', 'train', or 'valid'). + """ + + if torch.distributed.get_rank() != 0: + return + + log_retro_rank_0(" > build %s chunk db." % db_type) + + # Count chunks. + if db_type == "sampled": + n_chunks_key = "n_chunks_sampled" + n_docs_key = None + elif db_type == "train": + n_chunks_key = "n_chunks_train" + n_docs_key = "n_docs_train" + elif db_type == "valid": + n_docs_key = None + else: + raise Exception("handle db_type '%s'." % db_type) + + if db_type == "valid": + n_chunks = sum(m["n_chunks"] - m["n_chunks_train"] for m in indexed_dataset_infos) + else: + n_chunks = sum(m[n_chunks_key] for m in indexed_dataset_infos) + n_docs = None if n_docs_key is None else sum(m[n_docs_key] for m in indexed_dataset_infos) + + # DB path. + db_path = get_merged_db_path_map(project_dir)[db_type] + + # Delete existing chunk db if incorrect size. + if os.path.exists(db_path): + + try: + + f = h5py.File(db_path) + n_alloc = len(f["chunks"]) # total allocated + n_written = f["n_written"][0].item() # total written + f.close() + + if n_chunks != n_alloc or n_chunks != n_written: + os.remove(db_path) + + except Exception as e: + if isinstance(e, OSError): + os.remove(db_path) + elif isinstance(e, KeyError): + f.close() + os.remove(db_path) + else: + raise e + + # Build merged chunk db. + if not os.path.exists(db_path): + + os.makedirs(os.path.dirname(db_path), exist_ok=True) + f = h5py.File(db_path, "w") + + # Initialize output arrays. + merged_chunk_db: np.ndarray = f.create_dataset("chunks", (n_chunks, 5), dtype="uint32") + merged_doc_offsets: np.ndarray = ( + None + if n_docs_key is None + else f.create_dataset("doc_offsets", (n_docs, 3), dtype="uint64") + ) + n_written = f.create_dataset("n_written", (1,), dtype="uint64") + n_written[0] = 0 + + # Iterate indexed datasets & collect chunks. + chunk_start_index = 0 + doc_start_index = 0 + doc_start_offset = 0 + for ds_idx, ds_info in enumerate(indexed_dataset_infos): + log_retro_rank_0( + " > merging dbs; '%s', dataset %d / %d ... '%s'." + % (db_type, ds_idx, len(indexed_dataset_infos), ds_info["prefix"]), + ) + individual_chunk_db: np.ndarray = get_individual_chunk_db(project_dir, ds_idx, ds_info) + individual_doc_offsets: np.ndarray = ( + None + if n_docs_key is None + else get_individual_doc_offsets(project_dir, ds_idx, ds_info) + ) + + if db_type == "valid": + individual_chunk_db = individual_chunk_db[ds_info["n_chunks_train"] :] + if n_docs_key is None: + individual_doc_offsets = None + else: + train_doc_offset = individual_doc_offsets[ds_info["n_docs_train"] - 1, 2] + individual_doc_offsets = np.copy( + individual_doc_offsets[ds_info["n_docs_train"] :] + ) + individual_doc_offsets[:, 2] -= train_doc_offset + + log_retro_rank_0("~~~") + log_retro_rank_0(individual_doc_offsets) + log_retro_rank_0(train_doc_offset) + raise Exception("test me.") + else: + individual_chunk_db = individual_chunk_db[: ds_info[n_chunks_key]] + individual_doc_offsets = ( + None + if n_docs_key is None + else np.copy(individual_doc_offsets[: ds_info[n_docs_key]]) + ) + + merged_chunk_db[ + chunk_start_index : chunk_start_index + len(individual_chunk_db) + ] = individual_chunk_db + chunk_start_index += len(individual_chunk_db) + n_written[0] = chunk_start_index + if n_docs_key is not None: + individual_doc_offsets[:, 2] += doc_start_offset + doc_end_index = doc_start_index + individual_doc_offsets.shape[0] + merged_doc_offsets[doc_start_index:doc_end_index] = individual_doc_offsets + doc_start_index = doc_end_index + doc_start_offset = individual_doc_offsets[-1, 2].item() + + f.close() + + +def build_merged_dbs(project_dir: str, indexed_dataset_infos: List[Dict]) -> None: + """Merge individual dataset components into single database. + + This method merges databases for DB types: + - 'sampled': used for training the vector index. + - 'train': used for adding to the trained vector index. + - 'valid': can be used for validating/testing the vector index. + + Args: + project_dir (str): Retro project dir. + indexed_dataset_infos (List[Dict]): Preprocessing metadata for each dataset (i.e., 'prefix', 'ratio', 'n_chunks', etc.). + """ + merge_dbs(project_dir, indexed_dataset_infos, "sampled") + merge_dbs(project_dir, indexed_dataset_infos, "train") + merge_dbs(project_dir, indexed_dataset_infos, "valid") + + +def build_db(config: RetroPreprocessingConfig) -> None: + """Extract token chunks from each indexed dataset. + + Iterate each document of each indexed dataset, extract that document's chunks, and save to a 'DB' (hdf5 file). + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + project_dir = config.retro_project_dir + + # Indexed dataset info. + if config.retro_task_validate is None: + indexed_dataset_infos = init_indexed_dataset_infos(config) + else: + indexed_dataset_infos = get_indexed_dataset_infos(config.retro_project_dir) + # Build individual dbs. + build_individual_dbs(config, indexed_dataset_infos) + + # If validating, return here. + if config.retro_task_validate is not None: + return + + # Single-process going forward. + if torch.distributed.get_rank() != 0: + return + + # Update n_chunks & save indexed dataset infos. + if not os.path.exists(get_indexed_dataset_infos_path(project_dir)): + update_chunk_counts(config, indexed_dataset_infos) + save_indexed_dataset_infos(project_dir, indexed_dataset_infos) + indexed_dataset_infos = get_indexed_dataset_infos(project_dir) + + # Builded merged dbs. + build_merged_dbs(project_dir, indexed_dataset_infos) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/db/dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/db/dataset.py new file mode 100644 index 0000000..1de6e02 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/db/dataset.py @@ -0,0 +1,108 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""A DBDataset is for iterating the chunks of the chunk database. + +This dataset is used for both training a vector index, and adding vectors to a +trained index. +""" + +from typing import List + +import numpy as np +import torch +from tqdm import tqdm + +from megatron.core.datasets.indexed_dataset import IndexedDataset + + +class DBDataset(torch.utils.data.Dataset): + """Dataset for iterating chunks. + + Args: + db_path (str): Path of HDF5-format chunk database. + indexed_datasets (List[IndexedDataset]): Indexed datasets used to build database. + chunks (np.ndarray): Array of chunk indexes, for indexing into indexed datasets. Format [dataset_idx, doc_id, start_idx, end_idx, bert_length]. + chunk_length (int): Max GPT chunk length (e.g., 64). + eod_token_id (int): EOD token ID. + """ + + def __init__( + self, + db_path: str, + indexed_datasets: List[IndexedDataset], + chunks: np.ndarray, + chunk_length: int, + eod_token_id: int, + ): + + assert chunks.shape[1] == 5, ( + "expected 5 columns (dataset_idx, " + "doc_idx, token_start_idx, token_end_idx, bert_chunk_length); " + "found %d columns." % chunks.shape[1] + ) + + self.db_path = db_path + self.indexed_datasets = indexed_datasets + self.chunks = chunks + self.doc_chunk_map = None + + self.max_chunk_length = chunk_length + self.eod_token_id = eod_token_id + + def __len__(self) -> int: + """Length of DB dataset. + + Returns: + Number of chunks contained in the dataset. + """ + return self.chunks.shape[0] + + def __getitem__(self, chunk_id: int) -> dict: + """DB dataset sample. + + Args: + chunk_id (int): Index of chunk within dataset. + + Returns: + A dict containing: + - 'doc_id': Document index within indexed dataset. + - 'text': GPT token IDs. + """ + + # Chunk start/end indexes. + indexed_dataset_id, doc_id, token_start_idx, token_end_idx, _ = [ + value.item() for value in self.chunks[chunk_id] + ] + chunk_length = token_end_idx - token_start_idx + indexed_dataset = self.indexed_datasets[indexed_dataset_id] + + # Chunk token ids. + token_ids = indexed_dataset.get(doc_id, offset=token_start_idx, length=chunk_length) + + # Extend chunks to max_chunk_length by padding with EOD tokens. + if chunk_length != self.max_chunk_length: + assert chunk_length < self.max_chunk_length, "invalid chunk len." + token_ids = token_ids.tolist() + token_ids += [self.eod_token_id] * (self.max_chunk_length - chunk_length) + + return { + "doc_id": doc_id, + "text": np.array(token_ids, dtype=np.int64), + } + + def load_doc_tuples(self) -> None: + """Load the dataset & document ids. + + Load the dataset id & document id of each chunk in the database, to + be used for causality filtering during querying. + """ + self.doc_tuples = np.zeros(shape=(len(self), 2), dtype="uint32") + block_size = int(1e6) + for start_idx in tqdm( + range(0, len(self), block_size), + "load doc tuples", + miniters=(len(self) // block_size) // 10, + disable=torch.distributed.get_rank() != 0, + ): + end_idx = min(len(self), start_idx + block_size) + self.doc_tuples[start_idx:end_idx] = self.chunks[start_idx:end_idx, :2] diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/db/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/db/utils.py new file mode 100644 index 0000000..df13089 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/db/utils.py @@ -0,0 +1,369 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Utilities for building a chunk database.""" + +import glob +import json +import os +from typing import Dict, List, Optional + +import numpy as np + +from megatron.core.datasets.indexed_dataset import IndexedDataset +from megatron.core.datasets.retro.config import RetroPreprocessingConfig +from megatron.core.datasets.retro.external_libs import h5py +from megatron.core.models.retro.utils import get_gpt_data_dir + +from .dataset import DBDataset + + +def get_db_dir(project_dir: str) -> str: + """Sub-directory for DB data. + + Args: + project_dir (str): Path to Retro project dir. + + Returns: + Path of the DB sub-directory within the project. + """ + return os.path.join(project_dir, "db") + + +def init_indexed_dataset_infos(config: RetroPreprocessingConfig) -> List[Dict]: + """Gather meta-info about each indexed dataset. + + The returned info array allows for easy access to the configuration, and + helps remove ambiguity. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + List of processing metadata for each dataset, including: + - ratio: Data split weight. + - prefix: Relative path to dataset under DB sub-directory. + """ + + data_dir = get_gpt_data_dir(config.retro_project_dir) + data_blend: List[str] = config.retro_gpt_data_path + assert len(data_blend) % 2 == 0, "currently, only blended dataset is supported." + + # Dataset infos. + infos = [] + for i in range(0, len(data_blend), 2): + ratio = float(data_blend[i]) + prefix = data_blend[i + 1] + path = os.path.join(data_dir, prefix + ".bin") + assert os.path.exists(path), "couldn't find '%s'." % path + infos.append( + {"ratio": ratio, "prefix": prefix,} + ) + + # Load indexed datasets. + load_indexed_datasets(config.retro_project_dir, infos) + + return infos + + +def get_indexed_dataset_infos_path(project_dir: str) -> str: + """Path to indexed dataset meta-infos. + + Args: + project_dir (str): Path to Retro project dir. + + Returns: + Path to the `indexed_dataset_infos.json` file. + """ + return os.path.join(get_db_dir(project_dir), "indexed_dataset_infos.json") + + +def save_indexed_dataset_infos(project_dir: str, indexed_dataset_infos: List[Dict]) -> None: + """Save dataset order & meta-info. + + Args: + project_dir (str): Path to Retro project dir. + indexed_dataset_infos (List[Dict]): List of metadata for each dataset, with each entry containing: + + - ratio: Data split weight. + - prefix: Relative path to dataset under DB sub-directory. + - n_docs: Number of documents. + - n_docs_train: Number of documents used for pretraining. + - n_chunks: Number of valid chunks. + - n_chunks_train: Number of valid chunks used for pretraining. + - n_chunks_invalid: Number of invalid chunks. + - n_chunks_sampled: Number of valid chunks used for vector index training. + """ + + # Remove 'dataset' field. + clean_infos = [] + for info in indexed_dataset_infos: + info = dict(info) + del info["dataset"] + clean_infos.append(info) + + # Save. + with open(get_indexed_dataset_infos_path(project_dir), "w") as f: + json.dump(clean_infos, f, indent=4) + + +def load_indexed_datasets(project_dir: str, indexed_dataset_infos: List[Dict]) -> None: + """Loaded indexed datasets into memory-mapped datasets. + + Args: + project_dir (str): Path to Retro project dir. + indexed_dataset_infos (List[Dict]): List of metadata for each dataset (see `save_indexed_dataset_infos()` for more details. + """ + data_dir = get_gpt_data_dir(project_dir) + for info in indexed_dataset_infos: + info["dataset"] = IndexedDataset(os.path.join(data_dir, info["prefix"]), mmap=True) + + +def get_indexed_dataset_infos(project_dir: str) -> List[Dict]: + """Load indexed dataset meta-infos. + + Args: + project_dir (str): Path to Retro project dir. + + Returns: + List of metadata for each dataset (see `save_indexed_dataset_infos()` for more details. + """ + + # Load json. + path = get_indexed_dataset_infos_path(project_dir) + with open(path) as f: + infos = json.load(f) + + # Load indexed datasets. + load_indexed_datasets(project_dir, infos) + + return infos + + +def get_individual_db_dir(project_dir: str, prefix: str) -> str: + """Individual DB's directory. + + Args: + project_dir (str): Path to Retro project dir. + prefix (str): Unique relative path to dataset within project dir. + + Returns: + Path to the given datasets's chunk database. + """ + return os.path.join(get_db_dir(project_dir), "individual", prefix) + + +def get_individual_db_paths(project_dir: str, prefix: str) -> List[str]: + """Get paths of all database blocks of an individual dataset. + + Args: + project_dir (str): Path to Retro project dir. + prefix (str): Unique relative path to dataset within project dir. + + Returns: + Paths to each HDF5 chunk database files that comprises this datasets full chunk database. + """ + return sorted(glob.glob(get_individual_db_dir(project_dir, prefix) + "/*hdf5")) + + +def get_individual_chunk_db(project_dir: str, ds_id: int, ds_info: dict) -> np.ndarray: + """Load individual dataset's chunk DB. + + Args: + project_dir (str): Path to Retro project dir. + ds_id (int): Index of dataset within blended dataset. + ds_info (dict): Preprocessing metadata for dataset (see `save_indexed_dataset_infos()` for more detail). + + Returns: + Array of chunk start/end indexes for this dataset, where the chunk indexes can be used for indexing into the corresponding indexed dataset. + """ + paths = get_individual_db_paths(project_dir, ds_info["prefix"]) + # *Note*: convert to dataset, rather than copying to memory. + db = np.zeros((ds_info["n_chunks"], 5), dtype="uint32") + db[:, 0] = ds_id + start_idx = 0 + for path in paths: + f = h5py.File(path, "r") + n_chunks_current = f["chunks_valid"].shape[0] + db[start_idx : (start_idx + n_chunks_current), 1:] = f["chunks_valid"] + start_idx += n_chunks_current + f.close() + + assert start_idx == ds_info["n_chunks"] + + return db + + +def get_individual_doc_offsets(project_dir: str, ds_id: int, ds_info: dict) -> np.ndarray: + """Load individual dataset's document offsets. + + Args: + project_dir (str): Path to Retro project dir. + ds_id (int): Index of dataset within blended dataset. + ds_info (dict): Preprocessing metadata for dataset (see `save_indexed_dataset_infos()` for more detail). + + Returns: + Array of document offsets by chunk index for this dataset. + """ + paths = get_individual_db_paths(project_dir, ds_info["prefix"]) + # *Note*: convert to dataset, rather than copying to memory. + doc_offsets = np.zeros((ds_info["n_docs"], 3), dtype="uint64") + doc_offsets[:, 0] = ds_id + start_idx = 0 + start_offset = 0 + for path in paths: + with h5py.File(path) as f: + current_doc_offsets = np.copy(f["doc_offsets"]) + current_doc_offsets[:, 1] += start_offset + current_ndocs = current_doc_offsets.shape[0] + doc_offsets[start_idx : (start_idx + current_ndocs), 1:] = current_doc_offsets + start_idx += current_ndocs + start_offset = current_doc_offsets[-1, 1].item() + + return doc_offsets + + +def get_merged_db_path_map(project_dir: str) -> dict: + """Paths to merged datasets. + + Args: + project_dir (str): Path to Retro project dir. + + Returns: + A dict of chunk databases, one for each of: + - sampled: Chunks used for training the vector index. + - train: Chunks used for pretraining 'train' dataset. + - valid: Chunks used for pretraining 'valid' dataset. + """ + base_dir = get_db_dir(project_dir) + return { + "sampled": os.path.join(base_dir, "merged", "sampled.hdf5"), + "train": os.path.join(base_dir, "merged", "train.hdf5"), + "valid": os.path.join(base_dir, "merged", "valid.hdf5"), + } + + +def get_merged_dataset( + project_dir: str, + chunk_length: int, + eod_token_id: int, + db_type: str, + indexed_dataset_infos: Optional[List[Dict]] = None, +) -> DBDataset: + """Get merged dataset. + + Args: + project_dir (str): Path to Retro project dir. + chunk_length (int): GPT chunk length (e.g., 64). + eod_token_id (int): EOD token ID. + db_type (str): DB type (e.g., 'sampled', 'train', or 'valid'). + indexed_dataset_infos (Optional[List[Dict]]): Optionally, pre-loaded list of dataset metadata (see `save_indexed_dataset_infos()` for more detail). If not provided, the indexed dataset infos will be loaded from disk. + + Returns: + A DBDataset, which is a dataset that wraps the HDF5 chunk index array. + """ + + if not indexed_dataset_infos: + indexed_dataset_infos = get_indexed_dataset_infos(project_dir) + + # Load chunks. + db_path = get_merged_db_path_map(project_dir)[db_type] + f = h5py.File(db_path, "r") + chunks = f["chunks"] + + # DB dataset. + indexed_datasets = [info["dataset"] for info in indexed_dataset_infos] + dataset = DBDataset( + db_path=db_path, + indexed_datasets=indexed_datasets, + chunks=chunks, + chunk_length=chunk_length, + eod_token_id=eod_token_id, + ) + + return dataset + + +def get_merged_sampled_dataset( + project_dir: str, + chunk_length: int, + eod_token_id: int, + indexed_dataset_infos: Optional[List[Dict]] = None, +) -> DBDataset: + """Get sampled dataset (for training the vector index). + + Args: + project_dir (str): Path to Retro project dir. + chunk_length (int): GPT chunk length (e.g., 64). + eod_token_id (int): EOD token ID. + indexed_dataset_infos (Optional[List[Dict]]): Optionally, pre-loaded list of dataset metadata (see `save_indexed_dataset_infos()` for more detail). If not provided, the indexed dataset infos will be loaded from disk. + + Returns: + A DBDataset, which is a dataset that wraps the HDF5 chunk index array. + """ + return get_merged_dataset( + project_dir, chunk_length, eod_token_id, "sampled", indexed_dataset_infos + ) + + +def get_merged_train_dataset( + project_dir: str, + chunk_length: int, + eod_token_id: int, + indexed_dataset_infos: Optional[List[Dict]] = None, +) -> DBDataset: + """Get training dataset (for adding to the vector index). + + Args: + project_dir (str): Path to Retro project dir. + chunk_length (int): GPT chunk length (e.g., 64). + eod_token_id (int): EOD token ID. + indexed_dataset_infos (Optional[List[Dict]]): Optionally, pre-loaded list of dataset metadata (see `save_indexed_dataset_infos()` for more detail). If not provided, the indexed dataset infos will be loaded from disk. + + Returns: + A DBDataset, which is a dataset that wraps the HDF5 chunk index array. + """ + return get_merged_dataset( + project_dir, chunk_length, eod_token_id, "train", indexed_dataset_infos + ) + + +def get_merged_valid_dataset( + project_dir: str, + chunk_length: int, + eod_token_id: int, + indexed_dataset_infos: Optional[List[Dict]] = None, +) -> DBDataset: + """Get validation dataset (for testing the vector index). + + Args: + project_dir (str): Path to Retro project dir. + chunk_length (int): GPT chunk length (e.g., 64). + eod_token_id (int): EOD token ID. + indexed_dataset_infos (Optional[List[Dict]]): Optionally, pre-loaded list of dataset metadata (see `save_indexed_dataset_infos()` for more detail). If not provided, the indexed dataset infos will be loaded from disk. + + Returns: + A DBDataset, which is a dataset that wraps the HDF5 chunk index array. + """ + return get_merged_dataset( + project_dir, chunk_length, eod_token_id, "valid", indexed_dataset_infos + ) + + +def get_merged_datasets(project_dir: str, chunk_length: int, eod_token_id: int) -> dict: + """Get all merged datasets. + + Args: + project_dir (str): Path to Retro project dir. + chunk_length (int): GPT chunk length (e.g., 64). + eod_token_id (int): EOD token ID. + + Returns: + A dict mapping DB type ('sampled', 'train', or 'valid') to the corresponding DBDataset, which is a dataset that wraps the HDF5 chunk index array. + """ + fns = { + "sampled": get_merged_sampled_dataset, + "train": get_merged_train_dataset, + "valid": get_merged_valid_dataset, + } + datasets = {key: fn(project_dir, chunk_length, eod_token_id) for key, fn in fns.items()} + return datasets diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/external_libs.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/external_libs.py new file mode 100644 index 0000000..98b2872 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/external_libs.py @@ -0,0 +1,19 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Required external libraries for Retro preprocessing.""" + +import importlib + +required_libs = [ + "faiss", + "h5py", + "transformers", # for huggingface bert +] + +for lib in required_libs: + try: + globals()[lib] = importlib.import_module(lib) + except ImportError as e: + raise Exception( + f"Missing one or more packages required for Retro preprocessing: {required_libs}. Tried importing '{lib}'." + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/__init__.py new file mode 100644 index 0000000..d069f55 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +""" +Exports: + + - train_index: Train an index on representative vectors. + - add_to_index: Add vectors to a trained index. + - build_index: Wrapper function that calls above two functions. +""" + +from .build import add_to_index, build_index, train_index diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/build.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/build.py new file mode 100644 index 0000000..a5659e9 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/build.py @@ -0,0 +1,313 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Construct an index. + +Constructing an index generally happens in two phases: + + - index.train(): Train an index on a representative set of vectors. + - index.add(): Add vectors to an index, to be available for retrieval. +""" + +import os +import shutil + +import numpy as np +import torch +from tqdm import tqdm + +from megatron.core.datasets.retro.config import RetroPreprocessingConfig +from megatron.core.datasets.retro.db.utils import ( + get_merged_sampled_dataset, + get_merged_train_dataset, +) +from megatron.core.datasets.retro.external_libs import h5py +from megatron.core.datasets.retro.utils import GPTToTextDataset + +from .factory import IndexFactory +from .utils import ( + get_training_data_block_dir, + get_training_data_block_paths, + get_training_data_merged_path, + get_training_data_root_dir, +) + +################################################## +# Train index. +################################################## + + +def get_empty_index_path(config: RetroPreprocessingConfig) -> str: + """Path of empty index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + Path to the empty (trained, but without added samples) vector index. + """ + index = IndexFactory.get_index(config.retro_index_type) + empty_index_path = index.get_empty_index_path(config) + return empty_index_path + + +def get_block_nload(block_path: str, load_fraction: float) -> int: + """Compute number of blocks to load. + + This is computed by multiplying the total number of available blocks with the + fraction of blocks to load. + + Args: + block_path (str): Path to HDF5 file containing block of data. File must contain key 'data'. + load_fraction (float): Fraction (0 < load_fraction <= 1) of block samples to load. + + Returns: + Number of block samples to load. + """ + with h5py.File(block_path) as fi: + return int(load_fraction * fi["data"].shape[0]) + + +def merge_embedding_blocks(config: RetroPreprocessingConfig) -> None: + """Merge individual embedding blocks into a single binary mmap file. + + The embeddings are initially stored in block-sized (e.g., ~100k embeddings per + block) HDF5 files. These individual block files must be merged into a single + file before training, to be based as a numpy mmap array to the index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + if torch.distributed.get_rank() != 0: + return + + # Get block, merged paths. + load_fraction = config.retro_index_train_load_fraction + block_paths = get_training_data_block_paths(config) + bin_path = get_training_data_merged_path(config) + + # Skip, if already built. + if os.path.exists(bin_path): + return + + # Merge blocks. + with open(bin_path, "wb") as fo: + byte_offset = 0 + for block_idx, block_path in enumerate( + tqdm( + block_paths, + "merge train embeddings", + miniters=len(block_paths) // 10, + disable=torch.distributed.get_rank() != 0, + ) + ): + with h5py.File(block_path) as fi: + + nload = get_block_nload(block_path, load_fraction) + block = np.array(fi["data"][:nload], copy=False) + + fo.write(block.tobytes()) + + byte_offset += block.size * block.itemsize + fo.seek(byte_offset) + + +def get_text_dataset_for_training(config: RetroPreprocessingConfig) -> GPTToTextDataset: + """Convert GPT token chunk dataset to a text dataset for passing to the + embedder. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + The text dataset consisting of tokens converted from sampled chunk database. + """ + gpt_dataset = get_merged_sampled_dataset( + project_dir=config.retro_project_dir, + chunk_length=config.retro_gpt_chunk_length, + eod_token_id=config.retro_tokenizers.gpt.eod, + ) + text_dataset = GPTToTextDataset(gpt_dataset, config.retro_tokenizers.gpt) + return text_dataset + + +def embed_training_chunks(config: RetroPreprocessingConfig) -> None: + """Embed DB chunks. + + Store chunks in blocks on disk. These blocks will later be merged into + a single dataset for training the index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + merged_train_data_path = get_training_data_merged_path(config) + if os.path.exists(merged_train_data_path): + return + + # Get training text dataset. + text_dataset = get_text_dataset_for_training(config) + + # Embed dataset. + embedder = config.retro_bert_embedders.disk + embedder.embed_text_dataset("index", get_training_data_block_dir(config), text_dataset) + + # Merge embeddings. + merge_embedding_blocks(config) + + +def train_on_embeddings(config: RetroPreprocessingConfig) -> None: + """Train index on embedded DB chunks. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + index = IndexFactory.get_index(config.retro_index_type) + index.train(config) + + +def remove_embeddings(config: RetroPreprocessingConfig) -> None: + """Remove embeddings after training. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + torch.distributed.barrier() + if torch.distributed.get_rank() != 0: + return + empty_index_path = get_empty_index_path(config) + assert os.path.isfile(empty_index_path) + shutil.rmtree(get_training_data_root_dir(config), ignore_errors=True) + + +def _train_index(config: RetroPreprocessingConfig) -> None: + """Train index on DB chunks. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + # Check if trained index already exists. + if not os.path.isfile(get_empty_index_path(config)): + + # Embed training chunks. + embed_training_chunks(config) + + # Train index on embeddings. + train_on_embeddings(config) + + # Wait for (single-process) training to complete. + torch.distributed.barrier() + + # Remove embeddings. + if config.retro_index_delete_training_embeddings: + remove_embeddings(config) + + +def train_index(config: RetroPreprocessingConfig) -> None: + """Entry point for training the index. + + We select whether to train a new index, or validate an existing index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + # Train new index. + if config.retro_task_validate is None: + _train_index(config) + + # Validate existing trained index. + else: + from .validate import validate_training_embeddings + + validate_training_embeddings(config) + + +################################################## +# Add to index. +################################################## + + +def get_text_dataset_for_adding(config: RetroPreprocessingConfig) -> GPTToTextDataset: + """Convert GPT token chunk dataset to a text dataset for passing to the + embedder. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + The text dataset that consists of tokens converted from the 'train' chunk database. These are the chunks used for retrieval by the pretraining 'train' dataset. + """ + gpt_dataset = get_merged_train_dataset( + project_dir=config.retro_project_dir, + chunk_length=config.retro_gpt_chunk_length, + eod_token_id=config.retro_tokenizers.gpt.eod, + ) + text_dataset = GPTToTextDataset(gpt_dataset, config.retro_tokenizers.gpt) + return text_dataset + + +def _add_to_index(config: RetroPreprocessingConfig) -> str: + """Add DB chunks to index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + Path to the populated index. + """ + + # Get index. + index = IndexFactory.get_index(config.retro_index_type) + + # Get text dataset. + text_dataset = get_text_dataset_for_adding(config) + + # Add to index. + output_index_path = index.add(config, text_dataset) + + return output_index_path + + +def add_to_index(config: RetroPreprocessingConfig) -> None: + """Entry point for adding to the index. + + We select whether to add to a new index, or validate an existing index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + # Add to new index. + if config.retro_task_validate is None: + _add_to_index(config) + + # Validate existing encodings. + else: + from .validate import validate_added_encodings + + validate_added_encodings(config) + + +################################################## +# Build index (train + add). +################################################## + + +def build_index(config: RetroPreprocessingConfig) -> None: + """Build index. + + Building index involves sequentially running stages above: + - Train index (on sampled training chunks). + - Add to index (on all training chunks). + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + # Train index. + train_index(config) + + # Add to index. + add_to_index(config) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/factory.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/factory.py new file mode 100644 index 0000000..293d58c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/factory.py @@ -0,0 +1,40 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""The IndexFactory constructs an index from an index type string.""" + +from megatron.core.datasets.retro.index.index import Index + +from .indexes import FaissBaseIndex, FaissParallelAddIndex + + +class IndexFactory: + """Get index. + + Index type generally read from argument '--retro-index-ty'. + """ + + @classmethod + def get_index_class(cls, index_type: str) -> type: + """Get an index class, given a type string. + + Args: + index_type (str): One of 'faiss-base' (naive Faiss index wrapper) or 'faiss-par-add' (Faiss index wrapper with near embarrassingly parallel index.add(). + + Returns: + An `Index` sub-type corresponding to the `index_type`. + """ + return {"faiss-base": FaissBaseIndex, "faiss-par-add": FaissParallelAddIndex,}[index_type] + + @classmethod + def get_index(cls, index_type: str) -> Index: + """Construct an index from an index type string. + + Args: + index_type (str): One of 'faiss-base' (naive Faiss index wrapper) or 'faiss-par-add' (Faiss index wrapper with near embarrassingly parallel index.add(). + + Returns: + An `Index` instance corresponding to the `index_type`. + """ + index_class = cls.get_index_class(index_type) + index = index_class() + return index diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/index.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/index.py new file mode 100644 index 0000000..a8c086f --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/index.py @@ -0,0 +1,134 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Base class for all vector indexes. + +A vector index is a type of retrieval database that is queried using vectors, +and returns vectors that are 'similar' (e.g., by cosine distance) to the query +vector. The construction and usage of an index generally has the following +pattern: + + - Train the index on representative vectors. + - Add vectors to the index (i.e., vectors available for retrieval) + - Query index with new vector, to retrieve similar vector indexes. +""" + +import abc +import os +from typing import List, Tuple + +import numpy as np +import torch + +from megatron.core.datasets.retro.config import Embedder, RetroPreprocessingConfig +from megatron.core.datasets.retro.external_libs import faiss +from megatron.core.datasets.retro.utils import GPTToTextDataset + +from .utils import get_index_dir + + +class Index(abc.ABC): + + """Abstract base class for indexes. + + *Note* : While currently only Faiss-based classes are implemented, in the + future, this class will be extended with other types of indexes that have + different performance-accuracy trade-offs. + + The primary methods to override are: + - train() : Train index on the sampled training chunks. + - add() : Add all training chunks to index. + """ + + @classmethod + def make_object_verbose(cls, index: faiss.Index, verbose: bool) -> None: + """Make index object verbose. + + Args: + index (faiss.Index): Faiss object to set verbose. + verbose (bool): Sets whether index should log status updates during training and adding. + """ + assert isinstance(verbose, bool) + faiss.ParameterSpace().set_index_parameter(index, "verbose", verbose) + + def get_empty_index_path(self, config: RetroPreprocessingConfig) -> str: + """Get file path to empty index (i.e., trained, but unpopulated). + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + File path to empty index (i.e., this index has had index.train() called, but not yet index.add()). + """ + return os.path.join( + get_index_dir(config), "empty_%.3f.faissindex" % config.retro_index_train_load_fraction, + ) + + def get_empty_index(self, config: RetroPreprocessingConfig) -> faiss.Index: + """Get empty index (i.e., trained, but unpopulated). + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + Empty Faiss index, loaded from storage. + """ + return faiss.read_index(self.get_empty_index_path(config)) + + def get_added_index_path(self, config: RetroPreprocessingConfig) -> str: + """Get file path to index that has been populated with vectors. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + File path to added index (i.e., this index has had both index.train() and index.add() called). + """ + return os.path.join( + get_index_dir(config), + "added_%.3f_%.3f.faissindex" + % (config.retro_index_train_load_fraction, config.retro_index_add_load_fraction,), + ) + + def get_added_index(self, config: RetroPreprocessingConfig) -> faiss.Index: + """Get index that has been populated with vectors. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + 'Added' (i.e., populated) Faiss index, loaded from storage. + """ + return faiss.read_index(self.get_added_index_path(config)) + + @abc.abstractmethod + def train(self, config: RetroPreprocessingConfig) -> None: + """Train index on a representative set of vectors. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + @abc.abstractmethod + def add(self, config: RetroPreprocessingConfig, text_dataset: GPTToTextDataset) -> None: + """Add vectors to index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + text_dataset (GPTToTextDataset): Text dataset that will be embedded and added to the index. + """ + + def embed_text_dataset_block( + self, embedder: Embedder, text_dataset: GPTToTextDataset, _range: Tuple[int, int] + ) -> np.ndarray: + """Embed a range of a text dataset. + + Args: + embedder (Embedder): Embedder used for embedding a text dataset. + text_dataset (GPTToTextDataset): Text dataset that will be embedded. + _range (Tuple[int, int]): Start/end sample indices within text dataset used for embedding. + + Returns: + An array of embeddings, with shape (len(text_dataset), dimension(embedder)). + """ + sub_dataset = torch.utils.data.Subset(text_dataset, range(*_range)) + return embedder.embed_text_dataset(sub_dataset) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/indexes/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/indexes/__init__.py new file mode 100644 index 0000000..c445909 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/indexes/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +""" +Exports: +- FaissBaseIndex: Unoptimized Faiss index wrapper +- FaissParallelAddIndex: Optimized index.add() for Faiss index. +""" + +from .faiss_base import FaissBaseIndex +from .faiss_par_add import FaissParallelAddIndex diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/indexes/faiss_base.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/indexes/faiss_base.py new file mode 100644 index 0000000..1ffc725 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/indexes/faiss_base.py @@ -0,0 +1,150 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +""" +This class implements a simple, un-optimized wrapper around a Faiss index, that +implements the Index interface (see ..index.py). While this class is +instantiable, it is meant to be extended with optimizations in classes that +inherit from this class (see FaissParAddIndex, for an example). +""" + +import os + +import numpy as np +import torch +from tqdm import tqdm + +from megatron.core.datasets.retro.config import RetroPreprocessingConfig +from megatron.core.datasets.retro.external_libs import faiss +from megatron.core.datasets.retro.index.index import Index +from megatron.core.datasets.retro.index.utils import ( + get_training_data_merged_path, + num_samples_to_block_ranges, +) +from megatron.core.datasets.retro.utils import GPTToTextDataset, log_retro_rank_0 + + +class FaissBaseIndex(Index): + """Base class for Faiss-base indexes. + + This class wraps a Faiss index, and adds additional functionality for training + and adding codes. This base class performs a naive sequential code adding, + while the optimized FaissParallelAddIndex class performs a parallel + index.add(). + """ + + def _train(self, config: RetroPreprocessingConfig) -> None: + """Train index (rank 0's method). + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + assert torch.distributed.get_rank() == 0 + + # Set num threads (torch.distributed reset it to 1). + faiss.omp_set_num_threads(64) + + empty_index_path = self.get_empty_index_path(config) + + # Index already exists? -> return. + if os.path.isfile(empty_index_path): + return + + # Load data. + merged_path = get_training_data_merged_path(config) + inp = np.memmap(merged_path, dtype="f4", mode="r",).reshape((-1, config.hidden_size)) + + # Init index. + index = faiss.index_factory(config.hidden_size, config.retro_index_str) + + # Move to GPU. + log_retro_rank_0("> move faiss index to gpu.") + index_ivf = faiss.extract_index_ivf(index) + clustering_index = faiss.index_cpu_to_all_gpus(faiss.IndexFlatL2(index_ivf.d)) + index_ivf.clustering_index = clustering_index + log_retro_rank_0("> finished moving to gpu.") + self.make_object_verbose(index, True) + self.make_object_verbose(index_ivf, True) + self.make_object_verbose(index_ivf.quantizer, True) + self.make_object_verbose(index_ivf.clustering_index, True) + + # Train index. + index.train(inp) + + # Save index. + faiss.write_index(index, empty_index_path) + + def train(self, config: RetroPreprocessingConfig) -> None: + """Train index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + # Single process only. + if torch.distributed.get_rank() == 0: + self._train(config) + + torch.distributed.barrier() + + def _add(self, config: RetroPreprocessingConfig, text_dataset: GPTToTextDataset) -> None: + """Add to index (rank 0's method). + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + text_dataset (GPTToTextDataset): Text dataset that will be embedded and added to the index. + """ + + assert torch.distributed.get_rank() == 0 + + dataset_sample_ranges = num_samples_to_block_ranges(len(text_dataset)) + + # Set num threads (torch.distributed reset it to 1). + faiss.omp_set_num_threads(64) + + # Bert embedder. + embedder = config.bert_embedders.mem + + # Empty/added index paths. + empty_index_path = self.get_empty_index_path() + added_index_path = self.get_added_index_path() + + # Skip adding, if index exists. + if os.path.isfile(added_index_path): + return + + # Read trained index. + index = faiss.read_index(empty_index_path) + + # Iterate data blocks & add. + for sample_range in tqdm(dataset_sample_ranges, "faiss_base.add"): + + # Embed text. + embeds = self.embed_text_dataset_block(embedder, text_dataset, sample_range) + + # Add to index. + index.add(embeds) + + # Write index. + faiss.write_index(index, added_index_path) + + def add(self, config: RetroPreprocessingConfig, text_dataset: GPTToTextDataset) -> str: + """Add to index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + text_dataset (GPTToTextDataset): Text dataset that will be embedded and added to the index. + + Returns: + File path to the populated index. + """ + + # Single process only. + if torch.distributed.get_rank() == 0: + self._add(config, text_dataset) + + # Wait for rank 0. + torch.distributed.barrier() + + # Get output index path, for return. + return self.get_added_index_path(config) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/indexes/faiss_par_add.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/indexes/faiss_par_add.py new file mode 100644 index 0000000..6d9d68f --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/indexes/faiss_par_add.py @@ -0,0 +1,208 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Multi-process & multi-node version of Faiss's index.add(). + +This class inherits from FaissBaseIndex, and optimizes the 'add()' method by +making it multi-node and multi-process, with bit-wise equivalence to +FaissBaseIndex. This allows 'add()' to scale out to very large datasets, since +the vast majority of the computational effort is embarrassingly parallel. +""" + +import os +import shutil +from typing import Tuple + +import numpy as np +import psutil +import torch +from tqdm import tqdm + +from megatron.core.datasets.retro.config import Embedder, RetroPreprocessingConfig +from megatron.core.datasets.retro.external_libs import faiss, h5py +from megatron.core.datasets.retro.index.utils import get_added_code_paths, get_added_codes_dir +from megatron.core.datasets.retro.utils import ( + GPTToTextDataset, + get_blocks_by_rank, + log_retro_rank_0, + retro_makedir, +) + +from .faiss_base import FaissBaseIndex + + +class FaissParallelAddIndex(FaissBaseIndex): + """ + This class parallelizes both 1) encoding vectors, and 2) adding codes to the + index. This class is more performant than naive use of Faiss, because most + of the computational work is in encoding the vectors, which is an + embarassingly parallel operation. + """ + + def encode_block( + self, index: faiss.Index, embedder: Embedder, text_dataset: GPTToTextDataset, block: dict + ) -> Tuple[np.ndarray, np.ndarray]: + """Encode sub-dataset block, to be later added to index. + + Encode the data subset, generally in blocks of 1M vectors each. For + each block, the empty/trained index is loaded, codes are computed + via index.sa_encode(), and the resulting codes are saved to disk. + + Args: + index (faiss.Index): Faiss index object. + embedder (Embedder): Embedder used to embed text dataset. + text_dataset (GPTToTextDataset): Text dataset to be embedded and encoded. + block (dict): Range information specifying start/end indices within text dataset. + + Returns: + A tuple of (embeddings, encodings) for the given block subset of the text dataset. + """ + + # Embed block. + embeddings = self.embed_text_dataset_block(embedder, text_dataset, block["range"],) + + # Encode block. + log_retro_rank_0("encode.") + codes = index.sa_encode(embeddings) + + # Return embeddings for validation purposes. + return embeddings, codes + + def save_block(self, config: RetroPreprocessingConfig, block: dict, codes: np.ndarray) -> None: + """Save block of codes to disk. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + block (dict): Range information specifying the start/end indices within the encoded text dataset. Here, the 'path' item is used for writing the encodings to storage. + codes (np.ndarray): Block of encodings to be saved to storage. + """ + # Save neighbors. + log_retro_rank_0("save codes.") + retro_makedir(config, os.path.dirname(block["path"])) + with h5py.File(block["path"], "w") as f: + f.create_dataset("data", data=codes) + + def encode(self, config: RetroPreprocessingConfig, text_dataset: GPTToTextDataset) -> None: + """Encode text dataset, to be later added to index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + text_dataset (GPTToTextDataset): Text dataset to be encoded by the index. + """ + + codes_dir = get_added_codes_dir(config) + retro_makedir(config, codes_dir) + + # Index. + index = self.get_empty_index(config) + + # Bert embedder. + embedder = config.retro_bert_embedders.mem + + # Missing code blocks. + def validate(f: h5py.File) -> None: + """Validation method for validating loaded encodings. + + Args: + f (h5py.File): File that contains encodings. + """ + assert len(f["data"].shape) == 2 + + blocks = get_blocks_by_rank( + codes_dir, len(text_dataset), config.retro_block_size, validate=validate, + ) + + # Encode each block. + for block_index, block in enumerate(blocks.missing): + + if block is not None: + + # Progress. + log_retro_rank_0( + "encode block %d / %d ... %s." + % (block_index, len(blocks.missing), block["path"],) + ) + + # Encode and save. + _, codes = self.encode_block(index, embedder, text_dataset, block) + self.save_block(config, block, codes) + + # Synchronize progress across all ranks. (for easier observation) + log_retro_rank_0(" > waiting for other ranks to finish block.") + torch.distributed.barrier() + + def add_codes(self, config: RetroPreprocessingConfig) -> None: + """Read codes from disk, and add them to the index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + if torch.distributed.get_rank() != 0: + return + + added_index_path = self.get_added_index_path(config) + if os.path.exists(added_index_path): + return + + # Index. + log_retro_rank_0("read empty index.") + index = self.get_empty_index(config) + index_ivf = faiss.extract_index_ivf(index) + + # Add codes. + log_retro_rank_0("add codes.") + code_paths = get_added_code_paths(config) + pbar = tqdm(code_paths) + for code_path in pbar: + pbar.set_description( + "add codes, mem %.3f gb, %.1f%%" + % (psutil.virtual_memory()[3] / 1024 ** 3, psutil.virtual_memory()[2],) + ) + with h5py.File(code_path) as f: + + nload = int(config.retro_index_add_load_fraction * f["data"].shape[0]) + offset = int(os.path.basename(code_path).split("-")[0]) + xids = np.arange(offset, offset + nload) + codes = np.copy(f["data"][:nload]) + index_ivf.add_sa_codes(codes, xids) + + # Update index's ntotal. + index.ntotal = index_ivf.ntotal + + # Write index. + log_retro_rank_0("write added index.") + faiss.write_index(index, added_index_path) + + def remove_codes(self, config: RetroPreprocessingConfig) -> None: + """Remove added codes after adding to index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + if torch.distributed.get_rank() != 0: + return + assert os.path.isfile(self.get_added_index_path(config)) + + if config.retro_index_delete_added_codes: + raise Exception("remove?") + shutil.rmtree(get_added_codes_dir(config), ignore_errors=True) + + def add(self, config: RetroPreprocessingConfig, text_dataset: GPTToTextDataset) -> None: + """Add vectors to index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + text_dataset (GPTToTextDataset): Text dataset that will be embedded and added to the index. + """ + + # Encode chunks. + self.encode(config, text_dataset) + + # Add codes to index. + self.add_codes(config) + + # Wait for (single-process) adding to complete. + torch.distributed.barrier() + + # Remove codes. + self.remove_codes(config) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/utils.py new file mode 100644 index 0000000..321cd65 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/utils.py @@ -0,0 +1,126 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Utilities for building an index.""" + +import glob +import os +from typing import List, Tuple + +from megatron.core.datasets.retro.config import RetroPreprocessingConfig +from megatron.core.datasets.retro.utils import retro_makedir + + +def get_index_dir(config: RetroPreprocessingConfig) -> str: + """Create sub-directory for this index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + Path to index sub-directory within Retro project. + """ + + # Directory path. + index_dir_path = os.path.join( + config.retro_project_dir, "index", config.retro_index_type, config.retro_index_str, + ) + + # Make directory. + retro_makedir(config, index_dir_path) + + return index_dir_path + + +def num_samples_to_block_ranges( + config: RetroPreprocessingConfig, num_samples: int +) -> List[Tuple[int, int]]: + """Split a range (length num_samples) into sequence of block ranges + of size block_size. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + num_samples (int): Split `num_samples` into consecutive block ranges, where each block is size `config.retro_block_size`. + + Returns: + A list of tuples where each item is the (start, end) index for a given block. + """ + block_size = config.retro_block_size + start_idxs = list(range(0, num_samples, block_size)) + end_idxs = [min(num_samples, s + block_size) for s in start_idxs] + ranges = list(zip(start_idxs, end_idxs)) + return ranges + + +def get_training_data_root_dir(config: RetroPreprocessingConfig) -> str: + """Get root directory for embeddings (blocks and merged data). + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + Path to the training data directory, which contains both training embedding blocks and the final merged training embeddings. + """ + return os.path.join(config.retro_project_dir, "index", "train_emb") + + +def get_training_data_block_dir(config: RetroPreprocessingConfig) -> str: + """Get directory for of saved embedding blocks. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + Path to the directory containing the training embedding blocks, which will be later merged into a single embedding array. + """ + return os.path.join(get_training_data_root_dir(config), "blocks") + + +def get_training_data_block_paths(config: RetroPreprocessingConfig) -> List[str]: + """Get paths to saved embedding blocks. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + Paths of all training embedding blocks. + """ + return sorted(glob.glob(get_training_data_block_dir(config) + "/*.hdf5")) + + +def get_training_data_merged_path(config: RetroPreprocessingConfig) -> str: + """Get path to merged training embeddings. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + Path to the merged training embedding binary file. + """ + return os.path.join( + get_training_data_root_dir(config), + "train_%.3f.bin" % config.retro_index_train_load_fraction, + ) + + +def get_added_codes_dir(config: RetroPreprocessingConfig) -> str: + """Get directory of saved encodings. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + Path to the directory containing the vector encodings for adding to the index. + """ + return os.path.join(get_index_dir(config), "add_codes") + + +def get_added_code_paths(config: RetroPreprocessingConfig) -> List[str]: + """Get paths to all saved encodings. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + Paths of all vector encoding blocks, for adding to the index. + """ + return sorted(glob.glob(get_added_codes_dir(config) + "/*.hdf5")) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/validate.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/validate.py new file mode 100644 index 0000000..6783df6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/index/validate.py @@ -0,0 +1,191 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Validate an index's data. + +This module contains functionality for checking for bitwise equality across code +changes. The training and adding steps of index construction can be validated +separately. The following high-level checks are supported: + + - Training: Validate that saved training embeddings are bitwise equal with a + sample set of freshly computed embeddings. (*Note*: + `--no-retro-index-delete-training-embeddings` must be used.) + - Adding: Validate that the saved encodings are bitwise equal with a sample of + sample set of freshly computed encodings. (*Note*: + `--no-retro-index-delete-added-codes` must be used.) +""" + +import typing + +import numpy as np +import torch +from torch.utils.data import Subset + +from megatron.core.datasets.retro.config import RetroPreprocessingConfig +from megatron.core.datasets.retro.external_libs import h5py +from megatron.core.datasets.retro.utils import ( + GPTToTextDataset, + get_blocks_by_rank, + log_retro_rank_0, +) + +from .build import get_text_dataset_for_adding, get_text_dataset_for_training +from .factory import IndexFactory +from .utils import get_added_codes_dir, get_training_data_block_dir + +################################################## +# Validate trained index. +################################################## + + +def validate_training_embeddings(config: RetroPreprocessingConfig) -> None: + """Validate training embeddings. + + Steps: + - Randomly sample subset of text dataset blocks. + - Embed each block. + - Compare against saved embeddings. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + # Training text dataset. + text_dataset = get_text_dataset_for_training(config) + + # Sample existing blocks. + blocks = get_blocks_by_rank( + dirname=get_training_data_block_dir(config), + n_samples=len(text_dataset), + block_size=config.retro_block_size, + validate=None, + sample=config.retro_task_validate, + ) + + assert blocks.n_missing_world == 0 + + # Embed & validate blocks. + embedder = config.retro_bert_embedders.mem + for block_idx, block in enumerate(blocks.existing): + + # Missing block lists are extended with None to have equal-length + # lists. Skip the Nones. + if block is not None: + + # Progress. (*note*: move world progress to here.) + log_retro_rank_0( + "embed training block %d / %d ... %s." + % (block_idx, len(blocks.existing), block["path"],) + ) + + # Load existing block embeddings. + with h5py.File(block["path"]) as f: + existing_embeddings = np.copy(f["data"]) + + # Embed block. + sub_dataset = Subset(text_dataset, range(*block["range"])) + embeddings = embedder.embed_text_dataset(sub_dataset, "train") + + # Check equality. + log_retro_rank_0(" > validate.") + assert np.array_equal(existing_embeddings, embeddings) + + # Synchronize progress across all ranks. (for easier observation) + log_retro_rank_0(" > waiting for other ranks to finish block.") + torch.distributed.barrier() + + log_retro_rank_0(" > finished validating training embeddings.") + + +################################################## +# Validate filled index. +################################################## + + +def validate_added_encodings(config: RetroPreprocessingConfig) -> None: + """Validate added encodings. + + Steps: + - Randomly sample subset of text dataset blocks. + - Encode each block. + - Compare against saved encodings. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + # Index. + index = IndexFactory.get_index(config.retro_index_type) + inner_index = index.get_empty_index(config) + + # Text dataset. + text_dataset = get_text_dataset_for_adding(config) + + # Sample existing blocks. + def validate(f: h5py.File) -> None: + """Validation method for validating encoding blocks. + + Args: + f (h5py.File): File with block of encodings. + """ + assert len(f["data"].shape) == 2 + + blocks = get_blocks_by_rank( + dirname=get_added_codes_dir(config), + n_samples=len(text_dataset), + block_size=config.retro_block_size, + validate=validate, + sample=config.retro_task_validate, + ) + + assert blocks.n_missing_world == 0 + + # Encode and validate blocks. + embedder = config.retro_bert_embedders.mem + for block_idx, block in enumerate(blocks.existing): + + if block is not None: + + # Progress. + log_retro_rank_0( + "encode block %d / %d ... %s." % (block_idx, len(blocks.existing), block["path"],) + ) + + # Load existing codes. + with h5py.File(block["path"]) as f: + existing_codes = np.copy(f["data"]) + + # Encode block. + embeddings, codes = index.encode_block(inner_index, embedder, text_dataset, block) + + # Check equality. + log_retro_rank_0(" > validate.") + assert np.array_equal(existing_codes, codes) + + # Synchronize progress across all ranks. (for easier observation) + log_retro_rank_0(" > waiting for other ranks to finish block.") + torch.distributed.barrier() + + log_retro_rank_0(" > finished validating added encodings.") + + +################################################## +# Validate index (trained + filled). +################################################## + + +def validate_index(config: RetroPreprocessingConfig) -> None: + """Validate index. + + Validating index involves sequentially running stages above: + - Validate trained index. + - Validate filled index. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + # Validate training embeddings. + validate_training_embeddings(config) + + # Validate added codes. + validate_added_encodings(config) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/__init__.py new file mode 100644 index 0000000..ac94833 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/gpt_chunk_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/gpt_chunk_dataset.py new file mode 100644 index 0000000..34a2ee6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/gpt_chunk_dataset.py @@ -0,0 +1,110 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +""" +A GPTChunkDataset is a wrapper around a regular GPTDataset, that sequentially +chunks the sample tokens into `retro_chunk_length` sized smaller samples. + +For example, if the GPTDataset has 100 samples and a sequence length of 2048, and +retro_chunk_length is 64, then the GPTChunkDataset will contain 100*(2048/64) = +3200 samples, each with length 64. +""" + +import torch + +from megatron.core.datasets.gpt_dataset import GPTDataset +from megatron.core.datasets.retro.utils import get_num_chunks_per_sample + +from .utils import get_neighbor_dir + + +class GPTChunkDataset(torch.utils.data.Dataset): + """Pretraining chunk dataset wraps a standard GPT dataset. + + This dataset conceptually divides each sample (e.g., length 2048) + into chunks (e.g., length 64) and restructures them into a list of + chunks (e.g., length num_samples * num_chunks_per_sample). + + Args: + sample_dataset (GPTDataset): Original GPT dataset, with `sequence_length` size samples. + sample_length (int): Alias for `sequence_length`. + chunk_length (int): Retro chunk length (e.g., 64). + """ + + def __init__(self, sample_dataset: GPTDataset, sample_length: int, chunk_length: int): + + super().__init__() + + self.sample_dataset = sample_dataset + self.chunk_length = chunk_length + self.n_chunks_per_sample = get_num_chunks_per_sample(sample_length, chunk_length) + self.n_samples = len(sample_dataset) + self.n_chunks = self.n_samples * self.n_chunks_per_sample + + def __len__(self) -> int: + """Get dataset length. + + Returns: + Dataset length. + """ + return self.n_chunks + + def __getitem__(self, idx: int) -> dict: + """Get sample, including represented document IDs. + + Args: + idx (int): Sample index. + + Returns: + A sample, which contains both the chunk-length token sample ('text') along with all document_ids ('doc_ids') contained withing the full `sequence_length` sample. + """ + + # Convert global chunk index to global sample index & local chunk index. + sample_idx = idx // self.n_chunks_per_sample + chunk_idx = idx % self.n_chunks_per_sample + + # Extract sample data. + sample = self.sample_dataset[sample_idx] + sample_token_ids = sample["text"] + sample_doc_ids = sample["document_ids"] + + # Chunk start/end token idxs. + token_start_idx = chunk_idx * self.chunk_length + token_end_idx = token_start_idx + self.chunk_length + chunk_token_ids = sample_token_ids[token_start_idx:token_end_idx] + + # Sample. + return { + "doc_ids": sample_doc_ids, + "text": chunk_token_ids, + } + + +def build_gpt_chunk_datasets_from_gpt_datasets( + project_dir: str, gpt_datasets: dict, sample_length: int, chunk_length: int, +) -> dict: + """Get train, valid, test GPT chunk datasets. + + Args: + project_dir (str): Retro project dir. + gpt_datasets (dict): Mapping of 'train', 'valid', and 'test' GPT datasets (original, unchunked datasets). + sample_length (int): Alias of `sequence_length`. + chunk_length (int): Retro chunk length (e.g., 64). + + Returns: + A ? + """ + + # GPT chunk datasets. + chunk_datasets = { + key: { + "dataset": GPTChunkDataset(sample_ds, sample_length, chunk_length), + "neighbor_dir": get_neighbor_dir(project_dir, key, sample_ds), + "num_active_chunks": num_active_samples + * get_num_chunks_per_sample(sample_length, chunk_length), + } + if sample_ds + else None + for key, (sample_ds, num_active_samples) in gpt_datasets.items() + } + + return chunk_datasets diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/multi_split_gpt_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/multi_split_gpt_dataset.py new file mode 100644 index 0000000..7dc3f44 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/multi_split_gpt_dataset.py @@ -0,0 +1,106 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""A MultiSplitGPTDataset can handle multiple intersecting split strings, as well +as returning all of the document IDs of a sample.""" + +import logging +from dataclasses import dataclass +from typing import Dict, List + +import numpy + +from megatron.core.datasets.blended_megatron_dataset_config import ( + convert_split_vector_to_split_matrix, + parse_and_normalize_split, +) +from megatron.core.datasets.gpt_dataset import GPTDataset, GPTDatasetConfig +from megatron.core.datasets.indexed_dataset import IndexedDataset +from megatron.core.datasets.utils import Split, log_single_rank + +logger = logging.getLogger(__name__) + + +@dataclass +class MultiSplitGPTDatasetConfig(GPTDatasetConfig): + """Configuration object for Megatron Core blended and Retro datasets. + + Args: + return_document_ids (bool): Whether to return the document ids when querying the dataset. Turn this option on during preprocessing. + split_preprocessing (str): The Retro preprocessing split string. It follows the same pattern convention as 'split'. Not to be used with 'blend_per_split'. + """ + + return_document_ids: bool = None + + split_preprocessing: str = None + + def __post_init__(self) -> None: + """Validate config attributes.""" + super().__post_init__() + assert self.split is not None, "the Retro data pipeline does not support 'blend_per_split'" + assert self.return_document_ids is not None, "this attribute must be user defined" + assert self.split_preprocessing is not None, "this attribute must be user defined" + split_vector = parse_and_normalize_split(self.split) + split_preprocessing_vector = parse_and_normalize_split(self.split_preprocessing) + if not numpy.allclose(split_vector, split_preprocessing_vector): + self.split_matrix = convert_split_vector_to_split_matrix( + split_vector, split_preprocessing_vector + ) + log_single_rank( + logger, + logging.WARNING, + f"split =/= split_preprocessing. Let split_matrix = {self.split_matrix}", + ) + + +class MultiSplitGPTDataset(GPTDataset): + """Retro's customized GPT dataset. + + Args: + indexed_dataset (IndexedDataset): The IndexedDataset around which to build the MegatronDataset. + dataset_path (str): The real path on disk to the dataset, for bookkeeping. + indexed_indices (numpy.ndarray): The set of the documents indices to expose. + num_samples (int): The number of samples to draw from the indexed dataset. + index_split (Split): The indexed_indices Split. + config (MultiSplitGPTDatasetConfig): The Retro-specific container for all config sourced parameters. + """ + + def __init__( + self, + indexed_dataset: IndexedDataset, + dataset_path: str, + indexed_indices: numpy.ndarray, + num_samples: int, + index_split: Split, + config: MultiSplitGPTDatasetConfig, + ) -> None: + super().__init__( + indexed_dataset, dataset_path, indexed_indices, num_samples, index_split, config + ) + + def __getitem__(self, idx: int) -> Dict[str, numpy.ndarray]: + """Get dataset sample. + + Args: + idx (int): The index into the dataset. + + Returns: + Dict[str, numpy.ndarray]: The text ids and (optionally) the document ids wrapped in a dictionary. + """ + text, document_ids = self._query_document_sample_shuffle_indices(idx) + if self.config.return_document_ids: + return {"text": text, "document_ids": document_ids} + else: + return {"text": text} + + @staticmethod + def _key_config_attributes() -> List[str]: + """Add custom attributes for building unique dataset hash. + + The preprocessing split used for preprocessing will constrain the samples available for pretraining. + + Returns: + List[str]: The key config attributes. + """ + return super(MultiSplitGPTDataset, MultiSplitGPTDataset)._key_config_attributes() + [ + "split_preprocessing" + ] diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/query.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/query.py new file mode 100644 index 0000000..165792f --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/query.py @@ -0,0 +1,394 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Entry point for querying an index using a GPTChunkDataset. + +Querying involves: + + - Iterate all chunks in the GPTChunkDataset. + - Query index for neighbor chunk IDs (i.e., chunks from the chunk database). + - Save neighbor chunk IDs to disk, for use in building a RetroDataset sample + during pretraining. +""" + +import os +import time +import typing + +import numpy as np +import psutil +import torch +from tqdm import tqdm + +from megatron.core.datasets.retro.config import RetroPreprocessingConfig +from megatron.core.datasets.retro.db.dataset import DBDataset +from megatron.core.datasets.retro.db.utils import ( + get_merged_train_dataset as get_db_merged_train_dataset, +) +from megatron.core.datasets.retro.external_libs import faiss, h5py +from megatron.core.datasets.retro.index.factory import IndexFactory +from megatron.core.datasets.retro.index.index import Index +from megatron.core.datasets.retro.index.utils import get_index_dir +from megatron.core.datasets.retro.query.gpt_chunk_dataset import GPTChunkDataset +from megatron.core.datasets.retro.utils import ( + GPTToTextDataset, + get_blocks_by_rank, + log_retro_rank_0, + retro_makedir, +) + +from .gpt_chunk_dataset import build_gpt_chunk_datasets_from_gpt_datasets + + +def get_index(config: RetroPreprocessingConfig, ondisk: bool = False,) -> faiss.Index: + """Read index from disk. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + ondisk (bool): If `ondisk = True`, memory map the index. (For debugging purposes only; very non-performant.) + + Returns: + A Faiss index, loaded from storage. + """ + + # Load index. + index_wrapper = IndexFactory.get_index(config.retro_index_type) + index_dir = get_index_dir(config) + added_index_path = index_wrapper.get_added_index_path(config) + if ondisk: + index = faiss.read_index(added_index_path, faiss.IO_FLAG_MMAP) + else: + index = faiss.read_index(added_index_path) + + # Search parameters. + faiss.ParameterSpace().set_index_parameter(index, "efSearch", config.retro_query_ef_search) + faiss.ParameterSpace().set_index_parameter(index, "nprobe", config.retro_query_nprobe) + + return index + + +def embed_block( + config: RetroPreprocessingConfig, gpt_dataset: GPTChunkDataset, block: dict, +) -> np.ndarray: + """Embed block of chunks. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + gpt_dataset (GPTChunkDataset): Chunk dataset to be embedded. + block (dict): Range information containing start/end indices of subset of chunk dataset. + + Returns: + Embeddings array, with shape (len(block["range"]), dimension(embedder)). + """ + text_block_dataset = torch.utils.data.Subset( + GPTToTextDataset(gpt_dataset, config.retro_tokenizers.gpt), range(*block["range"]), + ) + return config.retro_bert_embedders.mem.embed_text_dataset(text_block_dataset) + + +def query_embeddings( + config: RetroPreprocessingConfig, + db_dataset: DBDataset, + index: Index, + embeddings: np.ndarray, + chunk_id_range: range, + sample_map: dict, + n_chunks_per_sample: int, + verbose: bool = True, +) -> typing.Tuple[np.ndarray, np.ndarray]: + """Query neighbors of a block of embeddings. + + Querying includes: + - Query index for neighbor chunk IDs. + - Filter chunk IDs that have the same document ID as the queried embedding. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + db_dataset (DBDataset): Dataset containing chunk database entries. + index (Index): Vector index populated with chunk database indices. + embeddings (np.ndarray): Embeddings from GPT chunk dataset. + chunk_id_range (range): Chunk ID range from GPT chunk dataset. + sample_map (dict): Mapping of sample_idx to dataset_idx and document_ids. Used for document filtering. + n_chunks_per_sample (int): Number of chunks per sample (e.g., sequence_length / chunk_length). + verbose (bool): Log querying progress. + + Returns: + A tuple of original (unfiltered) neighbor IDs, and filtered (by document ID) neighbor IDs. + """ + + # Query neighbor ids. + if verbose: + log_retro_rank_0("search.") + t = time.time() + assert index.ntotal > 0, "check we don't accidentally have an empty index." + _, query_neighbor_ids = index.search(embeddings, config.retro_query_num_neighbors_query) + if verbose: + log_retro_rank_0(" time : %.3f sec." % (time.time() - t)) + + # Filter banned neighbor ids. + if verbose: + log_retro_rank_0("filter banned neighbor ids.") + filtered_neighbor_ids = np.full( + shape=(len(query_neighbor_ids), config.retro_query_num_neighbors_save), + fill_value=-1, + dtype="int64", + ) + min_chunk_id, max_chunk_id = chunk_id_range + for chunk_id in range(min_chunk_id, max_chunk_id): + + sample_id = chunk_id // n_chunks_per_sample + sample = sample_map[sample_id] + sample_dataset_idx = sample["dataset_idx"].item() + sample_doc_ids = sample["doc_ids"].tolist() + sample_doc_tuples = [(sample_dataset_idx, d) for d in sample_doc_ids] + + # Get valid neighbors (!= -1). + query_row = [i for i in query_neighbor_ids[chunk_id - min_chunk_id] if i >= 0] + + # Filter row. + filtered_row = [ + i + for i in query_row + if tuple(db_dataset.doc_tuples[i].tolist()) not in sample_doc_tuples + ] + filtered_row = filtered_row[: config.retro_query_num_neighbors_save] + filtered_row += [-1] * (config.retro_query_num_neighbors_save - len(filtered_row)) + filtered_neighbor_ids[chunk_id - min_chunk_id] = filtered_row + + return query_neighbor_ids, filtered_neighbor_ids + + +def query_embedding_block( + config: RetroPreprocessingConfig, + db_dataset: DBDataset, + index: Index, + embeddings: np.ndarray, + chunk_id_range: range, + sample_map: dict, + n_chunks_per_sample: int, +) -> typing.Tuple[np.ndarray, np.ndarray]: + """Query a block of embeddings. + + The block is broken into smaller sub-blocks, for easier tracking of progress. + Both the raw neighbor IDs and the filtered neighbor IDs (i.e., chunks with the + same document ID are removed) are collected. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + db_dataset (DBDataset): Dataset containing chunk database entries. + index (Index): Vector index populated with chunk database indices. + embeddings (np.ndarray): Embeddings from GPT chunk dataset. + chunk_id_range (range): Chunk ID range from GPT chunk dataset. + sample_map (dict): Mapping of sample_idx to dataset_idx and document_ids. Used for document filtering. + n_chunks_per_sample (int): Number of chunks per sample (e.g., sequence_length / chunk_length). + + Returns: + A tuple of original (unfiltered) neighbor IDs, and filtered (by document ID) neighbor IDs. + """ + + query_neighbor_ids = [] + filtered_neighbor_ids = [] + + # Query in sub-blocks. + partial_block_size = 1000 + for partial_start_idx in tqdm( + range(0, len(embeddings), partial_block_size), + " search", + miniters=(len(embeddings) // partial_block_size) // 10, + disable=torch.distributed.get_rank() != 0, + ): + partial_end_idx = min(len(embeddings), partial_start_idx + partial_block_size) + partial_embeddings = embeddings[partial_start_idx:partial_end_idx] + partial_chunk_id_range = ( + chunk_id_range[0] + partial_start_idx, + chunk_id_range[0] + partial_end_idx, + ) + partial_query_neighbor_ids, partial_filtered_neighbor_ids = query_embeddings( + config, + db_dataset, + index, + partial_embeddings, + partial_chunk_id_range, + sample_map, + n_chunks_per_sample, + verbose=False, + ) + query_neighbor_ids.append(partial_query_neighbor_ids) + filtered_neighbor_ids.append(partial_filtered_neighbor_ids) + + # Concatenate. + query_neighbor_ids = np.concatenate(query_neighbor_ids, axis=0) + filtered_neighbor_ids = np.concatenate(filtered_neighbor_ids, axis=0) + + return query_neighbor_ids, filtered_neighbor_ids + + +def query_block_neighbors( + config: RetroPreprocessingConfig, + db_dataset: DBDataset, + query_dataset: GPTChunkDataset, + index: Index, + block: dict, +) -> None: + """Query neighbors of a dataset block (i.e., range). + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + db_dataset (DBDataset): Dataset containing chunk database entries. + query_dataset (GPTChunkDataset): GPT chunk dataset to be queried. + index (Index): Vector index populated with chunk database indices. + block (dict): Range information containing start/end indices for querying GPT chunk dataset. + """ + + n_chunks_per_sample = query_dataset.n_chunks_per_sample + + # Sample map. + sample_ids = sorted( + list(set(chunk_id // n_chunks_per_sample for chunk_id in range(*block["range"]))) + ) + sample_map = {} + for i in sample_ids: + sample = query_dataset.sample_dataset[i] + sample_map[i] = { + "dataset_idx": sample["dataset_id"], + "doc_ids": sample["document_ids"], + } + + # Embed block. + embeddings = embed_block(config, query_dataset, block) + + # Query embeddings. + _, filtered_neighbor_ids = query_embedding_block( + config, db_dataset, index, embeddings, block["range"], sample_map, n_chunks_per_sample, + ) + + if config.retro_task_validate is None: + # Save neighbors. + log_retro_rank_0("save neighbors.") + retro_makedir(config, os.path.dirname(block["path"])) + f = h5py.File(block["path"], "w") + f.create_dataset("neighbors", data=filtered_neighbor_ids) + f.close() + + else: + # Validate neighbors. + with h5py.File(block["path"]) as f: + existing_neighbor_ids = np.copy(f["neighbors"]) + assert np.array_equal(existing_neighbor_ids, filtered_neighbor_ids) + + +def query_dataset_neighbors( + config: RetroPreprocessingConfig, + db_dataset: DBDataset, + query_dataset: GPTChunkDataset, + num_active_chunks: int, + prefix: str, + neighbor_dir: str, + index: Index, +) -> None: + """Query neighbors of each chunk within a dataset. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + db_dataset (DBDataset): Dataset containing chunk database entries. + query_dataset (GPTChunkDataset): GPT chunk dataset to be queried. + num_active_chunks (int): The 'active' chunks are the subset of the GPT chunk dataset that aren't being queried. This argument is used when validating the correctness of a subset of the GPT chunk dataset. + prefix (str): Extra string for logging progress. + neighbor_dir (str): File path to directory for saving neighbor IDs. + index (Index): Vector index populated with chunk database indices. + """ + + def validate(f: h5py.File) -> None: + """Validation method for validating saved neighbor IDs. + + Args: + f (h5py.File): File containing save neighbor IDs. + """ + assert f["neighbors"].shape[1] == config.retro_query_num_neighbors_save, ( + "neighbors.shape == %s; num_neighbors_target == %d." + % (str(f["neighbors"].shape), config.retro_num_neighbors_target,) + ) + + if config.retro_task_validate is None: + retro_makedir(config, neighbor_dir) + blocks = get_blocks_by_rank( + neighbor_dir, num_active_chunks, config.retro_block_size, validate=validate, + ) + active_blocks = blocks.missing + else: + blocks = get_blocks_by_rank( + neighbor_dir, + num_active_chunks, + config.retro_block_size, + validate=validate, + sample=config.retro_task_validate, + ) + assert blocks.n_missing_world == 0 + active_blocks = blocks.existing + + # Query each block. + for block_index, block in enumerate(active_blocks): + + if block is not None: + + # Progress. + log_retro_rank_0( + "%squery '%s' block %d / %d ... %s ... mem %.3f gb, %.1f%%." + % ( + "" if config.retro_task_validate is None else "[validate] ", + prefix, + block_index, + len(active_blocks), + os.path.basename(block["path"]), + psutil.virtual_memory()[3] / 1024 ** 3, + psutil.virtual_memory()[2], + ) + ) + + # Query block neighbors. + query_block_neighbors(config, db_dataset, query_dataset, index, block) + + # Synchronize progress across all ranks. (for easier observation) + log_retro_rank_0(" > waiting for other ranks to finish block.") + torch.distributed.barrier() + + +def query_neighbors(config: RetroPreprocessingConfig) -> None: + """Query pretraining datasets (train & valid). + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + """ + + # Num threads. + faiss.omp_set_num_threads(64) + + # Load chunk db dataset. + log_retro_rank_0("load chunk db dataset.") + db_dataset = get_db_merged_train_dataset( + project_dir=config.retro_project_dir, + chunk_length=config.retro_gpt_chunk_length, + eod_token_id=config.retro_tokenizers.gpt.eod, + ) + db_dataset.load_doc_tuples() + + # Load index. + log_retro_rank_0(" > get index.") + index = get_index(config) + + # Query each (i.e., train, valid, test) dataset. + log_retro_rank_0(" > query.") + for prefix, info in vars(config.retro_gpt_chunk_datasets).items(): + if info is None: + continue + log_retro_rank_0( + " > query '%s' dataset ... %d samples." % (prefix, info["num_active_chunks"]) + ) + query_dataset_neighbors( + config, + db_dataset, + info["dataset"], + info["num_active_chunks"], + prefix, + info["neighbor_dir"], + index, + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/retro_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/retro_dataset.py new file mode 100644 index 0000000..07af161 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/retro_dataset.py @@ -0,0 +1,242 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +""" +A RetroDataset wraps both: + + - A GPTDataset (which is nested as GPTChunkDataset -> MultiSplitGPTDataset -> + GPTDataset). + - Neighbor IDs of chunks in the chunk database, that were saved during + preprocessing. + +Both the GPT sample data and the neighbor IDs are returned within a sample from +this dataset. +""" + +import os +from typing import Any, Dict, Optional, Tuple + +import numpy as np +import torch + +from megatron.core.datasets.retro.db.dataset import DBDataset +from megatron.core.datasets.retro.db.utils import get_merged_train_dataset as get_db_dataset +from megatron.core.datasets.retro.external_libs import h5py +from megatron.core.datasets.retro.utils import BlockPathMap, log_retro_rank_0 +from megatron.core.models.retro import RetroConfig + +from .gpt_chunk_dataset import GPTChunkDataset, build_gpt_chunk_datasets_from_gpt_datasets +from .utils import get_query_dir + + +class RetroDataset(torch.utils.data.Dataset): + """Dataset of retro samples. + + Each sample contains the original GPT sample, along with the token IDs + of each neighbor of each chunk within the sequence. Neighbor array has + shape (num_chunks_per_sample, num_neighbors, num_retrieved_tokens). + + ** Note: chunk dataset wraps original GPT dataset (see gpt_chunk_dataset.py). + + Args: + num_queried_samples (int): Total number of queried samples. + num_neighbors (int): Total number of saved neighbors. + num_retrieved_chunks (int): Number of retrieved chunks (e.g., 2 for neighbor + continuation). + block_size (int): Number of neighbor entries per file. + db_dataset (DBDataset): Chunk database used for retrieval. + chunk_dataset (GPTChunkDataset): GPT chunk dataset, which is a wrapper around a standard GPT dataset that breaks each sample into chunks. + neighbor_path_map (BlockPathMap): Mapping of neighbor ID to file path. + """ + + def __init__( + self, + num_queried_samples: int, + num_neighbors: int, + num_retrieved_chunks: int, + block_size: int, + db_dataset: DBDataset, + chunk_dataset: GPTChunkDataset, + neighbor_path_map: BlockPathMap, + ): + super().__init__() + + self.num_queried_samples = num_queried_samples + self.num_neighbors = num_neighbors + self.num_retrieved_chunks = num_retrieved_chunks + self.block_size = block_size + self.db_dataset = db_dataset + self.chunk_dataset = chunk_dataset + self.neighbor_path_map = neighbor_path_map + + def __len__(self) -> int: + """Dataset length. + + Returns: + Number of samples in dataset. + """ + return len(self.chunk_dataset.sample_dataset) + + def __getitem__(self, sample_idx: int) -> dict: + """Get dataset sample. + + Args: + sample_idx (int): Index of sample in dataset. + + Returns: + A dict consisting of GPT sample (attribute 'text') and corresponding neighbor chunk IDs ('neighbor_chunks', for indexing chunk database) and neighbor token IDs (corresponding chunk database GPT tokens). + """ + n_chunks_per_sample = self.chunk_dataset.n_chunks_per_sample + + # Wrap sample idx around number of queried samples. + sample_idx = sample_idx % self.num_queried_samples + + # Get standard sample. + sample = self.chunk_dataset.sample_dataset[sample_idx] + + # Sample idx to chunk idxs. + chunk_idxs = list( + range(sample_idx * n_chunks_per_sample, (sample_idx + 1) * n_chunks_per_sample,) + ) + + # Collect retrieved tokens. + all_retrieved_chunk_ids = [] + all_retrieved_token_ids = [] + for chunk_idx in chunk_idxs: + + # Neighbor chunk ids. + neighbor_path = self.neighbor_path_map[chunk_idx] + with h5py.File(neighbor_path, "r") as f: + neighbor_chunk_ids = f["neighbors"][ + chunk_idx % self.block_size, : self.num_neighbors + ].tolist() + + # Retrieved (neighbor + continuation) token ids. + retrieved_chunk_ids = [] + retrieved_token_ids = [] + for neighbor_chunk_id in neighbor_chunk_ids: + current_chunk_ids = [ + i % len(self.db_dataset) + for i in range(neighbor_chunk_id, neighbor_chunk_id + self.num_retrieved_chunks) + ] + current_token_ids = [self.db_dataset[ci]["text"] for ci in current_chunk_ids] + retrieved_chunk_ids.append(current_chunk_ids) + retrieved_token_ids.append(current_token_ids) + + # Collect retrieved tokens. + all_retrieved_chunk_ids.append(retrieved_chunk_ids) + all_retrieved_token_ids.append(retrieved_token_ids) + + # Reshape retrieved tokens. + all_retrieved_chunk_ids = np.array(all_retrieved_chunk_ids).reshape( + (n_chunks_per_sample, self.num_neighbors, -1) + ) + all_retrieved_token_ids = np.array(all_retrieved_token_ids).reshape( + (n_chunks_per_sample, self.num_neighbors, -1) + ) + + # Sample. + sample: Dict[str, np.ndarray] = { + **sample, + "neighbor_chunks": all_retrieved_chunk_ids, + "neighbor_tokens": all_retrieved_token_ids, + } + + return sample + + +def get_retro_datasets( + config: RetroConfig, gpt_datasets: dict, sample_length: int, eod_token_id: int, +) -> Tuple[Optional[RetroDataset], Optional[RetroDataset], Optional[RetroDataset]]: + """Get train, valid, test retro datasets. + + Args: + config (RetroConfig): Retro preprocessing config. + gpt_datasets (dict): Mapping of data split key ('train', 'valid', or 'test') to the original sequence-length GPT dataset (i.e., not the chunk dataset). + sample_length (int): Alias to `sequence_length`. + eod_token_id (int): GPT EOD token ID. + + Returns: + A tuple of 'train', 'valid', and 'test' `RetroDataset`s. + """ + + # DB dataset. + db_dataset = get_db_dataset( + project_dir=config.retro_project_dir, + chunk_length=config.retro_chunk_length, + eod_token_id=eod_token_id, + ) + + # GPT chunk datasets. + chunk_ds_info_map = build_gpt_chunk_datasets_from_gpt_datasets( + project_dir=config.retro_project_dir, + gpt_datasets=gpt_datasets, + sample_length=sample_length, + chunk_length=config.retro_chunk_length, + ) + + # Retro datasets. + retro_dataset_map: Dict[str, Optional[RetroDataset]] = {} + query_dir = get_query_dir(config.retro_project_dir) + for data_key, chunk_ds_info in chunk_ds_info_map.items(): + + # Skip unused datasets. + if chunk_ds_info is None: + retro_dataset_map[data_key] = None + continue + + # For consistency with preprocessing, the neighbor_dir is overwritten + # (from its setting in `build_gpt_chunk_datasets_from_gpt_datasets()` + # above). This is one piece -- along with setting data_path and + # train_samples from config.json -- of ensuring consistency between + # preprocessing and pretraining. + chunk_dataset = chunk_ds_info["dataset"] + chunk_ds_info["neighbor_dir"] = os.path.join( + query_dir, config.retro_neighbor_dirs[data_key], + ) + neighbor_dir = chunk_ds_info["neighbor_dir"] + neighbor_path_map = BlockPathMap.from_dir( + dir=neighbor_dir, block_size=config.retro_block_size + ) + + # Verify num chunks. + n_active_chunks = chunk_ds_info["num_active_chunks"] + n_neighbor_chunks = neighbor_path_map.max_idx + + if not os.path.isdir(neighbor_dir): + if torch.distributed.get_rank() == 0: + raise Exception( + "neighbor directory '%s' not found; please " + "compare --train-samples, --seq-length, --seed, " + "--eval-iters, and --eval-interval, with " + "retro preprocessing args." % neighbor_dir + ) + torch.distributed.barrier() + exit() + + if config.retro_verify_neighbor_count and n_active_chunks != n_neighbor_chunks: + if torch.distributed.get_rank() == 0: + log_retro_rank_0("neighbor_dir : %s" % neighbor_dir) + log_retro_rank_0("neighbor_path_map : %s" % neighbor_path_map) + raise Exception( + "num sampled chunks (%d) != num neighbor chunks " + "(%d); did you complete querying the entire " + "pretraining dataset?" % (n_active_chunks, n_neighbor_chunks) + ) + torch.distributed.barrier() + exit() + + # Retro dataset. + retro_dataset_map[data_key] = RetroDataset( + num_queried_samples=gpt_datasets[data_key][1], + num_neighbors=config.retro_num_neighbors, + num_retrieved_chunks=config.retro_num_retrieved_chunks, + block_size=config.retro_block_size, + db_dataset=db_dataset, + chunk_dataset=chunk_dataset, + neighbor_path_map=neighbor_path_map, + ) + + return ( + retro_dataset_map["train"], + retro_dataset_map["valid"], + retro_dataset_map["test"], + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/utils.py new file mode 100644 index 0000000..f07920d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/query/utils.py @@ -0,0 +1,35 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Utilities for querying the pretraining dataset.""" + +import os + +from megatron.core.datasets.megatron_dataset import MegatronDataset + + +def get_query_dir(project_dir: str) -> str: + """Get root directory of all saved query data. + + Args: + project_dir (str): Retro project dir. + + Returns: + Path to query sub-directory in Retro project. + """ + return os.path.join(project_dir, "query") + + +def get_neighbor_dir(project_dir: str, key: str, dataset: MegatronDataset) -> str: + """Get directory containing neighbor IDs for a dataset (i.e., train, valid, or test). + + Args: + project_dir (str): Retro project dir. + key (str): Dataset split key; 'train', 'valid', or 'test'. + dataset (MegatronDataset): Dataset containing unique hash for finding corresponding neighbors. + + Returns: + Path to directory containing this dataset's neighbors within Retro project. + """ + return os.path.join( + get_query_dir(project_dir), os.path.basename(f"{key}_{dataset.unique_description_hash}"), + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/utils.py new file mode 100644 index 0000000..1f3a258 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/retro/utils.py @@ -0,0 +1,349 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Utilities for Retro preprocessing.""" + +import glob +import logging +import os +from collections import defaultdict +from types import SimpleNamespace +from typing import Any, Callable, Dict, List, Optional + +import numpy as np +import torch +from tqdm import tqdm + +from megatron.core import parallel_state +from megatron.core.datasets.retro.config import RetroPreprocessingConfig +from megatron.core.datasets.retro.query.multi_split_gpt_dataset import ( + MultiSplitGPTDataset, + MultiSplitGPTDatasetConfig, +) +from megatron.core.datasets.utils import log_single_rank + +from .external_libs import h5py + +logger = logging.getLogger(__name__) + + +def log_retro_rank_0(message: str) -> None: + """Log on rank 0. + + Args: + message (str): Message to log. + """ + log_single_rank(logger, logging.INFO, "[RETRO] " + message) + + +def retro_makedir(config: RetroPreprocessingConfig, path: str) -> None: + """Make a directory, conditional on not being in validation mode. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + path (str): Path to directory. + """ + if config.retro_task_validate is None: + os.makedirs(path, exist_ok=True) + + +def extract_data_config(config: RetroPreprocessingConfig) -> MultiSplitGPTDatasetConfig: + """Extract data config from dataset. + + Args: + config (RetroPreprocessingConfig): Retro preprocessing config. + + Returns: + The config object used to build the dataset. + """ + return config.retro_gpt_chunk_datasets.train["dataset"].sample_dataset.config + + +def get_num_chunks_per_sample(sample_length: int, chunk_length: int) -> int: + """Compute seq_length // chunk_length. + + Args: + sample_length (int): Alias of `sequence_length`. + chunk_length (int): Retro chunk length (e.g., 64). + + Returns: + Number of chunks per sample (i.e., `sequence_length` / `chunk_length`). + """ + assert sample_length % chunk_length == 0 + return sample_length // chunk_length + + +class GPTToTextDataset(torch.utils.data.Dataset): + """Dataset to convert GPT tokens to text. + + Args: + gpt_dataset (MultiSplitGPTDataset): GPT dataset, which outputs GPT token samples. + gpt_tokenizer (Any): GPT tokenizer. + """ + + def __init__(self, gpt_dataset: MultiSplitGPTDataset, gpt_tokenizer: Any): + + super().__init__() + + self.gpt_dataset = gpt_dataset + self.gpt_tokenizer = gpt_tokenizer + + def __len__(self) -> int: + """Dataset length. + + Returns: + Number of samples in the dataset. + """ + return len(self.gpt_dataset) + + def __getitem__(self, idx: int) -> dict: + """Get dataset sample. + + Args: + idx (int): Index of sample. + + Returns: + A dict containing attribute 'text' of type string. + """ + gpt_token_ids = self.gpt_dataset[idx]["text"].tolist() + text = self.gpt_tokenizer.detokenize(gpt_token_ids) + return {"text": text} + + +def get_blocks( + dirname: str, n_samples: int, block_size: int, validate: Callable = None, +) -> SimpleNamespace: + """Divide range [0, num_samples) to sequence of block ranges. + + This is a core method within the concept of block processing. The idea + is to divide a range (size n_samples) into a sequence of blocks. Each + block corresponds to a file within 'dirname' with name + '{start_idx}-{end_idx}.hdf5'. This method checks for the existence of + these files, and returns two lists, one for existing blocks and one for + missing blocks. + + Args: + dirname (str): Path to directory containing block files. + n_samples (int): Ideal number of samples. The total number of saved block data is <=n_samples. + block_size (int): Max number of samples per block file (e.g., 100000). + validate (Callable): Method for validating each block file during load. + + Returns: + A namespace consisting of 2 lists: existing blocks, and missing blocks. The total number of samples between the existing and missing blocks should equal n_samples above. + """ + + assert os.path.isdir(dirname), "missing directory '%s.'" % dirname + + # Block ranges. + block_start_idxs = list(range(0, n_samples, block_size)) + block_end_idxs = [min(n_samples, i + block_size) for i in block_start_idxs] + block_ranges = list(zip(block_start_idxs, block_end_idxs)) + + # All block files (existing + missing). + n_digits = int(np.ceil(np.log(n_samples) / np.log(10)) + 1) + all_blocks = [ + { + "range": r, + "path": os.path.join( + dirname, "%s-%s.hdf5" % tuple([str(i).zfill(n_digits) for i in r]), + ), + } + for r in block_ranges + ] + all_block_path_set = set(block["path"] for block in all_blocks) + + # Validate function. + validate = (lambda f: None) if validate is None else validate + + # Delete corrupt files. + if torch.distributed.get_rank() == 0: + existing_block_paths = [ + block["path"] for block in all_blocks if os.path.exists(block["path"]) + ] + for index, path in enumerate(tqdm(existing_block_paths, "validating block.")): + + assert path in all_block_path_set, "unexpected filename, '%s'." % path + + try: + f = h5py.File(path, "r") + except: + os.remove(path) + continue + + try: + validate(f) + except: + os.remove(path) + finally: + f.close() + + # Wait for files to be deleted. + torch.distributed.barrier() + + # Collect blocks. + blocks = SimpleNamespace( + existing=[b for b in all_blocks if os.path.exists(b["path"])], + missing=[b for b in all_blocks if not os.path.exists(b["path"])], + ) + + return blocks + + +def get_blocks_by_rank( + dirname: str, + n_samples: int, + block_size: int, + validate: Callable = None, + sample: Optional[float] = None, +) -> SimpleNamespace: + """Divide existing and missing blocks evenly across all ranks. + + See 'get_blocks()' above for description. The returned lists of existing and + missing blocks are split evenly across ranks via interleaving. This way, + each rank has a roughly equal number of blocks to process for a + downstream operation. + + Args: + dirname (str): Path to directory containing block files. + n_samples (int): Ideal number of samples. The total number of saved block data is <=n_samples. + block_size (int): Max number of samples per block file (e.g., 100000). + validate (Callable): Method for validating each block file during load. + sample (Optional[float]): If provided, sample a random subset of the blocks. Used for validating preprocessing correctness. + + Returns: + A namespace consisting of 2 lists: existing blocks, and missing blocks. Each of these two lists is potentially a sub-sample of the total set of existing and missing blocks, depending on whether sampling is used. Additionally, the attributes n_existing_world and n_missing_world are the total number of existing and missing blocks, independent of samples. Therefore, (n_existing_world + n_missing_world) * block_size == n_samples. + """ + + # Get world blocks. + blocks = get_blocks(dirname, n_samples, block_size, validate) + + # This rank's existing and missing files. + data_parallel_rank = parallel_state.get_data_parallel_rank() + data_parallel_world_size = parallel_state.get_data_parallel_world_size() + rank_existing_blocks = blocks.existing[ + data_parallel_rank : len(blocks.existing) : data_parallel_world_size + ] + rank_missing_blocks = blocks.missing[ + data_parallel_rank : len(blocks.missing) : data_parallel_world_size + ] + + # Extend rank's existing and missing blocks (with None) such that all ranks + # have equal length lists. This allows for easier tracking of global progress. + def get_world_max(n: int) -> int: + """Get max value across ranks. + + Args: + n (int): Value on this rank. + + Returns: + Max value across all ranks. + """ + n_tensor = torch.cuda.LongTensor([n]) + torch.distributed.all_reduce(n_tensor, op=torch.distributed.ReduceOp.MAX) + return n_tensor.item() + + max_n_existing = get_world_max(len(rank_existing_blocks)) + max_n_missing = get_world_max(len(rank_missing_blocks)) + + rank_existing_blocks += [None] * (max_n_existing - len(rank_existing_blocks)) + rank_missing_blocks += [None] * (max_n_missing - len(rank_missing_blocks)) + + # Collect blocks. + blocks = SimpleNamespace( + n_existing_world=len(blocks.existing), + n_missing_world=len(blocks.missing), + existing=rank_existing_blocks, + missing=rank_missing_blocks, + ) + + if sample is not None: + # Sample existing and missing blocks evenly across all ranks. The + # returned lists of blocks are randomly sampled (without replacement) + # to yield `sample * len(blocks)` number of blocks. + + # Randomly sample blocks. + def sample_blocks(_blocks: List[Optional[Dict]]) -> List[Optional[Dict]]: + """Sample a random subset of all blocks. + + Args: + _blocks (List[Optional[Dict]]): List of all blocks. + + Returns: + A random subset of the blocks. + """ + n_blocks_sample = int(np.ceil(sample * len(_blocks))) + sampled_blocks: List[Optional[Dict]] = [b for b in _blocks if b is not None] + + np.random.seed(None) + np.random.shuffle(sampled_blocks) + + sampled_blocks = sampled_blocks[:n_blocks_sample] + sampled_blocks += [None] * (n_blocks_sample - len(sampled_blocks)) + + return sampled_blocks + + blocks.existing = sample_blocks(blocks.existing) + blocks.missing = sample_blocks(blocks.missing) + + return blocks + + +class BlockPathMap: + """Map an index to its containing block path. + + The common use for this class is to have a directory of files containing + blocks of processed data, of uniform block size (e.g., 100k samples per + file). Each file must follow a naming convention of 'startIdx-endIdx.[ext]', + where 'endIdx' minus 'startIdx' must equal the block size, with the possible + exception of the final block. Given an input index, this class maps the + index to the containing block file. + + Args: + block_paths (List[str]): List of paths to saved block files. + block_size (int): Max number of samples per block file (e.g., 100000). + """ + + @classmethod + def from_dir(cls, dir: str, block_size: int, ext: str = "hdf5") -> Any: + """Get list of block files, and create map. + + Args: + dir (str): Path to directory containing saved block files. + block_size (int): Max number of samples per block file (e.g., 100000). + ext (str): Block file extension (e.g., 'hdf5'). + + Returns: + A mapping of sample index to block file path. + """ + assert os.path.isdir(dir), f"directory not found, '{dir}'." + return cls(sorted(glob.glob(dir + f"/*.{ext}")), block_size) + + def __init__(self, block_paths: List[str], block_size: int): + self.max_idx = 0 + self.block_path_map = {} + for block_path in block_paths: + name = os.path.splitext(os.path.basename(block_path))[0] + start_idx, end_idx = [int(i) for i in name.split("-")] + self.block_path_map[start_idx] = block_path + self.max_idx = max(self.max_idx, end_idx) + self.block_size = block_size + + def __str__(self) -> str: + """Stringify the mapping. + + Returns: + A string representation of this block path map. + """ + return "%d paths" % len(self.block_path_map) + + def __getitem__(self, idx: int) -> str: + """Get block path from index. + + Args: + idx (int): Index of sample. + + Returns: + The path to the block file containing the sample index. + """ + block_start_idx = self.block_size * (idx // self.block_size) + block_path = self.block_path_map[block_start_idx] + return block_path diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/t5_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/t5_dataset.py new file mode 100644 index 0000000..6985bb9 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/t5_dataset.py @@ -0,0 +1,234 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from collections import deque +from dataclasses import dataclass, field +from typing import Dict, List, Optional, Union + +import numpy + +from megatron.core.datasets.indexed_dataset import IndexedDataset +from megatron.core.datasets.masked_dataset import ( + MaskedWordPieceDataset, + MaskedWordPieceDatasetConfig, +) +from megatron.core.datasets.utils import Split + + +@dataclass +class T5MaskedWordPieceDatasetConfig(MaskedWordPieceDatasetConfig): + """Configuration object for Megatron Core T5 WordPiece datasets + + NB: As a temporary holdover from Megatron-LM. The T5 tokenizer has an attribute which defines + a number of special sentinel tokens used during sampling. The assert in __post_init__ serves to + preserve compatibility with Megatron-LM until the T5 tokenizer is in Megatron Core. + """ + + sequence_length_encoder: Optional[int] = field(init=False, default=None) + """A sequence_length alias and the sequence length for the encoder""" + + sequence_length_decoder: int = None + """The sequence length for the decoder""" + + def __post_init__(self) -> None: + """Do asserts and set fields post init + """ + super().__post_init__() + + self.sequence_length_encoder = self.sequence_length + + assert self.sequence_length_encoder is not None + assert self.sequence_length_decoder is not None + + assert len(self.tokenizer.additional_special_tokens_ids) > 0 + + +class T5MaskedWordPieceDataset(MaskedWordPieceDataset): + """The T5 dataset that assumes WordPiece tokenization + + Args: + indexed_dataset (IndexedDataset): The IndexedDataset around which to build the MegatronDataset + + dataset_path (str): The real path on disk to the dataset, for bookkeeping + + indexed_indices (numpy.ndarray): The set of the documents indices to expose + + num_samples (int): The number of samples to draw from the indexed dataset + + index_split (Split): The indexed_indices Split + + config (T5MaskedWordPieceDatasetConfig): The config + """ + + def __init__( + self, + indexed_dataset: IndexedDataset, + dataset_path: str, + indexed_indices: numpy.ndarray, + num_samples: int, + index_split: Split, + config: T5MaskedWordPieceDatasetConfig, + ) -> None: + super().__init__( + indexed_dataset, dataset_path, indexed_indices, num_samples, index_split, config + ) + + def _finalize(self) -> None: + """Abstract method implementation + """ + self.token_lookup = list(self.config.tokenizer.inv_vocab.keys()) + # Account for the single and single token ids + self.sample_index = self._build_sample_index(self.config.sequence_length - 2, 1) + + @staticmethod + def _key_config_attributes() -> List[str]: + """Inherited method implementation + + Returns: + List[str]: The key config attributes + """ + return super( + T5MaskedWordPieceDataset, T5MaskedWordPieceDataset + )._key_config_attributes() + ["sequence_length_decoder",] + + def __getitem__(self, idx: int) -> Dict[str, Union[int, numpy.ndarray]]: + """Abstract method implementation + + Args: + idx (int): The index into the dataset + + Returns: + Dict[str, Union[int, numpy.ndarray]]: The + """ + idx_beg, idx_end, target_sequence_length = self.sample_index[idx] + sample = [self.dataset[i] for i in range(idx_beg, idx_end)] + + numpy_random_state = numpy.random.RandomState( + seed=(self.config.random_seed + idx) % 2 ** 32 + ) + + assert target_sequence_length <= self.config.sequence_length + + # Flatten the sample into a list of tokens + tokens = [token for sentence in sample for token in sentence] + + # Truncate the list of tokens to a desired length + truncated = len(tokens) > target_sequence_length + tokens = tokens[:target_sequence_length] + + # Masking + (tokens, _, _, _, masked_spans,) = self._create_masked_lm_predictions( + tokens, target_sequence_length, numpy_random_state + ) + + # Prepare the encoder input and decoder input and output + sentinels = deque(self.config.tokenizer.additional_special_tokens_ids) + encoder_input = [] + decoder_input = [self.config.tokenizer.bos] + decoder_output = [] + idx_beg = 0 + for indices, labels in masked_spans: + sentinel = sentinels.popleft() + + # set the end index + idx_end = indices[0] + + encoder_input.extend(tokens[idx_beg:idx_end]) + encoder_input.append(sentinel) + + decoder_input.append(sentinel) + decoder_input.extend(labels) + + decoder_output.append(sentinel) + decoder_output.extend(labels) + + # set the start index + idx_beg = indices[-1] + 1 + + encoder_input.extend(tokens[idx_beg:]) + decoder_output.append(self.config.tokenizer.eos) + + # Pad the sequences and convert to NumPy + length_toks_encoder = len(encoder_input) + length_toks_decoder = len(decoder_input) + length_pads_encoder = self.config.sequence_length_encoder - length_toks_encoder + length_pads_decoder = self.config.sequence_length_decoder - length_toks_decoder + assert length_pads_encoder >= 0 + assert length_pads_decoder >= 0 + + encoder_input = numpy.array(encoder_input, dtype=numpy.int64) + encoder_input = numpy.pad( + encoder_input, (0, length_pads_encoder), constant_values=self.config.tokenizer.pad + ) + + decoder_input = numpy.array(decoder_input, dtype=numpy.int64) + decoder_input = numpy.pad( + decoder_input, (0, length_pads_decoder), constant_values=self.config.tokenizer.pad + ) + + # Create attention and history masks + mask_encoder = self._make_attention_mask(encoder_input, encoder_input) + mask_encoder_decoder = self._make_attention_mask(decoder_input, encoder_input) + mask_decoder = self._make_attention_mask(decoder_input, decoder_input) + mask_decoder = mask_decoder * self._make_history_mask(decoder_input) + + # Mask the labels + decoder_output = numpy.array(decoder_output, dtype=numpy.int64) + decoder_output = numpy.pad(decoder_output, (0, length_pads_decoder), constant_values=-1) + + # Get the loss mask + loss_mask = numpy.zeros(self.config.sequence_length_decoder, dtype=numpy.int64) + loss_mask[:length_toks_decoder] = 1 + + return { + "text_enc": encoder_input, + "text_dec": decoder_input, + "labels": decoder_output, + "loss_mask": loss_mask, + "truncated": int(truncated), + "enc_mask": mask_encoder, + "dec_mask": mask_decoder, + "enc_dec_mask": mask_encoder_decoder, + } + + @staticmethod + def _make_attention_mask( + source_block: numpy.ndarray, target_block: numpy.ndarray + ) -> numpy.ndarray: + """Return a 2-D attention mask + + Args: + source_block (numpy.ndarray): A 1-D array + target_block (numpy.ndarray): A 1-D array + + Returns: + numpy.ndarray: The 2-D attention mask + """ + mask = (target_block[None, :] >= 1) * (source_block[:, None] >= 1) + return mask.astype(numpy.int64) + + @staticmethod + def _make_history_mask(block: numpy.ndarray) -> numpy.ndarray: + """Return a 2-D history (lower-left-triangular) mask + + Args: + block (numpy.ndarray): A 1-D array + + Returns: + numpy.ndarray: The 2-D history (lower-left-triangular) mask + """ + arange = numpy.arange(block.shape[0]) + mask = arange[None,] <= arange[:, None] + return mask.astype(numpy.int64) + + def _get_token_mask(self, numpy_random_state: numpy.random.RandomState) -> int: + """Abstract method implementation + + 100% of the time, replace the token id with mask token id. + + Args: + numpy_random_state (RandomState): The NumPy random state + + Returns: + int: The mask token id + """ + return self.config.tokenizer.mask diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/utils.py new file mode 100644 index 0000000..def0fb7 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/datasets/utils.py @@ -0,0 +1,64 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import logging +from enum import Enum +from typing import Any, List + +import numpy +import torch + +logger = logging.getLogger(__name__) + + +class Split(Enum): + train = 0 + valid = 1 + test = 2 + + +def compile_helpers(): + """Compile C++ helper functions at runtime. Make sure this is invoked on a single process. + """ + import os + import subprocess + + command = ["make", "-C", os.path.abspath(os.path.dirname(__file__))] + if subprocess.run(command).returncode != 0: + import sys + + log_single_rank(logger, logging.ERROR, "Failed to compile the C++ dataset helper functions") + sys.exit(1) + + +def log_single_rank(logger: logging.Logger, *args: Any, rank: int = 0, **kwargs: Any): + """If torch distributed is initialized, log only on rank + + Args: + logger (logging.Logger): The logger to write the logs + + args (Tuple[Any]): All logging.Logger.log positional arguments + + rank (int, optional): The rank to write on. Defaults to 0. + + kwargs (Dict[str, Any]): All logging.Logger.log keyword arguments + """ + if torch.distributed.is_initialized(): + if torch.distributed.get_rank() == rank: + logger.log(*args, **kwargs) + else: + logger.log(*args, **kwargs) + + +def normalize(weights: List[float]) -> List[float]: + """Do non-exponentiated normalization + + Args: + weights (List[float]): The weights + + Returns: + List[float]: The normalized weights + """ + w = numpy.array(weights, dtype=numpy.float64) + w_sum = numpy.sum(w) + w = (w / w_sum).tolist() + return w diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/__init__.py new file mode 100644 index 0000000..df08d7e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +from .core import check_is_distributed_checkpoint +from .mapping import LocalNonpersitentObject, ShardedTensor +from .serialization import ( + load, + load_common_state_dict, + load_plain_tensors, + load_tensors_metadata, + save, +) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/core.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/core.py new file mode 100644 index 0000000..50384e6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/core.py @@ -0,0 +1,77 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +""" Module for managing distributed checkpoints metadata. """ + +import json +from dataclasses import asdict, dataclass +from pathlib import Path +from typing import Optional + +CONFIG_FNAME = 'metadata.json' + + +class CheckpointingException(Exception): + """ Base checkpointing related exception """ + + pass + + +@dataclass +class CheckpointingConfig: + """ Documents backends used in the checkpoint. + + Checkpoint config keeps track of formats used for storing the sharded tensors + (sharded_backend) and other objects (common_backend). + + Note that versioning is not for the checkpoint content (which is application specific), + but for the checkpoint format itself. + """ + + sharded_backend: str + sharded_backend_version: int = 1 + common_backend: str = 'torch' + common_backend_version: int = 1 + + +def check_is_distributed_checkpoint(checkpoint_dir): + """ Checks if `metadata.json` exists in the checkpoint and is a valid config. + + Args: + checkpoint_dir: checkpoint directory + + Returns: + bool: True if `metadata.json` exists in the checkpoint and is a valid config. + """ + return maybe_load_config(checkpoint_dir) is not None + + +def maybe_load_config(checkpoint_dir: str) -> Optional[CheckpointingConfig]: + """ Returns checkpoint config if `checkpoint_dir` is a distributed checkpoint and None otherwise + + Args: + checkpoint_dir: checkpoint directory + + Returns: + CheckpointingConfig (optional): None if checkpoint is not a valid distributed checkpoint + """ + config_path = Path(checkpoint_dir, CONFIG_FNAME) + if not config_path.exists(): + return None + with config_path.open() as f: + config_dict = json.load(f) + return CheckpointingConfig(**config_dict) + + +def save_config(config: CheckpointingConfig, checkpoint_dir: str): + """ Save given config to checkpoint directory. + + Args: + config: checkpoint config + checkpoint_dir: checkpoint directory + + Returns: + None + """ + config_path = Path(checkpoint_dir, CONFIG_FNAME) + with config_path.open('w') as f: + json.dump(asdict(config), f) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/dict_utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/dict_utils.py new file mode 100644 index 0000000..95591cd --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/dict_utils.py @@ -0,0 +1,232 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +""" Utilities for operating with dicts and lists. + +All functions in this module handle nesting of dicts and lists. +Other objects (e.g. tuples) are treated as atomic leaf types that cannot be traversed. +""" + +from collections import defaultdict +from typing import Any, Callable, Iterable, Optional, Tuple, Union + +import torch + + +def extract_matching_values( + x: Union[dict, list], predicate: Callable[[Any], bool], return_lists_as_dicts: bool = False +) -> Tuple[Union[dict, list], Union[dict, list]]: + """ Return matching and nonmatching values. Keeps hierarchy. + + Args: + x (Union[dict, list]) : state dict to process. Top-level argument must be a dict or list + predicate (object -> bool): determines matching values + return_lists_as_dicts (bool): if True, matching lists will be turned + into dicts, with keys indicating the indices of original elements. + Useful for reconstructing the original hierarchy. + """ + + def _set_elem(target, k, v): + if return_lists_as_dicts: + target[k] = v + else: + target.append(v) + + if isinstance(x, dict): + matching_vals = {} + nonmatching_vals = {} + for k, v in x.items(): + if isinstance(v, (list, dict)): + match, nonmatch = extract_matching_values(v, predicate, return_lists_as_dicts) + if match: + matching_vals[k] = match + if nonmatch or not v: + nonmatching_vals[k] = nonmatch + elif predicate(v): + matching_vals[k] = v + else: + nonmatching_vals[k] = v + elif isinstance(x, list): + matching_vals = {} if return_lists_as_dicts else [] + nonmatching_vals = {} if return_lists_as_dicts else [] + for ind, v in enumerate(x): + if isinstance(v, (list, dict)) and v: + match, nonmatch = extract_matching_values(v, predicate, return_lists_as_dicts) + if match: + _set_elem(matching_vals, ind, match) + if nonmatch or not v: + _set_elem(nonmatching_vals, ind, nonmatch) + else: + target = matching_vals if predicate(v) else nonmatching_vals + _set_elem(target, ind, v) + else: + raise ValueError(f'Unexpected top-level object type: {type(x)}') + return matching_vals, nonmatching_vals + + +def diff(x1: Any, x2: Any, prefix: Tuple = ()) -> Tuple[list, list, list]: + """ Recursive diff of dicts. + + Args: + x1 (object): left dict + x2 (object): right dict + prefix (tuple): tracks recursive calls. Used for reporting differing keys. + + Returns: + Tuple[list, list, list]: tuple of: + - only_left: Prefixes present only in left dict + - only_right: Prefixes present only in right dict + - mismatch: values present in both dicts but not equal across dicts. + For tensors equality of all elems is checked. + Each element is a tuple (prefix, type of left value, type of right value). + """ + mismatch = [] + if isinstance(x1, dict) and isinstance(x2, dict): + only_left = [prefix + (k,) for k in x1.keys() - x2.keys()] + only_right = [prefix + (k,) for k in x2.keys() - x1.keys()] + for k in x2.keys() & x1.keys(): + _left, _right, _mismatch = diff(x1[k], x2[k], prefix + (k,)) + only_left.extend(_left) + only_right.extend(_right) + mismatch.extend(_mismatch) + elif isinstance(x1, list) and isinstance(x2, list): + only_left = list(range(len(x1) - 1, len(x2) - 1, -1)) + only_right = list(range(len(x1) - 1, len(x2) - 1, -1)) + for i, (v1, v2) in enumerate(zip(x1, x2)): + _left, _right, _mismatch = diff(v1, v2, prefix + (i,)) + only_left.extend(_left) + only_right.extend(_right) + mismatch.extend(_mismatch) + else: + only_left = [] + only_right = [] + if isinstance(x1, torch.Tensor) and isinstance(x2, torch.Tensor): + _is_mismatch = not torch.all(x1 == x2) + else: + try: + _is_mismatch = bool(x1 != x2) + except RuntimeError: + _is_mismatch = True + + if _is_mismatch: + mismatch.append((prefix, type(x1), type(x2))) + + return only_left, only_right, mismatch + + +def inspect_types(x: Any, prefix: Tuple = (), indent: int = 4): + """ Helper to print types of (nested) dict values. """ + print_indent = lambda: print(' ' * indent * len(prefix), end='') + if isinstance(x, dict): + print() + for k, v in x.items(): + print_indent() + print(f'> {k}: ', end='') + inspect_types(v, prefix + (k,), indent) + elif isinstance(x, list): + print() + for i, v in enumerate(x): + print_indent() + print(f'- {i}: ', end='') + inspect_types(v, prefix + (i,), indent) + else: + if isinstance(x, torch.Tensor): + print(f'Tensor of shape {x.shape}') + else: + try: + x_str = str(x) + except: + x_str = '' + if len(x_str) > 30: + x_str = x_str[:30] + '... (truncated)' + print(f'[{type(x)}]: {x_str}') + + +def nested_values(x: Union[dict, list]): + """ Returns iterator over (nested) values of a given dict or list. """ + x_iter = x.values() if isinstance(x, dict) else x + for v in x_iter: + if isinstance(v, (dict, list)): + yield from nested_values(v) + else: + yield v + + +def nested_items_iter(x: Union[dict, list]): + """ Returns iterator over (nested) tuples (container, key, value) of a given dict or list. """ + x_iter = x.items() if isinstance(x, dict) else enumerate(x) + for k, v in x_iter: + if isinstance(v, (dict, list)): + yield from nested_items_iter(v) + else: + yield x, k, v + + +def dict_map(f: Callable, d: dict): + """ `map` equivalent for dicts. """ + for sub_d, k, v in nested_items_iter(d): + sub_d[k] = f(v) + + +def dict_map_with_key(f: Callable, d: dict): + """ `map` equivalent for dicts with a function that accepts tuple (key, value). """ + for sub_d, k, v in nested_items_iter(d): + sub_d[k] = f(k, v) + + +def dict_list_map_inplace(f: Callable, x: Union[dict, list]): + """ Maps dicts and lists *in-place* with a given function. """ + if isinstance(x, dict): + for k, v in x.items(): + x[k] = dict_list_map_inplace(f, v) + elif isinstance(x, list): + x[:] = (dict_list_map_inplace(f, v) for v in x) + else: + return f(x) + return x + + +def dict_list_map_outplace(f: Callable, x: Union[dict, list]): + """ Maps dicts and lists *out-of-place* with a given function. """ + if isinstance(x, dict): + return {k: dict_list_map_outplace(f, v) for k, v in x.items()} + elif isinstance(x, list): + return [dict_list_map_outplace(f, v) for v in x] + else: + return f(x) + + +def merge(x1: dict, x2: dict, key: Tuple[str, ...] = ()): + """ Merges dicts and lists recursively. """ + if isinstance(x1, dict) and isinstance(x2, dict): + for k, v2 in x2.items(): + if k not in x1: + x1[k] = v2 + else: + x1[k] = merge(x1[k], v2, key=key + (k,)) + elif isinstance(x1, list) and isinstance(x2, list): + if len(x1) != len(x2): + raise ValueError( + f'Cannot merge two lists with different lengths ({len(x1)} and {len(x2)}, encountered at level {key})' + ) + for i, v2 in enumerate(x2): + x1[i] = merge(x1[i], v2, key=key + (i,)) + else: + raise ValueError( + f'Duplicate non-dict and non-list values encountered: `{x1}` and `{x2}` (at level {key})' + ) + return x1 + + +def map_reduce( + xs: Iterable, + key_fn: Callable = lambda x: x, + value_fn: Callable = lambda x: x, + reduce_fn: Callable = lambda x: x, +) -> dict: + """ Simple map-reduce implementation following `more_itertools.map_reduce` interface. """ + res = defaultdict(list) + for x in xs: + res[key_fn(x)].append(value_fn(x)) + for k in res: + res[k] = reduce_fn(res[k]) + return dict(res) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/mapping.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/mapping.py new file mode 100644 index 0000000..bdee641 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/mapping.py @@ -0,0 +1,358 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +""" Core library classes for representing sharding of tensors and objects. + +The main expected usage is wrapping torch.Tensors in state dicts with +ShardedTensor class (mostly with the ShardedTensor.from_rank_offsets classmethod). +""" + +import logging +from abc import ABC +from dataclasses import dataclass, replace +from itertools import chain +from typing import Any, Callable, Dict, Optional, Tuple, Union + +import numpy as np +import torch + +from .core import CheckpointingException +from .dict_utils import dict_list_map_inplace, dict_list_map_outplace + +logger = logging.getLogger(__name__) + +# These type definitions are just hints to differentiate a plain model state +# dict (StateDict) from a state dict with tensors replaced with ShardedTensors +# (ShardedStateDict). +StateDict = Dict[str, Any] +ShardedStateDict = Dict[str, Any] +ReplicaId = Union[int, Tuple[int, ...]] + + +class ShardedBase(ABC): + key: str + data: object + replica_id: ReplicaId + + +@dataclass +class ShardedTensor(ShardedBase): + """Represents a mapping between a local tensor and a global tensor. + + Global tensor is assumed to consist of many local tensors distributed + between different processes. + + Args: + key: unique identifier of a global tensor + data: local tensor data. Can be None only for consistency validation + dtype: tensor dtype + local_shape: local tensor shape + global_shape: global tensor shape + global_offset: offset of a local tensor in a global tensor, specified in number of tensor elements + axis_fragmentations: global tensor fragmentation of each axis + replica_id: indicates given local tensor's replication wrt. local tensors in different processes + prepend_axis_num: number of axes prepended to the local tensor to reflect global tensor shape. The behavior is similar to unsqueezing the local tensor. + allow_shape_mismatch: if True, during loading, the global shape of a stored tensor does not have to match the expected global shape. Useful for representing tensors with flexible shape, e.g. padded. + flattened_range: specifies a slice that should be applied to a flattened tensor with `local_shape` in order to get the tensor stored as `data` + """ + + key: str + data: Optional[torch.Tensor] + dtype: torch.dtype + local_shape: Tuple[int, ...] + global_shape: Tuple[int, ...] + global_offset: Tuple[int, ...] + axis_fragmentations: Optional[Tuple[int, ...]] + replica_id: ReplicaId = 0 + prepend_axis_num: int = 0 + allow_shape_mismatch: bool = False + flattened_range: Optional[slice] = None + + def global_slice(self) -> Tuple[Union[int, slice], ...]: + assert len(self.global_offset) == len(self.local_shape) + self.prepend_axis_num + return tuple( + chain( + (off for off in self.global_offset[: self.prepend_axis_num]), + ( + slice(off, off + sh) + for off, sh in zip( + self.global_offset[self.prepend_axis_num :], self.local_shape + ) + ), + ) + ) + + def global_coordinates(self) -> Tuple[np.ndarray, ...]: + if self.flattened_range is None: + raise CheckpointingException( + f'`global_coordinates` is undefined for' + f' {self.__class__.__name__} without `flattened_range`' + ) + + local_coords = self.local_coordinates() + assert len(local_coords) + self.prepend_axis_num == len(self.global_offset), ( + len(local_coords), + self, + ) + global_coords = tuple( + c + off + for c, off in zip((0,) * self.prepend_axis_num + local_coords, self.global_offset) + ) + return global_coords + + def local_coordinates(self) -> Tuple[np.ndarray, ...]: + if self.flattened_range is None: + raise CheckpointingException( + f'`local_coordinates` is undefined for' + f' {self.__class__.__name__} without `flattened_range`' + ) + + # TODO: np.unravel_index? + mask = np.zeros(np.product(self.local_shape), dtype=bool) + mask[self.flattened_range] = True + return np.nonzero(mask.reshape(self.local_shape)) + + def max_allowed_chunks(self) -> Tuple[int, ...]: + chunks = [] + for axis_sh, axis_fragm in zip(self.global_shape, self.axis_fragmentations): + if not self.allow_shape_mismatch and axis_sh % axis_fragm != 0: + raise CheckpointingException( + f'Axis shape ({axis_sh}) not divisible' f' by axis fragmentation ({axis_fragm}' + ) + axis_chunk_size = axis_sh // axis_fragm + chunks.append(axis_chunk_size) + return tuple(chunks) + + def without_data(self): + return replace(self, data=None) + + @classmethod + def from_rank_offsets( + cls, + key: str, + data: torch.Tensor, + *rank_offsets: Tuple[int, int, int], + replica_id: ReplicaId = 0, + prepend_axis_num: int = 0, + **init_kwargs, + ): + """Allows to construct the ShardedTensor given offset specified in process ranks. + + Args: + key: unique key + data: local tensor data + rank_offsets: each tuple (axis, axis_rank_offset, axis_fragm) says that if global tensor is divided into `axis_fragm` fragment along `axis` axis, then local tensor data corresponds to the `axis_rank_offset` chunk. + replica_id: see ShardedTensor + prepend_axis_num: see ShardedTensor + init_kwargs: passed to ShardedTensor.__init__ + """ + global_offset = [0] * (data.ndim + prepend_axis_num) + global_shape = ([1] * prepend_axis_num) + list(data.shape) + axis_fragmentations = [1] * (data.ndim + prepend_axis_num) + _seen_axis = set() + for axis, axis_rank_offset, axis_fragm in rank_offsets: + assert axis >= 0 and axis_rank_offset >= 0 and axis_fragm >= 0, ( + axis, + axis_rank_offset, + axis_fragm, + ) + assert ( + axis_rank_offset < axis_fragm + ), 'Rank offset must be lower than axis fragmentation' + if axis in _seen_axis: + raise CheckpointingException('Duplicated axis specified') + _seen_axis.add(axis) + + local_axis_shape = 1 if axis < prepend_axis_num else data.shape[axis - prepend_axis_num] + global_shape[axis] = axis_fragm * local_axis_shape + global_offset[axis] = axis_rank_offset * local_axis_shape + axis_fragmentations[axis] = axis_fragm + + return cls( + key, + data, + data.dtype, + tuple(data.shape), + tuple(global_shape), + tuple(global_offset), + tuple(axis_fragmentations), + replica_id, + prepend_axis_num, + **init_kwargs, + ) + + def init_data(self, device: torch.device, init_fn=torch.empty): + if self.data is not None: + return + self.data = init_fn(self.local_shape, dtype=self.dtype, device=device) + + def __str__(self): + return f'{self.__class__.__name__}(key=\'{self.key}\')' + + +def is_main_replica(replica_id: ReplicaId): + """ Checks if given `replica_id` is considered as main. + + "Main" replica is: + - integer 0 + - or an iterable with all 0 elements + + It is the application responsibility to set correct replicas for sharded tensors. + + Args: + replica_id (Union[int, Tuple[int, ...]]): replica id + + Returns: + (bool): True for a "main" replica + """ + if isinstance(replica_id, int): + return replica_id == 0 + return all(r == 0 for r in replica_id) + + +class LocalNonpersitentObject: + """Object that should not be stored in a checkpoint, but restored locally. + + Wrapping any object inside the state dict with LocalNonpersitentObject + will result in: + - during saving, this object will *not* be stored in the checkpoint + - during loading, a local version of this object will be placed in a state dict + """ + + def __init__(self, obj): + self.obj = obj + + def unwrap(self): + return self.obj + + +@dataclass +class ShardedObject(ShardedBase): + """Represents a mapping between a local object and a global object. + + Global object is assumed to consist of many local objects distributed + between different processes. + + NOTE: Contrary to ShardedTensor, it's impossible to change global object + sharding. Conceptually, ShardedObject is a fully-sharded ShardedTensor + with atomic arbitrary typed elements. + + Args: + key: unique identifier of a global tensor + data: local object data. Can be None only for consistency validation + global_shape: global object shape + global_offset: offset of a local object in a global object, specified in number of shards + replica_id: indicates local object replication wrt. local objects in different processes + """ + + key: str + data: object + global_shape: Tuple[int, ...] + global_offset: Tuple[int, ...] + replica_id: ReplicaId = 0 + + def without_data(self): + return replace(self, data=None) + + @property + def unique_key(self): + return f'{self.key}/shard_{".".join(map(str, self.global_offset))}_{".".join(map(str, self.global_shape))}' + + def __str__(self): + return f'{self.__class__.__name__}(key=\'{self.key}\')' + + +@dataclass +class ShardedTensorFactory(ShardedBase): + """ Allows to apply transformations to tensors before/after serialization. + + The essence of those transformations is that they can be applied to + optimizer states the same way they are applied to the model params. + + Builder creates a sub-state-dict out of a tensor before saving, and merger + merges the corresponding state dict after loading. + + Args: + key (str): unique identifier of the factory + data (torch.Tensor): original model parameter that will be further transformed by this factory + build_fn (callable): function that transforms the original tensor to a sharded state dict + merge_fn (callable): function that transforms loaded subtree back into a single tensor (inverse of `build_fn`) + replica_id (ReplicaId): indicates factory replication wrt. factories in different processes + """ + + key: str + data: torch.Tensor + build_fn: Callable[[str, torch.Tensor, ReplicaId], ShardedStateDict] + merge_fn: Callable[[StateDict], torch.Tensor] + replica_id: ReplicaId = 0 + + def build(self): + return self.build_fn(self.key, self.data, self.replica_id) + + +def apply_factories(sharded_state_dict: ShardedStateDict): + """ Turn ShardedTensorFactories into ShardedTensors *in-place*. + + Args: + sharded_state_dict (ShardedStateDict): state dict possibly containing ShardedTensorFactory objects + + Returns: + None: state dict is modified in place + """ + + def apply(x): + if isinstance(x, ShardedTensorFactory): + x = x.build() + return x + + dict_list_map_inplace(apply, sharded_state_dict) + + +def apply_factory_merges( + x1: StateDict, x2: ShardedStateDict, key: Tuple[str, ...] = () +) -> StateDict: + """ Apply merges defined by ShardedTensorFactories *in-place*. + + Args: + x1 (StateDict): state dict loaded from the checkpoint + x2 (ShardedStateDict): subset of `x1` (in terms of dict keys) with ShardedTensorFactory + as (possibly nested) values that define how to merge objects from the `x1` state dict + key (Tuple[str, ...]): current key in a recursive call. Used only for reporting meaningful errors + + Returns: + StateDict: `x1` modified in-place + """ + if isinstance(x2, ShardedTensorFactory): + return x2.merge_fn(x1) + + # There rest is almost the same as the `merge` function from `dict_utils` + if isinstance(x1, dict) and isinstance(x2, dict): + for k, v2 in x2.items(): + if k not in x1: + raise ValueError( + f'Different dict keys encountered in `apply_factory_merges` ({x1.keys()} vs {x2.keys()})' + ) + else: + x1[k] = apply_factory_merges(x1[k], v2, key=key + (k,)) + elif isinstance(x1, list) and isinstance(x2, list): + if len(x1) != len(x2): + err_msg = f'Cannot merge two lists with different lengths ({len(x1)} and {len(x2)}, encountered at key {key})' + logger.error(err_msg + f'\nx1: {x1}\nx2: {x2}') + raise ValueError(err_msg) + for i, v2 in enumerate(x2): + x1[i] = apply_factory_merges(x1[i], v2, key=key + (i,)) + elif isinstance(x1, list) and isinstance(x2, dict): + for k, v2 in x2.items(): + if not isinstance(k, int): + raise ValueError( + f'Invalid dict key {k} non-integer type encountered in a list-dict merge at level {key}' + ) + if k >= len(x1): + raise ValueError( + f'Dict key {k} out of bound for list of length {len(x1)} (encountered at level {key})' + ) + x1[k] = apply_factory_merges(x1[k], v2, key=key + (k,)) + else: + raise ValueError( + f'Duplicate non-dict and non-list values encountered: `{x1}` and `{x2} (at key {key})`' + ) + return x1 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/optimizer.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/optimizer.py new file mode 100644 index 0000000..bec1742 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/optimizer.py @@ -0,0 +1,127 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +""" Helpers for defining sharding for optimizer states based on existing sharding for model parameters. """ + +import logging +from copy import deepcopy +from dataclasses import replace +from itertools import chain +from typing import Dict, Iterable, List, Tuple, Union + +logger = logging.getLogger(__name__) + +import torch + +from .dict_utils import nested_values +from .mapping import ( + LocalNonpersitentObject, + ShardedStateDict, + ShardedTensor, + ShardedTensorFactory, + StateDict, +) +from .utils import extract_sharded_tensors_and_factories + + +def get_optim_param_to_id_map(optim_params_iter: Iterable[torch.nn.Parameter]) -> Dict[int, int]: + param_mappings = {} + for i, param in enumerate(optim_params_iter): + if id(param) not in param_mappings: + param_mappings[id(param)] = i + return param_mappings + + +def get_param_id_to_sharded_param_map( + model_sharded_state_dict: ShardedStateDict, optim_params_iter: Iterable[torch.nn.Parameter] +) -> Dict[int, Union[ShardedTensor, ShardedTensorFactory]]: + """ Generate mapping from optimizer state ids to model sharded parameters. + + Args: + model_sharded_state_dict: sharded state dict with all model sharded tensors (can have any structure) + optim_params_iter: iterable which iterates over model parameters tracked by the optimizer. + The iteration must be in the same order as in the optimizer parameters. + + Returns: + Dict[int, Union[ShardedTensor, ShardedTensorFactory]]: mapping from optimizer state ids + to model sharded parameters. + """ + model_sharded_state_dict, _ = extract_sharded_tensors_and_factories(model_sharded_state_dict) + id_to_sharded_param_map = {} + param_to_id_map = get_optim_param_to_id_map(optim_params_iter) + for ten in nested_values(model_sharded_state_dict): + if id(ten.data) in param_to_id_map: + id_to_sharded_param_map[param_to_id_map[id(ten.data)]] = ten + else: + logger.debug(f'{ten} is not tracked by the optimizer') + + if not id_to_sharded_param_map: + logger.warning( + "Sharded parameters mapping is empty. It means tensors in model state dict" + " do not correspond to tensors in optimizer parameters map." + " Make sure to call state_dict with `keep_vars=True`." + ) + return id_to_sharded_param_map + + +def make_sharded_optimizer_tensor( + model_param: Union[ShardedTensor, ShardedTensorFactory], optim_param: torch.Tensor, prefix: str +) -> Union[ShardedTensor, ShardedTensorFactory]: + """ Build a ShardedTensor or ShardedTensorFactory for optimizer param based on model param + + Args: + model_param (Union[ShardedTensor, ShardedTensorFactory]): model param + optim_param (torch.Tensor): corresponding optimizer param + prefix (str): optimizer prefix for the ShardedTensor or ShardedTensorFactory + + Returns: + Union[ShardedTensor, ShardedTensorFactory]: wrapped optimizer parameter + """ + if isinstance(model_param, ShardedTensorFactory): + return replace(model_param, key=f'{prefix}.{model_param.key}', data=optim_param) + + assert ( + tuple(optim_param.shape) == model_param.local_shape + ), f'Optimizer shape ({tuple(optim_param.shape)} does not match model shape ({model_param.local_shape})' + return replace( + model_param, key=f'{prefix}.{model_param.key}', data=optim_param, dtype=optim_param.dtype + ) + + +def optim_state_to_sharding_state( + optim_state_dict: StateDict, + id_to_sharded_param_map: Dict[int, ShardedTensor], + exclude_keys: Tuple[str] = (), +): + """ Turn optimizer state dict to sharded state dict based on model state dict *in-place*. + + Can be used to add sharding information to most common optimizer state dict. + Creates separate ShardedTensors for each key in `optim_state_dict['state']` + (e.g. for torch.optim.Adam there will be separate tensors for `exp_avg` and `exp_avg_sq`) + + Args: + optim_state_dict (StateDict): optimizer state dict with + state parameters under `state` key and group hyperparameters under `param_groups` -> `params` key. + id_to_sharded_param_map (Dict[int, ShardedTensor]): mapping from optimizer param ids to model sharded tensors. + Can be generated with `get_param_id_to_sharded_param_map` function + exclude_keys (Tuple[str]): optimizer state keys to exclude from the final state dict. + + Returns: + None: state dict is modified in place + """ + sharded_state = {} + for param_id, param_state in optim_state_dict['state'].items(): + sharded_state[param_id] = {} + for state_key, param in param_state.items(): + if state_key in exclude_keys: + continue + if param_id in id_to_sharded_param_map: + sharded_state[param_id][state_key] = make_sharded_optimizer_tensor( + id_to_sharded_param_map[param_id], param, prefix=f'optimizer.state.{state_key}' + ) + else: + raise ValueError(f'Param id {param_id} does not match any model sharded param') + + optim_state_dict['param_groups'] = deepcopy(optim_state_dict['param_groups']) + for group in optim_state_dict['param_groups']: + group['params'] = LocalNonpersitentObject(group['params']) + optim_state_dict['state'] = sharded_state diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/serialization.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/serialization.py new file mode 100644 index 0000000..dee4a10 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/serialization.py @@ -0,0 +1,499 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +""" Entrypoints for saving and loading the distributed checkpoints. + +Functions `load` and `save` are equivalents of `torch.load` and `torch.save` +but expect torch.Tensors to be wrapped with classes from the `mapping module`. +Additionally, `load` expects the sharded state dict argument as a guidance for loading the sharded tensors. +""" + +import logging +import os +from collections import Counter, defaultdict +from itertools import chain +from pathlib import Path +from typing import Iterable, List, Optional, Tuple, Union + +import numpy as np +import torch + +from .core import CheckpointingConfig, maybe_load_config, save_config +from .dict_utils import ( + dict_list_map_inplace, + diff, + extract_matching_values, + map_reduce, + merge, + nested_values, +) +from .mapping import ( + CheckpointingException, + ShardedObject, + ShardedStateDict, + ShardedTensor, + ShardedTensorFactory, + StateDict, + apply_factories, + apply_factory_merges, + is_main_replica, +) +from .strategies.base import ( + LoadCommonStrategy, + LoadShardedStrategy, + SaveCommonStrategy, + SaveShardedStrategy, + StrategyAction, + get_default_strategy, +) +from .utils import ( + extract_nonpersistent, + extract_sharded_base, + extract_sharded_tensors, + extract_sharded_tensors_or_nonpersistent, +) + +COMMON_STATE_FNAME = 'common.pt' + +logger = logging.getLogger(__name__) + + +def load( + sharded_state_dict: ShardedStateDict, + checkpoint_dir: str, + sharded_strategy: Union[LoadShardedStrategy, Tuple[str, int], None] = None, + common_strategy: Union[LoadCommonStrategy, Tuple[str, int], None] = None, + validate_access_integrity: bool = True, +) -> StateDict: + """Loading entrypoint. + + In the steps below, the following verbs refer to corresponding objects: + - load = load from checkpoint + - extract = extract from sharded_state_dict + - add = add to the final state dict + Steps: + 1. Load common state dict and form the base of the result state dict + 2. Apply factories to sharded_state_dict + 3. Extract LocalNonPersistentObject and add + 4. (optional) Extract ShardedObjects, load and add + 5. Extract ShardedBase, load, apply factory merges and add + + Args: + sharded_state_dict (ShardedStateDict): state dict of the existing model + populated with ShardedTensors. Used as a mapping to determine which + parts of global tensors stored in the checkpoint should be loaded. + checkpoint_dir (str): directory with the checkpoint + sharded_strategy (LoadShardedStrategy, Tuple[str, int], optional): configures loading behavior for sharded tensors + common_strategy (LoadCommonStrategy, Tuple[str, int], optional): configures loading behavior for common data + validate_access_integrity (bool default = True): checks if each tensor shard is accessed + exactly once (as main replica) by some process + """ + if common_strategy is not None: + raise NotImplementedError('The only supported common strategy is torch') + + sharded_strategy = _verify_checkpoint_and_load_strategy(checkpoint_dir, sharded_strategy) + + checkpoint_dir = Path(checkpoint_dir) + common_state_dict = load_common_state_dict(checkpoint_dir) + if not sharded_state_dict: + return common_state_dict + + # Create a copy of sharded_state_dict as the passed in state dict may have + # references that prevent tensors from being deallocated + sharded_state_dict, _ = extract_matching_values(sharded_state_dict, lambda x: True) + + sh_ten_factories, _ = extract_matching_values( + sharded_state_dict, + lambda x: isinstance(x, ShardedTensorFactory), + return_lists_as_dicts=True, + ) + apply_factories(sharded_state_dict) + # Data inside sh_ten_factories no longer needed so delete them to reduce memory usage + def unlink_data(x): + x.data = None + return x + + dict_list_map_inplace(unlink_data, sh_ten_factories) + # Non-persistent objects + nonpersistent_state_dict, sharded_state_dict = extract_nonpersistent(sharded_state_dict) + dict_list_map_inplace(lambda o: o.unwrap(), nonpersistent_state_dict) + merge(common_state_dict, nonpersistent_state_dict) + + # Sharded base + if not sharded_strategy.can_handle_sharded_objects: + # TODO: implement is a part of common strategy + sharded_objects, sharded_state_dict = load_sharded_objects( + sharded_state_dict, checkpoint_dir + ) + merge(common_state_dict, sharded_objects) + sharded_state_dict, _ = extract_sharded_base(sharded_state_dict) + + if validate_access_integrity: + validate_sharding_integrity(nested_values(sharded_state_dict)) + + loaded_state_dict = sharded_strategy.load(sharded_state_dict, checkpoint_dir) + + loaded_state_dict = apply_factory_merges(loaded_state_dict, sh_ten_factories) + + merge(common_state_dict, loaded_state_dict) + return common_state_dict + + +def _verify_checkpoint_and_load_strategy( + checkpoint_dir: str, sharded_strategy: Union[LoadShardedStrategy, Tuple[str, int], None] = None, +) -> LoadShardedStrategy: + """ Verifies if checkpoint metadata exists and matches given strategy. + + Args: + checkpoint_dir (str): checkpoint directory + sharded_strategy (LoadShardedStrategy, Tuple[str, int], optional): load strategy to be verified + if compatible with the checkpoint content. If None, the default load strategy + for the checkpoint backend will be returned. + """ + if not Path(checkpoint_dir).exists(): + raise CheckpointingException(f'Checkpoint directory {checkpoint_dir} does not exist') + + saved_config = maybe_load_config(checkpoint_dir) + if saved_config is None: + raise CheckpointingException(f'{checkpoint_dir} is not a distributed checkpoint') + + if sharded_strategy is None: + sharded_strategy = get_default_strategy( + StrategyAction.LOAD_SHARDED, + saved_config.sharded_backend, + saved_config.sharded_backend_version, + ) + elif isinstance(sharded_strategy, tuple): + sharded_strategy = get_default_strategy(StrategyAction.LOAD_SHARDED, *sharded_strategy) + + # TODO: implement consistency checks here + return sharded_strategy + + +# TODO: implement it as common torch strategy +def load_common_state_dict(checkpoint_dir: Path) -> StateDict: + """ Load common (non-sharded) objects state dict from the checkpoint. + + Args: + checkpoint_dir (Path): checkpoint directory + + Returns: + StateDict: state dict with non-sharded objects from the checkpoint + """ + load_path = Path(checkpoint_dir) / COMMON_STATE_FNAME + try: + return torch.load(load_path, map_location='cpu') + except FileNotFoundError as e: + err_msg = f'Common file {load_path} does not exist' + ckpt_files = [f.name for f in checkpoint_dir.iterdir()] + logger.debug(f'{err_msg}. Checkpoint directory content: {ckpt_files}') + raise CheckpointingException(err_msg) from e + + +def load_sharded_objects(sharded_state_dict: ShardedStateDict, checkpoint_dir: Path): + """ Replaces all ShardedObject from a given state dict with values loaded from the checkpoint. + + Args: + sharded_state_dict (ShardedStateDict): sharded state dict defining what objects should be loaded. + checkpoint_dir (Path): checkpoint directory + + Returns: + None: state dict is modified in place + """ + sharded_objects, sharded_state_dict = extract_matching_values( + sharded_state_dict, lambda v: isinstance(v, ShardedObject) + ) + + def load_sharded_object(sh_obj: ShardedObject): + sh_obj.data = None + load_path = (checkpoint_dir / sh_obj.unique_key).with_suffix('.pt') + try: + loaded_obj = torch.load(load_path) + except FileNotFoundError as e: + err_msg = f'Object shard {load_path} not found' + obj_subdir = checkpoint_dir / sh_obj.key + if obj_subdir.exists(): + obj_files = [f.name for f in obj_subdir.iterdir()] + logger.debug(f'{err_msg}. Object {sh_obj.key} directory content: {obj_files}') + else: + ckpt_files = [f.name for f in checkpoint_dir.iterdir()] + logger.debug( + f'{err_msg}. Object {sh_obj.key} directory does not exist. Checkpoint directory content: {ckpt_files}' + ) + raise CheckpointingException(err_msg) from e + return loaded_obj + + return dict_list_map_inplace(load_sharded_object, sharded_objects), sharded_state_dict + + +def load_tensors_metadata( + checkpoint_dir: str, sharded_strategy: Union[LoadShardedStrategy, None] = None +) -> ShardedStateDict: + """Load tensors metadata from the checkpoint. + + Returns a dictionary similar to a sharded state dict, but note that + the dictionary keys are simply ShardedTensor keys (contrary to the + actual sharded state dicts where keys correspond to state dict keys). + + Dict values are ShardedTensors without any sharding (so, the only useful + information is tensors global shape and dtype). + + Concrete implementation depends on the loading strategy. If no strategy is + given, a default for a given backend is used. + """ + sharded_strategy = _verify_checkpoint_and_load_strategy(checkpoint_dir, sharded_strategy) + return sharded_strategy.load_tensors_metadata(Path(checkpoint_dir)) + + +def load_plain_tensors(checkpoint_dir: str): + """Load checkpoint tensors without any sharding. + + NOTE: common state dict is NOT included.""" + sharded_state_dict = load_tensors_metadata(checkpoint_dir) + # Don't validate integrity because shards will be overlapped + # if world_size > 1 (all processes load whole tensors) + return load(sharded_state_dict, checkpoint_dir, validate_access_integrity=False) + + +def save( + sharded_state_dict: ShardedStateDict, + checkpoint_dir: str, + sharded_strategy: Union[SaveShardedStrategy, Tuple[str, int], None] = None, + common_strategy: Union[SaveCommonStrategy, Tuple[str, int], None] = None, + validate_access_integrity: bool = True, +) -> None: + """Saving entrypoint. + + Extracts ShardedTensors from the given state dict. Rank 0 saves the + "regular" part of the checkpoint to common torch file. + The ShardedTensors are saved according to a strategy specified by the + config. + + Steps: + 1. Apply factories + 2. Extract and discard LocalNonPersistentObject + 3. Extract all ShardedBase object + 4. Save all other objects to common.pt + 5. (optional) Extract and save ShardedObjects + 6. Save all ShardedBase objects + + Args: + sharded_state_dict (ShardedStateDict): state dict of the populated with + ShardedTensors. Used as a mapping to determine how local tensors + should be saved as global tensors in the checkpoint. + checkpoint_dir (str): directory to save the checkpoint to + sharded_strategy (SaveShardedStrategy, Tuple[str, int], optional): configures sharded tensors saving behavior and backend + common_strategy (SaveCommonStrategy, Tuple[str, int], optional): configures common data saving behavior and backend + validate_access_integrity (bool default = True): checks if each tensor shard is accessed + exactly once (as main replica) by some process + """ + checkpoint_dir = Path(checkpoint_dir) + + if torch.distributed.get_rank() == 0: + if not checkpoint_dir.exists(): + raise CheckpointingException( + f'Checkpoint destination directory does not exist: {checkpoint_dir}' + ) + + if next(checkpoint_dir.iterdir(), None) is not None: + raise CheckpointingException( + f'Checkpoint destination directory ({checkpoint_dir}) is not empty' + ) + + if common_strategy is not None: + raise NotImplementedError('The only supported common strategy is torch') + + if sharded_strategy is None: + sharded_strategy = ('zarr', 1) + if not isinstance(sharded_strategy, SaveShardedStrategy): + assert isinstance(sharded_strategy, tuple), type(sharded_strategy) + sharded_strategy = get_default_strategy(StrategyAction.SAVE_SHARDED, *sharded_strategy) + + apply_factories(sharded_state_dict) + _, sharded_state_dict = extract_nonpersistent(sharded_state_dict) + sharded_state_dict, state_dict = extract_sharded_base(sharded_state_dict) + _save_common_dict(state_dict, checkpoint_dir, True) + + if validate_access_integrity: + validate_sharding_integrity(list(nested_values(sharded_state_dict))) + + if not sharded_strategy.can_handle_sharded_objects: + # TODO: implement is a part of common strategy + sharded_state_dict = _extract_and_save_sharded_objects( + sharded_state_dict, checkpoint_dir, validate_access_integrity + ) + + sharded_strategy.save(sharded_state_dict, checkpoint_dir) + if torch.distributed.get_rank() == 0: + save_config( + CheckpointingConfig(sharded_strategy.backend, sharded_strategy.version), checkpoint_dir + ) + torch.distributed.barrier() + + +# TODO: implement it as common torch strategy +def _save_common_dict( + state_dict: StateDict, checkpoint_dir: Path, validate_consistency: bool = False +): + if torch.distributed.get_rank() == 0: + torch.save(state_dict, checkpoint_dir / COMMON_STATE_FNAME) + if validate_consistency: + # TODO: implement checking consistency with rank 0 common dict on other ranks + pass + # torch.distributed.barrier() + # if not torch.distributed.get_rank() == 0: + # rank_0_state_dict = torch.load(checkpoint_dir / COMMON_STATE_FNAME) + # print(diff(common_state_dict, rank_0_state_dict)) + + +def _extract_and_save_sharded_objects( + state_dict: StateDict, checkpoint_dir: Path, validate_consistency: bool = False +): + sharded_objects, state_dict = extract_matching_values( + state_dict, lambda v: isinstance(v, ShardedObject) + ) + sharded_objects = list(nested_values(sharded_objects)) + for sh_obj in sharded_objects: + if is_main_replica(sh_obj.replica_id): + save_path = (checkpoint_dir / sh_obj.unique_key).with_suffix('.pt') + os.makedirs(save_path.parent, exist_ok=True) + torch.save(sh_obj.data, save_path) + return state_dict + + +def validate_sharding_integrity(sharded_tensors: Iterable[ShardedTensor]): + """ Validate if the ShardedTensors from multiple processes define correct sharding of a global tensor. + + Local ShardedTensors metadata is exchanged with `torch.distributed.all_gather_object` + and then process with global rank 0 checks if main replicas of the shards: + - cover the whole global tensors + - don't overlap + + Args: + sharded_tensors (Iterable[ShardedTensor]): sharded tensors local to this process + + Returns: + None + + Raises: + CheckpointingException for invalid access pattern + """ + sharding = [ten.without_data() for ten in sharded_tensors] + all_sharding = [None] * torch.distributed.get_world_size() + torch.distributed.all_gather_object(all_sharding, sharding) + if torch.distributed.get_rank() != 0: + return + + key_shardings = defaultdict(list) + for rank, rank_shardings in enumerate(all_sharding): + for sharding in rank_shardings: + key_shardings[sharding.key].append((rank, sharding)) + for key, shardings in key_shardings.items(): + if isinstance(shardings[0][1], ShardedObject): + _validate_objects_for_key(shardings) + else: + _validate_sharding_for_key(shardings) + + +def _validate_sharding_for_key(rank_sharding: List[Tuple[int, ShardedTensor]]): + some_rank_shard = rank_sharding[0][1] + global_shape = some_rank_shard.global_shape + local_shape = some_rank_shard.local_shape + dtype = some_rank_shard.dtype + has_flattened_range = some_rank_shard.flattened_range is not None + for rank, sharding in rank_sharding: + assert sharding.dtype == dtype, (sharding.dtype, dtype, some_rank_shard) + assert sharding.global_shape == global_shape, ( + sharding.global_shape, + global_shape, + some_rank_shard, + ) + assert sharding.local_shape == local_shape, ( + sharding.local_shape, + local_shape, + some_rank_shard, + ) + assert (sharding.flattened_range is not None) == has_flattened_range, ( + (sharding.flattened_range is not None), + has_flattened_range, + some_rank_shard, + ) + + shard_access_cnt = _compute_shards_access(rank_sharding) + if has_flattened_range: + map_reduce( + rank_sharding, + lambda x: x[1].global_offset, + lambda x: x[1], + _validate_sharding_for_key_flattened, + ) + else: + if not torch.all(shard_access_cnt == 1): + logger.error(f'Invalid access pattern for {rank_sharding[0][1]}: {shard_access_cnt}') + raise CheckpointingException(f'Invalid access pattern for {rank_sharding[0][1]}') + + +def _compute_shards_access(rank_sharding): + def chunk_offset(sharding): + assert len(sharding.global_offset) == len(sharding.local_shape) + sharding.prepend_axis_num + return tuple( + chain( + (off for off in sharding.global_offset[: sharding.prepend_axis_num]), + ( + off // sh + for off, sh in zip( + sharding.global_offset[sharding.prepend_axis_num :], sharding.local_shape + ) + ), + ) + ) + + shard_access_cnt = torch.zeros( + rank_sharding[0][1].axis_fragmentations, dtype=torch.int, device='cpu' + ) + for rank, sharding in rank_sharding: + if is_main_replica(sharding.replica_id): + shard_access_cnt[chunk_offset(sharding)] += 1 + # TODO: consider validating different replicas too + return shard_access_cnt + + +def _validate_sharding_for_key_flattened(tensors_by_shard): + all_slices = [] + local_shape = tensors_by_shard[0].local_shape + for sharding in tensors_by_shard: + assert sharding.local_shape == local_shape + sharding: ShardedTensor + if not is_main_replica(sharding.replica_id): + # TODO: this checks only saving (and loading replica_id=0) consistency + continue + + all_slices.append((sharding.flattened_range.start, sharding.flattened_range.stop)) + + starts, stops = map(np.asarray, zip(*sorted(all_slices))) + if ( + starts[0] != 0 + or stops[-1] != np.product(local_shape) + or not np.all(starts[1:] == stops[:-1]) + ): + logger.error( + f'Flattened ranges dont cover the whole shard {tensors_by_shard[0]}. Ranges: {(starts, stops)}' + ) + raise CheckpointingException( + f'Flattened ranges dont cover the whole shard {tensors_by_shard[0]}' + ) + + +def _validate_objects_for_key(sharded_objects: List[ShardedObject]): + """ Ensure uniqueness of saved objects. """ + unique_keys = [ + sh_obj.unique_key for _, sh_obj in sharded_objects if is_main_replica(sh_obj.replica_id) + ] + if len(unique_keys) != len(set(unique_keys)): + duplicates = {k: cnt for k, cnt in Counter(unique_keys).items() if cnt > 1} + logger.error(f'Duplicate ShardedObject keys and counts: {duplicates}') + raise CheckpointingException(f'Duplicate ShardedObject keys: {list(duplicates.keys())}') + expected_shard_num = np.prod(sharded_objects[0][1].global_shape) + if len(unique_keys) != expected_shard_num: + err_msg = f'Invalid access pattern: {expected_shard_num - len(unique_keys)} ShardedObject are missing.' + logger.error(f'{err_msg} Existing shards: {unique_keys}') + raise CheckpointingException(err_msg) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/__init__.py new file mode 100644 index 0000000..1f03c10 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +""" Various loading and saving strategies """ diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/base.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/base.py new file mode 100644 index 0000000..3cba534 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/base.py @@ -0,0 +1,120 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +""" Strategies base interfaces. """ + +from abc import ABC, abstractmethod +from collections import defaultdict +from enum import Enum +from pathlib import Path +from typing import Dict, List, Optional + +from ..mapping import CheckpointingException, ShardedStateDict, ShardedTensor, StateDict + + +class StrategyAction(Enum): + LOAD_COMMON = 'load_common' + LOAD_SHARDED = 'load_sharded' + SAVE_COMMON = 'save_common' + SAVE_SHARDED = 'save_sharded' + + +default_strategies = defaultdict(dict) + + +def get_default_strategy(action: StrategyAction, backend: str, version: int): + """ Retrieves a default strategy for a given action, backend and version. """ + try: + if backend == 'zarr': + error_hint = ' Please install `zarr` and `tensorstore<=0.1.45` packages' + from .tensorstore import _import_trigger + from .zarr import _import_trigger + elif backend == 'torch_dist': + error_hint = ' Please use PyTorch version >=2.1' + from .torch import _import_trigger + except ImportError as e: + raise CheckpointingException( + f'Cannot import a default strategy for: {(action.value, backend, version)}. Error: {e}. Hint: {error_hint}' + ) from e + try: + return default_strategies[action.value][(backend, version)] + except KeyError as e: + raise CheckpointingException( + f'Cannot find a default strategy for: {(action.value, backend, version)}' + ) from e + + +class LoadStrategyBase(ABC): + """ Base class for a load strategy. Requires implementing checks for compatibility with a given checkpoint version. """ + + @abstractmethod + def check_backend_compatibility(self, loaded_version): + raise NotImplementedError + + @abstractmethod + def check_version_compatibility(self, loaded_version): + raise NotImplementedError + + @property + def can_handle_sharded_objects(self): + """ Returns whether or not this strategy can handle loading ShardedObjects. """ + return False + + +class SaveStrategyBase(ABC): + """ Base class for a save strategy. Requires defining a backend type and version of the saved format. """ + + def __init__(self, backend: str, version: int): + self.backend = backend + self.version = version + + @property + def can_handle_sharded_objects(self): + """ Returns whether or not this strategy can handle saving ShardedObjects. """ + return False + + +class LoadCommonStrategy(LoadStrategyBase): + """ Load strategy for common (non-sharded) objects """ + + @abstractmethod + def load(self, checkpoint_dir: Path): + raise NotImplementedError + + +class LoadShardedStrategy(LoadStrategyBase): + """ Load strategy for sharded tensors """ + + @abstractmethod + def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path): + raise NotImplementedError + + @abstractmethod + def load_tensors_metadata(self, checkpoint_dir: Path): + """Load tensors metadata from the checkpoint. + + Returns a dictionary similar to a sharded state dict, but note that + the dictionary keys are simply ShardedTensor keys (contrary to the + actual sharded state dicts where keys correspond to state dict keys). + + Dict values are ShardedTensors without any sharding (so, the only useful + information is tensors global shape and dtype). + """ + raise NotImplementedError( + f'{self.__class__.__name__} doesnt allow loading only sharded metadata' + ) + + +class SaveCommonStrategy(SaveStrategyBase): + """ Save strategy for common (non-sharded) objects """ + + @abstractmethod + def save(self, common_state_dict: StateDict, checkpoint_dir: Path): + raise NotImplementedError + + +class SaveShardedStrategy(SaveStrategyBase): + """ Save strategy for sharded tensors """ + + @abstractmethod + def save(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path): + raise NotImplementedError diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/filesystem_async.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/filesystem_async.py new file mode 100644 index 0000000..ea502f1 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/filesystem_async.py @@ -0,0 +1,288 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +""" Storage writer for PyT Distributed format allowing asynchronous save. """ + +import logging +import os +from itertools import chain +from pathlib import Path +from time import time +from typing import Callable, Dict, List, Optional, Tuple + +import psutil +import torch +from torch import multiprocessing as mp +from torch.distributed.checkpoint import FileSystemWriter +from torch.distributed.checkpoint.filesystem import DEFAULT_SUFFIX, _StoragePrefix, _write_item +from torch.distributed.checkpoint.planner import SavePlan, SavePlanner, WriteItem, WriteItemType +from torch.distributed.checkpoint.storage import WriteResult +from torch.futures import Future + +logger = logging.getLogger(__name__) + +WriteBucket = Tuple[Path, str, Tuple[list, list]] # represents writes to a single file + + +class FileSystemWriterAsync(FileSystemWriter): + """ + Async-enabled implementation of FileSystemWriter using file IO. + + This class doesn't spawn the async process itself, relies on the external async mechanism. + + Flow: + 1. Call `write_data` + 2. Externally start async process with `get_save_function_and_args` function and args + 3. The async function to call is `writer_proxy_func` which calls + `write_preloaded_data` in multiple processes + + After saving is finalized on all ranks: + 4. Call `super().finish` with the results gathered in `self.writer_result` + + Note that step (3) above can also be called synchronously. + + Currently, it's assumed that a separate writer is created for each ckpt save + (intermediate state is stored as writer attributes). + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + if not self.single_file_per_rank: + raise NotImplementedError( + 'single_file_per_rank flag not supported for FileSystemWriterAsync' + ) + + # Intermediate state between preparation and finalization + self.write_buckets: Optional[List[WriteBucket]] = None + self.write_results: Optional[Dict[int, List[WriteResult]]] = None + + def prepare_write_data(self, plan: SavePlan, planner: SavePlanner) -> None: + """ + First stage of async saving. Copy data to CPU and plan the local saving. + + Args: + plan (SavePlan): save plan generated by the PyT Distributed compatible planner + planner (SavePlanner): save planner used to resolve the bytes and tensor data + + Returns: None, but stores the save plan in `self.write_buckets` + """ + storage_plan: _StoragePrefix = plan.storage_data + start = time() + logger.debug(f"thread_count: {self.thread_count}, time: {start}") + item_buckets = _split_by_size_and_type(self.thread_count, plan.items) + logger.debug(f"bucket_prep, time: {time() - start}") + + start = time() + # move tensors from GPU to CPU before starting async writing + # We do D2H synchronously for now + file_count = 0 + + def gen_file(): + nonlocal file_count + file_name = f"{storage_plan.prefix}{file_count}{DEFAULT_SUFFIX}" + file_count += 1 + return file_name + + # Prepare bytes / tensor data in each bucket, which will be assigned to each writer process + self.write_buckets = [] + for bucket in item_buckets: + bytes_data = [ + (item, planner.resolve_data(item)) + for item in bucket + if item.type == WriteItemType.BYTE_IO + ] + tensor_data = [ + (item, planner.resolve_data(item).detach().to("cpu", non_blocking=True)) + for item in bucket + if item.type != WriteItemType.BYTE_IO + ] + if len(bytes_data) > 0 or len(tensor_data) > 0: + file_name = gen_file() + self.write_buckets.append( + (self.path / file_name, file_name, (bytes_data, tensor_data)) + ) + + # Check if there is anything to write on this rank + if len(self.write_buckets) > 0: + assert len(self.write_buckets) <= self.thread_count, ( + len(self.write_buckets), + self.thread_count, + ) + ctx = mp.get_context('fork') + self.write_results = ctx.Manager().dict() + else: + self.write_results = {} + logger.debug(f"D2H and push, time: {time() - start}") + + def get_save_function_and_args(self) -> Optional[Tuple[Callable, Tuple]]: + """ + Get function that saves the data to storage along with its arguments. + Allows the external caller to apply the save function synchronously or asynchronously. + + Returns: None (if there is nothing to write on this rank) or a tuple of: + - the function that saves the data + - arguments to that function + """ + if not self.write_buckets: + return None + return (self.write_preloaded_data_multiproc, (self.write_buckets, self.write_results)) + + @staticmethod + def write_preloaded_data_multiproc( + write_buckets: List[WriteBucket], write_results: Dict[int, List[WriteResult]] + ) -> None: + """ + Performs saving data to storage with multiple processes. + + Args: + write_buckets (List[WriteBucket]): write plan + write_results: (Dict[int, List[WriteResult]]): dict to store the write results to. + Assumes multiprocessing save, so keys are local process indices + Returns: None + """ + w_start = time() + ctx = mp.get_context('fork') + p_list = [ + ctx.Process( + target=FileSystemWriterAsync.write_preloaded_data, + args=(i, write_bucket, write_results, True), + ) + for i, write_bucket in enumerate(write_buckets) + ] + for p in p_list: + p.start() + for p in p_list: + p.join() + + w_end = time() + logger.debug( + f"{w_end}, rank: {torch.distributed.get_rank()}, write(sync,parallel): {w_end - w_start}" + ) + + @staticmethod + def write_preloaded_data( + local_proc_idx: int, + write_bucket: WriteBucket, + write_results: Dict[int, List[WriteResult]], + use_fsync: bool, + ) -> None: + """ + Performs actual data saving to storage. + + Args: + local_proc_idx (int): index of a local process that performs writing + write_bucket (WriteBucket): data to write to storage + write_results (Dict[int, List[WriteResult]]): dict to store the write results to. + Assumes multiprocessing save, so keys are local process indices + use_fsync (bool): if True, calls os.fsync at the end of saving + + Returns: None, the write result are written to the `write_results` dict + """ + mem_before = _process_memory() + + local_results = [] + file_name, storage_key, (bytes_data, tensor_data) = write_bucket + with open(file_name, "wb") as stream: + for write_item, data in bytes_data: + local_results.append(_write_item(stream, data, write_item, storage_key)) + + for write_item, tensor in tensor_data: + assert tensor.is_cpu + local_results.append(_write_item(stream, tensor, write_item, storage_key)) + + if use_fsync: + os.fsync(stream.fileno()) + write_results[local_proc_idx] = local_results + mem_after = _process_memory() + logger.debug( + f"{local_proc_idx} consumed: {mem_after - mem_before}, before: {mem_before}, after: {mem_after}" + ) + + def write_data(self, plan: SavePlan, planner: SavePlanner,) -> Future[List[WriteResult]]: + raise NotImplementedError('write_data not implemented for FileSystemWriterAsync') + + def retrieve_write_results(self) -> List[WriteResult]: + """ + Turn self.write_results into a single results lists. Includes error check. + + Returns (List[WriteResult]): the list of write results from all local processes performing the save. + + """ + assert self.write_results is not None + assert self.write_buckets is not None + if len(self.write_results) != len(self.write_buckets): + raise RuntimeError( + f'Incomplete worker results (expected {len(self.write_buckets)}, got {len(self.write_results)}.' + f' This probably indicates a worker failure.' + ) + return list(chain.from_iterable(self.write_results.values())) + + +def _split_by_size_and_type(bins: int, items: List[WriteItem]) -> List[List[WriteItem]]: + """ + Splits write items according to item size into close to uniform bins. + + Same as torch.distributed.checkpoint.filesystem._split_by_size_and_type, + but with a fixed _item_size function. + + Args: + bins (int): numbers of bins to split to + items (List[WriteItem]): list of write items + + Returns (List[List[WriteItem]]): write items split to bins + """ + if bins == 1: + return [items] + + bytes_items = [wi for wi in items if wi.type == WriteItemType.BYTE_IO] + tensor_items = [wi for wi in items if wi.type != WriteItemType.BYTE_IO] + + buckets: List[List[WriteItem]] = [[] for _ in range(bins)] + bucket_sizes = [0 for _ in range(bins)] + + tensor_items.sort(key=_item_size, reverse=True) + + # Assign bytes with a simple round-robin + for i, item in enumerate(bytes_items): + buckets[i % bins].append(item) + + # Then, assign tensors according to their sizes + for item in tensor_items: + # TODO replace with headq + idx = min(enumerate(bucket_sizes), key=lambda x: x[1])[0] + buckets[idx].append(item) + bucket_sizes[idx] += _item_size(item) + + return buckets + + +def _item_size(item: WriteItem) -> int: + """ + Calculates size (in bytes) of a single write item. + + Same as torch.distributed.checkpoint.filesystem._item_size, + but fixes computing chunk size (with item.tensor_data.chunk.sizes) + + Args: + item (WriteItem): write item to compute the size of + + Returns (int): size of an item in bytes + """ + size = 1 + assert item.tensor_data is not None + # can't use math.prod as PT needs to support older python + for s in item.tensor_data.chunk.sizes: + size *= s + + dtype = item.tensor_data.properties.dtype + return size * torch._utils._element_size(dtype) + + +def _process_memory() -> int: + """ + Get memory used by current process. + + Returns (int): memory used by current process + """ + process = psutil.Process(os.getpid()) + mem_info = process.memory_info() + return mem_info.rss diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/state_dict_saver.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/state_dict_saver.py new file mode 100644 index 0000000..db21889 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/state_dict_saver.py @@ -0,0 +1,134 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +""" State dict saver for PyT Distributed format allowing asynchronous save. """ + +from logging import getLogger +from time import time +from typing import TYPE_CHECKING, Optional, Tuple, cast + +import torch +import torch.distributed as dist +from torch.distributed.checkpoint import CheckpointException +from torch.distributed.checkpoint.default_planner import DefaultSavePlanner +from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE, Metadata +from torch.distributed.checkpoint.planner import SavePlanner +from torch.distributed.checkpoint.utils import _DistWrapper, _get_failure_dict + +if TYPE_CHECKING: + from .filesystem_async import FileSystemWriterAsync + + +logger = getLogger(__name__) + + +def save_state_dict_async_plan( + state_dict: STATE_DICT_TYPE, + storage_writer: 'FileSystemWriterAsync', + process_group: Optional[dist.ProcessGroup] = None, + coordinator_rank: int = 0, + planner: Optional[SavePlanner] = None, +) -> Tuple['FileSystemWriterAsync', Metadata, _DistWrapper]: + """ + First stage of saving a state dict to storage. + + This is an async adjustment of torch.distributed.checkpoint.state_dict_saver. + In order to support async save, saving should be split into three parts: + 1. Planning + 2. Actual saving + 3. Finalization + + Out of these, step (2) *must* happen asynchronously. + The first step is realized with this function. + + The planning part consists of several steps, described here: + https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.SavePlanner + + Args: + state_dict (STATE_DICT_TYPE): state dict to save + storage_writer (FileSystemWriterAsync): in current version only an instance of + FileSystemWriterAsync + process_group (dist.ProcessGroup, optional): process group used for save planning + coordinator_rank (int, optional): coordinator rank for planning. Defaults to 0. + planner (SavePlanner, optional): save planner for torch.distributed.checkpoint format + + Returns: Tuple of: + - storage writer (the one passed as input) + - metadata from planning + - distributed wrapper used for planning + The return value of this function should be passed as an input to + `save_state_dict_async_finalize`. + """ + rank = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0 + dist_wrapper = _DistWrapper(process_group, True, coordinator_rank) + if planner is None: + planner = DefaultSavePlanner() + assert planner is not None + + global_metadata = None + + def local_step(): + assert planner is not None + planner.set_up_planner(state_dict, dist_wrapper.is_coordinator) + storage_writer.set_up_storage_writer(dist_wrapper.is_coordinator) + local_plan = planner.create_local_plan() + local_plan = storage_writer.prepare_local_plan(local_plan) + return local_plan + + def global_step(all_local_plans): + nonlocal global_metadata + + assert planner is not None + all_local_plans, global_metadata = planner.create_global_plan(all_local_plans) + all_local_plans = storage_writer.prepare_global_plan(all_local_plans) + return all_local_plans + + # Execute local and global planning + start_plan = time() + central_plan = dist_wrapper.reduce_scatter("plan", local_step, global_step) + logger.debug(f"rank: {rank}, plan time: {time() - start_plan}") + + # Prepare async writing of tensors. + # The `storage_writer` will store the information about tensors it needs to save + start = time() + final_local_plan = planner.finish_plan(central_plan) + storage_writer.prepare_write_data(final_local_plan, planner) + end = time() + logger.debug(f"{time()} rank: {rank}, write(async) time: {end - start}") + return storage_writer, cast(Metadata, global_metadata), dist_wrapper + + +def save_state_dict_async_finalize( + storage_writer: 'FileSystemWriterAsync', global_metadata: Metadata, dist_wrapper: _DistWrapper, +) -> None: + """ + Finalization of save_state_dict_async_plan. + + The input arguments are the same as the save_state_dict_async_plan output, + the `write_results` are retrieved from the storage_writer. + + Args: + storage_writer (FileSystemWriterAsync): storage writer used for planning + global_metadata (Metadata): metadata created during planning + dist_wrapper (_DistWrapper): distributed wrapper created during planning + + Returns: None + """ + write_results = storage_writer.retrieve_write_results() + + # Gather the write results that will be saved to the metadata file. + gather_start = time() + all_results = dist_wrapper.gather_object(write_results) + gather_end = time() + logger.debug(f"{gather_end}, {torch.distributed.get_rank()}, gather: {gather_end-gather_start}") + + # Store the metadata on coordinator rank + if dist_wrapper.is_coordinator: + node_failures = _get_failure_dict(all_results) + if len(node_failures) == 0: + assert global_metadata is not None + write_start = time() + storage_writer.finish(global_metadata, all_results) + write_end = time() + logger.debug(f"{write_end}, metadata_write: {write_end - write_start}") + else: + raise CheckpointException("write", node_failures) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/tensorstore.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/tensorstore.py new file mode 100644 index 0000000..61972ec --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/tensorstore.py @@ -0,0 +1,131 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +""" Strategies using TensorStore to load and save Zarr arrays. """ + +from functools import partial +from itertools import starmap +from logging import getLogger +from pathlib import Path + +import tensorstore as ts +import torch + +from ..core import CheckpointingException +from ..dict_utils import dict_list_map_inplace +from ..mapping import ShardedStateDict, ShardedTensor +from .base import LoadShardedStrategy, StrategyAction, default_strategies +from .zarr import ( + load_zarr_based_sharded_metadata, + numpy_to_torch_dtype_dict, + postprocess_numpy_array, +) + +_import_trigger = None + +logger = getLogger(__name__) + + +class TensorStoreLoadShardedStrategy(LoadShardedStrategy): + def __init__(self, load_directly_on_device: bool = False): + super().__init__() + self.load_directly_on_device = load_directly_on_device + + def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path): + if torch.distributed.get_rank() == 0: + print(f'Loading distributed checkpoint with {self.__class__.__name__}') + if self.load_directly_on_device: + print(f'Loading distributed checkpoint directly on the GPU') + load_fn = partial( + _load_from_array, + checkpoint_dir=checkpoint_dir, + load_directly_on_device=self.load_directly_on_device, + ) + dict_list_map_inplace(load_fn, sharded_state_dict) + return sharded_state_dict + + def load_tensors_metadata(self, checkpoint_dir: Path): + def get_ts_shape_dtype(path): + arr = open_ts_array(path) + return arr.shape, arr.dtype.numpy_dtype + + return load_zarr_based_sharded_metadata(checkpoint_dir, get_ts_shape_dtype) + + def check_backend_compatibility(self, loaded_version): + pass # TODO + + def check_version_compatibility(self, loaded_version): + pass # TODO + + +def merge_global_slice_with_shape(global_slice, actual_shape, key): + def _merge_slice(dim_slice, dim_size): + if isinstance(dim_slice, slice): + assert ( + dim_slice.start < dim_size + ), f'Got empty slice for ShardedTensor {key} ({dim_slice}, {dim_size})' + if dim_slice.stop > dim_size: + dim_slice = slice(dim_slice.start, dim_size, dim_slice.step) + return dim_slice + + assert len(global_slice) == len(actual_shape), (global_slice, actual_shape, key) + return tuple(starmap(_merge_slice, zip(global_slice, actual_shape))) + + +def _load_from_array( + sharded_tensor: ShardedTensor, + checkpoint_dir: Path, + load_directly_on_device: bool = False, + apply_flattened_range: bool = True, +): + x = _load_regular_chunk(sharded_tensor, checkpoint_dir) + ten = postprocess_numpy_array(x, sharded_tensor, apply_flattened_range) + if load_directly_on_device: + sharded_tensor.data.data.copy_(ten) + return sharded_tensor.data + else: + return ten + + +def _load_regular_chunk(sharded_tensor: ShardedTensor, checkpoint_dir: Path): + assert isinstance(sharded_tensor, ShardedTensor), type(sharded_tensor) + arr = open_ts_array(checkpoint_dir / sharded_tensor.key) + if sharded_tensor.global_shape == arr.shape: + x = ( + arr[sharded_tensor.global_slice()].read().result() + ) # flattened tensors loading is delayed + elif sharded_tensor.allow_shape_mismatch: + global_slice = merge_global_slice_with_shape( + sharded_tensor.global_slice(), arr.shape, sharded_tensor.key + ) + x = arr[global_slice].read().result() # flattened tensors loading is delayed + else: + _msg = ( + f'Global shape mismatch for loaded ({arr.shape})' + f' and expected ({sharded_tensor.global_shape}) tensor' + f' for key {sharded_tensor.key}' + ) + raise CheckpointingException(_msg) + return x + + +def open_ts_array(arr_path: Path): + """Opens a Zarr file array with Tensorstore with basic setting. + + Args: + arr_path (Path): path to a Zarr (Tensorstore) array + """ + spec = {'driver': 'zarr', 'metadata_key': '.zarray', 'kvstore': {}} + spec['kvstore'] = { + 'driver': 'file', + 'path': str(arr_path), + } + try: + arr = ts.open(ts.Spec(spec), open=True).result() + except Exception as e: + raise CheckpointingException(f'Array {arr_path} could not be loaded. Error: {e}') from e + return arr + + +default_strategies[StrategyAction.LOAD_SHARDED.value][ + ('zarr', 1) +] = TensorStoreLoadShardedStrategy() diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/torch.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/torch.py new file mode 100644 index 0000000..7199b7e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/torch.py @@ -0,0 +1,525 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +""" Strategies using PyTorch distributed.checkpoint as an underlying format. """ +import dataclasses +import io +import itertools +from collections import defaultdict +from logging import getLogger +from pathlib import Path +from typing import Any, Dict, Iterable, List, Optional, Tuple, Union, cast + +import torch +from torch.distributed import checkpoint +from torch.distributed._shard.metadata import ShardMetadata +from torch.distributed._shard.sharded_tensor import Shard, ShardedTensorMetadata, TensorProperties +from torch.distributed._sharded_tensor import ShardedTensor as TorchShardedTensor +from torch.distributed.checkpoint import ( + DefaultLoadPlanner, + DefaultSavePlanner, + FileSystemReader, + LoadPlan, + SavePlan, + TensorStorageMetadata, + WriteItem, +) +from torch.distributed.checkpoint._nested_dict import FLATTEN_MAPPING, unflatten_state_dict +from torch.distributed.checkpoint._traverse import OBJ_PATH, traverse_state_dict +from torch.distributed.checkpoint.default_planner import create_default_local_save_plan +from torch.distributed.checkpoint.planner_helpers import _create_write_items + +from ..core import CheckpointingException +from ..dict_utils import nested_values +from ..mapping import ( + ShardedBase, + ShardedObject, + ShardedStateDict, + ShardedTensor, + StateDict, + is_main_replica, +) +from .base import LoadShardedStrategy, SaveShardedStrategy, StrategyAction, default_strategies +from .filesystem_async import FileSystemWriterAsync +from .state_dict_saver import save_state_dict_async_finalize, save_state_dict_async_plan + +_import_trigger = None + +logger = getLogger(__name__) + + +def flatten_state_dict( + state_dict: ShardedStateDict, +) -> Tuple[ShardedStateDict, Dict[str, OBJ_PATH]]: + """ Flattens state dict into a single level dict. + + It's a copy of torch.distributed.checkpoint._nested_dict.flatten_state_dict + which also accepts ShardedBase tensors as terminal objects + + Args: + state_dict (ShardedStateDict): state dict to be flattened + + Returns (tuple): flattened state dict and a mapping allowing to recreate the original one + + """ + flattened = {} + mappings = {} + + def flat_copy(path: OBJ_PATH, value: Any) -> None: + new_fqn = ".".join(map(str, path)) + if new_fqn in flattened: + raise ValueError(f"duplicated flatten key {new_fqn}") + flattened[new_fqn] = value + mappings[new_fqn] = path + + traverse_state_dict(state_dict, flat_copy, lambda x: isinstance(x, (torch.Tensor, ShardedBase))) + return flattened, mappings + + +def sharded_tensor_to_torch_sharded_tensor( + sh_tens: List[ShardedTensor], rank: Optional[int] = None +) -> TorchShardedTensor: + """Convert MCore ShardedTensor to PyT ShardedTensor. PyT requires information about all chunks. + + NOTE: this function assumes regular (grid) sharding of the MCore ShardedTensor. + The only local irregularities could be introduced with a `flattened_range` attribute. + + NOTE: `flattened_range` is currently supported only for 1D tensors. + + This function follows the logic of torch.distributed.fsdp._shard_utils._create_chunk_sharded_tensor. + Additionally, it saves `prepend_axis_num` (specific to MCore) as an attribute + for further restoration in `_unwrap_pyt_sharded_tensor`. + + Args: + sh_tens (List[ShardedTensor]): list of sharded tensors to convert + rank (int, optional): current process rank passed to PyT ShardedTensor. + If None, assumes rank in the default pg. + + Returns (TorchShardedTensor): PyT ShardedTensor containing all passed shards. + + """ + if rank is None: + rank = torch.distributed.get_rank() + + some_sh_ten = sh_tens[0] + has_flattened_range = some_sh_ten.flattened_range is not None + + prepend_axis_num = sh_tens[0].prepend_axis_num + # Determine local shards + if has_flattened_range: + if prepend_axis_num: + raise NotImplementedError( + '`prepend_axis_num` attribute of ShardedTensor not supported' + 'together with `flattened_range` for PyT Distributed format' + ) + for sh_ten in sh_tens: + assert sh_ten.flattened_range is not None + assert len(sh_ten.global_offset) == 1, sh_ten + + local_shards = [ + Shard.from_tensor_and_offsets( + sh_ten.data, [sh_ten.global_offset[0] + sh_ten.flattened_range.start], rank + ) + for sh_ten in sh_tens + ] + offsets_shape = some_sh_ten.local_shape # used to determine local offsets + else: + # Apply extra axes `prepend_axis_num` with a view + for sh_ten in sh_tens: + assert sh_ten.flattened_range is None, sh_ten.flattened_range + if prepend_axis_num: + sh_ten.data = sh_ten.data.view((1,) * prepend_axis_num + sh_ten.local_shape) + + local_shards = [ + Shard.from_tensor_and_offsets(sh_ten.data, list(sh_ten.global_offset), rank) + for sh_ten in sh_tens + ] + offsets_shape = some_sh_ten.data.shape # includes prepended axes + + local_global_offsets = {} + for sh_ten in sh_tens: + local_global_offsets.setdefault(sh_ten.global_offset, []).append(sh_ten) + + # Create a ShardedTensor without invoking communication. Determine global shards + shard_metadata = [] + # NOTE: here we assume a regular grid of shards + for fragment_offsets in itertools.product(*map(range, some_sh_ten.axis_fragmentations)): + offset = tuple(map(lambda x: x[0] * x[1], zip(fragment_offsets, offsets_shape))) + if offset in local_global_offsets: + # local shard + placement = f"rank:{rank}/cuda" + for sh_ten in local_global_offsets[offset]: + if has_flattened_range: + offset = (sh_ten.global_offset[0] + sh_ten.flattened_range.start,) + size = sh_ten.data.shape + shard_metadata.append(ShardMetadata(offset, size, placement)) + + else: + # for shards from other ranks we provide simplistic data - this information will be discarded + # during TorchShardedTensor._init_from_local_shards_and_global_metadata call + shard_metadata.append(ShardMetadata(offset, offsets_shape, "cuda")) + + tensor = some_sh_ten.data + sharded_tensor_metadata = ShardedTensorMetadata( + shards_metadata=shard_metadata, + size=torch.Size(some_sh_ten.global_shape), + tensor_properties=TensorProperties( + dtype=tensor.dtype, + layout=tensor.layout, + requires_grad=tensor.requires_grad, + memory_format=torch.contiguous_format, + pin_memory=tensor.is_pinned(), + ), + ) + pyt_sh_ten = TorchShardedTensor._init_from_local_shards_and_global_metadata( + local_shards, sharded_tensor_metadata=sharded_tensor_metadata, process_group=None + ) + pyt_sh_ten.prepend_axis_num = prepend_axis_num + return pyt_sh_ten + + +def mcore_to_pyt_state_dict( + state_dict: Dict[str, List[ShardedBase]], + is_loading: bool = False, + init_device: torch.device = torch.device("cpu"), +) -> Dict[str, Union[TorchShardedTensor, io.BytesIO]]: + """Turn state dict with ShardedTensors and ShardedObjects to state dict compatible with PyT Dist format. + + Operates in-place and returns the original state dict. + + Args: + state_dict (Dict[str, List[ShardedBase]]): flattened state dict, where values + are lists of either ShardedTensor or ShardedObjects. + is_loading (bool, optional): flag indicating if loading or saving. Defaults to False. + init_device (torch.device, optional): device to initialize potentially missing tensors + during loading. Defaults to 'cpu'. + + Returns (Dict[str, Union[TorchShardedTensor, io.BytesIO]]): original dictionary with values + converted either into PyT ShardedTensors or io.BytesIO. + + """ + rank = torch.distributed.get_rank() + pyt_state_dict = {} + + def _mcore_to_torch_sharded_tensor(sh_tens: List[ShardedTensor]) -> TorchShardedTensor: + """Build a PyT ShardedTensor from given shards. + + During loading: + - if data is None, initialize it with an empty tensor (will be used to copy the data into) + - if `allow_shape_mismatch` is True, the data is initialized with zeros + prior to loading (not all parts of the tensor will be read from the checkpoint) + """ + assert all(isinstance(sh_ten, ShardedTensor) for sh_ten in sh_tens), sh_tens + for sh_ten in sh_tens: + if sh_ten.data is None: + if is_loading: + sh_ten.init_data( + init_device, + init_fn=torch.zeros if sh_ten.allow_shape_mismatch else torch.empty, + ) + else: + raise CheckpointingException(f'`data` attr is None for {sh_ten}') + else: + sh_ten.data = sh_ten.data.detach() + if sh_ten.allow_shape_mismatch and is_loading: + sh_ten.data.zero_() + + torch_sh_ten = sharded_tensor_to_torch_sharded_tensor(sh_tens, rank) + torch_sh_ten.key = sh_tens[0].key + return torch_sh_ten + + def _mcore_to_torch_sharded_object(sh_objs: List[ShardedObject]) -> io.BytesIO: + """Build io.BytesIO from given sharded objects data.""" + assert all(isinstance(sh_obj, ShardedObject) for sh_obj in sh_objs), sh_objs + serialized_data = io.BytesIO() + torch.save([sh_obj.data for sh_obj in sh_objs], serialized_data) + return serialized_data + + for k, v in state_dict.items(): + if isinstance(v[0], ShardedTensor): + v = cast(List[ShardedTensor], v) + pyt_state_dict[k] = _mcore_to_torch_sharded_tensor(v) + else: + v = cast(List[ShardedObject], v) + pyt_state_dict[k] = _mcore_to_torch_sharded_object(v) + + return pyt_state_dict + + +def _unwrap_pyt_sharded_tensor(sh_ten: TorchShardedTensor) -> List[torch.Tensor]: + """ Unwrap tensor from PyT ShardedTensor instance. + + If `prepend_axis_num` was non-zero (which is specific to MCore ShardedTensor) + then the tensor has additional singleton dimensions which should be squeezed. + """ + prepend_axis_num = getattr(sh_ten, 'prepend_axis_num', 0) + if prepend_axis_num == 0: + return [sh.tensor for sh in sh_ten.local_shards()] + ret_tensors = [] + for sh in sh_ten.local_shards(): + ten = sh.tensor + for _ in range(prepend_axis_num): + ten = ten.squeeze(0) + ret_tensors.append(ten) + return ret_tensors + + +def _replace_state_dict_keys_with_sharded_keys( + sharded_state_dict: ShardedStateDict, keep_only_main_replica: bool = False +) -> Tuple[Dict[str, List[ShardedBase]], FLATTEN_MAPPING, Dict[str, List[str]]]: + """Group ShardedBase objects by keys and return mappings required for recreating the original dict. """ + flat_sd, flat_mapping = flatten_state_dict(sharded_state_dict) + rename_mapping = defaultdict(list) + new_flat_sd = defaultdict(list) + for k, sh_base in flat_sd.items(): + assert isinstance(sh_base, ShardedBase), type(sh_base) + key = sh_base.unique_key if isinstance(sh_base, ShardedObject) else sh_base.key + if is_main_replica(sh_base.replica_id) or not keep_only_main_replica: + rename_mapping[key].append(k) + new_flat_sd[key].append(sh_base) + return new_flat_sd, flat_mapping, rename_mapping + + +def _replace_sharded_keys_with_state_dict_keys( + state_dict: Dict[str, List[Union[torch.Tensor, io.BytesIO]]], + flat_mapping: FLATTEN_MAPPING, + rename_mapping: Dict[str, List[str]], +): + """ Inverse of _replace_state_dict_keys_with_sharded_keys. """ + recovered_sd = {} + for k, tensors in state_dict.items(): + assert len(tensors) == len(rename_mapping[k]) + for ten, recovered_k in zip(tensors, rename_mapping[k]): + recovered_sd[recovered_k] = ten + + return unflatten_state_dict(recovered_sd, flat_mapping) + + +def _restore_dict_types(x: Union[dict, list, Any], keys_template: Union[dict, list, Any]): + """ Recursively update `x` keys, based on `keys_template`. """ + if isinstance(keys_template, dict): + assert isinstance(x, dict), type(x) + for k, v in keys_template.items(): + if not isinstance(k, str): + assert str(k) in x, (k, x.keys) + x[k] = x.pop(str(k)) + _restore_dict_types(x[k], v) + elif isinstance(keys_template, list): + assert isinstance(x, list), type(x) + for x_val, templ_val in zip(x, keys_template): + _restore_dict_types(x_val, templ_val) + + +class MCoreSavePlanner(DefaultSavePlanner): + """Differs with the default planner by saving BytesIO objects on all ranks. + + In the integration of MCore with PyT Distributed format, BytesIO objects + come from ShardedObjects, which should be treated as separate objects on each rank + (not common on all ranks). + + Also, the objects are already packed in io.BytesIO, so no need to redo it + in transform_object. + """ + + def create_local_plan(self) -> SavePlan: + plan = create_default_local_save_plan(self.state_dict, self.is_coordinator) + self._add_non_coordinator_iobytes_request(plan) + if self.flatten_state_dict: + plan = dataclasses.replace(plan, planner_data=self.mappings) + self.plan = plan + + return self.plan + + def _add_non_coordinator_iobytes_request(self, plan): + if self.is_coordinator: + return + for fqn, obj in self.state_dict.items(): + if isinstance(obj, io.BytesIO): + plan.items.extend(_create_write_items(fqn, obj)) + + def transform_object(self, write_item: WriteItem, object: Any): + return object + + +class MCoreLoadPlanner(DefaultLoadPlanner): + """Adds global shape validation to the default planner. + + If global shape validation can be ignored (shouldn't!), the default + load planner can be used. + """ + + def __init__( + self, *args, shapes_validation_sharded_tensors: Iterable[ShardedTensor] = (), **kwargs + ) -> None: + super().__init__(*args, **kwargs) + self.shapes_validation_sharded_tensors = shapes_validation_sharded_tensors + + def _validate_global_shapes(self, metadata, sharded_tensors): + for sh_ten in sharded_tensors: + loaded_shape = metadata.state_dict_metadata[sh_ten.key].size + if loaded_shape != sh_ten.global_shape: + _msg = ( + f'Global shape mismatch for loaded ({loaded_shape})' + f' and expected ({sh_ten.global_shape}) tensor' + f' for key {sh_ten.key}' + ) + raise CheckpointingException(_msg) + + def create_local_plan(self) -> LoadPlan: + self._validate_global_shapes(self.metadata, self.shapes_validation_sharded_tensors) + return super().create_local_plan() + + +class TorchDistSaveShardedStrategy(SaveShardedStrategy): + """Basic save strategy for the PyT Distributed format. + + The idea is to translate MCore ShardedTensors into PyT ShardedTensors + and reuse the default torch.distributed.checkpoint saving mechanism. + """ + + def __init__( + self, backend: str, version: int, keep_only_main_replica: bool = True, thread_count: int = 2 + ): + """Adds parameters specific to PyT Distributed format + Args: + backend (str): format backend string + version (int): format version + keep_only_main_replica (bool, optional): PyT Distributed has a mechanism + for deduplication, but replica_id aware deduplication is more coherent. + Default is True (recommended to keep it). + thread_count (int, optional): threads to use during saving. + Affects the number of files in the checkpoint (saving ranks * num_threads). + """ + super().__init__(backend, version) + self.keep_only_main_replica = keep_only_main_replica + self.thread_count = thread_count + + # Intermediate state + self.save_state_dict_ret: Optional[Tuple[Any, ...]] = None + + def save(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path): + """ Translates MCore ShardedTensors to PyT ShardedTensors and saves in PyT Distributed format. + + Args: + sharded_state_dict (ShardedStateDict): sharded state dict to save + checkpoint_dir (Path): checkpoint directory + + Returns: None + """ + # Translate the state dict + ( + sharded_state_dict, + flat_mapping, + rename_mapping, + ) = _replace_state_dict_keys_with_sharded_keys( + sharded_state_dict, self.keep_only_main_replica + ) + pyt_state_dict = mcore_to_pyt_state_dict(sharded_state_dict, False) + + # Using async infrastructure for sync save + writer = FileSystemWriterAsync(checkpoint_dir, thread_count=self.thread_count) + self.save_state_dict_ret = save_state_dict_async_plan( + pyt_state_dict, + writer, + None, + planner=MCoreSavePlanner(dedup_replicated_tensors=not self.keep_only_main_replica), + ) + fun_args = writer.get_save_function_and_args() + if fun_args is not None: + fun, args = fun_args + fun(*args) + self._finalize_save() + + def _finalize_save(self) -> None: + """ Perform save finalization. + + Breakdown into `save` and `save_finalize` cn be useful for async saving. + """ + if self.save_state_dict_ret is None: + raise CheckpointingException('finalize_save called, but no ckpt save in progress') + + save_state_dict_async_finalize(*self.save_state_dict_ret) + self.save_state_dict_ret = None + torch.distributed.barrier() + + def can_handle_sharded_objects(self): + return True + + +class TorchDistLoadShardedStrategy(LoadShardedStrategy): + """Basic load strategy for the PyT Distributed format. """ + + def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path) -> StateDict: + """Translates MCore ShardedTensors to PyT ShardedTensors and loads from PyT Distributed format. + + Args: + sharded_state_dict (ShardedStateDict): sharded state dict with mapping + information to instruct loading + checkpoint_dir (Path): checkpoint directory + + Returns: loaded state dict + """ + flexible_shape_sharded_tensors = [ + sh_ten + for sh_ten in nested_values(sharded_state_dict) + if isinstance(sh_ten, ShardedTensor) and not sh_ten.allow_shape_mismatch + ] + + orig_sharded_state_dict = sharded_state_dict + # MCore state dict to PyT Distributed compatible + ( + sharded_state_dict, + flat_mapping, + rename_mapping, + ) = _replace_state_dict_keys_with_sharded_keys(sharded_state_dict) + pyt_state_dict = mcore_to_pyt_state_dict(sharded_state_dict, True) + # Load PyT Distributed format + checkpoint.load_state_dict( + pyt_state_dict, + FileSystemReader(checkpoint_dir), + planner=MCoreLoadPlanner( + shapes_validation_sharded_tensors=flexible_shape_sharded_tensors + ), + ) + pyt_state_dict = cast( + Dict[str, Union[TorchShardedTensor, List[io.BytesIO]]], pyt_state_dict + ) + # Unwrap ShardedTensors and return to original state dict + mcore_state_dict = { + k: v if not isinstance(v, TorchShardedTensor) else _unwrap_pyt_sharded_tensor(v) + for k, v in pyt_state_dict.items() + } + mcore_state_dict = _replace_sharded_keys_with_state_dict_keys( + mcore_state_dict, flat_mapping, rename_mapping + ) + _restore_dict_types(mcore_state_dict, orig_sharded_state_dict) + return mcore_state_dict + + def load_tensors_metadata(self, checkpoint_dir: Path): + """Uses tensors metadata stored in the metadata file.""" + fs_reader = FileSystemReader(checkpoint_dir) + metadata = fs_reader.read_metadata() + + return { + k: ShardedTensor.from_rank_offsets( + k, torch.empty(tp.size, **tp.properties.__dict__, device='meta') + ).without_data() + for k, tp in metadata.state_dict_metadata.items() + if isinstance(tp, TensorStorageMetadata) + } + + def can_handle_sharded_objects(self): + return True + + def check_backend_compatibility(self, loaded_version): + pass # TODO + + def check_version_compatibility(self, loaded_version): + pass # TODO + + +default_strategies[StrategyAction.LOAD_SHARDED.value][ + ('torch_dist', 1) +] = TorchDistLoadShardedStrategy() +default_strategies[StrategyAction.SAVE_SHARDED.value][ + ('torch_dist', 1) +] = TorchDistSaveShardedStrategy('torch_dist', 1) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/two_stage.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/two_stage.py new file mode 100644 index 0000000..8d20c32 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/two_stage.py @@ -0,0 +1,257 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +""" 2-stage checkpoint loading. """ +import os +import time +from collections import defaultdict +from dataclasses import dataclass +from functools import partial, wraps +from itertools import chain +from logging import DEBUG, INFO, StreamHandler, getLogger +from operator import attrgetter, itemgetter +from pathlib import Path +from typing import Iterable, List, NamedTuple, Optional, Tuple, Union + +import torch + +from ..dict_utils import dict_list_map_inplace, map_reduce, nested_values +from ..mapping import ShardedStateDict, ShardedTensor, StateDict +from .base import LoadShardedStrategy +from .tensorstore import TensorStoreLoadShardedStrategy, _load_from_array, open_ts_array +from .zarr import flatten_range, load_zarr_based_sharded_metadata + +_import_trigger = None + + +timers = defaultdict(list) + +logger = getLogger(__name__) + + +def timed(verbose=True): + def timed_dec(fn): + name = fn.__name__ + + @wraps(fn) + def wrapped(*args, **kwargs): + if verbose: + logger.debug(f'{name} init') + start = time.time() + ret = fn(*args, **kwargs) + took = time.time() - start + if verbose: + logger.debug(f'{name} took {took}s') + timers[name].append(took) + return ret + + return wrapped + + return timed_dec + + +@dataclass +class _ShardedTensorMetadata: + global_rank: int + sharded_tensor_no_data: ShardedTensor + dist_group_rank: Tuple[int] # id of distributed group + dist_group_ranks: Tuple[int] # id of distributed group + data_size: Optional[int] = None # bytes + + +def sharded_tensor_chunk_id(sharded_tensor: ShardedTensor): + return ( + sharded_tensor.key, + sharded_tensor.global_offset, + ) + + +class TwoStageDataParallelLoadShardedStrategy(LoadShardedStrategy): + """Loads one checkpoint replica from storage and broadcasts to other nodes. + + This strategy loads checkpoint from storage on minimal set of nodes + and distributes the checkpoint to other nodes with torch.distributed. + Loading is performed with tensorstore. + + Steps: + 0. (optional) create Gloo distributed groups + 1. Exchange ShardedTensors metadata between all nodes + 2. Align needed tensors within DP groups + 3. For each globally unique tensor: + 3.a) on one of the ranks load it from storage to CPU and move to CUDA + 3.b) allocate CUDA tensor on other ranks + 3.c) broadcast within DP group + 3.d) copy tensor content to the model param location + 3.e) free tensor buffers from a) and b) + + Notes: + 1. Loading and broadcasting is done sequentially to avoid both host and device OOMs + 2. There is a lot of overlap potential between all three steps done for each tensor: + 2.a) loading from storage to numpy + 2.b) moving CPU tensors to CUDA + 2.c) broadcast + """ + + def __init__(self, data_parallel_group, cpu_transfer=True): + super().__init__() + + self.cpu_transfer = cpu_transfer + self.data_parallel_group_orig = data_parallel_group + self.data_parallel_group = None if cpu_transfer else data_parallel_group + self.dp_group_ranks = tuple( + sorted(torch.distributed.get_process_group_ranks(data_parallel_group)) + ) + self.dp_group_rank = torch.distributed.get_rank(self.data_parallel_group_orig) + self.global_rank = torch.distributed.get_rank() + + def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path): + self.maybe_init_gloo_group() + all_tensors_sorted = self._build_load_plan(sharded_state_dict) + self._exchange_loaded_tensors(all_tensors_sorted, sharded_state_dict, checkpoint_dir) + # TODO: fix hang in summarize_load_times + # self.summarize_load_times() + return sharded_state_dict + + def summarize_load_times(self): + torch.distributed.barrier() + logger.info('Checkpoint loading finished. Summary:') + # TODO: `timers` keys are not guaranteed to be the same across ranks which causes hangs + for key, times in sorted(timers.items()): + times_sum = sum(times) + max_times = torch.tensor([times_sum], device='cuda') + avg_times = torch.tensor([times_sum], device='cuda') + torch.distributed.all_reduce(max_times, op=torch.distributed.ReduceOp.MAX) + torch.distributed.all_reduce(avg_times, op=torch.distributed.ReduceOp.SUM) + avg_times /= torch.distributed.get_world_size() + if torch.distributed.get_rank() == 0: + logger.info(f'{key}: max {max_times[0]}, avg {avg_times[0]}') + + @timed(verbose=False) + def load_tensor_from_storage(self, checkpoint_dir, ten_meta: _ShardedTensorMetadata): + logger.debug(f'_load_from_array({ten_meta.sharded_tensor_no_data.key}) init') + ret = _load_from_array( + ten_meta.sharded_tensor_no_data, + checkpoint_dir, + load_directly_on_device=False, + apply_flattened_range=False, + ) + logger.debug(f'_load_from_array({ten_meta.sharded_tensor_no_data.key}) DONE') + return ret + + @timed() + def maybe_init_gloo_group(self): + if not self.cpu_transfer: + return + all_groups = [None] * torch.distributed.get_world_size() + torch.distributed.all_gather_object(all_groups, self.dp_group_ranks) + all_groups = set(tuple(sorted(gr)) for gr in all_groups) + for group_ranks in sorted(all_groups): + gloo_pg = torch.distributed.new_group(ranks=group_ranks, backend='gloo') + if self.global_rank in group_ranks: + self.data_parallel_group = gloo_pg + assert self.dp_group_rank == torch.distributed.get_rank(self.data_parallel_group) + + def check_backend_compatibility(self, loaded_version): + pass # TODO + + def check_version_compatibility(self, loaded_version): + pass # TODO + + @timed() + def _build_load_plan( + self, sharded_state_dict: ShardedStateDict + ) -> List[_ShardedTensorMetadata]: + local_meta = [ + _ShardedTensorMetadata( + self.global_rank, + sharded_ten.without_data(), + self.dp_group_rank, + self.dp_group_ranks, + ) + for sharded_ten in nested_values(sharded_state_dict) + ] + all_meta = [None] * torch.distributed.get_world_size(group=self.data_parallel_group) + torch.distributed.all_gather_object(all_meta, local_meta, group=self.data_parallel_group) + all_meta = list(chain.from_iterable(all_meta)) + all_tensors_sorted = self.deduplicate_chunks(all_meta) + return all_tensors_sorted + + @timed() + def deduplicate_chunks(self, ten_metas: List[_ShardedTensorMetadata]): + """ Group tensors by chunk and then pick the tensor with the lowest rank. + + NOTE: with proper loading overlap, loading from randomized ranks + (instead of the smallest one) could be beneficial here. + """ + ten_metas = map_reduce( + ten_metas, + key_fn=lambda meta: sharded_tensor_chunk_id(meta.sharded_tensor_no_data), + reduce_fn=partial(min, key=attrgetter('dist_group_rank')), + ) + all_metas_sorted = list(map(itemgetter(1), sorted(ten_metas.items()))) + return all_metas_sorted + + @timed() + def _exchange_loaded_tensors( + self, ten_metas: List[_ShardedTensorMetadata], sharded_state_dict, checkpoint_dir + ): + logger.debug(f'_exchange_loaded_tensors, num ten_metas: {len(ten_metas)}') + for ten_meta in ten_metas: + + src_rank = torch.distributed.get_global_rank( + self.data_parallel_group, ten_meta.dist_group_rank + ) + + if self.dp_group_rank == ten_meta.dist_group_rank: + exchange_tensor = self.load_tensor_from_storage(checkpoint_dir, ten_meta) + if not self.cpu_transfer: + exchange_tensor = exchange_tensor.cuda() + else: + # TODO: for non-flattened ranges we could reuse the buffer from the start here + exchange_tensor = torch.empty( + ten_meta.sharded_tensor_no_data.local_shape, + device='cpu' if self.cpu_transfer else 'cuda', + dtype=ten_meta.sharded_tensor_no_data.dtype, + ) + + logger.debug( + f'exchange {ten_meta.sharded_tensor_no_data.key}, {exchange_tensor.shape}({exchange_tensor.numel()}), broadcast({src_rank} -> {self.dp_group_ranks})' + ) + torch.distributed.broadcast( + exchange_tensor, group=self.data_parallel_group, src=src_rank + ) + self._distribute_data_to_state_dict(ten_meta, exchange_tensor, sharded_state_dict) + logger.debug(f'exchange {ten_meta.sharded_tensor_no_data.key} done') + + # free buffer memory + exchange_tensor = None + + @timed(verbose=False) + def _distribute_data_to_state_dict( + self, + ten_meta: _ShardedTensorMetadata, + loaded_ten: torch.Tensor, + sharded_state_dict: ShardedStateDict, + ): + tensor_key = sharded_tensor_chunk_id(ten_meta.sharded_tensor_no_data) + + def _fill_in_data(t: Union[ShardedTensor, torch.Tensor]): + if not isinstance(t, ShardedTensor) or sharded_tensor_chunk_id(t) != tensor_key: + # already filled-in or key not matching + return t + sharded_tensor: ShardedTensor = t + x = loaded_ten + if sharded_tensor.flattened_range is not None: + x = flatten_range(sharded_tensor, x) + + # Reuse existing buffer + sharded_tensor.data.data.copy_(x) + return sharded_tensor.data + + dict_list_map_inplace(_fill_in_data, sharded_state_dict) + + def load_tensors_metadata(self, checkpoint_dir: Path): + def get_ts_shape_dtype(path): + arr = open_ts_array(path) + return arr.shape, arr.dtype.numpy_dtype + + return load_zarr_based_sharded_metadata(checkpoint_dir, get_ts_shape_dtype) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/zarr.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/zarr.py new file mode 100644 index 0000000..666e9f5 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/strategies/zarr.py @@ -0,0 +1,300 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +""" Strategies using Zarr as an underlying format. """ +import logging +import os +import threading +from functools import partial +from logging import getLogger +from pathlib import Path +from typing import Callable, List, Optional, Tuple + +import numpy as np +import torch +import zarr + +from ..core import CheckpointingException +from ..dict_utils import dict_list_map_inplace, nested_values +from ..mapping import ShardedStateDict, ShardedTensor, is_main_replica +from .base import LoadShardedStrategy, SaveShardedStrategy, StrategyAction, default_strategies + +logger = logging.getLogger(__name__) + +numpy_to_torch_dtype_dict = { + np.dtype('bool'): torch.bool, + np.dtype('uint8'): torch.uint8, + np.dtype('int8'): torch.int8, + np.dtype('int16'): torch.int16, + np.dtype('int32'): torch.int32, + np.dtype('int64'): torch.int64, + np.dtype('float16'): torch.float16, + np.dtype('float32'): torch.float32, + np.dtype('float64'): torch.float64, + np.dtype('complex64'): torch.complex64, + np.dtype('complex128'): torch.complex128, +} + +torch_to_numpy_dtype_dict = {v: k for k, v in numpy_to_torch_dtype_dict.items()} + + +try: + import tensorstore + + HAS_BFLOAT16 = True + numpy_to_torch_dtype_dict[np.dtype('bfloat16')] = torch.bfloat16 + torch_to_numpy_dtype_dict[torch.bfloat16] = np.dtype('bfloat16') +except ImportError: + HAS_BFLOAT16 = False + +_import_trigger = None + +logger = getLogger(__name__) + + +class ZarrSaveShardedStrategy(SaveShardedStrategy): + def save(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path): + sharded_tensors = list(nested_values(sharded_state_dict)) + arrays = _create_or_open_zarr_arrays(sharded_tensors, checkpoint_dir) + for ten, arr in zip(sharded_tensors, arrays): + _save_to_existing_array(ten, arr) + torch.distributed.barrier() + + +def _create_or_open_zarr_arrays( + sharded_tensors: List[ShardedTensor], checkpoint_dir: Path +) -> List[Optional[zarr.Array]]: + """ Returns list of zarr arrays corresponding to given tensors. + + For a sharded tensors that: + a) is main replica and represents the first chunk (all offsets 0), creates the Zarr array + b) is main replica but not the first chunk, opens the arrays created in (a) (possibly by other process) + c) otherwise, sets the corresponding array to None since it won't be used + + Args: + sharded_tensors (List[ShardedTensor]): sharded tensors from a given rank that will be saved to checkpoint + checkpoint_dir (Path): checkpoint in which the arrays will be created + """ + arrays = [] + for ten in sharded_tensors: + arr = _create_zarr_array(ten, checkpoint_dir) if _should_create_array(ten) else None + arrays.append(arr) + + torch.distributed.barrier() + # Open arrays created above by other processes + for arr_idx, ten in enumerate(sharded_tensors): + if arrays[arr_idx] is not None: + # array created by this process + assert _should_create_array(ten), ten + continue + if not is_main_replica(ten.replica_id): + # this array won't be needed for saving and can stay None + continue + open_kwargs = {} + if ten.flattened_range is not None: + open_kwargs['synchronizer'] = zarr.ProcessSynchronizer( + str(checkpoint_dir / f'{ten.key}.sync') + ) + arrays[arr_idx] = _open_zarr_array_verbose(checkpoint_dir / ten.key, 'r+', **open_kwargs) + return arrays + + +def _should_create_array(ten: ShardedTensor): + return ( + is_main_replica(ten.replica_id) + and set(ten.global_offset) == {0} + and (ten.flattened_range is None or ten.flattened_range.start == 0) + ) + + +def _save_to_existing_array(sharded_tensor: ShardedTensor, arr: Optional[zarr.Array]): + if not is_main_replica(sharded_tensor.replica_id): + return + assert arr is not None + x = sharded_tensor.data + x = x.detach().cpu() + torch.cuda.synchronize() + if x.dtype == torch.bfloat16: + x = x.float() + x = x.numpy() + x = x.astype('bfloat16') + else: + x = x.numpy() + + if sharded_tensor.flattened_range is None: + arr[sharded_tensor.global_slice()] = x + else: + arr.set_coordinate_selection(sharded_tensor.global_coordinates(), x) + + +def _create_zarr_array(sharded_tensor: ShardedTensor, checkpoint_dir: Path): + np_dtype = torch_to_numpy_dtype_dict[sharded_tensor.dtype] + try: + arr = zarr.create( + sharded_tensor.global_shape, + dtype=np_dtype, + store=checkpoint_dir / sharded_tensor.key, + chunks=sharded_tensor.max_allowed_chunks(), + compressor=None, + fill_value=None, + write_empty_chunks=True, + ) + logger.debug(f'Created a new Zarr array at {checkpoint_dir / sharded_tensor.key}') + except zarr.errors.ContainsArrayError as e: + raise CheckpointingException( + f'Array {checkpoint_dir / sharded_tensor.key} already exists' + ) from e + + if HAS_BFLOAT16 and np_dtype == np.dtype('bfloat16'): + arr._dtype = np_dtype + zarray = arr.store['.zarray'] + arr.store['.zarray'] = zarray.replace(b' exp_sh: + assert ( + False + ), f'Expected shape ({exp_sh}) smaller than actual ({x_sh}) for {repr(expected_sharded_ten)}' + else: + pad_args.extend((0, exp_sh - x_sh)) + # TODO: behavior control with envvar is for testing purposes only, remove it + if not int(os.environ.get('DIST_CKPT_PAD_REPLICATE', 0)): + return torch.nn.functional.pad(x, pad_args) + + # unsqueeze and squeeze to get shapes supported by cudnn + print(f'Replicating last row for {expected_sharded_ten.key}') + if x.dtype == torch.bfloat16: + return ( + torch.nn.functional.pad(x.float().unsqueeze(0), pad_args, mode='replicate') + .squeeze(0) + .bfloat16() + ) + return torch.nn.functional.pad(x.unsqueeze(0), pad_args, mode='replicate').squeeze(0) + + +def load_zarr_based_sharded_metadata( + checkpoint_dir: Path, get_shape_dtype_fn: Callable[[str], Tuple[Tuple[int], np.dtype]] +) -> ShardedStateDict: + """Load metadata of Zarr arrays. + + Args: + checkpoint_dir (str): checkpoint root directory + get_shape_dtype_fn (str -> ((int, ...), np.dtype)): a function returning + an array shape and dtype for a given Zarr array path + """ + sharded_state_dict = {} + for subdir in checkpoint_dir.iterdir(): + if not subdir.is_dir() or not (subdir / '.zarray').exists(): + continue + key = subdir.name + arr_shape, arr_dtype = get_shape_dtype_fn(str(subdir)) + + sharded_state_dict[key] = ShardedTensor( + key, + None, + numpy_to_torch_dtype_dict[arr_dtype], + arr_shape, + arr_shape, + tuple(0 for _ in arr_shape), + tuple(1 for _ in arr_shape), + ) + return sharded_state_dict + + +# default_strategies[StrategyAction.LOAD_SHARDED.value][('zarr', 1)] = ZarrLoadShardedStrategy() +default_strategies[StrategyAction.SAVE_SHARDED.value][('zarr', 1)] = ZarrSaveShardedStrategy( + 'zarr', 1 +) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/utils.py new file mode 100644 index 0000000..07062af --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/dist_checkpointing/utils.py @@ -0,0 +1,154 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved. + +""" Helpers for manipulating sharded tensors and sharded state dicts. """ + +from typing import Dict, Tuple + +from .dict_utils import dict_list_map_inplace, extract_matching_values +from .mapping import ( + LocalNonpersitentObject, + ShardedBase, + ShardedObject, + ShardedStateDict, + ShardedTensor, + ShardedTensorFactory, + StateDict, +) + + +def extract_sharded_tensors( + sharded_state_dict: ShardedStateDict, +) -> Tuple[ShardedStateDict, StateDict]: + """ Extract a dict consisting of only ShardedTensor objects from a given state dict with any objects. + + Args: + sharded_state_dict: state dict possibly containing ShardedTensor objects + + Returns: + Tuple[ShardedStateDict, StateDict]: tuple of: + - state dict with all ShardedTensor (keeping the original state dict structure) + - state dict with all objects other than ShardedTensor (keeping the original state dict structure) + """ + return extract_matching_values(sharded_state_dict, lambda v: isinstance(v, ShardedTensor)) + + +def extract_sharded_tensors_and_factories( + sharded_state_dict: ShardedStateDict, +) -> Tuple[ShardedStateDict, StateDict]: + """ Extract a dict consisting of only ShardedTensor and ShardedTensorFactory objects from a given state dict with any objects. + + Args: + sharded_state_dict: state dict possibly containing ShardedTensor and ShardedTensorFactory objects + + Returns: + Tuple[ShardedStateDict, StateDict]: tuple of: + - state dict with all ShardedTensor and ShardedTensorFactory (keeping the original state dict structure) + - state dict with all other objects (keeping the original state dict structure) + """ + return extract_matching_values( + sharded_state_dict, lambda v: isinstance(v, (ShardedTensor, ShardedTensorFactory)) + ) + + +def extract_sharded_tensors_or_nonpersistent( + sharded_state_dict: ShardedStateDict, +) -> Tuple[ShardedStateDict, StateDict]: + """ Extract a dict consisting of only ShardedTensor, ShardedTensorFactory and LocalNonpersitentObject + objects from a given state dict with any objects. + + Args: + sharded_state_dict: state dict possibly containing ShardedTensor, ShardedTensorFactory and LocalNonpersitentObject objects + + Returns: + Tuple[ShardedStateDict, StateDict]: tuple of: + - state dict with all ShardedTensor, ShardedTensorFactory and LocalNonpersitentObject (keeping the original state dict structure) + - state dict with all other objects (keeping the original state dict structure) + """ + return extract_matching_values( + sharded_state_dict, + lambda v: isinstance(v, (ShardedTensor, LocalNonpersitentObject, ShardedTensorFactory)), + ) + + +def extract_sharded_base( + sharded_state_dict: ShardedStateDict, +) -> Tuple[ShardedStateDict, StateDict]: + return extract_matching_values(sharded_state_dict, lambda v: isinstance(v, ShardedBase),) + + +def extract_nonpersistent( + sharded_state_dict: ShardedStateDict, +) -> Tuple[ShardedStateDict, StateDict]: + return extract_matching_values( + sharded_state_dict, lambda v: isinstance(v, LocalNonpersitentObject), + ) + + +def add_prefix_for_sharding(sharded_state_dict: ShardedStateDict, prefix: str): + """ Prepend a given prefix to all ShardedBase objects in a given state dict *in-place*. + + Args: + sharded_state_dict (ShardedStateDict): sharded state dict + prefix (str): prefix to be prepended + + Returns: + None: state dict is modified in-place + """ + + def add_prefix(t): + if isinstance(t, ShardedBase): + t.key = f'{prefix}{t.key}' + return t + + dict_list_map_inplace(add_prefix, sharded_state_dict) + + +def replace_prefix_for_sharding( + sharded_state_dict: ShardedStateDict, old_prefix: str, new_prefix: str +): + """ Replaces the given prefix in *all* sharded keys in a given state dict. + + Errors out if some key does not begin with a given prefix. + + Args: + sharded_state_dict (ShardedStateDict): sharded state dict to replace keys in + old_prefix (str): prefix to be replaced in each key + new_prefix (str): new prefix + + Returns: + None: state dict is modified in place + """ + + def _replace_prefix(x): + if isinstance(x, (ShardedTensor, ShardedTensorFactory, ShardedObject)): + if not x.key.startswith(old_prefix): + raise ValueError(f'Expected {x.key} to begin with prefix {old_prefix}') + x.key = f'{new_prefix}{x.key[len(old_prefix):]}' # str.removeprefix in Python >= 3.9 + return x + + dict_list_map_inplace(_replace_prefix, sharded_state_dict) + + +def apply_prefix_mapping(sharded_state_dict: ShardedStateDict, prefix_map: Dict[str, str]): + """ Replaces prefixes *only in keys matching* with one of prefixes in the map. + + Args: + sharded_state_dict (ShardedStateDict): sharded state dict to replace keys in + prefix_map (Dict[str, str]): map of old->new prefixes. The first matching prefix for each key is used + + Returns: + None: state dict is modified in place + """ + + def _replace_prefixes(x): + if not isinstance(x, (ShardedTensor, ShardedTensorFactory, ShardedObject)): + return x + for old_prefix, new_prefix in prefix_map.items(): + if x.key.startswith(old_prefix): + x.key = ( + f'{new_prefix}{x.key[len(old_prefix):]}' # str.removeprefix in Python >= 3.9 + ) + break + return x + + dict_list_map_inplace(_replace_prefixes, sharded_state_dict) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/__init__.py new file mode 100644 index 0000000..b375e37 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from .distributed_data_parallel import DistributedDataParallel +from .distributed_data_parallel_config import DistributedDataParallelConfig +from .finalize_model_grads import finalize_model_grads +from .param_and_grad_buffer import ParamAndGradBuffer, shard_buffer diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/distributed_data_parallel.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/distributed_data_parallel.py new file mode 100644 index 0000000..e600b14 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/distributed_data_parallel.py @@ -0,0 +1,299 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from contextlib import contextmanager +from logging import getLogger +from typing import Dict, Optional + +import torch + +from .. import parallel_state +from ..transformer.module import MegatronModule +from ..transformer.transformer_config import TransformerConfig +from .distributed_data_parallel_config import DistributedDataParallelConfig +from .param_and_grad_buffer import ParamAndGradBuffer + +logger = getLogger(__name__) + + +class DistributedDataParallel(MegatronModule): + """ + DDP wrapper which stores grads in contiguous buffers. Also has option of overlapping + communication with backprop computation by breaking up full model's gradients into smaller + buckets and running all-reduce / reduce-scatter on each bucket asynchronously. This class + also provides the option to do the gradient accumulation in a type other than the param type + (e.g., fp32 for a bf16 model). + + Args: + config: Transformer config object. + ddp_config: DistributedDataParallel config object. + module: Underlying model. + data_parallel_group: Data-parallel process group. + expert_data_parallel_group: Optional data-parallel process group for experts in a MoE. + disable_bucketing: If true, force assign all parameters to a single bucket. If false, + use standard bucketing policy: assign parameters to smaller buckets and all-reduce + per bucket _if_ overlap_grad_reduce is True and pp_rank is 0. + check_for_nan_in_grad: If true, check if local grad norm is NaN. + + """ + + def __init__( + self, + config: TransformerConfig, + ddp_config: DistributedDataParallelConfig, + module: torch.nn.Module, + data_parallel_group: torch.distributed.ProcessGroup, + expert_data_parallel_group: Optional[torch.distributed.ProcessGroup] = None, + disable_bucketing: bool = False, + ): + super().__init__(config=config) + self.module = module + + # If bucket_size is not provided as an input, use sane default. + # If using very large dp_sizes, make buckets larger to ensure that chunks used in NCCL + # ring-reduce implementations are large enough to remain bandwidth-bound rather than + # latency-bound. + if ddp_config.bucket_size is None: + dp_size = parallel_state.get_data_parallel_world_size() + ddp_config.bucket_size = max(40000000, 1000000 * dp_size) + # Set bucket_size to infinity if overlap_grad_reduce is False. + if not ddp_config.overlap_grad_reduce: + ddp_config.bucket_size = None + + self.ddp_config = ddp_config + if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0: + logger.info( + f'Setting up DistributedDataParallel with {type(self.ddp_config).__name__}: {self.ddp_config}' + ) + + # Turn off bucketing if we are on a pipeline stage that is not the first (since + # data-parallel communication on these stages is not on the critical path), or if + # disable_bucketing is True (e.g., we might not want to break up model parameters + # into buckets for model chunks after the first in the interleaved schedule). + self.bucket_size = self.ddp_config.bucket_size + if parallel_state.get_pipeline_model_parallel_rank() > 0: + self.bucket_size = None + if disable_bucketing: + self.bucket_size = None + + self.module = module + self.param_to_buffer = {} + + # Group parameters by their gradient type. + param_to_name = {} + dense_params = [] + expert_parallel_params = [] + for name, param in self.module.named_parameters(): + if not param.requires_grad: + continue + + param.grad_added_to_main_grad = False + param_to_name[param] = name + + if getattr(param, 'allreduce', True): + dense_params.append(param) + else: + expert_parallel_params.append(param) + + def allocate_buffers_for_parameters( + input_params, data_parallel_group, gradient_scaling_factor=1.0, + ): + param_and_grad_dtype_to_params = {} + + # Group parameters by their gradient type. + for param in input_params: + if not param.requires_grad: + continue + + param_dtype = param.dtype + grad_dtype = torch.float if self.ddp_config.grad_reduce_in_fp32 else param.dtype + + params = param_and_grad_dtype_to_params.get((param_dtype, grad_dtype), []) + params.append(param) + param_and_grad_dtype_to_params[(param_dtype, grad_dtype)] = params + + # Allocate the grad buffers and map the grads. + buffers = [] + for (param_dtype, grad_dtype), params in param_and_grad_dtype_to_params.items(): + buffers.append( + ParamAndGradBuffer( + self.ddp_config, + param_dtype, + grad_dtype, + params, + data_parallel_group, + self.bucket_size, + param_to_name, + gradient_scaling_factor, + ) + ) + for param in params: + self.param_to_buffer[param] = buffers[-1] + + return buffers + + data_parallel_world_size = torch.distributed.get_world_size(data_parallel_group) + + # Allocate the param+grad buffers for dense params' grads. + self.buffers = allocate_buffers_for_parameters( + dense_params, + data_parallel_group, + gradient_scaling_factor=1.0 / data_parallel_world_size, + ) + + # Allocate separate param+grad buffers for expert parallel params' grads. + self.expert_parallel_buffers = allocate_buffers_for_parameters( + expert_parallel_params, + expert_data_parallel_group, + gradient_scaling_factor=1.0 / data_parallel_world_size, + ) + + # Delete references to weight_tensor if they exist since we don't want two parameter copies + # if we re-mapped parameters (which happens when we use the distributed optimizer). + # This is a temporary workaround around a TE bug that is fixed with + # https://github.com/NVIDIA/TransformerEngine/pull/719. + if self.ddp_config.use_distributed_optimizer: + + @torch.no_grad() + def unmap_weight_tensor(m): + if hasattr(m, 'weight_tensor'): + m.weight_tensor = None + + self.module.apply(unmap_weight_tensor) + + # Register backward hook. + # Accumulation function for the gradients need to be stored so they + # don't go out of scope. + self.grad_accs = [] + for param in self.module.parameters(): + if param.requires_grad: + # Expand so we get access to grad_fn. + param_tmp = param.expand_as(param) + # Get the gradient accumulator function. + grad_acc = param_tmp.grad_fn.next_functions[0][0] + grad_acc.register_hook(self._make_param_hook(param, self.param_to_buffer)) + self.grad_accs.append(grad_acc) + + def forward(self, *inputs, **kwargs): + """ + Calls the wrapped module's forward() method. + """ + return self.module(*inputs, **kwargs) + + def _make_param_hook( + self, + param: torch.nn.Parameter, + param_to_buffer: Dict[torch.nn.Parameter, ParamAndGradBuffer], + ): + """ + Creates the all-reduce / reduce-scatter hook for backprop. + """ + + def param_hook(*unused): + if param.requires_grad: + if self.ddp_config.overlap_grad_reduce: + assert ( + param.grad is not None + ), 'param.grad being None is not safe when overlap_grad_reduce is True' + if param.grad is not None and ( + not param.grad_added_to_main_grad or getattr(param, 'zero_out_wgrad', False) + ): + param.main_grad.add_(param.grad.data) + param.grad = None + + if self.ddp_config.overlap_grad_reduce: + param_to_buffer[param].register_grad_ready(param) + + return param_hook + + @contextmanager + def no_sync(self): + """ + Context manager that turns off gradient synchronization. + """ + for buffer in self.buffers + self.expert_parallel_buffers: + buffer.is_last_microbatch = False + try: + yield + finally: + for buffer in self.buffers + self.expert_parallel_buffers: + buffer.is_last_microbatch = True + + def start_grad_sync(self, *unused): + """ + Initiates grad sync (all-reduce or reduce-scatter) communication operations + for all model gradients. + + When overlap_grad_reduce is set to True, dispatches asynchronous communication + calls. When overlap_grad_reduce is set to False, calls synchronous + communication ops. + """ + for buffer in self.buffers + self.expert_parallel_buffers: + buffer.start_grad_sync() + + def finish_grad_sync(self): + """ + Finishes grad sync (all-reduce or reduce-scatter) communication operations + for all model gradients. + + When overlap_grad_reduce is set to True, waits for asynchronous communication + calls to complete. When overlap_grad_reduce is set to False, calls synchronous + communication ops. + """ + for buffer in self.buffers + self.expert_parallel_buffers: + buffer.finish_grad_sync() + + def zero_grad_buffer(self): + """ + Zeros out all grad buffers. Needs to be called at the beginning of each + training iteration. + """ + for param in self.module.parameters(): + if param.requires_grad: + param.grad_added_to_main_grad = False + for buffer in self.buffers + self.expert_parallel_buffers: + buffer.reset() + + def broadcast_params(self): + """ + Syncs parameters across all DP ranks. + """ + for param in self.module.parameters(): + is_expert_parallel = not getattr(param, 'allreduce', True) + + if is_expert_parallel: + torch.distributed.broadcast( + param.data, + src=torch.distributed.get_process_group_ranks(self.expert_data_parallel_group), + group=self.expert_data_parallel_group, + ) + else: + torch.distributed.broadcast( + param.data, + src=torch.distributed.get_process_group_ranks(self.data_parallel_group), + group=self.data_parallel_group, + ) + + def state_dict(self, prefix='', keep_vars=False): + """ + Returns a dictionary containing references to the whole state of the + wrapped module. + + Both parameters and persistent buffers (e.g. running averages) are included. + Keys are corresponding parameter and buffer names. Parameters and buffers + set to None are not included. + """ + return self.module.state_dict(prefix=prefix, keep_vars=keep_vars) + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """ + Returns wrapped module's state_dict for checkpoint saving. + """ + return self.module.state_dict_for_save_checkpoint(prefix=prefix, keep_vars=keep_vars) + + def load_state_dict(self, state_dict, strict=True): + """ + Copies parameters and buffers from state_dict into the wrapped module and its + descendants. If strict is True, then the keys of state_dict must exactly match + the keys returned by this module’s state_dict() function. + """ + self.module.load_state_dict(state_dict, strict=strict) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/distributed_data_parallel_config.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/distributed_data_parallel_config.py new file mode 100644 index 0000000..b12be92 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/distributed_data_parallel_config.py @@ -0,0 +1,28 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from dataclasses import dataclass +from typing import Optional + + +@dataclass +class DistributedDataParallelConfig: + """Configuration for DistributedDataParallel.""" + + grad_reduce_in_fp32: bool = False + """If true, reduce grads in fp32.""" + + overlap_grad_reduce: bool = False + """If true, overlap grad all-reduce / reduce-scatter with backward compute.""" + + use_distributed_optimizer: bool = False + """If true, issue reduce-scatter collectives to aggregate gradients and clean up originally + allocated model parameters, otherwise issue all-reduce collectives. + """ + + check_for_nan_in_grad: bool = False + """ If true, check for NaNs in gradients _before_ communication collective.""" + + bucket_size: Optional[int] = None + """Maximum number of parameters in each bucket. If unspecified, MCore uses a default + value of max(40000000, 1000000 * dp_size) parameters (larger DP sizes need larger buckets + to ensure collectives do not become latency-bound).""" diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/finalize_model_grads.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/finalize_model_grads.py new file mode 100644 index 0000000..445f00a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/finalize_model_grads.py @@ -0,0 +1,131 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from typing import List + +import torch +from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors + +from .. import parallel_state +from ..transformer.transformer_config import TransformerConfig +from ..utils import get_attr_wrapped_model, get_model_config + + +def _allreduce_word_embedding_grads(model: List[torch.nn.Module], config: TransformerConfig): + """ + All-reduce word embedding grads. + + Reduce grads across first and last stages to ensure that word_embeddings parameters stay in + sync. This should only run for models that support pipelined model parallelism (BERT and GPT). + """ + + if ( + parallel_state.is_rank_in_embedding_group(ignore_virtual=True) + and parallel_state.get_pipeline_model_parallel_world_size() > 1 + ): + if parallel_state.is_pipeline_first_stage(ignore_virtual=True): + model_module = model[0] + elif parallel_state.is_pipeline_last_stage(ignore_virtual=True): + model_module = model[-1] + else: # We do not support the interleaved schedule for T5 yet. + model_module = model[0] + + # Look for module with 'pre_process' attribute to get around the fact that DDP and + # other wrapper classes inherit from non-core MegatronModule that has + # 'share_embeddings_and_output_weights' and 'shared_embedding_or_output_weight' + # attributes already, causing get_attr_wrapped_model() to not unwrap anything here. + # TODO: Clean this up once the wrapper classes inherit from core MegatronModule. + model_module = get_attr_wrapped_model(model_module, 'pre_process', return_model_obj=True) + if model_module.share_embeddings_and_output_weights: + weight = model_module.shared_embedding_or_output_weight() + grad = weight.main_grad + torch.distributed.all_reduce(grad, group=parallel_state.get_embedding_group()) + + +def _allreduce_position_embedding_grads(model: List[torch.nn.Module], config: TransformerConfig): + """ + All-reduce position_embeddings grad across first (encoder) and split (decoder) stages to + ensure that position embeddings parameters stay in sync. This should only run for T5 models + with pipeline parallelism. + """ + if ( + parallel_state.is_rank_in_position_embedding_group() + and parallel_state.get_pipeline_model_parallel_world_size() > 1 + and config.pipeline_model_parallel_split_rank is not None + ): + model_module = model[0] + grad = get_attr_wrapped_model( + model_module, 'language_model.embedding.position_embeddings.weight.main_grad' + ) + torch.distributed.all_reduce(grad, group=parallel_state.get_position_embedding_group()) + + +def _allreduce_embedding_grads(model: List[torch.nn.Module], config: TransformerConfig): + """ + All-reduce both word and position embeddings. + """ + _allreduce_word_embedding_grads(model, config) + _allreduce_position_embedding_grads(model, config) + + +def _allreduce_layernorm_grads(model: List[torch.nn.Module], config: TransformerConfig): + """ + All-reduce layernorm grads (for sequence parallelism). + """ + + # All-reduce layernorm parameters across model parallel nodes + # when sequence parallelism is used + if parallel_state.get_tensor_model_parallel_world_size() > 1 and ( + config.sequence_parallel or config.qk_layernorm + ): + grads = [] + for model_chunk in model: + for name, param in get_attr_wrapped_model(model_chunk, 'named_parameters')(): + if ( + getattr(param, 'sequence_parallel', False) + or 'q_layernorm' in name + or 'k_layernorm' in name + ): + grad = param.main_grad + grads.append(grad.data) + if grads: + coalesced = _flatten_dense_tensors(grads) + torch.distributed.all_reduce( + coalesced, group=parallel_state.get_tensor_model_parallel_group() + ) + for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)): + buf.copy_(synced) + + +def finalize_model_grads(model: List[torch.nn.Module]): + """ + All-reduce all model grads across DP replicas, layernorm grads for sequence parallelism, + embedding grads across first and last pipeline stages (if not tied). + """ + + config = get_model_config(model[0]) + + # All-reduce / reduce-scatter across DP replicas. + if config.timers is not None: + config.timers('all-grads-sync', log_level=1).start(barrier=config.barrier_with_L1_time) + for model_chunk in model: + model_chunk.finish_grad_sync() + if config.timers is not None: + config.timers('all-grads-sync').stop() + + # All-reduce layer-norm grads (for sequence parallelism). + if config.timers is not None: + config.timers('layernorm-grads-all-reduce', log_level=1).start( + barrier=config.barrier_with_L1_time + ) + _allreduce_layernorm_grads(model, config) + if config.timers is not None: + config.timers('layernorm-grads-all-reduce').stop() + + # All-reduce embedding grads (for pipeline parallelism). + if config.timers is not None: + config.timers('embedding-grads-all-reduce', log_level=1).start( + barrier=config.barrier_with_L1_time + ) + _allreduce_embedding_grads(model, config) + if config.timers is not None: + config.timers('embedding-grads-all-reduce').stop() diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/param_and_grad_buffer.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/param_and_grad_buffer.py new file mode 100644 index 0000000..91dbc7a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/distributed/param_and_grad_buffer.py @@ -0,0 +1,513 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import math +import os +from enum import Enum +from logging import getLogger +from typing import Dict, List, Optional + +import torch + +from .. import parallel_state +from .distributed_data_parallel_config import DistributedDataParallelConfig + +logger = getLogger(__name__) + + +class BufferType(Enum): + PARAM = 1 + GRAD = 2 + + +def shard_buffer(buffer: torch.Tensor, data_parallel_world_size: int): + """ + Shard buffer into data_parallel_world_size chunks of equal size. + """ + assert buffer.numel() % data_parallel_world_size == 0 + shard_size = buffer.numel() // data_parallel_world_size + sharded_buffer = [ + buffer[(r * shard_size) : ((r + 1) * shard_size)] for r in range(data_parallel_world_size) + ] + return sharded_buffer + + +class Bucket: + """ + Bucket to keep track of a subset of the model's gradients. Provides functionality to register + when params in the bucket have grads ready to be synced; an asynchronous communication call + is automatically launched when _all_ params in the bucket have grads ready. + + Args: + ddp_config: DistributedDataParallel config object. + params: List of parameters whose gradients are collated in this bucket. + param_data: View in larger ParamAndGradBuffer.param_data that this bucket is responsible for. + grad_data: View in larger ParamAndGradBuffer.grad_data that this bucket is responsible for. + offset: Offset of this bucket's view in the larger ParamAndGradBuffer. + numel_unpadded: Number of unpadded elements in bucket. + data_parallel_group: Data-parallel process group. + data_parallel_world_size: World size using the data-parallel group group. + gradient_scaling_factor: This factor is utilized to scale gradients prior to their + communication. Its application is twofold: it facilitates the averaging of gradients + and the scaling of gradients in the context of the Mixture of Experts (MoE) model. + """ + + def __init__( + self, + ddp_config: DistributedDataParallelConfig, + params: List[torch.nn.Parameter], + param_data: Optional[torch.Tensor], + grad_data: torch.Tensor, + offset: int, + numel_unpadded: int, + data_parallel_group: torch.distributed.ProcessGroup, + data_parallel_world_size: int, + gradient_scaling_factor: float, + ): + self.ddp_config = ddp_config + + # State for bookkeeping: params is the set of parameters this bucket is + # responsible for, params_with_grad is the set of parameters with grads + # available. When overlap_grad_reduce is True, communication (all-reduce + # or reduce-scatter) is issued when params_with_grad equals params. + self.params_list = params + self.params = set(params) + self.params_with_grad = set() + self.param_data = param_data + self.grad_data = grad_data + # The distributed optimizer needs to keep track of this bucket's offset + # within the full grad_buffer. + self.offset = offset + self.numel_unpadded = numel_unpadded + self.data_parallel_group = data_parallel_group + self.data_parallel_world_size = data_parallel_world_size + self.data_parallel_rank = torch.distributed.get_rank(group=data_parallel_group) + self.gradient_scaling_factor = gradient_scaling_factor + + self.reset() + + def reset(self): + """ + Reset metadata in bucket in preparation for the next iteration of training. + """ + self.params_with_grad = set() + self.communication_handle = None + self.communication_issued = False + + def start_grad_sync(self): + """ + Initiates grad sync (all-reduce or reduce-scatter) communication operation + for this bucket. + + When overlap_grad_reduce is set to True, dispatches an asynchronous + communication call. When overlap_grad_reduce is set to False, makes + synchronous call. + """ + assert ( + self.communication_handle is None and not self.communication_issued + ), 'Should not have multiple communication calls in flight at once' + + # Make sure norm of grads in bucket are not NaN + # prior to data-parallel all-reduce / reduce-scatter. + if self.ddp_config.check_for_nan_in_grad: + global_rank = torch.distributed.get_rank() + norm = self.grad_data.norm(p=2) + assert not norm.isnan(), ( + f'Rank {global_rank}: found NaN in local grad norm in ' + f'backward pass before data-parallel communication collective. ' + f'Device: {torch.cuda.current_device()}, node: {os.uname()[1]}' + ) + + self.grad_data *= self.gradient_scaling_factor + # Use async_op only when overlap_grad_reduce is True. + if self.ddp_config.use_distributed_optimizer: + local_data_view = shard_buffer(self.grad_data, self.data_parallel_world_size)[ + self.data_parallel_rank + ] + self.communication_handle = torch.distributed._reduce_scatter_base( + local_data_view, + self.grad_data, + group=self.data_parallel_group, + async_op=self.ddp_config.overlap_grad_reduce, + ) + else: + self.communication_handle = torch.distributed.all_reduce( + self.grad_data, + group=self.data_parallel_group, + async_op=self.ddp_config.overlap_grad_reduce, + ) + self.communication_issued = True + + def finish_grad_sync(self): + """ + Finishes grad sync (all-reduce or reduce-scatter) communication operation + for this bucket. + + When overlap_grad_reduce is set to True, waits for asynchronous communication + call to complete. When overlap_grad_reduce is set to False, makes synchronous call. + """ + # If overlap_grad_reduce is False, start (and finish) synchronous communication call here. + if not self.ddp_config.overlap_grad_reduce: + self.start_grad_sync() + return + assert self.communication_handle is not None and self.communication_issued, ( + f'Communication call has not been issued for this bucket ' + f'({len(self.params_with_grad)}/{len(self.params)} params have grad available)' + ) + self.communication_handle.wait() + + def register_grad_ready(self, param: torch.nn.Parameter): + """ + Registers grads for the passed-in param to be "ready" for grad sync. + + When the number of microbatches is greater than 1, we only want to register + grads as ready when processing the last microbatch and overlap_grad_reduce is True. + """ + assert param in self.params, 'Param is not in the bucket' + assert param not in self.params_with_grad, 'Cannot set grad twice' + assert ( + self.ddp_config.overlap_grad_reduce + ), 'register_grad_ready() should be called only when overlapping grad reduce' + self.params_with_grad.add(param) + # If all params in bucket have grads available, issue communication call. + if len(self.params_with_grad) == len(self.params): + self.start_grad_sync() + + +class ParamAndGradBuffer: + """ + Groups parameters and gradients into a contiguous buffer, and then breaks the buffer into + buckets with roughly `bucket_size` parameters each. + + Args: + ddp_config: DistributedDataParallel config object. + param_dtype: Type of param tensor. + grad_dtype: Type of grad tensor. + params: List of parameters whose parameters and gradients are collated in the underlying + tensor. + data_parallel_group: Data-parallel process group. + bucket_size: The rough size of each bucket in terms of number of parameters. + param_to_name: Mapping from `torch.nn.Parameter` to name (for logging purposes). + gradient_scaling_factor: This factor is utilized to scale gradients prior to their + communication. Its application is twofold: it facilitates the averaging of gradients + and the scaling of gradients in the context of the Mixture of Experts (MoE) model. + """ + + def __init__( + self, + ddp_config: DistributedDataParallelConfig, + param_dtype: torch.dtype, + grad_dtype: torch.dtype, + params: List[torch.nn.Parameter], + data_parallel_group: torch.distributed.ProcessGroup, + bucket_size: int, + param_to_name: Dict[torch.nn.Parameter, str], + gradient_scaling_factor: float, + ): + self.ddp_config = ddp_config + + # Check that params are unique. + unique_params = set() + for param in params: + assert param not in unique_params + unique_params.add(param) + del unique_params + + # Store attributes that will be needed later. + self.param_dtype = param_dtype + self.grad_dtype = grad_dtype + self.data_parallel_group = data_parallel_group + self.data_parallel_world_size = torch.distributed.get_world_size( + group=self.data_parallel_group + ) + self.gradient_scaling_factor = gradient_scaling_factor + self.is_last_microbatch = True + + # Data structures to store underlying buckets and relevant indexing data. + self.buckets = [] + self.param_to_bucket = {} # Param -> bucket mapping. + self.param_index_map = {} # Param -> location in buffer mapping (used in dist. optimizer). + + def _pad_if_needed(data_index: int) -> int: + """ + Pads data indices if using distributed optimizer (to ensure uniform sharding). + """ + if self.ddp_config.use_distributed_optimizer: + return ( + int(math.ceil(data_index / self.data_parallel_world_size)) + * self.data_parallel_world_size + ) + return data_index + + # First, figure out how many elements should be in the underlying buffer storage. + # Note that if we need to split the buffer into smaller buckets, each of these + # might need to be padded as well (if using the distributed optimizer). + data_start_index = 0 + bucket_data_start_index = data_start_index + bucket_params = set() + self.bucket_indices = [] + per_bucket_numel_unpadded = [] + bucket_id = 0 + + def _create_new_bucket(data_end_index: int) -> int: + """ + Create the bucket_id'th bucket with collected bucket_params, starting at + bucket_data_start_index. + """ + nonlocal bucket_data_start_index, bucket_params, bucket_id + per_bucket_numel_unpadded.append(data_end_index - bucket_data_start_index) + data_end_index = _pad_if_needed(data_end_index) + # Update bucket metadata. + self.bucket_indices.append((bucket_data_start_index, data_end_index)) + bucket_data_start_index = data_end_index + # Re-set bucket_params and increment bucket_id for next bucket. + bucket_params = set() + bucket_id += 1 + # Return the potentially padded data_end_index. + return data_end_index + + for param in params[::-1]: + # Iterate through parameters in reverse order to roughly follow backprop order, + # and skip parameters that don't require gradients. + if not param.requires_grad: + continue + this_numel = param.data.nelement() + data_end_index = data_start_index + this_numel + + def _does_param_require_new_bucket(param): + """ + Split shared embedding parameters into separate bucket if using distributed + optimizer that makes use of reduce-scatters instead of all-reduces. + This ensures that the first and last pipeline stage partition optimizer state + for the shared embedding parameters the same way across DP replicas, allowing + the DP reduce-scatter to be before the embedding all-reduce. + """ + return ( + getattr(param, "shared_embedding", False) + and self.ddp_config.use_distributed_optimizer + ) + + # Create bucket with already collected parameters if current param needs its own bucket. + if _does_param_require_new_bucket(param) and len(bucket_params) > 0: + # We are creating a bucket for the already accumulated parameters, whose params + # end at the current data_start_index. + if self.ddp_config.use_distributed_optimizer: + # data_start_index should already be padded. + assert data_start_index % self.data_parallel_world_size == 0 + _create_new_bucket(data_start_index) + + self.param_index_map[param] = ( + data_start_index, + data_end_index, + bucket_id, + ) + bucket_params.add(param) + + # If we have enough elements already or the current param is part of the shared embedding + # layer and needs a separate bucket, form a new bucket. + if ( + bucket_size is not None + and (data_end_index - bucket_data_start_index) >= bucket_size + ) or _does_param_require_new_bucket(param): + data_end_index = _create_new_bucket(data_end_index) + data_start_index = data_end_index + + # Add remaining params to a new bucket. + if len(bucket_params) > 0: + data_end_index = _create_new_bucket(data_end_index) + + # Next, create underlying storage for buffer (with numel elements that includes + # padding as necessary). + self.numel = data_end_index + if self.ddp_config.use_distributed_optimizer: + assert self.numel % self.data_parallel_world_size == 0 + self.param_data = None + # Only re-map param tensors if using distributed optimizer. + if self.ddp_config.use_distributed_optimizer: + self.param_data = torch.zeros( + self.numel, + dtype=self.param_dtype, + device=torch.cuda.current_device(), + requires_grad=False, + ) + self.grad_data = torch.zeros( + self.numel, + dtype=self.grad_dtype, + device=torch.cuda.current_device(), + requires_grad=False, + ) + + # Finally, map param.data and param.main_grad fields to buffers. + bucket_params = set() + bucket_data_start_index = 0 + cur_bucket_id = 0 + for param in params[::-1]: + if not param.requires_grad: + continue + data_start_index, data_end_index, bucket_id = self.param_index_map[param] + + # Assign param.data to appropriate segment of self.param_data. + if self.param_data is not None: + old_param_data = param.data + param.data = self._get( + param.data.shape, data_start_index, buffer_type=BufferType.PARAM + ) + assert old_param_data._base is None + # Copy tensor values (from initialization or checkpoint). + param.data.detach().copy_(old_param_data) + del old_param_data + + param.main_grad = self._get( + param.data.shape, data_start_index, buffer_type=BufferType.GRAD + ) + if bucket_id != cur_bucket_id: + bucket_data_end_index = _pad_if_needed(data_start_index) + self._set_bucket( + bucket_params=bucket_params, + start_index=bucket_data_start_index, + end_index=bucket_data_end_index, + numel_unpadded=per_bucket_numel_unpadded[cur_bucket_id], + bucket_id=cur_bucket_id, + ) + bucket_data_start_index = bucket_data_end_index + bucket_params = set() + assert cur_bucket_id + 1 == len(self.buckets) + assert bucket_id == cur_bucket_id + 1 + cur_bucket_id = bucket_id + bucket_params.add(param) + + # Add remaining params to a new bucket. + if len(bucket_params) > 0: + bucket_data_end_index = _pad_if_needed(data_end_index) + self._set_bucket( + bucket_params=bucket_params, + start_index=bucket_data_start_index, + end_index=bucket_data_end_index, + numel_unpadded=per_bucket_numel_unpadded[cur_bucket_id], + bucket_id=cur_bucket_id, + ) + + # Log buckets for all PP stages. + if ( + parallel_state.get_data_parallel_rank(with_context_parallel=True) == 0 + and parallel_state.get_tensor_model_parallel_rank() == 0 + ): + logger.info( + f'Number of buckets for gradient all-reduce / reduce-scatter: {len(self.buckets)}' + ) + for index, bucket in enumerate(self.buckets): + numel = 0 + for param in bucket.params: + numel += param.data.nelement() + logger.info(f'Params for bucket {index+1} ({numel} elements):') + for param in bucket.params: + logger.info(f' {param_to_name[param]}') + + def _get(self, shape: torch.Size, start_index: int, buffer_type: BufferType) -> torch.Tensor: + """ + Return a tensor with the input `shape` as a view into the 1-D data starting at + `start_index`. + """ + end_index = start_index + shape.numel() + assert end_index <= self.numel, 'Requested tensor is out of buffer range' + if buffer_type == BufferType.PARAM: + assert self.param_data is not None + buffer_tensor = self.param_data[start_index:end_index] + elif buffer_type == BufferType.GRAD: + buffer_tensor = self.grad_data[start_index:end_index] + else: + raise Exception("Illegal buffer type provided to GradBuffer._get() function") + buffer_tensor = buffer_tensor.view(shape) + return buffer_tensor + + def _set_bucket( + self, + bucket_params: List[torch.nn.Parameter], + start_index: int, + end_index: int, + numel_unpadded: int, + bucket_id: int, + ): + """ + Helper function to create new bucket, add it to list of buckets, and + also update param->bucket mapping. + """ + + # Assert that indices are correctly padded (if needed), and that bucket + # position is same as originally computed. + if self.ddp_config.use_distributed_optimizer: + assert start_index % self.data_parallel_world_size == 0 + assert end_index % self.data_parallel_world_size == 0 + assert (start_index, end_index) == self.bucket_indices[bucket_id] + + # Get appropriate view into global ParamAndGradBuffer. + bucketed_param_data = None + if self.param_data is not None: + bucketed_param_data = self._get( + torch.Size([end_index - start_index]), start_index, buffer_type=BufferType.PARAM + ) + bucketed_grad_data = self._get( + torch.Size([end_index - start_index]), start_index, buffer_type=BufferType.GRAD + ) + bucket = Bucket( + ddp_config=self.ddp_config, + params=bucket_params, + param_data=bucketed_param_data, + grad_data=bucketed_grad_data, + offset=start_index, + numel_unpadded=numel_unpadded, + data_parallel_group=self.data_parallel_group, + data_parallel_world_size=self.data_parallel_world_size, + gradient_scaling_factor=self.gradient_scaling_factor, + ) + self.buckets.append(bucket) + for bucket_param in bucket_params: + assert bucket_param not in self.param_to_bucket + self.param_to_bucket[bucket_param] = bucket + + def reset(self): + """ + Zero out the underlying grad_buffer and reset all buckets in preparation for the next + iteration of training. + """ + self.grad_data.zero_() + for bucket in self.buckets: + bucket.reset() + self.is_last_microbatch = True + + def start_grad_sync(self): + """ + Initiates grad sync (all-reduce or reduce-scatter) communication operations + for all buckets in the grad buffer. + + When overlap_grad_reduce is set to True, dispatches asynchronous communication + calls. When overlap_grad_reduce is set to False, calls synchronous + communication ops. + """ + for bucket in self.buckets: + bucket.start_grad_sync() + + def finish_grad_sync(self): + """ + Finishes grad sync (all-reduce or reduce-scatter) communication operations + for all buckets in the grad buffer. + + When overlap_grad_reduce is set to True, waits for asynchronous communication + calls to complete. When overlap_grad_reduce is set to False, calls synchronous + communication ops. + """ + for bucket in self.buckets: + bucket.finish_grad_sync() + + def register_grad_ready(self, param: torch.nn.Parameter): + """ + Registers grads for the passed-in param to be "ready" for grad sync. + + When the number of microbatches is greater than 1, we only want to register + grads as ready when processing the last microbatch and overlap_grad_reduce is True. + """ + assert ( + self.ddp_config.overlap_grad_reduce + ), 'register_grad_ready() should only be called when overlap_grad_reduce is True' + if self.is_last_microbatch: + bucket = self.param_to_bucket[param] + bucket.register_grad_ready(param) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/enums.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/enums.py new file mode 100644 index 0000000..46e7d3b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/enums.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import enum + + +class ModelType(enum.Enum): + encoder_or_decoder = 1 + encoder_and_decoder = 2 + retro_encoder = 3 + retro_decoder = 4 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_bias_dropout.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_bias_dropout.py new file mode 100644 index 0000000..08af02b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_bias_dropout.py @@ -0,0 +1,73 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +from typing import Optional, Tuple + +import torch + +from megatron.core.jit import jit_fuser + + +def _bias_dropout_add_func(x_with_bias, residual, prob, training): + # type: (Tuple[Tensor, Optional[Tensor]], Tensor, float, bool) -> Tensor + # NOTE: Previously, the argument `bias` used to be passed as + # `bias.expand_as(residual)` when the `bias_dropout_func` is called from the + # transformer layer but broadcasting should automatically take care of that. + # Also, looking at broadcasting semantics, `expand_as` and broadcasting + # seem to be identical performance-wise (both just change the view). + + x, bias = x_with_bias # unpack + + # If we want to train mixed precision, then the output of this function + # should be half precision. However, in AMP O1, the input (residual) is + # in fp32, and it will up-cast the result to fp32, causing pipeline parallel + # GPU communication to hang. Therefore, we need to cast residual to the same + # dtype as x. + residual = residual if residual.dtype == x.dtype else residual.to(x.dtype) + + # The Dropout operation, Residual Addition and the tensor returning can be + # done generically outside the if statement, but that stops fusing of Bias + # Addition-Dropout-Residual Addition operation. So doing it together inside + # the conditional branch to improve performance + if bias is not None: + x = x + bias + out = torch.nn.functional.dropout(x, p=prob, training=training) + out = residual + out + return out + else: + out = torch.nn.functional.dropout(x, p=prob, training=training) + out = residual + out + return out + + +def bias_dropout_add_unfused(training): + def _bias_dropout_add(x_with_bias, residual, prob): + return _bias_dropout_add_func(x_with_bias, residual, prob, training) + + return _bias_dropout_add + + +@jit_fuser +def bias_dropout_add_fused_train( + x_with_bias: Tuple[torch.Tensor, Optional[torch.Tensor]], residual: torch.Tensor, prob: float, +) -> torch.Tensor: + return _bias_dropout_add_func(x_with_bias, residual, prob, True) + + +@jit_fuser +def bias_dropout_add_fused_inference( + x_with_bias: Tuple[torch.Tensor, Optional[torch.Tensor]], residual: torch.Tensor, prob: float, +) -> torch.Tensor: + return _bias_dropout_add_func(x_with_bias, residual, prob, False) + + +def get_bias_dropout_add(training, fused): + if fused: + # jit scripting for a nn.module (with dropout) is not + # triggering the fusion kernel. For now, we use two + # different nn.functional routines to account for varying + # dropout semantics during training and inference phases. + if training: + return bias_dropout_add_fused_train + else: + return bias_dropout_add_fused_inference + else: + return bias_dropout_add_unfused(training) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_bias_geglu.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_bias_geglu.py new file mode 100644 index 0000000..70ef348 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_bias_geglu.py @@ -0,0 +1,85 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import torch + +from megatron.core.jit import jit_fuser + +###### BIAS GELU FUSION/ NO AUTOGRAD ################ +# 1/sqrt(2*pi)-> 0.3989423 +# 1/sqrt(2) -> 0.70710678 +# sqrt(2/pi) -> 0.79788456 +# this function is tanh approximation of gelu +# actual gelu is: +# x * 0.5 * (1.0 + torch.erf(x * 0.70710678)) + + +@jit_fuser +def geglu(y): + y_1, y_2 = torch.chunk(y, 2, -1) + return (y_1 * 0.5 * (1.0 + torch.tanh(0.79788456 * y_1 * (1 + 0.044715 * y_1 * y_1)))) * y_2 + + +@jit_fuser +def bias_geglu(bias, y): + y = y + bias + return geglu(y) + + +# gradient of tanh approximation of gelu +# gradient of actual gelu is: +# 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 * x * x) +@jit_fuser +def geglu_back(g, y): + y_1, y_2 = torch.chunk(y, 2, -1) + tanh_out = torch.tanh(0.79788456 * y_1 * (1 + 0.044715 * y_1 * y_1)) + # sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243 + ff = 0.5 * y_1 * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * y_1 * y_1)) + 0.5 * ( + 1 + tanh_out + ) + return torch.cat(((g * y_2) * ff, g * (y_1 * 0.5 * (1.0 + tanh_out))), -1) + + +@jit_fuser +def bias_geglu_back(g, y, bias): + y = y + bias + return geglu_back(g, y) + + +class BiasGeGLUFunction(torch.autograd.Function): + @staticmethod + # bias is an optional argument + def forward(ctx, input, bias): + ctx.save_for_backward(input, bias) + return bias_geglu(input, bias) + + @staticmethod + def backward(ctx, grad_output): + input, bias = ctx.saved_tensors + tmp = bias_geglu_back(grad_output, input, bias) + return tmp, tmp + + +class GeGLUFunction(torch.autograd.Function): + @staticmethod + # bias is an optional argument + def forward(ctx, input): + ctx.save_for_backward(input) + return geglu(input) + + @staticmethod + def backward(ctx, grad_output): + input = ctx.saved_tensors + tmp = geglu_back(grad_output, input[0]) + return tmp + + +def bias_geglu_impl(input, bias): + ori_shape = input.shape + assert len(ori_shape) in [2, 3] + input = input.view(-1, ori_shape[-1]) + if bias is not None: + output = BiasGeGLUFunction.apply(input, bias) + else: + output = GeGLUFunction.apply(input) + + return output if len(ori_shape) == 2 else output.view(ori_shape[0], ori_shape[1], -1) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_bias_gelu.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_bias_gelu.py new file mode 100644 index 0000000..2b54674 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_bias_gelu.py @@ -0,0 +1,50 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import torch + +from megatron.core.jit import jit_fuser + +###### BIAS GELU FUSION/ NO AUTOGRAD ################ +# 1/sqrt(2*pi)-> 0.3989423 +# 1/sqrt(2) -> 0.70710678 +# sqrt(2/pi) -> 0.79788456 +# this function is tanh approximation of gelu +# actual gelu is: +# x * 0.5 * (1.0 + torch.erf(x * 0.70710678)) + + +@jit_fuser +def bias_gelu(bias, y): + x = bias + y + return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))) + + +# gradient of tanh approximation of gelu +# gradient of actual gelu is: +# 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 * x * x) +@jit_fuser +def bias_gelu_back(g, bias, y): + x = bias + y + tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)) + # sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243 + ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * ( + 1 + tanh_out + ) + return ff * g + + +class GeLUFunction(torch.autograd.Function): + @staticmethod + # bias is an optional argument + def forward(ctx, input, bias): + ctx.save_for_backward(input, bias) + return bias_gelu(bias, input) + + @staticmethod + def backward(ctx, grad_output): + input, bias = ctx.saved_tensors + tmp = bias_gelu_back(grad_output, bias, input) + return tmp, tmp + + +bias_gelu_impl = GeLUFunction.apply diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_bias_swiglu.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_bias_swiglu.py new file mode 100644 index 0000000..fd3ac3e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_bias_swiglu.py @@ -0,0 +1,89 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import torch +import torch.nn.functional as F + +from megatron.core.jit import jit_fuser + +###### BIAS SWIGLU FUSION/ NO AUTOGRAD ################ + + +@jit_fuser +def swiglu(y): + y_1, y_2 = torch.chunk(y, 2, -1) + return F.silu(y_1) * y_2 + + +@jit_fuser +def bias_swiglu(y, bias): + y = y + bias + return swiglu(y) + + +# gradient of tanh approximation of gelu +# gradient of actual gelu is: +# 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 * x * x) +@jit_fuser +def swiglu_back(g, y): + y_1, y_2 = torch.chunk(y, 2, -1) + return torch.cat( + (g * torch.sigmoid(y_1) * (1 + y_1 * (1 - torch.sigmoid(y_1))) * y_2, g * F.silu(y_1)), -1 + ) + + +@jit_fuser +def bias_swiglu_back(g, y, bias): + y = y + bias + return swiglu_back(g, y) + + +class BiasSwiGLUFunction(torch.autograd.Function): + @staticmethod + # bias is an optional argument + def forward(ctx, input, bias, fp8_input_store): + input_for_backward = input.to(torch.float8_e4m3fn) if fp8_input_store else input + ctx.save_for_backward(input_for_backward, bias) + ctx.ori_input_dtype = input.dtype + ctx.fp8_input_store = fp8_input_store + return bias_swiglu(input, bias) + + @staticmethod + def backward(ctx, grad_output): + input, bias = ctx.saved_tensors + input = input.to(ctx.ori_input_dtype) if ctx.fp8_input_store else input + tmp = bias_swiglu_back(grad_output, input, bias) + return tmp, tmp, None + + +class SwiGLUFunction(torch.autograd.Function): + @staticmethod + # bias is an optional argument + def forward(ctx, input, fp8_input_store): + input_for_backward = input.to(torch.float8_e4m3fn) if fp8_input_store else input + ctx.save_for_backward(input_for_backward) + ctx.ori_input_dtype = input.dtype + ctx.fp8_input_store = fp8_input_store + return swiglu(input) + + @staticmethod + def backward(ctx, grad_output): + input = ctx.saved_tensors[0] + input = input.to(ctx.ori_input_dtype) if ctx.fp8_input_store else input + tmp = swiglu_back(grad_output, input) + return tmp, None + + +def bias_swiglu_impl(input, bias, fp8_input_store=False): + ori_shape = input.shape + assert len(ori_shape) in [2, 3] + input = input.view(-1, ori_shape[-1]) + if bias is not None: + output = BiasSwiGLUFunction.apply(input, bias, fp8_input_store) + else: + output = SwiGLUFunction.apply(input, fp8_input_store) + + return output if len(ori_shape) == 2 else output.view(ori_shape[0], ori_shape[1], -1) + + +# bias_swiglu_impl = BiasSwiGLUFunction.apply +# swiglu_impl = SwiGLUFunction.apply diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_layer_norm.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_layer_norm.py new file mode 100644 index 0000000..30fa5d4 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_layer_norm.py @@ -0,0 +1,169 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import importlib +import inspect +import numbers + +import torch +from torch import Tensor +from torch.nn import init +from torch.nn.parameter import Parameter + +from megatron.core.transformer import TransformerConfig +from megatron.core.utils import make_viewless_tensor + +try: + from apex.contrib.layer_norm.layer_norm import FastLayerNormFN + + HAVE_PERSIST_LAYER_NORM = True +except: + HAVE_PERSIST_LAYER_NORM = False + +try: + from apex.normalization.fused_layer_norm import FusedLayerNormAffineFunction + + HAVE_FUSED_LAYER_NORM = True +except: + HAVE_FUSED_LAYER_NORM = False + + +class FusedLayerNorm(torch.nn.Module): + + """Layer Norm, fused into a single CUDA kernel. + + Args: + hidden_size (int): Transformer hidden dimension. + + eps (float): Epsilon added to denominator, for numerical stability. + + persist_layer_norm (bool): Use persistent fused layer norm kernel. + This kernel supports only a set of hidden sizes. Please + check persist_ln_hidden_sizes if your hidden size is supported. + + zero_centered_gamma (bool): Adjust LayerNorm weights such that they are + centered around zero. This improves numerical stability. + + config (TransformerConfig): Transformer config. Include to match custom + layer norm interfaces. + + normalization (str): Normalization type, used for Transformer Engine. + Must equal 'LayerNorm' here. + """ + + def __init__( + self, + config: TransformerConfig, + hidden_size: int, + eps: float = 1e-5, + persist_layer_norm: bool = True, + zero_centered_gamma: bool = False, + normalization: str = "LayerNorm", # included to match TE interface + ): + super().__init__() + + self.config = config + + self.zero_centered_gamma = self.config.layernorm_zero_centered_gamma + assert ( + self.config.normalization == "LayerNorm" + ), f'({self.config.normalization}) is not supported in FusedLayerNorm' + + # List of hiddens sizes supported in the persistent layer norm kernel + # If the hidden size is not supported, fall back to the non-persistent + # kernel. + persist_ln_hidden_sizes = [ + 1024, + 1536, + 2048, + 2304, + 3072, + 3840, + 4096, + 5120, + 6144, + 8192, + 10240, + 12288, + 12800, + 15360, + 16384, + 18432, + 20480, + 24576, + 25600, + 30720, + 32768, + 40960, + 49152, + 65536, + ] + persist_layer_norm = self.config.persist_layer_norm + if hidden_size not in persist_ln_hidden_sizes or not HAVE_PERSIST_LAYER_NORM: + persist_layer_norm = False + + if not persist_layer_norm and not HAVE_FUSED_LAYER_NORM: + # TODO: Add pytorch only layer norm + raise ValueError(f'Apex must currently be installed to use megatron core.') + + if isinstance(hidden_size, numbers.Integral): + hidden_size = (hidden_size,) + self.hidden_size = torch.Size(hidden_size) + self.eps = eps + self.weight = Parameter(torch.Tensor(*hidden_size)) + self.bias = Parameter(torch.Tensor(*hidden_size)) + self.reset_parameters() + self.persist_layer_norm = persist_layer_norm + self.sequence_parallel = self.config.sequence_parallel + + # set sequence parallelism flag on weight and bias parameters + setattr(self.weight, 'sequence_parallel', self.sequence_parallel) + setattr(self.bias, 'sequence_parallel', self.sequence_parallel) + + def reset_parameters(self): + + if self.zero_centered_gamma: + init.zeros_(self.weight) + init.zeros_(self.bias) + else: + init.ones_(self.weight) + init.zeros_(self.bias) + + def forward(self, input: Tensor) -> Tensor: + + weight = self.weight + 1 if self.zero_centered_gamma else self.weight + + if self.persist_layer_norm: + if 'memory_efficient' in inspect.getfullargspec(FastLayerNormFN.forward).args: + output = FastLayerNormFN.apply( + input, weight, self.bias, self.eps, self.config.memory_efficient_layer_norm + ) + else: + output = FastLayerNormFN.apply(input, weight, self.bias, self.eps) + + # Apex's fast layer norm function outputs a 'view' tensor (i.e., has + # a populated '_base' field). This will result in schedule.py's + # deallocate_output_tensor() throwing an error, so a viewless tensor is + # created to prevent this. + output = make_viewless_tensor( + inp=output, requires_grad=input.requires_grad, keep_graph=True + ) + + else: + if ( + 'memory_efficient' + in inspect.getfullargspec(FusedLayerNormAffineFunction.forward).args + ): + return FusedLayerNormAffineFunction.apply( + input, + weight, + self.bias, + self.hidden_size, + self.eps, + self.config.memory_efficient_layer_norm, + ) + else: + return FusedLayerNormAffineFunction.apply( + input, weight, self.bias, self.hidden_size, self.eps + ) + + return output diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_softmax.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_softmax.py new file mode 100644 index 0000000..c7bfbb7 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/fusions/fused_softmax.py @@ -0,0 +1,220 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +from typing import Optional + +import torch +import torch.nn as nn + +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.utils import get_default_causal_mask + + +class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function): + """ + Fused operation which performs following three operations in sequence + 1. Scale the tensor. + 2. Apply upper triangular mask (typically used in gpt models). + 3. Perform softmax. + """ + + @staticmethod + def forward(ctx, inputs, scale): + import scaled_upper_triang_masked_softmax_cuda + + scale_t = torch.tensor([scale]) + softmax_results = scaled_upper_triang_masked_softmax_cuda.forward(inputs, scale_t[0]) + + ctx.save_for_backward(softmax_results, scale_t) + return softmax_results + + @staticmethod + def backward(ctx, output_grads): + import scaled_upper_triang_masked_softmax_cuda + + softmax_results, scale_t = ctx.saved_tensors + input_grads = scaled_upper_triang_masked_softmax_cuda.backward( + output_grads, softmax_results, scale_t[0] + ) + + return input_grads, None + + +class ScaledMaskedSoftmax(torch.autograd.Function): + """ + Fused operation which performs following three operations in sequence + 1. Scale the tensor. + 2. Apply the mask. + 3. Perform softmax. + """ + + @staticmethod + def forward(ctx, inputs, mask, scale): + import scaled_masked_softmax_cuda + + scale_t = torch.tensor([scale]) + + softmax_results = scaled_masked_softmax_cuda.forward(inputs, mask, scale_t[0]) + ctx.save_for_backward(softmax_results, scale_t) + return softmax_results + + @staticmethod + def backward(ctx, output_grads): + import scaled_masked_softmax_cuda + + softmax_results, scale_t = ctx.saved_tensors + + input_grads = scaled_masked_softmax_cuda.backward(output_grads, softmax_results, scale_t[0]) + return input_grads, None, None + + +class ScaledSoftmax(torch.autograd.Function): + """ + Fused operation which performs following two operations in sequence + 1. Scale the tensor. + 2. Perform softmax. + """ + + @staticmethod + def forward(ctx, inputs, scale): + import scaled_softmax_cuda + + scale_t = torch.tensor([scale]) + + softmax_results = scaled_softmax_cuda.forward(inputs, scale_t[0]) + ctx.save_for_backward(softmax_results, scale_t) + return softmax_results + + @staticmethod + def backward(ctx, output_grads): + import scaled_softmax_cuda + + softmax_results, scale_t = ctx.saved_tensors + + input_grads = scaled_softmax_cuda.backward(output_grads, softmax_results, scale_t[0]) + return input_grads, None, None + + +class FusedScaleMaskSoftmax(nn.Module): + """ + fused operation: scaling + mask + softmax + + Args: + input_in_fp16: flag to indicate if input in fp16 data format. + input_in_bf16: flag to indicate if input in bf16 data format. + attn_mask_type: attention mask type (pad or causal) + scaled_masked_softmax_fusion: flag to indicate user want to use softmax fusion + mask_func: mask function to be applied. + softmax_in_fp32: if true, softmax in performed at fp32 precision. + scale: scaling factor used in input tensor scaling. + """ + + def __init__( + self, + input_in_fp16, + input_in_bf16, + attn_mask_type, + scaled_masked_softmax_fusion, + mask_func, + softmax_in_fp32, + scale, + ): + super(FusedScaleMaskSoftmax, self).__init__() + self.input_in_fp16 = input_in_fp16 + self.input_in_bf16 = input_in_bf16 + assert not ( + self.input_in_fp16 and self.input_in_bf16 + ), "both fp16 and bf16 flags cannot be active at the same time." + self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16 + self.attn_mask_type = attn_mask_type + self.scaled_masked_softmax_fusion = scaled_masked_softmax_fusion + self.mask_func = mask_func + self.softmax_in_fp32 = softmax_in_fp32 + self.scale = scale + + assert self.scale is None or softmax_in_fp32, "softmax should be in fp32 when scaled" + + def forward(self, input: torch.Tensor, mask: Optional[torch.Tensor]): + """Forward pass of softmax with masked input. + + In case attn_mask_type is causal the mask is generated and None can be passed. + A user-defined mask is only needed when attn_mask_type is not causal. + """ + # [b, np, sq, sk] + assert input.dim() == 4 + + if self.is_kernel_available(mask, *input.size()): + return self.forward_fused_softmax(input, mask) + else: + return self.forward_torch_softmax(input, mask) + + def is_kernel_available(self, mask, b, np, sq, sk): + attn_batches = b * np + + if ( + self.scaled_masked_softmax_fusion # user want to fuse + and self.input_in_float16 # input must be fp16 + and 16 < sk <= 4096 # sk must be 16 ~ 2048 + and sq % 4 == 0 # sq must be divisor of 4 + and sk % 4 == 0 # sk must be divisor of 4 + and attn_batches % 4 == 0 # np * b must be divisor of 4 + ): + if 0 <= sk <= 4096: + batch_per_block = self.get_batch_per_block(sq, sk, b, np) + + if self.attn_mask_type == AttnMaskType.causal: + if attn_batches % batch_per_block == 0: + return True + else: + if sq % batch_per_block == 0: + return True + return False + + def forward_fused_softmax(self, input, mask): + b, np, sq, sk = input.size() + scale = self.scale if self.scale is not None else 1.0 + + if self.attn_mask_type == AttnMaskType.causal: + assert sq == sk, "causal mask is only for self attention" + + # input is 3D tensor (attn_batches, sq, sk) + input = input.view(-1, sq, sk) + probs = ScaledUpperTriangMaskedSoftmax.apply(input, scale) + return probs.view(b, np, sq, sk) + else: + # input is 4D tensor (b, np, sq, sk) + if mask is not None: + return ScaledMaskedSoftmax.apply(input, mask, scale) + else: + return ScaledSoftmax.apply(input, scale) + + def forward_torch_softmax(self, input, mask): + if self.input_in_float16 and self.softmax_in_fp32: + input = input.float() + + if self.scale is not None: + input = input * self.scale + + # Generate causal mask if not given + sq, sk = input.size(2), input.size(3) + if self.attn_mask_type == AttnMaskType.causal and mask is None and sq > 1: + # If sq == 1 then either KV cache is used or one-element context is passed + # so keeping mask=None in this case; subsequent code should handle it + assert sq == sk, "causal mask is only for self attention" + mask = get_default_causal_mask(sq) + + mask_output = self.mask_func(input, mask) if mask is not None else input + probs = torch.nn.Softmax(dim=-1)(mask_output) + + if self.input_in_float16 and self.softmax_in_fp32: + if self.input_in_fp16: + probs = probs.half() + else: + probs = probs.bfloat16() + + return probs + + @staticmethod + def get_batch_per_block(sq, sk, b, np): + import scaled_masked_softmax_cuda + + return scaled_masked_softmax_cuda.get_batch_per_block(sq, sk, b, np) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/inference/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/inference/__init__.py new file mode 100644 index 0000000..f801100 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/inference/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/inference/gpt/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/inference/gpt/__init__.py new file mode 100644 index 0000000..f801100 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/inference/gpt/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/inference/gpt/model_specs.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/inference/gpt/model_specs.py new file mode 100644 index 0000000..50467ef --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/inference/gpt/model_specs.py @@ -0,0 +1,50 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add +from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear +from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules +from megatron.core.transformer.custom_layers.transformer_engine import TENorm +from megatron.core.transformer.dot_product_attention import DotProductAttention +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules + + +# Use this spec for AMMO PTQ and TensorRT-LLM export +def get_gpt_layer_ammo_spec() -> ModuleSpec: + """Mix the native spec with TENorm. + + This is essentially the native local spec except for the layernorm implementation + is using TENorm from Transformer-Engine. This TENorm supports both FusedLayerNorm and RMSNorm and + prevents the apex dependency. + """ + return ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + input_layernorm=TENorm, + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=SelfAttentionSubmodules( + linear_qkv=ColumnParallelLinear, + core_attention=DotProductAttention, + linear_proj=RowParallelLinear, + ), + ), + self_attn_bda=get_bias_dropout_add, + pre_mlp_layernorm=TENorm, + mlp=ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear, + ), + ), + mlp_bda=get_bias_dropout_add, + # Map TE-layernorm-fusion keys back + sharded_state_dict_keys_map={ + 'input_layernorm.': 'self_attention.linear_qkv.layer_norm_', + 'pre_mlp_layernorm.': 'mlp.linear_fc1.layer_norm_', + }, + ), + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/inference/gpt/state_dict_hooks.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/inference/gpt/state_dict_hooks.py new file mode 100644 index 0000000..7d6197d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/inference/gpt/state_dict_hooks.py @@ -0,0 +1,133 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from logging import getLogger + +import torch + +logger = getLogger(__name__) + + +def mcore_gpt_load_classic_state_dict_pre_hook( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, +): + """Register a pre-hook to fix the state_dict key difference. + + This prehook is used when trying to load the classic Megatron-LM GPTModel into its + megatron/core variant that uses native ParallelLinear and Transformer-Engine Norm. + Only this particular spec supports post-training quantization and TensorRT-LLM + config export through `nvidia-ammo` package. + + Args: + state_dict: state dictionary + prefix: module name prefix + local_metadata: local metatdata + strict: whether is in strict mode + missing_keys: missing state dict keys + unexpected_keys: unexpected state dict keys + error_msgs: error messages + """ + if "modelopt_state" in state_dict: + state_dict.pop("modelopt_state") + + if "language_model" in state_dict: + language_model_state_dict = state_dict.pop("language_model") + if "embedding" in language_model_state_dict: + if "word_embeddings" in language_model_state_dict["embedding"]: + for key, param in language_model_state_dict["embedding"]["word_embeddings"].items(): + state_dict.update({"embedding.word_embeddings." + key: param}) + if "position_embeddings" in language_model_state_dict["embedding"]: + for key, param in language_model_state_dict["embedding"][ + "position_embeddings" + ].items(): + state_dict.update({"embedding.position_embeddings." + key: param}) + if "transformer" in language_model_state_dict: + for key, param in language_model_state_dict["transformer"].items(): + state_dict.update({"decoder." + key: param}) + else: + for key, param in language_model_state_dict["encoder"].items(): + state_dict.update({"decoder." + key: param}) + if "output_layer" in language_model_state_dict: + for key, param in language_model_state_dict["output_layer"].items(): + state_dict.update({"output_layer." + key: param}) + + if torch.distributed.get_rank() == 0: + logger.info("ModelOptGPTModel {}".format(state_dict.keys())) + + module_name_rewrite_list = [ + ("input_norm", "input_layernorm"), + (".attention.query_key_value", ".self_attention.linear_qkv"), + (".attention.dense", ".self_attention.linear_proj"), + ("self_attention.query_key_value", "self_attention.linear_qkv"), + ("self_attention.dense", "self_attention.linear_proj"), + ("post_attention_layernorm", "pre_mlp_layernorm"), + ("post_attention_norm", "pre_mlp_layernorm"), + ("dense_h_to_4h", "linear_fc1"), + ("dense_4h_to_h", "linear_fc2"), + ("final_norm", "final_layernorm"), + ] + + key_rewrite_list = [] + + for key, _ in state_dict.items(): + for old_name, new_name in module_name_rewrite_list: + if old_name in key: + key_rewrite_list += [(key, key.replace(old_name, new_name))] + + for old_key, new_key in key_rewrite_list: + if torch.distributed.get_rank() == 0: + logger.info("replace {} with {}".format(old_key, new_key)) + state_dict[new_key] = state_dict[old_key] + state_dict.pop(old_key) + + +def mcore_gpt_load_te_state_dict_pre_hook( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, +): + """Register a pre-hook to fix the state_dict key difference of. + + This prehook is used when trying to load the megatron/core GPTModel that uses a + fused Transformer-Engine ParallelLinear into the variant that uses native ParallelLinear + and Transformer-Engine Norm (effectively to restore the fusion). + Only this particular spec supports post-training quantization and TensorRT-LLM + config export through `nvidia-ammo` package. + + Args: + state_dict: state dictionary + prefix: module name prefix + local_metadata: local metatdata + strict: whether is in strict mode + missing_keys: missing state dict keys + unexpected_keys: unexpected state dict keys + error_msgs: error messages + """ + if "modelopt_state" in state_dict: + state_dict.pop("modelopt_state") + + key_with_te_extra_state_to_pop = [] + + for key, _ in state_dict.items(): + if "_extra_state" in key: + key_with_te_extra_state_to_pop += [key] + + for key in key_with_te_extra_state_to_pop: + state_dict.pop(key) + + module_name_rewrite_list = [ + ("self_attention.linear_qkv.layer_norm_weight", "input_layernorm.weight"), + ("self_attention.linear_qkv.layer_norm_bias", "input_layernorm.bias"), + ("mlp.linear_fc1.layer_norm_weight", "pre_mlp_layernorm.weight"), + ("mlp.linear_fc1.layer_norm_bias", "pre_mlp_layernorm.bias"), + ] + + key_rewrite_list = [] + + for key, _ in state_dict.items(): + for old_name, new_name in module_name_rewrite_list: + if old_name in key: + key_rewrite_list += [(key, key.replace(old_name, new_name))] + + for old_key, new_key in key_rewrite_list: + if torch.distributed.get_rank() == 0: + logger.info("replace {} with {}".format(old_key, new_key)) + state_dict[new_key] = state_dict[old_key] + state_dict.pop(old_key) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/inference_params.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/inference_params.py new file mode 100644 index 0000000..2879024 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/inference_params.py @@ -0,0 +1,27 @@ +class InferenceParams: + """Inference parameters that are passed to the main model in order + to efficienly calculate and store the context during inference.""" + + def __init__(self, max_batch_size, max_sequence_length): + self.max_sequence_length = max_sequence_length + self.max_batch_size = max_batch_size + self.sequence_len_offset = 0 + self.batch_size_offset = 0 + self.key_value_memory_dict = {} + + def swap_key_value_dict(self, batch_idx): + "swap between batches" + if len(self.key_value_memory_dict) == 0: + raise ValueError("should not swap when dict in empty") + + for layer_number in self.key_value_memory_dict.keys(): + inference_key_memory, inference_value_memory = self.key_value_memory_dict[layer_number] + assert ( + len(batch_idx) == inference_key_memory.shape[1] + ) # make sure batch size is the same + new_inference_key_memory = inference_key_memory[:, batch_idx] + new_inference_value_memory = inference_value_memory[:, batch_idx] + self.key_value_memory_dict[layer_number] = ( + new_inference_key_memory, + new_inference_value_memory, + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/jit.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/jit.py new file mode 100644 index 0000000..8bb18d3 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/jit.py @@ -0,0 +1,11 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import torch + +TORCH_MAJOR = int(torch.__version__.split(".")[0]) +TORCH_MINOR = int(torch.__version__.split(".")[1]) + +jit_fuser = torch.jit.script +# nvFuser is deprecated in PyTorch JIT starting from 2.2 +if (TORCH_MAJOR > 2) or (TORCH_MAJOR == 2 and TORCH_MINOR >= 2): + jit_fuser = torch.compile diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/model_parallel_config.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/model_parallel_config.py new file mode 100644 index 0000000..f9d2dea --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/model_parallel_config.py @@ -0,0 +1,310 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +from dataclasses import dataclass +from typing import Callable, ContextManager, Optional + +import torch + + +@dataclass +class ModelParallelConfig: + """Base configuration for Megatron Core + + The initialization function has an argument for each parameter. + """ + + ################### + # Model parallelism + ################### + tensor_model_parallel_size: int = 1 + """Intra-layer model parallelism. Splits tensors across GPU ranks.""" + + pipeline_model_parallel_size: int = 1 + """Inter-layer model parallelism. Splits transformer layers across GPU ranks.""" + + virtual_pipeline_model_parallel_size: Optional[int] = None + """Interleaved pipeline parallelism is used to improve performance by reducing the pipeline + bubble. Considers a transformer block as a list of smaller transformer (virtual) blocks. + The number of virtual blocks per pipeline model parallel rank is the virtual model parallel + size. See Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM: + arxiv.org/pdf/2104.04473.pdf for more details. + """ + + sequence_parallel: bool = False + """Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms + and dropout sequentially. See Reducing Activation Recomputation in Large Transformer Models + (https://arxiv.org/abs/2205.05198) for more details. + """ + + context_parallel_size: int = 1 + """Splits network input along sequence dimension across GPU ranks.""" + + expert_model_parallel_size: int = 1 + """Distributes Moe Experts across sub data parallel dimension.""" + + ################### + # Initialization + ################### + perform_initialization: bool = True + """If true, weights are initialized. This option can be useful when you know you are going to + load values from a checkpoint. + """ + + use_cpu_initialization: bool = False + """When set to False, we initialize the weights directly on the GPU. CPU initialization is the + same regardless of tensor model parallelism, but GPU initialization is not. Transferring + weights from CPU to GPU can take a significant amount of time for large models. + """ + + ################### + # Training + ################### + fp16: bool = False + """If true, train with fp16 mixed precision training.""" + + bf16: bool = False + """If true, train with bf16 mixed precision training.""" + + params_dtype: torch.dtype = torch.float32 + """dtype used when intializing the weights.""" + + timers: Callable = None + """Timers object to call for various timing functions. See megatron.core.timers.Timers""" + + finalize_model_grads_func: Callable = None + """Function that finalizes gradients on all workers. Could include ensuring that grads are + all-reduced across data parallelism, pipeline parallelism, and sequence parallelism + dimensions. + """ + + grad_scale_func: Callable = None + """If using loss scaling, this function should take the loss and return the scaled loss. If + None, no function is called on the loss. + """ + + no_sync_func: Callable = None + """Function that creates a context that suppresses asynchronous data-parallel communication. If + the model is an instance of core.distributed.DistributedDataParallel, the default is to use + core.distributed.DistributedDataParallel.no_sync. + """ + + grad_sync_func: Callable = None + """Function that launches asynchronous gradient reductions (e.g. distributed optimizer gradient + reduce-scatters). The function should take one argument: an iterable of parameters whose + gradients are to be synchronized. + """ + + param_sync_func: Callable = None + """Function that launches asynchronous parameter synchronizations (e.g. distributed optimizer + parameter all-gathers). The function should take one argument: an iterable of parameters to + be synchronized. + """ + + enable_autocast: bool = False + """If true runs the forward step function inside torch.autocast context.""" + + autocast_dtype: torch.dtype = None + """dtype to pass to torch.amp.autocast when enabled. If None, is set to pipeline_dtype.""" + + num_microbatches_with_partial_activation_checkpoints: Optional[int] = None + """If int, set the number of microbatches where not all of the layers will be checkpointed and + recomputed. The rest of the microbatches within the window of maximum outstanding + microbatches will recompute all layers (either full recompute or selective recompute). If + None, the checkpoint and recompute will be left up to the forward_step function. + + """ + + ################### + # Optimizations + ################### + gradient_accumulation_fusion: bool = False + """If true, fuses weight gradient accumulation to GEMMs. Requires the custom CUDA extension + fused_weight_gradient_mlp_cuda module. To use gradient_accumulation_fusion you must install + APEX with --cpp_ext and --cuda_ext. For example: "pip install --global-option=\"--cpp_ext\" + --global-option=\"--cuda_ext\" ". Note that the extension requires CUDA>=11. Otherwise, you + must turn off gradient accumulation fusion. + """ + + async_tensor_model_parallel_allreduce: bool = False + """If true, enables asynchronous execution of tensor-model-parallel all-reduce with weight + gradient compuation of a column-linear layer. + """ + + use_te_rng_tracker: bool = False + """If true, uses RNG state tracker in TransformerEngine if exists. + """ + + tp_comm_overlap: bool = False + """If true, allows overlapping of Linear layer execution with tensor parallel communication + collectives like AllGather/ReduceScatter. Overlapping is done for the linear layers wherever + possible during the forward and the backward pass. + """ + + tp_comm_bulk_wgrad: bool = True + """If true, allows All-Gather overlap with Bprop activation gradient GEMM. Don't care if + tp_comm_overlap is False. + """ + + tp_comm_bulk_dgrad: bool = True + """If true, allows Reduce-Scatter overlap with Bprop weight gradient GEMM. Don't care if + tp_comm_overlap is False. + """ + + tp_comm_overlap_ag: bool = True + """If true, allows All-Gather overlap with GEMM by pipelining the GEMM and All-Gather. + Don't care if tp_comm_overlap is False. + """ + + tp_comm_overlap_rs: bool = True + """If true, allows Reduce-Scatter overlap with GEMM by pipelining the GEMM and Reduce-Scatter. + Don't care if tp_comm_overlap is False. + """ + + tp_comm_overlap_rs_dgrad: bool = False + """If true, allows Reduce-Scatter overlap with DGRAD GEMM by pipelining the + GEMM and Reduce-Scatter splits. Don't care if tp_comm_overlap is False. + """ + + tp_comm_split_ag: bool = True + """Deprecated from TransformerEngine v1.6.0. + If true, allows All-Gather overlap with Fprop GEMM by pipelining the GEMM and All-Gather + splits. Don't care if tp_comm_overlap is False. + """ + + tp_comm_atomic_ag: bool = False + """Deprecated from TransformerEngine v1.6.0. + If true, allows All-Gather overlap with Fprop GEMM by pipelining the GEMM and All-Gather both + done atomically. Don't care if tp_comm_overlap is False. + """ + + tp_comm_split_rs: bool = True + """Deprecated from TransformerEngine v1.6.0. + If true, allows Reduce-Scatter overlap with Fprop GEMM by pipelining the GEMM and + Reduce-Scatter splits. Don't care if tp_comm_overlap is False. + """ + + tp_comm_atomic_rs: bool = False + """Deprecated from TransformerEngine v1.6.0. + If true, allows Reduce-Scatter overlap with Fprop GEMM by pipelining the GEMM and + Reduce-Scatter both done atomically. Don't care if tp_comm_overlap is False. + """ + + tp_comm_disable_qkv: bool = False + """ + If true, the AllGather -> Gemm overlap for QKV gets disabled + """ + + tp_comm_disable_fc1: bool = False + """ + If true, the AllGather -> Gemm overlap for FC1 layer of MLP gets disabled + """ + + ################### + # Pipeline Parallel + ################### + pipeline_dtype: torch.dtype = None + """dtype used in p2p communication, usually params_dtype""" + + variable_seq_lengths: bool = False + """Support for variable sequence lengths across microbatches. Setting this communicates the size + of tensors during pipeline parallelism communication, because of this extra overhead it + should only be set if the sequence length varies by microbatch within a global batch. + """ + + overlap_p2p_comm: bool = False + """When True some of the peer to peer communication for pipeline parallelism will overlap with + computation. Must be False if batch_p2p_comm is true. + """ + + batch_p2p_comm: bool = True + """Use batch_isend_irecv instead of individual isend/irecv calls. Must be False if + overlap_p2p_comm is True. + """ + + batch_p2p_sync: bool = True + """When using batch_isend_irecv, do a cuda.device.synchronize afterward to work around a bug in + older version of PyTorch. + """ + + use_ring_exchange_p2p: bool = False + """Use custom ring_exchange kernel instead of torch.distributed.batch_isend_irecv(). Requires + custom built torch with torch.distributed.ring_exchange. + """ + + deallocate_pipeline_outputs: bool = False + """If True, output data is deallocated after the tensor is sent to the next pipeline stage. + Helps with saving memory, does nothing when pipeline parallel is not used. + """ + + defer_embedding_wgrad_compute: bool = False + """If true, defers the embedding WGRAD GEMMs while pipeline flush is + taking place enabling us to hide pipeline flush latency. Defaults to False. + """ + + pipeline_model_parallel_split_rank: Optional[int] = None + """If int, rank where encoder and decoder should be split in cases where the model has both an + encoder and decoder (e.g., T5). Ignored if None. + """ + + ################### + # CPU Offloading + ################### + cpu_offloading: bool = False + """When set to True, all the activations are offloaded to the CPU asynchronously.""" + + cpu_offloading_num_layers: int = 0 + """Tells the number of transformer layers for which activations has to be offloaded.""" + + _cpu_offloading_context: ContextManager = None # Used for internal use only, not to be set by the user. TODO: Need to move to the 'right' place when possible. + """For internal use only, do not set.""" + + cpu_offloading_activations: bool = True + """If True, offloads the activations to CPU.""" + + cpu_offloading_weights: bool = True + """If True, offloads the weights to CPU.""" + + ################### + # Timing + ################### + barrier_with_L1_time: bool = True + """If true, use barrier with level 1 time measurements. It is up to the user to make sure + calling barrier with their timers will not result in hangs. This can happen if for example + the user adds a level 1 timer that is not called by all ranks. + """ + + def __post_init__(self): + """ Python dataclass method that is used to modify attributes after initialization. + See https://docs.python.org/3/library/dataclasses.html#post-init-processing for more details. + """ + if self.sequence_parallel: + if self.tensor_model_parallel_size <= 1: + raise ValueError("Can not use sequence paralllelism without tensor parallelism") + if self.async_tensor_model_parallel_allreduce: + # sequence_parallelism already does this async + self.async_tensor_model_parallel_allreduce = False + + if self.pipeline_model_parallel_size > 1: + if self.pipeline_dtype is None: + raise ValueError( + "When using pipeline parallelism, pipeline_dtype must be specified" + ) + + if self.autocast_dtype is None: + self.autocast_dtype = self.params_dtype + + if self.defer_embedding_wgrad_compute and self.pipeline_model_parallel_size == 1: + raise ValueError( + "Cannot defer embedding wgrad compute when pipeline model parallel is not used" + ) + + if self.defer_embedding_wgrad_compute and not self.gradient_accumulation_fusion: + raise ValueError( + "Cannot defer embedding wgrad compute when gradient accumulation fusion is not used" + ) + + if self.expert_model_parallel_size > 1 and self.tensor_model_parallel_size > 1: + if self.sequence_parallel is False: + raise ValueError( + "When using expert parallelism and tensor parallelism, sequence parallelism must be used" + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/T5/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/T5/__init__.py new file mode 100644 index 0000000..f65859a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/T5/__init__.py @@ -0,0 +1 @@ +from .t5_model import T5Model diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/T5/t5_model.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/T5/t5_model.py new file mode 100644 index 0000000..b00ae67 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/T5/t5_model.py @@ -0,0 +1,434 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import logging +from typing import List, Literal, Optional, Tuple + +import torch +from torch import Tensor + +from megatron.core import InferenceParams, parallel_state, tensor_parallel +from megatron.core.dist_checkpointing.mapping import ShardedStateDict +from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding +from megatron.core.models.common.embeddings.rotary_pos_embedding import RotaryEmbedding +from megatron.core.models.common.language_module.language_module import LanguageModule +from megatron.core.transformer.enums import AttnMaskType, ModelType +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_block import TransformerBlock +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.utils import make_tp_sharded_tensor_for_checkpoint + + +class T5LMHead(MegatronModule): + """Masked LM head for T5 + + Args: + config (TransformerConfig): transformer config + parallel_output (bool): wether output logits being distributed or not. + vocab_size (int): vocabulary size + pre_process (bool): Include embedding layer + share_embeddings_and_output_weights (bool): When True, input embeddings and output logit weights are + shared. + """ + + def __init__( + self, + config: TransformerConfig, + parallel_output: bool, + vocab_size: int, + pre_process: bool = True, + share_embeddings_and_output_weights: bool = False, + ): + super(T5LMHead, self).__init__(config=config) + + self.parallel_output = parallel_output + + self.output_layer = tensor_parallel.ColumnParallelLinear( + config.hidden_size, + vocab_size, + config=config, + init_method=config.init_method, + bias=share_embeddings_and_output_weights, + skip_bias_add=not share_embeddings_and_output_weights, + gather_output=not self.parallel_output, + skip_weight_param_allocation=pre_process and share_embeddings_and_output_weights, + ) + + def forward(self, hidden_states: Tensor, word_embeddings_weight: Tensor) -> Tensor: + """Forward pass. + + Args: + hidden_states (Tensor): output hidden states from decoder + word_embeddings_weight (Tensor): word embedding weight + + Returns: + Tensor: logits tensor + """ + + logits, _ = self.output_layer(hidden_states, weight=word_embeddings_weight) + return logits + + +class T5Model(LanguageModule): + """T5 Language model. + + Args: + config (TransformerConfig): transformer config + + transformer_encoder_layer_spec (ModuleSpec): transformer layer customization specs for encoder + + transformer_decoder_layer_spec (ModuleSpec): transformer layer customization specs for decoder + + vocab_size (int): vocabulary size + + max_sequence_length (int): maximum size of sequence. This is used for positional embedding + + pre_process (bool): Include embedding layer (used with pipeline parallelism) + post_process (bool): Include an output layer (used with pipeline parallelism) + + fp16_lm_cross_entropy (bool, optional): Defaults to False + + parallel_output (bool): Do not gather the outputs, keep them split across tensor parallel ranks + + share_embeddings_and_output_weights (bool): When True, input embeddings and output logit weights are + shared. Defaults to False. + + position_embedding_type (string): Position embedding type. Options ['learned_absolute', 'rope']. + Defaults is 'learned_absolute'. + + rotary_percent (float): Percent of rotary dimension to use for rotary position embeddings. + Defaults to 1.0 (100%). Ignored unless position_embedding_type is 'rope'. + + seq_len_interpolation_factor (float): scale of linearly interpolating RoPE for longer sequences. + The value must be a float larger than 1.0. Defaults to None. + """ + + def __init__( + self, + config: TransformerConfig, + transformer_encoder_layer_spec: ModuleSpec, + transformer_decoder_layer_spec: ModuleSpec, + vocab_size: int, + max_sequence_length: int, + pre_process: bool = True, + post_process: bool = True, + fp16_lm_cross_entropy: bool = False, + parallel_output: bool = True, + share_embeddings_and_output_weights: bool = False, + position_embedding_type: Literal['learned_absolute', 'rope'] = 'learned_absolute', + rotary_percent: float = 1.0, + seq_len_interpolation_factor: Optional[float] = None, + ): + + super(T5Model, self).__init__(config=config) + + self.config: TransformerConfig = config + self.transformer_encoder_layer_spec: ModuleSpec = transformer_encoder_layer_spec + self.transformer_decoder_layer_spec: ModuleSpec = transformer_decoder_layer_spec + self.vocab_size = vocab_size + self.max_sequence_length = max_sequence_length + self.pre_process = pre_process + self.post_process = post_process + self.add_encoder = True + self.add_decoder = True + self.fp16_lm_cross_entropy = fp16_lm_cross_entropy + self.parallel_output = parallel_output + self.share_embeddings_and_output_weights = share_embeddings_and_output_weights + self.position_embedding_type = position_embedding_type + + # megatron core pipelining currently depends on model type + self.model_type = ModelType.encoder_and_decoder + + # Embeddings. + if self.pre_process: + self.embedding = LanguageModelEmbedding( + config=self.config, + vocab_size=self.vocab_size, + max_sequence_length=self.max_sequence_length, + position_embedding_type=self.position_embedding_type, + ) + + # Rotary Position Embeddings + if self.position_embedding_type == 'rope': + self.rotary_pos_emb = RotaryEmbedding( + kv_channels=self.config.kv_channels, + rotary_percent=rotary_percent, + rotary_interleaved=self.config.rotary_interleaved, + seq_len_interpolation_factor=seq_len_interpolation_factor, + ) + + # Transformer encoder + encoder_spec, decoder_spec = ( + self.transformer_encoder_layer_spec, + self.transformer_decoder_layer_spec, + ) + self.encoder = TransformerBlock( + config=self.config, + spec=encoder_spec, + pre_process=self.pre_process, + post_process=self.post_process, + ) + # Transformer decoder + self.decoder = TransformerBlock( + config=self.config, + spec=decoder_spec, + pre_process=self.pre_process, + post_process=self.post_process, + ) + + # Output + if post_process: + self.lm_head = T5LMHead( + config, + parallel_output, + self.vocab_size, + self.pre_process, + self.share_embeddings_and_output_weights, + ) + self.output_layer = self.lm_head.output_layer + + if self.pre_process or self.post_process: + self.setup_embeddings_and_output_layer() + + def forward( + self, + encoder_input_ids: Tensor, + decoder_input_ids: Tensor, + encoder_attn_mask: Tensor, + decoder_attn_mask: Tensor, + encoder_decoder_attn_mask: Tensor, + lm_labels: Tensor = None, + inference_params: InferenceParams = None, + ) -> Tensor: + """Forward pass. + + Args: + encoder_input_ids (Tensor): input ids for encoder + decoder_input_ids (Tensor): input ids for decoder + encoder_attn_mask (Tensor): self-attention mask for encoder + decoder_attn_mask (Tensor): self-attention mask for decoder + encoder_decoder_attn_mask (Tensor): cross-attention mask between encoder and decoder + lm_labels (Tensor): labels for decoder output + inference_params (InferenceParams): relevant arguments for inferencing + + Returns: + Tensor: loss tensor + """ + + ( + encoder_attn_mask, + decoder_attn_mask, + encoder_decoder_attn_mask, + ) = t5_extended_attention_mask( + [encoder_attn_mask, decoder_attn_mask, encoder_decoder_attn_mask] + ) + encoder_position_ids = t5_position_ids(encoder_input_ids) + decoder_position_ids = t5_position_ids(decoder_input_ids) + + ## Encoder forward + # Encoder embedding. + if self.pre_process: + encoder_input = self.embedding( + input_ids=encoder_input_ids, position_ids=encoder_position_ids + ) + else: + # intermediate stage of pipeline + encoder_input = None + + # Rotary positional embeddings + rotary_pos_emb = None + if self.position_embedding_type == 'rope': + rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len( + inference_params, self.encoder, encoder_input, self.config + ) + rotary_pos_emb = self.rotary_pos_emb(rotary_seq_len) + + # Run encoder. + encoder_hidden_states = self.encoder( + hidden_states=encoder_input, + attention_mask=encoder_attn_mask, + inference_params=inference_params, + rotary_pos_emb=rotary_pos_emb, + ) + + ## Decoder forward + # Decoder embedding. + if self.pre_process: + decoder_input = self.embedding( + input_ids=decoder_input_ids, position_ids=decoder_position_ids + ) + else: + # intermediate stage of pipeline + decoder_input = None ### should it take encoder_hidden_states + + # Rotary positional embeddings + rotary_pos_emb = None + if self.position_embedding_type == 'rope': + rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len( + inference_params, self.decoder, decoder_input, self.config + ) + rotary_pos_emb = self.rotary_pos_emb(rotary_seq_len) + + # Run decoder. + decoder_hidden_states = self.decoder( + hidden_states=decoder_input, + attention_mask=decoder_attn_mask, + context=encoder_hidden_states, + context_mask=encoder_decoder_attn_mask, + inference_params=inference_params, + rotary_pos_emb=rotary_pos_emb, + ) + + # Return if not post_process + if not self.post_process: + return decoder_hidden_states + + # logits and loss + output_weight = None + if self.share_embeddings_and_output_weights: + output_weight = self.shared_embedding_or_output_weight() + logits = self.lm_head(decoder_hidden_states, word_embeddings_weight=output_weight) + + if lm_labels is None: + # [s b h] => [b s h] + return logits.transpose(0, 1).contiguous() + + loss = self.compute_language_model_loss(lm_labels, logits) + + return loss + + def set_input_tensor(self, input_tensor): + """ See megatron.model.transformer.set_input_tensor()""" + + # This is usually handled in schedules.py but some inference code still + # gives us non-lists or None + if not isinstance(input_tensor, list): + input_tensor = [input_tensor] + + if self.add_encoder and self.add_decoder: + assert ( + len(input_tensor) == 1 + ), 'input_tensor should only be length 1 for stage with both encoder and decoder' + self.encoder.set_input_tensor(input_tensor[0]) + elif self.add_encoder: + assert ( + len(input_tensor) == 1 + ), 'input_tensor should only be length 1 for stage with only encoder' + self.encoder.set_input_tensor(input_tensor[0]) + elif self.add_decoder: + if len(input_tensor) == 2: + self.decoder.set_input_tensor(input_tensor[0]) + self.encoder_hidden_state = input_tensor[1] + elif len(input_tensor) == 1: + self.decoder.set_input_tensor(None) + self.encoder_hidden_state = input_tensor[0] + else: + raise Exception('input_tensor must have either length 1 or 2') + else: + raise Exception('Stage must have at least either encoder or decoder') + + def shared_embedding_or_output_weight(self) -> Tensor: + """Function to share the input embeddings and output logit weights.""" + + if self.pre_process: + return self.embedding.word_embeddings.weight + elif self.post_process: + return self.lm_head.output_layer.weight + return None + + def sharded_state_dict( + self, prefix: str = '', sharded_offsets: tuple = (), metadata: Optional[dict] = None + ) -> ShardedStateDict: + assert not sharded_offsets, "Unexpected sharded offsets" + sharded_state_dict = {} + + if self.pre_process: + embedding_prefix = f'{prefix}embedding.' + embedding_sharded_state_dict = self.embedding.sharded_state_dict( + prefix=embedding_prefix, metadata=metadata + ) + sharded_state_dict.update(embedding_sharded_state_dict) + + encoder_prefix = f'{prefix}encoder.' + encoder_sharded_state_dict = self.encoder.sharded_state_dict( + prefix=encoder_prefix, metadata=metadata + ) + sharded_state_dict.update(encoder_sharded_state_dict) + + decoder_prefix = f'{prefix}decoder.' + decoder_sharded_state_dict = self.decoder.sharded_state_dict( + prefix=decoder_prefix, metadata=metadata + ) + sharded_state_dict.update(decoder_sharded_state_dict) + + if self.post_process: + output_layer_prefix = f'{prefix}output_layer.' + output_layer_weight_key = f'{output_layer_prefix}weight' + output_layer_bias_key = f'{output_layer_prefix}bias' + if self.share_embeddings_and_output_weights: + if not self.pre_process: + # when sharing embeddings with last stage, we need to use the weights from the first stage + # on pipeline first rank, word embeddings are saved to {prefix}embedding.word_embeddings.weight + tensor = self.shared_embedding_or_output_weight() + first_stage_word_emb_key = f'{prefix}embedding.word_embeddings.weight' + dp_rank = parallel_state.get_data_parallel_rank() + dp_size = parallel_state.get_data_parallel_world_size() + last_stage_word_emb_replica_id = ( + dp_rank + dp_size + ) # copy of first stage embedding + + sharded_output_layer_tensor = make_tp_sharded_tensor_for_checkpoint( + tensor=tensor, + key=first_stage_word_emb_key, + replica_id=last_stage_word_emb_replica_id, + allow_shape_mismatch=True, + ) + + sharded_state_dict[output_layer_weight_key] = sharded_output_layer_tensor + # output_layer.weight is shared, but we still need to process output_layer.bias + sharded_output_layer_tensor = make_tp_sharded_tensor_for_checkpoint( + tensor=self.lm_head.output_layer.bias, + key=output_layer_bias_key, + allow_shape_mismatch=True, + ) + sharded_state_dict[output_layer_bias_key] = sharded_output_layer_tensor + else: + output_layer_state_dict = self.output_layer.state_dict( + prefix=output_layer_prefix, keep_vars=True + ) + output_layer_tensor = output_layer_state_dict[output_layer_weight_key] + # independent output layer + sharded_output_layer_tensor = make_tp_sharded_tensor_for_checkpoint( + tensor=output_layer_tensor, + key=output_layer_weight_key, + replica_id=parallel_state.get_data_parallel_rank(), + allow_shape_mismatch=True, + ) + + sharded_state_dict[output_layer_weight_key] = sharded_output_layer_tensor + + return sharded_state_dict + + +def t5_extended_attention_mask(attention_mask_list: List[Tensor]) -> List[Tensor]: + def attn_mask_postprocess(attn_mask): + # [b, 1, s, s] + extended_attention_mask = attn_mask.unsqueeze(1) + return extended_attention_mask + + return [attn_mask_postprocess(attn_mask) for attn_mask in attention_mask_list] + + +def t5_position_ids(token_ids: Tensor) -> Tensor: + """Calculate position ids from token ids + Args: + token_ids (Tensor): input tokens + + Returns: + Tensor: position ids + """ + seq_length = token_ids.size(1) + position_ids = torch.arange(seq_length, dtype=torch.long, device=token_ids.device) + position_ids = position_ids.unsqueeze(0).expand_as(token_ids) + + return position_ids diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/T5/t5_spec.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/T5/t5_spec.py new file mode 100644 index 0000000..4776191 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/T5/t5_spec.py @@ -0,0 +1,229 @@ +from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add +from megatron.core.fusions.fused_layer_norm import FusedLayerNorm +from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear +from megatron.core.transformer.attention import ( + CrossAttention, + CrossAttentionSubmodules, + SelfAttention, + SelfAttentionSubmodules, +) +from megatron.core.transformer.custom_layers.transformer_engine import ( + TEColumnParallelLinear, + TEDotProductAttention, + TELayerNormColumnParallelLinear, + TENorm, + TERowParallelLinear, +) +from megatron.core.transformer.dot_product_attention import DotProductAttention +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.identity_op import IdentityOp +from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_block import ( + TransformerBlockSubmodules, + get_num_layers_to_build, +) +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules + + +def encoder_model_with_transformer_engine_default_spec() -> ModuleSpec: + """T5 encoder TE spec (uses Transformer Engine components).""" + + return ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": AttnMaskType.padding}, + submodules=SelfAttentionSubmodules( + linear_qkv=TELayerNormColumnParallelLinear, + core_attention=TEDotProductAttention, + linear_proj=TERowParallelLinear, + q_layernorm=IdentityOp, + k_layernorm=IdentityOp, + ), + ), + self_attn_bda=get_bias_dropout_add, + mlp=ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear, + ), + ), + mlp_bda=get_bias_dropout_add, + ), + ) + + +def decoder_model_with_transformer_engine_default_spec() -> ModuleSpec: + """T5 decoder TE spec (uses Transformer Engine components).""" + + return ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=SelfAttentionSubmodules( + linear_qkv=TELayerNormColumnParallelLinear, + core_attention=TEDotProductAttention, + linear_proj=TERowParallelLinear, + q_layernorm=IdentityOp, + k_layernorm=IdentityOp, + ), + ), + self_attn_bda=get_bias_dropout_add, + pre_cross_attn_layernorm=TENorm, + cross_attention=ModuleSpec( + module=CrossAttention, + submodules=CrossAttentionSubmodules( + linear_q=TEColumnParallelLinear, + linear_kv=TEColumnParallelLinear, + core_attention=TEDotProductAttention, + linear_proj=TERowParallelLinear, + ), + ), + cross_attn_bda=get_bias_dropout_add, + mlp=ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear, + ), + ), + mlp_bda=get_bias_dropout_add, + ), + ) + + +def encoder_model_with_local_spec() -> ModuleSpec: + """T5 encoder local spec (uses Megatron-Core components).""" + + return ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + input_layernorm=FusedLayerNorm, + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": AttnMaskType.padding}, + submodules=SelfAttentionSubmodules( + linear_qkv=ColumnParallelLinear, + core_attention=DotProductAttention, + linear_proj=RowParallelLinear, + q_layernorm=IdentityOp, + k_layernorm=IdentityOp, + ), + ), + self_attn_bda=get_bias_dropout_add, + pre_mlp_layernorm=FusedLayerNorm, + mlp=ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear, + ), + ), + mlp_bda=get_bias_dropout_add, + sharded_state_dict_keys_map={ + 'input_layernorm.': 'self_attention.linear_qkv.layer_norm_', + 'pre_mlp_layernorm.': 'mlp.linear_fc1.layer_norm_', + }, + ), + ) + + +def decoder_model_with_local_spec() -> ModuleSpec: + """T5 decoder local spec (uses Megatron-Core components).""" + + return ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + input_layernorm=FusedLayerNorm, + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=SelfAttentionSubmodules( + linear_qkv=ColumnParallelLinear, + core_attention=DotProductAttention, + linear_proj=RowParallelLinear, + q_layernorm=IdentityOp, + k_layernorm=IdentityOp, + ), + ), + self_attn_bda=get_bias_dropout_add, + pre_cross_attn_layernorm=FusedLayerNorm, + cross_attention=ModuleSpec( + module=CrossAttention, + submodules=CrossAttentionSubmodules( + linear_q=ColumnParallelLinear, + linear_kv=ColumnParallelLinear, + core_attention=DotProductAttention, + linear_proj=RowParallelLinear, + ), + ), + cross_attn_bda=get_bias_dropout_add, + pre_mlp_layernorm=FusedLayerNorm, + mlp=ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear, + ), + ), + mlp_bda=get_bias_dropout_add, + sharded_state_dict_keys_map={ + 'input_layernorm.': 'self_attention.linear_qkv.layer_norm_', + 'pre_mlp_layernorm.': 'mlp.linear_fc1.layer_norm_', + }, + ), + ) + + +def get_t5_encoder_with_transformer_engine_block_spec( + num_layers: int, +) -> TransformerBlockSubmodules: + """T5 encoder block spec for Transformer Engine + + Args: + config (TransformerConfig): config, containing number of layers for encoder + """ + + layer_spec = encoder_model_with_transformer_engine_default_spec() + block_spec = TransformerBlockSubmodules([layer_spec] * num_layers) + return block_spec + + +def get_t5_decoder_with_transformer_engine_block_spec( + num_layers: int, +) -> TransformerBlockSubmodules: + """T5 decoder block spec for Transformer Engine + + Args: + config (TransformerConfig): config, containing number of layers for decoder + """ + + layer_spec = decoder_model_with_transformer_engine_default_spec() + block_spec = TransformerBlockSubmodules([layer_spec] * num_layers) + return block_spec + + +def get_t5_encoder_with_local_block_spec(num_layers: int) -> TransformerBlockSubmodules: + """T5 encoder block spec for local (uses Megatron-Core components) + + Args: + num_layers (int): number of encoder layers + """ + + layer_spec = encoder_model_with_local_spec() + block_spec = TransformerBlockSubmodules([layer_spec] * num_layers) + return block_spec + + +def get_t5_decoder_with_local_block_spec(num_layers: int) -> TransformerBlockSubmodules: + """T5 decoder block spec for local (uses Megatron-Core components) + + Args: + num_layers (int): number of decoder layers + """ + + layer_spec = decoder_model_with_local_spec() + block_spec = TransformerBlockSubmodules([layer_spec] * num_layers) + return block_spec diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/bert_layer_specs.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/bert_layer_specs.py new file mode 100644 index 0000000..a668fcb --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/bert_layer_specs.py @@ -0,0 +1,73 @@ +from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add +from megatron.core.fusions.fused_layer_norm import FusedLayerNorm +from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear +from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules +from megatron.core.transformer.custom_layers.transformer_engine import ( + TEDotProductAttention, + TELayerNormColumnParallelLinear, + TERowParallelLinear, +) +from megatron.core.transformer.dot_product_attention import DotProductAttention +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.identity_op import IdentityOp +from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules + +# Use this spec to use lower level Transformer Engine modules (required for fp8 training) +bert_layer_with_transformer_engine_spec = ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": AttnMaskType.padding}, + submodules=SelfAttentionSubmodules( + linear_qkv=TELayerNormColumnParallelLinear, + core_attention=TEDotProductAttention, + linear_proj=TERowParallelLinear, + q_layernorm=IdentityOp, + k_layernorm=IdentityOp, + ), + ), + self_attn_bda=get_bias_dropout_add, + mlp=ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear, + ), + ), + mlp_bda=get_bias_dropout_add, + ), +) + +# Use this spec for an implementation using only modules in megatron core +bert_layer_local_spec = ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + input_layernorm=FusedLayerNorm, + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": AttnMaskType.padding}, + submodules=SelfAttentionSubmodules( + linear_qkv=ColumnParallelLinear, + core_attention=DotProductAttention, + linear_proj=RowParallelLinear, + q_layernorm=IdentityOp, + k_layernorm=IdentityOp, + ), + ), + self_attn_bda=get_bias_dropout_add, + pre_mlp_layernorm=FusedLayerNorm, + mlp=ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear, + ), + ), + mlp_bda=get_bias_dropout_add, + sharded_state_dict_keys_map={ + 'input_layernorm.': 'self_attention.linear_qkv.layer_norm_', + 'pre_mlp_layernorm.': 'mlp.linear_fc1.layer_norm_', + }, + ), +) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/bert_lm_head.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/bert_lm_head.py new file mode 100644 index 0000000..74f2bde --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/bert_lm_head.py @@ -0,0 +1,41 @@ +import torch +from torch import Tensor + +from megatron.core.fusions.fused_layer_norm import FusedLayerNorm +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.transformer.utils import get_linear_layer + + +class BertLMHead(MegatronModule): + """Masked LM head for Bert. + + Args: + hidden_size: hidden size + config (TransformerConfig): TransformerConfig object + """ + + def __init__( + self, hidden_size: int, config: TransformerConfig, + ): + super().__init__(config=config) + + # TODO: Should switch this to TE ? + self.dense = get_linear_layer( + hidden_size, hidden_size, config.init_method, config.perform_initialization + ) + + setattr(self.dense.weight, 'sequence_parallel', config.sequence_parallel) + setattr(self.dense.bias, 'sequence_parallel', config.sequence_parallel) + + self.layer_norm = FusedLayerNorm( + config=config, hidden_size=hidden_size, eps=config.layernorm_epsilon, + ) + + self.gelu = torch.nn.functional.gelu + + def forward(self, hidden_states: Tensor) -> Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.gelu(hidden_states) + hidden_states = self.layer_norm(hidden_states) + return hidden_states diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/bert_model.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/bert_model.py new file mode 100644 index 0000000..19f5759 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/bert_model.py @@ -0,0 +1,280 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +import os +from collections import OrderedDict +from typing import Dict, Literal, Optional + +import torch +from torch import Tensor + +from megatron.core import parallel_state, tensor_parallel +from megatron.core.dist_checkpointing.mapping import ShardedStateDict +from megatron.core.models.bert.bert_lm_head import BertLMHead +from megatron.core.models.bert.pooler import Pooler +from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding +from megatron.core.models.common.embeddings.rotary_pos_embedding import RotaryEmbedding +from megatron.core.models.common.language_module.language_module import LanguageModule +from megatron.core.transformer.enums import AttnMaskType, ModelType +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_block import TransformerBlock +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.transformer.utils import get_linear_layer +from megatron.core.utils import make_tp_sharded_tensor_for_checkpoint + + +class BertModel(LanguageModule): + """Transformer language model. + + Args: + config (TransformerConfig): transformer config + num_tokentypes (int) : Set to 2 when args.bert_binary_head is True, and 0 otherwise. Defaults to 0. + transformer_layer_spec (ModuleSpec): Specifies module to use for transformer layers + vocab_size (int): vocabulary size + max_sequence_length (int): maximum size of sequence. This is used for positional embedding + pre_process (bool): Include embedding layer (used with pipeline parallelism) + post_process (bool): Include an output layer (used with pipeline parallelism) + parallel_output (bool): Do not gather the outputs, keep them split across tensor parallel ranks + share_embeddings_and_output_weights (bool): When True, input embeddings and output logit weights are shared. Defaults to False. + position_embedding_type (string): Position embedding type. Options ['learned_absolute', 'rope']. + Defaults is 'learned_absolute'. + rotary_percent (float): Percent of rotary dimension to use for rotary position embeddings. + Defaults to 1.0 (100%). Ignored unless position_embedding_type is 'rope'. + """ + + def __init__( + self, + config: TransformerConfig, + num_tokentypes: int, + transformer_layer_spec: ModuleSpec, + vocab_size: int, + max_sequence_length: int, + pre_process: bool = True, + post_process: bool = True, + fp16_lm_cross_entropy: bool = False, + parallel_output: bool = True, + share_embeddings_and_output_weights: bool = False, + position_embedding_type: Literal['learned_absolute', 'rope'] = 'learned_absolute', + rotary_percent: float = 1.0, + seq_len_interpolation_factor: Optional[float] = None, + add_binary_head=True, + return_embeddings=False, + ): + super(BertModel, self).__init__(config=config) + + if return_embeddings: + assert self.post_process and self.add_binary_head + + assert ( + os.getenv('NVTE_ALLOW_NONDETERMINISTIC_ALGO') == '0' + or os.getenv('NVTE_FLASH_ATTN') == '0' + ), "Bert currently does not support flash attention. Please set env variable NVTE_FLASH_ATTN=0 or set NVTE_ALLOW_NONDETERMINISTIC_ALGO=0" + + self.config: TransformerConfig = config + self.transformer_layer_spec: ModuleSpec = transformer_layer_spec + self.vocab_size = vocab_size + self.max_sequence_length = max_sequence_length + self.pre_process = pre_process + self.post_process = post_process + self.fp16_lm_cross_entropy = fp16_lm_cross_entropy + self.parallel_output = parallel_output + self.share_embeddings_and_output_weights = share_embeddings_and_output_weights + self.position_embedding_type = position_embedding_type + self.add_binary_head = add_binary_head + self.return_embeddings = return_embeddings + + # megatron core pipelining currently depends on model type + self.model_type = ModelType.encoder_or_decoder + + # Embeddings. + if self.pre_process: + self.embedding = LanguageModelEmbedding( + config=self.config, + vocab_size=self.vocab_size, + max_sequence_length=self.max_sequence_length, + position_embedding_type=position_embedding_type, + num_tokentypes=num_tokentypes, + ) + + if self.position_embedding_type == 'rope': + self.rotary_pos_emb = RotaryEmbedding( + kv_channels=self.config.kv_channels, + rotary_percent=rotary_percent, + rotary_interleaved=self.config.rotary_interleaved, + seq_len_interpolation_factor=seq_len_interpolation_factor, + ) + + # Transformer. + self.encoder = TransformerBlock( + config=self.config, + spec=self.transformer_layer_spec, + pre_process=self.pre_process, + post_process=self.post_process, + ) + + # Output + if post_process: + # TODO: Make sure you are passing in the mpu_vocab_size properly + self.lm_head = BertLMHead(config.hidden_size, config,) + + self.output_layer = tensor_parallel.ColumnParallelLinear( + config.hidden_size, + self.vocab_size, + config=config, + init_method=config.init_method, + bias=True, + skip_bias_add=False, + gather_output=not self.parallel_output, + skip_weight_param_allocation=pre_process and share_embeddings_and_output_weights, + ) + + self.binary_head = None + if self.add_binary_head: + # TODO: Shoudl switch this to TE ? + self.binary_head = get_linear_layer( + config.hidden_size, 2, config.init_method, config.perform_initialization + ) + + self.pooler = Pooler( + config.hidden_size, config.init_method, config, config.sequence_parallel + ) + + if self.pre_process or self.post_process: + self.setup_embeddings_and_output_layer() + + def bert_extended_attention_mask(self, attention_mask: Tensor) -> Tensor: + """Creates the extended attention mask + + Converts the attention mask of dimension [batch size, 1, seq len] to [batch size, 1, seq len, seq len] and makes it binary + + Args: + attention_mask (Tensor): The input attention mask + + Returns: + Tensor: The extended binary attention mask + """ + # We create a 3D attention mask from a 2D tensor mask. + # [b, 1, s] + attention_mask_b1s = attention_mask.unsqueeze(1) + # [b, s, 1] + attention_mask_bs1 = attention_mask.unsqueeze(2) + # [b, s, s] + attention_mask_bss = attention_mask_b1s * attention_mask_bs1 + # [b, 1, s, s] + extended_attention_mask = attention_mask_bss.unsqueeze(1) + + # Convert attention mask to binary: + extended_attention_mask = extended_attention_mask < 0.5 + + return extended_attention_mask + + def bert_position_ids(self, token_ids): + # Create position ids + seq_length = token_ids.size(1) + position_ids = torch.arange(seq_length, dtype=torch.long, device=token_ids.device) + position_ids = position_ids.unsqueeze(0).expand_as(token_ids) + + return position_ids + + def set_input_tensor(self, input_tensor: Tensor) -> None: + """Sets input tensor to the model. + + See megatron.model.transformer.set_input_tensor() + + Args: + input_tensor (Tensor): Sets the input tensor for the model. + """ + # This is usually handled in schedules.py but some inference code still + # gives us non-lists or None + if not isinstance(input_tensor, list): + input_tensor = [input_tensor] + + assert len(input_tensor) == 1, 'input_tensor should only be length 1 for gpt/bert' + self.encoder.set_input_tensor(input_tensor[0]) + + def forward( + self, + input_ids: Tensor, + attention_mask: Tensor, + tokentype_ids: Tensor = None, + lm_labels: Tensor = None, + inference_params=None, + ): + """Forward function of BERT model + + Forward function of the BERT Model This function passes the input tensors + through the embedding layer, and then the encoder and finally into the post + processing layer (optional). + + It either returns the Loss values if labels are given or the final hidden units + """ + extended_attention_mask = self.bert_extended_attention_mask(attention_mask) + + if parallel_state.is_pipeline_first_stage(): + input_ids = input_ids + position_ids = self.bert_position_ids(input_ids) + else: + position_ids = None + input_ids = None + + # Encoder embedding. + if self.pre_process: + encoder_input = self.embedding( + input_ids=input_ids, position_ids=position_ids, tokentype_ids=tokentype_ids + ) + else: + # intermediate stage of pipeline + # encoder will get hidden_states from encoder.input_tensor + encoder_input = None + + # Rotary positional embeddings (Why not move this into BERT/GPTEmberdding ?) + rotary_pos_emb = None + if self.position_embedding_type == 'rope': + rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len( + inference_params, self.encoder, encoder_input, self.config + ) + rotary_pos_emb = self.rotary_pos_emb(rotary_seq_len) + + # Run encoder. + hidden_states = self.encoder( + hidden_states=encoder_input, + attention_mask=extended_attention_mask, + inference_params=inference_params, + rotary_pos_emb=rotary_pos_emb, + ) + if not self.post_process: + return hidden_states + + if self.add_binary_head: + pooled_output = self.pooler(hidden_states, 0) + + if self.return_embeddings: + embeddings = torch.transpose(hidden_states, 0, 1) + masks = torch.sum(attention_mask, dim=1) + # Collect masked embeddings. + output = torch.zeros( + size=(embeddings.shape[0], embeddings.shape[2]), + dtype=torch.float32, + device=torch.cuda.current_device(), + ) + for i, (embedding, mask) in enumerate(zip(embeddings, masks)): + output[i, :] = torch.mean(embedding[1 : mask - 1], dim=0) + return output + + # logits and loss + output_weight = None + if self.share_embeddings_and_output_weights: + output_weight = self.shared_embedding_or_output_weight() + + hidden_states_after_lm_head = self.lm_head(hidden_states=hidden_states) + logits, _ = self.output_layer(hidden_states_after_lm_head, weight=output_weight) + + binary_logits = None + if self.binary_head is not None: + binary_logits = self.binary_head(pooled_output) + + if lm_labels is None: + # [s b h] => [b s h] + return logits.transpose(0, 1).contiguous(), binary_logits + + loss = self.compute_language_model_loss(lm_labels, logits) + + return loss, binary_logits diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/pooler.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/pooler.py new file mode 100644 index 0000000..c144d8c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/bert/pooler.py @@ -0,0 +1,51 @@ +import torch +from torch import Tensor + +from megatron.core import tensor_parallel +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.transformer.utils import get_linear_layer + + +class Pooler(MegatronModule): + """Pooler layer. + + Pool hidden states of a specific token (for example start of the + sequence) and add a linear transformation followed by a tanh. + + Args: + hidden_size (int): The hidden size_ + init_method (callable): weight initialization method for the linear layer. bias is set to zero. + config (TransformerConfig): The transformer configuration + sequence_parallel (bool): Using squence parallel ? Defaults to False + """ + + def __init__( + self, + hidden_size: int, + init_method: callable, + config: TransformerConfig, + sequence_parallel: bool = False, + ): + super(Pooler, self).__init__(config) + # TODO: Shoudl switch this to TE ? + self.dense = get_linear_layer( + hidden_size, hidden_size, init_method, config.perform_initialization + ) + self.sequence_parallel = sequence_parallel + + def forward(self, hidden_states: Tensor, sequence_index=0): + # hidden_states: [s, b, h] + # sequence_index: index of the token to pool. + + # gather data along sequence dimensions + # same pooler is run on all tensor parallel nodes + if self.sequence_parallel: + hidden_states = tensor_parallel.gather_from_sequence_parallel_region( + hidden_states, tensor_parallel_output_grad=False + ) + + pooled = hidden_states[sequence_index, :, :] + pooled = self.dense(pooled) + pooled = torch.tanh(pooled) + return pooled diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/embeddings/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/embeddings/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/embeddings/language_model_embedding.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/embeddings/language_model_embedding.py new file mode 100644 index 0000000..d525a30 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/embeddings/language_model_embedding.py @@ -0,0 +1,129 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +from typing import Literal + +import torch +from torch import Tensor + +from megatron.core import tensor_parallel +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.transformer_config import TransformerConfig + + +class LanguageModelEmbedding(MegatronModule): + """Language model embeddings. + + Args: + config (TransformerConfig): config object with all necessary configs for TransformerBlock + vocab_size (int): vocabulary size + max_sequence_length (int): maximum size of sequence. This + is used for positional embedding + add_position_embedding (bool): Add a position embedding. + embedding_dropout_prob (float): dropout probability for embeddings + num_tokentypes (int): Set to 0 without binary head, and 2 with a binary head . Defaults to 0. + """ + + def __init__( + self, + config: TransformerConfig, + vocab_size: int, + max_sequence_length: int, + position_embedding_type: Literal['learned_absolute', 'rope'] = 'learned_absolute', + num_tokentypes: int = 0, + ): + super().__init__(config=config) + + self.config: TransformerConfig = config + self.vocab_size: int = vocab_size + self.max_sequence_length: int = max_sequence_length + self.add_position_embedding: bool = position_embedding_type == 'learned_absolute' + self.num_tokentypes = num_tokentypes + + # Word embeddings (parallel). + self.word_embeddings = tensor_parallel.VocabParallelEmbedding( + num_embeddings=self.vocab_size, + embedding_dim=self.config.hidden_size, + init_method=self.config.init_method, + config=self.config, + ) + + # Position embedding (serial). + if self.add_position_embedding: + self.position_embeddings = torch.nn.Embedding( + self.max_sequence_length, self.config.hidden_size + ) + + # Initialize the position embeddings. + if self.config.perform_initialization: + self.config.init_method(self.position_embeddings.weight) + + if self.num_tokentypes > 0: + self.tokentype_embeddings = torch.nn.Embedding( + self.num_tokentypes, self.config.hidden_size + ) + # Initialize the token-type embeddings. + if self.config.perform_initialization: + self.config.init_method(self.tokentype_embeddings.weight) + else: + self.tokentype_embeddings = None + + # Embeddings dropout + self.embedding_dropout = torch.nn.Dropout(self.config.hidden_dropout) + + def zero_parameters(self): + """Zero out all parameters in embedding.""" + self.word_embeddings.weight.data.fill_(0) + self.word_embeddings.weight.shared = True + self.position_embeddings.weight.data.fill_(0) + self.position_embeddings.weight.shared = True + if self.num_tokentypes > 0: + self.tokentype_embeddings.weight.data.fill_(0) + self.tokentype_embeddings.weight.shared = True + + def forward(self, input_ids: Tensor, position_ids: Tensor, tokentype_ids: int = None) -> Tensor: + """Forward pass of the embedding module. + + Args: + input_ids (Tensor): The input tokens + position_ids (Tensor): The position id's used to calculate position embeddings + tokentype_ids (int): The token type ids. Used when args.bert_binary_head is set to True. Defaults to None + + Returns: + Tensor: The output embeddings + """ + word_embeddings = self.word_embeddings(input_ids) + if self.add_position_embedding: + position_embeddings = self.position_embeddings(position_ids) + embeddings = word_embeddings + position_embeddings + else: + embeddings = word_embeddings + + # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + + if tokentype_ids is not None: + assert self.tokentype_embeddings is not None + # [b s h] -> [s b h] (So that it can be added with embeddings) + tokentype_embedding = self.tokentype_embeddings(tokentype_ids).permute(1, 0, 2) + embeddings = embeddings + tokentype_embedding + else: + assert self.tokentype_embeddings is None + + # If the input flag for fp32 residual connection is set, convert for float. + if self.config.fp32_residual_connection: + embeddings = embeddings.float() + + # Dropout. + if self.config.sequence_parallel: + embeddings = tensor_parallel.scatter_to_sequence_parallel_region(embeddings) + # `scatter_to_sequence_parallel_region` returns a view, which prevents + # the original tensor from being garbage collected. Clone to facilitate GC. + # Has a small runtime cost (~0.5%). + if self.config.clone_scatter_output_in_embedding: + embeddings = embeddings.clone() + with tensor_parallel.get_cuda_rng_tracker().fork(): + embeddings = self.embedding_dropout(embeddings) + else: + embeddings = self.embedding_dropout(embeddings) + + return embeddings diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/embeddings/rotary_pos_embedding.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/embeddings/rotary_pos_embedding.py new file mode 100644 index 0000000..d4e6be8 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/embeddings/rotary_pos_embedding.py @@ -0,0 +1,251 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +from __future__ import annotations + +from typing import TYPE_CHECKING, Optional + +if TYPE_CHECKING: + from megatron.core.transformer.transformer_config import TransformerConfig + from megatron.core.transformer.transformer_block import TransformerBlock + +import logging + +import torch +from torch import Tensor, nn + +from megatron.core import parallel_state + +logger = logging.getLogger(__name__) + +try: + from apex.transformer.functional import ( + fused_apply_rotary_pos_emb, + fused_apply_rotary_pos_emb_thd, + ) + + HAVE_APPLY_ROPE_FUSION = True +except: + HAVE_APPLY_ROPE_FUSION = False + + +__all__ = ['RotaryEmbedding', 'apply_rotary_pos_emb'] + + +def get_pos_emb_on_this_cp_rank(pos_emb, seq_dim): + cp_size = parallel_state.get_context_parallel_world_size() + cp_rank = parallel_state.get_context_parallel_rank() + cp_idx = torch.tensor( + [cp_rank, (2 * cp_size - cp_rank - 1)], device="cpu", pin_memory=True + ).cuda(non_blocking=True) + pos_emb = pos_emb.view( + *pos_emb.shape[:seq_dim], 2 * cp_size, -1, *pos_emb.shape[(seq_dim + 1) :] + ) + pos_emb = pos_emb.index_select(seq_dim, cp_idx) + pos_emb = pos_emb.view(*pos_emb.shape[:seq_dim], -1, *pos_emb.shape[(seq_dim + 2) :]) + return pos_emb + + +class RotaryEmbedding(nn.Module): + """Rotary Embedding for language model. + + Args: + kv_channels (int): Projection weights dimension in multi-head attention. Obtained from transformer config + rotary_percent (float): Percent of rotary dimension to use for rotary position embeddings. + seq_len_interpolation_factor (float, optional): scale of linearly interpolating RoPE for longer sequences. The value must be a float larger than 1.0. Defaults to None + rotary_base (int, optional): Base period for rotary position embeddings. Defaults to 10000. + """ + + def __init__( + self, + kv_channels: int, + rotary_percent: float, + rotary_interleaved: bool = False, + seq_len_interpolation_factor: float = None, + rotary_base: int = 10000, + ) -> None: + super().__init__() + + dim = kv_channels + if rotary_percent < 1.0: + dim = int(dim * rotary_percent) + self.rotary_interleaved = rotary_interleaved + + self.seq_len_interpolation_factor = seq_len_interpolation_factor + self.inv_freq = 1.0 / ( + rotary_base + ** ( + torch.arange(0, dim, 2, dtype=torch.float32, device=torch.cuda.current_device()) + / dim + ) + ) + + def forward(self, max_seq_len: int, offset: int = 0) -> Tensor: + """Forward pass of RoPE embedding. + + Args: + max_seq_len (int): Maximum size of sequence + offset (int, optional): _description_. Defaults to 0. + + Returns: + Tensor: Embeddings after applying RoPE. + """ + seq = ( + torch.arange(max_seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype) + + offset + ) + + if self.seq_len_interpolation_factor is not None: + seq *= 1 / self.seq_len_interpolation_factor + + freqs = torch.outer(seq, self.inv_freq) + # first part even vector components, second part odd vector components, + # 2 * dim in dimension size + if not self.rotary_interleaved: + emb = torch.cat((freqs, freqs), dim=-1) + else: + emb = torch.stack((freqs.view(-1, 1), freqs.view(-1, 1)), dim=-1).view( + freqs.shape[0], -1 + ) + # emb [seq_length, .., dim] + emb = emb[:, None, None, :] + if parallel_state.get_context_parallel_world_size() > 1: + # slice rotary_pos_emb along sequence dimension and select the parition of the current CP rank + emb = get_pos_emb_on_this_cp_rank(emb, 0) + return emb + + def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): + state_dict.pop(f'{prefix}inv_freq', None) + return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) + + def get_rotary_seq_len( + self, + inference_params, + transformer: TransformerBlock, + transformer_input: Tensor, + transformer_config: TransformerConfig, + ) -> float: + """Function to get the rotary sequence length. + + Args: + inference_params : Used during Inference time + transformer (TransformerBlock): The transformer block (decoder/encoder) used by the model + transformer_input (Tensor): _description_ + transformer_config (TransformerConfig): Transformer config used by the model + + Returns: + float: The rotary sequence length + """ + if inference_params is not None: + rotary_seq_len = inference_params.max_sequence_length + else: + if transformer.input_tensor is not None: + rotary_seq_len = transformer.input_tensor.size(0) + else: + rotary_seq_len = transformer_input.size(0) + + if transformer_config.sequence_parallel: + rotary_seq_len *= transformer_config.tensor_model_parallel_size + + rotary_seq_len *= transformer_config.context_parallel_size + + return rotary_seq_len + + +def _rotate_half(x: Tensor, rotary_interleaved: bool) -> Tensor: + """Change sign so the last dimension becomes [-odd, +even] + + Args: + x (Tensor): Input tensor + + Returns: + Tensor: Tensor rotated half + """ + if not rotary_interleaved: + x1, x2 = torch.chunk(x, 2, dim=-1) + return torch.cat((-x2, x1), dim=-1) + else: + x1 = x[:, :, :, ::2] + x2 = x[:, :, :, 1::2] + x_new = torch.stack((-x2, x1), dim=-1) + return x_new.view(x_new.shape[0], x_new.shape[1], x_new.shape[2], -1) + + +def apply_rotary_pos_emb_bshd(t: Tensor, freqs: Tensor, rotary_interleaved: bool = False) -> Tensor: + """Apply rotary positional embedding to input tensor T. + + check https://kexue.fm/archives/8265 for detailed formulas + + Args: + t (Tensor): Input tensor T is of shape [seq_length, ... , dim] + freqs (Tensor): Rotary Positional embedding tensor freq is of shape [seq_length, ..., dim] + + Returns: + Tensor: The input tensor after applying RoPE + """ + rot_dim = freqs.shape[-1] + + # ideally t_pass is empty so rotary pos embedding is applied to all tensor t + t, t_pass = t[..., :rot_dim], t[..., rot_dim:] + + # first part is cosine component + # second part is sine component, need to change signs with _rotate_half method + cos_ = torch.cos(freqs).to(t.dtype) + sin_ = torch.sin(freqs).to(t.dtype) + + t = (t * cos_) + (_rotate_half(t, rotary_interleaved) * sin_) + return torch.cat((t, t_pass), dim=-1) + + +def apply_rotary_pos_emb_thd( + t: Tensor, cu_seqlens: Tensor, freqs: Tensor, rotary_interleaved: bool = False +) -> Tensor: + + """A baseline implementation of applying RoPE for `thd` format. + + Args: + t (Tensor): Input tensor T is of shape [t, h, d] + cu_seqlens(Tensor): Cumulative sum of sequence lengths in a batch for `t`, + with shape [b + 1] and dtype torch.int32. + freqs (Tensor): Rotary Positional embedding tensor freq is of shape [max_s, 1, 1, d] + + Returns: + Tensor: Shape [t, h, d]. The input tensor after applying RoPE. + """ + + seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist() + return torch.cat( + [ + apply_rotary_pos_emb_bshd(x.unsqueeze(1), freqs[: x.size(0)]) + for x in torch.split(t, seqlens) + ] + ).squeeze(1) + + +def apply_rotary_pos_emb( + t: Tensor, freqs: Tensor, config: TransformerConfig, cu_seqlens: Optional[Tensor] = None, +): + """ + Reroute to the appropriate apply_rotary_pos_emb function depending on + fused/unfused kernels, or bshd (conventional) / thd (packed seq) format + """ + if config.apply_rope_fusion and not HAVE_APPLY_ROPE_FUSION: + # setting apply_rope_fusion in config to False so that subsequent queries to this config also return False + config.apply_rope_fusion = False + if not getattr(apply_rotary_pos_emb, "printed_fused_warning", False): + logger.warning( + "Setting apply_rope_fusion to false because its implementation" + " is not included in Apex. Try upgrading to the latest version" + ) + apply_rotary_pos_emb.printed_fused_warning = True + if config.apply_rope_fusion: + if cu_seqlens is None: + return fused_apply_rotary_pos_emb(t, freqs, transpose_output_memory=True) + else: + return fused_apply_rotary_pos_emb_thd(t, cu_seqlens, freqs) + else: + if cu_seqlens is None: + return apply_rotary_pos_emb_bshd(t, freqs, rotary_interleaved=config.rotary_interleaved) + else: + return apply_rotary_pos_emb_thd( + t, cu_seqlens, freqs, rotary_interleaved=config.rotary_interleaved + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/language_module/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/language_module/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/language_module/language_module.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/language_module/language_module.py new file mode 100644 index 0000000..78d9f86 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/language_module/language_module.py @@ -0,0 +1,200 @@ +import logging +from typing import Optional, Tuple + +import torch +from torch import Tensor + +from megatron.core import parallel_state, tensor_parallel +from megatron.core.dist_checkpointing.mapping import ShardedStateDict +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.utils import make_tp_sharded_tensor_for_checkpoint + + +class LanguageModule(MegatronModule): + """Base language module that has common helper functions used across GPT, BERT etc. + + Args: + config (TransformerConfig): Input transformer config for the model + """ + + def __init__(self, config: TransformerConfig) -> None: + super().__init__(config=config) + + def compute_language_model_loss(self, labels: Tensor, logits: Tensor) -> Tensor: + """Computes the language model loss (Cross entropy across vocabulary) + + Args: + labels (Tensor): The labels of dimension [batch size, seq length] + logits (Tensor): The final logits returned by the output layer of the transformer model + + Returns: + Tensor: Loss tensor of dimensions [batch size, sequence_length] + """ + # [b s] => [s b] + labels = labels.transpose(0, 1).contiguous() + loss = tensor_parallel.vocab_parallel_cross_entropy(logits.float(), labels) + + # [s b] => [b, s] + loss = loss.transpose(0, 1).contiguous() + return loss + + def setup_embeddings_and_output_layer(self) -> None: + """Sets up embedding layer in first stage and output layer in last stage. + + This function initalizes word embeddings in the final stage when we are + using pipeline parallelism and sharing word embeddings, and sets up param + attributes on the embedding and output layers. + """ + + # Set `is_embedding_or_output_parameter` attribute. + if self.pre_process: + self.embedding.word_embeddings.weight.is_embedding_or_output_parameter = True + if self.post_process and self.output_layer.weight is not None: + self.output_layer.weight.is_embedding_or_output_parameter = True + + if not self.share_embeddings_and_output_weights: + return + + if self.pre_process and self.post_process: + # Zero out wgrad if sharing embeddings between two layers on same + # pipeline stage to make sure grad accumulation into main_grad is + # correct and does not include garbage values (e.g., from torch.empty). + self.shared_embedding_or_output_weight().zero_out_wgrad = True + return + + if self.pre_process and not self.post_process: + assert parallel_state.is_pipeline_first_stage() + self.shared_embedding_or_output_weight().shared_embedding = True + + if self.post_process and not self.pre_process: + assert not parallel_state.is_pipeline_first_stage() + # set word_embeddings weights to 0 here, then copy first + # stage's weights using all_reduce below. + self.output_layer.weight.data.fill_(0) + self.output_layer.weight.shared = True + self.output_layer.weight.shared_embedding = True + + # Parameters are shared between the word embeddings layers, and the + # heads at the end of the model. In a pipelined setup with more than + # one stage, the initial embedding layer and the head are on different + # workers, so we do the following: + # 1. Create a second copy of word_embeddings on the last stage, with + # initial parameters of 0.0. + # 2. Do an all-reduce between the first and last stage to ensure that + # the two copies of word_embeddings start off with the same + # parameter values. + # 3. In the training loop, before an all-reduce between the grads of + # the two word_embeddings layers to ensure that every applied weight + # update is the same on both stages. + + # Ensure that first and last stages have the same initial parameter + # values. + if torch.distributed.is_initialized(): + if parallel_state.is_rank_in_embedding_group(): + weight = self.shared_embedding_or_output_weight() + weight.data = weight.data.cuda() + torch.distributed.all_reduce( + weight.data, group=parallel_state.get_embedding_group() + ) + + elif not getattr(LanguageModule, "embedding_warning_printed", False): + logging.getLogger(__name__).warning( + "Distributed processes aren't initialized, so the output layer " + "is not initialized with weights from the word embeddings. " + "If you are just manipulating a model this is fine, but " + "this needs to be handled manually. If you are training " + "something is definitely wrong." + ) + LanguageModule.embedding_warning_printed = True + + def shared_embedding_or_output_weight(self) -> Tensor: + """Gets the emedding weight or output logit weights when share embedding and output weights set to True. + + Returns: + Tensor: During pre processing it returns the input embeddings weight while during post processing it returns the final output layers weight + """ + if self.pre_process: + return self.embedding.word_embeddings.weight + elif self.post_process: + return self.output_layer.weight + return None + + def sharded_state_dict( + self, + prefix: str = '', + sharded_offsets: Tuple[Tuple[int, int, int]] = (), + metadata: Optional[dict] = None, + ) -> ShardedStateDict: + """ Sharded state dict implementation that handles the output layer weights tying. + + Args: + prefix (str): Module name prefix. + sharded_offsets (tuple): PP related offsets, expected to be empty at this module level. + metadata (Optional[Dict]): metadata controlling sharded state dict creation. + + Returns: + ShardedStateDict: sharded state dict for the LanguageModel + """ + assert not sharded_offsets, "Unexpected sharded offsets" + sharded_state_dict = super().sharded_state_dict(prefix, sharded_offsets, metadata) + + first_stage_word_emb_key = f'{prefix}embedding.word_embeddings.weight' + output_layer_weight_key = f'{prefix}output_layer.weight' + output_layer_bias_key = f'{prefix}output_layer.bias' + + if self.share_embeddings_and_output_weights: + self.tie_embeddings_and_output_weights_state_dict( + sharded_state_dict, output_layer_weight_key, first_stage_word_emb_key + ) + elif self.post_process: + # Make sure the output layer follows the embeddings padding logic + sharded_state_dict[output_layer_weight_key].allow_shape_mismatch = True + + # Regardless of sharing the output weights with embeddings, we must handle the bias padding + if self.post_process and output_layer_bias_key in sharded_state_dict: + sharded_state_dict[output_layer_bias_key].allow_shape_mismatch = True + + return sharded_state_dict + + def tie_embeddings_and_output_weights_state_dict( + self, + sharded_state_dict: ShardedStateDict, + output_layer_weight_key: str, + first_stage_word_emb_key: str, + ) -> None: + """Ties the embedding and output weights in a given sharded state dict. + + Args: + sharded_state_dict (ShardedStateDict): state dict with the weight to tie + output_layer_weight_key (str): key of the output layer weight in the state dict. + This entry will be replaced with a tied version + first_stage_word_emb_key (str): this must be the same as the + ShardedTensor.key of the first stage word embeddings. + + Returns: None, acts in-place + """ + if not self.post_process: + # No output layer + assert output_layer_weight_key not in sharded_state_dict, sharded_state_dict.keys() + return + + if self.pre_process: + # Output layer is equivalent to the embedding already + return + + # Replace the default output layer with a one sharing the weights with the embedding + del sharded_state_dict[output_layer_weight_key] + tensor = self.shared_embedding_or_output_weight() + last_stage_word_emb_replica_id = ( + 1, # copy of first stage embedding + 0, + parallel_state.get_data_parallel_rank(with_context_parallel=True), + ) + + sharded_state_dict[output_layer_weight_key] = make_tp_sharded_tensor_for_checkpoint( + tensor=tensor, + key=first_stage_word_emb_key, + replica_id=last_stage_word_emb_replica_id, + allow_shape_mismatch=True, + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/vision_module/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/vision_module/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/vision_module/vision_module.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/vision_module/vision_module.py new file mode 100644 index 0000000..5dc5187 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/common/vision_module/vision_module.py @@ -0,0 +1,17 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +"""Megatron Vision Module.""" + +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.transformer_config import TransformerConfig + + +# Note: This is only a stub at the moment. This will be expanded in follow-up changes. +class VisionModule(MegatronModule): + """Base vision module that has common helper functions used across CLIP, ViT, etc. + + Args: + config (TransformerConfig): Input transformer config for the model + """ + + def __init__(self, config: TransformerConfig) -> None: + super().__init__(config=config) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/gpt/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/gpt/__init__.py new file mode 100644 index 0000000..2d5eb86 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/gpt/__init__.py @@ -0,0 +1 @@ +from .gpt_model import GPTModel diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/gpt/gpt_layer_specs.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/gpt/gpt_layer_specs.py new file mode 100755 index 0000000..20461fa --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/gpt/gpt_layer_specs.py @@ -0,0 +1,106 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add +from megatron.core.fusions.fused_layer_norm import FusedLayerNorm +from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear +from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules +from megatron.core.transformer.custom_layers.transformer_engine import ( + TEDotProductAttention, + TELayerNormColumnParallelLinear, + TENorm, + TERowParallelLinear, +) +from megatron.core.transformer.dot_product_attention import DotProductAttention +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.identity_op import IdentityOp +from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.moe.moe_layer import MoELayer +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_block import TransformerBlockSubmodules +from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules + + +# Use this spec to use lower level Transformer Engine modules (required for fp8 training) +def get_gpt_layer_with_transformer_engine_spec( + num_experts: int = None, moe_grouped_gemm: bool = False, qk_layernorm: bool = False +) -> ModuleSpec: + mlp = _get_mlp_module_spec( + use_te=True, num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm + ) + return ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=SelfAttentionSubmodules( + linear_qkv=TELayerNormColumnParallelLinear, + core_attention=TEDotProductAttention, + linear_proj=TERowParallelLinear, + q_layernorm=TENorm if qk_layernorm else IdentityOp, + k_layernorm=TENorm if qk_layernorm else IdentityOp, + ), + ), + self_attn_bda=get_bias_dropout_add, + pre_mlp_layernorm=TENorm if num_experts else IdentityOp, + mlp=mlp, + mlp_bda=get_bias_dropout_add, + ), + ) + + +# Use this spec for an implementation using only modules in megatron core +def get_gpt_layer_local_spec( + num_experts: int = None, moe_grouped_gemm: bool = False, qk_layernorm: bool = False +) -> ModuleSpec: + mlp = _get_mlp_module_spec( + use_te=False, num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm + ) + return ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + input_layernorm=FusedLayerNorm, + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=SelfAttentionSubmodules( + linear_qkv=ColumnParallelLinear, + core_attention=DotProductAttention, + linear_proj=RowParallelLinear, + q_layernorm=FusedLayerNorm if qk_layernorm else IdentityOp, + k_layernorm=FusedLayerNorm if qk_layernorm else IdentityOp, + ), + ), + self_attn_bda=get_bias_dropout_add, + pre_mlp_layernorm=FusedLayerNorm, + mlp=mlp, + mlp_bda=get_bias_dropout_add, + sharded_state_dict_keys_map={ + 'input_layernorm.': 'self_attention.linear_qkv.layer_norm_', + 'pre_mlp_layernorm.': 'mlp.linear_fc1.layer_norm_', + }, + ), + ) + + +# Helper function to get module spec for MLP/MoE +def _get_mlp_module_spec( + use_te: bool = True, num_experts: int = None, moe_grouped_gemm: bool = False +) -> ModuleSpec: + if num_experts is None: + # Dense MLP w/ or w/o TE modules. + return ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=TELayerNormColumnParallelLinear if use_te else ColumnParallelLinear, + linear_fc2=TERowParallelLinear if use_te else RowParallelLinear, + ), + ) + else: + # Mixture of experts with modules in megatron core. + return ModuleSpec( + module=MoELayer, + submodules=MLPSubmodules(linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear,) + if not moe_grouped_gemm + else None, + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/gpt/gpt_model.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/gpt/gpt_model.py new file mode 100644 index 0000000..70f3f3b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/gpt/gpt_model.py @@ -0,0 +1,239 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import logging +from typing import Dict, Literal, Optional, Tuple, Union + +import torch +from torch import Tensor + +from megatron.core import InferenceParams, parallel_state, tensor_parallel +from megatron.core.dist_checkpointing.mapping import ShardedStateDict +from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding +from megatron.core.models.common.embeddings.rotary_pos_embedding import RotaryEmbedding +from megatron.core.models.common.language_module.language_module import LanguageModule +from megatron.core.packed_seq_params import PackedSeqParams +from megatron.core.transformer.enums import AttnMaskType, ModelType +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_block import TransformerBlock +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.utils import make_tp_sharded_tensor_for_checkpoint + + +class GPTModel(LanguageModule): + """GPT Transformer language model. + + Args: + config (TransformerConfig): Transformer config + transformer_layer_spec (ModuleSpec): Specifies module to use for transformer layers + vocab_size (int): Vocabulary size + max_sequence_length (int): maximum size of sequence. This is used for positional embedding + pre_process (bool, optional): Include embedding layer (used with pipeline parallelism). Defaults to True. + post_process (bool, optional): Include an output layer (used with pipeline parallelism). Defaults to True. + fp16_lm_cross_entropy (bool, optional): Defaults to False. + parallel_output (bool, optional): Do not gather the outputs, keep them split across tensor parallel ranks. Defaults to True. + share_embeddings_and_output_weights (bool, optional): When True, input embeddings and output logit weights are shared. Defaults to False. + position_embedding_type (Literal[learned_absolute,rope], optional): Position embedding type.. Defaults to 'learned_absolute'. + rotary_percent (float, optional): Percent of rotary dimension to use for rotary position embeddings. Ignored unless position_embedding_type is 'rope'. Defaults to 1.0. + rotary_base (int, optional): Base period for rotary position embeddings. Ignored unless position_embedding_type is 'rope'. Defaults to 10000. + seq_len_interpolation_factor (Optional[float], optional): scale of linearly interpolating RoPE for longer sequences. The value must be a float larger than 1.0. Defaults to None. + """ + + def __init__( + self, + config: TransformerConfig, + transformer_layer_spec: ModuleSpec, + vocab_size: int, + max_sequence_length: int, + pre_process: bool = True, + post_process: bool = True, + fp16_lm_cross_entropy: bool = False, + parallel_output: bool = True, + share_embeddings_and_output_weights: bool = False, + position_embedding_type: Literal['learned_absolute', 'rope'] = 'learned_absolute', + rotary_percent: float = 1.0, + rotary_base: int = 10000, + seq_len_interpolation_factor: Optional[float] = None, + ) -> None: + super().__init__(config=config) + + self.transformer_layer_spec: ModuleSpec = transformer_layer_spec + self.vocab_size = vocab_size + self.max_sequence_length = max_sequence_length + self.pre_process = pre_process + self.post_process = post_process + self.fp16_lm_cross_entropy = fp16_lm_cross_entropy + self.parallel_output = parallel_output + self.share_embeddings_and_output_weights = share_embeddings_and_output_weights + self.position_embedding_type = position_embedding_type + + # megatron core pipelining currently depends on model type + # TODO: remove this dependency ? + self.model_type = ModelType.encoder_or_decoder + + # These 2 attributes are needed for TensorRT-LLM export. + self.max_position_embeddings = max_sequence_length + self.rotary_percent = rotary_percent + + if self.pre_process: + self.embedding = LanguageModelEmbedding( + config=self.config, + vocab_size=self.vocab_size, + max_sequence_length=self.max_sequence_length, + position_embedding_type=position_embedding_type, + ) + + if self.position_embedding_type == 'rope': + self.rotary_pos_emb = RotaryEmbedding( + kv_channels=self.config.kv_channels, + rotary_percent=rotary_percent, + rotary_interleaved=self.config.rotary_interleaved, + seq_len_interpolation_factor=seq_len_interpolation_factor, + rotary_base=rotary_base, + ) + + # Transformer. + self.decoder = TransformerBlock( + config=self.config, + spec=transformer_layer_spec, + pre_process=self.pre_process, + post_process=self.post_process, + ) + + # Output + if post_process: + if self.config.defer_embedding_wgrad_compute: + # The embedding activation buffer preserves a reference to the input activations + # of the final embedding projection layer GEMM. It will hold the activations for + # all the micro-batches of a global batch for the last pipeline stage. Once we are + # done with all the back props for all the microbatches for the last pipeline stage, + # it will be in the pipeline flush stage. During this pipeline flush we use the + # input activations stored in embedding activation buffer and gradient outputs stored + # in gradient buffer to calculate the weight gradients for the embedding final linear layer. + self.embedding_activation_buffer = [] + self.grad_output_buffer = [] + else: + self.embedding_activation_buffer = None + self.grad_output_buffer = None + + self.output_layer = tensor_parallel.ColumnParallelLinear( + config.hidden_size, + self.vocab_size, + config=config, + init_method=config.init_method, + bias=False, + skip_bias_add=False, + gather_output=not self.parallel_output, + skip_weight_param_allocation=self.pre_process + and self.share_embeddings_and_output_weights, + embedding_activation_buffer=self.embedding_activation_buffer, + grad_output_buffer=self.grad_output_buffer, + ) + + if self.pre_process or self.post_process: + self.setup_embeddings_and_output_layer() + + def set_input_tensor(self, input_tensor: Tensor) -> None: + """Sets input tensor to the model. + + See megatron.model.transformer.set_input_tensor() + + Args: + input_tensor (Tensor): Sets the input tensor for the model. + """ + # This is usually handled in schedules.py but some inference code still + # gives us non-lists or None + if not isinstance(input_tensor, list): + input_tensor = [input_tensor] + + assert len(input_tensor) == 1, 'input_tensor should only be length 1 for gpt/bert' + self.decoder.set_input_tensor(input_tensor[0]) + + def forward( + self, + input_ids: Tensor, + position_ids: Tensor, + attention_mask: Tensor, + decoder_input: Tensor = None, + labels: Tensor = None, + inference_params: InferenceParams = None, + packed_seq_params: PackedSeqParams = None, + extra_block_kwargs: dict = None, + ) -> Tensor: + """Forward function of the GPT Model This function passes the input tensors + through the embedding layer, and then the decoeder and finally into the post + processing layer (optional). + + It either returns the Loss values if labels are given or the final hidden units + """ + # If decoder_input is provided (not None), then input_ids and position_ids are ignored. + # Otherwise, apply embedding layer on input_ids and position_ids to get decoder_input. + + # Decoder embedding. + if decoder_input is not None: + pass + elif self.pre_process: + decoder_input = self.embedding(input_ids=input_ids, position_ids=position_ids) + else: + # intermediate stage of pipeline + # decoder will get hidden_states from encoder.input_tensor + decoder_input = None + + # Rotary positional embeddings (embedding is None for PP intermediate devices) + rotary_pos_emb = None + if self.position_embedding_type == 'rope': + rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len( + inference_params, self.decoder, decoder_input, self.config + ) + rotary_pos_emb = self.rotary_pos_emb(rotary_seq_len) + + # Run decoder. + hidden_states = self.decoder( + hidden_states=decoder_input, + attention_mask=attention_mask, + inference_params=inference_params, + rotary_pos_emb=rotary_pos_emb, + packed_seq_params=packed_seq_params, + **(extra_block_kwargs or {}), + ) + + if not self.post_process: + return hidden_states + + # logits and loss + output_weight = None + if self.share_embeddings_and_output_weights: + output_weight = self.shared_embedding_or_output_weight() + logits, _ = self.output_layer(hidden_states, weight=output_weight) + + if labels is None: + # [s b h] => [b s h] + return logits.transpose(0, 1).contiguous() + + loss = self.compute_language_model_loss(labels, logits) + + return loss + + def sharded_state_dict( + self, prefix: str = '', sharded_offsets: tuple = (), metadata: Optional[Dict] = None + ) -> ShardedStateDict: + """ Sharded state dict implementation for GPTModel backward-compatibility (removing extra state). + + Args: + prefix (str): Module name prefix. + sharded_offsets (tuple): PP related offsets, expected to be empty at this module level. + metadata (Optional[Dict]): metadata controlling sharded state dict creation. + + Returns: + ShardedStateDict: sharded state dict for the GPTModel + """ + sharded_state_dict = super().sharded_state_dict(prefix, sharded_offsets, metadata) + output_layer_extra_state_key = f'{prefix}output_layer._extra_state' + + # Old GPT checkpoints only stored the output layer weight key. So we remove the _extra_state key + # but check that it doesn't contain any data anyway + output_extra_state = sharded_state_dict.pop(output_layer_extra_state_key, None) + assert not ( + output_extra_state and output_extra_state.data + ), f'Expected output layer extra state to be empty, got: {output_extra_state}' + + return sharded_state_dict diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/multimodal/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/multimodal/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/multimodal/llava_model.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/multimodal/llava_model.py new file mode 100644 index 0000000..08132fa --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/multimodal/llava_model.py @@ -0,0 +1,185 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +import logging +from collections import namedtuple +from functools import partial +from typing import List + +import torch + +from megatron.core import parallel_state, tensor_parallel +from megatron.core.models.gpt import GPTModel +from megatron.core.models.vision.clip_vit_model import CLIPViTModel +from megatron.core.models.vision.multimodal_projector import MultimodalProjector +from megatron.core.transformer import MegatronModule +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_config import TransformerConfig + + +# Note: This is under development and may be missing features. +class LLaVAModel(MegatronModule): + """LLaVA multi-modal model. + + Args: + language_transformer_config (TransformerConfig): Transformer config for the language model. + language_transformer_layer_spec (ModuleSpec): Specifies module to use for transformer layers of the language model. + vocab_size (int): Vocabulary size. + max_sequence_length (int): maximum sequence length. This is used for positional embedding. + vision_transformer_config (TransformerConfig): Transformer config for the vision model. + vision_transformer_layer_spec (ModuleSpec): Specifies module to use for transformer layers of the vision model. + vision_projection_config (TransformerConfig): Config for the projection from vision model outputs to language model inputs. + vision_projection_layer_spec (ModuleSpec): Specifies the module to use for the vision projection. + vision_projection_type (str): Type of the vision projection to use. Default is a 2-layer MLP. + allow_missing_vision_projection_checkpoint (bool): Allow vision projection weights to be missing when loading a checkpoint. Default False. + """ + + def __init__( + self, + language_transformer_config: TransformerConfig, + language_transformer_layer_spec: ModuleSpec, + vocab_size: int, + max_sequence_length: int, + vision_transformer_config: TransformerConfig, + vision_transformer_layer_spec: ModuleSpec, + vision_projection_config: TransformerConfig, + vision_projection_layer_spec: ModuleSpec, + vision_projection_type: str = "mlp", + allow_missing_vision_projection_checkpoint: bool = False, + ) -> None: + super().__init__(config=language_transformer_config) + + logging.getLogger(__name__).warning( + "LLaVA model is under development and may be missing features." + ) + + if parallel_state.get_pipeline_model_parallel_world_size() > 1: + raise NotImplementedError("pipeline parallelism is not supported in this model yet.") + + self.language_model = GPTModel( + language_transformer_config, + language_transformer_layer_spec, + vocab_size, + max_sequence_length, + ) + + self.vision_model = CLIPViTModel(vision_transformer_config, vision_transformer_layer_spec) + + # Map (intermediate) vision model outputs to the language model input dimension. + self.vision_projection = MultimodalProjector( + vision_projection_config, + vision_projection_layer_spec, + vision_projection_type, + vision_transformer_config.hidden_size, # input size to the projection. + ) + + # This allows ignoring missing weights for the vision projection during checkpoint loading. + # This should be disabled by default but can be enabled if your checkpoint contains pretrained + # vision and language models but not the projection from vision model outputs to language model inputs. + if allow_missing_vision_projection_checkpoint: + vision_projection_param_names = [ + f"vision_projection.{name}" for name in self.vision_projection.state_dict().keys() + ] + self.vision_projection.register_load_state_dict_post_hook( + partial(_load_state_dict_hook_ignore_param_names, vision_projection_param_names) + ) + + def set_input_tensor(self, input_tensor: torch.Tensor) -> None: + """Sets input tensor to the model. + + NOTE: Pipeline parallelism is not supported in this model yet. This is just a placeholder implementation. + + Args: + input_tensor (Tensor): Sets the input tensor for the model. + """ + self.vision_model.set_input_tensor(input_tensor) + + def freeze( + self, freeze_language_model: bool, freeze_vision_model: bool, freeze_vision_projection: bool + ): + """Freeze model modules. + + Make specific modules non-trainable by setting requires_grad to False for the module's parameters. + + Args: + freeze_language_model (bool): Freeze the language model module. + freeze_vision_model (bool): Freeze the vision model module. + freeze_vision_projection (bool): Freeze the vision projection module. + """ + modules = [] + if freeze_language_model: + modules.append(self.language_model) + if freeze_vision_model: + modules.append(self.vision_model) + if freeze_vision_projection: + modules.append(self.vision_projection) + + for module in modules: + for param in module.parameters(): + param.requires_grad = False + + def forward( + self, + images: torch.Tensor, + input_ids: torch.Tensor, + position_ids: torch.Tensor, + attention_mask: torch.Tensor, + labels: torch.Tensor = None, + ) -> torch.Tensor: + """Forward function of the LLaVA model. + + Args: + images (torch.Tensor): input image of shape [batch, img_h, img_w]. + input_ids (torch.Tensor): input text ids [batch, text_seq_len]. + position_ids (torch.Tensor): input text position ids [batch, text_seq_len]. + attention_mask (torch.Tensor): attention mask for the language model [batch, 1, combined_seq_len, combined_seq_len]. + labels (torch.Tensor): Optional target text labels [batch, combined_seq_len]. + + Returns: + output (torch.Tensor): Loss of shape [b, s] if labels are provided, otherwise logits of shape [b, s, vocab_size]. + """ + image_embeddings = self.vision_model(images) # [b, img_seq_len, h_vision] + + # map vision model output size to language model input size. + image_embeddings = self.vision_projection(image_embeddings) # [b, img_seq_len, h_language] + + image_embeddings = image_embeddings.permute(1, 0, 2) # [img_seq_len, b, h_language] + language_embeddings = self.language_model.embedding( + input_ids=input_ids, position_ids=position_ids + ) # [text_seq_len, b, h_language] + combined_embeddings = torch.cat( + [image_embeddings, language_embeddings], dim=0 + ) # [combined_seq_len, b, h_language] + + # Embedding is computed above so we can discard input and position ids. + input_ids = None + position_ids = None + + # Note: This returns loss if labels are provided, otherwise logits. + output = self.language_model( + input_ids, + position_ids, + attention_mask, + decoder_input=combined_embeddings, + labels=labels, + ) + + return output + + +def _load_state_dict_hook_ignore_param_names( + param_names: List[str], module: torch.nn.Module, incompatible_keys: namedtuple +): + """Hook to ignore missing keys during checkpoint loading. + + By default, this should not be used to avoid accidentally missing weights in checkpoint loading. + + Example use case: Use this for the vision projection if you want to load a checkpoint that contains vision and language model weights + but not the vision projection weights. + + Args: + param_names (list of str): Parameter names allowed to be missing when calling load_state_dict. + module (torch.nn.Module): The torch module this hook applies to. Unused here but required by the torch API. + incompatible_keys (namedtuple): Namedtuple with fields missing_keys and unexpected_keys, which collect the missing and unexpected + keys when calling load_state_dict on this torch module, respectively. + """ + for param_name in param_names: + incompatible_keys.missing_keys.remove(param_name) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/__init__.py new file mode 100644 index 0000000..ea7cea6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +""" +Exports: + + - RetroConfig: configuration dataclass for RetroModel. + - RetroModel: The Retro model. + - get_retro_decoder_block_spec: Get spec for Retro decoder transformer block. +""" + +from .config import RetroConfig +from .decoder_spec import get_retro_decoder_block_spec +from .model import RetroModel diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/base_attention.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/base_attention.py new file mode 100644 index 0000000..741f712 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/base_attention.py @@ -0,0 +1,44 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Base class for decoder and encoder attention modules.""" + +from megatron.core.models.retro.config import RetroConfig +from megatron.core.transformer.attention import CrossAttention, CrossAttentionSubmodules +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.module import MegatronModule + + +class BaseRetroCrossAttention(MegatronModule): + + """Base class for Retro cross attention, for both encoder & decoder layers. + + This class collects the retro arguments below (i.e., num neighbors, chunk + length, and retrieve length) for use in Retro's custom cross attention + operators. + + Args: + config (RetroConfig): Retro config. + submodules (CrossAttentionSubmodules): Cross attention submodules. + layer_number (int): Layer number within transformer block. + attn_mask_type (AttnMaskType): Mask type ('causal' or 'padding'). + """ + + def __init__( + self, + config: RetroConfig, + submodules: CrossAttentionSubmodules, + layer_number: int = 1, + attn_mask_type: AttnMaskType = AttnMaskType.padding, + ): + super().__init__(config=config) + + self.attn = CrossAttention( + config=config, + submodules=submodules, + layer_number=layer_number, + attn_mask_type=attn_mask_type, + ) + + self.retro_num_neighbors = config.retro_num_neighbors + self.retro_chunk_length = config.retro_chunk_length + self.retro_retrieved_length = config.retro_retrieved_length diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/config.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/config.py new file mode 100644 index 0000000..b9a5eb9 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/config.py @@ -0,0 +1,87 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Configuration dataclass for a RetroModel.""" + +import os +import types +from dataclasses import dataclass +from importlib.metadata import version + +from pkg_resources import packaging + +from megatron.core.transformer import TransformerConfig + + +@dataclass +class RetroConfig(TransformerConfig): + """Configuration object for Retro models. """ + + # Retro. + retro_project_dir: str = None + """Retro project directory, which contains the preprocessed data for for pretraining. This + directory is built during preprocessing (see tools/retro/README.md), and contains + subdirectories for the chunk database and pretraining neighbors. + """ + + retro_block_size: int = None + """Number of records to load per data file, as saved during preprocessing. Block processing is + used for efficient data preprocessing. + """ + + retro_chunk_length: int = None + """Chunk length used for performing chunked- cross-attention (CCA).""" + + retro_encoder_num_layers: int = 2 + """Number of layers to use for the retrieval encoder.""" + + retro_encoder_hidden_dropout: float = 0.1 + """Hidden dropout for retrieval encoder.""" + + retro_encoder_attention_dropout: float = 0.1 + """Attention dropout for retrieval encoder.""" + + retro_neighbor_dirs: dict = None + """Directory names of saved neighbor id files for train, valid, and test datasets.""" + + retro_num_neighbors: int = 2 + """Number of neighbors to retrieve during pretraining.""" + + retro_num_retrieved_chunks: int = 2 + """Number of chunks to retrieve from the retrieval database.""" + + retro_retrieved_length: int = None + """Cached value of retro_num_retrieved_chunks * retro_chunk_length (i.e., the total number of + retrieved tokens; neighbor + continuation). + """ + + retro_split_preprocessing: str = None + """Data split used during data preprocessing.""" + + retro_verify_neighbor_count: bool = True + """Verify that len(GPT dataset) == len(saved neighbors).""" + + def __post_init__(self) -> None: + """Validate Retro config.""" + + super().__post_init__() + + # Validate Transformer Engine version. + te_version = packaging.version.Version(version("transformer-engine")) + if te_version >= packaging.version.Version("1.3"): + try: + assert os.getenv("NVTE_FLASH_ATTN") == "0" + assert os.getenv("NVTE_FUSED_ATTN") == "0" + except Exception as e: + raise Exception( + "When using Transformer Engine >= 1.3, environment vars NVTE_FLASH_ATTN and NVTE_FUSED_ATTN most both be defined and set to '0'. Currently, NVTE_FLASH_ATTN == %s, NVTE_FUSED_ATTN == %s." + % ( + os.getenv("NVTE_FLASH_ATTN", "[unset]"), + os.getenv("NVTE_FUSED_ATTN", "[unset]"), + ) + ) + + # Preprocessing split should be defined. + assert self.retro_split_preprocessing is not None + + # Pre-compute retrieved length. + self.retro_retrieved_length = self.retro_num_retrieved_chunks * self.retro_chunk_length diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/decoder_attention.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/decoder_attention.py new file mode 100644 index 0000000..f459163 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/decoder_attention.py @@ -0,0 +1,309 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Retro's cross attention modules for the decoder block.""" + +from functools import partial +from typing import Callable + +import numpy as np +import torch +from torch import Tensor + +from megatron.core import InferenceParams +from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add +from megatron.core.models.retro.base_attention import BaseRetroCrossAttention +from megatron.core.models.retro.config import RetroConfig +from megatron.core.models.retro.utils import get_all_true_mask +from megatron.core.transformer import ModuleSpec +from megatron.core.transformer.attention import CrossAttentionSubmodules +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.transformer_block import TransformerBlock + + +class RetroDecoderCrossAttention(BaseRetroCrossAttention): + + """Retro decoder's chunked cross attention operator. + + See this paper for more details: https://arxiv.org/abs/2112.04426. + Neighboring chunks retrieved from the chunk database are used here for + chunked-cross attention. + + ** Note about 'encoder_block_spec' ** + + Retro is an encoder-decoder model that uses its encoder for encoding + neighboring chunks that are retrieved from a chunk database. These + encoded neighbors are then used in the decoder stack for performing + chunked-cross attention (see paper link above). + + In contrast to the T5 model, the encoder and decoder are computationally + intertwined, since the input to the encoder is the output of the self- + attention of the first decoder layer. As such, the encoder block itself + is instantiated within the first Retro decoder layer, in order to receive + the self-attention's output. (Note, that only the first decoder layer + instantiates an encoder block, and the remaining decoder layers use the + encoder output from the first decoder layer.) + + Args: + config (RetroConfig): Retro config. + submodules (CrossAttentionSubmodules): Cross attention submodules. + layer_number (int): Layer number within transformer block. + attn_mask_type (AttnMaskType): Mask type ('causal' or 'padding'). + encoder_block_spec (ModuleSpec): The first Retro decoder layer is provided with a transformer block spec to construct the neighbor encoder. + """ + + def __init__( + self, + config: RetroConfig, + submodules: CrossAttentionSubmodules, + layer_number: int = 1, + attn_mask_type: AttnMaskType = AttnMaskType.padding, + encoder_block_spec: ModuleSpec = None, + ): + super().__init__( + config=config, + submodules=submodules, + layer_number=layer_number, + attn_mask_type=attn_mask_type, + ) + + if encoder_block_spec: + self.encoder = TransformerBlock( + config=config, spec=encoder_block_spec, pre_process=True, post_process=False, + ) + # self._encoder_key = 'encoder' # ... necessary? + else: + self.encoder = None + + def forward( + self, + hidden_states: Tensor, + attention_mask: Tensor, + key_value_states: Tensor = None, + inference_params: InferenceParams = None, + # rotary_pos_emb: Tensor = None, # ... unsupported for retro. + ) -> dict: + """Cross attention for Retro decoder. + + Notation: + ns : Sequence length. + bs : Batch size. + d : Hidden size. + l : Number of chunks per sample (i.e., seq_length/chunk_length). + m : Number of tokens per chunk. + k : Number of neighbors. + r : Number of retrieved tokens (neighbors + continuation). + + Args: + hidden_states (Tensor): Transformer layer hidden states. + attention_mask (Tensor): Attention mask. + key_value_states (Tensor): Neighbor embeddings if first decoder layer, else encoder output. + inference_params (InferenceParams): Inference params. + + Returns: + A dict consisting of the attention output and context, along with other scalars necessary for performing the downstream bias-dropout-add. + """ + + # hidden_states: [ ns, bs, d ] + # key_value_states: [ r, k*bs*l, d ] + + ns, bs, d = hidden_states.shape + l = int(np.ceil(ns / self.retro_chunk_length)) + + # Retrieve neighbors. + if self.encoder: + + # Sequence length remainder. + first_ns = ns % self.retro_chunk_length + + # Case 1: Sequence length not divisible by chunk length. + if first_ns > 0: + + # Split sequence into first partial chunk & remaining chunks. + first_chunk, rest_chunk = hidden_states[:first_ns], hidden_states[first_ns:] + + # Pad partial chunk with zeros. + first_chunk = torch.nn.functional.pad( + first_chunk, (0, 0, 0, 0, 0, self.retro_chunk_length - first_ns), 'constant', 0, + ) + + # Concatenate padded chunk with remaining chunks. + chunked_output = torch.cat((first_chunk, rest_chunk), dim=0) # [ l*m, bs, d ] + + # Case 2: Sequence length is divisible by chunk length. + else: + chunked_output = hidden_states # [ l*m, bs, d ] + + # Chunk & permute hidden states. + # - hidden_states: [ l*m, bs, d ] + # - chunked_output: [ m, bs*l, d ] + chunked_output = ( + chunked_output.reshape(l, self.retro_chunk_length, bs, d) + .permute(1, 2, 0, 3) + .reshape(self.retro_chunk_length, bs * l, d) + .contiguous() + ) + + # flash attn: [ b, h, sq, sk ] + # fused attn: [ b, 1, 1, sq ] + chunked_output_mask = get_all_true_mask( + size=(1, 1, chunked_output.shape[0], key_value_states.shape[0]), + device=chunked_output.device, + ) + + # Encode neighbors. (Note: 'key_value_states' re-assigned here.) + key_value_states = self.encoder( + hidden_states=key_value_states, + attention_mask=attention_mask, + context=chunked_output, + context_mask=chunked_output_mask, + inference_params=inference_params, + ) # [ r, k*bs*l, d ] + key_value_states = key_value_states.reshape( + self.retro_retrieved_length * self.retro_num_neighbors, bs * l, d + ) # [ r*k, bs*l, d ] + + # Attend starting at last token of first chunk. + pad = (ns - 1) % self.retro_chunk_length + attending_chunks = hidden_states[pad:] + + # Pad attending tokens to sequence length. + padded_chunks = torch.nn.functional.pad( + attending_chunks, (0, 0, 0, 0, 0, self.retro_chunk_length - 1), 'constant', 0, + ) + + # Permute attending chunks. + # - padded_chunks: [ l*m, bs, d ] + # - padded_chunked_output: [ m, bs*l, d ] (matches 'chunked_output' above) + padded_chunked_output = padded_chunks.reshape(l, self.retro_chunk_length, bs, d).permute( + 1, 2, 0, 3 + ) + padded_chunked_output = padded_chunked_output.reshape( + self.retro_chunk_length, bs * l, d + ).contiguous() + + # flash attn: [ b, h, sq, sk ] + # fused attn: [ b, 1, 1, sq ] + padded_chunked_output_mask = get_all_true_mask( + size=(1, 1, padded_chunked_output.shape[0], key_value_states.shape[0]), + device=padded_chunked_output.device, + ) + + # Attend to encoded neighbors. + attention_output, attention_bias = self.attn( + hidden_states=padded_chunked_output, + attention_mask=padded_chunked_output_mask, + key_value_states=key_value_states, + ) + + # Return dimensions for bias-dropout step. + return { + "ns": ns, + "bs": bs, + "d": d, + "l": l, + "pad": pad, + "attention_output": attention_output, # [ m, bs*l, d ] + "attention_bias": attention_bias, # [ d ] + "context": key_value_states, # [ r*k, bs*l, d ] + } + + +class RetroDecoderBiasDropoutAdd(MegatronModule): + + """Retro decoder's bias-dropout-add operator. + + This operator takes care of reshaping and permuting the output from the + chunk dimension to the sequence dimension. + + Args: + config (RetroConfig): Retro config. + """ + + def __init__( + self, config: RetroConfig, + ): + super().__init__(config=config) + self.retro_chunk_length = config.retro_chunk_length + + @classmethod + def _forward( + cls, + x_with_bias: dict, + residual: Tensor, + prob: float, + retro_chunk_length: int, + bias_dropout_add: Callable, + ) -> Tensor: + """Per-chunk bias-dropout-add. + + Args: + x_with_bias (dict): Attention output and bias, along with other Retro relevant parameters. + residual (Tensor): Transformer layer residual. + prob (float): Dropout probability. + retro_chunk_length (int): Retro chunk length (e.g., 64). + bias_dropout_add (Callable): Bias-dropout-add function. + + Returns: + Output of bias-dropout-add. + """ + + # Extract input dict. + ns = x_with_bias["ns"] + bs = x_with_bias["bs"] + d = x_with_bias["d"] + l = x_with_bias["l"] + pad = x_with_bias["pad"] + attention_output = x_with_bias["attention_output"] # [ m, bs*l, d ] + attention_bias = x_with_bias["attention_bias"] # [ d ] + + # Re-enable torch grad to enable fused optimization. + with torch.enable_grad(): + + # Bias-dropout-add. + x = bias_dropout_add( + ( + attention_output, + None if attention_bias is None else attention_bias.expand_as(attention_output), + ), + torch.zeros_like(attention_output), + prob, + ) + + # Permute chunks back to sequence dimension. + # 1. [ m, bs*l, d ] + # 2. [ m, bs, l, d ] + # 3. [ l, m, bs, d ] + # 4. [ m*l, bs, d ] == [ ns, bs, d ] + x = ( + x.reshape(retro_chunk_length, bs, l, d) + .permute(2, 0, 1, 3) + .reshape(retro_chunk_length * l, bs, d) + ) + + # Prepend zeros for non-attending tokens. + x = torch.nn.functional.pad(x, (0, 0, 0, 0, pad, 0), 'constant', 0,)[ + :ns + ] # [ ns, bs, d ] + + # Add residual. [ ns, bs, d ] + x = x + residual + + # Output. [ ns, bs, d ] + return x + + def forward(self, training: bool, fused: bool) -> partial: + """Retro decoder bias-dropout-add. + + Args: + training (bool): If training, then apply dropout. + fused (bool): Fuse bias-dropout-add. + + Returns: + The partial function for performing bias-dropout-add. + """ + return partial( + self._forward, + retro_chunk_length=self.retro_chunk_length, + bias_dropout_add=get_bias_dropout_add(training, fused), + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/decoder_spec.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/decoder_spec.py new file mode 100644 index 0000000..e669ecc --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/decoder_spec.py @@ -0,0 +1,161 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Specs for Retro decoder.""" + +import typing + +from megatron.core import parallel_state +from megatron.core.fusions.fused_layer_norm import FusedLayerNorm +from megatron.core.models.gpt.gpt_layer_specs import ( + get_gpt_layer_local_spec, + get_gpt_layer_with_transformer_engine_spec, +) +from megatron.core.models.retro.config import RetroConfig +from megatron.core.models.retro.decoder_attention import ( + RetroDecoderBiasDropoutAdd, + RetroDecoderCrossAttention, +) +from megatron.core.models.retro.encoder_spec import get_retro_encoder_block_spec +from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear +from megatron.core.transformer import ModuleSpec +from megatron.core.transformer.attention import CrossAttentionSubmodules +from megatron.core.transformer.custom_layers.transformer_engine import ( + TEColumnParallelLinear, + TEDotProductAttention, + TENorm, + TERowParallelLinear, +) +from megatron.core.transformer.dot_product_attention import DotProductAttention +from megatron.core.transformer.transformer_block import ( + TransformerBlockSubmodules, + get_num_layers_to_build, +) + + +def get_retro_decoder_layer_te_spec( + encoder_block_spec: typing.Union[ModuleSpec, TransformerBlockSubmodules, None] = None +) -> ModuleSpec: + """Retro decoder TE spec (uses Transformer Engine components). + + A Retro decoder layer uses custom attention and bias-dropout-add operators + to perform chunked-cross attention. Additionally, the first Retro decoder + layer instantiates an entire encoder transformer block. As such, the decoder + cross attention module takes an optional encoder block spec, which is only + provided for the first Retro decoder layer. + + Args: + encoder_block_spec (ModuleSpec): Retro encoder block spec, to be provided for the first Retro decoder layer. + + Returns: + A module spec with Transformer Engine modules. + """ + spec = get_gpt_layer_with_transformer_engine_spec() + spec.submodules.pre_cross_attn_layernorm = TENorm + spec.submodules.cross_attention = ModuleSpec( + module=RetroDecoderCrossAttention, + params={"encoder_block_spec": encoder_block_spec,}, + submodules=CrossAttentionSubmodules( + linear_q=TEColumnParallelLinear, + linear_kv=TEColumnParallelLinear, + core_attention=TEDotProductAttention, + linear_proj=TERowParallelLinear, + ), + ) + spec.submodules.cross_attn_bda = ModuleSpec(module=RetroDecoderBiasDropoutAdd) + return spec + + +def get_retro_decoder_layer_local_spec( + encoder_block_spec: typing.Optional[ModuleSpec] = None, +) -> ModuleSpec: + """Retro decoder local spec (uses Megatron-Core components). + + A Retro decoder layer uses custom attention and bias-dropout-add operators + to perform chunked-cross attention. Additionally, the first Retro decoder + layer instantiates an entire encoder transformer block. As such, the decoder + cross attention module takes an optional encoder block spec, which is only + provided for the first Retro decoder layer. + + Args: + encoder_block_spec (ModuleSpec): Retro encoder block spec, to be provided for the first Retro decoder layer. + + Returns: + A module spec with local modules. + """ + spec = get_gpt_layer_local_spec() + spec.submodules.pre_cross_attn_layernorm = FusedLayerNorm + spec.submodules.cross_attention = ModuleSpec( + module=RetroDecoderCrossAttention, + params={"encoder_block_spec": encoder_block_spec,}, + submodules=CrossAttentionSubmodules( + linear_q=ColumnParallelLinear, + linear_kv=ColumnParallelLinear, + core_attention=DotProductAttention, + linear_proj=RowParallelLinear, + ), + ) + spec.submodules.cross_attn_bda = ModuleSpec(module=RetroDecoderBiasDropoutAdd) + return spec + + +def get_retro_decoder_block_spec( + config: RetroConfig, use_transformer_engine: bool +) -> TransformerBlockSubmodules: + + """Retro decoder block spec. + + Retro decoder block implementation details: + - The retro decoder block consists of interleaved GPT layers and customized Retro decoder layers. + - The Retro decoder layers are spaced three layers apart, and start on layer 6 or 9 (depending on the total number of layers). + - The first decoder layer instantiates an encoder block, and it therefore passes in an encoder_block_spec. + + Args: + config (RetroConfig): Retro config. + use_transformer_engine (bool): If True, use Transformer Engine (instead of local modules. + + Returns: + Transformer block submodules for the given spec. + """ + + # Num layers. + assert ( + parallel_state.get_pipeline_model_parallel_world_size() == 1 + ), "retro does not currently support pipeline parallelism." + assert ( + parallel_state.get_virtual_pipeline_model_parallel_world_size() is None + ), "retro does not currently support virtual pipeline parallelism." + num_layers = get_num_layers_to_build(config) + + # Retro layer numbers. + retro_layer_start = 6 if num_layers <= 15 else 9 + retro_layer_numbers = list(range(retro_layer_start, num_layers + 1, 3)) + + # Layer specs. + gpt_layer_spec = ( + get_gpt_layer_with_transformer_engine_spec() + if use_transformer_engine + else get_gpt_layer_local_spec() + ) + get_retro_decoder_layer_spec = ( + get_retro_decoder_layer_te_spec + if use_transformer_engine + else get_retro_decoder_layer_local_spec + ) + retro_layer_spec = get_retro_decoder_layer_spec() + retro_layer_spec_with_retriever = get_retro_decoder_layer_spec( + get_retro_encoder_block_spec(config, use_transformer_engine) + ) + + layer_specs = [] + for layer_number in range(1, num_layers + 1): + if layer_number == retro_layer_numbers[0]: + layer_specs.append(retro_layer_spec_with_retriever) + elif layer_number in retro_layer_numbers: + layer_specs.append(retro_layer_spec) + else: + layer_specs.append(gpt_layer_spec) + + # Block spec. + block_spec = TransformerBlockSubmodules(layer_specs=layer_specs) + + return block_spec diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/encoder_attention.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/encoder_attention.py new file mode 100644 index 0000000..a2226c0 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/encoder_attention.py @@ -0,0 +1,233 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Retro's cross attention modules for the encoder block.""" + +from functools import partial +from typing import Callable, List, Optional, Tuple, Type + +import torch +from torch import Tensor + +from megatron.core import InferenceParams +from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add +from megatron.core.models.retro.base_attention import BaseRetroCrossAttention +from megatron.core.models.retro.config import RetroConfig +from megatron.core.models.retro.utils import get_all_true_mask +from megatron.core.transformer.module import MegatronModule + + +class RetroEncoderCrossAttention(BaseRetroCrossAttention): + + """Retro encoder's cross attention operator. + + See this paper for more details: https://arxiv.org/abs/2112.04426. + Neighboring chunks are retrieved from the chunk database, encoded, and + used by the decoder layers for chunked cross attention. + + Args: + config (RetroConfig): Retro config. + submodules (CrossAttentionSubmodules): Cross attention submodules. + layer_number (int): Layer number within transformer block. + attn_mask_type (AttnMaskType): Mask type ('causal' or 'padding'). + """ + + def forward( + self, + hidden_states: Tensor, + attention_mask: Tensor, + key_value_states: Tensor = None, + inference_params: InferenceParams = None, + # rotary_pos_emb: Tensor = None, # unsupported for retro. + ) -> List[Tuple[Tensor, Optional[Tensor], Tensor]]: + """Cross attention for Retro encoder. + + Notation: + ns : Sequence length. + bs : Batch size. + d : Hidden size. + l : Number of chunks per sample (i.e., seq_length/chunk_length). + k : Number of neighbors. + r : Number of retrieved tokens (neighbors + continuation). + + Args: + hidden_states (Tensor): Transformer layer hidden states. + attention_mask (Tensor): Attention mask. + key_value_states (Tensor): Neighbor embeddings. + inference_params (InferenceParams): Inference params. + + Returns: + List of tuples, where each tuple is (attention_output, attention_bias, residual). + """ + + # Input shape. [ r, bs*l*k, d ] + ns, bs, d = hidden_states.shape + + # Reshape sequence into neighboring chunks. + # - hidden_states: [ r, bs*l*k, d ] + # - chunked_outputs: [ r, bs*l, k, d ] + chunked_outputs = hidden_states.reshape( + self.retro_retrieved_length, -1, self.retro_num_neighbors, d + ) + + # flash attn: [ b, h, sq, sk ] + # fused attn: [ b, 1, 1, sq ] + chunked_output_mask = get_all_true_mask( + size=(1, 1, chunked_outputs.shape[0], key_value_states.shape[0]), + device=chunked_outputs.device, + ) + + # Per-chunk attention. + attention_output_tuples = [] + for k in range(self.retro_num_neighbors): + + # Attend to current neighboring chunks. + # - chunked_output: [ r, bs*l, d ] + # - key_value_states: [ m, bs*l, d ] + # - attention_output: [ r, bs*l, d ] + # - attention_bias: [ d ] + chunked_output = chunked_outputs[:, :, k].contiguous() + attention_output, attention_bias = self.attn( + hidden_states=chunked_output, # Q (neighbor embedding) + attention_mask=chunked_output_mask, + key_value_states=key_value_states, # K, V (hidden act) + ) + + # Residual connection. [ r, bs*l, d ] + residual = chunked_output + + # Collect tensors. + attention_output_tuples.append((attention_output, attention_bias, residual,)) + + # Output. (List[Tuple[( [ r, bs*l, d ], [ d ] )]]) + return attention_output_tuples + + +class RetroEncoderBiasDropoutAdd(MegatronModule): + + """Retro encoder's bias-dropout-add operator. + + This operator applies bias-dropout-add individually on each neighboring + chunk that is retrieved from the chunk database. + + Args: + config (RetroConfig): Retro config. + """ + + def __init__( + self, config: RetroConfig, + ): + super().__init__(config=config) + self.retro_num_neighbors = config.retro_num_neighbors + + @classmethod + def _forward( + cls, + x_with_bias: List[Tuple[Tensor, Optional[Tensor], Tensor]], + residual: Tensor, + prob: float, + retro_num_neighbors: int, + bias_dropout_add: Callable, + ) -> Tensor: + """Per-chunk bias-dropout-add. + + Args: + x_with_bias (dict): Attention output and bias tuple. + residual (Tensor): Transformer layer residual. + prob (float): Dropout probability. + retro_num_neighbors (int): Number of retrieved neighbor chunks (e.g., 2). + bias_dropout_add (Callable): Bias-dropout-add function. + + Returns: + Output of bias-dropout-add. + """ + + # Re-enable torch grad to enable fused optimization. + with torch.enable_grad(): + + # Per-neighbor bias-dropout-add. + # - attention_output: [ r, bs*l, d ] + # - attention_bias: [ d ] + # - residual: [ r, bs*l, d ] + # - output: [ r, bs*l, d ] + outputs = [ + bias_dropout_add( + ( + attention_output, + None if attention_bias is None else attention_bias.expand_as(residual), + ), + residual, + prob, + ) + for attention_output, attention_bias, residual in x_with_bias + ] + + # Concatenate outputs (to shape [r, k*bs*l, d]; see notation above). + r, _, d = outputs[0].shape + output = torch.stack(outputs, dim=1).reshape(r, -1, d) + + # Output. [ r, k*bs*l, d ] + return output + + def forward(self, training: bool, fused: bool) -> partial: + """Retro decoder bias-dropout-add. + + Args: + training (bool): If training, then apply dropout. + fused (bool): Fuse bias-dropout-add. + + Returns: + A partial function for performing bias-dropout-add. + """ + return partial( + self._forward, + retro_num_neighbors=self.retro_num_neighbors, + bias_dropout_add=get_bias_dropout_add(training, fused), + ) + + +class RetroEncoderLayerNorm(MegatronModule): + + """Retro encoder's layernorm operator. + + This operator applies layernorm individually on each neighboring chunk that + is retrieved from the chunk database, and then concatenates the chunks into + a single tensor. + + Args: + config (RetroConfig): Retro config. + submodules (Type): Layer norm class. (Named 'submodules' to fit external interface.) + """ + + def __init__( + self, config: RetroConfig, submodules: Type, **kwargs: dict, + ): + super().__init__(config=config) + norm_class = submodules + self.norm = norm_class(config=config, **kwargs) + self.retro_num_neighbors = config.retro_num_neighbors + + def forward(self, input: Tensor) -> Tensor: + """Per-chunk layer norm. + + Args: + input (Tensor): Input chunks, concatenated into a single tensor. + + Returns: + Output of the layer norm. + """ + + # Input shape: [ r, k*bs*l, d ]. (see notation above in attention module) + + # Split input into 'num_neighbors' tensors. + chunk_size = input.shape[1] // self.retro_num_neighbors + inputs = torch.split(input, chunk_size, dim=1) + + # Norm. + outputs = [self.norm(inp.contiguous()) for inp in inputs] + + # Concatenate layer norms (to shape [r, k*bs*l, d]; see notation above). + r, _, d = inputs[0].shape + output = torch.stack(outputs, dim=1).reshape(r, -1, d) + + # Output. [ r, k*bs*l, d ] + return output diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/encoder_spec.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/encoder_spec.py new file mode 100644 index 0000000..4edd97b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/encoder_spec.py @@ -0,0 +1,153 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Specs for Retro encoder.""" + +from megatron.core.fusions.fused_layer_norm import FusedLayerNorm +from megatron.core.models.gpt.gpt_layer_specs import ( + get_gpt_layer_local_spec, + get_gpt_layer_with_transformer_engine_spec, +) +from megatron.core.models.retro.config import RetroConfig +from megatron.core.models.retro.encoder_attention import ( + RetroEncoderBiasDropoutAdd, + RetroEncoderCrossAttention, + RetroEncoderLayerNorm, +) +from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear +from megatron.core.transformer import ModuleSpec +from megatron.core.transformer.attention import CrossAttentionSubmodules +from megatron.core.transformer.custom_layers.transformer_engine import ( + TEColumnParallelLinear, + TEDotProductAttention, + TENorm, + TERowParallelLinear, +) +from megatron.core.transformer.dot_product_attention import DotProductAttention +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.transformer_block import TransformerBlockSubmodules + + +def get_retro_encoder_layer_te_spec() -> ModuleSpec: + """Retro encoder TE spec (uses Transformer Engine components). + + A Retro encoder layer uses custom attention, bias-dropout-add, and layernorm + operators to encode neighboring chunks that are retrieved from the chunk + database. Each operator is responsible for iterating the retrieved chunks + and processing them individually. + + Returns: + A module spec if Transformer Engine modules. + """ + spec = get_gpt_layer_with_transformer_engine_spec() + spec.submodules.pre_cross_attn_layernorm = TENorm + spec.submodules.cross_attention = ModuleSpec( + module=RetroEncoderCrossAttention, + params={"attn_mask_type": AttnMaskType.padding,}, + submodules=CrossAttentionSubmodules( + linear_q=TEColumnParallelLinear, + linear_kv=TEColumnParallelLinear, + core_attention=TEDotProductAttention, + linear_proj=TERowParallelLinear, + ), + ) + spec.submodules.cross_attn_bda = ModuleSpec(module=RetroEncoderBiasDropoutAdd) + spec.submodules.pre_mlp_layernorm = ModuleSpec(module=RetroEncoderLayerNorm, submodules=TENorm,) + spec.submodules.mlp = ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=TEColumnParallelLinear, linear_fc2=TERowParallelLinear, + ), + ) + return spec + + +def get_retro_encoder_layer_local_spec() -> ModuleSpec: + """Retro encoder local spec (uses Megatron-Core components). + + A Retro encoder layer uses custom attention, bias-dropout-add, and layernorm + operators to encode neighboring chunks that are retrieved from the chunk + database. Each operator is responsible for iterating the retrieved chunks + and processing them individually. + + Returns: + A module spec if local modules. + """ + spec = get_gpt_layer_local_spec() + spec.submodules.pre_cross_attn_layernorm = FusedLayerNorm + spec.submodules.cross_attention = ModuleSpec( + module=RetroEncoderCrossAttention, + params={"attn_mask_type": AttnMaskType.padding,}, + submodules=CrossAttentionSubmodules( + linear_q=ColumnParallelLinear, + linear_kv=ColumnParallelLinear, + core_attention=DotProductAttention, + linear_proj=RowParallelLinear, + ), + ) + spec.submodules.cross_attn_bda = ModuleSpec(module=RetroEncoderBiasDropoutAdd) + spec.submodules.pre_mlp_layernorm = ModuleSpec( + module=RetroEncoderLayerNorm, submodules=FusedLayerNorm, + ) + spec.submodules.mlp = ModuleSpec( + module=MLP, + submodules=MLPSubmodules(linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear,), + ) + spec.submodules.sharded_state_dict_keys_map = { + 'input_layernorm.': 'self_attention.linear_qkv.layer_norm_', + } # pre_mlp_layernorm doesn't need remapping + return spec + + +def get_retro_encoder_block_spec( + config: RetroConfig, use_transformer_engine: bool +) -> TransformerBlockSubmodules: + + """Retro encoder block spec. + + The retro encoder block consists of one customized Retro encoder layer + (layer 1), and all of the following layers are standard GPT layers. + + Args: + config (RetroConfig): Retro config. + use_transformer_engine (bool): If True, use Transformer Engine (instead of local modules). + + Returns: + Transformer block submodules for the given spec. + """ + + # Num layers. + num_layers = config.retro_encoder_num_layers + retro_layer_numbers = [1] + + # Layer specs. + gpt_layer_spec = ( + get_gpt_layer_with_transformer_engine_spec() + if use_transformer_engine + else get_gpt_layer_local_spec() + ) + get_retro_encoder_layer_spec = ( + get_retro_encoder_layer_te_spec + if use_transformer_engine + else get_retro_encoder_layer_local_spec + ) + retro_layer_spec = get_retro_encoder_layer_spec() + for spec in (gpt_layer_spec, retro_layer_spec): + spec.params["hidden_dropout"] = config.retro_encoder_hidden_dropout + spec.submodules.self_attention.params["attn_mask_type"] = AttnMaskType.padding + spec.submodules.self_attention.submodules.core_attention = ModuleSpec( + module=TEDotProductAttention if use_transformer_engine else DotProductAttention, + params={"attention_dropout": config.retro_encoder_attention_dropout,}, + ) + + layer_specs = [] + for layer_number in range(1, num_layers + 1): + if layer_number in retro_layer_numbers: + layer_specs.append(retro_layer_spec) + else: + layer_specs.append(gpt_layer_spec) + + # Block spec. + block_spec = TransformerBlockSubmodules(layer_specs=layer_specs) + + return block_spec diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/model.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/model.py new file mode 100644 index 0000000..32c6d26 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/model.py @@ -0,0 +1,100 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Retro Model.""" +from typing import Dict, Optional + +from torch import Tensor + +from megatron.core import InferenceParams +from megatron.core.dist_checkpointing.mapping import ShardedStateDict +from megatron.core.models.gpt import GPTModel + + +class RetroModel(GPTModel): + + """Retro Model. + + A Retro model mostly re-uses the GPTModel interface, with the only difference + being the embedding of the 'context' this is used by Retro for processing + neighbor tokens. This embedded context is then forwarded to the Transformer + Block. + """ + + def forward( + self, + input_ids: Tensor, + position_ids: Tensor, + attention_mask: Tensor, + context_input_ids: Tensor = None, + context_position_ids: Tensor = None, + context_mask: Tensor = None, + decoder_input: Tensor = None, + labels: Tensor = None, + inference_params: InferenceParams = None, + ) -> Tensor: + """RetroModel forward method. + + Foward input tokens & mask, along with neighbor tokens & mask, through + the Retro model.. + + Args: + input_ids (Tensor): Input token IDs. + position_ids (Tensor): Input position IDs. + attention_mask (Tensor): Input attention mask. + context_input_ids (Tensor): Context (i.e., neighbor) token IDs. + context_position_ids (Tensor): Context (i.e., neighbor) position IDs. + context_mask (Tensor): Context (i.e., neighbor) attention mask. + decoder_input (Tensor): When using pipeline parallelism, input_ids and position_ids will only be used on the first stage, and for all other stages decoder_input will be provided via communication from the previous stage. + labels (Tensor): The labels of dimension [batch size, seq length]. + inference_params (InferenceParams): Parameters for inference. + + Returns: + Output tensor of forward pass. + """ + + # Argument shapes: + # Notation: + # ns : Sequence length. + # bs : Batch size. + # d : Hidden size. + # l : Number of chunks per sample (i.e., seq_length/chunk_length). + # k : Number of neighbors. + # r : Number of retrieved tokens (neighbors + continuation). + # - input_ids: [ bs, ns ] + # - context_ids: [ k*bs*l, r ] + # - context: [ r, k*bs*l, d ] + # - output: [ ns, bs, d ] + + # Context embedding (e.g., for Retro neighbor tokens). + if context_input_ids is not None: + context = self.embedding(context_input_ids, context_position_ids) + else: + context = None + + # Call GPTModel.forward, and pass in embedded context. + return super().forward( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + decoder_input=decoder_input, + labels=labels, + inference_params=inference_params, + extra_block_kwargs={"context": context, "context_mask": context_mask,}, + ) + + def sharded_state_dict( + self, prefix: str = '', sharded_offsets: tuple = (), metadata: Optional[Dict] = None + ) -> ShardedStateDict: + """Get sharded state dict. + + Args: + prefix (str): Module name prefix. + sharded_offsets (tuple): Offsets of local shard within global tensor. + metadata (Optional[Dict]): Shard metadata. + + Returns: + A ? + """ + metadata = metadata or {} + metadata['non_homogeneous_layers'] = True + return super().sharded_state_dict(prefix, sharded_offsets, metadata) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/utils.py new file mode 100644 index 0000000..7d83c5d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/retro/utils.py @@ -0,0 +1,24 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import os + +import torch + + +def get_config_path(project_dir: str) -> str: + """Config copy stored within retro project dir.""" + return os.path.join(project_dir, "config.json") + + +def get_gpt_data_dir(project_dir: str) -> str: + """Get project-relative directory of GPT bin/idx datasets.""" + return os.path.join(project_dir, "data") + + +# ** Note ** : Retro's compatibility between cross attention and Flash/Fused +# Attention is currently a work in progress. We default to returning None for +# now. +# def get_all_true_mask(size, device): +# return torch.full(size=size, fill_value=True, dtype=torch.bool, device=device) +def get_all_true_mask(size, device): + return None diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/vision/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/vision/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/vision/clip_vit_model.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/vision/clip_vit_model.py new file mode 100644 index 0000000..56e017d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/vision/clip_vit_model.py @@ -0,0 +1,138 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from typing import Optional + +import torch + +from megatron.core import tensor_parallel +from megatron.core.models.common.vision_module.vision_module import VisionModule +from megatron.core.transformer.custom_layers.transformer_engine import TENorm +from megatron.core.transformer.enums import ModelType +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_block import TransformerBlock +from megatron.core.transformer.transformer_config import TransformerConfig + + +# Note: This is under development and is missing features like position embedding interpolation. +class CLIPViTModel(VisionModule): + """CLIP ViT vision model. + + Args: + transformer_config (TransformerConfig): Transformer config + transformer_layer_spec (ModuleSpec): Specifies module to use for transformer layers + patch_dim (int): Image patch size. + img_h (int): Input image height. + img_w (int): Input image width. + add_class_token (bool, optional): Include a class token. Defaults to True. + class_token_len (int): Class token length. Defaults to 1 but 8 may be faster. + """ + + def __init__( + self, + transformer_config: TransformerConfig, + transformer_layer_spec: ModuleSpec, + patch_dim: int = 14, + img_h: int = 336, + img_w: int = 336, + add_class_token: bool = True, + class_token_len: int = 1, + ) -> None: + super().__init__(config=transformer_config) + + self.visual_hidden_size = transformer_config.hidden_size + self.patch_dim = patch_dim + self.img_h = img_h + self.img_w = img_w + assert self.img_h % self.patch_dim == 0 + assert self.img_w % self.patch_dim == 0 + self.num_patches_per_dim_h = self.img_h // self.patch_dim + self.num_patches_per_dim_w = self.img_w // self.patch_dim + self.num_patches = self.num_patches_per_dim_h * self.num_patches_per_dim_w + + self.add_class_token = add_class_token + self.class_token_len = class_token_len + + self.seq_length = self.num_patches + (self.class_token_len if self.add_class_token else 0) + + self.conv1 = torch.nn.Conv2d( + in_channels=3, + out_channels=self.visual_hidden_size, + kernel_size=self.patch_dim, + stride=self.patch_dim, + bias=False, + ) + + self.position_ids = torch.arange(self.seq_length).expand(1, -1).cuda() + + self.position_embeddings = torch.nn.Embedding(self.seq_length, self.visual_hidden_size) + + self.add_class_token = add_class_token + if self.add_class_token: + self.class_token = torch.nn.Parameter( + torch.randn(1, self.class_token_len, self.visual_hidden_size) + ) + + self.ln_pre = TENorm( + config=self.config, + hidden_size=self.visual_hidden_size, + eps=self.config.layernorm_epsilon, + ) + + self.model_type = ModelType.encoder_or_decoder + + # Transformer + final layer norm (via post_process) + # TODO: Follow-up changes will make pre and post_process configurable. They are needed for supporting pipeline parallelism. + self.transformer = TransformerBlock( + config=transformer_config, + spec=transformer_layer_spec, + pre_process=True, + post_process=True, + ) + + # Note: a final linear layer present in some implementations is omitted here. It can be added separately where needed. + + def set_input_tensor(self, input_tensor: torch.Tensor) -> None: + """Sets input tensor to the model. + + Args: + input_tensor (Tensor): Sets the input tensor for the model. + """ + self.transformer.set_input_tensor(input_tensor) + + def forward( + self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + """Forward function of the CLIP ViT Model. This function passes the input tensors + through the embedding layer and then the transformer. + + Args: + x (torch.Tensor): input data of shape [batch, img_h, img_w] + attention_mask (torch.Tensor with dtype=bool): Attention mask to use. If none, all ones. + + Returns: + x (torch.Tensor): output after final transformer block of shape [b, s, h]. + """ + x = self.conv1(x) # shape = [batch, hidden_size, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # [batch, hidden_size, grid ** 2] + x = x.permute(0, 2, 1) # [batch, grid ** 2, hidden_size] + + if self.add_class_token: + class_token = self.class_token.expand( + x.shape[0], -1, -1 + ) # [batch, class_token_len, hidden_size] + x = torch.cat( + [class_token, x], dim=1 + ) # [batch, grid ** 2 + class_token_len, hidden_size] + + x = x + self.position_embeddings(self.position_ids) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # [b, s, h] -> [s, b, h] + if attention_mask is None: + attention_mask = torch.ones(1, 1, x.shape[0], x.shape[0]).cuda() # [1, 1, s, s] + attention_mask = attention_mask < 0.5 # to bool + x = self.transformer(x.contiguous(), attention_mask) + x = x.permute(1, 0, 2) # [s, b, h] -> [b, s, h] + x = x.contiguous() + + return x diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/vision/multimodal_projector.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/vision/multimodal_projector.py new file mode 100644 index 0000000..84cb24c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/vision/multimodal_projector.py @@ -0,0 +1,58 @@ +from megatron.core import tensor_parallel +from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.spec_utils import ModuleSpec, build_module +from megatron.core.transformer.transformer_config import TransformerConfig + + +class MultimodalProjector(MegatronModule): + """ + MultimodalProjector will take the encoded input with input_size hidden state and project + it into the hidden size of the language model for multimodal training. When projector is + type affine linear_fc1 from submodules is used. + + Args: + transformer_config (TransformerConfig): Transformer config + submodules (MLPSubmodules): Specifies MLP submodules for mlp type projector + projector_type (str): Projector type + input_size (int): Input size from feature encoder + """ + + def __init__( + self, + config: TransformerConfig, + submodules: MLPSubmodules, + projector_type: str, + input_size: int, + ): + super().__init__(config=config) + self.projector_type = projector_type + + assert submodules is not None, "MLPSubmodules must be provided" + + if self.projector_type == "mlp": + self.encoder = MLP(config=config, submodules=submodules, input_size=input_size) + elif self.projector_type == "affine": + self.encoder = build_module( + submodules.linear_fc1, + input_size, + config.hidden_size, + config=config, + init_method=config.init_method, + gather_output=True, + bias=config.add_bias_linear, + skip_bias_add=True, + is_expert=False, + tp_comm_buffer_name=None, + ) + else: + raise Exception(f"Unsupported multimodal projection type {self.projector_type}") + + def forward(self, hidden_states): + # Run encoder. + encoder_output, encoder_output_bias = self.encoder(hidden_states) + + if encoder_output_bias is not None: + encoder_output = encoder_output + encoder_output_bias + + return encoder_output diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/models/vision/vit_layer_specs.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/vision/vit_layer_specs.py new file mode 100644 index 0000000..26360da --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/models/vision/vit_layer_specs.py @@ -0,0 +1,50 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add +from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear +from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules +from megatron.core.transformer.custom_layers.transformer_engine import ( + TEDotProductAttention, + TELayerNormColumnParallelLinear, + TERowParallelLinear, +) +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.identity_op import IdentityOp +from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules + + +# Use this spec to use lower level Transformer Engine modules (required for fp8 training) +def get_vit_layer_with_transformer_engine_spec() -> ModuleSpec: + mlp = _get_mlp_module_spec(use_te=True) + return ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": AttnMaskType.no_mask}, + submodules=SelfAttentionSubmodules( + linear_qkv=TELayerNormColumnParallelLinear, + core_attention=TEDotProductAttention, + linear_proj=TERowParallelLinear, + ), + ), + self_attn_bda=get_bias_dropout_add, + pre_mlp_layernorm=IdentityOp, + mlp=mlp, + mlp_bda=get_bias_dropout_add, + ), + ) + + +# Helper function to get module spec for MLP/MoE +def _get_mlp_module_spec(use_te: bool = True,) -> ModuleSpec: + # Dense MLP w/ or w/o TE modules. + return ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=TELayerNormColumnParallelLinear if use_te else ColumnParallelLinear, + linear_fc2=TERowParallelLinear if use_te else RowParallelLinear, + ), + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/__init__.py new file mode 100644 index 0000000..3f3f3fe --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/__init__.py @@ -0,0 +1,342 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +from logging import getLogger +from typing import Callable, Dict, List, Optional + +import torch +from apex.optimizers import FusedAdam as Adam +from apex.optimizers import FusedSGD as SGD + +from megatron.core import mpu + +from ..distributed import ParamAndGradBuffer +from ..transformer.module import MegatronModule +from .distrib_optimizer import DistributedOptimizer +from .grad_scaler import ConstantGradScaler, DynamicGradScaler +from .optimizer import ( + ChainedOptimizer, + Float16OptimizerWithFloat16Params, + FP32Optimizer, + MegatronOptimizer, +) +from .optimizer_config import OptimizerConfig + +logger = getLogger(__name__) + + +def _get_param_groups( + model_chunks: List[MegatronModule], + no_weight_decay_cond: Callable, + scale_lr_cond: Callable, + lr_mult: float, + use_decoupled_learning_rate: bool, +) -> List[Dict]: + """Create parameter groups for optimizer. + + Creates parameter groups based on weight decay condition (regularized vs + non regularized), learning rate scale condition (lr vs lr_mult * lr), + and whether it is expert parameters. scale_lr_cond is used during finetuning + where head of the network requires a scaled version of the base learning rate. + + Args: + model_chunks (List[MegatronModule]): model chunks to create parameter + groups for. + no_weight_decay_cond (func): function to determine whether a parameter + should not perform weight decay. + scale_lr_cond (func): function to determine whether a parameter + should have a scaled learning rate. + lr_mult (float): learning rate multiplier for parameters that + satisfy scale_lr_cond. + use_decoupled_learning_rate (bool): true if using decoupled learning rate. + + Returns: + List of parameter groups. + """ + + # Map (wd_mult, lr_mult, is_expert_parallel, is_decoupled_lr) to params. + params_map = {} + for model_chunk in model_chunks: + for name, param in model_chunk.named_parameters(): + if not param.requires_grad: + continue + + is_expert_parallel = not getattr(param, 'allreduce', True) + + if no_weight_decay_cond is not None: + no_wd = no_weight_decay_cond(name, param) + else: + # Do not regularize biases and norm parameters. + no_wd = name.endswith(".bias") or len(param.shape) == 1 + + if scale_lr_cond is not None: + scale_lr = scale_lr_cond(name, param) + else: + scale_lr = False + + if not no_wd and not scale_lr: + wd_mult, lr_mult = 1.0, 1.0 + elif not no_wd and scale_lr: + wd_mult, lr_mult = 1.0, lr_mult + elif no_wd and not scale_lr: + wd_mult, lr_mult = 0.0, 1.0 + else: + wd_mult, lr_mult = 0.0, lr_mult + + is_decoupled_lr = False + # For input/embedding and output layer: embedding.word_embeddings.weight / output_layer.weight. + if use_decoupled_learning_rate and getattr( + param, 'is_embedding_or_output_parameter', False + ): + is_decoupled_lr = True + + key = (wd_mult, lr_mult, is_expert_parallel, is_decoupled_lr) + if key not in params_map: + params_map[key] = [] + params_map[key].append(param) + + param_groups = [] + for (wd_mult, lr_mult, is_expert_parallel, is_decoupled_lr), params in params_map.items(): + assert len(params) > 0 + param_groups.append( + { + 'params': params, + 'wd_mult': wd_mult, + 'lr_mult': lr_mult, + 'is_expert_parallel': is_expert_parallel, + 'is_decoupled_lr': is_decoupled_lr, + } + ) + + return param_groups + + +def _update_min_and_max_lr_in_param_groups( + param_groups: List[Dict], + lr: float, + min_lr: float, + decoupled_lr: Optional[float], + decoupled_min_lr: Optional[float], +) -> List[Dict]: + """ + Updates `max_lr` and `min_lr` values in each parameter group, and returns new list. + By default, each group will use `lr` / `min_lr` as `max_lr` / `min_lr`. + If `decoupled_lr` is provided, then `decoupled_lr` / `decoupled_min_lr` will be used + as `max_lr` / `min_lr` for the input and output layer. + + Args: + param_groups (List): parameter groups whose 'max_lr' and `min_lr` fields need to + be adjusted. + lr (float): learning rate. + min_lr (float): minimum learning rate. + decoupled_lr (Optional[float]): optional decoupled learning rate. + decoupled_min_lr (Optional[float]): optional decoupled minimum learning rate. + + Returns: + List of adjusted parameter groups. + """ + + if decoupled_min_lr is None: + decoupled_min_lr = min_lr + + for param_group in param_groups: + if param_group['is_decoupled_lr']: + assert decoupled_lr is not None + param_group['max_lr'] = decoupled_lr + param_group['min_lr'] = decoupled_min_lr + else: + param_group['max_lr'] = lr + param_group['min_lr'] = min_lr + return param_groups + + +def _get_megatron_optimizer_based_on_param_groups( + config: OptimizerConfig, + param_groups: List, + per_model_buffers: Optional[Dict[int, List[ParamAndGradBuffer]]] = None, + data_parallel_group: Optional[torch.distributed.ProcessGroup] = None, + data_parallel_group_gloo: Optional[torch.distributed.ProcessGroup] = None, + data_parallel_group_idx: Optional[int] = None, +) -> MegatronOptimizer: + """Get Megatron optimizer based on parameter groups. + + Args: + config (OptimizerConfig): optimizer configuration object. + param_groups (list): list of parameter groups. + per_model_buffers (dict, optional): buffers for distributed optimizer. Defaults to None. + data_parallel_group (torch.distributed.ProcessGroup, optional): data-parallel group for + distributed optimizer. Defaults to None. + data_parallel_group_gloo (torch.distributed.ProcessGroup, optional): gloo data-parallel + group for distributed optimizer. Defaults to None. + data_parallel_group_idx (int, optional): data-parallel group index for distributed + optimizer. Defaults to None. + + Returns: + Instance of MegatronOptimizer. + """ + if config.optimizer == 'adam': + optimizer = Adam( + param_groups, + lr=config.lr, + weight_decay=config.weight_decay, + betas=(config.adam_beta1, config.adam_beta2), + eps=config.adam_eps, + ) + + def init_state_fn(opt): + for group in opt.param_groups: + for p in group['params']: + if len(opt.state[p]) == 0: + opt.state[p]['exp_avg'] = torch.zeros_like(p.data) + opt.state[p]['exp_avg_sq'] = torch.zeros_like(p.data) + + elif config.optimizer == 'sgd': + optimizer = SGD( + param_groups, + lr=config.lr, + weight_decay=config.weight_decay, + momentum=config.sgd_momentum, + ) + init_state_fn = None + else: + raise Exception('{} optimizer is not supported.'.format(config.optimizer)) + + # Mixed precision optimizer. + # - Note: both the Float16Optimizer and the DistributedOptimizer inherit + # from the MixedPrecisionOptimizer, which manages any optimizer where + # the model params and main params are distinct. + if config.fp16 or config.bf16 or config.use_distributed_optimizer: + + # Grad scaler: + # if loss-scale is provided, instantiate the constant scaler. + # if we are using fp16 and loss-scale is not present, use a + # dynamic scaler. + # otherwise we are running in bf16 with no loss-scale so + # leave it as None. + grad_scaler = None + + # Constant loss scale. + if config.loss_scale: + grad_scaler = ConstantGradScaler(config.loss_scale) + + # Dynamic loss scale. + else: + if config.fp16: + grad_scaler = DynamicGradScaler( + initial_scale=config.initial_loss_scale, + min_scale=config.min_loss_scale, + growth_factor=2.0, + backoff_factor=0.5, + growth_interval=config.loss_scale_window, + hysteresis=config.hysteresis, + ) + + optimizer_args = [ + optimizer, + config, + grad_scaler, + init_state_fn, + ] + if config.use_distributed_optimizer: + optimizer = DistributedOptimizer( + *optimizer_args, + per_model_buffers=per_model_buffers, + data_parallel_group=data_parallel_group, + data_parallel_group_gloo=data_parallel_group_gloo, + data_parallel_group_idx=data_parallel_group_idx, + ) + else: + optimizer = Float16OptimizerWithFloat16Params(*optimizer_args) + + return optimizer + + # FP32. + return FP32Optimizer(optimizer, config, init_state_fn,) + + +def get_megatron_optimizer( + config: OptimizerConfig, + model_chunks: List[MegatronModule], + no_weight_decay_cond: Optional[Callable] = None, + scale_lr_cond: Optional[Callable] = None, + lr_mult: float = 1.0, +) -> MegatronOptimizer: + """Retrieve the Megatron optimizer for model chunks. + + We use separate optimizers for expert parameters and non-expert parameters. + + Args: + config (OptimizerConfig): optimizer configuration object. + model_chunks (List[MegatronModule]): model chunks to get optimizer for. + no_weight_decay_cond (func, optional): function to determine whether a parameter + should not perform weight decay. Defaults to None. + scale_lr_cond (func, optional): function to determine whether a parameter + should have a scaled learning rate. Defaults to None. + lr_mult (float, optional): learning rate multiplier for parameters that + satisfy scale_lr_cond. Defaults to 1.0. + + Returns: + Instance of MegatronOptimizer. + """ + + if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0: + logger.info(f'Setting up optimizer with {type(config).__name__}: {config}') + + # Collect param groups. + param_groups = _get_param_groups( + model_chunks, + no_weight_decay_cond, + scale_lr_cond, + lr_mult, + use_decoupled_learning_rate=config.decoupled_lr is not None, + ) + param_groups = _update_min_and_max_lr_in_param_groups( + param_groups, + lr=config.lr, + min_lr=config.min_lr, + decoupled_lr=config.decoupled_lr, + decoupled_min_lr=config.decoupled_min_lr, + ) + + # Collect grad buffers for distributed optimizer. + per_model_buffers = {} + per_model_ep_buffers = {} + for model_idx, model_chunk in enumerate(model_chunks): + if hasattr(model_chunk, 'buffers'): + per_model_buffers[model_idx] = model_chunk.buffers + per_model_ep_buffers[model_idx] = model_chunk.expert_parallel_buffers + + # Split param groups into dense and MoE params (since data-parallel groups for MoE + # parameters can be different with expert parallelism). + dense_param_groups = list(filter(lambda g: not g['is_expert_parallel'], param_groups)) + moe_param_groups = list(filter(lambda g: g['is_expert_parallel'], param_groups)) + + # Create optimizers. + model_parallel_rank = torch.distributed.get_rank(mpu.get_model_parallel_group()) + optimizers = [ + _get_megatron_optimizer_based_on_param_groups( + config, + param_groups=dense_param_groups, + per_model_buffers=per_model_buffers, + data_parallel_group=mpu.get_data_parallel_group(with_context_parallel=True), + data_parallel_group_gloo=mpu.get_data_parallel_group_gloo(with_context_parallel=True), + data_parallel_group_idx=model_parallel_rank, + ) + ] + if len(moe_param_groups) > 0: + model_parallel_world_size = torch.distributed.get_world_size(mpu.get_model_parallel_group()) + expert_parallel_rank = mpu.get_expert_model_parallel_rank() + optimizers.append( + _get_megatron_optimizer_based_on_param_groups( + config, + param_groups=moe_param_groups, + per_model_buffers=per_model_ep_buffers, + data_parallel_group=mpu.get_data_modulo_expert_parallel_group(), + data_parallel_group_gloo=mpu.get_data_modulo_expert_parallel_group_gloo(), + data_parallel_group_idx=expert_parallel_rank * model_parallel_world_size + + model_parallel_rank, + ) + ) + + if len(optimizers) == 1: + return optimizers[0] + + return ChainedOptimizer(optimizers) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/clip_grads.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/clip_grads.py new file mode 100644 index 0000000..cfb0c33 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/clip_grads.py @@ -0,0 +1,153 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Gradient clipping.""" + +import os +from typing import List, Optional, Union + +import amp_C +import torch +from apex.multi_tensor_apply import multi_tensor_applier +from torch import inf + +from ..tensor_parallel import param_is_not_tensor_parallel_duplicate +from ..transformer.module import param_is_not_shared + + +def clip_grad_norm_fp32( + parameters: Union[List[torch.Tensor], torch.Tensor], + grads_for_norm: Union[List[torch.Tensor], torch.Tensor], + max_norm: Union[int, float], + norm_type: Union[int, float] = 2, + model_parallel_group: Optional[torch.distributed.ProcessGroup] = None, +) -> float: + """Clips gradient norm of an iterable of parameters whose gradients + are in fp32. + + This is adapted from torch.nn.utils.clip_grad.clip_grad_norm_ and + added functionality to handle model parallel parameters. Note that + the gradients are modified in place. + + Args: + parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a + single Tensor that will have gradients normalized. + grads_for_norm (Iterable[Tensor]): an iterable of Tensors or a single + Tensor that will be used for calculating the grad norm. + max_norm (float or int): max norm of the gradients. + norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for + infinity norm. + model_parallel_group (torch.distributed.ProcessGroup, optional): model-parallel + group over which grad norm needs to be aggregated. + + Returns: + Total norm of the parameters (viewed as a single vector). + """ + + if isinstance(parameters, torch.Tensor): + parameters = [parameters] + if isinstance(grads_for_norm, torch.Tensor): + grads_for_norm = [grads_for_norm] + + # Grads. + grads = [] + for param in parameters: + if param.grad is not None: + assert param.grad.type() == 'torch.cuda.FloatTensor' + grads.append(param.grad.detach()) + + # Norm parameters. + max_norm = float(max_norm) + norm_type = float(norm_type) + total_norm = 0.0 + + # Calculate norm. + if norm_type == inf: + total_norm = max(grad.abs().max() for grad in grads_for_norm) + total_norm_cuda = torch.tensor([float(total_norm)], dtype=torch.float, device='cuda') + # Take max across all model-parallel GPUs. + torch.distributed.all_reduce( + total_norm_cuda, op=torch.distributed.ReduceOp.MAX, group=model_parallel_group + ) + total_norm = total_norm_cuda[0].item() + + else: + if norm_type == 2.0: + dummy_overflow_buf = torch.tensor([0], dtype=torch.int, device='cuda') + # Use apex's multi-tensor applier for efficiency reasons. + # Multi-tensor applier takes a function and a list of list + # and performs the operation on that list all in one kernel. + if grads_for_norm: + grad_norm, _ = multi_tensor_applier( + amp_C.multi_tensor_l2norm, + dummy_overflow_buf, + [grads_for_norm], + False, # no per-parameter norm + ) + else: + grad_norm = torch.tensor([0], dtype=torch.float, device='cuda') + # Since we will be summing across data parallel groups, + # we need the pow(norm-type). + total_norm = grad_norm ** norm_type + + else: + for grad in grads_for_norm: + grad_norm = torch.norm(grad, norm_type) + total_norm += grad_norm ** norm_type + + # Sum across all model-parallel GPUs. + torch.distributed.all_reduce( + total_norm, op=torch.distributed.ReduceOp.SUM, group=model_parallel_group + ) + total_norm = total_norm.item() ** (1.0 / norm_type) + + # Scale. + clip_coeff = max_norm / (total_norm + 1.0e-6) + if clip_coeff < 1.0: + dummy_overflow_buf = torch.tensor([0], dtype=torch.int, device='cuda') + multi_tensor_applier( + amp_C.multi_tensor_scale, dummy_overflow_buf, [grads, grads], clip_coeff + ) + + return total_norm + + +def count_zeros_fp32( + parameters: Union[List[torch.Tensor], torch.Tensor], + model_parallel_group: torch.distributed.ProcessGroup, +) -> float: + """Counts the number of zeros in gradients associated with the passed-in list of + parameters. + + Args: + parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a + single Tensor that will have the number of zeros in its corresponding + gradient counted. + model_parallel_group (torch.distributed.ProcessGroup, optional): model-parallel + group over which grad norm needs to be aggregated. + """ + + if isinstance(parameters, torch.Tensor): + parameters = [parameters] + + # Filter parameters based on: + # - grad should not be none + # - parameter should not be shared + # - should not be a replica due to tensor model parallelism + total_num_zeros = torch.tensor([0.0], dtype=torch.float, device='cuda') + for param in parameters: + grad_not_none = param.grad is not None + is_not_shared = param_is_not_shared(param) + is_not_tp_duplicate = param_is_not_tensor_parallel_duplicate(param) + if grad_not_none and is_not_shared and is_not_tp_duplicate: + grad = param.grad.detach() + num_zeros = grad.numel() - torch.count_nonzero(grad) + total_num_zeros = num_zeros + total_num_zeros + + # Sum across all model-parallel GPUs. + torch.distributed.all_reduce( + total_num_zeros, op=torch.distributed.ReduceOp.SUM, group=model_parallel_group + ) + + total_num_zeros = total_num_zeros.item() + + return total_num_zeros diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/distrib_optimizer.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/distrib_optimizer.py new file mode 100644 index 0000000..16df771 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/distrib_optimizer.py @@ -0,0 +1,1452 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Megatron distributed optimizer.""" + + +import itertools +from logging import getLogger +from typing import Callable, Dict, List, Optional, Tuple + +import torch +from apex.optimizers import FusedAdam as Adam + +from .. import parallel_state, tensor_parallel +from ..dist_checkpointing import ShardedTensor +from ..dist_checkpointing.mapping import LocalNonpersitentObject, ShardedObject, ShardedStateDict +from ..distributed import ParamAndGradBuffer, shard_buffer +from .grad_scaler import MegatronGradScaler +from .optimizer import MixedPrecisionOptimizer, _zero_grad_group_helper +from .optimizer_config import OptimizerConfig + +logger = getLogger(__name__) + + +class Range: + """ + A range represents a start and end points for indexing a shard + from a full tensor. + """ + + def __init__(self, start: int, end: int): + self.start = start + self.end = end + self.size = end - start + + def normalize(self, start: int = 0): + return Range(start, start + self.size) + + def __str__(self): + return "%d,%d [%d]" % (self.start, self.end, self.size) + + def __len__(self): + return self.end - self.start + + +class DistributedOptimizer(MixedPrecisionOptimizer): + @classmethod + def _build_model_gbuf_param_range_map( + cls, + param_world_index_map: Dict[torch.nn.Parameter, Tuple], + gbuf_world_range: Range, + bucket_offset: int, + ): + """ + Build mapping from param reference to grad buffer shard ranges. + + This method builds a mapping from parameter references to grad + buffer shard ranges, specific to each data-parallel (DP) rank's + set of 'owned' parameters. Each grad buffer (padded to be an even + multiple of DP-world-size) is conceptually divided into DP-world-size + contiguous regions, where each DP rank 'owns' a contiguous regions. + Ownership in this sense means DP rank is responsible for reducing + the relevant subset of grads, and updating the relevant subset of + params. + + This conceptual partitioning of the grad buffer does NOT respect + parameter boundaries, and as such it is assumed that each created + range references a shard (or subset) of the full parameter. It is + easiest to think of each DP rank as operating (i.e., reducing, + gathering) purely on views into the grad buffer, for all model-to- + main & main-to-model operations. + + This method creates four ranges: + - The param's range within the entire grad buffer (i.e., world index). + - The param's range within the relevant grad bucket's buffer. + - The param's range within the DP rank's local view of the grad buffer. + - The param's range within itself (i.e., its shard). + """ + + # Param range map. + param_range_map = {} + for param, param_world_indexes in param_world_index_map.items(): + + # Param range. + param_world_start, param_world_end, _ = param_world_indexes + param_local_start = max(0, param_world_start - gbuf_world_range.start) + param_local_end = min(gbuf_world_range.size, param_world_end - gbuf_world_range.start) + + # Add param, if within local gbuf range. + if param_local_end > param_local_start: + param_local_range = Range(param_local_start, param_local_end) + param_world_range = param_local_range.normalize( + param_local_start + gbuf_world_range.start + ) + param_world_range_in_bucket = Range( + param_world_range.start - bucket_offset, param_world_range.end - bucket_offset + ) + sub_param_start = max(0, gbuf_world_range.start - param_world_start) + sub_param_range = param_local_range.normalize(sub_param_start) + param_range_map[param] = { + "gbuf_world": param_world_range, + "gbuf_world_in_bucket": param_world_range_in_bucket, + "gbuf_local": param_local_range, + "param": sub_param_range, + } + + return param_range_map + + @classmethod + def _build_model_gbuf_range(cls, param_and_grad_buffer: ParamAndGradBuffer, bucket_index: int): + """ + Build mapping between params and their grad buffers. + + This method does the initial setup for the method above. This setup + includes determining the shard ranges into the param_and_grad_buffer + for each data-parallel (DP) rank. Each DP rank keeps range info for + all other DP ranks, for the purpose of creating args for + reduce-scatter and all-gather. + """ + + data_parallel_rank = torch.distributed.get_rank(param_and_grad_buffer.data_parallel_group) + data_parallel_world_size = param_and_grad_buffer.data_parallel_group.size() + + bucket = param_and_grad_buffer.buckets[bucket_index] + gbuf_size = bucket.grad_data.numel() + assert ( + gbuf_size % data_parallel_world_size == 0 + ), f"Each bucket's buffer size should be divisible by {data_parallel_world_size}" + max_gbuf_range_size = gbuf_size // data_parallel_world_size + + # All world ranges (i.e., across all data parallel ranks). + gbuf_world_all_ranges = [] + for r in range(data_parallel_world_size): + # Compute start of chunk in this bucket. + gbuf_world_start = r * max_gbuf_range_size + gbuf_world_end = min(gbuf_size, gbuf_world_start + max_gbuf_range_size) + # Add bucket's offset in grad buffer. + gbuf_world_range = Range( + gbuf_world_start + bucket.offset, gbuf_world_end + bucket.offset + ) + gbuf_world_all_ranges.append(gbuf_world_range) + + # Local DP's ranges. + gbuf_world_range = gbuf_world_all_ranges[data_parallel_rank] + + # Get each param's ranges. + param_range_map = cls._build_model_gbuf_param_range_map( + param_and_grad_buffer.param_index_map, gbuf_world_range, bucket.offset + ) + + # Group into dict. + data = { + "param_map": param_range_map, + } + + return data + + @classmethod + def _build_gbuf_range_map(cls, param_and_grad_buffer: ParamAndGradBuffer): + """ + Build mapping between params and their grad buffers. These mappings are + partitioned according to data type. + + Iterate through all buckets of grad buffer to construct param ranges + that this rank "owns" (the dp_rank'th shard of each bucket, where each + shard is 1/dp_world_size of the bucket). + + Args: + param_and_grad_buffer (ParamAndGradBuffer): buffer to build mapping for. + """ + return { + (param_and_grad_buffer.param_dtype, param_and_grad_buffer.grad_dtype): [ + cls._build_model_gbuf_range(param_and_grad_buffer, bucket_index) + for bucket_index in range(len(param_and_grad_buffer.buckets)) + ] + } + + @classmethod + def _build_model_param_gbuf_map( + cls, gbuf_ranges: List[Dict] + ) -> Dict[torch.nn.Parameter, Tuple]: + """ + Create a reverse of the gbuf_ranges, for referencing in opposite direction. + """ + param_gbuf_map = {} + for gbuf_index, gbuf_range_map in enumerate(gbuf_ranges): + for dtype, gbuf_range_map_for_all_buckets in gbuf_range_map.items(): + for bucket_index, gbuf_range_map in enumerate(gbuf_range_map_for_all_buckets): + for param, _ in gbuf_range_map["param_map"].items(): + assert ( + param not in param_gbuf_map + ), "Param should not be in param_gbuf_map; each param only belongs to a single bucket" + param_gbuf_map[param] = (gbuf_index, dtype, bucket_index) + return param_gbuf_map + + @classmethod + def _build_optimizer_group_ranges(cls, param_groups: List[Dict], gbuf_ranges: List[Dict]): + """ + Create optimizer groups. + + Given the set of parameter shard ranges that are owned by the current + data-parallel (DP) rank, gather the set of parameters that will be + used (in the method below) to create the current DP's optimizer + groups. + """ + + # Param group map. + # World param group map. + # - Store a mapping of for all parameters + # across all DP ranks. This is necessary because it is our first + # cross reference between the DDP mappings and the optimizer group + # parameters. This mapping only for use in the next step of building + # the local mapping over this DP rank's parameters. + world_param_group_map = {} + for group_index, group in enumerate(param_groups): + for param in group["params"]: + assert param.requires_grad + world_param_group_map[param] = group_index + + # Optimizer group ranges & param-group mapping. + # - Build a mapping from groups to their contained parameters, and also + # from parameters to their containing group index and order within + # the group. The group index and order are particularly important for + # saving and loading checkpoints. + local_param_group_map = {} + group_ranges = [{"params": []} for _ in param_groups] + for gbuf_range_map in gbuf_ranges: + for dtype, gbuf_range_map_for_all_buckets in gbuf_range_map.items(): + for gbuf_range_map in gbuf_range_map_for_all_buckets: + for param in gbuf_range_map["param_map"]: + group_index = world_param_group_map[param] + group_range = group_ranges[group_index] + group_range["params"].append(param) + local_param_group_map[param] = (group_index, len(group_range["params"]) - 1) + + # Squeeze zero-size group ranges. + for group_index, group_range in enumerate(group_ranges): + group_range["orig_group"] = param_groups[group_index] + group_range["orig_group_idx"] = param_groups[group_index] + + return local_param_group_map, group_ranges + + @classmethod + def _build_model_and_main_param_groups( + cls, + gbuf_ranges: List[Dict], + param_gbuf_map: Dict[torch.nn.Parameter, Tuple], + opt_group_ranges: List, + ): + """ + Create main parameter groups needed for the optimizer step. + + These groups encompass both: 1) groups used by this class, for + reducing/gather, and 2) groups used by the inner optimizer for the + parameter update. Given that the conceptual grad buffer partitioning + (created in earlier method) doesn't respect parameter boundaries, + the optimizer operates on shards of the model parameters, rather than + the full parameters. + """ + + # Parameter groups: + # model_float16_groups: original float16 parameters + # model_fp32_groups: original fp32 parameters + # shard_float16_groups: shards of original float16 parameters + # shard_fp32_groups: shards of original fp32 parameters + # shard_fp32_from_float16_groups: fp32 copy of float16 parameters + model_float16_groups = [] + model_fp32_groups = [] + shard_float16_groups = [] + shard_fp32_groups = [] + shard_fp32_from_float16_groups = [] + + # Allocate (or slice) each group's param shard. + for group_range in opt_group_ranges: + + # Params of this group. + model_float16_params_this_group = [] + model_fp32_params_this_group = [] + shard_float16_params_this_group = [] + shard_fp32_params_this_group = [] + shard_fp32_from_float16_params_this_group = [] + model_float16_groups.append(model_float16_params_this_group) + model_fp32_groups.append(model_fp32_params_this_group) + shard_float16_groups.append(shard_float16_params_this_group) + shard_fp32_groups.append(shard_fp32_params_this_group) + shard_fp32_from_float16_groups.append(shard_fp32_from_float16_params_this_group) + + for model_param in group_range["params"]: + + assert model_param.requires_grad + + gbuf_index, dtype, bucket_index = param_gbuf_map[model_param] + gbuf_range = gbuf_ranges[gbuf_index][dtype][bucket_index] + param_range = gbuf_range["param_map"][model_param]["param"] + + # fp16, bf16 params. + if model_param.type() in ['torch.cuda.HalfTensor', 'torch.cuda.BFloat16Tensor']: + + # Clone model -> main. + shard_model_param = model_param.detach().view(-1)[ + param_range.start : param_range.end + ] + shard_main_param = shard_model_param.clone().float() + tensor_parallel.copy_tensor_model_parallel_attributes( + shard_model_param, model_param + ) + tensor_parallel.copy_tensor_model_parallel_attributes( + shard_main_param, model_param + ) + if hasattr(model_param, 'shared'): + shard_model_param.shared = model_param.shared + shard_main_param.shared = model_param.shared + + # Add to group. + model_float16_params_this_group.append(model_param) + shard_float16_params_this_group.append(shard_model_param) + shard_fp32_from_float16_params_this_group.append(shard_main_param) + + # fp32 params. + elif model_param.type() == 'torch.cuda.FloatTensor': + shard_model_param = model_param.view(-1)[param_range.start : param_range.end] + model_fp32_params_this_group.append(model_param) + shard_fp32_params_this_group.append(shard_model_param) + tensor_parallel.copy_tensor_model_parallel_attributes( + shard_model_param, model_param + ) + if hasattr(model_param, 'shared'): + shard_model_param.shared = model_param.shared + + else: + raise TypeError( + 'Wrapped parameters must be one of ' + 'torch.cuda.FloatTensor, ' + 'torch.cuda.HalfTensor, or ' + 'torch.cuda.BFloat16Tensor. ' + 'Received {}'.format(model_param.type()) + ) + + # Update optimizer's params. + group_range["orig_group"]["params"] = [ + *shard_fp32_params_this_group, + *shard_fp32_from_float16_params_this_group, + ] + + return ( + model_float16_groups, + model_fp32_groups, + shard_float16_groups, + shard_fp32_groups, + shard_fp32_from_float16_groups, + ) + + def __init__( + self, + optimizer: torch.optim.Optimizer, + config: OptimizerConfig, + grad_scaler: MegatronGradScaler, + init_state_fn: Optional[Callable], + per_model_buffers: Dict[int, List[ParamAndGradBuffer]], + data_parallel_group: torch.distributed.ProcessGroup, + data_parallel_group_gloo: torch.distributed.ProcessGroup, + data_parallel_group_idx: int, + ): + """ + Distributed optimizer, for all data types (fp16, bf16, and fp32). + + The steps in this method create the core mapping between param and grad buffers, + parameters, and parameter shard ranges, that is needed for converting between model + param indexes and main parameter shard indexes. This method also updates the optimizer + parameter groups with the newly created shards. + + Args: + optimizer (torch.optim.Optimizer): base optimizer such as Adam or SGD. + config (OptimizerConfig): configuration object for optimizer. + grad_scaler (MegatronGradScaler): used for scaling gradients. Note that + this can be None. This case happens when `bf16 = True` and we don't + use any loss scale. Note that for `bf16 = True`, we can have + a constant gradient scaler. Also for `bf16 = False`, we + always require a grad scaler. + init_state_fn (Callable, optional): function to initialize state in the optimizer. + per_model_buffers (Dict[int, List[ParamAndGradBuffer]]): the implementation of the + distributed optimizer is centered on using a contiguous buffer for + communicating grads & params between the model state and the optimizer state. + You can find a more detailed description in + https://github.com/NVIDIA/Megatron-LM/blob/main/docs/source/distrib_optimizer.md. + data_parallel_group (torch.distributed.ProcessGroup): data-parallel group to use to + all-gather params after optimizer.step(). + data_parallel_group_gloo (torch.distributed.ProcessGroup): gloo data-parallel group + (used in checkpoint loading and saving). + data_parallel_group_idx (int): index in data-parallel group (used by + distributed checkpointing logic). + """ + + super().__init__( + optimizer, config, grad_scaler, init_state_fn, + ) + + assert isinstance( + optimizer, Adam + ), "Only Adam currently supported, due to checkpointing requirements." + + # Model grad buffer ranges. + assert per_model_buffers is not None, "per_model_buffers must be provided" + self.buffers = list(itertools.chain(*per_model_buffers.values())) + self.per_model_buffers = per_model_buffers + self.data_parallel_group = data_parallel_group + self.data_parallel_group_gloo = data_parallel_group_gloo + self.data_parallel_group_idx = data_parallel_group_idx + self.gbuf_idx_to_model_idx_map = {} + gbuf_idx = 0 + for model_idx, buffers in self.per_model_buffers.items(): + for _ in buffers: + self.gbuf_idx_to_model_idx_map[gbuf_idx] = model_idx + gbuf_idx += 1 + self.gbuf_ranges = [] + self.per_bucket_numel = [] + self.per_bucket_numel_unpadded = [] + for buffer in self.buffers: + + self.per_bucket_numel.append( + { + (buffer.param_dtype, buffer.grad_dtype): [ + bucket.grad_data.numel() for bucket in buffer.buckets + ] + } + ) + self.per_bucket_numel_unpadded.append( + { + (buffer.param_dtype, buffer.grad_dtype): [ + bucket.numel_unpadded for bucket in buffer.buckets + ] + } + ) + self.gbuf_ranges.append(self._build_gbuf_range_map(buffer)) + self.model_param_gbuf_map = self._build_model_param_gbuf_map(self.gbuf_ranges) + + # Optimizer ranges. + ( + self.model_param_group_index_map, + self.opt_group_ranges, + ) = self._build_optimizer_group_ranges(self.optimizer.param_groups, self.gbuf_ranges) + + # Allocate main param shards. + ( + self.model_float16_groups, + self.model_fp32_groups, + self.shard_float16_groups, + self.shard_fp32_groups, + self.shard_fp32_from_float16_groups, + ) = self._build_model_and_main_param_groups( + self.gbuf_ranges, self.model_param_gbuf_map, self.opt_group_ranges + ) + + # Now construct data structures to manage all-gather handles. + self.all_gather_handles = [] + self.all_gather_handle_index_to_bucket_index_map = [] + self.model_index_to_all_gather_handle_index_map = {} + self.all_gather_handle_indices = [] + self.param_to_all_gather_handle_index_map = {} + + self.pbuf_view_items = self._get_model_param_buffer_dp_views() + for (gbuf_index, dtype, bucket_index, _, _) in self.pbuf_view_items: + self.all_gather_handle_index_to_bucket_index_map.append( + (gbuf_index, dtype, bucket_index) + ) + all_gather_handle_index = len(self.all_gather_handle_index_to_bucket_index_map) - 1 + self.all_gather_handles.append(None) + + # Store all all_gather_handle_indices. + model_idx = self.gbuf_idx_to_model_idx_map[gbuf_index] + if model_idx not in self.model_index_to_all_gather_handle_index_map: + self.model_index_to_all_gather_handle_index_map[model_idx] = [] + self.model_index_to_all_gather_handle_index_map[model_idx].append( + all_gather_handle_index + ) + + for param in self.buffers[gbuf_index].buckets[bucket_index].params_list: + self.param_to_all_gather_handle_index_map[param] = all_gather_handle_index + self.num_all_gather_handles = len(self.all_gather_handle_index_to_bucket_index_map) + + self.overlap_param_gather = self.config.overlap_param_gather + self.remove_pre_hook_handle = None + if self.overlap_param_gather: + self.enable_pre_hook() + + self.update_successful = False + + # Update optimizer groups. + # - Also, leverage state_dict() and load_state_dict() to + # recast preexisting per-param state tensors. + self.optimizer.param_groups = [g["orig_group"] for g in self.opt_group_ranges] + self.optimizer.load_state_dict(self.optimizer.state_dict()) + + def enable_pre_hook(self): + """ + Enable forward pre-hook needed for param all-gather overlap with forward compute. + """ + assert self.remove_pre_hook_handle is None + self.remove_pre_hook_handle = torch.nn.modules.module.register_module_forward_pre_hook( + self._make_forward_pre_hook() + ) + + def disable_pre_hook(self): + """ + Disable forward pre-hook needed for param all-gather overlap with forward compute. + """ + assert self.remove_pre_hook_handle is not None + self.remove_pre_hook_handle.remove() + self.remove_pre_hook_handle = None + + # Make sure all-gathers are completed as needed. + self._reset_metadata_and_sync_gather_all_model_params(force_sync=True) + + def _get_model_param_range_map(self, param: torch.nn.Parameter): + """ + Given a model param, get the index sub-range of the param that this + data-parallel rank owns. + """ + gbuf_index, dtype, bucket_index = self.model_param_gbuf_map[param] + gbuf_range_map = self.gbuf_ranges[gbuf_index][dtype][bucket_index] + param_range_map = gbuf_range_map["param_map"][param] + return param_range_map + + def get_model_parallel_group(self) -> torch.distributed.ProcessGroup: + """ + With the distributed optimizer, the model parallel group is the + entire world. + """ + return None + + def state_dict(self): + """ + The state dict contains all non-DP-rank-dependent (i.e., non-parameter- + related) optimizer variables. The returned state dict can be stored in + the standard model/RNG checkpoint file. The parameter and dependent + optimizer state (e.g., exp_avg, exp_avg_sq) are stored in a separate + checkpoint file by calling 'save_parameter_state()'. + """ + + state_dict = {} + + # Optimizer state (do not store parameter state here). + state_dict['optimizer'] = { + k: v for k, v in self.optimizer.state_dict().items() if k != "state" + } + for param_group in state_dict["optimizer"]["param_groups"]: + del param_group["params"] + + # Grad scaler state. + if self.grad_scaler: + state_dict['grad_scaler'] = self.grad_scaler.state_dict() + + return state_dict + + def load_state_dict(self, state_dict): + """Load the state dict. + + As detailed in state_dict(), the state dict contains all non- + parameter-related variables. This method is notably longer than + state_dict(), because the Torch optimizers state has yet to be + allocated at this point, and so we must do a cross referencing between + the optimizers state (and the ordering it expects for parameter state) + and this DP rank's shards. The optimizer at this point does not contain + any tensor dimension information, so we must get these dimensions from + the DP shards mapped during DistributedOptimizer.__init__(). + + The tensor parameter state is loaded via load_parameter_state(), and + so this method also must populate the loaded state dict with dummy + tensor data (i.e., via torch.empty() below). This will be overwritten + during load_parameter_state(). + + ** Note: Torch optimizer's state structure. ** + The Torch optimizer stores its state in two levels. The top level is a + list of groups, where each group contains a list of integer indexes + (corresponding to parameters) that index into a master parameter list + that is shared by all groups. As such, three values are necessary for + maintaining this ordering: + + - group_index : The group to which a parameter belongs. + - group_order : The index of a parameter within its group. + - state_order : The index of a parameter within the shared parameter + list. + """ + + # Get the Torch optimizer's state dict. + # - This 'inner' optimizer at this point is unallocated, and only + # contains an integer odering of parameters within each group, and + # the ordering of parameters within its flattened parameter state + # list. + inner_state_dict = self.optimizer.state_dict() + state_dict_param_groups = [ + {**group, "params": list(inner_state_dict["param_groups"][idx]["params"]),} + for idx, group in enumerate(state_dict["optimizer"]["param_groups"]) + ] + + # Allocate 'dummy' data for optimizer state (i.e., torch.empty() below) + # - Real data is overwritten during load_parameter_state(). + state_dict_state = [] + for gbuf_range_maps in self.gbuf_ranges: + for gbuf_range_map_for_all_buckets in gbuf_range_maps.values(): + for gbuf_range_map in gbuf_range_map_for_all_buckets: + for model_param, param_range_map in gbuf_range_map["param_map"].items(): + + # Get parameter ordering information (see method docstring + # for details). + group_index, group_order = self.model_param_group_index_map[model_param] + state_order = inner_state_dict["param_groups"][group_index]["params"][ + group_order + ] + + # Allocate dummy tensors. + numel = len(param_range_map["gbuf_world"]) + init_shard = lambda: torch.empty( + (numel,), dtype=torch.float32, device=torch.cuda.current_device() + ) + + state_dict_state.append( + (state_order, {"exp_avg": init_shard(), "exp_avg_sq": init_shard(),}) + ) + + # Sort by state order (see method docstring for details). + state_dict_state.sort(key=lambda s: s[0]) + state_dict_state = {s[0]: s[1] for s in state_dict_state} + + # Optimizer. + self.optimizer.load_state_dict( + {"state": state_dict_state, "param_groups": state_dict_param_groups,} + ) + + # Grad scaler. + if 'grad_scaler' not in state_dict: + if self.config.fp16: + logger.info( + '***WARNING*** found an old checkpoint, will not ' 'load grad scaler ...' + ) + else: + if self.grad_scaler: + self.grad_scaler.load_state_dict(state_dict['grad_scaler']) + else: + logger.info( + '***WARNING*** fould the grad scaler in the ' + 'checkpoint but it is None in the class. ' + 'Skipping loading grad scaler ...' + ) + + if 'param_state' in state_dict: + assert 'param_state_sharding_type' in state_dict, state_dict.keys() + param_state = state_dict['param_state'] + sharding_type = state_dict['param_state_sharding_type'] + logger.info(f'Loading distributed optimizer sharded state of type {sharding_type}') + if sharding_type == 'dp_zero_gather_scatter': + self.load_parameter_state_from_dp_zero(param_state) + elif sharding_type == 'fully_sharded_bucket_space': + self.load_parameter_state_from_fs_bucket_space(param_state) + else: + raise NotImplementedError(f'Unknown sharding_type: {sharding_type}') + + def get_parameter_state_fs_bucket_space(self): + """Get internal representation of parameter state without any copies and modifications. + + This is referred to as "fully sharded bucket space" because the optimizer state is + fully sharded (e.g. no gather involved) and bucket-centric (the state + follows the internal structure of the Distributed Optimizer buckets) + as opposed to model-centric (typical structure of PyT optimizers) + """ + state = { + "per_bucket_numel": self.per_bucket_numel, + "per_bucket_numel_unpadded": self.per_bucket_numel_unpadded, + } + for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges): + + # Iterate grad buffers (by data type). + dtype_state = {} + assert len(gbuf_range_maps) == 1, "single dtype supported, for now." + for dtype, gbuf_range_map_for_all_buckets in gbuf_range_maps.items(): + buckets_state = [] + for bucket_idx, gbuf_range_map in enumerate(gbuf_range_map_for_all_buckets): + bucket_state = [] + for model_param, param_range_map in gbuf_range_map["param_map"].items(): + + # Main param & optimizer states. + group_index, group_order = self.model_param_group_index_map[model_param] + main_param = self.optimizer.param_groups[group_index]["params"][group_order] + optim_state = self.optimizer.state[main_param] + + tensors = { + "param": main_param, + **optim_state, + "gbuf_local_start": param_range_map["gbuf_local"].start, + "gbuf_local_end": param_range_map["gbuf_local"].end, + } + bucket_state.append(tensors) + buckets_state.append(bucket_state) + dtype_state[dtype] = buckets_state + state[gbuf_idx] = dtype_state + return state + + def get_parameter_state_dp_zero(self): + """Get parameter state (i.e., parameter & optimizer tensors). + + This method performs two steps: + - For each DP rank, copy param & optimizer shards to contiguous CPU + buffers (e.g., one buffer each for main_param, exp_avg, and + exp_avg_sq). + - Gather contiguous buffers on DP rank 0 and concatenate to world + buffers. + """ + + # Data parallelism variables. + data_parallel_world_size = self.data_parallel_group_gloo.size() + data_parallel_rank = torch.distributed.get_rank(self.data_parallel_group_gloo) + data_parallel_group_gloo = self.data_parallel_group_gloo + data_parallel_global_ranks = torch.distributed.get_process_group_ranks( + self.data_parallel_group_gloo + ) + + # Collect param states. + state = { + "per_bucket_numel": self.per_bucket_numel, + "per_bucket_numel_unpadded": self.per_bucket_numel_unpadded, + } + for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges): + + # Iterate grad buffers (by data type). + dtype_state = {} + assert len(gbuf_range_maps) == 1, "single dtype supported, for now." + for dtype, gbuf_range_map_for_all_buckets in gbuf_range_maps.items(): + world_tensors = {} + for bucket_idx, gbuf_range_map in enumerate(gbuf_range_map_for_all_buckets): + + # Compute local DP contiguous shard's size. + gbuf_world_numel = self.buffers[gbuf_idx].buckets[bucket_idx].grad_data.numel() + assert gbuf_world_numel % data_parallel_world_size == 0 + gbuf_local_numel = gbuf_world_numel // data_parallel_world_size + local_shards = { + key: torch.empty((gbuf_local_numel,), dtype=torch.float32, device="cpu") + for key in ("param", "exp_avg", "exp_avg_sq") + } + + # Build contiguous DP rank shards (for param + optim states). + for model_param, param_range_map in gbuf_range_map["param_map"].items(): + + # Main param & optimizer states. + group_index, group_order = self.model_param_group_index_map[model_param] + main_param = self.optimizer.param_groups[group_index]["params"][group_order] + optim_state = self.optimizer.state[main_param] + + tensors = { + "param": main_param, + **optim_state, + } + + # Copy states into contiguous shard. + gbuf_local_start = param_range_map["gbuf_local"].start + gbuf_local_end = param_range_map["gbuf_local"].end + for key in local_shards: + local_shards[key][gbuf_local_start:gbuf_local_end].data.copy_( + tensors[key].detach().cpu() + ) + + # Gather contiguous shards on DP rank 0. + for key, send_tensor in local_shards.items(): + + # Gather tensor list. + if data_parallel_rank == 0: + recv_tensors = [ + torch.empty((gbuf_local_numel,), dtype=torch.float32, device="cpu") + for _ in range(data_parallel_world_size) + ] + else: + recv_tensors = None + + # Gather. + torch.distributed.gather( + send_tensor, + recv_tensors, + data_parallel_global_ranks[0], + data_parallel_group_gloo, + ) + + # Concatenate. + if data_parallel_rank == 0: + if key not in world_tensors: + world_tensors[key] = [] + world_tensors[key].append(torch.cat(recv_tensors)) + + # Collect world state. + dtype_state[dtype] = world_tensors + state[gbuf_idx] = dtype_state + + return state + + def save_parameter_state(self, filename: str): + """Save the distributed parameter state on DP rank 0. + + Args: + filename (str): path to save parameter state to. + """ + + state_dict = self.get_parameter_state_dp_zero() + if torch.distributed.get_rank(self.data_parallel_group) == 0: + torch.save(state_dict, filename) + + def sharded_state_dict( + self, + model_sharded_state_dict: ShardedStateDict, + is_loading: bool = False, + sharding_type: str = 'fully_sharded_bucket_space', + ): + """ + Chooses between 3 param state sharding implementations as requested by `sharding_type`. + + Regular state dict parameters are saved on DP rank 0 and loaded on all ranks. + """ + + state_dict = { + k: ShardedObject( + f'optimizer.distributed.dp_group_idx_{self.data_parallel_group_idx}.{k}', + v, + (1,), + (0,), + replica_id=torch.distributed.get_rank(self.data_parallel_group), + ) + for k, v in self.state_dict().items() + } + + if is_loading: + self.init_state_fn(self.optimizer) + + if sharding_type == 'fully_sharded_bucket_space': + param_state = self.sharded_param_state_fs_bucket_space( + model_sharded_state_dict, is_loading + ) + elif sharding_type == 'dp_zero_gather_scatter': + param_state = self.sharded_param_state_dp_zero(model_sharded_state_dict, is_loading) + elif sharding_type == 'fully_sharded_model_space': + # In this approach the tensors could be directly related to model parameters + # by linking them with metadata from `model_sharded_state_dict`. + # This would allow changing TP and PP while using DistOpt (as with other optimizers). + # This implementation is more involved and left out for now. + raise NotImplementedError( + f'The fully sharded model space version for' + f' {self.__class__.__name__}.sharded_state_dict' + f' not implemented.' + ) + else: + raise NotImplementedError(f'Unknown sharding_type: {sharding_type}') + + state_dict['param_state'] = param_state + state_dict['param_state_sharding_type'] = sharding_type + return state_dict + + def sharded_param_state_dp_zero( + self, model_sharded_state_dict: ShardedStateDict, is_loading: bool = False + ): + """Naive implementation which reuses gather/scatter from the legacy ckpt format. + + During saving, gathers the parameters state on DP rank 0 and saves a ShardedObject + with fixed TPxPP structure. During loading, loads the saved data on DP rank 0 + (None on other ranks). Relies on the parameters scatter done in load_state_dict. + """ + if is_loading: + param_state_data = None + else: + # Gather on rank 0 + param_state_data = self.get_parameter_state_dp_zero() + + if torch.distributed.get_rank(self.data_parallel_group) == 0: + # Fixed TPxPP. Save on DP rank 0 only + param_state = ShardedObject( + f'optimizer.distributed.dp_group_idx_{self.data_parallel_group_idx}.param_state', + param_state_data, + (1,), + (0,), + ) + else: + # DP ranks > 0 don't save. During loading, the param_state needs to be None. + param_state = LocalNonpersitentObject(None) + + return param_state + + def sharded_param_state_fs_bucket_space( + self, model_sharded_state_dict: ShardedStateDict, is_loading: bool = False + ): + """Sharded state dict where each noncontiguous buffer is a separate ShardedTensor. + + Results in fully parallel save and load without any inter-process + communication or intermediate buffers/copies. + """ + data_parallel_rank = torch.distributed.get_rank(self.data_parallel_group) + data_parallel_world_size = torch.distributed.get_world_size(self.data_parallel_group) + + state = self.get_parameter_state_fs_bucket_space() + # per_bucket_numel metadata is saved separately for each TPxPP domain. + for per_bucket_key in ('per_bucket_numel', 'per_bucket_numel_unpadded'): + state[per_bucket_key] = ShardedObject( + f'optimizer.distributed.dp_group_idx_{self.data_parallel_group_idx}.{per_bucket_key}', + state[per_bucket_key], + (1,), + (0,), + replica_id=data_parallel_rank, + ) + + for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges): + for dtype, gbuf_range_map_for_all_buckets in state[gbuf_idx].items(): + for bucket_idx, bucket_state in enumerate(gbuf_range_map_for_all_buckets): + # Compute local DP contiguous shard's size. + gbuf_world_numel = self.buffers[gbuf_idx].buckets[bucket_idx].grad_data.numel() + assert gbuf_world_numel % data_parallel_world_size == 0 + gbuf_local_numel = gbuf_world_numel // data_parallel_world_size + + sharded_bucket_key = f'optimizer.distributed.dp_group_idx_{self.data_parallel_group_idx}.gbuf_idx_{gbuf_idx}.dtype_{dtype}.bucket_idx_{bucket_idx}' + + # The global ckpt tensors must be fully covered. + # We add extra empty padding if necessary + assert bucket_state, 'empty bucket encountered' + if bucket_state[-1]['gbuf_local_end'] != gbuf_local_numel: + assert ( + data_parallel_rank == data_parallel_world_size - 1 + ), 'encountered padding on non-last DP rank' + pad_tensors = { + k: torch.empty( + gbuf_local_numel - bucket_state[-1]['gbuf_local_end'], + dtype=v.dtype, + device=v.device, + ) + for k, v in bucket_state[-1].items() + if isinstance(v, torch.Tensor) + } + bucket_state.append( + { + **pad_tensors, + 'gbuf_local_start': bucket_state[-1]['gbuf_local_end'], + 'gbuf_local_end': gbuf_local_numel, + } + ) + + # Each tensor is mapped to a slice (`flattened_range`) + # of a DP-local shard of size `gbuf_local_numel`. + for bucket_params_idx in range(len(bucket_state)): + tensors = bucket_state[bucket_params_idx] + gbuf_local_start = tensors.pop('gbuf_local_start') + gbuf_local_end = tensors.pop('gbuf_local_end') + + for key in tensors: + assert tensors[key].shape == (gbuf_local_end - gbuf_local_start,), ( + tensors[key].shape, + gbuf_local_start, + gbuf_local_end, + ) + + tensors[key] = ShardedTensor( + f'{sharded_bucket_key}.{key}', + tensors[key], + tensors[key].dtype, + (gbuf_local_numel,), + (data_parallel_world_size * gbuf_local_numel,), + (data_parallel_rank * gbuf_local_numel,), + axis_fragmentations=(data_parallel_world_size,), + flattened_range=slice(gbuf_local_start, gbuf_local_end), + allow_shape_mismatch=True, + ) + return state + + def load_parameter_state_from_fs_bucket_space(self, state_dict): + """ Loads the parameter state from an internal representation. + + Inverse of the `get_parameter_state_internal_repr` method. + """ + if state_dict is not None and "per_bucket_numel_unpadded" in state_dict: + per_bucket_numel_unpadded_in_checkpoint = state_dict["per_bucket_numel_unpadded"] + assert self.per_bucket_numel_unpadded == per_bucket_numel_unpadded_in_checkpoint, ( + f"Number of unpadded elements in each bucket need to be the same in current run " + f"({self.per_bucket_numel_unpadded}) and checkpoint " + f"({per_bucket_numel_unpadded_in_checkpoint})" + ) + + for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges): + assert len(gbuf_range_maps) == 1, "single dtype supported, for now." + for dtype, gbuf_range_map_for_all_buckets in gbuf_range_maps.items(): + for bucket_idx, gbuf_range_map in enumerate(gbuf_range_map_for_all_buckets): + bucket_state = state_dict[gbuf_idx][dtype][bucket_idx] + + # State dict bucket state can be 1 entry longer in case of padding + assert len(bucket_state) in ( + len(gbuf_range_map["param_map"]), + len(gbuf_range_map["param_map"]) + 1, + ), (len(bucket_state), len(gbuf_range_map["param_map"])) + for src_tensors, (model_param, param_range_map) in zip( + bucket_state, gbuf_range_map["param_map"].items() + ): + # Main param & optimizer states. + group_index, group_order = self.model_param_group_index_map[model_param] + main_param = self.optimizer.param_groups[group_index]["params"][group_order] + optim_state = self.optimizer.state[main_param] + + dst_tensors = { + "param": main_param, + **optim_state, + } + for key in dst_tensors: + dst_tensors[key].copy_(src_tensors[key]) + + def load_parameter_state_from_dp_zero(self, state_dict): + """Load parameter state (i.e., parameter & optimizer tensors) from DP 0 rank. + + This method performs the reverse of get_parameter_state_dp_zero(): + - Scatter contiguous buffers from DP rank 0 to each DP rank (each DP + rank receives its relevant subset of the world buffers). + - For each DP rank, copy param & optimizer shards from contiguous CPU + buffers. (e.g., one buffer each for main_param, exp_avg, and + exp_avg_sq). + """ + if state_dict is not None and "per_bucket_numel_unpadded" in state_dict: + per_bucket_numel_unpadded_in_checkpoint = state_dict["per_bucket_numel_unpadded"] + assert self.per_bucket_numel_unpadded == per_bucket_numel_unpadded_in_checkpoint, ( + f"Number of unpadded elements in each bucket need to be the same in current run " + f"({self.per_bucket_numel_unpadded}) and checkpoint " + f"({per_bucket_numel_unpadded_in_checkpoint})" + ) + + # Data parallelism variables. + data_parallel_world_size = self.data_parallel_group_gloo.size() + data_parallel_rank = torch.distributed.get_rank(self.data_parallel_group_gloo) + data_parallel_group_gloo = self.data_parallel_group_gloo + data_parallel_global_ranks = torch.distributed.get_process_group_ranks( + self.data_parallel_group_gloo + ) + + # Scatter tensors to all DP ranks. + for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges): + for dtype, gbuf_range_map_for_all_buckets in gbuf_range_maps.items(): + for bucket_idx, gbuf_range_map in enumerate(gbuf_range_map_for_all_buckets): + + # Compute local DP contiguous shard's size. + gbuf_world_numel = self.buffers[gbuf_idx].buckets[bucket_idx].grad_data.numel() + assert gbuf_world_numel == self.per_bucket_numel[gbuf_idx][dtype][bucket_idx] + assert gbuf_world_numel % data_parallel_world_size == 0 + gbuf_local_numel = gbuf_world_numel // data_parallel_world_size + + # Contiguous local shards (received from DP rank 0). + local_shards = { + key: torch.empty((gbuf_local_numel,), dtype=torch.float32, device="cpu") + for key in ("param", "exp_avg", "exp_avg_sq") + } + + # Scatter local shards from DP rank 0. + for key, recv_tensor in local_shards.items(): + + # Scatter tensor list. + if data_parallel_rank == 0: + world_tensor_for_all_buckets = state_dict[gbuf_idx][dtype][key] + if not isinstance(world_tensor_for_all_buckets, list): + world_tensor_for_all_buckets = [world_tensor_for_all_buckets] + assert bucket_idx < len(world_tensor_for_all_buckets), ( + f"Trying to load state for bucket_id {bucket_idx} (out of " + f"{len(gbuf_range_map_for_all_buckets)} buckets) from checkpoint; " + f"checkpoint only has {len(world_tensor_for_all_buckets)} bucket(s)" + ) + # This tensor might be bigger or smaller than expected (depending on + # relative sizes of per_bucket_numel_in_checkpoint and self.per_bucket_numel). + world_tensor = world_tensor_for_all_buckets[bucket_idx] + if "per_bucket_numel" in state_dict: + numel_in_checkpoint = state_dict["per_bucket_numel"][gbuf_idx][ + dtype + ][bucket_idx] + numel = self.per_bucket_numel[gbuf_idx][dtype][bucket_idx] + numel_unpadded = self.per_bucket_numel_unpadded[gbuf_idx][dtype][ + bucket_idx + ] + assert world_tensor.numel() == numel_in_checkpoint + assert numel_unpadded <= world_tensor.numel(), ( + "True number of elements should be fewer than number of elements in " + "checkpoint tensor" + ) + if world_tensor.numel() > numel: + # Truncate extra values, which are padding anyway. + logger.info( + f"Truncating extra values from checkpoint (numel_in_checkpoint={numel_in_checkpoint}, " + f"numel={numel}, numel_unpadded={numel_unpadded})" + ) + world_tensor = world_tensor[:numel] + elif world_tensor.numel() < numel: + # In this case, numel > world_tensor.numel() (which is numel_in_checkpoint). + # Create new tensor with right number of values, then copy and use new tensor. + logger.info( + f"Expanding tensor from checkpoint (numel_in_checkpoint={numel_in_checkpoint}, " + f"numel={numel}, numel_unpadded={numel_unpadded})" + ) + world_tensor_reshaped = torch.empty( + (numel,), + dtype=world_tensor.dtype, + device=world_tensor.device, + ) + world_tensor_reshaped[:numel_in_checkpoint].copy_(world_tensor) + world_tensor = world_tensor_reshaped + else: + logger.info( + "***WARNING*** Using older checkpoint so skipping padding checks" + ) + gbuf_start_idxs = list(range(0, gbuf_world_numel, gbuf_local_numel)) + send_tensors = [ + world_tensor[i : (i + gbuf_local_numel)] for i in gbuf_start_idxs + ] + else: + send_tensors = None + + # Scatter. + torch.distributed.scatter( + recv_tensor, + send_tensors, + data_parallel_global_ranks[0], + data_parallel_group_gloo, + ) + + # Copy local contiguous shards to param/optim shards. + for model_param, param_range_map in gbuf_range_map["param_map"].items(): + + # Main param & optimizer states. + group_index, group_order = self.model_param_group_index_map[model_param] + main_param = self.optimizer.param_groups[group_index]["params"][group_order] + optim_state = self.optimizer.state[main_param] + + tensors = { + "param": main_param, + **optim_state, + } + + # Copy states into contiguous shard. + gbuf_local_start = param_range_map["gbuf_local"].start + gbuf_local_end = param_range_map["gbuf_local"].end + for key in local_shards: + tensors[key].data.copy_( + local_shards[key][gbuf_local_start:gbuf_local_end] + ) + + def load_parameter_state(self, filename: str): + """Load the distributed parameter state from disk. + + Args: + filename (str): path to load parameter state from. + """ + state_dict = None + if torch.distributed.get_rank(self.data_parallel_group) == 0: + state_dict = torch.load(filename) + + self.load_parameter_state_from_dp_zero(state_dict) + + def zero_grad(self, set_to_none: bool = True): + """ + Zeroes grads for the model related parameters, i.e., model_float16_groups + and model_fp32_groups. We additionally zero the remaining groups as a + memory optimization to reduce fragmentation; in the case of + set_to_none==True, the space used by this field can be safely deallocated. + + Args: + set_to_none (bool): if true, set grads to None. + """ + for groups in ( + self.model_float16_groups, + self.model_fp32_groups, + self.shard_float16_groups, # grad empty/unused here? + self.shard_fp32_groups, # throws grad-access warning + self.shard_fp32_from_float16_groups, + ): + for group in groups: + _zero_grad_group_helper(group, set_to_none) + + # If overlapping param all-gather with forward compute, launch all-gather + # for first accessed bucket here before forward compute is initiated. + # The all-gather for the next bucket will be launched in the forward + # pre-hook when this all-gather finishes (to ensure that the communication + # kernels don't head-of-line block the compute kernels since we run with + # CUDA_DEVICE_MAX_CONNECTIONS=1 to support sequence parallelism). + if self.overlap_param_gather: + self._dispatch_gather_model_params(all_gather_handle_index=0) + + def _get_model_param_buffer_dp_views(self): + """ + Get shard views of each of the param buffers. + + In this nested list, the top level is grouped by the virtual model + index and the buffer's data type. The sub-level is a list of + shards of that buffer, where each shard in the list represents + a contiguous view of the buffer, that is owned by a data-parallel + rank. The shard boundary does not respect parameter boundaries, and + so the elements of some parameters are split across data parallel + ranks. + + Additionally, return references to the entire buffers, for use + in _all_gather_base. + """ + + # Buffer views. + # Add in reverse order in each model chunk since buckets start from the end of the model but we want + # all-gathers to run first for the start of the model (same order as forward pass). + # We keep the view_items in model chunk order since we want to still first run all_gather and + # all_gather_handle.wait() for the first model chunk. + # In all cases, we want all_gather and all_gather_handle.wait() to be called in the same order, + # and all_gather_handle.wait() needs to be called just before the corresponding forward pass. + view_items = [] + for gbuf_index, buffer in enumerate(self.buffers): + view_items_per_model_chunk = [] + dtype = self.buffers[gbuf_index].param_dtype + for bucket_index, bucket in enumerate(buffer.buckets): + data_parallel_world_size = torch.distributed.get_world_size( + self.data_parallel_group + ) + buf_views = shard_buffer(bucket.param_data, data_parallel_world_size) + view_items_per_model_chunk.insert( + 0, (gbuf_index, dtype, bucket_index, bucket.param_data, buf_views) + ) + view_items.extend(view_items_per_model_chunk) + + return view_items + + def _dispatch_gather_model_params(self, all_gather_handle_index: int, force_sync: bool = False): + """ + All-gather updated model params. + + When using the distributed optimizer, the params are already laid out in a contiguous + buffer (see mcore/distributed/param_and_grad_buffer.py for details), and so the + all-gather will put the results in the right region of memory. + """ + async_op = self.overlap_param_gather and not force_sync + if self.update_successful: + data_parallel_group = self.data_parallel_group + data_parallel_rank = torch.distributed.get_rank(data_parallel_group) + + # All-gather updated main params. + # All param_buf views are guaranteed to have the same number of elements + # across all data-parallel ranks, due to padding done in + # param_and_grad_buffer.py). Thus, all sub-views will have consistent + # start / end indexes across data-parallel ranks. + (gbuf_index, dtype, bucket_index, pbuf, pbuf_views) = self.pbuf_view_items[ + all_gather_handle_index + ] + assert all_gather_handle_index < len(self.all_gather_handles) + all_gather_handle = torch.distributed._all_gather_base( + pbuf, pbuf_views[data_parallel_rank], group=data_parallel_group, async_op=async_op, + ) + self.all_gather_handles[all_gather_handle_index] = all_gather_handle + assert self.all_gather_handle_index_to_bucket_index_map[all_gather_handle_index] == ( + gbuf_index, + dtype, + bucket_index, + ) + + def _make_forward_pre_hook(self): + """ + Create a forward pre-hook to wait on all-gather handles when necessary (i.e., + when a module uses a parameter in a bucket with a still incomplete all-gather) + and then copy the results from the param_buffer into model_params. + """ + + def hook(module, *unused): + assert ( + self.overlap_param_gather + ), "Should use pre-hook only when overlap_param_gather is True" + + # Make sure all parameters in this module have been all-gathered as necessary. + for param in module.parameters(recurse=False): + # Skip parameters that don't require grad. + if not param.requires_grad: + continue + + # Some params might be handled in another DistributedOptimizer instance; for + # example, we use separate DistributedOptimizer instances for expert and + # non-expert params. + if param in self.param_to_all_gather_handle_index_map: + all_gather_handle_index = self.param_to_all_gather_handle_index_map[param] + self._finish_param_sync_helper(all_gather_handle_index) + + return hook + + def finish_param_sync(self, model_index: int, *unused): + """ + Finishes all necessary param syncs for the model_index'th model chunk. + + Args: + model_index (int): index of model chunk to synchronize params. + """ + if model_index not in self.model_index_to_all_gather_handle_index_map: + return + + all_gather_handle_indices = self.model_index_to_all_gather_handle_index_map[model_index] + for all_gather_handle_index in all_gather_handle_indices: + self._finish_param_sync_helper(all_gather_handle_index) + + def _finish_param_sync_helper(self, all_gather_handle_index: int): + """ + Waits on all_gather_handle if necessary, then dispatches the next all-gather + as necessary. + """ + + # First check if there is an outstanding all-gather handle for this param. + # If so, wait on the handle to ensure the communication is finished. + assert all_gather_handle_index < len(self.all_gather_handles) + all_gather_handle = self.all_gather_handles[all_gather_handle_index] + if all_gather_handle is not None: + all_gather_handle.wait() + self.all_gather_handles[all_gather_handle_index] = None + + # Launch the all-gather for the next bucket now. + # We can't pre-launch all-gathers for all buckets at once since we don't + # want to head-of-line block the compute kernels with communication kernels + # (since we run with CUDA_DEVICE_MAX_CONNECTIONS=1 to support sequence + # parallelism). + next_all_gather_handle_index = all_gather_handle_index + 1 + if next_all_gather_handle_index < self.num_all_gather_handles: + self._dispatch_gather_model_params(next_all_gather_handle_index) + + def _collect_main_grad_data_for_unscaling(self): + """ + Note: this should be equivalent to the float-16 optimizer's method, + but written differently, so the two should be combined. + """ + return [ + param.grad.data for group in self.optimizer.param_groups for param in group["params"] + ] + + def _get_model_and_main_params_data_float16(self): + """ + Get aligned list of model and main params. + """ + model_data = [] + main_data = [] + for model_group, main_group in zip( + self.shard_float16_groups, self.shard_fp32_from_float16_groups + ): + for model_param, main_param in zip(model_group, main_group): + model_data.append(model_param.data) + main_data.append(main_param.data) + return model_data, main_data + + def _copy_model_grads_to_main_grads(self): + """ + Copy model grads to main grads. + + Since this step follows a reduce-scatter through the DDP's grad + buffer, this method is responsible for copying the updated grads + from the grad buffer to the main shard's grad field. + """ + + # Utility method for copying group grads. + def copy_group_grads(model_groups, shard_main_groups): + for model_group, shard_main_group in zip(model_groups, shard_main_groups): + for model_param, shard_main_param in zip(model_group, shard_main_group): + + param_range_map = self._get_model_param_range_map(model_param) + param_range = param_range_map["param"] + assert param_range.size == shard_main_param.nelement() + + model_grad = model_param.main_grad + shard_model_grad = model_grad.view(-1)[param_range.start : param_range.end] + shard_main_param.grad = shard_model_grad.float() + + # Copy model groups to shard groups. + copy_group_grads(self.model_float16_groups, self.shard_fp32_from_float16_groups) + copy_group_grads(self.model_fp32_groups, self.shard_fp32_groups) + + def _copy_main_params_to_model_params(self): + """ + Copy main params to model params. + + Since this step is followed by an all-gather through the DDP's grad + buffer, this method is responsible for copying the updated params + from the main shards into the correct position in the grad buffer. + """ + + # Utility method for copying group params. + def copy_group_params(shard_main_groups, model_groups): + for shard_main_group, model_group in zip(shard_main_groups, model_groups): + for shard_main_param, model_param in zip(shard_main_group, model_group): + + param_range_map = self._get_model_param_range_map(model_param) + world_range = param_range_map["gbuf_world_in_bucket"] + + assert world_range.size == shard_main_param.nelement() + + gbuf_index, _, bucket_id = self.model_param_gbuf_map[model_param] + model_param_buffer = self.buffers[gbuf_index].buckets[bucket_id].param_data + + shard_model_param = model_param_buffer.view(-1)[ + world_range.start : world_range.end + ] + + shard_model_param.data.copy_(shard_main_param) + + # Copy shard groups to model groups. + copy_group_params(self.shard_fp32_from_float16_groups, self.model_float16_groups) + copy_group_params(self.shard_fp32_groups, self.model_fp32_groups) + + def _copy_model_params_to_main_params(self): + """ + Copy model params to main params. + + During finetuning, this method is used to reload the main params from + the model params. This copy does not make use of the grad buffer as + an intermediary. + """ + + # Utility method for copying group params. + def copy_group_params(model_groups, shard_main_groups): + for model_group, shard_main_group in zip(model_groups, shard_main_groups): + for model_param, shard_main_param in zip(model_group, shard_main_group): + + param_range_map = self._get_model_param_range_map(model_param) + param_range = param_range_map["param"] + assert param_range.size == shard_main_param.nelement() + + shard_model_param = model_param.view(-1)[param_range.start : param_range.end] + shard_main_param.data.copy_(shard_model_param) + + # Copy model groups to shard groups. + copy_group_params(self.model_float16_groups, self.shard_fp32_from_float16_groups) + copy_group_params(self.model_fp32_groups, self.shard_fp32_groups) + + def _reset_metadata_and_sync_gather_all_model_params(self, force_sync: bool): + """ + Reset metadata needed to track results of all-gathers. + """ + self.all_gather_handles = [None for _ in range(len(self.all_gather_handles))] + + # Launch synchronous all-gather if --overlap-param-gather is turned on or if force_sync + # is explicitly set to True (e.g., if we are going to turn off all-gather overlapping for + # validation / test iterations). + if not self.overlap_param_gather or force_sync: + for all_gather_handle_index in range(self.num_all_gather_handles): + self._dispatch_gather_model_params(all_gather_handle_index, force_sync=force_sync) + + @torch.no_grad() + def step(self): + """ + Step optimizer. + Under the hood, either launch synchronous param all-gathers or get ready to launch + asynchorous all-gathers that get overlapped with the next forward pass. + """ + self.update_successful, grad_norm, num_zeros_in_grad = super().step() + + timers = self.config.timers + if timers is not None: + timers('params-all-gather', log_level=1).start(barrier=self.config.barrier_with_L1_time) + # If not overlapping all-gather for parameters, launch synchronous all-gather + # communication calls here. If overlapping all-gather for parameters, the following + # call to _gather_all_model_params is a no-op: the first all-gather is launched + # asynchronously in the next optimizer.zero_grad() call and subsequent all-gathers + # are launched in the forward pre-hook. + self._reset_metadata_and_sync_gather_all_model_params(force_sync=False) + if timers is not None: + timers('params-all-gather').stop() + + return self.update_successful, grad_norm, num_zeros_in_grad diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/grad_scaler.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/grad_scaler.py new file mode 100644 index 0000000..abdd1e7 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/grad_scaler.py @@ -0,0 +1,142 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Megatron grad scaler.""" + +from abc import ABC, abstractmethod +from typing import Dict + +import torch + + +class MegatronGradScaler(ABC): + def __init__(self, initial_scale: float): + """Initialize scale value with the input initial scale.""" + assert initial_scale > 0.0 + self._scale = torch.tensor([initial_scale], dtype=torch.float, device='cuda') + + @property + def scale(self): + return self._scale + + @property + def inv_scale(self): + return self._scale.double().reciprocal().float() + + @abstractmethod + def update(self, found_inf: bool): + pass + + @abstractmethod + def state_dict(self): + pass + + @abstractmethod + def load_state_dict(self, state_dict: Dict): + pass + + +class ConstantGradScaler(MegatronGradScaler): + """ + Constant grad scaler (loss scale is never adjusted regardless of NaNs seen in gradients). + """ + + def update(self, found_inf: bool): + pass + + def state_dict(self): + return dict() + + def load_state_dict(self, state_dict): + pass + + +class DynamicGradScaler(MegatronGradScaler): + """ + Grad scaler with dynamic scale that gets adjusted during training. + + Reduces loss scale by `backoff_factor` if `hysteresis` number of NaNs are seen in a row. Increases + loss scale by `growth_factor` if NaNs are not seen for `growth_interval` iterations. + """ + + def __init__( + self, + initial_scale: float, + min_scale: float, + growth_factor: float, + backoff_factor: float, + growth_interval: int, + hysteresis: int, + ): + """ + Grad scaler with dynamic scale that gets adjusted during training. + + Args: + initial_scale (float): Initial loss scale value. + min_scale (float): Minimum loss scale value. + growth_factor (float): Factor to grow loss scale by if NaNs are not seen in `growth_interval` + training iterations. Must be greater than 1. + backoff_factor (float): Factor to decrease loss scale by if NaNs are seen in `hysteresis` + consecutive training iterations. Must be between 0 and 1. + growth_interval (int): Number of training iterations of no NaNs before loss scale is increased. + hysteresis (int): Number of training iterations of consecutive NaNs before loss scale is decreased. + """ + super(DynamicGradScaler, self).__init__(initial_scale) + + # Lower bound on the scale. + assert min_scale > 0.0 + assert min_scale <= initial_scale + self.min_scale = torch.tensor([min_scale], dtype=torch.float, device='cuda') + # Growth and backoff factors for the scale. + assert growth_factor > 1.0 + self.growth_factor = torch.tensor([growth_factor], dtype=torch.float, device='cuda') + assert backoff_factor < 1.0 + assert backoff_factor > 0.0 + self.backoff_factor = torch.tensor([backoff_factor], dtype=torch.float, device='cuda') + # Interval over which if we don't see any inf/nan, + # we will scale the grad scale by the growth factor. + assert growth_interval > 0 + self.growth_interval = growth_interval + # Number of inf/nans we should see before scaling down + # the grad scale by the backoff factor. + assert hysteresis > 0 + self.hysteresis = hysteresis + + # Trackers. + self._growth_tracker = 0 + self._hysteresis_tracker = self.hysteresis + + def update(self, found_inf: bool): + """ + Updates internal state in grad scaler based on whether NaNs are seen in grads or not. + """ + + # If we have an inf/nan, growth tracker is set to 0 + # and hysterisis tracker is reduced by 1. + if found_inf: + self._growth_tracker = 0 + self._hysteresis_tracker -= 1 + # Now if we are out of hysteresis count, scale down the loss. + if self._hysteresis_tracker <= 0: + self._scale = torch.max(self._scale * self.backoff_factor, self.min_scale) + else: + # If there is no nan/inf, increment the growth tracker. + self._growth_tracker += 1 + # If we have had enough consequitive intervals with no nan/inf: + if self._growth_tracker == self.growth_interval: + # Reset the tracker and hysteresis trackers, + self._growth_tracker = 0 + self._hysteresis_tracker = self.hysteresis + # and scale up the loss scale. + self._scale = self._scale * self.growth_factor + + def state_dict(self): + state_dict = {} + state_dict['scale'] = self._scale + state_dict['growth_tracker'] = self._growth_tracker + state_dict['hysteresis_tracker'] = self._hysteresis_tracker + return state_dict + + def load_state_dict(self, state_dict: Dict): + self._scale = state_dict['scale'].cuda(torch.cuda.current_device()) + self._growth_tracker = state_dict['growth_tracker'] + self._hysteresis_tracker = state_dict['hysteresis_tracker'] diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/optimizer.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/optimizer.py new file mode 100644 index 0000000..760e3d8 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/optimizer.py @@ -0,0 +1,836 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Megatron optimizer.""" + +import math +from abc import ABC, abstractmethod +from itertools import chain +from logging import getLogger +from typing import Callable, List, Optional + +import amp_C +import torch +from apex.multi_tensor_apply import multi_tensor_applier + +from .. import parallel_state, tensor_parallel +from ..dist_checkpointing.mapping import ShardedStateDict +from ..dist_checkpointing.optimizer import ( + get_param_id_to_sharded_param_map, + make_sharded_optimizer_tensor, + optim_state_to_sharding_state, +) +from ..dist_checkpointing.utils import add_prefix_for_sharding +from ..transformer.module import param_is_not_shared +from .clip_grads import clip_grad_norm_fp32, count_zeros_fp32 +from .grad_scaler import MegatronGradScaler +from .optimizer_config import OptimizerConfig + +logger = getLogger(__name__) + + +def _zero_grad_group_helper(group: List[torch.nn.Parameter], set_to_none: bool): + """ + Zero out the gradient for a group of parameters. + Note: copied from torch.optim.optimizer. + """ + for param in group: + if param.grad is not None: + if set_to_none: + param.grad = None + else: + if param.grad.grad_fn is not None: + param.grad.detach_() + else: + param.grad.requires_grad_(False) + param.grad.zero_() + + +def _multi_tensor_copy_this_to_that( + this: List[torch.Tensor], that: List[torch.Tensor], overflow_buf: Optional[torch.Tensor] = None +): + """ + Use multi-tensor-applier to copy values from one list to another. + We don't have a bfloat16 implementation so for now if the overflow_buf + is not provided, we default back to simple loop copy to be compatible + with bfloat16. + """ + if overflow_buf: + overflow_buf.fill_(0) + # Scaling with factor `1.0` is equivalent to copy. + multi_tensor_applier(amp_C.multi_tensor_scale, overflow_buf, [this, that], 1.0) + else: + for this_, that_ in zip(this, that): + that_.copy_(this_) + + +class MegatronOptimizer(ABC): + """ + Base class for all Megatron optimizers. + + Args: + optimizer (torch.optim.Optimizer): base optimizer such as Adam or SGD. + config (OptimizerConfig): configuration object for optimizer. + init_state_fn (Callable, optional): function to initialize state in the optimizer. + """ + + def __init__( + self, + optimizer: torch.optim.Optimizer, + config: OptimizerConfig, + init_state_fn: Callable = lambda x: None, + ): + + """Input optimizer is the base optimizer (e.g., Adam).""" + self.optimizer = optimizer + assert self.optimizer, 'no optimizer is provided.' + self.config = config + self.init_state_fn = init_state_fn + + def get_parameters(self) -> List[torch.nn.Parameter]: + """ + Get list of parameters wrapped in optimizer. + """ + params = [] + for param_group in self.optimizer.param_groups: + for param in param_group['params']: + params.append(param) + return params + + def get_main_grads_for_grad_norm(self) -> List[torch.Tensor]: + """ + Get main_grads that should be taken into account to compute the grad norm. + Filter parameters based on: + - grad should not be None. + - parameter should not be shared (i.e., grads shouldn't be double counted while + computing norms). + - should not be a replica due to tensor model parallelism. + """ + params = self.get_parameters() + grads_for_norm = [] + for param in params: + grad = param.grad + grad_not_none = grad is not None + is_not_shared = param_is_not_shared(param) + is_not_tp_duplicate = tensor_parallel.param_is_not_tensor_parallel_duplicate(param) + if grad_not_none and is_not_shared and is_not_tp_duplicate: + grads_for_norm.append(grad) + + return grads_for_norm + + def get_model_parallel_group(self) -> torch.distributed.ProcessGroup: + """Default returned here, but the distributed optimizer overrides this.""" + return parallel_state.get_model_parallel_group() + + def clip_grad_norm(self, clip_grad: float) -> float: + """Compute grad norm.""" + params = self.get_parameters() + grads_for_norm = self.get_main_grads_for_grad_norm() + return clip_grad_norm_fp32( + params, grads_for_norm, clip_grad, model_parallel_group=self.get_model_parallel_group(), + ) + + def count_zeros(self) -> float: + """Count number of zeros in model's gradients.""" + params = self.get_parameters() + return count_zeros_fp32(params, model_parallel_group=self.get_model_parallel_group()) + + @abstractmethod + def zero_grad(self, set_to_none: bool = True): + pass + + @abstractmethod + def get_loss_scale(self) -> torch.Tensor: + """ + Get current loss scale factor. + NOTE: The output should be a CUDA tensor of size 1. + """ + pass + + def scale_loss(self, loss: torch.Tensor) -> torch.Tensor: + """Simple scaling.""" + return self.get_loss_scale() * loss + + def finish_param_sync(self, model_index: int): + """ + Finish parameter synchronization for all optimizers. + This is a no-op for all non-distributed optimizers. + """ + pass + + @abstractmethod + def reload_model_params(self): + """Refreshes any internal state from the current model parameters. + Call whenever the parameters are changed outside of the optimizer. + For example, when we load a model from a checkpoint without loading + the optimizer, the model parameters are updated but for fp16 optimizer + with main parameters, the main parameters need to also be updated.""" + pass + + @abstractmethod + def state_dict(self): + pass + + @abstractmethod + def load_state_dict(self, state_dict): + pass + + # Promote state so it can be retrieved or set via + # "optimizer_instance.state" + def _get_state(self): + return self.optimizer.state + + def _set_state(self, value): + self.optimizer.state = value + + state = property(_get_state, _set_state) + + # Promote param_groups so it can be retrieved or set via + # "optimizer_instance.param_groups" + # (for example, to adjust the learning rate) + def _get_param_groups(self): + return self.optimizer.param_groups + + def _set_param_groups(self, value): + self.optimizer.param_groups = value + + param_groups = property(_get_param_groups, _set_param_groups) + + @abstractmethod + def step(self): + """Step the optimizer.""" + pass + + @abstractmethod + def sharded_state_dict( + self, model_sharded_state_dict: ShardedStateDict, is_loading: bool = False + ) -> ShardedStateDict: + """ Builds sharded state dict for the optimizer, based on model's sharded state dict. + + Args: + model_sharded_state_dict (ShardedStateDict): sharded state dict of the model + is_loading (bool, optional): flag indicating whether the state dict will be used to save or load the optimizer state. + Defaults to False. + + Returns: optimizer sharded state dict + """ + + +class MixedPrecisionOptimizer(MegatronOptimizer): + """Base class for both the float-16 and the distributed optimizer. + + Args: + optimizer (torch.optim.Optimizer): base optimizer such as Adam or SGD. + config (OptimizerConfig): configuration object for optimizer. + grad_scaler (MegatronGradScaler): used for scaling gradients. Note that + this can be None. This case happens when `bf16 = True` and we don't + use any loss scale. Note that for `bf16 = True`, we can have + a constant gradient scaler. Also for `bf16 = False`, we + always require a grad scaler. + init_state_fn (Callable, optional): function to initialize state in the optimizer. + """ + + def __init__( + self, + optimizer: torch.optim.Optimizer, + config: OptimizerConfig, + grad_scaler: Optional[MegatronGradScaler], + init_state_fn: Callable, + ): + + super().__init__( + optimizer, config, init_state_fn, + ) + self.grad_scaler = grad_scaler + + # None grad scaler is only supported for bf16. + if self.grad_scaler is None: + assert not self.config.fp16, 'fp16 expects a grad scaler.' + + # Tensor used to determine if a nan/if has happend. + # Any non-zero value indicates inf/nan. + # Note that we keep this for the cases that grad scaler is none. + # We still record nan/inf if we have a bfloat16 with a grad scaler. + if self.grad_scaler: + self.found_inf = torch.tensor([0.0], dtype=torch.float, device='cuda') + + # Dummy tensor needed for apex multi-apply tensor. + # For bfloat, we don't have multi-tensor apply and for now + # we set it to none so the multi-tensor apply gets ignored. + if self.config.bf16: + self._dummy_overflow_buf = None + else: + self._dummy_overflow_buf = torch.tensor([0], dtype=torch.int, device='cuda') + + # In case grad scaler is not passed, define the unity scale. + if self.grad_scaler is None: + self._scale_one = torch.tensor([1.0], dtype=torch.float, device='cuda') + + def get_loss_scale(self): + if self.grad_scaler is None: + return self._scale_one + return self.grad_scaler.scale + + def reload_model_params(self): + self._copy_model_params_to_main_params() + + def _unscale_main_grads_and_check_for_nan(self): + + # Collect main grads. + main_grads = self._collect_main_grad_data_for_unscaling() + + # Reset found inf. + self.found_inf.fill_(0.0) + + # Unscale and set found inf/nan + torch._amp_foreach_non_finite_check_and_unscale_( + main_grads, self.found_inf, self.grad_scaler.inv_scale + ) + + # Update across all model parallel instances. + torch.distributed.all_reduce( + self.found_inf, op=torch.distributed.ReduceOp.MAX, group=self.get_model_parallel_group() + ) + + # Check for nan. + found_inf_flag = self.found_inf.item() > 0 + + return found_inf_flag + + @torch.no_grad() + def step(self): + + timers = self.config.timers + + # Copy gradients from model params to main params. + if timers is not None: + timers('optimizer-copy-to-main-grad', log_level=1).start( + barrier=self.config.barrier_with_L1_time + ) + self._copy_model_grads_to_main_grads() + if timers is not None: + timers('optimizer-copy-to-main-grad').stop() + + # Do unscale, check for inf, and update grad scaler only for + # the case that grad scaler is provided. + if self.grad_scaler: + + # Unscale and check for inf/nan. + if timers is not None: + timers('optimizer-unscale-and-check-inf', log_level=1).start( + barrier=self.config.barrier_with_L1_time + ) + found_inf_flag = self._unscale_main_grads_and_check_for_nan() + if timers is not None: + timers('optimizer-unscale-and-check-inf').stop() + + # We are done with scaling gradients + # so we can update the loss scale. + self.grad_scaler.update(found_inf_flag) + + # If we found inf/nan, skip the update. + if found_inf_flag: + return False, None, None + + # Clip the main gradients. + if timers is not None: + timers('optimizer-clip-main-grad', log_level=1).start( + barrier=self.config.barrier_with_L1_time + ) + grad_norm = None + if self.config.clip_grad > 0.0: + grad_norm = self.clip_grad_norm(self.config.clip_grad) + if timers is not None: + timers('optimizer-clip-main-grad').stop() + + # Count the zeros in the grads. + if timers is not None: + timers('optimizer-count-zeros', log_level=1).start( + barrier=self.config.barrier_with_L1_time + ) + num_zeros_in_grad = self.count_zeros() if self.config.log_num_zeros_in_grad else None + if timers is not None: + timers('optimizer-count-zeros').stop() + + # Step the optimizer. + if timers is not None: + timers('optimizer-inner-step', log_level=1).start( + barrier=self.config.barrier_with_L1_time + ) + self.optimizer.step() + if timers is not None: + timers('optimizer-inner-step').stop() + + # Update params from main params. + if timers is not None: + timers('optimizer-copy-main-to-model-params', log_level=1).start( + barrier=self.config.barrier_with_L1_time + ) + self._copy_main_params_to_model_params() + if timers is not None: + timers('optimizer-copy-main-to-model-params').stop() + + # Successful update. + return True, grad_norm, num_zeros_in_grad + + +class Float16OptimizerWithFloat16Params(MixedPrecisionOptimizer): + """Float16 optimizer for fp16 and bf16 data types. + + Args: + optimizer (torch.optim.Optimizer): base optimizer such as Adam or SGD. + config (OptimizerConfig): configuration object for optimizer. + grad_scaler (MegatronGradScaler): used for scaling gradients. Note that + this can be None. This case happens when `bf16 = True` and we don't + use any loss scale. Note that for `bf16 = True`, we can have + a constant gradient scaler. Also for `bf16 = False`, we + always require a grad scaler. + init_state_fn (Callable, optional): function to initialize state in the optimizer. + """ + + def __init__( + self, + optimizer: torch.optim.Optimizer, + config: OptimizerConfig, + grad_scaler: MegatronGradScaler, + init_state_fn: Callable, + ): + + super().__init__( + optimizer, config, grad_scaler, init_state_fn, + ) + + # Handle main parameters. + + # Three groups of parameters: + # float16_groups: original float16 parameters + # fp32_from_float16_groups: fp32 copy of float16 parameters + # fp32_from_fp32_groups: original fp32 parameters + self.float16_groups = [] + self.fp32_from_float16_groups = [] + self.fp32_from_fp32_groups = [] + + # For all the groups in the original optimizer: + for param_group in self.optimizer.param_groups: + float16_params_this_group = [] + fp32_params_this_group = [] + fp32_from_float16_params_this_group = [] + # For all the parameters in this group: + for i, param in enumerate(param_group['params']): + if param.requires_grad: + + # float16 params: + if param.type() in ['torch.cuda.HalfTensor', 'torch.cuda.BFloat16Tensor']: + float16_params_this_group.append(param) + # Create a copy + main_param = param.detach().clone().float() + # Copy tensor model parallel attributes. + tensor_parallel.copy_tensor_model_parallel_attributes(main_param, param) + if hasattr(param, 'shared'): + main_param.shared = param.shared + # Replace the optimizer params with the new fp32 copy. + param_group['params'][i] = main_param + + fp32_from_float16_params_this_group.append(main_param) + # Reset existing state dict key to the new main param. + if param in self.optimizer.state: + self.optimizer.state[main_param] = self.optimizer.state.pop(param) + # fp32 params. + elif param.type() == 'torch.cuda.FloatTensor': + fp32_params_this_group.append(param) + param_group['params'][i] = param + + else: + raise TypeError( + 'Wrapped parameters must be one of ' + 'torch.cuda.FloatTensor, ' + 'torch.cuda.HalfTensor, or ' + 'torch.cuda.BFloat16Tensor. ' + 'Received {}'.format(param.type()) + ) + + self.float16_groups.append(float16_params_this_group) + self.fp32_from_float16_groups.append(fp32_from_float16_params_this_group) + self.fp32_from_fp32_groups.append(fp32_params_this_group) + + def zero_grad(self, set_to_none=True): + """We only need to zero the model related parameters, i.e., + float16_groups & fp32_from_fp32_groups. We additionally zero + fp32_from_float16_groups as a memory optimization to reduce + fragmentation; in the case of set_to_none==True, the space + used by this field can be safely deallocated at this point.""" + for group in self.float16_groups: + _zero_grad_group_helper(group, set_to_none) + for group in self.fp32_from_float16_groups: + _zero_grad_group_helper(group, set_to_none) + for group in self.fp32_from_fp32_groups: + _zero_grad_group_helper(group, set_to_none) + + def _collect_main_grad_data_for_unscaling(self): + + main_grads = [] + + # fp32 params from float16 ones. + for main_group in self.fp32_from_float16_groups: + for main_param in main_group: + if main_param.grad is not None: + main_grads.append(main_param.grad.data) + + # Append fp32 parameters. + for main_group in self.fp32_from_fp32_groups: + for main_param in main_group: + if main_param.grad is not None: + main_grads.append(main_param.grad.data) + + return main_grads + + def _get_model_and_main_params_data_float16(self): + model_data = [] + main_data = [] + for model_group, main_group in zip(self.float16_groups, self.fp32_from_float16_groups): + for model_param, main_param in zip(model_group, main_group): + model_data.append(model_param.data) + main_data.append(main_param.data) + return model_data, main_data + + def _copy_model_grads_to_main_grads(self): + # This only needs to be done for the float16 group. + for model_group, main_group in zip(self.float16_groups, self.fp32_from_float16_groups): + for model_param, main_param in zip(model_group, main_group): + if hasattr(model_param, 'main_grad'): + main_param.grad = model_param.main_grad.float() + else: + if model_param.grad is not None: + main_param.grad = model_param.grad.float() + + # Safe to deallocate model's grad/main_grad after copying. + # (If using contiguous buffers, main_grad's memory should + # persist and therefore should not be deallocated.) + model_param.grad = None + + # For fp32 grads, we need to reset the grads to main grad. + for model_group in self.fp32_from_fp32_groups: + for model_param in model_group: + model_param.grad = model_param.main_grad + + def _copy_main_params_to_model_params(self): + # Only needed for the float16 params. + model_data, main_data = self._get_model_and_main_params_data_float16() + _multi_tensor_copy_this_to_that( + this=main_data, that=model_data, overflow_buf=self._dummy_overflow_buf + ) + + def _copy_model_params_to_main_params(self): + # Only needed for the float16 params. + model_data, main_data = self._get_model_and_main_params_data_float16() + _multi_tensor_copy_this_to_that( + this=model_data, that=main_data, overflow_buf=self._dummy_overflow_buf + ) + + def state_dict(self): + state_dict = {} + state_dict['optimizer'] = self.optimizer.state_dict() + if self.grad_scaler: + state_dict['grad_scaler'] = self.grad_scaler.state_dict() + state_dict['fp32_from_fp16_params'] = self.fp32_from_float16_groups + return state_dict + + def sharded_state_dict( + self, model_sharded_state_dict: ShardedStateDict, is_loading: bool = False + ): + if is_loading: + self.init_state_fn(self.optimizer) + + state_dict = self.state_dict() + + id_to_sharded_param_map = get_param_id_to_sharded_param_map( + model_sharded_state_dict, chain.from_iterable(g for g in self.float16_groups) + ) + + # Convert fp32_from_fp16_params + assert len(state_dict['fp32_from_fp16_params']) == len( + state_dict['optimizer']['param_groups'] + ) + state_dict['fp32_from_fp16_params'] = [ + [ + make_sharded_optimizer_tensor( + id_to_sharded_param_map[param_id], + fp32_param, + prefix=f'optimizer.state.fp32_param', + ) + for param_id, fp32_param in zip(state_group['params'], fp32_group) + ] + for fp32_group, state_group in zip( + state_dict['fp32_from_fp16_params'], state_dict['optimizer']['param_groups'] + ) + ] + + # Convert regular optimizer state + optim_state_to_sharding_state(state_dict['optimizer'], id_to_sharded_param_map) + return state_dict + + def load_state_dict(self, state_dict): + # Optimizer. + optimizer_key = 'optimizer' + if optimizer_key not in state_dict: + optimizer_key = 'optimizer_state_dict' + logger.info('***WARNING*** loading optimizer from ' 'an old checkpoint ...') + self.optimizer.load_state_dict(state_dict[optimizer_key]) + + # Grad scaler. + if 'grad_scaler' not in state_dict: + if self.config.fp16: + logger.info( + '***WARNING*** found an old checkpoint, will not ' 'load grad scaler ...' + ) + else: + if self.grad_scaler: + self.grad_scaler.load_state_dict(state_dict['grad_scaler']) + else: + logger.info( + '***WARNING*** fould the grad scaler in the ' + 'checkpoint but it is None in the class. ' + 'Skipping loading grad scaler ...' + ) + + # Copy data for the main params. + fp32_from_float16_params_key = 'fp32_from_fp16_params' + if fp32_from_float16_params_key not in state_dict: + fp32_from_float16_params_key = 'fp32_from_fp16' + for current_group, saved_group in zip( + self.fp32_from_float16_groups, state_dict[fp32_from_float16_params_key] + ): + for current_param, saved_param in zip(current_group, saved_group): + current_param.data.copy_(saved_param.data) + + +class FP32Optimizer(MegatronOptimizer): + """Float32 optimizer. + + Args: + optimizer (torch.optim.Optimizer): base optimizer such as Adam or SGD. + config (OptimizerConfig): configuration object for optimizer. + init_state_fn (Callable, optional): function to initialize state in the optimizer. + """ + + def __init__( + self, optimizer: torch.optim.Optimizer, config: OptimizerConfig, init_state_fn: Callable, + ): + + super(FP32Optimizer, self).__init__( + optimizer, config, init_state_fn, + ) + + self._scale = torch.tensor([1.0], dtype=torch.float, device='cuda') + + def zero_grad(self, set_to_none=True): + """Copied from torch.optim.optimizer""" + for group in self.optimizer.param_groups: + _zero_grad_group_helper(group['params'], set_to_none) + + def get_loss_scale(self): + """FP32 optimizer does not do any scaling.""" + return self._scale + + @torch.no_grad() + def step(self): + """Clip gradients (if needed) and step the base optimizer. + Always return successful since there is no overflow.""" + + timers = self.config.timers + + # Copy main_grads to grads. + if timers is not None: + timers('optimizer-copy-to-main-grad', log_level=1).start( + barrier=self.config.barrier_with_L1_time + ) + for param_group in self.optimizer.param_groups: + for param in param_group['params']: + param.grad = param.main_grad + if timers is not None: + timers('optimizer-copy-to-main-grad').stop() + + # Clip gradients. + if timers is not None: + timers('optimizer-clip-main-grad', log_level=1).start( + barrier=self.config.barrier_with_L1_time + ) + grad_norm = None + if self.config.clip_grad > 0.0: + grad_norm = self.clip_grad_norm(self.config.clip_grad) + if timers is not None: + timers('optimizer-clip-main-grad').stop() + + # Count the zeros in the grads. + if timers is not None: + timers('optimizer-count-zeros', log_level=1).start( + barrier=self.config.barrier_with_L1_time + ) + num_zeros_in_grad = self.count_zeros() if self.config.log_num_zeros_in_grad else None + if timers is not None: + timers('optimizer-count-zeros').stop() + + # Update parameters. + if timers is not None: + timers('optimizer-inner-step', log_level=1).start( + barrier=self.config.barrier_with_L1_time + ) + self.optimizer.step() + if timers is not None: + timers('optimizer-inner-step').stop() + + # No overflow for FP32 optimizer. + return True, grad_norm, num_zeros_in_grad + + def reload_model_params(self): + pass + + def state_dict(self): + return self.optimizer.state_dict() + + def load_state_dict(self, state_dict): + self.optimizer.load_state_dict(state_dict) + + +class ChainedOptimizer(MegatronOptimizer): + """ChainedOptimizer is designed for a collection of optimizers. + + These optimizers are responsible for different parts of multiple models for + a training task and will be executed one-by-one when the model is updated. + + Args: + chained_optimizers: a list of optimizers. + """ + + # Remove these attributes which inherits from MegatronOptimizer. + state = None + param_groups = None + + def __init__(self, chained_optimizers: List[MegatronOptimizer]): + self.chained_optimizers = chained_optimizers + self.param_groups = [] + for optimizer in self.chained_optimizers: + self.param_groups += optimizer.param_groups + + def zero_grad(self, set_to_none=True): + for optimizer in self.chained_optimizers: + optimizer.zero_grad(set_to_none) + + def get_loss_scale(self): + return self.chained_optimizers[0].get_loss_scale() + + def reload_model_params(self): + for optimizer in self.chained_optimizers: + optimizer.reload_model_params() + + def state_dict(self): + return [optimizer.state_dict() for optimizer in self.chained_optimizers] + + def sharded_state_dict( + self, model_sharded_state_dict: ShardedStateDict, is_loading: bool = False, **kwargs + ): + sharded_state_dict = {} + for optimizer_idx, optimizer in enumerate(self.chained_optimizers): + optim_state_dict = optimizer.sharded_state_dict( + model_sharded_state_dict, is_loading, **kwargs + ) + add_prefix_for_sharding(optim_state_dict, f'chained_{optimizer_idx}.') + sharded_state_dict[optimizer_idx] = optim_state_dict + return sharded_state_dict + + def load_state_dict(self, state_dict): + if len(self.chained_optimizers) != len(state_dict): + raise RuntimeError( + f'Expected {len(self.chained_optimizers)} entries' + f' in state dict, but got {len(state_dict)}.' + ) + if isinstance(state_dict, dict): + state_dict = (v for k, v in sorted(state_dict.items())) + for optimizer, state in zip(self.chained_optimizers, state_dict): + optimizer.load_state_dict(state) + + # Reset param_groups as load_state_dict reset chained optimizers's attribute. + self.param_groups = [] + for optimizer in self.chained_optimizers: + self.param_groups += optimizer.param_groups + + def disable_pre_hook(self): + if not self.config.use_distributed_optimizer or not self.config.overlap_param_gather: + raise ValueError( + "disable_pre_hook should only be called with 'use_distributed_optimizer' " + "and 'overlap_param_gather' are both enabled." + ) + for optimizer in self.chained_optimizers: + optimizer.disable_pre_hook() + + def enable_pre_hook(self): + if not self.config.use_distributed_optimizer or not self.config.overlap_param_gather: + raise ValueError( + "enable_pre_hook should only be called with 'use_distributed_optimizer' " + "and 'overlap_param_gather' are both enabled." + ) + for optimizer in self.chained_optimizers: + optimizer.enable_pre_hook() + + def step(self): + """ChainedOptimizer will step all optimizers one by one. + """ + + update_successful, grad_norm, num_zeros_in_grad = True, 0, 0 + grad_norms = [] + for optimizer in self.chained_optimizers: + _update_successful, _grad_norm, _num_zeros_in_grad = optimizer.step() + update_successful &= _update_successful + grad_norms += [_grad_norm if _grad_norm else 0.0] + num_zeros_in_grad += _num_zeros_in_grad if _num_zeros_in_grad else 0 + grad_norm = math.sqrt(sum([x ** 2 for x in grad_norms])) + + return update_successful, grad_norm, num_zeros_in_grad + + def save_parameter_state(self, filename: str): + """Save the distributed parameter states of all optimizers to a file. + + Args: + filename (str): path to save parameter state to. + """ + save_states = False + states = [] + for optimizer in self.chained_optimizers: + if hasattr(optimizer, 'get_parameter_state_dp_zero'): + state_dict = optimizer.get_parameter_state_dp_zero() + + # Save checkpoint economically, only when DP rank = 0, state dict + # needs to be saved. + if torch.distributed.get_rank(optimizer.data_parallel_group) == 0: + states.append(state_dict) + save_states = True + else: + states.append(None) + else: + states.append(None) + + if save_states: + torch.save(states, filename) + + def load_parameter_state(self, filename: str): + """Load the distributed parameter states of all optimizers from a file. + + Args: + filename (str): path to load parameter state from. + """ + states = None + for idx, optimizer in enumerate(self.chained_optimizers): + if not hasattr(optimizer, 'load_parameter_state_from_dp_zero'): + continue + + # Lazy loading checkpoint, state dict is needed only when DP rank = 0. + if torch.distributed.get_rank(optimizer.data_parallel_group) == 0 and states is None: + states = torch.load(filename) + + state_dict = states[idx] if states else None + optimizer.load_parameter_state_from_dp_zero(state_dict) + + def finish_param_sync(self, model_index: int): + """Finish parameter synchronization for all optimizers. + """ + for optimizer in self.chained_optimizers: + optimizer.finish_param_sync(model_index) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/optimizer_config.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/optimizer_config.py new file mode 100644 index 0000000..66daea9 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/optimizer/optimizer_config.py @@ -0,0 +1,116 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from dataclasses import dataclass +from typing import Callable, Optional + +import torch + + +@dataclass +class OptimizerConfig: + """Configuration for optimizer.""" + + ############## + # General + ############## + optimizer: str = 'adam' + """Optimizer to use (one of Adam or SGD).""" + + lr: Optional[float] = None + """Initial learning rate. Depending on decay style and initial warmup, the learning rate at each + iteration would be different. + """ + + min_lr: Optional[float] = None + """Minumum value for learning rate. The scheduler clip values below this threshold.""" + + decoupled_lr: Optional[float] = None + """Separate learning rate for the input and output layer.""" + + decoupled_min_lr: Optional[float] = None + """Minimum value for learning rate for the input and output layer. The scheduler clip values + below this threshold. + """ + + weight_decay: float = 0.01 + """Weight decay coefficient for L2 regularization.""" + + ############## + # Precision + ############## + fp16: bool = False + """If true, train with fp16 mixed precision training. Defaults to False.""" + + bf16: bool = False + """If true, train with bf16 mixed precision training. Defaults to False.""" + + params_dtype: torch.dtype = torch.float32 + """dtype used when intializing the weights. Defaults to torch.float32.""" + + ############### + # Loss scaling + ############### + loss_scale: Optional[float] = None + """Static loss scaling, positive power of 2 values can improve fp16 convergence. If None, + dynamic loss scaling is used. + """ + + initial_loss_scale: float = 2 ** 32 + """Initial loss-scale for dynamic loss scaling.""" + + min_loss_scale: float = 1.0 + """Minimum loss scale for dynamic loss scaling.""" + + loss_scale_window: float = 1000 + """Window over which to raise/lower dynamic scale.""" + + hysteresis: int = 2 + """Hysteresis for dynamic loss scaling.""" + + ############## + # Optimizer + ############## + # Adam + adam_beta1: float = 0.9 + """First coefficient for computing running averages of gradient and its square in Adam + optimizer. + """ + + adam_beta2: float = 0.999 + """Second coefficient for computing running averages of gradient and its square in Adam + optimizer. + """ + + adam_eps: float = 1e-08 + """Term added to the denominator to improve numerical stability in Adam optimizer.""" + + # SGD. + sgd_momentum: float = 0.9 + """Momentum factor for SGD optimizer.""" + + ####################### + # Distributed optimizer + ####################### + use_distributed_optimizer: bool = False + """Distribute optimizer state over data-parallel replicas.""" + + overlap_grad_reduce: bool = False + """If true, overlap grad reduce-scatter with backward compute in distributed optimizer.""" + + overlap_param_gather: bool = False + """If true, overlap param all-gather with forward compute in distributed optimizer.""" + + ################ + # Miscellaneous + ################ + clip_grad: float = 1.0 + """Gradient clipping based on global L2 norm.""" + + log_num_zeros_in_grad: bool = False + """If true, calculate and log the number of zeros in gradient.""" + + barrier_with_L1_time: bool = False + """If true, use barrier with level 1 time measurements.""" + + timers: Callable = None + """Function to get timers.""" diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/package_info.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/package_info.py new file mode 100644 index 0000000..1caa0a4 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/package_info.py @@ -0,0 +1,29 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + + +MAJOR = 0 +MINOR = 7 +PATCH = 0 +PRE_RELEASE = 'b0' + +# Use the following formatting: (major, minor, patch, pre-release) +VERSION = (MAJOR, MINOR, PATCH, PRE_RELEASE) + +__shortversion__ = '.'.join(map(str, VERSION[:3])) +__version__ = '.'.join(map(str, VERSION[:3])) + ''.join(VERSION[3:]) + +__package_name__ = 'megatron_core' +__contact_names__ = 'NVIDIA' +__contact_emails__ = 'nemo-toolkit@nvidia.com' # use NeMo Email +__homepage__ = ( + 'https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/' # use NeMo homepage +) +__repository_url__ = 'https://github.com/NVIDIA/Megatron-LM/megatron/core' +__download_url__ = 'https://github.com/NVIDIA/Megatron-LM/releases' +__description__ = ( + 'Megatron Core - a library for efficient and scalable training of transformer based models' +) +__license__ = 'BSD-3' +__keywords__ = ( + 'deep learning, machine learning, gpu, NLP, NLU, language, transformer, nvidia, pytorch, torch' +) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/packed_seq_params.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/packed_seq_params.py new file mode 100644 index 0000000..478c172 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/packed_seq_params.py @@ -0,0 +1,13 @@ +from dataclasses import dataclass + +from torch import Tensor + + +@dataclass +class PackedSeqParams: + # parameters to TEDotProductAttention and fused rope kernels for the `thd` (packed) sequence format, + qkv_format: str = None + cu_seqlens_q: Tensor = None + cu_seqlens_kv: Tensor = None + max_seqlen_q: Tensor = None + max_seqlen_kv: Tensor = None diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/parallel_state.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/parallel_state.py new file mode 100644 index 0000000..338c1a5 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/parallel_state.py @@ -0,0 +1,1238 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Model and data parallel groups.""" + +import os +import warnings +from datetime import timedelta +from typing import List, Optional + +import torch + +from .utils import GlobalMemoryBuffer + +# Intra-layer model parallel group that the current rank belongs to. +_TENSOR_MODEL_PARALLEL_GROUP = None +# Inter-layer model parallel group that the current rank belongs to. +_PIPELINE_MODEL_PARALLEL_GROUP = None +# Model parallel group (both intra- and pipeline) that the current rank belongs to. +_MODEL_PARALLEL_GROUP = None +# Embedding group. +_EMBEDDING_GROUP = None +# Position embedding group. +_POSITION_EMBEDDING_GROUP = None +# Data parallel group that the current rank belongs to. +_DATA_PARALLEL_GROUP = None +_DATA_PARALLEL_GROUP_GLOO = None +# tensor model parallel group and data parallel group combined +# used for fp8 and moe training +_TENSOR_AND_DATA_PARALLEL_GROUP = None +# Expert parallel group that the current rank belongs to. +_EXPERT_MODEL_PARALLEL_GROUP = None +_TENSOR_AND_EXPERT_PARALLEL_GROUP = None +_DATA_MODULO_EXPERT_PARALLEL_GROUP = None +_DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO = None + + +_VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = None +_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None +_PIPELINE_MODEL_PARALLEL_SPLIT_RANK = None + +# These values enable us to change the mpu sizes on the fly. +_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = None +_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None +_MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE = None +_MPU_TENSOR_MODEL_PARALLEL_RANK = None +_MPU_PIPELINE_MODEL_PARALLEL_RANK = None +_MPU_EXPERT_MODEL_PARALLEL_RANK = None + +# A list of ranks that have a copy of the embedding. +_EMBEDDING_GLOBAL_RANKS = None + +# A list of ranks that have a copy of the position embedding. +_POSITION_EMBEDDING_GLOBAL_RANKS = None + +# A list of global ranks for each pipeline group to ease calculation of the source +# rank when broadcasting from the first or last pipeline stage. +_PIPELINE_GLOBAL_RANKS = None + +# A list of global ranks for each data parallel group to ease calculation of the source +# rank when broadcasting weights from src to all other data parallel ranks +_DATA_PARALLEL_GLOBAL_RANKS = None + +# A list of global ranks for each tensor model parallel group to ease calculation of +# the first local rank in the tensor model parallel group +_TENSOR_MODEL_PARALLEL_GLOBAL_RANKS = None + +# Context parallel group that the current rank belongs to +_CONTEXT_PARALLEL_GROUP = None +# A list of global ranks for each context parallel group to ease calculation of the +# destination rank when exchanging KV/dKV between context parallel_ranks +_CONTEXT_PARALLEL_GLOBAL_RANKS = None + +# Data parallel group information with context parallel combined. +_DATA_PARALLEL_GROUP_WITH_CP = None +_DATA_PARALLEL_GROUP_WITH_CP_GLOO = None +_DATA_PARALLEL_GLOBAL_RANKS_WITH_CP = None + +# combined parallel group of TP, DP, and CP used for fp8 +_TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP = None + +# Memory buffers to avoid dynamic memory allocation +_GLOBAL_MEMORY_BUFFER = None + +# MOE logging +_MOE_AUX_LOSSES_LOGGING_TRACKER = {} + + +def get_nccl_options(pg_name, nccl_comm_cfgs): + """Set the NCCL process group options. + + Args: + pg_name (str): process group name + nccl_comm_cfgs (dict): nccl communicator configurations + + When an option (e.g., max_ctas) is not found in the config, use the NCCL default setting. + """ + if pg_name in nccl_comm_cfgs: + nccl_options = torch.distributed.ProcessGroupNCCL.Options() + nccl_options.config.cga_cluster_size = nccl_comm_cfgs[pg_name].get('cga_cluster_size', 4) + nccl_options.config.max_ctas = nccl_comm_cfgs[pg_name].get('max_ctas', 32) + nccl_options.config.min_ctas = nccl_comm_cfgs[pg_name].get('min_ctas', 1) + return nccl_options + else: + return None + + +def generate_masked_orthogonal_rank_groups( + world_size: int, parallel_size: List[int], mask: List[bool], +) -> List[List[int]]: + """Generate orthogonal parallel groups based on the parallel size and mask. + + Arguments: + world_size (int): world size + + parallel_size (List[int]): + The parallel size of each orthogonal parallel type. For example, if + tensor_parallel_size = 2, pipeline_model_parallel_group = 3, data_parallel_size = 4, + and the parallel mapping order is tp-pp-dp, then the parallel_size = [2, 3, 4]. + + mask (List[bool]): + The mask controls which parallel methods the generated groups represent. If mask[i] is + True, it means the generated group contains the i-th parallelism method. For example, + if parallel_size = [tp_size, pp_size, dp_size], and mask = [True, False , True], then + the generated group is the `tp-dp` group, if the mask = [False, True, False], then the + generated group is the `pp` group. + + Algorithm: + For orthogonal parallelism, such as tp/dp/pp/cp, the global_rank and + local_rank satisfy the following equation: + global_rank = tp_rank + dp_rank * tp_size + pp_rank * tp_size * dp_size (1) + tp_rank \in [0, tp_size) + dp_rank \in [0, dp_size) + pp_rank \in [0, pp_size) + + If we want to get the `dp_group` (tp_size * pp_size groups of dp_size ranks each. + For example, if the gpu size is 8 and order is 'tp-pp-dp', size is '2-2-2', and the + dp_group here is [[0, 4], [1, 5], [2, 6], [3, 7]].) + The tp_rank and pp_rank will be combined to form the `dp_group_index`. + dp_group_index = tp_rank + pp_rank * tp_size (2) + + So, Given that tp_rank and pp_rank satisfy equation (2), and dp_rank in + range(0, dp_size), the ranks in dp_group[dp_group_index] satisfies the + equation (1). + + This function solve this math problem. + + For example, if the parallel_size = [tp_size, dp_size, pp_size] = [2, 3, 4], + and the mask = [False, True, False]. Then, + dp_group_index(0) = tp_rank(0) + pp_rank(0) * 2 + dp_group_index(1) = tp_rank(1) + pp_rank(0) * 2 + ... + dp_group_index(7) = tp_rank(1) + pp_rank(3) * 2 + + dp_group[0] = 0 + range(0, 3) * 2 + 0 = [0, 2, 4] + dp_group[1] = 1 + range(0, 3) * 2 + 0 = [1, 3, 5] + ... + dp_group[7] = 1 + range(0, 3) * 2 + 3 * 2 * 3 = [19, 21, 23] + """ + + def prefix_product(a: List[int], init=1) -> List[int]: + r = [init] + for v in a: + init = init * v + r.append(init) + return r + + def inner_product(a: List[int], b: List[int]) -> int: + return sum([x * y for x, y in zip(a, b)]) + + def decompose(index, shape, stride=None): + ''' + This function solve the math problem below: + There is an equation: + index = sum(idx[i] * stride[i]) + And given the value of index, stride. + Return the idx. + This function will used to get the pp/dp/pp_rank + from group_index and rank_in_group. + ''' + if stride is None: + stride = prefix_product(shape) + idx = [(index // d) % s for s, d in zip(shape, stride)] + # stride is a prefix_product result. And the value of stride[-1] + # is not used. + assert ( + sum([x * y for x, y in zip(idx, stride[:-1])]) == index + ), "idx {} with shape {} mismatch the return idx {}".format(index, shape, idx) + return idx + + masked_shape = [s for s, m in zip(parallel_size, mask) if m] + unmasked_shape = [s for s, m in zip(parallel_size, mask) if not m] + + global_stride = prefix_product(parallel_size) + masked_stride = [d for d, m in zip(global_stride, mask) if m] + unmasked_stride = [d for d, m in zip(global_stride, mask) if not m] + + group_size = prefix_product(masked_shape)[-1] + num_of_group = world_size // group_size + + ranks = [] + for group_index in range(num_of_group): + # get indices from unmaksed for group_index. + decomposed_group_idx = decompose(group_index, unmasked_shape) + rank = [] + for rank_in_group in range(group_size): + # get indices from masked for rank_in_group. + decomposed_rank_idx = decompose(rank_in_group, masked_shape) + rank.append( + inner_product(decomposed_rank_idx, masked_stride) + + inner_product(decomposed_group_idx, unmasked_stride) + ) + ranks.append(rank) + return ranks + + +class RankGenerator(object): + def __init__(self, tp: int, ep: int, dp: int, pp: int, cp: int, order: str) -> None: + self.tp = tp + self.ep = ep + self.dp = dp + self.pp = pp + self.cp = cp + self.world_size = tp * dp * pp * cp + + self.name_to_size = { + "tp": self.tp, + "pp": self.pp, + "dp": self.dp, + "ep": self.ep, + "cp": self.cp, + } + self.order = order + order = order.lower() + + if 'ep' in order: + if 'ep-dp' not in order and 'dp-ep' not in order: + raise RuntimeError(f"The ep and dp must be adjacent in order ({self.order}).") + + for name in self.name_to_size.keys(): + if name not in order and self.name_to_size[name] != 1: + raise RuntimeError( + f"The size of ({name}) is ({self.name_to_size[name]}), but you haven't specified the order ({self.order})." + ) + elif name not in order: + order = order + '-' + name + + self.order_w_ep = order + self.order_wo_ep = '-'.join([token for token in order.split('-') if token != 'ep']) + self.ordered_size_wo_ep = [] + self.ordered_size_w_ep = [] + + for token in order.split('-'): + if token == 'dp': + self.ordered_size_w_ep.append(self.dp // self.ep) + self.ordered_size_wo_ep.append(self.dp) + elif token == 'ep': + self.ordered_size_w_ep.append(self.ep) + else: + self.ordered_size_w_ep.append(self.name_to_size[token]) + self.ordered_size_wo_ep.append(self.name_to_size[token]) + + def get_mask(self, order: str, token: str): + ordered_token = order.split('-') + token = token.split('-') + mask = [False] * len(ordered_token) + for t in token: + mask[ordered_token.index(t)] = True + return mask + + def get_ranks(self, token, independent_ep=False): + '''Get rank group by input token. + + Arguments: + token (str): + Specify the ranks type that want to get. If we want + to obtain multiple parallel types, we can use a hyphen + '-' to separate them. For example, if we want to obtain + the TP_DP group, the token should be 'tp-dp'. + + independent_ep (bool: True): + This flag controls whether we treat EP and DP independently. + EP shares ranks with DP, if we want to get ranks related to + EP, we should set the flag. For example, get_ranks('dp', True) + will get DP modulo EP group, and get_ranks('dp', False) will + get full DP group. + ''' + if independent_ep: + parallel_size = self.ordered_size_w_ep + order = self.order_w_ep + else: + parallel_size = self.ordered_size_wo_ep + order = self.order_wo_ep + mask = self.get_mask(order, token) + ranks = generate_masked_orthogonal_rank_groups(self.world_size, parallel_size, mask) + return ranks + + +def initialize_model_parallel( + tensor_model_parallel_size: int = 1, + pipeline_model_parallel_size: int = 1, + virtual_pipeline_model_parallel_size: Optional[int] = None, + pipeline_model_parallel_split_rank: Optional[int] = None, + use_sharp: bool = False, + context_parallel_size: int = 1, + expert_model_parallel_size: int = 1, + nccl_communicator_config_path: Optional[str] = None, + distributed_timeout_minutes: int = 30, + order: str = "tp-cp-ep-dp-pp", +) -> None: + """Initialize model data parallel groups. + + Args: + tensor_model_parallel_size (int, default = 1): + The number of GPUs to split individual tensors across. + + pipeline_model_parallel_size (int, default = 1): + The number of tensor parallel GPU groups to split the + Transformer layers across. For example, if + tensor_model_parallel_size is 4 and + pipeline_model_parallel_size is 2, the model will be split + into 2 groups of 4 GPUs. + + virtual_pipeline_model_parallel_size (int, optional): + The number of stages that each pipeline group will have, + interleaving as necessary. If None, no interleaving is + performed. For example, if tensor_model_parallel_size is 1, + pipeline_model_parallel_size is 4, + virtual_pipeline_model_parallel_size is 2, and there are + 16 transformer layers in the model, the model will be + split into 8 stages with two layers each and each GPU + would get 2 stages as such (layer number starting with 1): + + GPU 0: [1, 2] [9, 10] + GPU 1: [3, 4] [11, 12] + GPU 2: [5, 6] [13, 14] + GPU 3: [7, 8] [15, 16] + + pipeline_model_parallel_split_rank (int, optional): + For models with both an encoder and decoder, the rank in + pipeline to switch between encoder and decoder (i.e. the + first rank of the decoder). This allows the user to set + the pipeline parallel size of the encoder and decoder + independently. For example, if + pipeline_model_parallel_size is 8 and + pipeline_model_parallel_split_rank is 3, then ranks 0-2 + will be the encoder and ranks 3-7 will be the decoder. + + use_sharp (bool, default = False): + Set the use of SHARP for the collective communications of + data-parallel process groups. When `True`, run barrier + within each data-parallel process group, which specifies + the SHARP application target groups. + + context_parallel_size (int, default = 1): + The number of tensor parallel GPU groups to split the + network input sequence length across. Compute of attention + module requires tokens of full sequence length, so GPUs + in a context parallel group need to communicate with each + other to exchange information of other sequence chunks. + Each GPU and its counterparts in other tensor parallel + groups compose a context parallel group. + + For example, assume we have 8 GPUs, if tensor model parallel + size is 4 and context parallel size is 2, the network input + will be split into two sequence chunks, which are processed + by 2 different groups of 4 GPUs. One chunk is processed by + GPU0-3, the other chunk is processed by GPU4-7. Four groups + are build to do context parallel communications: [GPU0, GPU4], + [GPU1, GPU5], [GPU2, GPU6], and [GPU3, GPU7]. + + Context parallelism partitions sequence length, so it has no + impact on weights, which means weights are duplicated among + GPUs in a context parallel group. Hence, weight gradients + all-reduce is required in backward. For simplicity, we piggyback + GPUs of context parallelism on data parallel group for + weight gradient all-reduce. + + expert_model_parallel_size (int, default = 1): + The number of Mixture of Experts parallel GPUs in each expert + parallel group. + + nccl_communicator_config_path (str, default = None): + Path to the yaml file of NCCL communicator configurations. + `min_ctas`, `max_ctas`, and `cga_cluster_size` can be set + for each communicator. + + distributed_timeout_minutes (int, default = 30): Timeout, in + minutes,for operations executed against distributed + process groups. See PyTorch documentation at + https://pytorch.org/docs/stable/distributed.html for + caveats. + + order (str, default=tp-dp-pp): + The rank initialization order of parallelism. Now we support + tp-dp-pp and tp-pp-dp orders. + + Let's say we have a total of 16 GPUs denoted by g0 ... g15 and we + use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize + the model pipeline. The present function will + create 8 tensor model-parallel groups, 4 pipeline model-parallel groups + and 8 data-parallel groups as: + 8 data_parallel groups: + [g0, g2], [g1, g3], [g4, g6], [g5, g7], [g8, g10], [g9, g11], [g12, g14], [g13, g15] + 8 tensor model-parallel groups: + [g0, g1], [g2, g3], [g4, g5], [g6, g7], [g8, g9], [g10, g11], [g12, g13], [g14, g15] + 4 pipeline model-parallel groups: + [g0, g4, g8, g12], [g1, g5, g9, g13], [g2, g6, g10, g14], [g3, g7, g11, g15] + Note that for efficiency, the caller should make sure adjacent ranks + are on the same DGX box. For example if we are using 2 DGX-1 boxes + with a total of 16 GPUs, rank 0 to 7 belong to the first box and + ranks 8 to 15 belong to the second box. + + """ + # Get world size and rank. Ensure some consistencies. + assert torch.distributed.is_initialized() + world_size: int = torch.distributed.get_world_size() + + if ( + world_size + % (tensor_model_parallel_size * pipeline_model_parallel_size * context_parallel_size) + != 0 + ): + raise RuntimeError( + f"world_size ({world_size}) is not divisible by tensor_model_parallel_size " + f"({tensor_model_parallel_size}) x pipeline_model_parallel_size ({pipeline_model_parallel_size}) " + f"x context_parallel_size ({context_parallel_size})" + ) + + data_parallel_size: int = world_size // ( + tensor_model_parallel_size * pipeline_model_parallel_size * context_parallel_size + ) + + if data_parallel_size % expert_model_parallel_size != 0: + raise RuntimeError( + f"data_parallel_size ({data_parallel_size}) is not divisible by expert_model_parallel_size " + ) + + if expert_model_parallel_size > 1 and context_parallel_size > 1: + raise RuntimeError( + f"combination of expert model prallellism and context parallelism is not supported" + ) + + num_tensor_model_parallel_groups: int = world_size // tensor_model_parallel_size + num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size + + if virtual_pipeline_model_parallel_size is not None: + if not pipeline_model_parallel_size > 2: + raise RuntimeError( + "pipeline-model-parallel size should be greater than 2 with interleaved schedule" + ) + global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK + global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE + _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = 0 + _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = virtual_pipeline_model_parallel_size + + if pipeline_model_parallel_split_rank is not None: + global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK + _PIPELINE_MODEL_PARALLEL_SPLIT_RANK = pipeline_model_parallel_split_rank + + rank = torch.distributed.get_rank() + + nccl_comm_cfgs = {} + if nccl_communicator_config_path is not None: + try: + import yaml + except ImportError: + raise RuntimeError( + "Cannot import `yaml`. Setting custom nccl communicator configs " + "requires the yaml package." + ) + + with open(nccl_communicator_config_path, "r") as stream: + nccl_comm_cfgs = yaml.safe_load(stream) + + rank_generator = RankGenerator( + tp=tensor_model_parallel_size, + ep=expert_model_parallel_size, + dp=data_parallel_size, + pp=pipeline_model_parallel_size, + cp=context_parallel_size, + order=order, + ) + timeout = timedelta(minutes=distributed_timeout_minutes) + + # Build the data-parallel groups. + global _DATA_PARALLEL_GROUP + global _DATA_PARALLEL_GROUP_GLOO + global _DATA_PARALLEL_GLOBAL_RANKS + global _DATA_PARALLEL_GROUP_WITH_CP + global _DATA_PARALLEL_GROUP_WITH_CP_GLOO + global _DATA_PARALLEL_GLOBAL_RANKS_WITH_CP + assert _DATA_PARALLEL_GROUP is None, 'data parallel group is already initialized' + + for ranks in rank_generator.get_ranks('dp'): + group = torch.distributed.new_group( + ranks, timeout=timeout, pg_options=get_nccl_options('dp', nccl_comm_cfgs) + ) + group_gloo = torch.distributed.new_group(ranks, timeout=timeout, backend="gloo") + if rank in ranks: + _DATA_PARALLEL_GROUP = group + _DATA_PARALLEL_GROUP_GLOO = group_gloo + _DATA_PARALLEL_GLOBAL_RANKS = ranks + for ranks_with_cp in rank_generator.get_ranks('dp-cp'): + group_with_cp = torch.distributed.new_group( + ranks_with_cp, timeout=timeout, pg_options=get_nccl_options('dp_cp', nccl_comm_cfgs) + ) + group_with_cp_gloo = torch.distributed.new_group( + ranks_with_cp, timeout=timeout, backend="gloo" + ) + if rank in ranks_with_cp: + _DATA_PARALLEL_GROUP_WITH_CP = group_with_cp + _DATA_PARALLEL_GROUP_WITH_CP_GLOO = group_with_cp_gloo + _DATA_PARALLEL_GLOBAL_RANKS_WITH_CP = ranks_with_cp + + # Apply SHARP to DP process groups + if use_sharp: + if rank == 0: + print( + "The number of process groups to use SHARP with depends on the type " + "of the network switch. Nvidia QM1 switch supports SAHRP up to 8 " + "process groups and QM2 supports up to 256 process groups. We apply " + "SHARP to the communications of the data-parallel domain. If the " + "number of data-parallel process groups is larger than the max " + "process groups that the network switch supports, the communication " + "will fall back to non-SHARP operators. To enable SHARP, " + "`#SBATCH_NETWORK=sharp` should be set in the sbatch script." + ) + torch.distributed.barrier( + group=get_data_parallel_group(with_context_parallel=True), + device_ids=[torch.cuda.current_device()], + ) + # Set `NCCL_COLLNET_ENABLE=0` to restrict SHARP application to DP process groups + os.environ["NCCL_COLLNET_ENABLE"] = "0" + + # Build the context-parallel groups. + global _CONTEXT_PARALLEL_GROUP + global _CONTEXT_PARALLEL_GLOBAL_RANKS + assert _CONTEXT_PARALLEL_GROUP is None, 'context parallel group is already initialized' + for ranks in rank_generator.get_ranks('cp'): + group = torch.distributed.new_group( + ranks, timeout=timeout, pg_options=get_nccl_options('cp', nccl_comm_cfgs) + ) + if rank in ranks: + _CONTEXT_PARALLEL_GROUP = group + _CONTEXT_PARALLEL_GLOBAL_RANKS = ranks + + # Build the model-parallel groups. + global _MODEL_PARALLEL_GROUP + assert _MODEL_PARALLEL_GROUP is None, 'model parallel group is already initialized' + for ranks in rank_generator.get_ranks('tp-pp'): + group = torch.distributed.new_group( + ranks, timeout=timeout, pg_options=get_nccl_options('mp', nccl_comm_cfgs) + ) + if rank in ranks: + _MODEL_PARALLEL_GROUP = group + + # Build the tensor model-parallel groups. + global _TENSOR_MODEL_PARALLEL_GROUP + global _TENSOR_MODEL_PARALLEL_GLOBAL_RANKS + assert ( + _TENSOR_MODEL_PARALLEL_GROUP is None + ), 'tensor model parallel group is already initialized' + for ranks in rank_generator.get_ranks('tp'): + group = torch.distributed.new_group( + ranks, timeout=timeout, pg_options=get_nccl_options('tp', nccl_comm_cfgs) + ) + if rank in ranks: + _TENSOR_MODEL_PARALLEL_GROUP = group + _TENSOR_MODEL_PARALLEL_GLOBAL_RANKS = ranks + + # Build the pipeline model-parallel groups and embedding groups + # (first and last rank in each pipeline model-parallel group). + global _PIPELINE_MODEL_PARALLEL_GROUP + global _PIPELINE_GLOBAL_RANKS + assert ( + _PIPELINE_MODEL_PARALLEL_GROUP is None + ), 'pipeline model parallel group is already initialized' + global _EMBEDDING_GROUP + global _EMBEDDING_GLOBAL_RANKS + assert _EMBEDDING_GROUP is None, 'embedding group is already initialized' + global _POSITION_EMBEDDING_GROUP + global _POSITION_EMBEDDING_GLOBAL_RANKS + assert _POSITION_EMBEDDING_GROUP is None, 'position embedding group is already initialized' + for ranks in rank_generator.get_ranks('pp'): + group = torch.distributed.new_group( + ranks, timeout=timeout, pg_options=get_nccl_options('pp', nccl_comm_cfgs) + ) + if rank in ranks: + _PIPELINE_MODEL_PARALLEL_GROUP = group + _PIPELINE_GLOBAL_RANKS = ranks + # Setup embedding group (to exchange gradients between + # first and last stages). + if len(ranks) > 1: + embedding_ranks = [ranks[0], ranks[-1]] + position_embedding_ranks = [ranks[0]] + if pipeline_model_parallel_split_rank is not None: + if ranks[pipeline_model_parallel_split_rank] not in embedding_ranks: + embedding_ranks = [ + ranks[0], + ranks[pipeline_model_parallel_split_rank], + ranks[-1], + ] + if ranks[pipeline_model_parallel_split_rank] not in position_embedding_ranks: + position_embedding_ranks = [ranks[0], ranks[pipeline_model_parallel_split_rank]] + else: + embedding_ranks = ranks + position_embedding_ranks = ranks + + group = torch.distributed.new_group( + embedding_ranks, timeout=timeout, pg_options=get_nccl_options('embd', nccl_comm_cfgs) + ) + if rank in embedding_ranks: + _EMBEDDING_GROUP = group + if rank in ranks: + _EMBEDDING_GLOBAL_RANKS = embedding_ranks + + group = torch.distributed.new_group( + position_embedding_ranks, + timeout=timeout, + pg_options=get_nccl_options('embd', nccl_comm_cfgs), + ) + if rank in position_embedding_ranks: + _POSITION_EMBEDDING_GROUP = group + if rank in ranks: + _POSITION_EMBEDDING_GLOBAL_RANKS = position_embedding_ranks + + # Build the tensor + data parallel groups. + global _TENSOR_AND_DATA_PARALLEL_GROUP + global _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP + assert ( + _TENSOR_AND_DATA_PARALLEL_GROUP is None + ), 'Tensor + data parallel group is already initialized' + for ranks in rank_generator.get_ranks('tp-dp-cp'): + group = torch.distributed.new_group( + ranks, timeout=timeout, pg_options=get_nccl_options('tp_dp_cp', nccl_comm_cfgs) + ) + if rank in ranks: + _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP = group + for ranks in rank_generator.get_ranks('tp-dp'): + group = torch.distributed.new_group( + ranks, timeout=timeout, pg_options=get_nccl_options('tp_dp', nccl_comm_cfgs) + ) + if rank in ranks: + _TENSOR_AND_DATA_PARALLEL_GROUP = group + + # Build the tensor + expert parallel groups + global _EXPERT_MODEL_PARALLEL_GROUP + assert _EXPERT_MODEL_PARALLEL_GROUP is None, 'Expert parallel group is already initialized' + global _TENSOR_AND_EXPERT_PARALLEL_GROUP + assert ( + _TENSOR_AND_EXPERT_PARALLEL_GROUP is None + ), 'Tensor + expert parallel group is already initialized' + global _DATA_MODULO_EXPERT_PARALLEL_GROUP + assert ( + _DATA_MODULO_EXPERT_PARALLEL_GROUP is None + ), 'Data modulo expert group is already initialized' + global _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO + + for ranks in rank_generator.get_ranks('tp-ep', independent_ep=True): + group = torch.distributed.new_group( + ranks, timeout=timeout, pg_options=get_nccl_options('tp_exp', nccl_comm_cfgs) + ) + if rank in ranks: + _TENSOR_AND_EXPERT_PARALLEL_GROUP = group + + for ranks in rank_generator.get_ranks('ep', independent_ep=True): + group = torch.distributed.new_group( + ranks, pg_options=get_nccl_options('exp', nccl_comm_cfgs) + ) + if rank in ranks: + _EXPERT_MODEL_PARALLEL_GROUP = group + + for ranks in rank_generator.get_ranks('dp', independent_ep=True): + group = torch.distributed.new_group( + ranks, timeout=timeout, pg_options=get_nccl_options('dp_modulo_exp', nccl_comm_cfgs) + ) + group_gloo = torch.distributed.new_group(ranks, backend="gloo") + if rank in ranks: + _DATA_MODULO_EXPERT_PARALLEL_GROUP = group + _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO = group_gloo + + # Initialize global memory buffer + # This isn't really "parallel state" but there isn't another good place to + # put this. If we end up with a more generic initialization of megatron-core + # we could stick it there + _set_global_memory_buffer() + + +def is_initialized(): + """Useful for code segments that may be accessed with or without mpu initialization""" + return _DATA_PARALLEL_GROUP is not None + + +def is_unitialized() -> bool: + """Check if parallel state has been initialized + + Deprecated. Use is_initialized instead. + + """ + warnings.warn( + "is_unitialized is deprecated, use is_initialized instead", DeprecationWarning, + ) + return not is_initialized() + + +def model_parallel_is_initialized(): + """Check if model and data parallel groups are initialized.""" + if ( + _TENSOR_MODEL_PARALLEL_GROUP is None + or _PIPELINE_MODEL_PARALLEL_GROUP is None + or _DATA_PARALLEL_GROUP is None + ): + return False + return True + + +def get_model_parallel_group(): + """Get the model parallel group the caller rank belongs to.""" + assert _MODEL_PARALLEL_GROUP is not None, 'model parallel group is not initialized' + return _MODEL_PARALLEL_GROUP + + +def get_tensor_model_parallel_group(check_initialized=True): + """Get the tensor model parallel group the caller rank belongs to.""" + if check_initialized: + assert ( + _TENSOR_MODEL_PARALLEL_GROUP is not None + ), 'tensor model parallel group is not initialized' + return _TENSOR_MODEL_PARALLEL_GROUP + + +def get_pipeline_model_parallel_group(): + """Get the pipeline model parallel group the caller rank belongs to.""" + assert ( + _PIPELINE_MODEL_PARALLEL_GROUP is not None + ), 'pipeline_model parallel group is not initialized' + return _PIPELINE_MODEL_PARALLEL_GROUP + + +def get_data_parallel_group(with_context_parallel=False): + """Get the data parallel group the caller rank belongs to.""" + if with_context_parallel: + assert ( + _DATA_PARALLEL_GROUP_WITH_CP is not None + ), 'data parallel group with context parallel combined is not initialized' + return _DATA_PARALLEL_GROUP_WITH_CP + else: + assert _DATA_PARALLEL_GROUP is not None, 'data parallel group is not initialized' + return _DATA_PARALLEL_GROUP + + +def get_data_parallel_group_gloo(with_context_parallel=False): + """Get the data parallel group-gloo the caller rank belongs to.""" + if with_context_parallel: + assert ( + _DATA_PARALLEL_GROUP_WITH_CP_GLOO is not None + ), 'data parallel group-gloo with context parallel combined is not initialized' + return _DATA_PARALLEL_GROUP_WITH_CP_GLOO + else: + assert _DATA_PARALLEL_GROUP_GLOO is not None, 'data parallel group-gloo is not initialized' + return _DATA_PARALLEL_GROUP_GLOO + + +def get_context_parallel_group(check_initialized=True): + """Get the context parallel group the caller rank belongs to.""" + if check_initialized: + assert _CONTEXT_PARALLEL_GROUP is not None, 'context parallel group is not initialized' + return _CONTEXT_PARALLEL_GROUP + + +def get_context_parallel_global_ranks(check_initialized=True): + """Get all global ranks of the context parallel group that the caller rank belongs to.""" + if check_initialized: + assert ( + _CONTEXT_PARALLEL_GLOBAL_RANKS is not None + ), 'context parallel group is not initialized' + return _CONTEXT_PARALLEL_GLOBAL_RANKS + + +def get_embedding_group(): + """Get the embedding group the caller rank belongs to.""" + assert _EMBEDDING_GROUP is not None, 'embedding group is not initialized' + return _EMBEDDING_GROUP + + +def get_position_embedding_group(): + """Get the position embedding group the caller rank belongs to.""" + assert _POSITION_EMBEDDING_GROUP is not None, 'position embedding group is not initialized' + return _POSITION_EMBEDDING_GROUP + + +def get_amax_reduction_group(with_context_parallel=False): + """Get the FP8 amax reduction group the caller rank belongs to.""" + if with_context_parallel: + assert ( + _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP is not None + ), 'FP8 amax reduction group is not initialized' + return _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP + else: + assert ( + _TENSOR_AND_DATA_PARALLEL_GROUP is not None + ), 'FP8 amax reduction group is not initialized' + return _TENSOR_AND_DATA_PARALLEL_GROUP + + +def get_tensor_and_data_parallel_group(with_context_parallel=False): + """Get the tensor and data parallel group the caller rank belongs to.""" + if with_context_parallel: + assert ( + _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP is not None + ), 'tensor and data parallel group is not initialized' + return _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP + else: + assert ( + _TENSOR_AND_DATA_PARALLEL_GROUP is not None + ), 'tensor and data parallel group is not initialized' + return _TENSOR_AND_DATA_PARALLEL_GROUP + + +def get_expert_model_parallel_group(): + assert ( + _EXPERT_MODEL_PARALLEL_GROUP is not None + ), 'expert model parallel group is not initialized' + return _EXPERT_MODEL_PARALLEL_GROUP + + +def get_tensor_and_expert_parallel_group(): + assert ( + _TENSOR_AND_EXPERT_PARALLEL_GROUP is not None + ), 'tensor and expert parallel group is not initialized' + return _TENSOR_AND_EXPERT_PARALLEL_GROUP + + +def get_data_modulo_expert_parallel_group(): + assert ( + _DATA_MODULO_EXPERT_PARALLEL_GROUP is not None + ), 'data modulo expert parallel group is not initialized' + return _DATA_MODULO_EXPERT_PARALLEL_GROUP + + +def get_data_modulo_expert_parallel_group_gloo(): + assert ( + _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO is not None + ), 'data modulo expert parallel group-gloo is not initialized' + return _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO + + +def set_expert_model_parallel_world_size(world_size): + global _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE + _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE = world_size + + +def set_tensor_model_parallel_world_size(world_size): + """Set the tensor model parallel size""" + global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE + _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = world_size + + +def set_pipeline_model_parallel_world_size(world_size): + """Set the pipeline model parallel size""" + global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE + _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = world_size + + +def set_virtual_pipeline_model_parallel_world_size(world_size): + """Set the pipeline model parallel size""" + global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE + _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = world_size + + +def get_tensor_model_parallel_world_size(): + """Return world size for the tensor model parallel group.""" + global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE + if _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE is not None: + return _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE + return torch.distributed.get_world_size(group=get_tensor_model_parallel_group()) + + +def get_pipeline_model_parallel_world_size(): + """Return world size for the pipeline model parallel group.""" + global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE + if _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE is not None: + return _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE + return torch.distributed.get_world_size(group=get_pipeline_model_parallel_group()) + + +def set_expert_model_parallel_rank(rank): + """Set expert model parallel rank.""" + global _MPU_EXPERT_MODEL_PARALLEL_RANK + _MPU_EXPERT_MODEL_PARALLEL_RANK = rank + + +def set_tensor_model_parallel_rank(rank): + """Set tensor model parallel rank.""" + global _MPU_TENSOR_MODEL_PARALLEL_RANK + _MPU_TENSOR_MODEL_PARALLEL_RANK = rank + + +def set_pipeline_model_parallel_rank(rank): + """Set pipeline model parallel rank.""" + global _MPU_PIPELINE_MODEL_PARALLEL_RANK + _MPU_PIPELINE_MODEL_PARALLEL_RANK = rank + + +def set_pipeline_model_parallel_split_rank(rank): + """Set pipeline model parallel split rank.""" + global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK + _PIPELINE_MODEL_PARALLEL_SPLIT_RANK = rank + + +def get_tensor_model_parallel_rank(): + """Return my rank for the tensor model parallel group.""" + global _MPU_TENSOR_MODEL_PARALLEL_RANK + if _MPU_TENSOR_MODEL_PARALLEL_RANK is not None: + return _MPU_TENSOR_MODEL_PARALLEL_RANK + return torch.distributed.get_rank(group=get_tensor_model_parallel_group()) + + +def get_pipeline_model_parallel_rank(): + """Return my rank for the pipeline model parallel group.""" + global _MPU_PIPELINE_MODEL_PARALLEL_RANK + if _MPU_PIPELINE_MODEL_PARALLEL_RANK is not None: + return _MPU_PIPELINE_MODEL_PARALLEL_RANK + return torch.distributed.get_rank(group=get_pipeline_model_parallel_group()) + + +def get_pipeline_model_parallel_split_rank(): + """Return pipeline model parallel split rank.""" + global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK + return _PIPELINE_MODEL_PARALLEL_SPLIT_RANK + + +def is_pipeline_first_stage(ignore_virtual=False): + """Return True if in the first pipeline model-parallel stage, False otherwise.""" + if not ignore_virtual: + if ( + get_virtual_pipeline_model_parallel_world_size() is not None + and get_virtual_pipeline_model_parallel_rank() != 0 + ): + return False + return get_pipeline_model_parallel_rank() == 0 + + +def is_pipeline_last_stage(ignore_virtual=False): + """Return True if in the last pipeline model-parallel stage, False otherwise.""" + if not ignore_virtual: + virtual_pipeline_model_parallel_world_size = ( + get_virtual_pipeline_model_parallel_world_size() + ) + if virtual_pipeline_model_parallel_world_size is not None and get_virtual_pipeline_model_parallel_rank() != ( + virtual_pipeline_model_parallel_world_size - 1 + ): + return False + return get_pipeline_model_parallel_rank() == (get_pipeline_model_parallel_world_size() - 1) + + +def is_rank_in_embedding_group(ignore_virtual=False): + """Return true if current rank is in embedding group, False otherwise.""" + rank = torch.distributed.get_rank() + global _EMBEDDING_GLOBAL_RANKS + if ignore_virtual: + return rank in _EMBEDDING_GLOBAL_RANKS + if rank in _EMBEDDING_GLOBAL_RANKS: + if rank == _EMBEDDING_GLOBAL_RANKS[0]: + return is_pipeline_first_stage(ignore_virtual=False) + elif rank == _EMBEDDING_GLOBAL_RANKS[-1]: + return is_pipeline_last_stage(ignore_virtual=False) + else: + return True + return False + + +def is_rank_in_position_embedding_group(): + """Return true if current rank is in position embedding group, False otherwise.""" + rank = torch.distributed.get_rank() + global _POSITION_EMBEDDING_GLOBAL_RANKS + return rank in _POSITION_EMBEDDING_GLOBAL_RANKS + + +def is_pipeline_stage_before_split(rank=None): + """Return True if pipeline stage executes encoder block for a model + with both encoder and decoder.""" + if get_pipeline_model_parallel_world_size() == 1: + return True + if rank is None: + rank = get_pipeline_model_parallel_rank() + global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK + if _PIPELINE_MODEL_PARALLEL_SPLIT_RANK is None: + return True + if rank < _PIPELINE_MODEL_PARALLEL_SPLIT_RANK: + return True + return False + + +def is_pipeline_stage_after_split(rank=None): + """Return True if pipeline stage executes decoder block for a model + with both encoder and decoder.""" + if get_pipeline_model_parallel_world_size() == 1: + return True + if rank is None: + rank = get_pipeline_model_parallel_rank() + global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK + if _PIPELINE_MODEL_PARALLEL_SPLIT_RANK is None: + return True + if rank >= _PIPELINE_MODEL_PARALLEL_SPLIT_RANK: + return True + return False + + +def is_pipeline_stage_at_split(): + """Return true if pipeline stage executes decoder block and next + stage executes encoder block for a model with both encoder and + decoder.""" + rank = get_pipeline_model_parallel_rank() + return is_pipeline_stage_before_split(rank) and is_pipeline_stage_after_split(rank + 1) + + +def get_virtual_pipeline_model_parallel_rank(): + """Return the virtual pipeline-parallel rank.""" + global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK + return _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK + + +def set_virtual_pipeline_model_parallel_rank(rank): + """Set the virtual pipeline-parallel rank.""" + global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK + _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = rank + + +def get_virtual_pipeline_model_parallel_world_size(): + """Return the virtual pipeline-parallel world size.""" + global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE + return _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE + + +def get_tensor_model_parallel_src_rank(): + """Calculate the global rank corresponding to the first local rank + in the tensor model parallel group.""" + assert ( + _TENSOR_MODEL_PARALLEL_GLOBAL_RANKS is not None + ), "Tensor model parallel group is not initialized" + return _TENSOR_MODEL_PARALLEL_GLOBAL_RANKS[0] + + +def get_data_parallel_src_rank(with_context_parallel=False): + """Calculate the global rank corresponding to the first local rank + in the data parallel group.""" + if with_context_parallel: + assert ( + _DATA_PARALLEL_GLOBAL_RANKS_WITH_CP is not None + ), "Data parallel group with context parallel combined is not initialized" + return _DATA_PARALLEL_GLOBAL_RANKS_WITH_CP[0] + else: + assert _DATA_PARALLEL_GLOBAL_RANKS is not None, "Data parallel group is not initialized" + return _DATA_PARALLEL_GLOBAL_RANKS[0] + + +def get_pipeline_model_parallel_first_rank(): + """Return the global rank of the first process in the pipeline for the + current tensor parallel group""" + assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized" + return _PIPELINE_GLOBAL_RANKS[0] + + +def get_pipeline_model_parallel_last_rank(): + """Return the global rank of the last process in the pipeline for the + current tensor parallel group""" + assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized" + last_rank_local = get_pipeline_model_parallel_world_size() - 1 + return _PIPELINE_GLOBAL_RANKS[last_rank_local] + + +def get_pipeline_model_parallel_next_rank(): + """Return the global rank that follows the caller in the pipeline""" + assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized" + rank_in_pipeline = get_pipeline_model_parallel_rank() + world_size = get_pipeline_model_parallel_world_size() + return _PIPELINE_GLOBAL_RANKS[(rank_in_pipeline + 1) % world_size] + + +def get_pipeline_model_parallel_prev_rank(): + """Return the global rank that preceeds the caller in the pipeline""" + assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized" + rank_in_pipeline = get_pipeline_model_parallel_rank() + world_size = get_pipeline_model_parallel_world_size() + return _PIPELINE_GLOBAL_RANKS[(rank_in_pipeline - 1) % world_size] + + +def get_data_parallel_world_size(with_context_parallel=False): + """Return world size for the data parallel group.""" + if torch.distributed.is_available() and torch.distributed.is_initialized(): + return torch.distributed.get_world_size( + group=get_data_parallel_group(with_context_parallel=with_context_parallel) + ) + else: + return 0 + + +def get_data_parallel_rank(with_context_parallel=False): + """Return my rank for the data parallel group.""" + if torch.distributed.is_available() and torch.distributed.is_initialized(): + return torch.distributed.get_rank( + group=get_data_parallel_group(with_context_parallel=with_context_parallel) + ) + else: + return 0 + + +def get_context_parallel_world_size(): + """Return world size for the context parallel group.""" + if torch.distributed.is_available() and torch.distributed.is_initialized(): + return torch.distributed.get_world_size(group=get_context_parallel_group()) + else: + return 0 + + +def get_context_parallel_rank(): + """Return my rank for the context parallel group.""" + if torch.distributed.is_available() and torch.distributed.is_initialized(): + return torch.distributed.get_rank(group=get_context_parallel_group()) + else: + return 0 + + +def get_expert_model_parallel_world_size(): + """Return world size for the expert model parallel group""" + if _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE: + return _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE + if torch.distributed.is_available() and torch.distributed.is_initialized(): + tensor_and_expert_parallel_world_size = torch.distributed.get_world_size( + group=get_tensor_and_expert_parallel_group() + ) + return tensor_and_expert_parallel_world_size // get_tensor_model_parallel_world_size() + else: + return 0 + + +def get_tensor_and_expert_parallel_world_size(): + """Return world size for the expert model parallel group times model parallel group. + Currently, each expert will also be distributed across TP group by default. + """ + if torch.distributed.is_available() and torch.distributed.is_initialized(): + tensor_and_expert_parallel_world_size = torch.distributed.get_world_size( + group=get_tensor_and_expert_parallel_group() + ) + return tensor_and_expert_parallel_world_size + else: + return 0 + + +def get_expert_model_parallel_rank(): + """Return my rank for the expert parallel group""" + if _MPU_EXPERT_MODEL_PARALLEL_RANK: + return _MPU_EXPERT_MODEL_PARALLEL_RANK + if torch.distributed.is_available() and torch.distributed.is_initialized(): + tensor_and_expert_parallel_rank = torch.distributed.get_rank( + group=get_tensor_and_expert_parallel_group() + ) + return tensor_and_expert_parallel_rank // get_tensor_model_parallel_world_size() + else: + return 0 + + +def get_data_modulo_expert_parallel_rank(): + """Return my rank for the context parallel group.""" + if torch.distributed.is_available() and torch.distributed.is_initialized(): + return torch.distributed.get_rank(group=get_data_modulo_expert_parallel_group()) + else: + return 0 + + +def _set_global_memory_buffer(): + """Initialize global buffer""" + global _GLOBAL_MEMORY_BUFFER + assert _GLOBAL_MEMORY_BUFFER is None, 'global memory buffer is already initialized' + _GLOBAL_MEMORY_BUFFER = GlobalMemoryBuffer() + + +def get_global_memory_buffer(): + """Return the global GlobalMemoryBuffer object""" + assert _GLOBAL_MEMORY_BUFFER is not None, 'global memory buffer is not initialized' + return _GLOBAL_MEMORY_BUFFER + + +def destroy_global_memory_buffer(): + """Sets the global memory buffer to None""" + global _GLOBAL_MEMORY_BUFFER + _GLOBAL_MEMORY_BUFFER = None + + +def destroy_model_parallel(): + """Set the groups to none.""" + global _MODEL_PARALLEL_GROUP + _MODEL_PARALLEL_GROUP = None + global _TENSOR_MODEL_PARALLEL_GROUP + _TENSOR_MODEL_PARALLEL_GROUP = None + global _PIPELINE_MODEL_PARALLEL_GROUP + _PIPELINE_MODEL_PARALLEL_GROUP = None + global _DATA_PARALLEL_GROUP + _DATA_PARALLEL_GROUP = None + global _DATA_PARALLEL_GROUP_WITH_CP + _DATA_PARALLEL_GROUP_WITH_CP = None + global _CONTEXT_PARALLEL_GROUP + _CONTEXT_PARALLEL_GROUP = None + global _CONTEXT_PARALLEL_GLOBAL_RANKS + _CONTEXT_PARALLEL_GLOBAL_RANKS = None + global _EMBEDDING_GROUP + _EMBEDDING_GROUP = None + global _POSITION_EMBEDDING_GROUP + _POSITION_EMBEDDING_GROUP = None + global _TENSOR_AND_DATA_PARALLEL_GROUP + _TENSOR_AND_DATA_PARALLEL_GROUP = None + global _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP + _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP = None + global _EXPERT_MODEL_PARALLEL_GROUP + _EXPERT_MODEL_PARALLEL_GROUP = None + global _TENSOR_AND_EXPERT_PARALLEL_GROUP + _TENSOR_AND_EXPERT_PARALLEL_GROUP = None + global _DATA_MODULO_EXPERT_PARALLEL_GROUP + _DATA_MODULO_EXPERT_PARALLEL_GROUP = None + global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK + _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = None + global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE + _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None + global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE + _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = None + global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE + _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None + global _MPU_TENSOR_MODEL_PARALLEL_RANK + _MPU_TENSOR_MODEL_PARALLEL_RANK = None + global _MPU_PIPELINE_MODEL_PARALLEL_RANK + _MPU_PIPELINE_MODEL_PARALLEL_RANK = None + global _GLOBAL_MEMORY_BUFFER + _GLOBAL_MEMORY_BUFFER = None + global _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE + _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE = None + global _MPU_EXPERT_MODEL_PARALLEL_RANK + _MPU_EXPERT_MODEL_PARALLEL_RANK = None diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/pipeline_parallel/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/pipeline_parallel/__init__.py new file mode 100644 index 0000000..00cd1ff --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/pipeline_parallel/__init__.py @@ -0,0 +1 @@ +from .schedules import get_forward_backward_func diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/pipeline_parallel/p2p_communication.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/pipeline_parallel/p2p_communication.py new file mode 100644 index 0000000..e5e7e5a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/pipeline_parallel/p2p_communication.py @@ -0,0 +1,570 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import operator +from functools import reduce +from typing import Callable, List, Optional, Tuple, Union + +import torch + +from megatron import core +from megatron.core import ModelParallelConfig +from megatron.core.parallel_state import ( + get_pipeline_model_parallel_group, + get_pipeline_model_parallel_next_rank, + get_pipeline_model_parallel_prev_rank, + get_pipeline_model_parallel_rank, +) + +# Types +Shape = Union[List[int], torch.Size] + + +def _communicate_shapes(tensor_send_next, tensor_send_prev, recv_prev, recv_next, config): + """Communicate tensor shapes between stages. Used to communicate + tensor shapes before the actual tensor communication happens. + This is required when the sequence lengths across micro batches + are not uniform. + + Args: + tensor_send_next: tensor to send to next rank (no tensor sent if + set to None). + tensor_send_prev: tensor to send to prev rank (no tensor sent if + set to None). + recv_prev: boolean for whether tensor should be received from + previous rank. + recv_next: boolean for whether tensor should be received from + next rank. + Returns: + (recv_prev_shape, recv_next_shape) + """ + + recv_prev_shape_tensor = None + recv_next_shape_tensor = None + send_prev_shape_tensor = None + send_next_shape_tensor = None + if recv_prev: + recv_prev_shape_tensor = torch.empty( + (3), device=torch.cuda.current_device(), dtype=torch.int64 + ) + if recv_next: + recv_next_shape_tensor = torch.empty( + (3), device=torch.cuda.current_device(), dtype=torch.int64 + ) + if tensor_send_prev is not None: + send_prev_shape_tensor = torch.tensor( + tensor_send_prev.size(), device=torch.cuda.current_device(), dtype=torch.int64 + ) + if tensor_send_next is not None: + send_next_shape_tensor = torch.tensor( + tensor_send_next.size(), device=torch.cuda.current_device(), dtype=torch.int64 + ) + + if config.use_ring_exchange_p2p: + torch.distributed.ring_exchange( + tensor_send_prev=send_prev_shape_tensor, + tensor_recv_prev=recv_prev_shape_tensor, + tensor_send_next=send_next_shape_tensor, + tensor_recv_next=recv_next_shape_tensor, + group=get_pipeline_model_parallel_group(), + ) + else: + ops = [] + if send_prev_shape_tensor is not None: + send_prev_op = torch.distributed.P2POp( + torch.distributed.isend, + send_prev_shape_tensor, + get_pipeline_model_parallel_prev_rank(), + ) + ops.append(send_prev_op) + if recv_prev_shape_tensor is not None: + recv_prev_op = torch.distributed.P2POp( + torch.distributed.irecv, + recv_prev_shape_tensor, + get_pipeline_model_parallel_prev_rank(), + ) + ops.append(recv_prev_op) + if send_next_shape_tensor is not None: + send_next_op = torch.distributed.P2POp( + torch.distributed.isend, + send_next_shape_tensor, + get_pipeline_model_parallel_next_rank(), + ) + ops.append(send_next_op) + if recv_next_shape_tensor is not None: + recv_next_op = torch.distributed.P2POp( + torch.distributed.irecv, + recv_next_shape_tensor, + get_pipeline_model_parallel_next_rank(), + ) + ops.append(recv_next_op) + if len(ops) > 0: + reqs = torch.distributed.batch_isend_irecv(ops) + for req in reqs: + req.wait() + + # To protect against race condition when using batch_isend_irecv(). + # should take this out once the bug with batch_isend_irecv is resolved. + torch.cuda.synchronize() + + recv_prev_shape = [0, 0, 0] + if recv_prev_shape_tensor is not None: + recv_prev_shape = recv_prev_shape_tensor.tolist() + + recv_next_shape = [0, 0, 0] + if recv_next_shape_tensor is not None: + recv_next_shape = recv_next_shape_tensor.tolist() + + return recv_prev_shape, recv_next_shape + + +def _batched_p2p_ops( + *, + tensor_send_prev: Optional[torch.Tensor], + tensor_recv_prev: Optional[torch.Tensor], + tensor_send_next: Optional[torch.Tensor], + tensor_recv_next: Optional[torch.Tensor], + group: torch.distributed.ProcessGroup +): + ops = [] + if tensor_send_prev is not None: + send_prev_op = torch.distributed.P2POp( + torch.distributed.isend, + tensor_send_prev, + get_pipeline_model_parallel_prev_rank(), + group, + ) + ops.append(send_prev_op) + if tensor_recv_prev is not None: + recv_prev_op = torch.distributed.P2POp( + torch.distributed.irecv, + tensor_recv_prev, + get_pipeline_model_parallel_prev_rank(), + group, + ) + ops.append(recv_prev_op) + if tensor_send_next is not None: + send_next_op = torch.distributed.P2POp( + torch.distributed.isend, + tensor_send_next, + get_pipeline_model_parallel_next_rank(), + group, + ) + ops.append(send_next_op) + if tensor_recv_next is not None: + recv_next_op = torch.distributed.P2POp( + torch.distributed.irecv, + tensor_recv_next, + get_pipeline_model_parallel_next_rank(), + group, + ) + ops.append(recv_next_op) + if len(ops) > 0: + reqs = torch.distributed.batch_isend_irecv(ops) + else: + reqs = [] + return reqs + + +def _p2p_ops( + *, + tensor_send_prev: Optional[torch.Tensor], + tensor_recv_prev: Optional[torch.Tensor], + tensor_send_next: Optional[torch.Tensor], + tensor_recv_next: Optional[torch.Tensor], + group: torch.distributed.ProcessGroup +): + reqs = [] + rank = get_pipeline_model_parallel_rank() + if get_pipeline_model_parallel_rank() % 2 == 0: + if tensor_send_next is not None: + send_next_req = torch.distributed.isend( + tensor=tensor_send_next, dst=get_pipeline_model_parallel_next_rank(), group=group, + ) + reqs.append(send_next_req) + + if tensor_recv_prev is not None: + recv_prev_req = torch.distributed.irecv( + tensor=tensor_recv_prev, src=get_pipeline_model_parallel_prev_rank(), group=group, + ) + reqs.append(recv_prev_req) + + if tensor_send_prev is not None: + send_prev_req = torch.distributed.isend( + tensor=tensor_send_prev, dst=get_pipeline_model_parallel_prev_rank(), group=group, + ) + reqs.append(send_prev_req) + + if tensor_recv_next is not None: + recv_next_req = torch.distributed.irecv( + tensor=tensor_recv_next, src=get_pipeline_model_parallel_next_rank(), group=group, + ) + reqs.append(recv_next_req) + + else: + if tensor_recv_prev is not None: + recv_prev_req = torch.distributed.irecv( + tensor=tensor_recv_prev, src=get_pipeline_model_parallel_prev_rank(), group=group, + ) + reqs.append(recv_prev_req) + + if tensor_send_next is not None: + send_next_req = torch.distributed.isend( + tensor=tensor_send_next, dst=get_pipeline_model_parallel_next_rank(), group=group, + ) + reqs.append(send_next_req) + + if tensor_recv_next is not None: + recv_next_req = torch.distributed.irecv( + tensor=tensor_recv_next, src=get_pipeline_model_parallel_next_rank(), group=group, + ) + reqs.append(recv_next_req) + + if tensor_send_prev is not None: + send_prev_req = torch.distributed.isend( + tensor=tensor_send_prev, dst=get_pipeline_model_parallel_prev_rank(), group=group, + ) + reqs.append(send_prev_req) + return reqs + + +def _communicate( + *, + tensor_send_next: Optional[torch.Tensor], + tensor_send_prev: Optional[torch.Tensor], + recv_prev: bool, + recv_next: bool, + tensor_shape: Shape, + config: ModelParallelConfig, + wait_on_reqs: bool = True +) -> Tuple[torch.Tensor, torch.Tensor]: + """Communicate tensors between stages. Used as helper method in other + communication methods that are used in megatron/schedules.py. + + Args: + tensor_send_next (torch.Tensor, optional): + Tensor to send to next rank (no tensor sent if None) + + tensor_send_prev (torch.Tensor, optional): + Tensor to send to prev rank (no tensor sent if None) + + recv_prev (boolean, required): + whether tensor should be received from previous rank. + + recv_next (boolean, required): + whether tensor should be received from next rank. + + tensor_shape (List[int] or torch.Size, required): + shape of tensor to receive (this method assumes that all + tensors sent and received in a single function call are + the same shape). + + wait_on_reqs (boolean, optional, default=False): + For non-batched p2p communication, wait on each request + before returning. + + Returns: + tuple containing + + - tensor_recv_prev: torch.Tensor if recv_prev is True, None otherwise. + - tensor_recv_next: torch.Tensor if recv_next is True, None otherwise. + + """ + + # Create placeholder tensors for receive in forward and backward directions + # if needed. + tensor_recv_prev = None + tensor_recv_next = None + + if not config.variable_seq_lengths: + recv_prev_shape = tensor_shape + recv_next_shape = tensor_shape + else: + recv_prev_shape, recv_next_shape = _communicate_shapes( + tensor_send_next, tensor_send_prev, recv_prev, recv_next, config + ) + + if recv_prev: + if config.pipeline_dtype is None: + raise RuntimeError("pipeline_dtype must be provided if recv_prev is True") + if tensor_shape is None: + raise RuntimeError( + "tensor_shape must be specified if recv_prev is True. " + "Common tensor_shape is (seq_length, micro_batch_size, hidden_size)" + ) + tensor_recv_prev = torch.empty( + recv_prev_shape, + requires_grad=True, + device=torch.cuda.current_device(), + dtype=config.pipeline_dtype, + ) + if recv_next: + if config.pipeline_dtype is None: + raise RuntimeError("dtype must be provided if recv_next is True") + if tensor_shape is None: + raise RuntimeError( + "tensor_shape must be specified if recv_next is True. " + "Common tensor_shape is (seq_length, micro_batch_size, hidden_size)" + ) + tensor_recv_next = torch.empty( + recv_next_shape, + requires_grad=True, + device=torch.cuda.current_device(), + dtype=config.pipeline_dtype, + ) + + # Send tensors in both the forward and backward directions as appropriate. + if config.use_ring_exchange_p2p: + + def _ring_exchange_wrapper(**kwargs): + torch.distributed.ring_exchange(**kwargs) + return [] + + p2p_func = _ring_exchange_wrapper + elif config.batch_p2p_comm: + assert wait_on_reqs + p2p_func = _batched_p2p_ops + else: + p2p_func = _p2p_ops + + reqs = p2p_func( + tensor_send_prev=tensor_send_prev, + tensor_recv_prev=tensor_recv_prev, + tensor_send_next=tensor_send_next, + tensor_recv_next=tensor_recv_next, + group=get_pipeline_model_parallel_group(), + ) + + if wait_on_reqs and len(reqs) > 0: + for req in reqs: + req.wait() + reqs = None + + if config.batch_p2p_comm and config.batch_p2p_sync: + # To protect against race condition when using batch_isend_irecv(). + # User should assert that we have a modern enough PyTorch to not need this + torch.cuda.synchronize() + + return tensor_recv_prev, tensor_recv_next, reqs + + +def recv_forward(tensor_shape: Shape, config: ModelParallelConfig) -> torch.Tensor: + """ Receive tensor from previous rank in pipeline (forward receive). + + See _communicate for argument details. + """ + + if core.parallel_state.is_pipeline_first_stage(): + input_tensor = None + else: + if config.timers is not None: + config.timers('forward-recv', log_level=2).start() + input_tensor, _, _ = _communicate( + tensor_send_next=None, + tensor_send_prev=None, + recv_prev=True, + recv_next=False, + tensor_shape=tensor_shape, + config=config, + ) + if config.timers is not None: + config.timers('forward-recv').stop() + return input_tensor + + +def recv_backward(tensor_shape: Shape, config: ModelParallelConfig) -> torch.Tensor: + """Receive tensor from next rank in pipeline (backward receive). + + See _communicate for argument details. + """ + if core.parallel_state.is_pipeline_last_stage(): + output_tensor_grad = None + else: + if config.timers is not None: + config.timers('backward-recv', log_level=2).start() + _, output_tensor_grad, _ = _communicate( + tensor_send_next=None, + tensor_send_prev=None, + recv_prev=False, + recv_next=True, + tensor_shape=tensor_shape, + config=config, + ) + if config.timers is not None: + config.timers('backward-recv').stop() + return output_tensor_grad + + +def send_forward(output_tensor: torch.Tensor, config: ModelParallelConfig) -> None: + """Send tensor to next rank in pipeline (forward send). + + See _communicate for argument details. + """ + + if not core.parallel_state.is_pipeline_last_stage(): + if config.timers is not None: + config.timers('forward-send', log_level=2).start() + _communicate( + tensor_send_next=output_tensor, + tensor_send_prev=None, + recv_prev=False, + recv_next=False, + tensor_shape=None, + config=config, + ) + if config.timers is not None: + config.timers('forward-send').stop() + + +def send_backward(input_tensor_grad: torch.Tensor, config: ModelParallelConfig) -> None: + """Send tensor to previous rank in pipeline (backward send). + + See _communicate for argument details. + """ + if not core.parallel_state.is_pipeline_first_stage(): + if config.timers is not None: + config.timers('backward-send', log_level=2).start() + _communicate( + tensor_send_next=None, + tensor_send_prev=input_tensor_grad, + recv_prev=False, + recv_next=False, + tensor_shape=None, + config=config, + ) + if config.timers is not None: + config.timers('backward-send').stop() + + +def send_forward_recv_backward( + output_tensor: torch.Tensor, tensor_shape: Shape, config: ModelParallelConfig +) -> torch.Tensor: + """Batched send and recv with next rank in pipeline. + + See _communicate for argument details. + """ + if core.parallel_state.is_pipeline_last_stage(): + output_tensor_grad = None + else: + if config.timers is not None: + config.timers('forward-send-backward-recv', log_level=2).start() + _, output_tensor_grad, _ = _communicate( + tensor_send_next=output_tensor, + tensor_send_prev=None, + recv_prev=False, + recv_next=True, + tensor_shape=tensor_shape, + config=config, + ) + if config.timers is not None: + config.timers('forward-send-backward-recv').stop() + return output_tensor_grad + + +def send_backward_recv_forward( + input_tensor_grad: torch.Tensor, tensor_shape: Shape, config: ModelParallelConfig +) -> torch.Tensor: + """Batched send and recv with previous rank in pipeline. + + See _communicate for argument details. + """ + if core.parallel_state.is_pipeline_first_stage(): + input_tensor = None + else: + if config.timers is not None: + config.timers('backward-send-forward-recv', log_level=2).start() + input_tensor, _, _ = _communicate( + tensor_send_next=None, + tensor_send_prev=input_tensor_grad, + recv_prev=True, + recv_next=False, + tensor_shape=tensor_shape, + config=config, + ) + if config.timers is not None: + config.timers('backward-send-forward-recv').stop() + return input_tensor + + +def send_forward_recv_forward( + output_tensor: torch.Tensor, + recv_prev: bool, + tensor_shape: Shape, + config: ModelParallelConfig, + overlap_p2p_comm: bool = False, +) -> torch.Tensor: + """Batched recv from previous rank and send to next rank in pipeline. + + See _communicate for argument details. + """ + if config.timers is not None: + config.timers('forward-send-forward-recv', log_level=2).start() + input_tensor, _, wait_handles = _communicate( + tensor_send_next=output_tensor, + tensor_send_prev=None, + recv_prev=recv_prev, + recv_next=False, + tensor_shape=tensor_shape, + wait_on_reqs=(not overlap_p2p_comm), + config=config, + ) + if config.timers is not None: + config.timers('forward-send-forward-recv').stop() + if overlap_p2p_comm: + return input_tensor, wait_handles + return input_tensor + + +def send_backward_recv_backward( + input_tensor_grad: torch.Tensor, + recv_next: bool, + tensor_shape: Shape, + config: ModelParallelConfig, + overlap_p2p_comm: bool = False, +) -> torch.Tensor: + """Batched recv from next rank and send to previous rank in pipeline. + + See _communicate for argument details. + """ + if config.timers is not None: + config.timers('backward-send-backward-recv', log_level=2).start() + _, output_tensor_grad, wait_handles = _communicate( + tensor_send_next=None, + tensor_send_prev=input_tensor_grad, + recv_prev=False, + recv_next=recv_next, + tensor_shape=tensor_shape, + wait_on_reqs=(not overlap_p2p_comm), + config=config, + ) + if config.timers is not None: + config.timers('backward-send-backward-recv').stop() + if overlap_p2p_comm: + return output_tensor_grad, wait_handles + return output_tensor_grad + + +def send_forward_backward_recv_forward_backward( + output_tensor: torch.Tensor, + input_tensor_grad: torch.Tensor, + recv_prev: bool, + recv_next: bool, + tensor_shape: Shape, + config: ModelParallelConfig, +) -> torch.Tensor: + """Batched send and recv with previous and next ranks in pipeline. + + See _communicate for argument details. + """ + if config.timers is not None: + config.timers('forward-backward-send-forward-backward-recv', log_level=2).start() + input_tensor, output_tensor_grad, _ = _communicate( + tensor_send_next=output_tensor, + tensor_send_prev=input_tensor_grad, + recv_prev=recv_prev, + recv_next=recv_next, + tensor_shape=tensor_shape, + config=config, + ) + if config.timers is not None: + config.timers('forward-backward-send-forward-backward-recv').stop() + return input_tensor, output_tensor_grad diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/pipeline_parallel/schedules.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/pipeline_parallel/schedules.py new file mode 100644 index 0000000..174c2fb --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/pipeline_parallel/schedules.py @@ -0,0 +1,1377 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import contextlib +from typing import Callable, Iterator, List, Optional, Union + +import torch +from torch.autograd.variable import Variable + +from megatron.core import parallel_state +from megatron.core.enums import ModelType +from megatron.core.pipeline_parallel import p2p_communication +from megatron.core.transformer.moe.router import MoEAuxLossAutoScaler +from megatron.core.utils import get_attr_wrapped_model, get_model_config, get_model_type + +# Types +Shape = Union[List[int], torch.Size] + + +def get_forward_backward_func(): + """Retrieves the appropriate forward_backward function given the + configuration of parallel_state. + + Returns a function that will perform all of the forward and + backward passes of the model given the pipeline model parallel + world size and virtual pipeline model parallel world size in the + global parallel_state. + + Note that if using sequence parallelism, the sequence length component of + the tensor shape is updated to original_sequence_length / + tensor_model_parallel_world_size. + + The function returned takes the following arguments: + + forward_step_func (required): A function that takes a data + iterator and a model as its arguments and return the model's + forward output and the loss function. The loss function should + take one torch.Tensor and return a torch.Tensor of loss and a + dictionary of string -> torch.Tensor. + + A third argument, checkpoint_activations_microbatch, indicates + that the activations for this microbatch should be + checkpointed. A None value for this argument indicates that + the default from the configuration should be used. This is + used when the + num_microbatches_with_partial_activation_checkpoints is used. + + For example: + + def loss_func(loss_mask, output_tensor): + losses = output_tensor.float() + loss_mask = loss_mask.view(-1).float() + loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum() + + # Reduce loss for logging. + averaged_loss = average_losses_across_data_parallel_group([loss]) + + return loss, {'lm loss': averaged_loss[0]} + + def forward_step(data_iterator, model): + data, loss_mask = next(data_iterator) + output = model(data) + return output, partial(loss_func, loss_mask) + + + forward_backward_func(forward_step_func=forward_step, ...) + + + data_iterator (required): an iterator over the data, will be + passed as is to forward_step_func. Expected to be a list of + iterators in the case of interleaved pipeline parallelism. + + model (required): the actual model. Expected to be a list of modules in the case of interleaved + pipeline parallelism. Must be a (potentially wrapped) megatron.core.models.MegatronModule. + + num_microbatches (int, required): + The number of microbatches to go through + + seq_length (int, required): Sequence length of the current global batch. If this is a dual-stack + transformer, this is the encoder's sequence length. This is ignored if variable_seq_lengths + in the config is True. Otherwise, each microbatch in the current global batch size must use + this sequence length. + + micro_batch_size (int, required): The number of sequences in a microbatch. + + decoder_seq_length (int, optional): The sequence length for the decoder in a dual-stack + transformer. This is ignored for a single-stack transformer. + + forward_only (optional, default = False): Perform only the forward step + + collect_non_loss_data (optional, bool, default=False): TODO + + first_val_step (bool, optional): Is the first step of the validation phase. Used by + Transformer Engine modules to only update their fp8 weights only on the first validation step. + + """ + pipeline_model_parallel_size = parallel_state.get_pipeline_model_parallel_world_size() + if pipeline_model_parallel_size > 1: + if parallel_state.get_virtual_pipeline_model_parallel_world_size() is not None: + forward_backward_func = forward_backward_pipelining_with_interleaving + else: + forward_backward_func = forward_backward_pipelining_without_interleaving + else: + forward_backward_func = forward_backward_no_pipelining + return forward_backward_func + + +def deallocate_output_tensor(out, deallocate_pipeline_outputs=False): + '''Pseudo-deallocate (i.e., set to scalar) the output tensor's '.data' field. + + This method should be called right after the output tensor has been + sent to the next pipeline stage. At this point, the output tensor is + only useful for its '.grad_fn' field, and not its '.data'. + ''' + if (out is None) or (not deallocate_pipeline_outputs): + return + assert isinstance(out, torch.Tensor), "expected Tensor, found %s." % type(out).__name__ + assert out._base is None, "counter-productive to free a view of another tensor." + out.data = torch.empty((1,), device=out.device, dtype=out.dtype,) + + +def custom_backward(output, grad_output): + '''Directly call C++ autograd engine. + + To make the 'deallocate_output_tensor' (above) optimization work, the C++ + autograd engine must be called directly, bypassing Pytorch's + torch.autograd.backward. Pytorch's 'backward' checks that the output and + grad have the same shape, while C++'s 'backward' does not. + ''' + + assert output.numel() == 1, "output should be pseudo-'freed' in schedule, to optimize memory" + assert isinstance(output, torch.Tensor), "output == '%s'." % type(output).__name__ + assert isinstance(grad_output, (torch.Tensor, type(None))), ( + "grad_output == '%s'." % type(grad_output).__name__ + ) + + # Handle scalar output + if grad_output is None: + assert output.numel() == 1, "implicit grad requires scalar output." + grad_output = torch.ones_like(output, memory_format=torch.preserve_format,) + + # Call c++ engine [ see torch/csrc/autograd/python_engine.cpp ] + Variable._execution_engine.run_backward( + tensors=(output,), + grad_tensors=(grad_output,), + keep_graph=False, + create_graph=False, + inputs=tuple(), + allow_unreachable=True, + accumulate_grad=True, + ) + + +def set_current_microbatch(model, microbatch_id): + decoder_exists = True + decoder = None + try: + decoder = get_attr_wrapped_model(model, "decoder") + except RuntimeError: + decoder_exists = False + if decoder_exists and decoder is not None: + decoder.current_microbatch = microbatch_id + + +def forward_step( + forward_step_func, + data_iterator, + model, + num_microbatches, + input_tensor, + forward_data_store, + config, + collect_non_loss_data=False, + checkpoint_activations_microbatch=None, + is_first_microbatch=False, + current_microbatch=None, +): + + """Forward step for passed-in model. + + If first stage, input tensor is obtained from data_iterator, otherwise + passed-in input_tensor is used. + + Returns output tensor.""" + if config.timers is not None: + config.timers('forward-compute', log_level=2).start() + + if is_first_microbatch and hasattr(model, 'set_is_first_microbatch'): + model.set_is_first_microbatch() + if current_microbatch is not None: + set_current_microbatch(model, current_microbatch) + + unwrap_output_tensor = False + if not isinstance(input_tensor, list): + input_tensor = [input_tensor] + unwrap_output_tensor = True + + set_input_tensor = get_attr_wrapped_model(model, "set_input_tensor") + set_input_tensor(input_tensor) + + if config.enable_autocast: + context_manager = torch.autocast("cuda", dtype=config.autocast_dtype) + else: + context_manager = contextlib.nullcontext() + with context_manager: + if checkpoint_activations_microbatch is None: + output_tensor, loss_func = forward_step_func(data_iterator, model) + else: + output_tensor, loss_func = forward_step_func( + data_iterator, model, checkpoint_activations_microbatch + ) + + if parallel_state.is_pipeline_last_stage(): + if not collect_non_loss_data: + output_tensor = loss_func(output_tensor) + loss, loss_reduced = output_tensor + output_tensor = loss / num_microbatches + forward_data_store.append(loss_reduced) + else: + data = loss_func(output_tensor, non_loss_data=True) + forward_data_store.append(data) + + if config.timers is not None: + config.timers('forward-compute').stop() + + # Set the loss scale for the auxiliary loss of the MoE layer. + # Since we use a trick to do backward on the auxiliary loss, we need to set the scale explicitly. + if hasattr(config, 'num_moe_experts') and config.num_moe_experts is not None: + # Calculate the loss scale based on the grad_scale_func if available, else default to 1. + loss_scale = ( + config.grad_scale_func(torch.tensor(1.0, device=output_tensor.device)) + if config.grad_scale_func is not None + else torch.tensor(1.0) + ) + # Set the loss scale + MoEAuxLossAutoScaler.set_loss_scale(loss_scale / num_microbatches) + + # If T5 model (or other model with encoder and decoder) + # and in decoder stack, then send encoder_hidden_state + # downstream as well. + model_type = get_model_type(model) + if ( + parallel_state.is_pipeline_stage_after_split() + and model_type == ModelType.encoder_and_decoder + ): + return [output_tensor, input_tensor[-1]] + if unwrap_output_tensor: + return output_tensor + return [output_tensor] + + +def backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, config): + """Backward step through passed-in output tensor. + + If last stage, output_tensor_grad is None, otherwise gradient of loss + with respect to stage's output tensor. + + Returns gradient of loss with respect to input tensor (None if first + stage).""" + + # NOTE: This code currently can handle at most one skip connection. It + # needs to be modified slightly to support arbitrary numbers of skip + # connections. + + if config.timers is not None: + config.timers('backward-compute', log_level=2).start() + + # Retain the grad on the input_tensor. + unwrap_input_tensor_grad = False + if not isinstance(input_tensor, list): + input_tensor = [input_tensor] + unwrap_input_tensor_grad = True + for x in input_tensor: + if x is not None: + x.retain_grad() + + if not isinstance(output_tensor, list): + output_tensor = [output_tensor] + if not isinstance(output_tensor_grad, list): + output_tensor_grad = [output_tensor_grad] + + # Backward pass. + if output_tensor_grad[0] is None and config.grad_scale_func is not None: + output_tensor[0] = config.grad_scale_func(output_tensor[0]) + + if config.deallocate_pipeline_outputs: + custom_backward(output_tensor[0], output_tensor_grad[0]) + else: + torch.autograd.backward(output_tensor[0], grad_tensors=output_tensor_grad[0]) + + # Collect the grad of the input_tensor. + input_tensor_grad = [None] + if input_tensor is not None: + input_tensor_grad = [] + for x in input_tensor: + if x is None: + input_tensor_grad.append(None) + else: + input_tensor_grad.append(x.grad) + + # Handle single skip connection if it exists (encoder_hidden_state in + # model with encoder and decoder). + if ( + parallel_state.get_pipeline_model_parallel_world_size() > 1 + and parallel_state.is_pipeline_stage_after_split() + and model_type == ModelType.encoder_and_decoder + ): + if output_tensor_grad[1] is not None: + input_tensor_grad[-1].add_(output_tensor_grad[1]) + if unwrap_input_tensor_grad: + input_tensor_grad = input_tensor_grad[0] + + if config.timers is not None: + config.timers('backward-compute').stop() + + return input_tensor_grad + + +def check_first_val_step(first_val_step, forward_only, cond): + if (first_val_step is not None) and forward_only: + return first_val_step and cond + else: + return cond + + +def forward_backward_no_pipelining( + *, + forward_step_func, + data_iterator: Union[Iterator, List[Iterator]], + model: Union[torch.nn.Module, List[torch.nn.Module]], + num_microbatches: int, + seq_length: int, # unused + micro_batch_size: int, # unused + decoder_seq_length: int = None, # unused + forward_only: bool = False, + collect_non_loss_data: bool = False, + first_val_step: bool = None, +): + """Run forward and backward passes with no pipeline parallelism + (no inter-stage communication). + + Returns dictionary with losses. + + + See get_forward_backward_func() for argument details + """ + + if isinstance(model, list): + assert len(model) == 1, "non-pipeline-parallel schedule does not support model chunking" + model = model[0] + if isinstance(data_iterator, list): + assert ( + len(data_iterator) == 1 + ), "non-pipeline-parallel schedule does not support model chunking" + data_iterator = data_iterator[0] + + config = get_model_config(model) + if config.timers is not None: + config.timers('forward-backward', log_level=1).start(barrier=config.barrier_with_L1_time) + + no_sync_func = config.no_sync_func + if no_sync_func is None: + no_sync_func = contextlib.nullcontext + + model_type = get_model_type(model) + + forward_data_store = [] + input_tensor, output_tensor_grad = None, None + with no_sync_func(): + for i in range(num_microbatches - 1): + output_tensor = forward_step( + forward_step_func, + data_iterator, + model, + num_microbatches, + input_tensor, + forward_data_store, + config, + collect_non_loss_data, + is_first_microbatch=check_first_val_step(first_val_step, forward_only, i == 0), + current_microbatch=i, + ) + if not forward_only: + backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, config) + + # Run computation for last microbatch out of context handler (want to + # synchronize gradients). + output_tensor = forward_step( + forward_step_func, + data_iterator, + model, + num_microbatches, + input_tensor, + forward_data_store, + config, + collect_non_loss_data, + is_first_microbatch=check_first_val_step( + first_val_step, forward_only, num_microbatches == 1 + ), + current_microbatch=num_microbatches - 1, + ) + + if not forward_only: + backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, config) + + if config.timers is not None: + config.timers('forward-backward').stop() + + if config.finalize_model_grads_func is not None and not forward_only: + # Finalize model grads (perform full grad all-reduce / reduce-scatter for + # data parallelism and layernorm all-reduce for sequence parallelism). + config.finalize_model_grads_func([model]) + + return forward_data_store + + +def forward_backward_pipelining_with_interleaving( + *, + forward_step_func, + data_iterator: Union[Iterator, List[Iterator]], + model: Union[torch.nn.Module, List[torch.nn.Module]], + num_microbatches: int, + seq_length: int, + micro_batch_size: int, + decoder_seq_length: int = None, + forward_only: bool = False, + collect_non_loss_data: bool = False, + first_val_step: bool = None, +): + """Run interleaved 1F1B schedule (model split into model chunks), with + communication between pipeline stages as needed. + + Returns dictionary with losses if the last stage, empty dict otherwise.""" + assert isinstance(model, list), "interleaved pipeline parallelism expected model chunking" + assert all(isinstance(chunk, torch.nn.Module) for chunk in model), "invalid model chunking" + assert isinstance( + data_iterator, list + ), "interleaved pipeline parallelism expected each model chunk to have a data iterator" + + config = get_model_config(model[0]) + if config.overlap_p2p_comm and config.batch_p2p_comm: + raise ValueError("Can not use both overlap_p2p_comm and batch_p2p_comm") + + if config.timers is not None: + config.timers('forward-backward', log_level=1).start(barrier=config.barrier_with_L1_time) + + # Disable async grad reductions + no_sync_func = config.no_sync_func + if isinstance(no_sync_func, list): + + def multi_no_sync(): + stack = contextlib.ExitStack() + for model_chunk_no_sync_func in config.no_sync_func: + stack.enter_context(model_chunk_no_sync_func()) + return stack + + no_sync_func = multi_no_sync + if no_sync_func is None: + no_sync_func = contextlib.nullcontext + no_sync_context = None + + if config.grad_sync_func is not None and not isinstance(config.grad_sync_func, list): + config.grad_sync_func = [config.grad_sync_func for _ in model] + + if config.param_sync_func is not None and not isinstance(config.param_sync_func, list): + config.param_sync_func = [config.param_sync_func for _ in model] + + def disable_grad_sync(): + """Disable asynchronous grad reductions""" + nonlocal no_sync_context + if no_sync_context is None: + no_sync_context = no_sync_func() + no_sync_context.__enter__() + + def enable_grad_sync(): + """Enable asynchronous grad reductions""" + nonlocal no_sync_context + if no_sync_context is not None: + no_sync_context.__exit__(None, None, None) + no_sync_context = None + + disable_grad_sync() + + # Model chunk IDs with synchronized grads + synchronized_model_chunks = set() + + input_tensors = [[] for _ in range(len(model))] + output_tensors = [[] for _ in range(len(model))] + forward_data_store = [] + if not forward_only: + output_tensor_grads = [[] for _ in range(len(model))] + + pipeline_parallel_size = parallel_state.get_pipeline_model_parallel_world_size() + pipeline_parallel_rank = parallel_state.get_pipeline_model_parallel_rank() + + if num_microbatches % pipeline_parallel_size != 0: + msg = f'number of microbatches ({num_microbatches}) is not divisible by ' + msg += f'pipeline-model-parallel-size ({pipeline_parallel_size}) ' + msg += 'when using interleaved schedule' + raise RuntimeError(msg) + + model_type = get_model_type(model[0]) + if model_type == ModelType.encoder_and_decoder: + raise RuntimeError("Interleaving is not supported with an encoder and decoder model.") + + if decoder_seq_length is not None and decoder_seq_length != seq_length: + raise RuntimeError( + "Interleaving is not supported with a different decoder sequence length." + ) + + tensor_shape = [seq_length, micro_batch_size, config.hidden_size] + tensor_shape[0] = tensor_shape[0] // parallel_state.get_context_parallel_world_size() + if config.sequence_parallel: + tensor_shape[0] = tensor_shape[0] // parallel_state.get_tensor_model_parallel_world_size() + + # Compute number of warmup and remaining microbatches. + num_model_chunks = len(model) + total_num_microbatches = num_microbatches * num_model_chunks + all_warmup_microbatches = False + if forward_only: + num_warmup_microbatches = total_num_microbatches + else: + # Run all forward passes and then all backward passes if number of + # microbatches is just the number of pipeline stages. + # Otherwise, perform (num_model_chunks-1)*pipeline_parallel_size on + # all workers, followed by more microbatches after depending on + # stage ID (more forward passes for earlier stages, later stages can + # immediately start with 1F1B). + if num_microbatches == pipeline_parallel_size: + num_warmup_microbatches = total_num_microbatches + all_warmup_microbatches = True + else: + num_warmup_microbatches = (pipeline_parallel_size - pipeline_parallel_rank - 1) * 2 + num_warmup_microbatches += (num_model_chunks - 1) * pipeline_parallel_size + num_warmup_microbatches = min(num_warmup_microbatches, total_num_microbatches) + num_microbatches_remaining = total_num_microbatches - num_warmup_microbatches + + # Checkpoint the activations of partial Transformer layers in a number of micro-batches + # within the maximum outstanding micro-batch backpropagations. + # Micro-batches with the ids less than 'num_microbatches_with_partial_activation_checkpoints' + # checkpoint partial Transformer layers (or skip checkpointing) and + # the rest of micro-batches within a window of micro-batches checkpoint + # all Transformer layers. The window of micro-batches is set by the maximum + # outstanding backpropagations and becomes smaller at later pipeline stages. + # Please refer the appendix C in https://arxiv.org/pdf/2205.05198.pdf + max_outstanding_backprops = None + if config.num_microbatches_with_partial_activation_checkpoints is not None: + max_outstanding_backprops = num_warmup_microbatches + 1 + + # Synchronize params for first two model chunks + if config.param_sync_func is not None: + config.param_sync_func[0](model[0].parameters()) + config.param_sync_func[1](model[1].parameters()) + + def get_model_chunk_id(microbatch_id, forward): + """Helper method to get the model chunk ID given the iteration number.""" + microbatch_id_in_group = microbatch_id % (pipeline_parallel_size * num_model_chunks) + model_chunk_id = microbatch_id_in_group // pipeline_parallel_size + if not forward: + model_chunk_id = num_model_chunks - model_chunk_id - 1 + return model_chunk_id + + def get_microbatch_id_in_model_chunk(iteration_id, forward): + """Helper method to get the microbatch_id within model chunk given the iteration number.""" + assert forward + iteration_group_id = iteration_id // (pipeline_parallel_size * num_model_chunks) + microbatch_id_in_model_chunk = (iteration_group_id * pipeline_parallel_size) + ( + iteration_id % pipeline_parallel_size + ) + return microbatch_id_in_model_chunk + + def is_first_microbatch_for_model_chunk(microbatch_id: int) -> bool: + """Check if an iteration is the first for a model chunk.""" + microbatch_group_size = pipeline_parallel_size * num_model_chunks + num_microbatch_groups = total_num_microbatches // microbatch_group_size + microbatch_group_id = microbatch_id // microbatch_group_size + microbatch_id_in_group = microbatch_id % microbatch_group_size + if microbatch_group_id == 0: + return microbatch_id_in_group % pipeline_parallel_size == 0 + else: + return False + + def is_last_microbatch_for_model_chunk(microbatch_id: int) -> bool: + """Check if an iteration is the last for a model chunk.""" + microbatch_group_size = pipeline_parallel_size * num_model_chunks + num_microbatch_groups = total_num_microbatches // microbatch_group_size + microbatch_group_id = microbatch_id // microbatch_group_size + microbatch_id_in_group = microbatch_id % microbatch_group_size + if microbatch_group_id == num_microbatch_groups - 1: + return microbatch_id_in_group % pipeline_parallel_size == pipeline_parallel_size - 1 + else: + return False + + def forward_step_helper(microbatch_id, current_microbatch, checkpoint_activations_microbatch): + """Helper method to run forward step with model split into chunks + (run set_virtual_pipeline_model_parallel_rank() before calling + forward_step()).""" + model_chunk_id = get_model_chunk_id(microbatch_id, forward=True) + parallel_state.set_virtual_pipeline_model_parallel_rank(model_chunk_id) + + # launch param synchronization for next model chunk + # Note: Asynchronous communication tends to slow down compute. + # To reduce idling from mismatched microbatch times, we launch + # asynchronous communication at the same time across the + # pipeline-parallel group. + if config.param_sync_func is not None: + param_sync_microbatch_id = microbatch_id + pipeline_parallel_rank + if ( + param_sync_microbatch_id < total_num_microbatches + and is_first_microbatch_for_model_chunk(param_sync_microbatch_id) + ): + param_sync_chunk_id = get_model_chunk_id(param_sync_microbatch_id, forward=True) + 1 + if 1 < param_sync_chunk_id < num_model_chunks: + config.param_sync_func[param_sync_chunk_id]( + model[param_sync_chunk_id].parameters() + ) + + # forward step + if parallel_state.is_pipeline_first_stage(): + if len(input_tensors[model_chunk_id]) == len(output_tensors[model_chunk_id]): + input_tensors[model_chunk_id].append(None) + input_tensor = input_tensors[model_chunk_id][-1] + + output_tensor = forward_step( + forward_step_func, + data_iterator[model_chunk_id], + model[model_chunk_id], + num_microbatches, + input_tensor, + forward_data_store, + config, + collect_non_loss_data, + checkpoint_activations_microbatch, + check_first_val_step( + first_val_step, forward_only, is_first_microbatch_for_model_chunk(microbatch_id), + ), + current_microbatch=current_microbatch, + ) + output_tensors[model_chunk_id].append(output_tensor) + + # if forward-only, no need to save tensors for a backward pass + if forward_only: + input_tensors[model_chunk_id].pop() + output_tensors[model_chunk_id].pop() + + return output_tensor + + def backward_step_helper(microbatch_id): + """Helper method to run backward step with model split into chunks + (run set_virtual_pipeline_model_parallel_rank() before calling + backward_step()).""" + model_chunk_id = get_model_chunk_id(microbatch_id, forward=False) + parallel_state.set_virtual_pipeline_model_parallel_rank(model_chunk_id) + + # launch grad synchronization (default) + if config.grad_sync_func is None and is_last_microbatch_for_model_chunk(microbatch_id): + enable_grad_sync() + synchronized_model_chunks.add(model_chunk_id) + + if parallel_state.is_pipeline_last_stage(): + if len(output_tensor_grads[model_chunk_id]) == 0: + output_tensor_grads[model_chunk_id].append(None) + input_tensor = input_tensors[model_chunk_id].pop(0) + output_tensor = output_tensors[model_chunk_id].pop(0) + output_tensor_grad = output_tensor_grads[model_chunk_id].pop(0) + input_tensor_grad = backward_step( + input_tensor, output_tensor, output_tensor_grad, model_type, config + ) + + # launch grad synchronization (custom grad sync) + # Note: Asynchronous communication tends to slow down compute. + # To reduce idling from mismatched microbatch times, we launch + # asynchronous communication at the same time across the + # pipeline-parallel group. + if config.grad_sync_func is not None: + grad_sync_microbatch_id = microbatch_id - pipeline_parallel_rank + if grad_sync_microbatch_id >= 0 and is_last_microbatch_for_model_chunk( + grad_sync_microbatch_id + ): + grad_sync_chunk_id = get_model_chunk_id(grad_sync_microbatch_id, forward=False) + enable_grad_sync() + config.grad_sync_func[grad_sync_chunk_id](model[grad_sync_chunk_id].parameters()) + synchronized_model_chunks.add(grad_sync_chunk_id) + disable_grad_sync() + + return input_tensor_grad + + # Run warmup forward passes. + parallel_state.set_virtual_pipeline_model_parallel_rank(0) + input_tensors[0].append(p2p_communication.recv_forward(tensor_shape, config)) + + fwd_wait_handles = None + bwd_wait_handles = None + + for k in range(num_warmup_microbatches): + + if fwd_wait_handles is not None: + for req in fwd_wait_handles: + req.wait() + + cur_model_chunk_id = get_model_chunk_id(k, forward=True) + # Decide to checkpoint all layers' activations of the current micro-batch + if max_outstanding_backprops is not None: + checkpoint_activations_microbatch = ( + k % max_outstanding_backprops + >= config.num_microbatches_with_partial_activation_checkpoints + ) + else: + checkpoint_activations_microbatch = None + + current_microbatch = get_microbatch_id_in_model_chunk(k, forward=True) + output_tensor = forward_step_helper( + k, current_microbatch, checkpoint_activations_microbatch + ) + + # Determine if tensor should be received from previous stage. + next_forward_model_chunk_id = get_model_chunk_id(k + 1, forward=True) + recv_prev = True + if parallel_state.is_pipeline_first_stage(ignore_virtual=True): + if next_forward_model_chunk_id == 0: + recv_prev = False + if k == (total_num_microbatches - 1): + recv_prev = False + + # Don't send tensor downstream if on last stage. + if parallel_state.is_pipeline_last_stage(): + output_tensor = None + + # Send and receive tensors as appropriate (send tensors computed + # in this iteration; receive tensors for next iteration). + if not config.overlap_p2p_comm: + if ( + k == (num_warmup_microbatches - 1) + and not forward_only + and not all_warmup_microbatches + ): + input_tensor_grad = None + recv_next = True + if parallel_state.is_pipeline_last_stage(ignore_virtual=True): + recv_next = False + ( + input_tensor, + output_tensor_grad, + ) = p2p_communication.send_forward_backward_recv_forward_backward( + output_tensor, + input_tensor_grad, + recv_prev=recv_prev, + recv_next=recv_next, + tensor_shape=tensor_shape, + config=config, + ) + output_tensor_grads[num_model_chunks - 1].append(output_tensor_grad) + else: + input_tensor = p2p_communication.send_forward_recv_forward( + output_tensor, recv_prev=recv_prev, tensor_shape=tensor_shape, config=config + ) + input_tensors[next_forward_model_chunk_id].append(input_tensor) + else: + input_tensor, fwd_wait_handles = p2p_communication.send_forward_recv_forward( + output_tensor, + recv_prev=recv_prev, + tensor_shape=tensor_shape, + config=config, + overlap_p2p_comm=True, + ) + + if ( + k == (num_warmup_microbatches - 1) + and not forward_only + and not all_warmup_microbatches + ): + input_tensor_grad = None + recv_next = True + if parallel_state.is_pipeline_last_stage(ignore_virtual=True): + recv_next = False + + ( + output_tensor_grad, + bwd_wait_handles, + ) = p2p_communication.send_backward_recv_backward( + input_tensor_grad, + recv_next=recv_next, + tensor_shape=tensor_shape, + config=config, + overlap_p2p_comm=True, + ) + + output_tensor_grads[num_model_chunks - 1].append(output_tensor_grad) + input_tensors[next_forward_model_chunk_id].append(input_tensor) + + deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs) + + # Run 1F1B in steady state. + for k in range(num_microbatches_remaining): + # Forward pass. + forward_k = k + num_warmup_microbatches + + # Decide to checkpoint all layers' activations of the current micro-batch + if max_outstanding_backprops is not None: + checkpoint_activations_microbatch = ( + forward_k % max_outstanding_backprops + >= config.num_microbatches_with_partial_activation_checkpoints + ) + else: + checkpoint_activations_microbatch = None + + cur_model_chunk_id = get_model_chunk_id(forward_k, forward=True) + current_microbatch = get_microbatch_id_in_model_chunk(forward_k, forward=True) + if config.overlap_p2p_comm: + if fwd_wait_handles is not None: + for req in fwd_wait_handles: + req.wait() + + deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs) + + output_tensor = forward_step_helper( + forward_k, current_microbatch, checkpoint_activations_microbatch + ) + + # Determine if current stage has anything to send in either direction, + # otherwise set tensor to None. + forward_model_chunk_id = get_model_chunk_id(forward_k, forward=True) + parallel_state.set_virtual_pipeline_model_parallel_rank(forward_model_chunk_id) + + # Last virtual stage no activation tensor to send + if parallel_state.is_pipeline_last_stage(): + output_tensor = None + + # Determine if peers are sending, and where in data structure to put + # received tensors. + recv_prev = True + if parallel_state.is_pipeline_first_stage(ignore_virtual=True): + # First stage is ahead of last stage by (pipeline_parallel_size - 1). + next_forward_model_chunk_id = get_model_chunk_id( + forward_k - (pipeline_parallel_size - 1), forward=True + ) + if next_forward_model_chunk_id == (num_model_chunks - 1): + recv_prev = False + next_forward_model_chunk_id += 1 + else: + next_forward_model_chunk_id = get_model_chunk_id(forward_k + 1, forward=True) + + # If last iteration, don't receive; we already received one extra + # before the start of the for loop. + if k == (num_microbatches_remaining - 1): + recv_prev = False + + # Send activation tensor to the next stage and receive activation tensor from the + # previous stage + input_tensor, fwd_wait_handles = p2p_communication.send_forward_recv_forward( + output_tensor, + recv_prev=recv_prev, + tensor_shape=tensor_shape, + config=config, + overlap_p2p_comm=True, + ) + # assert fwd_wait_handles is not None + + if bwd_wait_handles is not None: + for req in bwd_wait_handles: + req.wait() + + # Backward pass. + backward_k = k + input_tensor_grad = backward_step_helper(backward_k) + + backward_model_chunk_id = get_model_chunk_id(backward_k, forward=False) + parallel_state.set_virtual_pipeline_model_parallel_rank(backward_model_chunk_id) + + # First virtual stage no activation gradient tensor to send + if parallel_state.is_pipeline_first_stage(): + input_tensor_grad = None + + # Determine if the current virtual stage has an activation gradient tensor to receive + recv_next = True + if parallel_state.is_pipeline_last_stage(ignore_virtual=True): + # Last stage is ahead of first stage by (pipeline_parallel_size - 1). + next_backward_model_chunk_id = get_model_chunk_id( + backward_k - (pipeline_parallel_size - 1), forward=False + ) + if next_backward_model_chunk_id == 0: + recv_next = False + next_backward_model_chunk_id -= 1 + else: + next_backward_model_chunk_id = get_model_chunk_id(backward_k + 1, forward=False) + + output_tensor_grad, bwd_wait_handles = p2p_communication.send_backward_recv_backward( + input_tensor_grad, + recv_next=recv_next, + tensor_shape=tensor_shape, + config=config, + overlap_p2p_comm=True, + ) + + else: # no p2p overlap + output_tensor = forward_step_helper(forward_k, checkpoint_activations_microbatch) + + # Backward pass. + backward_k = k + input_tensor_grad = backward_step_helper(backward_k) + + # Send output_tensor and input_tensor_grad, receive input_tensor + # and output_tensor_grad. + + # Determine if current stage has anything to send in either direction, + # otherwise set tensor to None. + forward_model_chunk_id = get_model_chunk_id(forward_k, forward=True) + parallel_state.set_virtual_pipeline_model_parallel_rank(forward_model_chunk_id) + if parallel_state.is_pipeline_last_stage(): + output_tensor = None + + backward_model_chunk_id = get_model_chunk_id(backward_k, forward=False) + parallel_state.set_virtual_pipeline_model_parallel_rank(backward_model_chunk_id) + if parallel_state.is_pipeline_first_stage(): + input_tensor_grad = None + + # Determine if peers are sending, and where in data structure to put + # received tensors. + recv_prev = True + if parallel_state.is_pipeline_first_stage(ignore_virtual=True): + # First stage is ahead of last stage by (pipeline_parallel_size - 1). + next_forward_model_chunk_id = get_model_chunk_id( + forward_k - (pipeline_parallel_size - 1), forward=True + ) + if next_forward_model_chunk_id == (num_model_chunks - 1): + recv_prev = False + next_forward_model_chunk_id += 1 + else: + next_forward_model_chunk_id = get_model_chunk_id(forward_k + 1, forward=True) + + recv_next = True + if parallel_state.is_pipeline_last_stage(ignore_virtual=True): + # Last stage is ahead of first stage by (pipeline_parallel_size - 1). + next_backward_model_chunk_id = get_model_chunk_id( + backward_k - (pipeline_parallel_size - 1), forward=False + ) + if next_backward_model_chunk_id == 0: + recv_next = False + next_backward_model_chunk_id -= 1 + else: + next_backward_model_chunk_id = get_model_chunk_id(backward_k + 1, forward=False) + + # If last iteration, don't receive; we already received one extra + # before the start of the for loop. + if k == (num_microbatches_remaining - 1): + recv_prev = False + + # Communicate tensors. + ( + input_tensor, + output_tensor_grad, + ) = p2p_communication.send_forward_backward_recv_forward_backward( + output_tensor, + input_tensor_grad, + recv_prev=recv_prev, + recv_next=recv_next, + tensor_shape=tensor_shape, + config=config, + ) + deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs) + + # Put input_tensor and output_tensor_grad in data structures in the + # right location. + if recv_prev: + input_tensors[next_forward_model_chunk_id].append(input_tensor) + if recv_next: + output_tensor_grads[next_backward_model_chunk_id].append(output_tensor_grad) + + deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs) + + # Run cooldown backward passes (flush out pipeline). + if not forward_only: + if config.overlap_p2p_comm and bwd_wait_handles is not None: + for wait_handle in bwd_wait_handles: + wait_handle.wait() + + if all_warmup_microbatches: + output_tensor_grads[num_model_chunks - 1].append( + p2p_communication.recv_backward(tensor_shape, config=config) + ) + for k in range(num_microbatches_remaining, total_num_microbatches): + input_tensor_grad = backward_step_helper(k) + next_backward_model_chunk_id = get_model_chunk_id(k + 1, forward=False) + recv_next = True + if parallel_state.is_pipeline_last_stage(ignore_virtual=True): + if next_backward_model_chunk_id == (num_model_chunks - 1): + recv_next = False + if k == (total_num_microbatches - 1): + recv_next = False + output_tensor_grads[next_backward_model_chunk_id].append( + p2p_communication.send_backward_recv_backward( + input_tensor_grad, recv_next=recv_next, tensor_shape=tensor_shape, config=config + ) + ) + + # Launch any remaining grad reductions. + enable_grad_sync() + if config.grad_sync_func is not None: + for model_chunk_id in range(num_model_chunks): + if model_chunk_id not in synchronized_model_chunks: + config.grad_sync_func[model_chunk_id](model[model_chunk_id].parameters()) + synchronized_model_chunks.add(model_chunk_id) + + if config.timers is not None: + config.timers('forward-backward').stop() + + if config.finalize_model_grads_func is not None and not forward_only: + # Finalize model grads (perform full grad all-reduce / reduce-scatter for + # data parallelism, layernorm all-reduce for sequence parallelism, and + # embedding all-reduce for pipeline parallelism). + config.finalize_model_grads_func(model) + + return forward_data_store + + +def get_tensor_shapes( + *, + rank: int, + model_type: ModelType, + seq_length: int, + micro_batch_size: int, + decoder_seq_length: int, + config, +): + # Determine right tensor sizes (based on position of rank with respect to split + # rank) and model size. + # Send two tensors if model is T5 and rank is in decoder stage: + # first tensor is decoder (pre-transpose), + # second tensor is encoder (post-transpose). + # If model is T5 and rank is at the boundary: + # send one tensor (post-transpose from encoder). + # Otherwise, send one tensor (pre-transpose). + tensor_shapes = [] + + seq_length = seq_length // parallel_state.get_context_parallel_world_size() + if model_type == ModelType.encoder_and_decoder: + decoder_seq_length = decoder_seq_length // parallel_state.get_context_parallel_world_size() + + if config.sequence_parallel: + seq_length = seq_length // parallel_state.get_tensor_model_parallel_world_size() + if model_type == ModelType.encoder_and_decoder: + decoder_seq_length = ( + decoder_seq_length // parallel_state.get_tensor_model_parallel_world_size() + ) + + if model_type == ModelType.encoder_and_decoder: + if parallel_state.is_pipeline_stage_before_split(rank): + tensor_shapes.append((seq_length, micro_batch_size, config.hidden_size)) + else: + tensor_shapes.append((decoder_seq_length, micro_batch_size, config.hidden_size)) + tensor_shapes.append((seq_length, micro_batch_size, config.hidden_size)) + else: + tensor_shapes.append((seq_length, micro_batch_size, config.hidden_size)) + return tensor_shapes + + +def recv_forward(tensor_shapes, config): + input_tensors = [] + for tensor_shape in tensor_shapes: + if tensor_shape is None: + input_tensors.append(None) + else: + input_tensors.append(p2p_communication.recv_forward(tensor_shape, config)) + return input_tensors + + +def recv_backward(tensor_shapes, config): + output_tensor_grads = [] + for tensor_shape in tensor_shapes: + if tensor_shape is None: + output_tensor_grads.append(None) + else: + output_tensor_grads.append(p2p_communication.recv_backward(tensor_shape, config)) + return output_tensor_grads + + +def send_forward(output_tensors, tensor_shapes, config): + if not isinstance(output_tensors, list): + output_tensors = [output_tensors] + for (output_tensor, tensor_shape) in zip(output_tensors, tensor_shapes): + if tensor_shape is None: + continue + p2p_communication.send_forward(output_tensor, config) + + +def send_backward(input_tensor_grads, tensor_shapes, config): + if not isinstance(input_tensor_grads, list): + input_tensor_grads = [input_tensor_grads] + for (input_tensor_grad, tensor_shape) in zip(input_tensor_grads, tensor_shapes): + if tensor_shape is None: + continue + p2p_communication.send_backward(input_tensor_grad, config) + + +def send_forward_recv_backward(output_tensors, tensor_shapes, config): + if not isinstance(output_tensors, list): + output_tensors = [output_tensors] + output_tensor_grads = [] + for (output_tensor, tensor_shape) in zip(output_tensors, tensor_shapes): + if tensor_shape is None: + output_tensor_grads.append(None) + continue + output_tensor_grad = p2p_communication.send_forward_recv_backward( + output_tensor, tensor_shape, config + ) + output_tensor_grads.append(output_tensor_grad) + return output_tensor_grads + + +def send_backward_recv_forward(input_tensor_grads, tensor_shapes, config): + if not isinstance(input_tensor_grads, list): + input_tensor_grads = [input_tensor_grads] + input_tensors = [] + for (input_tensor_grad, tensor_shape) in zip(input_tensor_grads, tensor_shapes): + if tensor_shape is None: + input_tensors.append(None) + continue + input_tensor = p2p_communication.send_backward_recv_forward( + input_tensor_grad, tensor_shape, config + ) + input_tensors.append(input_tensor) + return input_tensors + + +def forward_backward_pipelining_without_interleaving( + *, + forward_step_func, + data_iterator: Union[Iterator, List[Iterator]], + model: Union[torch.nn.Module, List[torch.nn.Module]], + num_microbatches: int, + seq_length: int, + micro_batch_size: int, + decoder_seq_length: int = None, + forward_only: bool = False, + collect_non_loss_data: bool = False, + first_val_step: bool = None, +): + """Run non-interleaved 1F1B schedule, with communication between pipeline + stages. + + Returns dictionary with losses if the last stage, empty dict otherwise.""" + + if isinstance(model, list): + assert ( + len(model) == 1 + ), "non-interleaved pipeline parallelism does not support model chunking" + model = model[0] + if isinstance(data_iterator, list): + assert ( + len(data_iterator) == 1 + ), "non-pipeline-parallel schedule does not support model chunking" + data_iterator = data_iterator[0] + + config = get_model_config(model) + if config.overlap_p2p_comm: + raise ValueError( + "Non-interleaved pipeline parallelism does not support overlapping p2p communication" + ) + + if config.timers is not None: + config.timers('forward-backward', log_level=1).start(barrier=config.barrier_with_L1_time) + + # Disable async grad reductions + no_sync_func = config.no_sync_func + if no_sync_func is None: + no_sync_func = contextlib.nullcontext + no_sync_context = None + + def disable_grad_sync(): + """Disable asynchronous grad reductions""" + nonlocal no_sync_context + if no_sync_context is None: + no_sync_context = no_sync_func() + no_sync_context.__enter__() + + def enable_grad_sync(): + """Enable asynchronous grad reductions""" + nonlocal no_sync_context + if no_sync_context is not None: + no_sync_context.__exit__(None, None, None) + no_sync_context = None + + disable_grad_sync() + + # Compute number of warmup microbatches. + num_warmup_microbatches = ( + parallel_state.get_pipeline_model_parallel_world_size() + - parallel_state.get_pipeline_model_parallel_rank() + - 1 + ) + num_warmup_microbatches = min(num_warmup_microbatches, num_microbatches) + num_microbatches_remaining = num_microbatches - num_warmup_microbatches + + # Checkpoint the activations of partial Transformer layers in a number of micro-batches + # within the maximum outstanding micro-batch backpropagations. + # Micro-batches with the ids less than 'num_microbatches_with_partial_activation_checkpoints' + # checkpoint partial Transformer layers (or skip checkpointing) and + # the rest of micro-batches within a window of micro-batches checkpoint + # all Transformer layers. The window of micro-batches is set by the maximum + # outstanding backpropagations and becomes smaller at later pipeline stages. + # Please refer the appendix C in https://arxiv.org/pdf/2205.05198.pdf + max_outstanding_backprops = None + if config.num_microbatches_with_partial_activation_checkpoints is not None: + max_outstanding_backprops = num_warmup_microbatches + 1 + + model_type = get_model_type(model) + + rank = parallel_state.get_pipeline_model_parallel_rank() + recv_tensor_shapes = get_tensor_shapes( + rank=rank - 1, + model_type=model_type, + seq_length=seq_length, + micro_batch_size=micro_batch_size, + decoder_seq_length=decoder_seq_length, + config=config, + ) + send_tensor_shapes = get_tensor_shapes( + rank=rank, + model_type=model_type, + seq_length=seq_length, + micro_batch_size=micro_batch_size, + decoder_seq_length=decoder_seq_length, + config=config, + ) + + # Input, output tensors only need to be saved when doing backward passes + input_tensors = None + output_tensors = None + if not forward_only: + input_tensors = [] + output_tensors = [] + forward_data_store = [] + + # Run warmup forward passes. + for i in range(num_warmup_microbatches): + # Decide to checkpoint all layers' activations of the current micro-batch + if max_outstanding_backprops is not None: + checkpoint_activations_microbatch = ( + i % max_outstanding_backprops + >= config.num_microbatches_with_partial_activation_checkpoints + ) + else: + checkpoint_activations_microbatch = None + + input_tensor = recv_forward(recv_tensor_shapes, config) + output_tensor = forward_step( + forward_step_func, + data_iterator, + model, + num_microbatches, + input_tensor, + forward_data_store, + config, + collect_non_loss_data, + checkpoint_activations_microbatch, + check_first_val_step(first_val_step, forward_only, i == 0), + current_microbatch=i, + ) + send_forward(output_tensor, send_tensor_shapes, config) + + if not forward_only: + input_tensors.append(input_tensor) + output_tensors.append(output_tensor) + deallocate_output_tensor(output_tensor[0], config.deallocate_pipeline_outputs) + + # Before running 1F1B, need to receive first forward tensor. + # If all microbatches are run in warmup / cooldown phase, then no need to + # receive this tensor here. + if num_microbatches_remaining > 0: + input_tensor = recv_forward(recv_tensor_shapes, config) + + # Run 1F1B in steady state. + for i in range(num_microbatches_remaining): + last_iteration = i == (num_microbatches_remaining - 1) + + # Decide to checkpoint all layers' activations of the current micro-batch + if max_outstanding_backprops is not None: + checkpoint_activations_microbatch = ( + (i + num_warmup_microbatches) % max_outstanding_backprops + ) >= config.num_microbatches_with_partial_activation_checkpoints + else: + checkpoint_activations_microbatch = None + + output_tensor = forward_step( + forward_step_func, + data_iterator, + model, + num_microbatches, + input_tensor, + forward_data_store, + config, + collect_non_loss_data, + checkpoint_activations_microbatch, + check_first_val_step( + first_val_step, forward_only, (i == 0) and (num_warmup_microbatches == 0) + ), + current_microbatch=i + num_warmup_microbatches, + ) + + if forward_only: + send_forward(output_tensor, send_tensor_shapes, config) + + if not last_iteration: + input_tensor = recv_forward(recv_tensor_shapes, config) + + else: + output_tensor_grad = send_forward_recv_backward( + output_tensor, send_tensor_shapes, config + ) + + # Add input_tensor and output_tensor to end of list. + input_tensors.append(input_tensor) + output_tensors.append(output_tensor) + deallocate_output_tensor(output_tensor[0], config.deallocate_pipeline_outputs) + + # Pop input_tensor and output_tensor from the start of the list for + # the backward pass. + input_tensor = input_tensors.pop(0) + output_tensor = output_tensors.pop(0) + + # Enable grad sync for the last microbatch in the batch if the full + # backward pass completes in the 1F1B stage. + if num_warmup_microbatches == 0 and last_iteration: + if config.grad_sync_func is None or rank == 0: + enable_grad_sync() + + input_tensor_grad = backward_step( + input_tensor, output_tensor, output_tensor_grad, model_type, config + ) + + if last_iteration: + input_tensor = None + send_backward(input_tensor_grad, recv_tensor_shapes, config) + else: + input_tensor = send_backward_recv_forward( + input_tensor_grad, recv_tensor_shapes, config + ) + + # Run cooldown backward passes. + if not forward_only: + for i in range(num_warmup_microbatches): + + # Enable async grad reduction in the last backward pass + # Note: If grad sync function is provided, only enable + # async grad reduction in first pipeline stage. Other + # pipeline stages do grad reduction during pipeline + # bubble. + if i == num_warmup_microbatches - 1: + if config.grad_sync_func is None or rank == 0: + enable_grad_sync() + + input_tensor = input_tensors.pop(0) + output_tensor = output_tensors.pop(0) + + output_tensor_grad = recv_backward(send_tensor_shapes, config) + + input_tensor_grad = backward_step( + input_tensor, output_tensor, output_tensor_grad, model_type, config + ) + + send_backward(input_tensor_grad, recv_tensor_shapes, config) + + # Launch any remaining grad reductions. + if no_sync_context is not None: + enable_grad_sync() + if config.grad_sync_func is not None: + config.grad_sync_func(model.parameters()) + + if config.timers is not None: + config.timers('forward-backward').stop() + + if config.finalize_model_grads_func is not None and not forward_only: + # Finalize model grads (perform full grad all-reduce / reduce-scatter for + # data parallelism, layernorm all-reduce for sequence parallelism, and + # embedding all-reduce for pipeline parallelism). + config.finalize_model_grads_func([model]) + + return forward_data_store diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/requirements.txt b/Megatron-LM-core_r0.7.0.beta/megatron/core/requirements.txt new file mode 100644 index 0000000..08ed5ee --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/requirements.txt @@ -0,0 +1 @@ +torch \ No newline at end of file diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/__init__.py new file mode 100644 index 0000000..6b0aa59 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/__init__.py @@ -0,0 +1,70 @@ +from .cross_entropy import vocab_parallel_cross_entropy +from .data import broadcast_data +from .layers import ( + ColumnParallelLinear, + RowParallelLinear, + VocabParallelEmbedding, + copy_tensor_model_parallel_attributes, + linear_with_grad_accumulation_and_async_allreduce, + param_is_not_tensor_parallel_duplicate, + set_defaults_if_not_set_tensor_model_parallel_attributes, + set_tensor_model_parallel_attributes, +) +from .mappings import ( + all_gather_last_dim_from_tensor_parallel_region, + all_to_all, + all_to_all_hp2sp, + all_to_all_sp2hp, + copy_to_tensor_model_parallel_region, + gather_from_sequence_parallel_region, + gather_from_sequence_parallel_region_to_moe, + gather_from_tensor_model_parallel_region, + reduce_scatter_last_dim_to_tensor_parallel_region, + reduce_scatter_to_sequence_parallel_region_from_moe, + scatter_to_sequence_parallel_region, + scatter_to_tensor_model_parallel_region, +) +from .random import ( + checkpoint, + get_cuda_rng_tracker, + get_data_parallel_rng_tracker_name, + model_parallel_cuda_manual_seed, +) +from .utils import ( + gather_split_1d_tensor, + split_tensor_along_last_dim, + split_tensor_into_1d_equal_chunks, +) + +__all__ = [ + # cross_entropy.py + "vocab_parallel_cross_entropy", + # data.py + "broadcast_data", + # layers.py + "ColumnParallelLinear", + "RowParallelLinear", + "VocabParallelEmbedding", + "set_tensor_model_parallel_attributes", + "set_defaults_if_not_set_tensor_model_parallel_attributes", + "copy_tensor_model_parallel_attributes", + "param_is_not_tensor_parallel_duplicate", + "linear_with_grad_accumulation_and_async_allreduce", + # mappings.py + "copy_to_tensor_model_parallel_region", + "gather_from_tensor_model_parallel_region", + "gather_from_sequence_parallel_region", + # "reduce_from_tensor_model_parallel_region", + "scatter_to_tensor_model_parallel_region", + "scatter_to_sequence_parallel_region", + # random.py + "checkpoint", + "get_cuda_rng_tracker", + "model_parallel_cuda_manual_seed", + # utils.py + "split_tensor_along_last_dim", + "split_tensor_into_1d_equal_chunks", + "gather_split_1d_tensor", + "gather_from_sequence_parallel_region_to_moe", + "reduce_scatter_to_sequence_parallel_region_from_moe", +] diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/cross_entropy.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/cross_entropy.py new file mode 100644 index 0000000..1614dbb --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/cross_entropy.py @@ -0,0 +1,142 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import torch + +from megatron.core.parallel_state import ( + get_tensor_model_parallel_group, + get_tensor_model_parallel_rank, + get_tensor_model_parallel_world_size, +) + +from .utils import VocabUtility + + +class _VocabParallelCrossEntropy(torch.autograd.Function): + @staticmethod + def forward(ctx, vocab_parallel_logits, target, label_smoothing=0.0): + + # Maximum value along vocab dimension across all GPUs. + logits_max = torch.max(vocab_parallel_logits, dim=-1)[0] + torch.distributed.all_reduce( + logits_max, op=torch.distributed.ReduceOp.MAX, group=get_tensor_model_parallel_group() + ) + # Subtract the maximum value. + vocab_parallel_logits = vocab_parallel_logits - logits_max.unsqueeze(dim=-1) + + # Get the partition's vocab indecies + get_vocab_range = VocabUtility.vocab_range_from_per_partition_vocab_size + partition_vocab_size = vocab_parallel_logits.size()[-1] + rank = get_tensor_model_parallel_rank() + world_size = get_tensor_model_parallel_world_size() + vocab_start_index, vocab_end_index = get_vocab_range(partition_vocab_size, rank, world_size) + + # Create a mask of valid vocab ids (1 means it needs to be masked). + target_mask = (target < vocab_start_index) | (target >= vocab_end_index) + masked_target = target.clone() - vocab_start_index + masked_target[target_mask] = 0 + + # Get predicted-logits = logits[target]. + # For Simplicity, we convert logits to a 2-D tensor with size + # [*, partition-vocab-size] and target to a 1-D tensor of size [*]. + logits_2d = vocab_parallel_logits.view(-1, partition_vocab_size) + masked_target_1d = masked_target.view(-1) + arange_1d = torch.arange(start=0, end=logits_2d.size()[0], device=logits_2d.device) + predicted_logits_1d = logits_2d[arange_1d, masked_target_1d] + predicted_logits_1d = predicted_logits_1d.clone().contiguous() + predicted_logits = predicted_logits_1d.view_as(target) + predicted_logits[target_mask] = 0.0 + # All reduce is needed to get the chunks from other GPUs. + torch.distributed.all_reduce( + predicted_logits, + op=torch.distributed.ReduceOp.SUM, + group=get_tensor_model_parallel_group(), + ) + + # Sum of exponential of logits along vocab dimension across all GPUs. + exp_logits = vocab_parallel_logits + torch.exp(vocab_parallel_logits, out=exp_logits) + sum_exp_logits = exp_logits.sum(dim=-1) + torch.distributed.all_reduce( + sum_exp_logits, + op=torch.distributed.ReduceOp.SUM, + group=get_tensor_model_parallel_group(), + ) + + # Loss = log(sum(exp(logits))) - predicted-logit. + loss = torch.log(sum_exp_logits) - predicted_logits + + # Normalize and optionally smooth logits + exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1)) + + vocab_size = exp_logits.size(-1) + if label_smoothing > 0: + """ + We'd like to assign 1 / (K - 1) probability mass to every index that is not the ground truth. + = (1 - alpha) * y_gt + alpha * mean(y_{i for i != gt}) + = (1 - alpha) * y_gt + (alpha / (K - 1)) * \sum_{i != gt} y_i + = ((K - 1) * (1 - alpha) / (K - 1)) * y_gt + (alpha / (K - 1)) * \sum_{i != gt} y_i + = (K * (1 - alpha) - 1) / (K - 1)) * y_gt + (alpha / (K - 1)) * \sum_{i} y_i + = (1 - (alpha * K) / (K - 1)) * y_gt + ( (alpha * K) / (K - 1) ) * \sum_{i} y_i / K + From: https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/losses/smoothed_cross_entropy.py + """ + assert 1.0 > label_smoothing > 0.0 + smoothing = label_smoothing * vocab_size / (vocab_size - 1) + + # Exp logits at this point are normalized probabilities. So we can just take the log to get log-probs. + log_probs = torch.log(exp_logits) + mean_log_probs = log_probs.mean(dim=-1) + loss = (1.0 - smoothing) * loss - smoothing * mean_log_probs + + ctx.label_smoothing, ctx.vocab_size = label_smoothing, vocab_size + + # Store softmax, target-mask and masked-target for backward pass. + ctx.save_for_backward(exp_logits, target_mask, masked_target_1d) + + return loss + + @staticmethod + def backward(ctx, grad_output): + + # Retreive tensors from the forward path. + softmax, target_mask, masked_target_1d = ctx.saved_tensors + label_smoothing, vocab_size = ctx.label_smoothing, ctx.vocab_size + + # All the inputs have softmax as thier gradient. + grad_input = softmax + # For simplicity, work with the 2D gradient. + partition_vocab_size = softmax.size()[-1] + grad_2d = grad_input.view(-1, partition_vocab_size) + + # Add the gradient from matching classes. + arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=grad_2d.device) + + softmax_update = 1.0 - target_mask.view(-1).float() + + if label_smoothing > 0: + smoothing = label_smoothing * vocab_size / (vocab_size - 1) + grad_2d[arange_1d, masked_target_1d] -= (1.0 - smoothing) * softmax_update + average_grad = 1 / vocab_size + grad_2d[arange_1d, :] -= smoothing * average_grad + else: + grad_2d[arange_1d, masked_target_1d] -= softmax_update + + # Finally elementwise multiplication with the output gradients. + grad_input.mul_(grad_output.unsqueeze(dim=-1)) + + return grad_input, None, None + + +def vocab_parallel_cross_entropy(vocab_parallel_logits, target, label_smoothing=0.0): + """ + Performs cross entropy loss when logits are split across tensor parallel ranks + + Args: + vocab_parallel_logits: logits split across tensor parallel ranks + dimension is [sequence_length, batch_size, hidden_size] + + target: correct vocab ids of dimseion [sequence_length, micro_batch_size] + + lobal_smoothing: smoothing factor, must be in range [0.0, 1.0) + default is no smoothing (=0.0) + """ + return _VocabParallelCrossEntropy.apply(vocab_parallel_logits, target, label_smoothing) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/data.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/data.py new file mode 100644 index 0000000..01dd90d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/data.py @@ -0,0 +1,104 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import torch + +from megatron.core.parallel_state import ( + get_tensor_model_parallel_group, + get_tensor_model_parallel_rank, + get_tensor_model_parallel_src_rank, +) + +_MAX_DATA_DIM = 5 + + +def _check_data_types(keys, data, target_dtype): + """Check that all the keys have the same target data type.""" + for key in keys: + assert data[key].dtype == target_dtype, ( + '{} has data type {} which ' + 'is different than {}'.format(key, data[key].dtype, target_dtype) + ) + + +def _build_key_size_numel_dictionaries(keys, data): + """Build the size on rank 0 and broadcast.""" + max_dim = _MAX_DATA_DIM + sizes = [0 for _ in range(max_dim) for _ in keys] + + # Pack the sizes on rank zero. + if get_tensor_model_parallel_rank() == 0: + offset = 0 + for key in keys: + assert data[key].dim() < max_dim, 'you should increase MAX_DATA_DIM' + size = data[key].size() + for i, s in enumerate(size): + sizes[i + offset] = s + offset += max_dim + + # Move to GPU and broadcast. + sizes_cuda = torch.tensor(sizes, dtype=torch.long, device='cuda') + torch.distributed.broadcast( + sizes_cuda, get_tensor_model_parallel_src_rank(), group=get_tensor_model_parallel_group() + ) + + # Move back to cpu and unpack. + sizes_cpu = sizes_cuda.cpu() + key_size = {} + key_numel = {} + total_numel = 0 + offset = 0 + for key in keys: + i = 0 + size = [] + numel = 1 + while sizes_cpu[offset + i] > 0: + this_size = sizes_cpu[offset + i] + size.append(this_size) + numel *= this_size + i += 1 + key_size[key] = size + key_numel[key] = numel + total_numel += numel + offset += max_dim + + return key_size, key_numel, total_numel + + +def broadcast_data(keys, data, datatype): + """Broadcast data from rank zero of each model parallel group to the + members of the same model parallel group. + + Args: + keys: list of keys in the data disctionary to be broadcasted + data: data dictionary of string keys and cpu tensor values. + datatype: torch data type of all tensors in data associated + with keys. + """ + # Build (key, size) and (key, number of elements) dictionaries along + # with the total number of elements on all ranks. + key_size, key_numel, total_numel = _build_key_size_numel_dictionaries(keys, data) + + # Pack on rank zero. + if get_tensor_model_parallel_rank() == 0: + # Check that all keys have the same data type. + _check_data_types(keys, data, datatype) + # Flatten the data associated with the keys + flatten_data = torch.cat([data[key].contiguous().view(-1) for key in keys], dim=0).cuda() + else: + flatten_data = torch.empty(total_numel, device=torch.cuda.current_device(), dtype=datatype) + + # Broadcast + torch.distributed.broadcast( + flatten_data, get_tensor_model_parallel_src_rank(), group=get_tensor_model_parallel_group() + ) + + # Unpack + output = {} + offset = 0 + for key in keys: + size = key_size[key] + numel = key_numel[key] + output[key] = flatten_data.narrow(0, offset, numel).view(size) + offset += numel + + return output diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/layers.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/layers.py new file mode 100644 index 0000000..177efc3 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/layers.py @@ -0,0 +1,1042 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +# Parts of the code here are adapted from PyTorch +# repo: https://github.com/pytorch/pytorch + +import io +import math +import os +import warnings +from typing import Any, Callable, List, Optional, Tuple + +import torch +import torch.nn.functional as F +import torch.nn.init as init +from torch.cuda.amp import custom_bwd, custom_fwd +from torch.nn.parameter import Parameter + +from megatron.core.model_parallel_config import ModelParallelConfig +from megatron.core.parallel_state import ( + get_global_memory_buffer, + get_tensor_model_parallel_group, + get_tensor_model_parallel_rank, + get_tensor_model_parallel_world_size, +) + +from ..dist_checkpointing.mapping import ShardedStateDict +from ..transformer.utils import make_sharded_tensors_for_checkpoint +from ..utils import make_tp_sharded_tensor_for_checkpoint, prepare_input_tensors_for_wgrad_compute +from .mappings import ( + copy_to_tensor_model_parallel_region, + gather_from_sequence_parallel_region, + gather_from_tensor_model_parallel_region, + reduce_from_tensor_model_parallel_region, + reduce_scatter_to_sequence_parallel_region, + scatter_to_tensor_model_parallel_region, +) +from .random import get_cuda_rng_tracker, get_expert_parallel_rng_tracker_name +from .utils import VocabUtility, divide, split_tensor_along_last_dim + +_grad_accum_fusion_available = True +try: + import fused_weight_gradient_mlp_cuda +except ImportError: + _grad_accum_fusion_available = False + +_MODEL_PARALLEL_ATTRIBUTE_DEFAULTS = { + 'tensor_model_parallel': False, + 'partition_dim': -1, + 'partition_stride': 1, +} + + +def param_is_not_tensor_parallel_duplicate(param): + return (hasattr(param, 'tensor_model_parallel') and param.tensor_model_parallel) or ( + get_tensor_model_parallel_rank() == 0 + ) + + +def set_tensor_model_parallel_attributes(tensor, is_parallel, dim, stride): + # Make sure the attributes are not set. + for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS: + assert not hasattr(tensor, attribute) + # Set the attributes. + setattr(tensor, 'tensor_model_parallel', is_parallel) + setattr(tensor, 'partition_dim', dim) + setattr(tensor, 'partition_stride', stride) + + +def set_defaults_if_not_set_tensor_model_parallel_attributes(tensor): + def maybe_set(attribute, value): + if not hasattr(tensor, attribute): + setattr(tensor, attribute, value) + + for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS: + maybe_set(attribute, _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS[attribute]) + + +def copy_tensor_model_parallel_attributes(destination_tensor, source_tensor): + def maybe_copy(attribute): + if hasattr(source_tensor, attribute): + setattr(destination_tensor, attribute, getattr(source_tensor, attribute)) + + for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS: + maybe_copy(attribute) + + +def _initialize_affine_weight_gpu( + weight, init_method, partition_dim, stride=1, expert_parallel=False +): + """Initialize affine weight for model parallel on GPU.""" + + set_tensor_model_parallel_attributes( + tensor=weight, is_parallel=True, dim=partition_dim, stride=stride + ) + + if not expert_parallel: + with get_cuda_rng_tracker().fork(): + init_method(weight) + else: + with get_cuda_rng_tracker().fork(get_expert_parallel_rng_tracker_name()): + init_method(weight) + + +def _initialize_affine_weight_cpu( + weight, + output_size, + input_size, + per_partition_size, + partition_dim, + init_method, + stride=1, + return_master_weight=False, + *, + params_dtype=torch.float32, +): + """Initialize affine weight for model parallel. + + Build the master weight on all processes and scatter + the relevant chunk.""" + + set_tensor_model_parallel_attributes( + tensor=weight, is_parallel=True, dim=partition_dim, stride=stride + ) + + # Initialize master weight + master_weight = torch.empty(output_size, input_size, dtype=torch.float, requires_grad=False) + init_method(master_weight) + master_weight = master_weight.to(dtype=params_dtype) + + # Split and copy + per_partition_per_stride_size = divide(per_partition_size, stride) + weight_list = torch.split(master_weight, per_partition_per_stride_size, dim=partition_dim) + rank = get_tensor_model_parallel_rank() + world_size = get_tensor_model_parallel_world_size() + my_weight_list = weight_list[rank::world_size] + + with torch.no_grad(): + # all tensors must live on the same device + cpu_weight = torch.cat(my_weight_list, dim=partition_dim).to_dense() + weight.data.copy_(cpu_weight) + if return_master_weight: + return master_weight + return None + + +class VocabParallelEmbedding(torch.nn.Module): + """Embedding parallelized in the vocabulary dimension. + + This is mainly adapted from torch.nn.Embedding and all the default + values are kept. + + Args: + num_embeddings: vocabulary size. + embedding_dim: size of hidden state. + + Keyword Args: + config: A megatron.core.ModelParallelConfig object + """ + + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + *, + init_method: Callable, + config: ModelParallelConfig, + ): + super(VocabParallelEmbedding, self).__init__() + # Keep the input dimensions. + self.num_embeddings = num_embeddings + self.embedding_dim = embedding_dim + self.tensor_model_parallel_size = get_tensor_model_parallel_world_size() + # Divide the weight matrix along the vocaburaly dimension. + ( + self.vocab_start_index, + self.vocab_end_index, + ) = VocabUtility.vocab_range_from_global_vocab_size( + self.num_embeddings, get_tensor_model_parallel_rank(), self.tensor_model_parallel_size + ) + self.num_embeddings_per_partition = self.vocab_end_index - self.vocab_start_index + + # Allocate weights and initialize. + if config.use_cpu_initialization: + self.weight = Parameter( + torch.empty( + self.num_embeddings_per_partition, self.embedding_dim, dtype=config.params_dtype + ) + ) + if config.perform_initialization: + _initialize_affine_weight_cpu( + self.weight, + self.num_embeddings, + self.embedding_dim, + self.num_embeddings_per_partition, + 0, + init_method, + params_dtype=config.params_dtype, + ) + else: + self.weight = Parameter( + torch.empty( + self.num_embeddings_per_partition, + self.embedding_dim, + device=torch.cuda.current_device(), + dtype=config.params_dtype, + ) + ) + if config.perform_initialization: + _initialize_affine_weight_gpu(self.weight, init_method, partition_dim=0, stride=1) + + def forward(self, input_): + if self.tensor_model_parallel_size > 1: + # Build the mask. + input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index) + # Mask the input. + masked_input = input_.clone() - self.vocab_start_index + masked_input[input_mask] = 0 + else: + masked_input = input_ + # Get the embeddings. + output_parallel = self.weight[masked_input] + # Mask the output embedding. + if self.tensor_model_parallel_size > 1: + output_parallel[input_mask, :] = 0.0 + # Reduce across all the model parallel GPUs. + output = reduce_from_tensor_model_parallel_region(output_parallel) + return output + + def sharded_state_dict( + self, + prefix: str = '', + sharded_offsets: Tuple[Tuple[int, int, int]] = (), + metadata: Optional[dict] = None, + ) -> ShardedStateDict: + """ Non-default implementation for embeddings due to `allow_shape_mismatch` param """ + state_dict = self.state_dict(prefix='', keep_vars=True) + + weight_prefix = f'{prefix}weight' + return { + weight_prefix: make_tp_sharded_tensor_for_checkpoint( + tensor=state_dict['weight'], + key=weight_prefix, + allow_shape_mismatch=True, + prepend_offsets=sharded_offsets, + ) + } + + +class LinearWithFrozenWeight(torch.autograd.Function): + """Linear operator that does not calculate gradient for weight. + This op and LinearWithGradAccumulationAndAsyncCommunication performs + mathematically-identical forward and DGRAD. + + Conceptually this op is the same as torch.nn.functional.linear with + weight.requires_grad==False, but in experiments they are not identical + mathematically. """ + + @staticmethod + @custom_fwd + def forward( + ctx, input, weight, bias, + ): + ctx.save_for_backward(weight) + output = torch.matmul(input, weight.t()) + if bias is not None: + output = output + bias + return output + + @staticmethod + @custom_bwd + def backward(ctx, grad_output): + (weight,) = ctx.saved_tensors + grad_input = grad_output.matmul(weight) + return grad_input, None, None + + +def linear_with_frozen_weight( + input: torch.Tensor, + weight: torch.Tensor, + bias: Optional[torch.Tensor], + gradient_accumulation_fusion: bool, + async_grad_allreduce: bool, + sequence_parallel: bool, + grad_output_buffer: Optional[List[torch.Tensor]] = None, +) -> torch.Tensor: + """Linear layer execution with weight.requires_grad == False. + + This function handles linear layers with weight frozen (untrainable). + In the forward, it only saves weight and does not save input activations. + In the backward, it does not perform weight gradient calculation, or + weight gradient allreduce. + + Args: + + input (torch.Tensor required): input like torch.nn.functional.linear + + weight (torch.Tensor required): weight like torch.nn.functional.linear + + bias (torch.Tensor optional): bias like torch.nn.functional.linear + + gradient_accumulation_fusion (bool required): dummy argument, used to + keep the API unified between all forward implementation functions. + + async_grad_allreduce (bool required): dummy argument, used to + keep the API unified between all forward implementation functions. + + sequence_parallel (bool required): Indicates that sequence + parallelism is used and thus in the forward pass the input is + all gathered, and the backward pass the input gradients are + reduce scattered. + + grad_output_buffer (List[torch.Tensor] optional): dummy argument, used to + keep the API unified between all forward implementation functions. + + """ + + assert grad_output_buffer is None, ( + "grad_output_buffer kwarg is only supported with " + "linear_with_grad_accumulation_and_async_allreduce" + ) + + if sequence_parallel: + input = gather_from_sequence_parallel_region(input, tensor_parallel_output_grad=True) + else: + input = input + + args = [ + input, + weight, + bias, + ] + + return LinearWithFrozenWeight.apply(*args) + + +class LinearWithGradAccumulationAndAsyncCommunication(torch.autograd.Function): + """See linear_with_grad_accumulation_and_async_allreduce""" + + @staticmethod + @custom_fwd + def forward( + ctx, + input, + weight, + bias, + gradient_accumulation_fusion, + async_grad_allreduce, + sequence_parallel, + grad_output_buffer, + ): + ctx.save_for_backward(input, weight) + ctx.use_bias = bias is not None + ctx.gradient_accumulation_fusion = gradient_accumulation_fusion + ctx.async_grad_allreduce = async_grad_allreduce + ctx.sequence_parallel = sequence_parallel + ctx.grad_output_buffer = grad_output_buffer + + if sequence_parallel: + world_size = get_tensor_model_parallel_world_size() + dim_size = list(input.size()) + dim_size[0] = dim_size[0] * world_size + + all_gather_buffer = get_global_memory_buffer().get_tensor(dim_size, input.dtype, "mpu") + torch.distributed._all_gather_base( + all_gather_buffer, input, group=get_tensor_model_parallel_group() + ) + total_input = all_gather_buffer + else: + total_input = input + + output = torch.matmul(total_input, weight.t()) + if bias is not None: + output = output + bias + return output + + @staticmethod + @custom_bwd + def backward(ctx, grad_output): + input, weight = ctx.saved_tensors + use_bias = ctx.use_bias + grad_output_buffer = ctx.grad_output_buffer + + wgrad_compute = True + if grad_output_buffer is not None: + grad_output_buffer.append(grad_output) + wgrad_compute = False + + if wgrad_compute: + if ctx.sequence_parallel: + world_size = get_tensor_model_parallel_world_size() + dim_size = list(input.size()) + dim_size[0] = dim_size[0] * world_size + + all_gather_buffer = get_global_memory_buffer().get_tensor( + dim_size, input.dtype, "mpu" + ) + handle = torch.distributed._all_gather_base( + all_gather_buffer, input, group=get_tensor_model_parallel_group(), async_op=True + ) + + # Here we rely on CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that the + # gather is scheduled before the input gradient computation + total_input = all_gather_buffer + else: + total_input = input + grad_input = grad_output.matmul(weight) + + if ctx.sequence_parallel and wgrad_compute: + handle.wait() + + if wgrad_compute: + grad_output, total_input = prepare_input_tensors_for_wgrad_compute( + grad_output, total_input + ) + + if ctx.async_grad_allreduce: + # Asynchronous all-reduce + handle = torch.distributed.all_reduce( + grad_input, group=get_tensor_model_parallel_group(), async_op=True + ) + # Here we rely on CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that the + # all-reduce is scheduled before the weight gradient computation + + if ctx.sequence_parallel: + assert not ctx.async_grad_allreduce + dim_size = list(input.size()) + sub_grad_input = torch.empty( + dim_size, dtype=input.dtype, device=torch.cuda.current_device(), requires_grad=False + ) + # reduce_scatter + handle = torch.distributed._reduce_scatter_base( + sub_grad_input, grad_input, group=get_tensor_model_parallel_group(), async_op=True + ) + # Here we rely on CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that the + # reduce scatter is scheduled before the weight gradient computation + + if ctx.gradient_accumulation_fusion: + if wgrad_compute: + if weight.main_grad.dtype == torch.float32: + fused_weight_gradient_mlp_cuda.wgrad_gemm_accum_fp32( + total_input, grad_output, weight.main_grad + ) + elif weight.main_grad.dtype in (torch.float16, torch.bfloat16): + fused_weight_gradient_mlp_cuda.wgrad_gemm_accum_fp16( + total_input, grad_output, weight.main_grad + ) + else: + raise RuntimeError("Unsupported gradient type for gradient accumulation fusion") + + if hasattr(weight, 'grad_added_to_main_grad'): + # When overlap_grad_reduce is True, need to ensure that backward hooks + # are all run on the main backprop thread to prevent deadlocks. Setup + # dummy grad_weight tensor to prevent backward hooks from being run + # in a background thread. + if getattr(weight, 'zero_out_wgrad', False): + grad_weight = torch.zeros( + weight.main_grad.shape, + dtype=input.dtype, + device=torch.cuda.current_device(), + requires_grad=False, + ) + else: + grad_weight = torch.empty( + weight.main_grad.shape, + dtype=input.dtype, + device=torch.cuda.current_device(), + requires_grad=False, + ) + weight.grad_added_to_main_grad = True + else: + grad_weight = None + else: + grad_weight = grad_output.t().matmul(total_input) + grad_bias = grad_output.sum(dim=0) if use_bias else None + + if ctx.sequence_parallel: + handle.wait() + # Need to return None's as gradient has to flow for all the input arguments + # provided during forward + return sub_grad_input, grad_weight, grad_bias, None, None, None, None + + if ctx.async_grad_allreduce: + handle.wait() + + return grad_input, grad_weight, grad_bias, None, None, None, None + + +def linear_with_grad_accumulation_and_async_allreduce( + input: torch.Tensor, + weight: torch.Tensor, + bias: Optional[torch.Tensor], + gradient_accumulation_fusion: bool, + async_grad_allreduce: bool, + sequence_parallel: bool, + grad_output_buffer: Optional[List[torch.Tensor]] = None, +) -> torch.Tensor: + """Linear layer execution with asynchronous communication and + gradient accumulation fusion in backprop. + + This has the option to accumulate the result of backprop + calculation into an existing gradient buffer, preventing the need + to do an additional addition kernel after the gradient + calculation. + + Additionally, the tensor parallel all reduce of the input + gradients can be done asynchronously with the calculation of + the weight gradients. + + In the case of sequence parallelism, the reduce scatter of the + input gradients is done asynchronously with the calcluation of the + weight gradients. + + Use of this module requires that the environment variable + CUDA_DEVICE_MAX_CONNECTIONS=1. There are a few collective + operations, noted in the code, that should be scheduled before + compute kernels to overlap the communication with the computation, + which is necessary for a speedup but not for correctness so that + ordering isn't imposed by the scheduler. Setting + CUDA_DEVICE_MAX_CONNECTIONS=1 forces the kernels to be scheduled + in the order they are called. + + Args: + + input (torch.Tensor required): input like torch.nn.functional.linear + + weight (torch.Tensor required): weight like torch.nn.functional.linear + + bias (torch.Tensor optional): bias like torch.nn.functional.linear + + gradient_accumulation_fusion (bool required): Perform the gradient + accumulation fusion, requires the custom CUDA extension + fused_weight_gradient_mlp_cuda module. To use + gradient_accumulation_fusion you must install APEX with + --cpp_ext and --cuda_ext. For example: "pip install + --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext .\" + " Note that the extension requires CUDA>=11. Otherwise, you + must turn off gradient accumulation fusion." + + async_grad_allreduce (bool required): Do the allreduce of input + gradients asyncronously with the computation of weight + gradients. If sequence_parallel is True, this must be + False, as no all reduce is performed. + + sequence_parallel (bool required): Indicates that sequence + parallelism is used and thus in the forward pass the input is + all gathered, and the backward pass the input gradients are + reduce scattered. + + grad_output_buffer (List[torch.Tensor] optional): Buffer used to save + output gradients when embedding table wgrad compute is deferred. + Defaults to None. + """ + args = [ + input, + weight, + bias, + gradient_accumulation_fusion, + async_grad_allreduce, + sequence_parallel, + grad_output_buffer, + ] + + if not linear_with_grad_accumulation_and_async_allreduce.warned: + if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1": + if sequence_parallel: + warnings.warn( + "When using sequence parallelism it is recommended to set the " + "environment variable CUDA_DEVICE_MAX_CONNECTIONS to 1 for " + "maximum speedup" + ) + linear_with_grad_accumulation_and_async_allreduce.warned = True + + if async_grad_allreduce: + warnings.warn( + "When using async grad allreduce it is recommended to set the " + "environment variable CUDA_DEVICE_MAX_CONNECTIONS to 1 for " + "maximum speedup" + ) + linear_with_grad_accumulation_and_async_allreduce.warned = True + + return LinearWithGradAccumulationAndAsyncCommunication.apply(*args) + + +linear_with_grad_accumulation_and_async_allreduce.warned = False + + +class ColumnParallelLinear(torch.nn.Module): + """Linear layer with column parallelism. + + The linear layer is defined as Y = XA + b. A is parallelized along + its second dimension as A = [A_1, ..., A_p]. + + Args: + input_size: first dimension of matrix A. + output_size: second dimension of matrix A. + bias: If true, add bias + gather_output: If true, call all-gather on output and make Y available to all GPUs, otherwise, every GPU will have its output which is Y_i = XA_i + init_method: method to initialize weights. Note that bias is always set to zero. + stride: For the strided linear layers. + keep_master_weight_for_test: This was added for testing and should be set to False. It returns the master weights used for initialization. + skip_bias_add: If True, do not add the bias term, instead return it to be added by the caller. This enables performance optimations where bias can be fused with other elementwise operations. + skip_weight_param_allocation: If True, weight parameter is not allocated and must be passed as a keyword argument `weight` during the forward pass. Note that this does not affect bias, which will be allocated if bias is True. Defaults to False. + embedding_activation_buffer: This buffer holds the input activations of the final embedding linear layer on the last pipeline stage when defer_embedding_wgrad_compute is enabled. + grad_output_buffer: This buffer holds the gradient outputs of the final embedding linear layer on the last pipeline stage when defer_embedding_wgrad_compute is enabled. + is_expert: If True, the layer is treated as an MoE expert layer. + config: ModelParallelConfig object + tp_comm_buffer_name: Communication buffer name is not used in non-Transformer-Engine modules. + disable_grad_reduce: If True, reduction of output gradients across tensor-parallel ranks will be disabled. Defaults to False. This feature is used by Lora Adapter in Nemo to delay and fuse reduction along with other gradients for performance optimization. + """ + + def __init__( + self, + input_size, + output_size, + *, + config: ModelParallelConfig, + init_method: Callable, + bias=True, + gather_output=False, + stride=1, + keep_master_weight_for_test=False, + skip_bias_add=False, + skip_weight_param_allocation: bool = False, + embedding_activation_buffer: Optional[List[torch.Tensor]] = None, + grad_output_buffer: Optional[List[torch.Tensor]] = None, + is_expert: bool = False, + tp_comm_buffer_name: str = None, # Not used + disable_grad_reduce: bool = False, + ): + super(ColumnParallelLinear, self).__init__() + + # Keep input parameters + self.input_size = input_size + self.output_size = output_size + self.gather_output = gather_output + # Divide the weight matrix along the last dimension. + world_size = get_tensor_model_parallel_world_size() + self.output_size_per_partition = divide(output_size, world_size) + self.skip_bias_add = skip_bias_add + self.is_expert = is_expert + self.expert_parallel = config.expert_model_parallel_size > 1 + self.embedding_activation_buffer = embedding_activation_buffer + self.grad_output_buffer = grad_output_buffer + self.config = config + self.disable_grad_reduce = disable_grad_reduce + + # Parameters. + # Note: torch.nn.functional.linear performs XA^T + b and as a result + # we allocate the transpose. + # Initialize weight. + if not skip_weight_param_allocation: + if config.use_cpu_initialization: + self.weight = Parameter( + torch.empty( + self.output_size_per_partition, self.input_size, dtype=config.params_dtype + ) + ) + if config.perform_initialization: + self.master_weight = _initialize_affine_weight_cpu( + self.weight, + self.output_size, + self.input_size, + self.output_size_per_partition, + 0, + init_method, + stride=stride, + return_master_weight=keep_master_weight_for_test, + ) + else: + self.weight = Parameter( + torch.empty( + self.output_size_per_partition, + self.input_size, + device=torch.cuda.current_device(), + dtype=config.params_dtype, + ) + ) + if config.perform_initialization: + _initialize_affine_weight_gpu( + self.weight, + init_method, + partition_dim=0, + stride=stride, + expert_parallel=(self.is_expert and self.expert_parallel), + ) + + setattr(self.weight, 'allreduce', not (self.is_expert and self.expert_parallel)) + else: + self.weight = None + + if bias: + if config.use_cpu_initialization: + self.bias = Parameter( + torch.empty(self.output_size_per_partition, dtype=config.params_dtype) + ) + else: + self.bias = Parameter( + torch.empty( + self.output_size_per_partition, + device=torch.cuda.current_device(), + dtype=config.params_dtype, + ) + ) + set_tensor_model_parallel_attributes(self.bias, True, 0, stride) + if config.perform_initialization: + # Always initialize bias to zero. + with torch.no_grad(): + self.bias.zero_() + setattr(self.bias, 'allreduce', not (self.is_expert and self.expert_parallel)) + else: + self.register_parameter('bias', None) + + self.async_tensor_model_parallel_allreduce = ( + config.async_tensor_model_parallel_allreduce and world_size > 1 + ) + + self.sequence_parallel = config.sequence_parallel + if self.sequence_parallel and world_size <= 1: + warnings.warn( + f"`sequence_parallel` is set to `True`, but tensor model parallel size is {world_size}. " + f"Disabling sequence parallel." + ) + self.sequence_parallel = False + + if config.gradient_accumulation_fusion and not _grad_accum_fusion_available: + raise RuntimeError( + "ColumnParallelLinear was called with gradient_accumulation_fusion set " + "to True but the custom CUDA extension fused_weight_gradient_mlp_cuda " + "module is not found. To use gradient_accumulation_fusion you must " + "install APEX with --cpp_ext and --cuda_ext. For example: " + "pip install --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext .\" " + "Note that the extension requires CUDA>=11. Otherwise, you must turn off " + "gradient accumulation fusion." + ) + self.gradient_accumulation_fusion = config.gradient_accumulation_fusion + + if self.async_tensor_model_parallel_allreduce and self.sequence_parallel: + raise RuntimeError( + "`async_tensor_model_parallel_allreduce` and `sequence_parallel` " + "cannot be enabled at the same time." + ) + + self._forward_impl = linear_with_grad_accumulation_and_async_allreduce + self.explicit_expert_comm = self.is_expert and ( + self.sequence_parallel or self.expert_parallel + ) + + # Hook adding a default empty _extra_state for state dict + self._register_load_state_dict_pre_hook( + lambda state_dict, prefix, *args, **kwargs: state_dict.setdefault( + f'{prefix}_extra_state' + ) + ) + + def forward(self, input_: torch.Tensor, weight: Optional[torch.Tensor] = None): + """Forward of ColumnParallelLinear + + Args: + input_: 3D tensor whose order of dimension is [sequence, batch, hidden] + + weight (optional): weight tensor to use, compulsory when + skip_weight_param_allocation is True. + + Returns: + - output + - bias + + """ + if weight is None: + if self.weight is None: + raise RuntimeError( + "weight was not supplied to ColumnParallelLinear forward pass " + "and skip_weight_param_allocation is True." + ) + weight = self.weight + else: + # Check the weight passed in is the correct shape + expected_shape = (self.output_size_per_partition, self.input_size) + if weight.shape != expected_shape: + raise RuntimeError( + f"supplied weight's shape is {tuple(weight.shape)}, " + f"not {expected_shape} as expected" + ) + + if self.config._cpu_offloading_context is not None: + if self.config._cpu_offloading_context.inside_context == True: + assert ( + self.config.cpu_offloading == False + ), "CPU Offloading cannot be enabled while using non-TE modules" + + bias = self.bias if not self.skip_bias_add else None + + if ( + self.async_tensor_model_parallel_allreduce + or self.sequence_parallel + or self.explicit_expert_comm + or self.disable_grad_reduce + ): + input_parallel = input_ + else: + input_parallel = copy_to_tensor_model_parallel_region(input_) + + if self.config.defer_embedding_wgrad_compute: + self.embedding_activation_buffer.append(input_parallel) + + # Matrix multiply. + if not weight.requires_grad: + self._forward_impl = linear_with_frozen_weight + else: + self._forward_impl = linear_with_grad_accumulation_and_async_allreduce + + output_parallel = self._forward_impl( + input=input_parallel, + weight=weight, + bias=bias, + gradient_accumulation_fusion=self.gradient_accumulation_fusion, + async_grad_allreduce=False + if self.explicit_expert_comm + else self.async_tensor_model_parallel_allreduce, + sequence_parallel=False if self.explicit_expert_comm else self.sequence_parallel, + grad_output_buffer=self.grad_output_buffer + if self.config.defer_embedding_wgrad_compute + else None, + ) + if self.gather_output: + # All-gather across the partitions. + assert not self.sequence_parallel + output = gather_from_tensor_model_parallel_region(output_parallel) + else: + output = output_parallel + output_bias = self.bias if self.skip_bias_add else None + return output, output_bias + + def sharded_state_dict(self, prefix='', sharded_offsets=(), metadata=None): + """ Sharding along axis 0, bias sharded """ + state_dict = self.state_dict(prefix='', keep_vars=True) + return make_sharded_tensors_for_checkpoint( + state_dict, prefix, {'weight': 0, 'bias': 0}, sharded_offsets + ) + + def set_extra_state(self, state: Any): + """ Extra state is ignored """ + + def get_extra_state(self) -> None: + """ Keep compatibility with TE state dict. """ + return None + + +class RowParallelLinear(torch.nn.Module): + """Linear layer with row parallelism. + + The linear layer is defined as Y = XA + b. A is parallelized along its first dimension and X along its second dimension. A = transpose([A_1 .. A_p]) X = [X_1, ..., X_p] + + Args: + input_size: first dimension of matrix A. + output_size: second dimension of matrix A. + bias: If true, add bias. Note that bias is not parallelized. + input_is_parallel: If true, we assume that the input is already split across the GPUs and we do not split again. + init_method: method to initialize weights. Note that bias is always set to zero. + stride: For the strided linear layers. + keep_master_weight_for_test: This was added for testing and should be set to False. It returns the master weights used for initialization. + skip_bias_add: If True, do not add the bias term, instead return it to be added by the caller. This enables performance optimations where bias can be fused with other elementwise operations. + is_expert: If True, the layer is treated as an MoE expert layer + tp_comm_buffer_name: Communication buffer name. Not used in + non-Transformer-Engine modules. + config: ModelParallelConfig object + + """ + + def __init__( + self, + input_size: int, + output_size: int, + *, + config: ModelParallelConfig, + init_method: Callable, + bias: bool, + input_is_parallel: bool, + skip_bias_add: bool, + stride: int = 1, + keep_master_weight_for_test: bool = False, + is_expert: bool = False, + tp_comm_buffer_name: str = None, # Not used + ): + super(RowParallelLinear, self).__init__() + + # Keep input parameters + self.input_size = input_size + self.output_size = output_size + self.input_is_parallel = input_is_parallel + # Divide the weight matrix along the last dimension. + world_size = get_tensor_model_parallel_world_size() + self.input_size_per_partition = divide(input_size, world_size) + self.skip_bias_add = skip_bias_add + self.config = config + self.is_expert = is_expert + self.expert_parallel = config.expert_model_parallel_size > 1 + self.gradient_accumulation_fusion = config.gradient_accumulation_fusion + self.sequence_parallel = config.sequence_parallel + if self.sequence_parallel and not self.input_is_parallel: + raise RuntimeError("To enable `sequence_parallel`, `input_is_parallel` must be `True`") + + # Parameters. + # Note: torch.nn.functional.linear performs XA^T + b and as a result + # we allocate the transpose. + # Initialize weight. + if config.use_cpu_initialization: + self.weight = Parameter( + torch.empty( + self.output_size, self.input_size_per_partition, dtype=config.params_dtype + ) + ) + if config.perform_initialization: + self.master_weight = _initialize_affine_weight_cpu( + self.weight, + self.output_size, + self.input_size, + self.input_size_per_partition, + 1, + init_method, + stride=stride, + return_master_weight=keep_master_weight_for_test, + params_dtype=config.params_dtype, + ) + else: + self.weight = Parameter( + torch.empty( + self.output_size, + self.input_size_per_partition, + device=torch.cuda.current_device(), + dtype=config.params_dtype, + ) + ) + if config.perform_initialization: + _initialize_affine_weight_gpu( + self.weight, + init_method, + partition_dim=1, + stride=stride, + expert_parallel=(self.is_expert and self.expert_parallel), + ) + setattr(self.weight, 'allreduce', not (self.is_expert and self.expert_parallel)) + + if bias: + if config.use_cpu_initialization: + self.bias = Parameter(torch.empty(self.output_size, dtype=config.params_dtype)) + else: + self.bias = Parameter( + torch.empty( + self.output_size, + device=torch.cuda.current_device(), + dtype=config.params_dtype, + ) + ) + + if config.perform_initialization: + # Always initialize bias to zero. + with torch.no_grad(): + self.bias.zero_() + setattr(self.bias, 'allreduce', not (self.is_expert and self.expert_parallel)) + setattr(self.bias, 'sequence_parallel', self.sequence_parallel) + else: + self.register_parameter('bias', None) + + self._forward_impl = linear_with_grad_accumulation_and_async_allreduce + self.explicit_expert_comm = self.is_expert and ( + self.sequence_parallel or self.expert_parallel + ) + + # Hook adding a default empty _extra_state for state dict + self._register_load_state_dict_pre_hook( + lambda state_dict, prefix, *args, **kwargs: state_dict.setdefault( + f'{prefix}_extra_state' + ) + ) + + def forward(self, input_): + """Forward of RowParallelLinear + + Args: + input_: 3D tensor whose order of dimension is [sequence, batch, hidden] + + Returns: + - output + - bias + """ + + if self.config._cpu_offloading_context is not None: + if self.config._cpu_offloading_context.inside_context == True: + assert ( + self.config.cpu_offloading == False + ), "CPU Offloading cannot be enabled while using non-TE modules" + + # Set up backprop all-reduce. + if self.input_is_parallel: + input_parallel = input_ + else: + assert not self.sequence_parallel + input_parallel = scatter_to_tensor_model_parallel_region(input_) + # Matrix multiply. + if not self.weight.requires_grad: + self._forward_impl = linear_with_frozen_weight + else: + self._forward_impl = linear_with_grad_accumulation_and_async_allreduce + output_parallel = self._forward_impl( + input=input_parallel, + weight=self.weight, + bias=None, + gradient_accumulation_fusion=self.gradient_accumulation_fusion, + async_grad_allreduce=False, + sequence_parallel=False, + ) + + # All-reduce across all the partitions. + if self.explicit_expert_comm: + assert self.skip_bias_add + output_ = output_parallel + elif self.sequence_parallel: + output_ = reduce_scatter_to_sequence_parallel_region(output_parallel) + else: + output_ = reduce_from_tensor_model_parallel_region(output_parallel) + if not self.skip_bias_add: + output = (output_ + self.bias) if self.bias is not None else output_ + output_bias = None + else: + output = output_ + output_bias = self.bias + return output, output_bias + + def sharded_state_dict(self, prefix='', sharded_offsets=(), metadata=None): + """ Sharding along axis 1, bias not sharded """ + state_dict = self.state_dict(prefix='', keep_vars=True) + return make_sharded_tensors_for_checkpoint( + state_dict, prefix, {'weight': 1}, sharded_offsets + ) + + def set_extra_state(self, state: Any): + """ Extra state is ignored """ + + def get_extra_state(self) -> None: + """ Keep compatibility with TE state dict. """ + return None diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/mappings.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/mappings.py new file mode 100644 index 0000000..93c793f --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/mappings.py @@ -0,0 +1,501 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import torch + +from megatron.core.parallel_state import ( + get_expert_model_parallel_group, + get_tensor_and_expert_parallel_group, + get_tensor_model_parallel_group, + get_tensor_model_parallel_rank, + get_tensor_model_parallel_world_size, +) + +from .utils import split_tensor_along_last_dim + + +def _reduce(input_): + """All-reduce the input tensor across model parallel group.""" + + # Bypass the function if we are using only 1 GPU. + if get_tensor_model_parallel_world_size() == 1: + return input_ + + # All-reduce. + torch.distributed.all_reduce(input_, group=get_tensor_model_parallel_group()) + + return input_ + + +def _split_along_last_dim(input_): + """Split the tensor along its last dimension and keep the + corresponding slice.""" + + world_size = get_tensor_model_parallel_world_size() + # Bypass the function if we are using only 1 GPU. + if world_size == 1: + return input_ + + # Split along last dimension. + input_list = split_tensor_along_last_dim(input_, world_size) + + # Note: torch.split does not create contiguous tensors by default. + rank = get_tensor_model_parallel_rank() + output = input_list[rank].contiguous() + + return output + + +def _split_along_first_dim(input_): + """Split the tensor along its first dimension and keep the + corresponding slice.""" + + world_size = get_tensor_model_parallel_world_size() + # Bypass the function if we are using only 1 GPU. + if world_size == 1: + return input_ + + # Split along first dimension. + dim_size = input_.size()[0] + assert ( + dim_size % world_size == 0 + ), "First dimension of the tensor should be divisible by tensor parallel size" + local_dim_size = dim_size // world_size + rank = get_tensor_model_parallel_rank() + dim_offset = rank * local_dim_size + + output = input_[dim_offset : dim_offset + local_dim_size].contiguous() + + return output + + +def _gather_along_last_dim(input_): + """Gather tensors and concatinate along the last dimension.""" + + world_size = get_tensor_model_parallel_world_size() + # Bypass the function if we are using only 1 GPU. + if world_size == 1: + return input_ + + # Size and dimension. + last_dim = input_.dim() - 1 + rank = get_tensor_model_parallel_rank() + + tensor_list = [torch.empty_like(input_) for _ in range(world_size)] + tensor_list[rank] = input_ + torch.distributed.all_gather(tensor_list, input_, group=get_tensor_model_parallel_group()) + + # Note: torch.cat already creates a contiguous tensor. + output = torch.cat(tensor_list, dim=last_dim).contiguous() + + return output + + +def _reduce_scatter_along_last_dim(input_): + """Reduce-scatter tensors on the last dimension.""" + num_dims = input_.dim() + permute_order = (num_dims - 1,) + tuple(range(num_dims - 1)) + input_ = input_.permute(permute_order).contiguous() + + output = _reduce_scatter_along_first_dim(input_) + + permute_order = tuple(range(1, num_dims)) + (0,) + output = output.permute(permute_order).contiguous() + + return output + + +def _gather_along_first_dim(input_): + """Gather tensors and concatinate along the first dimension.""" + + world_size = get_tensor_model_parallel_world_size() + # Bypass the function if we are using only 1 GPU. + if world_size == 1: + return input_ + + dim_size = list(input_.size()) + dim_size[0] = dim_size[0] * world_size + + output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device()) + torch.distributed._all_gather_base( + output, input_.contiguous(), group=get_tensor_model_parallel_group() + ) + + return output + + +def _reduce_scatter_along_first_dim(input_): + """Reduce-scatter the input tensor across model parallel group.""" + world_size = get_tensor_model_parallel_world_size() + # Bypass the function if we are using only 1 GPU. + if world_size == 1: + return input_ + + dim_size = list(input_.size()) + assert ( + dim_size[0] % world_size == 0 + ), "First dimension of the tensor should be divisible by tensor parallel size" + + dim_size[0] = dim_size[0] // world_size + + output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device()) + torch.distributed._reduce_scatter_base( + output, input_.contiguous(), group=get_tensor_model_parallel_group() + ) + return output + + +def _gather_along_first_dim_moe(input_): + """Gather tensors and concatenate along the first dimension.""" + group = get_tensor_and_expert_parallel_group() + world_size = torch.distributed.get_world_size(group=group) + # Bypass the function if we are using only 1 GPU. + if world_size == 1: + return input_ + + dim_size = list(input_.size()) + dim_size[0] = dim_size[0] * world_size + + output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device()) + torch.distributed._all_gather_base(output, input_.contiguous(), group=group) + + return output + + +def _reduce_scatter_along_first_dim_moe(input_): + """Reduce-scatter the input tensor across model parallel group.""" + group = get_tensor_and_expert_parallel_group() + world_size = torch.distributed.get_world_size(group=group) + # Bypass the function if we are using only 1 GPU. + if world_size == 1: + return input_ + + dim_size = list(input_.size()) + assert dim_size[0] % world_size == 0 + dim_size[0] = dim_size[0] // world_size + + output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device()) + torch.distributed._reduce_scatter_base(output, input_.contiguous(), group=group) + return output + + +def _gather_along_first_dim_expert_parallel(input_): + """Gather tensors and concatenate along the first dimension.""" + group = get_expert_model_parallel_group() + world_size = torch.distributed.get_world_size(group=group) + # Bypass the function if we are using only 1 GPU. + if world_size == 1: + return input_ + + dim_size = list(input_.size()) + dim_size[0] = dim_size[0] * world_size + + output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device()) + torch.distributed._all_gather_base(output, input_.contiguous(), group=group) + + return output + + +class _CopyToModelParallelRegion(torch.autograd.Function): + """Pass the input to the model parallel region.""" + + @staticmethod + def symbolic(graph, input_): + return input_ + + @staticmethod + def forward(ctx, input_): + return input_ + + @staticmethod + def backward(ctx, grad_output): + return _reduce(grad_output) + + +class _ReduceFromModelParallelRegion(torch.autograd.Function): + """All-reduce the input from the model parallel region.""" + + @staticmethod + def symbolic(graph, input_): + return _reduce(input_) + + @staticmethod + def forward(ctx, input_): + return _reduce(input_) + + @staticmethod + def backward(ctx, grad_output): + return grad_output + + +class _ScatterToModelParallelRegion(torch.autograd.Function): + """Split the input and keep only the corresponding chuck to the rank.""" + + @staticmethod + def symbolic(graph, input_): + return _split_along_last_dim(input_) + + @staticmethod + def forward(ctx, input_): + return _split_along_last_dim(input_) + + @staticmethod + def backward(ctx, grad_output): + return _gather_along_last_dim(grad_output) + + +class _GatherFromModelParallelRegion(torch.autograd.Function): + """Gather the input from model parallel region and concatinate.""" + + @staticmethod + def symbolic(graph, input_): + return _gather_along_last_dim(input_) + + @staticmethod + def forward(ctx, input_): + return _gather_along_last_dim(input_) + + @staticmethod + def backward(ctx, grad_output): + return _split_along_last_dim(grad_output) + + +class _ScatterToSequenceParallelRegion(torch.autograd.Function): + """Split the input and keep only the corresponding chuck to the rank.""" + + @staticmethod + def symbolic(graph, input_): + return _split_along_first_dim(input_) + + @staticmethod + def forward(ctx, input_): + return _split_along_first_dim(input_) + + @staticmethod + def backward(ctx, grad_output): + return _gather_along_first_dim(grad_output) + + +class _GatherFromSequenceParallelRegion(torch.autograd.Function): + """Gather the input from sequence parallel region and concatinate.""" + + @staticmethod + def symbolic(graph, input_, tensor_parallel_output_grad=True): + return _gather_along_first_dim(input_) + + @staticmethod + def forward(ctx, input_, tensor_parallel_output_grad=True): + ctx.tensor_parallel_output_grad = tensor_parallel_output_grad + return _gather_along_first_dim(input_) + + @staticmethod + def backward(ctx, grad_output): + tensor_parallel_output_grad = ctx.tensor_parallel_output_grad + + # If the computation graph after the gather operation is + # in the tensor parallel mode, output gradients need to reduce + # scattered and whereas if the computation is duplicated, + # output gradients need to be scattered. + if tensor_parallel_output_grad: + return _reduce_scatter_along_first_dim(grad_output), None + else: + return _split_along_first_dim(grad_output), None + + +class _ReduceScatterToSequenceParallelRegion(torch.autograd.Function): + """Reduce scatter the input from the model parallel region.""" + + @staticmethod + def symbolic(graph, input_): + return _reduce_scatter_along_first_dim(input_) + + @staticmethod + def forward(ctx, input_): + return _reduce_scatter_along_first_dim(input_) + + @staticmethod + def backward(ctx, grad_output): + return _gather_along_first_dim(grad_output) + + +class _GatherFromSequenceParallelRegionToMOE(torch.autograd.Function): + """Gather the input from model parallel region and concatenate.""" # TODO + + @staticmethod + def symbolic(graph, input_): + return _gather_along_first_dim_moe(input_) + + @staticmethod + def forward(ctx, input_): + return _gather_along_first_dim_moe(input_,) + + @staticmethod + def backward(ctx, grad_output): + return _reduce_scatter_along_first_dim_moe(grad_output) + + +class _ReduceScatterToSequenceParallelRegionFromMOE(torch.autograd.Function): + """Reduce scatter the input from the model parallel region.""" + + @staticmethod + def symbolic(graph, input_): + return _reduce_scatter_along_first_dim_moe(input_) + + @staticmethod + def forward(ctx, input_): + return _reduce_scatter_along_first_dim_moe(input_,) + + @staticmethod + def backward(ctx, grad_output): + return _gather_along_first_dim_moe(grad_output) + + +class _AllGatherFromTensorParallelRegion(torch.autograd.Function): + """Gather the input from model parallel region and concatenate.""" + + @staticmethod + def symbolic(graph, input_): + return _gather_along_last_dim(input_) + + @staticmethod + def forward(ctx, input_): + return _gather_along_last_dim(input_,) + + @staticmethod + def backward(ctx, grad_output): + return _reduce_scatter_along_last_dim(grad_output) + + +class _ReduceScatterToTensorParallelRegion(torch.autograd.Function): + """Reduce scatter the input from the model parallel region.""" + + @staticmethod + def symbolic(graph, input_): + return _reduce_scatter_along_last_dim(input_) + + @staticmethod + def forward(ctx, input_): + return _reduce_scatter_along_last_dim(input_,) + + @staticmethod + def backward(ctx, grad_output): + return _gather_along_last_dim(grad_output) + + +class _AllToAll(torch.autograd.Function): + @staticmethod + def forward(ctx, group, input, output_split_sizes, input_split_sizes): + ctx.group = group + ctx.output_split_sizes = output_split_sizes + ctx.input_split_sizes = input_split_sizes + + world_size = torch.distributed.get_world_size(group=group) + # Bypass the function if we are using only 1 GPU. + if world_size == 1: + return input + + input = input.contiguous() + if output_split_sizes is None: + # Equal split (all2all) + output = torch.empty_like(input) + else: + # Unequal split (all2all-v) + output = input.new_empty( + size=[sum(output_split_sizes)] + list(input.size()[1:]), + dtype=input.dtype, + device=torch.cuda.current_device(), + ) + torch.distributed.all_to_all_single( + output, + input, + output_split_sizes=output_split_sizes, + input_split_sizes=input_split_sizes, + group=group, + ) + return output + + @staticmethod + def backward(ctx, *grad_output): + return ( + None, + _AllToAll.apply(ctx.group, *grad_output, ctx.input_split_sizes, ctx.output_split_sizes), + None, + None, + ) + + +# ----------------- +# Helper functions. +# ----------------- + + +def copy_to_tensor_model_parallel_region(input_): + return _CopyToModelParallelRegion.apply(input_) + + +def reduce_from_tensor_model_parallel_region(input_): + return _ReduceFromModelParallelRegion.apply(input_) + + +def scatter_to_tensor_model_parallel_region(input_): + return _ScatterToModelParallelRegion.apply(input_) + + +def gather_from_tensor_model_parallel_region(input_): + return _GatherFromModelParallelRegion.apply(input_) + + +def scatter_to_sequence_parallel_region(input_): + return _ScatterToSequenceParallelRegion.apply(input_) + + +def gather_from_sequence_parallel_region(input_, tensor_parallel_output_grad=True): + return _GatherFromSequenceParallelRegion.apply(input_, tensor_parallel_output_grad) + + +def reduce_scatter_to_sequence_parallel_region(input_): + return _ReduceScatterToSequenceParallelRegion.apply(input_) + + +def gather_from_sequence_parallel_region_to_moe(input_): + return _GatherFromSequenceParallelRegionToMOE.apply(input_) + + +def reduce_scatter_to_sequence_parallel_region_from_moe(input_): + return _ReduceScatterToSequenceParallelRegionFromMOE.apply(input_) + + +def all_gather_last_dim_from_tensor_parallel_region(input_): + return _AllGatherFromTensorParallelRegion.apply(input_) + + +def reduce_scatter_last_dim_to_tensor_parallel_region(input_): + return _ReduceScatterToTensorParallelRegion.apply(input_) + + +def all_to_all(group, input_, output_split_sizes_=None, input_split_sizes_=None): + return _AllToAll.apply(group, input_, output_split_sizes_, input_split_sizes_) + + +def all_to_all_sp2hp(input_): + world_size = get_tensor_model_parallel_world_size() + tp_group = get_tensor_model_parallel_group() + input_ = input_.reshape(-1, input_.shape[-1]) + split_tensors = torch.split( + input_, split_size_or_sections=input_.shape[-1] // world_size, dim=1 + ) + concat_tensor = torch.cat(split_tensors, dim=0) + output = all_to_all(tp_group, concat_tensor) + return output + + +def all_to_all_hp2sp(input_): + world_size = get_tensor_model_parallel_world_size() + input_ = input_.reshape(-1, input_.shape[-1]) + tp_group = get_tensor_model_parallel_group() + input_exchanged = all_to_all(tp_group, input_) + input_reshaped = input_exchanged.reshape(-1, input_exchanged.shape[-1]) + split_tensors = torch.split( + input_reshaped, split_size_or_sections=input_reshaped.shape[0] // world_size, dim=0 + ) + output = torch.cat(split_tensors, dim=-1) + return output diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/random.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/random.py new file mode 100644 index 0000000..20a2720 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/random.py @@ -0,0 +1,301 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +# Parts of the code here are adapted from PyTorch +# repo: https://github.com/pytorch/pytorch + +import contextlib +from importlib.metadata import version + +import torch +from pkg_resources import packaging +from torch import _C +from torch.cuda import _lazy_call +from torch.cuda import device as device_ctx_manager +from torch.utils.checkpoint import detach_variable + +from megatron.core.parallel_state import ( + get_data_parallel_rank, + get_expert_model_parallel_rank, + get_tensor_model_parallel_group, + get_tensor_model_parallel_rank, + get_tensor_model_parallel_world_size, +) +from megatron.core.utils import safely_set_viewless_tensor_data + +from .utils import gather_split_1d_tensor, split_tensor_into_1d_equal_chunks + +# Default name for the model parallel rng tracker. +_MODEL_PARALLEL_RNG_TRACKER_NAME = 'model-parallel-rng' +_EXPERT_PARALLEL_RNG_TRACKER_NAME = 'expert-parallel-rng' +_DATA_PARALLEL_RNG_TRACKER_NAME = 'data-parallel-rng' + + +def _set_cuda_rng_state(new_state, device=-1): + """Sets the random number generator state of the current GPU. + + Argumentss: + new_state (torch.ByteTensor): The desired state + This function is adapted from PyTorch repo (torch.cuda.set_rng_state) + with a single change: the input state is not cloned. Cloning caused + major performance issues for +4 GPU cases. + """ + if hasattr(_C, '_cuda_setRNGState') and callable(_C._cuda_setRNGState): + # older PyTorch + def cb(): + with device_ctx_manager(device): + _C._cuda_setRNGState(new_state) + + else: + # newer PyTorch + if device == -1: + device = torch.device('cuda') + elif isinstance(device, str): + device = torch.device(device) + elif isinstance(device, int): + device = torch.device('cuda', device) + + def cb(): + idx = device.index + if idx is None: + idx = torch.cuda.current_device() + default_generator = torch.cuda.default_generators[idx] + default_generator.set_state(new_state) + + _lazy_call(cb) + + +def get_expert_parallel_rng_tracker_name(): + global _EXPERT_PARALLEL_RNG_TRACKER_NAME + return _EXPERT_PARALLEL_RNG_TRACKER_NAME + + +def get_data_parallel_rng_tracker_name(): + global _DATA_PARALLEL_RNG_TRACKER_NAME + return _DATA_PARALLEL_RNG_TRACKER_NAME + + +class CudaRNGStatesTracker: + """Tracker for the cuda RNG states. + + Using the `add` method, a cuda rng state is initialized based on + the input `seed` and is assigned to `name`. Later, by forking the + rng state, we can perform operations and return to our starting + cuda state. + """ + + def __init__(self): + self.reset() + + def is_initialized(self): + return self._is_initialized + + def reset(self): + """Set to the initial state (no tracker).""" + + # Track if initialized. + self._is_initialized = False + + # Map from a string name to the cuda rng state. + self.states_ = {} + + # Seeds are just for book keeping and ensure no seed is set twice. + self.seeds_ = set() + + def get_states(self): + """Get rng states. Copy the dictionary so we have direct + pointers to the states, not just a pointer to the dictionary.""" + states = {} + for name in self.states_: + states[name] = self.states_[name] + return states + + def set_states(self, states): + """Set the rng states. For efficiency purposes, we do not check + the size of seed for compatibility.""" + self._is_initialized = True + self.states_ = states + + def add(self, name, seed): + """Track the rng state.""" + self._is_initialized = True + # Check seed is not already used. + if seed in self.seeds_: + raise Exception('seed {} already exists'.format(seed)) + self.seeds_.add(seed) + # Check that state is not already defined. + if name in self.states_: + raise Exception('cuda rng state {} already exists'.format(name)) + # Get the current rng state. + orig_rng_state = torch.cuda.get_rng_state() + # Set the new state and store it. + torch.cuda.manual_seed(seed) + self.states_[name] = torch.cuda.get_rng_state() + # Reset rng state to what it was. + _set_cuda_rng_state(orig_rng_state) + + @contextlib.contextmanager + def fork(self, name=_MODEL_PARALLEL_RNG_TRACKER_NAME): + """Fork the cuda rng state, perform operations, and exit with + the original state.""" + # Check if we have added the state + if name not in self.states_: + raise Exception('cuda rng state {} is not added'.format(name)) + # Store current rng state. + orig_cuda_rng_state = torch.cuda.get_rng_state() + # Set rng state to the desired one + _set_cuda_rng_state(self.states_[name]) + # Do the stuff we wanted to do. + try: + yield + finally: + # Update the current rng state for later use. + self.states_[name] = torch.cuda.get_rng_state() + # And set the state to the original state we started with. + _set_cuda_rng_state(orig_cuda_rng_state) + + +# RNG tracker object. +_CUDA_RNG_STATE_TRACKER = None +_CUDA_RNG_STATE_TRACKER_INITIALIZED = False + + +def initialize_rng_tracker(use_te_rng_tracker: bool = False): + global _CUDA_RNG_STATE_TRACKER + global _CUDA_RNG_STATE_TRACKER_INITIALIZED + if _CUDA_RNG_STATE_TRACKER_INITIALIZED: + return + if use_te_rng_tracker: + try: + import transformer_engine.pytorch as te + + _te_version = packaging.version.Version(version("transformer-engine")) + if _te_version < packaging.version.Version("1.5.0"): + raise RuntimeError("use_te_rng_tracker requires TransformerEngine version >= 1.5") + except: + raise RuntimeError("use_te_rng_tracker requires TransformerEngine, but not installed") + if use_te_rng_tracker: + _CUDA_RNG_STATE_TRACKER = te.distributed.CudaRNGStatesTracker() + else: + _CUDA_RNG_STATE_TRACKER = CudaRNGStatesTracker() + _CUDA_RNG_STATE_TRACKER_INITIALIZED = True + + +def get_cuda_rng_tracker(): + """Get cuda rng tracker.""" + initialize_rng_tracker() + return _CUDA_RNG_STATE_TRACKER + + +def model_parallel_cuda_manual_seed(seed): + """Initialize model parallel cuda seed. + + This function should be called after the model parallel is + initialized. Also, no torch.cuda.manual_seed should be called + after this function. Basically, this is replacement for that + function. + Two set of RNG states are tracked: + default state: This is for data parallelism and is the same among a set of model parallel GPUs but different across different model paralle groups. This is used for example for dropout in the non-tensor-model-parallel regions. + tensor-model-parallel state: This state is different among a set of model parallel GPUs, but the same across data parallel groups. This is used for example for dropout in model parallel regions. + """ + # 2718 is just for fun and any POSITIVE value will work. + offset = seed + 2718 + tensor_model_parallel_seed = offset + get_tensor_model_parallel_rank() + # Data parallel gets the original seed. + data_parallel_seed = seed + + initialize_rng_tracker() + _CUDA_RNG_STATE_TRACKER.reset() + # Set the default state. + torch.cuda.manual_seed(data_parallel_seed) + _CUDA_RNG_STATE_TRACKER.add(_DATA_PARALLEL_RNG_TRACKER_NAME, data_parallel_seed) + + # and model parallel state. + _CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME, tensor_model_parallel_seed) + + expert_parallel_seed = ( + seed + 1024 + 100 * get_expert_model_parallel_rank() + get_tensor_model_parallel_rank() + ) + _CUDA_RNG_STATE_TRACKER.add(_EXPERT_PARALLEL_RNG_TRACKER_NAME, expert_parallel_seed) + + +class CheckpointFunction(torch.autograd.Function): + """Checkpoint Function + + This function is adapted from torch.utils.checkpoint with two main changes: + 1) torch.cuda.set_rng_state is replaced with `_set_cuda_rng_state` + 2) the states in the model parallel tracker are also properly tracked/set/reset. + """ + + @staticmethod + def forward(ctx, run_function, distribute_saved_activations, *args): + ctx.run_function = run_function + ctx.distribute_saved_activations = distribute_saved_activations + + # Copy the rng states. + ctx.fwd_cpu_rng_state = torch.get_rng_state() + ctx.fwd_cuda_rng_state = torch.cuda.get_rng_state() + ctx.fwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states() + + with torch.no_grad(): + outputs = run_function(*args) + + # Divide hidden states across model parallel group and only keep + # the chunk corresponding to the current rank. + if distribute_saved_activations: + ctx.input_0_shape = args[0].data.shape + safely_set_viewless_tensor_data( + args[0], split_tensor_into_1d_equal_chunks(args[0].data, new_buffer=True) + ) + + # Store everything. + ctx.save_for_backward(*args) + + return outputs + + @staticmethod + def backward(ctx, *args): + if not torch.autograd._is_checkpoint_valid(): + raise RuntimeError( + "Checkpointing is not compatible with .grad(), " + "please use .backward() if possible" + ) + inputs = ctx.saved_tensors + if ctx.distribute_saved_activations: + safely_set_viewless_tensor_data( + inputs[0], gather_split_1d_tensor(inputs[0].data).view(ctx.input_0_shape) + ) + + # Store the current states. + bwd_cpu_rng_state = torch.get_rng_state() + bwd_cuda_rng_state = torch.cuda.get_rng_state() + bwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states() + + # Set the states to what it used to be before the forward pass. + torch.set_rng_state(ctx.fwd_cpu_rng_state) + _set_cuda_rng_state(ctx.fwd_cuda_rng_state) + get_cuda_rng_tracker().set_states(ctx.fwd_cuda_rng_state_tracker) + + # Compute the forward pass. + detached_inputs = detach_variable(inputs) + with torch.enable_grad(): + outputs = ctx.run_function(*detached_inputs) + + # Set the states back to what it was at the start of this function. + torch.set_rng_state(bwd_cpu_rng_state) + _set_cuda_rng_state(bwd_cuda_rng_state) + get_cuda_rng_tracker().set_states(bwd_cuda_rng_state_tracker) + + if isinstance(outputs, torch.Tensor): + outputs = (outputs,) + + # filter out non tensor outputs for backward pass + outputs, args = zip(*filter(lambda x: torch.is_tensor(x[0]), zip(outputs, args))) + torch.autograd.backward(outputs, args) + grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else inp for inp in detached_inputs) + return (None, None) + grads + + +def checkpoint(function, distribute_saved_activations, *args): + """Checkpoint a model or part of the model. + This has been directly copied from torch.utils.checkpoint.""" + return CheckpointFunction.apply(function, distribute_saved_activations, *args) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/utils.py new file mode 100644 index 0000000..fc0db15 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/tensor_parallel/utils.py @@ -0,0 +1,113 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +from typing import List, Sequence + +import torch + +from megatron.core import parallel_state +from megatron.core.utils import divide + + +def split_tensor_along_last_dim( + tensor: torch.Tensor, num_partitions: int, contiguous_split_chunks: bool = False, +) -> List[torch.Tensor]: + """ Split a tensor along its last dimension. + + Args: + tensor: input tensor. + num_partitions: number of partitions to split the tensor + contiguous_split_chunks: If True, make each chunk contiguous + in memory. + + Returns: + A list of Tensors + """ + # Get the size and dimension. + last_dim = tensor.dim() - 1 + last_dim_size = divide(tensor.size()[last_dim], num_partitions) + # Split. + tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) + # Note: torch.split does not create contiguous tensors by default. + if contiguous_split_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + + return tensor_list + + +def split_tensor_into_1d_equal_chunks(tensor, new_buffer=False): + """ Break a tensor into equal 1D chunks across tensor parallel ranks. + + Returns a Tensor or View with this rank's portion of the data. + + Args: + tensor: The tensor to split + + Keyword Args: + new_buffer (bool): If True, returns a new Tensor. + If False, returns a view into the existing Tensor. + Default is False + + """ + partition_size = torch.numel(tensor) // parallel_state.get_tensor_model_parallel_world_size() + start_index = partition_size * parallel_state.get_tensor_model_parallel_rank() + end_index = start_index + partition_size + if new_buffer: + data = torch.empty( + partition_size, + dtype=tensor.dtype, + device=torch.cuda.current_device(), + requires_grad=False, + ) + data.copy_(tensor.view(-1)[start_index:end_index]) + else: + data = tensor.view(-1)[start_index:end_index] + return data + + +def gather_split_1d_tensor(tensor): + """ Opposite of split_tensor_into_1d_equal_chunks. Gather values from tensor + model parallel ranks. + + Returns a new Tensor with the gathered data. + + Args: + tensor: A Tensor or view of this rank's portion of the data. + """ + numel_gathered = torch.numel(tensor) * parallel_state.get_tensor_model_parallel_world_size() + gathered = torch.empty( + numel_gathered, dtype=tensor.dtype, device=torch.cuda.current_device(), requires_grad=False + ) + # TODO: This API is experimental in pytorch (as of Feb 2022) and + # this might break in future pytorch releases. We chose this API + # as opposed to torch.distributed.all_gather for efficiency reasons. + # This API calls directly NCCL all-gather versus the former does + # internal copies and can potentially cause slow down. + torch.distributed._all_gather_base( + gathered, tensor, group=parallel_state.get_tensor_model_parallel_group() + ) + return gathered + + +class VocabUtility: + """ Split the vocabulary into `world_size` chunks and return the first + and last index of the vocabulary belonging to the `rank` + partition: Note that indices in [fist, last) + + """ + + @staticmethod + def vocab_range_from_per_partition_vocab_size( + per_partition_vocab_size: int, rank, world_size: int + ) -> Sequence[int]: + index_f = rank * per_partition_vocab_size + index_l = index_f + per_partition_vocab_size + return index_f, index_l + + @staticmethod + def vocab_range_from_global_vocab_size( + global_vocab_size: int, rank: int, world_size: int + ) -> Sequence[int]: + per_partition_vocab_size = divide(global_vocab_size, world_size) + return VocabUtility.vocab_range_from_per_partition_vocab_size( + per_partition_vocab_size, rank, world_size + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/timers.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/timers.py new file mode 100644 index 0000000..b61eb4e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/timers.py @@ -0,0 +1,398 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Megatron timers.""" + +import time +from abc import ABC, abstractmethod +from typing import List + +import torch + + +class TimerBase(ABC): + def __init__(self, name): + self.name = name + + @abstractmethod + def start(self, barrier=False): + pass + + @abstractmethod + def stop(self, barrier=False): + pass + + @abstractmethod + def reset(self): + pass + + @abstractmethod + def elapsed(self, reset=True, barrier=False): + pass + + +class DummyTimer(TimerBase): + def __init__(self): + super().__init__('dummy timer') + + def start(self, barrier=False): + return + + def stop(self, barrier=False): + return + + def reset(self): + return + + def elapsed(self, reset=True, barrier=False): + raise Exception('dummy timer should not be used to calculate elapsed time') + + +class Timer(TimerBase): + """ + Timer class with ability to start/stop. + + Comment on using `barrier`: If this flag is passed, then all + the caller processes will wait till all reach the timing routine. + It is up to the user to make sure all the ranks in `barrier_group` + call it otherwise, it will result in a hang. + Comment on `barrier_group`: By default it is set to None which + in torch distributed land, it will result in the global communicator. + """ + + def __init__(self, name): + """Initialize Timer. + + Args: + name (str): Name of the timer. + """ + super().__init__(name) + self._elapsed = 0.0 + self._active_time = 0.0 + self._started = False + # Note that None will default to the global process group + self._barrier_group = None + self._start_time = time.time() + + def set_barrier_group(self, barrier_group): + """Sets barrier group. + + Args: + barrier_group (ProcessGroup): Torch ProcessGroup for barrier. + """ + self._barrier_group = barrier_group + + def start(self, barrier=False): + """Start the timer. + + Args: + barrier (bool, optional): Synchronizes ranks before starting. Defaults to False. + """ + assert not self._started, 'timer has already been started' + if barrier: + torch.distributed.barrier(group=self._barrier_group) + torch.cuda.synchronize() + self._start_time = time.time() + self._started = True + + def stop(self, barrier=False): + """Stop the timer. + + Args: + barrier (bool, optional): Synchronizes ranks before stopping. Defaults to False. + """ + assert self._started, 'timer is not started' + if barrier: + torch.distributed.barrier(group=self._barrier_group) + torch.cuda.synchronize() + elapsed = time.time() - self._start_time + self._elapsed += elapsed + self._active_time += elapsed + self._started = False + + def reset(self): + """Reset timer. + """ + # Don't reset _active_time + self._elapsed = 0.0 + self._started = False + + def elapsed(self, reset=True, barrier=False): + """Calculates the elapsed time and restarts timer. + + Args: + reset (bool, optional): Resets timer before restarting. Defaults to True. + barrier (bool, optional): Synchronizes ranks before stopping. Defaults to False. + + Returns: + float: Elapsed time. + """ + _started = self._started + # If the timing in progress, end it first. + if self._started: + self.stop(barrier=barrier) + # Get the elapsed time. + _elapsed = self._elapsed + # Reset the elapsed time + if reset: + self.reset() + # If timing was in progress, set it back. + if _started: + self.start(barrier=barrier) + return _elapsed + + def active_time(self): + return self._active_time + + +class Timers: + """Class for a group of Timers. + """ + + def __init__(self, log_level, log_option): + """Initialize group of timers. + + Args: + log_level (int): Log level to control what timers are enabled. + log_option (str): Setting for logging statistics over ranks for all the timers. Allowed: ['max', 'minmax', 'all']. + """ + self._log_level = log_level + allowed_log_options = set(['max', 'minmax', 'all']) + assert ( + log_option in allowed_log_options + ), 'input log option {} is invalid. It must be one of {}'.format( + log_option, allowed_log_options + ) + self._log_option = log_option + self._timers = {} + self._log_levels = {} + self._dummy_timer = DummyTimer() + self._max_log_level = 2 + + def __call__(self, name, log_level=None): + """Call timer with name and log level.""" + # If the timer has already been set, then check if the log-level + # is provided, it matches the one that the timer was created with. + if name in self._timers: + if log_level is not None: + assert log_level == self._log_levels[name], ( + 'input log level {} does not match already existing ' + 'log level {} for {} timer'.format(log_level, self._log_levels[name], name) + ) + return self._timers[name] + # If timer does not exist and no log level is provided, + # set it to the max log level which is 2. + if log_level is None: + log_level = self._max_log_level + assert ( + log_level <= self._max_log_level + ), 'log level {} is larger than max supported log level {}'.format( + log_level, self._max_log_level + ) + # Now if the input log level is larger than the one set for + # the timers class, just ignore it and return a dummy timer. + if log_level > self._log_level: + return self._dummy_timer + # Otherwise, initalize the timer and set the level. + self._timers[name] = Timer(name) + self._log_levels[name] = log_level + return self._timers[name] + + def _get_elapsed_time_all_ranks(self, names, reset, barrier): + """Returns elapsed times of timers in names. + Assumptions: + - All the ranks call this function. + - `names` are identical on all ranks. + If the above assumptions are not met, calling this function will + result in hang. + + Args: + names (List[str]): list of timer names + reset (bool): reset the timer after recording the elapsed time + barrier (bool): if set, do a global barrier before time measurments + + Returns: + torch.tensor: Tensor of size [world_size, len(names)] with times in float. + """ + + # First make sure all the callers are in sync. + if barrier: + torch.distributed.barrier() + + world_size = torch.distributed.get_world_size() + rank = torch.distributed.get_rank() + + # Here we can use gather on the rank we want to print the + # timing, however, there is no gather_base support in + # pytorch yet. It is simpler to deal with a single tensor + # and since we are only gathering a small amount of data, + # it should be ok to use all-gather instead of gather. + rank_name_to_time = torch.zeros( + (world_size, len(names)), dtype=torch.float, device=torch.cuda.current_device() + ) + for i, name in enumerate(names): + if name in self._timers: + # Here we don't need to pass the barrier flag as all + # the processes are already in sync. This avoids the + # issue of different timers having different barrier + # groups inside their class. + rank_name_to_time[rank, i] = self._timers[name].elapsed(reset=reset) + + # See the note above for why we are not using gather. + torch.distributed._all_gather_base( + rank_name_to_time.view(-1), rank_name_to_time[rank, :].view(-1) + ) + + return rank_name_to_time + + def _get_global_min_max_time(self, names, reset, barrier, normalizer): + """Report only min and max times across all ranks.""" + + rank_name_to_time = self._get_elapsed_time_all_ranks(names, reset, barrier) + name_to_min_max_time = {} + for i, name in enumerate(names): + rank_to_time = rank_name_to_time[:, i] + # filter out the ones we did not have any timings for + rank_to_time = rank_to_time[rank_to_time > 0.0] + # If the timer exists: + if rank_to_time.numel() > 0: + name_to_min_max_time[name] = ( + rank_to_time.min().item() / normalizer, + rank_to_time.max().item() / normalizer, + ) + return name_to_min_max_time + + def _get_global_min_max_time_string(self, names, reset, barrier, normalizer, max_only): + """Report strings for max/minmax times across all ranks.""" + name_to_min_max_time = self._get_global_min_max_time(names, reset, barrier, normalizer) + if not name_to_min_max_time: + return None + if max_only: + output_string = 'max time across ranks (ms):' + else: + output_string = '(min, max) time across ranks (ms):' + for name in name_to_min_max_time: + min_time, max_time = name_to_min_max_time[name] + if max_only: + output_string += '\n {}: {:.2f}'.format((name + ' ').ljust(48, '.'), max_time) + else: + output_string += '\n {}: ({:.2f}, {:.2f})'.format( + (name + ' ').ljust(48, '.'), min_time, max_time + ) + return output_string + + def _get_all_ranks_time_string(self, names, reset, barrier, normalizer): + """Report times across all ranks.""" + rank_name_to_time = self._get_elapsed_time_all_ranks(names, reset, barrier) + + output_string = 'times across ranks (ms):' + no_reported_timing = True + for i, name in enumerate(names): + not_yet_found = True + for rank in range(torch.distributed.get_world_size()): + if rank_name_to_time[rank, i] > 0: + no_reported_timing = False + if not_yet_found: + not_yet_found = False + output_string += '\n {}:'.format(name) + output_string += '\n rank {:2d}: {:.2f}'.format( + rank, rank_name_to_time[rank, i] / normalizer + ) + if no_reported_timing: + return None + return output_string + + def get_all_timers_string( + self, + names: List[str] = None, + normalizer: float = 1.0, + reset: bool = True, + barrier: bool = False, + ): + """Returns the output string with logged timer values according to configured options. + + Args: + names (List[str]): Names of the timers to log. If None, all registered timers are fetched. Defaults to None. + normalizer (float, optional): Normalizes the timer values by the factor. Defaults to 1.0. + reset (bool, optional): Whether to reset timer values after logging. Defaults to True. + barrier (bool, optional): Whether to do a global barrier before time measurments. Defaults to False. + + Raises: + Exception: Raises if log option is invalid. + + Returns: + str: Formatted string with the timer values. + """ + + if names == None: # get all registered timers + names = self._timers.keys() + + assert normalizer > 0.0 + if self._log_option in ['max', 'minmax']: + max_only = False + if self._log_option == 'max': + max_only = True + output_string = self._get_global_min_max_time_string( + names, reset, barrier, normalizer / 1000.0, max_only + ) + elif self._log_option == 'all': + output_string = self._get_all_ranks_time_string( + names, reset, barrier, normalizer / 1000.0 + ) + else: + raise Exception('unknown timing log option {}'.format(self._log_option)) + return output_string + + def log( + self, + names: List[str], + rank: int = None, + normalizer: float = 1.0, + reset: bool = True, + barrier: bool = False, + ): + """logs the timers passed in names to stdout. Example usage is to log average per step value for timer 'foo', + this function can be called with normalizer factor set to logging interval. + + Args: + names (List[str]): Names of the timers to log. + rank (int, optional): logs the timers to a specific rank. If set to None, logs to the last rank. Defaults to None. + normalizer (float, optional): Normalizes the timer values by the factor. Defaults to 1.0. + reset (bool, optional): Whether to reset timer values after logging. Defaults to True. + barrier (bool, optional): Whether to do a global barrier before time measurments. Defaults to False. + """ + + output_string = self.get_all_timers_string(names, normalizer, reset, barrier) + # If no input rank is provided, log on last rank. + if rank is None: + rank = torch.distributed.get_world_size() - 1 + if rank == torch.distributed.get_rank() and output_string is not None: + print(output_string, flush=True) + + def write( + self, + names: List[str], + writer, + iteration: int, + normalizer: float = 1.0, + reset: bool = True, + barrier: bool = False, + ): + """Write timers to a tensorboard writer. Note that we only report maximum time across ranks to tensorboard. + + Args: + names (List[str]): Names of the timers to log. + writer (SummaryWriter): Tensorboard SummaryWriter object + iteration (int): Current iteration. + normalizer (float, optional): Normalizes the timer values by the factor. Defaults to 1.0. + reset (bool, optional): Whether to reset timer values after logging. Defaults to True. + barrier (bool, optional): Whether to do a global barrier before time measurments. Defaults to False. + """ + # currently when using add_scalars, + # torch.utils.add_scalars makes each timer its own run, which + # polutes the runs list, so we just add each as a scalar + assert normalizer > 0.0 + name_to_min_max_time = self._get_global_min_max_time(names, reset, barrier, normalizer) + if writer is not None: + for name in name_to_min_max_time: + _, max_time = name_to_min_max_time[name] + writer.add_scalar(name + '-time', max_time, iteration) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/__init__.py new file mode 100644 index 0000000..7cc1077 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +from .module import MegatronModule +from .spec_utils import ModuleSpec, build_module +from .transformer_config import TransformerConfig +from .transformer_layer import TransformerLayer, TransformerLayerSubmodules diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/attention.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/attention.py new file mode 100644 index 0000000..ab2f575 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/attention.py @@ -0,0 +1,595 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +from abc import ABC, abstractmethod +from dataclasses import dataclass +from importlib.metadata import version +from typing import Union + +import torch +from pkg_resources import packaging + +from megatron.core import parallel_state, tensor_parallel +from megatron.core.models.common.embeddings.rotary_pos_embedding import apply_rotary_pos_emb +from megatron.core.parallel_state import ( + get_data_parallel_group, + get_data_parallel_rank, + get_data_parallel_world_size, + get_tensor_model_parallel_group, + get_tensor_model_parallel_rank, + get_tensor_model_parallel_world_size, +) +from megatron.core.transformer.custom_layers.transformer_engine import SplitAlongDim +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.identity_op import IdentityFuncOp, IdentityOp +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.spec_utils import ModuleSpec, build_module +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.utils import divide + +from .enums import AttnMaskType +from .transformer_config import TransformerConfig + + +@dataclass +class SelfAttentionSubmodules: + linear_qkv: Union[ModuleSpec, type] = None + core_attention: Union[ModuleSpec, type] = None + linear_proj: Union[ModuleSpec, type] = None + q_layernorm: Union[ModuleSpec, type] = None + k_layernorm: Union[ModuleSpec, type] = None + + +@dataclass +class CrossAttentionSubmodules: + linear_q: Union[ModuleSpec, type] = None + linear_kv: Union[ModuleSpec, type] = None + core_attention: Union[ModuleSpec, type] = None + linear_proj: Union[ModuleSpec, type] = None + + +class Attention(MegatronModule, ABC): + """Attention layer abstract class. + + This layer only contains common modules required for the "self attn" and + "cross attn" specializations. + """ + + def __init__( + self, + config: TransformerConfig, + submodules: Union[SelfAttentionSubmodules, CrossAttentionSubmodules], + layer_number: int, + attn_mask_type: AttnMaskType, + attention_type: str, + ): + super().__init__(config=config) + + self.config = config + self.layer_number = layer_number + self.attn_mask_type = attn_mask_type + self.attention_type = attention_type + + # For normal attention without groups, num_query_groups == num_attention_heads, + # so these two will be the same + self.query_projection_size = self.config.kv_channels * self.config.num_attention_heads + self.kv_projection_size = self.config.kv_channels * self.config.num_query_groups + + # Per attention head and per partition values. + world_size = parallel_state.get_tensor_model_parallel_world_size() + self.hidden_size_per_attention_head = divide( + self.query_projection_size, self.config.num_attention_heads + ) + self.num_attention_heads_per_partition = divide(self.config.num_attention_heads, world_size) + self.num_query_groups_per_partition = divide(self.config.num_query_groups, world_size) + + self.core_attention = build_module( + submodules.core_attention, + config=self.config, + layer_number=self.layer_number, + attn_mask_type=self.attn_mask_type, + attention_type=self.attention_type, + ) + + self.checkpoint_core_attention = self.config.recompute_granularity == 'selective' + + # Output. + self.linear_proj = build_module( + submodules.linear_proj, + self.query_projection_size, + self.config.hidden_size, + config=self.config, + init_method=self.config.output_layer_init_method, + bias=self.config.add_bias_linear, + input_is_parallel=True, + skip_bias_add=True, + is_expert=False, + tp_comm_buffer_name='proj', + ) + + def _checkpointed_attention_forward( + self, + query, + key, + value, + attention_mask, + rotary_pos_emb=None, + attn_mask_type=None, + packed_seq_params=None, + ): + """Forward method with selective activation checkpointing.""" + + def custom_forward(*inputs): + query = inputs[0] + key = inputs[1] + value = inputs[2] + attention_mask = inputs[3] + attn_mask_type = inputs[5] + attn_mask_type = AttnMaskType(attn_mask_type.item()) + output_ = self.core_attention( + query, + key, + value, + attention_mask, + attn_mask_type=attn_mask_type, + packed_seq_params=packed_seq_params, + ) + return output_ + + if attn_mask_type is None: + attn_mask_type = self.attn_mask_type + attn_mask_type = torch.tensor([attn_mask_type.value], dtype=torch.int) + hidden_states = tensor_parallel.checkpoint( + custom_forward, + False, + query, + key, + value, + attention_mask, + rotary_pos_emb, + attn_mask_type, + ) + + return hidden_states + + def _allocate_memory(self, inference_max_sequence_length, batch_size, dtype): + """Allocate memory to store kv cache during inference.""" + + return torch.empty( + inference_max_sequence_length, + batch_size, + self.num_query_groups_per_partition, + self.hidden_size_per_attention_head, + dtype=dtype, + device=torch.cuda.current_device(), + ) + + def _adjust_key_value_for_inference(self, inference_params, key, value, rotary_pos_emb): + """ + Saves the generated key and value tensors to the end of the buffers in inference_params. + Returns the full size keys and values from the provided inference_params, as well as + adjusted rotary_pos_emb. + + Returns a tuple: (key, value, rotary_pos_emb) + + """ + attn_mask_type = self.attn_mask_type + if inference_params is None: + return key, value, rotary_pos_emb, attn_mask_type + + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + is_first_step = False + if self.layer_number not in inference_params.key_value_memory_dict: + inf_max_seq_length = inference_params.max_sequence_length + inf_max_batch_size = inference_params.max_batch_size + inference_key_memory = self._allocate_memory( + inf_max_seq_length, inf_max_batch_size, key.dtype + ) + inference_value_memory = self._allocate_memory( + inf_max_seq_length, inf_max_batch_size, value.dtype + ) + inference_params.key_value_memory_dict[self.layer_number] = ( + inference_key_memory, + inference_value_memory, + ) + is_first_step = True + else: + # Get the pre-allocated buffers for this layer + inference_key_memory, inference_value_memory = inference_params.key_value_memory_dict[ + self.layer_number + ] + attn_mask_type = AttnMaskType.no_mask + + batch_start = inference_params.batch_size_offset + batch_end = batch_start + key.size(1) + assert batch_end <= inference_key_memory.size(1) + sequence_start = inference_params.sequence_len_offset + sequence_end = sequence_start + key.size(0) + assert sequence_end <= inference_key_memory.size(0) + # Copy key and values. + inference_key_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = key + inference_value_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = value + key = inference_key_memory[:sequence_end, batch_start:batch_end, ...] + value = inference_value_memory[:sequence_end, batch_start:batch_end, ...] + + # adjust the key rotary positional embedding + if rotary_pos_emb is not None: + q_pos_emb, k_pos_emb = rotary_pos_emb + # need to cross check this condition during inference + # if not set_inference_key_value_memory: + if not is_first_step: + # In inference, we compute one token at a time. + # Select the correct positional embedding + # (only the last token in the sequence) + q_pos_emb = q_pos_emb[sequence_end - 1 : sequence_end] + else: + # In the first forward pass of inference, + # we use the entire provided prefix. + # q_pos_emb here has the rope embeddings of the entire + # prefix + to-be-generated output so + # we slice to just the prefix. + q_pos_emb = q_pos_emb[:sequence_end, :, :, :] + k_pos_emb = k_pos_emb[:sequence_end, :, :, :] + rotary_pos_emb = (q_pos_emb, k_pos_emb) + + return key, value, rotary_pos_emb, attn_mask_type + + @abstractmethod + def get_query_key_value_tensors(self, hidden_states, key_value_states): + """ + This method needs to be implemented based on whether the derived class + is "self-attn" or "cross-attn". + """ + + def forward( + self, + hidden_states, + attention_mask, + key_value_states=None, + inference_params=None, + rotary_pos_emb=None, + packed_seq_params=None, + ): + # hidden_states: [sq, b, h] + + # For self attention we just duplicate the rotary_pos_emb if it isn't already + if rotary_pos_emb is not None and not isinstance(rotary_pos_emb, tuple): + rotary_pos_emb = (rotary_pos_emb,) * 2 + + # ===================== + # Query, Key, and Value + # ===================== + # Get the query, key and value tensors based on the type of attention - + # self or cross attn. + query, key, value = self.get_query_key_value_tensors(hidden_states, key_value_states) + + # =================================================== + # Adjust key, value, and rotary_pos_emb for inference + # =================================================== + key, value, rotary_pos_emb, attn_mask_type = self._adjust_key_value_for_inference( + inference_params, key, value, rotary_pos_emb + ) + + if packed_seq_params is not None: + query = query.squeeze(1) + key = key.squeeze(1) + value = value.squeeze(1) + + # ================================================ + # relative positional embedding (rotary embedding) + # ================================================ + if rotary_pos_emb is not None: + q_pos_emb, k_pos_emb = rotary_pos_emb + + if packed_seq_params is not None: + cu_seqlens_q = packed_seq_params.cu_seqlens_q + cu_seqlens_kv = packed_seq_params.cu_seqlens_kv + else: + cu_seqlens_q = cu_seqlens_kv = None + query = apply_rotary_pos_emb( + query, q_pos_emb, config=self.config, cu_seqlens=cu_seqlens_q, + ) + key = apply_rotary_pos_emb( + key, k_pos_emb, config=self.config, cu_seqlens=cu_seqlens_kv, + ) + + # TODO, can apply positional embedding to value_layer so it has + # absolute positional embedding. + # otherwise, only relative positional embedding takes effect + # value_layer = apply_rotary_pos_emb(value_layer, k_pos_emb) + + # ================================== + # core attention computation + # ================================== + + if self.checkpoint_core_attention and self.training: + core_attn_out = self._checkpointed_attention_forward( + query, + key, + value, + attention_mask, + attn_mask_type=attn_mask_type, + packed_seq_params=packed_seq_params, + ) + else: + core_attn_out = self.core_attention( + query, + key, + value, + attention_mask, + attn_mask_type=attn_mask_type, + packed_seq_params=packed_seq_params, + ) + + if packed_seq_params is not None: + # reshape to same output shape as unpacked case + # (t, np, hn) -> (t, b=1, h=np*hn) + # t is the pack size = sum (sq_i) + # note that batch is a dummy dimension in the packed case + core_attn_out = core_attn_out.reshape(core_attn_out.size(0), 1, -1) + + # ================= + # Output. [sq, b, h] + # ================= + + output, bias = self.linear_proj(core_attn_out) + + return output, bias + + +class SelfAttention(Attention): + """Self-attention layer class + + Self-attention layer takes input with size [s, b, h] + and returns output of the same size. + """ + + def __init__( + self, + config: TransformerConfig, + submodules: SelfAttentionSubmodules, + layer_number: int, + attn_mask_type=AttnMaskType.padding, + ): + super().__init__( + config=config, + submodules=submodules, + layer_number=layer_number, + attn_mask_type=attn_mask_type, + attention_type="self", + ) + + self.linear_qkv = build_module( + submodules.linear_qkv, + self.config.hidden_size, + self.query_projection_size + 2 * self.kv_projection_size, + config=self.config, + init_method=self.config.init_method, + gather_output=False, + bias=self.config.add_bias_linear or self.config.add_qkv_bias, + skip_bias_add=False, + is_expert=False, + tp_comm_buffer_name='qkv', + ) + + if submodules.q_layernorm is not None: + self.q_layernorm = build_module( + submodules.q_layernorm, + hidden_size=self.hidden_size_per_attention_head, + config=self.config, + eps=self.config.layernorm_epsilon, + ) + else: + self.q_layernorm = None + + if submodules.k_layernorm is not None: + self.k_layernorm = build_module( + submodules.k_layernorm, + hidden_size=self.hidden_size_per_attention_head, + config=self.config, + eps=self.config.layernorm_epsilon, + ) + else: + self.k_layernorm = None + + def run_realtime_tests(self): + """Performs a consistency check. + + This function makes sure that tensors across devices are the same during an experiment. + This is often not guaranteed to be so because of silent hardware failures (eg, memory + corruption loading a checkpoint, network traffic corruption encountered during data transmission). + + (TODO) In the future, more tensors should be checked across the training run and + checked every X iterations. This is left for future work. Equality of tensors is probably not + required; transmitting hashes is sufficient.""" + + if self.config.qk_layernorm: + # check that all tensor parallel and data parallel ranks have the same + # Q & K layernorm parameters. + rank = get_data_parallel_rank() + inputs = torch.stack( + [ + self.q_layernorm.weight.data, + self.q_layernorm.bias.data, + self.k_layernorm.weight.data, + self.k_layernorm.bias.data, + ] + ) + dp_list = [torch.empty_like(inputs) for _ in range(get_data_parallel_world_size())] + dp_list[rank] = inputs + torch.distributed.all_gather(dp_list, inputs, group=get_data_parallel_group()) + + def _compare(srcs, tgts, names, parallelism): + assert len(srcs) == len(tgts) == len(names) + for src, tgt, name in zip(srcs, tgts, names): + assert torch.all( + src == tgt + ), f"Discrepancy between {name} in {parallelism} ranks {i} and {rank}. Diff: {torch.norm(src - tgt)}" + + for i, dp in enumerate(dp_list): + q_w, q_b, k_w, k_b = torch.unbind(dp) + _compare( + [q_w, q_b, k_w, k_b], + [ + self.q_layernorm.weight.data, + self.q_layernorm.bias.data, + self.k_layernorm.weight.data, + self.k_layernorm.bias.data, + ], + ["q_w", "q_b", "k_w", "k_b"], + "DP", + ) + + rank = get_tensor_model_parallel_rank() + tp_list = [ + torch.empty_like(inputs) for _ in range(get_tensor_model_parallel_world_size()) + ] + tp_list[rank] = inputs + torch.distributed.all_gather(tp_list, inputs, group=get_tensor_model_parallel_group()) + + for i, tp in enumerate(tp_list): + q_w, q_b, k_w, k_b = torch.unbind(tp) + _compare( + [q_w, q_b, k_w, k_b], + [ + self.q_layernorm.weight.data, + self.q_layernorm.bias.data, + self.k_layernorm.weight.data, + self.k_layernorm.bias.data, + ], + ["q_w", "q_b", "k_w", "k_b"], + "TP", + ) + + def get_query_key_value_tensors(self, hidden_states, key_value_states=None): + """ + Derives `query`, `key` and `value` tensors from `hidden_states`. + """ + # Attention heads [sq, b, h] --> [sq, b, ng * (np/ng + 2) * hn)] + mixed_qkv, _ = self.linear_qkv(hidden_states) + + # [sq, b, hp] --> [sq, b, ng, (np/ng + 2) * hn] + new_tensor_shape = mixed_qkv.size()[:-1] + ( + self.num_query_groups_per_partition, + ( + (self.num_attention_heads_per_partition // self.num_query_groups_per_partition + 2) + * self.hidden_size_per_attention_head + ), + ) + mixed_qkv = mixed_qkv.view(*new_tensor_shape) + + split_arg_list = [ + ( + self.num_attention_heads_per_partition + // self.num_query_groups_per_partition + * self.hidden_size_per_attention_head + ), + self.hidden_size_per_attention_head, + self.hidden_size_per_attention_head, + ] + + if SplitAlongDim is not None: + + # [sq, b, ng, (np/ng + 2) * hn] --> [sq, b, ng, np/ng * hn], [sq, b, ng, hn], [sq, b, ng, hn] + (query, key, value) = SplitAlongDim(mixed_qkv, 3, split_arg_list,) + else: + + # [sq, b, ng, (np/ng + 2) * hn] --> [sq, b, ng, np/ng * hn], [sq, b, ng, hn], [sq, b, ng, hn] + (query, key, value) = torch.split(mixed_qkv, split_arg_list, dim=3,) + + # [sq, b, ng, np/ng * hn] -> [sq, b, np, hn] + query = query.reshape(query.size(0), query.size(1), -1, self.hidden_size_per_attention_head) + + if self.q_layernorm is not None: + query = self.q_layernorm(query) + + if self.k_layernorm is not None: + key = self.k_layernorm(key) + + if self.config.test_mode: + self.run_realtime_tests() + + return query, key, value + + +class CrossAttention(Attention): + """Cross-attention layer class + + Cross-attention layer takes input with size [s, b, h] and context with size + [s, b, h] and returns output of the same size. + """ + + def __init__( + self, + config: TransformerConfig, + submodules: CrossAttentionSubmodules, + layer_number: int, + attn_mask_type=AttnMaskType.padding, + ): + super().__init__( + config=config, + submodules=submodules, + layer_number=layer_number, + attn_mask_type=attn_mask_type, + attention_type="cross", + ) + + if self.config.num_query_groups != self.config.num_attention_heads: + raise ValueError( + f"Group query attention is not currently supported in cross attention." + ) + assert self.query_projection_size == self.kv_projection_size + + self.linear_q = build_module( + submodules.linear_q, + self.config.hidden_size, + self.query_projection_size, + config=self.config, + init_method=self.config.init_method, + gather_output=False, + bias=self.config.add_bias_linear, + skip_bias_add=False, + is_expert=False, + ) + + self.linear_kv = build_module( + submodules.linear_kv, + self.config.hidden_size, + 2 * self.kv_projection_size, + config=self.config, + init_method=self.config.init_method, + gather_output=False, + bias=self.config.add_bias_linear, + skip_bias_add=False, + is_expert=False, + ) + + def get_query_key_value_tensors(self, hidden_states, key_value_states): + """ + Derives `query` tensor from `hidden_states`, and `key`/`value` tensors + from `key_value_states`. + """ + # Attention heads [sk, b, h] --> [sk, b, (np * 2 * hn)] + mixed_kv, _ = self.linear_kv(key_value_states) + + # [sk, b, (np * 2 * hn)] --> [sk, b, np, 2 * hn] + new_tensor_shape = mixed_kv.size()[:-1] + ( + self.num_attention_heads_per_partition, + 2 * self.hidden_size_per_attention_head, + ) + mixed_kv = mixed_kv.view(*new_tensor_shape) + + # [sk, b, np, 2 * hn] --> 2 [sk, b, np, hn] + (key, value) = tensor_parallel.split_tensor_along_last_dim(mixed_kv, 2) + + # Attention head [sq, b, h] --> [sq, b, hp] + query, _ = self.linear_q(hidden_states) + + # [sq, b, hp] --> [sq, b, np, hn] + new_tensor_shape = query.size()[:-1] + ( + self.num_attention_heads_per_partition, + self.hidden_size_per_attention_head, + ) + query = query.view(*new_tensor_shape) + + return query, key, value diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/custom_layers/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/custom_layers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/custom_layers/transformer_engine.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/custom_layers/transformer_engine.py new file mode 100644 index 0000000..04efc00 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/custom_layers/transformer_engine.py @@ -0,0 +1,623 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import dataclasses +import os +from importlib.metadata import version +from typing import Callable + +import torch +import transformer_engine as te +from pkg_resources import packaging +from torch import Tensor + +from megatron.core import ModelParallelConfig +from megatron.core.packed_seq_params import PackedSeqParams +from megatron.core.parallel_state import ( + get_context_parallel_global_ranks, + get_context_parallel_group, + get_tensor_model_parallel_group, +) +from megatron.core.tensor_parallel import get_cuda_rng_tracker +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.transformer.utils import make_sharded_tensors_for_checkpoint + +_te_version = packaging.version.Version(version("transformer-engine")) + + +def _get_extra_te_kwargs(config: TransformerConfig): + extra_transformer_engine_kwargs = { + "params_dtype": config.params_dtype, + } + + if _te_version >= packaging.version.Version("0.12.0"): + if config.use_cpu_initialization: + extra_transformer_engine_kwargs["device"] = 'cpu' + else: + extra_transformer_engine_kwargs["device"] = torch.cuda.current_device() + return extra_transformer_engine_kwargs + + +def condition_init_method(config, init_method): + return init_method if config.perform_initialization else (lambda w: None) + + +class TENorm: + """ + A conditional wrapper to initialize an instance of Transformer-Engine's + `LayerNorm` or `RMSNorm` based on input + """ + + # TODO should we ditch normalization config and just use spec to choose LayerNorm vs RMSNorm? + def __new__( + cls, config: TransformerConfig, hidden_size: int, eps: float = 1e-5, + ): + if config.normalization == "LayerNorm": + instance = te.pytorch.LayerNorm( + hidden_size=hidden_size, + eps=eps, + sequence_parallel=config.sequence_parallel, + zero_centered_gamma=config.layernorm_zero_centered_gamma, + **_get_extra_te_kwargs(config), + ) + elif config.normalization == "RMSNorm": + assert hasattr( + te.pytorch, "RMSNorm" + ), "Transformer-Engine >= v0.11 required to use this feature" + instance = te.pytorch.RMSNorm( + hidden_size=hidden_size, + eps=eps, + sequence_parallel=config.sequence_parallel, + zero_centered_gamma=config.layernorm_zero_centered_gamma, + **_get_extra_te_kwargs(config), + ) + else: + raise Exception('Only LayerNorm and RMSNorm are curently supported') + + return instance + + +class TELinear(te.pytorch.Linear): + """ + Wrapper for the Transformer-Engine's `Linear` layer. + + Note that if Megatron's parallel_state has not been initialized + yet, the tp_group passed to TE will be None and must be set later + via set_tensor_parallel_group(). + """ + + def __init__( + self, + input_size: int, + output_size: int, + *, + parallel_mode: str, + config: ModelParallelConfig, + init_method: Callable, + bias: bool, + skip_bias_add: bool, + skip_weight_param_allocation: bool, + tp_comm_buffer_name: str = None, + ): + self.config = config + + # TE returns a zero length Tensor when bias=False and + # return_bias=True, but we prefer None. So in that case we + # tell TE to not return the bias, and return None + # ourselves. This way our forward always returns two values + # and we don't have to deal with the zero length Tensor. + self.te_return_bias = skip_bias_add and bias + self.is_first_microbatch = True + self.disable_parameter_transpose_cache = self.config.disable_parameter_transpose_cache + if skip_weight_param_allocation: + raise ValueError( + 'Transformer Engine linear layers do not support skip_weight_param_allocation' + ) + + extra_kwargs = _get_extra_te_kwargs(config) + + if _te_version >= packaging.version.Version("0.8.0"): + if self.config.tp_comm_overlap: + if _te_version > packaging.version.Version("1.5.0"): + # Use old overlap flags if they were supplied instead + extra_kwargs["ub_overlap_ag"] = ( + self.config.tp_comm_overlap_ag + if hasattr(self.config, "tp_comm_overlap_ag") + else self.config.tp_comm_split_ag or self.config.tp_comm_atomic_ag + ) + extra_kwargs["ub_overlap_rs"] = ( + self.config.tp_comm_overlap_rs + if hasattr(self.config, "tp_comm_overlap_rs") + else self.config.tp_comm_split_rs or self.config.tp_comm_atomic_rs + ) + else: + extra_kwargs["ub_split_ag"] = self.config.tp_comm_split_ag + extra_kwargs["ub_atomic_gemm_ag"] = self.config.tp_comm_atomic_ag + extra_kwargs["ub_split_rs"] = self.config.tp_comm_split_rs + extra_kwargs["ub_atomic_gemm_rs"] = self.config.tp_comm_atomic_rs + if _te_version > packaging.version.Version("1.0.0"): + assert ( + tp_comm_buffer_name is not None + ), "Buffer name should be set to configure communication overlap settings" + extra_kwargs["ub_name"] = tp_comm_buffer_name + + super().__init__( + in_features=input_size, + out_features=output_size, + sequence_parallel=self.config.sequence_parallel, + fuse_wgrad_accumulation=self.config.gradient_accumulation_fusion, + tp_group=get_tensor_model_parallel_group(check_initialized=False), + tp_size=self.config.tensor_model_parallel_size, + get_rng_state_tracker=get_cuda_rng_tracker + if get_cuda_rng_tracker().is_initialized() + else None, + init_method=condition_init_method(config, init_method), + bias=bias, + return_bias=self.te_return_bias, + parallel_mode=parallel_mode, + **extra_kwargs, + ) + + def forward(self, x): + _is_first_microbatch = ( + None if self.disable_parameter_transpose_cache else self.is_first_microbatch + ) + out = super().forward(x, is_first_microbatch=_is_first_microbatch) + self.is_first_microbatch = False + + # TE only returns a tuple when return_bias is True, otherwise + # it returns a single Tensor, we always want to return two + # values regardless of the arguments. + if self.te_return_bias: + return out + return out, None + + +class TELayerNormColumnParallelLinear(te.pytorch.LayerNormLinear): + """ + Wrapper for the Transformer-Engine's `LayerNormLinear` layer that combines + layernorm and linear layers + """ + + def __init__( + self, + input_size: int, + output_size: int, + *, + config: TransformerConfig, + init_method: Callable, + gather_output: bool, + bias: bool, + skip_bias_add: bool, + is_expert: bool, + skip_weight_param_allocation: bool = False, + tp_comm_buffer_name: str = None, + ): + self.config = config + + if gather_output: + raise ValueError('Transformer Engine linear layers do not support gather_output = True') + + if is_expert: + raise ValueError('Transformer Engine linear layers do not yet support MoE') + + if skip_weight_param_allocation: + raise ValueError( + 'Transformer Engine linear layers do not support skip_weight_param_allocation' + ) + + # TE returns a zero length Tensor when bias=False and + # return_bias=True, but we prefer None. So in that case we + # tell TE to not return the bias, and return None + # ourselves. This way our forward always returns two values + # and we don't have to deal with the zero length Tensor. + self.te_return_bias = skip_bias_add and bias + self.is_first_microbatch = True + self.disable_parameter_transpose_cache = self.config.disable_parameter_transpose_cache + extra_kwargs = _get_extra_te_kwargs(config) + + # Only Transformer-Engine version >= 0.11.0 supports `RMSNorm` + if _te_version >= packaging.version.Version("0.11.0"): + extra_kwargs["normalization"] = self.config.normalization + elif self.config.normalization != "LayerNorm": + raise ValueError( + f"Transformer Engine v{_te_version} does not support {self.config.normalization}." + ) + + if _te_version >= packaging.version.Version("0.8.0"): + if self.config.tp_comm_overlap: + extra_kwargs["ub_bulk_wgrad"] = self.config.tp_comm_bulk_wgrad + extra_kwargs["ub_bulk_dgrad"] = self.config.tp_comm_bulk_dgrad + if _te_version > packaging.version.Version("1.5.0"): + # Use old overlap flags if they were supplied instead + extra_kwargs["ub_overlap_ag"] = ( + self.config.tp_comm_overlap_ag + if hasattr(self.config, "tp_comm_overlap_ag") + else self.config.tp_comm_split_ag or self.config.tp_comm_atomic_ag + ) + if _te_version > packaging.version.Version("1.6.0.dev0"): + extra_kwargs["ub_overlap_rs_dgrad"] = ( + self.config.tp_comm_overlap_rs_dgrad + if hasattr(self.config, "tp_comm_overlap_rs_dgrad") + else False + ) + if tp_comm_buffer_name == 'qkv' and self.config.tp_comm_disable_qkv: + extra_kwargs["ub_overlap_ag"] = False + extra_kwargs["ub_overlap_rs_dgrad"] = False + + if tp_comm_buffer_name == 'fc1' and self.config.tp_comm_disable_fc1: + extra_kwargs["ub_overlap_ag"] = False + extra_kwargs["ub_overlap_rs_dgrad"] = False + else: + extra_kwargs["ub_atomic_gemm_ag"] = self.config.tp_comm_atomic_ag + extra_kwargs["ub_split_ag"] = self.config.tp_comm_split_ag + if _te_version > packaging.version.Version("1.0.0"): + assert ( + tp_comm_buffer_name is not None + ), "Buffer name should be set to configure communication overlap settings" + extra_kwargs["ub_name"] = tp_comm_buffer_name + + super().__init__( + in_features=input_size, + out_features=output_size, + eps=self.config.layernorm_epsilon, + sequence_parallel=self.config.sequence_parallel, + fuse_wgrad_accumulation=self.config.gradient_accumulation_fusion, + tp_group=get_tensor_model_parallel_group(check_initialized=False), + tp_size=self.config.tensor_model_parallel_size, + get_rng_state_tracker=get_cuda_rng_tracker + if get_cuda_rng_tracker().is_initialized() + else None, + init_method=condition_init_method(config, init_method), + bias=bias, + return_bias=self.te_return_bias, + parallel_mode="column", + return_layernorm_output=False, + zero_centered_gamma=self.config.layernorm_zero_centered_gamma, + **extra_kwargs, + ) + + def forward(self, x): + _is_first_microbatch = ( + None if self.disable_parameter_transpose_cache else self.is_first_microbatch + ) + out = super().forward(x, is_first_microbatch=_is_first_microbatch) + self.is_first_microbatch = False + + # TE only returns a tuple when return_bias is True, otherwise + # it returns a single Tensor, we always want to return two + # values regardless of the arguments. + if self.te_return_bias: + return out + return out, None + + def sharded_state_dict(self, prefix='', sharded_offsets=(), metadata=None): + """ Sharding along axis 0, bias sharded """ + state_dict = self.state_dict(prefix='', keep_vars=True) + return make_sharded_tensors_for_checkpoint( + state_dict, prefix, {'weight': 0, 'bias': 0}, sharded_offsets + ) + + +class TEColumnParallelLinear(TELinear): + """ + Wrapper for the Transformer-Engine's `Linear` layer but specialized similar + to megatron's `ColumnParallelLinear` layer. + """ + + def __init__( + self, + input_size: int, + output_size: int, + *, + config: ModelParallelConfig, + init_method: Callable, + gather_output: bool, + bias: bool, + skip_bias_add: bool, + is_expert: bool, + skip_weight_param_allocation: bool = False, + tp_comm_buffer_name: str = None, + ): + if gather_output: + raise ValueError('Transformer Engine linear layers do not support gather_output = True') + + if is_expert: + raise ValueError('Transformer Engine linear layers do not yet support MoE') + + super().__init__( + input_size=input_size, + output_size=output_size, + parallel_mode="column", + config=config, + init_method=condition_init_method(config, init_method), + bias=bias, + skip_bias_add=skip_bias_add, + skip_weight_param_allocation=skip_weight_param_allocation, + tp_comm_buffer_name=tp_comm_buffer_name, + ) + + def sharded_state_dict(self, prefix='', sharded_offsets=(), metadata=None): + """ Sharding along axis 0, bias sharded """ + state_dict = self.state_dict(prefix='', keep_vars=True) + return make_sharded_tensors_for_checkpoint( + state_dict, prefix, {'weight': 0, 'bias': 0}, sharded_offsets + ) + + +class TERowParallelLinear(TELinear): + """ + Wrapper for the Transformer-Engine's `Linear` layer but specialized similar + to megatron's `RowParallelLinear` layer. + """ + + def __init__( + self, + input_size: int, + output_size: int, + *, + config: ModelParallelConfig, + init_method: Callable, + bias: bool, + input_is_parallel: bool, + skip_bias_add: bool, + is_expert: bool, + tp_comm_buffer_name: str = None, + ): + if not input_is_parallel: + raise ValueError( + "Transformer Engine linear layers do not support input_is_parallel = False" + ) + + if is_expert: + raise ValueError('Transformer Engine linear layers do not yet support MoE') + + super().__init__( + input_size=input_size, + output_size=output_size, + parallel_mode="row", + config=config, + init_method=condition_init_method(config, init_method), + bias=bias, + skip_bias_add=skip_bias_add, + skip_weight_param_allocation=False, # We don't currently use this for row parallel layers + tp_comm_buffer_name=tp_comm_buffer_name, + ) + + def sharded_state_dict(self, prefix='', sharded_offsets=(), metadata=None): + """ Sharding along axis 1, bias not sharded """ + state_dict = self.state_dict(prefix='', keep_vars=True) + return make_sharded_tensors_for_checkpoint( + state_dict, prefix, {'weight': 1}, sharded_offsets + ) + + +class TEDotProductAttention(te.pytorch.DotProductAttention): + """ + Wrapper for the Transformer-Engine's `DotProductAttention` layer that also + has "flash attention" enabled. + + Note that if Megatron's parallel_state has not been initialized yet, the + tp_group and cp_group passed to TE will be None and must be set later + via set_tensor_parallel_group() and set_context_parallel_group(). + """ + + cp_stream: torch.cuda.Stream = None + + def __init__( + self, + config: TransformerConfig, + layer_number: int, + attn_mask_type: AttnMaskType, + attention_type: str, + attention_dropout: float = None, + ): + self.config = config + self.te_forward_mask_type = False + self.qkv_format: str = 'sbhd' + + if self.config.apply_query_key_layer_scaling != bool( + int(os.getenv('NVTE_APPLY_QK_LAYER_SCALING', '0')) + ): + raise ValueError( + f"apply_query_key_layer_scaling is {self.config.apply_query_key_layer_scaling} " + f"but environment variable NVTE_APPLY_QK_LAYER_SCALING is " + f"{os.getenv('NVTE_APPLY_QK_LAYER_SCALING')}. Transformer Engine does not support " + f"setting query key layer scaling via argument, so these two must match." + ) + + extra_kwargs = {} + if _te_version >= packaging.version.Version("0.11.0"): + extra_kwargs["num_gqa_groups"] = self.config.num_query_groups + elif self.config.num_query_groups != self.config.num_attention_heads: + raise ValueError( + f"Transformer Engine v{_te_version} does not support Grouped Query Attention, " + f"use a newer version of Transformer Engine. " + f"(num_query_groups ({self.config.num_query_groups}) != " + f"num_attention_heads ({self.config.num_attention_heads}))" + ) + + if _te_version >= packaging.version.Version("0.10.0"): + extra_kwargs["attention_type"] = attention_type + # older version don't need attention_type + + if _te_version > packaging.version.Version("0.12.0"): + self.te_forward_mask_type = True + + # Only Transformer-Engine version >= 1.0.0 supports context parallelism + if _te_version >= packaging.version.Version("1.0.0"): + if getattr(TEDotProductAttention, "cp_stream") is None: + TEDotProductAttention.cp_stream = torch.cuda.Stream() + extra_kwargs["cp_group"] = get_context_parallel_group(check_initialized=False) + extra_kwargs["cp_global_ranks"] = get_context_parallel_global_ranks( + check_initialized=False + ) + extra_kwargs["cp_stream"] = TEDotProductAttention.cp_stream + else: + assert ( + self.config.context_parallel_size == 1 + ), "Only Transformer-Engine version >= 1.0.0 supports context parallelism!" + + if config.window_size is not None: + # Check version + assert _te_version >= packaging.version.Version( + "1.2.0" + ), f"Transformer-Engine version ({str(_te_version)}) must be >= 1.2.0 to support sliding window attention." + extra_kwargs['window_size'] = config.window_size + + super().__init__( + num_attention_heads=self.config.num_attention_heads, + kv_channels=self.config.kv_channels, + attention_dropout=self.config.attention_dropout + if attention_dropout is None + else attention_dropout, + attn_mask_type=attn_mask_type.name, + sequence_parallel=self.config.sequence_parallel, + tp_size=self.config.tensor_model_parallel_size, + get_rng_state_tracker=get_cuda_rng_tracker + if get_cuda_rng_tracker().is_initialized() + else None, + tp_group=get_tensor_model_parallel_group(check_initialized=False), + layer_number=layer_number, + **extra_kwargs, + ) + + def forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + attention_mask: Tensor, + attn_mask_type: AttnMaskType, + packed_seq_params: PackedSeqParams = None, + ): + packed_seq_kwargs = ( + dataclasses.asdict(packed_seq_params) if packed_seq_params is not None else {} + ) + # overwrite self.qkv_format depending on self.config.apply_rope_fusion, which can be set after init + if self.config.apply_rope_fusion and _te_version > packaging.version.Version("0.13.0"): + self.qkv_format = 'bshd' + + qkv_format = packed_seq_kwargs.get('qkv_format', self.qkv_format) + + if _te_version < packaging.version.Version("1.3.0"): + # TE 1.3.0 introduces precomputing max_seqlen to remove unnecessary kernels and D2H copies (#555) + # These two arguments did not exist prior to 1.3.0 + packed_seq_kwargs.pop("max_seqlen_q", None) + packed_seq_kwargs.pop("max_seqlen_kv", None) + + if self.config.apply_rope_fusion and qkv_format == 'bshd': + query, key, value = [x.transpose(0, 1).contiguous() for x in (query, key, value)] + # In PyTorch, the following two tensors are in fact the same: + # Tensor with shape (1, S, H, D) and stride (S*H*D, H*D, D, 1) + # Tensor with shape (1, S, H, D) and stride (H*D, H*D, D, 1) + # Stride for a dimension that is 1 has no meaning, so tensors created two different ways + # can have same shape but different strides. + # We unify them to the first one to pass the stride check in TE + if value.shape == key.shape and value.shape[0] == 1 and value.stride() != key.stride(): + value = value.as_strided(value.shape, key.stride()) + + if self.te_forward_mask_type: + core_attn_out = super().forward( + query, + key, + value, + attention_mask, + attn_mask_type=attn_mask_type.name, + **packed_seq_kwargs, + ) + else: + core_attn_out = super().forward(query, key, value, attention_mask, **packed_seq_kwargs,) + + if self.config.apply_rope_fusion and qkv_format == 'bshd': + return core_attn_out.transpose(0, 1) + else: + return core_attn_out + + +class TEDelayedScaling(te.common.recipe.DelayedScaling): + """ + Wrapper for the Transformer-Engine's `DelayedScaling` layer. + """ + + def __init__( + self, + config: ModelParallelConfig, + fp8_format: int, + override_linear_precision: tuple = (False, False, False), + ): + extra_kwargs = _get_extra_te_kwargs(config) + if _te_version >= packaging.version.Version("1.6.0.dev0"): + extra_kwargs["fp8_dpa"] = config.fp8_dot_product_attention + extra_kwargs["fp8_mha"] = config.fp8_multi_head_attention + + super().__init__( + margin=config.fp8_margin, + interval=config.fp8_interval, + fp8_format=fp8_format, + amax_compute_algo=config.fp8_amax_compute_algo, + amax_history_len=config.fp8_amax_history_len, + override_linear_precision=override_linear_precision, + **extra_kwargs, + ) + + +def te_checkpoint( + forward_func, + distribute_saved_activations, + get_rng_state_tracker, + tp_group, + hidden_states, + attention_mask, + context, + context_mask, + rotary_pos_emb, + packed_seq_params, +): + from transformer_engine.pytorch.distributed import checkpoint + + if _te_version >= packaging.version.Version("1.5.0"): + return checkpoint( + forward_func, + hidden_states, + attention_mask, + context, + context_mask, + rotary_pos_emb, + packed_seq_params, + distribute_saved_activations=distribute_saved_activations, + get_rng_state_tracker=get_rng_state_tracker, + tp_group=tp_group, + ) + else: + return checkpoint( + forward_func, + distribute_saved_activations, + get_rng_state_tracker, + tp_group, + hidden_states, + attention_mask, + context, + context_mask, + rotary_pos_emb, + packed_seq_params, + ) + + +try: + + from transformer_engine.pytorch.attention import _SplitAlongDim + + SplitAlongDim = _SplitAlongDim.apply + +except ImportError: + + SplitAlongDim = None + +try: + + from transformer_engine.pytorch.cpu_offload import get_cpu_offload_context + +except ImportError: + + get_cpu_offload_context = None diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/dot_product_attention.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/dot_product_attention.py new file mode 100644 index 0000000..967d0ce --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/dot_product_attention.py @@ -0,0 +1,205 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + + +import math + +import torch +from torch import Tensor + +from megatron.core import parallel_state, tensor_parallel +from megatron.core.fusions.fused_softmax import FusedScaleMaskSoftmax +from megatron.core.packed_seq_params import PackedSeqParams +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.transformer.utils import attention_mask_func +from megatron.core.utils import divide + + +class DotProductAttention(MegatronModule): + """ + Region where selective activation recomputation is applied. + This region is memory intensive but less compute intensive which + makes activation checkpointing more efficient for LLMs (20B+). + See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + + We use the following notation: + h: hidden size + n: number of attention heads + p: number of tensor model parallel partitions + b: batch size + s: sequence length + """ + + def __init__( + self, + config: TransformerConfig, + layer_number: int, + attn_mask_type: AttnMaskType, + attention_type: str, + attention_dropout: float = None, + ): + super().__init__(config=config) + + self.config: TransformerConfig = config + + assert ( + self.config.context_parallel_size == 1 + ), "Context parallelism is only supported by TEDotProductAttention!" + + assert ( + self.config.window_size is None + ), "Sliding Window Attention is only supported by TEDotProductAttention!" + + self.layer_number = max(1, layer_number) + self.attn_mask_type = attn_mask_type + self.attention_type = attention_type # unused for now + + projection_size = self.config.kv_channels * self.config.num_attention_heads + + # Per attention head and per partition values. + world_size = parallel_state.get_tensor_model_parallel_world_size() + self.hidden_size_per_partition = divide(projection_size, world_size) + self.hidden_size_per_attention_head = divide(projection_size, config.num_attention_heads) + self.num_attention_heads_per_partition = divide(self.config.num_attention_heads, world_size) + self.num_query_groups_per_partition = divide(self.config.num_query_groups, world_size) + + coeff = None + self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) + if self.config.apply_query_key_layer_scaling: + coeff = self.layer_number + self.norm_factor *= coeff + + self.scale_mask_softmax = FusedScaleMaskSoftmax( + input_in_fp16=self.config.fp16, + input_in_bf16=self.config.bf16, + attn_mask_type=self.attn_mask_type, + scaled_masked_softmax_fusion=self.config.masked_softmax_fusion, + mask_func=attention_mask_func, + softmax_in_fp32=self.config.attention_softmax_in_fp32, + scale=coeff, + ) + + # Dropout. Note that for a single iteration, this layer will generate + # different outputs on different number of parallel partitions but + # on average it should not be partition dependent. + self.attention_dropout = torch.nn.Dropout( + self.config.attention_dropout if attention_dropout is None else attention_dropout + ) + + def forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + attention_mask: Tensor, + attn_mask_type: AttnMaskType = None, + packed_seq_params: PackedSeqParams = None, + ): + assert packed_seq_params is None, ( + "Packed sequence is not supported by DotProductAttention." + "Please use TEDotProductAttention instead." + ) + + # =================================== + # Raw attention scores. [b, n/p, s, s] + # =================================== + + # expand the key and value [sk, b, ng, hn] -> [sk, b, np, hn] + # This is a noop for normal attention where ng == np. When using group query attention this + # creates a view that has the keys and values virtually repeated along their dimension to + # match the number of queries. + + # attn_mask_type is not used. + if self.num_attention_heads_per_partition // self.num_query_groups_per_partition > 1: + key = key.repeat_interleave( + self.num_attention_heads_per_partition // self.num_query_groups_per_partition, dim=2 + ) + value = value.repeat_interleave( + self.num_attention_heads_per_partition // self.num_query_groups_per_partition, dim=2 + ) + + # [b, np, sq, sk] + output_size = ( + query.size(1), + query.size(2), + query.size(0), + key.size(0), + ) + + # [sq, b, np, hn] -> [sq, b * np, hn] + # This will be a simple view when doing normal attention, but in group query attention + # the key and value tensors are repeated to match the queries so you can't use simple strides + # to extract the queries. + query = query.reshape(output_size[2], output_size[0] * output_size[1], -1) + # [sk, b, np, hn] -> [sk, b * np, hn] + key = key.view(output_size[3], output_size[0] * output_size[1], -1) + + # preallocting input tensor: [b * np, sq, sk] + matmul_input_buffer = parallel_state.get_global_memory_buffer().get_tensor( + (output_size[0] * output_size[1], output_size[2], output_size[3]), query.dtype, "mpu", + ) + + # Raw attention scores. [b * np, sq, sk] + matmul_result = torch.baddbmm( + matmul_input_buffer, + query.transpose(0, 1), # [b * np, sq, hn] + key.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] + beta=0.0, + alpha=(1.0 / self.norm_factor), + ) + + # change view to [b, np, sq, sk] + attention_scores = matmul_result.view(*output_size) + + # =========================== + # Attention probs and dropout + # =========================== + + # attention scores and attention mask [b, np, sq, sk] + attention_probs: Tensor = self.scale_mask_softmax(attention_scores, attention_mask) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + + if not self.config.sequence_parallel: + with tensor_parallel.get_cuda_rng_tracker().fork(): + attention_probs = self.attention_dropout(attention_probs) + else: + attention_probs = self.attention_dropout(attention_probs) + + # ========================= + # Context layer. [sq, b, hp] + # ========================= + + # value -> context layer. + # [sk, b, np, hn] --> [b, np, sq, hn] + + # context layer shape: [b, np, sq, hn] + output_size = ( + value.size(1), + value.size(2), + query.size(0), + value.size(3), + ) + + # change view [sk, b * np, hn] + value = value.view(value.size(0), output_size[0] * output_size[1], -1) + + # change view [b * np, sq, sk] + attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) + + # matmul: [b * np, sq, hn] + context = torch.bmm(attention_probs, value.transpose(0, 1)) + + # change view [b, np, sq, hn] + context = context.view(*output_size) + + # [b, np, sq, hn] --> [sq, b, np, hn] + context = context.permute(2, 0, 1, 3).contiguous() + + # [sq, b, np, hn] --> [sq, b, hp] + new_context_shape = context.size()[:-2] + (self.hidden_size_per_partition,) + context = context.view(*new_context_shape) + + return context diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/enums.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/enums.py new file mode 100644 index 0000000..ab72f35 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/enums.py @@ -0,0 +1,26 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import enum + + +# can we get rid of this? +# it's being used in pipeline schedules +class ModelType(enum.Enum): + encoder_or_decoder = 1 + encoder_and_decoder = 2 + + +# class LayerType(enum.Enum): +# encoder = 1 +# decoder = 2 + + +class AttnType(enum.Enum): + self_attn = 1 + cross_attn = 2 + + +class AttnMaskType(enum.Enum): + padding = 1 + causal = 2 + no_mask = 3 # only used for TE diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/identity_op.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/identity_op.py new file mode 100644 index 0000000..5d9388f --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/identity_op.py @@ -0,0 +1,28 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +import torch + + +class IdentityOp(torch.nn.Module): + """ + This is a placeholder for IdentityOp(x) -> x + """ + + def __init__(self, *args, **kwargs): + super().__init__() + + def forward(self, x, *args, **kwargs): + return x + + +class IdentityFuncOp(IdentityOp): + """ + This is a placeholder for IdentityFuncOp(...)(x) -> IdentityOp(x) -> x. + Such a func is handy for ops like `bias_dropout_fusion` which themselves + return a function at runtime based on passed arguments + """ + + def __init__(self, *args, **kwargs): + super().__init__() + + def forward(self, *args, **kwargs): + return super().forward diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/mlp.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/mlp.py new file mode 100644 index 0000000..426ef92 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/mlp.py @@ -0,0 +1,205 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch +import torch.nn.functional as F + +from megatron.core import parallel_state +from megatron.core.dist_checkpointing import ShardedTensor +from megatron.core.dist_checkpointing.mapping import ( + ReplicaId, + ShardedStateDict, + ShardedTensorFactory, +) +from megatron.core.fusions.fused_bias_geglu import bias_geglu_impl +from megatron.core.fusions.fused_bias_gelu import bias_gelu_impl +from megatron.core.fusions.fused_bias_swiglu import bias_swiglu_impl +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.spec_utils import ModuleSpec, build_module +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.transformer.utils import make_sharded_tensors_for_checkpoint + + +@dataclass +class MLPSubmodules: + linear_fc1: Union[ModuleSpec, type] = None + linear_fc2: Union[ModuleSpec, type] = None + + +class MLP(MegatronModule): + """ + MLP will take the input with h hidden state, project it to 4*h + hidden dimension, perform nonlinear transformation, and project the + state back into h hidden dimension. + + + Returns an output and a bias to be added to the output. + If config.add_bias_linear is False, the bias returned is None. + + We use the following notation: + h: hidden size + p: number of tensor model parallel partitions + b: batch size + s: sequence length + """ + + def __init__( + self, + config: TransformerConfig, + submodules: MLPSubmodules, + is_expert: bool = False, + input_size: int = None, + ): + super().__init__(config=config) + + self.config: TransformerConfig = config + + self.input_size = input_size if input_size != None else self.config.hidden_size + + # If this is a gated linear unit we double the output width, see https://arxiv.org/pdf/2002.05202.pdf + ffn_hidden_size = self.config.ffn_hidden_size + if self.config.gated_linear_unit: + ffn_hidden_size *= 2 + + self.linear_fc1 = build_module( + submodules.linear_fc1, + self.input_size, + ffn_hidden_size, + config=self.config, + init_method=self.config.init_method, + gather_output=False, + bias=self.config.add_bias_linear, + skip_bias_add=True, + is_expert=is_expert, + tp_comm_buffer_name='fc1', + ) + + self.activation_func = self.config.activation_func + + self.linear_fc2 = build_module( + submodules.linear_fc2, + self.config.ffn_hidden_size, + self.config.hidden_size, + config=self.config, + init_method=self.config.output_layer_init_method, + bias=self.config.add_bias_linear, + input_is_parallel=True, + skip_bias_add=True, + is_expert=is_expert, + tp_comm_buffer_name='fc2', + ) + + def forward(self, hidden_states): + + # [s, b, 4 * h/p] + intermediate_parallel, bias_parallel = self.linear_fc1(hidden_states) + + if self.config.bias_activation_fusion: + if self.activation_func == F.gelu: + if self.config.gated_linear_unit: + intermediate_parallel = bias_geglu_impl(intermediate_parallel, bias_parallel) + else: + assert self.config.add_bias_linear is True + intermediate_parallel = bias_gelu_impl(intermediate_parallel, bias_parallel) + elif self.activation_func == F.silu and self.config.gated_linear_unit: + intermediate_parallel = bias_swiglu_impl( + intermediate_parallel, + bias_parallel, + self.config.activation_func_fp8_input_store, + ) + else: + raise ValueError("Only support fusion of gelu and swiglu") + else: + if bias_parallel is not None: + intermediate_parallel = intermediate_parallel + bias_parallel + if self.config.gated_linear_unit: + + def glu(x): + x = torch.chunk(x, 2, dim=-1) + return self.config.activation_func(x[0]) * x[1] + + intermediate_parallel = glu(intermediate_parallel) + else: + intermediate_parallel = self.activation_func(intermediate_parallel) + + # [s, b, h] + output, output_bias = self.linear_fc2(intermediate_parallel) + + return output, output_bias + + def sharded_state_dict( + self, prefix: str = '', sharded_offsets: tuple = (), metadata: Optional[dict] = None + ) -> ShardedStateDict: + sharded_state_dict = {} + for name, module in self._modules.items(): + if name == 'linear_fc1' and self.config.gated_linear_unit: + sub_sd = self._sharded_state_dict_for_glu( + name, module, prefix, sharded_offsets, metadata + ) + else: + sub_sd = module.sharded_state_dict(f'{prefix}{name}.', sharded_offsets, metadata) + sharded_state_dict.update(sub_sd) + return sharded_state_dict + + def _sharded_state_dict_for_glu( + self, + module_name: str, + module: torch.nn.Module, + prefix: str, + sharded_offsets: Tuple[Tuple[int, int, int]], + metadata: Optional[dict] = None, + ): + assert module_name == 'linear_fc1', module_name + sharded_state_dict = module.sharded_state_dict( + f'{prefix}{module_name}.', sharded_offsets, metadata + ) + weight_key = f'{prefix}{module_name}.weight' + prev_sh_ten = sharded_state_dict[weight_key] + + # We must split the tensor into 2 parts, each sharded separately. + # This requires a ShardedTensorFactory which `chunk`s during saving + # and `cat`s during loading + tp_rank = parallel_state.get_tensor_model_parallel_rank() + tp_size = parallel_state.get_tensor_model_parallel_world_size() + + tp_shard_axis = 0 + prepend_axis_num = len(sharded_offsets) + + def sh_ten_build_fn(key: str, t: torch.Tensor, replica_id: ReplicaId): + offset_w = (tp_shard_axis + prepend_axis_num, tp_rank, tp_size * 2) + offset_v = (tp_shard_axis + prepend_axis_num, tp_size + tp_rank, tp_size * 2) + with torch.no_grad(): + tensor_w, tensor_v = torch.chunk(t, 2, dim=tp_shard_axis) + return [ + ShardedTensor.from_rank_offsets( + key, + tensor_w, + *sharded_offsets, + offset_w, + replica_id=replica_id, + prepend_axis_num=prepend_axis_num, + ), + ShardedTensor.from_rank_offsets( + key, + tensor_v, + *sharded_offsets, + offset_v, + replica_id=replica_id, + prepend_axis_num=prepend_axis_num, + ), + ] + + def sh_ten_merge_fn(sub_state_dict): + with torch.no_grad(): + return torch.cat(sub_state_dict) + + sharded_state_dict[weight_key] = ShardedTensorFactory( + prev_sh_ten.key, + prev_sh_ten.data, + sh_ten_build_fn, + sh_ten_merge_fn, + prev_sh_ten.replica_id, + ) + return sharded_state_dict diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/module.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/module.py new file mode 100644 index 0000000..007521d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/module.py @@ -0,0 +1,190 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Megatron Module.""" +from typing import Optional, Tuple + +import torch +from torch.autograd import Variable +from torch.nn.parameter import Parameter + +from megatron.core import parallel_state +from megatron.core.dist_checkpointing.mapping import ShardedStateDict +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.transformer.utils import ( + make_sharded_tensors_for_checkpoint, + sharded_state_dict_default, +) + +_FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor) +_HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor) +_BF16_TYPES = (torch.BFloat16Tensor, torch.cuda.BFloat16Tensor) + + +def param_is_not_shared(param): + return not hasattr(param, 'shared') or not param.shared + + +class MegatronModule(torch.nn.Module): + """Base Megatron module inhertied by all Models. + + Megatron specific extensions of torch Module with support + for pipelining + + Args: + config (TransformerConfig): Transformer config + """ + + # def __init__(self, config: TransformerConfig, share_word_embeddings=True): + def __init__(self, config: TransformerConfig): + super().__init__() + self.config = config + + def state_dict_for_save_checkpoint(self, prefix: str = '', keep_vars: bool = False): + """Override state dict for saving checkpoints Use this function to override the + state dict for saving checkpoints. + + Args: + prefix (str, optional): _description_. Defaults to ''. + keep_vars (bool, optional): _description_. Defaults to False. + + Returns: + _type_: _description_ + """ + + return self.state_dict(prefix=prefix, keep_vars=keep_vars) + + def sharded_state_dict( + self, + prefix: str = '', + sharded_offsets: Tuple[Tuple[int, int, int]] = (), + metadata: Optional[dict] = None, + ) -> ShardedStateDict: + """Default implementation for sharded state dict for distributed checkpointing. + + General definition of sharded_state_dict simply calls `sharded_state_dict_default` + (which call sharded_state_dict method if possible or a default implementation otherwise) + recursively on all submodules. + + Args: + prefix (str): prefix for the state dict keys + sharded_offsets (Tuple[Tuple[int, int, int]], optional): sharding already + applied (e.g. PP related) by sup-modules. Passed along to ShardedTensor + metadata (dict, optional): metadata passed recursively to sharded_state_dict methods + + Returns: + dict: dictionary of state dict keys mapped to ShardedTensors + """ + sharded_state_dict = {} + # Save parameters + self._save_to_state_dict(sharded_state_dict, '', keep_vars=True) + sharded_state_dict = make_sharded_tensors_for_checkpoint( + sharded_state_dict, prefix, sharded_offsets=sharded_offsets + ) + # Recurse into submodules + for name, module in self.named_children(): + sharded_state_dict.update( + sharded_state_dict_default(module, f'{prefix}{name}.', sharded_offsets, metadata) + ) + return sharded_state_dict + + def set_is_first_microbatch(self): + """Sets the is_first_microbatch flag if it exists. When this flag is set, TE modules will update their fp8 parameter cache. + + """ + for m in self.modules(): + if hasattr(m, "is_first_microbatch"): + m.is_first_microbatch = True + + +def conversion_helper(val, conversion): + if not isinstance(val, (tuple, list)): + return conversion(val) + rtn = [conversion_helper(v, conversion) for v in val] + if isinstance(val, tuple): + rtn = tuple(rtn) + return rtn + + +def fp32_to_float16(val, float16_convertor): + def half_conversion(val): + val_typecheck = val + if isinstance(val_typecheck, (Parameter, Variable)): + val_typecheck = val.data + if isinstance(val_typecheck, _FLOAT_TYPES): + val = float16_convertor(val) + return val + + return conversion_helper(val, half_conversion) + + +def float16_to_fp32(val): + def float_conversion(val): + val_typecheck = val + if isinstance(val_typecheck, (Parameter, Variable)): + val_typecheck = val.data + if isinstance(val_typecheck, (_BF16_TYPES, _HALF_TYPES)): + val = val.float() + return val + + return conversion_helper(val, float_conversion) + + +class Float16Module(MegatronModule): + """Float 16 Module. + + Attributes: + config (TransformerConfig): Transformer config + fp16 (bool) : Specifies if the model runs in fp16 mode + bf16 (bool) : Specifies if the model runs in bf16 mode + + Args: + config (TransformerConfig): The transformer config used to initalize the model + """ + + def __init__(self, config: TransformerConfig, module: torch.nn.Module): + super(Float16Module, self).__init__(config) + self.config = config + self.fp16 = config.fp16 + self.bf16 = config.bf16 + + if self.fp16: + self.add_module('module', module.half()) + + def float16_convertor(val): + return val.half() + + elif self.bf16: + self.add_module('module', module.bfloat16()) + + def float16_convertor(val): + return val.bfloat16() + + else: + raise Exception('Either config.fp16 or config.bf16 should be True.') + + self.float16_convertor = float16_convertor + + def set_input_tensor(self, input_tensor): + return self.module.set_input_tensor(input_tensor) + + def forward(self, *inputs, **kwargs): + if parallel_state.is_pipeline_first_stage(): + inputs = fp32_to_float16(inputs, self.float16_convertor) + outputs = self.module(*inputs, **kwargs) + if parallel_state.is_pipeline_last_stage(): + outputs = float16_to_fp32(outputs) + return outputs + + def state_dict(self, destination=None, prefix='', keep_vars=False): + return self.module.state_dict(destination=destination, prefix=prefix, keep_vars=keep_vars) + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """Retrieve state_dict from the module being wrapped.""" + return self.module.state_dict_for_save_checkpoint(prefix=prefix, keep_vars=keep_vars) + + def sharded_state_dict(self, prefix='', *args, **kwargs): + """Retrieve sharded_state_dict from the module being wrapped.""" + return self.module.sharded_state_dict(prefix, *args, **kwargs) + + def load_state_dict(self, state_dict, strict=True): + self.module.load_state_dict(state_dict, strict=strict) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/README.md b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/README.md new file mode 100644 index 0000000..737c228 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/README.md @@ -0,0 +1,194 @@ +# Megatron Core MoE Key Features + +### Parallelism + +- **Expert Parallel** + - A specific method of parallelism for MoE models, where experts are partitioned onto different workers and each worker processes a different batch of training samples, each worker process one or more experts for each MoE layer. +- **3D Parallel**: Data Parallel , Tensor Parallel, Pipeline Parallel, Sequence Parallel + - Note: When using MoE with expert parallelism and tensor parallelism, sequence parallelism must be used. +- **Richer parallel mappings**: EP can be combined with DP/TP/PP/SP for handling larger MoE variants. +- **Distributed optimizer.** + +### Router and Load Balancing + +- Router type: + - Top-K MLP router + - Expert Choice router (coming soon) +- Load Balancing algorithms: + - Sinkhorn (S-BASE) + - Aux loss / Load balancing loss + +### Performance Optimizations + +- GroupedGEMM when num local experts > 1 + - Supported dtype: bf16 + +### Token Dispatch Mechanism + +- Dropless / No token drop. +- Token drop. (coming soon) + +### Ease of use +- Checkpoint converter (coming soon) + +## Upcoming features + +- Enhanced cutlass GroupedGEMM kernels + - Reduced host-device syncs. + - More supported dtype: fp32/bf16/fp16 + - Kernel heuristics tuned for A100/A10/L40S + - BWD cutlass GroupedGEMM kernels supported +- Token permutation / unpermutation fusion +- Fused Sinkhorn Kernel +- Context Parallel with MoE +- FP8 training support +- Enable ’--tp-comm-overlap‘ for MoE +- Distributed optimizer for MoE params. + +# User Guide + +### MoE Related Arguments + +| Item | Description | +| --- | --- | +| num-experts | Number of Experts in MoE (None means no MoE) | +| expert-model-parallel-size | Degree of expert model parallelism. | +| moe-grouped-gemm | When there are multiple experts per rank, compress multiple local gemms into a single kernel launch to improve the utilization and performance by leveraging the Grouped GEMM feature introduced since CUTLASS 2.8 | +| moe-router-load-balancing-type | Determines the load balancing strategy for the router. "aux_loss" corresponds to the load balancing loss used in GShard and SwitchTransformer, "sinkhorn" corresponds to the balancing algorithm used in S-BASE, and "none" implies no load balancing. The default is "aux_loss". | +| moe-router-topk | Number of experts to route to for each token. The default is 2. | +| moe-aux-loss-coeff | Scaling coefficient for the aux loss: a starting value of 1e-2 is recommended. | +| moe-z-loss-coeff | Scaling coefficient for the z-loss: a starting value of 1e-3 is recommended. | +| moe-input-jitter-eps | Add noise to the input tensor by applying jitter with a specified epsilon value. | +| moe-token-dropping | This feature involves selectively dropping and padding tokens for each expert to achieve a specified capacity, similar to GShard, Switch-Transformer, and DeepSpeed-MoE. Note: Currently unsupported. | + +### Example + +To train a top-2 MoE model with an auxiliary loss, include the following arguments: + +```python +--num-experts 8 +--expert-model-parallel-size 8 +--moe-grouped-gemm +--moe-router-load-balancing-type aux_loss # options: aux_loss, sinkhorn, none. Default is aux_loss. +--moe-router-topk 2 +--moe-aux-loss-coeff 1e-2 +--use-distributed-optimizer +``` +## A detailed MoE script: +
+Click here. + +```bash +#!/bin/bash + +# Runs Mixtral 8x7B model on 16 A100 GPUs + +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=${MASTER_ADDR:-"localhost"} +MASTER_PORT=${MASTER_PORT:-"6000"} +NNODES=${NNODES:-"1"} +NODE_RANK=${RANK:-"0"} +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) + +CHECKPOINT_PATH=$1 +TOKENIZER_MODEL=$2 +DATA_PATH=$3 + +DISTRIBUTED_ARGS=( + --nproc_per_node $GPUS_PER_NODE + --nnodes $NNODES + --node_rank $NODE_RANK + --master_addr $MASTER_ADDR + --master_port $MASTER_PORT +) + +MODEL_ARGS=( + --use-mcore-models + --disable-bias-linear + --seq-length 2048 + --max-position-embeddings 32768 + --num-layers 32 + --hidden-size 4096 + --ffn-hidden-size 14336 + --num-attention-heads 32 + --init-method-std 0.01 + --attention-dropout 0.0 + --hidden-dropout 0.0 + --normalization RMSNorm + --position-embedding-type rope + --swiglu + --untie-embeddings-and-output-weights + --group-query-attention + --num-query-groups 8 + --no-masked-softmax-fusion + --no-position-embedding +) + +MOE_ARGS=( + --num-experts 8 + --expert-model-parallel-size 4 + --moe-router-load-balancing-type aux_loss # options: aux_loss, sinkhorn, None. Default is aux_loss. + --moe-router-topk 2 + --moe-aux-loss-coeff 1e-2 + --moe-grouped-gemm +) + +DATA_ARGS=( + --tokenizer-type Llama2Tokenizer + --tokenizer-model ${TOKENIZER_MODEL} + --data-path $DATA_PATH + --split 99990,8,2 +) + +TRAINING_ARGS=( + --micro-batch-size 1 + --global-batch-size 128 + --lr 1e-4 + --train-iters 500000 + --lr-decay-iters 320000 + --lr-decay-style cosine + --min-lr 1.0e-5 + --weight-decay 0.1 + --lr-warmup-iters 500 + --clip-grad 1.0 + --bf16 +) + +MODEL_PARALLEL_ARGS=( + --tensor-model-parallel-size 4 + --pipeline-model-parallel-size 1 + --sequence-parallel + --use-distributed-optimizer +) + +LOGGING_ARGS=( + --log-interval 1 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH \ + --tensorboard-dir "${CHECKPOINT_PATH}/tensorboard" \ + --no-load-optim \ + --no-load-rng +) + +if [ -n "${WANDB_API_KEY}" ]; then + LOGGING_ARGS+=( + --wandb-project ${WANDB_PROJECT:-"Mixtral-Finetuning"} + --wandb-exp-name ${WANDB_NAME:-"Mixtral_8x7B"} + ) +fi + +torchrun ${DISTRIBUTED_ARGS[@]} pretrain_gpt.py \ + ${MODEL_ARGS[@]} \ + ${MOE_ARGS[@]} \ + ${DATA_ARGS[@]} \ + ${TRAINING_ARGS[@]} \ + ${MODEL_PARALLEL_ARGS[@]} \ + ${LOGGING_ARGS[@]} +``` +
diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/experts.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/experts.py new file mode 100644 index 0000000..54c83ea --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/experts.py @@ -0,0 +1,248 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from typing import Tuple + +import numpy as np +import torch +import torch.nn.functional as F +from torch.nn.parameter import Parameter + +from megatron.core import parallel_state +from megatron.core.dist_checkpointing.mapping import ShardedStateDict +from megatron.core.dist_checkpointing.utils import replace_prefix_for_sharding +from megatron.core.jit import jit_fuser +from megatron.core.tensor_parallel.layers import ( + _initialize_affine_weight_cpu, + _initialize_affine_weight_gpu, +) +from megatron.core.tensor_parallel.utils import divide +from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.moe import grouped_gemm_util as gg +from megatron.core.transformer.transformer_config import TransformerConfig + + +class GroupedMLP(MegatronModule): + """An efficient implementation of the Experts layer using CUTLASS GroupedGEMM. + + This class is designed to execute multiple experts in parallel, thereby maximizing computational efficiency. + """ + + def __init__(self, num_local_experts: int, config: TransformerConfig): + super().__init__(config=config) + self.config: TransformerConfig = config + self.num_local_experts = num_local_experts + gg.assert_grouped_gemm_is_available() + assert ( + config.add_bias_linear == False + ), "bias in the expert layer is not supported in Grouped GEMM yet, please set '--disable-bias-linear' instead." + + self.expert_parallel = config.expert_model_parallel_size > 1 + if self.config.gated_linear_unit: + if self.config.activation_func != F.silu: + raise ValueError("Activation function must be silu when using GroupedMLP.") + + @jit_fuser + def glu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + + self.activation_func = glu + else: + self.activation_func = self.config.activation_func + + # How many feature each rank holds for fc1 and fc2, respectively. + tp_size = parallel_state.get_tensor_model_parallel_world_size() + fc1_output_size = self.config.ffn_hidden_size * self.num_local_experts + if config.gated_linear_unit: + # Project to 4h. If using swiglu double the output width, + # see https://arxiv.org/pdf/2002.05202.pdf + fc1_output_size *= 2 + fc1_output_size_per_partition = divide(fc1_output_size, tp_size) + + fc2_input_size = self.config.ffn_hidden_size * self.num_local_experts + fc2_input_size_per_partition = divide(fc2_input_size, tp_size) + + # Note: The current kernel implementations of grouped_gemm + # does not support transposition with CUTLASS grouped GEMM + # (https://github.com/fanshiqing/grouped_gemm/blob/main/csrc/grouped_gemm.cu#L355-L358) + # and as a result we avoid allocate the transpose of weights. + # Initialize weight. + if config.use_cpu_initialization: + self.weight1 = Parameter( + torch.empty( + self.config.hidden_size, + fc1_output_size_per_partition, + dtype=config.params_dtype, + ) + ) + self.weight2 = Parameter( + torch.empty( + fc2_input_size_per_partition, + self.config.hidden_size, + dtype=config.params_dtype, + ) + ) + if config.perform_initialization: + _initialize_affine_weight_cpu( + self.weight1, + self.config.hidden_size, + fc1_output_size, + fc1_output_size_per_partition, + partition_dim=1, + init_method=config.init_method, + params_dtype=config.params_dtype, + ) + _initialize_affine_weight_cpu( + self.weight2, + fc2_input_size, + self.config.hidden_size, + fc2_input_size_per_partition, + partition_dim=0, + init_method=config.output_layer_init_method, + params_dtype=config.params_dtype, + ) + else: + self.weight1 = Parameter( + torch.empty( + self.config.hidden_size, + fc1_output_size_per_partition, + device=torch.cuda.current_device(), + dtype=config.params_dtype, + ) + ) + self.weight2 = Parameter( + torch.empty( + fc2_input_size_per_partition, + self.config.hidden_size, + device=torch.cuda.current_device(), + dtype=config.params_dtype, + ) + ) + if config.perform_initialization: + _initialize_affine_weight_gpu( + self.weight1, + config.init_method, + partition_dim=1, + expert_parallel=self.expert_parallel, + ) + _initialize_affine_weight_gpu( + self.weight2, + config.output_layer_init_method, + partition_dim=0, + expert_parallel=self.expert_parallel, + ) + setattr(self.weight1, 'allreduce', not self.expert_parallel) + setattr(self.weight2, 'allreduce', not self.expert_parallel) + + def forward(self, permuted_local_hidden_states, tokens_per_expert): + if permuted_local_hidden_states.nelement() != 0: + # Reshape the weights for the grouped GEMMs. + w1 = self.weight1.view(self.num_local_experts, self.config.hidden_size, -1) + w2 = self.weight2.view(self.num_local_experts, -1, self.config.hidden_size) + + fc1_output = gg.ops.gmm( + permuted_local_hidden_states, w1, tokens_per_expert, trans_b=False + ) + + intermediate_parallel = self.activation_func(fc1_output) + + fc2_output = gg.ops.gmm(intermediate_parallel, w2, tokens_per_expert, trans_b=False) + else: + # No token is allocated for local experts. + assert torch.count_nonzero(tokens_per_expert) == 0 + + # Make sure parameters still have gradients when no tokens are routed to this set of experts. + w1 = self.weight1.view(self.config.hidden_size, -1) + w2 = self.weight2.view(-1, self.config.hidden_size) + h = torch.matmul(permuted_local_hidden_states, w1) + h = self.activation_func(h) + h = torch.matmul(h, w2) + + fc2_output = h + + return fc2_output, None + + def sharded_state_dict(self, prefix='', sharded_offsets=(), metadata=None): + raise NotImplementedError( + 'Currently distributed checkpointing is not supported for GroupedMLP' + ) + + +class SequentialMLP(MegatronModule): + """An implementation of the Experts layer using a sequence of MLP layers. + + This class executes each expert sequentially. + """ + + def __init__(self, num_local_experts, config: TransformerConfig, submodules: MLPSubmodules): + super().__init__(config=config) + self.add_bias = config.add_bias_linear + self.num_local_experts = num_local_experts + self.local_experts = torch.nn.ModuleList() + for _ in range(self.num_local_experts): + expert = MLP(self.config, submodules, is_expert=True) + self.local_experts.append(expert) + + def forward(self, permuted_local_hidden_states, tokens_per_expert): + output_local = torch.zeros_like(permuted_local_hidden_states) + output_bias_local = None + if self.add_bias: + output_bias_local = torch.zeros_like(permuted_local_hidden_states) + + cumsum_num_tokens = torch.cumsum(tokens_per_expert, dim=0) + # Insert zero at the begining for offset index's convenience + zero_tensor = torch.zeros(1, dtype=torch.long, device=cumsum_num_tokens.device) + cumsum_num_tokens = torch.cat((zero_tensor, cumsum_num_tokens)) + for expert_num, expert in enumerate(self.local_experts): + start = cumsum_num_tokens[expert_num] + end = cumsum_num_tokens[expert_num + 1] + hidden = permuted_local_hidden_states[start:end] + output, output_bias = expert(hidden) + + output_local[start:end] = output + if self.add_bias: + output_bias = output_bias.expand_as(output) + output_bias_local[start:end, :] = output_bias + + return output_local, output_bias_local + + def sharded_state_dict(self, prefix='', sharded_offsets=(), metadata=None): + """ Maps local expert to global experts. """ + sharded_state_dict = {} + num_global_experts = ( + parallel_state.get_expert_model_parallel_world_size() * self.num_local_experts + ) + local_expert_indices_offset = ( + parallel_state.get_expert_model_parallel_rank() * self.num_local_experts + ) + + expert_sharded_prefix = f'{prefix}experts.' + for expert_local_idx, expert in enumerate(self.local_experts): + expert_global_idx = local_expert_indices_offset + expert_local_idx + expert_state_dict_prefix = f'{prefix}local_experts.{expert_local_idx}.' + expert_sharded_offsets = ( + *sharded_offsets, + (len(sharded_offsets), expert_global_idx, num_global_experts), + ) + + expert_state_dict = expert.sharded_state_dict( + expert_state_dict_prefix, expert_sharded_offsets, metadata + ) + # Remove expert layers indexing from sharded keys + replace_prefix_for_sharding( + expert_state_dict, expert_state_dict_prefix, expert_sharded_prefix + ) + # Adjust replica ids - replication along DP modulo EP + for k, sh_ten in expert_state_dict.items(): + replica_id = sh_ten.replica_id + assert ( + len(replica_id) == 3 + ), f'Expected replica_id for {k} to be in (PP, TP, DP) format, got: {replica_id}' + sh_ten.replica_id = ( + *replica_id[:2], + parallel_state.get_data_modulo_expert_parallel_rank(), + ) + + sharded_state_dict.update(expert_state_dict) + return sharded_state_dict diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/grouped_gemm_util.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/grouped_gemm_util.py new file mode 100644 index 0000000..e7ef79d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/grouped_gemm_util.py @@ -0,0 +1,20 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +try: + import grouped_gemm +except ImportError: + grouped_gemm = None + + +def grouped_gemm_is_available(): + return grouped_gemm is not None + + +def assert_grouped_gemm_is_available(): + assert grouped_gemm_is_available(), ( + "Grouped GEMM is not available. Please run " + "`pip install git+https://github.com/fanshiqing/grouped_gemm@v1.0`." + ) + + +ops = grouped_gemm.ops if grouped_gemm_is_available() else None diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/moe_layer.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/moe_layer.py new file mode 100644 index 0000000..6b54cee --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/moe_layer.py @@ -0,0 +1,93 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +from abc import ABC, abstractmethod + +import torch + +from megatron.core import parallel_state +from megatron.core.transformer.mlp import MLPSubmodules +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.moe.experts import GroupedMLP, SequentialMLP +from megatron.core.transformer.moe.router import TopKRouter +from megatron.core.transformer.moe.token_dispatcher import ( + MoEAllGatherTokenDispatcher, + MoEAlltoAllTokenDispatcher, +) +from megatron.core.transformer.transformer_config import TransformerConfig + + +class BaseMoELayer(MegatronModule, ABC): + """Base class for a mixture of experts layer. + + Args: + config (TransformerConfig): Configuration object for the transformer model. + """ + + def __init__(self, config: TransformerConfig, layer_number: int = None): + super(BaseMoELayer, self).__init__(config) + self.config = config + self.expert_parallel_size = parallel_state.get_expert_model_parallel_world_size() + assert self.expert_parallel_size > 0, "Expected non-negative expert parallel size" + assert self.config.num_moe_experts % self.expert_parallel_size == 0 + self.num_local_experts = self.config.num_moe_experts // self.expert_parallel_size + local_expert_indices_offset = ( + parallel_state.get_expert_model_parallel_rank() * self.num_local_experts + ) + self.local_expert_indices = [ + local_expert_indices_offset + i for i in range(self.num_local_experts) + ] + assert all(map(lambda x: x < self.config.num_moe_experts, self.local_expert_indices)) + self.router = None + self.experts = None + self.token_dispatcher = None + self.layer_number = layer_number + + @abstractmethod + def forward(self, hidden_states): + pass + + def set_layer_number(self, layer_number: int): + self.layer_number = layer_number + self.router.set_layer_number(layer_number) + + +class MoELayer(BaseMoELayer): + """Mixture of experts Layer **currently only supports no token dropping**. + + Args: + BaseMoELayer (MegatronModule): Base class for MoE layers + """ + + def __init__( + self, config: TransformerConfig, submodules: MLPSubmodules = None, layer_number: int = None + ): + self.submodules = submodules + super(MoELayer, self).__init__(config=config, layer_number=layer_number) + self.router = TopKRouter(config=self.config) + if self.config.moe_grouped_gemm: + self.experts = GroupedMLP(self.num_local_experts, self.config) + else: + assert isinstance(self.submodules, MLPSubmodules) + self.experts = SequentialMLP(self.num_local_experts, self.config, self.submodules) + if config.moe_token_dispatcher_type == "allgather": + self.token_dispatcher = MoEAllGatherTokenDispatcher( + self.num_local_experts, self.local_expert_indices, config=self.config + ) + elif config.moe_token_dispatcher_type == "alltoall": + self.token_dispatcher = MoEAlltoAllTokenDispatcher( + self.num_local_experts, self.local_expert_indices, config=self.config + ) + else: + raise ValueError( + f"Unsupported token dispatcher type: {config.moe_token_dispatcher_type}" + ) + + def forward(self, hidden_states: torch.Tensor): + # process MoE + scores, indices = self.router(hidden_states) + (dispatched_input, tokens_per_expert) = self.token_dispatcher.token_permutation( + hidden_states, scores, indices + ) + expert_output, mlp_bias = self.experts(dispatched_input, tokens_per_expert) + output, mlp_bias = self.token_dispatcher.token_unpermutation(expert_output, mlp_bias) + return output, mlp_bias diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/moe_utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/moe_utils.py new file mode 100644 index 0000000..246572b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/moe_utils.py @@ -0,0 +1,229 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import torch + +from megatron.core import parallel_state + + +def switch_load_balancing_loss_func(gates, mask, moe_aux_loss_coeff): + """Calculate the auxiliary loss for better load balacing. + Please refer to the Switch Transformer paper (https://arxiv.org/abs/2101.03961) for details. + + Args: + gates (torch.Tensor): The gates tensor representing the routing probabilities for each expert. + mask (torch.Tensor): The 2D mask tensor indicating which experts are selected. + + Returns: + torch.Tensor: The auxiliary loss for load balancing. + """ + num_experts = mask.size(-1) + gates_mean = gates.mean(dim=0) + top_k = mask[0].count_nonzero() + selection_mean = mask.float().mean(dim=0) / top_k + aux_loss = torch.sum(gates_mean * selection_mean) * num_experts + aux_loss *= moe_aux_loss_coeff + return aux_loss + + +def z_loss_func(logits, z_loss_coeff): + """Encourages the router's logits to remain small to enhance stability. + Please refer to the ST-MoE paper (https://arxiv.org/pdf/2202.08906.pdf) for details. + + Args: + logits (torch.Tensor): The logits of the router. + + Returns: + torch.Tensor: The logits after applying the z-loss. + """ + + z_loss = torch.mean(torch.square(torch.logsumexp(logits, dim=-1))) * z_loss_coeff + return z_loss + + +def sinkhorn(cost: torch.Tensor, tol: float = 0.0001): + """Sinkhorn based MoE routing function""" + cost = torch.exp(cost) + d0 = torch.ones(cost.size(0), device=cost.device, dtype=cost.dtype) + d1 = torch.ones(cost.size(1), device=cost.device, dtype=cost.dtype) + + eps = 0.00000001 + error = 1e9 + d1_old = d1 + while error > tol: + d0 = (1 / d0.size(0)) * 1 / (torch.sum(d1 * cost, 1) + eps) + d1 = (1 / d1.size(0)) * 1 / (torch.sum(d0.unsqueeze(1) * cost, 0) + eps) + error = torch.mean(torch.abs(d1_old - d1)) + d1_old = d1 + return d1 * cost * d0.unsqueeze(1) + + +class MoEAuxLossAutoScaler(torch.autograd.Function): + """An AutoScaler that compute and scales the grad for auxiliary loss. + + """ + + main_loss_backward_scale: torch.Tensor = torch.tensor(1.0) + + @staticmethod + def forward(ctx, output: torch.Tensor, aux_loss: torch.Tensor): + """Preserve the aux_loss by storing it in the context to avoid garbage collection. + + Args: + output (torch.Tensor): The output tensor. + aux_loss (torch.Tensor): The auxiliary loss tensor. + + Returns: + torch.Tensor: The output tensor. + """ + ctx.save_for_backward(aux_loss) + return output + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + """Compute and scale the gradient for auxiliary loss.. + + Args: + grad_output (torch.Tensor): The gradient of the output. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: The gradient of the output, scaled auxiliary loss gradient. + """ + (aux_loss,) = ctx.saved_tensors + aux_loss_backward_scale = MoEAuxLossAutoScaler.main_loss_backward_scale + scaled_aux_loss_grad = torch.ones_like(aux_loss) * aux_loss_backward_scale + return grad_output, scaled_aux_loss_grad + + @staticmethod + def set_loss_scale(scale: torch.Tensor): + """set the scale of the aux loss. + + Args: + scale (torch.Tensor): The scale value to set. Please ensure that the scale passed in matches the scale of the main_loss. + """ + MoEAuxLossAutoScaler.main_loss_backward_scale = scale + + +def permute(tokens, indices, topk: int = 1): + """Permute the tokens based on the indices. Token with the same index will be grouped together. + + Args: + tokens (torch.Tensor): The input token tensor. + indices (torch.Tensor): The token to expert indices tensor, should have a shape of [num_tokens, topk]. + topk (int, optional): The topk value. Defaults to 1. + + Returns: + torch.Tensor: The permuted tensor. + """ + if topk > 1: + assert indices.size(1) == topk + flatten_indices = indices.view(-1) + sorted_indices = torch.argsort(flatten_indices, stable=True) + permuted_tokens = tokens.index_select(0, sorted_indices // topk) + return permuted_tokens, sorted_indices + + +def unpermute(permuted_tokens, sorted_indices, probs: torch.Tensor = None, topk: int = 1): + """Unpermute a tensor of permuted tokens based on sorted indices, and optionally merge the tokens with their corresponding probabilities. + + Args: + permuted_tokens (torch.Tensor): The tensor of permuted tokens to be unpermuted. + sorted_indices (torch.Tensor): The tensor of sorted indices used to unpermute the tokens. + probs (torch.Tensor, optional): The tensor of probabilities corresponding to the permuted tokens. If provided, the unpermuted tokens will be merged with their respective probabilities. + topk (int, optional): The number of top tokens to consider for merging with probabilities. Defaults to 1. + """ + if topk > 1: + assert probs is not None + assert ( + probs.size(0) == permuted_tokens.size(0) // topk + ), f"{probs.size()} {permuted_tokens.size()}" + if probs is not None: + assert probs.size(0) == permuted_tokens.size(0) // topk + assert probs.size(1) == topk, f"probs size {probs.size()} merge_factor {topk}" + + unpermuted_tokens = torch.zeros_like(permuted_tokens) + unpermuted_tokens.index_copy_(0, sorted_indices, permuted_tokens) + + unpermuted_tokens = unpermuted_tokens.reshape(-1, topk, permuted_tokens.size(-1)) + + if probs is not None: + unpermuted_tokens = unpermuted_tokens * probs.unsqueeze(-1) + + unpermuted_tokens = unpermuted_tokens.sum(dim=1) + + return unpermuted_tokens + + +def save_to_aux_losses_tracker(name: str, loss: torch.Tensor, layer_number: int, num_layers: int): + """Save the auxiliary loss for logging. + Args: + name (str): The name of the loss. + loss (torch.Tensor): The loss tensor. + layer_number (int): Layer index of the loss. + num_layers (int): The number of total layers. + """ + # Skip aux loss logging if layer_number is None. + if layer_number is None: + return + + if name not in parallel_state._MOE_AUX_LOSSES_LOGGING_TRACKER: + parallel_state._MOE_AUX_LOSSES_LOGGING_TRACKER[name] = torch.zeros( + num_layers, device=loss.device + ) + parallel_state._MOE_AUX_LOSSES_LOGGING_TRACKER[name][layer_number - 1] += loss.detach() + + +def clear_aux_losses_tracker(): + """Clear the auxiliary losses.""" + for name in parallel_state._MOE_AUX_LOSSES_LOGGING_TRACKER: + parallel_state._MOE_AUX_LOSSES_LOGGING_TRACKER[name].zero_() + + +def get_aux_losses_tracker(): + """Return the auxiliary losses.""" + return parallel_state._MOE_AUX_LOSSES_LOGGING_TRACKER + + +def aggregate_aux_losses_tracker_across_pipeline_parallel(): + """Sum aux losses across PP.""" + for name in parallel_state._MOE_AUX_LOSSES_LOGGING_TRACKER: + loss = parallel_state._MOE_AUX_LOSSES_LOGGING_TRACKER[name] + torch.distributed.all_reduce(loss, group=parallel_state.get_pipeline_model_parallel_group()) + + +def track_moe_metrics( + loss_scale, iteration, writer, wandb_writer=None, total_loss_dict=None, per_layer_logging=False +): + # Aux loss logging + aggregate_aux_losses_tracker_across_pipeline_parallel() + if writer is not None: + aux_losses = {k: v.float() * loss_scale for k, v in get_aux_losses_tracker().items()} + for name, loss_list in aux_losses.items(): + if total_loss_dict is not None: + if name not in total_loss_dict: + total_loss_dict[name] = loss_list.mean() + else: + total_loss_dict[name] += loss_list.mean() + + # currently when using add_scalars, + # torch.utils.add_scalars makes each timer its own run, which + # polutes the runs list, so we just add each as a scalar + writer.add_scalar(name, loss_list.mean(), iteration) + if per_layer_logging: + for i, loss in enumerate(loss_list.tolist()): + writer.add_scalar(f"moe/{name}_layer_{i}", loss, iteration) + + # W&B logging lacks support for logging multiple scalars simultaneously. + # As a workaround, we log each scalar individually first, then we can create + # a custom panel to manually group them to a single plot. + if wandb_writer: + wandb_writer.log({f"{name}": loss_list.mean()}, iteration) + if per_layer_logging: + wandb_writer.log( + { + f"moe/{name}_layer_{i}": loss + for i, loss in enumerate(loss_list.tolist()) + }, + iteration, + ) + + clear_aux_losses_tracker() diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/router.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/router.py new file mode 100644 index 0000000..d8d4c63 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/router.py @@ -0,0 +1,274 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import math +from abc import ABC, abstractmethod +from typing import Callable, List + +import torch + +from megatron.core.tensor_parallel import ( + gather_from_sequence_parallel_region, + get_cuda_rng_tracker, + get_data_parallel_rng_tracker_name, +) +from megatron.core.tensor_parallel.random import ( + get_cuda_rng_tracker, + get_data_parallel_rng_tracker_name, +) +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.moe.moe_utils import ( + MoEAuxLossAutoScaler, + save_to_aux_losses_tracker, + sinkhorn, + switch_load_balancing_loss_func, + z_loss_func, +) +from megatron.core.transformer.transformer_config import TransformerConfig + + +class Router(ABC, MegatronModule): + """Base Router class""" + + def __init__(self, config: TransformerConfig) -> None: + """ + Initialize the Router module. + + Args: + config (TransformerConfig): Configuration object for the Transformer model. + """ + super().__init__(config) + self.config = config + self.num_experts = self.config.num_moe_experts + self.moe_aux_loss_func = None + self.layer_number = None + + # Initialize the gate weights. + self.weight = torch.nn.Parameter( + torch.empty((self.config.num_moe_experts, self.config.hidden_size)) + ) + with get_cuda_rng_tracker().fork(get_data_parallel_rng_tracker_name()): + config.init_method(self.weight) + setattr(self.weight, 'sequence_parallel', config.sequence_parallel) + + def gating(self, input: torch.Tensor): + """Forward pass of the router gate. + + Args: + input (torch.Tensor): Input tensor. + + Returns: + torch.Tensor: Logits tensor. + """ + logits = torch.nn.functional.linear(input, self.weight) + return logits + + @abstractmethod + def routing(self, logits: torch.Tensor): + """Routing function. + + Args: + logits (torch.Tensor): Logits tensor. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of tensors representing max probs and the indices. + """ + raise NotImplementedError("Routing function not implemented.") + + @abstractmethod + def forward(self, input: torch.Tensor): + """ + Forward pass of the router. + + Args: + input (torch.Tensor): Input tensor. + """ + raise NotImplementedError("Forward function not implemented.") + + def set_layer_number(self, layer_number: int): + """Set the layer number for the router.""" + self.layer_number = layer_number + + +class TopKRouter(Router): + """Route each token to the top-k experts.""" + + def __init__(self, config: TransformerConfig,) -> None: + """Initialize the zero token dropping router. + + Args: + config (TransformerConfig): The configuration for the transformer model. + """ + super().__init__(config=config) + assert config.moe_token_dropping is False + self.topk = self.config.moe_router_topk + self.routing_type = self.config.moe_router_load_balancing_type + self.input_jitter = None + + def sinkhorn_load_balancing(self, logits: torch.Tensor): + """Apply sinkhorn routing to the logits tensor. + + Args: + logits (torch.Tensor): The logits tensor. + + Returns: + torch.Tensor: The logits tensor after applying sinkhorn routing. + """ + + def _sinkhorn_activation(logits): + if self.topk == 1: + logits = torch.sigmoid(logits) + else: # k > 1 + logits = torch.softmax(logits, dim=-1, dtype=torch.float32).type_as(logits) + return logits + + assert self.config.moe_aux_loss_coeff == 0, "Sinkhorn routing does not support aux loss." + if self.training: + with torch.no_grad(): + norm_logits = sinkhorn( + logits.to(dtype=torch.float32) + ) # explicit fp32 conversion for stability + _, indices = torch.topk(norm_logits, k=self.topk, dim=1) + logits = _sinkhorn_activation(logits) + scores = torch.gather(logits, 1, indices) + else: + logits = _sinkhorn_activation(logits) + scores, indices = torch.topk(logits, k=self.topk, dim=1) + return scores, indices + + def aux_loss_load_balancing(self, logits: torch.Tensor): + """Apply loss-based load balancing to the logits tensor. + + Args: + logits (torch.Tensor): The logits tensor. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: The scores and the indices tensor after applying load balancing. + """ + top_logits, indices = torch.topk(logits, k=self.topk, dim=1) + scores = torch.softmax(top_logits, dim=-1, dtype=torch.float32).type_as(logits) + # Apply load balancing loss + probs = torch.softmax(logits, dim=-1, dtype=torch.float32) + scores = self.apply_load_balancing_loss(probs, indices, activation=scores) + return scores, indices + + def apply_load_balancing_loss( + self, probs: torch.Tensor, indices: torch.Tensor, activation: torch.Tensor, + ): + """Applies auxiliary loss to the MoE layer. + + Args: + loss_func (callable): The loss function to be used. + probs (torch.Tensor): The probabilities output by the MoE layer. + indices (torch.Tensor): The indices of the selected experts. + activation (torch.Tensor): The activation tensor to attach the gradient function to. + + Returns: + torch.Tensor: The activation tensor with the attached gradient function. + """ + mask = torch.nn.functional.one_hot(indices, num_classes=self.num_experts).sum(dim=1) + aux_loss = switch_load_balancing_loss_func(probs, mask, self.config.moe_aux_loss_coeff) + save_to_aux_losses_tracker( + "load_balancing_loss", + aux_loss / self.config.moe_aux_loss_coeff, + self.layer_number, + self.config.num_layers, + ) + activation = MoEAuxLossAutoScaler.apply(activation, aux_loss) + return activation + + def apply_z_loss(self, logits): + """Encourages the router's logits to remain small to enhance stability. + Please refer to the ST-MoE paper (https://arxiv.org/pdf/2202.08906.pdf) for details. + + Args: + logits (torch.Tensor): The logits of the router. + + Returns: + torch.Tensor: The logits after applying the z-loss. + """ + if self.config.moe_z_loss_coeff is not None: + z_loss = z_loss_func(logits, self.config.moe_z_loss_coeff) + logits = MoEAuxLossAutoScaler.apply(logits, z_loss) + save_to_aux_losses_tracker( + "z_loss", + z_loss / self.config.moe_z_loss_coeff, + self.layer_number, + self.config.num_layers, + ) + return logits + + def apply_input_jitter(self, input: torch.Tensor): + """Add noise to the input tensor. + Refer to https://arxiv.org/abs/2101.03961. + + Args: + input (Tensor): Input tensor. + + Returns: + Tensor: Jittered input. + """ + if self.config.moe_input_jitter_eps is not None: + eps = self.config.moe_input_jitter_eps + if self.input_jitter is None: + self.input_jitter = torch.distributions.uniform.Uniform( + torch.tensor(1.0 - eps, device=input.device), + torch.tensor(1.0 + eps, device=input.device), + ).rsample + return input * self.input_jitter(input.shape) + else: + return input + + def routing(self, logits: torch.Tensor): + """Top-k routing function + + Args: + logits (torch.Tensor): Logits tensor. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Probs and the indices tensor. + """ + logits = logits.view(-1, self.config.num_moe_experts) + + # Apply Z-Loss + logits = self.apply_z_loss(logits) + + if ( + self.config.tensor_model_parallel_size > 1 + and self.config.moe_token_dispatcher_type == "alltoall" + ): + # Gather the logits from the TP region + logits = gather_from_sequence_parallel_region(logits) + + if self.routing_type == "sinkhorn": + scores, indices = self.sinkhorn_load_balancing(logits) + elif self.routing_type == "aux_loss": + scores, indices = self.aux_loss_load_balancing(logits) + elif self.routing_type == "none": + # A naive top-k routing without load balancing + top_logits, indices = torch.topk(logits, k=self.topk, dim=1) + scores = torch.softmax(top_logits, dim=-1, dtype=torch.float32).type_as(logits) + else: + raise ValueError(f"Unsupported MoE routing type: {self.routing_type}") + + return scores, indices + + def forward(self, input: torch.Tensor): + """ + Forward pass of the router. + + Args: + input (torch.Tensor): Input tensor. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: scores and indices. + """ + self.hidden = input.shape[-1] + + # Apply input jitter + input = self.apply_input_jitter(input) + logits = self.gating(input) + logits = logits.view(-1, self.config.num_moe_experts) + + scores, indices = self.routing(logits) + + return scores, indices diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/token_dispatcher.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/token_dispatcher.py new file mode 100644 index 0000000..d46448d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/moe/token_dispatcher.py @@ -0,0 +1,489 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from abc import abstractmethod +from typing import List, Optional, Tuple + +import torch + +from megatron.core import parallel_state, tensor_parallel +from megatron.core.tensor_parallel.mappings import _gather_along_first_dim_expert_parallel +from megatron.core.transformer.moe.moe_utils import permute, unpermute +from megatron.core.transformer.transformer_config import TransformerConfig + + +class MoETokenDispatcher: + """ + MoE Token Dispatcher + """ + + def __init__(self, config: TransformerConfig) -> None: + """ + Initialize the MoE Token Dispatcher. + """ + self.config = config + + @abstractmethod + def token_permutation( + self, tokens: torch.Tensor, indices: torch.Tensor, + ): + """Dispatch tokens to experts. + + Args: + tokens (torch.Tensor): Input tokens. + indices (torch.Tensor): indices tensor. + + Returns: + torch.Tensor: Tokens tensor. + """ + raise NotImplementedError("Dispatch function not implemented.") + + @abstractmethod + def token_unpermutation( + self, expert_output: torch.Tensor, scores: torch.Tensor, indices: torch.Tensor, + ): + """Restores the expert output to its original ordering. + + Args: + expert_output (torch.Tensor): The output tensor from the expert models. + scores (torch.Tensor): Each token's score with each expert. + indices (torch.Tensor): The indices used to reorder the expert output. + + Returns: + (torch.Tensor, torch.Tensor): Unpermuted activation and optional bias. + """ + raise NotImplementedError("Restore function not implemented.") + + +class MoEAllGatherTokenDispatcher(MoETokenDispatcher): + """ + AllGather Based Token dispatcher. + """ + + def __init__( + self, num_local_experts: int, local_expert_indices: List[int], config: TransformerConfig, + ) -> None: + """ + Initialize the zero token dropping router. + """ + super().__init__(config=config) + self.num_local_experts = num_local_experts + assert self.num_local_experts > 0, "Expected at least one expert" + self.local_expert_indices = local_expert_indices + assert len(self.local_expert_indices) > 0, "Expected at least one local expert index" + self.router_topk = config.moe_router_topk + self.add_bias = config.add_bias_linear + + # self.local_probs: probs of global token assignment to local experts. + self.local_probs = None + + # self.indices: The indices of `local_indices` (which holds the un-sorted expert indices of tokens that local expert can process) that give its sorted order along dim 0. + self.indices = None + + # self.global_local_map: 2D tensor. A mask of mapping between global and local tokens where each element is True if it's between the local_expert_indices. Only useful when cross device token permutation is enabled and **AllGahter** is performed. + self.global_local_map = None + + def token_permutation( + self, hidden_states: torch.Tensor, max_prob: torch.Tensor, max_ind: torch.Tensor + ): + """Dispatch tokens to local experts. It's composed of two stages: + (1) Permute the tokens across the expert parallel devices. After this stage, + each device receives all of the tokens assigned to its local set of experts + in its local HBM. + (2) Permute the tokens locally so that they are grouped by their expert + assignment. After the stage (1), the tokens are grouped by which device + they came from. We re-order them locally for subsequent efficient computation. + + Args: + hidden_states: input tokens of shape [SeqLen/TP, MBS, HiddenSize] + max_prob: probs of local token assignment to global experts. + max_ind: token assignment to local experts. + + Returns: + permuted_local_hidden_states: Permutation of tokens to local experts group. + tokens_per_expert: the number of tokens each local expert to process. + """ + self.hidden_shape = hidden_states.shape + # [S/TP, B, H] -> [S*B/TP, H] + hidden_states = hidden_states.view(-1, self.hidden_shape[-1]) + + # Permute the tokens across the expert parallel devices. + if self.config.sequence_parallel or (self.config.expert_model_parallel_size > 1): + # [S*B/TP, H] -> [S*B, H] + global_hidden_states = tensor_parallel.gather_from_sequence_parallel_region_to_moe( + hidden_states + ) + with torch.no_grad(): + global_indices = tensor_parallel.gather_from_sequence_parallel_region_to_moe( + max_ind + ) + # Create a mask of mapping between global and local tokens where each + # element is True if it's between the local_expert_indices + global_local_mask = (global_indices >= self.local_expert_indices[0]) & ( + global_indices <= self.local_expert_indices[-1] + ) + local_indices = global_indices.masked_select(global_local_mask) + + if self.router_topk > 1: # k > 1 + global_probs = tensor_parallel.gather_from_sequence_parallel_region_to_moe(max_prob) + self.local_probs = global_probs.masked_select(global_local_mask) + else: + self.local_probs = max_prob + + # Reshape global_local_mask to be compatible with Tensor.gather + global_local_map = global_local_mask.nonzero()[:, 0] + self.global_local_map = global_local_map.view(-1, 1).expand(-1, hidden_states.shape[-1]) + local_hidden_states = torch.gather(global_hidden_states, 0, self.global_local_map) + else: + if self.router_topk > 1: + global_local_mask = torch.ones_like(max_ind).bool() + local_indices = max_ind.masked_select(global_local_mask) + self.local_probs = max_prob.masked_select(global_local_mask) + global_local_map = global_local_mask.nonzero()[:, 0] + self.global_local_map = global_local_map.view(-1, 1).expand( + -1, hidden_states.shape[-1] + ) + local_hidden_states = torch.gather(hidden_states, 0, self.global_local_map) + else: + local_indices = max_ind + self.local_probs = max_prob + local_hidden_states = hidden_states + self.global_local_map = None + + with torch.no_grad(): + # The indices of local_indices that give its sorted order along dim 0. + self.indices = torch.argsort(local_indices, dim=0) + tokens_per_expert = torch.histc( + local_indices, + bins=self.num_local_experts, + min=self.local_expert_indices[0], + max=self.local_expert_indices[-1], + ) + tokens_per_expert = tokens_per_expert.cpu().to(torch.long) + + # Stage2: permute the tokens locally so that they are grouped by their expert assignment + # Reshape indices to be compatible with Tensor.gather + self.indices = self.indices.view(-1, 1).expand(-1, hidden_states.shape[-1]) + permuted_local_hidden_states = torch.gather(local_hidden_states, 0, self.indices) + return ( + permuted_local_hidden_states, + tokens_per_expert, + ) + + def token_unpermutation( + self, hidden_states: torch.Tensor, bias: torch.Tensor = None, + ): + """ + Reverse process of `dispatch()` which permutes the ouput of local + experts locallay and across expert parallel rank into the original order to + produce the final output. + + Args: + hidden_states: 2D tensor of shape [sum_tokens_of_all_local_experts, HiddenSize], + ouput of local experts. + bias (optional): The bias tensor. + + Returns: + output_total: un-permuted updated hidden states output from all local experts + with shape of [SeqLen/TP, MBS, HiddenSize] + """ + # Stage1: unpermute the tokens and bias locally respectively. + scores = self.local_probs.to(dtype=hidden_states.dtype) + unpermuted_local_hidden = torch.zeros_like(hidden_states) + assert self.indices.shape == hidden_states.shape + unpermuted_local_hidden = unpermuted_local_hidden.scatter(0, self.indices, hidden_states) + + # Scale the expert output prior to reduction and subsequent to local unpermutation if k > 1. + if self.router_topk > 1: + unpermuted_local_hidden = unpermuted_local_hidden * scores.view(-1, 1) + + unpermuted_local_bias = None + if self.add_bias: + assert bias is not None + unpermuted_local_bias = torch.zeros_like(hidden_states) + assert self.indices.shape == bias.shape + unpermuted_local_bias = unpermuted_local_bias.scatter(0, self.indices, bias) + if self.router_topk > 1: + unpermuted_local_bias = unpermuted_local_bias * scores.view(-1, 1) + + output_total = unpermuted_local_hidden + output_bias_total = unpermuted_local_bias + + # Unpermute the tokens across expert parallel devices. + if self.config.sequence_parallel or (self.config.expert_model_parallel_size > 1): + assert ( + self.global_local_map is not None + ), "global_local_map is necessary for `AllGather`." + ep_group_size = parallel_state.get_tensor_and_expert_parallel_world_size() + # hidden_shape: [SeqLen/TP, MBS, HiddenSize], glboal_num_tokens = SeqLen/TP*MBS*(TP*EP) + global_num_tokens = self.hidden_shape[0] * self.hidden_shape[1] * ep_group_size + global_hidden_shape = [global_num_tokens, hidden_states.shape[-1]] + unpermuted_global_hidden = torch.zeros( + global_hidden_shape, dtype=hidden_states.dtype, device=torch.cuda.current_device() + ) + # Reshape global_local_map to be compatible with Tensor.scatter + assert self.global_local_map.shape == unpermuted_local_hidden.shape + unpermuted_global_hidden = unpermuted_global_hidden.scatter_add( + 0, self.global_local_map, unpermuted_local_hidden + ) + output_total = tensor_parallel.reduce_scatter_to_sequence_parallel_region_from_moe( + unpermuted_global_hidden + ) + if self.add_bias: + # Unpermute the bias across expert parallel devices. + unpermuted_global_bias = torch.zeros_like(unpermuted_global_hidden) + unpermuted_global_bias = unpermuted_global_bias.scatter_add( + 0, self.global_local_map, unpermuted_local_bias + ) + output_bias_total = tensor_parallel.reduce_scatter_to_sequence_parallel_region_from_moe( + unpermuted_global_bias + ) + # bias is duplicated across tensor parallelism ranks; + # reduce scatter reduces bias across tensor parallel_ranks + output_bias_total = ( + output_bias_total / parallel_state.get_tensor_model_parallel_world_size() + ) + else: + if self.router_topk > 1: + global_num_tokens = self.hidden_shape[0] * self.hidden_shape[1] + global_hidden_shape = [global_num_tokens, hidden_states.shape[-1]] + unpermuted_global_hidden = torch.zeros( + global_hidden_shape, + dtype=hidden_states.dtype, + device=torch.cuda.current_device(), + ) + output_total = unpermuted_global_hidden.scatter_add( + 0, self.global_local_map, unpermuted_local_hidden + ) + if self.add_bias: + unpermuted_global_bias = torch.zeros_like(unpermuted_global_hidden) + output_bias_total = unpermuted_global_bias.scatter_add( + 0, self.global_local_map, unpermuted_local_bias + ) + + if self.router_topk == 1: + output_total = output_total * scores + output_total = output_total.view(self.hidden_shape) + if self.add_bias: + assert output_bias_total is not None + if self.router_topk == 1: + output_bias_total = output_bias_total * scores + output_bias_total = output_bias_total.view(self.hidden_shape) + else: + output_bias_total = None + + return output_total, output_bias_total + + +class MoEAlltoAllTokenDispatcher(MoETokenDispatcher): + """ + AlltoAll Based Token dispatcher. + """ + + def __init__( + self, num_local_experts: int, local_expert_indices: List[int], config: TransformerConfig, + ) -> None: + """ + Initialize the AlltoAll token dispatcher. + + Args: + num_local_experts (int): Number of local experts on the current device. + local_expert_indices (List[int]): Indices of local experts on the current device. + config (TransformerConfig): Configuration for the transformer model. + """ + super().__init__(config=config) + self.num_local_experts = num_local_experts + self.num_experts = config.num_moe_experts + assert self.num_local_experts > 0, "Expected at least one expert" + self.local_expert_indices = local_expert_indices + assert ( + len(self.local_expert_indices) == self.num_local_experts + ), "Invalid local expert indices" + self.router_topk = config.moe_router_topk + self.add_bias = config.add_bias_linear + self.ep_size = config.expert_model_parallel_size + self.scores: torch.Tensor = None + self.input_splits = None + self.output_splits = None + self.num_global_tokens_per_local_expert = None + + def preprocess(self, indices: torch.Tensor) -> torch.Tensor: + """ + Preprocess token indices for AlltoAll communication and token permutation. This method computes the number of tokens assigned to each expert based on the input indices. + It also initializes the necessary data structures for AlltoAll communication, such as input + and output splits, and the mapping between global tokens and local experts. + + Args: + indices (torch.Tensor): Tensor of indices mapping tokens to experts. + + Returns: + torch.Tensor: Tensor containing the number of tokens assigned to local expert. + """ + num_local_tokens_per_expert = torch.histc( + indices, bins=self.num_experts, min=0, max=self.num_experts + ) + # num_local_tokens_per_expert: [num_experts] + + ep_size = self.config.expert_model_parallel_size + if ep_size > 1: + # =================================================== + # Calculate input_splits, output_splits for alltoall-v. + # =================================================== + self.input_splits = ( + num_local_tokens_per_expert.reshape(ep_size, self.num_local_experts) + .sum(axis=1) + .to(torch.device("cpu")) + .numpy() + ) + num_global_tokens_per_expert = _gather_along_first_dim_expert_parallel( + num_local_tokens_per_expert + ).reshape(ep_size, self.num_experts) + self.num_global_tokens_per_local_expert = num_global_tokens_per_expert[ + :, self.local_expert_indices + ] + self.output_splits = ( + self.num_global_tokens_per_local_expert.sum(axis=-1).to(torch.device("cpu")).numpy() + ) + num_tokens_per_local_expert = self.num_global_tokens_per_local_expert.sum(axis=0).to( + torch.device("cpu"), non_blocking=True + ) + # =================================================== + # num_global_tokens_per_expert: [ep_size, num_experts] + # num_global_tokens_per_local_expert: [ep_size, num_local_experts] + # num_tokens_per_local_expert: [num_local_experts] + # =================================================== + else: + self.num_global_tokens_per_local_expert = num_local_tokens_per_expert.reshape( + -1, self.num_experts + ) + num_tokens_per_local_expert = num_local_tokens_per_expert.to( + torch.device("cpu"), non_blocking=True + ) + + if self.num_local_experts > 1: + expert_ids_per_ep_rank = torch.tensor( + [i % self.num_local_experts for i in range(self.config.num_moe_experts)], + dtype=torch.int32, + device=torch.cuda.current_device(), + ) + self.global_input_tokens_local_experts_indices = torch.repeat_interleave( + expert_ids_per_ep_rank, self.num_global_tokens_per_local_expert.ravel() + ) + + return num_tokens_per_local_expert + + def token_permutation( + self, hidden_states: torch.Tensor, scores: torch.Tensor, indices: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Dispatch tokens to local experts using AlltoAll communication. + + Args: + hidden_states (torch.Tensor): Input token embeddings. + scores (torch.Tensor): Scores of tokens assigned to experts. + indices (torch.Tensor): Indices of tokens assigned to experts. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: + - Permuted token embeddings for local experts. + - Number of tokens per expert. + """ + self.hidden_shape = hidden_states.shape + self.scores = scores + assert scores.dim() == 2, "Expected 2D tensor for scores" + assert indices.dim() == 2, "Expected 2D tensor for indices" + tokens_per_expert = self.preprocess(indices) + + # TODO Optimize EP=1 case + # Flatten the input tensor + # hidden_states: [S/TP, B, H] -> [S*B/TP, H] + hidden_states = hidden_states.view(-1, self.hidden_shape[-1]) + + # Perform tensor parallel AlltoAll communication + # hidden_states: [S*B/TP, H] -> [S*B, H/TP] + if parallel_state.get_tensor_model_parallel_world_size() > 1: + hidden_states = tensor_parallel.all_to_all_sp2hp(hidden_states) + + # Permutation 1: input to AlltoAll input + self.local_input_tokens_global_experts_indices = indices + permutated_local_input_tokens, self.reversed_local_input_permutation_mapping = permute( + hidden_states, self.local_input_tokens_global_experts_indices, topk=self.router_topk, + ) + + # Perform expert parallel AlltoAll communication + global_input_tokens = tensor_parallel.all_to_all( + parallel_state.get_expert_model_parallel_group(), + permutated_local_input_tokens, + self.output_splits, + self.input_splits, + ) + + # Permutation 2: AlltoAll output to expert input if num_local_experts > 1 + if self.num_local_experts > 1: + global_input_tokens, self.reversed_global_input_permutation_mapping = permute( + global_input_tokens, self.global_input_tokens_local_experts_indices + ) + + # Perform tensor parallel All-Gather + # global_input_tokens: [SEQL, H/TP] -> [SEQL, H] + if parallel_state.get_tensor_model_parallel_world_size() > 1: + global_input_tokens = tensor_parallel.all_gather_last_dim_from_tensor_parallel_region( + global_input_tokens + ) + + return global_input_tokens, tokens_per_expert + + def token_unpermutation( + self, hidden_states: torch.Tensor, bias: torch.Tensor = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + Reverse the token permutation to restore the original order. + + Args: + hidden_states (torch.Tensor): Output from local experts. + bias (torch.Tensor, optional): Bias tensor (not supported). + + Returns: + Tuple[torch.Tensor, Optional[torch.Tensor]]: + - Unpermuted token embeddings in the original order. + - None (bias is not supported). + """ + assert bias is None, "Bias is not supported in MoEAlltoAllTokenDispatcher" + + # Perform tensor parallel Reduce-Scatter + # hidden_states: [SEQL, H] -> [SEQL, H/TP] + if parallel_state.get_tensor_model_parallel_world_size() > 1: + hidden_states = tensor_parallel.reduce_scatter_last_dim_to_tensor_parallel_region( + hidden_states + ) + + # Unpermutation 2: expert output to AlltoAll input + # hidden_states: [SEQL, H] -> [SEQL, H/TP] + if self.num_local_experts > 1: + hidden_states = unpermute( + hidden_states, self.reversed_global_input_permutation_mapping, + ) + + # Perform expert parallel AlltoAll communication + permutated_local_input_tokens = tensor_parallel.all_to_all( + parallel_state.get_expert_model_parallel_group(), + hidden_states, + self.input_splits, + self.output_splits, + ) + + # Unpermutation 1: AlltoAll output to output + output = unpermute( + permutated_local_input_tokens, + self.reversed_local_input_permutation_mapping, + probs=self.scores, + topk=self.router_topk, + ) + + # Perform tensor parallel AlltoAll communication + if parallel_state.get_tensor_model_parallel_world_size() > 1: + # output: [S*B, H/TP] -> [S*B/TP, H] + output = tensor_parallel.all_to_all_hp2sp(output) + + # Reshape the output tensor + output = output.view(self.hidden_shape) + return output, None diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/spec_utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/spec_utils.py new file mode 100644 index 0000000..473933e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/spec_utils.py @@ -0,0 +1,109 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import types +from dataclasses import dataclass, field +from typing import Tuple, Union + +import torch + + +@dataclass +class ModuleSpec: + """This is a Module Specification dataclass. + + Specification defines the location of the module (to import dynamically) + or the imported module itself. It also defines the params that need to be + passed to initialize the module. + + Args: + module (Union[Tuple, type]): A tuple describing the location of the + module class e.g. `(module.location, ModuleClass)` or the imported + module class itself e.g. `ModuleClass` (which is already imported + using `from module.location import ModuleClass`). + params (dict): A dictionary of params that need to be passed while init. + + """ + + module: Union[Tuple, type] + params: dict = field(default_factory=lambda: {}) + submodules: type = None + + +def import_module(module_path: Tuple[str]): + """Import a named object from a module in the context of this function. + + TODO: make this importer module more robust, at least make sure there + are no side effects of using this as is + """ + base_path, name = module_path + try: + module = __import__(base_path, globals(), locals(), [name]) + except ImportError as e: + print(f"couldn't import module due to {e}") + return None + return vars(module)[name] + + +def get_module(spec_or_module: Union[ModuleSpec, type], **additional_kwargs): + # If a module clas is already provided return it as is + if isinstance(spec_or_module, (type, types.FunctionType)): + return spec_or_module + + # If the module is provided instead of module path, then return it as is + if isinstance(spec_or_module.module, (type, types.FunctionType)): + return spec_or_module.module + + # Otherwise, return the dynamically imported module from the module path + return import_module(spec_or_module.module) + + +def build_module(spec_or_module: Union[ModuleSpec, type], *args, **kwargs): + # If the passed `spec_or_module` is + # a `Function`, then return it as it is + # NOTE: to support an already initialized module add the following condition + # `or isinstance(spec_or_module, torch.nn.Module)` to the following if check + if isinstance(spec_or_module, types.FunctionType): + return spec_or_module + + # If the passed `spec_or_module` is actually a spec (instance of + # `ModuleSpec`) and it specifies a `Function` using its `module` + # field, return the `Function` as it is + if isinstance(spec_or_module, ModuleSpec) and isinstance( + spec_or_module.module, types.FunctionType + ): + return spec_or_module.module + + # Check if a module class is provided as a spec or if the module path + # itself is a class + if isinstance(spec_or_module, type): + module = spec_or_module + elif hasattr(spec_or_module, "module") and isinstance(spec_or_module.module, type): + module = spec_or_module.module + else: + # Otherwise, dynamically import the module from the module path + module = import_module(spec_or_module.module) + + # If the imported module is actually a `Function` return it as it is + if isinstance(module, types.FunctionType): + return module + + # Finally return the initialized module with params from the spec as well + # as those passed as **kwargs from the code + + # Add the `submodules` argument to the module init call if it exists in the + # spec. + if hasattr(spec_or_module, "submodules") and spec_or_module.submodules is not None: + kwargs["submodules"] = spec_or_module.submodules + + try: + return module( + *args, **spec_or_module.params if hasattr(spec_or_module, "params") else {}, **kwargs + ) + except Exception as e: + # improve the error message since we hide the module name in the line above + import sys + + tb = sys.exc_info()[2] + raise type(e)(f"{str(e)} when instantiating {module.__name__}").with_traceback( + sys.exc_info()[2] + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/transformer_block.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/transformer_block.py new file mode 100755 index 0000000..4924a2b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/transformer_block.py @@ -0,0 +1,469 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import re +import warnings +from contextlib import nullcontext +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import torch +from torch import Tensor + +from megatron.core import InferenceParams, parallel_state, tensor_parallel +from megatron.core.dist_checkpointing.mapping import ShardedStateDict +from megatron.core.dist_checkpointing.utils import replace_prefix_for_sharding +from megatron.core.fusions.fused_layer_norm import FusedLayerNorm +from megatron.core.packed_seq_params import PackedSeqParams +from megatron.core.transformer.custom_layers.transformer_engine import ( + TEDelayedScaling, + TENorm, + get_cpu_offload_context, + te_checkpoint, +) +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.spec_utils import ModuleSpec, build_module +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.transformer.transformer_layer import BaseTransformerLayer, TransformerLayer +from megatron.core.transformer.utils import sharded_state_dict_default +from megatron.core.utils import make_sharded_tensor_for_checkpoint, make_viewless_tensor + + +def get_num_layers_to_build(config: TransformerConfig) -> int: + + num_layers_per_pipeline_rank = ( + config.num_layers // parallel_state.get_pipeline_model_parallel_world_size() + ) + + if parallel_state.get_virtual_pipeline_model_parallel_world_size() is not None: + # Interleaved pipeline parallelism: + # Number of layers in each model chunk is the number of layers in the stage, + # divided by the number of model chunks in a stage. + # With 8 layers, 2 stages, and 4 model chunks, we want an assignment of + # layers to stages like (each list is a model chunk): + # Stage 0: [0] [2] [4] [6] + # Stage 1: [1] [3] [5] [7] + # With 8 layers, 2 stages, and 2 virtual stages, we want an assignment of + # layers to stages like (each list is a model chunk): + # Stage 0: [0, 1] [4, 5] + # Stage 1: [2, 3] [6, 7] + + vp_size = parallel_state.get_virtual_pipeline_model_parallel_world_size() + + num_layers_per_virtual_rank = num_layers_per_pipeline_rank // vp_size + + num_layers_to_build = num_layers_per_virtual_rank + + else: + # Non-interleaved pipeline parallelism: + # Each stage gets a contiguous set of layers. + + num_layers_to_build = num_layers_per_pipeline_rank + + return num_layers_to_build + + +@dataclass +class TransformerBlockSubmodules: + layer_specs: List[ModuleSpec] = None + layer_norm: Optional[Union[ModuleSpec, torch.nn.Module]] = None + + +def _get_block_submodules( + config: TransformerConfig, spec: Union[TransformerBlockSubmodules, ModuleSpec], +) -> TransformerBlockSubmodules: + + # Transformer block submodules. + if isinstance(spec, TransformerBlockSubmodules): + return spec + + # ModuleSpec here is generally assumed to be for a transformer layer that + # is implemented in `transformer_layer.py` or if it subclasses + # `BaseTransformerLayer` from the `transformer_layer.py` file. + elif isinstance(spec, ModuleSpec): + if issubclass(spec.module, TransformerBlock): + return spec.submodules + elif issubclass(spec.module, BaseTransformerLayer): + num_layers = get_num_layers_to_build(config) + return TransformerBlockSubmodules(layer_specs=[spec] * num_layers, layer_norm=TENorm,) + else: + raise Exception(f"specialize for {spec.module.__name__}.") + else: + raise Exception(f"specialize for {type(spec).__name__}.") + + +class TransformerBlock(MegatronModule): + """Transformer class.""" + + def __init__( + self, + config: TransformerConfig, + spec: Union[TransformerBlockSubmodules, ModuleSpec], + post_layer_norm: bool = True, + pre_process: bool = True, + post_process: bool = True, + ): + super().__init__(config=config) + + self.submodules = _get_block_submodules(config, spec) + self.post_layer_norm = post_layer_norm + self.pre_process = pre_process + self.post_process = post_process + # Dictionary to store CUDA graphs. Number of items in the dictionary = len(self.layers). + # Item `i` in the dictionary is a list of `N` CUDA graphs for layer 'i' where N is the + # number of microbatches. Multiple CUDA graphs per layer is required to support + # pipelining which requires running FWD graph of multiple microbatches before BWD graph. + self.cuda_graphs = {} + self.current_microbatch = -1 + + # required for pipeline parallel schedules + self.input_tensor = None + + self.checkpoint_core_attention = self.config.recompute_granularity == 'selective' + + if get_cpu_offload_context is not None: + ( + self.offload_context, + self.group_prefetch_offload_commit_async, + ) = get_cpu_offload_context( + self.config.cpu_offloading, + self.config.cpu_offloading_num_layers, + self.config.cpu_offloading_activations, + self.config.cpu_offloading_weights, + ) + self.config._cpu_offloading_context = ( + self.offload_context if self.config.cpu_offloading else None + ) + else: + assert ( + self.config.cpu_offloading == False + ), "CPU Offloading is enabled when TE is not present" + + self.offload_context, self.group_prefetch_offload_commit_async = nullcontext(), None + self.config._cpu_offloading_context = None + + self._build_layers() + self.num_layers_per_pipeline_rank = len(self.layers) + + def _build_layers(self): + # Transformer layers. + # @jcasper can we improve how we deal with layer_number? + # currently it's only used in CoreAttention? + # if self.apply_query_key_layer_scaling: + # coeff = self.layer_number + # self.norm_factor *= coeff + def build_layer(layer_spec, layer_number): + return build_module(layer_spec, config=self.config, layer_number=layer_number,) + + # offset is implicit in TransformerLayer + self.layers = torch.nn.ModuleList( + [ + build_layer(layer_spec, i + 1) + for i, layer_spec in enumerate(self.submodules.layer_specs) + ] + ) + + # # TODO: add back standalone_embedding_stage + # if self.num_layers == 0: + # # When a standalone embedding stage is used (e.g., + # # args.standalone_embedding_stage == True), virtual pipeline ranks + # # on pipeline rank 0 will have zero transformer layers assigned to + # # them. This results in the model's input and output tensors to be + # # the same, which will cause failure for certain output tensor + # # optimizations (e.g., pipeline output deallocation). To remedy + # # this, we assign a 'no-op' layer on these ranks, which will + # # disconnect the input tensor from the output tensor. + # self.num_layers = 1 + # self.layers = torch.nn.ModuleList([NoopTransformerLayer(1)]) + # else: + # self.layers = torch.nn.ModuleList([build_layer(i + 1 + offset) for i in range(self.num_layers)]) + + # In pipeline parallelism, we want to add this LN only to the last stage of the pipeline + # self.post_process and self.post_layer_norm guide this behavior + if self.submodules.layer_norm and self.post_process and self.post_layer_norm: + self.final_layernorm = build_module( + self.submodules.layer_norm, + config=self.config, + hidden_size=self.config.hidden_size, + eps=self.config.layernorm_epsilon, + ) + else: + self.final_layernorm = None # Either this or nn.Identity + + def _get_layer(self, layer_number: int): + return self.layers[layer_number] + + def _checkpointed_forward( + self, + hidden_states: Tensor, + attention_mask: Tensor, + context: Tensor, + context_mask: Tensor, + rotary_pos_emb: Tensor, + packed_seq_params: PackedSeqParams, + ): + """Forward method with activation checkpointing.""" + + def custom(start: int, end: int): + def custom_forward( + hidden_states, + attention_mask, + context, + context_mask, + rotary_pos_emb, + packed_seq_params, + ): + for index in range(start, end): + layer = self._get_layer(index) + hidden_states, context = layer( + hidden_states=hidden_states, + attention_mask=attention_mask, + context=context, + context_mask=context_mask, + rotary_pos_emb=rotary_pos_emb, + inference_params=None, + packed_seq_params=packed_seq_params, + ) + return hidden_states, context + + return custom_forward + + def checkpoint_handler(forward_func): + if self.config.fp8: + return te_checkpoint( + forward_func, + self.config.distribute_saved_activations, + tensor_parallel.random.get_cuda_rng_tracker, + parallel_state.get_tensor_model_parallel_group(), + hidden_states, + attention_mask, + context, + context_mask, + rotary_pos_emb, + packed_seq_params, + ) + else: + return tensor_parallel.checkpoint( + forward_func, + self.config.distribute_saved_activations, + hidden_states, + attention_mask, + context, + context_mask, + rotary_pos_emb, + packed_seq_params, + ) + + if self.config.recompute_method == 'uniform': + # Uniformly divide the total number of Transformer layers and checkpoint + # the input activation of each divided chunk. + # A method to further reduce memory usage reducing checkpoints. + l = 0 + while l < self.num_layers_per_pipeline_rank: + hidden_states, context = checkpoint_handler( + custom(l, l + self.config.recompute_num_layers) + ) + + l += self.config.recompute_num_layers + + elif self.config.recompute_method == 'block': + # Checkpoint the input activation of only a set number of individual + # Transformer layers and skip the rest. + # A method fully use the device memory removing redundant re-computation. + recompute_skip_num_layers = 0 + for l in range(self.num_layers_per_pipeline_rank): + # Skip recomputation when input grad computation is not needed. + # Need to have at least one input tensor with gradient computation + # for re-enterant autograd engine. + if self.config.fp8 and not hidden_states.requires_grad: + recompute_skip_num_layers += 1 + if ( + l >= recompute_skip_num_layers + and l < self.config.recompute_num_layers + recompute_skip_num_layers + ): + hidden_states, context = checkpoint_handler(custom(l, l + 1)) + else: + hidden_states, context = custom(l, l + 1)( + hidden_states, + attention_mask, + context, + context_mask, + rotary_pos_emb, + packed_seq_params, + ) + else: + raise ValueError("Invalid activation recompute method.") + + return hidden_states + + def set_input_tensor(self, input_tensor: Tensor): + """Set input tensor to be used instead of forward()'s input. + + When doing pipeline parallelism the input from the previous + stage comes from communication, not from the input, so the + model's forward_step_func won't have it. This function is thus + used by internal code to bypass the input provided by the + forward_step_func""" + self.input_tensor = input_tensor + + def forward( + self, + hidden_states: Tensor, + attention_mask: Tensor, + context: Tensor = None, + context_mask: Tensor = None, + rotary_pos_emb: Tensor = None, + inference_params: InferenceParams = None, + packed_seq_params: PackedSeqParams = None, + ): + # hidden_states (float): [s, b, h] + # attention_mask (bool): [1, 1, s, s] + + if not self.pre_process: + # See set_input_tensor() + hidden_states = self.input_tensor + + # Viewless tensor. + # - We only need to create a viewless tensor in the case of micro batch + # size (mbs) == 1, since in this case, 'hidden_states.transpose()' + # above creates a view tensor, and '.contiguous()' is a pass-through. + # For mbs >= 2, '.contiguous()' creates a new tensor, eliminating + # the need to make it viewless. + # + # However, we don't explicitly check mbs == 1 here because + # make_viewless_tensor() has negligible overhead when its input + # is already viewless. + # + # - For the 'else' case above, calling make_viewless_tensor() here is + # likely redundant, since p2p_communication.py (likely originator) + # already creates viewless tensors. That said, make_viewless_tensor() + # is called here to be future-proof and corner-case-proof. + hidden_states = make_viewless_tensor( + inp=hidden_states, requires_grad=True, keep_graph=True, + ) + + if self.config.sequence_parallel: + rng_context = tensor_parallel.get_cuda_rng_tracker().fork() + else: + rng_context = nullcontext() + + if self.config.fp8: + import transformer_engine # To keep out TE dependency when not training in fp8 + + if self.config.fp8 == "e4m3": + fp8_format = transformer_engine.common.recipe.Format.E4M3 + elif self.config.fp8 == "hybrid": + fp8_format = transformer_engine.common.recipe.Format.HYBRID + else: + raise ValueError("E4M3 and HYBRID are the only supported FP8 formats.") + + fp8_recipe = TEDelayedScaling( + config=self.config, + fp8_format=fp8_format, + override_linear_precision=(False, False, not self.config.fp8_wgrad), + ) + fp8_group = None + if parallel_state.model_parallel_is_initialized(): + fp8_group = parallel_state.get_tensor_model_parallel_group() + fp8_context = transformer_engine.pytorch.fp8_autocast( + enabled=True, fp8_recipe=fp8_recipe, fp8_group=fp8_group + ) + else: + fp8_context = nullcontext() + + with rng_context and fp8_context: + # Forward pass. + if self.config.recompute_granularity == 'full' and self.training: + hidden_states = self._checkpointed_forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + context=context, + context_mask=context_mask, + rotary_pos_emb=rotary_pos_emb, + packed_seq_params=packed_seq_params, + ) + else: + for l_no, layer in enumerate(self.layers): + with self.offload_context: + if (len(self.cuda_graphs) == 0) or (not self.training): + hidden_states, context = layer( + hidden_states=hidden_states, + attention_mask=attention_mask, + context=context, + context_mask=context_mask, + rotary_pos_emb=rotary_pos_emb, + inference_params=inference_params, + packed_seq_params=packed_seq_params, + ) + # CUDA graph doesn't output context and is expected to be None + assert ( + (context is None) + or (not self.config.enable_cuda_graph) + or (not self.training) + ) + else: + # CUDA graph replay for layer `l_no` and microbatch `self.current_microbatch` + # CUDA graph requires positional arguments with the exception of is_first_microbatch. + # Also CUDA graph accepts only Tensor inputs and outputs. Hence, the arg list and + # returned list is limited to `hidden_states`. + assert (len(self.cuda_graphs) > l_no) and ( + self.current_microbatch < len(self.cuda_graphs[l_no]) + ) + hidden_states = self.cuda_graphs[l_no][self.current_microbatch]( + hidden_states, is_first_microbatch=(self.current_microbatch == 0), + ) + + if ( + torch.is_grad_enabled() + and self.config.cpu_offloading + and self.group_prefetch_offload_commit_async is not None + ): + hidden_states = self.group_prefetch_offload_commit_async(hidden_states) + + # Final layer norm. + if self.final_layernorm is not None: + hidden_states = self.final_layernorm(hidden_states) + + return hidden_states + + def sharded_state_dict( + self, prefix: str = '', sharded_offsets: tuple = (), metadata: dict = None + ) -> ShardedStateDict: + assert not sharded_offsets, "Unexpected sharded offsets" + non_homogeneous_layers = metadata is not None and metadata.get( + 'non_homogeneous_layers', False + ) + sharded_state_dict = {} + + layer_prefix = f'{prefix}layers.' + num_layers = self.config.num_layers + for layer in self.layers: + offset = layer._get_layer_offset() + + global_layer_offset = layer.layer_number - 1 # self.layer_number starts at 1 + state_dict_prefix = f'{layer_prefix}{global_layer_offset - offset}.' # module list index in TransformerBlock + if non_homogeneous_layers: + sharded_prefix = f'{layer_prefix}{global_layer_offset}.' + sharded_pp_offset = [] + else: + sharded_prefix = layer_prefix + sharded_pp_offset = [ + (0, global_layer_offset, num_layers) + ] # PP sharding offset for ShardedTensors + layer_sharded_state_dict = layer.sharded_state_dict( + state_dict_prefix, sharded_pp_offset, metadata + ) + replace_prefix_for_sharding(layer_sharded_state_dict, state_dict_prefix, sharded_prefix) + + sharded_state_dict.update(layer_sharded_state_dict) + + # Add modules other than self.layers + for name, module in self.named_children(): + if not module is self.layers: + sharded_state_dict.update( + sharded_state_dict_default( + module, f'{prefix}{name}.', sharded_offsets, metadata + ) + ) + + return sharded_state_dict diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/transformer_config.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/transformer_config.py new file mode 100644 index 0000000..e809729 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/transformer_config.py @@ -0,0 +1,399 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import types +from dataclasses import dataclass +from typing import Callable, Optional, Tuple + +import torch +import torch.nn.functional as F + +from ..model_parallel_config import ModelParallelConfig +from ..utils import init_method_normal, scaled_init_method_normal + + +@dataclass +class TransformerConfig(ModelParallelConfig): + """Configuration object for megatron-core transformers. + + The initialization function has an argument for each parameter, including those in ModelParallelConfig. + """ + + #################### + # model architecture + #################### + num_layers: int = 0 + """Number of transformer layers in a transformer block.""" + + hidden_size: int = 0 + """Transformer hidden size.""" + + num_attention_heads: int = 0 + """Number of transformer attention heads.""" + + num_query_groups: int = None + """Number of query groups for group query attention. If None, normal attention is used.""" + + ffn_hidden_size: int = None + """Transformer Feed-Forward Network hidden size. This is set to 4*hidden_size if not provided.""" + + kv_channels: int = None + """Projection weights dimension in multi-head attention. This is set to hidden_size // + num_attention_heads if not provided.""" + + hidden_dropout: float = 0.1 + """Dropout probability for transformer hidden state.""" + + attention_dropout: float = 0.1 + """Post attention dropout probability.""" + + fp32_residual_connection: bool = False + """If true, move residual connections to fp32.""" + + # @jcasper should we keep this option? + apply_residual_connection_post_layernorm: bool = False + """If True, uses the original BERT residule connection ordering.""" + + layernorm_epsilon: float = 1e-5 + """Epsilon value for any LayerNorm operations.""" + + layernorm_zero_centered_gamma: bool = False + """If set to True, the LayerNorm is adjusted to center the gamma values around 0. This improves + numerical stability.""" + + add_bias_linear: bool = True + """Include a bias term in all linear layers (QKV projections, after core attention, and two in + MLP layer).""" + + add_qkv_bias: bool = False + """Add a bias term only for QKV projections.""" + + gated_linear_unit: bool = False + """Use a gated linear unit for the first linear layer in the MLP.""" + + activation_func: Callable = F.gelu + """Activation function to use for the non-linearity in the MLP.""" + + activation_func_fp8_input_store: bool = False + """Store the input of MLP activation function in FP8 for backprop to save memory. + The stored input is casted back to the original precision before backprop compuatation.""" + + num_moe_experts: int = None + """Number of experts to use for MoE layer. When set, it replaces MLP with MoE layer. Set to None + for no MoE.""" + + rotary_interleaved: bool = False + """True is rotate pairs of even and odd dimensions (RoFormer style), False is rotate pairs of + first half and second half (LLaMa style). Default to False.""" + + window_size: Optional[Tuple[int, int]] = None + """If not None, then will use sliding window attention. The size of the window is specified by + the numbers inside the tuple; -1 is special value meaning "infinite window size".""" + + normalization: bool = "LayerNorm" + """Which norm to use for normalization layers, valid options are `LayerNorm` and `RMSNorm`.""" + + qk_layernorm: bool = False + """Whether to apply LayerNorm to the query and key embeddings.""" + + test_mode: bool = False + """Whether to run real-time tests.""" + + #################### + # initialization + #################### + init_method: Callable = None + """Method to initialize weights. Note that bias is always set to zero. Should be a function that + takes a single Tensor and initializes it. If None, will be set to + megatron.core.utils.init_method_normal(init_method_std) which is torch nn init normal with + mean=0.0 and std=init_method_std.""" + + output_layer_init_method: Callable = None + """Method to initialize weights of the output layer of both attention and MLP blocks. If None, + will be set to megatron.core.utils.scaled_init_method_normal(init_method_std) which is torch nn + init normal with mean=0.0 and std=init_method_std / math.sqrt(2.0 * num_layers).""" + + init_method_std: float = 0.02 + """Standard deviation of the zero mean normal for the default initialization method, not used if + init_method and output_layer_init_method are provided.""" + + #################### + # mixed-precision + #################### + apply_query_key_layer_scaling: bool = False + """If true, scale Q * K^T by 1 / layer-number. This improve numeric stability when training with + fp16.""" + + attention_softmax_in_fp32: bool = True + """If True, run attention masking and softmax in fp32. This should be True if + apply_query_key_layer_scaling is True.""" + + #################### + # fusion + #################### + bias_activation_fusion: bool = False + """If True, fuses bias addition and the activation function when possible.""" + + masked_softmax_fusion: bool = False + """If True, uses softmax fusion.""" + + persist_layer_norm: bool = False + """If True, uses the persistent fused layer norm kernel. This kernel only supports a fixed set + of hidden sizes.""" + + memory_efficient_layer_norm: bool = False + """If True, and using local layers (not from TransformerEngine), tells Apex to use the memory + efficient fused LayerNorm kernel. Ignored if not using LayerNorm.""" + + bias_dropout_fusion: bool = False # TODO: this should be bias_dropout_add_fusion? + """If True, uses bias dropout fusion.""" + + apply_rope_fusion: bool = False + """If True, use fused RoPE kernel.""" + + #################### + # activation recomputation + #################### + recompute_granularity: str = None + recompute_granularity: str = None + """Determines which type of activation recompute to use. Megatron-core supports 'selective' + activation checkpointing where only the memory intensive part of attention is checkpointed. + These memory intensive activations are also less compute intensive which makes activation + checkpointing more efficient for LLMs (20B+). See Reducing Activation Recomputation in Large + Transformer Models (https://arxiv.org/abs/2205.05198) for more details. 'full' will checkpoint + the entire transformer layer. If None, no recompute is performed and all activations are saved. + If set, must be 'selective' or 'full'. 'selective' always uses all layers. + """ + + recompute_method: str = None + """Determines which transformer layers will be recomputed. uniform will uniformly divide the + total number of transformer layers in a transformer block and recompute the input activation of + each divided chunk at the specified granularity. block will recompute the input activations for + only a set number of transformer layers per pipeline stage. The rest of the layers in the + pipeline stage will not have any activations recomputed. If None, and recompute is enabled, all + layers will do recomputation. If set, must be 'uniform' or 'block'.""" + + recompute_num_layers: int = None + """When recompute_method is uniform, recompute_num_layers is the number of transformer layers in + each uniformly divided recompute unit. When recompute_method is block, recompute_num_layers is + the number of transformer layers to recompute within each pipeline stage. Must be None for + 'selective' activation checkpointing.""" + + distribute_saved_activations: bool = None + """If True, distribute recomputed activations across the model parallel group.""" + + #################### + # fp8 related + #################### + fp8: str = None + """If set, enables the use of FP8 precision through Transformer Engine. There are 2 predefined + choices (1) 'e4m3' uniformly uses e4m3 for all FP8 tensors, (2) 'hybrid' uses e4m3 for all FP8 + activation and weight tensors and e5m2 for all FP8 output activation gradient tensors.""" + + fp8_margin: int = 0 + """Margin for the scaling factor computation.""" + + fp8_interval: int = 1 + """Controls how often the scaling factor is recomputed.""" + + fp8_amax_history_len: int = 1 + """The length of the amax history window used for scaling factor computation.""" + + fp8_amax_compute_algo: str = "most_recent" + """Algorithm used for choosing the `amax` value for the scaling factor computation. There are 2 + predefined choices: `max` chooses the largest `amax` in the history window, while `most_recent` + always chooses the most recently seen value. + + """ + + fp8_wgrad: bool = True + """When set to False, override FP8 config options and do the wgrad computation in higher precision.""" + + fp8_dot_product_attention: bool = False + """When set to True, use the FP8 implementation of Dot Product Attention.""" + + fp8_multi_head_attention: bool = False + """When set to True, use the FP8 implementation of Multi Head Attention.""" + + #################### + # MoE related + #################### + moe_router_load_balancing_type: str = "aux_loss" + """Determines the load balancing strategy for the router. "aux_loss" corresponds to the load + balancing loss used in GShard and SwitchTransformer, "sinkhorn" corresponds to the balancing + algorithm used in S-BASE, and "none" implies no load balancing.""" + + moe_router_topk: int = 2 + """Number of experts to route to for each token.""" + + moe_grouped_gemm: bool = False + """When there are multiple experts per rank, compress multiple local (potentially small) gemms + in a single kernel launch to improve the utilization and performance by leveraging the Grouped + GEMM feature introduced since CUTLASS 2.8 (https://github.com/fanshiqing/grouped_gemm). + + """ + + moe_aux_loss_coeff: float = 0 # 1e-2 would be a good start value for load balance loss. + """Scaling coefficient for the aux loss. A starting value of 1e-2 is recommended.""" + + moe_z_loss_coeff: float = None # 1e-3 would be a good start value for z-loss + """Scaling coefficient for the z-loss. A starting value of 1e-3 is recommended.""" + + moe_input_jitter_eps: float = None + """Add noise to the input tensor by applying jitter with a specified epsilon value.""" + + moe_token_dropping: bool = False # TODO: Support token dropping. + """This feature involves selectively dropping and padding tokens for each expert to achieve a + specified capacity, similar to GShard, Switch-Transformer, and DeepSpeed-MoE. Note that this is + currently unsupported so should remain False.""" + + moe_token_dispatcher_type: str = "allgather" + """The type of token dispatcher to use. The default is 'allgather'. Options are 'allgather' and 'alltoall'.""" + moe_per_layer_logging: bool = False + """Enable per-layer logging for MoE, currently supports auxiliary loss and z loss.""" + + #################### + # miscellaneous + #################### + clone_scatter_output_in_embedding: bool = True + """When set to True, clone the output of scatter_to_sequence_parallel_region in embedding layer + to facilitate garbage collection of input.""" + + disable_parameter_transpose_cache: bool = False + """When set to true, the parameter transposes are not cached for subsequent iterations.""" + + enable_cuda_graph: bool = False + """When set to true, TransformerLayer blocks are wrapped with CUDA graph.""" + + # These 2 attributes are WAR for TRTLLM export. DO NOT USE!! WILL BE DEPRECATED SOON!! + max_position_embeddings: int = 0 + """Deprecated. Do not use.""" + + rotary_percent: float = 0 + """Deprecated. Do not use.""" + + def __post_init__(self): + """ Python dataclass method that is used to modify attributes after initialization. + See https://docs.python.org/3/library/dataclasses.html#post-init-processing for more details. + """ + super().__post_init__() + if self.fp16 and self.bf16: + raise ValueError( + f'Only one of self.fp16: {self.fp16} and self.bf16 {self.bf16} should be True.' + ) + + if self.num_attention_heads % self.tensor_model_parallel_size != 0: + raise ValueError( + f"num_attention_heads ({self.num_attention_heads}) must be a multiple of " + f"tensor_model_parallel_size ({self.tensor_model_parallel_size})." + ) + + if self.ffn_hidden_size is None: + self.ffn_hidden_size = 4 * self.hidden_size + + if self.kv_channels is None: + self.kv_channels = self.hidden_size // self.num_attention_heads + + if self.num_query_groups is None: + self.num_query_groups = self.num_attention_heads + + if self.num_query_groups % self.tensor_model_parallel_size != 0: + raise ValueError( + f"num_query_groups ({self.num_query_groups}) must be a multiple of " + f"tensor_model_parallel_size ({self.tensor_model_parallel_size})." + ) + + if self.apply_query_key_layer_scaling: + self.attention_softmax_in_fp32 = True + + if self.expert_model_parallel_size > 1 and self.num_moe_experts is None: + raise ValueError(f'num_moe_experts must be non None to use expert-parallel.') + + if self.num_moe_experts is not None and self.num_moe_experts <= 0: + raise ValueError(f'num_moe_experts must be non-negative.') + + if self.cpu_offloading and ( + self.cpu_offloading_num_layers < 0 or self.cpu_offloading_num_layers >= self.num_layers + ): + raise ValueError( + f'CPU offloading can be done only for layers less than {self.num_layers}' + ) + + if self.cpu_offloading and self.pipeline_model_parallel_size > 1: + raise ValueError( + f'Currently there is no support for Pipeline parallelism with CPU offloading' + ) + + if self.cpu_offloading and self.recompute_granularity is not None: + raise ValueError( + f'CPU offloading does not work when activation recomputation is enabled' + ) + + if self.recompute_granularity is not None: + if not self.recompute_granularity in ['full', 'selective']: + raise ValueError( + f'When using recompute_granuarlity: {self.recompute_granularity} must be "full" or "selective".' + ) + + if self.recompute_method is not None: + if not self.recompute_method in ['block', 'uniform']: + raise ValueError( + f'recompute_method: {self.recompute_method} must be "block" or "uniform".' + ) + elif self.recompute_granularity != 'selective': + raise ValueError( + f'Using recompute_granularity: {self.recompute_granularity} so recompute_method must be "block" or "uniform"' + ) + + if self.recompute_granularity != 'selective' and self.recompute_num_layers is None: + raise ValueError( + f'When using recompute_granularity: {self.recompute_granularity} recompute_num_layers must be between ' + f'1 and num_layers_per_pipeline_rank: {self.num_layers // self.pipeline_model_parallel_size}' + ) + elif ( + self.recompute_granularity == 'selective' and self.recompute_num_layers is not None + ): + raise ValueError( + f'When using recompute_granularity: {self.recompute_granularity} recompute_num_layers must be None.' + ) + + if self.distribute_saved_activations and self.sequence_parallel: + raise ValueError( + f'distribute_saved_activations: {self.distribute_saved_activations} must be false when sequence parallel is enabled: {self.sequence_parallel}' + ) + + if self.virtual_pipeline_model_parallel_size is not None: + if not self.num_layers % self.virtual_pipeline_model_parallel_size == 0: + raise ValueError( + f'num_layers: {self.num_layers} must be divisible by virtual_model_parallel_size {self.virtual_pipeline_model_parallel_size}' + ) + + if self.apply_query_key_layer_scaling: + self.attention_softmax_in_fp32 = True + + if self.bias_activation_fusion: + if self.activation_func not in [F.gelu, F.silu]: + raise ValueError( + "When bias_activation_fusion is True, activation function should be either gelu or swiglu" + ) + if ( + self.activation_func == F.gelu + and not self.gated_linear_unit + and not self.add_bias_linear + ): + raise ValueError( + "When bias_activation_fusion is True, gated_linear_unit is False, " + "and activation function is gelu, add_bias_linear must also be True." + ) + if self.activation_func_fp8_input_store: + if self.activation_func != F.silu or not self.gated_linear_unit: + raise ValueError("Storing activation input in FP8 is supported only for SwiGLU.") + if self.apply_rope_fusion and self.rotary_interleaved: + raise ValueError(f'rotary_interleaved does not work with apply_rope_fusion.') + + if self.init_method is None: + self.init_method = init_method_normal(self.init_method_std) + + if self.output_layer_init_method is None: + self.output_layer_init_method = scaled_init_method_normal( + self.init_method_std, self.num_layers + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/transformer_layer.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/transformer_layer.py new file mode 100644 index 0000000..631179e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/transformer_layer.py @@ -0,0 +1,255 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from abc import ABC +from dataclasses import dataclass, field +from typing import Dict, Optional, Union + +import torch + +from megatron.core import parallel_state +from megatron.core.dist_checkpointing.mapping import ShardedStateDict +from megatron.core.dist_checkpointing.utils import apply_prefix_mapping +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.identity_op import IdentityFuncOp, IdentityOp +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.spec_utils import ModuleSpec, build_module +from megatron.core.transformer.transformer_config import TransformerConfig +from megatron.core.utils import make_viewless_tensor + + +@dataclass +class TransformerLayerSubmodules: + input_layernorm: Union[ModuleSpec, type] = IdentityOp + self_attention: Union[ModuleSpec, type] = IdentityOp + self_attn_bda: Union[ModuleSpec, type] = IdentityFuncOp + + pre_cross_attn_layernorm: Union[ModuleSpec, type] = IdentityOp + cross_attention: Union[ModuleSpec, type] = IdentityOp + cross_attn_bda: Union[ModuleSpec, type] = IdentityFuncOp + + pre_mlp_layernorm: Union[ModuleSpec, type] = IdentityOp + mlp: Union[ModuleSpec, type] = IdentityOp + mlp_bda: Union[ModuleSpec, type] = IdentityFuncOp + + # Mapping for sharded tensor keys to be applied in `sharded_state_dict` method + sharded_state_dict_keys_map: Dict[str, str] = field(default_factory=dict) + + +class BaseTransformerLayer(ABC): + """ A common parent class for `TransformerLayer` like implementations. + + A dummy class that is subclassed by similar `TransformerLayer`s e.g. the + `TransformerLayer` in this file and possibly other `TransformerLayer` + implementations that aim to use `TransformerBlock` as the base module. + The main purpose is to check if any layer (or module) provided in the spec + is a subclass of this class to allow fanning-out of that spec for all the + layers in the `TransformerBlock`. See `_get_block_submodules` method + implementation in `transformer_block.py` file for more details. + """ + + def __init__(self): + pass + + +class TransformerLayer(MegatronModule, BaseTransformerLayer): + """A single transformer layer. + + Transformer layer takes input with size [s, b, h] and returns an + output of the same size. + """ + + def __init__( + self, + config: TransformerConfig, + submodules: TransformerLayerSubmodules, + layer_number: int = 1, + hidden_dropout: float = None, + ): + super().__init__(config=config) + self.submodules_config = submodules + + self.layer_number = layer_number + self._get_layer_offset() + self.hidden_dropout = config.hidden_dropout if hidden_dropout is None else hidden_dropout + + ## [Module 1: Input Layernorm] Optional Layernorm on the input data + # TODO: add pytorch only layernorm + self.input_layernorm = build_module( + submodules.input_layernorm, + config=self.config, + hidden_size=self.config.hidden_size, + eps=self.config.layernorm_epsilon, + ) + + ## [Module 2: SelfAttention] + self.self_attention = build_module( + submodules.self_attention, config=self.config, layer_number=layer_number, + ) + + ## [Module 3: BiasDropoutFusion] + self.self_attn_bda = build_module(submodules.self_attn_bda) + + ## [Module 4: Post SelfAttention] Optional Layernorm after self-attn + self.pre_cross_attn_layernorm = build_module( + submodules.pre_cross_attn_layernorm, + config=self.config, + hidden_size=self.config.hidden_size, + eps=self.config.layernorm_epsilon, + ) + + ## [Module 5: CrossAttention] + self.cross_attention = build_module( + submodules.cross_attention, config=self.config, layer_number=layer_number, + ) + + ## [Module 6: BiasDropoutFusion] + self.cross_attn_bda = build_module(submodules.cross_attn_bda, config=self.config,) + + ## [Module 7: Pre MLP] Optional Layernorm before MLP + self.pre_mlp_layernorm = build_module( + submodules.pre_mlp_layernorm, + config=self.config, + hidden_size=self.config.hidden_size, + eps=self.config.layernorm_epsilon, + ) + + ## [Module 8: MLP block] + # TODO how to set the gpt_layer_spec.py when we have moe_frequency > 1, + # where MLP and MoE layer both appear alternately? + self.mlp = build_module(submodules.mlp, config=self.config) + if hasattr(self.mlp, 'set_layer_number'): + self.mlp.set_layer_number(self.layer_number) + + ## [Module 9: BiasDropoutFusion] + self.mlp_bda = build_module(submodules.mlp_bda) + + # @jcasper how should we handle nvfuser? + # Set bias+dropout+add fusion grad_enable execution handler. + # TORCH_MAJOR = int(torch.__version__.split('.')[0]) + # TORCH_MINOR = int(torch.__version__.split('.')[1]) + # use_nvfuser = TORCH_MAJOR > 1 or (TORCH_MAJOR == 1 and TORCH_MINOR >= 10) + # self.bias_dropout_add_exec_handler = nullcontext if use_nvfuser else torch.enable_grad + self.bias_dropout_add_exec_handler = torch.enable_grad + + def _get_layer_offset(self): + + pipeline_rank = parallel_state.get_pipeline_model_parallel_rank() + + num_layers_per_pipeline_rank = ( + self.config.num_layers // parallel_state.get_pipeline_model_parallel_world_size() + ) + + if parallel_state.get_virtual_pipeline_model_parallel_world_size() is not None: + vp_rank = parallel_state.get_virtual_pipeline_model_parallel_rank() + vp_size = parallel_state.get_virtual_pipeline_model_parallel_world_size() + + total_num_layers = self.config.num_layers + num_layers_per_virtual_rank = num_layers_per_pipeline_rank // vp_size + total_virtual_chunks = total_num_layers // vp_size + offset = vp_rank * total_virtual_chunks + (pipeline_rank * num_layers_per_virtual_rank) + + else: + # Each stage gets a contiguous set of layers. + if parallel_state.get_pipeline_model_parallel_world_size() > 1: + offset = pipeline_rank * num_layers_per_pipeline_rank + else: + offset = 0 + + return offset + + def forward( + self, + hidden_states, + attention_mask, + context=None, + context_mask=None, + rotary_pos_emb=None, + inference_params=None, + packed_seq_params=None, + ): + # hidden_states: [s, b, h] + + # Residual connection. + residual = hidden_states + + # Optional Input Layer norm + input_layernorm_output = self.input_layernorm(hidden_states) + + # Self attention. + attention_output_with_bias = self.self_attention( + input_layernorm_output, + attention_mask=attention_mask, + inference_params=inference_params, + rotary_pos_emb=rotary_pos_emb, + packed_seq_params=packed_seq_params, + ) + + # TODO: could we move `bias_dropout_add_exec_handler` itself + # inside the module provided in the `bias_dropout_add_spec` module? + with self.bias_dropout_add_exec_handler(): + hidden_states = self.self_attn_bda(self.training, self.config.bias_dropout_fusion)( + attention_output_with_bias, residual, self.hidden_dropout + ) + + # Residual connection. + residual = hidden_states + + # Optional Layer norm after self-attention + pre_cross_attn_layernorm_output = self.pre_cross_attn_layernorm(hidden_states) + + # Cross attention. + attention_output_with_bias = self.cross_attention( + pre_cross_attn_layernorm_output, + attention_mask=context_mask, + key_value_states=context, + inference_params=inference_params, + ) + + if isinstance(attention_output_with_bias, dict) and "context" in attention_output_with_bias: + context = attention_output_with_bias["context"] + + # TODO: could we move `bias_dropout_add_exec_handler` itself + # inside the module provided in the `bias_dropout_add_spec` module? + with self.bias_dropout_add_exec_handler(): + hidden_states = self.cross_attn_bda(self.training, self.config.bias_dropout_fusion)( + attention_output_with_bias, residual, self.hidden_dropout + ) + + # Residual connection. + residual = hidden_states + + # Optional Layer norm post the cross-attention. + pre_mlp_layernorm_output = self.pre_mlp_layernorm(hidden_states) + + # MLP. + mlp_output_with_bias = self.mlp(pre_mlp_layernorm_output) + + # TODO: could we move `bias_dropout_add_exec_handler` itself + # inside the module provided in the `bias_dropout_add_spec` module? + with self.bias_dropout_add_exec_handler(): + hidden_states = self.mlp_bda(self.training, self.config.bias_dropout_fusion)( + mlp_output_with_bias, residual, self.hidden_dropout + ) + + # Jit compiled function creates 'view' tensor. This tensor + # potentially gets saved in the MPU checkpoint function context, + # which rejects view tensors. While making a viewless tensor here + # won't result in memory savings (like the data loader, or + # p2p_communication), it serves to document the origin of this + # 'view' tensor. + output = make_viewless_tensor( + inp=hidden_states, requires_grad=hidden_states.requires_grad, keep_graph=True + ) + + return output, context + + def sharded_state_dict( + self, prefix: str = '', sharded_offsets: tuple = (), metadata: Optional[dict] = None + ) -> ShardedStateDict: + sharded_state_dict = super().sharded_state_dict(prefix, sharded_offsets, metadata) + prefixed_map = { + f'{prefix}{k}': f'{prefix}{v}' + for k, v in self.submodules_config.sharded_state_dict_keys_map.items() + } + if prefixed_map: + apply_prefix_mapping(sharded_state_dict, prefixed_map) + return sharded_state_dict diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/utils.py new file mode 100644 index 0000000..025f7c2 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/transformer/utils.py @@ -0,0 +1,188 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Utilities for transformer layers.""" +from functools import lru_cache +from operator import itemgetter +from typing import Any, Dict, Iterable, Iterator, Optional, Tuple, Union + +import torch + +from megatron.core import parallel_state +from megatron.core.dist_checkpointing.mapping import ShardedObject, ShardedStateDict, StateDict +from megatron.core.jit import jit_fuser +from megatron.core.utils import ( + make_sharded_tensor_for_checkpoint, + make_tp_sharded_tensor_for_checkpoint, +) + + +def get_linear_layer(rows, columns, init_method, perform_initialization=True): + """Simple linear layer with weight initialization.""" + layer = torch.nn.Linear(rows, columns) + if perform_initialization: # Take from modelparallel config + init_method(layer.weight) + with torch.no_grad(): + layer.bias.zero_() + return layer + + +@lru_cache(maxsize=32) +def get_default_causal_mask(sq: int) -> torch.Tensor: + """Return the causal upper triangular mask for softmax input.""" + return torch.triu(torch.ones(sq, sq, device="cuda"), diagonal=1).bool() + + +def attention_mask_func(attention_scores, attention_mask): + attention_scores.masked_fill_(attention_mask, -10000.0) + return attention_scores + + +@jit_fuser +def gelu_impl(x): + """OpenAI's gelu implementation.""" + return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x * (1.0 + 0.044715 * x * x))) + + +def openai_gelu(x): + return gelu_impl(x) + + +# This is actually Python equivalent of torch.nn.functional.gelu(), also with type hints for ONNX exporter +@jit_fuser +def erf_gelu(x): + return ( + x * 0.5 * (torch.erf(x / 1.41421).to(dtype=x.dtype) + torch.ones_like(x).to(dtype=x.dtype)) + ) + + +def make_sharded_tensors_for_checkpoint( + state_dict: StateDict, + prefix: str, + tensor_parallel_layers_axis_map: Optional[Dict[str, int]] = None, + sharded_offsets: Iterable[Tuple[int, int, int]] = (), + extra_state_suffix: str = '_extra_state', +): + """Wraps tensors from transformer layers with ShardedTensor or ShardedObject. + + For a given `state_dict`, wraps: + - all _extra_states with ShardedObject + - all tensors specified in tensor_parallel_layers_axis_map with TP and DP sharded ShardedTensor + - other values with DP sharded ShardedTensor + + Args: + state_dict (StateDict): state_dict to convert + prefix (str): prefix appended to keys in final state dict + tensor_parallel_layers_axis_map (Dict[str, int], optional): dict mapping layer + names to the axis for TP sharding + sharded_offsets (Iterable[Tuple[int, int, int]], optional): sharding already + applied (e.g. PP related), passed along to ShardedTensor + extra_state_suffix (str, default = '_extra_state'): layers with this + suffix will be wrapped with ShardedObject instead of ShardedTensor. + + """ + + if tensor_parallel_layers_axis_map is None: + tensor_parallel_layers_axis_map = {} + + sharded_state_dict = {} + for layer_name in state_dict.keys(): + tensor = state_dict[layer_name] + layer_key = f'{prefix}{layer_name}' + + if layer_name.endswith(extra_state_suffix): + sharded_state_dict[layer_key] = make_sharded_object_for_checkpoint( + tensor, layer_key, sharded_offsets + ) + + elif layer_name in tensor_parallel_layers_axis_map: + tp_axis = tensor_parallel_layers_axis_map[layer_name] + sharded_state_dict[layer_key] = make_tp_sharded_tensor_for_checkpoint( + tensor, layer_key, tp_axis, prepend_offsets=sharded_offsets, + ) + + else: + sharded_state_dict[layer_key] = make_sharded_tensor_for_checkpoint( + tensor, layer_key, prepend_offsets=sharded_offsets, + ) + + return sharded_state_dict + + +def make_sharded_object_for_checkpoint( + obj: Any, + key: str, + sharded_offsets: Iterable[Tuple[int, int, int]] = (), + replica_id: Union[None, int, Tuple[int, ...]] = None, + **kwargs, +): + """ Helper for instantiating a non-sharded ShardedObject (replicated across TP and DP group). + + Args: + obj (object): any object to be sharded + key (str): unique identifier of the object + sharded_offsets (Iterable[Tuple[int, int, int]]): offsets normally + prepended to ShardedTensors, will be used as global offsets for + ShardedObject + replica_id (Union[None, int, Tuple[int, ...]]): replica id + """ + if replica_id is None: + replica_id = ( + 0, + parallel_state.get_tensor_model_parallel_rank(), + parallel_state.get_data_parallel_rank(with_context_parallel=True), + ) + + return ShardedObject(key, obj, *_get_extra_state_offsets(sharded_offsets), replica_id, **kwargs) + + +def _get_extra_state_offsets( + sharded_offsets: Iterable[Tuple[int, int, int]] +) -> Tuple[Tuple[int, ...], Tuple[int, ...]]: + """ Turns ShardedTensor offsets into offsets suitable for ShardedObject. """ + if sharded_offsets: + sharded_offsets = sorted(sharded_offsets, key=itemgetter(0)) # sort by axis + axis, extra_state_offset, extra_state_shape = zip(*sharded_offsets) + assert list(axis) == list( + range(len(axis)) + ), f'Expected contiguous axis for offsets: {sharded_offsets}' + else: + extra_state_shape = (1,) + extra_state_offset = (0,) + return extra_state_shape, extra_state_offset + + +def sharded_state_dict_default( + module: torch.nn.Module, + prefix: str = '', + sharded_offsets: Tuple[Tuple[int, int, int]] = (), + metadata: Optional[dict] = None, +) -> ShardedStateDict: + """Provides implementation for sharded_state_dict method for non-MegatronModules. + + Tries to call `module.sharded_state_dict` when possible, + otherwise uses regular state dict and assumes tensors are replicated across TP and DP. + + `keep_vars=True` is passed to module.state_dict so that optimizer states + can be sharded later on. + + Args: + module (torch.nn.Module): module which sharded state dict we want to obtain + prefix (str): prefix for the state dict keys + sharded_offsets (Tuple[Tuple[int, int, int]], optional): sharding already + applied (e.g. PP related) by sup-modules. Passed along to ShardedTensor + metadata (dict, optional): metadata passed to module sharded_state_dict method + + Returns: + dict: dictionary of state dict keys mapped to ShardedTensors + """ + + if hasattr(module, 'sharded_state_dict'): + module_sharded_sd = module.sharded_state_dict( + prefix=prefix, sharded_offsets=sharded_offsets, metadata=metadata + ) + else: + module_sd = module.state_dict(prefix='', keep_vars=True) + module_sharded_sd = make_sharded_tensors_for_checkpoint( + module_sd, prefix, {}, sharded_offsets, + ) + return module_sharded_sd diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/core/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/core/utils.py new file mode 100644 index 0000000..abd8416 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/core/utils.py @@ -0,0 +1,1098 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +"""Utility functions used throughout Megatron core""" +import logging +import math +import operator +import queue +import socket +import sys +import threading +import time +import traceback +from dataclasses import dataclass +from datetime import datetime +from functools import reduce +from types import TracebackType +from typing import List, Optional, Tuple, Type, Union + +import torch + +from megatron.core import parallel_state +from megatron.core.dist_checkpointing.mapping import ShardedTensor + + +def ensure_divisibility(numerator, denominator): + """Ensure that numerator is divisible by the denominator.""" + assert numerator % denominator == 0, "{} is not divisible by {}".format(numerator, denominator) + + +def divide(numerator, denominator): + """Ensure that numerator is divisible by the denominator and return + the division value.""" + ensure_divisibility(numerator, denominator) + return numerator // denominator + + +def get_attr_wrapped_model(model, attr, allow_none=True, return_model_obj=False): + """Get an attribute from a wrapped model. + If return_model_obj is true, return the object that has the 'attr' attribute; + otherwise, return the attribute directly.""" + if isinstance(model, list): + raise RuntimeError("_get_attr_wrapped_model given a list of models") + + if allow_none: + + def condition(model, attr): + return not hasattr(model, attr) + + else: + + def condition(model, attr): + return getattr(model, attr, None) is None + + while condition(model, attr): + if not hasattr(model, "module"): + raise RuntimeError(f"_get_attr_wrapped_model couldn't find attribute {attr}") + + model = model.module + + if return_model_obj: + return model + return getattr(model, attr) + + +def get_model_type(model): + return get_attr_wrapped_model(model, 'model_type') + + +def get_model_config(model): + return get_attr_wrapped_model(model, 'config', allow_none=False) + + +class GlobalMemoryBuffer: + """Global buffer to avoid dynamic memory allocations. + Caller should ensure that buffers of the same name + are not used concurrently.""" + + def __init__(self): + self.buffer = {} + + def get_tensor(self, tensor_shape, dtype, name): + required_len = reduce(operator.mul, tensor_shape, 1) + if ( + self.buffer.get((name, dtype), None) is None + or self.buffer[(name, dtype)].numel() < required_len + ): + self.buffer[(name, dtype)] = torch.empty( + required_len, dtype=dtype, device=torch.cuda.current_device(), requires_grad=False + ) + + return self.buffer[(name, dtype)][0:required_len].view(*tensor_shape) + + +def _kernel_make_viewless_tensor(inp, requires_grad): + '''Make a viewless tensor. + + View tensors have the undesirable side-affect of retaining a reference + to the originally-viewed tensor, even after manually setting the '.data' + field. This method creates a new tensor that links to the old tensor's + data, without linking the viewed tensor, referenced via the '._base' + field. + ''' + out = torch.empty((1,), dtype=inp.dtype, device=inp.device, requires_grad=requires_grad,) + out.data = inp.data + return out + + +class MakeViewlessTensor(torch.autograd.Function): + ''' + Autograd function to make a viewless tensor. + + This function should be used in cases where the computation graph needs + to be propagated, but we only want a viewless tensor (e.g., + ParallelTransformer's hidden_states). Call this function by passing + 'keep_graph = True' to 'make_viewless_tensor()'. + ''' + + @staticmethod + def forward(ctx, inp, requires_grad): + return _kernel_make_viewless_tensor(inp, requires_grad) + + @staticmethod + def backward(ctx, grad_output): + return grad_output, None + + +def make_viewless_tensor(inp, requires_grad, keep_graph): + ''' + Entry-point for creating viewless tensors. + + This method should be used, rather than calling 'MakeViewlessTensor' + or '_kernel_make_viewless_tensor' directly. This method acts as a + switch for determining if an autograd function or a regular method + should be used to create the tensor. + ''' + + # return tensor as-is, if not a 'view' + if inp._base is None: + return inp + + # create viewless tensor + if keep_graph: + return MakeViewlessTensor.apply(inp, requires_grad) + else: + return _kernel_make_viewless_tensor(inp, requires_grad) + + +def assert_viewless_tensor(tensor, extra_msg=None): + '''Assert that a tensor is not a view (i.e., its '._base' field is + not set).''' + if isinstance(tensor, list): + [assert_viewless_tensor(t) for t in tensor] + return tensor + if not isinstance(tensor, torch.Tensor): + return tensor + assert tensor._base is None, ( + "Ensure tensor._base is None before setting tensor.data or storing " + "tensor to memory buffer. Otherwise, a memory leak will occur (and " + "likely accumulate over iterations). %s" + ) % extra_msg + return tensor + + +def safely_set_viewless_tensor_data(tensor, new_data_tensor): + '''Safely set tensor's '.data' field. + + Check first that the tensor is viewless (i.e., '._base' not set). If not, + raise an exception. + ''' + assert_viewless_tensor( + tensor, + extra_msg="FYI, tensor._base has shape %s, and new_data_tensor has shape %s." + % ("--" if tensor._base is None else tensor._base.shape, new_data_tensor.shape), + ) + tensor.data = new_data_tensor + + +def init_method_normal(sigma): + """Init method based on N(0, sigma).""" + + def init_(tensor): + return torch.nn.init.normal_(tensor, mean=0.0, std=sigma) + + return init_ + + +def scaled_init_method_normal(sigma, num_layers): + """Init method based on N(0, sigma/sqrt(2*num_layers).""" + std = sigma / math.sqrt(2.0 * num_layers) + + def init_(tensor): + return torch.nn.init.normal_(tensor, mean=0.0, std=std) + + return init_ + + +def make_tp_sharded_tensor_for_checkpoint( + tensor, key, tp_axis=0, replica_id=None, prepend_offsets=(), **kwargs +): + """ Helper for instantiating a ShardedTensor where the `tp_axis` dimension is sharded across TP group. + + Optionally, can provide offsets which prepend new dimensions to the tensor. + """ + + prepend_axis_num = len(prepend_offsets) + + if replica_id is None: + replica_id = (0, 0, parallel_state.get_data_parallel_rank(with_context_parallel=True)) + + return ShardedTensor.from_rank_offsets( + key, + tensor, + *prepend_offsets, + ( + tp_axis + prepend_axis_num, + parallel_state.get_tensor_model_parallel_rank(), + parallel_state.get_tensor_model_parallel_world_size(), + ), + replica_id=replica_id, + prepend_axis_num=prepend_axis_num, + **kwargs, + ) + + +def make_sharded_tensor_for_checkpoint(tensor, key, prepend_offsets=(), replica_id=None, **kwargs): + """ Helper for instantiating a non-sharded ShardedTensor (replicated across TP and DP group). + + Optionally, can provide offsets which prepend new dimensions to the tensor. + """ + + prepend_axis_num = len(prepend_offsets) + + if replica_id is None: + replica_id = ( + 0, + parallel_state.get_tensor_model_parallel_rank(), + parallel_state.get_data_parallel_rank(with_context_parallel=True), + ) + + return ShardedTensor.from_rank_offsets( + key, + tensor, + *prepend_offsets, + replica_id=replica_id, + prepend_axis_num=prepend_axis_num, + **kwargs, + ) + + +def prepare_input_tensors_for_wgrad_compute(grad_output, all_gathered_input): + + # Doing gather + slicing during the NeMo forward pass can make this tensor + # not be contiguous. PyTorch only checks if the tensor is contiguous, and only + # clones it if it's not contiguous: + # https://github.com/pytorch/pytorch/blob/c47cf9bc7f9e02f649ab4ed53fe4d35732c92ab6/torch/_refs/__init__.py#L2761 + grad_output = grad_output.contiguous() + # Convert the tensor shapes to 2D for execution compatibility + if grad_output.dim() == 3: + grad_output = grad_output.view( + grad_output.shape[0] * grad_output.shape[1], grad_output.shape[2] + ) + all_gathered_input = all_gathered_input.view( + all_gathered_input.shape[0] * all_gathered_input.shape[1], all_gathered_input.shape[2] + ) + + return grad_output, all_gathered_input + + +def drain_embedding_wgrad_compute(config, embedding_activation_buffer, grad_output_buffer, weight): + """ Helper for performing embedding wgrad GEMM's during the pipeline drain phase, pipelines the AllGather and GEMM's. + + Should only be used when pipeline model parallelism and gradient accumulation fusion are enabled. + """ + + assert len(embedding_activation_buffer) == len( + grad_output_buffer + ), "Length of activation and gradient buffers need to be equal!" + + import fused_weight_gradient_mlp_cuda + + from megatron.core.parallel_state import ( + get_global_memory_buffer, + get_tensor_model_parallel_group, + get_tensor_model_parallel_world_size, + ) + + input = embedding_activation_buffer.pop(0) + world_size = get_tensor_model_parallel_world_size() + dim_size = list(input.size()) + dim_size[0] = dim_size[0] * world_size + + all_gathered_input = [None, None] + if config.sequence_parallel: + all_gather_buffer = get_global_memory_buffer().get_tensor(dim_size, input.dtype, "mpu_0") + handle = torch.distributed._all_gather_base( + all_gather_buffer, input, group=get_tensor_model_parallel_group(), async_op=False + ) + + all_gathered_input[0] = all_gather_buffer + all_gather_buffer = None + else: + all_gathered_input[0] = input + + input = None + + def wgrad_compute(all_gathered_input, grad_output, weight): + + grad_output, all_gathered_input = prepare_input_tensors_for_wgrad_compute( + grad_output, all_gathered_input + ) + + if config.gradient_accumulation_fusion: + if weight.main_grad.dtype == torch.float32: + fused_weight_gradient_mlp_cuda.wgrad_gemm_accum_fp32( + all_gathered_input, grad_output, weight.main_grad + ) + elif weight.main_grad.dtype in (torch.float16, torch.bfloat16): + fused_weight_gradient_mlp_cuda.wgrad_gemm_accum_fp16( + all_gathered_input, grad_output, weight.main_grad + ) + else: + raise RuntimeError("Unsupported gradient type for gradient accumulation fusion") + + # We have all_gathered_input list acting as a double buffer here, + # since we are pipelining the AllGather and GEMM,one buffer all gathers + # the input while the other buffer reads from it for the GEMM. We use i + # and (i+1) for indexing to enable this double buffering. + for i in range(len(embedding_activation_buffer)): + input = embedding_activation_buffer.pop(0) + if config.sequence_parallel: + name = "mpu_" + str((i + 1) % 2) + all_gather_buffer = get_global_memory_buffer().get_tensor(dim_size, input.dtype, name) + handle = torch.distributed._all_gather_base( + all_gather_buffer, input, group=get_tensor_model_parallel_group(), async_op=True + ) + + all_gathered_input[(i + 1) % 2] = all_gather_buffer + all_gather_buffer = None + else: + all_gathered_input[(i + 1) % 2] = input + + grad_output = grad_output_buffer.pop(0) + wgrad_compute(all_gathered_input[i % 2], grad_output, weight) + input, all_gathered_input[i % 2], grad_output = None, None, None + + if config.sequence_parallel: + handle.wait() + + grad_output = grad_output_buffer.pop(0) + wgrad_compute(all_gathered_input[1], grad_output, weight) + input, all_gathered_input[1], grad_output = None, None, None + + +class _ValueWithRank: + """This is an internal class, not for use outside this module + + Attributes: + _rank (int): rank for the value + _value (float) : the value it stores, eg elapsed time + _unit (str) : unit for the value + """ + + def __init__(self, value: float, rank: int, unit: str = "") -> None: + """Initializer + + Args: + _value (float): the initial value with which it is inited + _rank (int): the rank number + _unit (str) : the unit of the value, eg ms or flops + """ + self._rank = rank + self._value = value + self._unit = unit + + def __lt__(self, other) -> bool: + """ Check if value of self is smaller than other's value + + Args: + other (_ValueWithRank): The other object to compare with + + Returns: + bool: True if lhs._value of operand is less than rhs._value, else False + """ + return self._value < other._value + + def __gt__(self, other) -> bool: + """Check if value of self is larger than other's value + + Args: + other (_ValueWithRank): The other object to compare with + + Returns: + bool: True if lhs._value of operand is greater than rhs._value, else False + """ + return self._value > other._value + + def __call__(self) -> Tuple[float, int, str]: + """Returns the value, the rank, and unit as a Tuple + + Returns: + Tuple[float, int, str]: value, rank, unit + """ + return self._value, self._rank, self._unit + + def __str__(self) -> str: + """String representation of the object + + Returns: + str: strigified object + """ + + return f"{self._value:.2f}{self._unit}/{self._rank}" + + +@dataclass +class _StragglerData: + """This is an internal dataclass, not for use outside this module + + Attributes: + min_elapsed (_ValueWithRank) min iteration time across all ranks + max_elapsed (_ValueWithRank) max iteration time across all ranks + min_btime (_ValueWithRank) min cpu time across all ranks + max_btime (_ValueWithRank) max cpu time across all ranks + min_temp (_ValueWithRank): min gpu temp across all ranks + max_temp (_ValueWithRank): max gpu temp across all ranks + min_power (_ValueWithRank) min gpu power across all ranks + max_power (_ValueWithRank) max gpu power across all ranks + min_util (_ValueWithRank): min gpu util across all ranks + max_util (_ValueWithRank): max gpu util across all ranks + min_clock (_ValueWithRank): min gpu clock across all ranks + max_clock (_ValueWithRank) max gpu clock across all ranks + aflops (List[_ValueWithRank]): sorted array of (_ValueWithRank) + """ + + # gemm time + min_elapsed = _ValueWithRank(sys.float_info.max, 0, "ms") + max_elapsed = _ValueWithRank(sys.float_info.min, 0, "ms") + # get_batch time + min_btime = _ValueWithRank(sys.float_info.max, 0, "us") + max_btime = _ValueWithRank(sys.float_info.min, 0, "us") + # temp + min_temp = _ValueWithRank(sys.float_info.max, 0, "C") + max_temp = _ValueWithRank(sys.float_info.min, 0, "C") + # power + min_power = _ValueWithRank(sys.float_info.max, 0, "W") + max_power = _ValueWithRank(sys.float_info.min, 0, "W") + # util + min_util = _ValueWithRank(sys.float_info.max, 0, "%") + max_util = _ValueWithRank(sys.float_info.min, 0, "%") + # clock + min_clock = _ValueWithRank(sys.float_info.max, 0, "MHz") + max_clock = _ValueWithRank(sys.float_info.min, 0, "MHz") + aflops: List[_ValueWithRank] = None + + +class StragglerDetector: + """Singleton Class implementing per rank Straggler Detector + + It use cuda events to time operation of choice using the + start and stop methods which can be directly invoked using + the class instance or can be used like a python context. + After collection, a report() method is available to display + the collected metrics. It is only supported if CUDA is + available. megatron/core/README_STRAGGLER.md for more info + + Note: + The instance and class attributes mentioned below are all + private to the class and has no use outside the class + + Attributes: + _off (bool): current state of the toggle + start (FunctionType): start method + stop (FunctionType): stop method + world (int): world size + rank (int): rank for this instance + mmcnt (int): number of ranks to report + port (int): control port + amp (float): amplification factor for TFLOPs, default 3.0 + toggle (bool): whether to start/stop detector collection + bdata (bool): when true, just collect get_batch + dev (int): cuda device + idx (int): index into the list below + idx_q (LifoQueue): queue of index + evt_q (LifoQueue): cuda event queue + start_events (list[torch.cuda.Event]): cuda start event + stop_events (list[torch.cuda.Event]): cuda stop event + start_time (list[int]): start time (wallclock) + stop_time (list[int]): stop time (wallclock) + start_batch (list[int]): start time for get_batch + stop_batch (list[int]): stop time for get_batch + sock (socket): the controller socket + ctrlr (Thread): the controller thread + logger (Logger): the logger instance for this instance + """ + + _configured = False + """Indicates if the singleton instance is configured or not + """ + + def __new__(cls: Type["StragglerDetector"]) -> "StragglerDetector": + """Constructor + Creates an instance of the class if not created + + Args: + cls (Type['StragglerDetector']): The class type + + Returns: + StragglerDetector: the class instance + """ + + if not hasattr(cls, "_instance"): + cls._instance = super(StragglerDetector, cls).__new__(cls) + return cls._instance + + def __init__(self) -> None: + """Initializer + + The inital state of the StragglerDetector instance is disabled. + The enabled state is indicated using self._off member variable + and the proerty enabled. + """ + self._off = True + self.start = self.null_method + self.stop = self.null_method + self.world = 0 + self.rank = 0 + self.mmcnt = 1 + self.port = 0 + self.amp = 3.0 + self.toggle = False + self.bdata = False + self.dev = None + self.idx = 0 + self.idx_q = None + self.evt_q = None + self.start_events = None + self.stop_events = None + self.start_time = None + self.stop_time = None + self.start_batch = None + self.stop_batch = None + self.sock = None + self.ctrlr = None + self.logger = logging.getLogger(__name__) + + def configure( + self, + world: int, + rank: int, + mmcnt: int = 1, + amp: float = 3.0, + port: int = 65535, + prefill: int = 1024, + enabled: bool = False, + ) -> None: + """This method is called to configure the Singleton instance + + It should be called once per instantiation per process. + + Note: + The constructor keeps the state of instance disabled + i.e no collection will happen even when start/stop methods are + called. Only when enabled is True (self._off is True), the + start/stop method pointers get assigned the real collection + methods, otherwise they are initialized with null_method + + Args: + world (int): World Size + rank (int): The rank of this trainer + mmcnt (int, optional): Number of ranks to print for showing Min/Max Etpt. + Defaults to 1. + amp (float, optional): Set to 3.0 if we only use timers in fwd pass. + Defaults to 3.0. + port (int, optional): Control port, useful only for rank-0. Defaults to 65535. + prefill (int, optional): Howmany Events to pre-populate. Defaults to 1024. + enabled (bool, optional): Whether or not collection is enabled on startup. + Defaults to False. + """ + if StragglerDetector._configured: + # don't throw + return + StragglerDetector._configured = True + self.bdata = False + self.start = self.null_method + self.stop = self.null_method + self._off = True + # No CUDA, No Support + if torch.cuda.is_available(): + self._off = not enabled + self.world = world + self.rank = rank + self.mmcnt = mmcnt if mmcnt > 1 else 1 + self.amp = amp + self.port = port + self.toggle = False + self.bdata = False + self.idx = 0 + self.idx_q = queue.LifoQueue() + self.evt_q = queue.LifoQueue() + self.start_events = [] + self.stop_events = [] + self.start_time = [] + self.stop_time = [] + self.start_batch = [] + self.stop_batch = [] + backend = torch.distributed.get_backend() + if backend == "nccl": + self.dev = torch.cuda.current_device() + else: + self.dev = torch.device("cpu") + # cache some events + for _ in range(prefill): + self.evt_q.put(torch.cuda.Event(enable_timing=True)) + if self.rank == 0: + # Start the controller + self._controller() + if not self._off: + self.start = self.start_method + self.stop = self.stop_method + + def reset(self) -> None: + """This method is called to reset the metrics state of the instance + + It is generally called from within elapsed() after extracting per rank metrics. + """ + if self._off: + return + self.idx = 0 + self.idx_q = queue.LifoQueue() + # Pool them + _ = [self.evt_q.put(ev) for ev in self.start_events] + _ = [self.evt_q.put(ev) for ev in self.stop_events] + self.start_events = [] + self.stop_events = [] + # Use regular timers + self.start_time = [] + self.stop_time = [] + self.start_batch = [] + self.stop_batch = [] + self.bdata = False + + def start_method(self) -> None: + """This method adds the start timers. + + Both cuda event and perf_counter are added. If bdata is set to + true from __call__, this method skips inserting cuda + timer. This way it can be used to measure time spent on + CPU - generally useful for timing get_batch() + """ + # Not reentrant + # First check if this start is for data + if self.bdata: + self.start_batch.append(time.perf_counter_ns()) + self.stop_batch.append(0) # this indicate we need to add timer + self.bdata = False + return + if self.evt_q.qsize() > 1: + sev = self.evt_q.get() # no try-catch + eev = self.evt_q.get() # no try-catch + else: + sev = torch.cuda.Event(enable_timing=True) + eev = torch.cuda.Event(enable_timing=True) + self.start_events.append(sev) + self.stop_events.append(eev) + self.start_time.append(0) + self.stop_time.append(0) + self.idx_q.put(self.idx) + self.start_time[self.idx] = time.perf_counter_ns() + self.start_events[self.idx].record() + self.idx += 1 + + def stop_method(self) -> None: + """This method adds the stop timers. + + Both cuda event and perf_counter are added. If bdata is set to + true from __call__, this method skips inserting cuda + timer. Also see start_method() + """ + # Not reentrant + # First check if this stop is for data + dle = len(self.stop_batch) - 1 + if dle >= 0 and self.stop_batch[dle] == 0: + self.stop_batch[dle] = time.perf_counter_ns() + return + idx = self.idx_q.get() + self.stop_time[idx] = time.perf_counter_ns() + self.stop_events[idx].record() + + def elapsed(self) -> Tuple[float, float, int, int, int, int]: + """This method is called from report(), or can be called directly + + It is called to collect all the elapsed time since last reset(). + It finally calls reset() + + Returns: + Tuple[float, float, int, int, int, int]: see below for returns + delta : time spent in kernel + batch_delta : time spent in get_batch + temp : observed gpu temp + power : observed gpu power + util : observed gpu utilization + clock : observed gpu clock + """ + if self._off: + # match with return below + return 0, 0, 0, 0, 0, 0 + ls_ev = len(self.start_events) + le_ev = len(self.stop_events) + ls_bs = len(self.start_batch) + ls_be = len(self.stop_batch) + delta = 0.0 + batch_delta = 0.0 + temp = 0 + power = 0 + clock = 0 + if ls_ev != le_ev: + self.logger.warning(f"Event Start/Stop out of sync {ls_ev}/{le_ev}") + elif ls_bs != ls_be: + self.logger.warning(f"get_batch Start/Stop out of sync {ls_bs}/{ls_be}") + else: + temp = torch.cuda.temperature() + power = torch.cuda.power_draw() + util = torch.cuda.utilization() + clock = torch.cuda.clock_rate() + torch.cuda.synchronize() + # Process Events + for i in range(ls_ev): + e_ev = self.start_events[i].elapsed_time(self.stop_events[i]) + e_tm = (self.stop_time[i] - self.start_time[i]) / 1e6 # ns to ms + # Pick the larger of Event and perf_counter time? + delta += max(e_ev, e_tm) + # Process get_batch + for i in range(ls_bs): + batch_delta = (self.stop_batch[i] - self.start_batch[i]) / 1e3 # us + self.reset() # Prepare for next round + # time in ms, batch_delta in us, check return above + return delta, batch_delta, temp, power, util, clock + + def report(self, total_flops: float = 0.0, log_interval: int = 0) -> bool: + """Function to log the min/max metircs and the associated rank over a time period + + It finds the slowest and fastest rank among all ranks. It should be + called by all ranks, but only rank-0 prints the analysis + At the end it checks, if the straggler detector should + remain active or if it should be deactivated. + + Args: + total_flops (float, optional): The theoretical flops over the period. Defaults to 0.0. + log_interval (int, optional): The training interval over which reporting is called(ms) + Defaults to 0. + + Returns: + bool: True if reported, else False + """ + ret = False + if not self._off and total_flops > 0.0 and log_interval > 0: + elapsed, btime_us, temp, power, util, clock = self.elapsed() # get raw time + ptime = elapsed / (log_interval * 1.0) # avg per iteration elapsed time, ms + btime = btime_us / (log_interval * 1.0) # avg per iteration get_batch time, us + api_flops = total_flops / (log_interval * 1.0) # avg per iteration flops, ms + apir_flops = api_flops / ( + ptime * 10 ** 9 * self.world + ) # this is avg per iteration this rank's thruput, TFLOP/s (note 10**9), + et_flops = apir_flops / self.amp # Estimated TFLOPs, not tracing backward + + o_dt = self._min_max( + ptime, btime, float(temp), float(power), float(util), float(clock), et_flops, + ) + if self.rank == 0: + now = f"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}]" + min_flops, min_frank, _ = o_dt.aflops[0]() + max_flops, max_frank, _ = o_dt.aflops[-1]() + self.logger.info( + f"{now} | " + f"MnRtt/Rnk: {o_dt.min_elapsed} | " + f"MxRtt/Rnk: {o_dt.max_elapsed} | " + f"MnPwr/Rnk: {o_dt.min_power} | " + f"MxPwr/Rnk: {o_dt.max_power} | " + f"MnTmp/Rnk: {o_dt.min_temp} | " + f"MxTmp/Rnk: {o_dt.max_temp} | " + f"MnUtl/Rnk: {o_dt.min_util} | " + f"MxUtl/Rnk: {o_dt.max_util} | " + f"MnClk/Rnk: {o_dt.min_clock} | " + f"MxClk/Rnk: {o_dt.max_clock} | " + f"MnDRtt/Rnk: {o_dt.min_btime} | " + f"MxDRtt/Rnk: {o_dt.max_btime} | " + f"MnEtpt/Rnk: {min_flops:.2f}TF/{min_frank} | " + f"MxEtpt/Rnk: {max_flops:.2f}TF/{max_frank}" + ) + if self.mmcnt > 1 and self.mmcnt < self.world: + line = f"^^^^ Bottom {self.mmcnt} Ranks with lowest Etpt(TF):" + for i in range(self.mmcnt): + line += f" {o_dt.aflops[i]}," + self.logger.info(line) + line = f"^^^^ Top {self.mmcnt} Ranks with highest Etpt(TF):" + shift = self.world - self.mmcnt + for i in range(self.mmcnt): + line += f" {o_dt.aflops[i+shift]}," + self.logger.info(line) + ret = True + + # Check/Communicate if tracking is turned off or on + self._check_toggle() + return ret + + def _check_toggle(self) -> None: + """Helper method to check if a request to toggle the collection state was made + + It checks iof collection state toggle req was made via the server listening on + rank-0 since last call to report(). Called by report(). Calling this method + indirectly from report() is the only way to activate the change that is made + via rank-0 + """ + # If no change just commnunicate the current + off = self._off + if self.rank == 0 and self.toggle: + off = not self._off + self.toggle = False + state = torch.tensor(off, dtype=torch.bool, device=self.dev) + torch.distributed.broadcast(state, 0) # Blocking + self._off = state.item() + if not self._off: + self.start = self.start_method + self.stop = self.stop_method + state = "ON" + else: + self.start = self.null_method + self.stop = self.null_method + state = "OFF" + if self.rank == 0 and off is not self._off: + self.logger.info(f"Toggling StragglerDetector State {state}") + + def _handler(self) -> None: + """Thread function for the controller. + + It is a tcp-server that listens on a port. Uses HTTP protocol. + If connected to it using curl, it indicates a toggle of the + collection state. The actual toggling happens at the end of + calling report() when _check_toggle() is called. + """ + resp = f"HTTP/1.0 200 OK\r\nConnection: Close\r\nContent-length: " + + if self.rank == 0: + state = "OFF" if self._off else "ON" + self.logger.info( + f"Controller ready to recv " f"commands on port {self.port}. Current state {state}" + ) + while True: + try: + conn, _ = self.sock.accept() + _ = conn.recv(1024) + self.toggle = True + state = "ON" if self._off else "OFF" + msg = f"Will turn StragglerDetector {state} at next logging interval" + msg_len = len(msg) + final_resp = f"{resp}{msg_len}\r\n\r\n{msg}" + conn.send(final_resp.encode()) + conn.close() + self.logger.info(msg) + except Exception as err: + self.logger.error(f"Error in stragler handler.. {str(err)}") + return + + def _controller(self): + """Installs a controller listener that is used to toggle collection state. + + Called from configure(). Ignored for all ranks other than rank-0 + """ + try: + if self.rank == 0: + neth = "0.0.0.0" + netp = self.port + self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + self.sock.bind((neth, netp)) + self.sock.listen(128) + self.ctrlr = threading.Thread( + target=self._handler, args=(), name="straggler", daemon=True + ) + self.ctrlr.start() + except Exception as err: + self.logger.warning(f"StragglerDetector cannot be controlled.. {str(err)}") + + def _min_max( + self, + ptime: float, + btime: float, + temp: float, + power: float, + util: float, + clock: float, + flops: float, + ) -> Union[_StragglerData, None]: + """Helper function to find the min/max values + + Args: + ptime (float): avg per iteration gpu time + btime (float): avg per iteration cpu time + temp (float): gpu temp at the time of reporting + power (float): gpu power at the time of reporting + util (float): gpu util at the time of reporting + clock (float): gpu clock at the time of reporting + flops (float): estimated flops for the rank + + Returns: + Union[_StragglerData, None]: It contains the min/max of few metrics and the + corresponding rank it also has sorted list of + all (flops, rank) sorted by flops (aflops) + or returns None if collecton is disabled + """ + if self._off: + return None + # initialize output data object + o_dt = _StragglerData() + + prof_data = {} + prof_data["rank"] = self.rank + prof_data["time"] = ptime + prof_data["btime"] = btime + prof_data["temp"] = temp + prof_data["power"] = power + prof_data["util"] = util + prof_data["clock"] = clock + prof_data["flops"] = flops + + if self.rank == 0: + data_list = [prof_data] * self.world + else: + data_list = None + + # this is blocking by default + torch.distributed.gather_object(prof_data, object_gather_list=data_list, dst=0) + + if self.rank == 0: + min_ctime = min(data_list, key=lambda k: k["time"]) # elapsed + max_ctime = max(data_list, key=lambda k: k["time"]) # elapsed + + min_cbatch = min(data_list, key=lambda k: k["btime"]) # batch time + max_cbatch = max(data_list, key=lambda k: k["btime"]) # batch time + + min_ctemp = min(data_list, key=lambda k: k["temp"]) # temp + max_ctemp = max(data_list, key=lambda k: k["temp"]) # temp + + min_cpower = min(data_list, key=lambda k: k["power"]) # power + max_cpower = max(data_list, key=lambda k: k["power"]) # power + + min_cutil = min(data_list, key=lambda k: k["util"]) # gpu util + max_cutil = max(data_list, key=lambda k: k["util"]) # gpu util + + min_cclock = min(data_list, key=lambda k: k["clock"]) # gpu clock + max_cclock = max(data_list, key=lambda k: k["clock"]) # gpu clock + + min_val = min_ctime["time"] + min_rank = min_ctime["rank"] + max_val = max_ctime["time"] + max_rank = max_ctime["rank"] + o_dt.min_elapsed = _ValueWithRank(min_val, min_rank, "ms") + o_dt.max_elapsed = _ValueWithRank(max_val, max_rank, "ms") + + min_val = min_cbatch["btime"] + min_rank = min_cbatch["rank"] + max_val = max_cbatch["btime"] + max_rank = max_cbatch["rank"] + o_dt.min_btime = _ValueWithRank(min_val, min_rank, "us") + o_dt.max_btime = _ValueWithRank(max_val, max_rank, "us") + + min_val = min_ctemp["temp"] + min_rank = min_ctemp["rank"] + max_val = max_ctemp["temp"] + max_rank = max_ctemp["rank"] + o_dt.min_temp = _ValueWithRank(min_val, min_rank, "C") + o_dt.max_temp = _ValueWithRank(max_val, max_rank, "C") + + min_val = min_cpower["power"] + min_rank = min_cpower["rank"] + max_val = max_cpower["power"] + max_rank = max_cpower["rank"] + o_dt.min_power = _ValueWithRank(min_val, min_rank, "W") + o_dt.max_power = _ValueWithRank(max_val, max_rank, "W") + + min_val = min_cutil["util"] + min_rank = min_cutil["rank"] + max_val = max_cutil["util"] + max_rank = max_cutil["rank"] + o_dt.min_util = _ValueWithRank(min_val, min_rank, "%") + o_dt.max_util = _ValueWithRank(max_val, max_rank, "%") + + min_val = min_cclock["clock"] + min_rank = min_cclock["rank"] + max_val = max_cclock["clock"] + max_rank = max_cclock["rank"] + o_dt.min_clock = _ValueWithRank(min_val, min_rank, "MHz") + o_dt.max_clock = _ValueWithRank(max_val, max_rank, "MHz") + + o_dt.aflops = [ + _ValueWithRank(d.get("flops"), d.get("rank")) for _, d in enumerate(data_list) + ] + o_dt.aflops.sort(key=lambda val_with_rank: val_with_rank()[0]) + # wait for everyone here + torch.distributed.barrier() + + return o_dt + + @property + def enabled(self) -> bool: + """Can be called to check the enabled state of the instance + + Note: + After the request to toggle the state, the + actual state change happens at end of call + to report() + """ + return not self._off + + @property + def configured(self) -> bool: + """Can be called to check if the the instance is already configured + + Returns: + bool: returns True if configure was called and was a success, else False + """ + return StragglerDetector._configured + + @property + def my_rank(self): + """Can be called to get configured rank of this instance + + Returns: + int: Configured rank for this instance + """ + return self.rank + + @property + def world_size(self) -> int: + """Can be called to get configured world of this instance + + Returns: + int: World size configured for this instance + """ + return self.world + + def null_method(self) -> None: + """Default method to initialize start/stop method ptrs""" + pass + + def __enter__(self) -> "StragglerDetector": + """Define context/instance entry + + Returns: + StragglerDetector: the instance + """ + self.start() + return self + + def __call__(self, bdata: bool = False) -> "StragglerDetector": + """Callable for the instance. Set context state, + + Useful when the context is used for cpu timers only when bdata=True + + Args: + bdata (bool, optional): when true, only enables cpu timers. Defaults to False. + + Returns: + StragglerDetector: the instance + """ + self.bdata = bdata + return self + + def __exit__( + self, + ex_type: Optional[Type[BaseException]], + ex_val: Optional[BaseException], + ex_tb: Optional[TracebackType], + ) -> bool: + """Define context/instance exit, calls the stop method + + Args: + ex_type (Optional[Type[BaseException]]): Exception type + ex_val (Optional[BaseException]): _description_ + ex_tb (Optional[TracebackType]): _description_ + + Returns: + bool: True if the exception was handled + """ + # Should not suppress errors even if turned off + ret = False + if ex_type is not None: + err = traceback.format_exception(ex_tb) + self.logger.warning(f"{str(ex_val)}\n{err}") + ret = True + self.stop() + return ret + + +# Singleton, global visibility +__straggler__ = StragglerDetector() +"""StragglerDetector: private module variable, not be directly accessed +""" diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/__init__.py new file mode 100644 index 0000000..f801100 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/arguments.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/arguments.py new file mode 100644 index 0000000..c03e70c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/arguments.py @@ -0,0 +1,25 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +def add_ammo_args(parser): + """Add additional arguments for ammo.""" + group = parser.add_argument_group(title="ammo-generic") + + group.add_argument( + "--ammo-load-classic-megatron-to-mcore", + action="store_true", + help="Load a classic megatron-lm checkpoint to a new megatron-core model.", + ) + group.add_argument( + "--ammo-convert-te-to-local-spec", + action="store_true", + help="Load a megatron-core transformer-engine checkpoint to a model with local spec.", + ) + group.add_argument( + "--ammo-quant-cfg", + type=str, + default=None, + choices=["int8_sq", "fp8", "int4_awq", "None"], + help="Algorithms supported by atq.quantize.", + ) + + return parser diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/gpt/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/gpt/__init__.py new file mode 100644 index 0000000..f801100 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/gpt/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/gpt/model_provider.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/gpt/model_provider.py new file mode 100644 index 0000000..e0cc326 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/gpt/model_provider.py @@ -0,0 +1,73 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""ModelOpt GPT model provider.""" + +from typing import Union + +from megatron.training import get_args, print_rank_0 +from megatron.training.arguments import core_transformer_config_from_args +from megatron.core.inference.gpt.model_specs import get_gpt_layer_ammo_spec +from megatron.core.inference.gpt.state_dict_hooks import ( + mcore_gpt_load_classic_state_dict_pre_hook, + mcore_gpt_load_te_state_dict_pre_hook, +) +from megatron.core.models.gpt import GPTModel as MCoreGPTModel + + +def model_provider( + pre_process=True, post_process=True, parallel_output=True, +) -> Union[MCoreGPTModel]: + """Builds the GPT model. + + This model_provider only sypport use_mcore_models=True. + + Args: + pre_process (bool, optional): Set to true if you need to compute embedings. Defaults to True. + post_process (bool, optional): Set to true if you need to want to compute output logits/loss. Defaults to True. + parallel_output (bool): whether to allgather the output logits? This must be + True if `model_provider` is called in text_generation_server. + + Returns: + Union[MCoreGPTModel]: The returned model + """ + args = get_args() + + print_rank_0("building GPT model ...") + config = core_transformer_config_from_args(get_args()) + + if args.use_mcore_models: + if args.spec is not None: + raise ValueError("Custom layer specs are not supported!") + else: + if args.num_experts is None: + transformer_layer_spec = get_gpt_layer_ammo_spec() + else: + raise ValueError("MoE is not supported for now!") + + model_type = MCoreGPTModel + model_kwargs = { + "config": config, + "transformer_layer_spec": transformer_layer_spec, + "vocab_size": args.padded_vocab_size, + "max_sequence_length": args.max_position_embeddings, + "pre_process": pre_process, + "post_process": post_process, + "fp16_lm_cross_entropy": args.fp16_lm_cross_entropy, + "parallel_output": parallel_output, + "share_embeddings_and_output_weights": not args.untie_embeddings_and_output_weights, + "position_embedding_type": args.position_embedding_type, + "rotary_percent": args.rotary_percent, + } + else: + raise ValueError("Classic Megatron-LM models are not supported!") + + model = model_type(**model_kwargs) + print_rank_0(str(model)) + + if args.use_mcore_models: + if args.ammo_load_classic_megatron_to_mcore: + model._register_load_state_dict_pre_hook(mcore_gpt_load_classic_state_dict_pre_hook) + elif args.ammo_convert_te_to_local_spec: + model._register_load_state_dict_pre_hook(mcore_gpt_load_te_state_dict_pre_hook) + + return model diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/static/index.html b/Megatron-LM-core_r0.7.0.beta/megatron/inference/static/index.html new file mode 100644 index 0000000..8062879 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/static/index.html @@ -0,0 +1,124 @@ + + + + + + + +Megatron + + + +
+

Prompt Megatron

+ + + + + +
+0 +/ 1000 +
+ +
+ + + + + diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/__init__.py new file mode 100644 index 0000000..77da7be --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + + +from .api import ( + generate, + generate_and_post_process, + beam_search_and_post_process) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/api.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/api.py new file mode 100644 index 0000000..4557ff3 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/api.py @@ -0,0 +1,207 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Inference API.""" + + +import torch + +from megatron.core import mpu +from .communication import broadcast_float_list +from .generation import ( + generate_tokens_probs_and_return_on_first_stage, + score_and_return_on_first_stage, + beam_search_and_return_on_first_stage) +from .tokenization import ( + tokenize_prompts, + detokenize_generations) + +def generate_and_post_process(model, + prompts=None, + tokens_to_generate=0, + return_output_log_probs=False, + top_k_sampling=0, + top_p_sampling=0.0, + top_p_decay=0.0, + top_p_bound=0.0, + temperature=1.0, + add_BOS=False, + use_eod_token_for_early_termination=True, + stop_on_double_eol=False, + stop_on_eol=False, + prevent_newline_after_colon=False, + random_seed=-1, + return_logits=False): + """Run inference and post-process outputs, i.e., detokenize, + move to cpu and convert to list.""" + + # Main inference. + tokens, lengths, output_log_probs, logits = generate( + model, + prompts=prompts, + tokens_to_generate=tokens_to_generate, + return_output_log_probs=return_output_log_probs, + top_k_sampling=top_k_sampling, + top_p_sampling=top_p_sampling, + top_p_decay=top_p_decay, + top_p_bound=top_p_bound, + temperature=temperature, + add_BOS=add_BOS, + use_eod_token_for_early_termination=use_eod_token_for_early_termination, + stop_on_double_eol=stop_on_double_eol, + stop_on_eol=stop_on_eol, + prevent_newline_after_colon=prevent_newline_after_colon, + random_seed=random_seed) + + # Only post-process on first stage. + if mpu.is_pipeline_first_stage(): + tokens, prompts_plus_generations, prompts_plus_generations_segments = \ + detokenize_generations(tokens, lengths, True) + + if return_output_log_probs: + output_log_probs = output_log_probs.cpu().numpy().tolist() + for i, (prob, seg) in enumerate(zip(output_log_probs, prompts_plus_generations_segments)): + output_log_probs[i] = prob[:len(seg)-1] + + if return_logits: + assert(tokens_to_generate == 0) + assert(mpu.get_pipeline_model_parallel_world_size() == 1) + return prompts_plus_generations, prompts_plus_generations_segments, \ + output_log_probs, tokens, logits + else: + return prompts_plus_generations, prompts_plus_generations_segments, \ + output_log_probs, tokens + + return None + +def generate(model, + prompts=None, + tokens_to_generate=0, + return_output_log_probs=False, + top_k_sampling=0, + top_p_sampling=0.0, + top_p_decay=0.0, + top_p_bound=0.0, + temperature=1.0, + add_BOS=False, + use_eod_token_for_early_termination=True, + stop_on_double_eol=False, + stop_on_eol=False, + prevent_newline_after_colon=False, + random_seed=-1): + """Given prompts and input parameters, run inference and return: + tokens: prompts plus the generated tokens. + lengths: length of the prompt + generations. Note that we can + discard tokens in the tokens tensor that are after the + corresponding length. + output_log_probs: log probs of the tokens. + """ + + # Make sure input params are avaialble to all ranks. + values = [tokens_to_generate, + return_output_log_probs, + top_k_sampling, top_p_sampling, top_p_decay, top_p_bound, + temperature, add_BOS, use_eod_token_for_early_termination, + stop_on_double_eol, + stop_on_eol, + prevent_newline_after_colon, + random_seed] + values_float_tensor = broadcast_float_list(len(values), float_list=values) + tokens_to_generate = int(values_float_tensor[0].item()) + return_output_log_probs = bool(values_float_tensor[1].item()) + top_k_sampling = int(values_float_tensor[2].item()) + top_p_sampling = values_float_tensor[3].item() + top_p_decay = values_float_tensor[4].item() + top_p_bound = values_float_tensor[5].item() + temperature = values_float_tensor[6].item() + add_BOS = bool(values_float_tensor[7].item()) + use_eod_token_for_early_termination = bool(values_float_tensor[8].item()) + stop_on_double_eol = bool(values_float_tensor[9].item()) + stop_on_eol = bool(values_float_tensor[10].item()) + prevent_newline_after_colon = bool(values_float_tensor[11].item()) + random_seed = int(values_float_tensor[12].item()) + + if random_seed != -1: + torch.random.manual_seed(random_seed) + + # Tokenize prompts and get the batch. + # Note that these tensors are broadcaseted to all ranks. + if torch.distributed.get_rank() == 0: + assert prompts is not None + + context_tokens_tensor, context_length_tensor = tokenize_prompts( + prompts=prompts, tokens_to_generate=tokens_to_generate, add_BOS=add_BOS) + + if tokens_to_generate == 0: + return score_and_return_on_first_stage( + model, context_tokens_tensor, context_length_tensor) + + # Main inference function. + # Note that the outputs are available on the first stage. + return generate_tokens_probs_and_return_on_first_stage( + model, context_tokens_tensor, context_length_tensor, + return_output_log_probs=return_output_log_probs, + top_k=top_k_sampling, + top_p=top_p_sampling, + top_p_decay=top_p_decay, + top_p_bound=top_p_bound, + temperature=temperature, + use_eod_token_for_early_termination=use_eod_token_for_early_termination, + stop_on_double_eol=stop_on_double_eol, + stop_on_eol=stop_on_eol, + prevent_newline_after_colon=prevent_newline_after_colon) + +def beam_search_and_post_process(model, + prompts=None, + tokens_to_generate=0, + beam_size=0, + add_BOS=False, + stop_token=50256, + num_return_gen=1, + length_penalty=1, + prevent_newline_after_colon=False): + """Run beam search and post-process outputs, i.e., detokenize, + move to cpu and convert to list.""" + + # Main inference. + tokens, scores = beam_search(model, + prompts=prompts, + tokens_to_generate=tokens_to_generate, + beam_size=beam_size, + add_BOS=add_BOS, + stop_token=stop_token, + num_return_gen=num_return_gen, + length_penalty=length_penalty, + prevent_newline_after_colon=prevent_newline_after_colon) + # Only post-process on first stage. + if mpu.is_pipeline_first_stage(): + lengths = tokens.size(1)*torch.ones(beam_size, dtype=torch.int64, device=torch.cuda.current_device()) + tokens, prompts_plus_generations, prompts_plus_generations_segments = detokenize_generations(tokens, lengths, True) + scores = scores.cpu().numpy().tolist() + return prompts_plus_generations, prompts_plus_generations_segments, scores + + return None + +def beam_search(model, prompts=None, tokens_to_generate=0, beam_size=0, add_BOS=False, stop_token=50256, num_return_gen=1, length_penalty=1, prevent_newline_after_colon=False): + # Make sure input params are avaialble to all ranks. + values = [tokens_to_generate, + beam_size, + add_BOS, + stop_token, + num_return_gen, + length_penalty, + prevent_newline_after_colon] + values_float_tensor = broadcast_float_list(len(values), float_list=values) + tokens_to_generate = int(values_float_tensor[0].item()) + beam_size = int(values_float_tensor[1].item()) + add_BOS = bool(values_float_tensor[2].item()) + stop_token = int(values_float_tensor[3].item()) + num_return_gen = int(values_float_tensor[4].item()) + length_penalty = values_float_tensor[5].item() + prevent_newline_after_colon = values_float_tensor[6].item() + + context_tokens_tensor, context_length_tensor = tokenize_prompts( + prompts=prompts, tokens_to_generate=tokens_to_generate, add_BOS=add_BOS) + + return beam_search_and_return_on_first_stage(model, context_tokens_tensor, context_length_tensor, + beam_size, stop_token=stop_token, num_return_gen=num_return_gen, length_penalty=length_penalty, + prevent_newline_after_colon=prevent_newline_after_colon) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/beam_utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/beam_utils.py new file mode 100644 index 0000000..911a641 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/beam_utils.py @@ -0,0 +1,64 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +## from huggingface beam search +class BeamHypotheses(object): + def __init__(self, num_beams, length_penalty=1.0, early_stopping=False): + """ + Initialize n-best list of hypotheses. + """ + self.length_penalty = length_penalty + self.early_stopping = early_stopping + self.num_beams = num_beams + self.beams = [] + self.worst_score = 1e9 + + def __len__(self): + """ + Number of hypotheses in the list. + """ + return len(self.beams) + + def add(self, hyp, sum_logprobs, length): + """ + Add a new hypothesis to the list. + """ + score = sum_logprobs / length ** self.length_penalty + if len(self) < self.num_beams or score > self.worst_score: + self.beams.append((score, hyp)) + if len(self) > self.num_beams: + sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.beams)]) + del self.beams[sorted_scores[0][1]] + self.worst_score = sorted_scores[1][0] + else: + self.worst_score = min(score, self.worst_score) + + def is_done(self, best_sum_logprobs, cur_len): + """ + If there are enough hypotheses and that none of the hypotheses being generated + can become better than the worst one in the heap, then we are done with this sentence. + """ + + if len(self) < self.num_beams: + return False + elif self.early_stopping: + return True + else: + cur_score = best_sum_logprobs / cur_len ** self.length_penalty + ret = self.worst_score >= cur_score + return ret + diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/communication.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/communication.py new file mode 100644 index 0000000..dee3207 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/communication.py @@ -0,0 +1,185 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Communications utilities.""" + + +import torch + +from megatron.core import mpu + + + +# TODO: use functions from megatron/p2p +def recv_from_prev_pipeline_rank_(recv_buffer=None): + """Receive from previous pipeline stage and update the + input buffer inplace.""" + if not mpu.is_pipeline_first_stage(): + assert recv_buffer is not None + recv_prev_op = torch.distributed.P2POp( + torch.distributed.irecv, recv_buffer, + mpu.get_pipeline_model_parallel_prev_rank()) + reqs = torch.distributed.batch_isend_irecv([recv_prev_op]) + for req in reqs: + req.wait() + # To protect against race condition when using batch_isend_irecv(). + torch.cuda.synchronize() + + + +# TODO: use functions from megatron/p2p +def send_to_next_pipeline_rank(tensor=None): + """Send output to the next pipeline stage.""" + if not mpu.is_pipeline_last_stage(): + assert tensor is not None + send_next_op = torch.distributed.P2POp( + torch.distributed.isend, tensor, + mpu.get_pipeline_model_parallel_next_rank()) + reqs = torch.distributed.batch_isend_irecv([send_next_op]) + for req in reqs: + req.wait() + # To protect against race condition when using batch_isend_irecv(). + torch.cuda.synchronize() + + + +def _is_cuda(tensor): + """Check if a tensor is not none and is cuda.""" + assert tensor is not None + assert tensor.is_cuda + + + +def _is_cuda_contiguous(tensor): + """Check if a tensor is not none, is cuda, and is contiguous.""" + _is_cuda(tensor) + assert tensor.is_contiguous() + + + +def broadcast_from_last_pipeline_stage(size, dtype, tensor=None): + """Broadcast a tensor from last pipeline stage to all ranks.""" + + is_last_stage = mpu.is_pipeline_last_stage() + # If first stage and last state are the same, then there is no + # pipeline parallelism and no need to communicate. + if mpu.is_pipeline_first_stage() and is_last_stage: + return tensor + + if is_last_stage: + _is_cuda_contiguous(tensor) + else: + tensor = torch.empty(size, + dtype=dtype, + device=torch.cuda.current_device()) + # Get the group and corresponding source rank. + src = mpu.get_pipeline_model_parallel_last_rank() + group = mpu.get_pipeline_model_parallel_group() + torch.distributed.broadcast(tensor, src, group) + + return tensor + + + +def broadcast_from_last_to_first_pipeline_stage(size, dtype, tensor=None): + """Broadcast tensor values from last stage into the first stage.""" + + is_last_stage = mpu.is_pipeline_last_stage() + is_first_stage = mpu.is_pipeline_first_stage() + # If first stage and last state are the same, then there is no + # pipeline parallelism and no need to communicate. + if is_first_stage and is_last_stage: + return tensor + # Only first and last stage pipeline stages need to be involved. + if is_last_stage or is_first_stage: + if is_last_stage: + _is_cuda_contiguous(tensor) + else: + tensor = torch.empty(size, + dtype=dtype, + device=torch.cuda.current_device()) + src = mpu.get_pipeline_model_parallel_last_rank() + group = mpu.get_embedding_group() + # Broadcast from last stage into the first stage. + torch.distributed.broadcast(tensor, src, group) + else: + tensor = None + + return tensor + + + +def copy_from_last_to_first_pipeline_stage(size, dtype, tensor=None): + """Copy tensor values from last stage into the first stage. + Note that the input tensor is updated in place.""" + + is_last_stage = mpu.is_pipeline_last_stage() + is_first_stage = mpu.is_pipeline_first_stage() + # If first stage and last state are the same, then there is no + # pipeline parallelism and no need to communicate. + if is_first_stage and is_last_stage: + return + # Only first and last stage pipeline stages need to be involved. + if is_last_stage or is_first_stage: + _is_cuda(tensor) + is_contiguous = tensor.is_contiguous() + src = mpu.get_pipeline_model_parallel_last_rank() + group = mpu.get_embedding_group() + if is_contiguous: + tensor_ = tensor + else: + if is_last_stage: + tensor_ = tensor.contiguous() + else: + tensor_ = torch.empty(size, + dtype=dtype, + device=torch.cuda.current_device()) + # Broadcast from last stage into the first stage. + torch.distributed.broadcast(tensor_, src, group) + # Update the first stage tensor + if is_first_stage and not is_contiguous: + tensor[...] = tensor_ + + + +def broadcast_tensor(size, dtype, tensor=None, rank=0): + """ Given size and type of a tensor on all ranks and the tensor value + only on a specific rank, broadcast from that rank to all other ranks. + """ + + if torch.distributed.get_rank() == rank: + _is_cuda_contiguous(tensor) + else: + tensor = torch.empty(size, + dtype=dtype, + device=torch.cuda.current_device()) + + torch.distributed.broadcast(tensor, rank) + + return tensor + + + +def broadcast_list(size, dtype, list_values=None, rank=0): + """Broadcast a list of values with a given type.""" + + tensor = None + if torch.distributed.get_rank() == rank: + tensor = torch.tensor(list_values, dtype=dtype, + device=torch.cuda.current_device()) + + return broadcast_tensor(size, dtype, tensor=tensor, rank=rank) + + + +def broadcast_int_list(size, int_list=None, rank=0): + """Broadcast a list of interger values.""" + + return broadcast_list(size, torch.int64, list_values=int_list, rank=rank) + + + +def broadcast_float_list(size, float_list=None, rank=0): + """Broadcast a list of float values.""" + + return broadcast_list(size, torch.float32, list_values=float_list, + rank=rank) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/forward_step.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/forward_step.py new file mode 100644 index 0000000..e695196 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/forward_step.py @@ -0,0 +1,177 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Forward step utilities.""" + +from collections.abc import Iterable + +import torch + +from megatron.training import get_args +from megatron.core import mpu, InferenceParams +from .communication import ( + send_to_next_pipeline_rank, + recv_from_prev_pipeline_rank_) + + +class ForwardStep: + """Forward step function with all the communications. + We use a class here to hide the inference parameters + from the outside caller.""" + + def __init__(self, model, max_batch_size, max_sequence_length): + """Set values so we don't need to do it multiple times.""" + # Make sure model is in eval mode. + assert not isinstance(model, Iterable), \ + 'interleaving schedule is not supported for inference' + model.eval() + self.model = model + # Initialize inference parameters. + self.inference_params = InferenceParams(max_batch_size, + max_sequence_length) + # Pipelining arguments. + args = get_args() + self.pipeline_size_larger_than_one = ( + args.pipeline_model_parallel_size > 1) + # Threshold of pipelining. + self.pipelining_batch_x_seqlen = \ + args.inference_batch_times_seqlen_threshold + + + def __call__(self, tokens, position_ids, attention_mask): + """Invocation of the forward methods. Note that self.inference_params + is being modified by the forward step.""" + # Pipelining case. + if self.pipeline_size_larger_than_one: + current_batch_x_seqlen = tokens.size(0) * tokens.size(1) + if current_batch_x_seqlen >= self.pipelining_batch_x_seqlen: + micro_batch_size = \ + max(1, self.pipelining_batch_x_seqlen // tokens.size(1)) + return _with_pipelining_forward_step(self.model, + tokens, + position_ids, + attention_mask, + self.inference_params, + micro_batch_size) + + return _no_pipelining_forward_step(self.model, + tokens, + position_ids, + attention_mask, + self.inference_params) + + + +def _get_recv_buffer_dtype(args): + """Receive happens between the layers.""" + if args.fp32_residual_connection: + return torch.float + return args.params_dtype + + + +def _allocate_recv_buffer(batch_size, sequence_length): + """Receive happens between the layers with size [s, b, h].""" + if mpu.is_pipeline_first_stage(): + return None + args = get_args() + recv_size = (sequence_length, batch_size, args.hidden_size) + return torch.empty(recv_size, + dtype=_get_recv_buffer_dtype(args), + device=torch.cuda.current_device()) + + + +def _forward_step_helper(model, tokens, position_ids, attention_mask, + inference_params, recv_buffer=None): + """Single forward step. Update the allocate memory flag so + only the first time the memory is allocated.""" + batch_size = tokens.size(0) + sequence_length = tokens.size(1) + if recv_buffer is None: + recv_buffer = _allocate_recv_buffer(batch_size, sequence_length) + + # Receive from previous stage. + recv_from_prev_pipeline_rank_(recv_buffer) + + # Forward pass through the model. + model.set_input_tensor(recv_buffer) + output_tensor = model(tokens, position_ids, attention_mask, + inference_params=inference_params) + + # Send output to the next stage. + send_to_next_pipeline_rank(output_tensor) + + return output_tensor + + + +def _no_pipelining_forward_step(model, tokens, position_ids, attention_mask, + inference_params, recv_buffer=None): + """If recv_buffer is none, we will allocate one on the fly.""" + # Run a simple forward pass. + output_tensor = _forward_step_helper(model, tokens, position_ids, + attention_mask, inference_params, + recv_buffer=recv_buffer) + # Update the sequence length offset. + inference_params.sequence_len_offset += tokens.size(1) + + logits = None + if mpu.is_pipeline_last_stage(): + logits = output_tensor + + return logits + + + +def _with_pipelining_forward_step(model, tokens, position_ids, attention_mask, + inference_params, micro_batch_size): + """No interleaving is supported.""" + sequence_length = tokens.size(1) + batch_size = tokens.size(0) + + # Divide the batch dimension into micro batches. + num_micro_batches, last_chunk = divmod(batch_size, + micro_batch_size) + if last_chunk > 0: + num_micro_batches += 1 + + # Preallocate memory for output logits. + logits = None + if mpu.is_pipeline_last_stage(): + args = get_args() + logits = torch.empty( + (batch_size, sequence_length, args.padded_vocab_size), + dtype=torch.float32, device=torch.cuda.current_device()) + + # Preallocate recv buffer. + recv_buffer = _allocate_recv_buffer(micro_batch_size, sequence_length) + + for micro_batch_index in range(num_micro_batches): + # Slice among the batch dimenion. + start = micro_batch_index * micro_batch_size + end = min(start + micro_batch_size, batch_size) + this_micro_batch_size = end - start + tokens2use = tokens[start:end, ...] + position_ids2use = position_ids[start:end, ...] + + # Run a simple forward pass. + if this_micro_batch_size != micro_batch_size: + recv_buffer = None + output = _forward_step_helper(model, tokens2use, position_ids2use, + attention_mask, inference_params, + recv_buffer=recv_buffer) + + # Adjust the batch size offset to account for the micro-batch. + inference_params.batch_size_offset += this_micro_batch_size + + # Copy logits. + if mpu.is_pipeline_last_stage(): + logits[start:end, ...] = output + + # Once we are done with all the micro-batches, we can + # adjust the sequence length offset. + inference_params.sequence_len_offset += sequence_length + # and reset the batch size offset + inference_params.batch_size_offset = 0 + + return logits diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/generation.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/generation.py new file mode 100644 index 0000000..84e4af1 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/generation.py @@ -0,0 +1,432 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Generation utilities.""" + +import torch +import torch.nn.functional as F + +from megatron.training import get_args, get_tokenizer +from megatron.core import mpu +from megatron.training.utils import get_ltor_masks_and_position_ids +from .communication import ( + copy_from_last_to_first_pipeline_stage, + broadcast_from_last_pipeline_stage, + broadcast_from_last_to_first_pipeline_stage) +from .forward_step import ForwardStep +from .sampling import sample +from .beam_utils import BeamHypotheses + +def score_and_return_on_first_stage(model, tokens, lengths): + """Function for just scoring. + + Args: + model: no interleaving is supported. + tokens: prompt tokens extended to be of size [b, max_prompt_length] + lengths: original prompt length, size: [b] + Note: Outside of model, other parameters only need to be available on + rank 0. + + Returns: + output_log_probs: log probability of the selected tokens. size: [b, s] + """ + + args = get_args() + + batch_size = tokens.size(0) + max_prompt_length = lengths.max().item() + assert max_prompt_length == tokens.size(1) + + if max_prompt_length > args.max_position_embeddings: + raise ValueError("Length of prompt + tokens_to_generate longer than allowed") + + if max_prompt_length * batch_size > args.max_tokens_to_oom: + raise ValueError("Too many tokens. " + str(max_prompt_length*batch_size)+ " is greater than "+str(args.max_tokens_to_oom)) + + # forward step. + forward_step = ForwardStep(model, batch_size, max_prompt_length) + + # =================== + # Pre-allocate memory + # =================== + + # Log probability of the sequence (prompt + generated tokens). + output_log_probs = None + output_log_probs_size = (batch_size, max_prompt_length - 1) + + if mpu.is_pipeline_last_stage(): + output_log_probs = torch.empty(output_log_probs_size, + dtype=torch.float32, + device=torch.cuda.current_device()) + + # ============= + # Run infernece + # ============= + with torch.no_grad(): + attention_mask, position_ids = _build_attention_mask_and_position_ids(tokens) + + # logits will be meanigful only in the last pipeline stage. + logits = forward_step(tokens, position_ids, attention_mask) + + if mpu.is_pipeline_last_stage(): + # Always the last stage should have an output. + assert logits is not None + log_probs = F.log_softmax(logits, dim=2) + + # Pick the tokens that we need to get the log + # probabilities for. Note that next input token is + # the token which we selected in the current logits, + # so shift by 1. + indices = torch.unsqueeze(tokens[:, 1:], 2) + output_log_probs = torch.gather(log_probs, 2, indices).squeeze(2) + + # ====================================== + # Broadcast to the first pipeline stage. + # ====================================== + output_log_probs = broadcast_from_last_to_first_pipeline_stage( + output_log_probs_size, torch.float32, output_log_probs) + + return tokens, lengths, output_log_probs, logits + +def generate_tokens_probs_and_return_on_first_stage( + model, tokens, lengths, + return_output_log_probs=False, + top_k=0, top_p=0.0, top_p_decay=0.0, top_p_bound=0.0, + temperature=1.0, + use_eod_token_for_early_termination=True, + stop_on_double_eol=False, + stop_on_eol=False, + prevent_newline_after_colon=True + ): + """Main token generation function. + + Args: + model: no interleaving is supported. + tokens: prompt tokens extended to be of size [b, max-sequence-length] + lengths: original prompt length, size: [b] + return_output_log_probs: flag to calculate the log probability of + the generated tokens. Note that the log probability is the one + from the original logit. + top_k, top_p: top-k and top-p sampling parameters. + Note that top-k = 1 is gready. Also, these paramters are + exclusive meaning that: + if top-k > 0 then we expect top-p=0. + if top-p > 0 then we check for top-k=0. + temperature: sampling temperature. + use_eod_token_for_early_termination: if True, do early termination if + all the sequences have reached this token. + prevent_newline_after_colon: if True, it will disable generating new line \n after : + Note: Outside of model, other parameters only need to be available on + rank 0. + + Returns: Note that is size is adjusted to a lower value than + max-sequence-length if generation is terminated early. + tokens: prompt and generated tokens. size: [b, :] + generated_sequence_lengths: total length (including prompt) of + the generated sequence. size: [b] + output_log_probs: log probability of the selected tokens. size: [b, s] + """ + + args = get_args() + tokenizer = get_tokenizer() + + batch_size = tokens.size(0) + min_prompt_length = lengths.min().item() + max_sequence_length = tokens.size(1) + + if max_sequence_length > args.max_position_embeddings: + raise ValueError("Length of prompt + tokens_to_generate longer than allowed") + + if max_sequence_length * batch_size > args.max_tokens_to_oom: + raise ValueError("Too many tokens. " + str(max_sequence_length*batch_size)+ " is greater than "+str(args.max_tokens_to_oom)) + + # forward step. + forward_step = ForwardStep(model, batch_size, max_sequence_length) + + # Added termination_id to support the case that we want to terminate the + # generation once that id is generated. + if hasattr(args, 'eos_id'): + termination_id = args.eos_id + else: + termination_id = tokenizer.eod + + # =================== + # Pre-allocate memory + # =================== + + # Log probability of the sequence (prompt + generated tokens). + output_log_probs = None + output_log_probs_size = (batch_size, max_sequence_length - 1) + # Lengths of generated seuquence including including prompts. + generated_sequence_lengths = None + if mpu.is_pipeline_last_stage(): + if return_output_log_probs: + output_log_probs = torch.empty(output_log_probs_size, + dtype=torch.float32, + device=torch.cuda.current_device()) + generated_sequence_lengths = torch.ones( + batch_size, dtype=torch.int64, + device=torch.cuda.current_device()) * max_sequence_length + + # Whether we have reached a termination id. + is_generation_done = torch.zeros(batch_size, dtype=torch.uint8, + device=torch.cuda.current_device()) + + # ============= + # Run infernece + # ============= + + with torch.no_grad(): + attention_mask, position_ids = _build_attention_mask_and_position_ids( + tokens) + prev_context_length = 0 + for context_length in range(min_prompt_length, max_sequence_length): + + # Pick the slice that we need to pass through the network. + tokens2use = tokens[:, prev_context_length:context_length] + positions2use = position_ids[:, prev_context_length:context_length] + attention_mask2use = attention_mask[ + ..., prev_context_length:context_length, :context_length] + + # logits will be meanigful only in the last pipeline stage. + logits = forward_step(tokens2use, positions2use, attention_mask2use) + + if mpu.is_pipeline_last_stage(): + if prevent_newline_after_colon: + logits[tokens2use[:, -1] == tokenizer.tokenize(':')[0], -1, tokenizer.tokenize('\n')[0]] = -1e10 # disable "\n" after ":" + # Always the last stage should have an output. + assert logits is not None + + # Sample. + last_token_logits = logits[:, -1, :] + new_sample = sample(last_token_logits, + top_k=top_k, + top_p=top_p, + temperature=temperature, + vocab_size=tokenizer.vocab_size) + if top_p > 0.0 and top_p_decay > 0.0: + top_p = top_p * top_p_decay + if top_p_bound > 0.0: + top_p = max(top_p, top_p_bound) + + # If a prompt length is smaller or equal th current context + # length, it means we have started generating tokens + started = lengths <= context_length + # Update the tokens. + tokens[started, context_length] = new_sample[started] + + # Calculate the log probabilities. + if return_output_log_probs: + log_probs = F.log_softmax(logits, dim=2) + if return_output_log_probs: + # Pick the tokens that we need to get the log + # probabilities for. Note that next input token is + # the token which we selected in the current logits, + # so shift by 1. + indices = torch.unsqueeze( + tokens[ + :, + (prev_context_length + 1):(context_length + 1)], + 2) + output_log_probs[:, + prev_context_length:context_length] = \ + torch.gather(log_probs, 2, indices).squeeze(2) + + # Update the tokens on the first stage so the next input to + # the network is correct. + copy_from_last_to_first_pipeline_stage(batch_size, torch.int64, + tokens[:, context_length]) + + # Update the context length for the next token generation. + prev_context_length = context_length + + # Check if all the sequences have hit the termination_id. + done = None + if mpu.is_pipeline_last_stage(): + # TODO(rprenger) These stopping methods are tokenizer dependent + # instead tokenization should be in the inference loop so stop sequences can be used + if stop_on_double_eol: + hit_double_eol = (new_sample == 628).byte() & started.byte() + hit_two_eols = (new_sample == 198).byte() & (tokens[:, context_length-1] == 198).byte() & started.byte() + done_token = hit_double_eol | hit_two_eols + elif stop_on_eol: + hit_double_eol = (new_sample == 628).byte() & started.byte() + hit_eol = (new_sample == 198).byte() & started.byte() + done_token = hit_double_eol | hit_eol + else: + done_token = (new_sample == termination_id).byte() & \ + started.byte() + + just_finished = (done_token & ~is_generation_done).bool() + generated_sequence_lengths[just_finished.view(-1)] = \ + context_length + 1 + is_generation_done = is_generation_done | done_token + done = torch.all(is_generation_done) + done = broadcast_from_last_pipeline_stage(1, torch.uint8, + tensor=done) + if use_eod_token_for_early_termination and done: + break + + # =================================================== + # Update the length of based on max generated length. + # =================================================== + + tokens = tokens[:, :(context_length + 1)] + if mpu.is_pipeline_last_stage(): + if return_output_log_probs: + output_log_probs = output_log_probs[:, :context_length] + + # ====================================== + # Broadcast to the first pipeline stage. + # ====================================== + + generated_sequence_lengths = broadcast_from_last_to_first_pipeline_stage( + batch_size, torch.int64, generated_sequence_lengths) + if return_output_log_probs: + output_log_probs_size = (batch_size, context_length) + output_log_probs = broadcast_from_last_to_first_pipeline_stage( + output_log_probs_size, torch.float32, output_log_probs) + + return tokens, generated_sequence_lengths, output_log_probs, None + +def beam_search_and_return_on_first_stage(model, tokens, lengths, beam_size, stop_token, num_return_gen, length_penalty, prevent_newline_after_colon=True): + args = get_args() + tokenizer = get_tokenizer() + + batch_size = tokens.size(0) + assert(batch_size == 1) + prompt_length = lengths.item() + final_sequence_length = tokens.size(1) + final_sequence_length = min(final_sequence_length, args.max_position_embeddings) + + # If the context is too big, this happens + if prompt_length >= final_sequence_length: + raise ValueError("context length + tokens_to_generate too large") + + # forward step. + forward_step = ForwardStep(model, beam_size, final_sequence_length) + + beam_hyp = BeamHypotheses(beam_size, length_penalty) + best_batches = None + done = torch.zeros(1, dtype=torch.uint8, device=torch.cuda.current_device()) + scores = torch.zeros(beam_size, + dtype=torch.float32, + device=torch.cuda.current_device()).unsqueeze(1) + scores_size_tensor, tokens_size_tensor = None, None + # ============= + # Run infernece + # ============= + with torch.no_grad(): + tokens = tokens.repeat(beam_size, 1) + attention_mask, position_ids = _build_attention_mask_and_position_ids(tokens) + prev_context_length = 0 + for context_length in range(prompt_length, final_sequence_length): + + # Pick the slice that we need to pass through the network. + tokens2use = tokens[:, prev_context_length:context_length] + positions2use = position_ids[:, prev_context_length:context_length] + attention_mask2use = attention_mask[ + ..., prev_context_length:context_length, :context_length] + + # logits will be meanigful only in the last pipeline stage. + logits = forward_step(tokens2use, positions2use, attention_mask2use) + + if mpu.is_pipeline_last_stage(): + if prevent_newline_after_colon: + logits[tokens2use[:, -1] == tokenizer.tokenize(':')[0], -1, tokenizer.tokenize('\n')[0]] = -1e10 # disable "\n" after ":" + vocab_size = logits.size(2) + log_probs = F.log_softmax(logits, dim=2) + new_scores = log_probs[:, -1, :] + scores + + if context_length == prompt_length: # if this is the first one + sorted_scores, indices = torch.sort(new_scores[0,:], descending=True) + else: + sorted_scores, indices = torch.sort(new_scores.view(-1), descending=True) + + best_beam_ids = torch.div(indices[: 2 * beam_size], vocab_size).trunc().long() + best_words = indices[:2 * beam_size] % vocab_size + best_scores = sorted_scores[: 2 * beam_size] + + next_beams = [] + for beam_token_rank, (token_id, beam_score, beam_id) in enumerate( + zip(best_words, best_scores, best_beam_ids) + ): + if token_id.item() == stop_token: + # if beam_token does not belong to top num_beams tokens, it should not be added + is_beam_token_worse_than_top_num_beams = beam_token_rank >= beam_size + if is_beam_token_worse_than_top_num_beams: + continue + beam_hyp.add( + tokens[beam_id].clone(), + beam_score, + context_length + 1 - prompt_length + ) + else: + # add next predicted token since it is not eos_token + next_beams.append((token_id, beam_score, beam_id)) + + if len(next_beams) == beam_size: + break + + if beam_hyp.is_done(best_scores.max().item(), context_length + 1 - prompt_length): + done = torch.ones(1, dtype=torch.uint8, device=torch.cuda.current_device()) + + best_batches = tokens.new([item[2] for item in next_beams]) + tokens = tokens[best_batches,:] + tokens[:, context_length] = tokens.new([item[0] for item in next_beams]) + scores = scores.new([item[1] for item in next_beams]).unsqueeze(1) + + # torch.distributed.barrier() + done = broadcast_from_last_pipeline_stage(1, torch.uint8, done) + if done: + break + + # Update the tokens on the first stage so the next input to + # the network is correct. + copy_from_last_to_first_pipeline_stage(tokens.size(), torch.int64, + tokens) + + # set inference key values to make it consistent with best beam index + best_batches = broadcast_from_last_pipeline_stage(beam_size, torch.int64, best_batches) + forward_step.inference_params.swap_key_value_dict(best_batches) + + # Update the context length for the next token generation. + prev_context_length = context_length + + if mpu.is_pipeline_last_stage(): + # if cannot find stop token, add open beams to hyps + if not done: + for beam_id in range(beam_size): + beam_hyp.add(tokens[beam_id].clone(), scores[beam_id].squeeze(), context_length + 1 - prompt_length) + + # rank based on scores + sorted_hyps = sorted(beam_hyp.beams, key=lambda x: x[0], reverse=True) + num_return_gen = min(num_return_gen, len(sorted_hyps)) + scores = [sorted_hyps[i][0] for i in range(num_return_gen)] + tokens = [sorted_hyps[i][1] for i in range(num_return_gen)] + scores = torch.stack(scores, dim=0) + tokens = torch.stack(tokens, dim=0) + scores_size_tensor = torch.tensor(scores.shape, dtype=torch.int64, device=torch.cuda.current_device()) + tokens_size_tensor = torch.tensor(tokens.shape, dtype=torch.int64, device=torch.cuda.current_device()) + + scores_size_tensor = broadcast_from_last_pipeline_stage(1, torch.int64, scores_size_tensor) + tokens_size_tensor = broadcast_from_last_pipeline_stage(2, torch.int64, tokens_size_tensor) + + scores = broadcast_from_last_to_first_pipeline_stage(tuple(scores_size_tensor), torch.float32, scores) + tokens = broadcast_from_last_to_first_pipeline_stage(tuple(tokens_size_tensor), torch.int64, tokens) + + return tokens, scores + + +def _build_attention_mask_and_position_ids(tokens): + """Build the attention mask and postition ids for the input tokens.""" + + # Since we are not interested in loss-mask and reset attention/position + # is also False, eod_token is not used so it is safe to set it to None. + attention_mask, _, position_ids = get_ltor_masks_and_position_ids( + data=tokens, + eod_token=None, + reset_position_ids=False, + reset_attention_mask=False, + eod_mask_loss=False) + + return attention_mask, position_ids diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/sampling.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/sampling.py new file mode 100644 index 0000000..370773a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/sampling.py @@ -0,0 +1,93 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Sampling utilities. +Part of this code is inspired by: + - https://github.com/ari-holtzman/degen/blob/master/gen.py + - https://huggingface.co/transformers/_modules/transformers/generation_logits_process.html +""" + + +import torch + + + +def modify_logits_for_top_k_filtering(logits, top_k): + """Set the logits for none top-k values to -inf.""" + + filter_ = logits < torch.topk(logits, top_k)[0][..., -1, None] + logits.masked_fill_(filter_, float('-Inf')) + + + +def modify_logits_for_top_p_filtering(logits, top_p): + """Set the logits for none top-p values to -inf.""" + + # First sort and calculate cumulative sum of probabilities. + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1) + + # Filteration based on the cumulative sum. + filter_ = cumulative_probs > top_p + # This shift by 1 is weird and I cannot justify it. This existed + # in the original implementation: + # https://github.com/ari-holtzman/degen/blob/master/gen.py + # and I guess it is needed so keeping it for now. + filter_[:, 1:] = filter_[:, :-1].clone() + # Make sure we at least have one token to select from. + filter_[..., 0] = 0 + + # Fill in the filtered part + filter_ = filter_.scatter(1, sorted_indices, filter_) + logits.masked_fill_(filter_, float('-Inf')) + + + +def sample(logits, top_k=0, top_p=0.0, temperature=1.0, vocab_size=None): + """ Sample and generate a token. + Note: logits has the dimension [b, v] where b is the batch size + and v is the vocabulary size. + If vocab_size is provided, we will make sure the sample that is + generated is in [0, vocab-size). This will avoid out of vocabulary + generations due to padding. + """ + + # Check logits for consistency. + assert logits.ndim == 2, 'expected the logits to be of [b, v] shape.' + assert logits.type() == 'torch.cuda.FloatTensor', \ + 'input logits should be floats.' + + + # Greedy is just simple argmax. + if top_k == 1: + assert top_p == 0.0, 'cannot set both greedy and top-p samplings.' + samples = torch.argmax(logits, dim=-1) + + # Top-k or top-p sampling. + else: + # Clone so we do not modify the inputs, + logits = logits.clone() + # Apply temperature in place. + if temperature != 1.0: + logits.div_(temperature) + + if top_k > 1: + assert top_p == 0.0, 'cannot set both top-k and top-p samplings.' + assert top_k <= logits.size(1), 'top-k is larger than logit size.' + if vocab_size: + assert top_k < vocab_size, 'top-k is larger than vocab size.' + modify_logits_for_top_k_filtering(logits, top_k) + + elif top_p > 0.0: + assert top_p <= 1.0, 'top-p should be in (0, 1].' + modify_logits_for_top_p_filtering(logits, top_p) + + # After filtering, we need to recalculate the distribution. + probs = logits.softmax(dim=-1) + samples = torch.multinomial(probs, num_samples=1).view(-1) + + # If vocab size is provided, make sure the samples are in + # in the range [0, vocab-size). + if vocab_size: + samples = torch.clamp(samples, min=0, max=(vocab_size - 1)) + + return samples diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/tokenization.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/tokenization.py new file mode 100644 index 0000000..18cc077 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation/tokenization.py @@ -0,0 +1,125 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Tokenization utilities.""" + + +import torch + + +from megatron.training import get_tokenizer, get_args +from .communication import broadcast_int_list, broadcast_tensor + + +def detokenize_generations(tokens_gpu_tensor, + lengths_gpu_tensor, + return_segments): + """Detokenize the generated tokens.""" + + tokenizer = get_tokenizer() + args = get_args() + prompts_plus_generations = [] + if return_segments: + prompts_plus_generations_segments = [] + + tokens = tokens_gpu_tensor.cpu().numpy().tolist() + lengths = lengths_gpu_tensor.cpu().numpy().tolist() + for sequence_tokens, length in zip(tokens, lengths): + sequence_tokens = sequence_tokens[:length] + prompts_plus_generations.append( + tokenizer.detokenize(sequence_tokens)) + if return_segments: + words = [] + for token in sequence_tokens: + if args.tokenizer_type in ['SentencePieceTokenizer', + 'GPTSentencePieceTokenizer', + 'Llama2Tokenizer']: + word = tokenizer.decoder[token] + elif args.tokenizer_type == 'NullTokenizer': + word = str(token) + else: + word = tokenizer.tokenizer.decoder[token] + word = bytearray( + [tokenizer.tokenizer.byte_decoder[c] for c in word]).decode( + 'utf-8', errors='replace') + words.append(word) + prompts_plus_generations_segments.append(words) + + if return_segments: + return tokens, prompts_plus_generations, \ + prompts_plus_generations_segments + + return tokens, prompts_plus_generations + + +def tokenize_prompts(prompts=None, tokens_to_generate=None, + add_BOS=None, rank=0): + """Tokenize prompts and make them avaiable on all ranks.""" + + # On all ranks set to None so we can pass them to functions + sizes_list = None + prompts_tokens_cuda_long_tensor = None + prompts_length_cuda_long_tensor = None + + # On the specified rank, build the above. + if torch.distributed.get_rank() == rank: + assert prompts is not None + assert tokens_to_generate is not None + # Tensor of tokens padded and their unpadded length. + prompts_tokens_cuda_long_tensor, prompts_length_cuda_long_tensor = \ + _tokenize_prompts_and_batch(prompts, tokens_to_generate, add_BOS) + # We need the sizes of these tensors for the boradcast + sizes_list = [prompts_tokens_cuda_long_tensor.size(0), # Batch size + prompts_tokens_cuda_long_tensor.size(1)] # Sequence lenght + + # First, broadcast the sizes. + sizes_tensor = broadcast_int_list(2, int_list=sizes_list, rank=rank) + + # Now that we have the sizes, we can boradcast the tokens + # and length tensors. + sizes = sizes_tensor.tolist() + prompts_tokens_cuda_long_tensor = broadcast_tensor( + sizes, torch.int64, tensor=prompts_tokens_cuda_long_tensor, rank=rank) + prompts_length_cuda_long_tensor = broadcast_tensor( + sizes[0], torch.int64, tensor=prompts_length_cuda_long_tensor, + rank=rank) + + return prompts_tokens_cuda_long_tensor, prompts_length_cuda_long_tensor + + +def _tokenize_prompts_and_batch(prompts, tokens_to_generate, add_BOS): + """Given a set of prompts and number of tokens to generate: + - tokenize prompts + - set the sequence length to be the max of length of prompts + plus the number of tokens we would like to generate + - pad all the sequences to this length so we can convert them + into a 2D tensor. + """ + + # Tokenize all the prompts. + tokenizer = get_tokenizer() + if add_BOS: + prompts_tokens = [[tokenizer.eod] + tokenizer.tokenize(prompt) + for prompt in prompts] + else: + prompts_tokens = [tokenizer.tokenize(prompt) for prompt in prompts] + + # Now we have a list of list of tokens which each list has a different + # size. We want to extend this list to: + # - incorporate the tokens that need to be generated + # - make all the sequences equal length. + # Get the prompts length. + prompts_length = [len(prompt_tokens) for prompt_tokens in prompts_tokens] + # Get the max prompts length. + max_prompt_len = max(prompts_length) + # Number of tokens in the each sample of the batch. + samples_length = max_prompt_len + tokens_to_generate + # Now update the list of list to be of the same size: samples_length. + for prompt_tokens, prompt_length in zip(prompts_tokens, prompts_length): + padding_size = samples_length - prompt_length + prompt_tokens.extend([tokenizer.eod] * padding_size) + + # Now we are in a structured format, we can convert to tensors. + prompts_tokens_tensor = torch.tensor(prompts_tokens, dtype=torch.long, device='cuda') + prompts_length_tensor = torch.tensor(prompts_length, dtype=torch.long, device='cuda') + + return prompts_tokens_tensor, prompts_length_tensor diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation_server.py b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation_server.py new file mode 100644 index 0000000..2eba2e2 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/inference/text_generation_server.py @@ -0,0 +1,241 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +import datetime +import torch +import json +import threading +from flask import Flask, request, jsonify, current_app +from flask_restful import Resource, Api +from megatron.training import get_args +from megatron.inference.text_generation import generate_and_post_process +from megatron.inference.text_generation import beam_search_and_post_process + + +GENERATE_NUM = 0 +BEAM_NUM = 1 +lock = threading.Lock() + +class MegatronGenerate(Resource): + def __init__(self, model): + self.model = model + + @staticmethod + def send_do_generate(): + choice = torch.tensor([GENERATE_NUM], dtype=torch.long, device='cuda') + torch.distributed.broadcast(choice, 0) + + @staticmethod + def send_do_beam_search(): + choice = torch.tensor([BEAM_NUM], dtype=torch.long, device='cuda') + torch.distributed.broadcast(choice, 0) + + def put(self): + args = get_args() + + if not "prompts" in request.get_json(): + return "prompts argument required", 400 + + if "max_len" in request.get_json(): + return "max_len is no longer used. Replace with tokens_to_generate", 400 + + if "sentences" in request.get_json(): + return "sentences is no longer used. Replace with prompts", 400 + + prompts = request.get_json()["prompts"] + if not isinstance(prompts, list): + return "prompts is not a list of strings", 400 + + if len(prompts) == 0: + return "prompts is empty", 400 + + if len(prompts) > 128: + return "Maximum number of prompts is 128", 400 + + tokens_to_generate = 64 # Choosing hopefully sane default. Full sequence is slow + if "tokens_to_generate" in request.get_json(): + tokens_to_generate = request.get_json()["tokens_to_generate"] + if not isinstance(tokens_to_generate, int): + return "tokens_to_generate must be an integer greater than 0" + if tokens_to_generate < 0: + return "tokens_to_generate must be an integer greater than or equal to 0" + + logprobs = False + if "logprobs" in request.get_json(): + logprobs = request.get_json()["logprobs"] + if not isinstance(logprobs, bool): + return "logprobs must be a boolean value" + + if tokens_to_generate == 0 and not logprobs: + return "tokens_to_generate=0 implies logprobs should be True" + + temperature = 1.0 + if "temperature" in request.get_json(): + temperature = request.get_json()["temperature"] + if not (type(temperature) == int or type(temperature) == float): + return "temperature must be a positive number less than or equal to 100.0" + if not (0.0 < temperature <= 100.0): + return "temperature must be a positive number less than or equal to 100.0" + + top_k = 0.0 + if "top_k" in request.get_json(): + top_k = request.get_json()["top_k"] + if not (type(top_k) == int): + return "top_k must be an integer equal to or greater than 0 and less than or equal to 1000" + if not (0 <= top_k <= 1000): + return "top_k must be equal to or greater than 0 and less than or equal to 1000" + + top_p = 0.0 + if "top_p" in request.get_json(): + top_p = request.get_json()["top_p"] + if not (type(top_p) == float): + return "top_p must be a positive float less than or equal to 1.0" + if top_p > 0.0 and top_k > 0.0: + return "cannot set both top-k and top-p samplings." + if not (0 <= top_p <= 1.0): + return "top_p must be less than or equal to 1.0" + + top_p_decay = 0.0 + if "top_p_decay" in request.get_json(): + top_p_decay = request.get_json()["top_p_decay"] + if not (type(top_p_decay) == float): + return "top_p_decay must be a positive float less than or equal to 1.0" + if top_p == 0.0: + return "top_p_decay cannot be set without top_p" + if not (0 <= top_p_decay <= 1.0): + return "top_p_decay must be less than or equal to 1.0" + + top_p_bound = 0.0 + if "top_p_bound" in request.get_json(): + top_p_bound = request.get_json()["top_p_bound"] + if not (type(top_p_bound) == float): + return "top_p_bound must be a positive float less than or equal to top_p" + if top_p == 0.0: + return "top_p_bound cannot be set without top_p" + if not (0.0 < top_p_bound <= top_p): + return "top_p_bound must be greater than 0 and less than top_p" + + add_BOS = False + if "add_BOS" in request.get_json(): + add_BOS = request.get_json()["add_BOS"] + if not isinstance(add_BOS, bool): + return "add_BOS must be a boolean value" + + if any([len(prompt) == 0 for prompt in prompts]) and not add_BOS: + return "Empty prompts require add_BOS=true" + + stop_on_double_eol = False + if "stop_on_double_eol" in request.get_json(): + stop_on_double_eol = request.get_json()["stop_on_double_eol"] + if not isinstance(stop_on_double_eol, bool): + return "stop_on_double_eol must be a boolean value" + + stop_on_eol = False + if "stop_on_eol" in request.get_json(): + stop_on_eol = request.get_json()["stop_on_eol"] + if not isinstance(stop_on_eol, bool): + return "stop_on_eol must be a boolean value" + + prevent_newline_after_colon = False + if "prevent_newline_after_colon" in request.get_json(): + prevent_newline_after_colon = request.get_json()["prevent_newline_after_colon"] + if not isinstance(prevent_newline_after_colon, bool): + return "prevent_newline_after_colon must be a boolean value" + + random_seed = -1 + if "random_seed" in request.get_json(): + random_seed = request.get_json()["random_seed"] + if not isinstance(random_seed, int): + return "random_seed must be integer" + if random_seed < 0: + return "random_seed must be a positive integer" + + no_log = False + if "no_log" in request.get_json(): + no_log = request.get_json()["no_log"] + if not isinstance(no_log, bool): + return "no_log must be a boolean value" + + beam_width = None + if "beam_width" in request.get_json(): + beam_width = request.get_json()["beam_width"] + if not isinstance(beam_width, int): + return "beam_width must be integer" + if beam_width < 1: + return "beam_width must be an integer > 1" + if len(prompts) > 1: + return "When doing beam_search, batch size must be 1" + + stop_token=50256 + if "stop_token" in request.get_json(): + stop_token = request.get_json()["stop_token"] + if not isinstance(stop_token, int): + return "stop_token must be an integer" + + length_penalty = 1 + if "length_penalty" in request.get_json(): + length_penalty = request.get_json()["length_penalty"] + if not isinstance(length_penalty, float): + return "length_penalty must be a float" + + with lock: # Need to get lock to keep multiple threads from hitting code + + if not no_log: + print("request IP: " + str(request.remote_addr)) + print(json.dumps(request.get_json()),flush=True) + print("start time: ", datetime.datetime.now()) + + try: + if beam_width is not None: + MegatronGenerate.send_do_beam_search() # Tell other ranks we're doing beam_search + response, response_seg, response_scores = \ + beam_search_and_post_process( + self.model, + prompts=prompts, + tokens_to_generate=tokens_to_generate, + beam_size = beam_width, + add_BOS=add_BOS, + stop_token=stop_token, + num_return_gen=beam_width, # Returning whole beam + length_penalty=length_penalty, + prevent_newline_after_colon=prevent_newline_after_colon + ) + + return jsonify({"text": response, + "segments": response_seg, + "scores": response_scores}) + else: + MegatronGenerate.send_do_generate() # Tell other ranks we're doing generate + response, response_seg, response_logprobs, _ = \ + generate_and_post_process( + self.model, + prompts=prompts, + tokens_to_generate=tokens_to_generate, + return_output_log_probs=logprobs, + top_k_sampling=top_k, + top_p_sampling=top_p, + top_p_decay=top_p_decay, + top_p_bound=top_p_bound, + temperature=temperature, + add_BOS=add_BOS, + use_eod_token_for_early_termination=True, + stop_on_double_eol=stop_on_double_eol, + stop_on_eol=stop_on_eol, + prevent_newline_after_colon=prevent_newline_after_colon, + random_seed=random_seed) + + return jsonify({"text": response, + "segments": response_seg, + "logprobs": response_logprobs}) + + except ValueError as ve: + return ve.args[0] + print("end time: ", datetime.datetime.now()) + + +class MegatronServer(object): + def __init__(self, model): + self.app = Flask(__name__, static_url_path='') + api = Api(self.app) + api.add_resource(MegatronGenerate, '/api', resource_class_args=[model]) + + def run(self, url, port): + self.app.run(url, threaded=True, debug=False, port=port) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/autoaugment.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/autoaugment.py new file mode 100644 index 0000000..7f988c5 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/autoaugment.py @@ -0,0 +1,320 @@ +"""AutoAugment data augmentation policy for ImageNet. + +-- Begin license text. + +MIT License + +Copyright (c) 2018 Philip Popien + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +-- End license text. + +Code adapted from https://github.com/DeepVoltaire/AutoAugment. + +This module implements the fixed AutoAugment data augmentation policy for ImageNet provided in +Appendix A, Table 9 of reference [1]. It does not include any of the search code for augmentation +policies. + +Reference: +[1] https://arxiv.org/abs/1805.09501 +""" + +import random + +import numpy as np +from PIL import Image +from PIL import ImageEnhance +from PIL import ImageOps + +_MAX_LEVEL = 10 # Maximum integer strength of an augmentation, if applicable. + + +class ImageNetPolicy: + """Definition of an ImageNetPolicy. + + Implements a fixed AutoAugment data augmentation policy targeted at + ImageNet training by randomly applying at runtime one of the 25 pre-defined + data augmentation sub-policies provided in Reference [1]. + + Usage example as a Pytorch Transform: + >>> transform=transforms.Compose([transforms.Resize(256), + >>> ImageNetPolicy(), + >>> transforms.ToTensor()]) + """ + + def __init__(self, fillcolor=(128, 128, 128)): + """Initialize an ImageNetPolicy. + + Args: + fillcolor (tuple): RGB color components of the color to be used for + filling when needed (default: (128, 128, 128), which + corresponds to gray). + """ + # Instantiate a list of sub-policies. + # Each entry of the list is a SubPolicy which consists of + # two augmentation operations, + # each of those parametrized as operation, probability, magnitude. + # Those two operations are applied sequentially on the image upon call. + self.policies = [ + SubPolicy("posterize", 0.4, 8, "rotate", 0.6, 9, fillcolor), + SubPolicy("solarize", 0.6, 5, "autocontrast", 0.6, 5, fillcolor), + SubPolicy("equalize", 0.8, 8, "equalize", 0.6, 3, fillcolor), + SubPolicy("posterize", 0.6, 7, "posterize", 0.6, 6, fillcolor), + SubPolicy("equalize", 0.4, 7, "solarize", 0.2, 4, fillcolor), + SubPolicy("equalize", 0.4, 4, "rotate", 0.8, 8, fillcolor), + SubPolicy("solarize", 0.6, 3, "equalize", 0.6, 7, fillcolor), + SubPolicy("posterize", 0.8, 5, "equalize", 1.0, 2, fillcolor), + SubPolicy("rotate", 0.2, 3, "solarize", 0.6, 8, fillcolor), + SubPolicy("equalize", 0.6, 8, "posterize", 0.4, 6, fillcolor), + SubPolicy("rotate", 0.8, 8, "color", 0.4, 0, fillcolor), + SubPolicy("rotate", 0.4, 9, "equalize", 0.6, 2, fillcolor), + SubPolicy("equalize", 0.0, 7, "equalize", 0.8, 8, fillcolor), + SubPolicy("invert", 0.6, 4, "equalize", 1.0, 8, fillcolor), + SubPolicy("color", 0.6, 4, "contrast", 1.0, 8, fillcolor), + SubPolicy("rotate", 0.8, 8, "color", 1.0, 2, fillcolor), + SubPolicy("color", 0.8, 8, "solarize", 0.8, 7, fillcolor), + SubPolicy("sharpness", 0.4, 7, "invert", 0.6, 8, fillcolor), + SubPolicy("shearX", 0.6, 5, "equalize", 1.0, 9, fillcolor), + SubPolicy("color", 0.4, 0, "equalize", 0.6, 3, fillcolor), + SubPolicy("equalize", 0.4, 7, "solarize", 0.2, 4, fillcolor), + SubPolicy("solarize", 0.6, 5, "autocontrast", 0.6, 5, fillcolor), + SubPolicy("invert", 0.6, 4, "equalize", 1.0, 8, fillcolor), + SubPolicy("color", 0.6, 4, "contrast", 1.0, 8, fillcolor), + SubPolicy("equalize", 0.8, 8, "equalize", 0.6, 3, fillcolor), + ] + + def __call__(self, img): + """Define call method for ImageNetPolicy class.""" + policy_idx = random.randint(0, len(self.policies) - 1) + return self.policies[policy_idx](img) + + def __repr__(self): + """Define repr method for ImageNetPolicy class.""" + return "ImageNetPolicy" + + +class SubPolicy: + """Definition of a SubPolicy. + + A SubPolicy consists of two augmentation operations, + each of those parametrized as operation, probability, magnitude. + The two operations are applied sequentially on the image upon call. + """ + + def __init__( + self, + operation1, + probability1, + magnitude_idx1, + operation2, + probability2, + magnitude_idx2, + fillcolor, + ): + """Initialize a SubPolicy. + + Args: + operation1 (str): Key specifying the first augmentation operation. + There are fourteen key values altogether (see supported_ops below + listing supported operations). probability1 (float): Probability + within [0., 1.] of applying the first augmentation operation. + magnitude_idx1 (int): Integer specifiying the strength of the first + operation as an index further used to derive the magnitude from a + range of possible values. + operation2 (str): Key specifying the second augmentation operation. + probability2 (float): Probability within [0., 1.] of applying the + second augmentation operation. + magnitude_idx2 (int): Integer specifiying the strength of the + second operation as an index further used to derive the magnitude + from a range of possible values. + fillcolor (tuple): RGB color components of the color to be used for + filling. + Returns: + """ + # List of supported operations for operation1 and operation2. + supported_ops = [ + "shearX", + "shearY", + "translateX", + "translateY", + "rotate", + "color", + "posterize", + "solarize", + "contrast", + "sharpness", + "brightness", + "autocontrast", + "equalize", + "invert", + ] + assert (operation1 in supported_ops) and ( + operation2 in supported_ops + ), "SubPolicy:one of oper1 or oper2 refers to an unsupported operation." + + assert ( + 0.0 <= probability1 <= 1.0 and 0.0 <= probability2 <= 1.0 + ), "SubPolicy: prob1 and prob2 should be within [0., 1.]." + + assert ( + isinstance(magnitude_idx1, int) and 0 <= magnitude_idx1 <= 10 + ), "SubPolicy: idx1 should be specified as an integer within [0, 10]." + + assert ( + isinstance(magnitude_idx2, int) and 0 <= magnitude_idx2 <= 10 + ), "SubPolicy: idx2 should be specified as an integer within [0, 10]." + + # Define a dictionary where each key refers to a specific type of + # augmentation and the corresponding value is a range of ten possible + # magnitude values for that augmentation. + num_levels = _MAX_LEVEL + 1 + ranges = { + "shearX": np.linspace(0, 0.3, num_levels), + "shearY": np.linspace(0, 0.3, num_levels), + "translateX": np.linspace(0, 150 / 331, num_levels), + "translateY": np.linspace(0, 150 / 331, num_levels), + "rotate": np.linspace(0, 30, num_levels), + "color": np.linspace(0.0, 0.9, num_levels), + "posterize": np.round(np.linspace(8, 4, num_levels), 0).astype( + np.int32 + ), + "solarize": np.linspace(256, 0, num_levels), # range [0, 256] + "contrast": np.linspace(0.0, 0.9, num_levels), + "sharpness": np.linspace(0.0, 0.9, num_levels), + "brightness": np.linspace(0.0, 0.9, num_levels), + "autocontrast": [0] + * num_levels, # This augmentation doesn't use magnitude parameter. + "equalize": [0] + * num_levels, # This augmentation doesn't use magnitude parameter. + "invert": [0] + * num_levels, # This augmentation doesn't use magnitude parameter. + } + + def rotate_with_fill(img, magnitude): + """Define rotation transformation with fill. + + The input image is first rotated, then it is blended together with + a gray mask of the same size. Note that fillcolor as defined + elsewhere in this module doesn't apply here. + + Args: + magnitude (float): rotation angle in degrees. + Returns: + rotated_filled (PIL Image): rotated image with gray filling for + disoccluded areas unveiled by the rotation. + """ + rotated = img.convert("RGBA").rotate(magnitude) + rotated_filled = Image.composite( + rotated, Image.new("RGBA", rotated.size, (128,) * 4), rotated + ) + return rotated_filled.convert(img.mode) + + # Define a dictionary of augmentation functions where each key refers + # to a specific type of augmentation and the corresponding value defines + # the augmentation itself using a lambda function. + # pylint: disable=unnecessary-lambda + func_dict = { + "shearX": lambda img, magnitude: img.transform( + img.size, + Image.AFFINE, + (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0), + Image.BICUBIC, + fillcolor=fillcolor, + ), + "shearY": lambda img, magnitude: img.transform( + img.size, + Image.AFFINE, + (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0), + Image.BICUBIC, + fillcolor=fillcolor, + ), + "translateX": lambda img, magnitude: img.transform( + img.size, + Image.AFFINE, + ( + 1, + 0, + magnitude * img.size[0] * random.choice([-1, 1]), + 0, + 1, + 0, + ), + fillcolor=fillcolor, + ), + "translateY": lambda img, magnitude: img.transform( + img.size, + Image.AFFINE, + ( + 1, + 0, + 0, + 0, + 1, + magnitude * img.size[1] * random.choice([-1, 1]), + ), + fillcolor=fillcolor, + ), + "rotate": lambda img, magnitude: rotate_with_fill(img, magnitude), + "color": lambda img, magnitude: ImageEnhance.Color(img).enhance( + 1 + magnitude * random.choice([-1, 1]) + ), + "posterize": lambda img, magnitude: ImageOps.posterize( + img, magnitude + ), + "solarize": lambda img, magnitude: ImageOps.solarize( + img, magnitude + ), + "contrast": lambda img, magnitude: ImageEnhance.Contrast( + img + ).enhance(1 + magnitude * random.choice([-1, 1])), + "sharpness": lambda img, magnitude: ImageEnhance.Sharpness( + img + ).enhance(1 + magnitude * random.choice([-1, 1])), + "brightness": lambda img, magnitude: ImageEnhance.Brightness( + img + ).enhance(1 + magnitude * random.choice([-1, 1])), + "autocontrast": lambda img, magnitude: ImageOps.autocontrast(img), + "equalize": lambda img, magnitude: ImageOps.equalize(img), + "invert": lambda img, magnitude: ImageOps.invert(img), + } + + # Store probability, function and magnitude of the first augmentation + # for the sub-policy. + self.probability1 = probability1 + self.operation1 = func_dict[operation1] + self.magnitude1 = ranges[operation1][magnitude_idx1] + + # Store probability, function and magnitude of the second augmentation + # for the sub-policy. + self.probability2 = probability2 + self.operation2 = func_dict[operation2] + self.magnitude2 = ranges[operation2][magnitude_idx2] + + def __call__(self, img): + """Define call method for SubPolicy class.""" + # Randomly apply operation 1. + if random.random() < self.probability1: + img = self.operation1(img, self.magnitude1) + + # Randomly apply operation 2. + if random.random() < self.probability2: + img = self.operation2(img, self.magnitude2) + + return img diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/biencoder_dataset_utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/biencoder_dataset_utils.py new file mode 100644 index 0000000..4ea43cd --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/biencoder_dataset_utils.py @@ -0,0 +1,209 @@ +import os +import time + +import numpy as np +import torch + +from megatron.training import get_args, get_tokenizer, print_rank_0 +from megatron.core import mpu, tensor_parallel +from megatron.legacy.data.dataset_utils import create_masked_lm_predictions, \ + pad_and_convert_to_numpy +from megatron.legacy.data.data_samplers import MegatronPretrainingSampler + +def make_attention_mask(source_block, target_block): + """ + Returns a 2-dimensional (2-D) attention mask + :param source_block: 1-D array + :param target_block: 1-D array + """ + mask = (target_block[None, :] >= 1) * (source_block[:, None] >= 1) + mask = mask.astype(np.int64) + # (source_length, target_length) + return mask + +def get_one_epoch_dataloader(dataset, micro_batch_size=None): + """Specifically one epoch to be used in an indexing job.""" + args = get_args() + + if micro_batch_size is None: + micro_batch_size = args.micro_batch_size + num_workers = args.num_workers + + # Use megatron's sampler with consumed samples set to 0 as + # this is only for evaluation and don't intend to resume half way. + # Also, set the drop last to false as don't intend to remove + # the last batch + batch_sampler = MegatronPretrainingSampler( + total_samples=len(dataset), + consumed_samples=0, + micro_batch_size=args.micro_batch_size, + data_parallel_rank=mpu.get_data_parallel_rank(), + data_parallel_size=mpu.get_data_parallel_world_size(), + drop_last=False) + + return torch.utils.data.DataLoader(dataset, + batch_sampler=batch_sampler, + num_workers=num_workers, + pin_memory=True) + + +def get_ict_batch(data_iterator): + # Items and their type. + keys = ['query_tokens', 'query_mask', + 'context_tokens', 'context_mask', 'block_data'] + datatype = torch.int64 + + # Broadcast data. + if data_iterator is None: + data = None + else: + data = next(data_iterator) + data_b = tensor_parallel.broadcast_data(keys, data, datatype) + + # Unpack. + query_tokens = data_b['query_tokens'].long() + query_mask = data_b['query_mask'] < 0.5 + context_tokens = data_b['context_tokens'].long() + context_mask = data_b['context_mask'] < 0.5 + block_indices = data_b['block_data'].long() + + return query_tokens, query_mask,\ + context_tokens, context_mask, block_indices + + +def join_str_list(str_list): + """Join a list of strings, handling spaces appropriately""" + result = "" + for s in str_list: + if s.startswith("##"): + result += s[2:] + else: + result += " " + s + return result + + +class BlockSampleData(object): + """A struct for fully describing a fixed-size block of data as used in REALM + + :param start_idx: for first sentence of the block + :param end_idx: for last sentence of the block (may be partially truncated in sample construction) + :param doc_idx: the index of the document from which the block comes in the original indexed dataset + :param block_idx: a unique integer identifier given to every block. + """ + def __init__(self, start_idx, end_idx, doc_idx, block_idx): + self.start_idx = start_idx + self.end_idx = end_idx + self.doc_idx = doc_idx + self.block_idx = block_idx + + def as_array(self): + return np.array([self.start_idx, self.end_idx, self.doc_idx, self.block_idx]).astype(np.int64) + + def as_tuple(self): + return self.start_idx, self.end_idx, self.doc_idx, self.block_idx + + +class BlockSamplesMapping(object): + def __init__(self, mapping_array): + # make sure that the array is compatible with BlockSampleData + assert mapping_array.shape[1] == 4 + self.mapping_array = mapping_array + + def __len__(self): + return self.mapping_array.shape[0] + + def __getitem__(self, idx): + """Get the data associated with an indexed sample.""" + sample_data = BlockSampleData(*self.mapping_array[idx]) + return sample_data + + +def get_block_samples_mapping(block_dataset, title_dataset, data_prefix, num_epochs, + max_num_samples, max_seq_length, seed, name, use_one_sent_docs=False): + """Get samples mapping for a dataset over fixed size blocks. This function also requires + a dataset of the titles for the source documents since their lengths must be taken into account. + + :return: samples_mapping (BlockSamplesMapping) + """ + + if not num_epochs: + if not max_num_samples: + raise ValueError("Need to specify either max_num_samples " + "or num_epochs") + num_epochs = np.iinfo(np.int32).max - 1 + if not max_num_samples: + max_num_samples = np.iinfo(np.int64).max - 1 + + # Filename of the index mapping + indexmap_filename = data_prefix + indexmap_filename += '_{}_indexmap'.format(name) + if num_epochs != (np.iinfo(np.int32).max - 1): + indexmap_filename += '_{}ep'.format(num_epochs) + if max_num_samples != (np.iinfo(np.int64).max - 1): + indexmap_filename += '_{}mns'.format(max_num_samples) + indexmap_filename += '_{}msl'.format(max_seq_length) + indexmap_filename += '_{}s'.format(seed) + if use_one_sent_docs: + indexmap_filename += '_1sentok' + indexmap_filename += '.npy' + + # Build the indexed mapping if not exist. + if mpu.get_data_parallel_rank() == 0 and \ + not os.path.isfile(indexmap_filename): + print(' > WARNING: could not find index map file {}, building ' + 'the indices on rank 0 ...'.format(indexmap_filename)) + + # Make sure the types match the helpers input types. + assert block_dataset.document_indices.dtype == np.int64 + assert block_dataset.sequence_lengths.dtype == np.int32 + + # Build samples mapping + verbose = torch.distributed.get_rank() == 0 + start_time = time.time() + print_rank_0(' > building samples index mapping for {} ...'.format( + name)) + + from megatron.core.datasets import helpers + mapping_array = helpers.build_blocks_mapping( + block_dataset.document_indices, + block_dataset.sequence_lengths, + title_dataset.sequence_lengths, + num_epochs, + max_num_samples, + max_seq_length - 3, # account for added tokens + seed, + verbose, + use_one_sent_docs) + + + print_rank_0(' > done building samples index mapping') + np.save(indexmap_filename, mapping_array, allow_pickle=True) + print_rank_0(' > saved the index mapping in {}'.format( + indexmap_filename)) + # Make sure all the ranks have built the mapping + print_rank_0(' > elapsed time to build and save samples mapping ' + '(seconds): {:4f}'.format( + time.time() - start_time)) + + # This should be a barrier but nccl barrier assumes + # device_index=rank which is not the case for model + # parallel case + counts = torch.tensor([1], dtype=torch.long, device='cuda') + torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group()) + assert counts[0].item() == torch.distributed.get_world_size( + group=mpu.get_data_parallel_group()) + + # Load indexed dataset. + print_rank_0(' > loading indexed mapping from {}'.format( + indexmap_filename)) + start_time = time.time() + + mapping_array = np.load(indexmap_filename, allow_pickle=True, mmap_mode='r') + samples_mapping = BlockSamplesMapping(mapping_array) + + print_rank_0(' loaded indexed file in {:3.3f} seconds'.format( + time.time() - start_time)) + print_rank_0(' total number of samples: {}'.format( + mapping_array.shape[0])) + + return samples_mapping diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/data_samplers.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/data_samplers.py new file mode 100644 index 0000000..78c7e1a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/data_samplers.py @@ -0,0 +1,192 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Dataloaders.""" + + +import random +import torch +import numpy as np +from torch.utils.data import Dataset +from megatron.training import get_args +from megatron.core import mpu + + +def build_pretraining_data_loader(dataset, consumed_samples): + """Build dataloader given an input dataset.""" + + if dataset is None: + return None + args = get_args() + + # Megatron sampler + if args.dataloader_type == 'single': + batch_sampler = MegatronPretrainingSampler( + total_samples=len(dataset), + consumed_samples=consumed_samples, + micro_batch_size=args.micro_batch_size, + data_parallel_rank=mpu.get_data_parallel_rank(), + data_parallel_size=mpu.get_data_parallel_world_size()) + elif args.dataloader_type == 'cyclic': + batch_sampler = MegatronPretrainingRandomSampler( + dataset, + total_samples=len(dataset), + consumed_samples=consumed_samples, + micro_batch_size=args.micro_batch_size, + data_parallel_rank=mpu.get_data_parallel_rank(), + data_parallel_size=mpu.get_data_parallel_world_size(), + data_sharding=args.data_sharding) + elif args.dataloader_type == "external": + # External dataloaders are passed through. User is expected to provide a + # torch-compatible dataloader and define samplers, if needed. + return dataset + else: + raise Exception('{} dataloader type is not supported.'.format( + args.dataloader_type)) + + # Torch dataloader. + return torch.utils.data.DataLoader(dataset, + batch_sampler=batch_sampler, + num_workers=args.num_workers, + pin_memory=True, + persistent_workers=True if args.num_workers > 0 else False, + ) + +class MegatronPretrainingSampler: + + def __init__(self, total_samples, consumed_samples, micro_batch_size, + data_parallel_rank, data_parallel_size, drop_last=True): + # Keep a copy of input params for later use. + self.total_samples = total_samples + self.consumed_samples = consumed_samples + self.micro_batch_size = micro_batch_size + self.data_parallel_rank = data_parallel_rank + self.micro_batch_times_data_parallel_size = \ + self.micro_batch_size * data_parallel_size + self.drop_last = drop_last + + # Sanity checks. + assert self.total_samples > 0, \ + 'no sample to consume: {}'.format(self.total_samples) + assert self.consumed_samples < self.total_samples, \ + 'no samples left to consume: {}, {}'.format(self.consumed_samples, + self.total_samples) + assert self.micro_batch_size > 0 + assert data_parallel_size > 0 + assert self.data_parallel_rank < data_parallel_size, \ + 'data_parallel_rank should be smaller than data size: {}, ' \ + '{}'.format(self.data_parallel_rank, data_parallel_size) + + def __len__(self): + return self.total_samples + + def get_start_end_idx(self): + start_idx = self.data_parallel_rank * self.micro_batch_size + end_idx = start_idx + self.micro_batch_size + return start_idx, end_idx + + def __iter__(self): + batch = [] + # Last batch will be dropped if drop_last is not set False + for idx in range(self.consumed_samples, self.total_samples): + batch.append(idx) + if len(batch) == self.micro_batch_times_data_parallel_size: + start_idx, end_idx = self.get_start_end_idx() + yield batch[start_idx:end_idx] + batch = [] + + # Check the last partial batch and see drop_last is set + if len(batch) > 0 and not self.drop_last: + start_idx, end_idx = self.get_start_end_idx() + yield batch[start_idx:end_idx] + + +class RandomSeedDataset(Dataset): + + def __init__(self, dataset): + args = get_args() + self.base_seed = args.seed + self.curr_seed = args.seed + self.dataset = dataset + + def __len__(self): + return len(self.dataset) + + def set_epoch(self, epoch): + self.curr_seed = self.base_seed + epoch + + def __getitem__(self, idx): + seed = idx + self.curr_seed + torch.manual_seed(seed) + random.seed(seed) + np.random.seed(seed) + return self.dataset[idx] + + +class MegatronPretrainingRandomSampler: + + def __init__(self, dataset, total_samples, consumed_samples, micro_batch_size, + data_parallel_rank, data_parallel_size, data_sharding): + # Keep a copy of input params for later use. + self.dataset = dataset + self.total_samples = total_samples + self.consumed_samples = consumed_samples + self.micro_batch_size = micro_batch_size + self.data_parallel_rank = data_parallel_rank + self.data_parallel_size = data_parallel_size + self.data_sharding = data_sharding + self.micro_batch_times_data_parallel_size = \ + self.micro_batch_size * data_parallel_size + self.last_batch_size = \ + self.total_samples % self.micro_batch_times_data_parallel_size + + # Sanity checks. + assert self.total_samples > 0, \ + 'no sample to consume: {}'.format(self.total_samples) + assert self.micro_batch_size > 0 + assert data_parallel_size > 0 + assert self.data_parallel_rank < data_parallel_size, \ + 'data_parallel_rank should be smaller than data size: {}, ' \ + '{}'.format(self.data_parallel_rank, data_parallel_size) + + def __len__(self): + return self.total_samples + + def __iter__(self): + active_total_samples = self.total_samples - self.last_batch_size + self.epoch = self.consumed_samples // active_total_samples + current_epoch_samples = self.consumed_samples % active_total_samples + assert current_epoch_samples % self.micro_batch_times_data_parallel_size == 0 + + if isinstance(self.dataset, RandomSeedDataset): + self.dataset.set_epoch(self.epoch) + + # data sharding and random sampling + if self.data_sharding: + bucket_size = (self.total_samples // self.micro_batch_times_data_parallel_size) \ + * self.micro_batch_size + bucket_offset = current_epoch_samples // self.data_parallel_size + start_idx = self.data_parallel_rank * bucket_size + + g = torch.Generator() + g.manual_seed(self.epoch) + random_idx = torch.randperm(bucket_size, generator=g).tolist() + idx_range = [start_idx + x for x in random_idx[bucket_offset:]] + else: + full_bucket_size = (self.total_samples // self.micro_batch_size) \ + * self.micro_batch_size + full_bucket_offset = current_epoch_samples + g = torch.Generator() + g.manual_seed(self.epoch) + idx_range_total = \ + torch.randperm(full_bucket_size, generator=g).tolist() + idx_range_active = idx_range_total[full_bucket_offset:] + idx_range = idx_range_active[self.data_parallel_rank::self.data_parallel_size] + + batch = [] + # Last batch if not complete will be dropped. + for idx in idx_range: + batch.append(idx) + if len(batch) == self.micro_batch_size: + self.consumed_samples += self.micro_batch_times_data_parallel_size + yield batch + batch = [] diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/dataset_utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/dataset_utils.py new file mode 100644 index 0000000..f6ff472 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/dataset_utils.py @@ -0,0 +1,726 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors, and NVIDIA. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# Most of the code here has been copied from: +# https://github.com/google-research/albert/blob/master/create_pretraining_data.py +# with some modifications. + +import math +import os +import time +import collections + +import numpy as np +import torch + +from megatron.training import ( + get_args, + print_rank_0 +) +from megatron.core import mpu +from megatron.core.datasets.indexed_dataset import IndexedDataset + + +DSET_TYPE_BERT = 'standard_bert' +DSET_TYPE_ICT = 'ict' +DSET_TYPE_T5 = 't5' +DSET_TYPE_MULTIMODAL = 'multimodal' + +DSET_TYPES = [DSET_TYPE_BERT, DSET_TYPE_ICT, DSET_TYPE_T5, DSET_TYPE_MULTIMODAL] + + +def get_datasets_weights_and_num_samples(data_prefix, + train_valid_test_num_samples): + + # The data prefix should be in the format of: + # weight-1, data-prefix-1, weight-2, data-prefix-2, .. + assert len(data_prefix) % 2 == 0 + num_datasets = len(data_prefix) // 2 + weights = [0]*num_datasets + prefixes = [0]*num_datasets + for i in range(num_datasets): + weights[i] = float(data_prefix[2*i]) + prefixes[i] = (data_prefix[2*i+1]).strip() + # Normalize weights + weight_sum = 0.0 + for weight in weights: + weight_sum += weight + assert weight_sum > 0.0 + weights = [weight / weight_sum for weight in weights] + + # Add 0.5% (the 1.005 factor) so in case the bleding dataset does + # not uniformly distribute the number of samples, we still have + # samples left to feed to the network. + if isinstance(train_valid_test_num_samples, list): + datasets_train_valid_test_num_samples = [] + for weight in weights: + datasets_train_valid_test_num_samples.append( + [int(math.ceil(val * weight * 1.005)) + for val in train_valid_test_num_samples]) + else: + # Used when separate dataset files are provided for train, + # valid and test + datasets_train_valid_test_num_samples = [ + int(math.ceil(train_valid_test_num_samples * weight * 1.005)) + for weight in weights] + + return prefixes, weights, datasets_train_valid_test_num_samples + + +def get_a_and_b_segments(sample, np_rng): + """Divide sample into a and b segments.""" + + # Number of sentences in the sample. + n_sentences = len(sample) + # Make sure we always have two sentences. + assert n_sentences > 1, 'make sure each sample has at least two sentences.' + + # First part: + # `a_end` is how many sentences go into the `A`. + a_end = 1 + if n_sentences >= 3: + # Note that randin in numpy is exclusive. + a_end = np_rng.randint(1, n_sentences) + tokens_a = [] + for j in range(a_end): + tokens_a.extend(sample[j]) + + # Second part: + tokens_b = [] + for j in range(a_end, n_sentences): + tokens_b.extend(sample[j]) + + # Random next: + is_next_random = False + if np_rng.random() < 0.5: + is_next_random = True + tokens_a, tokens_b = tokens_b, tokens_a + + return tokens_a, tokens_b, is_next_random + + +def truncate_segments(tokens_a, tokens_b, len_a, len_b, max_num_tokens, np_rng): + """Truncates a pair of sequences to a maximum sequence length.""" + #print(len_a, len_b, max_num_tokens) + assert len_a > 0 + if len_a + len_b <= max_num_tokens: + return False + while len_a + len_b > max_num_tokens: + if len_a > len_b: + len_a -= 1 + tokens = tokens_a + else: + len_b -= 1 + tokens = tokens_b + if np_rng.random() < 0.5: + del tokens[0] + else: + tokens.pop() + return True + + +def create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id): + """Merge segments A and B, add [CLS] and [SEP] and build tokentypes.""" + + tokens = [] + tokentypes = [] + # [CLS]. + tokens.append(cls_id) + tokentypes.append(0) + # Segment A. + for token in tokens_a: + tokens.append(token) + tokentypes.append(0) + # [SEP]. + tokens.append(sep_id) + tokentypes.append(0) + # Segment B. + for token in tokens_b: + tokens.append(token) + tokentypes.append(1) + if tokens_b: + # [SEP]. + tokens.append(sep_id) + tokentypes.append(1) + + return tokens, tokentypes + + +MaskedLmInstance = collections.namedtuple("MaskedLmInstance", + ["index", "label"]) + + +def is_start_piece(piece): + """Check if the current word piece is the starting piece (BERT).""" + # When a word has been split into + # WordPieces, the first token does not have any marker and any subsequence + # tokens are prefixed with ##. So whenever we see the ## token, we + # append it to the previous set of word indexes. + return not piece.startswith("##") + + +def create_masked_lm_predictions(tokens, + vocab_id_list, vocab_id_to_token_dict, + masked_lm_prob, + cls_id, sep_id, mask_id, + max_predictions_per_seq, + np_rng, + max_ngrams=3, + do_whole_word_mask=True, + favor_longer_ngram=False, + do_permutation=False, + geometric_dist=False, + masking_style="bert"): + """Creates the predictions for the masked LM objective. + Note: Tokens here are vocab ids and not text tokens.""" + + cand_indexes = [] + # Note(mingdachen): We create a list for recording if the piece is + # the starting piece of current token, where 1 means true, so that + # on-the-fly whole word masking is possible. + token_boundary = [0] * len(tokens) + + for (i, token) in enumerate(tokens): + if token == cls_id or token == sep_id: + token_boundary[i] = 1 + continue + # Whole Word Masking means that if we mask all of the wordpieces + # corresponding to an original word. + # + # Note that Whole Word Masking does *not* change the training code + # at all -- we still predict each WordPiece independently, softmaxed + # over the entire vocabulary. + if (do_whole_word_mask and len(cand_indexes) >= 1 and + not is_start_piece(vocab_id_to_token_dict[token])): + cand_indexes[-1].append(i) + else: + cand_indexes.append([i]) + if is_start_piece(vocab_id_to_token_dict[token]): + token_boundary[i] = 1 + + output_tokens = list(tokens) + + masked_lm_positions = [] + masked_lm_labels = [] + + if masked_lm_prob == 0: + return (output_tokens, masked_lm_positions, + masked_lm_labels, token_boundary) + + num_to_predict = min(max_predictions_per_seq, + max(1, int(round(len(tokens) * masked_lm_prob)))) + + ngrams = np.arange(1, max_ngrams + 1, dtype=np.int64) + if not geometric_dist: + # Note(mingdachen): + # By default, we set the probilities to favor shorter ngram sequences. + pvals = 1. / np.arange(1, max_ngrams + 1) + pvals /= pvals.sum(keepdims=True) + if favor_longer_ngram: + pvals = pvals[::-1] + + ngram_indexes = [] + for idx in range(len(cand_indexes)): + ngram_index = [] + for n in ngrams: + ngram_index.append(cand_indexes[idx:idx + n]) + ngram_indexes.append(ngram_index) + + np_rng.shuffle(ngram_indexes) + + (masked_lms, masked_spans) = ([], []) + covered_indexes = set() + for cand_index_set in ngram_indexes: + if len(masked_lms) >= num_to_predict: + break + if not cand_index_set: + continue + # Note(mingdachen): + # Skip current piece if they are covered in lm masking or previous ngrams. + for index_set in cand_index_set[0]: + for index in index_set: + if index in covered_indexes: + continue + + if not geometric_dist: + n = np_rng.choice(ngrams[:len(cand_index_set)], + p=pvals[:len(cand_index_set)] / + pvals[:len(cand_index_set)].sum(keepdims=True)) + else: + # Sampling "n" from the geometric distribution and clipping it to + # the max_ngrams. Using p=0.2 default from the SpanBERT paper + # https://arxiv.org/pdf/1907.10529.pdf (Sec 3.1) + n = min(np_rng.geometric(0.2), max_ngrams) + + index_set = sum(cand_index_set[n - 1], []) + n -= 1 + # Note(mingdachen): + # Repeatedly looking for a candidate that does not exceed the + # maximum number of predictions by trying shorter ngrams. + while len(masked_lms) + len(index_set) > num_to_predict: + if n == 0: + break + index_set = sum(cand_index_set[n - 1], []) + n -= 1 + # If adding a whole-word mask would exceed the maximum number of + # predictions, then just skip this candidate. + if len(masked_lms) + len(index_set) > num_to_predict: + continue + is_any_index_covered = False + for index in index_set: + if index in covered_indexes: + is_any_index_covered = True + break + if is_any_index_covered: + continue + for index in index_set: + covered_indexes.add(index) + masked_token = None + if masking_style == "bert": + # 80% of the time, replace with [MASK] + if np_rng.random() < 0.8: + masked_token = mask_id + else: + # 10% of the time, keep original + if np_rng.random() < 0.5: + masked_token = tokens[index] + # 10% of the time, replace with random word + else: + masked_token = vocab_id_list[np_rng.randint(0, len(vocab_id_list))] + elif masking_style == "t5": + masked_token = mask_id + else: + raise ValueError("invalid value of masking style") + + output_tokens[index] = masked_token + masked_lms.append(MaskedLmInstance(index=index, label=tokens[index])) + + masked_spans.append(MaskedLmInstance( + index=index_set, + label=[tokens[index] for index in index_set])) + + assert len(masked_lms) <= num_to_predict + np_rng.shuffle(ngram_indexes) + + select_indexes = set() + if do_permutation: + for cand_index_set in ngram_indexes: + if len(select_indexes) >= num_to_predict: + break + if not cand_index_set: + continue + # Note(mingdachen): + # Skip current piece if they are covered in lm masking or previous ngrams. + for index_set in cand_index_set[0]: + for index in index_set: + if index in covered_indexes or index in select_indexes: + continue + + n = np.random.choice(ngrams[:len(cand_index_set)], + p=pvals[:len(cand_index_set)] / + pvals[:len(cand_index_set)].sum(keepdims=True)) + index_set = sum(cand_index_set[n - 1], []) + n -= 1 + + while len(select_indexes) + len(index_set) > num_to_predict: + if n == 0: + break + index_set = sum(cand_index_set[n - 1], []) + n -= 1 + # If adding a whole-word mask would exceed the maximum number of + # predictions, then just skip this candidate. + if len(select_indexes) + len(index_set) > num_to_predict: + continue + is_any_index_covered = False + for index in index_set: + if index in covered_indexes or index in select_indexes: + is_any_index_covered = True + break + if is_any_index_covered: + continue + for index in index_set: + select_indexes.add(index) + assert len(select_indexes) <= num_to_predict + + select_indexes = sorted(select_indexes) + permute_indexes = list(select_indexes) + np_rng.shuffle(permute_indexes) + orig_token = list(output_tokens) + + for src_i, tgt_i in zip(select_indexes, permute_indexes): + output_tokens[src_i] = orig_token[tgt_i] + masked_lms.append(MaskedLmInstance(index=src_i, label=orig_token[src_i])) + + masked_lms = sorted(masked_lms, key=lambda x: x.index) + # Sort the spans by the index of the first span + masked_spans = sorted(masked_spans, key=lambda x: x.index[0]) + + for p in masked_lms: + masked_lm_positions.append(p.index) + masked_lm_labels.append(p.label) + return (output_tokens, masked_lm_positions, masked_lm_labels, token_boundary, masked_spans) + + +def pad_and_convert_to_numpy(tokens, tokentypes, masked_positions, + masked_labels, pad_id, max_seq_length): + """Pad sequences and convert them to numpy.""" + + # Some checks. + num_tokens = len(tokens) + padding_length = max_seq_length - num_tokens + assert padding_length >= 0 + assert len(tokentypes) == num_tokens + assert len(masked_positions) == len(masked_labels) + + # Tokens and token types. + filler = [pad_id] * padding_length + tokens_np = np.array(tokens + filler, dtype=np.int64) + tokentypes_np = np.array(tokentypes + filler, dtype=np.int64) + + # Padding mask. + padding_mask_np = np.array([1] * num_tokens + [0] * padding_length, + dtype=np.int64) + + # Lables and loss mask. + labels = [-1] * max_seq_length + loss_mask = [0] * max_seq_length + for i in range(len(masked_positions)): + assert masked_positions[i] < num_tokens + labels[masked_positions[i]] = masked_labels[i] + loss_mask[masked_positions[i]] = 1 + labels_np = np.array(labels, dtype=np.int64) + loss_mask_np = np.array(loss_mask, dtype=np.int64) + + return tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np + + +def build_train_valid_test_datasets_with_prefixes(train_valid_test_num_samples, + max_seq_length, + seed, + train_data_prefix=None, + valid_data_prefix=None, + test_data_prefix=None, + binary_head=False, + max_seq_length_dec=None, + dataset_type='standard_bert'): + print_rank_0("Separate data paths provided for train, valid & test.") + + train_dataset, valid_dataset, test_dataset = None, None, None + # Single dataset. + if train_data_prefix is not None: + train_dataset = build_dataset("train", train_data_prefix, + train_valid_test_num_samples[0], + max_seq_length, seed, + binary_head, max_seq_length_dec, + dataset_type=dataset_type) + + if valid_data_prefix is not None: + valid_dataset = build_dataset("valid", valid_data_prefix, + train_valid_test_num_samples[1], + max_seq_length, seed, False, + binary_head, max_seq_length_dec, + dataset_type=dataset_type) + + if test_data_prefix is not None: + test_dataset = build_dataset("test", test_data_prefix, + train_valid_test_num_samples[2], + max_seq_length, seed, False, + binary_head, max_seq_length_dec, + dataset_type=dataset_type) + + return (train_dataset, valid_dataset, test_dataset) + + +def build_train_valid_test_datasets(data_prefix, splits_string, + train_valid_test_num_samples, + max_seq_length, seed, + binary_head=False, + max_seq_length_dec=None, + dataset_type='standard_bert'): + + if len(data_prefix) == 1: + return _build_train_valid_test_datasets(data_prefix[0], + splits_string, + train_valid_test_num_samples, + max_seq_length, seed, + binary_head, + max_seq_length_dec, + dataset_type=dataset_type) + + raise NotImplementedError("Blending currently unsupported for non-GPT dataset instances") + + +def _build_train_valid_test_datasets(data_prefix, splits_string, + train_valid_test_num_samples, + max_seq_length, seed, + binary_head, + max_seq_length_dec, + dataset_type='standard_bert'): + + # Indexed dataset. + indexed_dataset = get_indexed_dataset_(data_prefix, + dataset_type) + + # Get start and end indices of train/valid/train into doc-idx + # Note that doc-idx is desinged to be num-docs + 1 so we can + # easily iterate over it. + total_num_of_documents = indexed_dataset.document_indices.shape[0] - 1 + splits = get_train_valid_test_split_(splits_string, total_num_of_documents) + + # Print stats about the splits. + print_rank_0(' > dataset split:') + + def print_split_stats(name, index): + print_rank_0(' {}:'.format(name)) + print_rank_0(' document indices in [{}, {}) total of {} ' + 'documents'.format(splits[index], splits[index + 1], + splits[index + 1] - splits[index])) + start_index = indexed_dataset.document_indices[splits[index]] + end_index = indexed_dataset.document_indices[splits[index + 1]] + print_rank_0(' sentence indices in [{}, {}) total of {} ' + 'sentences'.format(start_index, end_index, + end_index - start_index)) + print_split_stats('train', 0) + print_split_stats('validation', 1) + print_split_stats('test', 2) + + def build_split_dataset(index, name): + dataset = None + if splits[index + 1] > splits[index]: + # Get the pointer to the original doc-idx so we can set it later. + doc_idx_ptr = indexed_dataset.get_document_indices() + # Slice the doc-idx + start_index = splits[index] + # Add +1 so we can index into the dataset to get the upper bound. + end_index = splits[index + 1] + 1 + # New doc_idx view. + indexed_dataset.set_document_indices(doc_idx_ptr[start_index:end_index]) + + dataset = build_dataset( + name, data_prefix, + train_valid_test_num_samples[index], max_seq_length, + seed, binary_head, max_seq_length_dec, + dataset_type, indexed_dataset) + + # Set the original pointer so dataset remains the main dataset. + indexed_dataset.set_document_indices(doc_idx_ptr) + # Checks. + assert indexed_dataset.document_indices[0] == 0 + assert indexed_dataset.document_indices.shape[0] == \ + (total_num_of_documents + 1) + return dataset + + train_dataset = build_split_dataset(0, 'train') + valid_dataset = build_split_dataset(1, 'valid') + test_dataset = build_split_dataset(2, 'test') + + return (train_dataset, valid_dataset, test_dataset) + + +def build_dataset(name, data_prefix, max_num_samples, + max_seq_length, seed, binary_head, + max_seq_length_dec, dataset_type='standard_bert', + indexed_dataset=None): + + from megatron.legacy.data.ict_dataset import ICTDataset + from megatron.legacy.data.multimodal_dataset import MultiModalDataset + + if dataset_type == DSET_TYPE_BERT or dataset_type == DSET_TYPE_T5: + raise ValueError("The Megatron-LM BERT and T5 datasets are deprecated.") + + if dataset_type not in DSET_TYPES: + raise ValueError("Invalid dataset_type: ", dataset_type) + + if indexed_dataset is None: + indexed_dataset = get_indexed_dataset_(data_prefix, + dataset_type) + + kwargs = dict( + name=name, + data_prefix=data_prefix, + num_epochs=None, + max_num_samples=max_num_samples, + max_seq_length=max_seq_length, + seed=seed, + ) + + if dataset_type == DSET_TYPE_ICT: + args = get_args() + + title_dataset = get_indexed_dataset_( + args.titles_data_path, + dataset_type) + + dataset = ICTDataset( + block_dataset=indexed_dataset, + title_dataset=title_dataset, + query_in_block_prob=args.query_in_block_prob, + use_one_sent_docs=args.use_one_sent_docs, + binary_head=binary_head, + **kwargs + ) + elif dataset_type == DSET_TYPE_MULTIMODAL: + args = get_args() + dataset = MultiModalDataset( + name=name, + data_prefix=data_prefix, + indexed_dataset=indexed_dataset, + num_samples=max_num_samples, + seq_length=max_seq_length, + seed=seed, + img_h=args.img_h, + img_w=args.img_w, + ) + else: + raise NotImplementedError("Dataset type not fully implemented.") + + return dataset + + +def get_indexed_dataset_(data_prefix, dataset_type): + + print_rank_0(' > building dataset index ...') + + start_time = time.time() + multimodal = dataset_type == DSET_TYPE_MULTIMODAL + indexed_dataset = IndexedDataset(data_prefix, multimodal) + assert indexed_dataset.sequence_lengths.shape[0] == indexed_dataset.document_indices[-1] + print_rank_0(' > finished creating indexed dataset in {:4f} ' + 'seconds'.format(time.time() - start_time)) + + print_rank_0(' > indexed dataset stats:') + print_rank_0(' number of documents: {}'.format( + indexed_dataset.document_indices.shape[0] - 1)) + print_rank_0(' number of sentences: {}'.format( + indexed_dataset.sequence_lengths.shape[0])) + + return indexed_dataset + + +def get_train_valid_test_split_(splits_string, size): + """ Get dataset splits from comma or '/' separated string list.""" + + splits = [] + if splits_string.find(',') != -1: + splits = [float(s) for s in splits_string.split(',')] + elif splits_string.find('/') != -1: + splits = [float(s) for s in splits_string.split('/')] + else: + splits = [float(splits_string)] + while len(splits) < 3: + splits.append(0.) + splits = splits[:3] + splits_sum = sum(splits) + assert splits_sum > 0.0 + splits = [split / splits_sum for split in splits] + splits_index = [0] + for index, split in enumerate(splits): + splits_index.append(splits_index[index] + + int(round(split * float(size)))) + diff = splits_index[-1] - size + for index in range(1, len(splits_index)): + splits_index[index] -= diff + assert len(splits_index) == 4 + assert splits_index[-1] == size + return splits_index + +def get_samples_mapping(indexed_dataset, + data_prefix, + num_epochs, + max_num_samples, + max_seq_length, + short_seq_prob, + seed, + name, + binary_head): + """Get a list that maps a sample index to a starting sentence index, end sentence index, and length""" + + if not num_epochs: + if not max_num_samples: + raise ValueError("Need to specify either max_num_samples " + "or num_epochs") + num_epochs = np.iinfo(np.int32).max - 1 + if not max_num_samples: + max_num_samples = np.iinfo(np.int64).max - 1 + + # Filename of the index mapping + indexmap_filename = data_prefix + indexmap_filename += '_{}_indexmap'.format(name) + if num_epochs != (np.iinfo(np.int32).max - 1): + indexmap_filename += '_{}ep'.format(num_epochs) + if max_num_samples != (np.iinfo(np.int64).max - 1): + indexmap_filename += '_{}mns'.format(max_num_samples) + indexmap_filename += '_{}msl'.format(max_seq_length) + indexmap_filename += '_{:0.2f}ssp'.format(short_seq_prob) + indexmap_filename += '_{}s'.format(seed) + indexmap_filename += '.npy' + + # Build the indexed mapping if not exist. + if torch.distributed.get_rank() == 0 and \ + not os.path.isfile(indexmap_filename): + print(' > WARNING: could not find index map file {}, building ' + 'the indices on rank 0 ...'.format(indexmap_filename)) + + # Make sure the types match the helpers input types. + assert indexed_dataset.document_indices.dtype == np.int64 + assert indexed_dataset.sequence_lengths.dtype == np.int32 + + # Build samples mapping + verbose = torch.distributed.get_rank() == 0 + start_time = time.time() + print_rank_0(' > building samples index mapping for {} ...'.format( + name)) + # First compile and then import. + from megatron.core.datasets import helpers + samples_mapping = helpers.build_mapping( + indexed_dataset.document_indices, + indexed_dataset.sequence_lengths, + num_epochs, + max_num_samples, + max_seq_length, + short_seq_prob, + seed, + verbose, + 2 if binary_head else 1) + print_rank_0(' > done building samples index maping') + np.save(indexmap_filename, samples_mapping, allow_pickle=True) + print_rank_0(' > saved the index mapping in {}'.format( + indexmap_filename)) + # Make sure all the ranks have built the mapping + print_rank_0(' > elasped time to build and save samples mapping ' + '(seconds): {:4f}'.format( + time.time() - start_time)) + # This should be a barrier but nccl barrier assumes + # device_index=rank which is not the case for model + # parallel case + counts = torch.tensor([1], dtype=torch.long, device='cuda') + torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group()) + torch.distributed.all_reduce(counts, group=mpu.get_pipeline_model_parallel_group()) + assert counts[0].item() == ( + torch.distributed.get_world_size() // + torch.distributed.get_world_size(group=mpu.get_tensor_model_parallel_group())) + + # Load indexed dataset. + print_rank_0(' > loading indexed mapping from {}'.format( + indexmap_filename)) + start_time = time.time() + samples_mapping = np.load(indexmap_filename, allow_pickle=True, mmap_mode='r') + print_rank_0(' loaded indexed file in {:3.3f} seconds'.format( + time.time() - start_time)) + print_rank_0(' total number of samples: {}'.format( + samples_mapping.shape[0])) + + return samples_mapping diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/ict_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/ict_dataset.py new file mode 100644 index 0000000..2c65f2c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/ict_dataset.py @@ -0,0 +1,156 @@ +import itertools +import random + +import numpy as np +from torch.utils.data import Dataset + +from megatron.training import get_tokenizer +from megatron.training import get_args +from megatron.legacy.data.dataset_utils import get_indexed_dataset_ +from megatron.legacy.data.realm_dataset_utils import get_block_samples_mapping + +def make_attention_mask(source_block, target_block): + """ + Returns a 2-dimensional (2-D) attention mask + :param source_block: 1-D array + :param target_block: 1-D array + """ + mask = (target_block[None, :] >= 1) * (source_block[:, None] >= 1) + mask = mask.astype(np.int64) + # (source_length, target_length) + return mask + +def get_ict_dataset(use_titles=True, query_in_block_prob=1): + """Get a dataset which uses block samples mappings to get ICT/block indexing data (via get_block()) + rather than for training, since it is only built with a single epoch sample mapping. + """ + args = get_args() + block_dataset = get_indexed_dataset_(args.data_path, 'mmap', True) + titles_dataset = get_indexed_dataset_(args.titles_data_path, 'mmap', True) + + kwargs = dict( + name='full', + block_dataset=block_dataset, + title_dataset=titles_dataset, + data_prefix=args.data_path, + num_epochs=1, + max_num_samples=None, + max_seq_length=args.seq_length, + seed=1, + query_in_block_prob=query_in_block_prob, + use_titles=use_titles, + use_one_sent_docs=args.use_one_sent_docs + ) + dataset = ICTDataset(**kwargs) + return dataset + + +class ICTDataset(Dataset): + """Dataset containing sentences and their blocks for an inverse cloze task.""" + def __init__(self, name, block_dataset, title_dataset, data_prefix, + num_epochs, max_num_samples, max_seq_length, query_in_block_prob, + seed, use_titles=True, use_one_sent_docs=False, binary_head=False): + self.name = name + self.seed = seed + self.max_seq_length = max_seq_length + self.query_in_block_prob = query_in_block_prob + self.block_dataset = block_dataset + self.title_dataset = title_dataset + self.rng = random.Random(self.seed) + self.use_titles = use_titles + self.use_one_sent_docs = use_one_sent_docs + + self.samples_mapping = get_block_samples_mapping( + block_dataset, title_dataset, data_prefix, num_epochs, + max_num_samples, max_seq_length, seed, name, use_one_sent_docs) + self.tokenizer = get_tokenizer() + self.vocab_id_list = list(self.tokenizer.inv_vocab.keys()) + self.vocab_id_to_token_list = self.tokenizer.inv_vocab + self.cls_id = self.tokenizer.cls + self.sep_id = self.tokenizer.sep + self.mask_id = self.tokenizer.mask + self.pad_id = self.tokenizer.pad + + def __len__(self): + return len(self.samples_mapping) + + def __getitem__(self, idx): + """Get an ICT example of a pseudo-query and the block of text from which it was extracted""" + sample_data = self.samples_mapping[idx] + start_idx, end_idx, doc_idx, block_idx = sample_data.as_tuple() + + if self.use_titles: + title = self.title_dataset[int(doc_idx)] + title_pad_offset = 3 + len(title) + else: + title = None + title_pad_offset = 2 + block = [self.block_dataset[i] for i in range(start_idx, end_idx)] + assert len(block) > 1 or self.use_one_sent_docs or self.query_in_block_prob == 1 + + # randint() is inclusive for Python rng + rand_sent_idx = self.rng.randint(0, len(block) - 1) + + # keep the query in the context query_in_block_prob fraction of the time. + if self.rng.random() < self.query_in_block_prob: + query = block[rand_sent_idx].copy() + else: + query = block.pop(rand_sent_idx) + + # still need to truncate because blocks are concluded when + # the sentence lengths have exceeded max_seq_length. + query = query[:self.max_seq_length - 2] + block = list(itertools.chain(*block))[:self.max_seq_length - title_pad_offset] + + query_tokens, query_pad_mask = self.concat_and_pad_tokens(query) + context_tokens, context_pad_mask = self.concat_and_pad_tokens(block, title) + + query_mask = make_attention_mask(query_tokens, query_tokens) + context_mask = make_attention_mask(context_tokens, context_tokens) + + block_data = sample_data.as_array() + + sample = { + 'query_tokens': query_tokens, + 'query_mask': query_mask, + 'query_pad_mask': query_pad_mask, + 'context_tokens': context_tokens, + 'context_mask': context_mask, + 'context_pad_mask': context_pad_mask, + 'block_data': block_data, + } + + return sample + + def get_block(self, start_idx, end_idx, doc_idx): + """Get the IDs for an evidence block plus the title of the corresponding document""" + block = [self.block_dataset[i] for i in range(start_idx, end_idx)] + title = self.title_dataset[int(doc_idx)] + + block = list(itertools.chain(*block))[:self.max_seq_length - (3 + len(title))] + block_tokens, block_pad_mask = self.concat_and_pad_tokens(block, title) + + return block_tokens, block_pad_mask + + def get_null_block(self): + """Get empty block and title - used in REALM pretraining""" + block, title = [], [] + block_tokens, block_pad_mask = self.concat_and_pad_tokens(block, title) + + return block_tokens, block_pad_mask + + def concat_and_pad_tokens(self, tokens, title=None): + """Concat with special tokens and pad sequence to self.max_seq_length""" + tokens = list(tokens) + if title is None: + tokens = [self.cls_id] + tokens + [self.sep_id] + else: + title = list(title) + tokens = [self.cls_id] + title + [self.sep_id] + tokens + [self.sep_id] + assert len(tokens) <= self.max_seq_length + + num_pad = self.max_seq_length - len(tokens) + pad_mask = [1] * len(tokens) + [0] * num_pad + tokens += [self.pad_id] * num_pad + + return np.array(tokens), np.array(pad_mask) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/image_folder.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/image_folder.py new file mode 100644 index 0000000..de15b29 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/image_folder.py @@ -0,0 +1,302 @@ +# BSD 3-Clause License +# +# Copyright (c) Soumith Chintala 2016, +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# * Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# * Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# * Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +# code taken from +# https://github.com/pytorch/vision/blob/main/torchvision/datasets/folder.py +# added support for classes_fraction and data_per_class_fraction + +from torchvision.datasets import VisionDataset +from PIL import Image + +import os +import os.path +from typing import Any, Callable, cast, Dict, List, Optional, Tuple +import numpy as np + +def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool: + """Checks if a file is an allowed extension. + Args: + filename (string): path to a file + extensions (tuple of strings): extensions to consider (lowercase) + Returns: + bool: True if the filename ends with one of given extensions + """ + return filename.lower().endswith(extensions) + + +def is_image_file(filename: str) -> bool: + """Checks if a file is an allowed image extension. + Args: + filename (string): path to a file + Returns: + bool: True if the filename ends with a known image extension + """ + return has_file_allowed_extension(filename, IMG_EXTENSIONS) + + +def make_dataset( + directory: str, + class_to_idx: Dict[str, int], + data_per_class_fraction: float, + extensions: Optional[Tuple[str, ...]] = None, + is_valid_file: Optional[Callable[[str], bool]] = None, +) -> List[Tuple[str, int]]: + """Generates a list of samples of a form (path_to_sample, class). + Args: + directory (str): root dataset directory + class_to_idx (Dict[str, int]): dictionary mapping class name to class index + extensions (optional): A list of allowed extensions. + Either extensions or is_valid_file should be passed. Defaults to None. + is_valid_file (optional): A function that takes path of a file + and checks if the file is a valid file + (used to check of corrupt files) both extensions and + is_valid_file should not be passed. Defaults to None. + Raises: + ValueError: In case ``extensions`` and ``is_valid_file`` are None or both are not None. + Returns: + List[Tuple[str, int]]: samples of a form (path_to_sample, class) + """ + instances = [] + directory = os.path.expanduser(directory) + both_none = extensions is None and is_valid_file is None + both_something = extensions is not None and is_valid_file is not None + if both_none or both_something: + raise ValueError("Both extensions and is_valid_file cannot be None or not None at the same time") + if extensions is not None: + def is_valid_file(x: str) -> bool: + return has_file_allowed_extension(x, cast(Tuple[str, ...], extensions)) + is_valid_file = cast(Callable[[str], bool], is_valid_file) + for target_class in sorted(class_to_idx.keys()): + class_index = class_to_idx[target_class] + target_dir = os.path.join(directory, target_class) + if not os.path.isdir(target_dir): + continue + local_instances = [] + for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)): + for fname in sorted(fnames): + path = os.path.join(root, fname) + if is_valid_file(path): + item = path, class_index + local_instances.append(item) + + instances.extend(local_instances[0:int(len(local_instances) * data_per_class_fraction)]) + + return instances + + +class DatasetFolder(VisionDataset): + """A generic data loader where the samples are arranged in this way: :: + root/class_x/xxx.ext + root/class_x/xxy.ext + root/class_x/[...]/xxz.ext + root/class_y/123.ext + root/class_y/nsdf3.ext + root/class_y/[...]/asd932_.ext + Args: + root (string): Root directory path. + loader (callable): A function to load a sample given its path. + extensions (tuple[string]): A list of allowed extensions. + both extensions and is_valid_file should not be passed. + transform (callable, optional): A function/transform that takes in + a sample and returns a transformed version. + E.g, ``transforms.RandomCrop`` for images. + target_transform (callable, optional): A function/transform that takes + in the target and transforms it. + is_valid_file (callable, optional): A function that takes path of a file + and check if the file is a valid file (used to check of corrupt files) + both extensions and is_valid_file should not be passed. + Attributes: + classes (list): List of the class names sorted alphabetically. + class_to_idx (dict): Dict with items (class_name, class_index). + samples (list): List of (sample path, class_index) tuples + targets (list): The class_index value for each image in the dataset + """ + + def __init__( + self, + root: str, + loader: Callable[[str], Any], + extensions: Optional[Tuple[str, ...]] = None, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + classes_fraction=1.0, + data_per_class_fraction=1.0, + is_valid_file: Optional[Callable[[str], bool]] = None, + ) -> None: + super(DatasetFolder, self).__init__(root, transform=transform, + target_transform=target_transform) + self.classes_fraction = classes_fraction + self.data_per_class_fraction = data_per_class_fraction + classes, class_to_idx = self._find_classes(self.root) + samples = self.make_dataset(self.root, + class_to_idx, + self.data_per_class_fraction, + extensions, + is_valid_file) + if len(samples) == 0: + msg = "Found 0 files in subfolders of: {}\n".format(self.root) + if extensions is not None: + msg += "Supported extensions are: {}".format(",".join(extensions)) + raise RuntimeError(msg) + + self.loader = loader + self.extensions = extensions + self.total = len(samples) + self.classes = classes + self.class_to_idx = class_to_idx + self.samples = samples + self.targets = [s[1] for s in samples] + + @staticmethod + def make_dataset( + directory: str, + class_to_idx: Dict[str, int], + data_per_class_fraction: float, + extensions: Optional[Tuple[str, ...]] = None, + is_valid_file: Optional[Callable[[str], bool]] = None, + ) -> List[Tuple[str, int]]: + return make_dataset(directory, + class_to_idx, + data_per_class_fraction, + extensions=extensions, + is_valid_file=is_valid_file) + + def _find_classes(self, dir: str) -> Tuple[List[str], Dict[str, int]]: + """ + Finds the class folders in a dataset. + Args: + dir (string): Root directory path. + Returns: + tuple: (classes, class_to_idx) where classes are relative to (dir), and class_to_idx is a dictionary. + Ensures: + No class is a subdirectory of another. + """ + all_classes = [d.name for d in os.scandir(dir) if d.is_dir()] + classes = all_classes[0:int(len(all_classes) * self.classes_fraction)] + classes.sort() + class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)} + return classes, class_to_idx + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + Returns: + tuple: (sample, target) where target is class_index of the target class. + """ + curr_index = index + for x in range(self.total): + try: + path, target = self.samples[curr_index] + sample = self.loader(path) + break + except Exception as e: + curr_index = np.random.randint(0, self.total) + + if self.transform is not None: + sample = self.transform(sample) + if self.target_transform is not None: + target = self.target_transform(target) + + return sample, target + + def __len__(self) -> int: + return len(self.samples) + + +IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp') + + +def pil_loader(path: str) -> Image.Image: + # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) + with open(path, 'rb') as f: + img = Image.open(f) + return img.convert('RGB') + + +# TODO: specify the return type +def accimage_loader(path: str) -> Any: + import accimage + try: + return accimage.Image(path) + except IOError: + # Potentially a decoding problem, fall back to PIL.Image + return pil_loader(path) + + +def default_loader(path: str) -> Any: + from torchvision import get_image_backend + if get_image_backend() == 'accimage': + return accimage_loader(path) + else: + return pil_loader(path) + + +class ImageFolder(DatasetFolder): + """A generic data loader where the images are arranged in this way: :: + root/dog/xxx.png + root/dog/xxy.png + root/dog/[...]/xxz.png + root/cat/123.png + root/cat/nsdf3.png + root/cat/[...]/asd932_.png + Args: + root (string): Root directory path. + transform (callable, optional): A function/transform that takes in an PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + loader (callable, optional): A function to load an image given its path. + is_valid_file (callable, optional): A function that takes path of an Image file + and check if the file is a valid file (used to check of corrupt files) + Attributes: + classes (list): List of the class names sorted alphabetically. + class_to_idx (dict): Dict with items (class_name, class_index). + imgs (list): List of (image path, class_index) tuples + """ + + def __init__( + self, + root: str, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + classes_fraction=1.0, + data_per_class_fraction=1.0, + loader: Callable[[str], Any] = default_loader, + is_valid_file: Optional[Callable[[str], bool]] = None, + ): + super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS if is_valid_file is None else None, + transform=transform, + target_transform=target_transform, + classes_fraction=classes_fraction, + data_per_class_fraction=data_per_class_fraction, + is_valid_file=is_valid_file) + self.imgs = self.samples + diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/multimodal_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/multimodal_dataset.py new file mode 100644 index 0000000..93ea790 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/multimodal_dataset.py @@ -0,0 +1,54 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +from PIL import Image, UnidentifiedImageError +import numpy as np +import io +import torch + +try: + from torchvision.transforms import InterpolationMode + BICUBIC = InterpolationMode.BICUBIC +except ImportError: + BICUBIC = Image.BICUBIC + +from torchvision.transforms import Compose, ToTensor, Normalize, ToPILImage, RandomResizedCrop, Resize + +def _convert_image_to_rgb(image): + return image.convert("RGB") + +def _transform(img_h, img_w): + return Compose([ + ToPILImage(), + RandomResizedCrop((img_h, img_w), scale=(0.5, 1.0), interpolation=BICUBIC), + _convert_image_to_rgb, + ToTensor(), + Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), + ]) + +class MultiModalDataset(torch.utils.data.Dataset): + + def __init__(self, name, data_prefix, indexed_dataset, + num_samples, seq_length, seed, img_h, img_w): + + self.name = name + self.indexed_dataset = indexed_dataset + self.doc_idx = indexed_dataset.get_document_indices() + self.visual_transform = _transform(img_h, img_w) + + def __len__(self): + return self.indexed_dataset.sequence_lengths.shape[0] + + def __getitem__(self, idx): + text_sample, mode = self.indexed_dataset.get(self.doc_idx[idx]) + assert mode == 0 + img_sample, mode = self.indexed_dataset.get(self.doc_idx[idx]+1) + assert mode == 1 + img_pad = img_sample[0].item() + xs = img_sample[1:].tobytes(order='C') + xs = xs[:len(xs)-img_pad] + + img_sample = np.array(Image.open(io.BytesIO(xs))) + img_sample = self.visual_transform(img_sample).reshape(-1) + + return {'text': np.array(text_sample, dtype=np.int64), + 'img': np.array(img_sample, dtype=np.float32)} diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/orqa_wiki_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/orqa_wiki_dataset.py new file mode 100644 index 0000000..99217d6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/orqa_wiki_dataset.py @@ -0,0 +1,193 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Wikipedia dataset from DPR code for ORQA.""" + +from abc import ABC +import csv +import numpy as np +import random +import torch +from torch.utils.data import Dataset + +from megatron.training import print_rank_0, get_args, get_tokenizer +from megatron.core import tensor_parallel +from megatron.legacy.data.biencoder_dataset_utils import make_attention_mask + +def get_open_retrieval_wiki_dataset(): + args = get_args() + tokenizer = get_tokenizer() + + dataset = OpenRetrievalEvidenceDataset('2018 Wikipedia from DPR codebase', + 'evidence', + args.evidence_data_path, + tokenizer, + args.retriever_seq_length) + return dataset + + +def get_open_retrieval_batch(data_iterator): + # Items and their type. + keys = ['row_id', 'context', 'context_mask', 'context_types', + 'context_pad_mask'] + datatype = torch.int64 + + # Broadcast data. + data = None if data_iterator is None else next(data_iterator) + data_b = tensor_parallel.broadcast_data(keys, data, datatype) + + # Unpack. + row_id = data_b['row_id'].long() + context = data_b['context'].long() + + # TODO: make the context mask a binary one + context_mask = (data_b['context_mask'] < 0.5) + + context_types = data_b['context_types'].long() + context_pad_mask = data_b['context_pad_mask'].long() + + return row_id, context, context_mask, context_types, context_pad_mask + + +def build_tokens_types_paddings_from_text(row, tokenizer, max_seq_length): + """Build token types and paddings, trim if needed, and pad if needed.""" + + title_ids = tokenizer.tokenize(row['title']) + context_ids = tokenizer.tokenize(row['text']) + + # Appending the title of the context at front + extended_context_ids = title_ids + [tokenizer.sep_id] + context_ids + + context_ids, context_types, context_pad_mask = \ + build_tokens_types_paddings_from_ids(extended_context_ids, + max_seq_length, tokenizer.cls, tokenizer.sep, tokenizer.pad) + + return context_ids, context_types, context_pad_mask + + +# noinspection DuplicatedCode +def build_tokens_types_paddings_from_ids(text_ids, max_seq_length, + cls_id, sep_id, pad_id): + """Build token types and paddings, trim if needed, and pad if needed.""" + enc_ids = [] + tokentypes_enc = [] + + # [CLS]. + enc_ids.append(cls_id) + tokentypes_enc.append(0) + + # A. + len_src = len(text_ids) + enc_ids.extend(text_ids) + tokentypes_enc.extend([0] * len_src) + + # Cap the size. + if len(enc_ids) > max_seq_length - 1: + enc_ids = enc_ids[0: max_seq_length - 1] + tokentypes_enc = tokentypes_enc[0: max_seq_length - 1] + + # [SEP]. + enc_ids.append(sep_id) + tokentypes_enc.append(0) + + num_tokens_enc = len(enc_ids) + # Padding. + padding_length = max_seq_length - len(enc_ids) + if padding_length > 0: + enc_ids.extend([pad_id] * padding_length) + tokentypes_enc.extend([pad_id] * padding_length) + + pad_mask = ([1] * num_tokens_enc) + ([0] * padding_length) + pad_mask = np.array(pad_mask, dtype=np.int64) + + return enc_ids, tokentypes_enc, pad_mask + + +def build_sample(row_id, context_ids, context_types, context_pad_mask): + """Convert to numpy and return a sample consumed by the batch producer.""" + + context_ids = np.array(context_ids, dtype=np.int64) + context_types = np.array(context_types, dtype=np.int64) + context_mask = make_attention_mask(context_ids, context_ids) + + sample = ({ + 'row_id': row_id, + 'context': context_ids, + 'context_mask': context_mask, + 'context_types': context_types, + 'context_pad_mask': context_pad_mask + }) + return sample + + +class OpenRetrievalEvidenceDataset(ABC, Dataset): + """Open Retrieval Evidence dataset class.""" + + def __init__(self, task_name, dataset_name, datapath, tokenizer, + max_seq_length): + # Store inputs. + self.task_name = task_name + self.dataset_name = dataset_name + self.tokenizer = tokenizer + self.max_seq_length = max_seq_length + print_rank_0(' > building {} dataset for {}:'.format(self.task_name, + self.dataset_name)) + # Process the files. + print_rank_0(datapath) + self.samples, self.id2text = self.process_samples_from_single_path( + datapath) + + args = get_args() + if args.sample_rate < 1: # subsample + k = int(len(self.samples) * args.sample_rate) + self.samples = random.sample(self.samples, k) + + print_rank_0(' >> total number of samples: {}'.format( + len(self.samples))) + + def __len__(self): + return len(self.samples) + + def __getitem__(self, idx): + row = self.samples[idx] + + context_ids, context_types, context_pad_mask = \ + build_tokens_types_paddings_from_text(row, self.tokenizer, + self.max_seq_length) + + sample = build_sample(row['doc_id'], + context_ids, + context_types, + context_pad_mask) + return sample + + @staticmethod + def process_samples_from_single_path(filename): + print_rank_0(' > Processing {} ...'.format(filename)) + total = 0 + + rows = [] + id2text = {} + + with open(filename) as tsvfile: + reader = csv.reader(tsvfile, delimiter='\t') + next(reader, None) # skip the headers + for row in reader: + # file format: doc_id, doc_text, title + doc_id = int(row[0]) + text = row[1] + title = row[2] + + rows.append({'doc_id': doc_id, + 'text': text, + 'title': title}) + + assert doc_id not in id2text + id2text[doc_id] = (text, title) + + total += 1 + if total % 100000 == 0: + print_rank_0(' > processed {} rows so far ...'.format( + total)) + + print_rank_0(' >> processed {} samples.'.format(len(rows))) + return rows, id2text diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/realm_dataset_utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/realm_dataset_utils.py new file mode 100644 index 0000000..50bf9bd --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/realm_dataset_utils.py @@ -0,0 +1,199 @@ +import os +import time + +import numpy as np +import torch + +from megatron.training import print_rank_0 +from megatron.core import mpu, tensor_parallel +from megatron.legacy.data.dataset_utils import create_masked_lm_predictions, pad_and_convert_to_numpy +from megatron.training import get_args, get_tokenizer, print_rank_0 + + +def get_one_epoch_dataloader(dataset, micro_batch_size=None): + """Specifically one epoch to be used in an indexing job.""" + args = get_args() + + world_size = mpu.get_data_parallel_world_size() + rank = mpu.get_data_parallel_rank() + if micro_batch_size is None: + micro_batch_size = args.micro_batch_size + global_batch_size = micro_batch_size * world_size + num_workers = args.num_workers + + sampler = torch.utils.data.SequentialSampler(dataset) + # importantly, drop_last must be False to get all the data. + assert False, 'DistributedBatchSampler deprecated, change the implementation' + from megatron.legacy.data.samplers import DistributedBatchSampler + batch_sampler = DistributedBatchSampler(sampler, + batch_size=global_batch_size, + drop_last=False, + rank=rank, + world_size=world_size) + + return torch.utils.data.DataLoader(dataset, + batch_sampler=batch_sampler, + num_workers=num_workers, + pin_memory=True) + + +def get_ict_batch(data_iterator): + # Items and their type. + keys = ['query_tokens', 'query_pad_mask', + 'block_tokens', 'block_pad_mask', 'block_data'] + datatype = torch.int64 + + # Broadcast data. + if data_iterator is None: + data = None + else: + data = next(data_iterator) + data_b = tensor_parallel.broadcast_data(keys, data, datatype) + + # Unpack. + query_tokens = data_b['query_tokens'].long() + query_pad_mask = data_b['query_pad_mask'].long() + block_tokens = data_b['block_tokens'].long() + block_pad_mask = data_b['block_pad_mask'].long() + block_indices = data_b['block_data'].long() + + return query_tokens, query_pad_mask,\ + block_tokens, block_pad_mask, block_indices + + +def join_str_list(str_list): + """Join a list of strings, handling spaces appropriately""" + result = "" + for s in str_list: + if s.startswith("##"): + result += s[2:] + else: + result += " " + s + return result + + +class BlockSampleData(object): + """A struct for fully describing a fixed-size block of data as used in REALM + + :param start_idx: for first sentence of the block + :param end_idx: for last sentence of the block (may be partially truncated in sample construction) + :param doc_idx: the index of the document from which the block comes in the original indexed dataset + :param block_idx: a unique integer identifier given to every block. + """ + def __init__(self, start_idx, end_idx, doc_idx, block_idx): + self.start_idx = start_idx + self.end_idx = end_idx + self.doc_idx = doc_idx + self.block_idx = block_idx + + def as_array(self): + return np.array([self.start_idx, self.end_idx, self.doc_idx, self.block_idx]).astype(np.int64) + + def as_tuple(self): + return self.start_idx, self.end_idx, self.doc_idx, self.block_idx + + +class BlockSamplesMapping(object): + def __init__(self, mapping_array): + # make sure that the array is compatible with BlockSampleData + assert mapping_array.shape[1] == 4 + self.mapping_array = mapping_array + + def __len__(self): + return self.mapping_array.shape[0] + + def __getitem__(self, idx): + """Get the data associated with an indexed sample.""" + sample_data = BlockSampleData(*self.mapping_array[idx]) + return sample_data + + +def get_block_samples_mapping(block_dataset, title_dataset, data_prefix, num_epochs, + max_num_samples, max_seq_length, seed, name, use_one_sent_docs=False): + """Get samples mapping for a dataset over fixed size blocks. This function also requires + a dataset of the titles for the source documents since their lengths must be taken into account. + + :return: samples_mapping (BlockSamplesMapping) + """ + + if not num_epochs: + if not max_num_samples: + raise ValueError("Need to specify either max_num_samples " + "or num_epochs") + num_epochs = np.iinfo(np.int32).max - 1 + if not max_num_samples: + max_num_samples = np.iinfo(np.int64).max - 1 + + # Filename of the index mapping + indexmap_filename = data_prefix + indexmap_filename += '_{}_indexmap'.format(name) + if num_epochs != (np.iinfo(np.int32).max - 1): + indexmap_filename += '_{}ep'.format(num_epochs) + if max_num_samples != (np.iinfo(np.int64).max - 1): + indexmap_filename += '_{}mns'.format(max_num_samples) + indexmap_filename += '_{}msl'.format(max_seq_length) + indexmap_filename += '_{}s'.format(seed) + if use_one_sent_docs: + indexmap_filename += '_1sentok' + indexmap_filename += '.npy' + + # Build the indexed mapping if not exist. + if mpu.get_data_parallel_rank() == 0 and \ + not os.path.isfile(indexmap_filename): + print(' > WARNING: could not find index map file {}, building ' + 'the indices on rank 0 ...'.format(indexmap_filename)) + + # Make sure the types match the helpers input types. + assert block_dataset.document_indices.dtype == np.int64 + assert block_dataset.sequence_lengths.dtype == np.int32 + + # Build samples mapping + verbose = torch.distributed.get_rank() == 0 + start_time = time.time() + print_rank_0(' > building samples index mapping for {} ...'.format( + name)) + + from megatron.core.datasets import helpers + mapping_array = helpers.build_blocks_mapping( + block_dataset.document_indices, + block_dataset.sequence_lengths, + title_dataset.sequence_lengths, + num_epochs, + max_num_samples, + max_seq_length - 3, # account for added tokens + seed, + verbose, + use_one_sent_docs) + + + print_rank_0(' > done building samples index mapping') + np.save(indexmap_filename, mapping_array, allow_pickle=True) + print_rank_0(' > saved the index mapping in {}'.format( + indexmap_filename)) + # Make sure all the ranks have built the mapping + print_rank_0(' > elapsed time to build and save samples mapping ' + '(seconds): {:4f}'.format( + time.time() - start_time)) + + # This should be a barrier but nccl barrier assumes + # device_index=rank which is not the case for model + # parallel case + counts = torch.tensor([1], dtype=torch.long, device='cuda') + torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group()) + assert counts[0].item() == torch.distributed.get_world_size( + group=mpu.get_data_parallel_group()) + + # Load indexed dataset. + print_rank_0(' > loading indexed mapping from {}'.format( + indexmap_filename)) + start_time = time.time() + + mapping_array = np.load(indexmap_filename, allow_pickle=True, mmap_mode='r') + samples_mapping = BlockSamplesMapping(mapping_array) + + print_rank_0(' loaded indexed file in {:3.3f} seconds'.format( + time.time() - start_time)) + print_rank_0(' total number of samples: {}'.format( + mapping_array.shape[0])) + + return samples_mapping diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/realm_index.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/realm_index.py new file mode 100644 index 0000000..2575af7 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/realm_index.py @@ -0,0 +1,224 @@ +import itertools +import os +import pickle +import shutil + +import numpy as np +import torch + +from megatron.training import get_args +from megatron.core import mpu + + +def detach(tensor): + return tensor.detach().cpu().numpy() + + +class OpenRetreivalDataStore(object): + """ + Serializable data structure for holding data for blocks -- + embeddings and necessary metadata for Retriever + """ + def __init__(self, embedding_path=None, load_from_path=True, rank=None): + self.embed_data = dict() + if embedding_path is None: + args = get_args() + embedding_path = args.embedding_path + rank = args.rank + self.embedding_path = embedding_path + self.rank = rank + + if load_from_path: + self.load_from_file() + + block_data_name = os.path.splitext(self.embedding_path)[0] + self.temp_dir_name = block_data_name + '_tmp' + + def state(self): + return { + 'embed_data': self.embed_data, + } + + def clear(self): + """ + Clear the embedding data structures to save memory. + The metadata ends up getting used, and is also much smaller in + dimensionality so it isn't really worth clearing. + """ + self.embed_data = dict() + + def load_from_file(self): + """Populate members from instance saved to file""" + + if not mpu.model_parallel_is_initialized() or mpu.get_data_parallel_rank() == 0: + print("\n> Unpickling BlockData", flush=True) + state_dict = pickle.load(open(self.embedding_path, 'rb')) + if not mpu.model_parallel_is_initialized() or mpu.get_data_parallel_rank() == 0: + print(">> Finished unpickling BlockData\n", flush=True) + + self.embed_data = state_dict['embed_data'] + + def add_block_data(self, row_id, block_embeds, allow_overwrite=False): + """ + Add data for set of blocks + :param row_id: 1D array of unique int ids for the blocks + :param block_embeds: 2D array of embeddings of the blocks + In the case of retriever this will be [start_idx, end_idx, doc_idx] + """ + for idx, embed in zip(row_id, block_embeds): + if not allow_overwrite and idx in self.embed_data: + raise ValueError("Unexpectedly tried to overwrite block data") + + self.embed_data[idx] = np.float16(embed) + + def save_shard(self): + """ + Save the block data that was created this in this process + """ + if not os.path.isdir(self.temp_dir_name): + os.makedirs(self.temp_dir_name, exist_ok=True) + + # save the data for each shard + with open('{}/{}.pkl'.format(self.temp_dir_name, self.rank), 'wb') \ + as writer: + pickle.dump(self.state(), writer) + + def merge_shards_and_save(self): + #Combine all the shards made using save_shard + shard_names = os.listdir(self.temp_dir_name) + seen_own_shard = False + + for fname in os.listdir(self.temp_dir_name): + shard_rank = int(os.path.splitext(fname)[0]) + if shard_rank == self.rank: + seen_own_shard = True + continue + + with open('{}/{}'.format(self.temp_dir_name, fname), 'rb') as f: + data = pickle.load(f) + old_size = len(self.embed_data) + shard_size = len(data['embed_data']) + + # add the shard's data and check to make sure there + # is no overlap + self.embed_data.update(data['embed_data']) + assert len(self.embed_data) == old_size + shard_size + + assert seen_own_shard + + # save the consolidated shards and remove temporary directory + with open(self.embedding_path, 'wb') as final_file: + pickle.dump(self.state(), final_file) + shutil.rmtree(self.temp_dir_name, ignore_errors=True) + + print("Finished merging {} shards for a total of {} embeds".format( + len(shard_names), len(self.embed_data)), flush=True) + + +class FaissMIPSIndex(object): + """ + Wrapper object for a BlockData which similarity search via FAISS under the hood + """ + def __init__(self, embed_size, embed_data=None, use_gpu=False): + self.embed_size = embed_size + self.embed_data = embed_data + self.use_gpu = use_gpu + + self.mips_index = None + self._set_mips_index() + + def _set_mips_index(self): + """ + Create a Faiss Flat index with inner product as the metric + to search against + """ + try: + import faiss + except ImportError: + raise Exception("Error: Please install faiss to use FaissMIPSIndex") + + if not mpu.model_parallel_is_initialized() or mpu.get_data_parallel_rank() == 0: + print("\n> Building index", flush=True) + + cpu_index = faiss.IndexFlatIP(self.embed_size) + + if self.use_gpu: + # create resources and config for GpuIndex + config = faiss.GpuMultipleClonerOptions() + config.shard = True + config.useFloat16 = True + gpu_index = faiss.index_cpu_to_all_gpus(cpu_index, co=config) + self.mips_index = faiss.IndexIDMap(gpu_index) + if not mpu.model_parallel_is_initialized() or mpu.get_data_parallel_rank() == 0: + print(">> Initialized index on GPU", flush=True) + else: + # CPU index supports IDs so wrap with IDMap + self.mips_index = faiss.IndexIDMap(cpu_index) + if not mpu.model_parallel_is_initialized() or mpu.get_data_parallel_rank() == 0: + print(">> Initialized index on CPU", flush=True) + + # if we were constructed with a BlockData, then automatically load it + # when the FAISS structure is built + if self.embed_data is not None: + self.add_embed_data(self.embed_data) + + def reset_index(self): + """Delete existing index and create a new""" + del self.mips_index + + # reset the block data so that _set_block_index will reload it as well + if self.embed_data is not None: + embed_data_path = self.embed_data.embedding_path + del self.embed_data + self.embed_data = OpenRetreivalDataStore(embed_data_path) + + self._set_mips_index() + + def update_index(self): + """Delete existing index and create a new""" + del self.mips_index + + # reset the block data so that _set_mips_index will reload it as well + if self.embed_data is not None: + self.embed_data.load_from_file() + self._set_mips_index() + + def add_embed_data(self, all_embed_data): + """Add the embedding of each block to the underlying FAISS index""" + + # this assumes the embed_data is a dict : {int: np.array} + block_indices, block_embeds = zip(*all_embed_data.embed_data.items()) + + # the embeddings have to be entered in as float32 even though the math + # internally is done with float16. + embeds_arr = np.float32(np.array(block_embeds)) + indices_arr = np.array(block_indices) + + # we no longer need the embedding data since it's in the index now + all_embed_data.clear() + + self.mips_index.add_with_ids(embeds_arr, indices_arr) + + if not mpu.model_parallel_is_initialized() or mpu.get_data_parallel_rank() == 0: + print(">>> Finished adding block data to index", flush=True) + + def search_mips_index(self, query_embeds, top_k, reconstruct=True): + """ + Get the top-k blocks by the index distance metric. + + :param reconstruct: if True: return a [num_queries x k x embed_dim] + array of blocks + if False: return [num_queries x k] array of + distances, and another for indices + """ + query_embeds = np.float32(detach(query_embeds)) + + if reconstruct: + # get the vectors themselves + top_k_block_embeds = self.mips_index.search_and_reconstruct(\ + query_embeds, top_k) + return top_k_block_embeds + else: + # get distances and indices of closest vectors + distances, block_indices = self.mips_index.search(query_embeds, top_k) + return distances, block_indices diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/vit_dataset.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/vit_dataset.py new file mode 100644 index 0000000..e65c536 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/data/vit_dataset.py @@ -0,0 +1,249 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +import os +import random +import numpy as np +import torch +import torchvision.transforms as T +from torchvision import datasets +from megatron.training import get_args +from megatron.legacy.data.image_folder import ImageFolder +from megatron.legacy.data.autoaugment import ImageNetPolicy +from megatron.legacy.data.data_samplers import RandomSeedDataset +from PIL import Image, ImageFilter, ImageOps + + +class GaussianBlur(object): + """ + Apply Gaussian Blur to the PIL image. + """ + def __init__(self, p=0.5, radius_min=0.1, radius_max=2.): + self.prob = p + self.radius_min = radius_min + self.radius_max = radius_max + + def __call__(self, img): + do_it = random.random() <= self.prob + if not do_it: + return img + + return img.filter( + ImageFilter.GaussianBlur( + radius=random.uniform(self.radius_min, self.radius_max) + ) + ) + + +class Solarization(object): + """ + Apply Solarization to the PIL image. + """ + def __init__(self, p): + self.p = p + + def __call__(self, img): + if random.random() < self.p: + return ImageOps.solarize(img) + else: + return img + + +class ClassificationTransform(): + def __init__(self, image_size, train=True): + args = get_args() + assert args.fp16 or args.bf16 + self.data_type = torch.half if args.fp16 else torch.bfloat16 + if train: + self.transform = T.Compose([ + T.RandomResizedCrop(image_size), + T.RandomHorizontalFlip(), + T.ColorJitter(0.4, 0.4, 0.4, 0.1), + ImageNetPolicy(), + T.ToTensor(), + T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + T.ConvertImageDtype(self.data_type) + ]) + else: + self.transform = T.Compose([ + T.Resize(image_size), + T.CenterCrop(image_size), + T.ToTensor(), + T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + T.ConvertImageDtype(self.data_type) + ]) + + def __call__(self, input): + output = self.transform(input) + return output + + +class InpaintingTransform(): + def __init__(self, image_size, train=True): + + args = get_args() + self.mask_factor = args.mask_factor + self.mask_type = args.mask_type + self.image_size = image_size + self.patch_size = args.patch_dim + self.mask_size = int(self.mask_factor*(image_size[0]/self.patch_size)*(image_size[1]/self.patch_size)) + self.train = train + assert args.fp16 or args.bf16 + self.data_type = torch.half if args.fp16 else torch.bfloat16 + + if self.train: + self.transform = T.Compose([ + T.RandomResizedCrop(self.image_size), + T.RandomHorizontalFlip(), + T.ColorJitter(0.4, 0.4, 0.4, 0.1), + ImageNetPolicy(), + T.ToTensor(), + T.ConvertImageDtype(self.data_type) + ]) + else: + self.transform = T.Compose([ + T.Resize(self.image_size, interpolation=2), + T.CenterCrop(self.image_size), + T.ToTensor(), + T.ConvertImageDtype(self.data_type) + ]) + + def gen_mask(self, image_size, mask_size, mask_type, patch_size): + # output: mask as a list with indices for missing patches + action_list = [[0, 1], [0, -1], [1, 0], [-1, 0]] + assert image_size[0] == image_size[1] + img_size_patch = image_size[0] // patch_size + + # drop masked patches + mask = torch.zeros((image_size[0], image_size[1]), dtype=torch.float) + + if mask_type == 'random': + x = torch.randint(0, img_size_patch, ()) + y = torch.randint(0, img_size_patch, ()) + for i in range(mask_size): + r = torch.randint(0, len(action_list), ()) + x = torch.clamp(x + action_list[r][0], min=0, max=img_size_patch - 1) + y = torch.clamp(y + action_list[r][1], min=0, max=img_size_patch - 1) + x_offset = x * patch_size + y_offset = y * patch_size + mask[x_offset:x_offset+patch_size, y_offset:y_offset+patch_size] = 1 + else: + assert mask_type == 'row' + count = 0 + for x in reversed(range(img_size_patch)): + for y in reversed(range(img_size_patch)): + if (count < mask_size): + count += 1 + x_offset = x * patch_size + y_offset = y * patch_size + mask[x_offset:x_offset+patch_size, y_offset:y_offset+patch_size] = 1 + return mask + + def __call__(self, input): + trans_input = self.transform(input) + mask = self.gen_mask(self.image_size, self.mask_size, + self.mask_type, self.patch_size) + mask = mask.unsqueeze(dim=0) + return trans_input, mask + + +class DinoTransform(object): + def __init__(self, image_size, train=True): + args = get_args() + self.data_type = torch.half if args.fp16 else torch.bfloat16 + + flip_and_color_jitter = T.Compose([ + T.RandomHorizontalFlip(p=0.5), + T.RandomApply( + [T.ColorJitter(brightness=0.4, contrast=0.4, + saturation=0.2, hue=0.1)], + p=0.8 + ), + T.RandomGrayscale(p=0.2), + ]) + + if args.fp16 or args.bf16: + normalize = T.Compose([ + T.ToTensor(), + T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + T.ConvertImageDtype(self.data_type) + ]) + else: + normalize = T.Compose([ + T.ToTensor(), + T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + + # first global crop + scale_const = 0.4 + self.global_transform1 = T.Compose([ + T.RandomResizedCrop(image_size, + scale=(scale_const, 1), + interpolation=Image.BICUBIC), + flip_and_color_jitter, + GaussianBlur(1.0), + normalize + ]) + # second global crop + self.global_transform2 = T.Compose([ + T.RandomResizedCrop(image_size, + scale=(scale_const, 1), + interpolation=Image.BICUBIC), + flip_and_color_jitter, + GaussianBlur(0.1), + Solarization(0.2), + normalize + ]) + # transformation for the local small crops + self.local_crops_number = args.dino_local_crops_number + self.local_transform = T.Compose([ + T.RandomResizedCrop(args.dino_local_img_size, + scale=(0.05, scale_const), + interpolation=Image.BICUBIC), + flip_and_color_jitter, + GaussianBlur(p=0.5), + normalize + ]) + + def __call__(self, image): + crops = [] + crops.append(self.global_transform1(image)) + crops.append(self.global_transform2(image)) + for _ in range(self.local_crops_number): + crops.append(self.local_transform(image)) + return crops + + +def build_train_valid_datasets(data_path, image_size=224): + args = get_args() + + if args.vision_pretraining_type == 'classify': + train_transform = ClassificationTransform(image_size) + val_transform = ClassificationTransform(image_size, train=False) + elif args.vision_pretraining_type == 'inpaint': + train_transform = InpaintingTransform(image_size, train=False) + val_transform = InpaintingTransform(image_size, train=False) + elif args.vision_pretraining_type == 'dino': + train_transform = DinoTransform(image_size, train=True) + val_transform = ClassificationTransform(image_size, train=False) + else: + raise Exception('{} vit pretraining type is not supported.'.format( + args.vit_pretraining_type)) + + # training dataset + train_data_path = data_path[0] if len(data_path) <= 2 else data_path[2] + train_data = ImageFolder( + root=train_data_path, + transform=train_transform, + classes_fraction=args.classes_fraction, + data_per_class_fraction=args.data_per_class_fraction + ) + train_data = RandomSeedDataset(train_data) + + # validation dataset + val_data_path = data_path[1] + val_data = ImageFolder( + root=val_data_path, + transform=val_transform + ) + val_data = RandomSeedDataset(val_data) + + return train_data, val_data diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fp16_deprecated/loss_scaler.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fp16_deprecated/loss_scaler.py new file mode 100755 index 0000000..cb64aa9 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fp16_deprecated/loss_scaler.py @@ -0,0 +1,26 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""For backward compatibility, we need the class definitions to deserialize.""" + +class LossScaler: + def __init__(self, scale=1): + self.cur_scale = scale + +class DynamicLossScaler: + def __init__(self, + init_scale=2**32, + scale_factor=2., + scale_window=1000, + min_scale=1, + delayed_shift=1, + consecutive_hysteresis=False): + self.cur_scale = init_scale + self.cur_iter = 0 + self.last_overflow_iter = -1 + self.scale_factor = scale_factor + self.scale_window = scale_window + self.min_scale = min_scale + self.delayed_shift = delayed_shift + self.cur_hysteresis = delayed_shift + self.consecutive_hysteresis = consecutive_hysteresis + diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/__init__.py new file mode 100644 index 0000000..87cceac --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/__init__.py @@ -0,0 +1,75 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import os +import pathlib +import subprocess + +from torch.utils import cpp_extension + +# Setting this param to a list has a problem of generating different +# compilation commands (with diferent order of architectures) and +# leading to recompilation of fused kernels. Set it to empty string +# to avoid recompilation and assign arch flags explicity in +# extra_cuda_cflags below +os.environ["TORCH_CUDA_ARCH_LIST"] = "" + + +def load(args): + + # Check if cuda 11 is installed for compute capability 8.0 + cc_flag = [] + _, bare_metal_major, bare_metal_minor = _get_cuda_bare_metal_version( + cpp_extension.CUDA_HOME + ) + if int(bare_metal_major) >= 11: + cc_flag.append('-gencode') + cc_flag.append('arch=compute_80,code=sm_80') + if int(bare_metal_minor) >= 8: + cc_flag.append('-gencode') + cc_flag.append('arch=compute_90,code=sm_90') + + # Build path + srcpath = pathlib.Path(__file__).parent.absolute() + buildpath = srcpath / "build" + _create_build_dir(buildpath) + + # Helper function to build the kernels. + def _cpp_extention_load_helper(name, sources, extra_cuda_flags): + return cpp_extension.load( + name=name, + sources=sources, + build_directory=buildpath, + extra_cflags=[ + "-O3", + ], + extra_cuda_cflags=[ + "-O3", + "-gencode", + "arch=compute_70,code=sm_70", + "--use_fast_math", + ] + + extra_cuda_flags + + cc_flag, + verbose=(args.rank == 0), + ) + + +def _get_cuda_bare_metal_version(cuda_dir): + raw_output = subprocess.check_output( + [cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True + ) + output = raw_output.split() + release_idx = output.index("release") + 1 + release = output[release_idx].split(".") + bare_metal_major = release[0] + bare_metal_minor = release[1][0] + + return raw_output, bare_metal_major, bare_metal_minor + + +def _create_build_dir(buildpath): + try: + os.mkdir(buildpath) + except OSError: + if not os.path.isdir(buildpath): + print(f"Creation of the build directory {buildpath} failed") diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/compat.h b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/compat.h new file mode 100644 index 0000000..5495d78 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/compat.h @@ -0,0 +1,17 @@ +/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. */ + +/*This code is copied fron NVIDIA apex: + * https://github.com/NVIDIA/apex + * with minor changes. */ + + + +#ifndef TORCH_CHECK +#define TORCH_CHECK AT_CHECK +#endif + +#ifdef VERSION_GE_1_3 +#define DATA_PTR data_ptr +#else +#define DATA_PTR data +#endif diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/tests/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/tests/test_fused_kernels.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/tests/test_fused_kernels.py new file mode 100644 index 0000000..adb9ac6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/tests/test_fused_kernels.py @@ -0,0 +1,388 @@ +import math + +import torch +from torch.nn import LayerNorm + +from megatron.legacy.model.enums import AttnMaskType +from megatron.legacy.model.fused_layer_norm import MixedFusedLayerNorm +from megatron.legacy.model.fused_softmax import FusedScaleMaskSoftmax +from megatron.legacy.model.utils import attention_mask_func +from megatron.legacy.fused_kernels import load + +def test_load_fused_kernels(): + try: + import fused_layer_norm_cuda + import scaled_masked_softmax_cuda + import scaled_upper_triang_masked_softmax_cuda + import torch + + print("[Success] load_fused_kernels") + except ImportError as e: + print("[Fail] load_fused_kernels") + raise e + +def test_fused_softmax(): + bert = BertModel.from_pretrained("bert-base-cased").cuda().half() + tokenizer = BertTokenizer.from_pretrained("bert-base-cased") + test_text = ( + "Hello. How are you? I am fine thank you and you? yes Good. " + "hi hi hi hi hi hi hi hi hi hi hi hi hi" # 32 + ) + + tokens = tokenizer( + [test_text] * 4, + return_tensors="pt", + ) + + embedding_output = bert.embeddings( + input_ids=tokens["input_ids"].cuda(), + position_ids=None, + token_type_ids=tokens["token_type_ids"].cuda(), + inputs_embeds=None, + past_key_values_length=0, + ) + + # (bsz, 1, 1, seq_len) + mask = bert.get_extended_attention_mask( + attention_mask=tokens["attention_mask"].cuda(), + input_shape=tokens["input_ids"].shape, + device=bert.device, + ) + # (bsz, 1, seq_len, seq_len) + mask = mask.repeat(1, 1, mask.size()[-1], 1) + + attention = bert.encoder.layer[0].attention.self + key_layer = attention.transpose_for_scores(attention.key(embedding_output)) + query_layer = attention.transpose_for_scores(attention.query(embedding_output)) + + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + attention_scores /= math.sqrt(key_layer.size()[-1]) + + fused_softmax = ( + FusedScaleMaskSoftmax( + input_in_fp16=True, + input_in_bf16=False, + mask_func=attention_mask_func, + scale=None, + softmax_in_fp32=False, + attn_mask_type=AttnMaskType.padding, + scaled_masked_softmax_fusion=True, + ) + .cuda() + .half() + ) + + fused_softmax_output = fused_softmax( + attention_scores, + (mask != 0), + ) + + torch_softmax = ( + FusedScaleMaskSoftmax( + input_in_fp16=True, + input_in_bf16=False, + mask_func=attention_mask_func, + scale=None, + softmax_in_fp32=False, + attn_mask_type=AttnMaskType.padding, + scaled_masked_softmax_fusion=False, + ) + .cuda() + .half() + ) + + torch_softmax_output = torch_softmax( + attention_scores, + (mask != 0), + ) + + test_result = (fused_softmax_output - torch_softmax_output).abs() + + while test_result.dim() != 1: + test_result = test_result.mean(dim=-1) + + diff = test_result.mean(dim=-1) + + if diff <= 1e-3: + print( + f"\n[Success] test_fused_softmax" + f"\n > mean_difference={diff}" + f"\n > fused_values={fused_softmax_output[-1][-1][-1][:5].tolist()}" + f"\n > torch_values={torch_softmax_output[-1][-1][-1][:5].tolist()}" + ) + else: + print( + f"\n[Fail] test_fused_softmax" + f"\n > mean_difference={diff}, " + f"\n > fused_values={fused_softmax_output[-1][-1][-1][:5].tolist()}, " + f"\n > torch_values={torch_softmax_output[-1][-1][-1][:5].tolist()}" + ) + + +def test_fused_upper_triangle_mask_softmax(): + gpt = GPT2Model.from_pretrained("gpt2").cuda().half() + tokenizer = GPT2Tokenizer.from_pretrained("gpt2") + test_text = ( + "Hello. How are you? I am fine thank you and you? yes Good. " + "hi hi hi hi hi hi hi" # 24 + ) + + tokens = tokenizer( + [test_text] * 4, + return_tensors="pt", + ) + + attention_mask = tokens["attention_mask"].cuda() + attention_mask = attention_mask.view(attention_mask.size(0), -1) + attention_mask = attention_mask[:, None, None, :] + attention_mask = (1.0 - attention_mask) * -10000.0 + attention_mask = attention_mask.repeat(1, 1, attention_mask.size()[-1], 1) + attn = gpt.h[0] + + hidden_states = gpt.wte(tokens["input_ids"].cuda()) + q, k, v = attn.attn.c_attn(hidden_states).split(768, dim=-1) + q = attn.attn._split_heads(q, attn.attn.num_heads, attn.attn.head_dim) + k = attn.attn._split_heads(k, attn.attn.num_heads, attn.attn.head_dim) + attn_weights = torch.matmul(q, k.transpose(-1, -2)) + + sq, sk = q.size(-2), k.size(-2) + causal_mask = attn.attn.bias[:, :, sk - sq : sk, :sk].bool() + total_mask = ~(causal_mask & (attention_mask == 0)) + """ + tensor([[[[False, True, True, ..., True, True, True], + [False, False, True, ..., True, True, True], + [False, False, False, ..., True, True, True], + ..., + [False, False, False, ..., False, True, True], + [False, False, False, ..., False, False, True], + [False, False, False, ..., False, False, False]]] + """ + + fused_softmax = ( + FusedScaleMaskSoftmax( + input_in_fp16=True, + input_in_bf16=False, + mask_func=attention_mask_func, + scale=None, + softmax_in_fp32=False, + attn_mask_type=AttnMaskType.causal, + scaled_masked_softmax_fusion=True, + ) + .cuda() + .half() + ) + + fused_softmax_output = fused_softmax( + attn_weights, + total_mask, + ) + + torch_softmax = ( + FusedScaleMaskSoftmax( + input_in_fp16=True, + input_in_bf16=False, + mask_func=attention_mask_func, + scale=None, + softmax_in_fp32=False, + attn_mask_type=AttnMaskType.causal, + scaled_masked_softmax_fusion=False, + ) + .cuda() + .half() + ) + + torch_softmax_output = torch_softmax( + attn_weights, + total_mask, + ) + + test_result = (fused_softmax_output - torch_softmax_output).abs() + + while test_result.dim() != 1: + test_result = test_result.mean(dim=-1) + + diff = test_result.mean(dim=-1) + + if diff <= 1e-3: + print( + f"\n[Success] test_fused_upper_triangle_mask_softmax" + f"\n > mean_difference={diff}" + f"\n > fused_values={fused_softmax_output[-1][-1][-1][:5].tolist()}" + f"\n > torch_values={torch_softmax_output[-1][-1][-1][:5].tolist()}" + ) + else: + print( + f"\n[Fail] test_fused_upper_triangle_mask_softmax" + f"\n > mean_difference={diff}, " + f"\n > fused_values={fused_softmax_output[-1][-1][-1][:5].tolist()}, " + f"\n > torch_values={torch_softmax_output[-1][-1][-1][:5].tolist()}" + ) + + +def test_layer_norm(): + bert = BertModel.from_pretrained("bert-base-cased").cuda().half() + tokenizer = BertTokenizer.from_pretrained("bert-base-cased") + test_text = ( + "Hello. How are you? I am fine thank you and you? yes Good. " + "hi hi hi hi hi hi hi hi hi hi hi hi hi" # 32 + ) + + tokens = tokenizer( + [test_text] * 4, + return_tensors="pt", + ) + + # [bsz, seq_len, d_model] + embedding_output = ( + bert.embeddings( + input_ids=tokens["input_ids"].cuda(), + position_ids=None, + token_type_ids=tokens["token_type_ids"].cuda(), + inputs_embeds=None, + past_key_values_length=0, + ) + .cuda() + .half() + ) + + fused_layernorm_layer = ( + MixedFusedLayerNorm(normalized_shape=embedding_output.size(-1)).cuda().half() + ) + + torch_layernorm_layer = ( + LayerNorm(normalized_shape=embedding_output.size(-1)).cuda().half() + ) + + fused_output = fused_layernorm_layer(embedding_output) + torch_output = torch_layernorm_layer(embedding_output) + test_result = (fused_output - torch_output).abs() + + while test_result.dim() != 1: + test_result = test_result.mean(dim=-1) + + diff = test_result.mean(dim=-1) + + if diff <= 1e-3: + print( + f"\n[Success] test_layer_norm" + f"\n > mean_difference={diff}" + f"\n > fused_values={fused_output[-1][-1][:5].tolist()}" + f"\n > torch_values={torch_output[-1][-1][:5].tolist()}" + ) + else: + print( + f"\n[Fail] test_layer_norm" + f"\n > mean_difference={diff}, " + f"\n > fused_values={fused_output[-1][-1][:5].tolist()}, " + f"\n > torch_values={torch_output[-1][-1][:5].tolist()}" + ) + + +def attention_mask_func(attention_scores, attention_mask): + attention_scores.masked_fill_(attention_mask, -10000.0) + return attention_scores + + +def forward_torch_softmax(input, mask, scale): + input = input * scale + mask_output = attention_mask_func(input, mask) if mask is not None else input + probs = torch.nn.Softmax(dim=-1)(mask_output) + return probs + + +def test_masked_softmax_forward(): + import scaled_masked_softmax_cuda + + batch = 2 + attn = 16 + scale_t = torch.tensor([1.0]) + for qlen in [128, 256, 1024, 2048, 4096]: + for klen in [128, 256, 1024, 2048]: + inputs = torch.normal(0, 2, (batch, attn, qlen, klen), dtype=torch.float16, device='cuda:0') + masks = torch.randint(0, 2, (batch, 1, qlen, klen), dtype=torch.bool, device='cuda:0') + softmax_results = scaled_masked_softmax_cuda.forward(inputs, masks, scale_t[0].item()) + softmax_results_torch = forward_torch_softmax(inputs, masks, scale_t[0].item()) + error = (softmax_results_torch - softmax_results).abs().max() + assert error < 1e-3 + +def test_masked_softmax_backward(): + import scaled_masked_softmax_cuda + + batch = 2 + attn = 16 + scale_t = torch.tensor([1.0]) + for qlen in [128, 256, 1024, 2048, 4096]: + for klen in [128, 256, 1024, 2048]: + inputs = torch.normal(0, 2, (batch, attn, qlen, klen), dtype=torch.float16, device='cuda:0') + backward = torch.rand_like(inputs, dtype=torch.float16, device='cuda:0') + masks = torch.randint(0, 2, (batch, 1, qlen, klen), dtype=torch.bool, device='cuda:0') + softmax_results = scaled_masked_softmax_cuda.forward(inputs, masks, scale_t[0].item()) + back_grad = scaled_masked_softmax_cuda.backward(backward, softmax_results, scale_t[0].item()) + + inputs.requires_grad = True + softmax_results_torch = forward_torch_softmax(inputs, masks, scale_t[0].item()) + softmax_results_torch.backward(backward) + error = (back_grad - inputs.grad).abs().max() + assert error < 1e-3 + + +def test_allmasked_softmax_forward(): + import scaled_masked_softmax_cuda + + batch = 2 + attn = 16 + scale_t = torch.tensor([1.0]) + for qlen in [128, 256, 1024, 2048, 4096]: + for klen in [128, 256, 1024, 2048]: + inputs = torch.normal(0, 2, (batch, attn, qlen, klen), dtype=torch.float16, device='cuda:0') + masks = torch.ones((batch, 1, qlen, klen), dtype=torch.bool, device='cuda:0') + softmax_results = scaled_masked_softmax_cuda.forward(inputs, masks, scale_t[0].item()) + softmax_results_torch = torch.zeros_like(inputs) + error = (softmax_results_torch - softmax_results).abs().max() + assert error == 0.0 + + +def test_allmasked_softmax_backward(): + import scaled_masked_softmax_cuda + + batch = 2 + attn = 16 + scale_t = torch.tensor([1.0]) + for qlen in [128, 256, 1024, 2048, 4096]: + for klen in [128, 256, 1024, 2048]: + inputs = torch.normal(0, 2, (batch, attn, qlen, klen), dtype=torch.float16, device='cuda:0') + backward = torch.rand_like(inputs, dtype=torch.float16, device='cuda:0') + masks = torch.ones((batch, 1, qlen, klen), dtype=torch.bool, device='cuda:0') + softmax_results = scaled_masked_softmax_cuda.forward(inputs, masks, scale_t[0].item()) + back_grad = scaled_masked_softmax_cuda.backward(backward, softmax_results, scale_t[0].item()) + inputs.requires_grad = True + softmax_results_torch = forward_torch_softmax(inputs, masks, scale_t[0].item()) + softmax_results_torch.backward(backward) + error = (back_grad - inputs.grad).abs().max() + assert error < 1e-3 + + +if __name__ == "__main__": + try: + from transformers import BertTokenizer, GPT2Tokenizer + from transformers.models.bert.modeling_bert import BertModel + from transformers.models.gpt2.modeling_gpt2 import GPT2Model + import transformers + + transformers.logging.set_verbosity( + transformers.logging.FATAL, + ) + + except: + print("\n[Fail] Please install `transformers` package to test fused kernels\n") + exit(-1) + + load() + test_masked_softmax_forward() + test_masked_softmax_backward() + test_allmasked_softmax_forward() + test_allmasked_softmax_backward() + test_load_fused_kernels() + test_fused_softmax() + test_fused_upper_triangle_mask_softmax() + test_layer_norm() diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/type_shim.h b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/type_shim.h new file mode 100644 index 0000000..d60a6f8 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/fused_kernels/type_shim.h @@ -0,0 +1,103 @@ +/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. */ + + +#include +#include "compat.h" + + +#define DISPATCH_HALF_AND_BFLOAT(TYPE, NAME, ...) \ + switch(TYPE) \ + { \ + case at::ScalarType::Half: \ + { \ + using scalar_t = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::BFloat16: \ + { \ + using scalar_t = at::BFloat16; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } + + +#define DISPATCH_HALF_BFLOAT_AND_FLOAT(TYPE, NAME, ...) \ + switch(TYPE) \ + { \ + case at::ScalarType::Half: \ + { \ + using scalar_t = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::BFloat16: \ + { \ + using scalar_t = at::BFloat16; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Float: \ + { \ + using scalar_t = float; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } + + + +#define DISPATCH_FLOAT_HALF_AND_BFLOAT_INOUT_TYPES(TYPEIN, TYPEOUT, NAME, ...) \ + switch(TYPEIN) \ + { \ + case at::ScalarType::Float: \ + { \ + using scalar_t_in = float; \ + switch(TYPEOUT) \ + { \ + case at::ScalarType::Float: \ + { \ + using scalar_t_out = float; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Half: \ + { \ + using scalar_t_out = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::BFloat16: \ + { \ + using scalar_t_out = at::BFloat16; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPEOUT), "'"); \ + } \ + break; \ + } \ + case at::ScalarType::Half: \ + { \ + using scalar_t_in = at::Half; \ + using scalar_t_out = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::BFloat16: \ + { \ + using scalar_t_in = at::BFloat16; \ + using scalar_t_out = at::BFloat16; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPEIN), "'"); \ + } + diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/indexer.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/indexer.py new file mode 100644 index 0000000..75851ad --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/indexer.py @@ -0,0 +1,129 @@ +import sys +import time +import torch +import torch.distributed as dist + +from megatron.training import get_args, print_rank_0 +from megatron.core import mpu +from megatron.training.checkpointing import load_biencoder_checkpoint +from megatron.legacy.data.orqa_wiki_dataset import get_open_retrieval_wiki_dataset +from megatron.legacy.data.orqa_wiki_dataset import get_open_retrieval_batch +from megatron.legacy.data.biencoder_dataset_utils import get_one_epoch_dataloader +from megatron.legacy.data.realm_index import detach, OpenRetreivalDataStore +from megatron.legacy.model.biencoder_model import get_model_provider +from megatron.training import get_model + + +class IndexBuilder(object): + """ + Object for taking one pass over a dataset and creating a BlockData of its + embeddings + """ + def __init__(self): + args = get_args() + self.model = None + self.dataloader = None + self.evidence_embedder_obj = None + self.biencoder_shared_query_context_model = \ + args.biencoder_shared_query_context_model + + # need to know whether we're using a REALM checkpoint (args.load) + # or ICT checkpoint + assert not (args.load and args.ict_load) + + self.log_interval = args.indexer_log_interval + self.batch_size = args.indexer_batch_size + + self.load_attributes() + self.is_main_builder = mpu.get_data_parallel_rank() == 0 + self.num_total_builders = mpu.get_data_parallel_world_size() + self.iteration = self.total_processed = 0 + + def load_attributes(self): + """ + Load the necessary attributes: model, dataloader and empty BlockData + """ + only_context_model = True + if self.biencoder_shared_query_context_model: + only_context_model = False + + model = get_model(get_model_provider(only_context_model=\ + only_context_model, biencoder_shared_query_context_model=\ + self.biencoder_shared_query_context_model)) + + self.model = load_biencoder_checkpoint(model, + only_context_model=only_context_model) + + assert len(self.model) == 1 + self.model[0].eval() + + self.dataset = get_open_retrieval_wiki_dataset() + self.dataloader = iter(get_one_epoch_dataloader(self.dataset, \ + self.batch_size)) + + self.evidence_embedder_obj = OpenRetreivalDataStore( \ + load_from_path=False) + + def track_and_report_progress(self, batch_size): + """ + Utility function for tracking progress + """ + self.iteration += 1 + self.total_processed += batch_size * self.num_total_builders + if self.is_main_builder and self.iteration % self.log_interval == 0: + print('Batch {:10d} | Total {:10d}'.format(self.iteration, + self.total_processed), flush=True) + + def build_and_save_index(self): + """ + Goes through one epoch of the dataloader and adds all data to this + instance's BlockData. + + The copy of BlockData is saved as a shard, which when run in a + distributed setting will be consolidated by the rank 0 process + and saved as a final pickled BlockData. + """ + assert len(self.model) == 1 + unwrapped_model = self.model[0] + + while not hasattr(unwrapped_model, 'embed_text'): + unwrapped_model = unwrapped_model.module + + while True: + try: + # batch also has query_tokens and query_pad_data + row_id, context_tokens, context_mask, context_types, \ + context_pad_mask = get_open_retrieval_batch( \ + self.dataloader) + except (StopIteration, IndexError): + break + + # TODO: can we add with torch.no_grad() to reduce memory usage + # detach, separate fields and add to BlockData + assert context_mask.dtype == torch.bool + context_logits = unwrapped_model.embed_text( + unwrapped_model.context_model, context_tokens, context_mask, + context_types) + + context_logits = detach(context_logits) + row_id = detach(row_id) + + self.evidence_embedder_obj.add_block_data(row_id, context_logits) + self.track_and_report_progress(batch_size=len(row_id)) + + # This process signals to finalize its shard and then synchronize with + # the other processes + self.evidence_embedder_obj.save_shard() + torch.distributed.barrier() + del self.model + + # rank 0 process builds the final copy + if self.is_main_builder: + self.evidence_embedder_obj.merge_shards_and_save() + # make sure that every single piece of data was embedded + assert len(self.evidence_embedder_obj.embed_data) == \ + len(self.dataset) + self.evidence_embedder_obj.clear() + + # complete building the final copy + torch.distributed.barrier() diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/__init__.py new file mode 100644 index 0000000..cb010e5 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +from .fused_layer_norm import MixedFusedLayerNorm as LayerNorm +from .rms_norm import RMSNorm + +from .bert_model import BertModel +from .gpt_model import GPTModel +from .t5_model import T5Model +from .language_model import get_language_model +from .module import Float16Module diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/bert_model.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/bert_model.py new file mode 100644 index 0000000..eca22f0 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/bert_model.py @@ -0,0 +1,257 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +"""BERT model.""" + +import torch + +from megatron.training import get_args +from megatron.core import tensor_parallel +from megatron.legacy.model.enums import AttnMaskType +from megatron.legacy.model.language_model import parallel_lm_logits +from megatron.legacy.model.language_model import get_language_model +from megatron.legacy.model.utils import get_norm +from megatron.legacy.model.utils import openai_gelu, erf_gelu +from megatron.legacy.model.utils import get_linear_layer +from megatron.legacy.model.utils import init_method_normal +from megatron.legacy.model.utils import scaled_init_method_normal +from .module import MegatronModule + + +def bert_extended_attention_mask(attention_mask): + # We create a 3D attention mask from a 2D tensor mask. + # [b, 1, s] + attention_mask_b1s = attention_mask.unsqueeze(1) + # [b, s, 1] + attention_mask_bs1 = attention_mask.unsqueeze(2) + # [b, s, s] + attention_mask_bss = attention_mask_b1s * attention_mask_bs1 + # [b, 1, s, s] + extended_attention_mask = attention_mask_bss.unsqueeze(1) + + # Convert attention mask to binary: + extended_attention_mask = (extended_attention_mask < 0.5) + + return extended_attention_mask + +def bert_position_ids(token_ids): + # Create position ids + seq_length = token_ids.size(1) + position_ids = torch.arange(seq_length, dtype=torch.long, + device=token_ids.device) + position_ids = position_ids.unsqueeze(0).expand_as(token_ids) + + return position_ids + + +class BertLMHead(MegatronModule): + """Masked LM head for Bert + + Args: + config: TransformerConfig object + mpu_vocab_size: model parallel size of vocabulary. + parallel_output: whether output logits being distributed or not. + """ + + def __init__(self, mpu_vocab_size, config, parallel_output): + super().__init__(config=config) + + args = get_args() + self.bias = torch.nn.Parameter(torch.zeros(mpu_vocab_size)) + tensor_parallel.set_tensor_model_parallel_attributes(self.bias, True, 0, 1) + self.parallel_output = parallel_output + + self.dense = get_linear_layer(config.hidden_size, config.hidden_size, config.init_method) + setattr(self.dense.weight, 'sequence_parallel', config.sequence_parallel) + setattr(self.dense.bias, 'sequence_parallel', config.sequence_parallel) + + self.norm = get_norm(config) + self.gelu = torch.nn.functional.gelu + if args.openai_gelu: + self.gelu = openai_gelu + elif args.onnx_safe: + self.gelu = erf_gelu + + def forward(self, hidden_states, word_embeddings_weight): + hidden_states = self.dense(hidden_states) + hidden_states = self.gelu(hidden_states) + hidden_states = self.norm(hidden_states) + output = parallel_lm_logits(hidden_states, + word_embeddings_weight, + self.parallel_output, + bias=self.bias) + return output + + def load_state_dict(self, state_dict, strict=True): + """Customize load.""" + + # Handle renaming layernorm -> norm in component names + state_dict_ = {} + for key in state_dict.keys(): + newkey = key.replace("layernorm", "norm") + state_dict_[newkey] = state_dict[key] + + super().load_state_dict(state_dict_, strict) + + +def post_language_model_processing(lm_output, pooled_output, + lm_head, binary_head, + lm_labels, + logit_weights, + fp16_lm_cross_entropy): + # Output. + lm_logits = lm_head( + lm_output, logit_weights) + + binary_logits = None + if binary_head is not None: + binary_logits = binary_head(pooled_output) + + if lm_labels is None: + # [s b h] => [b s h] + return lm_logits.transpose(0,1).contiguous(), binary_logits + else: + # [b s] => [s b] + lm_labels = lm_labels.transpose(0,1).contiguous() + # lm_logits : [s, b, h] and lm_labels: [s, b] + if fp16_lm_cross_entropy: + assert lm_logits.dtype == torch.half + lm_loss = tensor_parallel.vocab_parallel_cross_entropy(lm_logits, lm_labels) + else: + lm_loss = tensor_parallel.vocab_parallel_cross_entropy(lm_logits.float(), + lm_labels) + # [s, b] => [b s] + lm_loss = lm_loss.transpose(0,1).contiguous() + return lm_loss, binary_logits + + +class BertModel(MegatronModule): + """Bert Language model.""" + + def __init__(self, + config, + num_tokentypes=2, + add_binary_head=True, + parallel_output=True, + pre_process=True, + post_process=True): + super().__init__(config=config) + args = get_args() + + # TODO this option is not yet implemented in BERT + assert args.untie_embeddings_and_output_weights is False + + self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy + self.add_binary_head = add_binary_head + self.parallel_output = parallel_output + self.pre_process = pre_process + self.post_process = post_process + + self.return_embeddings = args.output_bert_embeddings + if self.return_embeddings: + assert self.post_process and self.add_binary_head + + self.language_model, self._language_model_key = get_language_model( + config=config, + num_tokentypes=num_tokentypes, + add_pooler=self.add_binary_head, + encoder_attn_mask_type=AttnMaskType.padding, + pre_process=self.pre_process, + post_process=self.post_process) + + self.initialize_word_embeddings() + if self.post_process: + self.lm_head = BertLMHead(self.shared_embedding_or_output_weight().size(0), config, parallel_output) + self._lm_head_key = 'lm_head' + self.binary_head = None + if self.add_binary_head: + self.binary_head = get_linear_layer(config.hidden_size, 2, + config.init_method) + self._binary_head_key = 'binary_head' + + def set_input_tensor(self, input_tensor): + """See megatron.legacy.model.transformer.set_input_tensor()""" + self.language_model.set_input_tensor(input_tensor) + + def forward(self, bert_model_input, attention_mask, + tokentype_ids=None, lm_labels=None): + + extended_attention_mask = bert_extended_attention_mask(attention_mask) + input_ids = bert_model_input + position_ids = bert_position_ids(input_ids) + + lm_output = self.language_model( + input_ids, + position_ids, + extended_attention_mask, + tokentype_ids=tokentype_ids + ) + + if self.post_process and self.add_binary_head: + lm_output, pooled_output = lm_output + + # Return pooled output (e.g., when computing Bert embeddings). + if self.return_embeddings: + + # Sum attention mask. + embeddings = torch.transpose(lm_output, 0, 1) + masks = torch.sum(attention_mask, dim=1) + + # Collect masked embeddings. + output = torch.zeros( + size=(embeddings.shape[0], embeddings.shape[2]), + dtype=torch.float32, + device=torch.cuda.current_device()) + for i, (embedding, mask) in enumerate(zip(embeddings, masks)): + output[i, :] = torch.mean(embedding[1: mask - 1], dim=0) + + return output + + else: + pooled_output = None + + if self.post_process: + return post_language_model_processing(lm_output, pooled_output, + self.lm_head, self.binary_head, + lm_labels, + self.shared_embedding_or_output_weight(), + self.fp16_lm_cross_entropy) + else: + return lm_output + + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """For easy load when model is combined with other heads, + add an extra key.""" + + state_dict_ = {} + state_dict_[self._language_model_key] \ + = self.language_model.state_dict_for_save_checkpoint(prefix=prefix, + keep_vars=keep_vars) + if self.post_process: + state_dict_[self._lm_head_key] \ + = self.lm_head.state_dict_for_save_checkpoint(prefix=prefix, + keep_vars=keep_vars) + if self.post_process and self.add_binary_head: + state_dict_[self._binary_head_key] \ + = self.binary_head.state_dict(prefix=prefix, keep_vars=keep_vars) + # Save word_embeddings. + if self.post_process and not self.pre_process: + state_dict_[self._word_embeddings_for_head_key] \ + = self.word_embeddings.state_dict(prefix=prefix, keep_vars=keep_vars) + return state_dict_ + + def load_state_dict(self, state_dict, strict=True): + """Customized load.""" + + self.language_model.load_state_dict( + state_dict[self._language_model_key], strict=strict) + if self.post_process: + self.lm_head.load_state_dict( + state_dict[self._lm_head_key], strict=strict) + if self.post_process and self.add_binary_head: + self.binary_head.load_state_dict( + state_dict[self._binary_head_key], strict=strict) + # Load word_embeddings. + if self.post_process and not self.pre_process: + self.word_embeddings.load_state_dict( + state_dict[self._word_embeddings_for_head_key], strict=strict) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/biencoder_model.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/biencoder_model.py new file mode 100644 index 0000000..8983cb5 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/biencoder_model.py @@ -0,0 +1,328 @@ +import os +import torch +import sys + +from megatron.training import get_args, print_rank_0, get_tokenizer +from megatron.core import mpu +from megatron.training.checkpointing import fix_query_key_value_ordering +from megatron.training.checkpointing import get_checkpoint_tracker_filename +from megatron.training.checkpointing import get_checkpoint_name +from megatron.legacy.model.bert_model import bert_position_ids +from megatron.legacy.model.enums import AttnMaskType +from megatron.legacy.model.language_model import get_language_model +from megatron.legacy.model.utils import get_linear_layer +from megatron.legacy.model.utils import init_method_normal +from megatron.legacy.model.utils import scaled_init_method_normal +from .module import MegatronModule + +def get_model_provider(only_query_model=False, only_context_model=False, + biencoder_shared_query_context_model=False): + + def model_provider(pre_process=True, post_process=True): + """Build the model.""" + + print_rank_0('building Bienoder model ...') + model = biencoder_model_provider(only_query_model=only_query_model, + only_context_model = only_context_model, + biencoder_shared_query_context_model = \ + biencoder_shared_query_context_model, + pre_process=pre_process, post_process=post_process) + + return model + + return model_provider + + +def biencoder_model_provider(only_query_model=False, + only_context_model=False, + biencoder_shared_query_context_model=False, + pre_process=True, + post_process=True): + """Build the model.""" + + assert mpu.get_tensor_model_parallel_world_size() == 1 and \ + mpu.get_pipeline_model_parallel_world_size() == 1, \ + "Model parallel size > 1 not supported for ICT" + + print_rank_0('building BiEncoderModel...') + + # simpler to just keep using 2 tokentypes since + # the LM we initialize with has 2 tokentypes + model = BiEncoderModel( + num_tokentypes=2, + parallel_output=False, + only_query_model=only_query_model, + only_context_model=only_context_model, + biencoder_shared_query_context_model=\ + biencoder_shared_query_context_model, + pre_process=pre_process, + post_process=post_process) + + return model + + +class BiEncoderModel(MegatronModule): + """Bert-based module for Biencoder model.""" + + def __init__(self, + num_tokentypes=1, + parallel_output=True, + only_query_model=False, + only_context_model=False, + biencoder_shared_query_context_model=False, + pre_process=True, + post_process=True): + super(BiEncoderModel, self).__init__() + args = get_args() + + bert_kwargs = dict( + num_tokentypes=num_tokentypes, + parallel_output=parallel_output, + pre_process=pre_process, + post_process=post_process) + + self.biencoder_shared_query_context_model = \ + biencoder_shared_query_context_model + assert not (only_context_model and only_query_model) + self.use_context_model = not only_query_model + self.use_query_model = not only_context_model + self.biencoder_projection_dim = args.biencoder_projection_dim + + if self.biencoder_shared_query_context_model: + self.model = PretrainedBertModel(**bert_kwargs) + self._model_key = 'shared_model' + self.query_model, self.context_model = self.model, self.model + else: + if self.use_query_model: + # this model embeds (pseudo-)queries - Embed_input in the paper + self.query_model = PretrainedBertModel(**bert_kwargs) + self._query_key = 'query_model' + + if self.use_context_model: + # this model embeds evidence blocks - Embed_doc in the paper + self.context_model = PretrainedBertModel(**bert_kwargs) + self._context_key = 'context_model' + + def set_input_tensor(self, input_tensor): + """See megatron.legacy.model.transformer.set_input_tensor()""" + # this is just a placeholder and will be needed when model + # parallelism will be used + # self.language_model.set_input_tensor(input_tensor) + return + + def forward(self, query_tokens, query_attention_mask, query_types, + context_tokens, context_attention_mask, context_types): + """Run a forward pass for each of the models and + return the respective embeddings.""" + + if self.use_query_model: + query_logits = self.embed_text(self.query_model, + query_tokens, + query_attention_mask, + query_types) + else: + raise ValueError("Cannot embed query without the query model.") + if self.use_context_model: + context_logits = self.embed_text(self.context_model, + context_tokens, + context_attention_mask, + context_types) + else: + raise ValueError("Cannot embed block without the block model.") + return query_logits, context_logits + + @staticmethod + def embed_text(model, tokens, attention_mask, token_types): + """Embed a batch of tokens using the model""" + logits = model(tokens, + attention_mask, + token_types) + return logits + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """Save dict with state dicts of each of the models.""" + state_dict_ = {} + if self.biencoder_shared_query_context_model: + state_dict_[self._model_key] = \ + self.model.state_dict_for_save_checkpoint( + prefix=prefix, keep_vars=keep_vars) + else: + if self.use_query_model: + state_dict_[self._query_key] = \ + self.query_model.state_dict_for_save_checkpoint( + prefix=prefix, keep_vars=keep_vars) + + if self.use_context_model: + state_dict_[self._context_key] = \ + self.context_model.state_dict_for_save_checkpoint( + prefix=prefix, keep_vars=keep_vars) + + return state_dict_ + + def load_state_dict(self, state_dict, strict=True): + """Load the state dicts of each of the models""" + if self.biencoder_shared_query_context_model: + print_rank_0("Loading shared query-context model") + self.model.load_state_dict(state_dict[self._model_key], \ + strict=strict) + else: + if self.use_query_model: + print_rank_0("Loading query model") + self.query_model.load_state_dict( \ + state_dict[self._query_key], strict=strict) + + if self.use_context_model: + print_rank_0("Loading context model") + self.context_model.load_state_dict( \ + state_dict[self._context_key], strict=strict) + + def init_state_dict_from_bert(self): + """Initialize the state from a pretrained BERT model + on iteration zero of ICT pretraining""" + args = get_args() + + if args.bert_load is None: + print_rank_0("bert-load argument is None") + return + + tracker_filename = get_checkpoint_tracker_filename(args.bert_load) + if not os.path.isfile(tracker_filename): + raise FileNotFoundError("Could not find BERT checkpoint") + with open(tracker_filename, 'r') as f: + iteration = int(f.read().strip()) + assert iteration > 0 + + checkpoint_name = get_checkpoint_name(args.bert_load, iteration, False) + if mpu.get_data_parallel_rank() == 0: + print('global rank {} is loading BERT checkpoint {}'.format( + torch.distributed.get_rank(), checkpoint_name)) + + # Load the checkpoint. + try: + state_dict = torch.load(checkpoint_name, map_location='cpu') + except ModuleNotFoundError: + from megatron.legacy.fp16_deprecated import loss_scaler + # For backward compatibility. + print_rank_0(' > deserializing using the old code structure ...') + sys.modules['fp16.loss_scaler'] = sys.modules[ + 'megatron.fp16_deprecated.loss_scaler'] + sys.modules['megatron.fp16.loss_scaler'] = sys.modules[ + 'megatron.fp16_deprecated.loss_scaler'] + state_dict = torch.load(checkpoint_name, map_location='cpu') + sys.modules.pop('fp16.loss_scaler', None) + sys.modules.pop('megatron.fp16.loss_scaler', None) + except BaseException: + print_rank_0('could not load the BERT checkpoint') + sys.exit() + + checkpoint_version = state_dict.get('checkpoint_version', 0) + + # load the LM state dict into each model + model_dict = state_dict['model']['language_model'] + + if self.biencoder_shared_query_context_model: + self.model.language_model.load_state_dict(model_dict) + fix_query_key_value_ordering(self.model, checkpoint_version) + else: + if self.use_query_model: + self.query_model.language_model.load_state_dict(model_dict) + # give each model the same ict_head to begin with as well + if self.biencoder_projection_dim > 0: + query_proj_state_dict = \ + self.state_dict_for_save_checkpoint()\ + [self._query_key]['projection_enc'] + fix_query_key_value_ordering(self.query_model, checkpoint_version) + + if self.use_context_model: + self.context_model.language_model.load_state_dict(model_dict) + if self.query_model is not None and \ + self.biencoder_projection_dim > 0: + self.context_model.projection_enc.load_state_dict\ + (query_proj_state_dict) + fix_query_key_value_ordering(self.context_model, checkpoint_version) + + +class PretrainedBertModel(MegatronModule): + """BERT-based encoder for queries or contexts used for + learned information retrieval.""" + + def __init__(self, num_tokentypes=2, + parallel_output=True, pre_process=True, post_process=True): + super(PretrainedBertModel, self).__init__() + + args = get_args() + tokenizer = get_tokenizer() + self.pad_id = tokenizer.pad + self.biencoder_projection_dim = args.biencoder_projection_dim + self.parallel_output = parallel_output + self.pre_process = pre_process + self.post_process = post_process + init_method = init_method_normal(args.init_method_std) + scaled_init_method = scaled_init_method_normal( + args.init_method_std, args.num_layers) + + self.language_model, self._language_model_key = get_language_model( + num_tokentypes=num_tokentypes, + add_pooler=False, + encoder_attn_mask_type=AttnMaskType.padding, + init_method=init_method, + scaled_init_method=scaled_init_method, + pre_process=self.pre_process, + post_process=self.post_process) + + if args.biencoder_projection_dim > 0: + self.projection_enc = get_linear_layer(args.hidden_size, + args.biencoder_projection_dim, + init_method) + self._projection_enc_key = 'projection_enc' + + def forward(self, input_ids, attention_mask, tokentype_ids=None): + extended_attention_mask = attention_mask.unsqueeze(1) + #extended_attention_mask = bert_extended_attention_mask(attention_mask) + position_ids = bert_position_ids(input_ids) + + lm_output = self.language_model(input_ids, + position_ids, + extended_attention_mask, + tokentype_ids=tokentype_ids) + # This mask will be used in average-pooling and max-pooling + pool_mask = (input_ids == self.pad_id).unsqueeze(2) + + # Taking the representation of the [CLS] token of BERT + pooled_output = lm_output[0, :, :] + + # Converting to float16 dtype + pooled_output = pooled_output.to(lm_output.dtype) + + # Output. + if self.biencoder_projection_dim: + pooled_output = self.projection_enc(pooled_output) + + return pooled_output + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """For easy load when model is combined with other heads, + add an extra key.""" + + state_dict_ = {} + state_dict_[self._language_model_key] \ + = self.language_model.state_dict_for_save_checkpoint( + prefix=prefix, keep_vars=keep_vars) + + if self.biencoder_projection_dim > 0: + state_dict_[self._projection_enc_key] = \ + self.projection_enc.state_dict(prefix=prefix, + keep_vars=keep_vars) + + return state_dict_ + + def load_state_dict(self, state_dict, strict=True): + """Customized load.""" + print_rank_0("loading pretrained weights") + self.language_model.load_state_dict( + state_dict[self._language_model_key], strict=strict) + + if self.biencoder_projection_dim > 0: + print_rank_0("loading projection head weights") + self.projection_enc.load_state_dict( + state_dict[self._projection_enc_key], strict=strict) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/classification.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/classification.py new file mode 100644 index 0000000..c9fe165 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/classification.py @@ -0,0 +1,101 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Classification model.""" + +import torch + +from megatron.training import get_args, print_rank_last +from megatron.legacy.model.enums import AttnMaskType +from megatron.legacy.model.bert_model import bert_extended_attention_mask, bert_position_ids +from megatron.legacy.model.language_model import get_language_model +from megatron.legacy.model.utils import get_linear_layer +from megatron.legacy.model.utils import init_method_normal +from megatron.legacy.model.utils import scaled_init_method_normal +from .module import MegatronModule + + +class Classification(MegatronModule): + + def __init__(self, + config, + num_classes, + num_tokentypes=2, + pre_process=True, + post_process=True): + super().__init__(config=config, share_embeddings_and_output_weights=False) + args = get_args() + + self.num_classes = num_classes + self.pre_process = pre_process + self.post_process = post_process + + self.language_model, self._language_model_key = get_language_model( + config=config, + num_tokentypes=num_tokentypes, + add_pooler=True, + encoder_attn_mask_type=AttnMaskType.padding, + pre_process=self.pre_process, + post_process=self.post_process) + + # Multi-choice head. + if self.post_process: + self.classification_dropout = torch.nn.Dropout(args.hidden_dropout) + self.classification_head = get_linear_layer(args.hidden_size, + self.num_classes, + config.init_method) + self._classification_head_key = 'classification_head' + + def set_input_tensor(self, input_tensor): + """See megatron.legacy.model.transformer.set_input_tensor()""" + self.language_model.set_input_tensor(input_tensor) + + def forward(self, model_input, attention_mask, tokentype_ids=None): + + extended_attention_mask = bert_extended_attention_mask(attention_mask) + input_ids = model_input + position_ids = bert_position_ids(input_ids) + + lm_output = self.language_model( + input_ids, + position_ids, + extended_attention_mask, + tokentype_ids=tokentype_ids + ) + + if self.post_process: + _, pooled_output = lm_output + classification_output = self.classification_dropout(pooled_output) + classification_logits = self.classification_head(classification_output) + + # Reshape back to separate choices. + classification_logits = classification_logits.view(-1, self.num_classes) + + return classification_logits + return lm_output + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """For easy load when model is combined with other heads, + add an extra key.""" + + state_dict_ = {} + state_dict_[self._language_model_key] \ + = self.language_model.state_dict_for_save_checkpoint(prefix=prefix, + keep_vars=keep_vars) + if self.post_process: + state_dict_[self._classification_head_key] \ + = self.classification_head.state_dict(prefix=prefix, keep_vars=keep_vars) + return state_dict_ + + def load_state_dict(self, state_dict, strict=True): + """Customized load.""" + + self.language_model.load_state_dict( + state_dict[self._language_model_key], strict=strict) + if self.post_process: + if self._classification_head_key in state_dict: + self.classification_head.load_state_dict( + state_dict[self._classification_head_key], strict=strict) + else: + print_rank_last('***WARNING*** could not find {} in the checkpoint, ' + 'initializing to random'.format( + self._classification_head_key)) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/enums.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/enums.py new file mode 100644 index 0000000..bc4e4aa --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/enums.py @@ -0,0 +1,21 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import enum + +class LayerType(enum.Enum): + encoder = 1 + decoder = 2 + retro_encoder = 3 + retro_decoder = 4 + retro_decoder_with_retriever = 5 + +class AttnType(enum.Enum): + self_attn = 1 + cross_attn = 2 + +class AttnMaskType(enum.Enum): + padding = 1 + causal = 2 + +# For backward compatibility with old model checkpoints +from megatron.core.enums import ModelType diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/fused_bias_gelu.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/fused_bias_gelu.py new file mode 100644 index 0000000..e00e631 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/fused_bias_gelu.py @@ -0,0 +1,44 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import torch +from megatron.core.jit import jit_fuser + + +###### BIAS GELU FUSION/ NO AUTOGRAD ################ +# 1/sqrt(2*pi)-> 0.3989423 +# 1/sqrt(2) -> 0.70710678 +# sqrt(2/pi) -> 0.79788456 +# this function is tanh approximation of gelu +# actual gelu is: +# x * 0.5 * (1.0 + torch.erf(x * 0.70710678)) + +@jit_fuser +def bias_gelu(bias, y): + x = bias + y + return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))) + +# gradient of tanh approximation of gelu +# gradient of actual gelu is: +# 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 * x * x) +@jit_fuser +def bias_gelu_back(g, bias, y): + x = bias + y + tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)) + # sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243 + ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (1 + tanh_out) + return ff*g + +class GeLUFunction(torch.autograd.Function): + @staticmethod + # bias is an optional argument + def forward(ctx, input, bias): + ctx.save_for_backward(input, bias) + return bias_gelu(bias, input) + + @staticmethod + def backward(ctx, grad_output): + input, bias = ctx.saved_tensors + tmp = bias_gelu_back(grad_output, bias, input) + return tmp, tmp + +bias_gelu_impl = GeLUFunction.apply diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/fused_layer_norm.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/fused_layer_norm.py new file mode 100644 index 0000000..f076302 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/fused_layer_norm.py @@ -0,0 +1,96 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""This code is copied fron NVIDIA apex: + https://github.com/NVIDIA/apex + with some changes. """ + +import numbers +import torch +from torch.nn.parameter import Parameter +from torch.nn import init +import importlib + +from megatron.core.utils import make_viewless_tensor + +try: + from apex.contrib.layer_norm.layer_norm import FastLayerNormFN + HAVE_PERSIST_LAYER_NORM = True +except: + HAVE_PERSIST_LAYER_NORM = False + +try: + from apex.normalization.fused_layer_norm import fused_layer_norm_affine +except: + fused_layer_norm_affine = None + +global fused_layer_norm_cuda +fused_layer_norm_cuda = None + + +class MixedFusedLayerNorm(torch.nn.Module): + + def __init__(self, normalized_shape, eps=1e-5, + no_persist_layer_norm=True, + sequence_parallel=False, + apply_layernorm_1p=False): + super(MixedFusedLayerNorm, self).__init__() + + self.apply_layernorm_1p = apply_layernorm_1p + + global fused_layer_norm_cuda + fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda") + + # List of hiddens sizes supported in the persistent layer norm kernel + # If the hidden size is not supported, fall back to the non-persistent + # kernel. + persist_ln_hidden_sizes = [1024, 1536, 2048, 2304, 3072, 3840, 4096, + 5120, 6144, 8192, 10240, 12288, 12800, 15360, 16384, 18432, 20480, + 24576, 25600, 30720, 32768, 40960, 49152, 65536] + if normalized_shape not in persist_ln_hidden_sizes or \ + not HAVE_PERSIST_LAYER_NORM: + no_persist_layer_norm = True + + if isinstance(normalized_shape, numbers.Integral): + normalized_shape = (normalized_shape,) + self.normalized_shape = torch.Size(normalized_shape) + self.eps = eps + self.weight = Parameter(torch.Tensor(*normalized_shape)) + self.bias = Parameter(torch.Tensor(*normalized_shape)) + self.reset_parameters() + self.no_persist_layer_norm = no_persist_layer_norm + self.sequence_parallel = sequence_parallel + + # set sequence parallelism flag on weight and bias parameters + setattr(self.weight, 'sequence_parallel', self.sequence_parallel) + setattr(self.bias, 'sequence_parallel', self.sequence_parallel) + + + def reset_parameters(self): + + if self.apply_layernorm_1p: + init.zeros_(self.weight) + init.zeros_(self.bias) + else: + init.ones_(self.weight) + init.zeros_(self.bias) + + def forward(self, input): + + weight = self.weight + 1 if self.apply_layernorm_1p else self.weight + + if self.no_persist_layer_norm: + assert fused_layer_norm_affine is not None, \ + "fused_layer_norm_affine is not available, please install apex from https://github.com/NVIDIA/apex" + return fused_layer_norm_affine(input, weight, self.bias, self.normalized_shape, eps=self.eps) + else: + output = FastLayerNormFN.apply(input, weight, self.bias, self.eps) + + # Apex's fast layer norm function outputs a 'view' tensor (i.e., has + # a populated '_base' field). This will result in schedule.py's + # deallocate_output_tensor() throwing an error, so a viewless tensor is + # created to prevent this. + output = make_viewless_tensor(inp = output, + requires_grad = input.requires_grad, + keep_graph = True) + + return output diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/fused_softmax.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/fused_softmax.py new file mode 100644 index 0000000..1a62b6a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/fused_softmax.py @@ -0,0 +1,213 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + + +import torch +import torch.nn as nn +from megatron.legacy.model.enums import AttnMaskType + + +class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function): + """ + Fused operation which performs following three operations in sequence + 1. Scale the tensor. + 2. Apply upper triangular mask (typically used in gpt models). + 3. Perform softmax. + """ + + @staticmethod + def forward(ctx, inputs, scale): + import scaled_upper_triang_masked_softmax_cuda + + scale_t = torch.tensor([scale]) + softmax_results = scaled_upper_triang_masked_softmax_cuda.forward( + inputs, scale_t[0] + ) + + ctx.save_for_backward(softmax_results, scale_t) + return softmax_results + + @staticmethod + def backward(ctx, output_grads): + import scaled_upper_triang_masked_softmax_cuda + + softmax_results, scale_t = ctx.saved_tensors + input_grads = scaled_upper_triang_masked_softmax_cuda.backward( + output_grads, softmax_results, scale_t[0] + ) + + return input_grads, None + + +class ScaledMaskedSoftmax(torch.autograd.Function): + """ + Fused operation which performs following three operations in sequence + 1. Scale the tensor. + 2. Apply the mask. + 3. Perform softmax. + """ + + @staticmethod + def forward(ctx, inputs, mask, scale): + import scaled_masked_softmax_cuda + + scale_t = torch.tensor([scale]) + + softmax_results = scaled_masked_softmax_cuda.forward(inputs, mask, scale_t[0]) + ctx.save_for_backward(softmax_results, scale_t) + return softmax_results + + @staticmethod + def backward(ctx, output_grads): + import scaled_masked_softmax_cuda + + softmax_results, scale_t = ctx.saved_tensors + + input_grads = scaled_masked_softmax_cuda.backward( + output_grads, softmax_results, scale_t[0] + ) + return input_grads, None, None + + +class ScaledSoftmax(torch.autograd.Function): + """ + Fused operation which performs following two operations in sequence + 1. Scale the tensor. + 2. Perform softmax. + """ + + @staticmethod + def forward(ctx, inputs, scale): + import scaled_softmax_cuda + + scale_t = torch.tensor([scale]) + + softmax_results = scaled_softmax_cuda.forward( + inputs, scale_t[0] + ) + ctx.save_for_backward(softmax_results, scale_t) + return softmax_results + + @staticmethod + def backward(ctx, output_grads): + import scaled_softmax_cuda + + softmax_results, scale_t = ctx.saved_tensors + + input_grads = scaled_softmax_cuda.backward( + output_grads, softmax_results, scale_t[0] + ) + return input_grads, None, None + + +class FusedScaleMaskSoftmax(nn.Module): + """ + fused operation: scaling + mask + softmax + + Args: + input_in_fp16: flag to indicate if input in fp16 data format. + input_in_bf16: flag to indicate if input in bf16 data format. + attn_mask_type: attention mask type (pad or causal) + scaled_masked_softmax_fusion: flag to indicate user want to use softmax fusion + mask_func: mask function to be applied. + softmax_in_fp32: if true, softmax in performed at fp32 precision. + scale: scaling factor used in input tensor scaling. + """ + + def __init__( + self, + input_in_fp16, + input_in_bf16, + attn_mask_type, + scaled_masked_softmax_fusion, + mask_func, + softmax_in_fp32, + scale, + ): + super(FusedScaleMaskSoftmax, self).__init__() + self.input_in_fp16 = input_in_fp16 + self.input_in_bf16 = input_in_bf16 + assert not ( + self.input_in_fp16 and self.input_in_bf16 + ), "both fp16 and bf16 flags cannot be active at the same time." + self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16 + self.attn_mask_type = attn_mask_type + self.scaled_masked_softmax_fusion = scaled_masked_softmax_fusion + self.mask_func = mask_func + self.softmax_in_fp32 = softmax_in_fp32 + self.scale = scale + + assert ( + self.scale is None or softmax_in_fp32 + ), "softmax should be in fp32 when scaled" + + def forward(self, input, mask): + # [b, np, sq, sk] + assert input.dim() == 4 + + if self.is_kernel_available(mask, *input.size()): + return self.forward_fused_softmax(input, mask) + else: + return self.forward_torch_softmax(input, mask) + + def is_kernel_available(self, mask, b, np, sq, sk): + attn_batches = b * np + + if ( + self.scaled_masked_softmax_fusion # user want to fuse + and self.input_in_float16 # input must be fp16 + and 16 < sk <= 16384 # sk must be 16 ~ 16384 + and sq % 4 == 0 # sq must be divisor of 4 + and sk % 4 == 0 # sk must be divisor of 4 + and attn_batches % 4 == 0 # np * b must be divisor of 4 + ): + if 0 <= sk <= 16384: + batch_per_block = self.get_batch_per_block(sq, sk, b, np) + + if self.attn_mask_type == AttnMaskType.causal: + if attn_batches % batch_per_block == 0: + return True + else: + if sq % batch_per_block == 0: + return True + return False + + def forward_fused_softmax(self, input, mask): + b, np, sq, sk = input.size() + scale = self.scale if self.scale is not None else 1.0 + + if self.attn_mask_type == AttnMaskType.causal: + assert sq == sk, "causal mask is only for self attention" + + # input is 3D tensor (attn_batches, sq, sk) + input = input.view(-1, sq, sk) + probs = ScaledUpperTriangMaskedSoftmax.apply(input, scale) + return probs.view(b, np, sq, sk) + else: + # input is 4D tensor (b, np, sq, sk) + if mask is not None: + return ScaledMaskedSoftmax.apply(input, mask, scale) + else: + return ScaledSoftmax.apply(input, scale) + + def forward_torch_softmax(self, input, mask): + if self.input_in_float16 and self.softmax_in_fp32: + input = input.float() + + if self.scale is not None: + input = input * self.scale + mask_output = self.mask_func(input, mask) if mask is not None else input + probs = torch.nn.Softmax(dim=-1)(mask_output) + + if self.input_in_float16 and self.softmax_in_fp32: + if self.input_in_fp16: + probs = probs.half() + else: + probs = probs.bfloat16() + + return probs + + @staticmethod + def get_batch_per_block(sq, sk, b, np): + import scaled_masked_softmax_cuda + + return scaled_masked_softmax_cuda.get_batch_per_block(sq, sk, b, np) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/gpt_model.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/gpt_model.py new file mode 100644 index 0000000..8e38019 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/gpt_model.py @@ -0,0 +1,122 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +"""GPT-2 model.""" + +import torch + +from megatron.training import get_args +from megatron.core import tensor_parallel +from .module import MegatronModule + +from .enums import AttnMaskType +from .language_model import parallel_lm_logits +from .language_model import get_language_model + + +def post_language_model_processing(lm_output, labels, logit_weights, + parallel_output, + fp16_lm_cross_entropy): + + # Output. Format [s b h] + output = parallel_lm_logits( + lm_output, + logit_weights, + parallel_output) + + if labels is None: + # [s b h] => [b s h] + return output.transpose(0,1).contiguous() + else: + # [b s] => [s b] + labels = labels.transpose(0,1).contiguous() + if fp16_lm_cross_entropy: + assert output.dtype == torch.half + loss = tensor_parallel.vocab_parallel_cross_entropy(output, labels) + else: + loss = tensor_parallel.vocab_parallel_cross_entropy(output.float(), labels) + + # [s b] => [b, s] + loss = loss.transpose(0,1).contiguous() + return loss + + +class GPTModel(MegatronModule): + """GPT-2 Language model.""" + + def __init__(self, + config, + num_tokentypes=0, + parallel_output=True, + pre_process=True, + post_process=True): + args = get_args() + super().__init__(config=config, share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights) + + self.parallel_output = parallel_output + self.pre_process = pre_process + self.post_process = post_process + self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy + self.untie_embeddings_and_output_weights = args.untie_embeddings_and_output_weights + + self.language_model, self._language_model_key = get_language_model( + config=config, + num_tokentypes=num_tokentypes, + add_pooler=False, + encoder_attn_mask_type=AttnMaskType.causal, + pre_process=self.pre_process, + post_process=self.post_process) + + if not args.untie_embeddings_and_output_weights: + self.initialize_word_embeddings() + + def set_input_tensor(self, input_tensor): + """See megatron.legacy.model.transformer.set_input_tensor()""" + self.language_model.set_input_tensor(input_tensor) + + def forward(self, input_ids, position_ids, attention_mask, + retriever_input_ids=None, + retriever_position_ids=None, + retriever_attn_mask=None, + labels=None, tokentype_ids=None, inference_params=None): + + lm_output = self.language_model( + input_ids, + position_ids, + attention_mask, + retriever_input_ids=retriever_input_ids, + retriever_position_ids=retriever_position_ids, + retriever_attn_mask=retriever_attn_mask, + inference_params=inference_params) + + if self.post_process: + return post_language_model_processing( + lm_output, labels, + self.language_model.output_layer.weight if self.untie_embeddings_and_output_weights else self.shared_embedding_or_output_weight(), + self.parallel_output, + self.fp16_lm_cross_entropy) + else: + return lm_output + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + + state_dict_ = {} + state_dict_[self._language_model_key] \ + = self.language_model.state_dict_for_save_checkpoint( + prefix=prefix, keep_vars=keep_vars) + # Save word_embeddings. + if self.post_process and not self.pre_process and not self.untie_embeddings_and_output_weights: + state_dict_[self._word_embeddings_for_head_key] \ + = self.word_embeddings.state_dict(prefix=prefix, + keep_vars=keep_vars) + return state_dict_ + + def load_state_dict(self, state_dict, strict=True): + """Customized load.""" + + # Load word_embeddings. + if self.post_process and not self.pre_process and not self.untie_embeddings_and_output_weights: + self.word_embeddings.load_state_dict( + state_dict[self._word_embeddings_for_head_key], strict=strict) + if self._language_model_key in state_dict: + state_dict = state_dict[self._language_model_key] + self.language_model.load_state_dict(state_dict, strict=strict) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/language_model.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/language_model.py new file mode 100644 index 0000000..4fb5ae0 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/language_model.py @@ -0,0 +1,626 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +"""Transformer based language model.""" + +import torch +import torch.nn.functional as F + +from megatron.training import get_args +from megatron.core import mpu, tensor_parallel +from megatron.core.enums import ModelType +from megatron.core.models.common.embeddings.rotary_pos_embedding import RotaryEmbedding + +from .enums import AttnMaskType, LayerType +from .module import MegatronModule +from .transformer import ParallelTransformer +from .utils import get_linear_layer +from .utils import init_method_normal, scaled_init_method_normal + + +def parallel_lm_logits(input_, word_embeddings_weight, parallel_output, + bias=None): + """LM logits using word embedding weights.""" + args = get_args() + # Parallel logits. + if args.async_tensor_model_parallel_allreduce or\ + args.sequence_parallel: + input_parallel = input_ + model_parallel = mpu.get_tensor_model_parallel_world_size() > 1 + async_grad_allreduce = args.async_tensor_model_parallel_allreduce and \ + model_parallel and not args.sequence_parallel + else: + input_parallel = tensor_parallel.copy_to_tensor_model_parallel_region(input_) + async_grad_allreduce = False + + # Matrix multiply. + logits_parallel = tensor_parallel.linear_with_grad_accumulation_and_async_allreduce( + input=input_parallel, + weight=word_embeddings_weight, + bias=bias, + gradient_accumulation_fusion=args.gradient_accumulation_fusion, + async_grad_allreduce=async_grad_allreduce, + sequence_parallel=args.sequence_parallel) + # Gather if needed. + + if parallel_output: + return logits_parallel + + return tensor_parallel.gather_from_tensor_model_parallel_region(logits_parallel) + + +def get_language_model(config, num_tokentypes, add_pooler, + encoder_attn_mask_type, + add_encoder=True, + add_decoder=False, + decoder_attn_mask_type=AttnMaskType.causal, + pre_process=True, post_process=True): + """Build language model and return along with the key to save.""" + args = get_args() + if config.init_method is None: + config.init_method = init_method_normal(config.init_method_std) + + if config.output_layer_init_method is None: + config.output_layer_init_method = scaled_init_method_normal(config.init_method_std, + config.num_layers) + + # Language model. + language_model = TransformerLanguageModel( + config, + encoder_attn_mask_type, + num_tokentypes=num_tokentypes, + add_encoder=add_encoder, + add_decoder=add_decoder, + decoder_attn_mask_type=decoder_attn_mask_type, + add_pooler=add_pooler, + pre_process=pre_process, + post_process=post_process + ) + # key used for checkpoints. + language_model_key = 'language_model' + + return language_model, language_model_key + + +class Pooler(MegatronModule): + """Pooler layer. + + Pool hidden states of a specific token (for example start of the + sequence) and add a linear transformation followed by a tanh. + + Args: + hidden_size: hidden size + init_method: weight initialization method for the linear layer. + bias is set to zero. + """ + + def __init__(self, hidden_size, init_method): + super(Pooler, self).__init__() + args = get_args() + self.dense = get_linear_layer(hidden_size, hidden_size, init_method) + self.sequence_parallel = args.sequence_parallel + + + def forward(self, hidden_states, sequence_index=0): + # hidden_states: [s, b, h] + # sequence_index: index of the token to pool. + + # gather data along sequence dimensions + # same pooler is run on all tensor parallel nodes + if self.sequence_parallel: + hidden_states = tensor_parallel.gather_from_sequence_parallel_region( + hidden_states, + tensor_parallel_output_grad=False) + + pooled = hidden_states[sequence_index, :, :] + pooled = self.dense(pooled) + pooled = torch.tanh(pooled) + return pooled + + +class Embedding(MegatronModule): + """Language model embeddings. + + Args: + hidden_size: hidden size + vocab_size: vocabulary size + max_sequence_length: maximum size of sequence. This + is used for positional embedding + embedding_dropout_prob: dropout probability for embeddings + init_method: weight initialization method + num_tokentypes: size of the token-type embeddings. 0 value + will ignore this embedding + """ + + def __init__(self, + hidden_size, + vocab_size, + max_sequence_length, + embedding_dropout_prob, + config, + num_tokentypes=0): + super(Embedding, self).__init__() + + self.hidden_size = hidden_size + self.init_method = config.init_method + self.num_tokentypes = num_tokentypes + + args = get_args() + + # Word embeddings (parallel). + self.params_dtype = args.params_dtype + self.word_embeddings = tensor_parallel.VocabParallelEmbedding( + vocab_size, self.hidden_size, config=config, init_method=config.init_method) + self._word_embeddings_key = 'word_embeddings' + + # Position embedding (serial). + self.add_position_embedding = args.position_embedding_type == 'learned_absolute' + if self.add_position_embedding: + self.position_embeddings = torch.nn.Embedding( + max_sequence_length, self.hidden_size) + self._position_embeddings_key = 'position_embeddings' + # Initialize the position embeddings. + if args.perform_initialization: + self.init_method(self.position_embeddings.weight) + + # Token type embedding. + # Add this as an optional field that can be added through + # method call so we can load a pretrain model without + # token types and add them as needed. + self._tokentype_embeddings_key = 'tokentype_embeddings' + if self.num_tokentypes > 0: + self.tokentype_embeddings = torch.nn.Embedding(self.num_tokentypes, + self.hidden_size) + # Initialize the token-type embeddings. + if args.perform_initialization: + self.init_method(self.tokentype_embeddings.weight) + else: + self.tokentype_embeddings = None + + self.fp32_residual_connection = args.fp32_residual_connection + self.sequence_parallel = args.sequence_parallel + self.clone_scatter_output_in_embedding = args.clone_scatter_output_in_embedding + # Embeddings dropout + self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob) + + def zero_parameters(self): + """Zero out all parameters in embedding.""" + self.word_embeddings.weight.data.fill_(0) + self.word_embeddings.weight.shared = True + if self.add_position_embedding: + self.position_embeddings.weight.data.fill_(0) + self.position_embeddings.weight.shared = True + if self.num_tokentypes > 0: + self.tokentype_embeddings.weight.data.fill_(0) + self.tokentype_embeddings.weight.shared = True + + def add_tokentype_embeddings(self, num_tokentypes): + """Add token-type embedding. This function is provided so we can add + token-type embeddings in case the pretrained model does not have it. + This allows us to load the model normally and then add this embedding. + """ + if self.tokentype_embeddings is not None: + raise Exception('tokentype embeddings is already initialized') + if torch.distributed.get_rank() == 0: + print('adding embedding for {} tokentypes'.format(num_tokentypes), + flush=True) + self.num_tokentypes = num_tokentypes + self.tokentype_embeddings = torch.nn.Embedding(num_tokentypes, + self.hidden_size) + # Initialize the token-type embeddings. + args = get_args() + self.init_method(self.tokentype_embeddings.weight) + + def forward(self, input_ids, position_ids, tokentype_ids=None): + # Embeddings. + words_embeddings = self.word_embeddings(input_ids) + if self.add_position_embedding: + position_embeddings = self.position_embeddings(position_ids) + embeddings = words_embeddings + position_embeddings + else: + embeddings = words_embeddings + + if tokentype_ids is not None: + assert self.tokentype_embeddings is not None + embeddings = embeddings + self.tokentype_embeddings(tokentype_ids) + else: + assert self.tokentype_embeddings is None + + # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + + # If the input flag for fp32 residual connection is set, convert for float. + if self.fp32_residual_connection: + embeddings = embeddings.float() + + # Dropout. + if self.sequence_parallel: + embeddings = tensor_parallel.scatter_to_sequence_parallel_region(embeddings) + # `scatter_to_sequence_parallel_region` returns a view, which prevents + # the original tensor from being garbage collected. Clone to facilitate GC. + # Has a small runtime cost (~0.5%). + if self.clone_scatter_output_in_embedding: + embeddings = embeddings.clone() + with tensor_parallel.get_cuda_rng_tracker().fork(): + embeddings = self.embedding_dropout(embeddings) + else: + embeddings = self.embedding_dropout(embeddings) + + return embeddings + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """For easy load.""" + + state_dict_ = {} + state_dict_[self._word_embeddings_key] \ + = self.word_embeddings.state_dict(prefix=prefix, + keep_vars=keep_vars) + if self.add_position_embedding: + state_dict_[self._position_embeddings_key] \ + = self.position_embeddings.state_dict(prefix=prefix, + keep_vars=keep_vars) + if self.num_tokentypes > 0: + state_dict_[self._tokentype_embeddings_key] \ + = self.tokentype_embeddings.state_dict(prefix=prefix, + keep_vars=keep_vars) + + return state_dict_ + + def load_state_dict(self, state_dict, strict=True): + """Customized load.""" + + # Word embedding. + if self._word_embeddings_key in state_dict: + state_dict_ = state_dict[self._word_embeddings_key] + else: + # for backward compatibility. + state_dict_ = {} + for key in state_dict.keys(): + if 'word_embeddings' in key: + state_dict_[key.split('word_embeddings.')[1]] \ + = state_dict[key] + self.word_embeddings.load_state_dict(state_dict_, strict=strict) + + # Position embedding. + if self.add_position_embedding: + if self._position_embeddings_key in state_dict: + state_dict_ = state_dict[self._position_embeddings_key] + else: + # for backward compatibility. + state_dict_ = {} + for key in state_dict.keys(): + if 'position_embeddings' in key: + state_dict_[key.split('position_embeddings.')[1]] \ + = state_dict[key] + self.position_embeddings.load_state_dict(state_dict_, strict=strict) + + # Tokentype embedding. + if self.num_tokentypes > 0: + state_dict_ = {} + if self._tokentype_embeddings_key in state_dict: + state_dict_ = state_dict[self._tokentype_embeddings_key] + else: + # for backward compatibility. + for key in state_dict.keys(): + if 'tokentype_embeddings' in key: + state_dict_[key.split('tokentype_embeddings.')[1]] \ + = state_dict[key] + if len(state_dict_.keys()) > 0: + self.tokentype_embeddings.load_state_dict(state_dict_, + strict=strict) + else: + print('***WARNING*** expected tokentype embeddings in the ' + 'checkpoint but could not find it', flush=True) + + +class TransformerLanguageModel(MegatronModule): + """Transformer language model. + + Args: + transformer_hparams: transformer hyperparameters + vocab_size: vocabulary size + max_sequence_length: maximum size of sequence. This + is used for positional embedding + embedding_dropout_prob: dropout probability for embeddings + num_tokentypes: size of the token-type embeddings. 0 value + will ignore this embedding + """ + + def __init__(self, + config, + encoder_attn_mask_type, + num_tokentypes=0, + add_encoder=True, + add_decoder=False, + decoder_attn_mask_type=AttnMaskType.causal, + add_pooler=False, + pre_process=True, + post_process=True): + args = get_args() + # TODO: passing share_embeddings_and_output_weights=False will not work correctly for T5 and embeddings will not be synced. Fix later for T5. + if args.untie_embeddings_and_output_weights: assert not add_decoder + super(TransformerLanguageModel, self).__init__(share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights) + + self.pre_process = pre_process + self.post_process = post_process + self.hidden_size = config.hidden_size + self.num_tokentypes = num_tokentypes + self.init_method = config.init_method + self.add_encoder = add_encoder + self.encoder_attn_mask_type = encoder_attn_mask_type + self.add_decoder = add_decoder + self.decoder_attn_mask_type = decoder_attn_mask_type + self.add_pooler = add_pooler + self.encoder_hidden_state = None + self.add_retriever = args.retro_add_retriever + self.untie_embeddings_and_output_weights = args.untie_embeddings_and_output_weights + + # Embeddings. + if self.pre_process: + self.embedding = Embedding(self.hidden_size, + args.padded_vocab_size, + args.max_position_embeddings, + args.hidden_dropout, + config, + self.num_tokentypes) + self._embedding_key = 'embedding' + + # Rotary positional embeddings + self.use_rotary_position_embeddings = \ + args.position_embedding_type == 'rope' + if self.use_rotary_position_embeddings: + self.seq_length = args.seq_length + rotary_dim = args.hidden_size // args.num_attention_heads \ + if args.kv_channels is None else args.kv_channels + + # partial rotary embeddings, which is better than full rotary + # Wang and Komatsuzaki et al + # https://github.com/kingoflolz/mesh-transformer-jax/ + self.rotary_pos_emb = RotaryEmbedding( + kv_channels=rotary_dim, + rotary_percent=args.rotary_percent, + seq_len_interpolation_factor=args.rotary_seq_len_interpolation_factor, + ) + + # Encoder (usually set to True, False if part of an encoder-decoder + # architecture and in encoder-only stage). + if self.add_encoder: + self.encoder = ParallelTransformer( + config, + model_type=args.model_type if not args.retro_add_retriever \ + else ModelType.retro_decoder, + self_attn_mask_type=self.encoder_attn_mask_type, + pre_process=self.pre_process, + post_process=self.post_process, + ) + self._encoder_key = 'encoder' + else: + self.encoder = None + + # Decoder (usually set to False, True if part of an encoder-decoder + # architecture and in decoder-only stage). + if self.add_decoder: + self.decoder = ParallelTransformer( + config, + model_type=args.model_type, + layer_type=LayerType.decoder, + self_attn_mask_type=self.decoder_attn_mask_type, + pre_process=self.pre_process, + post_process=self.post_process) + self._decoder_key = 'decoder' + else: + self.decoder = None + + if self.post_process: + # Pooler. + if self.add_pooler: + self.pooler = Pooler(self.hidden_size, self.init_method) + self._pooler_key = 'pooler' + + if self.untie_embeddings_and_output_weights: + self.output_layer = tensor_parallel.ColumnParallelLinear( + args.hidden_size, + args.padded_vocab_size, + config=config, + init_method=self.init_method, + bias=False) # Setting bias to False always to keep it consistent with embedding tying that also does not have a bias. + self._output_layer_key = 'output_layer' + + def set_input_tensor(self, input_tensor): + """ See megatron.legacy.model.transformer.set_input_tensor()""" + + # This is usually handled in schedules.py but some inference code still + # gives us non-lists or None + if not isinstance(input_tensor, list): + input_tensor = [input_tensor] + + if self.add_encoder and self.add_decoder: + assert len(input_tensor) == 1, \ + 'input_tensor should only be length 1 for stage with both encoder and decoder' + self.encoder.set_input_tensor(input_tensor[0]) + elif self.add_encoder: + assert len(input_tensor) == 1, \ + 'input_tensor should only be length 1 for stage with only encoder' + self.encoder.set_input_tensor(input_tensor[0]) + elif self.add_decoder: + if len(input_tensor) == 2: + self.decoder.set_input_tensor(input_tensor[0]) + self.encoder_hidden_state = input_tensor[1] + elif len(input_tensor) == 1: + self.decoder.set_input_tensor(None) + self.encoder_hidden_state = input_tensor[0] + else: + raise Exception('input_tensor must have either length 1 or 2') + else: + raise Exception('Stage must have at least either encoder or decoder') + + def forward(self, enc_input_ids, enc_position_ids, enc_attn_mask, + dec_input_ids=None, dec_position_ids=None, dec_attn_mask=None, + retriever_input_ids=None, + retriever_position_ids=None, + retriever_attn_mask=None, + enc_dec_attn_mask=None, tokentype_ids=None, + inference_params=None, + pooling_sequence_index=0, + enc_hidden_states=None, output_enc_hidden=False): + + # Encoder embedding. + if self.pre_process: + encoder_input = self.embedding(enc_input_ids, enc_position_ids, + tokentype_ids=tokentype_ids) + else: + encoder_input = None + + # Retriever embedding. + if self.add_retriever and self.pre_process: + retriever_input = self.embedding(retriever_input_ids, + retriever_position_ids, + tokentype_ids=tokentype_ids) + else: + retriever_input = None + + # Rotary positional embeddings + rotary_pos_emb = None + if self.use_rotary_position_embeddings: + if inference_params is not None: + rotary_pos_emb = \ + self.rotary_pos_emb(inference_params.max_sequence_length) + else: + rotary_pos_emb = self.rotary_pos_emb(self.seq_length) + + # Run encoder. + if enc_hidden_states is None: + if self.encoder is not None: + encoder_output = self.encoder( + encoder_input, + enc_attn_mask, + retriever_input=retriever_input, + retriever_attn_mask=retriever_attn_mask, + inference_params=inference_params, + rotary_pos_emb=rotary_pos_emb) + else: + encoder_output = self.encoder_hidden_state + else: + encoder_output = enc_hidden_states.to(encoder_input.dtype) + + if self.post_process: + if self.add_pooler: + pooled_output = self.pooler(encoder_output, + pooling_sequence_index) + + # output_enc_hidden refers to when we just need the encoder's + # output. For example, it is helpful to compute + # similarity between two sequences by average pooling + if not self.add_decoder or output_enc_hidden: + if self.add_pooler and self.post_process: + return encoder_output, pooled_output + else: + return encoder_output + + # Decoder embedding. + if self.pre_process: + decoder_input = self.embedding(dec_input_ids, + dec_position_ids) + else: + decoder_input = None + + # Run decoder. + decoder_output = self.decoder( + decoder_input, + dec_attn_mask, + encoder_output=encoder_output, + enc_dec_attn_mask=enc_dec_attn_mask, + inference_params=inference_params, + rotary_pos_emb=rotary_pos_emb) + + if self.add_pooler and self.post_process: + return decoder_output, encoder_output, pooled_output + else: + return decoder_output, encoder_output + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """For easy load.""" + + state_dict_ = {} + if self.pre_process: + state_dict_[self._embedding_key] \ + = self.embedding.state_dict_for_save_checkpoint(prefix=prefix, + keep_vars=keep_vars) + if self.add_encoder: + state_dict_[self._encoder_key] \ + = self.encoder.state_dict_for_save_checkpoint(prefix=prefix, + keep_vars=keep_vars) + if self.post_process: + if self.add_pooler: + state_dict_[self._pooler_key] \ + = self.pooler.state_dict_for_save_checkpoint(prefix=prefix, + keep_vars=keep_vars) + if self.untie_embeddings_and_output_weights: + state_dict_[self._output_layer_key] \ + = self.output_layer.state_dict(prefix=prefix, keep_vars=keep_vars) + + if self.add_decoder: + state_dict_[self._decoder_key] \ + = self.decoder.state_dict_for_save_checkpoint(prefix=prefix, + keep_vars=keep_vars) + + return state_dict_ + + def load_state_dict(self, state_dict, strict=True): + """Customized load.""" + + # Embedding. + if self.pre_process: + if self._embedding_key in state_dict: + state_dict_ = state_dict[self._embedding_key] + else: + # for backward compatibility. + state_dict_ = {} + for key in state_dict.keys(): + if '_embeddings' in key: + state_dict_[key] = state_dict[key] + self.embedding.load_state_dict(state_dict_, strict=strict) + + # Encoder. + if self.add_encoder: + if self._encoder_key in state_dict: + state_dict_ = state_dict[self._encoder_key] + # For backward compatibility. + elif 'transformer' in state_dict: + state_dict_ = state_dict['transformer'] + else: + # For backward compatibility. + state_dict_ = {} + for key in state_dict.keys(): + if 'transformer.' in key: + state_dict_[key.split('transformer.')[1]] = state_dict[key] + + # For backward compatibility. + state_dict_self_attention = {} + for key in state_dict_.keys(): + if '.attention.' in key: + state_dict_self_attention[key.replace(".attention.", + ".self_attention.")] = state_dict_[key] + else: + state_dict_self_attention[key] = state_dict_[key] + state_dict_ = state_dict_self_attention + + self.encoder.load_state_dict(state_dict_, strict=strict) + + # Pooler. + if self.post_process: + if self.add_pooler: + assert 'pooler' in state_dict, \ + 'could not find data for pooler in the checkpoint' + self.pooler.load_state_dict(state_dict[self._pooler_key], + strict=strict) + if self.untie_embeddings_and_output_weights: + assert 'output_layer' in state_dict, \ + 'could not find data for output_layer in the checkpoint' + self.output_layer.load_state_dict(state_dict[self._output_layer_key], + strict=strict) + # Decoder. + if self.add_decoder: + assert 'decoder' in state_dict, \ + 'could not find data for pooler in the checkpoint' + self.decoder.load_state_dict(state_dict[self._decoder_key], + strict=strict) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/module.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/module.py new file mode 100644 index 0000000..849fda7 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/module.py @@ -0,0 +1,206 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Megatron Module""" + +import torch +from torch.autograd import Variable +from torch.nn.parameter import Parameter + +from megatron.training import get_args +from megatron.core import mpu, tensor_parallel + + +_FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor) +_HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor) +_BF16_TYPES = (torch.BFloat16Tensor, torch.cuda.BFloat16Tensor) + + + +def param_is_not_shared(param): + return not hasattr(param, 'shared') or not param.shared + + + +class MegatronModule(torch.nn.Module): + """Megatron specific extensions of torch Module with support + for pipelining.""" + + def __init__(self, config=None, share_embeddings_and_output_weights=True): + super(MegatronModule, self).__init__() + self.config = config + self.share_embeddings_and_output_weights = share_embeddings_and_output_weights + + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """Use this function to override the state dict for + saving checkpoints.""" + return self.state_dict(prefix=prefix, keep_vars=keep_vars) + + + def shared_embedding_or_output_weight(self): + if self.pre_process: + return self.language_model.embedding.word_embeddings.weight + else: + if not self.share_embeddings_and_output_weights: + raise Exception('shared_embedding_or_output_weight() called for last ' + 'stage, but share_embeddings_and_output_weights is false') + return self.word_embeddings.weight + + + def initialize_word_embeddings(self): + args = get_args() + if not self.share_embeddings_and_output_weights: + raise Exception('initialize_word_embeddings() was called but ' + 'share_embeddings_and_output_weights is false') + + # This function just initializes the word embeddings in the final stage + # when we are using pipeline parallelism. Nothing to do if we aren't + # using pipeline parallelism. + if args.pipeline_model_parallel_size == 1: + # Zero out wgrad if sharing embeddings between two layers on same + # pipeline stage to make sure grad accumulation into main_grad is + # correct and does not include garbage values (e.g., from torch.empty). + self.shared_embedding_or_output_weight().zero_out_wgrad = True + return + + if mpu.is_pipeline_first_stage() and self.pre_process and not self.post_process: + self.shared_embedding_or_output_weight().shared_embedding = True + + # Parameters are shared between the word embeddings layers, and the + # heads at the end of the model. In a pipelined setup with more than + # one stage, the initial embedding layer and the head are on different + # workers, so we do the following: + # 1. Create a second copy of word_embeddings on the last stage, with + # initial parameters of 0.0. + # 2. Do an all-reduce between the first and last stage to ensure that + # the two copies of word_embeddings start off with the same + # parameter values. + # 3. In the training loop, before an all-reduce between the grads of + # the two word_embeddings layers to ensure that every applied weight + # update is the same on both stages. + if mpu.is_pipeline_last_stage() and not self.pre_process: + assert not mpu.is_pipeline_first_stage() + self._word_embeddings_for_head_key = 'word_embeddings_for_head' + # set word_embeddings weights to 0 here, then copy first + # stage's weights using all_reduce below. + self.word_embeddings = tensor_parallel.VocabParallelEmbedding( + args.padded_vocab_size, self.config.hidden_size, + config=self.config, init_method=self.config.init_method) + self.word_embeddings.weight.data.fill_(0) + self.word_embeddings.weight.shared = True + self.word_embeddings.weight.shared_embedding = True + + # Zero out initial weights for decoder embedding. + # NOTE: We don't currently support T5 with the interleaved schedule. + if not mpu.is_pipeline_first_stage(ignore_virtual=True) and \ + self.pre_process: + self.language_model.embedding.zero_parameters() + + if not torch.distributed.is_initialized(): + if not getattr(MegatronModule, "embedding_warning_printed", False): + print("WARNING! Distributed processes aren't initialized, so " + "word embeddings in the last layer are not initialized. " + "If you are just manipulating a model this is fine, but " + "this needs to be handled manually. If you are training " + "something is definitely wrong.") + MegatronModule.embedding_warning_printed = True + return + + # Ensure that first and last stages have the same initial parameter + # values. + if mpu.is_rank_in_embedding_group(): + self.shared_embedding_or_output_weight().data = self.shared_embedding_or_output_weight().data.cuda() + torch.distributed.all_reduce(self.shared_embedding_or_output_weight().data, + group=mpu.get_embedding_group()) + + # Ensure that encoder(first stage) and decoder(split stage) position + # embeddings have the same initial parameter values + # NOTE: We don't currently support T5 with the interleaved schedule. + if mpu.is_rank_in_position_embedding_group() and \ + args.pipeline_model_parallel_split_rank is not None: + # TODO: Support tokentype embedding. + self.language_model.embedding.cuda() + position_embeddings = self.language_model.embedding.position_embeddings + torch.distributed.all_reduce(position_embeddings.weight.data, + group=mpu.get_position_embedding_group()) + + +def conversion_helper(val, conversion): + """Apply conversion to val. Recursively apply conversion if `val` + #is a nested tuple/list structure.""" + if not isinstance(val, (tuple, list)): + return conversion(val) + rtn = [conversion_helper(v, conversion) for v in val] + if isinstance(val, tuple): + rtn = tuple(rtn) + return rtn + + +def fp32_to_float16(val, float16_convertor): + """Convert fp32 `val` to fp16/bf16""" + def half_conversion(val): + val_typecheck = val + if isinstance(val_typecheck, (Parameter, Variable)): + val_typecheck = val.data + if isinstance(val_typecheck, _FLOAT_TYPES): + val = float16_convertor(val) + return val + return conversion_helper(val, half_conversion) + + +def float16_to_fp32(val): + """Convert fp16/bf16 `val` to fp32""" + def float_conversion(val): + val_typecheck = val + if isinstance(val_typecheck, (Parameter, Variable)): + val_typecheck = val.data + if isinstance(val_typecheck, (_BF16_TYPES, _HALF_TYPES)): + val = val.float() + return val + return conversion_helper(val, float_conversion) + + + +class Float16Module(MegatronModule): + + def __init__(self, module, args): + super(Float16Module, self).__init__() + + if args.fp16: + self.add_module('module', module.half()) + def float16_convertor(val): + return val.half() + elif args.bf16: + self.add_module('module', module.bfloat16()) + def float16_convertor(val): + return val.bfloat16() + else: + raise Exception('should not be here') + + self.float16_convertor = float16_convertor + + + def set_input_tensor(self, input_tensor): + return self.module.set_input_tensor(input_tensor) + + + def forward(self, *inputs, **kwargs): + if mpu.is_pipeline_first_stage(): + inputs = fp32_to_float16(inputs, self.float16_convertor) + outputs = self.module(*inputs, **kwargs) + if mpu.is_pipeline_last_stage(): + outputs = float16_to_fp32(outputs) + return outputs + + + def state_dict(self, prefix='', keep_vars=False): + return self.module.state_dict(prefix=prefix, keep_vars=keep_vars) + + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + return self.module.state_dict_for_save_checkpoint(prefix=prefix, + keep_vars=keep_vars) + + + def load_state_dict(self, state_dict, strict=True): + self.module.load_state_dict(state_dict, strict=strict) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/multiple_choice.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/multiple_choice.py new file mode 100644 index 0000000..bec0548 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/multiple_choice.py @@ -0,0 +1,112 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Multiple choice model.""" + +import torch + +from megatron.training import get_args, print_rank_last +from megatron.legacy.model.enums import AttnMaskType +from megatron.legacy.model.bert_model import bert_extended_attention_mask, bert_position_ids +from megatron.legacy.model.language_model import get_language_model +from megatron.legacy.model.utils import get_linear_layer +from megatron.legacy.model.utils import init_method_normal +from megatron.legacy.model.utils import scaled_init_method_normal +from .module import MegatronModule + + +class MultipleChoice(MegatronModule): + + def __init__(self, + config, + num_tokentypes=2, + pre_process=True, + post_process=True): + super(MultipleChoice, self).__init__(share_embeddings_and_output_weights=False) + args = get_args() + + self.pre_process = pre_process + self.post_process = post_process + + self.language_model, self._language_model_key = get_language_model( + config=config, + num_tokentypes=num_tokentypes, + add_pooler=True, + encoder_attn_mask_type=AttnMaskType.padding, + pre_process=self.pre_process, + post_process=self.post_process) + + # Multi-choice head. + if self.post_process: + self.multichoice_dropout = torch.nn.Dropout(args.hidden_dropout) + self.multichoice_head = get_linear_layer(args.hidden_size, 1, + init_method) + self._multichoice_head_key = 'multichoice_head' + + def set_input_tensor(self, input_tensor): + """See megatron.legacy.model.transformer.set_input_tensor()""" + self.language_model.set_input_tensor(input_tensor) + + def forward(self, model_input, attention_mask, tokentype_ids=None): + + # [batch, choices, sequence] --> [batch * choices, sequence] --> + # transformer --> [batch, choices] --> softmax + + # Ensure the shape is [batch-size, choices, sequence] + assert len(attention_mask.shape) == 3 + num_choices = attention_mask.shape[1] + + # Reshape and treat choice dimension the same as batch. + attention_mask = attention_mask.view(-1, attention_mask.size(-1)) + extended_attention_mask = bert_extended_attention_mask(attention_mask) + + input_ids = model_input + # Do the same as attention_mask for input_ids, tokentype_ids + assert len(input_ids.shape) == 3 + assert len(tokentype_ids.shape) == 3 + input_ids = input_ids.view(-1, input_ids.size(-1)) + tokentype_ids = tokentype_ids.view(-1, tokentype_ids.size(-1)) + position_ids = bert_position_ids(input_ids) + + lm_output = self.language_model( + input_ids, + position_ids, + extended_attention_mask, + tokentype_ids=tokentype_ids + ) + if self.post_process: + _, pooled_output = lm_output + multichoice_output = self.multichoice_dropout(pooled_output) + multichoice_logits = self.multichoice_head(multichoice_output) + + # Reshape back to separate choices. + multichoice_logits = multichoice_logits.view(-1, num_choices) + + return multichoice_logits + return lm_output + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """For easy load when model is combined with other heads, + add an extra key.""" + + state_dict_ = {} + state_dict_[self._language_model_key] \ + = self.language_model.state_dict_for_save_checkpoint(prefix=prefix, + keep_vars=keep_vars) + if self.post_process: + state_dict_[self._multichoice_head_key] \ + = self.multichoice_head.state_dict(prefix=prefix, keep_vars=keep_vars) + return state_dict_ + + def load_state_dict(self, state_dict, strict=True): + """Customized load.""" + + self.language_model.load_state_dict( + state_dict[self._language_model_key], strict=strict) + if self.post_process: + if self._multichoice_head_key in state_dict: + self.multichoice_head.load_state_dict( + state_dict[self._multichoice_head_key], strict=strict) + else: + print_rank_last('***WARNING*** could not find {} in the checkpoint, ' + 'initializing to random'.format( + self._multichoice_head_key)) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/realm_model.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/realm_model.py new file mode 100644 index 0000000..5b2859a --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/realm_model.py @@ -0,0 +1,204 @@ +import os +import torch + +from megatron.training import get_args, print_rank_0 +from megatron.training.checkpointing import get_checkpoint_tracker_filename, get_checkpoint_name +from megatron.legacy.model import BertModel +from .module import MegatronModule +from megatron.core import mpu +from megatron.legacy.model.enums import AttnMaskType +from megatron.legacy.model.utils import get_linear_layer +from megatron.legacy.model.utils import init_method_normal +from megatron.legacy.model.language_model import get_language_model +from megatron.legacy.model.utils import scaled_init_method_normal +from megatron.legacy.model.bert_model import bert_extended_attention_mask, bert_position_ids + + +def general_ict_model_provider(only_query_model=False, only_block_model=False): + """Build the model.""" + args = get_args() + assert args.ict_head_size is not None, \ + "Need to specify --ict-head-size to provide an ICTBertModel" + assert mpu.get_tensor_model_parallel_world_size() == 1 and mpu.get_pipeline_model_parallel_world_size() == 1, \ + "Model parallel size > 1 not supported for ICT" + + print_rank_0('building ICTBertModel...') + + # simpler to just keep using 2 tokentypes since the LM we initialize with has 2 tokentypes + model = ICTBertModel( + ict_head_size=args.ict_head_size, + num_tokentypes=2, + parallel_output=True, + only_query_model=only_query_model, + only_block_model=only_block_model) + + return model + + +class ICTBertModel(MegatronModule): + """Bert-based module for Inverse Cloze task.""" + def __init__(self, + ict_head_size, + num_tokentypes=1, + parallel_output=True, + only_query_model=False, + only_block_model=False): + super(ICTBertModel, self).__init__() + bert_kwargs = dict( + ict_head_size=ict_head_size, + num_tokentypes=num_tokentypes, + parallel_output=parallel_output + ) + assert not (only_block_model and only_query_model) + self.use_block_model = not only_query_model + self.use_query_model = not only_block_model + + if self.use_query_model: + # this model embeds (pseudo-)queries - Embed_input in the paper + self.query_model = IREncoderBertModel(**bert_kwargs) + self._query_key = 'question_model' + + if self.use_block_model: + # this model embeds evidence blocks - Embed_doc in the paper + self.block_model = IREncoderBertModel(**bert_kwargs) + self._block_key = 'context_model' + + def forward(self, query_tokens, query_attention_mask, block_tokens, block_attention_mask): + """Run a forward pass for each of the models and return the respective embeddings.""" + query_logits = self.embed_query(query_tokens, query_attention_mask) + block_logits = self.embed_block(block_tokens, block_attention_mask) + return query_logits, block_logits + + def embed_query(self, query_tokens, query_attention_mask): + """Embed a batch of tokens using the query model""" + if self.use_query_model: + query_types = torch.cuda.LongTensor(*query_tokens.shape).fill_(0) + query_ict_logits, _ = self.query_model.forward(query_tokens, query_attention_mask, query_types) + return query_ict_logits + else: + raise ValueError("Cannot embed query without query model.") + + def embed_block(self, block_tokens, block_attention_mask): + """Embed a batch of tokens using the block model""" + if self.use_block_model: + block_types = torch.cuda.LongTensor(*block_tokens.shape).fill_(0) + block_ict_logits, _ = self.block_model.forward(block_tokens, block_attention_mask, block_types) + return block_ict_logits + else: + raise ValueError("Cannot embed block without block model.") + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """Save dict with state dicts of each of the models.""" + state_dict_ = {} + if self.use_query_model: + state_dict_[self._query_key] \ + = self.query_model.state_dict_for_save_checkpoint( + prefix=prefix, keep_vars=keep_vars) + + if self.use_block_model: + state_dict_[self._block_key] \ + = self.block_model.state_dict_for_save_checkpoint( + prefix=prefix, keep_vars=keep_vars) + + return state_dict_ + + def load_state_dict(self, state_dict, strict=True): + """Load the state dicts of each of the models""" + if self.use_query_model: + print("Loading ICT query model", flush=True) + self.query_model.load_state_dict( + state_dict[self._query_key], strict=strict) + + if self.use_block_model: + print("Loading ICT block model", flush=True) + self.block_model.load_state_dict( + state_dict[self._block_key], strict=strict) + + def init_state_dict_from_bert(self): + """Initialize the state from a pretrained BERT model on iteration zero of ICT pretraining""" + args = get_args() + tracker_filename = get_checkpoint_tracker_filename(args.bert_load) + if not os.path.isfile(tracker_filename): + raise FileNotFoundError("Could not find BERT load for ICT") + with open(tracker_filename, 'r') as f: + iteration = int(f.read().strip()) + assert iteration > 0 + + checkpoint_name = get_checkpoint_name(args.bert_load, iteration, False) + if mpu.get_data_parallel_rank() == 0: + print('global rank {} is loading checkpoint {}'.format( + torch.distributed.get_rank(), checkpoint_name)) + + try: + state_dict = torch.load(checkpoint_name, map_location='cpu') + except BaseException: + raise ValueError("Could not load checkpoint") + + # load the LM state dict into each model + model_dict = state_dict['model']['language_model'] + self.query_model.language_model.load_state_dict(model_dict) + self.block_model.language_model.load_state_dict(model_dict) + + # give each model the same ict_head to begin with as well + query_ict_head_state_dict = self.state_dict_for_save_checkpoint()[self._query_key]['ict_head'] + self.block_model.ict_head.load_state_dict(query_ict_head_state_dict) + + +class IREncoderBertModel(MegatronModule): + """BERT-based encoder for queries or blocks used for learned information retrieval.""" + def __init__(self, ict_head_size, num_tokentypes=2, parallel_output=True): + super(IREncoderBertModel, self).__init__() + args = get_args() + + self.ict_head_size = ict_head_size + self.parallel_output = parallel_output + init_method = init_method_normal(args.init_method_std) + scaled_init_method = scaled_init_method_normal(args.init_method_std, + args.num_layers) + + self.language_model, self._language_model_key = get_language_model( + num_tokentypes=num_tokentypes, + add_pooler=True, + encoder_attn_mask_type=AttnMaskType.padding, + init_method=init_method, + scaled_init_method=scaled_init_method) + + self.ict_head = get_linear_layer(args.hidden_size, ict_head_size, init_method) + self._ict_head_key = 'ict_head' + + def forward(self, input_ids, attention_mask, tokentype_ids=None): + extended_attention_mask = bert_extended_attention_mask( + attention_mask, next(self.language_model.parameters()).dtype) + position_ids = bert_position_ids(input_ids) + + lm_output, pooled_output = self.language_model( + input_ids, + position_ids, + extended_attention_mask, + tokentype_ids=tokentype_ids) + + # Output. + ict_logits = self.ict_head(pooled_output) + return ict_logits, None + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """For easy load when model is combined with other heads, + add an extra key.""" + + state_dict_ = {} + state_dict_[self._language_model_key] \ + = self.language_model.state_dict_for_save_checkpoint(prefix=prefix, + keep_vars=keep_vars) + state_dict_[self._ict_head_key] \ + = self.ict_head.state_dict(prefix=prefix, + keep_vars=keep_vars) + return state_dict_ + + def load_state_dict(self, state_dict, strict=True): + """Customized load.""" + self.language_model.load_state_dict( + state_dict[self._language_model_key], strict=strict) + self.ict_head.load_state_dict( + state_dict[self._ict_head_key], strict=strict) + + diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/rms_norm.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/rms_norm.py new file mode 100644 index 0000000..7e4424c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/rms_norm.py @@ -0,0 +1,31 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import torch +from torch import nn + +class RMSNorm(torch.nn.Module): + + def __init__(self, + dim: int, + eps: float = 1e-6, + sequence_parallel: bool = False): + """RMS Normaliation module + + Args: + dim (int): The width of input, i.e. hidden size + eps (float): epsilon to use for the norm, default to 1e-6 + sequence_parallel (bool): Set to true if sequence parallelism is being used, + this marks the weights as needing to be allreduced. + """ + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + setattr(self.weight, 'sequence_parallel', sequence_parallel) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + output = self._norm(x.float()).type_as(x) + return output * self.weight diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/t5_model.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/t5_model.py new file mode 100644 index 0000000..4c78922 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/t5_model.py @@ -0,0 +1,186 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""T5 model.""" + +import torch + +from megatron.training import get_args +from megatron.core import tensor_parallel +from megatron.legacy.model.enums import AttnMaskType +from megatron.legacy.model.language_model import parallel_lm_logits, get_language_model +from megatron.legacy.model import LayerNorm +from megatron.legacy.model.utils import ( + openai_gelu, + get_linear_layer +) +from .module import MegatronModule + + +def t5_extended_attention_mask(attention_mask_list): + + def attn_mask_postprocess(attn_mask): + # [b, 1, s, s] + extended_attention_mask = attn_mask.unsqueeze(1) + return extended_attention_mask + + return [attn_mask_postprocess(attn_mask) for attn_mask in attention_mask_list] + + +def t5_position_ids(token_ids): + # Create position ids + seq_length = token_ids.size(1) + position_ids = torch.arange(seq_length, dtype=torch.long, + device=token_ids.device) + position_ids = position_ids.unsqueeze(0).expand_as(token_ids) + + return position_ids + + +class T5LMHead(MegatronModule): + """Masked LM head for T5 + + Args: + mpu_vocab_size: model parallel size of vocabulary. + parallel_output: wether output logits being distributed or not. + """ + + def __init__(self, mpu_vocab_size, parallel_output): + super(T5LMHead, self).__init__() + + self.bias = torch.nn.Parameter(torch.zeros(mpu_vocab_size)) + self.bias.model_parallel = True + self.bias.partition_dim = 0 + self.bias.stride = 1 + self.parallel_output = parallel_output + + def forward(self, hidden_states, word_embeddings_weight): + output = parallel_lm_logits(hidden_states, + word_embeddings_weight, + self.parallel_output, + bias=self.bias) + return output + + +class T5Model(MegatronModule): + """T5 Language model.""" + + def __init__(self, + config, + num_tokentypes=0, + parallel_output=True, + pre_process=True, + post_process=True, + add_encoder=True, + add_decoder=True): + super().__init__(config=config) + args = get_args() + + self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy + self.parallel_output = parallel_output + self.pre_process = pre_process + self.post_process = post_process + self.add_encoder = add_encoder + self.add_decoder = add_decoder + + self.language_model, self._language_model_key = get_language_model( + config=config, + num_tokentypes=num_tokentypes, + add_pooler=False, + add_encoder=add_encoder, + add_decoder=add_decoder, + encoder_attn_mask_type=AttnMaskType.padding, + pre_process=self.pre_process, + post_process=self.post_process) + + self.initialize_word_embeddings() + + if self.post_process and self.add_decoder: + self.lm_head = T5LMHead( + self.shared_embedding_or_output_weight().size(0), + parallel_output) + self._lm_head_key = 'lm_head' + + def set_input_tensor(self, input_tensor): + """See megatron.legacy.model.transformer.set_input_tensor()""" + self.language_model.set_input_tensor(input_tensor) + + def forward(self, encoder_input_ids, decoder_input_ids, encoder_attn_mask, + decoder_attn_mask, encoder_decoder_attn_mask, + tokentype_ids=None, lm_labels=None, enc_hidden_states=None): + + # Converting the attention masks to proper parameter settings + encoder_attn_mask, decoder_attn_mask, encoder_decoder_attn_mask = t5_extended_attention_mask( + [encoder_attn_mask, decoder_attn_mask, encoder_decoder_attn_mask]) + + encoder_position_ids = t5_position_ids(encoder_input_ids) + decoder_position_ids = t5_position_ids(decoder_input_ids) + + lm_output = self.language_model(encoder_input_ids, + encoder_position_ids, + encoder_attn_mask, + decoder_input_ids, + decoder_position_ids, + decoder_attn_mask, + encoder_decoder_attn_mask, + tokentype_ids=tokentype_ids, + enc_hidden_states=enc_hidden_states) + + if self.post_process and self.add_decoder: + decoder_output, encoder_output = lm_output + # Output. [s, b, h] + lm_logits = self.lm_head(decoder_output, + self.shared_embedding_or_output_weight()) + + if lm_labels is None: + # [s b h] => [b s h] + return lm_logits.transpose(0,1).contiguous() + else: + # [b s] => [s b] + lm_labels = lm_labels.transpose(0,1).contiguous() + if self.fp16_lm_cross_entropy: + assert lm_logits.dtype == torch.half + lm_loss = tensor_parallel.vocab_parallel_cross_entropy(lm_logits, lm_labels) + else: + lm_loss = tensor_parallel.vocab_parallel_cross_entropy(lm_logits.float(), + lm_labels) + # [s b] => [b s] + lm_loss = lm_loss.transpose(0,1).contiguous() + return lm_loss + elif self.add_decoder and not self.add_encoder: + decoder_output, encoder_output = lm_output + return decoder_output + else: + encoder_output = lm_output + return encoder_output + + def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): + """For easy load when model is combined with other heads, + add an extra key.""" + + state_dict_ = {} + state_dict_[self._language_model_key] \ + = self.language_model.state_dict_for_save_checkpoint(prefix=prefix, + keep_vars=keep_vars) + if self.post_process and self.add_decoder: + state_dict_[self._lm_head_key] \ + = self.lm_head.state_dict_for_save_checkpoint(prefix=prefix, + keep_vars=keep_vars) + # Save word_embeddings. + if self.post_process and not self.pre_process and self.add_decoder: + state_dict_[self._word_embeddings_for_head_key] \ + = self.word_embeddings.state_dict(prefix=prefix, + keep_vars=keep_vars) + return state_dict_ + + def load_state_dict(self, state_dict, strict=True): + """Customized load.""" + + self.language_model.load_state_dict( + state_dict[self._language_model_key], strict=strict) + if self.post_process and self.add_decoder: + self.lm_head.load_state_dict(state_dict[self._lm_head_key], + strict=strict) + # Load word embeddings. + if self.post_process and not self.pre_process and self.add_decoder: + self.word_embeddings.load_state_dict( + state_dict[self._word_embeddings_for_head_key], strict=strict) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/transformer.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/transformer.py new file mode 100644 index 0000000..ef19656 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/transformer.py @@ -0,0 +1,1813 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Transformer.""" +from contextlib import nullcontext +import os +import math +import numpy as np +import torch +import torch.nn.functional as F +from typing import Optional + +from megatron import core +from megatron.training import get_timers, get_args, get_num_microbatches +from .module import MegatronModule +from megatron.core import mpu, tensor_parallel +from megatron.core.enums import ModelType +from megatron.legacy.model.enums import AttnMaskType, LayerType, AttnType +from megatron.legacy.model.fused_softmax import FusedScaleMaskSoftmax +from megatron.legacy.model.fused_bias_gelu import bias_gelu_impl +from megatron.core.models.common.embeddings.rotary_pos_embedding import RotaryEmbedding, apply_rotary_pos_emb +from megatron.legacy.model.utils import attention_mask_func, openai_gelu, erf_gelu, get_norm +from megatron.core.tensor_parallel import ( + gather_from_sequence_parallel_region_to_moe, + reduce_scatter_to_sequence_parallel_region_from_moe, + get_cuda_rng_tracker, + get_data_parallel_rng_tracker_name +) +from megatron.core.parallel_state import get_tensor_model_parallel_group, get_tensor_and_expert_parallel_group +from megatron.core.jit import jit_fuser + +try: + from einops import rearrange +except ImportError: + rearrange = None + +try: + from flash_attn.flash_attn_interface import flash_attn_unpadded_func +except ImportError: + try: + from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_unpadded_func + except ImportError: + flash_attn_unpadded_func = None + +""" We use the following notation throughout this file: + h: hidden size + n: number of attention heads + p: number of model parallel partitions + np: n/p + hp: h/p + hn: h/n + b: batch size + s: sequence length + l: number of layers + Transformer takes input of size [s, b, h] and returns a + tensor of the same size. We use the following arguments: + hyperparameters: transformer hyperparameters +""" + +class DropPath(MegatronModule): + """Drop paths (Stochastic Depth) per sample + (when applied in main path of residual blocks). + """ + + def __init__(self, drop_prob=0.): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_state): + if self.drop_prob == 0. or not self.training: + return hidden_state + keep_prob = 1 - self.drop_prob + # work with diff dim tensors, not just 2D ConvNets + # hidden_state: [s, b, h] + shape = (1,) + (hidden_state.shape[1],) + (1,) * (hidden_state.ndim - 2) + random_tensor = keep_prob + \ + torch.rand(shape, dtype=hidden_state.dtype, device=hidden_state.device) + random_tensor.floor_() # binarize + output = hidden_state.div(keep_prob) * random_tensor + return output + +class ParallelMLP(MegatronModule): + """MLP. + + MLP will take the input with h hidden state, project it to 4*h + hidden dimension, perform nonlinear transformation, and project the + state back into h hidden dimension. + """ + + def __init__(self, config, is_expert=False): + super(ParallelMLP, self).__init__() + args = get_args() + + self.add_bias = config.add_bias_linear + + ffn_hidden_size = config.ffn_hidden_size + if config.gated_linear_unit: + ffn_hidden_size *= 2 + + # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf + self.dense_h_to_4h = tensor_parallel.ColumnParallelLinear( + config.hidden_size, + ffn_hidden_size, + config=config, + init_method=config.init_method, + bias=self.add_bias, + gather_output=False, + skip_bias_add=True, + is_expert=is_expert, + ) + + self.bias_gelu_fusion = False + self.activation_func = None + self.swiglu = args.swiglu + + if args.openai_gelu: + self.activation_func = openai_gelu + elif args.onnx_safe: + self.activation_func = erf_gelu + elif args.swiglu: + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + self.activation_func = swiglu + elif args.squared_relu: + def squared_relu(x): + return torch.pow(F.relu(x), 2) + self.activation_func = squared_relu + else: + self.bias_gelu_fusion = args.bias_gelu_fusion + self.activation_func = F.gelu + + # Project back to h. + self.dense_4h_to_h = tensor_parallel.RowParallelLinear( + config.ffn_hidden_size, + config.hidden_size, + config=config, + init_method=config.output_layer_init_method, + bias=self.add_bias, + skip_bias_add=True, + input_is_parallel=True, + is_expert=is_expert, + ) + + def forward(self, hidden_states): + + # [s, b, 4hp] + intermediate_parallel, bias_parallel = self.dense_h_to_4h(hidden_states) + + if self.bias_gelu_fusion: + assert self.add_bias is True + assert self.activation_func == F.gelu + intermediate_parallel = bias_gelu_impl(intermediate_parallel, bias_parallel) + else: + if bias_parallel is not None: + intermediate_parallel = intermediate_parallel + bias_parallel + intermediate_parallel = self.activation_func(intermediate_parallel) + + # [s, b, h] + output, output_bias = self.dense_4h_to_h(intermediate_parallel) + return output, output_bias + +def sinkhorn(cost, tol=0.0001): + cost = torch.exp(cost) + d0 = torch.ones(cost.size(0), device=cost.device, dtype=cost.dtype) + d1 = torch.ones(cost.size(1), device=cost.device, dtype=cost.dtype) + + eps = 0.00000001 + error = 1e9 + d1_old = d1 + while error > tol: + d0 = (1/d0.size(0))*1/(torch.sum(d1*cost,1) + eps) + d1 = (1/d1.size(0))*1/(torch.sum(d0.unsqueeze(1)*cost,0)+eps) + error = torch.mean(torch.abs(d1_old-d1)) + d1_old = d1 + return d1*cost*d0.unsqueeze(1) + + +def get_router_linear_layer(config): + args = get_args() + router = torch.nn.Linear(args.hidden_size, args.num_experts, bias=False) + with get_cuda_rng_tracker().fork(get_data_parallel_rng_tracker_name()): + config.init_method(router.weight) + setattr(router.weight, 'sequence_parallel',config.sequence_parallel) + return router + + +class SwitchMLP(MegatronModule): + """ + Routes input to one of N MLP "experts" + """ + def __init__(self, config): + super(SwitchMLP, self).__init__() + args = get_args() + self.router = get_router_linear_layer(config) + self.expert_parallel_size = mpu.get_expert_model_parallel_world_size() + self.sequence_parallel = config.sequence_parallel + self.add_bias = config.add_bias_linear + + assert args.num_experts % self.expert_parallel_size == 0 + self.num_local_experts = args.num_experts // self.expert_parallel_size + local_expert_indices_offset = mpu.get_expert_model_parallel_rank() * self.num_local_experts + self.local_expert_indices = [local_expert_indices_offset + i for i in range(self.num_local_experts)] + + self.local_experts = torch.nn.ModuleList() + for i in range(self.num_local_experts): + self.local_experts.append(ParallelMLP(config, is_expert=True)) + + def gather_indices(self, local_indices): + """ Gather tensors and concatinate along the first dimension.""" + group = get_tensor_and_expert_parallel_group() + world_size = torch.distributed.get_world_size(group=group) + # Bypass the function if we are using only 1 GPU. + if world_size == 1: + return local_indices + + dim_size = list(local_indices.size()) + dim_size[0] = dim_size[0] * world_size + + # TODO pre allocate memory + output = torch.empty(dim_size, dtype=local_indices.dtype, + device=torch.cuda.current_device()) + torch.distributed._all_gather_base( + output, local_indices.contiguous(), group=group + ) + return output + + def forward(self, hidden_states): + # hidden_states: [b, s, h] + args = get_args() + s = hidden_states.size(0) + b = hidden_states.size(1) + h = hidden_states.size(2) + route = self.router(hidden_states).view(-1, args.num_experts) + + # TODO (rprenger) Right now we're just using the sinkhorn algorithm + # for load balancing. There should be an option to do no load balancing + # and the algorithm and parametets should be further tested + if self.training: + with torch.no_grad(): + sinkroute = sinkhorn(route.detach().to(dtype=torch.float32)) + _, max_ind = torch.max(sinkroute, dim=1) + route = torch.sigmoid(route) + max_prob = route[torch.arange(route.size(0)), max_ind] + else: + route = torch.sigmoid(route) + max_prob, max_ind = torch.max(route, dim=1) + + max_prob = torch.unsqueeze(max_prob, 1) + hidden_states = hidden_states.view(-1, hidden_states.size(2)) + + # TODO (rprenger) TODO this could be made easier to read + # Converting [s, b, h] to [s*b, h]. + # Each vector could be routed differently + if self.sequence_parallel or (self.expert_parallel_size > 1): + global_hidden_states = \ + gather_from_sequence_parallel_region_to_moe(hidden_states) + global_indices = self.gather_indices(max_ind) + else: + global_hidden_states = hidden_states + global_indices = max_ind + + output_total = torch.zeros_like(global_hidden_states) + if self.add_bias: + output_bias_total = torch.zeros_like(global_hidden_states) + + for expert_num, expert in enumerate(self.local_experts): + local_expert_index = self.local_expert_indices[expert_num] + local_indices = (global_indices == local_expert_index).nonzero() + hidden = global_hidden_states[local_indices, :] + output, output_bias = expert(hidden) + output_total[local_indices, :] = output + if self.add_bias: + output_bias = output_bias.expand_as(output) + output_bias_total[local_indices, :] = output_bias + + if self.sequence_parallel or (self.expert_parallel_size > 1): + output_total = \ + reduce_scatter_to_sequence_parallel_region_from_moe(output_total) + if self.add_bias: + output_bias_total = \ + reduce_scatter_to_sequence_parallel_region_from_moe(output_bias_total) + + # bias is duplicated across tensor parallelism ranks; + # reduce scatter reduces bias across tensor parallel_ranks + output_bias_total = \ + output_bias_total/mpu.get_tensor_model_parallel_world_size() + + output_total = output_total*max_prob + output_total = output_total.view(s, b, h) + if self.add_bias: + output_bias_total = output_bias_total*max_prob + output_bias_total = output_bias_total.view(s, b, h) + else: + output_bias_total = None + + return output_total, output_bias_total + + +class CoreAttention(MegatronModule): + + def __init__(self, layer_number, config, + attn_mask_type=AttnMaskType.padding): + super(CoreAttention, self).__init__() + self.fp16 = config.fp16 + self.bf16 = config.bf16 + + self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 + if self.apply_query_key_layer_scaling: + self.attention_softmax_in_fp32 = True + self.layer_number = max(1, layer_number) + self.attn_mask_type = attn_mask_type + self.sequence_parallel = config.sequence_parallel + + projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + world_size = mpu.get_tensor_model_parallel_world_size() + self.hidden_size_per_partition = core.utils.divide(projection_size, + world_size) + self.hidden_size_per_attention_head = core.utils.divide( + projection_size, config.num_attention_heads) + self.num_attention_heads_per_partition = core.utils.divide( + config.num_attention_heads, world_size) + + coeff = None + self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) + if self.apply_query_key_layer_scaling: + coeff = self.layer_number + self.norm_factor *= coeff + + self.scale_mask_softmax = FusedScaleMaskSoftmax( + self.fp16, self.bf16, + self.attn_mask_type, + config.masked_softmax_fusion, + attention_mask_func, + self.attention_softmax_in_fp32, + coeff) + + # Dropout. Note that for a single iteration, this layer will generate + # different outputs on different number of parallel partitions but + # on average it should not be partition dependent. + self.attention_dropout = torch.nn.Dropout(config.attention_dropout) + + def forward(self, query_layer, key_layer, + value_layer, attention_mask): + + # =================================== + # Raw attention scores. [b, np, s, s] + # =================================== + + # [b, np, sq, sk] + output_size = (query_layer.size(1), + query_layer.size(2), + query_layer.size(0), + key_layer.size(0)) + + # [sq, b, np, hn] -> [sq, b * np, hn] + query_layer = query_layer.reshape(output_size[2], + output_size[0] * output_size[1], -1) + # [sk, b, np, hn] -> [sk, b * np, hn] + key_layer = key_layer.view(output_size[3], + output_size[0] * output_size[1], -1) + + # preallocting input tensor: [b * np, sq, sk] + matmul_input_buffer = mpu.get_global_memory_buffer().get_tensor( + (output_size[0]*output_size[1], output_size[2], output_size[3]), + query_layer.dtype, "mpu") + + # Raw attention scores. [b * np, sq, sk] + matmul_result = torch.baddbmm( + matmul_input_buffer, + query_layer.transpose(0, 1), # [b * np, sq, hn] + key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] + beta=0.0, alpha=(1.0/self.norm_factor)) + + # change view to [b, np, sq, sk] + attention_scores = matmul_result.view(*output_size) + + # =========================== + # Attention probs and dropout + # =========================== + + # attention scores and attention mask [b, np, sq, sk] + attention_probs = self.scale_mask_softmax(attention_scores, + attention_mask) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + if not self.sequence_parallel: + with tensor_parallel.get_cuda_rng_tracker().fork(): + attention_probs = self.attention_dropout(attention_probs) + else: + attention_probs = self.attention_dropout(attention_probs) + + # ========================= + # Context layer. [sq, b, hp] + # ========================= + + # value_layer -> context layer. + # [sk, b, np, hn] --> [b, np, sq, hn] + + # context layer shape: [b, np, sq, hn] + output_size = (value_layer.size(1), + value_layer.size(2), + query_layer.size(0), + value_layer.size(3)) + + # change view [sk, b * np, hn] + value_layer = value_layer.view(value_layer.size(0), + output_size[0] * output_size[1], -1) + + # change view [b * np, sq, sk] + attention_probs = attention_probs.view(output_size[0] * output_size[1], + output_size[2], -1) + + # matmul: [b * np, sq, hn] + context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) + + # change view [b, np, sq, hn] + context_layer = context_layer.view(*output_size) + + # [b, np, sq, hn] --> [sq, b, np, hn] + context_layer = context_layer.permute(2, 0, 1, 3).contiguous() + + # [sq, b, np, hn] --> [sq, b, hp] + new_context_layer_shape = context_layer.size()[:-2] + \ + (self.hidden_size_per_partition,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer + + +class FlashSelfAttention(torch.nn.Module): + """Implement the scaled dot product attention with softmax. + Arguments + --------- + softmax_scale: The temperature to use for the softmax attention. + (default: 1/sqrt(d_keys) where d_keys is computed at + runtime) + attention_dropout: The dropout rate to apply to the attention + (default: 0.0) + """ + def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0, + device=None, dtype=None): + super().__init__() + assert flash_attn_unpadded_func is not None, ('Please install FlashAttention first, ' + 'e.g., with pip install flash-attn') + assert rearrange is not None, 'Please install einops first, e.g., with pip install einops' + self.causal = causal + self.softmax_scale = softmax_scale + self.dropout_p = attention_dropout + + def forward(self, q, k, v): + """Implements the multihead softmax attention. + Arguments + --------- + q, k, v: The tensor containing the query, key, and value. (B, S, H, D) + """ + + assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q,k,v))) + assert all((i.is_cuda for i in (q,k,v))) + + batch_size, seqlen_q = q.shape[0], q.shape[1] + seqlen_k = k.shape[1] + + q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [q, k, v]] + cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, + device=q.device) + + if self.training: + # during training q,k,v always have same seqlen + assert seqlen_k == seqlen_q + + is_causal = self.causal + cu_seqlens_k = cu_seqlens_q + dropout_p = self.dropout_p + else: + # turn off FA causal mask after first inference autoregressive iteration + # only on first autoregressive step q,k,v have same seqlen + is_causal = seqlen_q == seqlen_k + cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32, + device=q.device) + dropout_p = 0 + + output = flash_attn_unpadded_func( + q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k, + dropout_p, + softmax_scale=self.softmax_scale, causal=is_causal + ) + + output = rearrange(output, '(b s) ... -> b s ...', b=batch_size) + return output + + +class ParallelAttention(MegatronModule): + """Parallel self-attention layer abstract class. + + Self-attention layer takes input with size [s, b, h] + and returns output of the same size. + """ + + def __init__(self, config, layer_number, + attention_type=AttnType.self_attn, + attn_mask_type=AttnMaskType.padding): + super(ParallelAttention, self).__init__() + args = get_args() + self.layer_number = max(1, layer_number) + self.attention_type = attention_type + self.attn_mask_type = attn_mask_type + self.params_dtype = config.params_dtype + self.sequence_parallel = config.sequence_parallel + self.config = config + self.group_query_attention = args.group_query_attention + self.num_query_groups = args.num_query_groups + + query_projection_size = config.kv_channels * config.num_attention_heads + if self.group_query_attention: + kv_projection_size = args.kv_channels * args.num_query_groups + else: + kv_projection_size = args.kv_channels * args.num_attention_heads + + self.use_flash_attn = args.use_flash_attn \ + and attention_type == AttnType.self_attn \ + and self.attn_mask_type == AttnMaskType.causal + if self.use_flash_attn: + if flash_attn_unpadded_func is None: + raise ImportError('FlashAttention is not installed, please install with ' + 'pip install flash-attn') + assert attention_type == AttnType.self_attn, ('FlashAttention code path only supports ' + 'self-attention for now') + assert self.attn_mask_type == AttnMaskType.causal, ('FlashAttention code path only ' + 'supports causal mask for now') + if rearrange is None: + raise ImportError('einops is not installed, please install with pip install einops') + + # Per attention head and per partition values. + world_size = mpu.get_tensor_model_parallel_world_size() + self.hidden_size_per_attention_head = core.utils.divide( + query_projection_size, config.num_attention_heads) + self.num_attention_heads_per_partition = core.utils.divide( + config.num_attention_heads, world_size) + + if self.group_query_attention: + if args.num_query_groups % world_size != 0: + raise NotImplementedError('Currently the num_query_groups should be ' + 'a multiple of the tensor parallel size') + self.num_query_groups_per_partition = core.utils.divide( + args.num_query_groups, world_size) + else: + self.num_query_groups_per_partition = self.num_attention_heads_per_partition + + # Strided linear layer. + if attention_type == AttnType.self_attn: + self.query_key_value = tensor_parallel.ColumnParallelLinear( + config.hidden_size, + query_projection_size + 2 * kv_projection_size, + config=config, + init_method=config.init_method, + bias=args.add_bias_linear or args.add_qkv_bias, + gather_output=False) + else: + assert attention_type == AttnType.cross_attn + + if self.group_query_attention: + raise NotImplementedError("Grouped query attention not implemented for cross-attention.") + assert query_projection_size == kv_projection_size + + self.query = tensor_parallel.ColumnParallelLinear( + config.hidden_size, + query_projection_size, + config=config, + init_method=config.init_method, + bias=config.add_bias_linear, + gather_output=False) + + self.key_value = tensor_parallel.ColumnParallelLinear( + config.hidden_size, + 2 * kv_projection_size, + config=config, + init_method=config.init_method, + bias=config.add_bias_linear, + gather_output=False) + + self.core_attention = CoreAttention(self.layer_number, config, + self.attn_mask_type) + self.checkpoint_core_attention = config.recompute_granularity == 'selective' + + if self.use_flash_attn: + self.core_attention_flash = FlashSelfAttention( + causal=True, attention_dropout=config.attention_dropout + ) + + # Output. + self.dense = tensor_parallel.RowParallelLinear( + query_projection_size, + config.hidden_size, + config=config, + init_method=config.output_layer_init_method, + bias=args.add_bias_linear, + input_is_parallel=True, + skip_bias_add=True) + + def _checkpointed_attention_forward(self, query_layer, key_layer, + value_layer, attention_mask, + rotary_pos_emb=None): + """Forward method with activation checkpointing.""" + def custom_forward(*inputs): + query_layer = inputs[0] + key_layer = inputs[1] + value_layer = inputs[2] + attention_mask = inputs[3] + output_ = self.core_attention(query_layer, key_layer, + value_layer, attention_mask) + return output_ + + q_pos_emb, k_pos_emb = (None, None) if rotary_pos_emb is None \ + else rotary_pos_emb + + hidden_states = tensor_parallel.checkpoint( + custom_forward, + False, query_layer, key_layer, value_layer, attention_mask, + q_pos_emb, k_pos_emb) + + return hidden_states + + def _allocate_memory(self, inference_max_sequence_len, batch_size, num_attention_heads): + return torch.empty( + inference_max_sequence_len, + batch_size, + num_attention_heads, + self.hidden_size_per_attention_head, + dtype=self.params_dtype, + device=torch.cuda.current_device()) + + def forward(self, hidden_states, attention_mask, + encoder_output=None, inference_params=None, + rotary_pos_emb=None): + # hidden_states: [sq, b, h] + + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + is_first_step = False + if inference_params: + if self.layer_number not in inference_params.key_value_memory_dict: + inf_max_seq_len = inference_params.max_sequence_length + inf_max_batch_size = inference_params.max_batch_size + inference_key_memory = self._allocate_memory( + inf_max_seq_len, inf_max_batch_size, + self.num_query_groups_per_partition) + inference_value_memory = self._allocate_memory( + inf_max_seq_len, inf_max_batch_size, + self.num_query_groups_per_partition) + + inference_params.key_value_memory_dict[self.layer_number] = ( + inference_key_memory, inference_value_memory) + is_first_step = True + else: + inference_key_memory, inference_value_memory = \ + inference_params.key_value_memory_dict[self.layer_number] + + # ===================== + # Query, Key, and Value + # ===================== + if self.attention_type == AttnType.self_attn: + + # Attention heads [sq, b, h] --> [sq, b, ng * (np/ng + 2) * hn)] + mixed_x_layer, _ = self.query_key_value(hidden_states) + + # [sq, b, hp] --> [sq, b, ng, (np/ng + 2) * hn] + new_tensor_shape = mixed_x_layer.size()[:-1] + ( + self.num_query_groups_per_partition, + ( + (self.num_attention_heads_per_partition // self.num_query_groups_per_partition + 2) + * self.hidden_size_per_attention_head + ), + ) + mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + + # [sq, b, ng, (np/ng + 2) * hn] --> [sq, b, ng, np/ng * hn], [sq, b, ng, hn], [sq, b, ng, hn] + (query_layer, + key_layer, + value_layer) = torch.split( + mixed_x_layer, + [ + ( + self.num_attention_heads_per_partition // self.num_query_groups_per_partition + * self.hidden_size_per_attention_head + ), + self.hidden_size_per_attention_head, + self.hidden_size_per_attention_head + ], + dim=3) + + # [sq, b, ng, np/ng * hn] -> [sq, b, np, hn] - + query_layer = query_layer.view(query_layer.size(0), query_layer.size(1), -1, self.hidden_size_per_attention_head) + else: + # Attention heads [sk, b, h] --> [sk, b, (np * 2 * hn)] + mixed_kv_layer, _ = self.key_value(encoder_output) + + # [sk, b, (np * 2 * hn)] --> [sk, b, np, 2 * hn] + new_tensor_shape = mixed_kv_layer.size()[:-1] + \ + (self.num_attention_heads_per_partition, + 2 * self.hidden_size_per_attention_head) + mixed_kv_layer = mixed_kv_layer.view(*new_tensor_shape) + + # [sk, b, np, 2 * hn] --> 2 [sk, b, np, hn] + (key_layer, + value_layer) = tensor_parallel.split_tensor_along_last_dim(mixed_kv_layer, 2) + + # Attention head [sq, b, h] --> [sq, b, hp] + query_layer, _ = self.query(hidden_states) + # [sq, b, hp] --> [sq, b, np, hn] + new_tensor_shape = query_layer.size()[:-1] + \ + (self.num_attention_heads_per_partition, + self.hidden_size_per_attention_head) + query_layer = query_layer.view(*new_tensor_shape) + + # ================================== + # Adjust key and value for inference + # ================================== + + # duplicate the pos_emb for self attention + if rotary_pos_emb is not None: + if isinstance(rotary_pos_emb, tuple): + rotary_pos_emb = rotary_pos_emb + else: + rotary_pos_emb = ((rotary_pos_emb,) * 2) + + if inference_params: + batch_start = inference_params.batch_size_offset + batch_end = batch_start + key_layer.size(1) + assert batch_end <= inference_key_memory.size(1) + sequence_start = inference_params.sequence_len_offset + sequence_end = sequence_start + key_layer.size(0) + assert sequence_end <= inference_key_memory.size(0) + # Copy key and values. + inference_key_memory[sequence_start:sequence_end, + batch_start:batch_end, ...] = key_layer + inference_value_memory[sequence_start:sequence_end, + batch_start:batch_end, ...] = value_layer + key_layer = inference_key_memory[ + :sequence_end, batch_start:batch_end, ...] + value_layer = inference_value_memory[ + :sequence_end, batch_start:batch_end, ...] + + + # adjust the key rotary positional embedding + if rotary_pos_emb is not None: + q_pos_emb, k_pos_emb = rotary_pos_emb + # need to cross check this condition during inference + # if not set_inference_key_value_memory: + if not is_first_step: + # In inference, we compute one token at a time. + # Select the correct positional embedding + # (only the last token in the sequence) + q_pos_emb = q_pos_emb[sequence_end - 1 : sequence_end] + else: + # In the first forward pass of inference, + # we use the entire provided prefix. + # q_pos_emb here has the rope embeddings of the entire + # prefix + to-be-generated output so + # we slice to just the prefix. + q_pos_emb = q_pos_emb[:sequence_end, :, :, :] + k_pos_emb = k_pos_emb[:sequence_end, :, :, :] + rotary_pos_emb = (q_pos_emb, k_pos_emb) + + # ================================== + # core attention computation + # ================================== + + # expand the key_layer and value_layer [sk, b, ng, hn] -> [sk, b, np, hn] + if self.num_attention_heads_per_partition // self.num_query_groups_per_partition > 1: + key_layer = key_layer.repeat_interleave( + self.num_attention_heads_per_partition // self.num_query_groups_per_partition, + dim = 2 + ) + value_layer = value_layer.repeat_interleave( + self.num_attention_heads_per_partition // self.num_query_groups_per_partition, + dim = 2 + ) + + # apply relative positional encoding (rotary embedding) + if rotary_pos_emb is not None: + q_pos_emb, k_pos_emb = rotary_pos_emb + query_layer = apply_rotary_pos_emb(query_layer, q_pos_emb,self.config) + key_layer = apply_rotary_pos_emb(key_layer, k_pos_emb,self.config) + # TODO, can apply positional embedding to value_layer so it has + # absolute positional embedding. + # otherwise, only relative positional embedding takes effect + # value_layer = apply_rotary_pos_emb(value_layer, k_pos_emb) + + if not self.use_flash_attn: + if self.checkpoint_core_attention: + context_layer = self._checkpointed_attention_forward( + query_layer, key_layer, value_layer, attention_mask) + else: + context_layer = self.core_attention( + query_layer, key_layer, value_layer, attention_mask) + else: + q, k, v = [rearrange(x, 's b ... -> b s ...').contiguous() + for x in (query_layer, key_layer, value_layer)] + if not self.sequence_parallel: + with tensor_parallel.get_cuda_rng_tracker().fork(): + context_layer = self.core_attention_flash(q, k, v) + else: + context_layer = self.core_attention_flash(q, k, v) + context_layer = rearrange(context_layer, 'b s h d -> s b (h d)').contiguous() + + # ================= + # Output. [sq, b, h] + # ================= + + output, bias = self.dense(context_layer) + + return output, bias + + +def bias_dropout_add(x, bias, residual, prob, training): + # type: (Tensor, Optional[Tensor], Tensor, float, bool) -> Tensor + if bias is not None: + x = x + bias + out = torch.nn.functional.dropout(x, p=prob, training=training) + out = residual + out + return out + + +def get_bias_dropout_add(training): + def _bias_dropout_add(x, bias, residual, prob): + return bias_dropout_add(x, bias, residual, prob, training) + return _bias_dropout_add + + +@jit_fuser +def bias_dropout_add_fused_train(x: torch.Tensor, + bias: Optional[torch.Tensor], + residual: torch.Tensor, + prob: float) -> torch.Tensor: + return bias_dropout_add(x, bias, residual, prob, True) + + +@jit_fuser +def bias_dropout_add_fused_inference(x: torch.Tensor, + bias: Optional[torch.Tensor], + residual: torch.Tensor, + prob: float) -> torch.Tensor: + return bias_dropout_add(x, bias, residual, prob, False) + + +class ParallelTransformerLayer(MegatronModule): + """A single transformer layer. + + Transformer layer takes input with size [s, b, h] and returns an + output of the same size. + """ + + def __init__(self, config, + layer_number, layer_type=LayerType.encoder, + self_attn_mask_type=AttnMaskType.padding, + drop_path_rate=0.): + args = get_args() + + super(ParallelTransformerLayer, self).__init__() + self.layer_number = layer_number + self.layer_type = layer_type + + self.apply_residual_connection_post_norm \ + = config.apply_residual_connection_post_layernorm + + self.bf16 = config.bf16 + self.fp32_residual_connection = config.fp32_residual_connection + + # Normalize the input data. + self.input_norm = get_norm(config) + + # Self attention. + self.self_attention = ParallelAttention( + config, + layer_number, + attention_type=AttnType.self_attn, + attn_mask_type=self_attn_mask_type) + self.hidden_dropout = config.hidden_dropout + self.bias_dropout_fusion = config.bias_dropout_fusion + self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0.0 else None + + # Normalize the attention output + self.post_attention_norm = get_norm(config) + + # Cross attention. + if self.layer_type in (LayerType.decoder, + LayerType.retro_decoder, + LayerType.retro_decoder_with_retriever, + LayerType.retro_encoder): + self.inter_attention = ParallelAttention( + config, + layer_number, + attention_type=AttnType.cross_attn) + # Normalize the attention output. + self.post_inter_attention_norm = get_norm(config) + + # MLP + if args.num_experts is not None: + self.mlp = SwitchMLP(config) + else: + self.mlp = ParallelMLP(config) + + # Set bias+dropout+add fusion grad_enable execution handler. + TORCH_MAJOR = int(torch.__version__.split('.')[0]) + TORCH_MINOR = int(torch.__version__.split('.')[1]) + use_nvfuser = TORCH_MAJOR > 1 or (TORCH_MAJOR == 1 and TORCH_MINOR >= 10) + self.bias_dropout_add_exec_handler = \ + nullcontext if use_nvfuser else torch.enable_grad + + if args.retro_add_retriever: + self.retro_num_neighbors = args.retro_num_neighbors + self.retro_chunk_length = args.retro_chunk_length + self.retro_retrieved_length = \ + args.retro_num_retrieved_chunks * args.retro_chunk_length + + # Retriever (bi-directional transformer with cross attention) + if layer_type == LayerType.retro_decoder_with_retriever: + self.retriever = ParallelTransformer( + config=config, + model_type=ModelType.retro_encoder, + self_attn_mask_type=AttnMaskType.padding, + pre_process=True, + post_process=False, + ) + self._retriever_key = 'retriever' + else: + self.retriever = None + + def default_decoder_cross_attention(self, + encoder_output, + enc_dec_attn_mask, + norm_input, + norm_output, + bias_dropout_add_func): + '''Cross attention for a standard encoder-decoder model.''' + + # Attention. + attention_output, attention_bias = \ + self.inter_attention(norm_output, + enc_dec_attn_mask, + encoder_output=encoder_output) + + # Residual connection. + if self.apply_residual_connection_post_norm: + residual = norm_output + else: + residual = norm_input + + if attention_bias is not None: + attention_bias = attention_bias.expand_as(residual) + + # Bias-dropout-add. + with self.bias_dropout_add_exec_handler(): + norm_input = bias_dropout_add_func( + attention_output, + attention_bias, + residual, + self.hidden_dropout) + + # Normalize. + norm_output = self.post_inter_attention_norm(norm_input) + + return norm_input, norm_output + + def retro_encoder_cross_attention(self, + retriever_output, + norm_input, + norm_output, + bias_dropout_add_func): + """Cross attention for Retro encoder. + + Notation: + ns : Sequence length. + bs : Batch size. + d : Hidden size. + l : Number of chunks per sample (i.e., seq_length/chunk_length). + k : Number of neighbors. + r : Number of retrieved tokens (neighbors + continuation). + """ + + ns, bs, d = norm_output.shape # [r, bs * l * k, d] + + # Divide sequence dimension into chunks. + chunked_outputs = norm_output.reshape(self.retro_retrieved_length, + -1, + self.retro_num_neighbors, + d) + chunked_outputs_before_norm = \ + norm_input.reshape(self.retro_retrieved_length, -1, + self.retro_num_neighbors, d) # [r, bs*l, k, d] + + # Per-chunk attention. + norm_inputs = [] + norm_outputs = [] + for k in range(self.retro_num_neighbors): + + # Attention. + chunked_output = chunked_outputs[:,:,k].contiguous() + attention_output, attention_bias = \ + self.inter_attention( + chunked_output, # Q (neighbor embedding) + None, + encoder_output=retriever_output) # K, V (hidden act) + + # Residual connection. + if self.apply_residual_connection_post_norm: + residual = chunked_output + else: + residual = chunked_outputs_before_norm[:,:,k] + + # Re-enable torch grad to enable fused optimization. + with torch.enable_grad(): + norm_input = bias_dropout_add_func( + attention_output, + None if attention_bias is None else attention_bias.expand_as(residual), + residual, + self.hidden_dropout) + norm_inputs.append(norm_input) + + # Layer norm. + norm_output = self.post_inter_attention_norm(norm_input) + norm_outputs.append(norm_output) + + # Concatenate layer norms. + # norm_input : [r, k * bs * l, d] + # norm_output : [r, k * bs * l, d] + norm_input = torch.stack(norm_inputs, dim=1).reshape(ns, bs, d) + norm_output = torch.stack(norm_outputs, dim=1).reshape(ns, bs, d) + + return norm_input, norm_output + + def retro_decoder_cross_attention(self, + retriever_input, + retriever_output, + retriever_attn_mask, + norm_input, + norm_output, + inference_params, + bias_dropout_add_func): + """Cross attention for Retro decoder. + + Notation: + ns : Sequence length. + bs : Batch size. + d : Hidden size. + l : Number of chunks per sample (i.e., seq_length/chunk_length). + m : Number of tokens per chunk. + k : Number of neighbors. + r : Number of retrieved tokens (neighbors + continuation). + """ + + ns, bs, d = norm_output.shape + l = int(np.ceil(ns / self.retro_chunk_length)) + + # Retrieve neighbors. + if self.layer_type == LayerType.retro_decoder_with_retriever: + first_ns = ns % self.retro_chunk_length + if first_ns > 0: + first_chunk, rest_chunk = \ + norm_output[:first_ns], norm_output[first_ns:] + first_chunk = torch.nn.functional.pad( + first_chunk, + (0, 0, 0, 0, 0, self.retro_chunk_length - first_ns), + 'constant', + 0) + chunked_output = \ + torch.cat((first_chunk, rest_chunk), dim=0) # [l * m, bs, d] + else: + chunked_output = norm_output # [l * m, bs, d] + chunked_output = chunked_output \ + .reshape(l, self.retro_chunk_length, bs, d) \ + .permute(1, 2, 0, 3) \ + .reshape(self.retro_chunk_length, bs * l, d) \ + .contiguous() + + # Get Encoder Output + retriever_output = self.retriever( + hidden_states=retriever_input, + attention_mask=retriever_attn_mask, + retriever_output=chunked_output, + retriever_attn_mask=retriever_attn_mask, + inference_params=inference_params) # [r, k * bs * l , d] + retriever_output = retriever_output.reshape( + self.retro_retrieved_length * self.retro_num_neighbors, bs * l, d) # [r * k, bs * l, d] + + # Chunks. + pad = (ns - 1) % self.retro_chunk_length + attending_chunks = norm_output[pad:] + padded_chunks = torch.nn.functional.pad( + attending_chunks, + (0, 0, 0, 0, 0, self.retro_chunk_length - 1), + 'constant', 0) + padded_chunked_output = padded_chunks \ + .reshape(l, self.retro_chunk_length, bs, d) \ + .permute(1, 2, 0, 3) + padded_chunked_output = padded_chunked_output.reshape( + self.retro_chunk_length, bs * l, d).contiguous() + + # Encoder output. + attention_output, attention_bias = \ + self.inter_attention(padded_chunked_output, + None, + encoder_output=retriever_output) + + # Residual connection. + if self.apply_residual_connection_post_norm: + residual = norm_output + else: + residual = norm_input + + # Re-enable torch grad to enable fused optimization. + with torch.enable_grad(): + norm_input = bias_dropout_add_func( + attention_output, + None if attention_bias is None else attention_bias.expand_as(attention_output), + torch.zeros_like(attention_output), + self.hidden_dropout) + norm_input = norm_input \ + .reshape(self.retro_chunk_length, bs, l, d) \ + .permute(2, 0, 1, 3) # [l, m, bs, d] + norm_input = norm_input.reshape(self.retro_chunk_length * l, bs, d) + norm_input = torch.nn.functional.pad( + norm_input, + (0, 0, 0, 0, pad, 0), + 'constant', 0)[:ns] # [ns, b, d] + # TODO: better redesign with inference param + args = get_args() + norm_input = args.retro_attention_gate * norm_input + residual + + # Layer norm post the decoder attention + norm_output = self.post_inter_attention_norm(norm_input) + + return retriever_output, norm_input, norm_output + + def forward(self, hidden_states, attention_mask, + encoder_output=None, enc_dec_attn_mask=None, + retriever_input=None, + retriever_output=None, + retriever_attn_mask=None, + inference_params=None, + rotary_pos_emb=None): + + # Update the params in case the retro param changes during inference + # TODO: better redesign with inference param + args = get_args() + if args.retro_add_retriever: + self.retro_num_neighbors = args.retro_num_neighbors + self.retro_chunk_length = args.retro_chunk_length + self.retro_retrieved_length = \ + args.retro_num_retrieved_chunks * args.retro_chunk_length + + # hidden_states: [s, b, h] + + # Layer norm at the beginning of the transformer layer. + norm_output = self.input_norm(hidden_states) + + # Self attention. + attention_output, attention_bias = \ + self.self_attention( + norm_output, + attention_mask, + inference_params=inference_params, + rotary_pos_emb=rotary_pos_emb) + + # Residual connection. + if self.apply_residual_connection_post_norm: + residual = norm_output + else: + residual = hidden_states + + if self.drop_path is None: + # jit scripting for a nn.module (with dropout) is not + # trigerring the fusion kernel. For now, we use two + # different nn.functional routines to account for varying + # dropout semantics during training and inference phases. + if self.bias_dropout_fusion: + if self.training: + bias_dropout_add_func = bias_dropout_add_fused_train + else: + bias_dropout_add_func = bias_dropout_add_fused_inference + else: + bias_dropout_add_func = get_bias_dropout_add(self.training) + + if attention_bias is not None: + attention_bias = attention_bias.expand_as(residual) + with self.bias_dropout_add_exec_handler(): + norm_input = bias_dropout_add_func( + attention_output, + attention_bias, + residual, + self.hidden_dropout) + else: + out = torch.nn.functional.dropout(attention_output + attention_bias, + p=self.hidden_dropout, + training=self.training) + norm_input = residual + self.drop_path(out) + + # Layer norm post the self attention. + norm_output = self.post_attention_norm(norm_input) + + # Cross attention. + if self.layer_type == LayerType.encoder: + pass + elif self.layer_type == LayerType.decoder: + norm_input, norm_output = \ + self.default_decoder_cross_attention( + encoder_output, + enc_dec_attn_mask, + norm_input, + norm_output, + bias_dropout_add_func) + elif self.layer_type == LayerType.retro_encoder: + norm_input, norm_output = \ + self.retro_encoder_cross_attention( + retriever_output, + norm_input, + norm_output, + bias_dropout_add_func) + elif self.layer_type in (LayerType.retro_decoder, + LayerType.retro_decoder_with_retriever): + retriever_output, norm_input, norm_output = \ + self.retro_decoder_cross_attention( + retriever_input, + retriever_output, + retriever_attn_mask, + norm_input, + norm_output, + inference_params, + bias_dropout_add_func) + else: + raise Exception("Unsupported layer type, '%s'." % + self.layer_type.name) + + # MLP. + mlp_output, mlp_bias = self.mlp(norm_output) + + # Second residual connection. + if self.apply_residual_connection_post_norm: + residual = norm_output + else: + residual = norm_input + + if self.drop_path is None: + if mlp_bias is not None: + mlp_bias = mlp_bias.expand_as(residual) + with self.bias_dropout_add_exec_handler(): + output = bias_dropout_add_func( + mlp_output, + mlp_bias, + residual, + self.hidden_dropout) + + # Jit compiled function creates 'view' tensor. This tensor + # potentially gets saved in the MPU checkpoint function context, + # which rejects view tensors. While making a viewless tensor here + # won't result in memory savings (like the data loader, or + # p2p_communication), it serves to document the origin of this + # 'view' tensor. + output = core.utils.make_viewless_tensor(inp = output, + requires_grad = output.requires_grad, + keep_graph = True) + + else: + if mlp_bias is not None: + mlp_output = mlp_output + mlp_bias + out = torch.nn.functional.dropout(mlp_output, + p=self.hidden_dropout, + training=self.training) + output = residual + self.drop_path(out) + + if self.layer_type == LayerType.retro_decoder_with_retriever: + return output, retriever_output + else: + return output + + +class NoopTransformerLayer(MegatronModule): + """A single 'no-op' transformer layer. + + The sole purpose of this layer is for when a standalone embedding layer + is used (i.e., args.standalone_embedding_stage == True). In this case, + zero transformer layers are assigned when pipeline rank == 0. Additionally, + when virtual pipeline rank >= 1, zero total model parameters are created + (virtual rank 0 contains the input embedding). This results in the model's + input and output tensors being the same, which causes an error when + performing certain memory optimiations on the output tensor (e.g., + deallocating it). Thus, this layer disconnects the input from the output + via a clone. Since ranks containing a no-op layer are generally under- + utilized (both compute and memory), there's no worry of any performance + degredation. + """ + + def __init__(self, layer_number): + super().__init__() + self.layer_number = layer_number + + def forward(self, hidden_states, attention_mask, + encoder_output=None, enc_dec_attn_mask=None, + inference_params=None): + return hidden_states.clone() + + +def _get_num_layers(args, model_type, is_decoder=False): + """Compute the number of transformer layers resident on the current rank.""" + is_encoder_and_decoder_model = (model_type == ModelType.encoder_and_decoder) + if model_type == ModelType.retro_encoder: + num_layers = args.retro_encoder_layers + elif mpu.get_pipeline_model_parallel_world_size() > 1: + if is_encoder_and_decoder_model: + assert args.pipeline_model_parallel_split_rank is not None + + # When a standalone embedding stage is used, a rank is taken from + # the encoder's ranks, to be used for the encoder's embedding + # layer. This way, the rank referenced by the 'split rank' remains + # the same whether or not a standalone embedding stage is used. + num_ranks_in_encoder = ( + args.pipeline_model_parallel_split_rank - 1 + if args.standalone_embedding_stage else + args.pipeline_model_parallel_split_rank + ) + num_ranks_in_decoder = args.transformer_pipeline_model_parallel_size - num_ranks_in_encoder + assert args.encoder_num_layers % num_ranks_in_encoder == 0, \ + 'encoder_num_layers (%d) must be divisible by number of ranks given to encoder (%d)' % (args.encoder_num_layers, num_ranks_in_encoder) + assert args.decoder_num_layers % num_ranks_in_decoder == 0, \ + 'decoder_num_layers (%d) must be divisible by number of ranks given to decoder (%d)' % (args.decoder_num_layers, num_ranks_in_decoder) + if mpu.is_pipeline_stage_before_split(): + num_layers = ( + 0 + if args.standalone_embedding_stage + and mpu.get_pipeline_model_parallel_rank() == 0 else + args.encoder_num_layers // num_ranks_in_encoder + ) + else: + num_layers = args.decoder_num_layers // num_ranks_in_decoder + else: + assert args.num_layers == args.encoder_num_layers + assert args.num_layers % args.transformer_pipeline_model_parallel_size == 0, \ + 'num_layers must be divisible by transformer_pipeline_model_parallel_size' + + # When a standalone embedding stage is used, all transformer layers + # are divided among pipeline rank >= 1, while on pipeline rank 0, + # ranks either contain the input embedding layer (virtual pp rank 0), + # or no layers at all (virtual pp rank >= 1). + num_layers = ( + 0 + if args.standalone_embedding_stage + and mpu.get_pipeline_model_parallel_rank() == 0 else + args.num_layers // args.transformer_pipeline_model_parallel_size + ) + else: + if not is_decoder: + num_layers = args.encoder_num_layers + else: + num_layers = args.decoder_num_layers + return num_layers + + +def _get_layer_type(model_type, default_layer_type, retro_layer_numbers, + layer_number): + args = get_args() + if args.retro_add_retriever and layer_number in retro_layer_numbers: + if model_type == ModelType.retro_decoder: + return LayerType.retro_decoder_with_retriever \ + if layer_number == retro_layer_numbers[0] \ + else LayerType.retro_decoder + elif model_type == ModelType.retro_encoder: + return LayerType.retro_encoder + else: + raise Exception("Unsupported model type, '%s'." % model_type) + else: + return default_layer_type + + +class ParallelTransformer(MegatronModule): + """Transformer class.""" + + def __init__(self, config, + model_type, layer_type=LayerType.encoder, + self_attn_mask_type=AttnMaskType.padding, + post_norm=True, + pre_process=True, + post_process=True, + drop_path_rate=0.0): + super(ParallelTransformer, self).__init__() + args = get_args() + + self.layer_type = layer_type + self.model_type = model_type + self.bf16 = config.bf16 + self.fp32_residual_connection = config.fp32_residual_connection + self.post_norm = post_norm + self.pre_process = pre_process + self.post_process = post_process + self.input_tensor = None + self.drop_path_rate = drop_path_rate + self.transformer_impl = args.transformer_impl + self.retro_add_retriever = args.retro_add_retriever + + # Store activation checkpoiting flag. + self.recompute_granularity = config.recompute_granularity + self.recompute_method = config.recompute_method + self.recompute_num_layers = config.recompute_num_layers + self.distribute_saved_activations = \ + config.distribute_saved_activations and not config.sequence_parallel + + self.sequence_parallel = config.sequence_parallel + + # Transformer Engine Init. + self.transformer_engine_v_0_10 = False + self.transformer_engine_v_0_11 = False + self.transformer_engine_v_0_8 = False + if self.transformer_impl == 'transformer_engine': + global transformer_engine + import transformer_engine + from importlib.metadata import version + from pkg_resources import packaging + + te_version = packaging.version.Version(version("transformer-engine")) + if te_version >= packaging.version.Version("0.8.0"): + self.transformer_engine_v_0_8 = True + if te_version >= packaging.version.Version("0.10.0"): + self.transformer_engine_v_0_10 = True + if te_version >= packaging.version.Version("0.11.0"): + self.transformer_engine_v_0_11 = True + + del version, packaging + + assert not args.squared_relu, "TransformerEngine does not support squared relu activation." + + self.use_fp8 = args.fp8 is not None + self.fp8_recipe = None + self.fp8_group = None + if self.use_fp8: + assert args.transformer_impl == 'transformer_engine', \ + 'transformer-engine required for fp8 training and inference' + self.fp8_group = mpu.get_amax_reduction_group() + if args.fp8 == "e4m3": + fp8_format = transformer_engine.common.recipe.Format.E4M3 + elif args.fp8 == "hybrid": + fp8_format = transformer_engine.common.recipe.Format.HYBRID + else: + raise ValueError("The DelayedScaling recipe only supports E4M3 and HYBRID formats.") + self.fp8_recipe = transformer_engine.common.recipe.DelayedScaling( + margin=args.fp8_margin, + interval=args.fp8_interval, + fp8_format=fp8_format, + amax_history_len=args.fp8_amax_history_len, + amax_compute_algo=args.fp8_amax_compute_algo, + override_linear_precision=(False, False, not args.fp8_wgrad), + ) + + self.num_microbatches_in_previous_step = -1 + self.microbatch_count = 0 + self.checkpoint_core_attention = config.recompute_granularity == 'selective' + + # Number of layers. + self.num_layers = _get_num_layers(args, model_type, + layer_type==LayerType.decoder) + + self.drop_path_rates = [ + rate.item() for rate in + torch.linspace(0, self.drop_path_rate, config.num_layers)] + + self.retro_layer_numbers = None + if model_type == ModelType.retro_decoder: + retro_layer_start = 6 if config.num_layers <= 15 else 9 + self.retro_layer_numbers = \ + np.arange(retro_layer_start, args.num_layers + 1, 3).tolist() + if model_type == ModelType.retro_encoder: + self.retro_layer_numbers = [1] + + # Transformer layers. + if args.retro_add_retriever: + assert self.recompute_granularity != 'full', \ + "Full recompute not supported for Retro." + assert args.transformer_impl == 'local', \ + "Transformer engine does not support Retro layers." + def build_layer(layer_number): + if args.transformer_impl == 'local': + current_layer_type = _get_layer_type( + model_type, layer_type, self.retro_layer_numbers, + layer_number) + return ParallelTransformerLayer( + config, + layer_number, + layer_type=current_layer_type, + self_attn_mask_type=self_attn_mask_type, + drop_path_rate=self.drop_path_rates[layer_number - 1]) + else: + # This argument is only available from TE v0.10 onwards. + extra_transformer_engine_kwargs = {} + if self.transformer_engine_v_0_8: + extra_transformer_engine_kwargs["bias"] = args.add_bias_linear + if self.transformer_engine_v_0_10: + extra_transformer_engine_kwargs["activation"] = "swiglu" if args.swiglu else "gelu" + if self.transformer_engine_v_0_11: + extra_transformer_engine_kwargs["normalization"] = args.normalization + assert config.attention_softmax_in_fp32, "TransformerEngine only supports softmax compute in FP32." + assert ( + (bool(int(os.getenv("NVTE_APPLY_QK_LAYER_SCALING", "0"))) and args.fp16) == config.apply_query_key_layer_scaling + ), "Unsupported config for apply_query_key_layer_scaling in TransformerEngine." + return transformer_engine.pytorch.TransformerLayer( + config.hidden_size, + config.ffn_hidden_size, + config.num_attention_heads, + layernorm_epsilon=config.layernorm_epsilon, + hidden_dropout=config.hidden_dropout, + attention_dropout=config.attention_dropout, + init_method=config.init_method, + output_layer_init_method=config.output_layer_init_method, + layer_number=layer_number, + kv_channels=config.kv_channels, + self_attn_mask_type=self_attn_mask_type.name, + tp_group=mpu.get_tensor_model_parallel_group(), + get_rng_state_tracker=tensor_parallel.get_cuda_rng_tracker, + fuse_wgrad_accumulation=config.gradient_accumulation_fusion, + seq_length=args.seq_length, + micro_batch_size=args.micro_batch_size, + sequence_parallel=config.sequence_parallel, + params_dtype=config.params_dtype, + apply_residual_connection_post_layernorm=config.apply_residual_connection_post_layernorm, + output_layernorm=False, + layer_type="encoder", + drop_path_rate=self.drop_path_rates[layer_number - 1], + set_parallel_mode=True, + fuse_qkv_params=True, + **extra_transformer_engine_kwargs) + + if config.virtual_pipeline_model_parallel_size is not None: + assert config.num_layers % config.virtual_pipeline_model_parallel_size == 0, \ + 'num_layers_per_stage must be divisible by ' \ + 'virtual_pipeline_model_parallel_size' + assert args.model_type != ModelType.encoder_and_decoder + # Number of layers in each model chunk is the number of layers in the stage, + # divided by the number of model chunks in a stage. + self.num_layers = self.num_layers // config.virtual_pipeline_model_parallel_size + # With 8 layers, 2 stages, and 4 model chunks, we want an assignment of + # layers to stages like (each list is a model chunk): + # Stage 0: [0] [2] [4] [6] + # Stage 1: [1] [3] [5] [7] + # With 8 layers, 2 stages, and 2 virtual stages, we want an assignment of + # layers to stages like (each list is a model chunk): + # Stage 0: [0, 1] [4, 5] + # Stage 1: [2, 3] [6, 7] + offset = mpu.get_virtual_pipeline_model_parallel_rank() * ( + config.num_layers // config.virtual_pipeline_model_parallel_size) + \ + (mpu.get_pipeline_model_parallel_rank() * self.num_layers) + else: + # Each stage gets a contiguous set of layers. + if args.model_type == ModelType.encoder_and_decoder and \ + mpu.get_pipeline_model_parallel_world_size() > 1: + pipeline_rank = mpu.get_pipeline_model_parallel_rank() + if layer_type == LayerType.encoder: + offset = pipeline_rank * self.num_layers + else: + num_ranks_in_enc = args.pipeline_model_parallel_split_rank + offset = (pipeline_rank - num_ranks_in_enc) * self.num_layers + else: + offset = mpu.get_pipeline_model_parallel_rank() * self.num_layers + + if self.num_layers == 0: + # When a standalone embedding stage is used (e.g., + # args.standalone_embedding_stage == True), virtual pipeline ranks + # on pipeline rank 0 will have zero transformer layers assigned to + # them. This results in the model's input and output tensors to be + # the same, which will cause failure for certain output tensor + # optimizations (e.g., pipeline output deallocation). To remedy + # this, we assign a 'no-op' layer on these ranks, which will + # disconnect the input tensor from the output tensor. + self.num_layers = 1 + self.layers = torch.nn.ModuleList([ NoopTransformerLayer(1) ]) + else: + self.layers = torch.nn.ModuleList( + [build_layer(i + 1 + offset) for i in range(self.num_layers)]) + + # Update dropout rate for Retro encoder. + if model_type == ModelType.retro_encoder: + for layer in self.layers: + if layer.self_attention.use_flash_attn: + layer.self_attention.core_attention_flash.dropout_p = \ + torch.nn.Dropout(args.retro_encoder_attention_dropout) + else: + layer.self_attention.core_attention.attention_dropout.p =\ + args.retro_encoder_attention_dropout + layer.hidden_dropout = args.retro_encoder_hidden_dropout + + if self.post_process and self.post_norm: + # Final layer norm before output. + self.final_norm = get_norm(config) + + def _get_layer(self, layer_number): + return self.layers[layer_number] + + def _checkpointed_forward(self, hidden_states, attention_mask, + encoder_output, enc_dec_attn_mask, + rotary_pos_emb, is_first_microbatch): + """Forward method with activation checkpointing.""" + def custom(start, end): + def custom_forward(*args, **kwargs): + x_, *args = args + for index in range(start, end): + layer = self._get_layer(index) + x_ = layer(x_, *args, **kwargs) + return x_ + return custom_forward + + te_forward_kwargs = {} + if self.transformer_impl == 'transformer_engine': + te_forward_kwargs['is_first_microbatch'] = is_first_microbatch + if self.transformer_engine_v_0_10: + te_forward_kwargs['rotary_pos_emb'] = rotary_pos_emb + + if self.recompute_method == 'uniform': + # Uniformly divide the total number of Transformer layers and + # checkpoint the input activation of each divided chunk. + # A method to further reduce memory usage reducing checkpoints. + l = 0 + while l < self.num_layers: + if self.transformer_impl == 'transformer_engine': + hidden_states = transformer_engine.pytorch.checkpoint( + custom(l, l + self.recompute_num_layers), + self.distribute_saved_activations, + tensor_parallel.get_cuda_rng_tracker, + mpu.get_tensor_model_parallel_group(), + hidden_states, attention_mask, encoder_output, + enc_dec_attn_mask, **te_forward_kwargs) + else: + hidden_states = tensor_parallel.checkpoint( + custom(l, l + self.recompute_num_layers), + self.distribute_saved_activations, + hidden_states, attention_mask, + encoder_output, enc_dec_attn_mask, + None, None, None, None, rotary_pos_emb) + + l += self.recompute_num_layers + + elif self.recompute_method == 'block': + # Checkpoint the input activation of only a set number of individual + # Transformer layers and skip the rest. + # A method fully use the device memory removing redundant re-computation. + for l in range(self.num_layers): + if l < self.recompute_num_layers: + if self.transformer_impl == 'transformer_engine': + hidden_states = transformer_engine.pytorch.checkpoint( + custom(l, l + 1), + self.distribute_saved_activations, + tensor_parallel.get_cuda_rng_tracker, + mpu.get_tensor_model_parallel_group(), + hidden_states, attention_mask, encoder_output, + enc_dec_attn_mask, **te_forward_kwargs) + else: + hidden_states = tensor_parallel.checkpoint( + custom(l, l + 1), + self.distribute_saved_activations, + hidden_states, attention_mask, + encoder_output, enc_dec_attn_mask, + None, None, None, None, rotary_pos_emb) + else: + if self.transformer_impl == 'transformer_engine': + hidden_states = custom(l, l + 1)( + hidden_states, attention_mask, encoder_output, + enc_dec_attn_mask, **te_forward_kwargs) + else: + hidden_states = custom(l, l + 1)( + hidden_states, attention_mask, + encoder_output, enc_dec_attn_mask, + None, None, None, None, rotary_pos_emb) + else: + raise ValueError("Invalid activation recompute method.") + + return hidden_states + + def set_input_tensor(self, input_tensor): + """Set input tensor to be used instead of forward()'s input. + + When doing pipeline parallelism the input from the previous + stage comes from communication, not from the input, so the + model's forward_step_func won't have it. This function is thus + used by internal code to bypass the input provided by the + forward_step_func""" + self.input_tensor = input_tensor + + def forward(self, hidden_states, attention_mask, + encoder_output=None, enc_dec_attn_mask=None, + retriever_input=None, + retriever_output=None, + retriever_attn_mask=None, + inference_params=None, + rotary_pos_emb=None): + # hidden_states: [s, b, h] + + # Checks. + if inference_params: + assert self.recompute_granularity is None, \ + 'inference does not work with activation checkpointing' + + if not self.pre_process: + # See set_input_tensor() + hidden_states = self.input_tensor + + # Viewless tensor. + # - We only need to create a viewless tensor in the case of micro batch + # size (mbs) == 1, since in this case, 'hidden_states.transpose()' + # above creates a view tensor, and '.contiguous()' is a pass-through. + # For mbs >= 2, '.contiguous()' creates a new tensor, eliminating + # the need to make it viewless. + # + # However, we don't explicitly check mbs == 1 here because + # make_viewless_tensor() has negligible overhead when its input + # is already viewless. + # + # - For the 'else' case above, calling make_viewless_tensor() here is + # likely redundant, since p2p_communication.py (likely originator) + # already creates viewless tensors. That said, make_viewless_tensor() + # is called here to be future-proof and corner-case-proof. + hidden_states = core.utils.make_viewless_tensor( + hidden_states, + requires_grad=True, + keep_graph=True, + ) + + # RNG context. + if self.sequence_parallel: + rng_context = tensor_parallel.get_cuda_rng_tracker().fork() + else: + rng_context = nullcontext() + + # Forward layers. + with rng_context: + # The fp8_autocast context manager is a no-op when enabled=True + # The if...else serves to short circuit name resolution for fp8_autocast + with transformer_engine.pytorch.fp8_autocast( + enabled=self.use_fp8, + fp8_recipe=self.fp8_recipe, + fp8_group=self.fp8_group + ) if self.use_fp8 else nullcontext(): + # Determine if the current iteration is first microbatch + if self.num_microbatches_in_previous_step != get_num_microbatches(): + self.microbatch_count = 0 # Reset count on new batch size rampup interval + self.num_microbatches_in_previous_step = get_num_microbatches() + is_first_microbatch = self.microbatch_count % get_num_microbatches() == 0 + + # Forward pass. + if self.recompute_granularity == 'full': + hidden_states = self._checkpointed_forward(hidden_states, + attention_mask, + encoder_output, + enc_dec_attn_mask, + rotary_pos_emb, + is_first_microbatch) + else: + forward_kwargs = { + 'encoder_output': encoder_output, + 'enc_dec_attn_mask': enc_dec_attn_mask, + 'inference_params': inference_params, + } + + if self.transformer_impl == 'transformer_engine': + forward_kwargs['is_first_microbatch'] = is_first_microbatch + forward_kwargs['checkpoint_core_attention'] = self.checkpoint_core_attention + if self.transformer_engine_v_0_10: + forward_kwargs['rotary_pos_emb'] = rotary_pos_emb + else: + forward_kwargs['rotary_pos_emb'] = rotary_pos_emb + forward_kwargs['retriever_input'] = retriever_input + forward_kwargs['retriever_output'] = retriever_output + forward_kwargs['retriever_attn_mask'] = retriever_attn_mask + + for index in range(self.num_layers): + layer = self._get_layer(index) + + hidden_states = layer( + hidden_states, + attention_mask, + **forward_kwargs) + + # First Retro decoder layer returns both hidden_states + # and retriever_output. Make retriever_output available + # to subsequence Retro layers. + if isinstance(hidden_states, tuple): + assert len(hidden_states) == 2 + hidden_states, retriever_output = hidden_states + forward_kwargs["retriever_output"] = retriever_output + + # Skip counter update for eval and activation checkpointing + if torch.is_grad_enabled() and self.training: + self.microbatch_count += 1 + + # Final layer norm. + if self.post_process and self.post_norm: + hidden_states = self.final_norm(hidden_states) + + return hidden_states + + def load_state_dict(self, state_dict, strict=True): + """Customize load.""" + + # Handle renaming layernorm -> norm in component names + state_dict_ = {} + for key in state_dict.keys(): + # Bypass TransformerEngine module parameters. + if "layernorm_qkv" in key or "layernorm_mlp" in key: + state_dict_[key] = state_dict[key] + continue + newkey = key.replace("layernorm", "norm") + state_dict_[newkey] = state_dict[key] + + super().load_state_dict(state_dict_, strict) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/utils.py new file mode 100644 index 0000000..5762000 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/utils.py @@ -0,0 +1,79 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +"""Utilities for models.""" + +import math + +import torch + +from megatron.training import get_args +from megatron.legacy.model import LayerNorm, RMSNorm +from megatron.core.jit import jit_fuser + +def init_method_normal(sigma): + """Init method based on N(0, sigma).""" + def init_(tensor): + return torch.nn.init.normal_(tensor, mean=0.0, std=sigma) + + return init_ + + +def scaled_init_method_normal(sigma, num_layers): + """Init method based on N(0, sigma/sqrt(2*num_layers).""" + std = sigma / math.sqrt(2.0 * num_layers) + + def init_(tensor): + return torch.nn.init.normal_(tensor, mean=0.0, std=std) + + return init_ + + +def attention_mask_func(attention_scores, attention_mask): + attention_scores.masked_fill_(attention_mask, -10000.0) + return attention_scores + + +def get_linear_layer(rows, columns, init_method): + """Simple linear layer with weight initialization.""" + layer = torch.nn.Linear(rows, columns) + if get_args().perform_initialization: + init_method(layer.weight) + with torch.no_grad(): + layer.bias.zero_() + return layer + + +@jit_fuser +def gelu_impl(x): + """OpenAI's gelu implementation.""" + return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x * + + (1.0 + 0.044715 * x * x))) +def openai_gelu(x): + return gelu_impl(x) + + +#This is actually Python equivalent of torch.nn.functional.gelu(), also with type hints for ONNX exporter +@jit_fuser +def erf_gelu(x): + return x * 0.5 * (torch.erf(x / 1.41421).to(dtype=x.dtype)+torch.ones_like(x).to(dtype=x.dtype)) + + +def get_norm(config): + args = get_args() + if args.normalization == "LayerNorm": + return LayerNorm( + config.hidden_size, + eps=config.layernorm_epsilon, + no_persist_layer_norm=not config.persist_layer_norm, + sequence_parallel=config.sequence_parallel, + apply_layernorm_1p=args.apply_layernorm_1p) + elif args.normalization == "RMSNorm": + if args.apply_layernorm_1p: + raise NotImplementedError('RMSNorm does not currently support the layernorm_1p formulation.') + + return RMSNorm(dim=config.hidden_size, + eps=config.layernorm_epsilon, + sequence_parallel=config.sequence_parallel) + else: + raise Exception(f"unsupported norm type '{args.normalization}'.") diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/classification.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/classification.py new file mode 100644 index 0000000..f9419c7 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/classification.py @@ -0,0 +1,86 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Vision Transformer(VIT) model.""" + +import torch +from torch.nn.init import trunc_normal_ +from megatron.training import get_args +from megatron.legacy.model.utils import get_linear_layer +from megatron.legacy.model.vision.vit_backbone import VitBackbone, VitMlpHead +from megatron.legacy.model.vision.mit_backbone import mit_b3_avg +from megatron.legacy.model.module import MegatronModule + +class VitClassificationModel(MegatronModule): + """Vision Transformer Model.""" + + def __init__(self, config, num_classes, finetune=False, + pre_process=True, post_process=True): + super(VitClassificationModel, self).__init__() + args = get_args() + self.config = config + + self.hidden_size = args.hidden_size + self.num_classes = num_classes + self.finetune = finetune + self.pre_process = pre_process + self.post_process = post_process + self.backbone = VitBackbone( + config=config, + pre_process=self.pre_process, + post_process=self.post_process, + single_token_output=True + ) + + if self.post_process: + if not self.finetune: + self.head = VitMlpHead(config, self.hidden_size, self.num_classes) + else: + self.head = get_linear_layer( + self.hidden_size, + self.num_classes, + torch.nn.init.zeros_ + ) + + def set_input_tensor(self, input_tensor): + """See megatron.legacy.model.transformer.set_input_tensor()""" + self.backbone.set_input_tensor(input_tensor) + + def forward(self, input): + hidden_states = self.backbone(input) + + if self.post_process: + hidden_states = self.head(hidden_states) + + return hidden_states + + +class MitClassificationModel(MegatronModule): + """Mix vision Transformer Model.""" + + def __init__(self, num_classes, + pre_process=True, post_process=True): + super(MitClassificationModel, self).__init__() + args = get_args() + + self.hidden_size = args.hidden_size + self.num_classes = num_classes + + self.backbone = mit_b3_avg() + self.head = torch.nn.Linear(512, num_classes) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, torch.nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, torch.nn.Linear) and m.bias is not None: + torch.nn.init.constant_(m.bias, 0) + + def set_input_tensor(self, input_tensor): + """See megatron.legacy.model.transformer.set_input_tensor()""" + pass + + def forward(self, input): + hidden_states = self.backbone(input) + hidden_states = self.head(hidden_states) + + return hidden_states diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/dino.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/dino.py new file mode 100644 index 0000000..20ca210 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/dino.py @@ -0,0 +1,291 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the Apache license found in the +# LICENSE file in the root directory of this source tree. + +# copied from https://github.com/facebookresearch/dino/blob/main/main_dino.py +# reworked/refactored some parts to make it run in Megatron. +import math +import apex +import einops +import torch +import numpy as np +import torch.nn.functional as F +from torch.nn.init import trunc_normal_ +from megatron.training import get_args, print_rank_0 +from megatron.legacy.model.utils import get_linear_layer +from megatron.legacy.model.vision.vit_backbone import VitBackbone +from megatron.legacy.model.module import MegatronModule +from megatron.legacy.model.vision.mit_backbone import mit_b5_avg +from megatron.legacy.model.vision.esvit_swin_backbone import get_swin + + +class DINOLoss(torch.nn.Module): + def __init__(self, out_dim, ncrops, warmup_teacher_temp, teacher_temp, + warmup_teacher_temp_epochs, nepochs, student_temp=0.1, + center_momentum=0.9): + super().__init__() + self.student_temp = student_temp + self.center_momentum = center_momentum + self.ncrops = ncrops + self.register_buffer("center", torch.zeros(1, out_dim)) + # we apply a warm up for the teacher temperature because + # a too high temperature makes the training instable at the beginning + self.teacher_temp_schedule = np.concatenate(( + np.linspace(warmup_teacher_temp, + teacher_temp, warmup_teacher_temp_epochs), + np.ones(nepochs - warmup_teacher_temp_epochs) * teacher_temp + )) + self.teacher_temp = teacher_temp + + def forward(self, student_output, teacher_output, iteration): + """ + Cross-entropy between softmax outputs of the teacher + and student network. + """ + args = get_args() + student_out = student_output / self.student_temp + student_out = student_out.chunk(self.ncrops) + + epoch = iteration // args.iter_per_epoch + + # teacher centering and sharpening + temp = self.teacher_temp_schedule[epoch] + teacher_out = F.softmax((teacher_output - self.center) / temp, dim=-1) + + teacher_out = teacher_out.detach().chunk(2) + + total_loss = 0 + n_loss_terms = 0 + for iq, q in enumerate(teacher_out): + for v in range(len(student_out)): + if v == iq: + # we skip cases where student and teacher operate on the same view + continue + loss = torch.sum(-q * F.log_softmax(student_out[v], dim=-1), dim=-1) + total_loss += loss.mean() + n_loss_terms += 1 + total_loss /= n_loss_terms + self.update_center(teacher_output) + return total_loss + + @torch.no_grad() + def update_center(self, teacher_output): + """ + Update center used for teacher output. + """ + batch_center = torch.sum(teacher_output, dim=0, keepdim=True) + torch.distributed.all_reduce(batch_center) + batch_center = batch_center / (len(teacher_output) * torch.distributed.get_world_size()) + self.center = self.center * self.center_momentum + batch_center * (1 - self.center_momentum) + +class DINOHead(torch.nn.Module): + def __init__(self, in_dim, out_dim, norm_last_layer=True, nlayers=3): + super().__init__() + args = get_args() + hidden_dim = args.dino_head_hidden_size + bottleneck_dim = args.dino_bottleneck_size + nlayers = max(nlayers, 1) + if nlayers == 1: + self.mlp = torch.nn.Linear(in_dim, bottleneck_dim) + else: + layers = [torch.nn.Linear(in_dim, hidden_dim)] + layers.append(torch.nn.GELU()) + for _ in range(nlayers - 2): + layers.append(torch.nn.Linear(hidden_dim, hidden_dim)) + layers.append(torch.nn.GELU()) + layers.append(torch.nn.Linear(hidden_dim, bottleneck_dim)) + self.mlp = torch.nn.Sequential(*layers) + self.apply(self._init_weights) + self.last_layer = torch.nn.utils.weight_norm(torch.nn.Linear(bottleneck_dim, out_dim, bias=False)) + self.last_layer.weight_g.data.fill_(1) + if norm_last_layer: + self.last_layer.weight_g.requires_grad = False + + def _init_weights(self, m): + if isinstance(m, torch.nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, torch.nn.Linear) and m.bias is not None: + torch.nn.init.constant_(m.bias, 0) + + def forward(self, x): + x = self.mlp(x) + x = torch.nn.functional.normalize(x, dim=-1, p=2) + x = self.last_layer(x) + return x + + +class MultiCropWrapper(MegatronModule): + + """ + Perform forward pass separately on each resolution input. + The inputs corresponding to a single resolution are clubbed and single + forward is run on the same resolution inputs. Hence we do several + forward passes = number of different resolutions used. We then + concatenate all the output features and run the head forward on these + concatenated features. + """ + def __init__(self, backbone, head): + super(MultiCropWrapper, self).__init__() + # disable layers dedicated to ImageNet labels classification + #backbone.fc, backbone.head = torch.nn.Identity(), torch.nn.Identity() + self.backbone = backbone + self.head = head + + def forward(self, x): + # convert to list + if not isinstance(x, list): + x = [x] + idx_crops = torch.cumsum(torch.unique_consecutive( + torch.tensor([inp.shape[-1] for inp in x]), + return_counts=True, + )[1], 0) + + start_idx = 0 + for end_idx in idx_crops: + _out = self.backbone(torch.cat(x[start_idx: end_idx])) + if start_idx == 0: + output = _out + else: + output = torch.cat((output, _out)) + start_idx = end_idx + # Run the head forward on the concatenated features. + if self.training: + return self.head(output) + else: + return output + + +def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, + warmup_epochs=0, start_warmup_value=0): + warmup_schedule = np.array([]) + warmup_iters = warmup_epochs * niter_per_ep + if warmup_epochs > 0: + warmup_schedule = \ + np.linspace(start_warmup_value, base_value, warmup_iters) + + iters = np.arange(epochs * niter_per_ep - warmup_iters) + schedule = final_value + 0.5 * (base_value - final_value) \ + * (1 + np.cos(np.pi * iters / len(iters))) + + schedule = np.concatenate((warmup_schedule, schedule)) + assert len(schedule) == epochs * niter_per_ep + return schedule + + +def get_student_backbone_and_num_features(config, pre_process=True, post_process=True): + args = get_args() + + if args.vision_backbone_type == 'vit': + student = VitBackbone(config, + pre_process=pre_process, + post_process=post_process, + drop_path_rate=0.1, + single_token_output=True) + num_features = args.hidden_size + elif args.vision_backbone_type == 'mit': + student = mit_b5_avg(drop_path_rate=0.1) + num_features = 512 + elif args.vision_backbone_type == 'swin': + student = get_swin() + num_features = student.num_features + else: + raise Exception('{} vision backbone is not supported.'.format( + args.vision_backbone_type)) + + return student, num_features + +def get_teacher_backbone_and_num_features(config, pre_process=True, post_process=True): + args = get_args() + + if args.vision_backbone_type == 'vit': + teacher = VitBackbone(config, + pre_process=pre_process, + post_process=post_process, + single_token_output=True) + num_features = args.hidden_size + elif args.vision_backbone_type == 'mit': + teacher = mit_b5_avg(drop_path_rate=0.0) + num_features = 512 + elif args.vision_backbone_type == 'swin': + teacher = get_swin(is_teacher=True) + num_features = teacher.num_features + else: + raise Exception('{} vision backbone is not supported.'.format( + args.vision_backbone_type)) + return teacher, num_features + + +class DINOPretrainModel(MegatronModule): + def __init__(self, config, pre_process=True, post_process=True): + super(DINOPretrainModel, self).__init__() + args = get_args() + self.config = config + self.out_dim = 65536 + + self.dino_loss = DINOLoss( + self.out_dim, + args.dino_local_crops_number + 2, + args.dino_warmup_teacher_temp, + args.dino_teacher_temp, + args.dino_warmup_teacher_temp_epochs, + 300, + ) + + self.pre_process = pre_process + self.post_process = post_process + self.momentum_teacher = 0.996 + + student_backbone, num_features = \ + get_student_backbone_and_num_features(config, pre_process, post_process) + + self.student = MultiCropWrapper( + student_backbone, + DINOHead(num_features, self.out_dim, + norm_last_layer=args.dino_norm_last_layer) + ) + + self.momentum_schedule = cosine_scheduler( + self.momentum_teacher, 1, + args.train_iters // args.iter_per_epoch, + args.iter_per_epoch + ) + + teacher_backbone, num_features = \ + get_teacher_backbone_and_num_features(config, pre_process, post_process) + self.teacher = MultiCropWrapper( + teacher_backbone, + DINOHead(num_features, self.out_dim) + ) + self.teacher.load_state_dict(self.student.state_dict()) + + for p in self.teacher.parameters(): + if hasattr(p, "requires_grad") and p.requires_grad is not None: + p.requires_grad = False + + def set_input_tensor(self, tensor): + pass + + def forward(self, input): + student_output = None + if self.training: + student_output = self.student(input) + teacher_output = self.teacher(input[:2]) + else: + teacher_output = self.teacher(input) + return student_output, teacher_output + + def cancel_gradients_last_layer(self, iteration): + args = get_args() + epoch = iteration // args.iter_per_epoch + if epoch < args.dino_freeze_last_layer: + for n, p in self.student.named_parameters(): + if "last_layer" in n: + p.grad = None + + def update_momentum(self, iteration): + with torch.no_grad(): + m = self.momentum_schedule[iteration] + for param_q, param_k in zip(self.student.parameters(), self.teacher.parameters()): + param_k.data.mul_(m).add_((1 - m) * param_q.detach().data) + diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/esvit_swin_backbone.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/esvit_swin_backbone.py new file mode 100644 index 0000000..8793204 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/esvit_swin_backbone.py @@ -0,0 +1,849 @@ +# Copyright (c) 2021 Microsoft +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------- +# Modified by Chunyuan Li (chunyl@microsoft.com) +# Swin Transformer +# -------------------------------------------------------- + +import os +import logging +import torch +import torch.nn as nn +import torch.nn.functional as F +from functools import partial +import torch.distributed as dist +from torch.nn.init import trunc_normal_ +from megatron.legacy.model.transformer import DropPath +from megatron.training import get_args +from megatron.legacy.model import LayerNorm +import numpy as np +from math import sqrt + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, + out_features=None, act_layer=nn.GELU, drop=0.): + super(Mlp, self).__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + r"""Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): + + super(WindowAttention, self).__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2 Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0).type(attn.type()) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn_out = attn + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x, attn_out + + def extra_repr(self) -> str: + return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + + @staticmethod + def compute_macs(module, input, output): + B, N, C = input[0].shape + + module.__flops__ += module.flops(N) * B + + +class SwinTransformerBlock(nn.Module): + r"""Swin Transformer Block. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, + qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + self.H = input_resolution[0] + self.W = input_resolution[1] + + self.attn_mask_dict = {} + + + def create_attn_mask(self, H, W): + # calculate attention mask for SW-MSA + + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1)) # 1 Hp Wp 1 + h_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask + + + def forward(self, x): + B, L, C = x.shape + H = int(sqrt(L)) + W = H + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + + if H in self.attn_mask_dict.keys(): + attn_mask = self.attn_mask_dict[H] + else: + self.attn_mask_dict[H] = self.create_attn_mask(self.H, self.W).to(x.device) + attn_mask = self.attn_mask_dict[H] + + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows, attn = self.attn(x_windows, attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x, attn + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ + f"window_size={self.window_size}, shift_size={self.shift_size} mlp_ratio={self.mlp_ratio}" + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + + +class PatchMerging(nn.Module): + r"""Patch Merging Layer. + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + H = int(sqrt(L)) + W = H + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + H, W = self.input_resolution + flops = H * W * self.dim + flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + return flops + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + """ + + def __init__(self, dim, input_resolution, depth, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None): + + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + + self.blocks = nn.ModuleList([ + SwinTransformerBlock(dim=dim, input_resolution=input_resolution, + num_heads=num_heads, window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop, attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer) + for i in range(depth)]) + if downsample is not None: + self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x): + for blk in self.blocks: + x, _ = blk(x) + if self.downsample is not None: + x = self.downsample(x) + return x + + def forward_with_features(self, x): + fea = [] + for blk in self.blocks: + x, _ = blk(x) + fea.append(x) + if self.downsample is not None: + x = self.downsample(x) + return x, fea + + def forward_with_attention(self, x): + attns = [] + for blk in self.blocks: + x, attn = blk(x) + attns.append(attn) + if self.downsample is not None: + x = self.downsample(x) + return x, attns + + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None): + super().__init__() + img_size = (img_size, img_size) + patch_size = (patch_size, patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + B, C, H, W = x.shape + + x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C + if self.norm is not None: + x = self.norm(x) + return x + + + def flops(self): + Ho, Wo = self.patches_resolution + flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops + +class SwinTransformer(nn.Module): + r""" Swin Transformer + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + img_size (int | tuple(int)): Input image size. + patch_size (int | tuple(int)): Patch size. + in_chans (int): Number of input channels. + num_classes (int): Number of classes for classification head. + embed_dim (int): Embedding dimension. + depths (tuple(int)): Depth of Swin Transformer layers. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: Truee + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. + drop_path_rate (float): Stochastic depth rate. + norm_layer (nn.Module): normalization layer. + ape (bool): If True, add absolute position embedding to the patch embedding. + patch_norm (bool): If True, add normalization after patch embedding. + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, + embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], + window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, + norm_layer=nn.LayerNorm, ape=False, patch_norm=True, **kwargs): + super().__init__() + + self.num_classes = num_classes + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = int(embed_dim * 2 ** (self.num_layers - 1)) + self.mlp_ratio = mlp_ratio + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) + trunc_normal_(self.absolute_pos_embed, std=.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer), + input_resolution=(patches_resolution[0] // (2 ** i_layer), + patches_resolution[1] // (2 ** i_layer)), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None) + self.layers.append(layer) + + self.norm = norm_layer(self.num_features) + self.avgpool = nn.AdaptiveAvgPool1d(1) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'absolute_pos_embed'} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + # todo: to be implemented + return {'relative_position_bias_table'} + + def forward(self, x): + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x) + + x_region = self.norm(x) # B L C + x = self.avgpool(x_region.transpose(1, 2)) # B C 1 + x = torch.flatten(x, 1) + + return x + + + def forward_feature_maps(self, x): + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x) + + x_grid = self.norm(x) # B L C + x = self.avgpool(x_grid.transpose(1, 2)) # B C 1 + x = torch.flatten(x, 1) + + return x, x_grid + + + def forward_selfattention(self, x, n=1): + # n=1 return the last layer attn map; otherwise return attn maps in all layers + + + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + if n==1: + return self.forward_last_selfattention(x) + else: + return self.forward_all_selfattention(x) + + def forward_last_selfattention(self, x): + + for i, layer in enumerate(self.layers): + if i < len(self.layers) - 1: + x = layer(x) + else: + x, attns = layer.forward_with_attention(x) + return attns[-1] + + def forward_all_selfattention(self, x): + attn_out = [] + + for layer in self.layers: + x, attns = layer.forward_with_attention(x) + attn_out += attns + + return attn_out + + + def forward_return_n_last_blocks(self, x, n=1, return_patch_avgpool=False, depth=[]): + + num_blks = sum(depth) + start_idx = num_blks - n + + sum_cur = 0 + for i, d in enumerate(depth): + sum_cur_new = sum_cur + d + if start_idx >= sum_cur and start_idx < sum_cur_new: + start_stage = i + start_blk = start_idx - sum_cur + sum_cur = sum_cur_new + + + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + # we will return the averaged token features from the `n` last blocks + # note: there is no [CLS] token in Swin Transformer + output = [] + s = 0 + for i, layer in enumerate(self.layers): + x, fea = layer.forward_with_features(x) + + if i >= start_stage: + for x_ in fea[start_blk:]: + + if i == len(self.layers)-1: # use the norm in the last stage + x_ = self.norm(x_) + + x_avg = torch.flatten(self.avgpool(x_.transpose(1, 2)), 1) # B C + # print(f'Stage {i}, x_avg {x_avg.shape}') + output.append(x_avg) + + start_blk = 0 + + return torch.cat(output, dim=-1) + + + + def flops(self): + flops = 0 + flops += self.patch_embed.flops() + for i, layer in enumerate(self.layers): + flops += layer.flops() + if dist.get_rank() == 0: + print(f"GFLOPs layer_{i}: {layer.flops() / 1e9}") + flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers) + flops += self.num_features * self.num_classes + return flops + + def init_weights(self, pretrained='', pretrained_layers=[], verbose=True): + if os.path.isfile(pretrained): + pretrained_dict = torch.load(pretrained, map_location='cpu') + logging.info(f'=> loading pretrained model {pretrained}') + model_dict = self.state_dict() + pretrained_dict = { + k: v for k, v in pretrained_dict.items() + if k in model_dict.keys() + } + need_init_state_dict = {} + for k, v in pretrained_dict.items(): + need_init = ( + k.split('.')[0] in pretrained_layers + or pretrained_layers[0] is '*' + or 'relative_position_index' not in k + or 'attn_mask' not in k + ) + + if need_init: + if verbose: + logging.info(f'=> init {k} from {pretrained}') + + if 'relative_position_bias_table' in k and v.size() != model_dict[k].size(): + relative_position_bias_table_pretrained = v + relative_position_bias_table_current = model_dict[k] + L1, nH1 = relative_position_bias_table_pretrained.size() + L2, nH2 = relative_position_bias_table_current.size() + if nH1 != nH2: + logging.info(f"Error in loading {k}, passing") + else: + if L1 != L2: + logging.info( + '=> load_pretrained: resized variant: {} to {}' + .format((L1, nH1), (L2, nH2)) + ) + S1 = int(L1 ** 0.5) + S2 = int(L2 ** 0.5) + relative_position_bias_table_pretrained_resized = torch.nn.functional.interpolate( + relative_position_bias_table_pretrained.permute(1, 0).view(1, nH1, S1, S1), + size=(S2, S2), + mode='bicubic') + v = relative_position_bias_table_pretrained_resized.view(nH2, L2).permute(1, 0) + + if 'absolute_pos_embed' in k and v.size() != model_dict[k].size(): + absolute_pos_embed_pretrained = v + absolute_pos_embed_current = model_dict[k] + _, L1, C1 = absolute_pos_embed_pretrained.size() + _, L2, C2 = absolute_pos_embed_current.size() + if C1 != C1: + logging.info(f"Error in loading {k}, passing") + else: + if L1 != L2: + logging.info( + '=> load_pretrained: resized variant: {} to {}' + .format((1, L1, C1), (1, L2, C2)) + ) + S1 = int(L1 ** 0.5) + S2 = int(L2 ** 0.5) + absolute_pos_embed_pretrained = absolute_pos_embed_pretrained.reshape(-1, S1, S1, C1) + absolute_pos_embed_pretrained = absolute_pos_embed_pretrained.permute(0, 3, 1, 2) + absolute_pos_embed_pretrained_resized = torch.nn.functional.interpolate( + absolute_pos_embed_pretrained, size=(S2, S2), mode='bicubic') + v = absolute_pos_embed_pretrained_resized.permute(0, 2, 3, 1).flatten(1, 2) + + need_init_state_dict[k] = v + self.load_state_dict(need_init_state_dict, strict=False) + + def freeze_pretrained_layers(self, frozen_layers=[]): + for name, module in self.named_modules(): + if ( + name.split('.')[0] in frozen_layers + or '.'.join(name.split('.')[0:2]) in frozen_layers + or (len(frozen_layers) > 0 and frozen_layers[0] is '*') + ): + for _name, param in module.named_parameters(): + param.requires_grad = False + logging.info( + '=> set param {} requires grad to False' + .format(name) + ) + for name, param in self.named_parameters(): + if ( + name.split('.')[0] in frozen_layers + or (len(frozen_layers) > 0 and frozen_layers[0] is '*') + and param.requires_grad is True + ): + param.requires_grad = False + logging.info( + '=> set param {} requires grad to False' + .format(name) + ) + return self + + +def get_swin(is_teacher=False): + args = get_args() + + if args.swin_backbone_type == "tiny": + embed_dim = 96 + depths = [2, 2, 6, 2] + num_heads = [3, 6, 12, 24] + drop_path_rate = 0.1 + elif args.swin_backbone_type == 'h3': + embed_dim = 384 + depths = [2, 2, 18, 2] + num_heads = [6, 12, 24, 48] + drop_path_rate = 0.2 + else: + embed_dim = 128 + depths = [2, 2, 18, 2] + num_heads = [4, 8, 16, 32] + drop_path_rate = 0.2 + + swin = SwinTransformer( + img_size=224, + in_chans=3, + num_classes=1000, + patch_size=4, + embed_dim=embed_dim, + depths=depths, + num_heads=num_heads, + window_size=7, + mlp_ratio=4, + qkv_bias=True, + drop_rate=0, + attn_drop_rate=0, + drop_path_rate=(0.0 if is_teacher else drop_path_rate), + norm_layer=partial(LayerNorm, eps=1e-6), + ape=False, + patch_norm=True, + ) + + return swin + diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/inpainting.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/inpainting.py new file mode 100644 index 0000000..f71f5e3 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/inpainting.py @@ -0,0 +1,152 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# This source code is licensed under the BSD license found in the +# LICENSE file in the root directory of this source tree. + +import math +import apex +import einops +import torch +import torch.nn.functional as F +from megatron.training import get_args, print_rank_0 +from megatron.legacy.model.utils import get_linear_layer +from megatron.legacy.model.vision.vit_backbone import VitBackbone +from megatron.legacy.model.module import MegatronModule +from megatron.legacy.model.vision.mit_backbone import mit_b3 +from megatron.legacy.model.vision.utils import resize + + +class VitInpaintingModel(MegatronModule): + + def __init__(self, config, pre_process=True, post_process=True): + super(VitInpaintingModel, self).__init__() + args = get_args() + + self.config = config + self.pre_process = pre_process + self.post_process = post_process + self.hidden_size = config.hidden_size + self.backbone = VitBackbone( + config=config, + pre_process=self.pre_process, + post_process=self.post_process, + class_token=False, + ) + self.patch_dim = args.patch_dim + self.img_h = args.img_h + self.img_w = args.img_w + self.seq_length = args.seq_length + # full mask + + if self.post_process: + self.linear_decoder = get_linear_layer( + self.hidden_size, + self.backbone.flatten_dim, + torch.nn.init.zeros_ + ) + + def set_input_tensor(self, input_tensor): + self.backbone.set_input_tensor(input_tensor) + + def forward(self, input): + + hidden_states = self.backbone(input) + + if not self.post_process: + return hidden_states + decoded_output = self.linear_decoder(hidden_states) + output = einops.rearrange( + decoded_output, + "b (h w) (p1 p2 c) -> b c (h p1) (w p2)", + p1=self.patch_dim, + p2=self.patch_dim, + h=self.img_h//self.patch_dim, + w=self.img_w//self.patch_dim, + ) + + return output + + +class MLP(torch.nn.Module): + """ + Linear Embedding + """ + def __init__(self, input_dim=2048, embed_dim=768): + super().__init__() + self.proj = torch.nn.Linear(input_dim, embed_dim) + + def forward(self, x): + x = x.flatten(2).transpose(1, 2) + x = self.proj(x) + return x + + +class MitInpaintingModel(MegatronModule): + """Mix vision Transformer Model.""" + + def __init__(self, pre_process=True, post_process=True): + super(MitInpaintingModel, self).__init__() + self.pre_process = pre_process + self.post_process = post_process + + args = get_args() + self.patch_dim = args.patch_dim + self.img_h = args.img_h + self.img_w = args.img_w + self.flatten_dim = self.patch_dim * self.patch_dim * 3 + self.backbone = mit_b3() + + self.in_channels = [64, 128, 320, 512] + self.embedding_dim = 768 + + c1_in_channels, c2_in_channels, c3_in_channels, c4_in_channels = self.in_channels + + self.linear_c4 = MLP(input_dim=c4_in_channels, embed_dim=self.embedding_dim) + self.linear_c3 = MLP(input_dim=c3_in_channels, embed_dim=self.embedding_dim) + self.linear_c2 = MLP(input_dim=c2_in_channels, embed_dim=self.embedding_dim) + self.linear_c1 = MLP(input_dim=c1_in_channels, embed_dim=self.embedding_dim) + + self.conv_fuse = torch.nn.Conv2d(self.embedding_dim*4, self.embedding_dim, 1, 1, bias=False) + self.norm = apex.parallel.SyncBatchNorm(self.embedding_dim) + self.dropout = torch.nn.Dropout2d(0.1) + + self.linear_pred = torch.nn.Conv2d(self.embedding_dim, self.flatten_dim, kernel_size=1) + + def set_input_tensor(self, input_tensor): + """See megatron.legacy.model.transformer.set_input_tensor()""" + pass + + def forward(self, input): + c1, c2, c3, c4 = self.backbone(input) + + n, _, h, w = c4.shape + _c4 = self.linear_c4(c4).permute(0, 2, 1).reshape(n, -1, c4.shape[2], c4.shape[3]) + _c4 = resize(_c4, size=c1.size()[2:], mode='bilinear', align_corners=False) + + _c3 = self.linear_c3(c3).permute(0, 2, 1).reshape(n, -1, c3.shape[2], c3.shape[3]) + _c3 = resize(_c3, size=c1.size()[2:], mode='bilinear', align_corners=False) + + _c2 = self.linear_c2(c2).permute(0, 2, 1).reshape(n, -1, c2.shape[2], c2.shape[3]) + _c2 = resize(_c2, size=c1.size()[2:], mode='bilinear', align_corners=False) + + _c1 = self.linear_c1(c1).permute(0, 2, 1).reshape(n, -1, c1.shape[2], c1.shape[3]) + + _c = torch.cat([_c4, _c3, _c2, _c1], dim=1) + _c = self.conv_fuse(_c) + + x = self.norm(_c) + x = F.relu(x, inplace=True) + x = self.dropout(x) + + x = self.linear_pred(x) + + output = einops.rearrange( + x, + "b (c p1 p2) h w -> b c (h p1) (w p2)", + p1=self.patch_dim, + p2=self.patch_dim, + h=self.img_h//self.patch_dim, + w=self.img_w//self.patch_dim, + ) + + return output diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/knn_monitor.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/knn_monitor.py new file mode 100644 index 0000000..ad796d1 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/knn_monitor.py @@ -0,0 +1,129 @@ +import torch.nn.functional as F +import torch +from megatron.training import print_rank_0, get_args +from megatron.core import mpu +from megatron.legacy.data.vit_dataset import ClassificationTransform +from megatron.legacy.data.image_folder import ImageFolder + +_FEATURE_BANK = None + + +def build_data_loader(dataset, drop_last=True, shuffle=False): + """Data loader. Note that batch-size is the local (per GPU) batch-size.""" + # Sampler. + args = get_args() + micro_batch_size = 16 + num_workers = args.num_workers + world_size = mpu.get_data_parallel_world_size() + rank = mpu.get_data_parallel_rank() + sampler = torch.utils.data.distributed.DistributedSampler( + dataset, num_replicas=world_size, rank=rank, + drop_last=drop_last, shuffle=shuffle + ) + + # Data loader. Note that batch size is the per GPU batch size. + data_loader = torch.utils.data.DataLoader( + dataset, + batch_size=micro_batch_size, + sampler=sampler, + shuffle=False, + num_workers=num_workers, + drop_last=not drop_last, + pin_memory=True, + ) + return data_loader + + +def compute_feature_bank(model): + args = get_args() + global _FEATURE_BANK + feature_bank = [] + feature_label = [] + + train_ds = ImageFolder( + root=args.data_path[0], + transform=ClassificationTransform((args.img_h, args.img_w), train=False), + data_per_class_fraction=1.0 + ) + classes = len(train_ds.classes) + dataloader = build_data_loader(train_ds) + + for m in model: + m.eval() + + with torch.no_grad(): + for i, batch in enumerate(dataloader): + images = batch[0].cuda().contiguous() + labels = batch[1].cuda().contiguous() + student_feature, teacher_feature = model[0](images) + feature = F.normalize(teacher_feature.float(), dim=1) + feature_bank.append(feature) + feature_label.append(labels) + + for m in model: + m.train() + + # [N', D] + feature_bank = torch.cat(feature_bank, dim=0).contiguous() + feature_label = torch.cat(feature_label, dim=0).contiguous() + + feature_banks = [torch.zeros_like(feature_bank) + for i in range(mpu.get_data_parallel_world_size())] + torch.distributed.all_gather(feature_banks, + feature_bank, + group=mpu.get_data_parallel_group()) + + assert torch.all(torch.eq(feature_banks[mpu.get_data_parallel_rank()], + feature_bank)) + + feature_labels = [torch.zeros_like(feature_label) + for i in range(mpu.get_data_parallel_world_size())] + torch.distributed.all_gather(feature_labels, + feature_label, + group=mpu.get_data_parallel_group()) + + # [D, N] + feature_banks = torch.cat(feature_banks, dim=0).t().contiguous() + # [N] + feature_labels = torch.cat(feature_labels, dim=0).contiguous() + print_rank_0("feature_banks size is {}".format(feature_banks.size())) + print_rank_0("feature labels size is {}".format(feature_labels.size())) + + _FEATURE_BANK = (feature_banks, feature_labels, classes) + + +def get_feature_bank(): + global _FEATURE_BANK + assert _FEATURE_BANK is not None + return _FEATURE_BANK + + +# knn monitor as in InstDisc https://arxiv.org/abs/1805.01978 +# implementation follows http://github.com/zhirongw/lemniscate.pytorch and +# https://github.com/leftthomas/SimCLR +def knn_predict(feature, feature_bank, feature_labels, classes, knn_k, knn_t): + # compute cos similarity between each feature vector and feature bank ---> [B, N] + sim_matrix = torch.mm(feature, feature_bank) + # [B, K] + sim_weight, sim_indices = sim_matrix.topk(k=knn_k, dim=-1) + # [B, K] + sim_labels = torch.gather(feature_labels.expand(feature.size(0), -1), + dim=-1, + index=sim_indices) + sim_weight = (sim_weight / knn_t).exp() + + # counts for each class + one_hot_label = torch.zeros(feature.size(0) * knn_k, + classes, + device=sim_labels.device) + # [B*K, C] + one_hot_label = one_hot_label.scatter(dim=-1, + index=sim_labels.view(-1, 1), + value=1.0) + # weighted score ---> [B, C] + pred_scores = torch.sum( + one_hot_label.view(feature.size(0), -1, classes) * sim_weight.unsqueeze(dim=-1), + dim=1) + + pred_labels = pred_scores.argsort(dim=-1, descending=True) + return pred_labels diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/mit_backbone.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/mit_backbone.py new file mode 100644 index 0000000..3ca2303 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/mit_backbone.py @@ -0,0 +1,415 @@ +# Copyright (c) 2023, NVIDIA Corporation. All rights reserved. + +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +from functools import partial +from torch.nn.init import trunc_normal_ +from megatron.legacy.model.transformer import DropPath +from megatron.legacy.model import LayerNorm + + +class Mlp(nn.Module): + def __init__(self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.dwconv = DWConv(hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + elif isinstance(m, nn.Conv2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + fan_out //= m.groups + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + + def forward(self, x, H, W): + x = self.fc1(x) + x = self.dwconv(x, H, W) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0., + proj_drop=0., + sr_ratio=1): + super().__init__() + assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." + + self.dim = dim + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.q = nn.Linear(dim, dim, bias=qkv_bias) + self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.sr_ratio = sr_ratio + if sr_ratio > 1: + self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) + self.norm = LayerNorm(dim) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + elif isinstance(m, nn.Conv2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + fan_out //= m.groups + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + + def forward(self, x, H, W): + B, N, C = x.shape + q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) + + if self.sr_ratio > 1: + x_ = x.permute(0, 2, 1).reshape(B, C, H, W) + x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1) + x_ = self.norm(x_) + kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + else: + kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + k, v = kv[0], kv[1] + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=LayerNorm, sr_ratio=1): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, + num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + elif isinstance(m, nn.Conv2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + fan_out //= m.groups + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + + def forward(self, x, H, W): + x = x + self.drop_path(self.attn(self.norm1(x), H, W)) + x = x + self.drop_path(self.mlp(self.norm2(x), H, W)) + + return x + + +class OverlapPatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + + def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768): + super().__init__() + img_size = (img_size, img_size) + patch_size = (patch_size, patch_size) + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, + padding=(patch_size[0] // 2, patch_size[1] // 2)) + self.norm = LayerNorm(embed_dim) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + elif isinstance(m, nn.Conv2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + fan_out //= m.groups + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + + def forward(self, x): + x = self.proj(x) + _, _, H, W = x.shape + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + + return x, H, W + + +class MixVisionTransformer(nn.Module): + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512], + num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0., + attn_drop_rate=0., drop_path_rate=0., norm_layer=LayerNorm, + depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], output_avg=False): + super().__init__() + self.num_classes = num_classes + self.depths = depths + self.output_avg = output_avg + + # patch_embed + self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_chans=in_chans, + embed_dim=embed_dims[0]) + self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0], + embed_dim=embed_dims[1]) + self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1], + embed_dim=embed_dims[2]) + self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_chans=embed_dims[2], + embed_dim=embed_dims[3]) + + # transformer encoder + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + cur = 0 + self.block1 = nn.ModuleList([Block( + dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, + sr_ratio=sr_ratios[0]) + for i in range(depths[0])]) + self.norm1 = norm_layer(embed_dims[0]) + + cur += depths[0] + self.block2 = nn.ModuleList([Block( + dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, + sr_ratio=sr_ratios[1]) + for i in range(depths[1])]) + self.norm2 = norm_layer(embed_dims[1]) + + cur += depths[1] + self.block3 = nn.ModuleList([Block( + dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, + sr_ratio=sr_ratios[2]) + for i in range(depths[2])]) + self.norm3 = norm_layer(embed_dims[2]) + + cur += depths[2] + self.block4 = nn.ModuleList([Block( + dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, + sr_ratio=sr_ratios[3]) + for i in range(depths[3])]) + self.norm4 = norm_layer(embed_dims[3]) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + elif isinstance(m, nn.Conv2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + fan_out //= m.groups + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + + def reset_drop_path(self, drop_path_rate): + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))] + cur = 0 + for i in range(self.depths[0]): + self.block1[i].drop_path.drop_prob = dpr[cur + i] + + cur += self.depths[0] + for i in range(self.depths[1]): + self.block2[i].drop_path.drop_prob = dpr[cur + i] + + cur += self.depths[1] + for i in range(self.depths[2]): + self.block3[i].drop_path.drop_prob = dpr[cur + i] + + cur += self.depths[2] + for i in range(self.depths[3]): + self.block4[i].drop_path.drop_prob = dpr[cur + i] + + def freeze_patch_emb(self): + self.patch_embed1.requires_grad = False + + def forward_features(self, x): + B = x.shape[0] + outs = [] + + # stage 1 + x, H, W = self.patch_embed1(x) + for i, blk in enumerate(self.block1): + x = blk(x, H, W) + x = self.norm1(x) + x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() + outs.append(x) + + # stage 2 + x, H, W = self.patch_embed2(x) + for i, blk in enumerate(self.block2): + x = blk(x, H, W) + x = self.norm2(x) + x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() + outs.append(x) + + # stage 3 + x, H, W = self.patch_embed3(x) + for i, blk in enumerate(self.block3): + x = blk(x, H, W) + x = self.norm3(x) + x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() + outs.append(x) + + # stage 4 + x, H, W = self.patch_embed4(x) + for i, blk in enumerate(self.block4): + x = blk(x, H, W) + x = self.norm4(x) + if not self.output_avg: + x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() + outs.append(x) + + return outs + + def forward(self, x): + x = self.forward_features(x) + + if self.output_avg: + x = x[3].mean(dim=1) + + return x + + +class DWConv(nn.Module): + def __init__(self, dim=768): + super(DWConv, self).__init__() + self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim) + + def forward(self, x, H, W): + B, N, C = x.shape + x = x.transpose(1, 2).view(B, C, H, W) + x = self.dwconv(x) + x = x.flatten(2).transpose(1, 2) + + return x + +class mit_b0(MixVisionTransformer): + def __init__(self, **kwargs): + super(mit_b0, self).__init__( + patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], + qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], + drop_rate=0.0, drop_path_rate=0.1) + + +class mit_b1(MixVisionTransformer): + def __init__(self, **kwargs): + super(mit_b1, self).__init__( + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], + qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], + drop_rate=0.0, drop_path_rate=0.1) + + +class mit_b2(MixVisionTransformer): + def __init__(self, **kwargs): + super(mit_b2, self).__init__( + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], + qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], + drop_rate=0.0, drop_path_rate=0.1) + + +class mit_b3(MixVisionTransformer): + def __init__(self, **kwargs): + super(mit_b3, self).__init__( + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], + qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1], + drop_rate=0.0, drop_path_rate=0.1) + +class mit_b3_avg(MixVisionTransformer): + def __init__(self, drop_path_rate=0.1, **kwargs): + super(mit_b3_avg, self).__init__( + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], + qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1], + drop_rate=0.0, drop_path_rate=drop_path_rate, output_avg=True) + +class mit_b4(MixVisionTransformer): + def __init__(self, **kwargs): + super(mit_b4, self).__init__( + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], + qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1], + drop_rate=0.0, drop_path_rate=0.1) + +class mit_b5(MixVisionTransformer): + def __init__(self, **kwargs): + super(mit_b5, self).__init__( + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], + qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1], + drop_rate=0.0, drop_path_rate=0.1) + +class mit_b5_avg(MixVisionTransformer): + def __init__(self, drop_path_rate=0.1, **kwargs): + super(mit_b5_avg, self).__init__( + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], + qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1], + drop_rate=0.0, drop_path_rate=drop_path_rate, output_avg=True) + diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/swin_backbone.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/swin_backbone.py new file mode 100644 index 0000000..231802c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/swin_backbone.py @@ -0,0 +1,625 @@ +# Copyright (c) 2021 Microsoft +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------- +# Swin Transformer +# -------------------------------------------------------- + +import torch +import torch.nn as nn +import torch.utils.checkpoint as checkpoint +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ +from math import sqrt + +from megatron.training import get_args +from functools import partial + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, + out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + r""" Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + + +class SwinTransformerBlock(nn.Module): + r""" Swin Transformer Block. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, + qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + self.H = input_resolution[0] + self.W = input_resolution[1] + + self.attn_mask_dict = {} + + def create_attn_mask(self, H, W): + # calculate attention mask for SW-MSA + + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1)) # 1 Hp Wp 1 + h_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask + + + def forward(self, x): + B, L, C = x.shape + H = int(sqrt(L)) + W = H + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + else: + shifted_x = x + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + + +class PatchMerging(nn.Module): + r""" Patch Merging Layer. + + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + H, W = self.input_resolution + flops = H * W * self.dim + flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + return flops + + +class BasicLayer(nn.Module): + """ A basic Swin Transformer layer for one stage. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__(self, dim, input_resolution, depth, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False): + + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + SwinTransformerBlock(dim=dim, input_resolution=input_resolution, + num_heads=num_heads, window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop, attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer) + for i in range(depth)]) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + x_b4_ds = x + if self.downsample is not None: + x = self.downsample(x) + return x_b4_ds, x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + +class PatchEmbed(nn.Module): + r""" Image to Patch Embedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + Ho, Wo = self.patches_resolution + flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops + + +class SwinTransformer(nn.Module): + r""" Swin Transformer + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + + Args: + img_size (int | tuple(int)): Input image size. Default 224 + patch_size (int | tuple(int)): Patch size. Default: 4 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, + embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], + window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.3, + norm_layer=partial(nn.LayerNorm, eps=1e-6), ape=False, patch_norm=True, + use_checkpoint=False, output_avg=False, **kwargs): + super().__init__() + + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = int(embed_dim * 2 ** (self.num_layers - 1)) + self.mlp_ratio = mlp_ratio + self.img_size = to_2tuple(img_size) + self.patch_size = to_2tuple(patch_size) + self.output_avg = output_avg + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) + trunc_normal_(self.absolute_pos_embed, std=.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer), + input_resolution=(patches_resolution[0] // (2 ** i_layer), + patches_resolution[1] // (2 ** i_layer)), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint) + self.layers.append(layer) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'absolute_pos_embed'} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {'relative_position_bias_table'} + + def forward(self, x): + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + h = self.img_size[0] // self.patch_size[0] + w = self.img_size[1] // self.patch_size[1] + outs = [] + + for i, layer in enumerate(self.layers): + px, x = layer(x) + b, n, c = px.shape + + if i != len(self.layers) - 1 or not self.output_avg: + px = px.permute(0, 2, 1).contiguous() + px = px.reshape(b, c, h, w) + # is this a fair assumption ?? i think it's baked into the architecture + h, w = h//2, w//2 + outs.append(px) + + if self.output_avg: + return outs[-1].mean(dim=1) + + return outs + + def flops(self): + flops = 0 + flops += self.patch_embed.flops() + for i, layer in enumerate(self.layers): + flops += layer.flops() + flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers) + flops += self.num_features * self.num_classes + return flops + + +def get_swin(drop_path_rate=0.3, output_avg=False): + args = get_args() + + window_size = 7 + embed_dim = 128 + depths = [2, 2, 18, 2] + num_heads = [4, 8, 16, 32] + swin = SwinTransformer( + img_size=(args.img_h, args.img_w,), + in_chans=3, + patch_size=args.patch_dim, + embed_dim=embed_dim, + depths=depths, + num_heads=num_heads, + window_size=window_size, + drop_path_rate=drop_path_rate, + output_avg=output_avg, + ) + + return swin + diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/utils.py new file mode 100644 index 0000000..b406891 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/utils.py @@ -0,0 +1,27 @@ +import warnings +import torch +import torch.nn.functional as F + + +def resize(input, + size=None, + scale_factor=None, + mode='nearest', + align_corners=None, + warning=True): + if warning: + if size is not None and align_corners: + input_h, input_w = tuple(int(x) for x in input.shape[2:]) + output_h, output_w = tuple(int(x) for x in size) + if output_h > input_h or output_w > output_h: + if ((output_h > 1 and output_w > 1 and input_h > 1 + and input_w > 1) and (output_h - 1) % (input_h - 1) + and (output_w - 1) % (input_w - 1)): + warnings.warn( + f'When align_corners={align_corners}, ' + 'the output would more aligned if ' + f'input size {(input_h, input_w)} is `x+1` and ' + f'out size {(output_h, output_w)} is `nx+1`') + if isinstance(size, torch.Size): + size = tuple(int(x) for x in size) + return F.interpolate(input, size, scale_factor, mode, align_corners) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/vit_backbone.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/vit_backbone.py new file mode 100644 index 0000000..b46f6f7 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/model/vision/vit_backbone.py @@ -0,0 +1,248 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Vision Transformer(VIT) model.""" + +import math +import einops +import torch +import apex +import torch.nn.functional as F +from megatron.training import get_args +from megatron.legacy.model.transformer import ParallelTransformer +from megatron.legacy.model.utils import ( + get_linear_layer, + init_method_normal, + scaled_init_method_normal, +) +from megatron.legacy.model.module import MegatronModule + +CLASS_TOKEN_LENGTH = 8 + +class VitMlpHead(MegatronModule): + """Pooler layer. + + Pool hidden states of a specific token (for example start of the + sequence) and add a linear transformation followed by a tanh. + + Args: + hidden_size: hidden size + init_method: weight initialization method for the linear layer. + bias is set to zero. + """ + + def __init__(self, config, hidden_size, num_classes): + super(VitMlpHead, self).__init__() + self.config = config + self.dense_in = torch.nn.Linear(hidden_size, hidden_size) + self.relu = torch.nn.ReLU() + self.dense_out = torch.nn.Linear(hidden_size, num_classes) + torch.nn.init.constant_(self.dense_out.bias, -10) + + def forward(self, hidden_states): + # hidden_states: [b, 1, h] + # sequence_index: index of the token to pool. + dense_in_result = self.dense_in(hidden_states) + tanh_result = torch.tanh(dense_in_result) + dense_out_result = self.dense_out(tanh_result) + return dense_out_result + + +def isPerfectSquare(x): + if(x >= 0): + sr = math.sqrt(x) + return (int(sr) * int(sr) == x) + return False + + +def twod_interpolate_position_embeddings_hook( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, +): + + args = get_args() + num_patches_per_dim_h = args.img_h // args.patch_dim + num_patches_per_dim_w = args.img_w // args.patch_dim + num_patches = num_patches_per_dim_h * num_patches_per_dim_w + hidden_size = args.hidden_size + + key = prefix + "weight" + + assert key in state_dict + if key in state_dict: + input_param = state_dict[key] + + input_seq_len = input_param.shape[0] + assert(isPerfectSquare(input_seq_len) or isPerfectSquare(input_seq_len - CLASS_TOKEN_LENGTH)) + input_has_class_token = not isPerfectSquare(input_seq_len) + num_tok_input = input_seq_len - CLASS_TOKEN_LENGTH if input_has_class_token else input_seq_len + num_tok_output = num_patches + output_has_class_token = args.class_token_present + + # update input_param and load it to state_dict[key] + if input_has_class_token: + input_param_tok = input_param[:CLASS_TOKEN_LENGTH, :] + input_param_grid = input_param[CLASS_TOKEN_LENGTH:, :] + else: + input_param_tok = torch.zeros(CLASS_TOKEN_LENGTH, hidden_size) + input_param_grid = input_param + + assert input_param.shape[1] == hidden_size + + if num_tok_input != num_tok_output: + + gs_input = int(math.sqrt(num_tok_input)) + gs_new = (num_patches_per_dim_h, num_patches_per_dim_w) + + input_param_grid = input_param_grid.transpose(0, 1).contiguous() + input_param_grid = input_param_grid.reshape( + (1, -1, gs_input, gs_input) + ) + input_param_grid = input_param_grid.float() + scale_factor = (gs_new[0] / gs_input, gs_new[1] / gs_input) + + input_param_grid = F.interpolate( + input_param_grid, scale_factor=scale_factor, mode="bilinear" + ) + + input_param_grid = input_param_grid.half() + input_param_grid = input_param_grid.reshape((-1, num_tok_output)) + input_param_grid = input_param_grid.transpose(0, 1).contiguous() + + assert input_param_grid.shape[1] == hidden_size + + input_param = input_param_grid + assert ( + input_param.shape[0] == num_tok_output + and input_param.shape[1] == hidden_size + ) + + if output_has_class_token: + input_param = torch.cat((input_param_tok, input_param), dim=0) + + state_dict[key] = input_param + + +class VitBackbone(MegatronModule): + """Vision Transformer Model.""" + + def __init__(self, + config, + pre_process=True, + post_process=True, + class_token=True, + single_token_output=False, + post_layer_norm=True, + drop_path_rate=0.0): + super(VitBackbone, self).__init__(share_embeddings_and_output_weights=False) + args = get_args() + self.config = config + + self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy + + self.pre_process = pre_process + self.post_process = post_process + self.class_token = class_token + self.post_layer_norm = post_layer_norm + self.hidden_size = args.hidden_size + self.patch_dim = args.patch_dim + self.img_h = args.img_h + self.img_w = args.img_w + self.micro_batch_size = args.micro_batch_size + self.single_token_output = single_token_output + self.drop_path_rate = drop_path_rate + + assert self.img_h % self.patch_dim == 0 + assert self.img_w % self.patch_dim == 0 + self.num_patches_per_dim_h = self.img_h // self.patch_dim + self.num_patches_per_dim_w = self.img_w // self.patch_dim + self.num_patches = self.num_patches_per_dim_h * self.num_patches_per_dim_w + self.seq_length = self.num_patches + (CLASS_TOKEN_LENGTH if self.class_token else 0) + self.flatten_dim = self.patch_dim * self.patch_dim * args.num_channels + self.input_tensor = None + self.position_ids = None + + if self.pre_process: + # cls_token + if self.class_token: + self.cls_token = torch.nn.Parameter( + torch.randn(1, CLASS_TOKEN_LENGTH, self.hidden_size) + ) + torch.nn.init.zeros_(self.cls_token) + self.position_ids = torch.arange(self.seq_length).expand(1, -1).cuda() + + # Linear encoder + self.linear_encoder = torch.nn.Linear( + self.flatten_dim, self.hidden_size + ) + + # embedding + self.position_embeddings = torch.nn.Embedding( + self.seq_length, self.hidden_size + ) + init_method_normal(args.init_method_std)( + self.position_embeddings.weight + ) + + args.class_token_present = self.class_token + self.position_embeddings._register_load_state_dict_pre_hook( + twod_interpolate_position_embeddings_hook + ) + + self.embedding_dropout = torch.nn.Dropout(args.hidden_dropout) + + # Transformer + self.transformer = ParallelTransformer( + config, + model_type=args.model_type, + pre_process=self.pre_process, + post_process=self.post_process, + post_layer_norm=self.post_layer_norm, + drop_path_rate=self.drop_path_rate + ) + + def set_input_tensor(self, input_tensor): + """See megatron.legacy.model.transformer.set_input_tensor()""" + self.transformer.set_input_tensor(input_tensor) + + def forward(self, input): + + if self.pre_process: + rearranged_input = einops.rearrange( + input, + "b c (h p1) (w p2) -> b (h w) (p1 p2 c)", + p1=self.patch_dim, + p2=self.patch_dim, + ) + + assert rearranged_input.dtype == torch.half + encoder_output = self.linear_encoder(rearranged_input) + + concatenated_tokens = encoder_output + if self.class_token: + cls_tokens = self.cls_token.expand(encoder_output.shape[0], -1, -1) + concatenated_tokens = torch.cat((cls_tokens, encoder_output), dim=1) + + token_embeddings = concatenated_tokens + \ + self.position_embeddings(self.position_ids[:, :concatenated_tokens.shape[1]]) + # [b, s, h] => [s, b, h] + token_embeddings = token_embeddings.transpose(0, 1).contiguous() + hidden_states = self.embedding_dropout(token_embeddings) + else: + hidden_states = input + + hidden_states = self.transformer(hidden_states, None) + + if self.post_process: + # [s b h] => [b s h] + if self.single_token_output: + hidden_states = hidden_states[0] + else: + hidden_states = hidden_states.transpose(0, 1).contiguous() + + return hidden_states + diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/commons.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/commons.py new file mode 100644 index 0000000..611daf0 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/commons.py @@ -0,0 +1,70 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import argparse +import os +import random +import numpy +import torch + +import mpu + + +class IdentityLayer(torch.nn.Module): + def __init__(self, size, scale=1.0): + super(IdentityLayer, self).__init__() + self.weight = torch.nn.Parameter(scale * torch.randn(size)) + + def forward(self): + return self.weight + + +def set_random_seed(seed): + """Set random seed for reproducability.""" + random.seed(seed) + numpy.random.seed(seed) + torch.manual_seed(seed) + mpu.model_parallel_cuda_manual_seed(seed) + + +def initialize_distributed(backend='nccl'): + """Initialize torch.distributed.""" + # Get local rank in case it is provided. + parser = argparse.ArgumentParser() + parser.add_argument('--local_rank', type=int, default=None, + help='local rank passed from distributed launcher') + args = parser.parse_args() + local_rank = args.local_rank + + # Get rank and world size. + rank = int(os.getenv('RANK', '0')) + world_size = int(os.getenv("WORLD_SIZE", '1')) + + print('> initializing torch.distributed with local rank: {}, ' + 'rank: {}, world size: {}'.format(local_rank, rank, world_size)) + + # Set the device id. + device = rank % torch.cuda.device_count() + if local_rank is not None: + device = local_rank + torch.cuda.set_device(device) + + # Call the init process. + init_method = 'tcp://' + master_ip = os.getenv('MASTER_ADDR', 'localhost') + master_port = os.getenv('MASTER_PORT', '6000') + init_method += master_ip + ':' + master_port + torch.distributed.init_process_group( + backend=backend, + world_size=world_size, + rank=rank, + init_method=init_method) + + +def print_separator(message): + torch.distributed.barrier() + filler_len = (78 - len(message)) // 2 + filler = '-' * filler_len + string = '\n' + filler + ' {} '.format(message) + filler + if torch.distributed.get_rank() == 0: + print(string, flush=True) + torch.distributed.barrier() diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_cross_entropy.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_cross_entropy.py new file mode 100644 index 0000000..00ae422 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_cross_entropy.py @@ -0,0 +1,95 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +from commons import set_random_seed +from commons import IdentityLayer +from commons import print_separator +from commons import initialize_distributed +from mpu.cross_entropy import vocab_parallel_cross_entropy +import mpu +import torch.nn.functional as F +import torch +import random +import sys +sys.path.append("../..") + + +def torch_cross_entropy(batch_size, seq_length, vocab_size, + logits_scale, seed): + set_random_seed(seed) + identity = IdentityLayer((batch_size, seq_length, vocab_size), + scale=logits_scale).cuda() + logits = identity() + target = torch.cuda.LongTensor( + size=(batch_size, seq_length)).random_(0, vocab_size) + loss = F.cross_entropy(logits.view(-1, logits.size()[-1]), + target.view(-1), + reduction='none').view_as(target).mean() + loss.backward() + return loss, identity.weight.grad + + +def mpu_cross_entropy(batch_size, seq_length, vocab_size, + logits_scale, seed): + set_random_seed(seed) + identity = IdentityLayer((batch_size, seq_length, vocab_size), + scale=logits_scale).cuda() + logits = identity() + logits_parallel = mpu.scatter_to_tensor_model_parallel_region(logits) + target = torch.cuda.LongTensor( + size=(batch_size, seq_length)).random_(0, vocab_size) + loss = vocab_parallel_cross_entropy(logits_parallel, target).mean() + loss.backward() + return loss, identity.weight.grad + + +def test_cross_entropy(tensor_model_parallel_size): + + if torch.distributed.get_rank() == 0: + print('> testing cross entropy with model parallel size {} ...'. + format(tensor_model_parallel_size)) + + mpu.initialize_model_parallel(tensor_model_parallel_size) + tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() + + batch_size = 13 + seq_length = 17 + vocab_size_per_partition = 11 + logits_scale = 1000.0 + vocab_size = vocab_size_per_partition * tensor_model_parallel_size + seed = 1234 + + loss_torch, grad_torch = torch_cross_entropy(batch_size, seq_length, + vocab_size, logits_scale, + seed) + loss_mpu, grad_mpu = mpu_cross_entropy(batch_size, seq_length, + vocab_size, logits_scale, + seed) + + error = loss_torch.sub_(loss_mpu).abs().max() + print(' max error in loss on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 1.0e-6 + + error = grad_torch.sub_(grad_mpu).abs().max() + print(' max error in grad on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 1.0e-6 + + # Reset groups + mpu.destroy_tensor_model_parallel() + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print('>> passed the test :-)') + + +if __name__ == '__main__': + + initialize_distributed() + world_size = torch.distributed.get_world_size() + + tensor_model_parallel_size = 1 + while tensor_model_parallel_size <= world_size: + print_separator('test cross entropy') + test_cross_entropy(tensor_model_parallel_size) + tensor_model_parallel_size *= 2 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_data.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_data.py new file mode 100644 index 0000000..c30bf4b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_data.py @@ -0,0 +1,75 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +from commons import print_separator +from commons import initialize_distributed +from mpu import data as data_utils +import mpu +import torch +import functools +import operator +import sys +sys.path.append("../..") + + +def test_broadcast_data(tensor_model_parallel_size): + + if torch.distributed.get_rank() == 0: + print('> testing broadcast_data with model parallel size {} ...'. + format(tensor_model_parallel_size)) + + mpu.initialize_model_parallel(tensor_model_parallel_size) + torch.manual_seed(1234 + mpu.get_data_parallel_rank()) + tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() + + key_size_t = {'key1': [7, 11], + 'key2': [8, 2, 1], + 'key3': [13], + 'key4': [5, 1, 2], + 'key5': [5, 12]} + keys = list(key_size_t.keys()) + + data = {} + data_t = {} + for key in key_size_t: + data[key] = torch.LongTensor(size=key_size_t[key]).random_(0, 1000) + data_t[key] = data[key].clone() + data['keyX'] = torch.FloatTensor(size=(5, )).random_(0, 1000) + data_t['keyX'] = data['keyX'].clone() + if mpu.get_tensor_model_parallel_rank() != 0: + data = None + + data_utils._check_data_types(keys, data_t, torch.int64) + key_size, key_numel, \ + total_numel = data_utils._build_key_size_numel_dictionaries(keys, data) + for key in keys: + assert key_size[key] == key_size_t[key] + total_numel_t = 0 + for key in keys: + target_size = functools.reduce(operator.mul, key_size_t[key], 1) + assert key_numel[key] == target_size + total_numel_t += target_size + assert total_numel == total_numel_t + + data_b = data_utils.broadcast_data(keys, data, torch.int64) + for key in keys: + tensor = data_t[key].cuda() + assert data_b[key].sub(tensor).abs().max() == 0 + + # Reset groups + mpu.destroy_tensor_model_parallel() + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print('>> passed the test :-)') + + +if __name__ == '__main__': + + initialize_distributed() + world_size = torch.distributed.get_world_size() + + tensor_model_parallel_size = 1 + while tensor_model_parallel_size <= world_size: + print_separator('test test broadcast data') + test_broadcast_data(tensor_model_parallel_size) + tensor_model_parallel_size *= 2 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_initialize.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_initialize.py new file mode 100644 index 0000000..e5d2be3 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_initialize.py @@ -0,0 +1,82 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +from commons import print_separator +from commons import initialize_distributed +import mpu +import torch +import sys +sys.path.append("../..") + + +def test_initialize_model_parallel(tensor_model_parallel_size): + + if torch.distributed.get_rank() == 0: + print('> testing initialize_model_parallel with size {} ...'.format( + tensor_model_parallel_size)) + tensor_model_parallel_size_ = min(tensor_model_parallel_size, + torch.distributed.get_world_size()) + assert not mpu.model_parallel_is_initialized() + mpu.initialize_model_parallel(tensor_model_parallel_size_) + assert mpu.model_parallel_is_initialized() + + # Checks. + def check(group, world_size, rank): + assert world_size == torch.distributed.get_world_size(group=group) + assert rank == torch.distributed.get_rank(group=group) + + # Model parallel. + world_size = tensor_model_parallel_size_ + rank = torch.distributed.get_rank() % tensor_model_parallel_size_ + assert world_size == mpu.get_tensor_model_parallel_world_size() + assert rank == mpu.get_tensor_model_parallel_rank() + check(mpu.get_tensor_model_parallel_group(), world_size, rank) + + # Data parallel. + world_size = torch.distributed.get_world_size() // tensor_model_parallel_size_ + rank = torch.distributed.get_rank() // tensor_model_parallel_size + assert world_size == mpu.get_data_parallel_world_size() + assert rank == mpu.get_data_parallel_rank() + check(mpu.get_data_parallel_group(), world_size, rank) + + # Reset groups + mpu.destroy_model_parallel() + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print('>> passed the test :-)') + + +def test_get_tensor_model_parallel_src_rank(tensor_model_parallel_size_): + + if torch.distributed.get_rank() == 0: + print('> testing get_tensor_model_parallel_src_rank with size {} ...'.format( + tensor_model_parallel_size_)) + tensor_model_parallel_size = min(tensor_model_parallel_size_, + torch.distributed.get_world_size()) + assert not mpu.model_parallel_is_initialized() + mpu.initialize_model_parallel(tensor_model_parallel_size) + assert mpu.model_parallel_is_initialized() + + # Checks + src_rank = torch.distributed.get_rank() - mpu.get_tensor_model_parallel_rank() + assert mpu.get_tensor_model_parallel_src_rank() == src_rank + + # Reset groups + mpu.destroy_model_parallel() + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print('>> passed the test :-)') + + +if __name__ == '__main__': + + initialize_distributed() + world_size = torch.distributed.get_world_size() + tensor_model_parallel_size = 1 + while tensor_model_parallel_size <= world_size: + print_separator('test initialize model parallel') + test_initialize_model_parallel(tensor_model_parallel_size) + print_separator('test model parallel source rank') + test_get_tensor_model_parallel_src_rank(tensor_model_parallel_size) + tensor_model_parallel_size *= 2 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_layers.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_layers.py new file mode 100644 index 0000000..73ad4b9 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_layers.py @@ -0,0 +1,517 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +from mpu import layers +from commons import set_random_seed +from commons import print_separator +from commons import initialize_distributed +import mpu +from torch.nn.parameter import Parameter +import torch.nn.init as init +import torch +import random +import sys +sys.path.append("../..") + + +def test_parallel_embedding(tensor_model_parallel_size): + + if torch.distributed.get_rank() == 0: + print('> testing parallel embedding with model parallel size {} ...'. + format(tensor_model_parallel_size)) + + mpu.initialize_model_parallel(tensor_model_parallel_size) + tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() + + batch_size = 17 + seq_length = 23 + vocab_size = 48 + hidden_size = 16 + seed = 1236 + + set_random_seed(123) + input_data = torch.LongTensor( + size=(batch_size, seq_length)).random_(0, vocab_size).cuda() + loss_weight = torch.randn([batch_size, seq_length, hidden_size]).cuda() + + set_random_seed(seed) + embedding_original = torch.nn.Embedding(vocab_size, hidden_size).cuda() + + output = embedding_original(input_data) + loss_original = torch.mul(output, loss_weight).sum() + loss_original.backward() + + set_random_seed(seed) + embedding_parallel = layers.ParallelEmbedding( + vocab_size, hidden_size, init_method=init.normal_).cuda() + output = embedding_parallel(input_data) + loss_parallel = torch.mul(output, loss_weight).sum() + loss_parallel.backward() + + set_random_seed(seed) + embedding_vocab_parallel = layers.VocabParallelEmbedding( + vocab_size, hidden_size, init_method=init.normal_).cuda() + output = embedding_vocab_parallel(input_data) + loss_vocab_parallel = torch.mul(output, loss_weight).sum() + loss_vocab_parallel.backward() + + torch.distributed.barrier() + error = loss_parallel.sub(loss_original).abs() + print(' error in loss (parallel) on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 1.0e-12, 'error: {}'.format(error) + + torch.distributed.barrier() + error = loss_vocab_parallel.sub(loss_original).abs() + print(' error in loss (vocab parallel) on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 1.0e-12, 'error: {}'.format(error) + + weight_grad_orig = torch.split(embedding_original.weight.grad, + hidden_size // tensor_model_parallel_size, + 1)[mpu.get_tensor_model_parallel_rank()] + error = embedding_parallel.weight.grad.sub(weight_grad_orig).abs().max() + print(' error in grad (parallel) on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 1.0e-12, 'error: {}'.format(error) + + weight_grad_orig = torch.split(embedding_original.weight.grad, + vocab_size // tensor_model_parallel_size, + 0)[mpu.get_tensor_model_parallel_rank()] + error = embedding_vocab_parallel.weight.grad.sub( + weight_grad_orig).abs().max() + print(' error in grad (vocab parallel) on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 1.0e-12, 'error: {}'.format(error) + + # Reset groups + mpu.destroy_model_parallel() + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print('>> passed the test :-)') + + +def test_initialize_affine_weight(tensor_model_parallel_size): + + mpu.initialize_model_parallel(tensor_model_parallel_size) + if torch.distributed.get_rank() == 0: + print('> testing initialize_affine_weight with model parallel ' + 'size: {}'.format(tensor_model_parallel_size)) + tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() + + seed = 12345 + input_size_coeff = 13 + input_size = input_size_coeff * tensor_model_parallel_size + output_size_coeff = 17 + output_size = output_size_coeff * tensor_model_parallel_size + + # --------------- + # Column parallel + # --------------- + weight = torch.empty(output_size_coeff, input_size) + set_random_seed(seed) + layers._initialize_affine_weight(weight, output_size, input_size, + + output_size_coeff, 0, + torch.nn.init.normal_) + # Target. + set_random_seed(seed) + master_weight = torch.empty(output_size, input_size) + torch.nn.init.normal_(master_weight) + rank = mpu.get_tensor_model_parallel_rank() + my_weight = torch.split(master_weight, output_size_coeff, + dim=0)[rank].contiguous().clone() + + # Compare. + error = weight.sub(my_weight).abs().max() + torch.distributed.barrier() + print(' column parallel max error (should be zero) on global rank ' + '{}: {}'.format(torch.distributed.get_rank(), error)) + assert error < 1.0e-6 + + # ------------ + # Row parallel + # ------------ + weight = torch.empty(output_size, input_size_coeff) + set_random_seed(seed) + mpu.layers._initialize_affine_weight(weight, output_size, input_size, + input_size_coeff, 1, + torch.nn.init.normal_) + # Target. + set_random_seed(seed) + master_weight = torch.empty(output_size, input_size) + torch.nn.init.normal_(master_weight) + rank = mpu.get_tensor_model_parallel_rank() + my_weight = torch.split(master_weight, input_size_coeff, + dim=1)[rank].contiguous().clone() + + # Compare. + error = weight.sub(my_weight).abs().max() + torch.distributed.barrier() + print(' row parallel max error (should be zero) on global rank ' + '{}: {}'.format(torch.distributed.get_rank(), error)) + assert error < 1.0e-6 + + # Reset groups + mpu.destroy_model_parallel() + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print(' >> passed the test :-)') + + +class IdentityLayer2D(torch.nn.Module): + def __init__(self, m, n): + super(IdentityLayer2D, self).__init__() + self.weight = Parameter(torch.Tensor(m, n)) + torch.nn.init.xavier_normal_(self.weight) + + def forward(self): + return self.weight + + +def test_column_parallel_linear(tensor_model_parallel_size): + + mpu.initialize_model_parallel(tensor_model_parallel_size) + if torch.distributed.get_rank() == 0: + print('> testing ColumnParallelLinear with model parallel ' + 'size: {}'.format(tensor_model_parallel_size)) + tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() + + seed = 12345 + set_random_seed(seed) + input_size_coeff = 13 + input_size = input_size_coeff * tensor_model_parallel_size + output_size_coeff = 17 + output_size = output_size_coeff * tensor_model_parallel_size + batch_size = 7 + + # Network + identity_layer = IdentityLayer2D(batch_size, input_size).cuda() + linear_layer = mpu.ColumnParallelLinear( + input_size, output_size, keep_master_weight_for_test=True).cuda() + loss_weight = torch.randn([batch_size, output_size]).cuda() + # Forward + input_ = identity_layer() + output = linear_layer(input_) + loss = torch.mul(output, loss_weight).sum() + # Backward + loss.backward() + + # Values. + dLdY = loss_weight + X = identity_layer.weight + A = linear_layer.master_weight.cuda() + dLdA = torch.matmul(dLdY.t(), X) + dLdb = torch.matmul(torch.ones(batch_size, 1).cuda().t(), dLdY).view(-1) + dLdX = torch.matmul(dLdY, A) + + rank = mpu.get_tensor_model_parallel_rank() + my_dLdA = torch.split(dLdA, output_size_coeff, + dim=0)[rank].contiguous().clone() + error = my_dLdA.sub(linear_layer.weight.grad).abs().max() + torch.distributed.barrier() + print(' error in dLdA on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 1.0e-6 + + my_dLdb = torch.split(dLdb, output_size_coeff, + dim=0)[rank].contiguous().clone() + error = my_dLdb.sub(linear_layer.bias.grad).abs().max() + torch.distributed.barrier() + print(' error in dLdb on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 1.0e-6 + + error = dLdX.sub(identity_layer.weight.grad).abs().max() + torch.distributed.barrier() + print(' error in dLdX on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 1.0e-6 + + # Reset groups + mpu.destroy_model_parallel() + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print(' >> passed the test :-)') + + +def test_row_parallel_linear(tensor_model_parallel_size): + + mpu.initialize_model_parallel(tensor_model_parallel_size) + if torch.distributed.get_rank() == 0: + print('> testing RowParallelLinear with model parallel ' + 'size: {}'.format(tensor_model_parallel_size)) + tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() + + seed = 12345 + set_random_seed(seed) + input_size_coeff = 13 + input_size = input_size_coeff * tensor_model_parallel_size + output_size_coeff = 17 + output_size = output_size_coeff * tensor_model_parallel_size + batch_size = 7 + + # Network + identity_layer = IdentityLayer2D(batch_size, input_size).cuda() + linear_layer = mpu.RowParallelLinear( + input_size, output_size, keep_master_weight_for_test=True).cuda() + loss_weight = torch.randn([batch_size, output_size]).cuda() + # Forward + input_ = identity_layer() + output = linear_layer(input_) + loss = torch.mul(output, loss_weight).sum() + # Backward + loss.backward() + + # Values. + dLdY = loss_weight + X = identity_layer.weight + A = linear_layer.master_weight.cuda() + dLdA = torch.matmul(dLdY.t(), X) + dLdb = torch.matmul(torch.ones(batch_size, 1).cuda().t(), dLdY).view(-1) + dLdX = torch.matmul(dLdY, A) + + rank = mpu.get_tensor_model_parallel_rank() + my_dLdA = torch.split(dLdA, input_size_coeff, + dim=1)[rank].contiguous().clone() + error = my_dLdA.sub(linear_layer.weight.grad).abs().max() + torch.distributed.barrier() + print(' error in dLdA on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 1.0e-6 + + error = dLdb.sub(linear_layer.bias.grad).abs().max() + torch.distributed.barrier() + print(' error in dLdb on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 1.0e-6 + + error = dLdX.sub(identity_layer.weight.grad).abs().max() + torch.distributed.barrier() + print(' error in dLdX on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 1.0e-6 + + # Reset groups + mpu.destroy_model_parallel() + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print(' >> passed the test :-)') + + +class IdentityLayer3D(torch.nn.Module): + def __init__(self, m, n, k): + super(IdentityLayer3D, self).__init__() + self.weight = Parameter(torch.Tensor(m, n, k)) + torch.nn.init.xavier_normal_(self.weight) + + def forward(self): + return self.weight + + +def parallel_self_attention(tensor_model_parallel_size, num_att_heads_per_partition, + hidden_size_per_att_head, dropout_prob, batch_size, + sequence_length): + mpu.initialize_model_parallel(tensor_model_parallel_size) + tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() + + seed = 12345 + set_random_seed(seed) + + num_att_heads = num_att_heads_per_partition * \ + torch.distributed.get_world_size() + hidden_size = hidden_size_per_att_head * num_att_heads + + # Network + identity_layer = IdentityLayer3D(batch_size, sequence_length, + hidden_size).cuda() + attention_layer = mpu.BertParallelSelfAttention(hidden_size, num_att_heads, + dropout_prob).cuda() + loss_weight = torch.randn([batch_size, sequence_length, hidden_size]).cuda() + attention_mask = torch.randn([batch_size, 1, 1, sequence_length]).cuda() + # Forward + input_ = identity_layer() + output = attention_layer(input_, attention_mask) + loss = torch.mul(output, loss_weight).sum() + # Backward + loss.backward() + + rank = mpu.get_tensor_model_parallel_rank() + mpu.destroy_model_parallel() + return rank, hidden_size, tensor_model_parallel_size, loss, \ + attention_layer, identity_layer + + +def test_parallel_self_attention(tensor_model_parallel_size): + + if torch.distributed.get_rank() == 0: + print('> testing ParallelSelfAttention with model parallel ' + 'size: {}'.format(tensor_model_parallel_size)) + + num_att_heads_per_partition = 3 + hidden_size_per_att_head = 7 + dropout_prob = 0.0 # has to be zero + batch_size = 5 + sequence_length = 13 + + rank_1, hideen_size_1, tensor_model_parallel_size_1, loss_1, \ + attention_layer_1, identity_layer_1 = parallel_self_attention( + 1, num_att_heads_per_partition, + hidden_size_per_att_head, dropout_prob, batch_size, sequence_length) + + rank, hidden_size, tensor_model_parallel_size, loss, \ + attention_layer, identity_layer = parallel_self_attention( + tensor_model_parallel_size, num_att_heads_per_partition, + hidden_size_per_att_head, dropout_prob, batch_size, sequence_length) + assert hideen_size_1 == hidden_size + + error = loss_1.sub(loss).abs().max() + torch.distributed.barrier() + print(' loss error on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 5.0e-6 + + my_lin_grad_list = torch.split( + attention_layer_1.query_key_value.weight.grad, + hidden_size // tensor_model_parallel_size, 0)[rank::tensor_model_parallel_size] + my_lin_grad = torch.cat(my_lin_grad_list, dim=0) + error = my_lin_grad.sub( + attention_layer.query_key_value.weight.grad).abs().max() + torch.distributed.barrier() + print(' weight gradient error on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 5.0e-6 + + error = identity_layer_1.weight.grad.sub( + identity_layer.weight.grad).abs().max() + torch.distributed.barrier() + print(' input gradient error on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 5.0e-6 + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print(' >> passed the test :-)') + + +def parallel_transformer(tensor_model_parallel_size, num_att_heads_per_partition, + hidden_size_per_att_head, batch_size, sequence_length): + + mpu.initialize_model_parallel(tensor_model_parallel_size) + tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() + + seed = 12345 + set_random_seed(seed) + + num_att_heads = num_att_heads_per_partition * \ + torch.distributed.get_world_size() + hidden_size = hidden_size_per_att_head * num_att_heads + intermediate_size = 4 * hidden_size + + # Network + identity_layer = IdentityLayer3D(batch_size, sequence_length, + hidden_size).cuda() + transformer_layer = mpu.BertParallelTransformerLayer( + hidden_size, intermediate_size, num_att_heads, 0.0, 0.0, + torch.nn.functional.relu, 1.0e-5).cuda() + + loss_weight = torch.randn([batch_size, sequence_length, hidden_size]).cuda() + attention_mask = torch.randn([batch_size, 1, 1, sequence_length]).cuda() + # Forward + input_ = identity_layer() + output = transformer_layer(input_, attention_mask) + loss = torch.mul(output, loss_weight).sum() + # Backward + loss.backward() + + rank = mpu.get_tensor_model_parallel_rank() + mpu.destroy_model_parallel() + return rank, hidden_size, tensor_model_parallel_size, loss, \ + transformer_layer, identity_layer + + +def test_parallel_transformer_layer(tensor_model_parallel_size): + + if torch.distributed.get_rank() == 0: + print('> testing ParallelTransformerLayer with model parallel ' + 'size: {}'.format(tensor_model_parallel_size)) + + num_att_heads_per_partition = 3 + hidden_size_per_att_head = 7 + batch_size = 5 + sequence_length = 13 + + rank_1, hidden_size_1, tensor_model_parallel_size_1, loss_1, \ + transformer_layer_1, identity_layer_1 = parallel_transformer( + 1, num_att_heads_per_partition, + hidden_size_per_att_head, batch_size, sequence_length) + + rank, hidden_size, tensor_model_parallel_size, loss, \ + transformer_layer, identity_layer = parallel_transformer( + tensor_model_parallel_size, num_att_heads_per_partition, + hidden_size_per_att_head, batch_size, sequence_length) + + error = loss_1.sub(loss).abs().max() + torch.distributed.barrier() + print(' loss error on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 5.0e-5, 'error: {}'.format(error) + + error = identity_layer_1.weight.grad.sub( + identity_layer.weight.grad).abs().max() + torch.distributed.barrier() + print(' input gradient error on global rank {}: {}'.format( + torch.distributed.get_rank(), error)) + assert error < 5.0e-5, 'error: {}'.format(error) + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print(' >> passed the test :-)') + + +if __name__ == '__main__': + + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + initialize_distributed() + world_size = torch.distributed.get_world_size() + + print_separator('test initialize affine weight') + tensor_model_parallel_size = 1 + while tensor_model_parallel_size <= world_size: + test_initialize_affine_weight(tensor_model_parallel_size) + tensor_model_parallel_size *= 2 + + tensor_model_parallel_size = 1 + while tensor_model_parallel_size <= world_size: + print_separator('test parallel embedding') + test_parallel_embedding(tensor_model_parallel_size) + tensor_model_parallel_size *= 2 + + print_separator('test column-parallel linear') + tensor_model_parallel_size = 1 + while tensor_model_parallel_size <= world_size: + test_column_parallel_linear(tensor_model_parallel_size) + tensor_model_parallel_size *= 2 + + print_separator('test row-parallel linear') + tensor_model_parallel_size = 1 + while tensor_model_parallel_size <= world_size: + test_row_parallel_linear(tensor_model_parallel_size) + tensor_model_parallel_size *= 2 + + print_separator('test parallel self-attention') + tensor_model_parallel_size = 1 + while tensor_model_parallel_size <= world_size: + test_parallel_self_attention(tensor_model_parallel_size) + tensor_model_parallel_size *= 2 + + print_separator('test parallel transformer') + tensor_model_parallel_size = 1 + while tensor_model_parallel_size <= world_size: + test_parallel_transformer_layer(tensor_model_parallel_size) + tensor_model_parallel_size *= 2 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_random.py b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_random.py new file mode 100644 index 0000000..2609277 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/legacy/mpu/tests/test_random.py @@ -0,0 +1,191 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +from commons import print_separator +from commons import initialize_distributed +import mpu +import torch +import sys +sys.path.append("../..") + + +def test_set_cuda_rng_state(tensor_model_parallel_size): + + if torch.distributed.get_rank() == 0: + print('> testing set_rng_state with size {} ...'. + format(tensor_model_parallel_size)) + + mpu.initialize_model_parallel(tensor_model_parallel_size) + tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() + + size = 123 + seed = 1234 + torch.cuda.manual_seed(1234) + tensor = torch.tensor(size, dtype=torch.float, device='cuda') + + # Get the state + rng_state = torch.cuda.get_rng_state() + rng_state_copy = rng_state.clone() + + # Do some stuff. + for _ in range(5): + torch.randn(size, out=tensor) + result_1 = tensor.clone() + + assert rng_state.sub(rng_state_copy).max() == 0 + assert torch.cuda.get_rng_state().sub(rng_state_copy).max() > 0 + + # State should be different. + new_rng_state = torch.cuda.get_rng_state() + max_diff = new_rng_state.sub(rng_state).max() + print(' max diff in rng state (should be non-zero) on global rank {}: {}'. + format(torch.distributed.get_rank(), max_diff)) + assert max_diff > 0 + + # Reset the rng state and do the same stuff. + mpu.random._set_cuda_rng_state(rng_state) + for _ in range(5): + torch.randn(size, out=tensor) + mpu.random._set_cuda_rng_state(rng_state) + for _ in range(5): + torch.randn(size, out=tensor) + result_2 = tensor.clone() + + # Results should be the same + error = result_2.sub(result_1).abs().max() + print(' max error in generated tensors (should be zero) on ' + 'global rank {}: {}'.format(torch.distributed.get_rank(), error)) + assert error < 1.0e-6 + + # Input state should have remained intact. + error = rng_state.sub(rng_state_copy).max() + print(' max error in rng state (should be zero) on global rank {}: {}'. + format(torch.distributed.get_rank(), error)) + assert error == 0 + + # Reset groups + mpu.destroy_model_parallel() + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print('>> passed the test :-)') + + +def test_cuda_rng_tracker(tensor_model_parallel_size): + + if torch.distributed.get_rank() == 0: + print('> testing cuda rng tracker with size {} ...'. + format(tensor_model_parallel_size)) + + mpu.initialize_model_parallel(tensor_model_parallel_size) + tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() + + seed_1 = 1234 + seed_2 = 4321 + size = [12, 21] + tensor = torch.tensor(size, dtype=torch.float, device='cuda') + + # Set to seed_1 and generate two tensors. + torch.cuda.manual_seed(seed_1) + torch.randn(size, out=tensor) + target_11 = tensor.clone() + torch.randn(size, out=tensor) + target_12 = tensor.clone() + + # Set to seed_2 and generate two tensors. + torch.cuda.manual_seed(seed_2) + torch.randn(size, out=tensor) + target_21 = tensor.clone() + torch.randn(size, out=tensor) + target_22 = tensor.clone() + + # Now if we interleave seed_1 and seed_2, + # we should still get the same tensors + torch.cuda.manual_seed(seed_1) + mpu.get_cuda_rng_tracker().add('test', seed_2) + + torch.randn(size, out=tensor) + result_11 = tensor.clone() + + with mpu.get_cuda_rng_tracker().fork('test'): + torch.randn(size, out=tensor) + result_21 = tensor.clone() + + torch.randn(size, out=tensor) + result_12 = tensor.clone() + + with mpu.get_cuda_rng_tracker().fork('test'): + torch.randn(size, out=tensor) + result_22 = tensor.clone() + + diff = result_11.sub(result_21).abs().max() + diff = min(diff, result_12.sub(result_22).abs().max()) + print(' max diff in generated tensors (should be non-zero) on ' + 'global rank {}: {}'.format(torch.distributed.get_rank(), diff)) + assert diff > 1.0e-6 + error = max(result_11.sub(target_11).abs().max(), + result_12.sub(target_12).abs().max()) + error = max(error, result_21.sub(target_21).abs().max()) + error = max(error, result_22.sub(target_22).abs().max()) + print(' max error in generated tensors (should be zero) on ' + 'global rank {}: {}'.format(torch.distributed.get_rank(), error)) + assert error < 1.0e-6 + + # Reset the tracker + mpu.get_cuda_rng_tracker().reset() + + # Reset groups + mpu.destroy_model_parallel() + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print('>> passed the test :-)') + + +def test_model_parallel_cuda_manual_seed(tensor_model_parallel_size): + + if torch.distributed.get_rank() == 0: + print('> testing model parallel cuda manual seed with size {} ...'. + format(tensor_model_parallel_size)) + + mpu.initialize_model_parallel(tensor_model_parallel_size) + tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() + + mpu.model_parallel_cuda_manual_seed(12345) + assert torch.cuda.initial_seed() == 12345 + with mpu.get_cuda_rng_tracker().fork(): + assert torch.cuda.initial_seed() == (12345 + 2718 + + mpu.get_tensor_model_parallel_rank()) + + # Reset the tracker + mpu.get_cuda_rng_tracker().reset() + + # Reset groups + mpu.destroy_model_parallel() + + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print('>> passed the test :-)') + + +if __name__ == '__main__': + + initialize_distributed() + world_size = torch.distributed.get_world_size() + + tensor_model_parallel_size = 1 + while tensor_model_parallel_size <= world_size: + print_separator('test set rng state') + test_set_cuda_rng_state(tensor_model_parallel_size) + tensor_model_parallel_size *= 2 + + tensor_model_parallel_size = 1 + while tensor_model_parallel_size <= world_size: + print_separator('test cuda rng tracker') + test_cuda_rng_tracker(tensor_model_parallel_size) + tensor_model_parallel_size *= 2 + + tensor_model_parallel_size = 1 + while tensor_model_parallel_size <= world_size: + print_separator('test model parallel cuda manual seed') + test_model_parallel_cuda_manual_seed(tensor_model_parallel_size) + tensor_model_parallel_size *= 2 diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/__init__.py new file mode 100644 index 0000000..facb63c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/__init__.py @@ -0,0 +1,21 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import torch + +from .global_vars import get_args +from .global_vars import get_current_global_batch_size +from .global_vars import get_num_microbatches +from .global_vars import get_signal_handler +from .global_vars import update_num_microbatches +from .global_vars import get_tokenizer +from .global_vars import get_tensorboard_writer +from .global_vars import get_wandb_writer +from .global_vars import get_one_logger +from .global_vars import get_adlr_autoresume +from .global_vars import get_timers +from .initialize import initialize_megatron +from .training import pretrain, get_model, get_train_valid_test_num_samples + +from .utils import (print_rank_0, + is_last_rank, + print_rank_last) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/arguments.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/arguments.py new file mode 100644 index 0000000..85c5821 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/arguments.py @@ -0,0 +1,1639 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Megatron arguments.""" + +import argparse +import dataclasses +import json +import os +import torch +import types + +import torch.nn.functional as F +from megatron.core.models.retro.utils import ( + get_config_path as get_retro_config_path, + get_gpt_data_dir as get_retro_data_dir, +) +from megatron.core.transformer import TransformerConfig + + +def parse_args(extra_args_provider=None, ignore_unknown_args=False): + """Parse all arguments.""" + parser = argparse.ArgumentParser(description='Megatron-LM Arguments', + allow_abbrev=False) + + # Standard arguments. + parser = _add_network_size_args(parser) + parser = _add_regularization_args(parser) + parser = _add_training_args(parser) + parser = _add_initialization_args(parser) + parser = _add_learning_rate_args(parser) + parser = _add_checkpointing_args(parser) + parser = _add_mixed_precision_args(parser) + parser = _add_distributed_args(parser) + parser = _add_validation_args(parser) + parser = _add_data_args(parser) + parser = _add_autoresume_args(parser) + parser = _add_biencoder_args(parser) + parser = _add_vision_args(parser) + parser = _add_moe_args(parser) + parser = _add_logging_args(parser) + parser = _add_straggler_detector_args(parser) + parser = _add_inference_args(parser) + parser = _add_transformer_engine_args(parser) + parser = _add_retro_args(parser) + parser = _add_experimental_args(parser) + + # Custom arguments. + if extra_args_provider is not None: + parser = extra_args_provider(parser) + + # Parse. + if ignore_unknown_args: + args, _ = parser.parse_known_args() + else: + args = parser.parse_args() + + # Experimental yaml + if args.yaml_cfg is not None: + from .yaml_arguments import load_yaml + assert args.yaml_cfg and args.use_mcore_models, "To use yaml, mcore must be enabled" + args = load_yaml(args.yaml_cfg) + + + # Args from environment + args.rank = int(os.getenv('RANK', '0')) + args.world_size = int(os.getenv("WORLD_SIZE", '1')) + + return args + + +def load_retro_config(retro_project_dir): + '''Load Retro's config.json.''' + + # Retro config path. + retro_config_path = get_retro_config_path(retro_project_dir) + assert os.path.exists(retro_config_path), \ + "Retro project dir missing config.json." + + # Load retro config. + with open(retro_config_path) as f: + retro_config = types.SimpleNamespace(**json.load(f)) + + return retro_config + + +def load_retro_args(args): + """Load predefined args from Retro config (if applicable). + + When using Retro (or GPT for comparison purposes), data arguments are + overridden by the saved config.json within the Retro project directory. This + is to ensure that the data used for pretraining is consistent with the data + that was preprocessed using the Retro preprocessing pipeline (see + `tools/retro/preprocess_data.py`). + """ + + # Return if no project directory is specified. + if args.retro_project_dir is None: + return + + # Load retro config. + retro_config = load_retro_config(args.retro_project_dir) + + # Retro data path is relative to project dir (via hard or soft links). + data_dir = get_retro_data_dir(args.retro_project_dir) + data_path = list(retro_config.retro_gpt_data_path) + if len(data_path) % 2 == 0: + for i in range(len(data_path) - 1, -1, -2): + data_path[i] = os.path.join(data_dir, data_path[i]) + else: + assert len(data_path) == 1 + data_path[0] = os.path.join(data_dir, data_path[0]) + + # Update args. + args.data_cache_path = retro_config.retro_gpt_data_cache_path + args.data_path = data_path if args.data_path is None else args.data_path + args.eval_interval = retro_config.retro_gpt_eval_interval + args.eval_iters = retro_config.retro_gpt_eval_iters + args.global_batch_size = retro_config.retro_gpt_global_batch_size + args.max_position_embeddings = retro_config.retro_gpt_seq_length + args.merge_file = os.path.join( + args.retro_project_dir, + retro_config.retro_gpt_merge_file, + ) if retro_config.retro_gpt_merge_file is not None else None + args.seed = retro_config.retro_gpt_seed + args.seq_length = retro_config.retro_gpt_seq_length + args.tokenizer_model = os.path.join( + args.retro_project_dir, + retro_config.retro_gpt_tokenizer_model, + ) if retro_config.retro_gpt_tokenizer_model is not None else None + args.tokenizer_type = retro_config.retro_gpt_tokenizer_type + args.train_samples = retro_config.retro_gpt_train_samples + args.vocab_file = os.path.join( + args.retro_project_dir, + retro_config.retro_gpt_vocab_file, + ) if retro_config.retro_gpt_vocab_file is not None else None + + # Retro-specific args. + args.retro_block_size = retro_config.retro_block_size + args.retro_chunk_length = retro_config.retro_gpt_chunk_length + args.retro_neighbor_dirs = retro_config.retro_neighbor_dirs + args.retro_split_preprocessing = retro_config.retro_gpt_split + args.retro_bert_tokenizer_type = retro_config.retro_bert_tokenizer_type + args.retro_bert_vocab_file = retro_config.retro_bert_vocab_file + + +def validate_args(args, defaults={}): + + # Load saved args from Retro (if applicable). + load_retro_args(args) + + # Tensor model parallel size. + args.tensor_model_parallel_size = min( + args.tensor_model_parallel_size, args.world_size) + assert args.world_size % args.tensor_model_parallel_size == 0, 'world size'\ + ' ({}) is not divisible by tensor model parallel size ({})'.format( + args.world_size, args.tensor_model_parallel_size) + + # Pipeline model parallel size. + args.pipeline_model_parallel_size = min( + args.pipeline_model_parallel_size, + (args.world_size // args.tensor_model_parallel_size)) + args.transformer_pipeline_model_parallel_size = ( + args.pipeline_model_parallel_size - 1 + if args.standalone_embedding_stage else + args.pipeline_model_parallel_size + ) + + # Checks. + model_parallel_size = args.pipeline_model_parallel_size * \ + args.tensor_model_parallel_size + assert args.world_size % (model_parallel_size * args.context_parallel_size) == 0, \ + 'world size ({}) is not divisible by tensor parallel size ({}) times ' \ + 'pipeline parallel size ({}) times context parallel size ({})'.format( + args.world_size, args.tensor_model_parallel_size, + args.pipeline_model_parallel_size, args.context_parallel_size) + args.data_parallel_size = args.world_size // (model_parallel_size * args.context_parallel_size) + if args.rank == 0: + print('using world size: {}, data-parallel size: {}, ' + 'context-parallel size: {} ' + 'tensor-model-parallel size: {}, ' + 'pipeline-model-parallel size: {} '.format( + args.world_size, args.data_parallel_size, + args.context_parallel_size, + args.tensor_model_parallel_size, + args.pipeline_model_parallel_size), flush=True) + if args.pipeline_model_parallel_size > 1: + if args.pipeline_model_parallel_split_rank is not None: + assert args.pipeline_model_parallel_split_rank < \ + args.pipeline_model_parallel_size, 'split rank needs'\ + ' to be less than pipeline model parallel size ({})'.format( + args.pipeline_model_parallel_size) + + if args.tp_comm_overlap: + assert args.sequence_parallel == True, 'Tensor parallel communication/GEMM overlap can happen only when sequence parallelism is enabled' + + # Deprecated arguments + assert args.batch_size is None, '--batch-size argument is no longer ' \ + 'valid, use --micro-batch-size instead' + del args.batch_size + assert args.warmup is None, '--warmup argument is no longer valid, use ' \ + '--lr-warmup-fraction instead' + del args.warmup + assert args.model_parallel_size is None, '--model-parallel-size is no ' \ + 'longer valid, use --tensor-model-parallel-size instead' + del args.model_parallel_size + + if args.checkpoint_activations: + if args.rank == 0: + print('--checkpoint-activations is no longer valid, use --recompute-activations, ' + 'or, for more control, --recompute-granularity and --recompute-method.') + exit() + del args.checkpoint_activations + + if args.recompute_activations: + args.recompute_granularity = 'selective' + del args.recompute_activations + + # Set input defaults. + for key in defaults: + # For default to be valid, it should not be provided in the + # arguments that are passed to the program. We check this by + # ensuring the arg is set to None. + if getattr(args, key, None) is not None: + if args.rank == 0: + print('WARNING: overriding default arguments for {key}:{v} \ + with {key}:{v2}'.format(key=key, v=defaults[key], + v2=getattr(args, key)), + flush=True) + else: + setattr(args, key, defaults[key]) + + # Batch size. + assert args.micro_batch_size is not None + assert args.micro_batch_size > 0 + if args.global_batch_size is None: + args.global_batch_size = args.micro_batch_size * args.data_parallel_size + if args.rank == 0: + print('setting global batch size to {}'.format( + args.global_batch_size), flush=True) + assert args.global_batch_size > 0 + if args.num_layers_per_virtual_pipeline_stage is not None: + assert args.pipeline_model_parallel_size > 2, \ + 'pipeline-model-parallel size should be greater than 2 with ' \ + 'interleaved schedule' + assert args.num_layers % args.transformer_pipeline_model_parallel_size == 0, \ + 'number of layers should be divisible by the pipeline parallel size' + num_layers_per_pipeline_stage = args.num_layers // args.transformer_pipeline_model_parallel_size + assert num_layers_per_pipeline_stage % args.num_layers_per_virtual_pipeline_stage == 0, \ + 'number of layers per pipeline stage must be divisible number of layers per virtual pipeline stage' + args.virtual_pipeline_model_parallel_size = num_layers_per_pipeline_stage // \ + args.num_layers_per_virtual_pipeline_stage + else: + args.virtual_pipeline_model_parallel_size = None + # Overlap P2P communication is disabled if not using the interleaved schedule. + args.overlap_p2p_comm = False + if args.rank == 0: + print('WARNING: Setting args.overlap_p2p_comm to False since non-interleaved ' + 'schedule does not support overlapping p2p communication') + + if args.overlap_param_gather: + assert args.use_distributed_optimizer, \ + '--overlap-param-gather only supported with distributed optimizer' + assert args.overlap_grad_reduce, \ + '--overlap-grad-reduce should be turned on when using --overlap-param-gather' + assert args.use_mcore_models, \ + '--overlap-param-gather only supported with MCore models' + + # Parameters dtype. + args.params_dtype = torch.float + if args.fp16: + assert not args.bf16 + args.params_dtype = torch.half + # Turn off checking for NaNs in loss and grads if using dynamic loss scaling, + # where NaNs in grads / loss are signal to the loss scaler. + if not args.loss_scale: + args.check_for_nan_in_loss_and_grad = False + if args.rank == 0: + print('WARNING: Setting args.check_for_nan_in_loss_and_grad to False since ' + 'dynamic loss scaling is being used') + if args.bf16: + assert not args.fp16 + args.params_dtype = torch.bfloat16 + # bfloat16 requires gradient accumulation and all-reduce to + # be done in fp32. + if not args.accumulate_allreduce_grads_in_fp32: + args.accumulate_allreduce_grads_in_fp32 = True + if args.rank == 0: + print('accumulate and all-reduce gradients in fp32 for ' + 'bfloat16 data type.', flush=True) + + if args.rank == 0: + print('using {} for parameters ...'.format(args.params_dtype), + flush=True) + + if args.dataloader_type is None: + args.dataloader_type = 'single' + + # Consumed tokens. + args.consumed_train_samples = 0 + args.consumed_valid_samples = 0 + + # Support for variable sequence lengths across batches/microbatches. + # set it if the dataloader supports generation of variable sequence lengths + # across batches/microbatches. Due to additional communication overhead + # during pipeline parallelism, it should not be set if sequence length + # is constant during training. + args.variable_seq_lengths = False + + # Iteration-based training. + if args.train_iters: + # If we use iteration-based training, make sure the + # sample-based options are off. + assert args.train_samples is None, \ + 'expected iteration-based training' + assert args.lr_decay_samples is None, \ + 'expected iteration-based learning rate decay' + assert args.lr_warmup_samples == 0, \ + 'expected iteration-based learning rate warmup' + assert args.rampup_batch_size is None, \ + 'expected no batch-size rampup for iteration-based training' + if args.lr_warmup_fraction is not None: + assert args.lr_warmup_iters == 0, \ + 'can only specify one of lr-warmup-fraction and lr-warmup-iters' + + # Sample-based training. + if args.train_samples: + # If we use sample-based training, make sure the + # iteration-based options are off. + assert args.train_iters is None, \ + 'expected sample-based training' + assert args.lr_decay_iters is None, \ + 'expected sample-based learning rate decay' + assert args.lr_warmup_iters == 0, \ + 'expected sample-based learnig rate warmup' + if args.lr_warmup_fraction is not None: + assert args.lr_warmup_samples == 0, \ + 'can only specify one of lr-warmup-fraction ' \ + 'and lr-warmup-samples' + + if args.num_layers is not None: + assert args.encoder_num_layers is None, \ + 'cannot have both num-layers and encoder-num-layers specified' + args.encoder_num_layers = args.num_layers + else: + assert args.encoder_num_layers is not None, \ + 'either num-layers or encoder-num-layers should be specified' + args.num_layers = args.encoder_num_layers + + # Check required arguments. + required_args = ['num_layers', 'hidden_size', 'num_attention_heads', + 'max_position_embeddings'] + for req_arg in required_args: + _check_arg_is_not_none(args, req_arg) + + # Checks. + if args.ffn_hidden_size is None: + if args.swiglu: + # reduce the dimnesion for MLP since projections happens on + # two linear layers. this keeps the number of paramters in + # the same ballpark as the counterpart with 4*h size + # we keep it a multiple of 64, which means the actual tensor size + # will be a multiple of 64 / tp_size + args.ffn_hidden_size = int((4 * args.hidden_size * 2 / 3) / 64) * 64 + else: + args.ffn_hidden_size = 4 * args.hidden_size + + if args.kv_channels is None: + assert args.hidden_size % args.num_attention_heads == 0 + args.kv_channels = args.hidden_size // args.num_attention_heads + + if args.seq_length is not None: + assert args.encoder_seq_length is None + args.encoder_seq_length = args.seq_length + else: + assert args.encoder_seq_length is not None + args.seq_length = args.encoder_seq_length + + if args.seq_length is not None: + assert args.max_position_embeddings >= args.seq_length + if args.decoder_seq_length is not None: + assert args.max_position_embeddings >= args.decoder_seq_length + if args.lr is not None: + assert args.min_lr <= args.lr + if args.save is not None: + assert args.save_interval is not None + # Mixed precision checks. + if args.fp16_lm_cross_entropy: + assert args.fp16, 'lm cross entropy in fp16 only support in fp16 mode.' + if args.fp32_residual_connection: + assert args.fp16 or args.bf16, \ + 'residual connection in fp32 only supported when using fp16 or bf16.' + + if args.moe_grouped_gemm: + assert args.bf16, 'Currently GroupedGEMM for MoE only supports bf16 dtype.' + dc = torch.cuda.get_device_capability() + assert dc[0] >= 8, "Unsupported compute capability for GroupedGEMM kernels." + + if args.weight_decay_incr_style == 'constant': + assert args.start_weight_decay is None + assert args.end_weight_decay is None + args.start_weight_decay = args.weight_decay + args.end_weight_decay = args.weight_decay + else: + assert args.start_weight_decay is not None + assert args.end_weight_decay is not None + + TORCH_MAJOR = int(torch.__version__.split('.')[0]) + TORCH_MINOR = int(torch.__version__.split('.')[1]) + # Persistent fused layer norm. + if TORCH_MAJOR < 1 or (TORCH_MAJOR == 1 and TORCH_MINOR < 11): + args.no_persist_layer_norm = True + if args.rank == 0: + print('Persistent fused layer norm kernel is supported from ' + 'pytorch v1.11 (nvidia pytorch container paired with v1.11). ' + 'Defaulting to no_persist_layer_norm=True') + + # Activation recomputing. + if args.distribute_saved_activations: + assert args.tensor_model_parallel_size > 1, 'can distribute ' \ + 'recomputed activations only across tensor model ' \ + 'parallel groups' + assert args.recompute_granularity == 'full', \ + 'distributed recompute activations is only '\ + 'application to full recompute granularity' + assert args.recompute_method is not None, \ + 'for distributed recompute activations to work you '\ + 'need to use a recompute method ' + assert (TORCH_MAJOR, TORCH_MINOR) >= (1, 10), \ + 'distributed recompute activations are supported for pytorch ' \ + 'v1.10 and above (Nvidia Pytorch container >= 21.07). Current ' \ + 'pytorch version is v%s.%s.' % (TORCH_MAJOR, TORCH_MINOR) + + if args.recompute_granularity == 'selective': + assert args.recompute_method is None, \ + 'recompute method is not yet supported for ' \ + 'selective recomputing granularity' + + # disable sequence parallelism when tp=1 + # to avoid change in numerics when + # sequence_parallelism is enabled. + if args.tensor_model_parallel_size == 1: + args.sequence_parallel = False + + # disable async_tensor_model_parallel_allreduce when + # model parallel memory optimization is enabled + if args.sequence_parallel: + args.async_tensor_model_parallel_allreduce = False + + if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1": + if args.sequence_parallel: + raise RuntimeError( + "Using sequence parallelism requires setting the environment variable " + "CUDA_DEVICE_MAX_CONNECTIONS to 1") + if args.async_tensor_model_parallel_allreduce: + raise RuntimeError( + "Using async gradient all reduce requires setting the environment " + "variable CUDA_DEVICE_MAX_CONNECTIONS to 1") + + # Disable bias gelu fusion if we are disabling bias altogether + if not args.add_bias_linear: + args.bias_gelu_fusion = False + + # Retro checks. + if args.retro_add_retriever: + + # Train samples should be auto-loaded. + assert args.train_samples is not None, \ + "args.train_samples should be auto-loaded from the retro config." + + # Sequence parallelism unsupported. + assert not args.sequence_parallel, \ + "retro currently does not support sequence parallelism." + + # Pipeline parallelism unsupported. + assert args.pipeline_model_parallel_size == 1, \ + "retro currently does not support pipeline parallelism." + + if args.decoupled_lr is not None or args.decoupled_min_lr is not None: + assert args.use_mcore_models, \ + '--decoupled-lr and --decoupled-min-lr only supported by Megatron Core, please add --use-mcore-models.' + + # Legacy RoPE arguments + if args.use_rotary_position_embeddings: + args.position_embedding_type = 'rope' + if args.rotary_interleaved and args.apply_rope_fusion: + raise RuntimeError('--rotary-interleaved does not work with rope_fusion.') + if args.rotary_interleaved and not args.use_mcore_models: + raise RuntimeError('--rotary-interleaved only support Megatron Core, please add --use-mcore-models.') + + # Would just need to add 'NoPE' as a position_embedding_type to support this, but for now + # don't allow it to keep things simple + if not args.add_position_embedding and args.position_embedding_type != 'rope': + raise RuntimeError('--no-position-embedding is deprecated, use --position-embedding-type') + + # MoE Spec check + if args.num_experts is not None: + assert args.spec is None, "Model Spec must be None when using MoEs" + if args.tensor_model_parallel_size > 1: + assert args.sequence_parallel, \ + "When using MoE and tensor parallelism, sequence parallelism must be used." + + # Expert parallelism check + if args.expert_model_parallel_size > 1: + assert args.num_experts is not None, "num_experts must be non None to use expert model parallelism" + assert args.num_experts % args.expert_model_parallel_size == 0, \ + "Number of experts should be a multiple of expert model parallel_size." + assert not args.fp16, \ + "Expert parallelism is not supported with fp16 training." + + # Distributed checkpointing checks + if args.use_dist_ckpt and not args.use_mcore_models: + raise RuntimeError('--use-dist-ckpt only support Megatron Core, please add --use-mcore-models.') + + if args.use_tp_pp_dp_mapping: + assert args.context_parallel_size * args.expert_model_parallel_size <= 1, \ + "context_parallel and expert_model_parallel can't be used with tp-pp-dp mapping." + + # Print arguments. + _print_args("arguments", args) + + return args + + +def _print_args(title, args): + """Print arguments.""" + if args.rank == 0: + print(f'------------------------ {title} ------------------------', + flush=True) + str_list = [] + for arg in vars(args): + dots = '.' * (48 - len(arg)) + str_list.append(' {} {} {}'.format(arg, dots, getattr(args, arg))) + for arg in sorted(str_list, key=lambda x: x.lower()): + print(arg, flush=True) + print(f'-------------------- end of {title} ---------------------', + flush=True) + + +def _check_arg_is_not_none(args, arg): + assert getattr(args, arg) is not None, '{} argument is None'.format(arg) + + +def core_transformer_config_from_args(args, config_class=None): + + # Config class. + config_class = config_class or TransformerConfig + + # Translate args to core transformer configuration + kw_args = {} + for f in dataclasses.fields(config_class): + if hasattr(args, f.name): + kw_args[f.name] = getattr(args, f.name) + kw_args['persist_layer_norm'] = not args.no_persist_layer_norm + kw_args['layernorm_zero_centered_gamma'] = args.apply_layernorm_1p + kw_args['layernorm_epsilon'] = args.norm_epsilon + kw_args['deallocate_pipeline_outputs'] = True + kw_args['pipeline_dtype'] = args.params_dtype + kw_args['batch_p2p_comm'] = not args.overlap_p2p_comm + kw_args['num_moe_experts'] = args.num_experts + kw_args['rotary_interleaved'] = args.rotary_interleaved + if args.swiglu: + kw_args['activation_func'] = F.silu + kw_args['gated_linear_unit'] = True + kw_args['bias_activation_fusion'] = args.bias_swiglu_fusion + else: + kw_args['bias_activation_fusion'] = args.bias_gelu_fusion + if args.squared_relu: + assert not args.swiglu + def squared_relu(x): + return torch.pow(F.relu(x), 2) + kw_args['activation_func'] = squared_relu + if args.init_method_xavier_uniform: + kw_args['init_method'] = torch.nn.init.xavier_uniform_ + kw_args['scaled_init_method'] = torch.nn.init.xavier_uniform_ + if args.group_query_attention: + kw_args['num_query_groups'] = args.num_query_groups + else: + kw_args['num_query_groups'] = None + + # Return config. + return config_class(**kw_args) + + +def _add_transformer_engine_args(parser): + group = parser.add_argument_group(title='Transformer-Engine') + + group.add_argument('--fp8-format', default=None, + choices=['e4m3', 'hybrid'], + help='Which fp8 format scheme to use for FP8 tensors in the forward and backward pass', + dest='fp8') + group.add_argument('--fp8-margin', type=int, default=0, + help='Scaling margin for fp8', + dest='fp8_margin') + group.add_argument('--fp8-interval', type=int, default=1, + help='Scaling update interval for fp8', + dest='fp8_interval') + group.add_argument('--fp8-amax-history-len', type=int, default=1, + help='Number of steps for which amax history is recorded per tensor', + dest='fp8_amax_history_len') + group.add_argument('--fp8-amax-compute-algo', default='most_recent', + choices=['most_recent', 'max'], + help='Algorithm for computing amax from history', + dest='fp8_amax_compute_algo') + group.add_argument('--no-fp8-wgrad', action='store_false', + help='Execute wgrad in higher precision even for FP8 runs', + dest='fp8_wgrad') + group.add_argument('--transformer-impl', default='transformer_engine', + choices=['local', 'transformer_engine'], + help='Which Transformer implementation to use.') + + return parser + +def _add_inference_args(parser): + group = parser.add_argument_group(title='inference') + + group.add_argument('--inference-batch-times-seqlen-threshold', + type=int, default=512, + help='During inference, if batch-size times ' + 'sequence-length is smaller than this threshold ' + 'then we will not use pipelining, otherwise we will.') + group.add_argument('--max-tokens-to-oom', + type=int, default=12000, + help='Maximum number of tokens during inference' + 'tokens here is # in prompt + # to generate' + 'Allows us to throw an error before OOM crashes server') + group.add_argument('--output-bert-embeddings', action='store_true', + help='Output Bert embeddings (via mean pooling) from ' + 'model, rather than its binary head output or entire ' + 'hidden batch.') + group.add_argument('--bert-embedder-type', default="megatron", + choices=["megatron", "huggingface"], + help='Select either Megatron or Huggingface as the ' + 'Bert embedder.') + + return parser + + +def _add_retro_args(parser): + group = parser.add_argument_group(title='retro') + + group.add_argument('--retro-project-dir', default=None, + help='Retro project directory, which contains the ' + 'preprocessed data for pretraining. This directory ' + 'is built during preprocessing (see ' + 'tools/retro/README.md), and contains subdirectories ' + 'for the chunk database and pretraining neighbors.') + group.add_argument('--retro-add-retriever', + action='store_true', default=False, + help='Add a retriever to the transformer, for use in ' + 'pretraining a Retro model.') + group.add_argument('--retro-cyclic-train-iters', type=int, default=None, + help='Set number of training iterations for cyclic ' + 'Retro training.') + group.add_argument('--retro-encoder-layers', type=int, default=2, + help='Number of layers to use for the retrieval ' + 'encoder.') + group.add_argument('--retro-encoder-hidden-dropout', + type=float, default=0.1, help='Hidden dropout for ' + 'retrieval encoder.') + group.add_argument('--retro-encoder-attention-dropout', + type=float, default=0.1, help='Attention dropout for ' + 'retrieval encoder.') + group.add_argument("--retro-num-neighbors", type=int, default=2, + help='Number of neighbors to retrieve during ' + 'pretraining.') + group.add_argument("--retro-num-retrieved-chunks", type=int, default=2, + help='Number of chunks to retrieve from the retrieval ' + 'database.') + group.add_argument("--retro-attention-gate", type=float, default=1, + help="Gated cross attention.") + group.add_argument("--retro-no-verify-neighbor-count", action="store_false", + dest="retro_verify_neighbor_count", + help="Skip verifying that len(GPT dataset) == len(saved " + "neighbors).") + + # Enforce argument naming convention. + for action in group._group_actions: + prefix = action.dest.split("_")[0] + assert prefix == "retro", \ + "Retro args must be prefixed with '--retro-*', for consistent " \ + "styling. Please fix '%s'." % ", ".join(action.option_strings) + + return parser + + +def _add_network_size_args(parser): + group = parser.add_argument_group(title='network size') + + group.add_argument('--num-layers', type=int, default=None, + help='Number of transformer layers.') + group.add_argument('--encoder-num-layers', type=int, default=None, + help='Number of encoder transformer layers.') + group.add_argument('--decoder-num-layers', type=int, default=None, + help='Number of decoder transformer layers.') + group.add_argument('--hidden-size', type=int, default=None, + help='Tansformer hidden size.') + group.add_argument('--ffn-hidden-size', type=int, default=None, + help='Transformer Feed-Forward Network hidden size. ' + 'This is set to 4*hidden-size if not provided') + group.add_argument('--num-attention-heads', type=int, default=None, + help='Number of transformer attention heads.') + group.add_argument('--kv-channels', type=int, default=None, + help='Projection weights dimension in multi-head ' + 'attention. This is set to ' + ' args.hidden_size // args.num_attention_heads ' + 'if not provided.') + group.add_argument('--group-query-attention', action='store_true', + help='Use group-query attention.') + group.add_argument('--num-query-groups', type=int, default=1) + + group.add_argument('--max-position-embeddings', type=int, default=None, + help='Maximum number of position embeddings to use. ' + 'This is the size of position embedding.') + group.add_argument('--position-embedding-type', type=str, default='learned_absolute', + choices=['learned_absolute', 'rope'], + help='Position embedding type.') + group.add_argument('--use-rotary-position-embeddings', action='store_true', + help='Use rotary positional embeddings or not. ' + 'Deprecated: use --position-embedding-type') + group.add_argument('--rotary-percent', type=float, default=1.0, + help='Percent of rotary dimension to use, default 100%%') + group.add_argument('--rotary-interleaved', action='store_true', + help='Use interleaved rotary embedding.') + group.add_argument('--rotary-seq-len-interpolation-factor', type=int, default=None, + help='Sequence length interpolation factor for rotary embeddings.') + group.add_argument('--no-position-embedding', + action='store_false', + help='Disable position embedding. Deprecated: use --position-embedding-type', + dest='add_position_embedding') + group.add_argument('--make-vocab-size-divisible-by', type=int, default=128, + help='Pad the vocab size to be divisible by this value.' + 'This is added for computational efficieny reasons.') + group.add_argument('--normalization', default='LayerNorm', + choices=['LayerNorm', 'RMSNorm'], + help='Which normalization technique to use.') + group.add_argument('--norm-epsilon', type=float, default=1e-5, + help='Epsilon for layer norm and RMS norm.') + group.add_argument('--apply-layernorm-1p', action='store_true', + help='Adjust LayerNorm weights such that they are centered ' + 'around zero. This improves numerical stability.') + group.add_argument('--apply-residual-connection-post-layernorm', + action='store_true', + help='If set, use original BERT residula connection ' + 'ordering.') + group.add_argument('--openai-gelu', action='store_true', + help='Use OpenAIs GeLU implementation. This option' + 'should not be used unless for backward compatibility' + 'reasons.') + group.add_argument('--squared-relu', action='store_true', + help='Use squared relu activation instead of default gelu') + group.add_argument('--swiglu', action='store_true', + help='Use gated linear units and SiLU activation instead of default gelu') + group.add_argument('--onnx-safe', type=bool, required=False, + help='Use workarounds for known problems with ' + 'Torch ONNX exporter') + group.add_argument('--bert-no-binary-head', action='store_false', + help='Disable BERT binary head.', + dest='bert_binary_head') + group.add_argument('--untie-embeddings-and-output-weights', action='store_true', + help='Untie embeddings and output weights.'), + return parser + +def _add_straggler_detector_args(parser): + group = parser.add_argument_group(title='straggler') + group.add_argument('--log-straggler', action='store_true', + help='If set, tracks and logs straggler per GPU.') + group.add_argument('--disable-straggler-on-startup', action='store_true', + help='If set, StragglerDetector is disabled on startup.') + group.add_argument('--straggler-ctrlr-port', type=int, default=65535, + help='Port number to toggle StragglerDetector on/off at runtime') + group.add_argument('--straggler-minmax-count', type=int, default=1, + help='Number of ranks to report with high/low estimated throughput') + return parser + +def _add_logging_args(parser): + group = parser.add_argument_group(title='logging') + + group.add_argument('--log-params-norm', action='store_true', + help='If set, calculate and log parameters norm.') + group.add_argument('--log-num-zeros-in-grad', action='store_true', + help='If set, calculate and log the number of zeros in gradient.') + group.add_argument('--log-throughput', action='store_true', + help='If set, calculate and log throughput per GPU.') + group.add_argument('--log-progress', action='store_true', + help='If set, log progress (in terms of number of processed tokens and ' + 'number of floating-point operations) to progress.txt file in checkpoint ' + 'directory.') + group.add_argument('--timing-log-level', type=int, + default=0, choices=range(0,3), + help='Granularity level to measure and report timing. ' + ' 0: report only iteration time and make sure timing ' + ' does not introduce extra overhead.' + ' 1: report timing for operations that are executed ' + ' very limited times (basically once) during ' + ' each iteration (such as gradient all-reduce) ' + ' 2: report timing for operations that migh be ' + ' executed numerous times during each iteration. ' + 'Note that setting the level to 1 or 2 might ' + 'cause increase in iteration time.') + group.add_argument('--no-barrier-with-level-1-timing', action='store_false', + help='If not set, use barrier with level 1 time ' + 'measurements. Note that this is up to the user ' + 'to make sure calling barrier with their timers ' + 'will not result in hangs. This can happen if for ' + 'example the user adds a level 1 timer that is not ' + 'called by all ranks.', + dest='barrier_with_L1_time') + group.add_argument('--timing-log-option', type=str, default='minmax', + choices=['max', 'minmax', 'all'], + help='Options for logging timing:' + ' max: report the max timing across all ranks' + ' minmax: report min and max timings across all ranks' + ' all: report timings of all ranks.') + group.add_argument('--tensorboard-log-interval', type=int, default=1, + help='Report to tensorboard interval.') + group.add_argument('--tensorboard-queue-size', type=int, default=1000, + help='Size of the tensorboard queue for pending events ' + 'and summaries before one of the ‘add’ calls forces a ' + 'flush to disk.') + group.add_argument('--log-timers-to-tensorboard', action='store_true', + help='If set, write timers to tensorboard.') + group.add_argument('--log-batch-size-to-tensorboard', action='store_true', + help='If set, write batch-size to tensorboard.') + group.add_argument('--no-log-learnig-rate-to-tensorboard', + action='store_false', + help='Disable learning rate logging to tensorboard.', + dest='log_learning_rate_to_tensorboard') + group.add_argument('--no-log-loss-scale-to-tensorboard', + action='store_false', + help='Disable loss-scale logging to tensorboard.', + dest='log_loss_scale_to_tensorboard') + group.add_argument('--log-validation-ppl-to-tensorboard', + action='store_true', + help='If set, write validation perplexity to ' + 'tensorboard.') + group.add_argument('--log-memory-to-tensorboard', + action='store_true', + help='Enable memory logging to tensorboard.') + group.add_argument('--log-world-size-to-tensorboard', + action='store_true', + help='Enable world size logging to tensorboard.') + group.add_argument('--wandb-project', type=str, default='', + help='The wandb project name. Ignore wandb by default.') + group.add_argument('--wandb-exp-name', type=str, default='', + help='The wandb experiment name.') + group.add_argument('--wandb-save-dir', type=str, default='', + help='Path to save the wandb results locally.') + group.add_argument('--enable-one-logger', action='store_true', + help='If set, use one_logger to track E2E metrics' + 'Note that one_logger is an internal tool and not available externally. ' + 'For installation, please try command: `pip install ' + '--index-url=https://sc-hw-artf.nvidia.com/api/pypi/hwinf-ml-pypi/simple' + ' one_logger` or go to https://gitlab-master.nvidia.com/hwinf-dcm/onelogger ' + 'for more details') + group.add_argument('--one-logger-project', type=str, default='e2e-tracking', + help='The one-logger project name. Will ignore if ' + '--enable-one-logger is not set') + group.add_argument('--one-logger-entity', type=str, default='hwinf_dcm', + help='The one-logger username or team name. Will ignore if ' + '--enable-one-logger is not set') + group.add_argument('--one-logger-run-name', type=str, default=None, + help='The one-logger run name displayed. Will ignore if ' + '--enable-one-logger is not set') + return parser + + +def _add_regularization_args(parser): + group = parser.add_argument_group(title='regularization') + + group.add_argument('--attention-dropout', type=float, default=0.1, + help='Post attention dropout probability.') + group.add_argument('--hidden-dropout', type=float, default=0.1, + help='Dropout probability for hidden state transformer.') + group.add_argument('--weight-decay', type=float, default=0.01, + help='Weight decay coefficient for L2 regularization.') + group.add_argument('--start-weight-decay', type=float, + help='Initial weight decay coefficient for L2 regularization.') + group.add_argument('--end-weight-decay', type=float, + help='End of run weight decay coefficient for L2 regularization.') + group.add_argument('--weight-decay-incr-style', type=str, default='constant', + choices=['constant', 'linear', 'cosine'], + help='Weight decay increment function.') + group.add_argument('--clip-grad', type=float, default=1.0, + help='Gradient clipping based on global L2 norm.') + group.add_argument('--adam-beta1', type=float, default=0.9, + help='First coefficient for computing running averages ' + 'of gradient and its square') + group.add_argument('--adam-beta2', type=float, default=0.999, + help='Second coefficient for computing running averages ' + 'of gradient and its square') + group.add_argument('--adam-eps', type=float, default=1e-08, + help='Term added to the denominator to improve' + 'numerical stability') + group.add_argument('--sgd-momentum', type=float, default=0.9, + help='Momentum factor for sgd') + return parser + + +def _add_training_args(parser): + group = parser.add_argument_group(title='training') + + group.add_argument('--micro-batch-size', type=int, default=None, + help='Batch size per model instance (local batch size). ' + 'Global batch size is local batch size times data ' + 'parallel size times number of micro batches.') + group.add_argument('--batch-size', type=int, default=None, + help='Old batch size parameter, do not use. ' + 'Use --micro-batch-size instead') + group.add_argument('--global-batch-size', type=int, default=None, + help='Training batch size. If set, it should be a ' + 'multiple of micro-batch-size times data-parallel-size. ' + 'If this value is None, then ' + 'use micro-batch-size * data-parallel-size as the ' + 'global batch size. This choice will result in 1 for ' + 'number of micro-batches.') + group.add_argument('--rampup-batch-size', nargs='*', default=None, + help='Batch size ramp up with the following values:' + ' --rampup-batch-size ' + ' ' + ' ' + 'For example:' + ' --rampup-batch-size 16 8 300000 \ ' + ' --global-batch-size 1024' + 'will start with global batch size 16 and over ' + ' (1024 - 16) / 8 = 126 intervals will increase' + 'the batch size linearly to 1024. In each interval' + 'we will use approximately 300000 / 126 = 2380 samples.') + group.add_argument('--recompute-activations', action='store_true', + help='recompute activation to allow for training ' + 'with larger models, sequences, and batch sizes.') + group.add_argument('--recompute-granularity', type=str, default=None, + choices=['full', 'selective'], + help='Checkpoint activations to allow for training ' + 'with larger models, sequences, and batch sizes. ' + 'It is supported at two granularities 1) full: ' + 'whole transformer layer is recomputed, ' + '2) selective: core attention part of the transformer ' + 'layer is recomputed.') + group.add_argument('--no-check-for-nan-in-loss-and-grad', action='store_false', + help='Check for NaNs in loss and grad', + dest='check_for_nan_in_loss_and_grad') + group.add_argument('--distribute-saved-activations', + action='store_true', + help='If set, distribute recomputed activations ' + 'across model parallel group.') + group.add_argument('--recompute-method', type=str, default=None, + choices=['uniform', 'block'], + help='1) uniform: uniformly divide the total number of ' + 'Transformer layers and recompute the input activation of ' + 'each divided chunk at specified granularity, ' + '2) recompute the input activations of only a set number of ' + 'individual Transformer layers per pipeline stage and do the ' + 'rest without any recomputing at specified granularity' + 'default) do not apply activations recompute to any layers') + group.add_argument('--recompute-num-layers', type=int, default=None, + help='1) uniform: the number of Transformer layers in each ' + 'uniformly divided recompute unit, ' + '2) block: the number of individual Transformer layers ' + 'to recompute within each pipeline stage.') + group.add_argument('--no-clone-scatter-output-in-embedding', action='store_false', + help='If not set, clone the output of the scatter in embedding layer to GC original tensor.', + dest='clone_scatter_output_in_embedding') + group.add_argument('--profile', action='store_true', + help='Enable nsys profiling. When using this option, nsys ' + 'options should be specified in commandline. An example ' + 'nsys commandline is `nsys profile -s none -t nvtx,cuda ' + '-o --force-overwrite true ' + '--capture-range=cudaProfilerApi ' + '--capture-range-end=stop`.') + group.add_argument('--profile-step-start', type=int, default=10, + help='Global step to start profiling.') + group.add_argument('--profile-step-end', type=int, default=12, + help='Global step to stop profiling.') + group.add_argument('--profile-ranks', nargs='+', type=int, default=[0], + help='Global ranks to profile.') + group.add_argument('--tp-comm-overlap', action='store_true', help='Enables the ' + ' overlap of Tensor parallel communication and GEMM kernels.') + group.add_argument('--tp-comm-overlap-cfg', type=str, default=None, + help='Config file when tp_comm_overlap is enabled.') + group.add_argument('--disable-tp-comm-overlap-ag', action='store_false', + help=('Disables the All-Gather overlap with GEMM by ' + 'pipelining the GEMM and All-Gather.'), + dest='tp_comm_overlap_ag') + group.add_argument('--disable-tp-comm-overlap-rs', action='store_false', + help=('Disables the Reduce-Scatter overlap with GEMM by ' + 'pipelining the GEMM and Reduce-Scatter.'), + dest='tp_comm_overlap_rs') + group.add_argument('--tp-comm-overlap-rs-dgrad', action='store_true', + help = 'Enables the Reduce-Scatter overlap with dgrad GEMM.', + dest='tp_comm_overlap_rs_dgrad') + group.add_argument('--disable-tp-comm-bulk-dgrad', action='store_false', + help='Disables the All-Gather overlap with bprop activation gradient GEMM.', + dest='tp_comm_bulk_dgrad') + group.add_argument('--disable-tp-comm-bulk-wgrad', action='store_false', + help='Disables the Reduce-Scatter overlap with bprop weight gradient GEMM.', + dest='tp_comm_bulk_wgrad') + group.add_argument('--use-cpu-initialization', action='store_true', + default=None, + help='If set, initialize weights on the CPU. This eliminates init differences based on tensor parallelism.') + group.add_argument('--empty-unused-memory-level', default=0, type=int, + choices=[0, 1, 2], + help='Call torch.cuda.empty_cache() each iteration ' + '(training and eval), to reduce fragmentation.' + '0=off, 1=moderate, 2=aggressive.') + + # deprecated + group.add_argument('--checkpoint-activations', action='store_true', + help='Checkpoint activation to allow for training ' + 'with larger models, sequences, and batch sizes.') + group.add_argument('--train-iters', type=int, default=None, + help='Total number of iterations to train over all ' + 'training runs. Note that either train-iters or ' + 'train-samples should be provided.') + group.add_argument('--train-samples', type=int, default=None, + help='Total number of samples to train over all ' + 'training runs. Note that either train-iters or ' + 'train-samples should be provided.') + group.add_argument('--log-interval', type=int, default=100, + help='Report loss and timing interval.') + group.add_argument('--exit-interval', type=int, default=None, + help='Exit the program after the iteration is divisible ' + 'by this value.') + group.add_argument('--exit-duration-in-mins', type=int, default=None, + help='Exit the program after this many minutes.') + group.add_argument('--exit-signal-handler', action='store_true', + help='Dynamically save the checkpoint and shutdown the ' + 'training if SIGTERM is received') + group.add_argument('--tensorboard-dir', type=str, default=None, + help='Write TensorBoard logs to this directory.') + group.add_argument('--no-masked-softmax-fusion', + action='store_false', + help='Disable fusion of query_key_value scaling, ' + 'masking, and softmax.', + dest='masked_softmax_fusion') + group.add_argument('--no-bias-gelu-fusion', action='store_false', + help='Disable bias and gelu fusion.', + dest='bias_gelu_fusion') + group.add_argument('--no-bias-swiglu-fusion', action='store_false', + help='Disable bias and swiglu fusion, the fusion is ' + 'available only when using megatron-core.', + dest='bias_swiglu_fusion') + group.add_argument('--no-bias-dropout-fusion', action='store_false', + help='Disable bias and dropout fusion.', + dest='bias_dropout_fusion') + group.add_argument('--no-rope-fusion', action='store_false', + help='Disable rope fusion, the fusion is available ' + 'only when using megatron-core.', + dest='apply_rope_fusion') + group.add_argument('--use-flash-attn', action='store_true', + help='use FlashAttention implementation of attention. ' + 'https://arxiv.org/abs/2205.14135') + group.add_argument('--disable-bias-linear', action='store_false', + help='Disable bias in the linear layers', + dest='add_bias_linear') + group.add_argument('--add-qkv-bias', action='store_true', + help='Enable bias only in the QKV linear layers', + dest='add_qkv_bias') + group.add_argument('--optimizer', type=str, default='adam', + choices=['adam', 'sgd'], + help='Optimizer function') + group.add_argument('--dataloader-type', type=str, default=None, + choices=['single', 'cyclic', 'external'], + help='Single pass vs multiple pass data loader') + group.add_argument('--no-async-tensor-model-parallel-allreduce', + action='store_false', + help='Disable asynchronous execution of ' + 'tensor-model-parallel all-reduce with weight ' + 'gradient compuation of a column-linear layer.', + dest='async_tensor_model_parallel_allreduce') + group.add_argument('--no-persist-layer-norm', action='store_true', + help='Disable using persistent fused layer norm kernel. ' + 'This kernel supports only a set of hidden sizes. Please ' + 'check persist_ln_hidden_sizes if your hidden ' + 'size is supported.') + group.add_argument('--sequence-parallel', action='store_true', + help='Enable sequence parallel optimization.') + group.add_argument('--no-gradient-accumulation-fusion', + action='store_false', + help='Disable fusing gradient accumulation to weight ' + 'gradient computation of linear layers', + dest='gradient_accumulation_fusion') + group.add_argument('--use-mcore-models', action='store_true', + help='Use the implementation from megatron core') + group.add_argument('--manual-gc', action='store_true', + help='Disable the threshold-based default garbage ' + 'collector and trigger the garbage collection manually. ' + 'Manual garbage collection helps to align the timing of ' + 'the collection across ranks which mitigates the impact ' + 'of CPU-associated jitters. When the manual gc is enabled, ' + 'garbage collection is performed only at the start and the ' + 'end of the validation routine by default.') + group.add_argument('--manual-gc-interval', type=int, default=0, + help='Training step interval to trigger manual garbage ' + 'collection. When the value is set to 0, garbage ' + 'collection is not triggered between training steps.') + group.add_argument('--no-manual-gc-eval', action='store_false', + help='When using manual garbage collection, disable ' + 'garbage collection at the start and the end of each ' + 'evaluation run.', dest='manual_gc_eval') + group.add_argument('--disable-tp-comm-split-ag', action='store_false', + help='Disables the All-Gather overlap with fprop GEMM.', + dest='tp_comm_split_ag') + group.add_argument('--disable-tp-comm-split-rs', action='store_false', + help='Disables the Reduce-Scatter overlap with fprop GEMM.', + dest='tp_comm_split_rs') + + return parser + + +def _add_initialization_args(parser): + group = parser.add_argument_group(title='initialization') + + group.add_argument('--seed', type=int, default=1234, + help='Random seed used for python, numpy, ' + 'pytorch, and cuda.') + group.add_argument('--data-parallel-random-init', action='store_true', + help='Enable random initialization of params ' + 'across data parallel ranks') + group.add_argument('--init-method-std', type=float, default=0.02, + help='Standard deviation of the zero mean normal ' + 'distribution used for weight initialization.') + group.add_argument('--init-method-xavier-uniform', action='store_true', + help='Enable Xavier uniform parameter initialization') + + return parser + + +def _add_learning_rate_args(parser): + group = parser.add_argument_group(title='learning rate') + + group.add_argument('--lr', type=float, default=None, + help='Initial learning rate. Depending on decay style ' + 'and initial warmup, the learning rate at each ' + 'iteration would be different.') + group.add_argument('--lr-decay-style', type=str, default='linear', + choices=['constant', 'linear', 'cosine', 'inverse-square-root'], + help='Learning rate decay function.') + group.add_argument('--lr-decay-iters', type=int, default=None, + help='number of iterations to decay learning rate over,' + ' If None defaults to `--train-iters`') + group.add_argument('--lr-decay-samples', type=int, default=None, + help='number of samples to decay learning rate over,' + ' If None defaults to `--train-samples`') + group.add_argument('--lr-warmup-fraction', type=float, default=None, + help='fraction of lr-warmup-(iters/samples) to use ' + 'for warmup (as a float)') + group.add_argument('--lr-warmup-iters', type=int, default=0, + help='number of iterations to linearly warmup ' + 'learning rate over.') + group.add_argument('--lr-warmup-samples', type=int, default=0, + help='number of samples to linearly warmup ' + 'learning rate over.') + group.add_argument('--lr-warmup-init', type=float, default=0.0, + help='Initial value for learning rate warmup. The ' + 'scheduler starts warmup from this value.') + group.add_argument('--warmup', type=int, default=None, + help='Old lr warmup argument, do not use. Use one of the' + '--lr-warmup-* arguments above') + group.add_argument('--min-lr', type=float, default=0.0, + help='Minimum value for learning rate. The scheduler' + 'clip values below this threshold.') + group.add_argument('--override-opt_param-scheduler', action='store_true', + help='Reset the values of the scheduler (learning rate,' + 'warmup iterations, minimum learning rate, maximum ' + 'number of iterations, and decay style from input ' + 'arguments and ignore values from checkpoints. Note' + 'that all the above values will be reset.') + group.add_argument('--use-checkpoint-opt_param-scheduler', action='store_true', + help='Use checkpoint to set the values of the scheduler ' + '(learning rate, warmup iterations, minimum learning ' + 'rate, maximum number of iterations, and decay style ' + 'from checkpoint and ignore input arguments.') + group.add_argument('--decoupled-lr', type=float, default=None, + help='Separate learning rate for the input and output layer') + group.add_argument('--decoupled-min-lr', type=float, default=None, + help='Minimum value for learning rate for the input and output layer. The scheduler' + 'clip values below this threshold') + + return parser + + +def _add_checkpointing_args(parser): + group = parser.add_argument_group(title='checkpointing') + + group.add_argument('--save', type=str, default=None, + help='Output directory to save checkpoints to.') + group.add_argument('--save-interval', type=int, default=None, + help='Number of iterations between checkpoint saves.') + group.add_argument('--no-save-optim', action='store_true', default=None, + help='Do not save current optimizer.') + group.add_argument('--no-save-rng', action='store_true', default=None, + help='Do not save current rng state.') + group.add_argument('--load', type=str, default=None, + help='Directory containing a model checkpoint.') + group.add_argument('--no-load-optim', action='store_true', default=None, + help='Do not load optimizer when loading checkpoint.') + group.add_argument('--no-load-rng', action='store_true', default=None, + help='Do not load rng state when loading checkpoint.') + group.add_argument('--finetune', action='store_true', + help='Load model for finetuning. Do not load optimizer ' + 'or rng state from checkpoint and set iteration to 0. ' + 'Assumed when loading a release checkpoint.') + group.add_argument('--pretrained-checkpoint', type=str, default=None, + help='Directory containing a pretrained model checkpoint for finetuning.') + group.add_argument('--ckpt-step', type=int, default=None, + help='Checkpoint step to load model from.') + group.add_argument('--no-initialization', action='store_false', + help='Do not perform initialization when building model, ' + 'can reduce startup time when definitely loading from a ' + 'checkpoint', + dest='perform_initialization') + group.add_argument('--use-checkpoint-args', action='store_true', + help='Override any command line arguments with arguments ' + 'from the checkpoint') + group.add_argument('--exit-on-missing-checkpoint', action='store_true', + help="If '--load' is set, but checkpoint is not found " + "(e.g., path typo), then exit instead of random " + "initialization.") + group.add_argument('--use-dist-ckpt', action='store_true', + help='Use distributed checkpoint format.') + group.add_argument('--auto-detect-ckpt-format', action='store_true', + help='Determine if the checkpoint format is in legacy or distributed format.' + ' If False, expects distributed checkpoint iff args.use_dist_ckpt.' + ' Might slow down loading a bit (double rank0 ckpt load).') + group.add_argument('--dist-ckpt-format', type=str, default='torch_dist', + choices=['zarr', 'torch_dist'], + help='Distributed checkpoint format to use.') + group.add_argument('--ckpt-fully-parallel-save', action='store_true', + help='Apply full save parallelization across DP for' + ' distributed checkpoints. Depending on ckpt format' + ' might increase number of files in the checkpoint.') + + return parser + + +def _add_mixed_precision_args(parser): + group = parser.add_argument_group(title='mixed precision') + + group.add_argument('--fp16', action='store_true', + help='Run model in fp16 mode.') + group.add_argument('--bf16', action='store_true', + help='Run model in bfloat16 mode.') + group.add_argument('--loss-scale', type=float, default=None, + help='Static loss scaling, positive power of 2 ' + 'values can improve fp16 convergence. If None, dynamic' + 'loss scaling is used.') + group.add_argument('--initial-loss-scale', type=float, default=2**32, + help='Initial loss-scale for dynamic loss scaling.') + group.add_argument('--min-loss-scale', type=float, default=1.0, + help='Minimum loss scale for dynamic loss scaling.') + group.add_argument('--loss-scale-window', type=float, default=1000, + help='Window over which to raise/lower dynamic scale.') + group.add_argument('--hysteresis', type=int, default=2, + help='hysteresis for dynamic loss scaling') + group.add_argument('--fp32-residual-connection', action='store_true', + help='Move residual connections to fp32.') + group.add_argument('--apply-query-key-layer-scaling', action='store_true', + help='Scale Q * K^T by 1 / layer-number. ' + 'Useful for fp16 training.') + group.add_argument('--attention-softmax-in-fp32', action='store_true', + help='Run attention masking and softmax in fp32. ' + 'This flag is ignored unless ' + '--no-query-key-layer-scaling is specified.') + group.add_argument('--accumulate-allreduce-grads-in-fp32', + action='store_true', + help='Gradient accumulation and all-reduce in fp32.') + group.add_argument('--fp16-lm-cross-entropy', action='store_true', + help='Move the cross entropy unreduced loss calculation' + 'for lm head to fp16.') + + return parser + + +def _add_distributed_args(parser): + group = parser.add_argument_group(title='distributed') + + group.add_argument('--tensor-model-parallel-size', type=int, default=1, + help='Degree of tensor model parallelism.') + group.add_argument('--pipeline-model-parallel-size', type=int, default=1, + help='Degree of pipeline model parallelism.') + group.add_argument('--pipeline-model-parallel-split-rank', + type=int, default=None, + help='Rank where encoder and decoder should be split.') + group.add_argument('--model-parallel-size', type=int, default=None, + help='Old model parallel argument, do not use. Use ' + '--tensor-model-parallel-size instead.') + group.add_argument('--num-layers-per-virtual-pipeline-stage', type=int, default=None, + help='Number of layers per virtual pipeline stage') + group.add_argument('--no-overlap-p2p-communication', action='store_false', + help='overlap pipeline parallel communication with forward and backward chunks', + dest='overlap_p2p_comm') + group.add_argument('--distributed-backend', default='nccl', + choices=['nccl', 'gloo'], + help='Which backend to use for distributed training.') + group.add_argument('--distributed-timeout-minutes', type=int, default=10, + help='Timeout minutes for torch.distributed.') + group.add_argument('--overlap-grad-reduce', action='store_true', + default=False, help='If set, overlap DDP grad reduce.') + group.add_argument('--no-delay-grad-reduce', action='store_false', + help='If not set, delay / synchronize grad reductions in all but first PP stage.', + dest='delay_grad_reduce') + group.add_argument('--ddp-bucket-size', type=int, default=None, + help='Bucket size for data-parallel communication') + group.add_argument('--overlap-param-gather', action='store_true', + default=False, help='If set, overlap param all-gather in distributed optimizer.') + group.add_argument('--delay-param-gather', action='store_true', + default=False, help='If set, delay / synchronize param all-gathers in all but first PP stage.') + group.add_argument('--no-scatter-gather-tensors-in-pipeline', action='store_false', + help='If not set, use scatter/gather to optimize communication of tensors in pipeline.', + dest='scatter_gather_tensors_in_pipeline') + group.add_argument('--use-ring-exchange-p2p', action='store_true', + default=False, help='If set, use custom-built ring exchange ' + 'for p2p communications. Note that this option will require ' + 'a custom built image that support ring-exchange p2p.') + group.add_argument('--local_rank', type=int, default=None, + help='local rank passed from distributed launcher.') + group.add_argument('--lazy-mpu-init', type=bool, required=False, + help='If set to True, initialize_megatron() ' + 'skips DDP initialization and returns function to ' + 'complete it instead.Also turns on ' + '--use-cpu-initialization flag. This is for ' + 'external DDP manager.' ) + group.add_argument('--standalone-embedding-stage', action='store_true', + default=False, help='If set, *input* embedding layer ' + 'is placed on its own pipeline stage, without any ' + 'transformer layers. (For T5, this flag currently only ' + 'affects the encoder embedding.)') + group.add_argument('--use-distributed-optimizer', action='store_true', + help='Use distributed optimizer.') + group.add_argument('--context-parallel-size', type=int, default=1, + help='Degree of context parallelism.') + group.add_argument('--nccl-communicator-config-path', type=str, default=None, + help='Path to the yaml file with NCCL communicator ' + 'configurations. The number of min/max thread groups and thread ' + 'group cluster size of each communicator can be configured by ' + 'setting `min_ctas`, `max_ctas`, and `cga_cluster_size`.') + group.add_argument('--use-tp-pp-dp-mapping', action='store_true', default=False, + help='If set, distributed ranks initialize order is changed ' + 'from tp-dp-pp to tp-pp-dp. Make sure EP and CP aren\'t used ' + 'with this option enabled') + return parser + + +def _add_validation_args(parser): + group = parser.add_argument_group(title='validation') + + group.add_argument('--eval-iters', type=int, default=100, + help='Number of iterations to run for evaluation' + 'validation/test for.') + group.add_argument('--eval-interval', type=int, default=1000, + help='Interval between running evaluation on ' + 'validation set.') + group.add_argument("--test-mode", action="store_true", help='Run all real-time test alongside the experiment.') + group.add_argument('--skip-train', action='store_true', + default=False, help='If set, bypass the training loop, ' + 'optionally do evaluation for validation/test, and exit.') + + return parser + + +def _add_data_args(parser): + group = parser.add_argument_group(title='data and dataloader') + + group.add_argument('--data-path', nargs='*', default=None, + help='Path to the training dataset. Accepted format:' + '1) a single data path, 2) multiple datasets in the' + 'form: dataset1-weight dataset1-path dataset2-weight ' + 'dataset2-path ... It is used with --split when a ' + 'single dataset used for all three: train, valid ' + 'and test. It is exclusive to the other ' + '--*-data-path args') + group.add_argument('--split', type=str, default='969, 30, 1', + help='Comma-separated list of proportions for training,' + ' validation, and test split. For example the split ' + '`90,5,5` will use 90%% of data for training, 5%% for ' + 'validation and 5%% for test.') + group.add_argument('--train-data-path', nargs='*', default=None, + help='Path to the training dataset. Accepted format:' + '1) a single data path, 2) multiple datasets in the' + 'form: dataset1-weight dataset1-path dataset2-weight ' + 'dataset2-path ...') + group.add_argument('--valid-data-path', nargs='*', default=None, + help='Path to the validation dataset. Accepted format:' + '1) a single data path, 2) multiple datasets in the' + 'form: dataset1-weight dataset1-path dataset2-weight ' + 'dataset2-path ...') + group.add_argument('--test-data-path', nargs='*', default=None, + help='Path to the test dataset. Accepted format:' + '1) a single data path, 2) multiple datasets in the' + 'form: dataset1-weight dataset1-path dataset2-weight ' + 'dataset2-path ...') + group.add_argument('--data-cache-path', default=None, + help='Path to a directory to hold cached index files.') + group.add_argument('--no-mmap-bin-files', action='store_false', + help='Disable mmap-ing of .bin files.', + dest='mmap_bin_files') + group.add_argument('--mock-data', action='store_true', + help='Skip data loading and validation and opt for artificial ' + 'generation of mock data when an implementation is available.') + + group.add_argument('--vocab-size', type=int, default=None, + help='Size of vocab before EOD or padding.') + group.add_argument('--vocab-file', type=str, default=None, + help='Path to the vocab file.') + group.add_argument('--merge-file', type=str, default=None, + help='Path to the BPE merge file.') + group.add_argument('--vocab-extra-ids', type=int, default=0, + help='Number of additional vocabulary tokens. ' + 'They are used for span masking in the T5 model') + group.add_argument('--seq-length', type=int, default=None, + help='Maximum sequence length to process.') + group.add_argument('--encoder-seq-length', type=int, default=None, + help='Maximum encoder sequence length to process.' + 'This should be exclusive of --seq-length') + group.add_argument('--decoder-seq-length', type=int, default=None, + help="Maximum decoder sequence length to process.") + group.add_argument('--retriever-seq-length', type=int, default=256, + help='Maximum sequence length for the biencoder model ' + 'for retriever') + group.add_argument('--sample-rate', type=float, default=1.0, + help='sample rate for training data. Supposed to be 0 ' + ' < sample_rate < 1') + group.add_argument('--mask-prob', type=float, default=0.15, + help='Probability of replacing a token with mask.') + group.add_argument('--short-seq-prob', type=float, default=0.1, + help='Probability of producing a short sequence.') + group.add_argument('--num-workers', type=int, default=2, + help="Dataloader number of workers.") + group.add_argument('--tokenizer-type', type=str, + default=None, + choices=['BertWordPieceLowerCase', + 'BertWordPieceCase', + 'GPT2BPETokenizer', + 'SentencePieceTokenizer', + 'GPTSentencePieceTokenizer', + 'Llama2Tokenizer', + 'NullTokenizer'], + help='What type of tokenizer to use.') + group.add_argument('--tokenizer-model', type=str, default=None, + help='Sentencepiece tokenizer model.') + group.add_argument('--reset-position-ids', action='store_true', + help='Reset posistion ids after end-of-document token.') + group.add_argument('--reset-attention-mask', action='store_true', + help='Reset self attention maske after ' + 'end-of-document token.') + group.add_argument('--eod-mask-loss', action='store_true', + help='Mask loss for the end of document tokens.') + group.add_argument('--no-create-attention-mask-in-dataloader', action='store_false', + help='If set, do not create attention_masks in dataloader.', + dest='create_attention_mask_in_dataloader') + + return parser + + +def _add_autoresume_args(parser): + group = parser.add_argument_group(title='autoresume') + + group.add_argument('--adlr-autoresume', action='store_true', + help='Enable autoresume on adlr cluster.') + group.add_argument('--adlr-autoresume-interval', type=int, default=1000, + help='Intervals over which check for autoresume' + 'termination signal') + + return parser + + +def _add_biencoder_args(parser): + group = parser.add_argument_group(title='biencoder') + + # network size + group.add_argument('--ict-head-size', type=int, default=None, + help='Size of block embeddings to be used in ICT and ' + 'REALM (paper default: 128)') + group.add_argument('--biencoder-projection-dim', type=int, default=0, + help='Size of projection head used in biencoder (paper' + ' default: 128)') + group.add_argument('--biencoder-shared-query-context-model', action='store_true', + help='Whether to share the parameters of the query ' + 'and context models or not') + + # checkpointing + group.add_argument('--ict-load', type=str, default=None, + help='Directory containing an ICTBertModel checkpoint') + group.add_argument('--bert-load', type=str, default=None, + help='Directory containing an BertModel checkpoint ' + '(needed to start ICT and REALM)') + + # data + group.add_argument('--titles-data-path', type=str, default=None, + help='Path to titles dataset used for ICT') + group.add_argument('--query-in-block-prob', type=float, default=0.1, + help='Probability of keeping query in block for ' + 'ICT dataset') + group.add_argument('--use-one-sent-docs', action='store_true', + help='Whether to use one sentence documents in ICT') + group.add_argument('--evidence-data-path', type=str, default=None, + help='Path to Wikipedia Evidence frm DPR paper') + + # training + group.add_argument('--retriever-report-topk-accuracies', nargs='+', type=int, + default=[], help="Which top-k accuracies to report " + "(e.g. '1 5 20')") + group.add_argument('--retriever-score-scaling', action='store_true', + help='Whether to scale retriever scores by inverse ' + 'square root of hidden size') + + # faiss index + group.add_argument('--block-data-path', type=str, default=None, + help='Where to save/load BlockData to/from') + group.add_argument('--embedding-path', type=str, default=None, + help='Where to save/load Open-Retrieval Embedding' + ' data to/from') + + # indexer + group.add_argument('--indexer-batch-size', type=int, default=128, + help='How large of batches to use when doing indexing ' + 'jobs') + group.add_argument('--indexer-log-interval', type=int, default=1000, + help='After how many batches should the indexer ' + 'report progress') + return parser + + +def _add_vision_args(parser): + group = parser.add_argument_group(title="vision") + + # general vision arguements + group.add_argument('--num-classes', type=int, default=1000, + help='num of classes in vision classificaiton task') + group.add_argument('--img-h', type=int, default=224, + help='Image height for vision classification task') + group.add_argument('--img-w', type=int, default=224, + help='Image height for vision classification task') + group.add_argument('--num-channels', type=int, default=3, + help='Number of channels in input image data') + group.add_argument('--patch-dim', type=int, default=16, + help='patch dimension') + group.add_argument('--classes-fraction', type=float, default=1.0, + help='training with fraction of classes.') + group.add_argument('--data-per-class-fraction', type=float, default=1.0, + help='training with fraction of data per class.') + group.add_argument('--no-data-sharding', action='store_false', + help='Disable data sharding.', + dest='data_sharding') + group.add_argument('--head-lr-mult', type=float, default=1.0, + help='learning rate multiplier for head during finetuning') + + # pretraining type and backbone selection` + group.add_argument('--vision-pretraining', action='store_true', + help='flag to indicate vision pretraining') + group.add_argument('--vision-pretraining-type', type=str, default='classify', + choices=['classify', 'inpaint', 'dino'], + help='pretraining objectives') + group.add_argument('--vision-backbone-type', type=str, default='vit', + choices=['vit', 'mit', 'swin'], + help='backbone types types') + group.add_argument('--swin-backbone-type', type=str, default='tiny', + choices=['tiny', 'base', 'h3'], + help='pretraining objectives') + # inpainting arguments + group.add_argument('--mask-type', type=str, default='random', + choices=['random', 'row'], + help='mask types') + group.add_argument('--mask-factor', type=float, default=1.0, + help='mask size scaling parameter') + + # dino arguments + group.add_argument('--iter-per-epoch', type=int, default=1250, + help='iterations per epoch') + group.add_argument('--dino-local-img-size', type=int, default=96, + help='Image size for vision classification task') + group.add_argument('--dino-local-crops-number', type=int, default=10, + help='Number of local crops') + group.add_argument('--dino-head-hidden-size', type=int, default=2048, + help='Hidden dimension size in dino head') + group.add_argument('--dino-bottleneck-size', type=int, default=256, + help='Bottle neck dimension in dino head ') + group.add_argument('--dino-freeze-last-layer', type=float, default=1, + help='Freezing last layer weights') + group.add_argument('--dino-norm-last-layer', action='store_true', + help='Disable Norm in last layer.') + group.add_argument('--dino-warmup-teacher-temp', type=float, default=0.04, + help='warump teacher temperature') + group.add_argument('--dino-teacher-temp', type=float, default=0.07, + help='teacher temperature') + group.add_argument('--dino-warmup-teacher-temp-epochs', type=int, default=30, + help='warmup teacher temperaure epochs') + + # regularization arguments + group.add_argument('--qk-layernorm', action='store_true', + help='Whether to layer normalize the q and k attention embeddings.') + + return parser + +def _add_moe_args(parser): + group = parser.add_argument_group(title="moe") + group.add_argument('--expert-model-parallel-size', type=int, default=1, + help='Degree of expert model parallelism.') + group.add_argument('--num-experts', type=int, default=None, + help='Number of Experts in MoE (None means no MoE)') + group.add_argument('--moe-router-load-balancing-type', type=str, + choices=['aux_loss', 'sinkhorn', "none"], + default='aux_loss', + help='Determines the load balancing strategy for the router. "aux_loss" corresponds to the load balancing loss used in GShard and SwitchTransformer, "sinkhorn" corresponds to the balancing algorithm used in S-BASE, and "none" implies no load balancing. The default is "aux_loss".') + group.add_argument('--moe-router-topk', type=int, default=2, + help='Number of experts to route to for each token. The default is 2.') + group.add_argument('--moe-grouped-gemm', action='store_true', + help='When there are multiple experts per rank, compress multiple local (potentially small) gemms in a single kernel launch to improve the utilization and performance by leveraging the Grouped GEMM feature introduced since CUTLASS 2.8 (https://github.com/fanshiqing/grouped_gemm).') + group.add_argument('--moe-aux-loss-coeff', type=float, default=0.0, + help='Scaling coefficient for the aux loss: a starting value of 1e-2 is recommended.') + group.add_argument('--moe-z-loss-coeff', type=float, default=None, + help='Scaling coefficient for the z-loss: a starting value of 1e-3 is recommended.') + group.add_argument('--moe-input-jitter-eps', type=float, default=None, + help='Add noise to the input tensor by applying jitter with a specified epsilon value.') + group.add_argument('--moe-token-dropping', action='store_true', + help='This feature involves selectively dropping and padding tokens for each expert to achieve a specified capacity, similar to GShard, Switch-Transformer, and DeepSpeed-MoE. Note: Currently unsupported.') + group.add_argument('--moe-token-dispatcher-type', type=str, + choices=['allgather', 'alltoall'], + default='allgather', + help='.') + group.add_argument('--moe-per-layer-logging', action='store_true', + help='Enable per-layer logging for MoE, currently supports auxiliary loss and z loss.') + + return parser + +def _add_experimental_args(parser): + group = parser.add_argument_group(title='experimental') + + group.add_argument('--spec', type=str, default=None, nargs='*', + help='Specify the pair ' + 'that returns a spec to customize a model, transformer ' + 'block, or transformer layer, depending on the use case.' + 'To use local spec specify local as the argument.' + 'For more details, see the model class, ' + '`transformer_block.py`, or `transformer_layer.py`') + group.add_argument('--yaml-cfg', type=str, default=None, + help = 'Config file to add additional arguments') + + return parser diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/checkpointing.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/checkpointing.py new file mode 100644 index 0000000..efda88c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/checkpointing.py @@ -0,0 +1,851 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Input/output checkpointing.""" + +import os +import random +import sys +import numpy as np + +import torch + +from megatron.training import update_num_microbatches +from megatron.core import mpu, tensor_parallel, dist_checkpointing +from ..core.dist_checkpointing.mapping import ShardedObject +from .global_vars import get_args +from .utils import (unwrap_model, + print_rank_0) + + +_CHECKPOINT_VERSION = None + + +def set_checkpoint_version(value): + global _CHECKPOINT_VERSION + if _CHECKPOINT_VERSION is not None: + assert _CHECKPOINT_VERSION == value, \ + "checkpoint versions do not match" + _CHECKPOINT_VERSION = value + + +def get_checkpoint_version(): + global _CHECKPOINT_VERSION + return _CHECKPOINT_VERSION + + +def check_checkpoint_args(checkpoint_args): + """Ensure fixed arguments for a model are the same for the input + arguments and the one retrieved from checkpoint.""" + args = get_args() + + def _compare(arg_name, old_arg_name=None, default=None): + if old_arg_name is not None: + ckpt_arg_name = old_arg_name + else: + ckpt_arg_name = arg_name + if default is not None: + checkpoint_value = getattr(checkpoint_args, ckpt_arg_name, default) + else: + checkpoint_value = getattr(checkpoint_args, ckpt_arg_name) + args_value = getattr(args, arg_name) + error_message = '{} value from checkpoint ({}) is not equal to the ' \ + 'input argument value ({}).'.format( + arg_name, checkpoint_value, args_value) + assert checkpoint_value == args_value, error_message + + _compare('num_layers') + _compare('hidden_size') + _compare('num_attention_heads') + _compare('add_position_embedding', default=True) + if args.vocab_file: + _compare('max_position_embeddings') + _compare('make_vocab_size_divisible_by') + if not args.use_dist_ckpt: + _compare('padded_vocab_size') + _compare('tokenizer_type') + if args.data_parallel_random_init: + _compare('data_parallel_random_init') + if get_checkpoint_version() < 3.0: + _compare('tensor_model_parallel_size', + old_arg_name='model_parallel_size') + if get_checkpoint_version() >= 3.0 and not args.use_dist_ckpt: + _compare('tensor_model_parallel_size') + _compare('pipeline_model_parallel_size') + +def ensure_directory_exists(filename, check_parent=True): + """Build filename's path if it does not already exists.""" + dirname = os.path.dirname(filename) if check_parent else filename + os.makedirs(dirname, exist_ok=True) + + +def get_checkpoint_name(checkpoints_path, iteration, release=False, + pipeline_parallel=None, + tensor_rank=None, pipeline_rank=None, + expert_parallel=None, expert_rank=None, + return_base_dir=False): + """Determine the directory name for this rank's checkpoint.""" + if release: + directory = 'release' + else: + directory = 'iter_{:07d}'.format(iteration) + if return_base_dir: + common_path = os.path.join(checkpoints_path, directory) + return common_path + + # Use both the tensor and pipeline MP rank. + if pipeline_parallel is None: + pipeline_parallel = (mpu.get_pipeline_model_parallel_world_size() > 1) + if tensor_rank is None: + tensor_rank = mpu.get_tensor_model_parallel_rank() + if pipeline_rank is None: + pipeline_rank = mpu.get_pipeline_model_parallel_rank() + if expert_parallel is None: + expert_parallel = (mpu.get_expert_model_parallel_world_size() > 1) + if expert_rank is None: + expert_rank = mpu.get_expert_model_parallel_rank() + + # Use both the tensor and pipeline MP rank. If using the distributed + # optimizer, then the optimizer's path must additionally include the + # data parallel rank. + if not pipeline_parallel: + common_path = os.path.join(checkpoints_path, directory, + f'mp_rank_{tensor_rank:02d}') + else: + common_path = os.path.join(checkpoints_path, directory, + f'mp_rank_{tensor_rank:02d}_{pipeline_rank:03d}') + + if expert_parallel: + common_path = common_path + f'_{expert_rank:03d}' + + return os.path.join(common_path, "model_optim_rng.pt") + + +def get_distributed_optimizer_checkpoint_name(model_checkpoint_name): + return os.path.join(os.path.dirname(model_checkpoint_name), + "distrib_optim.pt") + + +def find_checkpoint_rank_0(checkpoints_path, iteration, release=False): + """Finds the checkpoint for rank 0 without knowing if we are using + pipeline parallelism/expert parallelism or not. + + Since the checkpoint naming scheme changes if pipeline or expert + parallelism is present, we need to look for both naming schemes if + we don't know if the checkpoint has pipeline or expert parallelism. + """ + + # Look for checkpoint with no pipelining and no expert parallelism + filename = get_checkpoint_name(checkpoints_path, iteration, release, + pipeline_parallel=False, + tensor_rank=0, pipeline_rank=0, + expert_parallel=False, expert_rank=0) + if os.path.isfile(filename): + return filename + + # Look for checkpoint with no pipelining and expert parallelism + filename = get_checkpoint_name(checkpoints_path, iteration, release, + pipeline_parallel=False, + tensor_rank=0, pipeline_rank=0, + expert_parallel=True, expert_rank=0) + if os.path.isfile(filename): + return filename + + # Look for checkpoint with pipelining and no expert parallelism + filename = get_checkpoint_name(checkpoints_path, iteration, release, + pipeline_parallel=True, + tensor_rank=0, pipeline_rank=0, + expert_parallel=False, expert_rank=0) + if os.path.isfile(filename): + return filename + + # Look for checkpoint with pipelining and expert parallelism + filename = get_checkpoint_name(checkpoints_path, iteration, release, + pipeline_parallel=True, + tensor_rank=0, pipeline_rank=0, + expert_parallel=True, expert_rank=0) + if os.path.isfile(filename): + return filename + + # Look for a distributed checkpoint + filename = get_checkpoint_name(checkpoints_path, iteration, release, + pipeline_parallel=True, + return_base_dir=True) + if dist_checkpointing.check_is_distributed_checkpoint(filename): + return filename + + return None + + +def get_checkpoint_tracker_filename(checkpoints_path): + + """Tracker file rescords the latest chckpoint during + training to restart from.""" + return os.path.join(checkpoints_path, 'latest_checkpointed_iteration.txt') + + +def checkpoint_exists(checkpoints_path): + if checkpoints_path is None: + return False + load_step = 'latest_checkpointed_iteration.txt' + return os.path.exists(os.path.join(checkpoints_path, load_step)) + + +def read_metadata(tracker_filename): + # Read the tracker file and either set the iteration or + # mark it as a release checkpoint. + iteration = 0 + release = False + with open(tracker_filename, 'r') as f: + metastring = f.read().strip() + try: + iteration = int(metastring) + except ValueError: + release = metastring == 'release' + if not release: + print_rank_0('ERROR: Invalid metadata file {}. Exiting'.format( + tracker_filename)) + sys.exit() + assert iteration > 0 or release, 'error parsing metadata file {}'.format( + tracker_filename) + + # Get the max iteration retrieved across the ranks. + if torch.distributed.is_initialized(): + iters_cuda = torch.tensor([iteration], dtype=torch.long, device='cuda') + torch.distributed.all_reduce(iters_cuda, op=torch.distributed.ReduceOp.MAX) + max_iter = iters_cuda[0].item() + + # We should now have all the same iteration. + # If not, print a warning and chose the maximum + # iteration across all ranks. + if iteration != max_iter: + rank = torch.distributed.get_rank() + print('WARNING: on rank {} found iteration {} in the ' + 'metadata while max iteration across the ranks ' + 'is {}, replacing it with max iteration.'.format( + rank, iteration, max_iter), flush=True) + else: + # When loading a checkpoint outside of training (for example, + # when editing it), we might not have torch distributed + # initialized, in this case, just assume we have the latest + max_iter = iteration + return max_iter, release + + +def get_rng_state(use_dist_ckpt: bool = False): + """ collect rng state across data parallel ranks """ + args = get_args() + rng_state = { + 'random_rng_state': random.getstate(), + 'np_rng_state': np.random.get_state(), + 'torch_rng_state': torch.get_rng_state(), + 'cuda_rng_state': torch.cuda.get_rng_state(), + 'rng_tracker_states': tensor_parallel.get_cuda_rng_tracker().get_states()} + + rng_state_list = None + if torch.distributed.is_initialized() and \ + mpu.get_data_parallel_world_size() > 1 and \ + args.data_parallel_random_init: + rng_state_list = \ + [None for i in range(mpu.get_data_parallel_world_size())] + torch.distributed.all_gather_object( + rng_state_list, + rng_state, + group=mpu.get_data_parallel_group()) + else: + rng_state_list = [rng_state] + + if use_dist_ckpt: + pp_rank = mpu.get_pipeline_model_parallel_rank() + pp_size = mpu.get_pipeline_model_parallel_world_size() + tp_rank = mpu.get_tensor_model_parallel_rank() + tp_size = mpu.get_tensor_model_parallel_world_size() + rng_state_list = ShardedObject('rng_state', rng_state_list, (pp_size, tp_size), (pp_rank, tp_rank), + replica_id=mpu.get_data_parallel_rank(with_context_parallel=True)) + + return rng_state_list + + +def save_checkpoint(iteration, model, optimizer, opt_param_scheduler, + num_floating_point_operations_so_far): + """Save a model checkpoint.""" + args = get_args() + + # Only rank zero of the data parallel writes to the disk. + model = unwrap_model(model) + + ckpt_format = args.dist_ckpt_format if args.use_dist_ckpt else 'torch' + print_rank_0('saving checkpoint at iteration {:7d} to {} in {} format'.format( + iteration, args.save, ckpt_format)) + + # Collect rng state across data parallel ranks. + rng_state = get_rng_state(args.use_dist_ckpt) + + # Checkpoint name. + checkpoint_name = get_checkpoint_name(args.save, iteration, return_base_dir=args.use_dist_ckpt) + + # Save distributed optimizer's custom parameter state. + if args.use_distributed_optimizer and not args.no_save_optim and optimizer is not None and not args.use_dist_ckpt: + optim_checkpoint_name = \ + get_distributed_optimizer_checkpoint_name(checkpoint_name) + ensure_directory_exists(optim_checkpoint_name) + optimizer.save_parameter_state(optim_checkpoint_name) + + # Collect args, model, RNG. + if not torch.distributed.is_initialized() \ + or mpu.get_data_modulo_expert_parallel_rank() == 0 \ + or args.use_dist_ckpt: + + optim_sd_kwargs = {} + if args.use_dist_ckpt and args.use_distributed_optimizer: + optim_sd_kwargs['sharding_type'] = ('fully_sharded_bucket_space' + if args.ckpt_fully_parallel_save + else 'dp_zero_gather_scatter') + print_rank_0(f'Storing distributed optimizer sharded state of type {optim_sd_kwargs["sharding_type"]}') + state_dict = generate_state_dict(args, model, optimizer, opt_param_scheduler, rng_state, + args.use_dist_ckpt, iteration, optim_sd_kwargs=optim_sd_kwargs) + + state_dict['num_floating_point_operations_so_far'] = num_floating_point_operations_so_far + if args.use_dist_ckpt: + if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0: + ensure_directory_exists(checkpoint_name, + check_parent=False) + dist_checkpointing.save(state_dict, checkpoint_name, (args.dist_ckpt_format, 1)) + + else: + # Save. + ensure_directory_exists(checkpoint_name) + torch.save(state_dict, checkpoint_name) + + # Wait so everyone is done (necessary) + if torch.distributed.is_initialized(): + torch.distributed.barrier() + + print_rank_0(' successfully saved checkpoint at iteration {:7d} to {}' \ + .format(iteration, args.save)) + + # And update the latest iteration + if not torch.distributed.is_initialized() \ + or torch.distributed.get_rank() == 0: + tracker_filename = get_checkpoint_tracker_filename(args.save) + with open(tracker_filename, 'w') as f: + f.write(str(iteration)) + + # Wait so everyone is done (not necessary) + if torch.distributed.is_initialized(): + torch.distributed.barrier() + + +def generate_state_dict(args, model, optimizer, opt_param_scheduler, + rng_state, use_dist_ckpt=False, iteration=None, + optim_sd_kwargs=None): + # Arguments, iteration, and model. + state_dict = {} + state_dict['args'] = args + state_dict['checkpoint_version'] = 3.0 + if iteration is not None: + state_dict['iteration'] = iteration + + if len(model) == 1: + state_dict['model'] = (model[0].sharded_state_dict() + if use_dist_ckpt else + model[0].state_dict_for_save_checkpoint()) + else: + for i in range(len(model)): + mpu.set_virtual_pipeline_model_parallel_rank(i) + state_dict['model%d' % i] = ( + model[i].sharded_state_dict() + if use_dist_ckpt else + model[i].state_dict_for_save_checkpoint()) + # Optimizer stuff. + if not args.no_save_optim: + if optimizer is not None: + state_dict['optimizer'] = (optimizer.sharded_state_dict(state_dict, **(optim_sd_kwargs or {})) + if use_dist_ckpt else + optimizer.state_dict()) + if opt_param_scheduler is not None: + state_dict['opt_param_scheduler'] = \ + opt_param_scheduler.state_dict() + # RNG states. + if not args.no_save_rng: + state_dict["rng_state"] = rng_state + return state_dict + + +def _transpose_first_dim(t, num_splits, num_splits_first, model): + input_shape = t.size() + # We use a self_attention module but the values extracted aren't + # specific to self attention so should work for cross attention as well + while hasattr(model, 'module'): + model = model.module + attention_module = model.language_model.encoder.layers[0].self_attention + hidden_size_per_attention_head = attention_module.hidden_size_per_attention_head + num_attention_heads_per_partition = attention_module.num_attention_heads_per_partition + if num_splits_first: + """[num_splits * np * hn, h] + -->(view) [num_splits, np, hn, h] + -->(tranpose) [np, num_splits, hn, h] + -->(view) [np * num_splits * hn, h] """ + + intermediate_shape = \ + (num_splits, num_attention_heads_per_partition, + hidden_size_per_attention_head) + input_shape[1:] + + t = t.view(*intermediate_shape) + t = t.transpose(0, 1).contiguous() + else: + """[np * hn * num_splits, h] + -->(view) [np, hn, num_splits, h] + -->(tranpose) [np, num_splits, hn, h] + -->(view) [np * num_splits * hn, h] """ + + intermediate_shape = \ + (num_attention_heads_per_partition, + hidden_size_per_attention_head, num_splits) +\ + input_shape[1:] + + t = t.view(*intermediate_shape) + t = t.transpose(1, 2).contiguous() + t = t.view(*input_shape) + + return t + + +def fix_query_key_value_ordering(model, checkpoint_version): + """Fix up query/key/value matrix ordering if checkpoint + version is smaller than 2.0 + """ + if checkpoint_version < 2.0: + if isinstance(model, list): + assert len(model)==1 + model = model[0] + for name, param in model.named_parameters(): + if name.endswith(('.query_key_value.weight', '.query_key_value.bias')): + if checkpoint_version == 0: + fixed_param = _transpose_first_dim(param.data, 3, True, model) + elif checkpoint_version == 1.0: + fixed_param = _transpose_first_dim(param.data, 3, False, model) + else: + print_rank_0(f"Invalid checkpoint version {checkpoint_version}.") + sys.exit() + param.data.copy_(fixed_param) + if name.endswith(('.key_value.weight', '.key_value.bias')): + if checkpoint_version == 0: + fixed_param = _transpose_first_dim(param.data, 2, True, model) + elif checkpoint_version == 1.0: + fixed_param = _transpose_first_dim(param.data, 2, False, model) + else: + print_rank_0(f"Invalid checkpoint version {checkpoint_version}.") + sys.exit() + param.data.copy_(fixed_param) + print_rank_0(" succesfully fixed query-key-values ordering for" + " checkpoint version {}".format(checkpoint_version)) + + +def _load_base_checkpoint(load_dir, rank0=False, sharded_state_dict=None, + exit_on_missing_checkpoint=False, checkpoint_step = None): + """ Load the base state_dict from the given directory + + If rank0 is true, just loads rank 0 checkpoint, ignoring arguments. + """ + + # Read the tracker file and set the iteration. + tracker_filename = get_checkpoint_tracker_filename(load_dir) + + # If no tracker file, return nothing + if not os.path.isfile(tracker_filename): + if not rank0: + print_rank_0('WARNING: could not find the metadata file {} '.format( + tracker_filename)) + print_rank_0(' will not load any checkpoints and will start from ' + 'random') + + # Conditionally exit if checkpoint not found. + if exit_on_missing_checkpoint: + print_rank_0(">> '--exit-on-missing-checkpoint' set ... exiting. <<") + if torch.distributed.is_initialized(): + torch.distributed.barrier() + sys.exit() + + return None, "", False + + # Otherwise, read the tracker file and either set the iteration or + # mark it as a release checkpoint. + if checkpoint_step is not None: + iteration = checkpoint_step + release = False + else: + iteration, release = read_metadata(tracker_filename) + + # Checkpoint. + if rank0: + checkpoint_name = find_checkpoint_rank_0(load_dir, iteration, release) + is_dist_ckpt = checkpoint_name is not None and dist_checkpointing.check_is_distributed_checkpoint(checkpoint_name) + else: + checkpoint_name = get_checkpoint_name(load_dir, iteration, release, + return_base_dir=True) + is_dist_ckpt = dist_checkpointing.check_is_distributed_checkpoint(checkpoint_name) + if not is_dist_ckpt: + checkpoint_name = get_checkpoint_name(load_dir, iteration, release, + return_base_dir=False) + dist_infix = "distributed " if is_dist_ckpt else "" + if release: + print_rank_0(f' loading release {dist_infix}checkpoint from {load_dir}') + else: + print_rank_0(f' loading {dist_infix}checkpoint from {load_dir} at iteration {iteration}') + + # Load the checkpoint. + if is_dist_ckpt: + if rank0: + state_dict = dist_checkpointing.load_common_state_dict(checkpoint_name) + return state_dict, checkpoint_name, release + + if sharded_state_dict is None: + args = get_args() + assert not args.auto_detect_ckpt_format and not args.use_dist_ckpt, (args.auto_detect_ckpt_format, args.use_dist_ckpt) + raise RuntimeError('Detected load from a distributed checkpoint, but neither --use-dist-ckpt nor --auto-detect-ckpt-format is set.') + state_dict = dist_checkpointing.load(sharded_state_dict, checkpoint_name) + return state_dict, checkpoint_name, release + + try: + state_dict = torch.load(checkpoint_name, map_location='cpu') + except ModuleNotFoundError: + from megatron.legacy.fp16_deprecated import loss_scaler + # For backward compatibility. + if not rank0: + print_rank_0(' > deserializing using the old code structure ...') + sys.modules['fp16.loss_scaler'] = sys.modules[ + 'megatron.legacy.fp16_deprecated.loss_scaler'] + sys.modules['megatron.fp16.loss_scaler'] = sys.modules[ + 'megatron.legacy.fp16_deprecated.loss_scaler'] + sys.modules['megatron.model'] = sys.modules['megatron.legacy.model'] + state_dict = torch.load(checkpoint_name, map_location='cpu') + sys.modules.pop('fp16.loss_scaler', None) + sys.modules.pop('megatron.fp16.loss_scaler', None) + sys.modules.pop('megatron.model', None) + except BaseException as e: + print_rank_0('could not load the checkpoint') + print_rank_0(e) + sys.exit() + + return state_dict, checkpoint_name, release + + +def load_args_from_checkpoint(args, load_arg='load', + exit_on_missing_checkpoint=False): + """Set required arguments from the checkpoint specified in the + arguments. + + Will overwrite arguments that have a non-None default value, but + will leave any arguments that default to None as set. + + Returns the same args NameSpace with the new values added/updated. + + If no checkpoint is specified in args, or if the checkpoint is + there but invalid, the arguments will not be modified + + """ + load_dir = getattr(args, load_arg) + + if load_dir is None: + print_rank_0('No load directory specified, using provided arguments.') + return args + + state_dict, checkpoint_name, release = _load_base_checkpoint( + load_dir, + rank0=True, + exit_on_missing_checkpoint=exit_on_missing_checkpoint, + checkpoint_step=args.ckpt_step + ) + + # Args. + if not state_dict: + print_rank_0('Checkpoint not found to provide arguments, using provided arguments.') + return args + + if 'args' not in state_dict: + print_rank_0('Checkpoint provided does not have arguments saved, using provided arguments.') + return args + + checkpoint_args = state_dict['args'] + checkpoint_version = state_dict.get('checkpoint_version', 0) + args.iteration = state_dict['iteration'] + + # One-off conversion for foundation models + if hasattr(checkpoint_args, 'disable_bias_linear'): + setattr(checkpoint_args, 'add_bias_linear', not getattr(checkpoint_args, 'disable_bias_linear')) + + def _set_arg(arg_name, old_arg_name=None, force=False): + if not force and getattr(args, arg_name, None) is not None: + return + + if old_arg_name is not None: + checkpoint_value = getattr(checkpoint_args, old_arg_name, None) + else: + checkpoint_value = getattr(checkpoint_args, arg_name, None) + + if checkpoint_value is not None: + print_rank_0(f"Setting {arg_name} to {checkpoint_value} from checkpoint") + setattr(args, arg_name, checkpoint_value) + else: + print_rank_0(f"Checkpoint did not provide arguments {arg_name}") + + _set_arg('num_layers') + _set_arg('hidden_size') + _set_arg('ffn_hidden_size') + _set_arg('seq_length') + _set_arg('num_attention_heads') + _set_arg('num_query_groups', force=True) + _set_arg('group_query_attention', force=True) + _set_arg('kv_channels') + _set_arg('max_position_embeddings') + _set_arg('position_embedding_type', force=True) + _set_arg('add_position_embedding', force=True) + _set_arg('use_rotary_position_embeddings', force=True) + _set_arg('rotary_percent', force=True) + _set_arg('rotary_interleaved', force=True) + _set_arg('add_bias_linear', force=True) + _set_arg('add_qkv_bias', force=True) + _set_arg('swiglu', force=True) + _set_arg('untie_embeddings_and_output_weights', force=True) + _set_arg('apply_layernorm_1p', force=True) + _set_arg('normalization', force=True) + _set_arg('tokenizer_type') + _set_arg('padded_vocab_size') + _set_arg('apply_query_key_layer_scaling', force=True) + if checkpoint_version < 3.0: + _set_arg('tensor_model_parallel_size', + 'model_parallel_size') + else: + _set_arg('tensor_model_parallel_size', force=True) + _set_arg('pipeline_model_parallel_size', force=True) + _set_arg('virtual_pipeline_model_parallel_size', force=True) + _set_arg('num_layers_per_virtual_pipeline_stage') + return args, checkpoint_args + + +def load_checkpoint(model, optimizer, opt_param_scheduler, load_arg='load', strict=True): + """Load a model checkpoint and return the iteration. + strict (bool): whether to strictly enforce that the keys in + :attr:`state_dict` of the checkpoint match the names of + parameters and buffers in model. + """ + args = get_args() + load_dir = getattr(args, load_arg) + + # Finetuning directories + pretrained_dir = getattr(args,'pretrained_checkpoint', None) + if pretrained_dir is not None and not checkpoint_exists(load_dir): + print_rank_0(f'Checkpoint file not found in load directory {load_dir} attempting to finetune with checkpoint in {pretrained_dir}') + load_dir = pretrained_dir + if not checkpoint_exists(load_dir): + raise FileNotFoundError("No checkpoint found in load directory or pretrained directory") + args.finetune = True + + + model = unwrap_model(model) + + load_kwargs = {} + is_dist_ckpt = False + if args.auto_detect_ckpt_format or args.use_dist_ckpt: + state_dict, checkpoint_name, release = _load_base_checkpoint(load_dir, rank0=True, exit_on_missing_checkpoint=args.exit_on_missing_checkpoint) + is_dist_ckpt = dist_checkpointing.check_is_distributed_checkpoint(checkpoint_name) + if is_dist_ckpt: + ckpt_tp_pp = (state_dict['args'].tensor_model_parallel_size, state_dict['args'].pipeline_model_parallel_size) + run_tp_pp = (mpu.get_tensor_model_parallel_world_size(), mpu.get_pipeline_model_parallel_world_size()) + mismatch_msg = "(TP, PP) mismatch after resume ({} vs {} from checkpoint)".format(ckpt_tp_pp, run_tp_pp) + + if ckpt_tp_pp == run_tp_pp and not getattr(state_dict['args'], 'no_save_rng', False): + rng_state = get_rng_state(True) # we can load the rng state + else: + rng_state = None + print_rank_0("{}: RNG state will be ignored".format(mismatch_msg)) + + # TODO: add DistributedOptimizer support for differing TPxPP + if ckpt_tp_pp != run_tp_pp and not release and not args.finetune and not args.no_load_optim and args.use_distributed_optimizer: + raise RuntimeError("{}: not supported for DistributedOptimizer".format(mismatch_msg)) + + optim_sd_kwargs = dict(is_loading=True) + if args.use_distributed_optimizer: + optim_sd_kwargs['sharding_type'] = ('fully_sharded_bucket_space' + if getattr(state_dict['args'], 'ckpt_fully_parallel_save', False) + else 'dp_zero_gather_scatter') + load_kwargs['sharded_state_dict'] = generate_state_dict(args, model, optimizer, opt_param_scheduler, + rng_state, args.use_dist_ckpt, optim_sd_kwargs=optim_sd_kwargs) + load_kwargs['exit_on_missing_checkpoint'] = args.exit_on_missing_checkpoint + + state_dict, checkpoint_name, release = _load_base_checkpoint(load_dir, rank0=False, **load_kwargs) + + # Checkpoint not loaded. + if state_dict is None: + # Iteration and num_floating_point_operations_so_far default to 0. + return 0, 0 + + # Set checkpoint version. + set_checkpoint_version(state_dict.get('checkpoint_version', 0)) + + # Set iteration. + if args.finetune or release: + iteration = 0 + else: + try: + iteration = state_dict['iteration'] + except KeyError: + try: # Backward compatible with older checkpoints + iteration = state_dict['total_iters'] + except KeyError: + print_rank_0('A metadata file exists but unable to load ' + 'iteration from checkpoint {}, exiting'.format(checkpoint_name)) + sys.exit() + num_floating_point_operations_so_far = state_dict.get('num_floating_point_operations_so_far', 0) + + # Check arguments. + assert args.consumed_train_samples == 0 + assert args.consumed_valid_samples == 0 + if 'args' in state_dict and not args.finetune: + checkpoint_args = state_dict['args'] + check_checkpoint_args(checkpoint_args) + args.consumed_train_samples = getattr(checkpoint_args, + 'consumed_train_samples', 0) + update_num_microbatches(consumed_samples=args.consumed_train_samples) + args.consumed_valid_samples = getattr(checkpoint_args, + 'consumed_valid_samples', 0) + else: + print_rank_0('could not find arguments in the checkpoint ...') + + # Model. + strict = False if args.retro_add_retriever else strict + if len(model) == 1: + model[0].load_state_dict(state_dict['model'], strict=strict) + else: + for i in range(len(model)): + mpu.set_virtual_pipeline_model_parallel_rank(i) + model[i].load_state_dict(state_dict['model%d' % i], strict=strict) + + # Fix up query/key/value matrix ordering if needed. + checkpoint_version = get_checkpoint_version() + print_rank_0(f' checkpoint version {checkpoint_version}') + fix_query_key_value_ordering(model, checkpoint_version) + + # Optimizer. + if not release and not args.finetune and not args.no_load_optim: + try: + # Load state dict. + if optimizer is not None: + optimizer.load_state_dict(state_dict['optimizer']) + + # Load distributed optimizer's custom parameter state. + # For distributed checkpoint it's already loaded in load_state_dict above + if args.use_distributed_optimizer and not is_dist_ckpt: + tracker_filename = get_checkpoint_tracker_filename(load_dir) + iteration, release = read_metadata(tracker_filename) + model_checkpoint_name = \ + get_checkpoint_name(load_dir, iteration, release) + optim_checkpoint_name = \ + get_distributed_optimizer_checkpoint_name( + model_checkpoint_name) + optimizer.load_parameter_state(optim_checkpoint_name) + + # Load scheduler. + if opt_param_scheduler is not None: + if 'lr_scheduler' in state_dict: # backward compatbility + opt_param_scheduler.load_state_dict(state_dict['lr_scheduler']) + else: + opt_param_scheduler.load_state_dict(state_dict['opt_param_scheduler']) + except KeyError: + print_rank_0('Unable to load optimizer from checkpoint {}. ' + 'Specify --no-load-optim or --finetune to prevent ' + 'attempting to load the optimizer state, ' + 'exiting ...'.format(checkpoint_name)) + sys.exit() + else: + if (args.fp16 or args.bf16) and optimizer is not None: + optimizer.reload_model_params() + + # rng states. + if not release and not args.finetune and not args.no_load_rng: + try: + if 'rng_state' in state_dict: + # access rng_state for data parallel rank + if args.data_parallel_random_init: + rng_state = state_dict['rng_state'][mpu.get_data_parallel_rank()] + else: + rng_state = state_dict['rng_state'][0] + random.setstate(rng_state['random_rng_state']) + np.random.set_state(rng_state['np_rng_state']) + torch.set_rng_state(rng_state['torch_rng_state']) + torch.cuda.set_rng_state(rng_state['cuda_rng_state']) + # Check for empty states array + if not rng_state['rng_tracker_states']: + raise KeyError + tensor_parallel.get_cuda_rng_tracker().set_states( + rng_state['rng_tracker_states']) + else: # backward compatability + random.setstate(state_dict['random_rng_state']) + np.random.set_state(state_dict['np_rng_state']) + torch.set_rng_state(state_dict['torch_rng_state']) + torch.cuda.set_rng_state(state_dict['cuda_rng_state']) + # Check for empty states array + if not state_dict['rng_tracker_states']: + raise KeyError + tensor_parallel.get_cuda_rng_tracker().set_states( + state_dict['rng_tracker_states']) + except KeyError: + print_rank_0('Unable to load rng state from checkpoint {}. ' + 'Specify --no-load-rng or --finetune to prevent ' + 'attempting to load the rng state, ' + 'exiting ...'.format(checkpoint_name)) + sys.exit() + + # Some utilities want to load a checkpoint without distributed being initialized + if torch.distributed.is_initialized(): + torch.distributed.barrier() + + print_rank_0(f' successfully loaded checkpoint from {load_dir} ' + f'[ t {mpu.get_tensor_model_parallel_rank()}, ' + f'p {mpu.get_pipeline_model_parallel_rank()} ] ' + f'at iteration {iteration}') + + return iteration, num_floating_point_operations_so_far + + +def load_biencoder_checkpoint(model, only_query_model=False, + only_context_model=False, custom_load_path=None): + """ + selectively load retrieval models for indexing/retrieving + from saved checkpoints + """ + + args = get_args() + + model = unwrap_model(model) + + load_path = custom_load_path if custom_load_path is not None else args.load + + tracker_filename = get_checkpoint_tracker_filename(load_path) + with open(tracker_filename, 'r') as f: + iteration = int(f.read().strip()) + + checkpoint_name = get_checkpoint_name(load_path, iteration, + args.use_distributed_optimizer, + release=False) + + if mpu.get_data_parallel_rank() == 0: + print('global rank {} is loading checkpoint {}'.format( + torch.distributed.get_rank(), checkpoint_name)) + + state_dict = torch.load(checkpoint_name, map_location='cpu') + ret_state_dict = state_dict['model'] + + if only_query_model: + ret_state_dict.pop('context_model') + if only_context_model: + ret_state_dict.pop('query_model') + + assert len(model) == 1 + model[0].load_state_dict(ret_state_dict) + torch.distributed.barrier() + + if mpu.get_data_parallel_rank() == 0: + print(' successfully loaded {}'.format(checkpoint_name)) + + return model diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/dist_signal_handler.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/dist_signal_handler.py new file mode 100644 index 0000000..a60204f --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/dist_signal_handler.py @@ -0,0 +1,81 @@ +import signal + +import torch + + +def get_world_size(): + if torch.distributed.is_available() and torch.distributed.is_initialized(): + world_size = torch.distributed.get_world_size() + else: + world_size = 1 + return world_size + + +def get_device(local_rank=None): + backend = torch.distributed.get_backend() + if backend == 'nccl': + if local_rank is None: + device = torch.device('cuda') + else: + device = torch.device(f'cuda:{local_rank}') + elif backend == 'gloo': + device = torch.device('cpu') + else: + raise RuntimeError + return device + + +def all_gather_item(item, dtype, group=None, async_op=False, local_rank=None): + if not torch.distributed.is_available() or \ + not torch.distributed.is_initialized(): + return [item] + + device = get_device(local_rank) + + if group is not None: + group_size = group.size() + else: + group_size = get_world_size() + + tensor = torch.tensor([item], device=device, dtype=dtype) + output_tensors = [ + torch.zeros(1, dtype=tensor.dtype, device=tensor.device) + for _ in range(group_size) + ] + torch.distributed.all_gather(output_tensors, tensor, group, async_op) + output = [elem.item() for elem in output_tensors] + return output + + +class DistributedSignalHandler: + def __init__(self, sig=signal.SIGTERM): + self.sig = sig + + def signals_received(self): + all_received = all_gather_item( + self._signal_received, dtype=torch.int32 + ) + return all_received + + def __enter__(self): + self._signal_received = False + self.released = False + self.original_handler = signal.getsignal(self.sig) + + def handler(signum, frame): + self._signal_received = True + + signal.signal(self.sig, handler) + + return self + + def __exit__(self, type, value, tb): + self.release() + + def release(self): + if self.released: + return False + + signal.signal(self.sig, self.original_handler) + self.released = True + return True diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/global_vars.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/global_vars.py new file mode 100644 index 0000000..ce68d8e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/global_vars.py @@ -0,0 +1,238 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Megatron global variables.""" + +import os +import sys +import torch + +from megatron.training import dist_signal_handler +from megatron.core import Timers +from megatron.training.tokenizer import build_tokenizer +from .microbatches import build_num_microbatches_calculator + +_GLOBAL_ARGS = None +_GLOBAL_NUM_MICROBATCHES_CALCULATOR = None +_GLOBAL_TOKENIZER = None +_GLOBAL_TENSORBOARD_WRITER = None +_GLOBAL_WANDB_WRITER = None +_GLOBAL_ONE_LOGGER = None +_GLOBAL_ADLR_AUTORESUME = None +_GLOBAL_TIMERS = None +_GLOBAL_SIGNAL_HANDLER = None + +def get_args(): + """Return arguments.""" + _ensure_var_is_initialized(_GLOBAL_ARGS, 'args') + return _GLOBAL_ARGS + + +def get_num_microbatches(): + return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get() + + +def get_current_global_batch_size(): + return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get_current_global_batch_size() + + +def update_num_microbatches(consumed_samples, consistency_check=True): + _GLOBAL_NUM_MICROBATCHES_CALCULATOR.update(consumed_samples, + consistency_check) + + +def get_tokenizer(): + """Return tokenizer.""" + _ensure_var_is_initialized(_GLOBAL_TOKENIZER, 'tokenizer') + return _GLOBAL_TOKENIZER + + +def get_tensorboard_writer(): + """Return tensorboard writer. It can be None so no need + to check if it is initialized.""" + return _GLOBAL_TENSORBOARD_WRITER + + +def get_wandb_writer(): + """Return tensorboard writer. It can be None so no need + to check if it is initialized.""" + return _GLOBAL_WANDB_WRITER + + +def get_one_logger(): + """Return one logger. It can be None so no need + to check if it is initialized.""" + return _GLOBAL_ONE_LOGGER + +def get_adlr_autoresume(): + """ADLR autoresume object. It can be None so no need + to check if it is initialized.""" + return _GLOBAL_ADLR_AUTORESUME + + +def get_timers(): + """Return timers.""" + _ensure_var_is_initialized(_GLOBAL_TIMERS, 'timers') + return _GLOBAL_TIMERS + + +def get_signal_handler(): + _ensure_var_is_initialized(_GLOBAL_SIGNAL_HANDLER, 'signal handler') + return _GLOBAL_SIGNAL_HANDLER + + +def _set_signal_handler(): + global _GLOBAL_SIGNAL_HANDLER + _ensure_var_is_not_initialized(_GLOBAL_SIGNAL_HANDLER, 'signal handler') + _GLOBAL_SIGNAL_HANDLER = dist_signal_handler.DistributedSignalHandler().__enter__() + + + +def set_global_variables(args, build_tokenizer=True): + """Set args, tokenizer, tensorboard-writer, adlr-autoresume, and timers.""" + + assert args is not None + + _ensure_var_is_not_initialized(_GLOBAL_ARGS, 'args') + set_args(args) + + _build_num_microbatches_calculator(args) + if build_tokenizer: + _ = _build_tokenizer(args) + _set_tensorboard_writer(args) + _set_wandb_writer(args) + _set_one_logger(args) + _set_adlr_autoresume(args) + _set_timers(args) + + if args.exit_signal_handler: + _set_signal_handler() + + +def set_args(args): + global _GLOBAL_ARGS + _GLOBAL_ARGS = args + + +def _build_num_microbatches_calculator(args): + + global _GLOBAL_NUM_MICROBATCHES_CALCULATOR + _ensure_var_is_not_initialized(_GLOBAL_NUM_MICROBATCHES_CALCULATOR, + 'num microbatches calculator') + + _GLOBAL_NUM_MICROBATCHES_CALCULATOR = build_num_microbatches_calculator( + args) + + +def _build_tokenizer(args): + """Initialize tokenizer.""" + global _GLOBAL_TOKENIZER + _ensure_var_is_not_initialized(_GLOBAL_TOKENIZER, 'tokenizer') + _GLOBAL_TOKENIZER = build_tokenizer(args) + return _GLOBAL_TOKENIZER + + +def rebuild_tokenizer(args): + global _GLOBAL_TOKENIZER + _GLOBAL_TOKENIZER = None + return _build_tokenizer(args) + + +def _set_tensorboard_writer(args): + """Set tensorboard writer.""" + global _GLOBAL_TENSORBOARD_WRITER + _ensure_var_is_not_initialized(_GLOBAL_TENSORBOARD_WRITER, + 'tensorboard writer') + + if hasattr(args, 'tensorboard_dir') and \ + args.tensorboard_dir and args.rank == (args.world_size - 1): + try: + from torch.utils.tensorboard import SummaryWriter + print('> setting tensorboard ...') + _GLOBAL_TENSORBOARD_WRITER = SummaryWriter( + log_dir=args.tensorboard_dir, + max_queue=args.tensorboard_queue_size) + except ModuleNotFoundError: + print('WARNING: TensorBoard writing requested but is not ' + 'available (are you using PyTorch 1.1.0 or later?), ' + 'no TensorBoard logs will be written.', flush=True) + + +def _set_wandb_writer(args): + global _GLOBAL_WANDB_WRITER + _ensure_var_is_not_initialized(_GLOBAL_WANDB_WRITER, + 'wandb writer') + if getattr(args, 'wandb_project', '') and args.rank == (args.world_size - 1): + if args.wandb_exp_name == '': + raise ValueError("Please specify the wandb experiment name!") + + import wandb + if args.wandb_save_dir: + save_dir = args.wandb_save_dir + else: + # Defaults to the save dir. + save_dir = os.path.join(args.save, 'wandb') + wandb_kwargs = { + 'dir': save_dir, + 'name': args.wandb_exp_name, + 'project': args.wandb_project, + 'config': vars(args)} + os.makedirs(wandb_kwargs['dir'], exist_ok=True) + wandb.init(**wandb_kwargs) + _GLOBAL_WANDB_WRITER = wandb + + +def _set_one_logger(args): + global _GLOBAL_ONE_LOGGER + _ensure_var_is_not_initialized(_GLOBAL_ONE_LOGGER, 'one logger') + + if args.enable_one_logger and args.rank == (args.world_size - 1): + try: + from one_logger.core import OneLogger + config = { + 'project': args.one_logger_project, + 'entity': args.one_logger_entity, + 'name': args.one_logger_run_name + } + one_logger = OneLogger(config=config) + _GLOBAL_ONE_LOGGER = one_logger + except BaseException: + print('WARNING: one_logger package is required to enable e2e metrics ' + 'tracking. Try pip install ' + '--index-url=https://sc-hw-artf.nvidia.com/api/pypi/hwinf-ml-pypi/simple' + ' one_logger to install it') + +def _set_adlr_autoresume(args): + """Initialize ADLR autoresume.""" + global _GLOBAL_ADLR_AUTORESUME + _ensure_var_is_not_initialized(_GLOBAL_ADLR_AUTORESUME, 'adlr autoresume') + + if args.adlr_autoresume: + if args.rank == 0: + print('enabling autoresume ...', flush=True) + sys.path.append(os.environ.get('SUBMIT_SCRIPTS', '.')) + try: + from userlib.auto_resume import AutoResume + except BaseException: + print('ADLR autoresume is not available, exiting ...') + sys.exit() + + _GLOBAL_ADLR_AUTORESUME = AutoResume + + +def _set_timers(args): + """Initialize timers.""" + global _GLOBAL_TIMERS + _ensure_var_is_not_initialized(_GLOBAL_TIMERS, 'timers') + _GLOBAL_TIMERS = Timers(args.timing_log_level, args.timing_log_option) + + +def _ensure_var_is_initialized(var, name): + """Make sure the input variable is not None.""" + assert var is not None, '{} is not initialized.'.format(name) + + +def _ensure_var_is_not_initialized(var, name): + """Make sure the input variable is not None.""" + assert var is None, '{} is already initialized.'.format(name) + + diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/initialize.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/initialize.py new file mode 100644 index 0000000..a49d4ee --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/initialize.py @@ -0,0 +1,394 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Megatron initialization.""" + +import random +import os +import time + +import numpy as np +import torch +from datetime import timedelta + +from megatron.legacy import fused_kernels +from megatron.training import get_adlr_autoresume +from megatron.training import get_args +from megatron.training import get_tensorboard_writer +from megatron.core import mpu, tensor_parallel +from megatron.training.arguments import parse_args, validate_args +from megatron.training.yaml_arguments import validate_yaml +from megatron.training.checkpointing import load_args_from_checkpoint +from megatron.training.global_vars import set_global_variables +from megatron.legacy.model.transformer import bias_dropout_add_fused_train +from megatron.legacy.model.fused_bias_gelu import bias_gelu + +def initialize_megatron( + extra_args_provider=None, + args_defaults={}, + ignore_unknown_args=False, + allow_no_cuda=False, + skip_mpu_initialization=False, +): + """Set global variables, initialize distributed, and + set autoresume and random seeds. + `allow_no_cuda` should not be set unless using megatron for cpu only + data processing. In general this arg should not be set unless you know + what you are doing. + Returns a function to finalize distributed env initialization + (optionally, only when args.lazy_mpu_init == True) + """ + if not allow_no_cuda: + # Make sure cuda is available. + assert torch.cuda.is_available(), "Megatron requires CUDA." + + # Parse arguments + args = parse_args(extra_args_provider, ignore_unknown_args) + + if args.use_checkpoint_args or args_defaults.get("use_checkpoint_args", False): + assert args.load is not None, "--use-checkpoints-args requires --load argument" + load_args_from_checkpoint(args) + + if args.yaml_cfg is not None: + args = validate_yaml(args, args_defaults) + else: + validate_args(args, args_defaults) + + + # set global args, build tokenizer, and set adlr-autoresume, + # tensorboard-writer, and timers. + set_global_variables(args) + + # torch.distributed initialization + def finish_mpu_init(): + args = get_args() + # Pytorch distributed. + _initialize_distributed() + + # Random seeds for reproducibility. + if args.rank == 0: + print("> setting random seeds to {} ...".format(args.seed)) + _set_random_seed(args.seed, args.data_parallel_random_init) + + if skip_mpu_initialization: + return None + + args = get_args() + if args.lazy_mpu_init: + # TODO is this still a necessary option? + args.use_cpu_initialization = True + # delayed initialization of DDP-related stuff + # We only set basic DDP globals + mpu.set_tensor_model_parallel_world_size(args.tensor_model_parallel_size) + # and return function for external DDP manager + # to call when it has DDP initialized + mpu.set_tensor_model_parallel_rank(args.rank) + return finish_mpu_init + else: + # Megatron's MPU is the master. Complete initialization right away. + finish_mpu_init() + + # Autoresume. + _init_autoresume() + + # Compile dependencies. + _compile_dependencies() + + if args.tp_comm_overlap: + _initialize_tp_communicators() + + # No continuation function + return None + + +def _compile_dependencies(): + + args = get_args() + + # ========================= + # Compile dataset C++ code. + # ========================= + # TODO: move this to ninja + if torch.distributed.get_rank() == 0: + start_time = time.time() + print("> compiling dataset index builder ...") + from megatron.core.datasets.utils import compile_helpers + + compile_helpers() + print( + ">>> done with dataset index builder. Compilation time: {:.3f} " + "seconds".format(time.time() - start_time), + flush=True, + ) + + # ================== + # Load fused kernels + # ================== + + # Custom kernel constraints check. + seq_len = args.seq_length + attn_batch_size = ( + args.num_attention_heads / args.tensor_model_parallel_size + ) * args.micro_batch_size + # Constraints on sequence length and attn_batch_size to enable warp based + # optimization and upper triangular optimization (for causal mask) + custom_kernel_constraint = ( + seq_len > 16 + and seq_len <= 16384 + and seq_len % 4 == 0 + and attn_batch_size % 4 == 0 + ) + # Print a warning. + if not ( + (args.fp16 or args.bf16) + and custom_kernel_constraint + and args.masked_softmax_fusion + ): + if args.rank == 0: + print( + "WARNING: constraints for invoking optimized" + " fused softmax kernel are not met. We default" + " back to unfused kernel invocations.", + flush=True, + ) + + # Always build on rank zero first. + if torch.distributed.get_rank() == 0: + start_time = time.time() + print("> compiling and loading fused kernels ...", flush=True) + fused_kernels.load(args) + torch.distributed.barrier() + else: + torch.distributed.barrier() + fused_kernels.load(args) + # Simple barrier to make sure all ranks have passed the + # compilation phase successfully before moving on to the + # rest of the program. We think this might ensure that + # the lock is released. + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + print( + ">>> done with compiling and loading fused kernels. " + "Compilation time: {:.3f} seconds".format(time.time() - start_time), + flush=True, + ) + +def _initialize_tp_communicators(): + """ initializing the communicators with user buffers for high-performance tensor-model-parallel + communication overlap """ + + try: + import yaml + + import transformer_engine + from transformer_engine.pytorch import module as te_module + + except ImportError: + raise RuntimeError("Tensor Parallel Communication/GEMM Overlap optimization needs 'yaml' and " + "'transformer_engine' packages") + + args = get_args() + + if args.tp_comm_overlap_cfg is not None: + with open(args.tp_comm_overlap_cfg,"r") as stream: + ub_cfgs = yaml.safe_load(stream) + else: + ub_cfgs = {} + + input_shape = [(args.seq_length * args.micro_batch_size) // args.context_parallel_size , args.hidden_size] + + #We create a MPI process group, which is needed to bootstrap the pipelined + #tensor-model-parallel communication overlap + torch.distributed.new_group(backend='mpi') + + te_module.base.initialize_ub(shape = input_shape, tp_size = args.tensor_model_parallel_size, + use_fp8 = (args.fp8 is not None) , ub_cfgs = ub_cfgs,) + +def _initialize_distributed(): + """Initialize torch.distributed and core model parallel.""" + args = get_args() + + device_count = torch.cuda.device_count() + if torch.distributed.is_initialized(): + + if args.rank == 0: + print( + "torch distributed is already initialized, " + "skipping initialization ...", + flush=True, + ) + args.rank = torch.distributed.get_rank() + args.world_size = torch.distributed.get_world_size() + + else: + + if args.rank == 0: + print("> initializing torch distributed ...", flush=True) + # Manually set the device ids. + if device_count > 0: + device = args.rank % device_count + if args.local_rank is not None: + assert ( + args.local_rank == device + ), "expected local-rank to be the same as rank % device-count." + else: + args.local_rank = device + torch.cuda.set_device(device) + # Call the init process + torch.distributed.init_process_group( + backend=args.distributed_backend, + world_size=args.world_size, + rank=args.rank, + timeout=timedelta(minutes=args.distributed_timeout_minutes), + ) + + # Set the tensor model-parallel, pipeline model-parallel, and + # data-parallel communicators. + if device_count > 0: + if mpu.model_parallel_is_initialized(): + print("model parallel is already initialized") + else: + mpu.initialize_model_parallel( + args.tensor_model_parallel_size, + args.pipeline_model_parallel_size, + args.virtual_pipeline_model_parallel_size, + args.pipeline_model_parallel_split_rank, + context_parallel_size=args.context_parallel_size, + expert_model_parallel_size=args.expert_model_parallel_size, + distributed_timeout_minutes=args.distributed_timeout_minutes, + nccl_communicator_config_path=args.nccl_communicator_config_path, + order='tp-cp-ep-dp-pp' if not args.use_tp_pp_dp_mapping else 'tp-pp-dp', + ) + if args.rank == 0: + print( + f"> initialized tensor model parallel with size " + f"{mpu.get_tensor_model_parallel_world_size()}" + ) + print( + f"> initialized pipeline model parallel with size " + f"{mpu.get_pipeline_model_parallel_world_size()}" + ) + + +def _init_autoresume(): + """Set autoresume start time.""" + autoresume = get_adlr_autoresume() + if autoresume: + torch.distributed.barrier() + autoresume.init() + torch.distributed.barrier() + + +def _set_random_seed(seed_, data_parallel_random_init=False): + """Set random seed for reproducability.""" + if seed_ is not None and seed_ > 0: + # Ensure that different pipeline MP stages get different seeds. + seed = seed_ + (100 * mpu.get_pipeline_model_parallel_rank()) + # Ensure different data parallel ranks get different seeds + if data_parallel_random_init: + seed = seed + (10 * mpu.get_data_parallel_rank()) + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.device_count() > 0: + tensor_parallel.model_parallel_cuda_manual_seed(seed) + else: + raise ValueError("Seed ({}) should be a positive integer.".format(seed)) + + +def write_args_to_tensorboard(): + """Write arguments to tensorboard.""" + args = get_args() + writer = get_tensorboard_writer() + if writer: + for arg in vars(args): + writer.add_text(arg, str(getattr(args, arg)), global_step=args.iteration) + + +def set_jit_fusion_options(): + """Set PyTorch JIT layer fusion options.""" + # flags required to enable jit fusion kernels + TORCH_MAJOR = int(torch.__version__.split(".")[0]) + TORCH_MINOR = int(torch.__version__.split(".")[1]) + if (TORCH_MAJOR > 1) or (TORCH_MAJOR == 1 and TORCH_MINOR >= 10): + # nvfuser + torch._C._jit_set_profiling_executor(True) + torch._C._jit_set_profiling_mode(True) + torch._C._jit_override_can_fuse_on_cpu(False) + torch._C._jit_override_can_fuse_on_gpu(False) + torch._C._jit_set_texpr_fuser_enabled(False) + torch._C._jit_set_nvfuser_enabled(True) + torch._C._debug_set_autodiff_subgraph_inlining(False) + else: + # legacy pytorch fuser + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + torch._C._jit_override_can_fuse_on_cpu(True) + torch._C._jit_override_can_fuse_on_gpu(True) + + _warmup_jit_function() + + +def _warmup_jit_function(): + """Compilie JIT functions before the main training steps""" + args = get_args() + if args.bf16: + dtype = torch.bfloat16 + elif args.fp16: + dtype = torch.float16 + else: + dtype = torch.float32 + + # Warmup fused bias+gelu + bias = torch.rand( + args.ffn_hidden_size // args.tensor_model_parallel_size, + dtype=dtype, + device="cuda", + ) + input = torch.rand( + ( + args.seq_length, + args.micro_batch_size, + args.ffn_hidden_size // args.tensor_model_parallel_size, + ), + dtype=dtype, + device="cuda", + ) + # Warmup JIT fusions with the input grad_enable state of both forward + # prop and recomputation + for bias_grad, input_grad in zip([True, True], [False, True]): + bias.requires_grad, input.requires_grad = bias_grad, input_grad + for _ in range(5): + output = bias_gelu(bias, input) + del bias, input, output + + # Warmup fused bias+dropout+add + if args.sequence_parallel: + seq_length = args.seq_length // mpu.get_tensor_model_parallel_world_size() + else: + seq_length = args.seq_length + input = torch.rand( + (seq_length, args.micro_batch_size, args.hidden_size), + dtype=dtype, + device="cuda", + ) + residual = torch.rand( + (seq_length, args.micro_batch_size, args.hidden_size), + dtype=dtype, + device="cuda", + ) + bias = torch.rand((args.hidden_size), dtype=dtype, device="cuda").expand_as( + residual + ) + dropout_rate = 0.1 + # Warmup JIT fusions with the input grad_enable state of both forward + # prop and recomputation + for input_grad, bias_grad, residual_grad in zip( + [False, True], [True, True], [True, True] + ): + input.requires_grad = input_grad + bias.requires_grad = bias_grad + residual.requires_grad = residual_grad + for _ in range(5): + output = bias_dropout_add_fused_train(input, bias, residual, dropout_rate) + del bias, input, residual, output + torch.cuda.empty_cache() diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/log_handler.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/log_handler.py new file mode 100644 index 0000000..06f5d18 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/log_handler.py @@ -0,0 +1,24 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import sys +from logging import LogRecord, StreamHandler + +BLACKLISTED_MODULES = ["torch.distributed"] + + +class CustomHandler(StreamHandler): + """ + Custom handler to filter out logging from code outside of + Megatron Core, and dump to stdout. + """ + + def __init__(self): + super().__init__(stream=sys.stdout) + + def filter(self, record: LogRecord) -> bool: + # Prevent log entries that come from the blacklisted modules + # through (e.g., PyTorch Distributed). + for blacklisted_module in BLACKLISTED_MODULES: + if record.name.startswith(blacklisted_module): + return False + return True diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/microbatches.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/microbatches.py new file mode 100644 index 0000000..729202e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/microbatches.py @@ -0,0 +1,145 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Megatron number of micro-batches calculators.""" + +from abc import ABC +from abc import abstractmethod + + +def build_num_microbatches_calculator(args): + + # Constant num micro-batches. + if args.rampup_batch_size is None: + num_microbatches_calculator = ConstantNumMicroBatches( + args.global_batch_size, args.micro_batch_size, + args.data_parallel_size) + if args.rank == 0: + print('setting number of micro-batches to constant {}'.format( + num_microbatches_calculator.get()), flush=True) + + else: + assert len(args.rampup_batch_size) == 3, 'expected the following ' \ + 'format: --rampup-batch-size ' \ + ' ' + start_batch_size = int(args.rampup_batch_size[0]) + batch_size_increment = int(args.rampup_batch_size[1]) + ramup_samples = int(args.rampup_batch_size[2]) + if args.rank == 0: + print('will use batch size rampup starting from global batch ' + 'size {} to global batch size {} with batch size increments ' + '{} over {} samples.'.format(start_batch_size, + args.global_batch_size, + batch_size_increment, + ramup_samples), flush=True) + num_microbatches_calculator = RampupBatchsizeNumMicroBatches( + start_batch_size, batch_size_increment, ramup_samples, + args.global_batch_size, args.micro_batch_size, + args.data_parallel_size) + + return num_microbatches_calculator + + +class NumMicroBatchesCalculator(ABC): + + def __init__(self): + self.num_micro_batches = None + self.current_global_batch_size = None + + def get(self): + return self.num_micro_batches + + def get_current_global_batch_size(self): + return self.current_global_batch_size + + @abstractmethod + def update(self, consumed_samples, consistency_check): + pass + + +class ConstantNumMicroBatches(NumMicroBatchesCalculator): + + def __init__(self, global_batch_size, micro_batch_size, data_parallel_size): + micro_batch_times_data_parallel = micro_batch_size * \ + data_parallel_size + assert global_batch_size % micro_batch_times_data_parallel == 0, \ + 'global batch size ({}) is not divisible by micro batch size ({})' \ + ' times data parallel size ({})'.format(global_batch_size, + micro_batch_size, + data_parallel_size) + self.num_micro_batches = global_batch_size // \ + micro_batch_times_data_parallel + assert self.num_micro_batches >= 1 + self.current_global_batch_size = global_batch_size + + def update(self, consumed_samples, consistency_check): + pass + + +class RampupBatchsizeNumMicroBatches(NumMicroBatchesCalculator): + + def __init__(self, start_batch_size, batch_size_increment, ramup_samples, + global_batch_size, micro_batch_size, data_parallel_size): + """Batch size ramp up. + Over + steps = (global-batch-size - start-batch-size) / batch_size_increment + increment batch size from start-batch-size to global-batch-size using + rampup-samples / steps + samples. + + Args: + start_batch_size: global batch size to start with + batch_size_increment: global batch size increments + ramup_samples: number of samples to use ramp up global + batch size from `start_batch_size` to `global_batch_size` + global_batch_size: global batch size post rampup + micro_batch_size: micro batch size + data_parallel_size: data parallel size. + """ + + self.micro_batch_size = micro_batch_size + self.data_parallel_size = data_parallel_size + self.micro_batch_times_data_parallel_size = self.micro_batch_size * \ + self.data_parallel_size + assert self.micro_batch_times_data_parallel_size > 0 + + assert start_batch_size > 0 + self.start_batch_size = start_batch_size + + assert global_batch_size > 0 + self.global_batch_size = global_batch_size + diff_batch_size = self.global_batch_size - self.start_batch_size + assert diff_batch_size >= 0 + assert batch_size_increment > 0 + self.batch_size_increment = batch_size_increment + assert diff_batch_size % batch_size_increment == 0, 'expected ' \ + 'global batch size interval ({}) to be divisible by global batch ' \ + 'size increment ({})'.format(diff_batch_size, batch_size_increment) + + num_increments = diff_batch_size // self.batch_size_increment + self.ramup_samples = ramup_samples + assert self.ramup_samples >= 0 + self.rampup_samples_per_increment = self.ramup_samples / num_increments + + # Initialize number of microbatches. + self.update(0, False) + + + def update(self, consumed_samples, consistency_check): + + if consumed_samples > self.ramup_samples: + self.current_global_batch_size = self.global_batch_size + else: + steps = int(consumed_samples / self.rampup_samples_per_increment) + self.current_global_batch_size = self.start_batch_size + \ + steps * self.batch_size_increment + assert self.current_global_batch_size <= self.global_batch_size + + if consistency_check: + assert self.current_global_batch_size % \ + self.micro_batch_times_data_parallel_size == 0, 'current global ' \ + 'batch size ({}) is not divisible by micro-batch-size ({}) times' \ + 'data parallel size ({})'.format(self.current_global_batch_size, + self.micro_batch_size, + self.data_parallel_size) + self.num_micro_batches = self.current_global_batch_size // \ + self.micro_batch_times_data_parallel_size diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/optimizer_param_scheduler.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/optimizer_param_scheduler.py new file mode 100644 index 0000000..54a45ef --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/optimizer_param_scheduler.py @@ -0,0 +1,230 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Learning rate decay and weight decay incr functions.""" + +import math + +from .utils import print_rank_0 + +class OptimizerParamScheduler(object): + """Anneals learning rate and weight decay""" + + def __init__(self, optimizer, init_lr, max_lr, min_lr, + lr_warmup_steps, lr_decay_steps, lr_decay_style, + start_wd, end_wd, wd_incr_steps, wd_incr_style, + use_checkpoint_opt_param_scheduler=True, + override_opt_param_scheduler=False): + + # Class values. + self.optimizer = optimizer + + self.init_lr = init_lr + self.max_lr = float(max_lr) + self.min_lr = min_lr + assert self.min_lr >= 0.0 + assert self.max_lr >= self.min_lr + assert self.init_lr <= self.max_lr + + self.lr_warmup_steps = lr_warmup_steps + self.num_steps = 0 + self.lr_decay_steps = lr_decay_steps + assert self.lr_decay_steps > 0 + assert self.lr_warmup_steps < self.lr_decay_steps + + self.lr_decay_style = lr_decay_style + + self.start_wd = start_wd + self.end_wd = end_wd + assert self.start_wd >= 0.0 + assert self.end_wd >= self.start_wd + self.wd_incr_steps = wd_incr_steps + self.wd_incr_style = wd_incr_style + + self.override_opt_param_scheduler = override_opt_param_scheduler + self.use_checkpoint_opt_param_scheduler = use_checkpoint_opt_param_scheduler + if self.override_opt_param_scheduler: + assert not self.use_checkpoint_opt_param_scheduler, 'both override and '\ + 'use-checkpoint are set.' + + # Set the learning rate + self.step(0) + print_rank_0('> learning rate decay style: {}'.format(self.lr_decay_style)) + + + def get_wd(self): + """ Weight decay incr functions""" + if self.num_steps > self.wd_incr_steps: + return self.end_wd + + if self.wd_incr_style == 'constant': + assert self.start_wd == self.end_wd + return self.end_wd + + incr_ratio = float(self.num_steps) / float(self.wd_incr_steps) + assert incr_ratio >= 0.0 + assert incr_ratio <= 1.0 + delta_wd = self.end_wd - self.start_wd + + if self.wd_incr_style == 'linear': + coeff = incr_ratio + elif self.wd_incr_style == 'cosine': + coeff = 0.5 * (math.cos(math.pi * (1 - incr_ratio)) + 1.0) + else: + raise Exception('{} weight decay increment style is not supported.'.format( + self.wd_incr_style)) + + return self.start_wd + coeff * delta_wd + + + def get_lr(self, param_group): + """Learning rate decay functions from: + https://openreview.net/pdf?id=BJYwwY9ll pg. 4""" + + max_lr = param_group.get('max_lr', self.max_lr) + min_lr = param_group.get('min_lr', self.min_lr) + + # Use linear warmup for the initial part. + if self.lr_warmup_steps > 0 and self.num_steps <= self.lr_warmup_steps: + return ( + self.init_lr + + ( + (max_lr - self.init_lr) + * float(self.num_steps) + / float(self.lr_warmup_steps) + ) + ) + + # If the learning rate is constant, just return the initial value. + if self.lr_decay_style == 'constant': + return max_lr + + # For any steps larger than `self.lr_decay_steps`, use `min_lr`. + if self.num_steps > self.lr_decay_steps: + return min_lr + + # If we are done with the warmup period, use the decay style. + if self.lr_decay_style == 'inverse-square-root': + warmup_steps = max(self.lr_warmup_steps, 1) + num_steps = max(self.num_steps, 1) + lr = max_lr * warmup_steps ** 0.5 / (num_steps ** 0.5) + return max(min_lr, lr) + + num_steps_ = self.num_steps - self.lr_warmup_steps + decay_steps_ = self.lr_decay_steps - self.lr_warmup_steps + decay_ratio = float(num_steps_) / float(decay_steps_) + assert decay_ratio >= 0.0 + assert decay_ratio <= 1.0 + delta_lr = max_lr - min_lr + + if self.lr_decay_style == 'linear': + coeff = (1.0 - decay_ratio) + elif self.lr_decay_style == 'cosine': + coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0) + else: + raise Exception('{} decay style is not supported.'.format( + self.lr_decay_style)) + + return min_lr + coeff * delta_lr + + + def step(self, increment): + """Set lr for all parameters groups.""" + self.num_steps += increment + new_wd = self.get_wd() + for param_group in self.optimizer.param_groups: + new_lr = self.get_lr(param_group) + param_group['lr'] = new_lr * param_group.get('lr_mult', 1.0) + param_group['weight_decay'] = new_wd * param_group.get('wd_mult', 1.0) + + + def state_dict(self): + state_dict = { + 'max_lr': self.max_lr, + 'lr_warmup_steps': self.lr_warmup_steps, + 'num_steps': self.num_steps, + 'lr_decay_style': self.lr_decay_style, + 'lr_decay_steps': self.lr_decay_steps, + 'min_lr': self.min_lr, + 'start_wd': self.start_wd, + 'end_wd': self.end_wd, + 'wd_incr_style': self.wd_incr_style, + 'wd_incr_steps': self.wd_incr_steps + } + return state_dict + + + def _check_and_set(self, cls_value, sd_value, name): + """Auxiliary function for checking the values in the checkpoint and + setting them.""" + if self.override_opt_param_scheduler: + print_rank_0(' > overriding {} value to {}'.format(name, cls_value)) + return cls_value + + if not self.use_checkpoint_opt_param_scheduler: + assert cls_value == sd_value, \ + f'OptimizerParamScheduler: class input value {cls_value} and checkpoint' \ + f'value {sd_value} for {name} do not match' + print_rank_0(' > using checkpoint value {} for {}'.format(sd_value, + name)) + return sd_value + + + def load_state_dict(self, sd): + + if 'start_lr' in sd: + max_lr_ = sd['start_lr'] + else: + max_lr_ = sd['max_lr'] + self.max_lr = self._check_and_set(self.max_lr, max_lr_, + 'learning rate') + + self.min_lr = self._check_and_set(self.min_lr, sd['min_lr'], + 'minimum learning rate') + + if 'warmup_iter' in sd: + lr_warmup_steps_ = sd['warmup_iter'] + elif 'warmup_steps' in sd: + lr_warmup_steps_ = sd['warmup_steps'] + else: + lr_warmup_steps_ = sd['lr_warmup_steps'] + self.lr_warmup_steps = self._check_and_set(self.lr_warmup_steps, + lr_warmup_steps_, + 'warmup iterations') + + if 'end_iter' in sd: + lr_decay_steps_ = sd['end_iter'] + elif 'decay_steps' in sd: + lr_decay_steps_ = sd['decay_steps'] + else: + lr_decay_steps_ = sd['lr_decay_steps'] + self.lr_decay_steps = self._check_and_set(self.lr_decay_steps, lr_decay_steps_, + 'total number of iterations') + + if 'decay_style' in sd: + lr_decay_style_ = sd['decay_style'] + else: + lr_decay_style_ = sd['lr_decay_style'] + self.lr_decay_style = self._check_and_set(self.lr_decay_style, + lr_decay_style_, + 'learning rate decay style') + + if 'num_iters' in sd: + num_steps = sd['num_iters'] + else: + num_steps = sd['num_steps'] + self.step(increment=num_steps) + + + if 'start_wd' in sd: + self.start_wd = self._check_and_set(self.start_wd, + sd['start_wd'], + "start weight decay") + self.end_wd = self._check_and_set(self.end_wd, + sd['end_wd'], + "end weight decay") + self.wd_incr_steps = self._check_and_set(self.wd_incr_steps, + sd['wd_incr_steps'], + "total number of weight decay iterations") + self.wd_incr_style = self._check_and_set(self.wd_incr_style, + sd['wd_incr_style'], + "weight decay incr style") \ No newline at end of file diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/theoretical_memory_usage.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/theoretical_memory_usage.py new file mode 100644 index 0000000..f9b7503 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/theoretical_memory_usage.py @@ -0,0 +1,187 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +"""Computes theoretical memory footprint for model training.""" + + +import math + +NUM_BYTES_IN_MEGABYTE = 1024 * 1024 + + +def compute_weight_and_optimizer_memory(args, verbose=False): + # Attention projection size. + query_projection_size = args.kv_channels * args.num_attention_heads + query_projection_to_hidden_size_ratio = query_projection_size / args.hidden_size + # Group Query Attention. + if not args.group_query_attention: + args.num_query_groups = args.num_attention_heads + # MoE. + num_experts = 1 if args.num_experts is None else args.num_experts + gated_linear_multiplier = 3 / 2 if args.swiglu else 1 + num_parameters_in_transformer_layers = ( + 2 + * args.num_layers + * args.hidden_size + * args.hidden_size + * ( + # Attention. + ( + (1 + (args.num_query_groups / args.num_attention_heads)) + * query_projection_to_hidden_size_ratio + ) + # MLP. + + ((args.ffn_hidden_size / args.hidden_size) * num_experts * gated_linear_multiplier) + # Transformer layernorms. + + (2 / args.hidden_size) + # Final layernorm. + + (1 / (args.num_layers * args.hidden_size)) + ) + ) + embedding_size = args.hidden_size * args.padded_vocab_size + if args.untie_embeddings_and_output_weights: + num_parameters_in_embedding_layers = 2 * embedding_size + else: + num_parameters_in_embedding_layers = embedding_size + num_total_parameters = num_parameters_in_transformer_layers + num_parameters_in_embedding_layers + if verbose: + print( + f"Number of parameters in transformer layers in billions: " + f"{num_parameters_in_transformer_layers / 10**9: .2f}" + ) + print( + f"Number of parameters in embedding layers in billions: " + f"{num_parameters_in_embedding_layers / 10**9:.2f}" + ) + print(f"Total number of parameters in billions: {num_total_parameters / 10**9:.2f}") + + # Most loaded model shard has (1/pp_size transformer layers + 1 embedding layer) / tp_size. + num_parameters_on_most_loaded_model_shard = ( + (num_parameters_in_transformer_layers / args.pipeline_model_parallel_size) + embedding_size + ) / args.tensor_model_parallel_size + if args.untie_embeddings_and_output_weights and args.pipeline_model_parallel_size == 1: + num_parameters_on_most_loaded_model_shard += ( + embedding_size / args.tensor_model_parallel_size + ) + if verbose: + print( + f"Number of parameters in most loaded shard in billions: " + f"{num_parameters_on_most_loaded_model_shard / 10**9:.4f}" + ) + + if args.pipeline_model_parallel_size > 1: + # Other shards just have (1/pp_size transformer layers) / tp_size. + num_parameters_on_other_model_shards = num_parameters_in_transformer_layers / ( + args.pipeline_model_parallel_size * args.tensor_model_parallel_size + ) + if verbose: + print( + f"Number of parameters in other shards in billions: " + f"{num_parameters_on_other_model_shards / 10**9:.4f}" + ) + + num_bytes_per_parameter = ( + 18 if not args.use_distributed_optimizer else 6 + (12 / args.data_parallel_size) + ) + weight_and_optimizer_memory = ( + num_parameters_on_most_loaded_model_shard * num_bytes_per_parameter + ) + + return weight_and_optimizer_memory + + +def compute_activation_memory(args, num_microbatches, verbose=False): + # Using formula in Table 2 of https://arxiv.org/pdf/2205.05198.pdf. + # We are trying to compute the maximum activation footprint, so all calculations in this + # function are for the first pipeline stage. + + # TODO: This function needs to take into account query_projection_size potentially being + # different from hidden_size. + + # Memory footprint from transformer layer (self-attention and MLP). + activation_memory = (args.seq_length * args.micro_batch_size * args.hidden_size) * ( + 18 + (4 * (args.ffn_hidden_size / args.hidden_size)) + ) + if verbose: + print( + f"Activation memory footprint per transformer layer: " + f"{activation_memory / NUM_BYTES_IN_MEGABYTE / args.tensor_model_parallel_size:.1f} MB" + ) + activation_memory *= args.num_layers + + # Now add activation memory required for input embeddings, last LayerNorm and output layer. + + # Input to embedding (pp_size microbatches in flight). + activation_memory += ( + 8 * args.seq_length * args.micro_batch_size * args.pipeline_model_parallel_size + ) + # Dropout in embedding layer (pp_size microbatches in flight). + activation_memory += ( + args.seq_length + * args.micro_batch_size + * args.hidden_size + * args.pipeline_model_parallel_size + ) + + # Multiply by interleaved PP memory factor. + if args.virtual_pipeline_model_parallel_size is not None: + interleaved_schedule_memory_penalty = 1 + ( + (args.pipeline_model_parallel_size - 1) + / (args.pipeline_model_parallel_size * args.virtual_pipeline_model_parallel_size) + ) + in_flight_microbatches = math.ceil( + interleaved_schedule_memory_penalty * args.pipeline_model_parallel_size + ) + if verbose: + print( + f"Memory penalty from interleaved schedule: {interleaved_schedule_memory_penalty:.2f}" + ) + print(f"Number of in-flight microbatches: {in_flight_microbatches}") + activation_memory *= interleaved_schedule_memory_penalty + + # If using non-interleaved schedule, number of microbatches in pipeline can be less than pp_size, + # so discount accordingly. + if args.virtual_pipeline_model_parallel_size is None and args.pipeline_model_parallel_size > 1: + if num_microbatches is not None: + activation_memory *= min(1, num_microbatches / args.pipeline_model_parallel_size) + in_flight_microbatches = min(num_microbatches, args.pipeline_model_parallel_size) + else: + in_flight_microbatches = args.pipeline_model_parallel_size + if verbose: + print(f"Number of in-flight microbatches: {in_flight_microbatches}") + + if args.pipeline_model_parallel_size == 1: + # Inputs to output layer and CE loss. + activation_memory += ( + args.seq_length + * args.micro_batch_size + * args.hidden_size + * 4 + * (1 + (args.padded_vocab_size / args.hidden_size)) + ) + + # Activation memory is partitioned by TP size due to tensor and sequence model parallelism. + return activation_memory / args.tensor_model_parallel_size + + +def report_theoretical_memory(args, num_microbatches=None, verbose=False): + weight_and_optimizer_memory = ( + compute_weight_and_optimizer_memory(args, verbose=verbose) / NUM_BYTES_IN_MEGABYTE + ) + + # Formulae here assume sequence parallelism and selective activation recomputation. + if not args.sequence_parallel or args.recompute_granularity != 'selective': + print( + f"Theoretical memory footprints: weight and optimizer={weight_and_optimizer_memory:.2f} MB" + ) + return + + activation_memory = ( + compute_activation_memory(args, num_microbatches=num_microbatches, verbose=verbose) + / NUM_BYTES_IN_MEGABYTE + ) + total_memory = weight_and_optimizer_memory + activation_memory + + print( + f"Theoretical memory footprints: weight and optimizer={weight_and_optimizer_memory:.2f} MB, " + f"activation={activation_memory:.2f} MB, total={total_memory:.2f} MB\n" + ) diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/tokenizer/__init__.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/tokenizer/__init__.py new file mode 100644 index 0000000..59ceb33 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/tokenizer/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + + +from .tokenizer import build_tokenizer diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/tokenizer/bert_tokenization.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/tokenizer/bert_tokenization.py new file mode 100644 index 0000000..642041e --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/tokenizer/bert_tokenization.py @@ -0,0 +1,431 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tokenization classes.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import re +import unicodedata +import six + + +def validate_case_matches_checkpoint(do_lower_case, init_checkpoint): + """Checks whether the casing config is consistent with the checkpoint name.""" + + # The casing has to be passed in by the user and there is no explicit check + # as to whether it matches the checkpoint. The casing information probably + # should have been stored in the bert_config.json file, but it's not, so + # we have to heuristically detect it to validate. + + if not init_checkpoint: + return + + m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint) + if m is None: + return + + model_name = m.group(1) + + lower_models = [ + "uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12", + "multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12" + ] + + cased_models = [ + "cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16", + "multi_cased_L-12_H-768_A-12" + ] + + is_bad_config = False + if model_name in lower_models and not do_lower_case: + is_bad_config = True + actual_flag = "False" + case_name = "lowercased" + opposite_flag = "True" + + if model_name in cased_models and do_lower_case: + is_bad_config = True + actual_flag = "True" + case_name = "cased" + opposite_flag = "False" + + if is_bad_config: + raise ValueError( + "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. " + "However, `%s` seems to be a %s model, so you " + "should pass in `--do_lower_case=%s` so that the fine-tuning matches " + "how the model was pre-training. If this error is wrong, please " + "just comment out this check." % (actual_flag, init_checkpoint, + model_name, case_name, opposite_flag)) + + +def convert_to_unicode(text): + """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" + if six.PY3: + if isinstance(text, str): + return text + elif isinstance(text, bytes): + return text.decode("utf-8", "ignore") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + elif six.PY2: + if isinstance(text, str): + return text.decode("utf-8", "ignore") + elif isinstance(text, unicode): + return text + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + else: + raise ValueError("Not running on Python2 or Python 3?") + + +def printable_text(text): + """Returns text encoded in a way suitable for print or `tf.logging`.""" + + # These functions want `str` for both Python2 and Python3, but in one case + # it's a Unicode string and in the other it's a byte string. + if six.PY3: + if isinstance(text, str): + return text + elif isinstance(text, bytes): + return text.decode("utf-8", "ignore") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + elif six.PY2: + if isinstance(text, str): + return text + elif isinstance(text, unicode): + return text.encode("utf-8") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + else: + raise ValueError("Not running on Python2 or Python 3?") + + +def load_vocab(vocab_file): + """Loads a vocabulary file into a dictionary.""" + vocab = collections.OrderedDict() + index = 0 + with open(vocab_file, "r", encoding = "utf-8") as reader: + while True: + token = convert_to_unicode(reader.readline()) + if not token: + break + token = token.strip() + vocab[token] = index + index += 1 + return vocab + + +def convert_by_vocab(vocab, items): + """Converts a sequence of [tokens|ids] using the vocab.""" + output = [] + for item in items: + output.append(vocab[item]) + return output + + +def convert_tokens_to_ids(vocab, tokens): + return convert_by_vocab(vocab, tokens) + + +def convert_ids_to_tokens(inv_vocab, ids): + return convert_by_vocab(inv_vocab, ids) + + +def whitespace_tokenize(text): + """Runs basic whitespace cleaning and splitting on a piece of text.""" + text = text.strip() + if not text: + return [] + tokens = text.split() + return tokens + + +class FullTokenizer(object): + """Runs end-to-end tokenziation.""" + + def __init__(self, vocab_file, do_lower_case=True): + self.vocab = load_vocab(vocab_file) + self.inv_vocab = {v: k for k, v in self.vocab.items()} + self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) + self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) + + def tokenize(self, text): + split_tokens = [] + for token in self.basic_tokenizer.tokenize(text): + for sub_token in self.wordpiece_tokenizer.tokenize(token): + split_tokens.append(sub_token) + + return split_tokens + + def convert_tokens_to_ids(self, tokens): + return convert_by_vocab(self.vocab, tokens) + + def convert_ids_to_tokens(self, ids): + return convert_by_vocab(self.inv_vocab, ids) + + @staticmethod + def convert_tokens_to_string(tokens, clean_up_tokenization_spaces=True): + """ Converts a sequence of tokens (string) in a single string. """ + + def clean_up_tokenization(out_string): + """ Clean up a list of simple English tokenization artifacts + like spaces before punctuations and abreviated forms. + """ + out_string = ( + out_string.replace(" .", ".") + .replace(" ?", "?") + .replace(" !", "!") + .replace(" ,", ",") + .replace(" ' ", "'") + .replace(" n't", "n't") + .replace(" 'm", "'m") + .replace(" 's", "'s") + .replace(" 've", "'ve") + .replace(" 're", "'re") + ) + return out_string + + text = ' '.join(tokens).replace(' ##', '').strip() + if clean_up_tokenization_spaces: + clean_text = clean_up_tokenization(text) + return clean_text + else: + return text + + def vocab_size(self): + return len(self.vocab) + + +class BasicTokenizer(object): + """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" + + def __init__(self, do_lower_case=True): + """Constructs a BasicTokenizer. + + Args: + do_lower_case: Whether to lower case the input. + """ + self.do_lower_case = do_lower_case + + def tokenize(self, text): + """Tokenizes a piece of text.""" + text = convert_to_unicode(text) + text = self._clean_text(text) + + # This was added on November 1st, 2018 for the multilingual and Chinese + # models. This is also applied to the English models now, but it doesn't + # matter since the English models were not trained on any Chinese data + # and generally don't have any Chinese data in them (there are Chinese + # characters in the vocabulary because Wikipedia does have some Chinese + # words in the English Wikipedia.). + text = self._tokenize_chinese_chars(text) + + orig_tokens = whitespace_tokenize(text) + split_tokens = [] + for token in orig_tokens: + if self.do_lower_case: + token = token.lower() + token = self._run_strip_accents(token) + split_tokens.extend(self._run_split_on_punc(token)) + + output_tokens = whitespace_tokenize(" ".join(split_tokens)) + return output_tokens + + def _run_strip_accents(self, text): + """Strips accents from a piece of text.""" + text = unicodedata.normalize("NFD", text) + output = [] + for char in text: + cat = unicodedata.category(char) + if cat == "Mn": + continue + output.append(char) + return "".join(output) + + def _run_split_on_punc(self, text): + """Splits punctuation on a piece of text.""" + chars = list(text) + i = 0 + start_new_word = True + output = [] + while i < len(chars): + char = chars[i] + if _is_punctuation(char): + output.append([char]) + start_new_word = True + else: + if start_new_word: + output.append([]) + start_new_word = False + output[-1].append(char) + i += 1 + + return ["".join(x) for x in output] + + def _tokenize_chinese_chars(self, text): + """Adds whitespace around any CJK character.""" + output = [] + for char in text: + cp = ord(char) + if self._is_chinese_char(cp): + output.append(" ") + output.append(char) + output.append(" ") + else: + output.append(char) + return "".join(output) + + def _is_chinese_char(self, cp): + """Checks whether CP is the codepoint of a CJK character.""" + # This defines a "chinese character" as anything in the CJK Unicode block: + # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + # + # Note that the CJK Unicode block is NOT all Japanese and Korean characters, + # despite its name. The modern Korean Hangul alphabet is a different block, + # as is Japanese Hiragana and Katakana. Those alphabets are used to write + # space-separated words, so they are not treated specially and handled + # like the all of the other languages. + if ((cp >= 0x4E00 and cp <= 0x9FFF) or # + (cp >= 0x3400 and cp <= 0x4DBF) or # + (cp >= 0x20000 and cp <= 0x2A6DF) or # + (cp >= 0x2A700 and cp <= 0x2B73F) or # + (cp >= 0x2B740 and cp <= 0x2B81F) or # + (cp >= 0x2B820 and cp <= 0x2CEAF) or + (cp >= 0xF900 and cp <= 0xFAFF) or # + (cp >= 0x2F800 and cp <= 0x2FA1F)): # + return True + + return False + + def _clean_text(self, text): + """Performs invalid character removal and whitespace cleanup on text.""" + output = [] + for char in text: + cp = ord(char) + if cp == 0 or cp == 0xfffd or _is_control(char): + continue + if _is_whitespace(char): + output.append(" ") + else: + output.append(char) + return "".join(output) + + +class WordpieceTokenizer(object): + """Runs WordPiece tokenziation.""" + + def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200): + self.vocab = vocab + self.unk_token = unk_token + self.max_input_chars_per_word = max_input_chars_per_word + + def tokenize(self, text): + """Tokenizes a piece of text into its word pieces. + + This uses a greedy longest-match-first algorithm to perform tokenization + using the given vocabulary. + + For example: + input = "unaffable" + output = ["un", "##aff", "##able"] + + Args: + text: A single token or whitespace separated tokens. This should have + already been passed through `BasicTokenizer. + + Returns: + A list of wordpiece tokens. + """ + + text = convert_to_unicode(text) + + output_tokens = [] + for token in whitespace_tokenize(text): + chars = list(token) + if len(chars) > self.max_input_chars_per_word: + output_tokens.append(self.unk_token) + continue + + is_bad = False + start = 0 + sub_tokens = [] + while start < len(chars): + end = len(chars) + cur_substr = None + while start < end: + substr = "".join(chars[start:end]) + if start > 0: + substr = "##" + substr + if substr in self.vocab: + cur_substr = substr + break + end -= 1 + if cur_substr is None: + is_bad = True + break + sub_tokens.append(cur_substr) + start = end + + if is_bad: + output_tokens.append(self.unk_token) + else: + output_tokens.extend(sub_tokens) + return output_tokens + + +def _is_whitespace(char): + """Checks whether `chars` is a whitespace character.""" + # \t, \n, and \r are technically contorl characters but we treat them + # as whitespace since they are generally considered as such. + if char == " " or char == "\t" or char == "\n" or char == "\r": + return True + cat = unicodedata.category(char) + if cat == "Zs": + return True + return False + + +def _is_control(char): + """Checks whether `chars` is a control character.""" + # These are technically control characters but we count them as whitespace + # characters. + if char == "\t" or char == "\n" or char == "\r": + return False + cat = unicodedata.category(char) + if cat in ("Cc", "Cf"): + return True + return False + + +def _is_punctuation(char): + """Checks whether `chars` is a punctuation character.""" + cp = ord(char) + # We treat all non-letter/number ASCII as punctuation. + # Characters such as "^", "$", and "`" are not in the Unicode + # Punctuation class but we treat them as punctuation anyways, for + # consistency. + if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or + (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): + return True + cat = unicodedata.category(char) + if cat.startswith("P"): + return True + return False diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/tokenizer/gpt2_tokenization.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/tokenizer/gpt2_tokenization.py new file mode 100644 index 0000000..3f37e44 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/tokenizer/gpt2_tokenization.py @@ -0,0 +1,321 @@ +# coding=utf-8 +# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tokenization classes for OpenAI GPT.""" + +from __future__ import (absolute_import, division, print_function, + unicode_literals) + +import sys +import json +import logging +import os +import regex as re +from io import open + +try: + from functools import lru_cache +except ImportError: + # Just a dummy decorator to get the checks to run on python2 + # because honestly I don't want to support a byte-level unicode BPE + # tokenizer on python 2 right now. + def lru_cache(): + return lambda func: func + + +logger = logging.getLogger(__name__) + +PRETRAINED_VOCAB_ARCHIVE_MAP = { + 'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json", +} +PRETRAINED_MERGES_ARCHIVE_MAP = { + 'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt", +} +PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = { + 'gpt2': 1024, +} +VOCAB_NAME = 'vocab.json' +MERGES_NAME = 'merges.txt' +SPECIAL_TOKENS_NAME = 'special_tokens.txt' + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + _chr = unichr if sys.version_info[0] == 2 else chr + bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + \ + list(range(ord("®"), ord("ÿ") + 1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8 + n) + n += 1 + cs = [_chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +class GPT2Tokenizer(object): + """ + GPT-2 BPE tokenizer. Peculiarities: + - Byte-level BPE + """ + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs): + """ + Instantiate a PreTrainedBertModel from a pre-trained model file. + Download and cache the pre-trained model file if needed. + """ + if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP: + vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path] + merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path] + special_tokens_file = None + else: + vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME) + merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME) + special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME) + if not os.path.exists(special_tokens_file): + special_tokens_file = None + else: + logger.info("loading special tokens file {}".format(special_tokens_file)) + # redirect to the cache, if necessary + try: + from .file_utils import cached_path + resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir) + resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir) + except EnvironmentError: + logger.error( + "Model name '{}' was not found in model name list ({}). " + "We assumed '{}' was a path or url but couldn't find files {} and {} " + "at this path or url.".format( + pretrained_model_name_or_path, + ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()), + pretrained_model_name_or_path, + vocab_file, merges_file)) + return None + if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file: + logger.info("loading vocabulary file {}".format(vocab_file)) + logger.info("loading merges file {}".format(merges_file)) + else: + logger.info("loading vocabulary file {} from cache at {}".format( + vocab_file, resolved_vocab_file)) + logger.info("loading merges file {} from cache at {}".format( + merges_file, resolved_merges_file)) + if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP: + # if we're using a pretrained model, ensure the tokenizer wont index sequences longer + # than the number of positional embeddings + max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path] + kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len) + # Instantiate tokenizer. + if special_tokens_file and 'special_tokens' not in kwargs: + special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1] + else: + special_tokens = kwargs.pop('special_tokens', []) + tokenizer = cls( + resolved_vocab_file, + resolved_merges_file, + special_tokens=special_tokens, + *inputs, + **kwargs) + return tokenizer + + def __init__(self, vocab_file, merges_file, errors='replace', + special_tokens=None, max_len=None): + self.max_len = max_len if max_len is not None else int(1e12) + self.encoder = json.load(open(vocab_file)) + self.decoder = {v: k for k, v in self.encoder.items()} + self.errors = errors # how to handle errors in decoding + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1] + bpe_merges = [tuple(merge.split()) for merge in bpe_data] + self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) + self.cache = {} + + # Should haved added re.IGNORECASE so BPE merges can happen for + # capitalized versions of contractions + self.pat = re.compile( + r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") + + self.special_tokens = {} + self.special_tokens_decoder = {} + self.set_special_tokens(special_tokens) + + def __len__(self): + return len(self.encoder) + len(self.special_tokens) + + def set_special_tokens(self, special_tokens): + """ Add a list of additional tokens to the encoder. + The additional tokens are indexed starting from the last index of the + current vocabulary in the order of the `special_tokens` list. + """ + if not special_tokens: + self.special_tokens = {} + self.special_tokens_decoder = {} + return + self.special_tokens = dict((tok, len(self.encoder) + i) + for i, tok in enumerate(special_tokens)) + self.special_tokens_decoder = {v: k for k, v in self.special_tokens.items()} + logger.info("Special tokens {}".format(self.special_tokens)) + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token) + pairs = get_pairs(word) + + if not pairs: + return token + + while True: + bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except BaseException: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word) - 1 and word[i + 1] == second: + new_word.append(first + second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def tokenize(self, text): + """ Tokenize a string. """ + bpe_tokens = [] + for token in re.findall(self.pat, text): + if sys.version_info[0] == 2: + token = ''.join(self.byte_encoder[ord(b)] for b in token) + else: + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def convert_tokens_to_ids(self, tokens): + """ Converts a sequence of tokens into ids using the vocab. """ + ids = [] + if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)): + if tokens in self.special_tokens: + return self.special_tokens[tokens] + else: + return self.encoder.get(tokens, 0) + for token in tokens: + if token in self.special_tokens: + ids.append(self.special_tokens[token]) + else: + ids.append(self.encoder.get(token, 0)) + if len(ids) > self.max_len: + logger.warning( + "Token indices sequence length is longer than the specified maximum " + " sequence length for this OpenAI GPT model ({} > {}). Running this" + " sequence through the model will result in indexing errors".format( + len(ids), self.max_len) + ) + return ids + + def convert_ids_to_tokens(self, ids, skip_special_tokens=False): + """Converts a sequence of ids in BPE tokens using the vocab.""" + tokens = [] + for i in ids: + if i in self.special_tokens_decoder: + if not skip_special_tokens: + tokens.append(self.special_tokens_decoder[i]) + else: + tokens.append(self.decoder[i]) + return tokens + + def encode(self, text): + return self.convert_tokens_to_ids(self.tokenize(text)) + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors) + return text + + def save_vocabulary(self, vocab_path): + """Save the tokenizer vocabulary and merge files to a directory.""" + if not os.path.isdir(vocab_path): + logger.error("Vocabulary path ({}) should be a directory".format(vocab_path)) + return + vocab_file = os.path.join(vocab_path, VOCAB_NAME) + merge_file = os.path.join(vocab_path, MERGES_NAME) + special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME) + + with open(vocab_file, 'w', encoding='utf-8') as f: + f.write(json.dumps(self.encoder, ensure_ascii=False)) + + index = 0 + with open(merge_file, "w", encoding="utf-8") as writer: + writer.write(u'#version: 0.2\n') + for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): + if index != token_index: + logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive." + " Please check that the tokenizer is not corrupted!".format(merge_file)) + index = token_index + writer.write(' '.join(bpe_tokens) + u'\n') + index += 1 + + index = len(self.encoder) + with open(special_tokens_file, 'w', encoding='utf-8') as writer: + for token, token_index in sorted(self.special_tokens.items(), key=lambda kv: kv[1]): + if index != token_index: + logger.warning("Saving special tokens vocabulary to {}: BPE indices are not consecutive." + " Please check that the tokenizer is not corrupted!".format(special_tokens_file)) + index = token_index + writer.write(token + u'\n') + index += 1 + + return vocab_file, merge_file, special_tokens_file diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/tokenizer/tokenizer.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/tokenizer/tokenizer.py new file mode 100644 index 0000000..1d60489 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/tokenizer/tokenizer.py @@ -0,0 +1,522 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +"""Megatron tokenizers.""" + +from abc import ABC +from abc import abstractmethod + +from megatron.core.datasets.megatron_tokenizer import MegatronTokenizer + +from .bert_tokenization import FullTokenizer as FullBertTokenizer +from .gpt2_tokenization import GPT2Tokenizer + + +def build_tokenizer(args): + """Initialize tokenizer.""" + if args.rank == 0: + print('> building {} tokenizer ...'.format(args.tokenizer_type), + flush=True) + + # Select and instantiate the tokenizer. + if args.tokenizer_type == 'BertWordPieceLowerCase': + assert args.vocab_file is not None + tokenizer = _BertWordPieceTokenizer(vocab_file=args.vocab_file, + lower_case=True, + vocab_extra_ids=args.vocab_extra_ids) + elif args.tokenizer_type == 'BertWordPieceCase': + assert args.vocab_file is not None + tokenizer = _BertWordPieceTokenizer(vocab_file=args.vocab_file, + lower_case=False, + vocab_extra_ids=args.vocab_extra_ids) + elif args.tokenizer_type == 'GPT2BPETokenizer': + assert args.vocab_file is not None + assert args.merge_file is not None + tokenizer = _GPT2BPETokenizer(args.vocab_file, args.merge_file) + elif args.tokenizer_type == 'SentencePieceTokenizer': + assert args.tokenizer_model is not None + tokenizer = _SentencePieceTokenizer(args.tokenizer_model, vocab_extra_ids=args.vocab_extra_ids) + elif args.tokenizer_type == 'GPTSentencePieceTokenizer': + assert args.tokenizer_model is not None + tokenizer = _GPTSentencePieceTokenizer(args.tokenizer_model) + elif args.tokenizer_type == 'Llama2Tokenizer': + assert args.tokenizer_model is not None + tokenizer = _Llama2Tokenizer(args.tokenizer_model) + elif args.tokenizer_type == 'NullTokenizer': + assert args.vocab_size is not None + tokenizer = _NullTokenizer(args.vocab_size) + else: + raise NotImplementedError('{} tokenizer is not ' + 'implemented.'.format(args.tokenizer_type)) + + # Add vocab size (if not already set from a checkpoint). + if getattr(args, "padded_vocab_size", None) is None: + args.padded_vocab_size = _vocab_size_with_padding(tokenizer.vocab_size, + args) + + return tokenizer + + +def _vocab_size_with_padding(orig_vocab_size, args): + """Pad vocab size so it is divisible by model parallel size and + still having GPU friendly size.""" + + after = orig_vocab_size + multiple = args.make_vocab_size_divisible_by * \ + args.tensor_model_parallel_size + while (after % multiple) != 0: + after += 1 + if args.rank == 0: + print(' > padded vocab (size: {}) with {} dummy tokens ' + '(new size: {})'.format( + orig_vocab_size, after - orig_vocab_size, after), flush=True) + return after + + +class _BertWordPieceTokenizer(MegatronTokenizer): + """Original BERT wordpiece tokenizer.""" + + def __init__(self, vocab_file, lower_case=True, vocab_extra_ids=0): + super().__init__(vocab_file, lower_case=lower_case, vocab_extra_ids=vocab_extra_ids) + self.tokenizer = FullBertTokenizer(vocab_file, do_lower_case=lower_case) + self.cls_id = self.tokenizer.vocab['[CLS]'] + self.sep_id = self.tokenizer.vocab['[SEP]'] + self.pad_id = self.tokenizer.vocab['[PAD]'] + self.mask_id = self.tokenizer.vocab['[MASK]'] + self._additional_special_tokens = [] + + # (dsachan) Add BOS and EOS tokens + SPECIAL_TOKENS = {'eos_token': '[EOS]', + 'bos_token': '[BOS]'} + self._bos_token = '[BOS]' + self.add_token(self._bos_token) + self._bos_token_id = self.vocab.get(self._bos_token) + + self._eos_token = '[EOS]' + self.add_token(self._eos_token) + self._eos_token_id = self.vocab.get(self._eos_token) + + # (dsachan) Add additional special tokens + # These can be used as sentinel tokens in T5 model inputs + additional_special_tokens = [] + additional_special_tokens.extend( + ["".format(i) for i in range(vocab_extra_ids)]) + self.add_additional_special_tokens(additional_special_tokens) + + def add_token(self, token): + if token not in self.vocab: + self.inv_vocab[self.vocab_size] = token + # self.vocab_size comes from len(vocab) + # and it will increase as we add elements + self.vocab[token] = self.vocab_size + + def add_additional_special_tokens(self, tokens_list): + setattr(self, "additional_special_tokens", tokens_list) + for value in tokens_list: + self.add_token(value) + + @property + def vocab_size(self): + return self.tokenizer.vocab_size() + + @property + def vocab(self): + return self.tokenizer.vocab + + @property + def inv_vocab(self): + return self.tokenizer.inv_vocab + + def tokenize(self, text): + text_tokens = self.tokenizer.tokenize(text) + return self.tokenizer.convert_tokens_to_ids(text_tokens) + + def decode(self, ids): + tokens = self.tokenizer.convert_ids_to_tokens(ids) + return self.tokenizer.convert_tokens_to_string(tokens) + + def decode_token_ids(self, token_ids): + tokens = self.tokenizer.convert_ids_to_tokens(token_ids) + exclude_list = ['[PAD]', '[CLS]'] + non_pads = [t for t in tokens if t not in exclude_list] + + result = "" + for s in non_pads: + if s.startswith("##"): + result += s[2:] + else: + result += " " + s + + return result + + @property + def cls(self): + return self.cls_id + + @property + def sep(self): + return self.sep_id + + @property + def pad(self): + return self.pad_id + + @property + def mask(self): + return self.mask_id + + @property + def bos(self): + """ Id of the beginning of sentence token in the vocabulary.""" + return self._bos_token_id + + @property + def eos(self): + """ Id of the end of sentence token in the vocabulary.""" + return self._eos_token_id + + @property + def bos_token(self): + """ Beginning of sentence token id """ + return self._bos_token + + @property + def eos_token(self): + """ End of sentence token id """ + return self._eos_token + + @property + def additional_special_tokens(self): + """ All the additional special tokens you may want to use (list of strings).""" + return self._additional_special_tokens + + @property + def additional_special_tokens_ids(self): + """ Ids of all the additional special tokens in the vocabulary (list of integers).""" + return [self.vocab.get(token) for token in self._additional_special_tokens] + + @additional_special_tokens.setter + def additional_special_tokens(self, value): + self._additional_special_tokens = value + + +class _GPT2BPETokenizer(MegatronTokenizer): + """Original GPT2 BPE tokenizer.""" + + def __init__(self, vocab_file, merge_file): + super().__init__(vocab_file, merge_file) + + self.tokenizer = GPT2Tokenizer(vocab_file, merge_file, errors='replace', + special_tokens=[], max_len=None) + self.eod_id = self.tokenizer.encoder['<|endoftext|>'] + + @property + def vocab_size(self): + return len(self.tokenizer.encoder) + + @property + def vocab(self): + return self.tokenizer.encoder + + @property + def inv_vocab(self): + return self.tokenizer.decoder + + def tokenize(self, text): + return self.tokenizer.encode(text) + + def detokenize(self, token_ids): + return self.tokenizer.decode(token_ids) + + @property + def eod(self): + return self.eod_id + + +class _SentencePieceTokenizer(MegatronTokenizer): + """SentencePieceTokenizer-Megatron wrapper""" + + def __init__(self, model_file, vocab_extra_ids=0): + super().__init__(model_file, vocab_extra_ids=vocab_extra_ids) + + import sentencepiece + self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=model_file) + self._initalize(vocab_extra_ids) + + def _populate_vocab(self): + self._vocab = {} + self._inv_vocab = {} + + for i in range(len(self.tokenizer)): + t = self.tokenizer.id_to_piece(i) + self._inv_vocab[i] = t + self._vocab[t] = i + + def _initalize(self, vocab_extra_ids): + self._populate_vocab() + self._special_tokens = {} + self._inv_special_tokens = {} + + self._t5_tokens = [] + + def _add_special_token(t): + if t not in self._vocab: + next_id = len(self._vocab) + self._vocab[t] = next_id + self._inv_vocab[next_id] = t + self._special_tokens[t] = self._vocab[t] + self._inv_special_tokens[self._vocab[t]] = t + + _add_special_token('') + self._cls_id = self._vocab[''] + _add_special_token('') + self._sep_id = self._vocab[''] + _add_special_token('') + self._eod_id = self._vocab[''] + _add_special_token('') + self._mask_id = self._vocab[''] + + pad_id = self.tokenizer.pad_id() + try: + pad_token = self.tokenizer.id_to_piece(pad_id) + except IndexError: + pad_token = '' + _add_special_token(pad_token) + self._pad_id = self._vocab[pad_token] + + bos_id = self.tokenizer.bos_id() + try: + bos_token = self.tokenizer.id_to_piece(bos_id) + except IndexError: + bos_token = '' + _add_special_token(bos_token) + self._bos_id = self._vocab[bos_token] + + eos_id = self.tokenizer.eos_id() + try: + eos_token = self.tokenizer.id_to_piece(eos_id) + except IndexError: + eos_token = '' + _add_special_token(eos_token) + self._eos_id = self._vocab[eos_token] + + for i in range(vocab_extra_ids): + t = "".format(i) + _add_special_token(t) + self._t5_tokens += [t] + + @property + def vocab_size(self): + return len(self._vocab) + + @property + def vocab(self): + return self._vocab + + @property + def inv_vocab(self): + return self._inv_vocab + + @property + def decoder(self): + return self._inv_vocab + + @property + def encoder(self): + return self._vocab + + # From: + # https://github.com/NVIDIA/NeMo/blob/c8fa217e811d60d11d014827c7f3845ff6c99ae7/nemo/collections/common/tokenizers/sentencepiece_tokenizer.py#L89 + def tokenize(self, text): + ids = [] + idx = 0 + + while 1: + indices = {} + for token in self._special_tokens: + try: + indices[token] = text[idx:].index(token) + except ValueError: + continue + if len(indices) == 0: + break + + next_token = min(indices, key=indices.get) + next_idx = idx + indices[next_token] + + ids.extend(self.tokenizer.encode_as_ids(text[idx:next_idx])) + ids.append(self._special_tokens[next_token]) + idx = next_idx + len(next_token) + + ids.extend(self.tokenizer.encode_as_ids(text[idx:])) + return ids + + # From: + # https://github.com/NVIDIA/NeMo/blob/c8fa217e811d60d11d014827c7f3845ff6c99ae7/nemo/collections/common/tokenizers/sentencepiece_tokenizer.py#L125 + def detokenize(self, ids): + text = "" + last_i = 0 + + for i, id in enumerate(ids): + if id in self._inv_special_tokens: + text += self.tokenizer.decode_ids(ids[last_i:i]) + " " + text += self._inv_special_tokens[id] + " " + last_i = i + 1 + + text += self.tokenizer.decode_ids(ids[last_i:]) + return text + + @property + def cls(self): + return self._cls_id + + @property + def sep(self): + return self._sep_id + + @property + def pad(self): + return self._pad_id + + @property + def bos(self): + return self._bos_id + + @property + def eod(self): + return self._eod_id + + @property + def eos(self): + return self._eos_id + + @property + def mask(self): + return self._mask_id + + @property + def additional_special_tokens_ids(self): + return [self.vocab[k] for k in self._t5_tokens] + + +class _GPTSentencePieceTokenizer(_SentencePieceTokenizer): + """SentencePieceTokenizer-Megatron wrapper""" + + def __init__(self, model_file,): + super().__init__(model_file, vocab_extra_ids=0) + + def _initalize(self, vocab_extra_ids): + self._populate_vocab() + + self._pad_id = self.tokenizer.pad_id() + self._bos_id = self.tokenizer.bos_id() + self._eos_id = self.tokenizer.eos_id() + + def tokenize(self, text): + return self.tokenizer.encode_as_ids(text) + + def detokenize(self, ids): + return self.tokenizer.decode_ids(ids) + + @property + def cls(self): + return -1 + + @property + def sep(self): + return -1 + + @property + def mask(self): + return -1 + + @property + def eod(self): + return self._eos_id + + @property + def additional_special_tokens_ids(self): + return None + + +class _Llama2Tokenizer(_SentencePieceTokenizer): + """SentencePieceTokenizer-Megatron wrapper""" + + def __init__(self, model_file,): + super().__init__(model_file, vocab_extra_ids=0) + + def _initalize(self, vocab_extra_ids): + self._populate_vocab() + + # BOS / EOS token IDs + self.n_words: int = self.tokenizer.vocab_size() + self.bos_id: int = self.tokenizer.bos_id() + self.eos_id: int = self.tokenizer.eos_id() + self.pad_id: int = self.tokenizer.pad_id() + assert self.tokenizer.vocab_size() == self.tokenizer.get_piece_size() + + def tokenize(self, s: str, bos=True, eos=False): + '''Default args for text completion, not chat/dialog.''' + assert type(s) is str + t = self.tokenizer.encode(s) + if bos: + t = [self.bos_id] + t + if eos: + t = t + [self.eos_id] + return t + + def detokenize(self, ids): + return self.tokenizer.decode_ids(ids) + + @property + def cls(self): + return -1 + + @property + def sep(self): + return -1 + + @property + def mask(self): + return -1 + + @property + def eod(self): + return self.eos_id + + @property + def additional_special_tokens_ids(self): + return None + + +class _NullTokenizer: + def __init__(self, vocab_size): + vocab_size = int(vocab_size) + self._eos_id = vocab_size + self.vocab_size = vocab_size+1 + + def tokenize(self, text): + return [int(x) for x in text.split(' ')] + + def detokenize(self, ids): + text = [str(x) for x in ids] + return ' '.join(text) + + @property + def cls(self): + return -1 + + @property + def sep(self): + return -1 + + @property + def mask(self): + return -1 + + @property + def eod(self): + return self._eos_id + + @property + def additional_special_tokens_ids(self): + return None diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/training.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/training.py new file mode 100644 index 0000000..f0194ef --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/training.py @@ -0,0 +1,1458 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Pretrain utilities.""" + +import gc +import dataclasses +from datetime import datetime +import math +import logging +import os +import sys +from .log_handler import CustomHandler +# Make default logging level INFO, but filter out all log messages not from MCore. +logging.basicConfig(handlers=[CustomHandler()], level=logging.INFO) +from .theoretical_memory_usage import report_theoretical_memory +import time +# The earliest we can measure the start time. +_TRAIN_START_TIME = time.time() +import torch + +from megatron.core import mpu, tensor_parallel +from megatron.core.utils import get_model_config, StragglerDetector +from megatron.training.checkpointing import load_checkpoint +from megatron.training.checkpointing import save_checkpoint +from megatron.legacy.model import Float16Module +from megatron.core.distributed import DistributedDataParallelConfig +from megatron.core.distributed import DistributedDataParallel as DDP +from megatron.core.distributed import finalize_model_grads +from megatron.core.enums import ModelType +from megatron.core.optimizer import get_megatron_optimizer, OptimizerConfig +from megatron.training.initialize import initialize_megatron +from megatron.training.initialize import write_args_to_tensorboard +from megatron.training.initialize import set_jit_fusion_options +from megatron.training.optimizer_param_scheduler import OptimizerParamScheduler +from megatron.legacy.data.data_samplers import build_pretraining_data_loader +from megatron.core.transformer.moe.moe_utils import track_moe_metrics +from megatron.core.pipeline_parallel import get_forward_backward_func + +from .utils import ( + calc_params_l2_norm, + check_adlr_autoresume_termination, + is_last_rank, + print_rank_0, + print_rank_last, + report_memory, + unwrap_model) +from .global_vars import ( + get_args, + get_signal_handler, + get_timers, + get_tensorboard_writer, + get_wandb_writer, + get_one_logger, + get_current_global_batch_size, + get_num_microbatches, + update_num_microbatches) + + +stimer = StragglerDetector() + +def print_datetime(string): + """Note that this call will sync across all ranks.""" + torch.distributed.barrier() + time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S') + print_rank_0('[' + string + '] datetime: {} '.format(time_str)) + + +def num_floating_point_operations(args, batch_size): + # Attention projection size. + query_projection_size = args.kv_channels * args.num_attention_heads + query_projection_to_hidden_size_ratio = query_projection_size / args.hidden_size + # Group Query Attention. + if not args.group_query_attention: + args.num_query_groups = args.num_attention_heads + # MoE. + num_experts_routed_to = 1 if args.num_experts is None else args.moe_router_topk + gated_linear_multiplier = 3 / 2 if args.swiglu else 1 + return ( + 12 + * batch_size + * args.seq_length + * args.num_layers + * args.hidden_size + * args.hidden_size + * ( + # Attention. + ( + ( + 1 + + (args.num_query_groups / args.num_attention_heads) + + (args.seq_length / args.hidden_size) + ) * query_projection_to_hidden_size_ratio + ) + # MLP. + + ( + (args.ffn_hidden_size / args.hidden_size) + * num_experts_routed_to + * gated_linear_multiplier + ) + # Logit. + + (args.padded_vocab_size / (2 * args.num_layers * args.hidden_size)) + ) + ) + + +def append_to_progress_log(string): + args = get_args() + if args.save is None: + return + progress_log_filename = os.path.join(args.save, "progress.txt") + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + with open(progress_log_filename, 'a') as f: + job_id = os.getenv('SLURM_JOB_ID', '') + num_gpus = args.world_size + f.write(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\tJob ID: {job_id}\t" + f"# GPUs: {num_gpus}\t{string}\n") + + +def get_start_time_from_progress_log(): + """ + Gets start time of earliest job with same world size. Also returns the number + of floating-point operations completed in last saved checkpoint. + """ + args = get_args() + assert args.save is not None + progress_log_filename = os.path.join(args.save, "progress.txt") + + # start_time is time when job with same world size started. + # start_num_floating_point_operations is the number of floating-point operations + # completed when this job started. + # latest_num_floating_point_operations is the number of floating-point operations + # completed in most recent saved checkpoint. + start_time = None + start_num_floating_point_operations = None + latest_num_floating_point_operations = 0 + + def _get_field(string, type): + return type(string.split(': ')[1]) + + with open(progress_log_filename, 'r') as f: + for line in f: + line = line.strip() + line_tokens = line.split('\t') + world_size_in_line = _get_field(line_tokens[2], int) + if line_tokens[3] == "Saved checkpoint": + latest_num_floating_point_operations = \ + _get_field(line_tokens[7], float) + if world_size_in_line != args.world_size: + # Re-start search if we see a different world size. + start_time = None + start_num_floating_point_operations = None + continue + if line_tokens[3] == "Starting job": + if start_time is None: + start_time = line_tokens[0] + start_num_floating_point_operations = \ + latest_num_floating_point_operations + assert start_time is not None and start_num_floating_point_operations is not None, \ + "Should have seen at least one 'Starting job' entry with same world_size" + return datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S'), \ + start_num_floating_point_operations + + +def pretrain(train_valid_test_dataset_provider, + model_provider, + model_type, + forward_step_func, + process_non_loss_data_func=None, + extra_args_provider=None, + args_defaults={}): + """Main training program. + + This function will run the followings in the order provided: + 1) initialize Megatron. + 2) setup model, optimizer and lr schedule using the model_provider. + 3) call train_val_test_data_provider to get train/val/test datasets. + 4) train the modle using the forward_step_func. + + Args: + train_valid_test_dataset_provider: a function that takes the size of + train/valid/test dataset and returns `train, valid, test` datasets. + model_provider: a function that returns a vanilla version of the + model. By vanilla we mean a simple model on cpu with no fp16 or ddp. + model_type: an enum that specifies the type of model being trained. + forward_step_func: a function that takes a `data iterator` and `model`, + and returns a `loss` scalar with a dictionary with key:values being + the info we would like to monitor during training, for example + `lm-loss: value`. We also require that this function add + `batch generator` to the timers class. + process_non_loss_data_func: a function to post process outputs of the + network. It can be used for dumping output tensors (e.g images) to + tensorboard. It takes `collected data`(list of tensors), + `current iteration index` and `tensorboard writer` as arguments. + extra_args_provider: a function that takes a parser and adds arguments + to it. It is used for programs to add their own arguments. + args_defaults: a dictionary from argument-name to argument-value. It + to set already parse arguments. + """ + + # Initalize and get arguments, timers, and Tensorboard writer. + initialize_megatron(extra_args_provider=extra_args_provider, + args_defaults=args_defaults) + + args = get_args() + timers = get_timers() + + if args.log_progress: + append_to_progress_log("Starting job") + + # Set pytorch JIT layer fusion options and warmup JIT functions. + set_jit_fusion_options() + + # Adjust the startup time so it reflects the largest value. + # This will be closer to what scheduler will see (outside of + # image ... launches. + global _TRAIN_START_TIME + start_time_tensor = torch.tensor([_TRAIN_START_TIME], + dtype=torch.double, + device='cuda') + torch.distributed.all_reduce(start_time_tensor, + op=torch.distributed.ReduceOp.MIN) + _TRAIN_START_TIME = start_time_tensor.item() + print_rank_0('time to initialize megatron (seconds): {:.3f}'.format( + time.time() - _TRAIN_START_TIME)) + print_datetime('after megatron is initialized') + + args = get_args() + timers = get_timers() + + one_logger = get_one_logger() + if one_logger: + one_logger.log_metrics({ + 'train_iterations_warmup': 5 + }) + + # Model, optimizer, and learning rate. + timers('model-and-optimizer-setup', log_level=0).start(barrier=True) + model, optimizer, opt_param_scheduler = setup_model_and_optimizer( + model_provider, model_type) + + timers('model-and-optimizer-setup').stop() + print_datetime('after model, optimizer, and learning rate ' + 'scheduler are built') + config = get_model_config(model[0]) + + # Data stuff. + timers('train/valid/test-data-iterators-setup', log_level=0).start( + barrier=True) + if args.virtual_pipeline_model_parallel_size is not None: + train_data_iterator = [] + valid_data_iterator = [] + test_data_iterator = [] + for i in range(len(model)): + mpu.set_virtual_pipeline_model_parallel_rank(i) + iterators = build_train_valid_test_data_iterators( + train_valid_test_dataset_provider) + train_data_iterator.append(iterators[0]) + valid_data_iterator.append(iterators[1]) + test_data_iterator.append(iterators[2]) + else: + train_data_iterator, valid_data_iterator, test_data_iterator \ + = build_train_valid_test_data_iterators( + train_valid_test_dataset_provider) + timers('train/valid/test-data-iterators-setup').stop() + print_datetime('after dataloaders are built') + + # Print setup timing. + print_rank_0('done with setup ...') + timers.log(['model-and-optimizer-setup', + 'train/valid/test-data-iterators-setup'], barrier=True) + + if not args.skip_train: + print_rank_0('training ...') + + if args.dataloader_type == 'cyclic' and args.retro_project_dir: + assert args.retro_cyclic_train_iters is not None + args.train_iters = args.retro_cyclic_train_iters + print_rank_0("retro cyclic train iters : %d" % args.train_iters) + + iteration = 0 + if args.do_train and args.train_iters > 0: + iteration, num_floating_point_operations_so_far = train( + forward_step_func, + model, optimizer, opt_param_scheduler, + train_data_iterator, valid_data_iterator, + process_non_loss_data_func, config) + + print_datetime('after training is done') + + if args.save and iteration != 0 and iteration % args.save_interval != 0: + save_checkpoint(iteration, model, optimizer, opt_param_scheduler, + num_floating_point_operations_so_far) + else: + print_rank_0('skipping training (--skip-train is on) ...') + + iteration = args.iteration + + if args.do_valid: + prefix = f'iteration {iteration} on validation set' + evaluate_and_print_results(prefix, forward_step_func, + valid_data_iterator, model, + iteration, process_non_loss_data_func, config, + verbose=True, write_to_tensorboard=not args.skip_train) + + if args.do_test: + prefix = f'iteration {iteration} on test set' + evaluate_and_print_results(prefix, forward_step_func, + test_data_iterator, model, + iteration, process_non_loss_data_func, config, + verbose=True, write_to_tensorboard=not args.skip_train) + + + +def update_train_iters(args): + + # For iteration-based training, we don't need to do anything + if args.train_iters: + return + + # Constant batch size with sample-based training. + if args.rampup_batch_size is None: + args.train_iters = args.train_samples // args.global_batch_size + + else: + # Sample based training with rampup batch size. + iterations = 0 + consumed_samples = 0 + # Rampup phase. + while consumed_samples <= int(args.rampup_batch_size[2]): + update_num_microbatches(consumed_samples, consistency_check=False) + consumed_samples += get_current_global_batch_size() + iterations += 1 + # Reset + update_num_microbatches(0, consistency_check=False) + # Constant phase + # Note that we throw away any partial last batch. + iterations += (args.train_samples - consumed_samples) // \ + args.global_batch_size + args.train_iters = iterations + + print_rank_0('setting training iterations to {}'.format(args.train_iters)) + + +def get_model(model_provider_func, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=True): + """Build the model.""" + args = get_args() + args.model_type = model_type + + # Build model. + if mpu.get_pipeline_model_parallel_world_size() > 1 and \ + args.virtual_pipeline_model_parallel_size is not None: + assert model_type != ModelType.encoder_and_decoder, \ + "Interleaved schedule not supported for model with both encoder and decoder" + model = [] + for i in range(args.virtual_pipeline_model_parallel_size): + mpu.set_virtual_pipeline_model_parallel_rank(i) + # Set pre_process and post_process only after virtual rank is set. + pre_process = mpu.is_pipeline_first_stage() + post_process = mpu.is_pipeline_last_stage() + this_model = model_provider_func( + pre_process=pre_process, + post_process=post_process + ) + this_model.model_type = model_type + model.append(this_model) + else: + pre_process = mpu.is_pipeline_first_stage() + post_process = mpu.is_pipeline_last_stage() + add_encoder = True + add_decoder = True + if model_type == ModelType.encoder_and_decoder: + if mpu.get_pipeline_model_parallel_world_size() > 1: + assert args.pipeline_model_parallel_split_rank is not None, \ + "Split rank needs to be specified for model with both encoder and decoder" + rank = mpu.get_pipeline_model_parallel_rank() + split_rank = args.pipeline_model_parallel_split_rank + world_size = mpu.get_pipeline_model_parallel_world_size() + pre_process = rank == 0 or rank == split_rank + post_process = (rank == (split_rank - 1)) or ( + rank == (world_size - 1)) + add_encoder = mpu.is_pipeline_stage_before_split() + add_decoder = mpu.is_pipeline_stage_after_split() + model = model_provider_func( + pre_process=pre_process, + post_process=post_process, + add_encoder=add_encoder, + add_decoder=add_decoder) + else: + model = model_provider_func( + pre_process=pre_process, + post_process=post_process + ) + model.model_type = model_type + + if not isinstance(model, list): + model = [model] + + # Set tensor model parallel attributes if not set. + # Only parameters that are already tensor model parallel have these + # attributes set for them. We should make sure the default attributes + # are set for all params so the optimizer can use them. + for model_module in model: + for param in model_module.parameters(): + tensor_parallel.set_defaults_if_not_set_tensor_model_parallel_attributes(param) + + # Print number of parameters. + if mpu.get_data_parallel_rank() == 0: + print(' > number of parameters on (tensor, pipeline) ' + 'model parallel rank ({}, {}): {}'.format( + mpu.get_tensor_model_parallel_rank(), + mpu.get_pipeline_model_parallel_rank(), + sum([sum([p.nelement() for p in model_module.parameters()]) + for model_module in model])), flush=True) + + # GPU allocation. + for model_module in model: + model_module.cuda(torch.cuda.current_device()) + + # Fp16 conversion. + if args.fp16 or args.bf16: + model = [Float16Module(model_module, args) for model_module in model] + + if wrap_with_ddp: + config = get_model_config(model[0]) + ddp_config = DistributedDataParallelConfig( + grad_reduce_in_fp32=args.accumulate_allreduce_grads_in_fp32, + overlap_grad_reduce=args.overlap_grad_reduce, + use_distributed_optimizer=args.use_distributed_optimizer, + check_for_nan_in_grad=args.check_for_nan_in_loss_and_grad, + bucket_size=args.ddp_bucket_size) + model = [DDP(config, + ddp_config, + model_chunk, + data_parallel_group=mpu.get_data_parallel_group(with_context_parallel=True), + expert_data_parallel_group=mpu.get_data_modulo_expert_parallel_group(), + # Turn off bucketing for model_chunk 2 onwards, since communication for these + # model chunks is overlapped with compute anyway. + disable_bucketing=(model_chunk_idx > 0)) + for (model_chunk_idx, model_chunk) in enumerate(model)] + + # Broadcast params from data parallel src rank to other data parallel ranks. + if args.data_parallel_random_init: + for model_module in model: + model_module.broadcast_params() + + return model + + +def get_optimizer_param_scheduler(optimizer): + """Build the learning rate scheduler.""" + args = get_args() + + # Iteration-based training. + if args.train_iters: + if args.lr_decay_iters is None: + args.lr_decay_iters = args.train_iters + lr_decay_steps = args.lr_decay_iters * args.global_batch_size + wd_incr_steps = args.train_iters * args.global_batch_size + if args.lr_warmup_fraction is not None: + lr_warmup_steps = args.lr_warmup_fraction * lr_decay_steps + else: + lr_warmup_steps = args.lr_warmup_iters * args.global_batch_size + # Sample-based training. + elif args.train_samples: + # We need to set training iters for later use. Technically + # we need to adjust the training samples too (due to last + # batch being incomplete) but we leave it as is for now. + update_train_iters(args) + if args.lr_decay_samples is None: + args.lr_decay_samples = args.train_samples + lr_decay_steps = args.lr_decay_samples + wd_incr_steps = args.train_samples + if args.lr_warmup_fraction is not None: + lr_warmup_steps = args.lr_warmup_fraction * lr_decay_steps + else: + lr_warmup_steps = args.lr_warmup_samples + else: + raise Exception( + 'either train-iters or train-samples should be provided.') + + opt_param_scheduler = OptimizerParamScheduler( + optimizer, + init_lr=args.lr_warmup_init, + max_lr=args.lr, + min_lr=args.min_lr, + lr_warmup_steps=lr_warmup_steps, + lr_decay_steps=lr_decay_steps, + lr_decay_style=args.lr_decay_style, + start_wd=args.start_weight_decay, + end_wd=args.end_weight_decay, + wd_incr_steps=wd_incr_steps, + wd_incr_style=args.weight_decay_incr_style, + use_checkpoint_opt_param_scheduler=args.use_checkpoint_opt_param_scheduler, + override_opt_param_scheduler=args.override_opt_param_scheduler) + + return opt_param_scheduler + + +def setup_model_and_optimizer(model_provider_func, + model_type, + no_wd_decay_cond=None, + scale_lr_cond=None, + lr_mult=1.0): + """Setup model and optimizer.""" + args = get_args() + timers = get_timers() + + model = get_model(model_provider_func, model_type) + unwrapped_model = unwrap_model(model) + + kwargs = {} + for f in dataclasses.fields(OptimizerConfig): + if hasattr(args, f.name): + kwargs[f.name] = getattr(args, f.name) + config = OptimizerConfig(**kwargs) + config.timers = timers + optimizer = get_megatron_optimizer(config, model, no_wd_decay_cond, + scale_lr_cond, lr_mult) + opt_param_scheduler = get_optimizer_param_scheduler(optimizer) + + if args.load is not None or args.pretrained_checkpoint is not None: + timers('load-checkpoint', log_level=0).start(barrier=True) + args.iteration, args.num_floating_point_operations_so_far = load_checkpoint( + model, optimizer, opt_param_scheduler) + timers('load-checkpoint').stop(barrier=True) + timers.log(['load-checkpoint']) + else: + args.iteration = 0 + args.num_floating_point_operations_so_far = 0 + + # get model without FP16 and/or DDP wrappers + if args.iteration == 0 and len(unwrapped_model) == 1 \ + and hasattr(unwrapped_model[0], 'init_state_dict_from_bert'): + print_rank_0("Initializing ICT from pretrained BERT model") + unwrapped_model[0].init_state_dict_from_bert() + if args.fp16: + optimizer.reload_model_params() + + return model, optimizer, opt_param_scheduler + + + +def train_step(forward_step_func, data_iterator, + model, optimizer, opt_param_scheduler, config): + """Single training step.""" + args = get_args() + timers = get_timers() + + # Set grad to zero. + for model_chunk in model: + model_chunk.zero_grad_buffer() + optimizer.zero_grad() + + # Forward pass. + forward_backward_func = get_forward_backward_func() + losses_reduced = forward_backward_func( + forward_step_func=forward_step_func, + data_iterator=data_iterator, + model=model, + num_microbatches=get_num_microbatches(), + seq_length=args.seq_length, + micro_batch_size=args.micro_batch_size, + decoder_seq_length=args.decoder_seq_length, + forward_only=False) + + # Empty unused memory. + if args.empty_unused_memory_level >= 1: + torch.cuda.empty_cache() + + # Vision gradients. + if getattr(args, 'vision_pretraining', False) and args.vision_pretraining_type == "dino": + unwrapped_model = unwrap_model(model[0]) + unwrapped_model.cancel_gradients_last_layer(args.curr_iteration) + + # Update parameters. + timers('optimizer', log_level=1).start(barrier=args.barrier_with_L1_time) + update_successful, grad_norm, num_zeros_in_grad = optimizer.step() + timers('optimizer').stop() + + # Vision momentum. + if getattr(args, 'vision_pretraining', False) and args.vision_pretraining_type == "dino": + unwrapped_model = unwrap_model(model[0]) + unwrapped_model.update_momentum(args.curr_iteration) + + # Update learning rate. + if update_successful: + increment = get_num_microbatches() * \ + args.micro_batch_size * \ + args.data_parallel_size + opt_param_scheduler.step(increment=increment) + skipped_iter = 0 + else: + skipped_iter = 1 + + # Empty unused memory. + if args.empty_unused_memory_level >= 2: + torch.cuda.empty_cache() + + if mpu.is_pipeline_last_stage(ignore_virtual=True): + # Average loss across microbatches. + loss_reduced = {} + for key in losses_reduced[0]: + losses_reduced_for_key = [x[key] for x in losses_reduced] + loss_reduced[key] = sum(losses_reduced_for_key) / len(losses_reduced_for_key) + return loss_reduced, skipped_iter, grad_norm, num_zeros_in_grad + return {}, skipped_iter, grad_norm, num_zeros_in_grad + + +def training_log(loss_dict, total_loss_dict, learning_rate, decoupled_learning_rate, iteration, + loss_scale, report_memory_flag, skipped_iter, + grad_norm, params_norm, num_zeros_in_grad): + """Log training information such as losses, timing, ....""" + args = get_args() + timers = get_timers() + writer = get_tensorboard_writer() + wandb_writer = get_wandb_writer() + one_logger = get_one_logger() + + # Advanced, skipped, and Nan iterations. + advanced_iters_key = 'advanced iterations' + skipped_iters_key = 'skipped iterations' + nan_iters_key = 'nan iterations' + # Advanced iterations. + if not skipped_iter: + total_loss_dict[advanced_iters_key] = total_loss_dict.get( + advanced_iters_key, 0) + 1 + else: + if advanced_iters_key not in total_loss_dict: + total_loss_dict[advanced_iters_key] = 0 + # Skipped iterations. + total_loss_dict[skipped_iters_key] = total_loss_dict.get( + skipped_iters_key, 0) + skipped_iter + # Update losses and set nan iterations + got_nan = False + for key in loss_dict: + if not skipped_iter: + total_loss_dict[key] = total_loss_dict.get( + key, torch.tensor([0.0], dtype=torch.float, device='cuda')) + loss_dict[key] + else: + value = loss_dict[key].float().sum().item() + is_nan = value == float('inf') or \ + value == -float('inf') or \ + value != value + got_nan = got_nan or is_nan + total_loss_dict[nan_iters_key] = total_loss_dict.get( + nan_iters_key, 0) + int(got_nan) + + # Logging. + timers_to_log = [ + 'forward-backward', + 'forward-compute', + 'backward-compute', + 'batch-generator', + 'forward-recv', + 'forward-send', + 'backward-recv', + 'backward-send', + 'forward-send-forward-recv', + 'forward-send-backward-recv', + 'backward-send-forward-recv', + 'backward-send-backward-recv', + 'forward-backward-send-forward-backward-recv', + 'layernorm-grads-all-reduce', + 'embedding-grads-all-reduce', + 'all-grads-sync', + 'params-all-gather', + 'optimizer-copy-to-main-grad', + 'optimizer-unscale-and-check-inf', + 'optimizer-clip-main-grad', + 'optimizer-count-zeros', + 'optimizer-inner-step', + 'optimizer-copy-main-to-model-params', + 'optimizer'] + + # Calculate batch size. + batch_size = args.micro_batch_size * args.data_parallel_size * \ + get_num_microbatches() + + # Track app tag & app tag ID + if one_logger: + job_name = os.environ.get('SLURM_JOB_NAME', None) + current_app_tag = f'{job_name}_{batch_size}_{args.world_size}' + one_logger.log_app_tag(current_app_tag) + + total_iterations = total_loss_dict[advanced_iters_key] + \ + total_loss_dict[skipped_iters_key] + + # Tensorboard values. + # Timer requires all the ranks to call. + if args.log_timers_to_tensorboard and \ + (iteration % args.tensorboard_log_interval == 0): + timers.write(timers_to_log, writer, iteration, + normalizer=total_iterations) + if writer and (iteration % args.tensorboard_log_interval == 0): + if wandb_writer: + wandb_writer.log({'samples vs steps': args.consumed_train_samples}, + iteration) + if args.log_learning_rate_to_tensorboard: + writer.add_scalar('learning-rate', learning_rate, iteration) + if args.decoupled_lr is not None: + writer.add_scalar('decoupled-learning-rate', decoupled_learning_rate, iteration) + writer.add_scalar('learning-rate vs samples', learning_rate, + args.consumed_train_samples) + if wandb_writer: + wandb_writer.log({'learning-rate': learning_rate}, iteration) + if args.log_batch_size_to_tensorboard: + writer.add_scalar('batch-size', batch_size, iteration) + writer.add_scalar('batch-size vs samples', batch_size, + args.consumed_train_samples) + if wandb_writer: + wandb_writer.log({'batch-size': batch_size}, iteration) + for key in loss_dict: + writer.add_scalar(key , loss_dict[key], iteration) + writer.add_scalar(key + ' vs samples', loss_dict[key], + args.consumed_train_samples) + if wandb_writer: + wandb_writer.log({key: loss_dict[key]}, iteration) + if args.log_loss_scale_to_tensorboard: + writer.add_scalar('loss-scale', loss_scale, iteration) + writer.add_scalar('loss-scale vs samples', loss_scale, + args.consumed_train_samples) + if wandb_writer: + wandb_writer.log({'loss-scale': loss_scale}, iteration) + if args.log_world_size_to_tensorboard: + writer.add_scalar('world-size', args.world_size, iteration) + writer.add_scalar('world-size vs samples', args.world_size, + args.consumed_train_samples) + if wandb_writer: + wandb_writer.log({'world-size': args.world_size}, iteration) + if grad_norm is not None: + writer.add_scalar('grad-norm', grad_norm, iteration) + writer.add_scalar('grad-norm vs samples', grad_norm, + args.consumed_train_samples) + if wandb_writer: + wandb_writer.log({'grad-norm': grad_norm}, iteration) + if num_zeros_in_grad is not None: + writer.add_scalar('num-zeros', num_zeros_in_grad, iteration) + writer.add_scalar('num-zeros vs samples', num_zeros_in_grad, + args.consumed_train_samples) + if wandb_writer: + wandb_writer.log({'num-zeros': num_zeros_in_grad}, iteration) + if params_norm is not None: + writer.add_scalar('params-norm', params_norm, iteration) + writer.add_scalar('params-norm vs samples', params_norm, + args.consumed_train_samples) + if wandb_writer: + wandb_writer.log({'params-norm': params_norm}, iteration) + if args.log_memory_to_tensorboard: + mem_stats = torch.cuda.memory_stats() + writer.add_scalar( + "mem-reserved-bytes", + mem_stats["reserved_bytes.all.current"], + iteration, + ) + writer.add_scalar( + "mem-allocated-bytes", + mem_stats["allocated_bytes.all.current"], + iteration, + ) + writer.add_scalar( + "mem-allocated-count", + mem_stats["allocation.all.current"], + iteration, + ) + if args.num_experts is not None: + moe_loss_scale = 1 / get_num_microbatches() + track_moe_metrics(moe_loss_scale, iteration, writer, wandb_writer, total_loss_dict, args.moe_per_layer_logging) + + if iteration % args.log_interval == 0: + elapsed_time = timers('interval-time').elapsed(barrier=True) + elapsed_time_per_iteration = elapsed_time / total_iterations + + throughput = num_floating_point_operations(args, batch_size) / ( + elapsed_time_per_iteration * 10**12 * args.world_size) + if args.log_timers_to_tensorboard: + if writer: + writer.add_scalar('iteration-time', + elapsed_time_per_iteration, iteration) + if wandb_writer: + wandb_writer.log({'iteration-time': elapsed_time_per_iteration}, + iteration) + log_string = f" [{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}]" + log_string += ' iteration {:8d}/{:8d} |'.format( + iteration, args.train_iters) + log_string += ' consumed samples: {:12d} |'.format( + args.consumed_train_samples) + log_string += ' elapsed time per iteration (ms): {:.1f} |'.format( + elapsed_time_per_iteration * 1000.0) + if args.log_throughput: + log_string += f' throughput per GPU (TFLOP/s/GPU): {throughput:.1f} |' + if args.log_timers_to_tensorboard: + if writer: + writer.add_scalar('throughput', throughput, iteration) + if wandb_writer: + wandb_writer.log({'throughput': throughput}, iteration) + assert learning_rate is not None + # Decoupled_learning_rate should be not None only on first and last pipeline stage. + log_string += ' learning rate: {:.6E} |'.format(learning_rate) + if args.decoupled_lr is not None and (mpu.is_pipeline_first_stage(ignore_virtual=True) or + mpu.is_pipeline_last_stage(ignore_virtual=True)): + assert decoupled_learning_rate is not None + log_string += ' decoupled learning rate: {:.6E} |'.format(decoupled_learning_rate) + else: + assert decoupled_learning_rate is None + log_string += ' global batch size: {:5d} |'.format(batch_size) + for key in total_loss_dict: + if key not in [advanced_iters_key, skipped_iters_key, + nan_iters_key]: + avg = total_loss_dict[key].item() / \ + float(max(1, total_loss_dict[advanced_iters_key])) + if avg > 0.0: + log_string += ' {}: {:.6E} |'.format(key, avg) + total_loss_dict[key] = torch.tensor([0.0], dtype=torch.float, device='cuda') + log_string += ' loss scale: {:.1f} |'.format(loss_scale) + if grad_norm is not None: + log_string += ' grad norm: {:.3f} |'.format(grad_norm) + if num_zeros_in_grad is not None: + log_string += ' num zeros: {:.1f} |'.format(num_zeros_in_grad) + if params_norm is not None: + log_string += ' params norm: {:.3f} |'.format(params_norm) + log_string += ' number of skipped iterations: {:3d} |'.format( + total_loss_dict[skipped_iters_key]) + log_string += ' number of nan iterations: {:3d} |'.format( + total_loss_dict[nan_iters_key]) + total_loss_dict[advanced_iters_key] = 0 + total_loss_dict[skipped_iters_key] = 0 + total_loss_dict[nan_iters_key] = 0 + print_rank_last(log_string) + if report_memory_flag and learning_rate > 0.: + # Report memory after optimizer state has been initialized. + if torch.distributed.get_rank() == 0: + num_microbatches = get_num_microbatches() + report_theoretical_memory(args, num_microbatches=num_microbatches, verbose=True) + report_memory('(after {} iterations)'.format(iteration)) + report_memory_flag = False + timers.log(timers_to_log, normalizer=args.log_interval) + + return report_memory_flag + + +def compute_throughputs_and_append_to_progress_log(iteration, + num_floating_point_operations_so_far): + args = get_args() + if args.save is None: + return + + # Compute job throughput. + # args.num_floating_point_operations_so_far keeps track of floating-point operations + # completed at the start of job. + global _TRAIN_START_TIME + job_throughput = \ + (num_floating_point_operations_so_far - + args.num_floating_point_operations_so_far) / ( + (time.time() - _TRAIN_START_TIME) * 10**12 * args.world_size) + + # Compute cumulative throughput since jobs of this world size were launched. + # `get_start_time_from_progress_log` returns start time and number of floating-point + # operations of first job of this world size. + start_time, start_num_floating_point_operations = get_start_time_from_progress_log() + elapsed_time = (datetime.now() - start_time).total_seconds() + cumulative_throughput = \ + (num_floating_point_operations_so_far - + start_num_floating_point_operations) / ( + elapsed_time * 10**12 * args.world_size) + + tokens_so_far = args.consumed_train_samples * args.seq_length + + append_to_progress_log(f"Saved checkpoint\tIteration: {iteration}\t" + f"Job throughput: {job_throughput:.1f} TFLOP/s/GPU\t" + f"Cumulative throughput: {cumulative_throughput:.1f} TFLOP/s/GPU\t" + f"Floating-point operations: {num_floating_point_operations_so_far:.2e}\t" + f"Tokens (in billions): {tokens_so_far / 10**9:.2f}") + + +def save_checkpoint_and_time(iteration, model, optimizer, opt_param_scheduler, + num_floating_point_operations_so_far): + args = get_args() + timers = get_timers() + # Extra barrier is added to make sure all ranks report the max time. + timers('save-checkpoint', log_level=0).start(barrier=True) + save_checkpoint(iteration, model, optimizer, opt_param_scheduler, + num_floating_point_operations_so_far) + timers('save-checkpoint').stop(barrier=True) + timers.log(['save-checkpoint']) + + if args.log_progress: + compute_throughputs_and_append_to_progress_log(iteration, + num_floating_point_operations_so_far) + + +def train(forward_step_func, model, optimizer, opt_param_scheduler, + train_data_iterator, valid_data_iterator, + process_non_loss_data_func, config): + """Train the model function.""" + args = get_args() + timers = get_timers() + + # Write args to tensorboard + write_args_to_tensorboard() + + # Turn on training mode which enables dropout. + for model_module in model: + model_module.train() + + # Tracking loss. + total_loss_dict = {} + + # Iterations. + iteration = args.iteration + one_logger = get_one_logger() + if one_logger: + iteration_start = iteration + train_samples_start = args.consumed_train_samples + train_samples_target = args.train_samples + one_logger.log_metrics({ + 'train_samples_start': args.consumed_train_samples, + 'train_iterations_start': iteration, + 'train_samples_target': train_samples_target, + 'train_iterations_target': args.train_iters, + }) + + num_floating_point_operations_so_far = args.num_floating_point_operations_so_far + + # Setup some training config params + config.grad_scale_func = optimizer.scale_loss + config.timers = timers + if isinstance(model[0], DDP) and args.overlap_grad_reduce: + assert config.no_sync_func is None, \ + ('When overlap_grad_reduce is True, config.no_sync_func must be None; ' + 'a custom no_sync_func is not supported when overlapping grad-reduce') + config.no_sync_func = [model_chunk.no_sync for model_chunk in model] + if len(model) == 1: + config.no_sync_func = config.no_sync_func[0] + if args.delay_grad_reduce: + config.grad_sync_func = [model_chunk.start_grad_sync for model_chunk in model] + if len(model) == 1: + config.grad_sync_func = config.grad_sync_func[0] + if args.overlap_param_gather and args.delay_param_gather: + config.param_sync_func = [lambda x: optimizer.finish_param_sync(model_index, x) + for model_index in range(len(model))] + if len(model) == 1: + config.param_sync_func = config.param_sync_func[0] + config.finalize_model_grads_func = finalize_model_grads + + timers('interval-time', log_level=0).start(barrier=True) + print_datetime('before the start of training step') + report_memory_flag = True + exit = False + + if args.manual_gc: + # Disable the default garbage collector and perform the collection manually. + # This is to align the timing of garbage collection across ranks. + assert args.manual_gc_interval >= 0, \ + 'Manual garbage collection interval should be laerger than or equal to 0.' + gc.disable() + gc.collect() + + # Singleton Initialization + if args.log_straggler: + global stimer + world = torch.distributed.get_world_size() + rank = torch.distributed.get_rank() + mmcnt = args.straggler_minmax_count + stimer.configure(world, rank, + mmcnt = mmcnt, + enabled = not args.disable_straggler_on_startup, + port = args.straggler_ctrlr_port) + total_flops = 0.0 + + num_microbatches = get_num_microbatches() + eval_duration = 0.0 + eval_iterations = 0 + def track_e2e_metrics(): + # Nested function to track a bunch of E2E APP metrics + if one_logger: + train_duration = timers('interval-time').active_time() # overall_elapsed + train_samples = args.consumed_train_samples - train_samples_start + train_iterations = iteration - iteration_start + train_iterations_time_msecs_avg = (train_duration * 1000.0) / train_iterations + if eval_iterations: + validation_iterations_time_msecs_avg = (eval_duration * 1000.0) / eval_iterations + else: + validation_iterations_time_msecs_avg = None + + one_logger.log_metrics({ + 'train_iterations_end': iteration, + 'train_samples_end': args.consumed_train_samples, + 'train_iterations': train_iterations, + 'train_samples': train_samples, + 'train_iterations_time_msecs_avg': train_iterations_time_msecs_avg, + 'validation_iterations_time_msecs_avg': validation_iterations_time_msecs_avg + }) + + while iteration < args.train_iters: + if args.profile and \ + iteration == args.profile_step_start and \ + torch.distributed.get_rank() in args.profile_ranks: + torch.cuda.cudart().cudaProfilerStart() + torch.autograd.profiler.emit_nvtx(record_shapes=True).__enter__() + + # Update number of microbatches first without consistency check to decide if a + # checkpoint should be saved. If the number of microbatches is different + # from the previous iteration, save a checkpoint. Then run consistency check + # to make sure training configuration is still valid. + update_num_microbatches(args.consumed_train_samples, consistency_check=False) + if get_num_microbatches() != num_microbatches and iteration != 0: + assert get_num_microbatches() > num_microbatches, \ + "number of microbatches should be increasing due to batch size rampup" + save_checkpoint_and_time(iteration, model, optimizer, + opt_param_scheduler, + num_floating_point_operations_so_far) + num_microbatches = get_num_microbatches() + update_num_microbatches(args.consumed_train_samples, consistency_check=True) + + args.curr_iteration = iteration + loss_dict, skipped_iter, grad_norm, num_zeros_in_grad = \ + train_step(forward_step_func, + train_data_iterator, + model, + optimizer, + opt_param_scheduler, + config) + iteration += 1 + batch_size = mpu.get_data_parallel_world_size() * \ + args.micro_batch_size * \ + get_num_microbatches() + args.consumed_train_samples += batch_size + num_fp_ops = num_floating_point_operations(args, batch_size) + num_floating_point_operations_so_far += num_fp_ops + total_flops += num_fp_ops + + # Logging. + loss_scale = optimizer.get_loss_scale().item() + params_norm = None + if args.log_params_norm: + params_norm = calc_params_l2_norm(model) + + if iteration % args.log_interval == 0: + track_e2e_metrics() + + learning_rate = None + decoupled_learning_rate = None + for param_group in optimizer.param_groups: + if param_group['is_decoupled_lr']: + decoupled_learning_rate = param_group['lr'] + else: + learning_rate = param_group['lr'] + report_memory_flag = training_log(loss_dict, total_loss_dict, + learning_rate, + decoupled_learning_rate, + iteration, loss_scale, + report_memory_flag, skipped_iter, + grad_norm, params_norm, num_zeros_in_grad) + # StragglerDetector + if iteration % args.log_interval == 0 and args.log_straggler: + stimer.report(total_flops, args.log_interval) + total_flops = 0.0 + + # Autoresume + if args.adlr_autoresume and \ + (iteration % args.adlr_autoresume_interval == 0): + check_adlr_autoresume_termination(iteration, model, optimizer, + opt_param_scheduler) + + # Evaluation + if args.eval_interval and iteration % args.eval_interval == 0 and \ + args.do_valid: + timers('interval-time').stop() + if args.use_distributed_optimizer and args.overlap_param_gather: + optimizer.disable_pre_hook() + if args.manual_gc and args.manual_gc_eval: + # Collect all objects. + gc.collect() + prefix = 'iteration {}'.format(iteration) + timers('eval-time', log_level=0).start(barrier=True) + evaluate_and_print_results(prefix, forward_step_func, + valid_data_iterator, model, + iteration, process_non_loss_data_func, + config, False) + eval_duration += timers('eval-time').elapsed() + eval_iterations += args.eval_iters + timers('eval-time').stop() + if args.manual_gc and args.manual_gc_eval: + # Collect only the objects created and used in evaluation. + gc.collect(generation=0) + if args.use_distributed_optimizer and args.overlap_param_gather: + optimizer.enable_pre_hook() + timers('interval-time', log_level=0).start(barrier=True) + + # Checkpointing + saved_checkpoint = False + if args.exit_signal_handler: + signal_handler = get_signal_handler() + if any(signal_handler.signals_received()): + save_checkpoint_and_time(iteration, model, optimizer, + opt_param_scheduler, + num_floating_point_operations_so_far) + print_datetime('exiting program after receiving SIGTERM.') + exit = True + break + + if args.save and args.save_interval and \ + iteration % args.save_interval == 0: + timers('interval-time').stop() + save_checkpoint_and_time(iteration, model, optimizer, + opt_param_scheduler, + num_floating_point_operations_so_far) + saved_checkpoint = True + timers('interval-time', log_level=0).start(barrier=True) + + # Exiting based on duration + if args.exit_duration_in_mins: + train_time = (time.time() - _TRAIN_START_TIME) / 60.0 + done_cuda = torch.tensor( + [train_time > args.exit_duration_in_mins], + dtype=torch.int, device='cuda') + torch.distributed.all_reduce( + done_cuda, op=torch.distributed.ReduceOp.MAX) + done = done_cuda.item() + if done: + if not saved_checkpoint: + save_checkpoint_and_time(iteration, model, optimizer, + opt_param_scheduler, + num_floating_point_operations_so_far) + print_datetime('exiting program after {} minutes'.format(train_time)) + exit = True + break + + # Exiting based on iterations + if args.exit_interval and iteration % args.exit_interval == 0: + if args.save and not saved_checkpoint: + save_checkpoint_and_time(iteration, model, optimizer, + opt_param_scheduler, + num_floating_point_operations_so_far) + torch.distributed.barrier() + print_datetime('exiting program at iteration {}'.format(iteration)) + exit = True + break + + if args.profile and \ + iteration == args.profile_step_end and \ + torch.distributed.get_rank() in args.profile_ranks: + torch.cuda.cudart().cudaProfilerStop() + + if args.manual_gc: + if args.manual_gc_interval != 0 and iteration % args.manual_gc_interval == 0: + gc.collect() + + track_e2e_metrics() + + # Flush TensorBoard and WandB writers. + writer = get_tensorboard_writer() + if writer: + writer.flush() + wandb_writer = get_wandb_writer() + if wandb_writer: + wandb_writer.finish() + + # Close out pre-hooks if using distributed optimizer and overlapped param gather. + if args.use_distributed_optimizer and args.overlap_param_gather: + optimizer.disable_pre_hook() + + # If any exit conditions (signal handler, duration, iterations) have been reached, exit. + if exit: + sys.exit() + + return iteration, num_floating_point_operations_so_far + + +def evaluate(forward_step_func, + data_iterator, + model, + process_non_loss_data_func, + config, + verbose=False): + """Evaluation.""" + args = get_args() + timers = get_timers() + + timers('evaluate', log_level=0).start(barrier=True) + + if args.vision_pretraining and args.vision_pretraining_type == "dino": + from megatron.legacy.model.vision.knn_monitor import compute_feature_bank + compute_feature_bank(model) + + # Turn on evaluation mode which disables dropout. + for model_module in model: + model_module.eval() + + total_loss_dict = {} + + # make validation batch size independent from training batch size + eval_batch_size = args.global_batch_size + eval_num_microbatches = eval_batch_size // \ + (args.micro_batch_size * args.data_parallel_size) + + with torch.no_grad(): + iteration = 0 + if verbose: + print_rank_0(f'Evaluating on {args.eval_iters * eval_batch_size} samples') + while iteration < args.eval_iters: + iteration += 1 + if verbose: + print_rank_0(f'Evaluating iter {iteration}/{args.eval_iters}') + + forward_backward_func = get_forward_backward_func() + # Don't care about timing during evaluation + config.timers = None + loss_dicts = forward_backward_func( + forward_step_func=forward_step_func, + data_iterator=data_iterator, + model=model, + num_microbatches=eval_num_microbatches, + seq_length=args.seq_length, + micro_batch_size=args.micro_batch_size, + decoder_seq_length=args.decoder_seq_length, + forward_only=True) + config.timers = get_timers() + + # Empty unused memory + if args.empty_unused_memory_level >= 1: + torch.cuda.empty_cache() + + if mpu.is_pipeline_last_stage(ignore_virtual=True): + # Reduce across processes. + for loss_dict in loss_dicts: + for key in loss_dict: + total_loss_dict[key] = total_loss_dict.get( + key, torch.tensor([0.0], dtype=torch.float, device='cuda')) + loss_dict[key] + + args.consumed_valid_samples += eval_batch_size + + if args.exit_duration_in_mins: + train_time = (time.time() - _TRAIN_START_TIME) / 60.0 + done_cuda = torch.tensor( + [train_time > args.exit_duration_in_mins], + dtype=torch.int, device='cuda') + torch.distributed.all_reduce( + done_cuda, op=torch.distributed.ReduceOp.MAX) + done = done_cuda.item() + if done: + print_rank_0('Exiting during evaluation, timelimit reached') + return None, None, True + + collected_non_loss_data = None + if process_non_loss_data_func is not None and is_last_rank(): + collected_non_loss_data = forward_backward_func( + forward_step_func=forward_step_func, + data_iterator=data_iterator, + model=model, + num_microbatches=get_num_microbatches(), + seq_length=args.seq_length, + micro_batch_size=args.micro_batch_size, + decoder_seq_length=args.decoder_seq_length, + forward_only=True, + collect_non_loss_data=True) + + # Move model back to the train mode. + for model_module in model: + model_module.train() + + for key in total_loss_dict: + total_loss_dict[key] /= args.eval_iters * eval_num_microbatches + + timers('evaluate').stop() + timers.log(['evaluate']) + + return total_loss_dict, collected_non_loss_data, False + +def evaluate_and_print_results(prefix, forward_step_func, + data_iterator, model, + iteration, process_non_loss_data_func, config, + verbose=False, write_to_tensorboard=True): + """Helper function to evaluate and dump results on screen.""" + args = get_args() + if write_to_tensorboard: + writer = get_tensorboard_writer() + else: + writer = None + + wandb_writer = get_wandb_writer() + + total_loss_dict, collected_non_loss_data, timelimit = evaluate( + forward_step_func, data_iterator, model, + process_non_loss_data_func, config, verbose) + # Timelimit hit during evaluation + if timelimit: + return + string = ' validation loss at {} | '.format(prefix) + for key in total_loss_dict: + string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item()) + ppl = math.exp(min(20, total_loss_dict[key].item())) + string += '{} PPL: {:.6E} | '.format(key, ppl) + if writer: + writer.add_scalar('{} validation'.format(key), + total_loss_dict[key].item(), + iteration) + writer.add_scalar('{} validation vs samples'.format(key), + total_loss_dict[key].item(), + args.consumed_train_samples) + if args.log_validation_ppl_to_tensorboard: + writer.add_scalar('{} validation ppl'.format(key), ppl, + iteration) + writer.add_scalar('{} validation ppl vs samples'.format(key), + ppl, args.consumed_train_samples) + if wandb_writer and is_last_rank(): + wandb_writer.log({ + '{} validation'.format(key): total_loss_dict[key].item()}, + iteration) + + if process_non_loss_data_func is not None and writer and is_last_rank(): + process_non_loss_data_func(collected_non_loss_data, iteration, writer) + + length = len(string) + 1 + print_rank_last('-' * length) + print_rank_last(string) + print_rank_last('-' * length) + + +def cyclic_iter(iter): + while True: + for x in iter: + yield x + + +def get_train_valid_test_num_samples(): + """Train/valid/test num samples.""" + + args = get_args() + + # Number of train/valid/test samples. + if args.train_samples: + train_samples = args.train_samples + else: + train_samples = args.train_iters * args.global_batch_size + eval_iters = (args.train_iters // args.eval_interval + 1) * \ + args.eval_iters + test_iters = args.eval_iters + + return ( + train_samples, + eval_iters * args.global_batch_size, + test_iters * args.global_batch_size, + ) + + +def build_train_valid_test_datasets(build_train_valid_test_datasets_provider): + """Build pretraining datasets.""" + train_valid_test_num_samples = get_train_valid_test_num_samples() + print_rank_0(' > datasets target sizes (minimum size):') + print_rank_0(' train: {}'.format(train_valid_test_num_samples[0])) + print_rank_0(' validation: {}'.format(train_valid_test_num_samples[1])) + print_rank_0(' test: {}'.format(train_valid_test_num_samples[2])) + return build_train_valid_test_datasets_provider(train_valid_test_num_samples) + + +def build_train_valid_test_data_loaders( + build_train_valid_test_datasets_provider): + """Build pretraining data loaders.""" + + args = get_args() + + (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None) + + print_rank_0('> building train, validation, and test datasets ...') + + # Backward compatibility, assume fixed batch size. + if args.iteration > 0 and args.consumed_train_samples == 0: + assert args.train_samples is None, \ + 'only backward compatiblity support for iteration-based training' + args.consumed_train_samples = args.iteration * args.global_batch_size + if args.iteration > 0 and args.consumed_valid_samples == 0: + if args.train_samples is None: + args.consumed_valid_samples = (args.iteration // args.eval_interval) * \ + args.eval_iters * args.global_batch_size + + # Rely on distributed-aware core datasets, temporary + is_distributed = getattr(build_train_valid_test_datasets_provider, "is_distributed", False) + + # Construct the data pipeline + if is_distributed or mpu.get_tensor_model_parallel_rank() == 0: + + # Build datasets. + train_ds, valid_ds, test_ds = build_train_valid_test_datasets( + build_train_valid_test_datasets_provider) + # Build dataloders. + train_dataloader = build_pretraining_data_loader( + train_ds, args.consumed_train_samples) + if args.skip_train: + valid_dataloader = build_pretraining_data_loader(valid_ds, 0) + else: + valid_dataloader = build_pretraining_data_loader( + valid_ds, args.consumed_valid_samples) + test_dataloader = build_pretraining_data_loader(test_ds, 0) + + # Flags to know if we need to do training/validation/testing. + do_train = train_dataloader is not None and args.train_iters > 0 + do_valid = valid_dataloader is not None and args.eval_iters > 0 + do_test = test_dataloader is not None and args.eval_iters > 0 + flags = torch.tensor( + [int(do_train), int(do_valid), int(do_test)], + dtype=torch.long, device='cuda') + else: + flags = torch.tensor([0, 0, 0], dtype=torch.long, device='cuda') + + torch.distributed.broadcast(flags, 0) + + args.do_train = getattr(args, "do_train", False) or flags[0].item() + args.do_valid = getattr(args, "do_valid", False) or flags[1].item() + args.do_test = getattr(args, "do_test", False) or flags[2].item() + + return train_dataloader, valid_dataloader, test_dataloader + + +def build_train_valid_test_data_iterators( + build_train_valid_test_datasets_provider): + """Build pretraining data iterators.""" + + args = get_args() + + # Build loaders. + train_dataloader, valid_dataloader, test_dataloader = \ + build_train_valid_test_data_loaders( + build_train_valid_test_datasets_provider) + + # Build iterators. + dl_type = args.dataloader_type + assert dl_type in ['single', 'cyclic', 'external'] + + def _get_iterator(dataloader_type, dataloader): + """Return dataset iterator.""" + if dataloader_type == "single": + return iter(dataloader) + elif dataloader_type == "cyclic": + return iter(cyclic_iter(dataloader)) + elif dataloader_type == "external": + # External dataloader is passed through. User is expected to define how to iterate. + return dataloader + else: + raise RuntimeError("unexpected dataloader type") + + if train_dataloader is not None: + train_data_iterator = _get_iterator(dl_type, train_dataloader) + else: + train_data_iterator = None + + if valid_dataloader is not None: + valid_data_iterator = _get_iterator(dl_type, valid_dataloader) + else: + valid_data_iterator = None + + if test_dataloader is not None: + test_data_iterator = _get_iterator(dl_type, test_dataloader) + else: + test_data_iterator = None + + return train_data_iterator, valid_data_iterator, test_data_iterator diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/utils.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/utils.py new file mode 100644 index 0000000..ef2ec1c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/utils.py @@ -0,0 +1,360 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""General utilities.""" + +import sys + +import torch + +try: + from apex.multi_tensor_apply import multi_tensor_applier +except ImportError: + multi_tensor_applier = None + +try: + import amp_C +except ImportError: + amp_C = None + +from megatron.training import ( + get_args, + get_adlr_autoresume, +) +from megatron.core import DistributedDataParallel as DDP +from megatron.core import mpu +from megatron.core.tensor_parallel import param_is_not_tensor_parallel_duplicate +from megatron.legacy.model import Float16Module +from megatron.legacy.model.module import param_is_not_shared + + +ALL_MODULE_WRAPPER_CLASSNAMES = (DDP, Float16Module) + + +def unwrap_model(model, module_instances=ALL_MODULE_WRAPPER_CLASSNAMES): + return_list = True + if not isinstance(model, list): + model = [model] + return_list = False + unwrapped_model = [] + for model_module in model: + while isinstance(model_module, module_instances): + model_module = model_module.module + unwrapped_model.append(model_module) + if not return_list: + return unwrapped_model[0] + return unwrapped_model + + +def calc_params_l2_norm(model): + """Calculate l2 norm of parameters """ + args = get_args() + if not isinstance(model, list): + model = [model] + # Remove duplicate params. + params_data = [] + for model_ in model: + for param in model_.parameters(): + is_not_tp_duplicate = param_is_not_tensor_parallel_duplicate(param) + if mpu.get_expert_model_parallel_rank() > 0: + if not getattr(param, 'allreduce', True) and is_not_tp_duplicate: + assert param_is_not_shared(param) + params_data.append(param.data.float() if args.bf16 else param.data) + else: + is_not_shared = param_is_not_shared(param) + if is_not_shared and is_not_tp_duplicate: + params_data.append(param.data.float() if args.bf16 else param.data) + + # Check the availability of apex + assert multi_tensor_applier is not None and amp_C is not None, \ + "apex is not available, please install it from https://github.com/NVIDIA/apex" + + # Calculate norm + dummy_overflow_buf = torch.tensor([0], dtype=torch.int, device='cuda') + norm, _ = multi_tensor_applier( + amp_C.multi_tensor_l2norm, + dummy_overflow_buf, + [params_data], + False # no per-parameter norm + ) + norm_2 = norm * norm + if mpu.get_expert_model_parallel_world_size() == 1: + # Sum across all model-parallel GPUs(tensor + pipeline). + torch.distributed.all_reduce(norm_2, + op=torch.distributed.ReduceOp.SUM, + group=mpu.get_model_parallel_group()) + else: + # Sum across tensor, pipeline and expert model-parallel GPUs. + torch.distributed.all_reduce(norm_2, + op=torch.distributed.ReduceOp.SUM, + group=mpu.get_tensor_and_expert_parallel_group()) + torch.distributed.all_reduce(norm_2, + op=torch.distributed.ReduceOp.SUM, + group=mpu.get_pipeline_model_parallel_group()) + return norm_2.item() ** 0.5 + + +def average_losses_across_data_parallel_group(losses): + """Reduce a tensor of losses across all GPUs.""" + averaged_losses = torch.cat( + [loss.clone().detach().view(1) for loss in losses]) + torch.distributed.all_reduce(averaged_losses, + group=mpu.get_data_parallel_group()) + averaged_losses = averaged_losses / \ + torch.distributed.get_world_size(group=mpu.get_data_parallel_group()) + + return averaged_losses + + +def report_memory(name): + """Simple GPU memory report.""" + mega_bytes = 1024.0 * 1024.0 + string = name + ' memory (MB)' + string += ' | allocated: {}'.format( + torch.cuda.memory_allocated() / mega_bytes) + string += ' | max allocated: {}'.format( + torch.cuda.max_memory_allocated() / mega_bytes) + string += ' | reserved: {}'.format( + torch.cuda.memory_reserved() / mega_bytes) + string += ' | max reserved: {}'.format( + torch.cuda.max_memory_reserved() / mega_bytes) + if mpu.get_data_parallel_rank() == 0: + print("[Rank {}] {}".format(torch.distributed.get_rank(), string), + flush=True) + + +def print_params_min_max_norm(optimizer, iteration): + """Print min, max, and norm of all parameters.""" + index = 0 + rank = torch.distributed.get_rank() + string = 'iteration, rank, index, tensor-model-parallel, min, max, norm\n' + optimizer_ = optimizer.optimizer + for param_group in optimizer_.param_groups: + for param in param_group['params']: + index += 1 + min_ = param.data.min() + max_ = param.data.max() + norm = torch.linalg.norm(param.data) + string += '{:7d}, {:4d}, {:4d}, {:2d}, '.format( + iteration, rank, index, int(param.tensor_model_parallel)) + string += '{:.6E}, {:.6E}, {:.6E}\n'.format(min_, max_, norm) + print(string, flush=True) + + +def check_adlr_autoresume_termination(iteration, model, + optimizer, opt_param_scheduler): + """Check for autoresume signal and exit if it is received.""" + from megatron.training.checkpointing import save_checkpoint + + args = get_args() + autoresume = get_adlr_autoresume() + # Add barrier to ensure consistnecy. + torch.distributed.barrier() + if autoresume.termination_requested(): + if args.save: + save_checkpoint(iteration, model, optimizer, opt_param_scheduler) + print_rank_0(">>> autoresume termination request found!") + if torch.distributed.get_rank() == 0: + autoresume.request_resume() + print_rank_0(">>> training terminated. Returning") + sys.exit(0) + + +def get_ltor_masks_and_position_ids(data, + eod_token, + reset_position_ids, + reset_attention_mask, + eod_mask_loss): + """Build masks and position id for left to right model.""" + + # Extract batch size and sequence length. + micro_batch_size, seq_length = data.size() + + # Attention mask (lower triangular). + if reset_attention_mask: + att_mask_batch = micro_batch_size + else: + att_mask_batch = 1 + attention_mask = torch.tril(torch.ones( + (att_mask_batch, seq_length, seq_length), device=data.device)).view( + att_mask_batch, 1, seq_length, seq_length) + + # Loss mask. + loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device) + if eod_mask_loss: + loss_mask[data == eod_token] = 0.0 + + # Position ids. + position_ids = torch.arange(seq_length, dtype=torch.long, + device=data.device) + position_ids = position_ids.unsqueeze(0).expand_as(data) + # We need to clone as the ids will be modifed based on batch index. + if reset_position_ids: + position_ids = position_ids.clone() + + if reset_position_ids or reset_attention_mask: + # Loop through the batches: + for b in range(micro_batch_size): + + # Find indecies where EOD token is. + eod_index = position_ids[b, data[b] == eod_token] + # Detach indecies from positions if going to modify positions. + if reset_position_ids: + eod_index = eod_index.clone() + + # Loop through EOD indecies: + prev_index = 0 + for j in range(eod_index.size()[0]): + i = eod_index[j] + # Mask attention loss. + if reset_attention_mask: + attention_mask[b, 0, (i + 1):, :(i + 1)] = 0 + # Reset positions. + if reset_position_ids: + position_ids[b, (i + 1):] -= (i + 1 - prev_index) + prev_index = i + 1 + + # Convert attention mask to binary: + attention_mask = (attention_mask < 0.5) + + return attention_mask, loss_mask, position_ids + + +def get_batch_on_this_cp_rank(batch): + """ Slice batch input along sequence dimension into multiple chunks, + which are parallelized across GPUs in a context parallel group. + """ + + # With causal masking, each token only attends to its prior tokens. Simply split + # sequence into CP chunks can result in severe load imbalance. That's to say, chunks + # at the end of sequence have bigger workload than others. To address this issue, + # we split sequence into 2*CP ranks. Assuming CP=2, we then get 4 chunks, chunk_0 + # and chunk_3 are assigned to GPU0, chunk_1 and chunk_2 are assigned to GPU1, so + # that we can get balanced workload among GPUs in a context parallel group. + args = get_args() + cp_size = args.context_parallel_size + if cp_size > 1: + cp_rank = mpu.get_context_parallel_rank() + for key, val in batch.items(): + if val is not None: + seq_dim = 1 if key != 'attention_mask' else 2 + val = val.view( + *val.shape[0:seq_dim], + 2 * cp_size, + val.shape[seq_dim] // (2 * cp_size), + *val.shape[(seq_dim + 1) :], + ) + index = torch.tensor([cp_rank, (2 * cp_size - cp_rank - 1)], + device="cpu", pin_memory=True).cuda(non_blocking=True) + val = val.index_select(seq_dim, index) + val = val.view(*val.shape[0:seq_dim], -1, *val.shape[(seq_dim + 2) :]) + batch[key] = val + + return batch + + +def print_rank_0(message): + """If distributed is initialized, print only on rank 0.""" + if torch.distributed.is_initialized(): + if torch.distributed.get_rank() == 0: + print(message, flush=True) + else: + print(message, flush=True) + +def is_last_rank(): + return torch.distributed.get_rank() == ( + torch.distributed.get_world_size() - 1) + +def print_rank_last(message): + """If distributed is initialized, print only on last rank.""" + if torch.distributed.is_initialized(): + if is_last_rank(): + print(message, flush=True) + else: + print(message, flush=True) + + +def get_batch_on_this_tp_rank(data_iterator): + + args = get_args() + + def _broadcast(item): + if item is not None: + torch.distributed.broadcast(item, mpu.get_tensor_model_parallel_src_rank(), group=mpu.get_tensor_model_parallel_group()) + + if mpu.get_tensor_model_parallel_rank() == 0: + + if data_iterator is not None: + data = next(data_iterator) + else: + data = None + + batch = { + 'tokens': data["tokens"].cuda(non_blocking = True), + 'labels': data["labels"].cuda(non_blocking = True), + 'loss_mask': data["loss_mask"].cuda(non_blocking = True), + 'attention_mask': None if "attention_mask" not in data else data["attention_mask"].cuda(non_blocking = True), + 'position_ids': data["position_ids"].cuda(non_blocking = True) + } + + if args.pipeline_model_parallel_size == 1: + _broadcast(batch['tokens']) + _broadcast(batch['labels']) + _broadcast(batch['loss_mask']) + _broadcast(batch['attention_mask']) + _broadcast(batch['position_ids']) + + elif mpu.is_pipeline_first_stage(): + _broadcast(batch['tokens']) + _broadcast(batch['attention_mask']) + _broadcast(batch['position_ids']) + + elif mpu.is_pipeline_last_stage(): + _broadcast(batch['labels']) + _broadcast(batch['loss_mask']) + _broadcast(batch['attention_mask']) + + else: + + tokens=torch.empty((args.micro_batch_size,args.seq_length), dtype = torch.int64 , device = torch.cuda.current_device()) + labels=torch.empty((args.micro_batch_size,args.seq_length), dtype = torch.int64 , device = torch.cuda.current_device()) + loss_mask=torch.empty((args.micro_batch_size,args.seq_length), dtype = torch.float32 , device = torch.cuda.current_device()) + if args.create_attention_mask_in_dataloader: + attention_mask=torch.empty( + (args.micro_batch_size,1,args.seq_length,args.seq_length), dtype = torch.bool , device = torch.cuda.current_device() + ) + else: + attention_mask=None + position_ids=torch.empty((args.micro_batch_size,args.seq_length), dtype = torch.int64 , device = torch.cuda.current_device()) + + if args.pipeline_model_parallel_size == 1: + _broadcast(tokens) + _broadcast(labels) + _broadcast(loss_mask) + _broadcast(attention_mask) + _broadcast(position_ids) + + elif mpu.is_pipeline_first_stage(): + labels=None + loss_mask=None + + _broadcast(tokens) + _broadcast(attention_mask) + _broadcast(position_ids) + + elif mpu.is_pipeline_last_stage(): + tokens=None + position_ids=None + + _broadcast(labels) + _broadcast(loss_mask) + _broadcast(attention_mask) + + batch = { + 'tokens': tokens, + 'labels': labels, + 'loss_mask': loss_mask, + 'attention_mask': attention_mask, + 'position_ids': position_ids + } + + return batch diff --git a/Megatron-LM-core_r0.7.0.beta/megatron/training/yaml_arguments.py b/Megatron-LM-core_r0.7.0.beta/megatron/training/yaml_arguments.py new file mode 100644 index 0000000..f81d4de --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/megatron/training/yaml_arguments.py @@ -0,0 +1,456 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Megatron arguments.""" + +import argparse +import dataclasses +import json +import os +import torch +import types + +from itertools import chain, starmap +from types import SimpleNamespace +import yaml, re, os +from types import SimpleNamespace + +import torch.nn.functional as F + +from megatron.core.transformer import TransformerConfig + +# Taken from https://stackoverflow.com/questions/65414773/parse-environment-variable-from-yaml-with-pyyaml +# Allows for yaml to use environment variables +env_pattern = re.compile(r".*?\${(.*?)}.*?") +def env_constructor(loader, node): + value = loader.construct_scalar(node) + for group in env_pattern.findall(value): + assert os.environ.get(group) is not None, f"environment variable {group} in yaml not found" + value = value.replace(f"${{{group}}}", os.environ.get(group)) + return value +yaml.add_implicit_resolver("!pathex", env_pattern) +yaml.add_constructor("!pathex", env_constructor) + + +str_dtype_to_torch = { + "float32" : torch.float32, + "float16" : torch.float16, + "bfloat16" : torch.bfloat16 +} + +def validate_yaml(args, defaults={}): + + # This is for legacy script env var setting + if type(args.data_path) is str: + # If no white space its a single path + split_data_path = args.data_path.split() + if len(split_data_path) != 1: + args.data_path = split_data_path + + # Tensor model parallel size. + args.model_parallel.tensor_model_parallel_size = min( + args.model_parallel.tensor_model_parallel_size, args.world_size) + assert args.world_size % args.model_parallel.tensor_model_parallel_size == 0, 'world size'\ + ' ({}) is not divisible by tensor model parallel size ({})'.format( + args.world_size, args.model_parallel.tensor_model_parallel_size) + # Pipeline model parallel size. + args.model_parallel.pipeline_model_parallel_size = min( + args.model_parallel.pipeline_model_parallel_size, + (args.world_size // args.model_parallel.tensor_model_parallel_size)) + args.model_parallel.transformer_pipeline_model_parallel_size = ( + args.model_parallel.pipeline_model_parallel_size - 1 + if args.standalone_embedding_stage else + args.model_parallel.pipeline_model_parallel_size + ) + # Checks. + model_parallel_size = args.model_parallel.pipeline_model_parallel_size * \ + args.model_parallel.tensor_model_parallel_size + assert args.world_size % (model_parallel_size * args.model_parallel.context_parallel_size) == 0, \ + 'world size ({}) is not divisible by tensor parallel size ({}) times ' \ + 'pipeline parallel size ({}) times context parallel size ({})'.format( + args.world_size, args.model_parallel.tensor_model_parallel_size, + args.model_parallel.pipeline_model_parallel_size, args.model_parallel.context_parallel_size) + + # data_parallel_size is not in model parallel config + args.data_parallel_size = args.world_size // (model_parallel_size * args.model_parallel.context_parallel_size) + if args.rank == 0: + print('using world size: {}, data-parallel size: {}, ' + 'context-parallel size: {} ' + 'tensor-model-parallel size: {}, ' + 'pipeline-model-parallel size: {} '.format( + args.world_size, args.data_parallel_size, + args.model_parallel.context_parallel_size, + args.model_parallel.tensor_model_parallel_size, + args.model_parallel.pipeline_model_parallel_size), flush=True) + if args.model_parallel.pipeline_model_parallel_size > 1: + if args.model_parallel.pipeline_model_parallel_split_rank is not None: + assert args.model_parallel.pipeline_model_parallel_split_rank < \ + args.model_parallel.pipeline_model_parallel_size, 'split rank needs'\ + ' to be less than pipeline model parallel size ({})'.format( + args.model_parallel.pipeline_model_parallel_size) + + if args.model_parallel.tp_comm_overlap: + assert args.model_parallel.sequence_parallel == True, 'Tensor parallel communication/GEMM overlap can happen only when sequence parallelism is enabled' + + # Set input defaults. + for key in defaults: + # For default to be valid, it should not be provided in the + # arguments that are passed to the program. We check this by + # ensuring the arg is set to None. + if getattr(args, key, None) is not None: + if args.rank == 0: + print('WARNING: overriding default arguments for {key}:{v} \ + with {key}:{v2}'.format(key=key, v=defaults[key], + v2=getattr(args, key)), + flush=True) + else: + setattr(args, key, defaults[key]) + + # Batch size. + assert args.micro_batch_size is not None + assert args.micro_batch_size > 0 + if args.global_batch_size is None: + args.global_batch_size = args.micro_batch_size * args.data_parallel_size + if args.rank == 0: + print('setting global batch size to {}'.format( + args.global_batch_size), flush=True) + assert args.global_batch_size > 0 + + # num_layers_per_virtual_pipeline_stage is not insde model parallel for checkpointing + if args.num_layers_per_virtual_pipeline_stage is not None: + assert args.model_parallel.pipeline_model_parallel_size > 2, \ + 'pipeline-model-parallel size should be greater than 2 with ' \ + 'interleaved schedule' + assert args.language_model.num_layers % args.model_parallel.transformer_pipeline_model_parallel_size == 0, \ + 'number of layers should be divisible by the pipeline parallel size' + num_layers_per_pipeline_stage = args.language_model.num_layers // args.model_parallel.transformer_pipeline_model_parallel_size + assert num_layers_per_pipeline_stage % args.num_layers_per_virtual_pipeline_stage == 0, \ + 'number of layers per pipeline stage must be divisible number of layers per virtual pipeline stage' + args.model_parallel.virtual_pipeline_model_parallel_size = num_layers_per_pipeline_stage // \ + args.num_layers_per_virtual_pipeline_stage + else: + args.model_parallel.virtual_pipeline_model_parallel_size = None + # Overlap P2P communication is disabled if not using the interleaved schedule. + args.model_parallel.overlap_p2p_comm = False + if args.rank == 0: + print('WARNING: Setting args.overlap_p2p_comm to False since non-interleaved ' + 'schedule does not support overlapping p2p communication') + + if args.overlap_param_gather: + assert args.use_distributed_optimizer, \ + '--overlap-param-gather only supported with distributed optimizer' + assert args.overlap_grad_reduce, \ + '--overlap-grad-reduce should be turned on when using --overlap-param-gather' + + # Parameters dtype. + if args.model_parallel.fp16: + assert not args.model_parallel.bf16 + args.model_parallel.params_dtype = torch.half + if args.model_parallel.bf16: + assert not args.model_parallel.fp16 + args.model_parallel.params_dtype = torch.bfloat16 + # bfloat16 requires gradient accumulation and all-reduce to + # be done in fp32. + if not args.accumulate_allreduce_grads_in_fp32: + args.accumulate_allreduce_grads_in_fp32 = True + if args.rank == 0: + print('accumulate and all-reduce gradients in fp32 for ' + 'bfloat16 data type.', flush=True) + + if args.rank == 0: + print('using {} for parameters ...'.format(args.model_parallel.params_dtype), + flush=True) + + if args.dataloader_type is None: + args.dataloader_type = 'single' + + # Consumed tokens. + args.consumed_train_samples = 0 + args.consumed_valid_samples = 0 + + # Support for variable sequence lengths across batches/microbatches. + # set it if the dataloader supports generation of variable sequence lengths + # across batches/microbatches. Due to additional communication overhead + # during pipeline parallelism, it should not be set if sequence length + # is constant during training. + args.model_parallel.variable_seq_lengths = False + + # Iteration-based training. + if args.train_iters: + # If we use iteration-based training, make sure the + # sample-based options are off. + assert args.train_samples is None, \ + 'expected iteration-based training' + assert args.lr_decay_samples is None, \ + 'expected iteration-based learning rate decay' + assert args.lr_warmup_samples == 0, \ + 'expected iteration-based learning rate warmup' + assert args.rampup_batch_size is None, \ + 'expected no batch-size rampup for iteration-based training' + if args.lr_warmup_fraction is not None: + assert args.lr_warmup_iters == 0, \ + 'can only specify one of lr-warmup-fraction and lr-warmup-iters' + + # Sample-based training. + if args.train_samples: + # If we use sample-based training, make sure the + # iteration-based options are off. + assert args.train_iters is None, \ + 'expected sample-based training' + assert args.lr_decay_iters is None, \ + 'expected sample-based learning rate decay' + assert args.lr_warmup_iters == 0, \ + 'expected sample-based learnig rate warmup' + if args.lr_warmup_fraction is not None: + assert args.lr_warmup_samples == 0, \ + 'can only specify one of lr-warmup-fraction ' \ + 'and lr-warmup-samples' + + # How to handle this better + if args.language_model.num_layers is not None: + assert args.encoder_num_layers is None, \ + 'cannot have both num-layers and encoder-num-layers specified' + args.encoder_num_layers = args.language_model.num_layers + else: + assert args.encoder_num_layers is not None, \ + 'either num-layers or encoder-num-layers should be specified' + args.language_model.num_layers = args.encoder_num_layers + + # Check required arguments. + # removed max_position_embeddings from reqs + required_args = ['num_layers', 'hidden_size', 'num_attention_heads'] + for req_arg in required_args: + _check_arg_is_not_none(args.language_model, req_arg) + + # Checks. + if args.language_model.ffn_hidden_size is None: + if args.language_model.activation_func == "swiglu": + # reduce the dimnesion for MLP since projections happens on + # two linear layers. this keeps the number of paramters in + # the same ballpark as the counterpart with 4*h size + # we keep it a multiple of 64, which means the actual tensor size + # will be a multiple of 64 / tp_size + args.language_model.ffn_hidden_size = int((4 * args.language_model.hidden_size * 2 / 3) / 64) * 64 + else: + args.language_model.ffn_hidden_size = 4 * args.language_model.hidden_size + + if args.language_model.kv_channels is None: + assert args.language_model.hidden_size % args.language_model.num_attention_heads == 0 + args.language_model.kv_channels = args.language_model.hidden_size // args.language_model.num_attention_heads + + #TODO: Implement arguments for encoder-decoder + if args.seq_length is not None: + assert args.encoder_seq_length is None + args.encoder_seq_length = args.seq_length + else: + assert args.encoder_seq_length is not None + args.seq_length = args.encoder_seq_length + + if args.seq_length is not None: + assert args.max_position_embeddings >= args.seq_length + if args.decoder_seq_length is not None: + assert args.max_position_embeddings >= args.decoder_seq_length + if args.lr is not None: + assert args.min_lr <= args.lr + if args.save is not None: + assert args.save_interval is not None + # Mixed precision checks. + if args.fp16_lm_cross_entropy: + assert args.fp16, 'lm cross entropy in fp16 only support in fp16 mode.' + if args.language_model.fp32_residual_connection: + assert args.model_parallel.fp16 or args.model_parallel.bf16, \ + 'residual connection in fp32 only supported when using fp16 or bf16.' + + if args.language_model.moe_grouped_gemm: + assert args.model_parallel.bf16, 'Currently GroupedGEMM for MoE only supports bf16 dtype.' + dc = torch.cuda.get_device_capability() + assert dc[0] >= 8, "Unsupported compute capability for GroupedGEMM kernels." + + if args.weight_decay_incr_style == 'constant': + assert args.start_weight_decay is None + assert args.end_weight_decay is None + args.start_weight_decay = args.weight_decay + args.end_weight_decay = args.weight_decay + else: + assert args.start_weight_decay is not None + assert args.end_weight_decay is not None + + TORCH_MAJOR = int(torch.__version__.split('.')[0]) + TORCH_MINOR = int(torch.__version__.split('.')[1]) + # Persistent fused layer norm. + if TORCH_MAJOR < 1 or (TORCH_MAJOR == 1 and TORCH_MINOR < 11): + args.language_model.persist_layer_norm = False + if args.rank == 0: + print('Persistent fused layer norm kernel is supported from ' + 'pytorch v1.11 (nvidia pytorch container paired with v1.11). ' + 'Defaulting to no_persist_layer_norm=True') + + # Activation recomputing. + if args.language_model.distribute_saved_activations: + assert args.model_parallel.tensor_model_parallel_size > 1, 'can distribute ' \ + 'recomputed activations only across tensor model ' \ + 'parallel groups' + assert args.language_model.recompute_granularity == 'full', \ + 'distributed recompute activations is only '\ + 'application to full recompute granularity' + assert args.language_model.recompute_method is not None, \ + 'for distributed recompute activations to work you '\ + 'need to use a recompute method ' + assert (TORCH_MAJOR, TORCH_MINOR) >= (1, 10), \ + 'distributed recompute activations are supported for pytorch ' \ + 'v1.10 and above (Nvidia Pytorch container >= 21.07). Current ' \ + 'pytorch version is v%s.%s.' % (TORCH_MAJOR, TORCH_MINOR) + + if args.language_model.recompute_granularity == 'selective': + assert args.language_model.recompute_method is None, \ + 'recompute method is not yet supported for ' \ + 'selective recomputing granularity' + + # disable sequence parallelism when tp=1 + # to avoid change in numerics when + # sequence_parallelism is enabled. + if args.model_parallel.tensor_model_parallel_size == 1: + args.model_parallel.sequence_parallel = False + + # disable async_tensor_model_parallel_allreduce when + # model parallel memory optimization is enabled + if args.model_parallel.sequence_parallel: + args.model_parallel.async_tensor_model_parallel_allreduce = False + + if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1": + if args.model_parallel.sequence_parallel: + raise RuntimeError( + "Using sequence parallelism requires setting the environment variable " + "CUDA_DEVICE_MAX_CONNECTIONS to 1") + if args.model_parallel.async_tensor_model_parallel_allreduce: + raise RuntimeError( + "Using async gradient all reduce requires setting the environment " + "variable CUDA_DEVICE_MAX_CONNECTIONS to 1") + + # Retro checks. + if getattr(args, 'retro_add_retriever', False): + raise Exception("Retro untested for yaml args. See arguments.py.") + + # Sequence parallelism unsupported. + assert not args.sequence_parallel, \ + "retro currently does not support sequence parallelism." + + # Pipeline parallelism unsupported. + assert args.pipeline_model_parallel_size == 1, \ + "retro currently does not support pipeline parallelism." + + #TODO: Retro args loading not tested + # Load retro args (used by both Retro & GPT). + if getattr(args, 'retro_project_dir', None) is not None: + raise Exception("Retro untested for yaml args. See arguments.py.") + + if args.language_model.rotary_interleaved and args.language_model.apply_rope_fusion: + raise RuntimeError('--rotary-interleaved does not work with rope_fusion.') + + # MoE Spec check + if args.language_model.num_moe_experts is not None: + assert args.spec is None, "Model Spec must be None when using MoEs" + if args.model_parallel.tensor_model_parallel_size > 1: + assert args.model_parallel.sequence_parallel, \ + "When using MoE and tensor parallelism, sequence parallelism must be used." + + # Expert parallelism check + if args.model_parallel.expert_model_parallel_size > 1: + assert args.language_model.num_moe_experts is not None, "num_experts must be non None to use expert model parallelism" + assert args.language_model.num_moe_experts % args.model_parallel.expert_model_parallel_size == 0, \ + "Number of experts should be a multiple of expert model parallel_size." + assert not args.model_parallel.fp16, \ + "Expert parallelism is not supported with fp16 training." + + # Print arguments. + _print_args("arguments", args) + + #TODO: Added as much of the global initialization requires the model parallel arguments + args = SimpleNamespace(**args.__dict__, **args.model_parallel.__dict__) + args = SimpleNamespace(**args.__dict__, **args.language_model.__dict__) + # For GPT Layer spec in pretrain_gpt + args.num_experts = args.language_model.num_moe_experts + + return args + +def _print_args(title, args): + """Print arguments.""" + if args.rank == 0: + print(f'------------------------ {title} ------------------------', + flush=True) + str_list = [] + for arg in vars(args): + dots = '.' * (48 - len(arg)) + str_list.append(' {} {} {}'.format(arg, dots, getattr(args, arg))) + for arg in sorted(str_list, key=lambda x: x.lower()): + print(arg, flush=True) + print(f'-------------------- end of {title} ---------------------', + flush=True) + +def core_config_from_args(args, dataclass=TransformerConfig): + """Builds core config object from namespace args from given dataclass + + Raises exception if argument missing in args + + Args: + args(SimpleNamespace, optional): Namespace to pull argument values from + dataclass (dataclass, optional): Core dataclass config to pull argument names from + + + Returns: + SimpleNamespace: The returned namespace to build core config from + """ + kw_args = {} + for f in dataclasses.fields(dataclass): + if hasattr(args, f.name): + kw_args[f.name] = getattr(args, f.name) + else: + raise Exception(f"Missing argument {f.name} for {str(dataclass)} config") + return kw_args + +def _check_arg_is_not_none(args, arg): + assert getattr(args, arg) is not None, '{} argument is None'.format(arg) + +def core_transformer_config_from_yaml(args, transfomer_key = "language_model"): + # Combine transfomer config with model parallel args + args = SimpleNamespace(**vars(getattr(args, transfomer_key)), **vars(args.model_parallel)) + # Translate args to core transformer configuration + kw_args = core_config_from_args(args, TransformerConfig) + + # Hardcoded + kw_args['deallocate_pipeline_outputs'] = True + kw_args['pipeline_dtype'] = kw_args['params_dtype'] + kw_args['batch_p2p_comm'] = not args.overlap_p2p_comm + + assert args.activation_func in ["swiglu","squaredrelu","gelu"], f"{args.activation_func} is not a supported activation function" + if args.activation_func == "swiglu": + kw_args['activation_func'] = F.silu + kw_args['gated_linear_unit'] = True + kw_args['bias_activation_fusion'] = args.bias_swiglu_fusion + elif args.activation_func == "squaredrelu": + def squared_relu(x): + return torch.pow(F.relu(x), 2) + kw_args['activation_func'] = squared_relu + elif args.activation_func == "gelu": + kw_args['activation_func'] = F.gelu + if args.add_bias_linear: + kw_args['bias_activation_fusion'] = False + else: + kw_args['bias_activation_fusion'] = args.bias_activation_fusion + + if args.init_method == "xavier_uniform": + kw_args['init_method'] = torch.nn.init.xavier_uniform_ + kw_args['scaled_init_method'] = torch.nn.init.xavier_uniform_ + + # Return Transformer config. + return TransformerConfig(**kw_args) + +def load_yaml(yaml_path): + print(f"warning using experimental yaml arguments feature, argparse arguments will be ignored") + with open(yaml_path, "r") as f: + config = yaml.load(f,Loader=yaml.FullLoader) + # Convert to nested namespace + config_namespace = json.loads(json.dumps(config), object_hook=lambda item: SimpleNamespace(**item)) + # Add config location to namespace + config_namespace.yaml_cfg = yaml_path + return config_namespace + diff --git a/Megatron-LM-core_r0.7.0.beta/setup.py b/Megatron-LM-core_r0.7.0.beta/setup.py new file mode 100644 index 0000000..c1666b6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/setup.py @@ -0,0 +1,129 @@ +"""Setup for pip package.""" + +import importlib.util +import os +import subprocess +import sys + +import setuptools +from setuptools import Extension, setup +from setuptools.command.build_ext import build_ext + +spec = importlib.util.spec_from_file_location('package_info', 'megatron/core/package_info.py') +package_info = importlib.util.module_from_spec(spec) +spec.loader.exec_module(package_info) + + +__contact_emails__ = package_info.__contact_emails__ +__contact_names__ = package_info.__contact_names__ +__description__ = package_info.__description__ +__download_url__ = package_info.__download_url__ +__homepage__ = package_info.__homepage__ +__keywords__ = package_info.__keywords__ +__license__ = package_info.__license__ +__package_name__ = package_info.__package_name__ +__repository_url__ = package_info.__repository_url__ +__version__ = package_info.__version__ + + +if os.path.exists('megatron/core/README.md'): + with open("megatron/core/README.md", "r", encoding='utf-8') as fh: + long_description = fh.read() + long_description_content_type = "text/markdown" + +else: + long_description = 'See ' + __homepage__ + long_description_content_type = "text/plain" + + +############################################################################### +# Dependency Loading # +# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # + + +def req_file(filename, folder="megatron/core"): + with open(os.path.join(folder, filename), encoding='utf-8') as f: + content = f.readlines() + # you may also want to remove whitespace characters + # Example: `\n` at the end of each line + return [x.strip() for x in content] + + +install_requires = req_file("requirements.txt") + + +############################################################################### +# Extension Making # +# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # + +extra_compile_args = subprocess.check_output(["python3", "-m", "pybind11", "--includes"]).decode("utf-8").strip().split() + +############################################################################### + +setuptools.setup( + name=__package_name__, + # Versions should comply with PEP440. For a discussion on single-sourcing + # the version across setup.py and the project code, see + # https://packaging.python.org/en/latest/single_source_version.html + version=__version__, + description=__description__, + long_description=long_description, + long_description_content_type=long_description_content_type, + # The project's main homepage. + url=__repository_url__, + download_url=__download_url__, + # Author details + author=__contact_names__, + author_email=__contact_emails__, + # maintainer Details + maintainer=__contact_names__, + maintainer_email=__contact_emails__, + # The licence under which the project is released + license=__license__, + classifiers=[ + # How mature is this project? Common values are + # 1 - Planning + # 2 - Pre-Alpha + # 3 - Alpha + # 4 - Beta + # 5 - Production/Stable + # 6 - Mature + # 7 - Inactive + 'Development Status :: 5 - Production/Stable', + # Indicate who your project is intended for + 'Intended Audience :: Developers', + 'Intended Audience :: Science/Research', + 'Intended Audience :: Information Technology', + # Indicate what your project relates to + 'Topic :: Scientific/Engineering', + 'Topic :: Scientific/Engineering :: Mathematics', + 'Topic :: Scientific/Engineering :: Image Recognition', + 'Topic :: Scientific/Engineering :: Artificial Intelligence', + 'Topic :: Software Development :: Libraries', + 'Topic :: Software Development :: Libraries :: Python Modules', + 'Topic :: Utilities', + # Pick your license as you wish (should match "license" above) + 'License :: OSI Approved :: BSD License', + # Supported python versions + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.8', + 'Programming Language :: Python :: 3.9', + # Additional Setting + 'Environment :: Console', + 'Natural Language :: English', + 'Operating System :: OS Independent', + ], + packages=setuptools.find_packages(include=['megatron.core', 'megatron.core.*'],), + ext_modules=[ + Extension( + "megatron.core.datasets.helpers", + sources=["megatron/core/datasets/helpers.cpp"], + language="c++", + extra_compile_args=extra_compile_args, + ) + ], + # Add in any packaged data. + include_package_data=True, + # PyPI package information. + keywords=__keywords__, +) diff --git a/Megatron-LM-core_r0.7.0.beta/tools/autoformat.sh b/Megatron-LM-core_r0.7.0.beta/tools/autoformat.sh new file mode 100755 index 0000000..e2b5bf5 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/autoformat.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) + +# for now we just format core + +black ${SCRIPT_DIR}/../megatron/core +isort ${SCRIPT_DIR}/../megatron/core diff --git a/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/__init__.py b/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/__init__.py new file mode 100644 index 0000000..766a66b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +from .embed import BertEmbedder, DiskDataParallelBertEmbedder diff --git a/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/dataset.py b/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/dataset.py new file mode 100644 index 0000000..da165b8 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/dataset.py @@ -0,0 +1,55 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import numpy as np +import torch + +from megatron.training import get_args, get_tokenizer + + +class BertEmbeddingDataset(torch.utils.data.Dataset): + '''Dataset to convert a text dataset to Bert tokens.''' + + def __init__(self, text_dataset, max_seq_length): + + super().__init__() + + args = get_args() + + # Dataset, tokenizer. + self.text_dataset = text_dataset + self.max_seq_length = max_seq_length + self.bert_tokenizer = get_tokenizer() + + def __len__(self): + return len(self.text_dataset) + + @classmethod + def build_sample(cls, tokenizer, token_ids): + get_constant_array = lambda c : np.full((len(token_ids) + 2,), c, "int64") + return { + "text" : np.array([ tokenizer.cls, *token_ids, tokenizer.sep ], dtype="int64"), + "types" : get_constant_array(0), + "labels" : get_constant_array(-1), + "is_random" : 0, + "loss_mask" : get_constant_array(0), + "padding_mask" : get_constant_array(1), + "truncated" : 0, + } + + def __getitem__(self, idx): + + # Text. + text_sample = self.text_dataset[idx] + text = text_sample["text"] + text = text.replace("<|endoftext|>", "") + + # Bert/Wordpiece tokens (+truncate). + bert_token_ids = self.bert_tokenizer.tokenize(text) + bert_token_ids = bert_token_ids[:self.max_seq_length - 2] # cls+sep. + if not bert_token_ids: + bert_token_ids = [ self.bert_tokenizer.pad_id ] # hack when empty seq + + # Bert sample. + sample = self.build_sample(self.bert_tokenizer, bert_token_ids) + + return sample diff --git a/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/embed.py b/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/embed.py new file mode 100644 index 0000000..b1f7eb8 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/embed.py @@ -0,0 +1,278 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from functools import partial +import numpy as np +import os +import time +import torch +from torch.utils.data import BatchSampler, DataLoader, SequentialSampler, Subset +from torch.utils.data._utils.collate import default_collate +from tqdm import tqdm + +from megatron.training import get_args, get_tokenizer, print_rank_0 +from megatron import core +from megatron.training.arguments import core_transformer_config_from_args +from megatron.core.datasets.retro.utils import get_blocks_by_rank +from megatron.core.enums import ModelType +from megatron.core.pipeline_parallel import get_forward_backward_func +from megatron.legacy.model import BertModel +from megatron.training import setup_model_and_optimizer +from pretrain_bert import model_provider, get_batch, loss_func, forward_step + +from .dataset import BertEmbeddingDataset +from .external_libs import h5py +from .huggingface import HuggingfaceEmbedder + + +def collate_batch(samples): + """Collate samples of various lengths. + + This collate function handles samples with various sequence lengths, by + padding 'text' arrays with pad_id, and other arrays with 0. + """ + + n_samples = len(samples) + keys = list(samples[0].keys()) + tokenizer = get_tokenizer() + + # Max sample length across all samples. + max_length_map = { key:0 for key in keys } + for sample in samples: + for key in keys: + value_length = \ + len(sample[key]) if isinstance(sample[key], np.ndarray) else None + max_length_map[key] = None \ + if value_length is None else \ + max(max_length_map[key], value_length) + + # Pad samples. + padded_samples = [] + for sample in samples: + padded_sample = {} + for key in keys: + padded_sample[key] = \ + np.pad( + sample[key], + (0, max_length_map[key] - len(sample[key])), + mode="constant", + constant_values=tokenizer.pad_id if key == "text" else 0, + ) \ + if isinstance(sample[key], np.ndarray) else \ + sample[key] + padded_samples.append(padded_sample) + + # Build batch with padded samples. + batch = default_collate(padded_samples) + + return batch + + +def get_data_loader(dataset, batch_size): + """Build data loader over data subset. + + Get a subset of the dataset (from start_idx -> end_idx), and wrap it in + a sequential sampler and data loader. + """ + + args = get_args() + + # Sequential & batch samplers. + batch_sampler = BatchSampler( + sampler=SequentialSampler(dataset), + batch_size=batch_size, + drop_last=False, + ) + + # Data loader. + data_loader = DataLoader(dataset, + batch_sampler=batch_sampler, + num_workers=args.num_workers, + pin_memory=True, + collate_fn=collate_batch) + + return data_loader + + +def embed_data_loader(models, data_loader, tag): + '''Iterate data loader and compute embeddings.''' + + # Verify no model parallelism. + args = get_args() + assert args.tensor_model_parallel_size == 1 and \ + args.pipeline_model_parallel_size == 1, \ + "since we call forward_step directly, only tp == pp == 1 allowed." + + # Data iterator. + data_iterator = iter(data_loader) + + # Eval mode. + for m in models: + m.eval() + + # Embed. + embeddings = [] + for _ in tqdm( + range(len(data_loader)), + " embed%s" % ("" if tag is None else " / '%s'" % tag), + miniters=len(data_loader) // 10, + disable=torch.distributed.get_rank() != 0, + ): + with torch.no_grad(): + result = forward_step(data_iterator, models[0]) + embeddings.append(result[0].detach().cpu().numpy()) + + # Concatenate embeddings. + embeddings = np.concatenate(embeddings, axis=0) + + return embeddings + + +class TextDataset(torch.utils.data.Dataset): + '''Dataset that holds a list of strings.''' + + def __init__(self, texts): + assert isinstance(texts, list) + for t in texts: + assert isinstance(t, str) + self.texts = texts + + def __len__(self): + return len(self.texts) + + def __getitem__(self, i): + return {"text": self.texts[i]} + + +class BertEmbedder: + '''Compute Bert embeddings, from a text dataset.''' + + def __init__(self, batch_size, max_bert_seq_length, embedder_type, warmup=True): + + args = get_args() + + assert args.output_bert_embeddings + + self.models, optimizer, opt_param_scheduler = \ + setup_model_and_optimizer(model_provider, + ModelType.encoder_or_decoder) + self.batch_size = batch_size + self.max_bert_seq_length = max_bert_seq_length + + # Init Huggingface, if in use. + if embedder_type == "megatron": + self.huggingface_embedder = None + elif embedder_type == "huggingface": + self.huggingface_embedder = HuggingfaceEmbedder(batch_size, + max_bert_seq_length) + else: + raise Exception("specialize for embedder type '%s'." % embedder_type) + + # Warm-up JIT. + # - Important to separately warm up: + # 1. batch_size == 1 + # 2. batch_size > 1 + if warmup: + warmup_dataset = TextDataset([ + "great fleas have lesser fleas, upon their backs to bite’em,", + "and lesser fleas have lesser fleas, and so, ad infinitum,", + "and those great fleas, themselves, in turn have greater fleas to go on,", + "while those again have greater still, and greater still, and so on.", + ]) + print_rank_0("bert / warmup single.") + for _ in range(3): + self.embed_text("hi, bert.") # batch size == 1 + print_rank_0("bert / warmup batch.") + for _ in range(3): + self.embed_text_dataset(warmup_dataset) # batch size > 1 + + def embed_text_dataset(self, text_dataset, tag=None): + '''Embed a text dataset.''' + + # Huggingface. + if self.huggingface_embedder: + return self.huggingface_embedder.embed_text_dataset(text_dataset) + + # Wrap in a BertEmbeddingDataset to tokenize samples. + bert_dataset = BertEmbeddingDataset(text_dataset, + self.max_bert_seq_length) + + # Embed. + data_loader = get_data_loader(bert_dataset, self.batch_size) + embeddings = embed_data_loader(self.models, data_loader, tag) + + return embeddings + + def embed_text(self, text): + '''Embed a single text string. + + Primarily used for on-the-fly embeddings, particularly during + analysis or debugging. For large scale, use 'embed_text_dataset()'. + ''' + + # Embed text. + text_ds = TextDataset([ text ]) + embed = self.embed_text_dataset(text_ds)[0] + + return embed + + +class DiskDataParallelBertEmbedder: + '''Process embeddings in blocks & save to disk.''' + + def __init__(self, embedder, block_size): + assert isinstance(embedder, BertEmbedder) + self.embedder = embedder + self.block_size = block_size + + def embed_text_blocks(self, name, dirname, text_dataset, + missing_embedding_blocks): + '''Process a text dataset in blocks.''' + + # Iterate blocks. + for block_index, block_info in enumerate(missing_embedding_blocks): + + # Missing block lists are extended with None to have equal-length + # lists. Skip the Nones. + if block_info is not None: + + # Progress. (*note*: move world progress to here.) + print_rank_0("embed '%s' block %d / %d ... %s." % ( + name, + block_index, + len(missing_embedding_blocks), + block_info["path"], + )) + + # Embed block. + sub_dataset = Subset(text_dataset, range(*block_info["range"])) + embeddings = self.embedder.embed_text_dataset(sub_dataset) + + # Save embeddings. + f = h5py.File(block_info["path"], "w") + f.create_dataset("data", data=embeddings) + f.close() + + # Synchronize progress across all ranks. (for easier observation) + print_rank_0(" > waiting for other ranks to finish block.") + torch.distributed.barrier() + + def embed_text_dataset(self, name, dirname, text_dataset): + '''Embed a text dataset.''' + + # Dataset dir. + os.makedirs(dirname, exist_ok=True) + + # Missing embedding blocks (stored on disk). + def validate(f): + assert f["data"].shape[1] == 1024 + blocks = get_blocks_by_rank( + dirname, + len(text_dataset), + self.block_size, + validate=validate) + + # Prevent missing file race condition. + torch.distributed.barrier() + + # Embed batches. + self.embed_text_blocks(name, dirname, text_dataset, blocks.missing) diff --git a/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/external_libs.py b/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/external_libs.py new file mode 100644 index 0000000..fb8e69f --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/external_libs.py @@ -0,0 +1,14 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import importlib + +required_libs = [ + "h5py", + "transformers", # for huggingface bert +] + +for lib in required_libs: + try: + globals()[lib] = importlib.import_module(lib) + except ImportError as e: + raise Exception(f"Missing one or more packages required for Bert embedding: {required_libs}. Tried importing '{lib}'.") diff --git a/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/huggingface.py b/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/huggingface.py new file mode 100644 index 0000000..1a08a80 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/bert_embedding/huggingface.py @@ -0,0 +1,126 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import numpy as np +import torch +from tqdm import tqdm + +from .external_libs import transformers + + +class IterableTextDataset(torch.utils.data.IterableDataset): + '''Iterable over a text dataset.''' + + def __init__(self, text_dataset): + self.text_dataset = text_dataset + + def __iter__(self): + '''Remove 'endoftext' string.''' + for sample_idx in range(len(self.text_dataset)): + sample = self.text_dataset[sample_idx] + text = sample["text"].replace("<|endoftext|>", "") + yield text + + +class MyFeatureExtractionPipeline(transformers.FeatureExtractionPipeline): + def _forward(self, model_inputs): + + # Embed inputs. + model_outputs = self.model(**model_inputs) + + # Attention mask. + embeddings = model_outputs[0] + masks = torch.sum(model_inputs['attention_mask'], dim=1) + + # Collect embeddings & check for nan. + outputs = [] + for embedding, mask in zip(embeddings, masks): + output = torch.mean(embedding[1: mask - 1], dim=0) + + # Nans due to empty input sequences; so only check first element. + if torch.isnan(output.view(-1)[0]).any(): + output.zero_() + + outputs.append(output) + + # Sample. + data = { + "input" : model_inputs["input_ids"], + "output" : outputs, + } + + return data + + def postprocess(self, model_outputs): + # Return input for analysis. + return { + "input" : model_outputs["input"].numpy(), + "output" : model_outputs["output"].numpy(), + } + + +class HuggingfaceEmbedder: + + def __init__(self, batch_size, max_seq_length): + + # Model, tokenizer. + self.model = transformers.BertModel.from_pretrained("bert-large-cased") + self.tokenizer = transformers.AutoTokenizer.from_pretrained( + "bert-large-cased", model_max_length=max_seq_length) + + # Feature extraction pipeline. + self.pipe = MyFeatureExtractionPipeline( + model=self.model, + tokenizer=self.tokenizer, + device=torch.cuda.current_device(), + truncation=True, + max_length=max_seq_length, + ) + + self.batch_size = batch_size + + def embed_text_dataset(self, text_dataset, verbose=True): + + # Wrap dataset in iterable. + dataset = IterableTextDataset(text_dataset) + + # Allocate output array. + n_samples = len(text_dataset) + embeddings = np.zeros((n_samples, 1024), dtype="f4") + start_idx = 0 + + # Wrap iterator in tqdm for verbose output. + _iter = self.pipe(dataset, batch_size=self.batch_size) + if verbose: + _iter = tqdm(_iter, "hf embed", total=n_samples) + + # Embed dataset. + for idx, out_dict in enumerate(_iter): + inp = out_dict["input"] + out = out_dict["output"] + embeddings[start_idx] = out + start_idx += 1 + + return embeddings + + def embed_text(self, text): + '''Embed a single text string. + + Primarily used for on-the-fly embeddings, particularly during + analysis or debugging. For large scale, use 'embed_text_dataset()'. + ''' + + class SingleTextDataset(torch.utils.data.Dataset): + '''Dataset that holds single string.''' + def __init__(self, text): + assert isinstance(text, str) + self.text = text + def __len__(self): + return 1 + def __getitem__(self, i): + return {"text": self.text} + + # Embed text. + text_ds = SingleTextDataset(text) + embed = self.embed_text_dataset(text_ds, verbose=False)[0] + + return embed diff --git a/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/convert.py b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/convert.py new file mode 100644 index 0000000..b6b739d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/convert.py @@ -0,0 +1,155 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import argparse +import importlib +import torch.multiprocessing as mp +import os +import sys + +# A loader is a python file with at least two functions +# - add_arguments - takes in a parser and adds any arguments needed +# - load_checkpoint - takes in the queue and parsed arguments + +# A saver is similar but has save_checkpoint instead of +# load_checkpoint + +# The loader and saver process are each given a queue, the loader +# should load the checkpoint and send the weights in messages in the +# following order, the saver should receive them in this order and +# save the checkpoints. A message consists of a python dictionary with +# a "name" for error checking and an entry for each tensor as +# indicated below. Note that the weight sent over the queue are the +# full model weights, nothing split. + +# If the loader ever sends "exit" to the queue, that means something +# went wrong and it is exiting. + +# - Metadata Namespace with the following attributes: +# model_type - GPT, BERT, T5, etc. (Part of protocol to allow this to be deduced later instead of given on command line) +# num_layers - Number of transformer layers +# hidden_size +# seq_length +# num_attention_heads +# max_position_embeddings +# tokenizer_type +# iteration +# params_dtype +# bert_binary_head - Used only if model_type is BERT +# previous_tensor_parallel_size - Optional +# previous_pipeline_parallel_size - Optional +# true_vocab_size +# make_vocab_size_divisble_by +# consumed_train_samples +# consumed_valid_samples +# messages +# { +# "name": "embeddings" +# "position embeddings" +# "word embeddings" +# } +# (for each transformer layer): +# { +# "name": "transformer layer N" +# "input norm weight" +# "input norm bias" +# "qkv weight" +# "qkv bias" +# "dense weight" +# "dense bias" +# "post norm weight" +# "post norm bias" +# "mlp l0 weight" +# "mlp l0 bias" +# "mlp l1 weight" +# "mlp l1 bias" +# } +# { +# "name": "final layer norm" +# "weight" +# "bias" +# } +# if present (i.e. for BERT): +# { +# "name": "pooler" +# "weight" +# "bias" +# } +# { +# "name": "lm head" +# "dense weight" +# "dense bias" +# "norm weight" +# "norm bias" +# } +# { +# "name": "binary head" +# "weight" +# "bias" +# } +# - "done" + +def load_plugin(plugin_type, name): + module_name = f"{plugin_type}_{name}" + try: + plugin = importlib.import_module(module_name) + except ModuleNotFoundError as e: + print(e) + module_name = name + try: + plugin = importlib.import_module(module_name) + except ModuleNotFoundError as e: + print(e) + sys.exit(f"Unable to load {plugin_type} plugin {name}. Exiting.") + + if not hasattr(plugin, 'add_arguments'): + sys.exit(f"{module_name} module is not a plugin. Exiting.") + + print(f"Loaded {module_name} as the {plugin_type}.") + return plugin + +def main(): + import argparse + parser = argparse.ArgumentParser(description="Megatron Checkpoint Converter Arguments", + allow_abbrev=False, conflict_handler='resolve') + + parser.add_argument('--model-type', type=str, required=True, + choices=['GPT', 'BERT'], + help='Type of the model') + parser.add_argument('--loader', type=str, default='megatron', + help='Module name to load checkpoint, should be on python path') + parser.add_argument('--saver', type=str, default='megatron', + help='Module name to save checkpoint, shdoul be on python path') + parser.add_argument('--load-dir', type=str, required=True, + help='Directory to load model checkpoint from') + parser.add_argument('--save-dir', type=str, required=True, + help='Directory to save model checkpoint to') + parser.add_argument('--max-queue-size', type=int, default=50, + help='Maximum number of tensors in the queue') + parser.add_argument('--no-checking', action='store_false', + help='Do not perform checking on the name and ordering of weights', + dest='checking') + + known_args, _ = parser.parse_known_args() + loader = load_plugin('loader', known_args.loader) + saver = load_plugin('saver', known_args.saver) + + loader.add_arguments(parser) + saver.add_arguments(parser) + + args = parser.parse_args() + + queue = mp.Queue(maxsize=args.max_queue_size) + + print("Starting saver...") + saver_proc = mp.Process(target=saver.save_checkpoint, args=(queue, args)) + saver_proc.start() + + print("Starting loader...") + loader.load_checkpoint(queue, args) + + print("Waiting for saver to complete...") + saver_proc.join() + + +if __name__ == '__main__': + main() diff --git a/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/loader_llama2_hf.py b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/loader_llama2_hf.py new file mode 100644 index 0000000..46bc049 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/loader_llama2_hf.py @@ -0,0 +1,364 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import json +import os +import sys +import torch +import transformers +from tqdm import tqdm +import types + + +def add_arguments(parser): + group = parser.add_argument_group(title='Llama-2 HF loader.') + + group.add_argument('--true-vocab-size', type=int, default=None, + help='original size of vocab, if specified will trim padding from embedding table.') + group.add_argument('--vocab-file', type=str, default=None, + help='Path to the vocab file. If specified will use this to get vocab size and ' + 'trim padding from the embedding table.') + group.add_argument('--tokenizer-model', required=True, + help='Sentencepiece tokenizer model.') + group.add_argument('--megatron-path', type=str, default=None, + help='Base directory of deepspeed repository') + + +def verify_transformers_version(): + major, minor, patch = map(int, transformers.__version__.split('.')) + assert major >= 4 and minor >= 31 + + +def load_args_from_checkpoint(args): + + # Read Llama args. + llama_args_path = os.path.join(args.load, "config.json") + with open(llama_args_path) as f: + llama_args = json.load(f) + + # Update Megatron args. + args.seq_length = 4096 + args.max_position_embeddings = 4096 + args.hidden_size = llama_args["hidden_size"] + args.num_attention_heads = llama_args["num_attention_heads"] + args.num_layers = llama_args["num_hidden_layers"] + args.global_batch_size = 1024 + args.norm_epsilon = llama_args["rms_norm_eps"] + args.iteration = 1 # '0', 'release' don't work + args.add_position_embedding = False + args.use_rotary_position_embeddings = True + args.swiglu = True + args.tokenizer_type = "Llama2Tokenizer" + args.fp16 = True + args.normalization = "RMSNorm" + args.add_bias_linear = False + args.untie_embeddings_and_output_weights = True + args.vocab_size = llama_args["vocab_size"] + args.padded_vocab_size = llama_args["vocab_size"] + args.llama = llama_args + args.ffn_hidden_size = llama_args["intermediate_size"] + + if "num_key_value_heads" in llama_args: + args.group_query_attention = True + args.num_query_groups = llama_args["num_key_value_heads"] + + +def set_preprocess_state(args, model, hf_model): + '''Set embedding params.''' + model.language_model.embedding.word_embeddings.weight.data.copy_( + hf_model.model.embed_tokens.weight) + + +def set_postprocess_state(args, model, hf_model): + '''Set output layer & norm params.''' + model.language_model.encoder.final_norm.weight.data.copy_(hf_model.model.norm.weight) + model.language_model.output_layer.weight.data.copy_(hf_model.lm_head.weight) + + +def set_attn_state(args, layer, hf_layer): + '''Set self-attention params.''' + + # Get attention layer & state. + attn = layer.self_attention + hf_attn = hf_layer.self_attn + + # Reshape loaded weights. + tp = args.tensor_model_parallel_size + nh = args.num_attention_heads // tp + ng = (args.num_query_groups if args.group_query_attention \ + else args.num_attention_heads) // tp + dim = args.kv_channels + assert nh % ng == 0 + + # Copy weights (re-order dimensions for Megatron). + attn.query_key_value.weight.data.copy_(torch.cat([ + hf_attn.q_proj.weight.reshape((ng, dim*nh//ng, -1)), + hf_attn.k_proj.weight.reshape((ng, dim, -1)), + hf_attn.v_proj.weight.reshape((ng, dim, -1)), + ], dim=1).reshape((-1, args.hidden_size))) + attn.dense.weight.data.copy_(hf_attn.o_proj.weight) + + +def set_mlp_state(args, layer, hf_layer): + '''Set MLP params.''' + + mlp = layer.mlp + hf_mlp = hf_layer.mlp + + mlp.dense_h_to_4h.weight.data.copy_(torch.cat([ + hf_mlp.gate_proj.weight, + hf_mlp.up_proj.weight, + ], dim=0)) + mlp.dense_4h_to_h.weight.data.copy_(hf_mlp.down_proj.weight) + + +def set_layer_state(args, model, hf_model, layer_idx): + '''Set transformer layer params.''' + + layer = model.language_model.encoder.layers[layer_idx] + hf_layer = hf_model.model.layers[layer_idx] + + set_attn_state(args, layer, hf_layer) + set_mlp_state(args, layer, hf_layer) + layer.input_norm.weight.data.copy_(hf_layer.input_layernorm.weight) + layer.post_attention_norm.weight.data.copy_(hf_layer.post_attention_layernorm.weight) + + +def load_checkpoint_to_model(args): + '''Set model params.''' + + from pretrain_gpt import model_provider + from transformers import LlamaForCausalLM + + # Load Huggingface model. + hf_model = LlamaForCausalLM.from_pretrained(args.load, device_map="cpu") + + # Init Megatron model. + model = model_provider(True, True).to(args.params_dtype) + + # Set model state. + set_preprocess_state(args, model, hf_model) + set_postprocess_state(args, model, hf_model) + for layer_idx in tqdm(range(args.num_layers), "set layer states"): + set_layer_state(args, model, hf_model, layer_idx) + + return model + + +def _load_checkpoint(queue, args): + + # Llama-2 requires HF transformers >=4.31.0. + verify_transformers_version() + + # Search in directory above this. + sys.path.append(os.path.abspath( + os.path.join(os.path.dirname(__file__), + os.path.pardir, + os.path.pardir))) + if args.megatron_path is not None: + sys.path.insert(0, args.megatron_path) + + try: + from megatron.training.arguments import parse_args, validate_args + from megatron.training.global_vars import set_args, set_global_variables + from megatron.legacy.model import module + from megatron.core import mpu + from megatron.core.enums import ModelType + from megatron.legacy import fused_kernels + except ModuleNotFoundError: + print("Unable to import Megatron, please specify the path to Megatron using --megatron-path. Exiting.") + queue.put("exit") + exit(1) + + # We want all arguments to come from us. + sys.argv = ['script.py', + '--no-masked-softmax-fusion', + '--no-bias-gelu-fusion', + '--no-bias-dropout-fusion', + '--no-async-tensor-model-parallel-allreduce', + '--use-cpu-initialization', + '--micro-batch-size', '1', + '--no-load-optim', + '--no-load-rng', + '--no-save-optim', + '--no-save-rng', + '--no-initialization', + '--load', args.load_dir + ] + + margs = parse_args() + margs.tokenizer_model = args.tokenizer_model + load_args_from_checkpoint(margs) + + # Arguments do sanity checks on the world size, but we don't care, + # so trick it into thinking we are plenty of processes. + margs.world_size = margs.tensor_model_parallel_size * margs.pipeline_model_parallel_size + + margs = validate_args(margs) + + def check_for_arg(arg_name, default=None): + if getattr(margs, arg_name, None) is None: + if default is not None: + setattr(margs, arg_name, default) + else: + print(f"Checkpoint does not specify the argument {arg_name}. Exiting.") + print(f"Arguments: {margs}") + queue.put("exit") + exit(1) + + check_for_arg('tensor_model_parallel_size') + check_for_arg('pipeline_model_parallel_size') + check_for_arg('num_layers') + check_for_arg('hidden_size') + check_for_arg('seq_length') + check_for_arg('num_attention_heads') + check_for_arg('max_position_embeddings') + check_for_arg('position_embedding_type') + check_for_arg('tokenizer_type') + check_for_arg('iteration') + check_for_arg('bert_binary_head') + check_for_arg('disable_bias_linear', False) + check_for_arg('params_dtype') + check_for_arg('swiglu', False) + + # Determine how to make our models. + assert args.model_type == 'GPT', 'Llama-2 is a GPT model.' + margs.model_type = ModelType.encoder_or_decoder + + # Suppress warning about torch.distributed not being initialized. + module.MegatronModule.embedding_warning_printed = True + + set_global_variables(margs, build_tokenizer=False) + mpu.set_tensor_model_parallel_world_size(margs.tensor_model_parallel_size) + mpu.set_pipeline_model_parallel_world_size(margs.pipeline_model_parallel_size) + mpu.set_virtual_pipeline_model_parallel_world_size(margs.virtual_pipeline_model_parallel_size) + fused_kernels.load(margs) + + # Short aliases. + tp_size = margs.tensor_model_parallel_size + pp_size = margs.pipeline_model_parallel_size + vp_size = margs.virtual_pipeline_model_parallel_size + if vp_size is None: + vp_size = 1 + + # Metadata. + md = types.SimpleNamespace() + md.model_type = args.model_type + md.num_layers = margs.num_layers + md.hidden_size = margs.hidden_size + md.seq_length = margs.seq_length + md.num_attention_heads = margs.num_attention_heads + md.max_position_embeddings = margs.max_position_embeddings + md.tokenizer_type = margs.tokenizer_type + md.iteration = margs.iteration + md.params_dtype = margs.params_dtype + md.bert_binary_head = margs.bert_binary_head + md.output_layer = margs.untie_embeddings_and_output_weights + md.position_embedding_type = margs.position_embedding_type + md.linear_bias = margs.add_bias_linear + md.norm_has_bias = False + md.swiglu = margs.swiglu + md.previous_tensor_parallel_size = margs.tensor_model_parallel_size + md.previous_pipeline_parallel_size = margs.pipeline_model_parallel_size + md.true_vocab_size = None # skips padding in saver + md.make_vocab_size_divisible_by = None + md.checkpoint_args = margs + md.consumed_train_samples = 0 + md.consumed_valid_samples = 0 + + # Get first pipe stage. + mpu.set_tensor_model_parallel_rank(0) + mpu.set_pipeline_model_parallel_rank(0) + model = load_checkpoint_to_model(margs) + + queue.put(md) + + def queue_put(name, msg): + print(f"sending {name}") + msg["name"] = name + queue.put(msg) + + # Send embeddings. + message = { + "word embeddings": model.language_model.embedding.word_embeddings.weight.data + } + if md.position_embedding_type == 'learned_absolute': + message["position embeddings"] = model.language_model.embedding.position_embeddings.weight.data + else: + assert not hasattr(model.language_model.embedding, 'position_embeddings') + + queue_put("embeddings", message) + + for layer_num in range(margs.num_layers): + message = {} + + # Get non-parallel tensors from tp_rank 0. + layer = model.language_model.encoder.layers[layer_num] + message["input norm weight"] = layer.input_norm.weight.data + message["post norm weight"] = layer.post_attention_norm.weight.data + if md.linear_bias: + message["dense bias"] = layer.self_attention.dense.bias.data + message["mlp l1 bias"] = layer.mlp.dense_4h_to_h.bias.data + + # Grab all parallel tensors for this layer. + qkv_weight = [] + qkv_bias = [] + dense_weight = [] + mlp_l0_weight = [] + mlp_l0_bias = [] + mlp_l1_weight = [] + layer = model.language_model.encoder.layers[layer_num] + qkv_weight.append(layer.self_attention.query_key_value.weight.data) + dense_weight.append(layer.self_attention.dense.weight.data) + mlp_l0_weight.append(layer.mlp.dense_h_to_4h.weight.data) + mlp_l1_weight.append(layer.mlp.dense_4h_to_h.weight.data) + if md.linear_bias: + qkv_bias.append(layer.self_attention.query_key_value.bias.data) + mlp_l0_bias.append(layer.mlp.dense_h_to_4h.bias.data) + + # Handle gated linear units. + if md.swiglu: + # Concat all the first halves ('W's) and all the second halves ('V's). + for tp_rank in range(tp_size): + mlp_l0_weight[tp_rank] = torch.chunk(mlp_l0_weight[tp_rank], 2, dim=0) + message["mlp l0 weight W"] = torch.cat([w[0] for w in mlp_l0_weight], dim=0) + message["mlp l0 weight V"] = torch.cat([w[1] for w in mlp_l0_weight], dim=0) + else: + message["mlp l0 weight"] = torch.cat(mlp_l0_weight, dim=0) + + # Simple concat of the rest. + message["qkv weight"] = torch.cat(qkv_weight, dim=0) + message["dense weight"] = torch.cat(dense_weight, dim=1) + message["mlp l1 weight"] = torch.cat(mlp_l1_weight, dim=1) + if md.linear_bias: + message["qkv bias"] = torch.cat(qkv_bias, dim=0) + if md.swiglu: + for tp_rank in range(tp_size): + mlp_l0_bias[tp_rank] = torch.chunk(mlp_l0_bias[tp_rank], 2, dim=0) + message["mlp l0 bias W"] = torch.cat([b[0] for b in mlp_l0_bias],dim=0) + message["mlp l0 bias V"] = torch.cat([b[1] for b in mlp_l0_bias],dim=0) + else: + message["mlp l0 bias"] = torch.cat(mlp_l0_bias, dim=0) + + queue_put(f"transformer layer {layer_num}", message) + + # Send final norm from tp_rank 0. + message = { + "weight": model.language_model.encoder.final_norm.weight.data, + } + queue_put("final norm", message) + + if md.output_layer: + message = { + "weight": model.language_model.output_layer.weight.data + } + queue_put("output layer", message) + + queue.put("done") + + +def load_checkpoint(queue, args): + try: + _load_checkpoint(queue, args) + except: + queue.put("exit") + raise diff --git a/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/loader_mcore.py b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/loader_mcore.py new file mode 100644 index 0000000..1f734a7 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/loader_mcore.py @@ -0,0 +1,382 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import json +import os +import sys +import torch +import types + +from utils import get_mcore_transformer_block_key, print_memory_usage + + +def add_arguments(parser): + group = parser.add_argument_group(title='Megatron loader') + + group.add_argument('--true-vocab-size', type=int, default=None, + help='original size of vocab, if specified will trim padding from embedding table.') + group.add_argument('--vocab-file', type=str, default=None, + help='Path to the vocab file. If specified will use this to get vocab size and ' + 'trim padding from the embedding table.') + group.add_argument('--megatron-path', type=str, default=None, + help='Base directory of deepspeed repository') + group.add_argument('--position-embedding-type', + type=str, + default='learned_absolute', + choices=['learned_absolute', 'rope'], + help='Position embedding type.') + group.add_argument('--loader-transformer-impl', default='transformer_engine', + choices=['local', 'transformer_engine'], + help='Which Transformer implementation to use.') + + +def _load_checkpoint(queue, args): + + # Search in directory above this + sys.path.append(os.path.abspath( + os.path.join(os.path.dirname(__file__), + os.path.pardir))) + if args.megatron_path is not None: + sys.path.insert(0, args.megatron_path) + + try: + from megatron.training.arguments import parse_args, validate_args + from megatron.training.global_vars import set_args, set_global_variables + from megatron.training.checkpointing import load_args_from_checkpoint, load_checkpoint + from megatron.legacy.model import module + from megatron.core import mpu + from megatron.core.enums import ModelType + from megatron.legacy import fused_kernels + except ModuleNotFoundError: + print("Unable to import Megatron, please specify the path to Megatron using --megatron-path. Exiting.") + queue.put("exit") + exit(1) + + # We want all arguments to come from us + sys.argv = ['script.py', + '--no-masked-softmax-fusion', + '--no-bias-gelu-fusion', + '--no-bias-dropout-fusion', + '--no-async-tensor-model-parallel-allreduce', + '--use-cpu-initialization', + '--micro-batch-size', '1', + '--no-load-optim', + '--no-load-rng', + '--no-save-optim', + '--no-save-rng', + '--no-initialization', + '--load', args.load_dir, + '--position-embedding-type', args.position_embedding_type, + ] + + margs = parse_args() + margs, checkpoint_args = load_args_from_checkpoint(margs, exit_on_missing_checkpoint=True) + + # Arguments do sanity checks on the world size, but we don't care, + # so trick it into thinking we are plenty of processes + margs.world_size = margs.tensor_model_parallel_size * margs.pipeline_model_parallel_size + + # Explicitly copy data types from checkpoint. + margs.fp16 = checkpoint_args.fp16 + margs.bf16 = checkpoint_args.bf16 + + # Validate margs. + margs = validate_args(margs) + + margs.use_mcore_models = True + margs.transformer_impl = args.loader_transformer_impl + + def check_for_arg(arg_name, default=None): + if getattr(margs, arg_name, None) is None: + if default is not None: + setattr(margs, arg_name, default) + else: + print(f"Checkpoint does not specify the argument {arg_name}. Exiting.") + print(f"Arguments: {margs}") + queue.put("exit") + exit(1) + + check_for_arg('tensor_model_parallel_size') + check_for_arg('pipeline_model_parallel_size') + check_for_arg('num_layers') + check_for_arg('hidden_size') + check_for_arg('seq_length') + check_for_arg('num_attention_heads') + check_for_arg('max_position_embeddings') + check_for_arg('position_embedding_type') + check_for_arg('tokenizer_type') + check_for_arg('iteration') + check_for_arg('bert_binary_head') + check_for_arg('disable_bias_linear', False) + check_for_arg('params_dtype') + check_for_arg('swiglu', False) + + # Determine how to make our models + if args.model_type == 'GPT': + from pretrain_gpt import model_provider + margs.model_type = ModelType.encoder_or_decoder + elif args.model_type == 'BERT': + from pretrain_bert import model_provider + margs.model_type = ModelType.encoder_or_decoder + else: + raise Exception(f'unrecognized model type: {args.model_type}') + + # supress warning about torch.distributed not being initialized + module.MegatronModule.embedding_warning_printed = True + + consumed_train_samples = None + consumed_valid_samples = None + def get_models(count, dtype): + nonlocal consumed_train_samples + nonlocal consumed_valid_samples + model_array_len = margs.virtual_pipeline_model_parallel_size + if model_array_len is None: + model_array_len = 1 + models = [[] for _ in range(model_array_len)] + pre_process = mpu.is_pipeline_first_stage() + post_process = mpu.is_pipeline_last_stage() + for rank in range(count): + mpu.set_tensor_model_parallel_rank(rank) + if margs.virtual_pipeline_model_parallel_size is not None: + model_ = [] + for i in range(margs.virtual_pipeline_model_parallel_size): + mpu.set_virtual_pipeline_model_parallel_rank(i) + # Set pre_process and post_process only after virtual rank is set. + pre_process = mpu.is_pipeline_first_stage() + post_process = mpu.is_pipeline_last_stage() + this_model = model_provider( + pre_process=pre_process, + post_process=post_process + ).to(dtype) + model_.append(this_model) + else: + pre_process = mpu.is_pipeline_first_stage() + post_process = mpu.is_pipeline_last_stage() + model_rank = 0 + model_ = [model_provider(pre_process, post_process).to(dtype)] + margs.consumed_train_samples = 0 + margs.consumed_valid_samples = 0 + margs.exit_on_missing_checkpoint = True + load_checkpoint(model_, None, None) + + if consumed_train_samples is not None: + assert(margs.consumed_train_samples == consumed_train_samples) + else: + consumed_train_samples = margs.consumed_train_samples + if consumed_valid_samples is not None: + assert(margs.consumed_valid_samples == consumed_valid_samples) + else: + consumed_valid_samples = margs.consumed_valid_samples + for vp_rank in range(model_array_len): + models[vp_rank].append(model_[vp_rank]) + + # Print memory usage. + print_memory_usage("loader", rank, count) + + return models + + set_global_variables(margs, build_tokenizer=False) + mpu.set_tensor_model_parallel_world_size(margs.tensor_model_parallel_size) + mpu.set_pipeline_model_parallel_world_size(margs.pipeline_model_parallel_size) + mpu.set_virtual_pipeline_model_parallel_world_size(margs.virtual_pipeline_model_parallel_size) + fused_kernels.load(margs) + + # Get true (non-padded) vocab size + if args.true_vocab_size is not None: + true_vocab_size = args.true_vocab_size + elif args.vocab_file is not None: + vocab = json.load(open(args.vocab_file)) + true_vocab_size = len(vocab) + if args.true_vocab_size is not None and true_vocab_size != args.true_vocab_size: + print("Both --true-vocab-size and --vocab-file specified and the vocab size does not match, aborting.") + queue.put("exit") + exit(1) + else: + true_vocab_size = None + + # short aliases + tp_size = margs.tensor_model_parallel_size + pp_size = margs.pipeline_model_parallel_size + vp_size = margs.virtual_pipeline_model_parallel_size + if vp_size is None: + vp_size = 1 + + # Layernorm has bias; RMSNorm does not. + if hasattr(checkpoint_args, 'normalization'): + norm_has_bias = checkpoint_args.normalization == "LayerNorm" + else: + # older models only supported LayerNorm + norm_has_bias = True + + # metadata + md = types.SimpleNamespace() + md.model_type = args.model_type + md.num_layers = margs.num_layers + md.hidden_size = margs.hidden_size + md.seq_length = margs.seq_length + md.num_attention_heads = margs.num_attention_heads + md.max_position_embeddings = margs.max_position_embeddings + md.tokenizer_type = margs.tokenizer_type + md.iteration = margs.iteration + md.params_dtype = margs.params_dtype + md.bert_binary_head = margs.bert_binary_head + md.output_layer = margs.untie_embeddings_and_output_weights + md.position_embedding_type = margs.position_embedding_type + md.linear_bias = margs.add_bias_linear + md.norm_has_bias = norm_has_bias + md.swiglu = margs.swiglu + md.previous_tensor_parallel_size = margs.tensor_model_parallel_size + md.previous_pipeline_parallel_size = margs.pipeline_model_parallel_size + md.true_vocab_size = true_vocab_size + md.make_vocab_size_divisible_by = margs.make_vocab_size_divisible_by + md.checkpoint_args = checkpoint_args + md.use_mcore_models = margs.use_mcore_models + + # Get transformer block (named either 'encoder' or 'decoder'). + transformer_block_key = get_mcore_transformer_block_key(md.model_type) + def get_transformer_block(_model): + return getattr(_model, transformer_block_key) + + # Get first pipe stage + mpu.set_pipeline_model_parallel_rank(0) + all_models = [get_models(tp_size, md.params_dtype)] + models = all_models[0][0] + + md.consumed_train_samples = consumed_train_samples + md.consumed_valid_samples = consumed_valid_samples + queue.put(md) + + def queue_put(name, msg): + print(f"sending {name}") + msg["name"] = name + queue.put(msg) + + # Send embeddings + message = { + "word embeddings": torch.cat( + [models[tp_rank].embedding.word_embeddings.weight.data for tp_rank in range(tp_size)], + dim = 0) + } + if md.position_embedding_type == 'learned_absolute': + message["position embeddings"] = models[0].embedding.position_embeddings.weight.data + else: + assert not hasattr(models[0].embedding, 'position_embeddings') + + queue_put("embeddings", message) + + total_layer_num = 0 + for vp_rank in range(vp_size): + mpu.set_virtual_pipeline_model_parallel_rank(vp_rank) + for pp_rank in range(pp_size): + if pp_rank > 0: + mpu.set_pipeline_model_parallel_rank(pp_rank) + if vp_rank == 0: + all_models.append(get_models(tp_size, md.params_dtype)) + models = all_models[pp_rank][vp_rank] + for layer_num in range(len(get_transformer_block(models[0]).layers)): + message = {} + + # Get non-parallel tensors from tp_rank 0 + layer = get_transformer_block(models[0]).layers[layer_num] + message["input norm weight"] = layer.self_attention.linear_qkv.layer_norm_weight.data + if norm_has_bias: + message["input norm bias"] = layer.self_attention.linear_qkv.layer_norm_bias.data + message["post norm weight"] = layer.mlp.linear_fc1.layer_norm_weight.data + if norm_has_bias: + message["post norm bias"] = layer.mlp.linear_fc1.layer_norm_bias.data + if md.linear_bias: + message["dense bias"] = layer.self_attention.linear_proj.bias.data + message["mlp l1 bias"] = layer.mlp.linear_fc2.bias.data + + # Grab all parallel tensors for this layer + qkv_weight = [] + qkv_bias = [] + dense_weight = [] + mlp_l0_weight = [] + mlp_l0_bias = [] + mlp_l1_weight = [] + for tp_rank, model in enumerate(models): + layer = get_transformer_block(model).layers[layer_num] + qkv_weight.append(layer.self_attention.linear_qkv.weight.data) + dense_weight.append(layer.self_attention.linear_proj.weight.data) + mlp_l0_weight.append(layer.mlp.linear_fc1.weight.data) + mlp_l1_weight.append(layer.mlp.linear_fc2.weight.data) + if md.linear_bias: + qkv_bias.append(layer.self_attention.linear_qkv.bias.data) + mlp_l0_bias.append(layer.mlp.linear_fc1.bias.data) + + # Handle gated linear units + if md.swiglu: + # concat all the first halves ('W's) and all the second halves ('V's) + for tp_rank in range(tp_size): + mlp_l0_weight[tp_rank] = torch.chunk(mlp_l0_weight[tp_rank], 2, dim=0) + message["mlp l0 weight W"] = torch.cat([w[0] for w in mlp_l0_weight], dim=0) + message["mlp l0 weight V"] = torch.cat([w[1] for w in mlp_l0_weight], dim=0) + else: + message["mlp l0 weight"] = torch.cat(mlp_l0_weight, dim=0) + + # simple concat of the rest + message["qkv weight"] = torch.cat(qkv_weight, dim=0) + message["dense weight"] = torch.cat(dense_weight, dim=1) + message["mlp l1 weight"] = torch.cat(mlp_l1_weight, dim=1) + if md.linear_bias: + message["qkv bias"] = torch.cat(qkv_bias, dim=0) + if md.swiglu: + for tp_rank in range(tp_size): + mlp_l0_bias[tp_rank] = torch.chunk(mlp_l0_bias[tp_rank], 2, dim=0) + message["mlp l0 bias W"] = torch.cat([b[0] for b in mlp_l0_bias],dim=0) + message["mlp l0 bias V"] = torch.cat([b[1] for b in mlp_l0_bias],dim=0) + else: + message["mlp l0 bias"] = torch.cat(mlp_l0_bias, dim=0) + + queue_put(f"transformer layer {total_layer_num}", message) + + total_layer_num = total_layer_num + 1 + + # Send final norm from tp_rank 0 + message = { + "weight": get_transformer_block(models[0]).final_layernorm.weight.data, + } + if norm_has_bias: + message["bias"] = get_transformer_block(models[0]).final_layernorm.bias.data + queue_put("final norm", message) + + if md.output_layer: + message = { + "weight": torch.cat( + [models[tp_rank].output_layer.weight.data for tp_rank in range(tp_size)], + dim = 0) + } + queue_put("output layer", message) + + + # Send BERT lm head and binary head if it exists + if md.model_type == 'BERT': + message = { + "weight": models[0].pooler.dense.weight.data, + "bias": models[0].pooler.dense.bias.data + } + queue_put("pooler", message) + + message = { + "dense weight": models[0].lm_head.dense.weight.data, + "dense bias": models[0].lm_head.dense.bias.data, + "norm weight": models[0].lm_head.layer_norm.weight.data, + } + if norm_has_bias: + message["norm bias"] = models[0].lm_head.layer_norm.bias.data + queue_put("lm head", message) + + if md.bert_binary_head: + message = { + "weight": models[0].binary_head.weight.data, + "bias": models[0].binary_head.bias.data + } + queue_put("binary head", message) + queue.put("done") + +def load_checkpoint(queue, args): + try: + _load_checkpoint(queue, args) + except: + queue.put("exit") + raise diff --git a/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/loader_megatron.py b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/loader_megatron.py new file mode 100644 index 0000000..371e426 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/loader_megatron.py @@ -0,0 +1,370 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import json +import os +import sys +import types + +import torch + + +def add_arguments(parser): + group = parser.add_argument_group(title='Megatron loader') + + group.add_argument('--true-vocab-size', type=int, default=None, + help='original size of vocab, if specified will trim padding from embedding table.') + group.add_argument('--vocab-file', type=str, default=None, + help='Path to the vocab file. If specified will use this to get vocab size and ' + 'trim padding from the embedding table.') + group.add_argument('--megatron-path', type=str, default=None, + help='Base directory of deepspeed repository') + group.add_argument('--position-embedding-type', + type=str, + default='learned_absolute', + choices=['learned_absolute', 'rope'], + help='Position embedding type.') + group.add_argument('--loader-transformer-impl', default='local', + choices=['local', 'transformer_engine'], + help='Which Transformer implementation to use.') + +def _load_checkpoint(queue, args): + + # Search in directory above this + sys.path.append(os.path.abspath( + os.path.join(os.path.dirname(__file__), + os.path.pardir))) + if args.megatron_path is not None: + sys.path.insert(0, args.megatron_path) + + try: + from megatron.training.arguments import parse_args, validate_args + from megatron.training.global_vars import set_args, set_global_variables + from megatron.training.checkpointing import load_args_from_checkpoint, load_checkpoint + from megatron.legacy.model import module + from megatron.core import mpu + from megatron.core.enums import ModelType + from megatron.legacy import fused_kernels + except ModuleNotFoundError: + print("Unable to import Megatron, please specify the path to Megatron using --megatron-path. Exiting.") + queue.put("exit") + exit(1) + + # We want all arguments to come from us + sys.argv = ['script.py', + '--no-masked-softmax-fusion', + '--no-bias-gelu-fusion', + '--no-bias-dropout-fusion', + '--no-async-tensor-model-parallel-allreduce', + '--use-cpu-initialization', + '--micro-batch-size', '1', + '--no-load-optim', + '--no-load-rng', + '--no-save-optim', + '--no-save-rng', + '--no-initialization', + '--load', args.load_dir, + '--position-embedding-type', args.position_embedding_type, + ] + + margs = parse_args() + margs, checkpoint_args = load_args_from_checkpoint(margs, exit_on_missing_checkpoint=True) + + # Arguments do sanity checks on the world size, but we don't care, + # so trick it into thinking we are plenty of processes + margs.world_size = margs.tensor_model_parallel_size * margs.pipeline_model_parallel_size + + # Explicitly copy data types from checkpoint. + margs.fp16 = checkpoint_args.fp16 + margs.bf16 = checkpoint_args.bf16 + + # Validate margs. + margs = validate_args(margs) + + margs.use_mcore_models = False + margs.transformer_impl = args.loader_transformer_impl + + def check_for_arg(arg_name, default=None): + if getattr(margs, arg_name, None) is None: + if default is not None: + setattr(margs, arg_name, default) + else: + print(f"Checkpoint does not specify the argument {arg_name}. Exiting.") + print(f"Arguments: {margs}") + queue.put("exit") + exit(1) + + check_for_arg('tensor_model_parallel_size') + check_for_arg('pipeline_model_parallel_size') + check_for_arg('num_layers') + check_for_arg('hidden_size') + check_for_arg('seq_length') + check_for_arg('num_attention_heads') + check_for_arg('max_position_embeddings') + check_for_arg('position_embedding_type') + check_for_arg('tokenizer_type') + check_for_arg('iteration') + check_for_arg('bert_binary_head') + check_for_arg('disable_bias_linear', False) + check_for_arg('params_dtype') + check_for_arg('swiglu', False) + + # Determine how to make our models + if args.model_type == 'GPT': + from pretrain_gpt import model_provider + margs.model_type = ModelType.encoder_or_decoder + elif args.model_type == 'BERT': + from pretrain_bert import model_provider + margs.model_type = ModelType.encoder_or_decoder + else: + raise Exception(f'unrecognized model type: {args.model_type}') + + # supress warning about torch.distributed not being initialized + module.MegatronModule.embedding_warning_printed = True + + consumed_train_samples = None + consumed_valid_samples = None + def get_models(count, dtype): + nonlocal consumed_train_samples + nonlocal consumed_valid_samples + model_array_len = margs.virtual_pipeline_model_parallel_size + if model_array_len is None: + model_array_len = 1 + models = [[] for _ in range(model_array_len)] + pre_process = mpu.is_pipeline_first_stage() + post_process = mpu.is_pipeline_last_stage() + for rank in range(count): + mpu.set_tensor_model_parallel_rank(rank) + if margs.virtual_pipeline_model_parallel_size is not None: + model_ = [] + for i in range(margs.virtual_pipeline_model_parallel_size): + mpu.set_virtual_pipeline_model_parallel_rank(i) + # Set pre_process and post_process only after virtual rank is set. + pre_process = mpu.is_pipeline_first_stage() + post_process = mpu.is_pipeline_last_stage() + this_model = model_provider( + pre_process=pre_process, + post_process=post_process + ).to(dtype) + model_.append(this_model) + else: + pre_process = mpu.is_pipeline_first_stage() + post_process = mpu.is_pipeline_last_stage() + model_rank = 0 + model_ = [model_provider(pre_process, post_process).to(dtype)] + margs.consumed_train_samples = 0 + margs.consumed_valid_samples = 0 + margs.exit_on_missing_checkpoint = True + load_checkpoint(model_, None, None) + + if consumed_train_samples is not None: + assert(margs.consumed_train_samples == consumed_train_samples) + else: + consumed_train_samples = margs.consumed_train_samples + if consumed_valid_samples is not None: + assert(margs.consumed_valid_samples == consumed_valid_samples) + else: + consumed_valid_samples = margs.consumed_valid_samples + for vp_rank in range(model_array_len): + models[vp_rank].append(model_[vp_rank]) + return models + + set_global_variables(margs, build_tokenizer=False) + mpu.set_tensor_model_parallel_world_size(margs.tensor_model_parallel_size) + mpu.set_pipeline_model_parallel_world_size(margs.pipeline_model_parallel_size) + mpu.set_virtual_pipeline_model_parallel_world_size(margs.virtual_pipeline_model_parallel_size) + fused_kernels.load(margs) + + # Get true (non-padded) vocab size + if args.true_vocab_size is not None: + true_vocab_size = args.true_vocab_size + elif args.vocab_file is not None: + vocab = json.load(open(args.vocab_file)) + true_vocab_size = len(vocab) + if args.true_vocab_size is not None and true_vocab_size != args.true_vocab_size: + print("Both --true-vocab-size and --vocab-file specified and the vocab size does not match, aborting.") + queue.put("exit") + exit(1) + else: + true_vocab_size = None + + # short aliases + tp_size = margs.tensor_model_parallel_size + pp_size = margs.pipeline_model_parallel_size + vp_size = margs.virtual_pipeline_model_parallel_size + if vp_size is None: + vp_size = 1 + + # Layernorm has bias; RMSNorm does not. + if hasattr(checkpoint_args, 'normalization'): + norm_has_bias = checkpoint_args.normalization == "LayerNorm" + else: + # older models only supported LayerNorm + norm_has_bias = True + + # metadata + md = types.SimpleNamespace() + md.model_type = args.model_type + md.num_layers = margs.num_layers + md.hidden_size = margs.hidden_size + md.seq_length = margs.seq_length + md.num_attention_heads = margs.num_attention_heads + md.max_position_embeddings = margs.max_position_embeddings + md.tokenizer_type = margs.tokenizer_type + md.iteration = margs.iteration + md.params_dtype = margs.params_dtype + md.bert_binary_head = margs.bert_binary_head + md.output_layer = margs.untie_embeddings_and_output_weights + md.position_embedding_type = margs.position_embedding_type + md.linear_bias = margs.add_bias_linear + md.norm_has_bias = norm_has_bias + md.swiglu = margs.swiglu + md.previous_tensor_parallel_size = margs.tensor_model_parallel_size + md.previous_pipeline_parallel_size = margs.pipeline_model_parallel_size + md.true_vocab_size = true_vocab_size + md.make_vocab_size_divisible_by = margs.make_vocab_size_divisible_by + md.checkpoint_args = checkpoint_args + + # Get first pipe stage + mpu.set_pipeline_model_parallel_rank(0) + all_models = [get_models(tp_size, md.params_dtype)] + models = all_models[0][0] + + md.consumed_train_samples = consumed_train_samples + md.consumed_valid_samples = consumed_valid_samples + queue.put(md) + + def queue_put(name, msg): + print(f"sending {name}") + msg["name"] = name + queue.put(msg) + + # Send embeddings + message = { + "word embeddings": torch.cat( + [models[tp_rank].language_model.embedding.word_embeddings.weight.data for tp_rank in range(tp_size)], + dim = 0) + } + if md.position_embedding_type == 'learned_absolute': + message["position embeddings"] = models[0].language_model.embedding.position_embeddings.weight.data + else: + assert not hasattr(models[0].language_model.embedding, 'position_embeddings') + + queue_put("embeddings", message) + + total_layer_num = 0 + for vp_rank in range(vp_size): + mpu.set_virtual_pipeline_model_parallel_rank(vp_rank) + for pp_rank in range(pp_size): + if pp_rank > 0: + mpu.set_pipeline_model_parallel_rank(pp_rank) + if vp_rank == 0: + all_models.append(get_models(tp_size, md.params_dtype)) + models = all_models[pp_rank][vp_rank] + for layer_num in range(len(models[0].language_model.encoder.layers)): + message = {} + + # Get non-parallel tensors from tp_rank 0 + layer = models[0].language_model.encoder.layers[layer_num] + message["input norm weight"] = layer.input_norm.weight.data + if norm_has_bias: + message["input norm bias"] = layer.input_norm.bias.data + message["post norm weight"] = layer.post_attention_norm.weight.data + if norm_has_bias: + message["post norm bias"] = layer.post_attention_norm.bias.data + if md.linear_bias: + message["dense bias"] = layer.self_attention.dense.bias.data + message["mlp l1 bias"] = layer.mlp.dense_4h_to_h.bias.data + + # Grab all parallel tensors for this layer + qkv_weight = [] + qkv_bias = [] + dense_weight = [] + mlp_l0_weight = [] + mlp_l0_bias = [] + mlp_l1_weight = [] + for tp_rank, model in enumerate(models): + layer = model.language_model.encoder.layers[layer_num] + qkv_weight.append(layer.self_attention.query_key_value.weight.data) + dense_weight.append(layer.self_attention.dense.weight.data) + mlp_l0_weight.append(layer.mlp.dense_h_to_4h.weight.data) + mlp_l1_weight.append(layer.mlp.dense_4h_to_h.weight.data) + if md.linear_bias: + qkv_bias.append(layer.self_attention.query_key_value.bias.data) + mlp_l0_bias.append(layer.mlp.dense_h_to_4h.bias.data) + + # Handle gated linear units + if md.swiglu: + # concat all the first halves ('W's) and all the second halves ('V's) + for tp_rank in range(tp_size): + mlp_l0_weight[tp_rank] = torch.chunk(mlp_l0_weight[tp_rank], 2, dim=0) + message["mlp l0 weight W"] = torch.cat([w[0] for w in mlp_l0_weight], dim=0) + message["mlp l0 weight V"] = torch.cat([w[1] for w in mlp_l0_weight], dim=0) + else: + message["mlp l0 weight"] = torch.cat(mlp_l0_weight, dim=0) + + # simple concat of the rest + message["qkv weight"] = torch.cat(qkv_weight, dim=0) + message["dense weight"] = torch.cat(dense_weight, dim=1) + message["mlp l1 weight"] = torch.cat(mlp_l1_weight, dim=1) + if md.linear_bias: + message["qkv bias"] = torch.cat(qkv_bias, dim=0) + if md.swiglu: + for tp_rank in range(tp_size): + mlp_l0_bias[tp_rank] = torch.chunk(mlp_l0_bias[tp_rank], 2, dim=0) + message["mlp l0 bias W"] = torch.cat([b[0] for b in mlp_l0_bias],dim=0) + message["mlp l0 bias V"] = torch.cat([b[1] for b in mlp_l0_bias],dim=0) + else: + message["mlp l0 bias"] = torch.cat(mlp_l0_bias, dim=0) + + queue_put(f"transformer layer {total_layer_num}", message) + + total_layer_num = total_layer_num + 1 + + # Send final norm from tp_rank 0 + message = { + "weight": models[0].language_model.encoder.final_norm.weight.data, + } + if norm_has_bias: + message["bias"] = models[0].language_model.encoder.final_norm.bias.data + queue_put("final norm", message) + + if md.output_layer: + message = { + "weight": torch.cat( + [models[tp_rank].language_model.output_layer.weight.data for tp_rank in range(tp_size)], + dim = 0) + } + queue_put("output layer", message) + + + # Send BERT lm head and binary head if it exists + if md.model_type == 'BERT': + message = { + "weight": models[0].language_model.pooler.dense.weight.data, + "bias": models[0].language_model.pooler.dense.bias.data + } + queue_put("pooler", message) + + message = { + "dense weight": models[0].lm_head.dense.weight.data, + "dense bias": models[0].lm_head.dense.bias.data, + "norm weight": models[0].lm_head.norm.weight.data, + } + if norm_has_bias: + message["norm bias"] = models[0].lm_head.norm.bias.data + queue_put("lm head", message) + + if md.bert_binary_head: + message = { + "weight": models[0].binary_head.weight.data, + "bias": models[0].binary_head.bias.data + } + queue_put("binary head", message) + queue.put("done") + +def load_checkpoint(queue, args): + try: + _load_checkpoint(queue, args) + except: + queue.put("exit") + raise diff --git a/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/saver_mcore.py b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/saver_mcore.py new file mode 100644 index 0000000..656103f --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/saver_mcore.py @@ -0,0 +1,665 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import os +import sys +import torch +from importlib.metadata import version +from pkg_resources import packaging + +from setter import ModelSetter +from utils import get_mcore_transformer_block_key, print_memory_usage + + +class MCoreSetter(ModelSetter): + + transformer_block_key = None + + @classmethod + def get_transformer_block(cls, model): + return getattr(model, cls.transformer_block_key) + + @classmethod + def has_position_embeddings(cls, model): + return hasattr(model.embedding, "position_embeddings") + + @classmethod + def set_embeddings( + cls, + model, + word=None, + pos=None, + ): + cls.set_tensor(model.embedding.word_embeddings.weight, word) + if pos is not None: + cls.set_tensor(model.embedding.position_embeddings.weight, pos) + + @classmethod + def set_final_norm( + cls, + model, + weight=None, + bias=None, + ): + block = cls.get_transformer_block(model) + cls.set_tensor(block.final_layernorm.weight, weight) + if bias is not None: + cls.set_tensor(block.final_layernorm.bias, bias) + + @classmethod + def set_output_word_embeddings( + cls, + model, + emb=None, + ): + cls.set_tensor(model.embedding.word_embeddings.weight, emb) + + @classmethod + def set_output_layer( + cls, + model, + weight=None, + ): + cls.set_tensor(model.output_layer.weight, weight) + + @classmethod + def set_pooler( + cls, + model, + weight=None, + bias=None, + ): + cls.set_tensor(model.pooler.dense.weight, weight) + if bias is not None: + cls.set_tensor(model.pooler.dense.bias, bias) + + @classmethod + def set_lm_head( + cls, + model, + dense_weight=None, + dense_bias=None, + norm_weight=None, + norm_bias=None, + ): + + cls.set_tensor(model.lm_head.dense.weight, dense_weight) + if dense_bias is not None: + cls.set_tensor(model.lm_head.dense.bias, dense_bias) + + cls.set_tensor(model.lm_head.layer_norm.weight, norm_weight) + if norm_bias is not None: + cls.set_tensor(model.lm_head.layer_norm.bias, norm_bias) + + @classmethod + def set_binary_head( + cls, + model, + weight=None, + bias=None, + ): + cls.set_tensor(model.binary_head.weight, weight) + if bias is not None: + cls.set_tensor(model.binary_head.bias, bias) + + +class MCoreLocalSetter(MCoreSetter): + + @classmethod + def set_layer( + cls, + model, + layer_idx, + self_attn_norm_weight=None, + self_attn_norm_bias=None, + self_attn_qkv_weight=None, + self_attn_qkv_bias=None, + self_attn_proj_weight=None, + self_attn_proj_bias=None, + mlp_norm_weight=None, + mlp_norm_bias=None, + mlp_fc1_weight=None, + mlp_fc1_bias=None, + mlp_fc2_weight=None, + mlp_fc2_bias=None, + ): + + block = cls.get_transformer_block(model) + l = block.layers[layer_idx] + + # Self attention. + cls.set_tensor(l.input_layernorm.weight, self_attn_norm_weight) + if self_attn_norm_bias is not None: + cls.set_tensor(l.input_layernorm.bias, self_attn_norm_bias) + + cls.set_tensor(l.self_attention.linear_qkv.weight, self_attn_qkv_weight) + if self_attn_qkv_bias is not None: + cls.set_tensor(l.self_attention.linear_qkv.bias, self_attn_qkv_bias) + + cls.set_tensor(l.self_attention.linear_proj.weight, self_attn_proj_weight) + if self_attn_proj_bias is not None: + cls.set_tensor(l.self_attention.linear_proj.bias, self_attn_proj_bias) + + # MLP. + cls.set_tensor(l.pre_mlp_layernorm.weight, mlp_norm_weight) + if mlp_norm_bias is not None: + cls.set_tensor(l.pre_mlp_layernorm.bias, mlp_norm_bias) + + cls.set_tensor(l.mlp.linear_fc1.weight, mlp_fc1_weight) + if mlp_fc1_bias is not None: + cls.set_tensor(l.mlp.linear_fc1.bias, mlp_fc1_bias) + + cls.set_tensor(l.mlp.linear_fc2.weight, mlp_fc2_weight) + if mlp_fc2_bias is not None: + cls.set_tensor(l.mlp.linear_fc2.bias, mlp_fc2_bias) + + +class MCoreTESetter(MCoreSetter): + + @classmethod + def set_layer( + cls, + model, + layer_idx, + self_attn_norm_weight=None, + self_attn_norm_bias=None, + self_attn_qkv_weight=None, + self_attn_qkv_bias=None, + self_attn_proj_weight=None, + self_attn_proj_bias=None, + mlp_norm_weight=None, + mlp_norm_bias=None, + mlp_fc1_weight=None, + mlp_fc1_bias=None, + mlp_fc2_weight=None, + mlp_fc2_bias=None, + ): + + block = cls.get_transformer_block(model) + l = block.layers[layer_idx] + + # Self attention. + cls.set_tensor(l.self_attention.linear_qkv.layer_norm_weight, self_attn_norm_weight) + if self_attn_norm_bias is not None: + cls.set_tensor(l.self_attention.linear_qkv.layer_norm_bias, self_attn_norm_bias) + + cls.set_tensor(l.self_attention.linear_qkv.weight, self_attn_qkv_weight) + if self_attn_qkv_bias is not None: + cls.set_tensor(l.self_attention.linear_qkv.bias, self_attn_qkv_bias) + + cls.set_tensor(l.self_attention.linear_proj.weight, self_attn_proj_weight) + if self_attn_proj_bias is not None: + cls.set_tensor(l.self_attention.linear_proj.bias, self_attn_proj_bias) + + # MLP. + cls.set_tensor(l.mlp.linear_fc1.layer_norm_weight, mlp_norm_weight) + if mlp_norm_bias is not None: + cls.set_tensor(l.mlp.linear_fc1.layer_norm_bias, mlp_norm_bias) + + cls.set_tensor(l.mlp.linear_fc1.weight, mlp_fc1_weight) + if mlp_fc1_bias is not None: + cls.set_tensor(l.mlp.linear_fc1.bias, mlp_fc1_bias) + + cls.set_tensor(l.mlp.linear_fc2.weight, mlp_fc2_weight) + if mlp_fc2_bias is not None: + cls.set_tensor(l.mlp.linear_fc2.bias, mlp_fc2_bias) + + +def get_model_setter(model_type, transformer_impl): + setter = { + "local" : MCoreLocalSetter, + "transformer_engine" : MCoreTESetter, + }[transformer_impl] + setter.transformer_block_key = get_mcore_transformer_block_key(model_type) + return setter + + +def add_arguments(parser): + group = parser.add_argument_group(title='M-Core saver') + + group.add_argument('--megatron-path', type=str, default=None, + help='Base directory of Megatron repository') + + group.add_argument('--target-tensor-parallel-size', type=int, + help='Target tensor model parallel size, defaults to the tensor parallel size ' + 'in the input checkpoint if provided by the loader, otherwise to 1') + group.add_argument('--target-pipeline-parallel-size', type=int, + help='Target tensor model parallel size, default to the pipeline parall size ' + 'in the input checkpoint if provided by the loader, otherwise to 1') + group.add_argument('--saver-transformer-impl', default='transformer_engine', + choices=['local', 'transformer_engine'], + help='Which Transformer implementation to use.') + + +def save_checkpoint(queue, args): + + # Transformer engine >= 0.12.0, for CPU initialization. + te_version = packaging.version.Version(version("transformer-engine")) + assert te_version >= packaging.version.Version("0.12.0"), \ + "transformer engine version: %s (>=0.12.0 required)." % te_version + + # Search in directory above this + sys.path.append(os.path.abspath( + os.path.join(os.path.dirname(__file__), + os.path.pardir, + os.path.pardir))) + if args.megatron_path is not None: + sys.path.insert(0, args.megatron_path) + + try: + from megatron.training.arguments import (parse_args, validate_args) + from megatron.training.checkpointing import save_checkpoint + from megatron.training.global_vars import set_global_variables, get_args + from megatron.core.enums import ModelType + from megatron.training.tokenizer.tokenizer import _vocab_size_with_padding + from megatron.legacy import fused_kernels + from megatron.core import mpu + except ModuleNotFoundError: + print("Unable to import Megatron, please specify the path to Megatron using --megatron-path. Exiting.") + exit(1) + + def queue_get(name=None): + val = queue.get() + if val == "exit": + print("Loader exited, exiting saver") + exit(1) + if name is not None and args.checking and val["name"] != name: + val_name = val["name"] + print(f'Unexpected message. Expecting "{name}" but got "{val_name}". Exiting saver.') + exit(1) + if name is not None: + print(f"received {name}") + return val + + def check_message(msg): + if not args.checking: + return + msg_name = msg.pop("name") + if len(msg.keys()) > 0: + print(f"Unexpected values in {msg_name}:") + for key in msg.keys(): + print(f" {key}") + print(f"Exiting. If you want to ignore this, use the argument --no-checking.") + exit(1) + + + md = queue_get() + + if args.target_tensor_parallel_size is None: + if hasattr(md, 'previous_tensor_parallel_size'): + args.target_tensor_parallel_size = md.previous_tensor_parallel_size + else: + print("loader did not provide a tensor parallel size and --target-tensor-parallel-size not provided on command line. " + "Default to 1.") + args.target_tensor_parallel_size = 1 + + if args.target_pipeline_parallel_size is None: + if hasattr(md, 'previous_pipeline_parallel_size'): + args.target_pipeline_parallel_size = md.previous_pipeline_parallel_size + else: + print("loader did not provide a pipeline parallel size and --target-pipeline-parallel-size not provided on command line. " + "Default to 1.") + args.target_pipeline_parallel_size = 1 + + + # Arguments do sanity checks on the world size, but we don't care, + # so trick it into thinking we are plenty of processes + if args.target_tensor_parallel_size is not None and args.target_pipeline_parallel_size is not None: + os.environ["WORLD_SIZE"] = f'{args.target_tensor_parallel_size * args.target_pipeline_parallel_size}' + + # We want all arguments to come from us + sys.argv = ['script.py', + '--num-layers', str(md.num_layers), + '--hidden-size', str(md.hidden_size), + '--seq-length', str(md.seq_length), + '--num-attention-heads', str(md.num_attention_heads), + '--max-position-embeddings', str(md.max_position_embeddings), + '--position-embedding-type', str(md.position_embedding_type), + '--tokenizer-type', str(md.tokenizer_type), + '--tensor-model-parallel-size', str(args.target_tensor_parallel_size), + '--pipeline-model-parallel-size', str(args.target_pipeline_parallel_size), + '--no-masked-softmax-fusion', + '--no-bias-gelu-fusion', + '--no-bias-dropout-fusion', + '--no-async-tensor-model-parallel-allreduce', + '--use-cpu-initialization', + '--micro-batch-size', '1', + '--no-load-optim', + '--no-load-rng', + '--no-save-optim', + '--no-save-rng', + '--no-initialization', + '--save-interval', '1', + '--save', args.save_dir + ] + + if md.make_vocab_size_divisible_by is not None: + sys.argv.extend(['--make-vocab-size-divisible-by', str(md.make_vocab_size_divisible_by)]) + if md.params_dtype == torch.float16: + sys.argv.append('--fp16') + elif md.params_dtype == torch.bfloat16: + sys.argv.append('--bf16') + + if md.output_layer: + sys.argv.append('--untie-embeddings-and-output-weights') + if not md.linear_bias: + sys.argv.append('--disable-bias-linear') + + if md.model_type == 'BERT' and not md.bert_binary_head: + sys.argv.append('--bert-no-binary-head') + + margs = parse_args() + + if hasattr (md, 'checkpoint_args'): + # These are arguments that we are either changing, or cause problems for validation if they are set + # Note that some of these deal with T5 so will need to be changed if we support T5. + args_to_keep = ['tensor_model_parallel_size', 'pipeline_model_parallel_size', 'world_size', 'params_dtype', + 'num_layers_per_virtual_pipeline_stage', 'virtual_pipeline_model_parallel_size', + 'masked_softmax_fusion', 'bias_gelu_fusion', 'bias_dropout_fusion', + 'sequence_parallel', 'async_tensor_model_parallel_allreduce', + 'no_load_optim', 'no_load_rng', 'no_save_optim', 'no_save_rng', + 'vocab_file', 'tokenizer_model', + 'save_interval', 'save', + 'perform_initialization', 'use_cpu_initialization', + 'recompute_granularity', 'recompute_num_layers', 'recompute_method', + 'encoder_num_layers', 'encoder_seq_length', + 'distribute_saved_activations', + 'train_iters', 'lr_decay_iters', 'lr_warmup_iters', 'lr_warmup_fraction', + 'start_weight_decay', 'end_weight_decay'] + + for arg, value in vars(md.checkpoint_args).items(): + if arg in args_to_keep: + continue + if not hasattr(margs, arg): + print(f"Checkpoint had argument {arg} but new arguments does not have this.") + continue + if getattr(margs, arg) != value: + print(f"Overwriting default {arg} value {getattr(margs, arg)} with value from checkpoint {value}.") + setattr(margs, arg, value) + + # Explicitly copy sequence_parallel, apply_query_key_layer_scaling. + margs.sequence_parallel = md.checkpoint_args.sequence_parallel + margs.apply_query_key_layer_scaling = md.checkpoint_args.apply_query_key_layer_scaling + + validate_args(margs) + + # Use M-core models & unset loaded paths. + margs.use_mcore_models = True + margs.blendable_index_path = None + margs.data_path = [] + margs.load = None + margs.save = args.save_dir + margs.tensorboard_dir = None + margs.tokenizer_model = None + margs.transformer_impl = args.saver_transformer_impl + + set_global_variables(margs, build_tokenizer=False) + + # Megatron args. (i.e., 'margs') + margs = get_args() + + if hasattr(md, 'consumed_train_samples'): + margs.consumed_train_samples = md.consumed_train_samples + margs.consumed_valid_samples = md.consumed_valid_samples + print(f"Setting consumed_train_samples to {margs.consumed_train_samples}" + f" and consumed_valid_samples to {margs.consumed_valid_samples}") + else: + print("consumed_train_samples not provided.") + + # Determine how to make our models + if md.model_type == 'GPT': + from pretrain_gpt import model_provider + margs.model_type = ModelType.encoder_or_decoder + elif md.model_type == 'BERT': + from pretrain_bert import model_provider + margs.model_type = ModelType.encoder_or_decoder + else: + raise Exception(f'unrecognized model type: {args.model_type}') + + # fake initializing distributed + mpu.set_tensor_model_parallel_world_size(args.target_tensor_parallel_size) + mpu.set_pipeline_model_parallel_world_size(args.target_pipeline_parallel_size) + mpu.set_tensor_model_parallel_rank(0) + mpu.set_pipeline_model_parallel_rank(0) + fused_kernels.load(margs) + + # Embeddings + #----------- + embeddings_msg = queue_get("embeddings") + + pos_embed = None + if md.position_embedding_type == 'learned_absolute': + pos_embed = embeddings_msg.pop("position embeddings") + orig_word_embed = embeddings_msg.pop("word embeddings") + check_message(embeddings_msg) + + # Deal with padding + if md.true_vocab_size is not None: + # figure out what our padded vocab size is + orig_vocab_size = orig_word_embed.shape[0] + margs.padded_vocab_size = _vocab_size_with_padding(md.true_vocab_size, margs) + + # Cut out extra padding we don't need + if orig_vocab_size > margs.padded_vocab_size: + full_word_embed = orig_word_embed[0:margs.padded_vocab_size,:] + + # Expanding embedding to larger size by replicating final entry + elif orig_vocab_size < margs.padded_vocab_size: + padding_size = margs.padded_vocab_size - orig_vocab_size + + full_word_embed = torch.cat(( + orig_word_embed, + orig_word_embed[-1].unsqueeze(0).expand(padding_size, -1))) + + # Same size! + else: + full_word_embed = orig_word_embed + else: + print("Original vocab size not specified, leaving embedding table as-is. " + "If you've changed the tensor parallel size this could cause problems.") + margs.padded_vocab_size = orig_word_embed.shape[0] + full_word_embed = orig_word_embed + + # Split into new tensor model parallel sizes + out_word_embed = torch.chunk(full_word_embed, args.target_tensor_parallel_size, dim=0) + + # Parameter setter class. + setter = get_model_setter(md.model_type, margs.transformer_impl) + + # Get models. + def get_models(count, dtype, pre_process, post_process): + models = [] + for rank in range(count): + models.append(model_provider(pre_process, post_process).to(dtype)) + print_memory_usage("saver", rank, count) + return models + + # Make models for first pipeline stage and fill in embeddings + mpu.set_pipeline_model_parallel_rank(0) + post_process = args.target_pipeline_parallel_size == 1 + models = get_models(args.target_tensor_parallel_size, md.params_dtype, True, post_process) + + # Set embeddings. + # -------------- + for tp_rank, model in enumerate(models): + if pos_embed is None: + assert not setter.has_position_embeddings(model) + setter.set_embeddings( + model, + word=out_word_embed[tp_rank], + pos=pos_embed, + ) + + # Transformer layers. + # ------------------ + total_layer_num = 0 + for pp_rank in range(args.target_pipeline_parallel_size): + # For later pipeline parallel ranks, make the new models + if pp_rank > 0: + mpu.set_pipeline_model_parallel_rank(pp_rank) + post_process = pp_rank == args.target_pipeline_parallel_size - 1 + models = get_models(args.target_tensor_parallel_size, md.params_dtype, False, post_process) + + for layer in range(len(setter.get_transformer_block(models[0]).layers)): + msg = queue_get(f"transformer layer {total_layer_num}") + + # duplicated tensors + input_norm_weight = msg.pop("input norm weight") + if md.norm_has_bias: + input_norm_bias = msg.pop("input norm bias") + post_norm_weight = msg.pop("post norm weight") + if md.norm_has_bias: + post_norm_bias = msg.pop("post norm bias") + if md.linear_bias: + dense_bias = msg.pop("dense bias") + mlp_l1_bias = msg.pop("mlp l1 bias") + + # Split up the parallel tensors + qkv_weight = torch.chunk(msg.pop("qkv weight"), args.target_tensor_parallel_size, dim=0) + dense_weight = torch.chunk(msg.pop("dense weight"), args.target_tensor_parallel_size, dim=1) + mlp_l1_weight = torch.chunk(msg.pop("mlp l1 weight"), args.target_tensor_parallel_size, dim=1) + + # Special handling for swiglu + if md.swiglu: + mlp_l0_weight_W = torch.chunk(msg.pop("mlp l0 weight W"), args.target_tensor_parallel_size, dim=0) + mlp_l0_weight_V = torch.chunk(msg.pop("mlp l0 weight V"), args.target_tensor_parallel_size, dim=0) + mlp_l0_weight = [torch.cat(weights, dim=0) for weights in zip(mlp_l0_weight_W, mlp_l0_weight_V)] + else: + mlp_l0_weight = torch.chunk(msg.pop("mlp l0 weight"), args.target_tensor_parallel_size, dim=0) + + if md.linear_bias: + qkv_bias = torch.chunk(msg.pop("qkv bias"), args.target_tensor_parallel_size, dim=0) + if md.swiglu: + mlp_l0_bias_W = torch.chunk(msg.pop("mlp l0 bias W"), args.target_tensor_parallel_size, dim=0) + mlp_l0_bias_V = torch.chunk(msg.pop("mlp l0 bias V"), args.target_tensor_parallel_size, dim=0) + mlp_l0_bias = [torch.cat(bias, dim=0) for bias in zip(mlp_l0_bias_W, mlp_l0_bias_V)] + else: + mlp_l0_bias = torch.chunk(msg.pop("mlp l0 bias"), args.target_tensor_parallel_size, dim=0) + + # Save them to the model + for tp_rank in range(args.target_tensor_parallel_size): + params_dict = { + "self_attn_norm_weight" : input_norm_weight, + "self_attn_qkv_weight" : qkv_weight[tp_rank], + "self_attn_proj_weight" : dense_weight[tp_rank], + "mlp_norm_weight" : post_norm_weight, + "mlp_fc1_weight" : mlp_l0_weight[tp_rank], + "mlp_fc2_weight" : mlp_l1_weight[tp_rank], + } + if md.norm_has_bias: + params_dict.update({ + "self_attn_norm_bias" : + input_norm_bias if md.norm_has_bias else None, + "mlp_norm_bias" : + post_norm_bias if md.norm_has_bias else None, + }) + if md.linear_bias: + params_dict.update({ + "self_attn_qkv_bias" : qkv_bias[tp_rank], + "self_attn_proj_bias" : dense_bias, + "mlp_fc1_bias" : mlp_l0_bias[tp_rank], + "mlp_fc2_bias" : mlp_l1_bias, + }) + setter.set_layer(models[tp_rank], layer, **params_dict) + + total_layer_num = total_layer_num + 1 + check_message(msg) + + + if post_process: + msg = queue_get("final norm") + final_norm_weight = msg.pop("weight") + if md.norm_has_bias: + final_norm_bias = msg.pop("bias") + for tp_rank, model in enumerate(models): + setter.set_final_norm( + model, + weight=final_norm_weight, + bias=final_norm_bias if md.norm_has_bias else None, + ) + if pp_rank != 0 and not md.output_layer: + # Copy word embeddings to final pipeline rank + setter.set_output_word_embeddings( + model, + emb=out_word_embed[tp_rank], + ) + del final_norm_weight + if md.norm_has_bias: + del final_norm_bias + check_message(msg) + + if md.output_layer: + msg = queue_get("output layer") + if not hasattr(models[0], 'output_layer'): + print("ERROR: got an output layer, but model does not have one") + exit(1) + output_layer_weight = torch.chunk(msg.pop("weight"), args.target_tensor_parallel_size, dim=0) + for tp_rank, model in enumerate(models): + setter.set_output_layer(model, output_layer_weight[tp_rank]) + del output_layer_weight + check_message(msg) + + msg = queue_get() + if msg != "done" and msg["name"] == "pooler": + if not hasattr(models[0], 'pooler'): + print("ERROR: got a pooler, but model does not have one") + exit(1) + print("received pooler") + pooler_weight = msg.pop("weight") + pooler_bias = msg.pop("bias") + for tp_rank in range(args.target_tensor_parallel_size): + setter.set_pooler( + model=models[tp_rank], + weight=pooler_weight, + bias=pooler_bias, + ) + del pooler_weight + del pooler_bias + check_message(msg) + msg = queue_get() + + if msg != "done" and msg["name"] == "lm head": + if not hasattr(models[0], 'lm_head'): + print("ERROR: got an lm head, but model does not have one") + exit(1) + print("received lm head") + lm_head_dense_weight = msg.pop("dense weight") + lm_head_dense_bias = msg.pop("dense bias") + lm_head_norm_weight = msg.pop("norm weight") + if md.norm_has_bias: + lm_head_norm_bias = msg.pop("norm bias") + for tp_rank in range(args.target_tensor_parallel_size): + setter.set_lm_head( + model=models[tp_rank], + dense_weight=lm_head_dense_weight, + dense_bias=lm_head_dense_bias, + norm_weight=lm_head_norm_weight, + norm_bias=lm_head_norm_bias if md.norm_has_bias else None, + ) + check_message(msg) + msg = queue_get() + + if msg != "done" and msg["name"] == "binary head": + if not hasattr(models[0], 'binary_head'): + print("ERROR: got a binary head, but model does not have one") + exit(1) + print("received binary head") + binary_head_weight = msg.pop("weight") + binary_head_bias = msg.pop("bias") + for tp_rank in range(args.target_tensor_parallel_size): + setter.set_binary_head( + model=models[tp_rank], + weight=binary_head_weight, + bias=binary_head_bias, + ) + check_message(msg) + msg = queue_get() + + if msg != "done": + print("ERROR: got some more data but was expecting to be done") + + for tp_rank in range(args.target_tensor_parallel_size): + mpu.set_tensor_model_parallel_rank(tp_rank) + save_checkpoint(md.iteration, [models[tp_rank]], None, None, + num_floating_point_operations_so_far=0) + + print("Done!") diff --git a/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/saver_megatron.py b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/saver_megatron.py new file mode 100644 index 0000000..d09f772 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/saver_megatron.py @@ -0,0 +1,413 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import os +import sys +import torch + + +def add_arguments(parser): + group = parser.add_argument_group(title='Megatron saver') + + group.add_argument('--megatron-path', type=str, default=None, + help='Base directory of Megatron repository') + + group.add_argument('--target-tensor-parallel-size', type=int, + help='Target tensor model parallel size, defaults to the tensor parallel size ' + 'in the input checkpoint if provided by the loader, otherwise to 1') + group.add_argument('--target-pipeline-parallel-size', type=int, + help='Target tensor model parallel size, default to the pipeline parall size ' + 'in the input checkpoint if provided by the loader, otherwise to 1') + group.add_argument('--saver-transformer-impl', default='local', + choices=['local', 'transformer_engine'], + help='Which Transformer implementation to use.') + +def save_checkpoint(queue, args): + + # Search in directory above this + sys.path.append(os.path.abspath( + os.path.join(os.path.dirname(__file__), + os.path.pardir, + os.path.pardir))) + if args.megatron_path is not None: + sys.path.insert(0, args.megatron_path) + + try: + from megatron.training.arguments import (parse_args, validate_args) + from megatron.training.checkpointing import save_checkpoint + from megatron.training.global_vars import set_global_variables, get_args + from megatron.core.enums import ModelType + from megatron.training.tokenizer.tokenizer import _vocab_size_with_padding + from megatron.legacy import fused_kernels + from megatron.core import mpu + except ModuleNotFoundError: + print("Unable to import Megatron, please specify the path to Megatron using --megatron-path. Exiting.") + exit(1) + + def queue_get(name=None): + val = queue.get() + if val == "exit": + print("Loader exited, exiting saver") + exit(1) + if name is not None and args.checking and val["name"] != name: + val_name = val["name"] + print(f'Unexpected message. Expecting "{name}" but got "{val_name}". Exiting saver.') + exit(1) + if name is not None: + print(f"received {name}") + return val + + def check_message(msg): + if not args.checking: + return + msg_name = msg.pop("name") + if len(msg.keys()) > 0: + print(f"Unexpected values in {msg_name}:") + for key in msg.keys(): + print(f" {key}") + print(f"Exiting. If you want to ignore this, use the argument --no-checking.") + exit(1) + + + md = queue_get() + + if args.target_tensor_parallel_size is None: + if hasattr(md, 'previous_tensor_parallel_size'): + args.target_tensor_parallel_size = md.previous_tensor_parallel_size + else: + print("loader did not provide a tensor parallel size and --target-tensor-parallel-size not provided on command line. " + "Default to 1.") + args.target_tensor_parallel_size = 1 + + if args.target_pipeline_parallel_size is None: + if hasattr(md, 'previous_pipeline_parallel_size'): + args.target_pipeline_parallel_size = md.previous_pipeline_parallel_size + else: + print("loader did not provide a pipeline parallel size and --target-pipeline-parallel-size not provided on command line. " + "Default to 1.") + args.target_pipeline_parallel_size = 1 + + + # Arguments do sanity checks on the world size, but we don't care, + # so trick it into thinking we are plenty of processes + if args.target_tensor_parallel_size is not None and args.target_pipeline_parallel_size is not None: + os.environ["WORLD_SIZE"] = f'{args.target_tensor_parallel_size * args.target_pipeline_parallel_size}' + + # We want all arguments to come from us + sys.argv = ['script.py', + '--num-layers', str(md.num_layers), + '--hidden-size', str(md.hidden_size), + '--seq-length', str(md.seq_length), + '--num-attention-heads', str(md.num_attention_heads), + '--max-position-embeddings', str(md.max_position_embeddings), + '--position-embedding-type', str(md.position_embedding_type), + '--tokenizer-type', str(md.tokenizer_type), + '--tensor-model-parallel-size', str(args.target_tensor_parallel_size), + '--pipeline-model-parallel-size', str(args.target_pipeline_parallel_size), + '--no-masked-softmax-fusion', + '--no-bias-gelu-fusion', + '--no-bias-dropout-fusion', + '--no-async-tensor-model-parallel-allreduce', + '--use-cpu-initialization', + '--micro-batch-size', '1', + '--no-load-optim', + '--no-load-rng', + '--no-save-optim', + '--no-save-rng', + '--no-initialization', + '--save-interval', '1', + '--save', args.save_dir + ] + + if md.make_vocab_size_divisible_by is not None: + sys.argv.extend(['--make-vocab-size-divisible-by', str(md.make_vocab_size_divisible_by)]) + if md.params_dtype == torch.float16: + sys.argv.append('--fp16') + elif md.params_dtype == torch.bfloat16: + sys.argv.append('--bf16') + + if md.output_layer: + sys.argv.append('--untie-embeddings-and-output-weights') + if not md.linear_bias: + sys.argv.append('--disable-bias-linear') + + if md.model_type == 'BERT' and not md.bert_binary_head: + sys.argv.append('--bert-no-binary-head') + + margs = parse_args() + + + if hasattr (md, 'checkpoint_args'): + # These are arguments that we are either changing, or cause problems for validation if they are set + # Note that some of these deal with T5 so will need to be changed if we support T5. + args_to_keep = ['tensor_model_parallel_size', 'pipeline_model_parallel_size', 'world_size', 'params_dtype', + 'num_layers_per_virtual_pipeline_stage', 'virtual_pipeline_model_parallel_size', + 'masked_softmax_fusion', 'bias_gelu_fusion', 'bias_dropout_fusion', + 'sequence_parallel', 'async_tensor_model_parallel_allreduce', + 'no_load_optim', 'no_load_rng', 'no_save_optim', 'no_save_rng', + 'vocab_file', 'tokenizer_model', + 'save_interval', 'save', + 'perform_initialization', 'use_cpu_initialization', + 'recompute_granularity', 'recompute_num_layers', 'recompute_method', + 'encoder_num_layers', 'encoder_seq_length', + 'distribute_saved_activations', + 'train_iters', 'lr_decay_iters', 'lr_warmup_iters', 'lr_warmup_fraction', + 'start_weight_decay', 'end_weight_decay'] + + + for arg, value in vars(md.checkpoint_args).items(): + if arg in args_to_keep: + continue + if not hasattr(margs, arg): + print(f"Checkpoint had argument {arg} but new arguments does not have this.") + continue + if getattr(margs, arg) != value: + print(f"Overwriting default {arg} value {getattr(margs, arg)} with value from checkpoint {value}.") + setattr(margs, arg, value) + + validate_args(margs) + + # Use MLM models. + margs.use_mcore_models = False + margs.transformer_impl = args.saver_transformer_impl + + # Do not instantiate Tensorboard + margs.tensorboard_dir = None + + set_global_variables(margs, build_tokenizer=False) + + # margs = megatron args + margs = get_args() + + if hasattr(md, 'consumed_train_samples'): + margs.consumed_train_samples = md.consumed_train_samples + margs.consumed_valid_samples = md.consumed_valid_samples + print(f"Setting consumed_train_samples to {margs.consumed_train_samples}" + f" and consumed_valid_samples to {margs.consumed_valid_samples}") + else: + print("consumed_train_samples not provided.") + + # Determine how to make our models + if md.model_type == 'GPT': + from pretrain_gpt import model_provider + margs.model_type = ModelType.encoder_or_decoder + elif md.model_type == 'BERT': + from pretrain_bert import model_provider + margs.model_type = ModelType.encoder_or_decoder + else: + raise Exception(f'unrecognized model type: {args.model_type}') + + def get_models(count, dtype, pre_process, post_process): + models = [model_provider(pre_process, post_process).to(dtype) for _ in range(count)] + return models + + # fake initializing distributed + mpu.set_tensor_model_parallel_world_size(args.target_tensor_parallel_size) + mpu.set_pipeline_model_parallel_world_size(args.target_pipeline_parallel_size) + mpu.set_tensor_model_parallel_rank(0) + mpu.set_pipeline_model_parallel_rank(0) + fused_kernels.load(margs) + + # Embeddings + #----------- + embeddings_msg = queue_get("embeddings") + + pos_embed = None + if md.position_embedding_type == 'learned_absolute': + pos_embed = embeddings_msg.pop("position embeddings") + orig_word_embed = embeddings_msg.pop("word embeddings") + check_message(embeddings_msg) + + # Deal with padding + if md.true_vocab_size is not None: + # figure out what our padded vocab size is + orig_vocab_size = orig_word_embed.shape[0] + margs.padded_vocab_size = _vocab_size_with_padding(md.true_vocab_size, margs) + + # Cut out extra padding we don't need + if orig_vocab_size > margs.padded_vocab_size: + full_word_embed = orig_word_embed[0:margs.padded_vocab_size,:] + + # Expanding embedding to larger size by replicating final entry + elif orig_vocab_size < margs.padded_vocab_size: + padding_size = margs.padded_vocab_size - orig_vocab_size + + full_word_embed = torch.cat(( + orig_word_embed, + orig_word_embed[-1].unsqueeze(0).expand(padding_size, -1))) + + # Same size! + else: + full_word_embed = orig_word_embed + else: + print("Original vocab size not specified, leaving embedding table as-is. " + "If you've changed the tensor parallel size this could cause problems.") + margs.padded_vocab_size = orig_word_embed.shape[0] + full_word_embed = orig_word_embed + + # Split into new tensor model parallel sizes + out_word_embed = torch.chunk(full_word_embed, args.target_tensor_parallel_size, dim=0) + + # Make models for first pipeline stage and fill in embeddings + mpu.set_pipeline_model_parallel_rank(0) + post_process = args.target_pipeline_parallel_size == 1 + models = get_models(args.target_tensor_parallel_size, md.params_dtype, True, post_process) + for tp_rank, model in enumerate(models): + model.language_model.embedding.word_embeddings.weight.data.copy_(out_word_embed[tp_rank]) + if pos_embed is not None: + model.language_model.embedding.position_embeddings.weight.data.copy_(pos_embed) + else: + assert not hasattr(model.language_model.embedding, "position_embeddings") + + # Transformer layers + #------------------- + total_layer_num = 0 + for pp_rank in range(args.target_pipeline_parallel_size): + # For later pipeline parallel ranks, make the new models + if pp_rank > 0: + mpu.set_pipeline_model_parallel_rank(pp_rank) + post_process = pp_rank == args.target_pipeline_parallel_size - 1 + models = get_models(args.target_tensor_parallel_size, md.params_dtype, False, post_process) + + for layer in range(len(models[0].language_model.encoder.layers)): + msg = queue_get(f"transformer layer {total_layer_num}") + + # duplicated tensors + input_norm_weight = msg.pop("input norm weight") + if md.norm_has_bias: + input_norm_bias = msg.pop("input norm bias") + post_norm_weight = msg.pop("post norm weight") + if md.norm_has_bias: + post_norm_bias = msg.pop("post norm bias") + if md.linear_bias: + dense_bias = msg.pop("dense bias") + mlp_l1_bias = msg.pop("mlp l1 bias") + + # Split up the parallel tensors + qkv_weight = torch.chunk(msg.pop("qkv weight"), args.target_tensor_parallel_size, dim=0) + dense_weight = torch.chunk(msg.pop("dense weight"), args.target_tensor_parallel_size, dim=1) + mlp_l1_weight = torch.chunk(msg.pop("mlp l1 weight"), args.target_tensor_parallel_size, dim=1) + + # Special handling for swiglu + if md.swiglu: + mlp_l0_weight_W = torch.chunk(msg.pop("mlp l0 weight W"), args.target_tensor_parallel_size, dim=0) + mlp_l0_weight_V = torch.chunk(msg.pop("mlp l0 weight V"), args.target_tensor_parallel_size, dim=0) + mlp_l0_weight = [torch.cat(weights, dim=0) for weights in zip(mlp_l0_weight_W, mlp_l0_weight_V)] + else: + mlp_l0_weight = torch.chunk(msg.pop("mlp l0 weight"), args.target_tensor_parallel_size, dim=0) + + if md.linear_bias: + qkv_bias = torch.chunk(msg.pop("qkv bias"), args.target_tensor_parallel_size, dim=0) + if md.swiglu: + mlp_l0_bias_W = torch.chunk(msg.pop("mlp l0 bias W"), args.target_tensor_parallel_size, dim=0) + mlp_l0_bias_V = torch.chunk(msg.pop("mlp l0 bias V"), args.target_tensor_parallel_size, dim=0) + mlp_l0_bias = [torch.cat(bias, dim=0) for bias in zip(mlp_l0_bias_W, mlp_l0_bias_V)] + else: + mlp_l0_bias = torch.chunk(msg.pop("mlp l0 bias"), args.target_tensor_parallel_size, dim=0) + + # Save them to the model + for tp_rank in range(args.target_tensor_parallel_size): + l = models[tp_rank].language_model.encoder.layers[layer] + l.input_norm.weight.data.copy_(input_norm_weight) + if md.norm_has_bias: + l.input_norm.bias.data.copy_(input_norm_bias) + l.self_attention.query_key_value.weight.data.copy_(qkv_weight[tp_rank]) + l.self_attention.dense.weight.data.copy_(dense_weight[tp_rank]) + l.post_attention_norm.weight.data.copy_(post_norm_weight) + if md.norm_has_bias: + l.post_attention_norm.bias.data.copy_(post_norm_bias) + l.mlp.dense_h_to_4h.weight.data.copy_(mlp_l0_weight[tp_rank]) + l.mlp.dense_4h_to_h.weight.data.copy_(mlp_l1_weight[tp_rank]) + if md.linear_bias: + l.self_attention.query_key_value.bias.data.copy_(qkv_bias[tp_rank]) + l.self_attention.dense.bias.data.copy_(dense_bias) + l.mlp.dense_h_to_4h.bias.data.copy_(mlp_l0_bias[tp_rank]) + l.mlp.dense_4h_to_h.bias.data.copy_(mlp_l1_bias) + + total_layer_num = total_layer_num + 1 + check_message(msg) + + + if post_process: + msg = queue_get("final norm") + final_norm_weight = msg.pop("weight") + if md.norm_has_bias: + final_norm_bias = msg.pop("bias") + for tp_rank in range(args.target_tensor_parallel_size): + models[tp_rank].language_model.encoder.final_norm.weight.data.copy_(final_norm_weight) + if md.norm_has_bias: + models[tp_rank].language_model.encoder.final_norm.bias.data.copy_(final_norm_bias) + if pp_rank != 0 and not md.output_layer: + # Copy word embeddings to final pipeline rank + models[tp_rank].word_embeddings.weight.data.copy_(out_word_embed[tp_rank]) + del final_norm_weight + if md.norm_has_bias: + del final_norm_bias + check_message(msg) + + if md.output_layer: + msg = queue_get("output layer") + if not hasattr(models[0].language_model, 'output_layer'): + print("ERROR: got an output layer, but model does not have one") + exit(1) + output_layer_weight = torch.chunk(msg.pop("weight"), args.target_tensor_parallel_size, dim=0) + for tp_rank in range(args.target_tensor_parallel_size): + models[tp_rank].language_model.output_layer.weight.data.copy_(output_layer_weight[tp_rank]) + del output_layer_weight + check_message(msg) + + msg = queue_get() + if msg != "done" and msg["name"] == "pooler": + if not hasattr(models[0].language_model, 'pooler'): + print("ERROR: got a pooler, but model does not have one") + exit(1) + print("received pooler") + pooler_weight = msg.pop("weight") + pooler_bias = msg.pop("bias") + for tp_rank in range(args.target_tensor_parallel_size): + models[tp_rank].language_model.pooler.dense.weight.data.copy_(pooler_weight) + models[tp_rank].language_model.pooler.dense.bias.data.copy_(pooler_bias) + del pooler_weight + del pooler_bias + check_message(msg) + msg = queue_get() + + if msg != "done" and msg["name"] == "lm head": + if not hasattr(models[0], 'lm_head'): + print("ERROR: got an lm head, but model does not have one") + exit(1) + print("received lm head") + lm_head_dense_weight = msg.pop("dense weight") + lm_head_dense_bias = msg.pop("dense bias") + lm_head_norm_weight = msg.pop("norm weight") + if md.norm_has_bias: + lm_head_norm_bias = msg.pop("norm bias") + for tp_rank in range(args.target_tensor_parallel_size): + models[tp_rank].lm_head.dense.weight.data.copy_(lm_head_dense_weight) + models[tp_rank].lm_head.dense.bias.data.copy_(lm_head_dense_bias) + models[tp_rank].lm_head.norm.weight.data.copy_(lm_head_norm_weight) + if md.norm_has_bias: + models[tp_rank].lm_head.norm.bias.data.copy_(lm_head_norm_bias) + check_message(msg) + msg = queue_get() + + if msg != "done" and msg["name"] == "binary head": + if not hasattr(models[0], 'binary_head'): + print("ERROR: got a binary head, but model does not have one") + exit(1) + print("received binary head") + binary_head_weight = msg.pop("weight") + binary_head_bias = msg.pop("bias") + for tp_rank in range(args.target_tensor_parallel_size): + models[tp_rank].binary_head.weight.data.copy_(binary_head_weight) + models[tp_rank].binary_head.bias.data.copy_(binary_head_bias) + check_message(msg) + msg = queue_get() + + if msg != "done": + print("ERROR: got some more data but was expecting to be done") + + for tp_rank in range(args.target_tensor_parallel_size): + mpu.set_tensor_model_parallel_rank(tp_rank) + save_checkpoint(md.iteration, [models[tp_rank]], None, None, + num_floating_point_operations_so_far=0) + print("Done!") diff --git a/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/setter.py b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/setter.py new file mode 100644 index 0000000..5e84cff --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/setter.py @@ -0,0 +1,113 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + + +class ModelSetter: + '''Model parameter setter. + + See convert.py for a full list of supported parameters and their names. + ''' + + @classmethod + def set_tensor(cls, dst, src): + '''Copy (in-place) src tensor to dst tensor.''' + if src is not None: + dst.data.copy_(src) + + @classmethod + def has_position_embeddings(cls, model): + ''' + Return True if learned parameters exist for position embeddings (e.g., + learned absolute), and False otherwise (e.g., RoPE). + ''' + raise NotImplementedError + + @classmethod + def set_embeddings( + cls, + model, + word=None, + pos=None, + ): + '''Set word and position embeddings.''' + raise NotImplementedError + + @classmethod + def set_output_word_embeddings( + cls, + model, + emb=None, + ): + '''Set output word embeddings for final pipeline stage.''' + raise NotImplementedError + + @classmethod + def set_layer( + cls, + model, + layer_idx, + self_attn_norm_weight=None, + self_attn_norm_bias=None, + self_attn_qkv_weight=None, + self_attn_qkv_bias=None, + self_attn_proj_weight=None, + self_attn_proj_bias=None, + mlp_norm_weight=None, + mlp_norm_bias=None, + mlp_fc1_weight=None, + mlp_fc1_bias=None, + mlp_fc2_weight=None, + mlp_fc2_bias=None, + ): + '''Set layer parameters.''' + raise NotImplementedError + + @classmethod + def set_final_norm( + cls, + model, + weight=None, + bias=None, + ): + '''Set final norm parameters (i.e., after last transformer layer).''' + raise NotImplementedError + + @classmethod + def set_output_layer( + cls, + model, + weight=None, + ): + '''Set output (i.e., 'dense') weights.''' + raise NotImplementedError + + @classmethod + def set_pooler( + cls, + model, + weight=None, + bias=None, + ): + '''Set pooler parameters (e.g., for Bert).''' + raise NotImplementedError + + @classmethod + def set_lm_head( + cls, + model, + dense_weight=None, + dense_bias=None, + norm_weight=None, + norm_bias=None, + ): + '''Set LM head parameters.''' + raise NotImplementedError + + @classmethod + def set_binary_head( + cls, + model, + weight=None, + bias=None, + ): + '''Set binary head parameters.''' + raise NotImplementedError diff --git a/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/utils.py b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/utils.py new file mode 100644 index 0000000..a604619 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/checkpoint/utils.py @@ -0,0 +1,23 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import psutil + + +def print_memory_usage(key, rank, num_ranks): + '''Print memory usage.''' + process = psutil.Process() + mem_info = process.memory_info() + print("> memory usage: '%s', rank %d / %d, mem %.1f/%.1f gb." % ( + key, + rank, + num_ranks, + mem_info.rss / 1024**3, + 100 * mem_info.rss / process.memory_percent() / 1024**3, + )) + + +def get_mcore_transformer_block_key(model_key): + return { + "GPT" : "decoder", + "BERT" : "encoder", + }[model_key] diff --git a/Megatron-LM-core_r0.7.0.beta/tools/linter.py b/Megatron-LM-core_r0.7.0.beta/tools/linter.py new file mode 100644 index 0000000..5b14007 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/linter.py @@ -0,0 +1,36 @@ +import os +import os.path as osp +import pathlib +import subprocess + + +def recursively_lint_files(): + """Recursively lint all python files in chosen subdirectories of megatron-lm""" + + try: + import autopep8 + except ModuleNotFoundError: + print("Please first install autopep8 via `pip install autopep8`") + return + + # get all python file paths from top level directory + file_dir = str(pathlib.Path(__file__).parent.absolute()) + working_dir = osp.join(file_dir, os.pardir) + all_py_paths = set(os.path.join(working_dir, fname) + for fname in os.listdir(working_dir) if ".py" in fname) + + # get all python file paths from chosen subdirectories + check_dirs = ['docker', 'megatron', 'openwebtext', 'scripts', 'tasks'] + for sub_dir in check_dirs: + for path, _, fnames in os.walk(osp.join(working_dir, sub_dir)): + all_py_paths.update(set(osp.join(path, fname) for fname in fnames if ".py" in fname)) + + print("Linting the following: ") + for py_path in all_py_paths: + print(py_path) + command = 'autopep8 --max-line-length 100 --aggressive --in-place {}'.format(py_path) + subprocess.check_call(command) + + +if __name__ == "__main__": + recursively_lint_files() diff --git a/Megatron-LM-core_r0.7.0.beta/tools/merge_datasets.py b/Megatron-LM-core_r0.7.0.beta/tools/merge_datasets.py new file mode 100644 index 0000000..c615558 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/merge_datasets.py @@ -0,0 +1,93 @@ +import os +import sys +import json +import argparse + +sys.path.append( + os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)) +) + +from megatron.core.datasets.indexed_dataset import ( + IndexedDataset, + IndexedDatasetBuilder, + get_bin_path, + get_idx_path, +) + + +def get_args(): + parser = argparse.ArgumentParser() + + group = parser.add_argument_group(title="input data") + group.add_argument( + "--input", + type=str, + required=True, + help="Path to directory containing all document files to merge", + ) + + group = parser.add_argument_group(title="output data") + group.add_argument( + "--output-prefix", + type=str, + required=True, + help="Path to binary output file without suffix", + ) + + group = parser.add_argument_group(title="miscellaneous") + group.add_argument( + "--multimodal", + action="store_true", + help="Whether the datasets are assumed to be multimodal" + ) + + args = parser.parse_args() + + assert os.path.isdir( + args.input + ), f"ERROR: {args.input} is not a directory or does not exist" + + assert os.path.isdir( + os.path.dirname(args.output_prefix) + ), f"ERROR: {os.path.dirname(args.output_prefix)} is not a directory or does not exist" + + return args + + +def main(): + args = get_args() + + prefixes = set() + for basename in os.listdir(args.input): + prefix, ext = os.path.splitext(basename) + + if prefix in prefixes: + continue + + if not os.path.isfile(os.path.join(args.input, basename)): + continue + + ext_pair = ".bin" if ext == ".idx" else ".idx" + assert os.path.isfile( + os.path.join(args.input, prefix) + ext_pair + ), f"ERROR: {ext_pair} file not provided for {os.path.join(args.input, prefix)}" + + prefixes.add(prefix) + + builder = None + for prefix in sorted(prefixes): + if builder is None: + dataset = IndexedDataset(os.path.join(args.input, prefix), multimodal=args.multimodal) + builder = IndexedDatasetBuilder( + get_bin_path(args.output_prefix), dtype=dataset.index.dtype, multimodal=args.multimodal + ) + del dataset + + builder.add_index(os.path.join(args.input, prefix)) + + builder.finalize(get_idx_path(args.output_prefix)) + + +if __name__ == '__main__': + + main() diff --git a/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/README.md b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/README.md new file mode 100644 index 0000000..d7707c6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/README.md @@ -0,0 +1,59 @@ +The following steps show how to prepare training dataset to train the mode. + +# Libraries to install + +``` + pip install ftfy langdetect numpy torch pandas nltk sentencepiece boto3 tqdm regex bs4 newspaper3k htmlmin tldextract + git clone https://github.com/mattilyra/LSH + cd LSH + python setup.py install +``` + +# Download the dataset + +1. Download the deduplicated URLs from [jcpeterson](https://mega.nz/#F!EZZD0YwJ!9_PlEQzdMVLaNdKv_ICNVQ!cc4RgQQZ) +2. Remove blacklisted URLs. +``` +python blacklist_urls.py +``` +3. Download the content from the clean urls with [openwebtext's utilities](https://github.com/eukaryote31/openwebtext/blob/master/download.py). + +4. Merge the contents into one loose json file with 1 json per newline of the format `{'text': text, 'url': unique_url}`. It is important for the url to be unique. + +# Prepare the data for GPT training: + +1. Perform ftfy, english detection and remove documents with less than 128 tokens. This step can be sharded and run on shards. +``` +python cleanup_dataset.py +``` +Additional cleanup (e.g. remove documents less than 512 characters or dataset specific cleaning like stories, realnews datasets) can be done using `cleanup_fix_dataset.py`. More details can be found by running `python cleanup_fix_dataset.py --help`. +2. Using LSH, find possible duplicates and store then in a file for later processing. The code supports saving and loading fingerprints for recurrent deduplications, and is also multithreaded for faster processing. More details are can be found by `python find_duplicate.py --help`. +``` +python find_duplicates.py --inputs --output +``` +3. Based on similarity measure defind inside function `is_similar` (default: 0.9), group urls that are similar. Basically, for each group, only one url we should keep and remove the rest. +``` +python group_duplicate_urls.py +``` +4. Remove similar documents that were detected in the last step. +``` +python remove_group_duplicates.py +``` + +5. Shuffle the dataset. +``` +shuf -o train_data.json +``` + +# Deduplicating ngrams + +To deduplicate the downstream tasks (e.g. lambada, squad) from the training dataset, we run the following command. + +``` +python filter_ngrams.py --tasks --dedup-dataset --output +``` +We use 13-grams by default for the deduplication. When we find a 13-gram match in a training document, we split the document into two pieces and remove the 13-gram along with 200 characters from the both side of the 13-gram. We also remove any splitted document with less than 200 characters or if a document got splitted more than 10 times. These parameters can be changed using corresponding arguments. + +Only for the lambada task, we need to provide the path, `--lambada-path `. + +Several other features (e.g. save and load dictionary) have been added, look at `python filter_ngrams.py --help` for details. diff --git a/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/add_id.py b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/add_id.py new file mode 100644 index 0000000..7bea7ee --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/add_id.py @@ -0,0 +1,54 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import argparse +import json +import os +import time + +""" +This code adds id to each json object in a json file. User can add prefix +to the ids. +""" + +if __name__ == '__main__': + + print('parsing the arguments ...') + + parser = argparse.ArgumentParser() + parser.add_argument('--input-file', type=str, default=None, help='Input'\ + ' json file where id needs to be added') + parser.add_argument('--output-file', type=str, default=None, help=\ + 'Output file name with id') + parser.add_argument('--id-prefix', type=str, default=None, help=\ + 'Id prefix') + parser.add_argument('--log-interval', type=int, default=100, + help='Log interval') + args = parser.parse_args() + + print('Adding ids to dataset ...') + + f_input = open(args.input_file, 'r', encoding='utf-8') + f_output = open(args.output_file, 'wb') + + unique_ids = 1 + start_time = time.time() + for row in f_input: + each_row = json.loads(row) + adlr_id_string = args.id_prefix + '-{:010d}'.format(int(unique_ids)) + each_row['adlr_id'] = adlr_id_string + myjson = json.dumps(each_row, ensure_ascii=False) + + f_output.write(myjson.encode('utf-8')) + f_output.write('\n'.encode('utf-8')) + + if unique_ids % args.log_interval == 0: + print(' processed {:9d} documents in {:.2f} seconds ...'.format( \ + unique_ids, time.time() - start_time), flush=True) + + unique_ids += 1 + + # Close the file. + f_input.close() + f_output.close() + + print('done :-)', flush=True) diff --git a/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/blacklist_urls.py b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/blacklist_urls.py new file mode 100644 index 0000000..f54f661 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/blacklist_urls.py @@ -0,0 +1,302 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +# WARNING! This file contains a blacklist of known malicious sites and thus contains some NSFW language. + + +import glob +import re +import time +import tldextract +import sys + + +# List of the domains to blacklist. +domain_blacklist = set([ + '500px', + 'aapks', + 'akamaihd', + 'amazon', + 'apple', + 'artifactfire', + 'artstation', + 'awwni', + 'bandcamp', + 'battleforthenet', + 'coinscalendar', + 'dailymotion', + 'deviantart', + 'discord', + 'discordapp', + 'dlapkandroid', + 'dropbox', + 'e621', + 'ebay', + 'edealinfo', + 'erome', + 'eroshare', + 'explosm', + 'facebook', + 'fbcdn', + 'flickr', + 'furaffinity', + 'futhead', + 'gatopardo', + 'gfycat', + 'gifsound', + 'gifsoup', + 'giphy', + 'github', + 'google', + 'gunprime', + 'gyazo', + 'horsefucker', + 'hotdealstar', + 'imagefap', + 'imageshack', + 'imgflip', + 'imgur', + 'instagram', + 'karmadecay', + 'kryptocal', + 'kym-cdn', + 'liveleak', + 'livememe', + 'lmgtfy', + 'magaimg', + 'memegenerator', + 'minorplanetcenter', + 'minus', + 'mobafire', + 'morejpeg', + 'nocookie', + 'pcpartpicker', + 'photobucket', + 'pinimg', + 'pinterest', + 'pixiv', + 'pornhub', + 'prntscr', + 'puu', + 'qkme', + 'quickmeme', + 'radd', + 'redd', + 'reddit', + 'reddit-stream', + 'redditlog', + 'redditmedia', + 'reddituploads', + 'redtube', + 'reupp', + 'reverb', + 'roanoke', + 'rollingstone', + 'sli', + 'soundcloud', + 'soundgasm', + 'spankbang', + 'spotify', + 'strawpoll', + 'streamable', + 'timeanddate', + 'tinypic', + 'touhouradio', + 'tumblr', + 'twimg', + 'twitch', + 'twitter', + 'vid', + 'vimeo', + 'vine', + 'vkaao', + 'vocaroo', + 'voyagefusion', + 'walmart', + 'wciu', + 'wikimedia', + 'wikipedia', + 'xhamster', + 'xkcd', + 'xvideos', + 'youtu', + 'youtube', + 'youtubedoubler', + 'ytimg', + 'zillexplorer', +]) + +def domain_is_in_blacklist(url): + domain = tldextract.extract(url).domain + return domain in domain_blacklist + + +# List of extentions to blacklist. +extentions_blacklist = ( + '.3gp', + '.7z' + '.ai', + '.aif', + '.apk', + '.app', + '.avi', + '.bin', + '.bmp', + '.bz2', + '.css', + '.csv', + '.dat', + '.deb', + '.dmg', + '.doc', + '.docx', + '.exe', + '.gif', + '.gifv', + '.gz', + '.iso', + '.jar', + '.jpeg', + '.jpg', + '.js', + '.log', + '.mid', + '.midi', + '.mkv', + '.mov', + '.mp3', + '.mp4', + '.mpeg', + '.mpg', + '.ogg', + '.ogv', + '.otf', + '.pdf', + '.pkg', + '.png', + '.pps', + '.ppt', + '.pptx', + '.psd', + '.py', + '.qt', + '.ram', + '.rar', + '.sql', + '.svg', + '.swf', + '.tar.gz', + '.tar', + '.tgz', + '.tiff', + '.ttf', + '.txt', + '.wav', + '.webm', + '.wma', + '.wmv', + '.xls', + '.xlsx', + '.xml', + '.xz', + '.zip', +) + +def extention_is_in_blacklist(url): + if url.split('?')[0].lower().endswith(extentions_blacklist): + return True + return False + + +# Malformed urls. +# This function is adapted from: +# https://stackoverflow.com/questions/7160737/python-how-to-validate-a-url-in-python-malformed-or-not +url_regex = re.compile( + r'^(?:http)s?://' # http:// or https:// + r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|' #domain... + r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' # ...or ip + r'(?::\d+)?' # optional port + r'(?:/?|[/?]\S+)$', re.IGNORECASE) +def url_is_malformed(url): + return re.match(url_regex, url) is None + + +def print_progress(prefix, start_time, urls_counter, + domain_blacklist_counter, + extention_blacklist_counter, + short_url_counter, malformed_url_counter, + duplicate_url_counter): + string = prefix + ' | ' + string += 'time elapsed (s): {:.2f} | '.format(time.time() - start_time) + string += 'number of urls: {} | '.format(urls_counter) + string += 'domain blacklisted: {} | '.format(domain_blacklist_counter) + string += 'extention blacklisted: {} | '.format(extention_blacklist_counter) + string += 'short urls (<=8): {} | '.format(short_url_counter) + string += 'malformed urls: {} | '.format(malformed_url_counter) + string += 'duplicate urls: {}'.format(duplicate_url_counter) + print(string, flush=True) + + +if __name__ == '__main__': + + + print('remove blacklisted urls ..') + + # Path to the url files. + path = sys.argv[1] + # Output url file. + output = sys.argv[2] + + # Get the list of url files. + files = glob.glob(path + '/*.txt') + print('> found {} files'.format(len(files))) + + urls = set() + urls_counter = 0 + domain_blacklist_counter = 0 + extention_blacklist_counter = 0 + short_url_counter = 0 + malformed_url_counter = 0 + duplicate_url_counter = 0 + start_time = time.time() + for filename in files: + with open(filename, 'r') as f: + for line in f: + url = line.strip() + urls_counter += 1 + if domain_is_in_blacklist(url): + print('[DOMAIN BLACKLIST]: {}'.format(url), flush=True) + domain_blacklist_counter += 1 + elif extention_is_in_blacklist(url): + print('[EXTENTION BLACKLIST]: {}'.format(url), flush=True) + extention_blacklist_counter += 1 + elif len(url) <= 8: + print('[SHORT URL]: {}'.format(url), flush=True) + short_url_counter += 1 + elif url_is_malformed(url): + print('[MALFORMED URL]: {}'.format(url), flush=True) + malformed_url_counter += 1 + elif url in urls: + print('[DUPLICATE URL]: {}'.format(url), flush=True) + duplicate_url_counter += 1 + else: + urls.add(url) + if urls_counter % 100000 == 0: + print_progress('PROGRESS', start_time, urls_counter, + domain_blacklist_counter, + extention_blacklist_counter, + short_url_counter, malformed_url_counter, + duplicate_url_counter) + + print_progress('FINAL', start_time, urls_counter, + domain_blacklist_counter, + extention_blacklist_counter, + short_url_counter, malformed_url_counter, + duplicate_url_counter) + + # Write the final set of urls. + print('> writing cleaned up url list to {}'.format(output)) + with open(output, 'w') as f: + for url in urls: + f.write(url + '\n') + + print('done :-)') diff --git a/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/cleanup_dataset.py b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/cleanup_dataset.py new file mode 100644 index 0000000..3a2eba4 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/cleanup_dataset.py @@ -0,0 +1,102 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + + +import ftfy +import json +from langdetect import detect +import numpy as np +import time +import os +import sys + +from tokenizer import Tokenizer + +MIN_DOCUMENT_LENGHT = 128 + + +def print_progress(prefix, start_time, num_docs, num_fixed_text, + num_non_english_docs, chars_non_english_docs, + num_small_docs, chars_small_docs): + + string = prefix + ' | ' + string += 'elapsed time: {:.2f} | '.format(time.time() - start_time) + string += 'documents: {} | '.format(num_docs) + string += 'fixed text: {} | '.format(num_fixed_text) + string += 'non-english: {} | '.format(num_non_english_docs) + string += 'non-english chars: {} | '.format(chars_non_english_docs) + string += 'small docs: {} | '.format(num_small_docs) + string += 'small docs chars: {}'.format(chars_small_docs) + print(string, flush=True) + + +def filter_corpus(filename, out_filename, print_interval=10000): + + print(' > filtering {}'.format(filename)) + + tokenizer = Tokenizer(cache_dir='./cache') + + num_docs = 0 + num_written_docs = 0 + num_small_docs = 0 + num_fixed_text = 0 + num_non_english_docs = 0 + chars_non_english_docs = 0 + chars_small_docs = 0 + start_time = time.time() + with open(out_filename, 'wb') as f: + with open(filename, 'r') as fin: + for line in fin: + try: + num_docs += 1 + myjson = json.loads(line) + # Fix text + text = ftfy.fix_text(myjson['text']) + if text != myjson['text']: + num_fixed_text += 1 + myjson['text'] = text + # Detect language. + if detect(text) != 'en': + print('[non-english text]', myjson) + num_non_english_docs += 1 + chars_non_english_docs += len(text) + continue + # On average each token is 5 characters so 8 is an + # upper bound. + if len(text) < (8 * MIN_DOCUMENT_LENGHT): + tokens = tokenizer.tokenize_document(text) + if len(tokens) < MIN_DOCUMENT_LENGHT: + print('[small document, skipping]:', myjson) + num_small_docs += 1 + chars_small_docs += len(text) + continue + myjson = json.dumps(myjson, ensure_ascii=False) + f.write(myjson.encode('utf-8')) + f.write('\n'.encode('utf-8')) + num_written_docs += 1 + if num_docs % print_interval == 0: + print_progress('[PROGRESS]', start_time, num_docs, + num_fixed_text, num_non_english_docs, + chars_non_english_docs, + num_small_docs, chars_small_docs) + except Exception as e: + print(' skipping ', line, e) + + print_progress('[FINAL]', start_time, num_docs, + num_fixed_text, num_non_english_docs, + chars_non_english_docs, + num_small_docs, chars_small_docs) + + +if __name__ == '__main__': + + print('building gpt2 dataset ...') + + input_filename = sys.argv[1] + output_filename = sys.argv[2] + + print('will be reading {}'.format(input_filename)) + print('and will write the results to {}'.format(output_filename)) + + filter_corpus(input_filename, output_filename) + + diff --git a/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/cleanup_fix_dataset.py b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/cleanup_fix_dataset.py new file mode 100644 index 0000000..c7f6cf2 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/cleanup_fix_dataset.py @@ -0,0 +1,178 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +""" +Filter and clean documents: +Capable to clean docs with less than 512 characters, less than +256 characters and contains javascript, fix text and dataset specific +cleaning like stories and realnews datasets. +Program arguments have the details. +""" + +import argparse +from functools import partial +import glob +import ftfy +import json +from langdetect import detect +import multiprocessing +import os +from pathlib import Path +import re +import time + +def process_doc(json_line, args): + + # Read the line. + document = json.loads(json_line) + text = document['text'] + + output = {'remove_512': False, 'remove_256_javascript': False, \ + 'remove_512_non_english': False, 'ftfy_fix_text': False, \ + 'general_cleaning': False} + + try: + # Reomove all docs with less than 512 characters + if "remove_512" in args.tasks: + if len(text) < 512: + output['remove_512'] = True + return output, text, document, True + + # Remove docs if less than 256 character length and contains Javascript + if "remove_256_javascript" in args.tasks: + if len(text) < 256 and 'javascript' in text.lower(): + output['remove_256_javascript'] = True + return output, text, document, True + + # Remove docs < 512 and nonenglish + if "remove_512_non_english" in args.tasks: + if len(text) < 512 and detect(text) != 'en': + output['remove_512_non_english'] = True + return output, text, document, True + + # Fix the text using ftfy, don't remove the text, hence return False + if "ftfy_fix_text" in args.tasks: + fixed_text = ftfy.fix_text(text) + output['ftfy_fix_text'] = True + return output, fixed_text, document, False + + # Cleaning extra spaces and newlines + if "general_cleaning" in args.tasks: + cleaned_text = re.sub(r" +|\b\n+ |\b\n+", " ", text) + #cleaned_text = re.sub(r"\n\n+", "\n\n", text) # used this for Gutenberg dataset + #cleaned_text = re.sub(r"\n", "\n\n", text) # Used this for realnews + + # stories datasets + #cleaned_text = re.sub(r" \'", "'", text) + #cleaned_text = re.sub(r" \!", "!", cleaned_text) + #cleaned_text = re.sub(r" \.", ".", cleaned_text) + #cleaned_text = re.sub(r" \?", "?", cleaned_text) + #cleaned_text = re.sub(r" - ", "-", cleaned_text) + ##cleaned_text = re.sub(r"\" ", "\"", cleaned_text) + #cleaned_text = re.sub(r" @ ", "@", cleaned_text) + + output['general_cleaning'] = True + return output, cleaned_text, document, False + + except Exception as e: + print('Error: *************************\n{}\ntext: {}'.format(e, \ + text), flush=True) + return output, text, document, True + + # don't remove + return output, text, document, False + + +def process_set(args, input_file, output_f_cleaned, output_f_filtered): + + print(' > working on {} ...'.format(input_file), flush=True) + + num_docs = num_remove_512 = num_remove_java = num_remove_512_non_english \ + = num_ftfy_fix_text = num_general_cleaning = 0 + + # Output file and counters. + output_cleaned = open(output_f_cleaned, 'wb') + output_filtered = open(output_f_filtered, 'wb') + + start_time = time.time() + + # Setup multi-processing. + num_workers = 40 + fin = open(input_file, 'r', encoding='utf-8') + pool = multiprocessing.Pool(num_workers) + process_doc_partial = partial(process_doc, args=args) + processed_docs = pool.imap(process_doc_partial, fin, 500) + + # Process documents. + for output, text, document, to_filter in processed_docs: + num_docs += 1 + + num_remove_512 += 1 if output['remove_512'] else 0 + num_remove_java += 1 if output['remove_256_javascript'] else 0 + num_remove_512_non_english += 1 if output['remove_512_non_english'] \ + else 0 + num_ftfy_fix_text += 1 if output['ftfy_fix_text'] else 0 + num_general_cleaning += 1 if output['general_cleaning'] else 0 + + document['text'] = text + myjson = json.dumps(document, ensure_ascii=False) + + if to_filter: + output_filtered.write(myjson.encode('utf-8')) + output_filtered.write('\n'.encode('utf-8')) + else: + output_cleaned.write(myjson.encode('utf-8')) + output_cleaned.write('\n'.encode('utf-8')) + + if num_docs % args.log_interval == 0: + print(' processed {:9d} documents in {:.2f} seconds ...'.format( + num_docs, time.time() - start_time), flush=True) + + # Close the file. + output_cleaned.close() + output_filtered.close() + fin.close() + + # Print stats. + print(' >> total docs: {} remove_512 {} remove_256_javascript {} '\ + 'remove_512_non_english {} ftfy_fix_text {} general_cleaning {}'.\ + format(num_docs, num_remove_512, num_remove_java,\ + num_remove_512_non_english, num_ftfy_fix_text, \ + num_general_cleaning), flush=True) + +if __name__ == '__main__': + + + print('parsing the arguments ...') + + parser = argparse.ArgumentParser() + parser.add_argument('--input-files', nargs = '*', required=True, default=\ + None, help = 'Input json files that needs to be'\ + ' cleaned') + parser.add_argument('--tasks', nargs = '*', required=True, default=None,\ + help = 'Tasks to perform on the input files, ' \ + 'such as remove_512, remove_256_javascript, ' \ + 'remove_512_non_english, ftfy_fix_text, and ' \ + 'general_cleaning. 256 or 512 means the number' \ + ' of characters.') + + parser.add_argument('--output-path', type=str, default=None, + help='Directory where the output should go') + parser.add_argument('--log-interval', type=int, default=100, + help='Log interval') + + args = parser.parse_args() + + print('cleanup dataset ...') + + for input_file in args.input_files: + input_filename, input_filename_ext = os.path.splitext(Path(input_file)\ + .name) + + output_f_cleaned = os.path.join(args.output_path, input_filename + \ + "_cleaned" + input_filename_ext) + output_f_filtered = os.path.join(args.output_path, input_filename + \ + "_filtered" + input_filename_ext) + + process_set(args, input_file, output_f_cleaned, output_f_filtered) + + print('done :-)', flush=True) diff --git a/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/filter_ngrams.py b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/filter_ngrams.py new file mode 100644 index 0000000..7327a16 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/filter_ngrams.py @@ -0,0 +1,479 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +""" +Deduplicate downstream tasks from training dataset. 13-grams have been used. +All split documents with less than 200 characters got filtered. Any document +with more than 10 splits got filtered as well. +""" + +import argparse +from functools import partial +import json +import multiprocessing +import nltk +import pickle +import re +import string +import sys +import time + +def get_words(text): + # get all the lowercase words from text + words, positions = [], [] + for match in re.finditer(r'\w+', text.lower()): + words.append(match.group(0)) + positions.append(match.start()) + return words, positions + +# splits the text +def split_text(text, start_position, remove_char_each_side, seq): + # first part of the text + punctuations = ".!?" + pos = start_position - remove_char_each_side + text_first = "" + while pos > 0 and not text[pos] in punctuations: + pos -= 1 + if pos > 0: + text_first = text[0:pos+1] + + # add length of seq and remove_char_each_side + pos = start_position + len(seq) + remove_char_each_side + + # last part of the text + text_second = "" + while pos < len(text) and not text[pos] in punctuations: + pos += 1 + if pos + 1 < len(text): + text_second = text[pos+1:len(text)] + + return text_first, text_second + +def check_and_clean_text(args, words, ngrams, text, start_position, \ + text_buf_ngram_free, text_buf, local_ngram): + + seq = " ".join(words) + if seq in ngrams: + print(" [matched]: {}".format(seq), flush=True) + + if args.get_ngram_freq_only: + # increase freq of this seq and then only consider the later part + # of the text for further processing + if seq in local_ngram: + local_ngram[seq] += 1 + else: + local_ngram[seq] = 1 + #print(" [increased]: {} {}".format(seq, ngrams[seq]), flush=True) + if (start_position + len(seq) + 1) < len(text): + text_buf.append(text[start_position + len(seq) + 1:len(text)]) + return False + + # split the text + text_first, text_second = split_text(text, start_position, \ + args.remove_char_each_side, seq) + + # first part of ngrams free + if len(text_first) > args.filter_text_char_len: + text_buf_ngram_free.append(text_first) + + # add second part for further processing + if len(text_second) > args.filter_text_char_len: + text_buf.append(text_second) + + return False # not ngram free + + # ngram free + return True + + +def free_ngram(line, args, key, ngrams, ngrams_freq_sorted): + # remove all the ngrams + + try: + myjson = json.loads(line) + text_buf = [myjson[key]] + except Exception as e: + print("Error: {}".format(e), flush=True) + text_buf = [] + + text_buf_ngram_free = [] + local_ngram = {} + while len(text_buf) > 0: + + # get the first one from the buffer + text = text_buf.pop(0) + words, positions = get_words(text) + + ngram_free = True + # find each max n-grams and check dictionary + for i in range(len(words) - args.max_ngram_size + 1): + check_ngram_free = check_and_clean_text(args, words[i:\ + i+args.max_ngram_size], ngrams, text, positions[i], \ + text_buf_ngram_free, text_buf, local_ngram) + + # the seq is ngram free? if yes, break + if not check_ngram_free: + ngram_free = False + break + + # if max ngrams doesn't match, check if any other lower n-grams + # within max ngram macthes + for ngram_len, _ in ngrams_freq_sorted: + check_ngram_free = check_and_clean_text(args, words[i:\ + i+ngram_len], ngrams, text, positions[i], \ + text_buf_ngram_free, text_buf, local_ngram) + + # same check as above + if not check_ngram_free: + ngram_free = False + break + + # check break from lower than max ngram loop above + if not ngram_free: + break + + # for the last max n-gram, check all the lower ngrams in it + if ngram_free and len(words) - args.max_ngram_size > 0: + # get the last words of the lax max ngram + last_seq_words = words[(len(words)-args.max_ngram_size):len(words)] + last_seq_start_position = len(words) - args.max_ngram_size + + # check all n-grams lower than the max + for pos, (ngram_len, _) in enumerate(ngrams_freq_sorted): + + # ignore the max ngram as has been considered already + if ngram_len == args.max_ngram_size: + continue + + # find each ngram of ngram_len in max n-grams and check + for i in range(len(last_seq_words) - ngram_len + 1): + check_ngram_free = check_and_clean_text(args, \ + last_seq_words[i:i+ngram_len], ngrams, text,\ + positions[last_seq_start_position+i], \ + text_buf_ngram_free, text_buf, local_ngram) + + if not check_ngram_free: + ngram_free = False + break + + if not ngram_free: + break + + # texts are ngram free + if ngram_free and not args.get_ngram_freq_only: + text_buf_ngram_free.append(text) + + # check if the text has only been trimmed + trimmed = 0 + if not args.get_ngram_freq_only and len(text_buf_ngram_free) == 1 and \ + len(text_buf_ngram_free[0]) < len(myjson[key]): + trimmed = 1 + + return text_buf_ngram_free, trimmed, myjson, local_ngram + +# insert word sequence into dictionary +def insert_dict(words, ngrams, pos): + seq = " ".join(words) + if seq not in ngrams: + ngrams[seq] = 0 + #ngrams[seq] = pos + +# insert each ngram from text into the ngrams dictionary +def compute_ngrams_insert_dict(args, text, ngrams): + words, positions = get_words(text) + if len(words) < args.min_ngram_size: + return + + if len(words) < args.max_ngram_size: + insert_dict(words, ngrams, positions[0]) + + for i in range(len(words) - args.max_ngram_size+1): + insert_dict(words[i:i+args.max_ngram_size], ngrams, positions[i]) + + +# Build ngrams for the lambada dataset +def process_task_lambda(args, task_file, ngrams): + print(' reading from {} and computing ngrams'.format(task_file)) + with open(task_file, 'r') as f: + for line in f: + try: + myjson = json.loads(line) + text = myjson['text'] + compute_ngrams_insert_dict(args, text, ngrams) + except Exception as e: + print('Error:', e) + print(" Entities in ngrams {}".format(len(ngrams)), flush=True) + + +# Build ngrams for the dataset of the given task +def process_task(args, task_name, ngrams): + + print(' reading from {} and computing ngrams'.format('import datasets')) + print(" Current entities in ngrams {}".format(len(ngrams)), flush=True) + # using validation/test data from datasets + from datasets import load_dataset + + entities_in_ngrams = len(ngrams) + + # load the dataset + if task_name == 'squad': + dataset = load_dataset('squad_v2', split='validation') + elif task_name == 'natural_questions': + dataset = load_dataset('natural_questions', split='validation') + elif task_name == 'triviaqa': + dataset = load_dataset('trivia_qa', 'unfiltered', split='test') + elif task_name == 'webqa': + dataset = load_dataset('web_questions', split='test') + elif task_name == 'race': + dataset = load_dataset('race', 'all', split='test') + elif task_name == 'drop': + dataset = load_dataset('drop', split='validation') + elif task_name == 'coqa': + dataset = load_dataset('coqa', split='validation') + elif task_name == 'piqa': + dataset = load_dataset('piqa', split='test') + else: + print("Invalid task name: {}".format(task_name), flush=True) + return + + # read the dataset and add to ngrams + for line in dataset: + try: + if task_name in ['squad', 'triviaqa', 'webqa', 'race', 'drop']: + text = line['question'] + compute_ngrams_insert_dict(args, text, ngrams) + elif task_name == 'natural_questions': + text = line['question']['text'] + compute_ngrams_insert_dict(args, text, ngrams) + elif task_name == 'coqa': + all_questions = line['questions'] + for question in all_questions: + compute_ngrams_insert_dict(args, question, ngrams) + elif task_name == 'piqa': + text = line['goal'] + compute_ngrams_insert_dict(args, text, ngrams) + except Exception as e: + print('Error:', e) + + print(" After task {} entities in ngrams {}, added {}".format(task_name, \ + len(ngrams), len(ngrams) - entities_in_ngrams), flush=True) + +def compute_tasks_ngrams(args, ngrams): + start_time = time.time() + for _, task_name in enumerate(args.tasks): + print('Task: {}'.format(task_name), flush=True) + if task_name == 'lambada': + assert args.lambada_path is not None + process_task_lambda(args, args.lambada_path, ngrams) + else: + process_task(args, task_name, ngrams) + print(" Taken time to compute ngrams {:.2f}".format(time.time() - \ + start_time), flush=True) + +def compute_ngram_freq_sorted(args, ngrams): + ngrams_freq = {} + for ngram_key in ngrams.keys(): + length = len(ngram_key.split()) + ngrams_freq[length] = ngrams_freq[length] + 1 if length in \ + ngrams_freq else 1 + + ngrams_freq_sorted = sorted(ngrams_freq.items(), key=lambda item: item[0]) + print(" Ngram frequencies: {}".format(ngrams_freq_sorted), flush=True) + print(" Entities in ngrams {} min_ngram_size {} max_ngram_size {}".format(\ + len(ngrams), ngrams_freq_sorted[0][0], ngrams_freq_sorted[len(\ + ngrams_freq_sorted) -1 ][0]), flush=True) + return ngrams_freq_sorted + +def get_ngrams_below_threshold(args, ngrams, ngrams_below_threshold, \ + dedup_file, dedup_key, ngrams_freq_sorted): + + start_time = time.time() + # get the ngrams frequency + args.get_ngram_freq_only = True + + # Open the large file to process in parallel + num_workers = args.num_threads + pool = multiprocessing.Pool(num_workers) + fin = open(dedup_file, 'r', encoding='utf-8') + free_ngram_abt_partial=partial(free_ngram, args=args, key=dedup_key, \ + ngrams=ngrams, ngrams_freq_sorted=ngrams_freq_sorted) + free_ngrams_abt = pool.imap(free_ngram_abt_partial, fin, 500) + + counter = 0 + for _, _, _, local_ngram in free_ngrams_abt: + counter += 1 + if counter % 1000 == 0: + print(' [compute_stat]> processed {} documents in {:.2f} seconds ...'. + format(counter, time.time() - start_time), flush=True) + for local_key in local_ngram: + if local_key in ngrams: + ngrams[local_key] += 1 + local_ngram = {} + + print(' Time taken to compute statistics {:.2f} seconds'.format(time.time() - \ + start_time), flush=True) + pool.close() + pool.join() + + start_time = time.time() + counter_threshold = 0 + # Get ngram below theadhold + for local_key, local_val in ngrams.items(): + if ngrams[local_key] < args.key_threshold: + print(" [threshold] {} {}".format(local_key, local_val), flush=True) + counter_threshold += 1 + ngrams_below_threshold[local_key] = 1 + + print(' Ngrams below threshold {}'.format(counter_threshold), flush=True) + fin.close() + +def clean_ngrams_below_threshold(args, ngrams_below_threshold, dedup_file, \ + dedup_key): + + start_time = time.time() + # Now actually filter the dataset + args.get_ngram_freq_only = False + #id_prefix = '-'.join(args.tasks[::2]) + id_prefix = '-'.join(args.tasks[::1]) + + # get the range of the size of the ngrams + ngrams_freq_sorted = compute_ngram_freq_sorted(args, ngrams_below_threshold) + + # Open the large file to process in parallel + counter = splitted = ignored = split_mt_thld = trimmed_count = 0 + num_workers = args.num_threads + pool = multiprocessing.Pool(num_workers) + fin = open(dedup_file, 'r', encoding='utf-8') + free_ngram_clean_partial=partial(free_ngram, args=args, key=dedup_key, \ + ngrams=ngrams_below_threshold, ngrams_freq_sorted=ngrams_freq_sorted) + free_ngrams_clean = pool.imap(free_ngram_clean_partial, fin, 500) + + out_f = open(args.output, 'wb') + + for text_buf_ngram_free, trimmed, myjson, _ in free_ngrams_clean: + counter += 1 + try: + + trimmed_count += trimmed + + if len(text_buf_ngram_free) > 1: + splitted += 1 + if len(text_buf_ngram_free) == 0: + ignored += 1 + # more than 10 splits ignored + if len(text_buf_ngram_free) > args.splits_count: + text_buf_ngram_free = [] + split_mt_thld += 1 + + if args.output is not None: + if "split_id" in myjson: + use_prefix = myjson["split_id"] + "-" + else: + use_prefix = "" + + for i in range(len(text_buf_ngram_free)): + split_id_string = id_prefix + '-{:010d}'.format(int(\ + counter)) + '-{:04d}'.format(int(i)) + myjson[dedup_key] = text_buf_ngram_free[i] + myjson["split_id"] = use_prefix + split_id_string + outjson = json.dumps(myjson, ensure_ascii=False) + #outjson = json.dumps({"text":text_buf_ngram_free[i], + # id_prefix+"_split_id":split_id_string}, + # ensure_ascii=False) + out_f.write(outjson.encode('utf-8')) + out_f.write('\n'.encode('utf-8')) + + if counter % 1000 == 0: + print(' [final]> processed {} documents in {:.2f} seconds ...'. + format(counter, time.time() - start_time), flush=True) + except Exception as e: + print('Error:', e) + + print(' [final]> processed {} documents in {:.2f} seconds ...'. + format(counter, time.time() - start_time), flush=True) + + print(' Total docs {} splitted {} ignored {} splits > theshold {} trimmed'\ + ' {}'.format(counter, splitted, ignored, split_mt_thld, trimmed_count)\ + , flush=True) + + pool.close() + pool.join() + + out_f.close() + fin.close() + +if __name__ == '__main__': + + # we use 13-grams, any text less than 200 characters got removed + # any text splitted more than 10 got removed as well + + print('parsing the arguments ...') + + parser = argparse.ArgumentParser() + parser.add_argument('--tasks', nargs = '*', required=True, default=None, \ + help = 'Tasks to use for deduplication: currently ' + ' suuport [lambada, squad, natural_questions,' + ' triviaqa, webqa, race, drop, coqa, and piqa]') + parser.add_argument('--lambada-path', type=str, default=None, + help='Only Lambada task needs the path') + parser.add_argument('--dedup-dataset', nargs = '*', default=None, + help='Dataset to deduplicate with the key to use' + ' e.g. cc.json text') + parser.add_argument('--output', type=str, default=None, + help='Output file name to save dedup dataset') + parser.add_argument('--num-threads', type=int, default=40, + help='Number of threads to use') + # Default dedup values + parser.add_argument('--max-ngram-size', type=int, default=13, + help='Maximum size of ngram to use.') + parser.add_argument('--min-ngram-size', type=int, default=8, + help='Minimum size of ngram to use.') + parser.add_argument('--filter-text-char-len', type=int, default=200, + help='Remove any text below this length.') + parser.add_argument('--key-threshold', type=int, default=10, + help='Number of keys to consider as threshold') + parser.add_argument('--save-dictionary', type=str, default=None, + help='Save the dictionary') + parser.add_argument('--load-dictionary', type=str, default=None, + help='Load the dictionary') + parser.add_argument('--splits-count', type=int, default=10, + help='Remove any documents more than this many splits') + parser.add_argument('--remove-char-each-side', type=int, default=200, + help='Maximum size of ngram to use.') + + args = parser.parse_args() + + assert len(args.dedup_dataset) == 2 + dedup_file = args.dedup_dataset[0] + dedup_key = args.dedup_dataset[1] + + # Setup multi-processing + num_workers = args.num_threads + if args.load_dictionary is None: + + # Build ngrams + ngrams = {} + compute_tasks_ngrams(args, ngrams) + + # get the range of the size of the ngrams + ngrams_freq_sorted = compute_ngram_freq_sorted(args, ngrams) + + # get ngram freq from large file in parallel + # get ngrams below threshold + ngrams_below_threshold = {} + get_ngrams_below_threshold(args, ngrams, ngrams_below_threshold, \ + dedup_file, dedup_key, ngrams_freq_sorted) + + # save the dictionary if needed + if args.save_dictionary is not None: + with open(args.save_dictionary, 'wb') as save_dict_handle: + pickle.dump(ngrams_below_threshold, save_dict_handle) + else: + with open(args.load_dictionary, 'rb') as load_dict_handle: + ngrams_below_threshold = pickle.load(load_dict_handle) + + # filter the large file + if args.output is not None: + clean_ngrams_below_threshold(args, ngrams_below_threshold, \ + dedup_file, dedup_key) + + print('done :-)') diff --git a/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/find_duplicates.py b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/find_duplicates.py new file mode 100644 index 0000000..7f6cb57 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/find_duplicates.py @@ -0,0 +1,292 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import argparse +from functools import partial +import itertools +import json +from lsh import cache, minhash +import multiprocessing +import numpy as np +import time +import pickle +import sys +import os + +# This function is adapted from: +# https://github.com/mattilyra/LSH/blob/master/examples/Introduction.ipynb +def shingles(text, char_ngram=5): + return set(text[head:head + char_ngram] + for head in range(0, len(text) - char_ngram)) + + +# This function is adapted from: +# https://github.com/mattilyra/LSH/blob/master/examples/Introduction.ipynb +def jaccard(set_a, set_b, args): + if len(set_a) < 1 or len(set_b) < 1: + return 0.0 + + intersection = set_a & set_b + union = set_a | set_b + + if args.jaccard == 'min': + return len(intersection) / min(len(set_a), len(set_b)) + elif args.jaccard == 'max': + return len(intersection) / max(len(set_a), len(set_b)) + else: + return len(intersection) / len(union) + +def compute_fingerprint(line, key): + try: + myjson = json.loads(line) + url = myjson[key] + text = myjson['text'] + fingerprint = hasher.fingerprint(text) + except Exception as e: + print('Error:', e) + return None, None, None, False + + return url, text, fingerprint, True + +def url_pairs_to_remove(args, bucket_urls, url_doc): + remove_urls_list = [] + deduped_local, counter_local = 0, 0 + iteration = 0 + while len(bucket_urls) > 1: + if args.heuristic_iter != -1 and \ + iteration == args.heuristic_iter: + break + + items = list(bucket_urls) + remove_urls = [] + main_url = items[np.random.randint(0, len(items))] + main_dhingles = shingles(url_doc[main_url]) + + for i in range(0, len(items)): + counter_local += 1 + other_url = items[i] + if other_url == main_url: + continue + other_shingles = shingles(url_doc[other_url]) + try: + jaccard_sim = jaccard(main_dhingles, other_shingles, args) + except Exception as e: + print('Error:', e) + jaccard_sim = 0.0 + if jaccard_sim > 0.5: + remove_urls.append({other_url: jaccard_sim}) + deduped_local += 1 + bucket_urls.remove(other_url) + + bucket_urls.remove(main_url) + if len(remove_urls) > 0: + remove_urls_list.append({main_url: remove_urls}) + iteration += 1 + return remove_urls_list, deduped_local, counter_local + +def write_remove_urls_list(remove_urls_list, f_out): + if len(remove_urls_list) > 0: + for each_url_remove in remove_urls_list: + myjson = json.dumps(each_url_remove, ensure_ascii=False) + f_out.write(myjson.encode('utf-8')) + f_out.write('\n'.encode('utf-8')) + +def compute_jaccard(each_bin, num_bins, start_time_local): + + remove_urls_list = [] + deduped_local, counter_local, bucket_local = 0, 0, 0 + + for bucket_id in each_bin: + bucket_local += 1 + if os.getpid() % num_bins == 0 and bucket_local % 100000 == 0: + print("Counter {}, progress {:.2f} time {:.2f}".\ + format(bucket_local, float(bucket_local)/float(len(each_bin)),\ + time.time() - start_time_local), flush=True) + + if len(each_bin[bucket_id]) <= 1: + continue + + bucket_urls = each_bin[bucket_id].copy() + remove_urls_list_sub, deduped_local_sub, counter_local_sub = \ + url_pairs_to_remove(args, bucket_urls, url_doc) + + deduped_local += deduped_local_sub + counter_local += counter_local_sub + if len(remove_urls_list_sub) > 0: + remove_urls_list.extend(remove_urls_list_sub) + + return remove_urls_list, deduped_local, counter_local + +def find_pair_urls_parallel(args, lshcache, url_doc): + start_time = time.time() + f_out = open(args.output, 'wb') + deduped, counter = 0, 0 + + # compute jaccards of buckets in bin in parallel (parallelism + # limited to # of bins) + num_bins = len(lshcache.bins) + pool = multiprocessing.Pool(num_bins) + compute_jaccard_partial = partial(compute_jaccard, num_bins=num_bins, \ + start_time_local=start_time) + # don't need to pass args and url_doc as they are already shared + compute_jaccard_iter = pool.imap(compute_jaccard_partial, lshcache.bins) + + print("multiprocessing init took {:.2f}".format(time.time() - start_time),\ + flush=True) + for remove_urls_list, deduped_local, counter_local in compute_jaccard_iter: + deduped += deduped_local + counter += counter_local + write_remove_urls_list(remove_urls_list, f_out) + print(' [write]> processed {} documents in {:.2f} ' + 'seoncds and deduped {} documents ...'.format(counter, time.time()\ + - start_time, deduped), flush=True) + + pool.close() + pool.join() + f_out.close() + + print(' Taken time for jaccard similariries {:.2f} seconds'.format(\ + time.time() - start_time), flush=True) + +def find_pair_urls_sequential(args, lshcache, url_doc): + start_time = time.time() + f_out = open(args.output, 'wb') + deduped, counter = 0, 0 + for b in lshcache.bins: + for bucket_id in b: + if len(b[bucket_id]) <= 1: + continue + + bucket_urls = b[bucket_id].copy() + remove_urls_list_sub, deduped_local_sub, counter_local_sub = \ + url_pairs_to_remove(args, bucket_urls, url_doc) + + deduped += deduped_local_sub + counter += counter_local_sub + write_remove_urls_list(remove_urls_list_sub, f_out) + if counter % 10000 == 0: + print(' [write]> processed {} documents in {:.2f} ' + 'seoncds and deduped {} documents ...'. + format(counter, time.time() - start_time, + deduped), flush=True) + f_out.close() + print(' [write]> processed {} documents in {:.2f} ' + 'seoncds and deduped {} documents ...'. + format(counter, time.time() - start_time, + deduped), flush=True) + +if __name__ == '__main__': + + print('parsing the arguments ...') + + parser = argparse.ArgumentParser() + parser.add_argument('--seed', type=int, default=1234, + help='Random seed used for python, numpy') + parser.add_argument('--inputs', nargs = '*', default=None, help = \ + 'Pairwise list of the input files and keys, ' + 'e.g. --inputs cc.json cc_id news.json news_id') + parser.add_argument('--load-fingerprints', nargs = '*', default=None, + help='Load fingerprints from a list of pickle files,' + ' e.g. cc.pkl news.pkl') + parser.add_argument('--save-fingerprints', type=str, default=None, + help='Save the fingerprints of the inputs.') + parser.add_argument('--output', type=str, default=None, + help='Output file name that consists of all ids' + ' with matching similarities') + parser.add_argument('--jaccard', type=str, default='union', + choices=['union', 'min', 'max'], help='Jaccard'\ + ' similarity computation') + parser.add_argument('--heuristic-iter', type=int, default=1, + help='Number of iterations to run the heuristics' + ': use -1 for exact') + parser.add_argument('--num-bands', type=int, default=10, + help='Number of bands to use in cache') + parser.add_argument('--num-seeds', type=int, default=100, + help='Number of seeds to use for minhash. Note that' + ' this value should be divisible by num-bands') + parser.add_argument('--jaccard-parallel', action='store_true', + help='Use this to process large number of documents.') + args = parser.parse_args() + + print('finding possible duplicate content ...') + + # set seed and get an array of seeds of 100 integers + np.random.seed(args.seed) + seeds = np.random.randint(0, 1e6, size=args.num_seeds) + + # initialize minhash and lsh cache + hasher = minhash.MinHasher(seeds=seeds, char_ngram=5, hashbytes=4) + lshcache = cache.Cache(num_bands=args.num_bands, hasher=hasher) + + url_doc = {} + + # load fingerprints from pickle file if needed + if args.load_fingerprints is not None: + for count_fp, fp_file_name in enumerate(args.load_fingerprints): + print("Loading fingerprints from pickle file {}".format( + fp_file_name), flush=True) + fp = open(fp_file_name, "rb") + if count_fp == 0: + # assign directory for the first pkl + lshcache = pickle.load(fp) + url_doc = pickle.load(fp) + else: + # append these to lshcache and url_doc + local_lshcache = pickle.load(fp) + local_url_doc = pickle.load(fp) + for url in local_lshcache.fingerprints.keys(): + url_doc[url] = local_url_doc[url] + lshcache.add_fingerprint(local_lshcache.fingerprints[url], url) + fp.close() + + counter = 0 + start_time = time.time() + + # compute finger prints of the inputs if any + # input file and the key to use as id + if args.inputs is not None: + print("Computing fingerprints", flush=True) + assert len(args.inputs) % 2 == 0 + for input_file, key in zip(args.inputs[::2], args.inputs[1::2]): + print(' document processing {} with key {}'.format(input_file, key), + flush=True) + + # compute fingerprints in parallel + num_workers = 40 + pool = multiprocessing.Pool(num_workers) + fin = open(input_file, 'r', encoding='utf-8') + compute_fingerprint_partial = partial(compute_fingerprint, key=key) + compute_fingerprint_iter = pool.imap(compute_fingerprint_partial, + fin, 512) + # traverse all the texts and add fingerprints + for url, text, fingerprint, flag in compute_fingerprint_iter: + counter += 1 + if flag: + url_doc[url] = text + lshcache.add_fingerprint(fingerprint, url) + if counter % 10000 == 0: + print(' [read]> processed {} documents in {:.2f} ' + 'seconds ...'.format(counter, time.time() - \ + start_time), flush=True) + + fin.close() + pool.close() + pool.join() + + # Save the fingerprints if needed + if args.save_fingerprints is not None: + print("Saving fingerprints to pickle file {}".format( + args.save_fingerprints), flush=True) + with open(args.save_fingerprints, 'wb') as f_save: + pickle.dump(lshcache, f_save) + pickle.dump(url_doc, f_save) + + # compute jaccard index of the input texts and write to file if needed + if args.output is not None: + print("Compute jaccard similarity", flush=True) + if args.jaccard_parallel: + find_pair_urls_parallel(args, lshcache, url_doc) + else: + find_pair_urls_sequential(args, lshcache, url_doc) + + print('done :-)') + diff --git a/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/group_duplicate_url.py b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/group_duplicate_url.py new file mode 100644 index 0000000..16a0354 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/group_duplicate_url.py @@ -0,0 +1,77 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import json +import time +import sys + + +if __name__ == '__main__': + + + print('grouping duplicate urls ...') + + input = sys.argv[1] + output = sys.argv[2] + if len(sys.argv) > 3: + jaccard_similarity_threshold = float(sys.argv[3]) + else: + jaccard_similarity_threshold = 0.7 + + url_to_index = {} + index_to_urls = [] + counter = 0 + start_time = time.time() + with open(input, 'r') as f: + for line in f: + counter += 1 + myjson = json.loads(line) + urls = [] + for main_url in myjson.keys(): + urls.append(main_url) + for value in myjson[main_url]: + for other_url, js in value.items(): + if js >= jaccard_similarity_threshold: + urls.append(other_url) + current_index = -1 + other_indices = set() + for url in urls: + if url in url_to_index: + if current_index == -1: + current_index = url_to_index[url] + elif current_index != url_to_index[url]: + other_indices.add(url_to_index[url]) + if current_index == -1: + current_index = len(index_to_urls) + index_to_urls.append(set()) + for url in urls: + url_to_index[url] = current_index + index_to_urls[current_index].add(url) + for index in other_indices: + for url in index_to_urls[index]: + index_to_urls[current_index].add(url) + url_to_index[url] = current_index + index_to_urls[index] = None + + if counter % 100000 == 0: + print(' > processed {} lines in {} seconds ...'.format( + counter, time.time() - start_time)) + + + total_remove = 0 + total_remain = 0 + for urls in index_to_urls: + if urls is not None: + if len(urls) > 1: + total_remove += (len(urls) - 1) + total_remain += 1 + print('out of {} urls, only {} are unique and {} should be removed'.format( + total_remove+total_remain, total_remain, total_remove)) + + with open(output, 'wb') as f: + for i, urls in enumerate(index_to_urls): + if urls is not None: + if len(urls) > 1: + myjson = json.dumps({str(i): list(urls)}, + ensure_ascii=False) + f.write(myjson.encode('utf-8')) + f.write('\n'.encode('utf-8')) diff --git a/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/merge_jsons.py b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/merge_jsons.py new file mode 100644 index 0000000..fb11fe4 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/merge_jsons.py @@ -0,0 +1,42 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + + +import glob +import sys +import json +import argparse + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument("--json_path", type=str, default=".", + help="path where all the json files are located") + + parser.add_argument("--output_file", type=str, default="merged_output.json", + help="filename where the merged json should go") + + args = parser.parse_args() + + json_path = args.json_path + out_file = args.output_file + + json_files = glob.glob(json_path + '/*.json') + + counter = 0 + + with open(out_file, 'w') as outfile: + for fname in json_files: + counter += 1 + + if counter % 1024 == 0: + print("Merging at ", counter, flush=True) + + with open(fname, 'r') as infile: + for row in infile: + each_row = json.loads(row) + outfile.write(row) + + + print("Merged file", out_file, flush=True) + + diff --git a/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/remove_group_duplicates.py b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/remove_group_duplicates.py new file mode 100644 index 0000000..44b62d6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/openwebtext/remove_group_duplicates.py @@ -0,0 +1,56 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + + +import json +import time +import sys + + +if __name__ == '__main__': + + url_filename = sys.argv[1] + data_filename = sys.argv[2] + output_filename = sys.argv[3] + + urls = set() + with open(url_filename, 'r') as f: + for line in f: + myjson = json.loads(line) + for key in myjson: + this_urls = myjson[key] + for i in range(1, len(this_urls)): + urls.add(this_urls[i]) + print('will be removing {} urls'.format(len(urls)), flush=True) + + written_docs = 0 + removed_docs = 0 + removed_chars = 0 + start_time = time.time() + with open(output_filename, 'wb') as fout: + with open(data_filename, 'r') as fin: + for line in fin: + try: + myjson = json.loads(line) + url = myjson['url'] + if url in urls: + print('removing', myjson) + removed_docs += 1 + removed_chars += len(myjson['text']) + continue + myjson = json.dumps(myjson, ensure_ascii=False) + fout.write(myjson.encode('utf-8')) + fout.write('\n'.encode('utf-8')) + written_docs += 1 + if written_docs % 10000 == 0: + print(' [PROCESSED] time (s): {:.2f} | written: {} ' + '| removed: {} (char: {})'.format( + time.time() - start_time, + written_docs, removed_docs, removed_chars)) + except Exception as e: + print('[SKIPPING]', line, e) + + print(' [PROCESSED] time (s): {:.2f} | written: {} ' + '| removed: {} (char: {})'.format( + time.time() - start_time, + written_docs, removed_docs, removed_chars)) + print('done :-)') diff --git a/Megatron-LM-core_r0.7.0.beta/tools/preprocess_data.py b/Megatron-LM-core_r0.7.0.beta/tools/preprocess_data.py new file mode 100644 index 0000000..55d9d6c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/preprocess_data.py @@ -0,0 +1,409 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Processing large data for pretraining.""" +import argparse +import math +import json +import os +import sys +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), + os.path.pardir))) +import time +import gzip +import glob +import torch +import numpy as np +import multiprocessing +try: + import nltk + nltk_available = True +except ImportError: + nltk_available = False + +from megatron.training.tokenizer import build_tokenizer +from megatron.core.datasets import indexed_dataset + + +# https://stackoverflow.com/questions/33139531/preserve-empty-lines-with-nltks-punkt-tokenizer +class CustomLanguageVars(nltk.tokenize.punkt.PunktLanguageVars): + + _period_context_fmt = r""" + \S* # some word material + %(SentEndChars)s # a potential sentence ending + \s* # <-- THIS is what I changed + (?=(?P + %(NonWord)s # either other punctuation + | + (?P\S+) # <-- Normally you would have \s+ here + ))""" + +class IdentitySplitter(object): + def tokenize(self, *text): + return text + + +class Encoder(object): + def __init__(self, args): + self.args = args + + def initializer(self): + # Use Encoder class as a container for global data + Encoder.tokenizer = build_tokenizer(self.args) + if self.args.split_sentences: + if not nltk_available: + print("NLTK is not available to split sentences.") + exit() + if os.environ.get("NLTK_DATA"): + library = os.path.join(os.environ.get("NLTK_DATA"), "tokenizers", "punkt", f"{self.args.lang}.pickle") + url = f"file:{library}" + else: + library = os.path.join("tokenizers", "punkt", f"{self.args.lang}.pickle") + url = f"nltk:{library}" + splitter = nltk.load(url) + if self.args.keep_newlines: + # this prevents punkt from eating newlines after sentences + Encoder.splitter = nltk.tokenize.punkt.PunktSentenceTokenizer( + train_text = splitter._params, + lang_vars = CustomLanguageVars()) + else: + Encoder.splitter = splitter + + else: + Encoder.splitter = IdentitySplitter() + + def split(self, json_line): + data = json.loads(json_line) + output = {} + for key in self.args.json_keys: + text = data[key] + max_len = 1000000 + tokens_list = [Encoder.splitter.tokenize(text[i:i+max_len]) for i in range(0, len(text), max_len)] + output[key] = [tokens for partial in tokens_list for tokens in partial] + return json.dumps(output), len(json_line) + + def encode(self, json_line): + data = json.loads(json_line) + ids = {} + lens = {} + for key in self.args.json_keys: + text = data[key] + if isinstance(text, list): + sentences = text + else: + sentences = [text] + doc_ids = [] + sentence_lens = [] + for sentence in sentences: + sentence_ids = Encoder.tokenizer.tokenize(sentence) + if len(sentence_ids) > 0: + doc_ids.extend(sentence_ids) + sentence_lens.append(len(sentence_ids)) + if len(doc_ids) > 0 and self.args.append_eod: + doc_ids.append(Encoder.tokenizer.eod) + sentence_lens[-1] += 1 + ids[key] = doc_ids + lens[key] = sentence_lens + return ids, lens, len(json_line) + + +class Partition(object): + def __init__(self, args, workers): + self.args = args + self.workers = workers + + def print_processing_stats(self, count, proc_start, total_bytes_processed): + if count % self.args.log_interval == 0: + current = time.time() + elapsed = current - proc_start + mbs = total_bytes_processed/elapsed/1024/1024 + print(f"Processed {count} documents", + f"({count/elapsed} docs/s, {mbs} MB/s).", + file=sys.stderr) + + def split_sentences(self, file_name): + input_file_name, output_file_name = file_name + print("Opening", input_file_name) + fin = open(input_file_name, 'r', encoding='utf-8') + fout = open(output_file_name, 'w') + + encoder = Encoder(self.args) + pool = multiprocessing.Pool(self.workers, initializer=encoder.initializer) + split_docs = pool.imap(encoder.split, fin, 32) + + proc_start = time.time() + total_bytes_processed = 0 + for i, (doc, bytes_processed) in enumerate(split_docs, start=1): + total_bytes_processed += bytes_processed + fout.write(doc + "\n") + self.print_processing_stats(i, proc_start, total_bytes_processed) + + fin.close() + fout.close() + + + def process_json_file(self, file_name): + input_file_name, output_prefix = file_name + print("Opening", input_file_name) + fin = open(input_file_name, 'r', encoding='utf-8') + + startup_start = time.time() + encoder = Encoder(self.args) + tokenizer = build_tokenizer(self.args) + pool = multiprocessing.Pool(self.workers, initializer=encoder.initializer) + encoded_docs = pool.imap(encoder.encode, fin, 32) + + level = "document" + if self.args.split_sentences: + level = "sentence" + + output_bin_files = {} + output_idx_files = {} + builders = {} + + for key in self.args.json_keys: + output_bin_files[key] = "{}_{}_{}.bin".format(output_prefix, + key, level) + output_idx_files[key] = "{}_{}_{}.idx".format(output_prefix, + key, level) + builders[key] = indexed_dataset.IndexedDatasetBuilder( + output_bin_files[key], + dtype=indexed_dataset.DType.optimal_dtype(tokenizer.vocab_size), + ) + + startup_end = time.time() + proc_start = time.time() + total_bytes_processed = 0 + print("Time to startup:", startup_end - startup_start) + for i, (doc, sentence_lens, bytes_processed) in enumerate(encoded_docs, start=1): + total_bytes_processed += bytes_processed + for key in doc.keys(): + builders[key].add_document(doc[key], sentence_lens[key]) + self.print_processing_stats(i, proc_start, total_bytes_processed) + + fin.close() + builders[key].finalize(output_idx_files[key]) + + +def get_args(): + parser = argparse.ArgumentParser() + group = parser.add_argument_group(title='input data') + group.add_argument('--input', type=str, required=True, + help='Path to input JSON') + group.add_argument('--json-keys', nargs='+', default=['text'], + help='space separate listed of keys to extract from json') + group.add_argument('--split-sentences', action='store_true', + help='Split documents into sentences.') + group.add_argument('--keep-newlines', action='store_true', + help='Keep newlines between sentences when splitting.') + + group = parser.add_argument_group(title='tokenizer') + group.add_argument('--tokenizer-type', type=str, required=True, + choices=['BertWordPieceLowerCase','BertWordPieceCase', + 'GPT2BPETokenizer', 'SentencePieceTokenizer', + 'GPTSentencePieceTokenizer', 'Llama2Tokenizer', + 'NullTokenizer'], + help='What type of tokenizer to use.') + group.add_argument('--tokenizer-model', type=str, default=None, + help='YTTM tokenizer model.') + group.add_argument('--vocab-file', type=str, default=None, + help='Path to the vocab file') + group.add_argument('--vocab-size', default=786, + help='size of vocab for use with NullTokenizer') + group.add_argument('--merge-file', type=str, default=None, + help='Path to the BPE merge file (if necessary).') + group.add_argument('--append-eod', action='store_true', + help='Append an token to the end of a document.') + group.add_argument('--lang', type=str, default='english', + help='Language to use for NLTK-powered sentence splitting.') + group = parser.add_argument_group(title='output data') + group.add_argument('--output-prefix', type=str, required=True, + help='Path to binary output file without suffix') + + group = parser.add_argument_group(title='runtime') + group.add_argument('--workers', type=int, required=True, + help=('Number of worker processes to launch.' + 'A good default for fast pre-processing ' + 'is: (workers * partitions) = available CPU cores.')) + group.add_argument('--partitions', type=int, default=1, + help='Number of file partitions') + group.add_argument('--log-interval', type=int, default=1000, + help='Interval between progress updates') + group.add_argument('--keep-sequential-samples', action='store_true', + help='Ensure ordering of samples in .jsonl files is ' + 'preserved when using partitions>1.') + args = parser.parse_args() + args.keep_empty = False + + if args.tokenizer_type.lower().startswith('bert') and not args.split_sentences: + print("Are you sure you don't want to split sentences?") + + # some default/dummy values for the tokenizer + args.rank = 1 + args.make_vocab_size_divisible_by = 128 + args.tensor_model_parallel_size = 1 + args.vocab_extra_ids = 0 + + return args + + +def get_file_name(args, file_id): + file_name, extension = os.path.splitext(args.input) + input_file_name = file_name + "_" + str(file_id) + extension + sentence_split_file = file_name + "_ss_" + str(file_id) + extension + output_prefix = args.output_prefix + "_" + str(file_id) + file_names = { + 'partition': input_file_name, + 'sentence_split': sentence_split_file, + 'output_prefix': output_prefix} + return file_names + + +def check_files_exist(in_ss_out_names, key, num_partitions): + for i in range(num_partitions): + if not os.path.exists(in_ss_out_names[i][key]): + return False + return True + + +def main(): + args = get_args() + + if args.split_sentences: + if nltk_available: + nltk.download("punkt", quiet=True, download_dir=os.environ.get("NLTK_DATA")) + else: + raise Exception( + "nltk library required for sentence splitting is not available.") + + in_ss_out_names = [] + if args.partitions == 1: + file_name, extension = os.path.splitext(args.input) + sentence_split_file = file_name + "_ss" + extension + file_names = { + 'partition': args.input, + 'sentence_split': sentence_split_file, + 'output_prefix': args.output_prefix} + in_ss_out_names.append(file_names) + else: + in_file_names = glob.glob(args.input) + + # Count total number of lines across .jsonl files + if args.keep_sequential_samples: + total_sample_count = 0 + for filename in in_file_names: + with open(filename, "r") as fin: + for fc, _ in enumerate(fin): + pass + total_sample_count += (fc + 1) + partition_size = math.ceil(total_sample_count / args.partitions) + + # create .jsonl parition files + for idx in range(args.partitions): + in_ss_out_name = get_file_name(args, idx) + in_ss_out_names.append(in_ss_out_name) + + # check to see if paritions were already created + partitions_present = check_files_exist(in_ss_out_names, 'partition', args.partitions) + + # check to see if paritions with split sentences already created + split_sentences_present = check_files_exist(in_ss_out_names, 'sentence_split', args.partitions) + + if not partitions_present and not split_sentences_present: + # populate .jsonl partition files from parent files + partitioned_input_files = [] + for idx in range(args.partitions): + partitioned_input_file = open(in_ss_out_names[idx]['partition'], 'w') + partitioned_input_files.append(partitioned_input_file) + + index = 0 + if args.keep_sequential_samples: line_count = 0 + for in_file_name in in_file_names: + # support for gzip files + if in_file_name.endswith(".gz"): + fin = gzip.open(in_file_name, 'rt') + else: + fin = open(in_file_name, 'r', encoding='utf-8') + + for line in fin: + partitioned_input_files[index].write(line) + if args.keep_sequential_samples: + line_count += 1 + if line_count % partition_size == 0: + index += 1 + else: + index = (index + 1)%args.partitions + + fin.close() + + for idx in range(args.partitions): + partitioned_input_files[idx].close() + + assert args.workers % args.partitions == 0 + partition = Partition(args, args.workers//args.partitions) + + # check to see if paritions with split sentences already created + split_sentences_present = check_files_exist(in_ss_out_names, 'sentence_split', args.partitions) + + # split sentences in partition files + if args.split_sentences and not split_sentences_present: + processes = [] + for name in in_ss_out_names: + p = multiprocessing.Process(target=partition.split_sentences, + args=((name['partition'], name['sentence_split']),)) + p.start() + processes.append(p) + + for p in processes: + p.join() + + if args.partitions == 1: + return + + + # encode partition files in parallel + processes = [] + input_key = 'sentence_split' if args.split_sentences else 'partition' + for name in in_ss_out_names: + p = multiprocessing.Process(target=partition.process_json_file, + args=((name[input_key], name['output_prefix']),)) + p.start() + processes.append(p) + + for p in processes: + p.join() + + if args.partitions == 1: + return + + # merge bin/idx partitions + level = "document" + if args.split_sentences: + level = "sentence" + + output_bin_files = {} + output_idx_files = {} + builders = {} + tokenizer = build_tokenizer(args) + + for key in args.json_keys: + output_bin_files[key] = "{}_{}_{}.bin".format(args.output_prefix, + key, level) + output_idx_files[key] = "{}_{}_{}.idx".format(args.output_prefix, + key, level) + builders[key] = indexed_dataset.IndexedDatasetBuilder( + output_bin_files[key], + dtype=indexed_dataset.DType.optimal_dtype(tokenizer.vocab_size), + ) + + for name in in_ss_out_names: + parition_output_prefix = name['output_prefix'] + full_partition_output_prefix = "{}_{}_{}".format(parition_output_prefix, + key, level) + builders[key].add_index(full_partition_output_prefix) + builders[key].finalize(output_idx_files[key]) + + +if __name__ == '__main__': + + main() + diff --git a/Megatron-LM-core_r0.7.0.beta/tools/preprocess_data_nmt.py b/Megatron-LM-core_r0.7.0.beta/tools/preprocess_data_nmt.py new file mode 100644 index 0000000..13a04f6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/preprocess_data_nmt.py @@ -0,0 +1,111 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Processing nmt data for finetuning.""" + +import argparse +import json +import multiprocessing +import os +import sys +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), + os.path.pardir))) +import time +import torch +from megatron.training.tokenizer import build_tokenizer +from megatron.core.datasets import indexed_dataset + + +class Encoder(object): + def __init__(self, args): + self.args = args + + def initializer(self): + # Use Encoder class as a container for global data + Encoder.tokenizer = build_tokenizer(self.args) + + def encode(self, text): + ids = {} + ids = Encoder.tokenizer.tokenize(text) + assert len(ids) > 0 + return ids, len(text) + + +def get_args(): + parser = argparse.ArgumentParser() + group = parser.add_argument_group(title='input data') + group.add_argument('--input', type=str, required=True, + help='Path to input JSON') + + group = parser.add_argument_group(title='tokenizer') + group.add_argument('--tokenizer-type', type=str, default='YTTMTokenizer', + choices=['BertWordPieceLowerCase','BertWordPieceCase', + 'GPT2BPETokenizer', 'SentencePieceTokenizer'], + help='What type of tokenizer to use.') + group.add_argument('--vocab-file', type=str, default=None, + help='Path to the vocab file') + group.add_argument('--merge-file', type=str, default=None, + help='Path to the BPE merge file (if necessary).') + + group = parser.add_argument_group(title='output data') + group.add_argument('--output-prefix', type=str, required=True, + help='Path to binary output file without suffix') + + group = parser.add_argument_group(title='runtime') + group.add_argument('--workers', type=int, default=1, + help='Number of worker processes to launch') + group.add_argument('--log-interval', type=int, default=100, + help='Interval between progress updates') + args = parser.parse_args() + args.keep_empty = False + + # some default/dummy values for the tokenizer + args.rank = 0 + args.make_vocab_size_divisible_by = 128 + args.tensor_model_parallel_size = 1 + args.vocab_extra_ids = 0 + + return args + +def main(): + args = get_args() + startup_start = time.time() + + print("Opening", args.input) + fin = open(args.input, 'r', encoding='utf-8') + + encoder = Encoder(args) + tokenizer = build_tokenizer(args) + pool = multiprocessing.Pool(args.workers, initializer=encoder.initializer) + encoded_sentences = pool.imap(encoder.encode, fin, 25) + + print(f"Vocab size: {tokenizer.vocab_size}") + print(f"Output prefix: {args.output_prefix}") + output_bin_file = "{}.bin".format(args.output_prefix) + output_idx_file = "{}.idx".format(args.output_prefix) + builder = indexed_dataset.IndexedDatasetBuilder( + output_bin_file, dtype=indexed_dataset.DType.optimal_dtype(tokenizer.vocab_size) + ) + + startup_end = time.time() + proc_start = time.time() + total_bytes_processed = 0 + print("Time to startup:", startup_end - startup_start) + + for i, (sentence, bytes_processed) in enumerate(encoded_sentences, start=1): + total_bytes_processed += bytes_processed + builder.add_item(torch.IntTensor(sentence)) + # documents contain only one sentence. + builder.end_document() + if i % args.log_interval == 0: + current = time.time() + elapsed = current - proc_start + mbs = total_bytes_processed/elapsed/1024/1024 + print(f"Processed {i} sentences", + f"({i/elapsed} sentences/s, {mbs} MB/s).", + file=sys.stderr) + + builder.finalize(output_idx_file) + +if __name__ == '__main__': + main() + diff --git a/Megatron-LM-core_r0.7.0.beta/tools/preprocess_mmdata.py b/Megatron-LM-core_r0.7.0.beta/tools/preprocess_mmdata.py new file mode 100755 index 0000000..247b66b --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/preprocess_mmdata.py @@ -0,0 +1,170 @@ +# coding=utf-8 +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +"""Processing text modality data for MultiModal pretraining.""" + +import argparse +import json +import multiprocessing +import os +import sys +import numpy as np +from torchvision.transforms import ToTensor +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), + os.path.pardir))) +import time + +import torch +try: + import nltk + nltk_available = True +except ImportError: + nltk_available = False + +from megatron.training.tokenizer import build_tokenizer +from megatron.core.datasets.indexed_dataset import IndexedDatasetBuilder + + +# https://stackoverflow.com/questions/33139531/preserve-empty-lines-with-nltks-punkt-tokenizer +class CustomLanguageVars(nltk.tokenize.punkt.PunktLanguageVars): + + _period_context_fmt = r""" + \S* # some word material + %(SentEndChars)s # a potential sentence ending + \s* # <-- THIS is what I changed + (?=(?P + %(NonWord)s # either other punctuation + | + (?P\S+) # <-- Normally you would have \s+ here + ))""" + +class IdentitySplitter(object): + def tokenize(self, *text): + return text + +class Encoder(object): + def __init__(self, args): + self.args = args + + def initializer(self): + # Use Encoder class as a container for global data + Encoder.tokenizer = build_tokenizer(self.args) + + def encode(self, input_pair): + json_line, img_path = input_pair + data = json.loads(json_line) + key = "text" + text = data[key] + sentence_ids = Encoder.tokenizer.tokenize(text) + pad_len = self.args.pad_length + if len(sentence_ids) > 0 and self.args.append_eod: + sentence_ids = sentence_ids[:pad_len] + current_length = len(sentence_ids) + sentence_ids.extend([Encoder.tokenizer.eod for _ in range(max(0,pad_len-current_length))]) + + with open(img_path, "rb") as tf: + xs = bytearray(tf.read()) + img_pad = (4 - len(xs) % 4) % 4 + xs.extend([0 for _ in range(img_pad)]) + img_raw = np.frombuffer(xs, dtype=np.int32) + img_raw = np.insert(img_raw, 0, img_pad) + + return sentence_ids, img_raw, len(json_line) + +def get_args(): + parser = argparse.ArgumentParser() + group = parser.add_argument_group(title='input data') + group.add_argument('--input', type=str, required=True, + help='Path to input JSON') + group.add_argument('--input-image', type=str, required=True, + help='Path to input image folder') + + group.add_argument('--pad-length', type=int, required=True, + help='Pad length of preprocessed text') + + group.add_argument('--split-sentences', action='store_true', + help='Split documents into sentences.') + group.add_argument('--keep-newlines', action='store_true', + help='Keep newlines between sentences when splitting.') + + group = parser.add_argument_group(title='tokenizer') + group.add_argument('--tokenizer-type', type=str, required=True, + choices=['BertWordPieceLowerCase','BertWordPieceCase', + 'GPT2BPETokenizer', 'SentencePieceTokenizer', 'GPTSentencePieceTokenizer'], + help='What type of tokenizer to use.') + group.add_argument('--vocab-file', type=str, default=None, + help='Path to the vocab file') + group.add_argument('--merge-file', type=str, default=None, + help='Path to the BPE merge file (if necessary).') + group.add_argument('--append-eod', action='store_true', + help='Append an token to the end of a document.') + group.add_argument('--lang', type=str, default='english', + help='Language to use for NLTK-powered sentence splitting.') + group.add_argument('--tokenizer-model', type=str, default=None, + help='sentencepeice tokenizer model.') + + group = parser.add_argument_group(title='output data') + group.add_argument('--output-prefix', type=str, required=True, + help='Path to binary output file without suffix') + group = parser.add_argument_group(title='runtime') + group.add_argument('--workers', type=int, default=1, + help='Number of worker processes to launch') + group.add_argument('--log-interval', type=int, default=100, + help='Interval between progress updates') + args = parser.parse_args() + args.keep_empty = False + + # some default/dummy values for the tokenizer + args.rank = 0 + args.make_vocab_size_divisible_by = 128 + args.tensor_model_parallel_size = 1 + args.vocab_extra_ids = 0 + + return args + +def main(): + args = get_args() + startup_start = time.time() + + encoder = Encoder(args) + tokenizer = build_tokenizer(args) + pool = multiprocessing.Pool(args.workers, initializer=encoder.initializer) + + fin = open(args.input, 'r', encoding='utf-8') + img_paths = [os.path.join(args.input_image, basename) for basename in os.listdir(args.input_image)] + + encoded_docs = pool.imap(encoder.encode, zip(fin, img_paths), 25) + + print(f"Vocab size: {tokenizer.vocab_size}") + print(f"Output prefix: {args.output_prefix}") + + output_bin_files = "{}.bin".format(args.output_prefix) + output_idx_files = "{}.idx".format(args.output_prefix) + + builders = IndexedDatasetBuilder(output_bin_files, dtype=np.int32, multimodal=True) + + startup_end = time.time() + proc_start = time.time() + total_bytes_processed = 0 + + print("Time to startup:", startup_end - startup_start) + + for i, (sentence, img_raw, bytes_processed) in enumerate(encoded_docs, start=1): + total_bytes_processed += bytes_processed + builders.add_item(torch.IntTensor(sentence)) + builders.add_item(torch.from_numpy(img_raw), 1) + builders.end_document() + if i % args.log_interval == 0: + current = time.time() + elapsed = current - proc_start + mbs = total_bytes_processed/elapsed/1024/1024 + print(f"Processed {i} documents", + f"({i/elapsed} docs/s, {mbs} MB/s).", + file=sys.stderr) + + builders.finalize(output_idx_files) + + +if __name__ == '__main__': + main() + diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/README.md b/Megatron-LM-core_r0.7.0.beta/tools/retro/README.md new file mode 100644 index 0000000..f7a38c8 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/README.md @@ -0,0 +1,256 @@ +# Retro and InstructRetro + +Retro [(Borgeaud et al., 2022)](https://arxiv.org/abs/2112.04426) is an autoregressive decoder-only language model (LM) +pretrained with retrieval-augmentation. +Retro features practical scalability to support large-scale pretraining from scratch by retrieving from trillions of +tokens. +Pretraining with retrieval provides a more efficient storage mechanism of factual knowledge, when compared to storing +factual knowledge implicitly within the network's parameters, thus largely reducing model parameters while achieving +lower perplexity than standard GPT. +Retro also provides the flexibility to update the +knowledge stored in LMs [(Wang et al., 2023a)](https://arxiv.org/abs/2304.06762) +by updating the retrieval database without training LMs again. + +InstructRetro [(Wang et al., 2023b)](https://arxiv.org/abs/2310.07713) further scales up the size of Retro to 48B, +featuring the largest LLM pretrained with retrieval (as of December 2023). +The obtained foundation model, Retro 48B, largely outperforms the GPT counterpart in terms of perplexity. +With instruction tuning on Retro, InstructRetro demonstrates significant improvement over the instruction tuned GPT on +downstream tasks in the zero-shot setting. Specifically, the average improvement of InstructRetro is 7% over its GPT +counterpart across 8 short-form QA tasks, 10% over GPT across 4 challenging long-form QA tasks, and 16% over GPT across +3 summarization tasks. We also find that one can ablate the encoder from InstructRetro architecture and directly use the +InstructRetro decoder backbone as GPT, while achieving comparable results. + +This README provides an end-to-end tutorial to reproduce Retro and InstructRetro. + +# Contents + +* [Checkpoints](#checkpoints) +* [End-to-end Reproduction Guide](#end-to-end-reproduction-guide) + * [Step 0: Prepare the environment](#step-0-prepare-the-environment) + * [Docker image](#docker-image) + * [Install dependencies](#install-dependencies) + * [Step 1: Build retrieval database](#step-1-build-retrieval-database) + * [Step 2: Pretraining](#step-2-pretraining) + * [Step 3: Perplexity evaluation](#step-3-perplexity-evaluation) + * [Step 4: Instruction tuning](#step-4-instruction-tuning) + * [Step 5: Downstream task evaluation](#step-5-downstream-task-evaluation) +* [Citations](#citations) + +# Checkpoints + +We provide the pretrained checkpoints of Retro and InstructRetro in the following table. The checkpoints are available +to download through the following links: + +| Model | Size | Instruction Tuning | Download Link 1 | Download Link 2 | Download Link 3 | +|-------------------------|------|--------------------|--------------------------------------------------------------------|--------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------| +| `retro-8b-base-4k` | 8b | | [Huggingface](https://huggingface.co/nvidia/retro-8b-base-4k) | [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/models/retro-8b-base-4k) | [Google Drive](https://drive.google.com/drive/folders/1uSQ5DAsuvx_8XcbtnVfs_MGvEOcx0uK_?usp=sharing) | +| `retro-8b-instruct-4k` | 8b | ✅ | [Huggingface](https://huggingface.co/nvidia/retro-8b-instruct-4k) | [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/models/retro-8b-instruct-4k) | [Google Drive](https://drive.google.com/drive/folders/1v5dKaSN0cm2lwyAWpFaJtlTrLhtMZXsI?usp=sharing) | +| `retro-48b-base-4k` | 48b | | [Huggingface](https://huggingface.co/nvidia/retro-48b-base-4k) | [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/models/retro-48b-base-4k) | [Google Drive](https://drive.google.com/drive/folders/1rtNpf0CiLElSHQcr3aLI3zgfI3teGTP5?usp=sharing) | +| `retro-48b-instruct-4k` | 48b | ✅ | [Huggingface](https://huggingface.co/nvidia/retro-48b-instruct-4k) | [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/models/retro-48b-instruct-4k) | [Google Drive](https://drive.google.com/drive/folders/1qdb0AQjSsAPGlWaIu3wgHPjf_nwLeY5h?usp=sharing) | + +# End-to-end Reproduction Guide + +In this README, we provide an end-to-end reproduction guide for InstructRetro, covering from large-scale retrieval +construction, pretraining, perplexity evaluation, instruction tuning, to downstream task evaluation. + +If you are interested in evaluation only, we also [open-sourced our checkpoints](#checkpoints) and you can directly go +to [Step 5](#step-5-downstream-task-evaluation) to evaluate the checkpoints on downstream tasks. + +## Step 0: Prepare the environment + +We recommend using docker environment to run the code. + +### Docker image + +We provide a docker build file in [tools/retro/examples/Dockerfile](examples/Dockerfile) for the reproduction. The +docker image is based on the [NGC docker](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch/tags) `nvcr.io/nvidia/pytorch:23.09-py3`. + +### Install dependencies + +Clone the Megatron repo: + +```bash +git clone --branch InstructRetro https://github.com/NVIDIA/Megatron-LM.git +``` + +If docker is not available, we recommend starting from a clean conda environment with the following runtime +dependencies: + +- Python 3.10 +- NVIDIA CUDA® 12.2.1 +- NVIDIA cuBLAS 12.2.5.6 +- NVIDIA cuDNN 8.9.5 +- NVIDIA NCCL 2.18.5 +- PyTorch 2.1.0a0+32f93b1 + +Then install Retro-specific dependencies, including: + +```bash +pip install -U faiss-gpu +pip install -U transformers +pip install -U sentencepiece +pip install -U h5py +pip install -U nltk +pip install -U einops +``` + +## Step 1: Build retrieval database + +In this step, we build a large-scale retrieval database for InstructRetro +through [Faiss](https://github.com/facebookresearch/faiss) to retrieve from trillions of tokens, and preprocess (and +save) the retrieval neighbors for the pretraining step. + +Please refer to [tools/retro/build_db.md](build_db.md) for more details. + +## Step 2: Pretraining + +*Please strictly follow Step 1 to build the retrieval database before pretraining to make sure the preprocessed +retrieval neighbors match the pretraining corpus.* + +In the pretraining step, we support both pretraining from scratch and continued pretraining from a pretrained GPT model. + +We provide a template pretraining script to pretrain 843M Retro from scratch. Prepare your own arguments and update our +templates in [tools/retro/examples/pretrain_model.sh](examples/pretrain_model.sh). Please note that the data path should +be exactly matching the one used in Step 1 to make sure the preprocessed retrieval neighbors match the pretraining +corpus. + +[//]: # (Take the example of the Wikipedia corpus) + +```bash +bash tools/retro/examples/pretrain_model.sh +``` + +After pretraining, the model checkpoints will be saved in the `--save` directory if you specified the arg +in `pretrain_model.sh`. + +To continue pretraining with retrieval from a pretrained GPT model, please specify `--load` in `pretrain_model.sh` to +load the pretrained GPT model checkpoint (the architecture of GPT, including hidden size, number of layers, and +activation methods, should be exactly the same as the one used for Retro). You should also +specify `--no-load-optim --finetune` to make sure the optimizer state is not loaded from the pretrained GPT model and +the continued pretraining with retrieval is from a clean start. After the first job / the first run, you will continue +pretraining with retrieval from your last checkpoint. In the follow-up jobs, you should launch the pretraining without +the flags `--no-load-optim --finetune` to make sure the optimizer state is correctly loaded from your last job. + +## Step 3: Perplexity evaluation + +During pretraining, we will automatically evaluate the model perplexity on the specified validation corpus +every `--eval-interval` steps. The validation corpus should be exactly the same as the one used in Step 1 to make sure +the preprocessed retrieval neighbors match the pretraining corpus. + +To evaluate the perplexity of a pretrained model, please add `--skip-train` in `pretrain_model.sh` to skip the +pretraining step and only evaluate the perplexity of the model specified in `--load` on the validation corpus. Run the +above command again to evaluate the perplexity of a pretrained model: + +```bash +bash tools/retro/examples/pretrain_model.sh +``` + +## Step 4: Instruction tuning + +In this step, we fine-tune the pretrained model on the downstream task with instructions. We provide a template +instruction tuning script to fine-tune 843M Retro. + +We also provide an open-source blend of instruction tuning datasets. The dataset is available to download +through [here](https://drive.google.com/file/d/1nzKwwYf8lYb9gN3P4YO8pFNU_B2nMYe1/view?usp=sharing). The blendable +dataset consists of the following open-source instruction tuning datasets: + +### Instruction Tuning Dataset Breakdown + +| Dataset | Samples | Epochs | Sampling Prob | +|------------------------------------------------------------|--------:|-------:|--------------:| +| [soda](https://arxiv.org/abs/2212.10465) | 2560 | 0.005 | 0.020 | +| [eli5](https://arxiv.org/abs/1907.09190) | 2561 | 0.055 | 0.020 | +| [self_instruct_short](https://arxiv.org/abs/2212.10560) | 1280 | 0.043 | 0.010 | +| [self_instruct_long](https://arxiv.org/abs/2212.10560) | 2560 | 0.333 | 0.020 | +| [unnatural-instructions](https://arxiv.org/abs/2212.09689) | 2560 | 0.024 | 0.020 | +| [flan_cot](https://arxiv.org/abs/2210.11416) | 1280 | 0.093 | 0.010 | +| [dolly](https://arxiv.org/abs/2305.13735) | 6400 | 0.938 | 0.050 | +| [oasst-skip-noncode](https://open-assistant.io/) | 104558 | 1.839 | 0.817 | +| [oasst-skip-code](https://open-assistant.io/) | 4243 | 1.839 | 0.033 | + +Refer to the paper links above for more details about each instruction tuning dataset. + +*We note that the provided instruction tuning dataset is all from open-source instruction tuning datasets. It is +slightly different from what we use in [InstructRetro](https://arxiv.org/abs/2310.07713), which contains private and +proprietary datasets. Thus a 1-2% accuracy difference in downstream tasks may be expected.* + +### Instruction tuning script + +Download +the [blended instruction tuning dataset](https://drive.google.com/file/d/1nzKwwYf8lYb9gN3P4YO8pFNU_B2nMYe1/view?usp=sharing) +in your data home directory `$DATA_HOME` and update our templates +in [tools/retro/sft/sft_retro_lm.sh](sft/sft_retro_lm.sh). + +An example command to run instruction tuning on 843M Retro is as follows: + +```bash + [blend-dataset-name] [model-size] [batch-size] [lr] [checkpoints] +bash tools/retro/sft/sft_retro_lm.sh open_inst 843m 128 5e-6 +``` + +The `blend_dataset_name` argument will blend all the datasets within the `$DATA_HOME` following the weights and +configurations specified in the `${blend_dataset_name}.sh` ([open_inst.sh](sft/open_inst.sh) in the example above). +The checkpoints will be saved in the `--save` directory. For example, it will be saved to +`/checkpoints/applications/retro-sft_pp1_same_format_ctx1_843m_128_5e-6`. + +## Step 5: Downstream task evaluation + +In this step, we demonstrate how to run InstructRetro for zero-shot evaluation on downstream question answering (QA) +tasks. We provide the pre-processed open-source evaluation datasets with a unified format for different tasks. The +evaluation datasets used in our paper are available to download +through [here](https://drive.google.com/drive/folders/1xw-N0LJR_lIWnH6BKzHIb49quVCS_V72?usp=sharing). Please stick to +the same retro workdir used in Step 0-4 to make sure the preprocessed retrieval neighbors match the pretraining corpus. +If you directly come to Step 5, an example retro workdir with `args.json` for 800M Retro is +provided [here](https://drive.google.com/file/d/121GqAdMvf8bJEBZRt-SD4uhW-SRWgI3s/view?usp=sharing). Note that the args +in the json can be overwritten through the command line. + +We present an example command to run retro generation given the InstructRetro checkpoints and the Natural Question (NQ) +task. The example command is for the 843m InstructRetro obtained in Step 4. Please specify the directory for the NQ +dataset and update the command accordingly for other checkpoints. + +```bash +bash tools/retro/text_generation/retro_generate.sh nq 843m greedy test 0 20000 1000 5 pp1 /checkpoints/applications/retro-sft_pp1_same_format_ctx1_843m_128_5e-6 2 +``` + +The generated responses will be saved in the corresponding checkpoint directory. For example, for the 843m +InstructRetro, it will be saved to +`/checkpoints/applications/retro-sft_pp1_same_format_ctx1_843m_128_5e-6/retro-generate-nq_5_2_843m_test_greedy_0_20000_1000.txt`. + +To evaluate the F1 / Exact Match (EM) scores of the generated responses, we provide an example script to run the +evaluation on the NQ dataset. Please specify the directory for the NQ dataset and update the command accordingly for +other checkpoints and downstream tasks. + +```bash +python3 tools/retro/text_generation/evaluate.py +``` + +# Citations + +See more details from our papers: + +[Shall we Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study.](https://arxiv.org/abs/2304.06762) + +_Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei +Xiao, Anima Anandkumar, Bryan Catanzaro._ (EMNLP 2023) + +[InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining.](https://arxiv.org/abs/2310.07713) + +_Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, Bryan Catanzaro._ + +Please cite the papers as follows if you use the data or code from this repo: + +```bibtex +@inproceedings{wang2023shall, + title = {Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study}, + author = {Boxin Wang and Wei Ping and Peng Xu and Lawrence McAfee and Zihan Liu and Mohammad Shoeybi and Yi Dong and Oleksii Kuchaiev and Bo Li and Chaowei Xiao and Anima Anandkumar and Bryan Catanzaro}, + journal = {The 2023 Conference on Empirical Methods in Natural Language Processing}, + year = {2023} +} + +@article{wang2023instructretro, + title = {InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining}, + author = {Boxin Wang and Wei Ping and Lawrence McAfee and Peng Xu and Bo Li and Mohammad Shoeybi and Bryan Catanzaro}, + year = {2023}, + journal = {arXiv preprint arXiv: 2310.07713} +} +``` diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/build_db.md b/Megatron-LM-core_r0.7.0.beta/tools/retro/build_db.md new file mode 100644 index 0000000..c999524 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/build_db.md @@ -0,0 +1,421 @@ +This directory contains a collection of tools for building the retrieval database and pretraining neighbors for Retro. This preprocessing pipeline is broken into 3 main stages: + +1. **Build retrieval chunk database** : Used for retrieving neighbors and continuation chunks, which are then passed through the retrieval encoder. +2. **Build index for similarity search** : Train and build a search index for querying chunk neighbors. +3. **Query pretraining neighbors** : For matching pretraining samples to database chunks. Neighbors are generated separately for training, validation, and test datasets. + +The following overview goes into more detail on the pipeline, code structure, usage, and pretraining. + + +# Contents + + * [Quick start](#quick-start) + * [Tutorial](#tutorial) + * [Code structure](#code-structure) + * [Arguments](#arguments) + + + + +# Quick Start +Key files: + +- `main.py` : Entry point for processing. +- `examples/preprocess_data.sh` : Example preprocessing launch (calls `main.py`). +- `examples/pretrain_data.sh` : Example pretraining launch (calls `pretrain_retro.py`). + +Use `--retro-tasks` to move through the preprocessing pipeline. + +- Simplest setup (builds everything): `--retro-tasks build` +- Alternatively, for tuning compute resources, run stages independently: + - Build retrieval database: `--retro-tasks db-build` + - Build search index: `--retro-tasks index-build` + - Query neighbors: `--retro-tasks pretraining-query-neighbors` + +Sample code flow: + +- `main.py` : Entry point (e.g., using `--retro-tasks X`). +- `db/build.py` : Build retrieval database. +- `index/build.py` : Build search index. Calls the following two files: + - `index/train.py` : Train index on subset of database. + - `index/add.py` : Add database chunks to index. +- `pretraining/query.py` : Query pretraining samples for database neighbors (saved to disk and used during pretraining). + + + +# Tutorial + +In this tutorial example, we use the Wikipedia corpus to demonstrate how we build a retrieval database and index for this corpus, and then query the pretraining datasets for their neighbors. + +## Step 1: Prepare your retrieval text corpus + +The format of text corpus follows the same format as in Megatron training. See [data precessing](../../README.md#data-preprocessing) for more details on how to convert your json dataset into the mmap format. + +Assume we have the Wikipedia corpus in the following format: + +``` +/Wikipedia_shuf_text_document.bin +/Wikipedia_shuf_text_document.idx +``` + +We note that the retrieval database can also be a blend of multiple text corpus. + +## Step 2: Build retrieval chunk database + +This *database* (stored as a 2-D array, NOT a relational database) consists of a list of chunks (traditionally length 64) extracted from the original GPT token dataset. This is simply a consecutive, non-overlapping chunking of the token dataset. Chunking only takes place within a document, and therefore the final chunk of each document has length: 1 <= chunk_length <= max_chunk_length. + +We discard chunks that would convert to an empty Bert sequence (rare case, happens ~1/100,000 chunks in our case), since we use Bert embeddings for building our index. Thus, the total number of chunks in the database will be slightly less than a naive calculation. + +Take the Wikipedia corpus as an example to build the retrieval chunk database: + +Prepare the following arguments and update our templates in [tools/retro/examples/preprocess_data.sh](examples/preprocess_data.sh): +- `--retro-workdir`: The directory in which the preprocessing pipeline saves its datasets and configuration files. + **This argument should remain consistent for a full pass through the pipeline, and for pretraining.** +- `--data-path`: text corpus path to build retrieval database. In the case of Wikipedia corpus, it could be +```bash +WIK="${DATA_HOME}/Wikipedia_shuf_text_document" + +DATA_BLEND=" \ + 1 ${WIK} \ +" +``` +- `--load`: bert path to load bert embedder +- `--vocab-file` and `--retro-bert-vocab-file`: bert vocab file +- `--retro-gpt-tokenizer-model`: gpt tokenizer model file + +Then launch the script: +```bash +bash tools/retro/examples/preprocess_data.sh db-build +``` + +After the `db-build` is finished, the output includes: +- The launching args will be saved in your `/args.json` for the following steps. +- The retrieval chunk database will be saved in your `/db/` with your dataset information in `/db/indexed_dataset_infos.json`. + +## Step 3: Build index for similarity search + +To match pretraining chunks to database chunks, a search index must be built to perform this querying. We use Faiss (https://github.com/facebookresearch/faiss) for training and building this index. Generally, the index is trained on a subset of all chunks in the database (specified via `--retro-index-ntrain`). After training, all chunks are added into the index, to be available during querying. + +Indexes only accept 1-D floating point vectors for training and adding, so each chunk must first be embedded before passing to the index for either training or adding. We use Bert embeddings for this purpose, and the embeddings are generated automatically within the pipeline. + +Take the Wikipedia corpus as an example to build the retrieval chunk database: + +```bash +bash tools/retro/examples/preprocess_data.sh index-train +``` +The `index-train` step is expected to take less than 4-hour on a single DGX-A100 node given the template index configuration. +To scale up for larger retrieval database, please carefully tune the faiss hyper-parameters specified in `--retro-index-str`. Please refer to [Faiss](https://github.com/facebookresearch/faiss/wiki/The-index-factory) to learn more about the index configuration. + +After the index is trained, the centroids, HNSW graph, and product quantizer is determined. However, the index is still empty, as there is no chunk added. + +Take the example of the Wikipedia corpus, with the default template, the output of `index-train` includes: +- The embedded Bert embeddings of the sampled chunks for `index-train` is saved in `/index/train_emb/`. +- The empty index is saved in `/index/faiss-par-add/OPQ32_64,IVF65536_HNSW8,PQ32/empty_0.970.faissindex`. + +Then we add all chunks in the retrieval database into the index so that we perform fast query over the whole retrieval database: +```bash +bash tools/retro/examples/preprocess_data.sh index-add +``` + +We note that this step can be time-consuming as it will go through the whole retrieval database, embed chunk tokens to BERT embeddings, and add them into the index. Please make sure you successfully add the whole retrieval database before moving on to the next stage. + +*In case your job is interrupted in the middle, you can just run the script again, and it will automatically skip the chunks that have been added into the index and start from the chunk where it is interrupted.* + + +Following the Wikipedia configuration, an example output of the step `index-add` includes: +- The index with retrieval data chunks added is saved in `/index/faiss-par-add/OPQ32_64,IVF65536_HNSW8,PQ32/added_0.970_0.950.faissindex`, which can be used to query the neighbors for pretraining. + +## Step 4: Query pretraining neighbors + +To ensure fast Retro pretraining, the database neighbors for pretraining samples are pre-computed and saved to disk, for efficient access within the Retro dataset. In this stage, the pretraining datasets (training, validation, and test) are iterated, each sample is broken into chunks, and the chunks are used for querying the index. Similar to when building the index, each chunk is embedded (via Bert) before querying the index. + +The saved neighbors are labeled with unique dataset properties (i.e., seed, sequence length, number of samples, etc.) to ensure the neighbors generated during preprocessing match the neighbors requested during pretraining. Please also make sure the pretraining configuration is the same as this step so that the neighbors are aligned. + +There are query-time hyper-parameters that can be tuned to improve the quality of the neighbors. These are specified in `RETRO_QUERY_EF_SEARCH` and `RETRO_QUERY_NPROBE`. The most important parameter is `RETRO_QUERY_NPROBE`, which controls the number of clusters to search during querying. This parameter can be tuned to improve the quality of the neighbors, but will also increase the query time. +We recommend following the tutorial of [faiss](https://github.com/facebookresearch/faiss/wiki/Index-IO,-cloning-and-hyper-parameter-tuning) to tune the hyper-parameters for your own retrieval database. + +Take the Wikipedia corpus as an example to query the neighbors in the retrieval database: + +```bash +bash tools/retro/examples/preprocess_data.sh query-pretraining-neighbors +``` + +The output of `query-pretraining-neighbors` on the Wikipedia corpus includes: +- `/wiki/query/train_855ab50e05151610301e2a74c4030fbc`, which contains the pre-retrieved neighbors for the pretraining dataset. +- `/wiki/query/valid_40bc7330318d64accec28e1e63c59bad`, which contains the pre-retrieved neighbors for the validation set of the pretraining corpus. + +## Step 5: Visualization of retrieval neighbors + +We also provide cli tools to help visualize and inspect the quality of your retrieved neighbors. + +To use the CLI, open a Python terminal via the `python` command, and then load a Retro workdir with the following: + +``` +from tools.retro.cli import retro +retro.init("/path/to/retro/workdir") +``` + +This initializes Megatron, and prepares the Retro data for inspection. We also print out some example commands to help you get familiar with the command lines. + +An example output for the Wikipedia Corpus: + +```text +setting number of micro-batches to constant 32 +> building BertWordPieceLowerCase tokenizer ... +> initializing torch distributed ... +> initialized tensor model parallel with size 1 +> initialized pipeline model parallel with size 1 +> compiling dataset index builder ... +... +... + > sample ratios: + dataset 0, input: 1, achieved: 1 +> size of blendable dataset: 201000 samples +> elapsed time for building blendable dataset indices: 0.00 (sec) +> building indices for blendable datasets ... + > sample ratios: + dataset 0, input: 1, achieved: 1 +> size of blendable dataset: 12864 samples +> finished creating pretrained GPT datasets ... + ++++++++++++++++++++++++++++++++++++++++++++++++++++ +examples ... [ *note*: 'db' = chunk db; 'pt' = pretraining corpus. ] ++++++++++++++++++++++++++++++++++++++++++++++++++++ + +~~~~ indexed datasets ~~~~ +retro.get_db_num_indexed_datasets() : 1 +retro.get_db_indexed_dataset_infos() : + [(1.000000, Wikipedia_shuf_text_document)] + +~~~~ counts ~~~~ +retro.get_db_num_chunks : 68104992. + +retro.get_pt_num_samples('train') : 201000. +retro.get_pt_num_samples('valid') : 12864. +retro.get_pt_num_chunks('train') : 1608000. +retro.get_pt_num_chunks('valid') : 102912. + +~~~~ tokens, text ~~~~ +retro.get_db_chunk_gpt(chunk_id) : [46809, 218340, 716, 647, ... , 251525, 872, 692, 4042] +retro.get_db_chunk_bert(chunk_id) : [10680, 16216, 4313, 1745 ... , 8117, 1007, 1012, 1997] +retro.get_db_chunk_text(chunk_id) : Jonas Geirnaert\n\nJonas ... ort Flatlife (11 min). Of +retro.get_db_chunk_and_continuation_text(chunk_id) : + ['Jonas Geirnaert Jonas Ge ... ort Flatlife (11 min). Of', + 'the copy he sent in for s ... abet, clearly has one. On'] + +retro.get_pt_sample('train', sample_id) : + { + 'dataset_idx' : 0 + 'text' : [ 676 14 40656 184 ... 4\n 276 17361 251542] + 'doc_ids' : [1246422 1596948 2403969] + 'neighbor_chunks' : [[[ 657380 657381]\n ... \n [34108760 34108761]]] + 'neighbor_tokens' : [[[ 276 9596 251511 . ... . 889 646 1723]]] + } + +(e.g., sample = retro.get_pt_sample(...)) + + sample['text'].shape : (513,) + sample['neighbor_tokens'].shape : (8, 20, 128) + sample['text'] : [ 676 14 40656 184 ... 4\n 276 17361 251542] + sample['neighbor_tokens'][17][1] : [ 14 14 30291 1 ... 682 328 379 251527] + retro.gpt_to_text(sample['text']) : also\nLatgalians (modern) ... ission criticised the AVN + retro.gpt_to_text(sample['neighbor_tokens']) : \n\nHis second marriage o ... Augusta Eardley-Wilmot (2 ++++++++++++++++++++++++++++++++++++++++++++++++++++ +``` + +We can also directly call the function `retro.print_neighbor_texts(sample_id, chunk_id)` to inspect the retrieval neighbors for a specific sample and chunk within the pretraining corpus. For example, + +```text +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +PRETRAINING CHUNK: + - also\nLatgalians (modern)\n\nReferences\n\nCategory:Defunct political parti ... e.\n\nAbout \nThe company was established established in 1997. It is listed +NEIGHBOR_CHUNKS: + - the sides.\n\nNotes\n\nReferences\n\nCategory:Obaku Zen\n*\nCategory:Japane ... 2, 2008. It was founded by Anand Jagannathan, CEO of parent company Kriyari + - 2007).\n\nSee also\n Satellite Communications\n Tonga\n\nReferences\n\nExte ... y Procter & Gamble (P&G) in 1985 in order for P&G to compete in the "beauty + - Japan\nCategory:Fish of Russia\nCategory:Fish described in 1845 Mareco Inde ... lic Opinion (WAPOR)\n European Society for Opinion and Marketing Research ( + - The current director of the company is Albert Bosch.\n\nSee also\n Coupon\n ... some articles in Basque. Deia is the main product of the Editorial Iparrag + - A.Ş have been traded on the Istanbul Stock Exchange since 2000.\n\nReferenc ... with stores in California, New York City, and London.\n\nHistory \nSnapette + - \nCategory:Hawaiian mythology\nCategory:Hawaiian religion\nCategory:Religio ... crative state contracts. In 2008 Prokom became a part of the Asseco capital + - , and the Baltic countries, as well as an online store.\n\nReferences\n\nEx ... nd are involved in intracellular trafficking. This protein does not contain + - juice producer\nFood industry of Russia\n\nReferences\n\nExternal links\nWi ... panies formerly listed on the New York Stock Exchange General Grant's March + - is in private ownership.\n\nReferences\n\nExternal links\n\nCategory:Online ... ten and directed by Brent Hodge. The film stars Aubrey Plaza, Molly Hawkey, + - company's display technology to manufacture and sell display-only engines.\ ... for a group of naval vessels (a division in naval usage).\n\nUsage\n Russia + - .\n\nCarrols also operated a chain of outlets in neighbouring Estonia from ... rama film directed by Raajeev Walia. It is produced by Aman Mehta and Bijal + - \n\nExternal links\nHightail website\nThe Next Web on YouSendIt rebrand to ... eptember 2014, sitting mainly in the criminal division of that court.\n\nBe + - American television seasons\nCategory:2014 American television seasons\nCat ... Canada and larger European cities.\n\nIn 2010, advertising in New Zealand, + - .\n\nNotes\n\nCategory:Trade unions\nCategory:Industrial Workers of the Wor ... x people, some of whom may have been working on a part-time basis. Its head + - \n List of podcasting companies\n\nReferences\n\nExternal links\n \n\nCateg ... ct.\n\nCategory:Populated places in the Ashanti Region Nkeirouka Ezekh\n\nN + - \n\nReferences\n\nExternal links\n ADESE official website\n\nCategory:Compa ... State Street, and UBS Warburg. Its first CEO was Ian M. Drachman. The firm + - Hotel\n Sulake Corporation\n Sulake Press Room\n Habbo Hotel - Blog\n\nCate ... l: 김진태; born December 19, 1980), better known by his stage name Verbal Jint + - hockey player\n Ruutu.fi, a Finnish television streaming service operated b ... from the bottom, a BDSM term\n Topping cycle, a cycle used in power plants + - of Surakarta\nCategory:Indonesian names\nCategory:Indonesian families\nCate ... mber 13, 2013 in Izhevsk on Universitetskaya Street (later it was given the + - facilities are also in Ankara and the company HQ is in Istanbul.\n\nReferen ... is currently a World Wide Web Consortium Working Draft.\n\nSee also\n Voice +``` + +The code snippet for the above example is also equivalent to +```python +tokens = retro.get_pt_sample('train', 0) +for token_ids in tokens["neighbor_tokens"][0]: + print("- %s" % (retro.gpt_to_text(token_ids))) + print("-" * 20) +``` + +# Code structure + +### `tools/retro/main.py` + +This is the main entry point for Retro preprocessing. Call `main.py --help` to see arguments. Additionally, some Retro arguments are in Megatron's core arguments, so also see `add_retro_args()` section of `megatron/arguments.py` for additional arguments. Two of the most important arguments to customize are `--retro-workdir` and `--retro-tasks`. + +- **`--retro-workdir`** : Set the directory in which the preprocessing pipeline saves its datasets and configuration files. This argument should remain consistent for a full pass through the pipeline, and for pretraining. + +- **`--retro-tasks`** : Set the stages of preprocessing to perform. As mentioned previously, the three high-level stages are: 1) build retrieval database, 2) build search index, and 3) query pretraining neighbors. `--retro-tasks` can be used to either run the full pipeline, or run each of these stages in isolation. The latter case is useful for tuning compute resources for each stage. For example, index training utilizes GPUs and requires relatively less time, while querying neighbors uses the CPU and is a relatively slow process. Example tasks include: + + - **`--retro-tasks build`** : Run entire preprocessing pipeline. + - **`--retro-tasks db-build`** : Build retrieval database. + - **`--retro-tasks index-build`** : Train and build search index. + - **`--retro-tasks pretraining-query-neighbors`** : Query pretraining neighbors. + +Multiple tasks can be specified by separating with commas (e.g., `--retro-tasks db-build,index-build`). Additionally, various 'miscellaneous' tasks are currently including, primarily for validating data for each stage; these task names can be seen in `main.py`. + +### `tools/retro/examples` + +Example scripts for setting arguments and launch Retro preprocessing. The key files here are: + +- **`preprocess_data.sh`** : Example launch script for preprocessing retro data. +- **`pretrain_model.sh`** : Example launch script for pretraining a retro model. + +### `tools/retro/db` + +Build the retrieval chunk database. The key files here are: + +- **`build.py`** : Entry point for building the database. This code is responsible for iterating the input datasets (i.e., `--data-path`), parsing each dataset into consecutive chunks, checking for empty Bert (Wordpiece) conversions, and storing this information to disk. Two databases are created: 1) the retrieval database, and 2) a sampled database used for training the search index. +- **`dataset.py`** : Defines database class, for iterating or accessing chunks in the database. Each chunk contains its tokens, Bert conversion length, and dataset index. + +Input data: + + +- Token datasets, as loaded by `gpt_dataset.py`. Multiple datasets can be specified by using a blended configuration (see `--data-path` in `megatron/arguments.py`). + +Output data: + +- **`/db/merged/train.hdf5`** : The main retrieval database. (*Database* here is used to denote a list of indexed chunks, rather than a *relational database*.) The chunks in this database are added to the search index, and are used for retrieval during pretraining. This file contains a single dataset `'chunks'`, which contains 5 columns: + + - `dataset_idx` : Dataset index, from list of blended indexed datasets. + - `document_idx` : Document index within dataset. + - `chunk_start_idx` : Chunk's starting token index within document. + - `chunk_end_idx` : Chunk's ending token index (exclusive) within document. + - `bert_chunk_length` : Length of Bert token sequence, after converting from GPT. + +- **`/db/merged/sampled.hdf5`** : Subset of training database that is used for training the search index. This file has the same structure as detailed above. In general, this database is significanly smaller than the `train.hdf5` database, since the search index only needs a relatively small number of samples to understand the data's structure. After training, all chunks in the main database (`train.hdf5`) are *added* to the search index. + +### `tools/retro/index` + +Build the search index. The key files here are: + +- `build.py` : Entry point for building the search index. First, the index is trained on the sampled chunk database (see above) by calling `train.py`, and then all chunks for the full database are added to the index by calling `add.py`. Note that training requires first embedding (using Bert) all chunks (a parallel operation), and then loading these embeddings and training the index (a sequential operation), so it's best to change one's compute setup after all chunks have been embedded and saved to disk. +- `indexes/faiss_base.py` : Wrapper class for building a Faiss index, following the standard `train()` and `add()` operations. +- `indexes/faiss_par_add.py` : Similar to above, except it uses an embarrassingly parallel (multi-node, multi-process) `add()` operation. Vectors are first added to separate index copies, and then merged together. + +Input data: + +- **`/db/merged/sampled.hdf5`** : Chunks used for training the search index. +- **`/db/merged/train.hdf5`** : Chunks used for adding to the *trained* search index. + +Output data: + +- **`/index///added.faissindex`** : The final index, which has been trained and has had all database chunks added to it. This index is ready for querying neighbors. Here, `RETRO_INDEX_TYPE` and `RETRO_INDEX_STR` correspond to the same-name arguments `--retro-index-type` (e.g., `faiss-par-add`) and `--retro-index-str` (e.g., `OPQ32_256,IVF4194304_HNSW32,PQ32`). +- **`/index///empty.faissindex`** : Generally can be discarded once `added.faissindex` has been built, but this file contains the *post-training*, *pre-adding* index. Useful for debugging or building other indexes. + +### `tools/retro/pretraining` + +Query the pretraining datasets (training, validation, test) for their neighbors within the database. Neighbors are queried during preprocessing -- rather than during pretraining -- because querying is a fairly slow operation, so it would be a bottleneck if performed during pretraining. Queried neighbors are tagged with their unique identifying information (e.g., `train_indexmap_27662746ns_2048sl_1234s`), so as to avoid incorrect references during pretraining. The key files here are: + +- **`query.py`** : Entry point for querying. The pretraining datasets are iterated, and each chunk within each sample is queried using the search index. These neighbors are filtered by discarding any database chunks that fall within the same document as any chunk within a pretraining sample. +- **`chunk_dataset.py`** : This creates an iterable 'chunk' dataset form of a pretraining dataset. This is just a light wrapper, but makes it easier to deterministically iterate and assign IDs to each chunk in a sample dataset. +- **`retro_dataset.py`** : The Retro dataset used for pretraining (not used in preprocessing). Each sample returns the sample tokens, along with neighbor tokens for each chunk within the sample. + +Input data: + +- Token datasets, as loaded by `gpt_dataset.py`. +- **`/index///added.faissindex`** : The trained index, with all database chunks added to it (see previous section for details). + +Output data: + +- **`/{train,valid,test}_XXns_YYsl_ZZs/WW.hdf5`** : These directories/files contain the indexes of neighbors for each chunk within each sample of the pretraining datasets. Each directory (e.g., `train_indexmap_2047435ns_2048sl_1234s`) contains a list of HDF5 files (e.g., one file might be called `0075700000-0075800000.hdf5`). Each HDF5 file contains a consecutive subset of neighbor IDs for a given chunk, for indexing into the main retrieval database. All HDF5 files taken together within a given directory, represent the entire set of neighbors for a dataset. The size of these HDF5 files is determined by the argument `--retro-block-size`. The `XX`, `YY`, `ZZ`, `WW` notation above denotes the dataset properties that are used for uniquely tagging the neighbor files, to ensure compatibility during model pretraining. These neighbor files are ultimated used by `retro_dataset.py` during pretraining, for building Retro samples. + +### `tools/retro/cli` + +Inspect preprocessed data. To use the CLI, open a Python terminal via the `python` command, and then load a Retro workdir with the following: + +``` +from tools.retro.cli import retro +retro.init("/path/to/retro/workdir") +``` + +This initializes Megatron, and prepares the Retro data for inspection. See the printed usage for available functions. Several routines are included for viewing data in the retrieval database and viewing pretraining samples and neighbors. For example: + +```python +retro.get_db_num_indexed_datasets() # 15 +retro.get_db_chunk_text(92874113) # 'research project at ... and philosophy' +retro.get_pt_sample('train', 62005) # '[16084, 26158, 25387 ..., 6898, 9568]' +``` + +Most methods within the CLI are prefixed to denote the data being inspected: + +- **'db'** : Retrieval database (i.e., chunk tokens, document IDs, and dataset IDs) +- **'pt'** : Pretraining datasets (i.e., sample tokens and neighbor tokens) + +### `tools/retro/utils.py` + +A collection of utility methods. Most importantly, this contains: + +- **`def get_gpt_tokenizer()`** : Get the GPT tokenizer. +- **`def get_bert_tokenizer()`** : Get the Bert tokenizer. +- **`class GPTToTextDataset`** : Wrapper class that converts GPT (BPE) samples to raw text. + +### `tools/bert_embedding` + +Generate Bert embeddings. The main files here are: + +- **`embed.py`** : Entry point for generating embeddings, and contains the two main embedding classes, `BertEmbedder` and `DiskDataParallelBertEmbedder` (more below). This file contains code for generating Megatron embeddings, while the file below contains code for Huggingface embeddings. +- **`huggingface.py`** : Used by `embed.py` when the embedder is configured (see below) to output Huggingface embeddings. +- **`dataset.py`** : Wrapper class for converting a raw-text dataset to Bert (Wordpiece) tokens. + +The Bert embeddings can be configured along two axes. The first axis is the output type: + +- **`class BertEmbedder`** : This class takes a raw-text dataset as input, generates its embeddings, and returns a Numpy array. The main functions are `embed_text_dataset` (accepts a raw-text dataset) and `embed_text` (accepts a string). +- **`class DiskDataParallelBertEmbedder`** : This class wraps `BertEmbedder`, and rather than returning a Numpy array, it saves the embeddings to disk. Additionally, this class automatically splits data across data parallel ranks (using interleaving), and also processes data in a specified `block_size` (e.g., 1,000,000). + +The second axis is the type of embedding model to use, controlled by the argument `--bert-embedder-type`: + +- **`--bert-embedder-type megatron`** : Use Megatron's Bert model. The specific model used is dependent on the loaded checkpoint, vocab file, and tokenizer. +- **`--bert-embedder-type huggingface`** : Use Huggingface's `bert-large-cased`. (*Note*: Huggingface's inclusion is likely to be deprecated; and there is no ability to configure cased/uncased.) + +### Pretraining + +- **`pretrain_retro.py`** : Launch script for pretraining Retro. Similar to `pretrain_gpt.py`, except this script handles loading neighbor tokens and setting up the neighbor attention mask. + +- **`megatron/model/retro_transformer.py`** : Implementation of Retro model, including the main transformer, the retrieval encoder, and chunked cross-attention layers. Note that currently, `retro_transformer.py` contains several classes that are nearly identical to `transformer.py`, except for 1 or 2 lines, due to code changes that are yet to be integrated. +- **`tools/retro/pretraining/retro_dataset.py`** : The Retro dataset used for pretraining (not used in preprocessing). Each sample returns the sample tokens, along with neighbor tokens for each chunk within the sample. + + + +# Arguments + +See `tools/retro/main.py`'s `add_retro_args()` and `megatron/arguments.py`'s `_add_retro_args()` for details and descriptions. Here we list some particularly important arguments: + +- `--retro-workdir` : Mentioned previously, this argument determines the directory in which a set of Retro data is stored (during preprocessing) and loaded (during pretraining). Any change in this directory during preprocessing may result in preprocessing starting over from scratch, and any change before pretraining will result in pretraining throwing an error. +- Preprocessing + - `--retro-gpt-chunk-length` : Retro chunk length (e.g., 64 in original paper). + - `--retro-tasks` : Comma-separated list of preprocessing tasks. Generally, the `build` task is the simplest way to run the preprocessing pipeline. For finer control, individual stages can be run by using tasks (in order): `db-build`, `index-build`, and `pretraining-query-neighbors`. + - `--retro-index-str` : Faiss index string that defines the index configuration. This will vary based on data size, compute/disk setup, and user needs. For example, this string looks something like `IVF262144_HNSW32,Flat` or `OPQ32_256,IVF4194304_HNSW32,PQ32`. +- Pretraining + - `--retro-add-retriever` : Must be used to select Retro model. + - `--retro-num-neighbors` : Number of neighbors to retrieve from the retrieval database (defaults to 2). + - `--retro-num-retrieved-chunks` : For each neighbor, the number consecutive chunks to retrieve, including the initial neighbor (defaults to 2). + - `--retro-attention-gate` : Gated mechanism to incorporate information of cross attention from retrieved neighbor (defaults to 1 during pretraining). + + + + + diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/cli/__init__.py b/Megatron-LM-core_r0.7.0.beta/tools/retro/cli/__init__.py new file mode 100644 index 0000000..2531017 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/cli/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +from .cli import retro diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/cli/__main__.py b/Megatron-LM-core_r0.7.0.beta/tools/retro/cli/__main__.py new file mode 100644 index 0000000..7c196fe --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/cli/__main__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import os + +from . import retro + + +if __name__ == "__main__": + retro.init(os.environ["RETRO_WORKDIR"]) diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/cli/cli.py b/Megatron-LM-core_r0.7.0.beta/tools/retro/cli/cli.py new file mode 100644 index 0000000..18da6c7 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/cli/cli.py @@ -0,0 +1,301 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +import json +import numpy as np +import os +import typing as T +from types import SimpleNamespace + +from megatron.training.arguments import load_retro_config, parse_args, validate_args +from megatron.core.datasets.retro.db.dataset import DBDataset +from megatron.core.datasets.retro.db.utils import ( + get_indexed_dataset_infos as get_db_indexed_dataset_infos, + get_merged_train_dataset as get_db_dataset, +) +from megatron.core.datasets.retro.query.retro_dataset import get_retro_datasets, RetroDataset +from megatron.global_vars import set_global_variables +from megatron.training import build_train_valid_test_datasets, update_train_iters +from pretrain_retro import train_valid_test_datasets_provider +from tools.retro.preprocess_data import get_tokenizers + + +def shorten_str(s: str, n: int) -> str: + s = "\\n".join(s.splitlines()) + return s if len(s) <= n else "%s ... %s" % (s[: n // 2], s[-n // 2 :]) + + +class retro: + + config = None + + ############################################## + # initialize. + ############################################## + + @classmethod + def init(cls, project_dir: str) -> None: + '''Initialize Megatron, tokenizers, and datasets.''' + + # Megatron args. + args = parse_args(extra_args_provider=None, ignore_unknown_args=False) + args.retro_project_dir = project_dir + args.micro_batch_size = 1 + args.num_layers = 1 + args.hidden_size = 1 + args.num_attention_heads = 1 + args.async_tensor_model_parallel_allreduce = False + args.retro_add_retriever = True # for building RetroDataset + validate_args(args) + set_global_variables(args) + update_train_iters(args) + + # Retro config. + cls.config = load_retro_config(project_dir) + cls.config.retro_project_dir = project_dir + cls.config.retro_tokenizers = get_tokenizers(cls.config) + + # Chunk database dataset. + cls.db_indexed_dataset_infos = get_db_indexed_dataset_infos(project_dir) + cls.db_dataset = get_db_dataset(project_dir, + cls.config.retro_gpt_chunk_length, + cls.config.retro_tokenizers.gpt.eod) + + # Pretraining datasets. + pt_train_ds, pt_valid_ds, pt_test_ds = build_train_valid_test_datasets( + train_valid_test_datasets_provider) + cls.pt_datasets = SimpleNamespace( + train=pt_train_ds, + valid=pt_valid_ds, + test=pt_test_ds, + ) + + # Print usage. + cls.print_usage() + + ############################################## + # utils. + ############################################## + + @classmethod + def gpt_to_text(cls, token_ids: np.ndarray) -> str: + '''GPT tokens to text.''' + return cls.config.retro_tokenizers.gpt.detokenize( + token_ids.tolist() if isinstance(token_ids, np.ndarray) else token_ids + ) + + @classmethod + def text_to_bert(cls, text: str) -> np.ndarray: + '''Text to Bert tokens.''' + return cls.config.retro_tokenizers.bert.tokenize(text) + + ############################################## + # chunk db. + ############################################## + + @classmethod + def get_db_num_indexed_datasets(cls) -> int: + '''Number of indexed datasets within blended dataset.''' + return len(cls.db_indexed_dataset_infos) + + @classmethod + def get_db_indexed_dataset_infos(cls) -> T.List[T.Tuple[float, str]]: + '''Dataset infos, including number of training & sampled sets.''' + return [(info["ratio"], info["prefix"]) for info in cls.db_indexed_dataset_infos] + + @classmethod + def get_db_dataset(cls) -> DBDataset: + return cls.db_dataset + + @classmethod + def get_db_num_chunks(cls) -> int: + '''Number of DB chunks.''' + return len(cls.get_db_dataset()) + + @classmethod + def get_db_chunk_gpt(cls, idx: int) -> T.List[int]: + '''Get DB chunk as GPT token ids.''' + return cls.get_db_dataset()[idx]["text"].tolist() + + @classmethod + def get_db_chunk_bert(cls, idx: int) -> T.List[int]: + '''Get DB chunk as Bert token ids.''' + return cls.text_to_bert(cls.get_db_chunk_text(idx)) + + @classmethod + def get_db_chunk_text(cls, idx: int) -> str: + '''Get DB chunk as text.''' + return cls.gpt_to_text(cls.get_db_chunk_gpt(idx)) + + @classmethod + def get_db_chunk_and_continuation_text(cls, idx: int) -> T.List[str]: + '''Get DB chunk along with continuation, as text.''' + + # Modulus used here to match original implementation (i.e., last + # chunks continuation wraps around to first chunk). + return [ + cls.get_db_chunk_text(idx), + cls.get_db_chunk_text((idx + 1) % len(cls.get_db_dataset())), + ] + + ############################################## + # pretraining corpus. + ############################################## + + @classmethod + def get_pt_num_samples_and_chunks(cls, data_key: str) -> T.Tuple[int, int]: + '''Number of samples & chunks (e.g., 32*n_samples) in corpus.''' + assert hasattr(cls.pt_datasets, data_key), ( + "pretraining set '%s' not found (choices: %s)." + % (data_key, ", ".join(vars(cls.pt_datasets).keys())) + ) + chunk_dataset = getattr(cls.pt_datasets, data_key).chunk_dataset + return ( + len(chunk_dataset.sample_dataset), + len(chunk_dataset), + ) + + @classmethod + def get_pt_num_samples(cls, data_key: str) -> int: + '''Number of pretraining samples.''' + return cls.get_pt_num_samples_and_chunks(data_key)[0] + + @classmethod + def get_pt_num_chunks(cls, data_key: str) -> int: + '''Number of pretraining chunks (e.g., 32*n_samples).''' + return cls.get_pt_num_samples_and_chunks(data_key)[1] + + @classmethod + def get_pt_dataset(cls, data_key: str) -> RetroDataset: + return getattr(cls.pt_datasets, data_key) + + @classmethod + def get_pt_sample(cls, data_key: str, idx: int) -> dict: + return getattr(cls.pt_datasets, data_key)[idx] + + @classmethod + def get_neighbor_tokens(cls, sample_id: int, chunk_id: int, data_key: str="train") -> T.Optional[dict]: + try: + sample = cls.get_pt_sample(data_key, sample_id) + sample_token_ids = sample["text"] + chunk_length = cls.args.retro_gpt_chunk_length + chunk_start_idx = chunk_id * chunk_length + chunk_end_idx = min(sample_token_ids.shape[0], chunk_start_idx + chunk_length) + chunk_token_ids = sample_token_ids[chunk_start_idx:chunk_end_idx] + neighbor_token_ids = sample["neighbor_tokens"][chunk_id] + return { + "chunk_tokens": chunk_token_ids, + "neighbor_tokens": neighbor_token_ids, + } + except: + return None + + @classmethod + def print_neighbor_texts(cls, sample_id: int, chunk_id: int, data_key: str="train") -> None: + tokens: dict = cls.get_neighbor_tokens(sample_id, chunk_id, data_key) + print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~") + try: + print("PRETRAINING CHUNK:") + print(" - %s" % shorten_str(cls.gpt_to_text(tokens["chunk_tokens"]), 150)) + print("NEIGHBOR_CHUNKS:") + for token_ids in tokens["neighbor_tokens"]: + print(" - %s" % shorten_str(cls.gpt_to_text(token_ids), 150)) + except: + print("" % sample_id) + + ############################################## + # usage. + ############################################## + + @classmethod + def print_usage(cls) -> None: + '''Print usage.''' + + print() + print("+++++++++++++++++++++++++++++++++++++++++++++++++++") + print("examples ... [ *note*: 'db' = chunk db; 'pt' = pretraining corpus. ]") + print("+++++++++++++++++++++++++++++++++++++++++++++++++++") + + print() + print("~~~~ indexed datasets ~~~~") + print("retro.get_db_num_indexed_datasets() : %s" % cls.get_db_num_indexed_datasets()) + print("retro.get_db_indexed_dataset_infos() :") + for i, (ratio, prefix) in enumerate(cls.get_db_indexed_dataset_infos()): + print( + " %s(%f, %s)%s" + % ( + "[" if i == 0 else " ", + ratio, + prefix, + "]" if i == len(cls.db_indexed_dataset_infos) - 1 else ",", + ) + ) + + print() + print("~~~~ counts ~~~~") + print("retro.get_db_num_chunks : %d." % cls.get_db_num_chunks()) + + print() + for sq_key in ("sample", "chunk"): + for data_key in ("train", "valid"): # test? + print( + "retro.get_pt_num_%ss('%s') : %d." + % (sq_key, data_key, getattr(cls, f"get_pt_num_{sq_key}s")(data_key)) + ) + + print() + print("~~~~ tokens, text ~~~~") + print( + "retro.get_db_chunk_gpt(chunk_id) : %s" + % shorten_str(str(retro.get_db_chunk_gpt(0)), 50) + ) + print( + "retro.get_db_chunk_bert(chunk_id) : %s" + % shorten_str(str(retro.get_db_chunk_bert(0)), 50) + ) + print( + "retro.get_db_chunk_text(chunk_id) : %s" + % shorten_str(retro.get_db_chunk_text(0).strip(), 50) + ) + print("retro.get_db_chunk_and_continuation_text(chunk_id) :") + for i, t in enumerate(retro.get_db_chunk_and_continuation_text(0)): + print( + " %s'%s'%s" + % ( + "[" if i == 0 else " ", + shorten_str(t.strip().replace("\n", " "), 50), + "]" if i == 1 else ",", + ) + ) + + sample = cls.get_pt_sample("train", 0) + sample_chunk_id = sample["neighbor_tokens"].shape[0] // 2 + sample_neighbor_id = 0 + print() + print("retro.get_pt_sample('train', sample_id) :") + print(" {") + for k, v in sample.items(): + print(" '%s' : %s" % (k, shorten_str(str(v), 50))) + print(" }") + + print() + print("(e.g., sample = retro.get_pt_sample(...))") + print() + print(" sample['text'].shape : %s" % str(sample["text"].shape)) + print(" sample['neighbor_tokens'].shape : %s" % str(sample["neighbor_tokens"].shape)) + print(" sample['text'] : %s" % shorten_str(str(sample["text"]), 50)) + print( + " sample['neighbor_tokens'][17][1] : %s" + % shorten_str(str(sample["neighbor_tokens"][sample_chunk_id][sample_neighbor_id]), 50) + ) + print( + " retro.gpt_to_text(sample['text']) : %s" + % shorten_str(cls.gpt_to_text(sample["text"]), 50) + ) + print( + " retro.gpt_to_text(sample['neighbor_tokens']) : %s" + % shorten_str( + cls.gpt_to_text(sample["neighbor_tokens"][sample_chunk_id][sample_neighbor_id]), 50 + ) + ) + + print("+++++++++++++++++++++++++++++++++++++++++++++++++++") diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/config_utils.py b/Megatron-LM-core_r0.7.0.beta/tools/retro/config_utils.py new file mode 100644 index 0000000..00676c6 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/config_utils.py @@ -0,0 +1,632 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Config utils.""" + +import argparse +from collections import namedtuple, OrderedDict +import dataclasses +import enum +import inspect +import os +import re +import types +import typing as T + + +PARAM_KEYWORDS = { + "param", + "parameter", + "arg", + "argument", + "attribute", + "key", + "keyword", +} +RAISES_KEYWORDS = {"raises", "raise", "except", "exception"} +DEPRECATION_KEYWORDS = {"deprecation", "deprecated"} +RETURNS_KEYWORDS = {"return", "returns"} +YIELDS_KEYWORDS = {"yield", "yields"} +EXAMPLES_KEYWORDS = {"example", "examples"} + + +class ParseError(RuntimeError): + """Base class for all parsing related errors.""" + + +class DocstringStyle(enum.Enum): + """Docstring style.""" + + REST = 1 + GOOGLE = 2 + NUMPYDOC = 3 + EPYDOC = 4 + AUTO = 255 + + +class RenderingStyle(enum.Enum): + """Rendering style when unparsing parsed docstrings.""" + + COMPACT = 1 + CLEAN = 2 + EXPANDED = 3 + + +class DocstringMeta: + """Docstring meta information. + + Symbolizes lines in form of + + :param arg: description + :raises ValueError: if something happens + """ + + def __init__( + self, args: T.List[str], description: T.Optional[str] + ) -> None: + """Initialize self. + + :param args: list of arguments. The exact content of this variable is + dependent on the kind of docstring; it's used to distinguish + between custom docstring meta information items. + :param description: associated docstring description. + """ + self.args = args + self.description = description + + +class DocstringParam(DocstringMeta): + """DocstringMeta symbolizing :param metadata.""" + + def __init__( + self, + args: T.List[str], + description: T.Optional[str], + arg_name: str, + type_name: T.Optional[str], + is_optional: T.Optional[bool], + default: T.Optional[str], + ) -> None: + """Initialize self.""" + super().__init__(args, description) + self.arg_name = arg_name + self.type_name = type_name + self.is_optional = is_optional + self.default = default + + +class DocstringReturns(DocstringMeta): + """DocstringMeta symbolizing :returns or :yields metadata.""" + + def __init__( + self, + args: T.List[str], + description: T.Optional[str], + type_name: T.Optional[str], + is_generator: bool, + return_name: T.Optional[str] = None, + ) -> None: + """Initialize self.""" + super().__init__(args, description) + self.type_name = type_name + self.is_generator = is_generator + self.return_name = return_name + + +class DocstringRaises(DocstringMeta): + """DocstringMeta symbolizing :raises metadata.""" + + def __init__( + self, + args: T.List[str], + description: T.Optional[str], + type_name: T.Optional[str], + ) -> None: + """Initialize self.""" + super().__init__(args, description) + self.type_name = type_name + self.description = description + + +class DocstringDeprecated(DocstringMeta): + """DocstringMeta symbolizing deprecation metadata.""" + + def __init__( + self, + args: T.List[str], + description: T.Optional[str], + version: T.Optional[str], + ) -> None: + """Initialize self.""" + super().__init__(args, description) + self.version = version + self.description = description + + +class DocstringExample(DocstringMeta): + """DocstringMeta symbolizing example metadata.""" + + def __init__( + self, + args: T.List[str], + snippet: T.Optional[str], + description: T.Optional[str], + ) -> None: + """Initialize self.""" + super().__init__(args, description) + self.snippet = snippet + self.description = description + + +class Docstring: + """Docstring object representation.""" + + def __init__( + self, + style=None, # type: T.Optional[DocstringStyle] + ) -> None: + """Initialize self.""" + self.short_description = None # type: T.Optional[str] + self.long_description = None # type: T.Optional[str] + self.blank_after_short_description = False + self.blank_after_long_description = False + self.meta = [] # type: T.List[DocstringMeta] + self.style = style # type: T.Optional[DocstringStyle] + + @property + def params(self) -> T.List[DocstringParam]: + """Return a list of information on function params.""" + return {m.arg_name:m for m in self.meta if isinstance(m, DocstringParam)} + + @property + def raises(self) -> T.List[DocstringRaises]: + """Return a list of information on the exceptions that the function + may raise. + """ + return [ + item for item in self.meta if isinstance(item, DocstringRaises) + ] + + @property + def returns(self) -> T.Optional[DocstringReturns]: + """Return a single information on function return. + + Takes the first return information. + """ + for item in self.meta: + if isinstance(item, DocstringReturns): + return item + return None + + @property + def many_returns(self) -> T.List[DocstringReturns]: + """Return a list of information on function return.""" + return [ + item for item in self.meta if isinstance(item, DocstringReturns) + ] + + @property + def deprecation(self) -> T.Optional[DocstringDeprecated]: + """Return a single information on function deprecation notes.""" + for item in self.meta: + if isinstance(item, DocstringDeprecated): + return item + return None + + @property + def examples(self) -> T.List[DocstringExample]: + """Return a list of information on function examples.""" + return [ + item for item in self.meta if isinstance(item, DocstringExample) + ] + + +class SectionType(enum.IntEnum): + """Types of sections.""" + + SINGULAR = 0 + """For sections like examples.""" + + MULTIPLE = 1 + """For sections like params.""" + + SINGULAR_OR_MULTIPLE = 2 + """For sections like returns or yields.""" + + +class Section(namedtuple("SectionBase", "title key type")): + """A docstring section.""" + + +GOOGLE_TYPED_ARG_REGEX = re.compile(r"\s*(.+?)\s*\(\s*(.*[^\s]+)\s*\)") +GOOGLE_ARG_DESC_REGEX = re.compile(r".*\. Defaults to (.+)\.") +MULTIPLE_PATTERN = re.compile(r"(\s*[^:\s]+:)|([^:]*\]:.*)") + +DEFAULT_SECTIONS = [ + Section("Arguments", "param", SectionType.MULTIPLE), + Section("Args", "param", SectionType.MULTIPLE), + Section("Parameters", "param", SectionType.MULTIPLE), + Section("Params", "param", SectionType.MULTIPLE), + Section("Raises", "raises", SectionType.MULTIPLE), + Section("Exceptions", "raises", SectionType.MULTIPLE), + Section("Except", "raises", SectionType.MULTIPLE), + Section("Attributes", "attribute", SectionType.MULTIPLE), + Section("Example", "examples", SectionType.SINGULAR), + Section("Examples", "examples", SectionType.SINGULAR), + Section("Returns", "returns", SectionType.SINGULAR_OR_MULTIPLE), + Section("Yields", "yields", SectionType.SINGULAR_OR_MULTIPLE), +] + + +class GoogleDocstringParser: + """Parser for Google-style docstrings.""" + + def __init__( + self, sections: T.Optional[T.List[Section]] = None, title_colon=True + ): + """Setup sections. + + :param sections: Recognized sections or None to defaults. + :param title_colon: require colon after section title. + """ + if not sections: + sections = DEFAULT_SECTIONS + self.sections = {s.title: s for s in sections} + self.title_colon = title_colon + self._setup() + + def _setup(self): + if self.title_colon: + colon = ":" + else: + colon = "" + self.titles_re = re.compile( + "^(" + + "|".join(f"({t})" for t in self.sections) + + ")" + + colon + + "[ \t\r\f\v]*$", + flags=re.M, + ) + + def _build_meta(self, text: str, title: str) -> DocstringMeta: + """Build docstring element. + + :param text: docstring element text + :param title: title of section containing element + :return: + """ + + section = self.sections[title] + + if ( + section.type == SectionType.SINGULAR_OR_MULTIPLE + and not MULTIPLE_PATTERN.match(text) + ) or section.type == SectionType.SINGULAR: + return self._build_single_meta(section, text) + + if ":" not in text: + # raise ParseError(f"Expected a colon in {text!r}.") + return None + + # Split spec and description + before, desc = text.split(":", 1) + if desc: + desc = desc[1:] if desc[0] == " " else desc + if "\n" in desc: + first_line, rest = desc.split("\n", 1) + desc = first_line + "\n" + inspect.cleandoc(rest) + desc = desc.strip("\n") + + return self._build_multi_meta(section, before, desc) + + @staticmethod + def _build_single_meta(section: Section, desc: str) -> DocstringMeta: + if section.key in RETURNS_KEYWORDS | YIELDS_KEYWORDS: + return DocstringReturns( + args=[section.key], + description=desc, + type_name=None, + is_generator=section.key in YIELDS_KEYWORDS, + ) + if section.key in RAISES_KEYWORDS: + return DocstringRaises( + args=[section.key], description=desc, type_name=None + ) + if section.key in EXAMPLES_KEYWORDS: + return DocstringExample( + args=[section.key], snippet=None, description=desc + ) + if section.key in PARAM_KEYWORDS: + raise ParseError("Expected paramenter name.") + return DocstringMeta(args=[section.key], description=desc) + + @staticmethod + def _build_multi_meta( + section: Section, before: str, desc: str + ) -> DocstringMeta: + if section.key in PARAM_KEYWORDS: + match = GOOGLE_TYPED_ARG_REGEX.match(before) + if match: + arg_name, type_name = match.group(1, 2) + if type_name.endswith(", optional"): + is_optional = True + type_name = type_name[:-10] + elif type_name.endswith("?"): + is_optional = True + type_name = type_name[:-1] + else: + is_optional = False + else: + arg_name, type_name = before, None + is_optional = None + + match = GOOGLE_ARG_DESC_REGEX.match(desc) + default = match.group(1) if match else None + + return DocstringParam( + args=[section.key, before], + description=desc, + arg_name=arg_name, + type_name=type_name, + is_optional=is_optional, + default=default, + ) + if section.key in RETURNS_KEYWORDS | YIELDS_KEYWORDS: + return DocstringReturns( + args=[section.key, before], + description=desc, + type_name=before, + is_generator=section.key in YIELDS_KEYWORDS, + ) + if section.key in RAISES_KEYWORDS: + return DocstringRaises( + args=[section.key, before], description=desc, type_name=before + ) + return DocstringMeta(args=[section.key, before], description=desc) + + def add_section(self, section: Section): + """Add or replace a section. + + :param section: The new section. + """ + + self.sections[section.title] = section + self._setup() + + def parse(self, text: str) -> Docstring: + """Parse the Google-style docstring into its components. + + :returns: parsed docstring + """ + ret = Docstring(style=DocstringStyle.GOOGLE) + if not text: + return ret + + # Clean according to PEP-0257 + text = inspect.cleandoc(text) + + # Find first title and split on its position + match = self.titles_re.search(text) + if match: + desc_chunk = text[: match.start()] + meta_chunk = text[match.start() :] + else: + desc_chunk = text + meta_chunk = "" + + # Break description into short and long parts + parts = desc_chunk.split("\n", 1) + ret.short_description = parts[0] or None + if len(parts) > 1: + long_desc_chunk = parts[1] or "" + ret.blank_after_short_description = long_desc_chunk.startswith( + "\n" + ) + ret.blank_after_long_description = long_desc_chunk.endswith("\n\n") + ret.long_description = long_desc_chunk.strip() or None + + # Split by sections determined by titles + matches = list(self.titles_re.finditer(meta_chunk)) + if not matches: + return ret + splits = [] + for j in range(len(matches) - 1): + splits.append((matches[j].end(), matches[j + 1].start())) + splits.append((matches[-1].end(), len(meta_chunk))) + + chunks = OrderedDict() # type: T.Mapping[str,str] + for j, (start, end) in enumerate(splits): + title = matches[j].group(1) + if title not in self.sections: + continue + + # Clear Any Unknown Meta + # Ref: https://github.com/rr-/docstring_parser/issues/29 + meta_details = meta_chunk[start:end] + unknown_meta = re.search(r"\n\S", meta_details) + if unknown_meta is not None: + meta_details = meta_details[: unknown_meta.start()] + + chunks[title] = meta_details.strip("\n") + if not chunks: + return ret + + # Add elements from each chunk + for title, chunk in chunks.items(): + # Determine indent + indent_match = re.search(r"^\s*", chunk) + if not indent_match: + raise ParseError(f'Can\'t infer indent from "{chunk}"') + indent = indent_match.group() + + # Check for singular elements + if self.sections[title].type in [ + SectionType.SINGULAR, + SectionType.SINGULAR_OR_MULTIPLE, + ]: + part = inspect.cleandoc(chunk) + ret.meta.append(self._build_meta(part, title)) + continue + + # Split based on lines which have exactly that indent + _re = "^" + indent + r"(?=\S)" + c_matches = list(re.finditer(_re, chunk, flags=re.M)) + if not c_matches: + raise ParseError(f'No specification for "{title}": "{chunk}"') + c_splits = [] + for j in range(len(c_matches) - 1): + c_splits.append((c_matches[j].end(), c_matches[j + 1].start())) + c_splits.append((c_matches[-1].end(), len(chunk))) + for j, (start, end) in enumerate(c_splits): + part = chunk[start:end].strip("\n") + ret.meta.append(self._build_meta(part, title)) + + return ret + + +def verify_and_get_config_attr_descs(config_cls, strict_docstring_match=True): + + assert dataclasses.is_dataclass(config_cls), f"uh oh <{config_cls.__name__}>." + + # Parse docstring. + try: + docstring = GoogleDocstringParser().parse(config_cls.__doc__) + except Exception as e: + raise Exception(f"error parsing {config_cls.__name__} docstring.") + + # Get attributes and types. + config_attrs = docstring.params + config_types = config_cls.__annotations__ + + # Verify attribute names. + config_attr_keys = set(config_attrs.keys()) + config_type_keys = set(config_types.keys()) + missing_attr_keys = config_type_keys - config_attr_keys + extra_attr_keys = config_attr_keys - config_type_keys + if strict_docstring_match: + assert not missing_attr_keys and not extra_attr_keys, f"{config_cls.__name__} docstring is either missing attributes ({', '.join(missing_attr_keys) if missing_attr_keys else '--'}) or contains extra attributes ({', '.join(extra_attr_keys) if extra_attr_keys else '--'})." + + # @todo + # Verify attribute type names. + # for key in config_attr_keys: + # ... todo ... + + # Verify base class attributes. + attrs = {k:v for base_cls in config_cls.__bases__ if dataclasses.is_dataclass(base_cls) for k,v in verify_and_get_config_attr_descs(base_cls, strict_docstring_match=strict_docstring_match).items()} + for key in config_attr_keys: + if key in config_types: + attrs[key] = { + "desc" : config_attrs[key].description, + "type" : config_types[key], + } + + return attrs + + +def add_config_args(parser, config_cls): + attrs = verify_and_get_config_attr_descs(config_cls, strict_docstring_match=False) + for key, attr in attrs.items(): + _type = attr["type"] + if dataclasses.is_dataclass(_type): + group = parser.add_argument_group(title=attr["desc"]) + add_config_args(group, _type) + else: + + default_value = getattr(config_cls, key) + args = { + "help" : attr["desc"], + "default" : default_value, + } + + if _type == bool: + assert isinstance(args["default"], (bool, type(None))), \ + f"boolean attribute '{key}' of {config_cls.__name__} " \ + "has non-boolean default value." + + # When default=True, add 'no-{key}' arg. + if default_value: + args["action"] = "store_false" + args["dest"] = key + key = "no-" + key + else: + args["action"] = "store_true" + + elif _type in (int, float): + args["type"] = _type + + elif _type == list: + args["nargs"] = "*" + + # else: ....... treat as string arg + # raise Exception(f"specialize action for '{key}', type <{_type}>.") + + try: + parser.add_argument(f"--{key.replace('_', '-')}", **args) + except argparse.ArgumentError as e: + pass + + +def get_config_leaf_field_names(config_cls): + names = set() + for field in dataclasses.fields(config_cls): + if dataclasses.is_dataclass(field.type): + names.update(get_config_leaf_field_names(field.type)) + else: + names.add(field.name) + return names + + +def config_from_args(args, config_cls, add_custom_args=False): + + # Collect config data in a dict. + data = {} + for field in dataclasses.fields(config_cls): + if dataclasses.is_dataclass(field.type): + data[field.name] = config_from_args(args, field.type) + else: + data[field.name] = getattr(args, field.name) + + # Add custom args. (e.g., for tools, tasks) + if add_custom_args: + + config_keys = get_config_leaf_field_names(config_cls) + arg_keys = set(vars(args).keys()) + custom_keys = arg_keys - config_keys + + custom_data = {k:v for k, v in vars(args).items() if k in custom_keys} + custom_config_cls = dataclasses.make_dataclass( + "CustomConfig", + [(k, type(v)) for k, v in custom_data.items()]) + custom_config = custom_config_cls(**custom_data) + data["custom"] = custom_config + + # Create config. [ todo: programmatically create dataclass that inherits + # TransformerConfig. ] + config = config_cls(**data) + + return config + + +def flatten_config(config, base_config_cls=None): + + # Lift sub-config data. + flat_config = {} + for field in dataclasses.fields(config): + value = getattr(config, field.name) + if dataclasses.is_dataclass(value): + flat_config = { **flat_config, **flatten_config(value) } + else: + flat_config[field.name] = value + + # Convert to dataclass. + if base_config_cls: + base_keys = set(field.name for field in dataclasses.fields(base_config_cls)) + flat_config_cls = dataclasses.make_dataclass( + cls_name="FlatMegatronConfig", + fields=[(k, T.Any, dataclasses.field(default=None)) + for k, v in flat_config.items() + if k not in base_keys], + bases=(base_config_cls,)) + flat_config = flat_config_cls(**flat_config) + + return flat_config diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/docker/Dockerfile b/Megatron-LM-core_r0.7.0.beta/tools/retro/docker/Dockerfile new file mode 100644 index 0000000..e8945b3 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/docker/Dockerfile @@ -0,0 +1,19 @@ +FROM nvcr.io/nvidia/pytorch:23.09-py3 + +RUN pip install -U faiss-gpu + +RUN apt update + +RUN apt install -qy htop + +RUN pip install -U transformers + +RUN pip install --upgrade google-api-python-client + +RUN pip install sentencepiece + +RUN pip install h5py + +RUN pip install nltk + +RUN pip install einops diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/preprocess_data.py b/Megatron-LM-core_r0.7.0.beta/tools/retro/preprocess_data.py new file mode 100644 index 0000000..1e0fdb5 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/preprocess_data.py @@ -0,0 +1,291 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Preprocess data for Retro. + +Stages (see argument '--retro-tasks'): +- Build chunk database (DB). +- Build index (train, add). +- Query pretraining neighbors. +""" + +import json +import os +import sys +import torch + +from megatron import get_args, initialize_megatron, print_rank_0 +from megatron.arguments import core_transformer_config_from_args +from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder +from megatron.core.datasets.retro.db import build_db +from megatron.core.datasets.retro.index import add_to_index, train_index +from megatron.core.datasets.retro.config import ( + RetroBertEmbedders, + RetroGPTChunkDatasets, + RetroPreprocessingConfig, + RetroTokenizers, +) +from megatron.core.datasets.retro.query.gpt_chunk_dataset import build_gpt_chunk_datasets_from_gpt_datasets +from megatron.core.datasets.retro.query.multi_split_gpt_dataset import ( + MultiSplitGPTDataset, + MultiSplitGPTDatasetConfig, +) +from megatron.core.datasets.retro.query.query import query_neighbors +from megatron.core.datasets.retro.query.utils import get_query_dir +from megatron.core.datasets.retro.utils import retro_makedir +from megatron.core.models.retro.utils import ( + get_config_path, + get_gpt_data_dir, +) +from megatron.training.tokenizer.tokenizer import ( + _BertWordPieceTokenizer, + _GPT2BPETokenizer, + _GPTSentencePieceTokenizer, +) +from megatron.training import get_train_valid_test_num_samples +from pretrain_gpt import is_dataset_built_on_rank +from tools.bert_embedding import BertEmbedder, DiskDataParallelBertEmbedder +from tools.retro.config_utils import add_config_args + + +def add_retro_args(parser): + group = parser.add_argument_group(title="Retro preprocessing") + add_config_args(group, RetroPreprocessingConfig) + return parser + + +def initialize_megatron_retro(): + '''Initialize megatron & save Retro config.''' + + # Prevent arguments.py from overriding preprocessing args. + project_dir_idx = sys.argv.index("--retro-project-dir") + retro_project_dir = sys.argv[project_dir_idx + 1] + del sys.argv[project_dir_idx] # delete key + del sys.argv[project_dir_idx] # delete value + + # Initialize. + initialize_megatron(extra_args_provider=add_retro_args) + + args = get_args() + args.retro_project_dir = retro_project_dir + + # Retro config. + config = get_retro_preprocessing_config() + + # Save retro config. + if config.retro_task_validate is None: + retro_makedir(config, config.retro_project_dir) + save_config(config) + + return config + + +def get_bert_embedders(config): + mem_embedder = BertEmbedder( + batch_size = config.retro_bert_batch_size, + max_bert_seq_length = config.retro_bert_max_chunk_length, + embedder_type = "megatron", + ) + return RetroBertEmbedders( + mem = mem_embedder, + disk = DiskDataParallelBertEmbedder(mem_embedder, config.retro_block_size), + ) + + +def get_gpt_chunk_datasets(config): + + args = get_args() + + # Dataset config. + data_dir = get_gpt_data_dir(config.retro_project_dir) + blend = list(config.retro_gpt_data_path) + for i in range(len(blend) - 1, -1, -2): + blend[i] = os.path.join(data_dir, blend[i]) + data_config = MultiSplitGPTDatasetConfig( + random_seed=config.retro_gpt_seed, + sequence_length=config.retro_gpt_seq_length, + blend=blend, + blend_per_split=[args.train_data_path, args.valid_data_path, args.test_data_path], + split=config.retro_gpt_split, + split_preprocessing=config.retro_gpt_split, + path_to_cache=config.retro_gpt_data_cache_path, + return_document_ids=True, + tokenizer=config.retro_tokenizers.gpt, + mock=args.mock_data, + reset_position_ids=args.reset_position_ids, + reset_attention_mask=args.reset_attention_mask, + eod_mask_loss=args.eod_mask_loss, + ) + + # GPT datasets. + print_rank_0(" > multi-split gpt datasets.") + train_valid_test_num_samples = get_train_valid_test_num_samples() + train_ds, valid_ds, test_ds = BlendedMegatronDatasetBuilder( + MultiSplitGPTDataset, + train_valid_test_num_samples, + is_dataset_built_on_rank, + data_config, + ).build() + + gpt_datasets = { + "train" : (train_ds, train_valid_test_num_samples[0]), + "valid" : (valid_ds, train_valid_test_num_samples[1]), + "test" : (test_ds, train_valid_test_num_samples[2]), + } + + # Chunk datasets. + chunk_datasets = build_gpt_chunk_datasets_from_gpt_datasets( + project_dir=config.retro_project_dir, + gpt_datasets=gpt_datasets, + sample_length=config.retro_gpt_seq_length, + chunk_length=config.retro_gpt_chunk_length, + ) + chunk_datasets = RetroGPTChunkDatasets(**chunk_datasets) + + return chunk_datasets + + +def get_gpt_tokenizer(config): + '''GPT (BPE) tokenizer.''' + tokenizer_type = config.retro_gpt_tokenizer_type + if tokenizer_type == "GPT2BPETokenizer": + assert config.retro_gpt_vocab_file and config.retro_gpt_merge_file + return _GPT2BPETokenizer( + vocab_file=os.path.join( + config.retro_project_dir, + config.retro_gpt_vocab_file, + ), + merge_file=os.path.join( + config.retro_project_dir, + config.retro_gpt_merge_file, + ), + ) + elif tokenizer_type == 'GPTSentencePieceTokenizer': + assert config.retro_gpt_tokenizer_model is not None + return _GPTSentencePieceTokenizer(os.path.join( + config.retro_project_dir, + config.retro_gpt_tokenizer_model, + )) + else: + raise Exception("unrecognized gpt tokenizer, '%s'." % tokenizer_type) + + +def get_bert_tokenizer(config): + '''Bert (Wordpiece) tokenizer.''' + lower_case = { + "BertWordPieceLowerCase" : True, + "BertWordPieceCase" : False, + }[config.retro_bert_tokenizer_type] + return _BertWordPieceTokenizer( + vocab_file=os.path.join( + config.retro_project_dir, + config.retro_bert_vocab_file, + ), + lower_case=lower_case, + ) + + +def get_tokenizers(config): + return RetroTokenizers( + gpt = get_gpt_tokenizer(config), + bert = get_bert_tokenizer(config), + ) + + +def get_retro_preprocessing_config(): + + # Arguments. + args = get_args() + + # Retro config. + config = core_transformer_config_from_args( + args, config_class=RetroPreprocessingConfig) + + # Add tools. + config.retro_tokenizers = get_tokenizers(config) + config.retro_bert_embedders = get_bert_embedders(config) + config.retro_gpt_chunk_datasets = get_gpt_chunk_datasets(config) + + return config + + +def save_config(config): + '''Save copy of config within retro project dir.''' + + if torch.distributed.get_rank() == 0: + + # GPT config + block size. + config_subset = { + k:v for k,v in vars(config).items() + if k.startswith("retro_gpt") and k != "retro_gpt_chunk_datasets" + } + config_subset["retro_block_size"] = config.retro_block_size + + # Bert config. + config_subset["retro_bert_tokenizer_type"] = config.retro_bert_tokenizer_type + config_subset["retro_bert_vocab_file"] = config.retro_bert_vocab_file + + # Neighbor directories. + query_dir = get_query_dir(config.retro_project_dir) + config_subset["retro_neighbor_dirs"] = { + k : (os.path.relpath(v["neighbor_dir"], query_dir) if v is not None else None) + for k, v in vars(config.retro_gpt_chunk_datasets).items() + } + + # Save. + config_path = get_config_path(config.retro_project_dir) + with open(config_path, "w") as f: + json.dump(config_subset, f, indent=4, sort_keys=True) + + torch.distributed.barrier() + + +if __name__ == "__main__": + + # Initalize Megatron. + config = initialize_megatron_retro() + + # Expand tasks. + task_remap = { + "build" : [ "db-build", "index-train", "index-add", "query-neighbors" ], + "index-build" : [ "index-train", "index-add" ], + "db-build" : [ "db-build" ], + "index-train" : [ "index-train" ], + "index-add" : [ "index-add" ], + "query-neighbors" : [ "query-neighbors" ], + } + tasks = [] + for task in config.retro_tasks: + tasks.extend(task_remap[task]) + config.retro_tasks = tasks + + # Select task to run. + for task in tasks: + + print_rank_0("start '%s%s'." % ( + "" if config.retro_task_validate is None else "[validate] ", + task, + )) + + # DB (i.e., chunk db). + if task == "db-build": + build_db(config) + + # Index. + elif task == "index-train": + train_index(config) + elif task == "index-add": + add_to_index(config) + + # Query. + elif task == "query-neighbors": + query_neighbors(config) + + else: + raise Exception("specialize for task '%s'." % task) + + torch.distributed.barrier() + + print_rank_0("end '%s%s'." % ( + "" if config.retro_task_validate is None else "[validate] ", + task, + )) diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/README.md b/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/README.md new file mode 100644 index 0000000..e589879 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/README.md @@ -0,0 +1,3 @@ +## Note + +The content within this `sft` directory is still under active development and will be updated soon. \ No newline at end of file diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/dataset_conv.py b/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/dataset_conv.py new file mode 100644 index 0000000..d7bde54 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/dataset_conv.py @@ -0,0 +1,446 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +import re +import json +import os +from typing import Any, Iterable, Dict + +from numpy import ndarray +from megatron.core.datasets.blended_megatron_dataset_config import BlendedMegatronDatasetConfig +from megatron.core.datasets.utils import Split +import torch +import numpy +import glob +from collections import OrderedDict + +from megatron.core.datasets.blended_megatron_dataset_config import BlendedMegatronDatasetConfig +from megatron.core.datasets.megatron_dataset import LowLevelDataset, MegatronDataset +from megatron.core.datasets.utils import Split +from dataclasses import dataclass + + +_DATASET_NAME_PATTERNS = { + Split.train: r"(?P[^\0]+)\/(?P=name)\_QA\_train.json", + Split.valid: r"(?P[^\0]+)\/(?P=name)\_QA\_dev.json", +} + + +@dataclass +class JsonQADatasetConfig(BlendedMegatronDatasetConfig): + """Configuration object for the QA finetuning pipeline + """ + ft_neighbours: int = 1 + + bert_retriever_neighbours: bool = False + + longform_answer: bool = False + + inference_only: bool = False + + retrieved_neighbours: bool = False + + fix_newsqa: bool = True + + def __post_init__(self) -> None: + super().__post_init__() + assert self.blend_per_split is not None + + +@dataclass +class RetroJsonQADatasetConfig(JsonQADatasetConfig): + """Configuration object for the Retro QA finetuning pipeline + """ + retro_num_neighbors: int = None + + retro_gpt_retrieved_length: int = None + + def __post_init__(self) -> None: + super().__post_init__() + assert self.retro_num_neighbors is not None + assert self.retro_gpt_retrieved_length is not None + + +class JsonQADataset(MegatronDataset): + + def __init__(self, dataset: Any, dataset_path: str, indices: ndarray, num_samples: int, index_split: Split, config: BlendedMegatronDatasetConfig) -> None: + super().__init__(dataset, dataset_path, indices, num_samples, index_split, config) + matches = re.findall(_DATASET_NAME_PATTERNS[index_split], dataset_path) + assert len(matches) == 1 + assert len(matches[0]) > 0 + self.dataset_name = matches[0] + + @staticmethod + def numel_low_level_dataset(low_level_dataset: LowLevelDataset) -> int: + return len(low_level_dataset) + + @staticmethod + def build_low_level_dataset(dataset_path: str, config: JsonQADatasetConfig) -> Iterable: + assert os.path.isfile(dataset_path), f"{dataset_path} does not exist on disk" + return preprocess(dataset_path, config) + + def __len__(self) -> int: + return len(self.dataset) + + def __getitem__(self, idx: int) -> Dict[str, ndarray]: + sample = self.dataset[idx % len(self.dataset)] + + # unpack tokens + query, answer, neighbours = sample + + # tokenization + output_tokens = self.config.tokenizer.tokenize(answer) + + input_tokens = reformat_prompt( + query, + neighbours, + self.dataset_name, + self.config.ft_neighbours, + len(output_tokens), + self.config.tokenizer, + self.config.sequence_length + ) + + # padding + tokens, answer_mask = pad_and_convert_to_numpy( + input_tokens, output_tokens, self.config.tokenizer.pad, self.config.sequence_length, self.config.tokenizer.eos + ) + + train_sample = { + 'text': tokens, + 'answer_mask': answer_mask, + } + + return train_sample + + +class RetroJsonQADataset(JsonQADataset): + + def __getitem__(self, idx: int) -> Dict[str, ndarray]: + + sample = self.dataset[idx % len(self.dataset)] + + # unpack tokens + query, answer, neighbours = sample + + # tokenization + output_tokens = self.config.tokenizer.tokenize(answer) + + input_tokens = reformat_prompt_retro( + query, + neighbours, + self.dataset_name, + self.config.ft_neighbours, + len(output_tokens), + self.config.tokenizer, + self.config.sequence_length + ) + + # padding + tokens, answer_mask = pad_and_convert_to_numpy( + input_tokens, + output_tokens, + self.config.tokenizer.pad, + self.config.sequence_length, + self.config.tokenizer.eos + ) + + # get retro neighbors + # context chunk and answer chunk + n_chunks_per_sample = 2 + num_neighbors = self.config.retro_num_neighbors + # disable retro encoder + neighbor_tokens = numpy.zeros( + [n_chunks_per_sample, num_neighbors, self.config.retro_gpt_retrieved_length], + dtype=numpy.int64 + ) + + train_sample = { + 'text': tokens, + 'answer_mask': answer_mask, + 'neighbor_tokens': neighbor_tokens, + 'context_len': len(input_tokens) + } + + return train_sample + + +def format_multichoice(multichoice_options): + options_text = ["({}) {}".format(chr(ord('A') + i), option) for i, option in + zip(range(len(multichoice_options)), multichoice_options)] + return "Choose one based on the following options: {}".format(" ".join(options_text)) + + +def format_multichoice_question(question, multichoice_options): + return "{}\n{}".format(question, format_multichoice(multichoice_options)) + + +def format_answer(answer): + return " {}".format(answer) + + +def preprocess(dataset_path: str, config: JsonQADatasetConfig): + assert config.ft_neighbours > 0 + if config.longform_answer: + nq_examples = [] + with open(dataset_path, "r") as f: + for fn in f: + nq_examples.append(json.loads(fn)) + else: + nq_examples = [] + for my_data_file in sorted(glob.glob(dataset_path)): + with open(my_data_file, "r", encoding='utf-8') as f: + nq_examples.extend(json.load(f)) + + data = [] + for instance in nq_examples: + question = instance["question"] + if 'qa_type' in instance and instance['qa_type'] == "multi_choice_qa": + question = format_multichoice_question(question, instance["multichoice_options"]) + if config.bert_retriever_neighbours: + contexts = instance["bert_pretrain_corpus_neighbours"] + neighbours = ["source: " + ctx for ctx in contexts] + else: + if config.retrieved_neighbours: + contexts = instance["ctxs"] + neighbours = ["title: " + ctx["title"] + ", source: " + ctx["text"] for ctx in contexts] + else: + if "sub-paragraphs" in instance: + if type(instance["sub-paragraphs"]) == list: # doc2dial: + neighbours = [ + "title: " + instance["sub-paragraphs"][0] + ", source: " + instance["sub-paragraphs"][1]] + else: + neighbours = ["title: , source: " + instance["sub-paragraphs"]] + elif config.fix_newsqa and "sub_paragraph" in instance: + neighbours = ["title: , source: " + instance["sub_paragraph"]] + else: + neighbours = ["title: , source: "] + + if config.inference_only: + data.append((question, None, neighbours)) + else: + if config.longform_answer: + if "longform_answer" in instance: + answers = [instance["longform_answer"]] + else: + continue + else: + if "answers" in instance: + answers = instance["answers"] + elif "answer" in instance: + if type(instance["answer"]) is str: + answers = [instance["answer"]] + elif type(instance["answer"]) is list: + answers = instance["answer"] + else: + answers = [str(instance["answer"])] + else: + raise ValueError("need to have answer or answers") + if len(answers) < 1: + continue + else: + if type(answers[0]) is dict: + answers = [answers[0]["text"].strip()] + elif type(answers[0]) is str: + answers = [answers[0]] + else: + raise ValueError("unsupported type for answer(s)") + + for answer in answers: + answer = format_answer(answer) + data.append((question, answer, neighbours)) + + return data + + +def count_stat(dataset, tokenizer, k): + nb_lens = [] + for i, d in enumerate(dataset): + query, answer, neighbours = d + nb_lens.extend([len(tokenizer.tokenize(neighbour)) for neighbour in neighbours[:k]]) + + print("len of nb", len(nb_lens)) + print("max of len nb", max(nb_lens)) + print("num of cut ", sum([l > 128 for l in nb_lens]), sum([l > 128 for l in nb_lens]) // len(nb_lens)) + print("last max", sorted(nb_lens)[-10:]) + + +def reformat_prompt_retro(query, neighbours, dataset_name, ft_neighbours, \ + max_output_len, tokenizer, max_seq_length): + system = ("System: This is a chat between a user and an artificial intelligence assistant. The assistant gives " + "helpful, detailed, and polite answers to the user's questions.\n\n") + + if dataset_name in ["oasst", "quiet_cockatoo", "open_inst", "quiet-cockatoo_commercial"]: + input_tokens = tokenizer.tokenize(system + query) + return input_tokens + + short_span_with_context = ["drop", "NarrativeQA", "QASC", "Quoref", "ROPES", "squad1.1", "squad2.0", "newsqa", "nq", + "tqa", "quac"] + yes_no_without_context = ["BoolQ"] + multichoices = [""] + formatted_dataset_name = ["doc2dial", "quac", "qrecc", "sharc"] + + if dataset_name in formatted_dataset_name: + dialogue_turn = query + else: + if dataset_name in short_span_with_context: + user = "{} Answer the above question with a short phrase.".format(query) + elif dataset_name in yes_no_without_context: + user = "{} Answer the above question with True or False.".format(query) + else: + user = "{} Answer the above question with a long complete answer.".format(query) + + if dataset_name in short_span_with_context: + dialogue_format = "User: {}\n\nAssistant: The answer is" + dialogue_turn = dialogue_format.format(user) + else: + dialogue_format = "User: {}\n\nAssistant:" + dialogue_turn = dialogue_format.format(user) + + if ft_neighbours > 0: + context = "\n\n".join(neighbours[0:ft_neighbours]) + "\n\n" + context_tokens = tokenizer.tokenize(context) + dialogue_tokens = tokenizer.tokenize(dialogue_turn) + system_tokens = tokenizer.tokenize(system) + context_tokens = context_tokens[:max_seq_length - max_output_len - len(dialogue_tokens) - len(system_tokens)] + context = tokenizer.detokenize(context_tokens) + + all_input = system + context + dialogue_turn + print(all_input) + input_tokens = tokenizer.tokenize(all_input) + else: + all_input = system + dialogue_turn + input_tokens = tokenizer.tokenize(all_input) + + return input_tokens + + +def flan_format(system, context, dialogue_turn, template_id=0): + templates = [ + "{}User: Answer based on context:\n\n{}{}", + "{}User: {}Answer this question based on the article: {}", + "{}User: {}{}", + "{}User: {}Answer this question: {}", + "{}User: Read this article and answer this question {}{}", + "{}User: {}Based on the above article, answer a question. {}", + "{}User: Context: {}Question: {}" + ] + template = templates[template_id - 1].format(system, context, dialogue_turn) + return template + + +def reformat_prompt(query, neighbours, dataset_name, ft_neighbours, \ + max_output_len, tokenizer, max_seq_length, template_id=0): + system = ("System: This is a chat between a user and an artificial intelligence assistant. The assistant gives " + "helpful, detailed, and polite answers to the user's questions based on the context. The assistant " + "should also indicate when the answer cannot be found in the context.\n\n") + + if dataset_name in ["oasst", "quiet_cockatoo", "open_inst", "quiet-cockatoo_commercial"]: + input_tokens = tokenizer.tokenize(system + query) + return input_tokens + + short_span_with_context = ["drop", "NarrativeQA", "QASC", "Quoref", "ROPES", "squad1.1", "squad2.0", "newsqa", "nq", + "BioASQ", "DuoRC_ParaphraseRC", "TextbookQA", "tqa"] + yes_no_without_context = ["boolq", "multirc"] + multichoices = ["race"] + # multi-turn qa datasets + formatted_dataset_name = ["convqa", "chatgptgen", "doc2dial", "quac", "qrecc", "sharc"] + + if dataset_name in formatted_dataset_name: + dialogue_turn = query + else: + if dataset_name in short_span_with_context: + if template_id == 0: + user = "Answer the following question with a short span. {}".format(query) + else: + user = query + elif dataset_name in yes_no_without_context: + user = "Answer the following question with True or False. {}".format(query) + elif dataset_name in multichoices: + user = "Answer the following question by selecting one of the provided options. {}".format(query) + else: + if template_id == 0: + user = "Please give a full and complete answer for the question. {}".format(query) + else: + user = query + + if dataset_name in short_span_with_context: + if template_id == 0: + dialogue_format = "User: {}\n\nAssistant: The answer is" + else: + dialogue_format = "{}\n\nAssistant: The answer is" + dialogue_turn = dialogue_format.format(user) + else: + if template_id == 0: + dialogue_format = "User: {}\n\nAssistant:" + else: + dialogue_format = "{}\n\nAssistant:" + dialogue_turn = dialogue_format.format(user) + + if ft_neighbours > 0: + context = "\n\n".join(neighbours[0:ft_neighbours]) + "\n\n" + context_tokens = tokenizer.tokenize(context) + dialogue_tokens = tokenizer.tokenize(dialogue_turn) + system_tokens = tokenizer.tokenize(system) + context_tokens = context_tokens[:max_seq_length - max_output_len - len(dialogue_tokens) - len(system_tokens)] + context = tokenizer.detokenize(context_tokens) + + if template_id == 0: + all_input = system + context + dialogue_turn + else: + all_input = flan_format(system, context, dialogue_turn, template_id=template_id) + input_tokens = tokenizer.tokenize(all_input) + else: + all_input = system + dialogue_turn + input_tokens = tokenizer.tokenize(all_input) + + return input_tokens + + +def reformat_prompt_short(query, neighbours, dataset_name, ft_neighbours, \ + max_output_len, tokenizer, max_seq_length): + if not query.endswith("?"): + query = query + "?" + query = "Question: {} Answer: The answer is".format(query) + + if ft_neighbours > 0: + context = "\n\n".join(neighbours[0:ft_neighbours]) + "\n\n" + context_tokens = tokenizer.tokenize(context) + dialogue_tokens = tokenizer.tokenize(query) + context_tokens = context_tokens[:max_seq_length - max_output_len - len(dialogue_tokens)] + context = tokenizer.detokenize(context_tokens) + all_input = context + query + input_tokens = tokenizer.tokenize(all_input) + else: + all_input = query + input_tokens = tokenizer.tokenize(all_input) + + return input_tokens + + +def pad_and_convert_to_numpy(input_ids, output_ids, + pad_id, max_seq_length, + eos_id): + """Pad sequences and convert them to numpy.""" + if len(input_ids) > max_seq_length: + input_ids = input_ids[:max_seq_length - 1] + + if len(input_ids + output_ids) > max_seq_length: + output_ids = output_ids[:max_seq_length - len(input_ids)] + + tokens = input_ids + output_ids + answer_mask = [0] * len(input_ids) + [1] * len(output_ids) + + # padding + num_tokens = len(tokens) + padding_length = max_seq_length - num_tokens + assert padding_length >= 0 + + # Tokens. + filler = [pad_id] * padding_length + tokens = numpy.array(tokens + [eos_id] + filler, dtype=numpy.int64) + + # answer mask + answer_mask = answer_mask + [1] + [0] * padding_length + answer_mask = numpy.array(answer_mask, dtype=numpy.int64) + + return tokens, answer_mask diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/open_inst.sh b/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/open_inst.sh new file mode 100644 index 0000000..9ebe063 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/open_inst.sh @@ -0,0 +1 @@ +DATA_BLEND="1.0 open_inst" diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/sft_retro.py b/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/sft_retro.py new file mode 100644 index 0000000..2cbea02 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/sft_retro.py @@ -0,0 +1,273 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +"""Pretrain GPT""" + +import torch +from functools import partial, reduce +import sys, os + +sys.path.append(os.path.abspath(os.path.join( + os.path.join(os.path.dirname(__file__), "../../../")))) +from megatron.training import get_args, get_retro_args +from megatron.training import print_rank_0 +from megatron.training import get_timers +from megatron.training import get_tokenizer +from megatron.core import tensor_parallel +from megatron.core.enums import ModelType +from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder +from megatron.training import pretrain +from megatron.training.utils import get_ltor_masks_and_position_ids +from megatron.training.utils import average_losses_across_data_parallel_group +from pretrain_gpt import model_provider, is_dataset_built_on_rank +from tools.retro.sft.dataset_conv import JsonQADataset, JsonQADatasetConfig, RetroJsonQADataset, RetroJsonQADatasetConfig + + +def get_tasks_args(parser): + """Provide extra arguments required for tasks.""" + group = parser.add_argument_group(title='tasks') + + # parameters for the knowledgeable dialogue generation + group.add_argument('--task', type=str, default=None, + help='Task name.') + group.add_argument('--epochs', type=int, default=None, + help='Number of finetunning epochs. Zero results in ' + 'evaluation only.') + group.add_argument('--keep-last', action='store_true', + help='Keep the last batch (maybe incomplete) in' + 'the data loader') + group.add_argument('--pretrained-checkpoint', type=str, default=None, + help='Pretrained checkpoint used for finetunning.') + group.add_argument('--data-folder', type=str, default=None, + help='dataset folder') + group.add_argument('--answer-loss-only', action='store_true', default=False, + help='take the loss from answer part, ignore the context') + group.add_argument('--weight', type=float, default=1) + group.add_argument('--adaptor', action='store_true', default=False) + group.add_argument('--project-size', type=int, default=256) + group.add_argument('--cyclic-train-iters', type=int, default=None) + group.add_argument('--stored_params', type=dict, default=dict()) + group.add_argument('--eval_ppl', action='store_true', default=False) + group.add_argument('--debug', action='store_true', default=False) + group.add_argument('--add_retriever', action='store_true', default=False) + group.add_argument('--return_doc_ids', action='store_true', default=False) + group.add_argument('--return_neighbor_ids', action='store_true', default=False) + group.add_argument('--add_offset_doc_ids', action='store_true', default=False) + group.add_argument('--offset_dict_path', type=str, default='') + group.add_argument('--neighbors_path', type=str, default='') + group.add_argument('--valid_neighbors_path', type=str, default='') + group.add_argument('--database_path', type=str, default='') + group.add_argument('--valid_database_path', type=str, default='') + group.add_argument('--encoder-layers', type=int, default=12) + group.add_argument('--encoder-hidden-dropout', type=float, default=0.1) + group.add_argument('--encoder-attention-dropout', type=float, default=0.1) + group.add_argument('--k', type=int, default=2) + group.add_argument('--r', type=int, default=128) + group.add_argument('--m', type=int, default=64) + group.add_argument('--dpr-mode', type=str, default="multi") + group.add_argument('--faiss-ckpt', type=str, default='') + group.add_argument('--original-db-file', type=str, default="") + group.add_argument('--ft_neighbours', type=int, default=1) + group.add_argument('--reuse-top', action='store_true', default=False) + group.add_argument('--shuffle_topn', action='store_true', default=False) + group.add_argument('--chunk0', action='store_true', default=False) + group.add_argument('--disable-encoder', action='store_true', default=False) + group.add_argument('--qa-space-pad', action='store_true', default=False) + group.add_argument('--retro-mask-encoder', action='store_true', default=False) + group.add_argument('--without-title', action='store_true', default=False) + group.add_argument('--longform-answer', action='store_true', default=False) + group.add_argument('--bert-retriever-neighbours', action='store_true', default=False) + group.add_argument('--prefix', action='store_true', default=False) + group.add_argument('--question-in-encoder', action='store_true', default=False) + group.add_argument('--reset_eval', type=bool, default=True) ## by default reset eval for each eval + return parser + + +def get_batch(data_iterator): + """Generate a batch""" + args = get_args() + tokenizer = get_tokenizer() + + # Items and their type. + keys = ['text', 'answer_mask'] + datatype = torch.int64 + + if args.retro_add_retriever: + keys += 'neighbor_tokens', 'context_len' + + # Broadcast data. + if data_iterator is not None: + try: + data = next(data_iterator) + + except BaseException: + data = data_iterator + raise ValueError("error with data_iterator") + else: + data = None + + data_b = tensor_parallel.broadcast_data(keys, data, datatype) + chunk_size = torch.min(data_b['context_len']) + retro_args = get_retro_args() + # two chunk retro has at least seq_len / 2 of chunk size + retro_args.retro_gpt_chunk_length = max(args.seq_length // 2, args.seq_length - chunk_size.item()) + + # Unpack. + tokens_ = data_b['text'].long() + labels = tokens_[:, 1:].contiguous() + tokens = tokens_[:, :-1].contiguous() + + answer_mask = data_b["answer_mask"].float()[:, 1:].contiguous() + + if args.retro_add_retriever: + neighbor_tokens = data_b['neighbor_tokens'].view(-1, + retro_args.retro_gpt_retrieved_length).long() # [bs * l * k, r] + + # Get the masks and postition ids. + attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids( + tokens, + tokenizer.eod, + args.reset_position_ids, + args.reset_attention_mask, + args.eod_mask_loss) + + if args.answer_loss_only: + loss_mask = loss_mask * answer_mask + + if args.retro_add_retriever: + _, _, neighbor_position_ids = get_ltor_masks_and_position_ids( + neighbor_tokens, + tokenizer.eod, + args.reset_position_ids, + args.reset_attention_mask, + args.eod_mask_loss) + neighbor_attention_mask = None + return tokens, labels, loss_mask, attention_mask, position_ids, \ + neighbor_tokens, neighbor_attention_mask, neighbor_position_ids + else: + return tokens, labels, loss_mask, attention_mask, position_ids + + +def loss_func(loss_mask, output_tensor): + losses = output_tensor.float() + loss_mask = loss_mask.view(-1).float() + loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum() + + # Reduce loss for logging. + averaged_loss = average_losses_across_data_parallel_group([loss]) + + return loss, {'lm loss': averaged_loss[0]} + + +def forward_step(data_iterator, model): + """Forward step.""" + args = get_args() + timers = get_timers() + + if args.retro_add_retriever: + timers('batch-generator', log_level=2).start() + tokens, labels, loss_mask, attention_mask, position_ids, \ + neighbor_tokens, neighbor_attention_mask, neighbor_position_ids = get_batch( + data_iterator) + timers('batch-generator').stop() + output_tensor = model(tokens, position_ids, attention_mask, + retriever_input_ids=neighbor_tokens, + retriever_position_ids=neighbor_position_ids, + retriever_attn_mask=neighbor_attention_mask, + labels=labels) + else: + timers('batch-generator', log_level=2).start() + tokens, labels, loss_mask, attention_mask, position_ids = get_batch( + data_iterator) + timers('batch-generator').stop() + output_tensor = model(tokens, position_ids, attention_mask, + labels=labels) + + return output_tensor, partial(loss_func, loss_mask) + + +def train_valid_test_datasets_provider(train_val_test_num_samples): + """Build train, valid, and test datasets.""" + args = get_args() + retro_args = get_retro_args() + + tokenizer = get_tokenizer() + + def fix_and_split_blend_pair(pair): + weight, name = pair + return [ + [weight, os.path.join(args.data_folder, name, f"{name}_QA_train.json")], + [weight, os.path.join(args.data_folder, name, f"{name}_QA_dev.json")], + None, + ] + + blend = [args.data_path[i:i+2] for i in range(0, len(args.data_path), 2)] + + if len(blend) == 1: + blend_per_split = [ + os.path.join(args.data_folder, blend[0], f"{blend[0]}_QA_train.json"), + os.path.join(args.data_folder, blend[0], f"{blend[0]}_QA_dev.json"), + None, + ] + else: + blend_per_split = [ + list( + reduce( + lambda x, y: x + y, + list(zip(*map(fix_and_split_blend_pair, blend)))[0] + ) + ), + None, + None, + ] + + extra_kwargs = {} + + if args.retro_add_retriever: + dataset_cls = RetroJsonQADataset + config_cls = RetroJsonQADatasetConfig + extra_kwargs["retro_num_neighbors"] = args.retro_num_neighbors + extra_kwargs["retro_gpt_retrieved_length"] = retro_args.retro_gpt_retrieved_length + else: + dataset_cls = JsonQADataset + config_cls = JsonQADatasetConfig + + config = config_cls( + random_seed=args.seed, + sequence_length=args.seq_length, + blend_per_split=blend_per_split, + split=args.split, + path_to_cache=args.data_cache_path, + mock=args.mock_data, + tokenizer=tokenizer, + ft_neighbours=args.ft_neighbours, + bert_retriever_neighbours=args.bert_retriever_neighbours, + longform_answer=args.longform_answer, + inference_only=False, + retrieved_neighbours=False, + fix_newsqa=True, + **extra_kwargs + ) + + print_rank_0('> building train, validation, and test datasets ' + 'for GPT ...') + train_ds, valid_ds, test_ds = BlendedMegatronDatasetBuilder( + dataset_cls, + train_val_test_num_samples, + is_dataset_built_on_rank, + config + ).build() + print_rank_0("> finished creating GPT datasets ...") + + return train_ds, valid_ds, test_ds + + +if __name__ == "__main__": + + # Temporary for transition to core datasets + train_valid_test_datasets_provider.is_distributed = True + + pretrain(train_valid_test_datasets_provider, model_provider, + ModelType.retro_decoder, # ModelType.encoder_or_decoder, + forward_step, + extra_args_provider=get_tasks_args + ) diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/sft_retro_lm.sh b/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/sft_retro_lm.sh new file mode 100644 index 0000000..8c13f10 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/sft/sft_retro_lm.sh @@ -0,0 +1,150 @@ +#!/bin/bash +# bash examples/qa/finetune_normal_lm.sh landrover_tasb_retrieved 843m 1 3e-6 1 + +blend_name=$1 +model_size=$2 +global_bsz=$3 +lr=$4 +ft_neighbours=1 +model_card=pp1 +ckpt=$5 +TASK=none + +train_iters=1000 + + +DATA_HOME="" +data_folder="$DATA_HOME" + +SFT_HOME="" + +TOKENIZER_MODEL="" + +RETRO_WORKDIR="" + +K=2 + +PRETRAINED_CHECKPOINT=${ckpt} + +SAVENAME="retro-${blend_name}_${model_card}_same_format_ctx${ft_neighbours}_${model_size}_${global_bsz}_${lr}" +CHECKPOINT_PATH="${SFT_HOME}/checkpoints/applications/${SAVENAME}" +TENSORBOARD_DIR="${SFT_HOME}/tensorboard/${SAVENAME}" +mkdir -p ${TENSORBOARD_DIR} + +. ./tools/retro/sft/"${blend_name}".sh + + +if [[ $model_size == "843m" ]]; then + # model param + mod_par=1 + layers=24 + hid_dim=1024 + heads=16 + pip_par=1 + + # node param + num_nodes=1 + lr=5e-6 + min_lr=5e-6 +fi + + +GPT_ARGS="--apply-layernorm-1p \ + --untie-embeddings-and-output-weights \ + --disable-bias-linear \ + --no-position-embedding \ + --use-rotary-position-embeddings \ + --rotary-percent 0.5 \ + --swiglu \ + --attention-dropout 0.0 \ + --hidden-dropout 0.0 \ + --pipeline-model-parallel-size $pip_par \ + --tensor-model-parallel-size $mod_par \ + --num-layers $layers \ + --hidden-size $hid_dim \ + --num-attention-heads $heads \ + --seq-length 4096 \ + --max-position-embeddings 4096 \ + --lr-decay-style cosine \ + --tokenizer-type GPTSentencePieceTokenizer \ + --tokenizer-model ${TOKENIZER_MODEL} \ + --clip-grad 1.0 \ + --weight-decay 0.01 \ + --adam-beta1 0.9 \ + --adam-beta2 0.98 \ + --log-params-norm \ + --log-num-zeros-in-grad \ + --bf16 \ + --use-distributed-optimizer \ +" + +FT_ARGS="--eod-mask-loss \ + --answer-loss-only \ + --ft_neighbours ${ft_neighbours} \ + --task $TASK" + + +OUTPUT_ARGS="--log-interval 10 \ + --save-interval 500 \ + --eval-interval 200 \ + --tensorboard-dir ${TENSORBOARD_DIR} \ + --log-validation-ppl-to-tensorboard \ + --eval-iters 100" + +options=" \ + $GPT_ARGS \ + --retro-workdir ${RETRO_WORKDIR} \ + --retro-add-retriever \ + --retro-num-neighbors ${K} \ + --retro-attention-gate 0 \ + --data-path ${DATA_BLEND} \ + --data-folder ${data_folder} \ + --recompute-activations \ + --lr $lr \ + --micro-batch-size 1 \ + --global-batch-size ${global_bsz} \ + --min-lr ${min_lr} \ + --retro-cyclic-train-iters ${train_iters} \ + --train-iters ${train_iters} \ + --dataloader-type cyclic \ + --save $CHECKPOINT_PATH \ + $OUTPUT_ARGS \ + $FT_ARGS" + +if [[ -d "$CHECKPOINT_PATH" ]]; then + options="$options \ + --load $CHECKPOINT_PATH " +else + echo $PRETRAINED_CHECKPOINT + options="$options \ + --load $PRETRAINED_CHECKPOINT \ + --finetune \ + --no-load-rng \ + --no-load-optim " +fi + +######## Command. ######## + +run_cmd="python -u ${SFT_HOME}/tools/retro/sft/sft_retro.py ${options}" + +export NCCL_DEBUG=INFO +export NCCL_IB_TIMEOUT=19 +export NCCL_IB_SL=1 +export CUDA_DEVICE_MAX_CONNECTIONS=1 + +NPROCS=8 +CMD="\ + pwd && cd ${SFT_HOME} && pwd && \ + export PYTHONPATH=$PYTHONPATH:${SFT_HOME} && \ + python -m torch.distributed.run \ + --nproc_per_node ${NPROCS} \ + --nnodes 1 \ + --node_rank 0 \ + --master_port 6000 \ + ${run_cmd} \ +" +echo "~~~~~~~~~~~~~~~~~~~~~~~~~~" +echo "CMD = '$CMD'." +echo "~~~~~~~~~~~~~~~~~~~~~~~~~~" +eval $CMD + diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/evaluate.py b/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/evaluate.py new file mode 100755 index 0000000..2031118 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/evaluate.py @@ -0,0 +1,200 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + + +import sys +import os +from tqdm import tqdm +import string +import json +import regex +import numpy as np + +sys.path.append(os.path.abspath(os.path.join( + os.path.join(os.path.dirname(__file__), "../../../")))) +from tools.retro.text_generation.metrics import F1Metric + + +def normalize_answer(s): + def remove_articles(text): + return regex.sub(r'\b(a|an|the)\b', ' ', text) + + def white_space_fix(text): + return ' '.join(text.split()) + + def remove_punc(text): + exclude = set(string.punctuation) + return ''.join(ch for ch in text if ch not in exclude) + + def lower(text): + return text.lower() + + return white_space_fix(remove_articles(remove_punc(lower(s)))) + + +def compute_f1_score(predicted_answers, groundtruth_answer, exp_name="default"): + """Evaluating F1 Score""" + print(len(predicted_answers), len(groundtruth_answer)) + if len(predicted_answers) != len(groundtruth_answer): + groundtruth_answer = groundtruth_answer[:len(predicted_answers)] + + guess_list = [] + answer_list = [] + + assert len(guess_list) == len(answer_list), \ + "lengths of guess and answer are different!" + + for pred, ans in zip(predicted_answers, groundtruth_answer): + pred = pred.strip() + if type(ans) == str: + ans = ans.strip() + elif type(ans) == dict: + ans = ans['text'].strip() + elif ans == None: + continue + if "<|endoftext|>" in pred: + pred = pred.replace("<|endoftext|>", "") + if ans == "no_passages_used": + ans = "" + guess_list.append(pred) + answer_list.append(ans) + + precision, recall, f1 = F1Metric.compute_all_pairs(guess_list, answer_list) + print('Method: %s; Precision: %.4f; recall: %.4f; f1: %.4f' % ( \ + exp_name, precision, recall, f1)) + + +def load_groundtruth_file(data_file): + with open(data_file, "r") as f: + nq_examples = json.load(f) + + data = [] + for instance in nq_examples: + if "answers" in instance: + answers = instance["answers"] + if len(answers) < 1: + answers = [None] + elif "answer" in instance: + if type(instance["answer"]) is str: + answers = [instance["answer"]] + elif type(instance["answer"]) is list: + answers = instance["answer"] + else: + answers = [str(instance["answer"])] + else: + raise ValueError("need to have answer or answers") + data.append(answers[0]) + + return data + + +def read_prediction(prediction_file): + prediction_list = [] + print('reading %s' % prediction_file) + with open(prediction_file, "r") as f: + for i, line in enumerate(tqdm(f)): + if prediction_file.endswith("jsonl"): + line = json.loads(line)["pred"] + # print(line) + line = line.replace("Answer:", "") + line = line.replace("Answer: ", "") + line = line.replace('???? ', "") + line = line.replace('A: ', "") + line = line.replace("A:", "") + + line = line.strip() + + if "<|endoftext|>" in line: + line = line.replace("<|endoftext|>", "") + line = normalize_answer(line) # normalize the answer + prediction_list.append(line) + + return prediction_list + + +def exact_match_score(prediction, ground_truth): + return normalize_answer(prediction) == normalize_answer(ground_truth) + + +def ems(prediction, ground_truths): + return max([exact_match_score(prediction, gt) for gt in ground_truths]) + + +def evaluate_ems(prediction_file, ground_truth_file, dev_num=3000): + prediction_list = read_prediction(prediction_file) + ground_truths_list = [] + + if ground_truth_file.endswith(('txt', 'lst')): + raw_data = open(ground_truth_file, 'r') + else: + with open(ground_truth_file, 'r') as f: + raw_data = json.load(f) + if "dev" in ground_truth_file: + raw_data = raw_data[:dev_num] + prediction_list = prediction_list[:dev_num] + + for each in raw_data: + if ground_truth_file.endswith('txt'): + each = json.loads(each) + + if 'answers' in each: + ground_truths_list.append(each['answers']) + elif 'answer' in each: + ground_truths_list.append(each['answer']) + else: + ground_truths_list.append([each]) + + exactmatch = [] + + good_example_list = [] + for i, each in enumerate(prediction_list): + score = ems(each, ground_truths_list[i]) + exactmatch.append(score) + if score: + good_example_list.append(i) + + final_em_score = np.mean(exactmatch) + + print('Exact Match: %.4f;' % final_em_score) + + print('done :-)') + + return final_em_score, exactmatch + + +def load_prediction(data_file): + data = [] + with open(data_file, "r") as f: + for line in f.readlines(): + data.append(line.strip()) + + return data + + +def evaluate_f1(ground_truth_file, prediction_file, reduced_test_only=False): + groundtruth_answer = load_groundtruth_file(ground_truth_file) + predicted_answers = load_prediction(prediction_file) + if not reduced_test_only: + compute_f1_score(predicted_answers, groundtruth_answer) + + +if __name__ == "__main__": + model_names = [] + model_names += "retro-open_inst_pp1_same_format_ctx1_843m_128_5e-6", + + for model_name in model_names: + ckpt_path = "/path/to/checkpoints/{}/".format(model_name) + + n_ctx = 5 + n_enc = 2 + iter = 1000 + model_param = "843m" + + prediction_file = ckpt_path + "/retro-generate-nq_{}_{}_{}_test_greedy_0_20000_{}.txt".format( + n_ctx, n_enc, model_param, iter) + ground_truth_file = "/path/to/NQ/test.json" + print(prediction_file) + print(ground_truth_file) + evaluate_f1(ground_truth_file, prediction_file) + evaluate_ems(prediction_file, ground_truth_file) + + print("=====================================") diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/metrics.py b/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/metrics.py new file mode 100755 index 0000000..bd0b5fe --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/metrics.py @@ -0,0 +1,80 @@ + +# The following code is adapted from +# https://github.com/facebookresearch/ParlAI/blob/master/parlai/core/metrics.py, +# which is licensed under the MIT license. More details on the license can be +# found at https://github.com/facebookresearch/ParlAI/blob/master/LICENSE. + +"""Provides standard metric evaluations for dialog.""" + +from collections import Counter +from typing import List +import numpy as np +import re +from nltk import ngrams + +re_art = re.compile(r'\b(a|an|the)\b') +re_punc = re.compile(r'[!"#$%&()*+,-./:;<=>?@\[\]\\^`{|}~_\']') + + +def normalize_answer(s): + """ + Lower text and remove punctuation, articles and extra whitespace. + """ + s = s.lower() + s = re_punc.sub(' ', s) + s = re_art.sub(' ', s) + s = ' '.join(s.split()) + return s + + +class F1Metric: + """ + Helper class which computes token-level F1. + """ + + @staticmethod + def _prec_recall_f1_score(pred_items, gold_items): + """ + Compute precision, recall and f1 given a set of gold and prediction items. + :param pred_items: iterable of predicted values + :param gold_items: iterable of gold values + :return: tuple (p, r, f1) for precision, recall, f1 + """ + common = Counter(gold_items) & Counter(pred_items) + num_same = sum(common.values()) + if num_same == 0: + return 0, 0, 0 + precision = 1.0 * num_same / len(pred_items) + recall = 1.0 * num_same / len(gold_items) + f1 = (2 * precision * recall) / (precision + recall) + return precision, recall, f1 + + @staticmethod + def compute_each_pair(guess: str, answer: str, n=1): + if answer == "": + return None, None, None + if guess == "": + return 0, 0, 0 + g_tokens = normalize_answer(guess).split() + a_tokens = normalize_answer(answer).split() + g_tokens = list(ngrams(g_tokens, n)) + a_tokens = list(ngrams(a_tokens, n)) + precision, recall, f1 = F1Metric._prec_recall_f1_score(g_tokens, a_tokens) + return precision, recall, f1 + + @staticmethod + def compute_all_pairs(guesses: List[str], answers: List[str], n=1): + # additional augment: + print("guess:", len(guesses), ", answers:", len(answers)) + assert len(guesses) == len(answers) + + precision_list, recall_list, f1_list = [], [], [] + for guess, answer in zip(guesses, answers): + precision, recall, f1 = F1Metric.compute_each_pair(guess, answer, n) + if precision is None or recall is None or f1 is None: + continue + precision_list.append(precision) + recall_list.append(recall) + f1_list.append(f1) + + return np.mean(precision_list), np.mean(recall_list), np.mean(f1_list) diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/retro_api.py b/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/retro_api.py new file mode 100644 index 0000000..b706774 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/retro_api.py @@ -0,0 +1,221 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + + +"""Inference API.""" +import numpy as np +import torch +from megatron.core import mpu +from megatron.training import print_rank_0, get_retro_args, get_args, get_tokenizer +from megatron.inference.text_generation.communication import broadcast_float_list, broadcast_tensor, broadcast_int_list +from megatron.inference.text_generation.generation import ( + score_and_return_on_first_stage) +from tools.retro.text_generation.retro_generation import ( + retro_generate_tokens_probs_and_return_on_first_stage) +from megatron.inference.text_generation.tokenization import ( + detokenize_generations) + + +def tokenize_prompts(prompts=None, tokens_to_generate=None, + add_BOS=None, rank=0): + """Tokenize prompts and make them avaiable on all ranks.""" + + # On all ranks set to None so we can pass them to functions + sizes_list = None + prompts_tokens_cuda_long_tensor = None + prompts_length_cuda_long_tensor = None + + # On the specified rank, build the above. + if torch.distributed.get_rank() == rank: + assert prompts is not None + assert tokens_to_generate is not None + # Tensor of tokens padded and their unpadded length. + prompts_tokens_cuda_long_tensor, prompts_length_cuda_long_tensor = \ + _tokenize_prompts_and_batch(prompts, tokens_to_generate, add_BOS) + # We need the sizes of these tensors for the boradcast + sizes_list = [prompts_tokens_cuda_long_tensor.size(0), # Batch size + prompts_tokens_cuda_long_tensor.size(1)] # Sequence lenght + + # First, broadcast the sizes. + sizes_tensor = broadcast_int_list(2, int_list=sizes_list, rank=rank) + + # Now that we have the sizes, we can boradcast the tokens + # and length tensors. + sizes = sizes_tensor.tolist() + prompts_tokens_cuda_long_tensor = broadcast_tensor( + sizes, torch.int64, tensor=prompts_tokens_cuda_long_tensor, rank=rank) + prompts_length_cuda_long_tensor = broadcast_tensor( + sizes[0], torch.int64, tensor=prompts_length_cuda_long_tensor, + rank=rank) + + return prompts_tokens_cuda_long_tensor, prompts_length_cuda_long_tensor + + +def _tokenize_prompts_and_batch(prompts, tokens_to_generate, add_BOS): + """Given a set of prompts and number of tokens to generate: + - tokenize prompts + - set the sequence length to be the max of length of prompts + plus the number of tokens we would like to generate + - pad all the sequences to this length so we can convert them + into a 2D tensor. + """ + + # Tokenize all the prompts. + tokenizer = get_tokenizer() + if add_BOS: + prompts_tokens = [[tokenizer.eod] + tokenizer.tokenize(prompt) + for prompt in prompts] + else: + prompts_tokens = [tokenizer.tokenize(prompt) for prompt in prompts] + + # Now we have a list of list of tokens which each list has a different + # size. We want to extend this list to: + # - incorporate the tokens that need to be generated + # - make all the sequences equal length. + # Get the prompts length. + prompts_length = [len(prompt_tokens) for prompt_tokens in prompts_tokens] + # Get the max prompts length. + max_prompt_len = max(prompts_length) + # Set the tokens to generate to the max prompts length for Retro + args = get_args() + if args.retro_add_retriever: + tokens_to_generate = max_prompt_len + # Number of tokens in the each sample of the batch. + samples_length = max_prompt_len + tokens_to_generate + # Now update the list of list to be of the same size: samples_length. + for prompt_tokens, prompt_length in zip(prompts_tokens, prompts_length): + padding_size = samples_length - prompt_length + prompt_tokens.extend([tokenizer.eod] * padding_size) + + # Now we are in a structured format, we can convert to tensors. + prompts_tokens_tensor = torch.cuda.LongTensor(prompts_tokens) + prompts_length_tensor = torch.cuda.LongTensor(prompts_length) + + return prompts_tokens_tensor, prompts_length_tensor + + +def retro_generate_and_post_process(model, + prompts=None, + neighbours_array=None, + tokens_to_generate=0, + return_output_log_probs=False, + top_k_sampling=0, + top_p_sampling=0.0, + temperature=1.0, + add_BOS=False, + use_eod_token_for_early_termination=True, + random_seed=-1, + logits_mask=None): + """Run inference and post-process outputs, i.e., detokenize, + move to cpu and convert to list.""" + + # Main inference. + tokens, lengths, output_log_probs = retro_generate( + model, + prompts=prompts, + neighbours_array=neighbours_array, + tokens_to_generate=tokens_to_generate, + return_output_log_probs=return_output_log_probs, + top_k_sampling=top_k_sampling, + top_p_sampling=top_p_sampling, + temperature=temperature, + add_BOS=add_BOS, + use_eod_token_for_early_termination=use_eod_token_for_early_termination, + random_seed=random_seed, + logits_mask=logits_mask) + + # Only post-process on first stage. + if mpu.is_pipeline_first_stage(): + tokens, prompts_plus_generations, prompts_plus_generations_segments = \ + detokenize_generations(tokens, lengths, True) + + if return_output_log_probs: + output_log_probs = output_log_probs.cpu().numpy().tolist() + for i, (prob, seg) in enumerate(zip(output_log_probs, prompts_plus_generations_segments)): + output_log_probs[i] = prob[:len(seg) - 1] + + return prompts_plus_generations, prompts_plus_generations_segments, \ + output_log_probs, tokens + + return None + + +def retro_generate(model, + prompts=None, + neighbours_array=None, + tokens_to_generate=0, + return_output_log_probs=False, + top_k_sampling=0, + top_p_sampling=0.0, + temperature=1.0, + add_BOS=False, + use_eod_token_for_early_termination=True, + stop_on_double_eol=False, + stop_on_eol=False, + random_seed=-1, + logits_mask=None): + """Given prompts and input parameters, run inference and return: + tokens: prompts plus the generated tokens. + lengths: length of the prompt + generations. Note that we can + discard tokens in the tokens tensor that are after the + corresponding length. + output_log_probs: log probs of the tokens. + """ + + # Make sure input params are avaialble to all ranks. + values = [tokens_to_generate, + return_output_log_probs, + top_k_sampling, top_p_sampling, + temperature, add_BOS, use_eod_token_for_early_termination, + stop_on_double_eol, + stop_on_eol, + random_seed] + values_float_tensor = broadcast_float_list(10, float_list=values) + tokens_to_generate = int(values_float_tensor[0].item()) + return_output_log_probs = bool(values_float_tensor[1].item()) + top_k_sampling = int(values_float_tensor[2].item()) + top_p_sampling = values_float_tensor[3].item() + temperature = values_float_tensor[4].item() + add_BOS = bool(values_float_tensor[5].item()) + use_eod_token_for_early_termination = bool(values_float_tensor[6].item()) + stop_on_double_eol = bool(values_float_tensor[7].item()) + stop_on_eol = bool(values_float_tensor[8].item()) + random_seed = int(values_float_tensor[9].item()) + + if random_seed != -1: + torch.random.manual_seed(random_seed) + + # Tokenize prompts and get the batch. + # Note that these tensors are broadcaseted to all ranks. + if torch.distributed.get_rank() == 0: + assert prompts is not None + + context_tokens_tensor, context_length_tensor = tokenize_prompts( + prompts=prompts, tokens_to_generate=tokens_to_generate, add_BOS=add_BOS) + + retro_args = get_retro_args() + retro_args.retro_gpt_chunk_length = context_length_tensor.item() + + retro_args = get_retro_args() + args = get_args() + r = retro_args.retro_gpt_retrieved_length + l = int(np.ceil(min(args.max_position_embeddings, context_tokens_tensor.size(1)) / retro_args.retro_gpt_chunk_length)) + if torch.distributed.get_rank() == 0: + neighbours_array = neighbours_array.reshape(1, args.retro_num_neighbors, r).repeat(l, axis=0) ## dim (l, k, r) + + if tokens_to_generate == 0: + return score_and_return_on_first_stage( + model, context_tokens_tensor, context_length_tensor) + + # Main inference function. + # Note that the outputs are available on the first stage. + return retro_generate_tokens_probs_and_return_on_first_stage( + model, context_tokens_tensor, context_length_tensor, + neighbours_array=neighbours_array, + return_output_log_probs=return_output_log_probs, + top_k=top_k_sampling, + top_p=top_p_sampling, + temperature=temperature, + use_eod_token_for_early_termination=use_eod_token_for_early_termination, + stop_on_double_eol=stop_on_double_eol, + stop_on_eol=stop_on_eol, + logits_mask=logits_mask) \ No newline at end of file diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/retro_generate.sh b/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/retro_generate.sh new file mode 100755 index 0000000..53f7d76 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/retro_generate.sh @@ -0,0 +1,125 @@ +#!/bin/bash + +TASK=$1 +model_size=$2 +sampling=$3 +split=$4 +gen_start=$5 +num_gen=$6 +ckpt_step=${7} +ft_neighbours=${8} +model_card=${9} +ckpt=${10} +K=${11} +retrieve=${12} + +QA_HOME="" + +TOKENIZER_MODEL="" + +RETRO_WORKDIR="" + + +if [[ $model_size == "843m" ]]; then + mod_par=1 + layers=24 + hid_dim=1024 + heads=16 + pip_par=1 +fi + +GPT_ARGS="--apply-layernorm-1p \ + --untie-embeddings-and-output-weights \ + --disable-bias-linear \ + --no-position-embedding \ + --use-rotary-position-embeddings \ + --rotary-percent 0.5 \ + --swiglu \ + --attention-dropout 0.0 \ + --hidden-dropout 0.0 \ + --pipeline-model-parallel-size $pip_par \ + --tensor-model-parallel-size $mod_par \ + --num-layers $layers \ + --hidden-size $hid_dim \ + --num-attention-heads $heads \ + --seq-length 4096 \ + --max-position-embeddings 4096 \ + --lr-decay-style cosine \ + --tokenizer-type GPTSentencePieceTokenizer \ + --tokenizer-model ${TOKENIZER_MODEL} \ + --clip-grad 1.0 \ + --weight-decay 0.01 \ + --adam-beta1 0.9 \ + --adam-beta2 0.98 \ + --log-params-norm \ + --log-num-zeros-in-grad \ + --bf16 \ +" + + +sample_input_file="/path/to/instruct_tuning/data/$TASK/${split}.json" + +top_k=1 +micro_bsz=1 +SAMPLE_ARGS="--top_k $top_k" + +CHECKPOINT_PATH=${ckpt} +sample_output_file="${CHECKPOINT_PATH}/retro-generate-${TASK}_${ft_neighbours}_${K}_${model_size}_${split}_${sampling}_${gen_start}_${num_gen}_${ckpt_step}.txt" + +DIR=`pwd` + +echo $sample_input_file +echo $sample_output_file + + +GEN_ARGS="$SAMPLE_ARGS \ + --gen-start-idx $gen_start \ + --num-gen $num_gen \ + --ckpt-step ${ckpt_step} \ + --sample-input-file $sample_input_file \ + --sample-output-file $sample_output_file \ + --retro-workdir ${RETRO_WORKDIR} \ + --retro-add-retriever \ + --retro-num-neighbors ${K} \ + --reuse-top \ + --retro-attention-gate 0 \ + " + +if [[ $retrieve == 1 ]]; then + GEN_ARGS="$GEN_ARGS \ + --use-retrieved-neighbours \ + " +fi + +FT_ARGS="--eod-mask-loss \ + --answer-loss-only \ + --ft_neighbours ${ft_neighbours} \ + --task $TASK" + +DISTRIBUTED_ARGS="--nproc_per_node ${mod_par} \ + --nnodes ${pip_par} \ + --node_rank 0 \ + --master_port 8889" + +######## Command. ######## + +COMMAND="python -m torch.distributed.run $DISTRIBUTED_ARGS ${DIR}/tools/retro/text_generation/retro_text_generation.py" + +COMMAND="$COMMAND \ + $GPT_ARGS \ + $GEN_ARGS \ + --load $CHECKPOINT_PATH \ + --micro-batch-size $micro_bsz \ + $FT_ARGS" + +export NCCL_DEBUG=INFO +export NCCL_IB_TIMEOUT=19 +export NCCL_IB_SL=1 +export CUDA_DEVICE_MAX_CONNECTIONS=1 + + +echo "~~~~~~~~~~~~~~~~~~~~~~~~~~" +echo "CMD = '$CMD'." +echo "~~~~~~~~~~~~~~~~~~~~~~~~~~" +eval $COMMAND + diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/retro_generation.py b/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/retro_generation.py new file mode 100644 index 0000000..f69103d --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/retro_generation.py @@ -0,0 +1,250 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + + +"""Generation utilities.""" +import torch +import torch.nn.functional as F +from megatron.training import get_args, get_tokenizer +from megatron.training import get_retro_args +from megatron.core import mpu +from megatron.training.utils import get_ltor_masks_and_position_ids, unwrap_model +from megatron.inference.text_generation.communication import ( + copy_from_last_to_first_pipeline_stage, + broadcast_from_last_pipeline_stage, + broadcast_from_last_to_first_pipeline_stage, broadcast_int_list, broadcast_tensor) +from megatron.inference.text_generation.generation import _build_attention_mask_and_position_ids +from megatron.inference.text_generation.sampling import sample + + + +def retro_generate_tokens_probs_and_return_on_first_stage( + model, tokens, lengths, neighbours_array=None, + return_output_log_probs=False, + top_k=0, top_p=0.0, + temperature=1.0, + use_eod_token_for_early_termination=True, + stop_on_double_eol=False, + stop_on_eol=False, + logits_mask=None): + """Main token generation function. + + Args: + model: no interleaving is supported. + tokens: prompt tokens extended to be of size [b, max-sequence-length] + lengths: original prompt length, size: [b] + neighbours_array: neighbours array of size [b, l, k, r] + return_output_log_probs: flag to calculate the log probability of + the generated tokens. Note that the log probability is the one + from the original logit. + top_k, top_p: top-k and top-p sampling parameters. + Note that top-k = 1 is gready. Also, these paramters are + exclusive meaning that: + if top-k > 0 then we expect top-p=0. + if top-p > 0 then we check for top-k=0. + temperature: sampling temperature. + use_eod_token_for_early_termination: if True, do early termination if + all the sequences have reached this token. + Note: Outside of model, other parameters only need to be available on + rank 0. + + Returns: Note that is size is adjusted to a lower value than + max-sequence-length if generation is terminated early. + tokens: prompt and generated tokens. size: [b, :] + generated_sequence_lengths: total length (including prompt) of + the generated sequence. size: [b] + output_log_probs: log probability of the selected tokens. size: [b, s] + """ + + args = get_args() + retro_args = get_retro_args() + + tokenizer = get_tokenizer() + + batch_size = tokens.size(0) + min_prompt_length = lengths.min().item() + max_sequence_length = tokens.size(1) + print("max_sequence_length", max_sequence_length) + print("min_prompt_length", min_prompt_length) + max_sequence_length = min(max_sequence_length, args.max_position_embeddings) + + # If the context is too big, this happens + if min_prompt_length >= max_sequence_length: + raise ValueError("context length + tokens_to_generate too large") + + # forward step. + unwrapped_model = unwrap_model( + model) + unwrapped_model.language_model.seq_length = max_sequence_length + + # Added termination_id to support the case that we want to terminate the + # generation once that id is generated. + if hasattr(args, 'eos_id'): + termination_id = args.eos_id + else: + termination_id = tokenizer.eod + + # =================== + # Pre-allocate memory + # =================== + + # Log probability of the sequence (prompt + generated tokens). + output_log_probs = None + output_log_probs_size = (batch_size, max_sequence_length - 1) + # Lengths of generated seuquence including including prompts. + generated_sequence_lengths = None + if mpu.is_pipeline_last_stage(): + if return_output_log_probs: + output_log_probs = torch.empty(output_log_probs_size, + dtype=torch.float32, + device=torch.cuda.current_device()) + generated_sequence_lengths = torch.ones( + batch_size, dtype=torch.int64, + device=torch.cuda.current_device()) * max_sequence_length + + # Whether we have reached a termination id. + is_generation_done = torch.zeros(batch_size, dtype=torch.uint8, + device=torch.cuda.current_device()) + + # ============= + # Run infernece + # ============= + + with torch.no_grad(): + attention_mask, position_ids = _build_attention_mask_and_position_ids( + tokens) + for context_length in range(min_prompt_length, max_sequence_length): + prev_context_length = 0 + sizes_list = None + neighbor_tokens_cuda_long_tensor = None + + # get the chunks for retrieval + if torch.distributed.get_rank() == 0: + neighbor_tokens = neighbours_array + neighbor_tokens_cuda_long_tensor = torch.cuda.LongTensor( + neighbor_tokens.reshape((-1, retro_args.retro_gpt_retrieved_length))) + sizes_list = [neighbor_tokens_cuda_long_tensor.size(0), # Batch size + neighbor_tokens_cuda_long_tensor.size(1)] # Sequence lenght + sizes_tensor = broadcast_int_list(2, int_list=sizes_list) + sizes = sizes_tensor.tolist() + neighbor_tokens_cuda_long_tensor = broadcast_tensor( + sizes, torch.int64, tensor=neighbor_tokens_cuda_long_tensor) + + _, _, neighbor_position_ids = get_ltor_masks_and_position_ids( + neighbor_tokens_cuda_long_tensor, + tokenizer.eod, + args.reset_position_ids, + args.reset_attention_mask, + args.eod_mask_loss) + neighbor_attention_mask = None + + # Pick the slice that we need to pass through the network. + tokens2use = tokens[:, prev_context_length:4096] + positions2use = position_ids[:, prev_context_length:4096] + attention_mask2use = attention_mask[ + ..., prev_context_length:4096, :4096] + + logits = model(tokens2use, positions2use, attention_mask2use, + retriever_input_ids=neighbor_tokens_cuda_long_tensor, + retriever_position_ids=neighbor_position_ids, retriever_attn_mask=neighbor_attention_mask, + ) + + if mpu.is_pipeline_last_stage(): + # Always the last stage should have an output. + assert logits is not None + + # Sample. + last_token_logits = logits[:, context_length - 1, :] + # last_token_logits = logits[:, -1, :] + + # word banning + if logits_mask is not None: + last_token_logits[:, logits_mask] = float('-Inf') + + new_sample = sample(last_token_logits, + top_k=top_k, + top_p=top_p, + temperature=temperature, + vocab_size=tokenizer.vocab_size) + + # If a prompt length is smaller or equal th current context + # length, it means we have started generating tokens + started = lengths <= context_length + # Update the tokens. + tokens[started, context_length] = new_sample[started] + + # Calculate the log probabilities. + if return_output_log_probs: + log_probs = F.log_softmax(logits, dim=2) + if return_output_log_probs: + # Pick the tokens that we need to get the log + # probabilities for. Note that next input token is + # the token which we selected in the current logits, + # so shift by 1. + indices = torch.unsqueeze( + tokens[ + :, + (prev_context_length + 1):(context_length + 1)], + 2) + output_log_probs[:, + prev_context_length:context_length] = \ + torch.gather(log_probs, 2, indices).squeeze(2) + + # Update the tokens on the first stage so the next input to + # the network is correct. + copy_from_last_to_first_pipeline_stage(batch_size, torch.int64, + tokens[:, context_length]) + + # Update the context length for the next token generation. + prev_context_length = context_length + + # Check if all the sequences have hit the termination_id. + done = None + if mpu.is_pipeline_last_stage(): + # TODO(rprenger) These stopping methods are tokenizer dependent + # instead tokenization should be in the inference loop so stop sequences can be used + if stop_on_double_eol: + hit_double_eol = (new_sample == 628).byte() & started.byte() + hit_two_eols = (new_sample == 198).byte() & ( + tokens[:, context_length - 1] == 198).byte() & started.byte() + done_token = hit_double_eol | hit_two_eols + elif stop_on_eol: + hit_double_eol = (new_sample == 628).byte() & started.byte() + hit_eol = (new_sample == 198).byte() & started.byte() + done_token = hit_double_eol | hit_eol + elif context_length > min_prompt_length + 64: # previous retrov1 limitations + done_token = 1 + else: + done_token = (new_sample == termination_id).byte() & \ + started.byte() + + just_finished = (done_token & ~is_generation_done).bool() + generated_sequence_lengths[just_finished.view(-1)] = \ + context_length + 1 + is_generation_done = is_generation_done | done_token + done = torch.all(is_generation_done) + done = broadcast_from_last_pipeline_stage(1, torch.uint8, + tensor=done) + if use_eod_token_for_early_termination and done: + break + + # =================================================== + # Update the length of based on max generated length. + # =================================================== + + tokens = tokens[:, :(context_length + 1)] + if mpu.is_pipeline_last_stage(): + if return_output_log_probs: + output_log_probs = output_log_probs[:, :context_length] + + # ====================================== + # Broadcast to the first pipeline stage. + # ====================================== + + generated_sequence_lengths = broadcast_from_last_to_first_pipeline_stage( + batch_size, torch.int64, generated_sequence_lengths) + if return_output_log_probs: + output_log_probs_size = (batch_size, context_length) + output_log_probs = broadcast_from_last_to_first_pipeline_stage( + output_log_probs_size, torch.float32, output_log_probs) + + return tokens, generated_sequence_lengths, output_log_probs diff --git a/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/retro_text_generation.py b/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/retro_text_generation.py new file mode 100755 index 0000000..c1cdcaf --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/retro/text_generation/retro_text_generation.py @@ -0,0 +1,262 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +"""Sample Generate GPT""" +import torch +import os +import sys +from typing import Union + +sys.path.append(os.path.abspath(os.path.join( + os.path.join(os.path.dirname(__file__), "../../../")))) +from megatron.training import get_args, get_retro_args +from megatron.training import print_rank_0 +from megatron.training import get_tokenizer +from megatron.training.checkpointing import load_checkpoint +from megatron.training.initialize import initialize_megatron +from megatron.core.models.gpt import GPTModel +from megatron.training import get_model +from tools.retro.text_generation.retro_api import retro_generate_and_post_process +from tools.retro.sft.sft_retro import get_tasks_args +from tools.retro.sft.dataset_conv import reformat_prompt, preprocess, reformat_prompt_short +import numpy as np +import time +import megatron.legacy.model +from megatron.training.arguments import core_transformer_config_from_args + + + +def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megatron.legacy.model.GPTModel]: + """Builds the model. + + If you set the use_mcore_models to True, it will return the mcore GPT model and if not the legacy GPT model. + + Args: + pre_process (bool, optional): Set to true if you need to compute embedings. Defaults to True. + post_process (bool, optional): Set to true if you need to want to compute output logits/loss. Defaults to True. + + + Returns: + Union[GPTModel, megatron.legacy.model.GPTModel]: The returned model + """ + print_rank_0('building GPT model ...') + config = core_transformer_config_from_args(get_args()) + + # not support core model yet + model = megatron.legacy.model.GPTModel( + config, + num_tokentypes=0, + parallel_output=False, + pre_process=pre_process, + post_process=post_process + ) + + return model + + +def pad_neighbours_for_query_only(args, nb_tokens, pad_id, ft_neighbours): + # take top k neighbours and padding + neighbours_tokens = [] + retro_args = get_retro_args() + r = retro_args.retro_gpt_retrieved_length + + if args.reuse_top: + valid_nb_tokens = nb_tokens[:args.retro_num_neighbors] + else: + valid_nb_tokens = nb_tokens[ft_neighbours:args.retro_num_neighbors + ft_neighbours] + + for nb_token in valid_nb_tokens: + if len(nb_token) >= r: + nb_token = nb_token[:r] + else: + nb_token = nb_token + [pad_id] * (r - len(nb_token)) + neighbours_tokens.append(nb_token) + print("len(nb_tokens)", len(nb_tokens)) + print("len(neighbours_tokens)", len(neighbours_tokens)) + print("args.retro_num_neighbors", args.retro_num_neighbors) + + if len(neighbours_tokens) < args.retro_num_neighbors: + assert ValueError("neighbours are not enough, add empty ones and create mask for those empty ones") + neighbours_tokens = np.array(neighbours_tokens) + return neighbours_tokens + + +def add_text_generate_args(parser): + """Text generation arguments.""" + + parser = get_tasks_args(parser) + group = parser.add_argument_group(title='text generation') + + group.add_argument("--temperature", type=float, default=1.0, + help='Sampling temperature.') + group.add_argument("--greedy", action='store_true', default=False, + help='Use greedy sampling.') + group.add_argument("--top_p", type=float, default=0.0, + help='Top p sampling.') + group.add_argument("--top_k", type=int, default=0, + help='Top k sampling.') + group.add_argument("--out-seq-length", type=int, default=256, + help='Size of the output generated text.') + group.add_argument("--sample-input-file", type=str, default=None, + help='Get input from file instead of interactive mode, ' + 'each line is an input.') + group.add_argument("--sample-output-file", type=str, default=None, + help='Output file got from --sample-input-file') + group.add_argument("--num-samples", type=int, default=0, + help='Number of samples to generate unconditionally, ' + 'defaults to 0 and interactive conditional sampling') + group.add_argument("--genfile", type=str, + help='Output file when generating unconditionally') + group.add_argument("--recompute", action='store_true', + help='During generation recompute all attention ' + 'instead of using previously computed keys/values.') + group.add_argument("--epsilon", type=float, default=0.01, + help="Minimum factor by which each probability is multiplied") + group.add_argument("--debug-gen", action='store_true', + help="If set, additional debugging output is printed to stdout") + group.add_argument('--length-penalty', type=float, default=1.0, + help='length penalty') + group.add_argument('--gen-start-idx', type=int, default=0, + help='project size for adapters') + group.add_argument('--num-gen', type=int, default=-1, + help='project size for adapters') + group.add_argument('--ckpt-step', type=int, default=None, + help='setting ckpt step manually') + group.add_argument("--short-format", action='store_true', + help='Use short format QA') + group.add_argument("--use-retrieved-neighbours", action='store_true', default=False, + help='Use retrieved neighbours') + group.add_argument('--template-id', type=int, default=0, + help='template id for generation,') + return parser + + +def generate_samples_conditional(model): + args = get_args() + start = time.time() + avg_time = [] + tokenizer = get_tokenizer() + model.eval() + if torch.distributed.get_rank() == 0: + + data = preprocess(args.sample_input_file, inference_only=True, + retrieved_neighbours=args.use_retrieved_neighbours) + print("total rows {}".format(len(data))) + all_data = data[args.gen_start_idx:] # start from gen_start_idx + if args.num_gen > 0: + all_data = all_data[:args.num_gen] + input_count = len(all_data) + input_pos = 0 + + terminate_runs = 0 + while True: + torch.distributed.barrier() + if torch.distributed.get_rank() == 0: + sentences = [] + n_arrays = [] + print("global batch size", args.global_batch_size) + for _ in range(args.global_batch_size): + print(input_pos) + if input_pos >= input_count: + print("reach the last row") + break + else: + sample = all_data[input_pos] + input_pos += 1 + + if True: + max_target_len = args.out_seq_length + query, _, neighbours = sample + + neighbours_array = pad_neighbours_for_query_only(args, + [tokenizer.tokenize(neighbour) for neighbour in + neighbours], tokenizer.eod, args.ft_neighbours) + print("neighbours_array.shape", neighbours_array.shape) + tokenizer = get_tokenizer() + + if args.short_format: + input_tokens = reformat_prompt_short(query, neighbours, args.task, args.ft_neighbours, + max_target_len, + tokenizer, args.seq_length) + else: + input_tokens = reformat_prompt(query, neighbours, args.task, args.ft_neighbours, max_target_len, + tokenizer, args.seq_length, template_id=args.template_id) + raw_text = tokenizer.detokenize(input_tokens) + print(raw_text) + else: + raise ValueError("invalid arg for task") + sentences.append(raw_text) + retro_args = get_retro_args() + + resp_sentences, resp_sentences_seg, scores, \ + tokens = retro_generate_and_post_process(model, prompts=sentences, + neighbours_array=neighbours_array, + tokens_to_generate=args.seq_length - retro_args.retro_gpt_chunk_length, + return_output_log_probs=False, + top_k_sampling=args.top_k, + top_p_sampling=args.top_p, + add_BOS=False, + temperature=1.0) + print("len of resp_sentences", len(resp_sentences)) + for prompt, generation in zip(sentences, resp_sentences): + datum = generation[len(prompt):] + print("prompt:", generation[:len(prompt)]) + if "<|endoftext|>" in datum: + datum = datum[:datum.find("<|endoftext|>")].strip() + datum = datum.replace("\n", " ") + print("cont:", datum) + yield datum + avg_time.append((time.time() - start) / args.global_batch_size) + print("avg time for each sample: ", sum(avg_time) / len(avg_time)) + start = time.time() + if input_pos >= input_count: + print("finish all lines") + terminate_runs = 1 + else: + retro_generate_and_post_process(model) + + terminate_runs_tensor = torch.cuda.LongTensor([terminate_runs]) + torch.distributed.broadcast(terminate_runs_tensor, 0) + terminate_runs = terminate_runs_tensor[0].item() + + if terminate_runs == 1: + return + + +def generate_and_write_samples_conditional(model): + args = get_args() + if args.sample_output_file is None: + sample_output_file = args.sample_input_file + ".out" + print('`sample-output-file` not specified, setting ' + 'it to {}'.format(sample_output_file)) + else: + sample_output_file = args.sample_output_file + with open(sample_output_file, 'w') as f: + for datum in generate_samples_conditional(model): + if torch.distributed.get_rank() == 0: + f.write(datum + '\n') + + +def main(): + """Main program.""" + + initialize_megatron(extra_args_provider=add_text_generate_args, + args_defaults={'no_load_rng': True, + 'no_load_optim': True}) + + # Set up model and load checkpoint + model = get_model(model_provider, wrap_with_ddp=False) + print(model) + args = get_args() + + if args.load is not None: + _ = load_checkpoint(model, None, None) + model = model[0] + + # Generate samples. + if args.sample_input_file is not None: + print(f"{args.sample_input_file}") + generate_and_write_samples_conditional(model) + + +if __name__ == "__main__": + main() diff --git a/Megatron-LM-core_r0.7.0.beta/tools/run_text_generation_server.py b/Megatron-LM-core_r0.7.0.beta/tools/run_text_generation_server.py new file mode 100644 index 0000000..5c98bb3 --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/run_text_generation_server.py @@ -0,0 +1,66 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Sample Generate GPT""" +import os +import sys +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), + os.path.pardir))) +from megatron.training import get_args +from megatron.training import print_rank_0 +from megatron.core import mpu +from megatron.training.checkpointing import load_checkpoint +from megatron.training.initialize import initialize_megatron +from megatron.training import get_model +from megatron.inference.text_generation_server import MegatronServer +from megatron.inference.text_generation import generate_and_post_process +from megatron.inference.text_generation import beam_search_and_post_process +from pretrain_gpt import model_provider + +import torch + + +def add_text_generate_args(parser): + group = parser.add_argument_group(title='text generation') + group.add_argument("--port", type=int, default=5000, + help='port for text generation server to run on') + return parser + + +if __name__ == "__main__": + initialize_megatron(extra_args_provider=add_text_generate_args, + args_defaults={'tokenizer_type': 'GPT2BPETokenizer', + 'no_load_rng': True, + 'no_load_optim': True}) + + args = get_args() + if args.num_layers_per_virtual_pipeline_stage is not None: + print("Interleaved pipeline schedule is not yet supported for text generation.") + exit() + print_rank_0("WARNING: Forcing exit_on_missing_checkpoint to True for text " + "generation.") + args.exit_on_missing_checkpoint = True + # Set up model and load checkpoint + model = get_model(model_provider, wrap_with_ddp=False) + + if args.load is not None: + _ = load_checkpoint(model, None, None) + + assert len(model) == 1, "Above condition should have caught this" + model = model[0] + if mpu.is_pipeline_first_stage() and mpu.get_tensor_model_parallel_rank() == 0: + server = MegatronServer(model) + server.run("0.0.0.0",port=args.port) + + while True: + choice = torch.tensor(1, dtype=torch.long, device='cuda') + torch.distributed.broadcast(choice, 0) + if choice.item() == 0: + try: + generate_and_post_process(model) + except ValueError as ve: + pass + elif choice.item() == 1: + try: + beam_search_and_post_process(model) + except ValueError as ve: + pass diff --git a/Megatron-LM-core_r0.7.0.beta/tools/text_generation_cli.py b/Megatron-LM-core_r0.7.0.beta/tools/text_generation_cli.py new file mode 100644 index 0000000..223928c --- /dev/null +++ b/Megatron-LM-core_r0.7.0.beta/tools/text_generation_cli.py @@ -0,0 +1,23 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +import sys +import json +import requests + + +if __name__ == "__main__": + url = sys.argv[1] + url = 'http://' + url + '/api' + headers = {'Content-Type': 'application/json'} + + while True: + sentence = input("Enter prompt: ") + tokens_to_generate = int(eval(input("Enter number of tokens to generate: "))) + + data = {"prompts": [sentence], "tokens_to_generate": tokens_to_generate} + response = requests.put(url, data=json.dumps(data), headers=headers) + + if response.status_code != 200: + print(f"Error {response.status_code}: {response.json()['message']}") + else: + print("Megatron Response: ") + print(response.json()['text'][0]) diff --git a/NeMo-2.0.0.rc0.beta/Dockerfile b/NeMo-2.0.0.rc0.beta/Dockerfile new file mode 100644 index 0000000..970c34a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/Dockerfile @@ -0,0 +1,180 @@ +# syntax=docker/dockerfile:experimental + +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +ARG BASE_IMAGE=nvcr.io/nvidia/pytorch:24.01-py3 + +# build an image that includes only the nemo dependencies, ensures that dependencies +# are included first for optimal caching, and useful for building a development +# image (by specifying build target as `nemo-deps`) +FROM ${BASE_IMAGE} as nemo-deps + +# dependency flags; should be declared after FROM +# torchaudio: not required by default +ARG REQUIRE_TORCHAUDIO=false +# k2: not required by default +ARG REQUIRE_K2=false +# ais cli: not required by default, install only if required +ARG REQUIRE_AIS_CLI=false + +# Ensure apt-get won't prompt for selecting options +ENV DEBIAN_FRONTEND=noninteractive +# libavdevice-dev required for latest torchaudio +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install -y \ + libsndfile1 sox \ + libfreetype6 \ + swig \ + ffmpeg \ + libavdevice-dev && \ + rm -rf /var/lib/apt/lists/* + +# libtool, ... , libgts-dev are required for graphviz +# graphviz is required for k2 and pynini visualization +RUN apt-get update && \ + apt-get install -y \ + libtool \ + libltdl-dev \ + automake \ + autoconf \ + bison \ + flex \ + tcl \ + ghostscript \ + libgd-dev \ + fontconfig \ + libcairo2-dev \ + libpango1.0-dev \ + libgts-dev && \ + rm -rf /var/lib/apt/lists/* + +WORKDIR /workspace/ +# Install megatron core, this can be removed once 0.3 pip package is released +# We leave it here in case we need to work off of a specific commit in main +RUN git clone https://github.com/NVIDIA/Megatron-LM.git && \ + cd Megatron-LM && \ + git checkout 36e9b6bf3d8034b10c9bbd9fc357c2df2bd1515c && \ + pip install . + +# Performance optimizations for distributed optimizer: https://github.com/NVIDIA/apex/pull/1771 +RUN git clone https://github.com/NVIDIA/apex.git && \ + cd apex && \ + git checkout f058162b215791b15507bb542f22ccfde49c872d && \ + pip install -v --no-build-isolation --disable-pip-version-check --no-cache-dir --config-settings "--build-option=--cpp_ext --cuda_ext --fast_layer_norm --distributed_adam --deprecated_fused_adam" ./ + +# Transformer Engine 1.2.0 +RUN git clone https://github.com/NVIDIA/TransformerEngine.git && \ + cd TransformerEngine && \ + git fetch origin da30634a6c9ccdbb6c587b6c93b1860e4b038204 && \ + git checkout FETCH_HEAD && \ + git submodule init && git submodule update && \ + NVTE_FRAMEWORK=pytorch NVTE_WITH_USERBUFFERS=1 MPI_HOME=/usr/local/mpi pip install . + +WORKDIR /tmp/ + +# uninstall stuff from base container +RUN pip3 uninstall -y sacrebleu torchtext + +# build torchaudio +WORKDIR /tmp/torchaudio_build +COPY scripts/installers /tmp/torchaudio_build/scripts/installers/ +RUN INSTALL_MSG=$(/bin/bash /tmp/torchaudio_build/scripts/installers/install_torchaudio_latest.sh); INSTALL_CODE=$?; \ + echo ${INSTALL_MSG}; \ + if [ ${INSTALL_CODE} -ne 0 ]; then \ + echo "torchaudio installation failed"; \ + if [ "${REQUIRE_TORCHAUDIO}" = true ]; then \ + exit ${INSTALL_CODE}; \ + else echo "Skipping failed torchaudio installation"; fi \ + else echo "torchaudio installed successfully"; fi + +COPY scripts /tmp/nemo/scripts/ +# install correct graphviz version (k2 and pynini visualization tool), skip if installation fails +RUN INSTALL_MSG=$(/bin/bash /tmp/nemo/scripts/installers/install_graphviz.sh --docker); INSTALL_CODE=$?; \ + echo ${INSTALL_MSG}; \ + if [ ${INSTALL_CODE} -ne 0 ]; then \ + echo "graphviz installation failed"; \ + if [ "${REQUIRE_K2}" = true ]; then \ + exit ${INSTALL_CODE}; \ + else echo "Skipping failed graphviz installation"; fi \ + else echo "graphviz installed successfully"; fi + +# install k2, skip if installation fails +COPY scripts /tmp/nemo/scripts/ +RUN INSTALL_MSG=$(/bin/bash /tmp/nemo/scripts/installers/install_k2.sh); INSTALL_CODE=$?; \ + echo ${INSTALL_MSG}; \ + if [ ${INSTALL_CODE} -ne 0 ]; then \ + echo "k2 installation failed"; \ + if [ "${REQUIRE_K2}" = true ]; then \ + exit ${INSTALL_CODE}; \ + else echo "Skipping failed k2 installation"; fi \ + else echo "k2 installed successfully"; fi + +# install nemo dependencies +WORKDIR /tmp/nemo +ENV LHOTSE_REQUIRE_TORCHAUDIO=0 +COPY requirements . +RUN for f in $(ls requirements*.txt); do pip3 install --disable-pip-version-check --no-cache-dir -r $f; done + +# install flash attention +RUN pip install flash-attn +# install numba for latest containers +RUN pip install numba>=0.57.1 +# install ammo +RUN pip install nvidia-ammo~=0.7.0 --extra-index-url https://pypi.nvidia.com --no-cache-dir + +# copy nemo source into a scratch image +FROM scratch as nemo-src +COPY . . + +# start building the final container +FROM nemo-deps as nemo +ARG NEMO_VERSION=1.23.0 + +# Check that NEMO_VERSION is set. Build will fail without this. Expose NEMO and base container +# version information as runtime environment variable for introspection purposes +RUN /usr/bin/test -n "$NEMO_VERSION" && \ + /bin/echo "export NEMO_VERSION=${NEMO_VERSION}" >> /root/.bashrc && \ + /bin/echo "export BASE_IMAGE=${BASE_IMAGE}" >> /root/.bashrc + +# Install NeMo +RUN --mount=from=nemo-src,target=/tmp/nemo,rw cd /tmp/nemo && pip install ".[all]" + +# Check install +RUN python -c "import nemo.collections.nlp as nemo_nlp" && \ + python -c "import nemo.collections.tts as nemo_tts" && \ + python -c "import nemo_text_processing.text_normalization as text_normalization" + + +# copy scripts/examples/tests into container for end user +WORKDIR /workspace/nemo +COPY scripts /workspace/nemo/scripts +COPY examples /workspace/nemo/examples +COPY tests /workspace/nemo/tests +COPY tutorials /workspace/nemo/tutorials +# COPY README.rst LICENSE /workspace/nemo/ + +RUN printf "#!/bin/bash\njupyter lab --no-browser --allow-root --ip=0.0.0.0" >> start-jupyter.sh && \ + chmod +x start-jupyter.sh + +# If required, install AIS CLI +RUN if [ "${REQUIRE_AIS_CLI}" = true ]; then \ + INSTALL_MSG=$(/bin/bash scripts/installers/install_ais_cli_latest.sh); INSTALL_CODE=$?; \ + echo ${INSTALL_MSG}; \ + if [ ${INSTALL_CODE} -ne 0 ]; then \ + echo "AIS CLI installation failed"; \ + exit ${INSTALL_CODE}; \ + else echo "AIS CLI installed successfully"; fi \ + else echo "Skipping AIS CLI installation"; fi diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/README.md b/NeMo-2.0.0.rc0.beta/examples/asr/README.md new file mode 100644 index 0000000..1ab4048 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/README.md @@ -0,0 +1,28 @@ +# Automatic Speech Recognition + +This directory contains example scripts to train ASR models using various methods such as Connectionist Temporal Classification loss, RNN Transducer Loss. + +Speech pre-training via self supervised learning, voice activity detection and other sub-domains are also included as part of this domain's examples. + +# ASR Model inference execution overview + +The inference scripts in this directory execute in the following order. When preparing your own inference scripts, please follow this order for correct inference. + +```mermaid + +graph TD + A[Hydra Overrides + Config Dataclass] --> B{Config} + B --> |Init| C[Model] + B --> |Init| D[Trainer] + C & D --> E[Set trainer] + E --> |Optional| F[Change Transducer Decoding Strategy] + F --> H[Load Manifest] + E --> |Skip| H + H --> I["model.transcribe(...)"] + I --> J[Write output manifest] + K[Ground Truth Manifest] + J & K --> |Optional| L[Evaluate CER/WER] + +``` + +During restoration of the model, you may pass the Trainer to the restore_from / from_pretrained call, or set it after the model has been initialized by using `model.set_trainer(Trainer)`. \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_adapters/README.md b/NeMo-2.0.0.rc0.beta/examples/asr/asr_adapters/README.md new file mode 100644 index 0000000..aa9b61d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_adapters/README.md @@ -0,0 +1,66 @@ +# ASR Adapters support + +This examples directory contains scripts to enable Adapters support for supported ASR models in NeMo. + +For further discussion of what are adapters, how they are trained and how are they used, please refer to the ASR tutorials. + +# Train one-or-more adapters to a pre-trained model. + +Using the `train_asr_adapter.py` script, you can provide the path to a pre-trained model, a config to define and add an adapter module to this pre-trained model, some information to setup datasets for training / validation - and then easily add any number of adapter modules to this network. + +**Note**: In order to train multiple adapters on a single model, provide the `model.nemo_model` (in the config) to be a previously adapted model! Ensure that you use a new unique `model.adapter.adapter_name` in the config. + +## Training execution flow diagram + +```mermaid + +graph TD + A[Hydra Overrides + Yaml Config] --> Bo{Config} + Bo --> B[Update Config for Adapter Supported Modules] + B --> |Init| C[Trainer] + C --> D[ExpManager] + B --> D[ExpManager] + C --> E[Pretrained Model Restore] + B --> |Init| E[Pretrained Model Restore] + E --> |Constructor| F1(Change Vocabulary) + F1 --> G(Setup Train + Validation + Test Data loaders) + G --> H1(Setup Optimization) + H1 --> H2(Setup Older Adapters) + H2 --> H3[Add New Adapters] + H3 --> H4[Disable all adapters, Enable newest adapter] + H4 --> H5[Freeze all model parameters, Unfreeze newest adapter parameters] + H5 --> I["trainer.fit(model)"] +``` + +# Evaluate adapted models + +In order to easily evaluate adapted models, you can use the `eval_asr_adapter.py` script, which takes in the path / name of an adapted model, and then selects one of the any number of adapter names to evaluate over. + +## Evaluation execution flow diagram + +```mermaid + +graph TD + A[Hydra Overrides + Yaml Config] --> Bo{Config} + Bo --> B[Update Config for Adapter Supported Modules] + B --> |Init| C[Trainer] + C --> E[Pretrained Adapted Model Restore] + E --> |Constructor| F1(Change Vocabulary) + F1 --> G(Setup Test Data loaders) + G --> H1(Setup Optimization) + H1 --> H2(Setup Older Adapters) + H2 --> H4{Adapter Name Provided} + H4 --> |Yes| H5[Disable all Adapters, Enable selected Adapter] + H4 --> |No| H6[Disable all Adapters] + H5 --> Ho[Freeze Weights] + H6 --> Ho[Freeze Weights] + Ho --> I["trainer.test(model)"] +``` + +**Note**: If you wish to evaluate the base model (with all adapters disabled), simply pass `model.adapter.adapter_name=null` to the config of this script to disable all adapters and evaluate just the base model. + +# Scoring and Analysis of Results + +The script `scoring_and_analysis.py` can be used to calculate the scoring metric for selecting hyperparameters for constrained and unconstrained adaptation experiments as outlined in [Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition](https://arxiv.org/abs/2210.03255). + +The script takes in as input a csv file containing all the hyperparameters and their corresponding WERs. Currently, it shows how it can be used to perform analysis on the [Crowdsourced high-quality UK and Ireland English Dialect speech data set](http://www.openslr.org/83/). To use it for other experiments, please updated the global variables outlined in the beginning of the script accordingly. These global variables correspond to the column names within the input csv file. diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_adapters/eval_asr_adapter.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_adapters/eval_asr_adapter.py new file mode 100644 index 0000000..bc5947f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_adapters/eval_asr_adapter.py @@ -0,0 +1,115 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Evaluate an adapted model + +python eval_asr_adapter.py \ + --config-path="../conf/asr_adapters" \ + --config-name="asr_adaptation.yaml" \ + model.pretrained_model=null \ + model.nemo_model=null \ + model.adapter.adapter_name= \ + model.test_ds.manifest_filepath="" \ + model.test_ds.batch_size=16 \ + model.train_ds.manifest_filepath=null \ + model.validation_ds.manifest_filepath=null \ + model.adapter.in_features=null \ + trainer.devices=1 \ + trainer.precision=32 + +# Pretrained Models + +For documentation on existing pretrained models, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/results.html + +""" + +import pytorch_lightning as pl +from omegaconf import OmegaConf, open_dict + +from nemo.collections.asr.models import ASRModel +from nemo.core import adapter_mixins +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +def update_encoder_config_to_support_adapter(model_cfg): + with open_dict(model_cfg): + adapter_metadata = adapter_mixins.get_registered_adapter(model_cfg.encoder._target_) + if adapter_metadata is not None: + model_cfg.encoder._target_ = adapter_metadata.adapter_class_path + + +def update_model_cfg(original_cfg, new_cfg): + with open_dict(new_cfg): + # drop keys which dont exist in old config + new_keys = list(new_cfg.keys()) + for key in new_keys: + if key not in original_cfg: + new_cfg.pop(key) + print("Removing unavailable key from config :", key) + + new_cfg = OmegaConf.merge(original_cfg, new_cfg) + return new_cfg + + +@hydra_runner(config_path="../conf/asr_adapters", config_name="asr_adaptation.yaml") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + if cfg.model.pretrained_model is None and cfg.model.nemo_model is None: + raise ValueError("Either set `cfg.model.nemo_model` or `cfg.model.pretrained_model`") + if cfg.model.pretrained_model is not None and cfg.model.nemo_model is not None: + raise ValueError("Cannot set `cfg.model.nemo_model` and `cfg.model.pretrained_model`. Select one only.") + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + if cfg.model.pretrained_model is not None: + model_cfg = ASRModel.from_pretrained(cfg.model.pretrained_model, return_config=True) + update_encoder_config_to_support_adapter(model_cfg) + model = ASRModel.from_pretrained(cfg.model.pretrained_model, override_config_path=model_cfg, trainer=trainer) + + else: + model_cfg = ASRModel.restore_from(cfg.model.nemo_model, return_config=True) + update_encoder_config_to_support_adapter(model_cfg) + model = ASRModel.restore_from(cfg.model.nemo_model, override_config_path=model_cfg, trainer=trainer) + + # Setup model for finetuning (train and validation only) + cfg.model.test_ds = update_model_cfg(model.cfg.test_ds, cfg.model.test_ds) + + # Call the dataloaders and optimizer + scheduler + model.setup_multiple_test_data(cfg.model.test_ds) + + # Setup adapters + with open_dict(cfg.model.adapter): + adapter_name = cfg.model.adapter.pop("adapter_name", None) + + # Disable all other adapters, enable just the current adapter. + model.set_enabled_adapters(enabled=False) # disable all adapters prior to training + + if adapter_name is not None: + model.set_enabled_adapters(adapter_name, enabled=True) # enable just one adapter by name if provided + + # First, Freeze all the weights of the model (not just encoder, everything) + model.freeze() + + # Finally, train model + trainer.test(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_adapters/scoring_and_analysis.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_adapters/scoring_and_analysis.py new file mode 100644 index 0000000..2a5602a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_adapters/scoring_and_analysis.py @@ -0,0 +1,377 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script is used to analyze the results of the experiments from a CSV file. + +Basic Usage: + To perform analysis on the adapters experiment results:: + + python scoring_and_analysis.py \ + --csv \ + --dataset_type_column + + To perform analysis on the finetuning experiment results:: + + $ python scoring_and_analysis.py \ + --csv \ + --dataset_type_column \ + -ft + +Advanced Usage: + The script by default shows only the best hyperparameters for each crietria. + To see a ranking of all the hyperparameters for each criteria in order to visualize + how the results were selected use the `--show_analysis` flag. Moreover, instead of + displaying only the best hyperparameters, you can use the `--topk` flag to show the + top *k* hyperparameters:: + + $ python scoring_and_analysis.py \ + --csv \ + --dataset_type_column \ + --show_analysis \ + --topk 3 + + Instead of doing the analysis over all possible combinations of all the hyperparameters, + you can restrict the search space only to a subset of experiments. This can be achieved + by the `-uargs` and the `-cargs` flag for the unconstrained and the constrained + experiments respectively:: + + $ python scoring_and_analysis.py \ + --csv \ + --dataset_type_column \ + -cargs 'Adapter Position' encoder \ + -cargs 'Adapter Dropout' 0.5 \ + -uargs 'Train Steps' 5000 +""" + +import argparse +from typing import Tuple + +import numpy as np +import pandas as pd + +# CHANGE: Specify the column names and their attributes to consider for the selection +# of the best results +UNCONSTRAINED_EXP_KEY = {'name': 'WER: Test', 'attribute': min} +CONSTRAINED_EXP_KEY = {'name': 'Score', 'attribute': max} + +# CHANGE: Hyperparamters of the best run to display in the output +ADAPTER_HYPERPARAMTER_COLUMNS = ['Adapter Dimensions', 'Adapter Dropout', 'Stochastic Depth', 'Train Steps'] +FINETUNING_HYPERPARAMETER_COLUMNS = ['Train Steps', 'Learning Rate'] + +# CHANGE: Column name for the test set WER on the new domain +TEST_WER_COLUMN = 'WER: Test' + +# CHANGE: Column name for the test set WER on the original domain +ORIGINAL_TEST_WER_COLUMN = 'WER: Librispeech Test Other' + +# CHANGE: Based on the experiment type, get the column name for categorizing the results +EXP_CATEGORY_KEY = {'adapters': 'Adapter Position', 'finetuning': 'Frozen Module'} + +# CHANGE: Maximum absolute WER degradation allowed in the original domain +MAX_DEGRADATION_PERCENTAGE = 3 + +# CHANGE: Baseline WER in the original domain +BASELINE_ORIGINAL_WER = 5.118 + +# CHANGE: Baseline WER in the domain to be adapted +# The keys of this dictionary should cover all values of the `dataset_type_column` +BASELINE_ADAPTED_WER = { + 'irish_english_male': 20.690, + 'midlands_english_female': 9.612, + 'midlands_english_male': 11.253, + 'northern_english_female': 11.108, + 'northern_english_male': 10.180, + 'scottish_english_female': 12.309, + 'scottish_english_male': 11.942, + 'southern_english_female': 9.701, + 'southern_english_male': 10.215, + 'welsh_english_female': 8.514, + 'welsh_english_male': 11.463, +} + + +def calculate_original_scale(original_wer): + wer_do = abs(original_wer - BASELINE_ORIGINAL_WER) + return (MAX_DEGRADATION_PERCENTAGE - min(MAX_DEGRADATION_PERCENTAGE, wer_do)) / MAX_DEGRADATION_PERCENTAGE + + +def calculate_adapt_werr(adapted_wer, group): + return max(BASELINE_ADAPTED_WER[group] - adapted_wer, 0) / BASELINE_ADAPTED_WER[group] + + +def parse_results(filepath: str, dataset_type_col: str, exp_type: str) -> Tuple[pd.DataFrame]: + """Calculate the scoring metric for each experiment + + Args: + filepath: Path to the csv file containing the results + dataset_type_col: Name of the column containing the dataset types + exp_type: Type of experiments in the csv file + + Returns: + Dataframes of all the experiments with scores + """ + global UNCONSTRAINED_EXP_KEY, TEST_WER_COLUMN + + df = pd.read_csv(filepath) + df.drop(columns=['Model', 'Model Size'], errors='ignore', inplace=True) # Drop columns if exists + + if exp_type == 'finetuning': + df['Frozen Module'] = df['Frozen Module'].replace('-', 'null') + + if 'Score' not in df: + # Calculate the selection scoring metric + df['Original Scale'] = df.apply(lambda x: calculate_original_scale(x[ORIGINAL_TEST_WER_COLUMN]), axis=1) + df['Adapt WERR'] = df.apply(lambda x: calculate_adapt_werr(x[TEST_WER_COLUMN], x[dataset_type_col]), axis=1) + df['Score'] = df['Original Scale'] * df['Adapt WERR'] + + # Round off the values to 4 decimal places + df = df.round({'Original Scale': 4, 'Adapt WERR': 4, 'Score': 4}) + + # Save the updated csv with scores + df.to_csv(filepath, index=False) + + return df + + +def display_analysis_table(df_analysis: pd.DataFrame, key_info: dict): + """Display the analysis table used to select the best hyperparameter configuration + + Args: + df_analysis: Dataframe of the analysis table + key_info: Dictionary containing the name of the column and the attribute to use for analysis + """ + # Calculate each column length for the table + column_lengths = {x: max(len(x), df_analysis[x].map(str).apply(len).max()) for x in df_analysis.columns} + + print(' | '.join([f'{x:^{column_lengths[x]}}' for x in df_analysis.columns])) + print('-' * sum([column_lengths[x] + 3 for x in df_analysis.columns])) + + for idx in range(len(df_analysis)): + row_str = [] + for column in df_analysis.columns: + row_str.append(f'{df_analysis.iloc[idx][column]:^{column_lengths[column]}}') + print(' | '.join(row_str)) + + +def display_results(df_all: pd.DataFrame, category: str, best_config: pd.Series, dataset_type_col: str, exp_type: str): + """Display the Test and the Librispeech Test Other WER for the best configuration. + + Args: + df_all: Dataframe of all the experiments + category: Adapter position or frozen module in case of finetuning + best_config: Best hyperparameter configurations + dataset_type_col: Name of the column containing the dataset types + exp_type: Type of experiments in the dataframe + """ + test_wer_values, ls_test_other_wer_values = [], [] + + print(f'{dataset_type_col:^25} | {TEST_WER_COLUMN:<20} | {ORIGINAL_TEST_WER_COLUMN:<20}') + print('-' * 70) + for dtype in df_all[dataset_type_col].unique(): + df_filtered = df_all[(df_all[dataset_type_col] == dtype) & (df_all[EXP_CATEGORY_KEY[exp_type]] == category)] + for col in ADAPTER_HYPERPARAMTER_COLUMNS if exp_type == 'adapters' else FINETUNING_HYPERPARAMETER_COLUMNS: + df_filtered = df_filtered[df_filtered[col] == best_config[col]] + + if len(df_filtered) == 0: + continue + + if len(df_filtered) > 1: + raise ValueError(f'More than one row found for dtype: {dataset_type_col} and category: {category}') + + dtype_data = df_filtered.iloc[0] + test_wer_values.append(dtype_data[TEST_WER_COLUMN]) + ls_test_other_wer_values.append(dtype_data[ORIGINAL_TEST_WER_COLUMN]) + print( + f'{dtype_data[dataset_type_col]:^25} | {dtype_data[TEST_WER_COLUMN]:^20} | {dtype_data[ORIGINAL_TEST_WER_COLUMN]:^20}' + ) + print('-' * 70) + print(f'{"Average":^25} | {np.mean(test_wer_values):^20} | {np.mean(ls_test_other_wer_values):^20}') + print('\n') + + +def get_best_config( + df_exp: pd.DataFrame, dataset_type_col: str, key_info: dict, topk: int, show_analysis: bool, exp_type: str, +): + """Get the best hyperparameter configuration for a given subset of experiments. + + Args: + df_exp: Dataframe of all experiments + dataset_type_col: Name of the column containing the dataset types + key_info: Dictionary containing the name of the column and the attribute to use for analysis + topk: Number of top-k results to display + show_analysis: Whether to display the analysis table + exp_type: Type of experiments in the dataframe + """ + # Columns to consider for hyperparameter combinations + hyperparamter_cols = ADAPTER_HYPERPARAMTER_COLUMNS if exp_type == 'adapters' else FINETUNING_HYPERPARAMETER_COLUMNS + + # Columns to display in the analysis table + analysis_columns = list(set([key_info['name'], TEST_WER_COLUMN, ORIGINAL_TEST_WER_COLUMN])) + + df_analyze = df_exp.drop( + columns=[ + x + for x in df_exp.columns + if x not in set(hyperparamter_cols + [EXP_CATEGORY_KEY[exp_type]] + analysis_columns) + ] + ) + + for category in df_exp[EXP_CATEGORY_KEY[exp_type]].unique(): + # Group all hyperparameter configurations and do mean across all speakers + df_category_mean = ( + df_analyze[df_analyze[EXP_CATEGORY_KEY[exp_type]] == category] + .groupby(hyperparamter_cols, as_index=False)[analysis_columns] + .mean() + ) + + # Sort the values by the key in order to get the top-k results + df_category_mean.sort_values( + by=key_info['name'], ascending=True if key_info['attribute'].__qualname__ == 'min' else False, inplace=True + ) + + print('=' * len(category)) + print(category.upper()) + print('=' * len(category) + '\n') + + if show_analysis: + display_analysis_table(df_category_mean, key_info) + print('\n') + + for idx in range(min(topk, len(df_category_mean))): + print('-----') + print(f'Top-{idx + 1}') + print('-----') + + df_category_best = df_category_mean.iloc[idx] + + print(f'\nHyperparamters') + print('---------------\n') + for hyperparamter in hyperparamter_cols + [key_info['name']]: + print(f'{hyperparamter:<20}: {df_category_best[hyperparamter]}') + print() + + print('\nResults') + print('-------\n') + display_results(df_exp, category, df_category_best, dataset_type_col, exp_type) + + +def analyze_results( + df_exp: pd.DataFrame, + fixed_hyperparameters: list, + title: str, + dataset_type_col: str, + key_info: dict, + topk: int, + show_analysis: bool, + exp_type: str, +): + """Perform analysis on a given subset of experiments + + Args: + df_exp: Dataframe of all experiments + fixed_hyperparameters: List of pair of hyperparamters and their values to fix in the analysis + title: Title of the analysis (for logging) + dataset_type_col: Name of the column containing the dataset types + key_info: Dictionary containing the name of the column and the attribute to use for analysis + topk: Number of top-k results to display + show_analysis: Whether to display the analysis table + exp_type: Type of experiments in the dataframe + """ + # Filter experiments based on the fixed hyperparameters + for hyperparameter_name, hyperparameter_value in fixed_hyperparameters: + df_exp = df_exp[df_exp[hyperparameter_name] == hyperparameter_value] + + # Perform analysis + print('+' * len(title)) + print(title) + print('+' * len(title) + '\n') + get_best_config(df_exp, dataset_type_col, key_info, topk, show_analysis, exp_type) + print() + + +def __validate_arg_type(arg): + """Validate the type of the command line argument value.""" + dtype = float if '.' in arg else int + try: + return dtype(arg) + except ValueError: + return arg + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-c', '--csv', required=True, help='Path to the cleaned results CSV file') + parser.add_argument( + '-dtype', + '--dataset_type_column', + required=True, + help='Name of the column containing the dataset type. Example: For SLR83 it is "Group", for GSC it is "Dataset Size"', + ) + parser.add_argument( + '-cargs', + '--constrained_args', + nargs=2, + action='append', + default=[], + type=__validate_arg_type, + help='Hyperparameters to fix for the constrained experiments', + ) + parser.add_argument( + '-uargs', + '--unconstrained_args', + nargs=2, + action='append', + default=[], + type=__validate_arg_type, + help='Hyperparameters to fix for the unconstrained experiments', + ) + parser.add_argument('-k', '--topk', type=int, default=1, help='Number of top-k results to display') + parser.add_argument( + '-ft', '--finetuning', action='store_true', help='True if the CSV contains Finetuning experiments' + ) + parser.add_argument( + '-s', '--show_analysis', action='store_true', help='Show the key values of all the dataset types' + ) + args = parser.parse_args() + + # Get the experiment type + exp_type = 'finetuning' if args.finetuning else 'adapters' + + # Parse CSV file + df = parse_results(args.csv, args.dataset_type_column, exp_type) + + # Perform analysis - Constrained Adaptation + analyze_results( + df, + args.constrained_args, + 'Constrained Experiment Results', + args.dataset_type_column, + CONSTRAINED_EXP_KEY, + args.topk, + args.show_analysis, + exp_type, + ) + + # Perform analysis - Unconstrained Adaptation + analyze_results( + df, + args.unconstrained_args, + 'Unconstrained Experiment Results', + args.dataset_type_column, + UNCONSTRAINED_EXP_KEY, + args.topk, + args.show_analysis, + exp_type, + ) diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_adapters/train_asr_adapter.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_adapters/train_asr_adapter.py new file mode 100644 index 0000000..5a94e2b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_adapters/train_asr_adapter.py @@ -0,0 +1,254 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Adapting the model + +python train_asr_adapter.py \ + --config-path="../conf/asr_adapters" \ + --config-name="asr_adaptation.yaml" \ + model.pretrained_model=null \ + model.nemo_model=null \ + model.adapter.adapter_name= \ + model.adapter.adapter_type="" \ + model.adapter.adapter_module_name= \ + model.adapter.linear.in_features= \ + model.adapter.linear.dim=32 \ + model.adapter.linear.dropout=0.0 \ + model.train_ds.manifest_filepath= \ + model.train_ds.batch_size=16 \ + model.validation_ds.manifest_filepath= \ + model.validation_ds.batch_size=16 \ + model.optim.lr=0.001 \ + model.optim.weight_decay=0.0 \ + model.optim.sched.warmup_steps=100 \ + trainer.max_steps=300 \ + trainer.devices=1 \ + trainer.precision=32 \ + exp_manager.exp_dir= + +# Hyper Parmaeter Search + +python train_asr_adapter.py \ + --config-path="../conf/asr_adapters" \ + --config-name="asr_adaptation_hp.yaml" \ + -m \ + model.pretrained_model=null \ + model.nemo_model=null \ + model.adapter.adapter_name= \ + model.adapter.adapter_type="" \ + model.adapter.adapter_module_name= \ + model.adapter.linear.in_features= \ + model.train_ds.manifest_filepath= \ + model.train_ds.batch_size=16 \ + model.validation_ds.manifest_filepath= \ + model.validation_ds.batch_size=16 \ + exp_manager.exp_dir="" \ + exp_manager.create_wandb_logger=true \ + exp_manager.wandb_logger_kwargs.project="" \ + ++delete_ckpt_after_train=True + +# Fine-tune a model + +While adaptation is very efficient for low-resource datasets, it imposes several restrictions - + +- The vocabulary of the new dataset must be supported by the pre-existing vocabulary or tokenizer. + If tokens exist outside this scope, the adapter will have to learn UNK tokens (or fail entirely + for character based models). + +- As a consequence of the above, the language of the new dataset must be the same as the original model. + There is ongoing research to enable more sophisticated adapters for other languages. + +When adapters cannot be readily used due to the above limitations, fine-tuning may be a better alternative. + +For documentation on fine-tuning a model, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations + +# Pretrained Models + +For documentation on existing pretrained models, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/results.html + +""" +import os +from dataclasses import is_dataclass + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf, open_dict + +from nemo.collections.asr.models import ASRModel +from nemo.core import adapter_mixins +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import clean_exp_ckpt, exp_manager + + +def update_model_config_to_support_adapter(model_cfg, current_cfg): + with open_dict(model_cfg): + # Override prediction logging in config + model_cfg.log_prediction = current_cfg.model.get('log_prediction', False) + + # Update encoder adapter compatible config + adapter_metadata = adapter_mixins.get_registered_adapter(model_cfg.encoder._target_) + if adapter_metadata is not None: + model_cfg.encoder._target_ = adapter_metadata.adapter_class_path + + +def update_model_cfg(original_cfg, new_cfg): + with open_dict(original_cfg), open_dict(new_cfg): + # force inject some keys into the config + whitelist_keys = ['num_workers', 'pin_memory'] + for wkey in whitelist_keys: + if wkey in new_cfg: + original_cfg[wkey] = new_cfg[wkey] + print(f"Injecting white listed key `{wkey}` into config") + + # drop keys which don't exist in old config and are not whitelisted + new_keys = list(new_cfg.keys()) + for key in new_keys: + if key not in original_cfg: + new_cfg.pop(key) + print("Removing unavailable key from config :", key) + + new_cfg = OmegaConf.merge(original_cfg, new_cfg) + return new_cfg + + +def add_global_adapter_cfg(model, global_adapter_cfg): + # Convert to DictConfig from dict or Dataclass + if is_dataclass(global_adapter_cfg): + global_adapter_cfg = OmegaConf.structured(global_adapter_cfg) + + if not isinstance(global_adapter_cfg, DictConfig): + global_adapter_cfg = DictConfig(global_adapter_cfg) + + # Update the model.cfg with information about the new adapter global cfg + with open_dict(global_adapter_cfg), open_dict(model.cfg): + if 'adapters' not in model.cfg: + model.cfg.adapters = OmegaConf.create({}) + + # Add the global config for adapters to the model's internal config + model.cfg.adapters[model.adapter_global_cfg_key] = global_adapter_cfg + + # Update all adapter modules (that already exist) with this global adapter config + model.update_adapter_cfg(model.cfg.adapters) + + +@hydra_runner(config_path="../conf/asr_adapters", config_name="asr_adaptation.yaml") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + if cfg.model.pretrained_model is None and cfg.model.nemo_model is None: + raise ValueError("Either set `cfg.model.nemo_model` or `cfg.model.pretrained_model`") + if cfg.model.pretrained_model is not None and cfg.model.nemo_model is not None: + raise ValueError("Cannot set both `cfg.model.nemo_model` and `cfg.model.pretrained_model`. Select one only.") + + trainer = pl.Trainer(**cfg.trainer) + exp_log_dir = exp_manager(trainer, cfg.get("exp_manager", None)) + + if cfg.model.pretrained_model is not None: + model_cfg = ASRModel.from_pretrained(cfg.model.pretrained_model, return_config=True) + update_model_config_to_support_adapter(model_cfg, cfg) + model = ASRModel.from_pretrained(cfg.model.pretrained_model, override_config_path=model_cfg, trainer=trainer) + + else: + model_cfg = ASRModel.restore_from(cfg.model.nemo_model, return_config=True) + update_model_config_to_support_adapter(model_cfg, cfg) + model = ASRModel.restore_from(cfg.model.nemo_model, override_config_path=model_cfg, trainer=trainer) + + # Setup model for finetuning (train and validation only) + cfg.model.train_ds = update_model_cfg(model.cfg.train_ds, cfg.model.train_ds) + model.setup_training_data(cfg.model.train_ds) + + if 'validation_ds' in cfg.model: + cfg.model.validation_ds = update_model_cfg(model.cfg.validation_ds, cfg.model.validation_ds) + model.setup_multiple_validation_data(cfg.model.validation_ds) + + # Setup optimizer + model.setup_optimization(cfg.model.optim) + + # Setup spec augmentation + if 'spec_augment' in cfg.model: + model.spec_augmentation = model.from_config_dict(cfg.model.spec_augment) + else: + model.spec_augmentation = None + del model.cfg.spec_augment + + # Setup adapters + with open_dict(cfg.model.adapter): + # Extract the name of the adapter (must be give for training) + adapter_name = cfg.model.adapter.pop("adapter_name") + adapter_type = cfg.model.adapter.pop("adapter_type") + adapter_module_name = cfg.model.adapter.pop("adapter_module_name", None) + adapter_state_dict_name = cfg.model.adapter.pop("adapter_state_dict_name", None) + + # Resolve the config of the specified `adapter_type` + if adapter_type not in cfg.model.adapter.keys(): + raise ValueError( + f"Adapter type ({adapter_type}) config could not be found. Adapter setup config - \n" + f"{OmegaConf.to_yaml(cfg.model.adapter)}" + ) + + adapter_type_cfg = cfg.model.adapter[adapter_type] + print(f"Found `{adapter_type}` config :\n" f"{OmegaConf.to_yaml(adapter_type_cfg)}") + + # Augment adapter name with module name, if not provided by user + if adapter_module_name is not None and ':' not in adapter_name: + adapter_name = f'{adapter_module_name}:{adapter_name}' + + # Extract the global adapter config, if provided + adapter_global_cfg = cfg.model.adapter.pop(model.adapter_global_cfg_key, None) + if adapter_global_cfg is not None: + add_global_adapter_cfg(model, adapter_global_cfg) + + model.add_adapter(adapter_name, cfg=adapter_type_cfg) + assert model.is_adapter_available() + + # Disable all other adapters, enable just the current adapter. + model.set_enabled_adapters(enabled=False) # disable all adapters prior to training + model.set_enabled_adapters(adapter_name, enabled=True) # enable just one adapter by name + + # First, Freeze all the weights of the model (not just encoder, everything) + model.freeze() + # Activate dropout() and other modules that depend on train mode. + model = model.train() + # Then, Unfreeze just the adapter weights that were enabled above (no part of encoder/decoder/joint/etc) + model.unfreeze_enabled_adapters() + + # Update model config prior to training + model.cfg = model.cfg + + # Finally, train model + trainer.fit(model) + + # Save the adapter state dict + if adapter_state_dict_name is not None: + state_path = exp_log_dir if exp_log_dir is not None else os.getcwd() + ckpt_path = os.path.join(state_path, "checkpoints") + if os.path.exists(ckpt_path): + state_path = ckpt_path + state_path = os.path.join(state_path, adapter_state_dict_name) + + # Save the adapter modules in a seperate file + model.save_adapters(str(state_path)) + + if 'delete_ckpt_after_train' in cfg: + delete_ckpt_after_train = cfg.delete_ckpt_after_train + if delete_ckpt_after_train: + # Remove PTL ckpt file, and potentially also remove .nemo file to conserve storage space. + clean_exp_ckpt(exp_log_dir, remove_ckpt=True, remove_nemo=False) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py new file mode 100644 index 0000000..7726c2b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py @@ -0,0 +1,451 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script can be used to simulate cache-aware streaming for ASR models. The ASR model to be used with this script need to get trained in streaming mode. Currently only Conformer models supports this streaming mode. +You may find examples of streaming models under 'NeMo/example/asr/conf/conformer/streaming/'. + +It works both on a manifest of audio files or a single audio file. It can perform streaming for a single stream (audio) or perform the evalution in multi-stream model (batch_size>1). +The manifest file must conform to standard ASR definition - containing `audio_filepath` and `text` as the ground truth. + +# Usage + +## To evaluate a model in cache-aware streaming mode on a single audio file: + +python speech_to_text_streaming_infer.py \ + --asr_model=asr_model.nemo \ + --audio_file=audio_file.wav \ + --compare_vs_offline \ + --use_amp \ + --debug_mode + +## To evaluate a model in cache-aware streaming mode on a manifest file: + +python speech_to_text_streaming_infer.py \ + --asr_model=asr_model.nemo \ + --manifest_file=manifest_file.json \ + --batch_size=16 \ + --compare_vs_offline \ + --use_amp \ + --debug_mode + +You may drop the '--debug_mode' and '--compare_vs_offline' to speedup the streaming evaluation. +If compare_vs_offline is not used, then significantly larger batch_size can be used. +Setting `--pad_and_drop_preencoded` would perform the caching for all steps including the first step. +It may result in slightly different outputs from the sub-sampling module compared to offline mode for some techniques like striding and sw_striding. +Enabling it would make it easier to export the model to ONNX. + +## Hybrid ASR models +For Hybrid ASR models which have two decoders, you may select the decoder by --set_decoder DECODER_TYPE, where DECODER_TYPE can be "ctc" or "rnnt". +If decoder is not set, then the default decoder would be used which is the RNNT decoder for Hybrid ASR models. + +## Multi-lookahead models +For models which support multiple lookaheads, the default is the first one in the list of model.encoder.att_context_size. To change it, you may use --att_context_size, for example --att_context_size [70,1]. + + +## Evaluate a model trained with full context for offline mode + +You may try the cache-aware streaming with a model trained with full context in offline mode. +But the accuracy would not be very good with small chunks as there is inconsistency between how the model is trained and how the streaming inference is done. +The accuracy of the model on the borders of chunks would not be very good. + +To use a model trained with full context, you need to pass the chunk_size and shift_size arguments. +If shift_size is not passed, chunk_size would be used as the shift_size too. +Also argument online_normalization should be enabled to simulate a realistic streaming. +The following command would simulate cache-aware streaming on a pretrained model from NGC with chunk_size of 100, shift_size of 50 and 2 left chunks as left context. +The chunk_size of 100 would be 100*4*10=4000ms for a model with 4x downsampling and 10ms shift in feature extraction. + +python speech_to_text_streaming_infer.py \ + --asr_model=stt_en_conformer_ctc_large \ + --chunk_size=100 \ + --shift_size=50 \ + --left_chunks=2 \ + --online_normalization \ + --manifest_file=manifest_file.json \ + --batch_size=16 \ + --compare_vs_offline \ + --use_amp \ + --debug_mode + +""" + + +import contextlib +import json +import os +import time +from argparse import ArgumentParser + +import torch +from omegaconf import open_dict + +import nemo.collections.asr as nemo_asr +from nemo.collections.asr.metrics.wer import word_error_rate +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis +from nemo.collections.asr.parts.utils.streaming_utils import CacheAwareStreamingAudioBuffer +from nemo.utils import logging + + +def extract_transcriptions(hyps): + """ + The transcribed_texts returned by CTC and RNNT models are different. + This method would extract and return the text section of the hypothesis. + """ + if isinstance(hyps[0], Hypothesis): + transcriptions = [] + for hyp in hyps: + transcriptions.append(hyp.text) + else: + transcriptions = hyps + return transcriptions + + +def calc_drop_extra_pre_encoded(asr_model, step_num, pad_and_drop_preencoded): + # for the first step there is no need to drop any tokens after the downsampling as no caching is being used + if step_num == 0 and not pad_and_drop_preencoded: + return 0 + else: + return asr_model.encoder.streaming_cfg.drop_extra_pre_encoded + + +def perform_streaming( + asr_model, streaming_buffer, compare_vs_offline=False, debug_mode=False, pad_and_drop_preencoded=False +): + batch_size = len(streaming_buffer.streams_length) + if compare_vs_offline: + # would pass the whole audio at once through the model like offline mode in order to compare the results with the stremaing mode + # the output of the model in the offline and streaming mode should be exactly the same + with torch.inference_mode(): + with autocast(): + processed_signal, processed_signal_length = streaming_buffer.get_all_audios() + with torch.no_grad(): + ( + pred_out_offline, + transcribed_texts, + cache_last_channel_next, + cache_last_time_next, + cache_last_channel_len, + best_hyp, + ) = asr_model.conformer_stream_step( + processed_signal=processed_signal, + processed_signal_length=processed_signal_length, + return_transcription=True, + ) + final_offline_tran = extract_transcriptions(transcribed_texts) + logging.info(f" Final offline transcriptions: {final_offline_tran}") + else: + final_offline_tran = None + + cache_last_channel, cache_last_time, cache_last_channel_len = asr_model.encoder.get_initial_cache_state( + batch_size=batch_size + ) + + previous_hypotheses = None + streaming_buffer_iter = iter(streaming_buffer) + pred_out_stream = None + for step_num, (chunk_audio, chunk_lengths) in enumerate(streaming_buffer_iter): + with torch.inference_mode(): + with autocast(): + # keep_all_outputs needs to be True for the last step of streaming when model is trained with att_context_style=regular + # otherwise the last outputs would get dropped + + with torch.no_grad(): + ( + pred_out_stream, + transcribed_texts, + cache_last_channel, + cache_last_time, + cache_last_channel_len, + previous_hypotheses, + ) = asr_model.conformer_stream_step( + processed_signal=chunk_audio, + processed_signal_length=chunk_lengths, + cache_last_channel=cache_last_channel, + cache_last_time=cache_last_time, + cache_last_channel_len=cache_last_channel_len, + keep_all_outputs=streaming_buffer.is_buffer_empty(), + previous_hypotheses=previous_hypotheses, + previous_pred_out=pred_out_stream, + drop_extra_pre_encoded=calc_drop_extra_pre_encoded( + asr_model, step_num, pad_and_drop_preencoded + ), + return_transcription=True, + ) + + if debug_mode: + logging.info(f"Streaming transcriptions: {extract_transcriptions(transcribed_texts)}") + + final_streaming_tran = extract_transcriptions(transcribed_texts) + logging.info(f"Final streaming transcriptions: {final_streaming_tran}") + + if compare_vs_offline: + # calculates and report the differences between the predictions of the model in offline mode vs streaming mode + # Normally they should be exactly the same predictions for streaming models + pred_out_stream_cat = torch.cat(pred_out_stream) + pred_out_offline_cat = torch.cat(pred_out_offline) + if pred_out_stream_cat.size() == pred_out_offline_cat.size(): + diff_num = torch.sum(pred_out_stream_cat != pred_out_offline_cat).cpu().numpy() + logging.info( + f"Found {diff_num} differences in the outputs of the model in streaming mode vs offline mode." + ) + else: + logging.info( + f"The shape of the outputs of the model in streaming mode ({pred_out_stream_cat.size()}) is different from offline mode ({pred_out_offline_cat.size()})." + ) + + return final_streaming_tran, final_offline_tran + + +def main(): + parser = ArgumentParser() + parser.add_argument( + "--asr_model", type=str, required=True, help="Path to an ASR model .nemo file or name of a pretrained model.", + ) + parser.add_argument( + "--device", type=str, help="The device to load the model onto and perform the streaming", default="cuda" + ) + parser.add_argument("--audio_file", type=str, help="Path to an audio file to perform streaming", default=None) + parser.add_argument( + "--manifest_file", + type=str, + help="Path to a manifest file containing audio files to perform streaming", + default=None, + ) + parser.add_argument("--use_amp", action="store_true", help="Whether to use AMP") + parser.add_argument("--debug_mode", action="store_true", help="Whether to print more detail in the output.") + parser.add_argument( + "--compare_vs_offline", + action="store_true", + help="Whether to compare the output of the model with the offline mode.", + ) + parser.add_argument( + "--batch_size", + type=int, + default=32, + help="The batch size to be used to perform streaming in batch mode with multiple streams", + ) + parser.add_argument( + "--chunk_size", + type=int, + default=-1, + help="The chunk_size to be used for models trained with full context and offline models", + ) + parser.add_argument( + "--shift_size", + type=int, + default=-1, + help="The shift_size to be used for models trained with full context and offline models", + ) + parser.add_argument( + "--left_chunks", + type=int, + default=2, + help="The number of left chunks to be used as left context via caching for offline models", + ) + + parser.add_argument( + "--online_normalization", + default=False, + action='store_true', + help="Perform normalization on the run per chunk.", + ) + parser.add_argument( + "--output_path", type=str, help="path to output file when manifest is used as input", default=None + ) + parser.add_argument( + "--pad_and_drop_preencoded", + action="store_true", + help="Enables padding the audio input and then dropping the extra steps after the pre-encoding for all the steps including the the first step. It may make the outputs of the downsampling slightly different from offline mode for some techniques like striding or sw_striding.", + ) + + parser.add_argument( + "--set_decoder", + choices=["ctc", "rnnt"], + default=None, + help="Selects the decoder for Hybrid ASR models which has both the CTC and RNNT decoder. Supported decoders are ['ctc', 'rnnt']", + ) + + parser.add_argument( + "--att_context_size", + type=str, + default=None, + help="Sets the att_context_size for the models which support multiple lookaheads", + ) + + args = parser.parse_args() + if (args.audio_file is None and args.manifest_file is None) or ( + args.audio_file is not None and args.manifest_file is not None + ): + raise ValueError("One of the audio_file and manifest_file should be non-empty!") + + if args.asr_model.endswith('.nemo'): + logging.info(f"Using local ASR model from {args.asr_model}") + asr_model = nemo_asr.models.ASRModel.restore_from(restore_path=args.asr_model) + else: + logging.info(f"Using NGC cloud ASR model {args.asr_model}") + asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name=args.asr_model) + + logging.info(asr_model.encoder.streaming_cfg) + if args.set_decoder is not None: + if hasattr(asr_model, "cur_decoder"): + asr_model.change_decoding_strategy(decoder_type=args.set_decoder) + else: + raise ValueError("Decoder cannot get changed for non-Hybrid ASR models.") + + if args.att_context_size is not None: + if hasattr(asr_model.encoder, "set_default_att_context_size"): + asr_model.encoder.set_default_att_context_size(att_context_size=json.loads(args.att_context_size)) + else: + raise ValueError("Model does not support multiple lookaheads.") + + global autocast + if ( + args.use_amp + and torch.cuda.is_available() + and hasattr(torch.cuda, 'amp') + and hasattr(torch.cuda.amp, 'autocast') + ): + logging.info("AMP enabled!\n") + autocast = torch.cuda.amp.autocast + else: + + @contextlib.contextmanager + def autocast(): + yield + + # configure the decoding config + decoding_cfg = asr_model.cfg.decoding + with open_dict(decoding_cfg): + decoding_cfg.strategy = "greedy" + decoding_cfg.preserve_alignments = False + if hasattr(asr_model, 'joint'): # if an RNNT model + decoding_cfg.greedy.max_symbols = 10 + decoding_cfg.fused_batch_size = -1 + asr_model.change_decoding_strategy(decoding_cfg) + + asr_model = asr_model.to(args.device) + asr_model.eval() + + # chunk_size is set automatically for models trained for streaming. For models trained for offline mode with full context, we need to pass the chunk_size explicitly. + if args.chunk_size > 0: + if args.shift_size < 0: + shift_size = args.chunk_size + else: + shift_size = args.shift_size + asr_model.encoder.setup_streaming_params( + chunk_size=args.chunk_size, left_chunks=args.left_chunks, shift_size=shift_size + ) + + # In streaming, offline normalization is not feasible as we don't have access to the whole audio at the beginning + # When online_normalization is enabled, the normalization of the input features (mel-spectrograms) are done per step + # It is suggested to train the streaming models without any normalization in the input features. + if args.online_normalization: + if asr_model.cfg.preprocessor.normalize not in ["per_feature", "all_feature"]: + logging.warning( + "online_normalization is enabled but the model has no normalization in the feature extration part, so it is ignored." + ) + online_normalization = False + else: + online_normalization = True + + else: + online_normalization = False + + streaming_buffer = CacheAwareStreamingAudioBuffer( + model=asr_model, + online_normalization=online_normalization, + pad_and_drop_preencoded=args.pad_and_drop_preencoded, + ) + if args.audio_file is not None: + # stream a single audio file + processed_signal, processed_signal_length, stream_id = streaming_buffer.append_audio_file( + args.audio_file, stream_id=-1 + ) + perform_streaming( + asr_model=asr_model, + streaming_buffer=streaming_buffer, + compare_vs_offline=args.compare_vs_offline, + pad_and_drop_preencoded=args.pad_and_drop_preencoded, + ) + else: + # stream audio files in a manifest file in batched mode + samples = [] + all_streaming_tran = [] + all_offline_tran = [] + all_refs_text = [] + + with open(args.manifest_file, 'r') as f: + for line in f: + item = json.loads(line) + samples.append(item) + + logging.info(f"Loaded {len(samples)} from the manifest at {args.manifest_file}.") + + start_time = time.time() + for sample_idx, sample in enumerate(samples): + processed_signal, processed_signal_length, stream_id = streaming_buffer.append_audio_file( + sample['audio_filepath'], stream_id=-1 + ) + if "text" in sample: + all_refs_text.append(sample["text"]) + logging.info(f'Added this sample to the buffer: {sample["audio_filepath"]}') + + if (sample_idx + 1) % args.batch_size == 0 or sample_idx == len(samples) - 1: + logging.info(f"Starting to stream samples {sample_idx - len(streaming_buffer) + 1} to {sample_idx}...") + streaming_tran, offline_tran = perform_streaming( + asr_model=asr_model, + streaming_buffer=streaming_buffer, + compare_vs_offline=args.compare_vs_offline, + debug_mode=args.debug_mode, + pad_and_drop_preencoded=args.pad_and_drop_preencoded, + ) + all_streaming_tran.extend(streaming_tran) + if args.compare_vs_offline: + all_offline_tran.extend(offline_tran) + streaming_buffer.reset_buffer() + + if args.compare_vs_offline and len(all_refs_text) == len(all_offline_tran): + offline_wer = word_error_rate(hypotheses=all_offline_tran, references=all_refs_text) + logging.info(f"WER% of offline mode: {round(offline_wer * 100, 2)}") + if len(all_refs_text) == len(all_streaming_tran): + streaming_wer = word_error_rate(hypotheses=all_streaming_tran, references=all_refs_text) + logging.info(f"WER% of streaming mode: {round(streaming_wer*100, 2)}") + + end_time = time.time() + logging.info(f"The whole streaming process took: {round(end_time - start_time, 2)}s") + + # stores the results including the transcriptions of the streaming inference in a json file + if args.output_path is not None and len(all_refs_text) == len(all_streaming_tran): + fname = ( + "streaming_out_" + + os.path.splitext(os.path.basename(args.asr_model))[0] + + "_" + + os.path.splitext(os.path.basename(args.test_manifest))[0] + + ".json" + ) + + hyp_json = os.path.join(args.output_path, fname) + os.makedirs(args.output_path, exist_ok=True) + with open(hyp_json, "w") as out_f: + for i, hyp in enumerate(all_streaming_tran): + record = { + "pred_text": hyp, + "text": all_refs_text[i], + "wer": round(word_error_rate(hypotheses=[hyp], references=[all_refs_text[i]]) * 100, 2), + } + out_f.write(json.dumps(record) + '\n') + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_chunked_inference/README.md b/NeMo-2.0.0.rc0.beta/examples/asr/asr_chunked_inference/README.md new file mode 100644 index 0000000..5b4c796 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_chunked_inference/README.md @@ -0,0 +1,11 @@ +# Streaming / Buffered ASR + +Contained within this directory are scripts to perform streaming or buffered inference of audio files using CTC / Transducer ASR models. + +## Difference between streaming and buffered ASR + +While we primarily showcase the defaults of these models in buffering mode, note that the major difference between streaming ASR and buffered ASR is the chunk size and the total context buffer size. + +If you reduce your chunk size, the latency for your first prediction is reduced, and the model appears to predict the text with shorter delay. On the other hand, since the amount of information in the chunk is reduced, it causes higher WER. + +On the other hand, if you increase your chunk size, then the delay between spoken sentence and the transcription increases (this is buffered ASR). While the latency is increased, you are able to obtain more accurate transcripts since the model has more context to properly transcribe the text. diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_chunked_inference/ctc/speech_to_text_buffered_infer_ctc.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_chunked_inference/ctc/speech_to_text_buffered_infer_ctc.py new file mode 100644 index 0000000..1d01e8e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_chunked_inference/ctc/speech_to_text_buffered_infer_ctc.py @@ -0,0 +1,253 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script serves three goals: + (1) Demonstrate how to use NeMo Models outside of PytorchLightning + (2) Shows example of batch ASR inference + (3) Serves as CI test for pre-trained checkpoint + +python speech_to_text_buffered_infer_ctc.py \ + model_path=null \ + pretrained_name=null \ + audio_dir="" \ + dataset_manifest="" \ + output_filename="" \ + total_buffer_in_secs=4.0 \ + chunk_len_in_secs=1.6 \ + model_stride=4 \ + batch_size=32 \ + clean_groundtruth_text=True \ + langid='en' + +# NOTE: + You can use `DEBUG=1 python speech_to_text_buffered_infer_ctc.py ...` to print out the + predictions of the model, and ground-truth text if presents in manifest. +""" +import contextlib +import copy +import glob +import math +import os +from dataclasses import dataclass, is_dataclass +from typing import Optional + +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf + +from nemo.collections.asr.models import EncDecCTCModel, EncDecHybridRNNTCTCModel +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecodingConfig +from nemo.collections.asr.parts.utils.eval_utils import cal_write_wer +from nemo.collections.asr.parts.utils.streaming_utils import FrameBatchASR +from nemo.collections.asr.parts.utils.transcribe_utils import ( + compute_output_filename, + get_buffered_pred_feat, + setup_model, + write_transcription, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging + +can_gpu = torch.cuda.is_available() + + +@dataclass +class TranscriptionConfig: + # Required configs + model_path: Optional[str] = None # Path to a .nemo file + pretrained_name: Optional[str] = None # Name of a pretrained model + audio_dir: Optional[str] = None # Path to a directory which contains audio files + dataset_manifest: Optional[str] = None # Path to dataset's JSON manifest + + # General configs + output_filename: Optional[str] = None + batch_size: int = 32 + num_workers: int = 0 + append_pred: bool = False # Sets mode of work, if True it will add new field transcriptions. + pred_name_postfix: Optional[str] = None # If you need to use another model name, rather than standard one. + random_seed: Optional[int] = None # seed number going to be used in seed_everything() + + # Set to True to output greedy timestamp information (only supported models) + compute_timestamps: bool = False + + # Set to True to output language ID information + compute_langs: bool = False + + # Chunked configs + chunk_len_in_secs: float = 1.6 # Chunk length in seconds + total_buffer_in_secs: float = 4.0 # Length of buffer (chunk + left and right padding) in seconds + model_stride: int = 8 # Model downsampling factor, 8 for Citrinet and FasConformer models and 4 for Conformer models. + + # Decoding strategy for CTC models + decoding: CTCDecodingConfig = CTCDecodingConfig() + + # Set `cuda` to int to define CUDA device. If 'None', will look for CUDA + # device anyway, and do inference on CPU only if CUDA device is not found. + # If `cuda` is a negative number, inference will be on CPU only. + cuda: Optional[int] = None + amp: bool = False + audio_type: str = "wav" + + # Recompute model transcription, even if the output folder exists with scores. + overwrite_transcripts: bool = True + + # Config for word / character error rate calculation + calculate_wer: bool = True + clean_groundtruth_text: bool = False + langid: str = "en" # specify this for convert_num_to_words step in groundtruth cleaning + use_cer: bool = False + + +@hydra_runner(config_name="TranscriptionConfig", schema=TranscriptionConfig) +def main(cfg: TranscriptionConfig) -> TranscriptionConfig: + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + torch.set_grad_enabled(False) + + for key in cfg: + cfg[key] = None if cfg[key] == 'None' else cfg[key] + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if cfg.random_seed: + pl.seed_everything(cfg.random_seed) + + if cfg.model_path is None and cfg.pretrained_name is None: + raise ValueError("Both cfg.model_path and cfg.pretrained_name cannot be None!") + if cfg.audio_dir is None and cfg.dataset_manifest is None: + raise ValueError("Both cfg.audio_dir and cfg.dataset_manifest cannot be None!") + + filepaths = None + manifest = cfg.dataset_manifest + if cfg.audio_dir is not None: + filepaths = list(glob.glob(os.path.join(cfg.audio_dir, f"**/*.{cfg.audio_type}"), recursive=True)) + manifest = None # ignore dataset_manifest if audio_dir and dataset_manifest both presents + + # setup GPU + if cfg.cuda is None: + if torch.cuda.is_available(): + device = [0] # use 0th CUDA device + accelerator = 'gpu' + else: + device = 1 + accelerator = 'cpu' + else: + device = [cfg.cuda] + accelerator = 'gpu' + map_location = torch.device('cuda:{}'.format(device[0]) if accelerator == 'gpu' else 'cpu') + logging.info(f"Inference will be done on device : {device}") + + asr_model, model_name = setup_model(cfg, map_location) + + model_cfg = copy.deepcopy(asr_model._cfg) + OmegaConf.set_struct(model_cfg.preprocessor, False) + # some changes for streaming scenario + model_cfg.preprocessor.dither = 0.0 + model_cfg.preprocessor.pad_to = 0 + + if model_cfg.preprocessor.normalize != "per_feature": + logging.error("Only EncDecCTCModelBPE models trained with per_feature normalization are supported currently") + + # Disable config overwriting + OmegaConf.set_struct(model_cfg.preprocessor, True) + + # setup AMP (optional) + if cfg.amp and torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and hasattr(torch.cuda.amp, 'autocast'): + logging.info("AMP enabled!\n") + autocast = torch.cuda.amp.autocast + else: + + @contextlib.contextmanager + def autocast(): + yield + + # Compute output filename + cfg = compute_output_filename(cfg, model_name) + + # if transcripts should not be overwritten, and already exists, skip re-transcription step and return + if not cfg.overwrite_transcripts and os.path.exists(cfg.output_filename): + logging.info( + f"Previous transcripts found at {cfg.output_filename}, and flag `overwrite_transcripts`" + f"is {cfg.overwrite_transcripts}. Returning without re-transcribing text." + ) + return cfg + + # Setup decoding strategy + if hasattr(asr_model, 'change_decoding_strategy'): + if not isinstance(asr_model, EncDecCTCModel) and not isinstance(asr_model, EncDecHybridRNNTCTCModel): + raise ValueError("The script supports ctc model and hybrid model with ctc decodng!") + + else: + if cfg.compute_langs: + raise ValueError("CTC models do not support `compute_langs` at the moment.") + + if hasattr( + asr_model, 'cur_decoder' + ): # hybrid model with ctc decoding or potential other models containing decoding switch feature + asr_model.change_decoding_strategy(cfg.decoding, decoder_type='ctc') + + else: # ctc model + asr_model.change_decoding_strategy(cfg.decoding) + + asr_model.eval() + asr_model = asr_model.to(asr_model.device) + + feature_stride = model_cfg.preprocessor['window_stride'] + model_stride_in_secs = feature_stride * cfg.model_stride + total_buffer = cfg.total_buffer_in_secs + chunk_len = float(cfg.chunk_len_in_secs) + + tokens_per_chunk = math.ceil(chunk_len / model_stride_in_secs) + mid_delay = math.ceil((chunk_len + (total_buffer - chunk_len) / 2) / model_stride_in_secs) + logging.info(f"tokens_per_chunk is {tokens_per_chunk}, mid_delay is {mid_delay}") + + frame_asr = FrameBatchASR( + asr_model=asr_model, frame_len=chunk_len, total_buffer=cfg.total_buffer_in_secs, batch_size=cfg.batch_size, + ) + + hyps = get_buffered_pred_feat( + frame_asr, + chunk_len, + tokens_per_chunk, + mid_delay, + model_cfg.preprocessor, + model_stride_in_secs, + asr_model.device, + manifest, + filepaths, + ) + output_filename, pred_text_attr_name = write_transcription( + hyps, cfg, model_name, filepaths=filepaths, compute_langs=False, compute_timestamps=False + ) + logging.info(f"Finished writing predictions to {output_filename}!") + + if cfg.calculate_wer: + output_manifest_w_wer, total_res, _ = cal_write_wer( + pred_manifest=output_filename, + pred_text_attr_name=pred_text_attr_name, + clean_groundtruth_text=cfg.clean_groundtruth_text, + langid=cfg.langid, + use_cer=cfg.use_cer, + output_filename=None, + ) + if output_manifest_w_wer: + logging.info(f"Writing prediction and error rate of each sample to {output_manifest_w_wer}!") + logging.info(f"{total_res}") + + return cfg + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_chunked_inference/rnnt/speech_to_text_buffered_infer_rnnt.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_chunked_inference/rnnt/speech_to_text_buffered_infer_rnnt.py new file mode 100644 index 0000000..ea82796 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_chunked_inference/rnnt/speech_to_text_buffered_infer_rnnt.py @@ -0,0 +1,301 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Script to perform buffered inference using RNNT models. + +Buffered inference is the primary form of audio transcription when the audio segment is longer than 20-30 seconds. +This is especially useful for models such as Conformers, which have quadratic time and memory scaling with +audio duration. + +The difference between streaming and buffered inference is the chunk size (or the latency of inference). +Buffered inference will use large chunk sizes (5-10 seconds) + some additional buffer for context. +Streaming inference will use small chunk sizes (0.1 to 0.25 seconds) + some additional buffer for context. + +# Middle Token merge algorithm + +python speech_to_text_buffered_infer_rnnt.py \ + model_path=null \ + pretrained_name=null \ + audio_dir="" \ + dataset_manifest="" \ + output_filename="" \ + total_buffer_in_secs=4.0 \ + chunk_len_in_secs=1.6 \ + model_stride=4 \ + batch_size=32 \ + clean_groundtruth_text=True \ + langid='en' + +# Longer Common Subsequence (LCS) Merge algorithm + +python speech_to_text_buffered_infer_rnnt.py \ + model_path=null \ + pretrained_name=null \ + audio_dir="" \ + dataset_manifest="" \ + output_filename="" \ + total_buffer_in_secs=4.0 \ + chunk_len_in_secs=1.6 \ + model_stride=4 \ + batch_size=32 \ + merge_algo="lcs" \ + lcs_alignment_dir= + +# NOTE: + You can use `DEBUG=1 python speech_to_text_buffered_infer_ctc.py ...` to print out the + predictions of the model, and ground-truth text if presents in manifest. +""" +import copy +import glob +import math +import os +from dataclasses import dataclass, is_dataclass +from typing import Optional + +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf, open_dict + +from nemo.collections.asr.models import EncDecHybridRNNTCTCModel, EncDecRNNTModel +from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTDecodingConfig +from nemo.collections.asr.parts.utils.eval_utils import cal_write_wer +from nemo.collections.asr.parts.utils.streaming_utils import ( + BatchedFrameASRRNNT, + LongestCommonSubsequenceBatchedFrameASRRNNT, +) +from nemo.collections.asr.parts.utils.transcribe_utils import ( + compute_output_filename, + get_buffered_pred_feat_rnnt, + setup_model, + write_transcription, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging + +can_gpu = torch.cuda.is_available() + + +@dataclass +class TranscriptionConfig: + # Required configs + model_path: Optional[str] = None # Path to a .nemo file + pretrained_name: Optional[str] = None # Name of a pretrained model + audio_dir: Optional[str] = None # Path to a directory which contains audio files + dataset_manifest: Optional[str] = None # Path to dataset's JSON manifest + + # General configs + output_filename: Optional[str] = None + batch_size: int = 32 + num_workers: int = 0 + append_pred: bool = False # Sets mode of work, if True it will add new field transcriptions. + pred_name_postfix: Optional[str] = None # If you need to use another model name, rather than standard one. + random_seed: Optional[int] = None # seed number going to be used in seed_everything() + + # Set to True to output greedy timestamp information (only supported models) + compute_timestamps: bool = False + + # Set to True to output language ID information + compute_langs: bool = False + + # Chunked configs + chunk_len_in_secs: float = 1.6 # Chunk length in seconds + total_buffer_in_secs: float = 4.0 # Length of buffer (chunk + left and right padding) in seconds + model_stride: int = 8 # Model downsampling factor, 8 for Citrinet and FastConformer models and 4 for Conformer models. + + # Set `cuda` to int to define CUDA device. If 'None', will look for CUDA + # device anyway, and do inference on CPU only if CUDA device is not found. + # If `cuda` is a negative number, inference will be on CPU only. + cuda: Optional[int] = None + audio_type: str = "wav" + + # Recompute model transcription, even if the output folder exists with scores. + overwrite_transcripts: bool = True + + # Decoding strategy for RNNT models + decoding: RNNTDecodingConfig = RNNTDecodingConfig() + + # Decoding configs + max_steps_per_timestep: int = 5 #'Maximum number of tokens decoded per acoustic timestep' + stateful_decoding: bool = False # Whether to perform stateful decoding + + # Merge algorithm for transducers + merge_algo: Optional[str] = 'middle' # choices=['middle', 'lcs'], choice of algorithm to apply during inference. + lcs_alignment_dir: Optional[str] = None # Path to a directory to store LCS algo alignments + + # Config for word / character error rate calculation + calculate_wer: bool = True + clean_groundtruth_text: bool = False + langid: str = "en" # specify this for convert_num_to_words step in groundtruth cleaning + use_cer: bool = False + + +@hydra_runner(config_name="TranscriptionConfig", schema=TranscriptionConfig) +def main(cfg: TranscriptionConfig) -> TranscriptionConfig: + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + torch.set_grad_enabled(False) + + for key in cfg: + cfg[key] = None if cfg[key] == 'None' else cfg[key] + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if cfg.random_seed: + pl.seed_everything(cfg.random_seed) + + if cfg.model_path is None and cfg.pretrained_name is None: + raise ValueError("Both cfg.model_path and cfg.pretrained_name cannot be None!") + if cfg.audio_dir is None and cfg.dataset_manifest is None: + raise ValueError("Both cfg.audio_dir and cfg.dataset_manifest cannot be None!") + + filepaths = None + manifest = cfg.dataset_manifest + if cfg.audio_dir is not None: + filepaths = list(glob.glob(os.path.join(cfg.audio_dir, f"**/*.{cfg.audio_type}"), recursive=True)) + manifest = None # ignore dataset_manifest if audio_dir and dataset_manifest both presents + + # setup GPU + if cfg.cuda is None: + if torch.cuda.is_available(): + device = [0] # use 0th CUDA device + accelerator = 'gpu' + else: + device = 1 + accelerator = 'cpu' + else: + device = [cfg.cuda] + accelerator = 'gpu' + map_location = torch.device('cuda:{}'.format(device[0]) if accelerator == 'gpu' else 'cpu') + logging.info(f"Inference will be done on device : {device}") + + asr_model, model_name = setup_model(cfg, map_location) + + model_cfg = copy.deepcopy(asr_model._cfg) + OmegaConf.set_struct(model_cfg.preprocessor, False) + # some changes for streaming scenario + model_cfg.preprocessor.dither = 0.0 + model_cfg.preprocessor.pad_to = 0 + + if model_cfg.preprocessor.normalize != "per_feature": + logging.error("Only EncDecRNNTBPEModel models trained with per_feature normalization are supported currently") + + # Disable config overwriting + OmegaConf.set_struct(model_cfg.preprocessor, True) + + # Compute output filename + cfg = compute_output_filename(cfg, model_name) + + # if transcripts should not be overwritten, and already exists, skip re-transcription step and return + if not cfg.overwrite_transcripts and os.path.exists(cfg.output_filename): + logging.info( + f"Previous transcripts found at {cfg.output_filename}, and flag `overwrite_transcripts`" + f"is {cfg.overwrite_transcripts}. Returning without re-transcribing text." + ) + return cfg + + asr_model.freeze() + asr_model = asr_model.to(asr_model.device) + + # Change Decoding Config + with open_dict(cfg.decoding): + if cfg.stateful_decoding: + cfg.decoding.strategy = "greedy" + else: + cfg.decoding.strategy = "greedy_batch" + cfg.decoding.preserve_alignments = True # required to compute the middle token for transducers. + cfg.decoding.fused_batch_size = -1 # temporarily stop fused batch during inference. + cfg.decoding.beam.return_best_hypothesis = True # return and write the best hypothsis only + + # Setup decoding strategy + if hasattr(asr_model, 'change_decoding_strategy'): + if not isinstance(asr_model, EncDecRNNTModel) and not isinstance(asr_model, EncDecHybridRNNTCTCModel): + raise ValueError("The script supports rnnt model and hybrid model with rnnt decodng!") + else: + # rnnt model + if isinstance(asr_model, EncDecRNNTModel): + asr_model.change_decoding_strategy(cfg.decoding) + + # hybrid ctc rnnt model with decoder_type = rnnt + if hasattr(asr_model, 'cur_decoder'): + asr_model.change_decoding_strategy(cfg.decoding, decoder_type='rnnt') + + feature_stride = model_cfg.preprocessor['window_stride'] + model_stride_in_secs = feature_stride * cfg.model_stride + total_buffer = cfg.total_buffer_in_secs + chunk_len = float(cfg.chunk_len_in_secs) + + tokens_per_chunk = math.ceil(chunk_len / model_stride_in_secs) + mid_delay = math.ceil((chunk_len + (total_buffer - chunk_len) / 2) / model_stride_in_secs) + logging.info(f"tokens_per_chunk is {tokens_per_chunk}, mid_delay is {mid_delay}") + + if cfg.merge_algo == 'middle': + frame_asr = BatchedFrameASRRNNT( + asr_model=asr_model, + frame_len=chunk_len, + total_buffer=cfg.total_buffer_in_secs, + batch_size=cfg.batch_size, + max_steps_per_timestep=cfg.max_steps_per_timestep, + stateful_decoding=cfg.stateful_decoding, + ) + + elif cfg.merge_algo == 'lcs': + frame_asr = LongestCommonSubsequenceBatchedFrameASRRNNT( + asr_model=asr_model, + frame_len=chunk_len, + total_buffer=cfg.total_buffer_in_secs, + batch_size=cfg.batch_size, + max_steps_per_timestep=cfg.max_steps_per_timestep, + stateful_decoding=cfg.stateful_decoding, + alignment_basepath=cfg.lcs_alignment_dir, + ) + # Set the LCS algorithm delay. + frame_asr.lcs_delay = math.floor(((total_buffer - chunk_len)) / model_stride_in_secs) + + else: + raise ValueError("Invalid choice of merge algorithm for transducer buffered inference.") + + hyps = get_buffered_pred_feat_rnnt( + asr=frame_asr, + tokens_per_chunk=tokens_per_chunk, + delay=mid_delay, + model_stride_in_secs=model_stride_in_secs, + batch_size=cfg.batch_size, + manifest=manifest, + filepaths=filepaths, + ) + + output_filename, pred_text_attr_name = write_transcription( + hyps, cfg, model_name, filepaths=filepaths, compute_langs=False, compute_timestamps=False + ) + logging.info(f"Finished writing predictions to {output_filename}!") + + if cfg.calculate_wer: + output_manifest_w_wer, total_res, _ = cal_write_wer( + pred_manifest=output_filename, + pred_text_attr_name=pred_text_attr_name, + clean_groundtruth_text=cfg.clean_groundtruth_text, + langid=cfg.langid, + use_cer=cfg.use_cer, + output_filename=None, + ) + if output_manifest_w_wer: + logging.info(f"Writing prediction and error rate of each sample to {output_manifest_w_wer}!") + logging.info(f"{total_res}") + + return cfg + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_ctc/README.md b/NeMo-2.0.0.rc0.beta/examples/asr/asr_ctc/README.md new file mode 100644 index 0000000..f1751da --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_ctc/README.md @@ -0,0 +1,32 @@ +# ASR with CTC Models + +This directory contains example scripts to train ASR models using Connectionist Temporal Classification Loss. + +Currently supported models are - + +* Character based CTC model +* Subword based CTC model + +# Model execution overview + +The training scripts in this directory execute in the following order. When preparing your own training-from-scratch / fine-tuning scripts, please follow this order for correct training/inference. + +```mermaid + +graph TD + A[Hydra Overrides + Yaml Config] --> B{Config} + B --> |Init| C[Trainer] + C --> D[ExpManager] + B --> D[ExpManager] + C --> E[Model] + B --> |Init| E[Model] + E --> |Constructor| F1(Change Vocabulary) + F1 --> F2(Setup InterCTC if available) + F2 --> F3(Setup Adapters if available) + F3 --> G(Setup Train + Validation + Test Data loaders) + G --> H(Setup Optimization) + H --> I[Maybe init from pretrained] + I --> J["trainer.fit(model)"] +``` + +During restoration of the model, you may pass the Trainer to the restore_from / from_pretrained call, or set it after the model has been initialized by using `model.set_trainer(Trainer)`. \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_ctc/speech_to_text_ctc.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_ctc/speech_to_text_ctc.py new file mode 100644 index 0000000..a39a0ea --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_ctc/speech_to_text_ctc.py @@ -0,0 +1,99 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Training the model + +Basic run (on CPU for 50 epochs): + python examples/asr/asr_ctc/speech_to_text_ctc.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath="" \ + trainer.devices=1 \ + trainer.accelerator='cpu' \ + trainer.max_epochs=50 + + +Add PyTorch Lightning Trainer arguments from CLI: + python speech_to_text_ctc.py \ + ... \ + +trainer.fast_dev_run=true + +Hydra logs will be found in "$(./outputs/$(date +"%y-%m-%d")/$(date +"%H-%M-%S")/.hydra)" +PTL logs will be found in "$(./outputs/$(date +"%y-%m-%d")/$(date +"%H-%M-%S")/lightning_logs)" + +Override some args of optimizer: + python speech_to_text_ctc.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath="./an4/train_manifest.json" \ + model.validation_ds.manifest_filepath="./an4/test_manifest.json" \ + trainer.devices=2 \ + trainer.max_epochs=2 \ + model.optim.args.betas=[0.8,0.5] \ + model.optim.args.weight_decay=0.0001 + +Override optimizer entirely + python speech_to_text_ctc.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath="./an4/train_manifest.json" \ + model.validation_ds.manifest_filepath="./an4/test_manifest.json" \ + trainer.devices=2 \ + trainer.max_epochs=2 \ + model.optim.name=adamw \ + model.optim.lr=0.001 \ + ~model.optim.args \ + +model.optim.args.betas=[0.8,0.5]\ + +model.optim.args.weight_decay=0.0005 + +# Fine-tune a model + +For documentation on fine-tuning this model, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations + +# Pretrained Models + +For documentation on existing pretrained models, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/results.html + +""" + +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models import EncDecCTCModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="../conf", config_name="config") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + asr_model.maybe_init_from_pretrained_checkpoint(cfg) + + trainer.fit(asr_model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if asr_model.prepare_test(trainer): + trainer.test(asr_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py new file mode 100644 index 0000000..5f36f3b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py @@ -0,0 +1,95 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Preparing the Tokenizer for the dataset +Use the `process_asr_text_tokenizer.py` script under /scripts/tokenizers/ in order to prepare the tokenizer. + +```sh +python /scripts/tokenizers/process_asr_text_tokenizer.py \ + --manifest= + OR + --data_file= \ + --data_root="" \ + --vocab_size= \ + --tokenizer=<"spe" or "wpe"> \ + --no_lower_case \ + --spe_type=<"unigram", "bpe", "char" or "word"> \ + --spe_character_coverage=1.0 \ + --log +``` + +# Training the model +```sh +python speech_to_text_ctc_bpe.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath= \ + model.validation_ds.manifest_filepath= \ + model.tokenizer.dir= \ + model.tokenizer.type= \ + trainer.devices=-1 \ + trainer.accelerator="gpu" \ + trainer.strategy="ddp" \ + trainer.max_epochs=100 \ + model.optim.name="adamw" \ + model.optim.lr=0.001 \ + model.optim.betas=[0.9,0.999] \ + model.optim.weight_decay=0.0001 \ + model.optim.sched.warmup_steps=2000 + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="" \ + exp_manager.wandb_logger_kwargs.project="" +``` + +# Fine-tune a model + +For documentation on fine-tuning this model, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations + +# Pretrained Models + +For documentation on existing pretrained models, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/results.html + +""" + +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models.ctc_bpe_models import EncDecCTCModelBPE +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="../conf/citrinet/", config_name="config_bpe") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + asr_model = EncDecCTCModelBPE(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + asr_model.maybe_init_from_pretrained_checkpoint(cfg) + + trainer.fit(asr_model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if asr_model.prepare_test(trainer): + trainer.test(asr_model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_hybrid_transducer_ctc/README.md b/NeMo-2.0.0.rc0.beta/examples/asr/asr_hybrid_transducer_ctc/README.md new file mode 100644 index 0000000..bb52de4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_hybrid_transducer_ctc/README.md @@ -0,0 +1,32 @@ +# ASR with Hybrid Transducer/CTC Models + +This directory contains example scripts to train ASR models with two decoders of Transducer and CTC Loss. + +Currently supported models are - + +* Character based Hybrid RNNT/CTC model +* Subword based Hybrid RNNT/CTC model + +# Model execution overview + +The training scripts in this directory execute in the following order. When preparing your own training-from-scratch / fine-tuning scripts, please follow this order for correct training/inference. + +```mermaid + +graph TD + A[Hydra Overrides + Yaml Config] --> B{Config} + B --> |Init| C[Trainer] + C --> D[ExpManager] + B --> D[ExpManager] + C --> E[Model] + B --> |Init| E[Model] + E --> |Constructor| F1(Change Vocabulary) + F1 --> F2(Setup Adapters if available) + F2 --> G(Setup Train + Validation + Test Data loaders) + G --> H1(Setup Optimization) + H1 --> H2(Change Transducer Decoding Strategy) + H2 --> I[Maybe init from pretrained] + I --> J["trainer.fit(model)"] +``` + +During restoration of the model, you may pass the Trainer to the restore_from / from_pretrained call, or set it after the model has been initialized by using `model.set_trainer(Trainer)`. \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_hybrid_transducer_ctc/helpers/convert_nemo_asr_hybrid_to_ctc.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_hybrid_transducer_ctc/helpers/convert_nemo_asr_hybrid_to_ctc.py new file mode 100644 index 0000000..199e399 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_hybrid_transducer_ctc/helpers/convert_nemo_asr_hybrid_to_ctc.py @@ -0,0 +1,184 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +A script to convert a Nemo ASR Hybrid model file (.nemo) to a Nemo ASR CTC or RNNT model file (.nemo) + +This allows you to train a RNNT-CTC Hybrid model, but then convert it into a pure CTC or pure RNNT model for use +in NeMo. The resulting .nemo file will be a pure CTC or RNNT model, and can be used like any other .nemo model +including in nemo2riva. + +Usage: python convert_nemo_asr_hybrid_to_ctc.py -i /path/to/hybrid.nemo -o /path/to/saved_ctc_model.nemo -m ctc|rnnt + +""" + + +import argparse +import os +from copy import deepcopy + +import torch +from omegaconf import OmegaConf + +from nemo.collections.asr.models import ( + ASRModel, + EncDecCTCModel, + EncDecCTCModelBPE, + EncDecRNNTBPEModel, + EncDecRNNTModel, +) +from nemo.utils import logging + + +def extract_model_ctc(args, hybrid_model): + """ + A function which converts a hybrid model to a pure ctc model. + Args: + args (argparse): the args collection from ArgumentParser created by running this script + hybrid_model (ASRModel): the loaded hybrid RNNT-CTC Nemo model + """ + BPE = False + ctc_class = EncDecCTCModel + if 'tokenizer' in hybrid_model.cfg.keys(): + BPE = True + ctc_class = EncDecCTCModelBPE + + hybrid_model_cfg = OmegaConf.to_container(hybrid_model.cfg) + + new_cfg = deepcopy(hybrid_model_cfg) + new_cfg['ctc_reduction'] = hybrid_model_cfg['aux_ctc']['ctc_reduction'] + new_cfg['decoder'] = hybrid_model_cfg['aux_ctc']['decoder'] + del new_cfg['compute_eval_loss'] + del new_cfg['model_defaults'] + del new_cfg['joint'] + del new_cfg['decoding'] + del new_cfg['aux_ctc'] + del new_cfg['loss'] + if BPE and 'labels' in new_cfg: + del new_cfg['labels'] + elif (not BPE) and 'tokenizer' in new_cfg: + del new_cfg['tokenizer'] + del new_cfg['target'] + del new_cfg['nemo_version'] + + new_cfg_oc = OmegaConf.create(new_cfg) + + # we call restore_from with strict=False because the .nemo file we're restoring from is a hybrid model, which will have named + # tensors in the state_dict that do not exist in the pure CTC model class, which would result in an exception with strict=True + ctc_model = ctc_class.restore_from( + args.input, map_location=torch.device('cpu'), override_config_path=new_cfg_oc, strict=False + ) + + assert all( + [ + torch.allclose(hybrid_model.state_dict()[x], ctc_model.state_dict()[x]) + for x in hybrid_model.state_dict().keys() + if x.split('.')[0] in ['preprocessor', 'encoder'] + ] + ), "Encoder and preprocessor state dicts don't match!" + + ctc_model.decoder.load_state_dict(hybrid_model.ctc_decoder.state_dict()) + + assert all( + [ + torch.allclose(hybrid_model.ctc_decoder.state_dict()[x], ctc_model.decoder.state_dict()[x]) + for x in hybrid_model.ctc_decoder.state_dict().keys() + ] + ), "Decoder state_dict load failed!" + + assert isinstance(ctc_model, ctc_class), "Extracted CTC model is of the wrong expected class!" + + return ctc_model + + +def extract_model_rnnt(args, hybrid_model): + """ + A function which converts a hybrid model to a pure rnnt model. + Args: + args (argparse): the args collection from ArgumentParser created by running this script + hybrid_model (ASRModel): the loaded hybrid RNNT-CTC Nemo model + """ + BPE = False + rnnt_class = EncDecRNNTModel + if 'tokenizer' in hybrid_model.cfg.keys(): + BPE = True + rnnt_class = EncDecRNNTBPEModel + + hybrid_model_cfg = OmegaConf.to_container(hybrid_model.cfg) + + new_cfg = deepcopy(hybrid_model_cfg) + del new_cfg['aux_ctc'] + if BPE and 'labels' in new_cfg: + del new_cfg['labels'] + elif (not BPE) and 'tokenizer' in new_cfg: + del new_cfg['tokenizer'] + del new_cfg['target'] + del new_cfg['nemo_version'] + + new_cfg_oc = OmegaConf.create(new_cfg) + + # we call restore_from with strict=False because the .nemo file we're restoring from is a hybrid model, which will have named + # tensors in the state_dict that do not exist in the pure RNNT model class, which would result in an exception with strict=True + rnnt_model = rnnt_class.restore_from( + args.input, map_location=torch.device('cpu'), override_config_path=new_cfg_oc, strict=False + ) + + assert all( + [ + torch.allclose(hybrid_model.state_dict()[x], rnnt_model.state_dict()[x]) + for x in hybrid_model.state_dict().keys() + if x.split('.')[0] in ['preprocessor', 'encoder', 'decoder', 'joint'] + ] + ), "State dict values mismatch, something went wrong!" + + assert isinstance(rnnt_model, rnnt_class), "Extracted RNNT model is of the wrong expected class!" + + return rnnt_model + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-i', '--input', required=True, type=str, help='path to Nemo Hybrid model .nemo file') + parser.add_argument('-o', '--output', required=True, type=str, help='path and name of output .nemo file') + parser.add_argument( + '-t', + '--model_type', + required=False, + type=str, + default='ctc', + choices=['ctc', 'rnnt'], + help='whether to output a ctc or rnnt model from the hybrid', + ) + + args = parser.parse_args() + + if not os.path.exists(args.input): + logging.critical(f'Input file [ {args.input} ] does not exist or cannot be found. Aborting.') + exit(255) + + hybrid_model = ASRModel.restore_from(args.input, map_location=torch.device('cpu')) + + if args.model_type == 'ctc': + output_model = extract_model_ctc(args, hybrid_model) + elif args.model_type == 'rnnt': + output_model = extract_model_rnnt(args, hybrid_model) + else: + logging.critical( + f"the model_type arg must be one of 'ctc' or 'rnnt', received unknown value: '{args.model_type}'. Aborting." + ) + exit(255) + + output_model.save_to(args.output) + logging.info(f'Converted {args.model_type.upper()} model was successfully saved to {args.output}') diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py new file mode 100644 index 0000000..2de150c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py @@ -0,0 +1,91 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Preparing the Tokenizer for the dataset +Use the `process_asr_text_tokenizer.py` script under /scripts/tokenizers/ in order to prepare the tokenizer. + +```sh +python /scripts/tokenizers/process_asr_text_tokenizer.py \ + --manifest= + OR + --data_file= \ + --data_root="" \ + --vocab_size= \ + --tokenizer=<"spe" or "wpe"> \ + --no_lower_case \ + --spe_type=<"unigram", "bpe", "char" or "word"> \ + --spe_character_coverage=1.0 \ + --log +``` + +# Training the model +```sh +python speech_to_text_hybrid_rnnt_ctc_bpe.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath= \ + model.validation_ds.manifest_filepath= \ + model.tokenizer.dir= \ + model.tokenizer.type= \ + model.aux_ctc.ctc_loss_weight=0.3 \ + trainer.devices=-1 \ + trainer.max_epochs=100 \ + model.optim.name="adamw" \ + model.optim.lr=0.001 \ + model.optim.betas=[0.9,0.999] \ + model.optim.weight_decay=0.0001 \ + model.optim.sched.warmup_steps=2000 + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="" \ + exp_manager.wandb_logger_kwargs.project="" +``` + +# Fine-tune a model + +For documentation on fine-tuning this model, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations + +""" + +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models import EncDecHybridRNNTCTCBPEModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner( + config_path="../conf/conformer/hybrid_transducer_ctc/", config_name="conformer_hybrid_transducer_ctc_bpe" +) +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + asr_model = EncDecHybridRNNTCTCBPEModel(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + asr_model.maybe_init_from_pretrained_checkpoint(cfg) + + trainer.fit(asr_model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if asr_model.prepare_test(trainer): + trainer.test(asr_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_char.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_char.py new file mode 100644 index 0000000..532e2c9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_char.py @@ -0,0 +1,100 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Training the model + +Basic run (on CPU for 50 epochs): + python examples/asr/asr_transducer/speech_to_text_hybrid_rnnt_ctc.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath="" \ + trainer.devices=1 \ + trainer.accelerator='cpu' \ + trainer.max_epochs=50 + + +Add PyTorch Lightning Trainer arguments from CLI: + python speech_to_text_rnnt.py \ + ... \ + +trainer.fast_dev_run=true + +Hydra logs will be found in "$(./outputs/$(date +"%y-%m-%d")/$(date +"%H-%M-%S")/.hydra)" +PTL logs will be found in "$(./outputs/$(date +"%y-%m-%d")/$(date +"%H-%M-%S")/lightning_logs)" + +Override some args of optimizer: + python speech_to_text_hybrid_rnnt_ctc.py \ + --config-path="../conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc" \ + --config-name="config_rnnt" \ + model.train_ds.manifest_filepath="./an4/train_manifest.json" \ + model.validation_ds.manifest_filepath="./an4/test_manifest.json" \ + trainer.devices=2 \ + model.aux_ctc.ctc_loss_weight=0.3 \ + trainer.precision=16 \ + trainer.max_epochs=2 \ + model.optim.betas=[0.8,0.5] \ + model.optim.weight_decay=0.0001 + +Override optimizer entirely + python speech_to_text_hybrid_rnnt_ctc.py \ + --config-path="../conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc" \ + --config-name="config_rnnt" \ + model.train_ds.manifest_filepath="./an4/train_manifest.json" \ + model.validation_ds.manifest_filepath="./an4/test_manifest.json" \ + model.aux_ctc.ctc_loss_weight=0.3 \ + trainer.devices=2 \ + trainer.precision=16 \ + trainer.max_epochs=2 \ + model.optim.name=adamw \ + model.optim.lr=0.001 \ + ~model.optim.args \ + +model.optim.args.betas=[0.8,0.5]\ + +model.optim.args.weight_decay=0.0005 + +# Fine-tune a model + +For documentation on fine-tuning this model, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations + +""" + +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models import EncDecHybridRNNTCTCModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="../conf/conformer/hybrid_transducer_ctc/", config_name="conformer_hybrid_transducer_ctc") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + asr_model = EncDecHybridRNNTCTCModel(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + asr_model.maybe_init_from_pretrained_checkpoint(cfg) + + trainer.fit(asr_model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if asr_model.prepare_test(trainer): + trainer.test(asr_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_transducer/README.md b/NeMo-2.0.0.rc0.beta/examples/asr/asr_transducer/README.md new file mode 100644 index 0000000..e1b3335 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_transducer/README.md @@ -0,0 +1,32 @@ +# ASR with Transducer Models + +This directory contains example scripts to train ASR models using Transducer Loss (often termed RNNT Loss). + +Currently supported models are - + +* Character based RNNT model +* Subword based RNNT model + +# Model execution overview + +The training scripts in this directory execute in the following order. When preparing your own training-from-scratch / fine-tuning scripts, please follow this order for correct training/inference. + +```mermaid + +graph TD + A[Hydra Overrides + Yaml Config] --> B{Config} + B --> |Init| C[Trainer] + C --> D[ExpManager] + B --> D[ExpManager] + C --> E[Model] + B --> |Init| E[Model] + E --> |Constructor| F1(Change Vocabulary) + F1 --> F2(Setup Adapters if available) + F2 --> G(Setup Train + Validation + Test Data loaders) + G --> H1(Setup Optimization) + H1 --> H2(Change Transducer Decoding Strategy) + H2 --> I[Maybe init from pretrained] + I --> J["trainer.fit(model)"] +``` + +During restoration of the model, you may pass the Trainer to the restore_from / from_pretrained call, or set it after the model has been initialized by using `model.set_trainer(Trainer)`. \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_transducer/speech_to_text_rnnt.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_transducer/speech_to_text_rnnt.py new file mode 100644 index 0000000..bc75a01 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_transducer/speech_to_text_rnnt.py @@ -0,0 +1,98 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Training the model + +Basic run (on CPU for 50 epochs): + python examples/asr/asr_transducer/speech_to_text_rnnt.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath="" \ + trainer.devices=1 \ + trainer.accelerator='cpu' \ + trainer.max_epochs=50 + + +Add PyTorch Lightning Trainer arguments from CLI: + python speech_to_text_rnnt.py \ + ... \ + +trainer.fast_dev_run=true + +Hydra logs will be found in "$(./outputs/$(date +"%y-%m-%d")/$(date +"%H-%M-%S")/.hydra)" +PTL logs will be found in "$(./outputs/$(date +"%y-%m-%d")/$(date +"%H-%M-%S")/lightning_logs)" + +Override some args of optimizer: + python speech_to_text_rnnt.py \ + --config-path="experimental/contextnet_rnnt" \ + --config-name="config_rnnt" \ + model.train_ds.manifest_filepath="./an4/train_manifest.json" \ + model.validation_ds.manifest_filepath="./an4/test_manifest.json" \ + trainer.devices=2 \ + trainer.precision=16 \ + trainer.max_epochs=2 \ + model.optim.betas=[0.8,0.5] \ + model.optim.weight_decay=0.0001 + +Override optimizer entirely + python speech_to_text_rnnt.py \ + --config-path="experimental/contextnet_rnnt" \ + --config-name="config_rnnt" \ + model.train_ds.manifest_filepath="./an4/train_manifest.json" \ + model.validation_ds.manifest_filepath="./an4/test_manifest.json" \ + trainer.devices=2 \ + trainer.precision=16 \ + trainer.max_epochs=2 \ + model.optim.name=adamw \ + model.optim.lr=0.001 \ + ~model.optim.args \ + +model.optim.args.betas=[0.8,0.5]\ + +model.optim.args.weight_decay=0.0005 + +# Fine-tune a model + +For documentation on fine-tuning this model, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations + +""" + +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models import EncDecRNNTModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="experimental/contextnet_rnnt", config_name="config_rnnt") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + asr_model = EncDecRNNTModel(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + asr_model.maybe_init_from_pretrained_checkpoint(cfg) + + trainer.fit(asr_model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if asr_model.prepare_test(trainer): + trainer.test(asr_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py new file mode 100644 index 0000000..339f65a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py @@ -0,0 +1,90 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Preparing the Tokenizer for the dataset +Use the `process_asr_text_tokenizer.py` script under /scripts/tokenizers/ in order to prepare the tokenizer. + +```sh +python /scripts/tokenizers/process_asr_text_tokenizer.py \ + --manifest= + OR + --data_file= \ + --data_root="" \ + --vocab_size= \ + --tokenizer=<"spe" or "wpe"> \ + --no_lower_case \ + --spe_type=<"unigram", "bpe", "char" or "word"> \ + --spe_character_coverage=1.0 \ + --log +``` + +# Training the model +```sh +python speech_to_text_rnnt_bpe.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath= \ + model.validation_ds.manifest_filepath= \ + model.tokenizer.dir= \ + model.tokenizer.type= \ + trainer.devices=-1 \ + trainer.accelerator="gpu" \ + trainer.strategy="ddp" \ + trainer.max_epochs=100 \ + model.optim.name="adamw" \ + model.optim.lr=0.001 \ + model.optim.betas=[0.9,0.999] \ + model.optim.weight_decay=0.0001 \ + model.optim.sched.warmup_steps=2000 + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="" \ + exp_manager.wandb_logger_kwargs.project="" +``` + +# Fine-tune a model + +For documentation on fine-tuning this model, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations + +""" + +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models import EncDecRNNTBPEModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="experimental/contextnet_rnnt", config_name="config_rnnt_bpe") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + asr_model = EncDecRNNTBPEModel(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + asr_model.maybe_init_from_pretrained_checkpoint(cfg) + + trainer.fit(asr_model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if asr_model.prepare_test(trainer): + trainer.test(asr_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_vad/README.md b/NeMo-2.0.0.rc0.beta/examples/asr/asr_vad/README.md new file mode 100644 index 0000000..f39b973 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_vad/README.md @@ -0,0 +1,60 @@ +# NeMo ASR+VAD Inference + +This example provides the ASR+VAD inference pipeline, with the option to perform only ASR or VAD alone. + +## Input + +There are two types of input +- A manifest passed to `manifest_filepath`, +- A directory containing audios passed to `audio_dir` and also specify `audio_type` (default to `wav`). + +The input manifest must be a manifest json file, where each line is a Python dictionary. The fields ["audio_filepath", "offset", "duration"] are required. An example of a manifest file is: +```json +{"audio_filepath": "/path/to/audio_file1", "offset": 0, "duration": 10000} +{"audio_filepath": "/path/to/audio_file2", "offset": 0, "duration": 10000} +``` + +If you want to calculate WER, provide `text` in manifest as groundtruth. An example of a manifest file is: +```json +{"audio_filepath": "/path/to/audio_file1", "offset": 0, "duration": 10000, "text": "hello world"} +{"audio_filepath": "/path/to/audio_file2", "offset": 0, "duration": 10000, "text": "hello world"} +``` + +## Output +Output will be a folder storing the VAD predictions and/or a manifest containing the audio transcriptions. Some temporary data will also be stored. + + +## Usage + +To run the code with ASR+VAD default settings: + +```bash +python speech_to_text_with_vad.py \ + manifest_filepath=/PATH/TO/MANIFEST.json \ + vad_model=vad_multilingual_frame_marblenet \ + asr_model=stt_en_conformer_ctc_large \ + vad_config=../conf/vad/frame_vad_infer_postprocess.yaml +``` + +- To use only ASR and disable VAD, set `vad_model=None` and `use_rttm=False`. + +- To use only VAD, set `asr_model=None` and specify both `vad_model` and `vad_config`. + +- To enable profiling, set `profiling=True`, but this will significantly slow down the program. + +### Using RTTM to handle non-speech audio segments +- To use or disable RTTM usage, set `use_rttm` to `True` or `False`. There are two options to use RTTM files, as specified by the parameter `rttm_mode`, which must be one of `mask` or `drop`. For `mask`, the RTTM file will be used to mask the non-speech features. For `drop`, the RTTM file will be used to drop the non-speech features. + +- It's recommended that for `rttm_mode='drop'`, use larger `pad_onset` and `pad_offset` to avoid dropping speech features. + +- To use a specific value for feature masking, set `feat_mask_val` to the desired value. +Default is `feat_mask_val=None`, where -16.530 (zero log mel-spectrogram value) will be used for `post_norm` and 0 (same as SpecAugment) will be used for `pre_norm`. + +- To normalize feature before masking, set `normalize=pre_norm`, and set `normalize=post_norm` for masking before normalization. + +### Frame-VAD and Segment-VAD +- By default, `speech_to_text_with_vad.py` and `vad_config=../conf/vad/frame_vad_infer_postprocess.yaml` will use a frame-VAD model, which generates a speech/non-speech prediction for each audio frame of 20ms. +- To use segment-VAD, use `speech_to_text_with_vad.py vad_type='segment' vad_config=../conf/vad/vad_inference_postprocessing.yaml` instead. In segment-VAD, the audio is split into segments and VAD is performed on each segment. The segments are then stitched together to form the final output. The segment size and stride can be specified by `window_length_in_sec` and `shift_length_in_sec` in the VAD config (e.g., `../conf/vad/vad_inference_postprocessing.yaml`) respectively. The default values are 0.63 seconds and 0.08 seconds respectively. + +### More options +- See more options in the `InferenceConfig` data class. diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_vad/speech_to_text_with_vad.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_vad/speech_to_text_with_vad.py new file mode 100644 index 0000000..391f299 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_vad/speech_to_text_with_vad.py @@ -0,0 +1,644 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This file provides the ASR+VAD inference pipeline, with the option to perform only ASR or VAD alone. + +There are two types of input, the first one is a manifest passed to `manifest_filepath`, +and the other one is to pass a directory containing audios to `audio_dir` and specify `audio_type`. + +The input manifest must be a manifest json file, where each line is a Python dictionary. The fields ["audio_filepath", "offset", "duration", "text"] are required. An example of a manifest file is: +``` +{"audio_filepath": "/path/to/audio_file1", "offset": 0, "duration": 10000, "text": "a b c d e"} +{"audio_filepath": "/path/to/audio_file2", "offset": 0, "duration": 10000, "text": "f g h i j"} +``` + +To run the code with ASR+VAD default settings: + +```bash +python speech_to_text_with_vad.py \ + manifest_filepath=/PATH/TO/MANIFEST.json \ + vad_model=vad_multilingual_frame_marblenet\ + asr_model=stt_en_conformer_ctc_large \ + vad_config=../conf/vad/frame_vad_inference_postprocess.yaml +``` + +To use only ASR and disable VAD, set `vad_model=None` and `use_rttm=False`. + +To use only VAD, set `asr_model=None` and specify both `vad_model` and `vad_config`. + +To enable profiling, set `profiling=True`, but this will significantly slow down the program. + +To use or disable feature masking/droping based on RTTM files, set `use_rttm` to `True` or `False`. +There are two ways to use RTTM files, either by masking the features (`rttm_mode=mask`) or by dropping the features (`rttm_mode=drop`). +For audios that have long non-speech audios between speech segments, dropping frames is recommended. + +To normalize feature before masking, set `normalize=pre_norm`, +and set `normalize=post_norm` for masking before normalization. + +To use a specific value for feature masking, set `feat_mask_val` to the desired value. +Default is `feat_mask_val=None`, where -16.635 will be used for `post_norm` and 0 will be used for `pre_norm`. + +See more options in the `InferenceConfig` class. +""" + + +import contextlib +import json +import os +import time +from dataclasses import dataclass, is_dataclass +from pathlib import Path +from typing import Callable, Optional + +import torch +import yaml +from omegaconf import DictConfig, OmegaConf +from torch.profiler import ProfilerActivity, profile, record_function +from tqdm import tqdm + +from nemo.collections.asr.data import feature_to_text_dataset +from nemo.collections.asr.metrics.wer import word_error_rate +from nemo.collections.asr.models import ASRModel, EncDecClassificationModel +from nemo.collections.asr.parts.submodules import CTCDecodingConfig +from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTDecodingConfig +from nemo.collections.asr.parts.utils.manifest_utils import read_manifest, write_manifest +from nemo.collections.asr.parts.utils.vad_utils import ( + generate_overlap_vad_seq, + generate_vad_segment_table, + get_vad_stream_status, + init_frame_vad_model, + init_vad_model, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging + +try: + from torch.cuda.amp import autocast +except ImportError: + + @contextlib.contextmanager + def autocast(enabled=None): + yield + + +@dataclass +class InferenceConfig: + # Required configs + asr_model: Optional[str] = None # Path to a .nemo file or a pretrained NeMo model on NGC + vad_model: Optional[str] = None # Path to a .nemo file or a pretrained NeMo model on NGC + vad_config: Optional[str] = None # Path to a yaml file containing VAD post-processing configs + manifest_filepath: Optional[str] = None # Path to dataset's JSON manifest + audio_dir: Optional[str] = None # Path to a directory containing audio files, use this if no manifest is provided + + use_rttm: bool = True # whether to use RTTM + rttm_mode: str = "mask" # how to use RTTM files, choices=[`mask`, `drop`] + feat_mask_val: Optional[float] = None # value used to mask features based on RTTM, set None to use defaults + normalize: Optional[ + str + ] = "post_norm" # whether and where to normalize audio feature, choices=[None, `pre_norm`, `post_norm`] + normalize_type: str = "per_feature" # how to determine mean and std used for normalization + normalize_audio_db: Optional[float] = None # set to normalize RMS DB of audio before extracting audio features + + profiling: bool = False # whether to enable pytorch profiling + + # General configs + batch_size: int = 1 # batch size for ASR. Feature extraction and VAD only support single sample per batch. + num_workers: int = 8 + sample_rate: int = 16000 + frame_unit_time_secs: float = 0.01 # unit time per frame in seconds, equal to `window_stride` in ASR configs, typically 10ms. + audio_type: str = "wav" + + # Output settings, no need to change + output_dir: Optional[str] = None # will be automatically set by the program + output_filename: Optional[str] = None # will be automatically set by the program + pred_name_postfix: Optional[str] = None # If you need to use another model name, other than the standard one. + + # Set to True to output language ID information + compute_langs: bool = False + + # Decoding strategy for CTC models + ctc_decoding: CTCDecodingConfig = CTCDecodingConfig() + + # Decoding strategy for RNNT models + rnnt_decoding: RNNTDecodingConfig = RNNTDecodingConfig(fused_batch_size=-1) + + # VAD model type + vad_type: str = "frame" # which type of VAD to use, choices=[`frame`, `segment`] + + +@hydra_runner(config_name="InferenceConfig", schema=InferenceConfig) +def main(cfg): + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if cfg.output_dir is None: + cfg.output_dir = "./outputs" + output_dir = Path(cfg.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + # setup profiling, note that profiling will significantly increast the total runtime + if cfg.profiling: + logging.info("Profiling enabled") + profile_fn = profile + record_fn = record_function + else: + logging.info("Profiling disabled") + + @contextlib.contextmanager + def profile_fn(*args, **kwargs): + yield + + @contextlib.contextmanager + def record_fn(*args, **kwargs): + yield + + input_manifest_file = prepare_inference_manifest(cfg) + + if cfg.manifest_filepath is None: + cfg.manifest_filepath = str(input_manifest_file) + + with profile_fn( + activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True, profile_memory=True + ) as prof: + + input_manifest_file = extract_audio_features(input_manifest_file, cfg, record_fn) + + if cfg.vad_model is not None: + logging.info(f"Running VAD with model: {cfg.vad_model}") + input_manifest_file = run_vad_inference(input_manifest_file, cfg, record_fn) + + if cfg.asr_model is not None: + logging.info(f"Running ASR with model: {cfg.asr_model}") + run_asr_inference(input_manifest_file, cfg, record_fn) + + if cfg.profiling: + print("--------------------------------------------------------------------\n") + print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=15)) + print("--------------------------------------------------------------------\n") + logging.info("Done.") + + +def prepare_inference_manifest(cfg: DictConfig) -> str: + + if cfg.audio_dir is not None and cfg.manifest_filepath is None: + manifest_data = [] + for audio_file in Path(cfg.audio_dir).glob(f"**/*.{cfg.audio_type}"): + item = {"audio_filepath": str(audio_file.absolute()), "duration": 1000000, "offset": 0} + manifest_data.append(item) + parent_dir = Path(cfg.audio_dir) + else: + manifest_data = read_manifest(cfg.manifest_filepath) + parent_dir = Path(cfg.manifest_filepath).parent + + new_manifest_data = [] + + for item in manifest_data: + audio_file = Path(item["audio_filepath"]) + if len(str(audio_file)) < 255 and not audio_file.is_file() and not audio_file.is_absolute(): + new_audio_file = parent_dir / audio_file + if new_audio_file.is_file(): + item["audio_filepath"] = str(new_audio_file.absolute()) + else: + item["audio_filepath"] = os.path.expanduser(str(audio_file)) + else: + item["audio_filepath"] = os.path.expanduser(str(audio_file)) + item["label"] = "infer" + item["text"] = "-" + new_manifest_data.append(item) + + new_manifest_filepath = str(Path(cfg.output_dir) / Path("temp_manifest_input.json")) + write_manifest(new_manifest_filepath, new_manifest_data) + return new_manifest_filepath + + +def extract_audio_features(manifest_filepath: str, cfg: DictConfig, record_fn: Callable) -> str: + file_list = [] + manifest_data = [] + out_dir = Path(cfg.output_dir) / Path("features") + new_manifest_filepath = str(Path(cfg.output_dir) / Path("temp_manifest_input_feature.json")) + + if Path(new_manifest_filepath).is_file(): + logging.info("Features already exist in output_dir, skipping feature extraction.") + return new_manifest_filepath + + has_feat = False + with open(manifest_filepath, 'r', encoding='utf-8') as fin: + for line in fin.readlines(): + item = json.loads(line.strip()) + manifest_data.append(item) + file_list.append(Path(item['audio_filepath']).stem) + if "feature_file" in item: + has_feat = True + if has_feat: + logging.info("Features already exist in manifest, skipping feature extraction.") + return manifest_filepath + + out_dir.mkdir(parents=True, exist_ok=True) + torch.set_grad_enabled(False) + if cfg.vad_model: + vad_model = init_frame_vad_model(cfg.vad_model) + else: + vad_model = EncDecClassificationModel.from_pretrained("vad_multilingual_marblenet") + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + vad_model = vad_model.to(device) + vad_model.eval() + vad_model.setup_test_data( + test_data_config={ + 'batch_size': 1, + 'vad_stream': False, + 'sample_rate': cfg.sample_rate, + 'manifest_filepath': manifest_filepath, + 'labels': ['infer',], + 'num_workers': cfg.num_workers, + 'shuffle': False, + 'normalize_audio_db': cfg.normalize_audio_db, + } + ) + + logging.info(f"Extracting features on {len(file_list)} audio files...") + with record_fn("feat_extract_loop"): + for i, test_batch in enumerate(tqdm(vad_model.test_dataloader(), total=len(vad_model.test_dataloader()))): + test_batch = [x.to(vad_model.device) for x in test_batch] + with autocast(): + with record_fn("feat_extract_infer"): + processed_signal, processed_signal_length = vad_model.preprocessor( + input_signal=test_batch[0], length=test_batch[1], + ) + with record_fn("feat_extract_other"): + processed_signal = processed_signal.squeeze(0)[:, :processed_signal_length] + processed_signal = processed_signal.cpu() + outpath = os.path.join(out_dir, file_list[i] + ".pt") + outpath = str(Path(outpath).absolute()) + torch.save(processed_signal, outpath) + manifest_data[i]["feature_file"] = outpath + del test_batch + + logging.info(f"Features saved at: {out_dir}") + write_manifest(new_manifest_filepath, manifest_data) + return new_manifest_filepath + + +def run_vad_inference(manifest_filepath: str, cfg: DictConfig, record_fn: Callable) -> str: + logging.info("Start VAD inference pipeline...") + if cfg.vad_type == "segment": + vad_model = init_vad_model(cfg.vad_model) + elif cfg.vad_type == "frame": + vad_model = init_frame_vad_model(cfg.vad_model) + else: + raise ValueError(f"Unknown VAD type: {cfg.vad_type}, supported types: ['segment', 'frame']") + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + vad_model = vad_model.to(device) + vad_model.eval() + + vad_yaml = Path(cfg.vad_config) + if not vad_yaml.is_file(): + raise ValueError(f"VAD config file not found: {cfg.vad_config}") + + with vad_yaml.open("r") as fp: + vad_cfg = yaml.safe_load(fp) + vad_cfg = DictConfig(vad_cfg) + + test_data_config = { + 'vad_stream': True, + 'manifest_filepath': manifest_filepath, + 'labels': ['infer',], + 'num_workers': cfg.num_workers, + 'shuffle': False, + 'window_length_in_sec': vad_cfg.vad.parameters.window_length_in_sec, + 'shift_length_in_sec': vad_cfg.vad.parameters.shift_length_in_sec, + } + vad_model.setup_test_data(test_data_config=test_data_config, use_feat=True) + + pred_dir = Path(cfg.output_dir) / Path("vad_frame_pred") + if pred_dir.is_dir(): + logging.info(f"VAD frame-level prediction already exists: {pred_dir}, skipped") + else: + logging.info("Generating VAD frame-level prediction") + pred_dir.mkdir(parents=True) + t0 = time.time() + pred_dir = generate_vad_frame_pred( + vad_model=vad_model, + window_length_in_sec=vad_cfg.vad.parameters.window_length_in_sec, + shift_length_in_sec=vad_cfg.vad.parameters.shift_length_in_sec, + manifest_vad_input=manifest_filepath, + out_dir=str(pred_dir), + use_feat=True, + record_fn=record_fn, + ) + t1 = time.time() + logging.info(f"Time elapsed: {t1 - t0: .2f} seconds") + logging.info( + f"Finished generating VAD frame level prediction with window_length_in_sec={vad_cfg.vad.parameters.window_length_in_sec} and shift_length_in_sec={vad_cfg.vad.parameters.shift_length_in_sec}" + ) + + frame_length_in_sec = vad_cfg.vad.parameters.shift_length_in_sec + # overlap smoothing filter + if vad_cfg.vad.parameters.smoothing: + # Generate predictions with overlapping input segments. Then a smoothing filter is applied to decide the label for a frame spanned by multiple segments. + # smoothing_method would be either in majority vote (median) or average (mean) + logging.info("Generating predictions with overlapping input segments") + t0 = time.time() + smoothing_pred_dir = generate_overlap_vad_seq( + frame_pred_dir=pred_dir, + smoothing_method=vad_cfg.vad.parameters.smoothing, + overlap=vad_cfg.vad.parameters.overlap, + window_length_in_sec=vad_cfg.vad.parameters.window_length_in_sec, + shift_length_in_sec=vad_cfg.vad.parameters.shift_length_in_sec, + num_workers=cfg.num_workers, + out_dir=vad_cfg.smoothing_out_dir, + ) + logging.info( + f"Finish generating predictions with overlapping input segments with smoothing_method={vad_cfg.vad.parameters.smoothing} and overlap={vad_cfg.vad.parameters.overlap}" + ) + t1 = time.time() + logging.info(f"Time elapsed: {t1 - t0: .2f} seconds") + pred_dir = smoothing_pred_dir + frame_length_in_sec = 0.01 + + # Turn frame-wise prediction into speech intervals + logging.info(f"Generating segment tables with postprocessing params: {vad_cfg.vad.parameters.postprocessing}") + segment_dir_name = "vad_rttm" + for key, val in vad_cfg.vad.parameters.postprocessing.items(): + segment_dir_name = segment_dir_name + "-" + str(key) + str(val) + + segment_dir = Path(cfg.output_dir) / Path(segment_dir_name) + if segment_dir.is_dir(): + logging.info(f"VAD speech segments already exists: {segment_dir}, skipped") + else: + segment_dir.mkdir(parents=True) + t0 = time.time() + segment_dir = generate_vad_segment_table( + vad_pred_dir=pred_dir, + postprocessing_params=vad_cfg.vad.parameters.postprocessing, + frame_length_in_sec=frame_length_in_sec, + num_workers=cfg.num_workers, + out_dir=segment_dir, + use_rttm=True, + ) + t1 = time.time() + logging.info(f"Time elapsed: {t1 - t0: .2f} seconds") + logging.info("Finished generating RTTM files from VAD predictions.") + + rttm_map = {} + for filepath in Path(segment_dir).glob("*.rttm"): + rttm_map[filepath.stem] = str(filepath.absolute()) + + manifest_data = read_manifest(manifest_filepath) + for i in range(len(manifest_data)): + key = Path(manifest_data[i]["audio_filepath"]).stem + manifest_data[i]["rttm_file"] = rttm_map[key] + + new_manifest_filepath = str(Path(cfg.output_dir) / Path(f"temp_manifest_{segment_dir_name}.json")) + write_manifest(new_manifest_filepath, manifest_data) + return new_manifest_filepath + + +def generate_vad_frame_pred( + vad_model: EncDecClassificationModel, + window_length_in_sec: float, + shift_length_in_sec: float, + manifest_vad_input: str, + out_dir: str, + use_feat: bool = False, + record_fn: Callable = None, +) -> str: + """ + Generate VAD frame level prediction and write to out_dir + """ + time_unit = int(window_length_in_sec / shift_length_in_sec) + trunc = int(time_unit / 2) + trunc_l = time_unit - trunc + all_len = 0 + + data = [] + with open(manifest_vad_input, 'r', encoding='utf-8') as fin: + for line in fin.readlines(): + file = json.loads(line)['audio_filepath'].split("/")[-1] + data.append(file.split(".wav")[0]) + logging.info(f"Inference on {len(data)} audio files/json lines!") + + status = get_vad_stream_status(data) + + with record_fn("vad_infer_loop"): + for i, test_batch in enumerate(tqdm(vad_model.test_dataloader(), total=len(vad_model.test_dataloader()))): + test_batch = [x.to(vad_model.device) for x in test_batch] + with autocast(): + with record_fn("vad_infer_model"): + if use_feat: + log_probs = vad_model(processed_signal=test_batch[0], processed_signal_length=test_batch[1]) + else: + log_probs = vad_model(input_signal=test_batch[0], input_signal_length=test_batch[1]) + + with record_fn("vad_infer_other"): + probs = torch.softmax(log_probs, dim=-1) + if len(probs.shape) == 3: + # squeeze the batch dimension, since batch size is 1 + probs = probs.squeeze(0) # [1,T,C] -> [T,C] + pred = probs[:, 1] + + if window_length_in_sec == 0: + to_save = pred + elif status[i] == 'start': + to_save = pred[:-trunc] + elif status[i] == 'next': + to_save = pred[trunc:-trunc_l] + elif status[i] == 'end': + to_save = pred[trunc_l:] + else: + to_save = pred + + to_save = to_save.cpu().tolist() + all_len += len(to_save) + + outpath = os.path.join(out_dir, data[i] + ".frame") + with open(outpath, "a", encoding='utf-8') as fout: + for p in to_save: + fout.write(f'{p:0.4f}\n') + + del test_batch + if status[i] == 'end' or status[i] == 'single': + all_len = 0 + return out_dir + + +def init_asr_model(model_path: str) -> ASRModel: + if model_path.endswith('.nemo'): + logging.info(f"Using local ASR model from {model_path}") + asr_model = ASRModel.restore_from(restore_path=model_path) + elif model_path.endswith('.ckpt'): + asr_model = ASRModel.load_from_checkpoint(checkpoint_path=model_path) + else: + logging.info(f"Using NGC ASR model {model_path}") + asr_model = ASRModel.from_pretrained(model_name=model_path) + return asr_model + + +def run_asr_inference(manifest_filepath, cfg, record_fn) -> str: + logging.info("Start ASR inference pipeline...") + asr_model = init_asr_model(cfg.asr_model) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + asr_model = asr_model.to(device) + asr_model.eval() + + # Setup decoding strategy + decode_function = None + decoder_type = cfg.get("decoder_type", None) + if not hasattr(asr_model, 'change_decoding_strategy'): + raise ValueError(f"ASR model {cfg.asr_model} does not support decoding strategy.") + if decoder_type is not None: # Hybrid model + if decoder_type == 'rnnt': + cfg.rnnt_decoding.fused_batch_size = -1 + cfg.rnnt_decoding.compute_langs = cfg.compute_langs + asr_model.change_decoding_strategy(cfg.rnnt_decoding, decoder_type=decoder_type) + decode_function = asr_model.decoding.rnnt_decoder_predictions_tensor + elif decoder_type == 'ctc': + asr_model.change_decoding_strategy(cfg.ctc_decoding, decoder_type=decoder_type) + decode_function = asr_model.decoding.ctc_decoder_predictions_tensor + else: + raise ValueError( + f"Unknown decoder type for hybrid model: {decoder_type}, supported types: ['rnnt', 'ctc']" + ) + elif hasattr(asr_model, 'joint'): # RNNT model + cfg.rnnt_decoding.fused_batch_size = -1 + cfg.rnnt_decoding.compute_langs = cfg.compute_langs + asr_model.change_decoding_strategy(cfg.rnnt_decoding) + decode_function = asr_model.decoding.rnnt_decoder_predictions_tensor + else: + asr_model.change_decoding_strategy(cfg.ctc_decoding) + decode_function = asr_model.decoding.ctc_decoder_predictions_tensor + + # Compute output filename + if cfg.output_filename is None: + # create default output filename + if cfg.pred_name_postfix is not None: + cfg.output_filename = cfg.manifest_filepath.replace('.json', f'_{cfg.pred_name_postfix}.json') + else: + tag = f"{cfg.normalize}_{cfg.normalize_type}" + if cfg.use_rttm: + vad_tag = Path(manifest_filepath).stem + vad_tag = vad_tag[len("temp_manifest_vad_rttm_") :] + if cfg.rttm_mode == "mask": + tag += f"-mask{cfg.feat_mask_val}-{vad_tag}" + else: + tag += f"-dropframe-{vad_tag}" + cfg.output_filename = cfg.manifest_filepath.replace('.json', f'-{Path(cfg.asr_model).stem}-{tag}.json') + cfg.output_filename = Path(cfg.output_dir) / Path(cfg.output_filename).name + + logging.info("Setting up dataloader for ASR...") + data_config = { + "manifest_filepath": manifest_filepath, + "normalize": cfg.normalize, + "normalize_type": cfg.normalize_type, + "use_rttm": cfg.use_rttm, + "rttm_mode": cfg.rttm_mode, + "feat_mask_val": cfg.feat_mask_val, + "frame_unit_time_secs": cfg.frame_unit_time_secs, + } + logging.info(f"use_rttm = {cfg.use_rttm}, rttm_mode = {cfg.rttm_mode}, feat_mask_val = {cfg.feat_mask_val}") + + if hasattr(asr_model, "tokenizer"): + dataset = feature_to_text_dataset.get_bpe_dataset(config=data_config, tokenizer=asr_model.tokenizer) + else: + data_config["labels"] = asr_model.decoder.vocabulary + dataset = feature_to_text_dataset.get_char_dataset(config=data_config) + + dataloader = torch.utils.data.DataLoader( + dataset=dataset, + batch_size=cfg.batch_size, + collate_fn=dataset._collate_fn, + drop_last=False, + shuffle=False, + num_workers=cfg.get('num_workers', 0), + pin_memory=cfg.get('pin_memory', False), + ) + + logging.info("Start transcribing...") + hypotheses = [] + all_hypotheses = [] + t0 = time.time() + with autocast(): + with torch.no_grad(): + with record_fn("asr_infer_loop"): + for test_batch in tqdm(dataloader, desc="Transcribing"): + with record_fn("asr_infer_model"): + outputs = asr_model.forward( + processed_signal=test_batch[0].to(device), + processed_signal_length=test_batch[1].to(device), + ) + + with record_fn("asr_infer_other"): + logits, logits_len = outputs[0], outputs[1] + + current_hypotheses, all_hyp = decode_function(logits, logits_len, return_hypotheses=False,) + if isinstance(current_hypotheses, tuple) and len(current_hypotheses) == 2: + current_hypotheses = current_hypotheses[0] # handle RNNT output + + hypotheses += current_hypotheses + if all_hyp is not None: + all_hypotheses += all_hyp + else: + all_hypotheses += current_hypotheses + + del logits + del test_batch + t1 = time.time() + logging.info(f"Time elapsed: {t1 - t0: .2f} seconds") + + logging.info("Finished transcribing.") + # Save output to manifest + input_manifest_data = read_manifest(manifest_filepath) + manifest_data = read_manifest(cfg.manifest_filepath) + + if "text" not in manifest_data[0]: + has_groundtruth = False + else: + has_groundtruth = True + + groundtruth = [] + for i in range(len(manifest_data)): + if has_groundtruth: + groundtruth.append(manifest_data[i]["text"]) + manifest_data[i]["pred_text"] = hypotheses[i] + manifest_data[i]["feature_file"] = input_manifest_data[i]["feature_file"] + if "rttm_file" in input_manifest_data[i]: + manifest_data[i]["feature_file"] = input_manifest_data[i]["feature_file"] + + write_manifest(cfg.output_filename, manifest_data) + + if not has_groundtruth: + hypotheses = " ".join(hypotheses) + words = hypotheses.split() + chars = "".join(words) + logging.info("-----------------------------------------") + logging.info(f"Number of generated characters={len(chars)}") + logging.info(f"Number of generated words={len(words)}") + logging.info("-----------------------------------------") + else: + wer_score = word_error_rate(hypotheses=hypotheses, references=groundtruth) + cer_score = word_error_rate(hypotheses=hypotheses, references=groundtruth, use_cer=True) + logging.info("-----------------------------------------") + logging.info(f"WER={wer_score:.4f}, CER={cer_score:.4f}") + logging.info("-----------------------------------------") + + logging.info(f"ASR output saved at {cfg.output_filename}") + return cfg.output_filename + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_with_tts/speech_to_text_bpe_with_text.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_with_tts/speech_to_text_bpe_with_text.py new file mode 100644 index 0000000..9462023 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_with_tts/speech_to_text_bpe_with_text.py @@ -0,0 +1,92 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Training hybrid ASR-TTS model using text-only data and/or audio-text pairs. +Provide ASR model config, add options related to TTS and text-only data. + +```shell +python speech_to_text_bpe_with_text.py \ + # (Optional: --config-path= --config-name=) \ + ++asr_model_type= \ + ++tts_model_path= \ + ++enhancer_model_path= \ + model.tokenizer.dir= \ + model.tokenizer.type="bpe" \ + model.train_ds.manifest_filepath= \ + ++model.train_ds.text_data.manifest_filepath= \ + ++model.train_ds.text_data.speakers_filepath= \ + ++model.train_ds.text_data.min_words=1 \ + ++model.train_ds.text_data.max_words=45 \ + ++model.train_ds.text_data.tokenizer_workers=4 \ + model.validation_ds.manifest_filepath= \ + model.train_ds.batch_size= \ + trainer.max_epochs= \ + trainer.num_nodes= \ + trainer.accumulate_grad_batches= \ + ++trainer.precision= \ + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="" \ + exp_manager.wandb_logger_kwargs.project="" \ + ++exp_manager.wandb_logger_kwargs.resume=auto \ + ++exp_manager.wandb_logger_kwargs.id="" \ + exp_manager.resume_if_exists=true \ + exp_manager.resume_ignore_no_checkpoint=true \ + exp_manager.exp_dir= \ + exp_manager.name= +``` +""" + + +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models.hybrid_asr_tts_models import ASRWithTTSModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="examples/asr/conf/conformer", config_name="conformer_transducer_bpe") +def main(cfg): + """ + Training hybrid ASR-TTS model using text-only data and/or audio-text pairs. + Provide ASR model config, add options related to TTS and text-only data. + """ + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + OmegaConf.resolve(cfg) + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + asr_model = ASRWithTTSModel.from_asr_config( + asr_cfg=cfg.model, + asr_model_type=cfg.asr_model_type, + tts_model_path=cfg.tts_model_path, + enhancer_model_path=cfg.get("enhancer_model_path", None), + trainer=trainer, + ) + + # Initialize the weights of the model from another model, if provided via config + asr_model.maybe_init_from_pretrained_checkpoint(cfg) + + trainer.fit(asr_model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if asr_model.prepare_test(trainer): + trainer.test(asr_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/asr_with_tts/speech_to_text_bpe_with_text_finetune.py b/NeMo-2.0.0.rc0.beta/examples/asr/asr_with_tts/speech_to_text_bpe_with_text_finetune.py new file mode 100644 index 0000000..5ded1ff --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/asr_with_tts/speech_to_text_bpe_with_text_finetune.py @@ -0,0 +1,80 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Finetuning pretrained ASR model with text-only data (can be mixed with audio-text pairs) +```shell +python speech_to_text_bpe_with_text_finetune.py \ + # (Optional: --config-path= --config-name=) \ + model.asr_model_path= \ + model.tts_model_path= \ + model.enhancer_model_path= \ + model.asr_model_fuse_bn= \ + model.train_ds.manifest_filepath= \ + model.train_ds.text_data.manifest_filepath= \ + model.train_ds.text_data.speakers_filepath= \ + model.train_ds.text_data.tokenizer_workers=4 \ + model.validation_ds.manifest_filepath= \ + model.train_ds.batch_size={args.batch_size} \ + trainer.max_epochs= \ + trainer.num_nodes= \ + trainer.accumulate_grad_batches= \ + trainer.precision= \ + model.optim.lr=1e-4 \ + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="" \ + exp_manager.wandb_logger_kwargs.project="" \ + ++exp_manager.wandb_logger_kwargs.resume=auto \ + ++exp_manager.wandb_logger_kwargs.id="" \ + exp_manager.resume_if_exists=true \ + exp_manager.resume_ignore_no_checkpoint=true \ + exp_manager.exp_dir= \ + exp_manager.name= +``` +""" + + +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models.hybrid_asr_tts_models import ASRWithTTSModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="examples/asr/asr_tts", config_name="hybrid_asr_tts") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + OmegaConf.resolve(cfg) + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + asr_model = ASRWithTTSModel(cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + asr_model.maybe_init_from_pretrained_checkpoint(cfg) + + # validate before training to get baseline metrics + trainer.validate(asr_model) + trainer.fit(asr_model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if asr_model.prepare_test(trainer): + trainer.test(asr_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_adapters/asr_adaptation.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_adapters/asr_adaptation.yaml new file mode 100644 index 0000000..6ab3f12 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_adapters/asr_adaptation.yaml @@ -0,0 +1,220 @@ +# Config to perform ASR adaptation using any pre-trained model (local nemo model or pre-trained checkpoint). +############################################################################################################ +# This config is special in that it is used alongside the scripts in the asr_adapters examples directory, +# but does not directly construct a model itself. Instead it mimics the usual ASR model configs, and initializes +# a pre-trained model (either local or via network), and overrides its many data loaders / optimizer / scheduler +# and other arguments +# +# **Note**: This config does *not* get stored in the adapted model, since this config is merely to setup the +# adapter training / inference script. This file can be considered a config not for the model, but for the +# script that will adapt the model or infer an adapted model. +# +# You can therefore call this script multiple times to add as many adapters as you need in a single model, +# by providing the previous adapted checkpoint as `model.nemo_model`. +# +# **Note**: Any config value in this yaml file *overrides* the equivalent config inside the model ! +# +# There are some important paramters of this config that must be updated by the user : +# - model.pretrained_model or model.nemo_model: str name or path to some pretrained model. Only one of the +# two should be passed. Selects the pre-trained model to be loaded and adapted. +# +# - model.adapter.adapter_name: Globally unique name, assigned to the adapter itself. Every adapter of a +# model must have a unique name. +# +# - model.adapter.in_features: The output dimension of each block of the model. This is model dependent. +# For example, Conformer dimension can be found via `model.encoder.d_model` in its config. +# For Citrinets/ContextNets, the dimension can be found usually in `model.encoder.jasper.0.filters`. +# +# - model.train_ds.manifest_filepath / model.validation_ds.manifest_filepath: Data filepaths to train the +# adapter module. +############################################################################################################ +# The recommendations during training of adapters is significantly different than general ASR training or +# fine-tuning. Below are some recommended configuration values. +# +# - model.adapter.dim: Usually we chose a small bottleneck dim here. 16 to 32 is generally enough. +# +# - model.optim.lr: We generally chose a very small LR, and a very short training schedule of just a few hundred +# steps - depending on the size of the dataset. Usually just a few epochs over the dataset with a low LR is +# sufficient for adaptation. +# +# - model.optim.weight_decay: We find that strong weight decay prevents significant degradation of prior training, +# but also limits the capacity of the model to learn the adapted domain. Usually as a baseline we use 0.0 +# +# - model.optim.sched.warmup_steps: We encourage warmup steps to be modified to suit the smaller training schedule. +# +# - trainer.max_steps: We recommend using trainer.max_steps to limit the training duration to just 10-20 epochs. +# Adapters converge very fast, and prolonged training may cause overfitting to the new domain, consequently, +# leading to catastrophic forgetting of the old domain. You can equivalently use small number of epochs using +# trainer.max_epochs. +# +# - trainer.check_val_every_n_epoch: Since the training run is short, and very fast usually, it is recommended to +# reduce the amount of validation to once every few epochs, rather than after every epoch, to speed up training. + +name: "ASR-Adapter" + +model: + # One of the below two values must be set ! + pretrained_model: null # name of a pretrained model + nemo_model: null # path to a ASR model file (.nemo) + + log_prediction: false # enables logging sample predictions in the output during training + + adapter: + ### Config of the adapter training/eval script ### + adapter_name: ??? # Name of the adapter, used by the script + adapter_type: "linear" # Type of the adapter. Corresponds to the subconfigs below. + adapter_module_name: null # Name of the adapter module. Combine multiple modules with '+' between module names. + adapter_state_dict_name: "adapters.pt" # If the individual adapters must be saved, a file name can be provided here. null disables this. + + ### Adapter Configs ### + # Linear / Houlsby Adapter (https://arxiv.org/abs/1902.00751) + linear: + # Config of the adapter module itself + _target_: nemo.collections.common.parts.adapter_modules.LinearAdapter + in_features: ??? # User must provide the output dimension of the layers of the model, which is the input dimension of this adapter. + dim: 32 # The hidden dimension of the adapter, as chosen by user, but small values are preferred to reduce param count. + activation: swish + norm_position: 'pre' # Can be `pre` or `post` + dropout: 0.0 # float, dropout for the adapter + + # Adapter strategy config + adapter_strategy: + _target_: nemo.core.classes.mixins.adapter_mixin_strategies.ResidualAddAdapterStrategy + stochastic_depth: 0.0 # float, setting to > 0 will enable stochastic depth for each adapter block. + l2_lambda: 0.0 # float, setting to > 0 will enable l2 norm auxiliary loss for each adapter's output. + + # Tiny-Attention Adapter (https://arxiv.org/abs/2211.01979) + # NOTE: Only supported for Attention based encoders. Make sure to pass `adapter_module_name` as "encoder" + tiny_attn: + # Config of the adapter module itself + # Defaults to Relative Positional Encoding MHA + # _target_ can instead be .MultiHeadAttentionAdapter if Conformer was originally using Absolute Positional Encoding. + _target_: nemo.collections.asr.parts.submodules.adapters.multi_head_attention_adapter_module.RelPositionMultiHeadAttentionAdapter + n_feat: ??? # User must provide the output dimension of the layers of the model, which is the input dimension of this adapter. + n_head: 1 # Number of heads for attention. + proj_dim: -1 # Can be `null` - to avoid projection, > 0 for explicit dim, or -1 to default to `n_head` + dropout_rate: 0.0 # float, dropout for the adapter + + # Adapter strategy config + adapter_strategy: + _target_: nemo.collections.asr.parts.submodules.adapters.multi_head_attention_adapter_module.MHAResidualAddAdapterStrategy + stochastic_depth: 0.0 # float, setting to > 0 will enable stochastic depth for each adapter block. + l2_lambda: 0.0 # float, setting to > 0 will enable l2 norm auxiliary loss for each adapter's output. + + # Optional global config available to all adapters at a global level. + # A global config is shared across every layer of the adapters, defining global properties rather + # than properties local to the adapter (as defined above). + # This can be useful in order to select *which type of adapter* is added, *what adapters to enable*, + # and further global operations that can decide dynamically how to support the requested adapter. + global_cfg: + check_encoder_adapter: True # ASR adapter key, determines whether to check if encoder adapter modules is supported + check_decoder_adapter: True # ASR adapter key, determines whether to check if decoder adapter modules is supported + check_joint_adapter: True # ASR adapter key, determines whether to check if joint adapter modules is supported + + # Overrides the model's internal spec augment configuration + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 0 + time_masks: 0 + freq_width: 27 + time_width: 0.05 + + train_ds: + # train dataset + dataloader config + # sample_rate will be merged with model config + # use_start_end_token will be merged with model config + # trim_silence will be merged with model config + # max_duration will be merged with model config + # min_duration will be merged with model config + manifest_filepath: ??? + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + # sample_rate will be merged with model config + # use_start_end_token will be merged with model config + manifest_filepath: ??? + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + + test_ds: + # sample_rate will be merged with model config + # use_start_end_token will be merged with model config + manifest_filepath: null + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + + optim: + # optimizer arguments + name: adamw + betas: [0.9, 0.98] + lr: 0.001 # LR depends on the scheduler used by the base model. Noam prefers 0.5, Cosine Annealing prefers 0.02 + weight_decay: 0 # During adaptation, since training run is short, WD is not required. Can be set if needed. + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: null # Warmup steps should be set, and smaller than the trainer.max_steps set below. + warmup_ratio: 0.1 # Warmup steps will be 10% of the training steps. + min_lr: 1e-5 + last_epoch: -1 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: null + max_steps: 1000 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: null + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + save_dir: null + offline: false # If true, wandb logging will be done offline and would require manual syncing. + tags: null # List of tags to assign to the run + + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_adapters/asr_adaptation_hp.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_adapters/asr_adaptation_hp.yaml new file mode 100644 index 0000000..4afbc3b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_adapters/asr_adaptation_hp.yaml @@ -0,0 +1,262 @@ +# Config to perform ASR adaptation using any pre-trained model (local nemo model or pre-trained checkpoint). +############################################################################################################ +# This config is special in that it is used alongside the scripts in the asr_adapters examples directory, +# but does not directly construct a model itself. Instead it mimics the usual ASR model configs, and initializes +# a pre-trained model (either local or via network), and overrides its many data loaders / optimizer / scheduler +# and other arguments +# +# **Note**: This config does *not* get stored in the adapted model, since this config is merely to setup the +# adapter training / inference script. This file can be considered a config not for the model, but for the +# script that will adapt the model or infer an adapted model. +# +# You can therefore call this script multiple times to add as many adapters as you need in a single model, +# by providing the previous adapted checkpoint as `model.nemo_model`. +# +# **Note**: Any config value in this yaml file *overrides* the equivalent config inside the model ! +# +# There are some important paramters of this config that must be updated by the user : +# - model.pretrained_model or model.nemo_model: str name or path to some pretrained model. Only one of the +# two should be passed. Selects the pre-trained model to be loaded and adapted. +# +# - model.adapter.adapter_name: Globally unique name, assigned to the adapter itself. Every adapter of a +# model must have a unique name. +# +# - model.adapter.in_features: The output dimension of each block of the model. This is model dependent. +# For example, Conformer dimension can be found via `model.encoder.d_model` in its config. +# For Citrinets/ContextNets, the dimension can be found usually in `model.encoder.jasper.0.filters`. +# +# - model.train_ds.manifest_filepath / model.validation_ds.manifest_filepath: Data filepaths to train the +# adapter module. +############################################################################################################ +# The recommendations during training of adapters is significantly different than general ASR training or +# fine-tuning. Below are some recommended configuration values. +# +# - model.adapter.dim: Usually we chose a small bottleneck dim here. 16 to 32 is generally enough. +# +# - model.optim.lr: We generally chose a very small LR, and a very short training schedule of just a few hundred +# steps - depending on the size of the dataset. Usually just a few epochs over the dataset with a low LR is +# sufficient for adaptation. +# +# - model.optim.weight_decay: We find that strong weight decay prevents significant degradation of prior training, +# but also limits the capacity of the model to learn the adapted domain. Usually as a baseline we use 0.0 +# +# - model.optim.sched.warmup_steps: We encourage warmup steps to be modified to suit the smaller training schedule. +# +# - trainer.max_steps: We recommend using trainer.max_steps to limit the training duration to just 10-20 epochs. +# Adapters converge very fast, and prolonged training may cause overfitting to the new domain, consequently, +# leading to catastrophic forgetting of the old domain. You can equivalently use small number of epochs using +# trainer.max_epochs. +# +# - trainer.check_val_every_n_epoch: Since the training run is short, and very fast usually, it is recommended to +# reduce the amount of validation to once every few epochs, rather than after every epoch, to speed up training. + +name: "ASR-Adapter-hp" + +model: + # One of the below two values must be set ! + pretrained_model: null # name of a pretrained model + nemo_model: null # path to a ASR model file (.nemo) + + log_prediction: false # enables logging sample predictions in the output during training + + adapter: + ### Config of the adapter training/eval script ### + adapter_name: ??? # Name of the adapter, used by the script + adapter_type: "linear" # Type of the adapter. Corresponds to the subconfigs below. + adapter_module_name: null # Name of the adapter module. Combine multiple modules with '+' between module names. + adapter_state_dict_name: "adapters.pt" # If the individual adapters must be saved, a file name can be provided here. null disables this. + + ### Adapter Configs ### + # Linear / Houlsby Adapter (https://arxiv.org/abs/1902.00751) + linear: + # Config of the adapter module itself + _target_: nemo.collections.common.parts.adapter_modules.LinearAdapter + in_features: ??? # User must provide the output dimension of the layers of the model, which is the input dimension of this adapter. + dim: 32 # The hidden dimension of the adapter, as chosen by user, but small values are preferred to reduce param count. + activation: swish + norm_position: 'pre' # Can be `pre` or `post` + dropout: 0.0 # float, dropout for the adapter + + # Adapter strategy config + adapter_strategy: + _target_: nemo.core.classes.mixins.adapter_mixin_strategies.ResidualAddAdapterStrategy + stochastic_depth: 0.0 # float, setting to > 0 will enable stochastic depth for each adapter block. + l2_lambda: 0.0 # float, setting to > 0 will enable l2 norm auxiliary loss for each adapter's output. + + # Tiny-Attention Adapter (https://arxiv.org/abs/2211.01979) + # NOTE: Only supported for Attention based encoders. Make sure to pass `adapter_module_name` as "encoder" + tinyattn: + # Config of the adapter module itself + # Defaults to Relative Positional Encoding MHA + # _target_ can instead be .MultiHeadAttentionAdapter if Conformer was originally using Absolute Positional Encoding. + _target_: nemo.collections.asr.parts.submodules.adapters.multi_head_attention_adapter_module.RelPositionMultiHeadAttentionAdapter + n_feat: ??? # User must provide the output dimension of the layers of the model, which is the input dimension of this adapter. + n_head: 1 # Number of heads for attention. + proj_dim: -1 # Can be `null` - to avoid projection, > 0 for explicit dim, or -1 to default to `n_head` + dropout_rate: 0.0 # float, dropout for the adapter + + # Adapter strategy config + adapter_strategy: + _target_: nemo.collections.asr.parts.submodules.adapters.multi_head_attention_adapter_module.MHAResidualAddAdapterStrategy + stochastic_depth: 0.0 # float, setting to > 0 will enable stochastic depth for each adapter block. + l2_lambda: 0.0 # float, setting to > 0 will enable l2 norm auxiliary loss for each adapter's output. + + # Optional global config available to all adapters at a global level. + # A global config is shared across every layer of the adapters, defining global properties rather + # than properties local to the adapter (as defined above). + # This can be useful in order to select *which type of adapter* is added, *what adapters to enable*, + # and further global operations that can decide dynamically how to support the requested adapter. + global_cfg: + check_encoder_adapter: True # ASR adapter key, determines whether to check if encoder adapter modules is supported + check_decoder_adapter: True # ASR adapter key, determines whether to check if decoder adapter modules is supported + check_joint_adapter: True # ASR adapter key, determines whether to check if joint adapter modules is supported + + # Overrides the model's internal spec augment configuration + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 0 + time_masks: 0 + freq_width: 27 + time_width: 0.05 + + train_ds: + # train dataset + dataloader config + # sample_rate will be merged with model config + # use_start_end_token will be merged with model config + # trim_silence will be merged with model config + # max_duration will be merged with model config + # min_duration will be merged with model config + manifest_filepath: ??? + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + # sample_rate will be merged with model config + # use_start_end_token will be merged with model config + manifest_filepath: ??? + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + + test_ds: + # sample_rate will be merged with model config + # use_start_end_token will be merged with model config + manifest_filepath: null + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + + optim: + # optimizer arguments + name: adamw + betas: [0.9, 0.98] + lr: 0.001 # LR depends on the scheduler used by the base model. Noam prefers 0.5, Cosine Annealing prefers 0.02 + weight_decay: 0 # During adaptation, since training run is short, WD is not required. Can be set if needed. + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: null # Warmup steps should be set, and smaller than the trainer.max_steps set below. + warmup_ratio: 0.1 # Warmup steps will be 10% of the training steps. + min_lr: 1e-5 + last_epoch: -1 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: null + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 10 # Interval of logging. + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + + # Add a unique name for all hyperparameter arguments to allow continued training. + # NOTE: It is necessary to add all hyperparameter arguments to the name ! + # This ensures successful restoration of model runs in case HP search crashes. + + ### `linear` adapter experiment name ### + name: ${name}-lr-${model.optim.lr}-adim-${model.adapter.linear.dim}-sd-${model.adapter.linear.adapter_strategy.stochastic_depth} + + ### `tiny_attn` adapter experiment name ### + # name: ${name}-lr-${model.optim.lr}-pdim-${model.adapter.tiny_attn.proj_dim}-nhead-${model.adapter.tiny_attn.n_head}-sd-${model.adapter.tiny_attn.adapter_strategy.stochastic_depth} + + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 1 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + create_wandb_logger: false + wandb_logger_kwargs: + name: ${exp_manager.name} + project: null + entity: null + save_dir: null + offline: false # If true, wandb logging will be done offline and would require manual syncing. + tags: null # List of tags to assign to the run + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # HP Search may crash due to various reasons, best to attempt continuation in order to + # resume from where the last failure case occured. + resume_if_exists: true + resume_ignore_no_checkpoint: true + +# Required for Hydra launch of hyperparameter search +defaults: + - override hydra/launcher: nemo_launcher + +# Hydra arguments necessary for hyperparameter optimization +# NOTE: This is for the `linear` adapter type ! Please change sweep.params for other adapter types ! +hydra: + sweep: + dir: "." + subdir: "." + + sweeper: + ### `linear` adapter configuration ### + params: # place all the parameters you wish to search over here (corresponding to the rest of the config) + model.optim.lr: 0.001,0.0001 + model.adapter.linear.dim: 32,64,96,128 + model.adapter.linear.adapter_strategy.stochastic_depth: 0.0,0.5,0.6,0.7,0.8,0.9 + + ### `tiny_attn` adapter configuration ### +# params: +# model.optim.lr: 0.001,0.0001 +# model.adapter.tiny_attn.n_head: 1 # Note if you use > 1 heads, the *minimum* proj_dim below should match your *max* n_head ! +# model.adapter.tiny_attn.proj_dim: 1,8,16,32,64 # 1,4,8,16,32,64 +# model.adapter.tiny_attn.adapter_strategy.stochastic_depth: 0.0,0.5,0.6,0.7,0.8,0.9 + + # Arguments to the hyperparameter runner + launcher: + num_gpus: -1 # Number of gpus to use. Each run works on a single GPU. + jobs_per_gpu: 1 # If each GPU has large memory, you can run multiple jobs on the same GPU for faster results (until OOM). diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_finetune/speech_to_text_finetune.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_finetune/speech_to_text_finetune.yaml new file mode 100644 index 0000000..415172b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_finetune/speech_to_text_finetune.yaml @@ -0,0 +1,118 @@ +name: "Speech_To_Text_Finetuning" + +# use `init_from_nemo_model` or `init_from_pretrained_model` to initialize the model +# We do not currently support `init_from_ptl_ckpt` to create a single script for all types of models. +init_from_nemo_model: null # path to nemo model + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + rnnt_reduction: 'mean_volume' + skip_nan_grad: false + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 20 + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "fully_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + char_labels: # use for char based models + update_labels: false + labels: null # example list config: \[' ', 'a', 'b', 'c'\] + + tokenizer: # use for spe/bpe based tokenizer models + update_tokenizer: false + dir: null # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + optim: + name: adamw + lr: 1e-4 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: CosineAnnealing + # scheduler config override + warmup_steps: 5000 + warmup_ratio: null + min_lr: 5e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 50 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files along with PTL checkpoints + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_finetune/speech_to_text_hf_finetune.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_finetune/speech_to_text_hf_finetune.yaml new file mode 100644 index 0000000..b8d84d1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_finetune/speech_to_text_hf_finetune.yaml @@ -0,0 +1,189 @@ +name: "Speech_To_Text_HF_Finetuning_using_HF_Datasets" + +# use `init_from_nemo_model` or `init_from_pretrained_model` to initialize the model +# We do not currently support `init_from_ptl_ckpt` to create a single script for all types of models. +init_from_nemo_model: null # path to nemo model +init_from_pretrained_model: null # name of pretrained NeMo model, e.g., `stt_en_fastconformer_transducer_large` + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + rnnt_reduction: 'mean_volume' + skip_nan_grad: false + + # configs for huggingface load_dataset function + data_path: "librispeech_asr" + data_name: null # name for the specific dataset to load, e.g., 'en' for MCV datasets, but some datasets don't require this field. + streaming: false # set True to use streaming mode, which doesn't wait for data downloading but each training step takes longer in the first epoch. If True, you'll need to specify trainer.max_steps instead of trainer.max_epochs. + + # keys for audio, sample_rate and transcription in the huggingface dataset, keys seperated by `.` for nested fields. See example at the bottom of this file. + audio_key: "audio.array" + sample_rate_key: "audio.sampling_rate" + text_key: "text" # the key for groundtruth transcription, e.g., MCV usually uses "sentence" while some others use "text" + + # simple text cleaning, by default converts all chars to lower-case and only keeps alpha-numeric chars. + normalize_text: true + symbols_to_keep: ["'"] # a list of symbols to keep during text cleaning. + + train_ds: + manifest_filepath: "hugginface" # set to a not None value to avoid breaking existing code + streaming: ${model.streaming} + normalize_text: ${model.normalize_text} + symbols_to_keep: ${model.symbols_to_keep} + audio_key: ${model.audio_key} + sample_rate_key: ${model.sample_rate_key} + text_key: ${model.text_key} + hf_data_cfg: # hf_data_cfg can be a ListConfig or DictConfig. Params for each data are passed into huggingface load_dataset(). Add more params if needed + - path: ${model.data_path} + name: ${model.data_name} + split: 'train.clean.360' + streaming: ${model.streaming} + - path: ${model.data_path} + name: ${model.data_name} + split: 'train.clean.100' + streaming: ${model.streaming} + - path: ${model.data_path} + name: ${model.data_name} + split: 'train.other.500' + streaming: ${model.streaming} + + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + shuffle_n: 2048 + num_workers: 8 + pin_memory: true + use_start_end_token: false + + validation_ds: + manifest_filepath: "hugginface" # set to a not None value to avoid breaking existing code + streaming: ${model.streaming} + normalize_text: ${model.normalize_text} + symbols_to_keep: ${model.symbols_to_keep} + audio_key: ${model.audio_key} + sample_rate_key: ${model.sample_rate_key} + text_key: ${model.text_key} + hf_data_cfg: # An example of using only one dataset + path: ${model.data_path} + name: ${model.data_name} + split: 'validation.other' + streaming: ${model.streaming} + + sample_rate: ${model.sample_rate} + batch_size: 8 + shuffle: false + shuffle_n: 2048 + num_workers: 8 + pin_memory: true + use_start_end_token: false + + test_ds: + manifest_filepath: "hugginface" # set to a not None value to avoid breaking existing code + streaming: ${model.streaming} + normalize_text: ${model.normalize_text} + symbols_to_keep: ${model.symbols_to_keep} + audio_key: ${model.audio_key} + sample_rate_key: ${model.sample_rate_key} + text_key: ${model.text_key} + hf_data_cfg: # hf_data_cfg can be a ListConfig or DictConfig. Params for each data are passed into huggingface load_dataset(). Add more params if needed + - path: ${model.data_path} + name: ${model.data_name} + split: 'test.other' + streaming: ${model.streaming} + - path: ${model.data_path} + name: ${model.data_name} + split: 'test.clean' + streaming: ${model.streaming} + + sample_rate: ${model.sample_rate} + batch_size: 8 + shuffle: false + shuffle_n: 2048 + num_workers: 8 + pin_memory: true + use_start_end_token: false + + char_labels: # use for char based models + update_labels: false + labels: null # example list config: \[' ', 'a', 'b', 'c'\] + + tokenizer: # use for spe/bpe based tokenizer models + update_tokenizer: false + dir: null # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + optim: + name: adamw + lr: 1e-4 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: CosineAnnealing + # scheduler config override + warmup_steps: 5000 + warmup_ratio: null + min_lr: 5e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files along with PTL checkpoints + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + + +# An example item in the HuggingFace `librispeech_asr` dataset: +# {'chapter_id': 141231, +# 'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', +# 'audio': { +# 'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', +# 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), +# 'sampling_rate': 16000 +# }, +# 'id': '1272-141231-0000', +# 'speaker_id': 1272, +# 'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'} diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_tts/hybrid_asr_tts.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_tts/hybrid_asr_tts.yaml new file mode 100644 index 0000000..e31234c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/asr_tts/hybrid_asr_tts.yaml @@ -0,0 +1,122 @@ +# Hybrid ASR-TTS model config to instantiate model from pretrained asr_model_path and tts_model_path .nemo checkpoints + +name: "Hybrid-Model-ASR-With-TTS" + +model: + sample_rate: 16000 + + # asr model + asr_model_path: ??? + asr_model: null + asr_model_type: null # rnnt_bpe, ctc_bpe or hybrid_rnnt_ctc_bpe; needed only if instantiating from config, otherwise type is auto inferred + asr_model_fuse_bn: false # only ConformerEncoder supported now, use false for other models + + # tts model + tts_model_path: ??? + tts_model: null + + # enhancer model + enhancer_model_path: null + enhancer_model: null + + train_ds: + text_data: + manifest_filepath: ??? + speakers_filepath: ??? + min_words: 1 + max_words: 45 # 45 - recommended value, ~16.7 sec for LibriSpeech + tokenizer_workers: 1 + asr_tts_sampling_technique: round-robin # random, round-robin, temperature + asr_tts_sampling_temperature: null + asr_tts_sampling_probabilities: null # [0.5,0.5] – ASR,TTS + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + use_start_end_token: false + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + optim: + name: adamw + lr: 1e-4 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: CosineAnnealing + # scheduler config override + warmup_steps: null + warmup_ratio: 0.2 + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/carnelinet/carnelinet_384.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/carnelinet/carnelinet_384.yaml new file mode 100644 index 0000000..6693247 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/carnelinet/carnelinet_384.yaml @@ -0,0 +1,276 @@ +# This config describes a CarneliNet model with 384 filters (CarneliNet-384) with CTC loss and word-piece tokenizer. +# The values in this config have tested on LibriSpeech dataset for effective batch size of 1K trained on 32 GPUs with AMP enabled. + +# Larger and smaller models, e.g. CarneliNet-1024, can reuse the same architecture and config file. However, for larger model +# SpecAugment regularization is stronger and number of filters in epilog layer is also increased. Specifically for LibriSpeech dataset +# activation function and weight initialization policy also change for the largest of models. If a training dataset is much larger than +# LibriSpeech, Swish and tds_uniform parameters may show improvements even for CarneliNet-1024. +# +# The changes between models is captured in the following table. The rest of the parameters is the same across all models. +# +-----------------+---------+---------+------------+----------------+------------+ +# | Model | filters | encoder | activation | weight | time_masks | +# | | | final | | init_mode | | +# | | | filters | | | | +# +-----------------+---------+---------+------------+----------------+------------+ +# | CarneliNet-256 | 256 | 640 | Swish | tds_uniform | 2 | +# +-----------------+---------+---------+------------+----------------+------------+ +# | CarneliNet-384 | 384 | 640 | Swish | tds_uniform | 2 | +# +-----------------+---------+---------+------------+----------------+------------+ +# | CarneliNet-512 | 512 | 640 | Swish | tds_uniform | 10 | +# +-----------------+---------+---------+------------+----------------+------------+ +# | CarneliNet-768 | 768 | 1024 | ReLu | xavier_uniform | 10 | +# +-----------------+---------+---------+------------+----------------+------------+ +# | CarneliNet-1024 | 1024 | 1024 | ReLu | xavier_uniform | 10 | +# +-----------------+---------+---------+------------+----------------+------------+ +name: &name "CarneliNet-384-8x-Stride" + +model: + sample_rate: &sample_rate 16000 + + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + trim_silence: false + use_start_end_token: false + max_duration: 16.7 + shuffle: true + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: 16000 + batch_size: 32 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + model_defaults: + repeat: 5 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + se_repeat: false + kernel_size: 11 + filters: 384 + encoder_final_filters: 640 + + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 + time_masks: 2 + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ParallelConvASREncoder + feat_in: *n_mels + activation: swish + init_mode: tds_uniform + conv_mask: true + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + se_repeat: ${model.model_defaults.se_repeat} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + se_repeat: ${model.model_defaults.se_repeat} + stride_last: true + residual_mode: "stride_add" + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: ["${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}"] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + se_repeat: ${model.model_defaults.se_repeat} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [7] + stride: [2] # *stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + se_repeat: ${model.model_defaults.se_repeat} + stride_last: true + residual_mode: "stride_add" + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: ["${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}"] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + se_repeat: ${model.model_defaults.se_repeat} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [2] # stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + se_repeat: ${model.model_defaults.se_repeat} + stride_last: true + residual_mode: "stride_add" + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: ["${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}","${model.model_defaults.kernel_size}"] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + se_repeat: ${model.model_defaults.se_repeat} + + - filters: ${model.model_defaults.encoder_final_filters} + repeat: 1 + kernel: ["${model.model_defaults.kernel_size}"] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + se_repeat: ${model.model_defaults.se_repeat} + + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: ${model.model_defaults.encoder_final_filters} + num_classes: -1 # filled with vocabulary size from tokenizer at runtime + vocabulary: [] # filled with vocabulary from tokenizer at runtime + + optim: + name: novograd + lr: 0.1 + + # optimizer arguments + betas: [0.8, 0.25] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: 1000 + warmup_ratio: null + min_lr: 1e-5 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 50 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + precision: 32 # If AMP is available, change to 16 to gain training speed increase and lower memory consumption (preferred). + sync_batchnorm: false + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + always_save_nemo: true + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/citrinet/citrinet_1024.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/citrinet/citrinet_1024.yaml new file mode 100644 index 0000000..0722a7e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/citrinet/citrinet_1024.yaml @@ -0,0 +1,480 @@ +# This config contains the default values for training a Citrinet model with CTC loss and BPE-based vocabulary. +# Default learning parameters in this config are set for effective batch size of 1k on 32 GPUs. +# To train it with smaller batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# If training for a short time, you can also reduce weight decay to 0. + +# Training Recipe +# This model can be trained using the default settings in this config with FP32 precision. +# When training under AMP, increase `warmup_steps` to 5000 for stable training. +# In order to create Citrinet-C, change the model.model_defaults.filters parameter. +# When reducing the receptive field of these models, it is advised to reduce the amount of augmentation +# for larger models from 10x time masking to 5x or 2x time masking. +# For further details regarding Citrinet, visit - https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#citrinet + +name: &name "Citrinet-1024-8x-Stride" + +model: + sample_rate: &sample_rate 16000 + log_prediction: true # enables logging sample predictions in the output during training + + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + trim_silence: false + max_duration: 20.0 + shuffle: true + use_start_end_token: false + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: 16000 + batch_size: 32 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + model_defaults: + repeat: 5 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 0.25 + filters: 1024 + enc_final: 1024 + + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 + time_masks: 10 + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [11] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [21] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [2] # *stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [21] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [23] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [25] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [25] + stride: [2] # stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [27] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [29] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [31] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [33] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [35] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [37] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [39] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.enc_final} + repeat: 1 + kernel: [41] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: ${model.model_defaults.enc_final} + num_classes: -1 # filled with vocabulary size from tokenizer at runtime + vocabulary: [] # filled with vocabulary from tokenizer at runtime + + + optim: + name: novograd + lr: 0.05 + + # optimizer arguments + betas: [0.8, 0.25] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: 5000 + warmup_ratio: null + min_lr: 1e-5 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + check_val_every_n_epoch: 1 + precision: 32 + sync_batchnorm: false + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + save_top_k: 3 + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/citrinet/citrinet_384.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/citrinet/citrinet_384.yaml new file mode 100644 index 0000000..f2ceb5f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/citrinet/citrinet_384.yaml @@ -0,0 +1,435 @@ +# This config contains the default values for training a Citrinet model with CTC loss and BPE-based vocabulary. +# Default learning parameters in this config are set for effective batch size of 1k on 32 GPUs. +# To train it with smaller batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# If training for a short time, you can also reduce weight decay to 0. + +# Training Recipe +# This model can be trained using the default settings in this config with FP32 precision. +# When training under AMP, increase `warmup_steps` to 5000 for stable training. +# In order to create Citrinet-C, find-replace `filters: 384` with `filters: C`. +# When reducing the receptive field of these models, it is advised to reduce the amount of augmentation +# for larger models from 10x time masking to 5x or 2x time masking. +# For further details regarding Citrinet, visit - https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#citrinet + +name: &name "Citrinet-384-8x-Stride" + +model: + sample_rate: &sample_rate 16000 + + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + trim_silence: false + max_duration: 16.7 + shuffle: true + use_start_end_token: false + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: 16000 + batch_size: 32 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + model_defaults: + repeat: 5 + dropout: 0.0 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 1.0 + enc_final: 640 + + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 + time_masks: 2 + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: 384 + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [11] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [21] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [2] # *stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [21] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [23] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [25] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [25] + stride: [2] # stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [27] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [29] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [31] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [33] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [35] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [37] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 384 + repeat: ${model.model_defaults.repeat} + kernel: [39] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.enc_final} + repeat: 1 + kernel: [41] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: ${model.model_defaults.enc_final} + num_classes: -1 # filled with vocabulary size from tokenizer at runtime + vocabulary: [] # filled with vocabulary from tokenizer at runtime + + optim: + name: novograd + lr: 0.05 + + # optimizer arguments + betas: [0.8, 0.25] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: 1000 + warmup_ratio: null + min_lr: 1e-5 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + check_val_every_n_epoch: 1 + precision: 32 + sync_batchnorm: false + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + save_top_k: 3 + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/citrinet/citrinet_512.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/citrinet/citrinet_512.yaml new file mode 100644 index 0000000..a36cb1d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/citrinet/citrinet_512.yaml @@ -0,0 +1,435 @@ +# This config contains the default values for training a Citrinet model with CTC loss and BPE-based vocabulary. +# Default learning parameters in this config are set for effective batch size of 1k on 32 GPUs. +# To train it with smaller batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# If training for a short time, you can also reduce weight decay to 0. + +# Training Recipe +# This model can be trained using the default settings in this config with FP32 precision. +# When training under AMP, increase `warmup_steps` to 5000 for stable training. +# In order to create Citrinet-C, find-replace `filters: 384` with `filters: C`. +# When reducing the receptive field of these models, it is advised to reduce the amount of augmentation +# for larger models from 10x time masking to 5x or 2x time masking. +# For further details regarding Citrinet, visit - https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#citrinet + +name: &name "Citrinet-512-8x-Stride" + +model: + sample_rate: &sample_rate 16000 + + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + trim_silence: false + max_duration: 16.7 + shuffle: true + num_workers: 8 + pin_memory: true + use_start_end_token: false + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + test_ds: + manifest_filepath: null + sample_rate: 16000 + batch_size: 32 + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + model_defaults: + repeat: 5 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 1.0 + enc_final: 640 + + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 + time_masks: 10 + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: 512 + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [11] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [21] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [2] # *stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [21] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [23] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [25] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [25] + stride: [2] # stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [27] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [29] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [31] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [33] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [35] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [37] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: 512 + repeat: ${model.model_defaults.repeat} + kernel: [39] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.enc_final} + repeat: 1 + kernel: [41] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: ${model.model_defaults.enc_final} + num_classes: -1 # filled with vocabulary size from tokenizer at runtime + vocabulary: [] # filled with vocabulary from tokenizer at runtime + + optim: + name: novograd + lr: 0.05 + + # optimizer arguments + betas: [0.8, 0.25] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: 1000 + warmup_ratio: null + min_lr: 1e-5 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + check_val_every_n_epoch: 1 + precision: 32 + sync_batchnorm: false + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + save_top_k: 3 + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false + diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/citrinet/config_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/citrinet/config_bpe.yaml new file mode 100644 index 0000000..8871601 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/citrinet/config_bpe.yaml @@ -0,0 +1,188 @@ +name: &name "ContextNet5x1" +sample_rate: &sample_rate 16000 +repeat: &repeat 1 +dropout: &dropout 0.0 +separable: &separable true + +model: + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + trim_silence: True + max_duration: 16.7 + shuffle: True + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shard_strategy: "scatter" + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + shuffle: False + num_workers: 8 + pin_memory: true + + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.02 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 64 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + rect_freq: 50 + rect_masks: 5 + rect_time: 120 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: 128 + repeat: 1 + kernel: [11] + stride: [1] + dilation: [1] + dropout: *dropout + residual: true + separable: *separable + se: true + se_context_size: -1 + + - filters: 256 + repeat: *repeat + kernel: [13] + stride: [1] + dilation: [1] + dropout: *dropout + residual: true + separable: *separable + se: true + se_context_size: -1 + + - filters: 256 + repeat: *repeat + kernel: [15] + stride: [1] + dilation: [1] + dropout: *dropout + residual: true + separable: *separable + se: true + se_context_size: -1 + + - filters: 256 + repeat: *repeat + kernel: [17] + stride: [1] + dilation: [1] + dropout: *dropout + residual: true + separable: *separable + se: true + se_context_size: -1 + + - filters: 256 + repeat: *repeat + kernel: [19] + stride: [1] + dilation: [1] + dropout: *dropout + residual: true + separable: *separable + se: true + se_context_size: -1 + + - filters: 256 + repeat: 1 + kernel: [21] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: *separable + se: true + se_context_size: -1 + + - filters: &enc_feat_out 1024 + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: *separable + se: true + se_context_size: -1 + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: 1024 + num_classes: -1 # filled with vocabulary size from tokenizer at runtime + vocabulary: [] # filled with vocabulary from tokenizer at runtime + + optim: + name: adam + # _target_: nemo.core.optim.optimizers.Adam + lr: .1 + + # optimizer arguments + betas: [0.9, 0.999] + weight_decay: 0.0001 + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: null + warmup_ratio: 0.05 + min_lr: 1e-6 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 5 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + create_wandb_logger: False + wandb_logger_kwargs: + name: null + project: null + diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/config.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/config.yaml new file mode 100644 index 0000000..6ab764c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/config.yaml @@ -0,0 +1,187 @@ +name: &name "QuartzNet15x5" +sample_rate: &sample_rate 16000 +repeat: &repeat 1 +dropout: &dropout 0.0 +separable: &separable true +labels: &labels [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + +model: + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: *labels + batch_size: 32 + trim_silence: True + max_duration: 16.7 + shuffle: True + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: *labels + batch_size: 32 + shuffle: False + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.02 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 64 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + rect_freq: 50 + rect_masks: 5 + rect_time: 120 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: 128 + repeat: 1 + kernel: [11] + stride: [1] + dilation: [1] + dropout: *dropout + residual: true + separable: *separable + se: true + se_context_size: -1 + + - filters: 256 + repeat: *repeat + kernel: [13] + stride: [1] + dilation: [1] + dropout: *dropout + residual: true + separable: *separable + se: true + se_context_size: -1 + + - filters: 256 + repeat: *repeat + kernel: [15] + stride: [1] + dilation: [1] + dropout: *dropout + residual: true + separable: *separable + se: true + se_context_size: -1 + + - filters: 256 + repeat: *repeat + kernel: [17] + stride: [1] + dilation: [1] + dropout: *dropout + residual: true + separable: *separable + se: true + se_context_size: -1 + + - filters: 256 + repeat: *repeat + kernel: [19] + stride: [1] + dilation: [1] + dropout: *dropout + residual: true + separable: *separable + se: true + se_context_size: -1 + + - filters: 256 + repeat: 1 + kernel: [21] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: *separable + se: true + se_context_size: -1 + + - filters: &enc_feat_out 1024 + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: *separable + se: true + se_context_size: -1 + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: 1024 + num_classes: 28 + vocabulary: *labels + + optim: + name: novograd + # _target_: nemo.core.optim.optimizers.Novograd + lr: .01 + # optimizer arguments + betas: [0.8, 0.5] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + + # Scheduler params + warmup_steps: null + warmup_ratio: null + min_lr: 0.0 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 5 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # check once per epoch .25 for 4 times per epoch + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/cache_aware_streaming/conformer_ctc_bpe_streaming.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/cache_aware_streaming/conformer_ctc_bpe_streaming.yaml new file mode 100644 index 0000000..80c6a90 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/cache_aware_streaming/conformer_ctc_bpe_streaming.yaml @@ -0,0 +1,211 @@ +# It contains the default values for training a streaming cache-aware Conformer-CTC ASR model, large size (~120M) with CTC loss and sub-word encoding. +# Models trained with this config have limited right context which make them efficient for streaming ASR. + +# You may find more detail: +# Conformer's architecture config: NeMo/examples/asr/conf/conformer/conformer_ctc_bpe.yaml +# Cache-aware Streaming Conformer: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#cache-aware-streaming-conformer + +# You may use NeMo/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py to simulate/evaluate this model in cache-aware streaming mode +# Pre-trained ASR models can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html + +# Note: if loss does not go down properly or diverges, you may try increasing the warmup steps from 10K to 20K. + +name: "Conformer-CTC-BPE-Streaming" + +model: + sample_rate: 16000 + log_prediction: true # enables logging sample predictions in the output during training + ctc_reduction: 'mean_batch' + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # recommend small vocab size of 128 or 256 when using 4x sub-sampling + # you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "NA" # No normalization for mel-spectogram makes streaming easier + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + # you may use lower time_masks for smaller models to have a faster convergence + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 18 + d_model: 512 + + # Sub-sampling params + # stacking_norm, stacking and dw_striding can be around 25% faster than striding during inference, while they may give similar or slightly worse results in terms of accuracy for Transducer models + subsampling: striding # vggnet, striding, stacking, stacking_norm, or dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # -1 sets it to d_model + causal_downsampling: true # enables causal convolutions during downsampling + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + # for att_context_style=regular, the right context is recommended to be a small number around 0 to 3 as multiple-layers may increase the effective right context too large + # for att_context_style=chunked_limited, the left context need to be dividable by the right context plus one + # look-ahead(secs) = att_context_size[1]*subsampling_factor*window_stride, example: 27*4*0.01=1.08s + + # For multi-lookahead models, you may specify a list of context sizes. During the training, different context sizes would be used randomly with the distribution specified by att_context_probs. + # The first item in the list would be the default during test/validation/inference. + # An example of settings for multi-lookahead: + # att_context_size: [[140,27],[140,13],[140,2],[140,0]] + # att_context_probs: [0.25, 0.25, 0.25, 0.25, 0.25] + att_context_size: [140, 27] # -1 means unlimited context + att_context_style: chunked_limited # regular or chunked_limited + att_context_probs: null + + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'layer_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + # Recommend to use causal convolutions as it would increase the effective right context and therefore the look-ahead significantly + conv_context_size: causal + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + optim: + name: adamw + lr: 2.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 0 # less necessity for weight_decay as we already have large augmentations with SpecAug and limited context + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 # you may try larger warmup like 20K is training is not stable + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/cache_aware_streaming/conformer_transducer_bpe_streaming.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/cache_aware_streaming/conformer_transducer_bpe_streaming.yaml new file mode 100644 index 0000000..c9cab3a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/cache_aware_streaming/conformer_transducer_bpe_streaming.yaml @@ -0,0 +1,265 @@ +# It contains the default values for training a streaming cache-aware Conformer-Transducer ASR model, large size (~120M) with Transducer loss and sub-word encoding. +# Models trained with this config have limited right context which make them efficient for streaming ASR. + +# You may find more detail: +# Conformer's architecture config: NeMo/examples/asr/conf/conformer/conformer_transducer_bpe.yaml +# Cache-aware Streaming Conformer: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#cache-aware-streaming-conformer + +# You may use NeMo/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py to simulate/evaluate this model in cache-aware streaming mode +# Pre-trained ASR models can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html + +# Note: if loss does not go down properly or diverges, you may try increasing the warmup steps from 10K to 20K. + +name: "Conformer-Transducer-BPE-Streaming" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "NA" # No normalization for mel-spectogram makes streaming easier + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + # stacking_norm, stacking and dw_striding can be around 25% faster than striding during inference, while they may give similar or slightly worse results in terms of accuracy for Transducer models + subsampling: striding # vggnet, striding, stacking, stacking_norm, or dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # -1 sets it to d_model + causal_downsampling: true # enables causal convolutions during downsampling + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + # for att_context_style=regular, the right context is recommended to be a small number around 0 to 3 as multiple-layers may increase the effective right context too large + # for att_context_style=chunked_limited, the left context need to be dividable by the right context plus one + # look-ahead(secs) = att_context_size[1]*subsampling_factor*window_stride, example: 13*8*0.01=1.04s + + # For multi-lookahead models, you may specify a list of context sizes. During the training, different context sizes would be used randomly with the distribution specified by att_context_probs. + # The first item in the list would be the default during test/validation/inference. + # An example of settings for multi-lookahead: + # att_context_size: [[140,27],[140,13],[140,2],[140,0]] + # att_context_probs: [0.25, 0.25, 0.25, 0.25, 0.25] + att_context_size: [140, 27] # -1 means unlimited context + att_context_style: chunked_limited # regular or chunked_limited + att_context_probs: null + + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'layer_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + # Recommend to use causal convolutions as it would increase the effective right context and therefore the look-ahead significantly + conv_context_size: causal + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 5 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + # It also helps to improve the accuracy of the model in streaming mode + fastemit_lambda: 1e-3 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 # you may try larger warmup like 20K is training is not stable + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/conformer_ctc_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/conformer_ctc_bpe.yaml new file mode 100644 index 0000000..8ac1646 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/conformer_ctc_bpe.yaml @@ -0,0 +1,234 @@ +# It contains the default values for training a Conformer-CTC ASR model, large size (~120M) with CTC loss and sub-word encoding. + +# Architecture and training config: +# Here are the recommended configs for different variants of Conformer-CTC, other parameters are the same as in this config file. +# One extra layer (compared to original paper) is added to the medium and large variants to compensate for replacing the LSTM decoder with a linear one. +# +# +--------------+---------+---------+----------+------------------+------------+-----+ +# | Model | d_model | n_heads | n_layers | conv_kernel_size | time_masks | lr | +# +==============+=========+========+===========+==================+============+=====+ +# | Small (13M)| 176 | 4 | 16 | 31 | 5 | 5.0 | +# +--------------+---------+--------+-----------+------------------+------------+-----+ +# | Medium (30M)| 256 | 4 | 18 | 31 | 5 | 5.0 | +# +--------------+---------+--------+-----------+------------------+------------+-----+ +# | Large (121M)| 512 | 8 | 18 | 31 | 10 | 2.0 | +# +------------------------+--------+-----------+------------------+------------+-----+ +# | XLarge (635M)| 1024 | 8 | 24 | 5 | 10 | 6.4 | +# +--------------+---------+--------+-----------+------------------+------------+-----+ +# +# Default learning parameters in this config are set for global batch size of 2K while you may use lower values. +# To increase the global batch size with limited number of GPUs, you may use higher accumulate_grad_batches. +# However accumulate_grad_batches is better to be avoided as long as the global batch size is large enough and training is stable. + +# You may find more info about Conformer-CTC here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-ctc +# Pre-trained models of Conformer-CTC can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html +# The checkpoint of the large model trained on LibriSpeech with this recipe can be found here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large_ls + +# We suggest to use trainer.precision=bf16 for GPUs which support it otherwise trainer.precision=16 is recommended. +# Using bf16 or 16 would make it possible to double the batch size and speedup training/inference. If fp16 is not stable and model diverges after some epochs, you may use fp32. +# Here are the suggested batch size per GPU for each precision and memory sizes: +# +-----------+------------+------------+ +# | Precision | GPU Memory | Batch Size | +# +===========+============+============+ +# | 32 | 16GB | 8 | +# | | 32GB | 16 | +# | | 80GB | 32 | +# +-----------+------------+------------+ +# | 16 or | 16GB | 16 | +# | bf16 | 32GB | 32 | +# | | 80GB | 64 | +# +-----------+------------+------------+ +# Note: They are based on the assumption of max_duration of 20. If you have longer or shorter max_duration, then batch sizes may need to get updated accordingly. + + +name: "Conformer-CTC-BPE" + +model: + sample_rate: 16000 + log_prediction: true # enables logging sample predictions in the output during training + ctc_reduction: 'mean_batch' + skip_nan_grad: false + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # recommend to SPE Unigram tokenizer with small vocab size of 128 or 256 when using 4x sub-sampling + # you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + # you may use lower time_masks for smaller models to have a faster convergence + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 18 + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # -1 sets it to d_model + causal_downsampling: false + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + optim: + name: adamw + lr: 2.0 + # optimizer arguments + betas: [0.9, 0.98] + # less necessity for weight_decay as we already have large augmentations with SpecAug + # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used + # weight decay of 0.0 with lr of 2.0 also works fine + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/conformer_ctc_char.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/conformer_ctc_char.yaml new file mode 100644 index 0000000..093efdc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/conformer_ctc_char.yaml @@ -0,0 +1,197 @@ +# It contains the default values for training a Conformer-CTC ASR model, large size (~120M) with CTC loss and char-based vocabulary. +# Char-based encoding may give lower accuracy than sub-word encoding for some languages (conformer_ctc_bpe.yaml). +# You may find more detail on Conformer-CTC at `examples/asr/conf/conformer/conformer_ctc_bpe.yaml` + +name: "Conformer-CTC-Char" + +model: + sample_rate: 16000 + labels: [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + log_prediction: true # enables logging sample predictions in the output during training + ctc_reduction: 'mean_batch' + skip_nan_grad: false + + train_ds: + manifest_filepath: ??? + labels: ${model.labels} + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + labels: ${model.labels} + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + labels: ${model.labels} + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + # you may use lower time_masks for smaller models to have a faster convergence + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 16 + d_model: 256 + + # Sub-sampling params + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: ${model.labels} + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + optim: + name: adamw + lr: 2.0 + # optimizer arguments + betas: [0.9, 0.98] + # less necessity for weight_decay as we already have large augmentations with SpecAug + # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used + # weight decay of 0.0 with lr of 2.0 also works fine + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/conformer_transducer_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/conformer_transducer_bpe.yaml new file mode 100644 index 0000000..8a15aab --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/conformer_transducer_bpe.yaml @@ -0,0 +1,286 @@ +# It contains the default values for training a Conformer-Transducer ASR model, large size (~120M) with Transducer loss and sub-word encoding. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-Transducer, other parameters are the same as in this config file. +# +# +--------------+---------+---------+----------+------------------+--------------+--------------------------+-----------------+ +# | Model | d_model | n_heads | n_layers | conv_kernel_size | weight_decay | pred_hidden/joint_hidden | pred_rnn_layers | +# +==============+=========+========+===========+==================+==============+==========================+=================+ +# | Small (14M)| 176 | 4 | 16 | 31 | 0.0 | 320 | 1 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ +# | Medium (32M)| 256 | 4 | 16 | 31 | 1e-3 | 640 | 1 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ +# | Large (120M)| 512 | 8 | 17 | 31 | 1e-3 | 640 | 1 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ +# | XLarge (644M)| 1024 | 8 | 24 | 5 | 1e-3 | 640 | 2 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ + +# Default learning parameters in this config are set for global batch size of 2K while you may use lower values. +# To increase the global batch size with limited number of GPUs, you may use higher accumulate_grad_batches. +# However accumulate_grad_batches is better to be avoided as long as the global batch size is large enough and training is stable. + +# You may find more info about Conformer-Transducer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-transducer +# Pre-trained models of Conformer-Transducer can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html +# The checkpoint of the large model trained on NeMo ASRSET with this recipe can be found here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_large + +# We suggest to use trainer.precision=bf16 for GPUs which support it otherwise trainer.precision=16 is recommended. +# Using bf16 or 16 would make it possible to double the batch size and speedup training/inference. If fp16 is not stable and model diverges after some epochs, you may use fp32. +# Here are the suggested batch size per GPU for each precision and memory sizes: +# +-----------+------------+------------+ +# | Precision | GPU Memory | Batch Size | +# +===========+============+============+ +# | 32 | 16GB | 8 | +# | | 32GB | 16 | +# | | 80GB | 32 | +# +-----------+------------+------------+ +# | 16 or | 16GB | 16 | +# | bf16 | 32GB | 32 | +# | | 80GB | 64 | +# +-----------+------------+------------+ +# Note: They are based on the assumption of max_duration of 20. If you have longer or shorter max_duration, then batch sizes may need to get updated accordingly. + +name: "Conformer-Transducer-BPE" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + # recommend to use SPE Unigram tokenizer with vocab size of 1K to 4k when using 4x sub-sampling + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling parameters + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + # Adds Gaussian noise to the gradients of the decoder to avoid overfitting + variational_noise: + start_step: 0 + std: 0.0 + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/conformer_transducer_char.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/conformer_transducer_char.yaml new file mode 100644 index 0000000..e6157f3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/conformer_transducer_char.yaml @@ -0,0 +1,247 @@ +# It contains the default values for training a Conformer-Transducer ASR model, large size (~120M) with Transducer loss and char-based vocabulary. +# You may find more detail on Conformer-Transducer at `examples/asr/conf/conformer/conformer_transducer_bpe.yaml` + +name: "Conformer-Transducer-Char" + +model: + sample_rate: &sample_rate 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + labels: [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + # Adds Gaussian noise to the gradients of the decoder to avoid overfitting + variational_noise: + start_step: 0 + std: 0.0 + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/hat/conformer_hat_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/hat/conformer_hat_bpe.yaml new file mode 100644 index 0000000..ba4e79f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/hat/conformer_hat_bpe.yaml @@ -0,0 +1,267 @@ +# It contains the default values for training a Conformer-HAT (Hybrid Autoregressive Transducer - https://arxiv.org/abs/2003.07705) ASR model, +# large size (~120M) with Transducer loss and sub-word encoding. +# The only difference from the standard Conformer-Transducer model (RNNT) is the use of "HATJiont" class (instead of "RNNTJoint") for joint module. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-HAT, other parameters are the same as in this config file. +# +# +--------------+---------+---------+----------+------------------+--------------+--------------------------+-----------------+ +# | Model | d_model | n_heads | n_layers | conv_kernel_size | weight_decay | pred_hidden/joint_hidden | pred_rnn_layers | +# +==============+=========+========+===========+==================+==============+==========================+=================+ +# | Small (14M)| 176 | 4 | 16 | 31 | 0.0 | 320 | 1 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ +# | Medium (32M)| 256 | 4 | 16 | 31 | 1e-3 | 640 | 1 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ +# | Large (120M)| 512 | 8 | 17 | 31 | 1e-3 | 640 | 1 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ +# | XLarge (644M)| 1024 | 8 | 24 | 5 | 1e-3 | 640 | 2 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ +# +# You may find more info about Conformer-Transducer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-hat-hybrid-autoregressive-transducer + +name: "Conformer-HAT-BPE" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + use_start_end_token: false + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling parameters + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.HATJoint # the only difference from the standard RNNT model + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 16 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + # Adds Gaussian noise to the gradients of the decoder to avoid overfitting + variational_noise: + start_step: 0 + std: 0.0 + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/hat/conformer_hat_char.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/hat/conformer_hat_char.yaml new file mode 100644 index 0000000..8da521d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/hat/conformer_hat_char.yaml @@ -0,0 +1,263 @@ +# It contains the default values for training a Conformer-HAT (Hybrid Autoregressive Transducer - https://arxiv.org/abs/2003.07705) ASR model, +# large size (~120M) with Transducer loss and char-based vocabulary. +# The only difference from the standard Conformer-Transducer model (RNNT) is the use of "HATJiont" class (instead of "RNNTJoint") for joint module. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-HAT, other parameters are the same as in this config file. +# +# +-------------+---------+---------+----------+--------------+--------------------------+ +# | Model | d_model | n_heads | n_layers | weight_decay | pred_hidden/joint_hidden | +# +=============+=========+========+===========+==============+==========================+ +# | Small (14M)| 176 | 4 | 16 | 0.0 | 320 | +# +-------------+---------+--------+-----------+--------------+--------------------------+ +# | Medium (32M)| 256 | 4 | 16 | 1e-3 | 640 | +# +-------------+---------+--------+-----------+--------------+--------------------------+ +# | Large (120M)| 512 | 8 | 17 | 1e-3 | 640 | +# +-----------------------------------------------------------+--------------------------+ +# +# You may find more info about Conformer-Transducer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-hat-hybrid-autoregressive-transducer + +name: "Conformer-HAT-Char" + +model: + sample_rate: &sample_rate 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + labels: [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.HATJoint # the only difference from the standard RNNT model + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 16 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + # Adds Gaussian noise to the gradients of the decoder to avoid overfitting + variational_noise: + start_step: 0 + std: 0.0 + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc_bpe.yaml new file mode 100644 index 0000000..80268e7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc_bpe.yaml @@ -0,0 +1,267 @@ +# It contains the default values for training a Conformer-Hybrid-Transducer-CTC ASR model, large size (~120M) with sub-word encoding. +# The model would have two decoders: RNNT (Transducer) and CTC + +# You may find more detail: +# Conformer's architecture config: NeMo/examples/asr/conf/conformer/conformer_ctc_bpe.yaml +# Hybrid ASR: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#hybrid-transducer-ctc + +# Pre-trained ASR models can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html + +name: "Conformer-Hybrid-Transducer-CTC-BPE" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling parameters + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + # The section which would contain the decoder and decoding configs of the auxiliary CTC decoder + aux_ctc: + ctc_loss_weight: 0.3 # the weight used to combine the CTC loss with the RNNT loss + use_cer: false + ctc_reduction: 'mean_batch' + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + decoding: + strategy: "greedy" + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc_char.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc_char.yaml new file mode 100644 index 0000000..fb932d9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc_char.yaml @@ -0,0 +1,270 @@ +# It contains the default values for training a Conformer-Hybrid-Transducer-CTC ASR model, large size (~120M) with char-based vocabulary. +# The model would have two decoders: RNNT (Transducer) and CTC + +# You may find more detail: +# Conformer's architecture config: NeMo/examples/asr/conf/conformer/conformer_ctc_char.yaml +# Hybrid ASR: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#hybrid-transducer-ctc + +# Pre-trained ASR models can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html + +name: "Conformer-Hybrid-Transducer-CTC-Char" + +model: + sample_rate: &sample_rate 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + labels: [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + # The section which would contain the decoder and decoding configs of the auxiliary CTC decoder + aux_ctc: + ctc_loss_weight: 0.3 # the weight used to combine the CTC loss with the RNNT loss + use_cer: false + ctc_reduction: 'mean_batch' + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: ${model.labels} + decoding: + strategy: "greedy" + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/multiblank/conformer_multiblank_transducer_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/multiblank/conformer_multiblank_transducer_bpe.yaml new file mode 100644 index 0000000..f844f1c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/multiblank/conformer_multiblank_transducer_bpe.yaml @@ -0,0 +1,256 @@ +# It contains the default values for training a Multiblank Conformer-Transducer ASR model with stateless decoders, large size (~120M) with Transducer loss and sub-word encoding. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-Transducer, other parameters are the same as in this config file. +# +# +-------------+---------+---------+----------+--------------+--------------------------+ +# | Model | d_model | n_heads | n_layers | weight_decay | pred_hidden/joint_hidden | +# +=============+=========+========+===========+==============+==========================+ +# | Small (14M)| 176 | 4 | 16 | 0.0 | 320 | +# +-------------+---------+--------+-----------+--------------+--------------------------+ +# | Medium (32M)| 256 | 4 | 16 | 1e-3 | 640 | +# +-------------+---------+--------+-----------+--------------+--------------------------+ +# | Large (120M)| 512 | 8 | 17 | 1e-3 | 640 | +# +-----------------------------------------------------------+--------------------------+ +# + +# You may find more info about Conformer-Transducer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-transducer +# Multiblank transducer is decribed in https://arxiv.org/pdf/2211.03541 +# Pre-trained models of Conformer-Transducer can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html + +name: "Multiblank-Conformer-Transducer-BPE" + +model: + loss: + loss_name: "multiblank_rnnt" + + multiblank_rnnt_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + # Multiblank RNN-T: https://arxiv.org/pdf/2211.03541.pdf + big_blank_durations: [2, 4, 8] + sigma: 0.05 + + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + num_extra_outputs: 3 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + model_type: "multiblank" + + # this must not be None in order to use the multi-blank specific decoding method. + # you could set this to [1, 1, 1] so that big blanks are treated the same + # as standard blanks during decoding, which usually improves accuracy although runs slower. + big_blank_durations: [2, 4, 8] + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/multilang/conformer_ctc_bpe_multilang.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/multilang/conformer_ctc_bpe_multilang.yaml new file mode 100644 index 0000000..8133c27 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/multilang/conformer_ctc_bpe_multilang.yaml @@ -0,0 +1,205 @@ +# It contains the default values for training a Conformer-CTC ASR model, large size (~120M) with CTC loss and sub-word encoding. +# This config file demonstrates the use of the AggregateTokenizer for multi-lingual model training + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-CTC, other parameters are the same as in this config file. +# One extra layer (compared to original paper) is added to the medium and large variants to compensate for replacing the LSTM decoder with a linear one. +# +# +-------------+---------+---------+----------+------------+-----+ +# | Model | d_model | n_heads | n_layers | time_masks | lr | +# +=============+=========+========+===========+============+=====+ +# | Small (13M)| 176 | 4 | 16 | 5 | 5.0 | +# +-------------+---------+--------+-----------+------------+-----+ +# | Medium (30M)| 256 | 4 | 18 | 5 | 5.0 | +# +-------------+---------+--------+-----------+------------+-----+ +# | Large (121M)| 512 | 8 | 18 | 10 | 2.0 | +# +---------------------------------------------------------------+ +# +# If you do not want to train with AMP, you may use weight decay of 0.0 or reduce the number of time maskings to 2 +# with time_width=100. It may help when you want to train for fewer epochs and need faster convergence. +# With weight_decay=0.0, learning rate may need to get reduced to 2.0. + +# You may find more info about Conformer-CTC here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-ctc +# Pre-trained models of Conformer-CTC can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html +# The checkpoint of the large model trained on LibriSpeech with this recipe can be found here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large_ls + +name: "Conformer-CTC-BPE-multilang" + +model: + sample_rate: 16000 + log_prediction: true # enables logging sample predictions in the output during training + ctc_reduction: 'mean_batch' + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # recommend small vocab size of 128 or 256 when using 4x sub-sampling + # you may find more detail on how to train a monolingual tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + type: agg # The AggregateTokenizer is an ordered dict of N monolingual tokenizers, one per language id + langs: + en: # this language id must match the 'lang' field for english samples in the manifest + dir: ??? # path to the en tokenizer which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + es: # this language id must match the 'lang' field for spanish samples in the manifest + dir: ??? # path to the es tokenizer which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + # you may use lower time_masks for smaller models to have a faster convergence + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 18 + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # -1 sets it to d_model + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + + optim: + name: adamw + lr: 2.0 + # optimizer arguments + betas: [0.9, 0.98] + # less necessity for weight_decay as we already have large augmentations with SpecAug + # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used + # weight decay of 0.0 with lr of 2.0 also works fine + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/multilang/conformer_transducer_bpe_multilang.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/multilang/conformer_transducer_bpe_multilang.yaml new file mode 100644 index 0000000..fc3202f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/multilang/conformer_transducer_bpe_multilang.yaml @@ -0,0 +1,260 @@ +# It contains the default values for training a Conformer-Transducer ASR model, large size (~120M) with Transducer loss and sub-word encoding. +# This config file demonstrates the use of the AggregateTokenizer for multi-lingual model training + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-Transducer, other parameters are the same as in this config file. +# +# +-------------+---------+---------+----------+--------------+--------------------------+ +# | Model | d_model | n_heads | n_layers | weight_decay | pred_hidden/joint_hidden | +# +=============+=========+========+===========+==============+==========================+ +# | Small (14M)| 176 | 4 | 16 | 0.0 | 320 | +# +-------------+---------+--------+-----------+--------------+--------------------------+ +# | Medium (32M)| 256 | 4 | 16 | 1e-3 | 640 | +# +-------------+---------+--------+-----------+--------------+--------------------------+ +# | Large (120M)| 512 | 8 | 17 | 1e-3 | 640 | +# +-----------------------------------------------------------+--------------------------+ +# + +# You may find more info about Conformer-Transducer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-transducer +# Pre-trained models of Conformer-Transducer can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html +# The checkpoint of the large model trained on NeMo ASRSET with this recipe can be found here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_large + +name: "Conformer-Transducer-BPE-multilang" + +model: + sample_rate: &sample_rate 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # You may find more detail on how to train a monolingual tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + type: agg # The AggregateTokenizer is an ordered dict of N monolingual tokenizers, one per language id + langs: + en: # this language id must match the 'lang' field for english samples in the manifest + dir: ??? # path to the en tokenizer which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + es: # this language id must match the 'lang' field for spanish samples in the manifest + dir: ??? # path to the es tokenizer which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.1 + + joint: + # If the vocabulary size is large, replace below _target_ with following lines + # _target_: nemo.collections.asr.modules.SampledRNNTJoint + # n_samples: 500 + + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.1 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 30 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/tdt/conformer_tdt_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/tdt/conformer_tdt_bpe.yaml new file mode 100644 index 0000000..068e7d6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/tdt/conformer_tdt_bpe.yaml @@ -0,0 +1,280 @@ +# This file contains the default values for training a Conformer-TDT ASR model, large size (~120M) with sub-word encoding. + +# You can find detailed info about TDT models at https://arxiv.org/abs/2304.06795. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-Transducer, other parameters are the same as in this config file. + +# Note: the added duration outputs from the joiner make TDT models slightly larger than corresponding conventional RNN-T models, +# although the difference is tiny -- the added number of params is roughly num-durations X (joint_hidden + pred_hidden), typically in the +# order of thousands of params. This is negligible even with the "Small" config with around 14 million params. +# Recommended duraction config is [0, 1, 2, ... , n] where optimal n is usually between 4 and 8 depending on the dataset. + +# +--------------+---------+---------+----------+------------------+--------------+--------------------------+-----------------+ +# | Model | d_model | n_heads | n_layers | conv_kernel_size | weight_decay | pred_hidden/joint_hidden | pred_rnn_layers | +# +==============+=========+========+===========+==================+==============+==========================+=================+ +# | Small (14M)| 176 | 4 | 16 | 31 | 0.0 | 320 | 1 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ +# | Medium (32M)| 256 | 4 | 16 | 31 | 1e-3 | 640 | 1 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ +# | Large (120M)| 512 | 8 | 17 | 31 | 1e-3 | 640 | 1 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ +# | XLarge (644M)| 1024 | 8 | 24 | 5 | 1e-3 | 640 | 2 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ + +# Default learning parameters in this config are set for global batch size of 2K while you may use lower values. +# To increase the global batch size with limited number of GPUs, you may use higher accumulate_grad_batches. +# However accumulate_grad_batches is better to be avoided as long as the global batch size is large enough and training is stable. + +name: "Conformer-TDT-BPE" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + # variables for TDT configs. + tdt_durations: [0, 1, 2, 3, 4] + num_tdt_durations: 5 + + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + use_start_end_token: false + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 16 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + num_extra_outputs: ${model.model_defaults.num_tdt_durations} + + decoding: + # Using greedy decoding is highly recommended for TDT models. Using greedy-batch will give very bad results + # if omega is 0; even if omega is non-zero, greedy-batch results are still going to be inaccurate. + strategy: "greedy" + + model_type: "tdt" + + # this must not be None in order to use the TDT specific decoding method. + durations: ${model.model_defaults.tdt_durations} + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + loss: + # This is the main different between a TDT model and a conventional RNNT model -- the loss function. + loss_name: "tdt" + + tdt_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.001 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + # refer to https://arxiv.org/abs/2304.06795 for the meaning of the following three configs. + durations: ${model.model_defaults.tdt_durations} + sigma: 0.05 # hyper-param for under-normalization. + omega: 0.1 # weight for regular RNN-T loss. + + # Adds Gaussian noise to the gradients of the decoder to avoid overfitting + variational_noise: + start_step: 0 + std: 0.0 + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/tdt/conformer_tdt_bpe_stateless.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/tdt/conformer_tdt_bpe_stateless.yaml new file mode 100644 index 0000000..2daf90f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/conformer/tdt/conformer_tdt_bpe_stateless.yaml @@ -0,0 +1,277 @@ +# This file contains the default values for training an TDT Conformer-Transducer ASR model, large size (~120M) with sub-word encoding. + +# You can find detailed info about TDT models at https://arxiv.org/abs/2304.06795. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-Transducer, other parameters are the same as in this config file. + +# Note: the added duration outputs from the joiner make TDT models slightly larger than corresponding conventional RNN-T models, +# although the difference is tiny -- the added number of params is roughly num-durations X (joint_hidden + pred_hidden), typically in the +# order of thousands of params. This is negligible even with the "Small" config with around 14 million params. +# Recommended duraction config is [0, 1, 2, ... , n] where optimal n is usually between 4 and 8 depending on the dataset. + +# +--------------+---------+---------+----------+------------------+--------------+--------------------------+-----------------+ +# | Model | d_model | n_heads | n_layers | conv_kernel_size | weight_decay | pred_hidden/joint_hidden | decoder_context | +# +==============+=========+========+===========+==================+==============+==========================+=================+ +# | Large (117M)| 512 | 8 | 17 | 31 | 1e-3 | 640 | 2 | +# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+ + +# Default learning parameters in this config are set for global batch size of 2K while you may use lower values. +# To increase the global batch size with limited number of GPUs, you may use higher accumulate_grad_batches. +# However accumulate_grad_batches is better to be avoided as long as the global batch size is large enough and training is stable. + + +name: "Conformer-TDT-BPE-Stateless" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + # variables for TDT configs. + tdt_durations: [0, 1, 2, 3, 4] + num_tdt_durations: 5 + + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + use_start_end_token: false + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + decoder: + _target_: nemo.collections.asr.modules.StatelessTransducerDecoder + context_size: 2 # The Stateless decoder uses 2 words as context by default. + normalization_mode: layer # This helps stabilize training for Stateless decoders. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 16 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + # this variable is non-zero for this TDT model, as well as multi-blank models. It represents the number of + # additional outputs from the joiner, besides all tokens in the BPE vocab plus the (standard) blank symbol. + num_extra_outputs: ${model.model_defaults.num_tdt_durations} + + decoding: + # Using greedy decoding is highly recommended for TDT models. Using greedy-batch will give very bad results + # if omega is 0; even if omega is non-zero, greedy-batch results are still going to be inaccurate. + strategy: "greedy" + + model_type: "tdt" + + # this must not be None in order to use the TDT specific decoding method. + durations: ${model.model_defaults.tdt_durations} + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + loss: + # This is the main different between a TDT model and a conventional RNNT model -- the loss function. + loss_name: "tdt" + + tdt_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.001 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + # refer to https://arxiv.org/abs/2304.06795 for the meaning of the following three configs. + durations: ${model.model_defaults.tdt_durations} + sigma: 0.05 # hyper-param for under-normalization. + omega: 0.1 # weight for regular RNN-T loss. + + # Adds Gaussian noise to the gradients of the decoder to avoid overfitting + variational_noise: + start_step: 0 + std: 0.0 + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/config_rnnt.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/config_rnnt.yaml new file mode 100644 index 0000000..ba8eca6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/config_rnnt.yaml @@ -0,0 +1,261 @@ +name: &name "ConvRNNT5x1" + +model: + sample_rate: 16000 + compute_eval_loss: true + + labels: [ " ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'" ] + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 32 + trim_silence: true + max_duration: 16.7 + labels: ${model.labels} + shuffle: true + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 32 + shuffle: false + labels: ${model.labels} + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 32 + shuffle: false + labels: ${model.labels} + num_workers: 8 + pin_memory: true + + model_defaults: + repeat: 5 + dropout: 0.0 + separable: true + se: true + se_context_size: -1 + # encoder / decoder / joint values + enc_hidden: 1024 + pred_hidden: 320 + joint_hidden: 320 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.02 + sample_rate: ${model.sample_rate} + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + rect_freq: 50 + rect_masks: 5 + rect_time: 120 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: 128 + repeat: 1 + kernel: [11] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: 256 + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + + - filters: 256 + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: 256 + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + + - filters: 256 + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: 256 + repeat: 1 + kernel: [21] + stride: [2] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + + - filters: ${model.model_defaults.enc_hidden} + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null + random_state_sampling: false + blank_as_pad: true + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.0 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # sets it according to cpu/gpu device + + # fused mode + fuse_loss_wer: false + fused_batch_size: 1 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.0 + + decoding: + strategy: "greedy_batch" + + # greedy strategy config + greedy: + max_symbols: 30 + + # beam strategy config + beam: + beam_size: 2 + score_norm: true + softmax_temperature: 1.0 # scale the logits by some temperature prior to softmax + tsd_max_sym_exp: 10 # for Time Synchronous Decoding, int > 0 + alsd_max_target_len: 5.0 # for Alignment-Length Synchronous Decoding, float > 1.0 + maes_num_steps: 2 # for modified Adaptive Expansion Search, int > 0 + maes_prefix_alpha: 1 # for modified Adaptive Expansion Search, int > 0 + maes_expansion_beta: 2 # for modified Adaptive Expansion Search, int >= 0 + maes_expansion_gamma: 2.3 # for modified Adaptive Expansion Search, float >= 0 + + loss: + loss_name: "default" + warprnnt_numba_kwargs: + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adam + # _target_: nemo.core.optim.optimizers.Adam + lr: .1 + + # optimizer arguments + betas: [0.9, 0.999] + weight_decay: 0.0001 + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: null + warmup_ratio: 0.05 + min_lr: 1e-6 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 5 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + precision: 32 + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + create_wandb_logger: False + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/config_rnnt_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/config_rnnt_bpe.yaml new file mode 100644 index 0000000..74cb0c9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/config_rnnt_bpe.yaml @@ -0,0 +1,261 @@ +name: &name "ConvRNNTBPE5x1" + +model: + sample_rate: 16000 + compute_eval_loss: true + + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 32 + trim_silence: true + max_duration: 16.7 + labels: [] + shuffle: true + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 32 + shuffle: false + labels: [] + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 32 + shuffle: false + labels: [] + num_workers: 8 + pin_memory: true + + model_defaults: + repeat: 5 + dropout: 0.0 + separable: true + se: true + se_context_size: -1 + # encoder / decoder / joint values + enc_hidden: 1024 + pred_hidden: 320 + joint_hidden: 320 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.02 + sample_rate: ${model.sample_rate} + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + rect_freq: 50 + rect_masks: 5 + rect_time: 120 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: 128 + repeat: 1 + kernel: [11] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: 256 + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + + - filters: 256 + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: 256 + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + + - filters: 256 + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: 256 + repeat: 1 + kernel: [21] + stride: [2] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + + - filters: ${model.model_defaults.enc_hidden} + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null + random_state_sampling: false + blank_as_pad: true + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.0 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # sets it according to cpu/gpu device + + # fused mode + fuse_loss_wer: false + fused_batch_size: 1 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.0 + + decoding: + strategy: "greedy_batch" + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + score_norm: true + softmax_temperature: 1.0 # scale the logits by some temperature prior to softmax + tsd_max_sym_exp: 10 # for Time Synchronous Decoding, int > 0 + alsd_max_target_len: 5.0 # for Alignment-Length Synchronous Decoding, float > 1.0 + maes_num_steps: 2 # for modified Adaptive Expansion Search, int > 0 + maes_prefix_alpha: 1 # for modified Adaptive Expansion Search, int > 0 + maes_expansion_beta: 2 # for modified Adaptive Expansion Search, int >= 0 + maes_expansion_gamma: 2.3 # for modified Adaptive Expansion Search, float >= 0 + + loss: + loss_name: "default" + warprnnt_numba_kwargs: + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adam + # _target_: nemo.core.optim.optimizers.Adam + lr: .1 + + # optimizer arguments + betas: [0.9, 0.999] + weight_decay: 0.0001 + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: null + warmup_ratio: 0.05 + min_lr: 1e-6 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 5 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + precision: 32 + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + create_wandb_logger: False + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/contextnet_rnnt.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/contextnet_rnnt.yaml new file mode 100644 index 0000000..501f11b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/contextnet_rnnt.yaml @@ -0,0 +1,509 @@ +# This config contains the default values for training a modified ContextNet model with Transducer loss and BPE-based vocabulary. +# In contrast to original ContextNet, the same number of filters is used throughout the model. +# Default learning parameters in this config are set for effective batch size of 1k on 32 GPUs. +# To train it with smaller batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. + +# It contains the default values for training a ContextNet ASR model, large size (~144M) with Transducer loss and sub-word encoding. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 1K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of ContextNet, other parameters are the same as in this config file. +# +# +-------------+---------+------------+ +# | Model | filters | time_masks | +# +=============+=========+============+ +# | Small (14M)| 256 | 2 | +# +-------------+---------+------------+ +# | Medium (40M)| 512 | 5 | +# +-------------+---------+------------+ +# | Large (145M)| 1024 | 10 | +# +------------------------------------- + +name: &name "ContextNet-8x-Stride-RNNT" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # Can be increased if memory allows or when using smaller model + trim_silence: false + max_duration: 16.7 + shuffle: true + use_start_end_token: false + num_workers: 16 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 8 + shuffle: false + use_start_end_token: false + num_workers: 16 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 8 + shuffle: false + use_start_end_token: false + num_workers: 16 + pin_memory: true + + model_defaults: + filters: 1024 + repeat: 5 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 1.0 + # encoder / decoder / joint values + enc_hidden: 640 + pred_hidden: 640 + joint_hidden: 640 + + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # should be kept at 2 + time_masks: 10 # can be 5 for small-med models, 10 for larger models. + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: swish + conv_mask: true + init_mode: "tds_uniform" + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] # *stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] # stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.enc_hidden} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 # only 1 layer LSTM networks are exportable. + t_max: null # Maximum possible target seq length used for Chrono Initialization - https://arxiv.org/abs/1804.11188. Disabled by default. + dropout: 0.1 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # sets it according to cpu/gpu device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.1 + + # RNNT decoding strategy + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 4 + score_norm: true + return_best_hypothesis: False + softmax_temperature: 1.0 # scale the logits by some temperature prior to softmax + tsd_max_sym_exp: 10 # for Time Synchronous Decoding, int > 0 + alsd_max_target_len: 5.0 # for Alignment-Length Synchronous Decoding, float > 1.0 + maes_num_steps: 2 # for modified Adaptive Expansion Search, int > 0 + maes_prefix_alpha: 1 # for modified Adaptive Expansion Search, int > 0 + maes_expansion_beta: 2 # for modified Adaptive Expansion Search, int >= 0 + maes_expansion_gamma: 2.3 # for modified Adaptive Expansion Search, float >= 0 + + # RNNT loss config + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + fastemit_lambda: 0.001 # Values can be in range [1e-4, 1e-2]. Generally, 0.001 is good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: novograd + lr: 0.05 + + # optimizer arguments + betas: [0.9, 0.0] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: 5000 + warmup_ratio: null + min_lr: 1e-6 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 # Should be set via SLURM variable `SLURM_JOB_NUM_NODES` + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + check_val_every_n_epoch: 1 # RNNT decoding is slower than CTC, so eval takes longer. Increase value to speed up training slightly. + precision: 32 # RNNT requires a lot of memory, so precision 16 is very important. Use very small batch size for precision 32. + gradient_clip_val: 1.0 # Gradient norm clip value + sync_batchnorm: true + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + save_top_k: 3 + always_save_nemo: true + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/contextnet_rnnt_char.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/contextnet_rnnt_char.yaml new file mode 100644 index 0000000..08ea55f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/contextnet_rnnt_char.yaml @@ -0,0 +1,511 @@ +# This config contains the default values for training a modified ContextNet model with Transducer loss and BPE-based vocabulary. +# In contrast to original ContextNet, the same number of filters is used throughout the model. +# Default learning parameters in this config are set for effective batch size of 1k on 32 GPUs. +# To train it with smaller batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. + +# It contains the default values for training a ContextNet ASR model, large size (~144M) with Transducer loss and sub-word encoding. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 1K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of ContextNet, other parameters are the same as in this config file. +# +# +-------------+---------+------------+ +# | Model | filters | time_masks | +# +=============+=========+============+ +# | Small (14M)| 256 | 2 | +# +-------------+---------+------------+ +# | Medium (40M)| 512 | 5 | +# +-------------+---------+------------+ +# | Large (145M)| 1024 | 10 | +# +------------------------------------- + +name: &name "ContextNet-8x-Stride-RNNT" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhause memory. Disable computation of transducer loss during validation/testing with this flag. + + labels: [ " ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'" ] + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # Can be increased if memory allows or when using smaller model + trim_silence: false + max_duration: 16.7 + labels: ${model.labels} + shuffle: true + use_start_end_token: false + num_workers: 16 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 8 + shuffle: false + labels: ${model.labels} + use_start_end_token: false + num_workers: 16 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 8 + shuffle: false + labels: ${model.labels} + use_start_end_token: false + num_workers: 16 + pin_memory: true + + model_defaults: + filters: 1024 + repeat: 5 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 1.0 + # encoder / decoder / joint values + enc_hidden: 640 + pred_hidden: 640 + joint_hidden: 640 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # should be kept at 2 + time_masks: 5 # can be 5 for small-med models, 10 for larger models. + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: swish + conv_mask: true + init_mode: "tds_uniform" + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] # *stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] # stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.enc_hidden} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 # only 1 layer LSTM networks are exportable. + t_max: null # Maximum possible target seq length used for Chrono Initialization - https://arxiv.org/abs/1804.11188. Disabled by default. + dropout: 0.1 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # sets it according to cpu/gpu device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.1 + + # RNNT decoding strategy + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 4 + score_norm: true + return_best_hypothesis: False + softmax_temperature: 1.0 # scale the logits by some temperature prior to softmax + tsd_max_sym_exp: 10 # for Time Synchronous Decoding, int > 0 + alsd_max_target_len: 5.0 # for Alignment-Length Synchronous Decoding, float > 1.0 + maes_num_steps: 2 # for modified Adaptive Expansion Search, int > 0 + maes_prefix_alpha: 1 # for modified Adaptive Expansion Search, int > 0 + maes_expansion_beta: 2 # for modified Adaptive Expansion Search, int >= 0 + maes_expansion_gamma: 2.3 # for modified Adaptive Expansion Search, float >= 0 + + # RNNT loss config + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + fastemit_lambda: 0.001 # Values can be in range [1e-4, 1e-2]. Generally, 0.001 is good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: novograd + lr: 0.05 + + # optimizer arguments + betas: [0.9, 0.0] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: 5000 + warmup_ratio: null + min_lr: 1e-6 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 # Should be set via SLURM variable `SLURM_JOB_NUM_NODES` + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + check_val_every_n_epoch: 1 # RNNT decoding is slower than CTC, so eval takes longer. Increase value to speed up training slightly. + precision: 32 # RNNT requires a lot of memory, so precision 16 is very important. Use very small batch size for precision 32. + gradient_clip_val: 1.0 # Gradient norm clip value + sync_batchnorm: true + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + save_top_k: 3 + always_save_nemo: true + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/contextnet_rnnt_multilang.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/contextnet_rnnt_multilang.yaml new file mode 100644 index 0000000..57ba09a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/contextnet_rnnt/contextnet_rnnt_multilang.yaml @@ -0,0 +1,516 @@ +# This config contains the default values for training a modified ContextNet model with Transducer loss and BPE-based vocabulary. +# It also uses the AggregateTokenizer, so that the model is trained on more than one language, one language per tokenizer +# In contrast to original ContextNet, the same number of filters is used throughout the model. +# Default learning parameters in this config are set for effective batch size of 1k on 32 GPUs. +# To train it with smaller batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. + +# It contains the default values for training a ContextNet ASR model, large size (~144M) with Transducer loss and sub-word encoding. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 1K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of ContextNet, other parameters are the same as in this config file. +# +# +-------------+---------+------------+ +# | Model | filters | time_masks | +# +=============+=========+============+ +# | Small (14M)| 256 | 2 | +# +-------------+---------+------------+ +# | Medium (40M)| 512 | 5 | +# +-------------+---------+------------+ +# | Large (145M)| 1024 | 10 | +# +------------------------------------- + +name: &name "ContextNet-8x-Stride-RNNT-mla" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # Can be increased if memory allows or when using smaller model + trim_silence: false + max_duration: 16.7 + shuffle: true + use_start_end_token: false + num_workers: 16 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 8 + shuffle: false + use_start_end_token: false + num_workers: 16 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 8 + shuffle: false + use_start_end_token: false + num_workers: 16 + pin_memory: true + + model_defaults: + filters: 1024 + repeat: 5 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 1.0 + # encoder / decoder / joint values + enc_hidden: 640 + pred_hidden: 640 + joint_hidden: 640 + + tokenizer: + type: agg # The AggregateTokenizer is an ordered dict of N monolingual tokenizers, one per language id + langs: + en: # this language id must match the 'lang' field for english samples in the manifest + dir: ??? # path to the en tokenizer which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + es: # this language id must match the 'lang' field for spanish samples in the manifest + dir: ??? # path to the es tokenizer which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # should be kept at 2 + time_masks: 10 # can be 5 for small-med models, 10 for larger models. + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: swish + conv_mask: true + init_mode: "tds_uniform" + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] # *stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] # stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.enc_hidden} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 # only 1 layer LSTM networks are exportable. + t_max: null # Maximum possible target seq length used for Chrono Initialization - https://arxiv.org/abs/1804.11188. Disabled by default. + dropout: 0.1 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # sets it according to cpu/gpu device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.1 + + # RNNT decoding strategy + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 4 + score_norm: true + return_best_hypothesis: False + softmax_temperature: 1.0 # scale the logits by some temperature prior to softmax + tsd_max_sym_exp: 10 # for Time Synchronous Decoding, int > 0 + alsd_max_target_len: 5.0 # for Alignment-Length Synchronous Decoding, float > 1.0 + maes_num_steps: 2 # for modified Adaptive Expansion Search, int > 0 + maes_prefix_alpha: 1 # for modified Adaptive Expansion Search, int > 0 + maes_expansion_beta: 2 # for modified Adaptive Expansion Search, int >= 0 + maes_expansion_gamma: 2.3 # for modified Adaptive Expansion Search, float >= 0 + + # RNNT loss config + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + fastemit_lambda: 0.001 # Values can be in range [1e-4, 1e-2]. Generally, 0.001 is good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: novograd + lr: 0.05 + + # optimizer arguments + betas: [0.9, 0.0] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: 5000 + warmup_ratio: null + min_lr: 1e-6 + last_epoch: -1 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 # Should be set via SLURM variable `SLURM_JOB_NUM_NODES` + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + check_val_every_n_epoch: 1 # RNNT decoding is slower than CTC, so eval takes longer. Increase value to speed up training slightly. + precision: 32 # RNNT requires a lot of memory, so precision 16 is very important. Use very small batch size for precision 32. + gradient_clip_val: 1.0 # Gradient norm clip value + sync_batchnorm: true + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + save_top_k: 3 + always_save_nemo: true + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_ctc_bpe_streaming.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_ctc_bpe_streaming.yaml new file mode 100644 index 0000000..a59a262 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_ctc_bpe_streaming.yaml @@ -0,0 +1,205 @@ +# It contains the default values for training a cache-aware streaming FastConformer-CTC ASR model, large size (~115M) with sub-word encoding. + +# You may find more detail: +# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer +# Cache-aware Conformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#cache-aware-streaming-conformer +# FastConformer-CTC's architecture config, along with the optimal batch size and precision: NeMo/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml + +name: "FastConformer-CTC-BPE-Streaming" + +model: + sample_rate: 16000 + log_prediction: true # enables logging sample predictions in the output during training + ctc_reduction: 'mean_batch' + skip_nan_grad: false + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 20 # you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + # We recommend to use vocab size of 1024 with SPE Unigram for most languages + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "NA" # No normalization for mel-spectogram makes streaming easier + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling parameters + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model + causal_downsampling: true + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + # for att_context_style=regular, the right context is recommended to be a small number around 0 to 3 as multiple-layers may increase the effective right context too large + # for att_context_style=chunked_limited, the left context need to be dividable by the right context plus one + # look-ahead(secs) = att_context_size[1]*subsampling_factor*window_stride, example: 13*8*0.01=1.04s + + # For multi-lookahead models, you may specify a list of context sizes. During the training, different context sizes would be used randomly with the distribution specified by att_context_probs. + # The first item in the list would be the default during test/validation/inference. + # An example of settings for multi-lookahead: + # att_context_size: [[70,13],[70,6],[70,1],[70,0]] + # att_context_probs: [0.25, 0.25, 0.25, 0.25, 0.25] + att_context_size: [70, 13] # -1 means unlimited context + att_context_style: chunked_limited # regular or chunked_limited + att_context_probs: null + + + xscaling: true # scales up the input embeddings by sqrt(d_model) + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'layer_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + # Recommend to use causal convolutions as it would increase the effective right context and therefore the look-ahead significantly + conv_context_size: causal + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + optim: + name: adamw + lr: 2.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_ctc_char_streaming.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_ctc_char_streaming.yaml new file mode 100644 index 0000000..8f8f7e4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_ctc_char_streaming.yaml @@ -0,0 +1,213 @@ +# It contains the default values for training a cache-aware streaming FastConformer-CTC ASR model, large size (~115M) with char-based vocabulary. + +# You may find more detail: +# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer +# Cache-aware Conformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#cache-aware-streaming-conformer +# FastConformer-CTC's architecture config, along with the optimal batch size and precision: NeMo/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml + +name: "FastConformer-CTC-Char-Streaming" + +model: + sample_rate: 16000 + log_prediction: true # enables logging sample predictions in the output during training + ctc_reduction: 'mean_batch' + skip_nan_grad: false + + labels: [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + trim_silence: false + max_duration: 20 # you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "NA" # No normalization for mel-spectogram makes streaming easier + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model + causal_downsampling: true + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + # for att_context_style=regular, the right context is recommended to be a small number around 0 to 3 as multiple-layers may increase the effective right context too large + # for att_context_style=chunked_limited, the left context need to be dividable by the right context plus one + # look-ahead(secs) = att_context_size[1]*subsampling_factor*window_stride, example: 13*8*0.01=1.04s + + # For multi-lookahead models, you may specify a list of context sizes. During the training, different context sizes would be used randomly with the distribution specified by att_context_probs. + # The first item in the list would be the default during test/validation/inference. + # An example of settings for multi-lookahead: + # att_context_size: [[70,13],[70,6],[70,1],[70,0]] + # att_context_probs: [0.25, 0.25, 0.25, 0.25, 0.25] + att_context_size: [70, 13] # -1 means unlimited context + att_context_style: chunked_limited # regular or chunked_limited + att_context_probs: null + + + xscaling: true # scales up the input embeddings by sqrt(d_model) + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'layer_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + # Recommend to use causal convolutions as it would increase the effective right context and therefore the look-ahead significantly + conv_context_size: causal + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + optim: + name: adamw + lr: 2.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_transducer_bpe_streaming.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_transducer_bpe_streaming.yaml new file mode 100644 index 0000000..69b21b4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_transducer_bpe_streaming.yaml @@ -0,0 +1,261 @@ +# It contains the default values for training a cache-aware streaming FastConformer-Transducer ASR model, large size (~115M) with sub-word encoding. + +# You may find more detail: +# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer +# Cache-aware Conformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#cache-aware-streaming-conformer +# FastConformer-Transducer's architecture config, along with the optimal batch size and precision: NeMo/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml + +name: "FastConformer-Transducer-BPE-Streaming" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 20 # you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + # We recommend to use vocab size of 1024 with SPE Unigram for most languages + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "NA" # No normalization for mel-spectogram makes streaming easier + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling parameters + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model + causal_downsampling: true + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + # for att_context_style=regular, the right context is recommended to be a small number around 0 to 3 as multiple-layers may increase the effective right context too large + # for att_context_style=chunked_limited, the left context need to be dividable by the right context plus one + # look-ahead(secs) = att_context_size[1]*subsampling_factor*window_stride, example: 13*8*0.01=1.04s + + # For multi-lookahead models, you may specify a list of context sizes. During the training, different context sizes would be used randomly with the distribution specified by att_context_probs. + # The first item in the list would be the default during test/validation/inference. + # An example of settings for multi-lookahead: + # att_context_size: [[70,13],[70,6],[70,1],[70,0]] + # att_context_probs: [0.25, 0.25, 0.25, 0.25, 0.25] + att_context_size: [70, 13] # -1 means unlimited context + att_context_style: chunked_limited # regular or chunked_limited + att_context_probs: null + + + xscaling: true # scales up the input embeddings by sqrt(d_model) + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'layer_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + # Recommend to use causal convolutions as it would increase the effective right context and therefore the look-ahead significantly + conv_context_size: causal + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + loss: + loss_name: "default" + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to increase the accuracy and reduce the latency of the model for streaming + # You may set it to lower values like 1e-3 for models with larger right context + fastemit_lambda: 5e-3 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_transducer_char_streaming.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_transducer_char_streaming.yaml new file mode 100644 index 0000000..8fd0965 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/cache_aware_streaming/fastconformer_transducer_char_streaming.yaml @@ -0,0 +1,270 @@ +# It contains the default values for training a cache-aware streaming FastConformer-Transducer ASR model, large size (~115M) with char-based vocabulary. + +# You may find more detail: +# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer +# Cache-aware Conformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#cache-aware-streaming-conformer +# FastConformer-CTC's architecture config: NeMo/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml +# FastConformer-Transducer's architecture config, along with the optimal batch size and precision: NeMo/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml + +name: "FastConformer-Transducer-Char-Streaming" + +model: + sample_rate: &sample_rate 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + labels: [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + trim_silence: false + max_duration: 20 # you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "NA" # No normalization for mel-spectogram makes streaming easier + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model + causal_downsampling: true + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + # for att_context_style=regular, the right context is recommended to be a small number around 0 to 3 as multiple-layers may increase the effective right context too large + # for att_context_style=chunked_limited, the left context need to be dividable by the right context plus one + # look-ahead(secs) = att_context_size[1]*subsampling_factor*window_stride, example: 13*8*0.01=1.04s + + # For multi-lookahead models, you may specify a list of context sizes. During the training, different context sizes would be used randomly with the distribution specified by att_context_probs. + # The first item in the list would be the default during test/validation/inference. + # An example of settings for multi-lookahead: + # att_context_size: [[70,13],[70,6],[70,1],[70,0]] + # att_context_probs: [0.25, 0.25, 0.25, 0.25, 0.25] + att_context_size: [70, 13] # -1 means unlimited context + att_context_style: chunked_limited # regular or chunked_limited + att_context_probs: null + + + xscaling: true # scales up the input embeddings by sqrt(d_model) + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'layer_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + # Recommend to use causal convolutions as it would increase the effective right context and therefore the look-ahead significantly + conv_context_size: causal + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + loss: + loss_name: "default" + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to increase the accuracy and reduce the latency of the model for streaming + # You may set it to lower values like 1e-3 for models with larger right context + fastemit_lambda: 5e-3 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml new file mode 100644 index 0000000..9b51edf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml @@ -0,0 +1,232 @@ +# It contains the default values for training a Fast Conformer-CTC ASR model, large size (~120M) with CTC loss and sub-word encoding. + +# You may find more info about FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer + +# We suggest to use trainer.precision=bf16 for GPUs which support it otherwise trainer.precision=16 is recommended. +# Using bf16 or 16 would make it possible to double the batch size and speedup training/inference. If fp16 is not stable and model diverges after some epochs, you may use fp32. +# Here are the suggested batch size per GPU for each precision and memory sizes: + +# +-----------+------------+------------+ +# | Precision | GPU Memory | Batch Size | +# +===========+============+============+ +# | 32 | 16GB | 16 | +# | | 32GB | 32 | +# | | 80GB | 64 | +# +-----------+------------+------------+ +# | fp16 or | 16GB | 32 | +# | bf16 | 32GB | 64 | +# | | 80GB | 128 | +# +-----------+------------+------------+ +# Here are the recommended configs for different variants of FastConformer-CTC-BPE, other parameters are the same as in this config file. +# +# +--------------+---------+---------+----------+----------------+--------------+--------------------------+-----------------+------------+ +# | Model | d_model | n_heads | n_layers |conv_kernel_size| weight_decay | pred_hidden/joint_hidden | pred_rnn_layers | xscaling | +# +==============+=========+========+===========+================+==============+==========================+=================+============+ +# | Small (14M) | 176 | 4 | 16 | 9 | 0.0 | 320 | 1 | True | +# +--------------+---------+--------+-----------+----------------+--------------+--------------------------+-----------------+------------+ +# | Medium (32M) | 256 | 4 | 16 | 9 | 1e-3 | 640 | 1 | True | +# +--------------+---------+--------+-----------+----------------+--------------+--------------------------+-----------------+------------+ +# | Large (120M) | 512 | 8 | 17 | 9 | 1e-3 | 640 | 1 | True | +# +--------------+---------+--------+-----------+----------------+--------------+--------------------------+-----------------+------------+ +# | XLarge (616M)| 1024 | 8 | 24 | 9 | 1e-3 | 640 | 2 | False | +# +--------------+---------+--------+-----------+----------------+--------------+--------------------------+-----------------+------------+ +# | XXLarge(1.2B)| 1024 | 8 | 42 | 5 | 1e-3 | 640 | 2 | False | +# +--------------------------------------------------------------+--------------+--------------------------+-----------------+------------+ + +# Note: They are based on the assumption of max_duration of 20. If you have longer or shorter max_duration, then batch sizes may need to get updated accordingly. + +# Default learning parameters in this config are set for global batch size of 2K while you may use lower values. +# To increase the global batch size with limited number of GPUs, you may use higher accumulate_grad_batches. +# However accumulate_grad_batches is better to be avoided as long as the global batch size is large enough and training is stable. + +name: "FastConformer-CTC-BPE" + +model: + sample_rate: 16000 + log_prediction: true # enables logging sample predictions in the output during training + ctc_reduction: 'mean_volume' + skip_nan_grad: false + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "fully_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # recommend vocab size of 128 or 256 when training on ~1k hr datasets and 1k vocab size on 10+k hr datasets + # you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + # you may use lower time_masks for smaller models to have a faster convergence + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 18 + d_model: 512 + + # Sub-sampling params + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # -1 sets it to d_model + causal_downsampling: false + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + optim: + name: adamw + lr: 1e-3 + # optimizer arguments + betas: [0.9, 0.98] + # less necessity for weight_decay as we already have large augmentations with SpecAug + # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used + # weight decay of 0.0 with lr of 2.0 also works fine + weight_decay: 1e-3 + + # scheduler setup + sched: + name: CosineAnnealing + # scheduler config override + warmup_steps: 15000 + warmup_ratio: null + min_lr: 1e-4 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml new file mode 100644 index 0000000..680d96e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml @@ -0,0 +1,283 @@ +# It contains the default values for training a Fast Conformer-Transducer ASR model, large size (~120M) with Transducer loss and sub-word encoding. + +# You may find more info about FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer + +# We suggest to use trainer.precision=bf16 for GPUs which support it otherwise trainer.precision=16 is recommended. +# Using bf16 or 16 would make it possible to double the batch size and speedup training/inference. If fp16 is not stable and model diverges after some epochs, you may use fp32. +# Here are the suggested batch size per GPU for each precision and memory sizes: + +# +-----------+------------+------------+ +# | Precision | GPU Memory | Batch Size | +# +===========+============+============+ +# | 32 | 16GB | 16 | +# | | 32GB | 32 | +# | | 80GB | 64 | +# +-----------+------------+------------+ +# | fp16 or | 16GB | 32 | +# | bf16 | 32GB | 64 | +# | | 80GB | 128 | +# +-----------+------------+------------+ +# Here are the recommended configs for different variants of FastConformer-Transducer-BPE, other parameters are the same as in this config file. +# +# +--------------+---------+---------+----------+----------------+--------------+--------------------------+-----------------+------------+ +# | Model | d_model | n_heads | n_layers |conv_kernel_size| weight_decay | pred_hidden/joint_hidden | pred_rnn_layers | xscaling | +# +==============+=========+========+===========+================+==============+==========================+=================+============+ +# | Small (14M) | 176 | 4 | 16 | 9 | 0.0 | 320 | 1 | True | +# +--------------+---------+--------+-----------+----------------+--------------+--------------------------+-----------------+------------+ +# | Medium (32M) | 256 | 4 | 16 | 9 | 1e-3 | 640 | 1 | True | +# +--------------+---------+--------+-----------+----------------+--------------+--------------------------+-----------------+------------+ +# | Large (120M) | 512 | 8 | 17 | 9 | 1e-3 | 640 | 1 | True | +# +--------------+---------+--------+-----------+----------------+--------------+--------------------------+-----------------+------------+ +# | XLarge (616M)| 1024 | 8 | 24 | 9 | 1e-3 | 640 | 2 | True | +# +--------------+---------+--------+-----------+----------------+--------------+--------------------------+-----------------+------------+ +# | XXLarge(1.2B)| 1024 | 8 | 42 | 5 | 1e-3 | 640 | 2 | False | +# +--------------------------------------------------------------+--------------+--------------------------+-----------------+------------+ + +# Note: They are based on the assumption of max_duration of 20. If you have longer or shorter max_duration, then batch sizes may need to get updated accordingly. + +# Default learning parameters in this config are set for global batch size of 2K while you may use lower values. +# To increase the global batch size with limited number of GPUs, you may use higher accumulate_grad_batches. +# However accumulate_grad_batches is better to be avoided as long as the global batch size is large enough and training is stable. + +name: "FastConformer-Transducer-BPE" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + rnnt_reduction: 'mean_volume' + skip_nan_grad: false + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "fully_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling parameters + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + # if a large vocabulary size is desired, you may wish to use SampleRNNTJoint module + # _target_: nemo.collections.asr.modules.SampledRNNTJoint + # n_samples: 500 # Specifies the minimum number of tokens to sample from the vocabulary space, excluding + # the RNNT blank token. If a given value is larger than the entire vocabulary size, then the full + # vocabulary will be used + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adamw + lr: 5e-3 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: CosineAnnealing + # scheduler config override + warmup_steps: 15000 + warmup_ratio: null + min_lr: 5e-4 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/hybrid_cache_aware_streaming/fastconformer_hybrid_transducer_ctc_bpe_streaming.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/hybrid_cache_aware_streaming/fastconformer_hybrid_transducer_ctc_bpe_streaming.yaml new file mode 100644 index 0000000..b0965b5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/hybrid_cache_aware_streaming/fastconformer_hybrid_transducer_ctc_bpe_streaming.yaml @@ -0,0 +1,278 @@ +# It contains the default values for training a cache-aware streaming FastConformer-Hybrid-Transducer-CTC ASR model, large size (~115M) with sub-word encoding. +# The model would have two decoders: RNNT (Transducer) and CTC + +# You may find more detail: +# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer +# Hybrid ASR: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#hybrid-transducer-ctc +# Cache-aware Conformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#cache-aware-streaming-conformer +# FastConformer-CTC's architecture config: NeMo/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml +# FastConformer-Transducer's architecture config, along with the optimal batch size and precision: NeMo/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml + +# Note: if training loss does not converge, you may increase warm-up to 20K. + +name: "FastConformer-Hybrid-Transducer-CTC-BPE-Streaming" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 20 # you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + # We recommend to use vocab size of 1024 with SPE Unigram for most languages + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "NA" # No normalization for mel-spectogram makes streaming easier + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling parameters + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model + causal_downsampling: true + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + # for att_context_style=regular, the right context is recommended to be a small number around 0 to 3 as multiple-layers may increase the effective right context too large + # for att_context_style=chunked_limited, the left context need to be dividable by the right context plus one + # look-ahead(secs) = att_context_size[1]*subsampling_factor*window_stride, example: 13*8*0.01=1.04s + + # For multi-lookahead models, you may specify a list of context sizes. During the training, different context sizes would be used randomly with the distribution specified by att_context_probs. + # The first item in the list would be the default during test/validation/inference. + # An example of settings for multi-lookahead: + # att_context_size: [[70,13],[70,6],[70,1],[70,0]] + # att_context_probs: [0.25, 0.25, 0.25, 0.25, 0.25] + att_context_size: [70, 13] # -1 means unlimited context + att_context_style: chunked_limited # regular or chunked_limited + att_context_probs: null + + xscaling: true # scales up the input embeddings by sqrt(d_model) + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'layer_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + # Recommend to use causal convolutions as it would increase the effective right context and therefore the look-ahead significantly + conv_context_size: causal + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + # The section which would contain the decoder and decoding configs of the auxiliary CTC decoder + aux_ctc: + ctc_loss_weight: 0.3 # the weight used to combine the CTC loss with the RNNT loss + use_cer: false + ctc_reduction: 'mean_batch' + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + decoding: + strategy: "greedy" + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + loss: + loss_name: "default" + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to increase the accuracy and reduce the latency of the model for streaming + # You may set it to lower values like 1e-3 for models with larger right context + fastemit_lambda: 5e-3 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/hybrid_cache_aware_streaming/fastconformer_hybrid_transducer_ctc_char_streaming.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/hybrid_cache_aware_streaming/fastconformer_hybrid_transducer_ctc_char_streaming.yaml new file mode 100644 index 0000000..9c144d2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/hybrid_cache_aware_streaming/fastconformer_hybrid_transducer_ctc_char_streaming.yaml @@ -0,0 +1,286 @@ +# It contains the default values for training a Conformer-Hybrid-Transducer-CTC ASR model, large size (~115M) with char-based vocabulary. +# The model would have two decoders: RNNT (Transducer) and CTC + +# You may find more detail: +# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer +# Hybrid ASR: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#hybrid-transducer-ctc +# Cache-aware Conformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#cache-aware-streaming-conformer +# FastConformer-CTC's architecture config: NeMo/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml +# FastConformer-Transducer's architecture config, along with the optimal batch size and precision: NeMo/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml + +# Note: if training loss does not converge, you may increase warm-up to 20K. + +name: "FastConformer-Hybrid-Transducer-CTC-Char-Streaming" + +model: + sample_rate: &sample_rate 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + labels: [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + trim_silence: false + max_duration: 20 # you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "NA" # No normalization for mel-spectogram makes streaming easier + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model + causal_downsampling: true + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + # for att_context_style=regular, the right context is recommended to be a small number around 0 to 3 as multiple-layers may increase the effective right context too large + # for att_context_style=chunked_limited, the left context need to be dividable by the right context plus one + # look-ahead(secs) = att_context_size[1]*subsampling_factor*window_stride, example: 13*8*0.01=1.04s + + # For multi-lookahead models, you may specify a list of context sizes. During the training, different context sizes would be used randomly with the distribution specified by att_context_probs. + # The first item in the list would be the default during test/validation/inference. + # An example of settings for multi-lookahead: + # att_context_size: [[70,13],[70,6],[70,1],[70,0]] + # att_context_probs: [0.25, 0.25, 0.25, 0.25, 0.25] + att_context_size: [70, 13] # -1 means unlimited context + att_context_style: chunked_limited # regular or chunked_limited + att_context_probs: null + + xscaling: true # scales up the input embeddings by sqrt(d_model) + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'layer_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + # Recommend to use causal convolutions as it would increase the effective right context and therefore the look-ahead significantly + conv_context_size: causal + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + # The section which would contain the decoder and decoding configs of the auxiliary CTC decoder + aux_ctc: + ctc_loss_weight: 0.3 # the weight used to combine the CTC loss with the RNNT loss + use_cer: false + ctc_reduction: 'mean_batch' + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: ${model.labels} + decoding: + strategy: "greedy" + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + loss: + loss_name: "default" + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to increase the accuracy and reduce the latency of the model for streaming + # You may set it to lower values like 1e-3 for models with larger right context + fastemit_lambda: 5e-3 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/hybrid_transducer_ctc/fastconformer_hybrid_transducer_ctc_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/hybrid_transducer_ctc/fastconformer_hybrid_transducer_ctc_bpe.yaml new file mode 100644 index 0000000..69e4546 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/hybrid_transducer_ctc/fastconformer_hybrid_transducer_ctc_bpe.yaml @@ -0,0 +1,257 @@ +# It contains the default values for training a FastConformer-Hybrid-Transducer-CTC ASR model, large size (~115M) with sub-word encoding. +# The model would have two decoders: RNNT (Transducer) and CTC + +# You may find more detail: +# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer +# Hybrid ASR: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#hybrid-transducer-ctc +# FastConformer-CTC's architecture config: NeMo/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml +# FastConformer-Transducer's architecture config, along with the optimal batch size and precision: NeMo/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml + +name: "FastConformer-Hybrid-Transducer-CTC-BPE" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 20 # you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + # We recommend to use vocab size of 1024 with SPE Unigram for most languages + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling parameters + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + # The section which would contain the decoder and decoding configs of the auxiliary CTC decoder + aux_ctc: + ctc_loss_weight: 0.3 # the weight used to combine the CTC loss with the RNNT loss + use_cer: false + ctc_reduction: 'mean_batch' + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + decoding: + strategy: "greedy" + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + loss: + loss_name: "default" + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + # It also helps to improve the accuracy of the model in streaming mode + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/hybrid_transducer_ctc/fastconformer_hybrid_transducer_ctc_char.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/hybrid_transducer_ctc/fastconformer_hybrid_transducer_ctc_char.yaml new file mode 100644 index 0000000..ea98d13 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/hybrid_transducer_ctc/fastconformer_hybrid_transducer_ctc_char.yaml @@ -0,0 +1,265 @@ +# It contains the default values for training a Conformer-Hybrid-Transducer-CTC ASR model, large size (~115M) with char-based vocabulary. +# The model would have two decoders: RNNT (Transducer) and CTC + +# You may find more detail: +# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer +# Hybrid ASR: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#hybrid-transducer-ctc +# FastConformer-CTC's architecture config: NeMo/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml +# FastConformer-Transducer's architecture config, along with the optimal batch size and precision: NeMo/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml + +name: "FastConformer-Hybrid-Transducer-CTC-Char" + +model: + sample_rate: &sample_rate 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + labels: [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + trim_silence: false + max_duration: 20 # you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + # The section which would contain the decoder and decoding configs of the auxiliary CTC decoder + aux_ctc: + ctc_loss_weight: 0.3 # the weight used to combine the CTC loss with the RNNT loss + use_cer: false + ctc_reduction: 'mean_batch' + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: ${model.labels} + decoding: + strategy: "greedy" + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + loss: + loss_name: "default" + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + # It also helps to improve the accuracy of the model in streaming mode + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/long_fastconformer/fast-conformer-long_ctc_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/long_fastconformer/fast-conformer-long_ctc_bpe.yaml new file mode 100644 index 0000000..2fab24f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/long_fastconformer/fast-conformer-long_ctc_bpe.yaml @@ -0,0 +1,204 @@ +# It contains the default values for training a Fast Conformer-CTC ASR model, large size (~120M) with CTC loss and sub-word encoding. +# This version uses Longformer-style attention in order to handle longer audio + +# You may find more detail: +# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer +# FastConformer-CTC's architecture config: NeMo/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml + +# Differences from baseline config are in +# model.encoder.global_tokens +# model.encoder.global_tokens_spacing +# model.encoder.global_attn_separate + +name: "FastConformer-Long-CTC-BPE" + +model: + sample_rate: 16000 + log_prediction: true # enables logging sample predictions in the output during training + ctc_reduction: 'mean_volume' + skip_nan_grad: false + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "fully_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # recommend vocab size of 128 or 256 when training on ~1k hr datasets and 1k vocab size on 10+k hr datasets + # you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + # you may use lower time_masks for smaller models to have a faster convergence + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 18 + d_model: 512 + + # Sub-sampling params + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # -1 sets it to d_model + causal_downsampling: false + + # Feed forward module's params + ff_expansion_factor: 4 + + self_attention_model: rel_pos_local_attn # longformer-style attention (sliding window + global tokens) + global_tokens: 1 # number of tokens that attend and are attended to by all tokens (put 0 to disable) + global_tokens_spacing: 1 # how far apart the global tokens are + global_attn_separate: false # whether global tokens should use separate q,k,v layers + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [128,128] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + optim: + name: adamw + lr: 1e-3 + # optimizer arguments + betas: [0.9, 0.98] + # less necessity for weight_decay as we already have large augmentations with SpecAug + # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used + # weight decay of 0.0 with lr of 2.0 also works fine + weight_decay: 1e-3 + + # scheduler setup + sched: + name: CosineAnnealing + # scheduler config override + warmup_steps: 15000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/long_fastconformer/fast-conformer-long_transducer_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/long_fastconformer/fast-conformer-long_transducer_bpe.yaml new file mode 100644 index 0000000..4d5f4db --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/fastconformer/long_fastconformer/fast-conformer-long_transducer_bpe.yaml @@ -0,0 +1,256 @@ +# It contains the default values for training a Fast Conformer-Transducer ASR model, large size (~120M) with Transducer loss and sub-word encoding. +# This version uses Longformer-style attention in order to handle longer audio + +# You may find more detail: +# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer +# FastConformer-Transducer's architecture config: NeMo/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml + +# Differences from baseline config are in +# model.encoder.global_tokens +# model.encoder.global_tokens_spacing +# model.encoder.global_attn_separate + +name: "FastConformer-Long-Transducer-BPE" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + rnnt_reduction: 'mean_volume' + skip_nan_grad: false + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "fully_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling parameters + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos_local_attn # longformer-style attention (sliding window + global tokens) + global_tokens: 1 # number of tokens that attend and are attended to by all tokens (put 0 to disable) + global_tokens_spacing: 1 # how far apart the global tokens are + global_attn_separate: false # whether global tokens should use separate q,k,v layers + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [128,128] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + # if a large vocabulary size is desired, you may wish to use SampleRNNTJoint module + # _target_: nemo.collections.asr.modules.SampledRNNTJoint + # n_samples: 500 # Specifies the minimum number of tokens to sample from the vocabulary space, excluding + # the RNNT blank token. If a given value is larger than the entire vocabulary size, then the full + # vocabulary will be used + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + optim: + name: adamw + lr: 2.5e-3 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: CosineAnnealing + # scheduler config override + warmup_steps: 15000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/jasper/jasper_10x5dr.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/jasper/jasper_10x5dr.yaml new file mode 100644 index 0000000..e93b8b6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/jasper/jasper_10x5dr.yaml @@ -0,0 +1,219 @@ +name: &name "Jasper10x5" + +model: + sample_rate: &sample_rate 16000 + labels: &labels [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: *labels + batch_size: 32 + trim_silence: True + max_duration: 16.7 + shuffle: True + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: *labels + batch_size: 32 + shuffle: False + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.02 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 64 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + rect_freq: 50 + rect_masks: 5 + rect_time: 120 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + jasper: + - dilation: [1] + dropout: 0.2 + filters: 256 + kernel: [11] + repeat: 1 + residual: false + stride: [2] + - dilation: [1] + dropout: 0.2 + filters: 256 + kernel: [11] + repeat: 5 + residual: true + residual_dense: true + stride: [1] + - dilation: [1] + dropout: 0.2 + filters: 256 + kernel: [11] + repeat: 5 + residual: true + residual_dense: true + stride: [1] + - dilation: [1] + dropout: 0.2 + filters: 384 + kernel: [13] + repeat: 5 + residual: true + residual_dense: true + stride: [1] + - dilation: [1] + dropout: 0.2 + filters: 384 + kernel: [13] + repeat: 5 + residual: true + residual_dense: true + stride: [1] + - dilation: [1] + dropout: 0.2 + filters: 512 + kernel: [17] + repeat: 5 + residual: true + residual_dense: true + stride: [1] + - dilation: [1] + dropout: 0.2 + filters: 512 + kernel: [17] + repeat: 5 + residual: true + residual_dense: true + stride: [1] + - dilation: [1] + dropout: 0.3 + filters: 640 + kernel: [21] + repeat: 5 + residual: true + residual_dense: true + stride: [1] + - dilation: [1] + dropout: 0.3 + filters: 640 + kernel: [21] + repeat: 5 + residual: true + residual_dense: true + stride: [1] + - dilation: [1] + dropout: 0.3 + filters: 768 + kernel: [25] + repeat: 5 + residual: true + residual_dense: true + stride: [1] + - dilation: [1] + dropout: 0.3 + filters: 768 + kernel: [25] + repeat: 5 + residual: true + residual_dense: true + stride: [1] + - dilation: [2] + dropout: 0.4 + filters: 896 + kernel: [29] + repeat: 1 + residual: false + stride: [1] + - dilation: [1] + dropout: 0.4 + filters: 1024 + kernel: [1] + repeat: 1 + residual: false + stride: [1] + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: 1024 + num_classes: 28 + vocabulary: *labels + + optim: + name: novograd + # _target_: nemo.core.optim.optimizers.Novograd + lr: .01 + # optimizer arguments + betas: [0.8, 0.5] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # pytorch lightning args + # monitor: val_loss + # reduce_on_plateau: false + + # Scheduler params + warmup_steps: null + warmup_ratio: null + min_lr: 0.0 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 5 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + create_wandb_logger: False + wandb_logger_kwargs: + name: null + project: null + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/lang_id/titanet_large.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/lang_id/titanet_large.yaml new file mode 100644 index 0000000..95aad89 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/lang_id/titanet_large.yaml @@ -0,0 +1,187 @@ +name: &name "TitaNet" +sample_rate: &sample_rate 16000 + +model: + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 128 + shuffle: True + is_tarred: False + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + num_workers: 16 + + augmentor: + noise: + manifest_path: null + prob: 0.8 + min_snr_db: 0 + max_snr_db: 15 + + impulse: + manifest_path: null + prob: 0.5 + + speed: + prob: 0.5 + sr: *sample_rate + resample_type: 'kaiser_fast' + min_speed_rate: 0.95 + max_speed_rate: 1.05 + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 128 + shuffle: False + num_workers: 16 + + test_ds: + manifest_filepath: null + sample_rate: 16000 + labels: null + batch_size: 128 + shuffle: False + num_workers: 16 + + model_defaults: + filters: 1024 + repeat: 3 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 1.0 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.025 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 3 + freq_width: 4 + time_masks: 5 + time_width: 0.03 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [3] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [7] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [11] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: &enc_feat_out 3072 + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + decoder: + _target_: nemo.collections.asr.modules.SpeakerDecoder + feat_in: *enc_feat_out + num_classes: 107 + pool_mode: 'xvector' # 'attention' + emb_sizes: 512 + + loss: + _target_: nemo.collections.common.losses.cross_entropy.CrossEntropyLoss + weight: 'auto' # could be 'auto' or 1D tensor or null + + optim: + name: sgd + lr: 0.001 #(original titanet-large was trained with 0.08 lr) + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + warmup_ratio: 0.1 + min_lr: 0.0001 + +trainer: + devices: 2 # number of gpus (original titanet-large was trained on 4 nodes with 8 gpus each) + max_epochs: 40 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + deterministic: True + enable_checkpointing: False + logger: False + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + gradient_clip_val: 1.0 + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: False + create_checkpoint_callback: True + checkpoint_callback_params: + save_best_model: True + always_save_nemo: True + create_wandb_logger: True + wandb_logger_kwargs: + name: null + project: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/lstm/lstm_ctc_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/lstm/lstm_ctc_bpe.yaml new file mode 100644 index 0000000..45642b9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/lstm/lstm_ctc_bpe.yaml @@ -0,0 +1,160 @@ +# It contains the default values for training an LSTM-CTC ASR model, large size (~170M for bidirectional and ~130M for unidirectional) with CTC loss and sub-word encoding. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. + +# Followed the architecture suggested in the following paper: +# 'STREAMING END-TO-END SPEECH RECOGNITION FOR MOBILE DEVICES' by Yanzhang He et al. (https://arxiv.org/pdf/1811.06621.pdf) + +# You may find more info about LSTM-CTC here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#lstm-transducer +# Pre-trained models of LSTM-CTC can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html + +name: "LSTM-CTC-BPE" + +model: + sample_rate: 16000 + log_prediction: true # enables logging sample predictions in the output during training + ctc_reduction: 'mean_batch' + skip_nan_grad: false + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 4 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 4 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 4 + pin_memory: true + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.RNNEncoder + feat_in: ${model.preprocessor.features} + n_layers: 8 + d_model: 2048 + proj_size: 640 # you may set it if you need different output size other than the default d_model + rnn_type: "lstm" # it can be lstm, gru or rnn + bidirectional: true # need to set it to false if you want to make the model causal + + # Sub-sampling params + subsampling: stacking # stacking, vggnet or striding + subsampling_factor: 4 + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + + ### regularization + dropout: 0.2 # The dropout used in most of the Conformer Modules + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-2 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.3 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/lstm/lstm_transducer_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/lstm/lstm_transducer_bpe.yaml new file mode 100644 index 0000000..8e4521d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/lstm/lstm_transducer_bpe.yaml @@ -0,0 +1,218 @@ +# It contains the default values for training an LSTM-Transducer ASR model, large size (~170M for bidirectional and ~130M for unidirectional) with Transducer loss and sub-word encoding. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. + +# Followed the architecture suggested in the following paper: +# 'STREAMING END-TO-END SPEECH RECOGNITION FOR MOBILE DEVICES' by Yanzhang He et al. (https://arxiv.org/pdf/1811.06621.pdf) + +# You may find more info about LSTM-Transducer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#lstm-transducer +# Pre-trained models of LSTM-Transducer can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html + +name: "LSTM-Transducer-BPE" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + model_defaults: + enc_hidden: 640 + pred_hidden: 640 + joint_hidden: 640 + rnn_hidden_size: 2048 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 4 + pin_memory: true + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 4 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 4 + pin_memory: true + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.RNNEncoder + feat_in: ${model.preprocessor.features} + n_layers: 8 + d_model: 2048 + proj_size: ${model.model_defaults.pred_hidden} # you may set it if you need different output size other than the default d_model + rnn_type: "lstm" # it can be lstm, gru or rnn + bidirectional: true # need to set it to false if you want to make the model causal + + # Sub-sampling params + subsampling: stacking # stacking, vggnet or striding + subsampling_factor: 4 + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + + ### regularization + dropout: 0.2 # The dropout used in most of the Conformer Modules + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 2 + t_max: null + dropout: 0.2 + rnn_hidden_size: 2048 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: null # 'null' would set it automatically according to CPU/GPU device + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: true + fused_batch_size: 4 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + # using fastemit_lambda=1e-3 can help the accuracy of the model when it is unidirectional + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-2 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.3 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/marblenet/marblenet_3x2x64.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/marblenet/marblenet_3x2x64.yaml new file mode 100644 index 0000000..f9b3f26 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/marblenet/marblenet_3x2x64.yaml @@ -0,0 +1,188 @@ +name: &name "MarbleNet-3x2x64" + +model: + sample_rate: 16000 + repeat: 2 + dropout: 0.0 + kernel_size_factor: 1.0 + + labels: ['background', 'speech'] + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + labels: ${model.labels} + batch_size: 128 + shuffle: True + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + shuffle_n: 2048 + num_workers: 8 + pin_memory: true + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + bucketing_weights: null + augmentor: + shift: + prob: 1.0 + min_shift_ms: -5.0 + max_shift_ms: 5.0 + white_noise: + prob: 1.0 + min_level: -90 + max_level: -46 + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + labels: ${model.labels} + batch_size: 128 + shuffle: False + num_workers: 8 + pin_memory: true + val_loss_idx: 0 + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + labels: ${model.labels} + batch_size: 128 + shuffle: False + num_workers: 8 + pin_memory: true + test_loss_idx: 0 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMFCCPreprocessor + window_size: 0.025 + window_stride: 0.01 + window: "hann" + n_mels: &n_mels 64 + n_mfcc: *n_mels + n_fft: 512 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 + time_masks: 2 + freq_width: 15 + time_width: 25 + rect_masks: 5 + rect_time: 25 + rect_freq: 15 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: 128 + repeat: 1 + kernel: [11] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: false + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 64 + repeat: ${model.repeat} + kernel: [13] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: true + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 64 + repeat: ${model.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: true + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 64 + repeat: ${model.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: true + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 128 + repeat: 1 + kernel: [29] + stride: [1] + dilation: [2] + dropout: ${model.dropout} + residual: false + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: &enc_final_filters 128 + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: false + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderClassification + feat_in: *enc_final_filters + return_logits: true + pooling_type: 'avg' + + optim: + name: sgd + lr: 0.01 + # optimizer arguments + weight_decay: 0.001 + momentum: 0.9 + + # scheduler setup + sched: + name: PolynomialHoldDecayAnnealing + # Scheduler params + power: 2.0 + warmup_ratio: 0.05 + hold_ratio: 0.45 + min_lr: 0.001 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 150 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + create_wandb_logger: False + wandb_logger_kwargs: + name: null + project: null + diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/marblenet/marblenet_3x2x64_20ms.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/marblenet/marblenet_3x2x64_20ms.yaml new file mode 100644 index 0000000..2c98c21 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/marblenet/marblenet_3x2x64_20ms.yaml @@ -0,0 +1,209 @@ +name: &name "MarbleNet-3x2x64" + +model: + sample_rate: 16000 + repeat: 2 + dropout: 0.0 + kernel_size_factor: 1.0 + + labels: ['0', '1'] + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + labels: ${model.labels} + batch_size: 128 + shuffle: True + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + shuffle_n: 2048 + num_workers: 8 + pin_memory: true + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + bucketing_weights: null + augmentor: + white_noise: + prob: 0.9 + min_level: -90 + max_level: -46 + gain: + prob: 0.5 + min_gain_dbfs: -10.0 + max_gain_dbfs: 10.0 + noise: + prob: 0.6 + manifest_path: /manifests/vad_noise/freesound_nonspeech_train_FL200.json + min_snr_db: 0 + max_snr_db: 20 + max_gain_db: 300.0 + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + labels: ${model.labels} + batch_size: 128 + shuffle: False + num_workers: 8 + pin_memory: true + val_loss_idx: 0 + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + labels: ${model.labels} + batch_size: 128 + shuffle: False + num_workers: 8 + pin_memory: true + test_loss_idx: 0 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "None" + window_size: 0.025 + sample_rate: ${model.sample_rate} + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + stft_conv: false + pad_to: 2 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: 128 + repeat: 1 + kernel: [11] + stride: [2] + dilation: [1] + dropout: ${model.dropout} + residual: false + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 64 + repeat: ${model.repeat} + kernel: [13] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: true + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 64 + repeat: ${model.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: true + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 64 + repeat: ${model.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: true + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 128 + repeat: 1 + kernel: [29] + stride: [1] + dilation: [2] + dropout: ${model.dropout} + residual: false + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: &enc_filters 128 + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: false + + decoder: + _target_: nemo.collections.common.parts.MultiLayerPerceptron + hidden_size: *enc_filters + num_classes: -1 + num_layers: 1 + activation: 'relu' + log_softmax: false + + optim: + name: sgd + lr: 0.01 + # optimizer arguments + weight_decay: 0.001 + momentum: 0.9 + + # scheduler setup + sched: + name: PolynomialHoldDecayAnnealing + # Scheduler params + power: 2.0 + warmup_ratio: 0.05 + hold_ratio: 0.45 + min_lr: 0.001 + last_epoch: -1 + +trainer: + devices: -1 # number of gpus, -1 to use all gpus + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 10 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + check_val_every_n_epoch: 1 + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: "val_acc_macro" + mode: "max" + save_top_k: 3 + always_save_nemo: true # saves the checkpoints as nemo files instead of PTL checkpoints + save_best_model: true + + # you need to set these two to True to continue the training + resume_if_exists: true + resume_ignore_no_checkpoint: true + + create_wandb_logger: False + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/matchboxnet/matchboxnet_3x1x64_v1.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/matchboxnet/matchboxnet_3x1x64_v1.yaml new file mode 100644 index 0000000..af054aa --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/matchboxnet/matchboxnet_3x1x64_v1.yaml @@ -0,0 +1,199 @@ +name: &name "MatchboxNet-3x1x64-v1" + +model: + sample_rate: 16000 + timesteps: 128 + repeat: 1 + dropout: 0.0 + kernel_size_factor: 1.0 + + labels_full: ['bed', 'bird', 'cat', 'dog', 'down', 'eight', 'five', 'four', 'go', 'happy', 'house', 'left', 'marvin', + 'nine', 'no', 'off', 'on', 'one', 'right', 'seven', 'sheila', 'six', 'stop', 'three', 'tree', 'two', 'up', + 'wow', 'yes', 'zero'] + + labels_subset: ["yes", "no", "up", "down", "left", "right", "on", "off", "stop", "go", "unknown", "silence"] + + labels: ${model.labels_full} + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + labels: ${model.labels} + batch_size: 128 + shuffle: True + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + bucketing_weights: null + augmentor: + shift: + prob: 1.0 + min_shift_ms: -5.0 + max_shift_ms: 5.0 + white_noise: + prob: 1.0 + min_level: -90 + max_level: -46 + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + labels: ${model.labels} + batch_size: 128 + shuffle: False + num_workers: 8 + pin_memory: true + val_loss_idx: 0 + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + labels: ${model.labels} + batch_size: 128 + shuffle: False + num_workers: 8 + pin_memory: true + test_loss_idx: 0 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMFCCPreprocessor + window_size: 0.025 + window_stride: 0.01 + window: "hann" + n_mels: &n_mels 64 + n_mfcc: *n_mels + n_fft: 512 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 + time_masks: 2 + freq_width: 15 + time_width: 25 + rect_masks: 5 + rect_time: 25 + rect_freq: 15 + + crop_or_pad_augment: + _target_: nemo.collections.asr.modules.CropOrPadSpectrogramAugmentation + audio_length: ${model.timesteps} + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: 128 + repeat: 1 + kernel: [11] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: false + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 64 + repeat: ${model.repeat} + kernel: [13] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: true + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 64 + repeat: ${model.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: true + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 64 + repeat: ${model.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: true + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 128 + repeat: 1 + kernel: [29] + stride: [1] + dilation: [2] + dropout: ${model.dropout} + residual: false + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: &enc_final_filters 128 + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: false + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderClassification + feat_in: *enc_final_filters + return_logits: true + pooling_type: 'avg' + + optim: + name: novograd + # _target_: nemo.core.optim.optimizers.Novograd + lr: 0.05 + # optimizer arguments + betas: [0.95, 0.5] + weight_decay: 0.001 + + # scheduler setup + sched: + name: PolynomialHoldDecayAnnealing + + # Scheduler params + power: 2.0 + warmup_ratio: 0.05 + hold_ratio: 0.45 + min_lr: 0.001 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 200 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + create_wandb_logger: False + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/matchboxnet/matchboxnet_3x1x64_v2.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/matchboxnet/matchboxnet_3x1x64_v2.yaml new file mode 100644 index 0000000..f3f4639 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/matchboxnet/matchboxnet_3x1x64_v2.yaml @@ -0,0 +1,200 @@ +name: &name "MatchboxNet-3x1x64-v2" + +model: + sample_rate: 16000 + timesteps: 128 + repeat: 1 + dropout: 0.0 + kernel_size_factor: 1.0 + + labels_full: ['visual', 'wow', 'learn', 'backward', 'dog', 'two', 'left', 'happy', 'nine', 'go', 'up', 'bed', 'stop', + 'one', 'zero', 'tree', 'seven', 'on', 'four', 'bird', 'right', 'eight', 'no', 'six', 'forward', 'house', + 'marvin', 'sheila', 'five', 'off', 'three', 'down', 'cat', 'follow', 'yes'] + + labels_subset: ["yes", "no", "up", "down", "left", "right", "on", "off", "stop", "go", "unknown", "silence"] + + labels: ${model.labels_full} + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + labels: ${model.labels} + batch_size: 128 + shuffle: True + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + bucketing_weights: null + augmentor: + shift: + prob: 1.0 + min_shift_ms: -5.0 + max_shift_ms: 5.0 + white_noise: + prob: 1.0 + min_level: -90 + max_level: -46 + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + labels: ${model.labels} + batch_size: 128 + shuffle: False + num_workers: 8 + pin_memory: true + val_loss_idx: 0 + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + labels: ${model.labels} + batch_size: 128 + shuffle: False + num_workers: 8 + pin_memory: true + test_loss_idx: 0 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMFCCPreprocessor + window_size: 0.025 + window_stride: 0.01 + window: "hann" + n_mels: &n_mels 64 + n_mfcc: *n_mels + n_fft: 512 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 + time_masks: 2 + freq_width: 15 + time_width: 25 + rect_masks: 5 + rect_time: 25 + rect_freq: 15 + + crop_or_pad_augment: + _target_: nemo.collections.asr.modules.CropOrPadSpectrogramAugmentation + audio_length: ${model.timesteps} + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: 128 + repeat: 1 + kernel: [11] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: false + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 64 + repeat: ${model.repeat} + kernel: [13] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: true + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 64 + repeat: ${model.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: true + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 64 + repeat: ${model.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: true + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: 128 + repeat: 1 + kernel: [29] + stride: [1] + dilation: [2] + dropout: ${model.dropout} + residual: false + separable: true + kernel_size_factor: ${model.kernel_size_factor} + + - filters: &enc_final_filters 128 + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: ${model.dropout} + residual: false + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderClassification + feat_in: *enc_final_filters + return_logits: true + pooling_type: 'avg' + + optim: + name: novograd + # _target_: nemo.core.optim.optimizers.Novograd + lr: 0.05 + # optimizer arguments + betas: [0.95, 0.5] + weight_decay: 0.001 + + # scheduler setup + sched: + name: PolynomialHoldDecayAnnealing + + # Scheduler params + power: 2.0 + warmup_ratio: 0.05 + hold_ratio: 0.45 + min_lr: 0.001 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 200 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + create_wandb_logger: False + wandb_logger_kwargs: + name: null + project: null + diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/quartznet/quartznet_15x5.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/quartznet/quartznet_15x5.yaml new file mode 100644 index 0000000..d5f2253 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/quartznet/quartznet_15x5.yaml @@ -0,0 +1,287 @@ +name: &name "QuartzNet15x5" + +model: + sample_rate: &sample_rate 16000 + repeat: &repeat 5 + dropout: &dropout 0.0 + separable: &separable true + labels: &labels [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: *labels + batch_size: 32 + trim_silence: True + max_duration: 16.7 + shuffle: True + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: *labels + batch_size: 32 + shuffle: False + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: 16000 + labels: *labels + batch_size: 32 + shuffle: False + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.02 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 64 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + rect_freq: 50 + rect_masks: 5 + rect_time: 120 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: 1 + residual: false + separable: *separable + stride: [2] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [2] + dropout: *dropout + filters: 512 + kernel: [87] + repeat: 1 + residual: false + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: &enc_filters 1024 + kernel: [1] + repeat: 1 + residual: false + stride: [1] + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: *enc_filters + num_classes: 28 + vocabulary: *labels + + optim: + name: novograd + # _target_: nemo.core.optim.optimizers.Novograd + lr: .01 + # optimizer arguments + betas: [0.8, 0.5] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # pytorch lightning args + # monitor: val_loss + # reduce_on_plateau: false + + # Scheduler params + warmup_steps: null + warmup_ratio: null + min_lr: 0.0 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 5 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + create_wandb_logger: False + wandb_logger_kwargs: + name: null + project: null + diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/quartznet/quartznet_15x5_aug.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/quartznet/quartznet_15x5_aug.yaml new file mode 100644 index 0000000..4daec79 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/quartznet/quartznet_15x5_aug.yaml @@ -0,0 +1,290 @@ +name: &name "QuartzNet15x5" + +model: + sample_rate: &sample_rate 16000 + repeat: &repeat 5 + dropout: &dropout 0.0 + separable: &separable true + labels: &labels [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: *labels + batch_size: 32 + trim_silence: True + max_duration: 16.7 + shuffle: True + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + augmentor: + rir_noise_aug: + prob: 0.5 + rir_manifest_path: ??? + rir_tar_filepaths: ??? + rir_prob: 0.5 + noise_manifest_paths: ??? + noise_tar_filepaths: ??? + min_snr_db: [0,0] + max_snr_db: [30,30] + orig_sample_rate: ??? + bg_noise_manifest_paths: ??? + bg_noise_tar_filepaths: ??? + bg_min_snr_db: [10,10] + bg_max_snr_db: [40,40] + bg_orig_sample_rate: ??? + transcode_aug: + prob: 0.1 + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: *labels + batch_size: 32 + shuffle: False + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.02 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 64 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + rect_freq: 50 + rect_masks: 5 + rect_time: 120 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: 1 + residual: false + separable: *separable + stride: [2] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [2] + dropout: *dropout + filters: 512 + kernel: [87] + repeat: 1 + residual: false + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: &enc_filters 1024 + kernel: [1] + repeat: 1 + residual: false + stride: [1] + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: *enc_filters + num_classes: 28 + vocabulary: *labels + + optim: + name: novograd + # _target_: nemo.core.optim.optimizers.Novograd + lr: .01 + # optimizer arguments + betas: [0.8, 0.5] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # pytorch lightning args + # monitor: val_loss + # reduce_on_plateau: false + + # Scheduler params + warmup_steps: null + warmup_ratio: null + min_lr: 0.0 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 5 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + create_wandb_logger: False + wandb_logger_kwargs: + name: null + project: null + diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/quartznet/quartznet_15x5_ru.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/quartznet/quartznet_15x5_ru.yaml new file mode 100644 index 0000000..37f58f0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/quartznet/quartznet_15x5_ru.yaml @@ -0,0 +1,284 @@ +name: &name "QuartzNet15x5_ru" + +model: + sample_rate: &sample_rate 16000 + repeat: &repeat 5 + dropout: &dropout 0.0 + separable: &separable true + labels: &labels [ " ", "а", "б", "в", "г", "д", "е", "ж", "з", "и", "й", "к", "л", "м", "н", "о", "п", + "р", "с", "т", "у", "ф", "х", "ц", "ч", "ш", "щ", "ъ", "ы", "ь", "э", "ю", "я" ] + + train_ds: + manifest_filepath: golos/train/manifest.jsonl # Can be found at https://sc.link/JpD + sample_rate: 16000 + labels: *labels + batch_size: 32 + trim_silence: True + max_duration: 20.0 + shuffle: True + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + parser: ru + + validation_ds: + manifest_filepath: golos/test/crowd/crowd.jsonl # Can be found at https://sc.link/Kqr + sample_rate: 16000 + labels: *labels + batch_size: 32 + shuffle: False + parser: ru + + test_ds: + manifest_filepath: golos/test/farfield/farfield.jsonl # Can be found at https://sc.link/Kqr + sample_rate: 16000 + labels: *labels + batch_size: 32 + shuffle: False + parser: ru + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.02 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 64 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + rect_freq: 50 + rect_masks: 2 + rect_time: 120 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: 1 + residual: false + separable: *separable + stride: [2] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [2] + dropout: *dropout + filters: 512 + kernel: [87] + repeat: 1 + residual: false + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: &enc_filters 1024 + kernel: [1] + repeat: 1 + residual: false + stride: [1] + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: *enc_filters + num_classes: 33 + vocabulary: *labels + + optim: + name: novograd + # _target_: nemo.core.optim.optimizers.Novograd + lr: .05 + # optimizer arguments + betas: [0.8, 0.5] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # pytorch lightning args + # monitor: val_loss + # reduce_on_plateau: false + + # Scheduler params + warmup_steps: null + warmup_ratio: null + min_lr: 0.0 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 5 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + create_wandb_logger: False + wandb_logger_kwargs: + name: null + project: null + diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/quartznet/quartznet_15x5_zh.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/quartznet/quartznet_15x5_zh.yaml new file mode 100644 index 0000000..c26b63b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/quartznet/quartznet_15x5_zh.yaml @@ -0,0 +1,483 @@ +name: &name "QuartzNet15x5" + +model: + sample_rate: &sample_rate 16000 + repeat: &repeat 5 + dropout: &dropout 0.0 + separable: &separable true + labels: &labels [' ', '''', A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, + S, T, U, V, W, X, Y, Z, 㶧, 䶮, 一, 丁, 七, 万, 丈, 三, 上, 下, 不, 与, 丐, 丑, 专, 且, 丕, + 世, 丘, 丙, 业, 丛, 东, 丝, 丞, 丢, 两, 严, 丧, 个, 丫, 中, 丰, 串, 临, 丸, 丹, 为, 主, 丽, 举, 乃, + 久, 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毛, 毡, 毫, 毯, 毽, 氏, 民, 氓, 气, 氚, 氛, 氟, 氢, 氤, 氦, 氧, 氨, 氪, 氮, 氯, 氰, 氲, 水, 永, + 汀, 汁, 求, 汇, 汉, 汊, 汐, 汕, 汗, 汛, 汝, 汞, 江, 池, 污, 汤, 汨, 汩, 汪, 汰, 汲, 汴, 汶, 汹, 汽, + 汾, 沁, 沂, 沃, 沅, 沈, 沉, 沌, 沏, 沐, 沓, 沙, 沛, 沟, 没, 沢, 沣, 沥, 沦, 沧, 沪, 沫, 沭, 沮, 沱, + 河, 沸, 油, 治, 沼, 沽, 沾, 沿, 泄, 泉, 泊, 泌, 泓, 泔, 法, 泖, 泗, 泛, 泞, 泠, 泡, 波, 泣, 泥, 注, + 泪, 泫, 泮, 泯, 泰, 泱, 泳, 泵, 泷, 泸, 泺, 泻, 泼, 泽, 泾, 洁, 洋, 洒, 洗, 洙, 洛, 洞, 津, 洪, 洮, + 洱, 洲, 洵, 洹, 洺, 活, 洼, 洽, 派, 流, 浃, 浅, 浆, 浇, 浈, 浊, 测, 济, 浏, 浐, 浑, 浒, 浓, 浔, 浙, + 浚, 浜, 浠, 浣, 浦, 浩, 浪, 浮, 浴, 海, 浸, 涂, 涅, 消, 涉, 涌, 涎, 涑, 涓, 涕, 涛, 涝, 涞, 涟, 涠, + 涡, 涣, 涤, 润, 涧, 涨, 涩, 涪, 涮, 涯, 液, 涵, 涸, 涿, 淀, 淄, 淅, 淆, 淇, 淋, 淌, 淑, 淖, 淘, 淝, + 淞, 淡, 淤, 淦, 淫, 淬, 淮, 深, 淳, 混, 淹, 添, 淼, 清, 渊, 渌, 渍, 渎, 渐, 渑, 渔, 渗, 渚, 渝, 渠, + 渡, 渣, 渤, 渥, 温, 渭, 港, 渲, 渴, 游, 渺, 湃, 湄, 湉, 湍, 湎, 湖, 湘, 湛, 湫, 湾, 湿, 溃, 溅, 溆, + 溉, 溏, 源, 溜, 溟, 溢, 溥, 溧, 溪, 溯, 溶, 溺, 滁, 滇, 滋, 滑, 滔, 滕, 滘, 滚, 滞, 满, 滢, 滤, 滥, + 滦, 滨, 滩, 滴, 滹, 漂, 漆, 漉, 漏, 漓, 演, 漕, 漠, 漩, 漪, 漫, 漭, 漯, 漱, 漳, 漾, 潆, 潇, 潋, 潍, + 潘, 潜, 潞, 潢, 潦, 潭, 潮, 潸, 潺, 潼, 澄, 澈, 澍, 澎, 澜, 澡, 澧, 澳, 澶, 激, 濂, 濑, 濒, 濠, 濡, + 濮, 濯, 瀑, 瀚, 瀛, 灌, 灏, 灞, 火, 灭, 灯, 灰, 灵, 灶, 灸, 灼, 灾, 灿, 炀, 炅, 炉, 炊, 炎, 炒, 炔, + 炕, 炖, 炙, 炜, 炫, 炬, 炭, 炮, 炯, 炳, 炷, 炸, 点, 炼, 炽, 烀, 烁, 烂, 烃, 烈, 烊, 烘, 烙, 烛, 烟, + 烤, 烦, 烧, 烨, 烩, 烫, 烬, 热, 烯, 烷, 烹, 烽, 焉, 焊, 焓, 焕, 焖, 焗, 焘, 焙, 焚, 焦, 焯, 焰, 焱, + 然, 煊, 煌, 煎, 煜, 煞, 煤, 煦, 照, 煨, 煮, 煲, 煳, 煽, 熄, 熊, 熏, 熔, 熙, 熟, 熠, 熨, 熬, 熵, 熹, + 燃, 燊, 燎, 燕, 燥, 燮, 爆, 爪, 爬, 爱, 爵, 父, 爷, 爸, 爹, 爽, 片, 版, 牌, 牍, 牒, 牙, 牛, 牟, 牠, + 牡, 牢, 牧, 物, 牲, 牵, 特, 牺, 牾, 犀, 犁, 犄, 犇, 犊, 犒, 犟, 犬, 犯, 状, 犷, 犸, 犹, 狂, 狄, 狈, + 狐, 狒, 狗, 狙, 狞, 狠, 狡, 狩, 独, 狭, 狮, 狰, 狱, 狸, 狼, 猁, 猎, 猖, 猛, 猜, 猝, 猥, 猩, 猪, 猫, + 猬, 献, 猴, 猷, 猹, 猾, 猿, 獒, 獗, 獭, 獾, 玄, 率, 玉, 王, 玑, 玖, 玛, 玟, 玥, 玩, 玫, 玮, 环, 现, + 玲, 玳, 玷, 玹, 玺, 玻, 珀, 珂, 珈, 珉, 珊, 珍, 珏, 珑, 珙, 珞, 珠, 珥, 班, 珮, 珲, 珺, 球, 琅, 理, + 琉, 琊, 琏, 琐, 琛, 琢, 琤, 琥, 琦, 琨, 琪, 琬, 琮, 琰, 琳, 琴, 琵, 琶, 琼, 瑁, 瑄, 瑕, 瑙, 瑚, 瑛, + 瑜, 瑞, 瑟, 瑠, 瑭, 瑰, 瑶, 瑷, 瑾, 璀, 璃, 璇, 璋, 璐, 璞, 璟, 璧, 璨, 瓜, 瓢, 瓣, 瓦, 瓮, 瓯, 瓶, + 瓷, 甄, 甘, 甚, 甜, 生, 甥, 用, 甩, 甫, 甬, 甭, 田, 由, 甲, 申, 电, 男, 甸, 町, 画, 畅, 畈, 畊, 界, + 畏, 畔, 留, 畜, 略, 番, 畴, 畸, 畿, 疃, 疆, 疏, 疑, 疖, 疗, 疙, 疚, 疝, 疟, 疡, 疣, 疤, 疫, 疮, 疯, + 疱, 疲, 疴, 疵, 疸, 疹, 疼, 疽, 疾, 病, 症, 痉, 痊, 痍, 痒, 痔, 痕, 痘, 痛, 痞, 痢, 痣, 痧, 痨, 痪, + 痫, 痰, 痱, 痴, 痹, 痼, 瘀, 瘁, 瘙, 瘟, 瘠, 瘢, 瘤, 瘦, 瘩, 瘪, 瘫, 瘳, 瘴, 瘸, 瘾, 癌, 癖, 癜, 癞, + 癣, 癫, 登, 白, 百, 皂, 的, 皆, 皇, 皋, 皎, 皑, 皓, 皖, 皙, 皮, 皱, 皿, 盂, 盅, 盆, 盈, 益, 盎, 盏, + 盐, 监, 盒, 盔, 盖, 盗, 盘, 盛, 盟, 目, 盯, 盱, 盲, 直, 相, 盹, 盼, 盾, 省, 眈, 眉, 看, 眙, 真, 眠, + 眨, 眩, 眬, 眯, 眶, 眷, 眸, 眺, 眼, 着, 睁, 睇, 睐, 睑, 睛, 睡, 睢, 督, 睦, 睫, 睬, 睹, 睽, 睾, 睿, + 瞄, 瞅, 瞌, 瞎, 瞑, 瞒, 瞟, 瞠, 瞥, 瞧, 瞩, 瞪, 瞬, 瞭, 瞰, 瞳, 瞻, 瞿, 矍, 矗, 矛, 矜, 矢, 矣, 知, + 矩, 矫, 矬, 短, 矮, 石, 矶, 矸, 矽, 矾, 矿, 砀, 码, 砂, 砌, 砍, 砒, 研, 砖, 砚, 砝, 砣, 砥, 砭, 砰, + 破, 砷, 砸, 砺, 砼, 砾, 础, 硅, 硌, 硒, 硕, 硖, 硚, 硝, 硫, 硬, 确, 硼, 碉, 碌, 碍, 碎, 碑, 碓, 碗, + 碘, 碚, 碜, 碟, 碣, 碧, 碰, 碱, 碳, 碴, 碾, 磁, 磅, 磊, 磋, 磐, 磕, 磨, 磴, 磷, 磺, 礁, 示, 礼, 社, + 祀, 祁, 祈, 祉, 祎, 祐, 祖, 祚, 祛, 祝, 神, 祟, 祠, 祢, 祥, 票, 祭, 祯, 祷, 祸, 祺, 禀, 禁, 禄, 禅, + 福, 禧, 禹, 禺, 离, 禽, 禾, 秀, 私, 秃, 秆, 秉, 秋, 种, 科, 秒, 秘, 租, 秣, 秤, 秦, 秧, 秩, 积, 称, + 秸, 移, 秽, 稀, 程, 稍, 税, 稔, 稚, 稞, 稠, 稣, 稳, 稷, 稹, 稻, 稼, 稽, 稿, 穆, 穗, 穴, 究, 穷, 穹, + 空, 穿, 突, 窃, 窄, 窈, 窍, 窑, 窒, 窕, 窖, 窗, 窘, 窜, 窝, 窟, 窠, 窥, 窦, 窨, 窿, 立, 竖, 站, 竞, + 竟, 章, 竣, 童, 竭, 端, 竹, 竺, 竽, 竿, 笃, 笆, 笈, 笋, 笑, 笔, 笙, 笛, 笠, 符, 笨, 第, 笳, 笸, 笼, + 等, 筋, 筏, 筐, 筑, 筒, 答, 策, 筛, 筝, 筠, 筱, 筵, 筷, 筹, 签, 简, 箍, 箔, 箕, 算, 管, 箩, 箫, 箭, + 箱, 箴, 篁, 篆, 篇, 篑, 篓, 篝, 篡, 篦, 篪, 篮, 篱, 篷, 篼, 簇, 簋, 簧, 簪, 簸, 簿, 籁, 籍, 米, 类, + 籼, 籽, 粉, 粑, 粒, 粕, 粗, 粘, 粟, 粤, 粥, 粪, 粮, 粱, 粲, 粳, 粹, 粼, 粽, 精, 糊, 糕, 糖, 糗, 糙, + 糟, 糠, 糯, 系, 紊, 素, 索, 紧, 紫, 累, 絮, 綦, 繁, 纂, 纠, 纡, 红, 纣, 纤, 约, 级, 纨, 纪, 纫, 纬, + 纭, 纯, 纰, 纱, 纲, 纳, 纵, 纶, 纷, 纸, 纹, 纺, 纽, 纾, 线, 绀, 练, 组, 绅, 细, 织, 终, 绉, 绊, 绋, + 绌, 绍, 绎, 经, 绑, 绒, 结, 绔, 绕, 绘, 给, 绚, 绛, 络, 绝, 绞, 统, 绢, 绣, 绥, 继, 绩, 绪, 绫, 续, + 绮, 绯, 绰, 绳, 维, 绵, 绷, 绸, 绻, 综, 绽, 绿, 缀, 缄, 缅, 缆, 缇, 缉, 缎, 缓, 缔, 缕, 编, 缘, 缙, + 缚, 缛, 缜, 缝, 缠, 缢, 缤, 缨, 缩, 缪, 缬, 缭, 缮, 缰, 缱, 缴, 缸, 缺, 罂, 罄, 罐, 网, 罔, 罕, 罗, + 罚, 罡, 罢, 罩, 罪, 置, 署, 罹, 羁, 羊, 羌, 美, 羔, 羚, 羞, 羡, 群, 羧, 羯, 羲, 羸, 羹, 羽, 羿, 翁, + 翅, 翊, 翌, 翎, 翔, 翘, 翟, 翠, 翡, 翩, 翰, 翱, 翻, 翼, 耀, 老, 考, 耄, 者, 耆, 耋, 而, 耍, 耐, 耒, + 耕, 耗, 耘, 耙, 耜, 耪, 耳, 耶, 耷, 耸, 耻, 耽, 耿, 聂, 聆, 聊, 聋, 职, 联, 聘, 聚, 聪, 肃, 肆, 肇, + 肉, 肋, 肌, 肖, 肘, 肚, 肛, 肝, 肠, 股, 肢, 肤, 肥, 肩, 肪, 肮, 肯, 肱, 育, 肴, 肺, 肾, 肿, 胀, 胁, + 胃, 胆, 背, 胎, 胖, 胗, 胚, 胛, 胜, 胞, 胡, 胤, 胥, 胧, 胫, 胭, 胯, 胰, 胱, 胳, 胶, 胸, 胺, 能, 脂, + 脆, 脉, 脊, 脍, 脏, 脐, 脑, 脓, 脖, 脚, 脯, 脱, 脸, 脾, 腆, 腈, 腊, 腋, 腌, 腐, 腑, 腓, 腔, 腕, 腥, + 腩, 腭, 腮, 腰, 腱, 腴, 腹, 腺, 腻, 腼, 腾, 腿, 膀, 膈, 膊, 膏, 膑, 膛, 膜, 膝, 膨, 膳, 膺, 臀, 臂, + 臃, 臆, 臊, 臣, 臧, 自, 臬, 臭, 至, 致, 臻, 臼, 舀, 舅, 舆, 舌, 舍, 舐, 舒, 舔, 舛, 舜, 舞, 舟, 航, + 舫, 般, 舰, 舱, 舵, 舶, 舷, 舸, 船, 艇, 艋, 艘, 艮, 良, 艰, 色, 艳, 艺, 艾, 艿, 节, 芊, 芋, 芍, 芒, + 芗, 芙, 芜, 芝, 芥, 芦, 芩, 芪, 芬, 芭, 芮, 芯, 花, 芳, 芷, 芸, 芹, 芽, 芾, 苇, 苋, 苍, 苏, 苑, 苓, + 苔, 苗, 苛, 苞, 苟, 苡, 苣, 若, 苦, 苫, 苯, 英, 苷, 苹, 茁, 茂, 范, 茄, 茅, 茆, 茉, 茌, 茎, 茗, 茛, + 茜, 茧, 茨, 茫, 茬, 茯, 茱, 茳, 茴, 茵, 茶, 茸, 茹, 茼, 荀, 荃, 荆, 荇, 草, 荏, 荐, 荒, 荔, 荚, 荛, + 荞, 荟, 荠, 荡, 荣, 荤, 荧, 荨, 荫, 药, 荷, 荸, 荻, 荼, 莅, 莆, 莉, 莎, 莒, 莓, 莘, 莜, 莞, 莠, 莪, + 莫, 莱, 莲, 莴, 获, 莹, 莺, 莽, 菀, 菁, 菅, 菇, 菊, 菌, 菏, 菖, 菘, 菜, 菠, 菡, 菩, 菱, 菲, 萃, 萄, + 萋, 萌, 萍, 萎, 萝, 萤, 营, 萦, 萧, 萨, 萱, 萸, 落, 葆, 著, 葚, 葛, 葡, 董, 葩, 葫, 葬, 葱, 葳, 葵, + 葺, 蒂, 蒋, 蒙, 蒜, 蒯, 蒲, 蒸, 蒿, 蓁, 蓄, 蓉, 蓓, 蓝, 蓟, 蓥, 蓦, 蓬, 蓼, 蔑, 蔓, 蔗, 蔚, 蔡, 蔫, + 蔬, 蔷, 蔺, 蔻, 蔼, 蔽, 蕃, 蕉, 蕊, 蕙, 蕨, 蕲, 蕴, 蕾, 薄, 薇, 薏, 薛, 薪, 薯, 薰, 薷, 藁, 藉, 藏, + 藐, 藓, 藕, 藜, 藠, 藤, 藩, 藻, 藿, 蘑, 蘸, 虎, 虏, 虐, 虑, 虔, 虚, 虞, 虫, 虱, 虹, 虻, 虽, 虾, 蚀, + 蚁, 蚂, 蚊, 蚌, 蚓, 蚕, 蚝, 蚣, 蚤, 蚪, 蚬, 蚯, 蚱, 蚴, 蛀, 蛆, 蛇, 蛉, 蛊, 蛋, 蛎, 蛐, 蛔, 蛙, 蛛, + 蛟, 蛤, 蛮, 蛰, 蛳, 蛹, 蛾, 蜀, 蜂, 蜃, 蜇, 蜈, 蜊, 蜍, 蜒, 蜓, 蜕, 蜗, 蜘, 蜚, 蜜, 蜡, 蜢, 蜥, 蜱, + 蜴, 蜷, 蜻, 蜿, 蝇, 蝈, 蝉, 蝌, 蝎, 蝗, 蝙, 蝠, 蝮, 蝴, 蝶, 蝽, 螂, 螃, 螈, 融, 螨, 螳, 螺, 蟀, 蟆, + 蟊, 蟋, 蟑, 蟒, 蟠, 蟹, 蟾, 蠊, 蠕, 蠡, 蠢, 血, 衅, 行, 衍, 衔, 街, 衙, 衡, 衢, 衣, 补, 表, 衩, 衫, + 衬, 衮, 衰, 衲, 衷, 袁, 袂, 袄, 袅, 袈, 袋, 袍, 袒, 袖, 袜, 被, 袭, 袱, 裁, 裂, 装, 裆, 裔, 裕, 裘, + 裙, 裟, 裤, 裨, 裱, 裳, 裴, 裸, 裹, 褂, 褐, 褒, 褓, 褔, 褚, 褛, 褥, 褪, 褴, 褶, 襁, 襄, 襟, 西, 要, + 覃, 覆, 见, 观, 规, 觅, 视, 览, 觉, 觊, 觎, 觐, 觑, 角, 觞, 解, 觥, 触, 言, 訾, 詹, 誉, 誓, 警, 譬, + 计, 订, 讣, 认, 讥, 讧, 讨, 让, 讪, 训, 议, 讯, 记, 讲, 讳, 讴, 讶, 讷, 许, 讹, 论, 讼, 讽, 设, 访, + 诀, 证, 诃, 评, 诅, 识, 诈, 诉, 诊, 诋, 词, 诏, 译, 诓, 试, 诗, 诘, 诙, 诚, 诛, 话, 诞, 诟, 诠, 诡, + 询, 诣, 诤, 该, 详, 诧, 诩, 诫, 诬, 语, 误, 诱, 诲, 说, 诵, 诶, 请, 诸, 诹, 诺, 读, 诽, 课, 诿, 谀, + 谁, 调, 谅, 谆, 谈, 谊, 谋, 谌, 谍, 谎, 谏, 谐, 谑, 谓, 谕, 谖, 谘, 谙, 谚, 谛, 谜, 谟, 谢, 谣, 谤, + 谦, 谧, 谨, 谩, 谬, 谭, 谮, 谯, 谱, 谴, 谶, 谷, 豁, 豆, 豇, 豉, 豌, 豚, 象, 豢, 豪, 豫, 豹, 豺, 貂, + 貅, 貉, 貌, 貔, 贝, 贞, 负, 贡, 财, 责, 贤, 败, 账, 货, 质, 贩, 贪, 贫, 贬, 购, 贮, 贯, 贰, 贱, 贲, + 贴, 贵, 贷, 贸, 费, 贺, 贻, 贼, 贾, 贿, 赁, 赂, 赃, 资, 赅, 赈, 赉, 赊, 赋, 赌, 赎, 赏, 赐, 赓, 赔, + 赖, 赘, 赚, 赛, 赝, 赞, 赠, 赡, 赢, 赣, 赤, 赦, 赫, 走, 赳, 赴, 赵, 赶, 起, 趁, 超, 越, 趋, 趟, 趣, + 足, 趴, 趵, 趸, 趺, 趾, 跃, 跄, 跆, 跋, 跌, 跎, 跑, 跚, 跛, 距, 跟, 跤, 跨, 跪, 跬, 路, 跳, 践, 跶, + 跷, 跹, 跺, 跻, 踉, 踊, 踌, 踏, 踝, 踞, 踢, 踩, 踪, 踮, 踯, 踱, 踵, 踹, 踺, 蹁, 蹂, 蹄, 蹈, 蹉, 蹊, + 蹋, 蹒, 蹚, 蹦, 蹩, 蹬, 蹭, 蹲, 蹴, 蹶, 蹼, 蹿, 躁, 躅, 躇, 躏, 身, 躬, 躯, 躲, 躺, 车, 轧, 轨, 轩, + 轫, 转, 轮, 软, 轰, 轱, 轲, 轳, 轴, 轶, 轸, 轻, 轼, 载, 轿, 较, 辄, 辅, 辆, 辈, 辉, 辊, 辍, 辐, 辑, + 输, 辕, 辖, 辗, 辘, 辙, 辛, 辜, 辞, 辟, 辣, 辨, 辩, 辫, 辰, 辱, 边, 辽, 达, 迁, 迂, 迄, 迅, 过, 迈, + 迎, 运, 近, 返, 还, 这, 进, 远, 违, 连, 迟, 迢, 迥, 迦, 迩, 迪, 迫, 迭, 述, 迷, 迸, 迹, 追, 退, 送, + 适, 逃, 逅, 逆, 选, 逊, 逋, 逍, 透, 逐, 逑, 递, 途, 逗, 通, 逛, 逝, 逞, 速, 造, 逡, 逢, 逮, 逯, 逵, + 逸, 逻, 逼, 逾, 遁, 遂, 遇, 遍, 遏, 遐, 遑, 道, 遗, 遛, 遢, 遣, 遥, 遨, 遭, 遮, 遴, 遵, 避, 邀, 邂, + 邃, 邋, 邑, 邓, 邕, 邙, 邛, 邝, 邡, 邢, 那, 邦, 邪, 邬, 邮, 邯, 邰, 邱, 邳, 邵, 邸, 邹, 邺, 邻, 郁, + 郅, 郇, 郊, 郎, 郑, 郓, 郜, 郝, 郡, 郧, 部, 郫, 郭, 郯, 郴, 郸, 都, 鄂, 鄙, 鄞, 鄢, 鄱, 酉, 酊, 酋, + 酌, 配, 酐, 酒, 酗, 酚, 酝, 酞, 酣, 酥, 酩, 酪, 酬, 酮, 酯, 酰, 酱, 酵, 酶, 酷, 酸, 酿, 醇, 醉, 醋, + 醍, 醐, 醒, 醛, 醺, 采, 釉, 释, 里, 重, 野, 量, 金, 釜, 鉴, 銮, 鏖, 鑫, 钇, 针, 钉, 钊, 钎, 钏, 钐, + 钒, 钓, 钗, 钙, 钛, 钜, 钝, 钞, 钟, 钠, 钢, 钣, 钥, 钦, 钧, 钨, 钩, 钮, 钯, 钰, 钱, 钲, 钳, 钴, 钵, + 钻, 钼, 钾, 钿, 铀, 铁, 铂, 铃, 铄, 铅, 铆, 铉, 铋, 铍, 铎, 铐, 铑, 铖, 铛, 铜, 铝, 铟, 铠, 铡, 铣, + 铤, 铧, 铨, 铩, 铬, 铭, 铮, 铰, 铲, 银, 铷, 铸, 铺, 链, 铿, 销, 锁, 锂, 锄, 锅, 锆, 锈, 锉, 锋, 锌, + 锏, 锐, 锑, 锒, 错, 锚, 锟, 锡, 锢, 锣, 锤, 锥, 锦, 锨, 锭, 键, 锯, 锰, 锲, 锴, 锵, 锷, 锹, 锻, 镀, + 镁, 镂, 镇, 镉, 镊, 镌, 镍, 镏, 镐, 镑, 镔, 镕, 镖, 镜, 镣, 镭, 镯, 镰, 镳, 镶, 长, 门, 闩, 闪, 闫, + 闭, 问, 闯, 闰, 闲, 闳, 间, 闵, 闷, 闸, 闹, 闺, 闻, 闽, 闾, 阀, 阁, 阂, 阄, 阅, 阆, 阉, 阎, 阐, 阑, + 阔, 阕, 阖, 阙, 阚, 阜, 队, 阡, 阪, 阮, 阱, 防, 阳, 阴, 阵, 阶, 阻, 阿, 陀, 陂, 附, 际, 陆, 陇, 陈, + 陉, 陋, 陌, 降, 限, 陕, 陛, 陡, 院, 除, 陨, 险, 陪, 陬, 陵, 陶, 陷, 隅, 隆, 隋, 隍, 随, 隐, 隔, 隗, + 隘, 隙, 障, 隧, 隶, 隼, 隽, 难, 雀, 雁, 雄, 雅, 集, 雇, 雉, 雌, 雍, 雏, 雒, 雕, 雨, 雪, 雯, 雳, 零, + 雷, 雹, 雾, 需, 霁, 霄, 霆, 震, 霈, 霉, 霍, 霎, 霏, 霓, 霖, 霜, 霞, 霪, 露, 霸, 霹, 霾, 靑, 青, 靓, + 靖, 静, 靛, 非, 靠, 靡, 面, 革, 靳, 靴, 靶, 鞅, 鞋, 鞍, 鞑, 鞘, 鞠, 鞭, 韦, 韧, 韩, 韫, 韬, 韭, 音, + 韵, 韶, 页, 顶, 顷, 项, 顺, 须, 顽, 顾, 顿, 颀, 颁, 颂, 预, 颅, 领, 颇, 颈, 颊, 颌, 颍, 颐, 频, 颓, + 颖, 颗, 题, 颚, 颜, 额, 颠, 颢, 颤, 颦, 颧, 风, 飒, 飓, 飘, 飙, 飚, 飞, 食, 飧, 餍, 餐, 餮, 饕, 饥, + 饨, 饪, 饭, 饮, 饯, 饰, 饱, 饲, 饴, 饵, 饶, 饷, 饺, 饼, 饽, 饿, 馀, 馁, 馄, 馅, 馆, 馈, 馊, 馋, 馍, + 馏, 馑, 馒, 馕, 首, 馗, 香, 馥, 馨, 马, 驭, 驮, 驯, 驰, 驱, 驳, 驴, 驶, 驷, 驸, 驹, 驻, 驼, 驾, 驿, + 骁, 骂, 骄, 骅, 骆, 骇, 骈, 骊, 骋, 验, 骏, 骐, 骑, 骓, 骗, 骚, 骛, 骜, 骝, 骞, 骠, 骡, 骤, 骥, 骨, + 骰, 骷, 骸, 骺, 骼, 髂, 髅, 髋, 髌, 髓, 高, 髦, 髯, 鬃, 鬓, 鬟, 鬼, 魁, 魂, 魄, 魅, 魇, 魉, 魍, 魏, + 魔, 魟, 鱼, 鱿, 鲁, 鲅, 鲈, 鲍, 鲑, 鲜, 鲟, 鲠, 鲢, 鲤, 鲨, 鲫, 鲭, 鲳, 鲶, 鲷, 鲸, 鲼, 鳃, 鳄, 鳅, + 鳌, 鳍, 鳕, 鳖, 鳗, 鳝, 鳞, 鳟, 鸟, 鸠, 鸡, 鸢, 鸣, 鸥, 鸦, 鸩, 鸪, 鸫, 鸭, 鸯, 鸳, 鸵, 鸽, 鸾, 鸿, + 鹁, 鹂, 鹃, 鹅, 鹉, 鹊, 鹌, 鹏, 鹑, 鹜, 鹞, 鹤, 鹦, 鹧, 鹫, 鹭, 鹰, 鹳, 鹿, 麂, 麋, 麒, 麓, 麝, 麟, + 麦, 麸, 麻, 麾, 黄, 黍, 黎, 黏, 黑, 黔, 默, 黛, 黝, 黟, 黯, 鼎, 鼓, 鼠, 鼬, 鼹, 鼻, 鼾, 齐, 齿, 龃, + 龄, 龅, 龈, 龉, 龊, 龌, 龙, 龚, 龟, "\U0002B5AF", "\U0002B689"] + + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: *labels + batch_size: 32 + trim_silence: True + normalize: False + max_duration: 16.7 + shuffle: True + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + normalize: False + labels: *labels + batch_size: 32 + shuffle: False + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.02 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 64 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + rect_freq: 50 + rect_masks: 5 + rect_time: 120 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: 1 + residual: false + separable: *separable + stride: [2] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [2] + dropout: *dropout + filters: 512 + kernel: [87] + repeat: 1 + residual: false + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: &enc_filters 1024 + kernel: [1] + repeat: 1 + residual: false + stride: [1] + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: *enc_filters + num_classes: 5206 + vocabulary: *labels + + optim: + name: novograd + # _target_: nemo.core.optim.optimizers.Novograd + lr: .01 + # optimizer arguments + betas: [0.8, 0.5] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # pytorch lightning args + # monitor: val_loss + # reduce_on_plateau: false + + # Scheduler params + warmup_steps: null + warmup_ratio: null + min_lr: 0.0 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 5 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + create_wandb_logger: False + wandb_logger_kwargs: + name: null + project: null + diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/speech_multitask/fast-conformer_aed.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/speech_multitask/fast-conformer_aed.yaml new file mode 100644 index 0000000..77260e5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/speech_multitask/fast-conformer_aed.yaml @@ -0,0 +1,284 @@ +# It contains the default values for training an autoregressive FastConformer-Transformer AED model with sub-word encoding. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of FastConformer-Transformer, other parameters are the same as in this config file. +# One extra (linear projection) layer is added between FastConformer encoder and Transformer decoder if they have different hidden sizes +# It is recommended to initialize FastConformer with ASR/SSL pre-trained encoder for better accuracy and faster convergence + +name: "FastConformer-Transformer-MultiTask" + +# Note: for larger models (1B+ params) initializing from a pretrained encoder +# may help (or even be required to) stabilize the training. +init_from_nemo_model: null + +# If using example training script, below will be used to instantiate spl_tokens tokenizer. +# Similar can be done by calling CanaryTokenizer.build_special_tokenizer(tokens, output_dir). +# If a tokenizer exists in dir, will skip building and use already built tokenizer. +spl_tokens: + model_dir: ??? + tokens: ["translate", "transcribe", "en", "es", "de", "fr"] + force_rebuild: False # Set to True to build new tokenizer each time. + +model: + sample_rate: 16000 + label_smoothing: 0.0 + context_len_for_AR_decoding: 5 # Length of input prompt tokens. For example, in Canary models, we use [BOS,src_lang,task,tgt_lang,pnc] and thus the length is 5 + log_prediction: true # enables logging sample predictions in the output during training + + # Important ! Set the prompt format to the class you need + prompt_format: ??? # Options supported: ["canary"] + + model_defaults: + asr_enc_hidden: 1024 + lm_enc_hidden: 512 + lm_dec_hidden: 1024 + + train_ds: + use_lhotse: true + tarred_audio_filepaths: null + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + shuffle: true + num_workers: 8 + # To understand the settings below, please refer to Lhotse Dataloading documentation: + # https://github.com/NVIDIA/NeMo/blob/main/docs/source/asr/datasets.rst#lhotse-dataloading + # You can also check the following configuration dataclass: + # https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/data/lhotse/dataloader.py#L36 + batch_size: null + batch_duration: 360 + quadratic_duration: 15 + use_bucketing: True + num_buckets: 20 + bucket_buffer_size: 20000 + shuffle_buffer_size: 10000 + + validation_ds: + use_lhotse: true + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 8 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 4 + pin_memory: true + use_start_end_token: true + use_bucketing: false + + test_ds: + use_lhotse: true + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 8 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 4 + pin_memory: true + use_start_end_token: true + use_bucketing: false + + # recommend small vocab size of 128 or 256 when using 4x sub-sampling + # you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: null # Null for aggregate tokenizers + type: agg # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) or `agg` for aggregate tokenizers + langs: + spl_tokens: # special tokens model + dir: null # Passed in training script + type: bpe + en: # English tokenizer (example, replace with whichever language you would like or add tokenizers to add tokenizer for additional languages) + dir: ??? + type: bpe + + custom_tokenizer: + _target_: nemo.collections.common.tokenizers.canary_tokenizer.CanaryTokenizer # Can be replaced with other tokenizer for different prompt formats + tokenizers: null # Filled at runtime by all the tokenizers inside the aggregate tokenizer + + # Audio Preprocessor + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + pad_value: 0.0 + + # SpecAugment is applied either in the model or in the data layer + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + # you may use lower time_masks for smaller models to have a faster convergence + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + # FastConformer Encoder + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 24 + d_model: ${model.model_defaults.asr_enc_hidden} + + # Sub-sampling params + subsampling: dw_striding # vggnet or striding, vggnet may give better results but needs more memory + subsampling_factor: 8 # must be power of 2 + subsampling_conv_channels: 256 # -1 sets it to d_model + causal_downsampling: false + reduction: null + reduction_position: null + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + xscaling: false # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: batch_norm + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # Optional Transformer Encoder sandwitched between ASR Encoder and Transformer Ddcoder. + # Only used if num_layers > 0 + transf_encoder: + _target_: nemo.collections.asr.modules.transformer.transformer_encoders.TransformerEncoder + num_layers: 0 + hidden_size: ${model.model_defaults.lm_enc_hidden} + inner_size: ${multiply:${model.model_defaults.lm_enc_hidden}, 4} + num_attention_heads: 8 + ffn_dropout: 0.1 + attn_score_dropout: 0.1 + attn_layer_dropout: 0.1 + mask_future: False + pre_ln: True + pre_ln_final_layer_norm: True + + transf_decoder: + _target_: nemo.collections.asr.modules.transformer.get_nemo_transformer + model_name: null + pretrained: false + encoder: null + pre_ln_final_layer_norm: true + + config_dict: + max_sequence_length: 512 + num_token_types: 0 + embedding_dropout: 0.1 + learn_positional_encodings: false + hidden_size: ${model.model_defaults.lm_dec_hidden} + inner_size: ${multiply:${model.model_defaults.lm_dec_hidden}, 4} + num_layers: 24 + num_attention_heads: 8 + ffn_dropout: 0.1 + attn_score_dropout: 0.1 + attn_layer_dropout: 0.1 + hidden_act: relu + pre_ln: true + vocab_size: None # Will be set by the model at runtime + + # Label Prediction Head (Token Classifier) + head: + _target_: nemo.collections.asr.parts.submodules.token_classifier.TokenClassifier + num_layers: 1 + activation: relu + log_softmax: true + hidden_size: ${model.transf_decoder.config_dict.hidden_size} + num_classes: None # Will be set by the model at runtime + dropout: 0.0 + use_transformer_init: true + + # Decoding Strategy + decoding: + strategy: beam + return_best_hypothesis: true # Returns the most probably hypothesis after beam search + + beam: + beam_size: 1 + len_pen: 0.0 + max_generation_delta: 50 + + # Loss Config + loss: + _target_: nemo.collections.common.losses.smoothed_cross_entropy.SmoothedCrossEntropyLoss + label_smoothing: ${model.label_smoothing} + pad_id: null + + optim: + name: adamw + lr: 3e-4 + # optimizer arguments + betas: [0.9, 0.98] + # less necessity for weight_decay as we already have large augmentations with SpecAug + # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used + # weight decay of 0.0 with lr of 2.0 also works fine + weight_decay: 1e-3 + + # scheduler setup + sched: + name: InverseSquareRootAnnealing + # scheduler config override + warmup_steps: 2500 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: -1 + max_steps: 100000 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 16 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 100 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 2 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_sacreBLEU" + mode: "max" + save_top_k: 3 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: true + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/speech_translation/fast-conformer_transformer.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/speech_translation/fast-conformer_transformer.yaml new file mode 100644 index 0000000..6b6dbf1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/speech_translation/fast-conformer_transformer.yaml @@ -0,0 +1,218 @@ +# It contains the default values for training an autoregressive FastConformer-Transformer ST model with sub-word encoding. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of FastConformer-Transformer, other parameters are the same as in this config file. +# One extra (linear projection) layer is added between FastConformer encoder and Transformer decoder if they have different hidden sizes +# It is recommended to initialize FastConformer with ASR pre-trained encoder for better accuracy and faster convergence + +name: "FastConformer-Transformer-BPE-st" + +# Initialize model encoder with pre-trained ASR FastConformer encoder for faster convergence and improved accuracy +init_from_nemo_model: + model0: + path: ??? + include: ["preprocessor", "encoder"] + +model: + sample_rate: 16000 + label_smoothing: 0.0 + log_prediction: true # enables logging sample predictions in the output during training + + train_ds: + is_tarred: true + tarred_audio_filepaths: ??? + manifest_filepath: ??? + sample_rate: 16000 + shuffle: false + trim_silence: false + batch_size: 4 + num_workers: 8 + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 4 + pin_memory: true + use_start_end_token: true + + test_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 4 + pin_memory: true + use_start_end_token: true + + # recommend small vocab size of 128 or 256 when using 4x sub-sampling + # you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + # you may use lower time_masks for smaller models to have a faster convergence + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: dw_striding # vggnet or striding, vggnet may give better results but needs more memory + subsampling_factor: 8 # must be power of 2 + subsampling_conv_channels: 256 # -1 sets it to d_model + causal_downsampling: false + reduction: null + reduction_position: null + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: batch_norm + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + transf_encoder: + num_layers: 0 + hidden_size: 512 + inner_size: 2048 + num_attention_heads: 8 + ffn_dropout: 0.1 + attn_score_dropout: 0.1 + attn_layer_dropout: 0.1 + + transf_decoder: + library: nemo + model_name: null + pretrained: false + max_sequence_length: 512 + num_token_types: 0 + embedding_dropout: 0.1 + learn_positional_encodings: false + hidden_size: 512 + inner_size: 2048 + num_layers: 6 + num_attention_heads: 4 + ffn_dropout: 0.1 + attn_score_dropout: 0.1 + attn_layer_dropout: 0.1 + hidden_act: relu + pre_ln: true + pre_ln_final_layer_norm: true + + head: + num_layers: 1 + activation: relu + log_softmax: true + dropout: 0.0 + use_transformer_init: true + + beam_search: + beam_size: 4 + len_pen: 0.0 + max_generation_delta: 50 + + optim: + name: adam + lr: 0.0001 + # optimizer arguments + betas: [0.9, 0.98] + # less necessity for weight_decay as we already have large augmentations with SpecAug + # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used + # weight decay of 0.0 with lr of 2.0 also works fine + #weight_decay: 1e-3 + + # scheduler setup + sched: + name: InverseSquareRootAnnealing + #d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 1000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 16 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 100 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_sacreBLEU" + mode: "max" + save_top_k: 3 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/squeezeformer/squeezeformer_ctc_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/squeezeformer/squeezeformer_ctc_bpe.yaml new file mode 100644 index 0000000..26257e2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/squeezeformer/squeezeformer_ctc_bpe.yaml @@ -0,0 +1,209 @@ +# It contains the default values for training a Squeezeformer-CTC ASR model, large size (~120M) with CTC loss and sub-word encoding. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Squeezeformer-CTC, other parameters are the same as in this config file. +# One extra layer (compared to original paper) is added to the medium and large variants to compensate for replacing the LSTM decoder with a linear one. +# +# | Model | d_model | n_layers | n_heads | time_masks | lr | time_reduce_idx | GBS | +# |--------------|---------|----------|---------|------------|--------|-----------------|------| +# | Extra-Small | 144 | 16 | 4 | 5 | 2e-3 | 7 | 1024 | +# | Small | 196 | 18 | 4 | 5 | 2e-3 | 8 | 1024 | +# | Small-Medium | 256 | 16 | 4 | 5 | 1.5e-3 | 7 | 1024 | +# | Medium | 324 | 20 | 4 | 7 | 1.5e-3 | 9 | 1024 | +# | Medium-Large | 512 | 18 | 8 | 10 | 1e-3 | 8 | 2048 | +# | Large | 640 | 22 | 8 | 10 | 5e-4 | 10 | 2048 | +# +# You may find more info about Squeezeformer-CTC here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#squeezeformer-ctc +# Pre-trained models of Squeezeformer-CTC can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html + +name: "Squeezeformer-CTC-BPE" + +model: + sample_rate: 16000 + log_prediction: true # enables logging sample predictions in the output during training + ctc_reduction: 'mean_batch' + skip_nan_grad: false + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 8 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + use_start_end_token: false + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 8 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 8 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + # recommend small vocab size of 128 or 256 when using 4x sub-sampling + # you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + # you may use lower time_masks for smaller models to have a faster convergence + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.SqueezeformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 18 + d_model: 512 + + # Squeezeformer params + adaptive_scale: true + time_reduce_idx: 8 + time_recovery_idx: null + + # Sub-sampling params + subsampling: dw_striding # dw_striding, vggnet, striding or stacking, vggnet may give better results but needs more memory + subsampling_factor: 4 # must be power of 2 + subsampling_conv_channels: -1 # -1 sets it to d_model + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm + + ### regularization + dropout: 0.1 # The dropout used in most of the Squeezeformer Modules + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + optim: + name: adamw + lr: 0.001 + # optimizer arguments + betas: [0.9, 0.98] + # less necessity for weight_decay as we already have large augmentations with SpecAug + # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used + # weight decay of 0.0 with lr of 2.0 also works fine + weight_decay: 4e-5 + + # scheduler setup + sched: + name: NoamHoldAnnealing + # scheduler config override + warmup_steps: 5000 # paper uses ~ 6500 steps (20 epochs) out of 500 epochs. + warmup_ratio: null + hold_steps: 40000 + hold_ratio: null # paper uses ~ 40000 steps (160 epochs) out of 500 epochs. + decay_rate: 1.0 # Noam decay = 0.5 and no hold steps. For Squeezeformer, use hold ~ 10-30% of training, then faster decay. + min_lr: 1e-5 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/squeezeformer/squeezeformer_ctc_char.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/squeezeformer/squeezeformer_ctc_char.yaml new file mode 100644 index 0000000..7d4f492 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/squeezeformer/squeezeformer_ctc_char.yaml @@ -0,0 +1,195 @@ +# It contains the default values for training a Squeezeformer-CTC ASR model, large size (~120M) with CTC loss and char encoding. + +# You may find more detail on Conformer-CTC at `examples/asr/conf/conformer/conformer_ctc_bpe.yaml` +# You may find more info about Squeezeformer-CTC here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#squeezeformer-ctc +# Pre-trained models of Squeezeformer-CTC can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html + +name: "Squeezeformer-CTC-BPE" + +model: + sample_rate: 16000 + labels: [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + log_prediction: true # enables logging sample predictions in the output during training + ctc_reduction: 'mean_batch' + skip_nan_grad: false + + train_ds: + manifest_filepath: ??? + labels: ${model.labels} + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + use_start_end_token: false + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + labels: ${model.labels} + sample_rate: ${model.sample_rate} + batch_size: 8 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + test_ds: + manifest_filepath: null + labels: ${model.labels} + sample_rate: ${model.sample_rate} + batch_size: 8 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + # you may use lower time_masks for smaller models to have a faster convergence + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.SqueezeformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 18 + d_model: 512 + + # Squeezeformer params + adaptive_scale: true + time_reduce_idx: 8 + time_recovery_idx: null + + # Sub-sampling params + subsampling: dw_striding # dw_striding, vggnet, striding or stacking, vggnet may give better results but needs more memory + subsampling_factor: 4 # must be power of 2 + subsampling_conv_channels: -1 # -1 sets it to d_model + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm + + ### regularization + dropout: 0.1 # The dropout used in most of the Squeezeformer Modules + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: ${model.labels} + + # config for InterCTC loss: https://arxiv.org/abs/2102.03216 + # specify loss weights and which layers to use for InterCTC + # e.g., to reproduce the paper results, set loss_weights: [0.3] + # and apply_at_layers: [8] (assuming 18 layers). Note that final + # layer loss coefficient is automatically adjusted (to 0.7 in above example) + interctc: + loss_weights: [] + apply_at_layers: [] + + optim: + name: adamw + lr: 0.001 + # optimizer arguments + betas: [0.9, 0.98] + # less necessity for weight_decay as we already have large augmentations with SpecAug + # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used + # weight decay of 0.0 with lr of 2.0 also works fine + weight_decay: 4e-5 + + # scheduler setup + sched: + name: NoamHoldAnnealing + # scheduler config override + warmup_steps: 5000 # paper uses ~ 6500 steps (20 epochs) out of 500 epochs. + warmup_ratio: null + hold_steps: 40000 + hold_ratio: null # paper uses ~ 40000 steps (160 epochs) out of 500 epochs. + decay_rate: 1.0 # Noam decay = 0.5 and no hold steps. For Squeezeformer, use hold ~ 10-30% of training, then faster decay. + min_lr: 1e-5 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/citrinet/citrinet_ssl_1024.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/citrinet/citrinet_ssl_1024.yaml new file mode 100644 index 0000000..2dd6750 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/citrinet/citrinet_ssl_1024.yaml @@ -0,0 +1,511 @@ +# This config contains the default values for self-supervised pre-training of a Citrinet model with contrastive loss. +# Default learning parameters in this config are set for effective batch size of 1k on 32 GPUs. +# To train it with smaller batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# If training for a short time, you can also reduce weight decay to 0. + +# Training Recipe +# This model can be trained using the default settings in this config with FP32 precision. +# When training under AMP, increase `warmup_steps` to 5000 for stable training. +# In order to create Citrinet-C, change the model.model_defaults.filters parameter. +# When reducing the receptive field of these models, it is advised to reduce the amount of augmentation +# for larger models from 10x time masking to 5x or 2x time masking. +# For further details regarding Citrinet, visit - https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#citrinet + +name: &name "Citrinet-1024-SSL-Contrastive" + +model: + sample_rate: &sample_rate 16000 + + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + trim_silence: false + max_duration: 16.7 + min_duration: 8.0 + shuffle: true + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + use_start_end_token: false + num_workers: 8 + pin_memory: true + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + max_duration: 35.0 + min_duration: 8.0 + + model_defaults: + repeat: 5 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 0.25 + filters: 1024 + decoder_out_channels: 128 + enc_final: 1024 + + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.MaskedPatchAugmentation + freq_masks: 3 + freq_width: 20 + patch_size: 48 + mask_patches: 0.5 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: ${model.preprocessor.features} + activation: relu + conv_mask: true + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [11] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [21] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [2] # *stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [21] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [23] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [25] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [25] + stride: [2] # stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [27] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [29] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [31] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [33] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [35] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [37] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [39] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.enc_final} + repeat: 1 + kernel: [41] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + loss_list: + contrastive: + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderReconstruction + feat_in: ${model.model_defaults.enc_final} + feat_hidden: 128 + # features in hidden layer of decoder + feat_out: ${model.model_defaults.decoder_out_channels} + stride_layers: 1 + # if loss.combine_time_steps is different than the encoder stride, + # then a corresponding amount of stride_layers needs to + # be added to the decoder (here stride is 8 and combine_time_steps is 4) + non_stride_layers: 0 + stride_transpose: true + apply_softmax: false + loss: + _target_: nemo.collections.asr.losses.ContrastiveLoss + in_dim: ${model.preprocessor.features} + proj_dim: ${model.model_defaults.decoder_out_channels} + combine_time_steps: 4 #how many spectrogram time steps are used for one target/representation for contrastive task + quantized_targets: true #should quantizer or linear layer be used + # (quantizer is required to extract pseudo-labels for other losses) + codebook_size: 300 # number of vectors in the quantization codebook per group + num_groups: 2 # number of groups in the quantizer codebook + num_negatives: 100 # number of sampled negatives for each target + sample_from_same_utterance_only: true #should negatives be sampled only from the same utterance + sample_from_non_masked: false #should negatives be sampled from non-masked steps + + mlm: + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderReconstruction + feat_in: ${model.model_defaults.enc_final} + feat_hidden: 128 + # features in hidden layer of decoder + feat_out: 90000 + # this should be equal to codebook_size^groups in the contrastive loss to match the targets + stride_layers: 1 + stride_transpose: true + activation: "identity" + apply_softmax: true + loss: + _target_: nemo.collections.asr.losses.MLMLoss + combine_time_steps: 4 + targets_from_loss: "contrastive" + loss_alpha: 1000. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.model_defaults.enc_final} + # scheduler config override + warmup_steps: 25000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_loss" + mode: "min" + save_top_k: 5 + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/citrinet/citrinet_ssl_ci.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/citrinet/citrinet_ssl_ci.yaml new file mode 100644 index 0000000..749b975 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/citrinet/citrinet_ssl_ci.yaml @@ -0,0 +1,470 @@ +# This config is used for the CI test of self-supervised learning for CitriNet + +name: &name "Citrinet-SSL-CI" + +model: + sample_rate: &sample_rate 16000 + + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + trim_silence: false + max_duration: 35.0 + min_duration: 4.0 + shuffle: true + is_tarred: false + tarred_audio_filepaths: null + use_start_end_token: false + num_workers: 8 + pin_memory: true + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + shuffle: false + use_start_end_token: false + max_duration: 35.0 + min_duration: 4.0 + + model_defaults: + repeat: 1 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 0.25 + filters: 128 + decoder_out_channels: 128 + + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.MaskedPatchAugmentation + freq_masks: 3 + freq_width: 20 + patch_size: 16 + mask_patches: 10 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [11] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [21] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [2] # *stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [21] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [23] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [25] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [25] + stride: [2] # stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [27] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [29] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [31] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [33] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [35] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [37] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [39] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: &enc_final 256 + repeat: 1 + kernel: [41] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderReconstruction + feat_in: *enc_final + feat_hidden: 128 + feat_out: ${model.model_defaults.decoder_out_channels} + stride_layers: 1 + + + loss: + _target_: nemo.collections.asr.losses.ContrastiveLoss + in_dim: *n_mels + proj_dim: ${model.model_defaults.decoder_out_channels} + combine_time_steps: 4 + sample_from_non_masked: false + num_negatives: 30 + quantized_targets: false + sample_from_same_utterance_only: true + + optim: + name: novograd + lr: 0.05 + + # optimizer arguments + betas: [0.8, 0.25] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: 5000 + warmup_ratio: null + min_lr: 1e-5 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + check_val_every_n_epoch: 1 + precision: 32 + sync_batchnorm: false + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: "val_loss" + mode: "min" + save_top_k: 1 + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/conformer/conformer_ssl.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/conformer/conformer_ssl.yaml new file mode 100644 index 0000000..a0fc5e4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/conformer/conformer_ssl.yaml @@ -0,0 +1,221 @@ +# This config contains the default values for self-supervised pre-training of a Conformer ASR model, large size (~120M). + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-CTC, other parameters are the same as in this config file. +# One extra layer (compared to original paper) is added to the medium and large variants to compensate for replacing the LSTM decoder with a linear one. +# +# +-------------+---------+---------+----------+------------+-----+ +# | Model | d_model | n_heads | n_layers | time_masks | lr | +# +=============+=========+========+===========+============+=====+ +# | Small (13M)| 176 | 4 | 16 | 5 | 5.0 | +# +-------------+---------+--------+-----------+------------+-----+ +# | Medium (30M)| 256 | 4 | 18 | 5 | 5.0 | +# +-------------+---------+--------+-----------+------------+-----+ +# | Large (121M)| 512 | 8 | 18 | 10 | 2.0 | +# +---------------------------------------------------------------+ +# +# If you do not want to train with AMP, you may use weight decay of 0.0 or reduce the number of time maskings to 2 +# with time_width=100. It may help when you want to train for fewer epochs and need faster convergence. +# With weight_decay=0.0, learning rate may need to get reduced to 2.0. + +name: "Conformer-SSL" + +model: + sample_rate: 16000 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: false + use_start_end_token: true + trim_silence: false + max_duration: 16.7 + min_duration: 8.0 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + min_duration: 8.0 + + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.MaskedPatchAugmentation + freq_masks: 3 + freq_width: 20 + patch_size: 48 + mask_patches: 0.5 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 18 + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # -1 sets it to d_model + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + decoder_out: 128 + + loss_list: + contrastive: + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderReconstruction + feat_in: ${model.encoder.d_model} + feat_hidden: 128 + # features in hidden layer of decoder + feat_out: ${model.decoder_out} + stride_layers: 0 + # if loss.combine_time_steps is less than the encoder stride, then a corresponding amount of stride_layers needs to + # be added to the decoder (here stride and combine_time_steps are both 4) + non_stride_layers: 0 + loss: + _target_: nemo.collections.asr.losses.ContrastiveLoss + in_dim: ${model.preprocessor.features} + proj_dim: ${model.decoder_out} + combine_time_steps: 4 # how many spectrogram time steps are used for one target/representation for contrastive task + quantized_targets: true # should quantizer or linear layer be used + # (quantizer is required to extract pseudo-labels for other losses) + codebook_size: 300 # number of vectors in the quantization codebook per group + num_groups: 2 # number of groups in the quantizer codebook + num_negatives: 100 # number of sampled negatives for each target + sample_from_same_utterance_only: true # should negatives be sampled only from the same utterance + sample_from_non_masked: false # should negatives be sampled from non-masked steps + + mlm: + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: ${model.encoder.d_model} + num_classes: 90000 + # set this to be equal to codebook_size^groups in the contrastive loss + loss: + _target_: nemo.collections.asr.losses.MLMLoss + combine_time_steps: 4 + targets_from_loss: "contrastive" + # since this loss requires targets, we can either get them from a manifest or from a quantized contrastive loss + loss_alpha: 1000. + # multiplier applied to this loss relative to others + transpose_encoded: false + # transposing input may be necessary depending on which layer is used as input to decoder + start_step: 0 + # determines what global step this loss starts being used at; + # this can be set to a higher number if your training is long enough, + # which may increase early training stability + output_from_layer: null + # if we wanted to use outputs from non-final encoder layer as input to this decoder, + # the layer name should be specified here + + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 25000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_loss" + mode: "min" + save_top_k: 5 + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/contextnet/contextnet_ssl.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/contextnet/contextnet_ssl.yaml new file mode 100644 index 0000000..6ce2d07 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/contextnet/contextnet_ssl.yaml @@ -0,0 +1,475 @@ +# This config contains the default values for self-supervised pre-training of ContextNet encoder. +# In contrast to original ContextNet, the same number of filters is used throughout the model. +# Default learning parameters in this config are set for effective batch size of 1K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of ContextNet, other parameters are the same as in this config file. +# +# +-------------+---------+------------+ +# | Model | filters | time_masks | +# +=============+=========+============+ +# | Small (14M)| 256 | 2 | +# +-------------+---------+------------+ +# | Medium (40M)| 512 | 5 | +# +-------------+---------+------------+ +# | Large (145M)| 1024 | 10 | +# +------------------------------------- + +name: &name "ContextNet-8x-Stride-SSL" + +model: + sample_rate: &sample_rate 16000 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # Can be increased if memory allows or when using smaller model + trim_silence: false + max_duration: 16.7 + min_duration: 8.0 + shuffle: true + use_start_end_token: false + num_workers: 16 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 8 + shuffle: false + use_start_end_token: false + num_workers: 16 + pin_memory: true + min_duration: 8.0 + + model_defaults: + filters: 1024 + repeat: 5 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 1.0 + enc_hidden: 640 + decoder_out_channels: 128 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.MaskedPatchAugmentation + freq_masks: 3 + freq_width: 20 + patch_size: 48 + mask_patches: 0.5 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: swish + conv_mask: true + init_mode: "tds_uniform" + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] # *stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [2] # stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [5] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + - filters: ${model.model_defaults.enc_hidden} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + loss_list: + contrastive: + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderReconstruction + feat_in: ${model.model_defaults.enc_hidden} + feat_hidden: 128 + # features in hidden layer of decoder + feat_out: ${model.model_defaults.decoder_out_channels} + stride_layers: 1 + # if loss.combine_time_steps is different than the encoder stride, + # then a corresponding amount of stride_layers needs to + # be added to the decoder (here stride is 8 and combine_time_steps is 4) + non_stride_layers: 0 + stride_transpose: true + apply_softmax: false + loss: + _target_: nemo.collections.asr.losses.ContrastiveLoss + in_dim: ${model.preprocessor.features} + proj_dim: ${model.model_defaults.decoder_out_channels} + combine_time_steps: 4 #how many spectrogram time steps are used for one target/representation for contrastive task + quantized_targets: true #should quantizer or linear layer be used + # (quantizer is required to extract pseudo-labels for other losses) + codebook_size: 300 # number of vectors in the quantization codebook per group + num_groups: 2 # number of groups in the quantizer codebook + num_negatives: 100 # number of sampled negatives for each target + sample_from_same_utterance_only: true #should negatives be sampled only from the same utterance + sample_from_non_masked: false #should negatives be sampled from non-masked steps + + mlm: + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderReconstruction + feat_in: ${model.model_defaults.enc_hidden} + feat_hidden: 128 + # features in hidden layer of decoder + feat_out: 90000 + # this should be equal to codebook_size^groups in the contrastive loss to match the targets + stride_layers: 1 + stride_transpose: true + activation: "identity" + apply_softmax: true + loss: + _target_: nemo.collections.asr.losses.MLMLoss + combine_time_steps: 4 + targets_from_loss: "contrastive" + loss_alpha: 1000. + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.model_defaults.enc_hidden} + # scheduler config override + warmup_steps: 25000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_loss" + mode: "min" + save_top_k: 5 + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/fastconformer/fast-conformer.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/fastconformer/fast-conformer.yaml new file mode 100644 index 0000000..47ad5aa --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/fastconformer/fast-conformer.yaml @@ -0,0 +1,235 @@ +# This config contains the default values for self-supervised pre-training of a Conformer ASR model, large size (~120M). + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-CTC, other parameters are the same as in this config file. +# One extra layer (compared to original paper) is added to the medium and large variants to compensate for replacing the LSTM decoder with a linear one. +# +# +-------------+---------+---------+----------+------------+-----+ +# | Model | d_model | n_heads | n_layers | time_masks | lr | +# +=============+=========+========+===========+============+=====+ +# | Large (121M)| 512 | 8 | 17 | 10 | 2.0 | +# +---------------------------------------------------------------+ +# +# If you do not want to train with AMP, you may use weight decay of 0.0 or reduce the number of time maskings to 2 +# with time_width=100. It may help when you want to train for fewer epochs and need faster convergence. +# With weight_decay=0.0, learning rate may need to get reduced to 2.0. + +name: "FastConformer-SSL" + +model: + sample_rate: 16000 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: false + use_start_end_token: true + trim_silence: false + max_duration: 16.7 + min_duration: 8.0 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + min_duration: 8.0 + + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.MaskedPatchAugmentation + freq_masks: 3 + freq_width: 20 + patch_size: 48 + mask_patches: 0.5 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling params + subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 8 # must be power of 2 for striding and vggnet + subsampling_conv_channels: 256 # -1 sets it to d_model + causal_downsampling: false + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 9 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + # set to non-zero to enable stochastic depth + stochastic_depth_drop_prob: 0.0 + stochastic_depth_mode: linear # linear or uniform + stochastic_depth_start_layer: 1 + + decoder_out: 256 + + loss_list: + contrastive: + is_active: true # indicates whether to use this loss + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderReconstruction + feat_in: ${model.encoder.d_model} + feat_hidden: ${model.decoder_out} + # features in hidden layer of decoder + feat_out: ${model.decoder_out} + stride_layers: 0 + # if loss.combine_time_steps is less than the encoder stride, then a corresponding amount of stride_layers needs to + # be added to the decoder (here stride and combine_time_steps are both 4) + non_stride_layers: 0 + loss: + _target_: nemo.collections.asr.losses.ContrastiveLoss + in_dim: ${model.preprocessor.features} + proj_dim: ${model.decoder_out} + combine_time_steps: 8 # how many spectrogram time steps are used for one target/representation for contrastive task + quantized_targets: false # should quantizer or linear layer be used + # (quantizer is required to extract pseudo-labels for other losses) + codebook_size: 300 # number of vectors in the quantization codebook per group + num_groups: 2 # number of groups in the quantizer codebook + num_negatives: 100 # number of sampled negatives for each target + sample_from_same_utterance_only: true # should negatives be sampled only from the same utterance + sample_from_non_masked: false # should negatives be sampled from non-masked steps + + mlm: + is_active: false # indicates whether to use this loss + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: ${model.encoder.d_model} + num_classes: 90000 + # set this to be equal to codebook_size^groups in the contrastive loss + loss: + _target_: nemo.collections.asr.losses.MLMLoss + combine_time_steps: 4 + targets_from_loss: "contrastive" + # since this loss requires targets, we can either get them from a manifest or from a quantized contrastive loss + loss_alpha: 1000. + # multiplier applied to this loss relative to others + transpose_encoded: false + # transposing input may be necessary depending on which layer is used as input to decoder + start_step: 0 + # determines what global step this loss starts being used at; + # this can be set to a higher number if your training is long enough, + # which may increase early training stability + output_from_layer: null + # if we wanted to use outputs from non-final encoder layer as input to this decoder, + # the layer name should be specified here + + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 25000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_loss" + mode: "min" + save_top_k: 3 + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/wav2vec/wav2vec_ci.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/wav2vec/wav2vec_ci.yaml new file mode 100644 index 0000000..2a12482 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/wav2vec/wav2vec_ci.yaml @@ -0,0 +1,169 @@ +# This config file contains parameters for training a Wav2Vec semi-supervised encoder +# These parameters are based off the FairSeq implementation. +# See here: https://github.com/pytorch/fairseq/blob/master/examples/wav2vec/config/pretraining/wav2vec2_base_librispeech.yaml + +# kernel: \[([0-9]+)\] +# filters: ([0-9]+) +name: &name "Wav2vec_Pretrain" + +model: + sample_rate: &sample_rate 16000 + feature_penalty: 0.0 + dropout_features: 0.1 # Dropout applied to inputs to context encoder + dropout_features_q: 0.1 # Dropout applied to inputs to target quantizer + embedding_dim: &emb_dim 768 # Project size of embedidng dimension for transformer + final_dim: &final_dim 256 # Project final representations and targets to this dimension (target embeddings). + + train_ds: + manifest_filepath: ??? + sample_rate: *sample_rate + batch_size: 8 + trim_silence: false + max_duration: 20.0 + min_duration: 8.0 + shuffle: true + is_tarred: false + tarred_audio_filepaths: null + use_start_end_token: false + num_workers: 8 + pin_memory: true + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: *sample_rate + batch_size: 8 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + max_duration: 20.0 + min_duration: 8.0 + + preprocessor: + _target_: nemo.collections.asr.modules.wav2vec_modules.ConvFeatureEncoder + extractor_mode: layer_norm # Mode for feature extractor. [group_norm, layer_norm] + conv_bias: False # Include bias in convolution feature extractor model + feature_grad_mult: 1.0 # Multiply extracted feature gradients + normalize_audio: true + embedding_dim: *emb_dim # projected final depth of feature embeddings + conv_layers: + - emb_dim: 512 + kernel_size: 10 + stride: 5 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 2 + stride: 2 + - emb_dim: 512 + kernel_size: 2 + stride: 2 + + spec_augment: + _target_: nemo.collections.asr.modules.MaskedPatchAugmentation + freq_masks: 3 + freq_width: 20 + patch_size: 12 + mask_patches: 0.5 + + encoder: + _target_: nemo.collections.asr.modules.wav2vec_modules.Wav2VecTransformerEncoder + layer_drop: 0.05 + pos_embed: # Config for convolutional model that generates positional embeddings required for attention layer + embedding_dim: *emb_dim + conv_pos: 128 # Number of filters for convolutional positional embeddings + conv_pos_groups: 16 # Number of groups for convolutional positional embeddings + transformer: # Config for nemo.collections.nlp.modules.common.transformer.TransformerEncoder + num_layers: 6 # Number of encoder layers in transformer model + hidden_size: *emb_dim # Encoder embedding dim + inner_size: 1536 # Encoder embedding dim for feed forward + num_attention_heads: 4 # Number of encoder attention heads + attn_score_dropout: .1 # probability of dropout applied to attention scores + attn_layer_dropout: .1 # probability of dropout applied to the output of the attention layers, but before layer normalization + ffn_dropout: .1 # probability of dropout applied to FFN output + hidden_act: gelu # Activation for transformer + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderReconstruction + feat_in: *emb_dim + feat_hidden: *emb_dim + feat_out: *final_dim + stride_layers: 0 + + loss: + _target_: nemo.collections.asr.losses.ContrastiveLoss + in_dim: *emb_dim + proj_dim: *final_dim + quantized_targets: true # should quantizer or linear layer be used + sample_from_same_utterance_only: true # should negatives be sampled only from the same utterance + sample_from_non_masked: false # should negatives be sampled from non-masked steps + + optim: + name: adamw + lr: 2 + eps: 1e-06 + # optimizer arguments + betas: [ 0.9, 0.98 ] + weight_decay: 0.0 + + # scheduler setup + sched: + name: NoamAnnealing + min_lr: 0.001 + d_model: ${model.encoder.transformer.hidden_size} + # Scheduler params + warmup_steps: 15000 + warmup_ratio: null + +trainer: + devices: 1 # number of gpus + num_nodes: 1 + max_steps: -1 # computed at runtime if not set + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 100 # Interval of logging. + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: false + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + create_wandb_logger: false + checkpoint_callback_params: + monitor: "val_loss" + mode: "min" + save_top_k: 1 + always_save_nemo: true + wandb_logger_kwargs: + name: null + project: null + resume_if_exists: false + resume_ignore_no_checkpoint: false + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/wav2vec/wav2vec_pretrain.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/wav2vec/wav2vec_pretrain.yaml new file mode 100644 index 0000000..757503b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/wav2vec/wav2vec_pretrain.yaml @@ -0,0 +1,167 @@ +# This config file contains parameters for training a Wav2Vec semi-supervised encoder +# These parameters are based off the FairSeq implementation. +# See here: https://github.com/pytorch/fairseq/blob/master/examples/wav2vec/config/pretraining/wav2vec2_base_librispeech.yaml + +# kernel: \[([0-9]+)\] +# filters: ([0-9]+) +name: &name "Wav2vec_Pretrain" + +model: + sample_rate: &sample_rate 16000 + feature_penalty: 0.0 + dropout_features: 0.1 # Dropout applied to inputs to context encoder + dropout_features_q: 0.1 # Dropout applied to inputs to target quantizer + embedding_dim: &emb_dim 768 # Project size of embedidng dimension for transformer + final_dim: &final_dim 256 # Project final representations and targets to this dimension (target embeddings). + + train_ds: + manifest_filepath: ??? + sample_rate: *sample_rate + batch_size: ??? + trim_silence: false + max_duration: null + min_duration: 8.0 + shuffle: true + is_tarred: false + tarred_audio_filepaths: null + use_start_end_token: false + num_workers: 8 + pin_memory: true + + validation_ds: + manifest_filepath: ??? + sample_rate: *sample_rate + batch_size: ??? + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + min_duration: 8.0 + + preprocessor: + _target_: nemo.collections.asr.modules.wav2vec_modules.ConvFeatureEncoder + extractor_mode: layer_norm # Mode for feature extractor. [group_norm, layer_norm] + conv_bias: False # Include bias in convolution feature extractor model + feature_grad_mult: 1.0 # Multiply extracted feature gradients + normalize_audio: true + embedding_dim: *emb_dim # projected final depth of feature embeddings + conv_layers: + - emb_dim: 512 + kernel_size: 10 + stride: 5 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 2 + stride: 2 + - emb_dim: 512 + kernel_size: 2 + stride: 2 + + spec_augment: + _target_: nemo.collections.asr.modules.MaskedPatchAugmentation + freq_masks: 3 + freq_width: 20 + patch_size: 12 + mask_patches: 0.5 + + encoder: + _target_: nemo.collections.asr.modules.wav2vec_modules.Wav2VecTransformerEncoder + layer_drop: 0.05 + pos_embed: # Config for convolutional model that generates positional embeddings required for attention layer + embedding_dim: *emb_dim + conv_pos: 128 # Number of filters for convolutional positional embeddings + conv_pos_groups: 16 # Number of groups for convolutional positional embeddings + transformer: # Config for nemo.collections.nlp.modules.common.transformer.TransformerEncoder + num_layers: 12 # Number of encoder layers in transformer model + hidden_size: *emb_dim # Encoder embedding dim + inner_size: 3072 # Encoder embedding dim for feed forward + num_attention_heads: 8 # Number of encoder attention heads + attn_score_dropout: .1 # probability of dropout applied to attention scores + attn_layer_dropout: .1 # probability of dropout applied to the output of the attention layers, but before layer normalization + ffn_dropout: .1 # probability of dropout applied to FFN output + hidden_act: gelu # Activation for transformer + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderReconstruction + feat_in: *emb_dim + feat_hidden: *emb_dim + feat_out: *final_dim + stride_layers: 0 + + loss: + _target_: nemo.collections.asr.losses.ContrastiveLoss + in_dim: *emb_dim + proj_dim: *final_dim + quantized_targets: true # should quantizer or linear layer be used + sample_from_same_utterance_only: true # should negatives be sampled only from the same utterance + sample_from_non_masked: false # should negatives be sampled from non-masked steps + + optim: + name: adamw + lr: 2 + eps: 1e-06 + # optimizer arguments + betas: [ 0.9, 0.98 ] + weight_decay: 0.0 + + # scheduler setup + sched: + name: NoamAnnealing + min_lr: 0.001 + d_model: ${model.encoder.transformer.hidden_size} + # Scheduler params + warmup_steps: 15000 + warmup_ratio: null + +trainer: + devices: 1 # number of gpus + num_nodes: 1 + max_steps: 400000 # computed at runtime if not set + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 100 # Interval of logging. + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + create_wandb_logger: false + checkpoint_callback_params: + monitor: "val_loss" + mode: "min" + save_top_k: 5 + every_n_epochs: 1 + always_save_nemo: true + wandb_logger_kwargs: + name: null + project: null + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/wav2vec/wav2vec_pretrain_large.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/wav2vec/wav2vec_pretrain_large.yaml new file mode 100644 index 0000000..b9e2397 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/ssl/wav2vec/wav2vec_pretrain_large.yaml @@ -0,0 +1,161 @@ +# This config file contains parameters for training a Wav2Vec semi-supervised encoder +# These parameters are based off the FairSeq implementation. +# See here: https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml + +# kernel: \[([0-9]+)\] +# filters: ([0-9]+) +name: &name "Wav2vec_Pretrain_Large" +model: + sample_rate: &sample_rate 16000 + feature_penalty: 1.0 + dropout_features: 0.1 # Dropout applied to inputs to context encoder + dropout_features_q: 0.1 # Dropout applied to inputs to target quantizer + embedding_dim: &emb_dim 1024 # Project size of embedidng dimension for transformer + final_dim: &final_dim 768 # Project final representations and targets to this dimension (target embeddings) + + train_ds: + manifest_filepath: ??? + sample_rate: *sample_rate + batch_size: ??? + trim_silence: false + max_duration: null + min_duration: 8 + shuffle: true + is_tarred: false + tarred_audio_filepaths: null + use_start_end_token: false + num_workers: 8 + pin_memory: true + + validation_ds: + manifest_filepath: ??? + sample_rate: *sample_rate + batch_size: ??? + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + min_duration: 8 + + preprocessor: + _target_: nemo.collections.asr.modules.wav2vec_modules.ConvFeatureEncoder + extractor_mode: layer_norm # Mode for feature extractor. [group_norm, layer_norm] + conv_bias: False # Include bias in convolution feature extractor model + feature_grad_mult: 1.0 # Multiply extracted feature gradients + normalize_audio: true + embedding_dim: *emb_dim # Final dimensions of output + conv_layers: + - emb_dim: 512 + kernel_size: 10 + stride: 5 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 2 + stride: 2 + - emb_dim: 512 + kernel_size: 2 + stride: 2 + + spec_augment: + _target_: nemo.collections.asr.modules.MaskedPatchAugmentation + freq_masks: 3 + freq_width: 20 + patch_size: 12 + mask_patches: 0.5 + + encoder: + _target_: nemo.collections.asr.modules.wav2vec_modules.Wav2VecTransformerEncoder + layer_drop: 0.2 + pos_embed: # Config for convolutional model that generates positional embeddings required for attention layer + embedding_dim: *emb_dim + conv_pos: 128 # Number of filters for convolutional positional embeddings + conv_pos_groups: 16 # Number of groups for convolutional positional embeddings + transformer: # Config for nemo.collections.nlp.modules.common.transformer.TransformerEncoder + num_layers: 24 # Number of encoder layers in transformer model + hidden_size: *emb_dim # Encoder embedding dim + inner_size: 4096 # Encoder embedding dim for feed forward + num_attention_heads: 16 # Number of encoder attention heads + attn_score_dropout: .1 #probability of dropout applied to attention scores + attn_layer_dropout: .1 #probability of dropout applied to the output of the attention layers, but before layer normalization + ffn_dropout: .1 # probability of dropout applied to FFN output + hidden_act: gelu # Activation for transformer + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoderReconstruction + feat_in: *emb_dim + feat_hidden: *emb_dim + feat_out: *final_dim + stride_layers: 0 + + loss: + _target_: nemo.collections.asr.losses.ContrastiveLoss + in_dim: *emb_dim + proj_dim: *final_dim + quantized_targets: true # should quantizer or linear layer be used + sample_from_same_utterance_only: true # should negatives be sampled only from the same utterance + sample_from_non_masked: false # should negatives be sampled from non-masked steps + + optim: + name: adamw + lr: 2 + eps: 1e-06 + # optimizer arguments + betas: [ 0.9, 0.98 ] + weight_decay: 0.0 + + # scheduler setup + sched: + name: NoamAnnealing + min_lr: 0.001 + d_model: ${model.encoder.transformer.hidden_size} + # Scheduler params + warmup_steps: 15000 + warmup_ratio: null + +trainer: + devices: 1 # number of gpus + num_nodes: 1 + max_steps: 250000 # computed at runtime if not set + accelerator: cpu + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + amp_backend: apex + amp_level: O1 + precision: 16 # Should be set to 16 for O1 and O2 to enable the AMP. # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 100 # Interval of logging. + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + create_wandb_logger: false + checkpoint_callback_params: + monitor: "val_loss" + mode: "min" + save_top_k: 5 + every_n_epochs: 1 + always_save_nemo: true + wandb_logger_kwargs: + name: null + project: null + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/vad/frame_vad_infer_postprocess.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/vad/frame_vad_infer_postprocess.yaml new file mode 100644 index 0000000..30c082a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/vad/frame_vad_infer_postprocess.yaml @@ -0,0 +1,39 @@ +name: &name "vad_inference_postprocessing" + +input_manifest: null # Path of json file of evaluation data. Audio files should have unique names +output_dir: null # Path to output directory where results will be stored +num_workers: 12 +sample_rate: 16000 +evaluate: False # whether to get AUROC and DERs, the manifest must contains groundtruth if enabled + +prepare_manifest: + auto_split: True # whether to automatically split manifest entry by split_duration to avoid potential CUDA out of memory issue. + split_duration: 400 # try smaller number if you still have CUDA memory issue + +vad: + model_path: "vad_multilingual_frame_marblenet" #.nemo local model path or pretrained model name or none + use_rttm: True # set True to output as RTTM format + parameters: # Parameters not tuned on large datasets, please use default parameters with caution + normalize_audio_db: null # set to non null value to normalize RMS DB of audio before preprocessing + window_length_in_sec: 0.0 # window length in sec for VAD context input, must be 0 for frame-VAD + shift_length_in_sec: 0.02 # frame-length in seconds for frame-VAD, must be 0.02 for the pretrained NeMo VAD model + smoothing: False # Deprecated for Frame-VAD. false or type of smoothing method (eg: median, mean) + overlap: 0.875 # Deprecated for Frame-VAD. overlap ratio for overlapped mean/median smoothing filter. If smoothing=False, ignore this value. + postprocessing: + onset: 0.3 # onset threshold for detecting the beginning and end of a speech + offset: 0.3 # offset threshold for detecting the end of a speech. + pad_onset: 0.2 # adding durations before each speech segment + pad_offset: 0.2 # adding durations after each speech segment + min_duration_on: 0.2 # threshold for short speech deletion + min_duration_off: 0.2 # threshold for short non-speech segment deletion + filter_speech_first: True + +prepared_manifest_vad_input: null # if not specify, it will automatically generated be "manifest_vad_input.json" +frame_out_dir: "vad_frame_outputs" +smoothing_out_dir: null # if not specify, it will automatically generated be frame_out_dir + "/overlap_smoothing_output" + "_" + smoothing_method + "_" + str(overlap) +rttm_out_dir: null # if not specify, it will automatically be frame_out_dir + "/seg_output_" + key and value in postprocessing params +out_manifest_filepath: null # if not specify it will automatically be "manifest_vad_out.json" + + +# json manifest line example +# {"audio_filepath": "/path/to/audio_file.wav", "offset": 0, "duration": 1.23, "label": "infer", "text": "-"} diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/vad/vad_inference_postprocessing.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/vad/vad_inference_postprocessing.yaml new file mode 100644 index 0000000..88ea3c8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/vad/vad_inference_postprocessing.yaml @@ -0,0 +1,40 @@ +name: &name "vad_inference_postprocessing" + +dataset: null # Path of json file of evaluation data. Audio files should have unique names +num_workers: 4 +sample_rate: 16000 + +# functionality +gen_seg_table: True # whether to converting frame level prediction to speech/no-speech segment in start and end times format +write_to_manifest: True # whether to writing above segments to a single manifest json file. + +prepare_manifest: + auto_split: True # whether to automatically split manifest entry by split_duration to avoid potential CUDA out of memory issue. + split_duration: 400 # try smaller number if you still have CUDA memory issue + + +vad: + model_path: "vad_multilingual_marblenet" #.nemo local model path or pretrained model name or none + parameters: # Parameters were tuned on 0~20db SNR noisy and clean multilingual ASR data. + normalize_audio: False + window_length_in_sec: 0.63 # window length in sec for VAD context input + shift_length_in_sec: 0.08 # shift length in sec for generate frame level VAD prediction, Here we use 0.08 for faster inferene + smoothing: False # false or type of smoothing method (eg: median, mean) + overlap: 0.875 # overlap ratio for overlapped mean/median smoothing filter. If smoothing=False, ignore this value. + postprocessing: + onset: 0.5 # onset threshold for detecting the beginning and end of a speech + offset: 0.3 # offset threshold for detecting the end of a speech. + pad_onset: 0.2 # adding durations before each speech segment + pad_offset: 0.2 # adding durations after each speech segment + min_duration_on: 0.5 # threshold for small non_speech deletion + min_duration_off: 0.5 # threshold for short speech segment deletion + filter_speech_first: True + +prepared_manifest_vad_input: null # if not specify, it will automatically generated be "manifest_vad_input.json" +frame_out_dir: "vad_frame" +smoothing_out_dir: null # if not specify, it will automatically generated be frame_out_dir + "/overlap_smoothing_output" + "_" + smoothing_method + "_" + str(overlap) +table_out_dir: null # if not specify, it will automatically be frame_out_dir + "/table_output_tmp_" + key and value in postprocessing params +out_manifest_filepath: null # if not specify it will automatically be "vad_out.json" + +# json manifest line example +# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer", "text": "-"} \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/wav2vec_ctc/wav2vecCTC.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/wav2vec_ctc/wav2vecCTC.yaml new file mode 100644 index 0000000..4baad73 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/wav2vec_ctc/wav2vecCTC.yaml @@ -0,0 +1,167 @@ + +# This config contains the default values for training a wav2vec model with CTC loss and BPE-based vocabulary. +# Default learning parameters in this config are set for effective batch size of 1k on 32 GPUs. +# To train it with smaller batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. + +name: &name Wav2Vec_CTC + +model: + sample_rate: &sample_rate 16000 + embedding_dim: &emb_dim 768 # Project size of embedding dimension for transformer + + train_ds: + manifest_filepath: ??? + sample_rate: *sample_rate + batch_size: ??? + trim_silence: false + max_duration: null + shuffle: true + is_tarred: false + tarred_audio_filepaths: null + use_start_end_token: false + num_workers: 8 + pin_memory: true + + validation_ds: + manifest_filepath: ??? + sample_rate: *sample_rate + batch_size: ?? + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: *sample_rate + batch_size: null + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe or wpe + + preprocessor: + _target_: nemo.collections.asr.modules.wav2vec_modules.ConvFeatureEncoder + extractor_mode: layer_norm # Mode for feature extractor. [group_norm, layer_norm] + conv_bias: False # Include bias in convolution feature extractor model + feature_grad_mult: 1.0 # Multiply extracted feature gradients + normalize_audio: true + embedding_dim: *emb_dim # Final dimensions of output + conv_layers: + - emb_dim: 512 + kernel_size: 10 + stride: 5 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 2 + stride: 2 + - emb_dim: 512 + kernel_size: 2 + stride: 2 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 4 + time_masks: 10 + freq_width: 27 + time_width: 0.05 + mask_value: 0.0 + + encoder: + _target_: nemo.collections.asr.modules.wav2vec_modules.Wav2VecTransformerEncoder + layer_drop: 0.05 + pos_embed: # Config for convolutional model that generates positional embeddings required for attention layer + embedding_dim: *emb_dim + conv_pos: 128 # Number of filters for convolutional positional embeddings + conv_pos_groups: 16 # Number of groups for convolutional positional embeddings + transformer: # Config for nemo.collections.nlp.modules.common.transformer.TransformerEncoder + num_layers: 12 # Number of encoder layers in transformer model + hidden_size: *emb_dim # Encoder embedding dim + inner_size: 3072 # Encoder embedding dim for feed forward + num_attention_heads: 8 # Number of encoder attention heads + attn_score_dropout: .1 #probability of dropout applied to attention scores + attn_layer_dropout: .1 #probability of dropout applied to the output of the attention layers, but before layer normalization + ffn_dropout: .1 # probability of dropout applied to FFN output + hidden_act: gelu # Activation for transformer + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: *emb_dim + num_classes: -1 # filled with vocabulary size from tokenizer at runtime + vocabulary: [] # filled with vocabulary from tokenizer at runtime + + optim: + name: adamw + lr: 2 + eps: 1e-06 + # optimizer arguments + betas: [ 0.9, 0.98 ] + weight_decay: 0.0 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.transformer.hidden_size} + min_lr: 0.001 + # Scheduler params + warmup_steps: 1500 + warmup_ratio: null + +trainer: + devices: 1 # number of gpus + num_nodes: 1 + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # 16, 32, or bf16 + log_every_n_steps: 100 # Interval of logging. + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + save_top_k: 5 + every_n_epochs: 1 + always_save_nemo: true + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + +hydra: + run: + dir: . + job_logging: + root: + handlers: null + diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/conf/wav2vec_ctc/wav2vecCTC_large.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/conf/wav2vec_ctc/wav2vecCTC_large.yaml new file mode 100644 index 0000000..1cccb38 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/conf/wav2vec_ctc/wav2vecCTC_large.yaml @@ -0,0 +1,166 @@ +# This config contains the default values for training a wav2vec model with CTC loss and BPE-based vocabulary. +# Default learning parameters in this config are set for effective batch size of 1k on 32 GPUs. +# To train it with smaller batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. + +name: &name Wav2Vec_CTC_Large + +model: + sample_rate: &sample_rate 16000 + embedding_dim: &emb_dim 1024 # Project size of embedding dimension for transformer + + train_ds: + manifest_filepath: ??? + sample_rate: *sample_rate + batch_size: ??? + trim_silence: false + max_duration: null + shuffle: true + is_tarred: false + tarred_audio_filepaths: null + use_start_end_token: false + num_workers: 8 + pin_memory: true + + validation_ds: + manifest_filepath: ??? + sample_rate: *sample_rate + batch_size: 4 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: *sample_rate + batch_size: null + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe or wpe + + preprocessor: + _target_: nemo.collections.asr.modules.wav2vec_modules.ConvFeatureEncoder + extractor_mode: layer_norm # Mode for feature extractor. [group_norm, layer_norm] + conv_bias: False # Include bias in convolution feature extractor model + feature_grad_mult: 1.0 # Multiply extracted feature gradients + normalize_audio: true + embedding_dim: *emb_dim # Final dimensions of output + conv_layers: + - emb_dim: 512 + kernel_size: 10 + stride: 5 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 3 + stride: 2 + - emb_dim: 512 + kernel_size: 2 + stride: 2 + - emb_dim: 512 + kernel_size: 2 + stride: 2 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 4 + time_masks: 10 + freq_width: 27 + time_width: 0.05 + mask_value: 0.0 + + encoder: + _target_: nemo.collections.asr.modules.wav2vec_modules.Wav2VecTransformerEncoder + layer_drop: 0.2 + pos_embed: # Config for convolutional model that generates positional embeddings required for attention layer + embedding_dim: *emb_dim + conv_pos: 128 # Number of filters for convolutional positional embeddings + conv_pos_groups: 16 # Number of groups for convolutional positional embeddings + transformer: # Config for nemo.collections.nlp.modules.common.transformer.TransformerEncoder + num_layers: 24 # Number of encoder layers in transformer model + hidden_size: *emb_dim # Encoder embedding dim + inner_size: 4096 # Encoder embedding dim for feed forward + num_attention_heads: 16 # Number of encoder attention heads + attn_score_dropout: .1 #probability of dropout applied to attention scores + attn_layer_dropout: .1 #probability of dropout applied to the output of the attention layers, but before layer normalization + ffn_dropout: .1 # probability of dropout applied to FFN output + hidden_act: gelu # Activation for transformer + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: *emb_dim + num_classes: -1 # filled with vocabulary size from tokenizer at runtime + vocabulary: [] # filled with vocabulary from tokenizer at runtime + + optim: + name: adamw + lr: 2 + eps: 1e-06 + # optimizer arguments + betas: [ 0.9, 0.98 ] + weight_decay: 0.0 + + # scheduler setup + sched: + name: NoamAnnealing + min_lr: 0.001 + d_model: ${model.encoder.transformer.hidden_size} + # Scheduler params + warmup_steps: 15000 + warmup_ratio: null + +trainer: + devices: 1 # number of gpus + num_nodes: 1 + max_steps: 250000 # computed at runtime if not set + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + amp_backend: apex + amp_level: O1 + precision: 16 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 100 # Interval of logging. + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + create_wandb_logger: false + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + save_top_k: 5 + every_n_epochs: 1 + always_save_nemo: true + wandb_logger_kwargs: + name: null + project: null + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/align_speech_parallel.py b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/align_speech_parallel.py new file mode 100644 index 0000000..abfffa0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/align_speech_parallel.py @@ -0,0 +1,202 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Based on examples/asr/transcribe_speech_parallel.py +# ASR alignment with multi-GPU/multi-node support for large datasets +# It supports both tarred and non-tarred datasets +# Arguments +# model: path to a nemo/PTL checkpoint file or name of a pretrained model +# predict_ds: config of the dataset/dataloader +# aligner_args: aligner config +# output_path: path to store the predictions +# model_stride: model downsampling factor, 8 for Citrinet models and 4 for Conformer models +# +# Results of each GPU/worker is written into a file named 'predictions_{rank}.json, and aggregated results of all workers are written into 'predictions_all.json' + +Example for non-tarred datasets: + +python align_speech_parallel.py \ + model=stt_en_conformer_ctc_large \ + predict_ds.manifest_filepath=/dataset/manifest_file.json \ + predict_ds.batch_size=16 \ + output_path=/tmp/ + +Example for tarred datasets: + +python align_speech_parallel.py \ + predict_ds.is_tarred=true \ + predict_ds.manifest_filepath=/tarred_dataset/tarred_audio_manifest.json \ + predict_ds.tarred_audio_filepaths=/tarred_dataset/audio__OP_0..127_CL_.tar \ + ... + +By default the trainer uses all the GPUs available and default precision is FP32. +By setting the trainer config you may control these configs. For example to do the predictions with AMP on just two GPUs: + +python align_speech_parallel.py \ + trainer.precision=16 \ + trainer.devices=2 \ + ... + +You may control the dataloader's config by setting the predict_ds: + +python align_speech_parallel.py \ + predict_ds.num_workers=8 \ + predict_ds.min_duration=2.0 \ + predict_ds.sample_rate=16000 \ + model=stt_en_conformer_ctc_small \ + ... + +You may control the aligner's config by setting the aligner_args: + aligner_args.alignment_type=argmax \ + aligner_args.word_output=False \ + aligner_args.cpu_decoding=True \ + aligner_args.decode_batch_size=8 \ + aligner_args.ctc_cfg.prob_suppress_index=-1 \ + aligner_args.ctc_cfg.prob_suppress_value=0.5 \ + aligner_args.rnnt_cfg.predictor_window_size=10 \ + aligner_args.decoder_module_cfg.intersect_pruned=true \ + aligner_args.decoder_module_cfg.intersect_conf.search_beam=40 \ + ... + +""" + + +import os +from dataclasses import dataclass, field, is_dataclass +from typing import Optional + +import pytorch_lightning as ptl +import torch +from omegaconf import MISSING, OmegaConf + +from nemo.collections.asr.data.audio_to_ctm_dataset import ASRCTMPredictionWriter +from nemo.collections.asr.models import ASRModel +from nemo.collections.asr.models.configs.aligner_config import K2AlignerWrapperModelConfig +from nemo.collections.asr.models.configs.asr_models_config import ASRDatasetConfig +from nemo.collections.asr.models.k2_aligner_model import AlignerWrapperModel +from nemo.core.config import TrainerConfig, hydra_runner +from nemo.utils import logging +from nemo.utils.get_rank import is_global_rank_zero + + +@dataclass +class ParallelAlignmentConfig: + model: Optional[str] = None # name + predict_ds: ASRDatasetConfig = field( + default_factory=lambda: ASRDatasetConfig(return_sample_id=True, num_workers=4) + ) + aligner_args: K2AlignerWrapperModelConfig = field(default_factory=lambda: K2AlignerWrapperModelConfig()) + output_path: str = MISSING + model_stride: int = 8 + + trainer: TrainerConfig = field(default_factory=lambda: TrainerConfig(devices=-1, accelerator="ddp")) + + # there arguments will be ignored + return_predictions: bool = False + use_cer: bool = False + + +def match_train_config(predict_ds, train_ds): + # It copies the important configurations from the train dataset of the model + # into the predict_ds to be used for prediction. It is needed to match the training configurations. + if train_ds is None: + return + + predict_ds.sample_rate = train_ds.get("sample_rate", 16000) + cfg_name_list = [ + "int_values", + "use_start_end_token", + "blank_index", + "unk_index", + "normalize", + "parser", + "eos_id", + "bos_id", + "pad_id", + ] + + if is_dataclass(predict_ds): + predict_ds = OmegaConf.structured(predict_ds) + for cfg_name in cfg_name_list: + if hasattr(train_ds, cfg_name): + setattr(predict_ds, cfg_name, getattr(train_ds, cfg_name)) + + return predict_ds + + +@hydra_runner(config_name="AlignmentConfig", schema=ParallelAlignmentConfig) +def main(cfg: ParallelAlignmentConfig): + if cfg.model.endswith(".nemo"): + logging.info("Attempting to initialize from .nemo file") + model = ASRModel.restore_from(restore_path=cfg.model, map_location="cpu") + elif cfg.model.endswith(".ckpt"): + logging.info("Attempting to initialize from .ckpt file") + model = ASRModel.load_from_checkpoint(checkpoint_path=cfg.model, map_location="cpu") + else: + logging.info( + "Attempting to initialize from a pretrained model as the model name does not have the extension of .nemo or .ckpt" + ) + model = ASRModel.from_pretrained(model_name=cfg.model, map_location="cpu") + + trainer = ptl.Trainer(**cfg.trainer) + + cfg.predict_ds.return_sample_id = True + cfg.return_predictions = False + cfg.use_cer = False + cfg.predict_ds = match_train_config(predict_ds=cfg.predict_ds, train_ds=model._cfg.train_ds) + data_loader = model._setup_dataloader_from_config(cfg.predict_ds) + + os.makedirs(cfg.output_path, exist_ok=True) + # trainer.global_rank is not valid before predict() is called. Need this hack to find the correct global_rank. + global_rank = trainer.node_rank * trainer.num_devices + int(os.environ.get("LOCAL_RANK", 0)) + output_file = os.path.join(cfg.output_path, f"predictions_{global_rank}.json") + output_ctm_dir = os.path.join(cfg.output_path, "ctm") + predictor_writer = ASRCTMPredictionWriter( + dataset=data_loader.dataset, + output_file=output_file, + output_ctm_dir=output_ctm_dir, + time_per_frame=cfg.model_stride * model._cfg.preprocessor['window_stride'], + ) + trainer.callbacks.extend([predictor_writer]) + + aligner_wrapper = AlignerWrapperModel(model=model, cfg=cfg.aligner_args) + trainer.predict(model=aligner_wrapper, dataloaders=data_loader, return_predictions=cfg.return_predictions) + samples_num = predictor_writer.close_output_file() + + logging.info( + f"Prediction on rank {global_rank} is done for {samples_num} samples and results are stored in {output_file}." + ) + + if torch.distributed.is_initialized(): + torch.distributed.barrier() + + samples_num = 0 + if is_global_rank_zero(): + output_file = os.path.join(cfg.output_path, f"predictions_all.json") + logging.info(f"Prediction files are being aggregated in {output_file}.") + with open(output_file, 'tw', encoding="utf-8") as outf: + for rank in range(trainer.world_size): + input_file = os.path.join(cfg.output_path, f"predictions_{rank}.json") + with open(input_file, 'r', encoding="utf-8") as inpf: + lines = inpf.readlines() + samples_num += len(lines) + outf.writelines(lines) + logging.info( + f"Prediction is done for {samples_num} samples in total on all workers and results are aggregated in {output_file}." + ) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/conf/citrinet/citrinet_mmi_1024.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/conf/citrinet/citrinet_mmi_1024.yaml new file mode 100644 index 0000000..b254b07 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/conf/citrinet/citrinet_mmi_1024.yaml @@ -0,0 +1,499 @@ +# This config contains the default values for training a Citrinet model with CTC-MMI loss and BPE-based vocabulary. +# Default learning parameters in this config are set for effective batch size of 1k on 32 GPUs. +# To train it with smaller batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# If training for a short time, you can also reduce weight decay to 0. + +# Training Recipe +# This model can be trained using the default settings in this config with FP32 precision. +# When training under AMP, increase `warmup_steps` to 5000 for stable training. +# In order to create Citrinet-C, change the model.model_defaults.filters parameter. +# When reducing the receptive field of these models, it is advised to reduce the amount of augmentation +# for larger models from 10x time masking to 5x or 2x time masking. +# For further details regarding Citrinet, visit - https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#citrinet + +name: &name "Citrinet-MMI-1024-8x-Stride" + +model: + sample_rate: &sample_rate 16000 + log_prediction: true # enables logging sample predictions in the output during training + + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + trim_silence: false + max_duration: 20.0 + shuffle: true + use_start_end_token: false + num_workers: 8 + pin_memory: true + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + batch_size: 32 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + test_ds: + manifest_filepath: null + sample_rate: 16000 + batch_size: 32 + shuffle: false + use_start_end_token: false + num_workers: 8 + pin_memory: true + + model_defaults: + repeat: 5 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 0.25 + filters: 1024 + enc_final: 1024 + + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: ??? # Can be either bpe or wpe + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: *sample_rate + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 + time_masks: 10 + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [5] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [11] + stride: [2] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [21] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [13] + stride: [2] # *stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [17] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [19] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [21] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [23] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [25] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [25] + stride: [2] # stride + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + stride_last: true + residual_mode: "stride_add" + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [27] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [29] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [31] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [33] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [35] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [37] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [39] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + + - filters: ${model.model_defaults.enc_final} + repeat: 1 + kernel: [41] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + kernel_size_factor: ${model.model_defaults.kernel_size_factor} + + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: ${model.model_defaults.enc_final} + num_classes: -1 # filled with vocabulary size from tokenizer at runtime + vocabulary: [] # filled with vocabulary from tokenizer at runtime + + + optim: + name: novograd + lr: 0.05 + + # optimizer arguments + betas: [0.8, 0.25] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # scheduler config override + warmup_steps: 5000 + warmup_ratio: null + min_lr: 1e-5 + last_epoch: -1 + + graph_module_cfg: + criterion_type: map + loss_type: mmi + transcribe_training: false + split_batch_size: 0 + backend_cfg: + token_lm: ??? + topo_type: default + topo_with_self_loops: true + intersect_pruned: false + boost_coeff: 0.0 + +trainer: + devices: 1 # number of gpus + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + check_val_every_n_epoch: 1 + precision: 32 + sync_batchnorm: false + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: "val_wer" + mode: "min" + save_top_k: 3 + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/conf/conformer/conformer_ctc_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/conf/conformer/conformer_ctc_bpe.yaml new file mode 100644 index 0000000..19f53b2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/conf/conformer/conformer_ctc_bpe.yaml @@ -0,0 +1,216 @@ +# It contains the default values for training a Conformer-MMI (CTC) ASR model, large size (~120M) with CTC loss and sub-word encoding. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-CTC, other parameters are the same as in this config file. +# One extra layer (compared to original paper) is added to the medium and large variants to compensate for replacing the LSTM decoder with a linear one. +# +# +-------------+---------+---------+----------+------------+-----+ +# | Model | d_model | n_heads | n_layers | time_masks | lr | +# +=============+=========+========+===========+============+=====+ +# | Small (13M)| 176 | 4 | 16 | 5 | 5.0 | +# +-------------+---------+--------+-----------+------------+-----+ +# | Medium (30M)| 256 | 4 | 18 | 5 | 5.0 | +# +-------------+---------+--------+-----------+------------+-----+ +# | Large (121M)| 512 | 8 | 18 | 10 | 2.0 | +# +---------------------------------------------------------------+ +# +# If you do not want to train with AMP, you may use weight decay of 0.0 or reduce the number of time maskings to 2 +# with time_width=100. It may help when you want to train for fewer epochs and need faster convergence. +# With weight_decay=0.0, learning rate may need to get reduced to 2.0. + +# You may find more info about Conformer-CTC here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-ctc + +name: "Conformer-MMI-BPE" + +model: + sample_rate: 16000 + log_prediction: true # enables logging sample predictions in the output during training + ctc_reduction: 'mean_batch' + skip_nan_grad: false + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + use_start_end_token: false + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + # recommend small vocab size of 128 or 256 when using 4x sub-sampling + # you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + # you may use lower time_masks for smaller models to have a faster convergence + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 18 + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # -1 sets it to d_model + causal_downsampling: false + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null + num_classes: -1 + vocabulary: [] + + optim: + name: adamw + lr: 2.0 + # optimizer arguments + betas: [0.9, 0.98] + # less necessity for weight_decay as we already have large augmentations with SpecAug + # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used + # weight decay of 0.0 with lr of 2.0 also works fine + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + + graph_module_cfg: + criterion_type: map + loss_type: mmi + transcribe_training: false + split_batch_size: 0 + backend_cfg: + token_lm: ??? + topo_type: default + topo_with_self_loops: true + intersect_pruned: false + boost_coeff: 0.0 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/conf/conformer/conformer_transducer_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/conf/conformer/conformer_transducer_bpe.yaml new file mode 100644 index 0000000..82e1bd4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/conf/conformer/conformer_transducer_bpe.yaml @@ -0,0 +1,268 @@ +# It contains the default values for training a Conformer-Transducer ASR model, large size (~120M) with Transducer loss and sub-word encoding. + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-Transducer, other parameters are the same as in this config file. +# +# +-------------+---------+---------+----------+--------------+--------------------------+ +# | Model | d_model | n_heads | n_layers | weight_decay | pred_hidden/joint_hidden | +# +=============+=========+========+===========+==============+==========================+ +# | Small (14M)| 176 | 4 | 16 | 0.0 | 320 | +# +-------------+---------+--------+-----------+--------------+--------------------------+ +# | Medium (32M)| 256 | 4 | 16 | 1e-3 | 640 | +# +-------------+---------+--------+-----------+--------------+--------------------------+ +# | Large (120M)| 512 | 8 | 17 | 1e-3 | 640 | +# +-----------------------------------------------------------+--------------------------+ +# + +# You may find more info about Conformer-Transducer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-transducer +# Pre-trained models of Conformer-Transducer can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html +# The checkpoint of the large model trained on NeMo ASRSET with this recipe can be found here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_large + +name: "Conformer-Transducer-BPE" + +model: + sample_rate: 16000 + compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag. + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + model_defaults: + enc_hidden: ${model.encoder.d_model} + pred_hidden: 640 + joint_hidden: 640 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # you may increase batch_size if your memory allows + shuffle: true + num_workers: 8 + pin_memory: true + use_start_end_token: false + trim_silence: false + max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset + min_duration: 0.1 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 16 + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: false + + # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 + d_model: 512 + + # Sub-sampling parameters + subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding + subsampling_factor: 4 # must be power of 2 for striding and vggnet + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + causal_downsampling: false + + # Reduction parameters: Can be used to add another subsampling layer at a given position. + # Having a 2x reduction will speedup the training and inference speech while keeping similar WER. + # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup. + reduction: null # pooling, striding, or null + reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder + reduction_factor: 1 + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + att_context_style: regular # regular or chunked_limited + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups) + # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size + # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0] + conv_context_size: null + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + decoder: + _target_: nemo.collections.asr.modules.RNNTDecoder + normalization_mode: null # Currently only null is supported for export. + random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf + blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference. + + prednet: + pred_hidden: ${model.model_defaults.pred_hidden} + pred_rnn_layers: 1 + t_max: null + dropout: 0.2 + + joint: + _target_: nemo.collections.asr.modules.RNNTJoint + log_softmax: true # should be always true for k2-based lattice loss + preserve_memory: false # dramatically slows down training, but might preserve some memory + + # Fuses the computation of prediction net + joint net + loss + WER calculation + # to be run on sub-batches of size `fused_batch_size`. + # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size. + # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss. + # Using small values here will preserve a lot of memory during training, but will make training slower as well. + # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1. + # However, to preserve memory, this ratio can be 1:8 or even 1:16. + # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow. + fuse_loss_wer: false + fused_batch_size: 16 + + jointnet: + joint_hidden: ${model.model_defaults.joint_hidden} + activation: "relu" + dropout: 0.2 + + decoding: + strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd. + + # greedy strategy config + greedy: + max_symbols: 10 + + # beam strategy config + beam: + beam_size: 2 + return_best_hypothesis: False + score_norm: true + tsd_max_sym_exp: 50 # for Time Synchronous Decoding + alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding + + loss: + loss_name: "default" + + warprnnt_numba_kwargs: + # FastEmit regularization: https://arxiv.org/abs/2010.11148 + # You may enable FastEmit to reduce the latency of the model for streaming + fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start. + clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only. + + graph_module_cfg: + criterion_type: ml + loss_type: rnnt + split_batch_size: 0 + backend_cfg: + topo_type: minimal + intersect_pruned: false + # Adds Gaussian noise to the gradients of the decoder to avoid overfitting + variational_noise: + start_step: 0 + std: 0.0 + + optim: + name: adamw + lr: 5.0 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 500 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 10 # Interval of logging. + enable_progress_bar: True + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + resume_if_exists: false + resume_ignore_no_checkpoint: false + + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/make_token_lm.py b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/make_token_lm.py new file mode 100644 index 0000000..c9c80d5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/make_token_lm.py @@ -0,0 +1,144 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import json +import os +from pathlib import Path +from subprocess import PIPE, Popen +from threading import Thread + +from nemo.collections.common import tokenizers +from nemo.utils import logging + + +def main(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + description="""Create token LM for input manifest and tokenizer.""", + ) + parser.add_argument( + "--manifest", required=True, type=str, help="Comma separated list of manifest files", + ) + parser.add_argument( + "--tokenizer_dir", + required=True, + type=str, + help="The directory path to the tokenizer vocabulary + additional metadata", + ) + parser.add_argument( + "--tokenizer_type", + required=True, + type=str, + choices=["bpe", "wpe"], + help="The type of the tokenizer. Currently supports `bpe` and `wpe`", + ) + parser.add_argument( + "--lm_builder", + default="chain-est-phone-lm", + type=str, + help=( + "The path or name of an LM builder. Supported builders: chain-est-phone-lm " + "and scripts/asr_language_modeling/ngram_lm/make_phone_lm.py" + ), + ) + parser.add_argument( + "--ngram_order", type=int, default=2, choices=[2, 3, 4, 5], help="Order of n-gram to use", + ) + parser.add_argument( + "--output_file", required=True, type=str, help="The path to store the token LM", + ) + parser.add_argument( + "--do_lowercase", action="store_true", help="Whether to apply lower case conversion on the text", + ) + args = parser.parse_args() + + is_chain_builder = Path(args.lm_builder).stem == "chain-est-phone-lm" + + """ TOKENIZER SETUP """ + logging.info(f"Loading {args.tokenizer_type} tokenizer from '{args.tokenizer_dir}' ...") + if args.tokenizer_type == "bpe": + # This is a BPE Tokenizer + model_path = os.path.join(args.tokenizer_dir, "tokenizer.model") + + # Update special tokens + tokenizer = tokenizers.SentencePieceTokenizer(model_path=model_path) + else: + # This is a WPE Tokenizer + vocab_path = os.path.join(args.tokenizer_dir, "vocab.txt") + tokenizer = tokenizers.AutoTokenizer(pretrained_model_name="bert-base-cased", vocab_file=vocab_path) + + logging.info(f"Tokenizer {tokenizer.__class__.__name__} loaded with {tokenizer.vocab_size} tokens") + + """ DATA PROCESSING """ + if "," in args.manifest: + manifests = args.manifest.split(",") + else: + manifests = [args.manifest] + + offset = 1 # tokens in token LM cannot be 0 + tok_text_list = [] + num_lines = 0 + for manifest in manifests: + logging.info(f"Processing manifest : {manifest} ...") + with open(manifest, "r") as in_reader: + for line in in_reader: + item = json.loads(line) + text = item["text"] + if args.do_lowercase: + text = text.lower() + tok_text = " ".join([str(i + offset) for i in tokenizer.text_to_ids(text)]) + if is_chain_builder: + tok_text = f"line_{num_lines} " + tok_text + tok_text_list.append(tok_text) + num_lines += 1 + + tok_texts = "\n".join(tok_text_list) + del tok_text_list + logging.info("Finished processing all manifests ! Number of sentences : {}".format(num_lines)) + + """ LM BUILDING """ + logging.info(f"Calling {args.lm_builder} ...") + if is_chain_builder: + pipe_args = [ + args.lm_builder, + f"--ngram-order={args.ngram_order}", + f"--no-prune-ngram-order={args.ngram_order}", + "ark:-", + "-", + ] + p1 = Popen(pipe_args, stdin=PIPE, stdout=PIPE, text=True) + p2 = Popen(["fstprint"], stdin=p1.stdout, stdout=PIPE, text=True) + p1.stdout.close() + p1.stdout = None + Thread(target=p1.communicate, args=[tok_texts]).start() + out, err = p2.communicate() + else: + pipe_args = [ + args.lm_builder, + f"--ngram-order={args.ngram_order}", + f"--no-backoff-ngram-order={args.ngram_order}", + "--phone-disambig-symbol=-11", + ] + p1 = Popen(pipe_args, stdout=PIPE, stdin=PIPE, text=True) + out, err = p1.communicate(tok_texts) + + logging.info(f"LM is built, writing to {args.output_file} ...") + with open(args.output_file, "w", encoding="utf-8") as f: + f.write(out) + logging.info(f"Done writing to '{args.output_file}'.") + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/speech_to_text_bpe.py b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/speech_to_text_bpe.py new file mode 100644 index 0000000..ee3924c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/speech_to_text_bpe.py @@ -0,0 +1,106 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Preparing the Tokenizer for the dataset +Use the `process_asr_text_tokenizer.py` script under /scripts/tokenizers/ in order to prepare the tokenizer. + +```sh +python /scripts/tokenizers/process_asr_text_tokenizer.py \ + --manifest= + OR + --data_file= \ + --data_root="" \ + --vocab_size= \ + --tokenizer=<"spe" or "wpe"> \ + --no_lower_case \ + --spe_type=<"unigram", "bpe", "char" or "word"> \ + --spe_character_coverage=1.0 \ + --log +``` + +# [FOR MMI LOSS ONLY] Building a token-level LM for the model training +```sh +python experimental/k2/make_token_lm.py \ + --manifest= \ + --tokenizer_dir= \ + --tokenizer_type= \ + --output_file= \ + --lm_builder=/scripts/asr_language_modeling/ngram_lm/make_phone_lm.py \ + --ngram_order=2 \ + --do_lowercase +``` + +# Training the model +```sh +python speech_to_text_ctc_bpe.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath= \ + model.validation_ds.manifest_filepath= \ + model.tokenizer.dir= \ + model.tokenizer.type= \ + trainer.devices=-1 \ + trainer.accelerator="ddp" \ + trainer.max_epochs=100 \ + model.optim.name="adamw" \ + model.optim.lr=0.001 \ + model.optim.betas=[0.9,0.999] \ + model.optim.weight_decay=0.0001 \ + model.optim.sched.warmup_steps=2000 + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="" \ + exp_manager.wandb_logger_kwargs.project="" \ + model.graph_module_cfg.criterion_type= \ + model.graph_module_cfg.loss_type= \ + model.graph_module_cfg.transcribe_training=False \ + model.graph_module_cfg.split_batch_size=0 \ + model.graph_module_cfg.background_cfg.topo_type=<`default` or `compact` or `shared_blank` or `minimal`> \ + model.graph_module_cfg.background_cfg.topo_with_self_loops=True \ +``` + +# If graph_module_cfg.criterion_type=`map`, you can set the following parameters: + model.graph_module_cfg.background_cfg.token_lm= \ + model.graph_module_cfg.background_cfg.intersect_pruned=False \ + model.graph_module_cfg.background_cfg.boost_coeff=0.0 +""" +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models.configs.k2_sequence_models_config import EncDecK2SeqModelConfig +from nemo.collections.asr.models.k2_sequence_models import EncDecK2SeqModelBPE +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="experimental/k2/conf/citrinet", config_name="citrinet_mmi_1024.yaml") +def main(cfg: EncDecK2SeqModelConfig): + logging.info(f"Hydra config: {OmegaConf.to_yaml(cfg)}") + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + asr_model = EncDecK2SeqModelBPE(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + asr_model.maybe_init_from_pretrained_checkpoint(cfg) + + trainer.fit(asr_model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if asr_model.prepare_test(trainer): + trainer.test(asr_model) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/speech_to_text_rnnt_bpe.py b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/speech_to_text_rnnt_bpe.py new file mode 100644 index 0000000..a0031fb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/k2/speech_to_text_rnnt_bpe.py @@ -0,0 +1,95 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Preparing the Tokenizer for the dataset +Use the `process_asr_text_tokenizer.py` script under /scripts/tokenizers/ in order to prepare the tokenizer. + +```sh +python /scripts/tokenizers/process_asr_text_tokenizer.py \ + --manifest= + OR + --data_file= \ + --data_root="" \ + --vocab_size= \ + --tokenizer=<"spe" or "wpe"> \ + --no_lower_case \ + --spe_type=<"unigram", "bpe", "char" or "word"> \ + --spe_character_coverage=1.0 \ + --log +``` + +# Training the model +```sh +python speech_to_text_rnnt_bpe.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath= \ + model.validation_ds.manifest_filepath= \ + model.tokenizer.dir= \ + model.tokenizer.type= \ + trainer.devices=-1 \ + trainer.accelerator="gpu" \ + trainer.strategy="ddp" \ + trainer.max_epochs=100 \ + model.optim.name="adamw" \ + model.optim.lr=0.001 \ + model.optim.betas=[0.9,0.999] \ + model.optim.weight_decay=0.0001 \ + model.optim.sched.warmup_steps=2000 + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="" \ + exp_manager.wandb_logger_kwargs.project="" \ + model.graph_module_cfg.criterion_type=ml \ + model.graph_module_cfg.loss_type=rnnt \ + model.graph_module_cfg.split_batch_size=0 \ + model.graph_module_cfg.background_cfg.topo_type=minimal +``` + +# Fine-tune a model + +For documentation on fine-tuning this model, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations + +""" + +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models import EncDecK2RnntSeqModelBPE +from nemo.collections.asr.models.configs.k2_sequence_models_config import EncDecK2SeqModelConfig +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="experimental/k2/conf/conformer", config_name="conformer_transducer_bpe.yaml") +def main(cfg: EncDecK2SeqModelConfig): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + asr_model = EncDecK2RnntSeqModelBPE(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + asr_model.maybe_init_from_pretrained_checkpoint(cfg) + + trainer.fit(asr_model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if asr_model.prepare_test(trainer): + trainer.test(asr_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/experimental/sclite/speech_to_text_sclite.py b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/sclite/speech_to_text_sclite.py new file mode 100644 index 0000000..80a4758 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/sclite/speech_to_text_sclite.py @@ -0,0 +1,148 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script is based on speech_to_text_eval.py and allows you to score the hypotheses +with sclite. A local installation from https://github.com/usnistgov/SCTK is required. +Hypotheses and references are first saved in trn format and are scored after applying a glm +file (if provided). + +# Usage + +python speech_to_text_sclite.py \ + --asr_model="" \ + --dataset="" \ + --out_dir="" \ + --sctk_dir="" \ + --glm="" \ + --batch_size=4 + +""" + +import errno +import json +import os +import subprocess +from argparse import ArgumentParser + +import torch + +from nemo.collections.asr.models import ASRModel +from nemo.collections.asr.parts.utils.manifest_utils import read_manifest +from nemo.utils import logging + +try: + from torch.cuda.amp import autocast +except ImportError: + from contextlib import contextmanager + + @contextmanager + def autocast(enabled=None): + yield + + +def score_with_sctk(sctk_dir, ref_fname, hyp_fname, out_dir, glm=""): + sclite_path = os.path.join(sctk_dir, "bin", "sclite") + if not os.path.exists(sclite_path): + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), sclite_path) + # apply glm + if os.path.exists(glm): + rfilter_path = os.path.join(sctk_dir, "bin", "rfilter1") + if not os.path.exists(rfilter_path): + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), rfilter_path) + hypglm = os.path.join(out_dir, os.path.basename(hyp_fname)) + ".glm" + rfilt_cmd = [rfilter_path] + [glm] + with open(hypglm, "w", encoding='utf-8') as hypf, open(hyp_fname, "r", encoding='utf-8') as hyp_in: + subprocess.run(rfilt_cmd, stdin=hyp_in, stdout=hypf) + refglm = os.path.join(out_dir, os.path.basename(ref_fname)) + ".glm" + with open(refglm, "w", encoding='utf-8') as reff, open(ref_fname, "r", encoding='utf-8') as ref_in: + subprocess.run(rfilt_cmd, stdin=ref_in, stdout=reff) + else: + refglm = ref_fname + hypglm = hyp_fname + + _ = subprocess.check_output(f"{sclite_path} -h {hypglm} -r {refglm} -i wsj -o all", shell=True) + + +can_gpu = torch.cuda.is_available() + + +def get_utt_info(manifest_path): + info_list = [] + with open(manifest_path, "r", encoding='utf-8') as utt_f: + for line in utt_f: + utt = json.loads(line) + info_list.append(utt) + + return info_list + + +def main(): + parser = ArgumentParser() + parser.add_argument( + "--asr_model", type=str, default="QuartzNet15x5Base-En", required=False, help="Pass: 'QuartzNet15x5Base-En'", + ) + parser.add_argument("--dataset", type=str, required=True, help="path to evaluation data") + parser.add_argument("--batch_size", type=int, default=4) + parser.add_argument("--out_dir", type=str, required=True, help="Destination dir for output files") + parser.add_argument("--sctk_dir", type=str, required=False, default="", help="Path to sctk root dir") + parser.add_argument("--glm", type=str, required=False, default="", help="Path to glm file") + args = parser.parse_args() + torch.set_grad_enabled(False) + + if not os.path.exists(args.out_dir): + os.makedirs(args.out_dir, exist_ok=True) + + use_sctk = os.path.exists(args.sctk_dir) + + if args.asr_model.endswith('.nemo'): + logging.info(f"Using local ASR model from {args.asr_model}") + asr_model = ASRModel.restore_from(restore_path=args.asr_model, map_location='cpu') + else: + logging.info(f"Using NGC cloud ASR model {args.asr_model}") + asr_model = ASRModel.from_pretrained(model_name=args.asr_model, map_location='cpu') + + if can_gpu: + asr_model = asr_model.cuda() + + asr_model.eval() + + manifest_data = read_manifest(args.dataset) + + references = [data['text'] for data in manifest_data] + audio_filepaths = [data['audio_filepath'] for data in manifest_data] + + with autocast(): + hypotheses = asr_model.transcribe(audio_filepaths, batch_size=args.batch_size) + + # if transcriptions form a tuple (from RNNT), extract just "best" hypothesis + if type(hypotheses) == tuple and len(hypotheses) == 2: + hypotheses = hypotheses[0] + + info_list = get_utt_info(args.dataset) + hypfile = os.path.join(args.out_dir, "hyp.trn") + reffile = os.path.join(args.out_dir, "ref.trn") + with open(hypfile, "w") as hyp_f, open(reffile, "w") as ref_f: + for i in range(len(hypotheses)): + utt_id = os.path.splitext(os.path.basename(info_list[i]['audio_filepath']))[0] + # rfilter in sctk likes each transcript to have a space at the beginning + hyp_f.write(" " + hypotheses[i] + " (" + utt_id + ")" + "\n") + ref_f.write(" " + references[i] + " (" + utt_id + ")" + "\n") + + if use_sctk: + score_with_sctk(args.sctk_dir, reffile, hypfile, args.out_dir, glm=args.glm) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/experimental/structured/conf/quartznet_15x5.yaml b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/structured/conf/quartznet_15x5.yaml new file mode 100644 index 0000000..a3f6cae --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/structured/conf/quartznet_15x5.yaml @@ -0,0 +1,237 @@ +name: &name "QuartzNet15x5" + +model: + sample_rate: &sample_rate 16000 + repeat: &repeat 5 + dropout: &dropout 0.0 + separable: &separable true + labels: &labels [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", + "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] + + train_ds: + manifest_filepath: ??? + sample_rate: *sample_rate + labels: *labels + batch_size: 32 + max_duration: 16.7 + is_tarred: False + tarred_audio_filepaths: null + + validation_ds: + labels: *labels + manifest_filepath: ??? + sample_rate: *sample_rate + batch_size: 32 + + preprocessor: + sample_rate: *sample_rate + window_size: 0.02 + window_stride: 0.01 + features: &n_mels 64 + + spec_augment: + rect_freq: 50 + rect_masks: 5 + rect_time: 120 + + encoder: + feat_in: *n_mels + activation: relu + + jasper: + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: 1 + residual: false + separable: *separable + stride: [2] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [33] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 256 + kernel: [39] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [51] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [63] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: 512 + kernel: [75] + repeat: *repeat + residual: true + separable: *separable + stride: [1] + + - dilation: [2] + dropout: *dropout + filters: 512 + kernel: [87] + repeat: 1 + residual: false + separable: *separable + stride: [1] + + - dilation: [1] + dropout: *dropout + filters: &enc_filters 1024 + kernel: [1] + repeat: 1 + residual: false + stride: [1] + + decoder: + feat_in: *enc_filters + num_classes: 28 + vocabulary: *labels + + optim: + name: novograd + lr: .01 + + # optimizer arguments + betas: [0.8, 0.5] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + + # Scheduler params + warmup_steps: null + warmup_ratio: null + min_lr: 0.0 + +trainer: + devices: 1 # number of gpus + max_epochs: 5 + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + name: *name diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/experimental/structured/speech_to_text_hybrid.py b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/structured/speech_to_text_hybrid.py new file mode 100644 index 0000000..2653063 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/structured/speech_to_text_hybrid.py @@ -0,0 +1,56 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.asr.models import EncDecCTCModel, configs +from nemo.core.config import hydra_runner +from nemo.utils.config_utils import update_model_config +from nemo.utils.exp_manager import exp_manager + + +""" +python speech_to_text_hybrid.py \ + --config-path="conf/quartznet" \ + --config-name="quartznet_15x5" \ + model.train_ds.manifest_filepath="/home/smajumdar/PycharmProjects/NeMo-som/examples/asr/an4/train_manifest.json" \ + model.validation_ds.manifest_filepath="/home/smajumdar/PycharmProjects/NeMo-som/examples/asr/an4/test_manifest.json" \ + trainer.devices=1 +""" + + +@hydra_runner(config_path="conf/quartznet", config_name="quartznet_15x5") +def main(cfg): + # Generate default asr model config + asr_model_config = configs.EncDecCTCModelConfig() + + # Merge hydra updates with model config + # `drop_missing_subconfig=True` is necessary here. Without it, while the data class will instantiate and be added + # to the config, it contains test_ds.sample_rate = MISSING and test_ds.labels = MISSING. + # This will raise a OmegaConf MissingMandatoryValue error when processing the dataloaders inside + # model_utils.resolve_test_dataloaders(model=self) (used for multi data loader support). + # In general, any operation that tries to use a DictConfig with MISSING in it will fail, + # other than explicit update operations to change MISSING to some actual value. + asr_model_config = update_model_config(asr_model_config, cfg, drop_missing_subconfigs=True) + + # From here on out, its a general OmegaConf DictConfig, directly usable by our code. + trainer = pl.Trainer(**asr_model_config.trainer) + exp_manager(trainer, asr_model_config.get("exp_manager", None)) + asr_model = EncDecCTCModel(cfg=asr_model_config.model, trainer=trainer) + + trainer.fit(asr_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/experimental/structured/speech_to_text_structured.py b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/structured/speech_to_text_structured.py new file mode 100644 index 0000000..366c6d8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/structured/speech_to_text_structured.py @@ -0,0 +1,146 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import asdict + +import pytorch_lightning as pl + +import nemo.collections.asr as nemo_asr +from nemo.collections.asr.models import EncDecCTCModel, configs +from nemo.utils.exp_manager import exp_manager + +""" +python speech_to_text_structured.py +""" + +# Generate default asr model config +cfg = configs.EncDecCTCModelConfig() + +# set global values +cfg.model.repeat = 5 +cfg.model.separable = True + +# fmt: off +LABELS = [ + " ", "a", "b", "c", "d", "e", + "f", "g", "h", "i", "j", "k", + "l", "m", "n", "o", "p", "q", + "r", "s", "t", "u", "v", "w", + "x", "y", "z", "'", +] +# fmt: on + +qn_15x5 = [ + nemo_asr.modules.conv_asr.JasperEncoderConfig( + filters=256, + repeat=1, + kernel=[33], + stride=[2], + separable=cfg.model.separable, + dilation=[1], + dropout=cfg.model.dropout, + residual=False, + ), + nemo_asr.modules.conv_asr.JasperEncoderConfig( + filters=256, + repeat=1, + kernel=[33], + stride=[1], + separable=cfg.model.separable, + dilation=[1], + dropout=cfg.model.dropout, + residual=True, + ), + # ... repeat 14 more times + nemo_asr.modules.conv_asr.JasperEncoderConfig( + filters=1024, repeat=1, kernel=[1], stride=[1], dilation=[1], dropout=cfg.model.dropout, residual=False, + ), +] + + +def main(): + # Update values + # MODEL UPDATES + cfg.name = "Mini QuartzNet" + cfg.model.labels = LABELS + + # train ds + cfg.model.train_ds.manifest_filepath = "" + cfg.model.train_ds.labels = LABELS + cfg.model.train_ds.sample_rate = cfg.model.sample_rate + + # validation ds + cfg.model.validation_ds.manifest_filepath = "" + cfg.model.validation_ds.labels = LABELS + cfg.model.validation_ds.sample_rate = cfg.model.sample_rate + + # del `test_ds` does not work! + # Refer - https://stackoverflow.com/questions/58119758/how-to-remove-dataclass-attributes + # Hydra/OmegaConf dont allow custom .asdict() methods either + # For now, explicitly set parameters + cfg.model.test_ds.sample_rate = cfg.model.sample_rate + cfg.model.test_ds.labels = cfg.model.labels + + # preprocessor + cfg.model.preprocessor.sample_rate = cfg.model.sample_rate + + # spec aug + cfg.model.spec_augment.rect_masks = 5 + cfg.model.spec_augment.rect_freq = 50 + cfg.model.spec_augment.rect_time = 120 + + # encoder + cfg.model.encoder.feat_in = cfg.model.preprocessor.features + cfg.model.encoder.activation = 'relu' + cfg.model.encoder.jasper = qn_15x5 + + # decoder + cfg.model.decoder.feat_in = qn_15x5[-1].filters + cfg.model.decoder.num_classes = len(LABELS) + cfg.model.decoder.vocabulary = LABELS + + # optim + cfg.model.optim.name = 'novograd' + cfg.model.optim.lr = 0.01 + + # `betas` dont exist inside the base config, + # so they cannot be added as such! + # Same for `weight_decay`. + cfg.model.optim.betas = [0.8, 0.5] + cfg.model.optim.weight_decay = 0.001 + + # sched + # As parameters such as warmup_steps and warmup_ratio + # dont exist inside the shell config, these values are not added! + cfg.model.optim.sched.name = "CosineAnnealing" + cfg.model.optim.sched.warmup_steps = None + cfg.model.optim.sched.warmup_ratio = 0.01 + + # Trainer config + cfg.trainer.devices = 1 + cfg.trainer.max_epochs = 5 + + # Exp Manager config + cfg.exp_manager.name = cfg.name + + # Note usage of asdict + trainer = pl.Trainer(**asdict(cfg.trainer)) + exp_manager(trainer, asdict(cfg.exp_manager)) + asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer) + + trainer.fit(asr_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/experimental/structured/speech_to_text_structured_v2.py b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/structured/speech_to_text_structured_v2.py new file mode 100644 index 0000000..e8a865a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/experimental/structured/speech_to_text_structured_v2.py @@ -0,0 +1,90 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import asdict + +import pytorch_lightning as pl + +from nemo.collections.asr.models import EncDecCTCModel, configs +from nemo.core.config import modelPT, optimizers, schedulers +from nemo.utils.exp_manager import exp_manager + +""" +python speech_to_text_structured_v2.py +""" + +# fmt: off +LABELS = [ + " ", "a", "b", "c", "d", "e", + "f", "g", "h", "i", "j", "k", + "l", "m", "n", "o", "p", "q", + "r", "s", "t", "u", "v", "w", + "x", "y", "z", "'", +] + +optim_cfg = optimizers.NovogradParams( + lr=0.01, + betas=(0.8, 0.5), + weight_decay=0.001 +) + +sched_cfg = schedulers.CosineAnnealingParams( + warmup_steps=None, + warmup_ratio=None, + min_lr=0.0, +) +# fmt: on + + +def main(): + # NeMo Model config + cfg = modelPT.NemoConfig(name='Custom QuartzNet') + + # Generate default asr model config + builder = configs.EncDecCTCModelConfigBuilder(name='quartznet_15x5') + + # set model global values + builder.set_labels(LABELS) + builder.set_optim(cfg=optim_cfg, sched_cfg=sched_cfg) + + model_cfg = builder.build() + + # set the model config to the NeMo Model + cfg.model = model_cfg + + # Update values + # MODEL UPDATES + # train ds + model_cfg.train_ds.manifest_filepath = "" + + # validation ds + model_cfg.validation_ds.manifest_filepath = "" + + # Trainer config + cfg.trainer.devices = 1 + cfg.trainer.max_epochs = 5 + + # Exp Manager config + cfg.exp_manager.name = cfg.name + + # Note usage of asdict + trainer = pl.Trainer(**asdict(cfg.trainer)) + exp_manager(trainer, asdict(cfg.exp_manager)) + asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer) + + trainer.fit(asr_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/export/transducer/infer_transducer_onnx.py b/NeMo-2.0.0.rc0.beta/examples/asr/export/transducer/infer_transducer_onnx.py new file mode 100644 index 0000000..2d39941 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/export/transducer/infer_transducer_onnx.py @@ -0,0 +1,220 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import glob +import json +import os +import tempfile +from argparse import ArgumentParser + +import torch +from tqdm import tqdm + +from nemo.collections.asr.metrics.wer import word_error_rate +from nemo.collections.asr.models import ASRModel +from nemo.collections.asr.parts.submodules.rnnt_greedy_decoding import ONNXGreedyBatchedRNNTInfer +from nemo.utils import logging + + +""" +Script to compare the outputs of a NeMo Pytorch based RNNT Model and its ONNX exported representation. + +# Compare a NeMo and ONNX model +python infer_transducer_onnx.py \ + --nemo_model="" \ + OR + --pretrained_model="" \ + --onnx_encoder="" \ + --onnx_decoder="" \ + --dataset_manifest="" \ + --audio_dir="" \ + --max_symbold_per_step=5 \ + --batch_size=32 \ + --log + +# Export and compare a NeMo and ONNX model +python infer_transducer_onnx.py \ + --nemo_model="" \ + OR + --pretrained_model="" \ + --export \ + --dataset_manifest="" \ + --audio_dir="" \ + --max_symbold_per_step=5 \ + --batch_size=32 \ + --log +""" + + +def parse_arguments(): + parser = ArgumentParser() + parser.add_argument( + "--nemo_model", type=str, default=None, required=False, help="Path to .nemo file", + ) + parser.add_argument( + '--pretrained_model', type=str, default=None, required=False, help='Name of a pretrained NeMo file' + ) + parser.add_argument('--onnx_encoder', type=str, default=None, required=False, help="Path to onnx encoder model") + parser.add_argument( + '--onnx_decoder', type=str, default=None, required=False, help="Path to onnx decoder + joint model" + ) + parser.add_argument('--threshold', type=float, default=0.01, required=False) + + parser.add_argument('--dataset_manifest', type=str, default=None, required=False, help='Path to dataset manifest') + parser.add_argument('--audio_dir', type=str, default=None, required=False, help='Path to directory of audio files') + parser.add_argument('--audio_type', type=str, default='wav', help='File format of audio') + + parser.add_argument('--export', action='store_true', help="Whether to export the model into onnx prior to eval") + parser.add_argument('--max_symbold_per_step', type=int, default=5, required=False, help='Number of decoding steps') + parser.add_argument('--batch_size', type=int, default=32, help='Batchsize') + parser.add_argument('--log', action='store_true', help='Log the predictions between pytorch and onnx') + + args = parser.parse_args() + return args + + +def assert_args(args): + if args.nemo_model is None and args.pretrained_model is None: + raise ValueError( + "`nemo_model` or `pretrained_model` must be passed ! It is required for decoding the RNNT tokens " + "and ensuring predictions match between Torch and ONNX." + ) + + if args.nemo_model is not None and args.pretrained_model is not None: + raise ValueError( + "`nemo_model` and `pretrained_model` cannot both be passed ! Only one can be passed to this script." + ) + + if args.export and (args.onnx_encoder is not None or args.onnx_decoder is not None): + raise ValueError("If `export` is set, then `onnx_encoder` and `onnx_decoder` arguments must be None") + + if args.audio_dir is None and args.dataset_manifest is None: + raise ValueError("Both `dataset_manifest` and `audio_dir` cannot be None!") + + if args.audio_dir is not None and args.dataset_manifest is not None: + raise ValueError("Submit either `dataset_manifest` or `audio_dir`.") + + if int(args.max_symbold_per_step) < 1: + raise ValueError("`max_symbold_per_step` must be an integer > 0") + + +def export_model_if_required(args, nemo_model): + if args.export: + nemo_model.export("temp_rnnt.onnx") + args.onnx_encoder = "encoder-temp_rnnt.onnx" + args.onnx_decoder = "decoder_joint-temp_rnnt.onnx" + + +def resolve_audio_filepaths(args): + # get audio filenames + if args.audio_dir is not None: + filepaths = list(glob.glob(os.path.join(args.audio_dir.audio_dir, f"*.{args.audio_type}"))) + else: + # get filenames from manifest + filepaths = [] + with open(args.dataset_manifest, 'r', encoding='utf-8') as f: + for line in f: + item = json.loads(line) + filepaths.append(item['audio_filepath']) + + logging.info(f"\nTranscribing {len(filepaths)} files...\n") + + return filepaths + + +def main(): + args = parse_arguments() + + device = 'cuda' if torch.cuda.is_available() else 'cpu' + + # Instantiate pytorch model + if args.nemo_model is not None: + nemo_model = args.nemo_model + nemo_model = ASRModel.restore_from(nemo_model, map_location=device) # type: ASRModel + nemo_model.freeze() + elif args.pretrained_model is not None: + nemo_model = args.pretrained_model + nemo_model = ASRModel.from_pretrained(nemo_model, map_location=device) # type: ASRModel + nemo_model.freeze() + else: + raise ValueError("Please pass either `nemo_model` or `pretrained_model` !") + + if torch.cuda.is_available(): + nemo_model = nemo_model.to('cuda') + + export_model_if_required(args, nemo_model) + + # Instantiate RNNT Decoding loop + encoder_model = args.onnx_encoder + decoder_model = args.onnx_decoder + max_symbols_per_step = args.max_symbold_per_step + decoding = ONNXGreedyBatchedRNNTInfer(encoder_model, decoder_model, max_symbols_per_step) + + audio_filepath = resolve_audio_filepaths(args) + + # Evaluate Pytorch Model (CPU/GPU) + actual_transcripts = nemo_model.transcribe(audio_filepath, batch_size=args.batch_size)[0] + + # Evaluate ONNX model + with tempfile.TemporaryDirectory() as tmpdir: + with open(os.path.join(tmpdir, 'manifest.json'), 'w', encoding='utf-8') as fp: + for audio_file in audio_filepath: + entry = {'audio_filepath': audio_file, 'duration': 100000, 'text': 'nothing'} + fp.write(json.dumps(entry) + '\n') + + config = {'paths2audio_files': audio_filepath, 'batch_size': args.batch_size, 'temp_dir': tmpdir} + + nemo_model.preprocessor.featurizer.dither = 0.0 + nemo_model.preprocessor.featurizer.pad_to = 0 + + temporary_datalayer = nemo_model._setup_transcribe_dataloader(config) + + all_hypothesis = [] + for test_batch in tqdm(temporary_datalayer, desc="ONNX Transcribing"): + input_signal, input_signal_length = test_batch[0], test_batch[1] + input_signal = input_signal.to(device) + input_signal_length = input_signal_length.to(device) + + # Acoustic features + processed_audio, processed_audio_len = nemo_model.preprocessor( + input_signal=input_signal, length=input_signal_length + ) + # RNNT Decoding loop + hypotheses = decoding(audio_signal=processed_audio, length=processed_audio_len) + + # Process hypothesis (map char/subword token ids to text) + hypotheses = nemo_model.decoding.decode_hypothesis(hypotheses) # type: List[str] + + # Extract text from the hypothesis + texts = [h.text for h in hypotheses] + + all_hypothesis += texts + del processed_audio, processed_audio_len + del test_batch + + if args.log: + for pt_transcript, onnx_transcript in zip(actual_transcripts, all_hypothesis): + print(f"Pytorch Transcripts : {pt_transcript}") + print(f"ONNX Transcripts : {onnx_transcript}") + print() + + # Measure error rate between onnx and pytorch transcipts + pt_onnx_cer = word_error_rate(all_hypothesis, actual_transcripts, use_cer=True) + assert pt_onnx_cer < args.threshold, "Threshold violation !" + + print("Character error rate between Pytorch and ONNX :", pt_onnx_cer) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/export/transducer/infer_transducer_ts.py b/NeMo-2.0.0.rc0.beta/examples/asr/export/transducer/infer_transducer_ts.py new file mode 100644 index 0000000..8e7b71a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/export/transducer/infer_transducer_ts.py @@ -0,0 +1,238 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import glob +import json +import os +import tempfile +from argparse import ArgumentParser + +import torch +from omegaconf import OmegaConf +from tqdm import tqdm + +from nemo.collections.asr.metrics.wer import word_error_rate +from nemo.collections.asr.models import ASRModel +from nemo.collections.asr.parts.submodules.rnnt_greedy_decoding import TorchscriptGreedyBatchedRNNTInfer +from nemo.utils import logging + + +""" +Script to compare the outputs of a NeMo Pytorch based RNNT Model and its Torchscript exported representation. + +# Compare a NeMo and Torchscript model +python infer_transducer_ts.py \ + --nemo_model="" \ + OR + --pretrained_model="" \ + --ts_encoder="" \ + --ts_decoder="" \ + --ts_cfg="" \ + --dataset_manifest="" \ + --audio_dir="" \ + --max_symbold_per_step=5 \ + --batch_size=32 \ + --log + +# Export and compare a NeMo and Torchscript model +python infer_transducer_ts.py \ + --nemo_model="" \ + OR + --pretrained_model="" \ + --export \ + --dataset_manifest="" \ + --audio_dir="" \ + --max_symbold_per_step=5 \ + --batch_size=32 \ + --log + +""" + + +def parse_arguments(): + parser = ArgumentParser() + parser.add_argument( + "--nemo_model", type=str, default=None, required=False, help="Path to .nemo file", + ) + parser.add_argument( + '--pretrained_model', type=str, default=None, required=False, help='Name of a pretrained NeMo file' + ) + parser.add_argument('--ts_encoder', type=str, default=None, required=False, help="Path to ts encoder model") + parser.add_argument( + '--ts_decoder', type=str, default=None, required=False, help="Path to ts decoder + joint model" + ) + parser.add_argument( + '--ts_cfg', type=str, default=None, required=False, help='Path to the yaml config of the exported model' + ) + parser.add_argument('--threshold', type=float, default=0.01, required=False) + + parser.add_argument('--dataset_manifest', type=str, default=None, required=False, help='Path to dataset manifest') + parser.add_argument('--audio_dir', type=str, default=None, required=False, help='Path to directory of audio files') + parser.add_argument('--audio_type', type=str, default='wav', help='File format of audio') + + parser.add_argument( + '--export', action='store_true', help="Whether to export the model into torchscript prior to eval" + ) + parser.add_argument('--max_symbold_per_step', type=int, default=5, required=False, help='Number of decoding steps') + parser.add_argument('--batch_size', type=int, default=32, help='Batchsize') + parser.add_argument('--log', action='store_true', help='Log the predictions between pytorch and torchscript') + + args = parser.parse_args() + return args + + +def assert_args(args): + if args.nemo_model is None and args.pretrained_model is None: + raise ValueError( + "`nemo_model` or `pretrained_model` must be passed ! It is required for decoding the RNNT tokens " + "and ensuring predictions match between Torch and Torchscript." + ) + + if args.nemo_model is not None and args.pretrained_model is not None: + raise ValueError( + "`nemo_model` and `pretrained_model` cannot both be passed ! Only one can be passed to this script." + ) + + if args.ts_cfg is None: + raise ValueError( + "Must provide the yaml config of the exported model. You can obtain it by loading the " + "nemo model and then using OmegaConf.save(model.cfg, 'cfg.yaml')" + ) + + if args.export and (args.ts_encoder is not None or args.ts_decoder is not None): + raise ValueError("If `export` is set, then `ts_encoder` and `ts_decoder` arguments must be None") + + if args.audio_dir is None and args.dataset_manifest is None: + raise ValueError("Both `dataset_manifest` and `audio_dir` cannot be None!") + + if args.audio_dir is not None and args.dataset_manifest is not None: + raise ValueError("Submit either `dataset_manifest` or `audio_dir`.") + + if int(args.max_symbold_per_step) < 1: + raise ValueError("`max_symbold_per_step` must be an integer > 0") + + +def export_model_if_required(args, nemo_model): + if args.export: + nemo_model.export(output="temp_rnnt.ts", check_trace=True) + OmegaConf.save(nemo_model.cfg, "ts_cfg.yaml") + + args.ts_encoder = "encoder-temp_rnnt.ts" + args.ts_decoder = "decoder_joint-temp_rnnt.ts" + args.ts_cfg = "ts_cfg.yaml" + + +def resolve_audio_filepaths(args): + # get audio filenames + if args.audio_dir is not None: + filepaths = list(glob.glob(os.path.join(args.audio_dir.audio_dir, f"*.{args.audio_type}"))) + else: + # get filenames from manifest + filepaths = [] + with open(args.dataset_manifest, 'r', encoding='utf-8') as f: + for line in f: + item = json.loads(line) + filepaths.append(item['audio_filepath']) + + logging.info(f"\nTranscribing {len(filepaths)} files...\n") + + return filepaths + + +def main(): + args = parse_arguments() + + device = 'cuda' if torch.cuda.is_available() else 'cpu' + + # Instantiate pytorch model + if args.nemo_model is not None: + nemo_model = args.nemo_model + nemo_model = ASRModel.restore_from(nemo_model, map_location=device) # type: ASRModel + nemo_model.freeze() + elif args.pretrained_model is not None: + nemo_model = args.pretrained_model + nemo_model = ASRModel.from_pretrained(nemo_model, map_location=device) # type: ASRModel + nemo_model.freeze() + else: + raise ValueError("Please pass either `nemo_model` or `pretrained_model` !") + + if torch.cuda.is_available(): + nemo_model = nemo_model.to('cuda') + + export_model_if_required(args, nemo_model) + + # Instantiate RNNT Decoding loop + encoder_model = args.ts_encoder + decoder_model = args.ts_decoder + ts_cfg = OmegaConf.load(args.ts_cfg) + max_symbols_per_step = args.max_symbold_per_step + decoding = TorchscriptGreedyBatchedRNNTInfer(encoder_model, decoder_model, ts_cfg, device, max_symbols_per_step) + + audio_filepath = resolve_audio_filepaths(args) + + # Evaluate Pytorch Model (CPU/GPU) + actual_transcripts = nemo_model.transcribe(audio_filepath, batch_size=args.batch_size)[0] + + # Evaluate Torchscript model + with tempfile.TemporaryDirectory() as tmpdir: + with open(os.path.join(tmpdir, 'manifest.json'), 'w', encoding='utf-8') as fp: + for audio_file in audio_filepath: + entry = {'audio_filepath': audio_file, 'duration': 100000, 'text': 'nothing'} + fp.write(json.dumps(entry) + '\n') + + config = {'paths2audio_files': audio_filepath, 'batch_size': args.batch_size, 'temp_dir': tmpdir} + + nemo_model.preprocessor.featurizer.dither = 0.0 + nemo_model.preprocessor.featurizer.pad_to = 0 + + temporary_datalayer = nemo_model._setup_transcribe_dataloader(config) + + all_hypothesis = [] + for test_batch in tqdm(temporary_datalayer, desc="Torchscript Transcribing"): + input_signal, input_signal_length = test_batch[0], test_batch[1] + input_signal = input_signal.to(device) + input_signal_length = input_signal_length.to(device) + + # Acoustic features + processed_audio, processed_audio_len = nemo_model.preprocessor( + input_signal=input_signal, length=input_signal_length + ) + # RNNT Decoding loop + hypotheses = decoding(audio_signal=processed_audio, length=processed_audio_len) + + # Process hypothesis (map char/subword token ids to text) + hypotheses = nemo_model.decoding.decode_hypothesis(hypotheses) # type: List[str] + + # Extract text from the hypothesis + texts = [h.text for h in hypotheses] + + all_hypothesis += texts + del processed_audio, processed_audio_len + del test_batch + + if args.log: + for pt_transcript, ts_transcript in zip(actual_transcripts, all_hypothesis): + print(f"Pytorch Transcripts : {pt_transcript}") + print(f"Torchscript Transcripts : {ts_transcript}") + print() + + # Measure error rate between torchscript and pytorch transcipts + pt_ts_cer = word_error_rate(all_hypothesis, actual_transcripts, use_cer=True) + assert pt_ts_cer < args.threshold, "Threshold violation !" + + print("Character error rate between Pytorch and Torchscript :", pt_ts_cer) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/quantization/speech_to_text_calibrate.py b/NeMo-2.0.0.rc0.beta/examples/asr/quantization/speech_to_text_calibrate.py new file mode 100644 index 0000000..264806c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/quantization/speech_to_text_calibrate.py @@ -0,0 +1,160 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Script for calibrating a pretrained ASR model for quantization +""" + +from argparse import ArgumentParser + +import torch +from omegaconf import open_dict + +from nemo.collections.asr.models import EncDecCTCModel +from nemo.utils import logging + +try: + from pytorch_quantization import calib + from pytorch_quantization import nn as quant_nn + from pytorch_quantization import quant_modules + from pytorch_quantization.tensor_quant import QuantDescriptor +except ImportError: + raise ImportError( + "pytorch-quantization is not installed. Install from " + "https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization." + ) + +try: + from torch.cuda.amp import autocast +except ImportError: + from contextlib import contextmanager + + @contextmanager + def autocast(enabled=None): + yield + + +can_gpu = torch.cuda.is_available() + + +def main(): + parser = ArgumentParser() + parser.add_argument( + "--asr_model", type=str, default="QuartzNet15x5Base-En", required=True, help="Pass: 'QuartzNet15x5Base-En'", + ) + parser.add_argument("--dataset", type=str, required=True, help="path to evaluation data") + parser.add_argument("--batch_size", type=int, default=256) + parser.add_argument( + "--dont_normalize_text", + default=False, + action='store_false', + help="Turn off trasnscript normalization. Recommended for non-English.", + ) + parser.add_argument('--num_calib_batch', default=1, type=int, help="Number of batches for calibration.") + parser.add_argument('--calibrator', type=str, choices=["max", "histogram"], default="max") + parser.add_argument('--percentile', nargs='+', type=float, default=[99.9, 99.99, 99.999, 99.9999]) + parser.add_argument("--amp", action="store_true", help="Use AMP in calibration.") + parser.set_defaults(amp=False) + + args = parser.parse_args() + torch.set_grad_enabled(False) + + # Initialize quantization + quant_desc_input = QuantDescriptor(calib_method=args.calibrator) + quant_nn.QuantConv2d.set_default_quant_desc_input(quant_desc_input) + quant_nn.QuantConvTranspose2d.set_default_quant_desc_input(quant_desc_input) + quant_nn.QuantLinear.set_default_quant_desc_input(quant_desc_input) + + if args.asr_model.endswith('.nemo'): + logging.info(f"Using local ASR model from {args.asr_model}") + asr_model_cfg = EncDecCTCModel.restore_from(restore_path=args.asr_model, return_config=True) + with open_dict(asr_model_cfg): + asr_model_cfg.encoder.quantize = True + asr_model = EncDecCTCModel.restore_from(restore_path=args.asr_model, override_config_path=asr_model_cfg) + + else: + logging.info(f"Using NGC cloud ASR model {args.asr_model}") + asr_model_cfg = EncDecCTCModel.from_pretrained(model_name=args.asr_model, return_config=True) + with open_dict(asr_model_cfg): + asr_model_cfg.encoder.quantize = True + asr_model = EncDecCTCModel.from_pretrained(model_name=args.asr_model, override_config_path=asr_model_cfg) + + asr_model.setup_test_data( + test_data_config={ + 'sample_rate': 16000, + 'manifest_filepath': args.dataset, + 'labels': asr_model.decoder.vocabulary, + 'batch_size': args.batch_size, + 'normalize_transcripts': args.dont_normalize_text, + 'shuffle': True, + } + ) + asr_model.preprocessor.featurizer.dither = 0.0 + asr_model.preprocessor.featurizer.pad_to = 0 + if can_gpu: + asr_model = asr_model.cuda() + asr_model.eval() + + # Enable calibrators + for name, module in asr_model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer): + if module._calibrator is not None: + module.disable_quant() + module.enable_calib() + else: + module.disable() + + for i, test_batch in enumerate(asr_model.test_dataloader()): + if can_gpu: + test_batch = [x.cuda() for x in test_batch] + if args.amp: + with autocast(): + _ = asr_model(input_signal=test_batch[0], input_signal_length=test_batch[1]) + else: + _ = asr_model(input_signal=test_batch[0], input_signal_length=test_batch[1]) + if i >= args.num_calib_batch: + break + + # Save calibrated model(s) + model_name = args.asr_model.replace(".nemo", "") if args.asr_model.endswith(".nemo") else args.asr_model + if not args.calibrator == "histogram": + compute_amax(asr_model, method="max") + asr_model.save_to(F"{model_name}-max-{args.num_calib_batch*args.batch_size}.nemo") + else: + for percentile in args.percentile: + print(F"{percentile} percentile calibration") + compute_amax(asr_model, method="percentile") + asr_model.save_to(F"{model_name}-percentile-{percentile}-{args.num_calib_batch*args.batch_size}.nemo") + + for method in ["mse", "entropy"]: + print(F"{method} calibration") + compute_amax(asr_model, method=method) + asr_model.save_to(F"{model_name}-{method}-{args.num_calib_batch*args.batch_size}.nemo") + + +def compute_amax(model, **kwargs): + for name, module in model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer): + if module._calibrator is not None: + if isinstance(module._calibrator, calib.MaxCalibrator): + module.load_calib_amax() + else: + module.load_calib_amax(**kwargs) + print(F"{name:40}: {module}") + if can_gpu: + model.cuda() + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/quantization/speech_to_text_quant_infer.py b/NeMo-2.0.0.rc0.beta/examples/asr/quantization/speech_to_text_quant_infer.py new file mode 100644 index 0000000..029623c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/quantization/speech_to_text_quant_infer.py @@ -0,0 +1,219 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Script for post training quantization of ASR models +""" + +import collections +from argparse import ArgumentParser +from pprint import pprint + +import torch +from omegaconf import open_dict + +from nemo.collections.asr.metrics.wer import WER, word_error_rate +from nemo.collections.asr.models import EncDecCTCModel +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecoding, CTCDecodingConfig +from nemo.utils import logging + +try: + from pytorch_quantization import nn as quant_nn + from pytorch_quantization import quant_modules +except ImportError: + raise ImportError( + "pytorch-quantization is not installed. Install from " + "https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization." + ) + + +try: + from torch.cuda.amp import autocast +except ImportError: + from contextlib import contextmanager + + @contextmanager + def autocast(enabled=None): + yield + + +can_gpu = torch.cuda.is_available() + + +def main(): + parser = ArgumentParser() + parser.add_argument( + "--asr_model", type=str, default="QuartzNet15x5Base-En", required=True, help="Pass: 'QuartzNet15x5Base-En'", + ) + parser.add_argument("--dataset", type=str, required=True, help="path to evaluation data") + parser.add_argument("--wer_target", type=float, default=None, help="used by test") + parser.add_argument("--batch_size", type=int, default=4) + parser.add_argument("--wer_tolerance", type=float, default=1.0, help="used by test") + parser.add_argument( + "--dont_normalize_text", + default=False, + action='store_false', + help="Turn off trasnscript normalization. Recommended for non-English.", + ) + parser.add_argument( + "--use_cer", default=False, action='store_true', help="Use Character Error Rate as the evaluation metric" + ) + parser.add_argument('--sensitivity', action="store_true", help="Perform sensitivity analysis") + parser.add_argument('--onnx', action="store_true", help="Export to ONNX") + parser.add_argument('--quant-disable-keyword', type=str, nargs='+', help='disable quantizers by keyword') + args = parser.parse_args() + torch.set_grad_enabled(False) + + quant_modules.initialize() + + if args.asr_model.endswith('.nemo'): + logging.info(f"Using local ASR model from {args.asr_model}") + asr_model_cfg = EncDecCTCModel.restore_from(restore_path=args.asr_model, return_config=True) + with open_dict(asr_model_cfg): + asr_model_cfg.encoder.quantize = True + asr_model = EncDecCTCModel.restore_from(restore_path=args.asr_model, override_config_path=asr_model_cfg) + + else: + logging.info(f"Using NGC cloud ASR model {args.asr_model}") + asr_model_cfg = EncDecCTCModel.from_pretrained(model_name=args.asr_model, return_config=True) + with open_dict(asr_model_cfg): + asr_model_cfg.encoder.quantize = True + asr_model = EncDecCTCModel.from_pretrained(model_name=args.asr_model, override_config_path=asr_model_cfg) + asr_model.setup_test_data( + test_data_config={ + 'sample_rate': 16000, + 'manifest_filepath': args.dataset, + 'labels': asr_model.decoder.vocabulary, + 'batch_size': args.batch_size, + 'normalize_transcripts': args.dont_normalize_text, + } + ) + asr_model.preprocessor.featurizer.dither = 0.0 + asr_model.preprocessor.featurizer.pad_to = 0 + if can_gpu: + asr_model = asr_model.cuda() + asr_model.eval() + + if args.quant_disable_keyword: + for name, module in asr_model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer): + for keyword in args.quant_disable_keyword: + if keyword in name: + logging.warning(F"Disable {name}") + module.disable() + + labels_map = dict([(i, asr_model.decoder.vocabulary[i]) for i in range(len(asr_model.decoder.vocabulary))]) + decoding_cfg = CTCDecodingConfig() + char_decoding = CTCDecoding(decoding_cfg, vocabulary=labels_map) + wer = WER(char_decoding, use_cer=args.use_cer) + wer_quant = evaluate(asr_model, labels_map, wer) + logging.info(f'Got WER of {wer_quant}. Tolerance was {args.wer_tolerance}') + + if args.sensitivity: + if wer_quant < args.wer_tolerance: + logging.info("Tolerance is already met. Skip sensitivity analyasis.") + return + quant_layer_names = [] + for name, module in asr_model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer): + module.disable() + layer_name = name.replace("._input_quantizer", "").replace("._weight_quantizer", "") + if layer_name not in quant_layer_names: + quant_layer_names.append(layer_name) + logging.info(F"{len(quant_layer_names)} quantized layers found.") + + # Build sensitivity profile + quant_layer_sensitivity = {} + for i, quant_layer in enumerate(quant_layer_names): + logging.info(F"Enable {quant_layer}") + for name, module in asr_model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer) and quant_layer in name: + module.enable() + logging.info(F"{name:40}: {module}") + + # Eval the model + wer_value = evaluate(asr_model, labels_map, wer) + logging.info(F"WER: {wer_value}") + quant_layer_sensitivity[quant_layer] = args.wer_tolerance - wer_value + + for name, module in asr_model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer) and quant_layer in name: + module.disable() + logging.info(F"{name:40}: {module}") + + # Skip most sensitive layers until WER target is met + for name, module in asr_model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer): + module.enable() + quant_layer_sensitivity = collections.OrderedDict(sorted(quant_layer_sensitivity.items(), key=lambda x: x[1])) + pprint(quant_layer_sensitivity) + skipped_layers = [] + for quant_layer, _ in quant_layer_sensitivity.items(): + for name, module in asr_model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer): + if quant_layer in name: + logging.info(F"Disable {name}") + if not quant_layer in skipped_layers: + skipped_layers.append(quant_layer) + module.disable() + wer_value = evaluate(asr_model, labels_map, wer) + if wer_value <= args.wer_tolerance: + logging.info( + F"WER tolerance {args.wer_tolerance} is met by skipping {len(skipped_layers)} sensitive layers." + ) + print(skipped_layers) + export_onnx(args, asr_model) + return + raise ValueError(f"WER tolerance {args.wer_tolerance} can not be met with any layer quantized!") + + export_onnx(args, asr_model) + + +def export_onnx(args, asr_model): + if args.onnx: + if args.asr_model.endswith("nemo"): + onnx_name = args.asr_model.replace(".nemo", ".onnx") + else: + onnx_name = args.asr_model + logging.info(F"Export to {onnx_name}") + quant_nn.TensorQuantizer.use_fb_fake_quant = True + asr_model.export(onnx_name, onnx_opset_version=13) + quant_nn.TensorQuantizer.use_fb_fake_quant = False + + +def evaluate(asr_model, labels_map, wer): + # Eval the model + hypotheses = [] + references = [] + for test_batch in asr_model.test_dataloader(): + if can_gpu: + test_batch = [x.cuda() for x in test_batch] + with autocast(): + log_probs, encoded_len, greedy_predictions = asr_model( + input_signal=test_batch[0], input_signal_length=test_batch[1] + ) + hypotheses += wer.decoding.ctc_decoder_predictions_tensor(greedy_predictions)[0] + for batch_ind in range(greedy_predictions.shape[0]): + seq_len = test_batch[3][batch_ind].cpu().detach().numpy() + seq_ids = test_batch[2][batch_ind].cpu().detach().numpy() + reference = ''.join([labels_map[c] for c in seq_ids[0:seq_len]]) + references.append(reference) + del test_batch + wer_value = word_error_rate(hypotheses=hypotheses, references=references, use_cer=wer.use_cer) + + return wer_value + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/quantization/speech_to_text_quant_infer_trt.py b/NeMo-2.0.0.rc0.beta/examples/asr/quantization/speech_to_text_quant_infer_trt.py new file mode 100644 index 0000000..e9916d6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/quantization/speech_to_text_quant_infer_trt.py @@ -0,0 +1,233 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Script for inference ASR models using TensorRT +""" + +import os +from argparse import ArgumentParser + +import numpy as np +import pycuda.driver as cuda +import tensorrt as trt +import torch +from omegaconf import open_dict + +from nemo.collections.asr.metrics.wer import WER, word_error_rate +from nemo.collections.asr.models import EncDecCTCModel +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecoding, CTCDecodingConfig +from nemo.utils import logging + +# Use autoprimaryctx if available (pycuda >= 2021.1) to +# prevent issues with other modules that rely on the primary +# device context. +try: + import pycuda.autoprimaryctx +except ModuleNotFoundError: + import pycuda.autoinit + +TRT_LOGGER = trt.Logger() + + +can_gpu = torch.cuda.is_available() + +try: + from torch.cuda.amp import autocast +except ImportError: + from contextlib import contextmanager + + @contextmanager + def autocast(enabled=None): + yield + + +def main(): + parser = ArgumentParser() + parser.add_argument( + "--asr_model", type=str, default="QuartzNet15x5Base-En", required=True, help="Pass: 'QuartzNet15x5Base-En'", + ) + parser.add_argument( + "--asr_onnx", + type=str, + default="./QuartzNet15x5Base-En-max-32.onnx", + help="Pass: 'QuartzNet15x5Base-En-max-32.onnx'", + ) + parser.add_argument("--dataset", type=str, required=True, help="path to evaluation data") + parser.add_argument("--batch_size", type=int, default=4) + parser.add_argument( + "--dont_normalize_text", + default=False, + action='store_false', + help="Turn off trasnscript normalization. Recommended for non-English.", + ) + parser.add_argument( + "--use_cer", default=False, action='store_true', help="Use Character Error Rate as the evaluation metric" + ) + parser.add_argument('--qat', action="store_true", help="Use onnx file exported from QAT tools") + args = parser.parse_args() + torch.set_grad_enabled(False) + + if args.asr_model.endswith('.nemo'): + logging.info(f"Using local ASR model from {args.asr_model}") + asr_model_cfg = EncDecCTCModel.restore_from(restore_path=args.asr_model, return_config=True) + with open_dict(asr_model_cfg): + asr_model_cfg.encoder.quantize = True + asr_model = EncDecCTCModel.restore_from(restore_path=args.asr_model, override_config_path=asr_model_cfg) + + else: + logging.info(f"Using NGC cloud ASR model {args.asr_model}") + asr_model_cfg = EncDecCTCModel.from_pretrained(model_name=args.asr_model, return_config=True) + with open_dict(asr_model_cfg): + asr_model_cfg.encoder.quantize = True + asr_model = EncDecCTCModel.from_pretrained(model_name=args.asr_model, override_config_path=asr_model_cfg) + asr_model.setup_test_data( + test_data_config={ + 'sample_rate': 16000, + 'manifest_filepath': args.dataset, + 'labels': asr_model.decoder.vocabulary, + 'batch_size': args.batch_size, + 'normalize_transcripts': args.dont_normalize_text, + } + ) + asr_model.preprocessor.featurizer.dither = 0.0 + asr_model.preprocessor.featurizer.pad_to = 0 + if can_gpu: + asr_model = asr_model.cuda() + asr_model.eval() + labels_map = dict([(i, asr_model.decoder.vocabulary[i]) for i in range(len(asr_model.decoder.vocabulary))]) + decoding_cfg = CTCDecodingConfig() + char_decoding = CTCDecoding(decoding_cfg, vocabulary=labels_map) + wer = WER(char_decoding, use_cer=args.use_cer) + wer_result = evaluate(asr_model, args.asr_onnx, labels_map, wer, args.qat) + logging.info(f'Got WER of {wer_result}.') + + +def get_min_max_input_shape(asr_model): + max_shape = (1, 64, 1) + min_shape = (64, 64, 99999) + for test_batch in asr_model.test_dataloader(): + test_batch = [x.cuda() for x in test_batch] + processed_signal, processed_signal_length = asr_model.preprocessor( + input_signal=test_batch[0], length=test_batch[1] + ) + shape = processed_signal.cpu().numpy().shape + if shape[0] > max_shape[0]: + max_shape = (shape[0], *max_shape[1:]) + if shape[0] < min_shape[0]: + min_shape = (shape[0], *min_shape[1:]) + if shape[2] > max_shape[2]: + max_shape = (*max_shape[0:2], shape[2]) + if shape[2] < min_shape[2]: + min_shape = (*min_shape[0:2], shape[2]) + return min_shape, max_shape + + +def build_trt_engine(asr_model, onnx_path, qat): + trt_engine_path = "{}.trt".format(onnx_path) + if os.path.exists(trt_engine_path): + return trt_engine_path + + min_input_shape, max_input_shape = get_min_max_input_shape(asr_model) + workspace_size = 512 + with trt.Builder(TRT_LOGGER) as builder: + network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + if qat: + network_flags |= 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_PRECISION) + with builder.create_network(flags=network_flags) as network, trt.OnnxParser( + network, TRT_LOGGER + ) as parser, builder.create_builder_config() as builder_config: + parser.parse_from_file(onnx_path) + builder_config.max_workspace_size = workspace_size * (1024 * 1024) + if qat: + builder_config.set_flag(trt.BuilderFlag.INT8) + + profile = builder.create_optimization_profile() + profile.set_shape("audio_signal", min=min_input_shape, opt=max_input_shape, max=max_input_shape) + builder_config.add_optimization_profile(profile) + + engine = builder.build_engine(network, builder_config) + serialized_engine = engine.serialize() + with open(trt_engine_path, "wb") as fout: + fout.write(serialized_engine) + return trt_engine_path + + +def trt_inference(stream, trt_ctx, d_input, d_output, input_signal, input_signal_length): + print("infer with shape: {}".format(input_signal.shape)) + + trt_ctx.set_binding_shape(0, input_signal.shape) + assert trt_ctx.all_binding_shapes_specified + + h_output = cuda.pagelocked_empty(tuple(trt_ctx.get_binding_shape(1)), dtype=np.float32) + + h_input_signal = cuda.register_host_memory(np.ascontiguousarray(input_signal.cpu().numpy().ravel())) + cuda.memcpy_htod_async(d_input, h_input_signal, stream) + trt_ctx.execute_async_v2(bindings=[int(d_input), int(d_output)], stream_handle=stream.handle) + cuda.memcpy_dtoh_async(h_output, d_output, stream) + stream.synchronize() + + greedy_predictions = torch.tensor(h_output).argmax(dim=-1, keepdim=False) + return greedy_predictions + + +def evaluate(asr_model, asr_onnx, labels_map, wer, qat): + # Eval the model + hypotheses = [] + references = [] + stream = cuda.Stream() + vocabulary_size = len(labels_map) + 1 + engine_file_path = build_trt_engine(asr_model, asr_onnx, qat) + with open(engine_file_path, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime: + trt_engine = runtime.deserialize_cuda_engine(f.read()) + trt_ctx = trt_engine.create_execution_context() + + profile_shape = trt_engine.get_profile_shape(profile_index=0, binding=0) + print("profile shape min:{}, opt:{}, max:{}".format(profile_shape[0], profile_shape[1], profile_shape[2])) + max_input_shape = profile_shape[2] + input_nbytes = trt.volume(max_input_shape) * trt.float32.itemsize + d_input = cuda.mem_alloc(input_nbytes) + max_output_shape = [max_input_shape[0], vocabulary_size, (max_input_shape[-1] + 1) // 2] + output_nbytes = trt.volume(max_output_shape) * trt.float32.itemsize + d_output = cuda.mem_alloc(output_nbytes) + + for test_batch in asr_model.test_dataloader(): + if can_gpu: + test_batch = [x.cuda() for x in test_batch] + processed_signal, processed_signal_length = asr_model.preprocessor( + input_signal=test_batch[0], length=test_batch[1] + ) + + greedy_predictions = trt_inference( + stream, + trt_ctx, + d_input, + d_output, + input_signal=processed_signal, + input_signal_length=processed_signal_length, + ) + hypotheses += wer.decoding.ctc_decoder_predictions_tensor(greedy_predictions)[0] + for batch_ind in range(greedy_predictions.shape[0]): + seq_len = test_batch[3][batch_ind].cpu().detach().numpy() + seq_ids = test_batch[2][batch_ind].cpu().detach().numpy() + reference = ''.join([labels_map[c] for c in seq_ids[0:seq_len]]) + references.append(reference) + del test_batch + wer_value = word_error_rate(hypotheses=hypotheses, references=references, use_cer=wer.use_cer) + + return wer_value + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/README.md b/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/README.md new file mode 100644 index 0000000..bdd3aea --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/README.md @@ -0,0 +1,105 @@ +# Speech Classification + +This directory contains example scripts to train speech classification and voice activity detection models. There are two types of VAD models: Frame-VAD and Segment-VAD. + +## Frame-VAD + +The frame-level VAD model predicts for each frame of the audio whether it has speech or not. For example, with the default config file (`../conf/marblenet/marblenet_3x2x64_20ms.yaml`), the model provides a probability for each frame of 20ms length. + +### Training +```sh +python speech_to_label.py \ + --config-path= + --config-name= \ + model.train_ds.manifest_filepath="[,]" \ + model.validation_ds.manifest_filepath=["",""] \ + trainer.devices=-1 \ + trainer.accelerator="gpu" \ + strategy="ddp" \ + trainer.max_epochs=100 +``` + +The input manifest must be a manifest json file, where each line is a Python dictionary. The fields ["audio_filepath", "offset", "duration", "label"] are required. An example of a manifest file is: +``` +{"audio_filepath": "/path/to/audio_file1", "offset": 0, "duration": 10000, "label": "0 1 0 0 1"} +{"audio_filepath": "/path/to/audio_file2", "offset": 0, "duration": 10000, "label": "0 0 0 1 1 1 1 0 0"} +``` +For example, if you have a 1s audio file, you'll need to have 50 frame labels in the manifest entry like "0 0 0 0 1 1 0 1 .... 0 1". +However, shorter label strings are also supported for smaller file sizes. For example, you can prepare the `label` in 40ms frame, and the model will properly repeat the label for each 20ms frame. + + +### Inference +python frame_vad_infer.py \ + --config-path="../conf/vad" --config-name="frame_vad_infer_postprocess" \ + dataset= + +The manifest json file should have the following format (each line is a Python dictionary): +``` +{"audio_filepath": "/path/to/audio_file1.wav", "offset": 0, "duration": 10000} +{"audio_filepath": "/path/to/audio_file2.wav", "offset": 0, "duration": 10000} +``` + +#### Evaluation +If you want to evaluate tne model's AUROC and DER performance, you need to set `evaluate: True` in config yaml (e.g., `../conf/vad/frame_vad_infer_postprocess.yaml`), and also provide groundtruth in label strings: +``` +{"audio_filepath": "/path/to/audio_file1.wav", "offset": 0, "duration": 10000, "label": "0 1 0 0 0 1 1 1 0"} +``` +or RTTM files: +``` +{"audio_filepath": "/path/to/audio_file1.wav", "offset": 0, "duration": 10000, "rttm_filepath": "/path/to/rttm_file1.rttm"} +``` + + +## Segment-VAD + +Segment-level VAD predicts a single label for each segment of audio (e.g., 0.63s by default). + +### Training +```sh +python speech_to_label.py \ + --config-path= \ + --config-name= \ + model.train_ds.manifest_filepath="[,]" \ + model.validation_ds.manifest_filepath=["",""] \ + trainer.devices=-1 \ + trainer.accelerator="gpu" \ + strategy="ddp" \ + trainer.max_epochs=100 +``` + +The input manifest must be a manifest json file, where each line is a Python dictionary. The fields ["audio_filepath", "offset", "duration", "label"] are required. An example of a manifest file is: +``` +{"audio_filepath": "/path/to/audio_file1", "offset": 0, "duration": 0.63, "label": "0"} +{"audio_filepath": "/path/to/audio_file2", "offset": 0, "duration": 0.63, "label": "1"} +``` + + +### Inference +```sh +python vad_infer.py \ + --config-path="../conf/vad" \ + --config-name="vad_inference_postprocessing.yaml" + dataset= +``` +The manifest json file should have the following format (each line is a Python dictionary): +``` +{"audio_filepath": "/path/to/audio_file1.wav", "offset": 0, "duration": 10000} +{"audio_filepath": "/path/to/audio_file2.wav", "offset": 0, "duration": 10000} +``` + + +## Visualization + +To visualize the VAD outputs, you can use the `nemo.collections.asr.parts.utils.vad_utils.plot_sample_from_rttm` function, which takes an audio file and an RTTM file as input, and plots the audio waveform and the VAD labels. Since the VAD inference script will output a json manifest `manifest_vad_out.json` by default, you can create a Jupyter Notebook with the following script and fill in the paths using the output manifest: +```python +from nemo.collections.asr.parts.utils.vad_utils import plot_sample_from_rttm + +plot_sample_from_rttm( + audio_file="/path/to/audio_file.wav", + rttm_file="/path/to/rttm_file.rttm", + offset=0.0, + duration=1000, + save_path="vad_pred.png" +) +``` + diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/frame_vad_infer.py b/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/frame_vad_infer.py new file mode 100644 index 0000000..594cc96 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/frame_vad_infer.py @@ -0,0 +1,211 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script peforms VAD on each 20ms frames of the input audio files. +Postprocessing is also performed to generate speech segments and store them as RTTM files. +Long audio files will be splitted into smaller chunks to avoid OOM issues, but the frames close +to the split points might have worse performance due to truncated context. + +## Usage: +python frame_vad_infer.py \ + --config-path="../conf/vad" --config-name="frame_vad_infer_postprocess" \ + input_manifest= \ + output_dir= + +The manifest json file should have the following format (each line is a Python dictionary): +{"audio_filepath": "/path/to/audio_file1", "offset": 0, "duration": 10000} +{"audio_filepath": "/path/to/audio_file2", "offset": 0, "duration": 10000} + +If you want to evaluate tne model's AUROC and DER performance, you need to set `evaluate=True` in config yaml, +and also provide groundtruth in either RTTM files or label strings: +{"audio_filepath": "/path/to/audio_file1", "offset": 0, "duration": 10000, "label": "0 1 0 0 0 1 1 1 0"} +or +{"audio_filepath": "/path/to/audio_file1", "offset": 0, "duration": 10000, "rttm_filepath": "/path/to/rttm_file1.rttm"} + +""" + +import os +from pathlib import Path + +import torch + +from nemo.collections.asr.parts.utils.manifest_utils import write_manifest +from nemo.collections.asr.parts.utils.vad_utils import ( + frame_vad_eval_detection_error, + frame_vad_infer_load_manifest, + generate_overlap_vad_seq, + generate_vad_frame_pred, + generate_vad_segment_table, + init_frame_vad_model, + prepare_manifest, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +@hydra_runner(config_path="../conf/vad", config_name="frame_vad_infer_postprocess") +def main(cfg): + if not cfg.input_manifest: + raise ValueError("You must input the path of json file of evaluation data") + output_dir = cfg.output_dir if cfg.output_dir else "frame_vad_outputs" + if os.path.exists(output_dir): + logging.warning( + f"Output directory {output_dir} already exists, use this only if you're tuning post-processing params." + ) + Path(output_dir).mkdir(parents=True, exist_ok=True) + + cfg.frame_out_dir = os.path.join(output_dir, "frame_preds") + cfg.smoothing_out_dir = os.path.join(output_dir, "smoothing_preds") + cfg.rttm_out_dir = os.path.join(output_dir, "rttm_preds") + + # each line of input_manifest should be have different audio_filepath and unique name to simplify edge cases or conditions + logging.info(f"Loading manifest file {cfg.input_manifest}") + manifest_orig, key_labels_map, key_rttm_map = frame_vad_infer_load_manifest(cfg) + + # Prepare manifest for streaming VAD + manifest_vad_input = cfg.input_manifest + if cfg.prepare_manifest.auto_split: + logging.info("Split long audio file to avoid CUDA memory issue") + logging.debug("Try smaller split_duration if you still have CUDA memory issue") + config = { + 'input': manifest_vad_input, + 'window_length_in_sec': cfg.vad.parameters.window_length_in_sec, + 'split_duration': cfg.prepare_manifest.split_duration, + 'num_workers': cfg.num_workers, + 'prepared_manifest_vad_input': cfg.prepared_manifest_vad_input, + 'out_dir': output_dir, + } + manifest_vad_input = prepare_manifest(config) + else: + logging.warning( + "If you encounter CUDA memory issue, try splitting manifest entry by split_duration to avoid it." + ) + + torch.set_grad_enabled(False) + vad_model = init_frame_vad_model(cfg.vad.model_path) + + # setup_test_data + vad_model.setup_test_data( + test_data_config={ + 'batch_size': 1, + 'sample_rate': 16000, + 'manifest_filepath': manifest_vad_input, + 'labels': ['infer'], + 'num_workers': cfg.num_workers, + 'shuffle': False, + 'normalize_audio_db': cfg.vad.parameters.normalize_audio_db, + } + ) + + vad_model = vad_model.to(device) + vad_model.eval() + + if not os.path.exists(cfg.frame_out_dir): + logging.info(f"Frame predictions do not exist at {cfg.frame_out_dir}, generating frame prediction.") + os.mkdir(cfg.frame_out_dir) + extract_frame_preds = True + else: + logging.info(f"Frame predictions already exist at {cfg.frame_out_dir}, skipping frame prediction generation.") + extract_frame_preds = False + + if extract_frame_preds: + logging.info("Generating frame-level prediction ") + pred_dir = generate_vad_frame_pred( + vad_model=vad_model, + window_length_in_sec=cfg.vad.parameters.window_length_in_sec, + shift_length_in_sec=cfg.vad.parameters.shift_length_in_sec, + manifest_vad_input=manifest_vad_input, + out_dir=cfg.frame_out_dir, + ) + logging.info(f"Finish generating VAD frame level prediction. You can find the prediction in {pred_dir}") + else: + pred_dir = cfg.frame_out_dir + + frame_length_in_sec = cfg.vad.parameters.shift_length_in_sec + + # overlap smoothing filter + if cfg.vad.parameters.smoothing: + # Generate predictions with overlapping input segments. Then a smoothing filter is applied to decide the label for a frame spanned by multiple segments. + # smoothing_method would be either in majority vote (median) or average (mean) + logging.info("Generating predictions with overlapping input segments") + smoothing_pred_dir = generate_overlap_vad_seq( + frame_pred_dir=pred_dir, + smoothing_method=cfg.vad.parameters.smoothing, + overlap=cfg.vad.parameters.overlap, + window_length_in_sec=cfg.vad.parameters.window_length_in_sec, + shift_length_in_sec=cfg.vad.parameters.shift_length_in_sec, + num_workers=cfg.num_workers, + out_dir=cfg.smoothing_out_dir, + ) + logging.info( + f"Finish generating predictions with overlapping input segments with smoothing_method={cfg.vad.parameters.smoothing} and overlap={cfg.vad.parameters.overlap}" + ) + pred_dir = smoothing_pred_dir + + # postprocessing and generate speech segments + logging.info("Converting frame level prediction to RTTM files.") + rttm_out_dir = generate_vad_segment_table( + vad_pred_dir=pred_dir, + postprocessing_params=cfg.vad.parameters.postprocessing, + frame_length_in_sec=frame_length_in_sec, + num_workers=cfg.num_workers, + use_rttm=cfg.vad.use_rttm, + out_dir=cfg.rttm_out_dir, + ) + logging.info( + f"Finish generating speech semgents table with postprocessing_params: {cfg.vad.parameters.postprocessing}" + ) + + logging.info("Writing VAD output to manifest") + key_pred_rttm_map = {} + manifest_new = [] + for entry in manifest_orig: + key = Path(entry['audio_filepath']).stem + entry['rttm_filepath'] = Path(os.path.join(rttm_out_dir, key + ".rttm")).absolute().as_posix() + if not Path(entry['rttm_filepath']).is_file(): + logging.warning(f"Not able to find {entry['rttm_filepath']} for {entry['audio_filepath']}") + entry['rttm_filepath'] = "" + manifest_new.append(entry) + key_pred_rttm_map[key] = entry['rttm_filepath'] + + if not cfg.out_manifest_filepath: + out_manifest_filepath = os.path.join(output_dir, "manifest_vad_output.json") + else: + out_manifest_filepath = cfg.out_manifest_filepath + write_manifest(out_manifest_filepath, manifest_new) + logging.info(f"Finished writing VAD output to manifest: {out_manifest_filepath}") + + if cfg.get("evaluate", False): + logging.info("Evaluating VAD results") + auroc, report = frame_vad_eval_detection_error( + pred_dir=pred_dir, + key_labels_map=key_labels_map, + key_rttm_map=key_rttm_map, + key_pred_rttm_map=key_pred_rttm_map, + frame_length_in_sec=frame_length_in_sec, + ) + DetER = report.iloc[[-1]][('detection error rate', '%')].item() + FA = report.iloc[[-1]][('false alarm', '%')].item() + MISS = report.iloc[[-1]][('miss', '%')].item() + logging.info(f"AUROC: {auroc:.4f}") + logging.info(f"DetER={DetER:0.4f}, False Alarm={FA:0.4f}, Miss={MISS:0.4f}") + logging.info(f"with params: {cfg.vad.parameters.postprocessing}") + logging.info("Done!") + + +if __name__ == "__main__": + main() # pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/speech_to_frame_label.py b/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/speech_to_frame_label.py new file mode 100644 index 0000000..04fcbdd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/speech_to_frame_label.py @@ -0,0 +1,70 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +The script trains a model that peforms classification on each frame of the input audio. +The default config (i.e., marblenet_3x2x64_20ms.yaml) outputs 20ms frames. + +## Training +```sh +python speech_to_label.py \ + --config-path= + --config-name= \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath=["",""] \ + trainer.devices=2 \ + trainer.accelerator="gpu" \ + strategy="ddp" \ + trainer.max_epochs=200 +``` + +The input manifest must be a manifest json file, where each line is a Python dictionary. The fields ["audio_filepath", "offset", "duration", "label"] are required. An example of a manifest file is: +``` +{"audio_filepath": "/path/to/audio_file1", "offset": 0, "duration": 10000, "label": "0 1 0 0 1"} +{"audio_filepath": "/path/to/audio_file2", "offset": 0, "duration": 10000, "label": "0 0 0 1 1 1 1 0 0"} +``` +For example, if you have a 1s audio file, you'll need to have 50 frame labels in the manifest entry like "0 0 0 0 1 1 0 1 .... 0 1". +However, shorter label strings are also supported for smaller file sizes. For example, you can prepare the `label` in 40ms frame, and the model will properly repeat the label for each 20ms frame. + +""" + +import pytorch_lightning as pl +from omegaconf import OmegaConf +from nemo.collections.asr.models.classification_models import EncDecFrameClassificationModel + +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="../conf/marblenet", config_name="marblenet_3x2x64_20ms") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + model = EncDecFrameClassificationModel(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + model.maybe_init_from_pretrained_checkpoint(cfg) + + trainer.fit(model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if model.prepare_test(trainer): + trainer.test(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/speech_to_label.py b/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/speech_to_label.py new file mode 100644 index 0000000..b3deb5a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/speech_to_label.py @@ -0,0 +1,182 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Task 1: Speech Command Recognition + +## Preparing the dataset +Use the `process_speech_commands_data.py` script under /scripts/dataset_processing in order to prepare the dataset. + +```sh +python /scripts/dataset_processing/process_speech_commands_data.py \ + --data_root= \ + --data_version= \ + --class_split= \ + --rebalance \ + --log +``` + +## Train to convergence +```sh +python speech_to_label.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath=["",""] \ + trainer.devices=2 \ + trainer.accelerator="gpu" \ + strategy="ddp" \ + trainer.max_epochs=200 \ + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="MatchboxNet-3x1x64-v1" \ + exp_manager.wandb_logger_kwargs.project="MatchboxNet-v1" \ + +trainer.precision=16 \ + +trainer.amp_level=O1 # needed if using PyTorch < 1.6 +``` + + +# Task 2: Voice Activity Detection + +## Preparing the dataset +Use the `process_vad_data.py` script under /scripts/dataset_processing in order to prepare the dataset. + +```sh +python process_vad_data.py \ + --out_dir= \ + --speech_data_root= \ + --background_data_root= \ + --rebalance_method=<'under' or 'over' of 'fixed'> \ + --log + (Optional --demo (for demonstration in tutorial). If you want to use your own background noise data, make sure to delete --demo) +``` + +## Train to convergence +```sh +python speech_to_label.py \ + --config-path= + --config-name= \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath=["",""] \ + trainer.devices=2 \ + trainer.accelerator="gpu" \ + strategy="ddp" \ + trainer.max_epochs=200 \ + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="MatchboxNet-3x1x64-vad" \ + exp_manager.wandb_logger_kwargs.project="MatchboxNet-vad" \ + +trainer.precision=16 \ + +trainer.amp_level=O1 # needed if using PyTorch < 1.6 +``` + +# Task 3: Language Identification + +## Preparing the dataset +Use the `filelist_to_manifest.py` script under /scripts/speaker_tasks in order to prepare the dataset. +``` + +## Train to convergence +```sh +python speech_to_label.py \ + --config-path= + --config-name= \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath="" \ + model.train_ds.augmentor.noise.manifest_path="" \ + model.train_ds.augmentor.impulse.manifest_path="" \ + model.decoder.num_classes= \ + trainer.devices=2 \ + trainer.max_epochs=40 \ + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="titanet" \ + exp_manager.wandb_logger_kwargs.project="langid" \ + +exp_manager.checkpoint_callback_params.monitor="val_acc_macro" \ + +exp_manager.checkpoint_callback_params.mode="max" \ + +trainer.precision=16 \ +``` + + +# Optional: Use tarred dataset to speed up data loading. Apply to both tasks. +## Prepare tarred dataset. + Prepare ONE manifest that contains all training data you would like to include. Validation should use non-tarred dataset. + Note that it's possible that tarred datasets impacts validation scores because it drop values in order to have same amount of files per tarfile; + Scores might be off since some data is missing. + + Use the `convert_to_tarred_audio_dataset.py` script under /scripts/speech_recognition in order to prepare tarred audio dataset. + For details, please see TarredAudioToClassificationLabelDataset in /nemo/collections/asr/data/audio_to_label.py + +python speech_to_label.py \ + --config-path= + --config-name= \ + model.train_ds.manifest_filepath= \ + model.train_ds.is_tarred=True \ + model.train_ds.tarred_audio_filepaths= \ + +model.train_ds.num_worker= \ + model.validation_ds.manifest_filepath=\ + trainer.devices=2 \ + trainer.accelerator="gpu" \ + strategy="ddp" \ \ + trainer.max_epochs=200 \ + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="MatchboxNet-3x1x64-vad" \ + exp_manager.wandb_logger_kwargs.project="MatchboxNet-vad" \ + +trainer.precision=16 \ + +trainer.amp_level=O1 # needed if using PyTorch < 1.6 + +# Fine-tune a model + +For documentation on fine-tuning this model, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations + +# Pretrained Models + +For documentation on existing pretrained models, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speech_classification/results.html# + +""" +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf + +from nemo.collections.asr.models import EncDecClassificationModel, EncDecSpeakerLabelModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="../conf/matchboxnet", config_name="matchboxnet_3x1x64_v1") +def main(cfg): + + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + if 'titanet' in cfg.name.lower(): + model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer) + else: + model = EncDecClassificationModel(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + model.maybe_init_from_pretrained_checkpoint(cfg) + trainer.fit(model) + torch.distributed.destroy_process_group() + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if trainer.is_global_zero: + trainer = pl.Trainer(devices=1, accelerator=cfg.trainer.accelerator, strategy=cfg.trainer.strategy) + if model.prepare_test(trainer): + trainer.test(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/vad_infer.py b/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/vad_infer.py new file mode 100644 index 0000000..8ab040b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_classification/vad_infer.py @@ -0,0 +1,174 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +During inference, we perform frame-level prediction by two approaches: + 1) shift the window of length window_length_in_sec (e.g. 0.63s) by shift_length_in_sec (e.g. 10ms) to generate the frame and use the prediction of the window to represent the label for the frame; + [this script demonstrate how to do this approach] + 2) generate predictions with overlapping input segments. Then a smoothing filter is applied to decide the label for a frame spanned by multiple segments. + [get frame level prediction by this script and use vad_overlap_posterior.py in NeMo/scripts/voice_activity_detection + One can also find posterior about converting frame level prediction + to speech/no-speech segment in start and end times format in that script.] + + Image https://raw.githubusercontent.com/NVIDIA/NeMo/main/tutorials/asr/images/vad_post_overlap_diagram.png + will help you understand this method. + +This script will also help you perform postprocessing and generate speech segments if needed + +Usage: +python vad_infer.py --config-path="../conf/vad" --config-name="vad_inference_postprocessing.yaml" dataset= + +""" +import json +import os + +import torch + +from nemo.collections.asr.parts.utils.speaker_utils import write_rttm2manifest +from nemo.collections.asr.parts.utils.vad_utils import ( + generate_overlap_vad_seq, + generate_vad_frame_pred, + generate_vad_segment_table, + init_vad_model, + prepare_manifest, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging + +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + +@hydra_runner(config_path="../conf/vad", config_name="vad_inference_postprocessing.yaml") +def main(cfg): + if not cfg.dataset: + raise ValueError("You must input the path of json file of evaluation data") + + # each line of dataset should be have different audio_filepath and unique name to simplify edge cases or conditions + key_meta_map = {} + with open(cfg.dataset, 'r') as manifest: + for line in manifest.readlines(): + audio_filepath = json.loads(line.strip())['audio_filepath'] + uniq_audio_name = audio_filepath.split('/')[-1].rsplit('.', 1)[0] + if uniq_audio_name in key_meta_map: + raise ValueError("Please make sure each line is with different audio_filepath! ") + key_meta_map[uniq_audio_name] = {'audio_filepath': audio_filepath} + + # Prepare manifest for streaming VAD + manifest_vad_input = cfg.dataset + if cfg.prepare_manifest.auto_split: + logging.info("Split long audio file to avoid CUDA memory issue") + logging.debug("Try smaller split_duration if you still have CUDA memory issue") + config = { + 'input': manifest_vad_input, + 'window_length_in_sec': cfg.vad.parameters.window_length_in_sec, + 'split_duration': cfg.prepare_manifest.split_duration, + 'num_workers': cfg.num_workers, + 'prepared_manifest_vad_input': cfg.prepared_manifest_vad_input, + } + manifest_vad_input = prepare_manifest(config) + else: + logging.warning( + "If you encounter CUDA memory issue, try splitting manifest entry by split_duration to avoid it." + ) + + torch.set_grad_enabled(False) + vad_model = init_vad_model(cfg.vad.model_path) + + # setup_test_data + vad_model.setup_test_data( + test_data_config={ + 'vad_stream': True, + 'sample_rate': 16000, + 'manifest_filepath': manifest_vad_input, + 'labels': ['infer',], + 'num_workers': cfg.num_workers, + 'shuffle': False, + 'window_length_in_sec': cfg.vad.parameters.window_length_in_sec, + 'shift_length_in_sec': cfg.vad.parameters.shift_length_in_sec, + 'trim_silence': False, + 'normalize_audio': cfg.vad.parameters.normalize_audio, + } + ) + + vad_model = vad_model.to(device) + vad_model.eval() + + if not os.path.exists(cfg.frame_out_dir): + os.mkdir(cfg.frame_out_dir) + else: + logging.warning( + "Note frame_out_dir exists. If new file has same name as file inside existing folder, it will append result to existing file and might cause mistakes for next steps." + ) + + logging.info("Generating frame level prediction ") + pred_dir = generate_vad_frame_pred( + vad_model=vad_model, + window_length_in_sec=cfg.vad.parameters.window_length_in_sec, + shift_length_in_sec=cfg.vad.parameters.shift_length_in_sec, + manifest_vad_input=manifest_vad_input, + out_dir=cfg.frame_out_dir, + ) + logging.info( + f"Finish generating VAD frame level prediction with window_length_in_sec={cfg.vad.parameters.window_length_in_sec} and shift_length_in_sec={cfg.vad.parameters.shift_length_in_sec}" + ) + frame_length_in_sec = cfg.vad.parameters.shift_length_in_sec + + # overlap smoothing filter + if cfg.vad.parameters.smoothing: + # Generate predictions with overlapping input segments. Then a smoothing filter is applied to decide the label for a frame spanned by multiple segments. + # smoothing_method would be either in majority vote (median) or average (mean) + logging.info("Generating predictions with overlapping input segments") + smoothing_pred_dir = generate_overlap_vad_seq( + frame_pred_dir=pred_dir, + smoothing_method=cfg.vad.parameters.smoothing, + overlap=cfg.vad.parameters.overlap, + window_length_in_sec=cfg.vad.parameters.window_length_in_sec, + shift_length_in_sec=cfg.vad.parameters.shift_length_in_sec, + num_workers=cfg.num_workers, + out_dir=cfg.smoothing_out_dir, + ) + logging.info( + f"Finish generating predictions with overlapping input segments with smoothing_method={cfg.vad.parameters.smoothing} and overlap={cfg.vad.parameters.overlap}" + ) + pred_dir = smoothing_pred_dir + frame_length_in_sec = 0.01 + + # postprocessing and generate speech segments + if cfg.gen_seg_table: + logging.info("Converting frame level prediction to speech/no-speech segment in start and end times format.") + table_out_dir = generate_vad_segment_table( + vad_pred_dir=pred_dir, + postprocessing_params=cfg.vad.parameters.postprocessing, + frame_length_in_sec=frame_length_in_sec, + num_workers=cfg.num_workers, + out_dir=cfg.table_out_dir, + ) + logging.info( + f"Finish generating speech semgents table with postprocessing_params: {cfg.vad.parameters.postprocessing}" + ) + + if cfg.write_to_manifest: + for i in key_meta_map: + key_meta_map[i]['rttm_filepath'] = os.path.join(table_out_dir, i + ".txt") + + if not cfg.out_manifest_filepath: + out_manifest_filepath = "vad_out.json" + else: + out_manifest_filepath = cfg.out_manifest_filepath + out_manifest_filepath = write_rttm2manifest(key_meta_map, out_manifest_filepath) + logging.info(f"Writing VAD output to manifest: {out_manifest_filepath}") + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_multitask/speech_to_text_aed.py b/NeMo-2.0.0.rc0.beta/examples/asr/speech_multitask/speech_to_text_aed.py new file mode 100644 index 0000000..b0e5333 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_multitask/speech_to_text_aed.py @@ -0,0 +1,91 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Training the model +```sh +python speech_to_text_aed.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.tarred_audio_filepaths= \ + model.train_ds.manifest_filepath= \ + model.train_ds.batch_duration=360 \ + model.train_ds.num_buckets=30 \ + model.train_ds.bucket_duration_bins= \ + model.validation_ds.manifest_filepath= \ + model.test_ds.manifest_filepath= \ + model.model_defaults.asr_enc_hidden=1024 \ + model.model_defaults.lm_enc_hidden=512 \ + model.model_defaults.lm_dec_hidden=1024 \ + model.tokenizer.langs.spl_tokens.dir= \ + model.tokenizer.langs.spl_tokens.type=bpe \ + model.tokenizer.langs.en.dir= \ + model.tokenizer.langs.en.type=bpe \ + model.prompt_format="canary" \ + trainer.devices=-1 \ + trainer.accelerator="ddp" \ + trainer.max_steps=100000 \ + +trainer.limit_train_batches=20000 \ + trainer.val_check_interval=5000 \ + +trainer.use_distributed_sampler=false \ + model.optim.name="adamw" \ + model.optim.lr=0.001 \ + model.optim.betas=[0.9,0.999] \ + model.optim.weight_decay=0.0001 \ + model.optim.sched.warmup_steps=2000 \ + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="" \ + exp_manager.wandb_logger_kwargs.project="" +``` + + +""" +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models import EncDecMultiTaskModel +from nemo.core.config import hydra_runner +from nemo.utils import logging, model_utils +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="../conf/speech_multitask/", config_name="fast-conformer_aed") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + # Check for spl tokens to create spl_tokenizer. + if cfg.get("spl_tokens"): + logging.info("Detected spl_tokens config. Building tokenizer.") + spl_cfg = cfg["spl_tokens"] + spl_tokenizer_cls = model_utils.import_class_by_path(cfg.model.tokenizer.custom_tokenizer["_target_"]) + spl_tokenizer_cls.build_special_tokenizer( + spl_cfg["tokens"], spl_cfg["model_dir"], force_rebuild=spl_cfg["force_rebuild"] + ) + cfg.model.tokenizer.langs.spl_tokens.dir = spl_cfg["model_dir"] + + aed_model = EncDecMultiTaskModel(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + aed_model.maybe_init_from_pretrained_checkpoint(cfg) + trainer.fit(aed_model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if aed_model.prepare_test(trainer): + trainer.test(aed_model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_multitask/speech_to_text_aed_chunked_infer.py b/NeMo-2.0.0.rc0.beta/examples/asr/speech_multitask/speech_to_text_aed_chunked_infer.py new file mode 100644 index 0000000..52d3a86 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_multitask/speech_to_text_aed_chunked_infer.py @@ -0,0 +1,237 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script chunks long audios into non-overlapping segments of `chunk_len_in_secs` seconds and performs inference on each +segment individually. The results are then concatenated to form the final output. + +Below is an example of how to run this script with the Canary-1b model. +It's recommended to use manifest input, otherwise the model will perform English ASR with punctuations and capitalizations. +An example manifest line: +{ + "audio_filepath": "/path/to/audio.wav", # path to the audio file + "duration": 10000.0, # duration of the audio + "taskname": "asr", # use "s2t_translation" for AST + "source_lang": "en", # Set `source_lang`==`target_lang` for ASR, choices=['en','de','es','fr'] + "target_lang": "de", # choices=['en','de','es','fr'] + "pnc": "yes", # whether to have PnC output, choices=['yes', 'no'] +} + +Example Usage: +python speech_to_text_aed_chunked_infer.py \ + model_path=null \ + pretrained_name="nvidia/canary-1b" \ + audio_dir="<(optional) path to folder of audio files>" \ + dataset_manifest="<(optional) path to manifest>" \ + output_filename="<(optional) specify output filename>" \ + chunk_len_in_secs=40.0 \ + batch_size=16 \ + decoding.beam.beam_size=5 + +""" + +import contextlib +import copy +import glob +import os +from dataclasses import dataclass, is_dataclass +from typing import Optional + +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf + +from nemo.collections.asr.parts.submodules.multitask_decoding import MultiTaskDecodingConfig +from nemo.collections.asr.parts.utils.eval_utils import cal_write_wer +from nemo.collections.asr.parts.utils.streaming_utils import FrameBatchMultiTaskAED +from nemo.collections.asr.parts.utils.transcribe_utils import ( + compute_output_filename, + get_buffered_pred_feat_multitaskAED, + setup_model, + write_transcription, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@dataclass +class TranscriptionConfig: + # Required configs + model_path: Optional[str] = None # Path to a .nemo file + pretrained_name: Optional[str] = None # Name of a pretrained model + audio_dir: Optional[str] = None # Path to a directory which contains audio files + dataset_manifest: Optional[str] = None # Path to dataset's JSON manifest + + # General configs + output_filename: Optional[str] = None # if None, output will be stored in the same directory as the input + batch_size: int = 8 # number of chunks to process in parallel. + append_pred: bool = False # Sets mode of work, if True it will add new field transcriptions. + pred_name_postfix: Optional[str] = None # If you need to use another model name, rather than standard one. + random_seed: Optional[int] = None # seed number going to be used in seed_everything() + + # Set to True to output greedy timestamp information (only supported models) + compute_timestamps: bool = False + + # Set to True to output language ID information + compute_langs: bool = False + + # Chunked configs + chunk_len_in_secs: float = 40.0 # Chunk length in seconds + model_stride: int = 8 # Model downsampling factor, 8 for Citrinet and FasConformer models and 4 for Conformer models. + + # Decoding strategy for MultitaskAED models + decoding: MultiTaskDecodingConfig = MultiTaskDecodingConfig() + + # Set `cuda` to int to define CUDA device. If 'None', will look for CUDA + # device anyway, and do inference on CPU only if CUDA device is not found. + # If `cuda` is a negative number, inference will be on CPU only. + cuda: Optional[int] = None + amp: bool = False + amp_dtype: str = "float16" # can be set to "float16" or "bfloat16" when using amp + matmul_precision: str = "highest" # Literal["highest", "high", "medium"] + audio_type: str = "wav" + + # Recompute model transcription, even if the output folder exists with scores. + overwrite_transcripts: bool = True + + # Config for word / character error rate calculation + calculate_wer: bool = True + clean_groundtruth_text: bool = False + langid: str = "en" # specify this for convert_num_to_words step in groundtruth cleaning + use_cer: bool = False + + +@hydra_runner(config_name="TranscriptionConfig", schema=TranscriptionConfig) +def main(cfg: TranscriptionConfig) -> TranscriptionConfig: + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + torch.set_grad_enabled(False) + + for key in cfg: + cfg[key] = None if cfg[key] == 'None' else cfg[key] + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if cfg.random_seed: + pl.seed_everything(cfg.random_seed) + + if cfg.model_path is None and cfg.pretrained_name is None: + raise ValueError("Both cfg.model_path and cfg.pretrained_name cannot be None!") + if cfg.audio_dir is None and cfg.dataset_manifest is None: + raise ValueError("Both cfg.audio_dir and cfg.dataset_manifest cannot be None!") + + filepaths = None + manifest = cfg.dataset_manifest + if cfg.audio_dir is not None: + filepaths = list(glob.glob(os.path.join(cfg.audio_dir, f"**/*.{cfg.audio_type}"), recursive=True)) + manifest = None # ignore dataset_manifest if audio_dir and dataset_manifest both presents + + # setup GPU + torch.set_float32_matmul_precision(cfg.matmul_precision) + if cfg.cuda is None: + if torch.cuda.is_available(): + device = [0] # use 0th CUDA device + accelerator = 'gpu' + else: + device = 1 + accelerator = 'cpu' + else: + device = [cfg.cuda] + accelerator = 'gpu' + map_location = torch.device('cuda:{}'.format(device[0]) if accelerator == 'gpu' else 'cpu') + logging.info(f"Inference will be done on device : {device}") + + asr_model, model_name = setup_model(cfg, map_location) + + model_cfg = copy.deepcopy(asr_model._cfg) + OmegaConf.set_struct(model_cfg.preprocessor, False) + # some changes for streaming scenario + model_cfg.preprocessor.dither = 0.0 + model_cfg.preprocessor.pad_to = 0 + + if model_cfg.preprocessor.normalize != "per_feature": + logging.error( + "Only EncDecMultiTaskModel models trained with per_feature normalization are supported currently" + ) + + # Disable config overwriting + OmegaConf.set_struct(model_cfg.preprocessor, True) + + # setup AMP (optional) + if cfg.amp and torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and hasattr(torch.cuda.amp, 'autocast'): + logging.info("AMP enabled!\n") + autocast = torch.cuda.amp.autocast + else: + + @contextlib.contextmanager + def autocast(*args, **kwargs): + yield + + # Compute output filename + cfg = compute_output_filename(cfg, model_name) + + # if transcripts should not be overwritten, and already exists, skip re-transcription step and return + if not cfg.overwrite_transcripts and os.path.exists(cfg.output_filename): + logging.info( + f"Previous transcripts found at {cfg.output_filename}, and flag `overwrite_transcripts`" + f"is {cfg.overwrite_transcripts}. Returning without re-transcribing text." + ) + return cfg + + asr_model.change_decoding_strategy(cfg.decoding) + + asr_model.eval() + asr_model = asr_model.to(asr_model.device) + + feature_stride = model_cfg.preprocessor['window_stride'] + model_stride_in_secs = feature_stride * cfg.model_stride + + frame_asr = FrameBatchMultiTaskAED( + asr_model=asr_model, + frame_len=cfg.chunk_len_in_secs, + total_buffer=cfg.chunk_len_in_secs, + batch_size=cfg.batch_size, + ) + + amp_dtype = torch.float16 if cfg.amp_dtype == "float16" else torch.bfloat16 + + with autocast(dtype=amp_dtype): + with torch.no_grad(): + hyps = get_buffered_pred_feat_multitaskAED( + frame_asr, model_cfg.preprocessor, model_stride_in_secs, asr_model.device, manifest, filepaths, + ) + + output_filename, pred_text_attr_name = write_transcription( + hyps, cfg, model_name, filepaths=filepaths, compute_langs=False, compute_timestamps=False + ) + logging.info(f"Finished writing predictions to {output_filename}!") + + if cfg.calculate_wer: + output_manifest_w_wer, total_res, _ = cal_write_wer( + pred_manifest=output_filename, + pred_text_attr_name=pred_text_attr_name, + clean_groundtruth_text=cfg.clean_groundtruth_text, + langid=cfg.langid, + use_cer=cfg.use_cer, + output_filename=None, + ) + if output_manifest_w_wer: + logging.info(f"Writing prediction and error rate of each sample to {output_manifest_w_wer}!") + logging.info(f"{total_res}") + + return cfg + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_pretraining/README.md b/NeMo-2.0.0.rc0.beta/examples/asr/speech_pretraining/README.md new file mode 100644 index 0000000..75ae9e5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_pretraining/README.md @@ -0,0 +1,27 @@ +# Speech Pre-training via Self Supervised Learning + +This directory contains example scripts to train ASR models using various Self Supervised Losses. + +The model's pretrained here can further be finetuned on specific labeled data in further steps. + +# Model execution overview + +The training scripts in this directory execute in the following order. When preparing your own training-from-scratch / fine-tuning scripts, please follow this order for correct training/inference. + +```mermaid + +graph TD + A[Hydra Overrides + Yaml Config] --> B{Config} + B --> |Init| C[Trainer] + C --> D[ExpManager] + B --> D[ExpManager] + C --> E[Model] + B --> |Init| E[Model] + E --> |Constructor| G(Setup Train + Validation Data loaders) + G --> H(Setup Optimization) + H --> I[Maybe init from pretrained] + I --> J["trainer.fit(model)"] + +``` + +During restoration of the model, you may pass the Trainer to the restore_from / from_pretrained call, or set it after the model has been initialized by using `model.set_trainer(Trainer)`. \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_pretraining/speech_pre_training.py b/NeMo-2.0.0.rc0.beta/examples/asr/speech_pretraining/speech_pre_training.py new file mode 100644 index 0000000..a7200a1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_pretraining/speech_pre_training.py @@ -0,0 +1,68 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models.ssl_models import SpeechEncDecSelfSupervisedModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +""" +# Example of unsupervised pre-training of a model +```sh +python speech_pre_training.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath= \ + model.validation_ds.manifest_filepath= \ + trainer.devices=-1 \ + trainer.accelerator="gpu" \ + strategy="ddp" \ + trainer.max_epochs=100 \ + model.optim.name="adamw" \ + model.optim.lr=0.001 \ + model.optim.betas=[0.9,0.999] \ + model.optim.weight_decay=0.0001 \ + model.optim.sched.warmup_steps=2000 + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="" \ + exp_manager.wandb_logger_kwargs.project="" +``` + +For documentation on fine-tuning, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations +When doing supervised fine-tuning from unsupervised pre-trained encoder, set flag init_strict to False + +""" + + +@hydra_runner(config_path="../conf/ssl/citrinet/", config_name="citrinet_ssl_1024") +def main(cfg): + logging.info(f"Hydra config: {OmegaConf.to_yaml(cfg)}") + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + asr_model = SpeechEncDecSelfSupervisedModel(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + asr_model.maybe_init_from_pretrained_checkpoint(cfg) + + trainer.fit(asr_model) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_to_text_eval.py b/NeMo-2.0.0.rc0.beta/examples/asr/speech_to_text_eval.py new file mode 100644 index 0000000..7b59ffe --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_to_text_eval.py @@ -0,0 +1,225 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Script to compute the Word or Character Error Rate of a given ASR model for a given manifest file for some dataset. +The manifest file must conform to standard ASR definition - containing `audio_filepath` and `text` as the ground truth. + +Note: This script depends on the `transcribe_speech.py` script, and therefore both scripts should be located in the +same directory during execution. + +# Arguments + +<< All arguments of `transcribe_speech.py` are inherited by this script, so please refer to `transcribe_speech.py` +for full list of arguments >> + + dataset_manifest: Required - path to dataset JSON manifest file (in NeMo format) + output_filename: Optional - output filename where the transcriptions will be written. (if scores_per_sample=True, + metrics per sample will be written there too) + + use_cer: Bool, whether to compute CER or WER + use_punct_er: Bool, compute dataset Punctuation Error Rate (set the punctuation marks for metrics computation with + "text_processing.punctuation_marks") + + tolerance: Float, minimum WER/CER required to pass some arbitrary tolerance. + + only_score_manifest: Bool, when set will skip audio transcription and just calculate WER of provided manifest. + scores_per_sample: Bool, compute metrics for each sample separately (if only_score_manifest=True, scores per sample + will be added to the manifest at the dataset_manifest path) + +# Usage + +## To score a dataset with a manifest file that does not contain previously transcribed `pred_text`. + +python speech_to_text_eval.py \ + model_path=null \ + pretrained_name=null \ + dataset_manifest= \ + output_filename= \ + batch_size=32 \ + amp=True \ + use_cer=False + +## To score a manifest file which has been previously augmented with transcribed text as `pred_text` +This is useful when one uses `transcribe_speech_parallel.py` to transcribe larger datasets, and results are written +to a manifest which has the two keys `text` (for ground truth) and `pred_text` (for model's transcription) + +python speech_to_text_eval.py \ + dataset_manifest= \ + use_cer=False \ + only_score_manifest=True + +""" + +import json +import os +from dataclasses import dataclass, is_dataclass +from typing import Optional + +import torch +import transcribe_speech +from omegaconf import MISSING, OmegaConf, open_dict + +from nemo.collections.asr.metrics.wer import word_error_rate +from nemo.collections.asr.parts.utils.transcribe_utils import ( + PunctuationCapitalization, + TextProcessingConfig, + compute_metrics_per_sample, +) +from nemo.collections.common.metrics.punct_er import DatasetPunctuationErrorRate +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@dataclass +class EvaluationConfig(transcribe_speech.TranscriptionConfig): + dataset_manifest: str = MISSING + output_filename: Optional[str] = "evaluation_transcripts.json" + + # decoder type: ctc or rnnt, can be used to switch between CTC and RNNT decoder for Joint RNNT/CTC models + decoder_type: Optional[str] = None + # att_context_size can be set for cache-aware streaming models with multiple look-aheads + att_context_size: Optional[list] = None + + use_cer: bool = False + use_punct_er: bool = False + tolerance: Optional[float] = None + + only_score_manifest: bool = False + scores_per_sample: bool = False + + text_processing: Optional[TextProcessingConfig] = TextProcessingConfig( + punctuation_marks=".,?", separate_punctuation=False, do_lowercase=False, rm_punctuation=False, + ) + + +@hydra_runner(config_name="EvaluationConfig", schema=EvaluationConfig) +def main(cfg: EvaluationConfig): + torch.set_grad_enabled(False) + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if cfg.audio_dir is not None: + raise RuntimeError( + "Evaluation script requires ground truth labels to be passed via a manifest file. " + "If manifest file is available, submit it via `dataset_manifest` argument." + ) + + if not os.path.exists(cfg.dataset_manifest): + raise FileNotFoundError(f"The dataset manifest file could not be found at path : {cfg.dataset_manifest}") + + if not cfg.only_score_manifest: + # Transcribe speech into an output directory + transcription_cfg = transcribe_speech.main(cfg) # type: EvaluationConfig + + # Release GPU memory if it was used during transcription + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + logging.info("Finished transcribing speech dataset. Computing ASR metrics..") + + else: + cfg.output_filename = cfg.dataset_manifest + transcription_cfg = cfg + + ground_truth_text = [] + predicted_text = [] + invalid_manifest = False + with open(transcription_cfg.output_filename, 'r') as f: + for line in f: + data = json.loads(line) + + if "pred_text" not in data: + invalid_manifest = True + break + + ground_truth_text.append(data[cfg.gt_text_attr_name]) + + predicted_text.append(data["pred_text"]) + + pc = PunctuationCapitalization(cfg.text_processing.punctuation_marks) + if cfg.text_processing.separate_punctuation: + ground_truth_text = pc.separate_punctuation(ground_truth_text) + predicted_text = pc.separate_punctuation(predicted_text) + if cfg.text_processing.do_lowercase: + ground_truth_text = pc.do_lowercase(ground_truth_text) + predicted_text = pc.do_lowercase(predicted_text) + if cfg.text_processing.rm_punctuation: + ground_truth_text = pc.rm_punctuation(ground_truth_text) + predicted_text = pc.rm_punctuation(predicted_text) + + # Test for invalid manifest supplied + if invalid_manifest: + raise ValueError( + f"Invalid manifest provided: {transcription_cfg.output_filename} does not " + f"contain value for `pred_text`." + ) + + if cfg.use_punct_er: + dper_obj = DatasetPunctuationErrorRate( + hypotheses=predicted_text, + references=ground_truth_text, + punctuation_marks=list(cfg.text_processing.punctuation_marks), + ) + dper_obj.compute() + + if cfg.scores_per_sample: + metrics_to_compute = ["wer", "cer"] + + if cfg.use_punct_er: + metrics_to_compute.append("punct_er") + + samples_with_metrics = compute_metrics_per_sample( + manifest_path=cfg.dataset_manifest, + reference_field=cfg.gt_text_attr_name, + hypothesis_field="pred_text", + metrics=metrics_to_compute, + punctuation_marks=cfg.text_processing.punctuation_marks, + output_manifest_path=cfg.output_filename, + ) + + # Compute the WER + cer = word_error_rate(hypotheses=predicted_text, references=ground_truth_text, use_cer=True) + wer = word_error_rate(hypotheses=predicted_text, references=ground_truth_text, use_cer=False) + + if cfg.use_cer: + metric_name = 'CER' + metric_value = cer + else: + metric_name = 'WER' + metric_value = wer + + if cfg.tolerance is not None: + if metric_value > cfg.tolerance: + raise ValueError(f"Got {metric_name} of {metric_value}, which was higher than tolerance={cfg.tolerance}") + + logging.info(f'Got {metric_name} of {metric_value}. Tolerance was {cfg.tolerance}') + + logging.info(f"Dataset WER/CER {wer:.2%}/{cer:.2%}") + + if cfg.use_punct_er: + dper_obj.print() + dper_obj.reset() + + # Inject the metric name and score into the config, and return the entire config + with open_dict(cfg): + cfg.metric_name = metric_name + cfg.metric_value = metric_value + + return cfg + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_to_text_finetune.py b/NeMo-2.0.0.rc0.beta/examples/asr/speech_to_text_finetune.py new file mode 100644 index 0000000..dbdefef --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_to_text_finetune.py @@ -0,0 +1,219 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script can used to fine-tune a speech-to-text model of any instance type when users want to +fine-tune an existing model without changing its core architecture but may change the tokenizer. +One can mention the pretrained model in two ways: +1) `init_from_nemo_model` or +2) `init_from_pretrained_model` in the configuration. + +To update the model architecture in conjunction with other modifications, it is advisable to use the primary 'speech_to_text_rnnt/ctc_*.py' script. + +Note: To create a single script for all model types, we currently only support two types of +initializations: +1) `init_from_nemo_model`, and +2) `init_from_pretrained_model`, +but not `init_from_ptl_ckpt`. + +To train with prior base model tokenizer keep `model.tokenizer.update_tokenizer` as false else +make it true and provide tokenizer dir along with tokenizer type. + +To fine-tune the model, use the following commands: + +For initialization from a NEMO model: +```sh +python /examples/asr/speech_to_text_finetune.py \ + init_from_nemo_model= +``` + +For initialization from a pretrained model: +```sh +python /examples/asr/speech_to_text_finetune.py \ + init_from_pretrained_model= +``` + +# Fine-Tune a Model + +For documentation on fine-tuning this model, please visit: +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations +""" +import time +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models import ASRModel +from nemo.core.config import hydra_runner +from nemo.utils import logging, model_utils +from nemo.utils.exp_manager import exp_manager +from nemo.utils.get_rank import is_global_rank_zero + + +def get_base_model(trainer, cfg): + """ + Returns the base model to be fine-tuned. + Currently supports two types of initializations: + 1) `init_from_nemo_model`, and + 2) `init_from_pretrained_model`. + Args: + trainer: PyTorch Lightning Trainer + cfg: config + Returns: + asr_model: ASRModel instance + """ + asr_model = None + nemo_model_path = cfg.get('init_from_nemo_model', None) + pretrained_name = cfg.get('init_from_pretrained_model', None) + if nemo_model_path is not None and pretrained_name is not None: + raise ValueError("Only pass `init_from_nemo_model` or `init_from_pretrained_model` but not both") + elif nemo_model_path is None and pretrained_name is None: + raise ValueError( + "Both `init_from_nemo_model` and `init_from_pretrained_model cannot be None, should pass atleast one of them" + ) + elif nemo_model_path is not None: + asr_model = ASRModel.restore_from(restore_path=nemo_model_path) + elif pretrained_name is not None: + # Due to potential first time download of the model on the cluster, we need to make sure that only one + # rank downloads the model and the others wait for the download to finish. + num_ranks = trainer.num_devices * trainer.num_devices + + if num_ranks > 1 and is_global_rank_zero(): + asr_model = ASRModel.from_pretrained(model_name=pretrained_name) + else: + # Sleep on all ranks for at least 60 seconds + wait_time = int(cfg.get('exp_manager', {}).get('seconds_to_sleep', 60)) + if wait_time < 60: + wait_time = 60 + + logging.info(f"Sleeping for at least {wait_time} seconds to wait for model download to finish.") + + time.sleep(wait_time) + + # restore model from cached model dir + asr_model = ASRModel.from_pretrained(model_name=pretrained_name) + + return asr_model + + +def check_vocabulary(asr_model, cfg): + """ + Checks if the decoder and vocabulary of the model needs to be updated. + If either of them needs to be updated, it updates them and returns the updated model. + else vocabulary will be reused from the pre-trained model. + Args: + asr_model: ASRModel instance + cfg: config + Returns: + asr_model: ASRModel instance with updated decoder and vocabulary + """ + if hasattr(cfg.model.tokenizer, 'update_tokenizer') and cfg.model.tokenizer.update_tokenizer: + if hasattr(cfg.model.char_labels, 'update_labels') and cfg.model.char_labels.update_labels: + raise ValueError( + "Both `model.tokenizer.update_tokenizer` and `model.char_labels.update_labels` cannot be passed together" + ) + else: + asr_model = update_tokenizer(asr_model, cfg.model.tokenizer.dir, cfg.model.tokenizer.type) + elif hasattr(cfg.model, 'char_labels') and cfg.model.char_labels.update_labels: + asr_model.change_vocabulary(new_vocabulary=cfg.model.char_labels.labels) + logging.warning("The vocabulary of the model has been updated with provided char labels.") + else: + logging.info("Reusing the vocabulary from the pre-trained model.") + + return asr_model + + +def update_tokenizer(asr_model, tokenizer_dir, tokenizer_type): + """ + Updates the tokenizer of the model and also reinitializes the decoder if the vocabulary size + of the new tokenizer differs from that of the loaded model. + Args: + asr_model: ASRModel instance + tokenizer_dir: tokenizer directory + tokenizer_type: tokenizer type + Returns: + asr_model: ASRModel instance with updated tokenizer and decoder + """ + vocab_size = asr_model.tokenizer.vocab_size + decoder = asr_model.decoder.state_dict() + if hasattr(asr_model, 'joint'): + joint_state = asr_model.joint.state_dict() + else: + joint_state = None + + if tokenizer_dir is None: + raise ValueError("dir must be specified if update_tokenizer is True") + logging.info("Using the tokenizer provided through config") + asr_model.change_vocabulary(new_tokenizer_dir=tokenizer_dir, new_tokenizer_type=tokenizer_type) + if asr_model.tokenizer.vocab_size != vocab_size: + logging.warning( + "The vocabulary size of the new tokenizer differs from that of the loaded model. As a result, finetuning will proceed with the new vocabulary, and the decoder will be reinitialized." + ) + else: + asr_model.decoder.load_state_dict(decoder) + if joint_state is not None: + asr_model.joint.load_state_dict(joint_state) + + return asr_model + + +def setup_dataloaders(asr_model, cfg): + """ + Sets up the training, validation and test dataloaders for the model. + Args: + asr_model: ASRModel instance + cfg: config + Returns: + asr_model: ASRModel instance with updated dataloaders + """ + cfg = model_utils.convert_model_config_to_dict_config(cfg) + asr_model.setup_training_data(cfg.model.train_ds) + asr_model.setup_multiple_validation_data(cfg.model.validation_ds) + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + asr_model.setup_multiple_test_data(cfg.model.test_ds) + + return asr_model + + +@hydra_runner(config_path="conf/asr_finetune", config_name="speech_to_text_finetune") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + if hasattr(cfg, 'init_from_ptl_ckpt') and cfg.init_from_ptl_ckpt is not None: + raise NotImplementedError( + "Currently for simplicity of single script for all model types, we only support `init_from_nemo_model` and `init_from_pretrained_model`" + ) + + asr_model = get_base_model(trainer, cfg) + + # Check vocabulary type and update if needed + asr_model = check_vocabulary(asr_model, cfg) + + # Setup Data + asr_model = setup_dataloaders(asr_model, cfg) + + # Setup Optimizer + asr_model.setup_optimization(cfg.model.optim) + + # Setup SpecAug + if hasattr(cfg.model, 'spec_augment') and cfg.model.spec_augment is not None: + asr_model.spec_augment = ASRModel.from_config_dict(cfg.model.spec_augment) + + trainer.fit(asr_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_translation/speech_to_text_transformer.py b/NeMo-2.0.0.rc0.beta/examples/asr/speech_translation/speech_to_text_transformer.py new file mode 100644 index 0000000..56b600e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_translation/speech_to_text_transformer.py @@ -0,0 +1,70 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Training the model +```sh +python speech_to_text_transformer.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.audio.tarred_audio_filepaths= \ + model.train_ds.audio_manifest_filepath= \ + model.validation_ds.manifest_filepath= \ + model.test_ds.manifest_filepath= \ + model.tokenizer.dir= \ + model.tokenizer.model_path= \ + model.tokenizer.type= \ + trainer.devices=-1 \ + trainer.accelerator="ddp" \ + trainer.max_epochs=100 \ + model.optim.name="adamw" \ + model.optim.lr=0.001 \ + model.optim.betas=[0.9,0.999] \ + model.optim.weight_decay=0.0001 \ + model.optim.sched.warmup_steps=2000 + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="" \ + exp_manager.wandb_logger_kwargs.project="" +``` + + +""" + +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.asr.models import EncDecTransfModelBPE +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="../conf/speech_translation/", config_name="fast-conformer_transformer") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + asr_model = EncDecTransfModelBPE(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + asr_model.maybe_init_from_pretrained_checkpoint(cfg) + trainer.fit(asr_model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if asr_model.prepare_test(trainer): + trainer.test(asr_model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/speech_translation/translate_speech.py b/NeMo-2.0.0.rc0.beta/examples/asr/speech_translation/translate_speech.py new file mode 100644 index 0000000..203852b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/speech_translation/translate_speech.py @@ -0,0 +1,210 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import contextlib +import json +import os +from dataclasses import dataclass, is_dataclass +from typing import List, Optional, Union + +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf + +from nemo.collections.asr.modules.conformer_encoder import ConformerChangeConfig +from nemo.collections.asr.parts.utils.transcribe_utils import compute_output_filename, prepare_audio_data, setup_model +from nemo.core.config import hydra_runner +from nemo.utils import logging + +""" +Translate audio file on a single CPU/GPU. Useful for translations of moderate amounts of audio data. + +# Arguments + model_path: path to .nemo ST checkpoint + pretrained_name: name of pretrained ST model (from NGC registry) + audio_dir: path to directory with audio files + dataset_manifest: path to dataset JSON manifest file (in NeMo format) + + output_filename: Output filename where the translations will be written + batch_size: batch size during inference + + cuda: Optional int to enable or disable execution of model on certain CUDA device. + allow_mps: Bool to allow using MPS (Apple Silicon M-series GPU) device if available + amp: Bool to decide if Automatic Mixed Precision should be used during inference + audio_type: Str filetype of the audio. Supported = wav, flac, mp3 + + overwrite_translations: Bool which when set allows repeated translations to overwrite previous results. + +# Usage +ST model can be specified by either "model_path" or "pretrained_name". +Data for translation can be defined with either "audio_dir" or "dataset_manifest". +Results are returned in a JSON manifest file. + +python translate_speech.py \ + model_path=null \ + pretrained_name=null \ + audio_dir="" \ + dataset_manifest="" \ + output_filename="" \ + batch_size=32 \ + cuda=0 \ + amp=True \ +""" + + +@dataclass +class ModelChangeConfig: + + # Sub-config for changes specific to the Conformer Encoder + conformer: ConformerChangeConfig = ConformerChangeConfig() + + +@dataclass +class TranslationConfig: + # Required configs + model_path: Optional[str] = None # Path to a .nemo file + pretrained_name: Optional[str] = None # Name of a pretrained model + audio_dir: Optional[str] = None # Path to a directory which contains audio files + dataset_manifest: Optional[str] = None # Path to dataset's JSON manifest + audio_key: str = 'audio_filepath' # Used to override the default audio key in dataset_manifest + eval_config_yaml: Optional[str] = None # Path to a yaml file of config of evaluation + + # General configs + output_filename: Optional[str] = None + batch_size: int = 32 + random_seed: Optional[int] = None # seed number going to be used in seed_everything() + + # Set `cuda` to int to define CUDA device. If 'None', will look for CUDA + # device anyway, and do inference on CPU only if CUDA device is not found. + # If `cuda` is a negative number, inference will be on CPU only. + cuda: Optional[int] = None + allow_mps: bool = False # allow to select MPS device (Apple Silicon M-series GPU) + amp: bool = False + audio_type: str = "wav" + + # Recompute model translation, even if the output folder exists with scores. + overwrite_translations: bool = True + + # can be set to True to return list of translations instead of the config + # if True, will also skip writing anything to the output file + return_translations: bool = False + + +@hydra_runner(config_name="TranslationConfig", schema=TranslationConfig) +def main(cfg: TranslationConfig) -> Union[TranslationConfig, List[str]]: + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + for key in cfg: + cfg[key] = None if cfg[key] == 'None' else cfg[key] + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if cfg.random_seed: + pl.seed_everything(cfg.random_seed) + + if cfg.model_path is None and cfg.pretrained_name is None: + raise ValueError("Both cfg.model_path and cfg.pretrained_name cannot be None!") + if cfg.audio_dir is None and cfg.dataset_manifest is None: + raise ValueError("Both cfg.audio_dir and cfg.dataset_manifest cannot be None!") + + # Load augmentor from exteranl yaml file which contains eval info, could be extend to other feature such VAD, P&C + augmentor = None + if cfg.eval_config_yaml: + eval_config = OmegaConf.load(cfg.eval_config_yaml) + augmentor = eval_config.test_ds.get("augmentor") + logging.info(f"Will apply on-the-fly augmentation on samples during translation: {augmentor} ") + + # setup GPU + if cfg.cuda is None: + if torch.cuda.is_available(): + device = [0] # use 0th CUDA device + accelerator = 'gpu' + map_location = torch.device('cuda:0') + elif cfg.allow_mps and hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): + logging.warning( + "MPS device (Apple Silicon M-series GPU) support is experimental." + " Env variable `PYTORCH_ENABLE_MPS_FALLBACK=1` should be set in most cases to avoid failures." + ) + device = [0] + accelerator = 'mps' + map_location = torch.device('mps') + else: + device = 1 + accelerator = 'cpu' + map_location = torch.device('cpu') + else: + device = [cfg.cuda] + accelerator = 'gpu' + map_location = torch.device(f'cuda:{cfg.cuda}') + + logging.info(f"Inference will be done on device: {map_location}") + + asr_model, model_name = setup_model(cfg, map_location) + trainer = pl.Trainer(devices=device, accelerator=accelerator) + asr_model.set_trainer(trainer) + asr_model = asr_model.eval() + + # collect additional translation information + return_hypotheses = False + + # prepare audio filepaths and decide wether it's partial audio + filepaths, partial_audio = prepare_audio_data(cfg) + + # setup AMP (optional) + if cfg.amp and torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and hasattr(torch.cuda.amp, 'autocast'): + logging.info("AMP enabled!\n") + autocast = torch.cuda.amp.autocast + else: + + @contextlib.contextmanager + def autocast(): + yield + + # Compute output filename + cfg = compute_output_filename(cfg, model_name) + + # if translations should not be overwritten, and already exists, skip re-translation step and return + if not cfg.return_translations and not cfg.overwrite_translations and os.path.exists(cfg.output_filename): + logging.info( + f"Previous translations found at {cfg.output_filename}, and flag `overwrite_translations`" + f"is {cfg.overwrite_translations}. Returning without re-translating text." + ) + return cfg + + # translate audio + with autocast(): + with torch.no_grad(): + translations = asr_model.translate( + paths2audio_files=filepaths, batch_size=cfg.batch_size, return_hypotheses=return_hypotheses, + ) + + logging.info(f"Finished translating {len(filepaths)} files !") + logging.info(f"Writing translations into file: {cfg.output_filename}") + + if cfg.return_translations: + return translations + + # write audio translations + with open(cfg.output_filename, 'w', encoding='utf-8', newline='\n') as f: + for filepath, translation in zip(filepaths, translations): + item = {'audio_filepath': filepath, 'pred_translation': translation} + f.write(json.dumps(item, ensure_ascii=False) + "\n") + logging.info(f"Finished writing predictions to {cfg.output_filename}!") + + return cfg + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/transcribe_speech.py b/NeMo-2.0.0.rc0.beta/examples/asr/transcribe_speech.py new file mode 100644 index 0000000..c8372c4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/transcribe_speech.py @@ -0,0 +1,466 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import contextlib +import glob +import json +import os +from dataclasses import dataclass, is_dataclass +from tempfile import NamedTemporaryFile +from typing import List, Optional, Union + +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf, open_dict + +from nemo.collections.asr.models import EncDecCTCModel, EncDecHybridRNNTCTCModel, EncDecMultiTaskModel +from nemo.collections.asr.modules.conformer_encoder import ConformerChangeConfig +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecodingConfig +from nemo.collections.asr.parts.submodules.multitask_decoding import MultiTaskDecoding, MultiTaskDecodingConfig +from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTDecodingConfig +from nemo.collections.asr.parts.utils.eval_utils import cal_write_wer +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis +from nemo.collections.asr.parts.utils.transcribe_utils import ( + compute_output_filename, + prepare_audio_data, + read_and_maybe_sort_manifest, + restore_transcription_order, + setup_model, + transcribe_partial_audio, + write_transcription, +) +from nemo.collections.common.parts.preprocessing.manifest import get_full_path +from nemo.core.config import hydra_runner +from nemo.utils import logging + +""" +Transcribe audio file on a single CPU/GPU. Useful for transcription of moderate amounts of audio data. + +# Arguments + model_path: path to .nemo ASR checkpoint + pretrained_name: name of pretrained ASR model (from NGC registry) + audio_dir: path to directory with audio files + dataset_manifest: path to dataset JSON manifest file (in NeMo format) + + compute_timestamps: Bool to request greedy time stamp information (if the model supports it) + compute_langs: Bool to request language ID information (if the model supports it) + + (Optionally: You can limit the type of timestamp computations using below overrides) + ctc_decoding.ctc_timestamp_type="all" # (default all, can be [all, char, word]) + rnnt_decoding.rnnt_timestamp_type="all" # (default all, can be [all, char, word]) + + (Optionally: You can limit the type of timestamp computations using below overrides) + ctc_decoding.ctc_timestamp_type="all" # (default all, can be [all, char, word]) + rnnt_decoding.rnnt_timestamp_type="all" # (default all, can be [all, char, word]) + + output_filename: Output filename where the transcriptions will be written + batch_size: batch size during inference + + cuda: Optional int to enable or disable execution of model on certain CUDA device. + allow_mps: Bool to allow using MPS (Apple Silicon M-series GPU) device if available + amp: Bool to decide if Automatic Mixed Precision should be used during inference + audio_type: Str filetype of the audio. Supported = wav, flac, mp3 + + overwrite_transcripts: Bool which when set allows repeated transcriptions to overwrite previous results. + + ctc_decoding: Decoding sub-config for CTC. Refer to documentation for specific values. + rnnt_decoding: Decoding sub-config for RNNT. Refer to documentation for specific values. + + calculate_wer: Bool to decide whether to calculate wer/cer at end of this script + clean_groundtruth_text: Bool to clean groundtruth text + langid: Str used for convert_num_to_words during groundtruth cleaning + use_cer: Bool to use Character Error Rate (CER) or Word Error Rate (WER) + +# Usage +ASR model can be specified by either "model_path" or "pretrained_name". +Data for transcription can be defined with either "audio_dir" or "dataset_manifest". +append_pred - optional. Allows you to add more than one prediction to an existing .json +pred_name_postfix - optional. The name you want to be written for the current model +Results are returned in a JSON manifest file. + +python transcribe_speech.py \ + model_path=null \ + pretrained_name=null \ + audio_dir="" \ + dataset_manifest="" \ + output_filename="" \ + clean_groundtruth_text=True \ + langid='en' \ + batch_size=32 \ + compute_timestamps=False \ + compute_langs=False \ + cuda=0 \ + amp=True \ + append_pred=False \ + pred_name_postfix="" +""" + + +@dataclass +class ModelChangeConfig: + + # Sub-config for changes specific to the Conformer Encoder + conformer: ConformerChangeConfig = ConformerChangeConfig() + + +@dataclass +class TranscriptionConfig: + # Required configs + model_path: Optional[str] = None # Path to a .nemo file + pretrained_name: Optional[str] = None # Name of a pretrained model + audio_dir: Optional[str] = None # Path to a directory which contains audio files + dataset_manifest: Optional[str] = None # Path to dataset's JSON manifest + channel_selector: Optional[ + Union[int, str] + ] = None # Used to select a single channel from multichannel audio, or use average across channels + audio_key: str = 'audio_filepath' # Used to override the default audio key in dataset_manifest + eval_config_yaml: Optional[str] = None # Path to a yaml file of config of evaluation + presort_manifest: bool = True # Significant inference speedup on short-form data due to padding reduction + + # General configs + output_filename: Optional[str] = None + batch_size: int = 32 + num_workers: int = 0 + append_pred: bool = False # Sets mode of work, if True it will add new field transcriptions. + pred_name_postfix: Optional[str] = None # If you need to use another model name, rather than standard one. + random_seed: Optional[int] = None # seed number going to be used in seed_everything() + + # Set to True to output greedy timestamp information (only supported models) + compute_timestamps: bool = False + # set to True if need to return full alignment information + preserve_alignment: bool = False + + # Set to True to output language ID information + compute_langs: bool = False + + # Set `cuda` to int to define CUDA device. If 'None', will look for CUDA + # device anyway, and do inference on CPU only if CUDA device is not found. + # If `cuda` is a negative number, inference will be on CPU only. + cuda: Optional[int] = None + allow_mps: bool = False # allow to select MPS device (Apple Silicon M-series GPU) + amp: bool = False + amp_dtype: str = "float16" # can be set to "float16" or "bfloat16" when using amp + matmul_precision: str = "highest" # Literal["highest", "high", "medium"] + audio_type: str = "wav" + + # Recompute model transcription, even if the output folder exists with scores. + overwrite_transcripts: bool = True + + # Decoding strategy for CTC models + ctc_decoding: CTCDecodingConfig = CTCDecodingConfig() + + # Decoding strategy for RNNT models + rnnt_decoding: RNNTDecodingConfig = RNNTDecodingConfig(fused_batch_size=-1) + + # Decoding strategy for AED models + multitask_decoding: MultiTaskDecodingConfig = MultiTaskDecodingConfig() + + # decoder type: ctc or rnnt, can be used to switch between CTC and RNNT decoder for Hybrid RNNT/CTC models + decoder_type: Optional[str] = None + # att_context_size can be set for cache-aware streaming models with multiple look-aheads + att_context_size: Optional[list] = None + + # Use this for model-specific changes before transcription + model_change: ModelChangeConfig = ModelChangeConfig() + + # Config for word / character error rate calculation + calculate_wer: bool = True + clean_groundtruth_text: bool = False + langid: str = "en" # specify this for convert_num_to_words step in groundtruth cleaning + use_cer: bool = False + + # can be set to True to return list of transcriptions instead of the config + # if True, will also skip writing anything to the output file + return_transcriptions: bool = False + + # Set to False to return text instead of hypotheses from the transcribe function, so as to save memory + return_hypotheses: bool = True + + # key for groundtruth text in manifest + gt_text_attr_name: str = "text" + gt_lang_attr_name: str = "lang" + + # Use model's transcribe() function instead of transcribe_partial_audio() by default + # Only use transcribe_partial_audio() when the audio is too long to fit in memory + # Your manifest input should have `offset` field to use transcribe_partial_audio() + allow_partial_transcribe: bool = False + extract_nbest: bool = False # Extract n-best hypotheses from the model + + +@hydra_runner(config_name="TranscriptionConfig", schema=TranscriptionConfig) +def main(cfg: TranscriptionConfig) -> Union[TranscriptionConfig, List[Hypothesis]]: + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + for key in cfg: + cfg[key] = None if cfg[key] == 'None' else cfg[key] + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if cfg.random_seed: + pl.seed_everything(cfg.random_seed) + + if cfg.model_path is None and cfg.pretrained_name is None: + raise ValueError("Both cfg.model_path and cfg.pretrained_name cannot be None!") + if cfg.audio_dir is None and cfg.dataset_manifest is None: + raise ValueError("Both cfg.audio_dir and cfg.dataset_manifest cannot be None!") + + # Load augmentor from exteranl yaml file which contains eval info, could be extend to other feature such VAD, P&C + augmentor = None + if cfg.eval_config_yaml: + eval_config = OmegaConf.load(cfg.eval_config_yaml) + augmentor = eval_config.test_ds.get("augmentor") + logging.info(f"Will apply on-the-fly augmentation on samples during transcription: {augmentor} ") + + # setup GPU + torch.set_float32_matmul_precision(cfg.matmul_precision) + if cfg.cuda is None: + if torch.cuda.is_available(): + device = [0] # use 0th CUDA device + accelerator = 'gpu' + map_location = torch.device('cuda:0') + elif cfg.allow_mps and hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): + logging.warning( + "MPS device (Apple Silicon M-series GPU) support is experimental." + " Env variable `PYTORCH_ENABLE_MPS_FALLBACK=1` should be set in most cases to avoid failures." + ) + device = [0] + accelerator = 'mps' + map_location = torch.device('mps') + else: + device = 1 + accelerator = 'cpu' + map_location = torch.device('cpu') + else: + device = [cfg.cuda] + accelerator = 'gpu' + map_location = torch.device(f'cuda:{cfg.cuda}') + + logging.info(f"Inference will be done on device: {map_location}") + + asr_model, model_name = setup_model(cfg, map_location) + + trainer = pl.Trainer(devices=device, accelerator=accelerator) + asr_model.set_trainer(trainer) + asr_model = asr_model.eval() + + # we will adjust this flag if the model does not support it + compute_timestamps = cfg.compute_timestamps + compute_langs = cfg.compute_langs + # has to be True if timestamps are required + preserve_alignment = True if cfg.compute_timestamps else cfg.preserve_alignment + + # Check whether model and decoder type match + if isinstance(asr_model, EncDecCTCModel): + if cfg.decoder_type and cfg.decoder_type != 'ctc': + raise ValueError('CTC model only support ctc decoding!') + elif isinstance(asr_model, EncDecHybridRNNTCTCModel): + if cfg.decoder_type and cfg.decoder_type not in ['ctc', 'rnnt']: + raise ValueError('Hybrid model only support ctc or rnnt decoding!') + else: # rnnt model, there could be other models needs to be addressed. + if cfg.decoder_type and cfg.decoder_type != 'rnnt': + raise ValueError('RNNT model only support rnnt decoding!') + + if cfg.decoder_type and hasattr(asr_model.encoder, 'set_default_att_context_size'): + asr_model.encoder.set_default_att_context_size(cfg.att_context_size) + + # Setup decoding strategy + if hasattr(asr_model, 'change_decoding_strategy') and hasattr(asr_model, 'decoding'): + if isinstance(asr_model.decoding, MultiTaskDecoding): + cfg.multitask_decoding.compute_langs = cfg.compute_langs + cfg.multitask_decoding.preserve_alignments = cfg.preserve_alignment + if cfg.extract_nbest: + cfg.multitask_decoding.beam.return_best_hypothesis = False + cfg.return_hypotheses = True + asr_model.change_decoding_strategy(cfg.multitask_decoding) + elif cfg.decoder_type is not None: + # TODO: Support compute_langs in CTC eventually + if cfg.compute_langs and cfg.decoder_type == 'ctc': + raise ValueError("CTC models do not support `compute_langs` at the moment") + + decoding_cfg = cfg.rnnt_decoding if cfg.decoder_type == 'rnnt' else cfg.ctc_decoding + if cfg.extract_nbest: + decoding_cfg.beam.return_best_hypothesis = False + cfg.return_hypotheses = True + decoding_cfg.compute_timestamps = cfg.compute_timestamps # both ctc and rnnt support it + if 'preserve_alignments' in decoding_cfg: + decoding_cfg.preserve_alignments = preserve_alignment + if 'compute_langs' in decoding_cfg: + decoding_cfg.compute_langs = cfg.compute_langs + if hasattr(asr_model, 'cur_decoder'): + asr_model.change_decoding_strategy(decoding_cfg, decoder_type=cfg.decoder_type) + else: + asr_model.change_decoding_strategy(decoding_cfg) + + # Check if ctc or rnnt model + elif hasattr(asr_model, 'joint'): # RNNT model + if cfg.extract_nbest: + cfg.rnnt_decoding.beam.return_best_hypothesis = False + cfg.return_hypotheses = True + cfg.rnnt_decoding.fused_batch_size = -1 + cfg.rnnt_decoding.compute_timestamps = cfg.compute_timestamps + cfg.rnnt_decoding.compute_langs = cfg.compute_langs + if 'preserve_alignments' in cfg.rnnt_decoding: + cfg.rnnt_decoding.preserve_alignments = preserve_alignment + + asr_model.change_decoding_strategy(cfg.rnnt_decoding) + else: + if cfg.compute_langs: + raise ValueError("CTC models do not support `compute_langs` at the moment.") + cfg.ctc_decoding.compute_timestamps = cfg.compute_timestamps + if cfg.extract_nbest: + cfg.ctc_decoding.beam.return_best_hypothesis = False + cfg.return_hypotheses = True + + asr_model.change_decoding_strategy(cfg.ctc_decoding) + + # Setup decoding config based on model type and decoder_type + with open_dict(cfg): + if isinstance(asr_model, EncDecCTCModel) or ( + isinstance(asr_model, EncDecHybridRNNTCTCModel) and cfg.decoder_type == "ctc" + ): + cfg.decoding = cfg.ctc_decoding + elif isinstance(asr_model.decoding, MultiTaskDecoding): + cfg.decoding = cfg.multitask_decoding + else: + cfg.decoding = cfg.rnnt_decoding + + remove_path_after_done = None + if isinstance(asr_model, EncDecMultiTaskModel): + # Special case for EncDecMultiTaskModel, where the input manifest is directly passed into the model's transcribe() function + partial_audio = False + if cfg.audio_dir is not None and not cfg.append_pred: + filepaths = list(glob.glob(os.path.join(cfg.audio_dir, f"**/*.{cfg.audio_type}"), recursive=True)) + else: + assert cfg.dataset_manifest is not None + if cfg.presort_manifest: + with NamedTemporaryFile("w", suffix=".json", delete=False) as f: + for item in read_and_maybe_sort_manifest(cfg.dataset_manifest, try_sort=True): + item["audio_filepath"] = get_full_path(item["audio_filepath"], cfg.dataset_manifest) + print(json.dumps(item), file=f) + cfg.dataset_manifest = f.name + remove_path_after_done = f.name + filepaths = cfg.dataset_manifest + else: + # prepare audio filepaths and decide wether it's partial audio + filepaths, partial_audio = prepare_audio_data(cfg) + + if not cfg.allow_partial_transcribe: + # by defatul, use model's transcribe() function, unless partial audio is required + partial_audio = False + + # setup AMP (optional) + if cfg.amp and torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and hasattr(torch.cuda.amp, 'autocast'): + logging.info("AMP enabled!\n") + autocast = torch.cuda.amp.autocast + else: + + @contextlib.contextmanager + def autocast(dtype=None): + yield + + # Compute output filename + cfg = compute_output_filename(cfg, model_name) + + # if transcripts should not be overwritten, and already exists, skip re-transcription step and return + if not cfg.return_transcriptions and not cfg.overwrite_transcripts and os.path.exists(cfg.output_filename): + logging.info( + f"Previous transcripts found at {cfg.output_filename}, and flag `overwrite_transcripts`" + f"is {cfg.overwrite_transcripts}. Returning without re-transcribing text." + ) + return cfg + + # transcribe audio + + amp_dtype = torch.float16 if cfg.amp_dtype == "float16" else torch.bfloat16 + + with autocast(dtype=amp_dtype): + with torch.no_grad(): + if partial_audio: + transcriptions = transcribe_partial_audio( + asr_model=asr_model, + path2manifest=cfg.dataset_manifest, + batch_size=cfg.batch_size, + num_workers=cfg.num_workers, + return_hypotheses=cfg.return_hypotheses, + channel_selector=cfg.channel_selector, + augmentor=augmentor, + decoder_type=cfg.decoder_type, + ) + else: + override_cfg = asr_model.get_transcribe_config() + override_cfg.batch_size = cfg.batch_size + override_cfg.num_workers = cfg.num_workers + override_cfg.return_hypotheses = cfg.return_hypotheses + override_cfg.channel_selector = cfg.channel_selector + override_cfg.augmentor = augmentor + override_cfg.text_field = cfg.gt_text_attr_name + override_cfg.lang_field = cfg.gt_lang_attr_name + transcriptions = asr_model.transcribe(audio=filepaths, override_config=override_cfg,) + + if cfg.dataset_manifest is not None: + logging.info(f"Finished transcribing from manifest file: {cfg.dataset_manifest}") + if cfg.presort_manifest: + transcriptions = restore_transcription_order(cfg.dataset_manifest, transcriptions) + else: + logging.info(f"Finished transcribing {len(filepaths)} files !") + logging.info(f"Writing transcriptions into file: {cfg.output_filename}") + + # if transcriptions form a tuple of (best_hypotheses, all_hypotheses) + if type(transcriptions) == tuple and len(transcriptions) == 2: + if cfg.extract_nbest: + # extract all hypotheses if exists + transcriptions = transcriptions[1] + else: + # extract just best hypothesis + transcriptions = transcriptions[0] + + if cfg.return_transcriptions: + return transcriptions + + # write audio transcriptions + output_filename, pred_text_attr_name = write_transcription( + transcriptions, + cfg, + model_name, + filepaths=filepaths, + compute_langs=compute_langs, + compute_timestamps=compute_timestamps, + ) + logging.info(f"Finished writing predictions to {output_filename}!") + + # clean-up + if cfg.presort_manifest is not None: + if remove_path_after_done is not None: + os.unlink(remove_path_after_done) + + if cfg.calculate_wer: + output_manifest_w_wer, total_res, _ = cal_write_wer( + pred_manifest=output_filename, + gt_text_attr_name=cfg.gt_text_attr_name, + pred_text_attr_name=pred_text_attr_name, + clean_groundtruth_text=cfg.clean_groundtruth_text, + langid=cfg.langid, + use_cer=cfg.use_cer, + output_filename=None, + ) + if output_manifest_w_wer: + logging.info(f"Writing prediction and error rate of each sample to {output_manifest_w_wer}!") + logging.info(f"{total_res}") + + return cfg + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/asr/transcribe_speech_parallel.py b/NeMo-2.0.0.rc0.beta/examples/asr/transcribe_speech_parallel.py new file mode 100644 index 0000000..c0af8f9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/asr/transcribe_speech_parallel.py @@ -0,0 +1,208 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# ASR transcribe/inference with multi-GPU/multi-node support for large datasets +# It supports both tarred and non-tarred datasets +# Arguments +# model: path to a nemo/PTL checkpoint file or name of a pretrained model +# predict_ds: config of the dataset/dataloader +# output_path: path to store the predictions +# return_predictions: whether to return the predictions as output other than writing into the files +# use_cer: whether to calculate the error in terms of CER or use the default WER +# +# Results of each GPU/worker is written into a file named 'predictions_{rank}.json, and aggregated results of all workers are written into 'predictions_all.json' + +Example for non-tarred datasets: + +python transcribe_speech_parallel.py \ + model=stt_en_conformer_ctc_large \ + predict_ds.manifest_filepath=/dataset/manifest_file.json \ + predict_ds.batch_size=16 \ + output_path=/tmp/ + +Example for Hybrid-CTC/RNNT models with non-tarred datasets: + +python transcribe_speech_parallel.py \ + model=stt_en_fastconformer_hybrid_large \ + decoder_type=ctc \ + predict_ds.manifest_filepath=/dataset/manifest_file.json \ + predict_ds.batch_size=16 \ + output_path=/tmp/ + +Example for tarred datasets: + +python transcribe_speech_parallel.py \ + predict_ds.is_tarred=true \ + predict_ds.manifest_filepath=/tarred_dataset/tarred_audio_manifest.json \ + predict_ds.tarred_audio_filepaths=/tarred_dataset/audio__OP_0..127_CL_.tar \ + ... + +By default the trainer uses all the GPUs available and default precision is FP32. +By setting the trainer config you may control these configs. For example to do the predictions with AMP on just two GPUs: + +python transcribe_speech_parallel.py \ + trainer.precision=16 \ + trainer.devices=2 \ + ... + +You may control the dataloader's config by setting the predict_ds: + +python transcribe_speech_parallel.py \ + predict_ds.num_workers=8 \ + predict_ds.min_duration=2.0 \ + predict_ds.sample_rate=16000 \ + model=stt_en_conformer_ctc_small \ + ... + +""" + + +import itertools +import json +import os +from dataclasses import dataclass, is_dataclass +from typing import Optional + +import pytorch_lightning as ptl +import torch +from omegaconf import MISSING, OmegaConf + +from nemo.collections.asr.data.audio_to_text_dataset import ASRPredictionWriter +from nemo.collections.asr.metrics.wer import word_error_rate +from nemo.collections.asr.models import ASRModel, EncDecHybridRNNTCTCModel +from nemo.collections.asr.models.configs.asr_models_config import ASRDatasetConfig +from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTDecodingConfig +from nemo.core.config import TrainerConfig, hydra_runner +from nemo.utils import logging +from nemo.utils.get_rank import is_global_rank_zero + + +@dataclass +class ParallelTranscriptionConfig: + model: Optional[str] = None # name + predict_ds: ASRDatasetConfig = ASRDatasetConfig(return_sample_id=True, num_workers=4) + output_path: str = MISSING + + # when return_predictions is enabled, the prediction call would keep all the predictions in memory and return them when prediction is done + return_predictions: bool = False + use_cer: bool = False + + # decoding strategy for RNNT models + rnnt_decoding: RNNTDecodingConfig = RNNTDecodingConfig() + + # decoder type: ctc or rnnt, can be used to switch between CTC and RNNT decoder for Hybrid RNNT/CTC models + decoder_type: Optional[str] = None + # att_context_size can be set for cache-aware streaming models with multiple look-aheads + att_context_size: Optional[list] = None + + trainer: TrainerConfig = TrainerConfig(devices=-1, accelerator="gpu", strategy="ddp") + + +def match_train_config(predict_ds, train_ds): + # It copies the important configurations from the train dataset of the model + # into the predict_ds to be used for prediction. It is needed to match the training configurations. + if train_ds is None: + return + + predict_ds.sample_rate = train_ds.get("sample_rate", 16000) + cfg_name_list = [ + "int_values", + "use_start_end_token", + "blank_index", + "unk_index", + "normalize", + "parser", + "eos_id", + "bos_id", + "pad_id", + ] + + if is_dataclass(predict_ds): + predict_ds = OmegaConf.structured(predict_ds) + for cfg_name in cfg_name_list: + if hasattr(train_ds, cfg_name): + setattr(predict_ds, cfg_name, getattr(train_ds, cfg_name)) + + return predict_ds + + +@hydra_runner(config_name="TranscriptionConfig", schema=ParallelTranscriptionConfig) +def main(cfg: ParallelTranscriptionConfig): + if cfg.model.endswith(".nemo"): + logging.info("Attempting to initialize from .nemo file") + model = ASRModel.restore_from(restore_path=cfg.model, map_location="cpu") + elif cfg.model.endswith(".ckpt"): + logging.info("Attempting to initialize from .ckpt file") + model = ASRModel.load_from_checkpoint(checkpoint_path=cfg.model, map_location="cpu") + else: + logging.info( + "Attempting to initialize from a pretrained model as the model name does not have the extension of .nemo or .ckpt" + ) + model = ASRModel.from_pretrained(model_name=cfg.model, map_location="cpu") + + if isinstance(model, EncDecHybridRNNTCTCModel) and cfg.decoder_type is not None: + model.change_decoding_strategy(decoder_type=cfg.decoder_type) + + trainer = ptl.Trainer(**cfg.trainer) + + cfg.predict_ds.return_sample_id = True + cfg.predict_ds = match_train_config(predict_ds=cfg.predict_ds, train_ds=model.cfg.train_ds) + data_loader = model._setup_dataloader_from_config(cfg.predict_ds) + + os.makedirs(cfg.output_path, exist_ok=True) + # trainer.global_rank is not valid before predict() is called. Need this hack to find the correct global_rank. + global_rank = trainer.node_rank * trainer.num_devices + int(os.environ.get("LOCAL_RANK", 0)) + output_file = os.path.join(cfg.output_path, f"predictions_{global_rank}.json") + predictor_writer = ASRPredictionWriter(dataset=data_loader.dataset, output_file=output_file) + trainer.callbacks.extend([predictor_writer]) + + predictions = trainer.predict(model=model, dataloaders=data_loader, return_predictions=cfg.return_predictions) + if predictions is not None: + predictions = list(itertools.chain.from_iterable(predictions)) + samples_num = predictor_writer.close_output_file() + + logging.info( + f"Prediction on rank {global_rank} is done for {samples_num} samples and results are stored in {output_file}." + ) + + if torch.distributed.is_initialized(): + torch.distributed.barrier() + + samples_num = 0 + pred_text_list = [] + text_list = [] + if is_global_rank_zero(): + output_file = os.path.join(cfg.output_path, f"predictions_all.json") + logging.info(f"Prediction files are being aggregated in {output_file}.") + with open(output_file, 'w') as outf: + for rank in range(trainer.world_size): + input_file = os.path.join(cfg.output_path, f"predictions_{rank}.json") + with open(input_file, 'r') as inpf: + lines = inpf.readlines() + for line in lines: + item = json.loads(line) + pred_text_list.append(item["pred_text"]) + text_list.append(item["text"]) + outf.write(json.dumps(item) + "\n") + samples_num += 1 + wer_cer = word_error_rate(hypotheses=pred_text_list, references=text_list, use_cer=cfg.use_cer) + logging.info( + f"Prediction is done for {samples_num} samples in total on all workers and results are aggregated in {output_file}." + ) + logging.info("{} for all predictions is {:.4f}.".format("CER" if cfg.use_cer else "WER", wer_cer)) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/audio_tasks/audio_to_audio_eval.py b/NeMo-2.0.0.rc0.beta/examples/audio_tasks/audio_to_audio_eval.py new file mode 100644 index 0000000..57d7095 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/audio_tasks/audio_to_audio_eval.py @@ -0,0 +1,278 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Script to compute metrics for a given audio-to-audio model for a given manifest file for some dataset. +The manifest file must include path to input audio and path to target (ground truth) audio. + +Note: This scripts depends on the `process_audio.py` script, and therefore both scripts should be +located in the same directory during execution. + +# Arguments + +<< All arguments of `process_audio.py` are inherited by this script, so please refer to `process_audio.py` +for full list of arguments >> + + dataset_manifest: Required - path to dataset JSON manifest file (in NeMo format) + output_dir: Optional - output directory where the processed audio will be saved + metrics: Optional - list of metrics to evaluate. Defaults to [sdr,estoi] + sample_rate: Optional - sample rate for loaded audio. Defaults to 16kHz. + only_score_manifest: Optional - If set, processing will be skipped and it is assumed the processed audio is available in dataset_manifest + +# Usage + +## To score a dataset with a manifest file that contains the input audio which needs to be processed and target audio + +python audio_to_audio_eval.py \ + model_path=null \ + pretrained_model=null \ + dataset_manifest= \ + output_dir= \ + processed_channel_selector= \ + target_key= \ + target_channel_selector= \ + metrics= + batch_size=32 \ + amp=True + +## To score a manifest file which has been previously processed and contains both processed audio and target audio + +python audio_to_audio_eval.py \ + dataset_manifest= \ + processed_key= + processed_channel_selector= \ + target_key= \ + target_channel_selector= \ + metrics= + batch_size=32 \ + amp=True +""" +import json +import os +import tempfile +from dataclasses import dataclass, field, is_dataclass +from typing import List, Optional + +import process_audio +import torch +from omegaconf import OmegaConf, open_dict +from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality +from torchmetrics.audio.sdr import ScaleInvariantSignalDistortionRatio, SignalDistortionRatio +from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility +from tqdm import tqdm + +from nemo.collections.asr.data import audio_to_audio_dataset +from nemo.collections.asr.metrics.audio import AudioMetricWrapper +from nemo.collections.common.parts.preprocessing import manifest +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@dataclass +class AudioEvaluationConfig(process_audio.ProcessConfig): + # Processed audio config + processed_channel_selector: Optional[List] = None + processed_key: str = 'processed_audio_filepath' + + # Target audio configs + target_dataset_dir: Optional[str] = None # If not provided, defaults to dirname(cfg.dataset_manifest) + target_channel_selector: Optional[List] = None + target_key: str = 'target_audio_filepath' + + # Sample rate for audio evaluation + sample_rate: int = 16000 + + # Score an existing manifest without running processing + only_score_manifest: bool = False + + # Metrics to calculate + metrics: List[str] = field(default_factory=lambda: ['sdr', 'estoi']) + + +def get_evaluation_dataloader(config): + """Prepare a dataloader for evaluation. + """ + dataset = audio_to_audio_dataset.get_audio_to_target_dataset(config=config) + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + collate_fn=dataset.collate_fn, + drop_last=config.get('drop_last', False), + shuffle=False, + num_workers=config.get('num_workers', min(config['batch_size'], os.cpu_count() - 1)), + pin_memory=True, + ) + + +def get_metrics(cfg: AudioEvaluationConfig): + """Prepare a dictionary with metrics. + """ + available_metrics = ['sdr', 'sisdr', 'stoi', 'estoi', 'pesq'] + + metrics = dict() + for name in sorted(set(cfg.metrics)): + name = name.lower() + if name == 'sdr': + metric = AudioMetricWrapper(metric=SignalDistortionRatio()) + elif name == 'sisdr': + metric = AudioMetricWrapper(metric=ScaleInvariantSignalDistortionRatio()) + elif name == 'stoi': + metric = AudioMetricWrapper(metric=ShortTimeObjectiveIntelligibility(fs=cfg.sample_rate, extended=False)) + elif name == 'estoi': + metric = AudioMetricWrapper(metric=ShortTimeObjectiveIntelligibility(fs=cfg.sample_rate, extended=True)) + elif name == 'pesq': + metric = AudioMetricWrapper(metric=PerceptualEvaluationSpeechQuality(fs=cfg.sample_rate, mode='wb')) + else: + raise ValueError(f'Unexpected metric: {name}. Currently available metrics: {available_metrics}') + + metrics[name] = metric + + return metrics + + +@hydra_runner(config_name="AudioEvaluationConfig", schema=AudioEvaluationConfig) +def main(cfg: AudioEvaluationConfig): + torch.set_grad_enabled(False) + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if cfg.audio_dir is not None: + raise RuntimeError( + "Evaluation script requires ground truth audio to be passed via a manifest file. " + "If manifest file is available, submit it via `dataset_manifest` argument." + ) + + if not os.path.exists(cfg.dataset_manifest): + raise FileNotFoundError(f'The dataset manifest file could not be found at path : {cfg.dataset_manifest}') + + if cfg.target_dataset_dir is None: + # Assume the target data is available in the same directory as the input data + cfg.target_dataset_dir = os.path.dirname(cfg.dataset_manifest) + elif not os.path.isdir(cfg.target_dataset_dir): + raise FileNotFoundError(f'Target dataset dir could not be found at path : {cfg.target_dataset_dir}') + + # Setup metrics + metrics = get_metrics(cfg) + + # Processing + if not cfg.only_score_manifest: + # Process audio using the configured model and save in the output directory + process_cfg = process_audio.main(cfg) # type: ProcessConfig + + # Release GPU memory if it was used during transcription + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + logging.info('Finished processing audio.') + else: + # Score the input manifest, no need to run a model + cfg.output_filename = cfg.dataset_manifest + process_cfg = cfg + + # Evaluation + with tempfile.TemporaryDirectory() as tmp_dir: + # Prepare a temporary manifest with processed audio and target + temporary_manifest_filepath = os.path.join(tmp_dir, 'manifest.json') + + num_files = 0 + + with open(process_cfg.output_filename, 'r') as f_processed, open( + temporary_manifest_filepath, 'w', encoding='utf-8' + ) as f_tmp: + for line_processed in f_processed: + data_processed = json.loads(line_processed) + + if cfg.processed_key not in data_processed: + raise ValueError( + f'Processed key {cfg.processed_key} not found in manifest: {process_cfg.output_filename}.' + ) + + if cfg.target_key not in data_processed: + raise ValueError( + f'Target key {cfg.target_key} not found in manifest: {process_cfg.output_filename}.' + ) + + item = { + 'processed': manifest.get_full_path( + audio_file=data_processed[cfg.processed_key], manifest_file=process_cfg.output_filename + ), + 'target': manifest.get_full_path( + audio_file=data_processed[cfg.target_key], data_dir=cfg.target_dataset_dir + ), + 'duration': data_processed.get('duration'), + } + + # Double-check files exist + for key in ['processed', 'target']: + if not os.path.isfile(item[key]): + raise ValueError(f'File for key "{key}" not found at: {item[key]}.\nCurrent item: {item}') + + # Warn if we're comparing the same files + if item['target'] == item['processed']: + logging.warning('Using the same file as processed and target: %s', item['target']) + + # Write the entry in the temporary manifest file + f_tmp.write(json.dumps(item) + '\n') + + num_files += 1 + + # Prepare dataloader + config = { + 'manifest_filepath': temporary_manifest_filepath, + 'sample_rate': cfg.sample_rate, + 'input_key': 'processed', + 'input_channel_selector': cfg.processed_channel_selector, + 'target_key': 'target', + 'target_channel_selector': cfg.target_channel_selector, + 'batch_size': min(cfg.batch_size, num_files), + 'num_workers': cfg.num_workers, + } + temporary_dataloader = get_evaluation_dataloader(config) + + # Calculate metrics + for eval_batch in tqdm(temporary_dataloader, desc='Evaluating'): + processed_signal, processed_length, target_signal, target_length = eval_batch + + if not torch.equal(processed_length, target_length): + raise RuntimeError(f'Length mismatch.') + + for name, metric in metrics.items(): + metric.update(preds=processed_signal, target=target_signal, input_length=target_length) + + # Convert to a dictionary with name: value + metrics_value = {name: metric.compute().item() for name, metric in metrics.items()} + + logging.info('Finished running evaluation.') + + # Show results + logging.info('Summary\n') + logging.info('Data') + logging.info('\tmanifest: %s', cfg.output_filename) + logging.info('\ttarget_dataset_dir: %s', cfg.target_dataset_dir) + logging.info('\tnum_files: %s', num_files) + logging.info('Metrics') + for name, value in metrics_value.items(): + logging.info('\t%10s: \t%6.2f', name, value) + + # Inject the metric name and score into the config, and return the entire config + with open_dict(cfg): + cfg.metrics_value = metrics_value + + return cfg + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/audio_tasks/conf/beamforming.yaml b/NeMo-2.0.0.rc0.beta/examples/audio_tasks/conf/beamforming.yaml new file mode 100644 index 0000000..18e04f0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/audio_tasks/conf/beamforming.yaml @@ -0,0 +1,126 @@ +# This configuration contains the exemplary values for training a multichannel speech enhancement model with a mask-based beamformer. +# +name: "beamforming" + +model: + sample_rate: 16000 + skip_nan_grad: false + num_outputs: 1 + + train_ds: + manifest_filepath: ??? + input_key: audio_filepath # key of the input signal path in the manifest + target_key: target_filepath # key of the target signal path in the manifest + target_channel_selector: 0 # target signal is the first channel from files in target_key + audio_duration: 4.0 # in seconds, audio segment duration for training + random_offset: true # if the file is longer than audio_duration, use random offset to select a subsegment + min_duration: ${model.train_ds.audio_duration} + batch_size: 64 # batch size may be increased based on the available memory + shuffle: true + num_workers: 8 + pin_memory: true + + validation_ds: + manifest_filepath: ??? + input_key: audio_filepath # key of the input signal path in the manifest + target_key: target_filepath + target_channel_selector: 0 # target signal is the first channel from files in target_key + batch_size: 1 # batch size may be increased based on the available memory + shuffle: false + num_workers: 4 + pin_memory: true + + test_ds: + manifest_filepath: ??? + input_key: audio_filepath # key of the input signal path in the manifest + target_key: target_filepath # key of the target signal path in the manifest + target_channel_selector: 0 # target signal is the first channel from files in target_key + batch_size: 1 # batch size may be increased based on the available memory + shuffle: false + num_workers: 4 + pin_memory: true + + encoder: + _target_: nemo.collections.asr.modules.audio_preprocessing.AudioToSpectrogram + fft_length: 512 # Length of the window and FFT for calculating spectrogram + hop_length: 256 # Hop length for calculating spectrogram + power: null + + decoder: + _target_: nemo.collections.asr.modules.audio_preprocessing.SpectrogramToAudio + fft_length: 512 # Length of the window and FFT for calculating spectrogram + hop_length: 256 # Hop length for calculating spectrogram + + mask_estimator: + _target_: nemo.collections.asr.modules.audio_modules.MaskEstimatorRNN + num_outputs: ${model.num_outputs} + num_subbands: 257 # Number of subbands of the input spectrogram + num_features: 256 # Number of features at RNN input + num_layers: 5 # Number of RNN layers + bidirectional: true # Use bi-directional RNN + + mask_processor: + _target_: nemo.collections.asr.modules.audio_modules.MaskBasedBeamformer # Mask-based multi-channel processing + ref_channel: 0 # Reference channel for the output + + loss: + _target_: nemo.collections.asr.losses.SDRLoss + scale_invariant: true # Use scale-invariant SDR + + metrics: + val: + sdr: # output SDR + _target_: torchmetrics.audio.SignalDistortionRatio + test: + sdr_ch0: # SDR on output channel 0 + _target_: torchmetrics.audio.SignalDistortionRatio + channel: 0 + + optim: + name: adamw + lr: 1e-4 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: -1 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: null + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 25 # Interval of logging. + enable_progress_bar: true + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_loss" + mode: "min" + save_top_k: 5 + always_save_nemo: true # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to true to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/audio_tasks/conf/beamforming_flex_channels.yaml b/NeMo-2.0.0.rc0.beta/examples/audio_tasks/conf/beamforming_flex_channels.yaml new file mode 100644 index 0000000..29fc87a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/audio_tasks/conf/beamforming_flex_channels.yaml @@ -0,0 +1,146 @@ +# This configuration contains the exemplary values for training a multichannel speech enhancement model with a mask-based beamformer. +# +name: beamforming_flex_channels + +model: + sample_rate: 16000 + skip_nan_grad: false + num_outputs: 1 + + train_ds: + manifest_filepath: ??? + input_key: audio_filepath # key of the input signal path in the manifest + input_channel_selector: null # load all channels from the input file + target_key: target_anechoic_filepath # key of the target signal path in the manifest + target_channel_selector: 0 # load only the first channel from the target file + audio_duration: 4.0 # in seconds, audio segment duration for training + random_offset: true # if the file is longer than audio_duration, use random offset to select a subsegment + min_duration: ${model.train_ds.audio_duration} + batch_size: 16 # batch size may be increased based on the available memory + shuffle: true + num_workers: 16 + pin_memory: true + + validation_ds: + manifest_filepath: ??? + input_key: audio_filepath # key of the input signal path in the manifest + input_channel_selector: null # load all channels from the input file + target_key: target_anechoic_filepath # key of the target signal path in the manifest + target_channel_selector: 0 # load only the first channel from the target file + batch_size: 8 + shuffle: false + num_workers: 8 + pin_memory: true + + channel_augment: + _target_: nemo.collections.asr.parts.submodules.multichannel_modules.ChannelAugment + num_channels_min: 2 # minimal number of channels selected for each batch + num_channels_max: null # max number of channels is determined by the batch size + permute_channels: true + + encoder: + _target_: nemo.collections.asr.modules.audio_preprocessing.AudioToSpectrogram + fft_length: 512 # Length of the window and FFT for calculating spectrogram + hop_length: 256 # Hop length for calculating spectrogram + + decoder: + _target_: nemo.collections.asr.modules.audio_preprocessing.SpectrogramToAudio + fft_length: ${model.encoder.fft_length} + hop_length: ${model.encoder.hop_length} + + mask_estimator: + _target_: nemo.collections.asr.modules.audio_modules.MaskEstimatorFlexChannels + num_outputs: ${model.num_outputs} # number of output masks + num_subbands: 257 # number of subbands for the input spectrogram + num_blocks: 5 # number of blocks in the model + channel_reduction_position: 3 # 0-indexed, apply channel reduction before this block + channel_reduction_type: average # channel-wise reduction + channel_block_type: transform_average_concatenate # channel block + temporal_block_type: conformer_encoder # temporal block + temporal_block_num_layers: 5 # number of layers for the temporal block + temporal_block_num_heads: 4 # number of heads for the temporal block + temporal_block_dimension: 128 # the hidden size of the temporal block + mag_reduction: null # channel-wise reduction of magnitude + mag_normalization: mean_var # normalization using mean and variance + use_ipd: true # use inter-channel phase difference + ipd_normalization: mean # mean normalization + + mask_processor: + # Mask-based multi-channel processor + _target_: nemo.collections.asr.modules.audio_modules.MaskBasedBeamformer + filter_type: pmwf # parametric multichannel wiener filter + filter_beta: 0.0 # mvdr + filter_rank: one + ref_channel: max_snr # select reference channel by maximizing estimated SNR + ref_hard: 1 # a one-hot reference. If false, a soft estimate across channels is used. + ref_hard_use_grad: false # use straight-through gradient when using hard reference + ref_subband_weighting: false # use subband weighting for reference estimation + num_subbands: ${model.mask_estimator.num_subbands} + + loss: + _target_: nemo.collections.asr.losses.SDRLoss + convolution_invariant: true # convolution-invariant loss + sdr_max: 30 # soft threshold for SDR + + metrics: + val: + sdr_0: + _target_: torchmetrics.audio.SignalDistortionRatio + channel: 0 # evaluate only on channel 0, if there are multiple outputs + + optim: + name: adamw + lr: 1e-4 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + + # scheduler setup + sched: + name: CosineAnnealing + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: -1 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: null + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 25 # Interval of logging. + enable_progress_bar: true + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_loss" + mode: "min" + save_top_k: 5 + always_save_nemo: true # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.pyth + # you need to set these two to true to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/audio_tasks/conf/masking.yaml b/NeMo-2.0.0.rc0.beta/examples/audio_tasks/conf/masking.yaml new file mode 100644 index 0000000..c667bec --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/audio_tasks/conf/masking.yaml @@ -0,0 +1,126 @@ +# This configuration contains the exemplary values for training a multichannel speech enhancement model with a mask-based beamformer. +# +name: "masking" + +model: + sample_rate: 16000 + skip_nan_grad: false + num_outputs: 1 + + train_ds: + manifest_filepath: ??? + input_key: audio_filepath # key of the input signal path in the manifest + target_key: target_filepath # key of the target signal path in the manifest + target_channel_selector: 0 # target signal is the first channel from files in target_key + audio_duration: 4.0 # in seconds, audio segment duration for training + random_offset: true # if the file is longer than audio_duration, use random offset to select a subsegment + min_duration: ${model.train_ds.audio_duration} + batch_size: 64 # batch size may be increased based on the available memory + shuffle: true + num_workers: 8 + pin_memory: true + + validation_ds: + manifest_filepath: ??? + input_key: audio_filepath # key of the input signal path in the manifest + target_key: target_filepath + target_channel_selector: 0 # target signal is the first channel from files in target_key + batch_size: 64 # batch size may be increased based on the available memory + shuffle: false + num_workers: 4 + pin_memory: true + + test_ds: + manifest_filepath: ??? + input_key: audio_filepath # key of the input signal path in the manifest + target_key: target_filepath # key of the target signal path in the manifest + target_channel_selector: 0 # target signal is the first channel from files in target_key + batch_size: 1 # batch size may be increased based on the available memory + shuffle: false + num_workers: 4 + pin_memory: true + + encoder: + _target_: nemo.collections.asr.modules.audio_preprocessing.AudioToSpectrogram + fft_length: 512 # Length of the window and FFT for calculating spectrogram + hop_length: 256 # Hop length for calculating spectrogram + power: null + + decoder: + _target_: nemo.collections.asr.modules.audio_preprocessing.SpectrogramToAudio + fft_length: 512 # Length of the window and FFT for calculating spectrogram + hop_length: 256 # Hop length for calculating spectrogram + + mask_estimator: + _target_: nemo.collections.asr.modules.audio_modules.MaskEstimatorRNN + num_outputs: ${model.num_outputs} + num_subbands: 257 # Number of subbands of the input spectrogram + num_features: 256 # Number of features at RNN input + num_layers: 5 # Number of RNN layers + bidirectional: true # Use bi-directional RNN + + mask_processor: + _target_: nemo.collections.asr.modules.audio_modules.MaskReferenceChannel # Apply mask on the reference channel + ref_channel: 0 # Reference channel for the output + + loss: + _target_: nemo.collections.asr.losses.SDRLoss + scale_invariant: true # Use scale-invariant SDR + + metrics: + val: + sdr: # output SDR + _target_: torchmetrics.audio.SignalDistortionRatio + test: + sdr_ch0: # SDR on output channel 0 + _target_: torchmetrics.audio.SignalDistortionRatio + channel: 0 + + optim: + name: adamw + lr: 1e-4 + # optimizer arguments + betas: [0.9, 0.98] + weight_decay: 1e-3 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: -1 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + gradient_clip_val: null + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 25 # Interval of logging. + enable_progress_bar: true + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_loss" + mode: "min" + save_top_k: 5 + always_save_nemo: true # saves the checkpoints as nemo files instead of PTL checkpoints + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to true to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/audio_tasks/process_audio.py b/NeMo-2.0.0.rc0.beta/examples/audio_tasks/process_audio.py new file mode 100644 index 0000000..e73831f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/audio_tasks/process_audio.py @@ -0,0 +1,246 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import contextlib +import glob +import json +import os +from dataclasses import dataclass, is_dataclass +from pathlib import Path +from typing import List, Optional + +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf + +from nemo.collections.asr.models import AudioToAudioModel +from nemo.core.config import hydra_runner +from nemo.utils import logging, model_utils + + +""" +Process audio file on a single CPU/GPU. Useful for processing of moderate amounts of audio data. + +# Arguments + model_path: path to .nemo checkpoint for an AudioToAudioModel + pretrained_name: name of a pretrained AudioToAudioModel model (from NGC registry) + audio_dir: path to directory with audio files + dataset_manifest: path to dataset JSON manifest file (in NeMo format) + max_utts: maximum number of utterances to process + + input_channel_selector: list of channels to take from audio files, defaults to `None` and takes all available channels + input_key: key for audio filepath in the manifest file, defaults to `audio_filepath` + + output_dir: Directory where processed files will be saved + output_filename: Output filename where manifest pointing to processed files will be written + batch_size: batch size during inference + + cuda: Optional int to enable or disable execution of model on certain CUDA device. + amp: Bool to decide if Automatic Mixed Precision should be used during inference + audio_type: Str filetype of the audio. Supported = wav, flac, mp3 + + overwrite_output: Bool which when set allowes repeated processing runs to overwrite previous results. + +# Usage +AudioToAudioModel can be specified by either `model_path` or `pretrained_name`. +Data for processing can be defined with either `audio_dir` or `dataset_manifest`. +Processed audio is saved in `output_dir`, and a manifest for processed files is saved +in `output_filename`. + +``` +python process_audio.py \ + model_path=null \ + pretrained_name=null \ + audio_dir="" \ + dataset_manifest="" \ + input_channel_selector=[] \ + output_dir="" \ + output_filename="" \ + batch_size=1 \ + cuda=0 \ + amp=True +``` +""" + + +@dataclass +class ProcessConfig: + # Required configs + model_path: Optional[str] = None # Path to a .nemo file + pretrained_name: Optional[str] = None # Name of a pretrained model + audio_dir: Optional[str] = None # Path to a directory which contains audio files + dataset_manifest: Optional[str] = None # Path to dataset's JSON manifest + max_utts: Optional[int] = None # max number of utterances to process + + # Audio configs + input_channel_selector: Optional[List] = None # Union types not supported Optional[Union[List, int]] + input_key: Optional[str] = None # Can be used with a manifest + + # General configs + output_dir: Optional[str] = None + output_filename: Optional[str] = None + batch_size: int = 1 + num_workers: int = 0 + + # Override model config + override_config_path: Optional[str] = None # path to a yaml config that will override the internal config file + + # Set `cuda` to int to define CUDA device. If 'None', will look for CUDA + # device anyway, and do inference on CPU only if CUDA device is not found. + # If `cuda` is a negative number, inference will be on CPU only. + cuda: Optional[int] = None + amp: bool = False + audio_type: str = "wav" + + # Recompute model predictions, even if the output folder exists. + overwrite_output: bool = False + + +@hydra_runner(config_name="ProcessConfig", schema=ProcessConfig) +def main(cfg: ProcessConfig) -> ProcessConfig: + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if cfg.model_path is None and cfg.pretrained_name is None: + raise ValueError("Both cfg.model_path and cfg.pretrained_name cannot be None!") + if cfg.audio_dir is None and cfg.dataset_manifest is None: + raise ValueError("Both cfg.audio_dir and cfg.dataset_manifest cannot be None!") + + # setup GPU + if cfg.cuda is None: + if torch.cuda.is_available(): + device = [0] # use 0th CUDA device + accelerator = 'gpu' + else: + device = 1 + accelerator = 'cpu' + else: + device = [cfg.cuda] + accelerator = 'gpu' + + map_location = torch.device('cuda:{}'.format(device[0]) if accelerator == 'gpu' else 'cpu') + + # setup model + if cfg.model_path is not None: + # restore model from .nemo file path + model_cfg = AudioToAudioModel.restore_from(restore_path=cfg.model_path, return_config=True) + classpath = model_cfg.target # original class path + imported_class = model_utils.import_class_by_path(classpath) # type: AudioToAudioModel + logging.info(f"Restoring model : {imported_class.__name__}") + audio_to_audio_model = imported_class.restore_from( + restore_path=cfg.model_path, override_config_path=cfg.override_config_path, map_location=map_location + ) # type: AudioToAudioModel + model_name = os.path.splitext(os.path.basename(cfg.model_path))[0] + else: + # restore model by name + audio_to_audio_model = AudioToAudioModel.from_pretrained( + model_name=cfg.pretrained_name, map_location=map_location + ) # type: AudioToAudioModel + model_name = cfg.pretrained_name + + trainer = pl.Trainer(devices=device, accelerator=accelerator) + audio_to_audio_model.set_trainer(trainer) + audio_to_audio_model = audio_to_audio_model.eval() + + if cfg.audio_dir is not None: + filepaths = list(glob.glob(os.path.join(cfg.audio_dir, f"**/*.{cfg.audio_type}"), recursive=True)) + else: + # get filenames from manifest + filepaths = [] + if os.stat(cfg.dataset_manifest).st_size == 0: + raise RuntimeError(f"The input dataset_manifest {cfg.dataset_manifest} is empty.") + + input_key = 'audio_filepath' if cfg.input_key is None else cfg.input_key + manifest_dir = Path(cfg.dataset_manifest).parent + with open(cfg.dataset_manifest, 'r') as f: + for line in f: + item = json.loads(line) + audio_file = Path(item[input_key]) + if not audio_file.is_file() and not audio_file.is_absolute(): + audio_file = manifest_dir / audio_file + filepaths.append(str(audio_file.absolute())) + + if cfg.max_utts is not None: + # Limit the number of utterances to process + filepaths = filepaths[: cfg.max_utts] + + logging.info(f"\nProcessing {len(filepaths)} files...\n") + + # setup AMP (optional) + if cfg.amp and torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and hasattr(torch.cuda.amp, 'autocast'): + logging.info("AMP enabled!\n") + autocast = torch.cuda.amp.autocast + else: + + @contextlib.contextmanager + def autocast(): + yield + + # Compute output filename + if cfg.output_dir is None: + # create default output filename + if cfg.audio_dir is not None: + cfg.output_dir = os.path.dirname(os.path.join(cfg.audio_dir, '.')) + f'_processed_{model_name}' + else: + cfg.output_dir = os.path.dirname(cfg.dataset_manifest) + f'_processed_{model_name}' + + # Compute output filename + if cfg.output_filename is None: + # create default output filename + cfg.output_filename = cfg.output_dir.rstrip('/') + '_manifest.json' + + # if transcripts should not be overwritten, and already exists, skip re-transcription step and return + if not cfg.overwrite_output and os.path.exists(cfg.output_dir): + raise RuntimeError( + f"Previous output found at {cfg.output_dir}, and flag `overwrite_output`" + f"is {cfg.overwrite_output}. Returning without processing." + ) + + # Process audio + with autocast(): + with torch.no_grad(): + paths2processed_files = audio_to_audio_model.process( + paths2audio_files=filepaths, + output_dir=cfg.output_dir, + batch_size=cfg.batch_size, + num_workers=cfg.num_workers, + input_channel_selector=cfg.input_channel_selector, + ) + + logging.info(f"Finished processing {len(filepaths)} files!") + logging.info(f"Processed audio is available in the output directory: {cfg.output_dir}") + + # Prepare new/updated manifest with a new key for processed audio + with open(cfg.output_filename, 'w', encoding='utf-8') as f: + if cfg.dataset_manifest is not None: + with open(cfg.dataset_manifest, 'r') as fr: + for idx, line in enumerate(fr): + item = json.loads(line) + item['processed_audio_filepath'] = paths2processed_files[idx] + f.write(json.dumps(item) + "\n") + + if cfg.max_utts is not None and idx >= cfg.max_utts - 1: + break + else: + for idx, processed_file in enumerate(paths2processed_files): + item = {'processed_audio_filepath': processed_file} + f.write(json.dumps(item) + "\n") + + return cfg + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/audio_tasks/speech_enhancement.py b/NeMo-2.0.0.rc0.beta/examples/audio_tasks/speech_enhancement.py new file mode 100644 index 0000000..5b32d9b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/audio_tasks/speech_enhancement.py @@ -0,0 +1,67 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Training the model + +Basic run (on CPU for 50 epochs): + python examples/audio_tasks/speech_enhancement.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath="" \ + trainer.devices=1 \ + trainer.accelerator='cpu' \ + trainer.max_epochs=50 + +PyTorch Lightning Trainer arguments and args of the model and the optimizer can be added or overriden from CLI +""" +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf + +from nemo.collections.asr.models import EncMaskDecAudioToAudioModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="./conf", config_name="masking") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg, resolve=True)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + model = EncMaskDecAudioToAudioModel(cfg=cfg.model, trainer=trainer) + + # Initialize the weights of the model from another model, if provided via config + model.maybe_init_from_pretrained_checkpoint(cfg) + + # Train the model + trainer.fit(model) + + # Run on test data, if available + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if trainer.is_global_zero: + # Destroy the current process group and let the trainer initialize it again with a single device. + if torch.distributed.is_initialized(): + torch.distributed.destroy_process_group() + + # Run test on a single device + trainer = pl.Trainer(devices=1, accelerator=cfg.trainer.accelerator) + if model.prepare_test(trainer): + trainer.test(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/convert_ckpt_to_nemo.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/convert_ckpt_to_nemo.py new file mode 100644 index 0000000..2bc0f5d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/convert_ckpt_to_nemo.py @@ -0,0 +1,192 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +r""" +Conversion script to convert PTL checkpoints into nemo checkpoint. + Example to run this conversion script: + python -m torch.distributed.launch --nproc_per_node= * \ + convert_ckpt_to_nemo.py \ + --checkpoint_folder \ + --checkpoint_name \ + --nemo_file_path \ + --tensor_model_parallel_size \ + --pipeline_model_parallel_size +""" + +import os +from argparse import ArgumentParser + +import torch +from omegaconf.omegaconf import OmegaConf, open_dict + +from nemo.collections.multimodal.models.multimodal_llm.neva.neva_model import MegatronNevaModel +from nemo.collections.multimodal.models.text_to_image.controlnet.controlnet import MegatronControlNet +from nemo.collections.multimodal.models.text_to_image.imagen.imagen import MegatronImagen +from nemo.collections.multimodal.models.text_to_image.instruct_pix2pix.ldm.ddpm_edit import MegatronLatentDiffusionEdit +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion +from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import MegatronCLIPModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector +from nemo.utils import AppState, logging +from nemo.utils.distributed import initialize_distributed +from nemo.utils.model_utils import inject_model_parallel_rank + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +def get_args(): + parser = ArgumentParser() + parser.add_argument( + "--checkpoint_folder", + type=str, + default=None, + required=True, + help="Path to PTL checkpoints saved during training. Ex: /raid/nemo_experiments/multimodal/checkpoints", + ) + parser.add_argument( + "--checkpoint_name", + type=str, + default=None, + required=True, + help="Name of checkpoint to be used. Ex: megatron_gpt--val_loss=6.34-step=649-last.ckpt", + ) + + parser.add_argument( + "--hparams_file", + type=str, + default=None, + required=False, + help="Path config for restoring. It's created during training and may need to be modified during restore if restore environment is different than training. Ex: /raid/nemo_experiments/megatron_gpt/hparams.yaml", + ) + parser.add_argument("--nemo_file_path", type=str, default=None, required=True, help="Path to output .nemo file.") + parser.add_argument("--gpus_per_node", type=int, required=False, default=1) + parser.add_argument("--tensor_model_parallel_size", type=int, required=False, default=1) + parser.add_argument("--pipeline_model_parallel_size", type=int, required=False, default=1) + parser.add_argument( + "--pipeline_model_parallel_split_rank", + type=int, + required=False, + default=None, + help="If pipeline parallel size > 1, this is the rank at which the encoder ends and the decoder begins.", + ) + parser.add_argument("--model_type", type=str, required=False, default="megatron_clip") + parser.add_argument("--local_rank", type=int, required=False, default=os.getenv('LOCAL_RANK', -1)) + parser.add_argument("--bcp", action="store_true", help="Whether on BCP platform") + + args = parser.parse_args() + return args + + +def convert(local_rank, rank, world_size, args): + app_state = AppState() + app_state.data_parallel_rank = 0 + + cfg = OmegaConf.load(args.hparams_file) + with open_dict(cfg): + cfg['model'] = cfg['cfg'] + cfg['trainer'] = {'precision': cfg['model']['precision']} + if args.bcp: + cfg['cluster_type'] = 'BCP' + trainer = MegatronTrainerBuilder(cfg).create_trainer() + + app_state.pipeline_model_parallel_size = args.pipeline_model_parallel_size + app_state.tensor_model_parallel_size = args.tensor_model_parallel_size + + # no use atm, use to split ranks in encoder/decoder models. + if args.pipeline_model_parallel_size > 1 and args.model_type in []: + if args.pipeline_model_parallel_split_rank is not None: + app_state.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_split_rank + else: + if args.pipeline_model_parallel_size % 2 != 0: + raise ValueError( + f"Pipeline model parallel size {args.pipeline_model_parallel_size} must be even if split rank is not specified." + ) + else: + # If split rank is not set, then we set it to be pipeline_model_parallel_size // 2 - this is because in most cases we have the same number of enc/dec layers. + app_state.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_size // 2 + else: + app_state.pipeline_model_parallel_split_rank = None + + app_state.model_parallel_size = app_state.tensor_model_parallel_size * app_state.pipeline_model_parallel_size + + parallel_state.initialize_model_parallel( + tensor_model_parallel_size=app_state.tensor_model_parallel_size, + pipeline_model_parallel_size=app_state.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank=app_state.pipeline_model_parallel_split_rank, + ) + + app_state.pipeline_model_parallel_rank = parallel_state.get_pipeline_model_parallel_rank() + app_state.tensor_model_parallel_rank = parallel_state.get_tensor_model_parallel_rank() + + # inject model parallel rank + checkpoint_path = inject_model_parallel_rank(os.path.join(args.checkpoint_folder, args.checkpoint_name)) + + logging.info( + f'rank: {rank}, local_rank: {local_rank}, is loading checkpoint: {checkpoint_path} for tp_rank: {app_state.tensor_model_parallel_rank} and pp_rank: {app_state.pipeline_model_parallel_rank}' + ) + + if args.model_type == 'megatron_clip': + model = MegatronCLIPModel.load_from_checkpoint( + checkpoint_path, hparams_file=args.hparams_file, trainer=trainer + ) + elif args.model_type == 'stable_diffusion': + model = MegatronLatentDiffusion.load_from_checkpoint( + checkpoint_path, hparams_file=args.hparams_file, trainer=trainer + ) + elif args.model_type == 'instruct_pix2pix': + model = MegatronLatentDiffusionEdit.load_from_checkpoint( + checkpoint_path, hparams_file=args.hparams_file, trainer=trainer + ) + elif args.model_type == 'dreambooth': + model = MegatronLatentDiffusion.load_from_checkpoint( + checkpoint_path, hparams_file=args.hparams_file, trainer=trainer + ) + elif args.model_type == 'imagen': + model = MegatronImagen.load_from_checkpoint(checkpoint_path, hparams_file=args.hparams_file, trainer=trainer) + elif args.model_type == 'controlnet': + model = MegatronControlNet.load_from_checkpoint( + checkpoint_path, hparams_file=args.hparams_file, trainer=trainer + ) + elif args.model_type == 'kosmos': + model = MegatronKosmosModel.load_from_checkpoint( + checkpoint_path, hparams_file=args.hparams_file, trainer=trainer + ) + elif args.model_type == 'neva': + model = MegatronNevaModel.load_from_checkpoint( + checkpoint_path, hparams_file=args.hparams_file, trainer=trainer + ) + else: + raise ValueError(f"Unrecognized model_type {args.model_type}.") + + model._save_restore_connector = NLPSaveRestoreConnector() + + if torch.distributed.is_initialized(): + torch.distributed.barrier() + + model.save_to(args.nemo_file_path) + + logging.info(f'NeMo model saved to: {args.nemo_file_path}') + + +if __name__ == '__main__': + args = get_args() + local_rank, rank, world_size = initialize_distributed(args) + convert(local_rank, rank, world_size, args) diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/llava_config.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/llava_config.yaml new file mode 100644 index 0000000..83c5a4b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/llava_config.yaml @@ -0,0 +1,213 @@ +name: nemo_neva +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: bf16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 4650 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + check_val_every_n_epoch: null + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: nemo_neva + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_clip--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + +model: + precision: ${trainer.precision} + + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + + # Batch size guideline for different types of dataset + micro_batch_size: 16 # limited by GPU memory + global_batch_size: 128 # will use more micro batches to reach global batch size + + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + restore_from_path: null # used in fine-tuning + + # Multimodal configs + mm_cfg: + llm: + from_pretrained: null # path to nemo checkpoint + freeze: False + model_type: llama_2 # Only support nvgpt or llama_2 + vision_encoder: + from_pretrained: "openai/clip-vit-large-patch14" # path or name + from_hf: True + patch_dim: 14 + hidden_size: 1024 # could be found from model but tricky in code + vision_select_layer: -2 # default to the last layer + class_token_length: 1 + freeze: True + pretrain_mm_mlp_adapter: null # path to pretrained mm adapter + mm_mlp_adapter_type: mlp2x_gelu + use_im_start_end: False + + + # LLM configs + # use GPTModel from megatron.core + mcore_gpt: False + + # model architecture + encoder_seq_length: 4096 + max_position_embeddings: ${.encoder_seq_length} + position_embedding_type: rope + num_layers: 24 + hidden_size: 2048 + ffn_hidden_size: 5440 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 16 + init_method_std: 0.014 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0.0 # Dropout probability for hidden state transformer. + attention_dropout: 0.0 # Dropout probability for attention + ffn_dropout: 0.0 # Dropout probability in the feed-forward layer. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: 'rmsnorm' # Normalization layer to use. Options are 'layernorm', 'rmsnorm' + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + bias: False # Whether to use bias terms in all weight matrices. + activation: 'fast-swiglu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] + headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head. + transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer'] + normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True. + rotary_percentage: 1.0 # If using position_embedding_type=rope, then the per head dim is multiplied by this. + attention_type: 'multihead' # Attention type. Options ['multihead'] + share_embeddings_and_output_weights: False # Share embedding and output layer weights. + overlap_p2p_comm: False # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + seq_len_interpolation_factor: null # RoPE Interpolation factor for sequence length. This is used to build long-context models with RoPE ex: https://arxiv.org/abs/2306.15595. + num_query_groups: null # Number of query groups for group query attention. If None, normal attention is used. + + ## Activation Checkpointing + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + activations_checkpoint_num_layers: null # not used with 'selective' + num_micro_batches_with_partial_activation_checkpoints: null + activations_checkpoint_layers_per_pipeline: null + sequence_parallel: False + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # model fusions + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + bias_dropout_add_fusion: False # Use a kernel that fuses the bias addition, dropout and residual connection addition. + + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism. + openai_gelu: False + bias_activation_fusion: False + megatron_legacy: False + + transformer_engine: False + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: False # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: most_recent # 'most_recent' or 'max'. Algorithm for computing amax from history + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + tokenizer: + library: 'sentencepiece' + type: null + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + additional_special_tokens: null # ["", "", "", "", "", ""] + + data: + num_workers: 8 + dataloader_type: cyclic + data_path: + lazy_preprocess: True + is_multimodal: True + sep_image_conv_front: False + image_token_len: 256 + conv_template: llama_2 # check `nemo/collections/multimodal/data/neva/conversation.py` + image_folder: null + image_aspect_ratio: 'square' + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: fused_adam + lr: 2e-3 + weight_decay: 0. + betas: + - 0.9 + - 0.95 + sched: + name: CosineAnnealing + warmup_steps: 140 + constant_steps: 0 + min_lr: 2e-5 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/neva_config.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/neva_config.yaml new file mode 100644 index 0000000..b41f15c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/neva_config.yaml @@ -0,0 +1,214 @@ +name: nemo_neva +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: bf16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 4650 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + check_val_every_n_epoch: null + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: nemo_neva + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_clip--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + +model: + precision: ${trainer.precision} + + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + + # Batch size guideline for different types of dataset + micro_batch_size: 16 # limited by GPU memory + global_batch_size: 128 # will use more micro batches to reach global batch size + + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + restore_from_path: null # used in fine-tuning + + # Multimodal configs + mm_cfg: + llm: + from_pretrained: null # path to nemo checkpoint + freeze: True + model_type: llama_2 # `nvgpt` or `llama_2` supported + vision_encoder: + from_pretrained: "" # path or name + from_hf: True + patch_dim: 14 + hidden_size: 1024 # could be found from model but tricky in code + vision_select_layer: -2 # default to the last layer + class_token_length: 1 + freeze: True + pretrain_mm_mlp_adapter: null # path to pretrained mm adapter + mm_mlp_adapter_type: linear + use_im_start_end: False + + + # LLM configs + # use GPTModel from megatron.core + mcore_gpt: False + + # model architecture + encoder_seq_length: 4096 + max_position_embeddings: ${.encoder_seq_length} + position_embedding_type: rope + num_layers: 40 + hidden_size: 5120 + ffn_hidden_size: 13824 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 40 + init_method_std: 0.014 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0.0 # Dropout probability for hidden state transformer. + attention_dropout: 0.0 # Dropout probability for attention + ffn_dropout: 0.0 # Dropout probability in the feed-forward layer. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: rmsnorm # Type of normalization layers + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + bias: False # Whether to use bias terms in all weight matrices. + activation: 'fast-swiglu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] + headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head. + transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer'] + normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True. + rotary_percentage: 1.0 # If using position_embedding_type=rope, then the per head dim is multiplied by this. + attention_type: 'multihead' # Attention type. Options ['multihead'] + share_embeddings_and_output_weights: False # Share embedding and output layer weights. + overlap_p2p_comm: False # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + seq_len_interpolation_factor: null # RoPE Interpolation factor for sequence length. This is used to build long-context models with RoPE ex: https://arxiv.org/abs/2306.15595. + num_query_groups: null # Number of query groups for group query attention. If None, normal attention is used. + use_flash_attention: True + + ## Activation Checkpointing + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + activations_checkpoint_num_layers: null # not used with 'selective' + num_micro_batches_with_partial_activation_checkpoints: null + activations_checkpoint_layers_per_pipeline: null + sequence_parallel: False + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # model fusions + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + bias_dropout_add_fusion: False # Use a kernel that fuses the bias addition, dropout and residual connection addition. + + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism. + openai_gelu: False + bias_activation_fusion: False + megatron_legacy: False + + transformer_engine: False + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: False # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: most_recent # 'most_recent' or 'max'. Algorithm for computing amax from history + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + + # Megatron O2-style half-precision + megatron_amp_O2: True # Enable O2-level automatic mixed precision using main parameters + async_grad_allreduce: False + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + tokenizer: + library: 'sentencepiece' + type: null + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + additional_special_tokens: null # ["", "", "", "", "", ""] + + data: + num_workers: 8 + dataloader_type: cyclic + data_path: + lazy_preprocess: True + is_multimodal: True + sep_image_conv_front: False + image_token_len: 256 + conv_template: ${model.mm_cfg.llm.model_type} # check `nemo/collections/multimodal/data/neva/conversation.py` + image_folder: null + image_aspect_ratio: 'square' + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: fused_adam + lr: 2e-3 + weight_decay: 0. + betas: + - 0.9 + - 0.95 + sched: + name: CosineAnnealing + warmup_steps: 140 + constant_steps: 0 + min_lr: 2e-5 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/neva_finetune.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/neva_finetune.yaml new file mode 100644 index 0000000..cafee11 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/neva_finetune.yaml @@ -0,0 +1,210 @@ +name: nemo_neva +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: bf16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 4900 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + check_val_every_n_epoch: null + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: nemo_neva + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_clip--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + +model: + precision: ${trainer.precision} + + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + + # Batch size guideline for different types of dataset + micro_batch_size: 4 # limited by GPU memory + global_batch_size: 32 # will use more micro batches to reach global batch size + + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + restore_from_path: null # used in fine-tuning + + # Multimodal configs + mm_cfg: + llm: + from_pretrained: null # path to nemo checkpoint + freeze: False + model_type: nvgpt # Only support nvgpt or llama_2 + vision_encoder: + from_pretrained: "" # path or name + from_hf: True + patch_dim: 14 + hidden_size: 1024 # could be found from model but tricky in code + vision_select_layer: -2 # default to the last layer + class_token_length: 1 + freeze: True + pretrain_mm_mlp_adapter: null # path to pretrained mm adapter + mm_mlp_adapter_type: linear + use_im_start_end: False # only support True now + + + # LLM configs + # use GPTModel from megatron.core + mcore_gpt: False + + # model architecture + encoder_seq_length: 4096 + max_position_embeddings: ${.encoder_seq_length} + position_embedding_type: rope + num_layers: 24 + hidden_size: 2048 + ffn_hidden_size: 5440 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 16 + init_method_std: 0.014 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0. # Dropout probability for hidden state transformer. + attention_dropout: 0. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: layernorm1p # Type of normalization layers + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + bias: False # Whether to use bias terms in all weight matrices. + activation: 'fast-swiglu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] + headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head. + transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer'] + normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True. + rotary_percentage: 0.5 # If using position_embedding_type=rope, then the per head dim is multiplied by this. + attention_type: 'multihead' # Attention type. Options ['multihead'] + share_embeddings_and_output_weights: False # Share embedding and output layer weights. + overlap_p2p_comm: False # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + seq_len_interpolation_factor: null # RoPE Interpolation factor for sequence length. This is used to build long-context models with RoPE ex: https://arxiv.org/abs/2306.15595. + num_query_groups: null # Number of query groups for group query attention. If None, normal attention is used. + + ## Activation Checkpointing + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + activations_checkpoint_num_layers: null # not used with 'selective' + num_micro_batches_with_partial_activation_checkpoints: null + activations_checkpoint_layers_per_pipeline: null + sequence_parallel: False + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # model fusions + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + bias_dropout_add_fusion: False # Use a kernel that fuses the bias addition, dropout and residual connection addition. + + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism. + openai_gelu: False + bias_activation_fusion: False + megatron_legacy: False + + transformer_engine: False + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: False # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: most_recent # 'most_recent' or 'max'. Algorithm for computing amax from history + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + + # Megatron O2-style half-precision + megatron_amp_O2: True # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + tokenizer: + library: 'megatron' + type: 'GPT2BPETokenizer' + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + + data: + num_workers: 8 + dataloader_type: cyclic + data_path: + lazy_preprocess: True + is_multimodal: True + sep_image_conv_front: False + image_token_len: 256 + conv_template: ${model.mm_cfg.llm.model_type} # check `nemo/collections/multimodal/data/neva/conversation.py` + image_folder: null + image_aspect_ratio: 'square' + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: fused_adam + lr: 2e-5 + weight_decay: 0. + betas: + - 0.9 + - 0.95 + sched: + name: CosineAnnealing + warmup_steps: 200 + constant_steps: 0 + min_lr: 2e-7 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/neva_inference.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/neva_inference.yaml new file mode 100644 index 0000000..c822237 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/neva_inference.yaml @@ -0,0 +1,54 @@ +inference: + greedy: False # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 0.2 # sampling temperature + add_BOS: False # add the bos token at the begining of the prompt + tokens_to_generate: 256 # The minimum length of the sequence to be generated. + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + end_strings: ["","",] # generation will stop when one of these tokens is generated + images_base_path: /pwd/images + insert_image_token: null # `left` or `right` or `null` + +trainer: + devices: 8 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: bf16 # 16, 32, or bf16 + +cluster_type: BCP +tensor_model_parallel_size: 8 +pipeline_model_parallel_size: 1 +pipeline_model_parallel_split_rank: 0 # used for encoder and decoder model (0 for others) +neva_model_file: /pwd/nemo_experiments/nemo_llava.nemo #neva_22b_tp8_finetuned_v1.nemo neva_8b_tp4_finetuned_v1.nemo +base_model_file: null +checkpoint_dir: null #/pwd/nemo_multimodal/nemo_experiments/nemo_llava_finetune/checkpoints # checkpoint file dir. This is used to load the PTL checkpoint generated during the Kosmos training +checkpoint_name: null #megatron_clip--val_loss=0.41-step=13499-consumed_samples=431904.0.ckpt # PTL checkpoint file name, only used for PTL checkpoint loading +hparams_file: null #/pwd/nemo_multimodal/nemo_experiments/nemo_llava_finetune/version_0/hparams.yaml # model configuration file, only used for PTL checkpoint loading +quality: 9 +toxicity: 0 +humor: 6 +creativity: 6 +violence: 0 +helpfulness: 6 +not_appropriate: 0 + +# MORE THAN ONE INFERENCE IS NOT RUNNING PROPERLY NEED TO CHECK WHY SECOND IS OUTPUTING JUNK N +prompt_file: /pwd/nemo_experiments/input_prompts.jsonl +output_file: /pwd/nemo_experiments/results.jsonl + +server: False # whether launch the API server +port: 5555 # the port number for the inference server +web_server: False # whether launch the web inference server +share: False # whether create a public URL +username: test # user name for web client +password: test2 # password for web client +web_port: 9889 # the port number of the web server + +quantization: + algorithm: awq # int8_sq, fp8, int8, awq + enable: False \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/neva_peft.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/neva_peft.yaml new file mode 100644 index 0000000..add113c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/conf/neva_peft.yaml @@ -0,0 +1,221 @@ +name: nemo_neva +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: bf16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 4900 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + check_val_every_n_epoch: null + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: nemo_neva + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_clip--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + +model: + precision: ${trainer.precision} + + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + + # Batch size guideline for different types of dataset + micro_batch_size: 4 # limited by GPU memory + global_batch_size: 32 # will use more micro batches to reach global batch size + + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + restore_from_path: null # used in fine-tuning + + # Multimodal configs + mm_cfg: + llm: + from_pretrained: null # path to nemo checkpoint + freeze: True # Set this to True in adapter learning! + model_type: nvgpt # Only support nvgpt or llama_2 + vision_encoder: + from_pretrained: "" # path or name + from_hf: True + patch_dim: 14 + hidden_size: 1024 # could be found from model but tricky in code + vision_select_layer: -2 # default to the last layer + class_token_length: 1 + freeze: True + pretrain_mm_mlp_adapter: null # path to pretrained mm adapter + mm_mlp_adapter_type: linear + use_im_start_end: False # only support True now + + peft: + peft_scheme: "lora" + restore_from_path: null + lora_tuning: + adapter_dim: 32 + adapter_dropout: 0.0 + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + layer_selection: null # selects in which layers to add lora adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + # LLM configs + # use GPTModel from megatron.core + mcore_gpt: False + + # model architecture + encoder_seq_length: 4096 + max_position_embeddings: ${.encoder_seq_length} + position_embedding_type: rope + num_layers: 24 + hidden_size: 2048 + ffn_hidden_size: 5440 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 16 + init_method_std: 0.014 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0. # Dropout probability for hidden state transformer. + attention_dropout: 0. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: layernorm1p # Type of normalization layers + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + bias: False # Whether to use bias terms in all weight matrices. + activation: 'fast-swiglu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] + headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head. + transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer'] + normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True. + rotary_percentage: 0.5 # If using position_embedding_type=rope, then the per head dim is multiplied by this. + attention_type: 'multihead' # Attention type. Options ['multihead'] + share_embeddings_and_output_weights: False # Share embedding and output layer weights. + overlap_p2p_comm: False # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + seq_len_interpolation_factor: null # RoPE Interpolation factor for sequence length. This is used to build long-context models with RoPE ex: https://arxiv.org/abs/2306.15595. + num_query_groups: null # Number of query groups for group query attention. If None, normal attention is used. + + ## Activation Checkpointing + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + activations_checkpoint_num_layers: null # not used with 'selective' + num_micro_batches_with_partial_activation_checkpoints: null + activations_checkpoint_layers_per_pipeline: null + sequence_parallel: False + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # model fusions + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + bias_dropout_add_fusion: False # Use a kernel that fuses the bias addition, dropout and residual connection addition. + + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism. + openai_gelu: False + bias_activation_fusion: False + megatron_legacy: False + + transformer_engine: False + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: False # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: most_recent # 'most_recent' or 'max'. Algorithm for computing amax from history + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + + # Megatron O2-style half-precision + megatron_amp_O2: True # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + tokenizer: + library: 'megatron' + type: 'GPT2BPETokenizer' + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + + data: + num_workers: 8 + dataloader_type: cyclic + data_path: + lazy_preprocess: True + is_multimodal: True + sep_image_conv_front: False + image_token_len: 256 + conv_template: ${model.mm_cfg.llm.model_type} # check `nemo/collections/multimodal/data/neva/conversation.py` + image_folder: null + image_aspect_ratio: 'square' + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: fused_adam + lr: 2e-4 + weight_decay: 0. + betas: + - 0.9 + - 0.95 + sched: + name: CosineAnnealing + warmup_steps: 200 + constant_steps: 0 + min_lr: 2e-7 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/convert_hf_llava_to_neva.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/convert_hf_llava_to_neva.py new file mode 100644 index 0000000..c9263ea --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/convert_hf_llava_to_neva.py @@ -0,0 +1,366 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +r""" +Script to convert HuggingFace LLaVA checkpoints into .nemo file. + Example to run this conversion script: + python convert_hf_llava_to_neva.py \ + --in-file \ + --out-file \ + --tokenizer-model \ + --conv-template llama_2 # nvgpt, llama_2, v1 (vicuna) +""" + +import os +from argparse import ArgumentParser +from collections import OrderedDict + +import torch +from llava import LlavaLlamaForCausalLM +from omegaconf import OmegaConf +from pytorch_lightning.core.saving import _load_state as ptl_load_state +from pytorch_lightning.trainer.trainer import Trainer +from transformers import LlamaTokenizer + +from nemo.collections.multimodal.models.multimodal_llm.neva.neva_model import MegatronNevaModel +from nemo.collections.nlp.parts.nlp_overrides import ( + GradScaler, + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, + NLPSaveRestoreConnector, + PipelineMixedPrecisionPlugin, +) +from nemo.utils import logging + + +def get_args(): + parser = ArgumentParser() + parser.add_argument( + "--in-file", type=str, default=None, required=True, help="Path to Huggingface LLaMA checkpoints", + ) + parser.add_argument("--out-file", type=str, default=None, required=True, help="Path to output .nemo file.") + parser.add_argument( + "--conv-template", + type=str, + default="llama_2", + required=False, + help="Conversation template: nvgpt, llama_2, v1 (vicuna)", + ) + parser.add_argument( + "--tokenizer-model", type=str, default=None, required=False, help="Path to sentencepiece tokenizer model." + ) + parser.add_argument("--precision", type=str, default="32", help="Model precision") + args = parser.parse_args() + return args + + +def load_model(cls, checkpoint, strict, **kwargs): + try: + if 'cfg' in kwargs: + model = ptl_load_state(cls, checkpoint, strict=strict, **kwargs) + else: + model = cls(cfg=checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY], **kwargs) + for name, module in model.named_parameters(): + if name in checkpoint['state_dict']: + if module.data.shape != checkpoint['state_dict'][name].shape: + print( + f"WARNING: Auto padding {name} from {checkpoint['state_dict'][name].shape} to {module.data.shape}" + ) + module.data[ + : checkpoint['state_dict'][name].size(0), : checkpoint['state_dict'][name].size(1) + ] = checkpoint['state_dict'][name] + else: + module.data = checkpoint['state_dict'][name] + checkpoint['state_dict'].pop(name) + else: + print(f"Unexpected key: {name} not in checkpoint but in model.") + + for name, buffer in model.named_buffers(): + if name in checkpoint['state_dict']: + buffer.data = checkpoint['state_dict'][name] + checkpoint['state_dict'].pop(name) + + if len(checkpoint['state_dict'].keys()) != 0: + raise RuntimeError( + f"Additional keys: {checkpoint['state_dict'].keys()} in checkpoint but not in model." + ) + + # register the artifacts + cfg = checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY] + if cfg.tokenizer.model is not None: + model.register_artifact("tokenizer.tokenizer_model", cfg.tokenizer.model) + if cfg.tokenizer.vocab_file is not None: + model.register_artifact("tokenizer.vocab_file", cfg.tokenizer.vocab_file) + if cfg.tokenizer.merge_file is not None: + model.register_artifact("tokenizer.merge_file", cfg.tokenizer.merge_file) + finally: + cls._set_model_restore_state(is_being_restored=False) + return model + + +def load_config(args, llava_config): + nemo_config = OmegaConf.load(os.path.join(os.path.dirname(__file__), 'conf/llava_config.yaml')).model + nemo_config.mm_cfg.mm_mlp_adapter_type = llava_config.get('mm_projector_type', 'linear') + nemo_config.mm_cfg.vision_encoder.from_pretrained = llava_config.get( + 'mm_vision_tower', 'openai/clip-vit-large-patch14' + ) + if '336' in nemo_config.mm_cfg.vision_encoder.from_pretrained: + nemo_config.data.image_token_len = 576 + nemo_config.encoder_seq_length = llava_config['max_position_embeddings'] + nemo_config.num_layers = int(llava_config['num_hidden_layers']) + nemo_config.hidden_size = llava_config['hidden_size'] + nemo_config.ffn_hidden_size = llava_config['intermediate_size'] + nemo_config.num_attention_heads = llava_config['num_attention_heads'] + nemo_config.max_position_embeddings = llava_config['max_position_embeddings'] + nemo_config.init_method_std = llava_config['initializer_range'] + nemo_config.layernorm_epsilon = llava_config['rms_norm_eps'] + if 'num_key_value_heads' in llava_config: + nemo_config.num_query_groups = llava_config['num_key_value_heads'] + nemo_config.use_cpu_initialization = True + nemo_config.activation = 'fast-swiglu' + nemo_config.data.conv_template = args.conv_template + nemo_config.mm_cfg.model_type = args.conv_template + if args.tokenizer_model is None: + nemo_config.tokenizer.model = llava_config['tokenizer_model'] + else: + nemo_config.tokenizer.model = args.tokenizer_model + if llava_config['rope_scaling'] is not None: + if llava_config['rope_scaling']['type'] == 'linear': + nemo_config['seq_len_interpolation_factor'] = llava_config['rope_scaling']['factor'] + else: + raise ValueError("Only linear rope scaling type is supported now") + + base = 128 + while llava_config['vocab_size'] % base != 0: + base //= 2 + nemo_config.make_vocab_size_divisible_by = base + + return nemo_config + + +def convert(args): + logging.info(f"loading checkpoint {args.in_file}") + model = LlavaLlamaForCausalLM.from_pretrained(args.in_file) + tokenizer = LlamaTokenizer.from_pretrained(args.in_file) + hf_config = vars(model.config) + hf_config['tokenizer_model'] = str(tokenizer.vocab_file) + print(f"hf_config: {hf_config}") + print("named parameters:") + for name, param in model.named_parameters(): + print(f"- {name}") + + nemo_config = load_config(args, hf_config) + print(nemo_config) + + if args.precision in ["32", "16"]: + precision = int(float(args.precision)) + elif args.precision in ["bf16", "bf16-mixed"]: + if torch.cuda.is_available() and torch.cuda.is_bf16_supported(): + precision = args.precision + else: + logging.warning("BF16 is not supported on this device. Using FP16 instead.") + precision = args.precision[2:] # prune bf in string + else: + precision = args.precision + + plugins = [] + if precision in [16, '16', 'bf16', '16-mixed', 'bf16-mixed']: + scaler = None + if precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=nemo_config.get('native_amp_init_scale', 2 ** 32), + growth_interval=nemo_config.get('native_amp_growth_interval', 1000), + hysteresis=nemo_config.get('hysteresis', 2), + ) + # MixedPrecisionPlugin in PTL >= 2.0 requires precision to be 16-mixed or bf16-mixed + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + + if nemo_config.get('megatron_amp_O2', False): + plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(PipelineMixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + + if precision == 32: + dtype = torch.float32 + elif precision in [16, "16", "16-mixed"]: + dtype = torch.float16 + elif precision in ["bf16", "bf16-mixed"]: + dtype = torch.bfloat16 + else: + dtype = torch.float32 # fallback + + nemo_config.precision = precision + print(f"nemo_config: {nemo_config}") + + trainer = Trainer(plugins=plugins, accelerator='cpu', precision=precision, strategy=NLPDDPStrategy()) + + hidden_size = hf_config["hidden_size"] + head_num = hf_config["num_attention_heads"] + head_size = hidden_size // head_num + num_layers = hf_config["num_hidden_layers"] + + mcore_gpt = nemo_config.mcore_gpt + + assert mcore_gpt == nemo_config.get( + 'transformer_engine', False + ), "mcore_gpt transformer_engine must be enabled (or disabled) together." + + param_to_weights = lambda param: param.float() + + checkpoint = OrderedDict() + checkpoint['state_dict'] = OrderedDict() + + # Multimodal projection + if mcore_gpt: + mm_projection_layer_base_name = ( + f'model.embedding.word_embeddings.adapter_layer.mm_projector_adapter.mm_projector' + ) + else: + mm_projection_layer_base_name = ( + f'model.language_model.embedding.word_embeddings.adapter_layer.mm_projector_adapter.mm_projector' + ) + for key in model.state_dict(): + if 'mm_projector' in key: + mm_projection_layer_suffix = key.split('mm_projector')[1] + checkpoint['state_dict'][ + f'{mm_projection_layer_base_name}{mm_projection_layer_suffix}' + ] = param_to_weights(model.state_dict()[key]) + + embed_weight = model.state_dict()[f'model.embed_tokens.weight'] + if mcore_gpt: + embed_weights_base_name = f'model.embedding.word_embeddings.weight' + else: + embed_weights_base_name = f'model.language_model.embedding.word_embeddings.weight' + checkpoint['state_dict'][embed_weights_base_name] = param_to_weights(embed_weight) + + # in hf, this is defined as register_buffer(..., persistent=False) so it won't be in the state dict + if f'model.layers.0.self_attn.rotary_emb.inv_freq' in model.state_dict(): + rotary_embed_weight = model.state_dict()[f'model.layers.0.self_attn.rotary_emb.inv_freq'] + if mcore_gpt: + rotary_embed_weight_base_name = f'model.rotary_pos_emb.inv_freq' + else: + rotary_embed_weight_base_name = f'model.language_model.rotary_pos_emb.inv_freq' + checkpoint['state_dict'][rotary_embed_weight_base_name] = param_to_weights(rotary_embed_weight) + + if nemo_config.num_query_groups is None or nemo_config.num_query_groups == head_num: + num_query_groups = head_num + else: + num_query_groups = nemo_config.num_query_groups + assert head_num % num_query_groups == 0, 'head_num must be divisible by num_query_groups' + if mcore_gpt: + assert nemo_config.activation.startswith('fast-'), 'mcore only supports fast version of gated linear unit.' + + for l in range(int(num_layers)): + print(f"converting layer {l}") + old_tensor_shape = model.state_dict()[f'model.layers.{l}.self_attn.q_proj.weight'].size() + new_q_tensor_shape = (head_num, head_size) + old_tensor_shape[1:] + new_kv_tensor_shape = (num_query_groups, head_size) + old_tensor_shape[1:] + q = model.state_dict()[f'model.layers.{l}.self_attn.q_proj.weight'].view(*new_q_tensor_shape) + k = model.state_dict()[f'model.layers.{l}.self_attn.k_proj.weight'].view(*new_kv_tensor_shape) + v = model.state_dict()[f'model.layers.{l}.self_attn.v_proj.weight'].view(*new_kv_tensor_shape) + qkv_weights = torch.empty((0, head_size) + old_tensor_shape[1:]) + heads_per_group = head_num // num_query_groups + for i in range(num_query_groups): + qkv_weights = torch.cat((qkv_weights, q[i * heads_per_group : (i + 1) * heads_per_group, :, :])) + qkv_weights = torch.cat((qkv_weights, k[i : i + 1, :, :])) + qkv_weights = torch.cat((qkv_weights, v[i : i + 1, :, :])) + qkv_weights = qkv_weights.reshape([head_size * (head_num + 2 * num_query_groups), hidden_size]) + if mcore_gpt: + qkv_weights_base_name = f'model.decoder.layers.{l}.self_attention.linear_qkv.weight' + else: + qkv_weights_base_name = f'model.language_model.encoder.layers.{l}.self_attention.query_key_value.weight' + checkpoint['state_dict'][qkv_weights_base_name] = param_to_weights(qkv_weights) + + # attention dense + o_weight = model.state_dict()[f'model.layers.{l}.self_attn.o_proj.weight'] + if mcore_gpt: + o_weight_base_name = f'model.decoder.layers.{l}.self_attention.linear_proj.weight' + else: + o_weight_base_name = f'model.language_model.encoder.layers.{l}.self_attention.dense.weight' + checkpoint['state_dict'][o_weight_base_name] = param_to_weights(o_weight) + + # MLP + mlp_down_weight = model.state_dict()[f'model.layers.{l}.mlp.gate_proj.weight'] + mlp_gate_weight = model.state_dict()[f'model.layers.{l}.mlp.up_proj.weight'] + if mcore_gpt: + mlp_down_base_name = f'model.decoder.layers.{l}.mlp.linear_fc1.weight' + else: + mlp_down_base_name = f'model.language_model.encoder.layers.{l}.mlp.dense_h_to_4h.weight' + mlp_down_weight = torch.cat((mlp_down_weight, mlp_gate_weight), axis=0) + checkpoint['state_dict'][mlp_down_base_name] = param_to_weights(mlp_down_weight) + + mlp_up_weight = model.state_dict()[f'model.layers.{l}.mlp.down_proj.weight'] + if mcore_gpt: + mlp_up_base_name = f'model.decoder.layers.{l}.mlp.linear_fc2.weight' + else: + mlp_up_base_name = f'model.language_model.encoder.layers.{l}.mlp.dense_4h_to_h.weight' + checkpoint['state_dict'][mlp_up_base_name] = param_to_weights(mlp_up_weight) + + # LayerNorm + input_ln_weight = model.state_dict()[f'model.layers.{l}.input_layernorm.weight'] + if mcore_gpt: + input_ln_base_name = f'model.decoder.layers.{l}.self_attention.linear_qkv.layer_norm_weight' + else: + input_ln_base_name = f'model.language_model.encoder.layers.{l}.input_layernorm.weight' + checkpoint['state_dict'][input_ln_base_name] = param_to_weights(input_ln_weight) + + post_attn_ln_weight = model.state_dict()[f'model.layers.{l}.post_attention_layernorm.weight'] + if mcore_gpt: + post_attn_ln_base_name = f'model.decoder.layers.{l}.mlp.linear_fc1.layer_norm_weight' + else: + post_attn_ln_base_name = f'model.language_model.encoder.layers.{l}.post_attention_layernorm.weight' + checkpoint['state_dict'][post_attn_ln_base_name] = param_to_weights(post_attn_ln_weight) + + print(f"done layer {l}") + + final_ln_weight = model.state_dict()[f'model.norm.weight'] + if mcore_gpt: + final_ln_base_name = f'model.decoder.final_layernorm.weight' + else: + final_ln_base_name = f'model.language_model.encoder.final_layernorm.weight' + checkpoint['state_dict'][final_ln_base_name] = param_to_weights(final_ln_weight) + + output_layer_weight = model.state_dict()[f'lm_head.weight'] + if mcore_gpt: + output_layer_base_name = f'model.output_layer.weight' + else: + output_layer_base_name = f'model.language_model.output_layer.weight' + checkpoint['state_dict'][output_layer_base_name] = param_to_weights(output_layer_weight) + + checkpoint[MegatronNevaModel.CHECKPOINT_HYPER_PARAMS_KEY] = nemo_config + + del model + + if nemo_config.get('megatron_amp_O2', False): + keys = list(checkpoint['state_dict'].keys()) + for key in keys: + checkpoint['state_dict'][key.replace('model.', 'model.module.', 1)] = checkpoint['state_dict'].pop(key) + + model = load_model(MegatronNevaModel, checkpoint, strict=False, trainer=trainer) + + model._save_restore_connector = NLPSaveRestoreConnector() + + # cast to target precision and disable cpu init + model = model.to(dtype=dtype) + model.cfg.use_cpu_initialization = False + + model.save_to(args.out_file) + logging.info(f'NeMo model saved to: {args.out_file}') + + +if __name__ == '__main__': + args = get_args() + convert(args) diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/eval/gradio_cli.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/eval/gradio_cli.py new file mode 100644 index 0000000..4f2136e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/eval/gradio_cli.py @@ -0,0 +1,41 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import base64 + +import requests + +# URL of the Gradio server +url = 'http://localhost:8890/api/predict/' + +# Prepare the text data +text_data = 'Describe this image please.' + +# Prepare the image data +with open("/path/to/images/001.jpg", "rb") as image_file: + encoded_string = base64.b64encode(image_file.read()).decode() + +# Data to send +data = {'data': [text_data, encoded_string]} + +# Sending a POST request to the Gradio server +response = requests.post(url, json=data) + +# Checking if the request was successful +if response.status_code == 200: + # Parsing the response + response_data = response.json() + print("Response from server:", response_data) +else: + print("Failed to get a response from the server, status code:", response.status_code) diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/eval/gradio_server.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/eval/gradio_server.py new file mode 100644 index 0000000..b1308a7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/eval/gradio_server.py @@ -0,0 +1,108 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import base64 +import io + +import gradio as gr +import PIL.Image +from omegaconf import OmegaConf + +from nemo.collections.multimodal.parts.utils import create_neva_model_and_processor + +CFG_STRING = """ +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: bf16 # 16, 32, or bf16 + +inference: + greedy: False # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 0.2 # sampling temperature + add_BOS: False # add the bos token at the begining of the prompt + tokens_to_generate: 256 # The minimum length of the sequence to be generated. + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + end_strings: ["","",] # generation will stop when one of these tokens is generated + images_base_path: /pwd/images + insert_image_token: null # `left` or `right` or `null` + +cluster_type: BCP +tensor_model_parallel_size: 1 +pipeline_model_parallel_size: 1 +pipeline_model_parallel_split_rank: 0 # used for encoder and decoder model (0 for others) + +neva_model_file: /pwd/nemo_experiments/nemo_llava.nemo #neva_22b_tp8_finetuned_v1.nemo neva_8b_tp4_finetuned_v1.nemo +base_model_file: null +checkpoint_dir: null #/pwd/nemo_multimodal/nemo_experiments/nemo_llava_finetune/checkpoints # checkpoint file dir. This is used to load the PTL checkpoint generated during the Kosmos training +checkpoint_name: null #megatron_clip--val_loss=0.41-step=13499-consumed_samples=431904.0.ckpt # PTL checkpoint file name, only used for PTL checkpoint loading +hparams_file: null #/pwd/nemo_multimodal/nemo_experiments/nemo_llava_finetune/version_0/hparams.yaml # model configuration file, only used for PTL checkpoint loading +""" + +cfg = OmegaConf.create(CFG_STRING) +cfg.neva_model_file = "/path/to/llava-v1.5-7b.nemo" +model, image_processor = create_neva_model_and_processor(cfg) + + +def predict(prompt, image_base64=None): + input_data = {"prompt": prompt} + if image_base64 is not None: + image_data = base64.b64decode(image_base64) + # image = PIL.Image.fromarray(image) + image = PIL.Image.open(io.BytesIO(image_data)) + input_data["image"] = image_processor(image) + + length_params: LengthParam = { + "max_length": cfg.inference.tokens_to_generate, + "min_length": cfg.inference.min_tokens_to_generate, + } + sampling_params: SamplingParam = { + "use_greedy": cfg.inference.greedy, + "temperature": cfg.inference.temperature, + "top_k": cfg.inference.top_k, + "top_p": cfg.inference.top_p, + "repetition_penalty": cfg.inference.repetition_penalty, + "add_BOS": cfg.inference.add_BOS, + "all_probs": cfg.inference.all_probs, + "compute_logprob": cfg.inference.compute_logprob, + "end_strings": cfg.inference.end_strings, + } + + # Generate model responses + responses = model.generate( + input_prompts=[input_data], # Adjust based on your model's requirements + length_params=length_params, # Define these parameters as in your original code + sampling_params=sampling_params, # Define these parameters as in your original code + inference_config=cfg, + ) + + return responses[0]["clean_response"] + + +iface = gr.Interface( + fn=predict, + inputs=[gr.Textbox(), gr.Textbox()], + outputs="text", + title="Multimodal Model Inference", + description="Enter a prompt and optionally upload an image for model inference.", +) + +if __name__ == "__main__": + iface.launch(server_port=8890, share=False) diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/eval/vqa_science.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/eval/vqa_science.py new file mode 100644 index 0000000..8ea267a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/eval/vqa_science.py @@ -0,0 +1,176 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import json +import math +import os + +import shortuuid +from omegaconf import OmegaConf +from tqdm import tqdm + +from nemo.collections.multimodal.parts.utils import create_neva_model_and_processor +from nemo.collections.nlp.modules.common.transformer.text_generation import LengthParam, SamplingParam +from nemo.utils.get_rank import is_global_rank_zero + +CFG_STRING = """ +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: bf16 # 16, 32, or bf16 + +inference: + greedy: True # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 0.2 # sampling temperature + add_BOS: True # add the bos token at the begining of the prompt + tokens_to_generate: 64 # The minimum length of the sequence to be generated. + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + end_strings: ["","",] # generation will stop when one of these tokens is generated + images_base_path: /pwd/images + insert_image_token: null # `left` or `right` or `null` + +cluster_type: BCP +tensor_model_parallel_size: 1 +pipeline_model_parallel_size: 1 +pipeline_model_parallel_split_rank: 0 # used for encoder and decoder model (0 for others) + +neva_model_file: /pwd/nemo_experiments/nemo_llava.nemo #neva_22b_tp8_finetuned_v1.nemo neva_8b_tp4_finetuned_v1.nemo +base_model_file: null +checkpoint_dir: null #/pwd/nemo_multimodal/nemo_experiments/nemo_llava_finetune/checkpoints # checkpoint file dir. This is used to load the PTL checkpoint generated during the Kosmos training +checkpoint_name: null #megatron_clip--val_loss=0.41-step=13499-consumed_samples=431904.0.ckpt # PTL checkpoint file name, only used for PTL checkpoint loading +hparams_file: null #/pwd/nemo_multimodal/nemo_experiments/nemo_llava_finetune/version_0/hparams.yaml # model configuration file, only used for PTL checkpoint loading +""" + + +def split_list(lst, n): + """Split a list into n (roughly) equal-sized chunks""" + chunk_size = math.ceil(len(lst) / n) # integer division + return [lst[i : i + chunk_size] for i in range(0, len(lst), chunk_size)] + + +def get_chunk(lst, n, k): + chunks = split_list(lst, n) + return chunks[k] + + +def eval_model(args): + # Model + cfg = OmegaConf.create(CFG_STRING) + cfg.neva_model_file = args.model_path + cfg.base_model_file = args.model_base + cfg.inference.images_base_path = args.image_folder + cfg.tensor_model_parallel_size = args.tp + cfg.trainer.devices = args.tp + + model, image_processor = create_neva_model_and_processor(cfg) + length_params: LengthParam = { + "max_length": cfg.inference.tokens_to_generate, + "min_length": cfg.inference.min_tokens_to_generate, + } + sampling_params: SamplingParam = { + "use_greedy": cfg.inference.greedy, + "temperature": cfg.inference.temperature, + "top_k": cfg.inference.top_k, + "top_p": cfg.inference.top_p, + "repetition_penalty": cfg.inference.repetition_penalty, + "add_BOS": cfg.inference.add_BOS, + "all_probs": cfg.inference.all_probs, + "compute_logprob": cfg.inference.compute_logprob, + "end_strings": cfg.inference.end_strings, + } + + questions = json.load(open(os.path.expanduser(args.question_file), "r")) + questions = get_chunk(questions, args.num_chunks, args.chunk_idx) + answers_file = os.path.expanduser(args.answers_file) + os.makedirs(os.path.dirname(answers_file), exist_ok=True) + ans_file = open(answers_file, "w") + for i, line in enumerate(tqdm(questions, disable=(not is_global_rank_zero()))): + idx = line["id"] + question = line['conversations'][0] + qs = question['value'].replace('', '').strip() + cur_prompt = qs + + if 'image' in line: + cur_prompt = qs = '' + cur_prompt + line['image'] = image_processor(os.path.join(cfg.inference.images_base_path, line['image'])) + + if args.single_pred_prompt: + qs = qs + '\n' + "Answer with the option's letter from the given choices directly." + cur_prompt = cur_prompt + '\n' + "Answer with the option's letter from the given choices directly." + + responses = model.generate( + input_prompts=[dict(prompt=qs, image=line.get('image', None))], + length_params=length_params, + sampling_params=sampling_params, + inference_config=cfg, + ) + # import pdb; pdb.set_trace() + outputs = responses[0]["clean_response"] + + # prompt for answer + if args.answer_prompter: + outputs_reasoning = outputs + + responses = model.generate( + input_prompts=[prompt + outputs_reasoning + ' ###\nANSWER:'], + length_params=length_params, + sampling_params=sampling_params, + inference_config=cfg, + ) + outputs = responses[0]["clean_response"] + outputs = outputs_reasoning + '\n The answer is ' + outputs + + ans_id = shortuuid.uuid() + ans_file.write( + json.dumps( + { + "question_id": idx, + "prompt": cur_prompt, + "text": outputs, + "answer_id": ans_id, + "model_id": args.model_path, + "metadata": {}, + } + ) + + "\n" + ) + ans_file.flush() + ans_file.close() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model-path", type=str, default="facebook/opt-350m") + parser.add_argument("--model-base", type=str, default=None) + parser.add_argument("--image-folder", type=str, default="") + parser.add_argument("--question-file", type=str, default="tables/question.json") + parser.add_argument("--answers-file", type=str, default="answer.jsonl") + parser.add_argument("--conv-mode", type=str, default="llava_v0") + parser.add_argument("--tp", type=int, default=1) + parser.add_argument("--num-chunks", type=int, default=1) + parser.add_argument("--chunk-idx", type=int, default=0) + parser.add_argument("--temperature", type=float, default=0.2) + parser.add_argument("--answer-prompter", action="store_true") + parser.add_argument("--single-pred-prompt", action="store_true") + args = parser.parse_args() + + eval_model(args) diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/neva_evaluation.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/neva_evaluation.py new file mode 100644 index 0000000..bd3f975 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/neva_evaluation.py @@ -0,0 +1,143 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import torch +from torch.utils.data import Dataset + +from nemo.collections.multimodal.parts.utils import create_neva_model_and_processor +from nemo.collections.nlp.modules.common.transformer.text_generation import LengthParam, SamplingParam +from nemo.core.config import hydra_runner + + +try: + import ammo.torch.quantization as atq + + HAVE_AMMO = True + +except (ImportError, ModuleNotFoundError): + + HAVE_AMMO = False + +if not torch.cuda.is_available(): + raise EnvironmentError("GPU is needed for the inference") + + +class RequestDataSet(Dataset): + def __init__(self, sentences): + super().__init__() + self.sentences = sentences + + def __len__(self,): + return len(self.sentences) + + def __getitem__(self, idx): + return self.sentences[idx] + + +@hydra_runner(config_path="conf", config_name="neva_inference") +def main(cfg) -> None: + model, image_processor = create_neva_model_and_processor(cfg) + + length_params: LengthParam = { + "max_length": cfg.inference.tokens_to_generate, + "min_length": cfg.inference.min_tokens_to_generate, + } + + sampling_params: SamplingParam = { + "use_greedy": cfg.inference.greedy, + "temperature": cfg.inference.temperature, + "top_k": cfg.inference.top_k, + "top_p": cfg.inference.top_p, + "repetition_penalty": cfg.inference.repetition_penalty, + "add_BOS": cfg.inference.add_BOS, + "all_probs": cfg.inference.all_probs, + "compute_logprob": cfg.inference.compute_logprob, + "end_strings": cfg.inference.end_strings, + } + + with open(cfg.prompt_file, 'r') as f: + lines = f.readlines() + + insert_image_token = cfg.inference.get("insert_image_token", None) + final_prompts = [] + for line in lines: + prompt_dict = json.loads(line) + assert 'prompt' in prompt_dict or 'text' in prompt_dict + if 'prompt' not in prompt_dict: + prompt_dict['prompt'] = prompt_dict['text'] + if insert_image_token == 'left': + prompt_dict['prompt'] = '' + prompt_dict['prompt'] + elif insert_image_token == 'right': + prompt_dict['prompt'] = prompt_dict['prompt'] + '' + if 'image' in prompt_dict: + prompt_dict['image_path'] = prompt_dict['image'] + prompt_dict['image'] = image_processor(os.path.join(cfg.inference.images_base_path, prompt_dict['image'])) + final_prompts.append(prompt_dict) + + responses = model.generate( + input_prompts=final_prompts, length_params=length_params, sampling_params=sampling_params, inference_config=cfg + ) + + # =================== Start Quantization ==================== + if HAVE_AMMO and cfg.quantization.enable == True: + print(f"Using quantization algorithm: {cfg.quantization.algorithm}") + if cfg.quantization.algorithm == "int8_sq": + atq_config = atq.INT8_SMOOTHQUANT_CFG + elif cfg.quantization.algorithm == "fp8": + atq_config = atq.FP8_DEFAULT_CFG + elif cfg.quantization.algorithm == "awq": + atq_config = atq.INT4_AWQ_CFG + else: + raise ValueError(f"Unsupported quantization algorithm: {cfg.quantization.algorithm}") + + def forward_loop(): + model.generate( + input_prompts=final_prompts, + length_params=length_params, + sampling_params=sampling_params, + inference_config=cfg, + ) + + atq.quantize(model, atq_config, forward_loop) + + responses = model.generate( + input_prompts=final_prompts, + length_params=length_params, + sampling_params=sampling_params, + inference_config=cfg, + ) + # ============== Quantization End ========================= + + results = [] + for response, prompt in zip(responses, final_prompts): + prompt['full_text'] = response["clean_text"] + prompt['text'] = response["clean_response"] + prompt['model_id'] = cfg.neva_model_file + if 'image_path' in prompt: + prompt['image'] = prompt.pop('image_path') + if 'answer_id' not in prompt: + prompt['answer_id'] = 0 + if 'metadata' not in prompt: + prompt['metadata'] = {} + results.append(prompt) + + with open(cfg.output_file, 'w') as f: + for result in results: + f.write(json.dumps(result) + '\n') + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/neva_finetune.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/neva_finetune.py new file mode 100644 index 0000000..8db1071 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/neva_finetune.py @@ -0,0 +1,51 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf + +from nemo.collections.multimodal.models.multimodal_llm.neva.neva_model import MegatronNevaModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +mp.set_start_method("spawn", force=True) + + +@hydra_runner(config_path="conf", config_name="neva_finetune") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + if cfg.model.restore_from_path is None: + model = MegatronNevaModel(cfg.model, trainer) + else: + model = MegatronNevaModel.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + override_config_path=cfg.model, + save_restore_connector=NLPSaveRestoreConnector(), + strict=False, + ) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/neva_peft.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/neva_peft.py new file mode 100644 index 0000000..2c0e1bc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/neva_peft.py @@ -0,0 +1,67 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf + +from nemo.collections.multimodal.models.multimodal_llm.neva.neva_model import MegatronNevaModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronLMPPTrainerBuilder +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +mp.set_start_method("spawn", force=True) + + +@hydra_runner(config_path="conf", config_name="neva_peft") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronLMPPTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + if cfg.model.restore_from_path is None: + model_cfg = cfg.model + model = MegatronNevaModel(cfg.model, trainer) + else: + model_cfg = MegatronNevaModel.merge_cfg_with(cfg.model.restore_from_path, cfg) + model = MegatronNevaModel.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + override_config_path=model_cfg, + save_restore_connector=NLPSaveRestoreConnector(), + strict=False, + ) + + peft_cfg_cls = PEFT_CONFIG_MAP[cfg.model.peft.peft_scheme] + + if cfg.model.peft.restore_from_path is not None: + # initialize peft weights from a checkpoint instead of randomly + # This is not the same as resume training because optimizer states are not restored. + logging.info("PEFT Weights will be loaded from", cfg.model.peft.restore_from_path) + model.load_adapters(cfg.model.peft.restore_from_path, peft_cfg_cls(model_cfg)) + elif peft_cfg_cls is not None: + logging.info("Adding adapter weights to the model for PEFT") + model.add_adapter(peft_cfg_cls(model_cfg)) + else: + logging.info(f"Running full finetuning since no peft scheme is given.\n{model.summarize()}") + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/neva_pretrain.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/neva_pretrain.py new file mode 100644 index 0000000..26e0dc2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/multimodal_llm/neva/neva_pretrain.py @@ -0,0 +1,42 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf + +from nemo.collections.multimodal.models.multimodal_llm.neva.neva_model import MegatronNevaModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +mp.set_start_method("spawn", force=True) + + +@hydra_runner(config_path="conf", config_name="neva_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model = MegatronNevaModel(cfg.model, trainer) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/controlnet/conf/controlnet_infer.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/controlnet/conf/controlnet_infer.yaml new file mode 100644 index 0000000..bcf56d5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/controlnet/conf/controlnet_infer.yaml @@ -0,0 +1,36 @@ +name: stable-diffusion-train + +infer: + unconditional_guidance_scale: 3 + num_images_per_prompt: 4 + hint_image_size: 512 + height: 512 + width: 512 + down_factor: 8 + inference_steps: 50 + sampler_type: 'DDIM' + eta: 0 + output_type: 'pil' + save_to_file: True + out_path: 'controlnet' + seed: 355 + prompts: + - high quality picture of a house in oil painting style + control: + - /datasets/coco-stuff/house.png #images/val2017/000000001584.jpg + # Depending on the input control, if the input control is already the conditioning image, null should be passed here + # If a reconstruction target is used as control, then preprocessing function that turns it into a conditioning image needs to be specified + control_image_preprocess: + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + +model: + restore_from_path: /ckpts/controlnet/30k.nemo + precision: ${trainer.precision} + strength: 2.0 + guess_mode: False \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/controlnet/conf/controlnet_v1-5.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/controlnet/conf/controlnet_v1-5.yaml new file mode 100644 index 0000000..2d1b3cf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/controlnet/conf/controlnet_v1-5.yaml @@ -0,0 +1,222 @@ +trainer: + devices: 2 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: True + max_epochs: 3 # PTL default. In practice, max_steps will be reached first. + max_steps: -1 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + limit_val_batches: 0 + + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: controlnet + create_wandb_logger: False + wandb_logger_kwargs: + project: stable-diffusion + group: controlnet + name: controlnet-v1.5 + resume: True + create_checkpoint_callback: True + create_tensorboard_logger: True + checkpoint_callback_params: + save_top_k: -1 + every_n_train_steps: 5000 + every_n_epochs: 0 + monitor: reduced_train_loss + filename: 'controlnet--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 4 # limited by GPU memory + global_batch_size: 8 + + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: images + cond_stage_key: captions + control_key: hint + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + scale_by_std: False + ckpt_path: + ignore_keys: [ ] + parameterization: eps + clip_denoised: True + load_only_unet: False + cosine_s: 8e-3 + given_betas: + original_elbo_weight: 0 + v_posterior: 0 + l_simple_weight: 1 + use_positional_encodings: False + learn_logvar: False + logvar_init: 0 + beta_schedule: linear + loss_type: l2 + learning_rate: 1.0e-04 + concat_mode: True + cond_stage_forward: + text_embedding_dropout_rate: 0.0 + fused_opt: True + inductor: False + inductor_cudagraphs: False + capture_cudagraph_iters: -1 # -1 to disable + channels_last: True + only_mid_control: False + sd_locked: True + + control_stage_config: + _target_: nemo.collections.multimodal.models.text_to_image.controlnet.controlnet.ControlNet + params: + from_pretrained_unet: /ckpts/v1-5-pruned.ckpt + from_NeMo: True + image_size: 32 # unused + in_channels: 4 + hint_channels: 3 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + use_linear_in_transformer: False + transformer_depth: 1 + context_dim: 768 + use_checkpoint: False + legacy: False + use_flash_attention: False + + unet_config: + _target_: nemo.collections.multimodal.models.text_to_image.controlnet.controlnet.ControlledUnetModel + from_pretrained: /ckpts/v1-5-pruned.ckpt + from_NeMo: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: False + legacy: False + use_flash_attention: False + + first_stage_config: + _target_: nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder.AutoencoderKL + from_pretrained: /ckpts/vae.bin + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder + version: openai/clip-vit-large-patch14 + device: cuda + max_length: 77 + + data: + num_workers: 16 + synthetic_data: False # dataset_path and local_root_path can be empty when using synthetic data + synthetic_data_length: 10000 + train: + dataset_path: + #- /datasets/tarfiles/fill50k.pkl + - /datasets/coco-stuff/coco-stuff-tarfiles/wdinfo-coco-stuff.pkl + augmentations: + resize_smallest_side: 512 + center_crop_h_w: 512, 512 + horizontal_flip: False + filterings: + + webdataset: + infinite_sampler: False + local_root_path: /datasets/coco-stuff/coco-stuff-tarfiles + + optim: + name: fused_adam + lr: 2e-5 + weight_decay: 0. + betas: + - 0.9 + - 0.999 + sched: + name: WarmupHoldPolicy + warmup_steps: 0 + hold_steps: 10000000000000 # Incredibly large value to hold the lr as constant + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + image_logger: + batch_frequency: 1000 + max_images: 4 + + #miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/controlnet/controlnet_infer.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/controlnet/controlnet_infer.py new file mode 100644 index 0000000..a33f3fc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/controlnet/controlnet_infer.py @@ -0,0 +1,246 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import time + +import cv2 +import einops +import torch +from PIL import Image + +from nemo.collections.multimodal.models.text_to_image.controlnet.controlnet import MegatronControlNet +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.ddim import DDIMSampler +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.plms import PLMSSampler +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.core.config import hydra_runner + + +def get_control_input(image_path, batch_size, hint_image_size, control_image_preprocess=None): + image = cv2.imread(image_path) + image = cv2.resize(image, (hint_image_size, hint_image_size)) + control = torch.from_numpy(image).float() / 255.0 + control = torch.stack([control for _ in range(batch_size)], dim=0) + control = einops.rearrange(control, 'b h w c -> b c h w') + return control + + +def encode_prompt(cond_stage_model, prompt, unconditional_guidance_scale, batch_size): + c = cond_stage_model.encode(batch_size * [prompt]) + if unconditional_guidance_scale != 1.0: + uc = cond_stage_model.encode(batch_size * [""]) + else: + uc = None + return c, uc + + +def initialize_sampler(model, sampler_type): + if sampler_type == 'DDIM': + sampler = DDIMSampler(model) + elif sampler_type == 'PLMS': + sampler = PLMSSampler(model) + else: + raise ValueError(f'Sampler {sampler_type} is not supported for {cls.__name__}') + return sampler + + +def decode_images(model, samples): + images = model.decode_first_stage(samples) + + images = torch.clamp((images + 1.0) / 2.0, min=0.0, max=1.0) + + return images + + +def torch_to_numpy(images): + numpy_images = [x.float().cpu().permute(0, 2, 3, 1).numpy() for x in images] + return numpy_images + + +def numpy_to_pil(images): + """ + Convert a numpy image or a batch of images to a PIL image. + """ + if images.ndim == 3: + images = images[None, ...] + images = (images * 255).round().astype("uint8") + pil_images = [Image.fromarray(image) for image in images] + + return pil_images + + +def pipeline(model, cfg, rng=None, verbose=True): + # setup default values for inference configs + unconditional_guidance_scale = cfg.infer.get("unconditional_guidance_scale", 7.5) + batch_size = cfg.infer.get('num_images_per_prompt', 1) + prompts = cfg.infer.get('prompts', []) + control = cfg.infer.get('control', []) + height = cfg.infer.get('height', 512) + width = cfg.infer.get('width', 512) + downsampling_factor = cfg.infer.get('down_factor', 8) + sampler_type = cfg.infer.get('sampler_type', 'DDIM') + inference_steps = cfg.infer.get('inference_steps', 50) + output_type = cfg.infer.get('output_type', 'pil') + save_to_file = cfg.infer.get('save_to_file', True) + out_path = cfg.infer.get('out_path', '') + eta = cfg.infer.get('eta', 0) + guess_mode = cfg.model.get('guess_mode', False) + hint_image_size = cfg.infer.get('hint_image_size', 512) + control_image_preprocess = cfg.infer.get('control_image_preprocess', None) + + # get autocast_dtype + if cfg.trainer.precision in ['bf16', 'bf16-mixed']: + autocast_dtype = torch.bfloat16 + elif cfg.trainer.precision in [32, '32', '32-true']: + autocast_dtype = torch.float + elif cfg.trainer.precision in [16, '16', '16-mixed']: + autocast_dtype = torch.half + else: + raise ValueError('precision must be in [32, 16, "bf16"]') + + with torch.no_grad(), torch.cuda.amp.autocast( + enabled=autocast_dtype in (torch.half, torch.bfloat16), dtype=autocast_dtype, + ): + + in_channels = model.model.diffusion_model.in_channels + + sampler = initialize_sampler(model, sampler_type.upper()) + + output = [] + throughput = [] + + if isinstance(prompts, str): + prompts = [prompts] + + assert len(prompts) == len(control) + + for control, prompt in zip(control, prompts): + tic = time.perf_counter() + tic_total = tic + txt_cond, txt_u_cond = encode_prompt( + model.cond_stage_model, prompt, unconditional_guidance_scale, batch_size + ) + + control = get_control_input(control, batch_size, hint_image_size, control_image_preprocess).to( + torch.cuda.current_device(), dtype=autocast_dtype + ) + + cond = {"c_concat": control, "c_crossattn": txt_cond} + u_cond = {"c_concat": None if guess_mode else control, "c_crossattn": txt_u_cond} + + toc = time.perf_counter() + conditioning_time = toc - tic + + latent_shape = [batch_size, height // downsampling_factor, width // downsampling_factor] + latents = torch.randn( + [batch_size, in_channels, height // downsampling_factor, width // downsampling_factor], generator=rng + ).to(torch.cuda.current_device()) + + tic = time.perf_counter() + samples, intermediates = sampler.sample( + S=inference_steps, + conditioning=cond, + batch_size=batch_size, + shape=latent_shape, + verbose=False, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=u_cond, + eta=eta, + x_T=latents, + ) + toc = time.perf_counter() + sampling_time = toc - tic + + tic = time.perf_counter() + images = decode_images(model, samples) + toc = time.perf_counter() + decode_time = toc - tic + + toc_total = time.perf_counter() + total_time = toc_total - tic_total + output.append(images) + + throughput.append( + { + 'text-conditioning-time': conditioning_time, + 'sampling-time': sampling_time, + 'decode-time': decode_time, + 'total-time': total_time, + 'sampling-steps': inference_steps, + } + ) + + # Convert output type and save to disk + if output_type == 'torch': + output = torch.cat(output, dim=0) + else: + output = torch_to_numpy(output) + if output_type == 'pil': + output = [numpy_to_pil(x) for x in output] + + if save_to_file: + os.makedirs(out_path, exist_ok=True) + # Saving control map + control_image = control[0].float().cpu().permute(1, 2, 0).numpy() + control_image = Image.fromarray((control_image * 255).round().astype("uint8")) + control_image.save(os.path.join(out_path, f'{prompt[:50]}_control.png')) + if output_type == 'pil': + for text_prompt, pils in zip(prompts, output): + for idx, image in enumerate(pils): + image.save(os.path.join(out_path, f'{text_prompt[:50]}_{idx}.png')) + else: + with open(os.path.join(out_path, 'output.pkl'), 'wb') as f: + pickle.dump(output, f) + else: + return output + + ave_metrics = {} + for key in throughput[0].keys(): + ave_metrics[f'avg-{key}'] = sum([dicts[key] for dicts in throughput]) / len(throughput) + if verbose: + print(ave_metrics) + + +@hydra_runner(config_path='conf', config_name='controlnet_infer') +def main(cfg): + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.ckpt_path = None + model_cfg.inductor = False + model_cfg.unet_config.from_pretrained = None + model_cfg.first_stage_config.from_pretrained = None + model_cfg.control_stage_config.from_pretrained_unet = None + model_cfg.channels_last = True + model_cfg.capture_cudagraph_iters = -1 + + torch.backends.cuda.matmul.allow_tf32 = True + trainer, megatron_diffusion_model = setup_trainer_and_model_for_inference( + model_provider=MegatronControlNet, cfg=cfg, model_cfg_modifier=model_cfg_modifier + ) + model = megatron_diffusion_model.model + model.cuda().eval() + + guess_mode = cfg.model.guess_mode + model.contol_scales = ( + [cfg.model.strength * (0.825 ** float(12 - i)) for i in range(13)] + if guess_mode + else ([cfg.model.strength] * 13) + ) + + rng = torch.Generator().manual_seed(cfg.infer.seed) + pipeline(model, cfg, rng=rng) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/controlnet/controlnet_train.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/controlnet/controlnet_train.py new file mode 100644 index 0000000..2bb8b66 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/controlnet/controlnet_train.py @@ -0,0 +1,50 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from pytorch_lightning import Trainer + +from nemo.collections.multimodal.models.text_to_image.controlnet.controlnet import MegatronControlNet +from nemo.collections.multimodal.models.text_to_image.controlnet.util import ImageLogger +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +class MegatronControlNetTrainerBuilder(MegatronTrainerBuilder): + """Builder for T5 model Trainer with overrides.""" + + def create_trainer(self, callbacks=[]) -> Trainer: + strategy = self._training_strategy() + plugins = self._plugins() + return Trainer(plugins=plugins, strategy=strategy, **self.cfg.trainer, callbacks=callbacks) + + +@hydra_runner(config_path='conf', config_name='controlnet_v1-5.yaml') +def main(cfg): + callbacks = [] + + if cfg.model.get('image_logger', None): + callbacks.append(ImageLogger(**cfg.model.image_logger)) + + trainer = MegatronControlNetTrainerBuilder(cfg).create_trainer(callbacks=callbacks) + + exp_manager(trainer, cfg.get("exp_manager", None)) + + model = MegatronControlNet(cfg.model, trainer) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/convert_hf_ckpt_to_nemo.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/convert_hf_ckpt_to_nemo.py new file mode 100644 index 0000000..31cddbf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/convert_hf_ckpt_to_nemo.py @@ -0,0 +1,226 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Usage example: + python /opt/NeMo/examples/multimodal/generative/stable_diffusion/convert_hf_ckpt_to_nemo.py + --ckpt_path=path/to/hf.ckpt + --hparams_file=path/to/saved.yaml + --nemo_file_path=hf2sd.nemo + +Additionally, provide a NeMo hparams file with the correct model architecture arguments. Refer to examples/multimodal/foundation/clip/conf/megatron_clip_config.yaml. +""" + +import os +import tempfile +from argparse import ArgumentParser + +import torch +from lightning_fabric.utilities.cloud_io import _load as pl_load +from omegaconf import OmegaConf +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.trainer.trainer import Trainer + +from nemo.collections.multimodal.models.text_to_image.controlnet.controlnet import MegatronControlNet +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector +from nemo.utils import AppState, logging +from nemo.utils.distributed import initialize_distributed + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +def get_args(): + parser = ArgumentParser() + parser.add_argument("--ckpt_path", type=str, default=None, required=True, help="Path to checkpoint.") + + parser.add_argument( + "--hparams_file", + type=str, + default=None, + required=False, + help="Path config for restoring. It's created during training and may need to be modified during restore if restore environment is different than training. Ex: /raid/nemo_experiments/megatron_gpt/hparams.yaml", + ) + parser.add_argument("--nemo_file_path", type=str, default=None, required=True, help="Path to output .nemo file.") + parser.add_argument("--gpus_per_node", type=int, required=False, default=1) + parser.add_argument("--tensor_model_parallel_size", type=int, required=False, default=1) + parser.add_argument("--pipeline_model_parallel_size", type=int, required=False, default=1) + parser.add_argument( + "--pipeline_model_parallel_split_rank", + type=int, + required=False, + default=None, + help="If pipeline parallel size > 1, this is the rank at which the encoder ends and the decoder begins.", + ) + parser.add_argument("--local_rank", type=int, required=False, default=os.getenv('LOCAL_RANK', -1)) + parser.add_argument("--bcp", action="store_true", help="Whether on BCP platform") + parser.add_argument("--model_type", type=str, required=False, default="stable_diffusion") + parser.add_argument("--nemo_clip_path", type=str, required=False, help="Path to clip ckpt file in .nemo format") + + args = parser.parse_args() + return args + + +def load_config_and_state_from_nemo(nemo_path): + if torch.cuda.is_available(): + map_location = torch.device('cuda') + else: + map_location = torch.device('cpu') + save_restore_connector = NLPSaveRestoreConnector() + cwd = os.getcwd() + + with tempfile.TemporaryDirectory() as tmpdir: + try: + save_restore_connector._unpack_nemo_file(path2file=nemo_path, out_folder=tmpdir) + + # Change current working directory to + os.chdir(tmpdir) + config_yaml = os.path.join(tmpdir, save_restore_connector.model_config_yaml) + cfg = OmegaConf.load(config_yaml) + + model_weights = os.path.join(tmpdir, save_restore_connector.model_weights_ckpt) + state_dict = save_restore_connector._load_state_dict_from_disk(model_weights, map_location=map_location) + finally: + os.chdir(cwd) + + return cfg, state_dict + + +def mapping_hf_state_dict(hf_state_dict, model, clip_dict=None): + nemo_state = model.state_dict() + new_state_dict = {} + for k, v in hf_state_dict.items(): + k = 'model.' + k + # This is not necessary when you turn off model.inductor in config file + # if 'diffusion_model' in k: + # k = k.replace('diffusion_model', 'diffusion_model._orig_mod') + if 'in_layers' in k or 'out_layers' in k: + s = k.split('.') + idx = int(s[-2]) + if idx != 0: + k = ".".join(s[:-2] + [str(int(idx - 1))] + [s[-1]]) + if k in nemo_state: + new_state_dict[k] = v + if clip_dict: + for k, v in clip_dict.items(): + k = k.replace("model.text_encoder", "model.cond_stage_model.model") + if k in nemo_state: + new_state_dict[k] = v + for k in [ + 'betas', + 'alphas_cumprod', + 'alphas_cumprod_prev', + 'sqrt_alphas_cumprod', + 'sqrt_one_minus_alphas_cumprod', + 'log_one_minus_alphas_cumprod', + 'sqrt_recip_alphas_cumprod', + 'sqrt_recipm1_alphas_cumprod', + 'posterior_variance', + 'posterior_log_variance_clipped', + 'posterior_mean_coef1', + 'posterior_mean_coef2', + ]: + new_state_dict['model.' + k] = nemo_state['model.' + k] + + return new_state_dict + + +def convert(local_rank, rank, world_size, args): + app_state = AppState() + app_state.data_parallel_rank = 0 + num_nodes = world_size // args.gpus_per_node + if args.bcp: + trainer = Trainer( + devices=args.gpus_per_node, num_nodes=num_nodes, accelerator='gpu', plugins=[TorchElasticEnvironment()] + ) + else: + trainer = Trainer(devices=args.gpus_per_node, num_nodes=num_nodes, accelerator='gpu') + + app_state.pipeline_model_parallel_size = args.pipeline_model_parallel_size + app_state.tensor_model_parallel_size = args.tensor_model_parallel_size + + # no use atm, use to split ranks in encoder/decoder models. + if args.pipeline_model_parallel_size > 1 and args.model_type in []: + if args.pipeline_model_parallel_split_rank is not None: + app_state.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_split_rank + else: + if args.pipeline_model_parallel_size % 2 != 0: + raise ValueError( + f"Pipeline model parallel size {args.pipeline_model_parallel_size} must be even if split rank is not specified." + ) + else: + # If split rank is not set, then we set it to be pipeline_model_parallel_size // 2 - this is because in most cases we have the same number of enc/dec layers. + app_state.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_size // 2 + else: + app_state.pipeline_model_parallel_split_rank = None + + app_state.model_parallel_size = app_state.tensor_model_parallel_size * app_state.pipeline_model_parallel_size + + parallel_state.initialize_model_parallel( + tensor_model_parallel_size=app_state.tensor_model_parallel_size, + pipeline_model_parallel_size=app_state.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank=app_state.pipeline_model_parallel_split_rank, + ) + + app_state.pipeline_model_parallel_rank = parallel_state.get_pipeline_model_parallel_rank() + app_state.tensor_model_parallel_rank = parallel_state.get_tensor_model_parallel_rank() + + if args.ckpt_path.endswith('safetensors'): + from safetensors.torch import load_file as load_safetensors + + checkpoint = load_safetensors(args.ckpt_path) + else: + checkpoint = pl_load(args.ckpt_path, map_location='cpu') + if 'state_dict' in checkpoint.keys(): + checkpoint = checkpoint['state_dict'] + cfg = OmegaConf.load(args.hparams_file) + cfg.model.inductor = False + if args.model_type == 'stable_diffusion': + model = MegatronLatentDiffusion(cfg.model, trainer) + elif args.model_type == 'controlnet': + model = MegatronControlNet(cfg.model, trainer) + + if 'nemo' in model.cfg.cond_stage_config._target_: + assert ( + args.nemo_clip_path is not None + ), "To align with current hparams file, you need to provide .nemo checkpoint of clip model for stable diffusion. If you want to convert HF clip checkpoint to .nemo checkpoint first, please refer to /opt/NeMo/examples/multimodal/foundation/clip/convert_external_clip_to_nemo.py" + _, clip_dict = load_config_and_state_from_nemo(args.nemo_clip_path) + else: + clip_dict = None + + state_dict = mapping_hf_state_dict(checkpoint, model, clip_dict=clip_dict) + + model._save_restore_connector = NLPSaveRestoreConnector() + + model.load_state_dict(state_dict) + + if torch.distributed.is_initialized(): + torch.distributed.barrier() + + model.save_to(args.nemo_file_path) + + logging.info(f'NeMo model saved to: {args.nemo_file_path}') + + +if __name__ == '__main__': + args = get_args() + local_rank, rank, world_size = initialize_distributed(args) + convert(local_rank, rank, world_size, args) diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/conf/dreambooth.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/conf/dreambooth.yaml new file mode 100644 index 0000000..a0886c5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/conf/dreambooth.yaml @@ -0,0 +1,224 @@ +name: Dreambooth + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: bf16-mixed + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 400 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + limit_val_batches: 0 + +exp_manager: + exp_dir: null + name: ${name} + create_checkpoint_callback: True + create_tensorboard_logger: True + checkpoint_callback_params: + every_n_train_steps: 200 + every_n_epochs: 0 + monitor: reduced_train_loss + save_on_train_epoch_end: False + filename: '${name}-{step}' + save_top_k: -1 + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 2 # limited by GPU memory + global_batch_size: 2 # will use more micro batches to reach global batch size + + with_prior_preservation: False + use_cached_latents: False + prior_loss_weight: 0.5 + train_text_encoder: False + restore_from_path: /ckpts/nemo-v1-5-188000-ema.nemo #This ckpt is only used to generate regularization images, thus .nemo ckpt is needed + + + + + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: images + cond_stage_key: captions + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn # check + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + scale_by_std: False + ckpt_path: + ignore_keys: [ ] + parameterization: eps + clip_denoised: True + load_only_unet: False + cosine_s: 8e-3 + given_betas: + original_elbo_weight: 0 + v_posterior: 0 + l_simple_weight: 1 + use_positional_encodings: False + learn_logvar: False + logvar_init: 0 + beta_schedule: linear + loss_type: l2 + + concat_mode: True + cond_stage_forward: + text_embedding_dropout_rate: 0.1 + fused_opt: True + inductor: False + inductor_cudagraphs: False + channels_last: False + + unet_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel.UNetModel + from_pretrained: /ckpts/unet.bin #load unet weights for finetuning, can use .ckpt ckpts from various sources + from_NeMo: False #Must be specified when from pretrained is not None, False means loading unet from HF ckpt + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + num_heads: 8 + use_spatial_transformer: true + transformer_depth: 1 + context_dim: 768 + use_checkpoint: False + legacy: False + use_flash_attention: False + + first_stage_config: + _target_: nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder.AutoencoderKL + from_pretrained: /ckpts/vae.bin + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 #Never used + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenMegatronCLIPEmbedder + restore_from_path: /ckpts/openai.nemo + device: cuda + freeze: True + layer: "last" + # For compatibility of history version that uses HF clip model + # _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder + # version: openai/clip-vit-large-patch14 + # device: cuda + # max_length: 77 + + noise_scheduler: + _target_: nemo.collections.multimodal.models.text_to_image.dreambooth.util.sd_noise_scheduler + parameterization: eps + v_posterior: 0 + given_betas: + beta_schedule: linear + timesteps: 1000 + linear_start: 0.00085 + linear_end: 0.012 + cosine_s: 8e-3 + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + optim: + name: fused_adam + lr: 1e-6 + weight_decay: 0. + betas: + - 0.9 + - 0.999 + sched: + name: WarmupHoldPolicy + warmup_steps: 1 + hold_steps: 10000000000000 # Incredibly large value to hold the lr as constant + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + data: + name: pbss + num_workers: 4 + instance_dir: /datasets/instance_dir + instance_prompt: a photo of a sks dog + regularization_dir: /datasets/nemo_dogs + regularization_prompt: a photo of a dog + num_reg_images: 10 + num_images_per_prompt: 4 + resolution: 512 + center_crop: True + cached_instance_dir: #/datasets/instance_dir_cached + cached_reg_dir: #/datasets/nemo_dogs_cached + +##The below infer config is to use inference script generating regularization images +infer: + unconditional_guidance_scale: 7.5 + num_images_per_prompt: ${model.data.num_images_per_prompt} + height: 512 + width: 512 + down_factor: 8 + inference_steps: 50 + sampler_type: 'PLMS' + eta: 0 + output_type: 'pil' + save_to_file: False + out_path: ${model.data.regularization_dir} + prompts: ${model.data.regularization_prompt} \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/conf/dreambooth_infer.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/conf/dreambooth_infer.yaml new file mode 100644 index 0000000..02faba0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/conf/dreambooth_infer.yaml @@ -0,0 +1,29 @@ +name: stable-diffusion-train + +infer: + unconditional_guidance_scale: 7.5 + num_images_per_prompt: 4 + height: 512 + width: 512 + down_factor: 8 + inference_steps: 100 + sampler_type: 'DDIM' + eta: 0 + output_type: 'pil' + save_to_file: True + out_path: 'dreambooth' + seed: 123 + prompts: + - 'a photo of a sks dog' + - 'a photo of a sks dog in a bucket' + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: bf16 + logger: False # logger provided by exp_manager + +model: + restore_from_path: null + precision: ${trainer.precision} \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/conf/dreambooth_lora_infer.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/conf/dreambooth_lora_infer.yaml new file mode 100644 index 0000000..b2af365 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/conf/dreambooth_lora_infer.yaml @@ -0,0 +1,33 @@ +name: stable-diffusion-train + +infer: + unconditional_guidance_scale: 7.5 + num_images_per_prompt: 4 + height: 512 + width: 512 + down_factor: 8 + inference_steps: 50 + sampler_type: 'DDIM' + eta: 0 + output_type: 'pil' + save_to_file: True + out_path: 'dreambooth' + seed: 123 + prompts: + - 'a photo of a sks dog' + - 'a photo of sks dog in a bucket' + + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + +model: + precision: ${trainer.precision} + peft: + restore_from_path: null + unet_config: + from_pretrained: null # In case user want to load lora weights to a different unet ckpt than that is used in training \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/conf/dreambooth_lora_train.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/conf/dreambooth_lora_train.yaml new file mode 100644 index 0000000..283fbda --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/conf/dreambooth_lora_train.yaml @@ -0,0 +1,241 @@ +name: Dreambooth-lora + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: bf16-mixed + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 500 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + limit_val_batches: 0 + +exp_manager: + exp_dir: null + name: ${name} + create_checkpoint_callback: True + create_tensorboard_logger: True + checkpoint_callback_params: + every_n_train_steps: 200 + every_n_epochs: 0 + monitor: reduced_train_loss + save_on_train_epoch_end: False + filename: '${name}-{step}' + save_top_k: -1 + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 1 # limited by GPU memory + global_batch_size: 1 # will use more micro batches to reach global batch size + + with_prior_preservation: False + use_cached_latents: False + prior_loss_weight: 0.5 + train_text_encoder: False + restore_from_path: /ckpts/nemo-v1-5-188000-ema.nemo #This ckpt is only used to generate regularization images, thus .nemo ckpt is needed + + + + + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: images + cond_stage_key: captions + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn # check + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + scale_by_std: False + ckpt_path: + ignore_keys: [ ] + parameterization: eps + clip_denoised: True + load_only_unet: False + cosine_s: 8e-3 + given_betas: + original_elbo_weight: 0 + v_posterior: 0 + l_simple_weight: 1 + use_positional_encodings: False + learn_logvar: False + logvar_init: 0 + beta_schedule: linear + loss_type: l2 + + concat_mode: True + cond_stage_forward: + text_embedding_dropout_rate: 0.1 + fused_opt: True + inductor: False + inductor_cudagraphs: False + channels_last: False + + unet_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel.UNetModel + from_pretrained: /ckpts/unet.bin #load unet weights for finetuning, can use .ckpt ckpts from various sources + from_NeMo: False #Must be specified when from pretrained is not None, False means loading unet from HF ckpt + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + num_heads: 8 + use_spatial_transformer: true + transformer_depth: 1 + context_dim: 768 + use_checkpoint: False + legacy: False + use_flash_attention: False + lora_network_alpha: null + + first_stage_config: + _target_: nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder.AutoencoderKL + from_pretrained: /ckpts/vae.bin + #ckpt_path: /ckpts/vae.ckpt #to support original opensource weights files, please use ckpt_path to load it. + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 #Never used + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenMegatronCLIPEmbedder + restore_from_path: /ckpts/openai.nemo + device: cuda + freeze: True + layer: "last" + enable_lora_finetune: False #to enable text encoder lora finetune, please enable both this one and "train_text_encoder" + # For compatibility of history version that uses HF clip model + # _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder + # version: openai/clip-vit-large-patch14 + # device: cuda + # max_length: 77 + # enable_lora_finetune: False #to enable text encoder lora finetune, please enable both this one and "train_text_encoder" + + noise_scheduler: + _target_: nemo.collections.multimodal.models.text_to_image.dreambooth.util.sd_noise_scheduler + parameterization: eps + v_posterior: 0 + given_betas: + beta_schedule: linear + timesteps: 1000 + linear_start: 0.00085 + linear_end: 0.012 + cosine_s: 8e-3 + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + optim: + name: fused_adam + lr: 1e-4 + weight_decay: 0. + betas: + - 0.9 + - 0.999 + sched: + name: WarmupHoldPolicy + warmup_steps: 1 + hold_steps: 10000000000000 # Incredibly large value to hold the lr as constant + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + data: + name: pbss + num_workers: 4 + instance_dir: /home/scratch.zhuoyaow_gpu/workspace/SD_NeMo_EA/launcher_scripts/data/inst_dir + instance_prompt: a photo of a sks dog + regularization_dir: /home/scratch.zhuoyaow_gpu/workspace/SD_NeMo_EA/launcher_scripts/data/nemo_dogs + regularization_prompt: a photo of a dog + num_reg_images: 10 + num_images_per_prompt: 4 + resolution: 512 + center_crop: True + cached_instance_dir: #/datasets/instance_dir_cached + cached_reg_dir: #/datasets/nemo_dogs_cached + + peft: + peft_scheme: "sdlora" + restore_from_path: null + lora_tuning: + adapter_dim: 32 + network_alpha: 16 + adapter_dropout: 0.0 + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + layer_selection: null # selects in which layers to add lora adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + +##The below infer config is to use inference script generating regularization images +infer: + unconditional_guidance_scale: 7.5 + num_images_per_prompt: ${model.data.num_images_per_prompt} + height: 512 + width: 512 + down_factor: 8 + inference_steps: 50 + sampler_type: 'PLMS' + eta: 0 + output_type: 'pil' + save_to_file: False + out_path: ${model.data.regularization_dir} + prompts: ${model.data.regularization_prompt} \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/dreambooth.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/dreambooth.py new file mode 100644 index 0000000..70231e5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/dreambooth.py @@ -0,0 +1,127 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os + +import torch +from omegaconf import OmegaConf + +from nemo.collections.multimodal.models.text_to_image.dreambooth.dreambooth import MegatronDreamBooth +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion +from nemo.collections.multimodal.parts.stable_diffusion.pipeline import pipeline +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder + +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +def prepare_reg_data(cfg): + reg_dir = cfg.model.data.regularization_dir + num_reg_images = cfg.model.data.num_reg_images + num_images_per_prompt = cfg.model.data.num_images_per_prompt + reg_prompt = cfg.model.data.regularization_prompt + os.makedirs(reg_dir, exist_ok=True) + NUM_REG_IMAGES = len(os.listdir(reg_dir)) + if NUM_REG_IMAGES < num_reg_images: + + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.ckpt_path = None + model_cfg.inductor = False + model_cfg.unet_config.use_flash_attention = False + model_cfg.micro_batch_size = cfg.model.micro_batch_size + model_cfg.global_batch_size = cfg.model.global_batch_size + model_cfg.unet_config.from_pretrained = None + model_cfg.first_stage_config.from_pretrained = None + model_cfg.target = ( + 'nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm.MegatronLatentDiffusion' + ) + + trainer, megatron_diffusion_model = setup_trainer_and_model_for_inference( + model_provider=MegatronLatentDiffusion, cfg=cfg, model_cfg_modifier=model_cfg_modifier + ) + model = megatron_diffusion_model.model + rng = torch.Generator() + rng.manual_seed(trainer.global_rank * 100 + cfg.model.seed) + images_to_generate = cfg.model.data.num_reg_images - NUM_REG_IMAGES + images_to_generate = images_to_generate // trainer.world_size + + logging.info( + f"No enough images in regularization folder, generating {images_to_generate} from provided ckpt on each device" + ) + + for i in range(images_to_generate // num_images_per_prompt + 1): + output = pipeline(model, cfg, verbose=False, rng=rng) + for text_prompt, pils in zip(reg_prompt, output): + for idx, image in enumerate(pils): + image.save( + os.path.join( + cfg.infer.out_path, + f'{reg_prompt}_{trainer.global_rank}_{NUM_REG_IMAGES + i * num_images_per_prompt + idx}.png', + ) + ) + del model + del trainer + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +@hydra_runner(config_path='conf', config_name='dreambooth.yaml') +def main(cfg): + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + torch.backends.cuda.matmul.allow_tf32 = True + + if cfg.model.with_prior_preservation: + prepare_reg_data(cfg) + parallel_state.destroy_model_parallel() + + trainer = MegatronTrainerBuilder(cfg).create_trainer() + + exp_manager(trainer, cfg.exp_manager) + + model = MegatronDreamBooth(cfg.model, trainer) + + if cfg.model.get('peft', None): + + peft_cfg_cls = PEFT_CONFIG_MAP[cfg.model.peft.peft_scheme] + + if cfg.model.peft.restore_from_path is not None: + # initialize peft weights from a checkpoint instead of randomly + # This is not the same as resume training because optimizer states are not restored. + logging.info("PEFT Weights will be loaded from", cfg.model.peft.restore_from_path) + model.load_adapters(cfg.model.peft.restore_from_path, peft_cfg_cls(model_cfg)) + elif peft_cfg_cls is not None: + logging.info("Adding adapter weights to the model for PEFT") + model.add_adapter(peft_cfg_cls(cfg.model)) + else: + logging.info(f"Running full finetuning since no peft scheme is given.\n{model.summarize()}") + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/dreambooth_infer.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/dreambooth_infer.py new file mode 100644 index 0000000..17952ce --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/dreambooth_infer.py @@ -0,0 +1,46 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion +from nemo.collections.multimodal.parts.stable_diffusion.pipeline import pipeline +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.core.config import hydra_runner + + +@hydra_runner(config_path='conf', config_name='dreambooth_infer') +def main(cfg): + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.ckpt_path = None + model_cfg.inductor = False + model_cfg.unet_config.use_flash_attention = False + model_cfg.unet_config.from_pretrained = None + model_cfg.first_stage_config.from_pretrained = None + model_cfg.target = ( + 'nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm.MegatronLatentDiffusion' + ) + + trainer, megatron_diffusion_model = setup_trainer_and_model_for_inference( + model_provider=MegatronLatentDiffusion, cfg=cfg, model_cfg_modifier=model_cfg_modifier + ) + model = megatron_diffusion_model.model + model.cuda().eval() + + rng = torch.Generator().manual_seed(cfg.infer.seed) + pipeline(model, cfg, rng=rng) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/dreambooth_lora_infer.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/dreambooth_lora_infer.py new file mode 100644 index 0000000..52f0aa2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/dreambooth/dreambooth_lora_infer.py @@ -0,0 +1,67 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +from omegaconf import open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion +from nemo.collections.multimodal.parts.stable_diffusion.pipeline import pipeline +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP +from nemo.core.config import hydra_runner + + +@hydra_runner(config_path='conf', config_name='dreambooth_lora_infer') +def main(cfg): + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.ckpt_path = None + model_cfg.inductor = False + model_cfg.target = ( + 'nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm.MegatronLatentDiffusion' + ) + if cfg.model.unet_config.from_pretrained: + model_cfg.unet_config.from_pretrained = cfg.model.unet_config.from_pretrained + + model_cfg = MegatronLatentDiffusion.restore_from( + restore_path=cfg.model.peft.restore_from_path, + trainer=None, + save_restore_connector=NLPSaveRestoreConnector(), + return_config=True, + ) + + with open_dict(model_cfg): + model_cfg_modifier(model_cfg) + + plugins = [] + plugins.append(TorchElasticEnvironment()) + strategy = NLPDDPStrategy(no_ddp_communication_hook=True, find_unused_parameters=False,) + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer) + + model = MegatronLatentDiffusion(model_cfg, trainer=trainer) + model.setup_complete = True + + peft_cfg_cls = PEFT_CONFIG_MAP[model_cfg.peft.peft_scheme] + + model.load_adapters(cfg.model.peft.restore_from_path, peft_cfg_cls(model_cfg)) + rng = torch.Generator().manual_seed(cfg.infer.seed) + + model = model.model.cuda().eval() + pipeline(model, cfg, rng=rng) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/README.md b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/README.md new file mode 100644 index 0000000..ba33b64 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/README.md @@ -0,0 +1,104 @@ +# Imagen +## A. Overview + +Imagen is a multi-stage text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Given a text prompt, Imagen first generates an image at a 64x64 resolution and then upsamples the generated image to 256x256 and 1024x1024 resolutions, all using diffusion models. + +**Table of Contents:** +- [Imagen](#imagen) + - [A. Overview](#a-overview) + - [B. Imagen Pipeline](#b-imagen-pipeline) + - [C. Files in this folder](#c-files-in-this-folder) + - [D. Imagen Training](#d-imagen-training) + - [D.1 Training Dataset](#d1-training-dataset) + - [D.2 Training configs](#d2-training-configs) + - [E. Imagen Inference](#e-imagen-inference) + - [E.1 Inference Settings](#e1-inference-settings) + - [E.2 Running the sample inference code](#e2-running-the-sample-inference-code) + - [E.3 Inference GPU Memory Usage](#e3-inference-gpu-memory-usage) + - [E.3.1 FP16 Inference](#e31-fp16-inference) + - [E.3.2 FP32 Inference](#e32-fp32-inference) + - [E.3.3 AMP Inference (Autocast Enabled)](#e33-amp-inference-autocast-enabled) + - [F. UNet Architecture](#f-unet-architecture) + - [F.1 U-Net (used for base model)](#f1-u-net-used-for-base-model) + - [F.2 Efficient U-Net (used for SR models)](#f2-efficient-u-net-used-for-sr-models) + +## B. Imagen Pipeline + +Imagen comprises a frozen text encoder (e.g. T5-XXL) to map input text into a sequence of embeddings, and a 64x64 image diffusion model, followed by two super-resolution diffusion models for generating 256x256 and 1024x1024 images. All diffusion models are conditioned on the text embedding sequence and use classifier-free guidance. + +## C. Files in this folder + +- [imagen_training.py](imagen_training.py): Script for running inference +- [imagen_generate_images.py](imagen_generate_images.py): Script for generating images for FID-CLIP analysis +- [imagen_infer.py](imagen_infer.py): Script for running inference + +## D. Imagen Training + +All three diffusion models (64x64, 256x256, 1024x1024) can be trained independently. + +### D.1 Training Dataset + +### D.2 Training configs +| configs | Description | +|---|---| +| base64-2b.yaml | 2b-parameter base 64x64 model as described in Imagen paper | +| base64-500m.yaml | 500m-parameter base 64x64 model with decreased number of embedding channels| +|sr256-400m.yaml| 400m-parameter sr 256x256 model as described in Imagen paper | +|sr1024-400m.yaml| 400m-parameter sr 1024x1024 model as described in Imagen paper | + +## E. Imagen Inference + +### E.1 Inference Settings + +[inference_pipeline.yaml](conf/inference_pipeline.yaml) specifies every config for running the sample inference code. Specifically: +- num_images_per_promt: The number of images you want to generate for each text prompt +- model_name: Different pre-defined configs (not used for now) +- run_ema_model: Either run reg/ema model for pretrained models +- customized_model: Instead of loading pre-defined models, load specified checkpoint. .ckpt checkpoint (generated during in-the-middle of training) and .nemo checkpoint (generated once training completed) are both acceptable +- target_resolution: should be one of [64, 256, 1024] +- inference_precision: Running inference in one of [16, 32, AMP] mode +- dynamic_thresholding: Whether to use dynamic thresholding when generating images +- texts: List of text prompts that are used to generate images +- output_path: The path to save generate images +- encoder_path: If not set (null), it will download text encoder first time running the inference code (and will be saved to HF_HOME), you can also load it offline by setting it to the prepared folder +- samplers: List of sampler settings that are used for each model. `step` (the number of iterations to denoise the image, ideally the larger the better, but also consume more time) and `cfg` for classifier free guidance value. You can tweak these values for better visual quality. + +### E.2 Running the sample inference code +``` +(inside NeMo root folder) +python examples/multimodal/generative/imagen/imagen_infer.py +``` + +### E.3 Inference GPU Memory Usage + +#### E.3.1 FP16 Inference +| Output\Batch size | 1 | 8 | +|-------------------|-------|-------| +| 64x64 | 11.7G | 11.9G | +| 256x256 | 12.5G | 13.0G | +| 1024x1024 | 14.1G | 21.6G | + +#### E.3.2 FP32 Inference +| Output\Batch size | 1 | 8 | +|-------------------|-------|-------| +| 64x64 | 21.7G | 22.6G | +| 256x256 | 23.4G | 24.5G | +| 1024x1024 | 26.6G | 40.6G | + +#### E.3.3 AMP Inference (Autocast Enabled) +| Output\Batch size | 1 | 8 | +|-------------------|-------|-------| +| 64x64 | 22.4G | 23.4G | +| 256x256 | 24.0G | 25.1G | +| 1024x1024 | 26.4G | 33.7G | + +## F. UNet Architecture + +We have prepared two types of UNet for Imagen according to the paper. Base model (64x64) and SR models (256x256, 1024x1024) are using different UNet models. + +### F.1 U-Net (used for base model) + + + +### F.2 Efficient U-Net (used for SR models) + diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/base64-2b.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/base64-2b.yaml new file mode 100644 index 0000000..4c02c97 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/base64-2b.yaml @@ -0,0 +1,142 @@ +name: imagen-nemo # The name of your model +allow_tf32: True + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + + +exp_manager: + exp_dir: /train/imagen-base64 # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: imagen-base64-nf512 + project: imagen + group: nemo-imagen + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 32 # limited by GPU memory + global_batch_size: 32 # will use more micro batches to reach global batch size + inductor: True + inductor_cudagraphs: False + unet_type: base + channels_last: True + + unet: + embed_dim: 512 + image_size: 64 + channels: 3 + num_res_blocks: 3 + channel_mult: [ 1, 2, 3, 4 ] + num_attn_heads: 4 + per_head_channels: 64 + cond_dim: 2048 + attention_type: fused + feature_pooling_type: attention + learned_sinu_pos_emb_dim: 0 + attention_resolutions: [ 8, 16, 32 ] + dropout: False + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: False + flash_attention: True + resblock_updown: False + resample_with_conv: True + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + ddp_overlap: True # True for using PyTorch default DDP overlap. False for using Megatron's default configuration for async grad allreduce + + preconditioning_type: EDM + preconditioning: + loss_type: l2 + sigma_data: 0.5 + p_mean: -1.2 + p_std: 1.2 + # If want to switch to continuous DDPM training, + # use the following config: + # preconditioning_type: DDPM + # preconditioning: + # loss_type: l2 + # pred_objective: noise + # noise_schedule: cosine + # timesteps: 1000 + + conditioning: + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + precached_key: embeddings_t5_xxl + out_key: t5_text + + data: + num_workers: 16 + train: + dataset_path: + - datasets/laion_aesthetic/wdinfo-selene.pkl # 48,874,000 + - datasets/coyo-700m/wdinfo-selene.pkl # 627,172,000 + augmentations: + resize_smallest_side: 64 + center_crop_h_w: 64, 64 + horizontal_flip: False + filterings: null + + webdataset: + use_webdataset: True + object_store: False + infinite_sampler: False + local_root_path: /datasets + verbose: False + + optim: + # We need weight decay for large-scale odel + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/base64-500m-edm.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/base64-500m-edm.yaml new file mode 100644 index 0000000..11224e3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/base64-500m-edm.yaml @@ -0,0 +1,136 @@ +name: imagen-nemo # The name of your model +allow_tf32: True + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + + +exp_manager: + exp_dir: /train/imagen-base64 # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: imagen-base64-nf256 + project: imagen + group: nemo-imagen + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 100 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 48 # limited by GPU memory + global_batch_size: 48 # will use more micro batches to reach global batch size + inductor: False + inductor_cudagraphs: False + unet_type: base + + unet: + embed_dim: 256 + image_size: 64 + channels: 3 + num_res_blocks: 3 + channel_mult: [ 1, 2, 3, 4 ] + num_attn_heads: 4 + per_head_channels: 64 + cond_dim: 512 + attention_type: fused + feature_pooling_type: attention + learned_sinu_pos_emb_dim: 0 + attention_resolutions: [ 8, 16, 32 ] + dropout: False + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: False + flash_attention: False + resblock_updown: False + resample_with_conv: True + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + preconditioning_type: EDM + preconditioning: + loss_type: l2 + sigma_data: 0.5 + p_mean: -1.2 + p_std: 1.2 + + conditioning: + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + precached_key: embeddings_t5_xxl + out_key: t5_text + + data: + num_workers: 16 + train: + dataset_path: + - datasets/laion_aesthetic/wdinfo-selene.pkl # 48,874,000 + - datasets/coyo-700m/wdinfo-selene.pkl # 627,172,000 + augmentations: + resize_smallest_side: 64 + center_crop_h_w: 64, 64 + horizontal_flip: False + filterings: null + + webdataset: + use_webdataset: True + object_store: False + infinite_sampler: False + local_root_path: /datasets + verbose: False + pbss_checkpoint_saving: + enable: False + pbss_credentials_file: pbss_credentials_joc.secret + save_frequency: 1000 + + optim: + # We need weight decay for large-scale odel + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/base64-500m.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/base64-500m.yaml new file mode 100644 index 0000000..eb66b5b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/base64-500m.yaml @@ -0,0 +1,144 @@ +name: imagen-nemo # The name of your model +allow_tf32: True + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + limit_val_batches: 0 + log_every_n_steps: 5 # Interval of logging. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + + +exp_manager: + exp_dir: /train/imagen-base64 # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: imagen-base64-nf256 + project: imagen + group: nemo-imagen + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 128 # limited by GPU memory + global_batch_size: 128 # will use more micro batches to reach global batch size + inductor: False + inductor_cudagraphs: False + unet_type: base + channels_last: True + + unet: + embed_dim: 256 + image_size: 64 + channels: 3 + num_res_blocks: 3 + channel_mult: [ 1, 2, 3, 4 ] + num_attn_heads: 4 + per_head_channels: 64 + cond_dim: 512 + attention_type: fused + feature_pooling_type: attention + learned_sinu_pos_emb_dim: 0 + attention_resolutions: [ 8, 16, 32 ] + dropout: False + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: False + flash_attention: True + resblock_updown: False + resample_with_conv: True + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + ddp_overlap: False # True for using PyTorch default DDP overlap. False for using Megatron's default configuration for async grad allreduce + + preconditioning_type: EDM + preconditioning: + loss_type: l2 + sigma_data: 0.5 + p_mean: -1.2 + p_std: 1.2 + # If want to switch to continuous DDPM training, + # use the following config: + # preconditioning_type: DDPM + # preconditioning: + # loss_type: l2 + # pred_objective: noise + # noise_schedule: cosine + # timesteps: 1000 + + conditioning: + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + precached_key: embeddings_t5_xxl + out_key: t5_text + + data: + num_workers: 16 + synthetic_data: False + synthetic_data_length: 800000 + train: + dataset_path: + - datasets/laion_aesthetic/wdinfo-selene.pkl # 48,874,000 + - datasets/coyo-700m/wdinfo-selene.pkl # 627,172,000 + augmentations: + resize_smallest_side: 64 + center_crop_h_w: 64, 64 + horizontal_flip: False + filterings: null + + webdataset: + use_webdataset: True + object_store: False + infinite_sampler: False + local_root_path: /datasets + verbose: False + + optim: + # We need weight decay for large-scale odel + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/base64-500m_online_encoding.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/base64-500m_online_encoding.yaml new file mode 100644 index 0000000..efbab7b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/base64-500m_online_encoding.yaml @@ -0,0 +1,137 @@ +name: imagen-nemo # The name of your model +allow_tf32: True + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + + +exp_manager: + exp_dir: /train/imagen-base64 # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: imagen-base64-nf256 + project: imagen + group: nemo-imagen + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 100 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 48 # limited by GPU memory + global_batch_size: 48 # will use more micro batches to reach global batch size + + unet_type: base + unet: + embed_dim: 256 + image_size: 64 + channels: 3 + num_res_blocks: 3 + channel_mult: [ 1, 2, 3, 4 ] + num_attn_heads: 4 + per_head_channels: 64 + cond_dim: 512 + attention_type: fused + feature_pooling_type: attention + learned_sinu_pos_emb_dim: 0 + attention_resolutions: [ 8, 16, 32 ] + dropout: False + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: True + flash_attention: False + resblock_updown: False + resample_with_conv: True + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + preconditioning_type: DDPM + preconditioning: + loss_type: l2 + pred_objective: noise + noise_schedule: cosine + timesteps: 1000 + + conditioning: + online_encoding: True # defaults to False (use precached encodings) if not specified + # Online encoding increases training time by about 3-4x, and is only for users who want to do a quick dev run of + # Imagen, and/or those who do not have the disk space to store precached embeddings. + # Optionally specify encoder_path if online_encoding; else, specify precached_key and out_key + encoder_path: # folder path to t5xxl-encoder.bin, or leave empty to download (and cache) t5-11b weights + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + + data: + num_workers: 16 + train: + dataset_path: + - datasets/laion_aesthetic/wdinfo-selene.pkl # 48,874,000 + - datasets/coyo-700m/wdinfo-selene.pkl # 627,172,000 + augmentations: + resize_smallest_side: 64 + center_crop_h_w: 64, 64 + horizontal_flip: False + filterings: null + + webdataset: + use_webdataset: True + object_store: False + infinite_sampler: False + local_root_path: /datasets + verbose: False + pbss_checkpoint_saving: + enable: False + pbss_credentials_file: pbss_credentials_joc.secret + save_frequency: 1000 + + optim: + # We need weight decay for large-scale odel + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/fid_inference.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/fid_inference.yaml new file mode 100644 index 0000000..413da2b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/fid_inference.yaml @@ -0,0 +1,26 @@ +num_images_per_promt: 8 # The number of images generated for each promt text +model_name: null # Avaliable model_name defined in pretrained_models.yaml +run_ema_model: True # Whether load the reg/ema model when using pretrained models +customized_model: # Mutually exclusive with model_name + base_ckpt: /aot/exp/nemo-megatron-stacked-ddpm-16n/imagen-nemo/checkpoints/imagen-nemo--reduced_train_loss=0.03-step=100000-consumed_samples=512000000.0.ckpt # Either .ckpt or .nemo is accepatable + base_cfg: examples/multimodal/generative/imagen/conf/base64-500m.yaml # Must provided if loading .ckpt checkpoint + sr256_ckpt: null + sr256_cfg: examples/multimodal/generative/imagen/conf/sr256-400m.yaml + sr1024_ckpt: null + sr1024_cfg: null +target_resolution: 64 # in [64, 256, 1024] +inference_precision: '32' # [16, 32, AMP] +thresholding_method: 'dynamic' +output_path: 'output/imagen-megatron-pipeline-fid' # Save location +record_time: True # Whether to record inference time meta +encoder_path: '/ckpts/encoders' # Set to null if you wish to download encoders on the fly +samplings: + - + step: 250 + cfg: 7.5 + - + step: 20 + cfg: 7.5 + + + diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/imagen_fid_images.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/imagen_fid_images.yaml new file mode 100644 index 0000000..5a5867c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/imagen_fid_images.yaml @@ -0,0 +1,57 @@ +name: imagen_fid_images + +fid: + classifier_free_guidance: + - 1 + - 1.5 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + nnodes_per_cfg: 1 + ntasks_per_node: 8 + local_task_id: null + num_images_to_eval: 30000 + coco_captions_path: /aot/datasets/coco2014/coco2014_val_sampled_30k/captions + coco_images_path: /aot/datasets/coco2014/coco2014_val/images_256 + save_path: output/fid-launcher-test + ncaptions_per_batch: 4 + save_all_res: False + save_text: False + +infer: + num_images_per_promt: 1 # The number of images generated for each promt text + model_name: null # Avaliable model_name defined in pretrained_models.yaml + run_ema_model: True # Whether load the reg/ema model when using pretrained models + customized_model: # Mutually exclusive with model_name + base_ckpt: /aot/exp/ckpts/imagen-megatron/edm-fused-1150k-ema.nemo # Either .ckpt or .nemo is accepatable + base_cfg: null # Must provided if loading .ckpt checkpoint + sr256_ckpt: /aot/exp/ckpts/imagen-megatron/sr-noise-aug-280k.nemo + sr256_cfg: null + sr1024_ckpt: null + sr1024_cfg: null + target_resolution: 256 # in [64, 256, 1024] + inference_precision: '32' # [16, 32, AMP] + thresholding_method: 'dynamic' + record_time: True # Whether to record inference time meta + encoder_path: '/ckpts/encoders' # Set to null if you wish to download encoders on the fly + samplings: + - + step: 30 + - + step: 20 + +models: + - + restore_from_path: /aot/exp/ckpts/imagen-megatron/edm-fused-1150k-ema.nemo + - + restore_from_path: /aot/exp/ckpts/imagen-megatron/sr-noise-aug-280k.nemo + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 32 + logger: False # logger provided by exp_manager diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/inference_pipeline.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/inference_pipeline.yaml new file mode 100644 index 0000000..1b4bbd9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/inference_pipeline.yaml @@ -0,0 +1,42 @@ +num_images_per_promt: 4 # The number of images generated for each promt text +model_name: null # Avaliable model_name defined in pretrained_models.yaml +run_ema_model: True # Whether load the reg/ema model when using pretrained models +customized_model: # Mutually exclusive with model_name + base_ckpt: null # Either .ckpt or .nemo is accepatable + base_cfg: examples/multimodal/generative/imagen/conf/base64-500m.yaml # Must provided if loading .ckpt checkpoint + sr256_ckpt: null + sr256_cfg: examples/multimodal/generative/imagen/conf/sr256-400m.yaml + sr1024_ckpt: null + sr1024_cfg: examples/multimodal/generative/imagen/conf/sr1024-400m.yaml +target_resolution: 64 # in [64, 256, 1024] +inference_precision: 32 # [16, 32, AMP] +thresholding_method: dynamic +texts: + - 'a photograph of an astronaut riding a horse' + - 'a highly detailed digital painting of a portal in a mystic forest with many beautiful trees. A person is standing in front of the portal' + - A photo of a Shiba Inu dog with a backpack riding a bike. It is wearing sunglasses and a beach hat. + - A cute corgi lives in a house made out of sushi. + - A high contrast portrait of a very happy fuzzy panda dressed as a chef in a high end kitchen making dough. There is a painting of flowers on the wall behind him. + - A brain riding a rocketship heading towards the moon. + - One cat and two dogs sitting on the grass. + - A wine glass on top of a dog. + - A blue coloured pizza. + - A transparent sculpture of a duck made out of glass. There is a painting on the wall behind it. + - A raccoon wearing cowboy hat and black leather jacket is behind the backyard window. Rain droplets on the window. + +output_path: 'output/imagen_output' # Save location +record_time: True # Whether to record inference time meta +encoder_path: '/ckpts/encoders' # Set to null if you wish to download encoders on the fly +samplings: + - # Base64 + step: 30 + cfg: 7.5 + - # SR256 + step: 20 + cfg: 8 + - # SR1024 + step: 20 + cfg: 7.5 + + + diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr1024-600m.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr1024-600m.yaml new file mode 100644 index 0000000..3652267 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr1024-600m.yaml @@ -0,0 +1,145 @@ +name: imagen-nemo # The name of your model +allow_tf32: True + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + + +exp_manager: + exp_dir: /train/imagen-1024 # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: imagen-sr1024-nf128 + project: imagen + group: nemo-imagen + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False +model: + + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 64 # limited by GPU memory + global_batch_size: 64 # will use more micro batches to reach global batch size + inductor: True + inductor_cudagraphs: False + unet_type: sr + channels_last: True + + unet: + embed_dim: 128 + image_size: 1024 + channels: 3 + channel_mult: [ 1, 2, 4, 8, 8 ] + num_attn_heads: 8 + per_head_channels: 64 + attention_type: cross + atnn_enabled_at: [ 0, 0, 0, 1, 1 ] + feature_pooling_type: attention + stride: 2 + num_resblocks: [ 2, 4, 8, 8, 8 ] + learned_sinu_pos_emb_dim: 0 + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: True + flash_attention: False + skip_connection_scaling: True + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + ddp_overlap: True # True for using PyTorch default DDP overlap. False for using Megatron's default configuration for async grad allreduce + + noise_cond_aug: True + preconditioning_type: EDM + preconditioning: + loss_type: l2 + sigma_data: 0.5 + p_mean: -1.2 + p_std: 1.2 + # If want to switch to continuous DDPM training, + # use the following config: + # preconditioning_type: DDPM + # preconditioning: + # loss_type: l2 + # pred_objective: noise + # noise_schedule: cosine + # timesteps: 1000 + + conditioning: + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + precached_key: embeddings_t5_xxl + out_key: t5_text + + data: + num_workers: 16 + train: + dataset_path: + - datasets/laion_aesthetic/wdinfo-selene.pkl # 48,874,000 + - datasets/coyo-700m/wdinfo-selene.pkl # 627,172,000 + augmentations: + resize_smallest_side: 1024 + center_crop_h_w: 256, 256 + horizontal_flip: False + filterings: + resolution: + method: larger + value: 1024 + estimated_portion: 0.2 # Estimated % of examples left after filtering. This is use to estimate # epoch + target_resolutions: [64, 256] + + webdataset: + use_webdataset: True + object_store: False + infinite_sampler: True + local_root_path: /datasets + verbose: False + + optim: + # We need weight decay for large-scale odel + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-400m-edm.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-400m-edm.yaml new file mode 100644 index 0000000..22ab067 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-400m-edm.yaml @@ -0,0 +1,222 @@ +name: imagen-nemo # The name of your model +allow_tf32: True + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + + +exp_manager: + exp_dir: /train/imagen-256 # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: imagen-sr256-nf128 + project: imagen + group: nemo-imagen + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 16 # limited by GPU memory + global_batch_size: 16 # will use more micro batches to reach global batch size + inductor: False + inductor_cudagraphs: False + + unet_type: sr-unet + unet: + embed_dim: 128 + image_size: 256 + channels: 3 + num_res_blocks: [2, 2, 3, 4, 3] + channel_mult: [ 1, 2, 4, 6, 6 ] + num_attn_heads: 4 + per_head_channels: 64 + cond_dim: 512 + attention_type: fused + feature_pooling_type: attention + learned_sinu_pos_emb_dim: 0 + attention_resolutions: [32, 16] + dropout: False + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: False + flash_attention: True + resblock_updown: False + resample_with_conv: True + low_res_cond: True + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + preconditioning_type: EDM + preconditioning: + loss_type: l2 + sigma_data: 0.5 + p_mean: -1.2 + p_std: 1.2 + + conditioning: + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + precached_key: embeddings_t5_xxl + out_key: t5_text + + data: + num_workers: 16 + train: + dataset_path: + - datasets/laion_aesthetic/wdinfo-selene.pkl # 48,874,000 + - datasets/coyo-700m/wdinfo-selene.pkl # 627,172,000 + # - datasets/improved-aesthetic/wdinfo-selene.pkl + augmentations: + resize_smallest_side: 256 + center_crop_h_w: 256, 256 + horizontal_flip: False + filterings: + resolution: + method: larger + value: 256 + estimated_portion: 0.8 # Estimated % of examples left after filtering. This is use to estimate # epoch + corruption_aug: + target_resolution: [ 64, 256 ] + kernel_radius_dict: # used for blurring & resizing, otherwise, not necessary. + 8: 1 + 16: 2 + 32: 3 + 64: 6 + 128: 11 + 256: 22 + 512: 44 + 1024: 88 + 2048: 176 + 4096: 352 + + blur: + add_random_blur: True + blur_prob1: 0.2 + blur_prob2: 0.2 + + blur_sigma_dict: + 8: 0.25 + 16: 0.5 + 32: 0.75 + 64: 1.5 + 128: 3 + 256: 6 + 512: 12 + 1024: 24 + 2048: 48 + 4096: 96 + + resize: + add_random_resize: True + + resize_prob1: + up: 0.2 + down: 0.2 + keep: 0.6 + resize_prob2: + up: 0.2 + down: 0.2 + keep: 0.6 + + resize_range1: + - 0.8 + - 1.2 + resize_range2: + - 0.8 + - 1.2 + + noise: + add_random_noise: True + gaussian_noise_prob1: 1.0 # 0.5 + gaussian_noise_prob2: 1.0 # 0.5 + gray_noise_prob1: 0.0 # 0.4 + gray_noise_prob2: 0.0 # 0.4 + + gaussian_sigma_range1: + - 0 + - 3 + gaussian_sigma_range2: + - 0 + - 2.5 + + poisson_scale_range1: + - 0.005 + - 3 + poisson_scale_range2: + - 0.005 + - 2.5 + + jpeg: + add_random_compression: False + jpeg_range1: + - 75 + - 95 + jpeg_range2: + - 75 + - 95 + + webdataset: + use_webdataset: True + object_store: False + infinite_sampler: True + local_root_path: /datasets + verbose: False + pbss_checkpoint_saving: + enable: False + pbss_credentials_file: pbss_credentials_joc.secret + save_frequency: 1000 + + optim: + # We need weight decay for large-scale odel + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-400m.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-400m.yaml new file mode 100644 index 0000000..984bddd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-400m.yaml @@ -0,0 +1,150 @@ +name: imagen-nemo # The name of your model +allow_tf32: True + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + + +exp_manager: + exp_dir: /train/imagen-256 # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: imagen-sr256-nf128 + project: imagen + group: nemo-imagen + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 16 # limited by GPU memory + global_batch_size: 16 # will use more micro batches to reach global batch size + inductor: True + inductor_cudagraphs: False + channels_last: True + + unet_type: sr-unet + unet: + embed_dim: 128 + image_size: 256 + channels: 3 + num_res_blocks: [2, 2, 3, 4, 3] + channel_mult: [ 1, 2, 4, 6, 6 ] + num_attn_heads: 4 + per_head_channels: 64 + cond_dim: 512 + attention_type: fused + feature_pooling_type: attention + learned_sinu_pos_emb_dim: 0 + attention_resolutions: [32, 16] + dropout: False + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: False + flash_attention: True + resblock_updown: False + resample_with_conv: True + low_res_cond: True + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + ddp_overlap: True # True for using PyTorch default DDP overlap. False for using Megatron's default configuration for async grad allreduce + + noise_cond_aug: True + preconditioning_type: EDM + preconditioning: + loss_type: l2 + sigma_data: 0.5 + p_mean: -1.2 + p_std: 1.2 + # If want to switch to continuous DDPM training, + # use the following config: + # preconditioning_type: DDPM + # preconditioning: + # loss_type: l2 + # pred_objective: noise + # noise_schedule: cosine + # timesteps: 1000 + + conditioning: + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + precached_key: embeddings_t5_xxl + out_key: t5_text + + data: + num_workers: 16 + train: + dataset_path: + - datasets/laion_aesthetic/wdinfo-selene.pkl # 48,874,000 + - datasets/coyo-700m/wdinfo-selene.pkl # 627,172,000 + augmentations: + resize_smallest_side: 256 + center_crop_h_w: 256, 256 + horizontal_flip: False + filterings: + resolution: + method: larger + value: 256 + estimated_portion: 0.8 # Estimated % of examples left after filtering. This is use to estimate # epoch + target_resolutions: [ 64, 256 ] + + webdataset: + use_webdataset: True + object_store: False + infinite_sampler: True + local_root_path: /datasets + verbose: False + + optim: + # We need weight decay for large-scale odel + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-450m-edm.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-450m-edm.yaml new file mode 100644 index 0000000..cbee92a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-450m-edm.yaml @@ -0,0 +1,222 @@ +name: imagen-nemo # The name of your model +allow_tf32: True + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + + +exp_manager: + exp_dir: /train/imagen-256 # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: imagen-sr256-nf128 + project: imagen + group: nemo-imagen + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 16 # limited by GPU memory + global_batch_size: 16 # will use more micro batches to reach global batch size + inductor: False + inductor_cudagraphs: False + + unet_type: sr-unet + unet: + embed_dim: 128 + image_size: 256 + channels: 3 + num_res_blocks: [2, 2, 3, 4, 3] + channel_mult: [ 1, 2, 4, 6, 6 ] + num_attn_heads: 4 + per_head_channels: 64 + cond_dim: 512 + attention_type: stacked + feature_pooling_type: attention + learned_sinu_pos_emb_dim: 0 + attention_resolutions: [32, 16] + dropout: False + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: False + flash_attention: True + resblock_updown: False + resample_with_conv: True + low_res_cond: True + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + preconditioning_type: EDM + preconditioning: + loss_type: l2 + sigma_data: 0.5 + p_mean: -1.2 + p_std: 1.2 + + conditioning: + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + precached_key: embeddings_t5_xxl + out_key: t5_text + + data: + num_workers: 16 + train: + dataset_path: + - datasets/laion_aesthetic/wdinfo-selene.pkl # 48,874,000 + - datasets/coyo-700m/wdinfo-selene.pkl # 627,172,000 + # - datasets/improved-aesthetic/wdinfo-selene.pkl + augmentations: + resize_smallest_side: 256 + center_crop_h_w: 256, 256 + horizontal_flip: False + filterings: + resolution: + method: larger + value: 256 + estimated_portion: 0.8 # Estimated % of examples left after filtering. This is use to estimate # epoch + corruption_aug: + target_resolution: [ 64, 256 ] + kernel_radius_dict: # used for blurring & resizing, otherwise, not necessary. + 8: 1 + 16: 2 + 32: 3 + 64: 6 + 128: 11 + 256: 22 + 512: 44 + 1024: 88 + 2048: 176 + 4096: 352 + + blur: + add_random_blur: True + blur_prob1: 0.2 + blur_prob2: 0.2 + + blur_sigma_dict: + 8: 0.25 + 16: 0.5 + 32: 0.75 + 64: 1.5 + 128: 3 + 256: 6 + 512: 12 + 1024: 24 + 2048: 48 + 4096: 96 + + resize: + add_random_resize: True + + resize_prob1: + up: 0.2 + down: 0.2 + keep: 0.6 + resize_prob2: + up: 0.2 + down: 0.2 + keep: 0.6 + + resize_range1: + - 0.8 + - 1.2 + resize_range2: + - 0.8 + - 1.2 + + noise: + add_random_noise: True + gaussian_noise_prob1: 1.0 # 0.5 + gaussian_noise_prob2: 1.0 # 0.5 + gray_noise_prob1: 0.0 # 0.4 + gray_noise_prob2: 0.0 # 0.4 + + gaussian_sigma_range1: + - 0 + - 3 + gaussian_sigma_range2: + - 0 + - 2.5 + + poisson_scale_range1: + - 0.005 + - 3 + poisson_scale_range2: + - 0.005 + - 2.5 + + jpeg: + add_random_compression: False + jpeg_range1: + - 75 + - 95 + jpeg_range2: + - 75 + - 95 + + webdataset: + use_webdataset: True + object_store: False + infinite_sampler: True + local_root_path: /datasets + verbose: False + pbss_checkpoint_saving: + enable: False + pbss_credentials_file: pbss_credentials_joc.secret + save_frequency: 1000 + + optim: + # We need weight decay for large-scale odel + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-600m-edm-noise.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-600m-edm-noise.yaml new file mode 100644 index 0000000..3e53181 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-600m-edm-noise.yaml @@ -0,0 +1,142 @@ +name: imagen-nemo # The name of your model +allow_tf32: True + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + + +exp_manager: + exp_dir: /train/imagen-256 # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: imagen-sr256-nf128 + project: imagen + group: nemo-imagen + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 32 # limited by GPU memory + global_batch_size: 32 # will use more micro batches to reach global batch size + inductor: False + inductor_cudagraphs: False + + unet_type: sr + unet: + embed_dim: 128 + image_size: 256 + channels: 3 + channel_mult: [ 1, 2, 4, 8, 8 ] + num_attn_heads: 8 + per_head_channels: 64 + attention_type: stacked + atnn_enabled_at: [ 0, 0, 0, 1, 1 ] + feature_pooling_type: attention + stride: 2 + num_resblocks: [ 2, 4, 8, 8, 8 ] + learned_sinu_pos_emb_dim: 0 + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: False + flash_attention: False + skip_connection_scaling: True + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + noise_cond_aug: True + preconditioning_type: EDM + preconditioning: + loss_type: l2 + sigma_data: 0.5 + p_mean: -1.2 + p_std: 1.2 + + conditioning: + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + precached_key: embeddings_t5_xxl + out_key: t5_text + + data: + num_workers: 16 + train: + dataset_path: + - datasets/laion_aesthetic/wdinfo-selene.pkl # 48,874,000 + - datasets/coyo-700m/wdinfo-selene.pkl # 627,172,000 + augmentations: + resize_smallest_side: 256 + center_crop_h_w: 256, 256 + horizontal_flip: False + filterings: + resolution: + method: larger + value: 256 + estimated_portion: 0.8 # Estimated % of examples left after filtering. This is use to estimate # epoch + corruption_aug: + target_resolution: [ 64, 256 ] + + webdataset: + use_webdataset: True + object_store: False + infinite_sampler: True + local_root_path: /datasets + verbose: False + pbss_checkpoint_saving: + enable: False + pbss_credentials_file: pbss_credentials_joc.secret + save_frequency: 1000 + + optim: + # We need weight decay for large-scale odel + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-600m-edm.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-600m-edm.yaml new file mode 100644 index 0000000..67f05c5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-600m-edm.yaml @@ -0,0 +1,219 @@ +name: imagen-nemo # The name of your model +allow_tf32: True + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + + +exp_manager: + exp_dir: /train/imagen-256 # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: imagen-sr256-nf128 + project: imagen + group: nemo-imagen + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 32 # limited by GPU memory + global_batch_size: 32 # will use more micro batches to reach global batch size + inductor: False + inductor_cudagraphs: False + + unet_type: sr + unet: + embed_dim: 128 + image_size: 256 + channels: 3 + channel_mult: [ 1, 2, 4, 8, 8 ] + num_attn_heads: 8 + per_head_channels: 64 + attention_type: stacked + atnn_enabled_at: [ 0, 0, 0, 1, 1 ] + feature_pooling_type: attention + stride: 2 + num_resblocks: [ 2, 4, 8, 8, 8 ] + learned_sinu_pos_emb_dim: 0 + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: False + flash_attention: False + skip_connection_scaling: True + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + preconditioning_type: EDM + preconditioning: + loss_type: l2 + sigma_data: 0.5 + p_mean: -1.2 + p_std: 1.2 + + conditioning: + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + precached_key: embeddings_t5_xxl + out_key: t5_text + + data: + num_workers: 16 + train: + dataset_path: + - datasets/laion_aesthetic/wdinfo-selene.pkl # 48,874,000 + - datasets/coyo-700m/wdinfo-selene.pkl # 627,172,000 + # - datasets/improved-aesthetic/wdinfo-selene.pkl + augmentations: + resize_smallest_side: 256 + center_crop_h_w: 256, 256 + horizontal_flip: False + filterings: + resolution: + method: larger + value: 256 + estimated_portion: 0.8 # Estimated % of examples left after filtering. This is use to estimate # epoch + corruption_aug: + target_resolution: [ 64, 256 ] + kernel_radius_dict: # used for blurring & resizing, otherwise, not necessary. + 8: 1 + 16: 2 + 32: 3 + 64: 6 + 128: 11 + 256: 22 + 512: 44 + 1024: 88 + 2048: 176 + 4096: 352 + + blur: + add_random_blur: True + blur_prob1: 0.2 + blur_prob2: 0.2 + + blur_sigma_dict: + 8: 0.25 + 16: 0.5 + 32: 0.75 + 64: 1.5 + 128: 3 + 256: 6 + 512: 12 + 1024: 24 + 2048: 48 + 4096: 96 + + resize: + add_random_resize: True + + resize_prob1: + up: 0.2 + down: 0.2 + keep: 0.6 + resize_prob2: + up: 0.2 + down: 0.2 + keep: 0.6 + + resize_range1: + - 0.8 + - 1.2 + resize_range2: + - 0.8 + - 1.2 + + noise: + add_random_noise: True + gaussian_noise_prob1: 1.0 # 0.5 + gaussian_noise_prob2: 1.0 # 0.5 + gray_noise_prob1: 0.0 # 0.4 + gray_noise_prob2: 0.0 # 0.4 + + gaussian_sigma_range1: + - 0 + - 3 + gaussian_sigma_range2: + - 0 + - 2.5 + + poisson_scale_range1: + - 0.005 + - 3 + poisson_scale_range2: + - 0.005 + - 2.5 + + jpeg: + add_random_compression: False + jpeg_range1: + - 75 + - 95 + jpeg_range2: + - 75 + - 95 + + webdataset: + use_webdataset: True + object_store: False + infinite_sampler: True + local_root_path: /datasets + verbose: False + pbss_checkpoint_saving: + enable: False + pbss_credentials_file: pbss_credentials_joc.secret + save_frequency: 1000 + + optim: + # We need weight decay for large-scale odel + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-600m.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-600m.yaml new file mode 100644 index 0000000..115e9dd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/conf/sr256-600m.yaml @@ -0,0 +1,146 @@ +name: imagen-nemo # The name of your model +allow_tf32: True + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + + +exp_manager: + exp_dir: /train/imagen-256 # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: imagen-sr256-nf128 + project: imagen + group: nemo-imagen + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 64 # limited by GPU memory + global_batch_size: 64 # will use more micro batches to reach global batch size + inductor: True + inductor_cudagraphs: False + channels_last: True + + unet_type: sr + unet: + embed_dim: 128 + image_size: 256 + channels: 3 + channel_mult: [ 1, 2, 4, 8, 8 ] + num_attn_heads: 8 + per_head_channels: 64 + attention_type: fused + atnn_enabled_at: [ 0, 0, 0, 1, 1 ] + feature_pooling_type: attention + stride: 2 + num_resblocks: [ 2, 4, 8, 8, 8 ] + learned_sinu_pos_emb_dim: 0 + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: False + flash_attention: True + skip_connection_scaling: True + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + ddp_overlap: True # True for using PyTorch default DDP overlap. False for using Megatron's default configuration for async grad allreduce + + noise_cond_aug: True + preconditioning_type: EDM + preconditioning: + loss_type: l2 + sigma_data: 0.5 + p_mean: -1.2 + p_std: 1.2 + # If want to switch to continuous DDPM training, + # use the following config: + # preconditioning_type: DDPM + # preconditioning: + # loss_type: l2 + # pred_objective: noise + # noise_schedule: cosine + # timesteps: 1000 + + conditioning: + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + precached_key: embeddings_t5_xxl + out_key: t5_text + + data: + num_workers: 16 + train: + dataset_path: + - datasets/laion_aesthetic/wdinfo-selene.pkl # 48,874,000 + - datasets/coyo-700m/wdinfo-selene.pkl # 627,172,000 + augmentations: + resize_smallest_side: 256 + center_crop_h_w: 256, 256 + horizontal_flip: False + filterings: + resolution: + method: larger + value: 256 + estimated_portion: 0.8 # Estimated % of examples left after filtering. This is use to estimate # epoch + target_resolutions: [64, 256] + webdataset: + use_webdataset: True + object_store: False + infinite_sampler: True + local_root_path: /datasets + verbose: False + + optim: + # We need weight decay for large-scale odel + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/generate_fid_images.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/generate_fid_images.py new file mode 100644 index 0000000..ea743e3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/generate_fid_images.py @@ -0,0 +1,116 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import torch +from pytorch_lightning import Trainer + +from nemo.collections.multimodal.models.text_to_image.imagen.imagen_pipeline import ImagenPipeline +from nemo.core.config import hydra_runner + + +@hydra_runner(config_path='conf', config_name='imagen_fid_images') +def main(cfg): + # Read configuration parameters + nnodes_per_cfg = cfg.fid.nnodes_per_cfg + ntasks_per_node = cfg.fid.ntasks_per_node + local_task_id = cfg.fid.local_task_id + num_images_to_eval = cfg.fid.num_images_to_eval + path = cfg.fid.coco_captions_path + save_text = cfg.fid.save_text + + node_id = int(os.environ.get("SLURM_ARRAY_TASK_ID", 0)) + node_id_per_cfg = node_id % nnodes_per_cfg + + current_node_cfg = cfg.fid.classifier_free_guidance[node_id // nnodes_per_cfg] + save_path = os.path.join(cfg.fid.save_path, str(current_node_cfg)) + + # Read and store captions + captions = [] + caption_files = sorted(os.listdir(path)) + assert len(caption_files) >= num_images_to_eval + for file in caption_files[:num_images_to_eval]: + with open(os.path.join(path, file), 'r') as f: + captions += f.readlines() + print(f"The total number of captions to generate is: {len(captions)}") + + # Calculate partition sizes and select the partition for the current node + partition_size_per_node = num_images_to_eval // nnodes_per_cfg + start_idx = node_id_per_cfg * partition_size_per_node + end_idx = (node_id_per_cfg + 1) * partition_size_per_node if node_id_per_cfg != nnodes_per_cfg - 1 else None + captions = captions[start_idx:end_idx] + print(f"Current node {node_id} will generate images from {start_idx} to {end_idx}") + + local_task_id = int(local_task_id) if local_task_id is not None else int(os.environ.get("SLURM_LOCALID", 0)) + partition_size_per_task = int(len(captions) // ntasks_per_node) + + # Select the partition for the current task + start_idx = local_task_id * partition_size_per_task + end_idx = (local_task_id + 1) * partition_size_per_task if local_task_id != ntasks_per_node - 1 else None + input = captions[start_idx:end_idx] + chunk_size = len(input) + + print(f"Current worker {node_id}:{local_task_id} will generate {len(input)} images") + os.makedirs(save_path, exist_ok=True) + + trainer = Trainer() + pipeline = ImagenPipeline.from_pretrained(cfg=cfg.infer, trainer=trainer, megatron_loading=True, megatron_cfg=cfg) + + # Generate images using the model and save them + batch_idx = 0 + batch_size = cfg.fid.ncaptions_per_batch + while True: + if batch_idx * batch_size >= len(input): + break + batch_captions = input[batch_idx * batch_size : (batch_idx + 1) * batch_size] + # Different seed for every image + seeds = [local_task_id * chunk_size + batch_idx * batch_size + idx for idx in range(len(batch_captions))] + with torch.no_grad(): + images, all_res_images, *_ = pipeline( + prompts=batch_captions, seed=seeds, single_batch_mode=True, classifier_free_guidance=current_node_cfg, + ) + + if cfg.fid.save_all_res: + all_res = [f'_RES{model.image_size}' for model in pipeline.models] + outpaths = [] + # for the highest resolution we save as its original name so that + # we can automate the CLIP & FID calculation process from Megatron-Launcher + all_res[-1] = '' + for res in all_res: + outpath = f"{save_path}{res}" + os.makedirs(outpath, exist_ok=True) + outpaths.append(outpath) + for outpath, one_res in zip(outpaths, all_res_images): + for idx, (caption, image) in enumerate(zip(batch_captions, one_res[0])): + image_idx = local_task_id * chunk_size + batch_idx * batch_size + idx + image.save(os.path.join(outpath, f'image{image_idx:06d}.png')) + if save_text: + with open(os.path.join(outpath, f'image{image_idx:06d}.txt'), 'w') as f: + f.writelines(caption) + else: + for idx, (caption, image) in enumerate(zip(batch_captions, images[0])): + image_idx = local_task_id * chunk_size + batch_idx * batch_size + idx + image.save(os.path.join(save_path, f'image{image_idx:06d}.png')) + if save_text: + with open(os.path.join(save_path, f'image{image_idx:06d}.txt'), 'w') as f: + f.writelines(caption) + print( + f'Save {len(images[0])} images to {save_path} with name from image{(local_task_id*chunk_size+batch_idx*batch_size):06d}.png to image{image_idx:06d}.png' + ) + batch_idx += 1 + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/imagen_generate_images.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/imagen_generate_images.py new file mode 100644 index 0000000..bc00205 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/imagen_generate_images.py @@ -0,0 +1,79 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import pickle + +import torch +from omegaconf import OmegaConf +from pytorch_lightning import Trainer + +from nemo.collections.multimodal.models.text_to_image.imagen.imagen_pipeline import ( + ImagenPipeline, + ImagenPipelineConfig, +) +from nemo.core.config import hydra_runner + + +@hydra_runner(config_path='conf', config_name='fid_inference.yaml') +def main(inference_config): + inference_config: ImagenPipelineConfig = OmegaConf.merge(ImagenPipelineConfig(), inference_config) + captions = pickle.load(open('coco_captions.pkl', 'rb')) + ntasks = 8 + if os.environ.get('CUDA_VISIBLE_DEVICES'): + # Multi-GPU + task_id = int(os.environ.get("CUDA_VISIBLE_DEVICES", 0)) + else: + # Single GPU + task_id = 0 + chuncksize = int(len(captions) // ntasks) + if task_id != ntasks - 1: + input = captions[task_id * chuncksize : (task_id + 1) * chuncksize] + else: + input = captions[task_id * chuncksize :] + captions = input + + trainer = Trainer() + pipeline = ImagenPipeline.from_pretrained(cfg=inference_config, trainer=trainer) + batch_size = 16 + batch_idx = 0 + + possible_res = [64, 256] # [64, 256] + outpaths = [] + for res in possible_res: + outpath = f'{inference_config.output_path}_RES{res}' + os.makedirs(outpath, exist_ok=True) + outpaths.append(outpath) + while True: + if batch_idx * batch_size >= len(captions): + break + batch_captions = captions[batch_idx * batch_size : (batch_idx + 1) * batch_size] + + # Different seed for every image + seeds = [task_id * chuncksize + batch_idx * batch_size + idx for idx in range(len(batch_captions))] + seed = batch_idx + chuncksize + + with torch.no_grad(): + images, all_res_images, throughput = pipeline(prompts=batch_captions, seed=seeds, single_batch_mode=True,) + + for outpath, one_res in zip(outpaths, all_res_images): + for idx, (caption, image) in enumerate(zip(batch_captions, one_res[0])): + image.save(os.path.join(outpath, f'image_{task_id*chuncksize+batch_idx*batch_size+idx}.png')) + with open(os.path.join(outpath, f'image_{task_id*chuncksize+batch_idx*batch_size+idx}.txt'), 'w') as f: + f.writelines(caption) + batch_idx += 1 + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/imagen_infer.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/imagen_infer.py new file mode 100644 index 0000000..0fb2917 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/imagen_infer.py @@ -0,0 +1,50 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +from omegaconf import OmegaConf +from pytorch_lightning import Trainer + +from nemo.collections.multimodal.models.text_to_image.imagen.imagen_pipeline import ( + ImagenPipeline, + ImagenPipelineConfig, +) +from nemo.core.config import hydra_runner + + +@hydra_runner(config_path='conf', config_name='inference_pipeline.yaml') +def main(inference_config): + if inference_config.get('infer'): + # invoking from launcher + trainer = Trainer(**inference_config.trainer) + inference_config = inference_config.infer + else: + trainer = Trainer() + inference_config: ImagenPipelineConfig = OmegaConf.merge(ImagenPipelineConfig(), inference_config) + pipeline = ImagenPipeline.from_pretrained(cfg=inference_config, trainer=trainer) + + # Texts are passed in the config files + images, all_res, throughput = pipeline() + + # Save images + outpath = inference_config.output_path + os.makedirs(outpath, exist_ok=True) + for text, pils in zip(inference_config.texts, images): + for idx, image in enumerate(pils): + image.save(os.path.join(outpath, f'{text}_{idx}.png')) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/imagen_training.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/imagen_training.py new file mode 100644 index 0000000..23c1c9c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/imagen/imagen_training.py @@ -0,0 +1,63 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl +import torch +from omegaconf.omegaconf import OmegaConf, open_dict +from torch._dynamo import disable +from torch._inductor import config as inductor_config + +from nemo.collections.multimodal.models.text_to_image.imagen.imagen import MegatronImagen +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path='conf', config_name='base64-500m') +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + # hydra interpolation does not work here as the interpolation key is lost when PTL saves hparams + with open_dict(cfg): + cfg.model.precision = cfg.trainer.precision + + model = MegatronImagen(cfg.model, trainer) + + if cfg.model.get("inductor", False): + # Temporary hack to get rid of TorchDynamo issue with DDP + # TODO: remove these if https://github.com/pytorch/pytorch/issues/94574 fixed + torch.arange = disable(torch.arange) + torch.ones = disable(torch.ones) + torch.zeros = disable(torch.zeros) + + # TODO: remove this if latest TorchDynamo fixed `t.uniform_(0, 1)` failure + torch.Tensor.uniform_ = disable(torch.Tensor.uniform_) + + # Disable TorchDynamo for unsupported function + pl.core.LightningModule.log = disable(pl.core.LightningModule.log) + + # TorchInductor with CUDA graph can lead to OOM + inductor_config.triton.cudagraphs = cfg.model.inductor_cudagraphs + model.model.model.unet = torch.compile(model.model.model.unet) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/instruct_pix2pix/conf/sd_edit.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/instruct_pix2pix/conf/sd_edit.yaml new file mode 100644 index 0000000..75eed9d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/instruct_pix2pix/conf/sd_edit.yaml @@ -0,0 +1,23 @@ +edit: + resolution: 256 + steps: 100 + input: path/to/input/picture + outpath: path/to/output/folder + prompt: "" + cfg_text: 7.5 + cfg_image: 1.2 + num_images_per_prompt: 8 + combine_images: [ 2, 4 ] # [row, column] + seed: 1234 + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: 16 # 16, 32, or bf16 + +model: + restore_from_path: null # Path to a trained instruct pix2pix .nemo file + precision: ${trainer.precision} + diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/instruct_pix2pix/conf/sd_finetune.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/instruct_pix2pix/conf/sd_finetune.yaml new file mode 100644 index 0000000..1c15b6e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/instruct_pix2pix/conf/sd_finetune.yaml @@ -0,0 +1,168 @@ +name: instruct-pix2pix-train + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 10000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 1 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: instruct-pix2pix + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + create_checkpoint_callback: True + create_tensorboard_logger: True + checkpoint_callback_params: + save_top_k: 4 + mode: min + monitor: val/loss + filename: 'instruct-pix2pix--{val/loss:.4f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + +model: + ckpt_path: null # load checkpoint weights from previous stages for fine-tuning + precision: ${trainer.precision} + micro_batch_size: 32 + global_batch_size: 32 # `= micro_batch_size * total_devices` fake global batch size for sampler + + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: edited + cond_stage_key: edit # txt for cifar, caption for pbss + image_size: 32 + channels: 4 + cond_stage_trainable: false + conditioning_key: hybrid + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + scale_by_std: False + + ignore_keys: [ ] + parameterization: eps + clip_denoised: True + load_only_unet: False + cosine_s: 8e-3 + given_betas: + original_elbo_weight: 0 + v_posterior: 0 + l_simple_weight: 1 + use_positional_encodings: False + learn_logvar: False + logvar_init: 0 + beta_schedule: linear + loss_type: l2 + concat_mode: True + cond_stage_forward: + text_embedding_dropout_rate: 0 + fused_opt: True + inductor: False + inductor_cudagraphs: False + + unet_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel.UNetModel + from_pretrained: + image_size: 32 # unused + in_channels: 8 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + num_heads: 8 + use_spatial_transformer: true + transformer_depth: 1 + context_dim: 768 + use_checkpoint: False + legacy: False + use_flash_attention: False + + first_stage_config: + _target_: nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder.AutoencoderKL + from_pretrained: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder + version: openai/clip-vit-large-patch14 + device: cuda + max_length: 77 + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + optim: + name: fused_adam + lr: 1e-4 + weight_decay: 0. + betas: + - 0.9 + - 0.999 + sched: + name: WarmupHoldPolicy + warmup_steps: 100 + hold_steps: 10000000000000 # Incredibly large value to hold the lr as constant + + data: + # Path to instruct-pix2pix dataset must be specified by the user. + # https://github.com/timothybrooks/instruct-pix2pix#generated-dataset + data_path: ??? + num_workers: 2 + dataloader_type: cyclic # cyclic + validation_drop_last: True # Set to false if the last partial validation samples is to be consumed diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/instruct_pix2pix/sd_edit_cli.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/instruct_pix2pix/sd_edit_cli.py new file mode 100644 index 0000000..f335406 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/instruct_pix2pix/sd_edit_cli.py @@ -0,0 +1,168 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import math +import os +import random + +import einops +import numpy as np +import torch +import torch.nn as nn +from einops import rearrange, repeat +from omegaconf import OmegaConf, open_dict +from PIL import Image, ImageOps + +from nemo.collections.multimodal.models.text_to_image.instruct_pix2pix.ldm.ddpm_edit import MegatronLatentDiffusionEdit +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.k_diffusion import ( + DiscreteEpsDDPMDenoiser, + sample_euler_ancestral, +) +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +class CFGDenoiser(nn.Module): + def __init__(self, model): + super().__init__() + self.inner_model = model + + def forward(self, z, sigma, cond, uncond, text_cfg_scale, image_cfg_scale): + cfg_z = einops.repeat(z, "b ... -> (n b) ...", n=3) + cfg_sigma = einops.repeat(sigma, "b ... -> (n b) ...", n=3) + cfg_cond = { + "c_crossattn": [torch.cat([cond["c_crossattn"][0], uncond["c_crossattn"][0], uncond["c_crossattn"][0]])], + "c_concat": [torch.cat([cond["c_concat"][0], cond["c_concat"][0], uncond["c_concat"][0]])], + } + out_cond, out_img_cond, out_uncond = self.inner_model(cfg_z, cfg_sigma, cond=cfg_cond).chunk(3) + out = out_uncond + text_cfg_scale * (out_cond - out_img_cond) + image_cfg_scale * (out_img_cond - out_uncond) + return out + + +@hydra_runner(config_path='conf', config_name='sd_edit') +def main(cfg): + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + with open_dict(cfg): + edit_cfg = cfg.pop("edit") + + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.ckpt_path = None + model_cfg.inductor = False + + trainer, megatron_diffusion_model = setup_trainer_and_model_for_inference( + model_provider=MegatronLatentDiffusionEdit, cfg=cfg, model_cfg_modifier=model_cfg_modifier, + ) + + # inference use the latent diffusion part of megatron wrapper + model = megatron_diffusion_model.model + model_wrap = DiscreteEpsDDPMDenoiser(model) + model_wrap_cfg = CFGDenoiser(model_wrap) + null_token = model.get_learned_conditioning([""]) + + seed = random.randint(0, 100000) if edit_cfg.seed is None else edit_cfg.seed + input_image = Image.open(edit_cfg.input).convert("RGB") + width, height = input_image.size + factor = edit_cfg.resolution / max(width, height) + factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height) + width = int((width * factor) // 64) * 64 + height = int((height * factor) // 64) * 64 + input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS) + + if edit_cfg.prompt == "": + input_image.save(edit_cfg.output) + return + + # get autocast_dtype + if trainer.precision in ['bf16', 'bf16-mixed']: + autocast_dtype = torch.bfloat16 + elif trainer.precision in [32, '32', '32-true']: + autocast_dtype = torch.float + elif trainer.precision in [16, '16', '16-mixed']: + autocast_dtype = torch.half + else: + raise ValueError('precision must be in ["32-true", "16-mixed", "bf16-mixed"]') + + num_images_per_prompt = edit_cfg.num_images_per_prompt + with torch.no_grad(), torch.cuda.amp.autocast( + enabled=autocast_dtype in (torch.half, torch.bfloat16), dtype=autocast_dtype, + ): + cond = {} + cond["c_crossattn"] = [ + repeat(model.get_learned_conditioning([edit_cfg.prompt]), "1 ... -> n ...", n=num_images_per_prompt) + ] + input_image = 2 * torch.tensor(np.array(input_image)).float() / 255 - 1 + input_image = rearrange(input_image, "h w c -> 1 c h w").cuda(non_blocking=True) + cond["c_concat"] = [ + repeat(model.encode_first_stage(input_image).mode(), "1 ... -> n ...", n=num_images_per_prompt) + ] + + uncond = {} + uncond["c_crossattn"] = [repeat(null_token, "1 ... -> n ...", n=num_images_per_prompt)] + uncond["c_concat"] = [torch.zeros_like(cond["c_concat"][0])] + + sigmas = model_wrap.get_sigmas(edit_cfg.steps) + + extra_args = { + "cond": cond, + "uncond": uncond, + "text_cfg_scale": edit_cfg.cfg_text, + "image_cfg_scale": edit_cfg.cfg_image, + } + torch.manual_seed(seed) + z = torch.randn_like(cond["c_concat"][0]) + z = z * sigmas[0] + z = sample_euler_ancestral(model_wrap_cfg, z, sigmas, extra_args=extra_args) + x = model.decode_first_stage(z) + x = torch.clamp((x + 1.0) / 2.0, min=0.0, max=1.0) + x = 255.0 * rearrange(x, "n c h w -> n h w c") + + os.makedirs(edit_cfg.outpath, exist_ok=True) + if edit_cfg.get("combine_images") is None: + for idx, image in enumerate(x): + edited_image = Image.fromarray(image.type(torch.uint8).cpu().numpy()) + save_path = os.path.join( + edit_cfg.outpath, + f'{edit_cfg.prompt.replace(" ", "_")}_{edit_cfg.cfg_text}_{edit_cfg.cfg_image}_{seed}_{idx}.jpg', + ) + edited_image.save(save_path) + logging.info(f"Edited image saved to: {save_path}") + else: + row, column = edit_cfg.combine_images + width, height = x.size(2), x.size(1) + total_width, total_height = width * column, height * row + edited_image = Image.new('RGB', (total_width, total_height)) + x_offset = 0 + y_offset = 0 + for idx, image in enumerate(x): + image = Image.fromarray(image.type(torch.uint8).cpu().numpy()) + edited_image.paste(image, (x_offset, y_offset)) + x_offset += image.size[0] + if (idx + 1) % column == 0: + x_offset = 0 + y_offset += height + save_path = os.path.join( + edit_cfg.outpath, + f'{edit_cfg.prompt.replace(" ", "_")}_{edit_cfg.cfg_text}_{edit_cfg.cfg_image}_{seed}_combine.jpg', + ) + edited_image.save(save_path) + logging.info(f"Edited image saved to: {save_path}") + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/instruct_pix2pix/sd_finetune.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/instruct_pix2pix/sd_finetune.py new file mode 100644 index 0000000..c7244de --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/instruct_pix2pix/sd_finetune.py @@ -0,0 +1,43 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from omegaconf.omegaconf import OmegaConf, open_dict + +from nemo.collections.multimodal.models.text_to_image.instruct_pix2pix.ldm.ddpm_edit import MegatronLatentDiffusionEdit +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="sd_finetune") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + # hydra interpolation does not work here as the interpolation key is lost when PTL saves hparams + with open_dict(cfg): + cfg.model.precision = cfg.trainer.precision + + model = MegatronLatentDiffusionEdit(cfg.model, trainer) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd2_train.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd2_train.yaml new file mode 100644 index 0000000..b725b15 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd2_train.yaml @@ -0,0 +1,192 @@ +name: stable-diffusion2-train + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 140000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + limit_val_batches: 0 + +exp_manager: + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: stable-diffusion + group: nemo-sd + name: ${name} + resume: True + create_checkpoint_callback: True + create_tensorboard_logger: True + checkpoint_callback_params: + every_n_train_steps: 1000 + every_n_epochs: 0 + monitor: reduced_train_loss + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 4 # limited by GPU memory + global_batch_size: 16 # will use more micro batches to reach global batch size + + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: images + cond_stage_key: captions # txt for cifar, caption for pbss + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn # check + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + scale_by_std: False + ckpt_path: + ignore_keys: [] + parameterization: eps + clip_denoised: True + load_only_unet: False + cosine_s: 8e-3 + given_betas: + original_elbo_weight: 0 + v_posterior: 0 + l_simple_weight: 1 + use_positional_encodings: False + learn_logvar: False + logvar_init: 0 + beta_schedule: linear + loss_type: l2 + + concat_mode: True + cond_stage_forward: + text_embedding_dropout_rate: 0.1 + fused_opt: True + inductor: True + inductor_cudagraphs: False + capture_cudagraph_iters: -1 # -1 to disable + channels_last: True + + unet_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel.UNetModel + from_pretrained: + from_NeMo: #Must be specified when from pretrained is not None, False means loading unet from HF ckpt + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + num_head_channels: 64 + use_spatial_transformer: true + use_linear_in_transformer: true + transformer_depth: 1 + context_dim: 1024 + use_checkpoint: False + legacy: False + use_flash_attention: False + + first_stage_config: + _target_: nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder.AutoencoderKL + from_pretrained: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 #Never used + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenMegatronCLIPEmbedder + restore_from_path: /path/to/clip.nemo + device: cuda + freeze: True + layer: "penultimate" + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + optim: + name: fused_adam + lr: 1e-4 + weight_decay: 0. + betas: + - 0.9 + - 0.999 + sched: + name: WarmupHoldPolicy + warmup_steps: 10000 + hold_steps: 10000000000000 # Incredibly large value to hold the lr as constant + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + data: + num_workers: 16 + synthetic_data: False # dataset_path and local_root_path can be empty when using synthetic data + synthetic_data_length: 10000 + train: + dataset_path: + - /datasets/coyo/test.pkl + augmentations: + resize_smallest_side: 512 + center_crop_h_w: 512, 512 + horizontal_flip: False + filterings: + + webdataset: + infinite_sampler: False + local_root_path: /datasets/coyo diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_fid_images.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_fid_images.yaml new file mode 100644 index 0000000..23a64a9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_fid_images.yaml @@ -0,0 +1,46 @@ +name: stable-diffusion-train + +fid: + classifier_free_guidance: + - 1.5 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + nnodes_per_cfg: 1 + ntasks_per_node: 8 + local_task_id: null + num_images_to_eval: 30000 + coco_captions_path: /coco2014/coco2014_val_sampled_30k/captions + coco_images_path: /coco2014/coco2014_val/images_256 + save_path: output + +infer: + unconditional_guidance_scale: null + num_images_per_prompt: 1 + height: 512 + width: 512 + down_factor: 8 + inference_steps: 50 + sampler_type: 'PLMS' + eta: 0 + output_type: 'pil' + save_to_file: False # We need to rename and maintain the order of images for clip score calculation, so we will save it outside the inference pipeline + out_path: ${fid.save_path} + seed: 123 + prompts: + batch_size: 8 + +trainer: + devices: ${fid.ntasks_per_node} + num_nodes: 1 + accelerator: gpu + precision: 32 + logger: False # logger provided by exp_manager + +model: + restore_from_path: null + precision: ${trainer.precision} diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_infer.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_infer.yaml new file mode 100644 index 0000000..5a34938 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_infer.yaml @@ -0,0 +1,32 @@ +name: stable-diffusion-train + +infer: + unconditional_guidance_scale: 7.5 + num_images_per_prompt: 4 + batch_size: 8 + height: 512 + width: 512 + down_factor: 8 + inference_steps: 25 + sampler_type: 'DPM' + eta: 0 + output_type: 'pil' + save_to_file: True + out_path: 'stable-diffusion' + seed: 123 + prompts: + - 'A photo of a Shiba Inu dog with a backpack riding a bike. It is wearing sunglasses and a beach hat.' + - 'A cute corgi lives in a house made out of sushi.' + - 'A high contrast portrait of a very happy fuzzy panda dressed as a chef in a high end kitchen making dough. There is a painting of flowers on the wall behind him.' + - 'A brain riding a rocketship heading towards the moon.' + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + +model: + restore_from_path: null + precision: ${trainer.precision} diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_lora_infer.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_lora_infer.yaml new file mode 100644 index 0000000..d77c24d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_lora_infer.yaml @@ -0,0 +1,34 @@ +name: stable-diffusion-train + +infer: + unconditional_guidance_scale: 7.5 + num_images_per_prompt: 4 + height: 512 + width: 512 + down_factor: 8 + inference_steps: 25 + sampler_type: 'DPM' + eta: 0 + output_type: 'pil' + save_to_file: True + out_path: 'stable-diffusion' + seed: 123 + prompts: + - 'A photo of a Shiba Inu dog with a backpack riding a bike. It is wearing sunglasses and a beach hat.' + - 'A cute corgi lives in a house made out of sushi.' + - 'A high contrast portrait of a very happy fuzzy panda dressed as a chef in a high end kitchen making dough. There is a painting of flowers on the wall behind him.' + - 'A brain riding a rocketship heading towards the moon.' + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + +model: + precision: ${trainer.precision} + peft: + restore_from_path: null + unet_config: + from_pretrained: null # In case user want to load lora weights to a different unet ckpt than that is used in training \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_lora_train.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_lora_train.yaml new file mode 100644 index 0000000..d9981a0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_lora_train.yaml @@ -0,0 +1,217 @@ +name: stable-diffusion-lora-train + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 2 # PTL default. In practice, max_steps will be reached first. + max_steps: -1 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + limit_val_batches: 0 + + +exp_manager: + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: stable-diffusion + group: nemo-sd + name: ${name} + resume: True + create_checkpoint_callback: True + create_tensorboard_logger: True + checkpoint_callback_params: + every_n_train_steps: 1000 + every_n_epochs: 0 + monitor: reduced_train_loss + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 1 # limited by GPU memory + global_batch_size: 1 # will use more micro batches to reach global batch size + native_amp_init_scale: 65536.0 # Init scale for grad scaler used at fp16 + + + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: images + cond_stage_key: captions # txt for cifar, caption for pbss + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn # check + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + scale_by_std: False + ckpt_path: + ignore_keys: [] + parameterization: eps + clip_denoised: True + load_only_unet: False + cosine_s: 8e-3 + given_betas: + original_elbo_weight: 0 + v_posterior: 0 + l_simple_weight: 1 + use_positional_encodings: False + learn_logvar: False + logvar_init: 0 + beta_schedule: linear + loss_type: l2 + + concat_mode: True + cond_stage_forward: + text_embedding_dropout_rate: 0.1 + fused_opt: True + inductor: False + inductor_cudagraphs: False + capture_cudagraph_iters: -1 # -1 to disable + channels_last: True + + unet_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel.UNetModel + from_pretrained: /ckpts/nemo-v1-2.ckpt + from_NeMo: True #Must be specified when from pretrained is not None, False means loading unet from HF ckpt + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + num_heads: 8 + use_spatial_transformer: true + transformer_depth: 1 + context_dim: 768 + use_checkpoint: False + legacy: False + use_flash_attention: True + unet_precision: fp32 + resblock_gn_groups: 32 + lora_network_alpha: null + + first_stage_config: + _target_: nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder.AutoencoderKL + from_pretrained: /ckpts/vae.bin + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 #Never used + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenMegatronCLIPEmbedder + restore_from_path: /ckpts/openai.nemo + device: cuda + freeze: True + layer: "last" + # For compatibility of history version that uses HF clip model + # _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder + # version: openai/clip-vit-large-patch14 + # device: cuda + # max_length: 77 + # capture_cudagraph_iters: ${model.capture_cudagraph_iters} + + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + ddp_overlap: True # True for using PyTorch DDP overlap. + + optim: + name: fused_adam + lr: 1e-4 + weight_decay: 0. + betas: + - 0.9 + - 0.999 + sched: + name: WarmupHoldPolicy + warmup_steps: 1 + hold_steps: 10000000000000 # Incredibly large value to hold the lr as constant + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + data: + num_workers: 16 + synthetic_data: False # dataset_path and local_root_path can be empty when using synthetic data + synthetic_data_length: 10000 + train: + dataset_path: + - /datasets/coyo/test.pkl + augmentations: + resize_smallest_side: 512 + center_crop_h_w: 512, 512 + horizontal_flip: False + filterings: + + webdataset: + infinite_sampler: False + local_root_path: /datasets/coyo + + peft: + peft_scheme: "sdlora" + restore_from_path: null + lora_tuning: + adapter_dim: 32 + adapter_dropout: 0.0 + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + layer_selection: null # selects in which layers to add lora adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_train.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_train.yaml new file mode 100644 index 0000000..8ce009d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_train.yaml @@ -0,0 +1,203 @@ +name: stable-diffusion-train + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 2 # PTL default. In practice, max_steps will be reached first. + max_steps: -1 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + limit_val_batches: 0 + + +exp_manager: + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: stable-diffusion + group: nemo-sd + name: ${name} + resume: True + create_checkpoint_callback: True + create_tensorboard_logger: True + checkpoint_callback_params: + every_n_train_steps: 1000 + every_n_epochs: 0 + monitor: reduced_train_loss + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 1 # limited by GPU memory + global_batch_size: 1 # will use more micro batches to reach global batch size + native_amp_init_scale: 65536.0 # Init scale for grad scaler used at fp16 + + + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: images + cond_stage_key: captions # txt for cifar, caption for pbss + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn # check + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + scale_by_std: False + ckpt_path: + ignore_keys: [] + parameterization: eps + clip_denoised: True + load_only_unet: False + cosine_s: 8e-3 + given_betas: + original_elbo_weight: 0 + v_posterior: 0 + l_simple_weight: 1 + use_positional_encodings: False + learn_logvar: False + logvar_init: 0 + beta_schedule: linear + loss_type: l2 + + concat_mode: True + cond_stage_forward: + text_embedding_dropout_rate: 0.1 + fused_opt: True + inductor: False + inductor_cudagraphs: False + capture_cudagraph_iters: -1 # -1 to disable + channels_last: True + + unet_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel.UNetModel + from_pretrained: #/ckpts/nemo-v1-2.ckpt + from_NeMo: True #Must be specified when from pretrained is not None, False means loading unet from HF ckpt + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + num_heads: 8 + use_spatial_transformer: true + transformer_depth: 1 + context_dim: 768 + use_checkpoint: False + legacy: False + use_flash_attention: True + unet_precision: fp32 + resblock_gn_groups: 32 + + first_stage_config: + _target_: nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder.AutoencoderKL + from_pretrained: /ckpts/vae.bin + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 #Never used + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenMegatronCLIPEmbedder + restore_from_path: /ckpts/openai.nemo + device: cuda + freeze: True + layer: "last" + # For compatibility of history version that uses HF clip model + # _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder + # version: openai/clip-vit-large-patch14 + # device: cuda + # max_length: 77 + + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + ddp_overlap: True # True for using PyTorch DDP overlap. + + optim: + name: fused_adam + lr: null + weight_decay: 0. + betas: + - 0.9 + - 0.999 + sched: + name: WarmupHoldPolicy + warmup_steps: 10000 + hold_steps: 10000000000000 # Incredibly large value to hold the lr as constant + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + data: + num_workers: 16 + synthetic_data: False # dataset_path and local_root_path can be empty when using synthetic data + synthetic_data_length: 10000 + train: + dataset_path: + - /datasets/coyo/test.pkl + augmentations: + resize_smallest_side: 512 + center_crop_h_w: 512, 512 + horizontal_flip: False + filterings: + + webdataset: + infinite_sampler: False + local_root_path: /datasets/coyo diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_base.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_base.yaml new file mode 100644 index 0000000..c536bae --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_base.yaml @@ -0,0 +1,102 @@ +model: + scale_factor: 0.13025 + disable_first_stage_autocast: True + is_legacy: False + + denoiser_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.denoiser.DiscreteDenoiser + num_idx: 1000 + + weighting_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.denoiser_weighting.EpsWeighting + scaling_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.denoiser_scaling.EpsScaling + discretization_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.discretizer.LegacyDDPMDiscretization + + + unet_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel.UNetModel + from_pretrained: /sdxl_ckpts/stable-diffusion-xl-base-1.0/unet/diffusion_pytorch_model.safetensors + from_NeMo: False + adm_in_channels: 2816 + num_classes: sequential + use_checkpoint: False + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4 ] + num_head_channels: 64 + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: [ 1, 2, 10 ] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16 + context_dim: 2048 + image_size: 64 # unused +# spatial_transformer_attn_type: softmax #note: only default softmax is supported now + legacy: False + use_flash_attention: False + + first_stage_config: + _target_: nemo.collections.multimodal.models.stable_diffusion.ldm.autoencoder.AutoencoderKLInferenceWrapper + from_pretrained: /sdxl_ckpts/stable-diffusion-xl-base-1.0/vae/diffusion_pytorch_model.safetensors + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + attn_type: vanilla + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [ 1, 2, 4, 4 ] + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + + + conditioner_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.GeneralConditioner + emb_models: + # crossattn cond + - is_trainable: False + input_key: txt + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder + layer: hidden + layer_idx: 11 + # crossattn and vector cond + - is_trainable: False + input_key: txt + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenOpenCLIPEmbedder2 + arch: ViT-bigG-14 + version: laion2b_s39b_b160k + freeze: True + layer: penultimate + always_return_pooled: True + legacy: False + # vector cond + - is_trainable: False + input_key: original_size_as_tuple + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.ConcatTimestepEmbedderND + outdim: 256 # multiplied by two + # vector cond + - is_trainable: False + input_key: crop_coords_top_left + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.ConcatTimestepEmbedderND + outdim: 256 # multiplied by two + # vector cond + - is_trainable: False + input_key: target_size_as_tuple + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.ConcatTimestepEmbedderND + outdim: 256 # multiplied by two + diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_base_train.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_base_train.yaml new file mode 100644 index 0000000..7aa765d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_base_train.yaml @@ -0,0 +1,212 @@ +name: stable-diffusion-xl-train + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: bf16-mixed + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 1 # PTL default. In practice, max_steps will be reached first. + max_steps: -1 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + limit_val_batches: 0 + +exp_manager: + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: stable-diffusion + group: nemo-sd + name: ${name} + resume: True + create_checkpoint_callback: True + create_tensorboard_logger: True + checkpoint_callback_params: + every_n_train_steps: 10000 + every_n_epochs: 0 + monitor: reduced_train_loss + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 1 # limited by GPU memory + global_batch_size: 1 # will use more micro batches to reach global batch size + + + scale_factor: 0.13025 + disable_first_stage_autocast: True + is_legacy: False + inductor: False # Not working right now + capture_cudagraph_iters: -1 + scale_by_std: False + channels_last: False + fsdp: True + fsdp_set_buffer_dtype: null + precache_mode: null # [text, both, null] + + loss_fn_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.loss.StandardDiffusionLoss + sigma_sampler: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.sigma_sampling.DiscreteSampling + num_idx: 1000 + discretization: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.discretizer.LegacyDDPMDiscretization + + + denoiser_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.denoiser.DiscreteDenoiser + num_idx: 1000 + + weighting_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.denoiser_weighting.EpsWeighting + scaling_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.denoiser_scaling.EpsScaling + discretization_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.discretizer.LegacyDDPMDiscretization + + + unet_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel.UNetModel + from_NeMo: False + adm_in_channels: 2816 + num_classes: sequential + use_checkpoint: False + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4 ] + num_head_channels: 64 + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: [ 1, 2, 10 ] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16 + context_dim: 2048 + image_size: 64 # unused + legacy: False + use_flash_attention: True + + first_stage_config: + _target_: nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder.AutoencoderKLInferenceWrapper + from_pretrained: /sdxl_ckpts/stable-diffusion-xl-base-1.0/vae/diffusion_pytorch_model.safetensors + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + attn_type: vanilla + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [ 1, 2, 4, 4 ] + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + + + conditioner_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.GeneralConditioner + emb_models: + # crossattn cond + - is_trainable: False + input_key: captions + ucg_rate: 0.1 + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder + layer: hidden + layer_idx: 11 + # crossattn and vector cond + - is_trainable: False + ucg_rate: 0.1 + input_key: captions + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenOpenCLIPEmbedder2 + arch: ViT-bigG-14 + version: laion2b_s39b_b160k + freeze: True + layer: penultimate + always_return_pooled: True + legacy: False + # vector cond + - is_trainable: False + ucg_rate: 0.1 + input_key: original_size_as_tuple + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.ConcatTimestepEmbedderND + outdim: 256 # multiplied by two + # vector cond + - is_trainable: False + ucg_rate: 0.1 + input_key: crop_coords_top_left + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.ConcatTimestepEmbedderND + outdim: 256 # multiplied by two + # vector cond + - is_trainable: False + ucg_rate: 0.1 + input_key: target_size_as_tuple + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.ConcatTimestepEmbedderND + outdim: 256 # multiplied by two + + + data: + num_workers: 16 + train: + dataset_path: + - YOUR_TRAINING_DATASET_WDINFO_FILE + augmentations: + resize_smallest_side: 256 + horizontal_flip: False + filterings: + + webdataset: + infinite_sampler: False + local_root_path: /datasets/coyo + + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + optim: + name: fused_adam + lr: 1e-4 # Need to adjust according to the global bs + weight_decay: 0. + betas: + - 0.9 + - 0.999 + sched: + name: WarmupHoldPolicy + warmup_steps: 10000 + hold_steps: 10000000000000 # Incredibly large value to hold the lr as constant + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_base_train_cache_both.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_base_train_cache_both.yaml new file mode 100644 index 0000000..43299fc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_base_train_cache_both.yaml @@ -0,0 +1,177 @@ +name: stable-diffusion-xl-train + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 32 + logger: False # logger provided by exp_manager + enable_checkpointing: False + replace_sampler_ddp: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: -1 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + limit_val_batches: 0 + +exp_manager: + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: stable-diffusion + group: nemo-sd + name: ${name} + resume: True + create_checkpoint_callback: True + create_tensorboard_logger: True + checkpoint_callback_params: + every_n_train_steps: 10000 + every_n_epochs: 0 + monitor: reduced_train_loss + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 1 # limited by GPU memory + global_batch_size: 1 # will use more micro batches to reach global batch size + + + scale_factor: 0.13025 + disable_first_stage_autocast: True + is_legacy: False + inductor: False # Not working right now + capture_cudagraph_iters: -1 + scale_by_std: False + channels_last: True + fsdp: True + precache_mode: both # [text, both, null] + input_key: latents_256 + + loss_fn_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.loss.StandardDiffusionLoss + sigma_sampler: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.sigma_sampling.DiscreteSampling + num_idx: 1000 + discretization: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.discretizer.LegacyDDPMDiscretization + + + denoiser_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.denoiser.DiscreteDenoiser + num_idx: 1000 + + weighting_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.denoiser_weighting.EpsWeighting + scaling_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.denoiser_scaling.EpsScaling + discretization_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.discretizer.LegacyDDPMDiscretization + + + unet_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel.UNetModel + from_NeMo: False + adm_in_channels: 1280 + num_classes: sequential + use_checkpoint: False + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4 ] + num_head_channels: 64 + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: [ 1, 2, 10 ] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16 + context_dim: 2048 + image_size: 64 # unused +# spatial_transformer_attn_type: softmax #note: only default softmax is supported now + legacy: False + use_flash_attention: True + + first_stage_config: + _target_: nemo.collections.multimodal.models.stable_diffusion.ldm.autoencoder.AutoencoderKLInferenceWrapper + from_pretrained: /sdxl_ckpts/stable-diffusion-xl-base-1.0/vae/diffusion_pytorch_model.safetensors + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + attn_type: vanilla + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [ 1, 2, 4, 4 ] + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + + + conditioner_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.GeneralConditioner + emb_models: + # crossattn cond + - is_trainable: False + ucg_rate: 0.1 + input_keys: [prompt_embeds, pooled_prompt_embeds] + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.PrecachedEmbModel + + data: + num_workers: 16 + train: + dataset_path: + - YOUR_TRAINING_DATASET_WDINFO_FILE + augmentations: + resize_smallest_side: 256 + horizontal_flip: False + filterings: + + webdataset: + infinite_sampler: False + local_root_path: DATASET_MOUNT_PATH + + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + optim: + name: fused_adam + lr: 1e-4 # Need to adjust based on global batch size + weight_decay: 0. + betas: + - 0.9 + - 0.999 + sched: + name: WarmupHoldPolicy + warmup_steps: 10000 + hold_steps: 10000000000000 # Incredibly large value to hold the lr as constant + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_base_train_no_conditions.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_base_train_no_conditions.yaml new file mode 100644 index 0000000..a9de572 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_base_train_no_conditions.yaml @@ -0,0 +1,204 @@ +name: stable-diffusion-xl-train + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: bf16-mixed + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: -1 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + limit_val_batches: 0 + +exp_manager: + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: stable-diffusion + group: nemo-sd + name: ${name} + resume: True + create_checkpoint_callback: True + create_tensorboard_logger: True + checkpoint_callback_params: + every_n_train_steps: 10000 + every_n_epochs: 0 + monitor: reduced_train_loss + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 1 # limited by GPU memory + global_batch_size: 2 # will use more micro batches to reach global batch size + + + scale_factor: 0.13025 + disable_first_stage_autocast: True + is_legacy: False + inductor: False + capture_cudagraph_iters: -1 + scale_by_std: False + channels_last: False + fsdp: True + precache_mode: null # [text, both, null] + + loss_fn_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.loss.StandardDiffusionLoss + sigma_sampler: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.sigma_sampling.DiscreteSampling + num_idx: 1000 + discretization: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.discretizer.LegacyDDPMDiscretization + + + denoiser_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.denoiser.DiscreteDenoiser + num_idx: 1000 + + weighting_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.denoiser_weighting.EpsWeighting + scaling_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.denoiser_scaling.EpsScaling + discretization_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.discretizer.LegacyDDPMDiscretization + + + unet_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel.UNetModel + from_NeMo: False + adm_in_channels: 1280 + num_classes: sequential + use_checkpoint: False + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4 ] + num_head_channels: 64 + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: [ 1, 2, 10 ] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16 + context_dim: 2048 + image_size: 64 # unused + legacy: False + use_flash_attention: True + + first_stage_config: + _target_: nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder.AutoencoderKLInferenceWrapper + from_pretrained: /sdxl_ckpts/stable-diffusion-xl-base-1.0/vae/diffusion_pytorch_model.safetensors + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + attn_type: vanilla + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [ 1, 2, 4, 4 ] + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + + + conditioner_config: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.GeneralConditioner + emb_models: + # crossattn cond + - is_trainable: False + input_key: captions + ucg_rate: 0.1 + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder + layer: hidden + layer_idx: 11 + # crossattn and vector cond + - is_trainable: False + ucg_rate: 0.1 + input_key: captions + emb_model: + _target_: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenOpenCLIPEmbedder2 + arch: ViT-bigG-14 + version: laion2b_s39b_b160k + freeze: True + layer: penultimate + always_return_pooled: True + legacy: False + + + + data: + num_workers: 16 + train: + dataset_path: + - YOUR_TRAINING_DATASET_WDINFO_FILE + augmentations: + resize_smallest_side: 256 + center_crop_h_w: 256, 256 + horizontal_flip: False + filterings: + + webdataset: + infinite_sampler: False + local_root_path: /datasets/coyo + + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + optim: + name: fused_adam + lr: 1e-4 # Need to adjust according to the global bs + weight_decay: 0. + betas: + - 0.9 + - 0.999 + sched: + name: WarmupHoldPolicy + warmup_steps: 10000 + hold_steps: 10000000000000 # Incredibly large value to hold the lr as constant + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + peft: + peft_scheme: null + restore_from_path: null + lora_tuning: + adapter_dim: 32 + adapter_dropout: 0.0 + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + layer_selection: null # selects in which layers to add lora adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_fid_images.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_fid_images.yaml new file mode 100644 index 0000000..647c7bb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_fid_images.yaml @@ -0,0 +1,95 @@ +name: stable-diffusion-train + +fid: + classifier_free_guidance: + - 1.5 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + nnodes_per_cfg: 1 + ntasks_per_node: 8 + local_task_id: null + num_images_to_eval: 30000 + coco_captions_path: /coco2014/coco2014_val_sampled_30k/captions + coco_images_path: /coco2014/coco2014_val/images_256 + save_path: output + +model: + restore_from_path: + is_legacy: False + +use_refiner: False +use_fp16: False # use fp16 model weights + +base_model_config: /opt/NeMo/examples/multimodal/generative/stable_diffusion/conf/sd_xl_base.yaml +refiner_config: /opt/NeMo/examples/multimodal/generative/stable_diffusion/conf/sd_xl_refiner.yaml + + +infer: + num_samples: 1 + prompt: + - "A professional photograph of an astronaut riding a pig" + negative_prompt: "" + seed: 123 + + +sampling: + base: + sampler: EulerEDMSampler + width: 1344 + height: 768 + steps: 40 + discretization: "LegacyDDPMDiscretization" + guider: "VanillaCFG" + thresholder: "None" + scale: 5.0 + aesthetic_score: 5.0 + negative_aesthetic_score: 5.0 + img2img_strength: 1.0 + orig_width: 1344 + orig_height: 768 + crop_coords_top: 0 + crop_coords_left: 0 + sigma_min: 0.0292 + sigma_max: 14.6146 + rho: 3.0 + s_churn: 0.0 + s_tmin: 0.0 + s_tmax: 999.0 + s_noise: 1.0 + eta: 1.0 + order: 4 + refiner: + sampler: EulerEDMSampler + width: 1344 + height: 768 + steps: 40 + discretization: "LegacyDDPMDiscretization" + guider: "VanillaCFG" + thresholder: "None" + scale: 5.0 + aesthetic_score: 6.0 + negative_aesthetic_score: 2.5 + img2img_strength: 0.15 + crop_coords_top: 0 + crop_coords_left: 0 + sigma_min: 0.0292 + sigma_max: 14.6146 + rho: 3.0 + s_churn: 0.0 + s_tmin: 0.0 + s_tmax: 999.0 + s_noise: 1.0 + eta: 1.0 + order: 4 + +trainer: + devices: ${evaluation.fid.ntasks_per_node} + num_nodes: 1 + accelerator: gpu + precision: 32 + logger: False # logger provided by exp_manager diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_infer.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_infer.yaml new file mode 100644 index 0000000..eb1f6d7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/conf/sd_xl_infer.yaml @@ -0,0 +1,67 @@ +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 32 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: -1 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + limit_val_batches: 0 + + +infer: + num_samples: 4 + prompt: + - "A professional photograph of an astronaut riding a pig" + - 'A photo of a Shiba Inu dog with a backpack riding a bike. It is wearing sunglasses and a beach hat.' + - 'A cute corgi lives in a house made out of sushi.' + - 'A high contrast portrait of a very happy fuzzy panda dressed as a chef in a high end kitchen making dough. There is a painting of flowers on the wall behind him.' + - 'A brain riding a rocketship heading towards the moon.' + negative_prompt: "" + seed: 123 + + +sampling: + base: + sampler: EulerEDMSampler + width: 256 + height: 256 + steps: 40 + discretization: "LegacyDDPMDiscretization" + guider: "VanillaCFG" + thresholder: "None" + scale: 5.0 + img2img_strength: 1.0 + sigma_min: 0.0292 + sigma_max: 14.6146 + rho: 3.0 + s_churn: 0.0 + s_tmin: 0.0 + s_tmax: 999.0 + s_noise: 1.0 + eta: 1.0 + order: 4 + orig_width: 1024 + orig_height: 1024 + crop_coords_top: 0 + crop_coords_left: 0 + aesthetic_score: 5.0 + negative_aesthetic_score: 5.0 + +model: + restore_from_path: + is_legacy: False + +use_refiner: False +use_fp16: False # use fp16 model weights +out_path: ./output + +base_model_config: /opt/NeMo/examples/multimodal/generative/stable_diffusion/conf/sd_xl_base.yaml +refiner_config: /opt/NeMo/examples/multimodal/generative/stable_diffusion/conf/sd_xl_refiner.yaml \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/generate_fid_images.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/generate_fid_images.py new file mode 100644 index 0000000..27ea591 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/generate_fid_images.py @@ -0,0 +1,96 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import torch +from omegaconf.omegaconf import open_dict + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion +from nemo.collections.multimodal.parts.stable_diffusion.pipeline import pipeline +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.core.config import hydra_runner + + +@hydra_runner(config_path='conf', config_name='sd_fid_images') +def main(cfg): + # Read configuration parameters + nnodes_per_cfg = cfg.fid.nnodes_per_cfg + ntasks_per_node = cfg.fid.ntasks_per_node + local_task_id = cfg.fid.local_task_id + num_images_to_eval = cfg.fid.num_images_to_eval + path = cfg.fid.coco_captions_path + + node_id = int(os.environ.get("SLURM_ARRAY_TASK_ID", 0)) + node_id_per_cfg = node_id % nnodes_per_cfg + + current_node_cfg = cfg.fid.classifier_free_guidance[node_id // nnodes_per_cfg] + with open_dict(cfg): + cfg.infer.unconditional_guidance_scale = current_node_cfg + save_path = os.path.join(cfg.fid.save_path, str(current_node_cfg)) + + # Read and store captions + captions = [] + caption_files = sorted(os.listdir(path)) + assert len(caption_files) >= num_images_to_eval + for file in caption_files[:num_images_to_eval]: + with open(os.path.join(path, file), 'r') as f: + captions += f.readlines() + + # Calculate partition sizes and select the partition for the current node + partition_size_per_node = num_images_to_eval // nnodes_per_cfg + start_idx = node_id_per_cfg * partition_size_per_node + end_idx = (node_id_per_cfg + 1) * partition_size_per_node if node_id_per_cfg != nnodes_per_cfg - 1 else None + captions = captions[start_idx:end_idx] + + local_task_id = int(local_task_id) if local_task_id is not None else int(os.environ.get("SLURM_LOCALID", 0)) + partition_size_per_task = int(len(captions) // ntasks_per_node) + + # Select the partition for the current task + start_idx = local_task_id * partition_size_per_task + end_idx = (local_task_id + 1) * partition_size_per_task if local_task_id != ntasks_per_node - 1 else None + input = captions[start_idx:end_idx] + + print(f"Current worker {node_id}:{local_task_id} will generate {len(input)} images") + + os.makedirs(save_path, exist_ok=True) + + # Modify the model configuration + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.ckpt_path = None + model_cfg.inductor = False + model_cfg.unet_config.use_flash_attention = False + model_cfg.unet_config.from_pretrained = None + model_cfg.first_stage_config.from_pretrained = None + model_cfg.global_batch_size = cfg.infer.batch_size * ntasks_per_node + + # Set up the trainer and model for inference + trainer, megatron_diffusion_model = setup_trainer_and_model_for_inference( + model_provider=MegatronLatentDiffusion, cfg=cfg, model_cfg_modifier=model_cfg_modifier + ) + model = megatron_diffusion_model.model + model.cuda().eval() + + # Generate images using the model and save them + cfg.infer.prompts = input + rng = torch.Generator().manual_seed(cfg.infer.seed + local_task_id * 10 + node_id_per_cfg * 100) + output = pipeline(model, cfg, rng=rng) + for i, image in enumerate(img for batch in output for img in batch): + image_num = i + partition_size_per_node * node_id_per_cfg + partition_size_per_task * local_task_id + image.save(os.path.join(save_path, f'image{image_num:06d}.png')) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/generate_xl_fid_images.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/generate_xl_fid_images.py new file mode 100644 index 0000000..b208308 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/generate_xl_fid_images.py @@ -0,0 +1,138 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import numpy as np +import torch +from einops import rearrange +from omegaconf.omegaconf import open_dict +from PIL import Image + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.diffusion_engine import MegatronDiffusionEngine +from nemo.collections.multimodal.parts.stable_diffusion.sdxl_pipeline import SamplingPipeline +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.core.config import hydra_runner + + +@hydra_runner(config_path='conf/stable_diffusion/conf', config_name='sd_xl_fid_images') +def main(cfg): + # Read configuration parameters + nnodes_per_cfg = cfg.fid.nnodes_per_cfg + ntasks_per_node = cfg.fid.ntasks_per_node + local_task_id = cfg.fid.local_task_id + num_images_to_eval = cfg.fid.num_images_to_eval + path = cfg.fid.coco_captions_path + use_refiner = cfg.get('use_refiner', False) + + node_id = int(os.environ.get("SLURM_ARRAY_TASK_ID", 0)) + node_id_per_cfg = node_id % nnodes_per_cfg + + current_node_cfg = cfg.fid.classifier_free_guidance[node_id // nnodes_per_cfg] + with open_dict(cfg): + cfg.sampling.base.scale = current_node_cfg + if use_refiner: + cfg.sampling.refiner.scale = current_node_cfg + save_path = os.path.join(cfg.fid.save_path, str(current_node_cfg)) + + # Read and store captions + captions = [] + caption_files = sorted(os.listdir(path)) + assert len(caption_files) >= num_images_to_eval + for file in caption_files[:num_images_to_eval]: + with open(os.path.join(path, file), 'r') as f: + captions += f.readlines() + + # Calculate partition sizes and select the partition for the current node + partition_size_per_node = num_images_to_eval // nnodes_per_cfg + start_idx = node_id_per_cfg * partition_size_per_node + end_idx = (node_id_per_cfg + 1) * partition_size_per_node if node_id_per_cfg != nnodes_per_cfg - 1 else None + captions = captions[start_idx:end_idx] + + local_task_id = int(local_task_id) if local_task_id is not None else int(os.environ.get("SLURM_LOCALID", 0)) + partition_size_per_task = int(len(captions) // ntasks_per_node) + + # Select the partition for the current task + start_idx = local_task_id * partition_size_per_task + end_idx = (local_task_id + 1) * partition_size_per_task if local_task_id != ntasks_per_node - 1 else None + input = captions[start_idx:end_idx] + + print(f"Current worker {node_id}:{local_task_id} will generate {len(input)} images") + + os.makedirs(save_path, exist_ok=True) + + torch.cuda.set_device(local_task_id) + + # base_model_config = cfg.base_model_config + # base = SamplingPipeline(base_model_config, use_fp16=cfg.use_fp16) + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.ckpt_path = None + model_cfg.inductor = False + model_cfg.unet_config.from_pretrained = None + model_cfg.first_stage_config.from_pretrained = None + model_cfg.fsdp = False + model_cfg.global_batch_size = model_cfg.micro_batch_size * ntasks_per_node + + torch.backends.cuda.matmul.allow_tf32 = True + trainer, megatron_diffusion_model = setup_trainer_and_model_for_inference( + model_provider=MegatronDiffusionEngine, cfg=cfg, model_cfg_modifier=model_cfg_modifier + ) + model = megatron_diffusion_model.model + model.cuda().eval() + base = SamplingPipeline(model, use_fp16=cfg.use_fp16, is_legacy=cfg.model.is_legacy) + + if use_refiner: + refiner_config = cfg.refiner_config + refiner = SamplingPipeline(refiner_config, use_fp16=cfg.use_fp16) + + # Generate images using the model and save them + for i, prompt in enumerate(input): + cfg.infer.prompt = [prompt] + seed = int(cfg.infer.seed + local_task_id * 10 + node_id_per_cfg * 100 + i * 1000) + output = base.text_to_image( + params=cfg.sampling.base, + prompt=cfg.infer.prompt, + negative_prompt=cfg.infer.negative_prompt, + samples=cfg.infer.num_samples, + return_latents=True if use_refiner else False, + seed=seed, + ) + + if use_refiner: + assert isinstance(output, (tuple, list)) + output, samples_z = output + assert output is not None + assert samples_z is not None + + # perform_save_locally(cfg.out_path, samples) + + output = refiner.refiner( + params=cfg.sampling.refiner, + image=samples_z, + prompt=cfg.infer.prompt, + negative_prompt=cfg.infer.negative_prompt, + samples=cfg.infer.num_samples, + seed=cfg.infer.seed, + ) + + for sample in output: + image_num = i + partition_size_per_node * node_id_per_cfg + partition_size_per_task * local_task_id + sample = 255.0 * rearrange(sample.cpu().numpy(), "c h w -> h w c") + image = Image.fromarray(sample.astype(np.uint8)) + image.save(os.path.join(save_path, f'image{image_num:06d}.png')) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_infer.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_infer.py new file mode 100644 index 0000000..f1e5e28 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_infer.py @@ -0,0 +1,44 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion +from nemo.collections.multimodal.parts.stable_diffusion.pipeline import pipeline +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.core.config import hydra_runner + + +@hydra_runner(config_path='conf', config_name='sd_infer') +def main(cfg): + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.ckpt_path = None + model_cfg.inductor = False + model_cfg.unet_config.use_flash_attention = False + model_cfg.unet_config.from_pretrained = None + model_cfg.first_stage_config.from_pretrained = None + + torch.backends.cuda.matmul.allow_tf32 = True + trainer, megatron_diffusion_model = setup_trainer_and_model_for_inference( + model_provider=MegatronLatentDiffusion, cfg=cfg, model_cfg_modifier=model_cfg_modifier + ) + model = megatron_diffusion_model.model + model.cuda().eval() + + rng = torch.Generator().manual_seed(cfg.infer.seed) + pipeline(model, cfg, rng=rng) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_lora_infer.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_lora_infer.py new file mode 100644 index 0000000..0877d4e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_lora_infer.py @@ -0,0 +1,64 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +from omegaconf import open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion +from nemo.collections.multimodal.parts.stable_diffusion.pipeline import pipeline +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP +from nemo.core.config import hydra_runner + + +@hydra_runner(config_path='conf', config_name='dreambooth_lora_infer') +def main(cfg): + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.ckpt_path = None + model_cfg.inductor = False + if cfg.model.unet_config.from_pretrained: + model_cfg.unet_config.from_pretrained = cfg.model.unet_config.from_pretrained + + model_cfg = MegatronLatentDiffusion.restore_from( + restore_path=cfg.model.peft.restore_from_path, + trainer=None, + save_restore_connector=NLPSaveRestoreConnector(), + return_config=True, + ) + + with open_dict(model_cfg): + model_cfg_modifier(model_cfg) + + plugins = [] + plugins.append(TorchElasticEnvironment()) + strategy = NLPDDPStrategy(no_ddp_communication_hook=True, find_unused_parameters=False,) + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer) + + model = MegatronLatentDiffusion(model_cfg, trainer=trainer) + model.setup_complete = True + + peft_cfg_cls = PEFT_CONFIG_MAP[model_cfg.peft.peft_scheme] + + model.load_adapters(cfg.model.peft.restore_from_path, peft_cfg_cls(model_cfg)) + rng = torch.Generator().manual_seed(cfg.infer.seed) + + model = model.model.cuda().eval() + pipeline(model, cfg, rng=rng) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_train.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_train.py new file mode 100644 index 0000000..b10eda5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_train.py @@ -0,0 +1,115 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import torch +from omegaconf.omegaconf import OmegaConf + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.callbacks import CUDAGraphCallback +from nemo.utils.exp_manager import exp_manager + + +class MegatronStableDiffusionTrainerBuilder(MegatronTrainerBuilder): + """Builder for SD model Trainer with overrides.""" + + def _training_strategy(self) -> NLPDDPStrategy: + """ + Returns a ddp strategy passed to Trainer.strategy. + """ + ddp_overlap = self.cfg.model.get('ddp_overlap', True) + if ddp_overlap: + return NLPDDPStrategy( + no_ddp_communication_hook=False, + gradient_as_bucket_view=self.cfg.model.gradient_as_bucket_view, + find_unused_parameters=True, + bucket_cap_mb=256, + ) + else: + return NLPDDPStrategy( + no_ddp_communication_hook=True, + gradient_as_bucket_view=self.cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + ) + + +@hydra_runner(config_path='conf', config_name='sd_train') +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + torch.backends.cuda.matmul.allow_tf32 = True + + callbacks = ( + None + if cfg.model.capture_cudagraph_iters < 0 + else [CUDAGraphCallback(capture_iteration=cfg.model.capture_cudagraph_iters)] + ) + trainer = MegatronStableDiffusionTrainerBuilder(cfg).create_trainer(callbacks) + + exp_manager(trainer, cfg.exp_manager) + + model = MegatronLatentDiffusion(cfg.model, trainer) + + if cfg.model.capture_cudagraph_iters >= 0: + # Warmup the model with random data + with torch.cuda.stream(torch.cuda.Stream()): + n, c, h = cfg.model.micro_batch_size, cfg.model.channels, cfg.model.image_size + x = torch.randn((n, c, h, h), dtype=torch.float32, device="cuda") + t = torch.randint(77, (n,), device="cuda") + cc = torch.randn((n, 77, cfg.model.unet_config.context_dim), dtype=torch.float32, device="cuda",) + if cfg.model.precision in [16, '16']: + x = x.type(torch.float16) + cc = cc.type(torch.float16) + autocast_enabled = False + dgrad_dtype = torch.float16 + else: + autocast_enabled = True + dgrad_dtype = torch.float16 + # akoumparouli: temp fix. + autocast_enabled = True + model = model.cuda() + for _ in range(5): + with torch.autocast(device_type="cuda", enabled=autocast_enabled, dtype=torch.float16): + out = model.model.model.diffusion_model(x, t, context=cc) + grad = torch.randn_like(out, dtype=dgrad_dtype) + out.backward(grad) + model.zero_grad() + + if cfg.model.get('peft', None): + + peft_cfg_cls = PEFT_CONFIG_MAP[cfg.model.peft.peft_scheme] + + if cfg.model.peft.restore_from_path is not None: + # initialize peft weights from a checkpoint instead of randomly + # This is not the same as resume training because optimizer states are not restored. + logging.info("PEFT Weights will be loaded from", cfg.model.peft.restore_from_path) + model.load_adapters(cfg.model.peft.restore_from_path, peft_cfg_cls(model_cfg)) + elif peft_cfg_cls is not None: + logging.info("Adding adapter weights to the model for PEFT") + model.add_adapter(peft_cfg_cls(cfg.model)) + else: + logging.info(f"Running full finetuning since no peft scheme is given.\n{model.summarize()}") + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_xl_infer.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_xl_infer.py new file mode 100644 index 0000000..8d18be5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_xl_infer.py @@ -0,0 +1,58 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.diffusion_engine import MegatronDiffusionEngine +from nemo.collections.multimodal.parts.stable_diffusion.sdxl_helpers import perform_save_locally +from nemo.collections.multimodal.parts.stable_diffusion.sdxl_pipeline import SamplingPipeline +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.core.config import hydra_runner + + +@hydra_runner(config_path='conf', config_name='sd_xl_infer') +def main(cfg): + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.ckpt_path = None + model_cfg.inductor = False + model_cfg.unet_config.from_pretrained = None + model_cfg.first_stage_config.from_pretrained = None + model_cfg.first_stage_config._target_ = 'nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder.AutoencoderKLInferenceWrapper' + model_cfg.fsdp = False + + torch.backends.cuda.matmul.allow_tf32 = True + trainer, megatron_diffusion_model = setup_trainer_and_model_for_inference( + model_provider=MegatronDiffusionEngine, cfg=cfg, model_cfg_modifier=model_cfg_modifier + ) + + model = megatron_diffusion_model.model + model.cuda().eval() + + base = SamplingPipeline(model, use_fp16=cfg.use_fp16, is_legacy=cfg.model.is_legacy) + use_refiner = cfg.get('use_refiner', False) + for i, prompt in enumerate(cfg.infer.prompt): + samples = base.text_to_image( + params=cfg.sampling.base, + prompt=[prompt], + negative_prompt=cfg.infer.negative_prompt, + samples=cfg.infer.num_samples, + return_latents=True if use_refiner else False, + seed=int(cfg.infer.seed + i * 100), + ) + + perform_save_locally(cfg.out_path, samples) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_xl_train.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_xl_train.py new file mode 100644 index 0000000..a91beca --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/text_to_image/stable_diffusion/sd_xl_train.py @@ -0,0 +1,102 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys +import torch +import torch._dynamo.config as dynamo_config +from omegaconf.omegaconf import OmegaConf +from pytorch_lightning import Trainer + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.diffusion_engine import MegatronDiffusionEngine +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPFSDPStrategy +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +class MegatronStableDiffusionTrainerBuilder(MegatronTrainerBuilder): + """Builder for SD model Trainer with overrides.""" + + def _training_strategy(self) -> NLPDDPStrategy: + """ + Returns a ddp strategy passed to Trainer.strategy. + """ + """ + Returns a DDP or a FSDP strategy passed to Trainer.strategy. + """ + # check interactive environment + _IS_INTERACTIVE = hasattr(sys, "ps1") or bool(sys.flags.interactive) + if _IS_INTERACTIVE and self.cfg.trainer.devices == 1: + logging.info("Detected interactive environment, using NLPDDPStrategyNotebook") + return NLPDDPStrategyNotebook(no_ddp_communication_hook=True, find_unused_parameters=False,) + + if self.cfg.model.get('fsdp', False): + assert ( + not self.cfg.model.optim.get('name') == 'distributed_fused_adam' + ), 'Distributed optimizer cannot be used with FSDP.' + if self.cfg.model.get('megatron_amp_O2', False): + logging.info('Torch FSDP is not compatible with O2 precision recipe. Setting O2 `False`.') + self.cfg.model.megatron_amp_O2 = False + return NLPFSDPStrategy( + limit_all_gathers=self.cfg.model.get('fsdp_limit_all_gathers', True), + sharding_strategy=self.cfg.model.get('fsdp_sharding_strategy', 'full'), + cpu_offload=self.cfg.model.get('fsdp_cpu_offload', False), + grad_reduce_dtype=self.cfg.model.get('fsdp_grad_reduce_dtype', 32), + precision=self.cfg.trainer.precision, + use_orig_params=self.cfg.model.inductor, + set_buffer_dtype=self.cfg.get('fsdp_set_buffer_dtype', None), + ) + + return NLPDDPStrategy( + no_ddp_communication_hook=(not self.cfg.model.get('ddp_overlap')), + gradient_as_bucket_view=self.cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + ) + + +@hydra_runner(config_path='conf', config_name='sd_xl_base_train') +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + torch.backends.cuda.matmul.allow_tf32 = True + + trainer = MegatronStableDiffusionTrainerBuilder(cfg).create_trainer() + + exp_manager(trainer, cfg.exp_manager) + + model = MegatronDiffusionEngine(cfg.model, trainer) + + if cfg.model.get('peft', None): + + peft_cfg_cls = PEFT_CONFIG_MAP[cfg.model.peft.peft_scheme] + + if cfg.model.peft.restore_from_path is not None: + # initialize peft weights from a checkpoint instead of randomly + # This is not the same as resume training because optimizer states are not restored. + logging.info("PEFT Weights will be loaded from", cfg.model.peft.restore_from_path) + model.load_adapters(cfg.model.peft.restore_from_path, peft_cfg_cls(model_cfg)) + elif peft_cfg_cls is not None: + logging.info("Adding adapter weights to the model for PEFT") + model.add_adapter(peft_cfg_cls(cfg.model)) + else: + logging.info(f"Running full finetuning since no peft scheme is given.") + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/conf/megatron_clip_VIT-L-14.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/conf/megatron_clip_VIT-L-14.yaml new file mode 100644 index 0000000..d8740bb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/conf/megatron_clip_VIT-L-14.yaml @@ -0,0 +1,203 @@ +model: + precision: 32 + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 64 # limited by GPU memory + global_batch_size: 2048 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + restore_from_pretrained: null # used in fine-tuning + # multimodal configs + output_dim: 768 + # As the number of devices used to train increases, so does the space complexity of + # the logit matrix. Using a naïve all-gather scheme, space complexity will be + # `O(n^2)`. Instead, complexity may become effectively linear if the flags + # `--gather-with-grad` and `--local-loss` are used. This alteration results in one-to-one + # numerical results as the naïve method. + local_loss: False # calculate loss w/ local features @ global (instead of realizing full global @ global matrix) + gather_with_grad: True # enable full distributed gradient for feature gather, set this to False may cause convergence issue + + vision: + precision: 32 + # vision configs + patch_dim: 14 + img_h: 224 + img_w: 224 + image_mean: null + image_std: null + num_channels: 3 + drop_patch_rate: 0.0 + drop_path_rate: 0.0 + global_average_pool: False + output_dim: ${model.output_dim} + class_token_length: 1 + preprocess_layernorm: True # apply layer norm to embedded tokens + + # model architecture + encoder_seq_length: 196 + max_position_embeddings: ${.encoder_seq_length} + position_embedding_type: learned_parameters + num_layers: 24 + hidden_size: 1024 + ffn_hidden_size: 4096 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 16 + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0. # Dropout probability for hidden state transformer. + attention_dropout: 0. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: layernorm # Type of normalization layers + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + + ## Activation Checkpointing + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + activations_checkpoint_num_layers: null # not used with 'selective' + sequence_parallel: False + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # model fusions + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + bias_dropout_add_fusion: True # Use a kernel that fuses the bias addition, dropout and residual connection addition. + + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism. + openai_gelu: False + bias_activation_fusion: False + megatron_legacy: True + activation: approx-gelu + + + + text: + precision: 32 + # text configs + output_dim: ${model.output_dim} + + # model architecture + encoder_seq_length: 77 + max_position_embeddings: ${.encoder_seq_length} + position_embedding_type: learned_parameters + num_layers: 12 + hidden_size: 768 + ffn_hidden_size: 3072 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 12 + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0. # Dropout probability for hidden state transformer. + attention_dropout: 0. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: layernorm # Type of normalization layers + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + + ## Activation Checkpointing + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + activations_checkpoint_num_layers: null # not used with 'selective' + num_micro_batches_with_partial_activation_checkpoints: null + activations_checkpoint_layers_per_pipeline: null + sequence_parallel: False + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # model fusions + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + bias_dropout_add_fusion: True # Use a kernel that fuses the bias addition, dropout and residual connection addition. + + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism. + openai_gelu: False + bias_activation_fusion: False + megatron_legacy: True + + transformer_engine: False + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: False # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: most_recent # 'most_recent' or 'max'. Algorithm for computing amax from history + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + activation: approx-gelu + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + tokenizer: + library: 'huggingface' + type: 'openai/clip-vit-large-patch14' + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + + data: + num_workers: 8 + train: + dataset_path: # List of paths to pkl files or tar files + - /datasets/coyo/test.pkl + validation: # List of paths to pkl files or tar files + dataset_path: + - /datasets/coyo/test.pkl + webdataset: + infinite_sampler: False + local_root_path: /datasets/coyo + + imagenet_val: null # Path to imagenet val set for conducting zero shot evaluation. + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: fused_adam + lr: 1e-3 + weight_decay: 0.2 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 2000 + constant_steps: 0 + min_lr: 1e-5 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/conf/megatron_clip_config.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/conf/megatron_clip_config.yaml new file mode 100644 index 0000000..a6b1928 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/conf/megatron_clip_config.yaml @@ -0,0 +1,250 @@ +name: megatron_clip +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 375000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + check_val_every_n_epoch: null + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_clip + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_clip--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 32 # limited by GPU memory + global_batch_size: 32 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + restore_from_path: null # used in fine-tuning + # multimodal configs + output_dim: 512 + # As the number of devices used to train increases, so does the space complexity of + # the logit matrix. Using a naïve all-gather scheme, space complexity will be + # `O(n^2)`. Instead, complexity may become effectively linear if the flags + # `--gather-with-grad` and `--local-loss` are used. This alteration results in one-to-one + # numerical results as the naïve method. + local_loss: False # calculate loss w/ local features @ global (instead of realizing full global @ global matrix) + gather_with_grad: True # enable full distributed gradient for feature gather, set this to False may cause convergence issue + + vision: + precision: ${trainer.precision} + # vision configs + patch_dim: 16 + img_h: 224 + img_w: 224 + image_mean: null + image_std: null + num_channels: 3 + drop_patch_rate: 0.0 + drop_path_rate: 0.0 + global_average_pool: False + output_dim: ${model.output_dim} + class_token_length: 8 + preprocess_layernorm: True # apply layer norm to embedded tokens + + # model architecture + encoder_seq_length: 196 + max_position_embeddings: ${.encoder_seq_length} + position_embedding_type: learned_absolute + num_layers: 12 + hidden_size: 768 + ffn_hidden_size: 3072 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 12 + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0. # Dropout probability for hidden state transformer. + attention_dropout: 0. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: layernorm # Type of normalization layers + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + + ## Activation Checkpointing + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + activations_checkpoint_num_layers: null # not used with 'selective' + sequence_parallel: False + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # model fusions + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + bias_dropout_add_fusion: True # Use a kernel that fuses the bias addition, dropout and residual connection addition. + + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism. + openai_gelu: False + bias_activation_fusion: False + megatron_legacy: False + + + text: + precision: ${trainer.precision} + # text configs + output_dim: ${model.output_dim} + + # model architecture + encoder_seq_length: 77 + max_position_embeddings: ${.encoder_seq_length} + position_embedding_type: learned_absolute + num_layers: 12 + hidden_size: 512 + ffn_hidden_size: 2048 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 8 + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0. # Dropout probability for hidden state transformer. + attention_dropout: 0. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: layernorm # Type of normalization layers + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + + ## Activation Checkpointing + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + activations_checkpoint_num_layers: null # not used with 'selective' + num_micro_batches_with_partial_activation_checkpoints: null + activations_checkpoint_layers_per_pipeline: null + sequence_parallel: False + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # model fusions + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + bias_dropout_add_fusion: True # Use a kernel that fuses the bias addition, dropout and residual connection addition. + + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism. + openai_gelu: False + bias_activation_fusion: False + megatron_legacy: False + + transformer_engine: False + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: False # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: most_recent # 'most_recent' or 'max'. Algorithm for computing amax from history + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + tokenizer: + library: 'huggingface' + type: 'openai/clip-vit-large-patch14' + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + + data: + num_workers: 8 + train: + dataset_path: # List of paths to pkl files or tar files + - /datasets/coyo/test.pkl + validation: # List of paths to pkl files or tar files + dataset_path: + - /datasets/coyo/test.pkl + webdataset: + infinite_sampler: False + local_root_path: /datasets/coyo + + imagenet_val: null # Path to imagenet val set for conducting zero shot evaluation. + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: fused_adam + lr: 1e-3 + weight_decay: 0.2 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 2000 + constant_steps: 0 + min_lr: 1e-5 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/conf/megatron_clip_imagenet_zeroshot.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/conf/megatron_clip_imagenet_zeroshot.yaml new file mode 100755 index 0000000..79bdac8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/conf/megatron_clip_imagenet_zeroshot.yaml @@ -0,0 +1,17 @@ +trainer: + devices: 8 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: bf16 # 16, 32, or bf16 + +model: + restore_from_path: null # Path to a trained ViT .nemo file + precision: ${trainer.precision} + micro_batch_size: 1000 + global_batch_size: 8000 + + data: + num_workers: 2 + imagenet_val: ??? # path to imagenet val folder + diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/conf/megatron_clip_infer.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/conf/megatron_clip_infer.yaml new file mode 100755 index 0000000..215cd17 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/conf/megatron_clip_infer.yaml @@ -0,0 +1,13 @@ +image_path: ??? # Path to a image for inference +texts: ??? # List of texts to compute similarity + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: 16 # 16, 32, or bf16 + +model: + restore_from_path: null # Path to a trained ViT .nemo file + precision: ${trainer.precision} diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/convert_external_clip_to_nemo.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/convert_external_clip_to_nemo.py new file mode 100644 index 0000000..631b3fa --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/convert_external_clip_to_nemo.py @@ -0,0 +1,276 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Usage example: + python /opt/NeMo/examples/multimodal/foundation/clip/convert_external_clip_to_nemo.py + --arch=ViT-H-14 + --version=laion2b_s32b_b79k + --hparams_file=path/to/saved.yaml + --nemo_file_path=open_clip.nemo + +If converting from OpenCLIP, specify the architecture (`arch`) and version (`version`) from the OpenCLIP model list (https://github.com/mlfoundations/open_clip#usage). + +If converting from Hugging Face, set the version to `huggingface` and the architecture (`arch`) to the Hugging Face model name (e.g., `yuvalkirstain/PickScore_v1`). + +Additionally, provide a NeMo hparams file with the correct model architecture arguments. Refer to examples/multimodal/foundation/clip/conf/megatron_clip_config.yaml. +""" + +import os +from argparse import ArgumentParser + +import einops +import open_clip +import torch +from omegaconf import OmegaConf +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.trainer.trainer import Trainer +from transformers import CLIPModel + +from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import MegatronCLIPModel +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector +from nemo.utils import AppState, logging +from nemo.utils.distributed import initialize_distributed + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +def get_args(): + parser = ArgumentParser() + parser.add_argument("--arch", type=str, default="openai/clip-vit-base-patch32") + parser.add_argument("--version", type=str, default="huggingface") + + parser.add_argument( + "--hparams_file", + type=str, + default=None, + required=False, + help="Path config for restoring. It's created during training and may need to be modified during restore if restore environment is different than training. Ex: /raid/nemo_experiments/megatron_gpt/hparams.yaml", + ) + parser.add_argument("--nemo_file_path", type=str, default=None, required=True, help="Path to output .nemo file.") + parser.add_argument("--gpus_per_node", type=int, required=False, default=1) + parser.add_argument("--tensor_model_parallel_size", type=int, required=False, default=1) + parser.add_argument("--pipeline_model_parallel_size", type=int, required=False, default=1) + parser.add_argument( + "--pipeline_model_parallel_split_rank", + type=int, + required=False, + default=None, + help="If pipeline parallel size > 1, this is the rank at which the encoder ends and the decoder begins.", + ) + parser.add_argument("--local_rank", type=int, required=False, default=os.getenv('LOCAL_RANK', -1)) + parser.add_argument("--bcp", action="store_true", help="Whether on BCP platform") + + args = parser.parse_args() + return args + + +def mapping_openclip_state_dict(open_model): + open_state_dict = open_model.state_dict() + key_mapping = { + "positional_embedding": "text_encoder.language_model.embedding.position_embeddings", + "token_embedding.weight": "text_encoder.language_model.embedding.word_embeddings.weight", + "ln_final.weight": "text_encoder.language_model.encoder.final_layernorm.weight", + "ln_final.bias": "text_encoder.language_model.encoder.final_layernorm.bias", + "text_projection": "text_encoder.head.weight", + } + layer_mapping = { + ".ln_1.weight": ".input_layernorm.weight", + ".ln_1.bias": ".input_layernorm.bias", + ".attn.in_proj_weight": ".self_attention.query_key_value.weight", + ".attn.in_proj_bias": ".self_attention.query_key_value.bias", + ".attn.out_proj.weight": ".self_attention.dense.weight", + ".attn.out_proj.bias": ".self_attention.dense.bias", + ".ln_2.weight": ".post_attention_layernorm.weight", + ".ln_2.bias": ".post_attention_layernorm.bias", + ".mlp.c_fc.weight": ".mlp.dense_h_to_4h.weight", + ".mlp.c_fc.bias": ".mlp.dense_h_to_4h.bias", + ".mlp.c_proj.weight": ".mlp.dense_4h_to_h.weight", + ".mlp.c_proj.bias": ".mlp.dense_4h_to_h.bias", + ".ln_pre.weight": ".preprocess_layernorm.weight", + ".ln_pre.bias": ".preprocess_layernorm.bias", + ".ln_post.weight": ".transformer.final_layernorm.weight", + ".ln_post.bias": ".transformer.final_layernorm.bias", + ".positional_embedding": ".position_embeddings", + ".backbone.proj": ".head.weight", + ".class_embedding": ".cls_token", + } + + nemo_state_dict = {} + for key in open_state_dict.keys(): + if key.startswith("transformer.resblocks."): + key_ = key.replace("transformer.resblocks.", "text_encoder.language_model.encoder.layers.") + elif key.startswith("visual.transformer.resblocks."): + key_ = key.replace("visual.transformer.resblocks.", "vision_encoder.backbone.transformer.layers.") + elif key.startswith('visual.'): + key_ = key.replace("visual.", "vision_encoder.backbone.") + else: + key_ = key + for pat in key_mapping: + if key_ == pat: + key_ = key_.replace(pat, key_mapping[pat]) + for pat in layer_mapping: + if key_.endswith(pat): + key_ = key_[: -len(pat)] + layer_mapping[pat] + break + nemo_state_dict[key_] = open_state_dict[key] + + nemo_state_dict["text_encoder.head.weight"] = nemo_state_dict["text_encoder.head.weight"].T + nemo_state_dict["vision_encoder.head.weight"] = nemo_state_dict["vision_encoder.head.weight"].T + nemo_state_dict["vision_encoder.backbone.cls_token"] = nemo_state_dict[ + "vision_encoder.backbone.cls_token" + ].reshape(1, 1, -1) + + return nemo_state_dict + + +def mapping_hf_state_dict(hf_model): + hf_state_dict = hf_model.state_dict() + key_mapping = { + "text_projection.weight": "text_encoder.head.weight", + "visual_projection.weight": "vision_encoder.head.weight", + } + + layer_mapping = { + ".layer_norm1.weight": ".input_layernorm.weight", + ".layer_norm1.bias": ".input_layernorm.bias", + ".self_attn.out_proj.weight": ".self_attention.dense.weight", + ".self_attn.out_proj.bias": ".self_attention.dense.bias", + ".layer_norm2.weight": ".post_attention_layernorm.weight", + ".layer_norm2.bias": ".post_attention_layernorm.bias", + ".mlp.fc1.weight": ".mlp.dense_h_to_4h.weight", + ".mlp.fc1.bias": ".mlp.dense_h_to_4h.bias", + ".mlp.fc2.weight": ".mlp.dense_4h_to_h.weight", + ".mlp.fc2.bias": ".mlp.dense_4h_to_h.bias", + ".pre_layrnorm.weight": ".preprocess_layernorm.weight", + ".pre_layrnorm.bias": ".preprocess_layernorm.bias", + ".post_layernorm.weight": ".transformer.final_layernorm.weight", + ".post_layernorm.bias": ".transformer.final_layernorm.bias", + ".backbone.embeddings.position_embedding.weight": ".backbone.position_embeddings.weight", + ".language_model.embeddings.position_embedding.weight": ".language_model.embedding.position_embeddings.weight", + ".embeddings.class_embedding": ".cls_token", + ".backbone.embeddings.patch_embedding.weight": ".backbone.conv1.weight", + ".final_layer_norm.weight": ".encoder.final_layernorm.weight", + ".final_layer_norm.bias": ".encoder.final_layernorm.bias", + ".embeddings.token_embedding.weight": ".embedding.word_embeddings.weight", + } + + nemo_state_dict = {} + for key in hf_state_dict.keys(): + if key.startswith("text_model.encoder.layers"): + key_ = key.replace("text_model.encoder.layers", "text_encoder.language_model.encoder.layers") + elif key.startswith("vision_model.encoder.layers"): + key_ = key.replace("vision_model.encoder.layers", "vision_encoder.backbone.transformer.layers") + elif key.startswith('vision_model.'): + key_ = key.replace("vision_model.", "vision_encoder.backbone.") + elif key.startswith('text_model.'): + key_ = key.replace('text_model.', 'text_encoder.language_model.') + else: + key_ = key + for pat in key_mapping: + if key_ == pat: + key_ = key_.replace(pat, key_mapping[pat]) + for pat in layer_mapping: + if key_.endswith(pat): + key_ = key_[: -len(pat)] + layer_mapping[pat] + break + if 'q_proj' in key_: + key_k = key.replace('q_proj', 'k_proj') + key_v = key.replace('q_proj', 'v_proj') + key_new = key_.replace('self_attn.q_proj', 'self_attention.query_key_value') + value_new = torch.concat((hf_state_dict[key], hf_state_dict[key_k], hf_state_dict[key_v]), dim=0) + nemo_state_dict[key_new] = value_new + elif not ('k_proj' in key_ or 'v_proj' in key_ or 'position_ids' in key_): + nemo_state_dict[key_] = hf_state_dict[key] + + nemo_state_dict["vision_encoder.backbone.cls_token"] = nemo_state_dict[ + "vision_encoder.backbone.cls_token" + ].reshape(1, 1, -1) + + return nemo_state_dict + + +def convert(local_rank, rank, world_size, args): + app_state = AppState() + app_state.data_parallel_rank = 0 + num_nodes = world_size // args.gpus_per_node + if args.bcp: + trainer = Trainer( + devices=args.gpus_per_node, num_nodes=num_nodes, accelerator='gpu', plugins=[TorchElasticEnvironment()] + ) + else: + trainer = Trainer(devices=args.gpus_per_node, num_nodes=num_nodes, accelerator='gpu') + + app_state.pipeline_model_parallel_size = args.pipeline_model_parallel_size + app_state.tensor_model_parallel_size = args.tensor_model_parallel_size + + # no use atm, use to split ranks in encoder/decoder models. + if args.pipeline_model_parallel_size > 1 and args.model_type in []: + if args.pipeline_model_parallel_split_rank is not None: + app_state.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_split_rank + else: + if args.pipeline_model_parallel_size % 2 != 0: + raise ValueError( + f"Pipeline model parallel size {args.pipeline_model_parallel_size} must be even if split rank is not specified." + ) + else: + # If split rank is not set, then we set it to be pipeline_model_parallel_size // 2 - this is because in most cases we have the same number of enc/dec layers. + app_state.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_size // 2 + else: + app_state.pipeline_model_parallel_split_rank = None + + app_state.model_parallel_size = app_state.tensor_model_parallel_size * app_state.pipeline_model_parallel_size + + parallel_state.initialize_model_parallel( + tensor_model_parallel_size=app_state.tensor_model_parallel_size, + pipeline_model_parallel_size=app_state.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank=app_state.pipeline_model_parallel_split_rank, + ) + + app_state.pipeline_model_parallel_rank = parallel_state.get_pipeline_model_parallel_rank() + app_state.tensor_model_parallel_rank = parallel_state.get_tensor_model_parallel_rank() + + cfg = OmegaConf.load(args.hparams_file) + model = MegatronCLIPModel(cfg.model, trainer) + + if args.version == "huggingface": + hf_model = CLIPModel.from_pretrained(args.arch) + state_dict = mapping_hf_state_dict(hf_model) + else: + open_model, _, _ = open_clip.create_model_and_transforms(args.arch, pretrained=args.version) + state_dict = mapping_openclip_state_dict(open_model) + + model.model.load_state_dict(state_dict) + + model._save_restore_connector = NLPSaveRestoreConnector() + + if torch.distributed.is_initialized(): + torch.distributed.barrier() + + model.save_to(args.nemo_file_path) + + logging.info(f'NeMo model saved to: {args.nemo_file_path}') + + +if __name__ == '__main__': + args = get_args() + local_rank, rank, world_size = initialize_distributed(args) + convert(local_rank, rank, world_size, args) diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/megatron_clip_imagenet_zeroshot.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/megatron_clip_imagenet_zeroshot.py new file mode 100644 index 0000000..ae481cf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/megatron_clip_imagenet_zeroshot.py @@ -0,0 +1,114 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn.functional as F +from omegaconf.omegaconf import OmegaConf, open_dict +from tqdm import tqdm + +from nemo.collections.multimodal.data.clip.clip_dataset import build_imagenet_validation_dataloader +from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import MegatronCLIPModel +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.collections.nlp.modules.common.megatron.utils import average_losses_across_data_parallel_group +from nemo.collections.nlp.parts.utils_funcs import torch_dtype_from_precision +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.get_rank import is_global_rank_zero + + +def accuracy(output, target, topk=(1,)): + pred = output.topk(max(topk), 1, True, True)[1].t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + return [float(correct[:k].reshape(-1).float().sum(0, keepdim=True).cpu().numpy()) for k in topk] + + +@hydra_runner(config_path="conf", config_name="megatron_clip_imagenet_zeroshot") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + # These configs are required to be off during inference. + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.vision.precision = cfg.trainer.precision + model_cfg.text.precision = cfg.trainer.precision + if cfg.trainer.precision != "bf16": + model_cfg.megatron_amp_O2 = False + model_cfg.sequence_parallel = False + model_cfg.activations_checkpoint_granularity = None + model_cfg.activations_checkpoint_method = None + + trainer, model = setup_trainer_and_model_for_inference( + model_provider=MegatronCLIPModel, cfg=cfg, model_cfg_modifier=model_cfg_modifier, + ) + + if model.cfg.get("megatron_amp_O2", False): + vision_encoder = model.model.module.vision_encoder + text_encoder = model.model.module.text_encoder + else: + vision_encoder = model.model.vision_encoder + text_encoder = model.model.text_encoder + + autocast_dtype = torch_dtype_from_precision(trainer.precision) + + with open_dict(cfg): + cfg.model["vision"] = model.cfg.vision + cfg.model["text"] = model.cfg.text + + imagenet_val = build_imagenet_validation_dataloader(cfg.model, model.tokenizer) + with torch.no_grad(), torch.cuda.amp.autocast( + enabled=autocast_dtype in (torch.half, torch.bfloat16), dtype=autocast_dtype, + ): + # build imagenet classification classifier + classifier = [] + for texts in imagenet_val["texts"]: + texts = texts.cuda(non_blocking=True) + class_embeddings = text_encoder(texts) + class_embedding = F.normalize(class_embeddings, dim=-1).mean(dim=0) + class_embedding /= class_embedding.norm() + classifier.append(class_embedding) + classifier = torch.stack(classifier, dim=1) + + top1, top5, n = 0.0, 0.0, 0.0 + for images, target in tqdm(imagenet_val["images"], desc="Imagenet Zero-shot Evaluation", leave=False): + if images is None or target is None: + continue + + images = images.cuda(non_blocking=True) + target = target.cuda(non_blocking=True) + # predict + image_features = vision_encoder(images) + image_features = F.normalize(image_features, dim=-1) + logits = 100.0 * image_features @ classifier + + # measure accuracy + acc1, acc5 = accuracy(logits, target, topk=(1, 5)) + top1 += acc1 + top5 += acc5 + n += images.size(0) + + logging.info('Finished zero-shot imagenet.') + top1 = top1 / n + top5 = top5 / n + + imagenet_metric = torch.zeros(2).cuda() + imagenet_metric[0], imagenet_metric[1] = top1, top5 + imagenet_metric = average_losses_across_data_parallel_group(imagenet_metric) + + if is_global_rank_zero: + logging.info(f"Zero-shot CLIP accuracy Top-1: {imagenet_metric[0]:.4f}; Top-5: {imagenet_metric[1]:.4f}") + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/megatron_clip_infer.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/megatron_clip_infer.py new file mode 100644 index 0000000..c99e7cb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/megatron_clip_infer.py @@ -0,0 +1,77 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from omegaconf.omegaconf import OmegaConf +from PIL import Image + +from nemo.collections.multimodal.data.clip.clip_dataset import get_preprocess_fns +from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import MegatronCLIPModel +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.collections.nlp.parts.utils_funcs import torch_dtype_from_precision +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.get_rank import is_global_rank_zero + + +@hydra_runner(config_path="conf", config_name="megatron_clip_infer") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + # These configs are required to be off during inference. + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.vision.precision = cfg.trainer.precision + model_cfg.text.precision = cfg.trainer.precision + if cfg.trainer.precision != "bf16": + model_cfg.megatron_amp_O2 = False + model_cfg.sequence_parallel = False + model_cfg.activations_checkpoint_granularity = None + model_cfg.activations_checkpoint_method = None + + trainer, model = setup_trainer_and_model_for_inference( + model_provider=MegatronCLIPModel, cfg=cfg, model_cfg_modifier=model_cfg_modifier, + ) + + if model.cfg.get("megatron_amp_O2", False): + vision_encoder = model.model.module.vision_encoder.eval() + text_encoder = model.model.module.text_encoder.eval() + else: + vision_encoder = model.model.vision_encoder.eval() + text_encoder = model.model.text_encoder.eval() + + val_image_transform, text_transform = get_preprocess_fns(model.cfg, model.tokenizer, is_train=False,) + + autocast_dtype = torch_dtype_from_precision(trainer.precision) + + image = Image.open(cfg.image_path).convert('RGB') + with torch.no_grad(), torch.cuda.amp.autocast( + enabled=autocast_dtype in (torch.half, torch.bfloat16), dtype=autocast_dtype, + ): + image = val_image_transform(image).unsqueeze(0).cuda() + texts = text_transform(cfg.texts).cuda() + image_features = vision_encoder(image) + text_features = text_encoder(texts) + image_features /= image_features.norm(dim=-1, keepdim=True) + text_features /= text_features.norm(dim=-1, keepdim=True) + + text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1) + + if is_global_rank_zero: + print(f"Given image's CLIP text probability: ", list(zip(cfg.texts, text_probs[0].cpu().numpy()))) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/megatron_clip_pretrain.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/megatron_clip_pretrain.py new file mode 100644 index 0000000..4462649 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/clip/megatron_clip_pretrain.py @@ -0,0 +1,48 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf + +from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import MegatronCLIPModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +mp.set_start_method("spawn", force=True) + + +@hydra_runner(config_path="conf", config_name="megatron_clip_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + assert ( + cfg.trainer.devices * cfg.trainer.num_nodes + ) * cfg.model.micro_batch_size == cfg.model.global_batch_size, ( + "Gradient accumulation is not supported in CLIP yet." + ) + + trainer = MegatronTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model = MegatronCLIPModel(cfg.model, trainer) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/nsfw/conf/megatron_nsfw_config.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/nsfw/conf/megatron_nsfw_config.yaml new file mode 100644 index 0000000..be820e8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/nsfw/conf/megatron_nsfw_config.yaml @@ -0,0 +1,230 @@ +name: megatron_clip +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + max_epochs: 10 + max_steps: 375000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + use_distributed_sampler: False + check_val_every_n_epoch: 1 + limit_val_batches: 1.0 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_nsfw + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_clip--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + ema: + enable: False + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 64 # limited by GPU memory + global_batch_size: 64 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + restore_from_pretrained: null # used in fine-tuning + # multimodal configs + output_dim: 768 + # As the number of devices used to train increases, so does the space complexity of + # the logit matrix. Using a naïve all-gather scheme, space complexity will be + # `O(n^2)`. Instead, complexity may become effectively linear if the flags + # `--gather-with-grad` and `--local-loss` are used. This alteration results in one-to-one + # numerical results as the naïve method. + local_loss: False # calculate loss w/ local features @ global (instead of realizing full global @ global matrix) + gather_with_grad: True # enable full distributed gradient for feature gather, set this to False may cause convergence issue + + vision: + precision: ${trainer.precision} + patch_dim: 14 + img_h: 224 + img_w: 224 + image_mean: null + image_std: null + num_channels: 3 + drop_patch_rate: 0.0 + drop_path_rate: 0.0 + global_average_pool: false + output_dim: ${model.output_dim} + class_token_length: 1 + preprocess_layernorm: true + encoder_seq_length: 196 + max_position_embeddings: 196 + position_embedding_type: learned_parameters + num_layers: 24 + hidden_size: 1024 + ffn_hidden_size: 4096 + num_attention_heads: 16 + init_method_std: 0.02 + use_scaled_init_method: true + hidden_dropout: 0.0 + attention_dropout: 0.0 + kv_channels: null + apply_query_key_layer_scaling: true + normalization: layernorm + layernorm_epsilon: 1.0e-05 + do_layer_norm_weight_decay: false + pre_process: true + post_process: true + persist_layer_norm: true + activations_checkpoint_granularity: null + activations_checkpoint_method: null + activations_checkpoint_num_layers: null + sequence_parallel: false + native_amp_init_scale: 4294967296 + native_amp_growth_interval: 1000 + hysteresis: 2 + fp32_residual_connection: false + fp16_lm_cross_entropy: false + masked_softmax_fusion: true + bias_dropout_add_fusion: true + use_cpu_initialization: false + onnx_safe: false + gradient_accumulation_fusion: false + openai_gelu: false + bias_activation_fusion: false + megatron_legacy: true + activation: approx-gelu + + text: + precision: ${trainer.precision} + # text configs + output_dim: ${model.output_dim} + + encoder_seq_length: 77 + max_position_embeddings: ${.encoder_seq_length} + position_embedding_type: learned_parameters + num_layers: 12 + hidden_size: 768 + ffn_hidden_size: 3072 + num_attention_heads: 12 + init_method_std: 0.02 + use_scaled_init_method: true + hidden_dropout: 0.0 + attention_dropout: 0.0 + kv_channels: null + apply_query_key_layer_scaling: true + normalization: layernorm + layernorm_epsilon: 1.0e-05 + do_layer_norm_weight_decay: false + pre_process: true + post_process: true + persist_layer_norm: true + activations_checkpoint_granularity: null + activations_checkpoint_method: null + activations_checkpoint_num_layers: null + num_micro_batches_with_partial_activation_checkpoints: null + activations_checkpoint_layers_per_pipeline: null + sequence_parallel: false + native_amp_init_scale: 4294967296 + native_amp_growth_interval: 1000 + hysteresis: 2 + fp32_residual_connection: false + fp16_lm_cross_entropy: false + masked_softmax_fusion: true + bias_dropout_add_fusion: true + use_cpu_initialization: false + onnx_safe: false + gradient_accumulation_fusion: false + openai_gelu: false + bias_activation_fusion: false + megatron_legacy: true + transformer_engine: false + fp8: false + fp8_e4m3: false + fp8_hybrid: false + fp8_margin: 0 + fp8_interval: 1 + fp8_amax_history_len: 1 + fp8_amax_compute_algo: most_recent + use_emha: false + activation: approx-gelu + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + sim_hidden_dim: 64 + cls_hidden_dim: 64 + + tokenizer: + library: 'huggingface' + type: 'openai/clip-vit-large-patch14' + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + + data: + num_workers: 8 + train: + dataset_path: /datasets/coyo/test.pkl + validation: # List of paths to pkl files or tar files + dataset_path: /datasets/coyo/test.pkl + webdataset: + infinite_sampler: False + local_root_path: /datasets/coyo + + imagenet_val: null # Path to imagenet val set for conducting zero shot evaluation. + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: adam + lr: 1e-3 + weight_decay: 0.0 + sched: + name: CosineAnnealing + warmup_steps: 200 + constant_steps: 0 + min_lr: 1e-5 + concepts: ??? + diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/nsfw/conf/megatron_nsfw_infer.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/nsfw/conf/megatron_nsfw_infer.yaml new file mode 100755 index 0000000..f78eba0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/nsfw/conf/megatron_nsfw_infer.yaml @@ -0,0 +1,12 @@ +image_path: ??? # Path to a image for inference + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: 16 # 16, 32, or bf16 + +model: + restore_from_path: null # Path to a trained ViT .nemo file + precision: ${trainer.precision} diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/nsfw/megatron_nsfw_infer.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/nsfw/megatron_nsfw_infer.py new file mode 100644 index 0000000..d16730d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/nsfw/megatron_nsfw_infer.py @@ -0,0 +1,78 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from omegaconf.omegaconf import OmegaConf +from PIL import Image + +from nemo.collections.multimodal.data.clip.augmentations.augmentations import image_transform +from nemo.collections.multimodal.models.vision_language_foundation.megatron_nsfw_clip_models import ( + MegatronContentFilteringModel, +) +from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.get_rank import is_global_rank_zero + + +def _get_autocast_dtype(precision: str): + if precision in ["bf16", "bf16-mixed"]: + return torch.bfloat16 + if precision in [32, "32", "32-true"]: + return torch.float + if precision in [16, "16", "16-mixed"]: + return torch.half + raise ValueError('precision must be in ["32-true", "16-mixed", "bf16-mixed"]') + + +@hydra_runner(config_path="conf", config_name="megatron_nsfw_infer") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + # These configs are required to be off during inference. + def model_cfg_modifier(model_cfg): + model_cfg.precision = cfg.trainer.precision + model_cfg.vision.precision = cfg.trainer.precision + if cfg.trainer.precision != "bf16": + model_cfg.megatron_amp_O2 = False + model_cfg.sequence_parallel = False + model_cfg.activations_checkpoint_granularity = None + model_cfg.activations_checkpoint_method = None + + trainer, model = setup_trainer_and_model_for_inference( + model_provider=MegatronContentFilteringModel, cfg=cfg, model_cfg_modifier=model_cfg_modifier, + ) + image_transform_fn = image_transform( + (model.cfg.vision.img_h, model.cfg.vision.img_w), + is_train=False, + mean=model.cfg.vision.image_mean, + std=model.cfg.vision.image_std, + resize_longest_max=True, + ) + + autocast_dtype = _get_autocast_dtype(trainer.precision) + image = Image.open(cfg.image_path).convert('RGB') + with torch.no_grad(), torch.cuda.amp.autocast( + enabled=autocast_dtype in (torch.half, torch.bfloat16), dtype=autocast_dtype, + ): + image = image_transform_fn(image).unsqueeze(0).cuda() + probability = model(image).sigmoid() + + if is_global_rank_zero: + print("Given image's NSFW probability: ", probability.cpu().item()) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/nsfw/megatron_nsfw_pretrain.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/nsfw/megatron_nsfw_pretrain.py new file mode 100644 index 0000000..a99a2c3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/vision_language_foundation/nsfw/megatron_nsfw_pretrain.py @@ -0,0 +1,58 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from omegaconf.omegaconf import OmegaConf + +from nemo.collections.multimodal.models.vision_language_foundation.megatron_nsfw_clip_models import ( + MegatronContentFilteringModel, +) +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="megatron_nsfw_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + assert ( + cfg.trainer.devices * cfg.trainer.num_nodes + ) * cfg.model.micro_batch_size == cfg.model.global_batch_size, ( + "Gradient accumulation is not supported in CLIP yet." + ) + + trainer = MegatronTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model = MegatronContentFilteringModel.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + override_config_path=cfg.model, + save_restore_connector=NLPSaveRestoreConnector(), + strict=False, + ) + + trainer.fit(model) + + if "save_path" in cfg.model: + logging.info(f"Saving model to path: {cfg.model.save_path}") + model.save_to(cfg.model.save_path) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/benchmark_callback.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/benchmark_callback.py new file mode 100644 index 0000000..fd7d5af --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/benchmark_callback.py @@ -0,0 +1,96 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import time +from typing import Optional + +from pytorch_lightning import Callback, LightningModule, Trainer + +from nemo.utils import logging + + +class BenchmarkCallback(Callback): + def __init__( + self, + start_benchmark_at_step: int = 0, + stop_benchmark_at_step: Optional[int] = None, + log_every_n_steps: int = 10, + ): + super().__init__() + self.start_benchmark_at_step = start_benchmark_at_step + self.stop_benchmark_at_step = stop_benchmark_at_step + self.log_every_n_steps = log_every_n_steps + self.train_times = [] + self.val_times = [] + self.train_steps_times = [] + self.val_steps_times = [] + + def should_benchmark(self, trainer: Trainer): + if self.stop_benchmark_at_step is None: + return trainer.global_step >= self.start_benchmark_at_step + return self.start_benchmark_at_step <= trainer.global_step <= self.stop_benchmark_at_step + + def on_train_epoch_start(self, trainer: Trainer, pl_module: LightningModule): + self.epoch_start_time = time.time() + + def on_train_epoch_end(self, trainer: Trainer, pl_module: LightningModule): + if self.should_benchmark(trainer): + epoch_time = time.time() - self.epoch_start_time + self.train_times.append(epoch_time) + logging.info(f'Training-Epoch-{trainer.current_epoch}-Time: {epoch_time} [sec]') + + def on_train_batch_start(self, trainer: Trainer, pl_module: LightningModule, batch, batch_idx: int): + self.step_start_time = time.time() + + def on_train_batch_end(self, trainer: Trainer, pl_module: LightningModule, outputs, batch, batch_idx: int): + if self.should_benchmark(trainer): + step_time = time.time() - self.step_start_time + self.train_steps_times.append(step_time) + if trainer.global_step % self.log_every_n_steps == 0: + logging.info(f'Training-Step-{trainer.global_step}-Time: {step_time} [sec]') + + def on_validation_epoch_start(self, trainer: Trainer, pl_module: LightningModule): + self.val_start_time = time.time() + + def on_validation_epoch_end(self, trainer: Trainer, pl_module: LightningModule): + if self.should_benchmark(trainer): + val_time = time.time() - self.val_start_time + self.val_times.append(val_time) + logging.info(f'Validation-Epoch-{trainer.current_epoch}-Time: {val_time} [sec]') + + def on_validation_batch_start( + self, trainer: Trainer, pl_module: LightningModule, batch, batch_idx: int, dataloader_idx: int + ): + self.val_step_start_time = time.time() + + def on_validation_batch_end( + self, trainer: Trainer, pl_module: LightningModule, outputs, batch, batch_idx: int, dataloader_idx: int + ): + if self.should_benchmark(trainer): + val_step_time = time.time() - self.val_step_start_time + self.val_steps_times.append(val_step_time) + if trainer.global_step % self.log_every_n_steps == 0: + logging.info(f'Validation-Step-{trainer.global_step}-Time: {val_step_time} [sec]') + + def on_fit_end(self, trainer: Trainer, pl_module: LightningModule): + if self.should_benchmark(trainer): + avg_train_time = sum(self.train_times) / len(self.train_times) + avg_val_time = sum(self.val_times) / len(self.val_times) + avg_train_step_time = sum(self.train_steps_times) / len(self.train_steps_times) + avg_val_step_time = sum(self.val_steps_times) / len(self.val_steps_times) + + logging.info(f'Average-Training-Epoch-Time: {avg_train_time} [sec]') + logging.info(f'Average-Validation-Epoch-Time: {avg_val_time} [sec]') + logging.info(f'Average-Training-Step-Time: {avg_train_step_time} [sec]') + logging.info(f'Average-Validation-Step-Time: {avg_val_step_time} [sec]') diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/config.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/config.yaml new file mode 100644 index 0000000..1adcbae --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/config.yaml @@ -0,0 +1,52 @@ +defaults: + - model: dreamfusion + - _self_ + +name: DreamFusion +seed: 2023 +mode: fit # fit, validate, test, export-mesh + +# export-mesh options +mesh_fname: /results/mesh.obj # mesh file name when mode=export-mesh +mesh_resolution: 128 # Mesh resolution when mode=export-mesh + +# benchmark options +enable_benchmark: False +benchmark_callback: + _target_: benchmark_callback.BenchmarkCallback + log_every_n_steps: 1 + +trainer: + devices: 1 + num_nodes: 1 + precision: 16 + max_steps: 10000 # example configs: dreamfuions=10000, dmtet=5000 + accelerator: gpu + enable_checkpointing: False + logger: False + log_every_n_steps: 1 + val_check_interval: 100 + accumulate_grad_batches: 1 + benchmark: False + enable_model_summary: True + +exp_manager: + name: ${name} + exp_dir: /results + create_tensorboard_logger: False + create_wandb_logger: False + wandb_logger_kwargs: + project: dreamfusion + group: nemo-df + name: ${name} + resume: True + create_checkpoint_callback: True + checkpoint_callback_params: + every_n_epochs: 0 + every_n_train_steps: 1000 # TODO(ahmadki): being ignored ? + monitor: loss + filename: '${name}-{step}' + save_top_k: -1 + always_save_nemo: False + resume_if_exists: True + resume_ignore_no_checkpoint: True diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/background/random.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/background/random.yaml new file mode 100644 index 0000000..9cfb09f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/background/random.yaml @@ -0,0 +1,3 @@ +_target_: nemo.collections.multimodal.modules.nerf.background.random_background.RandomBackground +base_background: [1, 1, 1] +random_ratio: 0.5 diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/background/static.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/background/static.yaml new file mode 100644 index 0000000..eb82f99 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/background/static.yaml @@ -0,0 +1,2 @@ +_target_: nemo.collections.multimodal.modules.nerf.background.static_background.StaticBackground +background: [0, 0, 1] # rgb diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/background/tcnn.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/background/tcnn.yaml new file mode 100644 index 0000000..8daf7bc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/background/tcnn.yaml @@ -0,0 +1,19 @@ +_target_: nemo.collections.multimodal.modules.nerf.background.tcnn_background.TCNNBackground +bound: 1 +encoder_num_input_dims: 3 # 3 directions +encoder_cfg: + otype: "HashGrid" + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + interpolation: "Smoothstep" + per_level_scale: # default is np.exp2(np.log2(2048 * bound / 16) / (16 - 1)) + +background_net_num_output_dims: 3 # rgb +background_net_cfg: + otype: "FullyFusedMLP" + activation: "ReLU" + output_activation: "None" + n_neurons: 32 + n_hidden_layers: 2 diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/background/torchngp.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/background/torchngp.yaml new file mode 100644 index 0000000..b777780 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/background/torchngp.yaml @@ -0,0 +1,11 @@ +_target_: nemo.collections.multimodal.modules.nerf.background.torchngp_background.TorchNGPBackground + +encoder_type: "frequency" +encoder_input_dims: 3 +encoder_multi_res: 6 + +num_output_dims: 3 +net_cfg: + num_hidden_dims: 32 + num_layers: 2 + bias: True diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/data/data.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/data/data.yaml new file mode 100644 index 0000000..0b5f88b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/data/data.yaml @@ -0,0 +1,41 @@ +_target_: data.AggregatorDataModule + +train_batch_size: 1 +train_shuffle: false +train_dataset: + _target_: nemo.collections.multimodal.data.nerf.random_poses.RandomPosesDataset + internal_batch_size: 100 + width: 64 + height: 64 + radius_range: [3.0, 3.5] + theta_range: [45, 105] + phi_range: [-180, 180] + fovx_range: [10, 30] + fovy_range: [10, 30] + jitter: False + jitter_center: 0.2 + jitter_target: 0.2 + jitter_up: 0.02 + uniform_sphere_rate: 0 + angle_overhead: 30 + angle_front: 60 + +val_batch_size: 1 +val_shuffle: false +val_dataset: + _target_: nemo.collections.multimodal.data.nerf.circle_poses.CirclePosesDataset + size: 5 + width: 800 + height: 800 + angle_overhead: 30 + angle_front: 60 + +test_batch_size: 1 +test_shuffle: false +test_dataset: + _target_: nemo.collections.multimodal.data.nerf.circle_poses.CirclePosesDataset + size: 100 + width: 800 + height: 800 + angle_overhead: 30 + angle_front: 60 diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/dreamfusion-dmtet.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/dreamfusion-dmtet.yaml new file mode 100644 index 0000000..bfadd4f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/dreamfusion-dmtet.yaml @@ -0,0 +1,40 @@ +_target_: nemo.collections.multimodal.models.nerf.dreamfusion.DreamFusion # TODO(ahmadki): dreamfusion-dmetet should have it's own class +defaults: + - nerf: torchngp + - background: torchngp + - material: basic_shading + - renderer: nvdiffrast + - guidance: sd_huggingface + - optim: adan + - loss: dmtet + - data: data + - _self_ + +### model options +resume_from_checkpoint: +prompt: 'a hamburger' +negative_prompt: '' +front_prompt: ', front view' +side_prompt: ', side view' +back_prompt: ', back view' +update_extra_interval: 16 +guidance_scale: 100 +export_video: False + +iters: ${trainer.max_steps} +# TODO(ahmadki): move to database +latent_iter_ratio: 0.0 +albedo_iter_ratio: 0 +min_ambient_ratio: 0.1 +textureless_ratio: 0.2 + +data: + train_dataset: + width: 512 + height: 512 + val_dataset: + width: 800 + height: 800 + test_dataset: + width: 800 + height: 800 diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/dreamfusion.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/dreamfusion.yaml new file mode 100644 index 0000000..a673933 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/dreamfusion.yaml @@ -0,0 +1,40 @@ +_target_: nemo.collections.multimodal.models.nerf.dreamfusion.DreamFusion +defaults: + - nerf: torchngp + - background: static + - material: basic_shading + - renderer: torchngp_raymarching + - guidance: sd_huggingface + - optim: adan + - loss: dreamfusion + - data: data + - _self_ + +### model options +resume_from_checkpoint: +prompt: 'a hamburger' +negative_prompt: '' +front_prompt: ', front view' +side_prompt: ', side view' +back_prompt: ', back view' +update_extra_interval: 16 +guidance_scale: 100 +export_video: False + +iters: ${trainer.max_steps} +# TODO(ahmadki): move to database +latent_iter_ratio: 0.2 +albedo_iter_ratio: 0.0 +min_ambient_ratio: 0.1 +textureless_ratio: 0.2 + +data: + train_dataset: + width: 64 + height: 64 + val_dataset: + width: 800 + height: 800 + test_dataset: + width: 800 + height: 800 diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/guidance/sd_huggingface.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/guidance/sd_huggingface.yaml new file mode 100644 index 0000000..a8b7adc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/guidance/sd_huggingface.yaml @@ -0,0 +1,4 @@ +_target_: nemo.collections.multimodal.modules.nerf.guidance.stablediffusion_huggingface_pipeline.StableDiffusion +precision: ${trainer.precision} +model_key: stabilityai/stable-diffusion-2-1-base +t_range: [0.02, 0.98] diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/guidance/sd_nemo.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/guidance/sd_nemo.yaml new file mode 100644 index 0000000..fd4517e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/guidance/sd_nemo.yaml @@ -0,0 +1,4 @@ +_target_: nemo.collections.multimodal.modules.nerf.guidance.stablediffusion_nemo_pipeline.StableDiffusion +checkpoint: /sd_checkpoints/nemo-1.5/sd-1.5.nemo +sampler_type: 'DDIM' +t_range: [0.02, 0.98] diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/guidance/sd_trt.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/guidance/sd_trt.yaml new file mode 100644 index 0000000..45c1e2a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/guidance/sd_trt.yaml @@ -0,0 +1,5 @@ +_target_: nemo.collections.multimodal.modules.nerf.guidance.stablediffusion_trt_pipeline.StableDiffusion +checkpoint: /sd_checkpoints/nemo-1.5/sd-1.5.nemo +plan_dir: /sd_checkpoints/nemo-1.5/plan +sampler_type=: DDIM" +t_range: [0.02, 0.98] diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/loss/dmtet.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/loss/dmtet.yaml new file mode 100644 index 0000000..188c103 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/loss/dmtet.yaml @@ -0,0 +1,8 @@ +lambda_sds: 1.0 +lambda_opacity: 0.0 +lambda_entropy: 0.0 +lambda_orientation: 0.0 +lambda_2d_normal_smooth: 0.0 +lambda_3d_normal_smooth: 0.0 +lambda_mesh_normal: 0.5 +lambda_mesh_laplacian: 0.5 diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/loss/dreamfusion.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/loss/dreamfusion.yaml new file mode 100644 index 0000000..8cfd4b4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/loss/dreamfusion.yaml @@ -0,0 +1,8 @@ +lambda_sds: 1.0 +lambda_opacity: 0.0 +lambda_entropy: 1e-3 +lambda_orientation: 1e-2 +lambda_2d_normal_smooth: 0.0 +lambda_3d_normal_smooth: 0.0 +lambda_mesh_normal: 0.0 +lambda_mesh_laplacian: 0.0 diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/material/basic_shading.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/material/basic_shading.yaml new file mode 100644 index 0000000..802defa --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/material/basic_shading.yaml @@ -0,0 +1 @@ +_target_: nemo.collections.multimodal.modules.nerf.materials.basic_shading.BasicShading diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/nerf/tcnn.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/nerf/tcnn.yaml new file mode 100644 index 0000000..0bf5ed6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/nerf/tcnn.yaml @@ -0,0 +1,32 @@ +_target_: nemo.collections.multimodal.modules.nerf.geometry.tcnn_nerf.TCNNNerf +num_input_dims: 3 # 3D space +bound: 1 +density_activation: softplus # softplus, exp +blob_radius: 0.5 +blob_density: 10 +normal_type: central_finite_difference + +encoder_cfg: + otype: "HashGrid" + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + interpolation: "Smoothstep" + per_level_scale: # default is np.exp2(np.log2(2048 * bound / 16) / (16 - 1)) + +sigma_net_num_output_dims: 1 # density +sigma_net_cfg: + otype: "FullyFusedMLP" + activation: "ReLU" + output_activation: "None" + n_neurons: 64 + n_hidden_layers: 3 + +features_net_num_output_dims: 3 # rgb +features_net_cfg: + otype: "FullyFusedMLP" + activation: "ReLU" + output_activation: "None" + n_neurons: 64 + n_hidden_layers: 3 diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/nerf/torchngp.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/nerf/torchngp.yaml new file mode 100644 index 0000000..48877dc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/nerf/torchngp.yaml @@ -0,0 +1,26 @@ +_target_: nemo.collections.multimodal.modules.nerf.geometry.torchngp_nerf.TorchNGPNerf +num_input_dims: 3 # 3D space +bound: 1 +density_activation: exp # softplus, exp +blob_radius: 0.2 +blob_density: 5 +normal_type: central_finite_difference + +encoder_cfg: + encoder_type: 'hashgrid' + encoder_max_level: + log2_hashmap_size: 19 + desired_resolution: 2048 + interpolation: smoothstep + +sigma_net_num_output_dims: 1 # density +sigma_net_cfg: + num_hidden_dims: 64 + num_layers: 3 + bias: True + +features_net_num_output_dims: 3 # rgb +features_net_cfg: + num_hidden_dims: 64 + num_layers: 3 + bias: True diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/optim/adan.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/optim/adan.yaml new file mode 100644 index 0000000..885c13f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/optim/adan.yaml @@ -0,0 +1,6 @@ +name: adan +lr: 5e-3 +eps: 1e-8 +weight_decay: 2e-5 +max_grad_norm: 5.0 +foreach: False diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/renderer/nerfacc.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/renderer/nerfacc.yaml new file mode 100644 index 0000000..73f48a7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/renderer/nerfacc.yaml @@ -0,0 +1,8 @@ +_target_: nemo.collections.multimodal.modules.nerf.renderers.nerfacc_volume_renderer.NerfaccVolumeBaseRenderer +grid_resolution: 128 +grid_levels: 3 +bound: ${model.nerf.bound} +render_step_size: 1.e-3 +near_plane: 0.2 +cone_angle: 0.004 +alpha_thre: 1.e-2 diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/renderer/nvdiffrast.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/renderer/nvdiffrast.yaml new file mode 100644 index 0000000..fefc217 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/renderer/nvdiffrast.yaml @@ -0,0 +1,6 @@ +_target_: nemo.collections.multimodal.modules.nerf.renderers.nvdiffrast_renderer.NVDiffRastRenderer +bound: ${model.nerf.bound} +grid_resolution: 128 +density_thresh: 10.0 +update_interval: 16 +quartet_file: "/results/tets/128_tets.npz" diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/renderer/torchngp_raymarching.yaml b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/renderer/torchngp_raymarching.yaml new file mode 100644 index 0000000..5075a5f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/config/model/renderer/torchngp_raymarching.yaml @@ -0,0 +1,7 @@ +_target_: nemo.collections.multimodal.modules.nerf.renderers.torchngp_volume_renderer.TorchNGPVolumeRenderer +bound: ${model.nerf.bound} +update_interval: 16 +grid_resolution: 128 +density_thresh: 10 +max_steps: 1024 +dt_gamma: 0 diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/data.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/data.py new file mode 100644 index 0000000..fe7c47a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/data.py @@ -0,0 +1,86 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl +from omegaconf.omegaconf import DictConfig +from torch.utils.data import DataLoader + + +# TODO(ahmadki): multi-GPU needs more work, we currently don't shard data +# across GPUs, which is OK for trainnig, but needs fixing for validation and testing. +class AggregatorDataModule(pl.LightningDataModule): + def __init__( + self, + train_dataset: DictConfig = None, + train_batch_size: int = 1, + train_shuffle: bool = False, + val_dataset: DictConfig = None, + val_batch_size: int = 1, + val_shuffle: bool = False, + test_dataset: DictConfig = None, + test_batch_size: int = 1, + test_shuffle: bool = False, + ): + super().__init__() + + self.train_dataset = train_dataset + self.train_batch_size = train_batch_size + self.train_shuffle = train_shuffle + self.val_dataset = val_dataset + self.val_batch_size = val_batch_size + self.val_shuffle = val_shuffle + self.test_dataset = test_dataset + self.test_batch_size = test_batch_size + self.test_shuffle = test_shuffle + + # TODO(ahmadki): lazy init + # def setup(self, stage=None) -> None: + # if stage in [None, "fit"]: + # self.train_dataset = instantiate(self.train_dataset) + # if stage in [None, "fit", "validate"]: + # self.val_dataset = instantiate(self.val_dataset) + # if stage in [None, "test", "predict"]: + # self.test_dataset = instantiate(self.test_dataset) + + def train_dataloader(self) -> DataLoader: + loader = DataLoader( + self.train_dataset, + batch_size=self.train_batch_size, + collate_fn=self.train_dataset.collate_fn, + pin_memory=True, + num_workers=4, + ) + return loader + + def val_dataloader(self) -> DataLoader: + loader = DataLoader( + self.val_dataset, + batch_size=self.val_batch_size, + collate_fn=self.val_dataset.collate_fn, + shuffle=self.val_shuffle, + pin_memory=True, + num_workers=0, + ) + return loader + + def test_dataloader(self) -> DataLoader: + loader = DataLoader( + self.test_dataset, + batch_size=self.test_batch_size, + collate_fn=self.test_dataset.collate_fn, + shuffle=self.test_shuffle, + pin_memory=True, + num_workers=0, + ) + return loader diff --git a/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/main.py b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/main.py new file mode 100644 index 0000000..5d7f616 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/multimodal/x_to_nerf/main.py @@ -0,0 +1,70 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from hydra.utils import get_class, instantiate +from omegaconf.omegaconf import DictConfig, OmegaConf +from pytorch_lightning import Trainer, seed_everything + +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path='config', config_name='config') +def main(cfg: DictConfig) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + seed_everything(cfg.seed, workers=True) + + mode = cfg.mode + logging.info(f"{mode=}") + + model = None + model_cls = get_class(cfg.model._target_) + if cfg.model.resume_from_checkpoint is None: + model = model_cls(cfg=cfg.model) + else: + logging.info(f"Loading model from checkpoint: {cfg.model.resume_from_checkpoint}") + model = model_cls.load_from_checkpoint(cfg.model.resume_from_checkpoint, strict=False, cfg=cfg.model) + + if mode == "export-mesh": + mesh = model.mesh(resolution=cfg.mesh_resolution) + mesh.export(cfg.mesh_fname) + return + + # Prepare callbacks + callbacks = [] + if cfg.enable_benchmark: + callbacks.append(instantiate(cfg.benchmark_callback)) + + # Setup trainer + trainer = Trainer(callbacks=callbacks, **cfg.trainer) + exp_manager(trainer, cfg.exp_manager) + + # Setup datamodule + dm = instantiate(cfg.model.data) + + if mode == "fit": + trainer.fit(model, datamodule=dm) + elif mode == "validate": + trainer.validate(model, datamodule=dm) + elif mode == "test": + trainer.test(model, datamodule=dm) + else: + raise ValueError(f"Invalid mode: {mode}") + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/dialogue/analyse_prediction_results.py b/NeMo-2.0.0.rc0.beta/examples/nlp/dialogue/analyse_prediction_results.py new file mode 100644 index 0000000..b97e886 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/dialogue/analyse_prediction_results.py @@ -0,0 +1,112 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import json +import re + +import numpy as np + +from nemo.collections.nlp.metrics.dialogue_metrics import DialogueGenerationMetrics + + +def read_jsonl(filename): + with open(filename, 'r', encoding="UTF-8") as f: + docs = [json.loads(line) for line in f.readlines()] + return docs + + +def get_incorrect_labels(docs): + incorrect_labels_docs = [] + for doc in docs: + if doc["ground_truth_labels"] != doc["generated_labels"]: + incorrect_labels_docs.append( + { + "input": doc["input"], + "ground_truth_labels": doc["ground_truth_labels"], + "generated_labels": doc["generated_labels"], + } + ) + return incorrect_labels_docs + + +def get_incorrect_slots(docs): + incorrect_slots_docs = [] + for doc in docs: + if doc["ground_truth_slots"] != doc["generated_slots"]: + incorrect_slots_docs.append( + { + "input": doc["input"], + "ground_truth_slots": doc["ground_truth_slots"], + "generated_slots": doc["generated_slots"], + } + ) + return incorrect_slots_docs + + +def sort_by_f1(docs): + for i in range(len(docs)): + doc = docs[i] + generated_field = doc["generated"] + ground_truth_field = doc["ground_truth"] + generated_field = remove_punctation(generated_field.lower()) + ground_truth_field = remove_punctation(ground_truth_field.lower()) + p, r, f1 = DialogueGenerationMetrics._get_one_f1(generated_field, ground_truth_field) + docs[i]["f1"] = f1 + docs[i]["generated"] = generated_field + docs[i]["ground_truth"] = ground_truth_field + docs.sort(key=lambda x: x["f1"]) + return docs + + +def remove_punctation(sentence): + return re.sub(r'[^\w\s]', '', sentence) + + +def generation_main(filename): + docs = read_jsonl(filename) + docs = sort_by_f1(docs) + bleu = DialogueGenerationMetrics.get_bleu( + [doc["generated"] for doc in docs], [doc["ground_truth"] for doc in docs] + ) + acc = np.mean([int(doc["generated"] == doc["ground_truth"]) for doc in docs]) * 100 + f1 = np.mean([doc["f1"] for doc in docs]) + print("Token level F1 is {:.3}".format(f1)) + print("BLEU is {:.3}".format(bleu)) + print("Exact match accuracy is {:.3}".format(acc)) + for i in range(0): + print(docs[i]) + + +def classification_main(filename): + docs = read_jsonl(filename) + incorrect_labels_docs = get_incorrect_labels(docs) + incorrect_slots_docs = get_incorrect_slots(docs) + + print("{} / {} have incorrect labels".format(len(incorrect_labels_docs), len(docs))) + print("{} / {} have incorrect slots".format(len(incorrect_slots_docs), len(docs))) + + for doc in incorrect_labels_docs: + print(doc) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--prediction_filename") + parser.add_argument("--mode", choices=['generation', 'classification'], default='classification') + args = parser.parse_args() + if args.mode == 'classification': + classification_main(args.prediction_filename) + else: + generation_main(args.prediction_filename) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/dialogue/conf/dialogue_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/dialogue/conf/dialogue_config.yaml new file mode 100644 index 0000000..6af9b5d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/dialogue/conf/dialogue_config.yaml @@ -0,0 +1,205 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +pretrained_model: null # pretrained model from list_available_models() +do_training: true # true for training mode, false for testing +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: 3 + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + gradient_clip_val: 1.0 + precision: 16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + +model: + # all models + tensor_model_parallel_size: 1 + nemo_path: null # filename to save the model and associated artifacts to .nemo file + library: huggingface # [huggingface, megatron] + save_model: False # save validation model checkpoints + + language_model: + pretrained_model_name: gpt2 # main config to select model (between bert, gpt2, t5/bart based models) see docs/source/nlp/dialogue.rst for full list of options + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + + # Dialogue GPT Classification/Generation and Dialogue S2S Generation Model args + tokens_to_generate: 32 # for generation mode only + + # Intent Slot Classification model args + class_balancing: ${model.dataset.class_balancing} + intent_loss_weight: 0.6 # relation of intent to slot loss in total loss (between 0 to 1) + data_dir: ${model.dataset.data_dir} + classifier_head: + num_output_layers: 2 + fc_dropout: 0.1 + + # Dialogue GPT Classification Megatron Prompt Learning model args + prompt_learning: false # please change to true to activate prompt learning + language_model_path: ${model.language_model.lm_checkpoint} + new_tasks: ['intent_and_slot'] + prompt_tuning: + new_prompt_init_methods: ['text'] + new_prompt_init_text: ['intent_and_slot'] + p_tuning: # P-tuning specific params + dropout: 0.0 + num_layers: 2 + encoder_type: mlp # lstm or tpmlp or embedding + prompt_learning_nemo_path: prompt_learning.nemo + data: {} + virtual_prompt_style: 'p-tuning' # 'prompt-tuning' + encoder_seq_length: 2048 + pipeline_model_parallel_size: 1 + data_parallel_size: 1 + global_batch_size: 8 + micro_batch_size: 8 + + task_templates: + - taskname: "intent_and_slot" + prompt_template: "<|VIRTUAL_PROMPT_0|> {utterance} \nintent: {intent} \nslot: {slot}" + total_virtual_tokens: 10 + answer_only_loss: True + virtual_token_splits: [10] + truncate_field: null + + # SGDQA args + encoder: + dropout: 0.1 + + # Zero Shot Intent Model args + original_nemo_checkpoint: null ## cannot directly load as .nemo uses the pre-refactor model, therefore transfer its attributes over + + dataset: + + ## All tasks/models + data_dir: ??? # location to load data from + dialogues_example_dir: ??? # store prediction files + task: sgd # [sgd, assistant, zero_shot, ms_marco, sgd_generation, design, mellon_qa] + debug_mode: false # small number of examples for debugging + max_seq_length: 128 # the maximum number of tokens per sample + + ## Dialogue S2S and GPT Generation Model params + input_field: utterance+response # passage+utterance, utterance, response, utterance+response, system_actions + output_field: fluent_response # response, fluent_response, system_utterance + + ## Dialogue GPT Classification Model params + field: intent # [intent, slots, service] + few_shot: 0 # int ; 0 to 10, for number of examples in prompt + eval_mode: ranking # ranking or generation or binary_score + binary_score_subsample: false # subsample negative examples for binary score training + binary_score_subsample_ratio: 2 # number of negative examples per postive example + prompt_template: default # default, prompt_tuning, i_want_to # "This example is" for zeroshotintentmodel #acts_slots_values, slots_values, values for DialogueS2SGenerationDataset + target_template: default # default, with_description, with_slots + + ## SGD task specific params + system_utterance: prev_turn # prev_turn, next_turn: prev_turn (default for sgdqa) takes the system utterance that precede the user utterance; next_turn (for sgd_generation) takes the system utterance that follows the user utterance + num_tasks: 1 # number of task heads 1 for DialogGPTClassification and 6 for SGDQA + + ## SGD and Zero Shot task specific params + preprocess_intent_function: default # default, lowercase, description # remove_domain for zero_shot task + + ## SGDQA model specific params + subsample: false # balances negative and positive training examples for improved performance + task_name: sgd_single_domain # or from [sgd_all, sgd_all_single, sgd_multi_domain, debug_sample] + state_tracker: nemotracker # or baseline + use_cache: false # uses a cache to store the processed dataset, you may use it for large datasets for speed up + use_fuzzy_match: true # Whether to use fuzzy string matching when comparing non-categorical slot values. Should be set to False when conducting multiwoz style evaluation. + joint_acc_across_turn: false # Whether to compute joint goal accuracy across turn instead of across service. Should be set to True when conducting multiwoz style evaluation. + max_num_cat_slot: 6 # maximum number of different categorical slots per service in dataset + max_num_noncat_slot: 12 # maximum number of different non-categorical slots per service in dataset + max_value_per_cat_slot: 12 # maximum number of different categorical slot values per service in dataset + max_num_intent: 4 # maximum number of different intents per service in dataset + num_samples: -1 # restrict num_samples to an int value, if -1 all samples will be used + pad_label: -1 # if -1 not slot token will be used + ignore_extra_tokens: false + ignore_start_end: true # do not use first and last token for slot training + do_lowercase: false + + #Zero Shot Intent Model args + class_balancing: null # or weighted_loss + num_classes: 3 + + # Mellon QA, MS Marco and Design task + dev_proportion: 10 # These datasets do not have a dedicated dev set, therefore need to split train into a new train and dev. Indicate an integer (5-90) for the proporton for dev set + + train_ds: + ds_item: "train" + prefix: train + batch_size: 16 + shuffle: true + num_workers: 3 + drop_last: false + pin_memory: false + + validation_ds: + prefix: test + ds_item: ["dev"] + batch_size: 8 + shuffle: false + num_workers: 3 + drop_last: false + pin_memory: false + + test_ds: + prefix: test + ds_item: ["test"] + batch_size: 8 + shuffle: false + num_workers: 3 + drop_last: false + pin_memory: false + + optim: + name: adamw + lr: 1e-4 + # optimizer arguments + betas: [0.9, 0.999] + weight_decay: 0.01 + + # scheduler setup + sched: + name: PolynomialDecayAnnealing + # Scheduler params + warmup_steps: null + warmup_ratio: 0.02 + last_epoch: -1 + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + +exp_manager: + exp_dir: null # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: "SGDGEN" # The name of your model + create_wandb_logger: True + wandb_logger_kwargs: + name: ??? + project: SGDGEN + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + resume_if_exists: false + resume_ignore_no_checkpoint: false \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/dialogue/dialogue.py b/NeMo-2.0.0.rc0.beta/examples/nlp/dialogue/dialogue.py new file mode 100644 index 0000000..de91b60 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/dialogue/dialogue.py @@ -0,0 +1,154 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script contains an example of how to train and test dialogue models in NeMo. + +***Setting the configs*** +The model and the PT trainer are defined in a config file that declares multiple important sections. +The most important ones are: + model: All arguments that are related to the Model - model, loss, optimizer, + schedulers, and datasets/data loaders. + trainer: Any argument to be passed to PyTorch Lightning including number of epochs, number of GPUs, + precision level, etc. + +This script uses the `/examples/nlp/dialogue_state_tracking/conf/dialog_config.yaml` config file +by default. You may update the config file from the file directly. The other option is to set another config file via command-line arguments by `--config-name=CONFIG_FILE_PATH'. + + +***Model Training*** + python dialogue.py + do_training=True + model.dataset.data_dir= + model.dataset.dialogues_example_dir= + model.dataset.task= e.g. sgd + model.language_model.pretrained_model_name= e.g. gpt2 + trainer.devices=[] + +***Model Evaluation*** + command as above, change do_training=False +""" + +import os + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models.dialogue.dialogue_gpt_classification_model import DialogueGPTClassificationModel +from nemo.collections.nlp.models.dialogue.dialogue_gpt_generation_model import DialogueGPTGenerationModel +from nemo.collections.nlp.models.dialogue.dialogue_nearest_neighbour_model import DialogueNearestNeighbourModel +from nemo.collections.nlp.models.dialogue.dialogue_s2s_generation_model import DialogueS2SGenerationModel +from nemo.collections.nlp.models.dialogue.dialogue_zero_shot_intent_model import DialogueZeroShotIntentModel +from nemo.collections.nlp.models.dialogue.intent_slot_classification_model import IntentSlotClassificationModel +from nemo.collections.nlp.models.dialogue.sgdqa_model import SGDQAModel +from nemo.collections.nlp.modules.common.megatron.megatron_utils import compute_model_parallel_rank +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.app_state import AppState +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="dialogue_config") +def main(cfg: DictConfig) -> None: + pl.seed_everything(42) + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + + try: + strategy = NLPDDPStrategy(no_ddp_communication_hook=True, find_unused_parameters=True,) + except (ImportError, ModuleNotFoundError): + strategy = 'auto' + + trainer = pl.Trainer(**cfg.trainer, strategy=strategy) + + exp_manager(trainer, cfg.get("exp_manager", None)) + + app_state = AppState() + app_state.data_parallel_size = cfg.model.data_parallel_size + if cfg.model.tensor_model_parallel_size > 1: + app_state.model_parallel_size = cfg.model.tensor_model_parallel_size + app_state.tensor_model_parallel_rank = compute_model_parallel_rank( + trainer.local_rank, app_state.model_parallel_size + ) + + if 'bert' in cfg.model.language_model.pretrained_model_name: + if cfg.model.dataset.task == 'sgd': + if cfg.model.original_nemo_checkpoint is not None: + model_class = DialogueZeroShotIntentModel + else: + model_class = SGDQAModel + elif cfg.model.dataset.task in ['zero_shot', 'design']: + model_class = DialogueZeroShotIntentModel + else: + model_class = IntentSlotClassificationModel + elif 'gpt' in cfg.model.language_model.pretrained_model_name.lower(): + if cfg.model.dataset.task in ['ms_marco', 'mellon_qa']: + model_class = DialogueGPTGenerationModel + else: + model_class = DialogueGPTClassificationModel + elif ( + 'bart' in cfg.model.language_model.pretrained_model_name.lower() + or 't5' in cfg.model.language_model.pretrained_model_name.lower() + ): + # please use bf16/32 with t5-large and above + # see https://github.com/huggingface/transformers/pull/10956 + model_class = DialogueS2SGenerationModel + elif 'sentence-transformers' in cfg.model.language_model.pretrained_model_name.lower(): + model_class = DialogueNearestNeighbourModel + + if cfg.pretrained_model or (cfg.model.nemo_path and os.path.exists(cfg.model.nemo_path)): + if cfg.pretrained_model: + logging.info(f'Loading pretrained model {cfg.pretrained_model}') + model = model_class.from_pretrained(cfg.pretrained_model) + else: + logging.info(f'Restoring model from {cfg.model.nemo_path}') + model = model_class.restore_from(cfg.model.nemo_path, trainer=trainer) + + if cfg.do_training: + model.setup_training_data(train_data_config=cfg.model.train_ds) + model.setup_multiple_validation_data(val_data_config=cfg.model.validation_ds) + else: + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + model = model_class(cfg.model, trainer=trainer) + + if cfg.do_training: + trainer.fit(model) + if cfg.model.nemo_path: + if not os.path.exists(cfg.model.nemo_path): + model.save_to(cfg.model.nemo_path) + else: + updated_nemo_path = cfg.model.nemo_path.replace(".nemo", "_new.nemo") + logging.warning("nemo path exists, saving at {} instead".format(updated_nemo_path)) + model.save_to(updated_nemo_path) + + else: + data_dir = cfg.model.dataset.get('data_dir', None) + dialogues_example_dir = cfg.model.dataset.get('dialogues_example_dir', None) + + if data_dir is None or dialogues_example_dir is None: + raise ValueError('No dataset directory provided. Skipping evaluation. ') + elif not os.path.exists(data_dir): + raise ValueError(f'{data_dir} is not found, skipping evaluation on the test set.') + else: + if hasattr(model, "update_data_dirs"): + model.update_data_dirs(data_dir=data_dir, dialogues_example_dir=dialogues_example_dir) + model._cfg.dataset = cfg.model.dataset + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.ds_item is not None: + model.setup_multiple_test_data(test_data_config=cfg.model.test_ds) + trainer.test(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/dialogue/remove_ms_marco_samples_without_wellFormedAnswers.py b/NeMo-2.0.0.rc0.beta/examples/nlp/dialogue/remove_ms_marco_samples_without_wellFormedAnswers.py new file mode 100644 index 0000000..53a7ecf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/dialogue/remove_ms_marco_samples_without_wellFormedAnswers.py @@ -0,0 +1,54 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import json +from ast import literal_eval + +import ijson + + +def main(filename): + with open(filename, 'r') as file: + objects = ijson.kvitems(file, 'wellFormedAnswers') + valid_old_key_to_new_key = {} + new_key = 0 + for key, well_formed_answer in objects: + value = well_formed_answer if isinstance(well_formed_answer, list) else literal_eval(well_formed_answer) + if len(value) > 0: + valid_old_key_to_new_key[key] = str(new_key) + new_key += 1 + filtered_data = {} + fieldnames = ['query', 'query_type', 'answers', 'wellFormedAnswers', 'passages'] + for fieldname in fieldnames: + add_data(filename, filtered_data, fieldname, valid_old_key_to_new_key) + + with open(filename, 'w') as fw: + json.dump(filtered_data, fw) + + +def add_data(filename, filtered_data, fieldname, valid_old_key_to_new_key): + with open(filename, 'r') as f: + objects = ijson.kvitems(f, fieldname) + filtered_data[fieldname] = { + valid_old_key_to_new_key[key]: query for key, query in objects if key in valid_old_key_to_new_key + } + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--filename") + args = parser.parse_args() + main(args.filename) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/analyze_errors.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/analyze_errors.py new file mode 100644 index 0000000..e3916b8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/analyze_errors.py @@ -0,0 +1,300 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script can be used to visualize the errors made by a (duplex) TN system. +More specifically, after running the evaluation script `duplex_text_normalization_test.py`, +a log file containing info about the errors will be generated. The location of this file +is determined by the argument `inference.errors_log_fp`. After that, we can use this +script to generate a HTML visualization. + +USAGE Example: +# python analyze_errors.py \ + --errors_log_fp=PATH_TO_ERRORS_LOG_FILE_PATH \ + --visualization_fp=PATH_TO_VISUALIZATION_FILE_PATH +""" + +from argparse import ArgumentParser +from typing import List + +from nemo.collections.nlp.data.text_normalization import constants + + +# Longest Common Subsequence +def lcs(X, Y): + """ Function for finding the longest common subsequence between two lists. + In this script, this function is particular used for aligning between the + ground-truth output string and the predicted string (for visualization purpose). + Args: + X: a list + Y: a list + + Returns: a list which is the longest common subsequence between X and Y + """ + m, n = len(X), len(Y) + L = [[0 for x in range(n + 1)] for x in range(m + 1)] + + # Following steps build L[m+1][n+1] in bottom up fashion. Note + # that L[i][j] contains length of LCS of X[0..i-1] and Y[0..j-1] + for i in range(m + 1): + for j in range(n + 1): + if i == 0 or j == 0: + L[i][j] = 0 + elif X[i - 1] == Y[j - 1]: + L[i][j] = L[i - 1][j - 1] + 1 + else: + L[i][j] = max(L[i - 1][j], L[i][j - 1]) + + # Following code is used to print LCS + index = L[m][n] + + # Create a character array to store the lcs string + lcs = [''] * (index + 1) + lcs[index] = '' + + # Start from the right-most-bottom-most corner and + # one by one store characters in lcs[] + i = m + j = n + while i > 0 and j > 0: + + # If current character in X[] and Y are same, then + # current character is part of LCS + if X[i - 1] == Y[j - 1]: + lcs[index - 1] = X[i - 1] + i -= 1 + j -= 1 + index -= 1 + + # If not same, then find the larger of two and + # go in the direction of larger value + elif L[i - 1][j] > L[i][j - 1]: + i -= 1 + else: + j -= 1 + + return lcs[:-1] + + +# Classes +class ErrorCase: + """ + This class represents an error case + + Args: + _input: Original input string + target: Ground-truth target string + pred: Predicted string + mode: A string indicates the mode (i.e., constants.ITN_MODE or constants.TN_MODE) + """ + + def __init__(self, _input: str, target: str, pred: str, classes: str, mode: str): + self._input = _input + self.target = target + self.pred = pred + self.mode = mode + self.classes = classes + + # Tokens + self.target_tokens = self.target.split(' ') + self.pred_tokens = self.pred.split(' ') + + # LCS + lcs_tokens = lcs(self.target_tokens, self.pred_tokens) + target_tokens_hightlight = [False] * len(self.target_tokens) + pred_tokens_hightlight = [False] * len(self.pred_tokens) + target_idx, pred_idx = 0, 0 + for token in lcs_tokens: + while self.target_tokens[target_idx] != token: + target_idx += 1 + while self.pred_tokens[pred_idx] != token: + pred_idx += 1 + target_tokens_hightlight[target_idx] = True + pred_tokens_hightlight[pred_idx] = True + target_idx += 1 + pred_idx += 1 + + # Spans + self.target_spans = self.get_spans(target_tokens_hightlight) + self.pred_spans = self.get_spans(pred_tokens_hightlight) + + # Determine unhighlighted target spans + unhighlighted_target_spans = [] + for ix, t in enumerate(self.target_spans): + if not t[-1]: + unhighlighted_target_spans.append((ix, t)) + # Determine unhighlighted pred spans + unhighlighted_pred_spans = [] + for ix, t in enumerate(self.pred_spans): + if not t[-1]: + unhighlighted_pred_spans.append((ix, t)) + + @classmethod + def from_lines(cls, lines: List[str], mode: str): + """ + This method returns an instance of ErrorCase from raw string lines. + + Args: + lines: A list of raw string lines for the error case. + mode: A string indicates the mode (i.e., constants.ITN_MODE or constants.TN_MODE) + + Returns: an instance of ErrorCase. + """ + for line in lines: + if line.startswith('Original Input'): + _input = line[line.find(':') + 1 :].strip() + elif line.startswith('Predicted Str'): + pred = line[line.find(':') + 1 :].strip() + elif line.startswith('Ground-Truth'): + target = line[line.find(':') + 1 :].strip() + elif line.startswith('Ground Classes'): + classes = line[line.find(':') + 1 :].strip() + return cls(_input, target, pred, classes, mode) + + def get_html(self): + """ + This method returns a HTML string representing this error case instance. + Returns: a string contains the HTML representing this error case instance. + """ + html_str = '' + # Input + input_form = 'Written' if self.mode == constants.TN_MODE else 'Spoken' + padding_multiplier = 1 if self.mode == constants.TN_MODE else 2 + padding_spaces = ''.join([' '] * padding_multiplier) + input_str = f'[Input ({input_form})]{padding_spaces}: {self._input}
\n' + html_str += input_str + ' ' + # Target + target_html = self.get_spans_html(self.target_spans, self.target_tokens) + target_form = 'Spoken' if self.mode == constants.TN_MODE else 'Written' + target_str = f'[Target ({target_form})]: {target_html}
\n' + html_str += target_str + ' ' + # Pred + pred_html = self.get_spans_html(self.pred_spans, self.pred_tokens) + padding_multiplier = 10 if self.mode == constants.TN_MODE else 11 + padding_spaces = ''.join([' '] * padding_multiplier) + pred_str = f'[Prediction]{padding_spaces}: {pred_html}
\n' + html_str += pred_str + ' ' + # Classes + padding_multiplier = 15 if self.mode == constants.TN_MODE else 16 + padding_spaces = ''.join([' '] * padding_multiplier) + class_str = f'[Classes]{padding_spaces}: {self.classes}
\n' + html_str += class_str + ' ' + # Space + html_str += '
\n' + return html_str + + def get_spans(self, tokens_hightlight): + """ + This method extracts the list of spans. + + Args: + tokens_hightlight: A list of boolean values where each value indicates whether a token needs to be hightlighted. + + Returns: + spans: A list of spans. Each span is represented by a tuple of 3 elements: (1) Start Index (2) End Index (3) A boolean value indicating whether the span needs to be hightlighted. + """ + spans, nb_tokens = [], len(tokens_hightlight) + cur_start_idx, cur_bool_val = 0, tokens_hightlight[0] + for idx in range(nb_tokens): + if idx == nb_tokens - 1: + if tokens_hightlight[idx] != cur_bool_val: + spans.append((cur_start_idx, nb_tokens - 2, cur_bool_val)) + spans.append((nb_tokens - 1, nb_tokens - 1, tokens_hightlight[idx])) + else: + spans.append((cur_start_idx, nb_tokens - 1, cur_bool_val)) + else: + if tokens_hightlight[idx] != cur_bool_val: + spans.append((cur_start_idx, idx - 1, cur_bool_val)) + cur_start_idx, cur_bool_val = idx, tokens_hightlight[idx] + return spans + + def get_spans_html(self, spans, tokens): + """ + This method generates a HTML string for a string sequence from its spans. + + Args: + spans: A list of contiguous spans in a sequence. Each span is represented by a tuple of 3 elements: (1) Start Index (2) End Index (3) A boolean value indicating whether the span needs to be hightlighted. + tokens: All tokens in the sequence + Returns: + html_str: A HTML string for the string sequence. + """ + html_str = '' + for start, end, type in spans: + color = 'red' if type else 'black' + span_tokens = tokens[start : end + 1] + span_str = '{} '.format(color, ' '.join(span_tokens)) + html_str += span_str + return html_str + + +# Main function for analysis +def analyze(errors_log_fp: str, visualization_fp: str): + """ + This method generates a HTML visualization of the error cases logged in a log file. + + Args: + errors_log_fp: Path to the error log file + visualization_fp: Path to the output visualization file + + """ + # Read lines from errors log + with open(errors_log_fp, 'r', encoding='utf-8') as f: + lines = f.readlines() + + # Process lines + tn_error_cases, itn_error_cases = [], [] + for ix in range(0, len(lines), 8): + mode_line = lines[ix] + info_lines = lines[ix + 1 : ix + 7] + # Append new error case + if mode_line.startswith('Forward Problem'): + mode = constants.TN_MODE + tn_error_cases.append(ErrorCase.from_lines(info_lines, mode)) + elif mode_line.startswith('Backward Problem'): + mode = constants.ITN_MODE + itn_error_cases.append(ErrorCase.from_lines(info_lines, mode)) + + # Basic stats + print('---- Text Normalization ----') + print('Number of TN errors: {}'.format(len(tn_error_cases))) + + print('---- Inverse Text Normalization ---- ') + print('Number of ITN errors: {}'.format(len(itn_error_cases))) + + # Produce a visualization + with open(visualization_fp, 'w+', encoding='utf-8') as f: + # Appendix + f.write('Appendix
') + f.write('Text Normalization Analysis.
') + f.write('Inverse Text Normalization Analysis.') + + # TN Section + f.write('

Text Normalization

\n') + for errorcase in tn_error_cases: + f.write(errorcase.get_html()) + + # ITN Section + f.write('

Inverse Text Normalization

\n') + for errorcase in itn_error_cases: + f.write(errorcase.get_html()) + + +if __name__ == '__main__': + # Parse argument + parser = ArgumentParser() + parser.add_argument('--errors_log_fp', help='Path to the error log file', required=True) + parser.add_argument('--visualization_fp', help='Path to the output visualization file', required=True) + args = parser.parse_args() + + analyze(args.errors_log_fp, args.visualization_fp) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/conf/duplex_tn_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/conf/duplex_tn_config.yaml new file mode 100644 index 0000000..9cb7059 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/conf/duplex_tn_config.yaml @@ -0,0 +1,159 @@ +name: &name DuplexTextNormalization +mode: joint # Three possible choices ['tn', 'itn', 'joint'] +lang: ??? # Supported languages are ['en', 'ru', 'de', 'multilingual'] + +# Pretrained Nemo Models +tagger_pretrained_model: null +decoder_pretrained_model: null + +# Tagger +tagger_trainer: + devices: 1 # the number of gpus, 0 for CPU + num_nodes: 1 + max_epochs: 5 # the number of training epochs (for ru or de or multilingual, try 10) + enable_checkpointing: False # provided by exp_manager + logger: false # provided by exp_manager + accumulate_grad_batches: 1 # accumulates grads every k batches + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + strategy: ddp + +tagger_model: + do_training: true + transformer: albert-base-v2 # For ru, try cointegrated/rubert-tiny | For de, try bert-base-german-cased | For multilingual, try bert-base-multilingual-cased + tokenizer: ${tagger_model.transformer} + max_sequence_len: 128 + nemo_path: ${tagger_exp_manager.exp_dir}/tagger_model.nemo # exported .nemo path + lang: ${lang} + mode: ${mode} + + optim: + name: adamw + lr: 5e-5 + weight_decay: 0.01 + + sched: + name: WarmupAnnealing + + # pytorch lightning args + monitor: val_token_precision + reduce_on_plateau: false + + # scheduler config override + warmup_steps: null + warmup_ratio: 0.1 + last_epoch: -1 + +tagger_exp_manager: + exp_dir: nemo_experiments # where to store logs and checkpoints + name: tagger_training # name of experiment + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + save_top_k: 3 + monitor: "val_token_precision" + mode: "max" + save_best_model: true + always_save_nemo: true + +# Decoder +decoder_trainer: + devices: 1 # the number of gpus, 0 for CPU + num_nodes: 1 + max_epochs: 3 # the number of training epochs + enable_checkpointing: False # provided by exp_manager + logger: false # provided by exp_manager + accumulate_grad_batches: 1 # accumulates grads every k batches + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + strategy: ddp + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + +decoder_model: + do_training: true + transformer: t5-small # For ru, try cointegrated/rut5-base | For de or multilingual, try google/mt5-base + max_sequence_len: 80 + tokenizer: ${decoder_model.transformer} + nemo_path: ${decoder_exp_manager.exp_dir}/decoder_model.nemo # exported .nemo path + lang: ${lang} + mode: ${mode} + + # Options related to covering grammars for TN + use_cg: false # Use covering grammars to avoid catastrophic errors + neural_confidence_threshold: 0.99 # If the neural model is not confident, then use the covering grammars + n_tagged: 1 # number of tagged options to consider, -1 - to get all possible tagged options + + optim: + name: adamw + lr: 2e-4 + weight_decay: 0.01 + + sched: + name: WarmupAnnealing + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + + # scheduler config override + warmup_steps: null + warmup_ratio: 0.0 + last_epoch: -1 + +decoder_exp_manager: + exp_dir: nemo_experiments # where to store logs and checkpoints + name: decoder_training # name of experiment + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + save_top_k: 3 + monitor: "val_loss" + mode: "min" + save_best_model: True + +# Data +data: + train_ds: + data_path: train.tsv # provide the full path to the file. Ignored when using tarred dataset, tar_metadata_file is used instead. + batch_size: 64 # local training batch size for each worker. Ignored when using tarred dataset, the batch size of the tarred dataset is used instead. + shuffle: true + max_insts: -1 # Maximum number of instances (-1 means no limit) + # Refer to the text_normalization doc for more information about data augmentation + tagger_data_augmentation: false + decoder_data_augmentation: true + use_cache: false # uses a cache to store the processed dataset, you may use it for large datasets for speed up (especially when using multi GPUs) + num_workers: 3 + pin_memory: false + drop_last: false + use_tarred_dataset: False # if true tar_metadata_file will be used + tar_metadata_file: null # metadata for tarred dataset. A JSON file containing the list of tar_files in "text_tar_filepaths" field + tar_shuffle_n: 100 # How many samples to look ahead and load to be shuffled + + validation_ds: + data_path: dev.tsv # provide the full path to the file. Provide multiple paths to run evaluation on multiple datasets + batch_size: 64 + shuffle: false + max_insts: -1 # Maximum number of instances (-1 means no limit) + use_cache: false # uses a cache to store the processed dataset, you may use it for large datasets for speed up (especially when using multi GPUs) + num_workers: 3 + pin_memory: false + drop_last: false + + test_ds: + data_path: test.tsv # provide the full path to the file + batch_size: 64 + shuffle: false + use_cache: false # uses a cache to store the processed dataset, you may use it for large datasets for speed up (especially when using multi GPUs) + num_workers: 3 + pin_memory: false + drop_last: false + errors_log_fp: errors.txt # Path to the file for logging the errors + +# Inference +inference: + interactive: false # Set to true if you want to enable the interactive mode when running duplex_text_normalization_test.py + from_file: null # Path to the raw text, no labels required. Each sentence on a separate line + batch_size: 16 # batch size for inference.from_file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/data/create_tarred_dataset.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/data/create_tarred_dataset.py new file mode 100644 index 0000000..cdedf45 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/data/create_tarred_dataset.py @@ -0,0 +1,302 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import json +import os +import pickle +import random +import tarfile +from glob import glob +from typing import List, Tuple + +from joblib import Parallel, delayed +from tqdm import tqdm +from transformers import AutoTokenizer + +import nemo.collections.nlp.data.text_normalization.constants as constants +from nemo.collections.nlp.data.text_normalization.decoder_dataset import TextNormalizationDecoderDataset +from nemo.utils import logging + + +""" +The script builds tar files for Tarred TextNormalizationDecoderDataset + +See `text_normalization doc ` +for more details on data format, and en/data_processing.py on how to pre-process the data before tarring. + +To run the script, use: + + python create_tarred_dataset.py \ + --input_files = "train_processed/output-00099-of-00100" \ + --input_files = "train_processed/output-00098-of-00100" \ + --lang = "en" \ + --out_dir="TARRED_DATA_OUTPUT_DIR" + +See the argparse help for more arguments. +""" + + +def _preprocess_file(input_file: str) -> List[Tuple[List[str]]]: + """ + Performs initial preprocessing, i.e., urls formatting, removal of "_trans" from Ru set + + Args: + input_file: path to a file in google TN format + + Returns: + Processed data. Each element is a Tuple(List[semiotic classes], List[written words], List[spoken words]) + """ + print(f"Reading and running initial pre-processing of {input_file}...") + cur_split = [] + with open(input_file, 'r', encoding='utf-8') as f: + # Loop through each line of the file + cur_classes, cur_tokens, cur_outputs = [], [], [] + for linectx, line in tqdm(enumerate(f)): + es = line.strip().split('\t') + if len(es) == 2 and es[0] == '': + cur_split.append((cur_classes, cur_tokens, cur_outputs)) + # Reset + cur_classes, cur_tokens, cur_outputs = [], [], [] + continue + assert len(es) == 3 + cur_classes.append(es[0]) + cur_tokens.append(es[1]) + cur_outputs.append(es[2]) + return cur_split + + +def _write_batches_to_tarfiles( + input_file: str, + tokenizer: AutoTokenizer, + tokenizer_name: str, + mode: str, + lang: str, + max_seq_len: int, + batch_size: int, + out_dir: str, + num_batches_per_tarfile: int, + decoder_data_augmentation: bool = False, +): + """ + Creates tar files for the input file, i.e.: + 1. Creates a TextNormalizationDecoderDataset from the input file + 2. Constructs batches of size `batch_size` + 3. Saves each created batch to a pickle file and then adds `num_batches_per_tarfile` + of the pickle files to a tarfile. + + Args: + input_file: path to cleaned data file. See en/data_processing.py for cleaning. + tokenizer: tokenizer + tokenizer_name: the name of the tokenizer, usually corresponds to the pre-trained LM + mode: model training mode + max_seq_len: maximum length of the sequence (examples that are longer will be discarded) + batch_size: batch size + out_dir: path to output directory + num_batches_per_tarfile: number of batches saved in each tar file + decoder_data_augmentation: Set to True to enable data augmentation for the decoder model + lang: data language + """ + + dataset = TextNormalizationDecoderDataset( + input_file=input_file, + raw_instances=_preprocess_file(input_file=input_file), + tokenizer=tokenizer, + tokenizer_name=tokenizer_name, + mode=mode, + max_len=max_seq_len, + decoder_data_augmentation=decoder_data_augmentation, + lang=lang, + use_cache=False, + max_insts=-1, + do_tokenize=False, + initial_shuffle=True, + ) + dataset.batchify(batch_size) + file_name = os.path.basename(input_file) + tar_file_ctr = 0 + tar_file_path = os.path.join( + out_dir, '%s-batches.%d.%d.%d.tar' % (file_name, batch_size, max_seq_len, tar_file_ctr) + ) + tar_file_ptr = tarfile.open(tar_file_path, 'w') + total_batch_ctr = 0 + batch_ctr = 0 + for batch in dataset.batches: + total_batch_ctr += 1 + batch_ctr += 1 + pickle_file = os.path.join(out_dir, '%s-batch-%d.pkl' % (file_name, total_batch_ctr)) + + pickle.dump(batch, open(pickle_file, 'wb')) + tar_file_ptr.add(pickle_file) + os.remove(pickle_file) + + if batch_ctr == num_batches_per_tarfile: + tar_file_ctr += 1 + tar_file_ptr.close() + tar_file_path = os.path.join( + out_dir, f'%s-batches.%d.%d.%d.tar' % (file_name, batch_size, max_seq_len, tar_file_ctr) + ) + tar_file_ptr = tarfile.open(tar_file_path, 'w',) + batch_ctr = 0 + + # return tar files paths that have batches remaining + remainder_tar_file_path = tar_file_ptr.name + tar_file_ptr.close() + + return total_batch_ctr, remainder_tar_file_path + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='(Inverse) Text Normalization tarred dataset creation') + parser.add_argument('--transformer_name', type=str, default="t5-small", help='Name of the pretrained LM.') + parser.add_argument('--mode', type=str, default='tn', choices=constants.MODES, help='(I)TN model training mode.') + parser.add_argument('--lang', type=str, default='en', choices=constants.SUPPORTED_LANGS, help='language.') + parser.add_argument( + '--decoder_data_augmentation', + action="store_true", + help='Set to True to use data augmentation for the decoder model.', + ) + parser.add_argument( + '-in', + '--input_files', + action='append', + required=True, + help="Example: -in train_processed/output-00099-of-00100 -in train_processed/output-00098-of-00100", + ) + parser.add_argument('--out_dir', type=str, required=True, help='Path to store dataloader and tokenizer models.') + parser.add_argument( + '--max_seq_length', type=int, default=80, help='Maximum sequence length, longer examples will be discarded.' + ) + parser.add_argument('--min_seq_length', type=int, default=1, help='Minimum sequence length.') + parser.add_argument( + '--num_batches_per_tarfile', + type=int, + default=2, + help='Number batches, i.e., pickle files, included in a single .tar file.', + ) + parser.add_argument('--n_jobs', type=int, default=-2, help='The maximum number of concurrently running jobs.') + parser.add_argument( + '--batch_size', + type=int, + default=16, + help='Batch size, i.e., number of examples in a single pickle file. This batch size will override the training size.', + ) + parser.add_argument( + '--factor', default=8, type=int, help='The final number of tar files will be divisible by the "factor" value' + ) + + args = parser.parse_args() + + # check if tar files exist + if os.path.exists(args.out_dir): + tar_files_in_out_dir = glob(f'{args.out_dir}/*.tar') + if tar_files_in_out_dir: + raise ValueError( + f'Tar files detected in {args.out_dir}. Delete the files to re-construct the dataset in the same directory.' + ) + else: + os.makedirs(args.out_dir) + + world_size = 1 + tokenizer = AutoTokenizer.from_pretrained(args.transformer_name) + + results_list = Parallel(n_jobs=args.n_jobs)( + delayed(_write_batches_to_tarfiles)( + input_file=input_file, + tokenizer=tokenizer, + tokenizer_name=args.transformer_name, + mode=args.mode, + lang=args.lang, + batch_size=args.batch_size, + max_seq_len=args.max_seq_length, + decoder_data_augmentation=args.decoder_data_augmentation, + out_dir=args.out_dir, + num_batches_per_tarfile=args.num_batches_per_tarfile, + ) + for input_file in args.input_files + ) + + total_batches = sum([batch_count for batch_count, _ in results_list]) + + # save batches from tar files containing the left over batches (if there's enough batches) + remainder_tar_file_ctr = 0 + remainder_tar_file_path = os.path.join( + args.out_dir, f'remainder-batches.tokens.{args.batch_size}.tar_file_{remainder_tar_file_ctr}.tar' + ) + remainder_tar_file_ptr = tarfile.open(remainder_tar_file_path, 'w') + batch_in_tar_ctr = 0 + for _, tar_file_path in results_list: + tar_file_ptr = tarfile.open(tar_file_path, 'r') + for member in tar_file_ptr.getmembers(): + remainder_tar_file_ptr.addfile(member, tar_file_ptr.extractfile(member.name)) + batch_in_tar_ctr += 1 + if batch_in_tar_ctr == args.num_batches_per_tarfile: + remainder_tar_file_ctr += 1 + remainder_tar_file_ptr.close() + remainder_tar_file_path = os.path.join( + args.out_dir, f'remainder-batches.tokens.{args.batch_size}.tar_file_{remainder_tar_file_ctr}.tar', + ) + remainder_tar_file_ptr = tarfile.open(remainder_tar_file_path, 'w',) + batch_in_tar_ctr = 0 + tar_file_ptr.close() + os.remove(tar_file_path) + + # log the number of batches remaining as they will be discarded + num_batches_discarded = len(remainder_tar_file_ptr.getmembers()) + remainder_tar_file_ptr.close() + os.remove(remainder_tar_file_path) + + tar_file_paths = glob(f'{args.out_dir}/*.tar') + if args.factor != 1: + num_tar_files = len(tar_file_paths) + num_tars_to_drop = num_tar_files % args.factor + num_batches_discarded += num_tars_to_drop * args.num_batches_per_tarfile + + random.shuffle(tar_file_paths) + for _ in range(num_tars_to_drop): + os.remove(tar_file_paths.pop(-1)) + + total_batches -= num_batches_discarded + logging.info(f'Number of batches discarded: {num_batches_discarded}, total batches kept: {total_batches}') + + # dump metadata to json + metadata = {} + metadata['num_batches'] = total_batches + + # rename tar files so they can be more easily used with CLI and YAML + file_name = f'{args.mode}.{args.batch_size}_bs.{args.num_batches_per_tarfile}_b_per_tar.{args.max_seq_length}_len' + for index, path in enumerate(tar_file_paths): + os.rename(path, os.path.join(args.out_dir, f'{file_name}.{index}.tar')) + + text_tar_filepaths = f'{file_name}._OP_0..{index}_CL_.tar' + logging.info(f'Files for brace expansion: "{text_tar_filepaths}"') + metadata['text_tar_filepaths'] = text_tar_filepaths + + # add tar files to metadata + tar_file_paths = glob(f'{args.out_dir}/*.tar') + metadata['tar_files'] = tar_file_paths + metadata_path = os.path.join(args.out_dir, 'metadata.json') + json.dump(metadata, open(metadata_path, 'w')) + + num_tar_files = len(tar_file_paths) + if num_tar_files < world_size: + raise ValueError( + ( + f'Number of tar files found: {num_tar_files} is less than world size: {world_size}. ' + f'There should be at least one tar file per GPU (ideally many tar files per GPU). ' + f'This may be due to dataset size. ' + f'Decrease num_batches_per_tarfile or num_tokens_per_batch to increase the number of tarfiles. ' + f'Also using shard_strategy=replicate will use all available tarfiles for every GPU. ' + ) + ) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/data/data_split.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/data/data_split.py new file mode 100644 index 0000000..b05cf6d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/data/data_split.py @@ -0,0 +1,152 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script creates data splits of the Google Text Normalization dataset +of the format mentioned in the `text_normalization doc `. + +USAGE Example: +1. Download the Google TN dataset from https://www.kaggle.com/google-nlu/text-normalization +2. Unzip the English subset (e.g., by running `tar zxvf en_with_types.tgz`). Then there will a folder named `en_with_types`. +3. Run this script +# python data_split.py \ + --data_dir=en_with_types/ \ + --output_dir=data_split/ \ + --lang=en + +In this example, the split files will be stored in the `data_split` folder. +The folder should contain three subfolders `train`, 'dev', and `test` with `.tsv` files. +""" + +from argparse import ArgumentParser +from os import listdir, mkdir +from os.path import isdir, isfile, join + +from tqdm import tqdm + +from nemo.collections.nlp.data.text_normalization import constants + +# Local Constants +TEST_SIZE_EN = 100002 +TEST_SIZE_RUS = 100007 + + +def read_google_data(data_file: str, lang: str, split: str, add_test_full=False): + """ + The function can be used to read the raw data files of the Google Text Normalization + dataset (which can be downloaded from https://www.kaggle.com/google-nlu/text-normalization) + + Args: + data_file: Path to the data file. Should be of the form output-xxxxx-of-00100 + lang: Selected language. + split: data split + add_test_full: do not truncate test data i.e. take the whole test file not #num of lines + Return: + data: list of examples + """ + data = [] + cur_classes, cur_tokens, cur_outputs = [], [], [] + with open(data_file, 'r', encoding='utf-8') as f: + for linectx, line in tqdm(enumerate(f)): + es = line.strip().split('\t') + if split == "test" and not add_test_full: + # For the results reported in the paper "RNN Approaches to Text Normalization: A Challenge": + # + For English, the first 100,002 lines of output-00099-of-00100 are used for the test set + # + For Russian, the first 100,007 lines of output-00099-of-00100 are used for the test set + if lang == constants.ENGLISH and linectx == TEST_SIZE_EN: + break + if lang == constants.RUSSIAN and linectx == TEST_SIZE_RUS: + break + if len(es) == 2 and es[0] == '': + data.append((cur_classes, cur_tokens, cur_outputs)) + # Reset + cur_classes, cur_tokens, cur_outputs = [], [], [] + continue + + # Remove _trans (for Russian) + if lang == constants.RUSSIAN: + es[2] = es[2].replace('_trans', '') + # Update the current example + assert len(es) == 3 + cur_classes.append(es[0]) + cur_tokens.append(es[1]) + cur_outputs.append(es[2]) + return data + + +if __name__ == '__main__': + parser = ArgumentParser(description='Preprocess Google text normalization dataset') + parser.add_argument('--data_dir', type=str, required=True, help='Path to folder with data') + parser.add_argument('--output_dir', type=str, default='preprocessed', help='Path to folder with preprocessed data') + parser.add_argument( + '--lang', type=str, default=constants.ENGLISH, choices=constants.SUPPORTED_LANGS, help='Language' + ) + parser.add_argument( + '--add_test_full', + action='store_true', + help='If True, additional folder test_full will be created without truncation of files', + ) + args = parser.parse_args() + + # Create the output dir (if not exist) + if not isdir(args.output_dir): + mkdir(args.output_dir) + mkdir(args.output_dir + '/train') + mkdir(args.output_dir + '/dev') + mkdir(args.output_dir + '/test') + if args.add_test_full: + mkdir(args.output_dir + '/test_full') + + for fn in sorted(listdir(args.data_dir))[::-1]: + fp = join(args.data_dir, fn) + if not isfile(fp): + continue + if not fn.startswith('output'): + continue + + # Determine the current split + split_nb = int(fn.split('-')[1]) + if split_nb < 90: + cur_split = "train" + elif split_nb < 95: + cur_split = "dev" + elif split_nb == 99: + cur_split = "test" + data = read_google_data(data_file=fp, lang=args.lang, split=cur_split) + # write out + output_file = join(args.output_dir, f'{cur_split}', f'{fn}.tsv') + print(fp) + print(output_file) + output_f = open(output_file, 'w', encoding='utf-8') + for inst in data: + cur_classes, cur_tokens, cur_outputs = inst + for c, t, o in zip(cur_classes, cur_tokens, cur_outputs): + output_f.write(f'{c}\t{t}\t{o}\n') + output_f.write('\t\n') + + print(f'{cur_split}_sentences: {len(data)}') + + # additionally generate full test files if needed + if cur_split == "test" and args.add_test_full: + data = read_google_data(data_file=fp, lang=args.lang, split=cur_split, add_test_full=True) + # write out + output_file = join(args.output_dir, 'test_full', f'{fn}.tsv') + output_f = open(output_file, 'w', encoding='utf-8') + for inst in data: + cur_classes, cur_tokens, cur_outputs = inst + for c, t, o in zip(cur_classes, cur_tokens, cur_outputs): + output_f.write(f'{c}\t{t}\t{o}\n') + output_f.write('\t\n') + + print(f'{cur_split}_sentences: {len(data)}') diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/data/en/data_preprocessing.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/data/en/data_preprocessing.py new file mode 100644 index 0000000..9523d09 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/data/en/data_preprocessing.py @@ -0,0 +1,402 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script can be used to clean the splits of English Google Text Normalization dataset +for better training performance. Without these processing steps we noticed that the model would have a hard time to learn certain input cases, and instead starts to either make unrecoverable errors +or hallucinate. For example, the model struggles to learn numbers with five or more digits due to limited examples in the training data, so we simplified the task for the model by letting it verbalize those cases +digit by digit. This makes the model more rebust to errors. +The operations include: + - numbers that are longer than `max_integer_length` will be verbalized digit by digit, e.g. the mapping "10001" -> "ten thousand and one" in the data +will be changed to "10001" -> "one zero zero zero one" + - denominators of fractions that are longer than `max_denominator_length` will be verbalized digit by digit + - sentences with non-English characters will be removed + - some class formats converted to standardized format, e.g. for `Fraction` "½" become "1/2" + - urls that have a spoken form of "*_letter" e.g. "dot h_letter _letter t_letter _letter m_letter _letter l_letter" are converted to "dot h t m l" + - for class types "PLAIN", "LETTERS", "ELECTRONIC", "VERBATIM", "PUNCT" the spoken form is changed to "" which means this class should be left unchanged + + +USAGE Example: +1. Download the Google TN dataset from https://www.kaggle.com/google-nlu/text-normalization +2. Unzip the English subset (e.g., by running `tar zxvf en_with_types.tgz`). Then there will a folder named `en_with_types`. +3. Run the data_split.py scripts to obtain the data splits +4. Run this script on the different splits +# python data_preprocessing.py \ + --input_path=data_split/train \ + --output_dir=train_processed \ + --max_integer_length=4 \ + --max_denominator_length=3 + +In this example, the cleaned files will be saved in train_processed/. + +After this script, you can use upsample.py to create a more class balanced training dataset for better performance. +""" + + +import os +from argparse import ArgumentParser + +import inflect +import regex as re +from tqdm import tqdm + +from nemo.collections.common.tokenizers.moses_tokenizers import MosesProcessor +from nemo.collections.nlp.data.text_normalization.constants import EN_GREEK_TO_SPOKEN +from nemo.collections.nlp.data.text_normalization.utils import ( + add_space_around_dash, + convert_fraction, + convert_superscript, +) +from nemo.utils import logging + +engine = inflect.engine() + +# these are all words that can appear in a verbalized number, this list will be used later as a filter to detect numbers in verbalizations +number_verbalizations = list(range(0, 20)) + list(range(20, 100, 10)) +number_verbalizations = ( + [engine.number_to_words(x, zero="zero").replace("-", " ").replace(",", "") for x in number_verbalizations] + + ["hundred", "thousand", "million", "billion", "trillion"] + + ["point"] +) +digit = "0123456789" +processor = MosesProcessor(lang_id="en") + + +def process_url(o): + """ + The function is used to process the spoken form of every URL in an example. + E.g., "dot h_letter _letter t_letter _letter m_letter _letter l_letter" -> + "dot h t m l" + Args: + o: The expected outputs for the spoken form + Return: + o: The outputs for the spoken form with preprocessed URLs. + """ + + def flatten(l): + """ flatten a list of lists """ + return [item for sublist in l for item in sublist] + + if o != '' and '_letter' in o: + o_tokens = o.split(' ') + all_spans, cur_span = [], [] + for j in range(len(o_tokens)): + if len(o_tokens[j]) == 0: + continue + if o_tokens[j] == '_letter': + all_spans.append(cur_span) + all_spans.append([' ']) + cur_span = [] + else: + o_tokens[j] = o_tokens[j].replace('_letter', '') + cur_span.append(o_tokens[j]) + if len(cur_span) > 0: + all_spans.append(cur_span) + o_tokens = flatten(all_spans) + + o = '' + for o_token in o_tokens: + if len(o_token) > 1: + o += ' ' + o_token + ' ' + else: + o += o_token + o = o.strip() + o_tokens = processor.tokenize(o).split() + o = ' '.join(o_tokens) + + return o + + +def convert2digits(digits: str): + """ + Verbalizes integer digit by digit, e.g. "12,000.12" -> "one two zero zero zero point one two" + It can also take in a string that has an integer as prefix and outputs only the verbalized part of that, e.g. "12 kg" -> "one two" + and outputs a warning + + Args: + digits: integer in string format + Return: + res: number verbalization of the integer prefix of the input + """ + res = [] + for i, x in enumerate(digits): + if x in digit: + res.append(engine.number_to_words(str(x), zero="zero").replace("-", " ").replace(",", "")) + elif x == ".": + res.append("point") + elif x in [" ", ","]: + continue + else: + # logging.warning(f"remove {digits[:i]} from {digits[i:]}") + break + res = " ".join(res) + return res, i + + +def convert(example): + cls, written, spoken = example + + written = convert_fraction(written) + written = re.sub("é", "e", written) + written = convert_superscript(written) + + if cls == "TIME": + written = re.sub("([0-9]): ([0-9])", "\\1:\\2", written) + if cls == "MEASURE": + written = re.sub("([0-9])\s?''", '\\1"', written) + + spoken = process_url(spoken) + + if cls in ["TELEPHONE", "DIGIT", "MEASURE", "DECIMAL", "MONEY", "ADDRESS"]: + spoken = re.sub(" o ", " zero ", spoken) + spoken = re.sub(" o ", " zero ", spoken) + spoken = re.sub("^o ", "zero ", spoken) + spoken = re.sub(" o$", " zero", spoken) + spoken = re.sub("^sil ", "", spoken) + spoken = re.sub(" sil ", " ", spoken) + spoken = re.sub(" sil ", " ", spoken) + spoken = re.sub(" sil$", "", spoken) + + if cls != "ELECTRONIC": + written = add_space_around_dash(written) + + example[1] = written + example[2] = spoken + + l = args.max_integer_length - 2 + + # if written form does not fulfill this format return + if not re.search("[0-9]{%s}[,\s]?[0-9]{3}" % l, written): + if cls != "FRACTION": + return + idx = written.index("/") + denominator = written[idx + 1 :].strip() + if not re.search(r"[0-9]{%s}" % (args.max_denominator_length + 1), denominator): + return + + # convert spoken forms for different classes + if cls == "CARDINAL": + if written[0] == "-": + digits = "minus " + convert2digits(written[1:])[0] + else: + digits = convert2digits(written)[0] + spoken = digits + elif cls == "ADDRESS": + idx = re.search("[0-9]", written).start() + number = convert2digits(written[idx:].strip())[0] + s_words = spoken.split() + for i, x in enumerate(s_words): + if x in number_verbalizations: + break + spoken = " ".join(s_words[:i]) + " " + number + elif cls == "DECIMAL": + res = [] + for i, x in enumerate(written): + if i == 0 and x == "-": + res.append("minus") + elif x in digit: + res.append(engine.number_to_words(str(x), zero="zero").replace("-", " ").replace(",", "")) + elif x == ".": + res.append("point") + spoken = " ".join(res) + m = re.search("([a-z]+)", written) + if m: + spoken += " " + m.group(1) + elif cls == "FRACTION": + res = [] + if written[0] == "-": + res.append("minus") + written = written[1:] + idx = written.index("/") + numerator = written[:idx].strip() + denominator = written[idx + 1 :].strip() + if len(numerator) > args.max_integer_length: + numerator = convert2digits(numerator)[0] + else: + numerator = engine.number_to_words(str(numerator), zero="zero").replace("-", " ").replace(",", "") + if len(denominator) > args.max_denominator_length: + denominator = convert2digits(denominator)[0] + else: + denominator = engine.number_to_words(str(denominator), zero="zero").replace("-", " ").replace(",", "") + spoken = numerator + " slash " + denominator + if res: + spoken = "minus " + spoken + elif cls == "MEASURE": + res = [] + if written[0] == "-": + res.append("minus") + written = written[1:] + idx = re.search("(?s:.*)([0-9]\s?[a-zA-Zµμ\/%Ω'])", written).end() + number, unit_idx = convert2digits(written[:idx].strip()) + s_words = spoken.split() + for i, x in enumerate(s_words): + if x not in number_verbalizations: + break + + spoken = number + " " + " ".join(s_words[i:]) + if res: + spoken = "minus " + spoken + elif cls == "MONEY": + res = [] + if written[0] == "-": + res.append("minus") + written = written[1:] + idx = re.search("[0-9]", written).start() + m = re.search("\.", written[idx:]) + idx_end = len(written) + if m: + idx_end = m.start() + idx + number, unit_idx = convert2digits(written[idx:idx_end].strip()) + s_words = spoken.split() + for i, x in enumerate(s_words): + if x not in number_verbalizations: + break + spoken = number + " " + " ".join(s_words[i:]) + if res: + spoken = "minus " + spoken + elif cls == "ORDINAL": + res = [] + if written[0] == "-": + res.append("minus") + written = written[1:] + if "th" in written.lower(): + idx = written.lower().index("th") + elif "rd" in written.lower(): + idx = written.lower().index("rd") + elif "nd" in written.lower(): + idx = written.lower().index("nd") + elif "st" in written.lower(): + idx = written.lower().index("st") + if re.search(r"[¿¡ºª]", written) is None: + spoken = convert2digits(written[:idx].strip())[0] + " " + written[idx:].lower() + if res: + spoken = "minus " + spoken + example[2] = spoken + + +def ignore(example): + """ + This function makes sure specific class types like 'PLAIN', 'ELECTRONIC' etc. are left unchanged. + + Args: + example: data example + """ + cls, _, _ = example + if cls in ["PLAIN", "LETTERS", "ELECTRONIC", "VERBATIM", "PUNCT"]: + example[2] = "" + if example[1] == 'I' and re.search("(first|one)", example[2]): + example[2] = "" + + +def process_file(fp): + """ Reading the raw data from a file of NeMo format and preprocesses it. Write is out to the output directory. + For more info about the data format, refer to the + `text_normalization doc `. + + Args: + fp: file path + """ + file_name = fp.split("/")[-1] + output_path = f"{args.output_dir}/{file_name}" + logging.info(f"-----input_file--------\n{fp}") + logging.info(f"-----output_file--------\n{output_path}") + + insts, w_words, s_words, classes = [], [], [], [] + delete_sentence = False + with open(fp, 'r', encoding='utf-8') as f: + for line in tqdm(f): + es = [e.strip() for e in line.strip().split('\t')] + if es[0] == '': + if not delete_sentence: + inst = (classes, w_words, s_words) + insts.append(inst) + # Reset + w_words, s_words, classes = [], [], [] + delete_sentence = False + else: + # convert data sample + convert(es) + # decide if this data sample's spoken form should be same as written form + ignore(es) + + characters_ignore = "¿¡ºª" + "".join(EN_GREEK_TO_SPOKEN.keys()) + # delete sentence with greek symbols, etc. + if re.search(rf"[{characters_ignore}]", es[1]) is not None: + delete_sentence = True + # delete characters from chinese, japanese, korean + if re.search(r'[\u4e00-\u9fff]+', es[1]) is not None: + delete_sentence = True + + if es[0] == 'MONEY' and re.search("\s?DM$", es[1]): + delete_sentence = True + + if es[0] == 'MEASURE' and re.search("\s?Da$", es[1]): + delete_sentence = True + + classes.append(es[0]) + w_words.append(es[1]) + s_words.append(es[2]) + + inst = (classes, w_words, s_words) + insts.append(inst) + + output_f = open(output_path, 'w+', encoding='utf-8') + for _, inst in enumerate(insts): + cur_classes, cur_tokens, cur_outputs = inst + for c, t, o in zip(cur_classes, cur_tokens, cur_outputs): + output_f.write(f'{c}\t{t}\t{o}\n') + + output_f.write(f'\t\n') + + +def main(): + if not os.path.exists(args.input_path): + raise ValueError(f"Input path {args.input_path} does not exist") + if os.path.exists(args.output_dir): + logging.info( + f"Output directory {args.output_dir} exists already. Existing files could be potentially overwritten." + ) + else: + logging.info(f"Creating output directory {args.output_dir}.") + os.makedirs(args.output_dir, exist_ok=True) + + if os.path.isdir(args.input_path): + input_paths = sorted([os.path.join(args.input_path, f) for f in os.listdir(args.input_path)]) + else: + input_paths = [args.input_path] + + for input_file in input_paths: + process_file(input_file) + + +if __name__ == "__main__": + + parser = ArgumentParser(description="Text Normalization Data Preprocessing for English") + parser.add_argument("--output_dir", required=True, type=str, help='Path to output directory.') + parser.add_argument("--input_path", required=True, type=str, help='Path to input file or input directory.') + parser.add_argument( + "--max_integer_length", + default=4, + type=int, + help='Maximum number of digits for integers that are allowed. Beyond this, the integers are verbalized digit by digit.', + ) + parser.add_argument( + "--max_denominator_length", + default=3, + type=int, + help='Maximum number of digits for denominators that are allowed. Beyond this, the denominator is verbalized digit by digit.', + ) + args = parser.parse_args() + + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/data/en/upsample.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/data/en/upsample.py new file mode 100644 index 0000000..e6331be --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/data/en/upsample.py @@ -0,0 +1,333 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script can be used to create a more class balanced file from a set of the data files of the English Google Text Normalization dataset +for better training performance. Currently this script upsamples the class types "MONEY", "MEASURE", "TIME", "FRACTION" since these are underrepresented in the Google Text Normalization dataset, but still diverse in its representations. +Of all the input files in `input_dir` this script takes the first file and computes the class patterns that occurs in it. +For those that are underrepresented, quantitatively defined as lower than `min_number`, the other files are scanned for sentences that have the missing patterns. +Those sentences are appended to the first file and outputted. + +USAGE Example: +1. Download the Google TN dataset from https://www.kaggle.com/google-nlu/text-normalization +2. Unzip the English subset (e.g., by running `tar zxvf en_with_types.tgz`). Then there will a folder named `en_with_types`. +3. Run the data_split.py, data_preprocessing.py scripts to obtain cleaned data files +4. Run this script on the training data portion +# python upsample.py \ + --input_dir=train_processed/ \ + --output_file=train_upsampled.tsv/ \ + --min_number=2000 + +In this example, the final file will be train_upsampled.tsv. +""" + + +import glob +from argparse import ArgumentParser +from collections import defaultdict +from typing import List + +import numpy as np +import regex as re + +parser = ArgumentParser(description="English Text Normalization upsampling") +parser.add_argument("--input_dir", required=True, type=str, help='Path to input directory with preprocessed data') +parser.add_argument("--output_file", required=True, type=str, help='Path to output file') +parser.add_argument("--min_number", default=2000, type=int, help='minimum number per pattern') +parser.add_argument("--pretty", action="store_true", help='Pretty print') +args = parser.parse_args() + +# global pattern tables +MONEY_PATTERNS = defaultdict(int) +MEASURE_PATTERNS = defaultdict(int) +TIME_PATTERNS = defaultdict(int) +FRACTION_PATTERNS = defaultdict(int) + +# global templates/stencils for creating patterns +money_templates = ["([0-9]|\.|,)+"] +measure_templates = ["^-?([0-9]|\.|,|/|\s)+"] +time_templates = [ + "^[0-9]+:[0-9][0-9]$", + "^[0-9]+:[0-9][0-9]\s?[a-zA-Z]+$", + "^[0-9]+\s(p|P|A|a)\.?(m|M)\.?", + "^[0-9]+(p|P|A|a)\.?(m|M)\.?", + "^[0-9]:[0-9][0-9]\s(p|P|A|a)\.?(m|M)\.?", + "^[0-9][0-9]:[0-9][0-9]\s(p|P|A|a)\.?(m|M)\.?", + "^[0-9]:[0-9][0-9](p|P|A|a)\.?(m|M)\.?", + "^[0-9][0-9]:[0-9][0-9](p|P|A|a)\.?(m|M)\.?", + "^[0-9]+.[0-9][0-9]\s?(p|P|A|a)\.?(m|M)\.?", + "^[0-9]+:[0-9]+:[0-9]+", + "^[0-9]+:[0-9]+.[0-9]+", + "^[0-9]+.[0-9]+$", + "^[0-9]+.[0-9]+\s?[a-zA-Z]+$", +] +fraction_templates = [ + "^-?[0-9]+\s?\/\s?[0-9]{3}$", + "^-?[0-9]{3}\s?\/\s?[0-9]+$", + "^[0-9]+\s[0-9]+\/[0-9]+$", + "^[0-9]+\s[0-9]+\/[0-9]+$", + "^[0-9]+\s[0-9]+\s\/\s[0-9]+$", + "^-?[0-9]+\s\/\s[0-9]+$", + "^-?[0-9]+\/[0-9]+$", +] + +# classes that still need to be upsampled, and required number of instances needed +classes_to_upsample = defaultdict(int) + + +def include_sentence(sentence_patterns) -> bool: + """ + Determines whether to use a sentence for upsampling whose patterns are provided as input. This will check the global pattern tables + if this sentence includes any patterns that are still needed. + + Args: + sentence_patterns: dictionary of patterns for a sentence grouped by class + Returns: + include: whether or not to use the sentence or for upsampling + """ + include = False + for k, v in sentence_patterns["MONEY"].items(): + if v > 0 and k in MONEY_PATTERNS and MONEY_PATTERNS[k] < args.min_number: + include = True + for k, v in sentence_patterns["MEASURE"].items(): + if v > 0 and k in MEASURE_PATTERNS and MEASURE_PATTERNS[k] < args.min_number: + include = True + for k, v in sentence_patterns["TIME"].items(): + if v > 0 and k in TIME_PATTERNS and TIME_PATTERNS[k] < args.min_number: + include = True + for k, v in sentence_patterns["FRACTION"].items(): + if v > 0 and k in FRACTION_PATTERNS and FRACTION_PATTERNS[k] < args.min_number: + include = True + + if include: + for k, v in sentence_patterns["MONEY"].items(): + if v > 0 and k in MONEY_PATTERNS: + MONEY_PATTERNS[k] += v + if MONEY_PATTERNS[k] - v < args.min_number and MONEY_PATTERNS[k] >= args.min_number: + classes_to_upsample["MONEY"] -= 1 + if classes_to_upsample["MONEY"] <= 0: + classes_to_upsample.pop("MONEY") + for k, v in sentence_patterns["MEASURE"].items(): + if v > 0 and k in MEASURE_PATTERNS: + MEASURE_PATTERNS[k] += v + if MEASURE_PATTERNS[k] - v < args.min_number and MEASURE_PATTERNS[k] >= args.min_number: + classes_to_upsample["MEASURE"] -= 1 + if classes_to_upsample["MEASURE"] <= 0: + classes_to_upsample.pop("MEASURE") + for k, v in sentence_patterns["TIME"].items(): + if v > 0 and k in TIME_PATTERNS: + TIME_PATTERNS[k] += v + if TIME_PATTERNS[k] - v < args.min_number and TIME_PATTERNS[k] >= args.min_number: + classes_to_upsample["TIME"] -= 1 + if classes_to_upsample["TIME"] <= 0: + classes_to_upsample.pop("TIME") + for k, v in sentence_patterns["FRACTION"].items(): + if v > 0 and k in FRACTION_PATTERNS: + FRACTION_PATTERNS[k] += v + if FRACTION_PATTERNS[k] - v < args.min_number and FRACTION_PATTERNS[k] >= args.min_number: + classes_to_upsample["FRACTION"] -= 1 + if classes_to_upsample["FRACTION"] <= 0: + classes_to_upsample.pop("FRACTION") + return include + + +def read_data_file(fp: str, upsample_file: bool = False): + """ Reading the raw data from a file of NeMo format + For more info about the data format, refer to the + `text_normalization doc `. + + Args: + fp: file paths + upsample_file: whether or not this input file should be used in full or only for upsampling, i.e. only as a subset + Returns: + insts: List of sentences parsed as list of words + """ + + insts, w_words, s_words, classes = [], [], [], [] + with open(fp, 'r', encoding='utf-8') as f: + sentence_patterns = { + "FRACTION": defaultdict(int), + "MEASURE": defaultdict(int), + "TIME": defaultdict(int), + "MONEY": defaultdict(int), + } + for line in f: + es = [e.strip() for e in line.strip().split('\t')] + if es[0] == '': + if not upsample_file: + inst = (classes, w_words, s_words) + insts.append(inst) + else: + ok = include_sentence(sentence_patterns) + if ok: + inst = (classes, w_words, s_words) + insts.append(inst) + # Reset + w_words, s_words, classes = [], [], [] + sentence_patterns = { + "FRACTION": defaultdict(int), + "MEASURE": defaultdict(int), + "TIME": defaultdict(int), + "MONEY": defaultdict(int), + } + + else: + classes.append(es[0]) + w_words.append(es[1]) + s_words.append(es[2]) + if not upsample_file: + register_patterns(cls=es[0], input_str=es[1], pretty=args.pretty) + else: + if es[0] in classes_to_upsample: + patterns = lookup_patterns(cls=es[0], input_str=es[1]) + update_patterns(sentence_patterns[es[0]], patterns) + if not upsample_file: + inst = (classes, w_words, s_words) + insts.append(inst) + return insts + + +def update_patterns(patterns: dict, new_patterns: dict): + """ + updates a given pattern table with counts from another table by adding them to the given table. + + Args: + patterns: main table + new_patterns: new table to update the main table with + """ + for k, v in new_patterns.items(): + patterns[k] += v + + +def register_patterns(cls: str, input_str: str, pretty: bool = False): + """ + Saves all patterns created from input string from global templates/stencils to global pattern table + + Args: + cls: class type of input_str + input_str: input string + pretty: used to pretty print patterns + """ + if cls == "MONEY": + new_dict = create_pattern(money_templates, input_str, pretty=pretty) + update_patterns(MONEY_PATTERNS, new_dict) + if cls == "MEASURE": + new_dict = create_pattern(measure_templates, input_str, pretty=pretty) + update_patterns(MEASURE_PATTERNS, new_dict) + if cls == "TIME": + new_dict = create_pattern(time_templates, input_str, pretty=pretty) + update_patterns(TIME_PATTERNS, new_dict) + if cls == "FRACTION": + new_dict = create_pattern(fraction_templates, input_str, pretty=pretty) + update_patterns(FRACTION_PATTERNS, new_dict) + + +def lookup_patterns(cls: str, input_str: str) -> dict: + """ + Look up all patterns that match an input string from global pattern table + + Args: + cls: class type of input_str + input_str: input string + """ + if cls == "MONEY": + new_dict = create_pattern(MONEY_PATTERNS.keys(), input_str) + if cls == "MEASURE": + new_dict = create_pattern(MEASURE_PATTERNS.keys(), input_str) + if cls == "TIME": + new_dict = create_pattern(TIME_PATTERNS.keys(), input_str) + if cls == "FRACTION": + new_dict = create_pattern(FRACTION_PATTERNS.keys(), input_str) + return new_dict + + +def create_pattern(templates: List[str], input_str: str, pretty: bool = False): + """ + create all patterns based on list of input templates using the input string. + + Args: + templates: list of templates/stencils + input_str: string to apply templates on to create patterns + pretty: used to pretty print patterns + """ + res = defaultdict(int) + for template in templates: + if re.search(template, input_str) is None: + continue + if not pretty: + res[re.sub(template, template, input_str)] += 1 + else: + res[re.sub(template, "@", input_str)] += 1 + return res + + +def print_stats(): + """ + print statistics on class patterns to be upsampled + """ + print("MONEY") + for k, v in MONEY_PATTERNS.items(): + print(f"\t{k}\t{v}") + print("no. patterns to upsample", classes_to_upsample["MONEY"]) + print("MEASURE") + for k, v in MEASURE_PATTERNS.items(): + print(f"\t{k}\t{v}") + print("no. patterns to upsample", classes_to_upsample["MEASURE"]) + print("TIME") + for k, v in TIME_PATTERNS.items(): + print(f"\t{k}\t{v}") + print("no. patterns to upsample", classes_to_upsample["TIME"]) + print("FRACTION") + for k, v in FRACTION_PATTERNS.items(): + print(f"\t{k}\t{v}") + print("no. patterns to upsample", classes_to_upsample["FRACTION"]) + + +def main(): + input_files = sorted(glob.glob(f"{args.input_dir}/output-*")) + print("Taking in full: ", input_files[0]) + inst_first_file = read_data_file(input_files[0]) + + measure_keys = list(MEASURE_PATTERNS.keys()) + for k in measure_keys: + if re.search("\s?st$", k) is not None or re.search("\s?Da$", k) is not None: + MEASURE_PATTERNS.pop(k) + + money_keys = list(MONEY_PATTERNS.keys()) + for k in money_keys: + if re.search("(DM|SHP|BMD|SCR|SHP|ARS|BWP|SBD)$", k) is not None: + MONEY_PATTERNS.pop(k) + + classes_to_upsample["FRACTION"] = sum(np.asarray(list(FRACTION_PATTERNS.values())) < args.min_number) + classes_to_upsample["MEASURE"] = sum(np.asarray(list(MEASURE_PATTERNS.values())) < args.min_number) + classes_to_upsample["TIME"] = sum(np.asarray(list(TIME_PATTERNS.values())) < args.min_number) + classes_to_upsample["MONEY"] = sum(np.asarray(list(MONEY_PATTERNS.values())) < args.min_number) + + print_stats() + for fp in input_files[1:]: + print("Upsamling: ", fp) + instances = read_data_file(fp, upsample_file=True) + inst_first_file.extend(instances) + print_stats() + + output_f = open(args.output_file, 'w+', encoding='utf-8') + for ix, inst in enumerate(inst_first_file): + cur_classes, cur_tokens, cur_outputs = inst + for c, t, o in zip(cur_classes, cur_tokens, cur_outputs): + output_f.write(f'{c}\t{t}\t{o}\n') + output_f.write(f'\t\n') + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/duplex_text_normalization_infer.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/duplex_text_normalization_infer.py new file mode 100644 index 0000000..3bb782e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/duplex_text_normalization_infer.py @@ -0,0 +1,166 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script contains an example on how to run inference with the DuplexTextNormalizationModel. +DuplexTextNormalizationModel is essentially a wrapper class around DuplexTaggerModel and DuplexDecoderModel. +Therefore, two trained NeMo models should be specified to run the joint evaluation +(one is a trained DuplexTaggerModel and the other is a trained DuplexDecoderModel). + +This script can perform inference for 2 settings: +1. inference from a raw file (no labels required). Each line of the file represents a single example for inference. + Specify in inference.from_file and inference.batch_size parameters. + + python duplex_text_normalization_infer.py \ + tagger_pretrained_model=PATH_TO_TRAINED_TAGGER \ + decoder_pretrained_model=PATH_TO_TRAINED_DECODER \ + mode={tn,itn,joint} \ + lang={en,ru,de} \ + inference.from_file=PATH_TO_RAW_TEXT_FILE + + The predictions will be saved at "_norm" and "_denorm" files. + +2. Interactive inference (one query at a time), set inference.interactive to True to enter the interactive mode + python duplex_text_normalization_infer.py \ + tagger_pretrained_model=PATH_TO_TRAINED_TAGGER \ + decoder_pretrained_model=PATH_TO_TRAINED_DECODER \ + mode={tn,itn,joint} \ + lang={en,ru,de} \ + inference.interactive=true + +This script uses the `/examples/nlp/duplex_text_normalization/conf/duplex_tn_config.yaml` +config file by default. The other option is to set another config file via command +line arguments by `--config-name=CONFIG_FILE_PATH'. +""" + + +import os +from typing import List + +from helpers import DECODER_MODEL, TAGGER_MODEL, instantiate_model_and_trainer +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.data.text_normalization import constants +from nemo.collections.nlp.models import DuplexTextNormalizationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging + +try: + from nemo_text_processing.text_normalization.data_loader_utils import post_process_punct + from nn_wfst.en.electronic.normalize import ElectronicNormalizer + from nn_wfst.en.whitelist.normalize import WhitelistNormalizer + + NEMO_TEXT_PROCESSING_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + NEMO_TEXT_PROCESSING_AVAILABLE = False + logging.warning( + " `nemo_text_processing` is not installed in this environment. Please refer to" + " https://github.com/NVIDIA/NeMo-text-processing and install this package before using " + " this script: `pip install nemo_text_processing`" + ) + + +@hydra_runner(config_path="conf", config_name="duplex_tn_config") +def main(cfg: DictConfig) -> None: + logging.debug(f'Config Params: {OmegaConf.to_yaml(cfg)}') + lang = cfg.lang + + if cfg.decoder_pretrained_model is None or cfg.tagger_pretrained_model is None: + raise ValueError("Both pre-trained models (DuplexTaggerModel and DuplexDecoderModel) should be provided.") + tagger_trainer, tagger_model = instantiate_model_and_trainer(cfg, TAGGER_MODEL, False) + decoder_trainer, decoder_model = instantiate_model_and_trainer(cfg, DECODER_MODEL, False) + decoder_model.max_sequence_len = 512 + tagger_model.max_sequence_len = 512 + tn_model = DuplexTextNormalizationModel(tagger_model, decoder_model, lang) + + if lang == constants.ENGLISH and NEMO_TEXT_PROCESSING_AVAILABLE: + normalizer_electronic = ElectronicNormalizer(input_case="cased", lang=lang, deterministic=True) + normalizer_whitelist = WhitelistNormalizer(input_case="cased", lang=lang, deterministic=True) + + if cfg.inference.get("from_file", False): + text_file = cfg.inference.from_file + logging.info(f'Running inference on {text_file}...') + if not os.path.exists(text_file): + raise ValueError(f'{text_file} not found.') + + with open(text_file, 'r') as f: + lines = f.readlines() + + if lang == constants.ENGLISH: + new_lines = normalizer_electronic.normalize_list(lines) + lines = [ + post_process_punct(input=input_, normalized_text=norm_) for input_, norm_ in zip(lines, new_lines) + ] + new_lines = normalizer_whitelist.normalize_list(lines) + lines = [ + post_process_punct(input=input_, normalized_text=norm_) for input_, norm_ in zip(lines, new_lines) + ] + + def _get_predictions(lines: List[str], mode: str, batch_size: int, text_file: str): + """ Runs inference on a batch data without labels and saved predictions to a file. """ + assert mode in ['tn', 'itn'] + file_name, extension = os.path.splitext(text_file) + batch, all_preds = [], [] + for i, line in enumerate(lines): + batch.append(line.strip()) + if len(batch) == batch_size or i == len(lines) - 1: + outputs = tn_model._infer(batch, [constants.DIRECTIONS_TO_MODE[mode]] * len(batch),) + all_preds.extend([x for x in outputs[-1]]) + batch = [] + assert len(all_preds) == len(lines) + out_file = f'{file_name}_{mode}{extension}' + with open(f'{out_file}', 'w') as f_out: + f_out.write("\n".join(all_preds)) + logging.info(f'Predictions for {mode} save to {out_file}.') + + batch_size = cfg.inference.get("batch_size", 8) + if cfg.mode in ['tn', 'joint']: + # TN mode + _get_predictions(lines, 'tn', batch_size, text_file) + if cfg.mode in ['itn', 'joint']: + # ITN mode + _get_predictions(lines, 'itn', batch_size, text_file) + + else: + print('Entering interactive mode.') + done = False + while not done: + print('Type "STOP" to exit.') + test_input = input('Input a test input:') + if test_input == "STOP": + done = True + if not done: + if lang == constants.ENGLISH and NEMO_TEXT_PROCESSING_AVAILABLE: + new_input = normalizer_electronic.normalize(test_input, verbose=False) + test_input = post_process_punct(input=test_input, normalized_text=new_input) + new_input = normalizer_whitelist.normalize(test_input, verbose=False) + test_input = post_process_punct(input=test_input, normalized_text=new_input) + directions = [] + inputs = [] + if cfg.mode in ['itn', 'joint']: + directions.append(constants.DIRECTIONS_TO_MODE[constants.ITN_MODE]) + inputs.append(test_input) + if cfg.mode in ['tn', 'joint']: + directions.append(constants.DIRECTIONS_TO_MODE[constants.TN_MODE]) + inputs.append(test_input) + outputs = tn_model._infer(inputs, directions)[-1] + if cfg.mode in ['joint', 'itn']: + print(f'Prediction (ITN): {outputs[0]}') + if cfg.mode in ['joint', 'tn']: + print(f'Prediction (TN): {outputs[-1]}') + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/duplex_text_normalization_test.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/duplex_text_normalization_test.py new file mode 100644 index 0000000..d80c88e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/duplex_text_normalization_test.py @@ -0,0 +1,89 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script runs evaluation on the test data. For more details on the data format refer to the +`text_normalization doc ` + +1. To evaluate the tagger model: + python duplex_text_normalization_test.py \ + tagger_pretrained_model=PATH_TO_TRAINED_TAGGER \ + mode={tn,itn,joint} \ + lang={en,ru,de} + +2. To evaluate the decoder model: + python duplex_text_normalization_test.py \ + decoder_pretrained_model=PATH_TO_TRAINED_DECODER \ + mode={tn,itn,joint} \ + lang={en,ru,de} + +3. To jointly evaluate "tagger -> decoder" pipeline the DuplexTextNormalizationModel will be used. + DuplexTextNormalizationModel is essentially a wrapper class around DuplexTaggerModel and DuplexDecoderModel. + Therefore, two trained NeMo models should be specified to run the joint evaluation + (one is a trained DuplexTaggerModel and the other is a trained DuplexDecoderModel). + Additionally, an error log will be saved in a file specified with data.test_ds.errors_log_fp (this file can be + later used with analyze_errors.py) + + python duplex_text_normalization_test.py \ + tagger_pretrained_model=PATH_TO_TRAINED_TAGGER \ + decoder_pretrained_model=PATH_TO_TRAINED_DECODER \ + mode={tn,itn,joint} \ + lang={en,ru,de} \ + data.test_ds.errors_log_fp=PATH_TO_FILE_TO_SAVE_ERROR_LOG \ + data.test_ds.use_cache=true \ + data.test_ds.batch_size=256 +""" + +from helpers import DECODER_MODEL, TAGGER_MODEL, instantiate_model_and_trainer +from omegaconf import DictConfig + +from nemo.collections.nlp.data.text_normalization import TextNormalizationTestDataset +from nemo.collections.nlp.models import DuplexTextNormalizationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@hydra_runner(config_path="conf", config_name="duplex_tn_config") +def main(cfg: DictConfig) -> None: + lang = cfg.lang + + if cfg.tagger_pretrained_model: + tagger_trainer, tagger_model = instantiate_model_and_trainer(cfg, TAGGER_MODEL, False) + tagger_model.max_sequence_len = 512 + tagger_model.setup_test_data(cfg.data.test_ds) + logging.info('Evaluating the tagger...') + tagger_trainer.test(model=tagger_model, verbose=False) + else: + logging.info('Tagger checkpoint is not provided, skipping tagger evaluation') + + if cfg.decoder_pretrained_model: + decoder_trainer, decoder_model = instantiate_model_and_trainer(cfg, DECODER_MODEL, False) + decoder_model.max_sequence_len = 512 + decoder_model.setup_multiple_test_data(cfg.data.test_ds) + logging.info('Evaluating the decoder...') + decoder_trainer.test(decoder_model) + else: + logging.info('Decoder checkpoint is not provided, skipping decoder evaluation') + + if cfg.tagger_pretrained_model and cfg.decoder_pretrained_model: + logging.info('Running evaluation of the duplex model (tagger + decoder) on the test set.') + tn_model = DuplexTextNormalizationModel(tagger_model, decoder_model, lang) + test_dataset = TextNormalizationTestDataset(cfg.data.test_ds.data_path, cfg.mode, lang) + results = tn_model.evaluate(test_dataset, cfg.data.test_ds.batch_size, cfg.data.test_ds.errors_log_fp) + print(f'\nTest results: {results}') + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/duplex_text_normalization_train.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/duplex_text_normalization_train.py new file mode 100644 index 0000000..f2daff7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/duplex_text_normalization_train.py @@ -0,0 +1,142 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script contains an example on how to train a DuplexTextNormalizationModel. +Note that DuplexTextNormalizationModel is essentially a wrapper class around +two other classes: + +(1) DuplexTaggerModel is a model for identifying spans in the input that need to +be normalized. Usually, such spans belong to semiotic classes (e.g., DATE, NUMBERS, ...). + +(2) DuplexDecoderModel is a model for normalizing the spans identified by the tagger. +For example, in the text normalization (TN) problem, each span will be converted to its +spoken form. In the inverse text normalization (ITN) problem, each span will be converted +to its written form. + +Therefore, this script consists of two parts, one is for training the tagger model +and the other is for training the decoder. + +This script uses the `/examples/nlp/duplex_text_normalization/conf/duplex_tn_config.yaml` +config file by default. The other option is to set another config file via command +line arguments by `--config-name=CONFIG_FILE_PATH'. Probably it is worth looking +at the example config file to see the list of parameters used for training. + +USAGE Example: +1. Obtain a processed dataset (refer to the `text_normalization doc `) +2. Run: +# python duplex_text_normalization_train.py \ + data.validation_ds.data_path=PATH_TO_VALIDATION_FILE \ + data.train_ds.data_path=PATH_TO_TRAIN_FILE \ + mode={tn,itn,joint} \ + lang={en,ru,de} + +There are 3 different modes. `tn` mode is for training a system for TN only. +`itn` mode is for training a system for ITN. `joint` is for training a system +that can do both TN and ITN at the same time. Note that the above command will +first train a tagger and then train a decoder sequentially. + +You can also train only a tagger (without training a decoder) by running the +following command: +# python duplex_text_normalization_train.py + data.validation_ds.data_path=PATH_TO_VALIDATION_FILE \ + data.train_ds.data_path=PATH_TO_TRAIN_FILE \ + data.test_ds.data_path=PATH_TO_TEST_FILE \ + mode={tn,itn,joint} + lang={en,ru,de} + decoder_model.do_training=false + +Or you can also train only a decoder (without training a tagger): +# python duplex_text_normalization_train.py \ + data.validation_ds.data_path=PATH_TO_VALIDATION_FILE \ + data.train_ds.data_path=PATH_TO_TRAIN_FILE \ + data.test_ds.data_path=PATH_TO_TEST_FILE \ + mode={tn,itn,joint} \ + lang={en,ru,de} \ + tagger_model.do_training=false + +To use tarred dataset for decoder training set: + + data.train_ds.use_tarred_dataset=True \ + data.train_ds.tar_metadata_file=\PATH_TO\\metadata.json + +Information on the arguments: + +Most arguments in the example config file are quite self-explanatory (e.g., +`decoder_model.optim.lr` refers to the learning rate for training the decoder). +Some arguments we want to mention are: + ++ lang: The language of the dataset. + ++ tagger_model.nemo_path: This is the path where the final trained tagger model +will be saved to. + ++ decoder_model.nemo_path: This is the path where the final trained decoder model +will be saved to. +""" + + +from helpers import DECODER_MODEL, TAGGER_MODEL, instantiate_model_and_trainer +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.data.text_normalization import TextNormalizationTestDataset +from nemo.collections.nlp.models import DuplexTextNormalizationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="duplex_tn_config") +def main(cfg: DictConfig) -> None: + logging.info(f'Config Params: {OmegaConf.to_yaml(cfg)}') + + # Train the tagger + if cfg.tagger_model.do_training: + logging.info( + "================================================================================================" + ) + logging.info('Starting training tagger...') + tagger_trainer, tagger_model = instantiate_model_and_trainer(cfg, TAGGER_MODEL, True) + tagger_exp_manager = cfg.get('tagger_exp_manager', None) + exp_manager(tagger_trainer, tagger_exp_manager) + tagger_trainer.fit(tagger_model) + logging.info('Training finished!') + + # Train the decoder + if cfg.decoder_model.do_training: + logging.info( + "================================================================================================" + ) + logging.info('Starting training decoder...') + decoder_trainer, decoder_model = instantiate_model_and_trainer(cfg, DECODER_MODEL, True) + decoder_exp_manager = cfg.get('decoder_exp_manager', None) + exp_manager(decoder_trainer, decoder_exp_manager) + decoder_trainer.fit(decoder_model) + logging.info('Training finished!') + + # Evaluation after training + if ( + hasattr(cfg.data, 'test_ds') + and cfg.data.test_ds.data_path is not None + and cfg.tagger_model.do_training + and cfg.decoder_model.do_training + ): + tn_model = DuplexTextNormalizationModel(tagger_model, decoder_model, cfg.lang) + test_dataset = TextNormalizationTestDataset(cfg.data.test_ds.data_path, cfg.mode, cfg.lang) + results = tn_model.evaluate(test_dataset, cfg.data.test_ds.batch_size, cfg.data.test_ds.errors_log_fp) + print(f'\nTest results: {results}') + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/helpers.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/helpers.py new file mode 100644 index 0000000..6c1cfe3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/helpers.py @@ -0,0 +1,100 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import pytorch_lightning as pl +from omegaconf import DictConfig + +from nemo.collections.nlp.data.text_normalization import constants +from nemo.collections.nlp.models import DuplexDecoderModel, DuplexTaggerModel +from nemo.utils import logging + +__all__ = ['TAGGER_MODEL', 'DECODER_MODEL', 'MODEL_NAMES', 'instantiate_model_and_trainer'] + +TAGGER_MODEL = 'tagger' +DECODER_MODEL = 'decoder' +MODEL_NAMES = [TAGGER_MODEL, DECODER_MODEL] + + +def instantiate_model_and_trainer(cfg: DictConfig, model_name: str, do_training: bool): + """ Function for instantiating a model and a trainer + Args: + cfg: The config used to instantiate the model and the trainer. + model_name: A str indicates whether the model to be instantiated is a tagger or a decoder (i.e., model_name should be either TAGGER_MODEL or DECODER_MODEL). + do_training: A boolean flag indicates whether the model will be trained or evaluated. + + Returns: + trainer: A PyTorch Lightning trainer + model: A NLPModel that can either be a DuplexTaggerModel or a DuplexDecoderModel + """ + assert model_name in MODEL_NAMES + + # Get configs for the corresponding models + trainer_cfg = cfg.get(f'{model_name}_trainer') + model_cfg = cfg.get(f'{model_name}_model') + pretrained_cfg = cfg.get(f'{model_name}_pretrained_model', None) + + trainer = pl.Trainer(**trainer_cfg) + if not pretrained_cfg: + logging.info(f'Initializing {model_name} model') + if model_name == TAGGER_MODEL: + model = DuplexTaggerModel(model_cfg, trainer=trainer) + if model_name == DECODER_MODEL: + model = DuplexDecoderModel(model_cfg, trainer=trainer) + elif os.path.exists(pretrained_cfg): + logging.info(f'Restoring pretrained {model_name} model from {pretrained_cfg}') + if model_name == TAGGER_MODEL: + model = DuplexTaggerModel.restore_from(pretrained_cfg) + if model_name == DECODER_MODEL: + model = DuplexDecoderModel.restore_from(pretrained_cfg) + else: + logging.info(f'Loading pretrained model {pretrained_cfg}') + if model_name == TAGGER_MODEL: + if pretrained_cfg not in DuplexTaggerModel.get_available_model_names(): + raise ( + ValueError( + f'{pretrained_cfg} not in the list of available Tagger models. Select from {DuplexTaggerModel.list_available_models()}' + ) + ) + model = DuplexTaggerModel.from_pretrained(pretrained_cfg) + if model_name == DECODER_MODEL: + if pretrained_cfg not in DuplexDecoderModel.get_available_model_names(): + raise ( + ValueError( + f'{pretrained_cfg} not in the list of available Decoder models. Select from {DuplexDecoderModel.list_available_models()}' + ) + ) + model = DuplexDecoderModel.from_pretrained(pretrained_cfg) + + # Set model.lang (if it is still None) + if model.lang is None: + model.lang = cfg.lang + assert model.lang in constants.SUPPORTED_LANGS + # Setup covering grammars (if enabled) + # We only support integrating with English TN covering grammars at the moment + if model_name == DECODER_MODEL and model_cfg.use_cg and cfg.lang == constants.ENGLISH: + if model.cg_normalizer is None: + model.setup_cgs(model_cfg) + + # Setup train and validation data + if do_training: + model.setup_training_data(train_data_config=cfg.data.train_ds) + if model_name == DECODER_MODEL: + model.setup_multiple_validation_data(val_data_config=cfg.data.validation_ds) + else: + model.setup_validation_data(val_data_config=cfg.data.validation_ds) + + logging.info(f'Model {model_name} -- Device {model.device}') + return trainer, model diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/__init__.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/__init__.py new file mode 100644 index 0000000..7d200df --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/__init__.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/__init__.py new file mode 100644 index 0000000..7d200df --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/__init__.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/__init__.py new file mode 100644 index 0000000..7d200df --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/normalize.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/normalize.py new file mode 100644 index 0000000..94eee52 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/normalize.py @@ -0,0 +1,62 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +try: + from nemo_text_processing.text_normalization.normalize import Normalizer + from nemo_text_processing.text_normalization.token_parser import TokenParser +except (ImportError, ModuleNotFoundError): + raise ModuleNotFoundError( + "The package `nemo_text_processing` was not installed in this environment. Please refer to" + " https://github.com/NVIDIA/NeMo-text-processing and install this package before using " + "this script" + ) + +from nemo.collections.common.tokenizers.moses_tokenizers import MosesProcessor + + +class ElectronicNormalizer(Normalizer): + """ + Normalizer for ELECTRONIC. + + Args: + input_case: accepting either "lower_cased" or "cased" input. + lang: language + deterministic: if True will provide a single transduction option, + for False multiple options (used for audio-based normalization) + cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache. + overwrite_cache: set to True to overwrite .far files + """ + + def __init__( + self, + input_case: str = 'cased', + lang: str = 'en', + deterministic: bool = True, + cache_dir: str = None, + overwrite_cache: bool = False, + max_number_of_permutations_per_split: int = 729, + ): + + from nn_wfst.en.electronic.tokenize_and_classify import ClassifyFst + from nn_wfst.en.electronic.verbalize_final import VerbalizeFinalFst + + self.tagger = ClassifyFst( + input_case=input_case, deterministic=deterministic, cache_dir=cache_dir, overwrite_cache=overwrite_cache + ) + self.verbalizer = VerbalizeFinalFst(deterministic=deterministic) + self.post_processor = None + self.parser = TokenParser() + self.lang = lang + self.processor = MosesProcessor(lang_id=lang) + self.max_number_of_permutations_per_split = max_number_of_permutations_per_split diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/tokenize_and_classify.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/tokenize_and_classify.py new file mode 100644 index 0000000..33694d1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/tokenize_and_classify.py @@ -0,0 +1,106 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import os + +try: + import pynini + from nemo_text_processing.text_normalization.en.graph_utils import ( + NEMO_WHITE_SPACE, + GraphFst, + delete_extra_space, + delete_space, + generator_main, + ) + from nemo_text_processing.text_normalization.en.taggers.electronic import ElectronicFst + from nemo_text_processing.text_normalization.en.taggers.punctuation import PunctuationFst + from nemo_text_processing.text_normalization.en.taggers.word import WordFst + from pynini.lib import pynutil +except (ImportError, ModuleNotFoundError): + raise ModuleNotFoundError( + "The package `nemo_text_processing` was not installed in this environment. Please refer to" + " https://github.com/NVIDIA/NeMo-text-processing and install this package before using " + "this script" + ) + +from nemo.utils import logging + + +class ClassifyFst(GraphFst): + """ + Final class that composes all other classification grammars. This class can process an entire sentence including punctuation. + For deployment, this grammar will be compiled and exported to OpenFst Finate State Archiv (FAR) File. + More details to deployment at NeMo/tools/text_processing_deployment. + + Args: + input_case: accepting either "lower_cased" or "cased" input. + deterministic: if True will provide a single transduction option, + for False multiple options (used for audio-based normalization) + cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache. + overwrite_cache: set to True to overwrite .far files + """ + + def __init__( + self, input_case: str, cache_dir: str = None, overwrite_cache: bool = False, deterministic: bool = True + ): + super().__init__(name="tokenize_and_classify", kind="classify", deterministic=deterministic) + + far_file = None + if cache_dir is not None and cache_dir != "None": + os.makedirs(cache_dir, exist_ok=True) + far_file = os.path.join(cache_dir, f"_{input_case}_en_tn_{deterministic}_deterministic.far") + if not overwrite_cache and far_file and os.path.exists(far_file): + self.fst = pynini.Far(far_file, mode="r")["tokenize_and_classify"] + logging.info(f'ClassifyFst.fst was restored from {far_file}.') + else: + logging.info(f"Creating ClassifyFst grammars.") + + punctuation = PunctuationFst(deterministic=deterministic) + punct_graph = punctuation.fst + word_graph = WordFst(deterministic=deterministic, punctuation=punctuation).fst + electonic_graph = ElectronicFst(cardinal=None, deterministic=deterministic).fst + + classify = pynutil.add_weight(electonic_graph, 1.1) | pynutil.add_weight(word_graph, 100) + + punct = pynutil.insert("tokens { ") + pynutil.add_weight(punct_graph, weight=2.1) + pynutil.insert(" }") + punct = pynini.closure( + pynini.compose(pynini.closure(NEMO_WHITE_SPACE, 1), delete_extra_space) + | (pynutil.insert(" ") + punct), + 1, + ) + token = pynutil.insert("tokens { ") + classify + pynutil.insert(" }") + token_plus_punct = ( + pynini.closure(punct + pynutil.insert(" ")) + token + pynini.closure(pynutil.insert(" ") + punct) + ) + + graph = ( + token_plus_punct + + pynini.closure( + ( + pynini.compose(pynini.closure(NEMO_WHITE_SPACE, 1), delete_extra_space) + | (pynutil.insert(" ") + punct + pynutil.insert(" ")) + ) + + token_plus_punct + ).optimize() + ) + + graph = delete_space + graph + delete_space + graph |= punct + + self.fst = graph.optimize() + + if far_file: + generator_main(far_file, {"tokenize_and_classify": self.fst}) + logging.info(f"ClassifyFst grammars are saved to {far_file}.") diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/verbalize.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/verbalize.py new file mode 100644 index 0000000..7236be7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/verbalize.py @@ -0,0 +1,41 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +try: + from nemo_text_processing.text_normalization.en.graph_utils import GraphFst + from nemo_text_processing.text_normalization.en.verbalizers.electronic import ElectronicFst +except (ImportError, ModuleNotFoundError): + raise ModuleNotFoundError( + "The package `nemo_text_processing` was not installed in this environment. Please refer to" + " https://github.com/NVIDIA/NeMo-text-processing and install this package before using " + "this script" + ) + + +class VerbalizeFst(GraphFst): + """ + Composes other verbalizer grammars. + For deployment, this grammar will be compiled and exported to OpenFst Finate State Archiv (FAR) File. + More details to deployment at NeMo/tools/text_processing_deployment. + + Args: + deterministic: if True will provide a single transduction option, + for False multiple options (used for audio-based normalization) + """ + + def __init__(self, deterministic: bool = True): + super().__init__(name="verbalize", kind="verbalize", deterministic=deterministic) + electronic_graph = ElectronicFst(deterministic=deterministic).fst + + self.fst = electronic_graph diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/verbalize_final.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/verbalize_final.py new file mode 100644 index 0000000..b2cc69c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/electronic/verbalize_final.py @@ -0,0 +1,57 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +try: + import pynini + from nemo_text_processing.text_normalization.en.graph_utils import GraphFst, delete_extra_space, delete_space + from nemo_text_processing.text_normalization.en.verbalizers.word import WordFst + from nn_wfst.en.electronic.verbalize import VerbalizeFst + from pynini.lib import pynutil +except (ImportError, ModuleNotFoundError): + raise ModuleNotFoundError( + "The package `nemo_text_processing` was not installed in this environment. Please refer to" + " https://github.com/NVIDIA/NeMo-text-processing and install this package before using " + "this script" + ) + + +class VerbalizeFinalFst(GraphFst): + """ + Finite state transducer that verbalizes an entire sentence. + + Args: + deterministic: if True will provide a single transduction option, + for False multiple options (used for audio-based normalization) + """ + + def __init__(self, deterministic: bool = True): + super().__init__(name="verbalize_final", kind="verbalize", deterministic=deterministic) + verbalize = VerbalizeFst(deterministic=deterministic).fst + word = WordFst(deterministic=deterministic).fst + types = verbalize | word + + if deterministic: + graph = ( + pynutil.delete("tokens") + + delete_space + + pynutil.delete("{") + + delete_space + + types + + delete_space + + pynutil.delete("}") + ) + else: + graph = delete_space + types + delete_space + graph = delete_space + pynini.closure(graph + delete_extra_space) + graph + delete_space + self.fst = graph diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/__init__.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/__init__.py new file mode 100644 index 0000000..7d200df --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/normalize.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/normalize.py new file mode 100644 index 0000000..6b9c0ad --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/normalize.py @@ -0,0 +1,68 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +try: + from nemo_text_processing.text_normalization.normalize import Normalizer + from nemo_text_processing.text_normalization.token_parser import TokenParser +except (ImportError, ModuleNotFoundError): + raise ModuleNotFoundError( + "The package `nemo_text_processing` was not installed in this environment. Please refer to" + " https://github.com/NVIDIA/NeMo-text-processing and install this package before using " + "this script" + ) + +from nemo.collections.common.tokenizers.moses_tokenizers import MosesProcessor + + +class WhitelistNormalizer(Normalizer): + """ + Normalizer for WHITELIST. + + Args: + input_case: accepting either "lower_cased" or "cased" input. + lang: language + deterministic: if True will provide a single transduction option, + for False multiple options (used for audio-based normalization) + cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache. + overwrite_cache: set to True to overwrite .far files + whitelist: path to a file with whitelist replacements + """ + + def __init__( + self, + input_case: str, + lang: str = 'en', + deterministic: bool = True, + cache_dir: str = None, + overwrite_cache: bool = False, + whitelist: str = None, + max_number_of_permutations_per_split: int = 729, + ): + + from nn_wfst.en.whitelist.tokenize_and_classify import ClassifyFst + from nn_wfst.en.whitelist.verbalize_final import VerbalizeFinalFst + + self.tagger = ClassifyFst( + input_case=input_case, + deterministic=deterministic, + cache_dir=cache_dir, + overwrite_cache=overwrite_cache, + whitelist=whitelist, + ) + self.verbalizer = VerbalizeFinalFst(deterministic=deterministic) + self.post_processor = None + self.parser = TokenParser() + self.lang = lang + self.processor = MosesProcessor(lang_id=lang) + self.max_number_of_permutations_per_split = max_number_of_permutations_per_split diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/tokenize_and_classify.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/tokenize_and_classify.py new file mode 100644 index 0000000..c2d69e7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/tokenize_and_classify.py @@ -0,0 +1,115 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import os + +try: + import pynini + from nemo_text_processing.text_normalization.en.graph_utils import ( + NEMO_WHITE_SPACE, + GraphFst, + delete_extra_space, + delete_space, + generator_main, + ) + from nemo_text_processing.text_normalization.en.taggers.punctuation import PunctuationFst + from nemo_text_processing.text_normalization.en.taggers.whitelist import WhiteListFst + from nemo_text_processing.text_normalization.en.taggers.word import WordFst + from pynini.lib import pynutil +except (ImportError, ModuleNotFoundError): + raise ModuleNotFoundError( + "The package `nemo_text_processing` was not installed in this environment. Please refer to" + " https://github.com/NVIDIA/NeMo-text-processing and install this package before using " + "this script" + ) + +from nemo.utils import logging + + +class ClassifyFst(GraphFst): + """ + Final class that composes all other classification grammars. This class can process an entire sentence including punctuation. + For deployment, this grammar will be compiled and exported to OpenFst Finate State Archiv (FAR) File. + More details to deployment at NeMo/tools/text_processing_deployment. + + Args: + input_case: accepting either "lower_cased" or "cased" input. + deterministic: if True will provide a single transduction option, + for False multiple options (used for audio-based normalization) + cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache. + overwrite_cache: set to True to overwrite .far files + whitelist: path to a file with whitelist replacements + """ + + def __init__( + self, + input_case: str, + cache_dir: str = None, + overwrite_cache: bool = False, + deterministic: bool = True, + whitelist: str = None, + ): + super().__init__(name="tokenize_and_classify", kind="classify", deterministic=deterministic) + + far_file = None + if cache_dir is not None and cache_dir != "None": + os.makedirs(cache_dir, exist_ok=True) + whitelist_file = os.path.basename(whitelist) if whitelist else "" + far_file = os.path.join( + cache_dir, f"_{input_case}_en_tn_{deterministic}_deterministic{whitelist_file}.far" + ) + if not overwrite_cache and far_file and os.path.exists(far_file): + self.fst = pynini.Far(far_file, mode="r")["tokenize_and_classify"] + logging.info(f'ClassifyFst.fst was restored from {far_file}.') + else: + logging.info(f"Creating ClassifyFst grammars.") + + punctuation = PunctuationFst(deterministic=deterministic) + punct_graph = punctuation.fst + word_graph = WordFst(deterministic=deterministic, punctuation=punctuation).fst + whitelist_graph = WhiteListFst(input_case=input_case, deterministic=deterministic).fst + + classify = pynutil.add_weight(whitelist_graph, 1) | pynutil.add_weight(word_graph, 100) + + punct = pynutil.insert("tokens { ") + pynutil.add_weight(punct_graph, weight=2.1) + pynutil.insert(" }") + punct = pynini.closure( + pynini.compose(pynini.closure(NEMO_WHITE_SPACE, 1), delete_extra_space) + | (pynutil.insert(" ") + punct), + 1, + ) + token = pynutil.insert("tokens { ") + classify + pynutil.insert(" }") + token_plus_punct = ( + pynini.closure(punct + pynutil.insert(" ")) + token + pynini.closure(pynutil.insert(" ") + punct) + ) + + graph = ( + token_plus_punct + + pynini.closure( + ( + pynini.compose(pynini.closure(NEMO_WHITE_SPACE, 1), delete_extra_space) + | (pynutil.insert(" ") + punct + pynutil.insert(" ")) + ) + + token_plus_punct + ).optimize() + ) + + graph = delete_space + graph + delete_space + graph |= punct + + self.fst = graph.optimize() + + if far_file: + generator_main(far_file, {"tokenize_and_classify": self.fst}) + logging.info(f"ClassifyFst grammars are saved to {far_file}.") diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/verbalize.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/verbalize.py new file mode 100644 index 0000000..c647a14 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/verbalize.py @@ -0,0 +1,41 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +try: + from nemo_text_processing.text_normalization.en.graph_utils import GraphFst + from nemo_text_processing.text_normalization.en.verbalizers.whitelist import WhiteListFst +except (ImportError, ModuleNotFoundError): + raise ModuleNotFoundError( + "The package `nemo_text_processing` was not installed in this environment. Please refer to" + " https://github.com/NVIDIA/NeMo-text-processing and install this package before using " + "this script" + ) + + +class VerbalizeFst(GraphFst): + """ + Composes other verbalizer grammars. + For deployment, this grammar will be compiled and exported to OpenFst Finate State Archiv (FAR) File. + More details to deployment at NeMo/tools/text_processing_deployment. + + Args: + deterministic: if True will provide a single transduction option, + for False multiple options (used for audio-based normalization) + """ + + def __init__(self, deterministic: bool = True): + super().__init__(name="verbalize", kind="verbalize", deterministic=deterministic) + whitelist_graph = WhiteListFst(deterministic=deterministic).fst + + self.fst = whitelist_graph diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/verbalize_final.py b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/verbalize_final.py new file mode 100644 index 0000000..550a8a8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/duplex_text_normalization/nn_wfst/en/whitelist/verbalize_final.py @@ -0,0 +1,58 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +try: + import pynini + from nemo_text_processing.text_normalization.en.graph_utils import GraphFst, delete_extra_space, delete_space + from nemo_text_processing.text_normalization.en.verbalizers.word import WordFst + from nn_wfst.en.electronic.verbalize import VerbalizeFst + from pynini.lib import pynutil +except (ImportError, ModuleNotFoundError): + raise ModuleNotFoundError( + "The package `nemo_text_processing` was not installed in this environment. Please refer to" + " https://github.com/NVIDIA/NeMo-text-processing and install this package before using " + "this script" + ) + + +class VerbalizeFinalFst(GraphFst): + """ + Finite state transducer that verbalizes an entire sentence. + + Args: + deterministic: if True will provide a single transduction option, + for False multiple options (used for audio-based normalization) + """ + + def __init__(self, deterministic: bool = True): + super().__init__(name="verbalize_final", kind="verbalize", deterministic=deterministic) + verbalize = VerbalizeFst(deterministic=deterministic).fst + word = WordFst(deterministic=deterministic).fst + types = verbalize | word + + if deterministic: + graph = ( + pynutil.delete("tokens") + + delete_space + + pynutil.delete("{") + + delete_space + + types + + delete_space + + pynutil.delete("}") + ) + else: + graph = delete_space + types + delete_space + graph = delete_space + pynini.closure(graph + delete_extra_space) + graph + delete_space + self.fst = graph diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/build_index.py b/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/build_index.py new file mode 100644 index 0000000..eeba5c8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/build_index.py @@ -0,0 +1,201 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import pickle as pkl +import random +from argparse import ArgumentParser + +import h5py +import numpy as np +import torch +from omegaconf import DictConfig, OmegaConf +from sklearn.decomposition import PCA +from tqdm import tqdm + +from nemo.collections.nlp.models import EntityLinkingModel +from nemo.utils import logging + +try: + import faiss +except ModuleNotFoundError: + logging.warning("Faiss is required for building the index. Please install faiss-gpu") + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + +def build_index(cfg: DictConfig, model: object): + """ + Builds faiss index from index dataset specified in the config. + + Args: + cfg (DictConfig): Config file specifying index parameters + model (object): Encoder model + """ + + # Get index dataset embeddings + # PCA model exists and index embeddings have already been PCAed, no need to re-extract/PCA them + if cfg.apply_pca and os.path.isfile(cfg.pca.pca_save_name) and os.path.isfile(cfg.pca_embeddings_save_name): + logging.info("Loading reduced dimensionality embeddings") + embeddings = h5py.File(cfg.pca_embeddings_save_name, "r") + embeddings = embeddings[cfg.index_ds.name][:] + + elif os.path.isfile(cfg.embedding_save_name): + logging.info("Loading previously extracted index dataset embeddings") + embeddings = h5py.File(cfg.embedding_save_name, "r") + embeddings = embeddings[cfg.index_ds.name][:] + + else: + logging.info("Encoding index dataset, this may take a while") + index_dataloader = model.setup_dataloader(cfg.index_ds, is_index_data=True) + embeddings, concept_ids = get_index_embeddings(cfg, index_dataloader, model) + + # Create pca model to reduce dimensionality of index dataset and decrease memory footprint + if cfg.apply_pca: + + # Need to train PCA model and apply PCA transformation with newly trained model + if not os.path.isfile(cfg.pca.pca_save_name): + logging.info("Fitting PCA model for embedding dimensionality reduction") + pca_train_set = random.sample(list(embeddings), k=int(len(embeddings) * cfg.pca.sample_fraction)) + pca = PCA(n_components=cfg.pca.output_dim) + pca.fit(pca_train_set) + pkl.dump(pca, open(cfg.pca.pca_save_name, "wb")) + embeddings = reduce_embedding_dim(pca, embeddings, cfg) + + # PCA model already trained, just need to reduce dimensionality of all embeddings + elif not os.path.isfile(cfg.pca_embeddings_save_name): + pca = pkl.load(open(cfg.pca.pca_save_name, "rb")) + embeddings = reduce_embedding_dim(pca, embeddings, cfg) + + # Build faiss index from embeddings + logging.info(f"Training index with embedding dim size {cfg.dims} using {faiss.get_num_gpus()} gpus") + quantizer = faiss.IndexFlatL2(cfg.dims) + index = faiss.IndexIVFFlat(quantizer, cfg.dims, cfg.nlist) + index = faiss.index_cpu_to_all_gpus(index) + index.train(embeddings) + + logging.info("Adding dataset embeddings to index") + for i in tqdm(range(0, embeddings.shape[0], cfg.index_batch_size)): + index.add(embeddings[i : i + cfg.index_batch_size]) + + logging.info("Saving index") + faiss.write_index(faiss.index_gpu_to_cpu(index), cfg.index_save_name) + logging.info("Index built and saved") + + +def reduce_embedding_dim(pca, embeddings, cfg): + """Apply PCA transformation to index dataset embeddings""" + + logging.info("Applying PCA transformation to entire index dataset") + embeddings = np.array(pca.transform(embeddings), dtype=np.float32) + emb_file = h5py.File(cfg.pca_embeddings_save_name, "w") + emb_file.create_dataset(cfg.index_ds.name, data=embeddings) + emb_file.close() + + return embeddings + + +def get_index_embeddings(cfg: DictConfig, dataloader: object, model: object): + """Use entity linking encoder to get embeddings for full index dataset""" + embeddings = [] + concept_ids = [] + + with torch.no_grad(): + for batch in tqdm(dataloader): + input_ids, token_type_ids, input_mask, batch_concept_ids = batch + input_ids = input_ids.to(device) + token_type_ids = token_type_ids.to(device) + input_mask = input_mask.to(device) + batch_embeddings = model.forward( + input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=input_mask + ) + + embeddings.extend(batch_embeddings.detach().cpu().numpy()) + concept_ids.extend(batch_concept_ids.numpy()) + + emb_file = h5py.File(cfg.embedding_save_name, "w") + emb_file.create_dataset(cfg.index_ds.name, data=embeddings) + emb_file.close() + + pkl.dump(concept_ids, open(cfg.concept_id_save_name, "wb")) + + return embeddings, concept_ids + + +def load_model(cfg: DictConfig, restore: bool): + """ + Loads encoder model. + + Args: + cfg: Config file specifying model parameters + restore: Whether to restore model weights trained + by the user. Otherwise will load weights + used before self alignment pretraining. + """ + + if restore: + model = EntityLinkingModel.restore_from(cfg.nemo_path) + else: + cfg.train_ds = None + cfg.validation_ds = None + cfg.test_ds = None + model = EntityLinkingModel(cfg) + + model = model.to(device) + + return model + + +def main(cfg: DictConfig, restore: bool): + """ + Builds new index if one hasn't been built yet. + + Args: + cfg: Config file specifying index parameters + restore: Whether to restore model weights trained + by the user. Otherwise will load weights + used before self alignment pretraining. + """ + + logging.info("Loading entity linking encoder model") + model = load_model(cfg.model, restore) + + if not os.path.isfile(cfg.index.index_save_name) or ( + cfg.apply_pca and not os.path.isfile(cfg.index.pca.pca_save_name) + ): + logging.info("Building index") + build_index(cfg.index, model) + else: + logging.info("Index and pca model (if required) already exists. Skipping build index step.") + + if not os.path.isfile(cfg.index.idx_to_id): + logging.info("Mapping entity index postions to ids") + map_idx_to_ids(cfg.index) + else: + logging.info("Map from concept index to id already exists. Skipping mapping step.") + + +if __name__ == '__main__': + parser = ArgumentParser() + parser.add_argument( + "--restore", action="store_true", help="Whether to restore encoder model weights from nemo path" + ) + parser.add_argument("--project_dir", required=False, type=str, default=".") + parser.add_argument("--cfg", required=False, type=str, default="./conf/umls_medical_entity_linking_config.yaml") + args = parser.parse_args() + + cfg = OmegaConf.load(args.cfg) + cfg.project_dir = args.project_dir + + main(cfg, args.restore) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/conf/tiny_example_entity_linking_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/conf/tiny_example_entity_linking_config.yaml new file mode 100644 index 0000000..b7f538c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/conf/tiny_example_entity_linking_config.yaml @@ -0,0 +1,90 @@ +project_dir: null +name: SelfAlignmentPretrainingForMedicalEntityLinking +trainer: + devices: 1 + num_nodes: 1 + max_epochs: 2 + max_steps: -1 + accumulate_grad_batches: 1 + precision: 16 + accelerator: gpu + strategy: ddp + gradient_clip_val: 0.0 + log_every_n_steps: 1 + val_check_interval: 2 + enable_checkpointing: False + logger: false +model: + nemo_path: ??? + max_seq_length: 128 + language_model: + pretrained_model_name: bert-base-uncased + config_file: null + config: null + lm_checkpoint: null + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} + vocab_file: null + tokenizer_model: null + do_lower_case: true + loss_params: null + train_ds: + data_file: ??? + max_seq_length: ${model.max_seq_length} + batch_size: 8 + shuffle: true + num_workers: 2 + pin_memory: false + drop_last: false + validation_ds: + data_file: ??? + max_seq_length: ${model.max_seq_length} + batch_size: 8 + shuffle: false + num_workers: 2 + pin_memory: false + drop_last: false + optim: + name: adam + lr: 3.0e-05 + weight_decay: 0.0 + sched: + name: CosineAnnealing + warmup_steps: null + warmup_ratio: 0.1 + min_lr: 0.0 + last_epoch: -1 +index: + dims: 768 + nlist: 2 + top_n: 3 + query_num_factor: 20 + index_save_name: ??? + index_batch_size: 10 + index_ds: + name: tiny_example + data_file: ??? + max_seq_length: ${model.max_seq_length} + batch_size: 100 + shuffle: false + num_workers: 2 + pin_memory: false + drop_last: false + idx_to_id: ${project_dir}/idx_to_id.pkl + id_to_string: ${project_dir}/id_to_string.pkl + concept_id_save_name: ${project_dir}/tiny_example_concept_ids.pkl + embedding_save_name: ${project_dir}/tiny_example_concept_embeddings.hdf5 + pca_embeddings_save_name: null + apply_pca: false + pca: null +exp_manager: + exp_dir: . + name: ${project_dir}/SelfAlignmentPretrainingTinyExample + create_tensorboard_logger: true + create_checkpoint_callback: true +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/conf/umls_medical_entity_linking_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/conf/umls_medical_entity_linking_config.yaml new file mode 100644 index 0000000..ad636ef --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/conf/umls_medical_entity_linking_config.yaml @@ -0,0 +1,95 @@ +project_dir: ??? +name: SelfAlignmentPretrainingForMedicalEntityLinking +trainer: + devices: 1 + num_nodes: 1 + max_epochs: 2 + max_steps: -1 + accumulate_grad_batches: 1 + precision: 16 + accelerator: gpu + strategy: ddp + gradient_clip_val: 0.0 + log_every_n_steps: 1 + val_check_interval: 1000 + enable_checkpointing: False + logger: false +model: + nemo_path: ${project_dir}/sap_bert_umls.nemo + raw_data: ${project_dir}/data/MRCONSO.RRF + max_seq_length: 128 + language_model: + pretrained_model_name: bert-base-uncased + config_file: null + config: null + lm_checkpoint: null + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} + vocab_file: null + tokenizer_model: null + do_lower_case: true + train_ds: + data_file: ${project_dir}/data/umls_train_pairs.tsv + max_seq_length: ${model.max_seq_length} + batch_size: 128 + shuffle: true + num_workers: 2 + pin_memory: false + drop_last: false + validation_ds: + data_file: ${project_dir}/data/umls_validation_pairs.tsv + max_seq_length: ${model.max_seq_length} + batch_size: 128 + shuffle: false + num_workers: 2 + pin_memory: false + drop_last: false + optim: + name: adam + lr: 3.0e-05 + weight_decay: 0.0 + sched: + name: CosineAnnealing + warmup_steps: null + warmup_ratio: 0.1 + min_lr: 0.0 + last_epoch: -1 +index: + dims: 256 + nlist: 300 + top_n: 5 + query_num_factor: 20 + index_save_name: ${project_dir}/medical_entity_linking_index + index_batch_size: 1000 + raw_data: ${model.raw_data} + index_ds: + name: umls + data_file: ${project_dir}/data/umls_index_concepts.tsv + max_seq_length: ${model.max_seq_length} + batch_size: 128 + shuffle: false + num_workers: 2 + pin_memory: false + drop_last: false + idx_to_id: ${project_dir}/data/idx_to_id.pkl + id_to_string: ${project_dir}/data/id_to_string.pkl + concept_id_save_name: ${project_dir}/data/concept_ids.pkl + embedding_save_name: ${project_dir}/data/medical_concept_embeddings.hdf5 + pca_embeddings_save_name: ${project_dir}/data/medical_concept_reduced_${index.dims}dim_embeddings.hdf5 + apply_pca: true + pca: + input_dim: 756 + output_dim: ${index.dims} + sample_fraction: 0.5 + pca_save_name: ${project_dir}/${index.pca.input_dim}_to_${index.pca.output_dim}_pca_model.pkl +exp_manager: + exp_dir: ${project_dir}/medical_entity_linking_experiments + name: sap_bert_umls + create_tensorboard_logger: true + create_checkpoint_callback: true +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/data/umls_dataset_processing.py b/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/data/umls_dataset_processing.py new file mode 100644 index 0000000..03a17da --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/data/umls_dataset_processing.py @@ -0,0 +1,189 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import itertools +import pickle as pkl +import random +from argparse import ArgumentParser + +import pandas as pd +from omegaconf import OmegaConf +from tqdm import tqdm + +# Info on these headers can be found here on the UMLS website https://www.ncbi.nlm.nih.gov/books/NBK9685/ +# section 3.3.4 Table 1 +HEADERS = [ + 'CUI', + 'LAT', + 'TS', + 'LUI', + 'STT', + 'SUI', + 'ISPREF', + 'AUI', + 'SAUI', + 'SCUI', + 'SDUI', + 'SAB', + 'TTY', + 'CODE', + 'STR', + 'SRL', + 'SUPPRESS', + 'CVF', +] + + +def process_umls_training_dataset(data_path, train_save_name, val_save_name, max_pairs, train_split, headers): + """ + Generates and saves UMLS self alignment pretraining train and validation data. Takes the raw .RRF UMLS + data file and creates different pair combinations for entities with the same CUI. Each row in the output + will be formatted as 'CUI EntitySynonym1 EntitySynonym2' with each item in a row separated by tabs. + Saves two .tsv output files, one for the train split and one for the validation split. + Only data marked as English is added to the train and val splits. + + Arguments: + data_path (str): path to MRCONSO.RRF UMLS data file + train_save_name (str): path to where training data will be saved + val_save_name (str): path to where validation data will be saved + max_pairs (int): max number of pairs for any one CUI added to the train + or validation splits + train_split (float): precentage of raw data to be added to train set split + headers (list): column lables within MRCONSO.RRF + """ + + print("Loading training data file...") + df = pd.read_table(data_path, names=headers, index_col=False, delimiter='|') + train_file = open(train_save_name, 'w') + val_file = open(val_save_name, 'w') + + cui = df["CUI"].iloc[0] + names = [] + random.seed(2021) + + for idx in tqdm(range(len(df))): + # Address incorrectly formatted data + if type(df["STR"].iloc[idx]) != str or "|" in df["STR"].iloc[idx]: + continue + + # Collect all english concept strings matching the current CUI + if df["CUI"].iloc[idx] == cui and df["LAT"].iloc[idx] == "ENG": + concept_string = df["STR"].iloc[idx] + names.append(concept_string) + + else: + # Pair off concept synonyms to make training and val sets + pairs = list(itertools.combinations(names, 2)) + + if len(pairs) == 0: + # Not enough concepts gathered to make a pair + cui = df["CUI"].iloc[idx] + names = [df["STR"].iloc[idx]] + continue + + # Removing leading C to convert label string to int + cui = int(cui[1:]) + random.shuffle(pairs) + + # Keep up to max pairs number pairs for any one concept + for pair in pairs[:max_pairs]: + + # Want concepts in train and val splits to be randomly selected and mutually exclusive + add_to_train = random.random() + + if add_to_train <= train_split: + train_file.write(f'{cui}\t{pair[0]}\t{pair[1]}\n') + else: + val_file.write(f'{cui}\t{pair[0]}\t{pair[1]}\n') + + # Switch to next concept + cui = df["CUI"].iloc[idx] + names = [df["STR"].iloc[idx]] + + train_file.close() + val_file.close() + print("Finished making training and validation data") + + +def process_umls_index_dataset(data_path, data_savename, id2string_savename, headers): + """ + Generates data file needed to build a UMLS index and a hash table mapping each + CUI to one canonical concept string. Takes the raw .RRF data file and saves + a .tsv indec concept file as well as the a .pkl file of cui to concept string + mappings. Only data marked as English is added to the index data file. + + Arguments: + data_path (str): path to MRCONSO.RRF UMLS data file + data_savename (str): path to where .tsv index data will be saved + id2string_savename (str): path to where .pkl cui to string mapping will + be saved + headers (list): column lables within MRCONSO.RRF + """ + + print("Loading index data file...") + df = pd.read_table(data_path, names=headers, index_col=False, delimiter='|') + id2string = {} + + with open(data_savename, "w") as outfile: + for idx, row in tqdm(df.iterrows(), total=df.shape[0]): + # Address incorrectly formatted data + if type(row["STR"]) != str or "|" in row["STR"]: + continue + + cui = row["CUI"] + sent = row["STR"] + + # Removing leading C to convert label string to int + cui = int(cui[1:]) + + # Only keeping english concepts + if row["LAT"] == "ENG": + outfile.write(f'{cui}\t{sent}\n') + + # Matching each cui to one canonical string represention + if cui not in id2string and ":" not in sent: + id2string[cui] = sent + + outfile.close() + pkl.dump(id2string, open(id2string_savename, "wb")) + print("Finished saving index data and id to concept mapping") + + +if __name__ == '__main__': + parser = ArgumentParser() + parser.add_argument("--index", action="store_true", help="Whether to process data for building an index") + parser.add_argument("--project_dir", required=False, type=str, default=".") + parser.add_argument("--cfg", required=False, type=str, default="conf/umls_medical_entity_linking_config.yaml") + parser.add_argument( + "--max_pairs", required=False, type=int, default=50, help="Max number of train pairs for a single concepts" + ) + parser.add_argument( + "--train_split", required=False, type=float, default=0.99, help="Precentage of data to add to train set" + ) + + args = parser.parse_args() + cfg = OmegaConf.load(args.cfg) + cfg.project_dir = args.project_dir + + if args.index: + process_umls_index_dataset(cfg.index.raw_data, cfg.index.index_ds.data_file, cfg.index.id_to_string, HEADERS) + else: + process_umls_training_dataset( + cfg.model.raw_data, + cfg.model.train_ds.data_file, + cfg.model.validation_ds.data_file, + args.max_pairs, + args.train_split, + HEADERS, + ) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/query_index.py b/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/query_index.py new file mode 100644 index 0000000..6cb51a7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/query_index.py @@ -0,0 +1,166 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import pickle as pkl +from argparse import ArgumentParser +from collections import OrderedDict +from typing import Dict + +import numpy as np +import torch +from build_index import load_model +from omegaconf import DictConfig, OmegaConf + +from nemo.utils import logging + +try: + import faiss +except ModuleNotFoundError: + logging.warning("Faiss is required for building the index. Please install faiss-gpu") + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + +def get_query_embedding(query, model): + """Use entity linking encoder to get embedding for index query""" + model_input = model.tokenizer( + query, + add_special_tokens=True, + padding=True, + truncation=True, + max_length=512, + return_token_type_ids=True, + return_attention_mask=True, + ) + + query_emb = model.forward( + input_ids=torch.LongTensor([model_input["input_ids"]]).to(device), + token_type_ids=torch.LongTensor([model_input["token_type_ids"]]).to(device), + attention_mask=torch.LongTensor([model_input["attention_mask"]]).to(device), + ) + + return query_emb + + +def query_index( + query: str, cfg: DictConfig, model: object, index: object, pca: object, idx2id: dict, id2string: dict, +) -> Dict: + + """ + Query the nearest neighbor index of entities to find the + concepts in the index dataset that are most similar to the + query. + + Args: + query (str): entity to look up in the index + cfg (DictConfig): config object to specifiy query parameters + model (EntityLinkingModel): entity linking encoder model + index (object): faiss index + pca (object): sklearn pca transformation to be applied to queries + idx2id (dict): dictionary mapping unique concept dataset index to + its CUI + id2string (dict): dictionary mapping each unqiue CUI to a + representative english description of + the concept + Returns: + A dictionary with the concept ids of the index's most similar + entities as the keys and a tuple containing the string + representation of that concept and its cosine similarity to + the query as the values. + """ + query_emb = get_query_embedding(query, model).detach().cpu().numpy() + + if cfg.apply_pca: + query_emb = pca.transform(query_emb) + + dist, neighbors = index.search(query_emb.astype(np.float32), cfg.query_num_factor * cfg.top_n) + dist, neighbors = dist[0], neighbors[0] + unique_ids = OrderedDict() + neighbor_idx = 0 + + # Many of nearest neighbors could map to the same concept id, their idx is their unique identifier + while len(unique_ids) < cfg.top_n and neighbor_idx < len(neighbors): + concept_id_idx = neighbors[neighbor_idx] + concept_id = idx2id[concept_id_idx] + + # Only want one instance of each unique concept + if concept_id not in unique_ids: + concept = id2string[concept_id] + unique_ids[concept_id] = (concept, 1 - dist[neighbor_idx]) + + neighbor_idx += 1 + + unique_ids = dict(unique_ids) + + return unique_ids + + +def main(cfg: DictConfig, restore: bool): + """ + Loads faiss index and allows commandline queries + to the index. Builds new index if one hasn't been built yet. + + Args: + cfg: Config file specifying index parameters + restore: Whether to restore model weights trained + by the user. Otherwise will load weights + used before self alignment pretraining. + """ + + if not os.path.isfile(cfg.index.index_save_name) or ( + cfg.apply_pca and not os.path.isfile(cfg.index.pca.pca_save_name) or not os.path.isfile(cfg.index.idx_to_id) + ): + logging.warning("Either no index and/or no mapping from entity idx to ids exists. Please run `build_index.py`") + return + + logging.info("Loading entity linking encoder model") + model = load_model(cfg.model, restore) + + logging.info("Loading index and associated files") + index = faiss.read_index(cfg.index.index_save_name) + idx2id = pkl.load(open(cfg.index.idx_to_id, "rb")) + id2string = pkl.load(open(cfg.index.id_to_string, "rb")) # Should be created during dataset prep + + if cfg.index.apply_pca: + pca = pkl.load(open(cfg.index.pca.pca_save_name, "rb")) + + while True: + query = input("enter index query: ") + output = query_index(query, cfg.top_n, cfg.index, model, index, pca, idx2id, id2string) + + if query == "exit": + break + + for concept_id in output: + concept_details = output[concept_id] + concept_id = "C" + str(concept_id).zfill(7) + print(concept_id, concept_details) + + print("----------------\n") + + +if __name__ == '__main__': + parser = ArgumentParser() + parser.add_argument( + "--restore", action="store_true", help="Whether to restore encoder model weights from nemo path" + ) + parser.add_argument("--project_dir", required=False, type=str, default=".") + parser.add_argument("--cfg", required=False, type=str, default="./conf/umls_medical_entity_linking_config.yaml") + args = parser.parse_args() + + cfg = OmegaConf.load(args.cfg) + cfg.project_dir = args.project_dir + + main(cfg, args.restore) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/self_alignment_pretraining.py b/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/self_alignment_pretraining.py new file mode 100644 index 0000000..a1ac1ac --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/entity_linking/self_alignment_pretraining.py @@ -0,0 +1,53 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# Please see tutorial at Nemo/tutorials/nlp/Entity_Linking_Medical.ipynb for +# more information on entity linking and self alignment pretraining. + +from omegaconf import DictConfig, OmegaConf +from pytorch_lightning import Trainer + +from nemo.collections.nlp.models import EntityLinkingModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="umls_medical_entity_linking_config.yaml") +def main(cfg: DictConfig) -> None: + # PTL 2.0 has find_unused_parameters as False by default, so its required to set it to True + # when there are unused parameters here + if cfg.trainer.strategy == 'ddp': + cfg.trainer.strategy = "ddp_find_unused_parameters_true" + logging.info(f"\nConfig Params:\n{OmegaConf.to_yaml(cfg)}") + trainer = Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + logging.info(f"Loading weights from pretrained model {cfg.model.language_model.pretrained_model_name}") + model = EntityLinkingModel(cfg=cfg.model, trainer=trainer) + logging.info("===========================================================================================") + logging.info('Starting training...') + trainer.fit(model) + logging.info('Training finished!') + logging.info("===========================================================================================") + + if cfg.model.nemo_path: + # '.nemo' file contains the last checkpoint and the params to initialize the model + model.save_to(cfg.model.nemo_path) + logging.info(f'Model is saved into `.nemo` file: {cfg.model.nemo_path}') + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/glue_benchmark/glue_benchmark.py b/NeMo-2.0.0.rc0.beta/examples/nlp/glue_benchmark/glue_benchmark.py new file mode 100644 index 0000000..3cb5f8e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/glue_benchmark/glue_benchmark.py @@ -0,0 +1,77 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +## Tasks +This script works with all GLUE Benchmark tasks, more details about the GLUE Benchmark could be found at +https://gluebenchmark.com/ + +More details on how to use this script could be found in tutorials/nlp/GLUE_Benchmark.ipynb + +## Model Training + +To train GLUEModel with the default config file, run: + python glue_benchmark.py \ + model.dataset.data_dir= \ + model.task_name=TASK_NAME \ + trainer.max_epochs= \ + trainer.devices="[] + +Supported task names: +["cola", "sst-2", "mrpc", "sts-b", "qqp", "mnli", "qnli", "rte", "wnli"] +Note, MNLI task includes both matched and mismatched dev sets +""" + +import os + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models import GLUEModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_name="glue_benchmark_config") +def main(cfg: DictConfig) -> None: + # PTL 2.0 has find_unused_parameters as False by default, so its required to set it to True + # when there are unused parameters like here + if cfg.trainer.strategy == 'ddp': + cfg.trainer.strategy = "ddp_find_unused_parameters_true" + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(**cfg.trainer) + exp_manager_cfg = cfg.get("exp_manager", None) + + if exp_manager_cfg: + exp_manager_cfg.name = cfg.model.task_name + logging.info(f'Setting task_name to {exp_manager_cfg.name} in exp_manager') + exp_manager(trainer, exp_manager_cfg) + + if cfg.model.nemo_path and os.path.exists(cfg.model.nemo_path): + model = GLUEModel.restore_from(cfg.model.nemo_path) + logging.info(f'Restoring model from {cfg.model.nemo_path}') + model.update_data_dir(data_dir=cfg.model.dataset.data_dir) + model.setup_training_data() + model.setup_multiple_validation_data() + trainer.fit(model) + else: + model = GLUEModel(cfg.model, trainer=trainer) + trainer.fit(model) + if cfg.model.nemo_path: + model.save_to(cfg.model.nemo_path) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/glue_benchmark/glue_benchmark_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/glue_benchmark/glue_benchmark_config.yaml new file mode 100644 index 0000000..21cdc04 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/glue_benchmark/glue_benchmark_config.yaml @@ -0,0 +1,82 @@ +# GLUE Benchmark with pre-trained BERT models +supported_tasks: &supported_tasks ['cola', 'sst-2', 'mrpc', 'sts-b', 'qqp', 'mnli', 'qnli', 'rte', 'wnli'] + +trainer: + devices: 1 # the number of gpus, 0 for CPU + num_nodes: 1 + max_epochs: 3 + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + precision: 16 + accelerator: gpu + strategy: ddp + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + +model: + task_name: &task_name mrpc # choose from: ["cola", "sst-2", "mrpc", "sts-b", "qqp", "mnli", "qnli", "rte", "wnli"] GLUE task name, MNLI includes both matched and mismatched dev sets + supported_tasks: *supported_tasks + output_dir: null # dir to write write predictions + nemo_path: null # filename to save the model and associated artifacts to .nemo file + dataset: + data_dir: ??? # /path/to/data + max_seq_length: 128 + use_cache: true + + # shared across dataloaders: + num_workers: 2 + pin_memory: false + drop_last: false + + train_ds: + ds_item: 'train.tsv' + shuffle: true + num_samples: -1 + batch_size: 32 + + validation_ds: + ds_item: 'dev.tsv' # for MNLI 'dev_matched.tsv' and 'dev_mismatched.tsv' will de used + shuffle: false + num_samples: -1 + batch_size: 32 + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null # only necessary for adding transformer/bert-specific special tokens to tokenizer if the tokenizer does not already have these inherently. + + language_model: + pretrained_model_name: bert-base-uncased + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + optim: + name: adam + lr: 5e-5 + weight_decay: 0.00 + + sched: + name: WarmupAnnealing + # Scheduler params + warmup_steps: null + warmup_ratio: 0.1 + last_epoch: -1 + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + +exp_manager: + exp_dir: null # exp_dir for your experiment, if None, defaults to "./NeMo_experiments" + name: *task_name # The name of your model + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/bert_dpr.py b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/bert_dpr.py new file mode 100644 index 0000000..2d9cd96 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/bert_dpr.py @@ -0,0 +1,35 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models import BertDPRModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="bert_ir_config") +def main(cfg: DictConfig) -> None: + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + bert_dpr_model = BertDPRModel(cfg.model, trainer=trainer) + trainer.fit(bert_dpr_model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/bert_joint_ir.py b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/bert_joint_ir.py new file mode 100644 index 0000000..1bb164e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/bert_joint_ir.py @@ -0,0 +1,35 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models import BertJointIRModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="bert_ir_config") +def main(cfg: DictConfig) -> None: + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + bert_joint_ir_model = BertJointIRModel(cfg.model, trainer=trainer) + trainer.fit(bert_joint_ir_model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/conf/bert_ir_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/conf/bert_ir_config.yaml new file mode 100644 index 0000000..56e573e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/conf/bert_ir_config.yaml @@ -0,0 +1,99 @@ +# Fine-tuning BERT model for information retrieval +name: &name BertIR +trainer: + devices: 1 # the number of gpus, 0 for CPU, or list with gpu indices + num_nodes: 1 + max_epochs: 2 # the number of training epochs + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + precision: 16 # 16 to use AMP + accelerator: gpu + strategy: ddp + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 0.05 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + enable_checkpointing: False # provided by exp_manager + logger: false # provided by exp_manager + +model: + nemo_path: null # exported .nemo path + + language_model: + pretrained_model_name: bert-base-uncased + sim_score_dropout: 0.1 + lm_checkpoint: null + config: + attention_probs_dropout_prob: 0.1 + hidden_act: gelu + hidden_dropout_prob: 0.1 + hidden_size: 768 + initializer_range: 0.02 + intermediate_size: 3072 + max_position_embeddings: 512 + num_attention_heads: 12 + num_hidden_layers: 12 + type_vocab_size: 2 + vocab_size: 30522 + config_file: null # json file, precedence over config + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # tokenizer that inherits from TokenizerSpec + vocab_file: null # path to vocab file + tokenizer_model: null # tokenizer model for sentencepiece + special_tokens: null + + train_ds: + passages: null # path to file with passages and their indices + queries: null # path to file with training queries and their indices + query_to_passages: null + # path to file with training examples which have the form of + # (query_id, relevant_passage_id, irrelevant_passage_1_id, ..., irrelevant_passage_n_id) + num_negatives: 10 + batch_size: 6 + psg_cache_format: npz + shuffle: true + num_samples: -1 # number of samples to be considered, -1 means all the dataset + num_workers: 1 + drop_last: false + pin_memory: false + + validation_ds: + passages: null # path to file with passages and their indices + queries: null # path to file with validation queries and their indices + query_to_passages: null # path to file with passages to re-rank for each validation query + num_negatives: 10 + batch_size: 6 + psg_cache_format: pkl + shuffle: false + num_samples: -1 # number of samples to be considered, -1 means all the dataset + num_workers: 1 + drop_last: false + pin_memory: false + + optim: + name: adam + lr: 1e-5 + betas: [0.9, 0.999] + weight_decay: 0 + + sched: + name: WarmupAnnealing + warmup_steps: null + warmup_ratio: 0.05 + last_epoch: -1 + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + +exp_manager: + exp_dir: null # where to store logs and checkpoints + name: *name # name of experiment + create_tensorboard_logger: True + create_checkpoint_callback: True + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/conf/megatron_bert_embedding_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/conf/megatron_bert_embedding_config.yaml new file mode 100644 index 0000000..0b57313 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/conf/megatron_bert_embedding_config.yaml @@ -0,0 +1,155 @@ +name: megatron_bert +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice we don't usually train for more than 1 epoch. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + benchmark: False + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_bert + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + filename: 'megatron_bert--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + + +model: + # model parallelism + mcore_bert: True + micro_batch_size: 4 + global_batch_size: 8 + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 + virtual_pipeline_model_parallel_size: null + + # model architecture + encoder_seq_length: 512 + max_position_embeddings: ${.encoder_seq_length} + position_embedding_type: 'learned_absolute' # Position embedding type. Options ['learned_absolute', 'rope', 'alibi', 'kerple' , 'xpos', 'sandwich'] xpos and sandwich are experimental. + num_layers: 24 + hidden_size: 1024 + ffn_hidden_size: 4096 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 16 + transformer_block_type: post_ln + add_pooler: True + add_lm_head: False + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + hidden_dropout: 0.1 # Dropout probability for hidden state transformer. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: False # scale Q * K^T by 1 / layer-number. + normalization: layernorm + layernorm_epsilon: 1e-12 + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + bert_binary_head: True # BERT binary head + megatron_legacy: False + tokenizer: + library: 'huggingface' + type: 'intfloat/e5-large-unsupervised' + model: null + vocab_file: null + merge_file: null + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: False + + # miscellaneous + seed: 1234 + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + ## Activation Checkpointing + # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed. + # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+). + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + # 'full' will checkpoint the entire transformer layer. + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model. + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null + # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory. + # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage. + num_micro_batches_with_partial_activation_checkpoints: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed + # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is + # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint + # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'. + # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage. + activations_checkpoint_layers_per_pipeline: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later + # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than + # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage + # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints', + # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path. + sequence_parallel: False + + data: + # Path to data must be specified by the user. + data_train: null + data_validation: null + hard_negatives_to_train: 4 + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap + splits_string: 900,50,50 + seq_length: ${model.encoder_seq_length} + skip_warmup: True + num_workers: 0 + dataloader_type: single # cyclic, LDDL + reset_position_ids: False # Reset position ids after end-of-document token + reset_attention_mask: False # Reset attention mask after end-of-document token + eod_mask_loss: False # Mask loss for the end of document tokens + masked_lm_prob: 0.15 # Probability of replacing a token with mask. + short_seq_prob: 0.1 # Probability of producing a short sequence. + + optim: + name: fused_adam + lr: 2e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 500 + constant_steps: 50000 + min_lr: 2e-5 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/conf/megatron_gpt_embedder_generate_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/conf/megatron_gpt_embedder_generate_config.yaml new file mode 100644 index 0000000..778dc93 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/conf/megatron_gpt_embedder_generate_config.yaml @@ -0,0 +1,216 @@ +name: megatron_gpt_peft_${model.peft.peft_scheme}_tuning + +trainer: + devices: 1 + accelerator: gpu + num_nodes: 1 + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 9999 + max_steps: 20000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 # frequency with which training steps are logged + val_check_interval: 200 # If is an int n > 1, will run val every n training steps, if a float 0.0 - 1.0 will run val every epoch fraction, e.g. 0.25 will run val every quarter epoch + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: validation_${model.data.test_ds.metric.name} + save_top_k: 1 + mode: min + save_nemo_on_train_end: True + filename: '${name}--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{consumed_samples}' + model_parallel_size: ${model.tensor_model_parallel_size} + always_save_nemo: True + save_best_model: True + +model: + seed: 1234 + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + + global_batch_size: 1 + micro_batch_size: 1 + restore_from_path: ??? # Path to an existing .nemo model you wish to add new tasks to or run inference with + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + save_nemo_on_validation_end: True # Saves an inference ready .nemo file every time a checkpoint is saved during training. + sync_batch_comm: False + megatron_amp_O2: False + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Activation Checkpoint + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null # not used with 'selective' + activations_checkpoint_layers_per_pipeline: null + gradient_as_bucket_view: False + + hidden_dropout: 0.0 + attention_dropout: 0.0 + ffn_dropout: 0.0 + temperature: 0.8 + num_soft_negatives: 0 # Number of soft negatives to use for contrastive loss,it should be max(batch_size - 1), 0 means use hard negatives only + + peft: + peft_scheme: "lora" # can be either adapter,ia3, or ptuning + restore_from_path: null + restore_from_ckpt: + checkpoint_dir: null + checkpoint_name: null + + # Used for adapter peft training + adapter_tuning: + type: 'parallel_adapter' # this should be either 'parallel_adapter' or 'linear_adapter' + adapter_dim: 32 + adapter_dropout: 0.0 + norm_position: 'pre' # This can be set to 'pre', 'post' or null, 'pre' is normally what is used. + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + norm_type: 'mixedfusedlayernorm' # IGNORED if layer_adapter is used, options are ['layernorm', 'mixedfusedlayernorm'] + layer_selection: null # selects in which layers to add adapters, e.g. [1,12] will add adapters to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + lora_tuning: + target_modules: ['attention_qkv','attention_dense','mlp_fc1','mlp_fc2'] # this can either be 'attention_qkv','attention_dense','mlp_fc1','mlp_fc2', attention (qkv & dense), mlp (fc1 & fc2) + adapter_dim: 32 + alpha: ${peft.lora_tuning.adapter_dim} + adapter_dropout: 0.0 + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + layer_selection: null # selects in which layers to add lora adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + # Used for p-tuning peft training + p_tuning: + virtual_tokens: 10 # The number of virtual tokens the prompt encoder should add at the start of the sequence + bottleneck_dim: 1024 # the size of the prompt encoder mlp bottleneck + embedding_dim: 1024 # the size of the prompt encoder embeddings + init_std: 0.023 + + ia3_tuning: + layer_selection: null # selects in which layers to add ia3 adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + + selective_tuning: + tunable_base_param_names: ["self_attention", "word_embeddings"] # TODO: regex support @adithyre + + data: + return_output_tensors: True + test_ds: + query_file_names: ??? # Path to a list of JSONL files corresponding to the query data. Data format is identical to validation_ds. + doc_file_names: ??? # Path to a list of JSONL files corresponding to the doc data. Data format is identical to validation_ds. + names: ["queries", "doc"] # Names of the corresponding datasets used to log metrics. + global_batch_size: 1 + micro_batch_size: 1 + shuffle: False + num_workers: 0 + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: False + add_eos: True + add_bos: False + write_embeddings_to_file: True + output_file_path_prefix: "test_embeddings" # Prefix of the file to write predictions to. + index_mapping_dir: null # Path to a directory to write index mapping files. + truncation_method: 'right' # Truncation from which position, Options: ['left', 'right'] + + metric: + name: "loss" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss'] + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + +inference: + greedy: True # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 1.0 # sampling temperature + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + outfile_path: output.txt + compute_attention_mask: True + +# server-related configs +server: False # whether launch the API server +port: 5555 # the port number for the inference server +web_server: False # whether launch the web inference server +share: True # whether create a public URL +username: test # user name for web client +password: test2 # password for web client +web_port: 9889 # the port number of the web server 1058 +chat: False # use the chat interface +chatbot_config: + value: False # whether to inject the value attributes + attributes: + - name: Quality + min: 0 + max: 4 + key: quality + type: int + default: 4 + - name: Toxicity + min: 0 + max: 4 + key: toxcity + type: int + default: 0 + - name: Humor + min: 0 + max: 4 + key: humor + type: int + default: 0 + - name: Creativity + min: 0 + max: 4 + key: creativity + type: int + default: 0 + - name: Violence + min: 0 + max: 4 + key: violence + type: int + default: 0 + - name: Helpfulness + min: 0 + max: 4 + key: helpfulness + type: int + default: 4 + - name: Not_Appropriate + min: 0 + max: 4 + key: not_appropriate + type: int + default: 0 + - name: Language + choices: ['ar', 'bg', 'bn', 'ca', 'cs', 'da', 'de', 'el', 'en', 'eo', 'es', 'eu', 'fa', 'fi', 'fr', 'gl', 'he', 'hu', 'id', 'it', 'ja', 'ko', 'nb', 'nl', 'pl', 'pt', 'ro', 'ru', 'sk', 'sv', 'th', 'tr', 'uk', 'vi', 'zh'] + key: lang + type: list + default: en + + user: User + assistant: Assistant + system: "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n" diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/conf/megatron_gpt_embedder_tuning_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/conf/megatron_gpt_embedder_tuning_config.yaml new file mode 100644 index 0000000..efd5271 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/conf/megatron_gpt_embedder_tuning_config.yaml @@ -0,0 +1,212 @@ +name: megatron_gpt_peft_${model.peft.peft_scheme}_tuning + +trainer: + devices: 1 + accelerator: gpu + num_nodes: 1 + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 9999 + max_steps: 20000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 # frequency with which training steps are logged + val_check_interval: 200 # If is an int n > 1, will run val every n training steps, if a float 0.0 - 1.0 will run val every epoch fraction, e.g. 0.25 will run val every quarter epoch + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: validation_${model.data.validation_ds.metric.name} + save_top_k: 1 + mode: min + save_nemo_on_train_end: True + filename: '${name}--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{consumed_samples}' + model_parallel_size: ${model.tensor_model_parallel_size} + always_save_nemo: False + save_best_model: True + create_early_stopping_callback: True + early_stopping_callback_params: + monitor: "val_loss" + mode: "min" + min_delta: 0.001 + patience: 10 + verbose: True + strict: False # Should be False to avoid a runtime error where EarlyStopping says monitor is unavailable, which sometimes happens with resumed training. + +model: + seed: 1234 + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + + global_batch_size: 128 + micro_batch_size: 4 + restore_from_path: ??? # Path to an existing .nemo model you wish to add new tasks to or run inference with + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + save_nemo_on_validation_end: False # Saves an inference ready .nemo file every time a checkpoint is saved during training. + sync_batch_comm: False + megatron_amp_O2: False + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Activation Checkpoint + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null # not used with 'selective' + activations_checkpoint_layers_per_pipeline: null + gradient_as_bucket_view: False + + hidden_dropout: 0.0 + attention_dropout: 0.0 + ffn_dropout: 0.0 + temperature: 0.8 + num_soft_negatives: 0 # Number of soft negatives to use for contrastive loss,it should be max(batch_size - 1), 0 means use hard negatives only + use_all_possible_negatives: False # If True, use all possible negatives for contrastive loss, otherwise use num_soft_negatives, if num_soft_negatives is 0, use hard negatives only + + peft: + peft_scheme: "lora" # can be either adapter,ia3, or ptuning + restore_from_path: null + + # Used for adapter peft training + adapter_tuning: + type: 'parallel_adapter' # this should be either 'parallel_adapter' or 'linear_adapter' + adapter_dim: 32 + adapter_dropout: 0.0 + norm_position: 'pre' # This can be set to 'pre', 'post' or null, 'pre' is normally what is used. + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + norm_type: 'mixedfusedlayernorm' # IGNORED if layer_adapter is used, options are ['layernorm', 'mixedfusedlayernorm'] + layer_selection: null # selects in which layers to add adapters, e.g. [1,12] will add adapters to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + lora_tuning: + target_modules: ['attention_qkv', 'attention_dense', 'mlp_fc1', 'mlp_fc2'] # + adapter_dim: 32 + adapter_dropout: 0.0 + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + layer_selection: null # selects in which layers to add lora adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + # Used for p-tuning peft training + p_tuning: + virtual_tokens: 10 # The number of virtual tokens the prompt encoder should add at the start of the sequence + bottleneck_dim: 1024 # the size of the prompt encoder mlp bottleneck + embedding_dim: 1024 # the size of the prompt encoder embeddings + init_std: 0.023 + + ia3_tuning: + layer_selection: null # selects in which layers to add ia3 adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + + selective_tuning: + tunable_base_param_names: ["self_attention", "word_embeddings"] # TODO: regex support @adithyre + + data: + return_output_tensors: True + train_ds: + # Example of how to specify paths to multiple datasets + # file_names: + # - /path/to/squad.jsonl + # - /path/to/mnli.jsonl + # - /path/to/boolq.jsonl + # Example of how each dataset is formatted + # {'input': 'John von Neumann\nVon Neumann made fundamental contributions .... Q: What did the math of artificial viscosity do?', 'output': 'smoothed the shock transition without sacrificing basic physics'} + file_names: ??? # Path to a list of JSONL files corresponding to the source data. + global_batch_size: ${model.global_batch_size} + micro_batch_size: ${model.micro_batch_size} + shuffle: True + num_workers: 0 + memmap_workers: 2 + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: True + # Example of how to specify concat_sampling_probabilities + # concat_sampling_probabilities: + # - 0.5 + # - 0.25 + # - 0.25 + concat_sampling_probabilities: null # When providing a list of datasets, this arg defines the sampling probabilities from each dataset when strategy='random' + label_key: 'output' + add_eos: True + add_bos: False + index_mapping_dir: null # Path to a directory to write index mapping files. + truncation_method: 'right' # Truncation from which position, Options: ['left', 'right'] + validation_ds: + query_file_names: ??? # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds. + doc_file_names: ??? # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds. + names: ["queries", "doc"] # Names of the corresponding datasets used to log metrics. + global_batch_size: ${model.global_batch_size} + micro_batch_size: ${model.micro_batch_size} + shuffle: False + num_workers: 0 + memmap_workers: ${model.data.train_ds.memmap_workers} + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: False + label_key: ${model.data.train_ds.label_key} + add_eos: ${model.data.train_ds.add_eos} + add_bos: ${model.data.train_ds.add_bos} + write_embeddings_to_file: False + output_file_path_prefix: "validation_embeddings" # Prefix of the file to write predictions to. + index_mapping_dir: null # Path to a directory to write index mapping files. + truncation_method: 'right' # Truncation from which position, Options: ['left', 'right'] + metric: + name: "loss" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss'] + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + test_ds: + file_names: null # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds. + names: null # Names of the corresponding datasets used to log metrics. + global_batch_size: ${model.global_batch_size} + micro_batch_size: ${model.micro_batch_size} + shuffle: False + num_workers: 0 + memmap_workers: ${model.data.train_ds.memmap_workers} + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: False + add_eos: ${model.data.train_ds.add_eos} + add_bos: ${model.data.train_ds.add_bos} + write_predictions_to_file: True + output_file_path_prefix: "test_embeddings" # Prefix of the file to write predictions to. + index_mapping_dir: null # Path to a directory to write index mapping files. + truncation_method: 'right' # Truncation from which position, Options: ['left', 'right'] + metric: + name: "loss" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss'] + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + + optim: + name: fused_adam + lr: 1e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 50 + min_lr: 0.0 # min_lr must be 0.0 for prompt learning when pipeline parallel > 1 + constant_steps: 0 # Constant steps should also be 0 when min_lr=0 + monitor: val_loss + reduce_on_plateau: false \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/megatron_bert_embedding_finetuning.py b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/megatron_bert_embedding_finetuning.py new file mode 100644 index 0000000..04d12fe --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/megatron_bert_embedding_finetuning.py @@ -0,0 +1,60 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf, open_dict + +from nemo.collections.nlp.models.information_retrieval.megatron_bert_embedding_model import MegatronBertEmbeddingModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronBertTrainerBuilder +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="megatron_bert_embedding_config") +def main(cfg) -> None: + if cfg.model.data.dataloader_type != "LDDL": + mp.set_start_method("spawn", force=True) + + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronBertTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model_cfg = MegatronBertEmbeddingModel.merge_cfg_with(cfg.restore_from_path, cfg) + + assert ( + model_cfg.micro_batch_size * cfg.trainer.devices == model_cfg.global_batch_size + ), "Gradiant accumulation is not supported for contrastive learning yet" + + OmegaConf.set_struct(model_cfg, True) + with open_dict(model_cfg): + model_cfg.precision = trainer.precision + + logging.info(f"Loading model from {cfg.restore_from_path}") + model = MegatronBertEmbeddingModel.restore_from( + restore_path=cfg.restore_from_path, + trainer=trainer, + save_restore_connector=NLPSaveRestoreConnector(), + override_config_path=model_cfg, + strict=True, + ) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/megatron_gpt_embedding_finetuning.py b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/megatron_gpt_embedding_finetuning.py new file mode 100644 index 0000000..e1fe28c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/megatron_gpt_embedding_finetuning.py @@ -0,0 +1,74 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import MutableMapping + +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf +from pytorch_lightning.loggers import WandbLogger + +from nemo.collections.nlp.models.information_retrieval.megatron_gpt_embedding_model import MegatronGPTEmbeddingModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronLMPPTrainerBuilder +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +mp.set_start_method("spawn", force=True) + + +def flatten_dict(d: MutableMapping, parent_key: str = '', sep: str = '.') -> MutableMapping: + items = [] + for k, v in d.items(): + new_key = parent_key + sep + k if parent_key else k + if isinstance(v, MutableMapping): + items.extend(flatten_dict(v, new_key, sep=sep).items()) + else: + items.append((new_key, v)) + return dict(items) + + +@hydra_runner(config_path="conf", config_name="megatron_gpt_embedder_tuning_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronLMPPTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model_cfg = MegatronGPTEmbeddingModel.merge_cfg_with(cfg.model.restore_from_path, cfg) + if trainer.global_rank == 0: + for logger in trainer.loggers: + if isinstance(logger, WandbLogger): + fd = flatten_dict(dict(model_cfg), sep="/") + logger.experiment.config.update(fd) + model = MegatronGPTEmbeddingModel.restore_from(cfg.model.restore_from_path, model_cfg, trainer=trainer) + peft_cfg_cls = PEFT_CONFIG_MAP[cfg.model.peft.peft_scheme] + + if cfg.model.peft.restore_from_path is not None: + # initialize peft weights from a checkpoint instead of randomly + # This is not the same as resume training because optimizer states are not restored. + logging.info("PEFT Weights will be loaded from", cfg.model.peft.restore_from_path) + model.load_adapters(cfg.model.peft.restore_from_path, peft_cfg_cls(model_cfg)) + elif peft_cfg_cls is not None: + logging.info("Adding adapter weights to the model for PEFT") + model.add_adapter(peft_cfg_cls(model_cfg)) + else: + logging.info(f"Running full finetuning since no peft scheme is given.\n{model.summarize()}") + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/megatron_gpt_embedding_generate.py b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/megatron_gpt_embedding_generate.py new file mode 100644 index 0000000..8cddceb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/information_retrieval/megatron_gpt_embedding_generate.py @@ -0,0 +1,135 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import asyncio +import os +import threading +from functools import partial + +import torch +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf, open_dict + +from nemo.collections.nlp.models.information_retrieval.megatron_gpt_embedding_model import MegatronGPTEmbeddingModel +from nemo.collections.nlp.modules.common.text_generation_server import MegatronServer +from nemo.collections.nlp.modules.common.text_generation_utils import generate +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronLMPPTrainerBuilder +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.model_utils import inject_model_parallel_rank + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + +mp.set_start_method("spawn", force=True) + + +def use_inference_server(cfg, model, trainer): + if not HAVE_MEGATRON_CORE: + raise ValueError('Megatron-core needs to be installed to use this feature!') + + from nemo.collections.nlp.modules.common.megatron_web_server import get_chatbot_demo, get_demo + + trainer.test(model, dataloaders=None) + + if parallel_state.is_pipeline_first_stage() and parallel_state.get_tensor_model_parallel_rank() == 0: + if cfg.web_server: + if cfg.chat: + defaults = { + 'user': cfg.chatbot_config.user, + 'assistant': cfg.chatbot_config.assistant, + 'system': cfg.chatbot_config.system, + } + web_ui = partial( + get_chatbot_demo, + defaults=defaults, + value=cfg.chatbot_config.value, + attributes=cfg.chatbot_config.attributes, + ) + else: + web_ui = get_demo + loop = asyncio.new_event_loop() + thread = threading.Thread( + target=web_ui, daemon=True, args=(cfg.share, cfg.username, cfg.password, cfg.port, cfg.web_port, loop), + ) + thread.start() + server = MegatronServer(model.cuda()) + server.run("0.0.0.0", port=cfg.port) + + while True: + choice = torch.cuda.LongTensor(1) + torch.distributed.broadcast(choice, 0) + if choice[0].item() == 0: + generate(model.cuda()) + + +@hydra_runner(config_path="conf", config_name="megatron_gpt_embedder_generate_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f"\n{OmegaConf.to_yaml(cfg)}") + trainer = MegatronLMPPTrainerBuilder(cfg).create_trainer() + + if cfg.model.peft.restore_from_path: + model_cfg = MegatronGPTEmbeddingModel.merge_inference_cfg(cfg.model.peft.restore_from_path, cfg) + else: + model_cfg = MegatronGPTEmbeddingModel.merge_inference_cfg(cfg.model.restore_from_path, cfg) + + with open_dict(model_cfg): + model_cfg.data.return_output_tensors = True + model_cfg.post_process = False + + model = MegatronGPTEmbeddingModel.restore_from(cfg.model.restore_from_path, model_cfg, trainer=trainer) + + if cfg.model.peft.restore_from_path: + model.load_adapters(cfg.model.peft.restore_from_path) + elif cfg.model.peft.restore_from_ckpt.checkpoint_dir and cfg.model.peft.restore_from_ckpt.checkpoint_name: + peft_cfg_cls = PEFT_CONFIG_MAP[cfg.model.peft.peft_scheme] + checkpoint_path = os.path.join( + cfg.model.peft.restore_from_ckpt.checkpoint_dir, cfg.model.peft.restore_from_ckpt.checkpoint_name + ) + # checkpoint_path is a dir in case of distributed checkpointing + if not os.path.isdir(checkpoint_path): + # legacy checkpoint needs model parallel rank injection + checkpoint_path = inject_model_parallel_rank( + os.path.join( + cfg.model.peft.restore_from_ckpt.checkpoint_dir, cfg.model.peft.restore_from_ckpt.checkpoint_name + ) + ) + model.load_adapters(checkpoint_path, peft_cfgs=peft_cfg_cls(model_cfg)) + else: + raise NotImplementedError("distributed checkpointing of PEFT weights is not supported") + + model.freeze() + logging.info(f"Freezing parameters for PEFT eval:\n{model.summarize()}") + + if not cfg.model.get('use_flash_attention', False): + cfg.inference.compute_attention_mask = True + config = OmegaConf.to_container(cfg.inference, resolve=True) + model.set_inference_config(config) + + if not cfg.server: + trainer.test(model) + else: + use_inference_server(cfg, model, trainer) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/intent_slot_classification/conf/intent_slot_classification_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/intent_slot_classification/conf/intent_slot_classification_config.yaml new file mode 100644 index 0000000..df66111 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/intent_slot_classification/conf/intent_slot_classification_config.yaml @@ -0,0 +1,110 @@ +# Intent and Slot classification with pretrained BERT models + +trainer: + devices: 1 # the number of gpus, 0 for CPU + num_nodes: 1 + max_epochs: 50 + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + precision: 32 # Should be set to 16 for O1 and O2 amp_level to enable the AMP. + accelerator: gpu + strategy: ddp + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + + enable_checkpointing: False + logger: false # Provided by exp_manager + +model: + nemo_path: null # filename to save the model and associated artifacts to .nemo file + data_dir: ??? # /path/to/data + class_labels: + intent_labels_file: intent_labels.csv + slot_labels_file: slot_labels.csv + class_balancing: null # or weighted_loss + intent_loss_weight: 0.6 # relation of intent to slot loss in total loss (between 0 to 1) + pad_label: -1 # if -1 not slot token will be used + ignore_extra_tokens: false + ignore_start_end: true # do not use first and last token for slot training + + train_ds: + prefix: train + batch_size: 32 + shuffle: true + num_samples: -1 + num_workers: 2 + drop_last: false + pin_memory: false + + validation_ds: + prefix: test + batch_size: 32 + shuffle: false + num_samples: -1 + num_workers: 2 + drop_last: false + pin_memory: false + + test_ds: + prefix: test + batch_size: 32 + shuffle: false + num_samples: -1 + num_workers: 2 + drop_last: false + pin_memory: false + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + + language_model: + max_seq_length: 50 + pretrained_model_name: bert-base-uncased + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + head: + num_output_layers: 2 + fc_dropout: 0.1 + + optim: + name: adam + lr: 2e-5 + args: + name: auto + params: + weight_decay: 0.01 + + sched: + name: WarmupAnnealing + iters_per_batch: null # computed at runtime + max_steps: -1 # computed at runtime or explicitly set here + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + + # scheduler config override + args: + name: auto + params: + warmup_steps: null + warmup_ratio: 0.1 + last_epoch: -1 + +exp_manager: + exp_dir: null # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: "IntentSlot" # The name of your model + create_tensorboard_logger: true # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: true # Whether you want exp_manager to create a modelcheckpoint callback + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/intent_slot_classification/conf/multi_label_intent_slot_classification_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/intent_slot_classification/conf/multi_label_intent_slot_classification_config.yaml new file mode 100644 index 0000000..c15c71e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/intent_slot_classification/conf/multi_label_intent_slot_classification_config.yaml @@ -0,0 +1,110 @@ +# Intent and Slot classification with pretrained BERT models + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 5 + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + precision: 32 # Should be set to 16 for O1 and O2 amp_level to enable the AMP. + accelerator: auto + strategy: ddp + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + +model: + nemo_path: null # filename to save the model and associated artifacts to .nemo file + data_dir: ??? # /path/to/data + class_labels: + intent_labels_file: intent_labels.csv + slot_labels_file: slot_labels.csv + class_balancing: null # or weighted_loss + intent_loss_weight: 0.6 # relation of intent to slot loss in total loss (between 0 to 1) + pad_label: -1 # if -1 not slot token will be used + ignore_extra_tokens: false + ignore_start_end: true # do not use first and last token for slot training + + train_ds: + prefix: train + batch_size: 32 + shuffle: true + num_samples: -1 + num_workers: 8 + drop_last: false + pin_memory: false + + validation_ds: + prefix: dev + batch_size: 32 + shuffle: false + num_samples: -1 + num_workers: 8 + drop_last: false + pin_memory: false + + test_ds: + prefix: dev + batch_size: 32 + shuffle: false + num_samples: -1 + num_workers: 8 + drop_last: false + pin_memory: false + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + + language_model: + max_seq_length: 50 + pretrained_model_name: bert-base-uncased + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + head: + num_output_layers: 2 + fc_dropout: 0.1 + + optim: + name: adam + lr: 2e-5 + args: + name: auto + params: + weight_decay: 0.01 + + sched: + name: WarmupAnnealing + iters_per_batch: null # computed at runtime + max_steps: -1 # computed at runtime or explicitly set here + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + + # scheduler config override + args: + name: auto + params: + warmup_steps: null + warmup_ratio: 0.1 + last_epoch: -1 + +language_model: + max_seq_length: 50 + pretrained_model_name: bert-base-uncased + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + +exp_manager: + exp_dir: null # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: "MultiLabelIntentSlot" # The name of your model + create_tensorboard_logger: False # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: False # Whether you want exp_manager to create a modelcheckpoint callback diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/intent_slot_classification/intent_slot_classification.py b/NeMo-2.0.0.rc0.beta/examples/nlp/intent_slot_classification/intent_slot_classification.py new file mode 100644 index 0000000..a112ea7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/intent_slot_classification/intent_slot_classification.py @@ -0,0 +1,89 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models import IntentSlotClassificationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="intent_slot_classification_config") +def main(cfg: DictConfig) -> None: + # PTL 2.0 has find_unused_parameters as False by default, so its required to set it to True + # when there are unused parameters like here + if cfg.trainer.strategy == 'ddp': + cfg.trainer.strategy = "ddp_find_unused_parameters_true" + logging.info(f'Config Params:\n {OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + # initialize the model using the config file + model = IntentSlotClassificationModel(cfg.model, trainer=trainer) + + # training + logging.info("================================================================================================") + logging.info('Starting training...') + trainer.fit(model) + logging.info('Training finished!') + + # Stop further testing as fast_dev_run does not save checkpoints + if trainer.fast_dev_run: + return + + # after model training is done, you can load the model from the saved checkpoint + # and evaluate it on a data file or on given queries. + logging.info("================================================================================================") + logging.info("Starting the testing of the trained model on test set...") + logging.info("We will load the latest model saved checkpoint from the training...") + + # for evaluation and inference you can load the previously trained model saved in .nemo file + # like this in your code, but we will just reuse the trained model here + # eval_model = IntentSlotClassificationModel.restore_from(restore_path=checkpoint_path) + eval_model = model + + # we will setup testing data reusing the same config (test section) + eval_model.update_data_dir_for_testing(data_dir=cfg.model.data_dir) + eval_model.setup_test_data(test_data_config=cfg.model.test_ds) + + trainer.test(model=eval_model, ckpt_path=None, verbose=False) + logging.info("Testing finished!") + + # run an inference on a few examples + logging.info("======================================================================================") + logging.info("Evaluate the model on the given queries...") + + # this will work well if you train the model on Assistant dataset + # for your own dataset change the examples appropriately + queries = [ + 'set alarm for seven thirty am', + 'lower volume by fifty percent', + 'what is my schedule for tomorrow', + ] + + pred_intents, pred_slots = eval_model.predict_from_examples(queries, cfg.model.test_ds) + + logging.info('The prediction results of some sample queries with the trained model:') + for query, intent, slots in zip(queries, pred_intents, pred_slots): + logging.info(f'Query : {query}') + logging.info(f'Predicted Intent: {intent}') + logging.info(f'Predicted Slots: {slots}') + + logging.info("Inference finished!") + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/intent_slot_classification/multi_label_intent_slot_classification.py b/NeMo-2.0.0.rc0.beta/examples/nlp/intent_slot_classification/multi_label_intent_slot_classification.py new file mode 100644 index 0000000..2441885 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/intent_slot_classification/multi_label_intent_slot_classification.py @@ -0,0 +1,104 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Sample command to run the script: + +python multi_label_intent_slot_classification.py \ + model.data_dir=/home/user/multiatis \ + model.validation_ds.prefix=dev \ + model.test_ds.prefix=dev \ + trainer.devices=[0] \ + +trainer.fast_dev_run=true \ + exp_manager.exp_dir=checkpoints + +fast_dev_run=false will save checkpoints for the model +""" + + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models import MultiLabelIntentSlotClassificationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="multi_label_intent_slot_classification_config") +def main(cfg: DictConfig) -> None: + logging.info(f'Config Params:\n {OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + # initialize the model using the config file + model = MultiLabelIntentSlotClassificationModel(cfg.model, trainer=trainer) + + # training + logging.info("================================================================================================") + logging.info('Starting training...') + trainer.fit(model) + logging.info('Training finished!') + + # Stop further testing as fast_dev_run does not save checkpoints + if trainer.fast_dev_run: + return + + # after model training is done, you can load the model from the saved checkpoint + # and evaluate it on a data file or on given queries. + logging.info("================================================================================================") + logging.info("Starting the testing of the trained model on test set...") + logging.info("We will load the latest model saved checkpoint from the training...") + + # for evaluation and inference you can load the previously trained model saved in .nemo file + # like this in your code, but we will just reuse the trained model here + # eval_model = MultiLabelIntentSlotClassificationModel.restore_from(restore_path=checkpoint_path) + eval_model = model + + # we will setup testing data reusing the same config (test section) + eval_model.update_data_dir_for_testing(data_dir=cfg.model.data_dir) + eval_model.setup_test_data(test_data_config=cfg.model.test_ds) + + trainer.test(model=eval_model, ckpt_path=None, verbose=False) + logging.info("Testing finished!") + + # Optimize Threshold + eval_model.optimize_threshold(cfg.model.test_ds, 'dev') + + # run an inference on a few examples + logging.info("======================================================================================") + logging.info("Evaluate the model on the given queries...") + + # this will work well if you train the model on ATIS dataset + # for your own dataset change the examples appropriately + queries = [ + 'i would like to find a flight from charlotte to las vegas that makes a stop in st. louis', + 'on april first i need a ticket from tacoma to san jose departing before 7 am', + 'how much is the limousine service in boston', + ] + + # We use the optimized threshold for predictions + pred_intents, pred_slots, pred_list = eval_model.predict_from_examples(queries, cfg.model.test_ds) + logging.info('The prediction results of some sample queries with the trained model:') + + for query, intent, slots in zip(queries, pred_intents, pred_slots): + logging.info(f'Query : {query}') + logging.info(f'Predicted Intents: {intent}') + logging.info(f'Predicted Slots: {slots}') + + logging.info("Inference finished!") + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/bert_pretraining.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/bert_pretraining.py new file mode 100644 index 0000000..75d0a10 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/bert_pretraining.py @@ -0,0 +1,38 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf +from pytorch_lightning.strategies import DDPStrategy + +from nemo.collections.nlp.models.language_modeling import BERTLMModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="bert_pretraining_from_text_config") +def main(cfg: DictConfig) -> None: + logging.info(f'Config:\n {OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(strategy=DDPStrategy(find_unused_parameters=True), **cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + bert_model = BERTLMModel(cfg.model, trainer=trainer) + trainer.fit(bert_model) + if cfg.model.nemo_path: + bert_model.save_to(cfg.model.nemo_path) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/bert_pretraining_from_preprocessed_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/bert_pretraining_from_preprocessed_config.yaml new file mode 100644 index 0000000..2c44c6c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/bert_pretraining_from_preprocessed_config.yaml @@ -0,0 +1,79 @@ +# BERT Pretraining from Preprocessed (tokenized) data +name: &name PretrainingBERTFromPreprocessed +trainer: + devices: 8 # the number of gpus, 0 for CPU, or list with gpu indices + num_nodes: 1 + max_steps: 2285714 # precedence over max_epochs + num_sanity_val_steps: 0 # needed for bert pretraining from preproc + use_distributed_sampler: false # needed for bert pretraining from preproc + accumulate_grad_batches: 1 # accumulates grads every k batches + precision: 16 # 16 to use AMP + accelerator: gpu + gradient_clip_val: 1.0 + log_every_n_steps: 1 + val_check_interval: 1.0 # check once per epoch .25 for 4 times per epoch + enable_checkpointing: False # provided by exp_manager + logger: false # provided by exp_manager + +model: + nemo_path: null # exported .nemo path + only_mlm_loss: true # only use masked language model without next sentence prediction + num_tok_classification_layers: 1 # number of token classification head output layers + num_seq_classification_layers: 2 # number of sequence classification head output layers + + + language_model: + pretrained_model_name: bert-base-uncased # huggingface model name + lm_checkpoint: null + config: + attention_probs_dropout_prob: 0.1 + hidden_act: gelu + hidden_dropout_prob: 0.1 + hidden_size: 768 + initializer_range: 0.02 + intermediate_size: 3072 + max_position_embeddings: 512 + num_attention_heads: 12 + num_hidden_layers: 12 + type_vocab_size: 2 + vocab_size: 30522 + config_file: null # json file, precedence over config + + tokenizer: null + + train_ds: + data_file: ??? # path to hdf5 file (or directory) + max_predictions_per_seq: 80 + batch_size: 16 + shuffle: true + num_samples: -1 + num_workers: 2 + drop_last: false + pin_memory: false + + optim: + name: adamw + lr: 0.4375e-4 + weight_decay: 0.01 + + sched: + name: SquareRootAnnealing + warmup_steps: null + warmup_ratio: 0.01 + min_lr: 0.0 + last_epoch: -1 + + +exp_manager: + exp_dir: null # where to store logs and checkpoints + name: *name # name of experiment + create_tensorboard_logger: True + create_checkpoint_callback: True + + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/bert_pretraining_from_text_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/bert_pretraining_from_text_config.yaml new file mode 100644 index 0000000..c29fcb3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/bert_pretraining_from_text_config.yaml @@ -0,0 +1,108 @@ +# BERT Pretraining from Text +name: &name PretrainingBERTFromText +trainer: + devices: 1 # the number of gpus, 0 for CPU, or list with gpu indices + num_nodes: 1 + max_epochs: 2 # the number of training epochs + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + precision: 16 # 16 to use AMP + accelerator: gpu + gradient_clip_val: 0.0 + log_every_n_steps: 1 + val_check_interval: 1.0 # check once per epoch .25 for 4 times per epoch + enable_checkpointing: False # provided by exp_manager + logger: false # provided by exp_manager + +model: + nemo_path: null # exported .nemo path + only_mlm_loss: false # only use masked language model without next sentence prediction + num_tok_classification_layers: 1 # number of token classification head output layers + num_seq_classification_layers: 2 # number of sequence classification head output layers + max_seq_length: 128 + # The maximum total input sequence length after tokenization. Sequences longer than this + # will be truncated, and sequences shorter than this will be padded. + mask_prob: 0.15 + # Probability of masking a token in the input text during data processing. + short_seq_prob: 0.1 + # Probability of having a sequence shorter than the maximum sequence length `max_seq_length` in data processing.", + + language_model: + pretrained_model_name: bert-base-uncased + lm_checkpoint: null + config: + attention_probs_dropout_prob: 0.1 + hidden_act: gelu + hidden_dropout_prob: 0.1 + hidden_size: 768 + initializer_range: 0.02 + intermediate_size: 3072 + max_position_embeddings: 512 + num_attention_heads: 12 + num_hidden_layers: 12 + type_vocab_size: 2 + vocab_size: 30522 + config_file: null # json file, precedence over config + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # tokenizer that inherits from TokenizerSpec + vocab_file: null # path to vocab file + tokenizer_model: null # tokenizer model for sentencepiece + special_tokens: # only necessary for adding transformer/bert-specific special tokens to tokenizer if the tokenizer does not already have these inherently. + unk_token: '[UNK]' + sep_token: '[SEP]' + pad_token: '[PAD]' + bos_token: '[CLS]' + mask_token: '[MASK]' + eos_token: '[SEP]' + cls_token: '[CLS]' + + train_ds: + data_file: ??? # path to data file + max_seq_length: ${model.max_seq_length} + mask_prob: ${model.mask_prob} + short_seq_prob: ${model.short_seq_prob} + batch_size: 16 # per GPU + shuffle: true + num_samples: -1 + num_workers: 2 + drop_last: false + pin_memory: false + + validation_ds: + data_file: ??? # path to data file + max_seq_length: ${model.max_seq_length} + mask_prob: ${model.mask_prob} + short_seq_prob: ${model.short_seq_prob} + batch_size: 16 # per GPU + shuffle: false + num_samples: -1 + num_workers: 2 + drop_last: false + pin_memory: false + + optim: + name: adamw + lr: 3e-5 + weight_decay: 0.0 + + sched: + name: CosineAnnealing + warmup_steps: null + warmup_ratio: 0.1 + min_lr: 0.0 + last_epoch: -1 + + +exp_manager: + exp_dir: null # where to store logs and checkpoints + name: *name # name of experiment + create_tensorboard_logger: True + create_checkpoint_callback: True + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_baichuan2_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_baichuan2_config.yaml new file mode 100644 index 0000000..6f90773 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_baichuan2_config.yaml @@ -0,0 +1,225 @@ +name: megatron_baichuan2 +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 32 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_baichuan2 + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +model: + mcore_gpt: True + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 4 # limited by GPU memory + global_batch_size: 8 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + # model architecture + encoder_seq_length: 4096 + max_position_embeddings: ${.encoder_seq_length} + num_layers: 32 # 7b: 32 | 13b: 40 + hidden_size: 4096 # 7b: 4096 | 13b: 5120 + ffn_hidden_size: 11008 # Transformer FFN hidden size. Usually 4 * hidden_size. | 7b: 11008 | 13b: 13696 + num_attention_heads: 32 # 7b: 32 | 13b: 40 + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0.0 # Dropout probability for hidden state transformer. + attention_dropout: 0.0 # Dropout probability for attention + ffn_dropout: 0.0 # Dropout probability in the feed-forward layer. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: 'rmsnorm' # Normalization layer to use. Options are 'layernorm', 'rmsnorm' + layernorm_epsilon: 1e-6 + do_layer_norm_weight_decay: False # True means weight decay on all params + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + bias: False # Whether to use bias terms in all weight matrices. + activation: 'swiglu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] + headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head. + transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer'] + openai_gelu: False # Use OpenAI's GELU instead of the default GeLU + normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True. + position_embedding_type: 'rope' # Position embedding type. Options ['learned_absolute', 'rope']. | 7b: 'rope' | 13b: 'alibi' + rotary_percentage: 1.0 # If using position_embedding_type=rope, then the per head dim is multiplied by this. + attention_type: 'multihead' # Attention type. Options ['multihead'] + share_embeddings_and_output_weights: False # Share embedding and output layer weights. + overlap_p2p_comm: False # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + num_query_groups: 32 # Number of query groups for group query attention. If None, normal attention is used. | 7b: 32 | 13b: 40 + + tokenizer: + library: 'sentencepiece' + type: null + model: ??? # /path/to/tokenizer.model + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + trust_remote_code: True + + # Mixed precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters # False!!! + grad_allreduce_chunk_size_mb: 125 + + # Fusion + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism.. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2. + bias_activation_fusion: False # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function. + bias_dropout_add_fusion: False # Use a kernel that fuses the bias addition, dropout and residual connection addition. + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + get_attention_mask_from_fusion: True # When using fused softmax it will create the attention mask so we won't copy it to the pipeline stages. + + + # Miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + sync_batch_comm: False # Enable stream synchronization after each p2p communication between pipeline stages + + ## Activation Checkpointing + # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed. + # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+). + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + # 'full' will checkpoint the entire transformer layer. + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model. + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null + # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory. + # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage. + num_micro_batches_with_partial_activation_checkpoints: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed + # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is + # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint + # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'. + # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage. + activations_checkpoint_layers_per_pipeline: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later + # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than + # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage + # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints', + # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path. + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Transformer Engine + transformer_engine: True + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: False # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: most_recent # 'most_recent' or 'max'. Algorithm for computing amax from history + reduce_amax: True # Perform reduction to sync amax tensors across GPUs after every iteration + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + + data: + # Path to data must be specified by the user. + # Supports List, String and Dictionary + # List : can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-gpt3_00_text_document + # - .5 + # - /raid/data/pile/my-gpt3_01_text_document + # Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test} + # Or see example below: + # "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}" + # data_prefix: ??? + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap + splits_string: 900,50,50 + seq_length: ${model.encoder_seq_length} + skip_warmup: True + num_workers: 2 + dataloader_type: single # cyclic + reset_position_ids: False # Reset position ids after end-of-document token + reset_attention_mask: False # Reset attention mask after end-of-document token + eod_mask_loss: False # Mask loss for the end of document tokens + validation_drop_last: True # Set to false if the last partial validation samples is to be consumed + no_seqlen_plus_one_input_tokens: False # Set to True to disable fetching (sequence length + 1) input tokens, instead get (sequence length) input tokens and mask the last token + pad_samples_to_global_batch_size: False # Set to True if you want to pad the last partial batch with -1's to equal global batch size + shuffle_documents: True # Set to False to disable documents shuffling. Sample index will still be shuffled + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [0] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: distributed_fused_adam + bucket_cap_mb: 100 + overlap_grad_sync: True + overlap_param_sync: True + contiguous_grad_buffer: True + grad_sync_dtype: bf16 + lr: 2e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 500 + constant_steps: 50000 + min_lr: 2e-5 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_baichuan2_inference.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_baichuan2_inference.yaml new file mode 100644 index 0000000..f2d9d6a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_baichuan2_inference.yaml @@ -0,0 +1,39 @@ +inference: + greedy: False # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 1.0 # sampling temperature + add_BOS: False # add the bos token at the begining of the prompt + tokens_to_generate: 30 # The minimum length of the sequence to be generated. + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + end_strings: [""] # generation will stop when one of these tokens is generated + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: bf16 # 16, 32, or bf16 + use_distributed_sampler: False + +tensor_model_parallel_size: -1 +pipeline_model_parallel_size: -1 +pipeline_model_parallel_split_rank: -1 # used for encoder and decoder model (0 for others) +megatron_amp_O2: False # Enable O2-level automatic mixed precision to save memory +gpt_model_file: null # GPT nemo file path +checkpoint_dir: null # checkpoint file dir. This is used to load the PTL checkpoint generated during the GPT training +checkpoint_name: null # PTL checkpoint file name, only used for PTL checkpoint loading +hparams_file: null # model configuration file, only used for PTL checkpoint loading +prompts: # prompts for GPT inference + - "Q: How are you?" + - "Q: How big is the universe?" +server: False # whether launch the API server +port: 5555 # the port number for the inference server +web_server: False # whether launch the web inference server +share: False # whether create a public URL +username: test # user name for web client +password: test2 # password for web client +web_port: 9889 # the port number of the web server diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_bart_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_bart_config.yaml new file mode 100644 index 0000000..02a41a9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_bart_config.yaml @@ -0,0 +1,151 @@ +defaults: + - .@model.encoder: megatron_model_base_config + - .@model.decoder: megatron_model_base_config + +name: megatron_bart +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + benchmark: False + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + filename: '${name}--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +model: + # model parallelism + micro_batch_size: 4 + global_batch_size: 8 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 + resume_from_checkpoint: null # manually set the checkpoint file to load from + pipeline_model_parallel_split_rank: 0 # rank at which decoder starts. + + # model architecture + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + + megatron_amp_O2: False # use AMP with O2 style mixed precision instead of native amp on-the-fly weight autocasting. + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + seq_length: 512 + max_position_embeddings: ${.seq_length} + + tokenizer: + library: 'megatron' + type: 'BertWordPieceCase' + model: null + vocab_file: null + merge_file: null + num_sentinel_tokens: 0 # expected to be 0 for BART + sentencepiece_legacy: True # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + + # weight init + embedding_init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + + # embedding dropout + embedding_dropout: 0.1 + + # embedding sharing + share_token_embeddings: True # If True share encoder/decoder embeddings + share_decoder_tokens_head_embeddings: True # If True share decoder embeddings and decoder projection to logits + + # token head + tokens_head_bias: True + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # miscellaneous + seed: 1234 + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + + data: + # Path to data must be specified by the user. + # can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-bart_00_text_document,.5,/raid/data/pile/my-bart_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-bart_00_text_document + # - .5 + # - /raid/data/pile/my-bart_01_text_document + data_prefix: ??? + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap + # data_impl_kwargs: # currently used only for text_mmap, csv_mmap (should be data_impl dependant) + # # defaults for text_memmap + # newline_int: 10 # byte-value of newline (Use ord('\n') to get value) + # header_lines: 0 # skip first N header lines + # workers: null # number of workers when creating missing index files (null defaults to cpu_num // 2) + # sort_dataset_paths: False # if True datasets will be sorted by name + # # defaults for csv_memmap + # newline_int: 10 # byte-value of newline + # header_lines: 1 # skip first N header lines + # workers: null # number of workers when creating missing index files (null defaults to cpu_num // 2) + # sort_dataset_paths: False # if True datasets will be sorted by name + # data_col: 1 # column to use for data + # data_sep: ',' # string to split text into columns + splits_string: 949,45,5 + seq_length: ${model.seq_length} + skip_warmup: True + num_workers: 0 + dataloader_type: single # cyclic + masked_lm_prob: 0.15 + dataset_type: 'bart' + short_seq_prob: 0.0 + max_ngram_size: 10 + mean_ngram_size: null + geometric_dist: True + permutation: False + whole_word_masking: True + favor_longer_ngrams: False + delete_mask_prob: 0.3 + respect_document_boundaries: True # If true, a single training exampl cannot cross document boundaries, increasing the fraction of tokens within a batch. + + optim: + name: fused_adam + lr: 0.0001 + betas: + - 0.9 + - 0.999 + eps: 1e-8 + weight_decay: 0.01 + sched: + name: WarmupAnnealing + min_lr: 0.00001 + last_epoch: -1 + warmup_ratio: 0.01 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_bert_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_bert_config.yaml new file mode 100644 index 0000000..58e8743 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_bert_config.yaml @@ -0,0 +1,161 @@ +name: megatron_bert +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice we don't usually train for more than 1 epoch. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + benchmark: False + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_bert + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + filename: 'megatron_bert--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + + +model: + # model parallelism + mcore_bert: False + micro_batch_size: 4 + global_batch_size: 8 + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 + virtual_pipeline_model_parallel_size: null + + # model architecture + encoder_seq_length: 512 + max_position_embeddings: ${.encoder_seq_length} + position_embedding_type: 'learned_absolute' # Position embedding type. Options ['learned_absolute', 'rope', 'alibi', 'kerple' , 'xpos', 'sandwich'] xpos and sandwich are experimental. + num_layers: 12 + hidden_size: 768 + ffn_hidden_size: 3072 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 12 + transformer_block_type: pre_ln + add_pooler: True + add_lm_head: True + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + hidden_dropout: 0.1 # Dropout probability for hidden state transformer. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: layernorm + layernorm_epsilon: 1e-5 + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + bert_binary_head: True # BERT binary head + megatron_legacy: False + + tokenizer: + library: 'megatron' + type: 'BertWordPieceLowerCase' + model: null + vocab_file: null + merge_file: null + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: False + + # miscellaneous + seed: 1234 + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + ## Activation Checkpointing + # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed. + # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+). + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + # 'full' will checkpoint the entire transformer layer. + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model. + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null + # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory. + # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage. + num_micro_batches_with_partial_activation_checkpoints: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed + # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is + # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint + # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'. + # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage. + activations_checkpoint_layers_per_pipeline: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later + # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than + # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage + # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints', + # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path. + sequence_parallel: False + + data: + # Path to data must be specified by the user. + # can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-gpt3_00_text_document + # - .5 + # - /raid/data/pile/my-gpt3_01_text_document + data_prefix: [1.0, /path/to/data] + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap + splits_string: 900,50,50 + seq_length: ${model.encoder_seq_length} + skip_warmup: True + num_workers: 0 + dataloader_type: single # cyclic, LDDL + reset_position_ids: False # Reset position ids after end-of-document token + reset_attention_mask: False # Reset attention mask after end-of-document token + eod_mask_loss: False # Mask loss for the end of document tokens + masked_lm_prob: 0.15 # Probability of replacing a token with mask. + short_seq_prob: 0.1 # Probability of producing a short sequence. + + optim: + name: fused_adam + lr: 2e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 500 + constant_steps: 50000 + min_lr: 2e-5 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_chatglm_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_chatglm_config.yaml new file mode 100644 index 0000000..84fbd1b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_chatglm_config.yaml @@ -0,0 +1,224 @@ +name: megatron_chatglm2 +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 32 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_chatglm2 + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +model: + mcore_gpt: True + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 4 # limited by GPU memory + global_batch_size: 8 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + # model architecture + encoder_seq_length: 32768 + max_position_embeddings: ${.encoder_seq_length} + num_layers: 28 + hidden_size: 4096 + ffn_hidden_size: 13696 + num_attention_heads: 32 + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0.0 # Dropout probability for hidden state transformer. + attention_dropout: 0.0 # Dropout probability for attention + ffn_dropout: 0.0 # Dropout probability in the feed-forward layer. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: 'rmsnorm' # Normalization layer to use. Options are 'layernorm', 'rmsnorm' + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + bias: False # Whether to use bias terms in all weight matrices. + qkv_bias: True # add bias for QKV linear + activation: 'fast-swiglu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] + headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head. + transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer'] + openai_gelu: False # Use OpenAI's GELU instead of the default GeLU + normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True. + position_embedding_type: 'rope' # Position embedding type. Options ['learned_absolute', 'rope'] + rotary_percentage: 0.5 # If using position_embedding_type=rope, then the per head dim is multiplied by this. For chatglm2, it is 0.5 (https://huggingface.co/THUDM/chatglm2-6b/blob/main/modeling_chatglm.py#L754) + rotary_interleaved: True # chatglm2 use interleaved rotary embedding + apply_rope_fusion: False + attention_type: 'multihead' # Attention type. Options ['multihead'] + share_embeddings_and_output_weights: False # Share embedding and output layer weights. + overlap_p2p_comm: False # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + num_query_groups: 2 # Number of query groups for group query attention. If None, normal attention is used. + override_vocab_size: null + + tokenizer: + library: huggingface #'sentencepiece' + type: THUDM/chatglm2-6b #null + model: null # /path/to/tokenizer.model + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + trust_remote_code: True + + # Mixed precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + + # Fusion + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism.. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2. + bias_activation_fusion: False # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function. + bias_dropout_add_fusion: False # Use a kernel that fuses the bias addition, dropout and residual connection addition. + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + get_attention_mask_from_fusion: True # When using fused softmax it will create the attention mask so we won't copy it to the pipeline stages. + + + # Miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + sync_batch_comm: False # Enable stream synchronization after each p2p communication between pipeline stages + + ## Activation Checkpointing + # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed. + # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+). + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + # 'full' will checkpoint the entire transformer layer. + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model. + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null + # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory. + # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage. + num_micro_batches_with_partial_activation_checkpoints: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed + # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is + # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint + # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'. + # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage. + activations_checkpoint_layers_per_pipeline: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later + # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than + # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage + # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints', + # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path. + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Transformer Engine + transformer_engine: True + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: False # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: most_recent # 'most_recent' or 'max'. Algorithm for computing amax from history + reduce_amax: True # Perform reduction to sync amax tensors across GPUs after every iteration + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + + data: + # Path to data must be specified by the user. + # Supports List, String and Dictionary + # List : can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-gpt3_00_text_document + # - .5 + # - /raid/data/pile/my-gpt3_01_text_document + # Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test} + # Or see example below: + # "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}" + # data_prefix: ??? + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap + splits_string: 900,50,50 + seq_length: ${model.encoder_seq_length} + skip_warmup: True + num_workers: 2 + dataloader_type: single # cyclic + reset_position_ids: False # Reset position ids after end-of-document token + reset_attention_mask: False # Reset attention mask after end-of-document token + eod_mask_loss: False # Mask loss for the end of document tokens + validation_drop_last: True # Set to false if the last partial validation samples is to be consumed + no_seqlen_plus_one_input_tokens: False # Set to True to disable fetching (sequence length + 1) input tokens, instead get (sequence length) input tokens and mask the last token + pad_samples_to_global_batch_size: False # Set to True if you want to pad the last partial batch with -1's to equal global batch size + shuffle_documents: True # Set to False to disable documents shuffling. Sample index will still be shuffled + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [0] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: fused_adam + lr: 2e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 500 + constant_steps: 50000 + min_lr: 2e-5 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_chatglm_inference.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_chatglm_inference.yaml new file mode 100644 index 0000000..e508b01 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_chatglm_inference.yaml @@ -0,0 +1,39 @@ +inference: + greedy: False # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 1.0 # sampling temperature + add_BOS: True # add the bos token at the begining of the prompt + tokens_to_generate: 30 # The minimum length of the sequence to be generated. + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + end_strings: [""] # generation will stop when one of these tokens is generated + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: 32 # 16, 32, or bf16 + use_distributed_sampler: False + +tensor_model_parallel_size: -1 +pipeline_model_parallel_size: -1 +pipeline_model_parallel_split_rank: -1 # used for encoder and decoder model (0 for others) +megatron_amp_O2: False # Enable O2-level automatic mixed precision to save memory +gpt_model_file: null # GPT nemo file path +checkpoint_dir: null # checkpoint file dir. This is used to load the PTL checkpoint generated during the GPT training +checkpoint_name: null # PTL checkpoint file name, only used for PTL checkpoint loading +hparams_file: null # model configuration file, only used for PTL checkpoint loading +prompts: # prompts for GPT inference + - "Q: How are you?" + - "Q: How big is the universe?" +server: False # whether launch the API server +port: 5555 # the port number for the inference server +web_server: False # whether launch the web inference server +share: False # whether create a public URL +username: test # user name for web client +password: test2 # password for web client +web_port: 9889 # the port number of the web server diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_falcon_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_falcon_config.yaml new file mode 100644 index 0000000..8905aba --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_falcon_config.yaml @@ -0,0 +1,220 @@ +name: megatron_falcon_gpt +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: bf16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_falcon_gpt + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_falcon--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +model: + mcore_gpt: True + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 1 # limited by GPU memory + global_batch_size: 1 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + # model architecture + encoder_seq_length: 2048 + max_position_embeddings: ${.encoder_seq_length} + num_layers: 32 # 7b: 32 | 40b: 60 | 180b: 80 + hidden_size: 4544 # 7b: 4544 | 40b: 8192 | 180b: 14848 + ffn_hidden_size: 18176 # Transformer FFN hidden size. Usually 4 * hidden_size. | 7b: 18176 | 40b: 32768 | 180b: 59392 + num_attention_heads: 71 # 7b: 71 | 40b: 128 | 180b: 232 + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0.0 # Dropout probability for hidden state transformer. + attention_dropout: 0.0 # Dropout probability for attention + ffn_dropout: 0.0 # Dropout probability in the feed-forward layer. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: 'layernorm' # Normalization layer to use. Options are 'layernorm', 'rmsnorm' + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + bias: False # Whether to use bias terms in all weight matrices. + activation: 'gelu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] + headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head. + transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer'] + openai_gelu: False # Use OpenAI's GELU instead of the default GeLU + normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True. + position_embedding_type: 'rope' # Position embedding type. Options ['learned_absolute', 'rope'] + rotary_percentage: 1.0 # If using position_embedding_type=rope, then the per head dim is multiplied by this. + attention_type: 'multihead' # Attention type. Options ['multihead'] + share_embeddings_and_output_weights: False # Share embedding and output layer weights. + overlap_p2p_comm: False # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + num_query_groups: 1 # Number of query groups for group query attention. If None, normal attention is used. | 7b: 1 | 40b: 8 | 180b: 8 + gc_interval: 0 + precision: bf16 + mcore_customization_config: + new_decoder_architecture: false + parallel_attention: true + + tokenizer: + library: 'huggingface' + type: 'tiiuae/falcon-7b' + use_fast: True + + # Mixed precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + + # Fusion + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism.. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2. + bias_activation_fusion: False # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function. + bias_dropout_add_fusion: False # Use a kernel that fuses the bias addition, dropout and residual connection addition. + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + get_attention_mask_from_fusion: True # When using fused softmax it will create the attention mask so we won't copy it to the pipeline stages. + apply_rope_fusion: False # Use a kernel to add rotary positional embeddings. Only used if position_embedding_type=rope + + + # Miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + sync_batch_comm: False # Enable stream synchronization after each p2p communication between pipeline stages + + ## Activation Checkpointing + # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed. + # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+). + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + # 'full' will checkpoint the entire transformer layer. + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model. + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null + # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory. + # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage. + num_micro_batches_with_partial_activation_checkpoints: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed + # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is + # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint + # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'. + # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage. + activations_checkpoint_layers_per_pipeline: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later + # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than + # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage + # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints', + # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path. + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Transformer Engine + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: False # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: most_recent # 'most_recent' or 'max'. Algorithm for computing amax from history + reduce_amax: True # Perform reduction to sync amax tensors across GPUs after every iteration + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + + data: + # Path to data must be specified by the user. + # Supports List, String and Dictionary + # List : can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-gpt3_00_text_document + # - .5 + # - /raid/data/pile/my-gpt3_01_text_document + # Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test} + # Or see example below: + # "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}" + # data_prefix: ??? + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap + splits_string: 900,50,50 + seq_length: ${model.encoder_seq_length} + skip_warmup: True + num_workers: 2 + dataloader_type: single # cyclic + reset_position_ids: False # Reset position ids after end-of-document token + reset_attention_mask: False # Reset attention mask after end-of-document token + eod_mask_loss: False # Mask loss for the end of document tokens + validation_drop_last: True # Set to false if the last partial validation samples is to be consumed + no_seqlen_plus_one_input_tokens: False # Set to True to disable fetching (sequence length + 1) input tokens, instead get (sequence length) input tokens and mask the last token + pad_samples_to_global_batch_size: False # Set to True if you want to pad the last partial batch with -1's to equal global batch size + shuffle_documents: True # Set to False to disable documents shuffling. Sample index will still be shuffled + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [0] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: distributed_fused_adam + lr: 2e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 500 + constant_steps: 50000 + min_lr: 2e-5 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_falcon_inference.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_falcon_inference.yaml new file mode 100644 index 0000000..1ccc9ed --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_falcon_inference.yaml @@ -0,0 +1,38 @@ +inference: + greedy: False # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 1.0 # sampling temperature + add_BOS: False # add the bos token at the begining of the prompt + tokens_to_generate: 30 # The minimum length of the sequence to be generated. + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + end_strings: ["<|endoftext|>"] # generation will stop when one of these tokens is generated + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: bf16 # 16, 32, or bf16 + use_distributed_sampler: False + +tensor_model_parallel_size: 1 +pipeline_model_parallel_size: 1 +megatron_amp_O2: True # Enable O2-level automatic mixed precision to save memory +gpt_model_file: null # GPT nemo file path +checkpoint_dir: null # checkpoint file dir. This is used to load the PTL checkpoint generated during the GPT training +checkpoint_name: null # PTL checkpoint file name, only used for PTL checkpoint loading +hparams_file: null # model configuration file, only used for PTL checkpoint loading +prompts: # prompts for GPT inference + - "Q: How are you?" + - "Q: How big is the universe?" +server: False # whether launch the API server +port: 5555 # the port number for the inference server +web_server: False # whether launch the web inference server +share: False # whether create a public URL +username: test # user name for web client +password: test2 # password for web client +web_port: 9889 # the port number of the web server diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gemma_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gemma_config.yaml new file mode 100644 index 0000000..bdc5e20 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gemma_config.yaml @@ -0,0 +1,220 @@ +name: megatron_llama +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 32 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_llama + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +model: + mcore_gpt: True + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 4 # limited by GPU memory + global_batch_size: 8 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + # model architecture + encoder_seq_length: 8192 + max_position_embeddings: ${.encoder_seq_length} + num_layers: 28 # 2b: 18 | 7b: 28 + hidden_size: 3072 # 2b: 2048 | 7b: 3072 + ffn_hidden_size: 24576 # Transformer FFN hidden size. Usually 4 * hidden_size. | 2b: 16384 | 7b: 24576 + num_attention_heads: 16 # 2b: 8 | 7b: 16 + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0.0 # Dropout probability for hidden state transformer. + attention_dropout: 0.0 # Dropout probability for attention + ffn_dropout: 0.0 # Dropout probability in the feed-forward layer. + kv_channels: 256 # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null | 2b: 256 | 7b: 256 + apply_embedding_scaling: True # scale sqrt(hidden_size) + apply_query_key_layer_scaling: False # scale Q * K^T by 1 / layer-number. + normalization: 'rmsnorm' # Normalization layer to use. Options are 'layernorm', 'rmsnorm' + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + bias: False # Whether to use bias terms in all weight matrices. + activation: 'geglu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] + headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head. + transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer'] + openai_gelu: True # Use OpenAI's GELU instead of the default GeLU + normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True. + position_embedding_type: 'rope' # Position embedding type. Options ['learned_absolute', 'rope'] + rotary_percentage: 1.0 # If using position_embedding_type=rope, then the per head dim is multiplied by this. + attention_type: 'multihead' # Attention type. Options ['multihead'] + share_embeddings_and_output_weights: True # Share embedding and output layer weights. + overlap_p2p_comm: False # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + num_query_groups: 16 # Number of query groups for group query attention. If None, normal attention is used. | 2b: 1 | 7b: 16 + + tokenizer: + library: 'sentencepiece' + type: null + model: ??? # /path/to/tokenizer.model + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + + # Mixed precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + + # Fusion + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism.. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2. + bias_activation_fusion: False # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function. + bias_dropout_add_fusion: False # Use a kernel that fuses the bias addition, dropout and residual connection addition. + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + get_attention_mask_from_fusion: True # When using fused softmax it will create the attention mask so we won't copy it to the pipeline stages. + + + # Miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + sync_batch_comm: False # Enable stream synchronization after each p2p communication between pipeline stages + + ## Activation Checkpointing + # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed. + # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+). + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + # 'full' will checkpoint the entire transformer layer. + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model. + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null + # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory. + # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage. + num_micro_batches_with_partial_activation_checkpoints: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed + # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is + # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint + # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'. + # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage. + activations_checkpoint_layers_per_pipeline: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later + # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than + # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage + # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints', + # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path. + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Transformer Engine + transformer_engine: True + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: True # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1024 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: max # 'most_recent' or 'max'. Algorithm for computing amax from history + reduce_amax: True # Perform reduction to sync amax tensors across GPUs after every iteration + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + + data: + # Path to data must be specified by the user. + # Supports List, String and Dictionary + # List : can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-gpt3_00_text_document + # - .5 + # - /raid/data/pile/my-gpt3_01_text_document + # Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test} + # Or see example below: + # "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}" + # data_prefix: ??? + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap + splits_string: 900,50,50 + seq_length: ${model.encoder_seq_length} + skip_warmup: True + num_workers: 2 + dataloader_type: single # cyclic + reset_position_ids: False # Reset position ids after end-of-document token + reset_attention_mask: False # Reset attention mask after end-of-document token + eod_mask_loss: False # Mask loss for the end of document tokens + validation_drop_last: True # Set to false if the last partial validation samples is to be consumed + no_seqlen_plus_one_input_tokens: False # Set to True to disable fetching (sequence length + 1) input tokens, instead get (sequence length) input tokens and mask the last token + pad_samples_to_global_batch_size: False # Set to True if you want to pad the last partial batch with -1's to equal global batch size + shuffle_documents: True # Set to False to disable documents shuffling. Sample index will still be shuffled + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [0] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: fused_adam + lr: 2e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 500 + constant_steps: 50000 + min_lr: 2e-5 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml new file mode 100755 index 0000000..ea37237 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml @@ -0,0 +1,281 @@ +defaults: + - _self_ + - optional tp_overlap@model.ub_tp_comm_overlap_cfg: + +name: megatron_gpt +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_gpt + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + create_neptune_logger: false + neptune_logger_kwargs: + project: null + name: null + prefix: train + log_model_checkpoints: false + tags: null # can specify as an array of strings in yaml array format + description: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +model: + # use GPTModel from megatron.core + mcore_gpt: False + + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 4 # limited by GPU memory + global_batch_size: 8 # will use more micro batches to reach global batch size + rampup_batch_size: null # Should be a list of 3 values: [, , ] + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + expert_model_parallel_size: 1 # expert model parallelism + + # model architecture + encoder_seq_length: 512 + max_position_embeddings: ${.encoder_seq_length} + num_layers: 12 + hidden_size: 768 + ffn_hidden_size: 3072 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 12 + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0.1 # Dropout probability for hidden state transformer. + attention_dropout: 0.1 # Dropout probability for attention + ffn_dropout: 0.0 # Dropout probability in the feed-forward layer. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: False # scale Q * K^T by 1 / layer-number. + normalization: 'layernorm' # Normalization layer to use. Options are 'layernorm', 'rmsnorm' + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + bias: True # Whether to use bias terms in all weight matrices. + activation: 'gelu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] + headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head. + transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer'] + openai_gelu: False # Use OpenAI's GELU instead of the default GeLU + normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True. + position_embedding_type: 'learned_absolute' # Position embedding type. Options ['learned_absolute', 'rope', 'alibi', 'kerple' , 'xpos', 'sandwich'] xpos and sandwich are experimental. + rotary_percentage: 1.0 # If using position_embedding_type=rope, then the per head dim is multiplied by this. + attention_type: 'multihead' # Attention type. Options ['multihead'] + share_embeddings_and_output_weights: True # Share embedding and output layer weights. + overlap_p2p_comm: False # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + seq_len_interpolation_factor: null # RoPE Interpolation factor for sequence length. This is used to build long-context models with RoPE ex: https://arxiv.org/abs/2306.15595. + num_query_groups: null # Number of query groups for group query attention. If None, normal attention is used. + + tokenizer: + library: 'megatron' + type: 'GPT2BPETokenizer' + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + + # Mixed precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + + # Fusion + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism.. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2. + bias_activation_fusion: True # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function. + bias_dropout_add_fusion: True # Use a kernel that fuses the bias addition, dropout and residual connection addition. + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + get_attention_mask_from_fusion: True # When using fused softmax it will create the attention mask so we won't copy it to the pipeline stages. + apply_rope_fusion: False # Use a kernel to add rotary positional embeddings. Only used if position_embedding_type=rope + + + # Miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + sync_batch_comm: False # Enable stream synchronization after each p2p communication between pipeline stages + nccl_communicator_config_path: null # Path to the yaml file with NCCL communicator options (min_ctas, max_ctas, and cga_cluster_size) + validation_param_sync_overlap: False # Overlap parameter AllGather with validation step. + + # FSDP + fsdp: False # Enable training with torch FSDP. + fsdp_sharding_strategy: 'full' # Method to shard model states. Available options are 'full', 'hybrid', and 'grad'. + fsdp_grad_reduce_dtype: 32 # Gradient reduction data type. + fsdp_sharded_checkpoint: False # Store and load FSDP shared checkpoint. + + # PyTorch distributed checkpoint + torch_distributed_checkpoint: False # Set to True to use PyTorch distributed checkpoint format. + + ## Activation Checkpointing + # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed. + # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+). + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + # 'full' will checkpoint the entire transformer layer. + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model. + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null + # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory. + # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage. + num_micro_batches_with_partial_activation_checkpoints: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed + # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is + # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint + # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'. + # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage. + activations_checkpoint_layers_per_pipeline: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later + # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than + # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage + # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints', + # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path. + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Transformer Engine + transformer_engine: False + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: True # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1024 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: max # 'most_recent' or 'max'. Algorithm for computing amax from history + reduce_amax: True # Perform reduction to sync amax tensors across GPUs after every iteration + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + ub_tp_comm_overlap: False + # Use userbuffer backend to overlap tensor-parallel communications with computes. + # This feature is only available with Transformer Engine and squence parallelism enabled and, currently, supports only GPT models. + ub_tp_comm_overlap_cfg: null + # A yaml file with userbuffer communicator configurations. This file should provide `method`, `dtype`, `num_sm`, `num_splits`, + # `cga_size`, `num_splits`, `set_sm_margin`, and `aggregate` for the communicators to use custom settings. + # If the configuration file is not provided a default setting is used for all communicators. + + ## Flash Attention + use_flash_attention: False # Use flash attention in self-attention module, this config does nothing when transformer_engine=True + + ##Offloading Activations/Weights to CPU + cpu_offloading: False + cpu_offloading_num_layers: ${sum:${.num_layers},-1} #This value should be between [1,num_layers-1] as we don't want to offload the final layer's activations and expose any offloading duration for the final layer + cpu_offloading_activations: True + cpu_offloading_weights: True + + ## Network + sharp: False # Enable the use of SHARP for NCCL data-parallel communications. This is going to be ignored if the network doesn't support SHARP. + + ## Megatron timers + enable_megatron_timers: False + megatron_timer_kwargs: + log_every_n_steps: 10 + log_mode: minmax + barrier: False + + data: + # Path to data must be specified by the user. + # Supports List, String and Dictionary + # List : can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-gpt3_00_text_document + # - .5 + # - /raid/data/pile/my-gpt3_01_text_document + # Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test} + # Or see example below: + # "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}" + data_prefix: ??? + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap + mmap_bin_files: True + splits_string: 900,50,50 + seq_length: ${model.encoder_seq_length} + skip_warmup: True + num_workers: 2 + dataloader_type: single # cyclic + reset_position_ids: False # Reset position ids after end-of-document token + reset_attention_mask: False # Reset attention mask after end-of-document token + eod_mask_loss: False # Mask loss for the end of document tokens + validation_drop_last: True # Set to false if the last partial validation samples is to be consumed + no_seqlen_plus_one_input_tokens: False # Set to True to disable fetching (sequence length + 1) input tokens, instead get (sequence length) input tokens and mask the last token + pad_samples_to_global_batch_size: False # Set to True if you want to pad the last partial batch with -1's to equal global batch size + shuffle_documents: True # Set to False to disable documents shuffling. Sample index will still be shuffled + exchange_indices_distributed: False # Set to True to exchange indices via torch.distributed instead of filesystem + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [0] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: fused_adam + lr: 2e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 500 + constant_steps: 0 + min_lr: 2e-5 + + gc_interval: 0 + # Interval of the host memory garbage collection. When it is zero, collectiion relies on the automatic garbage collector. + # If an interger value larger than zero is set, collection is done manually by the batch step interval of `gc_interval`. diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gpt_export.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gpt_export.yaml new file mode 100644 index 0000000..24d0c15 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gpt_export.yaml @@ -0,0 +1,25 @@ +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: bf16 # 16, 32, or bf16 + +model_type: gpt +tensor_model_parallel_size: 1 +pipeline_model_parallel_size: 1 +pipeline_model_parallel_split_rank: -1 # used for encoder and decoder model (0 for others) +gpt_model_file: null # GPT nemo file path +onnx_model_file: null # ONNX file path +checkpoint_dir: null # Checkpoint directory +checkpoint_name: null # Checkpoint name +hparams_file: null # hparams filepath + +export_options: + runtime_check: False + verbose: False + onnx_opset: 17 + do_constant_folding: True + cache_support: False + device: 'cuda' + check_tolerance: 0.01 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gpt_inference.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gpt_inference.yaml new file mode 100644 index 0000000..2570251 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gpt_inference.yaml @@ -0,0 +1,95 @@ +inference: + greedy: False # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 1.0 # sampling temperature + add_BOS: True # add the bos token at the begining of the prompt + tokens_to_generate: 30 # The minimum length of the sequence to be generated. + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + end_strings: ["<|endoftext|>"] # generation will stop when one of these tokens is generated + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: 16 # 16, 32, or bf16 + use_distributed_sampler: False + + +tensor_model_parallel_size: -1 +pipeline_model_parallel_size: -1 +pipeline_model_parallel_split_rank: -1 # used for encoder and decoder model (0 for others) +megatron_amp_O2: False # Enable O2-level automatic mixed precision to save memory +gpt_model_file: null # GPT nemo file path +checkpoint_dir: null # checkpoint file dir. This is used to load the PTL checkpoint generated during the GPT training +checkpoint_name: null # PTL checkpoint file name, only used for PTL checkpoint loading +hparams_file: null # model configuration file, only used for PTL checkpoint loading +prompts: # prompts for GPT inference + - "Q: How are you?" + - "Q: How big is the universe?" +server: False # whether launch the API server +port: 5555 # the port number for the inference server +web_server: False # whether launch the web inference server +share: False # whether create a public URL +username: test # user name for web client +password: test2 # password for web client +web_port: 9889 # the port number of the web server +chat: False # use the chat interface +chatbot_config: + value: False # whether to inject the value attributes + attributes: + - name: Quality + min: 0 + max: 4 + key: quality + type: int + default: 4 + - name: Toxicity + min: 0 + max: 4 + key: toxcity + type: int + default: 0 + - name: Humor + min: 0 + max: 4 + key: humor + type: int + default: 0 + - name: Creativity + min: 0 + max: 4 + key: creativity + type: int + default: 0 + - name: Violence + min: 0 + max: 4 + key: violence + type: int + default: 0 + - name: Helpfulness + min: 0 + max: 4 + key: helpfulness + type: int + default: 4 + - name: Not_Appropriate + min: 0 + max: 4 + key: not_appropriate + type: int + default: 0 + - name: Language + choices: ['ar', 'bg', 'bn', 'ca', 'cs', 'da', 'de', 'el', 'en', 'eo', 'es', 'eu', 'fa', 'fi', 'fr', 'gl', 'he', 'hu', 'id', 'it', 'ja', 'ko', 'nb', 'nl', 'pl', 'pt', 'ro', 'ru', 'sk', 'sv', 'th', 'tr', 'uk', 'vi', 'zh'] + key: lang + type: list + default: en + + user: User + assistant: Assistant + system: "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n" diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gpt_validate_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gpt_validate_config.yaml new file mode 100644 index 0000000..39b0c7e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_gpt_validate_config.yaml @@ -0,0 +1,22 @@ +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: 16 # 16, 32, or bf16 + log_every_n_steps: 1 + limit_val_batches: 10 + limit_test_batches: 50 + max_steps: 100 # needed to setup dataloaders + max_epochs: null + replace_sampler_ddp: False + +tensor_model_parallel_size: ??? # should be set the same as the pretrained model that is being restored from +pipeline_model_parallel_size: ??? # should be set the same as the pretrained model that is being restored from +micro_batch_size: null # limited by GPU memory, defaults to pretrained model config +global_batch_size: null # will use more micro batches to reach global batch size, defaults to pretrained model config +virtual_pipeline_model_parallel_size: null +gpt_model_file: null # GPT nemo file path +checkpoint_dir: null # checkpoint file dir. This is used to load the PTL checkpoint generated during the GPT training +checkpoint_name: null # PTL checkpoint file name, only used for PTL checkpoint loading +hparams_file: null # model configuration file, only used for PTL checkpoint loading diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_hiddens_base_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_hiddens_base_config.yaml new file mode 100644 index 0000000..560b966 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_hiddens_base_config.yaml @@ -0,0 +1,43 @@ +# this file main purpose is documentation, and it should not be used directly +enc_output_name: z # name of key in hidden transforms output to pass to decoder (default: hiddens). e.g., z for VAE/MIM. +tokens_loss_weight: 1.0 # weight of tokens loss (if not specified defaults to 1.0) +# the lists below are useful for adding multiple transforms and losses according to order +# if order is not important, you can use a single dictionary in the list with multiple keys +transform: # a list of dictionaries of transforms (or a joint dictionary) to apply to hiddens (list enforces order) + # - : # name of transform + # cls_name: # class name + # : # transform parameters + # ... + - q_z_given_x: # Gaussian posterior with reparameterization + cls_name: cond_gaussian # class name + hidden_size: 512 # hidden size of the encoder + min_logvar: -6.0 # minimum log variance + - logP_cls: # logP classifier logits + cls_name: guided_cls + input_name: hiddens + attr_name: logP + QED_cls: # QED classifier logits + cls_name: guided_cls + input_name: hiddens + attr_name: QED +loss: # a list of dictionaries of loss terms (or a joint dictionary) to add to reconstruction loss (list enforces order) + # - : # name of loss + # cls_name: # class name + # : # loss parameters + # ... + # below is example where order of losses does not matter so a single dictionary is enough + mim: # A-MIM example + cls_name: a_mim + loss_weight: 1.0 # weight of the MIM latent loss + vae: # VAE example + cls_name: vae + min_kl_value: null # minimum KL value if a float is provided + loss_weight: 1e-2 # weight of KL term in loss + logP_cls: # logP classifier loss (cross entropy) + cls_name: guided_cls_loss + input_name: logP + loss_weight: 0.1 + QED_cls: # QED classifier loss (cross entropy) + cls_name: guided_cls_loss + input_name: logP + loss_weight: 0.1 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_llama_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_llama_config.yaml new file mode 100644 index 0000000..965b511 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_llama_config.yaml @@ -0,0 +1,220 @@ +name: megatron_llama +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 32 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_llama + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +model: + mcore_gpt: True + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 4 # limited by GPU memory + global_batch_size: 8 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + # model architecture + encoder_seq_length: 4096 + max_position_embeddings: ${.encoder_seq_length} + num_layers: 32 # 7b: 32 | 13b: 40 | 70b: 80 + hidden_size: 4096 # 7b: 4096 | 13b: 5120 | 70b: 8192 + ffn_hidden_size: 11008 # Transformer FFN hidden size. Usually 4 * hidden_size. | 7b: 11008 | 13b: 13824 | 70b: 28672 + num_attention_heads: 32 # 7b: 32 | 13b: 40 | 70b: 64 + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0.0 # Dropout probability for hidden state transformer. + attention_dropout: 0.0 # Dropout probability for attention + ffn_dropout: 0.0 # Dropout probability in the feed-forward layer. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: 'rmsnorm' # Normalization layer to use. Options are 'layernorm', 'rmsnorm' + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + bias: False # Whether to use bias terms in all weight matrices. + activation: 'fast-swiglu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] + headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head. + transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer'] + openai_gelu: False # Use OpenAI's GELU instead of the default GeLU + normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True. + position_embedding_type: 'rope' # Position embedding type. Options ['learned_absolute', 'rope'] + rotary_percentage: 1.0 # If using position_embedding_type=rope, then the per head dim is multiplied by this. + attention_type: 'multihead' # Attention type. Options ['multihead'] + share_embeddings_and_output_weights: False # Share embedding and output layer weights. + overlap_p2p_comm: False # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + num_query_groups: 32 # Number of query groups for group query attention. If None, normal attention is used. | 7b: 32 | 13b: 40 | 70b: 8 + + tokenizer: + library: 'sentencepiece' + type: null + model: ??? # /path/to/tokenizer.model + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + + # Mixed precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + + # Fusion + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism.. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2. + bias_activation_fusion: False # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function. + bias_dropout_add_fusion: False # Use a kernel that fuses the bias addition, dropout and residual connection addition. + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + get_attention_mask_from_fusion: True # When using fused softmax it will create the attention mask so we won't copy it to the pipeline stages. + apply_rope_fusion: False # Use a kernel to add rotary positional embeddings. Only used if position_embedding_type=rope + + + # Miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + sync_batch_comm: False # Enable stream synchronization after each p2p communication between pipeline stages + + ## Activation Checkpointing + # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed. + # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+). + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + # 'full' will checkpoint the entire transformer layer. + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model. + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null + # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory. + # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage. + num_micro_batches_with_partial_activation_checkpoints: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed + # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is + # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint + # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'. + # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage. + activations_checkpoint_layers_per_pipeline: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later + # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than + # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage + # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints', + # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path. + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Transformer Engine + transformer_engine: True + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: True # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1024 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: max # 'most_recent' or 'max'. Algorithm for computing amax from history + reduce_amax: True # Perform reduction to sync amax tensors across GPUs after every iteration + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + + data: + # Path to data must be specified by the user. + # Supports List, String and Dictionary + # List : can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-gpt3_00_text_document + # - .5 + # - /raid/data/pile/my-gpt3_01_text_document + # Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test} + # Or see example below: + # "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}" + # data_prefix: ??? + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap + splits_string: 900,50,50 + seq_length: ${model.encoder_seq_length} + skip_warmup: True + num_workers: 2 + dataloader_type: single # cyclic + reset_position_ids: False # Reset position ids after end-of-document token + reset_attention_mask: False # Reset attention mask after end-of-document token + eod_mask_loss: False # Mask loss for the end of document tokens + validation_drop_last: True # Set to false if the last partial validation samples is to be consumed + no_seqlen_plus_one_input_tokens: False # Set to True to disable fetching (sequence length + 1) input tokens, instead get (sequence length) input tokens and mask the last token + pad_samples_to_global_batch_size: False # Set to True if you want to pad the last partial batch with -1's to equal global batch size + shuffle_documents: True # Set to False to disable documents shuffling. Sample index will still be shuffled + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [0] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: fused_adam + lr: 2e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 500 + constant_steps: 50000 + min_lr: 2e-5 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_llama_inference.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_llama_inference.yaml new file mode 100644 index 0000000..e508b01 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_llama_inference.yaml @@ -0,0 +1,39 @@ +inference: + greedy: False # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 1.0 # sampling temperature + add_BOS: True # add the bos token at the begining of the prompt + tokens_to_generate: 30 # The minimum length of the sequence to be generated. + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + end_strings: [""] # generation will stop when one of these tokens is generated + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: 32 # 16, 32, or bf16 + use_distributed_sampler: False + +tensor_model_parallel_size: -1 +pipeline_model_parallel_size: -1 +pipeline_model_parallel_split_rank: -1 # used for encoder and decoder model (0 for others) +megatron_amp_O2: False # Enable O2-level automatic mixed precision to save memory +gpt_model_file: null # GPT nemo file path +checkpoint_dir: null # checkpoint file dir. This is used to load the PTL checkpoint generated during the GPT training +checkpoint_name: null # PTL checkpoint file name, only used for PTL checkpoint loading +hparams_file: null # model configuration file, only used for PTL checkpoint loading +prompts: # prompts for GPT inference + - "Q: How are you?" + - "Q: How big is the universe?" +server: False # whether launch the API server +port: 5555 # the port number for the inference server +web_server: False # whether launch the web inference server +share: False # whether create a public URL +username: test # user name for web client +password: test2 # password for web client +web_port: 9889 # the port number of the web server diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_llama_quantization.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_llama_quantization.yaml new file mode 100644 index 0000000..ac10f72 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_llama_quantization.yaml @@ -0,0 +1,38 @@ +inference: + greedy: false # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 1.0 # sampling temperature + add_BOS: true # add the bos token at the begining of the prompt + tokens_to_generate: 30 # The minimum length of the sequence to be generated. + all_probs: false # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: false # a flag used to compute logprob of all the input text, a very special case of running inference, default False + batch_size: 64 # batch size for inference + max_context_length: 512 # max length of the context, input sequence will be truncated if it is longer than this + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: false # logger provided by exp_manager + precision: bf16 # 16, 32, or bf16 + enable_checkpointing: false + +quantization: + quantize_bmm1: false + algorithm: fp8 # int8_sq, fp8, int8, int4_awq, null + calib_dataset: cnn_dailymail # wikitext, cnn_dailymail, or a local dataset + num_calib_size: 512 # number of samples used for calibration + +export: + decoder_type: llama # gptnext, gpt2, llama + inference_tensor_parallel: 1 # Default using 1 TP for inference + dtype: 16 # Default precision data type + export_tensorrt_llm_config: true # export config to build TRT-LLM engine directly + +model_file: llama2-7b-fp16.nemo # Nemo file path +model_save: llama2-7b-fp8.qnemo # Path where the quantized model will be saved +tensor_model_parallel_size: 1 +pipeline_model_parallel_size: 1 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_model_base_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_model_base_config.yaml new file mode 100644 index 0000000..235bf3d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_model_base_config.yaml @@ -0,0 +1,41 @@ +num_layers: 12 # For perceiver models, this is the number of cross-attention blocks. Each layer has 1 cross-attention and "num_self_attention_per_cross_attention" self-attention layers. +hidden_size: 768 +ffn_hidden_size: 3072 # Transformer FFN hidden size. Usually 4 * hidden_size. +num_attention_heads: 12 +init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') +hidden_dropout: 0.1 # Dropout probability for hidden state transformer. +attention_dropout: 0.1 # Dropout probability in the attention layer. +ffn_dropout: 0.0 # Dropout probability in the feed-forward layer. +position_embedding_type: 'learned_absolute' # Position embedding type. Options ['learned_absolute', 'relative', 'alibi', 'kerple'] +relative_attention_num_buckets: 32 # Relative position number of buckets for computing the bias +relative_attention_max_distance: 128 # max_distance to keep relative distance in the attention_num_buckets. +relative_position_bias_self_attention_only: True # whether to only use relative position bias for self attention only. +kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null +apply_query_key_layer_scaling: False # scale Q * K^T by 1 / layer-number. +layernorm_epsilon: 1e-5 +persist_layer_norm: True # Use of persistent fused layer norm kernel. +bias_activation_fusion: True # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function. +grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce +masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. +bias_dropout_add_fusion: True # Use a kernel that fuses the bias addition, dropout and residual connection addition. +bias: True # Whether to use bias terms in all weight matrices. +normalization: 'layernorm' # Normalization layer to use. Options are 'layernorm', 'rmsnorm' +arch: 'transformer' # Options: ['transformer', 'perceiver'] +activation: 'gelu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] +headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head. +transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer'] +hidden_steps: 32 # Number of latent vectors to use for pereceiver encoders +num_self_attention_per_cross_attention: 1 # Number of self-attention layers for every cross-attention layer. +openai_gelu: False # Use OpenAI's GELU instead of the default GeLU +onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. +fp32_residual_connection: False # Use FP32 for residual connections. +activations_checkpoint_method: null # 'uniform', 'block' +activations_checkpoint_num_layers: 1 +activations_checkpoint_granularity: null +megatron_legacy: False # Whether to use the legacy Megatron model. This affects the way q,k,v is partitioned from the mixed q,k,v layer in ParallelAttention. This needs to be True for models converted from HF. +normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True. +num_moe_experts: 1 # When >1, FFNs are changed to MoE layers +moe_frequency: 1 # every Nth ffn layer will be made MoE +moe_dropout: 0.0 # Dropout value for MoE layers +use_flash_attention: false # Use flash attention in self-attention module +enable_megatron_timers: false # Megatron timers \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_retro_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_retro_config.yaml new file mode 100644 index 0000000..dafdcf5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_retro_config.yaml @@ -0,0 +1,127 @@ +defaults: + - .@model: megatron_model_base_config + +name: test_retro +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 2 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice we don't usually train for more than 1 epoch. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: null + limit_test_batches: null + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_retro + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + filename: 'megatron_retro--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + + +model: + version: 1 # indicate the retro model version + + # model parallelism + micro_batch_size: 4 + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 # has to be one. not supporting pipeline parallel yet + + # model architecture + encoder_seq_length: 2048 + max_position_embeddings: ${.encoder_seq_length} + + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + dump_debug_info: False # dump out the debug information + dump_debug_info_to_file: False # dump out the debug information to files + + # retro architecture + chunk_size: 64 # the chunk size used to retrive + enc_num_layers: 4 # total number of encoder layers + dec_num_layers: 6 # total number of decoder layers + enc_cross_attention: [3] # layer numbers for cross attention in encoder + dec_cross_attention: [3, 5] # layer numbers for chunked cross attention in decoder + add_position_embedding: False # whether use the absolute position encoding + + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + bert_binary_head: True # BERT binary head + + megatron_amp_O2: False # use AMP with O2 style mixed precision instead of native amp on-the-fly weight autocasting. + grad_allreduce_chunk_size_mb: 125 + + megatron_lm_compatible: False # a flag to indicate whether the model is compatible with Megatron LM + + tokenizer: + library: 'megatron' + type: 'GPT2BPETokenizer' + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # miscellaneous + seed: 1234 + + data: + # Path to data must be specified by the user. + # can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-gpt3_00_text_document + # - .5 + # - /raid/data/pile/my-gpt3_01_text_document + data_prefix: ??? # list of training datasets + knn_index: ??? # list of KNN map index files + retrieval_prefix: ??? # a singe path to retrieval data + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: retmmap # for retro model, this is the only allowed type + splits_string: 900,50,50 + seq_length: ${model.encoder_seq_length} # must be multiple of the chunk_size in your dataset + skip_warmup: True + num_workers: 0 + dataloader_type: single # cyclic + neighbors: 2 # number of retrieved neighbors + + optim: + name: fused_adam + lr: 1e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 500 + constant_steps: 50000 + min_lr: 1e-5 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_retro_finetune_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_retro_finetune_config.yaml new file mode 100644 index 0000000..7fa5c35 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_retro_finetune_config.yaml @@ -0,0 +1,105 @@ +name: fine_tune_retro + +trainer: + devices: 2 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice we don't usually train for more than 1 epoch. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: null + limit_test_batches: null + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_retro + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + filename: 'megatron_retro--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + + +model: + # model parallelism + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 # has to be one. not supporting pipeline parallel yet + + micro_batch_size: 4 + megatron_amp_O2: False # use AMP with O2 style mixed precision instead of native amp on-the-fly weight autocasting. + + tokenizer: + library: 'megatron' + type: 'GPT2BPETokenizer' + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # miscellaneous + seed: 1234 + + restore_path: null # the retro model restore path + + data: + train_ds: + file_name: ??? # train data file path + answer_only_loss: True # whether use answer only loss + seq_length: 128 # must be multiple of the chunk_size in your dataset + add_bos: True # whether to add bos at the beginning + add_eos: True # whether to add eos at the end + seed: 1234 + neighbors: 20 # number of retrieved neighbors + val_ds: + file_name: ??? # train data file path + answer_only_loss: True # whether use answer only loss + seq_length: 128 # must be multiple of the chunk_size in your dataset + add_bos: True # whether to add bos at the beginning + add_eos: True # whether to add eos at the end + seed: 1234 + neighbors: 20 # number of retrieved neighbors + test_ds: + file_name: ??? # train data file path + answer_only_loss: True # whether use answer only loss + seq_length: 128 # must be multiple of the chunk_size in your dataset + add_bos: True # whether to add bos at the beginning + add_eos: True # whether to add eos at the end + seed: 1234 + neighbors: 20 # number of retrieved neighbors + + + optim: + name: fused_adam + lr: 1e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 500 + constant_steps: 50000 + min_lr: 1e-5 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_retro_inference.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_retro_inference.yaml new file mode 100644 index 0000000..1b99a65 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_retro_inference.yaml @@ -0,0 +1,44 @@ +inference: + greedy: False # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 1.0 # sampling temperature + add_BOS: True # add the bos token at the begining of the prompt + tokens_to_generate: 30 # The minimum length of the sequence to be generated. + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: 16 # 16, 32, or bf16 + +inference_batch_size: 2 +tensor_model_parallel_size: -1 +pipeline_model_parallel_size: -1 +pipeline_model_parallel_split_rank: -1 # used for encoder and decoder model (0 for others) +retro_model_file: null # RETRO nemo file path + +use_predict_method: False # whether to use the predict method + +prompts: # prompts for RETRO model inference + - "hello," + - "good morning," + - "good afternoon," + - "good evening," + +########### Faiss service parameters ######## +retrieval_service: + strategy: RetroModelTextGenerationStrategy # choose customized inference strategy + neighbors: 4 + frequent_query: False # for the current token generation, frequently update the retrieval context. If false, update it every 64 tokens + pad_tokens: True # pad the tokens at the beginning to make it minimum of 64 tokens for retrieving at least once + store_retrieved: False # whether store the retrieved documents, so it can be checked + combo_service: + service_ip: '0.0.0.0' + service_port: 17181 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_retro_mutransfer.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_retro_mutransfer.yaml new file mode 100644 index 0000000..8010389 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_retro_mutransfer.yaml @@ -0,0 +1,224 @@ +defaults: + - .@base_model: megatron_model_base_config + - .@delta_model: megatron_model_base_config + - .@model: megatron_model_base_config + +name: mu_transfer_retro +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 2 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice we don't usually train for more than 1 epoch. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: null + limit_test_batches: null + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_retro + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + filename: 'megatron_retro--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +base_model: + version: 1 # indicate the retro model version + # model parallelism + micro_batch_size: 4 + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 # has to be one. not supporting pipeline parallel yet + + # model architecture + encoder_seq_length: 2048 + max_position_embeddings: ${.encoder_seq_length} + + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + # retro architecture + chunk_size: 64 # the chunk size used to retrive + enc_num_layers: 2 # total number of encoder layers + dec_num_layers: 12 # total number of decoder layers + enc_cross_attention: [0] # layer numbers for cross attention in encoder + dec_cross_attention: [5, 8, 11] # layer numbers for chunked cross attention in decoder + add_position_embedding: False # whether use the absolute position encoding + + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + bert_binary_head: True # BERT binary head + + megatron_amp_O2: False # use AMP with O2 style mixed precision instead of native amp on-the-fly weight autocasting. + grad_allreduce_chunk_size_mb: 125 + + tokenizer: + library: 'megatron' + type: 'GPT2BPETokenizer' + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # miscellaneous + seed: 1234 + +delta_model: + version: 1 # indicate the retro model version + # model parallelism + micro_batch_size: 4 + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 # has to be one. not supporting pipeline parallel yet + + # model architecture + encoder_seq_length: 2048 + max_position_embeddings: ${.encoder_seq_length} + + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + # retro architecture + chunk_size: 64 # the chunk size used to retrive + enc_num_layers: 2 # total number of encoder layers + dec_num_layers: 12 # total number of decoder layers + enc_cross_attention: [0] # layer numbers for cross attention in encoder + dec_cross_attention: [5, 8, 11] # layer numbers for chunked cross attention in decoder + add_position_embedding: False # whether use the absolute position encoding + + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + bert_binary_head: True # BERT binary head + + megatron_amp_O2: False # use AMP with O2 style mixed precision instead of native amp on-the-fly weight autocasting. + grad_allreduce_chunk_size_mb: 125 + + tokenizer: + library: 'megatron' + type: 'GPT2BPETokenizer' + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + # miscellaneous + seed: 1234 + +model: + version: 1 # indicate the retro model version + shape_file: null # the path to the shape file + # model parallelism + micro_batch_size: 4 + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 # has to be one. not supporting pipeline parallel yet + + # model architecture + encoder_seq_length: 2048 + max_position_embeddings: ${.encoder_seq_length} + + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + dump_debug_info: False # dump out the debug information + dump_debug_info_to_file: False # dump out the debug information to files + + # retro architecture + chunk_size: 64 # the chunk size used to retrive + enc_num_layers: 2 # total number of encoder layers + dec_num_layers: 12 # total number of decoder layers + enc_cross_attention: [0] # layer numbers for cross attention in encoder + dec_cross_attention: [5, 8, 11] # layer numbers for chunked cross attention in decoder + add_position_embedding: False # whether use the absolute position encoding + + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + bert_binary_head: True # BERT binary head + + megatron_amp_O2: False # use AMP with O2 style mixed precision instead of native amp on-the-fly weight autocasting. + grad_allreduce_chunk_size_mb: 125 + + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null # not used with 'selective' + # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory. + # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage. + + tokenizer: + library: 'megatron' + type: 'GPT2BPETokenizer' + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # miscellaneous + seed: 1234 + + data: + # Path to data must be specified by the user. + # can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-gpt3_00_text_document + # - .5 + # - /raid/data/pile/my-gpt3_01_text_document + data_prefix: ??? # list of training datasets + knn_index: ??? # list of KNN map index files + retrieval_prefix: ??? # a singe path to retrieval data + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: retmmap # for retro model, this is the only allowed type + splits_string: 900,50,50 + seq_length: ${model.encoder_seq_length} # must be multiple of the chunk_size in your dataset + skip_warmup: True + num_workers: 0 + dataloader_type: single # cyclic + neighbors: 2 # number of retrieved neighbors + + optim: + name: muadamw + lr: 1e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 500 + constant_steps: 50000 + min_lr: 1e-5 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_starcoder2_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_starcoder2_config.yaml new file mode 100644 index 0000000..6d47a14 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_starcoder2_config.yaml @@ -0,0 +1,222 @@ +name: megatron_starcoder2 +restore_from_path: null # used when starting from a .nemo file + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + precision: 32 + logger: false + enable_checkpointing: false + use_distributed_sampler: false + max_epochs: null + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 5000 + limit_val_batches: 50 + limit_test_batches: 50 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_starcoder2 + create_wandb_logger: true + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: true + resume_ignore_no_checkpoint: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: false + save_nemo_on_train_end: false + filename: megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples} + model_parallel_size: 2 + +model: + mcore_gpt: true + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 1 # limited by GPU memory + global_batch_size: 2 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + # model architecture + encoder_seq_length: 4096 + max_position_embeddings: ${.encoder_seq_length} + num_layers: 40 # 3b: 30 | 7b: 32 | 15b: 40 + hidden_size: 6144 # 3b: 3072 | 7b: 4608 | 15b: 6144 + ffn_hidden_size: 24576 # 3b: 12288 | 7b: 18432 | 15b: 24576 + num_attention_heads: 48 # 3b: 24 | 7b: 36 | 15b: 48 + init_method_std: 0.01275 # 3b: 0.018042 | 7b: 0.018042 | 15b: 0.01275 + hidden_dropout: 0 # Dropout probability for hidden state transformer. + attention_dropout: 0 # Dropout probability for attention + ffn_dropout: 0 # Dropout probability in the feed-forward layer. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: true # scale Q * K^T by 1 / layer-number. + layernorm_epsilon: 1.0e-05 + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: true # Add embedding + post_process: true # Add pooler + persist_layer_norm: true # Use of persistent fused layer norm kernel + position_embedding_type: rope # Position embedding type. Options ['learned_absolute', 'rope'] + rotary_base: 10000 # 3b: 1e5 | 7b: 1e5 | 15b: 1e4 + activation: gelu # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] + bias: true # Whether to use bias terms in all weight matrices. + normalization: layernorm # Normalization layer to use. Options are 'layernorm', 'rmsnorm' + transformer_block_type: pre_ln # Options ['pre_ln', 'post_ln', 'normformer'] + share_embeddings_and_output_weights: false # Share embedding and output layer weights. + + ## Fusion + grad_div_ar_fusion: true # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism.. + gradient_accumulation_fusion: true # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2. + bias_activation_fusion: true # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function. + bias_dropout_add_fusion: true # Use a kernel that fuses the bias addition, dropout and residual connection addition. + masked_softmax_fusion: true # Use a kernel that fuses the attention softmax with it's mask. + + ## Miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + sync_batch_comm: False # Enable stream synchronization after each p2p communication between pipeline stages + + overlap_p2p_comm: false + batch_p2p_comm: true + num_query_groups: 4 # 3b: 2 | 7b: 4 | 15b: 4 + tokenizer: + library: huggingface + type: bigcode/starcoder2-tokenizer + model: null + delimiter: null + vocab_file: null + merge_file: null + native_amp_init_scale: 4294967296 + native_amp_growth_interval: 1000 + hysteresis: 2 + fp32_residual_connection: false + fp16_lm_cross_entropy: false + megatron_amp_O2: false + grad_allreduce_chunk_size_mb: 125 + + ## Activation Checkpointing + # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed. + # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+). + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + # 'full' will checkpoint the entire transformer layer. + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model. + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null + # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory. + # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage. + num_micro_batches_with_partial_activation_checkpoints: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed + # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is + # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint + # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'. + # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage. + activations_checkpoint_layers_per_pipeline: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later + # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than + # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage + # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints', + # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path. + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Transformer Engine + transformer_engine: True + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: True # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1024 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: max # 'most_recent' or 'max'. Algorithm for computing amax from history + reduce_amax: True # Perform reduction to sync amax tensors across GPUs after every iteration + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + + ## Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [0] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: distributed_fused_adam + bucket_cap_mb: 128 + overlap_grad_sync: true + overlap_param_sync: true + contiguous_grad_buffer: true + lr: 0.0003 + weight_decay: 0.1 + betas: + - 0.9 + - 0.95 + sched: + name: CosineAnnealing + warmup_steps: 1000 + constant_steps: 0 + min_lr: 3.0e-05 + + data: + # Path to data must be specified by the user. + # Supports List, String and Dictionary + # List : can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-gpt3_00_text_document + # - .5 + # - /raid/data/pile/my-gpt3_01_text_document + # Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test} + # Or see example below: + # "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}" + # data_prefix: ??? + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap + splits_string: 900,50,50 + seq_length: ${model.encoder_seq_length} + skip_warmup: True + num_workers: 2 + dataloader_type: single # cyclic + reset_position_ids: False # Reset position ids after end-of-document token + reset_attention_mask: False # Reset attention mask after end-of-document token + eod_mask_loss: False # Mask loss for the end of document tokens + validation_drop_last: True # Set to false if the last partial validation samples is to be consumed + no_seqlen_plus_one_input_tokens: False # Set to True to disable fetching (sequence length + 1) input tokens, instead get (sequence length) input tokens and mask the last token + pad_samples_to_global_batch_size: False # Set to True if you want to pad the last partial batch with -1's to equal global batch size + shuffle_documents: True # Set to False to disable documents shuffling. Sample index will still be shuffled + add_fim: false # Enable FIM in training + fim: + rate: 0.5 + spm_rate: 0.5 + split_sample: + fragment_rate: 0.5 + no_prefix: + extra_tokens: + prefix: + middle: + suffix: + pad: + eod: <|endoftext|> + \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_starcoder_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_starcoder_config.yaml new file mode 100644 index 0000000..355e575 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_starcoder_config.yaml @@ -0,0 +1,257 @@ +name: megatron_starcoder +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_starcoder + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_starcoder--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +model: + # use GPTModel from megatron.core + mcore_gpt: True + + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + + micro_batch_size: 1 # limited by GPU memory + global_batch_size: 2 # will use more micro batches to reach global batch size + rampup_batch_size: null # Should be a list of 3 values: [, , ] + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + # model architecture + encoder_seq_length: 8192 + max_position_embeddings: ${.encoder_seq_length} + num_layers: 12 + hidden_size: 768 + ffn_hidden_size: 3072 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 12 + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0.1 # Dropout probability for hidden state transformer. + attention_dropout: 0.1 # Dropout probability for attention + ffn_dropout: 0.0 # Dropout probability in the feed-forward layer. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: False # scale Q * K^T by 1 / layer-number. + normalization: 'layernorm' # Normalization layer to use. Options are 'layernorm', 'rmsnorm' + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + bias: True # Whether to use bias terms in all weight matrices. + activation: 'gelu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu'] + headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head. + transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer'] + openai_gelu: False # Use OpenAI's GELU instead of the default GeLU + normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True. + position_embedding_type: 'rope' # Position embedding type. Options ['learned_absolute', 'rope', 'alibi', 'kerple' , 'xpos', 'sandwich'] xpos and sandwich are experimental. + rotary_percentage: 1.0 # If using position_embedding_type=rope, then the per head dim is multiplied by this. + attention_type: 'multihead' # Attention type. Options ['multihead'] + share_embeddings_and_output_weights: False # Share embedding and output layer weights. + overlap_p2p_comm: False # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1 + seq_len_interpolation_factor: null # RoPE Interpolation factor for sequence length. This is used to build long-context models with RoPE ex: https://arxiv.org/abs/2306.15595. + num_query_groups: null # Number of query groups for group query attention. If None, normal attention is used. + + tokenizer: + library: 'megatron' + type: 'GPT2BPETokenizer' + model: null + vocab_file: null + merge_file: null + delimiter: null # only used for tabular tokenizer + sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + + # Mixed precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + + # Fusion + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism.. + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2. + bias_activation_fusion: True # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function. + bias_dropout_add_fusion: True # Use a kernel that fuses the bias addition, dropout and residual connection addition. + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + get_attention_mask_from_fusion: True # When using fused softmax it will create the attention mask so we won't copy it to the pipeline stages. + apply_rope_fusion: False # Use a kernel to add rotary positional embeddings. Only used if position_embedding_type=rope + + # Miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + sync_batch_comm: False # Enable stream synchronization after each p2p communication between pipeline stages + + ## Activation Checkpointing + # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed. + # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+). + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + # 'full' will checkpoint the entire transformer layer. + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model. + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null + # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory. + # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage. + num_micro_batches_with_partial_activation_checkpoints: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed + # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is + # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint + # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'. + # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage. + activations_checkpoint_layers_per_pipeline: null + # This feature is valid only when used with pipeline-model-parallelism. + # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later + # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than + # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage + # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints', + # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path. + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Transformer Engine + transformer_engine: False + fp8: False # enables fp8 in TransformerLayer forward + fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3 + fp8_hybrid: True # sets fp8_format = recipe.Format.HYBRID + fp8_margin: 0 # scaling margin + fp8_interval: 1 # scaling update interval + fp8_amax_history_len: 1024 # Number of steps for which amax history is recorded per tensor + fp8_amax_compute_algo: max # 'most_recent' or 'max'. Algorithm for computing amax from history + reduce_amax: True # Perform reduction to sync amax tensors across GPUs after every iteration + use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False. + ub_tp_comm_overlap: False + # Use userbuffer backend to overlap tensor-parallel communications with computes. + # This feature is only available with Transformer Engine and squence parallelism enabled and, currently, supports only GPT models. + ub_tp_comm_overlap_cfg: null + # A yaml file with userbuffer communicator configurations. This file should provide `method`, `dtype`, `num_sm`, `num_splits`, + # `cga_size`, `num_splits`, `set_sm_margin`, and `aggregate` for the communicators to use custom settings. + # If the configuration file is not provided a default setting is used for all communicators. + + ## Flash Attention + use_flash_attention: False # Use flash attention in self-attention module, this config does nothing when transformer_engine=True + + data: + # Path to data must be specified by the user. + # Supports List, String and Dictionary + # List : can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-gpt3_00_text_document + # - .5 + # - /raid/data/pile/my-gpt3_01_text_document + # Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test} + # Or see example below: + # "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}" + data_prefix: ??? + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap + splits_string: 9998,1,1 + seq_length: ${model.encoder_seq_length} + skip_warmup: True + num_workers: 2 + dataloader_type: single # cyclic + reset_position_ids: False # Reset position ids after end-of-document token + reset_attention_mask: False # Reset attention mask after end-of-document token + eod_mask_loss: False # Mask loss for the end of document tokens + validation_drop_last: True # Set to false if the last partial validation samples is to be consumed + no_seqlen_plus_one_input_tokens: False # Set to True to disable fetching (sequence length + 1) input tokens, instead get (sequence length) input tokens and mask the last token + pad_samples_to_global_batch_size: False # Set to True if you want to pad the last partial batch with -1's to equal global batch size + shuffle_documents: True # Set to False to disable documents shuffling. Sample index will still be shuffled + exchange_indices_distributed: False # Set to True to exchange indices via torch.distributed instead of filesystem + add_fim: False # Set to True to use FIM + fim: + # fill in the middle + rate: 0.5 # Probability to convert a training sample into a "Fill-in-the-Middle" format. Must be between 0 and 1 + spm_rate: 0.5 # Probability that the a FIM sample uses the SPM format over the PSM format + split_sample: null # String around which to split the sample for FIM. If None (default), FIM is applied on the sample-level + fragment_rate: 0.5 # Rate of FIM on each fragment when fim_split_sample is not None + no_prefix: null # Do not apply FIM to fragments that start with this prefix + extra_tokens: + prefix: "" + middle: "" + suffix: "" + pad: "" + eod: "<|endoftext|>" + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [0] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: distributed_fused_adam + bucket_cap_mb: 128 + overlap_grad_sync: true + overlap_param_sync: true + contiguous_grad_buffer: true + lr: 0.0003 + weight_decay: 0.1 + betas: + - 0.9 + - 0.95 + sched: + name: CosineAnnealing + warmup_steps: 100 + constant_steps: 0 + min_lr: 3.0e-05 + + gc_interval: 0 + # Interval of the host memory garbage collection. When it is zero, collectiion relies on the automatic garbage collector. + # If an interger value larger than zero is set, collection is done manually by the batch step interval of `gc_interval`. diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t0_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t0_config.yaml new file mode 100644 index 0000000..0c76cd7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t0_config.yaml @@ -0,0 +1,95 @@ +name: megatron_t0 + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 300 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_t0 + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: validation_${model.data.validation_ds.metric.name} + save_top_k: 10 + mode: max + always_save_nemo: False # TODO: add support + filename: 'megatron_t0--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{consumed_samples}' + model_parallel_size: ${model.tensor_model_parallel_size} + save_best_model: True + +model: + restore_from_path: null # Path to a trained T5 .nemo file + pretrained_checkpoint: + checkpoint_dir: null # Path to a folder that contains a .ckpt file + checkpoint_name: null # Name of the .ckpt file within the checkpoint_dir. + hparams_file: null # Path to a .yaml file that contains the hyperparameters of the checkpoint. + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 + pipeline_model_parallel_split_rank: 0 + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + megatron_amp_O2: False # Enable O2 optimization for megatron amp + resume_from_checkpoint: null + hidden_dropout: 0.1 # Override dropout prob from pretraining + attention_dropout: 0.1 # Override attention dropout prob from pretraining + + data: + train_ds: + file_names: ??? # Path to a list of JSONL files corresponding to the source data. + global_batch_size: 128 + micro_batch_size: 16 + shuffle: True + num_workers: 8 + pin_memory: True + max_src_seq_length: 512 + max_tgt_seq_length: 512 + drop_last: True + concat_sampling_probabilities: ??? # When providing a list of datasets, this arg defines the sampling probabilities from each dataset when strategy='random' + replace_bos_with_pad: False # Replaces bos with pad for both the encoder and decoder. This is necessary when using Google's T5 checkpoints. + add_bos_to_input: False # Adds bos to the input sequence. + add_eos_to_input: False # Adds eos to the input sequence. + seed: 1234 + + validation_ds: + file_names: ??? # Path to a list of JSONL files corresponding to the source data. + names: null # Names of the corresponding datasets used to log metrics. + global_batch_size: 16 + micro_batch_size: 16 + shuffle: False + num_workers: 0 + pin_memory: True + max_src_seq_length: 512 + max_tgt_seq_length: 512 + drop_last: False # TODO: Figure out if there is a way to avoid dropping last. + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + metric: + name: "exact_string_match" # Name of the evaluation metric to use. + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + replace_bos_with_pad: ${data.train_ds.replace_bos_with_pad} + add_bos_to_input: ${data.train_ds.add_bos_to_input} + add_eos_to_input: ${data.train_ds.add_eos_to_input} + seed: 1234 + + optim: + name: fused_adam + lr: 5e-6 + weight_decay: 0.0 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config.yaml new file mode 100644 index 0000000..e51cfff --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config.yaml @@ -0,0 +1,155 @@ +defaults: + - .@model.encoder: megatron_model_base_config + - .@model.decoder: megatron_model_base_config + +name: megatron_t5 +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + benchmark: False + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + filename: '${name}--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +model: + # model parallelism + micro_batch_size: 4 + global_batch_size: 8 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 + resume_from_checkpoint: null # manually set the checkpoint file to load from + pipeline_model_parallel_split_rank: 0 # rank at which decoder starts. + + # model architecture + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + + megatron_amp_O2: False # use AMP with O2 style mixed precision instead of native amp on-the-fly weight autocasting. + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + seq_length: 512 + max_position_embeddings: ${.seq_length} + + tokenizer: + library: 'megatron' + type: 'BertWordPieceCase' + model: null + vocab_file: null + merge_file: null + num_sentinel_tokens: 100 + sentencepiece_legacy: True # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + + # weight init + embedding_init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + + # embedding dropout + embedding_dropout: 0.1 + + # embedding sharing + share_token_embeddings: True # If True share encoder/decoder embeddings + share_decoder_tokens_head_embeddings: True # If True share decoder embeddings and decoder projection to logits + + # token head + tokens_head_bias: True + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # miscellaneous + seed: 1234 + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + + data: + # Path to data must be specified by the user. + # Supports List, String and Dictionary + # List : can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-t5_00_text_document,.5,/raid/data/pile/my-t5_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-t5_00_text_document + # - .5 + # - /raid/data/pile/my-t5_01_text_document + # Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test} + # Or see example below: + # "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}" + data_prefix: ??? + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap # mmap, retmmap, text_mmap, csv_mmap + # data_impl_kwargs: # currently used only for text_mmap, csv_mmap (should be data_impl dependant) + # # defaults for text_memmap + # newline_int: 10 # byte-value of newline (Use ord('\n') to get value) + # header_lines: 0 # skip first N header lines + # workers: null # number of workers when creating missing index files (null defaults to cpu_num // 2) + # sort_dataset_paths: False # if True datasets will be sorted by name + # # defaults for csv_memmap + # newline_int: 10 # byte-value of newline + # header_lines: 1 # skip first N header lines + # workers: null # number of workers when creating missing index files (null defaults to cpu_num // 2) + # sort_dataset_paths: False # if True datasets will be sorted by name + # data_col: 1 # column to use for data + # data_sep: ',' # string to split text into columns + splits_string: 949,45,5 + seq_length: ${model.seq_length} + seq_length_dec: 128 + skip_warmup: True + num_workers: 0 + dataloader_type: single # cyclic + masked_lm_prob: 0.15 + dataset_type: 't5' + short_seq_prob: 0.0 + max_ngram_size: 10 + mean_ngram_size: null + geometric_dist: True + permutation: False + whole_word_masking: True + favor_longer_ngrams: False + respect_document_boundaries: True # If true, a single training exampl cannot cross document boundaries, increasing the fraction of tokens within a batch. + + optim: + name: fused_adam + lr: 0.0001 + betas: + - 0.9 + - 0.999 + eps: 1e-8 + weight_decay: 0.01 + sched: + name: WarmupAnnealing + min_lr: 0.00001 + last_epoch: -1 + warmup_ratio: 0.01 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config_finetune_eval.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config_finetune_eval.yaml new file mode 100644 index 0000000..40a061b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config_finetune_eval.yaml @@ -0,0 +1,52 @@ +name: megatron_t5_finetune_eval + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + benchmark: False + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_t5_finetune_eval + create_checkpoint_callback: False + +model: + restore_from_path: null # Path to a trained T5 .nemo file + pretrained_checkpoint: + checkpoint_dir: null # Path to a folder that contains a .ckpt file + checkpoint_name: null # Name of the .ckpt file within the checkpoint_dir. + hparams_file: null # Path to a .yaml file that contains the hyperparameters of the checkpoint. + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + megatron_amp_O2: False # Enable O2 optimization for megatron amp + + data: + validation_ds: + src_file_name: null # Path to the txt file corresponding to the source data. + tgt_file_name: null # Path to the txt file corresponding to the target data. + names: null # If src/tgt file names are ListConfigs, the corresponding label is used to log metrics. + global_batch_size: 64 + micro_batch_size: 64 + shuffle: False + num_workers: 0 + pin_memory: True + max_src_seq_length: 512 + max_tgt_seq_length: 128 + drop_last: False # TODO: Figure out if there is a way to avoid dropping last. + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + replace_bos_with_pad: False # Replaces bos with pad for both the encoder and decoder. This is necessary when using Google's T5 checkpoints. + add_bos_to_input: False # Adds bos to the input sequence. + add_eos_to_input: False # Adds eos to the input sequence. + + metric: + name: "exact_string_match" # Name of the evaluation metric to use. + average: micro # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null # Number of classes for the metric. Works only for 'F1', 'accuracy' and 'average_precision' etc. Refer to torchmetrics for metrics where this is supported. + class_labels: null # If the targets in your dataset are strings and not integers/float, you need to provide a list of class labels (size = num_classes) so we can convert from strings to integer categories to compute the metric. + labels_are_strings: True # NOTE: This is only required to properly handle metrics like f1, accuracy, average_precision etc. This does not affect extract_string_match. diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config_finetune_glue_eval.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config_finetune_glue_eval.yaml new file mode 100644 index 0000000..2c8994c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config_finetune_glue_eval.yaml @@ -0,0 +1,50 @@ +name: megatron_t5_glue_eval + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + benchmark: False + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_t5_glue_eval + create_checkpoint_callback: False + +model: + restore_from_path: null # Path to a trained T5 .nemo file + pretrained_checkpoint: + checkpoint_dir: null # Path to a folder that contains a .ckpt file + checkpoint_name: null # Name of the .ckpt file within the checkpoint_dir. + hparams_file: null # Path to a .yaml file that contains the hyperparameters of the checkpoint. + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + megatron_amp_O2: False # Enable O2 optimization for megatron amp + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 + pipeline_model_parallel_split_rank: 0 + + data: + validation_ds: + task_name: 'mnli' + file_path: ??? # Path to the TSV file for MNLI train ex: '/raid/Data/GLUE/MNLI/dev_matched.tsv' + global_batch_size: 1 + micro_batch_size: 1 + shuffle: False + num_workers: 0 + pin_memory: True + max_seq_length: 512 + drop_last: False + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + replace_bos_with_pad: False # Replaces bos with pad for both the encoder and decoder. This is necessary when using Google's T5 checkpoints. + add_bos_to_input: False # Adds bos to the input sequence. + add_eos_to_input: False # Adds eos to the input sequence. + metric: + name: "exact_string_match" # Name of the evaluation metric to use. + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config_finetune_glue_mnli.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config_finetune_glue_mnli.yaml new file mode 100644 index 0000000..459ca31 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config_finetune_glue_mnli.yaml @@ -0,0 +1,93 @@ +name: megatron_t5_glue + +trainer: + devices: 2 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 3 + max_steps: -1 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 300 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + benchmark: False + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_t5_glue + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: validation_${model.data.validation_ds.metric.name} + save_top_k: 10 + mode: max + always_save_nemo: False # TODO: add support + filename: 'megatron_t5--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}' + model_parallel_size: ${model.tensor_model_parallel_size} + save_best_model: True + +model: + restore_from_path: null # Path to a trained T5 .nemo file + pretrained_checkpoint: + checkpoint_dir: null # Path to a folder that contains a .ckpt file + checkpoint_name: null # Name of the .ckpt file within the checkpoint_dir. + hparams_file: null # Path to a .yaml file that contains the hyperparameters of the checkpoint. + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 + pipeline_model_parallel_split_rank: 0 + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + megatron_amp_O2: False # Enable O2 optimization for megatron amp + resume_from_checkpoint: null + hidden_dropout: 0.1 # Override dropout prob from pretraining + attention_dropout: 0.1 # Override attention dropout prob from pretraining + + data: + train_ds: + task_name: 'mnli' + file_path: ??? # Path to the TSV file for MNLI train ex: '/raid/Data/GLUE/MNLI/train.tsv' + global_batch_size: 128 + micro_batch_size: 64 + shuffle: True + num_workers: 0 + pin_memory: True + max_seq_length: 512 + drop_last: True + replace_bos_with_pad: False # Replaces bos with pad for both the encoder and decoder. This is necessary when using Google's T5 checkpoints. + add_bos_to_input: False # Adds bos to the input sequence. + add_eos_to_input: False # Adds eos to the input sequence. + + + validation_ds: + task_name: 'mnli' + file_path: ??? # Path to the TSV file for MNLI train ex: '/raid/Data/GLUE/MNLI/dev_matched.tsv' + global_batch_size: 128 + micro_batch_size: 64 + shuffle: False + num_workers: 0 + pin_memory: True + max_seq_length: 512 + drop_last: False # TODO: Figure out if there is a way to avoid dropping last. + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + replace_bos_with_pad: ${data.train_ds.replace_bos_with_pad} + add_bos_to_input: ${data.train_ds.add_bos_to_input} + add_eos_to_input: ${data.train_ds.add_eos_to_input} + metric: + name: "exact_string_match" # Name of the evaluation metric to use. + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + + optim: + name: fused_adam + lr: 5e-6 + weight_decay: 0.0 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config_finetune_glue_xnli.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config_finetune_glue_xnli.yaml new file mode 100644 index 0000000..44bf8ac --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_config_finetune_glue_xnli.yaml @@ -0,0 +1,116 @@ +name: megatron_t5_glue_xnli + +trainer: + devices: 2 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 3 + max_steps: -1 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 300 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + benchmark: False + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_t5_glue_xnli + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: validation_${model.data.validation_ds.metric.name} + save_top_k: 10 + mode: max + always_save_nemo: False # TODO: add support + filename: 'megatron_t5--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}' + model_parallel_size: ${model.tensor_model_parallel_size} + save_best_model: True + +model: + restore_from_path: null # Path to a trained T5 .nemo file + pretrained_checkpoint: + checkpoint_dir: null # Path to a folder that contains a .ckpt file + checkpoint_name: null # Name of the .ckpt file within the checkpoint_dir. + hparams_file: null # Path to a .yaml file that contains the hyperparameters of the checkpoint. + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 + pipeline_model_parallel_split_rank: 1 + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + resume_from_checkpoint: null + megatron_amp_O2: False # Enable O2 optimization for megatron amp + hidden_dropout: 0.1 # Override dropout prob from pretraining + attention_dropout: 0.1 # Override attention dropout prob from pretraining + eval_languages: ['fr', 'de', 'en', 'es'] # List of languages to evaluate zero-shot XNLI performance. + + data: + train_ds: + task_name: 'mnli' + file_path: ??? # Path to the TSV file for MNLI train ex: '/raid/Data/GLUE/MNLI/train.tsv' + global_batch_size: 128 + micro_batch_size: 64 + shuffle: True + num_workers: 0 + pin_memory: True + max_seq_length: 512 + drop_last: True + replace_bos_with_pad: False # Replaces bos with pad for both the encoder and decoder. This is necessary when using Google's T5 checkpoints. + add_bos_to_input: True # Adds bos to the input sequence. + add_eos_to_input: True # Adds eos to the input sequence. + + + validation_ds: + task_name: 'xnli' + file_path: ??? # Path to the TSV file for XNLI dev ex: '/raid/Data/GLUE/MNLI/dev_matched.tsv' + global_batch_size: 128 + micro_batch_size: 64 + shuffle: False + num_workers: 0 + pin_memory: True + max_seq_length: 512 + drop_last: False + write_predictions_to_file: False + prediction_file_path_prefix: null # Prefix of the file to write predictions to. + replace_bos_with_pad: ${data.train_ds.replace_bos_with_pad} + add_bos_to_input: ${data.train_ds.add_bos_to_input} + add_eos_to_input: ${data.train_ds.add_eos_to_input} + + metric: + name: "exact_string_match" # Name of the evaluation metric to use. + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + + test_ds: + task_name: 'xnli' + file_path: ??? # Path to the TSV file for XNLI dev ex: '/raid/Data/GLUE/MNLI/dev_matched.tsv' + global_batch_size: 128 + micro_batch_size: 64 + shuffle: False + num_workers: 0 + pin_memory: True + max_seq_length: 512 + drop_last: False + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + replace_bos_with_pad: ${data.train_ds.replace_bos_with_pad} + add_bos_to_input: ${data.train_ds.add_bos_to_input} + add_eos_to_input: ${data.train_ds.add_eos_to_input} + + metric: + name: "exact_string_match" # Name of the evaluation metric to use. + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + + optim: + name: fused_adam + lr: 5e-6 + weight_decay: 0.0 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_finetune.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_finetune.yaml new file mode 100644 index 0000000..2ba68cb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_finetune.yaml @@ -0,0 +1,99 @@ +name: megatron_t5_finetuning + +trainer: + devices: 2 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 10 + max_steps: -1 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 300 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_t5_finetune + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: validation_${model.data.validation_ds.metric.name} + save_top_k: 10 + mode: max + always_save_nemo: False # TODO: add support + filename: 'megatron_t5--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}' + model_parallel_size: ${model.tensor_model_parallel_size} + save_best_model: True + +model: + restore_from_path: null # Path to a trained T5 .nemo file + pretrained_checkpoint: + checkpoint_dir: null # Path to a folder that contains a .ckpt file + checkpoint_name: null # Name of the .ckpt file within the checkpoint_dir. + hparams_file: null # Path to a .yaml file that contains the hyperparameters of the checkpoint. + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 + pipeline_model_parallel_split_rank: 0 + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + megatron_amp_O2: False # Enable O2 optimization for megatron amp + resume_from_checkpoint: null + hidden_dropout: 0.1 # Override dropout prob from pretraining + attention_dropout: 0.1 # Override attention dropout prob from pretraining + + data: + train_ds: + src_file_name: ??? # Path to the txt file corresponding to the source data. + tgt_file_name: ??? # Path to the txt file corresponding to the target data. + global_batch_size: 128 + micro_batch_size: 64 + shuffle: True + num_workers: 0 + pin_memory: True + max_src_seq_length: 512 + max_tgt_seq_length: 128 + drop_last: True + concat_sampling_technique: temperature # When providing a list of datasets, this arg defines the sampling strategy. Options: ['temperature', 'random'] + concat_sampling_temperature: 5 # When providing a list of datasets, this arg defines the sampling temperature when strategy='temperature' + concat_sampling_probabilities: null # When providing a list of datasets, this arg defines the sampling probabilities from each dataset when strategy='random' + replace_bos_with_pad: False # Replaces bos with pad for both the encoder and decoder. This is necessary when using Google's T5 checkpoints. + add_bos_to_input: False # Adds bos to the input sequence. + add_eos_to_input: False # Adds eos to the input sequence. + + validation_ds: + src_file_name: ??? # Path to the txt file corresponding to the source data. + tgt_file_name: ??? # Path to the txt file corresponding to the target data. + names: null # If src/tgt file names are ListConfigs, the corresponding label is used to log metrics. + global_batch_size: 128 + micro_batch_size: 64 + shuffle: False + num_workers: 0 + pin_memory: True + max_src_seq_length: 512 + max_tgt_seq_length: 128 + drop_last: False # TODO: Figure out if there is a way to avoid dropping last. + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + replace_bos_with_pad: ${data.train_ds.replace_bos_with_pad} + add_bos_to_input: ${data.train_ds.add_bos_to_input} + add_eos_to_input: ${data.train_ds.add_eos_to_input} + metric: + name: "exact_string_match" # Name of the evaluation metric to use. Supported metrics: [`exact_string_match`, `rouge`, `pearson_corr_coef`, `spearman_corr_coef`, `f1`, `accuracy`, `average_precision`] + average: micro # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null # Number of classes for the metric. Works only for 'F1', 'accuracy' and 'average_precision' etc. Refer to torchmetrics for metrics where this is supported. + class_labels: null # If the targets in your dataset are strings and not integers/float, you need to provide a list of class labels (size = num_classes) so we can convert from strings to integer categories to compute the metric. + labels_are_strings: True # NOTE: This is only required to properly handle metrics like f1, accuracy, average_precision etc. This does not affect extract_string_match. + + optim: + name: fused_adam + lr: 5e-6 + weight_decay: 0.0 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_lm_adaptation_finetune.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_lm_adaptation_finetune.yaml new file mode 100644 index 0000000..8a00408 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_t5_lm_adaptation_finetune.yaml @@ -0,0 +1,101 @@ +name: megatron_t5_lm_adaptation_finetune +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_t5_lm_adaptation_finetune + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + filename: 'megatron_t5--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +model: + # pretrained model path + pretrained_model_path: ??? + + # model parallelism + micro_batch_size: 4 + global_batch_size: 8 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 2 + pipeline_model_parallel_size: 1 + resume_from_checkpoint: null # manually set the checkpoint file to load from + pipeline_model_parallel_split_rank: 1 + + # O2 mixed precision + megatron_amp_O2: False # use AMP with O2 style mixed precision instead of native amp on-the-fly weight autocasting. + + # JIT fusion params. + bias_activation_fusion: True # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function. + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + bias_dropout_add_fusion: True # Use a kernel that fuses the bias addition, dropout and residual connection addition. + + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + # Dropout + hidden_dropout: null + attention_dropout: null + + data: + # Path to data must be specified by the user. + # can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-t5_00_text_document,.5,/raid/data/pile/my-t5_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-t5_00_text_document + # - .5 + # - /raid/data/pile/my-t5_01_text_document + data_prefix: ??? + index_mapping_dir: null + data_impl: mmap + splits_string: 949,45,5 + seq_length: ${model.seq_length} + seq_length_dec: 128 + skip_warmup: True + num_workers: 0 + dataloader_type: single # cyclic + masked_lm_prob: 0.15 + dataset_type: 't5_prefix_lm' + short_seq_prob: 0.0 + max_ngram_size: 10 + mean_ngram_size: null + geometric_dist: True + permutation: False + whole_word_masking: True + favor_longer_ngrams: False + + optim: + name: fused_adam + lr: 5e-6 + betas: + - 0.9 + - 0.999 + eps: 1e-8 + weight_decay: 0.01 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_ul2_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_ul2_config.yaml new file mode 100644 index 0000000..86d02b0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/megatron_ul2_config.yaml @@ -0,0 +1,156 @@ +defaults: + - .@model.encoder: megatron_model_base_config + - .@model.decoder: megatron_model_base_config + +name: megatron_ul2 +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + filename: '${name}--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +model: + # model parallelism + micro_batch_size: 4 + global_batch_size: 8 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 + resume_from_checkpoint: null # manually set the checkpoint file to load from + pipeline_model_parallel_split_rank: 0 # rank at which decoder starts. + + # model architecture + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + + megatron_amp_O2: False # use AMP with O2 style mixed precision instead of native amp on-the-fly weight autocasting. + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + seq_length: 512 + max_position_embeddings: ${.seq_length} + + tokenizer: + library: 'megatron' + type: 'BertWordPieceCase' + model: null + vocab_file: null + merge_file: null + num_sentinel_tokens: 100 + sentencepiece_legacy: True # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + + # weight init + embedding_init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + + # embedding dropout + embedding_dropout: 0.1 + + # embedding sharing + share_token_embeddings: True # If True share encoder/decoder embeddings + share_decoder_tokens_head_embeddings: True # If True share decoder embeddings and decoder projection to logits + + # token head + tokens_head_bias: True + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # miscellaneous + seed: 1234 + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + + data: + # Path to data must be specified by the user. + # can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-t5_00_text_document,.5,/raid/data/pile/my-t5_01_text_document]", + # Or see example below: + # data_prefix: + # - .5 + # - /raid/data/pile/my-t5_00_text_document + # - .5 + # - /raid/data/pile/my-t5_01_text_document + data_prefix: ??? + index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix + data_impl: mmap + # data_impl_kwargs: # currently used only for text_mmap, csv_mmap (should be data_impl dependant) + # # defaults for text_memmap + # newline_int: 10 # byte-value of newline (Use ord('\n') to get value) + # header_lines: 0 # skip first N header lines + # workers: null # number of workers when creating missing index files (null defaults to cpu_num // 2) + # sort_dataset_paths: False # if True datasets will be sorted by name + # # defaults for csv_memmap + # newline_int: 10 # byte-value of newline + # header_lines: 1 # skip first N header lines + # workers: null # number of workers when creating missing index files (null defaults to cpu_num // 2) + # sort_dataset_paths: False # if True datasets will be sorted by name + # data_col: 1 # column to use for data + # data_sep: ',' # string to split text into columns + splits_string: 949,45,5 + seq_length: ${model.seq_length} + seq_length_dec: ${model.seq_length} + skip_warmup: True + num_workers: 0 + dataloader_type: single # cyclic + masked_lm_prob: 0.15 + extreme_masked_lm_prob: 0.5 + dataset_type: 'ul2' + short_seq_prob: 0.0 + max_ngram_size: 10 + extreme_max_ngram_size: 128 + extreme_min_ngram_size: 32 + extreme_mean_ngram_size: 64 + ngram_span_length_distribution: 'geometric' + extreme_ngram_span_length_distribution: 'truncated_normal' + prefix_lm_pivot_mean: 0.25 + mean_ngram_size: 3 + permutation: False + whole_word_masking: True + favor_longer_ngrams: False + respect_document_boundaries: True # If true, a single training exampl cannot cross document boundaries, increasing the fraction of tokens within a batch. + + optim: + name: fused_adam + lr: 0.0001 + betas: + - 0.9 + - 0.999 + eps: 1e-8 + weight_decay: 0.01 + sched: + name: WarmupAnnealing + min_lr: 0.00001 + last_epoch: -1 + warmup_ratio: 0.01 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h12288_tp4_mbs1_seqlen2048.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h12288_tp4_mbs1_seqlen2048.yaml new file mode 100644 index 0000000..c6e25c0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h12288_tp4_mbs1_seqlen2048.yaml @@ -0,0 +1,53 @@ +# UB communicator configurations +# Model configs: A100/175B/TP4/MBS1/SeqLen2K/BF16 + +# Bulk overlap with AllGather +qkv_dgrad: + method: bulk + num_sm: 2 + set_sm_margin: 0 + +qkv_wgrad: + method: bulk + num_sm: 2 + set_sm_margin: 0 + +fc1_dgrad: + method: bulk + num_sm: 2 + set_sm_margin: 0 + +fc1_wgrad: + method: bulk + num_sm: 2 + set_sm_margin: 0 + +## Ring-exchange overlap with AllGather +qkv_fprop: + method: ring_exchange + aggregate: 0 + +proj_dgrad: + method: ring_exchange + aggregate: 0 + +fc1_fprop: + method: ring_exchange + aggregate: 0 + +fc2_dgrad: + method: ring_exchange + aggregate: 0 + +# Chunked-collective overlap with ReduceScatter +proj_fprop: + method: pipeline + num_sm: 4 + num_splits: 4 + set_sm_margin: 0 + +fc2_fprop: + method: pipeline + num_sm: 4 + num_splits: 4 + set_sm_margin: 0 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h12288_tp4_mbs2_seqlen2048.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h12288_tp4_mbs2_seqlen2048.yaml new file mode 100644 index 0000000..434e0a2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h12288_tp4_mbs2_seqlen2048.yaml @@ -0,0 +1,53 @@ +# UB communicator configurations +# Model configs: A100/175B/TP4/MBS2/SeqLen2K/BF16 + +# Bulk overlap with AllGather +qkv_dgrad: + method: bulk + num_sm: 2 + set_sm_margin: 0 + +qkv_wgrad: + method: bulk + num_sm: 2 + set_sm_margin: 0 + +fc1_dgrad: + method: bulk + num_sm: 2 + set_sm_margin: 0 + +fc1_wgrad: + method: bulk + num_sm: 2 + set_sm_margin: 0 + +## Ring-exchange overlap with AllGather +qkv_fprop: + method: ring_exchange + aggregate: 0 + +proj_dgrad: + method: ring_exchange + aggregate: 0 + +fc1_fprop: + method: ring_exchange + aggregate: 0 + +fc2_dgrad: + method: ring_exchange + aggregate: 0 + +# Chunked-collective overlap with ReduceScatter +proj_fprop: + method: pipeline + num_sm: 8 + num_splits: 4 + set_sm_margin: 0 + +fc2_fprop: + method: pipeline + num_sm: 4 + num_splits: 4 + set_sm_margin: 0 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h6144_tp4_mbs4_seqlen2048.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h6144_tp4_mbs4_seqlen2048.yaml new file mode 100644 index 0000000..2c11fa8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h6144_tp4_mbs4_seqlen2048.yaml @@ -0,0 +1 @@ +# dummy file to build hydra configs diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h6144_tp8_mbs4_seqlen2048.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h6144_tp8_mbs4_seqlen2048.yaml new file mode 100644 index 0000000..2c11fa8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h6144_tp8_mbs4_seqlen2048.yaml @@ -0,0 +1 @@ +# dummy file to build hydra configs diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h8192_tp8_mbs4_seqlen2048.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h8192_tp8_mbs4_seqlen2048.yaml new file mode 100644 index 0000000..2c11fa8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_a100_h8192_tp8_mbs4_seqlen2048.yaml @@ -0,0 +1 @@ +# dummy file to build hydra configs diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h12288_tp4_mbs1_seqlen2048.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h12288_tp4_mbs1_seqlen2048.yaml new file mode 100644 index 0000000..21d02f3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h12288_tp4_mbs1_seqlen2048.yaml @@ -0,0 +1,59 @@ +# UB communicator configurations +# Model configs: H100/175B/TP4/MBS1/SeqLen2K/FP8 + +# Bulk overlap with AllGather / ReduceScatter +qkv_dgrad: + method: bulk + num_sm: 4 + cga_size: 2 + set_sm_margin: 0 + +qkv_wgrad: + method: bulk + num_sm: 8 + cga_size: 2 + set_sm_margin: 0 + +fc1_dgrad: + method: bulk + num_sm: 2 + cga_size: 2 + set_sm_margin: 0 + +fc1_wgrad: + method: bulk + num_sm: 4 + cga_size: 2 + set_sm_margin: 0 + +## Ring-exchange overlap with AllGather +qkv_fprop: + method: ring_exchange + aggregate: 0 + +proj_dgrad: + method: ring_exchange + aggregate: 0 + +fc1_fprop: + method: ring_exchange + aggregate: 0 + +fc2_dgrad: + method: ring_exchange + aggregate: 1 + +# Chunked-collective overlap with ReduceScatter +proj_fprop: + method: pipeline + num_sm: 24 + cga_size: 2 + num_splits: 4 + set_sm_margin: 1 + +fc2_fprop: + method: pipeline + num_sm: 20 + cga_size: 2 + num_splits: 4 + set_sm_margin: 1 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h12288_tp8_mbs2_seqlen2048.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h12288_tp8_mbs2_seqlen2048.yaml new file mode 100644 index 0000000..444c824 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h12288_tp8_mbs2_seqlen2048.yaml @@ -0,0 +1,59 @@ +# UB communicator configurations +# Model configs: H100/175B/TP8/MBS2/SeqLen2K/FP8 + +# Bulk overlap with AllGather +qkv_dgrad: + method: bulk + num_sm: 8 + cga_size: 2 + set_sm_margin: 0 + +qkv_wgrad: + method: bulk + num_sm: 16 + cga_size: 2 + set_sm_margin: 0 + +fc1_dgrad: + method: bulk + num_sm: 4 + cga_size: 2 + set_sm_margin: 0 + +fc1_wgrad: + method: bulk + num_sm: 16 + cga_size: 2 + set_sm_margin: 0 + +## Ring-exchange overlap with AllGather +qkv_fprop: + method: ring_exchange + aggregate: 0 + +proj_dgrad: + method: ring_exchange + aggregate: 1 + +fc1_fprop: + method: ring_exchange + aggregate: 0 + +fc2_dgrad: + method: ring_exchange + aggregate: 0 + +# Chunked-collective overlap with ReduceScatter +proj_fprop: + method: pipeline + num_sm: 16 + cga_size: 2 + num_splits: 4 + set_sm_margin: 1 + +fc2_fprop: + method: pipeline + num_sm: 24 + cga_size: 2 + num_splits: 4 + set_sm_margin: 1 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h6144_tp4_mbs4_seqlen2048.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h6144_tp4_mbs4_seqlen2048.yaml new file mode 100644 index 0000000..2c11fa8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h6144_tp4_mbs4_seqlen2048.yaml @@ -0,0 +1 @@ +# dummy file to build hydra configs diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h6144_tp8_mbs4_seqlen2048.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h6144_tp8_mbs4_seqlen2048.yaml new file mode 100644 index 0000000..2c11fa8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h6144_tp8_mbs4_seqlen2048.yaml @@ -0,0 +1 @@ +# dummy file to build hydra configs diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h8192_tp8_mbs4_seqlen2048.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h8192_tp8_mbs4_seqlen2048.yaml new file mode 100644 index 0000000..2c11fa8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/tp_overlap/ub_cfg_h100_h8192_tp8_mbs4_seqlen2048.yaml @@ -0,0 +1 @@ +# dummy file to build hydra configs diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/transformer_lm_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/transformer_lm_config.yaml new file mode 100644 index 0000000..9a94dc2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/conf/transformer_lm_config.yaml @@ -0,0 +1,102 @@ +name: TransformerLanguageModel +do_training: True # set to False if only preprocessing data + +model: + label_smoothing: 0.0 + preproc_out_dir: null # path to store data preprocessing outputs + + train_ds: + file_name: ??? # path to file with training data + tokens_in_batch: 4096 + clean: true + shuffle: true + num_workers: 8 + + # tarred dataset specific config + # use_tarred_dataset: true + # tar_files: ??? # path to tarred files + # metadata_file: ??? # metadata for tarred dataset + # shard_strategy: scatter + # tar_shuffle_n: 256 + + validation_ds: + file_name: ??? # path to file with validation data + tokens_in_batch: 512 + clean: false + shuffle: false + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + test_ds: + file_name: ??? # path to file with test data + tokens_in_batch: 512 + clean: false + shuffle: false + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + optim: + name: adam + lr: 0.001 + betas: + - 0.9 + - 0.98 + weight_decay: 0.0 + sched: + name: InverseSquareRootAnnealing + min_lr: 0.0 + last_epoch: -1 + warmup_ratio: 0.1 + + tokenizer: + tokenizer_name: sentencepiece + tokenizer_model: ??? + vocab_file: null + special_tokens: null + training_sample_size: null # valid for sentencepiece tokenizer + + encoder: + library: nemo + model_name: null + pretrained: false + max_sequence_length: 512 + num_token_types: 0 + embedding_dropout: 0.1 + learn_positional_encodings: false + hidden_size: 512 + num_layers: 6 + inner_size: 2048 + num_attention_heads: 8 + ffn_dropout: 0.1 + attn_score_dropout: 0.1 + attn_layer_dropout: 0.1 + hidden_act: relu + mask_future: true + pre_ln: false + + head: + num_layers: 1 + activation: relu + log_softmax: true + dropout: 0.0 + +trainer: + devices: 4 + num_nodes: 1 + max_epochs: 200 + precision: 16 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0 + accelerator: gpu + strategy: ddp + enable_checkpointing: False + logger: False + log_every_n_steps: 50 # Interval of logging. + check_val_every_n_epoch: 1 + benchmark: False + +exp_manager: + name: TransformerLM + files_to_copy: [] \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/convert_weights_to_nemo1.0.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/convert_weights_to_nemo1.0.py new file mode 100644 index 0000000..b58b339 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/convert_weights_to_nemo1.0.py @@ -0,0 +1,61 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Converts BERT NeMo0.* checkpoints to NeMo1.0 format. +""" + +from argparse import ArgumentParser + +import torch + +parser = ArgumentParser() +parser.add_argument("--bert_encoder", required=True, help="path to BERT encoder, e.g. /../BERT-STEP-2285714.pt") +parser.add_argument( + "--bert_token_classifier", + required=True, + help="path to BERT token classifier, e.g. /../BertTokenClassifier-STEP-2285714.pt", +) +parser.add_argument( + "--bert_sequence_classifier", + required=False, + default=None, + help="path to BERT sequence classifier, e.g /../SequenceClassifier-STEP-2285714.pt", +) +parser.add_argument( + "--output_path", required=False, default="converted_model.pt", help="output path to newly converted model" +) +args = parser.parse_args() + +bert_in = torch.load(args.bert_encoder) +tok_in = torch.load(args.bert_token_classifier) +if args.bert_sequence_classifier: + seq_in = torch.load(args.bert_sequence_classifier) + +new_dict = {} +new_model = {"state_dict": new_dict} +for k in bert_in: + new_name = k.replace("bert.", "bert_model.") + new_dict[new_name] = bert_in[k] + +for k in tok_in: + new_name = "mlm_classifier." + k + new_dict[new_name] = tok_in[k] + +if args.bert_sequence_classifier: + for k in seq_in: + new_name = "nsp_classifier." + k + new_dict[new_name] = seq_in[k] + +torch.save(new_model, args.output_path) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/get_wkt2.sh b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/get_wkt2.sh new file mode 100755 index 0000000..33b55b2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/get_wkt2.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +""" +This file is adapted from +https://github.com/salesforce/awd-lstm-lm/blob/master/getdata.sh +Copyright by the AWD LSTM authors. +""" +DATA_DIR=$1 +echo "- Downloading WikiText-2" + +wget --continue -P $DATA_DIR https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip +unzip -q $DATA_DIR/wikitext-2-v1.zip -d $DATA_DIR +cd $DATA_DIR/wikitext-2 +mv wiki.train.tokens train.txt +sed -i -e "s//[UNK]/g" train.txt +mv wiki.valid.tokens valid.txt +sed -i -e "s//[UNK]/g" valid.txt +mv wiki.test.tokens test.txt +sed -i -e "s//[UNK]/g" test.txt +cd .. +rm wikitext-2-v1.zip + +echo "- WikiText-2 saved at $DATA_DIR/wikitext-2" diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_bart_pretraining.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_bart_pretraining.py new file mode 100644 index 0000000..e45b5e0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_bart_pretraining.py @@ -0,0 +1,89 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.callbacks import ModelSummary +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector + +from nemo.collections.nlp.models.language_modeling.megatron_bart_model import MegatronBARTModel +from nemo.collections.nlp.parts.nlp_overrides import ( + CustomProgressBar, + GradScaler, + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, + PipelineMixedPrecisionPlugin, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="megatron_bart_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True, + gradient_as_bucket_view=cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + ) + if cfg.trainer.precision in [16, '16', 'bf16', '16-mixed', 'bf16-mixed']: + scaler = None + if cfg.trainer.precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + hysteresis=cfg.model.get('hysteresis', 2), + ) + # MixedPrecisionPlugin in PTL >= 2.0 requires precision to be 16-mixed or bf16-mixed + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + if megatron_amp_O2: + plugins.append(MegatronHalfPrecisionPlugin(plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(PipelineMixedPrecisionPlugin(plugin_precision, device='cuda', scaler=scaler)) + # Set precision None after precision plugins are created as PTL >= 2.1 does not allow both + # precision plugins and precision to exist + cfg.trainer.precision = None + + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + callbacks = [ModelSummary(max_depth=3)] + # enable_progress_bar is True by default. If cfg.trainer.enable_progress_bar=False, CustomProgressBar is not appended to callbacks + if 'enable_progress_bar' not in cfg.trainer or cfg.trainer.enable_progress_bar: + callbacks.append(CustomProgressBar()) + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=callbacks) + + exp_manager(trainer, cfg.exp_manager) + + # update resume from checkpoint found by exp_manager + if cfg.model.resume_from_checkpoint is not None: + trainer.ckpt_path = cfg.model.resume_from_checkpoint + + logging.info(f'Resuming training from checkpoint: {trainer.ckpt_path}') + + model = MegatronBARTModel(cfg.model, trainer) + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_bert_pretraining.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_bert_pretraining.py new file mode 100644 index 0000000..2901a58 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_bert_pretraining.py @@ -0,0 +1,42 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf, open_dict + +from nemo.collections.nlp.models.language_modeling.megatron_bert_model import MegatronBertModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronBertTrainerBuilder +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="megatron_bert_config") +def main(cfg) -> None: + if cfg.model.data.dataloader_type != "LDDL": + mp.set_start_method("spawn", force=True) + + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronBertTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model = MegatronBertModel(cfg.model, trainer) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_change_num_partitions.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_change_num_partitions.py new file mode 100644 index 0000000..436661e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_change_num_partitions.py @@ -0,0 +1,1511 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import shutil +import tarfile +import tempfile +from argparse import ArgumentParser +from typing import Dict, List + +import torch +import torch.nn as nn +from omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer + +from nemo.collections.nlp.parts.nlp_overrides import ( + NEMO_MEGATRON_MODEL_PARALLEL_APPSTATE_OVERRIDE, + GradScaler, + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, + NLPSaveRestoreConnector, + PipelineMixedPrecisionPlugin, +) +from nemo.utils import logging, model_utils +from nemo.utils.app_state import AppState + +""" +Usage: + +### Tensor Parallelism and Pipeline Parallelism conversion ### + +# Megatron GPT +python megatron_change_num_partitions.py \ + --model_file=PATH_TO_SRC_FILE \ + --target_file=PATH_TO_TGT_FILE \ + --tensor_model_parallel_size=-1 \ + --target_tensor_model_parallel_size=1 \ + --pipeline_model_parallel_size=-1 \ + --target_pipeline_model_parallel_size=1 \ + --precision=bf16 + +# Megatron T5 +python megatron_change_num_partitions.py \ + --model_file=PATH_TO_SRC_FILE \ + --target_file=PATH_TO_TGT_FILE \ + --model_class="nemo.collections.nlp.models.language_modeling.megatron_t5_model.MegatronT5Model" \ + --tensor_model_parallel_size=-1 \ + --target_tensor_model_parallel_size=1 \ + --pipeline_model_parallel_size=-1 \ + --target_pipeline_model_parallel_size=1 \ + --target_pipeline_model_parallel_split_rank=0 \ + --precision=bf16 + +# Megatron GPT + Virtual Pipeline parallelism + +python megatron_change_num_partitions.py \ + --model_extracted_dir="" \ + --target_file="" \ + --ckpt_name="" \ + --tensor_model_parallel_size= \ + --target_tensor_model_parallel_size= \ + --pipeline_model_parallel_size= \ + --target_pipeline_model_parallel_size= \ + --virtual_pipeline_model_parallel_size= \ + --hparams_file="" \ + --precision=bf16 + +### Only Tensor Parallelism conversion ### + +To the above commands, add the following argument: `--tp_conversion_only` + +# Note: This requires that the pipeline_model_parallel_size and tgt_pipeline_model_parallel_size is set to 1. + +### Large Models conversion ### + +When converting large models, ** always ** ensure that you pre-extract the nemo model and then only perform conversion + +$ mkdir "unpacked_nemo_file" +$ tar -xvf "" -C "/unpacked_nemo_file/" + +python megatron_change_num_partitions.py \ + ... + --model_extracted_dir="/unpacked_nemo_file/" + +### Model Classes ### + +# NOTE: Conversion of other model types. +# Default model type is MegatronGPTModel, if you want another model you need to pass classpath of the model +# For example - MegatronT5Model - + +python megatron_change_num_partitions.py \ + ... + --model_class="nemo.collections.nlp.models.language_modeling.megatron_t5_model.MegatronT5Model" + +# Additional arguments: + +--num_gpu_per_node: Number of GPUs per node. Default is 8. +--megatron_legacy: Whether the model is a legacy Megatron model or not. Default is False. May be unsuported for + Pipeline Parallelism change. +--tokenizer_model_path: Path to tokenizer model. Default is None. When not None, overrides the tokenizer model path + in the model config. +--tokenizer_vocab_file: Path to tokenizer vocab file. Default is None. When not None, overrides the tokenizer vocab + file in the model config. + +# Comments + +Passing --tensor_model_parallel_size=-1 or --pipeline_model_parallel_size=-1 will automatically infer the size from the +model config. + +""" + + +def set_virtual_parallel_rank_safely(rank: int): + AppState().virtual_pipeline_model_parallel_rank = rank + + try: + from megatron.core import parallel_state + + parallel_state.set_virtual_pipeline_model_parallel_rank(rank) + + if rank is None: + parallel_state.set_virtual_pipeline_model_parallel_world_size(None) + + except (ImportError, ModuleNotFoundError): + logging.warning("`megatron-core` not installed, cannot set virtual parallel rank !") + + +################# +### Utilities ### +################# + + +def force_cpu_model(cfg): + with open_dict(cfg): + # temporarily set to cpu + original_cpu_init = cfg.get('use_cpu_initialization', False) + if 'megatron_amp_O2' in cfg: + amp_o2_key = 'megatron_amp_O2' + original_amp_o2 = cfg.megatron_amp_O2 + elif 'megatron_amp_02' in cfg: + amp_o2_key = 'megatron_amp_02' + original_amp_o2 = cfg.megatron_amp_02 + else: + amp_o2_key, original_amp_o2 = None, None + + # Set new values + cfg.use_cpu_initialization = True + if amp_o2_key is not None: + cfg[amp_o2_key] = False + + # Disable sequence parallelism - Not disabling this gives error when converting the the model to TP=1 + original_sequence_parallel = cfg.get('sequence_parallel', None) + cfg.sequence_parallel = False + + # Setup restore dict + restore_dict = {'use_cpu_initialization': original_cpu_init} # 'megatron_amp_O2': original_amp_o2 + if amp_o2_key is not None: + restore_dict[amp_o2_key] = original_amp_o2 + if original_sequence_parallel is not None: + restore_dict['sequence_parallel'] = original_sequence_parallel + + return cfg, restore_dict + + +def restore_model_config(cfg, original_dict): + with open_dict(cfg): + for key, val in original_dict.items(): + logging.info(f"Restoring model config key ({key}) from {cfg[key]} to original value of {val}") + cfg[key] = val + return cfg + + +################# +### Utilities ### +################# + + +def compute_tp_splits( + param_name, param, partitions, global_idx, tp_size, pp_size, pp_rank, pp_split_rank, megatron_legacy, model_cfg +): + """ + Function to compute the splits required for tensor-parallelism. + + Args: + param_name: Name of the current parameter of the current model (TP X PP Y) + param: Value of the current parameter of the current model (TP X PP Y) + partitions: Partitions of the flattened parameter of the current model (TP 1 PP 1) + global_idx: The index used to select the parameter in the global partition. + tp_size: Int, tensor-parallelism size. + pp_size: Int, pipeline-parallelism size. + pp_rank: Int, pipeline-parallelism rank. + pp_split_rank: Int, pipeline-parallelism split rank. This should be > 1 if TP is being used with EncDec models (T5) + megatron_legacy: Bool, whether the model is a legacy Megatron model or not. + model_cfg: The model config as a OmegaConf DictConfig. + + Returns: + List of torch tensors, each of which is a split of the current parameter. + """ + # alias the global index to idx + idx = global_idx + + fast_glu_activation = str(model_cfg.get('activation', '')).lower() in ['fast-geglu', 'fast-swiglu', 'fast-reglu'] + + if param.shape == partitions[0][idx].shape: + split = [partitions[0][idx].data] * tp_size + logging.debug(">> Perfect match, no splitting needed") + elif param.shape[0] == partitions[0][idx].shape[0]: + split = torch.split(partitions[0][idx].data, param.shape[-1], dim=-1) + else: + # For T5-converted weights, the splitting needs to be strided such that q,k,v weights are bunched together on each tensor-parallel rank. + if '.query_key_value.' in param_name and megatron_legacy: # weight or bias + split_dim = partitions[0][idx].data.shape[0] + if split_dim % (tp_size * 3) != 0: + raise ValueError( + f"Can not split Q,K,V parameter {param_name} with shape {param.shape} into tensor parallel size {tp_size}. Not divisible by {tp_size * 3}." + ) + tp_qkv_splits = torch.chunk(partitions[0][idx].data, tp_size * 3, dim=0) + split = [] + for i in range(tp_size): + tp_qkv = torch.cat([tp_qkv_splits[item] for item in range(i, tp_size * 3, tp_size)]) + split.append(tp_qkv) + elif '.key_value.' in param_name and megatron_legacy: # weight or bias + split_dim = partitions[0][idx].data.shape[0] + if split_dim % (tp_size * 2) != 0: + raise ValueError( + f"Can not split K,V parameter {param_name} with shape {param.shape} into tensor parallel size {tp_size}. Not divisible by {tp_size * 2}." + ) + tp_qkv_splits = torch.chunk(partitions[0][idx].data, tp_size * 2, dim=0) + split = [] + for i in range(tp_size): + tp_qkv = torch.cat([tp_qkv_splits[item] for item in range(i, tp_size * 2, tp_size)]) + split.append(tp_qkv) + elif ('dense_h_to_4h' in param_name or 'linear_fc1' in param_name) and fast_glu_activation: + # For Megatron GPT model with Fast Glu activation + # Handle gated linear units + # concat all the first halves ('W's) and all the second halves ('V's) + w_split, k_split = torch.chunk(partitions[0][idx].data, 2, dim=0) + w_split = torch.chunk(w_split, tp_size, dim=0) + k_split = torch.chunk(k_split, tp_size, dim=0) + split = [torch.cat(weights, dim=0) for weights in zip(w_split, k_split)] # split per tp rank + + # Regular split for Megatron and NeMo-Megatron models. + else: + split = torch.split(partitions[0][idx].data, param.shape[0], dim=0) + + return split + + +def compute_tp_merge(idx, name, param, partitions_pp, model_cfg): + """ + Function to compute the partition merge required for tensor-parallelism. + + Args: + idx: The index used to select the parameter in the current pipeline partition. + name: + param: The parameter to be merged under TP 1 PP 1. + partitions_pp: List of all TP partitions of the flattened parameter of the current model for a given PP rank + (TP X PP Y). Indexed as partitions_pp[tp_rank][idx]. + model_cfg: The model config as an OmegaConf DictConfig. + + Returns: + The concatenated parameter for TP 1 PP 1. + """ + fast_glu_activation = str(model_cfg.get('activation', '')).lower() in ['fast-geglu', 'fast-swiglu', 'fast-reglu'] + + # Logic from original TP rank change + if param.shape == partitions_pp[0][idx].shape: + concated = partitions_pp[0][idx].data + elif param.shape[0] == partitions_pp[0][idx].shape[0]: + concated = torch.cat([partitions_pp[i][idx].data for i in range(len(partitions_pp))], dim=-1) + else: + concated = torch.cat([partitions_pp[i][idx].data for i in range(len(partitions_pp))], dim=0) + + # Logic for Fast Glu activation + if 'dense_h_to_4h' in name and fast_glu_activation: + # concat all the first halves ('W's) and all the second halves ('V's) + wk_splits = [] + for tpr in range(len(partitions_pp)): + wk_splits.append(torch.chunk(partitions_pp[tpr][idx].data, 2, dim=0)) + + w_split = torch.cat([w[0] for w in wk_splits], dim=0) + k_split = torch.cat([w[1] for w in wk_splits], dim=0) + concated = torch.cat([w_split, k_split], dim=0) + + # Trim padding + if concated.shape != param.shape: + logging.info( + f"Warning: Shape mismatch for parameter {name} required shape: {param.shape}, merged shape: {concated.shape}. Narrowing to match required size." + ) + if concated.shape[1:] == param.shape[1:]: + concated = torch.narrow(concated, 0, 0, param.shape[0]) + elif concated.shape[:-1] == param.shape[:-1]: + concated = torch.narrow(concated, -1, 0, param.shape[-1]) + else: + raise RuntimeError( + f"Can not handle parameter {name}, required shape: {param.shape}, merged shape: {concated.shape}." + ) + return concated + + +def write_tp_pp_split(model, splits, app_state, tp_size, pp_rank, write_path): + """ + Function to write the given TP PP split to NeMo File. + + Save each of the TP ranks in reverse order + This is done so that the last PP rank will save the last TP rank only after all other PP TP ranks are saved + The final rank will then save a new NeMo file with all other ranks inside. + + Args: + model: The model corresponding to the current TP PP split. Contains partial parameters. + splits: Nested List of tensors containing the TP splits of the current model given current PP rank. + Indexed as splits[idx][tp_rank]. + app_state: AppState object. + tp_size: The global tensor-parallel size of the final model. + pp_rank: The local pipeline parallel rank of the final model. + write_path: The path to save the NeMo file. + """ + for tp_rank in range(tp_size - 1, -1, -1): + app_state.pipeline_model_parallel_rank = pp_rank + app_state.tensor_model_parallel_rank = tp_rank + + idx = 0 + for name, param in model.named_parameters(): + split_val = splits[idx][tp_rank].clone() + + if param.shape != split_val.shape: + logging.info( + f"Warning: Shape mismatch for parameter {name} required shape: {param.shape}, split shape: {split_val.shape}. Padding to match required size." + ) + + if split_val.shape[1:] == param.shape[1:]: + pad = [0, 0] * len(split_val.shape) + pad[-1] = param.shape[0] - split_val.shape[0] + split_val = torch.nn.functional.pad(split_val, pad, 'constant') + elif split_val.shape[:-1] == param.shape[:-1]: + pad = [0, param.shape[-1] - split_val.shape[-1]] + split_val = torch.nn.functional.pad(split_val, pad, 'constant') + else: + raise RuntimeError( + f"Can not handle parameter {name}, required shape: {param.shape}, split shape: {split_val.shape}." + ) + + param.data = split_val + idx += 1 + + if write_path is not None: + logging.info(f"Writing pp rank {pp_rank} tp rank {tp_rank} to file {write_path}") + model.save_to(write_path) + + +def debug_log_split_param_diff(idx, param, param_name, partitions): + # Log some useful comparison of tensors that are being mapped. + # Note that the global param index for layers and modules may be different but the shapes + # and semantics of the layer should match. + logging.debug(f"Index: {idx} Model Params : {param_name} - {param.shape}") + logging.debug(f"Index: {idx} Global params: {partitions[1][idx]} - {partitions[0][idx].shape}") + + +################ +### Handlers ### +################ + + +class GPTHandler: + def __init__(self, megatron_legacy: bool): + self.duplicate_gpt_word_embedding_offset = 0 + self.untied_gpt_embedding = False + self.megatron_legacy = megatron_legacy + + def compute_split_index(self, model, idx, tp_rank, pp_rank, pp_split_rank, tp_size, pp_size): + if pp_rank == (pp_size - 1) and hasattr(model, 'model') and hasattr(model.model, 'word_embeddings'): + # duplicate embedding copy (tied weights) + self.duplicate_gpt_word_embedding_offset = 1 + + if model.cfg.get('share_embeddings_and_output_weights', True) is False: + self.untied_gpt_embedding = True + + if self.duplicate_gpt_word_embedding_offset > 0: + logging.info(f"GPT duplicate_gpt_word_embedding_offset: {self.duplicate_gpt_word_embedding_offset}") + + return idx + self.duplicate_gpt_word_embedding_offset + + def compute_splits(self, model, partitions, idx, tp_rank, pp_rank, pp_split_rank, tp_size, pp_size): + splits = [] + + # This is the PP X TP Y model with partial parameters present in correct order. + # We need to extract the parameters from the global map in reverse order to fill in the + # parameters of this model in forward order. + for param_name, param in model.named_parameters(): + + # Since we are moving forward, we may reach the end of the global map + # but GPT has an additional word embedding as its last parameter + # Therefore we check for this, and reset the index to the parameter of the PP 0 TP 0 rank + # which holds the parameters of the embedding. + if idx == (len(partitions[0])) and self.duplicate_gpt_word_embedding_offset > 0: + logging.info("Found duplicate embedding copy for GPT model, resetting index") + idx = 0 # reset idx parameter to 0 if we have duplicate embedding copy + + debug_log_split_param_diff(idx, param, param_name, partitions) + + # Tensor Parallel Splitting + split = compute_tp_splits( + param_name, + param, + partitions, + idx, + tp_size, + pp_size, + pp_rank, + pp_split_rank, + self.megatron_legacy, + model.cfg, + ) + + splits.append(split) + idx += 1 + + return idx, splits + + def compute_split_offset(self, offset_diff, tp_rank, pp_rank, pp_split_rank, tp_size, pp_size): + # GPT offset correction + if not self.untied_gpt_embedding and pp_size > 1 and pp_rank == (pp_size - 1) and pp_split_rank == 0: + offset_diff += 1 + + return offset_diff + + +class T5Handler: + def __init__(self, megatron_legacy: bool): + self.shared_enc_dec_embeddings = False + self.shared_enc_dec_embeddings_intermediate = False + self.enc_dec_share_token_embeddings_count = 0 + self.intermediate_shared_embedding_location = -1 + self.megatron_legacy = megatron_legacy + + def compute_split_index(self, model, idx, tp_rank, pp_rank, pp_split_rank, tp_size, pp_size): + final_idx = idx + + # Special case for T5 models - where the embeddings are shared between encoder and decoder + # and the rank of decoder split is arbitrary. + # Megatron T5 check for pipeline_model_parallel_split_rank in order to inject encoder embeddings + self.shared_enc_dec_embeddings = ( + pp_split_rank > 0 and pp_split_rank == pp_rank and model.cfg.get('share_token_embeddings', True) + ) + # If embedding sharing is active, both vocab and position embeddings are shared + if self.shared_enc_dec_embeddings: + self.enc_dec_share_token_embeddings_count = 2 + else: + self.enc_dec_share_token_embeddings_count = 0 + + # Start to calculate new idx + final_idx = final_idx + self.enc_dec_share_token_embeddings_count + + # Special case for T5 models - where the embeddings are shared between encoder and decoder + # For all decoder ranks which are not the pp_split_rank, we need to inject the vocab embeddings only at + # an intermediate location of the model (usually second last location). + # Megatron T5 check for pipeline_model_parallel_split_rank in order to inject encoder embeddings + # when the pipeline_model_parallel_split_rank is not the last PP rank + self.shared_enc_dec_embeddings_intermediate = ( + pp_split_rank > 0 + and pp_split_rank < pp_size + and hasattr(model, 'enc_dec_model') + and hasattr(model.enc_dec_model, 'word_embeddings') + ) + + if self.shared_enc_dec_embeddings_intermediate: + # Loop until we get the location of this tensor + self.intermediate_shared_embedding_location = -1 + for param_name, param in model.named_parameters(): # special case for T5 + if param_name == 'enc_dec_model.word_embeddings.weight': + self.intermediate_shared_embedding_location += 1 + break + self.intermediate_shared_embedding_location += 1 + else: + self.intermediate_shared_embedding_location = -1 + + # Re-evaluate the intermediate shared embedding flag + self.shared_enc_dec_embeddings_intermediate = self.shared_enc_dec_embeddings_intermediate and ( + self.intermediate_shared_embedding_location >= 0 + ) + # If module is present, add a module offset to the index + if self.shared_enc_dec_embeddings_intermediate: + final_idx += 1 + + if self.enc_dec_share_token_embeddings_count: + logging.info(f"EncDec share_token_embeddings_count: {self.enc_dec_share_token_embeddings_count}") + if self.shared_enc_dec_embeddings_intermediate: + logging.info( + f"EncDec share_enc_dec_embeddings_intermediate: {self.intermediate_shared_embedding_location}" + ) + + return final_idx + + def compute_splits(self, model, partitions, idx, tp_rank, pp_rank, pp_split_rank, tp_size, pp_size): + splits = [] + + # Backup index when EncDec models reset the index to fill in the first embedding matrices (when pp split rank == pp rank) + computed_index = idx + + # This is the PP X TP Y model with partial parameters present in correct order. + # We need to extract the parameters from the global map in reverse order to fill in the + # parameters of this model in forward order. + for param_name, param in model.named_parameters(): + + # Since we are moving forward, we may reach the end of the global map + # but T5 has an additional word embedding as its first two parameter when pp split rank == pp rank + # Therefore we check for this, and update the index to the parameter of the PP 0 TP 0 rank + # which holds the parameters of the embedding. + if self.enc_dec_share_token_embeddings_count: + logging.info("EncDec models decoder shares embedding with encoder, resetting index") + idx = ( + 2 - self.enc_dec_share_token_embeddings_count + ) # 0th index is vocab embedding, 1 is pos embedding, 2 is embedding count + + # Since we are moving forward, we may reach the end of the global map + # but T5 has an additional word embedding as randomly located in the decoder when + # when pp rank > pp_split_rank. + # Therefore we check for this, and skip the parameter of the current TP X PP Y module + # and fill this parameter later. + if self.shared_enc_dec_embeddings_intermediate and param_name == 'enc_dec_model.word_embeddings.weight': + logging.info( + "EncDec models decoder shares embedding with encoder in intermediate pos, skipping module for later update" + ) + continue + + debug_log_split_param_diff(idx, param, param_name, partitions) + + # Tensor Parallel Splitting + split = compute_tp_splits( + param_name, + param, + partitions, + idx, + tp_size, + pp_size, + pp_rank, + pp_split_rank, + self.megatron_legacy, + model.cfg, + ) + + splits.append(split) + idx += 1 + + # When pp split rank is equal to current pp rank, we need to first inject the encoder embeddings + # and then reset the index to the originally computed index + if self.enc_dec_share_token_embeddings_count > 0: + if self.enc_dec_share_token_embeddings_count - 1 == 0: + idx = computed_index + + self.enc_dec_share_token_embeddings_count -= 1 + + # Inject the EncDec shared embeddings intermediate tensor + # at one random location in the decoder of this TP PP rank. + # Note that usually it is the second last tensor, but to avoid specific index we search for it + # again. + if self.shared_enc_dec_embeddings_intermediate: + for param_name, param in model.named_parameters(): + if param_name == 'enc_dec_model.word_embeddings.weight': + logging.info("Found intermediate shared embedding, injecting") + split = compute_tp_splits( + param_name, + param, + partitions, + global_idx=0, + tp_size=tp_size, + pp_size=pp_size, + pp_rank=pp_rank, + pp_split_rank=pp_split_rank, + megatron_legacy=self.megatron_legacy, + model_cfg=model.cfg, + ) + splits.insert(self.intermediate_shared_embedding_location, split) + break + + return idx, splits + + def compute_split_offset(self, offset_diff, tp_rank, pp_rank, pp_split_rank, tp_size, pp_size): + # T5 offset correction for shared embedding when pp split rank == pp rank + if self.shared_enc_dec_embeddings: + offset_diff += 2 + + # T5 offset correction for intermediate shared embedding when pp rank > pp split rank + if self.shared_enc_dec_embeddings_intermediate: + offset_diff += 1 + + return offset_diff + + +################## +### Converters ### +################## + + +def merge_partition(model, partitions: Dict[int, List[List[torch.Tensor]]], write_path: str = None): + # Extract the pp_rank and number of modules per tp rank in each pp rank + pp_ranks = list(partitions.keys()) + pp_lens = [] + for pp_rank in pp_ranks: + partition_pp = partitions[pp_rank] + max_len = max([len(x) for x in partition_pp]) # Perform max as we need to iterate through all modules + pp_lens.append(max_len) + + total_params_merged = len([p for p in model.parameters()]) + pp_total_len = sum(pp_lens) + logging.info(f"Total layers in Merged Model: {total_params_merged}") + + og_pp_split_rank = 0 + if pp_total_len > total_params_merged: + og_pp_split_rank = model.cfg.get('pipeline_model_parallel_split_rank', 0) + + idx = 0 + pp_rank = 0 + global_idx = 0 + + # During merge - model is TP 1 PP 1 model with all parameters present in correct order. + # Merge the parameters of the various PP X TP Y models into the TP 1 PP 1 model. + for name, param in model.named_parameters(): + # Since the PP ranks each contain the list of all their TP rank parameters + # We need to detect if we need to move to the next PP rank when we run out of tensors in current PP rank + # Reset the index so that it indexes the new pp rank tensor list correctly + if idx >= pp_lens[pp_rank]: + pp_rank += 1 + idx = 0 + + # For EncDec models, after the encoder-decoder PP split occurs, + # the vocab and positional embeddings are duplicated across the PP ranks at the + # beginning of the decoder rank. We can skip them during the merge step. + if pp_total_len > total_params_merged: + if og_pp_split_rank > 0 and og_pp_split_rank == pp_rank: + logging.info( + f"Skipping duplicate vocab and positional embeddings for EncDec model " + f"at the pp split rank: {og_pp_split_rank}" + ) + idx += 2 + + # For EncDec models, after the pp split occurs, final pp rank of the decoder + # has an intermediate embedding tensor at the penultimate positon, skip that. + if og_pp_split_rank > 0 and global_idx == total_params_merged - 1: + logging.info( + f"Skipping intermediate embedding tensor for EncDec model at the final pp split " + f"rank: {og_pp_split_rank}", + ) + idx = pp_lens[pp_rank] - 1 + + # Extract all TP ranks out of current PP rank + partitions_pp = partitions[pp_rank] + + logging.debug( + f"Global idx: {global_idx} Index: {idx} Model Param: {name} " + f"Partition Params: {[p[idx].shape for p in partitions_pp]}" + ) + + # Original TP rank change logic + concated = compute_tp_merge(idx, name, param, partitions_pp, model.cfg) + + # Update the model parameter with the merged tensor + param.data = concated + idx += 1 + global_idx += 1 + + # Save the file iff the original file was PP 1 TP 1 + if write_path is not None: + model.save_to(write_path) + + +def split_partition( + model, + partitions, + pp_size: int, + tp_size: int, + pp_rank: int, + offset: int, + pp_split_rank: int = 0, + write_path: str = None, + megatron_legacy: bool = False, +): + if len(partitions) != 2: + raise ValueError( + "Can only split partitions of model with TP=1. For partitions of models with TP>1, merge first." + ) + + if tp_size < 1: + raise ValueError("TP size must to be >= 1.") + + if pp_size < 1: + raise ValueError("PP size must to be >= 1.") + + # Setup app state to mimic current PP and TP ranks with single merged module + app_state = AppState() + app_state.data_parallel_rank = 0 + app_state.pipeline_model_parallel_size = pp_size + app_state.tensor_model_parallel_size = tp_size + app_state.model_parallel_size = app_state.pipeline_model_parallel_size * app_state.tensor_model_parallel_size + + # Go in reverse for TP order, as PP 0 TP 0 will merge all preceding files + app_state.pipeline_model_parallel_rank = pp_rank + app_state.tensor_model_parallel_rank = tp_size - 1 + + # Compute reverse offset of parameter index from global map + num_params = sum([1 for _ in model.parameters()]) # Count number of parameters iteratively + idx = offset - num_params + 1 # start index of current PP TP rank in global map + + assert ( + idx + num_params - 1 == offset + ), f"idx = {idx}, num_params = {num_params}, sum = {idx + num_params}, offset = {offset}" + + # Special case for GPT models - whose last PP TP rank has a duplicate embedding tensor + + if 'gpt' in model.cfg.target.lower(): + logging.info("Splitting GPT model") + handler = GPTHandler(megatron_legacy=megatron_legacy) + + elif 't5' in model.cfg.target.lower(): + logging.info("Splitting T5 model") + handler = T5Handler(megatron_legacy=megatron_legacy) + + else: + raise ValueError(f"Unsupported model for Pipeline Parallelism change - {model.cfg.target}") + + idx = handler.compute_split_index(model, idx, 0, pp_rank, pp_split_rank, tp_size, pp_size) + + # Print some debug info + logging.info(f"Start Layer Idx: {idx} Number of layers in current rank: {num_params} Offset: {offset}") + logging.info("\n") + + # Split the model's parameters according to TP PP ranks + idx, splits = handler.compute_splits(model, partitions, idx, 0, pp_rank, pp_split_rank, tp_size, pp_size) + + # Compute the new offset for the next PP rank in reverse order + # Add 1 to offset to account for last PP rank's duplicated Embedding + offset_diff = offset - num_params + offset_diff = handler.compute_split_offset(offset_diff, 0, pp_rank, pp_split_rank, tp_size, pp_size) + + # Finalize the new offset + new_offset = offset_diff + + # Save each of the TP ranks in reverse order + # This is done so that the last PP rank will save the last TP rank only after all other PP TP ranks are saved + # The final rank will then save a new NeMo file with all other ranks inside. + write_tp_pp_split(model, splits, app_state, tp_size, pp_rank, write_path) + + return new_offset + + +def split_tp_partition_only(model, partitions, tp_size, write_path=None, megatron_legacy=False): + if len(partitions) != 2: + raise ValueError( + "Can only split partitions of model with TP=1. For partitions of models with TP>1, merge first." + ) + + if tp_size < 1: + raise ValueError("TP size must to be >= 1.") + + app_state = AppState() + app_state.data_parallel_rank = 0 + app_state.pipeline_model_parallel_size = 1 + app_state.tensor_model_parallel_size = tp_size + app_state.model_parallel_size = app_state.pipeline_model_parallel_size * app_state.tensor_model_parallel_size + + app_state.pipeline_model_parallel_rank = 0 + app_state.tensor_model_parallel_rank = tp_size - 1 + + idx = 0 + splits = [] + for param_name, param in model.named_parameters(): + split = compute_tp_splits( + param_name, + param, + partitions, + idx, + tp_size, + pp_size=1, + pp_rank=0, + pp_split_rank=0, + megatron_legacy=megatron_legacy, + model_cfg=model.cfg, + ) + splits.append(split) + idx += 1 + + # Save each of the TP ranks in reverse order + # This is done so that the last PP rank will save the last TP rank only after all other PP TP ranks are saved + # The final rank will then save a new NeMo file with all other ranks inside. + write_tp_pp_split(model, splits, app_state, tp_size, pp_rank=0, write_path=write_path) + + +def main(): + parser = ArgumentParser() + parser.add_argument("--model_file", type=str, default=None, required=False, help="Path to source .nemo file") + parser.add_argument("--target_file", type=str, required=True, help="Path to write target .nemo file") + parser.add_argument( + "--tensor_model_parallel_size", type=int, default=-1, required=False, help="TP size of source model" + ) + parser.add_argument("--target_tensor_model_parallel_size", type=int, required=True, help="TP size of target model") + parser.add_argument( + '--pipeline_model_parallel_size', type=int, default=-1, required=False, help='PP size of source model' + ) + parser.add_argument( + '--target_pipeline_model_parallel_size', type=int, required=True, help='PP size of target model' + ) + parser.add_argument( + '--target_pipeline_model_parallel_split_rank', type=int, default=0, help='PP rank to split for Enc-Dec models' + ) + parser.add_argument( + '--virtual_pipeline_model_parallel_size', type=int, default=None, help='Virtual Pipeline parallelism size' + ) + parser.add_argument( + '--ckpt_name', type=str, default=None, help='Checkpoint name to load from for Virtual Parallel' + ) + parser.add_argument( + "--model_class", + type=str, + default="nemo.collections.nlp.models.language_modeling.megatron_gpt_model.MegatronGPTModel", + help="NeMo model class. This script should support all NeMo megatron models that use Tensor Parallel", + ) + parser.add_argument("--precision", default=16, help="PyTorch Lightning Trainer precision flag") + parser.add_argument('--num_gpu_per_node', default=8, type=int, help='Number of GPUs per node') + parser.add_argument( + "--megatron_legacy", + action="store_true", + help="Converter for legacy megatron modles that have different q,k,v weight splits", + ) + parser.add_argument( + "--tokenizer_model_path", + type=str, + required=False, + default=None, + help="Path to the tokenizer model path if your model uses a tokenizer model as an artifact. This is needed if your model uses a sentencepiece tokenizer.", + ) + parser.add_argument( + "--tokenizer_vocab_file", + type=str, + required=False, + default=None, + help="Path to the tokenizer model path if your model uses a tokenizer model as an artifact. This is needed if your model uses a sentencepiece tokenizer.", + ) + parser.add_argument('--hparams_file', type=str, default=None, help='Path to hparams file from PTL training') + parser.add_argument('--tp_conversion_only', action='store_true', help='Only convert TP model to TP model') + parser.add_argument('--model_extracted_dir', type=str, default=None, help='Path to pre-extracted model directory') + + args = parser.parse_args() + + precision = args.precision + num_gpu_per_node = int(args.num_gpu_per_node) + if args.precision in ["32", "16"]: + precision = int(float(args.precision)) + + if precision in ["bf16", "bf16-mixed"]: + if torch.cuda.is_available() and torch.cuda.is_bf16_supported(): + pass + else: + logging.warning("BF16 is not supported on this device. Using FP16 instead.") + precision = precision[2:] + + if precision == 32: + dtype = torch.float32 + elif precision in [16, "16", "16-mixed"]: + dtype = torch.float16 + elif precision in ["bf16", "bf16-mixed"]: + dtype = torch.bfloat16 + else: + dtype = torch.float32 # fallback + + # Built target directory if it does not exist + target_dir = os.path.split(args.target_file)[0] + if not os.path.exists(target_dir): + os.makedirs(target_dir, exist_ok=True) + + tp_size = args.tensor_model_parallel_size + tgt_tp_size = args.target_tensor_model_parallel_size + pp_size = args.pipeline_model_parallel_size + tgt_pp_size = args.target_pipeline_model_parallel_size + pipeline_model_parallel_split_rank = args.target_pipeline_model_parallel_split_rank + vp_size = args.virtual_pipeline_model_parallel_size + if vp_size is None: + vp_size = 1 + + convert_vp = vp_size > 1 + if convert_vp: + from megatron.core import parallel_state + + parallel_state.set_virtual_pipeline_model_parallel_world_size(vp_size) + + hparams_filepath = args.hparams_file + if hparams_filepath is None: + logging.warning( + '\n\n\n!!!!!!!!!\n' + 'You are converting a model with virtual pipeline parallelism enabled, \n' + 'but have not passed `hparams_file` argument. \n' + 'This will cause each ckpt file to be temporarily laoded onto GPU memory!\n\n' + 'It is highly recommended to pass `hparams_file` argument to avoid this.\n' + ) + else: + hparams_filepath = None + + # Import the class of the model + cls = model_utils.import_class_by_path(args.model_class) + + if args.model_file is None and args.model_extracted_dir is None: + raise ValueError("Cannot pass model_file and model_extracted_dir as None at the same time.") + + tmp_cfg = cls.restore_from( + restore_path=args.model_file, + trainer=Trainer(devices=1, strategy=NLPDDPStrategy(), accelerator="cpu", precision=precision), + map_location=torch.device("cpu"), + return_config=True, + ) + plugins = [] + if precision in [16, '16', 'bf16', '16-mixed', 'bf16-mixed']: + scaler = None + if precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=tmp_cfg.get('native_amp_init_scale', 2 ** 32), + growth_interval=tmp_cfg.get('native_amp_growth_interval', 1000), + hysteresis=tmp_cfg.get('hysteresis', 2), + ) + # MixedPrecisionPlugin in PTL >= 2.0 requires precision to be 16-mixed or bf16-mixed + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + + if tmp_cfg.get('megatron_amp_O2', False): + plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(PipelineMixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + # Set precision None after precision plugins are created as PTL >= 2.1 does not allow both + # precision plugins and precision to exist + precision = None + trainer = Trainer(plugins=plugins, devices=1, strategy=NLPDDPStrategy(), accelerator="cpu", precision=precision) + + if tp_size < 0 or pp_size < 0: + logging.info(f"Loading model config from {args.model_file} to get TP and PP size") + model_config_internal = cls.restore_from( + restore_path=args.model_file, trainer=trainer, map_location=torch.device("cpu"), return_config=True, + ) + + tp_size = model_config_internal.get('tensor_model_parallel_size', 1) + pp_size = model_config_internal.get('pipeline_model_parallel_size', 1) + + # Check if TP conversion only + tp_conversion_only = args.tp_conversion_only + if tp_conversion_only: + logging.info("Converting TP model to TP model only") + + if pp_size > 1: + raise ValueError("Provided `--tp_conversion_only` but `--pipeline_model_parallel_size` > 1") + + if tgt_pp_size > 1: + raise ValueError("Provided `--tp_conversion_only` but `--target_pipeline_model_parallel_size` > 1") + + if pipeline_model_parallel_split_rank > 0: + raise ValueError("Provided `--tp_conversion_only` but `--target_pipeline_model_parallel_split_rank` > 0") + + # Force PP size to 1 + pp_size = 1 + tgt_pp_size = 1 + pipeline_model_parallel_split_rank = 0 + + if vp_size is None or vp_size < 0: + vp_size = 1 + + app_state = AppState() + app_state.data_parallel_rank = 0 + app_state.pipeline_model_parallel_size = pp_size + app_state.tensor_model_parallel_size = tp_size + + if vp_size > 1: + app_state.virtual_pipeline_model_parallel_size = vp_size + app_state.model_parallel_size = app_state.pipeline_model_parallel_size * app_state.tensor_model_parallel_size + + world_size = pp_size * tp_size # pseudo world size for simulating load of a specific rank on a single gpu + + app_state.tensor_model_parallel_rank = 0 + app_state.pipeline_model_parallel_rank = 0 + + if vp_size > 1: + set_virtual_parallel_rank_safely(0) + + # Extract tokenizer artifact from the model to temp directory + logging.info("Extracting tokenizer artifact from NeMo file...") + temp_dir = tempfile.mkdtemp() + tokenizer_model_path = None + with tarfile.open(args.model_file, "r") as tar: + for member in tar.getmembers(): + if '.model' in member.name: + extracted_file = tar.extractfile(member) + extracted_file_path = os.path.join(temp_dir, member.name) + + if tokenizer_model_path is None: + logging.info(f"Found tokenizer. Extracting {member.name} to {extracted_file_path}") + + tokenizer_model_path = extracted_file_path + with open(extracted_file_path, "wb") as f: + f.write(extracted_file.read()) + else: + if args.tokenizer_model_path is None: + logging.warning( + f"\n\nFound multiple tokenizer artifacts in the model file.\n" + f"Using only {tokenizer_model_path}.\n" + f"If this is incorrect, manually pass the correct tokenizer using " + f"`--tokenizer_model_path`.\n\n" + ) + + # If input model has TP > 1 or PP > 1 + # Reconstruct the model to have TP = 1 and PP = 1 + # Note that this is a forward loop that will process PP [0..N] TP [0..M] in sequential order. + if tp_size > 1 or pp_size > 1: + partitions = {} # 3d list of VP x PP x TP + model = None + + # Build partitions structure + for vp_idx in range(vp_size): + partitions[vp_idx] = [] # Build first layer - VP + + for pp_idx in range(pp_size): + # For each VP, build PP x TP holder + partitions[vp_idx].append({}) + partitions[vp_idx][pp_idx] = [] + + for vp_rank in range(vp_size): + if vp_size > 1: + set_virtual_parallel_rank_safely(vp_rank) + + for pp_rank in range(pp_size): + app_state.pipeline_model_parallel_rank = pp_rank + + for tp_rank in range(tp_size): + app_state.tensor_model_parallel_rank = tp_rank + + logging.info(f"Loading ------------ PP Rank: {pp_rank} TP Rank: {tp_rank}") + + # Override flag that forces Model to use AppState instead of Trainer + # to determine the world size, global and local rank + # Used for simulating load of a specific rank on a single gpu + os.environ[NEMO_MEGATRON_MODEL_PARALLEL_APPSTATE_OVERRIDE] = "true" + + # Compute the global rank to load the correct subset of parameters + global_rank = pp_rank * tp_size + tp_rank + + # Update AppState + app_state.world_size = world_size + app_state.global_rank = global_rank + app_state.local_rank = global_rank % num_gpu_per_node + app_state.pipeline_model_parallel_size = pp_size + app_state.tensor_model_parallel_size = tp_size + app_state.pipeline_model_parallel_split_rank = pipeline_model_parallel_split_rank + app_state.model_parallel_size = ( + app_state.pipeline_model_parallel_size * app_state.tensor_model_parallel_size + ) + + if vp_size > 1: + set_virtual_parallel_rank_safely(vp_rank) + + if vp_rank == 0: + save_restore_connector = NLPSaveRestoreConnector() + + if args.model_extracted_dir is not None: + logging.info(f"Using extracted model directory: {args.model_extracted_dir}") + save_restore_connector.model_extracted_dir = args.model_extracted_dir + + if args.model_file is not None: + model_filepath = args.model_file + else: + model_filepath = args.model_extracted_dir + + if vp_size == 1: + + # Get model config + tmp_cfg = cls.restore_from( + restore_path=model_filepath, + trainer=trainer, + map_location=torch.device("cpu"), + save_restore_connector=save_restore_connector, + return_config=True, + ) + + # Force model onto CPU + tmp_cfg, restore_dict = force_cpu_model(tmp_cfg) + + # Restore model + model = cls.restore_from( + restore_path=model_filepath, + trainer=trainer, + map_location=torch.device("cpu"), + save_restore_connector=save_restore_connector, + override_config_path=tmp_cfg, + ) + model.freeze() + + # Restore model config + restore_model_config(model.cfg, restore_dict) + + else: + if args.ckpt_name is None: + raise ValueError( + "For Virtual Parallel, ckpt name is required.\n" + "Please provide `--ckpt_name` argument." + ) + + # inject model parallel rank + checkpoint_path = model_utils.inject_model_parallel_rank( + os.path.join(model_filepath, args.ckpt_name) + ) + + vp_state_dict = torch.load(checkpoint_path, map_location="cpu") + + if hparams_filepath is not None: + # Force the model onto CPU + tmp_cfg = OmegaConf.load(hparams_filepath) + tmp_cfg, restore_dict = force_cpu_model(tmp_cfg) + + with tempfile.NamedTemporaryFile(mode='w', encoding='utf-8', suffix='.yml') as tmp: + OmegaConf.save(tmp_cfg, tmp, resolve=True) + tmp.seek(0) + + model = cls.load_from_checkpoint( + checkpoint_path=checkpoint_path, + trainer=trainer, + map_location=torch.device("cpu"), + hparams_file=tmp.name, + ) + model.freeze() + + restore_model_config(model.cfg, restore_dict) + + else: + model = cls.load_from_checkpoint( + checkpoint_path=checkpoint_path, trainer=trainer, map_location=torch.device("cpu"), + ) + model.freeze() + + model.to(dtype=dtype) + + # Reset env flag + os.environ.pop(NEMO_MEGATRON_MODEL_PARALLEL_APPSTATE_OVERRIDE, None) + + logging.info( + f"<<<<<<<< LOADED MODEL PP={pp_rank + 1} TP={tp_rank + 1} | " + f"GLOBAL RANK = {global_rank} >>>>>>>>>" + ) + + # Save the parameters + if vp_size == 1: + params = [p for p in model.parameters()] + partitions[vp_rank][pp_rank].append(params) # vp_rank = 0 + + else: + vp_params_tmp = [] + for vp_idx in range(vp_size): + set_virtual_parallel_rank_safely(vp_idx) + vp_params = vp_state_dict[f'model{vp_idx}'] + model.model[vp_idx].module.load_state_dict(vp_params, strict=True) + model.model[vp_idx].module.to('cpu') + params = [p for p in model.model[vp_idx].module.parameters()] + vp_params_tmp.append(params) + # partitions[pp_rank][vp_idx].append(params) + + for vp_idx in range(vp_size): + partitions[vp_idx][pp_rank].append(vp_params_tmp[vp_idx]) + + del vp_params_tmp + set_virtual_parallel_rank_safely(0) + + # app_state is being updated incorrectly during restore + app_state.data_parallel_rank = 0 + app_state.pipeline_model_parallel_rank = pp_rank + app_state.tensor_model_parallel_rank = tp_rank + app_state.pipeline_model_parallel_size = pp_size + app_state.tensor_model_parallel_size = tp_size + app_state.model_parallel_size = ( + app_state.pipeline_model_parallel_size * app_state.tensor_model_parallel_size + ) + + if vp_size > 1: + app_state.virtual_pipeline_model_parallel_size = vp_size + set_virtual_parallel_rank_safely(vp_rank) + + # Build a unified model with PP 1 TP 1 + with open_dict(model.cfg): + model.cfg.tensor_model_parallel_size = 1 + model.cfg.pipeline_model_parallel_size = 1 + model.cfg.virtual_pipeline_model_parallel_size = None + + app_state.global_rank = 0 + app_state.local_rank = 0 + app_state.data_parallel_rank = 0 + app_state.pipeline_model_parallel_rank = 0 + app_state.tensor_model_parallel_rank = 0 + app_state.pipeline_model_parallel_size = 1 + app_state.tensor_model_parallel_size = 1 + app_state.model_parallel_size = 1 + + if vp_size > 1: + set_virtual_parallel_rank_safely(None) + + trainer = Trainer( + plugins=plugins, devices=1, strategy=NLPDDPStrategy(), accelerator="cpu", precision=precision + ) + + with open_dict(model.cfg): + if args.tokenizer_model_path is not None: + model.cfg.tokenizer.model = args.tokenizer_model_path + if args.tokenizer_vocab_file is not None: + model.cfg.tokenizer.vocab_file = args.tokenizer_vocab_file + + model.cfg, restore_dict = force_cpu_model(model.cfg) + + # Remove Virtual Parallelism + model.cfg.virtual_pipeline_model_parallel_size = None + + logging.info(f"<<<<<<<< Building TP 1 PP 1 base model >>>>>>>>>") + model = cls(model.cfg, trainer) # type: nn.Module + model.freeze() + model = model.to('cpu') + model._save_restore_connector = NLPSaveRestoreConnector() + + restore_model_config(model.cfg, restore_dict) + + vp_param_count = 0 + for vp in range(vp_size): + for pp in range(pp_size): + for tp in range(tp_size): + vp_param_count += len(partitions[vp][pp][tp]) + + if vp_size > 1: + logging.debug(f"Total params in TP PP VP = 1 : {len(list(model.parameters()))}") + logging.debug(f"Total params in VP PP TP (og): {vp_param_count}") + + # Flatten Virtual Pipeline + if vp_size == 1: + # unpack vp container, pack pp tp container + partitions = partitions[0] + partitions = {idx: val for idx, val in enumerate(partitions)} + else: + flat_partitions = {idx: [] for idx in range(pp_size)} + + """ + Under VP convention + Notation : + Stage = PP rank + Number = GPT model / layer index + Ignore TP - every PP has all TP corresponding to that PP + chunk_index = the physical index of any [] in the list. Ex idx = 2 in below map corresponds to [2: PP 0 VP 1]] + + + For a PP 2 VP 4 model with 8 GPT layers- + + Indices + # Stage 0: [0:PP 0 VP 0] [2:PP 0 VP 1] [4:PP 0 VP 2] [6:PP 0 VP 3] + # Stage 1: [1:PP 1 VP 0] [3:PP 1 VP 1] [5:PP 1 VP 2] [7:PP 1 VP 3] + + after conversion will become + + # Stage 0: [0,1,2,3:PP 0] + # Stage 1: [4,5,6,7:PP 1] + + """ + pp_index = 0 + chunk_counter = 0 + tp_cache = [[] for _ in range(tp_size)] + + for vp in range(vp_size): + for pp in range(pp_size): + # Gather all TP under this VP PP combination. + # We will accumulate TP parameters from multiple layers in this cache. + for tp in range(tp_size): + tp_cache[tp].extend(partitions[vp][pp][tp]) + + # This counter indexes the global selection of a VP PP combination in the above map + chunk_counter += 1 + + # Log the mapping from old VP x PP to new PP index + logging.info(f"VP Conversion - vp: {vp} pp: {pp} -> pp_idx: {pp_index}") + + # Every vp_size chunks, we can fill a new PP index in the flat_partitions + if chunk_counter % vp_size == 0: + flat_partitions[pp_index].extend(tp_cache) + tp_cache = [[] for _ in range(tp_size)] + pp_index += 1 + + logging.debug( + f"VP merge step: \n" + f"vp: {vp} pp: {pp} pp_idx: {pp_index - 1} " + f"len(flat_partitions): {len(flat_partitions[pp_index - 1])}" + ) + + logging.debug(f"PP Size len(flat partitions) : {len(flat_partitions)}") + logging.debug(f"TP Size len(flat partitions[0]): {len(flat_partitions[0])}") + logging.debug(f"Layers len(flat partitions[0][0]) : {len(flat_partitions[0][0])}") + + partitions = flat_partitions + del tp_cache + + if tgt_tp_size > 1 or tgt_pp_size > 1: + merge_partition(model, partitions) + else: + # Write out the PP 1 TP 1 model to disk + merge_partition(model, partitions, args.target_file) + + # Empty cache memory of all parameters from all PP TP partitions + partitions.clear() + + else: + # If input model has TP = 1 and PP = 1 + app_state.model_parallel_size = 1 + + save_restore_connector = NLPSaveRestoreConnector() + + if args.model_extracted_dir is not None: + logging.info(f"Using extracted model directory: {args.model_extracted_dir}") + save_restore_connector.model_extracted_dir = args.model_extracted_dir + + if args.model_file is not None: + model_filepath = args.model_file + else: + model_filepath = args.model_extracted_dir + + tmp_cfg = cls.restore_from( + restore_path=model_filepath, + trainer=trainer, + map_location=torch.device("cpu"), + save_restore_connector=save_restore_connector, + return_config=True, + ) + + tmp_cfg, restore_dict = force_cpu_model(tmp_cfg) + + model = cls.restore_from( + restore_path=model_filepath, + trainer=trainer, + map_location=torch.device("cpu"), + save_restore_connector=save_restore_connector, + override_config_path=tmp_cfg, + ) + model.to(dtype=dtype) + + restore_model_config(model.cfg, restore_dict) + + # If target model has TP > 1 or PP > 1 + if tgt_pp_size > 1 or tgt_tp_size > 1: + + # Preserve the TP 1 PP 1 model parameters and names + global_params = [] + global_params.append([p for n, p in model.named_parameters()]) # params + global_params.append([n for n, p in model.named_parameters()]) # names + + logging.debug("Global parameters:") + for idx, (name, p) in enumerate(zip(global_params[1], global_params[0])): + logging.debug(f"{name} - {p.shape}") + + logging.info(f"TP 1 PP 1 Number of Parameters : {len(global_params[0])}") + + world_size = ( + tgt_pp_size * tgt_tp_size + ) # pseudo world size for simulating load of a specific rank on a single gpu + new_global_batch_size = model.cfg.micro_batch_size * world_size + old_global_batch_size = model.cfg.get('global_batch_size', model.cfg.micro_batch_size) + + global_offset = len(global_params[0]) - 1 # -1 cause this indexes the array, range [0, L-1] + logging.info(f"Final layer offset for parameters: {global_offset}") + + for pp_rank in range(tgt_pp_size - 1, -1, -1): # reverse order + + with open_dict(model.cfg): + model.cfg.pipeline_model_parallel_size = tgt_pp_size + model.cfg.tensor_model_parallel_size = tgt_tp_size + + if 'pipeline_model_parallel_split_rank' in model.cfg: + if pipeline_model_parallel_split_rank > 0: + model.cfg.pipeline_model_parallel_split_rank = pipeline_model_parallel_split_rank + elif pp_size > 1: + logging.warning( + f"Model config has `pipeline_model_parallel_split_rank` set to " + f"{model.cfg.pipeline_model_parallel_split_rank} and target PP " + f"size is {tgt_pp_size}. " + f"Provided `pipeline_model_parallel_split_rank` is " + f"{pipeline_model_parallel_split_rank}. " + f"Be careful that the model config is correct " + f"if encoder-decoder models are being converted." + ) + + model.cfg.global_batch_size = old_global_batch_size # Used for restoration + + # Override flag that forces Model to use AppState instead of Trainer + # to determine the world size, global and local rank + # Used for simulating load of a specific rank on a single gpu + os.environ[NEMO_MEGATRON_MODEL_PARALLEL_APPSTATE_OVERRIDE] = "true" + + # Compute the global rank + global_rank = ( + pp_rank * tgt_tp_size + 0 + ) # tp_rank = 0 needed just for modules, all TP will be merged to this PP rank + + # Update AppState + app_state.world_size = world_size + app_state.global_rank = global_rank + app_state.local_rank = global_rank % num_gpu_per_node + app_state.pipeline_model_parallel_size = tgt_pp_size + app_state.tensor_model_parallel_size = tgt_tp_size + app_state.model_parallel_size = ( + app_state.pipeline_model_parallel_size * app_state.tensor_model_parallel_size + ) + + trainer = Trainer( + plugins=plugins, devices=1, strategy=NLPDDPStrategy(), accelerator="cpu", precision=precision + ) + if args.tokenizer_model_path is not None: + with open_dict(model.cfg): + model.cfg.tokenizer.model = args.tokenizer_model_path + + else: + if tokenizer_model_path is None: + logging.warning("Could not extract tokenizer model file from checkpoint.") + + else: + # Extract tokenizer info + with open_dict(model.cfg): + model.cfg.tokenizer.model = tokenizer_model_path + + model.cfg, restore_dict = force_cpu_model(model.cfg) + + model = cls(model.cfg, trainer) + model = model.to('cpu') + model._save_restore_connector = NLPSaveRestoreConnector() + model.freeze() + model.to(dtype=dtype) + + restore_model_config(model.cfg, restore_dict) + + # Update global batch size + if old_global_batch_size % new_global_batch_size != 0 or old_global_batch_size < new_global_batch_size: + logging.info( + f"Global batch size {old_global_batch_size} is not divisible by new global batch size {new_global_batch_size}." + f" The model config will be updated with new global batch size {new_global_batch_size}." + ) + with open_dict(model.cfg): + model.cfg.global_batch_size = new_global_batch_size + + logging.info(f"Global rank: {global_rank} Local rank: {app_state.local_rank} World size: {world_size}") + logging.info(f"PP rank: {pp_rank} TP rank: {0}") + logging.info(f"TP 1 PP 1 Number of Layers : {len(global_params[0])}") + logging.info(f"Remaining layer offset for parameters: {global_offset}") + logging.info("\n") + + # Special case for TP conversion only mode + if tp_conversion_only: + logging.info(f"Skipping PP split due to flag `--tp_conversion_only`") + + split_tp_partition_only(model, global_params, tgt_tp_size, args.target_file, args.megatron_legacy) + break + + global_offset = split_partition( + model, + global_params, + tgt_pp_size, + tgt_tp_size, + pp_rank, + global_offset, + pipeline_model_parallel_split_rank, + args.target_file, + args.megatron_legacy, + ) + + # Reset env flag + os.environ.pop(NEMO_MEGATRON_MODEL_PARALLEL_APPSTATE_OVERRIDE, None) + + # Check if invalid global offset - after all PP splits, global offset should be -1 + if global_offset < -1 and not tp_conversion_only: + raise ValueError( + f"Invalid global offset {global_offset} found for global rank {app_state.global_rank} " + f"and local rank {app_state.local_rank}. Should be -1 if all parameters have been assigned. " + f"Currently, seems some parameters were duplicated." + ) + elif global_offset > -1 and not tp_conversion_only: + logging.error("\n") + logging.error("!" * 80) + logging.error("Error: Some parameters were not correctly added to model partitions.") + logging.error("Below is list of parameters skipped in reverse order: ") + + for param_id in range(global_offset, -1, -1): + logging.error( + f"Param ID: {param_id} : {global_params[1][param_id]} {global_params[0][param_id].shape}" + ) + logging.error("!" * 80) + + raise ValueError( + f"Invalid global offset {global_offset} found for global rank {app_state.global_rank} " + f"and local rank {app_state.local_rank}. Should be -1 if all parameters have been assigned. " + f"Currently, seems some parameters were not assigned." + ) + + logging.info("Successfully finished changing partitions!") + + if temp_dir is not None: + shutil.rmtree(temp_dir, ignore_errors=True) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_ckpt_to_nemo.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_ckpt_to_nemo.py new file mode 100644 index 0000000..40ba35f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_ckpt_to_nemo.py @@ -0,0 +1,243 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +r""" +Conversion script to convert PTL checkpoints into nemo checkpoint. + Example to run this conversion script: + python -m torch.distributed.launch --nproc_per_node= * \ + megatron_ckpt_to_nemo.py \ + --checkpoint_folder \ + --checkpoint_name \ + --nemo_file_path \ + --tensor_model_parallel_size \ + --pipeline_model_parallel_size +""" + +import dis +import os +from argparse import ArgumentParser + +import torch +from genericpath import isdir +from megatron.core import parallel_state +from omegaconf import OmegaConf, open_dict +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.trainer.trainer import Trainer + +from nemo.collections.nlp.models.language_modeling.megatron_bart_model import MegatronBARTModel +from nemo.collections.nlp.models.language_modeling.megatron_bert_model import MegatronBertModel +from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel +from nemo.collections.nlp.models.language_modeling.megatron_gpt_sft_model import MegatronGPTSFTModel +from nemo.collections.nlp.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel +from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model +from nemo.collections.nlp.models.machine_translation.megatron_nmt_model import MegatronNMTModel +from nemo.collections.nlp.parts.nlp_overrides import ( + GradScaler, + NLPDDPStrategy, + NLPSaveRestoreConnector, + PipelineMixedPrecisionPlugin, +) +from nemo.utils import AppState, logging +from nemo.utils.distributed import initialize_distributed +from nemo.utils.model_utils import inject_model_parallel_rank + + +def get_args(): + parser = ArgumentParser() + parser.add_argument( + "--checkpoint_folder", + type=str, + default=None, + required=True, + help="Path to PTL checkpoints saved during training. Ex: /raid/nemo_experiments/megatron_gpt/checkpoints", + ) + parser.add_argument( + "--checkpoint_name", + type=str, + default=None, + required=True, + help="Name of checkpoint to be used. Ex: megatron_gpt--val_loss=6.34-step=649-last.ckpt", + ) + + parser.add_argument( + "--hparams_file", + type=str, + default=None, + required=False, + help="Path config for restoring. It's created during training and may need to be modified during restore if restore environment is different than training. Ex: /raid/nemo_experiments/megatron_gpt/hparams.yaml", + ) + parser.add_argument("--nemo_file_path", type=str, default=None, required=True, help="Path to output .nemo file.") + parser.add_argument( + "--no_pack_nemo_file", + action="store_true", + help="If passed, output will be written under nemo_file_path as a directory instead of packed as a tarred .nemo file.", + ) + parser.add_argument("--gpus_per_node", type=int, required=True, default=None) + parser.add_argument("--tensor_model_parallel_size", type=int, required=True, default=None) + parser.add_argument("--pipeline_model_parallel_size", type=int, required=True, default=None) + parser.add_argument( + "--pipeline_model_parallel_split_rank", + type=int, + required=False, + default=None, + help="If pipeline parallel size > 1, this is the rank at which the encoder ends and the decoder begins.", + ) + parser.add_argument( + "--model_type", + type=str, + required=True, + default="gpt", + choices=["gpt", "sft", "t5", "bert", "nmt", "bart", "retro"], + ) + parser.add_argument("--local_rank", type=int, required=False, default=os.getenv('LOCAL_RANK', -1)) + parser.add_argument("--bcp", action="store_true", help="Whether on BCP platform") + parser.add_argument( + "--precision", + type=str, + required=False, + default='16-mixed', + choices=['32-true', '16-mixed', 'bf16-mixed'], + help="Precision value for the trainer that matches with precision of the ckpt", + ) + + args = parser.parse_args() + return args + + +def convert(local_rank, rank, world_size, args): + + app_state = AppState() + app_state.data_parallel_rank = 0 + num_nodes = world_size // args.gpus_per_node + plugins = [] + strategy = "auto" + if args.bcp: + plugins.append(TorchElasticEnvironment()) + if args.model_type == 'gpt': + strategy = NLPDDPStrategy() + + cfg = { + 'trainer': { + 'devices': args.gpus_per_node, + 'num_nodes': num_nodes, + 'accelerator': 'gpu', + 'precision': args.precision, + }, + 'model': {'native_amp_init_scale': 2 ** 32, 'native_amp_growth_interval': 1000, 'hysteresis': 2}, + } + cfg = OmegaConf.create(cfg) + + scaler = None + # If FP16 create a GradScaler as the build_model_parallel_config of MegatronBaseModel expects it + if cfg.trainer.precision == '16-mixed': + scaler = GradScaler( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + hysteresis=cfg.model.get('hysteresis', 2), + ) + plugins.append(PipelineMixedPrecisionPlugin(precision=cfg.trainer.precision, device='cuda', scaler=scaler)) + # Set precision None after precision plugins are created as PTL >= 2.1 does not allow both + # precision plugins and precision to exist + cfg.trainer.precision = None + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer) + + app_state.pipeline_model_parallel_size = args.pipeline_model_parallel_size + app_state.tensor_model_parallel_size = args.tensor_model_parallel_size + # Auto set split rank for T5, BART, NMT if split rank is None. + if args.pipeline_model_parallel_size > 1 and args.model_type in ['t5', 'bart', 'nmt']: + if args.pipeline_model_parallel_split_rank is not None: + app_state.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_split_rank + else: + if args.pipeline_model_parallel_size % 2 != 0: + raise ValueError( + f"Pipeline model parallel size {args.pipeline_model_parallel_size} must be even if split rank is not specified." + ) + else: + # If split rank is not set, then we set it to be pipeline_model_parallel_size // 2 - this is because in most cases we have the same number of enc/dec layers. + app_state.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_size // 2 + else: + app_state.pipeline_model_parallel_split_rank = None + + app_state.model_parallel_size = app_state.tensor_model_parallel_size * app_state.pipeline_model_parallel_size + + parallel_state.initialize_model_parallel( + tensor_model_parallel_size=app_state.tensor_model_parallel_size, + pipeline_model_parallel_size=app_state.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank=app_state.pipeline_model_parallel_split_rank, + ) + + app_state.pipeline_model_parallel_rank = parallel_state.get_pipeline_model_parallel_rank() + app_state.tensor_model_parallel_rank = parallel_state.get_tensor_model_parallel_rank() + + # check for distributed checkpoint + dist_ckpt_dir = os.path.join(args.checkpoint_folder, args.checkpoint_name) + if os.path.isdir(dist_ckpt_dir): + checkpoint_path = dist_ckpt_dir + else: + # legacy checkpoint needs model parallel injection + checkpoint_path = inject_model_parallel_rank(os.path.join(args.checkpoint_folder, args.checkpoint_name)) + + logging.info( + f'rank: {rank}, local_rank: {local_rank}, is loading checkpoint: {checkpoint_path} for tp_rank: {app_state.tensor_model_parallel_rank} and pp_rank: {app_state.pipeline_model_parallel_rank}' + ) + + if args.model_type == 'gpt': + model = MegatronGPTModel.load_from_checkpoint(checkpoint_path, hparams_file=args.hparams_file, trainer=trainer) + elif args.model_type == 'sft': + model = MegatronGPTSFTModel.load_from_checkpoint( + checkpoint_path, hparams_file=args.hparams_file, trainer=trainer + ) + # we force the target for the loaded model to have the correct target + # because the hparams.yaml sometimes contains MegatronGPTModel as the target. + with open_dict(model.cfg): + model.cfg.target = f"{MegatronGPTSFTModel.__module__}.{MegatronGPTSFTModel.__name__}" + + elif args.model_type == 'bert': + model = MegatronBertModel.load_from_checkpoint( + checkpoint_path, hparams_file=args.hparams_file, trainer=trainer + ) + elif args.model_type == 't5': + model = MegatronT5Model.load_from_checkpoint(checkpoint_path, hparams_file=args.hparams_file, trainer=trainer) + elif args.model_type == 'bart': + model = MegatronBARTModel.load_from_checkpoint( + checkpoint_path, hparams_file=args.hparams_file, trainer=trainer + ) + elif args.model_type == 'nmt': + model = MegatronNMTModel.load_from_checkpoint(checkpoint_path, hparams_file=args.hparams_file, trainer=trainer) + elif args.model_type == 'retro': + model = MegatronRetrievalModel.load_from_checkpoint( + checkpoint_path, hparams_file=args.hparams_file, trainer=trainer + ) + model._save_restore_connector = NLPSaveRestoreConnector() + save_file_path = args.nemo_file_path + if args.no_pack_nemo_file: + # With --no_pack_nemo_file, nemo_file_path is expected to be a directory. + # Adding a dummy model filename here conforms with SaveRestoreConnector's convention. + model._save_restore_connector.pack_nemo_file = False + save_file_path = os.path.join(save_file_path, 'model.nemo') + + if torch.distributed.is_initialized(): + torch.distributed.barrier() + + model.save_to(save_file_path) + + logging.info(f'NeMo model saved to: {args.nemo_file_path}') + + +if __name__ == '__main__': + args = get_args() + + local_rank, rank, world_size = initialize_distributed(args) + + convert(local_rank, rank, world_size, args) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_export.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_export.py new file mode 100644 index 0000000..bf91578 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_export.py @@ -0,0 +1,175 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright 2017 Johns Hopkins University (Shinji Watanabe) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +from omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer + +from nemo.collections.nlp.models.language_modeling.megatron_bart_model import MegatronBARTModel +from nemo.collections.nlp.models.language_modeling.megatron_bert_model import MegatronBertModel +from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel +from nemo.collections.nlp.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel +from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model +from nemo.collections.nlp.models.machine_translation.megatron_nmt_model import MegatronNMTModel +from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector +from nemo.core import ModelPT +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.app_state import AppState +from nemo.utils.model_utils import inject_model_parallel_rank + + +def get_model_class(cfg): + if cfg.model_type == 'gpt': + return MegatronGPTModel + elif cfg.model_type == 'bert': + return MegatronBertModel + elif cfg.model_type == 't5': + return MegatronT5Model + elif cfg.model_type == 'bart': + return MegatronBARTModel + elif cfg.model_type == 'nmt': + return MegatronNMTModel + elif cfg.model_type == 'retro': + return MegatronRetrievalModel + else: + raise ValueError("Invalid Model Type") + + +@hydra_runner(config_path="conf", config_name="megatron_gpt_export") +def nemo_export(cfg): + """Convert a nemo model into .onnx ONNX format.""" + nemo_in = None + if cfg.gpt_model_file: + nemo_in = cfg.gpt_model_file + elif cfg.checkpoint_dir: + nemo_in = os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name) + assert nemo_in is not None, "NeMo model not provided. Please provide the path to the .nemo or .ckpt file" + + onnx_out = cfg.onnx_model_file + + trainer = Trainer(strategy=NLPDDPStrategy(), **cfg.trainer) + assert ( + cfg.trainer.devices * cfg.trainer.num_nodes + == cfg.tensor_model_parallel_size * cfg.pipeline_model_parallel_size + ), "devices * num_nodes should equal tensor_model_parallel_size * pipeline_model_parallel_size" + + logging.info("Restoring NeMo model from '{}'".format(nemo_in)) + try: + if cfg.gpt_model_file: + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.gpt_model_file): + save_restore_connector.model_extracted_dir = cfg.gpt_model_file + + pretrained_cfg = ModelPT.restore_from( + restore_path=cfg.gpt_model_file, + trainer=trainer, + return_config=True, + save_restore_connector=save_restore_connector, + ) + OmegaConf.set_struct(pretrained_cfg, True) + with open_dict(pretrained_cfg): + pretrained_cfg.sequence_parallel = False + pretrained_cfg.activations_checkpoint_granularity = None + pretrained_cfg.activations_checkpoint_method = None + pretrained_cfg.precision = trainer.precision + if trainer.precision == "16": + pretrained_cfg.megatron_amp_O2 = False + model = ModelPT.restore_from( + restore_path=cfg.gpt_model_file, + trainer=trainer, + override_config_path=pretrained_cfg, + save_restore_connector=save_restore_connector, + ) + elif cfg.checkpoint_dir: + app_state = AppState() + if cfg.tensor_model_parallel_size > 1 or cfg.pipeline_model_parallel_size > 1: + app_state.model_parallel_size = cfg.tensor_model_parallel_size * cfg.pipeline_model_parallel_size + app_state.tensor_model_parallel_size = cfg.tensor_model_parallel_size + app_state.pipeline_model_parallel_size = cfg.pipeline_model_parallel_size + ( + app_state.tensor_model_parallel_rank, + app_state.pipeline_model_parallel_rank, + app_state.model_parallel_size, + app_state.data_parallel_size, + app_state.pipeline_model_parallel_split_rank, + app_state.virtual_pipeline_model_parallel_rank, + ) = fake_initialize_model_parallel( + world_size=app_state.model_parallel_size, + rank=trainer.global_rank, + tensor_model_parallel_size_=cfg.tensor_model_parallel_size, + pipeline_model_parallel_size_=cfg.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank_=cfg.pipeline_model_parallel_split_rank, + ) + checkpoint_path = inject_model_parallel_rank(os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name)) + model_cls = get_model_class(cfg) + model = model_cls.load_from_checkpoint(checkpoint_path, hparams_file=cfg.hparams_file, trainer=trainer) + else: + raise ValueError("need at least a nemo file or checkpoint dir") + except Exception as e: + logging.error( + "Failed to restore model from NeMo file : {}. Please make sure you have the latest NeMo package installed with [all] dependencies.".format( + nemo_in + ) + ) + raise e + + logging.info("Model {} restored from '{}'".format(model.__class__.__name__, nemo_in)) + + # Export + check_trace = cfg.export_options.runtime_check + + try: + model.to(device=cfg.export_options.device).freeze() + model.eval() + model.export( + onnx_out, + onnx_opset_version=cfg.export_options.onnx_opset, + do_constant_folding=cfg.export_options.do_constant_folding, + dynamic_axes={ + 'input_ids': {0: "sequence", 1: "batch"}, + 'position_ids': {0: "sequence", 1: "batch"}, + 'logits': {0: "sequence", 1: "batch"}, + }, + check_trace=check_trace, + check_tolerance=cfg.export_options.check_tolerance, + verbose=cfg.export_options.verbose, + ) + except Exception as e: + logging.error( + "Export failed. Please make sure your NeMo model class ({}) has working export() and that you have the latest NeMo package installed with [all] dependencies.".format( + model.__class__ + ) + ) + raise e + + +if __name__ == '__main__': + nemo_export() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_continue_training.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_continue_training.py new file mode 100755 index 0000000..73cbb2a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_continue_training.py @@ -0,0 +1,198 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import tempfile + +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector + +from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel +from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel +from nemo.collections.nlp.parts.nlp_overrides import ( + CustomProgressBar, + GradScaler, + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, + NLPSaveRestoreConnector, + PipelineMixedPrecisionPlugin, +) +from nemo.core.config import hydra_runner +from nemo.utils import AppState, logging +from nemo.utils.exp_manager import exp_manager +from nemo.utils.model_utils import inject_model_parallel_rank + + +def _modify_config(gpt_cfg, cfg, add_cfg_to_tree=False): + """ + This function modifies the original gpt pre-training config (t5_cfg) with attributes from the finetuning config (cfg). + The `add_cfg_to_tree` arg adds `cfg` to the top of the yaml tree which is needed for all `hparams.yaml` files when passed as an arg to `load_from_checkpoint()`. + """ + OmegaConf.set_struct(gpt_cfg, True) + OmegaConf.resolve(cfg) + with open_dict(gpt_cfg): + gpt_cfg.megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + gpt_cfg.micro_batch_size = cfg.model.micro_batch_size + gpt_cfg.global_batch_size = cfg.model.global_batch_size + gpt_cfg.sequence_parallel = cfg.model.get("sequence_parallel", False) + gpt_cfg.activations_checkpoint_granularity = cfg.model.get("activations_checkpoint_granularity", None) + gpt_cfg.activations_checkpoint_num_layers = cfg.model.get("activations_checkpoint_num_layers", None) + gpt_cfg.activations_checkpoint_method = cfg.model.get("activations_checkpoint_method", None) + gpt_cfg.data = cfg.model.data + gpt_cfg.optim = cfg.model.optim + gpt_cfg.precision = cfg.trainer.precision + gpt_cfg.restore_from_path = cfg.restore_from_path + gpt_cfg.resume_from_checkpoint = cfg.model.resume_from_checkpoint + gpt_cfg.gradient_as_bucket_view = cfg.model.gradient_as_bucket_view + gpt_cfg.encoder_seq_length = cfg.model.encoder_seq_length + gpt_cfg.max_position_embeddings = cfg.model.max_position_embeddings + gpt_cfg.seq_len_interpolation_factor = cfg.model.seq_len_interpolation_factor + gpt_cfg.use_flash_attention = cfg.model.use_flash_attention + gpt_cfg.tensor_model_parallel_size = cfg.model.get('tensor_model_parallel_size', 1) + gpt_cfg.pipeline_model_parallel_size = cfg.model.get('pipeline_model_parallel_size', 1) + gpt_cfg.pipeline_model_parallel_split_rank = cfg.model.get('pipeline_model_parallel_split_rank', 0) + + # This is needed when modifying a hparam file directly to load `.ckpt` files. + # This is not needed to modify the cfg in `.nemo` files. + if add_cfg_to_tree: + OmegaConf.resolve(gpt_cfg) + gpt_cfg.cfg = gpt_cfg + + return gpt_cfg + + +def load_from_nemo(cls, cfg, trainer, gpt_cfg, modify_confg_fn): + gpt_cfg = modify_confg_fn(gpt_cfg, cfg, add_cfg_to_tree=False) + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.restore_from_path): + save_restore_connector.model_extracted_dir = cfg.restore_from_path + model = cls.restore_from( + restore_path=cfg.restore_from_path, + trainer=trainer, + override_config_path=gpt_cfg, + save_restore_connector=save_restore_connector, + ) + return model + + +def load_from_checkpoint_dir(cls, cfg, trainer, modify_confg_fn): + app_state = AppState() + if cfg.model.tensor_model_parallel_size > 1 or cfg.model.pipeline_model_parallel_size > 1: + app_state.model_parallel_size = cfg.model.tensor_model_parallel_size * cfg.model.pipeline_model_parallel_size + app_state.tensor_model_parallel_size = cfg.model.tensor_model_parallel_size + app_state.pipeline_model_parallel_size = cfg.model.pipeline_model_parallel_size + ( + app_state.tensor_model_parallel_rank, + app_state.pipeline_model_parallel_rank, + app_state.model_parallel_size, + app_state.data_parallel_size, + app_state.pipeline_model_parallel_split_rank, + app_state.virtual_pipeline_model_parallel_rank, + ) = fake_initialize_model_parallel( + world_size=app_state.model_parallel_size, + rank=trainer.global_rank, + tensor_model_parallel_size_=cfg.model.tensor_model_parallel_size, + pipeline_model_parallel_size_=cfg.model.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank_=cfg.model.pipeline_model_parallel_split_rank, + ) + checkpoint_path = inject_model_parallel_rank( + os.path.join(cfg.model.pretrained_checkpoint.checkpoint_dir, cfg.model.pretrained_checkpoint.checkpoint_name) + ) + hparams_file = OmegaConf.load(cfg.model.pretrained_checkpoint.hparams_file) + gpt_cfg = modify_confg_fn(hparams_file.cfg, cfg, add_cfg_to_tree=True) + with tempfile.NamedTemporaryFile(suffix='.yaml') as f: + OmegaConf.save(config=gpt_cfg, f=f.name) + model = cls.load_from_checkpoint(checkpoint_path=checkpoint_path, trainer=trainer, hparams_file=f.name,) + return model + + +def validate_checkpoint_loading_args(cfg): + if cfg.checkpoint_dir is None or not os.path.isdir(cfg.checkpoint_dir): + raise ValueError(f'Checkpoint directory {cfg.checkpoint_dir} does not exist or is not a directory.') + if cfg.checkpoint_name is None: + raise ValueError(f'Checkpoint name {cfg.checkpoint_name} is not valid.') + if cfg.hparams_file is None or not os.path.isfile(cfg.hparams_file): + raise ValueError(f'Hparams file {cfg.hparams_file} does not exist or is not a file.') + + +@hydra_runner(config_path="conf", config_name="megatron_gpt_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + with_distributed_adam = cfg.model.optim.get('name', 'fused_adam') == 'distributed_fused_adam' + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True, + gradient_as_bucket_view=cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + ) + if cfg.trainer.precision in [16, '16', 'bf16', '16-mixed', 'bf16-mixed']: + scaler = None + if cfg.trainer.precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + hysteresis=cfg.model.get('hysteresis', 2), + ) + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + if megatron_amp_O2 and not with_distributed_adam: + plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(PipelineMixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + callbacks = [] + # enable_progress_bar is True by default. If cfg.trainer.enable_progress_bar=False, CustomProgressBar is not appended to callbacks + if 'enable_progress_bar' not in cfg.trainer or cfg.trainer.enable_progress_bar: + callbacks.append(CustomProgressBar()) + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=callbacks) + + exp_manager(trainer, cfg.exp_manager) + + # update resume from checkpoint found by exp_manager + if cfg.model.resume_from_checkpoint is not None: + trainer.ckpt_path = cfg.model.resume_from_checkpoint + + logging.info(f'Resuming training from checkpoint: {trainer.ckpt_path}') + + if cfg.restore_from_path: + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.restore_from_path): + save_restore_connector.model_extracted_dir = cfg.restore_from_path + gpt_cfg = MegatronGPTModel.restore_from( + restore_path=cfg.restore_from_path, + trainer=trainer, + return_config=True, + save_restore_connector=save_restore_connector, + ) + model = load_from_nemo(MegatronGPTModel, cfg, trainer, gpt_cfg, modify_confg_fn=_modify_config) + elif cfg.model.get("pretrained_checkpoint", None) is not None: + validate_checkpoint_loading_args(cfg.model.pretrained_checkpoint) + model = load_from_checkpoint_dir(MegatronGPTModel, cfg, trainer, modify_confg_fn=_modify_config) + else: + print(' > WARNING: No checkpoint provided. Starting from scratch.') + model = MegatronGPTModel(cfg.model, trainer) + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_eval.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_eval.py new file mode 100644 index 0000000..084a4b2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_eval.py @@ -0,0 +1,380 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import asyncio +import datetime +import os +import threading +from functools import partial + +import torch +from omegaconf import OmegaConf, open_dict +from pytorch_lightning.trainer.trainer import Trainer +from torch.utils.data import DataLoader, Dataset + +from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel +from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel +from nemo.collections.nlp.modules.common.text_generation_server import MegatronServer +from nemo.collections.nlp.modules.common.text_generation_utils import generate +from nemo.collections.nlp.modules.common.transformer.text_generation import LengthParam, SamplingParam +from nemo.collections.nlp.parts.nlp_overrides import CustomProgressBar, NLPDDPStrategy, NLPSaveRestoreConnector +from nemo.core.config import hydra_runner +from nemo.utils.app_state import AppState +from nemo.utils.model_utils import inject_model_parallel_rank + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + +""" +This is the script to run GPT text generation. + +Usage: + Assume the model has TP=1, PP=1 in the following use cases. + a. run greedy inference from a nemo file: + python megatron_gpt_eval.py \ + gpt_model_file=PATH_TO_MODEL \ + inference.greedy=True \ + inference.add_BOS=True \ + trainer.devices=1 \ + trainer.num_nodes=1 \ + tensor_model_parallel_size=-1 \ + pipeline_model_parallel_size=-1 \ + prompts=[prompt1,prompt2] + + b. run greedy inference from a PTL checkpoint file: + python megatron_gpt_eval.py \ + checkpoint_dir=PATH_TO_CHECKPOINT_FILE \ + checkpoint_name=CHECKPOINT_FILE_NAME \ + hparams_file=HPARAMS_FILE \ + inference.greedy=True \ + inference.add_BOS=True \ + trainer.devices=1 \ + trainer.num_nodes=1 \ + tensor_model_parallel_size=-1 \ + pipeline_model_parallel_size=-1 \ + prompts=[prompt1,prompt2] + + c. run top_p inference from a nemo file: + python megatron_gpt_eval.py \ + gpt_model_file=PATH_TO_MODEL \ + inference.greedy=False \ + inference.top_k=0 \ + inference.top_p=0.9 \ + inference.repetition_penalty=1.2 \ + inference.add_BOS=True \ + trainer.devices=1 \ + trainer.num_nodes=1 \ + tensor_model_parallel_size=-1 \ + pipeline_model_parallel_size=-1 \ + prompts=[prompt1,prompt2] + + d. If you don't need to generate tokens and need model to compute logprobs: + python megatron_gpt_eval.py \ + gpt_model_file=PATH_TO_MODEL \ + inference.compute_logprob=True \ + trainer.devices=1 \ + trainer.num_nodes=1 \ + tensor_model_parallel_size=-1 \ + pipeline_model_parallel_size=-1 \ + prompts=[text to get logprob] + + e. Launch the inference server + python megatron_gpt_eval.py \ + gpt_model_file=PATH_TO_MODEL \ + trainer.devices=1 \ + trainer.num_nodes=1 \ + tensor_model_parallel_size=-1 \ + pipeline_model_parallel_size=-1 \ + server=True + + To send a request to the server, here is one example code: + ```python + import json + import requests + + batch_size = 8 + port_num = 5555 + headers = {"Content-Type": "application/json"} + + + def request_data(data): + resp = requests.put('http://localhost:{}/generate'.format(port_num), + data=json.dumps(data), + headers=headers) + sentences = resp.json()['sentences'] + return sentences + + + data = { + "sentences": [""] * batch_size, + "tokens_to_generate": 300, + "temperature": 1.0, + "add_BOS": True, + "top_k": 0, + "top_p": 0.9, + "greedy": False, + "all_probs": False, + "repetition_penalty": 1.2, + "min_tokens_to_generate": 2, + } + + sentences = request_data(data) + ``` +""" + +if not torch.cuda.is_available(): + raise EnvironmentError("GPU is needed for the inference") + + +class RequestDataSet(Dataset): + def __init__(self, sentences): + super().__init__() + self.sentences = sentences + + def __len__(self,): + return len(self.sentences) + + def __getitem__(self, idx): + return self.sentences[idx] + + +def remove_padded_prompts(response, nb_paddings): + result = {} + for k, v in response.items(): + if v != None and (type(v) is list or type(v) is torch.Tensor): + v = v[:-nb_paddings] + result[k] = v + return result + + +@hydra_runner(config_path="conf", config_name="megatron_gpt_inference") +def main(cfg) -> None: + + callbacks = [] + # enable_progress_bar is True by default. If cfg.trainer.enable_progress_bar=False, CustomProgressBar is not appended to callbacks + if 'enable_progress_bar' not in cfg.trainer or cfg.trainer.enable_progress_bar: + callbacks.append(CustomProgressBar()) + # trainer required for restoring model parallel models + trainer = Trainer( + strategy=NLPDDPStrategy(timeout=datetime.timedelta(seconds=18000)), **cfg.trainer, callbacks=callbacks, + ) + + if cfg.gpt_model_file is not None: + if ( + cfg.tensor_model_parallel_size < 0 + or cfg.pipeline_model_parallel_size < 0 + or cfg.get('pipeline_model_parallel_split_rank', -1) < 0 + ): + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.gpt_model_file): + save_restore_connector.model_extracted_dir = cfg.gpt_model_file + model_config = MegatronGPTModel.restore_from( + restore_path=cfg.gpt_model_file, + trainer=trainer, + return_config=True, + save_restore_connector=save_restore_connector, + ) + + # with dist checkpointing we don't need to set this + if not model_config.get('mcore_gpt', False): + with open_dict(cfg): + cfg.tensor_model_parallel_size = model_config.get('tensor_model_parallel_size', 1) + cfg.pipeline_model_parallel_size = model_config.get('pipeline_model_parallel_size', 1) + cfg.pipeline_model_parallel_split_rank = model_config.get('pipeline_model_parallel_split_rank', 0) + + assert ( + cfg.trainer.devices * cfg.trainer.num_nodes + == cfg.tensor_model_parallel_size + * cfg.pipeline_model_parallel_size + * max(1, cfg.get('expert_model_parallel_size', 1)) + ), "devices * num_nodes should equal tensor_model_parallel_size * pipeline_model_parallel_size" + + if cfg.gpt_model_file: + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.gpt_model_file): + save_restore_connector.model_extracted_dir = cfg.gpt_model_file + + pretrained_cfg = MegatronGPTModel.restore_from( + restore_path=cfg.gpt_model_file, + trainer=trainer, + return_config=True, + save_restore_connector=save_restore_connector, + ) + OmegaConf.set_struct(pretrained_cfg, True) + with open_dict(pretrained_cfg): + pretrained_cfg.sequence_parallel = False + pretrained_cfg.activations_checkpoint_granularity = None + pretrained_cfg.activations_checkpoint_method = None + pretrained_cfg.precision = trainer.precision + pretrained_cfg["use_flash_attention"] = cfg.inference.get("use_flash_attention", False) + pretrained_cfg["apply_rope_fusion"] = False + if pretrained_cfg.get('mcore_gpt', False): + # with dist checkpointing we can use the model parallel config specified by the user + pretrained_cfg.tensor_model_parallel_size = cfg.tensor_model_parallel_size + pretrained_cfg.pipeline_model_parallel_size = cfg.pipeline_model_parallel_size + pretrained_cfg.expert_model_parallel_size = cfg.get('expert_model_parallel_size', 1) + pretrained_cfg.micro_batch_size = 1 + if trainer.precision == "16": + pretrained_cfg.megatron_amp_O2 = False + elif trainer.precision in ['bf16', 'bf16-mixed'] and cfg.get('megatron_amp_O2', False): + pretrained_cfg.megatron_amp_O2 = True + model = MegatronGPTModel.restore_from( + restore_path=cfg.gpt_model_file, + trainer=trainer, + override_config_path=pretrained_cfg, + save_restore_connector=save_restore_connector, + map_location=f'cuda:{trainer.local_rank}', # map_location is needed for converted models + ) + elif cfg.checkpoint_dir: + app_state = AppState() + if ( + cfg.tensor_model_parallel_size > 1 + or cfg.pipeline_model_parallel_size > 1 + or cfg.get('expert_model_parallel_size', 1) > 1 + ): + app_state.model_parallel_size = ( + cfg.tensor_model_parallel_size + * cfg.pipeline_model_parallel_size + * cfg.get('expert_model_parallel_size', 1) + ) + app_state.tensor_model_parallel_size = cfg.tensor_model_parallel_size + app_state.pipeline_model_parallel_size = cfg.pipeline_model_parallel_size + app_state.expert_model_parallel_size = cfg.get('expert_model_parallel_size', 1) + ( + app_state.tensor_model_parallel_rank, + app_state.pipeline_model_parallel_rank, + app_state.expert_model_parallel_rank, + app_state.model_parallel_size, + app_state.data_parallel_size, + app_state.pipeline_model_parallel_split_rank, + app_state.virtual_pipeline_model_parallel_rank, + ) = fake_initialize_model_parallel( + world_size=app_state.model_parallel_size, + rank=trainer.global_rank, + tensor_model_parallel_size_=cfg.tensor_model_parallel_size, + pipeline_model_parallel_size_=cfg.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank_=cfg.pipeline_model_parallel_split_rank, + expert_model_parallel_size_=cfg.get('expert_model_parallel_size', 1), + ) + checkpoint_path = os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name) + # checkpoint_path is a dir in case of distributed checkpointing + if not os.path.isdir(checkpoint_path): + # legacy checkpoint needs model parallel rank injection + checkpoint_path = inject_model_parallel_rank(os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name)) + model = MegatronGPTModel.load_from_checkpoint(checkpoint_path, hparams_file=cfg.hparams_file, trainer=trainer) + else: + raise ValueError("need at least a nemo file or checkpoint dir") + + model.freeze() + + # Have to turn off activations_checkpoint_method for inference + try: + model.model.language_model.encoder.activations_checkpoint_method = None + except AttributeError: + pass + + length_params: LengthParam = { + "max_length": cfg.inference.tokens_to_generate, + "min_length": cfg.inference.min_tokens_to_generate, + } + + sampling_params: SamplingParam = { + "use_greedy": cfg.inference.greedy, + "temperature": cfg.inference.temperature, + "top_k": cfg.inference.top_k, + "top_p": cfg.inference.top_p, + "repetition_penalty": cfg.inference.repetition_penalty, + "add_BOS": cfg.inference.add_BOS, + "all_probs": cfg.inference.all_probs, + "compute_logprob": cfg.inference.compute_logprob, + "end_strings": cfg.inference.end_strings, + } + + fp8_enabled = hasattr(model.cfg, "fp8") and (model.cfg.fp8 == True) + if fp8_enabled: + nb_paddings = 0 + while len(cfg.prompts) % 8 != 0: + cfg.prompts.append("") + nb_paddings += 1 + + # First method of running text generation, call model.generate method + response = model.generate( + inputs=OmegaConf.to_container(cfg.prompts), length_params=length_params, sampling_params=sampling_params + ) + + if fp8_enabled: + response = remove_padded_prompts(response, nb_paddings) + print("***************************") + print(response) + print("***************************") + + # Second method of running text generation, call trainer.predict [recommended] + bs = 8 if fp8_enabled else 2 + ds = RequestDataSet(OmegaConf.to_container(cfg.prompts)) + request_dl = DataLoader(dataset=ds, batch_size=bs) + config = OmegaConf.to_container(cfg.inference) + model.set_inference_config(config) + response = trainer.predict(model, request_dl) + + if fp8_enabled: + response[-1] = remove_padded_prompts(response[-1], nb_paddings) + print("***************************") + print(response) + print("***************************") + + # Third method of running text generation, use inference server + if cfg.server: + from nemo.collections.nlp.modules.common.megatron_web_server import get_chatbot_demo, get_demo + + if parallel_state.is_pipeline_first_stage() and parallel_state.get_tensor_model_parallel_rank() == 0: + if cfg.web_server: + if cfg.chat: + defaults = { + 'user': cfg.chatbot_config.user, + 'assistant': cfg.chatbot_config.assistant, + 'system': cfg.chatbot_config.system, + } + web_ui = partial( + get_chatbot_demo, + defaults=defaults, + value=cfg.chatbot_config.value, + attributes=cfg.chatbot_config.attributes, + ) + else: + web_ui = get_demo + loop = asyncio.new_event_loop() + thread = threading.Thread( + target=web_ui, + daemon=True, + args=(cfg.share, cfg.username, cfg.password, cfg.port, cfg.web_port, loop), + ) + thread.start() + server = MegatronServer(model.cuda()) + server.run("0.0.0.0", port=cfg.port) + + while True: + choice = torch.cuda.LongTensor(1) + torch.distributed.broadcast(choice, 0) + if choice[0].item() == 0: + generate(model.cuda()) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_pretraining.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_pretraining.py new file mode 100644 index 0000000..8015844 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_pretraining.py @@ -0,0 +1,46 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# To suppress BF16 compile related issue in the CI runs with turing/V100 +import torch._dynamo +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf, open_dict + +from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +torch._dynamo.config.suppress_errors = True + +mp.set_start_method("spawn", force=True) + + +@hydra_runner(config_path="conf", config_name="megatron_gpt_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model = MegatronGPTModel(cfg.model, trainer) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_test.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_test.py new file mode 100644 index 0000000..62a1d40 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_test.py @@ -0,0 +1,69 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from omegaconf.omegaconf import OmegaConf +from pytorch_lightning import Trainer + +from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel +from nemo.collections.nlp.modules.common.megatron.megatron_utils import compute_model_parallel_rank +from nemo.collections.nlp.parts.nlp_overrides import ( + NLPDDPStrategy, + NLPMixedPrecisionPlugin, + NLPPrecisionPlugin, + NLPSaveRestoreConnector, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.app_state import AppState + + +@hydra_runner(config_path="conf", config_name="megatron_gpt_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = None + if cfg.trainer.precision in [16, '16', '16-mixed']: + trainer = Trainer( + plugins=[ + NLPMixedPrecisionPlugin( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + ), + ], + strategy=NLPDDPStrategy(), + **cfg.trainer, + ) + elif cfg.trainer.precision in ['bf16', 'bf16-mixed']: + trainer = Trainer(plugins=[NLPNativeBfloat16PrecisionPlugin(),], strategy=NLPDDPStrategy(), **cfg.trainer,) + else: + trainer = Trainer(plugins=[NLPPrecisionPlugin()], strategy=NLPDDPStrategy(), **cfg.trainer) + + app_state = AppState() + app_state.model_parallel_size = cfg.model.tensor_model_parallel_size + app_state.model_parallel_rank = compute_model_parallel_rank(trainer.local_rank, app_state.model_parallel_size) + + model = MegatronGPTModel.restore_from( + cfg.restore_from_path, trainer=trainer, save_restore_connector=NLPSaveRestoreConnector(), + ) + + # Note: most nemo models must have the data paths configured before instantiating the model + # MegatronGPTMOdel sets up the data in the PTL method .setup which happens after DDP spawns. + model.cfg.data.splits_string = cfg.model.data.splits_string + + trainer.test(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_validate.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_validate.py new file mode 100644 index 0000000..b5a61e6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_gpt_validate.py @@ -0,0 +1,155 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import tempfile + +from omegaconf import OmegaConf, open_dict +from pytorch_lightning.trainer.trainer import Trainer + +from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel +from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel +from nemo.collections.nlp.parts.nlp_overrides import ( + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, + NLPSaveRestoreConnector, + PipelineMixedPrecisionPlugin, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.app_state import AppState +from nemo.utils.model_utils import inject_model_parallel_rank + +""" Example script showing how to run validation on a MegatronGPT model. + + Sample usage: + + From nemo model: + + python megatron_gpt_validate.py \ + trainer.devices=4 \ + trainer.num_nodes=1 \ + trainer.limit_val_batches=10 \ + trainer.max_steps=100 \ + tensor_model_parallel_size=1 \ + pipeline_model_parallel_size=4 \ + trainer.precision=bf16 \ + gpt_model_file=/path/to/megatron_gpt_tp_1_pp4.nemo + + from PTL checkpoint: + python megatron_gpt_validate.py \ + trainer.devices=4 \ + trainer.num_nodes=1 \ + trainer.limit_val_batches=10 \ + trainer.max_steps=100 \ + tensor_model_parallel_size=1 \ + pipeline_model_parallel_size=4 \ + virtual_pipeline_model_parallel_size=4 \ + trainer.precision=bf16 \ + checkpoint_dir='/path/to/experiment/checkpoints' \ + checkpoint_name='megatron_gpt--val_loss=7.78-step=100-consumed_samples=6336.0-last.ckpt' \ + hparams_file='/path/to/experiment/hparams.yaml + +""" + + +def modify_pretrained_cfg(pretrained_cfg, trainer, cfg): + with open_dict(pretrained_cfg): + OmegaConf.set_struct(pretrained_cfg, True) + pretrained_cfg.sequence_parallel = False + pretrained_cfg.activations_checkpoint_granularity = None + pretrained_cfg.activations_checkpoint_method = None + pretrained_cfg.precision = trainer.precision + if cfg.micro_batch_size is not None: + pretrained_cfg.micro_batch_size = cfg.micro_batch_size + if cfg.global_batch_size is not None: + pretrained_cfg.global_batch_size = cfg.global_batch_size + if trainer.precision == "16": + pretrained_cfg.megatron_amp_O2 = False + return pretrained_cfg + + +@hydra_runner(config_path="conf", config_name="megatron_gpt_validate_config") +def main(cfg) -> None: + + trainer = Trainer(strategy=NLPDDPStrategy(), **cfg.trainer) + + assert ( + cfg.trainer.devices * cfg.trainer.num_nodes + == cfg.tensor_model_parallel_size * cfg.pipeline_model_parallel_size + ), "devices * num_nodes should equal tensor_model_parallel_size * pipeline_model_parallel_size" + + if cfg.gpt_model_file: + logging.info(f"Restoring model from {cfg.gpt_model_file}") + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.gpt_model_file): + save_restore_connector.model_extracted_dir = cfg.gpt_model_file + + pretrained_cfg = MegatronGPTModel.restore_from( + restore_path=cfg.gpt_model_file, + trainer=trainer, + return_config=True, + save_restore_connector=save_restore_connector, + ) + pretrained_cfg = modify_pretrained_cfg(pretrained_cfg, trainer, cfg) + model = MegatronGPTModel.restore_from( + restore_path=cfg.gpt_model_file, + trainer=trainer, + override_config_path=pretrained_cfg, + save_restore_connector=save_restore_connector, + map_location=f'cuda:{trainer.local_rank}', # map_location is needed for converted models + ) + elif cfg.checkpoint_dir: + logging.info( + f"Restoring model from checkpoint_dir: {cfg.checkpoint_dir} with checkpoint name: {cfg.checkpoint_name}" + ) + app_state = AppState() + if cfg.tensor_model_parallel_size > 1 or cfg.pipeline_model_parallel_size > 1: + app_state.model_parallel_size = cfg.tensor_model_parallel_size * cfg.pipeline_model_parallel_size + app_state.tensor_model_parallel_size = cfg.tensor_model_parallel_size + app_state.pipeline_model_parallel_size = cfg.pipeline_model_parallel_size + app_state.virtual_pipeline_model_parallel_size = cfg.virtual_pipeline_model_parallel_size + ( + app_state.tensor_model_parallel_rank, + app_state.pipeline_model_parallel_rank, + app_state.model_parallel_size, + app_state.data_parallel_size, + app_state.pipeline_model_parallel_split_rank, + app_state.virtual_pipeline_model_parallel_rank, + ) = fake_initialize_model_parallel( + world_size=app_state.model_parallel_size, + rank=trainer.global_rank, + tensor_model_parallel_size_=cfg.tensor_model_parallel_size, + pipeline_model_parallel_size_=cfg.pipeline_model_parallel_size, + virtual_pipeline_model_parallel_size_=cfg.virtual_pipeline_model_parallel_size, + ) + checkpoint_path = inject_model_parallel_rank(os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name)) + pretrained_cfg = OmegaConf.load(cfg.hparams_file) + pretrained_cfg = modify_pretrained_cfg(pretrained_cfg.cfg, trainer, cfg) + with tempfile.NamedTemporaryFile(suffix='.yaml') as f: + OmegaConf.save(config=pretrained_cfg, f=f.name) + model = MegatronGPTModel.load_from_checkpoint( + checkpoint_path=checkpoint_path, trainer=trainer, hparams_file=f.name, + ) + else: + raise ValueError("need at least a nemo file or checkpoint dir") + + logging.info("\n\n************** Model configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(model.cfg)}') + + trainer.validate(model=model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_llama_quantization.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_llama_quantization.py new file mode 100644 index 0000000..92ead6b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_llama_quantization.py @@ -0,0 +1,90 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.multiprocessing as mp +from datasets import load_dataset + +from nemo.core.config import hydra_runner +from nemo.export.quantize import Quantizer + +mp.set_start_method("spawn", force=True) + +""" +Nemo quantization example script. + +Please consult nemo.export.quantize.Quantizer class +and examples/nlp/language_modeling/conf/megatron_llama_quantization.yaml config on available quantization methods, +models supported as well as how to set up data and inference for calibration (with defaults recommended). + +Example usage: +``` +python examples/nlp/language_modeling/megatron_llama_quantization.py \ + model_file=llama2-7b-fp16.nemo \ + model_save=llama2-7b-fp8.qnemo \ + quantization.algorithm=fp8 \ + export.decoder_type=llama \ + export.inference_tensor_parallel=1 +``` +""" + + +def get_calib_dataloader(data="cnn_dailymail", batch_size=64, calib_size=512, max_sequence_length=512): + if data == "wikitext": + dataset = load_dataset("wikitext", "wikitext-103-v1", split="train") + text_column = "text" + elif data == "cnn_dailymail": + dataset = load_dataset("cnn_dailymail", name="3.0.0", split="train") + text_column = "article" + else: + # Assume a local JSON dataset with a column named "text" + dataset = load_dataset("json", data_files=data, split="train") + text_column = "text" + calib_size = max(min(len(dataset), calib_size), batch_size) + for i in range(calib_size // batch_size): + batch = dataset[i * batch_size : (i + 1) * batch_size][text_column] + for j in range(len(batch)): + batch[j] = batch[j][:max_sequence_length] + yield batch + + +@hydra_runner(config_path="conf", config_name="megatron_llama_quantization") +def main(cfg) -> None: + if not torch.cuda.is_available(): + raise EnvironmentError("GPU is required for the inference.") + + quantizer = Quantizer(cfg.quantization, cfg.inference, cfg.export, cfg.trainer) + + # Quantization algorithm can be set to None. This is useful for baseline precision + # accuracy validation. In this case only weights export step will be performed: + if cfg.quantization.algorithm is not None: + dataloader = get_calib_dataloader( + cfg.quantization.calib_dataset, + cfg.inference.batch_size, + cfg.quantization.num_calib_size, + cfg.inference.max_context_length, + ) + dataloader = [data for data in dataloader] + else: + dataloader = None + + model = quantizer.quantize( + cfg.model_file, dataloader, cfg.tensor_model_parallel_size, cfg.pipeline_model_parallel_size + ) + + quantizer.export(model, cfg.model_save) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_lm_ckpt_to_nemo.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_lm_ckpt_to_nemo.py new file mode 100644 index 0000000..03d6fd9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_lm_ckpt_to_nemo.py @@ -0,0 +1,568 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +r""" +Conversion script to convert Megatron_LM checkpoints into nemo checkpoint. + Example to run this conversion script: + python -m torch.distributed.launch --nproc_per_node= megatron_lm_ckpt_to_nemo.py \ + --checkpoint_folder \ + --checkpoint_name megatron_gpt--val_loss=99.99-step={steps}-consumed_samples={consumed}.0 \ + --nemo_file_path \ + --model_type \ + --hparams_file + --tensor_model_parallel_size + --pipeline_model_parallel_size + --gpus_per_node +Note, hparams_file usually is generated by pytorch lightning when running the training job. +It is the model section of the model pretraining conf with an extra cfg key. +Check https://github.com/NVIDIA/NeMo/issues/4993 for an example. +To resume the training from converted MegatronLM checkpoint, make sure to set the +`trainer.max_steps=round(lr-warmup-fraction * lr-decay-iters + lr-decay-iters)` +where `lr-warmup-fraction` and `lr-decay-iters` are arguments from MegatronLM training +so the learning rate scheduler will follow the same curve. +""" + +import importlib +import os +import pathlib +import sys +from argparse import ArgumentParser +from collections import OrderedDict +from typing import Any, Optional + +import torch +from lightning_fabric.utilities.cloud_io import _load as pl_load +from megatron.core import parallel_state +from pytorch_lightning.core.saving import _load_state as ptl_load_state +from pytorch_lightning.core.saving import load_hparams_from_tags_csv, load_hparams_from_yaml +from pytorch_lightning.trainer.trainer import Trainer +from pytorch_lightning.utilities.migration import pl_legacy_patch + +from nemo.collections.nlp.models.language_modeling.megatron_bert_model import MegatronBertModel +from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel +from nemo.collections.nlp.modules.common.megatron.megatron_init import initialize_model_parallel_for_nemo +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector +from nemo.utils import AppState, logging +from nemo.utils.distributed import initialize_distributed +from nemo.utils.model_utils import inject_model_parallel_rank, uninject_model_parallel_rank + +# this enums code is copied from Megatron_LM +enum_code = ''' +import enum + +class ModelType(enum.Enum): + encoder_or_decoder = 1 + encoder_and_decoder = 2 + + +class LayerType(enum.Enum): + encoder = 1 + decoder = 2 + + +class AttnType(enum.Enum): + self_attn = 1 + cross_attn = 2 + + +class AttnMaskType(enum.Enum): + padding = 1 + causal = 2 +''' + + +def install_megatron_dependence(): + # this is a hack to install required modules for MegatronLM checkpoints + # run the following so we don't have to install Megatron_LM code + megatron_name = 'megatron' + megatron_spec = importlib.util.spec_from_loader(megatron_name, loader=None, is_package=True) + + megatron_module = importlib.util.module_from_spec(megatron_spec) + sys.modules[megatron_name] = megatron_module + + model_name = 'model' + model_spec = importlib.util.spec_from_loader(model_name, loader=None, is_package=True) + + model_module = importlib.util.module_from_spec(model_spec) + + megatron_module.__dict__['model'] = model_module + + sys.modules[megatron_name + '.' + model_name] = model_module + + enums_name = 'enums' + enums_spec = importlib.util.spec_from_loader(enums_name, loader=None, is_package=True) + enums_module = importlib.util.module_from_spec(enums_spec) + + model_module.__dict__['enums'] = enums_module + + sys.modules[megatron_name + '.' + model_name + '.' + enums_name] = enums_module + + exec(enum_code, enums_module.__dict__) + + +def get_args(): + parser = ArgumentParser() + parser.add_argument( + "--checkpoint_folder", + type=str, + default=None, + required=True, + help="Path to Megatron-LM checkpoints saved during training. Ex: /raid/Megatron_LM/checkpoints", + ) + parser.add_argument( + "--checkpoint_name", + type=str, + default='model_optim_rng.pt', + required=True, + help="Name of checkpoint to be used. Ex: model_optim_rng.pt", + ) + + parser.add_argument( + "--hparams_file", + type=str, + default=None, + required=False, + help="Path config for restoring. It's created during training and may need to be modified during restore if restore environment is different than training. Ex: /raid/nemo_experiments/megatron_gpt/hparams.yaml", + ) + parser.add_argument("--nemo_file_path", type=str, default=None, required=False, help="Path to output .nemo file.") + + parser.add_argument( + "--output_ckpt_file_path", type=str, default=None, required=False, help="Path to output .ckpt file." + ) + + parser.add_argument("--gpus_per_node", type=int, required=False, default=1) + + parser.add_argument("--tensor_model_parallel_size", type=int, required=True, default=None) + parser.add_argument("--pipeline_model_parallel_size", type=int, required=False, default=1) + + parser.add_argument("--local_rank", type=int, required=False, default=os.getenv('LOCAL_RANK', -1)) + + parser.add_argument("--model_type", type=str, required=True, default="gpt", choices=["gpt", "t5", "bert"]) + parser.add_argument("--mcore_input", action='store_true', help="input model is trained with Megatron Core") + + args = parser.parse_args() + return args + + +def parse_weights( + weight_dict: OrderedDict, + parent_key: str, + total: list, + converted: OrderedDict, + translator: dict, + mcore_translator: Optional[dict] = None, +): + for key in weight_dict: + if key.endswith('_extra_state'): + if weight_dict[key].read() == b'': + continue + else: + raise RuntimeError("encountered _extra_state that is non empty. I don't know what to do!!") + + new_key = key + name_translate = translator + + for replace_key in name_translate: + if key.find(replace_key) >= 0: + new_key = key.replace(replace_key, name_translate[replace_key]) + + if mcore_translator: + # convert to mcore key names + mcore_key = mcore_translator.get(f'model{parent_key}.{new_key}', new_key) + if mcore_key != new_key: + logging.info(f'successfully mapped model{parent_key}.{new_key} to {mcore_key}') + elif not (isinstance(weight_dict[key], OrderedDict) or isinstance(weight_dict[key], dict)): + logging.warning(f'cannot find nemo -> mcore mapping for: {new_key}') + + if isinstance(weight_dict[key], OrderedDict) or isinstance(weight_dict[key], dict): + parse_weights(weight_dict[key], parent_key + '.' + new_key, total, converted, translator, mcore_translator) + else: + num_parameters = torch.prod(torch.tensor(weight_dict[key].cpu().size())).item() + total[0] += num_parameters + final_key = mcore_key if mcore_translator else 'model' + parent_key + '.' + new_key + converted[final_key] = weight_dict[key] + + +def add_optimizer_state(lm_checkpoint, new_checkpoint, megatron_amp_O2=True): + # this method is to convert lm_checkpoint optimizer states for nemo checkpoint + OPTIMIZER_KEY = 'optimizer' + FP32_FP16_KEY = 'fp32_from_fp16_params' + NEW_OPTIMIZER_KEY = 'optimizer_states' + STEP_KEY = 'iteration' + NEW_STEP_KEY = 'global_step' + LR_SCHEDULER = 'lr_scheduler' + NEW_LR_SCHEDULER = 'lr_schedulers' + if OPTIMIZER_KEY in lm_checkpoint and OPTIMIZER_KEY in lm_checkpoint[OPTIMIZER_KEY]: + opt_state = lm_checkpoint[OPTIMIZER_KEY][OPTIMIZER_KEY] + if megatron_amp_O2: + opt_dict = dict() + if LR_SCHEDULER in lm_checkpoint: + sched = lm_checkpoint[LR_SCHEDULER] + for param_group in opt_state['param_groups']: + param_group['initial_lr'] = sched['max_lr'] + if FP32_FP16_KEY in lm_checkpoint[OPTIMIZER_KEY]: + fp32_state = lm_checkpoint[OPTIMIZER_KEY][FP32_FP16_KEY] + opt_dict[FP32_FP16_KEY] = fp32_state + opt_dict[OPTIMIZER_KEY] = opt_state + new_checkpoint[NEW_OPTIMIZER_KEY] = [opt_dict] + else: + new_checkpoint[NEW_OPTIMIZER_KEY] = [opt_state] + + if STEP_KEY in lm_checkpoint: + new_checkpoint[NEW_STEP_KEY] = lm_checkpoint[STEP_KEY] + new_checkpoint['epoch'] = 1 # always one epoch + if LR_SCHEDULER in lm_checkpoint: + gbs = lm_checkpoint['args'].global_batch_size + sched = lm_checkpoint[LR_SCHEDULER] + content = OrderedDict() + content['max_steps'] = int(sched['decay_steps']) // gbs + sched['warmup_steps'] // gbs + content['warmup_steps'] = int(sched['warmup_steps']) // gbs + content['constant_steps'] = 0 # no such conf in lm checkpoint + content['decay_steps'] = int(sched['decay_steps']) // gbs + content['min_lr'] = sched['min_lr'] + if OPTIMIZER_KEY in lm_checkpoint: + content['base_lrs'] = [ + i['initial_lr'] for i in new_checkpoint['optimizer_states'][0]['optimizer']['param_groups'] + ] + content['last_epoch'] = int(sched['num_steps']) // gbs + content['_last_lr'] = [i['lr'] for i in new_checkpoint['optimizer_states'][0]['optimizer']['param_groups']] + else: + content['base_lrs'] = [sched['max_lr']] + content['last_epoch'] = int(sched['num_steps']) // gbs + content['_step_count'] = int(sched['num_steps']) // gbs + content['verbose'] = False + content['_get_lr_called_within_step'] = False + new_checkpoint[NEW_LR_SCHEDULER] = [content] + + +def load_model(cls, checkpoint, strict, **kwargs): + try: + if 'cfg' in kwargs: + model = ptl_load_state(cls, checkpoint, strict=strict, **kwargs) + else: + cfg = checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY].cfg + + model = cls(cfg=cfg, **kwargs) + for name, module in model.named_parameters(): + if name in checkpoint['state_dict']: + module.data = checkpoint['state_dict'][name] + checkpoint['state_dict'].pop(name) + else: + print(f"Unexpected key: {name} not in checkpoint but in model.") + + for name, buffer in model.named_buffers(): + if name in checkpoint['state_dict']: + buffer.data = checkpoint['state_dict'][name] + checkpoint['state_dict'].pop(name) + + if len(checkpoint['state_dict'].keys()) != 0: + raise RuntimeError( + f"Additional keys: {checkpoint['state_dict'].keys()} in checkpoint but not in model." + ) + # register the artifacts + if cfg.tokenizer.model is not None: + model.register_artifact("tokenizer.tokenizer_model", cfg.tokenizer.model) + if cfg.tokenizer.vocab_file is not None: + model.register_artifact("tokenizer.vocab_file", cfg.tokenizer.vocab_file) + if cfg.tokenizer.merge_file is not None: + model.register_artifact("tokenizer.merge_file", cfg.tokenizer.merge_file) + finally: + cls._set_model_restore_state(is_being_restored=False) + return model + + +def load_from_checkpoint( + cls, + checkpoint_path: str, + map_location: Any = None, + hparams_file: Optional[str] = None, + strict: bool = True, + **kwargs, +): + """ + Loads Megatron_LM checkpoints, convert it, with some maintenance of restoration. + For documentation, please refer to LightningModule.load_from_checkpoin() documentation. + """ + checkpoint = None + try: + cls._set_model_restore_state(is_being_restored=True) + # TODO: replace with proper PTL API + + with pl_legacy_patch(): + if map_location is not None: + old_checkpoint = pl_load(checkpoint_path, map_location=map_location) + else: + old_checkpoint = pl_load(checkpoint_path, map_location=lambda storage, loc: storage) + + total_params = [0] + checkpoint = OrderedDict() + checkpoint['state_dict'] = OrderedDict() + parse_weights( + old_checkpoint['model'], + "", + total_params, + checkpoint['state_dict'], + translator=kwargs['translator'], + mcore_translator=kwargs.get('mcore_translator', None), + ) + print('converted {:.2f}M parameters'.format(total_params[0] / 1e6)) + + if hparams_file is not None: + extension = hparams_file.split(".")[-1] + if extension.lower() == "csv": + hparams = load_hparams_from_tags_csv(hparams_file) + elif extension.lower() in ("yml", "yaml"): + hparams = load_hparams_from_yaml(hparams_file) + else: + raise ValueError(".csv, .yml or .yaml is required for `hparams_file`") + + hparams["on_gpu"] = False + + # overwrite hparams by the given file + checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY] = hparams + + check_point_version = old_checkpoint.get('checkpoint_version', 0) + if check_point_version < 3: + # need to do the transpose of query_key_value variables + if hparams_file is not None: + np = hparams['cfg']['num_attention_heads'] + elif 'config' in old_checkpoint and 'num-attention-heads' in old_checkpoint['config']: + np = old_checkpoint['config']['num-attention-heads'] + + else: + logging.warning("cannot determine the number attention heads") + raise ValueError('need to know number of attention heads') + + if check_point_version == 0: + # 3, np, hn -> np, 3, hn + for key in checkpoint['state_dict']: + if key.find('query_key_value') >= 0: + weight = checkpoint['state_dict'][key] + if len(weight.size()) == 2: + # weight + weight = weight.view(3, np, -1, weight.size()[-1]) + weight = weight.transpose(0, 1).contiguous() + checkpoint['state_dict'][key] = weight.view(-1, weight.size()[-1]) + else: + # biase + weight = weight.view(3, np, -1) + weight = weight.transpose(0, 1).contiguous() + checkpoint['state_dict'][key] = weight.view(-1) + elif check_point_version == 1: + # np, hn, 3 -> np, 3, hn + for key in checkpoint['state_dict']: + if key.find('query_key_value') >= 0: + weight = checkpoint['state_dict'][key] + if len(weight.size()) == 2: + # weight + weight = weight.view(np, -1, 3, weight.size()[-1]) + weight = weight.transpose(1, 2).contiguous() + checkpoint['state_dict'][key] = weight + else: + # biase + weight = weight.view(np, -1, 3) + weight = weight.transpose(1, 2).contiguous() + checkpoint['state_dict'][key] = weight + + # for past checkpoint need to add the new key + if cls.CHECKPOINT_HYPER_PARAMS_KEY not in checkpoint: + checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY] = {} + # override the hparams with values that were passed in + # TODO: can we do this without overriding? + config_kwargs = kwargs.copy() + if 'trainer' in config_kwargs: + config_kwargs.pop('trainer') + checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY].update(config_kwargs) + add_optimizer_state(old_checkpoint, checkpoint) + consumed = None + if 'args' in old_checkpoint and hasattr(old_checkpoint['args'], 'consumed_train_samples'): + consumed = getattr(old_checkpoint['args'], 'consumed_train_samples') + steps = None + if 'iteration' in old_checkpoint: + steps = old_checkpoint['iteration'] + finally: + cls._set_model_restore_state(is_being_restored=False) + logging.warning(f"the checkpoint version is {check_point_version}") + return checkpoint, consumed, steps, check_point_version + + +def megatron_lm_inject_model_parallel_rank(filepath): + """ + Injects tensor/pipeline model parallel ranks into the filepath. + Does nothing if not using model parallelism. + """ + # first make sure filepath does not have rank + filepath = uninject_model_parallel_rank(filepath) + + app_state = AppState() + if app_state.model_parallel_size is not None and app_state.model_parallel_size > 1: + # filepath needs to be updated to include mp_rank + dirname = os.path.dirname(filepath) + basename = os.path.basename(filepath) + if app_state.pipeline_model_parallel_size is None or app_state.pipeline_model_parallel_size == 1: + filepath = f'{dirname}/mp_rank_{app_state.tensor_model_parallel_rank:02d}/{basename}' + else: + filepath = f'{dirname}/mp_rank_{app_state.tensor_model_parallel_rank:02d}_{app_state.pipeline_model_parallel_rank:03d}/{basename}' + return filepath + else: + return filepath + + +def convert(local_rank, rank, world_size, args): + + app_state = AppState() + initialize_model_parallel_for_nemo( + world_size=world_size, + global_rank=rank, + local_rank=local_rank, + tensor_model_parallel_size=args.tensor_model_parallel_size, + pipeline_model_parallel_size=args.pipeline_model_parallel_size, + virtual_pipeline_model_parallel_size=None, + pipeline_model_parallel_split_rank=0, + micro_batch_size=None, + global_batch_size=None, + seed=None, + apex_transformer_log_level=30, + ) + # @chcui we set seed to None to prevent a call to `get_expert_model_parallel_rank` + # which throw an error for nemo init + + # hard set the data parallel rank to 0, otherwiaze it is default to None + app_state.data_parallel_rank = 0 + + # tensor_model_parallel_size = args.tensor_model_parallel_size + num_nodes = world_size // args.gpus_per_node + assert world_size % args.gpus_per_node == 0, "world_size must be divisible by gpus_per_node" + + trainer = Trainer(devices=args.gpus_per_node, accelerator='gpu', num_nodes=num_nodes) + checkpoint_path = megatron_lm_inject_model_parallel_rank( + os.path.join(args.checkpoint_folder, args.checkpoint_name) + ) + logging.info(f"loading checkpoint {checkpoint_path}") + + if args.model_type == 'gpt': + # this dictionary is used to rename the model parameters + name_translate = {} + name_translate['transformer'] = 'encoder' + name_translate['.attention.'] = '.self_attention.' + # nemo megatron doesn't have _for_head key + name_translate['word_embeddings_for_head'] = 'word_embeddings' + name_translate['_norm.'] = '_layernorm.' # alternative layer norm key names + + mcore_translate = None + model_cfg = load_hparams_from_yaml(args.hparams_file).cfg + mcore_output = model_cfg.get("mcore_gpt", False) + if not mcore_output and args.mcore_input: + raise RuntimeError( + "Cannot convert from MCore Megatron-LM to legacy NeMo. " + "Please specify `mcore_gpt: true` in the hparams.yaml file." + ) + if mcore_output and not args.mcore_input: + # convert from legacy Megatron-LM to MCore NeMo. Initialize an mcore translation dict + from scripts.nlp_language_modeling.convert_nemo_gpt_to_mcore import build_key_mapping + + mcore_translate = {} + for k, v in build_key_mapping(model_cfg).items(): + mcore_translate[v] = k + # take into account alternative layer norm key names + mcore_translate[v.replace('_layernorm.', '_norm.')] = k + + checkpoint, consumed, steps, version = load_from_checkpoint( + MegatronGPTModel, + checkpoint_path, + hparams_file=args.hparams_file, + trainer=trainer, + translator=name_translate, + strict=False, + mcore_translator=mcore_translate, + ) + elif args.model_type == 'bert': + # this dictionary is used to rename the model parameters + name_translate = {} + name_translate['transformer'] = 'encoder' + name_translate['.attention.'] = '.self_attention.' + # nemo megatron doesn't have _for_head key + name_translate['word_embeddings_for_head'] = 'word_embeddings' + checkpoint, consumed, steps, version = load_from_checkpoint( + MegatronBertModel, + checkpoint_path, + hparams_file=args.hparams_file, + trainer=trainer, + translator=name_translate, + strict=False, + ) + else: + raise NotImplemented("{} is not supported".format(args.model_type)) + + if torch.distributed.is_initialized(): + torch.distributed.barrier() + + if args.output_ckpt_file_path: + filepath = args.output_ckpt_file_path + base_dir = pathlib.Path(filepath).parent + filename_str = pathlib.Path(filepath).name + suffix = '.ckpt' + content = {} + if consumed is not None: + content['consumed'] = consumed + else: + content['consumed'] = 0 + if steps is not None: + content['steps'] = steps + else: + content['steps'] = 0 + filename = filename_str.format(**content) + suffix + checkpoint_path_output = inject_model_parallel_rank(os.path.join(base_dir, filename)) + trainer.training_type_plugin.checkpoint_io.save_checkpoint(checkpoint, checkpoint_path_output) + logging.info(f'NeMo model checkpoint files saved to: {args.output_ckpt_file_path}') + + if args.nemo_file_path: + if args.model_type == 'gpt': + if mcore_output and not parallel_state.is_initialized(): + parallel_state.initialize_model_parallel( + tensor_model_parallel_size=args.tensor_model_parallel_size, + pipeline_model_parallel_size=args.pipeline_model_parallel_size, + virtual_pipeline_model_parallel_size=None, + pipeline_model_parallel_split_rank=0, + ) + model = load_model(MegatronGPTModel, checkpoint, strict=False, trainer=trainer) + elif args.model_type == 'bert': + model = load_model(MegatronBertModel, checkpoint, strict=False, trainer=trainer) + else: + raise NotImplemented("{} is not supported".format(args.model_type)) + + # verify tensor parallel rank id and pipeline parallel rank id matches + assert app_state.data_parallel_size == 1 + model._save_restore_connector = NLPSaveRestoreConnector() + model.save_to(args.nemo_file_path) + logging.info(f'NeMo model saved to: {args.nemo_file_path}') + + +if __name__ == '__main__': + install_megatron_dependence() + args = get_args() + if args.local_rank == -1: + device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") + rank = args.local_rank + local_rank = rank + world_size = 1 + else: + local_rank, rank, world_size = initialize_distributed(args) + + # make sure the world size is divisible by tensor model parallel_size + assert world_size % args.tensor_model_parallel_size == 0 + + torch.distributed.barrier() + convert(local_rank, rank, world_size, args) + torch.distributed.barrier() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_cal_shape.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_cal_shape.py new file mode 100644 index 0000000..a57a927 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_cal_shape.py @@ -0,0 +1,81 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.plugins.precision import MixedPrecisionPlugin + +from nemo.collections.nlp.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel +from nemo.collections.nlp.modules.common.megatron.mup.shape import make_base_shapes +from nemo.collections.nlp.parts.nlp_overrides import ( + CustomProgressBar, + GradScaler, + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@hydra_runner(config_path="conf", config_name="megatron_retro_mutransfer") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True if megatron_amp_O2 else False, + gradient_as_bucket_view=cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + ) + + if cfg.trainer.precision in [16, '16', 'bf16', '16-mixed', 'bf16-mixed']: + scaler = None + if cfg.trainer.precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + hysteresis=cfg.model.get('hysteresis', 2), + ) + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + if megatron_amp_O2: + plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(MixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + callbacks = [] + # enable_progress_bar is True by default. If cfg.trainer.enable_progress_bar=False, CustomProgressBar is not appended to callbacks + if 'enable_progress_bar' not in cfg.trainer or cfg.trainer.enable_progress_bar: + callbacks.append(CustomProgressBar()) + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=callbacks) + + # hydra interpolation does not work here as the interpolation key is lost when PTL saves hparams + with open_dict(cfg): + cfg.base_model.precision = cfg.trainer.precision + cfg.delta_model.precision = cfg.trainer.precision + + base_model = MegatronRetrievalModel(cfg.base_model, trainer) + delta_model = MegatronRetrievalModel(cfg.delta_model, trainer) + make_base_shapes(base_model, delta_model, savefile=cfg.model.shape_file) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_eval.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_eval.py new file mode 100644 index 0000000..9978bab --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_eval.py @@ -0,0 +1,144 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +from examples.nlp.language_modeling.megatron_gpt_eval import RequestDataSet +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from torch.utils.data import DataLoader + +from nemo.collections.nlp.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel +from nemo.collections.nlp.modules.common.transformer.text_generation import LengthParam, SamplingParam +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector +from nemo.core.config import hydra_runner + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + +""" +This is the script to run RETRO Model text generation. + +Usage: + Assume the model has TP=1, PP=1 + run greedy inference from a nemo file: + python megatron_retro_eval.py \ + trainer.devices=1 \ + trainer.num_nodes=1 \ + trainer.accelerator=gpu \ + trainer.precision=16 \ + inference.tokens_to_generate=128 \ + inference.greedy=True \ + retro_model_file=path_to_retro_nemo_file \ + tensor_model_parallel_size=-1 \ + pipeline_model_parallel_size=-1 \ + retrieval_service.faiss_devices='0' \ + retrieval_service.faiss_index=path_to_faiss_index \ + retrieval_service.retrieval_index=path_to_retrieval_dataset \ + retrieval_service.neighbors=20 +""" + + +@hydra_runner(config_path="conf", config_name="megatron_retro_inference") +def main(cfg) -> None: + trainer = Trainer(strategy=NLPDDPStrategy(), **cfg.trainer) + + model_path = cfg.retro_model_file + + save_restore_connector = NLPSaveRestoreConnector() + + if os.path.isdir(model_path): + save_restore_connector.model_extracted_dir = model_path + + model_cfg = MegatronRetrievalModel.restore_from( + model_path, trainer=trainer, return_config=True, save_restore_connector=save_restore_connector, + ) + + with open_dict(model_cfg): + model_cfg.precision = trainer.precision + model_cfg.sequence_parallel = False + model_cfg.activations_checkpoint_granularity = None + model_cfg.activations_checkpoint_method = None + + if ( + cfg.tensor_model_parallel_size < 0 + or cfg.pipeline_model_parallel_size < 0 + or cfg.get('pipeline_model_parallel_split_rank', -1) < 0 + ): + with open_dict(cfg): + cfg.tensor_model_parallel_size = model_cfg.get('tensor_model_parallel_size', 1) + cfg.pipeline_model_parallel_size = model_cfg.get('pipeline_model_parallel_size', 1) + cfg.pipeline_model_parallel_split_rank = model_cfg.get('pipeline_model_parallel_split_rank', 0) + + model = MegatronRetrievalModel.restore_from( + model_path, trainer=trainer, save_restore_connector=save_restore_connector, override_config_path=model_cfg, + ) + + length_params: LengthParam = { + "max_length": cfg.inference.tokens_to_generate, + "min_length": cfg.inference.min_tokens_to_generate, + } + + sampling_params: SamplingParam = { + "use_greedy": cfg.inference.greedy, + "temperature": cfg.inference.temperature, + "top_k": cfg.inference.top_k, + "top_p": cfg.inference.top_p, + "repetition_penalty": cfg.inference.repetition_penalty, + "add_BOS": cfg.inference.add_BOS, + "all_probs": cfg.inference.all_probs, + "compute_logprob": cfg.inference.compute_logprob, + } + + # check whether the DDP is initialized + if not parallel_state.is_initialized(): + + def dummy(): + return + + if model.trainer.strategy.launcher is not None: + model.trainer.strategy.launcher.launch(dummy, trainer=model.trainer) + model.trainer.strategy.setup_environment() + + config = OmegaConf.to_container(cfg.inference) + retrieval_service = OmegaConf.to_container(cfg.retrieval_service) + model.set_inference_config(config, retrieval_service) + + if not cfg.use_predict_method: + # First method of running text generation, call model.generate method + response = model.generate( + inputs=OmegaConf.to_container(cfg.prompts), + length_params=length_params, + sampling_params=sampling_params, + strategy=model.inference_strategy, + ) + else: + # Second method of running text generation, call trainer.predict + ds = RequestDataSet(OmegaConf.to_container(cfg.prompts)) + request_dl = DataLoader(dataset=ds, batch_size=cfg.inference_batch_size) + response = trainer.predict(model, request_dl) + + print("***************************") + print(response) + print("***************************") + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_fine_tune.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_fine_tune.py new file mode 100644 index 0000000..3fcaec1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_fine_tune.py @@ -0,0 +1,151 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import datetime +import os + +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.callbacks.timer import Timer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.plugins.precision import MixedPrecisionPlugin +from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector + +from nemo.collections.nlp.models.language_modeling.megatron_retro_fine_tune_model import MegatronRetroFinetuneModel +from nemo.collections.nlp.parts.nlp_overrides import ( + CustomProgressBar, + GradScaler, + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, + NLPSaveRestoreConnector, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import StatelessTimer, exp_manager + + +def _modify_config(retro_cfg, cfg, add_cfg_to_tree=False): + """ + This function modifies the original retro pre-training config with attributes from the finetuning config (cfg). + The `add_cfg_to_tree` arg adds `cfg` to the top of the yaml tree which is needed for all `hparams.yaml` files when passed as an arg to `load_from_checkpoint()`. + """ + OmegaConf.set_struct(retro_cfg, True) + with open_dict(retro_cfg): + retro_cfg.megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + retro_cfg.data = cfg.model.data + retro_cfg.precision = cfg.trainer.precision + retro_cfg.optim = cfg.model.optim + retro_cfg.micro_batch_size = cfg.model.micro_batch_size + # This is needed when modifying a hparam file directly to load `.ckpt` files. + # This is not needed to modify the cfg in `.nemo` files. + if add_cfg_to_tree: + OmegaConf.resolve(retro_cfg) + retro_cfg.cfg = retro_cfg + return retro_cfg + + +def load_from_nemo(cls, cfg, trainer, retro_cfg, modify_confg_fn, save_restore_connector): + retro_cfg = modify_confg_fn(retro_cfg, cfg, add_cfg_to_tree=False) + model = cls.restore_from( + restore_path=cfg.model.restore_path, + trainer=trainer, + override_config_path=retro_cfg, + save_restore_connector=save_restore_connector, + ) + return model + + +@hydra_runner(config_path="conf", config_name="megatron_retro_finetune_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + ###### following is the workaround for num_workers=0 issue ##### + # import torch.multiprocessing as mp + # mp.set_start_method("spawn", force=True) + ##################################################### + megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True if megatron_amp_O2 else False, + gradient_as_bucket_view=cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + timeout=datetime.timedelta(seconds=18000), + ) + + if cfg.trainer.precision in [16, '16', '16-mixed', 'bf16', 'bf16-mixed']: + scaler = None + if cfg.trainer.precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + hysteresis=cfg.model.get('hysteresis', 2), + ) + # MixedPrecisionPlugin in PTL >= 2.0 requires precision to be 16-mixed or bf16-mixed + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + if megatron_amp_O2: + plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(MixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + # Set precision None after precision plugins are created as PTL >= 2.1 does not allow both + # precision plugins and precision to exist + cfg.trainer.precision = None + + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + callbacks = [] + # enable_progress_bar is True by default. If cfg.trainer.enable_progress_bar=False, CustomProgressBar is not appended to callbacks + if 'enable_progress_bar' not in cfg.trainer or cfg.trainer.enable_progress_bar: + callbacks.append(CustomProgressBar()) + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=callbacks) + exp_manager(trainer, cfg.exp_manager) + + logging.info(f'Resuming training from checkpoint: {trainer.ckpt_path}') + + # Override timer callback to a stateless one + for idx, callback in enumerate(trainer.callbacks): + if isinstance(callback, Timer): + trainer.callbacks[idx] = StatelessTimer(cfg.trainer.max_time,) + + # load existing or init new soft prompt GPT model + if cfg.model.get("restore_path", None): + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.model.restore_path): + save_restore_connector.model_extracted_dir = cfg.model.restore_path + + model_cfg = MegatronRetroFinetuneModel.restore_from( + restore_path=cfg.model.restore_path, + trainer=trainer, + return_config=True, + save_restore_connector=save_restore_connector, + ) + # hydra interpolation does not work here as the interpolation key is lost when PTL saves hparams + model = load_from_nemo( + MegatronRetroFinetuneModel, + cfg, + trainer, + model_cfg, + modify_confg_fn=_modify_config, + save_restore_connector=save_restore_connector, + ) + else: + model = MegatronRetroFinetuneModel(cfg.model, trainer=trainer) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_mutransfer_pretrain.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_mutransfer_pretrain.py new file mode 100644 index 0000000..af6e220 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_mutransfer_pretrain.py @@ -0,0 +1,90 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.plugins.precision import MixedPrecisionPlugin +from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector + +from nemo.collections.nlp.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel +from nemo.collections.nlp.modules.common.megatron.mup.optim import MuAdam, MuAdamW +from nemo.collections.nlp.parts.nlp_overrides import ( + CustomProgressBar, + GradScaler, + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, +) +from nemo.core.config import hydra_runner +from nemo.core.config.optimizers import AdamParams, AdamWParams +from nemo.core.optim.optimizers import register_optimizer +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="megatron_retro_mutransfer") +def main(cfg) -> None: + register_optimizer("muadamw", MuAdamW, AdamWParams()) + register_optimizer("muadam", MuAdam, AdamParams()) + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True if megatron_amp_O2 else False, + gradient_as_bucket_view=cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + ) + + if cfg.trainer.precision in [16, '16', 'bf16', '16-mixed', 'bf16-mixed']: + scaler = None + if cfg.trainer.precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + hysteresis=cfg.model.get('hysteresis', 2), + ) + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + if megatron_amp_O2: + plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(MixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + # Set precision None after precision plugins are created as PTL >= 2.1 does not allow both + # precision plugins and precision to exist + cfg.trainer.precision = None + + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + callbacks = [] + # enable_progress_bar is True by default. If cfg.trainer.enable_progress_bar=False, CustomProgressBar is not appended to callbacks + if 'enable_progress_bar' not in cfg.trainer or cfg.trainer.enable_progress_bar: + callbacks.append(CustomProgressBar()) + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=callbacks) + + exp_manager(trainer, cfg.exp_manager) + + # resume_from_checkpoint = uninject_model_parallel_rank(resume_from_checkpoint) + logging.info(f'Resuming training from checkpoint: {trainer.ckpt_path}') + + model = MegatronRetrievalModel(cfg.model, trainer) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_pretraining.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_pretraining.py new file mode 100644 index 0000000..c84656d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_retro_pretraining.py @@ -0,0 +1,102 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.plugins.precision import MixedPrecisionPlugin +from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector + +from nemo.collections.nlp.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel +from nemo.collections.nlp.modules.common.megatron.megatron_init import initialize_model_parallel_for_nemo +from nemo.collections.nlp.parts.nlp_overrides import ( + CustomProgressBar, + GradScaler, + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, + NLPSaveRestoreConnector, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="megatron_retro_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True if megatron_amp_O2 else False, + gradient_as_bucket_view=cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + ) + + if cfg.trainer.precision in [16, '16', 'bf16', '16-mixed', 'bf16-mixed']: + scaler = None + if cfg.trainer.precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + hysteresis=cfg.model.get('hysteresis', 2), + ) + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + if megatron_amp_O2: + plugins.append(MegatronHalfPrecisionPlugin(plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(MixedPrecisionPlugin(plugin_precision, device='cuda', scaler=scaler)) + # Set precision None after precision plugins are created as PTL >= 2.1 does not allow both + # precision plugins and precision to exist + cfg.trainer.precision = None + + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + callbacks = [] + # enable_progress_bar is True by default. If cfg.trainer.enable_progress_bar=False, CustomProgressBar is not appended to callbacks + if 'enable_progress_bar' not in cfg.trainer or cfg.trainer.enable_progress_bar: + callbacks.append(CustomProgressBar()) + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=callbacks) + + exp_manager(trainer, cfg.exp_manager) + + # resume_from_checkpoint = uninject_model_parallel_rank(resume_from_checkpoint) + logging.info(f'Resuming training from checkpoint: {trainer.ckpt_path}') + + # load existing nemo retro model + if cfg.get("restore_from_path", None) is not None: + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.restore_from_path): + save_restore_connector.model_extracted_dir = cfg.restore_from_path + model = MegatronRetrievalModel.restore_from( + restore_path=cfg.restore_from_path, + trainer=trainer, + override_config_path=cfg.model, + save_restore_connector=save_restore_connector, + strict=False, + ) + else: + model = MegatronRetrievalModel(cfg.model, trainer) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_eval.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_eval.py new file mode 100644 index 0000000..0b6ea54 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_eval.py @@ -0,0 +1,145 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import os +from argparse import ArgumentParser + +import torch +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning.trainer.trainer import Trainer +from torch.utils.data import DataLoader + +from nemo.collections.nlp.data.language_modeling.megatron.request_dataset import T5RequestDataset +from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model +from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector +from nemo.utils.app_state import AppState + +assert torch.cuda.is_available() + + +def main(): + parser = ArgumentParser() + parser.add_argument("--model_file", type=str, default="", required=True, help="Pass path to model's .nemo file") + parser.add_argument( + "--prompt", type=str, default="", required=True, help="Prompt for the model (a text to complete)" + ) + parser.add_argument( + "--tokens_to_generate", type=int, default="16", required=False, help="How many tokens to add to prompt" + ) + parser.add_argument( + "--tensor_model_parallel_size", type=int, default=-1, required=False, + ) + parser.add_argument( + "--pipeline_model_parallel_size", type=int, default=-1, required=False, + ) + parser.add_argument( + "--pipeline_model_parallel_split_rank", type=int, default=-1, required=False, + ) + parser.add_argument("--precision", default="16", type=str, help="PyTorch Lightning Trainer precision flag") + parser.add_argument("--decoder_starts_with_pad", action="store_true", help="Decoder starts with pad token") + parser.add_argument("--add_eos_to_encoder_input", action="store_true", help="Encoder input ends with EOS token") + args = parser.parse_args() + + # cast precision to int if 32 or 16 + if args.precision in ["32", "16"]: + args.precision = int(float(args.precision)) + + if ( + args.tensor_model_parallel_size < 0 + or args.pipeline_model_parallel_size < 0 + or args.pipeline_model_parallel_split_rank < 0 + ): + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(args.model_file): + save_restore_connector.model_extracted_dir = args.model_file + + model_config = MegatronT5Model.restore_from( + restore_path=args.model_file, + trainer=Trainer(strategy=NLPDDPStrategy()), + return_config=True, + save_restore_connector=save_restore_connector, + ) + + args.tensor_model_parallel_size = model_config.get('tensor_model_parallel_size', 1) + args.pipeline_model_parallel_size = model_config.get('pipeline_model_parallel_size', 1) + args.pipeline_model_parallel_split_rank = model_config.get('pipeline_model_parallel_split_rank', 0) + + # trainer required for restoring model parallel models + trainer = Trainer( + strategy=NLPDDPStrategy(), + devices=args.tensor_model_parallel_size * args.pipeline_model_parallel_size, + accelerator='gpu', + precision=args.precision, + ) + + app_state = AppState() + if args.tensor_model_parallel_size > 1 or args.pipeline_model_parallel_size > 1: + app_state.model_parallel_size = args.tensor_model_parallel_size * args.pipeline_model_parallel_size + ( + app_state.tensor_model_parallel_rank, + app_state.pipeline_model_parallel_rank, + app_state.model_parallel_size, + app_state.data_parallel_size, + app_state.pipeline_model_parallel_split_rank, + app_state.virtual_pipeline_model_parallel_rank, + ) = fake_initialize_model_parallel( + world_size=app_state.model_parallel_size, + rank=trainer.global_rank, + tensor_model_parallel_size_=args.tensor_model_parallel_size, + pipeline_model_parallel_size_=args.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank_=args.pipeline_model_parallel_split_rank, + ) + + model_cfg = MegatronT5Model.restore_from( + restore_path=args.model_file, + trainer=trainer, + save_restore_connector=NLPSaveRestoreConnector(), + return_config=True, + ) + OmegaConf.set_struct(model_cfg, True) + with open_dict(model_cfg): + model_cfg.precision = trainer.precision + + model = MegatronT5Model.restore_from( + restore_path=args.model_file, + trainer=trainer, + save_restore_connector=NLPSaveRestoreConnector(), + override_config_path=model_cfg, + ) + model.freeze() + model.training = False + + request = { + "prompt": args.prompt, + "tokens_to_generate": args.tokens_to_generate, + "bos_id": model.tokenizer.pad_id if args.decoder_starts_with_pad else model.tokenizer.bos_id, + "add_eos_to_encoder_input": args.add_eos_to_encoder_input, + } + + dataset = T5RequestDataset(request, model.tokenizer) + + request_dl = DataLoader(dataset) + + response = trainer.predict(model, request_dl) + + print("***************************") + print(response) + print(response[0]['completion']['text']) + print("***************************") + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_lm_adaptation_finetune.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_lm_adaptation_finetune.py new file mode 100644 index 0000000..9e392d9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_lm_adaptation_finetune.py @@ -0,0 +1,139 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.callbacks import ModelSummary +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector + +from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model +from nemo.collections.nlp.parts.nlp_overrides import ( + CustomProgressBar, + GradScaler, + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, + NLPSaveRestoreConnector, + PipelineMixedPrecisionPlugin, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="megatron_t5_lm_adaptation_finetune") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True, # we don't use DDP for async grad allreduce + gradient_as_bucket_view=cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + ) + if cfg.trainer.precision in [16, '16', '16-mixed', 'bf16', 'bf16-mixed']: + scaler = None + if cfg.trainer.precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + hysteresis=cfg.model.get('hysteresis', 2), + ) + # MixedPrecisionPlugin in PTL >= 2.0 requires precision to be 16-mixed or bf16-mixed + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + if megatron_amp_O2: + plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(PipelineMixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + # Set precision None after precision plugins are created as PTL >= 2.1 does not allow both + # precision plugins and precision to exist + cfg.trainer.precision = None + + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + callbacks = [ModelSummary(max_depth=3)] + # enable_progress_bar is True by default. If cfg.trainer.enable_progress_bar=False, CustomProgressBar is not appended to callbacks + if 'enable_progress_bar' not in cfg.trainer or cfg.trainer.enable_progress_bar: + callbacks.append(CustomProgressBar()) + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=callbacks) + exp_manager(trainer, cfg.exp_manager) + + # update resume from checkpoint found by exp_manager + if cfg.model.resume_from_checkpoint is not None: + trainer.ckpt_path = cfg.model.resume_from_checkpoint + logging.info(f'Resuming training from checkpoint: {trainer.ckpt_path}') + + if hasattr(cfg.model, 'pretrained_model_path') and cfg.model.pretrained_model_path is not None: + pretrained_cfg = MegatronT5Model.restore_from( + cfg.model.pretrained_model_path, trainer=trainer, return_config=True + ) + OmegaConf.set_struct(pretrained_cfg, True) + with open_dict(pretrained_cfg): + + # Override data from T5 to Prefix-LM + encoder_seq_length = pretrained_cfg.data.seq_length + decoder_seq_length = ( + pretrained_cfg.data.seq_length + ) # Set decoder seq length to be enoder seq length for prefix-lm + pretrained_cfg.data = cfg.model.data + pretrained_cfg.data.seq_length = encoder_seq_length + pretrained_cfg.data.seq_length_dec = ( + decoder_seq_length - 1 + ) # -1 is to account for the addition of and and right shifting to create targets. + + # Override fusion params. + pretrained_cfg.masked_softmax_fusion = cfg.model.masked_softmax_fusion + pretrained_cfg.bias_dropout_add_fusion = cfg.model.bias_dropout_add_fusion + pretrained_cfg.bias_gelu_fusion = cfg.model.bias_gelu_fusion + + # Override dropout + if cfg.model.hidden_dropout is not None: + pretrained_cfg.hidden_dropout = cfg.model.hidden_dropout + + if cfg.model.attention_dropout is not None: + pretrained_cfg.attention_dropout = cfg.model.attention_dropout + + # Override precision + pretrained_cfg.precision = trainer.precision # Set above from trainer.precision + + # Override micro/global batch + pretrained_cfg.micro_batch_size = cfg.model.micro_batch_size + pretrained_cfg.global_batch_size = cfg.model.global_batch_size + + # O2 AMP + pretrained_cfg.megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + + # Optimizer overrides. + pretrained_cfg.optim = cfg.model.optim + + model = MegatronT5Model.restore_from( + cfg.model.pretrained_model_path, + trainer=trainer, + override_config_path=pretrained_cfg, + save_restore_connector=NLPSaveRestoreConnector(), + ) + else: + raise ValueError(f'No pretrained model path specified or does not exist {cfg.model.pretrained_model_path}') + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_pretraining.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_pretraining.py new file mode 100644 index 0000000..dd5dde2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_pretraining.py @@ -0,0 +1,38 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from omegaconf.omegaconf import OmegaConf, open_dict + +from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronT5TrainerBuilder +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="megatron_t5_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronT5TrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model = MegatronT5Model(cfg.model, trainer) + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_seq2seq_eval.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_seq2seq_eval.py new file mode 100644 index 0000000..ba8ea64 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_seq2seq_eval.py @@ -0,0 +1,143 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from megatron_t5_seq2seq_finetune import load_from_checkpoint_dir, load_from_nemo, validate_checkpoint_loading_args +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.plugins.precision import MixedPrecisionPlugin + +from nemo.collections.nlp.models.language_modeling.megatron_glue_model import MegatronT5GLUEModel +from nemo.collections.nlp.models.language_modeling.megatron_t0_model import MegatronT0Model +from nemo.collections.nlp.models.language_modeling.megatron_t5_sft_model import MegatronT5SFTModel +from nemo.collections.nlp.parts.nlp_overrides import GradScaler, MegatronHalfPrecisionPlugin, NLPDDPStrategy +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +def _modify_config(t5_cfg, cfg, add_cfg_to_tree=False): + """ + This function modifies the original t5 pre-training config (t5_cfg) with attributes from the finetuning config (cfg). + The `add_cfg_to_tree` arg adds `cfg` to the top of the yaml tree which is needed for all `hparams.yaml` files when passed as an arg to `load_from_checkpoint()`. + """ + OmegaConf.set_struct(t5_cfg, True) + with open_dict(t5_cfg): + t5_cfg.precision = cfg.trainer.precision + # Overwrite data configs + if cfg.model.data.validation_ds.get('src_file_name', None) is not None: + logging.info( + 'Found validation_ds.src_file_name in the config file. Overriding the finetuned model config file with the values from the new config file.' + ) + t5_cfg.data.validation_ds.src_file_name = cfg.model.data.validation_ds.src_file_name + if cfg.model.data.validation_ds.get('tgt_file_name', None) is not None: + logging.info( + 'Found validation_ds.tgt_file_name in the config file. Overriding the finetuned model config file with the values from the new config file.' + ) + t5_cfg.data.validation_ds.tgt_file_name = cfg.model.data.validation_ds.tgt_file_name + + if "write_predictions_to_file" in cfg.model.data.validation_ds: + t5_cfg.data.validation_ds.write_predictions_to_file = ( + cfg.model.data.validation_ds.write_predictions_to_file + ) + if "output_file_path_prefix" in cfg.model.data.validation_ds: + t5_cfg.data.validation_ds.output_file_path_prefix = cfg.model.data.validation_ds.output_file_path_prefix + + t5_cfg.data.validation_ds.micro_batch_size = cfg.model.data.validation_ds.micro_batch_size + t5_cfg.data.validation_ds.global_batch_size = cfg.model.data.validation_ds.global_batch_size + + # This is needed when modifying a hparam file directly to load `.ckpt` files. + # This is not needed to modify the cfg in `.nemo` files. + if add_cfg_to_tree: + OmegaConf.resolve(t5_cfg) + t5_cfg.cfg = t5_cfg + + return t5_cfg + + +@hydra_runner(config_path="conf", config_name="megatron_t5_config_finetune_glue_eval") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True, + gradient_as_bucket_view=cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + ) + if cfg.trainer.precision in [16, '16', '16-mixed', 'bf16', 'bf16-mixed']: + scaler = None + if cfg.trainer.precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + hysteresis=cfg.model.get('hysteresis', 2), + ) + # MixedPrecisionPlugin in PTL >= 2.0 requires precision to be 16-mixed or bf16-mixed + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + if megatron_amp_O2: + plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(MixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + # Set precision None after precision plugins are created as PTL >= 2.1 does not allow both + # precision plugins and precision to exist + cfg.trainer.precision = None + + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer) + + exp_manager(trainer, cfg.exp_manager) + + if hasattr(cfg.model.data.validation_ds, 'task_name'): + if cfg.model.restore_from_path: + t5_cfg = MegatronT5GLUEModel.restore_from( + restore_path=cfg.model.restore_from_path, trainer=trainer, return_config=True + ) + model = load_from_nemo(MegatronT5GLUEModel, cfg, trainer, t5_cfg, modify_confg_fn=_modify_config) + else: + validate_checkpoint_loading_args(cfg.model.pretrained_checkpoint) + model = load_from_checkpoint_dir(MegatronT5GLUEModel, cfg, trainer, modify_confg_fn=_modify_config) + elif hasattr(cfg.model.data.validation_ds, 'file_names'): + if cfg.model.restore_from_path: + t5_cfg = MegatronT0Model.restore_from( + restore_path=cfg.model.restore_from_path, trainer=trainer, return_config=True + ) + model = load_from_nemo(MegatronT0Model, cfg, trainer, t5_cfg, modify_confg_fn=_modify_config) + else: + validate_checkpoint_loading_args(cfg.model.pretrained_checkpoint) + model = load_from_checkpoint_dir(MegatronT0Model, cfg, trainer, modify_confg_fn=_modify_config) + else: + if cfg.model.restore_from_path: + t5_cfg = MegatronT5SFTModel.restore_from( + restore_path=cfg.model.restore_from_path, trainer=trainer, return_config=True + ) + model = load_from_nemo(MegatronT5SFTModel, cfg, trainer, t5_cfg, modify_confg_fn=_modify_config) + else: + validate_checkpoint_loading_args(cfg.model.pretrained_checkpoint) + model = load_from_checkpoint_dir(MegatronT5SFTModel, cfg, trainer, modify_confg_fn=_modify_config) + + model.freeze() + trainer.validate(model) + if hasattr(cfg.model.data, 'test_ds'): + trainer.test(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_seq2seq_finetune.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_seq2seq_finetune.py new file mode 100644 index 0000000..3cd4d74 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/megatron_t5_seq2seq_finetune.py @@ -0,0 +1,232 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import tempfile + +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector + +from nemo.collections.nlp.models.language_modeling.megatron_glue_model import MegatronT5GLUEModel +from nemo.collections.nlp.models.language_modeling.megatron_t0_model import MegatronT0Model +from nemo.collections.nlp.models.language_modeling.megatron_t5_sft_model import MegatronT5SFTModel +from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel +from nemo.collections.nlp.parts.nlp_overrides import ( + CustomProgressBar, + GradScaler, + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, + NLPSaveRestoreConnector, + PipelineMixedPrecisionPlugin, +) +from nemo.core.config import hydra_runner +from nemo.utils import AppState, logging +from nemo.utils.exp_manager import exp_manager +from nemo.utils.model_utils import inject_model_parallel_rank + +mp.set_start_method("spawn", force=True) + + +def _modify_config(t5_cfg, cfg, add_cfg_to_tree=False): + """ + This function modifies the original t5 pre-training config (t5_cfg) with attributes from the finetuning config (cfg). + The `add_cfg_to_tree` arg adds `cfg` to the top of the yaml tree which is needed for all `hparams.yaml` files when passed as an arg to `load_from_checkpoint()`. + """ + OmegaConf.set_struct(t5_cfg, True) + with open_dict(t5_cfg): + t5_cfg.megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + if hasattr(t5_cfg, 'encoder') and hasattr(t5_cfg, 'decoder'): + t5_cfg.encoder.masked_softmax_fusion = False + t5_cfg.decoder.masked_softmax_fusion = False + t5_cfg.encoder.hidden_dropout = cfg.model.get('hidden_dropout', 0.1) + t5_cfg.decoder.hidden_dropout = cfg.model.get('hidden_dropout', 0.1) + if hasattr(t5_cfg.encoder, 'ffn_dropout'): + t5_cfg.encoder.ffn_dropout = cfg.model.get('ffn_dropout', 0.1) + if hasattr(t5_cfg.decoder, 'ffn_dropout'): + t5_cfg.decoder.ffn_dropout = cfg.model.get('ffn_dropout', 0.1) + + if hasattr(cfg.model, 'encoder'): + if hasattr(cfg.model.encoder, 'position_embedding_type'): + t5_cfg.encoder.position_embedding_type = cfg.model.encoder.position_embedding_type + if hasattr(cfg.model.encoder, 'use_flash_attention'): + t5_cfg.encoder.use_flash_attention = cfg.model.encoder.use_flash_attention + if hasattr(cfg.model.encoder, 'attention_dropout'): + t5_cfg.encoder.attention_dropout = cfg.model.encoder.attention_dropout + if hasattr(cfg.model, 'decoder'): + if hasattr(cfg.model.decoder, 'position_embedding_type'): + t5_cfg.decoder.position_embedding_type = cfg.model.decoder.position_embedding_type + if hasattr(cfg.model.decoder, 'use_flash_attention'): + t5_cfg.decoder.use_flash_attention = cfg.model.decoder.use_flash_attention + if hasattr(cfg.model.decoder, 'attention_dropout'): + t5_cfg.decoder.attention_dropout = cfg.model.decoder.attention_dropout + else: + t5_cfg.hidden_dropout = cfg.model.get('hidden_dropout', 0.1) + t5_cfg.attention_dropout = cfg.model.get('attention_dropout', 0.1) + t5_cfg.masked_softmax_fusion = False + t5_cfg.data = cfg.model.data + t5_cfg.precision = cfg.trainer.precision + t5_cfg.optim = cfg.model.optim + t5_cfg.micro_batch_size = cfg.model.data.train_ds.micro_batch_size + t5_cfg.global_batch_size = cfg.model.data.train_ds.global_batch_size + # XNLI has eval languages in the yaml config. + if hasattr(cfg.model, 'eval_languages'): + t5_cfg.eval_languages = cfg.model.eval_languages + + # This is needed when modifying a hparam file directly to load `.ckpt` files. + # This is not needed to modify the cfg in `.nemo` files. + if add_cfg_to_tree: + OmegaConf.resolve(t5_cfg) + t5_cfg.cfg = t5_cfg + + return t5_cfg + + +def load_from_nemo(cls, cfg, trainer, t5_cfg, modify_confg_fn): + t5_cfg = modify_confg_fn(t5_cfg, cfg, add_cfg_to_tree=False) + model = cls.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + override_config_path=t5_cfg, + save_restore_connector=NLPSaveRestoreConnector(), + ) + return model + + +def load_from_checkpoint_dir(cls, cfg, trainer, modify_confg_fn): + app_state = AppState() + if cfg.model.tensor_model_parallel_size > 1 or cfg.model.pipeline_model_parallel_size > 1: + app_state.model_parallel_size = cfg.model.tensor_model_parallel_size * cfg.model.pipeline_model_parallel_size + app_state.tensor_model_parallel_size = cfg.model.tensor_model_parallel_size + app_state.pipeline_model_parallel_size = cfg.model.pipeline_model_parallel_size + ( + app_state.tensor_model_parallel_rank, + app_state.pipeline_model_parallel_rank, + app_state.model_parallel_size, + app_state.data_parallel_size, + app_state.pipeline_model_parallel_split_rank, + app_state.virtual_pipeline_model_parallel_rank, + ) = fake_initialize_model_parallel( + world_size=app_state.model_parallel_size, + rank=trainer.global_rank, + tensor_model_parallel_size_=cfg.model.tensor_model_parallel_size, + pipeline_model_parallel_size_=cfg.model.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank_=cfg.model.pipeline_model_parallel_split_rank, + ) + checkpoint_path = inject_model_parallel_rank( + os.path.join(cfg.model.pretrained_checkpoint.checkpoint_dir, cfg.model.pretrained_checkpoint.checkpoint_name) + ) + hparams_file = OmegaConf.load(cfg.model.pretrained_checkpoint.hparams_file) + t5_cfg = modify_confg_fn(hparams_file.cfg, cfg, add_cfg_to_tree=True) + with tempfile.NamedTemporaryFile(suffix='.yaml') as f: + OmegaConf.save(config=t5_cfg, f=f.name) + model = cls.load_from_checkpoint(checkpoint_path=checkpoint_path, trainer=trainer, hparams_file=f.name,) + return model + + +def validate_checkpoint_loading_args(cfg): + if cfg.checkpoint_dir is None or not os.path.isdir(cfg.checkpoint_dir): + raise ValueError(f'Checkpoint directory {cfg.checkpoint_dir} does not exist or is not a directory.') + if cfg.checkpoint_name is None: + raise ValueError(f'Checkpoint name {cfg.checkpoint_name} is not valid.') + if cfg.hparams_file is None or not os.path.isfile(cfg.hparams_file): + raise ValueError(f'Hparams file {cfg.hparams_file} does not exist or is not a file.') + + +@hydra_runner(config_path="conf", config_name="megatron_t5_config_finetune_glue_mnli") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True, + gradient_as_bucket_view=cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + ) + if cfg.trainer.precision in [16, '16', 'bf16', '16-mixed', 'bf16-mixed']: + scaler = None + if cfg.trainer.precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + hysteresis=cfg.model.get('hysteresis', 2), + ) + # MixedPrecisionPlugin in PTL >= 2.0 requires precision to be 16-mixed or bf16-mixed + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + if megatron_amp_O2: + plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(PipelineMixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + # Set precision None after precision plugins are created as PTL >= 2.1 does not allow both + # precision plugins and precision to exist + cfg.trainer.precision = None + + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + callbacks = [] + # enable_progress_bar is True by default. If cfg.trainer.enable_progress_bar=False, CustomProgressBar is not appended to callbacks + if 'enable_progress_bar' not in cfg.trainer or cfg.trainer.enable_progress_bar: + callbacks.append(CustomProgressBar()) + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=callbacks) + + exp_manager(trainer, cfg.exp_manager) + + # update resume from checkpoint found by exp_manager + if cfg.model.resume_from_checkpoint is not None: + trainer.ckpt_path = cfg.model.resume_from_checkpoint + logging.info(f'Resuming training from checkpoint: {trainer.ckpt_path}') + + if hasattr(cfg.model.data.train_ds, 'task_name'): + if cfg.model.restore_from_path: + t5_cfg = MegatronT5GLUEModel.restore_from( + restore_path=cfg.model.restore_from_path, trainer=trainer, return_config=True + ) + model = load_from_nemo(MegatronT5GLUEModel, cfg, trainer, t5_cfg, modify_confg_fn=_modify_config) + else: + validate_checkpoint_loading_args(cfg.model.pretrained_checkpoint) + model = load_from_checkpoint_dir(MegatronT5GLUEModel, cfg, trainer, modify_confg_fn=_modify_config) + elif hasattr(cfg.model.data.train_ds, 'file_names'): + if cfg.model.restore_from_path: + t5_cfg = MegatronT0Model.restore_from( + restore_path=cfg.model.restore_from_path, trainer=trainer, return_config=True + ) + model = load_from_nemo(MegatronT0Model, cfg, trainer, t5_cfg, modify_confg_fn=_modify_config) + else: + validate_checkpoint_loading_args(cfg.model.pretrained_checkpoint) + model = load_from_checkpoint_dir(MegatronT0Model, cfg, trainer, modify_confg_fn=_modify_config) + else: + if cfg.model.restore_from_path: + t5_cfg = MegatronT5SFTModel.restore_from( + restore_path=cfg.model.restore_from_path, trainer=trainer, return_config=True + ) + model = load_from_nemo(MegatronT5SFTModel, cfg, trainer, t5_cfg, modify_confg_fn=_modify_config) + else: + validate_checkpoint_loading_args(cfg.model.pretrained_checkpoint) + model = load_from_checkpoint_dir(MegatronT5SFTModel, cfg, trainer, modify_confg_fn=_modify_config) + + trainer.fit(model) + trainer.validate(model) + if hasattr(cfg.model.data, 'test_ds'): + trainer.test(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/transformer_lm.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/transformer_lm.py new file mode 100644 index 0000000..caaa0e0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/transformer_lm.py @@ -0,0 +1,35 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models.language_modeling import TransformerLMModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="transformer_lm_config") +def main(cfg: DictConfig) -> None: + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + transformer_lm = TransformerLMModel(cfg.model, trainer=trainer) + trainer.fit(transformer_lm) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_gpt_finetuning_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_gpt_finetuning_config.yaml new file mode 100644 index 0000000..40347f3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_gpt_finetuning_config.yaml @@ -0,0 +1,228 @@ +name: megatron_gpt_peft_${model.peft.peft_scheme}_tuning + +trainer: + devices: 1 + accelerator: gpu + num_nodes: 1 + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 9999 + max_steps: 20000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 # frequency with which training steps are logged + val_check_interval: 200 # If is an int n > 1, will run val every n training steps, if a float 0.0 - 1.0 will run val every epoch fraction, e.g. 0.25 will run val every quarter epoch + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: validation_${model.data.validation_ds.metric.name} + save_top_k: 1 + mode: min + save_nemo_on_train_end: True + filename: '${name}--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{consumed_samples}' + model_parallel_size: ${model.tensor_model_parallel_size} + always_save_nemo: False + save_best_model: True + create_early_stopping_callback: True + early_stopping_callback_params: + monitor: "val_loss" + mode: "min" + min_delta: 0.001 + patience: 10 + verbose: True + strict: False # Should be False to avoid a runtime error where EarlyStopping says monitor is unavailable, which sometimes happens with resumed training. + +model: + seed: 1234 + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + + global_batch_size: 128 + micro_batch_size: 4 + restore_from_path: ??? # Path to an existing .nemo model you wish to add new tasks to or run inference with + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + save_nemo_on_validation_end: False # Saves an inference ready .nemo file every time a checkpoint is saved during training. + sync_batch_comm: False + megatron_amp_O2: False + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Activation Checkpoint + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null # not used with 'selective' + activations_checkpoint_layers_per_pipeline: null + answer_only_loss: True + gradient_as_bucket_view: False + + hidden_dropout: 0.0 + attention_dropout: 0.0 + ffn_dropout: 0.0 + + # FSDP + fsdp: False # Enable training with torch FSDP. + fsdp_sharding_strategy: 'full' # Method to shard model states. Available options are 'full', 'hybrid', and 'grad'. + fsdp_grad_reduce_dtype: 'fp32' # Gradient reduction data type. + fsdp_sharded_checkpoint: False # Store and load FSDP shared checkpoint. + fsdp_use_orig_params: False # Set to True to use FSDP for specific peft scheme. + + peft: + peft_scheme: "adapter" # can be either adapter,ia3, or ptuning + restore_from_path: null + + # Used for adapter peft training + adapter_tuning: + type: 'parallel_adapter' # this should be either 'parallel_adapter' or 'linear_adapter' + adapter_dim: 32 + adapter_dropout: 0.0 + norm_position: 'pre' # This can be set to 'pre', 'post' or null, 'pre' is normally what is used. + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + norm_type: 'mixedfusedlayernorm' # IGNORED if layer_adapter is used, options are ['layernorm', 'mixedfusedlayernorm'] + layer_selection: null # selects in which layers to add adapters, e.g. [1,12] will add adapters to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + lora_tuning: + target_modules: ['attention_qkv'] # this can either be 'attention_qkv','attention_dense','mlp_fc1','mlp_fc2', attention (qkv & dense), mlp (fc1 & fc2) + adapter_dim: 32 + alpha: ${model.peft.lora_tuning.adapter_dim} + adapter_dropout: 0.0 + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + layer_selection: null # selects in which layers to add lora adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + # Used for p-tuning peft training + p_tuning: + virtual_tokens: 10 # The number of virtual tokens the prompt encoder should add at the start of the sequence + bottleneck_dim: 1024 # the size of the prompt encoder mlp bottleneck + embedding_dim: 1024 # the size of the prompt encoder embeddings + init_std: 0.023 + + ia3_tuning: + layer_selection: null # selects in which layers to add ia3 adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + + selective_tuning: + tunable_base_param_names: ["self_attention", "word_embeddings"] # TODO: regex support @adithyre + + data: + train_ds: + # Example of how to specify paths to multiple datasets + # file_names: + # - /path/to/squad.jsonl + # - /path/to/mnli.jsonl + # - /path/to/boolq.jsonl + # Example of how each dataset is formatted + # {'input': 'John von Neumann\nVon Neumann made fundamental contributions .... Q: What did the math of artificial viscosity do?', 'output': 'smoothed the shock transition without sacrificing basic physics'} + file_names: ??? # Path to a list of JSONL files corresponding to the source data. + global_batch_size: ${model.global_batch_size} + micro_batch_size: ${model.micro_batch_size} + shuffle: True + num_workers: 0 + memmap_workers: 2 + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: True + # Example of how to specify concat_sampling_probabilities + # concat_sampling_probabilities: + # - 0.5 + # - 0.25 + # - 0.25 + concat_sampling_probabilities: null # When providing a list of datasets, this arg defines the sampling probabilities from each dataset when strategy='random' + label_key: 'output' + add_eos: True + add_sep: False + add_bos: False + truncation_field: "input" # # Can be multiple keys separated with ',' Options: keys in prompt_template + index_mapping_dir: null # Path to a directory to write index mapping files. + prompt_template: "{input} {output}" # fstring to use for assistant prompt. Example: "Q: {input}\nA: {output}" + truncation_method: 'right' # Truncation from which position, Options: ['left', 'right'] + validation_ds: + file_names: ??? # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds. + names: null # Names of the corresponding datasets used to log metrics. + global_batch_size: ${model.global_batch_size} + micro_batch_size: ${model.micro_batch_size} + shuffle: False + num_workers: 0 + memmap_workers: ${model.data.train_ds.memmap_workers} + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: False + label_key: ${model.data.train_ds.label_key} + add_eos: ${model.data.train_ds.add_eos} + add_sep: ${model.data.train_ds.add_sep} + add_bos: ${model.data.train_ds.add_bos} + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + truncation_field: ${model.data.train_ds.truncation_field} # Options: keys in prompt_template + index_mapping_dir: null # Path to a directory to write index mapping files. + prompt_template: ${model.data.train_ds.prompt_template} # fstring to use for assistant prompt. Example: "Q: {input}\nA: {output}" + tokens_to_generate: 32 # decide how many tokens we want to generate to evaluate performance with string metrics + truncation_method: 'right' # Truncation from which position, Options: ['left', 'right'] + metric: + name: "loss" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss'] + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + test_ds: + file_names: null # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds. + names: null # Names of the corresponding datasets used to log metrics. + global_batch_size: ${model.global_batch_size} + micro_batch_size: ${model.micro_batch_size} + shuffle: False + num_workers: 0 + memmap_workers: ${model.data.train_ds.memmap_workers} + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: False + label_key: ${model.data.train_ds.label_key} + add_eos: ${model.data.train_ds.add_eos} + add_sep: ${model.data.train_ds.add_sep} + add_bos: ${model.data.train_ds.add_bos} + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + truncation_field: ${model.data.train_ds.truncation_field} # Options: keys in prompt_template + index_mapping_dir: null # Path to a directory to write index mapping files. + prompt_template: ${model.data.train_ds.prompt_template} + tokens_to_generate: 32 # decide how many tokens we want to generate to evaluate performance with string metrics + truncation_method: 'right' # Truncation from which position, Options: ['left', 'right'] + metric: + name: "loss" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss'] + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + + optim: + name: fused_adam + lr: 1e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 50 + min_lr: 0.0 # min_lr must be 0.0 for prompt learning when pipeline parallel > 1 + constant_steps: 0 # Constant steps should also be 0 when min_lr=0 + monitor: val_loss + reduce_on_plateau: false diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_gpt_generate_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_gpt_generate_config.yaml new file mode 100644 index 0000000..67d43eb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_gpt_generate_config.yaml @@ -0,0 +1,215 @@ +name: megatron_gpt_peft_${model.peft.peft_scheme}_tuning + +trainer: + devices: 1 + accelerator: gpu + num_nodes: 1 + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 9999 + max_steps: 20000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 # frequency with which training steps are logged + val_check_interval: 200 # If is an int n > 1, will run val every n training steps, if a float 0.0 - 1.0 will run val every epoch fraction, e.g. 0.25 will run val every quarter epoch + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: validation_${model.data.test_ds.metric.name} + save_top_k: 1 + mode: max + save_nemo_on_train_end: True + filename: '${name}--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{consumed_samples}' + model_parallel_size: ${model.tensor_model_parallel_size} + always_save_nemo: True + save_best_model: False + +model: + seed: 1234 + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + + global_batch_size: 1 + micro_batch_size: 1 + restore_from_path: ??? # Path to an existing .nemo model you wish to add new tasks to or run inference with + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + save_nemo_on_validation_end: True # Saves an inference ready .nemo file every time a checkpoint is saved during training. + sync_batch_comm: False + megatron_amp_O2: False + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Activation Checkpoint + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null # not used with 'selective' + activations_checkpoint_layers_per_pipeline: null + answer_only_loss: True + gradient_as_bucket_view: False + + hidden_dropout: 0.0 + attention_dropout: 0.0 + ffn_dropout: 0.0 + + peft: + peft_scheme: "adapter" # can be either adapter,ia3, or ptuning + restore_from_path: null + restore_from_ckpt: + checkpoint_dir: null + checkpoint_name: null + + # Used for adapter peft training + adapter_tuning: + type: 'parallel_adapter' # this should be either 'parallel_adapter' or 'linear_adapter' + adapter_dim: 32 + adapter_dropout: 0.0 + norm_position: 'pre' # This can be set to 'pre', 'post' or null, 'pre' is normally what is used. + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + norm_type: 'mixedfusedlayernorm' # IGNORED if layer_adapter is used, options are ['layernorm', 'mixedfusedlayernorm'] + layer_selection: null # selects in which layers to add adapters, e.g. [1,12] will add adapters to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + lora_tuning: + target_modules: ['attention_qkv'] # this can either be 'attention_qkv','attention_dense','mlp_fc1','mlp_fc2', attention (qkv & dense), mlp (fc1 & fc2) + adapter_dim: 32 + adapter_dropout: 0.0 + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + layer_selection: null # selects in which layers to add lora adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + # Used for p-tuning peft training + p_tuning: + virtual_tokens: 10 # The number of virtual tokens the prompt encoder should add at the start of the sequence + bottleneck_dim: 1024 # the size of the prompt encoder mlp bottleneck + embedding_dim: 1024 # the size of the prompt encoder embeddings + init_std: 0.023 + + ia3_tuning: + layer_selection: null # selects in which layers to add ia3 adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + + data: + test_ds: + file_names: ??? # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds. + names: ??? # Names of the corresponding datasets used to log metrics. + global_batch_size: 1 + micro_batch_size: 1 + shuffle: False + num_workers: 0 + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: False + context_key: 'input' + label_key: ${data.train_ds.label_key} + add_eos: ${data.train_ds.add_eos} + add_sep: ${data.train_ds.add_sep} + add_bos: ${data.train_ds.add_bos} + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + truncation_field: ${data.train_ds.truncation_field} # Options: keys in prompt_template + index_mapping_dir: null # Path to a directory to write index mapping files. + prompt_template: ${data.train_ds.prompt_template} + tokens_to_generate: 32 # decide how many tokens we want to generate to evaluate performance with string metrics + truncation_method: 'right' # Truncation from which position, Options: ['left', 'right'] + + metric: + name: "loss" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss'] + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + +inference: + greedy: True # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 1.0 # sampling temperature + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + outfile_path: output.txt + compute_attention_mask: True + +# server-related configs +server: False # whether launch the API server +port: 5555 # the port number for the inference server +web_server: False # whether launch the web inference server +share: True # whether create a public URL +username: test # user name for web client +password: test2 # password for web client +web_port: 9889 # the port number of the web server 1058 +chat: False # use the chat interface +chatbot_config: + value: False # whether to inject the value attributes + attributes: + - name: Quality + min: 0 + max: 4 + key: quality + type: int + default: 4 + - name: Toxicity + min: 0 + max: 4 + key: toxcity + type: int + default: 0 + - name: Humor + min: 0 + max: 4 + key: humor + type: int + default: 0 + - name: Creativity + min: 0 + max: 4 + key: creativity + type: int + default: 0 + - name: Violence + min: 0 + max: 4 + key: violence + type: int + default: 0 + - name: Helpfulness + min: 0 + max: 4 + key: helpfulness + type: int + default: 4 + - name: Not_Appropriate + min: 0 + max: 4 + key: not_appropriate + type: int + default: 0 + - name: Language + choices: ['ar', 'bg', 'bn', 'ca', 'cs', 'da', 'de', 'el', 'en', 'eo', 'es', 'eu', 'fa', 'fi', 'fr', 'gl', 'he', 'hu', 'id', 'it', 'ja', 'ko', 'nb', 'nl', 'pl', 'pt', 'ro', 'ru', 'sk', 'sv', 'th', 'tr', 'uk', 'vi', 'zh'] + key: lang + type: list + default: en + + user: User + assistant: Assistant + system: "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n" \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_gpt_sft.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_gpt_sft.yaml new file mode 100644 index 0000000..27e7399 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_gpt_sft.yaml @@ -0,0 +1,191 @@ +name: megatron_gpt_sft + +trainer: + devices: 1 + accelerator: gpu + num_nodes: 1 + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 9999 + max_steps: 20000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 # frequency with which training steps are logged + val_check_interval: 200 # If is an int n > 1, will run val every n training steps, if a float 0.0 - 1.0 will run val every epoch fraction, e.g. 0.25 will run val every quarter epoch + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: validation_${model.data.validation_ds.metric.name} + save_top_k: 2 + mode: max + save_nemo_on_train_end: False + filename: 'megatron_gpt_sft--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{consumed_samples}' + model_parallel_size: ${model.tensor_model_parallel_size} + save_best_model: True + +model: + seed: 1234 + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + global_batch_size: 128 + micro_batch_size: 4 + restore_from_path: ??? # Path to an existing p-tuned/prompt tuned .nemo model you wish to add new tasks to or run inference with + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + save_nemo_on_validation_end: True # Saves an inference ready .nemo file every time a checkpoint is saved during training. + sync_batch_comm: False + megatron_amp_O2: False + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Activation Checkpoint + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null # not used with 'selective' + activations_checkpoint_layers_per_pipeline: null + # This feature is valid only when used with pipeline-model-parallelism. More details in megatron_gpt_config.yaml. + answer_only_loss: False # not used right now + gradient_as_bucket_view: False + seq_len_interpolation_factor: null # if not None, seq_len_interpolation_factor will match the base model's value + use_flash_attention: null # if not None, will match the base model's value + + hidden_dropout: 0.0 + attention_dropout: 0.0 + ffn_dropout: 0.0 + + data: + chat: False # whether use chatbot data or not + chat_prompt_tokens: # special tokens for the chat prompts, a dictionary of {token_type: token}. note that some tokenizer may combine the characters at the junction between {end_of_turn}{turn_start}. e.g. '', the '><' sometimes is merged to be a single token. This is not supported, try to avoid + system_turn_start: '' + turn_start: '' + label_start: '' + end_of_turn: "\x0A" # \0x0A is '\n' + end_of_name: "\x0A" # \0x0A is '\n' + train_ds: + # Example of how to specify paths to multiple datasets + # file_names: + # - /path/to/squad.jsonl + # - /path/to/mnli.jsonl + # - /path/to/boolq.jsonl + # Example of how each dataset is formatted + # {'input': 'John von Neumann\nVon Neumann made fundamental contributions .... Q: What did the math of artificial viscosity do?', 'output': 'smoothed the shock transition without sacrificing basic physics'} + file_names: ??? # Path to a list of JSONL files corresponding to the source data. + global_batch_size: ${model.global_batch_size} + micro_batch_size: ${model.micro_batch_size} + shuffle: True + num_workers: 4 + memmap_workers: null + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: True + # Example of how to specify concat_sampling_probabilities + # concat_sampling_probabilities: + # - 0.5 + # - 0.25 + # - 0.25 + concat_sampling_probabilities: null # When providing a list of datasets, this arg defines the sampling probabilities from each dataset when strategy='random' + label_key: 'output' + add_eos: True + add_sep: False + add_bos: False + truncation_field: "input" # # Can be multiple keys separated with ',' Options: keys in prompt_template + index_mapping_dir: null # Path to a directory to write index mapping files. + prompt_template: "{input} {output}" # fstring to use for assistant prompt. Example: "Q: {input}\nA: {output}" + hf_dataset: False # Whether to load the json file with the HuggingFace dataset. otherwise, will load the jsonl file with the JSONLMemMapDataset. + truncation_method: 'right' # Truncation from which position, Options: ['left', 'right'] + + validation_ds: + file_names: ??? # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds. + names: null # Names of the corresponding datasets used to log metrics. + global_batch_size: ${model.global_batch_size} + micro_batch_size: ${model.micro_batch_size} + shuffle: False + num_workers: 4 + memmap_workers: ${model.data.train_ds.memmap_workers} + pin_memory: True + max_seq_length: ${model.data.train_ds.max_seq_length} + min_seq_length: 1 + drop_last: False + label_key: ${model.data.train_ds.label_key} + add_eos: ${model.data.train_ds.add_eos} + add_sep: ${model.data.train_ds.add_sep} + add_bos: ${model.data.train_ds.add_bos} + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + truncation_field: ${model.data.train_ds.truncation_field} # Options: keys in prompt_template + index_mapping_dir: null # Path to a directory to write index mapping files. + prompt_template: ${model.data.train_ds.prompt_template} # fstring to use for assistant prompt. Example: "Q: {input}\nA: {output}" + tokens_to_generate: 32 # decide how many tokens we want to generate to evaluate performance with string metrics + hf_dataset: False # Whether to load the json file with the HuggingFace dataset. otherwise, will load the jsonl file with the JSONLMemMapDataset. + truncation_method: 'right' # Truncation from which position, Options: ['left', 'right'] + + metric: + name: "loss" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss', 'rouge', 'token_f1'] + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + + test_ds: + file_names: ??? # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds. + names: null # Names of the corresponding datasets used to log metrics. + global_batch_size: ${model.global_batch_size} + micro_batch_size: ${model.micro_batch_size} + shuffle: False + num_workers: 4 + memmap_workers: ${model.data.train_ds.memmap_workers} + pin_memory: True + max_seq_length: ${model.data.train_ds.max_seq_length} + min_seq_length: 1 + drop_last: False + label_key: ${model.data.train_ds.label_key} + add_eos: ${model.data.train_ds.add_eos} + add_sep: ${model.data.train_ds.add_sep} + add_bos: ${model.data.train_ds.add_bos} + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + truncation_field: ${model.data.train_ds.truncation_field} # Options: keys in prompt_template + index_mapping_dir: null # Path to a directory to write index mapping files. + prompt_template: ${model.data.train_ds.prompt_template} # fstring to use for assistant prompt. Example: "Q: {input}\nA: {output}" + tokens_to_generate: 32 # decide how many tokens we want to generate to evaluate performance with string metrics + hf_dataset: False # Whether to load the json file with the HuggingFace dataset. otherwise, will load the jsonl file with the JSONLMemMapDataset. + truncation_method: 'right' # Truncation from which position, Options: Options: ['left', 'right'] + + metric: + name: "loss" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss'] + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + + optim: + name: fused_adam # Supports distributed optimizer for memory savings. To enable, set to 'distributed_fused_adam'. Needs Apex to be built with specific args to work. + lr: 3e-5 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + +inference: + greedy: True # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 1.0 # sampling temperature + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + compute_attention_mask: True \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_t5_finetuning_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_t5_finetuning_config.yaml new file mode 100644 index 0000000..d0ee2c4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_t5_finetuning_config.yaml @@ -0,0 +1,220 @@ +name: megatron_t5_peft_${model.peft.peft_scheme}_tuning + +trainer: + devices: 1 + accelerator: gpu + num_nodes: 1 + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 9999 + max_steps: 20000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 # frequency with which training steps are logged + val_check_interval: 200 # If is an int n > 1, will run val every n training steps, if a float 0.0 - 1.0 will run val every epoch fraction, e.g. 0.25 will run val every quarter epoch + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: validation_${model.data.validation_ds.metric.name} + save_top_k: 1 + mode: min + save_nemo_on_train_end: True + filename: '${name}--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{consumed_samples}' + model_parallel_size: ${model.tensor_model_parallel_size} + always_save_nemo: False + save_best_model: True + create_early_stopping_callback: True + early_stopping_callback_params: + monitor: "val_loss" + mode: "min" + min_delta: 0.001 + patience: 10 + verbose: True + strict: False # Should be False to avoid a runtime error where EarlyStopping says monitor is unavailable, which sometimes happens with resumed training. + +model: + seed: 1234 + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + + global_batch_size: 128 + micro_batch_size: 4 + restore_from_path: ??? # Path to an existing .nemo model you wish to add new tasks to or run inference with + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + save_nemo_on_validation_end: False # Saves an inference ready .nemo file every time a checkpoint is saved during training. + sync_batch_comm: False + megatron_amp_O2: False + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Activation Checkpoint + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null # not used with 'selective' + activations_checkpoint_layers_per_pipeline: null + answer_only_loss: True + gradient_as_bucket_view: False + + hidden_dropout: 0.0 + attention_dropout: 0.0 + ffn_dropout: 0.0 + + peft: + peft_scheme: "adapter" # can be either adapter,ia3, or ptuning + restore_from_path: null + + # Used for adapter peft training + adapter_tuning: + type: 'parallel_adapter' # this should be either 'parallel_adapter' or 'linear_adapter' + adapter_dim: 32 + adapter_dropout: 0.0 + norm_position: 'pre' # This can be set to 'pre', 'post' or null, 'pre' is normally what is used. + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + norm_type: 'mixedfusedlayernorm' # IGNORED if layer_adapter is used, options are ['layernorm', 'mixedfusedlayernorm'] + layer_selection: null # selects in which layers to add adapters, e.g. [1,12] will add adapters to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + lora_tuning: + adapter_dim: 32 + adapter_dropout: 0.0 + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + layer_selection: null # selects in which layers to add lora adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + # Used for p-tuning peft training + p_tuning: + virtual_tokens: 10 # The number of virtual tokens the prompt encoder should add at the start of the sequence + bottleneck_dim: 1024 # the size of the prompt encoder mlp bottleneck + embedding_dim: 1024 # the size of the prompt encoder embeddings + init_std: 0.023 + + ia3_tuning: + layer_selection: null # selects in which layers to add ia3 adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + + data: + train_ds: + # Example of how to specify paths to multiple datasets + # file_names: + # - /path/to/squad.jsonl + # - /path/to/mnli.jsonl + # - /path/to/boolq.jsonl + # Example of how each dataset is formatted + # {'input': 'John von Neumann\nVon Neumann made fundamental contributions .... Q: What did the math of artificial viscosity do?', 'output': 'smoothed the shock transition without sacrificing basic physics'} + file_names: ??? # Path to a list of JSONL files corresponding to the source data. + global_batch_size: ${model.global_batch_size} + micro_batch_size: ${model.micro_batch_size} + shuffle: True + num_workers: 0 + memmap_workers: 2 + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: True + # Example of how to specify concat_sampling_probabilities + # concat_sampling_probabilities: + # - 0.5 + # - 0.25 + # - 0.25 + concat_sampling_probabilities: null # When providing a list of datasets, this arg defines the sampling probabilities from each dataset when strategy='random' + context_key: 'input' + label_key: 'output' + add_eos: True + add_sep: False + add_bos: False + separate_prompt_and_response_with_newline: False + truncation_field: "context" # Options: ['context', 'answer'] + index_mapping_dir: null # Path to a directory to write index mapping files. + prompt_template: "{input} {output}" # fstring to use for assistant prompt. Example: "Q: {input}\nA: {output}" + + validation_ds: + file_names: ??? # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds. + names: null # Names of the corresponding datasets used to log metrics. + global_batch_size: ${model.global_batch_size} + micro_batch_size: ${model.micro_batch_size} + shuffle: False + num_workers: 0 + memmap_workers: ${model.data.train_ds.memmap_workers} + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: False + context_key: 'input' + label_key: 'output' + add_eos: ${model.data.train_ds.add_eos} + add_sep: ${model.data.train_ds.add_sep} + add_bos: ${model.data.train_ds.add_bos} + separate_prompt_and_response_with_newline: ${model.data.train_ds.separate_prompt_and_response_with_newline} + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + truncation_field: "context" # Options: ['context', 'answer'] + index_mapping_dir: null # Path to a directory to write index mapping files. + prompt_template: ${model.data.train_ds.prompt_template} # fstring to use for assistant prompt. Example: "Q: {input}\nA: {output}" + tokens_to_generate: 32 # decide how many tokens we want to generate to evaluate performance with string metrics + metric: + name: "loss" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss'] + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + test_ds: + file_names: null # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds. + names: null # Names of the corresponding datasets used to log metrics. + global_batch_size: ${model.global_batch_size} + micro_batch_size: ${model.micro_batch_size} + shuffle: False + num_workers: 0 + memmap_workers: ${model.data.train_ds.memmap_workers} + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: False + context_key: 'input' + label_key: 'output' + add_eos: ${model.data.train_ds.add_eos} + add_sep: ${model.data.train_ds.add_sep} + add_bos: ${model.data.train_ds.add_bos} + separate_prompt_and_response_with_newline: ${model.data.train_ds.separate_prompt_and_response_with_newline} + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + truncation_field: "context" # Options: ['context', 'answer'] + index_mapping_dir: null # Path to a directory to write index mapping files. + prompt_template: ${model.data.train_ds.prompt_template} + tokens_to_generate: 32 # decide how many tokens we want to generate to evaluate performance with string metrics + metric: + name: "loss" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss'] + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + + optim: + name: fused_adam + lr: 1e-4 + weight_decay: 0.01 + betas: + - 0.9 + - 0.98 + sched: + name: CosineAnnealing + warmup_steps: 50 + min_lr: 0.0 # min_lr must be 0.0 for prompt learning when pipeline parallel > 1 + constant_steps: 0 # Constant steps should also be 0 when min_lr=0 + monitor: val_loss + reduce_on_plateau: false \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_t5_generate_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_t5_generate_config.yaml new file mode 100644 index 0000000..c506f3d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/conf/megatron_t5_generate_config.yaml @@ -0,0 +1,213 @@ +name: megatron_t5_peft_${model.peft.peft_scheme}_tuning + +trainer: + devices: 1 + accelerator: gpu + num_nodes: 1 + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 9999 + max_steps: 20000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 # frequency with which training steps are logged + val_check_interval: 200 # If is an int n > 1, will run val every n training steps, if a float 0.0 - 1.0 will run val every epoch fraction, e.g. 0.25 will run val every quarter epoch + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: validation_${model.data.test_ds.metric.name} + save_top_k: 1 + mode: max + save_nemo_on_train_end: True + filename: '${name}--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{consumed_samples}' + model_parallel_size: ${model.tensor_model_parallel_size} + always_save_nemo: True + save_best_model: False + +model: + seed: 1234 + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + + global_batch_size: 1 + micro_batch_size: 1 + restore_from_path: ??? # Path to an existing .nemo model you wish to add new tasks to or run inference with + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + save_nemo_on_validation_end: True # Saves an inference ready .nemo file every time a checkpoint is saved during training. + sync_batch_comm: False + megatron_amp_O2: False + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + ## Activation Checkpoint + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null # not used with 'selective' + activations_checkpoint_layers_per_pipeline: null + answer_only_loss: True + gradient_as_bucket_view: False + + hidden_dropout: 0.0 + attention_dropout: 0.0 + ffn_dropout: 0.0 + + peft: + peft_scheme: "adapter" # can be either adapter,ia3, or ptuning + restore_from_path: null + restore_from_ckpt_name: null + restore_from_hparams_path: null + + # Used for adapter peft training + adapter_tuning: + type: 'parallel_adapter' # this should be either 'parallel_adapter' or 'linear_adapter' + adapter_dim: 32 + adapter_dropout: 0.0 + norm_position: 'pre' # This can be set to 'pre', 'post' or null, 'pre' is normally what is used. + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + norm_type: 'mixedfusedlayernorm' # IGNORED if layer_adapter is used, options are ['layernorm', 'mixedfusedlayernorm'] + layer_selection: null # selects in which layers to add adapters, e.g. [1,12] will add adapters to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + lora_tuning: + adapter_dim: 32 + adapter_dropout: 0.0 + column_init_method: 'xavier' # IGNORED if linear_adapter is used, options: xavier, zero or normal + row_init_method: 'zero' # IGNORED if linear_adapter is used, options: xavier, zero or normal + layer_selection: null # selects in which layers to add lora adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + weight_tying: False + position_embedding_strategy: null # used only when weight_tying is True + + # Used for p-tuning peft training + p_tuning: + virtual_tokens: 10 # The number of virtual tokens the prompt encoder should add at the start of the sequence + bottleneck_dim: 1024 # the size of the prompt encoder mlp bottleneck + embedding_dim: 1024 # the size of the prompt encoder embeddings + init_std: 0.023 + + ia3_tuning: + layer_selection: null # selects in which layers to add ia3 adapters. e.g. [1,12] will add lora to layer 1 (lowest) and 12. null will apply adapters to all layers + + data: + test_ds: + file_names: ??? # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds. + names: ??? # Names of the corresponding datasets used to log metrics. + global_batch_size: 1 #${model.global_batch_size} + micro_batch_size: 1 #${model.micro_batch_size} + shuffle: False + num_workers: 0 + pin_memory: True + max_seq_length: 2048 + min_seq_length: 1 + drop_last: False + context_key: 'input' + label_key: ${data.train_ds.label_key} + add_eos: ${data.train_ds.add_eos} + add_sep: ${data.train_ds.add_sep} + add_bos: ${data.train_ds.add_bos} + separate_prompt_and_response_with_newline: ${data.train_ds.separate_prompt_and_response_with_newline} + write_predictions_to_file: False + output_file_path_prefix: null # Prefix of the file to write predictions to. + truncation_field: ${data.train_ds.truncation_field} # Options: keys in prompt_template index_mapping_dir: null # Path to a directory to write index mapping files. + index_mapping_dir: null # Path to a directory to write index mapping files. + prompt_template: ${data.train_ds.prompt_template} + tokens_to_generate: 32 # decide how many tokens we want to generate to evaluate performance with string metrics + + metric: + name: "loss" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss'] + average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported. + num_classes: null + +inference: + greedy: True # Whether or not to use sampling ; use greedy decoding otherwise + top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + temperature: 1.0 # sampling temperature + all_probs: False # whether return the log prob for all the tokens in vocab + repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. + min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. + compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False + outfile_path: output.txt + compute_attention_mask: True + +# server-related configs +server: False # whether launch the API server +port: 5555 # the port number for the inference server +web_server: False # whether launch the web inference server +share: True # whether create a public URL +username: test # user name for web client +password: test2 # password for web client +web_port: 9889 # the port number of the web server 1058 +chat: False # use the chat interface +chatbot_config: + value: False # whether to inject the value attributes + attributes: + - name: Quality + min: 0 + max: 4 + key: quality + type: int + default: 4 + - name: Toxicity + min: 0 + max: 4 + key: toxcity + type: int + default: 0 + - name: Humor + min: 0 + max: 4 + key: humor + type: int + default: 0 + - name: Creativity + min: 0 + max: 4 + key: creativity + type: int + default: 0 + - name: Violence + min: 0 + max: 4 + key: violence + type: int + default: 0 + - name: Helpfulness + min: 0 + max: 4 + key: helpfulness + type: int + default: 4 + - name: Not_Appropriate + min: 0 + max: 4 + key: not_appropriate + type: int + default: 0 + - name: Language + choices: ['ar', 'bg', 'bn', 'ca', 'cs', 'da', 'de', 'el', 'en', 'eo', 'es', 'eu', 'fa', 'fi', 'fr', 'gl', 'he', 'hu', 'id', 'it', 'ja', 'ko', 'nb', 'nl', 'pl', 'pt', 'ro', 'ru', 'sk', 'sv', 'th', 'tr', 'uk', 'vi', 'zh'] + key: lang + type: list + default: en + + user: User + assistant: Assistant + system: "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n" \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py new file mode 100644 index 0000000..aaa087a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py @@ -0,0 +1,81 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf + +from nemo.collections.nlp.models.language_modeling.megatron_gpt_sft_model import MegatronGPTSFTModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronLMPPTrainerBuilder +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP + +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +mp.set_start_method("spawn", force=True) + +""" +This is the script to finetuning a GPT Model with any PEFT method. +A base GPT Model is required as a starting point. This script will then insert +Adapters into each Transformer layer and will train/update only these adapters +during training. The base GPT Model weights will remain frozen. + +During training this script will only save the newly trained Adapter weights +in checkpoints. At the end of training a .nemo file of Adapter weights will +be saved. + +Usage: + Assuming the base model is a 125m GPT Model, with TP=1, PP=1: + a. run a training run for a base gpt nemo file: + python megatron_gpt_finetuning.py \ + "model.data.train_ds.file_names=[PATH TO TRAINING JSONL FILE]", + "model.data.train_ds.concat_sampling_probabilities=[SAMPLING VAL]", + "model.data.validation_ds.file_names=[PATH TO VALIDATION JSONL FILE]", + "model.data.validation_ds.names=[NAME FOR METRIC LOGGING]", + model.restore_from_path="PATH TO BASE GPT MODEL .nemo FILE" + model.peft.peft_scheme='lora' # lora, ptuning, adapter, ia3, or none for full fineutning + name="NAME OF TRAINING RUN" + exp_manager.exp_dir="DIR TO SAVE CHECKPOINTS and .nemo FILE", +Please see lora.ipynb for a step-by-step guide. +""" + + +@hydra_runner(config_path="conf", config_name="megatron_gpt_finetuning_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronLMPPTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model_cfg = MegatronGPTSFTModel.merge_cfg_with(cfg.model.restore_from_path, cfg) + model = MegatronGPTSFTModel.restore_from(cfg.model.restore_from_path, model_cfg, trainer=trainer) + peft_cfg_cls = PEFT_CONFIG_MAP[cfg.model.peft.peft_scheme] + + if cfg.model.peft.restore_from_path is not None: + # initialize peft weights from a checkpoint instead of randomly + # This is not the same as resume training because optimizer states are not restored. + logging.info("PEFT Weights will be loaded from", cfg.model.peft.restore_from_path) + model.load_adapters(cfg.model.peft.restore_from_path, peft_cfg_cls(model_cfg)) + elif peft_cfg_cls is not None: + logging.info("Adding adapter weights to the model for PEFT") + model.add_adapter(peft_cfg_cls(model_cfg)) + else: + logging.info(f"Running full finetuning since no peft scheme is given.\n{model.summarize()}") + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_generate.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_generate.py new file mode 100644 index 0000000..0818f44 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_generate.py @@ -0,0 +1,170 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import asyncio +import os +import threading +from functools import partial + +import torch +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf + + +from nemo.collections.nlp.models.language_modeling.megatron_gpt_sft_model import MegatronGPTSFTModel +from nemo.collections.nlp.modules.common.text_generation_server import MegatronServer +from nemo.collections.nlp.modules.common.text_generation_utils import generate +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronLMPPTrainerBuilder +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.model_utils import inject_model_parallel_rank + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + +mp.set_start_method("spawn", force=True) +""" +This is the script to run inference with a PEFT model or an SFT Model. + +If you want to evaluate an SFT .nemo file: + +python examples/nlp/language_modeling/tuning/megatron_gpt_generate.py \ + model.restore_from_path= \ + model.peft.restore_from_path=null \ + trainer.devices=1 model.data.test_ds.file_names=\[, ] \ + model.data.test_ds.names=\['name_for_test_file1', 'name_for_test_file2'] \ # this is not the filename just some identifier + model.data.test_ds.global_batch_size=4 \ # or some other value + model.data.test_ds.micro_batch_size=4 \ + model.data.test_ds.tokens_to_generate=30 \ + inference.greedy=True \ + inference.outfile_path=\'' + +If you want to evaluate a PEFT Model, you should provide a base GPT model and a PEFT model .nemo file + +python examples/nlp/language_modeling/tuning/megatron_gpt_generate.py \ + model.restore_from_path= \ + model.peft.restore_from_path= \ # this will be created if you use `megatron_gpt_finetuning.py` + trainer.devices=1 model.data.test_ds.file_names=\[, ] \ + model.data.test_ds.names=\['name_for_test_file1', 'name_for_test_file2'] \ # this is not the filename just some identifier + model.data.test_ds.global_batch_size=4 \ # or some other value + model.data.test_ds.micro_batch_size=4 \ + model.data.test_ds.tokens_to_generate=30 \ + inference.greedy=True \ + inference.outfile_path=\'' + +[Advanced] If you want to evaluate a pretrained base model as if it was an SFT model, follow the command for +evaluating an SFT model, but set the following arguments with appropriate values for your finetuning dataset. +An example is below. + ... + model.data.test_ds.label_key='output' \ + model.data.test_ds.add_eos=True \ + model.data.test_ds.add_sep=False \ + model.data.test_ds.add_bos=False \ + model.data.test_ds.truncation_field="input" \ + model.data.test_ds.prompt_template="\{input\} \{output\}" \ +""" + + +def use_inference_server(cfg, model, trainer): + if not HAVE_MEGATRON_CORE: + raise ValueError('Megatron-core needs to be installed to use this feature!') + + from nemo.collections.nlp.modules.common.megatron_web_server import get_chatbot_demo, get_demo + + if parallel_state.is_pipeline_first_stage() and parallel_state.get_tensor_model_parallel_rank() == 0: + if cfg.web_server: + if cfg.chat: + defaults = { + 'user': cfg.chatbot_config.user, + 'assistant': cfg.chatbot_config.assistant, + 'system': cfg.chatbot_config.system, + } + web_ui = partial( + get_chatbot_demo, + defaults=defaults, + value=cfg.chatbot_config.value, + attributes=cfg.chatbot_config.attributes, + ) + else: + web_ui = get_demo + loop = asyncio.new_event_loop() + thread = threading.Thread( + target=web_ui, daemon=True, args=(cfg.share, cfg.username, cfg.password, cfg.port, cfg.web_port, loop), + ) + thread.start() + server = MegatronServer(model.cuda()) + server.run("0.0.0.0", port=cfg.port) + + while True: + choice = torch.cuda.LongTensor(1) + torch.distributed.broadcast(choice, 0) + if choice[0].item() == 0: + generate(model.cuda()) + + +@hydra_runner(config_path="conf", config_name="megatron_gpt_generate_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f"\n{OmegaConf.to_yaml(cfg)}") + trainer = MegatronLMPPTrainerBuilder(cfg).create_trainer() + + if cfg.model.peft.restore_from_path: + model_cfg = MegatronGPTSFTModel.merge_inference_cfg(cfg.model.peft.restore_from_path, cfg) + else: + model_cfg = MegatronGPTSFTModel.merge_inference_cfg(cfg.model.restore_from_path, cfg) + + model = MegatronGPTSFTModel.restore_from(cfg.model.restore_from_path, model_cfg, trainer=trainer) + + if cfg.model.peft.restore_from_path: + model.load_adapters(cfg.model.peft.restore_from_path) + elif cfg.model.peft.restore_from_ckpt.checkpoint_dir and cfg.model.peft.restore_from_ckpt.checkpoint_name: + peft_cfg_cls = PEFT_CONFIG_MAP[cfg.model.peft.peft_scheme] + checkpoint_path = os.path.join( + cfg.model.peft.restore_from_ckpt.checkpoint_dir, cfg.model.peft.restore_from_ckpt.checkpoint_name + ) + # checkpoint_path is a dir in case of distributed checkpointing + if not os.path.isdir(checkpoint_path): + # legacy checkpoint needs model parallel rank injection + checkpoint_path = inject_model_parallel_rank( + os.path.join( + cfg.model.peft.restore_from_ckpt.checkpoint_dir, cfg.model.peft.restore_from_ckpt.checkpoint_name + ) + ) + model.load_adapters(checkpoint_path, peft_cfgs=peft_cfg_cls(model_cfg)) + else: + raise NotImplementedError("distributed checkpointing of PEFT weights is not supported") + + model.freeze() + logging.info(f"Freezing parameters for PEFT eval:\n{model.summarize()}") + + if not cfg.model.get('use_flash_attention', False): + cfg.inference.compute_attention_mask = True + config = OmegaConf.to_container(cfg.inference, resolve=True) + model.set_inference_config(config) + + if not cfg.server: + trainer.test(model) + else: + use_inference_server(cfg, model, trainer) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_peft_eval.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_peft_eval.py new file mode 100644 index 0000000..11a3753 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_peft_eval.py @@ -0,0 +1,153 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################# +# THIS SCRIPT IS DEPRECATED # +############################# + +import asyncio +import threading +from functools import partial + +import torch +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf + + +from nemo.collections.nlp.models.language_modeling.megatron_gpt_sft_model import MegatronGPTSFTModel +from nemo.collections.nlp.modules.common.text_generation_server import MegatronServer +from nemo.collections.nlp.modules.common.text_generation_utils import generate +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronLMPPTrainerBuilder +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.decorators import deprecated + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True +except: + pass + +mp.set_start_method("spawn", force=True) +""" +This is the script to run inference with a PEFT model or an SFT Model. + +If you want to evaluate an SFT .nemo file: + +python examples/nlp/language_modeling/tuning/megatron_gpt_generate.py \ + model.restore_from_path= \ + model.peft.restore_from_path=null \ + trainer.devices=1 model.data.test_ds.file_names=\[, ] \ + model.data.test_ds.names=\['name_for_test_file1', 'name_for_test_file2'] \ # this is not the filename just some identifier + model.data.test_ds.global_batch_size=4 \ # or some other value + model.data.test_ds.micro_batch_size=4 \ + model.data.test_ds.tokens_to_generate=30 \ + inference.greedy=True \ + inference.outfile_path=\'' + +If you want to evaluate a PEFT Model, you should provide a base GPT model and a PEFT model .nemo file + +python examples/nlp/language_modeling/tuning/megatron_gpt_generate.py \ + model.restore_from_path= \ + model.peft.restore_from_path= \ # this will be created if you use `megatron_gpt_finetuning.py` + trainer.devices=1 model.data.test_ds.file_names=\[, ] \ + model.data.test_ds.names=\['name_for_test_file1', 'name_for_test_file2'] \ # this is not the filename just some identifier + model.data.test_ds.global_batch_size=4 \ # or some other value + model.data.test_ds.micro_batch_size=4 \ + model.data.test_ds.tokens_to_generate=30 \ + inference.greedy=True \ + inference.outfile_path=\'' + +""" + + +def use_inference_server(cfg, model, trainer): + if not HAVE_MEGATRON_CORE: + raise ValueError('Megatron-core needs to be installed to use this feature!') + + from nemo.collections.nlp.modules.common.megatron_web_server import get_chatbot_demo, get_demo + + trainer.test(model, dataloaders=None) + + if parallel_state.is_pipeline_first_stage() and parallel_state.get_tensor_model_parallel_rank() == 0: + if cfg.web_server: + if cfg.chat: + defaults = { + 'user': cfg.chatbot_config.user, + 'assistant': cfg.chatbot_config.assistant, + 'system': cfg.chatbot_config.system, + } + web_ui = partial( + get_chatbot_demo, + defaults=defaults, + value=cfg.chatbot_config.value, + attributes=cfg.chatbot_config.attributes, + ) + else: + web_ui = get_demo + loop = asyncio.new_event_loop() + thread = threading.Thread( + target=web_ui, daemon=True, args=(cfg.share, cfg.username, cfg.password, cfg.port, cfg.web_port, loop), + ) + thread.start() + server = MegatronServer(model.cuda()) + server.run("0.0.0.0", port=cfg.port) + + while True: + choice = torch.cuda.LongTensor(1) + torch.distributed.broadcast(choice, 0) + if choice[0].item() == 0: + generate(model.cuda()) + + +banner = '\n'.join(['' "*" * 80] * 5) + + +@deprecated( + wait_seconds=20, + explanation=f"\n{banner}\nmegatron_gpt_peft_eval.py is renamed to megatron_gpt_generate.py with the " + f"same functionality. \nPlease switch to the new name.\n{banner}\n", +) +@hydra_runner(config_path="conf", config_name="megatron_gpt_generate_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f"\n{OmegaConf.to_yaml(cfg)}") + trainer = MegatronLMPPTrainerBuilder(cfg).create_trainer() + + if cfg.model.peft.restore_from_path: + model_cfg = MegatronGPTSFTModel.merge_inference_cfg(cfg.model.peft.restore_from_path, cfg) + else: + model_cfg = MegatronGPTSFTModel.merge_inference_cfg(cfg.model.restore_from_path, cfg) + + model = MegatronGPTSFTModel.restore_from(cfg.model.restore_from_path, model_cfg, trainer=trainer) + + if cfg.model.peft.restore_from_path: + model.load_adapters(cfg.model.peft.restore_from_path) + + model.freeze() + logging.info(f"Freezing parameters for PEFT eval:\n{model.summarize()}") + + if not cfg.model.get('use_flash_attention', False): + cfg.inference.compute_attention_mask = True + config = OmegaConf.to_container(cfg.inference, resolve=True) + model.set_inference_config(config) + + if not cfg.server: + trainer.test(model) + else: + use_inference_server(cfg, model, trainer) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py new file mode 100644 index 0000000..1137866 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py @@ -0,0 +1,91 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################# +# THIS SCRIPT IS DEPRECATED # +############################# +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf + +from nemo.collections.nlp.models.language_modeling.megatron_gpt_sft_model import MegatronGPTSFTModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronLMPPTrainerBuilder +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP + +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.decorators import deprecated +from nemo.utils.exp_manager import exp_manager + +mp.set_start_method("spawn", force=True) + +""" +This is the script to finetuning a GPT Model with any PEFT method. +A base GPT Model is required as a starting point. This script will then insert +Adapters into each Transformer layer and will train/update only these adapters +during training. The base GPT Model weights will remain frozen. + +During training this script will only save the newly trained Adapter weights +in checkpoints. At the end of training a .nemo file of Adapter weights will +be saved. + +Usage: + Assuming the base model is a 125m GPT Model, with TP=1, PP=1: + a. run a training run for a base gpt nemo file: + python megatron_gpt_finetuning.py \ + "model.data.train_ds.file_names=[PATH TO TRAINING JSONL FILE]", + "model.data.train_ds.concat_sampling_probabilities=[SAMPLING VAL]", + "model.data.validation_ds.file_names=[PATH TO VALIDATION JSONL FILE]", + "model.data.validation_ds.names=[NAME FOR METRIC LOGGING]", + model.restore_from_path="PATH TO BASE GPT MODEL .nemo FILE" + model.peft.peft_scheme='lora' # lora, ptuning, adapter, ia3, or none for full fineutning + name="NAME OF TRAINING RUN" + exp_manager.exp_dir="DIR TO SAVE CHECKPOINTS and .nemo FILE", +Please see lora.ipynb for a step-by-step guide. +""" + +banner = '\n'.join(['' "*" * 80] * 5) + + +@deprecated( + wait_seconds=20, + explanation=f"\n{banner}\nmegatron_gpt_peft_tuning.py is renamed to megatron_gpt_finetuning.py with the " + f"same functionality. \nPlease switch to the new name.\n{banner}\n", +) +@hydra_runner(config_path="conf", config_name="megatron_gpt_finetuning_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronLMPPTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model_cfg = MegatronGPTSFTModel.merge_cfg_with(cfg.model.restore_from_path, cfg) + model = MegatronGPTSFTModel.restore_from(cfg.model.restore_from_path, model_cfg, trainer=trainer) + peft_cfg_cls = PEFT_CONFIG_MAP[cfg.model.peft.peft_scheme] + + if cfg.model.peft.restore_from_path is not None: + # initialize peft weights from a checkpoint instead of randomly + # This is not the same as resume training because optimizer states are not restored. + logging.info("PEFT Weights will be loaded from", cfg.model.peft.restore_from_path) + model.load_adapters(cfg.model.peft.restore_from_path, peft_cfg_cls(model_cfg)) + elif peft_cfg_cls is not None: + logging.info("Adding adapter weights to the model for PEFT") + model.add_adapter(peft_cfg_cls(model_cfg)) + else: + logging.info(f"Running full finetuning since no peft scheme is given.\n{model.summarize()}") + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_sft.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_sft.py new file mode 100644 index 0000000..506ddd0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_gpt_sft.py @@ -0,0 +1,247 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################# +# THIS SCRIPT IS DEPRECATED # +############################# +import os +import tempfile + +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment + +from nemo.collections.nlp.data.language_modeling.megatron.gpt_sft_chat_dataset import get_prompt_template_example +from nemo.collections.nlp.models.language_modeling.megatron_gpt_sft_model import MegatronGPTSFTModel +from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel +from nemo.collections.nlp.parts.nlp_overrides import ( + CustomProgressBar, + GradScaler, + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, + NLPSaveRestoreConnector, + PipelineMixedPrecisionPlugin, +) +from nemo.core.config import hydra_runner +from nemo.utils import AppState, logging +from nemo.utils.decorators import deprecated +from nemo.utils.exp_manager import exp_manager +from nemo.utils.model_utils import inject_model_parallel_rank + +mp.set_start_method("spawn", force=True) + + +def _modify_config(gpt_cfg, cfg, add_cfg_to_tree=False): + """ + This function modifies the original gpt pre-training config (gpt_cfg) with attributes from the finetuning config (cfg). + The `add_cfg_to_tree` arg adds `cfg` to the top of the yaml tree which is needed for all `hparams.yaml` files when passed as an arg to `load_from_checkpoint()`. + """ + OmegaConf.set_struct(gpt_cfg, True) + OmegaConf.resolve(cfg) + with open_dict(gpt_cfg): + gpt_cfg.megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + gpt_cfg.micro_batch_size = cfg.model.data.train_ds.micro_batch_size + gpt_cfg.global_batch_size = cfg.model.data.train_ds.global_batch_size + gpt_cfg.sequence_parallel = cfg.model.get("sequence_parallel", False) + gpt_cfg.activations_checkpoint_granularity = cfg.model.get("activations_checkpoint_granularity", None) + gpt_cfg.activations_checkpoint_num_layers = cfg.model.get("activations_checkpoint_num_layers", None) + gpt_cfg.activations_checkpoint_method = cfg.model.get("activations_checkpoint_method", None) + gpt_cfg.activations_checkpoint_layers_per_pipeline = cfg.model.get( + "activations_checkpoint_layers_per_pipeline", None + ) + gpt_cfg.data = cfg.model.data + gpt_cfg.optim = cfg.model.optim + gpt_cfg.precision = cfg.trainer.precision + gpt_cfg.answer_only_loss = cfg.model.answer_only_loss + gpt_cfg.restore_from_path = cfg.model.restore_from_path + gpt_cfg.resume_from_checkpoint = cfg.model.resume_from_checkpoint + gpt_cfg.save_nemo_on_validation_end = cfg.model.save_nemo_on_validation_end + gpt_cfg.gradient_as_bucket_view = cfg.model.gradient_as_bucket_view + gpt_cfg.hidden_dropout = cfg.model.get('hidden_dropout', 0.0) + gpt_cfg.attention_dropout = cfg.model.get('attention_dropout', 0.0) + gpt_cfg.ffn_dropout = cfg.model.ffn_dropout + gpt_cfg.use_flash_attention = cfg.model.get('use_flash_attention', False) + gpt_cfg.tensor_model_parallel_size = cfg.model.get('tensor_model_parallel_size', 1) + gpt_cfg.expert_model_parallel_size = cfg.model.get('expert_model_parallel_size', 1) + gpt_cfg.pipeline_model_parallel_size = cfg.model.get('pipeline_model_parallel_size', 1) + gpt_cfg.pipeline_model_parallel_split_rank = cfg.model.get('pipeline_model_parallel_split_rank', 0) + + if cfg.model.data.get('chat', False): + # chat model, overwrite the prompt template + prompt_template = get_prompt_template_example(cfg.model.data.chat_prompt_tokens) + gpt_cfg.data.train_ds.prompt_template = prompt_template + gpt_cfg.data.validation_ds.prompt_template = prompt_template + gpt_cfg.data.test_ds.prompt_template = prompt_template + + sft_cls = MegatronGPTSFTModel + gpt_cfg.target = f"{sft_cls.__module__}.{sft_cls.__name__}" + + if cfg.model.get('use_flash_attention', None) is not None: + gpt_cfg.use_flash_attention = cfg.model.use_flash_attention + + if cfg.model.get('seq_len_interpolation_factor', None) is not None: + gpt_cfg.seq_len_interpolation_factor = cfg.model.seq_len_interpolation_factor + + if cfg.model.get('rotary_base', None) is not None: + gpt_cfg.rotary_base = cfg.model.rotary_base + + sft_cls = MegatronGPTSFTModel + gpt_cfg.target = f"{sft_cls.__module__}.{sft_cls.__name__}" + + # This is needed when modifying a hparam file directly to load `.ckpt` files. + # This is not needed to modify the cfg in `.nemo` files. + if add_cfg_to_tree: + OmegaConf.resolve(gpt_cfg) + gpt_cfg.cfg = gpt_cfg + + return gpt_cfg + + +def load_from_nemo(cls, cfg, trainer, gpt_cfg, modify_confg_fn): + gpt_cfg = modify_confg_fn(gpt_cfg, cfg, add_cfg_to_tree=False) + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.model.restore_from_path): + save_restore_connector.model_extracted_dir = cfg.model.restore_from_path + model = cls.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + override_config_path=gpt_cfg, + save_restore_connector=save_restore_connector, + ) + return model + + +def load_from_checkpoint_dir(cls, cfg, trainer, modify_confg_fn): + app_state = AppState() + if cfg.model.tensor_model_parallel_size > 1 or cfg.model.pipeline_model_parallel_size > 1: + app_state.model_parallel_size = cfg.model.tensor_model_parallel_size * cfg.model.pipeline_model_parallel_size + app_state.tensor_model_parallel_size = cfg.model.tensor_model_parallel_size + app_state.pipeline_model_parallel_size = cfg.model.pipeline_model_parallel_size + ( + app_state.tensor_model_parallel_rank, + app_state.pipeline_model_parallel_rank, + app_state.model_parallel_size, + app_state.data_parallel_size, + app_state.pipeline_model_parallel_split_rank, + app_state.virtual_pipeline_model_parallel_rank, + ) = fake_initialize_model_parallel( + world_size=app_state.model_parallel_size, + rank=trainer.global_rank, + tensor_model_parallel_size_=cfg.model.tensor_model_parallel_size, + pipeline_model_parallel_size_=cfg.model.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank_=cfg.model.pipeline_model_parallel_split_rank, + ) + checkpoint_path = inject_model_parallel_rank( + os.path.join(cfg.model.pretrained_checkpoint.checkpoint_dir, cfg.model.pretrained_checkpoint.checkpoint_name) + ) + hparams_file = OmegaConf.load(cfg.model.pretrained_checkpoint.hparams_file) + gpt_cfg = modify_confg_fn(hparams_file.cfg, cfg, add_cfg_to_tree=True) + with tempfile.NamedTemporaryFile(suffix='.yaml') as f: + OmegaConf.save(config=gpt_cfg, f=f.name) + model = cls.load_from_checkpoint(checkpoint_path=checkpoint_path, trainer=trainer, hparams_file=f.name,) + return model + + +def validate_checkpoint_loading_args(cfg): + if cfg.checkpoint_dir is None or not os.path.isdir(cfg.checkpoint_dir): + raise ValueError(f'Checkpoint directory {cfg.checkpoint_dir} does not exist or is not a directory.') + if cfg.checkpoint_name is None: + raise ValueError(f'Checkpoint name {cfg.checkpoint_name} is not valid.') + if cfg.hparams_file is None or not os.path.isfile(cfg.hparams_file): + raise ValueError(f'Hparams file {cfg.hparams_file} does not exist or is not a file.') + + +banner = '\n'.join(['' "*" * 80] * 5) + + +@deprecated( + wait_seconds=20, + explanation=f"\n{banner}\n{__file__} is deprecated. PEFT and SFT scripts are now consolidated" + f"See updated scripts `megatron_gpt_finetuning.py` and `megatron_gpt_generate.py` for examples.\n{banner}\n", +) +@hydra_runner(config_path="conf", config_name="megatron_gpt_sft") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + with_distributed_adam = cfg.model.optim.get('name', 'fused_adam') == 'distributed_fused_adam' + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True, + gradient_as_bucket_view=cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + ) + if cfg.trainer.precision in [16, '16', 'bf16', '16-mixed', 'bf16-mixed']: + scaler = None + if cfg.trainer.precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + hysteresis=cfg.model.get('hysteresis', 2), + ) + # MixedPrecisionPlugin in PTL >= 2.0 requires precision to be 16-mixed or bf16-mixed + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + if megatron_amp_O2 and not with_distributed_adam: + plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(PipelineMixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + # Set precision None after precision plugins are created as PTL >= 2.1 does not allow both + # precision plugins and precision to exist + cfg.trainer.precision = None + + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + callbacks = [] + # enable_progress_bar is True by default. If cfg.trainer.enable_progress_bar=False, CustomProgressBar is not appended to callbacks + if 'enable_progress_bar' not in cfg.trainer or cfg.trainer.enable_progress_bar: + callbacks.append(CustomProgressBar()) + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=callbacks) + + exp_manager(trainer, cfg.exp_manager) + + # update resume from checkpoint found by exp_manager + if cfg.model.resume_from_checkpoint is not None: + trainer.ckpt_path = cfg.model.resume_from_checkpoint + logging.info(f'Resuming training from checkpoint: {trainer.ckpt_path}') + + if cfg.model.restore_from_path: + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.model.restore_from_path): + save_restore_connector.model_extracted_dir = cfg.model.restore_from_path + gpt_cfg = MegatronGPTSFTModel.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + return_config=True, + save_restore_connector=save_restore_connector, + ) + model = load_from_nemo(MegatronGPTSFTModel, cfg, trainer, gpt_cfg, modify_confg_fn=_modify_config) + else: + validate_checkpoint_loading_args(cfg.model.pretrained_checkpoint) + model = load_from_checkpoint_dir(MegatronGPTSFTModel, cfg, trainer, modify_confg_fn=_modify_config) + + if 'inference' in cfg: + if not cfg.model.use_flash_attention: + cfg.inference.compute_attention_mask = True + config = OmegaConf.to_container(cfg.inference, resolve=True) + model.set_inference_config(config) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_t5_finetuning.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_t5_finetuning.py new file mode 100644 index 0000000..d17b620 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_t5_finetuning.py @@ -0,0 +1,65 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf + +from nemo.collections.nlp.models.language_modeling.megatron_t5_sft_model import MegatronT5SFTModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronLMPPTrainerBuilder +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +mp.set_start_method("spawn", force=True) + +""" +This is the script to finetuning a T5 Model with any PEFT method. +A base T5 Model is required as a starting point. This script will then insert +Adapters into each Transformer layer and will train/update only these adapters +during training. The base T5 Model weights will remain frozen. + +This script is exactly the same as the peft tuning script for GPT. For more details +please refer to the GPT script and docs. +""" + + +@hydra_runner(config_path="conf", config_name="megatron_t5_finetuning_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronLMPPTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model_cfg = MegatronT5SFTModel.merge_cfg_with(cfg.model.restore_from_path, cfg) + model = MegatronT5SFTModel.restore_from(cfg.model.restore_from_path, model_cfg, trainer=trainer) + peft_cfg_cls = PEFT_CONFIG_MAP[cfg.model.peft.peft_scheme] + + if cfg.model.peft.restore_from_path is not None: + # initialize peft weights from a checkpoint instead of randomly + # This is not the same as resume training because optimizer states are not restored. + logging.info("PEFT Weights will be loaded from", cfg.model.peft.restore_from_path) + model.load_adapters(cfg.model.peft.restore_from_path, peft_cfg_cls(model_cfg)) + elif peft_cfg_cls is not None: + logging.info("Adding adapter weights to the model for PEFT") + model.add_adapter(peft_cfg_cls(model_cfg)) + else: + logging.info(f"Running full finetuning since no peft scheme is given.\n{model.summarize()}") + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_t5_generate.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_t5_generate.py new file mode 100644 index 0000000..d7328c5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_t5_generate.py @@ -0,0 +1,146 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################# +# THIS SCRIPT IS DEPRECATED # +############################# + +import asyncio +import threading +from functools import partial + +import torch +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf + + +from nemo.collections.nlp.models.language_modeling.megatron_t5_sft_model import MegatronT5SFTModel +from nemo.collections.nlp.modules.common.text_generation_server import MegatronServer +from nemo.collections.nlp.modules.common.text_generation_utils import generate +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronLMPPTrainerBuilder +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.decorators import deprecated + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True +except (ImportError, ModuleNotFoundError): + HAVE_MEGATRON_CORE = False + +mp.set_start_method("spawn", force=True) +""" +This is the script to run inference with a PEFT model or an SFT Model. + +If you want to evaluate an SFT .nemo file: + +python examples/nlp/language_modeling/tuning/megatron_t5_generate.py \ + model.restore_from_path= \ + model.peft.restore_from_path=null \ + trainer.devices=1 model.data.test_ds.file_names=\[, ] \ + model.data.test_ds.names=\['name_for_test_file1', 'name_for_test_file2'] \ # this is not the filename just some identifier + model.data.test_ds.global_batch_size=4 \ # or some other value + model.data.test_ds.micro_batch_size=4 \ + model.data.test_ds.tokens_to_generate=30 \ + inference.greedy=True \ + inference.outfile_path=\'' + +If you want to evaluate a PEFT Model, you should provide a base T5 model and a PEFT model .nemo file + +python examples/nlp/language_modeling/tuning/megatron_t5_generate.py \ + model.restore_from_path= \ + model.peft.restore_from_path= \ # this will be created if you use `megatron_t5_finetuning.py` + trainer.devices=1 model.data.test_ds.file_names=\[, ] \ + model.data.test_ds.names=\['name_for_test_file1', 'name_for_test_file2'] \ # this is not the filename just some identifier + model.data.test_ds.global_batch_size=4 \ # or some other value + model.data.test_ds.micro_batch_size=4 \ + model.data.test_ds.tokens_to_generate=30 \ + inference.greedy=True \ + inference.outfile_path=\'' + +""" + + +def use_inference_server(cfg, model, trainer): + if not HAVE_MEGATRON_CORE: + raise ValueError('Megatron-core needs to be installed to use this feature!') + + from nemo.collections.nlp.modules.common.megatron_web_server import get_chatbot_demo, get_demo + + trainer.test(model, dataloaders=None) + + if parallel_state.is_pipeline_first_stage() and parallel_state.get_tensor_model_parallel_rank() == 0: + if cfg.web_server: + if cfg.chat: + defaults = { + 'user': cfg.chatbot_config.user, + 'assistant': cfg.chatbot_config.assistant, + 'system': cfg.chatbot_config.system, + } + web_ui = partial( + get_chatbot_demo, + defaults=defaults, + value=cfg.chatbot_config.value, + attributes=cfg.chatbot_config.attributes, + ) + else: + web_ui = get_demo + loop = asyncio.new_event_loop() + thread = threading.Thread( + target=web_ui, daemon=True, args=(cfg.share, cfg.username, cfg.password, cfg.port, cfg.web_port, loop), + ) + thread.start() + server = MegatronServer(model.cuda()) + server.run("0.0.0.0", port=cfg.port) + + while True: + choice = torch.cuda.LongTensor(1) + torch.distributed.broadcast(choice, 0) + if choice[0].item() == 0: + generate(model.cuda()) + + +banner = '\n'.join(['' "*" * 80] * 5) + + +@deprecated( + wait_seconds=20, + explanation=f"\n{banner}\nmegatron_t5_peft_eval.py is renamed to megatron_t5_generate.py with the " + f"same functionality. \nPlease switch to the new name.\n{banner}\n", +) +@hydra_runner(config_path="conf", config_name="megatron_t5_generate_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f"\n{OmegaConf.to_yaml(cfg)}") + trainer = MegatronLMPPTrainerBuilder(cfg).create_trainer() + + model_cfg = MegatronT5SFTModel.merge_inference_cfg(cfg.model.peft.restore_from_path, cfg) + model = MegatronT5SFTModel.restore_from(cfg.model.restore_from_path, model_cfg, trainer=trainer) + + model.load_adapters(cfg.model.peft.restore_from_path) + + model.freeze() + logging.info(f"Freezing parameters for PEFT eval:\n{model.summarize()}") + + if not cfg.model.get('use_flash_attention', False): + cfg.inference.compute_attention_mask = True + + if not cfg.server: + trainer.test(model) + else: + use_inference_server(cfg, model, trainer) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_t5_peft_tuning.py b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_t5_peft_tuning.py new file mode 100644 index 0000000..ad4624e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/language_modeling/tuning/megatron_t5_peft_tuning.py @@ -0,0 +1,75 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################# +# THIS SCRIPT IS DEPRECATED # +############################# +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf + +from nemo.collections.nlp.models.language_modeling.megatron_t5_sft_model import MegatronT5SFTModel +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronLMPPTrainerBuilder +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.decorators import deprecated +from nemo.utils.exp_manager import exp_manager + +mp.set_start_method("spawn", force=True) + +""" +This is the script to finetuning a T5 Model with any PEFT method. +A base T5 Model is required as a starting point. This script will then insert +Adapters into each Transformer layer and will train/update only these adapters +during training. The base T5 Model weights will remain frozen. + +This script is exactly the same as the peft tuning script for GPT. For more details +please refer to the GPT script and docs. +""" + +banner = '\n'.join(['' "*" * 80] * 5) + + +@deprecated( + wait_seconds=20, + explanation=f"\n{banner}\nmegatron_t5_peft_tuning.py is renamed to megatron_t5_finetuning.py with the " + f"same functionality. \nPlease switch to the new name.\n{banner}\n", +) +@hydra_runner(config_path="conf", config_name="megatron_t5_finetuning_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronLMPPTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model_cfg = MegatronT5SFTModel.merge_cfg_with(cfg.model.restore_from_path, cfg) + model = MegatronT5SFTModel.restore_from(cfg.model.restore_from_path, model_cfg, trainer=trainer) + peft_cfg_cls = PEFT_CONFIG_MAP[cfg.model.peft.peft_scheme] + + if cfg.model.peft.restore_from_path is not None: + # initialize peft weights from a checkpoint instead of randomly + # This is not the same as resume training because optimizer states are not restored. + logging.info("PEFT Weights will be loaded from", cfg.model.peft.restore_from_path) + model.load_adapters(cfg.model.peft.restore_from_path, peft_cfg_cls(model_cfg)) + elif peft_cfg_cls is not None: + logging.info("Adding adapter weights to the model for PEFT") + model.add_adapter(peft_cfg_cls(model_cfg)) + else: + logging.info(f"Running full finetuning since no peft scheme is given.\n{model.summarize()}") + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/aayn_base.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/aayn_base.yaml new file mode 100644 index 0000000..ac18908 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/aayn_base.yaml @@ -0,0 +1,157 @@ +name: AttentionIsAllYouNeed +do_training: True # set to False if only preprocessing data +do_testing: False # set to True to run evaluation on test data after training + +model: + beam_size: 4 + len_pen: 0.6 + multilingual: False + max_generation_delta: -1 + label_smoothing: 0.1 + shared_tokenizer: True # train tokenizer model across src and tgt train data + preproc_out_dir: null # path to store data preprocessing outputs + src_language: 'en' + tgt_language: 'de' + shared_embeddings: false + + train_ds: + src_file_name: null + tgt_file_name: null + use_tarred_dataset: False # if true tar_file_name and meta_file_name will be used (or created automatically) + # config for preprocessing training data and creating a tarred datset automatically + tar_file_prefix: parallel # prefix for tar file names + tar_files: null # if data has already been preprocessed (rest of config ignored) + metadata_file: null # metadata for tarred dataset + lines_per_dataset_fragment: 1000000 # Number of lines to consider for bucketing and padding + num_batches_per_tarfile: 100 # Number of batches (pickle files) within each tarfile + tar_shuffle_n: 100 # How many samples to look ahead and load to be shuffled + shard_strategy: scatter # tarred dataset shard distribution strategy + n_preproc_jobs: -2 # number of processes to use for data preprocessing (-2 means all but 2) + tokens_in_batch: 512 + clean: true + max_seq_length: 512 + shuffle: true + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + concat_sampling_technique: temperature # only used with ConcatTranslationDataset + concat_sampling_temperature: 5 # only used with ConcatTranslationDataset + concat_sampling_probabilities: null # only used with ConcatTranslationDataset + + validation_ds: + src_file_name: ??? + tgt_file_name: ??? + tokens_in_batch: 512 + clean: false + max_seq_length: 512 + shuffle: false + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + test_ds: + src_file_name: ??? + tgt_file_name: ??? + tokens_in_batch: 512 + clean: false + max_seq_length: 512 + shuffle: false + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + optim: + name: adam + lr: 0.001 + betas: + - 0.9 + - 0.98 + weight_decay: 0.0 + sched: + name: InverseSquareRootAnnealing + min_lr: 0.0 + last_epoch: -1 + warmup_ratio: 0.1 + + encoder_tokenizer: + library: sentencepiece + tokenizer_model: null + vocab_size: null # vocab size for training bpe + bpe_dropout: null + vocab_file: null + special_tokens: null + r2l: false + + decoder_tokenizer: + library: sentencepiece + tokenizer_model: null + vocab_size: null # vocab size for training bpe + bpe_dropout: null + vocab_file: null + special_tokens: null + r2l: false + + encoder: + library: nemo + model_name: null + pretrained: false + max_sequence_length: 512 + num_token_types: 0 + embedding_dropout: 0.1 + learn_positional_encodings: false + hidden_size: 512 + num_layers: 6 + inner_size: 2048 + num_attention_heads: 8 + ffn_dropout: 0.1 + attn_score_dropout: 0.1 + attn_layer_dropout: 0.1 + hidden_act: relu + mask_future: false + pre_ln: false + pre_ln_final_layer_norm: true + + decoder: + library: nemo + model_name: null + pretrained: false + max_sequence_length: 512 + num_token_types: 0 + embedding_dropout: 0.1 + learn_positional_encodings: false + hidden_size: 512 + inner_size: 2048 + num_layers: 6 + num_attention_heads: 8 + ffn_dropout: 0.1 + attn_score_dropout: 0.1 + attn_layer_dropout: 0.1 + hidden_act: relu + pre_ln: false + pre_ln_final_layer_norm: true + + head: + num_layers: 1 + activation: relu + log_softmax: true + dropout: 0.0 + use_transformer_init: true + +trainer: + devices: 4 + num_nodes: 1 + max_epochs: 200 + precision: 16 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0 + accelerator: gpu + enable_checkpointing: False + logger: False + log_every_n_steps: 50 # Interval of logging. + check_val_every_n_epoch: 1 + benchmark: False + +exp_manager: + name: AAYNBase + files_to_copy: [] diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/aayn_base_megatron.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/aayn_base_megatron.yaml new file mode 100644 index 0000000..7023e4b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/aayn_base_megatron.yaml @@ -0,0 +1,179 @@ +defaults: + - ../../language_modeling/conf@model.encoder: megatron_model_base_config + - ../../language_modeling/conf@model.decoder: megatron_model_base_config + +name: megatron_nmt +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + precision: 16 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0 + accelerator: gpu + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: 1000 # PTL default. In practice, max_steps will be reached first. + max_steps: 400000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 1000 + accumulate_grad_batches: 1 + gradient_clip_val: 1.0 + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: ${name} + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + filename: '${name}--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + +model: + # NMT Params + multilingual: False + label_smoothing: 0.1 # TODO: Implement this. + shared_tokenizer: True # train tokenizer model across src and tgt train data + preproc_out_dir: null # path to store data preprocessing outputs + src_language: 'en' + tgt_language: 'de' + max_generation_delta: 20 # Maximum decoder sequence length is encoder sequence length + this parameter. + pretrained_model_path: null # Path to a pretrained model + pretrained_model_type: T5 + + # model parallelism + micro_batch_size: 32 + global_batch_size: 512 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 + pipeline_model_parallel_split_rank: 0 # rank at which decoder starts. + resume_from_checkpoint: null # manually set the checkpoint file to load from + + # model architecture + make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency. + + megatron_amp_O2: False # use AMP with O2 style mixed precision instead of native amp on-the-fly weight autocasting. + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce + gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + + seq_length: 512 + max_position_embeddings: ${.seq_length} + + # weight init + embedding_init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + + # embedding dropout + embedding_dropout: 0.1 + + # embedding sharing + share_token_embeddings: True # If True share encoder/decoder embeddings + share_decoder_tokens_head_embeddings: True # If True share decoder embeddings and decoder projection to logits + + # token head + tokens_head_bias: True + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # miscellaneous + seed: 1234 + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + + train_ds: + src_file_name: null + tgt_file_name: null + dataset_type: 'text_memmap' # Options ['bin_memmap', 'text_memmap'] + sampler: 'megatron' # Options ['megatron']. Note megatron samplers do not shuffle across epochs. + objective: 'nmt' # Options ['nmt', 'nmt-xlm'] + # NOTE: These ratios are used only when the objective is `nmt-xlm` + sampling_ratios: + x-masking: 0.17 # Extreme span masking task selection probability (either large spans or large masking prob) + r-masking: 0.17 # T5-style random span masking task selection probability + s-masking: 0.16 # Prefix-LM task selection probability + nmt: 0.5 # NMT selection probability + micro_batch_size: ${model.micro_batch_size} + global_batch_size: ${model.global_batch_size} + # config for preprocessing training data and creating a tarred datset automatically + max_seq_length: 512 + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + concat_sampling_probabilities: null # only used with ConcatTranslationDataset + + validation_ds: + src_file_name: ??? + tgt_file_name: ??? + dataset_type: 'text' # Options: ['text']. Validation data needs to be raw text. + tokens_in_batch: 512 + clean: false + max_seq_length: 512 + shuffle: false + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + test_ds: + src_file_name: ??? + tgt_file_name: ??? + dataset_type: 'text' # Options: ['text']. Validation data needs to be raw text. + tokens_in_batch: 512 + clean: false + max_seq_length: 512 + shuffle: false + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + optim: + name: fused_adam + lr: 0.0004 + betas: + - 0.9 + - 0.98 + weight_decay: 0.0 + sched: + name: InverseSquareRootAnnealing + min_lr: 0.0 + last_epoch: -1 + warmup_ratio: 0.1 + + encoder_tokenizer: + library: sentencepiece + model: null + vocab_size: null # vocab size for training bpe + bpe_dropout: null + vocab_file: null + special_tokens: null + training_sample_size: null # valid for sentencepiece tokenizer + r2l: false + sentencepiece_legacy: True # Legacy=True allows you to add special tokens to sentencepiece tokenizers. + num_sentinel_tokens: 0 + + decoder_tokenizer: + library: sentencepiece + model: null + vocab_size: null # vocab size for training bpe + bpe_dropout: null + vocab_file: null + special_tokens: null + training_sample_size: null # valid for sentencepiece tokenizer + r2l: false + sentencepiece_legacy: True + num_sentinel_tokens: 0 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/aayn_bottleneck.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/aayn_bottleneck.yaml new file mode 100644 index 0000000..463ee06 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/aayn_bottleneck.yaml @@ -0,0 +1,47 @@ +# The bottleneck architecture supports three learning framework (i.e., losses) +# via model.model_type: +# 1) nll - Conditional cross entropy (the usual NMT loss) +# 2) mim - MIM learning framework. A latent variable model with good +# reconstruction and compressed latent representation. +# https://arxiv.org/pdf/2003.02645.pdf +# 3) vae - VAE learning framework. A latent variable model which learns +# good probability estimation over observations and +# a regularized latent representation. +# https://arxiv.org/pdf/1312.6114.pdf +# The bottleneck architecture supports three encoder architectures via +# model.encoder.arch: +# 1) seq2seq - the usual NMT model without bottleneck +# 2) bridge - a bottleneck which projects the encoder output to a fixed +# number of steps using attention bridge (https://arxiv.org/pdf/1703.03130.pdf) +# 3) perceiver - a bottleneck by projecting inputs to a fixed +# number of steps using perceiver architecture (https://arxiv.org/pdf/2103.03206.pdf) +# 4) max_pool / avg_pool - a reduction by halving the number of steps at the end of every hidden block. +# reduction is using max pooling or average pooling. + +defaults: + - aayn_base + +name: AttentionIsAllYouNeedBottleneck + +do_training: True # set to False if only preprocessing data +do_testing: False # set to True to run evaluation on test data after training + +model: + model_type: 'nll' # learning (i.e., loss) type: nll (i.e., cross-entropy/auto-encoder), mim, vae (see description above) + min_logv: -6 # minimal allowed log variance for mim + latent_size: -1 # dimension of latent (projected from hidden) -1 will take value of hidden size + non_recon_warmup_batches: 200000 # warm-up steps for mim, and vae losses + recon_per_token: true # when false reconstruction is computed per sample, not per token + + encoder: + mask_future: false + arch: perceiver # avg_pool, max_pool, seq2seq, bridge, perceiver (see description above) + hidden_steps: 32 # fixed number of hidden steps + hidden_blocks: 1 # number of repeat blocks (see classes for description) + hidden_init_method: default # see classes for available values + + decoder: + arch: seq2seq # currently only seq2seq is supported + +exp_manager: + name: AAYNBottleneck diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/aayn_finetune.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/aayn_finetune.yaml new file mode 100644 index 0000000..f62e2a6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/aayn_finetune.yaml @@ -0,0 +1,77 @@ +name: AttentionIsAllYouNeedFinetune +do_training: True # set to False if only preprocessing data +do_testing: False # set to True to run evaluation on test data after training +model_path: ??? + +model: + train_ds: + src_file_name: null + tgt_file_name: null + use_tarred_dataset: False # if true tar_file_name and meta_file_name will be used (or created automatically) + # config for preprocessing training data and creating a tarred datset automatically + tar_file_prefix: parallel # prefix for tar file names + tar_files: null # if data has already been preprocessed (rest of config ignored) + metadata_file: null # metadata for tarred dataset + lines_per_dataset_fragment: 1000000 # Number of lines to consider for bucketing and padding + num_batches_per_tarfile: 100 # Number of batches (pickle files) within each tarfile + tar_shuffle_n: 100 # How many samples to look ahead and load to be shuffled + shard_strategy: scatter # tarred dataset shard distribution strategy + n_preproc_jobs: -2 # number of processes to use for data preprocessing (-2 means all but 2) + tokens_in_batch: 512 + clean: true + max_seq_length: 512 + shuffle: true + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + concat_sampling_technique: temperature # only used with ConcatTranslationDataset + concat_sampling_temperature: 5 # only used with ConcatTranslationDataset + concat_sampling_probabilities: null # only used with ConcatTranslationDataset + + validation_ds: + src_file_name: ??? + tgt_file_name: ??? + tokens_in_batch: 512 + clean: false + max_seq_length: 512 + shuffle: false + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + test_ds: + src_file_name: ??? + tgt_file_name: ??? + tokens_in_batch: 512 + clean: false + max_seq_length: 512 + shuffle: false + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + optim: + name: adam + lr: 0.00002 + betas: + - 0.9 + - 0.98 + weight_decay: 0.0 + +trainer: + devices: 4 + num_nodes: 1 + max_epochs: 200 + precision: 16 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0 + accelerator: gpu + enable_checkpointing: False + logger: False + log_every_n_steps: 50 # Interval of logging. + check_val_every_n_epoch: 1 + +exp_manager: + name: AAYNBaseFineTune + files_to_copy: [] diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/huggingface.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/huggingface.yaml new file mode 100644 index 0000000..b2567a0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/huggingface.yaml @@ -0,0 +1,132 @@ +name: HuggingFaceEncoder +do_training: True # set to False if only preprocessing data +do_testing: False # set to True to run evaluation on test data after training + +model: + beam_size: 4 + len_pen: 0.6 + max_generation_delta: -1 + label_smoothing: 0.1 + shared_tokenizer: false + preproc_out_dir: null + src_language: 'en' + tgt_language: 'de' + + train_ds: + src_file_name: null + tgt_file_name: null + use_tarred_dataset: False # if true tar_file_name and meta_file_name will be used (or created automatically) + # config for preprocessing training data and creating a tarred datset automatically + tar_file_prefix: parallel # prefix for tar file names + tar_files: null # if data has already been preprocessed (rest of config ignored) + metadata_file: null # metadata for tarred dataset + lines_per_dataset_fragment: 1000000 # Number of lines to consider for bucketing and padding + num_batches_per_tarfile: 100 # Number of batches (pickle files) within each tarfile + tar_shuffle_n: 100 # How many samples to look ahead and load to be shuffled + shard_strategy: scatter # tarred dataset shard distribution strategy + n_preproc_jobs: -2 # number of processes to use for data preprocessing (-2 means all but 2) + tokens_in_batch: 512 + clean: true + max_seq_length: 512 + shuffle: true + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + validation_ds: + src_file_name: null + tgt_file_name: null + tokens_in_batch: 512 + clean: false + max_seq_length: 512 + shuffle: false + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + test_ds: + src_file_name: null + tgt_file_name: null + tokens_in_batch: 512 + clean: false + max_seq_length: 512 + shuffle: false + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + optim: + name: adam + lr: 0.001 + betas: + - 0.9 + - 0.98 + weight_decay: 0.0 + sched: + name: InverseSquareRootAnnealing + min_lr: 0.0 + last_epoch: -1 + warmup_ratio: 0.1 + + encoder_tokenizer: + library: huggingface + tokenizer_model: null + vocab_file: null + special_tokens: null + vocab_size: null + + decoder_tokenizer: + library: huggingface + tokenizer_model: null + vocab_file: null + special_tokens: null + vocab_size: null + + encoder: + library: huggingface + model_name: bert-base-uncased + pretrained: false + + decoder: + library: nemo + model_name: null + pretrained: false + max_sequence_length: 512 + num_token_types: 2 + embedding_dropout: 0.1 + learn_positional_encodings: false + hidden_size: 512 + inner_size: 2048 + num_layers: 6 + num_attention_heads: 8 + ffn_dropout: 0.1 + attn_score_dropout: 0.1 + attn_layer_dropout: 0.1 + hidden_act: relu + pre_ln: false + + head: + num_layers: 1 + activation: relu + log_softmax: true + dropout: 0.0 + use_transformer_init: true + +trainer: + devices: 4 + num_nodes: 1 + max_epochs: 200 + precision: 16 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0 + accelerator: gpu + enable_checkpointing: False + logger: False + log_every_n_steps: 50 # Interval of logging. + check_val_every_n_epoch: 1 + benchmark: False + +exp_manager: + name: HuggingFaceEncoder + files_to_copy: [] diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/megatron.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/megatron.yaml new file mode 100644 index 0000000..ac7faed --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/megatron.yaml @@ -0,0 +1,160 @@ +name: MegatronEncoder +do_training: True # set to False if only preprocessing data +do_testing: False # set to True to run evaluation on test data after training + +model: + beam_size: 4 + len_pen: 0.6 + max_generation_delta: -1 + label_smoothing: 0.1 + shared_tokenizer: false + preproc_out_dir: null + src_language: 'en' + tgt_language: 'de' + + train_ds: + src_file_name: null + tgt_file_name: null + use_tarred_dataset: False # if true tar_file_name and meta_file_name will be used (or created automatically) + # config for preprocessing training data and creating a tarred datset automatically + tar_file_prefix: parallel # prefix for tar file names + tar_files: null # if data has already been preprocessed (rest of config ignored) + metadata_file: null # metadata for tarred dataset + lines_per_dataset_fragment: 1000000 # Number of lines to consider for bucketing and padding + num_batches_per_tarfile: 100 # Number of batches (pickle files) within each tarfile + tar_shuffle_n: 100 # How many samples to look ahead and load to be shuffled + shard_strategy: scatter # tarred dataset shard distribution strategy + n_preproc_jobs: -2 # number of processes to use for data preprocessing (-2 means all but 2) + tokens_in_batch: 512 + clean: true + max_seq_length: 512 + shuffle: true + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + validation_ds: + src_file_name: null + tgt_file_name: null + tokens_in_batch: 512 + clean: false + max_seq_length: 512 + shuffle: false + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + test_ds: + src_file_name: null + tgt_file_name: null + tokens_in_batch: 512 + clean: false + max_seq_length: 512 + shuffle: false + num_samples: -1 + drop_last: false + pin_memory: false + num_workers: 8 + + optim: + name: adam + lr: 0.001 + betas: + - 0.9 + - 0.98 + weight_decay: 0.0 + sched: + name: InverseSquareRootAnnealing + min_lr: 0.0 + last_epoch: -1 + warmup_ratio: 0.1 + + encoder_tokenizer: + library: megatron + tokenizer_model: null + vocab_file: null + special_tokens: null + vocab_size: null + model_name: null + + decoder_tokenizer: + library: sentencepiece + tokenizer_model: null + vocab_file: null + special_tokens: null + vocab_size: null + + encoder: + library: megatron + + # If using a pretrained megatron bert model from NGC, then use the corresponding model name + # For example, 'megatron-bert-345m-uncased'. + # If restoring from a local checkpoint, then use either 'megatron-bert-uncased' or 'megatron-bert-cased' + model_name: megatron-bert-uncased # or megatron-bert-cased + + # If restoring from a model parallel checkpoint, then checkpoint_file should be a path to + # the directory containing the megatron-lm checkpoints. The directory will have the structure: + + # /path/to/my/checkpoint/ + # ├── mp_rank_00 + # │ └── model_optim_rng.pt + # └── mp_rank_01 + # └── model_optim_rng.pt + + # If not using a model parallel checkpoint, then use the full path to the checkpoint: + + # /path/to/my/checkpoint/model_optim_rng.pt + checkpoint_file: null + vocab_file : null + + pretrained: true # only pretrained=true supported for now + + # model architecture configuration + hidden_size: 1024 + num_attention_heads: 16 + num_layers: 24 + max_position_embeddings: 512 + num_tokentypes: 0 + + decoder: + library: nemo + model_name: null + pretrained: false + max_sequence_length: 512 + num_token_types: 2 + embedding_dropout: 0.1 + learn_positional_encodings: false + hidden_size: 512 + inner_size: 2048 + num_layers: 6 + num_attention_heads: 8 + ffn_dropout: 0.1 + attn_score_dropout: 0.1 + attn_layer_dropout: 0.1 + hidden_act: relu + pre_ln: false + + head: + num_layers: 1 + activation: relu + log_softmax: true + dropout: 0.0 + use_transformer_init: true + +trainer: + devices: 4 + num_nodes: 1 + max_epochs: 200 + precision: 16 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0 + accelerator: gpu + strategy: ddp + enable_checkpointing: False + logger: False + log_every_n_steps: 50 # Interval of logging. + check_val_every_n_epoch: 1 + +exp_manager: + name: ${name} + files_to_copy: [] \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/nmt_megatron_infer.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/nmt_megatron_infer.yaml new file mode 100644 index 0000000..6ca1115 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/conf/nmt_megatron_infer.yaml @@ -0,0 +1,16 @@ +trainer: + devices: 1 + num_nodes: 1 + precision: 16 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0 + accelerator: gpu + +model_file: ??? +checkpoint_dir: null +srctext: ??? +tgtout: ??? +batch_size: 128 +source_lang: null +target_lang: null +tensor_model_parallel_size: 1 +pipeline_model_parallel_size: 1 +pipeline_model_parallel_split_rank: 0 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/create_tarred_monolingual_dataset.py b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/create_tarred_monolingual_dataset.py new file mode 100644 index 0000000..85dbc1c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/create_tarred_monolingual_dataset.py @@ -0,0 +1,70 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os + +from nemo.collections.nlp.data.machine_translation.preproc_mt_data import MTDataPreproc + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='NMT dataset pre-processing') + parser.add_argument( + '--tokenizer_name', type=str, default='sentencepiece', help='Supports entencepiece and HuggingFace tokenizers', + ) + parser.add_argument('--tokenizer_model', type=str, default=None, help='Path to tokenizer model') + parser.add_argument('--bpe_droput', type=float, default=0.0, help='BPE dropout to use') + parser.add_argument('--clean', action="store_true", help='Whether to clean dataset based on length diff') + parser.add_argument('--pkl_file_prefix', type=str, default='parallel', help='Prefix for tar and pickle files') + parser.add_argument('--fname', type=str, required=True, help='Path to monolingual data file') + parser.add_argument('--out_dir', type=str, required=True, help='Path to store dataloader and tokenizer models') + parser.add_argument('--max_seq_length', type=int, default=512, help='Max Sequence Length') + parser.add_argument('--min_seq_length', type=int, default=1, help='Min Sequence Length') + parser.add_argument('--tokens_in_batch', type=int, default=16000, help='# Tokens per batch per GPU') + parser.add_argument( + '--lines_per_dataset_fragment', + type=int, + default=1000000, + help='Number of lines to consider for bucketing and padding', + ) + parser.add_argument( + '--num_batches_per_tarfile', + type=int, + default=1000, + help='Number of batches (pickle files) within each tarfile', + ) + + args = parser.parse_args() + if not os.path.exists(args.out_dir): + os.mkdir(args.out_dir) + if (args.tokenizer_name == "sentencepiece") and not os.path.exists(args.tokenizer_model): + assert FileNotFoundError("Could not find tokenizer model %s" % (args.tokenizer)) + + tokenizer_model = MTDataPreproc.get_monolingual_tokenizer( + tokenizer_name=args.tokenizer_name, tokenizer_model=args.tokenizer_model, bpe_dropout=args.bpe_droput + ) + + MTDataPreproc.preprocess_monolingual_dataset( + clean=args.clean, + fname=args.fname, + out_dir=args.out_dir, + tokenizer=tokenizer_model, + max_seq_length=args.max_seq_length, + min_seq_length=args.min_seq_length, + tokens_in_batch=args.tokens_in_batch, + lines_per_dataset_fragment=args.lines_per_dataset_fragment, + num_batches_per_tarfile=args.num_batches_per_tarfile, + pkl_file_prefix=args.pkl_file_prefix, + global_rank=0, + world_size=1, + ) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/create_tarred_parallel_dataset.py b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/create_tarred_parallel_dataset.py new file mode 100644 index 0000000..506b307 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/create_tarred_parallel_dataset.py @@ -0,0 +1,187 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os + +from nemo.collections.nlp.data.machine_translation.preproc_mt_data import MTDataPreproc + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='NMT dataset pre-processing') + parser.add_argument('--shared_tokenizer', action="store_true", help='Whether to share encoder/decoder tokenizers') + parser.add_argument('--clean', action="store_true", help='Whether to clean dataset based on length diff') + parser.add_argument('--tar_file_prefix', type=str, default='parallel', help='Prefix for tar files') + parser.add_argument('--src_fname', type=str, required=True, help='Path to the source file') + parser.add_argument('--tgt_fname', type=str, required=True, help='Path to the target file') + parser.add_argument('--out_dir', type=str, required=True, help='Path to store dataloader and tokenizer models') + parser.add_argument('--encoder_model_name', type=str, default=None, help='For use with pretrained encoders') + parser.add_argument( + '--decoder_model_name', type=str, default=None, help='For use with pretrained decoders (not yet supported)' + ) + parser.add_argument( + '--encoder_tokenizer_model', type=str, default='None', help='Path to pre-trained encoder tokenizer model' + ) + parser.add_argument( + '--encoder_tokenizer_name', + type=str, + default='sentencepiece', + help='Encoder BPE Tokenizer Name, Options: [sentencepiece]', + ) + parser.add_argument('--encoder_tokenizer_vocab_size', type=int, default=32000, help='Encoder Vocab size after BPE') + parser.add_argument( + '--encoder_tokenizer_coverage', type=float, default=0.999, help='Encoder Character coverage for BPE' + ) + parser.add_argument('--encoder_tokenizer_bpe_dropout', type=float, default=0.1, help='Encoder BPE dropout prob') + parser.add_argument( + '--encoder_tokenizer_r2l', action="store_true", help='Whether to return encoded sequence from right to left' + ) + parser.add_argument( + '--encoder_tokenizer_legacy', + action="store_true", + help='Whether to use legacy tokenizer implementation of sentencepiece', + ) + parser.add_argument( + '--decoder_tokenizer_model', type=str, default='None', help='Path to pre-trained decoder tokenizer model' + ) + parser.add_argument( + '--decoder_tokenizer_name', + type=str, + default='sentencepiece', + help='Encoder BPE Tokenizer Name, Options: [sentencepiece]', + ) + parser.add_argument('--decoder_tokenizer_vocab_size', type=int, default=32000, help='Encoder Vocab size after BPE') + parser.add_argument( + '--decoder_tokenizer_coverage', type=float, default=0.999, help='Encoder Character coverage for BPE' + ) + parser.add_argument('--decoder_tokenizer_bpe_dropout', type=float, default=0.1, help='Encoder BPE dropout prob') + parser.add_argument( + '--decoder_tokenizer_r2l', action="store_true", help='Whether to return encoded sequence from right to left' + ) + parser.add_argument( + '--decoder_tokenizer_legacy', + action="store_true", + help='Whether to use legacy tokenizer implementation of sentencepiece', + ) + parser.add_argument('--max_seq_length', type=int, default=512, help='Max Sequence Length') + parser.add_argument('--min_seq_length', type=int, default=1, help='Min Sequence Length') + parser.add_argument('--tokens_in_batch', type=int, default=16000, help='# Tokens per batch per GPU') + parser.add_argument('--coverage', type=float, default=0.999, help='BPE character coverage [0-1]') + parser.add_argument( + '--lines_per_dataset_fragment', + type=int, + default=1000000, + help='Number of lines to consider for bucketing and padding', + ) + parser.add_argument( + '--num_batches_per_tarfile', + type=int, + default=1000, + help='Number of batches (pickle files) within each tarfile', + ) + parser.add_argument( + '--n_preproc_jobs', type=int, default=-2, help='Number of processes to use for creating the tarred dataset.', + ) + parser.add_argument( + '--byte_fallback', + action="store_true", + help='Whether to use byte fallback with sentencepiece for BPE tokenization.', + ) + parser.add_argument( + '--split_digits', action="store_true", help='Whether to split digits while tokenizing with sentencepiece.' + ) + parser.add_argument( + '--no_split_by_whitespace', + action="store_true", + help='If True, this will not respect whitepsaces while learning BPE merges.', + ) + args = parser.parse_args() + if not os.path.exists(args.out_dir): + os.mkdir(args.out_dir) + + if ( + args.encoder_tokenizer_model != 'None' + and args.decoder_tokenizer_model == 'None' + or args.decoder_tokenizer_model != 'None' + and args.encoder_tokenizer_model == 'None' + ): + if args.shared_tokenizer: + raise ValueError( + ''' + If using a pre-trained shared tokenizer, + both encoder and decoder tokenizers must be the same + ''' + ) + else: + raise ValueError('Both encoder and decoder pre-trained tokenizer models must be specified') + + if args.encoder_tokenizer_model == 'None' and args.decoder_tokenizer_model == 'None': + encoder_tokenizer_model, decoder_tokenizer_model = MTDataPreproc.train_tokenizers( + out_dir=args.out_dir, + src_fname=args.src_fname, + tgt_fname=args.tgt_fname, + shared_tokenizer=args.shared_tokenizer, + encoder_tokenizer_name=args.encoder_tokenizer_name, + encoder_tokenizer_vocab_size=args.encoder_tokenizer_vocab_size, + encoder_tokenizer_coverage=args.encoder_tokenizer_coverage, + decoder_tokenizer_name=args.decoder_tokenizer_name, + decoder_tokenizer_vocab_size=args.decoder_tokenizer_vocab_size, + decoder_tokenizer_coverage=args.decoder_tokenizer_coverage, + global_rank=0, + byte_fallback=args.byte_fallback, + split_digits=args.split_digits, + split_by_whitespace=not args.no_split_by_whitespace, + ) + else: + encoder_tokenizer_model, decoder_tokenizer_model = args.encoder_tokenizer_model, args.decoder_tokenizer_model + + encoder_tokenizer, decoder_tokenizer = MTDataPreproc.get_enc_dec_tokenizers( + encoder_tokenizer_name=args.encoder_tokenizer_name, + encoder_tokenizer_model=encoder_tokenizer_model, + encoder_bpe_dropout=args.encoder_tokenizer_bpe_dropout, + encoder_r2l=args.encoder_tokenizer_r2l, + decoder_tokenizer_name=args.decoder_tokenizer_name, + decoder_tokenizer_model=decoder_tokenizer_model, + decoder_bpe_dropout=args.decoder_tokenizer_bpe_dropout, + decoder_r2l=args.decoder_tokenizer_r2l, + encoder_tokenizer_legacy=args.encoder_tokenizer_legacy, + decoder_tokenizer_legacy=args.decoder_tokenizer_legacy, + ) + + _, _ = MTDataPreproc.preprocess_parallel_dataset( + clean=args.clean, + src_fname=args.src_fname, + tgt_fname=args.tgt_fname, + out_dir=args.out_dir, + encoder_tokenizer_name=args.encoder_tokenizer_name, + encoder_model_name=args.encoder_model_name, + encoder_tokenizer_model=encoder_tokenizer_model, + encoder_bpe_dropout=args.encoder_tokenizer_bpe_dropout, + encoder_tokenizer_r2l=args.encoder_tokenizer_r2l, + decoder_tokenizer_name=args.decoder_tokenizer_name, + decoder_model_name=args.decoder_model_name, + decoder_tokenizer_model=decoder_tokenizer_model, + decoder_tokenizer_r2l=args.decoder_tokenizer_r2l, + decoder_bpe_dropout=args.decoder_tokenizer_bpe_dropout, + max_seq_length=args.max_seq_length, + min_seq_length=args.min_seq_length, + tokens_in_batch=args.tokens_in_batch, + lines_per_dataset_fragment=args.lines_per_dataset_fragment, + num_batches_per_tarfile=args.num_batches_per_tarfile, + tar_file_prefix=args.tar_file_prefix, + global_rank=0, + world_size=1, + n_jobs=args.n_preproc_jobs, + encoder_tokenizer_legacy=args.encoder_tokenizer_legacy, + decoder_tokenizer_legacy=args.decoder_tokenizer_legacy, + ) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/enc_dec_nmt-bottleneck.py b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/enc_dec_nmt-bottleneck.py new file mode 100644 index 0000000..b1743e0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/enc_dec_nmt-bottleneck.py @@ -0,0 +1,146 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Optional + +from omegaconf import OmegaConf +from pytorch_lightning import Trainer + +from nemo.collections.nlp.data.machine_translation.preproc_mt_data import MTDataPreproc +from nemo.collections.nlp.models.machine_translation.mt_enc_dec_bottleneck_model import MTBottleneckModel +from nemo.collections.nlp.models.machine_translation.mt_enc_dec_config import MTBottleneckModelConfig +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy +from nemo.core.config import hydra_runner +from nemo.core.config.modelPT import NemoConfig +from nemo.core.config.pytorch_lightning import TrainerConfig +from nemo.utils import logging +from nemo.utils.config_utils import update_model_config +from nemo.utils.exp_manager import ExpManagerConfig, exp_manager + + +""" +Usage: + 1. If you need to start docker and install NeMo, otherwise skip this step: + + a. ```docker run --gpus all -it --rm -v /home/okuchaiev/repos/NeMo/:/NeMo -p 6006:6006 -v /mnt:/mnt --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:20.11-py3``` + b. ```cd /NeMo``` + c. ```./reinstall.sh``` + + 2. Train a new tokenizer (or use pre-trained one): + ```spm_train --input= --model_prefix= --vocab_size=8000 --character_coverage=1.0 --model_type=``` + +(To use WANDB, optionally, do login first) +``wandb login [YOUR WANDB login]`` + + 3. Start training: + + + (This example for "base" model on 2 GPUs for 150000 steps with batch size of 12500 tokens per GPU) + + python enc_dec_nmt-bottleneck.py \ + --config-path=conf \ + --config-name=aayn_bottleneck \ + trainer.devices=[0,1] \ + ~trainer.max_epochs \ + +trainer.max_steps=150000 \ + model.beam_size=4 \ + model.max_generation_delta=256 \ + model.label_smoothing=0.1 \ + model.model_type=nll \ + model.non_recon_warmup_batches=7500 \ + model.encoder_tokenizer.tokenizer_model=tokenizer.BPE.8192.model \ + model.decoder_tokenizer.tokenizer_model=tokenizer.BPE.8192.model \ + model.encoder.arch=perceiver \ + model.encoder.hidden_steps=32 \ + model.encoder.hidden_blocks=2 \ + model.encoder.hidden_init_method=bridge \ + model.encoder.num_layers=6 \ + model.encoder.hidden_size=512 \ + model.encoder.inner_size=2048 \ + model.encoder.num_attention_heads=8 \ + model.encoder.ffn_dropout=0.1 \ + model.decoder.num_layers=6 \ + model.decoder.hidden_size=512 \ + model.decoder.inner_size=2048 \ + model.decoder.num_attention_heads=8 \ + model.decoder.ffn_dropout=0.1 \ + model.train_ds.src_file_name=/mnt/D1/Data/NMT/wmt16_de_en/train.clean.de.shuffled \ + model.train_ds.tgt_file_name=/mnt/D1/Data/NMT/wmt16_de_en/train.clean.en.shuffled \ + model.train_ds.tokens_in_batch=12500 \ + model.validation_ds.src_file_name=/mnt/D1/Data/NMT/wmt16_de_en/wmt14-en-de.ref \ + model.validation_ds.tgt_file_name=/mnt/D1/Data/NMT/wmt16_de_en/wmt14-en-de.src \ + model.validation_ds.tokens_in_batch=8192 \ + model.test_ds.src_file_name=/mnt/D1/Data/NMT/wmt16_de_en/wmt14-en-de.ref \ + model.test_ds.tgt_file_name=/mnt/D1/Data/NMT/wmt16_de_en/wmt14-en-de.src \ + model.optim.lr=0.001 \ + model.optim.sched.warmup_ratio=0.05 \ + +exp_manager.create_wandb_logger=True \ + +exp_manager.wandb_logger_kwargs.name=TEST-nmt-base \ + +exp_manager.wandb_logger_kwargs.project=nmt-de-en \ + +exp_manager.create_checkpoint_callback=True \ + +exp_manager.checkpoint_callback_params.monitor=val_sacreBLEU \ + +exp_manager.exp_dir=nmt_base \ + +exp_manager.checkpoint_callback_params.mode=max +""" + + +@dataclass +class MTBottleneckConfig(NemoConfig): + name: Optional[str] = 'MTBottleneck' + do_training: bool = True + do_testing: bool = False + model: MTBottleneckModelConfig = MTBottleneckModelConfig() + trainer: Optional[TrainerConfig] = TrainerConfig() + exp_manager: Optional[ExpManagerConfig] = ExpManagerConfig(name='MTBottleneck', files_to_copy=[]) + + +@hydra_runner(config_path="conf", config_name="aayn_bottleneck") +def main(cfg: MTBottleneckConfig) -> None: + # merge default config with user specified config + default_cfg = MTBottleneckConfig() + cfg = update_model_config(default_cfg, cfg) + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + + # training is managed by PyTorch Lightning + trainer_cfg = OmegaConf.to_container(cfg.trainer) + trainer_cfg.pop('strategy', None) + trainer = Trainer(strategy=NLPDDPStrategy(), **trainer_cfg) + + # tokenizers will be trained and and tarred training data will be created if needed + # model config is then updated + if cfg.model.preproc_out_dir is not None: + MTDataPreproc(cfg=cfg.model, trainer=trainer) + + # experiment logs, checkpoints, and auto-resume are managed by exp_manager and PyTorch Lightning + exp_manager(trainer, cfg.exp_manager) + + # everything needed to train translation models is encapsulated in the NeMo MTEncdDecModel + mt_model = MTBottleneckModel(cfg.model, trainer=trainer) + + logging.info("\n\n************** Model parameters and their sizes ***********") + for name, param in mt_model.named_parameters(): + print(name, param.size()) + logging.info("***********************************************************\n\n") + + if cfg.do_training: + trainer.fit(mt_model) + + if cfg.do_testing: + trainer.test(mt_model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/enc_dec_nmt.py b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/enc_dec_nmt.py new file mode 100644 index 0000000..57b9f84 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/enc_dec_nmt.py @@ -0,0 +1,141 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Optional + +from omegaconf import OmegaConf +from pytorch_lightning import Trainer + +from nemo.collections.nlp.data.machine_translation.preproc_mt_data import MTDataPreproc +from nemo.collections.nlp.models.machine_translation.mt_enc_dec_config import MTEncDecModelConfig +from nemo.collections.nlp.models.machine_translation.mt_enc_dec_model import MTEncDecModel +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy +from nemo.core.config import hydra_runner +from nemo.core.config.modelPT import NemoConfig +from nemo.core.config.pytorch_lightning import TrainerConfig +from nemo.utils import logging +from nemo.utils.config_utils import update_model_config +from nemo.utils.exp_manager import ExpManagerConfig, exp_manager + + +""" +Usage: + 1. If you need to start docker and install NeMo, otherwise skip this step: + + a. ```docker run --gpus all -it --rm -v /home/okuchaiev/repos/NeMo/:/NeMo -p 6006:6006 -v /mnt:/mnt --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:20.11-py3``` + b. ```cd /NeMo``` + c. ```./reinstall.sh``` + + 2. Train a new tokenizer (or use pre-trained one): + ```spm_train --input= --model_prefix= --vocab_size=8000 --character_coverage=1.0 --model_type=``` + +(To use WANDB, optionally, do login first) +``wandb login [YOUR WANDB login]`` + + 3. Start training: + + + (This example for "base" model on 2 GPUs for 150000 steps with batch size of 12500 tokens per GPU) + + python enc_dec_nmt.py \ + --config-path=conf \ + --config-name=aayn_base \ + trainer.devices=[0,1] \ + ~trainer.max_epochs \ + +trainer.max_steps=150000 \ + model.beam_size=4 \ + model.max_generation_delta=5 \ + model.label_smoothing=0.1 \ + model.encoder_tokenizer.tokenizer_model=tokenizer.BPE.8192.model \ + model.decoder_tokenizer.tokenizer_model=tokenizer.BPE.8192.model \ + model.encoder.num_layers=6 \ + model.encoder.hidden_size=512 \ + model.encoder.inner_size=2048 \ + model.encoder.num_attention_heads=8 \ + model.encoder.ffn_dropout=0.1 \ + model.decoder.num_layers=6 \ + model.decoder.hidden_size=512 \ + model.decoder.inner_size=2048 \ + model.decoder.num_attention_heads=8 \ + model.decoder.ffn_dropout=0.1 \ + model.train_ds.src_file_name=/mnt/D1/Data/NMT/wmt16_de_en/train.clean.de.shuffled \ + model.train_ds.tgt_file_name=/mnt/D1/Data/NMT/wmt16_de_en/train.clean.en.shuffled \ + model.train_ds.tokens_in_batch=12500 \ + model.validation_ds.src_file_name=/mnt/D1/Data/NMT/wmt16_de_en/wmt14-en-de.ref \ + model.validation_ds.tgt_file_name=/mnt/D1/Data/NMT/wmt16_de_en/wmt14-en-de.src \ + model.validation_ds.tokens_in_batch=8192 \ + model.test_ds.src_file_name=/mnt/D1/Data/NMT/wmt16_de_en/wmt14-en-de.ref \ + model.test_ds.tgt_file_name=/mnt/D1/Data/NMT/wmt16_de_en/wmt14-en-de.src \ + model.optim.lr=0.001 \ + model.optim.sched.warmup_ratio=0.05 \ + +exp_manager.create_wandb_logger=True \ + +exp_manager.wandb_logger_kwargs.name=TEST-nmt-base \ + +exp_manager.wandb_logger_kwargs.project=nmt-de-en \ + +exp_manager.create_checkpoint_callback=True \ + +exp_manager.checkpoint_callback_params.monitor=val_sacreBLEU \ + +exp_manager.exp_dir=nmt_base \ + +exp_manager.checkpoint_callback_params.mode=max +""" + + +@dataclass +class MTEncDecConfig(NemoConfig): + name: Optional[str] = 'MTEncDec' + do_training: bool = True + do_testing: bool = False + model: MTEncDecModelConfig = MTEncDecModelConfig() + trainer: Optional[TrainerConfig] = TrainerConfig() + exp_manager: Optional[ExpManagerConfig] = ExpManagerConfig(name='MTEncDec', files_to_copy=[]) + + +@hydra_runner(config_path="conf", config_name="aayn_base") +def main(cfg: MTEncDecConfig) -> None: + # merge default config with user specified config + default_cfg = MTEncDecConfig() + cfg = update_model_config(default_cfg, cfg) + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + + # training is managed by PyTorch Lightning + trainer_cfg = OmegaConf.to_container(cfg.trainer) + trainer_cfg.pop('strategy', None) + trainer = Trainer(strategy=NLPDDPStrategy(), **trainer_cfg) + + # tokenizers will be trained and and tarred training data will be created if needed + # model config is then updated + if cfg.model.preproc_out_dir is not None: + MTDataPreproc(cfg=cfg.model, trainer=trainer) + + # experiment logs, checkpoints, and auto-resume are managed by exp_manager and PyTorch Lightning + exp_manager(trainer, cfg.exp_manager) + + # everything needed to train translation models is encapsulated in the NeMo MTEncdDecModel + mt_model = MTEncDecModel(cfg.model, trainer=trainer) + + logging.info("\n\n************** Model parameters and their sizes ***********") + for name, param in mt_model.named_parameters(): + print(name, param.size()) + logging.info("***********************************************************\n\n") + + if cfg.do_training: + trainer.fit(mt_model) + # Reset for PTL 2.0 as test uses the same ckpt as train via previously set self._ckpt_path + trainer.ckpt_path = None + if cfg.do_testing: + trainer.test(mt_model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/enc_dec_nmt_finetune.py b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/enc_dec_nmt_finetune.py new file mode 100644 index 0000000..16a635d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/enc_dec_nmt_finetune.py @@ -0,0 +1,106 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Optional + +from omegaconf import OmegaConf +from omegaconf.omegaconf import MISSING +from pytorch_lightning import Trainer + +from nemo.collections.nlp.models.machine_translation.mt_enc_dec_config import MTEncDecModelConfig +from nemo.collections.nlp.models.machine_translation.mt_enc_dec_model import MTEncDecModel +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy +from nemo.core.config import hydra_runner +from nemo.core.config.modelPT import NemoConfig +from nemo.core.config.pytorch_lightning import TrainerConfig +from nemo.utils import logging +from nemo.utils.config_utils import update_model_config +from nemo.utils.exp_manager import ExpManagerConfig, exp_manager + + +""" +Usage: + python enc_dec_nmt_finetune.py \ + model_path=/raid/models/de_en_24x6.nemo \ + trainer.devices=2 \ + ~trainer.max_epochs \ + +trainer.max_steps=4500 \ + +trainer.val_check_interval=500 \ + model.train_ds.tgt_file_name=/raid/data/train_lang_filtered.en \ + model.train_ds.src_file_name=/raid/data/train_lang_filtered.de \ + model.train_ds.tokens_in_batch=6000 \ + model.validation_ds.tgt_file_name=/raid/data/2015.norm.tok.en \ + model.validation_ds.src_file_name=/raid/data/2015.norm.tok.de \ + model.validation_ds.tokens_in_batch=4000 \ + model.test_ds.tgt_file_name=/raid/data/2015.en \ + model.test_ds.src_file_name=/raid/data/2015.de \ + +exp_manager.exp_dir=/raid/results/finetune-test \ + +exp_manager.create_checkpoint_callback=True \ + +exp_manager.checkpoint_callback_params.monitor=val_sacreBLEU \ + +exp_manager.checkpoint_callback_params.mode=max \ + +exp_manager.checkpoint_callback_params.save_best_model=true +""" + + +@dataclass +class MTFineTuneConfig(NemoConfig): + name: Optional[str] = 'MTEncDec' + model_path: str = MISSING + do_training: bool = True + do_testing: bool = False + model: MTEncDecModelConfig = MTEncDecModelConfig() + trainer: Optional[TrainerConfig] = TrainerConfig() + exp_manager: Optional[ExpManagerConfig] = ExpManagerConfig(name='MTEncDec', files_to_copy=[]) + + +@hydra_runner(config_path="conf", config_name="aayn_finetune") +def main(cfg: MTFineTuneConfig) -> None: + # merge default config with user specified config + default_cfg = MTFineTuneConfig() + default_cfg.model = MTEncDecModel.restore_from(restore_path=cfg.model_path, return_config=True) + del default_cfg.model.optim, default_cfg.model.train_ds, default_cfg.model.validation_ds, default_cfg.model.test_ds + cfg = update_model_config(default_cfg, cfg, drop_missing_subconfigs=False) + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + + # training is managed by PyTorch Lightning + trainer_cfg = OmegaConf.to_container(cfg.trainer) + trainer_cfg.pop('strategy', None) + trainer = Trainer(strategy=NLPDDPStrategy(), **trainer_cfg) + + # experiment logs, checkpoints, and auto-resume are managed by exp_manager and PyTorch Lightning + exp_manager(trainer, cfg.exp_manager) + + # everything needed to train translation models is encapsulated in the NeMo MTEncdDecModel + mt_model = MTEncDecModel.restore_from(restore_path=cfg.model_path, override_config_path=cfg.model, trainer=trainer) + + mt_model.setup_training_data(cfg.model.train_ds) + mt_model.setup_multiple_validation_data(val_data_config=cfg.model.validation_ds) + + logging.info("\n\n************** Model parameters and their sizes ***********") + for name, param in mt_model.named_parameters(): + print(name, param.size()) + logging.info("***********************************************************\n\n") + + if cfg.do_training: + trainer.fit(mt_model) + + if cfg.do_testing: + mt_model.setup_multiple_test_data(test_data_config=cfg.model.test_ds) + trainer.test(mt_model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/megatron_nmt_training.py b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/megatron_nmt_training.py new file mode 100644 index 0000000..7946500 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/megatron_nmt_training.py @@ -0,0 +1,183 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch.multiprocessing as mp +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.callbacks import ModelSummary +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector + +from nemo.collections.nlp.models.language_modeling.megatron_bart_model import MegatronBARTModel +from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model +from nemo.collections.nlp.models.machine_translation.megatron_nmt_model import MegatronNMTModel +from nemo.collections.nlp.parts.nlp_overrides import ( + GradScaler, + MegatronHalfPrecisionPlugin, + NLPDDPStrategy, + NLPSaveRestoreConnector, + PipelineMixedPrecisionPlugin, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +mp.set_start_method("spawn", force=True) + + +@hydra_runner(config_path="conf", config_name="aayn_base_megatron") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True, + gradient_as_bucket_view=cfg.model.gradient_as_bucket_view, + find_unused_parameters=False, + ) + if cfg.trainer.precision in [16, '16', 'bf16', '16-mixed', 'bf16-mixed']: + scaler = None + if cfg.trainer.precision in [16, '16', '16-mixed']: + scaler = GradScaler( + init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32), + growth_interval=cfg.model.get('native_amp_growth_interval', 1000), + hysteresis=cfg.model.get('hysteresis', 2), + ) + # MixedPrecisionPlugin in PTL >= 2.0 requires precision to be 16-mixed or bf16-mixed + plugin_precision = '16-mixed' + else: + plugin_precision = 'bf16-mixed' + + if megatron_amp_O2: + plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + else: + plugins.append(PipelineMixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler)) + # Set precision None after precision plugins are created as PTL >= 2.1 does not allow both + # precision plugins and precision to exist + cfg.trainer.precision = None + + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=[ModelSummary(max_depth=3)]) + + exp_manager(trainer, cfg.exp_manager) + + # update resume from checkpoint found by exp_manager + if cfg.model.resume_from_checkpoint is not None: + trainer.ckpt_path = cfg.model.resume_from_checkpoint + logging.info(f'Resuming training from checkpoint: {trainer.ckpt_path}') + + trainer._checkpoint_connector = _CheckpointConnector(trainer) + + if hasattr(cfg.model, 'pretrained_model_path') and cfg.model.pretrained_model_path is not None: + if not hasattr(cfg.model, 'pretrained_model_type'): + raise ValueError(f"Pretrained model type must be in [T5, BART].") + + assert cfg.model.pretrained_model_type in ['T5', 'BART'] + if cfg.model.pretrained_model_type == 'T5': + pretrained_cfg = MegatronT5Model.restore_from( + cfg.model.pretrained_model_path, trainer=trainer, return_config=True + ) + else: + pretrained_cfg = MegatronBARTModel.restore_from( + cfg.model.pretrained_model_path, trainer=trainer, return_config=True + ) + OmegaConf.set_struct(pretrained_cfg, True) + with open_dict(pretrained_cfg): + pretrained_cfg.masked_softmax_fusion = False + # Set source and target language/multilingual + pretrained_cfg.src_language = cfg.model.src_language + pretrained_cfg.tgt_language = cfg.model.tgt_language + pretrained_cfg.multilingual = cfg.model.multilingual + pretrained_cfg.shared_tokenizer = True + + # Max generation delta + pretrained_cfg.max_generation_delta = cfg.model.max_generation_delta + + # Set label smoothing + pretrained_cfg.label_smoothing = cfg.model.label_smoothing + + # Set tokenizer paths: + pretrained_cfg.encoder_tokenizer = pretrained_cfg.tokenizer + pretrained_cfg.decoder_tokenizer = pretrained_cfg.tokenizer + + # Pre-trained models should use the legacy sentencepiece tokenizer ex: mT5 + pretrained_cfg.encoder_tokenizer.sentencepiece_legacy = True + pretrained_cfg.decoder_tokenizer.sentencepiece_legacy = True + + # Override dropout + + # Old pre-trained checkpoints do not have separate encoder/decoder configurations, so replicate the config to encoder/decoder. + if not hasattr(pretrained_cfg, 'encoder'): + assert not hasattr(pretrained_cfg, 'decoder') + logging.warning( + "No separate configuration for encoder, found in pretrained model, using encoder dropout settings everywhere." + ) + pretrained_cfg.hidden_dropout = cfg.model.encoder.hidden_dropout + pretrained_cfg.attention_dropout = cfg.model.encoder.attention_dropout + else: + assert hasattr(pretrained_cfg, 'decoder') and hasattr(pretrained_cfg, 'encoder') + pretrained_cfg.encoder.hidden_dropout = cfg.model.encoder.hidden_dropout + pretrained_cfg.encoder.attention_dropout = cfg.model.encoder.attention_dropout + pretrained_cfg.decoder.hidden_dropout = cfg.model.decoder.hidden_dropout + pretrained_cfg.decoder.attention_dropout = cfg.model.decoder.attention_dropout + + # Override precision + pretrained_cfg.precision = trainer.precision # Set above from trainer.precision + + # Override micro/global batch + pretrained_cfg.micro_batch_size = cfg.model.micro_batch_size + pretrained_cfg.global_batch_size = cfg.model.global_batch_size + + # O2 AMP + pretrained_cfg.megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False) + + # Override data and global/micro batch size. + pretrained_cfg.train_ds = cfg.model.train_ds + pretrained_cfg.train_ds.micro_batch_size = cfg.model.micro_batch_size + pretrained_cfg.train_ds.global_batch_size = cfg.model.global_batch_size + if hasattr(cfg.model, 'validation_ds'): + pretrained_cfg.validation_ds = cfg.model.validation_ds + else: + raise AttributeError(f"No validation dataset found in config.") + if hasattr(cfg.model, 'test_ds'): + pretrained_cfg.test_ds = cfg.model.test_ds + + # Class target for the new class being restored. + pretrained_cfg.target = ( + "nemo.collections.nlp.models.machine_translation.megatron_nmt_model.MegatronNMTModel" + ) + + # Optimizer overrides. + pretrained_cfg.optim = cfg.model.optim + + model = MegatronNMTModel.restore_from( + cfg.model.pretrained_model_path, + trainer=trainer, + override_config_path=pretrained_cfg, + save_restore_connector=NLPSaveRestoreConnector(), + ) + else: + model = MegatronNMTModel(cfg.model, trainer) + + trainer.fit(model) + trainer.validate(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/nmt_transformer_infer.py b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/nmt_transformer_infer.py new file mode 100644 index 0000000..882350a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/nmt_transformer_infer.py @@ -0,0 +1,304 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Given NMT model's .nemo file(s), this script can be used to translate text. +USAGE Example: +1. Obtain text file in src language. You can use sacrebleu to obtain standard test sets like so: + sacrebleu -t wmt14 -l de-en --echo src > wmt14-de-en.src +2. Translate: + python nmt_transformer_infer.py --model=[Path to .nemo file(s)] --srctext=wmt14-de-en.src --tgtout=wmt14-de-en.pre +""" + + +import json +from argparse import ArgumentParser + +import torch + +import nemo.collections.nlp as nemo_nlp +from nemo.collections.nlp.modules.common.transformer import ( + BeamSearchSequenceGenerator, + BeamSearchSequenceGeneratorWithLanguageModel, + EnsembleBeamSearchSequenceGenerator, +) +from nemo.utils import logging + + +def translate_text( + models, args, src_text, tgt_text, tgt_text_all, src_texts, all_scores, all_timing, ensemble_generator +): + if len(models) > 1: + src_ids, src_mask = models[0].prepare_inference_batch(src_text) + best_translations = ensemble_generator(src_ids, src_mask, return_beam_scores=args.write_scores) + if args.write_scores: + all_results, scores, best_translations = ( + best_translations[0], + best_translations[1], + best_translations[2], + ) + scores = scores.view(-1).data.cpu().numpy().tolist() + all_scores += scores + src_texts += [item for item in src_text for i in range(args.beam_size)] + all_results = models[0].ids_to_postprocessed_text( + all_results, models[0].decoder_tokenizer, models[0].target_processor + ) + tgt_text_all += all_results + best_translations = models[0].ids_to_postprocessed_text( + best_translations, models[0].decoder_tokenizer, models[0].target_processor + ) + tgt_text += best_translations + else: + model = models[0] + best_translations = model.translate( + text=src_text, + source_lang=args.source_lang, + target_lang=args.target_lang, + return_beam_scores=args.write_scores, + log_timing=args.write_timing, + ) + + if args.write_timing: + *best_translations, timing_dict = best_translations + all_timing.append(timing_dict) + else: + best_translations = (best_translations,) + + if args.write_scores: + all_results, scores, best_translations = ( + best_translations[0], + best_translations[1], + best_translations[2], + ) + all_scores += scores + src_texts += [item for item in src_text for i in range(args.beam_size)] + tgt_text_all += all_results + else: + best_translations = best_translations[0] + + tgt_text += best_translations + + print(f"Translated {len(tgt_text)} sentences") + + +def main(): + parser = ArgumentParser() + parser.add_argument( + "--model", + type=str, + required=True, + help="Path to .nemo model file(s). If ensembling, provide comma separated paths to multiple models.", + ) + parser.add_argument("--srctext", type=str, required=True, help="Path to the file to translate.") + parser.add_argument( + "--tgtout", type=str, required=True, help="Path to the file where translations are to be written." + ) + parser.add_argument( + "--batch_size", type=int, default=256, help="Number of sentences to batch together while translatiing." + ) + parser.add_argument("--beam_size", type=int, default=4, help="Beam size.") + parser.add_argument( + "--len_pen", type=float, default=0.6, help="Length Penalty. Ref: https://arxiv.org/abs/1609.08144" + ) + parser.add_argument( + "--max_delta_length", + type=int, + default=5, + help="Stop generating if target sequence length exceeds source length by this number.", + ) + parser.add_argument( + "--target_lang", + type=str, + default=None, + help="Target language identifier ex: en,de,fr,es etc. If both `--target_lang` and `--source_lang` are " + "not set, then target language processing will be done the same way as during model training. If " + "`--target_lang` parameter is not set but `--source_lang` parameter is set, then target language " + "processing will not be performed. If `--target_lang` equals 'ignore', then target language processing " + "will not be performed regardless of value of `--source_lang` parameter.", + ) + parser.add_argument( + "--source_lang", + type=str, + default=None, + help="Source language identifier ex: en,de,fr,es etc. If both `--target_lang` and `--source_lang` are " + "not set, then source language processing will be done the same way as during model training. If " + "`--source_lang` parameter is not set but `--target_lang` parameter is set, then source language " + "processing will not be performed. If `--source_lang` equals 'ignore', then source language processing " + "will not be performed regardless of value of `--target_lang` parameter.", + ) + parser.add_argument( + "--write_scores", + action="store_true", + help="Whether to write a separate file with scores not including length penalties corresponding to each beam hypothesis (.score suffix)", + ) + parser.add_argument( + "--write_timing", + action="store_true", + help="Whether to write a separate file with detailed timing info (.timing.json suffix)", + ) + # shallow fusion specific parameters + parser.add_argument( + "--lm_model", + type=str, + default=None, + help="Optional path to an LM model that has the same tokenizer as NMT models for shallow fuison. Note: If using --write_scores, it will add LM scores as well.", + ) + parser.add_argument( + "--fusion_coef", type=float, default=0.07, help="Weight assigned to LM scores during shallow fusion." + ) + + args = parser.parse_args() + torch.set_grad_enabled(False) + logging.info("Attempting to initialize from .nemo file") + models = [] + for model_path in args.model.split(','): + if not model_path.endswith('.nemo'): + raise NotImplementedError(f"Only support .nemo files, but got: {model_path}") + model = nemo_nlp.models.machine_translation.MTEncDecModel.restore_from(restore_path=model_path).eval() + models.append(model) + + if (len(models) > 1) and (args.write_timing): + raise RuntimeError("Cannot measure timing when more than 1 model is used") + + src_text = [] + tgt_text = [] + tgt_text_all = [] + src_texts = [] + all_scores = [] + all_timing = [] + + if torch.cuda.is_available(): + models = [model.cuda() for model in models] + + if args.lm_model is not None: + lm_model = nemo_nlp.models.language_modeling.TransformerLMModel.restore_from(restore_path=args.lm_model).eval() + else: + lm_model = None + + if len(models) > 1: + ensemble_generator = EnsembleBeamSearchSequenceGenerator( + encoders=[model.encoder for model in models], + embeddings=[model.decoder.embedding for model in models], + decoders=[model.decoder.decoder for model in models], + log_softmaxes=[model.log_softmax for model in models], + max_sequence_length=512, + beam_size=args.beam_size, + bos=models[0].decoder_tokenizer.bos_id, + pad=models[0].decoder_tokenizer.pad_id, + eos=models[0].decoder_tokenizer.eos_id, + len_pen=args.len_pen, + max_delta_length=args.max_delta_length, + language_model=lm_model, + fusion_coef=args.fusion_coef, + ) + else: + model = models[0] + ensemble_generator = None + if lm_model is not None: + model.beam_search = BeamSearchSequenceGeneratorWithLanguageModel( + embedding=model.decoder.embedding, + decoder=model.decoder.decoder, + log_softmax=model.log_softmax, + bos=model.decoder_tokenizer.bos_id, + pad=model.decoder_tokenizer.pad_id, + eos=model.decoder_tokenizer.eos_id, + language_model=lm_model, + fusion_coef=args.fusion_coef, + max_sequence_length=model.decoder.max_sequence_length, + beam_size=args.beam_size, + len_pen=args.len_pen, + max_delta_length=args.max_delta_length, + ) + else: + model.beam_search = BeamSearchSequenceGenerator( + embedding=model.decoder.embedding, + decoder=model.decoder.decoder, + log_softmax=model.log_softmax, + bos=model.decoder_tokenizer.bos_id, + pad=model.decoder_tokenizer.pad_id, + eos=model.decoder_tokenizer.eos_id, + max_sequence_length=model.decoder.max_sequence_length, + beam_size=args.beam_size, + len_pen=args.len_pen, + max_delta_length=args.max_delta_length, + ) + + logging.info(f"Translating: {args.srctext}") + + with open(args.srctext, 'r') as src_f: + for line in src_f: + src_text.append(line.strip()) + if len(src_text) == args.batch_size: + # warmup when measuring timing + if args.write_timing and (not all_timing): + print("running a warmup batch") + translate_text( + models=models, + args=args, + src_text=src_text, + tgt_text=[], + tgt_text_all=[], + src_texts=[], + all_scores=[], + all_timing=[], + ensemble_generator=ensemble_generator, + ) + translate_text( + models=models, + args=args, + src_text=src_text, + tgt_text=tgt_text, + tgt_text_all=tgt_text_all, + src_texts=src_texts, + all_scores=all_scores, + all_timing=all_timing, + ensemble_generator=ensemble_generator, + ) + src_text = [] + + if len(src_text) > 0: + translate_text( + models=models, + args=args, + src_text=src_text, + tgt_text=tgt_text, + tgt_text_all=tgt_text_all, + src_texts=src_texts, + all_scores=all_scores, + all_timing=all_timing, + ensemble_generator=ensemble_generator, + ) + + with open(args.tgtout, 'w') as tgt_f: + for line in tgt_text: + tgt_f.write(line + "\n") + + if args.write_scores: + with open(args.tgtout + '.score', 'w') as tgt_f_scores: + for line, score, inp in zip(tgt_text_all, all_scores, src_texts): + tgt_f_scores.write(inp + "\t" + line + "\t" + str(score) + "\n") + + if args.write_timing: + # collect list of dicts to a dict of lists + timing_dict = {} + if len(all_timing): + for k in all_timing[0].keys(): + timing_dict[k] = [t[k] for t in all_timing] + + with open(args.tgtout + '.timing.json', 'w') as timing_fh: + json.dump(timing_dict, timing_fh) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/nmt_transformer_infer_megatron.py b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/nmt_transformer_infer_megatron.py new file mode 100644 index 0000000..c8ab668 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/nmt_transformer_infer_megatron.py @@ -0,0 +1,116 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Given NMT model's .nemo file(s), this script can be used to translate text. +USAGE Example: +1. Obtain text file in src language. You can use sacrebleu to obtain standard test sets like so: + sacrebleu -t wmt14 -l de-en --echo src > wmt14-de-en.src +2. Translate: + python nmt_transformer_infer.py --model=[Path to .nemo file(s)] --srctext=wmt14-de-en.src --tgtout=wmt14-de-en.pre +""" + + +import os + +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning.trainer.trainer import Trainer + +from nemo.collections.nlp.models.machine_translation.megatron_nmt_model import MegatronNMTModel +from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel +from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.app_state import AppState +from nemo.utils.model_utils import inject_model_parallel_rank + +try: + from apex.transformer.pipeline_parallel.utils import _reconfigure_microbatch_calculator + + HAVE_APEX = True +except (ImportError, ModuleNotFoundError): + ModelType = ApexGuardDefaults() + HAVE_APEX = False + + +@hydra_runner(config_path="conf", config_name="nmt_megatron_infer") +def main(cfg) -> None: + + # trainer required for restoring model parallel models + trainer = Trainer(strategy=NLPDDPStrategy(), **cfg.trainer) + assert ( + cfg.trainer.devices * cfg.trainer.num_nodes + == cfg.tensor_model_parallel_size * cfg.pipeline_model_parallel_size + ), "devices * num_nodes should equal tensor_model_parallel_size * pipeline_model_parallel_size" + + app_state = AppState() + app_state.model_parallel_size = cfg.tensor_model_parallel_size * cfg.pipeline_model_parallel_size + ( + app_state.tensor_model_parallel_rank, + app_state.pipeline_model_parallel_rank, + app_state.model_parallel_size, + app_state.data_parallel_size, + app_state.pipeline_model_parallel_split_rank, + app_state.virtual_pipeline_model_parallel_rank, + ) = fake_initialize_model_parallel( + world_size=app_state.model_parallel_size, + rank=trainer.global_rank, + tensor_model_parallel_size_=cfg.tensor_model_parallel_size, + pipeline_model_parallel_size_=cfg.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank_=cfg.pipeline_model_parallel_split_rank, + ) + + if cfg.model_file is not None: + if not os.path.exists(cfg.model_file): + raise ValueError(f"Model file {cfg.model_file} does not exist") + pretrained_cfg = MegatronNMTModel.restore_from(cfg.model_file, trainer=trainer, return_config=True) + OmegaConf.set_struct(pretrained_cfg, True) + with open_dict(pretrained_cfg): + pretrained_cfg.precision = trainer.precision + model = MegatronNMTModel.restore_from( + restore_path=cfg.model_file, + trainer=trainer, + save_restore_connector=NLPSaveRestoreConnector(), + override_config_path=pretrained_cfg, + ) + elif cfg.checkpoint_dir is not None: + checkpoint_path = inject_model_parallel_rank(os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name)) + model = MegatronNMTModel.load_from_checkpoint(checkpoint_path, hparams_file=cfg.hparams_file, trainer=trainer) + else: + raise ValueError("need at least a nemo file or checkpoint dir") + + model.freeze() + + logging.info(f"Translating: {cfg.srctext}") + src_text = [] + translations = [] + with open(cfg.srctext, 'r') as src_f, open(cfg.tgtout, 'w') as tgt_f: + for line in src_f: + src_text.append(line.strip()) + if len(src_text) == cfg.batch_size: + translations = model.translate( + text=src_text, source_lang=cfg.source_lang, target_lang=cfg.target_lang, + ) + for translation in translations: + tgt_f.write(translation + "\n") + src_text = [] + if len(src_text) > 0: + translations = model.translate(text=src_text, source_lang=cfg.source_lang, target_lang=cfg.target_lang,) + for translation in translations: + tgt_f.write(translation + "\n") + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/noisy_channel_reranking.py b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/noisy_channel_reranking.py new file mode 100644 index 0000000..c390722 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/noisy_channel_reranking.py @@ -0,0 +1,320 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script implemts Noisy Channel Reranking (NCR) - https://arxiv.org/abs/1908.05731 +Given .nemo files for a, reverse model (target -> source) and transformer LM (target LM) NMT model's .nemo file, +this script can be used to re-rank a forward model's (source -> target) beam candidates. + +This script can be used in two ways 1) Given the score file generated by `nmt_transformer_infer.py`, re-rank beam candidates and +2) Given NCR score file generated by 1), Re-rank beam candidates based only on cached scores in the ncr file. This is meant to tune NCR coeficients. + +Pre-requisite: Generating translations using `nmt_transformer_infer.py` +1. Obtain text file in src language. You can use sacrebleu to obtain standard test sets like so: + sacrebleu -t wmt14 -l de-en --echo src > wmt14-de-en.src +2. Translate using `nmt_transformer_infer.py` with a large beam size.: + python nmt_transformer_infer.py --model=[Path to .nemo file(s)] --srctext=wmt14-de-en.src --tgtout=wmt14-de-en.translations --beam_size 15 --write_scores + +USAGE Example (case 1): +Re-rank beam candidates: + python noisy_channel_reranking.py \ + --reverse_model=[Path to .nemo file] \ + --language_model=[Path to .nemo file] \ + --srctext=wmt14-de-en.translations.scores \ + --tgtout=wmt14-de-en.ncr.translations \ + --forward_model_coef=1.0 \ + --reverse_model_coef=0.7 \ + --target_lm_coef=0.05 \ + --write_scores \ + +USAGE Example (case 2): +Re-rank beam candidates using cached score file only + python noisy_channel_reranking.py \ + --cached_score_file=wmt14-de-en.ncr.translations.scores \ + --forward_model_coef=1.0 \ + --reverse_model_coef=0.7 \ + --target_lm_coef=0.05 \ + --tgtout=wmt14-de-en.ncr.translations \ +""" + + +from argparse import ArgumentParser + +import numpy as np +import torch + +import nemo.collections.nlp as nemo_nlp +from nemo.utils import logging + + +def score_fusion(args, forward_scores, rev_scores, lm_scores, src_lens, tgt_lens): + """ + Fuse forward, reverse and language model scores. + """ + fused_scores = [] + for forward_score, rev_score, lm_score, src_len, tgt_len in zip( + forward_scores, rev_scores, lm_scores, src_lens, tgt_lens + ): + score = 0 + + forward_score = forward_score / tgt_len if args.length_normalize_scores else forward_score + score += args.forward_model_coef * forward_score + + rev_score = rev_score / src_len if args.length_normalize_scores else rev_score + score += args.reverse_model_coef * rev_score + + lm_score = lm_score / tgt_len if args.length_normalize_scores else lm_score + score += args.target_lm_coef * lm_score + + if args.len_pen is not None: + score = score / (((5 + tgt_len) / 6) ** args.len_pen) + + fused_scores.append(score) + + return fused_scores + + +def main(): + parser = ArgumentParser() + parser.add_argument( + "--reverse_model", + type=str, + help="Path to .nemo model file(s). If ensembling, provide comma separated paths to multiple models.", + ) + parser.add_argument( + "--language_model", type=str, help="Optional path to an LM model that has the same tokenizer as NMT models.", + ) + parser.add_argument( + "--forward_model_coef", + type=float, + default=1.0, + help="Weight assigned to the forward NMT model for re-ranking.", + ) + parser.add_argument( + "--reverse_model_coef", + type=float, + default=0.7, + help="Weight assigned to the reverse NMT model for re-ranking.", + ) + parser.add_argument( + "--target_lm_coef", type=float, default=0.07, help="Weight assigned to the target LM model for re-ranking.", + ) + parser.add_argument( + "--srctext", + type=str, + default=None, + help="Path to a TSV file containing forward model scores of the format source \t beam_candidate_i \t forward_score", + ) + parser.add_argument( + "--cached_score_file", + type=str, + default=None, + help="Path to a TSV file containing cached scores for each beam candidate. Format source \t target \t forward_score \t reverse_score \t lm_score \t src_len \t tgt_len", + ) + parser.add_argument( + "--tgtout", type=str, required=True, help="Path to the file where re-ranked translations are to be written." + ) + parser.add_argument( + "--beam_size", + type=int, + default=4, + help="Beam size with which forward model translations were generated. IMPORTANT: mismatch can lead to wrong results and an incorrect number of generated translations.", + ) + parser.add_argument( + "--target_lang", type=str, default=None, help="Target language identifier ex: en,de,fr,es etc." + ) + parser.add_argument( + "--source_lang", type=str, default=None, help="Source language identifier ex: en,de,fr,es etc." + ) + parser.add_argument( + "--write_scores", action="store_true", help="Whether to write forward, reverse and lm scores to a file." + ) + parser.add_argument( + "--length_normalize_scores", + action="store_true", + help="If true, it will divide forward, reverse and lm scores by the corresponding sequence length.", + ) + parser.add_argument( + "--len_pen", + type=float, + default=None, + help="Apply a length penalty based on target lengths to the final NCR score.", + ) + + args = parser.parse_args() + torch.set_grad_enabled(False) + + if args.cached_score_file is None: + reverse_models = [] + for model_path in args.reverse_model.split(','): + if not model_path.endswith('.nemo'): + raise NotImplementedError(f"Only support .nemo files, but got: {model_path}") + model = nemo_nlp.models.machine_translation.MTEncDecModel.restore_from(restore_path=model_path).eval() + model.eval_loss_fn.reduction = 'none' + reverse_models.append(model) + + lm_model = nemo_nlp.models.language_modeling.TransformerLMModel.restore_from( + restore_path=args.language_model + ).eval() + + if args.srctext is not None and args.cached_score_file is not None: + raise ValueError("Only one of --srctext or --cached_score_file must be provided.") + + if args.srctext is None and args.cached_score_file is None: + raise ValueError("Neither --srctext nor --cached_score_file were provided.") + + if args.srctext is not None: + logging.info(f"Re-ranking: {args.srctext}") + else: + logging.info(f"Re-ranking from cached score file only: {args.cached_score_file}") + + if args.cached_score_file is None: + if torch.cuda.is_available(): + reverse_models = [model.cuda() for model in reverse_models] + lm_model = lm_model.cuda() + + src_text = [] + tgt_text = [] + all_reverse_scores = [] + all_lm_scores = [] + all_forward_scores = [] + all_src_lens = [] + all_tgt_lens = [] + + # Chceck args if re-ranking from cached score file. + if args.cached_score_file is not None: + if args.write_scores: + raise ValueError("--write_scores cannot be provided with a cached score file.") + if args.reverse_model is not None: + raise ValueError( + "--reverse_model cannot be provided with a cached score file since it assumes reverse scores already present in the cached file." + ) + if args.language_model is not None: + raise ValueError( + "--language_model cannot be provided with a cached score file since it assumes language model scores already present in the cached file." + ) + + if args.srctext is not None: + # Compute reverse scores and LM scores from the provided models since cached scores file is not provided. + with open(args.srctext, 'r') as src_f: + count = 0 + for line in src_f: + src_text.append(line.strip().split('\t')) + if len(src_text) == args.beam_size: + # Source and target sequences are flipped for the reverse direction model. + src_texts = [item[1] for item in src_text] + tgt_texts = [item[0] for item in src_text] + src, src_mask = reverse_models[0].prepare_inference_batch(src_texts) + tgt, tgt_mask = reverse_models[0].prepare_inference_batch(tgt_texts, target=True) + src_lens = src_mask.sum(1).data.cpu().tolist() + tgt_lens = tgt_mask.sum(1).data.cpu().tolist() + forward_scores = [float(item[2]) for item in src_text] + + # Ensemble of reverse model scores. + nmt_lls = [] + for model in reverse_models: + nmt_log_probs = model(src, src_mask, tgt[:, :-1], tgt_mask[:, :-1]) + nmt_nll = model.eval_loss_fn(log_probs=nmt_log_probs, labels=tgt[:, 1:]) + nmt_ll = nmt_nll.view(nmt_log_probs.size(0), nmt_log_probs.size(1)).sum(1) * -1.0 + nmt_ll = nmt_ll.data.cpu().numpy().tolist() + nmt_lls.append(nmt_ll) + reverse_scores = np.stack(nmt_lls).mean(0) + + # LM scores. + if lm_model is not None: + # Compute LM score for the src of the reverse model. + lm_log_probs = lm_model(src[:, :-1], src_mask[:, :-1]) + lm_nll = model.eval_loss_fn(log_probs=lm_log_probs, labels=src[:, 1:]) + lm_ll = lm_nll.view(lm_log_probs.size(0), lm_log_probs.size(1)).sum(1) * -1.0 + lm_ll = lm_ll.data.cpu().numpy().tolist() + else: + lm_ll = None + lm_scores = lm_ll + + all_reverse_scores.extend(reverse_scores) + all_lm_scores.extend(lm_scores) + all_forward_scores.extend(forward_scores) + + # Swapping source and target here back again since this is what gets written to the file. + all_src_lens.extend(tgt_lens) + all_tgt_lens.extend(src_lens) + + fused_scores = score_fusion(args, forward_scores, reverse_scores, lm_scores, src_lens, tgt_lens) + tgt_text.append(src_texts[np.argmax(fused_scores)]) + src_text = [] + count += 1 + print(f'Reranked {count} sentences') + + else: + # Use reverse and LM scores from the cached scores file to re-rank. + with open(args.cached_score_file, 'r') as src_f: + count = 0 + for line in src_f: + src_text.append(line.strip().split('\t')) + if len(src_text) == args.beam_size: + if not all([len(item) == 7 for item in src_text]): + raise IndexError( + "All lines did not contain exactly 5 fields. Format - src_txt \t tgt_text \t forward_score \t reverse_score \t lm_score \t src_len \t tgt_len" + ) + src_texts = [item[0] for item in src_text] + tgt_texts = [item[1] for item in src_text] + forward_scores = [float(item[2]) for item in src_text] + reverse_scores = [float(item[3]) for item in src_text] + lm_scores = [float(item[4]) for item in src_text] + src_lens = [float(item[5]) for item in src_text] + tgt_lens = [float(item[6]) for item in src_text] + + fused_scores = score_fusion(args, forward_scores, reverse_scores, lm_scores, src_lens, tgt_lens) + tgt_text.append(tgt_texts[np.argmax(fused_scores)]) + src_text = [] + count += 1 + print(f'Reranked {count} sentences') + + with open(args.tgtout, 'w') as tgt_f: + for line in tgt_text: + tgt_f.write(line + "\n") + + # Write scores file + if args.write_scores: + with open(args.tgtout + '.scores', 'w') as tgt_f, open(args.srctext, 'r') as src_f: + src_lines = [] + for line in src_f: + src_lines.append(line.strip().split('\t')) + if not (len(all_reverse_scores) == len(all_lm_scores) == len(all_forward_scores) == len(src_lines)): + raise ValueError( + f"Length of scores files do not match. {len(all_reverse_scores)} != {len(all_lm_scores)} != {len(all_forward_scores)} != {len(src_lines)}. This is most likely because --beam_size is set incorrectly. This needs to be set to the same value that was used to generate translations." + ) + for f, r, lm, sl, tl, src in zip( + all_forward_scores, all_reverse_scores, all_lm_scores, all_src_lens, all_tgt_lens, src_lines + ): + tgt_f.write( + src[0] + + '\t' + + src[1] + + '\t' + + str(f) + + '\t' + + str(r) + + '\t' + + str(lm) + + '\t' + + str(sl) + + '\t' + + str(tl) + + '\n' + ) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/translate_ddp.py b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/translate_ddp.py new file mode 100644 index 0000000..cbcc1af --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/machine_translation/translate_ddp.py @@ -0,0 +1,122 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import os +from argparse import ArgumentParser + +import torch +import torch.multiprocessing as mp +from torch.utils.data import DataLoader + +from nemo.collections.nlp.data.language_modeling import TarredSentenceDataset +from nemo.collections.nlp.data.machine_translation import TarredTranslationDataset +from nemo.collections.nlp.models.machine_translation.mt_enc_dec_model import MTEncDecModel +from nemo.utils import logging + + +def get_args(): + parser = ArgumentParser(description='Batch translation of sentences from a pre-trained model on multiple GPUs') + parser.add_argument("--model", type=str, required=True, help="Path to the .nemo translation model file") + parser.add_argument( + "--text2translate", type=str, required=True, help="Path to the pre-processed tarfiles for translation" + ) + parser.add_argument("--result_dir", type=str, required=True, help="Folder to write translation results") + parser.add_argument( + "--twoside", action="store_true", help="Set flag when translating the source side of a parallel dataset" + ) + parser.add_argument( + '--metadata_path', type=str, required=True, help="Path to the JSON file that contains dataset info" + ) + parser.add_argument('--topk', type=int, default=500, help="Value of k for topk sampling") + parser.add_argument('--src_language', type=str, required=True, help="Source lang ID for detokenization") + parser.add_argument('--tgt_language', type=str, required=True, help="Target lang ID for detokenization") + parser.add_argument( + '--reverse_lang_direction', + action="store_true", + help="Reverse source and target language direction for parallel dataset", + ) + parser.add_argument('--n_gpus', type=int, default=1, help="Number of GPUs to use") + args = parser.parse_args() + return args + + +def translate(rank, world_size, args): + if args.model.endswith(".nemo"): + logging.info("Attempting to initialize from .nemo file") + model = MTEncDecModel.restore_from(restore_path=args.model, map_location=f"cuda:{rank}") + elif args.model.endswith(".ckpt"): + logging.info("Attempting to initialize from .ckpt file") + model = MTEncDecModel.load_from_checkpoint(checkpoint_path=args.model, map_location=f"cuda:{rank}") + model.replace_beam_with_sampling(topk=args.topk) + model.eval() + if args.twoside: + dataset = TarredTranslationDataset( + text_tar_filepaths=args.text2translate, + metadata_path=args.metadata_path, + encoder_tokenizer=model.encoder_tokenizer, + decoder_tokenizer=model.decoder_tokenizer, + shuffle_n=100, + shard_strategy="scatter", + world_size=world_size, + global_rank=rank, + reverse_lang_direction=args.reverse_lang_direction, + ) + else: + dataset = TarredSentenceDataset( + text_tar_filepaths=args.text2translate, + metadata_path=args.metadata_path, + tokenizer=model.encoder_tokenizer, + shuffle_n=100, + shard_strategy="scatter", + world_size=world_size, + global_rank=rank, + ) + loader = DataLoader(dataset, batch_size=1) + result_dir = os.path.join(args.result_dir, f'rank{rank}') + os.makedirs(result_dir, exist_ok=True) + originals_file_name = os.path.join(result_dir, 'originals.txt') + translations_file_name = os.path.join(result_dir, 'translations.txt') + num_translated_sentences = 0 + + with open(originals_file_name, 'w') as of, open(translations_file_name, 'w') as tf: + for batch_idx, batch in enumerate(loader): + for i in range(len(batch)): + if batch[i].ndim == 3: + batch[i] = batch[i].squeeze(dim=0) + batch[i] = batch[i].to(rank) + if args.twoside: + src_ids, src_mask, _, _, _ = batch + else: + src_ids, src_mask = batch + if batch_idx % 100 == 0: + logging.info( + f"{batch_idx} batches ({num_translated_sentences} sentences) were translated by process with " + f"rank {rank}" + ) + num_translated_sentences += len(src_ids) + inputs, translations = model.batch_translate(src=src_ids, src_mask=src_mask) + for src, translation in zip(inputs, translations): + of.write(src + '\n') + tf.write(translation + '\n') + + +def main() -> None: + args = get_args() + world_size = torch.cuda.device_count() if args.n_gpus == -1 else args.n_gpus + mp.spawn(translate, args=(world_size, args), nprocs=world_size, join=True) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/conf/qa_conf.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/conf/qa_conf.yaml new file mode 100644 index 0000000..bb53054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/conf/qa_conf.yaml @@ -0,0 +1,157 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +pretrained_model: null # pretrained model from list_available_models() +do_training: true # true for training mode, false for testing +trainer: + devices: [0] # 0 for CPU, or list of the GPUs to use e.g. [0, 1] or [0] + num_nodes: 1 + max_epochs: 3 + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + gradient_clip_val: 1.0 + precision: 16 # should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # interval of logging. + val_check_interval: 1.0 # set to 0.25 to check 4 times per epoch, or an int for number of iterations + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # provided by exp_manager + logger: False # provided by exp_manager + strategy: ddp + +model: + tensor_model_parallel_size: 1 + nemo_path: null # filename to save the model and associated artifacts to .nemo file + library: huggingface # [huggingface, megatron]. Used by S2SQAModel and GPTQAModel + save_model: False # save validation model checkpoints + + tokens_to_generate: 32 # used by S2SQAModel and GPTQAModel to limit number of generated tokens + + dataset: + version_2_with_negative: true # if true, dataset contains some questions that do not have an answer + doc_stride: 128 # stride for splitting long documents into chunks + max_query_length: 64 + max_seq_length: 512 # max sequence length for input to the model + max_answer_length: 30 # max ground truth answer length + use_cache: false + do_lower_case: true + + # if true, context spans/chunks that do not contain answer are treated as unanswerable, + # useful for extractive datasets like SQuAD + # if false, all context spans/chunks are treated as relevant for answering given query, + # useful for generative datasets where answer is not necessarily in the context + # used by S2SQAModel and GPTQAModel + check_if_answer_in_context: true + + # if all, keep all doc spans + # if only_positive, keep doc spans containing answer only + # if limited_negative, keep 10 doc spans closest to answer per question + # used by BERTQAModel + keep_doc_spans: all # [all, only_positive, limited_negative] + + null_score_diff_threshold: 0.0 # If null_score - best_non_null is greater than the threshold predict null. + n_best_size: 20 + + num_workers: 1 + pin_memory: false + drop_last: false + + train_ds: + file: null # .json file + batch_size: 24 # per GPU + shuffle: true + num_samples: -1 + + # default values for the following params are retrieved from dataset config section, but you may override them + num_workers: ${model.dataset.num_workers} + drop_last: ${model.dataset.drop_last} + pin_memory: ${model.dataset.pin_memory} + + validation_ds: + file: null # .json file + batch_size: 24 # per GPU + shuffle: false + num_samples: -1 + + # default values for the following params are retrieved from dataset config section, but you may override them + num_workers: ${model.dataset.num_workers} + drop_last: ${model.dataset.drop_last} + pin_memory: ${model.dataset.pin_memory} + + test_ds: + file: null # .json file + batch_size: 24 # per GPU + shuffle: false + num_samples: -1 + + # default values for the following params are retrieved from dataset config section, but you may override them + num_workers: ${model.dataset.num_workers} + drop_last: ${model.dataset.drop_last} + pin_memory: ${model.dataset.pin_memory} + + language_model: + pretrained_model_name: bert-base-uncased # main config to select model (between bert, gpt2, t5/bart based models) + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + token_classifier: # used only by BERTQAModel for defining the extractive QA head + num_layers: 1 + dropout: 0. + num_classes: 2 + activation: relu + log_softmax: false + use_transformer_init: true + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # tokenizer that inherits from TokenizerSpec + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + + # expand the following to a dictionary if special tokens need to be added. + # only necessary for adding transformer/bert-specific special tokens to tokenizer if the tokenizer does not already have these inherently. + special_tokens: null + + optim: + name: adamw + lr: 5e-5 + + # optimizer arguments + betas: [0.9, 0.999] + weight_decay: 0. + + # scheduler setup + sched: + name: SquareRootAnnealing + + # scheduler params + warmup_steps: null + warmup_ratio: 0. + last_epoch: -1 + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + +exp_manager: + exp_dir: null # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: "QnA" # the name of your model + create_wandb_logger: False + wandb_logger_kwargs: + name: ??? + project: QnA + create_tensorboard_logger: True # whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # whether you want exp_manager to create a modelcheckpoint callback + resume_if_exists: false + resume_ignore_no_checkpoint: false \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/conf/question_answering_squad_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/conf/question_answering_squad_config.yaml new file mode 100644 index 0000000..2e54b6f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/conf/question_answering_squad_config.yaml @@ -0,0 +1,143 @@ +# Question Answering with SQUAD +name: &name QA + +pretrained_model: null # pretrained QAModel model from list_available_models() +do_training: true # training mode, for testing change to false +trainer: + devices: 1 # the number of gpus, 0 for CPU, or list with gpu indices + num_nodes: 1 + max_epochs: 2 # the number of training epochs + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + precision: 16 # 16 to use AMP + accelerator: gpu + strategy: ddp + gradient_clip_val: 0.0 + val_check_interval: 1.0 # check once per epoch .25 for 4 times per epoch + enable_checkpointing: False # provided by exp_manager + logger: false # provided by exp_manager + num_sanity_val_steps: 0 + log_every_n_steps: 1 # Interval of logging. + +model: + nemo_path: null # exported .nemo path + dataset: + version_2_with_negative: false + # If true, the examples contain some that do not have an answer. + doc_stride: 128 + # When splitting up a long document into chunks, + # how much stride to take between chunks. + max_query_length: 64 + # The maximum number of tokens for the question. + # Questions longer than this will be truncated to + # this length. + max_seq_length: 384 + # The maximum total input sequence length after + # WordPiece tokenization. Sequences longer than this + # will be truncated, and sequences shorter than this + # will be padded. + max_answer_length: 30 + # The maximum length of an answer that can be + # generated. This is needed because the start + # and end predictions are not conditioned + # on one another. + null_score_diff_threshold: 0.0 + # If null_score - best_non_null is greater than the threshold predict null. + n_best_size: 20 + # The total number of n-best predictions to generate at testing. + use_cache: true + do_lower_case: true + # if true does lower case + keep_doc_spans: all + # if all, keep all doc spans + # if only_positive, keep doc spans containing answer only + # if limited_negative, keep 10 doc spans closest to answer per question + + num_workers: 2 + pin_memory: false + drop_last: false + + train_ds: + file: null # .json file + batch_size: 24 # per GPU + shuffle: true + num_samples: -1 + # Default values for the following params are retrieved from dataset config section, but you may override them + num_workers: ${model.dataset.num_workers} + drop_last: ${model.dataset.drop_last} + pin_memory: ${model.dataset.pin_memory} + + validation_ds: + file: null # .json file + batch_size: 24 # per GPU + shuffle: false + num_samples: -1 + # Default values for the following params are retrieved from dataset config section, but you may override them + num_workers: ${model.dataset.num_workers} + drop_last: ${model.dataset.drop_last} + pin_memory: ${model.dataset.pin_memory} + + test_ds: + file: null # .json file + batch_size: 24 # per GPU + shuffle: false + num_samples: -1 + # Default values for the following params are retrieved from dataset config section, but you may override them + num_workers: ${model.dataset.num_workers} + drop_last: ${model.dataset.drop_last} + pin_memory: ${model.dataset.pin_memory} + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # tokenizer that inherits from TokenizerSpec + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null # expand the following to a dictionary if special tokens need to be added. + # only necessary for adding transformer/bert-specific special tokens to tokenizer if the tokenizer does not already have these inherently. + + language_model: + pretrained_model_name: bert-base-uncased # BERT-like model name + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null # if specified initializes model from scratch + + token_classifier: + num_layers: 1 + dropout: 0.0 + num_classes: 2 + activation: relu + log_softmax: false + use_transformer_init: true + + + optim: + name: adamw + lr: 3e-5 + weight_decay: 0.0 + sched: + name: SquareRootAnnealing + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + + # scheduler config override + warmup_steps: null + warmup_ratio: 0.0 + last_epoch: -1 + +exp_manager: + exp_dir: null # where to store logs and checkpoints + name: *name # name of experiment + create_tensorboard_logger: True + create_checkpoint_callback: True + create_wandb_logger: False + wandb_logger_kwargs: + name: ??? + project: QA + +hydra: + run: + dir: . + job_logging: + root: + handlers: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/convert_msmarco_to_squad_format.py b/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/convert_msmarco_to_squad_format.py new file mode 100644 index 0000000..4e1a997 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/convert_msmarco_to_squad_format.py @@ -0,0 +1,138 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import json +from ast import literal_eval + +from tqdm import tqdm + + +def load_json(filepath): + with open(filepath, "r") as f: + data = json.load(f) + return data + + +def dump_json(filepath, data): + with open(filepath, "w") as f: + json.dump(data, f) + + +def get_context_from_passages(passages, keep_only_relevant_passages): + contexts = [] + if keep_only_relevant_passages: + for passage in passages: + if passage["is_selected"] == 1: + contexts.append(passage["passage_text"]) + else: + contexts = [passage["passage_text"] for passage in passages] + + return " ".join(contexts) + + +def format_answers_into_squad_format(answers): + is_impossible = True if "No Answer Present." in answers else False + if is_impossible: + answers = [] + else: + answers = [{"text": ans, "answer_start": -1} for ans in answers] + + return answers + + +def convert_msmarco_to_squad_format(msmarco_data, args): + ids = list(msmarco_data["query"]) + squad_data = {"data": [{"title": "MSMARCO", "paragraphs": []}], "version": "v2.1"} + for index, _id in enumerate(tqdm(ids)): + + context = get_context_from_passages(msmarco_data["passages"][_id], args.keep_only_relevant_passages) + if not context: + continue + + query = msmarco_data["query"][_id] + + # use well formed answers if present, else use the 'answers' field + well_formed_answers = msmarco_data['wellFormedAnswers'][_id] + well_formed_answers = ( + well_formed_answers if isinstance(well_formed_answers, list) else literal_eval(well_formed_answers) + ) + answers = well_formed_answers if well_formed_answers else msmarco_data["answers"][_id] + answers = format_answers_into_squad_format(answers) + if args.exclude_negative_samples and (not answers): + continue + + squad_data["data"][0]["paragraphs"].append( + { + "context": context, + "qas": [ + {"id": index, "question": query, "answers": answers, "is_impossible": False if answers else True,} + ], + } + ) + + return squad_data + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--msmarco_train_input_filepath", default=None, type=str, required=True) + parser.add_argument("--msmarco_dev_input_filepath", default=None, type=str, required=True) + parser.add_argument("--converted_train_save_path", default=None, type=str, required=True) + parser.add_argument("--converted_dev_save_path", default=None, type=str, required=True) + parser.add_argument( + "--exclude_negative_samples", + default=False, + type=bool, + help="whether to keep No Answer samples in the dataset", + required=False, + ) + parser.add_argument( + "--keep_only_relevant_passages", + default=False, + type=bool, + help="if True, will only use passages with is_selected=True for context", + required=False, + ) + args = parser.parse_args() + + print("converting MS-MARCO train dataset...") + msmarco_train_data = load_json(args.msmarco_train_input_filepath) + squad_train_data = convert_msmarco_to_squad_format(msmarco_train_data, args) + dump_json(args.converted_train_save_path, squad_train_data) + + print("converting MS-MARCO dev dataset...") + msmarco_dev_data = load_json(args.msmarco_dev_input_filepath) + squad_dev_data = convert_msmarco_to_squad_format(msmarco_dev_data, args) + dump_json(args.converted_dev_save_path, squad_dev_data) + + +if __name__ == "__main__": + """ + Please agree to the Terms of Use at: + https://microsoft.github.io/msmarco/ + Download data at: + https://msmarco.blob.core.windows.net/msmarco/train_v2.1.json.gz + https://msmarco.blob.core.windows.net/msmarco/dev_v2.1.json.gz + + Example usage: + python convert_msmarco_to_squad_format.py \ + --msmarco_train_input_filepath=/path/to/msmarco_train_v2.1.json \ + --msmarco_dev_input_filepath=/path/to/msmarco_dev_v2.1.json \ + --converted_train_save_path=/path/to/msmarco_squad_format_train.json \ + --converted_dev_save_path=/path/to/msmarco_squad_format_dev.json \ + --exclude_negative_samples=False \ + --keep_only_relevant_passages=False + """ + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/get_squad.py b/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/get_squad.py new file mode 100755 index 0000000..255b040 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/get_squad.py @@ -0,0 +1,68 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import urllib.request + +from nemo.utils import logging + + +class SquadDownloader: + def __init__(self, save_path): + self.save_path = save_path + '/squad' + + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + + if not os.path.exists(self.save_path + '/v1.1'): + os.makedirs(self.save_path + '/v1.1') + + if not os.path.exists(self.save_path + '/v2.0'): + os.makedirs(self.save_path + '/v2.0') + + self.download_urls = { + 'https://rajpurkar.github.io/SQuAD-explorer' '/dataset/train-v1.1.json': 'v1.1/train-v1.1.json', + 'https://rajpurkar.github.io/SQuAD-explorer' '/dataset/dev-v1.1.json': 'v1.1/dev-v1.1.json', + 'https://rajpurkar.github.io/SQuAD-explorer' '/dataset/train-v2.0.json': 'v2.0/train-v2.0.json', + 'https://rajpurkar.github.io/SQuAD-explorer' '/dataset/dev-v2.0.json': 'v2.0/dev-v2.0.json', + } + + def download(self): + for item in self.download_urls: + url = item + file = self.download_urls[item] + + logging.info('Downloading: %s', url) + if os.path.isfile(self.save_path + '/' + file): + logging.info('** Download file already exists, skipping download') + else: + response = urllib.request.urlopen(url) + with open(self.save_path + '/' + file, "wb") as handle: + handle.write(response.read()) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Download Squad') + parser.add_argument( + '--destDir', + type=str, + required=False, + help='directory to store data', + default=os.path.split(os.path.abspath(__file__))[0], + ) + args = parser.parse_args() + logging.info(args.destDir) + squad_dl = SquadDownloader(args.destDir) + squad_dl.download() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/question_answering.py b/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/question_answering.py new file mode 100644 index 0000000..fcde035 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/question_answering/question_answering.py @@ -0,0 +1,92 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models.question_answering.qa_bert_model import BERTQAModel +from nemo.collections.nlp.models.question_answering.qa_gpt_model import GPTQAModel +from nemo.collections.nlp.models.question_answering.qa_s2s_model import S2SQAModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="qa_conf") +def main(cfg: DictConfig) -> None: + pl.seed_everything(42) + # PTL 2.0 has find_unused_parameters as False by default, so its required to set it to True + # when there are unused parameters like here + if cfg.trainer.strategy == 'ddp': + cfg.trainer.strategy = "ddp_find_unused_parameters_true" + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(**cfg.trainer) + exp_dir = exp_manager(trainer, cfg.get("exp_manager", None)) + + if "bert" in cfg.model.language_model.pretrained_model_name.lower(): + model_class = BERTQAModel + elif "gpt" in cfg.model.language_model.pretrained_model_name.lower(): + model_class = GPTQAModel + elif ( + "bart" in cfg.model.language_model.pretrained_model_name.lower() + or "t5" in cfg.model.language_model.pretrained_model_name.lower() + ): + model_class = S2SQAModel + + if cfg.pretrained_model or (cfg.model.nemo_path and os.path.exists(cfg.model.nemo_path)): + if cfg.pretrained_model: + logging.info(f'Loading pretrained model {cfg.pretrained_model}') + model = model_class.from_pretrained(cfg.pretrained_model) + else: + logging.info(f'Restoring model from {cfg.model.nemo_path}') + model = model_class.restore_from(cfg.model.nemo_path) + + if cfg.do_training: + model.setup_training_data(train_data_config=cfg.model.train_ds) + model.setup_multiple_validation_data(val_data_config=cfg.model.validation_ds) + else: + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + model = model_class(cfg.model, trainer=trainer) + + if cfg.do_training: + trainer.fit(model) + if cfg.model.nemo_path: + model.save_to(cfg.model.nemo_path) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.file is not None: + eval_device = [cfg.trainer.devices[0]] if isinstance(cfg.trainer.devices, list) else 1 + trainer = pl.Trainer(devices=eval_device, accelerator=cfg.trainer.accelerator, precision=16) + model.setup_test_data(test_data_config=cfg.model.test_ds) + trainer.test(model) + + # specifiy .json file to dump predictions. e.g. os.path.join(exp_dir, "output_nbest_file.json") + output_nbest_file = None + # specifiy .json file to dump predictions. e.g. os.path.join(exp_dir, "output_prediction_file.json") + output_prediction_file = None + inference_samples = 5 # for test purposes. To use entire inference dataset set to -1 + all_preds, all_nbest = model.inference( + cfg.model.test_ds.file, + output_prediction_file=output_prediction_file, + output_nbest_file=output_nbest_file, + num_samples=inference_samples, + ) + + for question_id in all_preds: + print(all_preds[question_id]) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/README.md b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/README.md new file mode 100644 index 0000000..9d2063e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/README.md @@ -0,0 +1,32 @@ +# SpellMapper - spellchecking model for ASR Customization +Paper: https://arxiv.org/abs/2306.02317 +This model was partly inspired by Microsoft's paper https://arxiv.org/pdf/2203.00888.pdf. +The goal is to build a model that gets as input a single ASR hypothesis (text) and a vocabulary of custom words/phrases and predicts which fragments in the ASR hypothesis should be replaced by which custom words/phrases if any. +Our model is non-autoregressive (NAR) based on transformer architecture (BERT with multiple separators). + +As initial data we use about 5 mln entities from [YAGO corpus](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/downloads/). These entities are short phrases from Wikipedia headings. +In order to get misspelled predictions we feed these data to TTS model and then to ASR model. +Having a "parallel" corpus of "correct + misspelled" phrases, we use statistical machine translation techniques to create a dictionary of possible ngram mappings with their respective frequencies. +We create an auxiliary algorithm that takes as input a sentence (ASR hypothesis) and a large custom dictionary (e.g. 5000 phrases) and selects top 10 candidate phrases that are probably contained in this sentence in a misspelled way. +The task of our final neural model is to predict which fragments in the ASR hypothesis should be replaced by which of top-10 candidate phrases if any. + +The pipeline consists of multiple steps: + +1. Download or generate training data. + See `https://github.com/bene-ges/nemo_compatible/tree/main/scripts/nlp/en_spellmapper/dataset_preparation` + +2. [Optional] Convert training dataset to tarred files. + `convert_dataset_to_tarred.sh` + +3. Train spellchecking model. + `run_training.sh` + or + `run_training_tarred.sh` + +4. Run evaluation. + - [test_on_kensho.sh](https://github.com/bene-ges/nemo_compatible/blob/main/scripts/nlp/en_spellmapper/evaluation/test_on_kensho.sh) + - [test_on_userlibri.sh](https://github.com/bene-ges/nemo_compatible/blob/main/scripts/nlp/en_spellmapper/evaluation/test_on_kensho.sh) + - [test_on_spoken_wikipedia.sh](https://github.com/bene-ges/nemo_compatible/blob/main/scripts/nlp/en_spellmapper/evaluation/test_on_kensho.sh) + +5. Run inference. + `python run_infer.sh` diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/checkpoint_to_nemo.py b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/checkpoint_to_nemo.py new file mode 100644 index 0000000..c2f514f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/checkpoint_to_nemo.py @@ -0,0 +1,38 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script converts checkpoint .ckpt to .nemo file. + +This script uses the `examples/nlp/spellchecking_asr_customization/conf/spellchecking_asr_customization_config.yaml` +config file by default. The other option is to set another config file via command +line arguments by `--config-name=CONFIG_FILE_PATH'. +""" + +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models import SpellcheckingAsrCustomizationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@hydra_runner(config_path="conf", config_name="spellchecking_asr_customization_config") +def main(cfg: DictConfig) -> None: + logging.debug(f'Config Params: {OmegaConf.to_yaml(cfg)}') + SpellcheckingAsrCustomizationModel.load_from_checkpoint(cfg.checkpoint_path).save_to(cfg.target_nemo_path) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/conf/spellchecking_asr_customization_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/conf/spellchecking_asr_customization_config.yaml new file mode 100644 index 0000000..f8dca7b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/conf/spellchecking_asr_customization_config.yaml @@ -0,0 +1,97 @@ +name: &name spellchecking +lang: ??? # e.g. 'ru', 'en' + +# Pretrained Nemo Models +pretrained_model: null + +trainer: + devices: 1 # the number of gpus, 0 for CPU + num_nodes: 1 + max_epochs: 3 # the number of training epochs + enable_checkpointing: false # provided by exp_manager + logger: false # provided by exp_manager + accumulate_grad_batches: 1 # accumulates grads every k batches + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + strategy: ddp + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + +model: + do_training: true + label_map: ??? # path/.../label_map.txt + semiotic_classes: ??? # path/.../semiotic_classes.txt + max_sequence_len: 128 + lang: ${lang} + hidden_size: 768 + + optim: + name: adamw + lr: 3e-5 + weight_decay: 0.1 + + sched: + name: WarmupAnnealing + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + + # scheduler config override + warmup_ratio: 0.1 + last_epoch: -1 + + language_model: + pretrained_model_name: bert-base-uncased # For ru, try DeepPavlov/rubert-base-cased | For de or multilingual, try bert-base-multilingual-cased + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + +exp_manager: + exp_dir: nemo_experiments # where to store logs and checkpoints + name: training # name of experiment + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + save_top_k: 3 + monitor: "val_loss" + mode: "min" + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + +tokenizer: + tokenizer_name: ${model.transformer} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + +# Data +data: + train_ds: + data_path: ??? # provide the full path to the file + batch_size: 8 + shuffle: true + num_workers: 3 + pin_memory: false + drop_last: false + + validation_ds: + data_path: ??? # provide the full path to the file. + batch_size: 8 + shuffle: false + num_workers: 3 + pin_memory: false + drop_last: false + + +# Inference +inference: + from_file: null # Path to the raw text, no labels required. Each sentence on a separate line + out_file: null # Path to the output file + batch_size: 16 # batch size for inference.from_file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/convert_data_to_tarred.sh b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/convert_data_to_tarred.sh new file mode 100644 index 0000000..d4265eb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/convert_data_to_tarred.sh @@ -0,0 +1,50 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# Path to NeMo repository +NEMO_PATH=NeMo + +DATA_PATH="data_folder" + +## data_folder_example +## ├── tarred_data +## | └── (output) +## ├── config.json +##   ├── label_map.txt +##   ├── semiotic_classes.txt +## ├── test.tsv +## ├── 1.tsv +## ├── ... +## └── 200.tsv + +## Each of {1-200}.tsv input files are 110'000 examples subsets of all.tsv (except for validation part), +## generated by https://github.com/bene-ges/nemo_compatible/blob/main/scripts/nlp/en_spellmapper/dataset_preparation/build_training_data.sh +## Note that in this example we use 110'000 as input and only pack 100'000 of them to tar file. +## This is because some input examples, e.g. too long, can be skipped during preprocessing, and we want all tar files to contain fixed equal number of examples. + +for part in {1..200} +do + python ${NEMO_PATH}/examples/nlp/spellchecking_asr_customization/create_tarred_dataset.py \ + lang="en" \ + data.train_ds.data_path=${DATA_PATH}/${part}.tsv \ + data.validation_ds.data_path=${DATA_PATH}/test.tsv \ + model.max_sequence_len=256 \ + model.language_model.pretrained_model_name=huawei-noah/TinyBERT_General_6L_768D \ + model.language_model.config_file=${DATA_PATH}/config.json \ + model.label_map=${DATA_PATH}/label_map.txt \ + model.semiotic_classes=${DATA_PATH}/semiotic_classes.txt \ + +output_tar_file=${DATA_PATH}/tarred_data/part${part}.tar \ + +take_first_n_lines=100000 +done diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/create_custom_vocab_index.py b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/create_custom_vocab_index.py new file mode 100644 index 0000000..68c55ff --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/create_custom_vocab_index.py @@ -0,0 +1,72 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script is used to create an index of custom vocabulary and save it to file. +See "examples/nlp/spellchecking_asr_customization/run_infer.sh" for the whole inference pipeline. +""" + +from argparse import ArgumentParser + +from nemo.collections.nlp.data.spellchecking_asr_customization.utils import get_index, load_ngram_mappings + +parser = ArgumentParser(description="Create an index of custom vocabulary and save it to file") + +parser.add_argument( + "--input_name", required=True, type=str, help="Path to input file with custom vocabulary (plain text)" +) +parser.add_argument( + "--ngram_mappings", required=True, type=str, help="Path to input file with n-gram mapping vocabulary" +) +parser.add_argument("--output_name", required=True, type=str, help="Path to output file with custom vocabulary index") +parser.add_argument("--min_log_prob", default=-4.0, type=float, help="Threshold on log probability") +parser.add_argument( + "--max_phrases_per_ngram", + default=500, + type=int, + help="Threshold on number of phrases that can be stored for one n-gram key in index. Keys with more phrases are discarded.", +) +parser.add_argument( + "--max_misspelled_freq", default=125000, type=int, help="Threshold on maximum frequency of misspelled n-gram" +) + +args = parser.parse_args() + +# Load custom vocabulary +custom_phrases = set() +with open(args.input_name, "r", encoding="utf-8") as f: + for line in f: + phrase = line.strip() + custom_phrases.add(" ".join(list(phrase.replace(" ", "_")))) +print("Size of customization vocabulary:", len(custom_phrases)) + +# Load n-gram mappings vocabulary +ngram_mapping_vocab, ban_ngram = load_ngram_mappings(args.ngram_mappings, max_misspelled_freq=args.max_misspelled_freq) + +# Generate index of custom phrases +phrases, ngram2phrases = get_index( + custom_phrases, + ngram_mapping_vocab, + ban_ngram, + min_log_prob=args.min_log_prob, + max_phrases_per_ngram=args.max_phrases_per_ngram, +) + +# Save index to file +with open(args.output_name, "w", encoding="utf-8") as out: + for ngram in ngram2phrases: + for phrase_id, begin, size, logprob in ngram2phrases[ngram]: + phrase = phrases[phrase_id] + out.write(ngram + "\t" + phrase + "\t" + str(begin) + "\t" + str(size) + "\t" + str(logprob) + "\n") diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/create_tarred_dataset.py b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/create_tarred_dataset.py new file mode 100644 index 0000000..d0bdc2c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/create_tarred_dataset.py @@ -0,0 +1,99 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script is used to create a tarred dataset for SpellcheckingAsrCustomizationModel. + +This script uses the `/examples/nlp/spellchecking_asr_customization/conf/spellchecking_asr_customization_config.yaml` +config file by default. The other option is to set another config file via command +line arguments by `--config-name=CONFIG_FILE_PATH'. Probably it is worth looking +at the example config file to see the list of parameters used for training. + +USAGE Example: +1. Obtain a processed dataset +2. Run: + python ${NEMO_PATH}/examples/nlp/spellchecking_asr_customization/create_tarred_dataset.py \ + lang=${LANG} \ + data.train_ds.data_path=${DATA_PATH}/train.tsv \ + model.language_model.pretrained_model_name=${LANGUAGE_MODEL} \ + model.label_map=${DATA_PATH}/label_map.txt \ + +output_tar_file=tarred/part1.tar \ + +take_first_n_lines=100000 + +""" +import pickle +import tarfile +from io import BytesIO + +from helpers import MODEL, instantiate_model_and_trainer +from omegaconf import DictConfig, OmegaConf + +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@hydra_runner(config_path="conf", config_name="spellchecking_asr_customization_config") +def main(cfg: DictConfig) -> None: + logging.info(f'Config Params: {OmegaConf.to_yaml(cfg)}') + logging.info("Start creating tar file from " + cfg.data.train_ds.data_path + " ...") + _, model = instantiate_model_and_trainer( + cfg, MODEL, True + ) # instantiate model like for training because we may not have pretrained model + dataset = model._train_dl.dataset + archive = tarfile.open(cfg.output_tar_file, mode="w") + max_lines = int(cfg.take_first_n_lines) + for i in range(len(dataset)): + if i >= max_lines: + logging.info("Reached " + str(max_lines) + " examples") + break + ( + input_ids, + input_mask, + segment_ids, + input_ids_for_subwords, + input_mask_for_subwords, + segment_ids_for_subwords, + character_pos_to_subword_pos, + labels_mask, + labels, + spans, + ) = dataset[i] + + # do not store masks as they are just arrays of 1 + content = { + "input_ids": input_ids, + "input_mask": input_mask, + "segment_ids": segment_ids, + "input_ids_for_subwords": input_ids_for_subwords, + "input_mask_for_subwords": input_mask_for_subwords, + "segment_ids_for_subwords": segment_ids_for_subwords, + "character_pos_to_subword_pos": character_pos_to_subword_pos, + "labels_mask": labels_mask, + "labels": labels, + "spans": spans, + } + b = BytesIO() + pickle.dump(content, b) + b.seek(0) + tarinfo = tarfile.TarInfo(name="example_" + str(i) + ".pkl") + tarinfo.size = b.getbuffer().nbytes + archive.addfile(tarinfo=tarinfo, fileobj=b) + + archive.close() + logging.info("Tar file " + cfg.output_tar_file + " created!") + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/helpers.py b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/helpers.py new file mode 100644 index 0000000..2db11b0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/helpers.py @@ -0,0 +1,86 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import os +from typing import Tuple + +import pytorch_lightning as pl +from omegaconf import DictConfig + +from nemo.collections.nlp.models import SpellcheckingAsrCustomizationModel +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector +from nemo.utils import logging + +__all__ = ["MODEL", "MODEL_NAMES", "instantiate_model_and_trainer"] + +MODEL = "spellchecking" +MODEL_NAMES = [MODEL] + + +def instantiate_model_and_trainer( + cfg: DictConfig, model_name: str, do_training: bool +) -> Tuple[pl.Trainer, SpellcheckingAsrCustomizationModel]: + """ Function for instantiating a model and a trainer + Args: + cfg: The config used to instantiate the model and the trainer. + model_name: A str indicates the model direction, currently only 'itn'. + do_training: A boolean flag indicates whether the model will be trained or evaluated. + + Returns: + trainer: A PyTorch Lightning trainer + model: A SpellcheckingAsrCustomizationModel + """ + + if model_name not in MODEL_NAMES: + raise ValueError(f"{model_name} is unknown model type") + + # Get configs for the corresponding models + trainer_cfg = cfg.get("trainer") + model_cfg = cfg.get("model") + pretrained_cfg = cfg.get("pretrained_model", None) + trainer = pl.Trainer(**trainer_cfg) + if not pretrained_cfg: + logging.info(f"Initializing {model_name} model") + if model_name == MODEL: + model = SpellcheckingAsrCustomizationModel(model_cfg, trainer=trainer) + else: + raise ValueError(f"{model_name} is unknown model type") + elif os.path.exists(pretrained_cfg): + logging.info(f"Restoring pretrained {model_name} model from {pretrained_cfg}") + save_restore_connector = NLPSaveRestoreConnector() + model = SpellcheckingAsrCustomizationModel.restore_from( + pretrained_cfg, save_restore_connector=save_restore_connector + ) + else: + logging.info(f"Loading pretrained model {pretrained_cfg}") + if model_name == MODEL: + if pretrained_cfg not in SpellcheckingAsrCustomizationModel.get_available_model_names(): + raise ( + ValueError( + f"{pretrained_cfg} not in the list of available Tagger models." + f"Select from {SpellcheckingAsrCustomizationModel.list_available_models()}" + ) + ) + model = SpellcheckingAsrCustomizationModel.from_pretrained(pretrained_cfg) + else: + raise ValueError(f"{model_name} is unknown model type") + + # Setup train and validation data + if do_training: + model.setup_training_data(train_data_config=cfg.data.train_ds) + model.setup_validation_data(val_data_config=cfg.data.validation_ds) + + logging.info(f"Model {model_name} -- Device {model.device}") + return trainer, model diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/postprocess_and_update_manifest.py b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/postprocess_and_update_manifest.py new file mode 100644 index 0000000..871d5e5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/postprocess_and_update_manifest.py @@ -0,0 +1,79 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script is used to postprocess SpellMapper results and generate an updated nemo ASR manifest. +See "examples/nlp/spellchecking_asr_customization/run_infer.sh" for the whole inference pipeline. +""" + +from argparse import ArgumentParser + +from nemo.collections.nlp.data.spellchecking_asr_customization.utils import ( + update_manifest_with_spellmapper_corrections, +) + +parser = ArgumentParser(description="Postprocess SpellMapper results and generate an updated nemo ASR manifest") + +parser.add_argument("--input_manifest", required=True, type=str, help="Path to input nemo ASR manifest") +parser.add_argument( + "--field_name", default="pred_text", type=str, help="Name of json field with original ASR hypothesis text" +) +parser.add_argument( + "--short2full_name", + required=True, + type=str, + help="Path to input file with correspondence between sentence fragments and full sentences", +) +parser.add_argument( + "--spellmapper_results", required=True, type=str, help="Path to input file with SpellMapper inference results" +) +parser.add_argument("--output_manifest", required=True, type=str, help="Path to output nemo ASR manifest") +parser.add_argument("--min_prob", default=0.5, type=float, help="Threshold on replacement probability") +parser.add_argument( + "--use_dp", + action="store_true", + help="Whether to use additional replacement filtering by using dynamic programming", +) +parser.add_argument( + "--replace_hyphen_to_space", + action="store_true", + help="Whether to use space instead of hyphen in replaced fragments", +) +parser.add_argument( + "--ngram_mappings", type=str, required=True, help="File with ngram mappings, only needed if use_dp=true" +) +parser.add_argument( + "--min_dp_score_per_symbol", + default=-1.5, + type=float, + help="Minimum dynamic programming sum score averaged by hypothesis length", +) + +args = parser.parse_args() + +update_manifest_with_spellmapper_corrections( + input_manifest_name=args.input_manifest, + short2full_name=args.short2full_name, + output_manifest_name=args.output_manifest, + spellmapper_results_name=args.spellmapper_results, + min_prob=args.min_prob, + replace_hyphen_to_space=args.replace_hyphen_to_space, + field_name=args.field_name, + use_dp=args.use_dp, + ngram_mappings=args.ngram_mappings, + min_dp_score_per_symbol=args.min_dp_score_per_symbol, +) + +print("Resulting manifest saved to: ", args.output_manifest) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/prepare_input_from_manifest.py b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/prepare_input_from_manifest.py new file mode 100644 index 0000000..6fd5e52 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/prepare_input_from_manifest.py @@ -0,0 +1,129 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script contains an example on how to prepare input for SpellMapper inference from a nemo ASR manifest. +It splits sentences to shorter fragments, runs candidate retrieval and generates input in the required format. +It produces two output files: + 1. File with correspondence between sentence fragments and full sentences. + 2. File that will serve as input for SpellMapper inference. + +See "examples/nlp/spellchecking_asr_customization/run_infer.sh" for the whole inference pipeline. +""" + +from argparse import ArgumentParser + +from nemo.collections.nlp.data.spellchecking_asr_customization.utils import ( + extract_and_split_text_from_manifest, + get_candidates, + load_index, +) + +parser = ArgumentParser(description="Prepare input for SpellMapper inference from a nemo ASR manifest") +parser.add_argument("--manifest", required=True, type=str, help="Path to input manifest file") +parser.add_argument( + "--custom_vocab_index", required=True, type=str, help="Path to input file with custom vocabulary index" +) +parser.add_argument( + "--big_sample", + required=True, + type=str, + help="Path to input file with big sample of phrases to sample dummy candidates if there less than 10 are found by retrieval", +) +parser.add_argument( + "--short2full_name", + required=True, + type=str, + help="Path to output file with correspondence between sentence fragments and full sentences", +) +parser.add_argument( + "--output_name", + required=True, + type=str, + help="Path to output file that will serve as input for SpellMapper inference", +) +parser.add_argument("--field_name", default="pred_text", type=str, help="Name of json field with ASR hypothesis text") +parser.add_argument("--len_in_words", default=16, type=int, help="Maximum fragment length in words") +parser.add_argument( + "--step_in_words", + default=8, + type=int, + help="Step in words for moving to next fragment. If less than len_in_words, fragments will intersect", +) + +args = parser.parse_args() + +# Split ASR hypotheses to shorter fragments, because SpellMapper can't handle arbitrarily long sequences. +# The correspondence between short and original fragments is saved to a file and will be used at post-processing. +extract_and_split_text_from_manifest( + input_name=args.manifest, + output_name=args.short2full_name, + field_name=args.field_name, + len_in_words=args.len_in_words, + step_in_words=args.step_in_words, +) + +# Load index of custom vocabulary from file +phrases, ngram2phrases = load_index(args.custom_vocab_index) + +# Load big sample of phrases to sample dummy candidates if there less than 10 are found by retrieval +big_sample_of_phrases = set() +with open(args.big_sample, "r", encoding="utf-8") as f: + for line in f: + phrase, freq = line.strip().split("\t") + if int(freq) > 50: # do not want to use frequent phrases as dummy candidates + continue + if len(phrase) < 6 or len(phrase) > 15: # do not want to use too short or too long phrases as dummy candidates + continue + big_sample_of_phrases.add(phrase) + +big_sample_of_phrases = list(big_sample_of_phrases) + +# Generate input for SpellMapper inference +out = open(args.output_name, "w", encoding="utf-8") +with open(args.short2full_name, "r", encoding="utf-8") as f: + for line in f: + short_sent, _ = line.strip().split("\t") + sent = "_".join(short_sent.split()) + letters = list(sent) + candidates = get_candidates(ngram2phrases, phrases, letters, big_sample_of_phrases) + if len(candidates) == 0: + continue + if len(candidates) != 10: + raise ValueError("expect 10 candidates, got: ", len(candidates)) + + # We add two columns with targets and span_info. + # They have same format as during training, but start and end positions are APPROXIMATE, they will be adjusted when constructing BertExample. + targets = [] + span_info = [] + for idx, c in enumerate(candidates): + if c[1] == -1: + continue + targets.append(str(idx + 1)) # targets are 1-based + start = c[1] + # ensure that end is not outside sentence length (it can happen because c[2] is candidate length used as approximation) + end = min(c[1] + c[2], len(letters)) + span_info.append("CUSTOM " + str(start) + " " + str(end)) + out.write( + " ".join(letters) + + "\t" + + ";".join([x[0] for x in candidates]) + + "\t" + + " ".join(targets) + + "\t" + + ";".join(span_info) + + "\n" + ) +out.close() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/run_infer.sh b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/run_infer.sh new file mode 100644 index 0000000..b4bbdc4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/run_infer.sh @@ -0,0 +1,99 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +## RUN INFERENCE ON NEMO MANIFEST AND CUSTOM VOCABULARY + +## Path to NeMo repository +NEMO_PATH=NeMo + +## Download model repo from Hugging Face (if clone doesn't work, run "git lfs install" and try again) +git clone https://huggingface.co/bene-ges/spellmapper_asr_customization_en +## Download repo with test data +git clone https://huggingface.co/datasets/bene-ges/spellmapper_en_evaluation + +## Files in model repo +PRETRAINED_MODEL=spellmapper_asr_customization_en/training_10m_5ep.nemo +NGRAM_MAPPINGS=spellmapper_asr_customization_en/replacement_vocab_filt.txt +BIG_SAMPLE=spellmapper_asr_customization_en/big_sample.txt + +## Override these two files if you want to test on your own data +## File with input nemo ASR manifest +INPUT_MANIFEST=spellmapper_en_evaluation/medical_manifest_ctc.json +## File containing custom words and phrases (plain text) +CUSTOM_VOCAB=spellmapper_en_evaluation/medical_custom_vocab.txt + +## Other files will be created +## File with index of custom vocabulary +INDEX="index.txt" +## File with short fragments and corresponding original sentences +SHORT2FULL="short2full.txt" +## File with input for SpellMapper inference +SPELLMAPPER_INPUT="spellmapper_input.txt" +## File with output of SpellMapper inference +SPELLMAPPER_OUTPUT="spellmapper_output.txt" +## File with output nemo ASR manifest +OUTPUT_MANIFEST="out_manifest.json" + + +# Create index of custom vocabulary +python ${NEMO_PATH}/examples/nlp/spellchecking_asr_customization/create_custom_vocab_index.py \ + --input_name ${CUSTOM_VOCAB} \ + --ngram_mappings ${NGRAM_MAPPINGS} \ + --output_name ${INDEX} \ + --min_log_prob -4.0 \ + --max_phrases_per_ngram 600 + +# Prepare input for SpellMapper inference +python ${NEMO_PATH}/examples/nlp/spellchecking_asr_customization/prepare_input_from_manifest.py \ + --manifest ${INPUT_MANIFEST} \ + --custom_vocab_index ${INDEX} \ + --big_sample ${BIG_SAMPLE} \ + --short2full_name ${SHORT2FULL} \ + --output_name ${SPELLMAPPER_INPUT} \ + --field_name "pred_text" \ + --len_in_words 16 \ + --step_in_words 8 + +# Run SpellMapper inference +python ${NEMO_PATH}/examples/nlp/spellchecking_asr_customization/spellchecking_asr_customization_infer.py \ + pretrained_model=${PRETRAINED_MODEL} \ + model.max_sequence_len=512 \ + inference.from_file=${SPELLMAPPER_INPUT} \ + inference.out_file=${SPELLMAPPER_OUTPUT} \ + inference.batch_size=16 \ + lang=en + +# Postprocess and create output corrected manifest +python ${NEMO_PATH}/examples/nlp/spellchecking_asr_customization/postprocess_and_update_manifest.py \ + --input_manifest ${INPUT_MANIFEST} \ + --short2full_name ${SHORT2FULL} \ + --output_manifest ${OUTPUT_MANIFEST} \ + --spellmapper_result ${SPELLMAPPER_OUTPUT} \ + --replace_hyphen_to_space \ + --field_name "pred_text" \ + --use_dp \ + --ngram_mappings ${NGRAM_MAPPINGS} \ + --min_dp_score_per_symbol -1.5 + +# Check WER of initial manifest +python ${NEMO_PATH}/examples/asr/speech_to_text_eval.py \ + dataset_manifest=${INPUT_MANIFEST} \ + use_cer=False \ + only_score_manifest=True + +# Check WER of corrected manifest +python ${NEMO_PATH}/examples/asr/speech_to_text_eval.py \ + dataset_manifest=${OUTPUT_MANIFEST} \ + use_cer=False \ + only_score_manifest=True diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/run_training.sh b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/run_training.sh new file mode 100644 index 0000000..85dddbb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/run_training.sh @@ -0,0 +1,56 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +## TRAIN WITH NON-TARRED DATA + +# Path to NeMo repository +NEMO_PATH=NeMo + +## Download repo with training data (very small example) +## If clone doesn't work, run "git lfs install" and try again +git clone https://huggingface.co/datasets/bene-ges/spellmapper_en_train_micro + +DATA_PATH=spellmapper_en_train_micro + +## Example of all files needed to run training with non-tarred data: +## spellmapper_en_train_micro +## ├── config.json +##   ├── label_map.txt +##   ├── semiotic_classes.txt +## ├── test.tsv +## └── train.tsv + +## To generate files config.json, label_map.txt, semiotic_classes.txt - run generate_configs.sh +## Files "train.tsv" and "test.tsv" contain training examples. +## For data preparation see https://github.com/bene-ges/nemo_compatible/blob/main/scripts/nlp/en_spellmapper/dataset_preparation/build_training_data.sh + +## Note that training with non-tarred data only works on single gpu. It makes sense if you use 1-2 million examples or less. + +python ${NEMO_PATH}/examples/nlp/spellchecking_asr_customization/spellchecking_asr_customization_train.py \ + lang="en" \ + data.validation_ds.data_path=${DATA_PATH}/test.tsv \ + data.train_ds.data_path=${DATA_PATH}/train.tsv \ + data.train_ds.batch_size=32 \ + data.train_ds.num_workers=8 \ + model.max_sequence_len=512 \ + model.language_model.pretrained_model_name=huawei-noah/TinyBERT_General_6L_768D \ + model.language_model.config_file=${DATA_PATH}/config.json \ + model.label_map=${DATA_PATH}/label_map.txt \ + model.semiotic_classes=${DATA_PATH}/semiotic_classes.txt \ + model.optim.lr=3e-5 \ + trainer.devices=[1] \ + trainer.num_nodes=1 \ + trainer.accelerator=gpu \ + trainer.strategy=ddp \ + trainer.max_epochs=5 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/run_training_tarred.sh b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/run_training_tarred.sh new file mode 100644 index 0000000..655c3e2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/run_training_tarred.sh @@ -0,0 +1,63 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +## TRAIN WITH TARRED DATA + +# Path to NeMo repository +NEMO_PATH=NeMo + +DATA_PATH=data_folder + +## data_folder_example +## ├── train_tarred +## | ├── part1.tar +## | ├── ... +## | └── part200.tar +## ├── config.json +##   ├── label_map.txt +##   ├── semiotic_classes.txt +## └── test.tsv +## To generate files config.json, label_map.txt, semiotic_classes.txt, run generate_configs.sh +## To prepare data, see ${NEMO_PATH}/examples/nlp/spellchecking_asr_customization/dataset_preparation/build_training_data.sh +## To convert data to tarred format, split all.tsv to pieces of 110'000 examples (except for validation part) and use ${NEMO_PATH}/examples/nlp/spellchecking_asr_customization/dataset_preparation/convert_data_to_tarred.sh +## To run training with tarred data, use ${NEMO_PATH}/examples/nlp/spellchecking_asr_customization/run_training_tarred.sh + +## ATTENTION: How to calculate model.optim.sched.max_steps: +## Suppose, you have 2'000'000 training examples, and want to train for 5 epochs on 4 gpus with batch size 32. +## 5 (epochs) * 32 (bs) * 4 (gpus) +## 1 step consumes 128 examples (32(bs) * 4(gpus)) +## 1 epoch makes 2000000/128=15625 steps (updates) +## 5 epochs make 5*15625=78125 steps + +python ${NEMO_PATH}/examples/nlp/spellchecking_asr_customization/spellchecking_asr_customization_train.py \ + lang="en" \ + data.validation_ds.data_path=${DATA_PATH}/test.tsv \ + data.train_ds.data_path=${DATA_PATH}/train_tarred/part_OP_1..100_CL_.tar \ + data.train_ds.batch_size=32 \ + data.train_ds.num_workers=16 \ + +data.train_ds.use_tarred_dataset=true \ + data.train_ds.shuffle=false \ + data.validation_ds.batch_size=16 \ + model.max_sequence_len=512 \ + model.language_model.pretrained_model_name=huawei-noah/TinyBERT_General_6L_768D \ + model.language_model.config_file=${DATA_PATH}/config.json \ + model.label_map=${DATA_PATH}/label_map.txt \ + model.semiotic_classes=${DATA_PATH}/semiotic_classes.txt \ + model.optim.sched.name=CosineAnnealing \ + +model.optim.sched.max_steps=195313 \ + trainer.devices=8 \ + trainer.num_nodes=1 \ + trainer.accelerator=gpu \ + trainer.strategy=ddp \ + trainer.max_epochs=5 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/spellchecking_asr_customization_infer.py b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/spellchecking_asr_customization_infer.py new file mode 100644 index 0000000..593264f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/spellchecking_asr_customization_infer.py @@ -0,0 +1,123 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script contains an example on how to run inference with the SpellcheckingAsrCustomizationModel. + +An input line should consist of 4 tab-separated columns: + 1. text of ASR-hypothesis + 2. texts of 10 candidates separated by semicolon + 3. 1-based ids of non-dummy candidates + 4. approximate start/end coordinates of non-dummy candidates (correspond to ids in third column) + +Example input (in one line): + t h e _ t a r a s i c _ o o r d a _ i s _ a _ p a r t _ o f _ t h e _ a o r t a _ l o c a t e d _ i n _ t h e _ t h o r a x + h e p a t i c _ c i r r h o s i s;u r a c i l;c a r d i a c _ a r r e s t;w e a n;a p g a r;p s y c h o m o t o r;t h o r a x;t h o r a c i c _ a o r t a;a v f;b l o c k a d e d + 1 2 6 7 8 9 10 + CUSTOM 6 23;CUSTOM 4 10;CUSTOM 4 15;CUSTOM 56 62;CUSTOM 5 19;CUSTOM 28 31;CUSTOM 39 48 + +Each line in SpellMapper output is tab-separated and consists of 4 columns: + 1. ASR-hypothesis (same as in input) + 2. 10 candidates separated with semicolon (same as in input) + 3. fragment predictions, separated with semicolon, each prediction is a tuple (start, end, candidate_id, probability) + 4. letter predictions - candidate_id predicted for each letter (this is only for debug purposes) + +Example output (in one line): + t h e _ t a r a s i c _ o o r d a _ i s _ a _ p a r t _ o f _ t h e _ a o r t a _ l o c a t e d _ i n _ t h e _ t h o r a x + h e p a t i c _ c i r r h o s i s;u r a c i l;c a r d i a c _ a r r e s t;w e a n;a p g a r;p s y c h o m o t o r;t h o r a x;t h o r a c i c _ a o r t a;a v f;b l o c k a d e d + 56 62 7 0.99998;4 20 8 0.95181;12 20 8 0.44829;4 17 8 0.99464;12 17 8 0.97645 + 8 8 8 0 8 8 8 8 8 8 8 8 8 8 8 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 7 7 7 7 7 + + +USAGE Example: +1. Train a model, or use a pretrained checkpoint. +2. Run on a single file: + python nemo/examples/nlp/spellchecking_asr_customization/spellchecking_asr_customization_infer.py \ + pretrained_model=${PRETRAINED_NEMO_CHECKPOINT} \ + model.max_sequence_len=512 \ + inference.from_file=input.txt \ + inference.out_file=output.txt \ + inference.batch_size=16 \ + lang=en +or on multiple files: + python ${NEMO_PATH}/examples/nlp/spellchecking_asr_customization/spellchecking_asr_customization_infer.py \ + pretrained_model=${PRETRAINED_NEMO_CHECKPOINT} \ + model.max_sequence_len=512 \ + +inference.from_filelist=filelist.txt \ + +inference.output_folder=output_folder \ + inference.batch_size=16 \ + lang=en + +This script uses the `/examples/nlp/spellchecking_asr_customization/conf/spellchecking_asr_customization_config.yaml` +config file by default. The other option is to set another config file via command +line arguments by `--config-name=CONFIG_FILE_PATH'. +""" + + +import os + +from helpers import MODEL, instantiate_model_and_trainer +from omegaconf import DictConfig, OmegaConf + +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@hydra_runner(config_path="conf", config_name="spellchecking_asr_customization_config") +def main(cfg: DictConfig) -> None: + logging.debug(f'Config Params: {OmegaConf.to_yaml(cfg)}') + + if cfg.pretrained_model is None: + raise ValueError("A pre-trained model should be provided.") + _, model = instantiate_model_and_trainer(cfg, MODEL, False) + + if cfg.model.max_sequence_len != model.max_sequence_len: + model.max_sequence_len = cfg.model.max_sequence_len + model.builder._max_seq_length = cfg.model.max_sequence_len + input_filenames = [] + output_filenames = [] + + if "from_filelist" in cfg.inference and "output_folder" in cfg.inference: + filelist_file = cfg.inference.from_filelist + output_folder = cfg.inference.output_folder + with open(filelist_file, "r", encoding="utf-8") as f: + for line in f: + path = line.strip() + input_filenames.append(path) + folder, name = os.path.split(path) + output_filenames.append(os.path.join(output_folder, name)) + else: + text_file = cfg.inference.from_file + logging.info(f"Running inference on {text_file}...") + if not os.path.exists(text_file): + raise ValueError(f"{text_file} not found.") + input_filenames.append(text_file) + output_filenames.append(cfg.inference.out_file) + + dataloader_cfg = { + "batch_size": cfg.inference.get("batch_size", 8), + "num_workers": cfg.inference.get("num_workers", 4), + "pin_memory": cfg.inference.get("num_workers", False), + } + for input_filename, output_filename in zip(input_filenames, output_filenames): + if not os.path.exists(input_filename): + logging.info(f"Skip non-existing {input_filename}.") + continue + model.infer(dataloader_cfg, input_filename, output_filename) + logging.info(f"Predictions saved to {output_filename}.") + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/spellchecking_asr_customization_train.py b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/spellchecking_asr_customization_train.py new file mode 100644 index 0000000..ac50b41 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/spellchecking_asr_customization/spellchecking_asr_customization_train.py @@ -0,0 +1,70 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script contains an example on how to train SpellMapper (SpellcheckingAsrCustomizationModel). +It uses the `examples/nlp/spellchecking_asr_customization/conf/spellchecking_asr_customization_config.yaml` +config file by default. The other option is to set another config file via command +line arguments by `--config-name=CONFIG_FILE_PATH'. Probably it is worth looking +at the example config file to see the list of parameters used for training. + +USAGE Example: + See `examples/nlp/spellchecking_asr_customization/run_training.sh` for training on non-tarred data. + and + `examples/nlp/spellchecking_asr_customization/run_training_tarred.sh` for training on tarred data. + +One (non-tarred) training example should consist of 4 tab-separated columns: + 1. text of ASR-hypothesis + 2. texts of 10 candidates separated by semicolon + 3. 1-based ids of correct candidates, or 0 if none + 4. start/end coordinates of correct candidates (correspond to ids in third column) +Example (in one line): + a s t r o n o m e r s _ d i d i e _ s o m o n _ a n d _ t r i s t i a n _ g l l o + d i d i e r _ s a u m o n;a s t r o n o m i e;t r i s t a n _ g u i l l o t;t r i s t e s s e;m o n a d e;c h r i s t i a n;a s t r o n o m e r;s o l o m o n;d i d i d i d i d i;m e r c y + 1 3 + CUSTOM 12 23;CUSTOM 28 41 +""" + +from helpers import MODEL, instantiate_model_and_trainer +from omegaconf import DictConfig, OmegaConf + +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="spellchecking_asr_customization_config") +def main(cfg: DictConfig) -> None: + # PTL 2.0 has find_unused_parameters as False by default, so its required to set it to True + # when there are unused parameters like here + if cfg.trainer.strategy == 'ddp': + cfg.trainer.strategy = "ddp_find_unused_parameters_true" + logging.info(f'Config Params: {OmegaConf.to_yaml(cfg)}') + + # Train the model + if cfg.model.do_training: + logging.info( + "================================================================================================" + ) + logging.info('Start training...') + trainer, model = instantiate_model_and_trainer(cfg, MODEL, True) + spellchecking_exp_manager = cfg.get('exp_manager', None) + exp_manager(trainer, spellchecking_exp_manager) + trainer.fit(model) + logging.info('Training finished!') + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text2sparql/conf/text2sparql_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/text2sparql/conf/text2sparql_config.yaml new file mode 100644 index 0000000..b9823e7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text2sparql/conf/text2sparql_config.yaml @@ -0,0 +1,106 @@ +# Text2Sparql with BART + +name: &name Text2Sparql + +trainer: + devices: 1 # the number of gpus, 0 for CPU, or list with gpu indices + num_nodes: 1 + max_epochs: 2 # the number of training epochs + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + accelerator: gpu + strategy: ddp + gradient_clip_val: 0.0 + log_every_n_steps: 1 + val_check_interval: 1.0 # check once per epoch .25 for 4 times per epoch + enable_checkpointing: False # provided by exp_manager + logger: false # provided by exp_manager + +model: + nemo_path: null # exported .nemo path + max_seq_length: 150 + batch_size: 16 + convert_labels: true # true if Bart, false otherwise (converts pad_id to -100 for masked loss) + data_dir: null + + language_model: + pretrained_model_name: facebook/bart-base # huggingface end-to-end model name + pretrained_encoder_model_name: null # huggingface encoder model name + pretrained_decoder_model_name: null # huggingface decoder model name + lm_checkpoint: null + config: null + config_file: null # json file, precedence over config + + encoder_tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # tokenizer that inherits from TokenizerSpec + vocab_file: null # path to vocab file + tokenizer_model: null # tokenizer model for sentencepiece + special_tokens: null + add_special_tokens: true + + decoder_tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # tokenizer that inherits from TokenizerSpec + vocab_file: null # path to vocab file + tokenizer_model: null # tokenizer model for sentencepiece + special_tokens: null + add_special_tokens: true + + train_ds: + filepath: ${model.data_dir}/train.tsv # path to data file + shuffle: true + num_samples: -1 + num_workers: 2 + drop_last: false + pin_memory: false + + validation_ds: + filepath: ${model.data_dir}/test_easy.tsv # path to data file + shuffle: false + num_samples: -1 + num_workers: 2 + drop_last: false + pin_memory: false + + test_ds: + filepath: ${model.data_dir}/test_hard.tsv # path to data file + shuffle: false + num_samples: -1 + num_workers: 2 + drop_last: false + pin_memory: false + + optim: + name: adamw + lr: 4e-5 + weight_decay: 0.0 + + sched: + name: CosineAnnealing + warmup_steps: null + warmup_ratio: 0.06 + min_lr: 0.0 + last_epoch: -1 + + generate: + max_length: ${model.max_seq_length} + num_beams: 1 + length_penalty: 2.0 + early_stopping: true + repetition_penalty: 1.0 + do_sample: false + top_k: null + top_p: null + num_return_sequences: 1 + +exp_manager: + exp_dir: null # where to store logs and checkpoints + name: *name # name of experiment + create_tensorboard_logger: True + create_checkpoint_callback: True + +hydra: + run: + dir: . + job_logging: + root: + handlers: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text2sparql/data/import_datasets.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text2sparql/data/import_datasets.py new file mode 100644 index 0000000..ce5f863 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text2sparql/data/import_datasets.py @@ -0,0 +1,134 @@ +# Copyright (c) 2020, MeetKai Inc. All rights reserved. +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script downloads Text2Sparql data and processes it into NeMo's neural machine translation dataset format. + +Text2Sparql data consists of 3 files which are saved to source_data_dir: + - train_queries_v3.tsv + - test_easy_queries_v3.tsv + - test_hard_queries_v3.tsv + +After processing, the script saves them to the target_data_dir as: + - train.tsv + - test_easy.tsv + - test_hard.tsv + + +You may run it with: + +python import_datasets \ + --source_data_dir ./text2sparql_src \ + --target_data_dir ./text2sparql_tgt +""" + +import argparse +import csv +import os +from urllib.request import Request, urlopen + +from nemo.collections.nlp.data.data_utils.data_preprocessing import MODE_EXISTS_TMP, if_exist +from nemo.utils import logging + +base_url = "https://m.meetkai.com/public_datasets/knowledge/" +prefix_map = { + "train_queries_v3.tsv": "train.tsv", + "test_easy_queries_v3.tsv": "test_easy.tsv", + "test_hard_queries_v3.tsv": "test_hard.tsv", +} + + +def download_text2sparql(infold: str): + """Downloads text2sparql train, test_easy, and test_hard data + + Args: + infold: save directory path + """ + os.makedirs(infold, exist_ok=True) + + for prefix in prefix_map: + url = base_url + prefix + + logging.info(f"Downloading: {url}") + if if_exist(infold, [prefix]): + logging.info("** Download file already exists, skipping download") + else: + req = Request(url, headers={"User-Agent": "Mozilla/5.0"}) + with open(os.path.join(infold, prefix), "wb") as handle: + handle.write(urlopen(req, timeout=20).read()) + + +def process_text2sparql(infold: str, outfold: str, do_lower_case: bool): + """ Process and convert MeetKai's text2sparql datasets to NeMo's neural machine translation format. + + Args: + infold: directory path to raw text2sparql data containing + train.tsv, test_easy.tsv, test_hard.tsv + outfold: output directory path to save formatted data for NeuralMachineTranslationDataset + the first line is header (sentence [tab] label) + each line should be [sentence][tab][label] + do_lower_case: if true, convert all sentences and labels to lower + """ + logging.info(f"Processing Text2Sparql dataset and storing at: {outfold}") + + os.makedirs(outfold, exist_ok=True) + + dataset_name = "Text2Sparql" + for prefix in prefix_map: + input_file = os.path.join(infold, prefix) + output_file = os.path.join(outfold, prefix_map[prefix]) + + if if_exist(outfold, [prefix_map[prefix]]): + logging.info(f"** {MODE_EXISTS_TMP.format(prefix_map[prefix], dataset_name, output_file)}") + continue + + if not if_exist(infold, [prefix]): + logging.info(f"** {prefix} of {dataset_name}" f" is skipped as it was not found") + continue + + assert input_file != output_file, "input file cannot equal output file" + with open(input_file, "r") as in_file: + with open(output_file, "w") as out_file: + reader = csv.reader(in_file, delimiter="\t") + + # replace headers + out_file.write("sentence\tlabel\n") + next(reader) + + for line in reader: + sentence = line[0] + label = line[1] + if do_lower_case: + sentence = sentence.lower() + label = label.lower() + out_file.write(f"{sentence}\t{label}\n") + + +if __name__ == "__main__": + # Parse the command-line arguments. + parser = argparse.ArgumentParser(description="Process and convert datasets into NeMo's format") + parser.add_argument( + "--source_data_dir", required=True, type=str, help="Path to the folder containing the dataset files" + ) + parser.add_argument("--target_data_dir", required=True, type=str, help="Path to save the processed dataset") + parser.add_argument("--do_lower_case", action="store_true") + args = parser.parse_args() + + source_dir = args.source_data_dir + target_dir = args.target_data_dir + do_lower_case = args.do_lower_case + + download_text2sparql(infold=source_dir) + process_text2sparql(infold=source_dir, outfold=target_dir, do_lower_case=do_lower_case) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text2sparql/evaluate_text2sparql.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text2sparql/evaluate_text2sparql.py new file mode 100644 index 0000000..52baa2a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text2sparql/evaluate_text2sparql.py @@ -0,0 +1,72 @@ +# Copyright (c) 2020, MeetKai Inc. All rights reserved. +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script contains an example on how to evaluate a NeuralMachineTranslationModel. +To load the example Text2Sparql dataset, please refer to ./data/import_datasets.py. +To train a model, please refer to text2sparql.py. + + +***Setting the configs*** +This script uses the `/examples/nlp/text2sparql/conf/text2sparql_config.yaml` config file by default. +You may update the config file from the file directly or by using the command line arguments. +Another other option is to set another config file via command line arguments by `--config-name=CONFIG_FILE_PATH'. + +Please refer to text2sparql.py for detailed instructions on setting the configuration. + + +***How to run the script?*** +- To reload and evaluate the model, run: + +python evaluate_text2sparql.py \ + model.test_ds.filepath="$TGT_DATA_DIR"/test_easy.tsv \ + model.batch_size=16 \ + model.nemo_path=./NeMo_logs/bart.nemo \ + exp_manager.exp_dir=./NeMo_logs +""" + +import os + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models.text2sparql import Text2SparqlModel +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@hydra_runner(config_path="conf", config_name="text2sparql_config") +def main(cfg: DictConfig) -> None: + logging.info(f"Config:\n {OmegaConf.to_yaml(cfg)}") + trainer = pl.Trainer(devices=cfg.trainer.devices, accelerator=cfg.trainer.accelerator) + nmt_model = Text2SparqlModel.restore_from(restore_path=cfg.model.nemo_path) + nmt_model.setup_test_data(cfg.model.test_ds) + results = trainer.test(nmt_model) + + with open(cfg.model.test_ds.filepath, "r", encoding='utf-8') as f: + lines = f.readlines() + + lines[0] = lines[0].strip() + f"\tpredictions\n" + for i, res in enumerate(results[0]["texts"]): + lines[i + 1] = lines[i + 1].strip() + f"\t{res}\n" + + savepath = os.path.join(cfg.exp_manager.exp_dir, os.path.basename(cfg.model.test_ds.filepath)) + with open(savepath, "w", encoding='utf-8') as f: + f.writelines(lines) + logging.info(f"Predictions saved to {savepath}") + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text2sparql/text2sparql.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text2sparql/text2sparql.py new file mode 100644 index 0000000..1353a39 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text2sparql/text2sparql.py @@ -0,0 +1,112 @@ +# Copyright (c) 2020, MeetKai Inc. All rights reserved. +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script contains an example on how to train and save a Text2SparqlModel. +Text2SparqlModel in NeMo supports sequence to sequence problems such as language translation +and text summarization, provided the data follows the format specified below. + + +***Data format*** +Text2SparqlModel requires the data to be stored in TAB separated files (.tsv) with two columns of +sentence and label, where the first line is a header of format: + sentence[TAB]label +And each line is of the format: + [SENTENCE][TAB][LABEL] + +If your dataset is stored in another format, you need to convert it to this format to use a +Text2SparqlModel. + + +***Setting the configs*** +This script uses the `/examples/nlp/text2sparql/conf/text2sparql_config.yaml` config file by default. +You may update the config file from the file directly or by using the command line arguments. +Another other option is to set another config file via command line arguments by `--config-name=CONFIG_FILE_PATH'. + +A Text2SparqlModel's config file declares multiple import sections. They are: + - trainer: Arguments to be passed to PyTorch Lightning. + - model: All arguments that relate to the Model - language_model, tokenizers, datasets, optimizer, generate. + - exp_manager: Arguments to be passed to NeMo's experiment manager. + - hydra: Arguments to be passed to Hydra. + +If using text2sparql_config.yaml, you must first update the following fields in the config: + - model.nemo_path: Model save path. Eg. [PATH]/bart.nemo + - model.data_dir: Path to data directory. Alternatively, you can adjust the file paths directly: + - model.train_ds.filepath + - model.validation_ds.filepath + - model.test_ds.filepath + - exp_manager.exp_dir: Directory to log results from the experiment. + +It is highly recommended to also adjust these parameters as necessary: + - trainer.devices: Set to 0 to use CPU. Otherwise the number denotes the number of GPUs. + - trainer.max_epochs: Maximum number of epochs to train for. + - model.batch_size: 8 is sufficient to train a decent Bart model for Text2Sparql. + - model.max_seq_length: Maximum (tokenized) sequence length. 150 is sufficient for Text2Sparql. + - model.language_model.pretrained_model_name: End2end pretrained model name from huggingface. + - model.encoder_tokenizer.tokenizer_name: Pretrained tokenizer name from huggingface. + - model.decoder_tokenizer.tokenizer_name: The same as above, as the tokenizer will handle encoding and decoding. + - model.optim.lr: Learning rate. + +You can also specify an encoder and decoder rather than using an end2end model like Bart by defining these parameters: + - model.language_model.pretrained_encoder_model_name: Pretrained huggingface encoder model name. + - model.encoder_tokenizer.tokenizer_name: Pretrained huggingface encoder tokenizer name. + - model.language_model.pretrained_decoder_model_name: Pretrained huggingface decoder model name. + - model.decoder_tokenizer.tokenizer_name: Pretrained huggingface decoder tokenizer name. + - model.language_model.pretrained_model_name: Set this to null. + + +***How to run the script?*** +- First, download the data to TGT_DATA_DIR (see ./data/import_datasets.py): + +SRC_DATA_DIR=./data/text2sparql_src +TGT_DATA_DIR=./data/text2sparql_tgt + +python ./data/import_datasets.py \ + --source_data_dir $SRC_DATA_DIR \ + --target_data_dir $TGT_DATA_DIR + +- And run the following to train and save the model: + +python text2sparql.py \ + model.train_ds.filepath="$TGT_DATA_DIR"/train.tsv \ + model.validation_ds.filepath="$TGT_DATA_DIR"/test_easy.tsv \ + model.test_ds.filepath="$TGT_DATA_DIR"/test_hard.tsv \ + model.batch_size=16 \ + model.nemo_path=./NeMo_logs/bart.nemo \ + exp_manager.exp_dir=./NeMo_logs +""" + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models.text2sparql import Text2SparqlModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="text2sparql_config") +def main(cfg: DictConfig) -> None: + logging.info(f"Config:\n {OmegaConf.to_yaml(cfg)}") + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + nmt_model = Text2SparqlModel(cfg.model, trainer=trainer) + trainer.fit(nmt_model) + if cfg.model.nemo_path: + nmt_model.save_to(cfg.model.nemo_path) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_classification/conf/ptune_text_classification_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/text_classification/conf/ptune_text_classification_config.yaml new file mode 100644 index 0000000..47180b5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_classification/conf/ptune_text_classification_config.yaml @@ -0,0 +1,114 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Config file for text classification with pre-trained BERT models + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: 100 + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + +model: + tensor_model_parallel_size: 1 # tensor model parallel size used in the LM model + seed: 1234 + nemo_path: null # filename to save the model and associated artifacts to .nemo file + use_lm_finetune: False # whether fine tune the language model + pseudo_token: '[PROMPT]' # pseudo prompt tokens + + tokenizer: + library: 'megatron' + type: 'GPT2BPETokenizer' + model: null + vocab_file: null + merge_file: null + + language_model: + nemo_file: null + + prompt_encoder: + template: [3, 3, 0] + dropout: 0.0 + num_layers: 2 + + dataset: + classes: ??? # The class labels, e.g. ['positive', 'neutral', 'negative'] + + train_ds: + file_path: null + batch_size: 64 + shuffle: true + num_samples: -1 # number of samples to be considered, -1 means all the dataset + num_workers: 3 + drop_last: false + pin_memory: false + + validation_ds: + file_path: null + batch_size: 64 + shuffle: false + num_samples: -1 # number of samples to be considered, -1 means all the dataset + num_workers: 3 + drop_last: false + pin_memory: false + + test_ds: + file_path: null + batch_size: 64 + shuffle: false + num_samples: -1 # number of samples to be considered, -1 means all the dataset + num_workers: 3 + drop_last: false + pin_memory: false + + optim: + name: adam + lr: 1e-5 + # optimizer arguments + betas: [0.9, 0.999] + weight_decay: 0.0005 + + # scheduler setup + sched: + name: WarmupAnnealing + # Scheduler params + warmup_steps: null + warmup_ratio: 0.1 + last_epoch: -1 + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + + # List of some sample queries for inference after training is done + infer_samples: [ + 'For example , net sales increased by 5.9 % from the first quarter , and EBITDA increased from a negative EUR 0.2 mn in the first quarter of 2009 .', + '8 May 2009 - Finnish liquid handling products and diagnostic test systems maker Biohit Oyj ( HEL : BIOBV ) said today ( 8 May 2009 ) its net loss narrowed to EUR0 .1 m ( USD0 .14 m ) for the first quarter of 2009 from EUR0 .4 m for the same period of 2008 .', + 'CHS Expo Freight is a major Finnish fair , exhibition and culture logistics company that provides logistics services to various events by land , air and sea .', + ] + +exp_manager: + exp_dir: null # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: "PTuneTextClassification" # The name of your model + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_classification/conf/text_classification_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/text_classification/conf/text_classification_config.yaml new file mode 100644 index 0000000..164bf34 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_classification/conf/text_classification_config.yaml @@ -0,0 +1,117 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Config file for text classification with pre-trained BERT models + +trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: 100 + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + +model: + nemo_path: text_classification_model.nemo # filename to save the model and associated artifacts to .nemo file + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + + language_model: + pretrained_model_name: bert-base-uncased + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + classifier_head: + num_output_layers: 2 + fc_dropout: 0.1 + + class_labels: + class_labels_file : null # optional to specify a file containing the list of the labels + + dataset: + num_classes: ??? # The number of classes. 0 < Label ", "") + outfiles[mode].write(f'{review}\t{label}\n') + + for mode in modes: + outfiles[mode].close() + + class_labels_file = open(os.path.join(outfold, 'label_ids.tsv'), 'w') + class_labels_file.write('negative\npositive\n') + class_labels_file.close() + + +def process_sst2(infold, outfold, uncased, splits=['train', 'dev']): + """Process sst2 dataset.""" + # "test" split doesn't have labels, so it is skipped + if not os.path.exists(infold): + link = 'https://dl.fbaipublicfiles.com/glue/data/SST-2.zip' + raise ValueError( + f'Data not found at {infold}. Please download SST-2 dataset from `{link}` and ' + f'extract it into the folder specified by `source_data_dir` argument.' + ) + + logging.info(f'Processing SST-2 dataset') + os.makedirs(outfold, exist_ok=True) + + def _read_tsv(input_file, quotechar=None): + """Read a tab separated value file.""" + with open(input_file, "r") as f: + reader = csv.reader(f, delimiter="\t", quotechar=quotechar) + lines = [] + for line in reader: + lines.append(line) + return lines + + for split in splits: + # Load input file. + input_file = os.path.join(infold, split + '.tsv') + lines = _read_tsv(input_file) + # Create output. + outfile = open(os.path.join(outfold, split + '.tsv'), 'w') + + # Copy lines, skip the header (line 0). + for line in lines[1:]: + text = line[0] + label = line[1] + # Lowercase when required. + if uncased: + text = text.lower() + # Write output. + outfile.write(f'{text}\t{label}\n') + # Close file. + outfile.close() + + class_labels_file = open(os.path.join(outfold, 'label_ids.tsv'), 'w') + class_labels_file.write('negative\npositive\n') + class_labels_file.close() + + logging.info(f'Result stored at {outfold}') + + +def process_chemprot(source_dir, target_dir, uncased, modes=['train', 'test', 'dev']): + if not os.path.exists(source_dir): + link = 'https://github.com/arwhirang/recursive_chemprot/tree/master/Demo/tree_LSTM/data' + raise ValueError(f'Data not found at {source_dir}. ' f'Please download ChemProt from {link}.') + + logging.info(f'Processing Chemprot dataset and store at {target_dir}') + os.makedirs(target_dir, exist_ok=True) + + naming_map = {'train': 'trainingPosit_chem', 'test': 'testPosit_chem', 'dev': 'developPosit_chem'} + + def _read_tsv(input_file, quotechar=None): + """Reads a tab separated value file.""" + with open(input_file, "r") as f: + reader = csv.reader(f, delimiter="\t", quotechar=quotechar) + lines = [] + for line in reader: + lines.append(line) + return lines + + outfiles = {} + label_mapping = {} + out_label_mapping = open(os.path.join(target_dir, 'label_mapping.tsv'), 'w') + for mode in modes: + outfiles[mode] = open(os.path.join(target_dir, mode + '.tsv'), 'w') + input_file = os.path.join(source_dir, naming_map[mode]) + lines = _read_tsv(input_file) + for line in lines: + text = line[1] + label = line[2] + if label == "True": + label = line[3] + if uncased: + text = text.lower() + if label not in label_mapping: + out_label_mapping.write(f'{label}\t{len(label_mapping)}\n') + label_mapping[label] = len(label_mapping) + label = label_mapping[label] + outfiles[mode].write(f'{text}\t{label}\n') + for mode in modes: + outfiles[mode].close() + out_label_mapping.close() + + +def process_thucnews(infold, outfold): + modes = ['train', 'test'] + train_size = 0.8 + if not os.path.exists(infold): + link = 'thuctc.thunlp.org/' + raise ValueError(f'Data not found at {infold}. ' f'Please download THUCNews from {link}.') + + logging.info(f'Processing THUCNews dataset and store at {outfold}') + os.makedirs(outfold, exist_ok=True) + + outfiles = {} + for mode in modes: + outfiles[mode] = open(os.path.join(outfold, mode + '.tsv'), 'a+', encoding='utf-8') + categories = ['体育', '娱乐', '家居', '彩票', '房产', '教育', '时尚', '时政', '星座', '游戏', '社会', '科技', '股票', '财经'] + for category in categories: + label = categories.index(category) + category_files = glob.glob(f'{infold}/{category}/*.txt') + test_num = int(len(category_files) * (1 - train_size)) + test_files = category_files[:test_num] + train_files = category_files[test_num:] + + for mode in modes: + logging.info(f'Processing {mode} data of the category {category}') + if mode == 'test': + files = test_files + else: + files = train_files + + if len(files) == 0: + logging.info(f'Skipping category {category} for {mode} mode') + continue + + for file in tqdm.tqdm(files): + with open(file, 'r', encoding='utf-8') as f: + news = f.read().strip().replace('\r', '') + news = news.replace('\n', '').replace('\t', ' ') + outfiles[mode].write(f'{news}\t{label}\n') + for mode in modes: + outfiles[mode].close() + + +if __name__ == "__main__": + # Parse the command-line arguments. + parser = argparse.ArgumentParser(description="Process and convert datasets into NeMo\'s format.") + parser.add_argument("--dataset_name", required=True, type=str, choices=['imdb', 'thucnews', 'chemprot']) + parser.add_argument( + "--source_data_dir", required=True, type=str, help='The path to the folder containing the dataset files.' + ) + parser.add_argument("--target_data_dir", required=True, type=str) + parser.add_argument("--do_lower_case", action='store_true') + args = parser.parse_args() + + dataset_name = args.dataset_name + do_lower_case = args.do_lower_case + source_dir = args.source_data_dir + target_dir = args.target_data_dir + + if not exists(source_dir): + raise FileNotFoundError(f"{source_dir} does not exist.") + + if dataset_name == 'imdb': + process_imdb(source_dir, target_dir, do_lower_case) + elif dataset_name == 'thucnews': + process_thucnews(source_dir, target_dir) + elif dataset_name == "chemprot": + process_chemprot(source_dir, target_dir, do_lower_case) + elif dataset_name == "sst-2": + process_sst2(source_dir, target_dir, do_lower_case) + else: + raise ValueError( + f'Dataset {dataset_name} is not supported.' + + "Please make sure that you build the preprocessing process for it. " + + "NeMo's format assumes that a data file has a header and each line of the file follows " + + "the format: text [TAB] label. Label is assumed to be an integer." + ) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_classification/model_parallel_text_classification_evaluation.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_classification/model_parallel_text_classification_evaluation.py new file mode 100644 index 0000000..ab3322f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_classification/model_parallel_text_classification_evaluation.py @@ -0,0 +1,41 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script runs model parallel text classification evaluation. +""" +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models.text_classification import TextClassificationModel +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="text_classification_config") +def main(cfg: DictConfig) -> None: + logging.info(f'\nConfig Params:\n{OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(strategy=NLPDDPStrategy(), **cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + # TODO: can we drop strict=False + model = TextClassificationModel.restore_from(cfg.model.nemo_path, trainer=trainer, strict=False) + model.setup_test_data(test_data_config=cfg.model.test_ds) + + trainer.test(model=model, ckpt_path=None) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_classification/text_classification_with_bert.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_classification/text_classification_with_bert.py new file mode 100644 index 0000000..01e8fae --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_classification/text_classification_with_bert.py @@ -0,0 +1,159 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script contains an example on how to train, evaluate and perform inference with the TextClassificationModel. +TextClassificationModel in NeMo supports text classification problems such as sentiment analysis or +domain/intent detection for dialogue systems, as long as the data follows the format specified below. + +***Data format*** +TextClassificationModel requires the data to be stored in TAB separated files (.tsv) with two columns of sentence and +label. Each line of the data file contains text sequences, where words are separated with spaces and label separated +with [TAB], i.e.: + +[WORD][SPACE][WORD][SPACE][WORD][TAB][LABEL] + +For example: + +hide new secretions from the parental units[TAB]0 +that loves its characters and communicates something rather beautiful about human nature[TAB]1 +... + +If your dataset is stored in another format, you need to convert it to this format to use the TextClassificationModel. + + +***Setting the configs*** +The model and the PT trainer are defined in a config file which declares multiple important sections. +The most important ones are: + model: All arguments that are related to the Model - language model, tokenizer, head classifier, optimizer, + schedulers, and datasets/data loaders. + trainer: Any argument to be passed to PyTorch Lightning including number of epochs, number of GPUs, + precision level, etc. + +This script uses the `/examples/nlp/text_classification/conf/text_classification_config.yaml` default config file +by default. You may update the config file from the file directly or by using the command line arguments. +Other option is to set another config file via command line arguments by `--config-name=CONFIG_FILE_PATH'. + +You first need to set the num_classes in the config file which specifies the number of classes in the dataset. +Notice that some config lines, including `model.dataset.classes_num`, have `???` as their value, this means that values +for these fields are required to be specified by the user. We need to specify and set the `model.train_ds.file_name`, +`model.validation_ds.file_name`, and `model.test_ds.file_name` in the config file to the paths of the train, validation, + and test files if they exist. We may do it by updating the config file or by setting them from the command line. + + +***How to run the script?*** +For example the following would train a model for 50 epochs in 2 GPUs on a classification task with 2 classes: + +# python text_classification_with_bert.py + model.dataset.num_classes=2 + model.train_ds=PATH_TO_TRAIN_FILE + model.validation_ds=PATH_TO_VAL_FILE + trainer.max_epochs=50 + trainer.devices=2 + +This script would also reload the last checkpoint after the training is done and does evaluation on the dev set, +then performs inference on some sample queries. + +By default, this script uses examples/nlp/text_classification/conf/text_classifciation_config.py config file, and +you may update all the params in the config file from the command line. You may also use another config file like this: + +# python text_classification_with_bert.py --config-name==PATH_TO_CONFIG_FILE + model.dataset.num_classes=2 + model.train_ds=PATH_TO_TRAIN_FILE + model.validation_ds=PATH_TO_VAL_FILE + trainer.max_epochs=50 + trainer.devices=2 + +***Load a saved model*** +This script would save the model after training into '.nemo' checkpoint file specified by nemo_path of the model config. +You may restore the saved model like this: + model = TextClassificationModel.restore_from(restore_path=NEMO_FILE_PATH) + +***Evaluation a saved model on another dataset*** +# If you wanted to evaluate the saved model on another dataset, you may restore the model and create a new data loader: + eval_model = TextClassificationModel.restore_from(restore_path=checkpoint_path) + +# Then, you may create a dataloader config for evaluation: + eval_config = OmegaConf.create( + {'file_path': cfg.model.test_ds.file_path, 'batch_size': 64, 'shuffle': False, 'num_workers': 3} + ) + eval_model.setup_test_data(test_data_config=eval_config) + +# You need to create a new trainer: + eval_trainer = pl.Trainer(devices=1) + eval_model.set_trainer(eval_trainer) + eval_trainer.test(model=eval_model, verbose=False) +""" +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models.text_classification import TextClassificationModel +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="text_classification_config") +def main(cfg: DictConfig) -> None: + logging.info(f'\nConfig Params:\n{OmegaConf.to_yaml(cfg)}') + try: + strategy = NLPDDPStrategy(find_unused_parameters=True) + except (ImportError, ModuleNotFoundError): + strategy = 'auto' + + trainer = pl.Trainer(strategy=strategy, **cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + if not cfg.model.train_ds.file_path: + raise ValueError("'train_ds.file_path' need to be set for the training!") + + model = TextClassificationModel(cfg.model, trainer=trainer) + logging.info("===========================================================================================") + logging.info('Starting training...') + trainer.fit(model) + logging.info('Training finished!') + logging.info("===========================================================================================") + + if cfg.model.nemo_path: + # '.nemo' file contains the last checkpoint and the params to initialize the model + model.save_to(cfg.model.nemo_path) + logging.info(f'Model is saved into `.nemo` file: {cfg.model.nemo_path}') + + # We evaluate the trained model on the test set if test_ds is set in the config file + if cfg.model.test_ds.file_path: + logging.info("===========================================================================================") + logging.info("Starting the testing of the trained model on test set...") + trainer.test(model=model, ckpt_path=None, verbose=False) + logging.info("Testing finished!") + logging.info("===========================================================================================") + + # perform inference on a list of queries. + if "infer_samples" in cfg.model and cfg.model.infer_samples: + logging.info("===========================================================================================") + logging.info("Starting the inference on some sample queries...") + + # max_seq_length=512 is the maximum length BERT supports. + results = model.classifytext(queries=cfg.model.infer_samples, batch_size=16, max_seq_length=512) + logging.info('The prediction results of some sample queries with the trained model:') + for query, result in zip(cfg.model.infer_samples, results): + logging.info(f'Query : {query}') + logging.info(f'Predicted label: {result}') + + logging.info("Inference finished!") + logging.info("===========================================================================================") + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/conf/thutmose_tagger_itn_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/conf/thutmose_tagger_itn_config.yaml new file mode 100644 index 0000000..7211b44 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/conf/thutmose_tagger_itn_config.yaml @@ -0,0 +1,97 @@ +name: &name itn +lang: ??? # e.g. 'ru', 'en' + +# Pretrained Nemo Models +pretrained_model: null + +trainer: + devices: 1 # the number of gpus, 0 for CPU + num_nodes: 1 + max_epochs: 3 # the number of training epochs + enable_checkpointing: false # provided by exp_manager + logger: false # provided by exp_manager + accumulate_grad_batches: 1 # accumulates grads every k batches + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + strategy: ddp + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + +model: + do_training: true + label_map: ??? # path/.../label_map.txt + semiotic_classes: ??? # path/to/.../semiotic_classes.txt + max_sequence_len: 128 + lang: ${lang} + hidden_size: 768 + + optim: + name: adamw + lr: 3e-5 + weight_decay: 0.1 + + sched: + name: WarmupAnnealing + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + + # scheduler config override + warmup_ratio: 0.1 + last_epoch: -1 + + language_model: + pretrained_model_name: bert-base-uncased # For ru, try DeepPavlov/rubert-base-cased | For de or multilingual, try bert-base-multilingual-cased + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + +exp_manager: + exp_dir: nemo_experiments # where to store logs and checkpoints + name: training # name of experiment + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + save_top_k: 3 + monitor: "val_loss" + mode: "min" + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + +tokenizer: + tokenizer_name: ${model.transformer} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + +# Data +data: + train_ds: + data_path: ??? # provide the full path to the file + batch_size: 8 + shuffle: true + num_workers: 3 + pin_memory: false + drop_last: false + + validation_ds: + data_path: ??? # provide the full path to the file. + batch_size: 8 + shuffle: false + num_workers: 3 + pin_memory: false + drop_last: false + + +# Inference +inference: + from_file: null # Path to the raw text, no labels required. Each sentence on a separate line + out_file: null # Path to the output file + batch_size: 16 # batch size for inference.from_file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/corpus_errors.ru b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/corpus_errors.ru new file mode 100644 index 0000000..e03168f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/corpus_errors.ru @@ -0,0 +1,643 @@ +CARDINAL два 132 +CARDINAL два 122 +CARDINAL два 102 +CARDINAL два 22 +CARDINAL два 72 +CARDINAL два 12 +CARDINAL два 62 +CARDINAL два 42 +CARDINAL два 4 2 +CARDINAL два 92 +CARDINAL два 212 +CARDINAL два 605792 +CARDINAL два 512 +CARDINAL два 82 +CARDINAL два 422 +CARDINAL два 41615252 +CARDINAL два 192 +CARDINAL два 32 +CARDINAL два 232 +CARDINAL два 162 +CARDINAL два 152 +CARDINAL два 432 +CARDINAL два 52 +CARDINAL два 112 +CARDINAL два 61417272 +CARDINAL два 852 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+CARDINAL двухстах пятидесяти 250-и +CARDINAL семисот пятидесяти 751 +CARDINAL ста четырнадцати 114-и +CARDINAL пятисот пятидесяти 551 +CARDINAL двух тысяч четырнадцати 2014и +CARDINAL шестистах десяти 610-и +CARDINAL ста семнадцати 117-и +CARDINAL ста тридцати восьми 138-и +CARDINAL трехсот шестидесяти 361 +CARDINAL тысячью девятистах четырнадцати 1914и +CARDINAL шестьюстами 600-и +CARDINAL один четыре xiv +CARDINAL тысячи девятисот восьмидесяти 1981 +CARDINAL двухстах семидесяти пяти 275-и +CARDINAL три один xxxi +CARDINAL семьсот семьдесят три 773-и +CARDINAL два три xxiii +CARDINAL тысячи сорока 1041 +CARDINAL тысячью двухстах семидесяти 1270-и +CARDINAL четырехсот семидесяти 471 +CARDINAL двумястами 200-и +CARDINAL трехстах семидесяти 370-и +CARDINAL нулю 400 +CARDINAL нулю 200 +CARDINAL нулем 510 +CARDINAL нулем 10 +CARDINAL нулем 200 +CARDINAL нулем 100 +CARDINAL нулем 400 +CARDINAL тысячи восьмисот сорока 1841 +CARDINAL двухстах десяти 210-и +CARDINAL тысячи восьмисот двадцати 1821 +CARDINAL пятисот сорока 541 diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/extract_giza_alignments.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/extract_giza_alignments.py new file mode 100644 index 0000000..f5a53b1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/extract_giza_alignments.py @@ -0,0 +1,522 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script can be used after GIZA++ alignment to extract final alignments for each semiotic class. +""" + +import re +from argparse import ArgumentParser + +from nemo.collections.nlp.data.text_normalization_as_tagging.utils import ( + check_monotonicity, + fill_alignment_matrix, + get_targets, + get_targets_from_back, +) + + +parser = ArgumentParser(description='Extract final alignments from GIZA++ alignments') +parser.add_argument('--mode', type=str, required=True, help='tn or itn') +parser.add_argument('--giza_dir', type=str, required=True, help='Path to folder with GIZA++ alignment') +parser.add_argument( + '--giza_suffix', type=str, required=True, help='suffix of alignment files, e.g. \"Ahmm.5\", \"A3.final\"' +) +parser.add_argument('--out_filename', type=str, required=True, help='Output file') +parser.add_argument('--lang', type=str, required=True, help="Language") +args = parser.parse_args() + + +def main() -> None: + g = open(args.giza_dir + "/GIZA++." + args.giza_suffix, "r", encoding="utf-8") + f = open(args.giza_dir + "/GIZA++reverse." + args.giza_suffix, "r", encoding="utf-8") + target_inner_delimiter = "" + if args.mode == "tn": + g, f = f, g + target_inner_delimiter = "_" + out = open(args.giza_dir + "/" + args.out_filename, "w", encoding="utf-8") + cache = {} + good_count, not_mono_count, not_covered_count, exception_count = 0, 0, 0, 0 + n = 0 + while True: + n += 3 + if n % 10000 == 0: + print(n, "lines processed") + fline1 = f.readline().strip() + fline2 = f.readline().strip() + fline3 = f.readline().strip() + gline1 = g.readline().strip() + gline2 = g.readline().strip() + gline3 = g.readline().strip() + if fline1 == "" and gline1 == "": + break + cache_key = fline1 + "\t" + fline2 + "\t" + gline1 + "\t" + gline2 + if cache_key in cache: + out.write(cache[cache_key] + "\n") + continue + if fline1 == "" or gline1 == "" or fline2 == "" or gline2 == "" or fline3 == "" or gline3 == "": + raise ValueError("Empty line: " + str(n)) + try: + matrix, srctokens, dsttokens = fill_alignment_matrix(fline2, fline3, gline2, gline3) + except Exception: + print(fline1) + print(fline2) + print(fline3) + print(gline1) + print(gline2) + print(gline3) + exception_count += 1 + out_str = "-exception:\t" + fline2 + "\t" + gline2 + out.write(out_str + "\n") + continue + else: + matrix[matrix <= 2] = 0 # leave only 1-to-1 alignment points + if check_monotonicity(matrix): + targets = get_targets(matrix, dsttokens, delimiter=target_inner_delimiter) + targets_from_back = get_targets_from_back(matrix, dsttokens, delimiter=target_inner_delimiter) + if len(targets) != len(srctokens): + raise ValueError( + "targets length doesn't match srctokens length: len(targets)=" + + str(len(targets)) + + "; len(srctokens)=" + + str(len(srctokens)) + ) + leftside_align = " ".join(targets) + rightside_align = " ".join(targets_from_back) + + rightside_align = rightside_align.replace(" _11100_", "_11 100_") + leftside_align = leftside_align.replace(" _11100_", "_11 100_") + + # _1 4000_ => _14 000_ + # 1 5,000 => 15 ,000 + rightside_align = re.sub(r"^_1 ([\d])(,?000)", r"_1\g<1> \g<2>", rightside_align) + leftside_align = re.sub(r"^_1 ([\d])(,?000)", r"_1\g<1> \g<2>", leftside_align) + + # "_2 10 0_" => "_2 100_" + rightside_align = re.sub(r"([\d]) 10 0_", r"\g<1> 100_", rightside_align) + leftside_align = re.sub(r"([\d]) 10 0_", r"\g<1> 100_", leftside_align) + + if srctokens[0] in [ + "ten", + "twenty", + "thirty", + "forty", + "fifty", + "sixty", + "seventy", + "eighty", + "ninety", + ]: + # ten thousand sixty _1 00 60_ => _10 0 60_ + rightside_align = re.sub(r"^(_\d) 00 (\d)", r"\g<1>0 0 \g<2>", rightside_align) + leftside_align = re.sub(r"^(_\d) 00 (\d)", r"\g<1>0 0 \g<2>", leftside_align) + + # ten thousand sixty three _1 0, 06 3_ => _10 ,0 6 3_ + rightside_align = re.sub(r"([ _]\d) 0, 0(\d)", r"\g<1>0 ,0 \g<2>", rightside_align) + leftside_align = re.sub(r"([ _]\d) 0, 0(\d)", r"\g<1>0 ,0 \g<2>", leftside_align) + + # _3 0, 7 7 4=> _30 , 7 7 4_ + rightside_align = re.sub(r"(\d) 0, ", r"\g<1>0 , ", rightside_align) + leftside_align = re.sub(r"(\d) 0, ", r"\g<1>0 , ", leftside_align) + + # _1 1, 1 40_ => _11 , 1 40_ + rightside_align = re.sub(r"1 1, (\d)", r"11 , \g<1>", rightside_align) + leftside_align = re.sub(r"1 1, (\d)", r"11 , \g<1>", leftside_align) + + if re.match(r".+надцат", srctokens[0]) or srctokens[0] in [ + "ten", + "eleven", + "twelve", + "thirteen", + "fourteen", + "fifteen", + "sixteen", + "seventeen", + "eighteen", + "nineteen", + ]: + # "_1 12 14_" -> "_11 2 14_" + rightside_align = re.sub( + r"^(_1) () ([\d])([\d])", r"\g<1>\g<3> \g<2> \g<4>", rightside_align + ) + leftside_align = re.sub( + r"^(_1) () ([\d])([\d])", r"\g<1>\g<3> \g<2> \g<4>", leftside_align + ) + + # "_1 10 10_" -> "_11 0 10_" + rightside_align = re.sub(r"^_1 ([\d])0 ([\d] ?[\d])", r"_1\g<1> 0 \g<2>", rightside_align) + leftside_align = re.sub(r"^_1 ([\d])0 ([\d] ?[\d])", r"_1\g<1> 0 \g<2>", leftside_align) + + if args.giza_dir.endswith("decimal") and args.lang == "ru": + # "_1 0, 5_" => "_10 , 5_" #десять целых и пять десятых + rightside_align = re.sub( + r"(\d) () ([0123456789])(,) ([\d])", r"\g<1>\g<3> \g<2> \g<4> \g<5>", rightside_align + ) + leftside_align = re.sub( + r"(\d) () ([0123456789])(,) ([\d])", r"\g<1>\g<3> \g<2> \g<4> \g<5>", leftside_align + ) + + if args.giza_dir.endswith("decimal") and args.lang == "en": + # "_7 0. 7_" => _70 . 7_ + rightside_align = re.sub(r"^(_\d) 0\. (\d)", r"\g<1>0 . \g<2>", rightside_align) + leftside_align = re.sub(r"^(_\d) 0\. (\d)", r"\g<1>0 . \g<2>", leftside_align) + + if args.giza_dir.endswith("money") and args.lang == "en": + # "_1 , 000__£<<" => "_1 ,000_ _£<<" + rightside_align = re.sub(r"(\d) , 000_(_[£$€])", r"\g<1> ,000_ \g<2>", rightside_align) + leftside_align = re.sub(r"(\d) , 000_(_[£$€])", r"\g<1> ,000_ \g<2>", leftside_align) + + if args.giza_dir.endswith("money"): + # "_5 000000__иен_" => "_5 000000_ _иен_" + rightside_align = re.sub( + r"([\d]) 000000_(_[^\d])", r"\g<1> 000000_ \g<2>", rightside_align + ) + leftside_align = re.sub(r"([\d]) 000000_(_[^\d])", r"\g<1> 000000_ \g<2>", leftside_align) + + # _5_ _m__£<< => "_5_ _m_ _£<<" + rightside_align = re.sub( + r"([\d]_) (_[mk]_)(_[^\d])", r"\g<1> \g<2> \g<3>", rightside_align + ) + leftside_align = re.sub(r"([\d]_) (_[mk]_)(_[^\d])", r"\g<1> \g<2> \g<3>", leftside_align) + + # "_3 0__m__£<<" => "_30 _m_ _£<<" + rightside_align = re.sub( + r"([\d]) 0_(_[mk]_)(_[^\d])", r"\g<1>0 \g<2> \g<3>", rightside_align + ) + leftside_align = re.sub( + r"([\d]) 0_(_[mk]_)(_[^\d])", r"\g<1>0 \g<2> \g<3>", leftside_align + ) + + # "_15 000__руб._" => "_15 000_ _руб._" + rightside_align = re.sub(r"([\d]) (000_)(_[^\d])", r"\g<1> \g<2> \g<3>", rightside_align) + leftside_align = re.sub(r"([\d]) (000_)(_[^\d])", r"\g<1> \g<2> \g<3>", leftside_align) + + # "_2 5 0 000__$<<" => "_2 50 000_ _$<<" + rightside_align = re.sub(r"([\d]) 0 000_(_[^\d])", r"\g<1>0 000_ \g<2>", rightside_align) + leftside_align = re.sub(r"([\d]) 0 000_(_[^\d])", r"\g<1>0 000_ \g<2>", leftside_align) + + # "_5 0 0000__$_" => "_500 000_ _$_" + rightside_align = re.sub(r"([\d]) 0 0000_(_[^\d])", r"\g<1>00 000_ \g<2>", rightside_align) + leftside_align = re.sub(r"([\d]) 0 0000_(_[^\d])", r"\g<1>00 000_ \g<2>", leftside_align) + + # "_1 000__руб._" => "_1000_ _руб._" + rightside_align = re.sub(r"_1 000_(_[^\d])", r"_1000_ \g<1>", rightside_align) + leftside_align = re.sub(r"_1 000_(_[^\d])", r"_1000_ \g<1>", leftside_align) + + # replace cases like "2 0__января" with "20_ _января" + leftside_align = re.sub(r"([\d]) (00?_)(_[^\d])", r"\g<1>\g<2> \g<3>", leftside_align) + rightside_align = re.sub(r"([\d]) (00?_)(_[^\d])", r"\g<1>\g<2> \g<3>", rightside_align) + + # "_3 0__september_ _2 014_" => "_30_ _september_ _2 014_" + # "_3 00__тыс.__руб._" => "_300_ _тыс.__руб._" + leftside_align = re.sub( + r"([\d]) (00?_)(_[^\d])", r"\g<1>\g<2> \g<3>", leftside_align + ) + rightside_align = re.sub( + r"([\d]) (00?_)(_[^\d])", r"\g<1>\g<2> \g<3>", rightside_align + ) + + # "_october_ _2 0,2 015_" => "_october_ _20 ,2 015_" + leftside_align = re.sub(r"([\d]) (0),(\d)", r"\g<1>\g<2> ,\g<3>", leftside_align) + rightside_align = re.sub(r"([\d]) (0),(\d)", r"\g<1>\g<2> ,\g<3>", rightside_align) + + # "_3 0_.10. _1 9 4 3_" => "_30_ .10. _1 9 4 3_" + leftside_align = re.sub(r"([\d]) (0_)(\.[\d])", r"\g<1>\g<2> \g<3>", leftside_align) + rightside_align = re.sub(r"([\d]) (0_)(\.[\d])", r"\g<1>\g<2> \g<3>", rightside_align) + + # replace cases like "_1 0000_" with "_10 000_" + # replace cases like "_5 00000_" with "_500 000_" + rightside_align = re.sub(r"([\d]) ([0][0]?)(000000000_)", r"\g<1>\g<2> \g<3>", rightside_align) + leftside_align = re.sub(r"([\d]) ([0][0]?)(000000000_)", r"\g<1>\g<2> \g<3>", leftside_align) + rightside_align = re.sub(r"([\d]) ([0][0]?)(000000_)", r"\g<1>\g<2> \g<3>", rightside_align) + leftside_align = re.sub(r"([\d]) ([0][0]?)(000000_)", r"\g<1>\g<2> \g<3>", leftside_align) + rightside_align = re.sub(r"([\d]) ([0][0]?)(000_)", r"\g<1>\g<2> \g<3>", rightside_align) + leftside_align = re.sub(r"([\d]) ([0][0]?)(000_)", r"\g<1>\g<2> \g<3>", leftside_align) + + # "_4 00,000_" -> "_400 ,000_" + rightside_align = re.sub(r"([\d]) ([0][0]?),(000_)", r"\g<1>\g<2> ,\g<3>", rightside_align) + leftside_align = re.sub(r"([\d]) ([0][0]?),(000_)", r"\g<1>\g<2> ,\g<3>", leftside_align) + + # "_9 3 ,0__²_> _км_" => "_9 3 ,0__²_> _км_" + rightside_align = re.sub(r"([\d]) (,00?_)(_[^\d])", r"\g<1>\g<2> \g<3>", rightside_align) + leftside_align = re.sub(r"([\d]) (,00?_)(_[^\d])", r"\g<1>\g<2> \g<3>", leftside_align) + + # "_0 , 01__г_" => "_0 , 01 _г_" + rightside_align = re.sub( + r"(,) 01_(_[^\d])", r"\g<1> 01_ \g<2>", rightside_align + ) + leftside_align = re.sub( + r"(,) 01_(_[^\d])", r"\g<1> 01_ \g<2>", leftside_align + ) + + # "_0 , 7 6 1__км_" => "_0 , 7 6 1_ _км_" + rightside_align = re.sub( + r"(,) (\d) (\d) 1_(_[^\d])", + r"\g<1> \g<2> \g<3> 1_ \g<4>", + rightside_align, + ) + leftside_align = re.sub( + r"(,) (\d) (\d) 1_(_[^\d])", + r"\g<1> \g<2> \g<3> 1_ \g<4>", + leftside_align, + ) + + # "_5 0000__рублей_" => "_50 000_ рублей" + rightside_align = re.sub( + r"([\d]) ([0][0]?)(000_)(_)", r"\g<1>\g<2> \g<3> \g<4>", rightside_align + ) + leftside_align = re.sub( + r"([\d]) ([0][0]?)(000_)(_)", r"\g<1>\g<2> \g<3> \g<4>", leftside_align + ) + + # "_1 115_" -> "_1 1 15_" + rightside_align = re.sub(r" ([1])([1][\d])", r"\g<1> \g<2>", rightside_align) + leftside_align = re.sub(r" ([1])([1][\d])", r"\g<1> \g<2>", leftside_align) + + # "_1 990-х_" -> "_1 9 90-х_" + rightside_align = re.sub(r" (9)(90)", r"\g<1> \g<2>", rightside_align) + leftside_align = re.sub(r" (9)(90)", r"\g<1> \g<2>", leftside_align) + rightside_align = re.sub(r" (8)(80)", r"\g<1> \g<2>", rightside_align) + leftside_align = re.sub(r" (8)(80)", r"\g<1> \g<2>", leftside_align) + rightside_align = re.sub(r" (7)(70)", r"\g<1> \g<2>", rightside_align) + leftside_align = re.sub(r" (7)(70)", r"\g<1> \g<2>", leftside_align) + rightside_align = re.sub(r" (6)(60)", r"\g<1> \g<2>", rightside_align) + leftside_align = re.sub(r" (6)(60)", r"\g<1> \g<2>", leftside_align) + rightside_align = re.sub(r" (5)(50)", r"\g<1> \g<2>", rightside_align) + leftside_align = re.sub(r" (5)(50)", r"\g<1> \g<2>", leftside_align) + rightside_align = re.sub(r" (4)(40)", r"\g<1> \g<2>", rightside_align) + leftside_align = re.sub(r" (4)(40)", r"\g<1> \g<2>", leftside_align) + rightside_align = re.sub(r" (3)(30)", r"\g<1> \g<2>", rightside_align) + leftside_align = re.sub(r" (3)(30)", r"\g<1> \g<2>", leftside_align) + rightside_align = re.sub(r" (2)(20)", r"\g<1> \g<2>", rightside_align) + leftside_align = re.sub(r" (2)(20)", r"\g<1> \g<2>", leftside_align) + + # восемь ноль ноль ноль ноль ноль ноль ноль _8 0 0 0 0 0 0 0_ + # _8 0000000_ + rightside_align = re.sub( + r" 0000000_", + r"0 0 0 0 0 0 0_", + rightside_align, + ) + leftside_align = re.sub( + r" 0000000_", + r"0 0 0 0 0 0 0_", + leftside_align, + ) + + # _8 000000_ + rightside_align = re.sub( + r" 000000_", r"0 0 0 0 0 0_", rightside_align + ) + leftside_align = re.sub( + r" 000000_", r"0 0 0 0 0 0_", leftside_align + ) + + # _8 00000_ + rightside_align = re.sub(r" 00000_", r"0 0 0 0 0_", rightside_align) + leftside_align = re.sub(r" 00000_", r"0 0 0 0 0_", leftside_align) + + # _8 0000_ + rightside_align = re.sub(r" 0000_", r"0 0 0 0_", rightside_align) + leftside_align = re.sub(r" 0000_", r"0 0 0 0_", leftside_align) + + # _8 000_ + rightside_align = re.sub(r" 000_", r"0 0 0_", rightside_align) + leftside_align = re.sub(r" 000_", r"0 0 0_", leftside_align) + + # "_2 010/11" => "_2 0 10 /11" + rightside_align = re.sub( + r" (0)([1][\d])/([\d])", r"\g<1> \g<2> /\g<3>", rightside_align + ) + leftside_align = re.sub( + r" (0)([1][\d])/([\d])", r"\g<1> \g<2> /\g<3>", leftside_align + ) + + # "_2 0 11/12_" => "_2 0 11 /12_" + rightside_align = re.sub(r" ([\d]+)/([\d])", r"\g<1> /\g<2>", rightside_align) + leftside_align = re.sub(r" ([\d]+)/([\d])", r"\g<1> /\g<2>", leftside_align) + + # "_2 0 1 0/2 0 11_" => "_2 0 10 /2 0 11_" + rightside_align = re.sub(r"([\d]) ([\d]+)/([\d])", r"\g<1>\g<2> /\g<3>", rightside_align) + leftside_align = re.sub(r"([\d]) ([\d]+)/([\d])", r"\g<1>\g<2> /\g<3>", leftside_align) + + # "_5 0%_" => "_50 %_" + # "_1 00%_" => "_100 %_" + # "_1 00,00%_" => "_100,00 %_" + rightside_align = re.sub(r"([\d]) ([0,]+)%", r"\g<1>\g<2> %", rightside_align) + leftside_align = re.sub(r"([\d]) ([0,]+)%", r"\g<1>\g<2> %", leftside_align) + + # ATTENTION: keep the order of next two rules + # "_2 0½_" => "_20 ½_" + rightside_align = re.sub(r"([\d]) ([\d]+)½", r"\g<1>\g<2> ½", rightside_align) + leftside_align = re.sub(r"([\d]) ([\d]+)½", r"\g<1>\g<2> ½", leftside_align) + # "_1 ½_ " => "_1 ½_" #одна целая и одна вторая + rightside_align = re.sub( + r"([\d]) (_?½_)? ", + r"\g<1> \g<2>", + rightside_align, + ) + leftside_align = re.sub( + r"([\d]) (_?½_)? ", + r"\g<1> \g<2>", + leftside_align, + ) + + if args.lang == "en" and srctokens[-1] == "half": + # _2 1/ 2_ => _2 ½_ + rightside_align = re.sub(r"(\d) 1/ 2_$", r"\g<1> ½_", rightside_align) + leftside_align = re.sub(r"(\d) 1/ 2_$", r"\g<1> ½_", leftside_align) + + # "_1 50_ _тыс.__руб._" => "_1 50_ _тыс._ _руб._" + rightside_align = re.sub(r"_ (_[^\d]+_)(_[^\d]+_)", r"_ \g<1> \g<2>", rightside_align) + leftside_align = re.sub(r"_ (_[^\d]+_)(_[^\d]+_)", r"_ \g<1> \g<2>", leftside_align) + + # _1000 000__$_ => "_1000000_ _$_" + rightside_align = re.sub(r"_1000 000_(_[^\d])", r"_1000000_ \g<1>", rightside_align) + leftside_align = re.sub(r"_1000 000_(_[^\d])", r"_1000000_ \g<1>", leftside_align) + + if args.giza_dir.endswith("date") and args.lang == "en": + # "_1 2_ _november_ _2 014_" => " _12_ _november_ _2 014_" + if srctokens[0] == "the": + leftside_align = re.sub(r"^_1 (\d_)", r" _1\g<1>", leftside_align) + rightside_align = re.sub(r"^_1 (\d_)", r" _1\g<1>", rightside_align) + + # " _12,2012_" => "_12_ ,20 12_" + leftside_align = re.sub(r"^ _12,2012_", r"_12_ ,20 12_", leftside_align) + rightside_align = re.sub(r"^ _12,2012_", r"_12_ ,20 12_", rightside_align) + + # " _1,20 14_" => "_1 ,20 14_" + leftside_align = re.sub(r"^ _1,(\d)", r"_1 ,\g<1>", leftside_align) + rightside_align = re.sub(r"^ _1,(\d)", r"_1 ,\g<1>", rightside_align) + + # "_2 1,20 14_" => "_2 1 ,20 14_" + leftside_align = re.sub(r" 1,(\d)", r"1 ,\g<1>", leftside_align) + rightside_align = re.sub(r" 1,(\d)", r"1 ,\g<1>", rightside_align) + + # _11,19 9 7_ => _11 ,19 9 7_ + leftside_align = re.sub(r" _11,(\d)", r"_11 ,\g<1>", leftside_align) + rightside_align = re.sub(r" _11,(\d)", r"_11 ,\g<1>", rightside_align) + + if len(srctokens) >= 2 and srctokens[-2] == "twenty": + # " _12,200 9_" => "_12 ,20 09_" + leftside_align = re.sub( + r"^ _12,200 (\d_)", r"_12_ ,20 0\g<1>", leftside_align + ) + rightside_align = re.sub( + r"^ _12,200 (\d_)", r"_12_ ,20 0\g<1>", rightside_align + ) + + # "_april_ _2 015_" => "_april_ _20 15_" + leftside_align = re.sub(r"2 0(\d\d_)$", r"20 \g<1>", leftside_align) + rightside_align = re.sub(r"2 0(\d\d_)$", r"20 \g<1>", rightside_align) + elif len(srctokens) >= 2 and srctokens[-2] == "thousand": + # " _12,200 9_" => "_12 ,2 00 9_" + leftside_align = re.sub( + r"^ _12,200 (\d_)", r"_12_ ,2 00 \g<1>", leftside_align + ) + rightside_align = re.sub( + r"^ _12,200 (\d_)", r"_12_ ,2 00 \g<1>", rightside_align + ) + + # thirtieth twenty fifteen _3 0th__,20 15_ => _30th_ _,20 15_ + leftside_align = re.sub(r"(\d) 0th_(_,\d)", r"\g<1>0th_ \g<2>", leftside_align) + rightside_align = re.sub(r"(\d) 0th_(_,\d)", r"\g<1>0th_ \g<2>", rightside_align) + + if args.giza_dir.endswith("date") and args.lang == "ru": + # тысяча девятьсот шестидесятого года _1 9 6 0_ => _1 9 60_ + if srctokens[-1] == "года": + leftside_align = re.sub(r"(\d) 0_", r"\g<1>0_ ", leftside_align) + rightside_align = re.sub(r"(\d) 0_", r"\g<1>0_ ", rightside_align) + + if args.giza_dir.endswith("time"): + if srctokens[-1] == "hundred": + # fifteen hundred _15:00_ + rightside_align = re.sub(r" (_\d\d:)00_", r"\g<1> 00_", rightside_align) + leftside_align = re.sub(r" (_\d\d:)00_", r"\g<1> 00_", leftside_align) + + # !! Do not change the order of next two rules + # twenty one hundred _2 1:00_ + rightside_align = re.sub(r"(_\d) (\d:)00_ ", r"\g<1> \g<2> 00_", rightside_align) + leftside_align = re.sub(r"(_\d) (\d:)00_ ", r"\g<1> \g<2> 00_", leftside_align) + # twenty hundred _2 0:00_ + rightside_align = re.sub(r"(_\d) (\d:)00_", r"\g<1>\g<2> 00_", rightside_align) + leftside_align = re.sub(r"(_\d) (\d:)00_", r"\g<1>\g<2> 00_", leftside_align) + + if srctokens[-1] == "o'clock": + # nine o'clock _09:00_ => "_09:00_ " + rightside_align = re.sub(r"^ ([^ ])$", r"\g<1> ", rightside_align) + leftside_align = re.sub(r"^ ([^ ])$", r"\g<1> ", leftside_align) + + # "_1 1:3 3_" => "_11: 3 3_" + rightside_align = re.sub(r"_(\d) (\d:)(\d)", r"\g<1>\g<2> \g<3>", rightside_align) + leftside_align = re.sub(r"_(\d) (\d:)(\d)", r"\g<1>\g<2> \g<3>", leftside_align) + + ban = False + if args.giza_dir.endswith("ordinal"): + if dsttokens[0] == "_—": # тысяча девятьсот сорок пятом _— 1 9 4 5_ + ban = True + + # ban roman numbers with at least two symbols, because we do not split them to parts + for t in rightside_align.split(): + if re.match(r"^_?[ivxl][ivxl]+_?$", t): + ban = True + + # ban cases like "_11/05/2013_", "_2005-11-25_", because they are source of incorrect alignments + if args.giza_dir.endswith("date") and args.lang == "en": + if "/" in rightside_align or "-" in rightside_align: + ban = True + + # ban brackets + if "(" in rightside_align or ")" in rightside_align: + ban = True + + if ban: + out_str = ( + "ban:\t" + + " ".join(srctokens) + + "\t" + + " ".join(dsttokens) + + "\t" + + leftside_align + + "\t" + + rightside_align + ) + else: + out_str = ( + "good:\t" + + " ".join(srctokens) + + "\t" + + " ".join(dsttokens) + + "\t" + + leftside_align + + "\t" + + rightside_align + ) + out.write(out_str + "\n") + cache[cache_key] = out_str + else: + out_str = "-mon:\t" + " ".join(srctokens) + "\t" + " ".join(dsttokens) + out.write(out_str + "\n") + cache[cache_key] = out_str + not_mono_count += 1 + + f.close() + g.close() + out.close() + + +# Main code +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/filter_sentences_with_errors.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/filter_sentences_with_errors.py new file mode 100644 index 0000000..3376a28 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/filter_sentences_with_errors.py @@ -0,0 +1,89 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script is used to filter sentences containing bad examples from Google TN Dataset. +""" + +from argparse import ArgumentParser +from os import listdir, mkdir +from os.path import exists, isfile, join +from typing import Set + +parser = ArgumentParser(description="Filter Google TN Dataset by error vocabulary") +parser.add_argument( + "--data_dir", required=True, type=str, help='Path to data directory with files like output-00000-of-00100.tsv' +) +parser.add_argument( + "--out_dir", required=True, type=str, help='Output data directory, same files (with some sentences filtered)' +) +parser.add_argument("--errors_vocab_filename", required=True, type=str, help='File with error vocabulary') +parser.add_argument("--lang", required=True, type=str, help="Language") +args = parser.parse_args() + + +def filter_file(inp_filename: str, out_filename: str, error_vcb: Set) -> None: + """Filter out whole sentences containing bad itn conversions. The output format is the same as input. + + Args: + inp_filename: Name of input file in Google TN Dataset format. + out_filename: Name of output file in Google TN Dataset format. + error_vcb: Set of tuples with erroneous conversion, e.g. ("CARDINAL", "two", "132") + """ + out = open(out_filename, "w", encoding="utf-8") + sent_lines = [] + sent_is_ok = True + with open(inp_filename, "r", encoding="utf-8") as f: + for line in f: + sent_lines.append(line.strip()) + if line.startswith(""): + if sent_is_ok and len(sent_lines) > 1: # there should be at least one line except + out.write("\n".join(sent_lines) + "\n") + sent_lines = [] + sent_is_ok = True + else: + cls, written, spoken = line.strip().split("\t") + k = (cls, spoken.casefold(), written.casefold()) + if k in error_vcb: + sent_is_ok = False + out.close() + + +def main() -> None: + if not exists(args.data_dir): + raise ValueError(f"Data dir {args.data_dir} does not exist") + + # load errors vocabulary + error_vcb = set() + with open(args.errors_vocab_filename, "r", encoding="utf-8") as f: + for line in f: + cls, spoken, written = line.strip().split("\t") + k = (cls, spoken, written) + error_vcb.add(k) + + for subdir in listdir(args.data_dir): + mkdir(join(args.out_dir, subdir)) + for filename in listdir(join(args.data_dir, subdir)): + if not filename.startswith('output'): + continue + inp_filename = join(args.data_dir, subdir, filename) + out_filename = join(args.out_dir, subdir, filename) + if not isfile(inp_filename): + continue + filter_file(inp_filename, out_filename, error_vcb) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/get_label_vocab.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/get_label_vocab.py new file mode 100644 index 0000000..bfeb1d3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/get_label_vocab.py @@ -0,0 +1,59 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script can be used to get label vocab from train and dev labeled files. +""" + +import sys +from argparse import ArgumentParser +from collections import Counter + +parser = ArgumentParser(description="Get label vocab") +parser.add_argument("--train_filename", required=True, type=str, help='File with training data') +parser.add_argument("--dev_filename", required=True, type=str, help='File with development data') +parser.add_argument("--out_filename", required=True, type=str, help='Output file') +args = parser.parse_args() + +vocab = Counter() + +n = 0 +for fn in [args.train_filename, args.dev_filename]: + with open(fn, "r", encoding="utf-8") as f: + for line in f: + parts = line.strip().split("\t") + if len(parts) < 2: + print("Warning: bad format in line: " + str(n) + ": " + line, file=sys.stderr) + continue + tags = parts[1].split(" ") + for t in tags: + if t == "": + vocab["KEEP"] += 1 + elif t == "": + vocab["DELETE"] += 1 + else: + vocab["DELETE|" + t] += 1 + n += 1 + +print("len(vocab)=", len(vocab)) +with open(args.out_filename, "w", encoding="utf-8") as out: + out.write("KEEP\n") + out.write("DELETE\n") + for t, freq in vocab.most_common(10000000): + if t == "KEEP": + continue + if t == "DELETE": + continue + out.write(t + "\n") diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_after_alignment.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_after_alignment.py new file mode 100644 index 0000000..33608b5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_after_alignment.py @@ -0,0 +1,254 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script can be used to combine joined giza alignments and Google Text Normalization dataset +to produce training corpus for the ThutmoseTaggerModel. +""" + +import glob +import os +from argparse import ArgumentParser +from collections import Counter +from typing import Dict, Optional, TextIO, Tuple + +from nemo.collections.nlp.data.text_normalization_as_tagging.utils import get_src_and_dst_for_alignment +from nemo.utils import logging + +parser = ArgumentParser(description="Produce data for the ThutmoseTaggerModel") +parser.add_argument( + "--mode", + required=True, + type=str, + help='Mode, one of ["get_replacement_vocab", "filter_by_vocab", "get_labeled_corpus"]', +) +parser.add_argument( + "--data_dir", required=True, type=str, help='Path to data directory with files like output-00000-of-00100.tsv' +) +parser.add_argument( + "--giza_dir", required=True, type=str, help='Path to directory with class folders like ordinal, date etc' +) +parser.add_argument( + "--alignment_filename", required=True, type=str, help='Name of alignment file, like "itn.out", "itn.out.vocab2000"' +) +parser.add_argument("--out_filename", required=True, type=str, help='Output file') +parser.add_argument("--vocab_filename", required=True, type=str, help='Vocab name') +parser.add_argument("--lang", required=True, type=str, help="Language") +args = parser.parse_args() + + +def process_file_itn(inputname: str, out: TextIO, keys2replacements: Dict[str, str]) -> None: + """Processes one file in Google TN Dataset format to get the labeled data for ThutmoseTaggerModel + + Args: + inputname: name of input file + out: output stream + keys2replacements: Mapping from (semiotic class, spoken, written) to the segmented written form, + which is aligned one-to-one to spoken words (this is the result obtained from Giza++ alignment pipeline) + + """ + words = [] + tags = [] + semiotic_info = [] + sent_is_ok = True + with open(inputname, "r", encoding="utf-8") as f: + for line in f: + if line.startswith(""): + if sent_is_ok and len(words) > 0: + out.write(" ".join(words) + "\t" + " ".join(tags) + "\t" + ";".join(semiotic_info) + "\n") + words = [] + tags = [] + semiotic_info = [] + sent_is_ok = True + else: + cls, written, spoken = line.strip().split("\t") + if spoken == "sil": + continue + if spoken == "": + words.append(written.casefold()) + tags.append("") + continue + src, dst, same_begin, same_end = get_src_and_dst_for_alignment( + cls.casefold(), written, spoken, args.lang + ) + same_from_begin = [] if same_begin == "" else same_begin.split(" ") + same_from_end = [] if same_end == "" else same_end.split(" ") + key = cls.casefold() + "\t" + src + "\t" + dst + if key in keys2replacements: + replacements = keys2replacements[key].split(" ") + spoken_words = dst.split(" ") + for w, r in zip( + same_from_begin + spoken_words + same_from_end, same_from_begin + replacements + same_from_end + ): + words.append(w) + if cls == "LETTERS" or cls == "PLAIN": + if w == r: + tags.append("") + else: + tags.append(r) + elif w == r.replace("_", ""): + tags.append("") + else: + tags.append(r) + semiotic_info.append( + cls + + " " + + str(len(words) - len(spoken_words) - len(same_from_begin) - len(same_from_end)) + + " " + + str(len(words)) + ) + else: + sent_is_ok = False + + +def process_line(semiotic_class: str, line: str) -> Optional[Tuple[str, str, str, int]]: + """A helper function to read the file with alignment results""" + + parts = line.strip().split("\t") + if len(parts) != 6: + return None + freq = int(parts[0]) + if parts[1] != "good:": + return None + + src, dst, leftside_align, rightside_align = parts[2], parts[3], parts[4], parts[5] + align = rightside_align + if semiotic_class == "letters" or semiotic_class == "plain": + align = leftside_align + + return src, dst, align, freq + + +def get_replacement_vocab() -> None: + """Loops through the files with alignment results in each semiotic class subfolder, counts frequencies of different + replacement segments. + """ + + full_vocab = Counter() + alignment_files = glob.glob(args.giza_dir + "/*/" + args.alignment_filename) + for fn in alignment_files: + fn_parts = fn.split("/") + if len(fn_parts) < 2: + raise ValueError("Bad filename: " + fn) + semiotic_class = fn_parts[-2] + class_vocab = Counter() + with open(fn, "r", encoding="utf-8") as f: + for line in f: + t = process_line(semiotic_class, line) + if t is None: + continue + src, dst, replacement, freq = t + inputs = src.split(" ") + replacements = replacement.split(" ") + if len(inputs) != len(replacements): + raise ValueError("Length mismatch in: " + line) + for inp, rep in zip(inputs, replacements): + if inp == rep: # skip same words + continue + full_vocab[rep] += freq + class_vocab[rep] += freq + with open(args.vocab_filename + "." + semiotic_class, "w", encoding="utf-8") as out: + for k, v in class_vocab.most_common(1000000000): + out.write(k + "\t" + str(v) + "\n") + + with open(args.vocab_filename, "w", encoding="utf-8") as out: + for k, v in full_vocab.most_common(1000000000): + out.write(k + "\t" + str(v) + "\n") + + +def filter_by_vocab() -> None: + """Given a restricted vocabulary of replacements, + loops through the files with alignment results in each semiotic class subfolder, + discards the examples containing a replacement which is not in our restricted vocabulary. + """ + + if not os.path.exists(args.vocab_filename): + raise ValueError(f"Alignments dir {args.giza_dir} does not exist") + # load vocab from file + vocab = {} + with open(args.vocab_filename, "r", encoding="utf-8") as f: + for line in f: + k, v = line.strip().split("\t") + vocab[k] = int(v) + print("len(vocab)=", len(vocab)) + alignment_files = glob.glob(args.giza_dir + "/*/" + args.alignment_filename) + for fn in alignment_files: + fn_parts = fn.split("/") + if len(fn_parts) < 2: + raise ValueError("Bad filename: " + fn) + semiotic_class = fn_parts[-2] + out = open(args.giza_dir + "/" + semiotic_class + "/" + args.out_filename, "w", encoding="utf-8") + with open(fn, "r", encoding="utf-8") as f: + for line in f: + t = process_line(semiotic_class, line) + if t is None: + continue + src, dst, replacement, freq = t + ok = True + for s, r in zip(src.split(" "), replacement.split(" ")): + if s != r and r not in vocab: + ok = False + if ok: + out.write(semiotic_class + "\t" + src + "\t" + dst + "\t" + replacement + "\n") + out.close() + + +def get_labeled_corpus() -> None: + """Loops through the files with alignment results in each semiotic class subfolder, + collects a mapping from (semiotic class, spoken, written) to the segmented written form, + which is aligned one-to-one to spoken words. + Then loops through the files in Google TN Dataset format to get the labeled data for ThutmoseTaggerModel. + It extracts the whole sentences and substitutes the semiotic spans to their aligned form from the dictionary. + """ + + if not os.path.exists(args.data_dir): + raise ValueError(f"Data dir {args.data_dir} does not exist") + + keys2replacements = {} + alignment_files = glob.glob(args.giza_dir + "/*/" + args.alignment_filename) + if len(alignment_files) == 0: + raise ValueError("Did not found any such files: " + args.giza_dir + "/*/" + args.alignment_filename) + for af in alignment_files: + with open(af, "r", encoding="utf-8") as f: + for line in f: + cls, src, dst, replacements = line.strip().split("\t") + key = cls + "\t" + dst + "\t" + src + if key in keys2replacements and keys2replacements[key] != replacements: + logging.warning("keys2replacements[key] != replacements", keys2replacements[key], replacements) + keys2replacements[key] = replacements + print("size of phrase-to-replacements dictionary =", len(keys2replacements)) + out = open(args.out_filename, "w", encoding="utf-8") + input_paths = sorted([os.path.join(args.data_dir, f) for f in os.listdir(args.data_dir)]) + for inputname in input_paths: + process_file_itn(inputname, out, keys2replacements) + out.close() + + +def main() -> None: + if not os.path.exists(args.giza_dir): + raise ValueError(f"Alignments dir {args.giza_dir} does not exist") + + if args.mode == "get_replacement_vocab": + get_replacement_vocab() + elif args.mode == "filter_by_vocab": + filter_by_vocab() + elif args.mode == "get_labeled_corpus": + get_labeled_corpus() + else: + raise ValueError("unknown mode: " + args.mode) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_for_alignment.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_for_alignment.py new file mode 100644 index 0000000..9fe64c1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_for_alignment.py @@ -0,0 +1,138 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script can be used to after google_data_preprocessing_before_alignment.py +to obtain separate "parallel" corpora for each semiotic class. + +USAGE Example: +1. Download the Google TN dataset from https://www.kaggle.com/google-nlu/text-normalization +2. Unzip the English subset (e.g., by running `tar zxvf en_with_types.tgz`). + Then there will a folder named `en_with_types`. +3. Run python google_data_preprocessing_before_alignment.py + which will produce a file data.tsv in its --output-dir +4. [Optional]. sort -u and rewrite data.tsv +5. Clone https://github.com/moses-smt/giza-pp.git, run "make" from its root folder. +6. Run this script + python ${NEMO}/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_for_alignment.py \ + --data_dir=<--output-dir from the previous step> \ + --out_dir= \ + --giza_dir=/.../giza-pp/GIZA++-v2 \ + --mckls_binary=/.../giza-pp/mkcls-v2/mkcls \ + --lang={en,ru} + + +Each corpus will be stored within <--data-dir> in the subdirectory with the name of the semiotic class, + containing files ready to be fed to Giza++: + src - written form, tokenized as characters + dst - spoken form, tokenized as words + run.sh - script for running Giza++ + +""" +from argparse import ArgumentParser +from collections import Counter +from os import listdir, mkdir +from os.path import isdir, join +from shutil import rmtree + +from nemo.collections.nlp.data.text_normalization_as_tagging.utils import get_src_and_dst_for_alignment + +parser = ArgumentParser(description='Split corpus to subcorpora for giza alignment') +parser.add_argument('--data_dir', type=str, required=True, help='Path to folder with data') +parser.add_argument('--out_dir', type=str, required=True, help='Path to output folder') +parser.add_argument('--giza_dir', type=str, required=True, help='Path to folder with GIZA++ binaries') +parser.add_argument('--mckls_binary', type=str, required=True, help='Path to mckls binary') +parser.add_argument('--lang', type=str, required=True, help='Language') +args = parser.parse_args() + + +def prepare_subcorpora_from_data() -> None: + """Preprocess a corpus in Google TN Dataset format, extract TN-ITN phrase pairs, prepare input for GIZA++ alignment. + """ + semiotic_vcb = Counter() + cache_vcb = {} + filenames = [] + for fn in listdir(args.data_dir + "/train"): + filenames.append(args.data_dir + "/train/" + fn) + for fn in listdir(args.data_dir + "/dev"): + filenames.append(args.data_dir + "/dev/" + fn) + for fn in filenames: + with open(fn, "r", encoding="utf-8") as f: + # Loop through each line of the file + for line in f: + parts = line.strip().split("\t") + if len(parts) < 3: + continue + if len(parts) != 3: + raise ValueError("Expect 3 parts, got " + str(len(parts))) + semiotic_class, written, spoken = parts[0], parts[1].strip(), parts[2].strip() + if spoken == "": + continue + semiotic_class = semiotic_class.casefold() + semiotic_vcb[semiotic_class] += 1 + classdir = join(args.out_dir, semiotic_class) + if not isdir(classdir): + mkdir(classdir) + src, dst, _, _ = get_src_and_dst_for_alignment(semiotic_class, written, spoken, args.lang) + if src == "" or dst == "": + continue + if len(src.split(" ")) >= 100: + continue + if semiotic_class not in cache_vcb: + cache_vcb[semiotic_class] = Counter() + cache_vcb[semiotic_class][(src, dst)] += 1 + for sem in semiotic_vcb: + classdir = join(args.out_dir, sem) + if not isdir(classdir): + raise ValueError("No such directory: " + classdir) + print(classdir, " has ", semiotic_vcb[sem], " instances") + with open(join(classdir, "run.sh"), "w") as out: + out.write("GIZA_PATH=\"" + args.giza_dir + "\"\n") + out.write("MKCLS=\"" + args.mckls_binary + "\"\n") + out.write("\n") + out.write("${GIZA_PATH}/plain2snt.out src dst\n") + out.write("${MKCLS} -m2 -psrc -c15 -Vsrc.classes opt >& mkcls1.log\n") + out.write("${MKCLS} -m2 -pdst -c15 -Vdst.classes opt >& mkcls2.log\n") + out.write("${GIZA_PATH}/snt2cooc.out src.vcb dst.vcb src_dst.snt > src_dst.cooc\n") + out.write( + "${GIZA_PATH}/GIZA++ -S src.vcb -T dst.vcb -C src_dst.snt -coocurrencefile src_dst.cooc -p0 0.98 -o GIZA++ >& GIZA++.log\n" + ) + out.write("##reverse direction\n") + out.write("${GIZA_PATH}/snt2cooc.out dst.vcb src.vcb dst_src.snt > dst_src.cooc\n") + out.write( + "${GIZA_PATH}/GIZA++ -S dst.vcb -T src.vcb -C dst_src.snt -coocurrencefile dst_src.cooc -p0 0.98 -o GIZA++reverse >& GIZA++reverse.log\n" + ) + out_src = open(join(classdir, "src"), 'w', encoding="utf-8") + out_dst = open(join(classdir, "dst"), 'w', encoding="utf-8") + out_freq = open(join(classdir, "freq"), 'w', encoding="utf-8") + for src, dst in cache_vcb[sem]: + freq = cache_vcb[sem][(src, dst)] + out_src.write(src + "\n") + out_dst.write(dst + "\n") + out_freq.write(str(freq) + "\n") + out_freq.close() + out_dst.close() + out_src.close() + + +# Main code +if __name__ == '__main__': + for name in listdir(args.out_dir): + path = join(args.out_dir, name) + if isdir(path): + rmtree(path) + + # Processing + prepare_subcorpora_from_data() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/sample_each_label.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/sample_each_label.py new file mode 100644 index 0000000..e3bf010 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/dataset_preparation/sample_each_label.py @@ -0,0 +1,58 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script can be used to sample each label from the labeled files. +""" + +import sys +from argparse import ArgumentParser +from collections import Counter + +parser = ArgumentParser(description="Sample labels") +parser.add_argument("--filename", required=True, type=str, help='File with input data') +parser.add_argument("--max_count", required=True, type=int, help='Count') +args = parser.parse_args() + + +vocab = Counter() + +out_sample = open(args.filename + ".sample_" + str(args.max_count), "w", encoding="utf-8") +out_rest = open(args.filename + ".rest_" + str(args.max_count), "w", encoding="utf-8") + +n = 0 +with open(args.filename, "r", encoding="utf-8") as f: + for line in f: + parts = line.strip().split("\t") + if len(parts) < 2: + print("Warning: bad format in line: " + str(n) + ": " + line, file=sys.stderr) + continue + + tags = parts[1].split(" ") + ok = False + for t in tags: + if t not in vocab: + vocab[t] = 0 + if vocab[t] < args.max_count: + ok = True + vocab[t] += 1 + if ok: + out_sample.write(line) + else: + out_rest.write(line) + n += 1 + +out_sample.close() +out_rest.close() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/evaluation/eval.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/evaluation/eval.py new file mode 100644 index 0000000..69521f9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/evaluation/eval.py @@ -0,0 +1,197 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script can be used to compare the inference output of Thutmose tagger with multi_reference file + +USAGE Example: + python eval.py \ + --inference_file= \ + --reference_file= \ + --print_other_errors + +The inference file is a tsv file in which the first column contains the predicted sentence text. +The reference file is a tsv file in which + the first column contains the input sentence text, + the second column contains the reference sentence text (taken from Google TN dataset) + the third column (optional) contains additional acceptable references for semiotic spans in this sentence. + E.g. + mizoguchi akiko september twenty ten mizoguchi akiko september 2010 DATE 2 5 | sept 2010 | sep. 2010 ... + (to get a reference file see the last steps in examples/nlp/text_normalization_as_tagging/prepare_dataset_en.sh, + starting from ".../examples/nlp/text_normalization_as_tagging/evaluation/get_multi_reference_vocab.py" + ) + +The script outputs the following metrics: + Word Error Rate (WER) - an automatic metric commonly used in ASR. + It does not take into account additional references. + Sentence accuracy: + The sentence is regarded as correct if its characters (without spaces) match to the reference, + It takes into account additional references. + + If at least one digital character doesn't match this sentence is regarded as containing Digit Error. + If all digital character match, but at least one non-digital character doesn't match + this sentence is regarded as containing Other Error. +""" + + +import re +from argparse import ArgumentParser + +from nemo.collections.asr.metrics.wer import word_error_rate + +parser = ArgumentParser(description="Compare inference output with multi-reference") +parser.add_argument("--inference_file", type=str, required=True, help="Path to inference file") +parser.add_argument( + "--print_other_errors", + action='store_true', + help="Whether to print other errors, if false only digit errors will be printed", +) +parser.add_argument("--reference_file", type=str, required=True, help="Path to reference file") +args = parser.parse_args() + +# Main code +if __name__ == "__main__": + inputs = [] + references = [] # list(size=len(inputs)) of lists + skip_ids = set() # sentences ids to be skipped during evaluation + with open(args.reference_file, "r", encoding="utf-8") as f: + for line in f: + multi_references = [] + parts = line.strip().split("\t") + if len(parts) < 2 or len(parts) > 3: + raise ValueError("Bad format: " + line) + words = parts[0].split() + inputs.append(words) + if len(parts) == 3: # there are non-trivial semiotic spans + multi_references.append("") + input_position = 0 + if "TELEPHONE" in parts[2] or "ELECTRONIC" in parts[2]: + skip_ids.add(len(references)) + spans = parts[2].split(";") + multi_references_updated = [] + for span in spans: + span_parts = span.split(" | ") + try: + sem, begin, end = span_parts[0].split(" ") + except Exception: + print("error: ", line) + continue + begin = int(begin) + end = int(end) + for ref in multi_references: + if len(span_parts) > 20 or len(multi_references_updated) > 20000: + print("warning: too many references: ", inputs[-1]) + break + for tr_variant in span_parts[1:]: + multi_references_updated.append( + ref + + " " + + " ".join(inputs[-1][input_position:begin]) # copy needed words from input + + " " + + tr_variant + ) + multi_references = multi_references_updated[:] # copy + multi_references_updated = [] + input_position = end + for i in range(len(multi_references)): # copy needed words from the input end + multi_references[i] += " " + " ".join(inputs[-1][input_position : len(inputs[-1])]) + # the last reference added is the actual one + multi_references.append(parts[1]) + references.append(multi_references) + + predictions = [] + predicted_tags = [] + predicted_semiotic = [] + # load predictions + with open(args.inference_file, "r", encoding="utf-8") as f: + for line in f: + parts = line.strip().split("\t") + if len(parts) == 1: + predictions.append(parts[0].casefold()) + predicted_tags.append([]) + continue + if len(parts) != 5: + raise ValueError("Bad format: " + line) + prediction, inp_str, tag_str, tags_with_swap_str, semiotic = parts + predictions.append(prediction.casefold()) + tags = tag_str.split(" ") + predicted_tags.append(tags) + predicted_semiotic.append(semiotic) + + sentences_with_errors_on_digits = 0 + correct_sentences_disregarding_space = 0 + + if len(inputs) != len(predictions) or len(inputs) != len(references): + raise ValueError( + "Length mismatch: len(inputs)=" + + str(len(inputs)) + + "; len(predictions)=" + + str(len(predictions)) + + "; len(references)=" + + str(len(references)) + ) + + refs_for_wer = [] + preds_for_wer = [] + for i in range(len(inputs)): + ok_digit = False + ok_all = False + if i in skip_ids: + continue + refs_for_wer.append(references[i][-1]) + preds_for_wer.append(predictions[i]) + for ref in references[i]: + ref_digit_fragments = re.findall(r"\d+", ref) + pred_digit_fragments = re.findall(r"\d+", predictions[i]) + if "".join(pred_digit_fragments) == "".join(ref_digit_fragments): + ok_digit = True + if predictions[i].replace("_", "").replace(" ", "") == ref.replace("_", "").replace(" ", ""): + ok_all = True + if not ok_digit: + print("digit error:") + print("\tinput=", " ".join(inputs[i])) + print("\ttags=", " ".join(predicted_tags[i])) + print("\tpred=", predictions[i]) + print("\tsemiotic=", predicted_semiotic[i]) + print("\tref=", references[i][-1]) # last reference is actual reference + sentences_with_errors_on_digits += 1 + elif ok_all: + correct_sentences_disregarding_space += 1 + elif args.print_other_errors: + print("other error:") + print("\tinput=", " ".join(inputs[i])) + print("\ttags=", " ".join(predicted_tags[i])) + print("\tpred=", predictions[i]) + print("\tsemiotic=", predicted_semiotic[i]) + print("\tref=", references[i][-1]) # last reference is actual reference + + wer = word_error_rate(refs_for_wer, preds_for_wer) + print("WER: ", wer) + print( + "Sentence accuracy: ", + correct_sentences_disregarding_space / (len(inputs) - len(skip_ids)), + correct_sentences_disregarding_space, + ) + print( + "digit errors: ", + sentences_with_errors_on_digits / (len(inputs) - len(skip_ids)), + sentences_with_errors_on_digits, + ) + print( + "other errors: ", + (len(inputs) - len(skip_ids) - correct_sentences_disregarding_space - sentences_with_errors_on_digits) + / (len(inputs) - len(skip_ids)), + len(inputs) - len(skip_ids) - correct_sentences_disregarding_space - sentences_with_errors_on_digits, + ) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/evaluation/eval_per_class.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/evaluation/eval_per_class.py new file mode 100644 index 0000000..1ed55ec --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/evaluation/eval_per_class.py @@ -0,0 +1,145 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script can be used to compare the inference output of Thutmose tagger with multi_reference file. +The additional report is stored to a separate file for each semiotic class. + +USAGE Example: + python eval_per_class.py \ + --inference_file= \ + --reference_file= \ + --output_file= + +The inference file is a tsv file in which the first column contains the predicted sentence text. +The reference file is a tsv file in which + the first column contains the input sentence text, + the second column contains the reference sentence text (taken from Google TN dataset) + the third column (optional) contains additional acceptable references for semiotic spans in this sentence. + E.g. + mizoguchi akiko september twenty ten mizoguchi akiko september 2010 DATE 2 5 | sept 2010 | sep. 2010 ... + +The script generates: + a file with report on accuracy per semiotiotic class (output_file). + files (.) with sentences, containing errors in this semiotic span. + +""" +import glob +import os +from argparse import ArgumentParser +from collections import Counter + +parser = ArgumentParser(description="Compare inference output with multi-reference, print report per class") +parser.add_argument("--inference_file", type=str, required=True, help="Path to inference file 1") +parser.add_argument("--reference_file", type=str, required=True, help="Path to reference file") +parser.add_argument("--output_file", type=str, required=True, help="Path to output file") +args = parser.parse_args() + +# Main code +if __name__ == '__main__': + + # delete all class-specific reports, as they are created in the append mode + for f in glob.glob(args.output_file + ".*"): + os.remove(f) + + total_count = Counter() + correct_count = Counter() + + f_ref = open(args.reference_file, "r", encoding="utf-8") + f_infer = open(args.inference_file, "r", encoding="utf-8") + f_out = open(args.output_file, "w", encoding="utf-8") + lines_ref = f_ref.readlines() + lines_infer = f_infer.readlines() + f_ref.close() + f_infer.close() + if len(lines_ref) != len(lines_infer): + raise ValueError( + "Number of lines doesn't match: len(lines_ref)=" + + str(len(lines_ref)) + + "; len(lines_infer)=" + + str(len(lines_infer)) + ) + for i in range(len(lines_infer)): + _, inp_str, _, tag_with_swap_str, semiotic = lines_infer[i].strip().split("\t") + input_words = inp_str.split(" ") + predicted_tags = tag_with_swap_str.split(" ") + predicted_words = predicted_tags[:] + for k in range(len(predicted_tags)): + t = predicted_tags[k] + if t == "": + predicted_words[k] = input_words[k] + elif t == "": + predicted_words[k] = "" + else: + predicted_words[k] = predicted_words[k].replace(">", "").replace("<", "") + + parts = lines_ref[i].strip().split("\t") + if len(parts) < 2 or len(parts) > 3: + raise ValueError("Bad format: " + lines_ref[i]) + if len(parts) == 3: # there are non-trivial semiotic spans + spans = parts[2].split(";") + for span in spans: + span_parts = span.split(" | ") + try: + sem, begin, end = span_parts[0].split(" ") + except Exception: + print("error: ", lines_ref[i]) + continue + begin = int(begin) + end = int(end) + + ok = False + predicted_span = " ".join(predicted_words[begin:end]).replace("_", " ").replace(" ", "").casefold() + input_span = " ".join(input_words[begin:end]) + total_count[sem] += 1 + for tr_variant in span_parts[1:]: + ref_span = tr_variant.replace("_", " ").replace(" ", "").casefold() + if ref_span == predicted_span: + ok = True + correct_count[sem] += 1 + break + if not ok: + out_sem = open(args.output_file + "." + sem, "a", encoding="utf-8") + out_sem.write( + "error: pred=" + + " ".join(predicted_words[begin:end]) + + "; inp=" + + input_span + + "; ref=" + + span + + "\n" + ) + out_sem.write("\tinput=" + " ".join(input_words) + "\n") + out_sem.write("\ttags=" + " ".join(predicted_tags) + "\n") + out_sem.write("\tpred=" + " ".join(predicted_words) + "\n") + out_sem.write("\tsemiotic=" + semiotic + "\n") + out_sem.write("\tref=" + parts[1] + "\n") + out_sem.close() + + f_out.write("class\ttotal\tcorrect\terrors\taccuracy\n") + for sem in total_count: + f_out.write( + sem + + "\t" + + str(total_count[sem]) + + "\t" + + str(correct_count[sem]) + + "\t" + + str(total_count[sem] - correct_count[sem]) + + "\t" + + str(correct_count[sem] / total_count[sem]) + + "\n" + ) + f_out.close() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/evaluation/get_multi_reference_vocab.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/evaluation/get_multi_reference_vocab.py new file mode 100644 index 0000000..43b0dd5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/evaluation/get_multi_reference_vocab.py @@ -0,0 +1,64 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script can be used to construct a vocabulary of multiple references +""" +from argparse import ArgumentParser +from collections import Counter +from os import listdir + +from nemo.collections.nlp.data.text_normalization_as_tagging.utils import spoken_preprocessing + +parser = ArgumentParser(description="Get reference vocabulary from corpus (it will be used in testing)") +parser.add_argument("--data_dir", type=str, required=True, help="Path to folder with data") +parser.add_argument("--out_filename", type=str, required=True, help="Path to output file") +args = parser.parse_args() + +if __name__ == "__main__": + + vcb = {} + filenames = [] + for fn in listdir(args.data_dir + "/train"): + filenames.append(args.data_dir + "/train/" + fn) + for fn in listdir(args.data_dir + "/dev"): + filenames.append(args.data_dir + "/dev/" + fn) + for fn in filenames: + print("Processing ", fn) + with open(fn, "r", encoding="utf-8") as f: + for line in f: + parts = line.strip().split("\t") + if len(parts) < 3: + continue + if len(parts) != 3: + raise ValueError("Expect 3 parts, got " + str(len(parts))) + semiotic_class, written, spoken = parts[0], parts[1].strip().casefold(), parts[2].strip().casefold() + spoken = spoken_preprocessing(spoken) + if spoken == "": + continue + if spoken == "" or written == "": + continue + if len(spoken.split(" ")) >= 100: + continue + k = (semiotic_class, spoken) + if k not in vcb: + vcb[k] = Counter() + vcb[k][written] += 1 + + with open(args.out_filename, "w", encoding="utf-8") as out: + for sem, spoken in vcb: + for written in vcb[(sem, spoken)]: + out.write(sem + "\t" + spoken + "\t" + written + "\t" + str(vcb[(sem, spoken)][written]) + "\n") + out.write(sem + "\t" + spoken + "\t" + spoken + "\t1\n") diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/evaluation/prepare_corpora_for_testing.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/evaluation/prepare_corpora_for_testing.py new file mode 100644 index 0000000..10cb916 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/evaluation/prepare_corpora_for_testing.py @@ -0,0 +1,152 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script can be used to prepare test corpus for the ThutmoseTaggerModel from Google Text Normalization dataset. +""" + +import os +import re +from argparse import ArgumentParser +from collections import Counter +from typing import Dict, TextIO, Tuple + +from nemo.collections.nlp.data.text_normalization_as_tagging.utils import spoken_preprocessing + +parser = ArgumentParser(description="Text Normalization Data Preprocessing for English") +parser.add_argument( + "--data_dir", required=True, type=str, help="Path to data directory with files like output-00000-of-00100.tsv" +) +parser.add_argument("--reference_vocab", required=True, type=str, help="Multi Reference vocabulary") +parser.add_argument("--output_file", required=True, type=str, help="Output file") +parser.add_argument( + "--sampling_count", required=True, type=int, help="Number of examples per class, you want, use -1 for all examples" +) +args = parser.parse_args() + + +def process_file( + inputname: str, + out: TextIO, + out_raw: TextIO, + reference_vcb: Dict[Tuple[str, str], Dict[str, int]], + sampling_vcb: Dict[str, int], +) -> None: + words = [] + reference_words = [] # size may be different + semiotic_info = [] + raw_lines = [] + sent_ok = True if args.sampling_count == -1 else False + with open(inputname, "r", encoding="utf-8") as f: + for line in f: + if line.startswith(""): + if len(words) > 0 and sent_ok: + out.write( + " ".join(words) + "\t" + " ".join(reference_words) + "\t" + ";".join(semiotic_info) + "\n" + ) + out_raw.write("\n".join(raw_lines) + "\n" + line) + words = [] + reference_words = [] + semiotic_info = [] + raw_lines = [] + sent_ok = True if args.sampling_count == -1 else False + else: + raw_lines.append(line.strip()) + cls, written, spoken = line.strip().split("\t") + spoken = spoken_preprocessing(spoken) + written = written.casefold() + references = set() + if spoken == "sil": + continue + if spoken == "": + words.append(written) + reference_words.append(written) + # if reference is , but the word has itn conversions in our dictionary, add them + for cls in ["CARDINAL", "ORDINAL", "DATE"]: # date, ex sixties -> 60s + k = (cls, written) + if k in reference_vcb: + for tr_variant in reference_vcb[k]: + references.add(tr_variant) + semiotic_info.append( + cls + + " " + + str(len(words) - 1) + + " " + + str(len(words)) + + " | " + + " | ".join(references) + ) + break + continue + + spoken_words = spoken.split() + words.extend(spoken_words) + + k = (cls, spoken) + if k in reference_vcb: + for tr_variant in reference_vcb[k]: + references.add(tr_variant) + references.add(spoken) + references.add(written) + for tr_variant in list(references): + # 6,51 km² => 6,51 km 2 + (tr_variant2, n2) = re.subn(r"²", " 2", tr_variant) + (tr_variant3, n3) = re.subn(r"³", " 3", tr_variant) + if n2 > 0: + references.add(tr_variant2) + if n3 > 0: + references.add(tr_variant3) + + semiotic_info.append( + cls + + " " + + str(len(words) - len(spoken_words)) + + " " + + str(len(words)) + + " | " + + " | ".join(list(references)) + ) + reference_words.append(written.casefold()) + + if cls not in sampling_vcb: + sampling_vcb[cls] = 0 + if sampling_vcb[cls] < args.sampling_count: + sent_ok = True + sampling_vcb[cls] += 1 + + +def main() -> None: + if not os.path.exists(args.data_dir): + raise ValueError(f"Data dir {args.data_dir} does not exist") + reference_vcb = {} + with open(args.reference_vocab, "r", encoding="utf-8") as f: + for line in f: + sem, spoken, written, freq = line.strip().split("\t") + k = (sem, spoken) + if k not in reference_vcb: + reference_vcb[k] = {} + reference_vcb[k][written] = int(freq) + sampling_vcb = Counter() + out = open(args.output_file, "w", encoding="utf-8") + out_raw = open(args.output_file + ".raw", "w", encoding="utf-8") + input_paths = sorted([os.path.join(args.data_dir, f) for f in os.listdir(args.data_dir)]) + for inputname in input_paths: + process_file(inputname, out, out_raw, reference_vcb, sampling_vcb) + out.close() + out_raw.close() + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/helpers.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/helpers.py new file mode 100644 index 0000000..347b05b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/helpers.py @@ -0,0 +1,82 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import os +from typing import Tuple + +import pytorch_lightning as pl +from omegaconf import DictConfig + +from nemo.collections.nlp.models import ThutmoseTaggerModel +from nemo.utils import logging + +__all__ = ["ITN_MODEL", "MODEL_NAMES", "instantiate_model_and_trainer"] + +ITN_MODEL = "itn" +MODEL_NAMES = [ITN_MODEL] + + +def instantiate_model_and_trainer( + cfg: DictConfig, model_name: str, do_training: bool +) -> Tuple[pl.Trainer, ThutmoseTaggerModel]: + """ Function for instantiating a model and a trainer + Args: + cfg: The config used to instantiate the model and the trainer. + model_name: A str indicates the model direction, currently only 'itn'. + do_training: A boolean flag indicates whether the model will be trained or evaluated. + + Returns: + trainer: A PyTorch Lightning trainer + model: A ThutmoseTaggerModel + """ + + if model_name not in MODEL_NAMES: + raise ValueError(f"{model_name} is unknown model type") + + # Get configs for the corresponding models + trainer_cfg = cfg.get("trainer") + model_cfg = cfg.get("model") + pretrained_cfg = cfg.get("pretrained_model", None) + trainer = pl.Trainer(**trainer_cfg) + if not pretrained_cfg: + logging.info(f"Initializing {model_name} model") + if model_name == ITN_MODEL: + model = ThutmoseTaggerModel(model_cfg, trainer=trainer) + else: + raise ValueError(f"{model_name} is unknown model type") + elif os.path.exists(pretrained_cfg): + logging.info(f"Restoring pretrained {model_name} model from {pretrained_cfg}") + model = ThutmoseTaggerModel.restore_from(pretrained_cfg) + else: + logging.info(f"Loading pretrained model {pretrained_cfg}") + if model_name == ITN_MODEL: + if pretrained_cfg not in ThutmoseTaggerModel.get_available_model_names(): + raise ( + ValueError( + f"{pretrained_cfg} not in the list of available Tagger models." + f"Select from {ThutmoseTaggerModel.list_available_models()}" + ) + ) + model = ThutmoseTaggerModel.from_pretrained(pretrained_cfg) + else: + raise ValueError(f"{model_name} is unknown model type") + + # Setup train and validation data + if do_training: + model.setup_training_data(train_data_config=cfg.data.train_ds) + model.setup_validation_data(val_data_config=cfg.data.validation_ds) + + logging.info(f"Model {model_name} -- Device {model.device}") + return trainer, model diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/install_requirements.sh b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/install_requirements.sh new file mode 100644 index 0000000..f54a6cb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/install_requirements.sh @@ -0,0 +1,6 @@ +#!/bin/bash + +git clone https://github.com/moses-smt/giza-pp.git giza-pp +cd giza-pp +make +cd .. diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py new file mode 100644 index 0000000..0516f3b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py @@ -0,0 +1,91 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script contains an example on how to run itn inference with the ThutmoseTaggerModel. + +The inference works on a raw file (no labels required). +Each line of the input file represents a single example for inference. + Specify inference.from_file and inference.batch_size parameters. + +USAGE Example: +1. Train a model, or use a pretrained checkpoint. +2. Run: + export TOKENIZERS_PARALLELISM=false + python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py \ + pretrained_model=./training.nemo \ + inference.from_file=./input.txt \ + inference.out_file=./output.txt \ + model.max_sequence_len=1024 #\ + inference.batch_size=128 + +This script uses the `/examples/nlp/text_normalization_as_tagging/conf/thutmose_tagger_itn_config.yaml` +config file by default. The other option is to set another config file via command +line arguments by `--config-name=CONFIG_FILE_PATH'. +""" + + +import os + +from helpers import ITN_MODEL, instantiate_model_and_trainer +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.data.text_normalization_as_tagging.utils import spoken_preprocessing +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@hydra_runner(config_path="conf", config_name="thutmose_tagger_itn_config") +def main(cfg: DictConfig) -> None: + logging.debug(f'Config Params: {OmegaConf.to_yaml(cfg)}') + + if cfg.pretrained_model is None: + raise ValueError("A pre-trained model should be provided.") + _, model = instantiate_model_and_trainer(cfg, ITN_MODEL, False) + + text_file = cfg.inference.from_file + logging.info(f"Running inference on {text_file}...") + if not os.path.exists(text_file): + raise ValueError(f"{text_file} not found.") + + with open(text_file, "r", encoding="utf-8") as f: + lines = f.readlines() + + batch_size = cfg.inference.get("batch_size", 8) + + batch, all_preds = [], [] + for i, line in enumerate(lines): + s = spoken_preprocessing(line) # this is the same input transformation as in corpus preparation + batch.append(s.strip()) + if len(batch) == batch_size or i == len(lines) - 1: + outputs = model._infer(batch) + for x in outputs: + all_preds.append(x) + batch = [] + if len(all_preds) != len(lines): + raise ValueError( + "number of input lines and predictions is different: predictions=" + + str(len(all_preds)) + + "; lines=" + + str(len(lines)) + ) + out_file = cfg.inference.out_file + with open(f"{out_file}", "w", encoding="utf-8") as f_out: + f_out.write("\n".join(all_preds)) + logging.info(f"Predictions saved to {out_file}.") + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_train.py b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_train.py new file mode 100644 index 0000000..36fe97d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_train.py @@ -0,0 +1,85 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script contains an example on how to train a ThutmoseTaggerModel for inverse text normalization(ITN). + +This script uses the `/examples/nlp/text_normalization_as_tagging/conf/thutmose_tagger_itn_config.yaml` +config file by default. The other option is to set another config file via command +line arguments by `--config-name=CONFIG_FILE_PATH'. Probably it is worth looking +at the example config file to see the list of parameters used for training. + +USAGE Example: +1. Obtain a processed dataset +2. Run: + python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_train.py \ + lang=${LANG} \ + data.validation_ds.data_path=${DATA_PATH}/valid.tsv \ + data.train_ds.data_path=${DATA_PATH}/train.tsv \ + data.train_ds.batch_size=128 \ + data.train_ds.num_workers=8 \ + model.language_model.pretrained_model_name=${LANGUAGE_MODEL} \ + model.label_map=${DATA_PATH}/label_map.txt \ + model.semiotic_classes=${DATA_PATH}/semiotic_classes.txt \ + model.optim.lr=3e-5 \ + trainer.devices=[1] \ + trainer.num_nodes=1 \ + trainer.accelerator=gpu \ + trainer.strategy=ddp \ + trainer.max_epochs=5 + +Information on the arguments: + +Most arguments in the example config file are quite self-explanatory (e.g., +`model.optim.lr` refers to the learning rate for training the model). + +Some arguments we want to mention are: + ++ lang: The language of the dataset. ++ model.language_model.pretrained_model_name: This is the backbone BERT model (depends on the language) +e.g. bert-base-uncased (English), DeepPavlov/rubert-base-cased (Russian) +""" + +from helpers import ITN_MODEL, instantiate_model_and_trainer +from omegaconf import DictConfig, OmegaConf + +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="thutmose_tagger_itn_config") +def main(cfg: DictConfig) -> None: + # PTL 2.0 has find_unused_parameters as False by default, so its required to set it to True + # when there are unused parameters like here + if cfg.trainer.strategy == 'ddp': + cfg.trainer.strategy = "ddp_find_unused_parameters_true" + logging.info(f'Config Params: {OmegaConf.to_yaml(cfg)}') + + # Train the model + if cfg.model.do_training: + logging.info( + "================================================================================================" + ) + logging.info('Start training...') + trainer, model = instantiate_model_and_trainer(cfg, ITN_MODEL, True) + thutmose_tagger_exp_manager = cfg.get('exp_manager', None) + exp_manager(trainer, thutmose_tagger_exp_manager) + trainer.fit(model) + logging.info('Training finished!') + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/prepare_dataset_en.sh b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/prepare_dataset_en.sh new file mode 100644 index 0000000..54fcdd2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/prepare_dataset_en.sh @@ -0,0 +1,334 @@ +#!/bin/bash + +## This bash-script reproduces the pipeline of data preparation for the Thutmose Tagger model (tagger-based ITN model) + +## In order to use it, you need: +## 1. install and compile GIZA++ +## git clone https://github.com/moses-smt/giza-pp.git giza-pp +## cd giza-pp +## make +## 2. Download Google TN Dataset +## https://www.kaggle.com/richardwilliamsproat/text-normalization-for-english-russian-and-polish +## 3. install NeMo +## git clone https://github.com/NVIDIA/NeMo +## 4. Specify the following paths + +## path to NeMo repository, e.g. /home/user/nemo +NEMO_PATH= +## path to GIZA++, e.g. /home/user/giza-pp/GIZA++-v2 +GIZA_BIN_DIR= +## path to MCKLS_BINARY, e.g. /home/user/giza-pp/mkcls-v2/mkcls +MCKLS_BINARY= +## initial unzipped Google Text Normalization Dataset, e.g. /home/user/data/en_with_types +GOOGLE_CORPUS_DIR= + + +## corpus language +CORPUS_LANG=en + +WORK_DIR=`pwd` # directory from which this bash-script is run +echo "Working directory:" ${WORK_DIR} + +## names of working subfolders +CORPUS_DIR=${WORK_DIR}/corpus +ALIGNMENT_DIR=${WORK_DIR}/alignment + +## read the data and split it into train, dev, test +## files in test folder is truncated to match the default test dataset from the paper on Google TN Dataset +## option --add_test_full=true creates additional test_full folder, which is not truncated +python ${NEMO_PATH}/examples/nlp/duplex_text_normalization/data/data_split.py \ + --data_dir=${GOOGLE_CORPUS_DIR} \ + --output_dir=${CORPUS_DIR} \ + --lang=${CORPUS_LANG} \ + --add_test_full + +## we need only output-00099-of-00100.tsv as the final test data +rm ${CORPUS_DIR}/test/output-00095-of-00100.tsv ${CORPUS_DIR}/test/output-00096-of-00100.tsv ${CORPUS_DIR}/test/output-00097-of-00100.tsv ${CORPUS_DIR}/test/output-00098-of-00100.tsv + +## This script extracts all unique ITN phrase-pairs from the Google TN dataset, tokenizes them and stores in separate +## folders for each semiotic class. In each folder we generate a bash script for running the alignment. +mkdir ${ALIGNMENT_DIR} +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_for_alignment.py \ + --data_dir=${CORPUS_DIR} \ + --out_dir=${ALIGNMENT_DIR} \ + --giza_dir=${GIZA_BIN_DIR} \ + --mckls_binary=${MCKLS_BINARY} \ + --lang=${CORPUS_LANG} + +##exclude punct class +rm -r ${ALIGNMENT_DIR}/punct + +## for better GIZA++ alignments mix in examples from other classes +## they will append to the tail of "src" and "dst" files and they will not have corresponding freqs in "freq" file +## all these appended lines will be skipped in the get_replacement_vocab step +for fn in "src" "dst" +do + cat ${ALIGNMENT_DIR}/money/${fn} \ + ${ALIGNMENT_DIR}/cardinal/${fn} \ + ${ALIGNMENT_DIR}/decimal/${fn} \ + ${ALIGNMENT_DIR}/fraction/${fn} \ + ${ALIGNMENT_DIR}/measure/${fn} > ${ALIGNMENT_DIR}/money/${fn}.new + + cat ${ALIGNMENT_DIR}/measure/${fn} \ + ${ALIGNMENT_DIR}/cardinal/${fn} \ + ${ALIGNMENT_DIR}/decimal/${fn} \ + ${ALIGNMENT_DIR}/fraction/${fn} \ + ${ALIGNMENT_DIR}/money/${fn} > ${ALIGNMENT_DIR}/measure/${fn}.new + + cat ${ALIGNMENT_DIR}/fraction/${fn} \ + ${ALIGNMENT_DIR}/cardinal/${fn} \ + ${ALIGNMENT_DIR}/measure/${fn} \ + ${ALIGNMENT_DIR}/money/${fn} > ${ALIGNMENT_DIR}/fraction/${fn}.new + + cat ${ALIGNMENT_DIR}/decimal/${fn} \ + ${ALIGNMENT_DIR}/cardinal/${fn} \ + ${ALIGNMENT_DIR}/measure/${fn} \ + ${ALIGNMENT_DIR}/money/${fn} > ${ALIGNMENT_DIR}/decimal/${fn}.new + +done + +for c in "decimal" "fraction" "measure" "money" +do + mv ${ALIGNMENT_DIR}/${c}/src.new ${ALIGNMENT_DIR}/${c}/src + mv ${ALIGNMENT_DIR}/${c}/dst.new ${ALIGNMENT_DIR}/${c}/dst +done + +for subfolder in ${ALIGNMENT_DIR}/* +do + echo ${subfolder} + chmod +x ${subfolder}/run.sh +done + +## Run alignment using multiple processes +for subfolder in ${ALIGNMENT_DIR}/* +do + cd ${subfolder} + ./run.sh & +done +wait + +## Extract final alignments for each semiotic class +for subfolder in ${ALIGNMENT_DIR}/* +do + python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/extract_giza_alignments.py \ + --mode=itn \ + --giza_dir=${subfolder} \ + --out_filename=itn.out \ + --giza_suffix=A3.final \ + --lang=en & +done +wait + +## add column with frequencies of phrase pairs in the corpus +for subfolder in ${ALIGNMENT_DIR}/* +do + paste -d"\t" ${subfolder}/freq ${subfolder}/itn.out > ${subfolder}/itn.out2 + awk 'BEGIN {FS="\t"} match($3, " "){print $0}' < ${subfolder}/itn.out2 | sort -rn > ${subfolder}/itn.debug +done + +## loop through the obtained alignments and collect vocabularies (for each semiotic class) +## of all possible replacement fragments (aka tags) +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_after_alignment.py \ + --mode=get_replacement_vocab \ + --giza_dir=${ALIGNMENT_DIR} \ + --alignment_filename=itn.out2 \ + --data_dir="" \ + --vocab_filename=${WORK_DIR}/replacement_vocab_full.txt \ + --out_filename="" \ + --lang=${CORPUS_LANG} + +## Here we put some voluntary thresholds on how many tags we take. +## Tags with low frequencies are likely to be derived from sporadic alignment mistakes +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.verbatim | head -n 108 > ${WORK_DIR}/replacement_vocab_verbatim.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.time | head -n 148 > ${WORK_DIR}/replacement_vocab_time.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.telephone | head -n 52 > ${WORK_DIR}/replacement_vocab_telephone.txt +head -n 0 ${WORK_DIR}/replacement_vocab_full.txt.plain > ${WORK_DIR}/replacement_vocab_plain.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.ordinal | head -n 251 > ${WORK_DIR}/replacement_vocab_ordinal.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.money | grep -v "a__" | head -n 532 > ${WORK_DIR}/replacement_vocab_money.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.measure | head -n 488 > ${WORK_DIR}/replacement_vocab_measure.txt +head -n 257 ${WORK_DIR}/replacement_vocab_full.txt.letters > ${WORK_DIR}/replacement_vocab_letters.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.fraction | head -n 169 > ${WORK_DIR}/replacement_vocab_fraction.txt +head -n 276 ${WORK_DIR}/replacement_vocab_full.txt.electronic > ${WORK_DIR}/replacement_vocab_electronic.txt +head -n 73 ${WORK_DIR}/replacement_vocab_full.txt.digit > ${WORK_DIR}/replacement_vocab_digit.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.decimal | head -n 149 > ${WORK_DIR}/replacement_vocab_decimal.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.date | grep -v "[0-9]-[0-9]" | grep -v "[0-9]\,[0-9]" | grep -v "[0-9]\.[0-9]" | grep -v "[0-9]\/[0-9]" | head -n 554 > ${WORK_DIR}/replacement_vocab_date.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.cardinal | head -n 402 > ${WORK_DIR}/replacement_vocab_cardinal.txt +head -n 137 ${WORK_DIR}/replacement_vocab_full.txt.address > ${WORK_DIR}/replacement_vocab_address.txt + +## concatenate all tags in a single vocabulary (repetitions don't matter) +cat ${WORK_DIR}/replacement_vocab_address.txt \ + ${WORK_DIR}/replacement_vocab_cardinal.txt \ + ${WORK_DIR}/replacement_vocab_date.txt \ + ${WORK_DIR}/replacement_vocab_decimal.txt \ + ${WORK_DIR}/replacement_vocab_digit.txt \ + ${WORK_DIR}/replacement_vocab_electronic.txt \ + ${WORK_DIR}/replacement_vocab_fraction.txt \ + ${WORK_DIR}/replacement_vocab_letters.txt \ + ${WORK_DIR}/replacement_vocab_measure.txt \ + ${WORK_DIR}/replacement_vocab_money.txt \ + ${WORK_DIR}/replacement_vocab_ordinal.txt \ + ${WORK_DIR}/replacement_vocab_plain.txt \ + ${WORK_DIR}/replacement_vocab_telephone.txt \ + ${WORK_DIR}/replacement_vocab_time.txt \ + ${WORK_DIR}/replacement_vocab_verbatim.txt > ${WORK_DIR}/replacement_vocab.select.txt + +## Here we loop once again through the alignments and discard those examples that are not fully covered +## by our restricted tag vocabulary +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_after_alignment.py \ + --mode=filter_by_vocab \ + --giza_dir=${ALIGNMENT_DIR} \ + --alignment_filename=itn.out2 \ + --data_dir="" \ + --vocab_filename=${WORK_DIR}/replacement_vocab.select.txt \ + --out_filename=itn.select.out \ + --lang=${CORPUS_LANG} + +## We now have a large collection of ITN phrase conversions that we know how to tag. +## Once again we loop through the Google TN dataset and create tag-labeled datasets, containing full sentences. +## If a sentence contains something that we do not know how to tag, we discard the whole sentence. +for subset in "train" "dev" +do + python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_after_alignment.py \ + --mode=get_labeled_corpus \ + --giza_dir=${ALIGNMENT_DIR} \ + --alignment_filename=itn.select.out \ + --data_dir=${CORPUS_DIR}/${subset} \ + --vocab_filename="" \ + --out_filename=${CORPUS_DIR}/${subset}.labeled \ + --lang=${CORPUS_LANG} +done + +## Loop through the obtained datasets and get final tag vocabulary +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/get_label_vocab.py \ + --train_filename=${CORPUS_DIR}/train.labeled \ + --dev_filename=${CORPUS_DIR}/dev.labeled \ + --out_filename=${CORPUS_DIR}/label_map.txt + +## The full dataset is very large, while some tags occur rarely. So we can try some sampling. +## Here we try to sample sentences that contain at least one tag that we have not yet seen at least for N times +## This script will split the input dataset into two parts: sampled sentences and the rest sentences. +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/sample_each_label.py \ + --filename=${CORPUS_DIR}/dev.labeled \ + --max_count=10 + +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/sample_each_label.py \ + --filename=${CORPUS_DIR}/train.labeled \ + --max_count=500 + +## Here we create final train and dev datasets, mixing sampled sentences and some quantity of the rest sentences. +mkdir ${WORK_DIR}/datasets +DATASET=${WORK_DIR}/datasets/itn_sample500k_rest1500k_select_vocab +mkdir $DATASET +cat ${CORPUS_DIR}/train.labeled.sample_500 > ${DATASET}/train.tsv +head -n 1500000 ${CORPUS_DIR}/train.labeled.rest_500 >> ${DATASET}/train.tsv +cat ${CORPUS_DIR}/dev.labeled.sample_10 > ${DATASET}/valid.tsv +head -n 12000 ${CORPUS_DIR}/dev.labeled.rest_10 >> ${DATASET}/valid.tsv +cp ${DATASET}/valid.tsv ${DATASET}/test.tsv + +## The model will also need a file with semiotic classes and label map (derived from tag vocabulary) +echo "ADDRESS" > ${CORPUS_DIR}/semiotic_classes.txt +echo "CARDINAL" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "DATE" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "DECIMAL" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "DIGIT" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "ELECTRONIC" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "FRACTION" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "LETTERS" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "MEASURE" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "MONEY" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "ORDINAL" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "PLAIN" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "PUNCT" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "TELEPHONE" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "TIME" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "VERBATIM" >> ${CORPUS_DIR}/semiotic_classes.txt + +cp ${CORPUS_DIR}/label_map.txt ${WORK_DIR}/datasets/label_map.txt +cp ${CORPUS_DIR}/semiotic_classes.txt ${WORK_DIR}/datasets/semiotic_classes.txt + +## Now all data is ready to train the model. + +## We also prepare the test data to test the model after the training. +## The test data knows nothing about alignment, it only contains input sentences and references. + +## The original test data from Google TN Dataset contains a single reference for each ITN span. +## In order to take into account more than one acceptable variant, +## we prepare a dictionary of multiple possible references. +## The following script maps the whole input text of ITN span to the list of different conversions that occurred +## with this input anywhere in the Google TN Dataset. +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/get_multi_reference_vocab.py \ + --data_dir=${CORPUS_DIR} \ + --out_filename=${CORPUS_DIR}/reference_vocab.txt + +## Filter some errors from the obtained multi-reference vocabulary +grep -P "[\d] m[\t]" ${CORPUS_DIR}/reference_vocab.txt | grep -v -P "^MEASURE" | grep -v -P "^MONEY" > ${CORPUS_DIR}/reference_vocab.bad1 +grep -P "[\d] a[\t]" ${CORPUS_DIR}/reference_vocab.txt | grep -v -P "^MEASURE" > ${CORPUS_DIR}/reference_vocab.bad2 +grep -P "[\d] b[\t]" ${CORPUS_DIR}/reference_vocab.txt | grep -v -P "^MEASURE" | grep -v -P "^MONEY" > ${CORPUS_DIR}/reference_vocab.bad3 +grep -P "[\d] i[\t]" ${CORPUS_DIR}/reference_vocab.txt | grep -v -P "^MEASURE" > ${CORPUS_DIR}/reference_vocab.bad4 +grep -P "[\d] i\-[\t]" ${CORPUS_DIR}/reference_vocab.txt > ${CORPUS_DIR}/reference_vocab.bad5 +grep -P "[\d] us[\t]" ${CORPUS_DIR}/reference_vocab.txt | grep -v -P "^MONEY" | grep -v -P "TELEPHONE" > ${CORPUS_DIR}/reference_vocab.bad6 +grep -P "[\d] u\.s\.[\t]" ${CORPUS_DIR}/reference_vocab.txt | grep -v -P "^MEASURE" | grep -v -P "^MONEY" > ${CORPUS_DIR}/reference_vocab.bad7 +cat ${CORPUS_DIR}/reference_vocab.bad* > ${CORPUS_DIR}/reference_vocab.bad +grep -Fvxf ${CORPUS_DIR}/reference_vocab.bad ${CORPUS_DIR}/reference_vocab.txt > ${CORPUS_DIR}/reference_vocab.filt + +## Generate the "default" test data for Google TN Dataset (same as usually used in papers) +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/prepare_corpora_for_testing.py \ + --data_dir=${CORPUS_DIR}/test \ + --reference_vocab=${CORPUS_DIR}/reference_vocab.filt \ + --output_file=${WORK_DIR}/datasets/test.labeled \ + --sampling_count=-1 +awk 'BEGIN {FS="\t"}{print $1}' < ${WORK_DIR}/datasets/test.labeled > ${WORK_DIR}/datasets/test.input +awk 'BEGIN {FS="\t"}{print $1 "\t" $3}' < ${WORK_DIR}/datasets/test.labeled > ${WORK_DIR}/datasets/test.input_ref + +## Generate the "hard" test data for Google TN Dataset. +## We try to sample at least 1000 examples per semiotic class. +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/prepare_corpora_for_testing.py \ + --data_dir=${CORPUS_DIR}/test_full \ + --reference_vocab=${CORPUS_DIR}/reference_vocab.txt \ + --output_file=${WORK_DIR}/datasets/test1000.labeled \ + --sampling_count=1000 +awk 'BEGIN {FS="\t"}{print $1}' < ${WORK_DIR}/datasets/test1000.labeled > ${WORK_DIR}/datasets/test1000.input +awk 'BEGIN {FS="\t"}{print $1 "\t" $3}' < ${WORK_DIR}/datasets/test1000.labeled > ${WORK_DIR}/datasets/test1000.input_ref + +## After we have train a model, we can run inference and evaluation like below + +##export TOKENIZERS_PARALLELISM=false +##PRETRAINED_MODEL=./nemo_experiments/training.nemo +### run inference on default Google Dataset test +#python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py \ +# pretrained_model=${PRETRAINED_MODEL} \ +# inference.from_file=${DATA_PATH}/test.input \ +# inference.out_file=./final_test.output \ +# model.max_sequence_len=1024 #\ +# inference.batch_size=128 +# +### run inference on "hard" test (sample of at least 1000 examples of each semiotic class) +#python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py \ +# pretrained_model=${PRETRAINED_MODEL} \ +# inference.from_file=${DATA_PATH}/test1000.input \ +# inference.out_file=./final_test1000.output \ +# model.max_sequence_len=1024 \ +# inference.batch_size=128 +# +### compare inference results to the reference +#python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/eval.py \ +# --reference_file=${DATA_PATH}/test.labeled \ +# --inference_file=final_test.output \ +# > final_test.report +# +#python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/eval.py \ +# --reference_file=${DATA_PATH}/test1000.labeled \ +# --inference_file=final_test1000.output \ +# --print_other_errors \ +# > final_test1000.report +# +### compare inference results to the reference, get separate report per semiotic class +#python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/eval_per_class.py \ +# --reference_file=${DATA_PATH}/test.labeled \ +# --inference_file=final_test.output \ +# --output_file=per_class.report +# +#python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/eval_per_class.py \ +# --reference_file=${DATA_PATH}/test1000.labeled \ +# --inference_file=final_test1000.output \ +# --output_file=per_class1000.report diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/prepare_dataset_ru.sh b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/prepare_dataset_ru.sh new file mode 100644 index 0000000..b16fb00 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/prepare_dataset_ru.sh @@ -0,0 +1,331 @@ +#!/bin/bash + +## This bash-script reproduces the pipeline of data preparation for the Thutmose Tagger model (tagger-based ITN model) + +## In order to use it, you need: +## 1. install and compile GIZA++ +## git clone https://github.com/moses-smt/giza-pp.git giza-pp +## cd giza-pp +## make +## 2. Download Google TN Dataset +## https://www.kaggle.com/richardwilliamsproat/text-normalization-for-english-russian-and-polish +## 3. install NeMo +## git clone https://github.com/NVIDIA/NeMo +## 4. Specify the following paths + +## path to NeMo repository, e.g. /home/user/nemo +NEMO_PATH= +## path to GIZA++, e.g. /home/user/giza-pp/GIZA++-v2 +GIZA_BIN_DIR= +## path to MCKLS_BINARY, e.g. /home/user/giza-pp/mkcls-v2/mkcls +MCKLS_BINARY= +## initial unzipped Google Text Normalization Dataset, e.g. /home/user/data/ru_with_types +GOOGLE_CORPUS_DIR= + + +## corpus language +CORPUS_LANG=ru + +WORK_DIR=`pwd` # directory from which this bash-script is run +echo "Working directory:" ${WORK_DIR} + +## names of working subfolders +CORPUS_DIR=${WORK_DIR}/corpus +ALIGNMENT_DIR=${WORK_DIR}/alignment + +## read the data and split it into train, dev, test +## files in test folder is truncated to match the default test dataset from the paper on Google TN Dataset +## option --add_test_full=true creates additional test_full folder, which is not truncated +python ${NEMO_PATH}/examples/nlp/duplex_text_normalization/data/data_split.py \ + --data_dir=${GOOGLE_CORPUS_DIR} \ + --output_dir=${CORPUS_DIR}_tmp \ + --lang=${CORPUS_LANG} \ + --add_test_full + +## we need only output-00099-of-00100.tsv as the final test data +rm ${CORPUS_DIR}_tmp/test/output-00095-of-00100.tsv ${CORPUS_DIR}_tmp/test/output-00096-of-00100.tsv ${CORPUS_DIR}_tmp/test/output-00097-of-00100.tsv ${CORPUS_DIR}_tmp/test/output-00098-of-00100.tsv + +## apply a blacklist of erroneous conversions to remove the sentences containing them from the corpus +mkdir ${CORPUS_DIR} +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/filter_sentences_with_errors.py \ + --data_dir=${CORPUS_DIR}_tmp \ + --out_dir=${CORPUS_DIR} \ + --lang=${CORPUS_LANG} \ + --errors_vocab_filename=${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/corpus_errors.${CORPUS_LANG} + +rm -r ${CORPUS_DIR}_tmp + +## This script extracts all unique ITN phrase-pairs from the Google TN dataset, tokenizes them and stores in separate +## folders for each semiotic class. In each folder we generate a bash script for running the alignment. +mkdir ${ALIGNMENT_DIR} +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_for_alignment.py \ + --data_dir=${CORPUS_DIR} \ + --out_dir=${ALIGNMENT_DIR} \ + --giza_dir=${GIZA_BIN_DIR} \ + --mckls_binary=${MCKLS_BINARY} \ + --lang=${CORPUS_LANG} + +##exclude punct class +rm -r ${ALIGNMENT_DIR}/punct + +## for better GIZA++ alignments mix in examples from other classes +## they will append to the tail of "src" and "dst" files and they will not have corresponding freqs in "freq" file +## all these appended lines will be skipped in the get_replacement_vocab step +for fn in "src" "dst" +do + cat ${ALIGNMENT_DIR}/money/${fn} \ + ${ALIGNMENT_DIR}/cardinal/${fn} \ + ${ALIGNMENT_DIR}/decimal/${fn} \ + ${ALIGNMENT_DIR}/fraction/${fn} \ + ${ALIGNMENT_DIR}/measure/${fn} > ${ALIGNMENT_DIR}/money/${fn}.new + + cat ${ALIGNMENT_DIR}/time/${fn} \ + ${ALIGNMENT_DIR}/cardinal/${fn} > ${ALIGNMENT_DIR}/time/${fn}.new + + cat ${ALIGNMENT_DIR}/measure/${fn} \ + ${ALIGNMENT_DIR}/cardinal/${fn} \ + ${ALIGNMENT_DIR}/decimal/${fn} \ + ${ALIGNMENT_DIR}/fraction/${fn} \ + ${ALIGNMENT_DIR}/money/${fn} > ${ALIGNMENT_DIR}/measure/${fn}.new + + cat ${ALIGNMENT_DIR}/fraction/${fn} \ + ${ALIGNMENT_DIR}/cardinal/${fn} \ + ${ALIGNMENT_DIR}/measure/${fn} \ + ${ALIGNMENT_DIR}/money/${fn} > ${ALIGNMENT_DIR}/fraction/${fn}.new + + cat ${ALIGNMENT_DIR}/decimal/${fn} \ + ${ALIGNMENT_DIR}/cardinal/${fn} \ + ${ALIGNMENT_DIR}/measure/${fn} \ + ${ALIGNMENT_DIR}/money/${fn} > ${ALIGNMENT_DIR}/decimal/${fn}.new +done + +for c in "decimal" "fraction" "measure" "time" "money" +do + mv ${ALIGNMENT_DIR}/${c}/src.new ${ALIGNMENT_DIR}/${c}/src + mv ${ALIGNMENT_DIR}/${c}/dst.new ${ALIGNMENT_DIR}/${c}/dst +done + +for subfolder in ${ALIGNMENT_DIR}/* +do + echo ${subfolder} + chmod +x ${subfolder}/run.sh +done + +## Run alignment using multiple processes +for subfolder in ${ALIGNMENT_DIR}/* +do + cd ${subfolder} + ./run.sh & +done +wait + +## Extract final alignments for each semiotic class +for subfolder in ${ALIGNMENT_DIR}/* +do + python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/extract_giza_alignments.py \ + --mode=itn \ + --giza_dir=${subfolder} \ + --out_filename=itn.out \ + --giza_suffix=A3.final \ + --lang=ru & +done +wait + +## add column with frequencies of phrase pairs in the corpus +for subfolder in ${ALIGNMENT_DIR}/* +do + paste -d"\t" ${subfolder}/freq ${subfolder}/itn.out > ${subfolder}/itn.out2 + awk 'BEGIN {FS="\t"} match($3, " "){print $0}' < ${subfolder}/itn.out2 | sort -rn > ${subfolder}/itn.debug +done + +## loop through the obtained alignments and collect vocabularies (for each semiotic class) +## of all possible replacement fragments (aka tags) +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_after_alignment.py \ + --mode=get_replacement_vocab \ + --giza_dir=${ALIGNMENT_DIR} \ + --alignment_filename=itn.out2 \ + --data_dir="" \ + --vocab_filename=${WORK_DIR}/replacement_vocab_full.txt \ + --out_filename="" \ + --lang=${CORPUS_LANG} + +## Here we put some voluntary thresholds on how many tags we take. +## Tags with low frequencies are likely to be derived from sporadic alignment mistakes +head -n 57 ${WORK_DIR}/replacement_vocab_full.txt.verbatim > ${WORK_DIR}/replacement_vocab_verbatim.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.time | head -n 106 > ${WORK_DIR}/replacement_vocab_time.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.telephone | head -n 134 > ${WORK_DIR}/replacement_vocab_telephone.txt +head -n 763 ${WORK_DIR}/replacement_vocab_full.txt.plain > ${WORK_DIR}/replacement_vocab_plain.txt +head -n 1397 ${WORK_DIR}/replacement_vocab_full.txt.ordinal > ${WORK_DIR}/replacement_vocab_ordinal.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.money | grep -v '0\$' | head -n 294 > ${WORK_DIR}/replacement_vocab_money.txt +grep -v "0__" ${WORK_DIR}/replacement_vocab_full.txt.measure | head -n 508 > ${WORK_DIR}/replacement_vocab_measure.txt +cp ${WORK_DIR}/replacement_vocab_full.txt.letters ${WORK_DIR}/replacement_vocab_letters.txt +head -n 163 ${WORK_DIR}/replacement_vocab_full.txt.fraction > ${WORK_DIR}/replacement_vocab_fraction.txt +head -n 262 ${WORK_DIR}/replacement_vocab_full.txt.electronic > ${WORK_DIR}/replacement_vocab_electronic.txt +cp ${WORK_DIR}/replacement_vocab_full.txt.digit ${WORK_DIR}/replacement_vocab_digit.txt +head -n 270 ${WORK_DIR}/replacement_vocab_full.txt.decimal > ${WORK_DIR}/replacement_vocab_decimal.txt +head -n 271 ${WORK_DIR}/replacement_vocab_full.txt.date > ${WORK_DIR}/replacement_vocab_date.txt +head -n 455 ${WORK_DIR}/replacement_vocab_full.txt.cardinal > ${WORK_DIR}/replacement_vocab_cardinal.txt + +## concatenate all tags in a single vocabulary (repetitions don't matter) +cat ${WORK_DIR}/replacement_vocab_cardinal.txt \ + ${WORK_DIR}/replacement_vocab_date.txt \ + ${WORK_DIR}/replacement_vocab_decimal.txt \ + ${WORK_DIR}/replacement_vocab_digit.txt \ + ${WORK_DIR}/replacement_vocab_electronic.txt \ + ${WORK_DIR}/replacement_vocab_fraction.txt \ + ${WORK_DIR}/replacement_vocab_letters.txt \ + ${WORK_DIR}/replacement_vocab_measure.txt \ + ${WORK_DIR}/replacement_vocab_money.txt \ + ${WORK_DIR}/replacement_vocab_ordinal.txt \ + ${WORK_DIR}/replacement_vocab_plain.txt \ + ${WORK_DIR}/replacement_vocab_telephone.txt \ + ${WORK_DIR}/replacement_vocab_time.txt \ + ${WORK_DIR}/replacement_vocab_verbatim.txt > ${WORK_DIR}/replacement_vocab.select.txt + +## Here we loop once again through the alignments and discard those examples that are not fully covered +## by our restricted tag vocabulary +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_after_alignment.py \ + --mode=filter_by_vocab \ + --giza_dir=${ALIGNMENT_DIR} \ + --alignment_filename=itn.out2 \ + --data_dir="" \ + --vocab_filename=${WORK_DIR}/replacement_vocab.select.txt \ + --out_filename=itn.select.out \ + --lang=${CORPUS_LANG} + +## We now have a large collection of ITN phrase conversions that we know how to tag. +## Once again we loop through the Google TN dataset and create tag-labeled datasets, containing full sentences. +## If a sentence contains something that we do not know how to tag, we discard the whole sentence. +for subset in "train" "dev" +do + python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/prepare_corpora_after_alignment.py \ + --mode=get_labeled_corpus \ + --giza_dir=${ALIGNMENT_DIR} \ + --alignment_filename=itn.select.out \ + --data_dir=${CORPUS_DIR}/${subset} \ + --vocab_filename="" \ + --out_filename=${CORPUS_DIR}/${subset}.labeled \ + --lang=${CORPUS_LANG} +done + +## Loop through the obtained datasets and get final tag vocabulary +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/get_label_vocab.py \ + --train_filename=${CORPUS_DIR}/train.labeled \ + --dev_filename=${CORPUS_DIR}/dev.labeled \ + --out_filename=${CORPUS_DIR}/label_map.txt + +## The full dataset is very large, while some tags occur rarely. So we can try some sampling. +## Here we try to sample sentences that contain at least one tag that we have not yet seen at least for N times +## This script will split the input dataset into two parts: sampled sentences and the rest sentences. +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/sample_each_label.py \ + --filename=${CORPUS_DIR}/dev.labeled \ + --max_count=10 + +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/dataset_preparation/sample_each_label.py \ + --filename=${CORPUS_DIR}/train.labeled \ + --max_count=500 + +## Here we create final train and dev datasets, mixing sampled sentences and some quantity of the rest sentences. +mkdir ${WORK_DIR}/datasets +DATASET=${WORK_DIR}/datasets/itn_sample500k_rest1500k_select_vocab +mkdir $DATASET +cat ${CORPUS_DIR}/train.labeled.sample_500 > ${DATASET}/train.tsv +head -n 1500000 ${CORPUS_DIR}/train.labeled.rest_500 >> ${DATASET}/train.tsv +cat ${CORPUS_DIR}/dev.labeled.sample_10 > ${DATASET}/valid.tsv +head -n 12000 ${CORPUS_DIR}/dev.labeled.rest_10 >> ${DATASET}/valid.tsv +cp ${DATASET}/valid.tsv ${DATASET}/test.tsv + +echo "CARDINAL" > ${CORPUS_DIR}/semiotic_classes.txt +echo "DATE" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "DECIMAL" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "DIGIT" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "ELECTRONIC" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "FRACTION" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "LETTERS" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "MEASURE" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "MONEY" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "ORDINAL" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "PLAIN" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "PUNCT" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "TELEPHONE" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "TIME" >> ${CORPUS_DIR}/semiotic_classes.txt +echo "VERBATIM" >> ${CORPUS_DIR}/semiotic_classes.txt + +cp ${CORPUS_DIR}/label_map.txt ${WORK_DIR}/datasets/label_map.txt +cp ${CORPUS_DIR}/semiotic_classes.txt ${WORK_DIR}/datasets/semiotic_classes.txt + +## Now all data is ready to train the model. + +## We also prepare the test data to test the model after the training. +## The test data knows nothing about alignment, it only contains input sentences and references. + +## The original test data from Google TN Dataset contains a single reference for each ITN span. +## In order to take into account more than one acceptable variant, +## we prepare a dictionary of multiple possible references. +## The following script maps the whole input text of ITN span to the list of different conversions that occurred +## with this input anywhere in the Google TN Dataset. +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/get_multi_reference_vocab.py \ + --data_dir=${CORPUS_DIR} \ + --out_filename=${CORPUS_DIR}/reference_vocab.txt + +## Generate the "default" test data for Google TN Dataset (same as usually used in papers) +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/prepare_corpora_for_testing.py \ + --data_dir=${CORPUS_DIR}/test \ + --reference_vocab=${CORPUS_DIR}/reference_vocab.txt \ + --output_file=${WORK_DIR}/datasets/test.labeled \ + --sampling_count=-1 +awk 'BEGIN {FS="\t"}{print $1}' < ${WORK_DIR}/datasets/test.labeled > ${WORK_DIR}/datasets/test.input +awk 'BEGIN {FS="\t"}{print $1 "\t" $3}' < ${WORK_DIR}/datasets/test.labeled > ${WORK_DIR}/datasets/test.input_ref + +## Generate the "hard" test data for Google TN Dataset. +## We try to sample at least 1000 examples per semiotic class. +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/prepare_corpora_for_testing.py \ + --data_dir=${CORPUS_DIR}/test_full \ + --reference_vocab=${CORPUS_DIR}/reference_vocab.txt \ + --output_file=${WORK_DIR}/datasets/test1000.labeled \ + --sampling_count=1000 +awk 'BEGIN {FS="\t"}{print $1}' < ${WORK_DIR}/datasets/test1000.labeled > ${WORK_DIR}/datasets/test1000.input +awk 'BEGIN {FS="\t"}{print $1 "\t" $3}' < ${WORK_DIR}/datasets/test1000.labeled > ${WORK_DIR}/datasets/test1000.input_ref + +## After we have train a model, we can run inference and evaluation like below + +##export TOKENIZERS_PARALLELISM=false +##PRETRAINED_MODEL=./nemo_experiments/training.nemo +### run inference on default Google Dataset test +#python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py \ +# pretrained_model=${PRETRAINED_MODEL} \ +# inference.from_file=${DATA_PATH}/test.input \ +# inference.out_file=./final_test.output \ +# model.max_sequence_len=1024 #\ +# inference.batch_size=128 +# +### run inference on "hard" test (sample of at least 1000 examples of each semiotic class) +#python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py \ +# pretrained_model=${PRETRAINED_MODEL} \ +# inference.from_file=${DATA_PATH}/test1000.input \ +# inference.out_file=./final_test1000.output \ +# model.max_sequence_len=1024 \ +# inference.batch_size=128 +# +### compare inference results to the reference +#python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/eval.py \ +# --reference_file=${DATA_PATH}/test.labeled \ +# --inference_file=final_test.output \ +# > final_test.report +# +#python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/eval.py \ +# --reference_file=${DATA_PATH}/test1000.labeled \ +# --inference_file=final_test1000.output \ +# --print_other_errors \ +# > final_test1000.report +# +### compare inference results to the reference, get separate report per semiotic class +#python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/eval_per_class.py \ +# --reference_file=${DATA_PATH}/test.labeled \ +# --inference_file=final_test.output \ +# --output_file=per_class.report +# +#python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/eval_per_class.py \ +# --reference_file=${DATA_PATH}/test1000.labeled \ +# --inference_file=final_test1000.output \ +# --output_file=per_class1000.report diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/readme.txt b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/readme.txt new file mode 100644 index 0000000..e439df0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/readme.txt @@ -0,0 +1,7 @@ +Thuthmose-tagger is a single-pass model for inverse text normalization (ITN). + +prepare_dataset_en.sh - English data preparation +prepare_dataset_ru.sh - Russian data preparation +normalization_as_tagging_train.py - Training a model +run_infer.sh - Inference and evaluation + diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/run_infer.sh b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/run_infer.sh new file mode 100644 index 0000000..5c9d70b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/text_normalization_as_tagging/run_infer.sh @@ -0,0 +1,44 @@ +#!/bin/bash + +## This bash-script demonstrates how to run inference and evaluation for the Thutmose Tagger model (tagger-based ITN model) + +## In order to use it, you need: +## 1. install NeMo +## git clone https://github.com/NVIDIA/NeMo +## 2. Specify the following paths + +## path to NeMo repository, e.g. /home/user/nemo +NEMO_PATH= + +## name or local path to pretrained model, e.g. ./nemo_experiments/training.nemo +PRETRAINED_MODEL= + +## path to input and reference files +# (see the last steps in examples/nlp/text_normalization_as_tagging/prepare_dataset_en.sh, +# starting from "python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/get_multi_reference_vocab.py" +#) +INPUT_FILE= +REFERENCE_FILE= + + +export TOKENIZERS_PARALLELISM=false + +### run inference on default Google Dataset test +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py \ + pretrained_model=${PRETRAINED_MODEL} \ + inference.from_file=${INPUT_FILE} \ + inference.out_file=./final_test.output \ + model.max_sequence_len=1024 #\ + inference.batch_size=128 + +### compare inference results to the reference +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/eval.py \ + --reference_file=${REFERENCE_FILE} \ + --inference_file=final_test.output \ + > final_test.report + +### compare inference results to the reference, get separate report per semiotic class +python ${NEMO_PATH}/examples/nlp/text_normalization_as_tagging/evaluation/eval_per_class.py \ + --reference_file=${REFERENCE_FILE} \ + --inference_file=final_test.output \ + --output_file=per_class.report diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/conf/punctuation_capitalization_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/conf/punctuation_capitalization_config.yaml new file mode 100644 index 0000000..cc374f5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/conf/punctuation_capitalization_config.yaml @@ -0,0 +1,179 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Punctuation and capitalization model with pretrained BERT-like models + +pretrained_model: null # pretrained Punctuation and Capitalization model from list_available_models(), for example: +# punctuation_en_bert or punctuation_en_distilbert +# or your_model.nemo +trainer: + devices: 1 # the number of gpus, 0 for CPU + num_nodes: 1 + max_epochs: 3 + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + gradient_clip_val: 0.0 + precision: 16 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0 + accelerator: gpu + strategy: ddp + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + +exp_manager: + exp_dir: null # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: Punctuation_and_Capitalization # The name of your model + create_tensorboard_logger: true # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: true # Whether you want exp_manager to create a model checkpoint callback + +model: + class_labels: + punct_labels_file: punct_label_ids.csv + capit_labels_file: capit_label_ids.csv + + common_dataset_parameters: + pad_label: 'O' + ignore_extra_tokens: false + ignore_start_end: true + punct_label_ids: null + capit_label_ids: null + label_vocab_dir: null + + train_ds: + # Tarred dataset is recommended if all dataset cannot be loaded in memory. Use script + # `examples/nlp/token_classification/create_punctuation_capitalization_tarred_dataset.py` for tarred dataset + # creation. + use_tarred_dataset: false + # A path to directory where `tar_metadata_file` or `text_file` and `labels_file` are stored. + ds_item: ??? + + text_file: text_train.txt + labels_file: labels_train.txt + # Permutes batches every epoch + shuffle: true + num_samples: -1 + # A max number of source text tokens in a batch. Examples are sorted by number of tokens in a source text before + # batching. Examples which number of tokens do not differ much are added to the batch. This procedure reduces + # number of pad tokens in a batch. A number of examples in a batch varies: longer input sequences -> less + # examples in a batch. + tokens_in_batch: 15000 + max_seq_length: 512 + # Number of jobs for tokenization and labels encoding. If 0, then multiprocessing is not used. If null, + # number of jobs is equal to the number of CPU cores. + # WARNING: can cause deadlocks with tokenizers, which use multiprocessing (e.g. SentencePiece) + n_jobs: 0 + + # Path to tarred dataset metadata file. Required if tarred dataset is used. Metadata file is a JSON file which + # contains total number of batches in the dataset, a list of paths to tar files and paths to label vocabularies. + # Metadata file is create by script + # `examples/nlp/token_classification/create_punctuation_capitalization_tarred_dataset.py` + tar_metadata_file: null + # Controls batch shuffling in tarred dataset. `tar_shuffle_n` is a size of shuffled batch buffer. Mind that this + # shuffling only permutes batches and doesn't exchange samples between batches. Proper shuffling is turned on in + # regular dataset. + tar_shuffle_n: 1 + + validation_ds: + # if evaluation data is not in the model.train_ds.ds_item as the training data or multiple datasets are used for + # evaluation is needed, specify ds_item, otherwise by default model.train_ds.ds_item is used + # See `train_ds` section for more details on tarred dataset + use_tarred_dataset: false + # expected format: `[PATH_TO_DEV1,PATH_TO_DEV2]` OR `PATH_TO_DEV` (Note no space between the paths and square + # brackets) + ds_item: ??? + + text_file: text_dev.txt + labels_file: labels_dev.txt + shuffle: false + num_samples: -1 + # See comment above `model.train_ds.tokens_in_batch` parameter for explanation. + tokens_in_batch: 15000 + max_seq_length: 512 + # Number of jobs for tokenization and labels encoding. If 0, then multiprocessing is not used. If null, + # number of jobs is equal to the number of CPU cores. + # WARNING: can cause deadlocks with tokenizers, which use multiprocessing (e.g. SentencePiece) + n_jobs: 0 + + # For more details see `train_ds` section. + tar_metadata_file: null + + test_ds: + # if evaluation data is not in the model.train_ds.ds_item as the training data or multiple datasets are used for + # evaluation is needed, specify ds_item, otherwise by default model.train_ds.ds_item is used + # See `train_ds` section for more details on tarred dataset + use_tarred_dataset: false + ds_item: ??? # expected format: [PATH_TO_DEV1,PATH_TO_DEV2] (Note no space between the paths and square brackets) + + text_file: text_dev.txt + labels_file: labels_dev.txt + shuffle: false + num_samples: -1 + # See comment above `model.train_ds.tokens_in_batch` parameter for explanation. + tokens_in_batch: 15000 + max_seq_length: 512 + # Number of jobs for tokenization and labels encoding. If 0, then multiprocessing is not used. If null, + # number of jobs is equal to the number of CPU cores. + # WARNING: can cause deadlocks with tokenizers, which use multiprocessing (e.g. SentencePiece) + n_jobs: 0 + + # For more details see `train_ds` section. + tar_metadata_file: null + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + + language_model: + pretrained_model_name: bert-base-uncased + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + punct_head: + num_fc_layers: 1 + fc_dropout: 0.1 + activation: 'relu' + use_transformer_init: True + + capit_head: + num_fc_layers: 1 + fc_dropout: 0.1 + activation: 'relu' + use_transformer_init: true + + optim: + name: adam + lr: 1e-4 + weight_decay: 0.00 + + sched: + name: WarmupAnnealing + # Scheduler params + warmup_steps: null + warmup_ratio: 0.1 + last_epoch: -1 + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/conf/punctuation_capitalization_lexical_audio_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/conf/punctuation_capitalization_lexical_audio_config.yaml new file mode 100644 index 0000000..e727d22 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/conf/punctuation_capitalization_lexical_audio_config.yaml @@ -0,0 +1,230 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Punctuation and capitalization lexical audio model with pretrained BERT-like models and Encoder-Decoder-like models. +pretrained_model: null # pretrained Punctuation and Capitalization Lexical Audio model from list_available_models(), for example: +# +# or your_model.nemo +trainer: + devices: -1 # the number of gpus, 0 for CPU + num_nodes: 1 + max_epochs: 5 + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0 + accelerator: gpu + strategy: ddp + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, + # LR schedulers, apex, etc. + log_every_n_steps: 50 + +exp_manager: + exp_dir: null # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: Punctuation_and_Capitalization_Lexical_Audio # The name of your model + create_tensorboard_logger: true # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: true # Whether you want exp_manager to create a model checkpoint callback + checkpoint_callback_params: + save_top_k: 3 + monitor: "val_loss" + mode: "min" + save_best_model: true + resume_from_checkpoint: null + +model: + audio_encoder: + pretrained_model: stt_en_conformer_ctc_medium # You can choose any pretrained ASR model from list_available_models() of EncDecCTCModel. + freeze: + is_enabled: false # If set to True weights of audio encoder will not be updated during training. + d_model: 256 # Input dimension of MultiheadAttentionMechanism and PositionwiseFeedForward + d_ff: 1024 # Hidden dimension of PositionwiseFeedForward + num_layers: 4 # Number of additional Conformer layers + adapter: + enable: false # If set to True will enable adapters for audio encoder. + config: + # For more details see `nemo.collections.common.parts.LinearAdapter` class + in_features: -1 # Will be replaced with size of audio encoder + dim: 128 # Hidden dimension of the feed forward network. + activation: 'swish' # Str name for an activation function. + fusion: + num_layers: 4 # Number of layers to use in fusion + num_attention_heads: 4 # Number of attention heads to use in fusion + inner_size: 2048 # Fusion inner size + + class_labels: + punct_labels_file: punct_label_ids.txt + capit_labels_file: capit_label_ids.txt + + common_dataset_parameters: + pad_label: 'O' + ignore_extra_tokens: false + ignore_start_end: true + punct_label_ids: null + capit_label_ids: null + label_vocab_dir: null + + train_ds: + # Tarred dataset is recommended if all dataset cannot be loaded in memory. Use script + # `examples/nlp/token_classification/create_punctuation_capitalization_tarred_dataset.py` for tarred dataset + # creation. + use_tarred_dataset: false + + # A path to directory where `tar_metadata_file` or `text_file` and `labels_file` and `audio_file` are stored. + ds_item: ??? + text_file: text_train.txt + labels_file: labels_train.txt + audio_file: audio_train.txt + + use_audio: true # Has to be set to true to use it for lexical audio model. + use_bucketing: true # If set to true batches will be sorted by length of audios and packed in batches limited by `tokens_in_batch`. Otherwise, provide `batch_size` parameter. + # If set to true audios will be loaded to memory during __init__ call of `BertPunctuationCapitalizationDataset`, consumes more RAM. + # Otherwise, audios will be loaded during `collate_fn` call of `BertPunctuationCapitalizationDataset`. + preload_audios: true + + # A max number of source text tokens in a batch. Examples are sorted by number of tokens in a source text before + # batching. Examples which number of tokens do not differ much are added to the batch. This procedure reduces + # number of pad tokens in a batch. A number of examples in a batch varies: longer input sequences -> less + # examples in a batch. + tokens_in_batch: 2048 + max_seq_length: 512 + + sample_rate: 16000 # Target sample rate of audios can be used for downsampling or upsamling. + num_workers: 0 + + # Number of jobs for tokenization and labels encoding. If 0, then multiprocessing is not used. If null, + # number of jobs is equal to the number of CPU cores. + # WARNING: can cause deadlocks with tokenizers, which use multiprocessing (e.g. SentencePiece) + n_jobs: 0 + + # Path to tarred dataset metadata file. Required if tarred dataset is used. Metadata file is a JSON file which + # contains total number of batches in the dataset, a list of paths to tar files and paths to label vocabularies. + # Metadata file is create by script + # `examples/nlp/token_classification/create_punctuation_capitalization_tarred_dataset.py` + tar_metadata_file: null + # Controls batch shuffling in tarred dataset. `tar_shuffle_n` is a size of shuffled batch buffer. Mind that this + # shuffling only permutes batches and doesn't exchange samples between batches. Proper shuffling is turned on in + # regular dataset. + tar_shuffle_n: 1 + + validation_ds: + # if evaluation data is not in the model.train_ds.ds_item as the training data or multiple datasets are used for + # evaluation is needed, specify ds_item, otherwise by default model.train_ds.ds_item is used + # See `train_ds` section for more details on tarred dataset + use_tarred_dataset: false + # expected format: `[PATH_TO_DEV1,PATH_TO_DEV2]` OR `PATH_TO_DEV` (Note no space between the paths and square + # brackets) + ds_item: ??? + + text_file: text_dev.txt + labels_file: labels_dev.txt + audio_file: audio_dev.txt + + use_audio: true + use_bucketing: false + preload_audios: false + + shuffle: false + num_samples: -1 + batch_size: 32 + # Number of jobs for tokenization and labels encoding. If 0, then multiprocessing is not used. If null, + # number of jobs is equal to the number of CPU cores. + # WARNING: can cause deadlocks with tokenizers, which use multiprocessing (e.g. SentencePiece) + n_jobs: 0 + + # For more details see `train_ds` section. + tar_metadata_file: null + + sample_rate: 16000 + num_workers: 0 + + test_ds: + # if evaluation data is not in the model.train_ds.ds_item as the training data or multiple datasets are used for + # evaluation is needed, specify ds_item, otherwise by default model.train_ds.ds_item is used + # See `train_ds` section for more details on tarred dataset + use_tarred_dataset: false + # expected format: `[PATH_TO_DEV1,PATH_TO_DEV2]` OR `PATH_TO_DEV` (Note no space between the paths and square + # brackets) + ds_item: ??? + + text_file: text_dev.txt + labels_file: labels_dev.txt + audio_file: audio_dev.txt + + use_audio: true + use_bucketing: false + preload_audios: false + + shuffle: false + num_samples: -1 + batch_size: 32 + # Number of jobs for tokenization and labels encoding. If 0, then multiprocessing is not used. If null, + # number of jobs is equal to the number of CPU cores. + # WARNING: can cause deadlocks with tokenizers, which use multiprocessing (e.g. SentencePiece) + n_jobs: 0 + + # For more details see `train_ds` section. + tar_metadata_file: null + + sample_rate: 16000 + num_workers: 0 + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + + language_model: + pretrained_model_name: bert-base-uncased + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + punct_head: + num_fc_layers: 1 + fc_dropout: 0.1 + activation: 'relu' + use_transformer_init: True + + capit_head: + num_fc_layers: 1 + fc_dropout: 0.1 + activation: 'relu' + use_transformer_init: true + + optim: + name: adam + lr: 1e-4 + weight_decay: 0.00 + + sched: + name: WarmupAnnealing + # Scheduler params + warmup_steps: null + warmup_ratio: 0.1 + last_epoch: -1 + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + +hydra: + run: + dir: . + job_logging: + root: + handlers: null \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/conf/token_classification_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/conf/token_classification_config.yaml new file mode 100644 index 0000000..05024c7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/conf/token_classification_config.yaml @@ -0,0 +1,117 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Token Classification tasks (for example, Named Entity Recognition) with pretrained BERT-like models + +pretrained_model: null # pretrained TokenClassification model from list_available_models() or path to a .nemo file, +# for example: ner_en_bert or your_model.nemo +trainer: + devices: 1 # the number of gpus, 0 for CPU + num_nodes: 1 + max_epochs: 5 + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + gradient_clip_val: 0.0 + precision: 16 # Should be set to 16 for O1 and O2, default is 16 as PT ignores it when am_level is O0 + accelerator: gpu + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + +exp_manager: + exp_dir: null # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: token_classification_model # The name of your model + create_tensorboard_logger: true # Whether you want exp_manager to create a tb logger + create_checkpoint_callback: true # Whether you want exp_manager to create a model checkpoint callback + +model: + label_ids: null # will be filled during training + class_labels: + class_labels_file: label_ids.csv # will be generated during training and saved in .nemo file + dataset: + data_dir: ??? # /path/to/data + class_balancing: null # choose from [null, weighted_loss]. Weighted_loss enables the weighted class balancing of the loss, may be used for handling unbalanced classes + max_seq_length: 128 + pad_label: 'O' + ignore_extra_tokens: false + ignore_start_end: false + use_cache: false + # shared among dataloaders + num_workers: 2 + pin_memory: false + drop_last: false + + train_ds: + text_file: text_train.txt + labels_file: labels_train.txt + shuffle: true + num_samples: -1 + batch_size: 64 + + validation_ds: + text_file: text_dev.txt + labels_file: labels_dev.txt + shuffle: false + num_samples: -1 + batch_size: 64 + + test_ds: + text_file: text_dev.txt + labels_file: labels_dev.txt + shuffle: false + num_samples: -1 + batch_size: 64 + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + + language_model: + pretrained_model_name: bert-base-uncased + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + + head: + num_fc_layers: 2 + fc_dropout: 0.5 + activation: 'relu' + use_transformer_init: True + + optim: + name: adam + lr: 5e-5 + weight_decay: 0.00 + + sched: + name: WarmupAnnealing + # Scheduler params + warmup_steps: null + warmup_ratio: 0.1 + last_epoch: -1 + + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + +hydra: + run: + dir: . + job_logging: + root: + handlers: null diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/create_punctuation_capitalization_tarred_dataset.py b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/create_punctuation_capitalization_tarred_dataset.py new file mode 100644 index 0000000..d74c2d8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/create_punctuation_capitalization_tarred_dataset.py @@ -0,0 +1,356 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import multiprocessing as mp +from pathlib import Path + +from nemo.collections.nlp.data.token_classification.punctuation_capitalization_tarred_dataset import ( + DEFAULT_CAPIT_LABEL_VOCAB_FILE_NAME, + DEFAULT_PUNCT_LABEL_VOCAB_FILE_NAME, + METADATA_CAPIT_LABEL_VOCAB_KEY, + METADATA_PUNCT_LABEL_VOCAB_KEY, + build_label_ids_from_list_of_labels, + check_labels_for_being_unique_before_building_label_ids, + check_tar_file_prefix, + create_tarred_dataset, +) + + +""" +A tarred dataset allows to train on large amounts without storing it all into memory simultaneously. In case of +punctuation and capitalization model, tarred dataset is a directory which contains metadata file, tar files with +batches, punct_label_vocab.csv and capit_label_vocab.csv files. + +A metadata file is a JSON file with 4 fields: 'num_batches', 'tar_files', 'punct_label_vocab_file', +'capit_label_vocab_file'. 'num_batches' (int) is a total number of batches in tarred dataset. 'tar_files' is a list of +paths to tar files relative to directory containing the metadata file. 'punct_label_vocab_file' and +'capit_label_vocab_file' are paths to .csv files containing all unique punctuation and capitalization labels. Each +label in these files is written in a separate line. The first labels in both files are equal and serve for padding and +as neutral labels. + +Every tar file contains objects written using `webdataset.TarWriter`. Each object is a dictionary with two items: +'__key__' and 'batch.pyd'. '__key__' is a name of a batch and 'batch.pyd' is a pickled dictionary which contains +'input_ids', 'subtokens_mask', 'punct_labels', 'capit_labels'. 'input_ids' is an array containing ids of source tokens, +'subtokens_mask' is a boolean array showing first tokens in words, 'punct_labels' and 'capit_labels' are arrays with +ids of labels. Metadata file should be passed to constructor of +`nemo.collections.nlp.data.token_classification.PunctuationCapitalizationTarredDataset` and the instance of +the class will handle iteration and constructing masks and token types for BERT model. + +Example of usage: + +python create_punctuation_capitalization_tarred_dataset.py \ + --text \ + --labels \ + --output_dir \ + --lines_per_dataset_fragment 10000 \ + --tokens_in_batch 8000 \ + --num_batches_per_tarfile 5 \ + --tokenizer_name char \ + --vocab_file +""" + + +def get_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + description=f"A tarred dataset allows to train on large amounts without storing it all into memory " + f"simultaneously. In case of punctuation and capitalization model, tarred dataset is a directory which " + f"contains metadata file, tar files with batches, {DEFAULT_PUNCT_LABEL_VOCAB_FILE_NAME} and " + f"{DEFAULT_CAPIT_LABEL_VOCAB_FILE_NAME} files. A metadata file is a JSON file with 4 fields: 'num_batches', " + f"'tar_files', '{METADATA_PUNCT_LABEL_VOCAB_KEY}', '{METADATA_CAPIT_LABEL_VOCAB_KEY}'. 'num_batches' (int) is " + f"a total number of batches in tarred dataset. 'tar_files' is a list of paths to tar files relative " + f"to directory containing the metadata file. '{METADATA_PUNCT_LABEL_VOCAB_KEY}' and " + f"'{METADATA_CAPIT_LABEL_VOCAB_KEY}' are paths to .csv files containing all unique punctuation and " + f"capitalization labels. Each label in these files is written in a separate line. The first labels in both " + f"files are equal and serve for padding and as neutral labels. Every tar file contains objects written " + f"using `webdataset.TarWriter`. Each object is a dictionary with two items: '__key__' and 'batch.pyd'. " + f"'__key__' is a name of a batch and 'batch.pyd' is a pickled dictionary which contains 'input_ids', " + f"'subtokens_mask', 'punct_labels', 'capit_labels'. 'input_ids' is an array containing ids of source tokens, " + f"'subtokens_mask' is a boolean array showing first tokens in words, 'punct_labels' and 'capit_labels' are " + f"arrays with ids of labels. Metadata file should be passed to constructor of " + "`nemo.collections.nlp.data.token_classification.PunctuationCapitalizationTarredDataset` and the instance of " + "the class will handle iteration and constructing masks and token types for BERT model.", + ) + parser.add_argument( + "--text", + "-t", + help="Path to source lowercased text without punctuation. Number of lines in `--text` file has to be equal " + "to number of lines in `--labels` file.", + type=Path, + required=True, + ) + parser.add_argument( + "--audio_file", + type=Path, + required=False, + help="Path to source file which contains paths to audio one path per line. " + "Number of lines in `--audio_file` has to be equal to number of lines in `--labels` file", + ) + parser.add_argument( + "--use_audio", + required=False, + action="store_true", + help="If set to `True` script creates lexical audio dataset which can be used with `PunctuationCapitalizationLexicalAudioModel`.", + ) + parser.add_argument( + "--sample_rate", + type=int, + required=False, + help="Target sample rate of audios. Can be used for downsampling or upsampling.", + ) + parser.add_argument( + "--labels", + "-L", + type=Path, + required=True, + help="Path to file with labels in the format described here " + "https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/punctuation_and_capitalization.html#" + "nemo-data-format . Number of lines in `--labels` file has to be equal to the number of lines in `--text` " + "file.", + ) + parser.add_argument( + "--output_dir", + "-o", + type=Path, + required=True, + help="Path to directory where .tar files, metadata file, label id files are stored.", + ) + parser.add_argument( + "--max_seq_length", + "-s", + type=int, + default=512, + help="Maximum number of subtokens in an input sequence. A source sequence which contain too many subtokens are " + "clipped to `--max_seq_length - 2` subtokens and then [CLS] token is prepended to the clipped sequence and " + "[SEP] token is appended to the clipped sequence. The clipping is performed via removal of subtokens in the " + "end of a source sequence.", + ) + parser.add_argument( + "--tokens_in_batch", + "-b", + type=int, + default=15000, + help="Maximum number of tokens in a batch including [CLS], [SEP], [UNK], and [PAD] tokens. Before packing into " + "batches source sequences are sorted by number of tokens in order to reduce number of pad tokens. So the " + "number of sequences in a batch may be different.", + ) + parser.add_argument( + "--lines_per_dataset_fragment", + type=int, + default=10 ** 6, + help="A number of lines processed by one worker during creation of tarred dataset. A worker tokenizes " + "`--lines_per_dataset_fragment` lines and keeps in RAM tokenized text labels before packing them into " + "batches. Reducing `--lines_per_dataset_fragment` leads to reducing of the amount of memory required by this " + "script.", + ) + parser.add_argument( + "--num_batches_per_tarfile", + type=int, + default=1000, + help="A number of batches saved in a tar file. If you increase `--num_batches_per_tarfile`, then there will " + "be less tar files in the dataset. There cannot be less then `--num_batches_per_tarfile` batches in a tar " + "file, and all excess batches are removed. Maximum number of discarded batches is " + "`--num_batches_per_tarfile - 1`.", + ) + parser.add_argument( + "--tokenizer_name", + "-T", + default="bert-base-uncased", + help="Name of the tokenizer used for tokenization of source sequences. Possible options are 'sentencepiece', " + "'word', 'char', HuggingFace tokenizers. For more options see function " + "`nemo.collections.nlp.modules.common.get_tokenizer`. The tokenizer has to have properties `cls_id`, " + "`pad_id`, `sep_id`, `unk_id`.", + ) + parser.add_argument( + "--tokenizer_model", "-m", type=Path, help="Path to tokenizer model required for 'sentencepiece' tokenizer." + ) + parser.add_argument( + "--vocab_file", + "-v", + type=Path, + help="Path to vocabulary file which can be used in 'word', 'char', and HuggingFace tokenizers.", + ) + parser.add_argument( + "--merges_file", "-M", type=Path, help="Path to merges file which can be used in HuggingFace tokenizers." + ) + parser.add_argument( + "--special_token_names", + "-n", + nargs="+", + help="Names of special tokens which may be passed to constructors of 'char', 'word', 'sentencepiece', and " + "HuggingFace tokenizers.", + ) + parser.add_argument( + "--special_token_values", + "-V", + nargs="+", + help="Values of special tokens which may be passed to constructors of 'char', 'word', 'sentencepiece', and " + "HuggingFace tokenizers.", + ) + parser.add_argument( + "--use_fast_tokenizer", "-f", action="store_true", help="Whether to use fast HuggingFace tokenizer." + ) + parser.add_argument( + "--pad_label", + "-P", + default='O', + help="Pad label both for punctuation and capitalization. This label is also is used for marking words which " + "do not need punctuation and capitalization. It is also a neutral label used for marking words which do " + "not require punctuation and capitalization.", + ) + punct = parser.add_mutually_exclusive_group(required=False) + punct.add_argument( + "--punct_labels", + "-p", + nargs="+", + help="All punctuation labels EXCEPT PAD LABEL. Punctuation labels are strings separated by spaces. " + "Alternatively you can use parameter `--punct_label_vocab_file`. If none of parameters `--punct_labels` " + "and `--punct_label_vocab_file` are provided, then punctuation label ids will be inferred from `--labels` " + "file.", + ) + punct.add_argument( + "--punct_label_vocab_file", + type=Path, + help="A path to file with punctuation labels. These labels include pad label. Pad label has to be the first " + "label in the file. Each label is written on separate line. Alternatively you can use `--punct_labels` " + "parameter. If none of parameters `--punct_labels` and `--punct_label_vocab_file` are provided, then " + "punctuation label ids will be inferred from `--labels` file.", + ) + capit = parser.add_mutually_exclusive_group(required=False) + capit.add_argument( + "--capit_labels", + "-c", + nargs="+", + help="All capitalization labels EXCEPT PAD LABEL. Capitalization labels are strings separated by spaces. " + "Alternatively you can use parameter `--capit_label_vocab_file`. If none of parameters `--capit_labels` " + "and `--capit_label_vocab_file` are provided, then capitalization label ids will be inferred from `--labels` " + "file.", + ) + capit.add_argument( + "--capit_label_vocab_file", + type=Path, + help="A path to file with capitalization labels. These labels include pad label. Pad label has to be the " + "first label in the file. Each label is written on separate line. Alternatively you can use `--capit_labels` " + "parameter. If none of parameters `--capit_labels` and `--capit_label_vocab_file` are provided, then " + "capitalization label ids will be inferred from `--labels` file.", + ) + parser.add_argument( + "--tar_file_prefix", + "-x", + default="punctuation_capitalization", + help="A string from which tar file names start. It can contain only characters 'A-Z', 'a-z', '0-9', '_', '-', " + "'.'.", + ) + parser.add_argument( + "--n_jobs", + "-j", + type=int, + default=mp.cpu_count(), + help="Number of workers for creating tarred dataset. By default it is equal to the number of CPU cores.", + ) + args = parser.parse_args() + for name in [ + "text", + "labels", + "output_dir", + "tokenizer_model", + "vocab_file", + "merges_file", + "punct_label_vocab_file", + "capit_label_vocab_file", + ]: + if getattr(args, name) is not None: + setattr(args, name, getattr(args, name).expanduser()) + if args.special_token_names is not None or args.special_token_values is not None: + if args.special_token_names is None: + parser.error( + "If you provide parameter `--special_token_values` you have to provide parameter " + "`--special_token_names`." + ) + if args.special_token_values is None: + parser.error( + "If you provide parameter `--special_token_names` you have to provide parameter " + "`--special_token_values`." + ) + if len(args.special_token_names) != len(args.special_token_values): + parser.error( + f"Parameters `--special_token_names` and `--special_token_values` have to have equal number of values " + f"whereas parameter `--special_token_names` has {len(args.special_token_names)} values and parameter " + f"`--special_token_values` has {len(args.special_token_values)} values." + ) + if len(set(args.special_token_names)) != len(args.special_token_names): + for i in range(len(args.special_token_names) - 1): + if args.special_token_names[i] in args.special_token_names[i + 1 :]: + parser.error( + f"Values of parameter `--special_token_names` has to be unique. Found duplicate value " + f"'{args.special_token_names[i]}'." + ) + if args.punct_labels is not None: + check_labels_for_being_unique_before_building_label_ids( + args.pad_label, args.punct_labels, '--pad_label', '--punct_labels', parser.error + ) + check_labels_for_being_unique_before_building_label_ids( + args.pad_label, args.capit_labels, '--pad_label', '--capit_labels', parser.error + ) + check_tar_file_prefix(args.tar_file_prefix, parser.error, '--tar_file_prefix') + return args + + +def main() -> None: + args = get_args() + if args.special_token_names is None: + special_tokens = None + else: + special_tokens = dict(zip(args.special_token_names, args.special_token_values)) + + if args.punct_labels is not None: + punct_label_ids = build_label_ids_from_list_of_labels(args.pad_label, args.punct_labels) + else: + punct_label_ids = None + + if args.capit_labels is not None: + capit_label_ids = build_label_ids_from_list_of_labels(args.pad_label, args.capit_labels) + else: + capit_label_ids = None + + create_tarred_dataset( + args.text, + args.labels, + args.output_dir, + args.max_seq_length, + args.tokens_in_batch, + args.lines_per_dataset_fragment, + args.num_batches_per_tarfile, + args.tokenizer_name, + tokenizer_model=args.tokenizer_model, + vocab_file=args.vocab_file, + merges_file=args.merges_file, + special_tokens=special_tokens, + use_fast_tokenizer=args.use_fast_tokenizer, + pad_label=args.pad_label, + punct_label_ids=punct_label_ids, + capit_label_ids=capit_label_ids, + punct_label_vocab_file=args.punct_label_vocab_file, + capit_label_vocab_file=args.capit_label_vocab_file, + tar_file_prefix=args.tar_file_prefix, + n_jobs=args.n_jobs, + audio_file=args.audio_file, + sample_rate=args.sample_rate, + use_audio=args.use_audio, + ) + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/get_libritts_data.py b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/get_libritts_data.py new file mode 100644 index 0000000..86a5d01 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/get_libritts_data.py @@ -0,0 +1,115 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This script downloads and unpacks LibriTTS data. And prepares it for punctuation and capitalization lexical audio model. +Data is being downloaded from www.openslr.org and then extracted via tar. +The script gathers text from every *.normalized.txt file inside of archive into single file with text and file with audio filepaths. +""" +import argparse +import glob +import os +import re +import shutil +import subprocess +import tarfile + +from tqdm import tqdm + +from nemo.collections.nlp.data.token_classification.token_classification_utils import create_text_and_labels +from nemo.utils import logging + +URL = { + 'train_clean_100': "https://www.openslr.org/resources/60/train-clean-100.tar.gz", + 'train_clean_360': "https://www.openslr.org/resources/60/train-clean-360.tar.gz", + 'train_other_500': "https://www.openslr.org/resources/60/train-other-500.tar.gz", + 'dev_clean': "https://www.openslr.org/resources/60/dev-clean.tar.gz", + 'dev_other': "https://www.openslr.org/resources/60/dev-other.tar.gz", + 'test_clean': "https://www.openslr.org/resources/60/test-clean.tar.gz", + 'test_other': "https://www.openslr.org/resources/60/test-other.tar.gz", +} + + +def __extract_file(filepath, data_dir): + try: + tar = tarfile.open(filepath) + tar.extractall(data_dir) + tar.close() + except Exception: + print(f"Error while extracting {filepath}. Already extracted?") + + +def __maybe_download_file(destination: str, source: str): + """ + Downloads source to destination if not exists. + If exists, skips download + Args: + destination: local filepath + source: url of resource + """ + source = URL[source] + if not os.path.exists(destination): + logging.info(f'Downloading {source} to {destination}') + subprocess.run(['wget', '-O', destination, source]) + return 1 + else: + logging.info(f'{destination} found. Skipping download') + return 0 + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description='Prepare LibriTTS dataset for punctuation capitalization lexical audio model training/evaluating.' + ) + parser.add_argument("--data_sets", default="dev_clean", type=str, help="List of subsets separated by comma") + parser.add_argument("--data_dir", required=True, type=str, help="Path to dir where data will be stored") + parser.add_argument( + "--clean", "-c", action="store_true", help="If set to True will delete all files except produced .txt and .wav" + ) + args = parser.parse_args() + + data_dir = args.data_dir + + if not os.path.exists(data_dir): + os.makedirs(data_dir) + + for subset in args.data_sets.split(','): + logging.info(f'Downloading {subset} subset') + if __maybe_download_file(data_dir + f'/{subset}.tar.gz', subset): + logging.info(f'Extracting {subset} subset') + __extract_file(data_dir + f'/{subset}.tar.gz', data_dir) + + logging.info(f'Processing data') + + splits = set([split.split('_')[0] for split in args.data_sets.split(',')]) + for split in splits: + os.makedirs(f'{data_dir}/audio/{split}', exist_ok=True) + with open(f'{data_dir}/{split}.txt', 'w') as text_data, open( + f'{data_dir}/audio_{split}.txt', 'w' + ) as audio_data: + for file in tqdm(glob.glob(f'{data_dir}/LibriTTS/{split}*/*/*/*.wav'), desc=f'Processing {split}'): + with open(file[:-4] + '.normalized.txt', 'r') as source_file: + lines = source_file.readlines() + text = lines[0] + text = re.sub(r"[^a-zA-Z\d,?!.']", ' ', text) + text = re.sub(' +', ' ', text) + shutil.copy(file.strip(), (f'{data_dir}/audio/{split}/' + file.split('/')[-1]).strip()) + text_data.write(text.strip() + "\n") + audio_data.write((f'{data_dir}/audio/{split}/' + file.split('/')[-1]).strip() + "\n") + create_text_and_labels(f'{data_dir}/', f'{data_dir}/{split}.txt') + logging.info(f'Processed {split} subset') + + if args.clean: + shutil.rmtree(f'{data_dir}/LibriTTS') + for tar in glob.glob(f'{data_dir}/**.tar.gz'): + os.remove(tar) diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/get_tatoeba_data.py b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/get_tatoeba_data.py new file mode 100644 index 0000000..6a4cd23 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/get_tatoeba_data.py @@ -0,0 +1,180 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import logging +import os +import random +import re +import subprocess + +from nemo.collections.nlp.data.token_classification.token_classification_utils import create_text_and_labels +from nemo.utils import logging + +URL = {'tatoeba': 'https://downloads.tatoeba.org/exports/sentences.csv'} + + +def __maybe_download_file(destination: str, source: str): + """ + Downloads source to destination if not exists. + If exists, skips download + Args: + destination: local filepath + source: url of resource + """ + source = URL[source] + if not os.path.exists(destination): + logging.info(f'Downloading {source} to {destination}') + subprocess.run(['wget', '-O', destination, source]) + else: + logging.info(f'{destination} found. Skipping download') + + +def __process_english_sentences( + in_file: str, out_file: str, percent_to_cut: float = 0, num_to_combine: int = 1, num_samples: int = -1 +): + """ + Extract English sentences from the Tatoeba dataset. + + Expected in_file format + that + contain letters and punctuation marks (,.?). + Chop and combine sentences. + Args: + in_file: local filepath to the tatoeba dataset. + Format: id [TAB] region_name [TAB] sentence, + for example: "1276\teng\tLet's try something.\n" + out_file: local filepath to the clean dataset + percent_to_cut: Percent of sentences to cut in the middle + to get examples of incomplete sentences. + This could be useful since ASR output not always + represents a complete sentence + num_to_combine: Number of sentences to combine into + a single example + num_samples: Number of samples in the final dataset + """ + if not os.path.exists(in_file): + raise FileNotFoundError(f'{in_file} not found.') + + in_file = open(in_file, 'r') + out_file = open(out_file, 'w') + lines_to_combine = [] + samples_count = 0 + + for line in in_file: + line = line.split('\t') + # use only English sentences + if line[1] == 'eng': + line = line[2].strip() + if re.match("^[A-Z][A-Za-z.,'?\s]+$", line): # nopep8 + # chop some sentences in the middle + if percent_to_cut > 0: + line = line.split() + if random.random() < percent_to_cut: + line = line[: len(line) // 2] + line = ' '.join(line) + + # combine multiple sentences into a single example + # to make it harder for the model to learn eos punctuation + if len(lines_to_combine) >= num_to_combine: + if samples_count == num_samples: + return + out_file.write(' '.join(lines_to_combine) + '\n') + lines_to_combine = [] + samples_count += 1 + lines_to_combine.append(line) + + if len(lines_to_combine) > 0 and (samples_count < num_samples or num_samples < 0): + out_file.write(' '.join(lines_to_combine) + '\n') + + +def __split_into_train_dev(in_file: str, train_file: str, dev_file: str, percent_dev: float): + """ + Create train and dev split of the dataset. + Args: + in_file: local filepath to the dataset + train_file: local filepath to the train dataset + dev_file: local filepath to the dev dataset + percent_dev: Percent of the sentences in the dev set + """ + if not os.path.exists(in_file): + raise FileNotFoundError(f'{in_file} not found.') + + lines = open(in_file, 'r').readlines() + train_file = open(train_file, 'w') + dev_file = open(dev_file, 'w') + + dev_size = int(len(lines) * percent_dev) + train_file.write(' '.join(lines[:-dev_size])) + dev_file.write(' '.join(lines[-dev_size:])) + + +def __delete_file(file_to_del: str): + """ + Deletes the file + Args: + file_to_del: local filepath to the file to delete + """ + if os.path.exists(file_to_del): + os.remove(file_to_del) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Prepare tatoeba dataset') + parser.add_argument("--data_dir", required=True, type=str) + parser.add_argument("--dataset", default='tatoeba', type=str) + parser.add_argument("--num_samples", default=-1, type=int, help='-1 to use the whole dataset') + parser.add_argument("--percent_to_cut", default=0, type=float, help='Percent of sentences to cut in the middle') + parser.add_argument( + "--num_lines_to_combine", default=1, type=int, help='Number of lines to combine into single example' + ) + parser.add_argument("--percent_dev", default=0.2, type=float, help='Size of the dev set, float') + parser.add_argument("--clean_dir", action='store_true') + args = parser.parse_args() + + if not os.path.exists(args.data_dir): + os.makedirs(args.data_dir) + + if args.dataset != 'tatoeba': + raise ValueError("Unsupported dataset.") + + logging.info(f'Downloading tatoeba dataset') + tatoeba_dataset = os.path.join(args.data_dir, 'sentences.csv') + __maybe_download_file(tatoeba_dataset, args.dataset) + + logging.info(f'Processing English sentences...') + clean_eng_sentences = os.path.join(args.data_dir, 'clean_eng_sentences.txt') + __process_english_sentences( + tatoeba_dataset, clean_eng_sentences, args.percent_to_cut, args.num_lines_to_combine, args.num_samples + ) + + train_file = os.path.join(args.data_dir, 'train.txt') + dev_file = os.path.join(args.data_dir, 'dev.txt') + + logging.info( + f'Splitting the {args.dataset} dataset into train and dev sets' + ' and creating labels and text files' + ) + __split_into_train_dev(clean_eng_sentences, train_file, dev_file, args.percent_dev) + + logging.info(f'Creating text and label files for training') + create_text_and_labels(args.data_dir, os.path.join(args.data_dir, 'train.txt')) + create_text_and_labels(args.data_dir, os.path.join(args.data_dir, 'dev.txt')) + + if args.clean_dir: + logging.info(f'Cleaning up {args.data_dir}') + __delete_file(clean_eng_sentences) + __delete_file(tatoeba_dataset) + __delete_file(train_file) + __delete_file(dev_file) + logging.info(f'Processing of the {args.dataset} is complete') diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/import_from_iob_format.py b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/import_from_iob_format.py new file mode 100644 index 0000000..4a6f154 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/import_from_iob_format.py @@ -0,0 +1,124 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os + +from nemo.utils import logging + + +def __convert_data(in_file: str, out_text_f: str, out_labels_f: str, max_length: int): + """ + Convert data from the IOB format to NeMo accepted format described below. + in_file should be in the IOB format, see example here: + https://www.clips.uantwerpen.be/conll2003/ner/. + + Args: + in_file: input file name + out_text_f: output file with text + out_labels_f: output file with labels + max_length: use -1 to leave the examples' length as is, otherwise long examples will be split into multiple + examples + After the conversion, the dataset is split into 2 files: text.txt + and labels.txt. + Each line of the text.txt file contains text sequences, where words + are separated with spaces. The labels.txt file contains corresponding + labels for each word in text.txt, the labels are separated with spaces. + Each line of the files should follow the format: + [WORD] [SPACE] [WORD] [SPACE] [WORD] (for text.txt) and + [LABEL] [SPACE] [LABEL] [SPACE] [LABEL] (for labels.txt). + + """ + in_file = open(in_file, 'r') + + if max_length == -1: + with open(out_text_f, 'w') as out_text, open(out_labels_f, 'w') as out_labels: + for line in in_file: + if line == '\n': + out_text.write(line) + out_labels.write(line) + else: + line = line.split() + out_text.write(line[0] + ' ') + out_labels.write(line[-1] + ' ') + + else: + words = [] + labels = [] + with open(out_text_f, 'w') as out_text, open(out_labels_f, 'w') as out_labels: + lines = in_file.readlines() + for line_id, line in enumerate(lines): + logging.info(f"{line_id} {len(lines)}") + contends = line.strip() + if len(contends) == 0: + assert len(words) == len(labels) + if len(words) > max_length: + # split if the sentence is longer than max_length + while len(words) > max_length: + tmplabel = labels[:max_length] + for iidx in range(len(tmplabel)): + if tmplabel.pop() == 'O': + break + l = ' '.join([label for label in labels[: len(tmplabel) + 1] if len(label) > 0]) + w = ' '.join([word for word in words[: len(tmplabel) + 1] if len(word) > 0]) + + out_text.write(w + "\n") + out_labels.write(l + "\n") + words = words[len(tmplabel) + 1 :] + labels = labels[len(tmplabel) + 1 :] + + if len(words) == 0: + continue + l = ' '.join([label for label in labels if len(label) > 0]) + w = ' '.join([word for word in words if len(word) > 0]) + + out_text.write(w + "\n") + out_labels.write(l + "\n") + words = [] + labels = [] + continue + + word = line.strip().split()[0] + label = line.strip().split()[-1] + words.append(word) + labels.append(label) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description='Convert data from IOB format to the format compatible with \ + nlp/examples/token_classification/scripts/token_classification_train.py and \ + token_classification_evaluate.py' + ) + parser.add_argument("--data_file", required=True, type=str, help='path to a file in IOB format') + parser.add_argument( + "--max_length", + default=-1, + type=int, + help='use -1 to leave the examples\'s length as is, ' + 'otherwise long examples will be split into multiple examples', + ) + args = parser.parse_args() + + data_dir, basename = os.path.split(args.data_file) + prefix = os.path.splitext(basename)[0] + if not os.path.exists(args.data_file): + raise FileNotFoundError(f"{args.data_file} not found") + + logging.info(f'Processing {args.data_file}') + out_text = os.path.join(data_dir, 'text_' + prefix + '.txt') + out_labels = os.path.join(data_dir, 'labels_' + prefix + '.txt') + + __convert_data(args.data_file, out_text, out_labels, args.max_length) + logging.info(f'Processing of the {args.data_file} is complete') diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/prepare_data_for_punctuation_capitalization.py b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/prepare_data_for_punctuation_capitalization.py new file mode 100644 index 0000000..78a0763 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/data/prepare_data_for_punctuation_capitalization.py @@ -0,0 +1,108 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +The script converts raw text to the NeMo format for punctuation and capitalization task. + +Raw Data Format +--------------- + +The Punctuation and Capitalization model can work with any text dataset, although it is recommended to balance the data, especially for the punctuation task. +Before pre-processing the data to the format expected by the model, the data should be split into train.txt and dev.txt (and optionally test.txt). +Each line in the **train.txt/dev.txt/test.txt** should represent one or more full and/or truncated sentences. + +Example of the train.txt/dev.txt file: + When is the next flight to New York? + The next flight is ... + .... + + +The `source_data_dir` structure should look like this: + . + |--sourced_data_dir + |-- dev.txt + |-- train.txt + + + +NeMo Data Format for training the model +--------------------------------------- + +The punctuation and capitalization model expects the data in the following format: + +The training and evaluation data is divided into 2 files: text.txt and labels.txt. \ +Each line of the **text.txt** file contains text sequences, where words are separated with spaces, i.e. + +[WORD] [SPACE] [WORD] [SPACE] [WORD], for example: + when is the next flight to new york + the next flight is ... + ... + +The **labels.txt** file contains corresponding labels for each word in text.txt, the labels are separated with spaces. \ +Each label in labels.txt file consists of 2 symbols: + +* the first symbol of the label indicates what punctuation mark should follow the word (where O means no punctuation needed); +* the second symbol determines if a word needs to be capitalized or not (where U indicates that the word should be upper-cased, and O - no capitalization needed.) + +By default, the following punctuation marks are considered: commas, periods, and question marks; the rest punctuation marks were removed from the data. +This can be changed by introducing new labels in the labels.txt files + +Each line of the labels.txt should follow the format: [LABEL] [SPACE] [LABEL] [SPACE] [LABEL] (for labels.txt). \ +For example, labels for the above text.txt file should be: + + OU OO OO OO OO OO OU ?U + OU OO OO OO ... + ... + +The complete list of all possible labels for this task used in this tutorial is: OO, ,O, .O, ?O, OU, ,U, .U, ?U. + +Converting Raw data to NeMo format +---------------------------------- + +To pre-process the raw text data, stored under :code:`sourced_data_dir` (see the :ref:`raw_data_format_punct` +section), run the following command: + + python examples/nlp/token_classification/data/prepare_data_for_punctuation_capitalization.py \ + -s \ + -o + +""" + +import argparse +import os + +from get_tatoeba_data import create_text_and_labels + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Prepare data for punctuation and capitalization tasks') + parser.add_argument("-s", "--source_file", required=True, type=str, help="Path to the source file") + parser.add_argument("-o", "--output_dir", required=True, type=str, help="Path to the output directory") + parser.add_argument( + "-p", + "--marks", + required=False, + type=str, + help="Punctuation marks to consider for dataset", + default=[",", ".", "?"], + nargs="+", + ) + args = parser.parse_args() + + if not os.path.exists(args.source_file): + raise ValueError(f'{args.source_file} was not found') + + os.makedirs(args.output_dir, exist_ok=True) + create_text_and_labels(args.output_dir, args.source_file, "".join(args.marks)) + + print(f'Processing of the {args.source_file} is complete') diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/punctuate_capitalize_infer.py b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/punctuate_capitalize_infer.py new file mode 100644 index 0000000..8fdb3ab --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/punctuate_capitalize_infer.py @@ -0,0 +1,282 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import json +from pathlib import Path +from typing import Dict, List, Union + +import torch.cuda + +from nemo.collections.nlp.models import PunctuationCapitalizationLexicalAudioModel, PunctuationCapitalizationModel + + +""" +This script is for restoring punctuation and capitalization. + +Usage example: + +python punctuate_capitalize.py \ + --input_manifest \ + --output_manifest + +Usage example for lexical audio model: +python punctuate_capitalize.py \ + --input_manifest \ + --output_manifest \ + --use_audio + + + is a path to NeMo ASR manifest. Usually it is an output of + NeMo/examples/asr/transcribe_speech.py but can be a manifest with 'text' key. Alternatively you can use + --input_text parameter for passing text for inference. + is a path to NeMo ASR manifest into which script output will be written. Alternatively + you can use parameter --output_text. + +For more details on this script usage look in argparse help. +""" + + +def get_args() -> argparse.Namespace: + default_model_parameter = "pretrained_name" + default_model = "punctuation_en_bert" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + description="The script is for restoring punctuation and capitalization in text or text and audio. To use text and audio use '--use_audio'. Long strings are split into " + "segments of length `--max_seq_length`. `--max_seq_length` is the length which includes [CLS] and [SEP] " + "tokens. If `--use_audio` is set, samples with texts longer than `--max_seq_length` will be ignored. Parameter `--step` controls segments overlapping. `--step` is a distance between beginnings of " + "consequent segments. Model outputs for tokens near the borders of tensors are less accurate and can be " + "discarded before final predictions computation. Parameter `--margin` is number of discarded outputs near " + "segments borders. Probabilities of tokens in overlapping parts of segments multiplied before selecting the " + "best prediction. Default values of parameters `--max_seq_length`, `--step`, and `--margin` are optimal for " + "IWSLT 2019 test dataset.", + ) + parser.add_argument( + '--use_audio', + required=False, + action="store_true", + help="If set `PunctuationCapitalizationLexicalAudioModel` will be used for inference", + ) + input_ = parser.add_mutually_exclusive_group(required=True) + input_.add_argument( + "--input_manifest", + "-m", + type=Path, + help="Path to the file with NeMo manifest which needs punctuation and capitalization. If the first element " + "of manifest contains key 'pred_text', 'pred_text' values are passed for tokenization. Otherwise 'text' " + "values are passed for punctuation and capitalization. Exactly one parameter of `--input_manifest` and " + "`--input_text` should be provided.", + ) + input_.add_argument( + "--input_text", + "-t", + type=Path, + help="Path to file with text which needs punctuation and capitalization. Exactly one parameter of " + "`--input_manifest` and `--input_text` should be provided.", + ) + parser.add_argument( + '--audio_file', + required=False, + type=Path, + help="Path to file with paths to audio. One path per row. Required if '--input_text' provided. Else 'audio_filepath' from manifest will be used.", + ) + output = parser.add_mutually_exclusive_group(required=True) + output.add_argument( + "--output_manifest", + "-M", + type=Path, + help="Path to output NeMo manifest. Text with restored punctuation and capitalization will be saved in " + "'pred_text' elements if 'pred_text' key is present in the input manifest. Otherwise text with restored " + "punctuation and capitalization will be saved in 'text' elements. Exactly one parameter of `--output_manifest` " + "and `--output_text` should be provided.", + ) + output.add_argument( + "--output_text", + "-T", + type=Path, + help="Path to file with text with restored punctuation and capitalization. Exactly one parameter of " + "`--output_manifest` and `--output_text` should be provided.", + ) + model = parser.add_mutually_exclusive_group(required=False) + model.add_argument( + "--pretrained_name", + "-p", + help=f"The name of NGC pretrained model. No more than one of parameters `--pretrained_name`, `--model_path`" + f"should be provided. If neither of parameters `--pretrained_name` and `--model_path` are provided, then the " + f"script is run with `--{default_model_parameter}={default_model}`.", + choices=[m.pretrained_model_name for m in PunctuationCapitalizationModel.list_available_models()] + + [m.pretrained_model_name for m in PunctuationCapitalizationLexicalAudioModel.list_available_models()], + ) + model.add_argument( + "--model_path", + "-P", + type=Path, + help=f"Path to .nemo checkpoint of punctuation and capitalization model. No more than one of parameters " + f"`--pretrained_name` and `--model_path` should be provided. If neither of parameters `--pretrained_name` and " + f"`--model_path` are provided, then the script is run with `--{default_model_parameter}={default_model}`.", + ) + parser.add_argument( + "--max_seq_length", + "-L", + type=int, + default=64, + help="Length of segments into which queries are split. `--max_seq_length` includes [CLS] and [SEP] tokens.", + ) + parser.add_argument( + "--step", + "-s", + type=int, + default=8, + help="Relative shift of consequent segments into which long queries are split. Long queries are split into " + "segments which can overlap. Parameter `step` controls such overlapping. Imagine that queries are " + "tokenized into characters, `max_seq_length=5`, and `step=2`. In such a case query 'hello' is tokenized " + "into segments `[['[CLS]', 'h', 'e', 'l', '[SEP]'], ['[CLS]', 'l', 'l', 'o', '[SEP]']]`.", + ) + parser.add_argument( + "--margin", + "-g", + type=int, + default=16, + help="A number of subtokens in the beginning and the end of segments which output probabilities are not used " + "for prediction computation. The first segment does not have left margin and the last segment does not have " + "right margin. For example, if input sequence is tokenized into characters, `max_seq_length=5`, `step=1`, " + "and `margin=1`, then query 'hello' will be tokenized into segments `[['[CLS]', 'h', 'e', 'l', '[SEP]'], " + "['[CLS]', 'e', 'l', 'l', '[SEP]'], ['[CLS]', 'l', 'l', 'o', '[SEP]']]`. These segments are passed to the " + "model. Before final predictions computation, margins are removed. In the next list, subtokens which logits " + "are not used for final predictions computation are marked with asterisk: `[['[CLS]'*, 'h', 'e', 'l'*, " + "'[SEP]'*], ['[CLS]'*, 'e'*, 'l', 'l'*, '[SEP]'*], ['[CLS]'*, 'l'*, 'l', 'o', '[SEP]'*]]`.", + ) + parser.add_argument( + "--batch_size", "-b", type=int, default=128, help="Number of segments which are processed simultaneously.", + ) + parser.add_argument( + "--save_labels_instead_of_text", + "-B", + action="store_true", + help="If this option is set, then punctuation and capitalization labels are saved instead text with restored " + "punctuation and capitalization. Labels are saved in format described here " + "https://docs.nvidia.com/deeplearning/nemo/" + "user-guide/docs/en/main/nlp/punctuation_and_capitalization.html#nemo-data-format", + ) + parser.add_argument( + "--device", + "-d", + choices=['cpu', 'cuda'], + help="Which device to use. If device is not set and CUDA is available, then GPU will be used. If device is " + "not set and CUDA is not available, then CPU is used.", + ) + parser.add_argument( + "--sample_rate", + type=int, + default=16000, + help="Target sample rate for audios if `--use_audio` was passed", + required=False, + ) + args = parser.parse_args() + if args.input_manifest is None and args.output_manifest is not None: + parser.error("--output_manifest requires --input_manifest") + if args.use_audio and (args.input_manifest is None and args.audio_file is None): + parser.error("--use_audio and --input_text require --audio_file") + if args.pretrained_name is None and args.model_path is None: + setattr(args, default_model_parameter, default_model) + for name in ["input_manifest", "input_text", "output_manifest", "output_text", "model_path", "audio_file"]: + if getattr(args, name) is not None: + setattr(args, name, getattr(args, name).expanduser()) + return args + + +def load_manifest(manifest: Path) -> List[Dict[str, Union[str, float]]]: + result = [] + with manifest.open() as f: + for i, line in enumerate(f): + data = json.loads(line) + result.append(data) + return result + + +def main() -> None: + args = get_args() + if args.pretrained_name is None: + model = ( + PunctuationCapitalizationModel.restore_from(args.model_path) + if not args.use_audio + else PunctuationCapitalizationLexicalAudioModel.restore_from(args.model_path) + ) + else: + model = ( + PunctuationCapitalizationModel.from_pretrained(args.pretrained_name) + if not args.use_audio + else PunctuationCapitalizationLexicalAudioModel.restore_from(args.model_path) + ) + if args.device is None: + if torch.cuda.is_available(): + model = model.cuda() + else: + model = model.cpu() + else: + model = model.to(args.device) + if args.input_manifest is None: + texts = [] + audios = [] + with args.input_text.open() as f: + for line in f: + texts.append(line.strip()) + if args.use_audio: + with args.audio_file.open() as f: + for line in f: + audios.append(line.strip()) + else: + manifest = load_manifest(args.input_manifest) + text_key = "pred_text" if "pred_text" in manifest[0] else "text" + texts = [] + audios = [] + for item in manifest: + texts.append(item[text_key]) + if args.use_audio: + audios.append(item["audio_filepath"]) + if args.use_audio: + processed_texts = model.add_punctuation_capitalization( + texts, + batch_size=args.batch_size, + max_seq_length=args.max_seq_length, + step=args.step, + margin=args.margin, + return_labels=args.save_labels_instead_of_text, + audio_queries=audios, + target_sr=args.sample_rate, + ) + else: + processed_texts = model.add_punctuation_capitalization( + texts, + batch_size=args.batch_size, + max_seq_length=args.max_seq_length, + step=args.step, + margin=args.margin, + return_labels=args.save_labels_instead_of_text, + ) + if args.output_manifest is None: + args.output_text.parent.mkdir(exist_ok=True, parents=True) + with args.output_text.open('w') as f: + for t in processed_texts: + f.write(t + '\n') + else: + args.output_manifest.parent.mkdir(exist_ok=True, parents=True) + with args.output_manifest.open('w') as f: + for item, t in zip(manifest, processed_texts): + item[text_key] = t + f.write(json.dumps(item) + '\n') + + +if __name__ == "__main__": + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/punctuation_capitalization_lexical_audio_train_evaluate.py b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/punctuation_capitalization_lexical_audio_train_evaluate.py new file mode 100644 index 0000000..149a9a4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/punctuation_capitalization_lexical_audio_train_evaluate.py @@ -0,0 +1,158 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import pytorch_lightning as pl +import torch +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models.token_classification.punctuation_capitalization_config import ( + PunctuationCapitalizationLexicalAudioConfig, +) +from nemo.collections.nlp.models.token_classification.punctuation_capitalization_lexical_audio_model import ( + PunctuationCapitalizationLexicalAudioModel, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +""" +This script show how to train a Punctuation and Capitalization Model with lexical and acoustic features. +More details on the task and data format could be found in tutorials/nlp/Punctuation_and_Capitalization.ipynb + +*** Setting the configs *** + +The model and the PT trainer are defined in a config file which declares multiple important sections. +The most important ones are: + model: All arguments that are related to the Model - language model, audio encoder, tokenizer, token classifier, optimizer, + schedulers, and datasets/data loaders. + trainer: Any argument to be passed to PyTorch Lightning including number of epochs, number of GPUs, + precision level, etc. +This script uses the `/examples/nlp/token_classification/conf/punctuation_capitalization_lexical_audio_config.yaml` config file +by default. You may update the config file from the file directly. +The other option is to set another config file via command line arguments by `--config-name=CONFIG_FILE_PATH'. + +*** Model training *** + +To run this script and train the model from scratch, use: + python punctuation_capitalization_lexical_audio_train_evaluate.py \ + model.train_ds.ds_item= \ + model.train_ds.text_file= \ + model.train_ds.labels_file= \ + model.train_ds.audio_file= \ + model.validation_ds.ds_item= \ + model.validation_ds.text_file= \ + model.validation_ds.labels_file= \ + model.validation_ds.audio_file= + +To use BERT-like pretrained P&C models' weights to initialize lexical encoder, use: + python punctuation_capitalization_lexical_audio_train_evaluate.py \ + model.train_ds.ds_item= \ + model.train_ds.text_file= \ + model.train_ds.labels_file= \ + model.train_ds.audio_file= \ + model.validation_ds.ds_item= \ + model.validation_ds.text_file= \ + model.validation_ds.labels_file= \ + model.validation_ds.audio_file= \ + model.restore_lexical_encoder_from= + + +If you wish to perform testing after training set `do_testing` to `true: + python punctuation_capitalization_lexical_audio_train_evaluate.py \ + +do_testing=true \ + pretrained_model= \ + model.train_ds.ds_item= \ + model.train_ds.text_file= \ + model.train_ds.labels_file= \ + model.train_ds.audio_file= \ + model.validation_ds.ds_item= \ + model.validation_ds.text_file= \ + model.validation_ds.labels_file= \ + model.validation_ds.audio_file= \ + model.test_ds.ds_item= \ + model.test_ds.text_file= \ + model.test_ds.labels_file= \ + model.test_ds.audio_file= + +Set `do_training` to `false` and `do_testing` to `true` to perform evaluation without training: + python punctuation_capitalization_lexical_audio_train_evaluate.py \ + +do_testing=true \ + +do_training=false \ + pretrained_model== \ + model.test_ds.ds_item= \ + model.test_ds.text_file= \ + model.test_ds.labels_file= \ + model.test_ds.audio_file= + +""" + + +@hydra_runner(config_path="conf", config_name="punctuation_capitalization_lexical_audio_config") +def main(cfg: DictConfig) -> None: + # PTL 2.0 has find_unused_parameters as False by default, so its required to set it to True + # when there are unused parameters like here + if cfg.trainer.strategy == 'ddp': + cfg.trainer.strategy = "ddp_find_unused_parameters_true" + torch.manual_seed(42) + cfg = OmegaConf.merge(OmegaConf.structured(PunctuationCapitalizationLexicalAudioConfig()), cfg) + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + if not cfg.do_training and not cfg.do_testing: + raise ValueError("At least one of config parameters `do_training` and `do_testing` has to be `true`.") + if cfg.do_training: + if cfg.model.get('train_ds') is None: + raise ValueError('`model.train_ds` config section is required if `do_training` config item is `True`.') + if cfg.do_testing: + if cfg.model.get('test_ds') is None: + raise ValueError('`model.test_ds` config section is required if `do_testing` config item is `True`.') + + if not cfg.pretrained_model: + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + model = PunctuationCapitalizationLexicalAudioModel(cfg.model, trainer=trainer) + else: + if os.path.exists(cfg.pretrained_model): + model = PunctuationCapitalizationLexicalAudioModel.restore_from(cfg.pretrained_model) + elif cfg.pretrained_model in PunctuationCapitalizationLexicalAudioModel.get_available_model_names(): + model = PunctuationCapitalizationLexicalAudioModel.from_pretrained(cfg.pretrained_model) + else: + raise ValueError( + f'Provide path to the pre-trained .nemo file or choose from ' + f'{PunctuationCapitalizationLexicalAudioModel.list_available_models()}' + ) + model.update_config_after_restoring_from_checkpoint( + class_labels=cfg.model.class_labels, + common_dataset_parameters=cfg.model.common_dataset_parameters, + train_ds=cfg.model.get('train_ds') if cfg.do_training else None, + validation_ds=cfg.model.get('validation_ds') if cfg.do_training else None, + test_ds=cfg.model.get('test_ds') if cfg.do_testing else None, + optim=cfg.model.get('optim') if cfg.do_training else None, + ) + model.set_trainer(trainer) + if cfg.do_training: + model.setup_training_data() + model.setup_multiple_validation_data(cfg.model.validation_ds) + model.setup_optimization() + else: + model.setup_multiple_test_data(cfg.model.test_ds) + if cfg.do_training: + trainer.fit(model) + if cfg.do_testing: + trainer.test(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py new file mode 100644 index 0000000..e983540 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py @@ -0,0 +1,161 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import pytorch_lightning as pl +import torch +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models import PunctuationCapitalizationModel +from nemo.collections.nlp.models.token_classification.punctuation_capitalization_config import ( + PunctuationCapitalizationConfig, +) +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +""" +This script show how to train a Punctuation and Capitalization Model. +More details on the task and data format could be found in tutorials/nlp/Punctuation_and_Capitalization.ipynb + +*** Setting the configs *** + +The model and the PT trainer are defined in a config file which declares multiple important sections. +The most important ones are: + model: All arguments that are related to the Model - language model, tokenizer, token classifier, optimizer, + schedulers, and datasets/data loaders. + trainer: Any argument to be passed to PyTorch Lightning including number of epochs, number of GPUs, + precision level, etc. +This script uses the `/examples/nlp/token_classification/conf/punctuation_capitalization_config.yaml` config file +by default. You may update the config file from the file directly. +The other option is to set another config file via command line arguments by `--config-name=CONFIG_FILE_PATH'. + +Additional default parameters could be found in PunctuationCapitalizationDataConfigBase from +/nemo/collections/nlp/data/token_classification/punctuation_capitalization_dataset.py, +use `+` to modify their values via command line, e.g.: `+model.train_ds.num_workers=2` + +For more details about the config files and different ways of model restoration, see tutorials/00_NeMo_Primer.ipynb + +*** Model training *** + +To run this script and train the model from scratch, use: + python punctuation_capitalization_train_evaluate.py \ + model.train_ds.ds_item= \ + model.train_ds.text_file= \ + model.train_ds.labels_file= \ + model.validation_ds.ds_item= \ + model.validation_ds.text_file= \ + model.validation_ds.labels_file= \ + ~model.test_ds + +To use one of the pretrained versions of the model and finetune it, run: + python punctuation_capitalization_train_evaluate.py \ + pretrained_model=punctuation_en_bert \ + model.train_ds.ds_item= \ + model.train_ds.text_file= \ + model.train_ds.labels_file= \ + model.validation_ds.ds_item= \ + model.validation_ds.text_file= \ + model.validation_ds.labels_file= \ + ~model.test_ds + + pretrained_model - pretrained PunctuationCapitalization model from list_available_models() or + path to a .nemo file, for example: punctuation_en_bert or model.nemo + +If you wish to perform testing after training set `do_testing` to `true: + python punctuation_capitalization_train_evaluate.py \ + +do_testing=true \ + pretrained_model=punctuation_en_bert \ + model.train_ds.ds_item= \ + model.train_ds.text_file= \ + model.train_ds.labels_file= \ + model.validation_ds.ds_item= \ + model.validation_ds.text_file= \ + model.validation_ds.labels_file= \ + model.test_ds.ds_item= \ + model.test_ds.text_file= \ + model.test_ds.labels_file= + +Set `do_training` to `false` and `do_testing` to `true` to perform evaluation without training: + python punctuation_capitalization_train_evaluate.py \ + +do_testing=true \ + +do_training=false \ + pretrained_model=punctuation_en_bert \ + model.test_ds.ds_item= \ + model.test_ds.text_file= \ + model.test_ds.labels_file= + +""" + + +@hydra_runner(config_path="conf", config_name="punctuation_capitalization_config") +def main(cfg: DictConfig) -> None: + # PTL 2.0 has find_unused_parameters as False by default, so its required to set it to True + # when there are unused parameters like here + if cfg.trainer.strategy == 'ddp': + cfg.trainer.strategy = "ddp_find_unused_parameters_true" + torch.manual_seed(42) + cfg = OmegaConf.merge(OmegaConf.structured(PunctuationCapitalizationConfig()), cfg) + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + if not cfg.do_training and not cfg.do_testing: + raise ValueError("At least one of config parameters `do_training` and `do_testing` has to `true`.") + if cfg.do_training: + if cfg.model.get('train_ds') is None: + raise ValueError('`model.train_ds` config section is required if `do_training` config item is `True`.') + if cfg.do_testing: + if cfg.model.get('test_ds') is None: + raise ValueError('`model.test_ds` config section is required if `do_testing` config item is `True`.') + + if not cfg.pretrained_model: + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + model = PunctuationCapitalizationModel(cfg.model, trainer=trainer) + else: + if os.path.exists(cfg.pretrained_model): + model = PunctuationCapitalizationModel.restore_from(cfg.pretrained_model) + elif cfg.pretrained_model in PunctuationCapitalizationModel.get_available_model_names(): + model = PunctuationCapitalizationModel.from_pretrained(cfg.pretrained_model) + else: + raise ValueError( + f'Config parameter `pretrained_model` should contain a path to the pre-trained .nemo file or a model ' + f'name from ' + f'{[m.pretrained_model_name for m in PunctuationCapitalizationModel.list_available_models()]}. ' + f'Provided `pretrained_model="{cfg.pretrained_model}"` is neither a valid path, nor a valid model ' + f'name.' + ) + model.update_config_after_restoring_from_checkpoint( + class_labels=cfg.model.class_labels, + common_dataset_parameters=cfg.model.common_dataset_parameters, + train_ds=cfg.model.get('train_ds') if cfg.do_training else None, + validation_ds=cfg.model.get('validation_ds') if cfg.do_training else None, + test_ds=cfg.model.get('test_ds') if cfg.do_testing else None, + optim=cfg.model.get('optim') if cfg.do_training else None, + ) + model.set_trainer(trainer) + if cfg.do_training: + model.setup_training_data() + model.setup_multiple_validation_data(cfg.model.validation_ds) + model.setup_optimization() + else: + model.setup_multiple_test_data(cfg.model.test_ds) + if cfg.do_training: + trainer.fit(model) + if cfg.do_testing: + trainer.test(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/token_classification_evaluate.py b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/token_classification_evaluate.py new file mode 100644 index 0000000..b69212f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/token_classification_evaluate.py @@ -0,0 +1,135 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import pytorch_lightning as pl +from omegaconf import DictConfig + +from nemo.collections.nlp.models import TokenClassificationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +""" +This script shows how to perform evaluation and runs inference of a few examples. + +More details on Token Classification model could be found in tutorials/nlp/Token_Classification_Named_Entity_Recognition.ipynb + +*** Setting the configs *** + +This script uses the `/examples/nlp/token_classification/conf/token_classification_config.yaml` config file +by default. You may update the config file from the file directly. +The other option is to set another config file via command line arguments by `--config-name=CONFIG_FILE_PATH'. + +For more details about the config files and different ways of model restoration, see tutorials/00_NeMo_Primer.ipynb + +*** Model Evaluation *** + +The script runs two types of evaluation: + * model.test() - this eval will use the config setting for evaluation such as model.dataset.max_seq_length + * model.evaluate_from_file(): + * disregards model.dataset.max_seq_length and evaluates all the tokens, BERT max seq length - 512 tokens after tokenization + * creates confusion matrix + * saves predictions and labels (if provided) + +To run the script: + + python token_classification_evaluate.py \ + model.dataset.data_dir= \ + pretrained_model=ner_en_bert + + - a directory that contains test_ds.text_file and test_ds.labels_file (see the config) +pretrained_model - pretrained TokenClassification model from list_available_models() or + path to a .nemo file, for example: ner_en_bert or your_model.nemo + +""" + + +@hydra_runner(config_path="conf", config_name="token_classification_config") +def main(cfg: DictConfig) -> None: + logging.info( + 'During evaluation/testing, it is currently advisable to construct a new Trainer with single GPU and \ + no DDP to obtain accurate results' + ) + + if not hasattr(cfg.model, 'test_ds'): + raise ValueError(f'model.test_ds was not found in the config, skipping evaluation') + + trainer = pl.Trainer( + devices=1, + precision=cfg.trainer.precision, + logger=False, + enable_checkpointing=False, + accelerator=cfg.trainer.accelerator, + ) + exp_dir = exp_manager(trainer, cfg.exp_manager) + + if not cfg.pretrained_model: + raise ValueError( + 'To run evaluation and inference script a pre-trained model or .nemo file must be provided.' + f'Choose from {TokenClassificationModel.list_available_models()} or "pretrained_model"="your_model.nemo"' + ) + + if os.path.exists(cfg.pretrained_model): + model = TokenClassificationModel.restore_from(cfg.pretrained_model) + elif cfg.pretrained_model in TokenClassificationModel.get_available_model_names(): + model = TokenClassificationModel.from_pretrained(cfg.pretrained_model) + else: + raise ValueError( + f'Provide path to the pre-trained .nemo checkpoint or choose from {TokenClassificationModel.list_available_models()}' + ) + + data_dir = cfg.model.dataset.get('data_dir', None) + if data_dir is None: + logging.error( + 'No dataset directory provided. Skipping evaluation. ' + 'To run evaluation on a file, specify path to the directory that contains test_ds.text_file and test_ds.labels_file with "model.dataset.data_dir" argument.' + ) + elif not os.path.exists(data_dir): + logging.error(f'{data_dir} is not found, skipping evaluation on the test set.') + else: + model.update_data_dir(data_dir=data_dir) + model._cfg.dataset = cfg.model.dataset + + if not hasattr(cfg.model, 'test_ds'): + logging.error(f'model.test_ds was not found in the config, skipping evaluation') + elif model.prepare_test(trainer): + model.setup_test_data(cfg.model.test_ds) + trainer.test(model) + + model.evaluate_from_file( + text_file=os.path.join(data_dir, cfg.model.test_ds.text_file), + labels_file=os.path.join(data_dir, cfg.model.test_ds.labels_file), + output_dir=exp_dir, + add_confusion_matrix=True, + normalize_confusion_matrix=True, + ) + else: + logging.error('Skipping the evaluation. The trainer is not setup properly.') + + # run an inference on a few examples + queries = ['we bought four shirts from the nvidia gear store in santa clara.', 'Nvidia is a company.'] + results = model.add_predictions(queries, output_file='predictions.txt') + + for query, result in zip(queries, results): + logging.info(f'Query : {query}') + logging.info(f'Result: {result.strip()}\n') + + logging.info(f'Results are saved at {exp_dir}') + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/token_classification_train.py b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/token_classification_train.py new file mode 100644 index 0000000..56c1487 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/token_classification/token_classification_train.py @@ -0,0 +1,152 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models import TokenClassificationModel +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +""" +This scripts shows how to train a Token Classification model. + +The Token Classification model supports Named Entity Recognition task and other token level classification tasks, +as long as the data follows the format specified below. + +More details on how to use this script could be found in +tutorials/nlp/Token_Classification_Named_Entity_Recognition.ipynb + +*** Data Format *** +Token Classification Model requires the data to be split into 2 files: text.txt and labels.txt. +Each line of the text.txt file contains text sequences, where words are separated with spaces, i.e.: +[WORD] [SPACE] [WORD] [SPACE] [WORD]. +The labels.txt file contains corresponding labels for each word in text.txt, the labels are separated with spaces, i.e.: +[LABEL] [SPACE] [LABEL] [SPACE] [LABEL]. + +Example of a text.txt file: +Jennifer is from New York City . +She likes ... +... + +Corresponding labels.txt file: +B-PER O O B-LOC I-LOC I-LOC O +O O ... +... + +*** Preparing the dataset *** + +To convert an IOB format data to the format required for training, run +examples/nlp/token_classification/data/import_from_iob_format.py on your train and dev files, as follows: + +python examples/nlp/token_classification/data/import_from_iob_format.py --data_file PATH_TO_IOB_FORMAT_DATAFILE + +*** Setting the configs *** + +The model and the PT trainer are defined in a config file which declares multiple important sections. +The most important ones are: + model: All arguments that are related to the Model - language model, tokenizer, token classifier, optimizer, + schedulers, and datasets/data loaders. + trainer: Any argument to be passed to PyTorch Lightning including number of epochs, number of GPUs, + precision level, etc. +This script uses the `/examples/nlp/token_classification/conf/token_classification_config.yaml` config file +by default. You may update the config file from the file directly. +The other option is to set another config file via command line arguments by `--config-name=CONFIG_FILE_PATH'. + +For more details about the config files and different ways of model restoration, see tutorials/00_NeMo_Primer.ipynb + +*** Model Training *** + +To train TokenClassification model from scratch with the default config file, run: + + python token_classification_train.py \ + model.dataset.data_dir= \ + trainer.max_epochs= \ + trainer.devices=[] + +To use one of the pretrained versions of the model specify a `pretrained_model` arg with either +TokenClassification model from list_available_models() or path to a .nemo file, for example: +ner_en_bert or model.nemo, run: + + python token_classification_train.py pretrained_model=ner_en_bert + +To use one of the pretrained versions of the model and fine-tune it, run: + + python token_classification_train.py \ + model.dataset.data_dir= \ + pretrained_model=ner_en_bert + + - a directory that contains test_ds.text_file and test_ds.labels_file (see the config) +pretrained_model - pretrained TokenClassification model from list_available_models() or + path to a .nemo file, for example: ner_en_bert or model.nemo + +For more ways of restoring a pre-trained model, see tutorials/00_NeMo_Primer.ipynb +""" + + +@hydra_runner(config_path="conf", config_name="token_classification_config") +def main(cfg: DictConfig) -> None: + try: + strategy = NLPDDPStrategy(find_unused_parameters=True) + except (ImportError, ModuleNotFoundError): + strategy = 'auto' + + trainer = pl.Trainer(strategy=strategy, **cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + if not cfg.pretrained_model: + logging.info(f'Config: {OmegaConf.to_yaml(cfg)}') + model = TokenClassificationModel(cfg.model, trainer=trainer) + else: + if os.path.exists(cfg.pretrained_model): + # TODO: can we drop strict=False? + model = TokenClassificationModel.restore_from(cfg.pretrained_model, trainer=trainer, strict=False) + elif cfg.pretrained_model in TokenClassificationModel.get_available_model_names(): + model = TokenClassificationModel.from_pretrained(cfg.pretrained_model) + else: + raise ValueError( + f'Provide path to the pre-trained .nemo file or choose from {TokenClassificationModel.list_available_models()}' + ) + + data_dir = cfg.model.dataset.get('data_dir', None) + if data_dir: + if not os.path.exists(data_dir): + raise ValueError(f'{data_dir} is not found at') + + # we can also do finetuning of the pretrained model but it will require + # setup the data dir to get class weights statistics + model.update_data_dir(data_dir=data_dir) + # finally, setup train and validation Pytorch DataLoaders + model.setup_training_data() + model.setup_validation_data() + # then we're setting up loss, use model.dataset.class_balancing, + # if you want to add class weights to the CrossEntropyLoss + model.setup_loss(class_balancing=cfg.model.dataset.class_balancing) + logging.info(f'Using config file of the pretrained model') + else: + raise ValueError( + 'Specify a valid dataset directory that contains test_ds.text_file and test_ds.labels_file \ + with "model.dataset.data_dir" argument' + ) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/zero_shot_intent_recognition/conf/zero_shot_intent_config.yaml b/NeMo-2.0.0.rc0.beta/examples/nlp/zero_shot_intent_recognition/conf/zero_shot_intent_config.yaml new file mode 100644 index 0000000..64fd5f8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/zero_shot_intent_recognition/conf/zero_shot_intent_config.yaml @@ -0,0 +1,108 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Config file for Zero Shot Intent Recognition (BERT model trained NLI) +trainer: + devices: 1 # the number of gpus, 0 for CPU + num_nodes: 1 + max_epochs: 1 + max_steps: -1 # precedence over max_epochs + accumulate_grad_batches: 1 # accumulates grads every k batches + precision: 16 + accelerator: gpu + strategy: ddp + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + +model: + dataset: + data_dir: ??? # /path/to/data + sentence_1_column: 8 # index of the column containing the premise or sentence 1 + sentence_2_column: 9 # index of the column containing the hypothesis or sentence 2 + label_column: -1 # index of the column containing labels. Labels should be "entailment", "contradiction", and "neutral". + class_balancing: null # null or 'weighted_loss'. 'weighted_loss' enables the weighted class balancing of the loss, may be used for handling unbalanced classes + use_cache: true # uses a cache to store the processed dataset, you may use it for large datasets for speed up + num_classes: 3 + max_seq_length: 128 + do_lower_case: true # true for uncased models, false for cased models, will be set automatically if pre-trained tokenizer model is used + + train_ds: + file_name: train.tsv + batch_size: 64 + shuffle: true + num_samples: -1 # number of samples to be considered, -1 means all the dataset + num_workers: 2 + drop_last: false + pin_memory: false + + validation_ds: + file_name: dev_matched.tsv + batch_size: 64 + shuffle: false + num_samples: -1 # number of samples to be considered, -1 means all the dataset + num_workers: 2 + drop_last: false + pin_memory: false + + test_ds: + file_name: null + batch_size: 64 + shuffle: false + num_samples: -1 # number of samples to be considered, -1 means all the dataset + num_workers: 2 + drop_last: false + pin_memory: false + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null # only necessary for adding transformer/bert-specific special tokens to tokenizer if the tokenizer does not already have these inherently. + + language_model: + pretrained_model_name: bert-base-uncased + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + classifier_head: + num_output_layers: 2 + fc_dropout: 0.1 + + optim: + name: adam + lr: 5e-5 + weight_decay: 0.00 + + sched: + name: WarmupAnnealing + # Scheduler params + warmup_steps: null + warmup_ratio: 0.1 + last_epoch: -1 + # pytorch lightning args + monitor: val_loss + reduce_on_plateau: false + +exp_manager: + exp_dir: null # exp_dir for your experiment, if None, defaults to "./NeMo_experiments" + name: "ZeroShotIntentRecognition" # The name of your model + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + +pretrained_model: # pretrained ZeroShotIntent model to be used for inference (.nemo file) \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/zero_shot_intent_recognition/zero_shot_intent_infer.py b/NeMo-2.0.0.rc0.beta/examples/nlp/zero_shot_intent_recognition/zero_shot_intent_infer.py new file mode 100644 index 0000000..eca8f1e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/zero_shot_intent_recognition/zero_shot_intent_infer.py @@ -0,0 +1,52 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os + +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models import ZeroShotIntentModel +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@hydra_runner(config_path="conf", config_name="zero_shot_intent_config") +def main(cfg: DictConfig) -> None: + logging.info(f'Config Params:\n {OmegaConf.to_yaml(cfg)}') + + # initialize the model using the config file + if cfg.pretrained_model and os.path.exists(cfg.pretrained_model): + model = ZeroShotIntentModel.restore_from(cfg.pretrained_model, strict=False) + else: + raise ValueError('Provide path to the pre-trained .nemo checkpoint') + + # predicting an intent of a query + queries = [ + "I'd like a veggie burger and fries", + "Turn off the lights in the living room", + ] + + candidate_labels = ['Food order', 'Play music', 'Request for directions', 'Change lighting', 'Calendar query'] + + predictions = model.predict(queries, candidate_labels, batch_size=4, multi_label=True) + + logging.info('The prediction results of some sample queries with the trained model:') + for query in predictions: + logging.info(json.dumps(query, indent=4)) + logging.info("Inference finished!") + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/nlp/zero_shot_intent_recognition/zero_shot_intent_train.py b/NeMo-2.0.0.rc0.beta/examples/nlp/zero_shot_intent_recognition/zero_shot_intent_train.py new file mode 100644 index 0000000..5b91049 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/nlp/zero_shot_intent_recognition/zero_shot_intent_train.py @@ -0,0 +1,43 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.nlp.models import ZeroShotIntentModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="zero_shot_intent_config") +def main(cfg: DictConfig) -> None: + logging.info(f'Config Params:\n {OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + # initialize the model using the config file + model = ZeroShotIntentModel(cfg.model, trainer=trainer) + + # training + logging.info("================================================================================================") + logging.info('Starting training...') + trainer.fit(model) + logging.info('Training finished!') + if cfg.model.nemo_path: + model.save_to(cfg.model.nemo_path) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/README.md b/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/README.md new file mode 100644 index 0000000..ac11e43 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/README.md @@ -0,0 +1,128 @@ +# NeMo End-to-End Speech Intent Classification and Slot Filling on SLURP Dataset + +## Introduction +This example shows how to train an end-to-end model for spoken language understanding on the SLURP dataset [2]. The model is an encoder-decoder framework, where the encoder is a Conformer-large [3] model initialized from [here](https://ngc.nvidia.com/models/nvidia:nemo:stt_en_conformer_ctc_large), while the decoder is a Transformer decoder [4] that is randomly initialized. The model is trained by minimizing the negative log-likelihood (NLL) loss with teacher forcing. + +## Results + +We present the main results of our models in the following table. +| | | | **Intent (Scenario_Action)** | | **Entity** | | | **SLURP Metrics** | | +|--------------------------------------------------|----------------|--------------------------|------------------------------|---------------|------------|--------|--------------|-------------------|---------------------| +| **Model** | **Params (M)** | **Pretrained** | **Accuracy** | **Precision** | **Recall** | **F1** | **Precision** | **Recall** | **F1** | +| NeMo-Conformer-Transformer-Large | 127 | NeMo ASR-Set 3.0 | 90.14 | 86.46 | 82.29 | 84.33 | 84.31 | 80.33 | 82.27 | +| NeMo-Conformer-Transformer-Large | 127 | NeMo SSL-LL60kh | 89.04 | 73.19 | 71.8 | 72.49 | 77.9 | 76.65 | 77.22 | +| NeMo-Conformer-Transformer-Large | 127 | None | 72.56 | 43.19 | 43.5 | 43.34 | 53.59 | 53.92 | 53.76 | +| NeMo-Conformer-Transformer-XLarge | 617 | NeMo SSL-LL60kh | 91.04 | 76.67 | 74.36 | 75.49 | 82.44 | 80.14 | 81.28 | + +Note: LL60kh refers to the Libri-Light dataset [7]. + +## Usage +Please install [NeMo](https://github.com/NVIDIA/NeMo) [1] before proceeding. **All following scripts are run under the current directory of this README.md file**. + +### Data Preparation +1. Under the current directory, run the following script to download and process data. +```bash +python ../../../scripts/dataset_processing/process_slurp_data.py \ + --data_dir="./slurp_data" \ + --text_key="semantics" \ + --suffix="slu" +``` + +2. Download evaluation code: +```bash +wget https://github.com/pswietojanski/slurp/raw/master/scripts/evaluation/util.py -P eval_utils/evaluation +wget https://github.com/pswietojanski/slurp/raw/master/scripts/evaluation/metrics/distance.py -P eval_utils/evaluation/metrics +wget https://github.com/pswietojanski/slurp/raw/master/scripts/evaluation/metrics/metrics.py -P eval_utils/evaluation/metrics +``` + + +### Building Tokenizers +1. Build the tokenizer for slu by running: +```bash +DATA_ROOT="./slurp_data" +python ../../../scripts/tokenizers/process_asr_text_tokenizer.py \ + --manifest="${DATA_ROOT}/train_slu.json,${DATA_ROOT}/train_synthetic_slu.json" \ + --data_root="${DATA_ROOT}/tokenizers_slu/" \ + --vocab_size=58 \ + --tokenizer="spe" \ + --spe_type="unigram" \ + --log \ + --spe_bos \ + --spe_eos \ + --spe_pad +``` + + +### Training +Run with the default config that uses ASR-pretrained encoder on NeMo ASR-set 3.0. The default batch size is set to 16 for a GPU with 32GB memory, please adjust it to your own case. + +```bash +DATA_DIR="./slurp_data" +EXP_NAME="slurp_conformer_transformer_large" +CUDA_VISIBLE_DEVICES=0 python run_speech_intent_slot_train.py \ + --config-path="./configs" --config-name=conformer_transformer_large_bpe \ + model.train_ds.manifest_filepath="[${DATA_DIR}/train_slu.json,${DATA_DIR}/train_synthetic_slu.json]" \ + model.validation_ds.manifest_filepath="${DATA_DIR}/devel_slu.json" \ + model.test_ds.manifest_filepath="${DATA_DIR}/test_slu.json" \ + model.tokenizer.dir="${DATA_DIR}/tokenizers_slu/tokenizer_spe_unigram_v58_pad_bos_eos" \ + model.train_ds.batch_size=16 \ + model.validation_ds.batch_size=16 \ + model.test_ds.batch_size=16 \ + trainer.devices=1 \ + trainer.max_epochs=100 \ + model.optim.sched.warmup_steps=2000 \ + exp_manager.name=$EXP_NAME \ + exp_manager.resume_if_exists=true \ + exp_manager.resume_ignore_no_checkpoint=true +``` + + +### Evaluation +After training, we can evaluate the model by running the following script, which will first perform checkpoint averaging and then run beam search with the averaged checkpoint on the test set. +```bash +DATA_DIR="./slurp_data" +EXP_NAME="slurp_conformer_transformer_large" +CKPT_DIR="./nemo_experiments/${EXP_NAME}/checkpoints" +CKPT_AVG_DIR="../../../examples/slu/speech_intent_slot/${CKPT_DIR}" + +python ../../../scripts/checkpoint_averaging/checkpoint_averaging.py $CKPT_AVG_DIR + +NEMO_MODEL="${CKPT_DIR}/${EXP_NAME}-averaged.nemo" +CUDA_VISIBLE_DEVICES=0 python run_speech_intent_slot_eval.py \ + dataset_manifest="${DATA_DIR}/test_slu.json" \ + model_path=$NEMO_MODEL \ + batch_size=32 \ + num_workers=8 \ + sequence_generator.type="beam" \ + sequence_generator.beam_size=32 \ + sequence_generator.temperature=1.25 \ + only_score_manifest=false +``` + +### Using Encoder Finetuned on SLURP Speech Recognition +To learn how to finetune the Conformer encoder on SLURP ASR, please refer to the tutorials at +- [Finetuning CTC models on other languages](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/ASR_CTC_Language_Finetuning.ipynb) +- [Self-Supervised pre-training for ASR](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Self_Supervised_Pre_Training.ipynb) + + +## Pretrained Models +The pretrained models and directions on how to use them are available [here](https://ngc.nvidia.com/catalog/models/nvidia:nemo:slu_conformer_transformer_large_slurp). + + +## Reference +[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) + +[2] [SLURP: A Spoken Language Understanding Resource Package](https://arxiv.org/abs/2011.13205) + +[3] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100) + +[4] [Attention Is All You Need](https://arxiv.org/abs/1706.03762?context=cs) + +[5] [Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding](https://arxiv.org/abs/2104.07253) + +[6] [SpeechBrain SLURP Recipe](https://github.com/speechbrain/speechbrain/tree/develop/recipes/SLURP) + +[7] [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://arxiv.org/abs/1912.07875) + +## Acknowledgments +The evaluation code is borrowed from the official [SLURP package](https://github.com/pswietojanski/slurp/tree/master/scripts/evaluation), and some data processing code is adapted from [SpeechBrain SLURP Recipe](https://github.com/speechbrain/speechbrain/tree/develop/recipes/SLURP). diff --git a/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/configs/conformer_transformer_large_bpe.yaml b/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/configs/conformer_transformer_large_bpe.yaml new file mode 100644 index 0000000..5d309f3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/configs/conformer_transformer_large_bpe.yaml @@ -0,0 +1,211 @@ +# Example config for speech intent classification and slot filling with Conformer-Transformer architecture. + +name: "Conformer-Transformer-BPE" + +pretrained_encoder: + name: stt_en_conformer_ctc_large + freeze: false + +model: + sample_rate: 16000 + log_prediction: true # enables logging sample predictions in the output during training + skip_nan_grad: false + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 16 # 16 for 32GB GPUs + shuffle: true + num_workers: 8 + pin_memory: true + use_start_end_token: true + trim_silence: false + max_duration: 11.0 + min_duration: 0.0 + # tarred datasets + is_tarred: false + tarred_audio_filepaths: null + shuffle_n: 2048 + # bucketing params + bucketing_strategy: "synced_randomized" + bucketing_batch_size: null + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + batch_size: 32 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: true + + test_ds: + manifest_filepath: null + sample_rate: ${model.sample_rate} + batch_size: 32 # you may increase batch_size if your memory allows + shuffle: false + num_workers: 8 + pin_memory: true + use_start_end_token: true + + # you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py + tokenizer: + dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: 0.025 + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 0 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 2 # set to zero to disable it + # you may use lower time_masks for smaller models to have a faster convergence + time_masks: 10 # set to zero to disable it + freq_width: 27 + time_width: 0.05 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 17 # SSL conformer-large have only 17 layers + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet or striding, vggnet may give better results but needs more memory + subsampling_factor: 4 # must be power of 2 + subsampling_conv_channels: -1 # -1 sets it to d_model + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + embedding: + _target_: nemo.collections.asr.modules.transformer.TransformerEmbedding + vocab_size: -1 + hidden_size: ${model.encoder.d_model} + max_sequence_length: 512 + num_token_types: 1 + embedding_dropout: 0.0 + learn_positional_encodings: false + + decoder: + _target_: nemo.collections.asr.modules.transformer.TransformerDecoder + num_layers: 3 + hidden_size: ${model.encoder.d_model} + inner_size: 2048 + num_attention_heads: 8 + attn_score_dropout: 0.0 + attn_layer_dropout: 0.0 + ffn_dropout: 0.0 + + classifier: + _target_: nemo.collections.common.parts.MultiLayerPerceptron + hidden_size: ${model.encoder.d_model} + num_classes: -1 + num_layers: 1 + activation: 'relu' + log_softmax: true + + loss: + label_smoothing: 0.0 + + sequence_generator: + type: greedy # choices=[greedy, topk, beam] + max_sequence_length: ${model.embedding.max_sequence_length} + temperature: 1.0 # for top-k sampling + beam_size: 1 # K for top-k sampling, N for beam search + len_pen: 0 # for beam-search + + optim_param_groups: + encoder: + lr: 0.0002 + + optim: + name: adamw + lr: 0.0003 + # optimizer arguments + betas: [0.9, 0.98] + # less necessity for weight_decay as we already have large augmentations with SpecAug + # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used + weight_decay: 0.0 + + # scheduler setup + sched: + name: CosineAnnealing # WarmupAnnealing + warmup_steps: 2000 + warmup_ratio: null + min_lr: 1e-5 + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 100 + max_steps: -1 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp_find_unused_parameters_true + accumulate_grad_batches: 1 + gradient_clip_val: 0.0 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 20 # Interval of logging. + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_wer" + mode: "min" + save_top_k: 5 + always_save_nemo: true # saves the checkpoints as nemo files instead of PTL checkpoints + save_best_model: false + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/eval_utils/evaluator.py b/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/eval_utils/evaluator.py new file mode 100644 index 0000000..e56711e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/eval_utils/evaluator.py @@ -0,0 +1,178 @@ +# ! /usr/bin/python +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import ast +from typing import Dict, List, Tuple, Union + +from .evaluation.metrics.metrics import ErrorMetric + + +def parse_semantics_str2dict(semantics_str: Union[List[str], str, Dict]) -> Tuple[Dict, bool]: + """ + This function parse the input string to a valid python dictionary for later evaluation. + Part of this function is adapted from + https://github.com/speechbrain/speechbrain/blob/develop/recipes/SLURP/direct/train_with_wav2vec2.py#L110-L127 + """ + invalid = False + if isinstance(semantics_str, dict): + return semantics_str, invalid + if isinstance(semantics_str, list): + semantics_str = " ".join(semantics_str) + + try: + if "|" in semantics_str: + semantics_str = semantics_str.replace("|", ",") + _dict = ast.literal_eval(semantics_str) + if not isinstance(_dict, dict): + _dict = { + "scenario": "none", + "action": "none", + "entities": [], + } + invalid = True + except Exception: # need this if the output is not a valid dict + _dict = { + "scenario": "none", + "action": "none", + "entities": [], + } + invalid = True + + if "scenario" not in _dict or not isinstance(_dict["scenario"], str): + _dict["scenario"] = "none" + invalid = True + if "action" not in _dict or not isinstance(_dict["action"], str): + _dict["action"] = "none" + invalid = True + if "entities" not in _dict: + _dict["entities"] = [] + invalid = True + else: + + def _parse_entity(item: Dict): + error = False + for key in ["type", "filler"]: + if key not in item or not isinstance(item[key], str): + item[key] = "none" + error = True + return item, error + + for i, x in enumerate(_dict["entities"]): + item, entity_error = _parse_entity(x) + invalid = invalid or entity_error + _dict["entities"][i] = item + + return _dict, invalid + + +class SLURPEvaluator: + """ + Evaluator class for calculating SLURP metrics + """ + + def __init__(self, average_mode: str = 'micro') -> None: + if average_mode not in ['micro', 'macro']: + raise ValueError(f"Only supports 'micro' or 'macro' average, but got {average_mode} instead.") + self.average_mode = average_mode + self.scenario_f1 = ErrorMetric.get_instance(metric="f1", average=average_mode) + self.action_f1 = ErrorMetric.get_instance(metric="f1", average=average_mode) + self.intent_f1 = ErrorMetric.get_instance(metric="f1", average=average_mode) + self.span_f1 = ErrorMetric.get_instance(metric="span_f1", average=average_mode) + self.distance_metrics = {} + for distance in ['word', 'char']: + self.distance_metrics[distance] = ErrorMetric.get_instance( + metric="span_distance_f1", average=average_mode, distance=distance + ) + self.slu_f1 = ErrorMetric.get_instance(metric="slu_f1", average=average_mode) + self.invalid = 0 + self.total = 0 + + def reset(self): + self.scenario_f1 = ErrorMetric.get_instance(metric="f1", average=self.average_mode) + self.action_f1 = ErrorMetric.get_instance(metric="f1", average=self.average_mode) + self.intent_f1 = ErrorMetric.get_instance(metric="f1", average=self.average_mode) + self.span_f1 = ErrorMetric.get_instance(metric="span_f1", average=self.average_mode) + self.distance_metrics = {} + for distance in ['word', 'char']: + self.distance_metrics[distance] = ErrorMetric.get_instance( + metric="span_distance_f1", average=self.average_mode, distance=distance + ) + self.slu_f1 = ErrorMetric.get_instance(metric="slu_f1", average=self.average_mode) + self.invalid = 0 + self.total = 0 + + def update(self, predictions: Union[List[str], str], groundtruth: Union[List[str], str]) -> None: + if isinstance(predictions, str): + predictions = [predictions] + if isinstance(groundtruth, str): + groundtruth = [groundtruth] + + for pred, truth in zip(predictions, groundtruth): + pred, syntax_error = parse_semantics_str2dict(pred) + truth, _ = parse_semantics_str2dict(truth) + self.scenario_f1(truth["scenario"], pred["scenario"]) + self.action_f1(truth["action"], pred["action"]) + self.intent_f1(f"{truth['scenario']}_{truth['action']}", f"{pred['scenario']}_{pred['action']}") + self.span_f1(truth["entities"], pred["entities"]) + for distance, metric in self.distance_metrics.items(): + metric(truth["entities"], pred["entities"]) + + self.total += 1 + self.invalid += int(syntax_error) + + def compute(self, aggregate=True) -> Dict: + scenario_results = self.scenario_f1.get_metric() + action_results = self.action_f1.get_metric() + intent_results = self.intent_f1.get_metric() + entity_results = self.span_f1.get_metric() + word_dist_results = self.distance_metrics['word'].get_metric() + char_dist_results = self.distance_metrics['char'].get_metric() + self.slu_f1(word_dist_results) + self.slu_f1(char_dist_results) + slurp_results = self.slu_f1.get_metric() + + if not aggregate: + return { + "scenario": scenario_results, + "action": action_results, + "intent": intent_results, + "entity": entity_results, + "word_dist": word_dist_results, + "char_dist": char_dist_results, + "slurp": slurp_results, + "invalid": self.invalid, + "total": self.total, + } + + scores = dict() + scores["invalid"] = self.invalid + scores["total"] = self.total + self.update_scores_dict(scenario_results, scores, "scenario") + self.update_scores_dict(action_results, scores, "action") + self.update_scores_dict(intent_results, scores, "intent") + self.update_scores_dict(entity_results, scores, "entity") + self.update_scores_dict(word_dist_results, scores, "word_dist") + self.update_scores_dict(char_dist_results, scores, "char_dist") + self.update_scores_dict(slurp_results, scores, "slurp") + + return scores + + def update_scores_dict(self, source: Dict, target: Dict, tag: str = '') -> Dict: + scores = source['overall'] + p, r, f1 = scores[:3] + target[f"{tag}_p"] = p + target[f"{tag}_r"] = r + target[f"{tag}_f1"] = f1 + return target diff --git a/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/eval_utils/inference.py b/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/eval_utils/inference.py new file mode 100644 index 0000000..d83d48b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/eval_utils/inference.py @@ -0,0 +1,240 @@ +# ! /usr/bin/python +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import contextlib +import glob +import json +import os +from dataclasses import dataclass, is_dataclass +from pathlib import Path +from typing import List, Optional + +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf +from tqdm.auto import tqdm + +from nemo.collections.asr.models import SLUIntentSlotBPEModel +from nemo.collections.asr.parts.utils.slu_utils import SequenceGeneratorConfig +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@dataclass +class InferenceConfig: + # Required configs + model_path: Optional[str] = None # Path to a .nemo file + pretrained_name: Optional[str] = None # Name of a pretrained model + audio_dir: Optional[str] = None # Path to a directory which contains audio files + dataset_manifest: Optional[str] = None # Path to dataset's JSON manifest + + # General configs + output_filename: Optional[str] = None + batch_size: int = 32 + num_workers: int = 8 + + # Set `cuda` to int to define CUDA device. If 'None', will look for CUDA + # device anyway, and do inference on CPU only if CUDA device is not found. + # If `cuda` is a negative number, inference will be on CPU only. + cuda: Optional[int] = None + amp: bool = False + audio_type: str = "wav" + + # Recompute model transcription, even if the output folder exists with scores. + overwrite_transcripts: bool = True + + # Decoding strategy for semantic outputs + sequence_generator: SequenceGeneratorConfig = SequenceGeneratorConfig(type="greedy") + + +def slurp_inference(model, path2manifest: str, batch_size: int = 4, num_workers: int = 0,) -> List[str]: + + if num_workers is None: + num_workers = min(batch_size, os.cpu_count() - 1) + + # We will store transcriptions here + hypotheses = [] + # Model's mode and device + mode = model.training + device = next(model.parameters()).device + dither_value = model.preprocessor.featurizer.dither + pad_to_value = model.preprocessor.featurizer.pad_to + + try: + model.preprocessor.featurizer.dither = 0.0 + model.preprocessor.featurizer.pad_to = 0 + # Switch model to evaluation mode + model.eval() + + logging_level = logging.get_verbosity() + logging.set_verbosity(logging.WARNING) + + config = { + 'manifest_filepath': path2manifest, + 'batch_size': batch_size, + 'num_workers': num_workers, + } + + temporary_datalayer = model._setup_transcribe_dataloader(config) + for test_batch in tqdm(temporary_datalayer, desc="Transcribing", ncols=80): + predictions = model.predict( + input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device) + ) + + hypotheses += predictions + + del predictions + del test_batch + + finally: + # set mode back to its original value + model.train(mode=mode) + model.preprocessor.featurizer.dither = dither_value + model.preprocessor.featurizer.pad_to = pad_to_value + logging.set_verbosity(logging_level) + return hypotheses + + +@hydra_runner(config_name="InferenceConfig", schema=InferenceConfig) +def run_inference(cfg: InferenceConfig) -> InferenceConfig: + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if cfg.model_path is None and cfg.pretrained_name is None: + raise ValueError("Both cfg.model_path and cfg.pretrained_name cannot be None!") + if cfg.audio_dir is None and cfg.dataset_manifest is None: + raise ValueError("Both cfg.audio_dir and cfg.dataset_manifest cannot be None!") + + # setup GPU + if cfg.cuda is None: + if torch.cuda.is_available(): + device = [0] # use 0th CUDA device + accelerator = 'gpu' + else: + device = 1 + accelerator = 'cpu' + else: + device = [cfg.cuda] + accelerator = 'gpu' + + map_location = torch.device('cuda:{}'.format(device[0]) if accelerator == 'gpu' else 'cpu') + + # setup model + if cfg.model_path is not None: + # restore model from .nemo file path + logging.info(f"Restoring model : {cfg.model_path}") + model = SLUIntentSlotBPEModel.restore_from(restore_path=cfg.model_path, map_location=map_location) + model_name = os.path.splitext(os.path.basename(cfg.model_path))[0] + else: + # restore model by name + model = SLUIntentSlotBPEModel.from_pretrained(model_name=cfg.pretrained_name, map_location=map_location) + model_name = cfg.pretrained_name + + trainer = pl.Trainer(devices=device, accelerator=accelerator) + model.set_trainer(trainer) + model = model.eval() + + # Setup decoding strategy + model.set_decoding_strategy(cfg.sequence_generator) + + # get audio filenames + if cfg.audio_dir is not None: + filepaths = list(glob.glob(os.path.join(cfg.audio_dir, f"**/*.{cfg.audio_type}"), recursive=True)) + else: + # get filenames from manifest + filepaths = [] + if os.stat(cfg.dataset_manifest).st_size == 0: + logging.error(f"The input dataset_manifest {cfg.dataset_manifest} is empty. Exiting!") + return None + + manifest_dir = Path(cfg.dataset_manifest).parent + with open(cfg.dataset_manifest, 'r') as f: + has_two_fields = [] + for line in f: + item = json.loads(line) + if "offset" in item and "duration" in item: + has_two_fields.append(True) + else: + has_two_fields.append(False) + audio_file = Path(item['audio_filepath']) + if not audio_file.is_file() and not audio_file.is_absolute(): + audio_file = manifest_dir / audio_file + filepaths.append(str(audio_file.absolute())) + + logging.info(f"\nStart inference with {len(filepaths)} files...\n") + + # setup AMP (optional) + if cfg.amp and torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and hasattr(torch.cuda.amp, 'autocast'): + logging.info("AMP enabled!\n") + autocast = torch.cuda.amp.autocast + else: + + @contextlib.contextmanager + def autocast(): + yield + + # Compute output filename + if cfg.output_filename is None: + # create default output filename + if cfg.audio_dir is not None: + cfg.output_filename = os.path.dirname(os.path.join(cfg.audio_dir, '.')) + '.json' + else: + cfg.output_filename = cfg.dataset_manifest.replace('.json', f'_{model_name}.json') + + # if transcripts should not be overwritten, and already exists, skip re-transcription step and return + if not cfg.overwrite_transcripts and os.path.exists(cfg.output_filename): + logging.info( + f"Previous transcripts found at {cfg.output_filename}, and flag `overwrite_transcripts`" + f"is {cfg.overwrite_transcripts}. Returning without re-transcribing text." + ) + + return cfg + + # transcribe audio + with autocast(): + with torch.no_grad(): + predictions = slurp_inference( + model=model, + path2manifest=cfg.dataset_manifest, + batch_size=cfg.batch_size, + num_workers=cfg.num_workers, + ) + + logging.info(f"Finished transcribing {len(filepaths)} files !") + + logging.info(f"Writing transcriptions into file: {cfg.output_filename}") + + # write audio transcriptions + with open(cfg.output_filename, 'w', encoding='utf-8') as f: + if cfg.audio_dir is not None: + for idx, text in enumerate(predictions): + item = {'audio_filepath': filepaths[idx], 'pred_text': text} + f.write(json.dumps(item) + "\n") + else: + with open(cfg.dataset_manifest, 'r') as fr: + for idx, line in enumerate(fr): + item = json.loads(line) + item['pred_text'] = predictions[idx] + f.write(json.dumps(item) + "\n") + + logging.info("Finished writing predictions !") + return cfg + + +if __name__ == '__main__': + run_inference() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/run_speech_intent_slot_eval.py b/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/run_speech_intent_slot_eval.py new file mode 100644 index 0000000..8ed0abe --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/run_speech_intent_slot_eval.py @@ -0,0 +1,185 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +from dataclasses import dataclass, is_dataclass +from pathlib import Path +from typing import Optional + +import torch +from eval_utils.evaluation.util import format_results +from eval_utils.evaluator import SLURPEvaluator +from eval_utils.inference import InferenceConfig, run_inference +from omegaconf import MISSING, OmegaConf, open_dict + +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +@dataclass +class EvaluationConfig(InferenceConfig): + dataset_manifest: str = MISSING + output_filename: Optional[str] = "evaluation_transcripts.json" + average: str = "micro" + full: bool = False + errors: bool = False + table_layout: str = "fancy_grid" + only_score_manifest: bool = False + + +@hydra_runner(config_name="EvaluationConfig", schema=EvaluationConfig) +def main(cfg: EvaluationConfig): + torch.set_grad_enabled(False) + + cfg.output_filename = str(Path(Path(cfg.model_path).parent) / Path("predictions.json")) + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if cfg.audio_dir is not None: + raise RuntimeError( + "Evaluation script requires ground truth labels to be passed via a manifest file. " + "If manifest file is available, submit it via `dataset_manifest` argument." + ) + + if not os.path.exists(cfg.dataset_manifest): + raise FileNotFoundError(f"The dataset manifest file could not be found at path : {cfg.dataset_manifest}") + + if not cfg.only_score_manifest: + # Transcribe speech into an output directory + transcription_cfg = run_inference(cfg) # type: EvaluationConfig + + # Release GPU memory if it was used during transcription + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + logging.info("Finished transcribing speech dataset. Computing metrics..") + + else: + cfg.output_filename = cfg.dataset_manifest + transcription_cfg = cfg + + ground_truth_text = [] + predicted_text = [] + invalid_manifest = False + + with open(transcription_cfg.output_filename, 'r') as f: + for line in f: + data = json.loads(line) + + if 'pred_text' not in data: + invalid_manifest = True + break + + ground_truth_text.append(data['text']) + predicted_text.append(data['pred_text']) + + # Test for invalid manifest supplied + if invalid_manifest: + raise ValueError( + f"Invalid manifest provided: {transcription_cfg.output_filename} does not " + f"contain value for `pred_text`." + ) + + # Compute the metrics + evaluator = SLURPEvaluator(cfg.average) + evaluator.update(predictions=predicted_text, groundtruth=ground_truth_text) + results = evaluator.compute(aggregate=False) + total = results["total"] + invalid = results["invalid"] + slurp_f1 = results["slurp"]["overall"][2] + + print("-------------- Results --------------") + print( + format_results( + results=results["scenario"], + label="scenario", + full=cfg.full, + errors=cfg.errors, + table_layout=cfg.table_layout, + ), + "\n", + ) + + print( + format_results( + results=results["action"], label="action", full=cfg.full, errors=cfg.errors, table_layout=cfg.table_layout + ), + "\n", + ) + + print( + format_results( + results=results["intent"], + label="intent (scen_act)", + full=cfg.full, + errors=cfg.errors, + table_layout=cfg.table_layout, + ), + "\n", + ) + + print( + format_results( + results=results["entity"], + label="entities", + full=cfg.full, + errors=cfg.errors, + table_layout=cfg.table_layout, + ), + "\n", + ) + + print( + format_results( + results=results["word_dist"], + label="entities (word distance)", + full=cfg.full, + errors=cfg.errors, + table_layout=cfg.table_layout, + ), + "\n", + ) + + print( + format_results( + results=results["char_dist"], + label="entities (char distance)", + full=cfg.full, + errors=cfg.errors, + table_layout=cfg.table_layout, + ), + "\n", + ) + + print( + format_results( + results=results["slurp"], label="SLU F1", full=cfg.full, errors=cfg.errors, table_layout=cfg.table_layout + ), + "\n", + ) + + print(f"Found {invalid} out of {total} predictions that have syntax error.") + + # Inject the metric name and score into the config, and return the entire config + with open_dict(cfg): + cfg.metric_name = "slurp_f1" + cfg.metric_value = slurp_f1 + + return cfg + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/run_speech_intent_slot_train.py b/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/run_speech_intent_slot_train.py new file mode 100644 index 0000000..d8989bf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/slu/speech_intent_slot/run_speech_intent_slot_train.py @@ -0,0 +1,127 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +# Preparing the Tokenizer for the dataset +Use the `process_asr_text_tokenizer.py` script under /scripts/tokenizers/ in order to prepare the tokenizer. + +```sh +python /scripts/tokenizers/process_asr_text_tokenizer.py \ + --manifest= + OR + --data_file= \ + --data_root="" \ + --vocab_size= \ + --tokenizer=<"spe" or "wpe"> \ + --no_lower_case \ + --spe_type=<"unigram", "bpe", "char" or "word"> \ + --spe_character_coverage=1.0 \ + --log +``` + +# Training the model +```sh +python run_speech_intent_slot_train.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath= \ + model.validation_ds.manifest_filepath= \ + model.tokenizer.dir= \ + model.tokenizer.type= \ + trainer.devices=-1 \ + trainer.accelerator="gpu" \ + trainer.strategy="ddp" \ + trainer.max_epochs=100 \ + model.optim.name="adamw" \ + model.optim.lr=0.001 \ + model.optim.betas=[0.9,0.999] \ + model.optim.weight_decay=0.0001 \ + model.optim.sched.warmup_steps=2000 + exp_manager.create_wandb_logger=True \ + exp_manager.wandb_logger_kwargs.name="" \ + exp_manager.wandb_logger_kwargs.project="" +``` + +# Fine-tune a model + +For documentation on fine-tuning this model, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations + +# Pretrained Models + +For documentation on existing pretrained models, please visit - +https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speech_intent_slot/results.html + +""" + +from pathlib import Path + +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf + +from nemo.collections.asr.models import ASRModel, SLUIntentSlotBPEModel, SpeechEncDecSelfSupervisedModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="./configs/", config_name="conformer_transformer_large_bpe") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + model = SLUIntentSlotBPEModel(cfg=cfg.model, trainer=trainer) + + # Init encoder from pretrained model + pretrained_encoder_name = cfg.pretrained_encoder.name + if pretrained_encoder_name is not None: + if Path(pretrained_encoder_name).is_file(): + logging.info(f"Loading pretrained encoder from local: {pretrained_encoder_name}") + pretraind_model = ASRModel.restore_from( + restore_path=pretrained_encoder_name, map_location=torch.device("cpu") + ) + model.encoder.load_state_dict(pretraind_model.encoder.state_dict(), strict=False) + del pretraind_model + else: + logging.info(f"Loading pretrained encoder from NGC: {pretrained_encoder_name}") + if pretrained_encoder_name.startswith("ssl_"): + model_cls = SpeechEncDecSelfSupervisedModel + elif pretrained_encoder_name.startswith("stt_"): + model_cls = ASRModel + else: + raise ValueError(f"Unknown pretrained encoder: {pretrained_encoder_name}") + pretraind_model = model_cls.from_pretrained( + model_name=pretrained_encoder_name, map_location=torch.device("cpu") + ) + model.encoder.load_state_dict(pretraind_model.encoder.state_dict(), strict=False) + del pretraind_model + else: + logging.info("Not using pretrained encoder.") + + if cfg.pretrained_encoder.freeze: + logging.info("Freezing encoder...") + model.encoder.freeze() + else: + model.encoder.unfreeze() + + trainer.fit(model) + + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if model.prepare_test(trainer): + trainer.test(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/README.md b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/README.md new file mode 100644 index 0000000..3ee67d7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/README.md @@ -0,0 +1,8 @@ +Speaker tasks in general are broadly classified into two tasks: +- [Speaker Recognition](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_recognition/intro.html) +- [Speaker Diarization](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_diarization/intro.html) + +**Speaker Recognition** is a research area which solves two major tasks: speaker identification (what is the identity of the speaker?) and speaker verification (is the speaker who they claim to be?). Whereas **Speaker Diarization** is a task segmenting audio recordings by speaker labels (Who Speaks When?). + +In *recognition* folder we provide scripts for training, inference and verification of audio samples. +In *diarization* folder we provide scripts for inference of speaker diarization using pretrained VAD (optional) and Speaker embedding extractor models diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/README.md b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/README.md new file mode 100644 index 0000000..a23984b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/README.md @@ -0,0 +1,326 @@ +# Speaker Diarization + +Documentation section for speaker related tasks can be found at: + - [Speaker Diarization](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_diarization/intro.html) + - [Speaker Identification and Verification](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_recognition/intro.html) + + +## Features of NeMo Speaker Diarization +- Provides pretrained speaker embedding extractor models and VAD models. +- Does not need to be tuned on dev-set while showing the better performance than AHC+PLDA method in general. +- Estimates the number of speakers in the given session. +- Provides example script for asr transcription with speaker labels. + +## Supported Pretrained Speaker Embedding Extractor models +- [titanet_large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/titanet_large) +- [ecapa_tdnn](https://ngc.nvidia.com/catalog/models/nvidia:nemo:ecapa_tdnn) +- [speakerverification_speakernet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/speakerverification_speakernet) + +## Supported Pretrained VAD models +- [vad_multilingual_marblenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet) +- [vad_marblenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_marblenet) +- [vad_telephony_marblenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_telephony_marblenet) + +## Supported ASR models +QuartzNet, CitriNet and Conformer-CTC models are supported. +Recommended models on NGC: +- [stt_en_quartznet15x5](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_quartznet15x5) +- [stt_en_conformer_ctc_large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_large) +- [stt_en_citrinet_1024](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_citrinet_1024) + +## Performance + +#### Clustering Diarizer +Diarization Error Rate (DER) table of `titanet_large.nemo` model on well known evaluation datasets. + +| Evaluation Condition | AMI(Lapel) | AMI(MixHeadset) | CH109 | NIST SRE 2000 | +|:--------------------------------------:|:--------------:|:-------------------:|:--------:|:-------------:| +| Domain Configuration | Meeting | Meeting |Telephonic| Telephonic | +| Oracle VAD
Known # of Speakers | 1.28 | 1.07 | 0.56 | 5.62 | +| Oracle VAD
Unknown # of Speakers | 1.28 | 1.4 | 0.88 | 4.33 | + +* All models were tested using the domain specific `.yaml` files which can be found in `conf/inference/` folder. +* The above result is based on the oracle Voice Activity Detection (VAD) result. +* This result is based on [titanet_large.nemo](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/titanet_large) model. + +#### Neural Diarizer +Multi-scale Diarization Decoder (MSDD) model [Multi-scale Diarization decoder](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_diarization/model.html) +Diarization Error Rate (DER) table of [diar_msdd_telephonic.nemo](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/diar_msdd_telephonic) model on telephonic speech datasets. + +| CH109| Forgiving | Fair | Full | +|-------------------------------------:|---------------------------------|----------------------------------|---------------------------------| +| (collar, ignore_overlap)| (0.25, True) | (0.25, True) | (0.0, False) | +| False Alarm | - | 0.62% | 1.80% | +| Miss | - | 2.47% | 5.96% | +| Confusion | - | 0.43% | 2.10% | +| DER | **0.58%** | **3.52%** | **9.86%** | + + +| CALLHOME | Forgiving | Fair | Full | +|-------------------------------------:|---------------------------------|----------------------------------|---------------------------------| +| (collar, ignore_overlap)| (0.25, True) | (0.25, True) | (0.0, False) | +| False Alarm | - | 1.05% | 2.24% | +| Miss | - | 7.62% | 11.09% | +| Confusion | - | 4.06% | 6.03% | +| DER | **4.15%** | **12.73%** | **19.37%** | + +* Evaluation setting: Oracle VAD
Unknown number of speakers (max. 8) +* Clustering parameter: `max_rp_threshold=0.15` +* All models were tested using the domain specific `.yaml` files which can be found in `conf/inference/` folder. +* The above result is based on the oracle Voice Activity Detection (VAD) result. + +## Run Speaker Diarization on Your Audio Files + +#### Example script for clustering diarizer: with system-VAD +```bash + python clustering_diarizer/offline_diar_infer.py \ + diarizer.manifest_filepath= \ + diarizer.out_dir='demo_output' \ + diarizer.speaker_embeddings.parameters.save_embeddings=False \ + diarizer.vad.model_path= \ + diarizer.speaker_embeddings.model_path= +``` + +#### Example script for neural diarizer: with system-VAD +```bash + python neural_diarizer/multiscale_diar_decoder_infer.py \ + diarizer.manifest_filepath= \ + diarizer.out_dir='demo_output' \ + diarizer.speaker_embeddings.parameters.save_embeddings=False \ + diarizer.vad.model_path= \ + diarizer.speaker_embeddings.model_path= \ + diarizer.msdd_model.model_path= \ +``` + +If you have oracle VAD files and groundtruth RTTM files for evaluation: +Provide rttm files in the input manifest file and enable oracle_vad as shown below. +```bash +... + diarizer.oracle_vad=True \ +... +``` + +#### Arguments +      To run speaker diarization on your audio recordings, you need to prepare the following file. + +- **`diarizer.manifest_filepath`: ** Path to manifest file + +Example: `manifest.json` + +```bash +{"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, label: "infer", "text": "-", "num_speakers": null, "rttm_filepath": "/path/to/rttm/file", "uem_filepath"="/path/to/uem/filepath"} +``` +Mandatory fields are `audio_filepath`, `offset`, `duration`, `label:"infer"` and `text: ` , and the rest are optional keys which can be passed based on the type of evaluation + +Some of important options in config file: + +- **`diarizer.vad.model_path`: voice activity detection model name or path to the model** + +Specify the name of VAD model, then the script will download the model from NGC. Currently, we have 'vad_multilingual_marblenet', 'vad_marblenet' and 'vad_telephony_marblenet' as options for VAD models. + +`diarizer.vad.model_path='vad_multilingual_marblenet'` + + +Instead, you can also download the model from [vad_multilingual_marblenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet), [vad_marblenet](https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_marblenet) and [vad_telephony_marblenet](https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_telephony_marblenet) and specify the full path name to the model as below. + +`diarizer.vad.model_path='path/to/vad_multilingual_marblenet.nemo'` + +- **`diarizer.speaker_embeddings.model_path`: speaker embedding model name** + +Specify the name of speaker embedding model, then the script will download the model from NGC. Currently, we support 'titanet_large', 'ecapa_tdnn' and 'speakerverification_speakernet'. + +`diarizer.speaker_embeddings.model_path='titanet_large'` + +You could also download *.nemo files from [this link](https://ngc.nvidia.com/catalog/models?orderBy=scoreDESC&pageNumber=0&query=SpeakerNet&quickFilter=&filters=) and specify the full path name to the speaker embedding model file (`*.nemo`). + +`diarizer.speaker_embeddings.model_path='path/to/titanet_large.nemo'` + + +- **`diarizer.speaker_embeddings.parameters.multiscale_weights`: multiscale diarization** + +Multiscale diarization system employs multiple scales at the same time to obtain a finer temporal resolution. To use multiscale feature, at least two scales and scale weights should be provided. The scales should be provided in descending order, from the longest scale to the base scale (the shortest). If multiple scales are provided, multiscale_weights must be provided in list format. The following example shows how multiscale parameters are specified and the recommended parameters. + +- **`diarizer.msdd_model.model_path`: neural diarizer (multiscale diarization decoder) name** + +If you want to use a neural diarizer model (e.g., MSDD model), specify the name of the neural diarizer model, then the script will download the model from NGC. Currently, we support 'diar_msdd_telephonic'. + +Note that you should not specify a scale setting that does not match with the MSDD model you are using. For example, `diar_msdd_telephonic` model is based on 5 scales as in the configs in model configs. + +`diarizer.speaker_embeddings.model_path='diar_msdd_telephonic' + +You could also download [diar_msdd_telephonic](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/diar_msdd_telephonic) +and specify the full path name to the speaker embedding model file (`*.nemo`). + +`diarizer.msdd_model.model_path='path/to/diar_msdd_telephonic.nemo'` + + +#### Example script: single-scale and multiscale + +Single-scale setting: +```bash + python offline_diar_infer.py \ + ... ... + parameters.window_length_in_sec=1.5 \ + parameters.shift_length_in_sec=0.75 \ + parameters.multiscale_weights=null \ +``` + +Multiscale setting (base scale - window_length 0.5 s and shift_length 0.25): + +```bash + python offline_diar_infer.py \ + ... ... + parameters.window_length_in_sec=[1.5,1.0,0.5] \ + parameters.shift_length_in_sec=[0.75,0.5,0.25] \ + parameters.multiscale_weights=[0.33,0.33,0.33] \ +``` + +
+ +## Run Speech Recognition with Speaker Diarization + +Using the script `offline_diar_with_asr_infer.py`, you can transcribe your audio recording with speaker labels as shown below: + +``` +[00:03.34 - 00:04.46] speaker_0: back from the gym oh good how's it going +[00:04.46 - 00:09.96] speaker_1: oh pretty well it was really crowded today yeah i kind of assumed everyone would be at the shore uhhuh +[00:12.10 - 00:13.97] speaker_0: well it's the middle of the week or whatever so +``` + +Currently, NeMo offline diarization inference supports QuartzNet English model and ConformerCTC ASR models (e.g.,`QuartzNet15x5Base-En`, `stt_en_conformer_ctc_large`). + +#### Example script + +```bash +python offline_diar_with_asr_infer.py \ + diarizer.manifest_filepath= \ + diarizer.out_dir='demo_asr_output' \ + diarizer.speaker_embeddings.model_path= \ + diarizer.asr.model_path= \ + diarizer.speaker_embeddings.parameters.save_embeddings=False \ + diarizer.asr.parameters.asr_based_vad=True +``` +If you have reference rttm files or oracle number of speaker information, you can provide those file paths and number of speakers in the manifest file path and pass `diarizer.clustering.parameters.oracle_num_speakers=True` as shown in the following example. + +```bash +python offline_diar_with_asr_infer.py \ + diarizer.manifest_filepath= \ + diarizer.out_dir='demo_asr_output' \ + diarizer.speaker_embeddings.model_path= \ + diarizer.asr.model_path= \ + diarizer.speaker_embeddings.parameters.save_embeddings=False \ + diarizer.asr.parameters.asr_based_vad=True \ + diarizer.clustering.parameters.oracle_num_speakers=True +``` + +#### Output folders + +The above script will create a folder named `./demo_asr_output/`. +For example, in `./demo_asr_output/`, you can check the results as below. + +```bash +./asr_with_diar +├── pred_rttms + └── my_audio1.json + └── my_audio1.txt + └── my_audio1.rttm + └── my_audio1_gecko.json +│ +└── speaker_outputs + └── oracle_vad_manifest.json + └── subsegments_scale2_cluster.label + └── subsegments_scale0.json + └── subsegments_scale1.json + └── subsegments_scale2.json +... +``` + +`my_audio1.json` file contains word-by-word json output with speaker label and time stamps. We also provide a json output file for [gecko](https://gong-io.github.io/gecko/) tool, where you can visualize the diarization result along with the ASR output. + +Example: `./demo_asr_output/pred_rttms/my_audio1.json` +```bash +{ + "status": "Success", + "session_id": "my_audio1", + "transcription": "back from the gym oh good ...", + "speaker_count": 2, + "words": [ + { + "word": "back", + "start_time": 0.44, + "end_time": 0.56, + "speaker_label": "speaker_0" + }, +... + { + "word": "oh", + "start_time": 1.74, + "end_time": 1.88, + "speaker_label": "speaker_1" + }, + { + "word": "good", + "start_time": 2.08, + "end_time": 3.28, + "speaker_label": "speaker_1" + }, +``` + +`*.txt` files in `pred_rttms` folder contain transcriptions with speaker labels and corresponding time. + +Example: `./demo_asr_output/pred_rttms/my_audio1.txt` +``` +[00:03.34 - 00:04.46] speaker_0: back from the gym oh good how's it going +[00:04.46 - 00:09.96] speaker_1: pretty well it was really crowded today yeah i kind of assumed everylonewould be at the shore uhhuh +[00:12.10 - 00:13.97] speaker_0: well it's the middle of the week or whatever so +[00:13.97 - 00:15.78] speaker_1: but it's the fourth of july mm +[00:16.90 - 00:21.80] speaker_0: so yeah people still work tomorrow do you have to work tomorrow did you drive off yesterday +``` + +In `speaker_outputs` folder we have three kinds of files as follows: + + - `oracle_vad_manifest.json` file contains oracle VAD labels that are extracted from RTTM files. + - `subsegments_scale.json` is a manifest file for subsegments, which includes segment-by-segment start and end time with original wav file path. In multi-scale mode, this file is generated for each ``. + - `subsegments_scale_cluster.label` file contains the estimated cluster labels for each segment. This file is only generated for the base scale index in multi-scale diarization mode. + + +### Optional Features for Speech Recognition with Speaker Diarization + +#### Beam Search Decoder + +Beam-search decoder can be applied to CTC based ASR models. To use this feature, [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) should be installed. [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) supports word timestamp generation and can be applied to speaker diarization. pyctcdecode also requires [KenLM](https://github.com/kpu/kenlm) and KenLM is recommended to be installed using PyPI. Install pyctcdecode in your environment with the following commands: +``` +pip install pyctcdecode +pip install https://github.com/kpu/kenlm/archive/master.zip +``` +You should provide a trained KenLM language model to use pyctcdecode. Binary or `.arpa` format can be provided to hydra configuration as below. + +```bash + python offline_diar_with_asr_infer.py \ + ... ... + diarizer.asr.ctc_decoder_parameters.pretrained_language_model="/path/to/kenlm_language_model.binary" +``` +You can download publicly available language models (`.arpa` files) at [KALDI Tedlium Language Models](https://kaldi-asr.org/models/m5). Download [4-gram Big ARPA](https://kaldi-asr.org/models/5/4gram_big.arpa.gz) and provide the model path. + +The following CTC decoder parameters can be modified to optimize the performance. +`diarizer.asr.ctc_decoder_parameters.beam_width` (default: 32) +`diarizer.asr.ctc_decoder_parameters.alpha` (default: 0.5) +`diarizer.asr.ctc_decoder_parameters.beta` (default: 2.5) + +#### Realign Words with a Language Model (Experimental) + +Diarization result with ASR transcript can be enhanced by applying a language model. To use this feature, python package [arpa](https://pypi.org/project/arpa/) should be installed. +``` +pip install arpa +``` +`diarizer.asr.realigning_lm_parameters.logprob_diff_threshold` can be modified to optimize the diarization performance (default value is 1.2). The lower the threshold, the more changes are expected to be seen in the output transcript. + +`arpa` package also uses KenLM language models as in pyctcdecode. You can download publicly available [4-gram Big ARPA](https://kaldi-asr.org/models/5/4gram_big.arpa.gz) model and provide the model path to hydra configuration as follows. + + +```bash +python offline_diar_with_asr_infer.py \ + ... ... + diarizer.asr.realigning_lm_parameters.logprob_diff_threshold=1.2 \ + diarizer.asr.realigning_lm_parameters.arpa_language_model="/path/to/4gram_big.arpa"\ +``` diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/clustering_diarizer/offline_diar_infer.py b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/clustering_diarizer/offline_diar_infer.py new file mode 100644 index 0000000..35077a5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/clustering_diarizer/offline_diar_infer.py @@ -0,0 +1,47 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from omegaconf import OmegaConf +from pytorch_lightning import seed_everything + +from nemo.collections.asr.models import ClusteringDiarizer +from nemo.core.config import hydra_runner +from nemo.utils import logging + +""" +This script demonstrates how to use run speaker diarization. +Usage: + python offline_diar_infer.py \ + diarizer.manifest_filepath= \ + diarizer.out_dir='demo_output' \ + diarizer.speaker_embeddings.model_path= \ + diarizer.vad.model_path='vad_marblenet' \ + diarizer.speaker_embeddings.parameters.save_embeddings=False + +Check out whole parameters in ./conf/offline_diarization.yaml and their meanings. +For details, have a look at /tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb +""" + +seed_everything(42) + + +@hydra_runner(config_path="../conf/inference", config_name="diar_infer_meeting.yaml") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + sd_model = ClusteringDiarizer(cfg=cfg).to(cfg.device) + sd_model.diarize() + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/clustering_diarizer/offline_diar_with_asr_infer.py b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/clustering_diarizer/offline_diar_with_asr_infer.py new file mode 100644 index 0000000..d15adb5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/clustering_diarizer/offline_diar_with_asr_infer.py @@ -0,0 +1,94 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from omegaconf import OmegaConf + +from nemo.collections.asr.parts.utils.decoder_timestamps_utils import ASRDecoderTimeStamps +from nemo.collections.asr.parts.utils.diarization_utils import OfflineDiarWithASR +from nemo.core.config import hydra_runner +from nemo.utils import logging + + +""" +This script demonstrates how to run offline speaker diarization with asr. +Usage: +python offline_diar_with_asr_infer.py \ + diarizer.manifest_filepath= \ + diarizer.out_dir='demo_asr_output' \ + diarizer.speaker_embeddings.model_path= \ + diarizer.asr.model_path= \ + diarizer.asr.parameters.asr_based_vad=True \ + diarizer.speaker_embeddings.parameters.save_embeddings=False + +Check out whole parameters in ./conf/offline_diarization_with_asr.yaml and their meanings. +For details, have a look at /tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb +Currently, the following NGC models are supported: + + stt_en_quartznet15x5 + stt_en_citrinet* + stt_en_conformer_ctc* + +""" + + +@hydra_runner(config_path="../conf/inference", config_name="diar_infer_meeting.yaml") +def main(cfg): + + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + # ASR inference for words and word timestamps + asr_decoder_ts = ASRDecoderTimeStamps(cfg.diarizer) + asr_model = asr_decoder_ts.set_asr_model() + word_hyp, word_ts_hyp = asr_decoder_ts.run_ASR(asr_model) + + # Create a class instance for matching ASR and diarization results + asr_diar_offline = OfflineDiarWithASR(cfg.diarizer) + asr_diar_offline.word_ts_anchor_offset = asr_decoder_ts.word_ts_anchor_offset + + # Diarization inference for speaker labels + diar_hyp, diar_score = asr_diar_offline.run_diarization(cfg, word_ts_hyp) + trans_info_dict = asr_diar_offline.get_transcript_with_speaker_labels(diar_hyp, word_hyp, word_ts_hyp) + + # If RTTM is provided and DER evaluation + if diar_score is not None: + # Get session-level diarization error rate and speaker counting error + der_results = OfflineDiarWithASR.gather_eval_results( + diar_score=diar_score, + audio_rttm_map_dict=asr_diar_offline.AUDIO_RTTM_MAP, + trans_info_dict=trans_info_dict, + root_path=asr_diar_offline.root_path, + ) + + # Calculate WER and cpWER if reference CTM files exist + wer_results = OfflineDiarWithASR.evaluate( + hyp_trans_info_dict=trans_info_dict, + audio_file_list=asr_diar_offline.audio_file_list, + ref_ctm_file_list=asr_diar_offline.ctm_file_list, + ) + + # Print average DER, WER and cpWER + OfflineDiarWithASR.print_errors(der_results=der_results, wer_results=wer_results) + + # Save detailed session-level evaluation results in `root_path`. + OfflineDiarWithASR.write_session_level_result_in_csv( + der_results=der_results, + wer_results=wer_results, + root_path=asr_diar_offline.root_path, + csv_columns=asr_diar_offline.csv_columns, + ) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/inference/diar_infer_general.yaml b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/inference/diar_infer_general.yaml new file mode 100644 index 0000000..6c24683 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/inference/diar_infer_general.yaml @@ -0,0 +1,93 @@ +# This YAML file is created for all types of offline speaker diarization inference tasks in `/example/speaker_tasks/diarization` folder. +# The inference parameters for VAD, speaker embedding extractor, clustering module, MSDD module, ASR decoder are all included in this YAML file. +# All the keys under `diarizer` key (`vad`, `speaker_embeddings`, `clustering`, `msdd_model`, `asr`) can be selectively used for its own purpose and also can be ignored if the module is not used. +# The configurations in this YAML file is optimized to show balanced performances on various types of domain. VAD is optimized on multilingual ASR datasets and diarizer is optimized on DIHARD3 development set. +# An example line in an input manifest file (`.json` format): +# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer", "text": "-", "num_speakers": null, "rttm_filepath": "/path/to/rttm/file", "uem_filepath": "/path/to/uem/file"} +name: &name "ClusterDiarizer" + +num_workers: 1 +sample_rate: 16000 +batch_size: 64 +device: null # can specify a specific device, i.e: cuda:1 (default cuda if cuda available, else cpu) +verbose: True # enable additional logging + +diarizer: + manifest_filepath: ??? + out_dir: ??? + oracle_vad: False # If True, uses RTTM files provided in the manifest file to get speech activity (VAD) timestamps + collar: 0.25 # Collar value for scoring + ignore_overlap: True # Consider or ignore overlap segments while scoring + + vad: + model_path: vad_multilingual_marblenet # .nemo local model path or pretrained VAD model name + external_vad_manifest: null # This option is provided to use external vad and provide its speech activity labels for speaker embeddings extraction. Only one of model_path or external_vad_manifest should be set + + parameters: # Tuned by detection error rate (false alarm + miss) on multilingual ASR evaluation datasets + window_length_in_sec: 0.63 # Window length in sec for VAD context input + shift_length_in_sec: 0.08 # Shift length in sec for generate frame level VAD prediction + smoothing: False # False or type of smoothing method (eg: median) + overlap: 0.5 # Overlap ratio for overlapped mean/median smoothing filter + onset: 0.5 # Onset threshold for detecting the beginning and end of a speech + offset: 0.3 # Offset threshold for detecting the end of a speech + pad_onset: 0.2 # Adding durations before each speech segment + pad_offset: 0.2 # Adding durations after each speech segment + min_duration_on: 0.5 # Threshold for small non_speech deletion + min_duration_off: 0.5 # Threshold for short speech segment deletion + filter_speech_first: True + + speaker_embeddings: + model_path: titanet_large # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet) + parameters: + window_length_in_sec: [1.9,1.2,0.5] # Window length(s) in sec (floating-point number). either a number or a list. ex) 1.5 or [1.5,1.0,0.5] + shift_length_in_sec: [0.95,0.6,0.25] # Shift length(s) in sec (floating-point number). either a number or a list. ex) 0.75 or [0.75,0.5,0.25] + multiscale_weights: [1,1,1] # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. ex) [0.33,0.33,0.33] + save_embeddings: True # If True, save speaker embeddings in pickle format. This should be True if clustering result is used for other models, such as `msdd_model`. + + clustering: + parameters: + oracle_num_speakers: False # If True, use num of speakers value provided in manifest file. + max_num_speakers: 8 # Max number of speakers for each recording. If an oracle number of speakers is passed, this value is ignored. + enhanced_count_thres: 80 # If the number of segments is lower than this number, enhanced speaker counting is activated. + max_rp_threshold: 0.25 # Determines the range of p-value search: 0 < p <= max_rp_threshold. + sparse_search_volume: 10 # The higher the number, the more values will be examined with more time. + maj_vote_spk_count: False # If True, take a majority vote on multiple p-values to estimate the number of speakers. + chunk_cluster_count: 50 # Number of forced clusters (overclustering) per unit chunk in long-form audio clustering. + embeddings_per_chunk: 10000 # Number of embeddings in each chunk for long-form audio clustering. Adjust based on GPU memory capacity. (default: 10000, approximately 40 mins of audio) + + msdd_model: + model_path: null # .nemo local model path or pretrained model name for multiscale diarization decoder (MSDD) + parameters: + use_speaker_model_from_ckpt: True # If True, use speaker embedding model in checkpoint. If False, the provided speaker embedding model in config will be used. + infer_batch_size: 25 # Batch size for MSDD inference. + sigmoid_threshold: [0.7] # Sigmoid threshold for generating binarized speaker labels. The smaller the more generous on detecting overlaps. + seq_eval_mode: False # If True, use oracle number of speaker and evaluate F1 score for the given speaker sequences. Default is False. + split_infer: True # If True, break the input audio clip to short sequences and calculate cluster average embeddings for inference. + diar_window_length: 50 # The length of split short sequence when split_infer is True. + overlap_infer_spk_limit: 5 # If the estimated number of speakers are larger than this number, overlap speech is not estimated. + + asr: + model_path: null # Provide NGC cloud ASR model name. stt_en_conformer_ctc_* models are recommended for diarization purposes. + parameters: + asr_based_vad: False # if True, speech segmentation for diarization is based on word-timestamps from ASR inference. + asr_based_vad_threshold: 1.0 # Threshold (in sec) that caps the gap between two words when generating VAD timestamps using ASR based VAD. + asr_batch_size: null # Batch size can be dependent on each ASR model. Default batch sizes are applied if set to null. + decoder_delay_in_sec: null # Native decoder delay. null is recommended to use the default values for each ASR model. + word_ts_anchor_offset: null # Offset to set a reference point from the start of the word. Recommended range of values is [-0.05 0.2]. + word_ts_anchor_pos: "start" # Select which part of the word timestamp we want to use. The options are: 'start', 'end', 'mid'. + fix_word_ts_with_VAD: False # Fix the word timestamp using VAD output. You must provide a VAD model to use this feature. + colored_text: False # If True, use colored text to distinguish speakers in the output transcript. + print_time: True # If True, the start and end time of each speaker turn is printed in the output transcript. + break_lines: False # If True, the output transcript breaks the line to fix the line width (default is 90 chars) + + ctc_decoder_parameters: # Optional beam search decoder (pyctcdecode) + pretrained_language_model: null # KenLM model file: .arpa model file or .bin binary file. + beam_width: 32 + alpha: 0.5 + beta: 2.5 + + realigning_lm_parameters: # Experimental feature + arpa_language_model: null # Provide a KenLM language model in .arpa format. + min_number_of_words: 3 # Min number of words for the left context. + max_number_of_words: 10 # Max number of words for the right context. + logprob_diff_threshold: 1.2 # The threshold for the difference between two log probability values from two hypotheses. \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/inference/diar_infer_meeting.yaml b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/inference/diar_infer_meeting.yaml new file mode 100644 index 0000000..738cbfd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/inference/diar_infer_meeting.yaml @@ -0,0 +1,93 @@ +# This YAML file is created for all types of offline speaker diarization inference tasks in `/example/speaker_tasks/diarization` folder. +# The inference parameters for VAD, speaker embedding extractor, clustering module, MSDD module, ASR decoder are all included in this YAML file. +# All the keys under `diarizer` key (`vad`, `speaker_embeddings`, `clustering`, `msdd_model`, `asr`) can be selectively used for its own purpose and also can be ignored if the module is not used. +# The configurations in this YAML file is suitable for 3~5 speakers participating in a meeting and may not show the best performance on other types of dialogues. +# An example line in an input manifest file (`.json` format): +# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer", "text": "-", "num_speakers": null, "rttm_filepath": "/path/to/rttm/file", "uem_filepath": "/path/to/uem/file"} +name: &name "ClusterDiarizer" + +num_workers: 1 +sample_rate: 16000 +batch_size: 64 +device: null # can specify a specific device, i.e: cuda:1 (default cuda if cuda available, else cpu) +verbose: True # enable additional logging + +diarizer: + manifest_filepath: ??? + out_dir: ??? + oracle_vad: False # If True, uses RTTM files provided in the manifest file to get speech activity (VAD) timestamps + collar: 0.25 # Collar value for scoring + ignore_overlap: True # Consider or ignore overlap segments while scoring + + vad: + model_path: vad_multilingual_marblenet # .nemo local model path or pretrained VAD model name + external_vad_manifest: null # This option is provided to use external vad and provide its speech activity labels for speaker embeddings extraction. Only one of model_path or external_vad_manifest should be set + + parameters: # Tuned parameters for CH109 (using the 11 multi-speaker sessions as dev set) + window_length_in_sec: 0.63 # Window length in sec for VAD context input + shift_length_in_sec: 0.01 # Shift length in sec for generate frame level VAD prediction + smoothing: False # False or type of smoothing method (eg: median) + overlap: 0.5 # Overlap ratio for overlapped mean/median smoothing filter + onset: 0.9 # Onset threshold for detecting the beginning and end of a speech + offset: 0.5 # Offset threshold for detecting the end of a speech + pad_onset: 0 # Adding durations before each speech segment + pad_offset: 0 # Adding durations after each speech segment + min_duration_on: 0 # Threshold for small non_speech deletion + min_duration_off: 0.6 # Threshold for short speech segment deletion + filter_speech_first: True + + speaker_embeddings: + model_path: titanet_large # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet) + parameters: + window_length_in_sec: [3.0,2.5,2.0,1.5,1.0,0.5] # Window length(s) in sec (floating-point number). either a number or a list. ex) 1.5 or [1.5,1.0,0.5] + shift_length_in_sec: [1.5,1.25,1.0,0.75,0.5,0.25] # Shift length(s) in sec (floating-point number). either a number or a list. ex) 0.75 or [0.75,0.5,0.25] + multiscale_weights: [1,1,1,1,1,1] # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. ex) [0.33,0.33,0.33] + save_embeddings: True # If True, save speaker embeddings in pickle format. This should be True if clustering result is used for other models, such as `msdd_model`. + + clustering: + parameters: + oracle_num_speakers: False # If True, use num of speakers value provided in manifest file. + max_num_speakers: 8 # Max number of speakers for each recording. If an oracle number of speakers is passed, this value is ignored. + enhanced_count_thres: 80 # If the number of segments is lower than this number, enhanced speaker counting is activated. + max_rp_threshold: 0.25 # Determines the range of p-value search: 0 < p <= max_rp_threshold. + sparse_search_volume: 30 # The higher the number, the more values will be examined with more time. + maj_vote_spk_count: False # If True, take a majority vote on multiple p-values to estimate the number of speakers. + chunk_cluster_count: 50 # Number of forced clusters (overclustering) per unit chunk in long-form audio clustering. + embeddings_per_chunk: 10000 # Number of embeddings in each chunk for long-form audio clustering. Adjust based on GPU memory capacity. (default: 10000, approximately 40 mins of audio) + + msdd_model: + model_path: null # .nemo local model path or pretrained model name for multiscale diarization decoder (MSDD) + parameters: + use_speaker_model_from_ckpt: True # If True, use speaker embedding model in checkpoint. If False, the provided speaker embedding model in config will be used. + infer_batch_size: 25 # Batch size for MSDD inference. + sigmoid_threshold: [0.7] # Sigmoid threshold for generating binarized speaker labels. The smaller the more generous on detecting overlaps. + seq_eval_mode: False # If True, use oracle number of speaker and evaluate F1 score for the given speaker sequences. Default is False. + split_infer: True # If True, break the input audio clip to short sequences and calculate cluster average embeddings for inference. + diar_window_length: 50 # The length of split short sequence when split_infer is True. + overlap_infer_spk_limit: 5 # If the estimated number of speakers are larger than this number, overlap speech is not estimated. + + asr: + model_path: stt_en_conformer_ctc_large # Provide NGC cloud ASR model name. stt_en_conformer_ctc_* models are recommended for diarization purposes. + parameters: + asr_based_vad: False # if True, speech segmentation for diarization is based on word-timestamps from ASR inference. + asr_based_vad_threshold: 1.0 # Threshold (in sec) that caps the gap between two words when generating VAD timestamps using ASR based VAD. + asr_batch_size: null # Batch size can be dependent on each ASR model. Default batch sizes are applied if set to null. + decoder_delay_in_sec: null # Native decoder delay. null is recommended to use the default values for each ASR model. + word_ts_anchor_offset: null # Offset to set a reference point from the start of the word. Recommended range of values is [-0.05 0.2]. + word_ts_anchor_pos: "start" # Select which part of the word timestamp we want to use. The options are: 'start', 'end', 'mid'. + fix_word_ts_with_VAD: False # Fix the word timestamp using VAD output. You must provide a VAD model to use this feature. + colored_text: False # If True, use colored text to distinguish speakers in the output transcript. + print_time: True # If True, the start and end time of each speaker turn is printed in the output transcript. + break_lines: False # If True, the output transcript breaks the line to fix the line width (default is 90 chars) + + ctc_decoder_parameters: # Optional beam search decoder (pyctcdecode) + pretrained_language_model: null # KenLM model file: .arpa model file or .bin binary file. + beam_width: 32 + alpha: 0.5 + beta: 2.5 + + realigning_lm_parameters: # Experimental feature + arpa_language_model: null # Provide a KenLM language model in .arpa format. + min_number_of_words: 3 # Min number of words for the left context. + max_number_of_words: 10 # Max number of words for the right context. + logprob_diff_threshold: 1.2 # The threshold for the difference between two log probability values from two hypotheses. \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/inference/diar_infer_telephonic.yaml b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/inference/diar_infer_telephonic.yaml new file mode 100644 index 0000000..8a75305 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/inference/diar_infer_telephonic.yaml @@ -0,0 +1,93 @@ +# This YAML file is created for all types of offline speaker diarization inference tasks in `/example/speaker_tasks/diarization` folder. +# The inference parameters for VAD, speaker embedding extractor, clustering module, MSDD module, ASR decoder are all included in this YAML file. +# All the keys under `diarizer` key (`vad`, `speaker_embeddings`, `clustering`, `msdd_model`, `asr`) can be selectively used for its own purpose and also can be ignored if the module is not used. +# The configurations in this YAML file is suitable for telephone recordings involving 2~8 speakers in a session and may not show the best performance on the other types of acoustic conditions or dialogues. +# An example line in an input manifest file (`.json` format): +# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer", "text": "-", "num_speakers": null, "rttm_filepath": "/path/to/rttm/file", "uem_filepath": "/path/to/uem/file"} +name: &name "ClusterDiarizer" + +num_workers: 1 +sample_rate: 16000 +batch_size: 64 +device: null # can specify a specific device, i.e: cuda:1 (default cuda if cuda available, else cpu) +verbose: True # enable additional logging + +diarizer: + manifest_filepath: ??? + out_dir: ??? + oracle_vad: False # If True, uses RTTM files provided in the manifest file to get speech activity (VAD) timestamps + collar: 0.25 # Collar value for scoring + ignore_overlap: True # Consider or ignore overlap segments while scoring + + vad: + model_path: vad_multilingual_marblenet # .nemo local model path or pretrained VAD model name + external_vad_manifest: null # This option is provided to use external vad and provide its speech activity labels for speaker embeddings extraction. Only one of model_path or external_vad_manifest should be set + + parameters: # Tuned parameters for CH109 (using the 11 multi-speaker sessions as dev set) + window_length_in_sec: 0.15 # Window length in sec for VAD context input + shift_length_in_sec: 0.01 # Shift length in sec for generate frame level VAD prediction + smoothing: "median" # False or type of smoothing method (eg: median) + overlap: 0.5 # Overlap ratio for overlapped mean/median smoothing filter + onset: 0.1 # Onset threshold for detecting the beginning and end of a speech + offset: 0.1 # Offset threshold for detecting the end of a speech + pad_onset: 0.1 # Adding durations before each speech segment + pad_offset: 0 # Adding durations after each speech segment + min_duration_on: 0 # Threshold for small non_speech deletion + min_duration_off: 0.2 # Threshold for short speech segment deletion + filter_speech_first: True + + speaker_embeddings: + model_path: titanet_large # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet) + parameters: + window_length_in_sec: [1.5,1.25,1.0,0.75,0.5] # Window length(s) in sec (floating-point number). either a number or a list. ex) 1.5 or [1.5,1.0,0.5] + shift_length_in_sec: [0.75,0.625,0.5,0.375,0.25] # Shift length(s) in sec (floating-point number). either a number or a list. ex) 0.75 or [0.75,0.5,0.25] + multiscale_weights: [1,1,1,1,1] # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. ex) [0.33,0.33,0.33] + save_embeddings: True # If True, save speaker embeddings in pickle format. This should be True if clustering result is used for other models, such as `msdd_model`. + + clustering: + parameters: + oracle_num_speakers: False # If True, use num of speakers value provided in manifest file. + max_num_speakers: 8 # Max number of speakers for each recording. If an oracle number of speakers is passed, this value is ignored. + enhanced_count_thres: 80 # If the number of segments is lower than this number, enhanced speaker counting is activated. + max_rp_threshold: 0.25 # Determines the range of p-value search: 0 < p <= max_rp_threshold. + sparse_search_volume: 30 # The higher the number, the more values will be examined with more time. + maj_vote_spk_count: False # If True, take a majority vote on multiple p-values to estimate the number of speakers. + chunk_cluster_count: 50 # Number of forced clusters (overclustering) per unit chunk in long-form audio clustering. + embeddings_per_chunk: 10000 # Number of embeddings in each chunk for long-form audio clustering. Adjust based on GPU memory capacity. (default: 10000, approximately 40 mins of audio) + + msdd_model: + model_path: diar_msdd_telephonic # .nemo local model path or pretrained model name for multiscale diarization decoder (MSDD) + parameters: + use_speaker_model_from_ckpt: True # If True, use speaker embedding model in checkpoint. If False, the provided speaker embedding model in config will be used. + infer_batch_size: 25 # Batch size for MSDD inference. + sigmoid_threshold: [0.7] # Sigmoid threshold for generating binarized speaker labels. The smaller the more generous on detecting overlaps. + seq_eval_mode: False # If True, use oracle number of speaker and evaluate F1 score for the given speaker sequences. Default is False. + split_infer: True # If True, break the input audio clip to short sequences and calculate cluster average embeddings for inference. + diar_window_length: 50 # The length of split short sequence when split_infer is True. + overlap_infer_spk_limit: 5 # If the estimated number of speakers are larger than this number, overlap speech is not estimated. + + asr: + model_path: stt_en_conformer_ctc_large # Provide NGC cloud ASR model name. stt_en_conformer_ctc_* models are recommended for diarization purposes. + parameters: + asr_based_vad: False # if True, speech segmentation for diarization is based on word-timestamps from ASR inference. + asr_based_vad_threshold: 1.0 # Threshold (in sec) that caps the gap between two words when generating VAD timestamps using ASR based VAD. + asr_batch_size: null # Batch size can be dependent on each ASR model. Default batch sizes are applied if set to null. + decoder_delay_in_sec: null # Native decoder delay. null is recommended to use the default values for each ASR model. + word_ts_anchor_offset: null # Offset to set a reference point from the start of the word. Recommended range of values is [-0.05 0.2]. + word_ts_anchor_pos: "start" # Select which part of the word timestamp we want to use. The options are: 'start', 'end', 'mid'. + fix_word_ts_with_VAD: False # Fix the word timestamp using VAD output. You must provide a VAD model to use this feature. + colored_text: False # If True, use colored text to distinguish speakers in the output transcript. + print_time: True # If True, the start and end time of each speaker turn is printed in the output transcript. + break_lines: False # If True, the output transcript breaks the line to fix the line width (default is 90 chars) + + ctc_decoder_parameters: # Optional beam search decoder (pyctcdecode) + pretrained_language_model: null # KenLM model file: .arpa model file or .bin binary file. + beam_width: 32 + alpha: 0.5 + beta: 2.5 + + realigning_lm_parameters: # Experimental feature + arpa_language_model: null # Provide a KenLM language model in .arpa format. + min_number_of_words: 3 # Min number of words for the left context. + max_number_of_words: 10 # Max number of words for the right context. + logprob_diff_threshold: 1.2 # The threshold for the difference between two log probability values from two hypotheses. \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/neural_diarizer/msdd_5scl_15_05_50Povl_256x3x32x2.yaml b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/neural_diarizer/msdd_5scl_15_05_50Povl_256x3x32x2.yaml new file mode 100644 index 0000000..03cbfd3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/neural_diarizer/msdd_5scl_15_05_50Povl_256x3x32x2.yaml @@ -0,0 +1,129 @@ +# Mutiscale diarization decoder (MSDD) is a speaker diarization model based on initializing clustering and multiscale segmentation input. +# Model name convention for MSDD: msdd_scl___Povl_xxx +# (Example) `msdd_5scl_15_05_50Povl_256x3x32x2.yaml` has 5 scales, the longest scale is 1.5 sec, the shortest scale is 0.5 sec, with 50 percent overlap, hidden layer size is 256, 3 LSTM layers, 32 CNN channels, 2 repeated Conv layers +# MSDD model checkpoint (.ckpt) and NeMo file (.nemo) contain speaker embedding model (TitaNet) and the speaker model is loaded along with standalone MSDD moodule. +# Note that MSDD models require more than one scale. Thus, the parameters in diarizer.speaker_embeddings.parameters should have more than one scale to function as a MSDD model. +# Example: a manifest line for training +# {"audio_filepath": "/path/to/audio01.wav", "offset": 390.83, "duration": 13.45, "text": "-", "num_speakers": 2, "rttm_filepath": "/path/to/audio01.rttm"} +name: "MultiscaleDiarDecoder" +sample_rate: 16000 +num_workers: 20 +batch_size: 7 + +model: + diarizer: + out_dir: null + oracle_vad: True # If True, uses RTTM files provided in manifest file to get speech activity (VAD) timestamps + speaker_embeddings: + model_path: ??? # .nemo local model path or pretrained model name (titanet_large is recommended) + parameters: + window_length_in_sec: [1.5,1.25,1.0,0.75,0.5] # Window length(s) in sec (floating-point number). either a number or a list. ex) 1.5 or [1.5,1.0,0.5] + shift_length_in_sec: [0.75,0.625,0.5,0.375,0.25] # Shift length(s) in sec (floating-point number). either a number or a list. ex) 0.75 or [0.75,0.5,0.25] + multiscale_weights: [1,1,1,1,1] # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. ex) [0.33,0.33,0.33] + save_embeddings: True # Save embeddings as pickle file for each audio input. + + num_workers: ${num_workers} + max_num_of_spks: 2 # Number of speakers per model. This is currently fixed at 2. + scale_n: 5 # Number of scales for MSDD model and initializing clustering. + soft_label_thres: 0.5 # Threshold for creating discretized speaker label from continuous speaker label in RTTM files. + emb_batch_size: 0 # If this value is bigger than 0, corresponding number of embedding vectors are attached to torch graph and trained. + + train_ds: + manifest_filepath: ??? + emb_dir: ??? + sample_rate: ${sample_rate} + num_spks: ${model.max_num_of_spks} + soft_label_thres: ${model.soft_label_thres} + labels: null + batch_size: ${batch_size} + emb_batch_size: ${model.emb_batch_size} + shuffle: True + + validation_ds: + manifest_filepath: ??? + emb_dir: ??? + sample_rate: ${sample_rate} + num_spks: ${model.max_num_of_spks} + soft_label_thres: ${model.soft_label_thres} + labels: null + batch_size: 2 + emb_batch_size: ${model.emb_batch_size} + shuffle: False + + test_ds: + manifest_filepath: null + emb_dir: null + sample_rate: 16000 + num_spks: ${model.max_num_of_spks} + soft_label_thres: ${model.soft_label_thres} + labels: null + batch_size: 2 + shuffle: False + seq_eval_mode: False + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.025 + sample_rate: ${sample_rate} + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + msdd_module: + _target_: nemo.collections.asr.modules.msdd_diarizer.MSDD_module + num_spks: ${model.max_num_of_spks} # Number of speakers per model. This is currently fixed at 2. + hidden_size: 256 # Hidden layer size for linear layers in MSDD module + num_lstm_layers: 3 # Number of stacked LSTM layers + dropout_rate: 0.5 # Dropout rate + cnn_output_ch: 32 # Number of filters in a conv-net layer. + conv_repeat: 2 # Determins the number of conv-net layers. Should be greater or equal to 1. + emb_dim: 192 # Dimension of the speaker embedding vectors + scale_n: ${model.scale_n} # Number of scales for multiscale segmentation input + weighting_scheme: 'conv_scale_weight' # Type of weighting algorithm. Options: ('conv_scale_weight', 'attn_scale_weight') + context_vector_type: 'cos_sim' # Type of context vector: options. Options: ('cos_sim', 'elem_prod') + + loss: + _target_: nemo.collections.asr.losses.bce_loss.BCELoss + weight: null # Weight for binary cross-entropy loss. Either `null` or list type input. (e.g. [0.5,0.5]) + + optim: + name: adam + lr: .001 + weight_decay: 0.001 + + sched: + name: CosineAnnealing + min_lr: 0.00001 + +trainer: + devices: 1 # number of gpus (devices) + accelerator: gpu + max_epochs: 200 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + strategy: ddp + accumulate_grad_batches: 1 + deterministic: True + enable_checkpointing: False + logger: False + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True + create_wandb_logger: False + checkpoint_callback_params: + monitor: "val_loss" + mode: "min" + save_top_k: 30 + every_n_epochs: 1 + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/neural_diarizer/msdd_6scl_30_05_50Povl_256x3x32x2.yaml b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/neural_diarizer/msdd_6scl_30_05_50Povl_256x3x32x2.yaml new file mode 100644 index 0000000..e4daf97 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/conf/neural_diarizer/msdd_6scl_30_05_50Povl_256x3x32x2.yaml @@ -0,0 +1,129 @@ +# Mutiscale diarization decoder (MSDD) is a speaker diarization model based on initializing clustering and multiscale segmentation input. +# Model name convention for MSDD: msdd_scl___Povl_xxx +# Example: `msdd_6scl_30_05_50Povl_256x3x32x2.yaml` has 6 scales, the longest scale is 3.0 sec, the shortest scale is 0.5 sec, with 50 percent overlap, hidden layer size is 256, 3 LSTM layers, 32 CNN channels, 2 repeated Conv layers +# MSDD model checkpoint (.ckpt) and NeMo file (.nemo) contain speaker embedding model (TitaNet) and the speaker model is loaded along with standalone MSDD moodule. +# Note that MSDD models require more than one scale. Thus, the parameters in diarizer.speaker_embeddings.parameters should have more than one scale to function as a MSDD model. +# Example: a manifest line for training +# {"audio_filepath": "/path/to/audio01.wav", "offset": 390.83, "duration": 13.45, "text": "-", "num_speakers": 2, "rttm_filepath": "/path/to/audio01.rttm"} +name: "MultiscaleDiarDecoder" +sample_rate: 16000 +num_workers: 20 +batch_size: 7 + +model: + diarizer: + out_dir: null + oracle_vad: True # If True, uses RTTM files provided in manifest file to get speech activity (VAD) timestamps + speaker_embeddings: + model_path: ??? # .nemo local model path or pretrained model name (titanet_large is recommended) + parameters: + window_length_in_sec: [3.0,2.5,2.0,1.5,1.0,0.5] # Window length(s) in sec (floating-point number). either a number or a list. ex) 1.5 or [1.5,1.0,0.5] + shift_length_in_sec: [1.5,1.25,1.0,0.75,0.5,0.25] # Shift length(s) in sec (floating-point number). either a number or a list. ex) 0.75 or [0.75,0.5,0.25] + multiscale_weights: [1,1,1,1,1,1] # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. ex) [0.33,0.33,0.33] + save_embeddings: True # Save embeddings as pickle file for each audio input. + + num_workers: ${num_workers} + max_num_of_spks: 2 # Number of speakers per model. This is currently fixed at 2. + scale_n: 6 # Number of scales for MSDD model and initializing clustering. + soft_label_thres: 0.5 # Threshold for creating discretized speaker label from continuous speaker label in RTTM files. + emb_batch_size: 0 # If this value is bigger than 0, corresponding number of embedding vectors are attached to torch graph and trained. + + train_ds: + manifest_filepath: ??? + emb_dir: ??? + sample_rate: ${sample_rate} + num_spks: ${model.max_num_of_spks} + soft_label_thres: ${model.soft_label_thres} + labels: null + batch_size: ${batch_size} + emb_batch_size: ${model.emb_batch_size} + shuffle: True + + validation_ds: + manifest_filepath: ??? + emb_dir: ??? + sample_rate: ${sample_rate} + num_spks: ${model.max_num_of_spks} + soft_label_thres: ${model.soft_label_thres} + labels: null + batch_size: 2 + emb_batch_size: ${model.emb_batch_size} + shuffle: False + + test_ds: + manifest_filepath: null + emb_dir: null + sample_rate: 16000 + num_spks: ${model.max_num_of_spks} + soft_label_thres: ${model.soft_label_thres} + labels: null + batch_size: 2 + shuffle: False + seq_eval_mode: False + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.025 + sample_rate: ${sample_rate} + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + msdd_module: + _target_: nemo.collections.asr.modules.msdd_diarizer.MSDD_module + num_spks: ${model.max_num_of_spks} # Number of speakers per model. This is currently fixed at 2. + hidden_size: 256 # Hidden layer size for linear layers in MSDD module + num_lstm_layers: 3 # Number of stacked LSTM layers + dropout_rate: 0.5 # Dropout rate + cnn_output_ch: 32 # Number of filters in a conv-net layer. + conv_repeat: 2 # Determins the number of conv-net layers. Should be greater or equal to 1. + emb_dim: 192 # Dimension of the speaker embedding vectors + scale_n: ${model.scale_n} # Number of scales for multiscale segmentation input + weighting_scheme: 'conv_scale_weight' # Type of weighting algorithm. Options: ('conv_scale_weight', 'attn_scale_weight') + context_vector_type: 'cos_sim' # Type of context vector: options. Options: ('cos_sim', 'elem_prod') + + loss: + _target_: nemo.collections.asr.losses.bce_loss.BCELoss + weight: null # Weight for binary cross-entropy loss. Either `null` or list type input. (e.g. [0.5,0.5]) + + optim: + name: adam + lr: .001 + weight_decay: 0.001 + + sched: + name: CosineAnnealing + min_lr: 0.00001 + +trainer: + devices: 1 # number of gpus (devices) + accelerator: gpu + max_epochs: 200 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + strategy: ddp + accumulate_grad_batches: 1 + deterministic: True + enable_checkpointing: False + logger: False + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True + create_wandb_logger: False + checkpoint_callback_params: + monitor: "val_loss" + mode: "min" + save_top_k: 30 + every_n_epochs: 1 + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder.py b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder.py new file mode 100644 index 0000000..984b5ce --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder.py @@ -0,0 +1,51 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl +from omegaconf import OmegaConf +from pytorch_lightning import seed_everything + +from nemo.collections.asr.models import EncDecDiarLabelModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +""" +Example training session (single GPU training on telephonic datasets) + +python ./multiscale_diar_decoder.py --config-path='../conf/neural_diarizer' --config-name='msdd_5scl_15_05_50Povl_256x3x32x2.yaml' \ + trainer.devices=1 \ + model.base.diarizer.speaker_embeddings.model_path="titanet_large" \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath="" \ + model.train_ds.emb_dir="" \ + model.validation_ds.emb_dir="" \ + exp_manager.name='sample_train' \ + exp_manager.exp_dir='./msdd_exp' +""" + +seed_everything(42) + + +@hydra_runner(config_path="../conf/neural_diarizer", config_name="msdd_5scl_15_05_50Povl_256x3x32x2.yaml") +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + msdd_model = EncDecDiarLabelModel(cfg=cfg.model, trainer=trainer) + trainer.fit(msdd_model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder_infer.py b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder_infer.py new file mode 100644 index 0000000..05d1b3c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder_infer.py @@ -0,0 +1,37 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.models.msdd_models import NeuralDiarizer +from nemo.core.config import hydra_runner + + +""" +Run the entire speaker diarization pipeline: VAD, clustering diarizer for initializing clustering then Multi-scale Diarization Decoder (MSDD). +python multiscale_diar_decoder_infer.py --config-path='../conf/inference' --config-name='diar_infer_telephonic.yaml' \ + diarizer.vad.model_path= \ + diarizer.msdd_model.model_path= \ + diarizer.oracle_vad=False \ + diarizer.manifest_filepath= \ + diarizer.out_dir= \ +""" + + +@hydra_runner(config_path="../conf/inference", config_name="diar_infer_telephonic.yaml") +def main(cfg): + diarizer_model = NeuralDiarizer(cfg=cfg).to(cfg.device) + diarizer_model.diarize() + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/README.md b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/README.md new file mode 100644 index 0000000..0e0f5ae --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/README.md @@ -0,0 +1,105 @@ +# Speaker Recognition + +Documentation section for speaker related tasks can be found at: + - [Speaker Identification and Verification](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_recognition/intro.html) + - [Speaker Diarization](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_diarization/intro.html) + +## Performance +| MODEL | type | EER (%)
Voxceleb-O (veri_test2.txt) | +|:------------------------------:|:---------------------:|:--------------------------------------:| +| [speakerverification_speakernet](https://ngc.nvidia.com/catalog/models/nvidia:nemo:speakerverification_speakernet) | xvector | 1.96 | +| [ecapa_tdnn](https://ngc.nvidia.com/catalog/models/nvidia:nemo:ecapa_tdnn) | channel-
attention | 0.92 | +| [titanet_large](https://ngc.nvidia.com/catalog/models/nvidia:nemo:ecapa_tdnn) | channel-
attention | 0.66 | + +## Training +Speaker Recognition models can be trained in a similar way as other models in NeMo using train and dev manifest files. Steps on how to create manifest files for voxceleb are provided below. +We provide three model configurations based on TitaNet, SpeakerNet and modified ECAPA_TDNN, with pretrained models provided for each of them. + +For training titanet_large (channel-attention) model: +```bash +python speaker_reco.py --config_path='conf' --config_name='titanet_large.yaml' +``` + +For training speakernet (x-vector) model: +```bash +python speaker_reco.py --config_path='conf' --config_name='SpeakerNet_verification_3x2x256.yaml' +``` + +For training ecapa_tdnn (channel-attention) model: +```bash +python speaker_reco.py --config_path='conf' --config_name='ecapa_tdnn.yaml' +``` +For step by step tutorial see [notebook](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb). + +### Fine Tuning +For fine tuning on a pretrained .nemo speaker recognition model, +```bash +python speaker_reco_finetune.py --config_path='conf' --config_name='titanet-finetune.yaml' +``` +for fine tuning tips see this [tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb) + +## Inference +We provide generic scripts for manifest file creation, embedding extraction, Voxceleb evaluation and speaker ID inference. Hence most of the steps would be common and differ slightly based on your end application. + +We explain here the process for voxceleb EER calculation on voxceleb-O cleaned [trail file](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test2.txt) + +### Manifest Creation +We first generate manifest file to get embeddings. The embeddings are then used by `voxceleb_eval.py` script to get EER + +```bash +# create list of files from voxceleb1 test folder (40 speaker test set) +find -iname '*.wav' > voxceleb1_test_files.txt +python /scripts/speaker_tasks/filelist_to_manifest.py --filelist voxceleb1_test_files.txt --id -3 --out voxceleb1_test_manifest.json +``` +### Embedding Extraction +Now using the manifest file created, we can extract embeddings to `data` folder using: +```bash +python extract_speaker_embeddings.py --manifest=voxceleb1_test_manifest.json --model_path='titanet_large' --embedding_dir='./' +``` +If you have a single file, you may also be using the following one liner to get embeddings for the audio file: + +```python +speaker_model = EncDecSpeakerLabelModel.from_pretrained(model_name="titanet_large") +embs = speaker_model.get_embedding('audio_path') +``` + +### Voxceleb Evaluation +``` bash +python voxceleb_eval.py --trial_file='/path/to/trail/file' --emb='./embeddings/voxceleb1_test_manifest_embeddings.pkl' +``` +The above command gives the performance of models on voxceleb-o cleaned trial file. + +### SpeakerID inference +Using data from an enrollment set, one can infer labels on a test set using various backends such as cosine-similarity or a neural classifier. + +To infer speaker labels using cosine_similarity backend +```bash +python speaker_identification_infer.py data.enrollment_manifest= data.test_manifest= backend.backend_model=cosine_similarity +``` +refer to conf/speaker_identification_infer.yaml for more options. + +## Voxceleb Data Preparation + +Scripts we provide for data preparation are very generic and can be applied to any dataset with a few path changes. +For VoxCeleb datasets, we first download the datasets individually and make a list of audio files. Then we use the script to generate manifest files for training and validation. +Download [voxceleb1](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) and [voxceleb2](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox2.html) data. + +Once downloaded and uncompressed, use programs such as ffmpeg to convert audio files from m4a format to wav format. +Refer to the following sample command +```bash +ffmpeg -v 8 -i -f wav -acodec pcm_s16le +``` + +Generate a list file that contains paths to all the dev audio files from voxceleb1 and voxceleb2 using find command as shown below: +```bash +find -iname '*.wav' > voxceleb1_dev.txt +find -iname '*.wav' > voxceleb2_dev.txt +cat voxceleb1_dev.txt voxceleb2_dev.txt > voxceleb12.txt +``` + +This list file is now used to generate training and validation manifest files using a script provided in `/scripts/speaker_tasks/`. This script has optional arguments to split the whole manifest file in to train and dev and also chunk audio files to smaller segments for robust training (for testing, we don't need this). + +```bash +python /scripts/speaker_tasks/filelist_to_manifest.py --filelist voxceleb12.txt --id -3 --out voxceleb12_manifest.json --split --create_segments +``` +This creates `train.json, dev.json` in the current working directory. diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/SpeakerNet_recognition_3x2x512.yaml b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/SpeakerNet_recognition_3x2x512.yaml new file mode 100644 index 0000000..35c0b65 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/SpeakerNet_recognition_3x2x512.yaml @@ -0,0 +1,154 @@ +name: &name "SpeakerNet" +sample_rate: &sample_rate 16000 +repeat: &rep 2 +dropout: &drop 0.5 +separable: &separable True +n_filters: &n_filters 512 + +model: + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 64 + shuffle: True + is_tarred: False + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 128 + shuffle: False + + test_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 1 + shuffle: False + embedding_dir: '.' + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.02 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 64 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: *n_filters + repeat: 1 + kernel: [3] + stride: [1] + dilation: [1] + dropout: *drop + residual: true + separable: *separable + + - filters: *n_filters + repeat: *rep + kernel: [7] + stride: [1] + dilation: [1] + dropout: *drop + residual: true + separable: *separable + + - filters: *n_filters + repeat: *rep + kernel: [11] + stride: [1] + dilation: [1] + dropout: *drop + residual: true + separable: *separable + + - filters: *n_filters + repeat: *rep + kernel: [15] + stride: [1] + dilation: [1] + dropout: *drop + residual: true + separable: *separable + + - filters: &enc_feat_out 1500 + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: *separable + + decoder: + _target_: nemo.collections.asr.modules.SpeakerDecoder + feat_in: *enc_feat_out + num_classes: 7205 + pool_mode: 'xvector' + emb_sizes: [512,512] + + loss: + _target_: from nemo.collections.common.losses.CrossEntropyLoss # you could also use AngularSoftmaxLoss + + optim: + name: novograd + # _target_: nemo.core.optim.optimizers.Novograd + lr: .008 + # optimizer arguments + args: + name: auto + # _target_: nemo.core.config.optimizers.NovogradParams + betas: [0.95, 0.5] + weight_decay: 0.001 + + # scheduler setup + sched: + name: CosineAnnealing + iters_per_batch: 1 # computed at runtime + max_steps: -1 # computed at runtime or explicitly set here + + # scheduler config override + args: + name: auto + # _target_: nemo.core.config.schedulers.CosineAnnealingParams + warmup_steps: null + warmup_ratio: 0.1 + min_lr: 0.0 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 200 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + deterministic: True + enable_checkpointing: False + logger: False + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + gradient_clip_val: 1.0 + + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/SpeakerNet_verification_3x2x256.yaml b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/SpeakerNet_verification_3x2x256.yaml new file mode 100644 index 0000000..dbb06bf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/SpeakerNet_verification_3x2x256.yaml @@ -0,0 +1,133 @@ +name: &name "SpeakerNet" +sample_rate: &sample_rate 16000 +repeat: &rep 2 +dropout: &drop 0.5 +separable: &separable True +n_filters: &n_filters 256 + +model: + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 64 + shuffle: True + is_tarred: False + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 128 + shuffle: False + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.02 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 64 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: *n_filters + repeat: 1 + kernel: [3] + stride: [1] + dilation: [1] + dropout: *drop + residual: true + separable: *separable + + - filters: *n_filters + repeat: *rep + kernel: [7] + stride: [1] + dilation: [1] + dropout: *drop + residual: true + separable: *separable + + - filters: *n_filters + repeat: *rep + kernel: [11] + stride: [1] + dilation: [1] + dropout: *drop + residual: true + separable: *separable + + - filters: *n_filters + repeat: *rep + kernel: [15] + stride: [1] + dilation: [1] + dropout: *drop + residual: true + separable: *separable + + - filters: &enc_feat_out 1500 + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: *separable + + decoder: + _target_: nemo.collections.asr.modules.SpeakerDecoder + feat_in: *enc_feat_out + num_classes: 7205 + pool_mode: 'xvector' + emb_sizes: 256 + + loss: + _target_: nemo.collections.asr.losses.angularloss.AngularSoftmaxLoss # you could also use cross-entrophy loss + scale: 30 + margin: 0.2 + + optim: + name: sgd + lr: .006 + weight_decay: 0.001 + momentum: 0.9 + + # scheduler setup + sched: + name: CosineAnnealing + warmup_ratio: 0.1 + min_lr: 0.0 + +trainer: + devices: 1 # number of gpus + max_epochs: 200 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + deterministic: True + enable_checkpointing: False + logger: False + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + gradient_clip_val: 1.0 + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/ecapa_tdnn.yaml b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/ecapa_tdnn.yaml new file mode 100644 index 0000000..3636d77 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/ecapa_tdnn.yaml @@ -0,0 +1,106 @@ +name: "ECAPA_TDNN" + +model: + + sample_rate: 16000 + + train_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + labels: null + batch_size: 64 + shuffle: True + augmentor: + noise: + manifest_path: null + prob: 0.5 + min_snr_db: 0 + max_snr_db: 15 + + speed: + prob: 0.5 + sr: ${model.sample_rate} + resample_type: 'kaiser_fast' + min_speed_rate: 0.95 + max_speed_rate: 1.05 + + validation_ds: + manifest_filepath: ??? + sample_rate: ${model.sample_rate} + labels: null + batch_size: 128 + shuffle: False + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.025 + sample_rate: ${model.sample_rate} + window_stride: 0.01 + window: "hann" + features: 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + stft_conv: false + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 3 + freq_width: 4 + time_masks: 5 + time_width: 0.03 + + + encoder: + _target_: nemo.collections.asr.modules.ECAPAEncoder + feat_in: ${model.preprocessor.features} + filters: [1024,1024,1024,1024,3072] + kernel_sizes: [5,3,3,3,1] + dilations: [1,1,1,1,1] + scale: 8 + + + decoder: + _target_: nemo.collections.asr.modules.SpeakerDecoder + feat_in: 3072 + num_classes: 7205 + pool_mode: 'attention' #xvector,tap or attention + emb_sizes: 192 + + loss: + _target_: nemo.collections.asr.losses.angularloss.AngularSoftmaxLoss # you could also use cross-entrophy loss + scale: 30 + margin: 0.2 + + optim: + name: sgd + lr: 0.08 + weight_decay: 0.0002 + + # scheduler setup + sched: + name: CosineAnnealing + warmup_ratio: 0.1 + min_lr: 0.0001 + +trainer: + devices: 1 # number of gpus (trained on four nodes - each node has 8 gpus) + max_epochs: 250 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + deterministic: False + enable_checkpointing: False + logger: False + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + gradient_clip_val: 1.0 + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/speaker_identification_infer.yaml b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/speaker_identification_infer.yaml new file mode 100644 index 0000000..31d1f2f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/speaker_identification_infer.yaml @@ -0,0 +1,27 @@ +name: &name "SpeakerIdentificationInfer" + +data: + enrollment_manifest: ??? + test_manifest: ??? + out_manifest: './infer_output.json' + sample_rate: 16000 + +backend: + backend_model: cosine_similarity # supported backends are cosine_similarity and neural_classifier + + cosine_similarity: + model_path: titanet_large # or path to .nemo file + batch_size: 32 + + neural_classifier: + model_path: ??? # path to neural model trained/finetuned with enrollment dataset + batch_size: 32 + +# json manifest line example +# +# enrollment_manifest: +# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": ""} +# +# test_manifest: +# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer"} +# diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/titanet-finetune.yaml b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/titanet-finetune.yaml new file mode 100644 index 0000000..ca3b2af --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/titanet-finetune.yaml @@ -0,0 +1,163 @@ +name: &name "TitaNet-Finetune" +sample_rate: &sample_rate 16000 + +init_from_pretrained_model: + speaker_tasks: + name: 'titanet_large' + include: ["preprocessor","encoder"] + exclude: ["decoder.final"] # Add specific layer names here to exlude or just ["decoder"] if to exclude all of decoder pretrained weights + +model: + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 64 + shuffle: True + is_tarred: False + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + augmentor: + speed: + prob: 0.3 + sr: *sample_rate + resample_type: 'kaiser_fast' + min_speed_rate: 0.95 + max_speed_rate: 1.05 + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 128 + shuffle: False + + model_defaults: + filters: 1024 + repeat: 3 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 1.0 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.025 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [3] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [7] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [11] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: &enc_feat_out 3072 + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + decoder: + _target_: nemo.collections.asr.modules.SpeakerDecoder + feat_in: *enc_feat_out + num_classes: ??? + pool_mode: 'attention' + emb_sizes: 192 + + loss: + _target_: nemo.collections.asr.losses.angularloss.AngularSoftmaxLoss # you could also use cross-entrophy loss + scale: 30 + margin: 0.2 + + optim_param_groups: + encoder: + lr: .001 + + optim: + name: adamw + lr: .0001 #(original titanet-large was trained with 0.08 lr) + weight_decay: 0.0002 + + # scheduler setup + sched: + name: CosineAnnealing + warmup_ratio: 0.1 + min_lr: 0.0 + +trainer: + devices: 1 # number of gpus (original titanet-large was trained on 4 nodes with 8 gpus each) + max_epochs: 10 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + deterministic: True + enable_checkpointing: False + logger: False + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + gradient_clip_val: 1.0 + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/titanet-large.yaml b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/titanet-large.yaml new file mode 100644 index 0000000..e485967 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/titanet-large.yaml @@ -0,0 +1,167 @@ +name: &name "TitaNet-L" +sample_rate: &sample_rate 16000 + +model: + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 64 + shuffle: True + is_tarred: False + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + augmentor: + noise: + manifest_path: null + prob: 0.5 + min_snr_db: 0 + max_snr_db: 15 + + speed: + prob: 0.3 + sr: *sample_rate + resample_type: 'kaiser_fast' + min_speed_rate: 0.95 + max_speed_rate: 1.05 + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 128 + shuffle: False + + model_defaults: + filters: 1024 + repeat: 3 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 1.0 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.025 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 3 + freq_width: 4 + time_masks: 5 + time_width: 0.03 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [3] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [7] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [11] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: &enc_feat_out 3072 + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + decoder: + _target_: nemo.collections.asr.modules.SpeakerDecoder + feat_in: *enc_feat_out + num_classes: 7205 + pool_mode: 'attention' + emb_sizes: 192 + + loss: + _target_: nemo.collections.asr.losses.angularloss.AngularSoftmaxLoss # you could also use cross-entrophy loss + scale: 30 + margin: 0.2 + + optim: + name: sgd + lr: .006 #(original titanet-large was trained with 0.08 lr) + weight_decay: 0.0002 + momentum: 0.9 + + # scheduler setup + sched: + name: CosineAnnealing + warmup_ratio: 0.1 + min_lr: 0.0 + +trainer: + devices: 1 # number of gpus (original titanet-large was trained on 4 nodes with 8 gpus each) + max_epochs: 250 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + deterministic: True + enable_checkpointing: False + logger: False + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + gradient_clip_val: 1.0 + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/titanet-small.yaml b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/titanet-small.yaml new file mode 100644 index 0000000..5f5bd3f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/conf/titanet-small.yaml @@ -0,0 +1,172 @@ +name: &name "TitaNet-S" +sample_rate: &sample_rate 16000 + +model: + train_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 64 + shuffle: True + is_tarred: False + tarred_audio_filepaths: null + tarred_shard_strategy: "scatter" + augmentor: + noise: + manifest_path: null + prob: 0.5 + min_snr_db: 5 + max_snr_db: 15 + + impulse: + manifest_path: null + prob: 0.5 + + speed: + prob: 0.5 + sr: *sample_rate + resample_type: 'kaiser_fast' + min_speed_rate: 0.95 + max_speed_rate: 1.05 + + validation_ds: + manifest_filepath: ??? + sample_rate: 16000 + labels: null + batch_size: 128 + shuffle: False + + model_defaults: + filters: 256 + repeat: 3 + dropout: 0.1 + separable: true + se: true + se_context_size: -1 + kernel_size_factor: 1.0 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + normalize: "per_feature" + window_size: 0.025 + sample_rate: *sample_rate + window_stride: 0.01 + window: "hann" + features: &n_mels 80 + n_fft: 512 + frame_splicing: 1 + dither: 0.00001 + + spec_augment: + _target_: nemo.collections.asr.modules.SpectrogramAugmentation + freq_masks: 3 + freq_width: 4 + time_masks: 5 + time_width: 0.03 + + encoder: + _target_: nemo.collections.asr.modules.ConvASREncoder + feat_in: *n_mels + activation: relu + conv_mask: true + + jasper: + - filters: ${model.model_defaults.filters} + repeat: 1 + kernel: [3] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [7] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [11] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: ${model.model_defaults.filters} + repeat: ${model.model_defaults.repeat} + kernel: [15] + stride: [1] + dilation: [1] + dropout: ${model.model_defaults.dropout} + residual: true + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + - filters: &enc_feat_out 3072 + repeat: 1 + kernel: [1] + stride: [1] + dilation: [1] + dropout: 0.0 + residual: false + separable: ${model.model_defaults.separable} + se: ${model.model_defaults.se} + se_context_size: ${model.model_defaults.se_context_size} + + decoder: + _target_: nemo.collections.asr.modules.SpeakerDecoder + feat_in: *enc_feat_out + num_classes: 7205 + pool_mode: 'attention' + emb_sizes: 192 + + loss: + _target_: nemo.collections.asr.losses.angularloss.AngularSoftmaxLoss + scale: 30 + margin: 0.2 + + optim: + name: sgd + lr: .006 #(original titanet-large was trained with 0.08 lr) + weight_decay: 0.001 + momentum: 0.9 + + # scheduler setup + sched: + name: CosineAnnealing + warmup_ratio: 0.1 + min_lr: 0.0 + +trainer: + devices: 1 # number of gpus (original titanet-large was trained on 4 nodes with 8 gpus each) + max_epochs: 250 + max_steps: -1 # computed at runtime if not set + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + deterministic: True + enable_checkpointing: False + logger: False + log_every_n_steps: 1 # Interval of logging. + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + gradient_clip_val: 1.0 + +exp_manager: + exp_dir: null + name: *name + create_tensorboard_logger: True + create_checkpoint_callback: True diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/extract_speaker_embeddings.py b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/extract_speaker_embeddings.py new file mode 100644 index 0000000..e67dc7d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/extract_speaker_embeddings.py @@ -0,0 +1,125 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This is a helper script to extract speaker embeddings based on manifest file +Usage: +python extract_speaker_embeddings.py --manifest=/path/to/manifest/file' +--model_path='/path/to/.nemo/file'(optional) +--embedding_dir='/path/to/embedding/directory' + +Args: +--manifest: path to manifest file containing audio_file paths for which embeddings need to be extracted +--model_path(optional): path to .nemo speaker verification model file to extract embeddings, if not passed SpeakerNet-M model would + be downloaded from NGC and used to extract embeddings +--embeddings_dir(optional): path to directory where embeddings need to stored default:'./' + + +""" + +import json +import os +import pickle as pkl +from argparse import ArgumentParser + +import numpy as np +import torch + +from nemo.collections.asr.models.label_models import EncDecSpeakerLabelModel +from nemo.collections.asr.parts.utils.speaker_utils import embedding_normalize +from nemo.utils import logging + + +def get_embeddings(speaker_model, manifest_file, batch_size=1, embedding_dir='./', device='cuda'): + """ + save embeddings to pickle file + Args: + speaker_model: NeMo model + manifest_file: path to the manifest file containing the audio file path from which the + embeddings should be extracted + batch_size: batch_size for inference + embedding_dir: path to directory to store embeddings file + device: compute device to perform operations + """ + + all_embs, _, _, _ = speaker_model.batch_inference(manifest_file, batch_size=batch_size, device=device) + all_embs = np.asarray(all_embs) + all_embs = embedding_normalize(all_embs) + out_embeddings = {} + + with open(manifest_file, 'r', encoding='utf-8') as manifest: + for i, line in enumerate(manifest.readlines()): + line = line.strip() + dic = json.loads(line) + uniq_name = '@'.join(dic['audio_filepath'].split('/')[-3:]) + out_embeddings[uniq_name] = all_embs[i] + + embedding_dir = os.path.join(embedding_dir, 'embeddings') + if not os.path.exists(embedding_dir): + os.makedirs(embedding_dir, exist_ok=True) + + prefix = manifest_file.split('/')[-1].rsplit('.', 1)[-2] + + name = os.path.join(embedding_dir, prefix) + embeddings_file = name + '_embeddings.pkl' + pkl.dump(out_embeddings, open(embeddings_file, 'wb')) + logging.info("Saved embedding files to {}".format(embedding_dir)) + + +def main(): + parser = ArgumentParser() + parser.add_argument( + "--manifest", type=str, required=True, help="Path to manifest file", + ) + parser.add_argument( + "--model_path", + type=str, + default='titanet_large', + required=False, + help="path to .nemo speaker verification model file to extract embeddings, if not passed SpeakerNet-M model would be downloaded from NGC and used to extract embeddings", + ) + parser.add_argument( + "--batch_size", type=int, default=1, required=False, help="batch size", + ) + parser.add_argument( + "--embedding_dir", + type=str, + default='./', + required=False, + help="path to directory where embeddings need to stored default:'./'", + ) + args = parser.parse_args() + torch.set_grad_enabled(False) + + if args.model_path.endswith('.nemo'): + logging.info(f"Using local speaker model from {args.model_path}") + speaker_model = EncDecSpeakerLabelModel.restore_from(restore_path=args.model_path) + elif args.model_path.endswith('.ckpt'): + speaker_model = EncDecSpeakerLabelModel.load_from_checkpoint(checkpoint_path=args.model_path) + else: + speaker_model = EncDecSpeakerLabelModel.from_pretrained(model_name="titanet_large") + logging.info(f"using pretrained titanet_large speaker model from NGC") + + device = 'cuda' + if not torch.cuda.is_available(): + device = 'cpu' + logging.warning("Running model on CPU, for faster performance it is adviced to use atleast one NVIDIA GPUs") + + get_embeddings( + speaker_model, args.manifest, batch_size=args.batch_size, embedding_dir=args.embedding_dir, device=device + ) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/speaker_identification_infer.py b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/speaker_identification_infer.py new file mode 100644 index 0000000..90f930f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/speaker_identification_infer.py @@ -0,0 +1,110 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json + +import numpy as np +import torch +from omegaconf import OmegaConf +from pytorch_lightning import seed_everything + +from nemo.collections.asr.data.audio_to_label import AudioToSpeechLabelDataset +from nemo.collections.asr.models import EncDecSpeakerLabelModel +from nemo.collections.asr.parts.features import WaveformFeaturizer +from nemo.core.config import hydra_runner +from nemo.utils import logging + +seed_everything(42) + + +@hydra_runner(config_path="conf", config_name="speaker_identification_infer") +def main(cfg): + + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + device = 'cuda' if torch.cuda.is_available() else 'cpu' + + enrollment_manifest = cfg.data.enrollment_manifest + test_manifest = cfg.data.test_manifest + out_manifest = cfg.data.out_manifest + sample_rate = cfg.data.sample_rate + + backend = cfg.backend.backend_model.lower() + + featurizer = WaveformFeaturizer(sample_rate=sample_rate) + dataset = AudioToSpeechLabelDataset(manifest_filepath=enrollment_manifest, labels=None, featurizer=featurizer) + enroll_id2label = dataset.id2label + + if backend == 'cosine_similarity': + model_path = cfg.backend.cosine_similarity.model_path + batch_size = cfg.backend.cosine_similarity.batch_size + if model_path.endswith('.nemo'): + speaker_model = EncDecSpeakerLabelModel.restore_from(model_path) + else: + speaker_model = EncDecSpeakerLabelModel.from_pretrained(model_path) + + enroll_embs, _, enroll_truelabels, _ = speaker_model.batch_inference( + enrollment_manifest, batch_size, sample_rate, device=device, + ) + + test_embs, _, _, _ = speaker_model.batch_inference(test_manifest, batch_size, sample_rate, device=device,) + + # length normalize + enroll_embs = enroll_embs / (np.linalg.norm(enroll_embs, ord=2, axis=-1, keepdims=True)) + test_embs = test_embs / (np.linalg.norm(test_embs, ord=2, axis=-1, keepdims=True)) + + # reference embedding + reference_embs = [] + keyslist = list(enroll_id2label.values()) + for label_id in keyslist: + indices = np.where(enroll_truelabels == label_id) + embedding = (enroll_embs[indices].sum(axis=0).squeeze()) / len(indices) + reference_embs.append(embedding) + + reference_embs = np.asarray(reference_embs) + + scores = np.matmul(test_embs, reference_embs.T) + matched_labels = scores.argmax(axis=-1) + + elif backend == 'neural_classifier': + model_path = cfg.backend.neural_classifier.model_path + batch_size = cfg.backend.neural_classifier.batch_size + + if model_path.endswith('.nemo'): + speaker_model = EncDecSpeakerLabelModel.restore_from(model_path) + else: + speaker_model = EncDecSpeakerLabelModel.from_pretrained(model_path) + + if speaker_model.decoder.final.out_features != len(enroll_id2label): + raise ValueError( + "number of labels mis match. Make sure you trained or finetuned neural classifier with labels from enrollement manifest_filepath" + ) + + _, test_logits, _, _ = speaker_model.batch_inference(test_manifest, batch_size, sample_rate, device=device,) + matched_labels = test_logits.argmax(axis=-1) + + with open(test_manifest, 'rb') as f1, open(out_manifest, 'w', encoding='utf-8') as f2: + lines = f1.readlines() + for idx, line in enumerate(lines): + line = line.strip() + item = json.loads(line) + item['infer'] = enroll_id2label[matched_labels[idx]] + json.dump(item, f2) + f2.write('\n') + + logging.info("Inference labels have been written to {} manifest file".format(out_manifest)) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/speaker_reco.py b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/speaker_reco.py new file mode 100644 index 0000000..a8acd4d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/speaker_reco.py @@ -0,0 +1,84 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf +from pytorch_lightning import seed_everything + +from nemo.collections.asr.models import EncDecSpeakerLabelModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +""" +Basic run (on GPU for 10 epochs for 2 class training): +EXP_NAME=sample_run +python ./speaker_reco.py --config-path='conf' --config-name='SpeakerNet_recognition_3x2x512.yaml' \ + trainer.max_epochs=10 \ + model.train_ds.batch_size=64 model.validation_ds.batch_size=64 \ + model.train_ds.manifest_filepath="" model.validation_ds.manifest_filepath="" \ + model.test_ds.manifest_filepath="" \ + trainer.devices=1 \ + model.decoder.params.num_classes=2 \ + exp_manager.name=$EXP_NAME +exp_manager.use_datetime_version=False \ + exp_manager.exp_dir='./speaker_exps' + +See https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb for notebook tutorial + +Optional: Use tarred dataset to speech up data loading. + Prepare ONE manifest that contains all training data you would like to include. Validation should use non-tarred dataset. + Note that it's possible that tarred datasets impacts validation scores because it drop values in order to have same amount of files per tarfile; + Scores might be off since some data is missing. + + Use the `convert_to_tarred_audio_dataset.py` script under /speech_recognition/scripts in order to prepare tarred audio dataset. + For details, please see TarredAudioToClassificationLabelDataset in /nemo/collections/asr/data/audio_to_label.py +""" + +seed_everything(42) + + +@hydra_runner(config_path="conf", config_name="SpeakerNet_verification_3x2x256.yaml") +def main(cfg): + + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(**cfg.trainer) + log_dir = exp_manager(trainer, cfg.get("exp_manager", None)) + speaker_model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer) + + # save labels to file + if log_dir is not None: + with open(os.path.join(log_dir, 'labels.txt'), 'w') as f: + if speaker_model.labels is not None: + for label in speaker_model.labels: + f.write(f'{label}\n') + + trainer.fit(speaker_model) + + if not trainer.fast_dev_run: + model_path = os.path.join(log_dir, '..', 'spkr.nemo') + speaker_model.save_to(model_path) + + torch.distributed.destroy_process_group() + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if trainer.is_global_zero: + trainer = pl.Trainer(devices=1, accelerator=cfg.trainer.accelerator, strategy=cfg.trainer.strategy) + if speaker_model.prepare_test(trainer): + trainer.test(speaker_model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/speaker_reco_finetune.py b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/speaker_reco_finetune.py new file mode 100644 index 0000000..884e5a6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/speaker_reco_finetune.py @@ -0,0 +1,57 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf +from pytorch_lightning import seed_everything + +from nemo.collections.asr.models import EncDecSpeakerLabelModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +seed_everything(42) + + +@hydra_runner(config_path="conf", config_name="titanet-finetune.yaml") +def main(cfg): + + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + trainer = pl.Trainer(**cfg.trainer) + log_dir = exp_manager(trainer, cfg.get("exp_manager", None)) + speaker_model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer) + speaker_model.maybe_init_from_pretrained_checkpoint(cfg) + + # save labels to file + if log_dir is not None: + with open(os.path.join(log_dir, 'labels.txt'), 'w') as f: + if speaker_model.labels is not None: + for label in speaker_model.labels: + f.write(f'{label}\n') + + trainer.fit(speaker_model) + + torch.distributed.destroy_process_group() + if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None: + if trainer.is_global_zero: + trainer = pl.Trainer(devices=1, accelerator=cfg.trainer.accelerator, strategy=cfg.trainer.strategy) + if speaker_model.prepare_test(trainer): + trainer.test(speaker_model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/voxceleb_eval.py b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/voxceleb_eval.py new file mode 100644 index 0000000..bf21c62 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/speaker_tasks/recognition/voxceleb_eval.py @@ -0,0 +1,111 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import pickle as pkl +import sys + +import numpy as np +from scipy.interpolate import interp1d +from scipy.optimize import brentq +from sklearn.metrics import roc_curve +from tqdm import tqdm + + +""" +This script faciliates to get EER % based on cosine-smilarity +for Voxceleb dataset. + +Args: + trial_file str: path to voxceleb trial file + emb : path to pickle file of embeddings dictionary (generated from spkr_get_emb.py) + save_kaldi_emb: if required pass this argument to save kaldi embeddings for KALDI PLDA training later + Note: order of audio files in manifest file should match the embeddings +""" + + +def get_acc(trial_file='', emb='', save_kaldi_emb=False): + + trial_score = open('trial_score.txt', 'w') + dirname = os.path.dirname(trial_file) + with open(emb, 'rb') as f: + emb = pkl.load(f) + trial_embs = [] + keys = [] + all_scores = [] + all_keys = [] + + # for each trials in trial file + with open(trial_file, 'r') as f: + tmp_file = f.readlines() + for line in tqdm(tmp_file): + line = line.strip() + truth, x_speaker, y_speaker = line.split() + + x_speaker = x_speaker.split('/') + x_speaker = '@'.join(x_speaker) + + y_speaker = y_speaker.split('/') + y_speaker = '@'.join(y_speaker) + + X = emb[x_speaker] + Y = emb[y_speaker] + + if save_kaldi_emb and x_speaker not in keys: + keys.append(x_speaker) + trial_embs.extend([X]) + + if save_kaldi_emb and y_speaker not in keys: + keys.append(y_speaker) + trial_embs.extend([Y]) + + score = np.dot(X, Y) / ((np.dot(X, X) * np.dot(Y, Y)) ** 0.5) + score = (score + 1) / 2 + + all_scores.append(score) + trial_score.write(str(score) + "\t" + truth) + truth = int(truth) + all_keys.append(truth) + + trial_score.write('\n') + trial_score.close() + + if save_kaldi_emb: + np.save(dirname + '/all_embs_voxceleb.npy', np.asarray(trial_embs)) + np.save(dirname + '/all_ids_voxceleb.npy', np.asarray(keys)) + print("Saved KALDI PLDA related embeddings to {}".format(dirname)) + + return np.asarray(all_scores), np.asarray(all_keys) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--trial_file", help="path to voxceleb trial file", type=str, required=True) + parser.add_argument("--emb", help="path to numpy file of embeddings", type=str, required=True) + parser.add_argument( + "--save_kaldi_emb", + help=":save kaldi embeddings for KALDI PLDA training later", + required=False, + action='store_true', + ) + + args = parser.parse_args() + trial_file, emb, save_kaldi_emb = args.trial_file, args.emb, args.save_kaldi_emb + + y_score, y = get_acc(trial_file=trial_file, emb=emb, save_kaldi_emb=save_kaldi_emb) + fpr, tpr, thresholds = roc_curve(y, y_score, pos_label=1) + + eer = brentq(lambda x: 1.0 - x - interp1d(fpr, tpr)(x), 0.0, 1.0) + sys.stdout.write("{0:.2f}\n".format(eer * 100)) diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/aligner.py b/NeMo-2.0.0.rc0.beta/examples/tts/aligner.py new file mode 100644 index 0000000..e32c044 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/aligner.py @@ -0,0 +1,33 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models import AlignerModel +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path='conf', config_name='aligner') +def main(cfg): + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get('exp_manager', None)) + model = AlignerModel(cfg=cfg.model, trainer=trainer) + trainer.callbacks.extend([pl.callbacks.LearningRateMonitor(), LogEpochTimeCallback()]) # noqa + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/aligner_heteronym_disambiguation.py b/NeMo-2.0.0.rc0.beta/examples/tts/aligner_heteronym_disambiguation.py new file mode 100644 index 0000000..c97d3db --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/aligner_heteronym_disambiguation.py @@ -0,0 +1,317 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import json +import os +import re + +import librosa +import soundfile as sf +import torch + +from nemo.collections.tts.models import AlignerModel +from nemo.collections.tts.parts.utils.tts_dataset_utils import general_padding + + +""" +G2P disambiguation using an Aligner model's input embedding distances. + +Does not handle OOV and leaves them as graphemes. + +The output will have each token's phonemes (or graphemes) bracketed, e.g. +<\"><, ><, >< >< ><.\"> + +Example: +python aligner_heteronym_disambiguation.py \ + --model= \ + --manifest= \ + --out= \ + --confidence=0.02 \ + --verbose +""" + + +def get_args(): + """Retrieve arguments for disambiguation. + """ + parser = argparse.ArgumentParser("G2P disambiguation using Aligner input embedding distances.") + # TODO(jocelynh): Make this required=False with default download from NGC once ckpt uploaded + parser.add_argument('--model', required=True, type=str, help="Path to Aligner model checkpoint (.nemo file).") + parser.add_argument( + '--manifest', + required=True, + type=str, + help="Path to data manifest. Each entry should contain the path to the audio file as well as the text in graphemes.", + ) + parser.add_argument( + '--out', required=True, type=str, help="Path to output file where disambiguations will be written." + ) + parser.add_argument( + '--sr', + required=False, + default=22050, + type=int, + help="Target sample rate to load the dataset. Should match what the model was trained on.", + ) + parser.add_argument( + '--heteronyms', + required=False, + type=str, + default='../../scripts/tts_dataset_files/heteronyms-052722', + help="Heteronyms file to specify which words should be disambiguated. All others will use default pron.", + ) + parser.add_argument( + '--confidence', required=False, type=float, default=0.0, help="Confidence threshold to keep a disambiguation." + ) + parser.add_argument( + '--verbose', + action='store_true', + help="If set to True, logs scores for each disambiguated word in disambiguation_logs.txt.", + ) + args = parser.parse_args() + return args + + +def load_and_prepare_audio(aligner, audio_path, target_sr, device): + """Loads and resamples audio to target sample rate (if necessary), and preprocesses for Aligner input. + """ + # Load audio and get length for preprocessing + audio_data, orig_sr = sf.read(audio_path) + if orig_sr != target_sr: + audio_data = librosa.core.resample(audio_data, orig_sr=orig_sr, target_sr=target_sr) + + audio = torch.tensor(audio_data, dtype=torch.float, device=device).unsqueeze(0) + audio_len = torch.tensor(audio_data.shape[0], device=device).long().unsqueeze(0) + + # Generate spectrogram + spec, spec_len = aligner.preprocessor(input_signal=audio, length=audio_len) + + return spec, spec_len + + +def disambiguate_candidates(aligner, text, spec, spec_len, confidence, device, heteronyms, log_file=None): + """Retrieves and disambiguate all candidate sentences for disambiguation of a given some text. + + Assumes that the max number of candidates per word is a reasonable batch size. + + Note: This could be sped up if multiple words' candidates were batched, but this is conceptually easier. + """ + # Grab original G2P result + aligner_g2p = aligner.tokenizer.g2p + base_g2p = aligner_g2p(text) + + # Tokenize text + words = [word for word, _ in aligner_g2p.word_tokenize_func(text)] + + ### Loop Through Words ### + result_g2p = [] + word_start_idx = 0 + + has_heteronym = False + + for word in words: + # Retrieve the length of the word in the default G2P conversion + g2p_default_len = len(aligner_g2p(word)) + + # Check if word needs to be disambiguated + if word in heteronyms: + has_heteronym = True + + # Add candidate for each ambiguous pronunciation + word_candidates = [] + candidate_prons_and_lengths = [] + + for pron in aligner_g2p.phoneme_dict[word]: + # Replace graphemes in the base G2P result with the current variant + candidate = base_g2p[:word_start_idx] + pron + base_g2p[word_start_idx + g2p_default_len :] + candidate_tokens = aligner.tokenizer.encode_from_g2p(candidate) + + word_candidates.append(candidate_tokens) + candidate_prons_and_lengths.append((pron, len(pron))) + + ### Inference ### + num_candidates = len(word_candidates) + + # If only one candidate, just convert and continue + if num_candidates == 1: + has_heteronym = False + result_g2p.append(f"<{' '.join(candidate_prons_and_lengths[0][0])}>") + word_start_idx += g2p_default_len + continue + + text_len = [len(toks) for toks in word_candidates] + text_len_in = torch.tensor(text_len, device=device).long() + + # Have to pad text tokens in case different pronunciations have different lengths + max_text_len = max(text_len) + text_stack = [] + for i in range(num_candidates): + padded_tokens = general_padding( + torch.tensor(word_candidates[i], device=device).long(), text_len[i], max_text_len + ) + text_stack.append(padded_tokens) + text_in = torch.stack(text_stack) + + # Repeat spectrogram and spec_len tensors to match batch size + spec_in = spec.repeat([num_candidates, 1, 1]) + spec_len_in = spec_len.repeat([num_candidates]) + + with torch.no_grad(): + soft_attn, _ = aligner(spec=spec_in, spec_len=spec_len_in, text=text_in, text_len=text_len_in) + + # Need embedding distances and duration preds to calculate mean distance for just the one word + text_embeddings = aligner.embed(text_in).transpose(1, 2) + l2_dists = aligner.alignment_encoder.get_dist(keys=text_embeddings, queries=spec_in).sqrt() + + durations = aligner.alignment_encoder.get_durations(soft_attn, text_len_in, spec_len_in).int() + + # Retrieve average embedding distances + min_dist = float('inf') + max_dist = 0.0 + best_candidate = None + for i in range(num_candidates): + candidate_mean_dist = aligner.alignment_encoder.get_mean_distance_for_word( + l2_dists=l2_dists[i], + durs=durations[i], + start_token=word_start_idx + (1 if aligner.tokenizer.pad_with_space else 0), + num_tokens=candidate_prons_and_lengths[i][1], + ) + if log_file: + log_file.write(f"{candidate_prons_and_lengths[i][0]} -- {candidate_mean_dist}\n") + + if candidate_mean_dist < min_dist: + min_dist = candidate_mean_dist + best_candidate = candidate_prons_and_lengths[i][0] + if candidate_mean_dist > max_dist: + max_dist = candidate_mean_dist + + # Calculate confidence score. If below threshold, skip and use graphemes. + disamb_conf = (max_dist - min_dist) / ((max_dist + min_dist) / 2.0) + if disamb_conf < confidence: + if log_file: + log_file.write(f"Below confidence threshold: {best_candidate} ({disamb_conf})\n") + + has_heteronym = False + result_g2p.append(f"<{' '.join(aligner_g2p(word))}>") + word_start_idx += g2p_default_len + continue + + # Otherwise, can write disambiguated word + if log_file: + log_file.write(f"best candidate: {best_candidate} (confidence: {disamb_conf})\n") + + result_g2p.append(f"<{' '.join(best_candidate)}>") + else: + if re.search("[a-zA-Z]", word) is None: + # Punctuation or space + result_g2p.append(f"<{word}>") + elif word in aligner_g2p.phoneme_dict: + # Take default pronunciation for everything else in the dictionary + result_g2p.append(f"<{' '.join(aligner_g2p.phoneme_dict[word][0])}>") + else: + # OOV + result_g2p.append(f"<{' '.join(aligner_g2p(word))}>") + + # Advance to phoneme index of next word + word_start_idx += g2p_default_len + + if log_file and has_heteronym: + log_file.write(f"{text}\n") + log_file.write(f"===\n{''.join(result_g2p)}\n===\n") + log_file.write(f"===============================\n") + + return result_g2p, has_heteronym + + +def disambiguate_dataset( + aligner, manifest_path, out_path, sr, heteronyms, confidence, device, verbose, heteronyms_only=True +): + """Disambiguates the phonemes for all words with ambiguous pronunciations in the given manifest. + """ + log_file = open('disambiguation_logs.txt', 'w') if verbose else None + + with open(out_path, 'w') as f_out: + with open(manifest_path, 'r') as f_in: + count = 0 + + for line in f_in: + # Retrieve entry and base G2P conversion for full text + entry = json.loads(line) + # Set punct_post_process=True in order to preserve words with apostrophes + text = aligner.normalizer.normalize(entry['text'], punct_post_process=True) + text = aligner.tokenizer.text_preprocessing_func(text) + + # Load and preprocess audio + audio_path = entry['audio_filepath'] + spec, spec_len = load_and_prepare_audio(aligner, audio_path, sr, device) + + # Get pronunciation candidates and disambiguate + disambiguated_text, has_heteronym = disambiguate_candidates( + aligner, text, spec, spec_len, confidence, device, heteronyms, log_file + ) + + # Skip writing entry if user only wants samples with heteronyms + if heteronyms_only and not has_heteronym: + continue + + # Save entry with disambiguation + entry['disambiguated_text'] = ''.join(disambiguated_text) + f_out.write(f"{json.dumps(entry)}\n") + + count += 1 + if count % 100 == 0: + print(f"Finished {count} entries.") + + print(f"Finished all entries, with a total of {count}.") + if log_file: + log_file.close() + + +def main(): + args = get_args() + + # Check file paths from arguments + if not os.path.exists(args.model): + print("Could not find model checkpoint file: ", args.model) + if not os.path.exists(args.manifest): + print("Could not find data manifest file: ", args.manifest) + if os.path.exists(args.out): + print("Output file already exists: ", args.out) + overwrite = input("Is it okay to overwrite it? (Y/N): ") + if overwrite.lower() != 'y': + print("Not overwriting output file, quitting.") + quit() + if not os.path.exists(args.heteronyms): + print("Could not find heteronyms list: ", args.heteronyms) + + # Read heteronyms list, one per line + heteronyms = set() + with open(args.heteronyms, 'r') as f_het: + for line in f_het: + heteronyms.add(line.strip().lower()) + + # Load model + print("Restoring Aligner model from checkpoint...") + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + aligner = AlignerModel.restore_from(args.model, map_location=device) + + # Disambiguation + print("Beginning disambiguation...") + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + disambiguate_dataset(aligner, args.manifest, args.out, args.sr, heteronyms, args.confidence, device, args.verbose) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/audio_codec.py b/NeMo-2.0.0.rc0.beta/examples/tts/audio_codec.py new file mode 100644 index 0000000..800edfb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/audio_codec.py @@ -0,0 +1,34 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl +from omegaconf import OmegaConf + +from nemo.collections.tts.models import AudioCodecModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf/audio_codec", config_name="audio_codec") +def main(cfg): + logging.info('\nConfig Params:\n%s', OmegaConf.to_yaml(cfg, resolve=True)) + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + model = AudioCodecModel(cfg=cfg.model, trainer=trainer) + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/aligner.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/aligner.yaml new file mode 100644 index 0000000..e6328ee --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/aligner.yaml @@ -0,0 +1,181 @@ +# This config contains the default values for training Aligner model on LJSpeech dataset. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: Aligner + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix" ] + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: hann + +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.10" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" + +model: + symbols_embedding_dim: 384 + bin_loss_start_ratio: 0.2 + bin_loss_warmup_epochs: 100 + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer + punct: true + stresses: true + chars: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 64 + num_workers: 4 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 64 + num_workers: 1 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: -11.52 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: clamp + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + alignment_encoder: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_mel_channels: ${model.n_mel_channels} + n_text_channels: ${model.symbols_embedding_dim} + n_att_channels: ${model.n_mel_channels} + + optim: + name: adam + lr: 1e-3 + weight_decay: 1e-6 + + sched: + name: CosineAnnealing + min_lr: 5e-5 + warmup_ratio: 0.35 + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + strategy: ddp + precision: 32 + max_epochs: 1000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 1 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_forward_sum_loss + mode: min + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/audio_codec/audio_codec_16000.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/audio_codec/audio_codec_16000.yaml new file mode 100644 index 0000000..7182414 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/audio_codec/audio_codec_16000.yaml @@ -0,0 +1,176 @@ +# This config contains the default values for training 16kHz NeMo Audio Codec model +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: AudioCodec + +max_epochs: ??? +max_steps: 200000 +# Adjust batch size based on GPU memory +batch_size: 32 +# When doing weighted sampling with multiple manifests, this defines how many training steps are in an epoch. +# If null, then weighted sampling is disabled. +weighted_sampling_steps_per_epoch: null + +# Dataset metadata for each manifest +# https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/data/vocoder_dataset.py#L39-L41 +train_ds_meta: ??? +val_ds_meta: ??? + +log_ds_meta: ??? +log_dir: ??? + +# Modify these values based on your sample rate +sample_rate: 16000 +train_n_samples: 16000 +down_sample_rates: [2, 4, 5, 5] +up_sample_rates: [5, 5, 4, 2] +# The number of samples per encoded audio frame. Should be the product of the down_sample_rates. +# For example 2 * 4 * 5 * 5 = 200. => frame_rate = 16000/200 = 80 +samples_per_frame: 200 + +model: + + max_epochs: ${max_epochs} + steps_per_epoch: ${weighted_sampling_steps_per_epoch} + max_steps: ${max_steps} + + sample_rate: ${sample_rate} + samples_per_frame: ${samples_per_frame} + + mel_loss_l1_scale: 1.0 + mel_loss_l2_scale: 1.0 + stft_loss_scale: 0.0 + time_domain_loss_scale: 0.1 + + # Probability of updating the discriminator during each training step + # For example, update the discriminator 2/3 times (2 updates for every 3 batches) + disc_updates_per_period: 2 + disc_update_period: 3 + + # All resolutions for reconstruction loss, ordered [num_fft, hop_length, window_length] + loss_resolutions: [ + [32, 8, 32], [64, 16, 64], [128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048] + ] + mel_loss_dims: [64, 64, 64, 64, 64, 64, 64] + mel_loss_log_guard: 1E-5 + stft_loss_log_guard: 1.0 + + train_ds: + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + weighted_sampling_steps_per_epoch: ${weighted_sampling_steps_per_epoch} + sample_rate: ${sample_rate} + n_samples: ${train_n_samples} + min_duration: 1.01 + max_duration: null + dataset_meta: ${train_ds_meta} + + dataloader_params: + batch_size: ${batch_size} + drop_last: true + num_workers: 4 + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + sample_rate: ${sample_rate} + n_samples: null + min_duration: null + max_duration: null + trunc_duration: 10.0 # Only use the first 10 seconds of audio for computing validation loss + dataset_meta: ${val_ds_meta} + + dataloader_params: + batch_size: 8 + num_workers: 2 + + # Configures how audio samples are generated and saved during training. + # Remove this section to disable logging. + log_config: + log_dir: ${log_dir} + log_epochs: [1, 2, 3, 4, 5, 6] + epoch_frequency: 1 + log_tensorboard: false + log_wandb: true + + generators: + - _target_: nemo.collections.tts.parts.utils.callbacks.AudioCodecArtifactGenerator + log_audio: true + log_encoding: true + log_dequantized: true + + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + sample_rate: ${sample_rate} + n_samples: null + min_duration: null + max_duration: null + trunc_duration: 15.0 # Only log the first 15 seconds of generated audio. + dataset_meta: ${log_ds_meta} + + dataloader_params: + batch_size: 4 + num_workers: 2 + + audio_encoder: + _target_: nemo.collections.tts.modules.encodec_modules.SEANetEncoder + down_sample_rates: ${down_sample_rates} + + audio_decoder: + _target_: nemo.collections.tts.modules.encodec_modules.SEANetDecoder + up_sample_rates: ${up_sample_rates} + + vector_quantizer: + _target_: nemo.collections.tts.modules.encodec_modules.ResidualVectorQuantizer + num_codebooks: 8 + + discriminator: + _target_: nemo.collections.tts.modules.encodec_modules.MultiResolutionDiscriminatorSTFT + resolutions: [[128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048]] + + # The original EnCodec uses hinged loss, but squared-GAN loss is more stable + # and reduces the need to tune the loss weights or use a gradient balancer. + generator_loss: + _target_: nemo.collections.tts.losses.audio_codec_loss.GeneratorSquaredLoss + + discriminator_loss: + _target_: nemo.collections.tts.losses.audio_codec_loss.DiscriminatorSquaredLoss + + optim: + _target_: torch.optim.AdamW + lr: 1e-4 + betas: [0.8, 0.9] + + sched: + name: StepLR + gamma: 0.999996 + step_size: 1 + + # Parameters above are tuned based on 8 GPUs with bs 32 for librilight dataset, based on number of GPUs, those parameters need to be updated accordingly +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp_find_unused_parameters_true + precision: 32 # Vector quantization only works with 32-bit precision. + max_steps: ${max_steps} + max_epochs: ${max_epochs} + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 1 + benchmark: false + + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + create_wandb_logger: false + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/audio_codec/audio_codec_24000.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/audio_codec/audio_codec_24000.yaml new file mode 100644 index 0000000..e5e3867 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/audio_codec/audio_codec_24000.yaml @@ -0,0 +1,177 @@ +# This config contains the default values for training 24khz audio codec model +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: EnCodec + +max_epochs: ??? +# Adjust batch size based on GPU memory +batch_size: 16 +# When doing weighted sampling with multiple manifests, this defines how many training steps are in an epoch. +# If null, then weighted sampling is disabled. +weighted_sampling_steps_per_epoch: null + +# Dataset metadata for each manifest +# https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/data/vocoder_dataset.py#L39-L41 +train_ds_meta: ??? +val_ds_meta: ??? + +log_ds_meta: ??? +log_dir: ??? + +# Modify these values based on your sample rate +sample_rate: 24000 +train_n_samples: 24000 +down_sample_rates: [2, 4, 5, 8] +up_sample_rates: [8, 5, 4, 2] +# The number of samples per encoded audio frame. Should be the product of the down_sample_rates. +# For example 2 * 4 * 5 * 8 = 320. +samples_per_frame: 320 + +model: + + max_epochs: ${max_epochs} + steps_per_epoch: ${weighted_sampling_steps_per_epoch} + + sample_rate: ${sample_rate} + samples_per_frame: ${samples_per_frame} + + mel_loss_l1_scale: 15.0 + mel_loss_l2_scale: 0.0 + stft_loss_scale: 15.0 + time_domain_loss_scale: 0.0 + + # Probability of updating the discriminator during each training step + # For example, update the discriminator 2/3 times (2 updates for every 3 batches) + disc_updates_per_period: 2 + disc_update_period: 3 + + # All resolutions for reconstruction loss, ordered [num_fft, hop_length, window_length] + loss_resolutions: [ + [32, 8, 32], [64, 16, 64], [128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048] + ] + mel_loss_dims: [5, 10, 20, 40, 80, 160, 320] + mel_loss_log_guard: 1.0 + stft_loss_log_guard: 1.0 + + train_ds: + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + weighted_sampling_steps_per_epoch: ${weighted_sampling_steps_per_epoch} + sample_rate: ${sample_rate} + n_samples: ${train_n_samples} + min_duration: 1.01 + max_duration: null + dataset_meta: ${train_ds_meta} + + dataloader_params: + batch_size: ${batch_size} + drop_last: true + num_workers: 4 + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + sample_rate: ${sample_rate} + n_samples: null + min_duration: null + max_duration: null + trunc_duration: 10.0 # Only use the first 10 seconds of audio for computing validation loss + dataset_meta: ${val_ds_meta} + + dataloader_params: + batch_size: 8 + num_workers: 2 + + # Configures how audio samples are generated and saved during training. + # Remove this section to disable logging. + log_config: + log_dir: ${log_dir} + log_epochs: [10, 50, 100, 150, 200] + epoch_frequency: 100 + log_tensorboard: false + log_wandb: true + + generators: + - _target_: nemo.collections.tts.parts.utils.callbacks.AudioCodecArtifactGenerator + log_audio: true + log_encoding: true + log_dequantized: true + + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + sample_rate: ${sample_rate} + n_samples: null + min_duration: null + max_duration: null + trunc_duration: 15.0 # Only log the first 15 seconds of generated audio. + dataset_meta: ${log_ds_meta} + + dataloader_params: + batch_size: 4 + num_workers: 2 + + audio_encoder: + _target_: nemo.collections.tts.modules.encodec_modules.SEANetEncoder + down_sample_rates: ${down_sample_rates} + + audio_decoder: + _target_: nemo.collections.tts.modules.encodec_modules.SEANetDecoder + up_sample_rates: ${up_sample_rates} + + vector_quantizer: + _target_: nemo.collections.tts.modules.encodec_modules.ResidualVectorQuantizer + num_codebooks: 8 + + discriminator: + _target_: nemo.collections.tts.modules.encodec_modules.MultiResolutionDiscriminatorSTFT + resolutions: [[128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048]] + + # The original EnCodec uses hinged loss, but squared-GAN loss is more stable + # and reduces the need to tune the loss weights or use a gradient balancer. + generator_loss: + _target_: nemo.collections.tts.losses.audio_codec_loss.GeneratorSquaredLoss + + discriminator_loss: + _target_: nemo.collections.tts.losses.audio_codec_loss.DiscriminatorSquaredLoss + + optim: + _target_: torch.optim.Adam + lr: 3e-4 + betas: [0.5, 0.9] + + sched: + name: ExponentialLR + gamma: 0.998 + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp_find_unused_parameters_true + precision: 32 # Vector quantization only works with 32-bit precision. + max_epochs: ${max_epochs} + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 10 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: false + create_wandb_logger: true + wandb_logger_kwargs: + name: null + project: null + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + mode: min + save_top_k: 5 + save_best_model: true + always_save_nemo: true + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/audio_codec/encodec_24000.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/audio_codec/encodec_24000.yaml new file mode 100644 index 0000000..4898d44 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/audio_codec/encodec_24000.yaml @@ -0,0 +1,177 @@ +# This config contains the default values for training 24khz EnCodec model +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: EnCodec + +max_epochs: ??? +# Adjust batch size based on GPU memory +batch_size: 16 +# When doing weighted sampling with multiple manifests, this defines how many training steps are in an epoch. +# If null, then weighted sampling is disabled. +weighted_sampling_steps_per_epoch: null + +# Dataset metadata for each manifest +# https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/data/vocoder_dataset.py#L39-L41 +train_ds_meta: ??? +val_ds_meta: ??? + +log_ds_meta: ??? +log_dir: ??? + +# Modify these values based on your sample rate +sample_rate: 24000 +train_n_samples: 24000 +down_sample_rates: [2, 4, 5, 8] +up_sample_rates: [8, 5, 4, 2] +# The number of samples per encoded audio frame. Should be the product of the down_sample_rates. +# For example 2 * 4 * 5 * 8 = 320. +samples_per_frame: 320 + +model: + + max_epochs: ${max_epochs} + steps_per_epoch: ${weighted_sampling_steps_per_epoch} + + sample_rate: ${sample_rate} + samples_per_frame: ${samples_per_frame} + + mel_loss_l1_scale: 1.0 + mel_loss_l2_scale: 1.0 + stft_loss_scale: 0.0 + time_domain_loss_scale: 0.1 + + # Probability of updating the discriminator during each training step + # For example, update the discriminator 2/3 times (2 updates for every 3 batches) + disc_updates_per_period: 2 + disc_update_period: 3 + + # All resolutions for reconstruction loss, ordered [num_fft, hop_length, window_length] + loss_resolutions: [ + [32, 8, 32], [64, 16, 64], [128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048] + ] + mel_loss_dims: [64, 64, 64, 64, 64, 64, 64] + mel_loss_log_guard: 1E-5 + stft_loss_log_guard: 1.0 + + train_ds: + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + weighted_sampling_steps_per_epoch: ${weighted_sampling_steps_per_epoch} + sample_rate: ${sample_rate} + n_samples: ${train_n_samples} + min_duration: 1.01 + max_duration: null + dataset_meta: ${train_ds_meta} + + dataloader_params: + batch_size: ${batch_size} + drop_last: true + num_workers: 4 + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + sample_rate: ${sample_rate} + n_samples: null + min_duration: null + max_duration: null + trunc_duration: 10.0 # Only use the first 10 seconds of audio for computing validation loss + dataset_meta: ${val_ds_meta} + + dataloader_params: + batch_size: 8 + num_workers: 2 + + # Configures how audio samples are generated and saved during training. + # Remove this section to disable logging. + log_config: + log_dir: ${log_dir} + log_epochs: [10, 50, 100, 150, 200] + epoch_frequency: 100 + log_tensorboard: false + log_wandb: true + + generators: + - _target_: nemo.collections.tts.parts.utils.callbacks.AudioCodecArtifactGenerator + log_audio: true + log_encoding: true + log_dequantized: true + + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + sample_rate: ${sample_rate} + n_samples: null + min_duration: null + max_duration: null + trunc_duration: 15.0 # Only log the first 15 seconds of generated audio. + dataset_meta: ${log_ds_meta} + + dataloader_params: + batch_size: 4 + num_workers: 2 + + audio_encoder: + _target_: nemo.collections.tts.modules.encodec_modules.SEANetEncoder + down_sample_rates: ${down_sample_rates} + + audio_decoder: + _target_: nemo.collections.tts.modules.encodec_modules.SEANetDecoder + up_sample_rates: ${up_sample_rates} + + vector_quantizer: + _target_: nemo.collections.tts.modules.encodec_modules.ResidualVectorQuantizer + num_codebooks: 8 + + discriminator: + _target_: nemo.collections.tts.modules.encodec_modules.MultiResolutionDiscriminatorSTFT + resolutions: [[128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048]] + + # The original EnCodec uses hinged loss, but squared-GAN loss is more stable + # and reduces the need to tune the loss weights or use a gradient balancer. + generator_loss: + _target_: nemo.collections.tts.losses.audio_codec_loss.GeneratorSquaredLoss + + discriminator_loss: + _target_: nemo.collections.tts.losses.audio_codec_loss.DiscriminatorSquaredLoss + + optim: + _target_: torch.optim.Adam + lr: 3e-4 + betas: [0.5, 0.9] + + sched: + name: ExponentialLR + gamma: 0.999 + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp_find_unused_parameters_true + precision: 32 # Vector quantization only works with 32-bit precision. + max_epochs: ${max_epochs} + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: false + create_wandb_logger: true + wandb_logger_kwargs: + name: null + project: null + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + mode: min + save_top_k: 5 + save_best_model: true + always_save_nemo: true + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/audio_codec/mel_codec_44100.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/audio_codec/mel_codec_44100.yaml new file mode 100644 index 0000000..15d12f0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/audio_codec/mel_codec_44100.yaml @@ -0,0 +1,196 @@ +# This config contains the default values for training 44.1kHz audio codec model which encodes mel spectrogram +# instead of raw audio. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: MelCodec + +max_epochs: ??? +# Adjust batch size based on GPU memory +batch_size: 16 +# When doing weighted sampling with multiple manifests, this defines how many training steps are in an epoch. +# If null, then weighted sampling is disabled. +weighted_sampling_steps_per_epoch: null + +# Dataset metadata for each manifest +# https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/data/vocoder_dataset.py#L39-L41 +train_ds_meta: ??? +val_ds_meta: ??? + +log_ds_meta: ??? +log_dir: ??? + +# Modify these values based on your sample rate +sample_rate: 44100 +win_length: 2048 +hop_length: 512 +train_n_samples: 16384 # ~0.37 seconds +# The product of the up_sample_rates should match the hop_length. +# For example 8 * 8 * 4 * 2 = 512. +up_sample_rates: [8, 8, 4, 2] + + +model: + + max_epochs: ${max_epochs} + steps_per_epoch: ${weighted_sampling_steps_per_epoch} + + sample_rate: ${sample_rate} + samples_per_frame: ${hop_length} + + mel_loss_l1_scale: 1.0 + mel_loss_l2_scale: 0.0 + stft_loss_scale: 20.0 + time_domain_loss_scale: 0.0 + commit_loss_scale: 0.0 + + # Probability of updating the discriminator during each training step + # For example, update the discriminator 1/2 times (1 update for every 2 batches) + disc_updates_per_period: 1 + disc_update_period: 2 + + # All resolutions for mel reconstruction loss, ordered [num_fft, hop_length, window_length] + loss_resolutions: [ + [32, 8, 32], [64, 16, 64], [128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048] + ] + mel_loss_dims: [5, 10, 20, 40, 80, 160, 320] + mel_loss_log_guard: 1.0 + stft_loss_log_guard: 1.0 + feature_loss_type: absolute + + train_ds: + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + dataset_meta: ${train_ds_meta} + weighted_sampling_steps_per_epoch: ${weighted_sampling_steps_per_epoch} + sample_rate: ${sample_rate} + n_samples: ${train_n_samples} + min_duration: 0.4 + max_duration: null + + dataloader_params: + batch_size: ${batch_size} + drop_last: true + num_workers: 4 + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + sample_rate: ${sample_rate} + n_samples: null + min_duration: null + max_duration: null + trunc_duration: 10.0 # Only use the first 10 seconds of audio for computing validation loss + dataset_meta: ${val_ds_meta} + + dataloader_params: + batch_size: 4 + num_workers: 2 + + # Configures how audio samples are generated and saved during training. + # Remove this section to disable logging. + log_config: + log_dir: ${log_dir} + log_epochs: [10, 50, 100, 150, 200] + epoch_frequency: 100 + log_tensorboard: false + log_wandb: true + + generators: + - _target_: nemo.collections.tts.parts.utils.callbacks.AudioCodecArtifactGenerator + log_audio: true + log_encoding: true + log_dequantized: true + + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + sample_rate: ${sample_rate} + n_samples: null + min_duration: null + max_duration: null + trunc_duration: 10.0 # Only log the first 10 seconds of generated audio. + dataset_meta: ${log_ds_meta} + + dataloader_params: + batch_size: 4 + num_workers: 2 + + audio_encoder: + _target_: nemo.collections.tts.modules.audio_codec_modules.MultiBandMelEncoder + mel_bands: [[0, 10], [10, 20], [20, 30], [30, 40], [40, 50], [50, 60], [60, 70], [70, 80]] + out_channels: 4 # The dimension of each codebook + hidden_channels: 128 + filters: 256 + mel_processor: + _target_: nemo.collections.tts.modules.audio_codec_modules.MelSpectrogramProcessor + mel_dim: 80 + sample_rate: ${sample_rate} + win_length: ${win_length} + hop_length: ${hop_length} + + audio_decoder: + _target_: nemo.collections.tts.modules.audio_codec_modules.HiFiGANDecoder + up_sample_rates: ${up_sample_rates} + input_dim: 32 # Should be equal to len(audio_encoder.mel_bands) * audio_encoder.out_channels + base_channels: 1024 # This is double the base channels of HiFi-GAN V1, making it approximately 4x larger. + + vector_quantizer: + _target_: nemo.collections.tts.modules.audio_codec_modules.GroupFiniteScalarQuantizer + num_groups: 8 # Should equal len(audio_encoder.mel_bands) + num_levels_per_group: [8, 5, 5, 5] # 8 * 5 * 5 * 5 = 1000 entries per codebook + + discriminator: + _target_: nemo.collections.tts.modules.audio_codec_modules.Discriminator + discriminators: + - _target_: nemo.collections.tts.modules.encodec_modules.MultiResolutionDiscriminatorSTFT + resolutions: [[128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048]] + - _target_: nemo.collections.tts.modules.audio_codec_modules.MultiPeriodDiscriminator + + # The original EnCodec uses hinged loss, but squared-GAN loss is more stable + # and reduces the need to tune the loss weights or use a gradient balancer. + generator_loss: + _target_: nemo.collections.tts.losses.audio_codec_loss.GeneratorSquaredLoss + + discriminator_loss: + _target_: nemo.collections.tts.losses.audio_codec_loss.DiscriminatorSquaredLoss + + optim: + _target_: torch.optim.Adam + lr: 2e-4 + betas: [0.8, 0.99] + + sched: + name: ExponentialLR + gamma: 0.998 + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp_find_unused_parameters_true + precision: 16 + max_epochs: ${max_epochs} + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: false + create_wandb_logger: true + wandb_logger_kwargs: + name: null + project: null + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + mode: min + save_top_k: 5 + save_best_model: true + always_save_nemo: true + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/de/fastpitch_align_22050_grapheme.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/de/fastpitch_align_22050_grapheme.yaml new file mode 100644 index 0000000..8f2acfe --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/de/fastpitch_align_22050_grapheme.yaml @@ -0,0 +1,242 @@ +# This config contains the default values for training FastPitch model with aligner using 22KHz sampling +# rate. If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch" ] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 132.524658203125 for https://zenodo.org/record/5525342/files/thorsten-neutral_v03.tgz?download=1 +pitch_std: ??? # e.g. 37.389366149902 for https://zenodo.org/record/5525342/files/thorsten-neutral_v03.tgz?download=1 + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: null +window: hann + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: de + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.GermanCharsTokenizer + punct: true + apostrophe: true + pad_with_space: true + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 # change to null to include longer audios. + min_duration: 0.1 + ignore_file: null + trim: true + trim_top_db: 50 + trim_frame_length: ${model.n_window_size} + trim_hop_length: ${model.n_window_stride} + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 # change to null to include longer audios. + min_duration: 0.1 + ignore_file: null + trim: true + trim_top_db: 50 + trim_frame_length: ${model.n_window_size} + trim_hop_length: ${model.n_window_stride} + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [ 0.9, 0.999 ] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: -1 # specify all GPUs regardless of its availability + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: 1500 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/de/fastpitch_align_22050_mix.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/de/fastpitch_align_22050_mix.yaml new file mode 100644 index 0000000..3ac4593 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/de/fastpitch_align_22050_mix.yaml @@ -0,0 +1,257 @@ +# This config contains the default values for training FastPitch model with aligner using 22KHz sampling +# rate. If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch" ] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 132.524658203125 for https://zenodo.org/record/5525342/files/thorsten-neutral_v03.tgz?download=1 +pitch_std: ??? # e.g. 37.389366149902 for https://zenodo.org/record/5525342/files/thorsten-neutral_v03.tgz?download=1 + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: null +window: hann + +phoneme_dict_path: "scripts/tts_dataset_files/de/de_nv230119.dict" +heteronyms_path: "scripts/tts_dataset_files/de/de_nv230119.heteronyms" + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: de + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer + locale: 'de-DE' + punct: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p + locale: 'de-DE' + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.8 + ignore_ambiguous_words: false + use_chars: true + use_stresses: true + grapheme_case: mixed + grapheme_prefix: '#' + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 # change to null to include longer audios. + min_duration: 0.1 + ignore_file: null + trim: true + trim_top_db: 50 + trim_frame_length: ${model.n_window_size} + trim_hop_length: ${model.n_window_stride} + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 # change to null to include longer audios. + min_duration: 0.1 + ignore_file: null + trim: true + trim_top_db: 50 + trim_frame_length: ${model.n_window_size} + trim_hop_length: ${model.n_window_stride} + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [ 0.9, 0.999 ] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: -1 # specify all GPUs regardless of its availability + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: 1500 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/de/fastpitch_align_44100_grapheme.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/de/fastpitch_align_44100_grapheme.yaml new file mode 100644 index 0000000..22cde5c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/de/fastpitch_align_44100_grapheme.yaml @@ -0,0 +1,242 @@ +# This config contains the default values for training FastPitch model with aligner using 44.1KHz sampling +# rate. If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch", "speaker_id" ] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 151.9578857421875 for top-5 speakers in https://github.com/iisys-hof/HUI-Audio-Corpus-German +pitch_std: ??? # e.g. 87.1997680664063 for top-5 speakers in https://github.com/iisys-hof/HUI-Audio-Corpus-German + +# Default values for dataset with sample_rate=44100 +sample_rate: 44100 +n_mel_channels: 80 +n_window_size: 2048 +n_window_stride: 512 +n_fft: 2048 +lowfreq: 0 +highfreq: null +window: hann + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: 119 # change to the number of speakers in your dataset. Make sure it is at least the largest speaker_id + 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: de + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.GermanCharsTokenizer + punct: true + apostrophe: true + pad_with_space: true + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 # change to null to include longer audios. + min_duration: 0.1 + ignore_file: null + trim: true + trim_top_db: 50 + trim_frame_length: ${model.n_window_size} + trim_hop_length: ${model.n_window_stride} + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 # change to null to include longer audios. + min_duration: 0.1 + ignore_file: null + trim: true + trim_top_db: 50 + trim_frame_length: ${model.n_window_size} + trim_hop_length: ${model.n_window_stride} + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [ 0.9, 0.999 ] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: -1 # specify all GPUs regardless of its availability + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: 1500 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/de/fastpitch_align_44100_phoneme.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/de/fastpitch_align_44100_phoneme.yaml new file mode 100644 index 0000000..6b8d6de --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/de/fastpitch_align_44100_phoneme.yaml @@ -0,0 +1,237 @@ +# This config contains the default values for training FastPitch model with aligner using 44.1KHz sampling +# rate. If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch", "speaker_id" ] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 151.9578857421875 for top-5 speakers in https://github.com/iisys-hof/HUI-Audio-Corpus-German +pitch_std: ??? # e.g. 87.1997680664063 for top-5 speakers in https://github.com/iisys-hof/HUI-Audio-Corpus-German + +# Default values for dataset with sample_rate=44100 +sample_rate: 44100 +n_mel_channels: 80 +n_window_size: 2048 +n_window_stride: 512 +n_fft: 2048 +lowfreq: 0 +highfreq: null +window: hann + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: 119 # change to the number of speakers in your dataset. Make sure it is at least the largest speaker_id + 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: de + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.GermanPhonemesTokenizer + punct: true + apostrophe: true + pad_with_space: true + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 # change to null to include longer audios. + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + use_beta_binomial_interpolator: false + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 # change to null to include longer audios. + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + use_beta_binomial_interpolator: false + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [ 0.9, 0.999 ] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: -1 # specify all GPUs regardless of its availability + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: 1500 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/es/fastpitch_align_44100.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/es/fastpitch_align_44100.yaml new file mode 100644 index 0000000..4cf2fb8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/es/fastpitch_align_44100.yaml @@ -0,0 +1,223 @@ +# This config contains the default values for training grapheme Spanish FastPitch model with aligner using +# 44.1KHz sampling rate. If you want to train model on other dataset, you can change config values according +# to your dataset. Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: ["align_prior_matrix", "pitch"] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 178.86880493164062 for 174 speakers in https://research.google/pubs/pub49150/ +pitch_std: ??? # e.g. 60.64979553222656 for 174 speakers in https://research.google/pubs/pub49150/ + +sample_rate: 44100 +n_mel_channels: 80 +n_window_size: 2048 +n_window_stride: 512 +n_fft: 2048 +lowfreq: 0 +highfreq: null +window: hann + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.SpanishCharsTokenizer + pad_with_space: true + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: -1 # number of gpus + accelerator: gpu + strategy: ddp + precision: 16 + max_steps: 1000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/es/fastpitch_align_44100_ipa.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/es/fastpitch_align_44100_ipa.yaml new file mode 100644 index 0000000..5ecefb3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/es/fastpitch_align_44100_ipa.yaml @@ -0,0 +1,236 @@ +# This config contains the default values for training IPA Spanish FastPitch model with aligner using +# 44.1KHz sampling rate. If you want to train model on other dataset, you can change config values according +# to your dataset. Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: ["align_prior_matrix", "pitch"] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 178.86880493164062 for 174 speakers in https://research.google/pubs/pub49150/ +pitch_std: ??? # e.g. 60.64979553222656 for 174 speakers in https://research.google/pubs/pub49150/ + +sample_rate: 44100 +n_mel_channels: 80 +n_window_size: 2048 +n_window_stride: 512 +n_fft: 2048 +lowfreq: 0 +highfreq: null +window: hann + +phoneme_dict_path: ??? + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer + locale: es-ES + punct: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p + locale: es-ES + phoneme_dict: ${phoneme_dict_path} + phoneme_probability: 0.5 + ignore_ambiguous_words: false + use_chars: true + use_stresses: true + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: -1 # number of gpus + accelerator: gpu + strategy: ddp + precision: 16 + max_steps: 1000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/es/fastpitch_align_44100_ipa_multi.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/es/fastpitch_align_44100_ipa_multi.yaml new file mode 100644 index 0000000..c55af51 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/es/fastpitch_align_44100_ipa_multi.yaml @@ -0,0 +1,231 @@ +# This config contains the default values for training multi-speaker IPA Spanish FastPitch model with aligner using +# 44.1KHz sampling rate. If you want to train model on other dataset, you can change config values according +# to your dataset. Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: ["align_prior_matrix", "pitch", "speaker_id"] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# See "scripts/tts_dataset_files/openslr_es/pitch_stats.json" for format example +pitch_stats_path: ??? + +sample_rate: 44100 +n_mel_channels: 80 +n_window_size: 2048 +n_window_stride: 512 +n_fft: 2048 +lowfreq: 0 +highfreq: null +window: hann + +phoneme_dict_path: ??? + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: ??? + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + speaker_emb_condition_prosody: true + speaker_emb_condition_aligner: true + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer + locale: es-ES + punct: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p + locale: es-ES + phoneme_dict: ${phoneme_dict_path} + phoneme_probability: 0.5 + ignore_ambiguous_words: false + use_chars: true + use_stresses: true + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_stats_path: ${pitch_stats_path} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: 15 + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_stats_path: ${pitch_stats_path} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: -1 # number of gpus + accelerator: gpu + strategy: ddp + precision: 16 + max_steps: 1000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch/fastpitch_22050.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch/fastpitch_22050.yaml new file mode 100644 index 0000000..846d09e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch/fastpitch_22050.yaml @@ -0,0 +1,286 @@ +# This config contains the default values for training an English 22.05kHz FastPitch model. +# If you want to train a model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +max_epochs: ??? +batch_size: 32 +weighted_sampling_steps_per_epoch: null + +n_speakers: ??? +speaker_path: null +feature_stats_path: null + +train_ds_meta: ??? +val_ds_meta: ??? +log_ds_meta: ??? + +phoneme_dict_path: ??? +heteronyms_path: ??? + +log_dir: ??? +vocoder_type: ??? +vocoder_name: null +vocoder_checkpoint_path: null + +# The below feature config should match the feature.yaml config used during preprocessing. +sample_rate: 22050 +win_length: 1024 +hop_length: 256 + +mel_feature: + _target_: nemo.collections.tts.parts.preprocessing.features.MelSpectrogramFeaturizer + sample_rate: ${sample_rate} + win_length: ${win_length} + hop_length: ${hop_length} + mel_dim: 80 + lowfreq: 0 + highfreq: null + +pitch_feature: + _target_: nemo.collections.tts.parts.preprocessing.features.PitchFeaturizer + sample_rate: ${sample_rate} + win_length: ${win_length} + hop_length: ${hop_length} + pitch_fmin: 60 + pitch_fmax: 640 + +energy_feature: + _target_: nemo.collections.tts.parts.preprocessing.features.EnergyFeaturizer + spec_featurizer: ${mel_feature} + +featurizers: + pitch: ${pitch_feature} + energy: ${energy_feature} + + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: ${n_speakers} + n_mel_channels: ${mel_feature.mel_dim} + min_token_duration: 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + energy_embedding_kernel_size: 3 + speaker_emb_condition_prosody: true + speaker_emb_condition_aligner: true + use_log_energy: false + dur_loss_scale: 0.1 + pitch_loss_scale: 0.1 + energy_loss_scale: 0.1 + aligner_loss_scale: 0.1 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${mel_feature.mel_dim} + lowfreq: ${mel_feature.lowfreq} + highfreq: ${mel_feature.highfreq} + n_fft: ${win_length} + n_window_size: ${win_length} + window_size: false + n_window_stride: ${hop_length} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${sample_rate} + window: hann + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1.0 + mag_power: 1.0 + mel_norm: null + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer + punct: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.8 + ignore_ambiguous_words: false + use_chars: true + use_stresses: true + + pitch_processor: + _target_: nemo.collections.tts.parts.preprocessing.feature_processors.MeanVarianceSpeakerNormalization + field: pitch + stats_path: ${feature_stats_path} + + energy_processor: + _target_: nemo.collections.tts.parts.preprocessing.feature_processors.MeanVarianceSpeakerNormalization + field: energy + stats_path: ${feature_stats_path} + + train_ds: + dataset: + _target_: nemo.collections.tts.data.text_to_speech_dataset.TextToSpeechDataset + dataset_meta: ${train_ds_meta} + weighted_sampling_steps_per_epoch: ${weighted_sampling_steps_per_epoch} + sample_rate: ${sample_rate} + speaker_path: ${speaker_path} + align_prior_hop_length: ${hop_length} + featurizers: ${featurizers} + feature_processors: + pitch: ${model.pitch_processor} + energy: ${model.energy_processor} + min_duration: 0.1 + max_duration: 10.0 + + dataloader_params: + batch_size: ${batch_size} + num_workers: 4 + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.text_to_speech_dataset.TextToSpeechDataset + dataset_meta: ${val_ds_meta} + sample_rate: ${sample_rate} + speaker_path: ${speaker_path} + align_prior_hop_length: ${hop_length} + featurizers: ${featurizers} + feature_processors: + pitch: ${model.pitch_processor} + energy: ${model.energy_processor} + + dataloader_params: + batch_size: ${batch_size} + num_workers: 2 + + log_config: + log_dir: ${log_dir} + log_epochs: [10, 50] + epoch_frequency: 100 + log_tensorboard: false + log_wandb: false + + generators: + - _target_: nemo.collections.tts.parts.utils.callbacks.FastPitchArtifactGenerator + log_spectrogram: true + log_alignment: true + audio_params: + _target_: nemo.collections.tts.parts.utils.callbacks.LogAudioParams + log_audio_gta: true + vocoder_type: ${vocoder_type} + vocoder_name: ${vocoder_name} + vocoder_checkpoint_path: ${vocoder_checkpoint_path} + + dataset: + _target_: nemo.collections.tts.data.text_to_speech_dataset.TextToSpeechDataset + text_tokenizer: ${model.text_tokenizer} + sample_rate: ${sample_rate} + speaker_path: ${speaker_path} + align_prior_hop_length: ${hop_length} + featurizers: ${featurizers} + + feature_processors: + pitch: ${model.pitch_processor} + energy: ${model.energy_processor} + + dataset_meta: ${log_ds_meta} + + dataloader_params: + batch_size: 8 + num_workers: 2 + + input_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 2 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + dist_type: cosine + temperature: 15.0 + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + energy_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: ${max_epochs} + accumulate_grad_batches: 1 + gradient_clip_val: 10.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 10 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch/fastpitch_44100.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch/fastpitch_44100.yaml new file mode 100644 index 0000000..da9e9a2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch/fastpitch_44100.yaml @@ -0,0 +1,286 @@ +# This config contains the default values for training an English 44.1kHz FastPitch model. +# If you want to train a model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +max_epochs: ??? +batch_size: 32 +weighted_sampling_steps_per_epoch: null + +n_speakers: ??? +speaker_path: null +feature_stats_path: null + +train_ds_meta: ??? +val_ds_meta: ??? +log_ds_meta: ??? + +phoneme_dict_path: ??? +heteronyms_path: ??? + +log_dir: ??? +vocoder_type: ??? +vocoder_name: null +vocoder_checkpoint_path: null + +# The below feature config should match the feature.yaml config used during preprocessing. +sample_rate: 44100 +win_length: 2048 +hop_length: 512 + +mel_feature: + _target_: nemo.collections.tts.parts.preprocessing.features.MelSpectrogramFeaturizer + sample_rate: ${sample_rate} + win_length: ${win_length} + hop_length: ${hop_length} + mel_dim: 80 + lowfreq: 0 + highfreq: null + +pitch_feature: + _target_: nemo.collections.tts.parts.preprocessing.features.PitchFeaturizer + sample_rate: ${sample_rate} + win_length: ${win_length} + hop_length: ${hop_length} + pitch_fmin: 60 + pitch_fmax: 640 + +energy_feature: + _target_: nemo.collections.tts.parts.preprocessing.features.EnergyFeaturizer + spec_featurizer: ${mel_feature} + +featurizers: + pitch: ${pitch_feature} + energy: ${energy_feature} + + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: ${n_speakers} + n_mel_channels: ${mel_feature.mel_dim} + min_token_duration: 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + energy_embedding_kernel_size: 3 + speaker_emb_condition_prosody: true + speaker_emb_condition_aligner: true + use_log_energy: false + dur_loss_scale: 0.1 + pitch_loss_scale: 0.1 + energy_loss_scale: 0.1 + aligner_loss_scale: 0.1 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${mel_feature.mel_dim} + lowfreq: ${mel_feature.lowfreq} + highfreq: ${mel_feature.highfreq} + n_fft: ${win_length} + n_window_size: ${win_length} + window_size: false + n_window_stride: ${hop_length} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${sample_rate} + window: hann + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1.0 + mag_power: 1.0 + mel_norm: null + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer + punct: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.8 + ignore_ambiguous_words: false + use_chars: true + use_stresses: true + + pitch_processor: + _target_: nemo.collections.tts.parts.preprocessing.feature_processors.MeanVarianceSpeakerNormalization + field: pitch + stats_path: ${feature_stats_path} + + energy_processor: + _target_: nemo.collections.tts.parts.preprocessing.feature_processors.MeanVarianceSpeakerNormalization + field: energy + stats_path: ${feature_stats_path} + + train_ds: + dataset: + _target_: nemo.collections.tts.data.text_to_speech_dataset.TextToSpeechDataset + dataset_meta: ${train_ds_meta} + weighted_sampling_steps_per_epoch: ${weighted_sampling_steps_per_epoch} + sample_rate: ${sample_rate} + speaker_path: ${speaker_path} + align_prior_hop_length: ${hop_length} + featurizers: ${featurizers} + feature_processors: + pitch: ${model.pitch_processor} + energy: ${model.energy_processor} + min_duration: 0.1 + max_duration: 10.0 + + dataloader_params: + batch_size: ${batch_size} + num_workers: 4 + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.text_to_speech_dataset.TextToSpeechDataset + dataset_meta: ${val_ds_meta} + sample_rate: ${sample_rate} + speaker_path: ${speaker_path} + align_prior_hop_length: ${hop_length} + featurizers: ${featurizers} + feature_processors: + pitch: ${model.pitch_processor} + energy: ${model.energy_processor} + + dataloader_params: + batch_size: ${batch_size} + num_workers: 2 + + log_config: + log_dir: ${log_dir} + log_epochs: [10, 50] + epoch_frequency: 100 + log_tensorboard: false + log_wandb: false + + generators: + - _target_: nemo.collections.tts.parts.utils.callbacks.FastPitchArtifactGenerator + log_spectrogram: true + log_alignment: true + audio_params: + _target_: nemo.collections.tts.parts.utils.callbacks.LogAudioParams + log_audio_gta: true + vocoder_type: ${vocoder_type} + vocoder_name: ${vocoder_name} + vocoder_checkpoint_path: ${vocoder_checkpoint_path} + + dataset: + _target_: nemo.collections.tts.data.text_to_speech_dataset.TextToSpeechDataset + text_tokenizer: ${model.text_tokenizer} + sample_rate: ${sample_rate} + speaker_path: ${speaker_path} + align_prior_hop_length: ${hop_length} + featurizers: ${featurizers} + + feature_processors: + pitch: ${model.pitch_processor} + energy: ${model.energy_processor} + + dataset_meta: ${log_ds_meta} + + dataloader_params: + batch_size: 8 + num_workers: 2 + + input_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 2 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + dist_type: cosine + temperature: 15.0 + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + energy_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: ${max_epochs} + accumulate_grad_batches: 1 + gradient_clip_val: 10.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 10 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_44100.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_44100.yaml new file mode 100644 index 0000000..13d631a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_44100.yaml @@ -0,0 +1,248 @@ +# This config contains the default values for training FastPitch model with aligner using 44.1KHz sampling +# rate. If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch" ] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 212.35873413085938 for LJSpeech +pitch_std: ??? # e.g. 68.52806091308594 for LJSpeech + +sample_rate: 44100 +n_mel_channels: 80 +n_window_size: 2048 +n_window_stride: 512 +n_fft: 2048 +lowfreq: 0 +highfreq: null +window: hann + +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.10" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer + punct: true + stresses: true + chars: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + use_beta_binomial_interpolator: true + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: null + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + use_beta_binomial_interpolator: true + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: -1 # number of gpus + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: 1500 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false + diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_44100_adapter.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_44100_adapter.yaml new file mode 100644 index 0000000..0478a00 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_44100_adapter.yaml @@ -0,0 +1,314 @@ +# This config contains the default values for training FastPitch speaker adaptation +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch", "speaker_id", "reference_audio"] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 212.35873413085938 for LJSpeech +pitch_std: ??? # e.g. 68.52806091308594 for LJSpeech + +# Default values for dataset with sample_rate=44100 +sample_rate: 44100 +n_mel_channels: 80 +n_window_size: 2048 +n_window_stride: 512 +n_fft: 2048 +lowfreq: 0 +highfreq: 8000 +window: hann + +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.10" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" + +reference_audio_type: "same-speaker" # options: ["same-speaker", "ground-truth"] + +model: + unfreeze_aligner: false + unfreeze_duration_predictor: false + unfreeze_pitch_predictor: false + learn_alignment: true + bin_loss_warmup_epochs: 100 + + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer + punct: true + stresses: true + chars: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.modules.EnglishG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + + adapter: + # Config of the adapter training/eval script. + adapter_name: "adapter" # Name of the adapter, used by the script + adapter_module_name: "encoder+decoder+duration_predictor+pitch_predictor+aligner" # Name of the adapter module. Combine multiple modules with '+' between module names. + adapter_state_dict_name: "adapters.pt" # If the individual adapters must be saved, a file name can be provided here. null disables this. + + # Config of the adapter module itself + _target_: nemo.collections.common.parts.adapter_modules.LinearAdapter + in_features: ${model.symbols_embedding_dim} # User must provide the output dimension of the layers of the model, which is the input dimension of this adapter. + dim: 256 # The hidden dimension of the adapter, as chosen by user, but small values are preferred to reduce param count. + activation: swish + norm_position: 'pre' # Can be `pre` or `post` + dropout: 0.0 # float, dropout for the adapter + + # Adapter strategy config + adapter_strategy: + _target_: nemo.core.classes.mixins.adapter_mixin_strategies.ResidualAddAdapterStrategy + stochastic_depth: 0.0 # float, setting to > 0 will enable stochastic depth for each adapter block. + l2_lambda: 0.0 # float, setting to > 0 will enable l2 norm auxiliary loss for each adapter's output. + + # Optional global config available to all adapters at a global level. + # A global config is shared across every layer of the adapters, defining global properties rather + # than properties local to the adapter (as defined above). + # This can be useful in order to select *which type of adapter* is added, *what adapters to enable*, + # and further global operations that can decide dynamically how to support the requested adapter. + global_cfg: + check_encoder_adapter: True # determines whether to check if encoder adapter modules is supported + check_decoder_adapter: True # determines whether to check if decoder adapter modules is supported + check_duration_predictor_adapter: True # determines whether to check if duration_predictor adapter modules is supported + check_pitch_predictor_adapter: True # determines whether to check if pitch_predictor adapter modules is supported + check_aligner_adapter: True # determines whether to check if aligner adapter modules is supported + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + use_beta_binomial_interpolator: true + reference_audio_type: ${reference_audio_type} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + use_beta_binomial_interpolator: true + reference_audio_type: ${reference_audio_type} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ] + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ] + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + condition_types: [ "add" ] # options: [ "add", "concat" ] + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ] + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ] + + speaker_encoder: + _target_: nemo.collections.tts.modules.submodules.SpeakerEncoder + precomputed_embedding_dim: null + lookup_module: + _target_: nemo.collections.tts.modules.submodules.SpeakerLookupTable + n_speakers: ??? + embedding_dim: ${model.symbols_embedding_dim} + gst_module: + _target_: nemo.collections.tts.modules.submodules.GlobalStyleToken + gst_size: ${model.symbols_embedding_dim} + n_style_token: 10 + n_style_attn_head: 4 + reference_encoder: + _target_: nemo.collections.tts.modules.submodules.ReferenceEncoder + n_mels: ${model.n_mel_channels} + cnn_filters: [32, 32, 64, 64, 128, 128] + dropout: 0.2 + gru_hidden: ${model.symbols_embedding_dim} + kernel_size: 3 + stride: 2 + padding: 1 + bias: true + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: 1000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 1 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_ipa.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_ipa.yaml new file mode 100644 index 0000000..3e515ca --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_ipa.yaml @@ -0,0 +1,247 @@ +# This config contains the default values for training a FastPitch model with aligner. +# If you want to train a model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch" ] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 212.35873413085938 for LJSpeech +pitch_std: ??? # e.g. 68.52806091308594 for LJSpeech + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: hann + +phoneme_dict_path: "scripts/tts_dataset_files/ipa_cmudict-0.7b_nv23.01.txt" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer + punct: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.8 + # Relies on the heteronyms list for anything that needs to be disambiguated + ignore_ambiguous_words: false + use_chars: true + use_stresses: true + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + use_beta_binomial_interpolator: true + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: null + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + use_beta_binomial_interpolator: true + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp + precision: 32 + max_epochs: 1000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_ipa_adapter.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_ipa_adapter.yaml new file mode 100644 index 0000000..2ef6bfc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_ipa_adapter.yaml @@ -0,0 +1,328 @@ +# This config contains the default values for training FastPitch speaker adaptation +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch", "speaker_id", "reference_audio"] + + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 212.35873413085938 for LJSpeech +pitch_std: ??? # e.g. 68.52806091308594 for LJSpeech + +# Default values for dataset with sample_rate=44100 +sample_rate: 44100 +n_mel_channels: 80 +n_window_size: 2048 +n_window_stride: 512 +n_fft: 2048 +lowfreq: 0 +highfreq: 8000 +window: hann + +phoneme_dict_path: "scripts/tts_dataset_files/ipa_cmudict-0.7b_nv23.01.txt" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" + +reference_audio_type: "same-speaker" # options: ["same-speaker", "ground-truth"] + +model: + unfreeze_aligner: false + unfreeze_duration_predictor: false + unfreeze_pitch_predictor: false + unfreeze_energy_predictor: false + learn_alignment: true + bin_loss_warmup_epochs: 100 + + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + energy_embedding_kernel_size: 3 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer + punct: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.modules.IPAG2P + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.8 + # Relies on the heteronyms list for anything that needs to be disambiguated + ignore_ambiguous_words: false + use_chars: true + use_stresses: true + + adapter: + # Config of the adapter training/eval script. + adapter_name: "adapter" # Name of the adapter, used by the script + adapter_module_name: "encoder+decoder+duration_predictor+pitch_predictor+aligner" # Name of the adapter module. Combine multiple modules with '+' between module names. + adapter_state_dict_name: "adapters.pt" # If the individual adapters must be saved, a file name can be provided here. null disables this. + + # Config of the adapter module itself + _target_: nemo.collections.common.parts.adapter_modules.LinearAdapter + in_features: ${model.symbols_embedding_dim} # User must provide the output dimension of the layers of the model, which is the input dimension of this adapter. + dim: 256 # The hidden dimension of the adapter, as chosen by user, but small values are preferred to reduce param count. + activation: swish + norm_position: 'pre' # Can be `pre` or `post` + dropout: 0.0 # float, dropout for the adapter + + # Adapter strategy config + adapter_strategy: + _target_: nemo.core.classes.mixins.adapter_mixin_strategies.ResidualAddAdapterStrategy + stochastic_depth: 0.0 # float, setting to > 0 will enable stochastic depth for each adapter block. + l2_lambda: 0.0 # float, setting to > 0 will enable l2 norm auxiliary loss for each adapter's output. + + # Optional global config available to all adapters at a global level. + # A global config is shared across every layer of the adapters, defining global properties rather + # than properties local to the adapter (as defined above). + # This can be useful in order to select *which type of adapter* is added, *what adapters to enable*, + # and further global operations that can decide dynamically how to support the requested adapter. + global_cfg: + check_encoder_adapter: True # determines whether to check if encoder adapter modules is supported + check_decoder_adapter: True # determines whether to check if decoder adapter modules is supported + check_duration_predictor_adapter: True # determines whether to check if duration_predictor adapter modules is supported + check_pitch_predictor_adapter: True # determines whether to check if pitch_predictor adapter modules is supported + check_aligner_adapter: True # determines whether to check if aligner adapter modules is supported + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + use_beta_binomial_interpolator: true + reference_audio_type: ${reference_audio_type} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + use_beta_binomial_interpolator: true + reference_audio_type: ${reference_audio_type} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ] + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ] + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + condition_types: [ "add" ] # options: [ "add", "concat" ] + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ] + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ] + + energy_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ] + + speaker_encoder: + _target_: nemo.collections.tts.modules.submodules.SpeakerEncoder + precomputed_embedding_dim: null + lookup_module: + _target_: nemo.collections.tts.modules.submodules.SpeakerLookupTable + n_speakers: ??? + embedding_dim: ${model.symbols_embedding_dim} + gst_module: + _target_: nemo.collections.tts.modules.submodules.GlobalStyleToken + gst_size: ${model.symbols_embedding_dim} + n_style_token: 10 + n_style_attn_head: 4 + reference_encoder: + _target_: nemo.collections.tts.modules.submodules.ReferenceEncoder + n_mels: ${model.n_mel_channels} + cnn_filters: [32, 32, 64, 64, 128, 128] + dropout: 0.2 + gru_hidden: ${model.symbols_embedding_dim} + kernel_size: 3 + stride: 2 + padding: 1 + bias: true + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: 1000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 1 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_v1.05.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_v1.05.yaml new file mode 100644 index 0000000..5d7c753 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_align_v1.05.yaml @@ -0,0 +1,248 @@ +# This config contains the default values for training FastPitch model with aligner on LJSpeech dataset. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch" ] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 212.35873413085938 for LJSpeech +pitch_std: ??? # e.g. 68.52806091308594 for LJSpeech + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: hann + +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.10" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer + punct: true + stresses: true + chars: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + use_beta_binomial_interpolator: true + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: null + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + use_beta_binomial_interpolator: true + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: 1000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_ssl.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_ssl.yaml new file mode 100644 index 0000000..12a3404 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/fastpitch_ssl.yaml @@ -0,0 +1,184 @@ +# This config contains the default values for training FastPitch model with aligner on LJSpeech dataset. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +ssl_model_ckpt_path: ??? +hifi_ckpt_path: ??? +sup_data_dir: null + +# LJSpeech stats (per frame) +# ignored if pitch_normalization: speaker_wise +pitch_mean: ??? #212.35873413085938 +pitch_std: ??? #68.52806091308594 + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: hann + + +ssl_content_emb_type: "embedding_and_probs" +speaker_stats_pitch_fp: null +pitch_normalization: speaker_wise +use_unique_tokens: true +speaker_conditioning_type: per_sample +segment_speaker_embedding: true +ssl_downsampling_factor: 4 # How many mel-spectrogram frames map to one content embedding in the SSL model + +model: + ssl_model_ckpt_path: ${ssl_model_ckpt_path} + ssl_downsampling_factor: ${ssl_downsampling_factor} + use_encoder: true + use_duration_predictor: ${use_unique_tokens} + pitch_conditioning: true + pitch_loss_scale: 1.0 + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: 1 + n_datasets: 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + content_emb_indim: 174 + speaker_emb_indim: 256 + content_emb_outdim: 192 + speaker_emb_outdim: 192 + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.FastPitchSSLDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + ssl_content_emb_type: ${ssl_content_emb_type} + pitch_conditioning: true + pitch_normalization: ${pitch_normalization} + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + speaker_stats_pitch_fp: ${speaker_stats_pitch_fp} + min_duration: 0.5 + max_duration: 16.0 + pad_multiple: 1024 + speaker_conditioning_type: ${speaker_conditioning_type} + sup_data_dir: ${sup_data_dir} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 2 + num_workers: 8 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.FastPitchSSLDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + ssl_content_emb_type: ${ssl_content_emb_type} + pitch_conditioning: true + pitch_normalization: ${pitch_normalization} + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + speaker_stats_pitch_fp: ${speaker_stats_pitch_fp} + min_duration: 0.5 + max_duration: 16.0 + pad_multiple: 1024 + speaker_conditioning_type: ${speaker_conditioning_type} + sup_data_dir: ${sup_data_dir} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 2 + num_workers: 0 + pin_memory: true + + # both encoder and decoder have same architecture, FFTransformerDecoder + encoder: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + _target_: torch.optim.AdamW + lr: 0.0002 + betas: [0.8, 0.99] + +trainer: + num_nodes: 1 + devices: -1 + accelerator: gpu + strategy: ddp + precision: 32 + max_epochs: 1000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: v_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/feature/feature_22050.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/feature/feature_22050.yaml new file mode 100644 index 0000000..8071eb7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/feature/feature_22050.yaml @@ -0,0 +1,28 @@ +sample_rate: 22050 +win_length: 1024 +hop_length: 256 + +mel_feature: + _target_: nemo.collections.tts.parts.preprocessing.features.MelSpectrogramFeaturizer + sample_rate: ${sample_rate} + win_length: ${win_length} + hop_length: ${hop_length} + mel_dim: 80 + lowfreq: 0 + highfreq: null + +pitch_feature: + _target_: nemo.collections.tts.parts.preprocessing.features.PitchFeaturizer + sample_rate: ${sample_rate} + win_length: ${win_length} + hop_length: ${hop_length} + pitch_fmin: 60 + pitch_fmax: 640 + +energy_feature: + _target_: nemo.collections.tts.parts.preprocessing.features.EnergyFeaturizer + spec_featurizer: ${mel_feature} + +featurizers: + pitch: ${pitch_feature} + energy: ${energy_feature} diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/feature/feature_44100.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/feature/feature_44100.yaml new file mode 100644 index 0000000..0cfc27f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/feature/feature_44100.yaml @@ -0,0 +1,28 @@ +sample_rate: 44100 +win_length: 2048 +hop_length: 512 + +mel_feature: + _target_: nemo.collections.tts.parts.preprocessing.features.MelSpectrogramFeaturizer + sample_rate: ${sample_rate} + win_length: ${win_length} + hop_length: ${hop_length} + mel_dim: 80 + lowfreq: 0 + highfreq: null + +pitch_feature: + _target_: nemo.collections.tts.parts.preprocessing.features.PitchFeaturizer + sample_rate: ${sample_rate} + win_length: ${win_length} + hop_length: ${hop_length} + pitch_fmin: 60 + pitch_fmax: 640 + +energy_feature: + _target_: nemo.collections.tts.parts.preprocessing.features.EnergyFeaturizer + spec_featurizer: ${mel_feature} + +featurizers: + pitch: ${pitch_feature} + energy: ${energy_feature} diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/hifigan.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/hifigan.yaml new file mode 100644 index 0000000..0f8be80 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/hifigan.yaml @@ -0,0 +1,99 @@ +# This config contains the default values for training HiFi-GAN model on LJSpeech dataset. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: "HifiGan" + +train_dataset: ??? +validation_datasets: ??? + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: hann + +train_n_segments: 8192 +train_max_duration: null +train_min_duration: 0.75 + +val_n_segments: 66048 +val_max_duration: null +val_min_duration: 3 + +defaults: + - model/generator: v1 + - model/train_ds: train_ds + - model/validation_ds: val_ds + +model: + preprocessor: + _target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures + nfilt: ${n_mel_channels} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + n_fft: ${n_fft} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + pad_to: 0 + pad_value: -11.52 + sample_rate: ${sample_rate} + window: ${window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: clamp + log_zero_guard_value: 1e-05 + mag_power: 1.0 + use_grads: false + exact_pad: true + + optim: + _target_: torch.optim.AdamW + lr: 0.0002 + betas: [0.8, 0.99] + + sched: + name: CosineAnnealing + min_lr: 1e-5 + warmup_ratio: 0.02 + + max_steps: 2500000 + l1_loss_factor: 45 + denoise_strength: 0.0025 + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp_find_unused_parameters_true + precision: 32 + max_steps: ${model.max_steps} + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 10 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + mode: min + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/hifigan_44100.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/hifigan_44100.yaml new file mode 100644 index 0000000..700bace --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/hifigan_44100.yaml @@ -0,0 +1,99 @@ +# This config contains the default values for training HiFi-GAN model on HiFi-TTS dataset. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: "HifiGan" + +train_dataset: ??? +validation_datasets: ??? + +# Default values for dataset with sample_rate=44100 +sample_rate: 44100 +n_mel_channels: 80 +n_window_size: 2048 +n_window_stride: 512 +n_fft: 2048 +lowfreq: 0 +highfreq: null +window: hann + +train_n_segments: 16384 +train_max_duration: null # change to null to include longer audios. +train_min_duration: 0.75 + +val_n_segments: 131072 +val_max_duration: null +val_min_duration: 3 + +defaults: + - model/generator: v1_44100 + - model/train_ds: train_ds + - model/validation_ds: val_ds + +model: + preprocessor: + _target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures + nfilt: ${n_mel_channels} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + n_fft: ${n_fft} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + pad_to: 0 + pad_value: -11.52 + sample_rate: ${sample_rate} + window: ${window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: clamp + log_zero_guard_value: 1e-05 + mag_power: 1.0 + use_grads: false + exact_pad: true + + optim: + _target_: torch.optim.AdamW + lr: 0.0002 + betas: [0.8, 0.99] + + sched: + name: CosineAnnealing + min_lr: 1e-5 + warmup_ratio: 0.02 + + max_steps: 2500000 + l1_loss_factor: 45 + denoise_strength: 0.0025 + +trainer: + num_nodes: 1 + devices: -1 + accelerator: gpu + strategy: ddp_find_unused_parameters_true + precision: 16 + max_steps: ${model.max_steps} + accumulate_grad_batches: 1 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 10 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + mode: min + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/generator/v1.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/generator/v1.yaml new file mode 100644 index 0000000..2698e15 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/generator/v1.yaml @@ -0,0 +1,7 @@ +_target_: nemo.collections.tts.modules.hifigan_modules.Generator +resblock: 1 +upsample_rates: [8,8,2,2] +upsample_kernel_sizes: [16,16,4,4] +upsample_initial_channel: 512 +resblock_kernel_sizes: [3,7,11] +resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]] diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/generator/v1_44100.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/generator/v1_44100.yaml new file mode 100644 index 0000000..3bd7352 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/generator/v1_44100.yaml @@ -0,0 +1,7 @@ +_target_: nemo.collections.tts.modules.hifigan_modules.Generator +resblock: 1 +upsample_rates: [8,8,4,2] +upsample_kernel_sizes: [16,16,4,4] +upsample_initial_channel: 512 +resblock_kernel_sizes: [3,7,11] +resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]] diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/generator/v2.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/generator/v2.yaml new file mode 100644 index 0000000..91e1719 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/generator/v2.yaml @@ -0,0 +1,7 @@ +_target_: nemo.collections.tts.modules.hifigan_modules.Generator +resblock: 1 +upsample_rates: [8,8,2,2] +upsample_kernel_sizes: [16,16,4,4] +upsample_initial_channel: 128 +resblock_kernel_sizes: [3,7,11] +resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]] diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/generator/v3.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/generator/v3.yaml new file mode 100644 index 0000000..e3639c1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/generator/v3.yaml @@ -0,0 +1,7 @@ +_target_: nemo.collections.tts.modules.hifigan_modules.Generator +resblock: 2 +upsample_rates: [8,8,4] +upsample_kernel_sizes: [16,16,8] +upsample_initial_channel: 256 +resblock_kernel_sizes: [3,5,7] +resblock_dilation_sizes: [[1,2], [2,6], [3,12]] diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/train_ds/train_ds.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/train_ds/train_ds.yaml new file mode 100644 index 0000000..46df72f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/train_ds/train_ds.yaml @@ -0,0 +1,13 @@ +dataset: + _target_: "nemo.collections.tts.data.dataset.VocoderDataset" + manifest_filepath: ${train_dataset} + sample_rate: ${sample_rate} + n_segments: ${train_n_segments} + max_duration: ${train_max_duration} + min_duration: ${train_min_duration} +dataloader_params: + drop_last: false + shuffle: true + batch_size: 16 + num_workers: 4 + pin_memory: true diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/train_ds/train_ds_finetune.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/train_ds/train_ds_finetune.yaml new file mode 100644 index 0000000..afee3b4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/train_ds/train_ds_finetune.yaml @@ -0,0 +1,15 @@ +dataset: + _target_: "nemo.collections.tts.data.dataset.VocoderDataset" + manifest_filepath: ${train_dataset} + sample_rate: ${sample_rate} + n_segments: ${train_n_segments} + max_duration: ${train_max_duration} + min_duration: ${train_min_duration} + load_precomputed_mel: true + hop_length: ${n_window_stride} +dataloader_params: + drop_last: false + shuffle: true + batch_size: 16 + num_workers: 4 + pin_memory: true diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/validation_ds/val_ds.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/validation_ds/val_ds.yaml new file mode 100644 index 0000000..e241f81 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/validation_ds/val_ds.yaml @@ -0,0 +1,13 @@ +dataset: + _target_: "nemo.collections.tts.data.dataset.VocoderDataset" + manifest_filepath: ${validation_datasets} + sample_rate: ${sample_rate} + n_segments: ${val_n_segments} + max_duration: ${val_max_duration} + min_duration: ${val_min_duration} +dataloader_params: + drop_last: false + shuffle: false + batch_size: 16 + num_workers: 1 + pin_memory: true diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/validation_ds/val_ds_finetune.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/validation_ds/val_ds_finetune.yaml new file mode 100644 index 0000000..6c5b79c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan/model/validation_ds/val_ds_finetune.yaml @@ -0,0 +1,15 @@ +dataset: + _target_: "nemo.collections.tts.data.dataset.VocoderDataset" + manifest_filepath: ${validation_datasets} + sample_rate: ${sample_rate} + n_segments: ${val_n_segments} + max_duration: ${val_max_duration} + min_duration: ${val_min_duration} + load_precomputed_mel: true + hop_length: ${n_window_stride} +dataloader_params: + drop_last: false + shuffle: false + batch_size: 16 + num_workers: 4 + pin_memory: true diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan_dataset/hifigan_22050.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan_dataset/hifigan_22050.yaml new file mode 100644 index 0000000..6086634 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan_dataset/hifigan_22050.yaml @@ -0,0 +1,151 @@ +# This config contains the default values for training a 22.05kHz HiFi-GAN model. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: "HifiGan" + +max_epochs: ??? +batch_size: 16 +weighted_sampling_steps_per_epoch: null + +train_ds_meta: ??? +val_ds_meta: ??? +log_ds_meta: ??? + +log_dir: ??? + +mel_dim: 80 +lowfreq: 0 +highfreq: null + +# Change these values depending on your sampling rate. +sample_rate: 22050 +win_length: 1024 +hop_length: 256 +upsample_rates: [8, 8, 2, 2] +train_n_samples: 8192 +val_min_duration_seconds: 3.0 +val_n_samples: 66048 + +model: + + max_epochs: ${max_epochs} + steps_per_epoch: ${weighted_sampling_steps_per_epoch} + l1_loss_factor: 60 + + preprocessor: + _target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures + nfilt: ${mel_dim} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + n_fft: ${win_length} + n_window_size: ${win_length} + n_window_stride: ${hop_length} + pad_to: 0 + pad_value: 0 + exact_pad: true + sample_rate: ${sample_rate} + window: hann + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1.0 + mag_power: 1.0 + mel_norm: null + use_grads: false + + generator: + _target_: nemo.collections.tts.modules.hifigan_modules.Generator + resblock: 1 + upsample_rates: ${upsample_rates} + upsample_kernel_sizes: [16, 16, 4, 4] + upsample_initial_channel: 512 + resblock_kernel_sizes: [3, 7, 11] + resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]] + + train_ds: + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + weighted_sampling_steps_per_epoch: ${weighted_sampling_steps_per_epoch} + sample_rate: ${sample_rate} + n_samples: ${train_n_samples} + min_duration: 0.4 + max_duration: null + dataset_meta: ${train_ds_meta} + + dataloader_params: + batch_size: ${batch_size} + num_workers: 4 + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + sample_rate: ${sample_rate} + n_samples: ${val_n_samples} + min_duration: ${val_min_duration_seconds} + max_duration: null + dataset_meta: ${val_ds_meta} + + dataloader_params: + batch_size: ${batch_size} + num_workers: 2 + + log_config: + log_dir: ${log_dir} + log_epochs: [10, 50] + epoch_frequency: 100 + log_tensorboard: false + log_wandb: false + + generators: + - _target_: nemo.collections.tts.parts.utils.callbacks.VocoderArtifactGenerator + + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + sample_rate: ${sample_rate} + n_samples: null + min_duration: null + max_duration: null + trunc_duration: 15.0 + dataset_meta: ${log_ds_meta} + + dataloader_params: + batch_size: 4 + num_workers: 2 + + optim: + _target_: torch.optim.AdamW + lr: 2e-4 + betas: [0.8, 0.99] + weight_decay: 1e-6 + sched: + name: ExponentialLR + gamma: 0.999 + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp_find_unused_parameters_true + precision: 16 + max_epochs: ${max_epochs} + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 10 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + create_wandb_logger: false + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan_dataset/hifigan_44100.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan_dataset/hifigan_44100.yaml new file mode 100644 index 0000000..5c5d1a7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/hifigan_dataset/hifigan_44100.yaml @@ -0,0 +1,151 @@ +# This config contains the default values for training a 44.1kHz HiFi-GAN model. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: "HifiGan" + +max_epochs: ??? +batch_size: 16 +weighted_sampling_steps_per_epoch: null + +train_ds_meta: ??? +val_ds_meta: ??? +log_ds_meta: ??? + +log_dir: ??? + +mel_dim: 80 +lowfreq: 0 +highfreq: null + +# Change these values depending on your sampling rate. +sample_rate: 44100 +win_length: 2048 +hop_length: 512 +upsample_rates: [8, 8, 4, 2] +train_n_samples: 16384 +val_min_duration_seconds: 3.0 +val_n_samples: 131072 + +model: + + max_epochs: ${max_epochs} + steps_per_epoch: ${weighted_sampling_steps_per_epoch} + l1_loss_factor: 60 + + preprocessor: + _target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures + nfilt: ${mel_dim} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + n_fft: ${win_length} + n_window_size: ${win_length} + n_window_stride: ${hop_length} + pad_to: 0 + pad_value: 0 + exact_pad: true + sample_rate: ${sample_rate} + window: hann + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1.0 + mag_power: 1.0 + mel_norm: null + use_grads: false + + generator: + _target_: nemo.collections.tts.modules.hifigan_modules.Generator + resblock: 1 + upsample_rates: ${upsample_rates} + upsample_kernel_sizes: [16, 16, 4, 4] + upsample_initial_channel: 512 + resblock_kernel_sizes: [3, 7, 11] + resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]] + + train_ds: + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + weighted_sampling_steps_per_epoch: ${weighted_sampling_steps_per_epoch} + sample_rate: ${sample_rate} + n_samples: ${train_n_samples} + min_duration: 0.4 + max_duration: null + dataset_meta: ${train_ds_meta} + + dataloader_params: + batch_size: ${batch_size} + num_workers: 4 + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + sample_rate: ${sample_rate} + n_samples: ${val_n_samples} + min_duration: ${val_min_duration_seconds} + max_duration: null + dataset_meta: ${val_ds_meta} + + dataloader_params: + batch_size: ${batch_size} + num_workers: 2 + + log_config: + log_dir: ${log_dir} + log_epochs: [10, 50] + epoch_frequency: 100 + log_tensorboard: false + log_wandb: false + + generators: + - _target_: nemo.collections.tts.parts.utils.callbacks.VocoderArtifactGenerator + + dataset: + _target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset + sample_rate: ${sample_rate} + n_samples: null + min_duration: null + max_duration: null + trunc_duration: 15.0 + dataset_meta: ${log_ds_meta} + + dataloader_params: + batch_size: 4 + num_workers: 2 + + optim: + _target_: torch.optim.AdamW + lr: 2e-4 + betas: [0.8, 0.99] + weight_decay: 1e-6 + sched: + name: ExponentialLR + gamma: 0.999 + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp_find_unused_parameters_true + precision: 16 + max_epochs: ${max_epochs} + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 10 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + create_wandb_logger: false + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/mixer-tts-x.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/mixer-tts-x.yaml new file mode 100644 index 0000000..736e0bc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/mixer-tts-x.yaml @@ -0,0 +1,249 @@ +# This config contains the default values for training Mixer-TTS-X model on LJSpeech dataset. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: MixerTTS-X + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch", "lm_tokens" ] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 212.35873413085938 for LJSpeech +pitch_std: ??? # e.g. 68.52806091308594 for LJSpeech + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: hann + +lm_model: albert + +model: + bin_loss_start_ratio: 0.2 + bin_loss_warmup_epochs: 100 + + symbols_embedding_dim: 384 + lm_model: ${lm_model} + cond_on_lm_embeddings: true + + pitch_loss_scale: 0.1 + durs_loss_scale: 0.1 + mel_loss_scale: 1.0 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.tts.common.tokenizers.text_to_speech.EnglishCharsTokenizer + punct: true + apostrophe: true + pad_with_space: true + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.MixerTTSXDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + lm_model: ${model.lm_model} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 64 + num_workers: 4 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.MixerTTSXDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + lm_model: ${model.lm_model} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 64 + num_workers: 1 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: -11.52 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: clamp + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + self_attention_module: + _target_: nemo.collections.tts.modules.mixer_tts.SelfAttentionModule + n_text_channels: ${model.symbols_embedding_dim} + n_lm_tokens_channels: 100 # dummy value, real value is set in model constructor + + encoder: + _target_: nemo.collections.tts.modules.mixer_tts.MixerTTSModule + num_tokens: 100 # dummy value, real value is set in model constructor + padding_idx: 100 # dummy value, real value is set in model constructor + feature_dim: 384 + kernel_sizes: [11, 13, 15, 17, 19, 21] + num_layers: 6 + expansion_factor: 4 + dropout: 0.15 + + decoder: + _target_: nemo.collections.tts.modules.mixer_tts.MixerTTSModule + num_tokens: -1 + feature_dim: 384 + kernel_sizes: [15, 17, 19, 21, 23, 25, 27, 29, 31] + num_layers: 9 + expansion_factor: 4 + dropout: 0.15 + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.15 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.15 + n_layers: 2 + + pitch_emb: + _target_: torch.nn.Conv1d + in_channels: 1 + out_channels: ${model.symbols_embedding_dim} + kernel_size: 3 + padding: 1 + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: 1000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 200 + check_val_every_n_epoch: 1 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_mel_loss + mode: min + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/mixer-tts.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/mixer-tts.yaml new file mode 100644 index 0000000..b7b2aa9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/mixer-tts.yaml @@ -0,0 +1,247 @@ +# This config contains the default values for training Mixer-TTS model on LJSpeech dataset. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: Mixer-TTS + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch" ] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 212.35873413085938 for LJSpeech +pitch_std: ??? # e.g. 68.52806091308594 for LJSpeech + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: hann + +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.10" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" + +model: + bin_loss_start_ratio: 0.2 + bin_loss_warmup_epochs: 100 + + symbols_embedding_dim: 384 + + pitch_loss_scale: 0.1 + durs_loss_scale: 0.1 + mel_loss_scale: 1.0 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer + punct: true + stresses: true + chars: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 64 + num_workers: 4 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: false + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 64 + num_workers: 1 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: -11.52 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: clamp + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + encoder: + _target_: nemo.collections.tts.modules.mixer_tts.MixerTTSModule + num_tokens: 100 # dummy value, real value is set in model constructor + padding_idx: 100 # dummy value, real value is set in model constructor + feature_dim: 384 + kernel_sizes: [11, 13, 15, 17, 19, 21] + num_layers: 6 + expansion_factor: 4 + dropout: 0.15 + + decoder: + _target_: nemo.collections.tts.modules.mixer_tts.MixerTTSModule + num_tokens: -1 + feature_dim: 384 + kernel_sizes: [15, 17, 19, 21, 23, 25, 27, 29, 31] + num_layers: 9 + expansion_factor: 4 + dropout: 0.15 + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.15 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.15 + n_layers: 2 + + pitch_emb: + _target_: torch.nn.Conv1d + in_channels: 1 + out_channels: ${model.symbols_embedding_dim} + kernel_size: 3 + padding: 1 + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: 1000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 200 + check_val_every_n_epoch: 1 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_mel_loss + mode: min + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/rad-tts_dec.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/rad-tts_dec.yaml new file mode 100644 index 0000000..e21168d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/rad-tts_dec.yaml @@ -0,0 +1,270 @@ +name: RadTTS +sample_rate: 22050 + +train_dataset: ??? +validation_datasets: ??? +ckpt_path: None +export_dir: ??? +sup_data_path: ??? +sup_data_types: ["log_mel", "align_prior_matrix", "pitch", "voiced_mask", "p_voiced", "energy"] + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 212.35873413085938 for LJSpeech +pitch_std: ??? # e.g. 68.52806091308594 for LJSpeech + +# default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# default values for sample_rate=22050 +n_mels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: "hann" + + +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.10" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" +mapping_file_path: "" + +model: + target: nemo.collections.tts.models.RadTTSModel + bin_loss_start_ratio: 0.2 + bin_loss_warmup_epochs: 100 + + symbols_embedding_dim: 384 + n_mel_channels: ${n_mels} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.tts.torch.tts_tokenizers.EnglishPhonemesTokenizer + punct: true + stresses: true + chars: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + mapping_file: ${mapping_file_path} + + train_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${train_dataset} + sample_rate: ${sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${n_fft} + win_length: ${n_window_size} + hop_length: ${n_window_stride} + window: ${window} + n_mels: ${n_mels} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + + + text_tokenizer: + _target_: "nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer" + punct: True + stresses: True + chars: True + space: ' ' + silence: null + apostrophe: True + sep: '|' + add_blank_at: null + pad_with_space: True + g2p: + _target_: "nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p" + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + dataloader_params: + drop_last: false + shuffle: true + batch_size: 8 + num_workers: 8 + pin_memory: false + + validation_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${validation_datasets} + sample_rate: ${sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${n_fft} + win_length: ${n_window_size} + hop_length: ${n_window_stride} + window: ${window} + n_mels: ${n_mels} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + text_tokenizer: + _target_: "nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer" + punct: True + stresses: True + chars: True + space: ' ' + silence: null + apostrophe: True + sep: '|' + add_blank_at: null + pad_with_space: True + g2p: + _target_: "nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p" + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + dataloader_params: + drop_last: false + shuffle: false + batch_size: 8 + num_workers: 8 + pin_memory: false + + optim: + name: RAdam + lr: 0.0001 + betas: [0.9, 0.98] + weight_decay: 0.000001 + + sched: + name: exp_decay + warmup_steps: 40000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + trainerConfig: + sigma: 1 + iters_per_checkpoint: 3000 + seed: null + ignore_layers: [] + finetune_layers: [] + include_layers: [] + with_tensorboard: true + dur_loss_weight: 1 + ctc_loss_weight: 1 + mask_unvoiced_f0: false + log_step: 1 + binarization_start_iter: 6000 + kl_loss_start_iter: 18000 + loss_weights: + ctc_loss_weight: 0.1 + dur_loss_weight: 1.0 + f0_loss_weight: 1.0 + energy_loss_weight: 1.0 + vpred_loss_weight: 1.0 + unfreeze_modules: "all" + + load_from_checkpoint: False + init_from_ptl_ckpt: ${ckpt_path} + modelConfig: + _target_: "nemo.collections.tts.modules.radtts.RadTTSModule" + n_speakers: 1 + n_speaker_dim: 16 + n_text: 384 #185 + n_text_dim: 512 + n_flows: 8 + n_conv_layers_per_step: 4 + n_mel_channels: 80 + n_hidden: 1024 + mel_encoder_n_hidden: 512 + dummy_speaker_embedding: false + n_early_size: 2 + n_early_every: 2 + n_group_size: 2 + affine_model: wavenet + include_modules: "decatnvpred" + scaling_fn: tanh + matrix_decomposition: LUS + learn_alignments: true + use_context_lstm: true + context_lstm_norm: spectral + context_lstm_w_f0_and_energy: true + text_encoder_lstm_norm: spectral + n_f0_dims: 1 + n_energy_avg_dims: 1 + use_first_order_features: false + unvoiced_bias_activation: "relu" + decoder_use_partial_padding: false + decoder_use_unvoiced_bias: true + ap_pred_log_f0: true + ap_use_unvoiced_bias: true + ap_use_voiced_embeddings: true + dur_model_config: null + f0_model_config: null + energy_model_config: null + v_model_config : + name : dap + hparams : + n_speaker_dim : 16 + take_log_of_input: false + bottleneck_hparams: + in_dim: 512 + reduction_factor: 16 + norm: weightnorm + non_linearity: relu + arch_hparams: + out_dim: 1 + n_layers: 2 + n_channels: 256 + kernel_size: 3 + p_dropout: 0.5 + +trainer: + devices: 8 + precision: 16 + max_epochs: 1000 + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False + logger: False + gradient_clip_val: 1 + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + +exp_manager: + exp_dir: ${export_dir} + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val/loss_ctc + mode: min + filepath: ${export_dir} + filename: model_checkpoint diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/rad-tts_dec_ipa.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/rad-tts_dec_ipa.yaml new file mode 100644 index 0000000..913eab4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/rad-tts_dec_ipa.yaml @@ -0,0 +1,273 @@ +name: RadTTS +sample_rate: 22050 + +train_dataset: ??? +validation_datasets: ??? +ckpt_path: None +export_dir: ??? +sup_data_path: ??? +sup_data_types: ["log_mel", "align_prior_matrix", "pitch", "voiced_mask", "p_voiced", "energy"] + + + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 212.35873413085938 for LJSpeech +pitch_std: ??? # e.g. 68.52806091308594 for LJSpeech + +# default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# default values for sample_rate=22050 +n_mels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: "hann" + + +phoneme_dict_path: "scripts/tts_dataset_files/ipa_cmudict-0.7b_nv23.01.txt" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" +mapping_file_path: "" + +model: + target: nemo.collections.tts.models.RadTTSModel + bin_loss_start_ratio: 0.2 + bin_loss_warmup_epochs: 100 + + symbols_embedding_dim: 384 + n_mel_channels: ${n_mels} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer + punct: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + # Relies on the heteronyms list for anything that needs to be disambiguated + ignore_ambiguous_words: true + use_chars: true + use_stresses: true + + train_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${train_dataset} + sample_rate: ${sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${n_fft} + win_length: ${n_window_size} + hop_length: ${n_window_stride} + window: ${window} + n_mels: ${n_mels} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + + + text_tokenizer: + _target_: "nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer" + punct: True + stresses: True + chars: True + space: ' ' + silence: null + apostrophe: True + sep: '|' + add_blank_at: null + pad_with_space: True + g2p: + _target_: "nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p" + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + dataloader_params: + drop_last: false + shuffle: true + batch_size: 8 + num_workers: 8 + pin_memory: false + + validation_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${validation_datasets} + sample_rate: ${sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${n_fft} + win_length: ${n_window_size} + hop_length: ${n_window_stride} + window: ${window} + n_mels: ${n_mels} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + text_tokenizer: + _target_: "nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer" + punct: True + stresses: True + chars: True + space: ' ' + silence: null + apostrophe: True + sep: '|' + add_blank_at: null + pad_with_space: True + g2p: + _target_: "nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p" + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + dataloader_params: + drop_last: false + shuffle: false + batch_size: 8 + num_workers: 8 + pin_memory: false + + optim: + name: RAdam + lr: 0.0001 + betas: [0.9, 0.98] + weight_decay: 0.000001 + + sched: + name: exp_decay + warmup_steps: 40000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + trainerConfig: + sigma: 1 + iters_per_checkpoint: 3000 + seed: null + ignore_layers: [] + finetune_layers: [] + include_layers: [] + with_tensorboard: true + dur_loss_weight: 1 + ctc_loss_weight: 1 + mask_unvoiced_f0: false + log_step: 1 + binarization_start_iter: 6000 + kl_loss_start_iter: 18000 + loss_weights: + ctc_loss_weight: 0.1 + dur_loss_weight: 1.0 + f0_loss_weight: 1.0 + energy_loss_weight: 1.0 + vpred_loss_weight: 1.0 + unfreeze_modules: "all" + + load_from_checkpoint: False + init_from_ptl_ckpt: ${ckpt_path} + modelConfig: + _target_: "nemo.collections.tts.modules.radtts.RadTTSModule" + n_speakers: 1 + n_speaker_dim: 16 + n_text: 384 #185 + n_text_dim: 512 + n_flows: 8 + n_conv_layers_per_step: 4 + n_mel_channels: 80 + n_hidden: 1024 + mel_encoder_n_hidden: 512 + dummy_speaker_embedding: false + n_early_size: 2 + n_early_every: 2 + n_group_size: 2 + affine_model: wavenet + include_modules: "decatnvpred" + scaling_fn: tanh + matrix_decomposition: LUS + learn_alignments: true + use_context_lstm: true + context_lstm_norm: spectral + context_lstm_w_f0_and_energy: true + text_encoder_lstm_norm: spectral + n_f0_dims: 1 + n_energy_avg_dims: 1 + use_first_order_features: false + unvoiced_bias_activation: "relu" + decoder_use_partial_padding: false + decoder_use_unvoiced_bias: true + ap_pred_log_f0: true + ap_use_unvoiced_bias: true + ap_use_voiced_embeddings: true + dur_model_config: null + f0_model_config: null + energy_model_config: null + v_model_config : + name : dap + hparams : + n_speaker_dim : 16 + take_log_of_input: false + bottleneck_hparams: + in_dim: 512 + reduction_factor: 16 + norm: weightnorm + non_linearity: relu + arch_hparams: + out_dim: 1 + n_layers: 2 + n_channels: 256 + kernel_size: 3 + p_dropout: 0.5 + +trainer: + devices: 8 + precision: 16 + max_epochs: 1000 + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False + logger: False + gradient_clip_val: 1 + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + +exp_manager: + exp_dir: ${export_dir} + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val/loss_ctc + mode: min + filepath: ${export_dir} + filename: model_checkpoint diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/rad-tts_feature_pred.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/rad-tts_feature_pred.yaml new file mode 100644 index 0000000..1b4aa02 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/rad-tts_feature_pred.yaml @@ -0,0 +1,332 @@ +name: RadTTS +sample_rate: 22050 + +train_dataset: ??? +validation_datasets: ??? +ckpt_path: ??? +export_dir: ??? +sup_data_path: ??? +sup_data_types: ["log_mel", "align_prior_matrix", "pitch", "voiced_mask", "p_voiced", "energy"] + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 212.35873413085938 for LJSpeech +pitch_std: ??? # e.g. 68.52806091308594 for LJSpeech + +# default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# default values for sample_rate=22050 +n_mels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: "hann" + +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.10" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" +mapping_file_path: "" + +model: + target: nemo.collections.tts.models.RadTTSModel + bin_loss_start_ratio: 0.2 + bin_loss_warmup_epochs: 100 + + symbols_embedding_dim: 384 + n_mel_channels: ${n_mels} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.tts.torch.tts_tokenizers.EnglishPhonemesTokenizer + punct: true + stresses: true + chars: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + mapping_file: ${mapping_file_path} + + train_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${train_dataset} + sample_rate: ${sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${n_fft} + win_length: ${n_window_size} + hop_length: ${n_window_stride} + window: ${window} + n_mels: ${n_mels} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + + + text_tokenizer: + _target_: "nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer" + punct: True + stresses: True + chars: True + space: ' ' + silence: null + apostrophe: True + sep: '|' + add_blank_at: null + pad_with_space: True + g2p: + _target_: "nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p" + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 8 + pin_memory: True + + validation_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${validation_datasets} + sample_rate: ${sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${n_fft} + win_length: ${n_window_size} + hop_length: ${n_window_stride} + window: ${window} + n_mels: ${n_mels} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + text_tokenizer: + _target_: "nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer" + punct: True + stresses: True + chars: True + space: ' ' + silence: null + apostrophe: True + sep: '|' + add_blank_at: null + pad_with_space: True + g2p: + _target_: "nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p" + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: True + + optim: + name: RAdam + lr: 0.001 + betas: [0.9, 0.98] + weight_decay: 0.000001 + + sched: + name: exp_decay + warmup_steps: 40000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + trainerConfig: + sigma: 1 + iters_per_checkpoint: 3000 + seed: null + ignore_layers: [] + finetune_layers: [] + include_layers: [] + with_tensorboard: true + dur_loss_weight: 1 + ctc_loss_weight: 1 + mask_unvoiced_f0: false + log_step: 1 + binarization_start_iter: 1000000 + kl_loss_start_iter: 1000000 + loss_weights: + ctc_loss_weight: 0.1 + dur_loss_weight: 1.0 + f0_loss_weight: 1.0 + energy_loss_weight: 1.0 + vpred_loss_weight: 1.0 + unfreeze_modules: "durf0energyvpred" + + load_from_checkpoint: True + init_from_ptl_ckpt: ${ckpt_path} + modelConfig: + _target_: "nemo.collections.tts.modules.radtts.RadTTSModule" + n_speakers: 1 + n_speaker_dim: 16 + n_text: 384 #185 + n_text_dim: 512 + n_flows: 8 + n_conv_layers_per_step: 4 + n_mel_channels: 80 + n_hidden: 1024 + mel_encoder_n_hidden: 512 + n_components: 0 + mean_scale: 0 + fixed_gaussian: true + dummy_speaker_embedding: false + use_positional_embedding: false + n_early_size: 2 + n_early_every: 2 + n_group_size: 2 + use_feature_gating: false + affine_model: wavenet + include_modules: "decatnunvbiasdpmvpredapm" + what_to_train: decatnunvbias + scaling_fn: tanh + reduction_norm: "" + matrix_decomposition: LUS + learn_alignments: true + use_query_proj: true + align_query_enc_type: 3xconv + lstm_applicable_steps: [] + use_context_lstm: true + context_lstm_norm: spectral + context_lstm_w_f0_and_energy: true + text_encoder_lstm_norm: spectral + use_text_conditional_priors: false + zero_out_context: false + n_aug_dims: 6 + n_f0_dims: 1 + n_energy_avg_dims: 1 + use_first_order_features: false + unvoiced_bias_activation: "relu" + decoder_use_partial_padding: false + decoder_use_unvoiced_bias: true + ap_pred_log_f0: true + ap_use_unvoiced_bias: true + ap_use_voiced_embeddings: true + p_dropout: 0.1 + noise_to_unvoiced_in_f0: 0 + noise_to_pvoiced: 0 + dur_model_config: + name: dap + hparams: + n_speaker_dim: 16 + bottleneck_hparams: + in_dim: 512 + reduction_factor: 16 + norm: weightnorm + non_linearity: relu + take_log_of_input: true + arch_hparams: + out_dim: 1 + n_layers: 2 + n_channels: 256 + kernel_size: 3 + p_dropout: 0.1 + f0_model_config: + name: dap + hparams: + n_speaker_dim: 16 + bottleneck_hparams: + in_dim: 512 + reduction_factor: 16 + norm: weightnorm + non_linearity: relu + take_log_of_input: false + arch_hparams: + out_dim: 1 + n_layers: 2 + n_channels: 256 + kernel_size: 11 + p_dropout: 0.5 + + energy_model_config: + name: dap + hparams: + n_speaker_dim: 16 + bottleneck_hparams: + in_dim: 512 + reduction_factor: 16 + norm: weightnorm + non_linearity: relu + take_log_of_input: false + arch_hparams: + out_dim: 1 + n_layers: 2 + n_channels: 256 + kernel_size: 3 + p_dropout: 0.5 + v_model_config : + name: dap + hparams: + n_speaker_dim: 16 + take_log_of_input: false + bottleneck_hparams: + in_dim: 512 + reduction_factor: 16 + norm: weightnorm + non_linearity: relu + arch_hparams: + out_dim: 1 + n_layers: 2 + n_channels: 256 + kernel_size: 3 + p_dropout: 0.5 + +trainer: + devices: 8 + precision: 16 + max_epochs: 1000 + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False + logger: False + gradient_clip_val: 1 + flush_logs_every_n_steps: 1000 + log_every_n_steps: 100 + check_val_every_n_epoch: 2 + +exp_manager: + exp_dir: ${export_dir} + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val/loss_energy + mode: min + filepath: ${export_dir} + filename: model_checkpoint diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/rad-tts_feature_pred_ipa.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/rad-tts_feature_pred_ipa.yaml new file mode 100644 index 0000000..d7a2730 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/rad-tts_feature_pred_ipa.yaml @@ -0,0 +1,339 @@ +name: RadTTS +sample_rate: 22050 + +train_dataset: ??? +validation_datasets: ??? +ckpt_path: ??? +export_dir: ??? +sup_data_path: ??? +sup_data_types: ["log_mel", "align_prior_matrix", "pitch", "voiced_mask", "p_voiced", "energy"] + + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 212.35873413085938 for LJSpeech +pitch_std: ??? # e.g. 68.52806091308594 for LJSpeech + +# default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +# default values for sample_rate=22050 +n_mels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: "hann" + +phoneme_dict_path: "scripts/tts_dataset_files/ipa_cmudict-0.7b_nv23.01.txt" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" +mapping_file_path: "" + +model: + target: nemo.collections.tts.models.RadTTSModel + bin_loss_start_ratio: 0.2 + bin_loss_warmup_epochs: 100 + + symbols_embedding_dim: 384 + n_mel_channels: ${n_mels} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer + punct: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + # Relies on the heteronyms list for anything that needs to be disambiguated + ignore_ambiguous_words: true + use_chars: true + use_stresses: true + + train_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${train_dataset} + sample_rate: ${sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${n_fft} + win_length: ${n_window_size} + hop_length: ${n_window_stride} + window: ${window} + n_mels: ${n_mels} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + + + text_tokenizer: + _target_: "nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer" + punct: True + stresses: True + chars: True + space: ' ' + silence: null + apostrophe: True + sep: '|' + add_blank_at: null + pad_with_space: True + g2p: + _target_: "nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p" + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 8 + pin_memory: True + + validation_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${validation_datasets} + sample_rate: ${sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${n_fft} + win_length: ${n_window_size} + hop_length: ${n_window_stride} + window: ${window} + n_mels: ${n_mels} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + text_tokenizer: + _target_: "nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer" + punct: True + stresses: True + chars: True + space: ' ' + silence: null + apostrophe: True + sep: '|' + add_blank_at: null + pad_with_space: True + g2p: + _target_: "nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p" + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.5 + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 8 + pin_memory: True + + optim: + name: RAdam + lr: 0.001 + betas: [0.9, 0.98] + weight_decay: 0.000001 + + sched: + name: exp_decay + warmup_steps: 40000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + trainerConfig: + sigma: 1 + iters_per_checkpoint: 3000 + seed: null + ignore_layers: [] + finetune_layers: [] + include_layers: [] + with_tensorboard: true + dur_loss_weight: 1 + ctc_loss_weight: 1 + mask_unvoiced_f0: false + log_step: 1 + binarization_start_iter: 1000000 + kl_loss_start_iter: 1000000 + loss_weights: + ctc_loss_weight: 0.1 + dur_loss_weight: 1.0 + f0_loss_weight: 1.0 + energy_loss_weight: 1.0 + vpred_loss_weight: 1.0 + unfreeze_modules: "durf0energyvpred" + + load_from_checkpoint: True + init_from_ptl_ckpt: ${ckpt_path} + modelConfig: + _target_: "nemo.collections.tts.modules.radtts.RadTTSModule" + n_speakers: 1 + n_speaker_dim: 16 + n_text: 384 #185 + n_text_dim: 512 + n_flows: 8 + n_conv_layers_per_step: 4 + n_mel_channels: 80 + n_hidden: 1024 + mel_encoder_n_hidden: 512 + n_components: 0 + mean_scale: 0 + fixed_gaussian: true + dummy_speaker_embedding: false + use_positional_embedding: false + n_early_size: 2 + n_early_every: 2 + n_group_size: 2 + use_feature_gating: false + affine_model: wavenet + include_modules: "decatnunvbiasdpmvpredapm" + what_to_train: decatnunvbias + scaling_fn: tanh + reduction_norm: "" + matrix_decomposition: LUS + learn_alignments: true + use_query_proj: true + align_query_enc_type: 3xconv + lstm_applicable_steps: [] + use_context_lstm: true + context_lstm_norm: spectral + context_lstm_w_f0_and_energy: true + text_encoder_lstm_norm: spectral + use_text_conditional_priors: false + zero_out_context: false + n_aug_dims: 6 + n_f0_dims: 1 + n_energy_avg_dims: 1 + use_first_order_features: false + unvoiced_bias_activation: "relu" + decoder_use_partial_padding: false + decoder_use_unvoiced_bias: true + ap_pred_log_f0: true + ap_use_unvoiced_bias: true + ap_use_voiced_embeddings: true + p_dropout: 0.1 + noise_to_unvoiced_in_f0: 0 + noise_to_pvoiced: 0 + dur_model_config: + name: dap + hparams: + n_speaker_dim: 16 + bottleneck_hparams: + in_dim: 512 + reduction_factor: 16 + norm: weightnorm + non_linearity: relu + take_log_of_input: true + use_transformer: true + arch_hparams: + out_dim: 1 + n_layers: 3 + n_head: 1 + d_head: 64 + d_inner: 1024 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0 + in_dim: 48 + f0_model_config: + name: dap + hparams: + n_speaker_dim: 16 + bottleneck_hparams: + in_dim: 512 + reduction_factor: 16 + norm: weightnorm + non_linearity: relu + take_log_of_input: false + arch_hparams: + out_dim: 1 + n_layers: 2 + n_channels: 256 + kernel_size: 11 + p_dropout: 0.5 + + energy_model_config: + name: dap + hparams: + n_speaker_dim: 16 + bottleneck_hparams: + in_dim: 512 + reduction_factor: 16 + norm: weightnorm + non_linearity: relu + take_log_of_input: false + arch_hparams: + out_dim: 1 + n_layers: 2 + n_channels: 256 + kernel_size: 3 + p_dropout: 0.5 + v_model_config : + name: dap + hparams: + n_speaker_dim: 16 + take_log_of_input: false + bottleneck_hparams: + in_dim: 512 + reduction_factor: 16 + norm: weightnorm + non_linearity: relu + arch_hparams: + out_dim: 1 + n_layers: 2 + n_channels: 256 + kernel_size: 3 + p_dropout: 0.5 + +trainer: + devices: 8 + precision: 16 + max_epochs: 1000 + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False + logger: False + gradient_clip_val: 1 + log_every_n_steps: 100 + check_val_every_n_epoch: 2 + +exp_manager: + exp_dir: ${export_dir} + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val/loss_energy + mode: min + filepath: ${export_dir} + filename: model_checkpoint diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/spectrogram-enhancer.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/spectrogram-enhancer.yaml new file mode 100644 index 0000000..ada3bc0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/spectrogram-enhancer.yaml @@ -0,0 +1,87 @@ +name: "spectrogram-enhancer" + +model: + n_bands: 80 + latent_dim: 192 + style_depth: 4 + network_capacity: 16 + mixed_prob: 0.9 + fmap_max: 192 + start_from_zero: true # might give better results at downstream tasks + + generator: + _target_: "nemo.collections.tts.modules.spectrogram_enhancer.Generator" + n_bands: ${model.n_bands} + latent_dim: ${model.latent_dim} + network_capacity: ${model.network_capacity} + style_depth: ${model.style_depth} + fmap_max: ${model.fmap_max} + + discriminator: + _target_: "nemo.collections.tts.modules.spectrogram_enhancer.Discriminator" + n_bands: ${model.n_bands} + network_capacity: ${model.network_capacity} + fmap_max: ${model.fmap_max} + + consistency_loss_weight: 10.0 # somewhere in [1., 100.], less for clean datasets, higher for noisier + gradient_penalty_loss_weight: 10.0 # read stylegan papers before changing + gradient_penalty_loss_every_n_steps: 4 + + # Spectrogram values range, calculated over your dataset with matching STFT parameters. + # Needed for treating spectrograms as images with pixel values around [0, 1]. + # For LibriTTS, you can try [-13.18, 4.78] + spectrogram_min_value: ??? + spectrogram_max_value: ??? + + train_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.PairedRealFakeSpectrogramsDataset" + manifest_filepath: ??? + dataloader_params: + drop_last: true + shuffle: true + batch_size: 8 + num_workers: 2 + + generator_opt: + _target_: torch.optim.Adam + lr: 2e-4 + betas: [0.5, 0.9] + + discriminator_opt: + _target_: torch.optim.Adam + lr: 2e-4 + betas: [0.5, 0.9] + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp + precision: 32 + max_epochs: 4 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + log_every_n_steps: 1000 + # we don't really need validation + check_val_every_n_epoch: null + limit_val_batches: 0.0 + benchmark: false + # provided by exp_manager + enable_checkpointing: False + logger: false + +exp_manager: + exp_dir: "" + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + # no good stopping rule, keep every checkpoint + # tune n_epochs for size of your dataset to avoid wasting space + checkpoint_callback_params: + every_n_epochs: 1 + save_on_train_epoch_end: true + save_top_k: -1 + monitor: "g_loss" + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/ssl_tts_22050.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/ssl_tts_22050.yaml new file mode 100644 index 0000000..2a10b40 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/ssl_tts_22050.yaml @@ -0,0 +1,191 @@ +# This config contains the default values for self-supervised pre-training of a Conformer ASR model, large size (~120M). + +# Architecture and training config: +# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective +# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches. +# Here are the recommended configs for different variants of Conformer-CTC, other parameters are the same as in this config file. +# One extra layer (compared to original paper) is added to the medium and large variants to compensate for replacing the LSTM decoder with a linear one. +# +# +-------------+---------+---------+----------+------------+-----+ +# | Model | d_model | n_heads | n_layers | time_masks | lr | +# +=============+=========+========+===========+============+=====+ +# | Small (13M)| 176 | 4 | 16 | 5 | 5.0 | +# +-------------+---------+--------+-----------+------------+-----+ +# | Medium (30M)| 256 | 4 | 18 | 5 | 5.0 | +# +-------------+---------+--------+-----------+------------+-----+ +# | Large (121M)| 512 | 8 | 18 | 10 | 2.0 | +# +---------------------------------------------------------------+ +# +# If you do not want to train with AMP, you may use weight decay of 0.0 or reduce the number of time maskings to 2 +# with time_width=100. It may help when you want to train for fewer epochs and need faster convergence. +# With weight_decay=0.0, learning rate may need to get reduced to 2.0. + +name: "Conformer-SSL" +init_from_pretrained_model: "ssl_en_conformer_large" + +model: + sample_rate: 22050 + combined_loss: true + pitch_augment: true + augment_sim_alpha: 1.0 + stop_gradient: false + augment_ctc: true + aug_loss_type: "cosine" + pad_multiple: 1 + train_ds: + manifest_speaker_verification_fp: ??? + manifest_content_fp: ??? + sample_rate: ${model.sample_rate} + batch_size_content: 8 # you may increase batch_size if your memory allows + batch_size_sv: 20 + shuffle: true + num_workers_sv: 4 + num_workers_content: 6 + pin_memory: false + max_duration_content: 16.7 + min_duration_content: 8.0 + segment_max_duration: 2 + sup_data_path: ??? + pitch_augment: ${model.pitch_augment} + cache_pitch_augment: true + pad_multiple: ${model.pad_multiple} + + validation_ds: + manifest_speaker_verification_fp: ??? + manifest_content_fp: ??? + sample_rate: ${model.sample_rate} + batch_size_content: 4 # you may increase batch_size if your memory allows + batch_size_sv: 8 + shuffle: false + num_workers_sv: 0 + num_workers_content: 0 + pin_memory: true + use_start_end_token: false + max_duration_content: 16.7 + min_duration_content: 8.0 + segment_max_duration: 2 + sup_data_path: ??? + pitch_augment: ${model.pitch_augment} + cache_pitch_augment: true + pad_multiple: ${model.pad_multiple} + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + sample_rate: ${model.sample_rate} + normalize: "per_feature" + window_size: null + window_stride: null + n_window_size: 1024 + n_window_stride: 256 + window: "hann" + features: 80 + n_fft: 1024 + log: true + frame_splicing: 1 + dither: 0.00001 + pad_to: 16 + pad_value: 0.0 + + spec_augment: + _target_: nemo.collections.asr.modules.MaskedPatchAugmentation + freq_masks: 3 + freq_width: 20 + patch_size: 48 + mask_patches: 0.5 + + downstream_heads: + task_names: ['speaker_verification', 'content'] + speaker_embed_size: 256 + num_speakers: 5994 + content_embed_size: 128 + + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.preprocessor.features} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 18 + d_model: 512 + + # Sub-sampling params + subsampling: striding # vggnet or striding, vggnet may give better results but needs more memory + subsampling_factor: 4 # must be power of 2 + subsampling_conv_channels: -1 # -1 sets it to d_model + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 8 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [-1, -1] # -1 means unlimited context + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + decoder_out: 128 + + + optim_backbone: + _target_: torch.optim.Adam + lr: 5e-5 + sched: + min_lr: 1e-6 + warmup_steps: 2000 + + optim_downstream: + _target_: torch.optim.Adam + lr: 1e-4 + sched: + min_lr: 1e-6 + warmup_steps: 1000 + + +trainer: + devices: -1 # number of GPUs, -1 would use all available GPUs + num_nodes: 1 + max_epochs: 1000 + max_steps: 500000 # computed at runtime if not set + val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations + accelerator: auto + strategy: ddp + accumulate_grad_batches: 1 + precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. + log_every_n_steps: 10 # Interval of logging. + num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs + sync_batchnorm: true + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + benchmark: false # needs to be false for models with variable-length speech input as it slows down training + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + # in case of multiple validation sets, first one is used + monitor: "val_loss" + mode: "min" + save_top_k: 5 + + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + # you need to set these two to True to continue the training + resume_if_exists: false + resume_ignore_no_checkpoint: false + + # You may use this section to create a W&B logger + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/tacotron2.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/tacotron2.yaml new file mode 100644 index 0000000..6daf628 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/tacotron2.yaml @@ -0,0 +1,195 @@ +# This config contains the default values for training Tacotron2 model on LJSpeech dataset. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: Tacotron2 + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: null +sup_data_types: null + +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.10" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" + +model: + pitch_fmin: 65.40639132514966 + pitch_fmax: 2093.004522404789 + + sample_rate: 22050 + n_mel_channels: 80 + n_window_size: 1024 + n_window_stride: 256 + n_fft: 1024 + lowfreq: 0 + highfreq: 8000 + window: hann + pad_value: -11.52 + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer + punct: true + stresses: true + chars: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + + train_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + dataloader_params: + drop_last: false + shuffle: true + batch_size: 48 + num_workers: 4 + pin_memory: true + + validation_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + dataloader_params: + drop_last: false + shuffle: false + batch_size: 24 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures + nfilt: ${model.n_mel_channels} + highfreq: ${model.highfreq} + log: true + log_zero_guard_type: clamp + log_zero_guard_value: 1e-05 + lowfreq: ${model.lowfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + n_window_stride: ${model.n_window_stride} + pad_to: 16 + pad_value: ${model.pad_value} + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + stft_conv: false + nb_augmentation_prob : 0 + mag_power: 1.0 + exact_pad: true + use_grads: false + + encoder: + _target_: nemo.collections.tts.modules.tacotron2.Encoder + encoder_kernel_size: 5 + encoder_n_convolutions: 3 + encoder_embedding_dim: 512 + + decoder: + _target_: nemo.collections.tts.modules.tacotron2.Decoder + decoder_rnn_dim: 1024 + encoder_embedding_dim: ${model.encoder.encoder_embedding_dim} + gate_threshold: 0.5 + max_decoder_steps: 1000 + n_frames_per_step: 1 # currently only 1 is supported + n_mel_channels: ${model.n_mel_channels} + p_attention_dropout: 0.1 + p_decoder_dropout: 0.1 + prenet_dim: 256 + prenet_p_dropout: 0.5 + # Attention parameters + attention_dim: 128 + attention_rnn_dim: 1024 + # AttentionLocation Layer parameters + attention_location_kernel_size: 31 + attention_location_n_filters: 32 + early_stopping: true + + postnet: + _target_: nemo.collections.tts.modules.tacotron2.Postnet + n_mel_channels: ${model.n_mel_channels} + p_dropout: 0.5 + postnet_embedding_dim: 512 + postnet_kernel_size: 5 + postnet_n_convolutions: 5 + + optim: + name: adam + lr: 1e-3 + weight_decay: 1e-6 + + # scheduler setup + sched: + name: CosineAnnealing + min_lr: 1e-5 + +trainer: + devices: 1 # number of gpus + max_epochs: ??? + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + gradient_clip_val: 1.0 + log_every_n_steps: 60 + check_val_every_n_epoch: 2 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + mode: min diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/tacotron2_44100.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/tacotron2_44100.yaml new file mode 100644 index 0000000..3965bfd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/tacotron2_44100.yaml @@ -0,0 +1,180 @@ +# TODO(Oktai15): update this config in 1.8.0 version + +name: Tacotron2 +sample_rate: 44100 +# , , will be added by the tacotron2.py script +labels: +- ' ' +- '!' +- '"' +- '''' +- ( +- ) +- ',' +- '-' +- . +- ':' +- ; +- '?' +- a +- b +- c +- d +- e +- f +- g +- h +- i +- j +- k +- l +- m +- 'n' +- o +- p +- q +- r +- s +- t +- u +- v +- w +- x +- 'y' +- z +n_fft: 2048 +n_mels: 80 +fmax: null +n_stride: 512 +pad_value: -11.52 +train_dataset: ??? +validation_datasets: ??? + +model: + labels: ${labels} + train_ds: + dataset: + _target_: "nemo.collections.asr.data.audio_to_text.AudioToCharDataset" + manifest_filepath: ${train_dataset} + max_duration: null + min_duration: 0.1 + trim: false + int_values: false + normalize: true + sample_rate: ${sample_rate} + # bos_id: 66 + # eos_id: 67 + # pad_id: 68 These parameters are added automatically in Tacotron2 + dataloader_params: + drop_last: false + shuffle: true + batch_size: 48 + num_workers: 4 + pin_memory: true + + + validation_ds: + dataset: + _target_: "nemo.collections.asr.data.audio_to_text.AudioToCharDataset" + manifest_filepath: ${validation_datasets} + max_duration: null + min_duration: 0.1 + int_values: false + normalize: true + sample_rate: ${sample_rate} + trim: false + # bos_id: 66 + # eos_id: 67 + # pad_id: 68 These parameters are added automatically in Tacotron2 + dataloader_params: + drop_last: false + shuffle: false + batch_size: 48 + num_workers: 8 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures + dither: 0.0 + nfilt: ${n_mels} + frame_splicing: 1 + highfreq: ${fmax} + log: true + log_zero_guard_type: clamp + log_zero_guard_value: 1e-05 + lowfreq: 0 + mag_power: 1.0 + n_fft: ${n_fft} + n_window_size: 2048 + n_window_stride: ${n_stride} + normalize: null + pad_to: 16 + pad_value: ${pad_value} + preemph: null + sample_rate: ${sample_rate} + window: hann + + encoder: + _target_: nemo.collections.tts.modules.tacotron2.Encoder + encoder_kernel_size: 5 + encoder_n_convolutions: 3 + encoder_embedding_dim: 512 + + decoder: + _target_: nemo.collections.tts.modules.tacotron2.Decoder + decoder_rnn_dim: 1024 + encoder_embedding_dim: ${model.encoder.encoder_embedding_dim} + gate_threshold: 0.5 + max_decoder_steps: 1000 + n_frames_per_step: 1 # currently only 1 is supported + n_mel_channels: ${n_mels} + p_attention_dropout: 0.1 + p_decoder_dropout: 0.1 + prenet_dim: 256 + prenet_p_dropout: 0.5 + # Attention parameters + attention_dim: 128 + attention_rnn_dim: 1024 + # AttentionLocation Layer parameters + attention_location_kernel_size: 31 + attention_location_n_filters: 32 + early_stopping: true + + postnet: + _target_: nemo.collections.tts.modules.tacotron2.Postnet + n_mel_channels: ${n_mels} + p_dropout: 0.5 + postnet_embedding_dim: 512 + postnet_kernel_size: 5 + postnet_n_convolutions: 5 + + optim: + name: adam + lr: 1e-3 + weight_decay: 1e-6 + + # scheduler setup + sched: + name: CosineAnnealing + min_lr: 1e-5 + + +trainer: + devices: 1 # number of gpus + max_epochs: ??? + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + gradient_clip_val: 1.0 + log_every_n_steps: 60 + check_val_every_n_epoch: 2 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/text/normalizer_en.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/text/normalizer_en.yaml new file mode 100644 index 0000000..aef1425 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/text/normalizer_en.yaml @@ -0,0 +1,3 @@ +_target_: nemo_text_processing.text_normalization.normalize.Normalizer +lang: en +input_case: cased \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/trim/energy.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/trim/energy.yaml new file mode 100644 index 0000000..475b621 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/trim/energy.yaml @@ -0,0 +1,7 @@ +_target_: nemo.collections.tts.parts.preprocessing.audio_trimming.EnergyAudioTrimmer + +db_threshold: 50.0 +speech_frame_threshold: 3 +trim_win_length: 4096 +trim_hop_length: 1024 +pad_seconds: 0.2 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/trim/vad.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/trim/vad.yaml new file mode 100644 index 0000000..38795d5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/trim/vad.yaml @@ -0,0 +1,10 @@ +_target_: nemo.collections.tts.parts.preprocessing.audio_trimming.VadAudioTrimmer + +model_name: "vad_multilingual_marblenet" +vad_sample_rate: 16000 +vad_threshold: 0.5 +device: "cpu" +speech_frame_threshold: 3 +trim_win_length: 4096 +trim_hop_length: 1024 +pad_seconds: 0.2 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/generator/c16.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/generator/c16.yaml new file mode 100644 index 0000000..7c15f2c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/generator/c16.yaml @@ -0,0 +1,7 @@ +_target_: nemo.collections.tts.modules.univnet_modules.Generator +noise_dim: 64 +channel_size: 16 +dilations: [1, 3, 9, 27] +strides: [8, 8, 4] +lrelu_slope: 0.2 +kpnet_conv_size: 3 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/generator/c32.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/generator/c32.yaml new file mode 100644 index 0000000..820f8fb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/generator/c32.yaml @@ -0,0 +1,7 @@ +_target_: nemo.collections.tts.modules.univnet_modules.Generator +noise_dim: 64 +channel_size: 32 +dilations: [1, 3, 9, 27] +strides: [8, 8, 4] +lrelu_slope: 0.2 +kpnet_conv_size: 3 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/train_ds/train_ds.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/train_ds/train_ds.yaml new file mode 100644 index 0000000..8f52e29 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/train_ds/train_ds.yaml @@ -0,0 +1,13 @@ +dataset: + _target_: "nemo.collections.tts.data.dataset.VocoderDataset" + manifest_filepath: ${train_dataset} + sample_rate: ${sample_rate} + n_segments: ${train_n_segments} + max_duration: ${train_max_duration} + min_duration: ${train_min_duration} +dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 4 + pin_memory: true diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/train_ds/train_ds_finetune.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/train_ds/train_ds_finetune.yaml new file mode 100644 index 0000000..5185bdb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/train_ds/train_ds_finetune.yaml @@ -0,0 +1,15 @@ +dataset: + _target_: "nemo.collections.tts.data.dataset.VocoderDataset" + manifest_filepath: ${train_dataset} + sample_rate: ${sample_rate} + n_segments: ${train_n_segments} + max_duration: ${train_max_duration} + min_duration: ${train_min_duration} + load_precomputed_mel: true + hop_length: ${n_window_stride} +dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 4 + pin_memory: true diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/validation_ds/val_ds.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/validation_ds/val_ds.yaml new file mode 100644 index 0000000..e241f81 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/validation_ds/val_ds.yaml @@ -0,0 +1,13 @@ +dataset: + _target_: "nemo.collections.tts.data.dataset.VocoderDataset" + manifest_filepath: ${validation_datasets} + sample_rate: ${sample_rate} + n_segments: ${val_n_segments} + max_duration: ${val_max_duration} + min_duration: ${val_min_duration} +dataloader_params: + drop_last: false + shuffle: false + batch_size: 16 + num_workers: 1 + pin_memory: true diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/validation_ds/val_ds_finetune.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/validation_ds/val_ds_finetune.yaml new file mode 100644 index 0000000..6c5b79c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/model/validation_ds/val_ds_finetune.yaml @@ -0,0 +1,15 @@ +dataset: + _target_: "nemo.collections.tts.data.dataset.VocoderDataset" + manifest_filepath: ${validation_datasets} + sample_rate: ${sample_rate} + n_segments: ${val_n_segments} + max_duration: ${val_max_duration} + min_duration: ${val_min_duration} + load_precomputed_mel: true + hop_length: ${n_window_stride} +dataloader_params: + drop_last: false + shuffle: false + batch_size: 16 + num_workers: 4 + pin_memory: true diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/univnet.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/univnet.yaml new file mode 100644 index 0000000..2f6b2f0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/univnet/univnet.yaml @@ -0,0 +1,105 @@ +# This config contains the default values for training UnivNet model on LJSpeech dataset. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: "UnivNet" + +train_dataset: ??? +validation_datasets: ??? + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: hann + +train_n_segments: 8192 +train_max_duration: null +train_min_duration: 0.75 + +val_n_segments: 66048 +val_max_duration: null +val_min_duration: 3 + +defaults: + - model/generator: c32 + - model/train_ds: train_ds + - model/validation_ds: val_ds + +model: + discriminator: + mpd: + periods: [2,3,5,7,11] + kernel_size: 5 + stride: 3 + use_spectral_norm: false + lrelu_slope: 0.2 + mrd: + resolutions: [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]] # (filter_length, hop_length, win_length) + use_spectral_norm: false + lrelu_slope: 0.2 + preprocessor: + _target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures + nfilt: ${n_mel_channels} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + n_fft: ${n_fft} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + pad_to: 0 + pad_value: -11.52 + sample_rate: ${sample_rate} + window: ${window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: clamp + log_zero_guard_value: 1e-05 + mag_power: 1.0 + use_grads: false + exact_pad: true + + optim: + _target_: torch.optim.AdamW + lr: 0.0001 + betas: [0.5, 0.9] + + max_steps: 1000000 + stft_lamb: 2.5 + denoise_strength: 0.0025 + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp_find_unused_parameters_true + precision: 32 + max_steps: ${model.max_steps} + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 10 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + mode: min + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/vits.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/vits.yaml new file mode 100644 index 0000000..c38229d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/vits.yaml @@ -0,0 +1,213 @@ +# This config contains the default values for training VITS model on LJSpeech dataset. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +# TODO: remove unnecessary arguments, refactoring + +name: VITS + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: null +sup_data_types: null + +phoneme_dict_path: "scripts/tts_dataset_files/ipa_cmudict-0.7b_nv23.01.txt" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: null +window: hann + +model: + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + mel_fmin: 0.0 + mel_fmax: null + + n_speakers: 0 + segment_size: 8192 + c_mel: 45 + c_kl: 1. + use_spectral_norm: false + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer + punct: true + apostrophe: true + pad_with_space: false + g2p: + _target_: nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.8 + # Relies on the heteronyms list for anything that needs to be disambiguated + ignore_ambiguous_words: false + use_chars: true + use_stresses: true + + train_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + + dataloader_params: + num_workers: 8 + pin_memory: false + + batch_sampler: + batch_size: 32 + boundaries: [32,300,400,500,600,700,800,900,1000] + num_replicas: ${trainer.devices} + shuffle: true + + validation_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 16 + num_workers: 4 + pin_memory: false + + preprocessor: + _target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures + nfilt: ${model.n_mel_channels} + highfreq: ${model.highfreq} + log: true + log_zero_guard_type: clamp + log_zero_guard_value: 1e-05 + lowfreq: ${model.lowfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + n_window_stride: ${model.n_window_stride} + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + stft_conv: false + nb_augmentation_prob : 0 + mag_power: 1.0 + exact_pad: true + use_grads: true + + synthesizer: + _target_: nemo.collections.tts.modules.vits_modules.SynthesizerTrn + inter_channels: 192 + hidden_channels: 192 + filter_channels: 768 + n_heads: 2 + n_layers: 6 + kernel_size: 3 + p_dropout: 0.1 + resblock: "1" + resblock_kernel_sizes: [3,7,11] + resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]] + upsample_rates: [8,8,2,2] + upsample_initial_channel: 512 + upsample_kernel_sizes: [16,16,4,4] + n_speakers: ${model.n_speakers} + gin_channels: 256 # for multi-speaker + + optim: + _target_: torch.optim.AdamW + lr: 2e-4 + betas: [0.9, 0.99] + eps: 1e-9 + + sched: + name: ExponentialLR + lr_decay: 0.999875 + +trainer: + num_nodes: 1 + devices: 2 + accelerator: gpu + strategy: ddp + precision: 32 + # amp_backend: 'apex' + # amp_level: 'O2' + # benchmark: true + max_epochs: -1 + accumulate_grad_batches: 1 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 50 + check_val_every_n_epoch: 1 + +exp_manager: + exp_dir: ??? + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: loss_gen_all + mode: min + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/vits_44100.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/vits_44100.yaml new file mode 100644 index 0000000..bcadb25 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/vits_44100.yaml @@ -0,0 +1,209 @@ +# This config contains the default values for training VITS model on LJSpeech dataset. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: VITS + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [speaker_id] + +pitch_fmin: 65.40639132514966 +pitch_fmax: 2093.004522404789 + +sample_rate: 44100 +n_mel_channels: 80 +n_window_size: 2048 +n_window_stride: 512 +n_fft: 2048 +lowfreq: 0 +highfreq: null +window: hann + +phoneme_dict_path: "scripts/tts_dataset_files/ipa_cmudict-0.7b_nv23.01.txt" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" + +model: + n_speakers: 13000 + segment_size: 16384 + c_mel: 45 + c_kl: 1. + use_spectral_norm: false + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: en + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer + punct: true + apostrophe: true + pad_with_space: false + g2p: + _target_: nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p + phoneme_dict: ${phoneme_dict_path} + heteronyms: ${heteronyms_path} + phoneme_probability: 0.8 + # Relies on the heteronyms list for anything that needs to be disambiguated + ignore_ambiguous_words: false + use_chars: true + use_stresses: true + + train_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + + dataloader_params: + num_workers: 8 + pin_memory: false + + batch_sampler: + batch_size: 32 + boundaries: [32,300,400,500,600,700,800,900,1000] + num_replicas: ${trainer.devices} + shuffle: true + + validation_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.TTSDataset" + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null + min_duration: 0.1 + ignore_file: null + trim: False + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 4 + pin_memory: false + + preprocessor: + _target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures + nfilt: ${model.n_mel_channels} + highfreq: ${model.highfreq} + log: true + log_zero_guard_type: clamp + log_zero_guard_value: 1e-05 + lowfreq: ${model.lowfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + n_window_stride: ${model.n_window_stride} + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + stft_conv: false + nb_augmentation_prob : 0 + mag_power: 1.0 + exact_pad: true + use_grads: true + + synthesizer: + _target_: nemo.collections.tts.modules.vits_modules.SynthesizerTrn + inter_channels: 192 + hidden_channels: 192 + filter_channels: 768 + n_heads: 2 + n_layers: 6 + kernel_size: 3 + p_dropout: 0.1 + resblock: "1" + resblock_kernel_sizes: [3,7,11] + resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]] + upsample_rates: [8,8,4,2] + upsample_initial_channel: 512 + upsample_kernel_sizes: [16,16,4,4] + n_speakers: ${model.n_speakers} + gin_channels: 256 # for multi-speaker + + optim: + _target_: torch.optim.AdamW + lr: 2e-4 + betas: [0.9, 0.99] + eps: 1e-9 + + sched: + name: CosineAnnealing + max_steps: 1000000 + min_lr: 1e-5 + +trainer: + num_nodes: 1 + devices: 2 + accelerator: gpu + strategy: ddp + precision: 32 + # amp_backend: 'apex' + # amp_level: 'O2' + # benchmark: true + max_epochs: -1 + accumulate_grad_batches: 1 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 50 + check_val_every_n_epoch: 1 + +exp_manager: + exp_dir: ??? + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: loss_gen_all + mode: min + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/waveglow.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/waveglow.yaml new file mode 100644 index 0000000..1d4d4bf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/waveglow.yaml @@ -0,0 +1,113 @@ +# This config contains the default values for training WaveGlow model on LJSpeech dataset. +# If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: "WaveGlow" + +train_dataset: ??? +validation_datasets: ??? + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: 8000 +window: hann + +model: + sigma: 1.0 + train_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.VocoderDataset" + manifest_filepath: ${train_dataset} + sample_rate: ${sample_rate} + max_duration: null + min_duration: 0.1 + n_segments: 16000 + dataloader_params: + drop_last: false + shuffle: true + batch_size: 12 + num_workers: 4 + pin_memory: true + + validation_ds: + dataset: + _target_: "nemo.collections.tts.data.dataset.VocoderDataset" + manifest_filepath: ${validation_datasets} + sample_rate: ${sample_rate} + max_duration: null + min_duration: 0.1 + dataloader_params: + drop_last: false + shuffle: false + batch_size: 8 + num_workers: 4 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures + nfilt: ${n_mel_channels} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + n_fft: ${n_fft} + # Changing these parameters are not recommended, because WaveGlow is currently hardcoded to these values + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + pad_to: 16 + pad_value: -11.52 + sample_rate: ${sample_rate} + window: ${window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: clamp + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + waveglow: + _target_: nemo.collections.tts.modules.waveglow.WaveGlowModule + n_early_every: 4 + n_early_size: 2 + n_flows: 12 + n_group: 8 + n_mel_channels: ${n_mel_channels} + n_wn_channels: 256 + n_wn_layers: 8 + wn_kernel_size: 3 + + optim: + name: adam + lr: 1e-4 + +trainer: + num_nodes: 1 + devices: 1 + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: ??? + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 200 + check_val_every_n_epoch: 25 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + create_wandb_logger: false + wandb_logger_kwargs: + name: null + project: null + entity: null + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/zh/fastpitch_align_22050.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/zh/fastpitch_align_22050.yaml new file mode 100644 index 0000000..21a84f3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/zh/fastpitch_align_22050.yaml @@ -0,0 +1,259 @@ +# This config contains the default values for training FastPitch model with aligner using 22KHz sampling +# rate. If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch" ] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 1986.977294921875 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 221.4948272705078 for SFbilingual dataset. +pitch_std: ??? # e.g. 64.6528930664063 for SFbilingual dataset. + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: null +window: hann + +# There are four candidates of `phoneme_dict_path` provided for Chinese as shown below, +# 1) 24-final Pinyin: "scripts/tts_dataset_files/zh/24finals/pinyin_dict_nv_22.10.txt", +# 2) IPA converted from 24-final Pinyin: "scripts/tts_dataset_files/zh/24finals/ipa_dict_nv23.05.txt", +# 3) 36-final Pinyin: "scripts/tts_dataset_files/zh/36finals/pinyin_dict_nv23.05.txt", +# 4) (default) IPA converted from 36-final Pinyin: "scripts/tts_dataset_files/zh/36finals/ipa_dict_nv23.05.txt" +# Suggest to choose IPA symbol set converted from 36-final Pinyin because better audio quality were observed. +phoneme_dict_path: "scripts/tts_dataset_files/zh/36finals/ipa_dict_nv23.05.txt" + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: 1 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: zh + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.ChinesePhonemesTokenizer + punct: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.zh_cn_pinyin.ChineseG2p + phoneme_dict: ${phoneme_dict_path} + word_segmenter: jieba # Only jieba is supported now. + phoneme_prefix: "" + phoneme_case: lower + tone_prefix: "#" + ascii_letter_prefix: "" + ascii_letter_case: upper + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null # change to null to include longer audios. + min_duration: 0.1 + ignore_file: null + trim: true + trim_top_db: 50 + trim_frame_length: ${model.n_window_size} + trim_hop_length: ${model.n_window_stride} + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null # change to null to include longer audios. + min_duration: 0.1 + ignore_file: null + trim: true + trim_top_db: 50 + trim_frame_length: ${model.n_window_size} + trim_hop_length: ${model.n_window_stride} + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 2 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: -1 # number of gpus + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: 5000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/conf/zh/fastpitch_align_multispeaker_22050.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/conf/zh/fastpitch_align_multispeaker_22050.yaml new file mode 100644 index 0000000..55c918c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/conf/zh/fastpitch_align_multispeaker_22050.yaml @@ -0,0 +1,261 @@ +# This config contains the default values for training FastPitch model with aligner using 22KHz sampling +# rate. If you want to train model on other dataset, you can change config values according to your dataset. +# Most dataset-specific arguments are in the head of the config file, see below. + +name: FastPitch + +train_dataset: ??? +validation_datasets: ??? +sup_data_path: ??? +sup_data_types: [ "align_prior_matrix", "pitch", "speaker_id"] + +# Default values from librosa.pyin +pitch_fmin: 65.40639132514966 +pitch_fmax: 1986.977294921875 + +# these frame-wise values depend on pitch_fmin and pitch_fmax, you can get values +# by running `scripts/dataset_processing/tts/extract_sup_data.py` +pitch_mean: ??? # e.g. 221.4948272705078 for SFbilingual dataset. +pitch_std: ??? # e.g. 64.6528930664063 for SFbilingual dataset. + +# Default values for dataset with sample_rate=22050 +sample_rate: 22050 +n_mel_channels: 80 +n_window_size: 1024 +n_window_stride: 256 +n_fft: 1024 +lowfreq: 0 +highfreq: null +window: hann + +# There are four candidates of `phoneme_dict_path` provided for Chinese as shown below, +# 1) 24-final Pinyin: "scripts/tts_dataset_files/zh/24finals/pinyin_dict_nv_22.10.txt", +# 2) IPA converted from 24-final Pinyin: "scripts/tts_dataset_files/zh/24finals/ipa_dict_nv23.05.txt", +# 3) 36-final Pinyin: "scripts/tts_dataset_files/zh/36finals/pinyin_dict_nv23.05.txt", +# 4) (default) IPA converted from 36-final Pinyin: "scripts/tts_dataset_files/zh/36finals/ipa_dict_nv23.05.txt" +# Suggest to choose IPA symbol set converted from 36-final Pinyin because better audio quality were observed. +phoneme_dict_path: "scripts/tts_dataset_files/zh/36finals/ipa_dict_nv23.05.txt" + +model: + learn_alignment: true + bin_loss_warmup_epochs: 100 + + n_speakers: 175 + max_token_duration: 75 + symbols_embedding_dim: 384 + pitch_embedding_kernel_size: 3 + speaker_emb_condition_prosody: true + speaker_emb_condition_aligner: true + + pitch_fmin: ${pitch_fmin} + pitch_fmax: ${pitch_fmax} + + pitch_mean: ${pitch_mean} + pitch_std: ${pitch_std} + + sample_rate: ${sample_rate} + n_mel_channels: ${n_mel_channels} + n_window_size: ${n_window_size} + n_window_stride: ${n_window_stride} + n_fft: ${n_fft} + lowfreq: ${lowfreq} + highfreq: ${highfreq} + window: ${window} + + text_normalizer: + _target_: nemo_text_processing.text_normalization.normalize.Normalizer + lang: zh + input_case: cased + + text_normalizer_call_kwargs: + verbose: false + punct_pre_process: true + punct_post_process: true + + text_tokenizer: + _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.ChinesePhonemesTokenizer + punct: true + apostrophe: true + pad_with_space: true + g2p: + _target_: nemo.collections.tts.g2p.models.zh_cn_pinyin.ChineseG2p + phoneme_dict: ${phoneme_dict_path} + word_segmenter: jieba # Only jieba is supported now. + phoneme_prefix: "" + phoneme_case: lower + tone_prefix: "#" + ascii_letter_prefix: "" + ascii_letter_case: upper + + train_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${train_dataset} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null # change to null to include longer audios. + min_duration: 0.1 + ignore_file: null + trim: true + trim_top_db: 50 + trim_frame_length: ${model.n_window_size} + trim_hop_length: ${model.n_window_stride} + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 12 + pin_memory: true + + validation_ds: + dataset: + _target_: nemo.collections.tts.data.dataset.TTSDataset + manifest_filepath: ${validation_datasets} + sample_rate: ${model.sample_rate} + sup_data_path: ${sup_data_path} + sup_data_types: ${sup_data_types} + n_fft: ${model.n_fft} + win_length: ${model.n_window_size} + hop_length: ${model.n_window_stride} + window: ${model.window} + n_mels: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + max_duration: null # change to null to include longer audios. + min_duration: 0.1 + ignore_file: null + trim: true + trim_top_db: 50 + trim_frame_length: ${model.n_window_size} + trim_hop_length: ${model.n_window_stride} + pitch_fmin: ${model.pitch_fmin} + pitch_fmax: ${model.pitch_fmax} + pitch_norm: true + pitch_mean: ${model.pitch_mean} + pitch_std: ${model.pitch_std} + + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 2 + pin_memory: true + + preprocessor: + _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor + features: ${model.n_mel_channels} + lowfreq: ${model.lowfreq} + highfreq: ${model.highfreq} + n_fft: ${model.n_fft} + n_window_size: ${model.n_window_size} + window_size: false + n_window_stride: ${model.n_window_stride} + window_stride: false + pad_to: 1 + pad_value: 0 + sample_rate: ${model.sample_rate} + window: ${model.window} + normalize: null + preemph: null + dither: 0.0 + frame_splicing: 1 + log: true + log_zero_guard_type: add + log_zero_guard_value: 1e-05 + mag_power: 1.0 + + input_fft: #n_embed and padding_idx are added by the model + _target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + d_embed: ${model.symbols_embedding_dim} + + output_fft: + _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder + n_layer: 6 + n_head: 1 + d_model: ${model.symbols_embedding_dim} + d_head: 64 + d_inner: 1536 + kernel_size: 3 + dropout: 0.1 + dropatt: 0.1 + dropemb: 0.0 + + alignment_module: + _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder + n_text_channels: ${model.symbols_embedding_dim} + + duration_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + pitch_predictor: + _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor + input_size: ${model.symbols_embedding_dim} + kernel_size: 3 + filter_size: 256 + dropout: 0.1 + n_layers: 2 + + optim: + name: adamw + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 1e-6 + + sched: + name: NoamAnnealing + warmup_steps: 1000 + last_epoch: -1 + d_model: 1 # Disable scaling based on model dim + +trainer: + num_nodes: 1 + devices: -1 # number of gpus + accelerator: gpu + strategy: ddp + precision: 16 + max_epochs: 5000 + accumulate_grad_batches: 1 + gradient_clip_val: 1000.0 + enable_checkpointing: false # Provided by exp_manager + logger: false # Provided by exp_manager + log_every_n_steps: 100 + check_val_every_n_epoch: 5 + benchmark: false + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: true + create_checkpoint_callback: true + checkpoint_callback_params: + monitor: val_loss + resume_if_exists: false + resume_ignore_no_checkpoint: false diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/fastpitch.py b/NeMo-2.0.0.rc0.beta/examples/tts/fastpitch.py new file mode 100644 index 0000000..a8e6ecd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/fastpitch.py @@ -0,0 +1,35 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models import FastPitchModel +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="fastpitch_align_v1.05") +def main(cfg): + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + model = FastPitchModel(cfg=cfg.model, trainer=trainer) + lr_logger = pl.callbacks.LearningRateMonitor() + epoch_time_logger = LogEpochTimeCallback() + trainer.callbacks.extend([lr_logger, epoch_time_logger]) + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/fastpitch_finetune.py b/NeMo-2.0.0.rc0.beta/examples/tts/fastpitch_finetune.py new file mode 100644 index 0000000..64b5e8b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/fastpitch_finetune.py @@ -0,0 +1,41 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models import FastPitchModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="fastpitch_align_44100") +def main(cfg): + if hasattr(cfg.model.optim, 'sched'): + logging.warning("You are using an optimizer scheduler while finetuning. Are you sure this is intended?") + if cfg.model.optim.lr > 1e-3 or cfg.model.optim.lr < 1e-5: + logging.warning("The recommended learning rate for finetuning is 2e-4") + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + model = FastPitchModel(cfg=cfg.model, trainer=trainer) + model.maybe_init_from_pretrained_checkpoint(cfg=cfg) + lr_logger = pl.callbacks.LearningRateMonitor() + epoch_time_logger = LogEpochTimeCallback() + trainer.callbacks.extend([lr_logger, epoch_time_logger]) + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/fastpitch_finetune_adapters.py b/NeMo-2.0.0.rc0.beta/examples/tts/fastpitch_finetune_adapters.py new file mode 100644 index 0000000..1361d63 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/fastpitch_finetune_adapters.py @@ -0,0 +1,153 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from dataclasses import is_dataclass + +import pytorch_lightning as pl +from omegaconf import DictConfig, OmegaConf, open_dict + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models import FastPitchModel +from nemo.core import adapter_mixins +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +def update_model_config_to_support_adapter(config) -> DictConfig: + with open_dict(config): + enc_adapter_metadata = adapter_mixins.get_registered_adapter(config.input_fft._target_) + if enc_adapter_metadata is not None: + config.input_fft._target_ = enc_adapter_metadata.adapter_class_path + + dec_adapter_metadata = adapter_mixins.get_registered_adapter(config.output_fft._target_) + if dec_adapter_metadata is not None: + config.output_fft._target_ = dec_adapter_metadata.adapter_class_path + + pitch_predictor_adapter_metadata = adapter_mixins.get_registered_adapter(config.pitch_predictor._target_) + if pitch_predictor_adapter_metadata is not None: + config.pitch_predictor._target_ = pitch_predictor_adapter_metadata.adapter_class_path + + duration_predictor_adapter_metadata = adapter_mixins.get_registered_adapter(config.duration_predictor._target_) + if duration_predictor_adapter_metadata is not None: + config.duration_predictor._target_ = duration_predictor_adapter_metadata.adapter_class_path + + aligner_adapter_metadata = adapter_mixins.get_registered_adapter(config.alignment_module._target_) + if aligner_adapter_metadata is not None: + config.alignment_module._target_ = aligner_adapter_metadata.adapter_class_path + + return config + + +def add_global_adapter_cfg(model, global_adapter_cfg): + # Convert to DictConfig from dict or Dataclass + if is_dataclass(global_adapter_cfg): + global_adapter_cfg = OmegaConf.structured(global_adapter_cfg) + + if not isinstance(global_adapter_cfg, DictConfig): + global_adapter_cfg = DictConfig(global_adapter_cfg) + + # Update the model.cfg with information about the new adapter global cfg + with open_dict(global_adapter_cfg), open_dict(model.cfg): + if 'adapters' not in model.cfg: + model.cfg.adapters = OmegaConf.create({}) + + # Add the global config for adapters to the model's internal config + model.cfg.adapters[model.adapter_global_cfg_key] = global_adapter_cfg + + # Update all adapter modules (that already exist) with this global adapter config + model.update_adapter_cfg(model.cfg.adapters) + + +@hydra_runner(config_path="conf", config_name="fastpitch_align_44100_adapter") +def main(cfg): + if hasattr(cfg.model.optim, 'sched'): + logging.warning("You are using an optimizer scheduler while finetuning. Are you sure this is intended?") + if cfg.model.optim.lr > 1e-3 or cfg.model.optim.lr < 1e-5: + logging.warning("The recommended learning rate for finetuning is 2e-4") + + trainer = pl.Trainer(**cfg.trainer) + exp_log_dir = exp_manager(trainer, cfg.get("exp_manager", None)) + # Initialize FastPitchModel + model = FastPitchModel(cfg=update_model_config_to_support_adapter(cfg.model), trainer=trainer) + model.maybe_init_from_pretrained_checkpoint(cfg=cfg) + + # Extract adapter parameters + with open_dict(cfg.model.adapter): + # Extract the name of the adapter (must be given for training) + adapter_name = cfg.model.adapter.pop("adapter_name", "adapter") + # Extract the name of the modules where adapters need to be added (must be given for training) + adapter_module_name = cfg.model.adapter.pop("adapter_module_name", None) + # Name of the adapter checkpoint which will be saved after training + adapter_state_dict_name = cfg.model.adapter.pop("adapter_state_dict_name", None) + + # augment adapter name with module name, if not provided by user + if adapter_module_name is not None and ':' not in adapter_name: + adapter_name = f'{adapter_module_name}:{adapter_name}' + + # Extract the global adapter config, if provided + adapter_global_cfg = cfg.model.adapter.pop(model.adapter_global_cfg_key, None) + + # Freeze model + model.freeze() + + # Setup adapters + if adapter_global_cfg is not None: + add_global_adapter_cfg(model, adapter_global_cfg) + + if cfg.model.get("unfreeze_aligner", False): + for name, param in model.fastpitch.aligner.named_parameters(): + param.requires_grad = True + + if cfg.model.get("unfreeze_duration_predictor", False): + for name, param in model.fastpitch.duration_predictor.named_parameters(): + param.requires_grad = True + + if cfg.model.get("unfreeze_pitch_predictor", False): + for name, param in model.fastpitch.pitch_predictor.named_parameters(): + param.requires_grad = True + + # Add adapters + model.add_adapter(name=adapter_name, cfg=cfg.model.adapter) + assert model.is_adapter_available() + # enable adapters + model.set_enabled_adapters(enabled=False) + model.set_enabled_adapters(adapter_name, enabled=True) + + # Set model to training mode. + model = model.train() + # Then, Unfreeze just the adapter weights that were enabled above (no part of model) + model.unfreeze_enabled_adapters() + # summarize the model + model.summarize() + + lr_logger = pl.callbacks.LearningRateMonitor() + epoch_time_logger = LogEpochTimeCallback() + trainer.callbacks.extend([lr_logger, epoch_time_logger]) + trainer.fit(model) + + # Save the adapter state dict after training has completed + if adapter_state_dict_name is not None: + state_path = exp_log_dir if exp_log_dir is not None else os.getcwd() + ckpt_path = os.path.join(state_path, "checkpoints") + if os.path.exists(ckpt_path): + state_path = ckpt_path + + # Save the adapter modules in a seperate file + model.save_adapters(os.path.join(state_path, adapter_state_dict_name)) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/fastpitch_ssl.py b/NeMo-2.0.0.rc0.beta/examples/tts/fastpitch_ssl.py new file mode 100644 index 0000000..1101ac1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/fastpitch_ssl.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models import fastpitch_ssl, hifigan +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="fastpitch_ssl") +def main(cfg): + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + vocoder = hifigan.HifiGanModel.load_from_checkpoint(cfg.hifi_ckpt_path).cpu() + vocoder.eval() + model = fastpitch_ssl.FastPitchModel_SSL(cfg=cfg.model, trainer=trainer, vocoder=vocoder) + if cfg.get("finetune", False): + model.maybe_init_from_pretrained_checkpoint(cfg=cfg) + lr_logger = pl.callbacks.LearningRateMonitor() + epoch_time_logger = LogEpochTimeCallback() + trainer.callbacks.extend([lr_logger, epoch_time_logger]) + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/g2p/README.md b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/README.md new file mode 100644 index 0000000..2862bd1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/README.md @@ -0,0 +1,2 @@ +This directory contains grapheme-to-phoneme (G2P) related work. + diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/g2p/conf/g2p_conformer_ctc.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/conf/g2p_conformer_ctc.yaml new file mode 100644 index 0000000..098e1fd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/conf/g2p_conformer_ctc.yaml @@ -0,0 +1,145 @@ +name: G2P-Conformer-CTC + +# Dataset info +train_manifest: ??? +validation_manifest: ??? +test_manifest: null +do_training: True +do_testing: False +pretrained_model: null # path to .nemo file or model name from list_available_models() + +model: + model_name: conformer_bpe + max_source_len: 512 + + tokenizer_grapheme: + dataset: + _target_: nemo.collections.common.tokenizers.char_tokenizer.CharTokenizer + unk_token: "҂" # in the data, T5 unk_token is still + vocab_file: null # will be filled during training + do_lower: true # whether to lower case graphemes + add_punctuation: true # whether to add punctuation symbols + + embedding: + d_model: 300 + encoder: + _target_: nemo.collections.asr.modules.ConformerEncoder + feat_in: ${model.embedding.d_model} + feat_out: -1 # you may set it if you need different output size other than the default d_model + n_layers: 16 + d_model: 176 + + # Sub-sampling params + subsampling: null # vggnet or striding, vggnet may give better results but needs more memory + subsampling_factor: 1 # must be power of 2 + subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model + + # Feed forward module's params + ff_expansion_factor: 4 + + # Multi-headed Attention Module's params + self_attention_model: rel_pos # rel_pos or abs_pos + n_heads: 4 # may need to be lower for smaller d_models + # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention + att_context_size: [ -1, -1 ] # -1 means unlimited context + xscaling: true # scales up the input embeddings by sqrt(d_model) + untie_biases: true # unties the biases of the TransformerXL layers + pos_emb_max_len: 5000 + + # Convolution module's params + conv_kernel_size: 31 + conv_norm_type: 'batch_norm' # batch_norm or layer_norm + + ### regularization + dropout: 0.1 # The dropout used in most of the Conformer Modules + dropout_pre_encoder: 0.1 # The dropout used before the encoder + dropout_emb: 0.0 # The dropout used for embeddings + dropout_att: 0.1 # The dropout for multi-headed attention modules + + decoder: + _target_: nemo.collections.asr.modules.ConvASRDecoder + feat_in: null # will be filled during training based on encoder model dim + num_classes: -1 + vocabulary: null # will be filled during training + + tokenizer: + dir: null # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe) + type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer) + + train_ds: + manifest_filepath: ${train_manifest} + dataset: + _target_: "nemo.collections.tts.g2p.data.ctc.CTCG2PBPEDataset" + phoneme_field: "text" # name of the field in manifest_filepath for ground truth phonemes + grapheme_field: "text_graphemes" # name of the field in manifest_filepath for input grapheme text + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 4 + + validation_ds: + manifest_filepath: ${validation_manifest} + dataset: + _target_: "nemo.collections.tts.g2p.data.ctc.CTCG2PBPEDataset" + phoneme_field: "text" # name of the field in manifest_filepath for ground truth phonemes + grapheme_field: "text_graphemes" # name of the field in manifest_filepath for input grapheme text + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 4 + + test_ds: + manifest_filepath: ${test_manifest} + dataset: + _target_: "nemo.collections.tts.g2p.data.ctc.CTCG2PBPEDataset" + phoneme_field: "text" # name of the field in manifest_filepath for ground truth phonemes + grapheme_field: "text_graphemes" # name of the field in manifest_filepath for input grapheme text + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 0 + + optim: + name: adamw + lr: 2.0 + # optimizer arguments + betas: [ 0.9, 0.98 ] + # less necessity for weight_decay as we already have large augmentations with SpecAug + # you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used + # weight decay of 0.0 with lr of 2.0 also works fine + weight_decay: 1e-3 + + # scheduler setup + sched: + name: NoamAnnealing + d_model: ${model.encoder.d_model} + # scheduler config override + warmup_steps: 10000 + warmup_ratio: null + min_lr: 1e-6 + +trainer: + devices: 1 # number of gpus + max_epochs: 5 + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 200 + check_val_every_n_epoch: 1 + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + save_top_k: 1 + monitor: "val_per" + mode: "min" + save_best_model: true diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/g2p/conf/g2p_heteronym_classification.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/conf/g2p_heteronym_classification.yaml new file mode 100644 index 0000000..31f39d8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/conf/g2p_heteronym_classification.yaml @@ -0,0 +1,104 @@ +name: HeteronymClassification + +# Dataset info +train_manifest: ??? +validation_manifest: ??? +test_manifest: null +do_training: True +do_testing: False +pretrained_model: null # path to .nemo file or model name from list_available_models() + +model: + wordids: ??? # path to wordids in WikiHomograph dataset format + max_seq_length: 256 # the maximum length BERT supports is 512 + label_ids: null # will be filled during training + class_labels: + class_labels_file: null # will be generated during training and saved in .nemo file + + language_model: + pretrained_model_name: bert-base-uncased # currently supports BERT or Distill BERT + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + + head: + num_fc_layers: 2 + fc_dropout: 0.5 + activation: 'relu' + use_transformer_init: True + + train_ds: + dataset: + _target_: "nemo.collections.tts.g2p.data.heteronym_classification.HeteronymClassificationDataset" + manifest: ${train_manifest} + grapheme_field: "text_graphemes" # name of the field in manifest for input grapheme text + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 0 + + validation_ds: + dataset: + _target_: "nemo.collections.tts.g2p.data.heteronym_classification.HeteronymClassificationDataset" + manifest: ${validation_manifest} + grapheme_field: "text_graphemes" # name of the field in manifest for input grapheme text + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 0 + + test_ds: + dataset: + _target_: "nemo.collections.tts.g2p.data.heteronym_classification.HeteronymClassificationDataset" + manifest: ${test_manifest} + grapheme_field: "text_graphemes" # name of the field in manifest for input grapheme text + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 #64 + num_workers: 0 + + optim: + name: adamw + lr: 5e-5 + weight_decay: 0.01 + # scheduler setup + sched: + name: WarmupAnnealing + # pytorch lightning args + reduce_on_plateau: false + # scheduler config override + warmup_steps: null + warmup_ratio: 0.1 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 10 + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 200 + check_val_every_n_epoch: 1 + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + save_top_k: 1 + monitor: "val_loss" + mode: "min" + save_best_model: true diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/g2p/conf/g2p_t5.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/conf/g2p_t5.yaml new file mode 100644 index 0000000..49082f0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/conf/g2p_t5.yaml @@ -0,0 +1,92 @@ +name: T5G2P + +# Dataset info +train_manifest: ??? +validation_manifest: ??? +test_manifest: null +do_training: True +do_testing: False +pretrained_model: null # path to .nemo file or model name from list_available_models() + +model: + model_name: "google/byt5-small" # One of: google/byt5-small/base/large/xl or t5-small/base/large/3b/11b + max_source_len: 256 + max_target_len: 512 + do_lower: false + + train_ds: + manifest_filepath: ${train_manifest} + dataset: + _target_: "nemo.collections.tts.g2p.data.t5.T5G2PDataset" + phoneme_field: "text" # name of the field in manifest_filepath for ground truth phonemes + grapheme_field: "text_graphemes" # name of the field in manifest_filepath for input grapheme text + dataloader_params: + drop_last: false + shuffle: true + batch_size: 20 + num_workers: 4 + + validation_ds: + manifest_filepath: ${validation_manifest} + dataset: + _target_: "nemo.collections.tts.g2p.data.t5.T5G2PDataset" + phoneme_field: "text" # name of the field in manifest_filepath for ground truth phonemes + grapheme_field: "text_graphemes" # name of the field in manifest_filepath for input grapheme text + dataloader_params: + drop_last: false + shuffle: false + batch_size: 20 + num_workers: 4 + + test_ds: + manifest_filepath: ${test_manifest} + dataset: + _target_: "nemo.collections.tts.g2p.data.t5.T5G2PDataset" + phoneme_field: "text" # name of the field in manifest_filepath for ground truth phonemes + grapheme_field: "text_graphemes" # name of the field in manifest_filepath for input grapheme text + dataloader_params: + drop_last: false + shuffle: false + batch_size: 20 + num_workers: 4 + + optim: + name: adamw + lr: 2e-4 + weight_decay: 0.01 + # scheduler setup + sched: + name: WarmupAnnealing + + # pytorch lightning args + monitor: val_token_precision + reduce_on_plateau: false + + # scheduler config override + warmup_steps: null + warmup_ratio: 0.1 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 5 + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 200 + check_val_every_n_epoch: 1 + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + save_top_k: 1 + monitor: "val_per" + mode: "min" + save_best_model: true + diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/g2p/conf/heteronym_classification_zh.yaml b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/conf/heteronym_classification_zh.yaml new file mode 100644 index 0000000..f0ca98e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/conf/heteronym_classification_zh.yaml @@ -0,0 +1,106 @@ +name: HeteronymClassification + +# Dataset info +# Chinese Polyphones with Pinyin (CPP) +# https://github.com/kakaobrain/g2pM#the-cpp-dataset +train_manifest: ??? +validation_manifest: ??? +test_manifest: ??? +do_training: True +do_testing: False +pretrained_model: null # path to .nemo file or model name from list_available_models() + +model: + wordids: ??? # path to CPP wordids in WikiHomograph dataset format e.g. ./cpp_manifest/wordid.tsv + max_seq_length: 256 # the maximum length BERT supports is 512 + label_ids: null # will be filled during training + class_labels: + class_labels_file: null # will be generated during training and saved in .nemo file + + language_model: + pretrained_model_name: bert-base-chinese # https://huggingface.co/bert-base-chinese/tree/main + lm_checkpoint: null + config_file: null # json file, precedence over config + config: null + + tokenizer: + tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece + vocab_file: null # path to vocab file + tokenizer_model: null # only used if tokenizer is sentencepiece + special_tokens: null + + head: + num_fc_layers: 2 + fc_dropout: 0.5 + activation: 'relu' + use_transformer_init: True + + train_ds: + dataset: + _target_: "nemo.collections.tts.g2p.data.heteronym_classification_data.HeteronymClassificationDataset" + manifest: ${train_manifest} + grapheme_field: "text_graphemes" # name of the field in manifest for input grapheme text + dataloader_params: + drop_last: false + shuffle: true + batch_size: 32 + num_workers: 0 + + validation_ds: + dataset: + _target_: "nemo.collections.tts.g2p.data.heteronym_classification_data.HeteronymClassificationDataset" + manifest: ${validation_manifest} + grapheme_field: "text_graphemes" # name of the field in manifest for input grapheme text + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 + num_workers: 0 + + test_ds: + dataset: + _target_: "nemo.collections.tts.g2p.data.heteronym_classification_data.HeteronymClassificationDataset" + manifest: ${test_manifest} + grapheme_field: "text_graphemes" # name of the field in manifest for input grapheme text + dataloader_params: + drop_last: false + shuffle: false + batch_size: 32 #64 + num_workers: 0 + + optim: + name: adamw + lr: 5e-5 + weight_decay: 0.01 + # scheduler setup + sched: + name: WarmupAnnealing + # pytorch lightning args + reduce_on_plateau: false + # scheduler config override + warmup_steps: null + warmup_ratio: 0.1 + last_epoch: -1 + +trainer: + devices: 1 # number of gpus + max_epochs: 10 + num_nodes: 1 + accelerator: gpu + strategy: ddp + accumulate_grad_batches: 1 + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 200 + check_val_every_n_epoch: 1 + +exp_manager: + exp_dir: null + name: ${name} + create_tensorboard_logger: True + create_checkpoint_callback: True + checkpoint_callback_params: + save_top_k: 1 + monitor: "val_loss" + mode: "min" + save_best_model: true diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/g2p/g2p_heteronym_classification_inference.py b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/g2p_heteronym_classification_inference.py new file mode 100644 index 0000000..61262c4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/g2p_heteronym_classification_inference.py @@ -0,0 +1,183 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import json +import os +from dataclasses import dataclass, is_dataclass +from typing import Optional + +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf + +from nemo.collections.tts.g2p.models.heteronym_classification import HeteronymClassificationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging + +""" +This script runs inference with HeteronymClassificationModel +If the input manifest contains target "word_id", evaluation will be also performed. + +To prepare dataset, see NeMo/scripts/dataset_processing/g2p/export_wikihomograph_data_to_manifest.py + +Inference form manifest: + +python g2p_heteronym_classification_inference.py \ + manifest="" \ + pretrained_model="" \ + output_manifest="" \ + wordid_to_phonemes_file="" + +Interactive inference: + +python g2p_heteronym_classification_inference.py \ + pretrained_model="" \ + wordid_to_phonemes_file="" # Optional + +""" + + +@dataclass +class TranscriptionConfig: + # Required configs + pretrained_model: str # Path to a .nemo file or Name of a pretrained model + + # path to .json manifest inference, if not provided, interactive mode will be enabled + manifest: Optional[str] = None # Path to .json manifest + output_manifest: Optional[ + str + ] = "predictions.json" # Path to .json manifest to save prediction, will be saved in "pred_text" field + grapheme_field: str = "text_graphemes" # name of the field in .json manifest for input grapheme text + + # mapping from wordid predicted by the model to phonemes, e.g., + # "../../../scripts/tts_dataset_files/wordid_to_ipa-0.7b_nv22.10.tsv" + wordid_to_phonemes_file: Optional[str] = None + + # if "word_id" targets are present in the manifest, evaluation will be performed and errors will be saved in errors_file + errors_file: Optional[str] = None # path to a file to save prediction errors + batch_size: int = 32 + num_workers: int = 0 + + +@hydra_runner(config_name="TranscriptionConfig", schema=TranscriptionConfig) +def main(cfg): + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if not cfg.pretrained_model: + raise ValueError( + 'To run evaluation and inference script a pre-trained model or .nemo file must be provided.' + f'Choose from {HeteronymClassificationModel.list_available_models()} or "pretrained_model"="your_model.nemo"' + ) + + logging.info( + 'During evaluation/testing, it is currently advisable to construct a new Trainer with single GPU and \ + no DDP to obtain accurate results' + ) + + # setup GPU + if torch.cuda.is_available(): + device = [0] # use 0th CUDA device + accelerator = 'gpu' + else: + device = 1 + accelerator = 'cpu' + + map_location = torch.device('cuda:{}'.format(device[0]) if accelerator == 'gpu' else 'cpu') + trainer = pl.Trainer(devices=device, accelerator=accelerator, logger=False, enable_checkpointing=False) + + if os.path.exists(cfg.pretrained_model): + model = HeteronymClassificationModel.restore_from(cfg.pretrained_model, map_location=map_location) + elif cfg.pretrained_model in HeteronymClassificationModel.get_available_model_names(): + model = HeteronymClassificationModel.from_pretrained(cfg.pretrained_model, map_location=map_location) + else: + raise ValueError( + f'Provide path to the pre-trained .nemo checkpoint or choose from {HeteronymClassificationModel.list_available_models()}' + ) + model.set_trainer(trainer) + model = model.eval() + + logging.info(f'Config Params: {model._cfg}') + + if cfg.manifest is not None: + if not os.path.exists(cfg.manifest): + raise ValueError(f"{cfg.manifest} not found.") + with torch.no_grad(): + model.disambiguate_manifest( + manifest=cfg.manifest, + output_manifest=cfg.output_manifest, + grapheme_field=cfg.grapheme_field, + batch_size=cfg.batch_size, + num_workers=cfg.num_workers, + ) + + # save predictions to a file + if cfg.errors_file is None: + cfg.errors_file = cfg.output_manifest.replace(".json", "_errors.txt") + + save_errors = True + correct = 0 + total = 0 + with open(cfg.output_manifest, "r", encoding="utf-8") as f_preds, open( + cfg.errors_file, "w", encoding="utf-8" + ) as f_errors: + for line in f_preds: + line = json.loads(line) + predictions = line["pred_wordid"] + # run evaluation if target word_id is available in the input manifest + if "word_id" in line: + targets = line["word_id"] + if isinstance(targets, str): + targets = [targets] + for idx, target_ in enumerate(targets): + total += 1 + if idx >= len(predictions) or target_ != predictions[idx]: + f_errors.write(f"INPUT: {line[cfg.grapheme_field]}\n") + f_errors.write(f"PRED : {predictions[idx]} -- GT: {target_}\n") + f_errors.write("===========================\n") + else: + correct += 1 + else: + save_errors = False + if save_errors: + logging.info(f"Accuracy: {round(correct / total * 100, 2)}% ({total - correct} errors out of {total})") + logging.info(f"Errors saved at {cfg.errors_file}") + else: + logging.info("No 'word_id' values found, skipping evaluation.") + if os.path.exists(cfg.errors_file): + os.remove(cfg.errors_file) + else: + print('Entering interactive mode.') + done = False + while not done: + print('Type "STOP" to exit.') + test_input = input('Input a test input:') + if test_input == "STOP": + done = True + if not done: + with torch.no_grad(): + _, sentences = model.disambiguate( + sentences=[test_input], + batch_size=1, + num_workers=cfg.num_workers, + wordid_to_phonemes_file=cfg.wordid_to_phonemes_file, + ) + print(sentences[0]) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/g2p/g2p_heteronym_classification_train_and_evaluate.py b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/g2p_heteronym_classification_train_and_evaluate.py new file mode 100644 index 0000000..6138656 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/g2p_heteronym_classification_train_and_evaluate.py @@ -0,0 +1,117 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import pytorch_lightning as pl +import torch + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.g2p.models.heteronym_classification import HeteronymClassificationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + +""" +This script runs training and evaluation of HeteronymClassificationModel + +To prepare dataset, see NeMo/scripts/dataset_processing/g2p/export_wikihomograph_data_to_manifest.py + +To run training: +python g2p_heteronym_classification_train_and_evaluate.py \ + train_manifest=" \ + validation_manifest=" \ + model.wordids="" \ + do_training=True + +To run training and testing (once the training is complete): +python g2p_heteronym_classification_train_and_evaluate.py \ + train_manifest=" \ + validation_manifest=" \ + model.test_ds.dataset.manifest=" \ + model.wordids="" \ + do_training=True \ + do_testing=True + +To run testing: +python g2p_heteronym_classification_train_and_evaluate.py \ + do_training=False \ + do_testing=True \ + model.test_ds.dataset.manifest=" \ + pretrained_model= + + +See https://github.com/google-research-datasets/WikipediaHomographData/blob/master/data/wordids.tsv for wordids file +format example + +See https://github.com/NVIDIA/NeMo/blob/main/scripts/dataset_processing/g2p/export_wikihomograph_data_to_manifest.py +on how to convert WikiHomograph data for HeteronymClassificationModel training/evaluation +""" + + +@hydra_runner(config_path="conf", config_name="g2p_heteronym_classification.yaml") +def main(cfg): + # PTL 2.0 has find_unused_parameters as False by default, so its required to set it to True + # when there are unused parameters like in this model + if cfg.trainer.strategy == 'ddp': + cfg.trainer.strategy = "ddp_find_unused_parameters_true" + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + model = None + if cfg.do_training: + model = HeteronymClassificationModel(cfg=cfg.model, trainer=trainer) + lr_logger = pl.callbacks.LearningRateMonitor() + epoch_time_logger = LogEpochTimeCallback() + trainer.callbacks.extend([lr_logger, epoch_time_logger]) + trainer.fit(model) + logging.info("Training is complete") + + if cfg.do_testing: + logging.info( + 'During evaluation/testing, it is currently advisable to construct a new Trainer with single GPU and \ + no DDP to obtain accurate results' + ) + # setup GPU + if torch.cuda.is_available(): + device = [0] # use 0th CUDA device + accelerator = 'gpu' + else: + device = 1 + accelerator = 'cpu' + + map_location = torch.device('cuda:{}'.format(device[0]) if accelerator == 'gpu' else 'cpu') + trainer = pl.Trainer(devices=device, accelerator=accelerator, logger=False, enable_checkpointing=False) + + if model is None: + if os.path.exists(cfg.pretrained_model): + # restore model from .nemo file path + model = HeteronymClassificationModel.restore_from(restore_path=cfg.pretrained_model) + elif cfg.pretrained_model in HeteronymClassificationModel.get_available_model_names(): + # restore model by name + model = HeteronymClassificationModel.from_pretrained(cfg.pretrained_model, map_location=map_location) + else: + raise ValueError( + f'Provide path to the pre-trained .nemo checkpoint or choose from {HeteronymClassificationModel.list_available_models()}' + ) + + if hasattr(cfg.model, "test_ds") and cfg.model.test_ds.dataset.manifest is not None: + model.setup_test_data(cfg.model.test_ds) + trainer.test(model) + else: + logging.info("test_ds not found, skipping evaluation") + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/g2p/g2p_inference.py b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/g2p_inference.py new file mode 100644 index 0000000..e7bffa8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/g2p_inference.py @@ -0,0 +1,123 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from dataclasses import dataclass, is_dataclass +from typing import Optional + +import pytorch_lightning as pl +import torch +from omegaconf import OmegaConf +from utils import get_metrics + +from nemo.collections.tts.models.base import G2PModel +from nemo.core.config import hydra_runner +from nemo.utils import logging + +""" +python g2p_inference.py \ + pretrained_model=" \ + manifest_filepath="" \ + output_file="" \ + batch_size=32 \ + num_workers=4 \ + pred_field=pred_text +""" + + +@dataclass +class TranscriptionConfig: + # Required configs + pretrained_model: str # Path to a .nemo file or Name of a pretrained model + manifest_filepath: str # Path to .json manifest file + phoneme_field: Optional[ + str + ] = None # name of the field in manifest_filepath for ground truth phonemes, default during training "text" + grapheme_field: Optional[str] = "text_graphemes" # name of the field in manifest_filepath for input grapheme text + + # General configs + output_file: Optional[ + str + ] = None # Path to .json manifest file to save predictions, will be saved in "target_field" + pred_field: Optional[str] = "pred_text" # name of the field in the output_file to save predictions + batch_size: int = 32 # Batch size to use for inference + num_workers: int = 0 # Number of workers to use for DataLoader during inference + + # Config for heteronyms correction + pretrained_heteronyms_model: Optional[ + str + ] = None # Path to a .nemo file or a Name of a pretrained model to disambiguate heteronyms (Optional) + + +@hydra_runner(config_name="TranscriptionConfig", schema=TranscriptionConfig) +def main(cfg: TranscriptionConfig) -> TranscriptionConfig: + logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') + + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if not cfg.pretrained_model: + raise ValueError( + 'To run evaluation and inference script a pre-trained model or .nemo file must be provided.' + f'Choose from {G2PModel.list_available_models()} or "pretrained_model"="your_model.nemo"' + ) + + logging.info( + 'During evaluation/testing, it is currently advisable to construct a new Trainer with single GPU and \ + no DDP to obtain accurate results' + ) + + # setup GPU + if torch.cuda.is_available(): + device = [0] # use 0th CUDA device + accelerator = 'gpu' + else: + device = 1 + accelerator = 'cpu' + + map_location = torch.device('cuda:{}'.format(device[0]) if accelerator == 'gpu' else 'cpu') + trainer = pl.Trainer(devices=device, accelerator=accelerator, logger=False, enable_checkpointing=False) + + if os.path.exists(cfg.pretrained_model): + model = G2PModel.restore_from(cfg.pretrained_model, map_location=map_location) + elif cfg.pretrained_model in G2PModel.get_available_model_names(): + model = G2PModel.from_pretrained(cfg.pretrained_model, map_location=map_location) + else: + raise ValueError( + f'Provide path to the pre-trained .nemo checkpoint or choose from {G2PModel.list_available_models()}' + ) + model._cfg.max_source_len = 512 + model.set_trainer(trainer) + model = model.eval() + + if cfg.output_file is None: + cfg.output_file = cfg.manifest_filepath.replace(".json", "_phonemes.json") + + with torch.no_grad(): + model.convert_graphemes_to_phonemes( + manifest_filepath=cfg.manifest_filepath, + output_manifest_filepath=cfg.output_file, + grapheme_field=cfg.grapheme_field, + batch_size=cfg.batch_size, + num_workers=cfg.num_workers, + pred_field=cfg.pred_field, + ) + print(f"IPA predictions saved in {cfg.output_file}") + + if cfg.phoneme_field is not None: + get_metrics(cfg.output_file, phoneme_field=cfg.phoneme_field, grapheme_field=cfg.grapheme_field) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/g2p/g2p_train_and_evaluate.py b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/g2p_train_and_evaluate.py new file mode 100644 index 0000000..ff7b2b0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/g2p_train_and_evaluate.py @@ -0,0 +1,121 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import pytorch_lightning as pl +import torch +from utils import get_model + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models.base import G2PModel +from nemo.core.config import hydra_runner +from nemo.utils import logging, model_utils +from nemo.utils.exp_manager import exp_manager + +""" +This script supports training of G2PModels +(for T5G2PModel use g2p_t5.yaml, for CTCG2PModel use either g2p_conformer.yaml or g2p_t5_ctc.yaml) + +# Training T5G2PModel and evaluation at the end of training: + python examples/text_processing/g2p/g2p_train_and_evaluate.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath="" \ + model.test_ds.manifest_filepath="" \ + trainer.devices=1 \ + do_training=True \ + do_testing=True + + Example of the config file: NeMo/examples/tts/g2p/conf/g2p_t5.yaml + +# Training Conformer-G2P Model and evaluation at the end of training: + python examples/text_processing/g2p/g2p_train_and_evaluate.py \ + # (Optional: --config-path= --config-name=) \ + model.train_ds.manifest_filepath="" \ + model.validation_ds.manifest_filepath="" \ + model.test_ds.manifest_filepath="" \ + model.tokenizer.dir= \ + trainer.devices=1 \ + do_training=True \ + do_testing=True + + Example of the config file: NeMo/examples/text_processing/g2p/conf/g2p_conformer_ctc.yaml + +# Run evaluation of the pretrained model: + python examples/text_processing/g2p/g2p_train_and_evaluate.py \ + # (Optional: --config-path= --config-name=) \ + pretrained_model="" \ + model.test_ds.manifest_filepath="" \ + trainer.devices=1 \ + do_training=False \ + do_testing=True +""" + + +@hydra_runner(config_path="conf", config_name="g2p_t5") +def main(cfg): + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + + g2p_model = None + if cfg.do_training: + g2p_model = get_model(cfg, trainer) + lr_logger = pl.callbacks.LearningRateMonitor() + epoch_time_logger = LogEpochTimeCallback() + trainer.callbacks.extend([lr_logger, epoch_time_logger]) + trainer.fit(g2p_model) + + if cfg.do_testing: + logging.info( + 'During evaluation/testing, it is currently advisable to construct a new Trainer with single GPU and \ + no DDP to obtain accurate results' + ) + # setup GPU + if torch.cuda.is_available(): + device = [0] # use 0th CUDA device + accelerator = 'gpu' + else: + device = 1 + accelerator = 'cpu' + + map_location = torch.device('cuda:{}'.format(device[0]) if accelerator == 'gpu' else 'cpu') + trainer = pl.Trainer(devices=device, accelerator=accelerator, logger=False, enable_checkpointing=False) + + if g2p_model is None: + if os.path.exists(cfg.pretrained_model): + # restore g2p_model from .nemo file path + model_cfg = G2PModel.restore_from(restore_path=cfg.pretrained_model, return_config=True) + classpath = model_cfg.target # original class path + imported_class = model_utils.import_class_by_path(classpath) + logging.info(f"Restoring g2p_model : {imported_class.__name__}") + g2p_model = imported_class.restore_from(restore_path=cfg.pretrained_model, map_location=map_location) + model_name = os.path.splitext(os.path.basename(cfg.pretrained_model))[0] + logging.info(f"Restored {model_name} g2p_model from {cfg.pretrained_model}.") + elif cfg.pretrained_model in G2PModel.get_available_model_names(): + # restore g2p_model by name + g2p_model = G2PModel.from_pretrained(cfg.pretrained_model, map_location=map_location) + else: + raise ValueError( + f'Provide path to the pre-trained .nemo checkpoint or choose from {G2PModel.list_available_models()}' + ) + + if hasattr(cfg.model, "test_ds") and cfg.model.test_ds.manifest_filepath is not None: + g2p_model.setup_multiple_test_data(cfg.model.test_ds) + if g2p_model.prepare_test(trainer): + trainer.test(g2p_model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/g2p/utils.py b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/utils.py new file mode 100644 index 0000000..d1870dd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/g2p/utils.py @@ -0,0 +1,93 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json + +from nemo.collections.asr.metrics.wer import word_error_rate +from nemo.collections.tts.g2p.models.ctc import CTCG2PModel +from nemo.collections.tts.g2p.models.t5 import T5G2PModel +from nemo.utils import logging + + +def get_model(cfg, trainer): + """ + Get model instance + + Args: + cfg: model's config file + trainer: trainer + Return: + G2PModel instance + """ + if "CTC" in cfg.name: + model = CTCG2PModel(cfg=cfg.model, trainer=trainer) + elif cfg.name == "T5G2P": + model = T5G2PModel(cfg=cfg.model, trainer=trainer) + else: + raise ValueError(f"{cfg.name} is not supported. Choose from [G2P-Conformer-CTC, T5G2P]") + return model + + +def get_metrics(manifest: str, pred_field="pred_text", phoneme_field="text", grapheme_field="text_graphemes"): + """ + Calculates WER and PER metrics (for duplicated grapheme entries with multiple reference values, + the best matching prediction will be used for evaluation.) + + Args: + manifest: Path to .json manifest file + pred_field: name of the field in the output_file to save predictions + phoneme_field: name of the field in manifest_filepath for ground truth phonemes + grapheme_field: name of the field in manifest_filepath for input grapheme text + + Returns: WER and PER values + """ + all_preds = [] + all_references = [] + all_graphemes = {} + with open(manifest, "r") as f: + for i, line in enumerate(f): + line = json.loads(line) + all_preds.append(line[pred_field]) + all_references.append(line[phoneme_field]) + + if line[grapheme_field] not in all_graphemes: + all_graphemes[line[grapheme_field]] = [] + all_graphemes[line[grapheme_field]].append(i) + + # collect all examples with multiple phoneme options and same grapheme form, choose the one with min PER + all_graphemes = {k: v for k, v in all_graphemes.items() if len(v) > 1} + lines_to_drop = [] + for phon_amb_indices in all_graphemes.values(): + refs, preds = [], [] + for phon_amb_indices_ in phon_amb_indices: + refs.append(all_references[phon_amb_indices_]) + preds.append(all_preds[phon_amb_indices_]) + pers = [] + for ref_, pred_ in zip(refs, preds): + pers.append(word_error_rate(hypotheses=[pred_], references=[ref_], use_cer=True)) + + min_idx = pers.index(min(pers)) + + phon_amb_indices.pop(min_idx) + lines_to_drop.extend(phon_amb_indices) + + # drop duplicated examples, only keep with min PER + all_preds = [x for i, x in enumerate(all_preds) if i not in lines_to_drop] + all_references = [x for i, x in enumerate(all_references) if i not in lines_to_drop] + + wer = word_error_rate(hypotheses=all_preds, references=all_references) + per = word_error_rate(hypotheses=all_preds, references=all_references, use_cer=True) + + logging.info(f"{manifest}: PER: {per * 100:.2f}%, WER: {wer * 100:.2f}%, lines: {len(all_references)}") + return wer, per diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/hifigan.py b/NeMo-2.0.0.rc0.beta/examples/tts/hifigan.py new file mode 100644 index 0000000..5c3406a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/hifigan.py @@ -0,0 +1,31 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.tts.models import HifiGanModel +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf/hifigan", config_name="hifigan") +def main(cfg): + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + model = HifiGanModel(cfg=cfg.model, trainer=trainer) + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/hifigan_finetune.py b/NeMo-2.0.0.rc0.beta/examples/tts/hifigan_finetune.py new file mode 100644 index 0000000..f0e2513 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/hifigan_finetune.py @@ -0,0 +1,32 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.tts.models import HifiGanModel +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf/hifigan", config_name="hifigan_44100") +def main(cfg): + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + model = HifiGanModel(cfg=cfg.model, trainer=trainer) + model.maybe_init_from_pretrained_checkpoint(cfg=cfg) + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/mixer_tts.py b/NeMo-2.0.0.rc0.beta/examples/tts/mixer_tts.py new file mode 100644 index 0000000..61a188f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/mixer_tts.py @@ -0,0 +1,33 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models import MixerTTSModel +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path='conf', config_name='mixer-tts') +def main(cfg): + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get('exp_manager', None)) + model = MixerTTSModel(cfg=cfg.model, trainer=trainer) + trainer.callbacks.extend([pl.callbacks.LearningRateMonitor(), LogEpochTimeCallback()]) # noqa + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/radtts.py b/NeMo-2.0.0.rc0.beta/examples/tts/radtts.py new file mode 100644 index 0000000..7dbdaed --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/radtts.py @@ -0,0 +1,75 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models.radtts import RadTTSModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +def freeze(model): + for p in model.parameters(): + p.requires_grad = False + + +def unfreeze(model): + for p in model.parameters(): + p.requires_grad = True + + +def prepare_model_weights(model, unfreeze_modules): + if unfreeze_modules != 'all': + model.freeze() # freeze everything + logging.info("module freezed, about to unfreeze modules to be trained") + if 'dur' in unfreeze_modules and hasattr(model.model, 'dur_pred_layer'): + logging.info("Training duration prediction") + unfreeze(model.model.dur_pred_layer) + if 'f0' in unfreeze_modules and hasattr(model.model, 'f0_pred_module'): + logging.info("Training F0 prediction") + unfreeze(model.model.f0_pred_module) + if 'energy' in unfreeze_modules and hasattr(model.model, 'energy_pred_module'): + logging.info("Training energy prediction") + unfreeze(model.model.energy_pred_module) + if 'vpred' in unfreeze_modules and hasattr(model.model, 'v_pred_module'): + logging.info("Training voiced prediction") + unfreeze(model.model.v_pred_module) + if hasattr(model, 'v_embeddings'): + logging.info("Training voiced embeddings") + unfreeze(model.model.v_embeddings) + if 'unvbias' in unfreeze_modules and hasattr(model.model, 'unvoiced_bias_module'): + logging.info("Training unvoiced bias") + unfreeze(model.model.unvoiced_bias_module) + else: + logging.info("Training everything") + + +@hydra_runner(config_path="conf", config_name="rad-tts_dec") +def main(cfg): + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get('exp_manager', None)) + model = RadTTSModel(cfg=cfg.model, trainer=trainer).cuda() + if cfg.model.load_from_checkpoint: + model.maybe_init_from_pretrained_checkpoint(cfg=cfg.model) + prepare_model_weights(model, cfg.model.trainerConfig.unfreeze_modules) + lr_logger = pl.callbacks.LearningRateMonitor() + epoch_time_logger = LogEpochTimeCallback() + trainer.callbacks.extend([lr_logger, epoch_time_logger]) + trainer.fit(model.cuda()) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/spectrogram_enhancer.py b/NeMo-2.0.0.rc0.beta/examples/tts/spectrogram_enhancer.py new file mode 100644 index 0000000..3367292 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/spectrogram_enhancer.py @@ -0,0 +1,33 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.tts.models import SpectrogramEnhancerModel +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="spectrogram-enhancer") +def main(cfg): + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg=cfg.get("exp_manager", None)) + model = SpectrogramEnhancerModel(cfg=cfg.model, trainer=trainer) + lr_logger = pl.callbacks.LearningRateMonitor() + trainer.callbacks.extend([lr_logger]) + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/ssl_tts.py b/NeMo-2.0.0.rc0.beta/examples/tts/ssl_tts.py new file mode 100644 index 0000000..a96dccb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/ssl_tts.py @@ -0,0 +1,36 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models import ssl_tts +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="ssl_tts_22050") +def main(cfg): + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + model = ssl_tts.SSLDisentangler(cfg=cfg.model, trainer=trainer) + model.maybe_init_from_pretrained_checkpoint(cfg=cfg) + lr_logger = pl.callbacks.LearningRateMonitor() + epoch_time_logger = LogEpochTimeCallback() + trainer.callbacks.extend([lr_logger, epoch_time_logger]) + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/tacotron2.py b/NeMo-2.0.0.rc0.beta/examples/tts/tacotron2.py new file mode 100755 index 0000000..a5446c3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/tacotron2.py @@ -0,0 +1,44 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models import Tacotron2Model +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +# hydra_runner is a thin NeMo wrapper around Hydra +# It looks for a config named tacotron2.yaml inside the conf folder +# Hydra parses the yaml and returns it as a Omegaconf DictConfig +@hydra_runner(config_path="conf", config_name="tacotron2") +def main(cfg): + # Define the Lightning trainer + trainer = pl.Trainer(**cfg.trainer) + # exp_manager is a NeMo construct that helps with logging and checkpointing + exp_manager(trainer, cfg.get("exp_manager", None)) + # Define the Tacotron 2 model, this will construct the model as well as + # define the training and validation dataloaders + model = Tacotron2Model(cfg=cfg.model, trainer=trainer) + # Let's add a few more callbacks + lr_logger = pl.callbacks.LearningRateMonitor() + epoch_time_logger = LogEpochTimeCallback() + trainer.callbacks.extend([lr_logger, epoch_time_logger]) + # Call lightning trainer's fit() to train the model + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/tacotron2_finetune.py b/NeMo-2.0.0.rc0.beta/examples/tts/tacotron2_finetune.py new file mode 100644 index 0000000..a0531f1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/tacotron2_finetune.py @@ -0,0 +1,45 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models import Tacotron2Model +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +# hydra_runner is a thin NeMo wrapper around Hydra +# It looks for a config named tacotron2.yaml inside the conf folder +# Hydra parses the yaml and returns it as a Omegaconf DictConfig +@hydra_runner(config_path="conf", config_name="tacotron2_44100") +def main(cfg): + # Define the Lightning trainer + trainer = pl.Trainer(**cfg.trainer) + # exp_manager is a NeMo construct that helps with logging and checkpointing + exp_manager(trainer, cfg.get("exp_manager", None)) + # Define the Tacotron 2 model, this will construct the model as well as + # define the training and validation dataloaders + model = Tacotron2Model(cfg=cfg.model, trainer=trainer) + model.maybe_init_from_pretrained_checkpoint(cfg=cfg) + # Let's add a few more callbacks + lr_logger = pl.callbacks.LearningRateMonitor() + epoch_time_logger = LogEpochTimeCallback() + trainer.callbacks.extend([lr_logger, epoch_time_logger]) + # Call lightning trainer's fit() to train the model + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/test_tts_infer.py b/NeMo-2.0.0.rc0.beta/examples/tts/test_tts_infer.py new file mode 100644 index 0000000..3b707ff --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/test_tts_infer.py @@ -0,0 +1,153 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script is used as a CI test and shows how to chain TTS and ASR models +""" + +from argparse import ArgumentParser +from math import ceil +from pathlib import Path + +import librosa +import soundfile +import torch + +from nemo.collections.asr.metrics.wer import word_error_rate +from nemo.collections.asr.models import EncDecCTCModel +from nemo.collections.common.parts.preprocessing import parsers +from nemo.collections.tts.models.base import SpectrogramGenerator, Vocoder +from nemo.utils import logging + +LIST_OF_TEST_STRINGS = [ + "Hey, this is a test of the speech synthesis system.", + "roupell received the announcement with a cheerful countenance.", + "with thirteen dollars, eighty-seven cents when considerably greater resources were available to him.", + "Two other witnesses were able to offer partial descriptions of a man they saw in the southeast corner window.", + "'just to steady their legs a little' in other words, to add his weight to that of the hanging bodies.", + "The discussion above has already set forth examples of his expression of hatred for the United States.", + "At two:thirty-eight p.m., Eastern Standard Time, Lyndon Baines Johnson took the oath of office as the thirty-sixth President of the United States.", + "or, quote, other high government officials in the nature of a complaint coupled with an expressed or implied determination to use a means.", + "As for my return entrance visa please consider it separately. End quote.", + "it appears that Marina Oswald also complained that her husband was not able to provide more material things for her.", + "appeared in The Dallas Times Herald on November fifteen, nineteen sixty-three.", + "The only exit from the office in the direction Oswald was moving was through the door to the front stairway.", +] + + +def main(): + parser = ArgumentParser() + parser.add_argument( + "--asr_model", + type=str, + default="QuartzNet15x5Base-En", + choices=[x.pretrained_model_name for x in EncDecCTCModel.list_available_models()], + ) + parser.add_argument( + "--tts_model_spec", + type=str, + default="tts_en_tacotron2", + choices=[x.pretrained_model_name for x in SpectrogramGenerator.list_available_models()], + ) + parser.add_argument( + "--tts_model_vocoder", + type=str, + default="tts_en_waveglow_88m", + choices=[x.pretrained_model_name for x in Vocoder.list_available_models()], + ) + parser.add_argument("--wer_tolerance", type=float, default=1.0, help="used by test") + parser.add_argument("--trim", action="store_true") + parser.add_argument("--debug", action="store_true") + args = parser.parse_args() + torch.set_grad_enabled(False) + + if args.debug: + logging.set_verbosity(logging.DEBUG) + + logging.info(f"Using NGC cloud ASR model {args.asr_model}") + asr_model = EncDecCTCModel.from_pretrained(model_name=args.asr_model) + logging.info(f"Using NGC cloud TTS Spectrogram Generator model {args.tts_model_spec}") + tts_model_spec = SpectrogramGenerator.from_pretrained(model_name=args.tts_model_spec) + logging.info(f"Using NGC cloud TTS Vocoder model {args.tts_model_vocoder}") + tts_model_vocoder = Vocoder.from_pretrained(model_name=args.tts_model_vocoder) + models = [asr_model, tts_model_spec, tts_model_vocoder] + + if torch.cuda.is_available(): + for i, m in enumerate(models): + models[i] = m.cuda() + for m in models: + m.eval() + + asr_model, tts_model_spec, tts_model_vocoder = models + + parser = parsers.make_parser( + labels=asr_model.decoder.vocabulary, name="en", unk_id=-1, blank_id=-1, do_normalize=True, + ) + labels_map = dict([(i, asr_model.decoder.vocabulary[i]) for i in range(len(asr_model.decoder.vocabulary))]) + + tts_input = [] + asr_references = [] + longest_tts_input = 0 + for test_str in LIST_OF_TEST_STRINGS: + tts_parsed_input = tts_model_spec.parse(test_str) + if len(tts_parsed_input[0]) > longest_tts_input: + longest_tts_input = len(tts_parsed_input[0]) + tts_input.append(tts_parsed_input.squeeze()) + + asr_parsed = parser(test_str) + asr_parsed = ''.join([labels_map[c] for c in asr_parsed]) + asr_references.append(asr_parsed) + + # Pad TTS Inputs + for i, text in enumerate(tts_input): + pad = (0, longest_tts_input - len(text)) + tts_input[i] = torch.nn.functional.pad(text, pad, value=68) + + logging.debug(tts_input) + + # Do TTS + tts_input = torch.stack(tts_input) + if torch.cuda.is_available(): + tts_input = tts_input.cuda() + specs = tts_model_spec.generate_spectrogram(tokens=tts_input) + audio = [] + step = ceil(len(specs) / 4) + for i in range(4): + audio.append(tts_model_vocoder.convert_spectrogram_to_audio(spec=specs[i * step : i * step + step])) + + audio = [item for sublist in audio for item in sublist] + audio_file_paths = [] + # Save audio + logging.debug(f"args.trim: {args.trim}") + for i, aud in enumerate(audio): + aud = aud.cpu().numpy() + if args.trim: + aud = librosa.effects.trim(aud, top_db=40)[0] + soundfile.write(f"{i}.wav", aud, samplerate=22050) + audio_file_paths.append(str(Path(f"{i}.wav"))) + + # Do ASR + hypotheses = asr_model.transcribe(audio_file_paths) + for i, _ in enumerate(hypotheses): + logging.debug(f"{i}") + logging.debug(f"ref:'{asr_references[i]}'") + logging.debug(f"hyp:'{hypotheses[i]}'") + wer_value = word_error_rate(hypotheses=hypotheses, references=asr_references) + if wer_value > args.wer_tolerance: + raise ValueError(f"Got WER of {wer_value}. It was higher than {args.wer_tolerance}") + logging.info(f'Got WER of {wer_value}. Tolerance was {args.wer_tolerance}') + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/univnet.py b/NeMo-2.0.0.rc0.beta/examples/tts/univnet.py new file mode 100644 index 0000000..91aafa6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/univnet.py @@ -0,0 +1,35 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models import UnivNetModel +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf/univnet", config_name="univnet") +def main(cfg): + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + model = UnivNetModel(cfg=cfg.model, trainer=trainer) + lr_logger = pl.callbacks.LearningRateMonitor() + epoch_time_logger = LogEpochTimeCallback() + trainer.callbacks.extend([lr_logger, epoch_time_logger]) + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/vits.py b/NeMo-2.0.0.rc0.beta/examples/tts/vits.py new file mode 100644 index 0000000..75e0d82 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/vits.py @@ -0,0 +1,33 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.tts.models.vits import VitsModel +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="vits") +def main(cfg): + trainer = pl.Trainer(use_distributed_sampler=False, **cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + model = VitsModel(cfg=cfg.model, trainer=trainer) + + trainer.callbacks.extend([pl.callbacks.LearningRateMonitor()]) + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/tts/waveglow.py b/NeMo-2.0.0.rc0.beta/examples/tts/waveglow.py new file mode 100755 index 0000000..66b1349 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/tts/waveglow.py @@ -0,0 +1,34 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytorch_lightning as pl + +from nemo.collections.common.callbacks import LogEpochTimeCallback +from nemo.collections.tts.models import WaveGlowModel +from nemo.core.config import hydra_runner +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="waveglow") +def main(cfg): + trainer = pl.Trainer(**cfg.trainer) + exp_manager(trainer, cfg.get("exp_manager", None)) + model = WaveGlowModel(cfg=cfg.model, trainer=trainer) + epoch_time_logger = LogEpochTimeCallback() + trainer.callbacks.extend([epoch_time_logger]) + trainer.fit(model) + + +if __name__ == '__main__': + main() # noqa pylint: disable=no-value-for-parameter diff --git a/NeMo-2.0.0.rc0.beta/examples/vision/convert_ckpt_to_nemo.py b/NeMo-2.0.0.rc0.beta/examples/vision/convert_ckpt_to_nemo.py new file mode 100644 index 0000000..14876f6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/vision/convert_ckpt_to_nemo.py @@ -0,0 +1,160 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +r""" +Conversion script to convert PTL checkpoints into nemo checkpoint. + Example to run this conversion script: + python -m torch.distributed.launch --nproc_per_node= * \ + convert_ckpt_to_nemo.py \ + --checkpoint_folder \ + --checkpoint_name \ + --nemo_file_path \ + --tensor_model_parallel_size \ + --pipeline_model_parallel_size +""" + +import os +from argparse import ArgumentParser + +import torch +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from pytorch_lightning.trainer.trainer import Trainer + +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector +from nemo.collections.vision.models.megatron_vit_classification_models import MegatronVitClassificationModel +from nemo.utils import AppState, logging +from nemo.utils.distributed import initialize_distributed +from nemo.utils.model_utils import inject_model_parallel_rank + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +def get_args(): + parser = ArgumentParser() + parser.add_argument( + "--checkpoint_folder", + type=str, + default=None, + required=True, + help="Path to PTL checkpoints saved during training. Ex: /raid/nemo_experiments/megatron_gpt/checkpoints", + ) + parser.add_argument( + "--checkpoint_name", + type=str, + default=None, + required=True, + help="Name of checkpoint to be used. Ex: megatron_gpt--val_loss=6.34-step=649-last.ckpt", + ) + + parser.add_argument( + "--hparams_file", + type=str, + default=None, + required=False, + help="Path config for restoring. It's created during training and may need to be modified during restore if restore environment is different than training. Ex: /raid/nemo_experiments/vision/hparams.yaml", + ) + parser.add_argument("--nemo_file_path", type=str, default=None, required=True, help="Path to output .nemo file.") + parser.add_argument("--gpus_per_node", type=int, required=True, default=None) + parser.add_argument("--tensor_model_parallel_size", type=int, required=True, default=None) + parser.add_argument("--pipeline_model_parallel_size", type=int, required=True, default=None) + parser.add_argument( + "--pipeline_model_parallel_split_rank", + type=int, + required=False, + default=None, + help="If pipeline parallel size > 1, this is the rank at which the encoder ends and the decoder begins.", + ) + parser.add_argument("--model_type", type=str, required=True, default="vit_classification") + parser.add_argument("--local_rank", type=int, required=False, default=os.getenv('LOCAL_RANK', -1)) + parser.add_argument("--bcp", action="store_true", help="Whether on BCP platform") + + args = parser.parse_args() + return args + + +def convert(local_rank, rank, world_size, args): + app_state = AppState() + app_state.data_parallel_rank = 0 + num_nodes = world_size // args.gpus_per_node + if args.bcp: + trainer = Trainer( + devices=args.gpus_per_node, num_nodes=num_nodes, accelerator='gpu', plugins=[TorchElasticEnvironment()] + ) + else: + trainer = Trainer(devices=args.gpus_per_node, num_nodes=num_nodes, accelerator='gpu') + + app_state.pipeline_model_parallel_size = args.pipeline_model_parallel_size + app_state.tensor_model_parallel_size = args.tensor_model_parallel_size + + # no use atm, use to split ranks in encoder/decoder models. + if args.pipeline_model_parallel_size > 1 and args.model_type in []: + if args.pipeline_model_parallel_split_rank is not None: + app_state.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_split_rank + else: + if args.pipeline_model_parallel_size % 2 != 0: + raise ValueError( + f"Pipeline model parallel size {args.pipeline_model_parallel_size} must be even if split rank is not specified." + ) + else: + # If split rank is not set, then we set it to be pipeline_model_parallel_size // 2 - this is because in most cases we have the same number of enc/dec layers. + app_state.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_size // 2 + else: + app_state.pipeline_model_parallel_split_rank = None + + app_state.model_parallel_size = app_state.tensor_model_parallel_size * app_state.pipeline_model_parallel_size + + parallel_state.initialize_model_parallel( + tensor_model_parallel_size=app_state.tensor_model_parallel_size, + pipeline_model_parallel_size=app_state.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank=app_state.pipeline_model_parallel_split_rank, + ) + + app_state.pipeline_model_parallel_rank = parallel_state.get_pipeline_model_parallel_rank() + app_state.tensor_model_parallel_rank = parallel_state.get_tensor_model_parallel_rank() + + # inject model parallel rank + checkpoint_path = inject_model_parallel_rank(os.path.join(args.checkpoint_folder, args.checkpoint_name)) + + logging.info( + f'rank: {rank}, local_rank: {local_rank}, is loading checkpoint: {checkpoint_path} for tp_rank: {app_state.tensor_model_parallel_rank} and pp_rank: {app_state.pipeline_model_parallel_rank}' + ) + + if args.model_type == 'vit_classification': + model = MegatronVitClassificationModel.load_from_checkpoint( + checkpoint_path, hparams_file=args.hparams_file, trainer=trainer + ) + else: + raise ValueError(f"Unrecognized model_type {args.model_type}.") + + model._save_restore_connector = NLPSaveRestoreConnector() + + if torch.distributed.is_initialized(): + torch.distributed.barrier() + + model.save_to(args.nemo_file_path) + + logging.info(f'NeMo model saved to: {args.nemo_file_path}') + + +if __name__ == '__main__': + args = get_args() + local_rank, rank, world_size = initialize_distributed(args) + convert(local_rank, rank, world_size, args) diff --git a/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/conf/megatron_vit_classification_config.yaml b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/conf/megatron_vit_classification_config.yaml new file mode 100755 index 0000000..264b49a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/conf/megatron_vit_classification_config.yaml @@ -0,0 +1,163 @@ +# shared by ViT classification pretraining and fine-tuning + +name: megatron_vit_classify +restore_from_path: null # used when starting from a .nemo file + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + precision: 16 + logger: False # logger provided by exp_manager + enable_checkpointing: False + use_distributed_sampler: False + max_epochs: -1 # PTL default. In practice, max_steps will be reached first. + max_steps: 95000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches + log_every_n_steps: 10 + val_check_interval: 100 + limit_val_batches: 50 + limit_test_batches: 500 + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually + +exp_manager: + explicit_log_dir: null + exp_dir: null + name: megatron_vit_classification + create_wandb_logger: False + wandb_logger_kwargs: + project: null + name: null + resume_if_exists: True + resume_ignore_no_checkpoint: True + create_checkpoint_callback: True + checkpoint_callback_params: + monitor: val_loss + save_top_k: 10 + mode: min + always_save_nemo: False # saves nemo file during validation, not implemented for model parallel + save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits + filename: 'megatron_vit_classification--{val_loss:.2f}-{step}-{consumed_samples}' + model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}} + + +model: + precision: ${trainer.precision} + # specify micro_batch_size, global_batch_size, and model parallelism + # gradient accumulation will be done automatically based on data_parallel_size + micro_batch_size: 256 # limited by GPU memory + global_batch_size: 4096 # will use more micro batches to reach global batch size + tensor_model_parallel_size: 1 # intra-layer model parallelism + pipeline_model_parallel_size: 1 # inter-layer model parallelism + virtual_pipeline_model_parallel_size: null # interleaved pipeline + + restore_from_path: null # used in fine-tuning + + # vision configs + vision_pretraining_type: "classify" + num_classes: 1000 + patch_dim: 16 + img_h: 224 + img_w: 224 + classes_fraction: 1.0 + data_per_class_fraction: 1.0 + num_channels: 3 + drop_path_rate: 0.0 + + # model architecture + encoder_seq_length: 196 + max_position_embeddings: ${.encoder_seq_length} + num_layers: 12 + hidden_size: 768 + ffn_hidden_size: 3072 # Transformer FFN hidden size. Usually 4 * hidden_size. + num_attention_heads: 12 + init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.') + use_scaled_init_method: True # use scaled residuals initialization + hidden_dropout: 0.1 # Dropout probability for hidden state transformer. + attention_dropout: 0. + kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null + apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number. + normalization: layernorm # Type of normalization layers + layernorm_epsilon: 1e-5 + do_layer_norm_weight_decay: False # True means weight decay on all params + pre_process: True # add embedding + post_process: True # add pooler + persist_layer_norm: True # Use of persistent fused layer norm kernel. + + # precision + native_amp_init_scale: 4294967296 # 2 ** 32 + native_amp_growth_interval: 1000 + hysteresis: 2 # Gradient scale hysteresis + fp32_residual_connection: False # Move residual connections to fp32 + fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16 + + # Megatron O2-style half-precision + megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters + grad_allreduce_chunk_size_mb: 125 + grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce + masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask. + bias_dropout_add_fusion: True # Use a kernel that fuses the bias addition, dropout and residual connection addition. + + # miscellaneous + seed: 1234 + resume_from_checkpoint: null # manually set the checkpoint file to load from + use_cpu_initialization: False # Init weights on the CPU (slow for large models) + onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter. + apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this + gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory) + gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism. + openai_gelu: False + bias_activation_fusion: False + megatron_legacy: False + + ## Activation Checkpointing + # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed. + # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+). + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + # 'full' will checkpoint the entire transformer layer. + activations_checkpoint_granularity: null # 'selective' or 'full' + activations_checkpoint_method: null # 'uniform', 'block', not used with 'selective' + # 'uniform' divides the total number of transformer layers and checkpoints the input activation + # of each chunk at the specified granularity + # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity + activations_checkpoint_num_layers: null # not used with 'selective' + # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory. + # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage. + + ## Sequence Parallelism + # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially + # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details. + sequence_parallel: False + + data: + # Path to image dataset must be specified by the user. + # Supports List + # List: can override from the CLI: "model.data.data_prefix=[/path/to/train, /path/to/val]", + data_path: ??? + num_workers: 2 + dataloader_type: cyclic # cyclic + validation_drop_last: True # Set to false if the last partial validation samples is to be consumed + data_sharding: False + + # Nsys profiling options + nsys_profile: + enabled: False + start_step: 10 # Global batch to start profiling + end_step: 10 # Global batch to end profiling + ranks: [ 0 ] # Global rank IDs to profile + gen_shape: False # Generate model and kernel details including input shapes + + optim: + name: fused_adam + lr: 5e-4 + weight_decay: 0.1 + betas: + - 0.9 + - 0.999 + sched: + name: CosineAnnealing + warmup_steps: 10000 + constant_steps: 0 + min_lr: 1e-5 \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/conf/megatron_vit_classification_evaluate.yaml b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/conf/megatron_vit_classification_evaluate.yaml new file mode 100755 index 0000000..4b9a71b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/conf/megatron_vit_classification_evaluate.yaml @@ -0,0 +1,15 @@ +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: 16 # 16, 32, or bf16 + +model: + restore_from_path: null # Path to a trained ViT .nemo file + precision: ${trainer.precision} + micro_batch_size: 512 # we only supports DP=1 eval at the moment, GBS=MBS + + data: + num_workers: 2 + imagenet_val: ??? # path to imagenet val folder \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/conf/megatron_vit_classification_infer.yaml b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/conf/megatron_vit_classification_infer.yaml new file mode 100755 index 0000000..553abb5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/conf/megatron_vit_classification_infer.yaml @@ -0,0 +1,12 @@ +data_path: ??? # Path to a image folder for inference + +trainer: + devices: 1 + num_nodes: 1 + accelerator: gpu + logger: False # logger provided by exp_manager + precision: 16 # 16, 32, or bf16 + +model: + restore_from_path: null # Path to a trained ViT .nemo file + precision: ${trainer.precision} diff --git a/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/megatron_vit_classification_evaluate.py b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/megatron_vit_classification_evaluate.py new file mode 100644 index 0000000..e827e4d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/megatron_vit_classification_evaluate.py @@ -0,0 +1,113 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import torch +from omegaconf.omegaconf import OmegaConf, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from torch.utils.data import DataLoader +from tqdm import tqdm + +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector +from nemo.collections.nlp.parts.utils_funcs import torch_dtype_from_precision +from nemo.collections.vision.data.megatron.image_folder import ImageFolder +from nemo.collections.vision.data.megatron.vit_dataset import ClassificationTransform +from nemo.collections.vision.models.megatron_vit_classification_models import MegatronVitClassificationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.get_rank import is_global_rank_zero + + +@hydra_runner(config_path="conf", config_name="megatron_vit_classification_evaluate") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True, find_unused_parameters=False, # we don't use DDP for async grad allreduce + ) + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + # trainer required for restoring model parallel models + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer) + + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.model.restore_from_path): + save_restore_connector.model_extracted_dir = cfg.model.restore_from_path + + model_cfg = MegatronVitClassificationModel.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + save_restore_connector=save_restore_connector, + return_config=True, + ) + + assert ( + cfg.trainer.devices * cfg.trainer.num_nodes + == model_cfg.tensor_model_parallel_size * model_cfg.pipeline_model_parallel_size + ), "devices * num_nodes should equal tensor_model_parallel_size * pipeline_model_parallel_size" + + # These configs are required to be off during inference. + with open_dict(model_cfg): + model_cfg.precision = trainer.precision + if trainer.precision != "bf16": + model_cfg.megatron_amp_O2 = False + model_cfg.sequence_parallel = False + model_cfg.activations_checkpoint_granularity = None + model_cfg.activations_checkpoint_method = None + + model = MegatronVitClassificationModel.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + override_config_path=model_cfg, + save_restore_connector=save_restore_connector, + strict=True, + ) + + model.eval() + + val_transform = ClassificationTransform(model.cfg, (model.cfg.img_h, model.cfg.img_w), train=False) + val_data = ImageFolder(root=cfg.model.data.imagenet_val, transform=val_transform,) + + def dummy(): + return + + if trainer.strategy.launcher is not None: + trainer.strategy.launcher.launch(dummy, trainer=trainer) + trainer.strategy.setup_environment() + + test_loader = DataLoader(val_data, batch_size=cfg.model.micro_batch_size, num_workers=cfg.model.data.num_workers,) + + autocast_dtype = torch_dtype_from_precision(trainer.precision) + + with torch.no_grad(), torch.cuda.amp.autocast( + enabled=autocast_dtype in (torch.half, torch.bfloat16), dtype=autocast_dtype, + ): + total = correct = 0.0 + for tokens, labels in tqdm(test_loader): + logits = model(tokens.cuda()) + class_indices = torch.argmax(logits, -1) + correct += (class_indices == labels.cuda()).float().sum() + total += len(labels) + + if is_global_rank_zero: + print(f"ViT Imagenet 1K Evaluation Accuracy: {correct / total:.4f}") + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/megatron_vit_classification_finetune.py b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/megatron_vit_classification_finetune.py new file mode 100644 index 0000000..5b4b19e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/megatron_vit_classification_finetune.py @@ -0,0 +1,51 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from omegaconf.omegaconf import OmegaConf, open_dict + +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector +from nemo.collections.vision.models.megatron_vit_classification_models import MegatronVitClassificationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="megatron_vit_classification_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + # hydra interpolation does not work here as the interpolation key is lost when PTL saves hparams + with open_dict(cfg): + cfg.model.finetune = True + cfg.model.precision = cfg.trainer.precision + + model = MegatronVitClassificationModel.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + override_config_path=cfg.model, + save_restore_connector=NLPSaveRestoreConnector(), + strict=False, + ) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/megatron_vit_classification_infer.py b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/megatron_vit_classification_infer.py new file mode 100644 index 0000000..a757eb7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/megatron_vit_classification_infer.py @@ -0,0 +1,137 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import glob +import os + +import torch +from omegaconf.omegaconf import OmegaConf, open_dict +from PIL import Image +from pytorch_lightning import Trainer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from torch.utils.data import DataLoader, Dataset + +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector +from nemo.collections.nlp.parts.utils_funcs import torch_dtype_from_precision +from nemo.collections.vision.data.imagenet_classnames import imagenet_classnames +from nemo.collections.vision.data.megatron.vit_dataset import ClassificationTransform +from nemo.collections.vision.models.megatron_vit_classification_models import MegatronVitClassificationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.get_rank import is_global_rank_zero + +_IMG_EXTENSIONS = "jpg jpeg png ppm pgm pbm pnm".split() + + +class ImageFolderDataset(Dataset): + def __init__(self, folder_path, transform=None): + self.folder_path = folder_path + self.transform = transform + # Use glob to find all image files in folder_path + image_paths = [] + for ext in _IMG_EXTENSIONS + [x.upper() for x in _IMG_EXTENSIONS]: + search_pattern = os.path.join(folder_path, f"*.{ext}") + image_paths += glob.glob(search_pattern) + self.image_paths = image_paths + + def __len__(self): + return len(self.image_paths) + + def __getitem__(self, idx): + image_path = self.image_paths[idx] + image = Image.open(image_path).convert('RGB') + if self.transform is not None: + image = self.transform(image) + return image + + +@hydra_runner(config_path="conf", config_name="megatron_vit_classification_infer") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + plugins = [] + strategy = NLPDDPStrategy( + no_ddp_communication_hook=True, find_unused_parameters=False, # we don't use DDP for async grad allreduce + ) + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + # trainer required for restoring model parallel models + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer) + + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.model.restore_from_path): + save_restore_connector.model_extracted_dir = cfg.model.restore_from_path + + model_cfg = MegatronVitClassificationModel.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + save_restore_connector=save_restore_connector, + return_config=True, + ) + + assert ( + cfg.trainer.devices * cfg.trainer.num_nodes + == model_cfg.tensor_model_parallel_size * model_cfg.pipeline_model_parallel_size + ), "devices * num_nodes should equal tensor_model_parallel_size * pipeline_model_parallel_size" + + # These configs are required to be off during inference. + with open_dict(model_cfg): + model_cfg.precision = trainer.precision + if trainer.precision != "bf16": + model_cfg.megatron_amp_O2 = False + model_cfg.sequence_parallel = False + model_cfg.activations_checkpoint_granularity = None + model_cfg.activations_checkpoint_method = None + + model = MegatronVitClassificationModel.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + override_config_path=model_cfg, + save_restore_connector=save_restore_connector, + strict=True, + ) + + model.eval() + + test_transform = ClassificationTransform(cfg.model, (model_cfg.img_h, model_cfg.img_w), train=False) + test_data = ImageFolderDataset(folder_path=cfg.data_path, transform=test_transform,) + test_loader = DataLoader(test_data, batch_size=8) + + def dummy(): + return + + if trainer.strategy.launcher is not None: + trainer.strategy.launcher.launch(dummy, trainer=trainer) + trainer.strategy.setup_environment() + + autocast_dtype = torch_dtype_from_precision(trainer.precision) + + with torch.no_grad(), torch.cuda.amp.autocast( + enabled=autocast_dtype in (torch.half, torch.bfloat16), dtype=autocast_dtype, + ): + class_names = [] + for tokens in test_loader: + logits = model(tokens.cuda()) + class_indices = torch.argmax(logits, -1) + class_names += [imagenet_classnames[x] for x in class_indices] + + if is_global_rank_zero: + filenames = [os.path.basename(f) for f in test_data.image_paths] + print(f"Predicted classes: ", list(zip(filenames, class_names))) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/megatron_vit_classification_pretrain.py b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/megatron_vit_classification_pretrain.py new file mode 100644 index 0000000..bacfb73 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/examples/vision/vision_transformer/megatron_vit_classification_pretrain.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from omegaconf.omegaconf import OmegaConf + +from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder +from nemo.collections.vision.models.megatron_vit_classification_models import MegatronVitClassificationModel +from nemo.core.config import hydra_runner +from nemo.utils import logging +from nemo.utils.exp_manager import exp_manager + + +@hydra_runner(config_path="conf", config_name="megatron_vit_classification_config") +def main(cfg) -> None: + logging.info("\n\n************** Experiment configuration ***********") + logging.info(f'\n{OmegaConf.to_yaml(cfg)}') + + trainer = MegatronTrainerBuilder(cfg).create_trainer() + exp_manager(trainer, cfg.exp_manager) + + model = MegatronVitClassificationModel(cfg.model, trainer) + + trainer.fit(model) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/external/get_collections.py b/NeMo-2.0.0.rc0.beta/external/get_collections.py new file mode 100644 index 0000000..d546ccb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/external/get_collections.py @@ -0,0 +1,90 @@ +# -*- coding: utf-8 -*- +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" Script responsible for generation of a JSON file with list of NeMo collections. """ + +import argparse +import importlib +import json +import os + +import nemo +from nemo.utils import logging + + +def process_collection(id, col): + """ Helper function processing the collection. + + Args: + id: (short) name of the collection. + col: a collection (python module). + """ + return { + "id": id, + "name": col.__name__, + "description": col.__description__, + "version": col.__version__, + "author": col.__author__, + } + + +def main(): + """ Main function generating a JSON file with list of NeMo collections. """ + # Parse filename. + parser = argparse.ArgumentParser() + parser.add_argument('--filename', help='Name of the output JSON file', type=str, default="collections.json") + args = parser.parse_args() + + # Get collections directory. + colletions_dir = os.path.dirname(nemo.collections.__file__) + logging.info('Analysing collections in `{}`'.format(colletions_dir)) + + # Generate list of NeMo collections - from the list of collection subfolders. + collections = {} + for sub_dir in os.listdir(colletions_dir): + # Skip cache. + if sub_dir == "__pycache__": + continue + # Check if it is a directory. + if os.path.isdir(os.path.join(colletions_dir, sub_dir)): + collections[sub_dir] = "nemo.collections." + sub_dir + + output_list = [] + # Iterate over all collections. + for key, val in collections.items(): + # Try to get module specification. + module_spec = importlib.util.find_spec(val) + if module_spec is None: + logging.warning(" * Failed to process `{}`".format(val)) + else: + try: + # Import the module from the module specification. + module = importlib.util.module_from_spec(module_spec) + module_spec.loader.exec_module(module) + # Add to list. + output_list.append(process_collection(key, module)) + logging.info(" * Processed `{}`".format(val)) + except AttributeError: + logging.warning(" * Failed to process `{}`".format(val)) + + # Export to JSON. + with open(args.filename, 'w', encoding='utf-8') as outfile: + json.dump(output_list, outfile) + + logging.info('Finshed the analysis, results exported to `{}`.'.format(args.filename)) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/external/get_modules.py b/NeMo-2.0.0.rc0.beta/external/get_modules.py new file mode 100644 index 0000000..c080be9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/external/get_modules.py @@ -0,0 +1,159 @@ +# -*- coding: utf-8 -*- +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" Script responsible for generation of a JSON file containing list of modules of a given collection. """ + +import argparse +import importlib +import inspect +import json +import os + +import nemo +from nemo.utils import logging + + +def process_member(name, obj, module_list): + """ Helper function processing the passed object and, if ok, adding a record to the module list. + + Args: + name: name of the member + obj: member (class/function etc.) + module_list: list of modules that (probably) will be expanded. + """ + # It is not a class - skip it. + if not inspect.isclass(obj): + return + + # Check inheritance - we know that all our datasets/modules/losses inherit from Serialization, + # Btw. Serialization is also required by this script. + if not issubclass(obj, nemo.core.Serialization): + return + + logging.info(" * Processing `{}`".format(str(obj))) + + module_list.append( + { + "name": name, + "cls": str(obj), + # Temporary solution: mockup arguments. + "arguments": [ + "jasper", + "activation", + "feat_in", + "normalization_mode", + "residual_mode", + "norm_groups", + "conv_mask", + "frame_splicing", + "init_mode", + ], + # Temporary solution: mockup input types. + "input_types": { + "audio_signal": "axes: (batch, dimension, time); elements_type: MelSpectrogramType", + "length": "axes: (batch,); elements_type: LengthType", + }, + # Temporary solution: mockup output types. + "output_types": { + "encoder_output": "axes: (batch, dimension, time); elements_type: AcousticEncodedRepresentation" + }, + } + ) + + +def main(): + """ Main function analysing the indicated NeMo collection and generating a JSON file with module descriptions. """ + # Parse filename. + parser = argparse.ArgumentParser() + parser.add_argument('--collection', help='ID of the collection', type=str) + parser.add_argument('--filename', help='Name of the output JSON file', type=str, default="modules.json") + args = parser.parse_args() + + # Get collections directory. + colletions_dir = os.path.dirname(nemo.collections.__file__) + logging.info('Analysing collections in `{}`'.format(colletions_dir)) + + # Generate list of NeMo collections - from the list of collection subfolders. + collections = {} + for sub_dir in os.listdir(colletions_dir): + # Skip cache. + if sub_dir == "__pycache__": + continue + # Check if it is a directory. + if os.path.isdir(os.path.join(colletions_dir, sub_dir)): + collections[sub_dir] = "nemo.collections." + sub_dir + + # Check the collection. + if args.collection not in collections.keys(): + logging.error("Coudn't process the incidated `{}` collection".format(args.collection)) + logging.info( + "Please select one of the existing collections using `--collection [{}]`".format("|".join(collections)) + ) + exit(-1) + + # Load the collection specification. + collection_spec = importlib.util.find_spec(collections[args.collection]) + if collection_spec is None: + logging.error("Failed to load the `{}` collection".format(val)) + + # Import the module from the module specification. + collection = importlib.util.module_from_spec(collection_spec) + collection_spec.loader.exec_module(collection) + + module_list = [] + # Iterate over the packages in the indicated collection. + logging.info("Analysing the `{}` collection".format(args.collection)) + + try: # Datasets in dataset folder + logging.info("Analysing the 'data' package") + for name, obj in inspect.getmembers(collection.data): + process_member(name, obj, module_list) + except AttributeError as e: + logging.info(" * No datasets found") + + try: # Datasets in dataset folder + logging.info("Analysing the 'datasets' package") + for name, obj in inspect.getmembers(collection.datasets): + process_member(name, obj, module_list) + except AttributeError as e: + logging.info(" * No datasets found") + + try: # Modules + logging.info("Analysing the 'modules' package") + for name, obj in inspect.getmembers(collection.modules): + process_member(name, obj, module_list) + except AttributeError as e: + logging.info(" * No modules found") + + try: # Losses + logging.info("Analysing the 'losses' package") + for name, obj in inspect.getmembers(collection.losses): + process_member(name, obj, module_list) + except AttributeError as e: + logging.info(" * No losses found") + + # Add prefix - only for default name. + filename = args.filename if args.filename != "modules.json" else args.collection + "_" + args.filename + # Export to JSON. + with open(filename, 'w', encoding='utf-8') as outfile: + json.dump(module_list, outfile) + + logging.info( + 'Finished analysis of the `{}` collection, results exported to `{}`.'.format(args.collection, filename) + ) + + +if __name__ == '__main__': + main() diff --git a/NeMo-2.0.0.rc0.beta/install_env.sh b/NeMo-2.0.0.rc0.beta/install_env.sh new file mode 100644 index 0000000..0a57ced --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/install_env.sh @@ -0,0 +1,26 @@ +apt-get update && \ + apt-get upgrade -y && \ + apt-get install -y \ + libsndfile1 sox \ + libfreetype6 \ + swig \ + ffmpeg \ + libavdevice-dev + +apt-get update && \ + apt-get install -y \ + libtool \ + libltdl-dev \ + automake \ + autoconf \ + bison \ + flex \ + tcl \ + ghostscript \ + libgd-dev \ + fontconfig \ + libcairo2-dev \ + libpango1.0-dev \ + libgts-dev + +pip install -r requirement.txt -i https://pypi.tuna.tsinghua.edu.cn/simple \ No newline at end of file diff --git a/NeMo-2.0.0.rc0.beta/nemo/README.md b/NeMo-2.0.0.rc0.beta/nemo/README.md new file mode 100644 index 0000000..91b734b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/README.md @@ -0,0 +1,11 @@ +NeMo (**Ne**ural **Mo**dules) is a toolkit for creating AI applications built around **neural modules**, conceptual blocks of neural networks that take *typed* inputs and produce *typed* outputs. + +**NeMo Core** provides common APIs all modules and models have to implement. + +**NeMo Collections** + +* ASR - collection of modules and models for building speech recognition networks +* TTS - collection of modules and models for building speech synthesis networks +* NLP - collection of modules and models for building NLP networks +* Vision - collection of modules and models for building computer vision networks +* Multimodal - collection of modules and models for building multimodal networks diff --git a/NeMo-2.0.0.rc0.beta/nemo/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/__init__.py new file mode 100644 index 0000000..5b9fedb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/__init__.py @@ -0,0 +1,28 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from nemo.package_info import ( + __contact_emails__, + __contact_names__, + __description__, + __download_url__, + __homepage__, + __keywords__, + __license__, + __package_name__, + __repository_url__, + __shortversion__, + __version__, +) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/__init__.py new file mode 100644 index 0000000..9e32500 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/README.md b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/README.md new file mode 100644 index 0000000..9a1b947 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/README.md @@ -0,0 +1,37 @@ +# Automatic Speech Recognition (ASR) + +## Key Features + +* [HuggingFace Space for Audio Transcription (File, Microphone and YouTube)](https://huggingface.co/spaces/smajumdar/nemo_multilingual_language_id) +* [Pretrained models](https://ngc.nvidia.com/catalog/collections/nvidia:nemo_asr) available in 14+ languages +* [Automatic Speech Recognition (ASR)](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/intro.html) + * Supported ASR [models](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html): + * Jasper, QuartzNet, CitriNet, ContextNet + * Conformer-CTC, Conformer-Transducer, FastConformer-CTC, FastConformer-Transducer + * Squeezeformer-CTC and Squeezeformer-Transducer + * LSTM-Transducer (RNNT) and LSTM-CTC + * Supports the following decoders/losses: + * CTC + * Transducer/RNNT + * Hybrid Transducer/CTC + * NeMo Original [Multi-blank Transducers](https://arxiv.org/abs/2211.03541) and [Token-and-Duration Transducers (TDT)](https://arxiv.org/abs/2304.06795) + * Streaming/Buffered ASR (CTC/Transducer) - [Chunked Inference Examples](https://github.com/NVIDIA/NeMo/tree/stable/examples/asr/asr_chunked_inference) + * [Cache-aware Streaming Conformer](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#cache-aware-streaming-conformer) with multiple lookaheads (including microphone streaming [tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Online_ASR_Microphone_Demo_Cache_Aware_Streaming.ipynb). + * Beam Search decoding + * [Language Modelling for ASR (CTC and RNNT)](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html): N-gram LM in fusion with Beam Search decoding, Neural Rescoring with Transformer + * [Support of long audios for Conformer with memory efficient local attention](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/results.html#inference-on-long-audio) +* [Speech Classification, Speech Command Recognition and Language Identification](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speech_classification/intro.html): MatchboxNet (Command Recognition), AmberNet (LangID) +* [Voice activity Detection (VAD)](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/speech_classification/models.html#marblenet-vad): MarbleNet + * ASR with VAD Inference - [Example](https://github.com/NVIDIA/NeMo/tree/stable/examples/asr/asr_vad) +* [Speaker Recognition](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_recognition/intro.html): TitaNet, ECAPA_TDNN, SpeakerNet +* [Speaker Diarization](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_diarization/intro.html) + * Clustering Diarizer: TitaNet, ECAPA_TDNN, SpeakerNet + * Neural Diarizer: MSDD (Multi-scale Diarization Decoder) +* [Speech Intent Detection and Slot Filling](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speech_intent_slot/intro.html): Conformer-Transformer + +You can also get a high-level overview of NeMo ASR by watching the talk *NVIDIA NeMo: Toolkit for Conversational AI*, presented at PyData Yerevan 2022: + + +[![NVIDIA NeMo: Toolkit for Conversational AI](https://img.youtube.com/vi/J-P6Sczmas8/maxres3.jpg +)](https://www.youtube.com/embed/J-P6Sczmas8?mute=0&start=14&autoplay=0 + "NeMo presentation at PyData@Yerevan 2022") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/__init__.py new file mode 100644 index 0000000..cd14a43 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/__init__.py @@ -0,0 +1,25 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr import data, losses, models, modules +from nemo.package_info import __version__ + +# Set collection version equal to NeMo version. +__version = __version__ + +# Authorship. +__author__ = "NVIDIA Corporation" + +# Set collection name. +__description__ = "Automatic Speech Recognition collection" diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/__init__.py new file mode 100644 index 0000000..9e32500 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_audio.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_audio.py new file mode 100644 index 0000000..a3c6dd0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_audio.py @@ -0,0 +1,1136 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import abc +import math +import random +from collections import OrderedDict, namedtuple +from dataclasses import dataclass +from typing import Callable, Dict, List, Optional, Tuple, Type, Union + +import librosa +import numpy as np +import torch + +from nemo.collections.asr.parts.preprocessing.segment import AudioSegment +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.common.parts.preprocessing import collections +from nemo.collections.common.parts.utils import flatten +from nemo.core.classes import Dataset +from nemo.core.neural_types import AudioSignal, EncodedRepresentation, LengthsType, NeuralType +from nemo.utils import logging + +__all__ = [ + 'AudioToTargetDataset', + 'AudioToTargetWithReferenceDataset', + 'AudioToTargetWithEmbeddingDataset', +] + + +def _audio_collate_fn(batch: List[dict]) -> Tuple[torch.Tensor]: + """Collate a batch of items returned by __getitem__. + Examples for each signal are zero padded to the same length + (batch_length), which is determined by the longest example. + Lengths of the original signals are returned in the output. + + Args: + batch: List of dictionaries. Each element of the list + has the following format + ``` + { + 'signal_0': 1D or 2D tensor, + 'signal_1': 1D or 2D tensor, + ... + 'signal_N': 1D or 2D tensor, + } + ``` + 1D tensors have shape (num_samples,) and 2D tensors + have shape (num_channels, num_samples) + + Returns: + A tuple containing signal tensor and signal length tensor (in samples) + for each signal. + The output has the following format: + ``` + (signal_0, signal_0_length, signal_1, signal_1_length, ..., signal_N, signal_N_length) + ``` + Note that the output format is obtained by interleaving signals and their length. + """ + signals = batch[0].keys() + + batched = tuple() + + for signal in signals: + signal_length = [b[signal].shape[-1] for b in batch] + # Batch length is determined by the longest signal in the batch + batch_length = max(signal_length) + b_signal = [] + for s_len, b in zip(signal_length, batch): + # check if padding is necessary + if s_len < batch_length: + if b[signal].ndim == 1: + # single-channel signal + pad = (0, batch_length - s_len) + elif b[signal].ndim == 2: + # multi-channel signal + pad = (0, batch_length - s_len, 0, 0) + else: + raise RuntimeError( + f'Signal {signal} has unsuported dimensions {signal.shape}. Currently, only 1D and 2D arrays are supported.' + ) + b[signal] = torch.nn.functional.pad(b[signal], pad) + # append the current padded signal + b_signal.append(b[signal]) + # (signal_batched, signal_length) + batched += (torch.stack(b_signal), torch.tensor(signal_length, dtype=torch.int32)) + + # Currently, outputs are expected to be in a tuple, where each element must correspond + # to the output type in the OrderedDict returned by output_types. + # + # Therefore, we return batched signals by interleaving signals and their length: + # (signal_0, signal_0_length, signal_1, signal_1_length, ...) + return batched + + +@dataclass +class SignalSetup: + signals: List[str] # signal names + duration: Optional[Union[float, list]] = None # duration for each signal + channel_selectors: Optional[List[ChannelSelectorType]] = None # channel selector for loading each signal + + +class ASRAudioProcessor: + """Class that processes an example from Audio collection and returns + a dictionary with prepared signals. + + For example, the output dictionary may be the following + ``` + { + 'input_signal': input_signal_tensor, + 'target_signal': target_signal_tensor, + 'reference_signal': reference_signal_tensor, + 'embedding_vector': embedding_vector + } + ``` + Keys in the output dictionary are ordered with synchronous signals given first, + followed by asynchronous signals and embedding. + + Args: + sample_rate: sample rate used for all audio signals + random_offset: If `True`, offset will be randomized when loading a subsegment + from a file. + """ + + def __init__( + self, sample_rate: float, random_offset: bool, + ): + self.sample_rate = sample_rate + self.random_offset = random_offset + + self.sync_setup = None + self.async_setup = None + self.embedding_setup = None + + @property + def sample_rate(self) -> float: + return self._sample_rate + + @sample_rate.setter + def sample_rate(self, value: float): + if value <= 0: + raise ValueError(f'Sample rate must be positive, received {value}') + + self._sample_rate = value + + @property + def random_offset(self) -> bool: + return self._random_offset + + @random_offset.setter + def random_offset(self, value: bool): + self._random_offset = value + + @property + def sync_setup(self) -> SignalSetup: + """Return the current setup for synchronous signals. + + Returns: + A dataclass containing the list of signals, their + duration and channel selectors. + """ + return self._sync_setup + + @sync_setup.setter + def sync_setup(self, value: Optional[SignalSetup]): + """Setup signals to be loaded synchronously. + + Args: + value: An instance of SignalSetup with the following fields + - signals: list of signals (keys of example.audio_signals) which will be loaded + synchronously with the same start time and duration. + - duration: Duration for each signal to be loaded. + If duration is set to None, the whole file will be loaded. + - channel_selectors: A list of channel selector for each signal. If channel selector + is None, all channels in the audio file will be loaded. + """ + if value is None or isinstance(value, SignalSetup): + self._sync_setup = value + else: + raise ValueError(f'Unexpected type {type(value)} for value {value}.') + + @property + def async_setup(self) -> SignalSetup: + """Return the current setup for asynchronous signals. + + Returns: + A dataclass containing the list of signals, their + duration and channel selectors. + """ + return self._async_setup + + @async_setup.setter + def async_setup(self, value: Optional[SignalSetup]): + """Setup signals to be loaded asynchronously. + + Args: + Args: + value: An instance of SignalSetup with the following fields + - signals: list of signals (keys of example.audio_signals) which will be loaded + asynchronously with signals possibly having different start and duration + - duration: Duration for each signal to be loaded. + If duration is set to None, the whole file will be loaded. + - channel_selectors: A list of channel selector for each signal. If channel selector + is None, all channels in the audio file will be loaded. + """ + if value is None or isinstance(value, SignalSetup): + self._async_setup = value + else: + raise ValueError(f'Unexpected type {type(value)} for value {value}.') + + @property + def embedding_setup(self) -> SignalSetup: + """Setup signals corresponding to an embedding vector. + """ + return self._embedding_setup + + @embedding_setup.setter + def embedding_setup(self, value: SignalSetup): + """Setup signals corresponding to an embedding vector. + + Args: + value: An instance of SignalSetup with the following fields + - signals: list of signals (keys of example.audio_signals) which will be loaded + as embedding vectors. + """ + if value is None or isinstance(value, SignalSetup): + self._embedding_setup = value + else: + raise ValueError(f'Unexpected type {type(value)} for value {value}.') + + def process(self, example: collections.Audio.OUTPUT_TYPE) -> Dict[str, torch.Tensor]: + """Process an example from a collection of audio examples. + + Args: + example: an example from Audio collection. + + Returns: + An ordered dictionary of signals and their tensors. + For example, the output dictionary may be the following + ``` + { + 'input_signal': input_signal_tensor, + 'target_signal': target_signal_tensor, + 'reference_signal': reference_signal_tensor, + 'embedding_vector': embedding_vector + } + ``` + Keys in the output dictionary are ordered with synchronous signals given first, + followed by asynchronous signals and embedding. + """ + audio = self.load_audio(example=example) + audio = self.process_audio(audio=audio) + return audio + + def load_audio(self, example: collections.Audio.OUTPUT_TYPE) -> Dict[str, torch.Tensor]: + """Given an example, load audio from `example.audio_files` and prepare + the output dictionary. + + Args: + example: An example from an audio collection + + Returns: + An ordered dictionary of signals and their tensors. + For example, the output dictionary may be the following + ``` + { + 'input_signal': input_signal_tensor, + 'target_signal': target_signal_tensor, + 'reference_signal': reference_signal_tensor, + 'embedding_vector': embedding_vector + } + ``` + Keys in the output dictionary are ordered with synchronous signals given first, + followed by asynchronous signals and embedding. + """ + output = OrderedDict() + + if self.sync_setup is not None: + # Load all signals with the same start and duration + sync_signals = self.load_sync_signals(example) + output.update(sync_signals) + + if self.async_setup is not None: + # Load each signal independently + async_signals = self.load_async_signals(example) + output.update(async_signals) + + # Load embedding vector + if self.embedding_setup is not None: + embedding = self.load_embedding(example) + output.update(embedding) + + if not output: + raise RuntimeError('Output dictionary is empty. Please use `_setup` methods to setup signals to be loaded') + + return output + + def process_audio(self, audio: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + """Process audio signals available in the input dictionary. + + Args: + audio: A dictionary containing loaded signals `signal: tensor` + + Returns: + An ordered dictionary of signals and their tensors. + """ + # Currently, not doing any processing of the loaded signals. + return audio + + def load_sync_signals(self, example: collections.Audio.OUTPUT_TYPE) -> Dict[str, torch.Tensor]: + """Load signals with the same start and duration. + + Args: + example: an example from audio collection + + Returns: + An ordered dictionary of signals and their tensors. + """ + output = OrderedDict() + sync_audio_files = [example.audio_files[s] for s in self.sync_setup.signals] + + sync_samples = self.get_samples_synchronized( + audio_files=sync_audio_files, + channel_selectors=self.sync_setup.channel_selectors, + sample_rate=self.sample_rate, + duration=self.sync_setup.duration, + fixed_offset=example.offset, + random_offset=self.random_offset, + ) + + for signal, samples in zip(self.sync_setup.signals, sync_samples): + output[signal] = torch.tensor(samples) + + return output + + def load_async_signals(self, example: collections.Audio.OUTPUT_TYPE) -> Dict[str, torch.Tensor]: + """Load each async signal independently, no constraints on starting + from the same time. + + Args: + example: an example from audio collection + + Returns: + An ordered dictionary of signals and their tensors. + """ + output = OrderedDict() + for idx, signal in enumerate(self.async_setup.signals): + samples = self.get_samples( + audio_file=example.audio_files[signal], + sample_rate=self.sample_rate, + duration=self.async_setup.duration[idx], + channel_selector=self.async_setup.channel_selectors[idx], + fixed_offset=example.offset, + random_offset=self.random_offset, + ) + output[signal] = torch.tensor(samples) + return output + + @classmethod + def get_samples( + cls, + audio_file: str, + sample_rate: int, + duration: Optional[float] = None, + channel_selector: ChannelSelectorType = None, + fixed_offset: float = 0, + random_offset: bool = False, + ) -> np.ndarray: + """Get samples from an audio file. + For a single-channel signal, the output is shape (num_samples,). + For a multi-channel signal, the output is shape (num_samples, num_channels). + + Args: + audio_file: path to an audio file + sample_rate: desired sample rate for output samples + duration: Optional desired duration of output samples. + If `None`, the complete file will be loaded. + If set, a segment of `duration` seconds will be loaded. + channel_selector: Optional channel selector, for selecting a subset of channels. + fixed_offset: Optional fixed offset when loading samples. + random_offset: If `True`, offset will be randomized when loading a short segment + from a file. The value is randomized between fixed_offset and + max_offset (set depending on the duration and fixed_offset). + + Returns: + Numpy array with samples from audio file. + The array has shape (num_samples,) for a single-channel signal + or (num_channels, num_samples) for a multi-channel signal. + """ + output = cls.get_samples_synchronized( + audio_files=[audio_file], + sample_rate=sample_rate, + duration=duration, + channel_selectors=[channel_selector], + fixed_offset=fixed_offset, + random_offset=random_offset, + ) + + return output[0] + + @classmethod + def get_samples_synchronized( + cls, + audio_files: List[str], + sample_rate: int, + duration: Optional[float] = None, + channel_selectors: Optional[List[ChannelSelectorType]] = None, + fixed_offset: float = 0, + random_offset: bool = False, + ) -> List[np.ndarray]: + """Get samples from multiple files with the same start and end point. + + Args: + audio_files: list of paths to audio files + sample_rate: desired sample rate for output samples + duration: Optional desired duration of output samples. + If `None`, the complete files will be loaded. + If set, a segment of `duration` seconds will be loaded from + all files. Segment is synchronized across files, so that + start and end points are the same. + channel_selectors: Optional channel selector for each signal, for selecting + a subset of channels. + fixed_offset: Optional fixed offset when loading samples. + random_offset: If `True`, offset will be randomized when loading a short segment + from a file. The value is randomized between fixed_offset and + max_offset (set depending on the duration and fixed_offset). + + Returns: + List with the same size as `audio_files` but containing numpy arrays + with samples from each audio file. + Each array has shape (num_samples,) or (num_channels, num_samples), for single- + or multi-channel signal, respectively. + For example, if `audio_files = [path/to/file_1.wav, path/to/file_2.wav]`, + the output will be a list `output = [samples_file_1, samples_file_2]`. + """ + if channel_selectors is None: + channel_selectors = [None] * len(audio_files) + + if duration is None: + # Load complete files starting from a fixed offset + offset = fixed_offset # fixed offset + num_samples = None # no constrain on the number of samples + + else: + # Fixed duration of the output + audio_durations = cls.get_duration(audio_files) + min_audio_duration = min(audio_durations) + available_duration = min_audio_duration - fixed_offset + + if available_duration <= 0: + raise ValueError(f'Fixed offset {fixed_offset}s is larger than shortest file {min_duration}s.') + + if duration + fixed_offset > min_audio_duration: + # The shortest file is shorter than the requested duration + logging.debug( + f'Shortest file ({min_audio_duration}s) is less than the desired duration {duration}s + fixed offset {fixed_offset}s. Returned signals will be shortened to {available_duration} seconds.' + ) + offset = fixed_offset + duration = available_duration + elif random_offset: + # Randomize offset based on the shortest file + max_offset = min_audio_duration - duration + offset = random.uniform(fixed_offset, max_offset) + else: + # Fixed offset + offset = fixed_offset + + # Fixed number of samples + num_samples = math.floor(duration * sample_rate) + + output = [] + + # Prepare segments + for idx, audio_file in enumerate(audio_files): + segment_samples = cls.get_samples_from_file( + audio_file=audio_file, + sample_rate=sample_rate, + offset=offset, + num_samples=num_samples, + channel_selector=channel_selectors[idx], + ) + output.append(segment_samples) + + return output + + @classmethod + def get_samples_from_file( + cls, + audio_file: Union[str, List[str]], + sample_rate: int, + offset: float, + num_samples: Optional[int] = None, + channel_selector: Optional[ChannelSelectorType] = None, + ) -> np.ndarray: + """Get samples from a single or multiple files. + If loading samples from multiple files, they will + be concatenated along the channel dimension. + + Args: + audio_file: path or a list of paths. + sample_rate: sample rate of the loaded samples + offset: fixed offset in seconds + num_samples: Optional, number of samples to load. + If `None`, all available samples will be loaded. + channel_selector: Select a subset of available channels. + + Returns: + An array with shape (samples,) or (channels, samples) + """ + if isinstance(audio_file, str): + # Load samples from a single file + segment_samples = cls.get_segment_from_file( + audio_file=audio_file, + sample_rate=sample_rate, + offset=offset, + num_samples=num_samples, + channel_selector=channel_selector, + ) + elif isinstance(audio_file, list): + # Load samples from multiple files and form a multi-channel signal + segment_samples = [] + for a_file in audio_file: + a_file_samples = cls.get_segment_from_file( + audio_file=a_file, + sample_rate=sample_rate, + offset=offset, + num_samples=num_samples, + channel_selector=channel_selector, + ) + segment_samples.append(a_file_samples) + segment_samples = cls.list_to_multichannel(segment_samples) + elif audio_file is None: + # Support for inference, when the target signal is `None` + segment_samples = [] + else: + raise RuntimeError(f'Unexpected audio_file type {type(audio_file)}') + return segment_samples + + @staticmethod + def get_segment_from_file( + audio_file: str, + sample_rate: int, + offset: float, + num_samples: Optional[int] = None, + channel_selector: Optional[ChannelSelectorType] = None, + ) -> np.ndarray: + """Get a segment of samples from a single audio file. + + Args: + audio_file: path to an audio file + sample_rate: sample rate of the loaded samples + offset: fixed offset in seconds + num_samples: Optional, number of samples to load. + If `None`, all available samples will be loaded. + channel_selector: Select a subset of available channels. + + Returns: + An array with shape (samples,) or (channels, samples) + """ + if num_samples is None: + segment = AudioSegment.from_file( + audio_file=audio_file, target_sr=sample_rate, offset=offset, channel_selector=channel_selector, + ) + + else: + segment = AudioSegment.segment_from_file( + audio_file=audio_file, + target_sr=sample_rate, + n_segments=num_samples, + offset=offset, + channel_selector=channel_selector, + ) + + if segment.samples.ndim == 1: + # Single-channel signal + return segment.samples + elif segment.samples.ndim == 2: + # Use multi-channel format as (channels, samples) + return segment.samples.T + else: + raise RuntimeError(f'Unexpected samples shape: {segment.samples.shape}') + + @staticmethod + def list_to_multichannel(signal: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray: + """Convert a list of signals into a multi-channel signal by concatenating + the elements of the list along the channel dimension. + + If input is not a list, it is returned unmodified. + + Args: + signal: list of arrays + + Returns: + Numpy array obtained by concatenating the elements of the list + along the channel dimension (axis=0). + """ + if not isinstance(signal, list): + # Nothing to do there + return signal + elif len(signal) == 0: + # Nothing to do, return as is + return signal + elif len(signal) == 1: + # Nothing to concatenate, return the original format + return signal[0] + + # If multiple signals are provided in a list, we concatenate them along the channel dimension + if signal[0].ndim == 1: + # Single-channel individual files + mc_signal = np.stack(signal, axis=0) + elif signal[0].ndim == 2: + # Multi-channel individual files + mc_signal = np.concatenate(signal, axis=0) + else: + raise RuntimeError(f'Unexpected target with {signal[0].ndim} dimensions.') + + return mc_signal + + @staticmethod + def get_duration(audio_files: List[str]) -> List[float]: + """Get duration for each audio file in `audio_files`. + + Args: + audio_files: list of paths to audio files + + Returns: + List of durations in seconds. + """ + duration = [librosa.get_duration(path=f) for f in flatten(audio_files)] + return duration + + def load_embedding(self, example: collections.Audio.OUTPUT_TYPE) -> Dict[str, torch.Tensor]: + """Given an example, load embedding from `example.audio_files[embedding]` + and return it in a dictionary. + + Args: + example: An example from audio collection + + Returns: + An dictionary of embedding keys and their tensors. + """ + output = OrderedDict() + for idx, signal in enumerate(self.embedding_setup.signals): + embedding_file = example.audio_files[signal] + embedding = self.load_embedding_vector(embedding_file) + output[signal] = torch.tensor(embedding) + return output + + @staticmethod + def load_embedding_vector(filepath: str) -> np.ndarray: + """Load an embedding vector from a file. + + Args: + filepath: path to a file storing a vector. + Currently, it is assumed the file is a npy file. + + Returns: + Array loaded from filepath. + """ + if filepath.endswith('.npy'): + with open(filepath, 'rb') as f: + embedding = np.load(f) + else: + raise RuntimeError(f'Unknown embedding file format in file: {filepath}') + + return embedding + + +class BaseAudioDataset(Dataset): + """Base class of audio datasets, providing common functionality + for other audio datasets. + + Args: + collection: Collection of audio examples prepared from manifest files. + audio_processor: Used to process every example from the collection. + A callable with `process` method. For reference, + please check ASRAudioProcessor. + """ + + @property + @abc.abstractmethod + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + + def __init__(self, collection: collections.Audio, audio_processor: Callable, output_type: Type[namedtuple]): + """Instantiates an audio dataset. + """ + super().__init__() + + self.collection = collection + self.audio_processor = audio_processor + self.output_type = output_type + + def num_channels(self, signal_key) -> int: + """Returns the number of channels for a particular signal in + items prepared by this dictionary. + + More specifically, this will get the tensor from the first + item in the dataset, check if it's a one- or two-dimensional + tensor, and return the number of channels based on the size + of the first axis (shape[0]). + + NOTE: + This assumes that all examples have the same number of channels. + + Args: + signal_key: string, used to select a signal from the dictionary + output by __getitem__ + + Returns: + Number of channels for the selected signal. + """ + # Assumption: whole dataset has the same number of channels + item = self.__getitem__(0) + + if item[signal_key].ndim == 1: + return 1 + elif item[signal_key].ndim == 2: + return item[signal_key].shape[0] + else: + raise RuntimeError( + f'Unexpected number of dimension for signal {signal_key} with shape {item[signal_key].shape}' + ) + + def __getitem__(self, index: int) -> Dict[str, torch.Tensor]: + """Return a single example from the dataset. + + Args: + index: integer index of an example in the collection + + Returns: + Dictionary providing mapping from signal to its tensor. + For example: + ``` + { + 'input_signal': input_signal_tensor, + 'target_signal': target_signal_tensor, + } + ``` + """ + example = self.collection[index] + output = self.audio_processor.process(example=example) + + return output + + def __len__(self) -> int: + """Return the number of examples in the dataset. + """ + return len(self.collection) + + def _collate_fn(self, batch) -> Tuple[torch.Tensor]: + """Collate items in a batch. + """ + return self.output_type(*_audio_collate_fn(batch)) + + +AudioToTargetExample = namedtuple( + typename='AudioToTargetExample', field_names='input_signal input_length target_signal target_length' +) + + +class AudioToTargetDataset(BaseAudioDataset): + """A dataset for audio-to-audio tasks where the goal is to use + an input signal to recover the corresponding target signal. + + Each line of the manifest file is expected to have the following format + ``` + { + 'input_key': 'path/to/input.wav', + 'target_key': 'path/to/path_to_target.wav', + 'duration': duration_of_input, + } + ``` + + Additionally, multiple audio files may be provided for each key in the manifest, for example, + ``` + { + 'input_key': 'path/to/input.wav', + 'target_key': ['path/to/path_to_target_ch0.wav', 'path/to/path_to_target_ch1.wav'], + 'duration': duration_of_input, + } + ``` + + Keys for input and target signals can be configured in the constructor (`input_key` and `target_key`). + + Args: + manifest_filepath: Path to manifest file in a format described above. + sample_rate: Sample rate for loaded audio signals. + input_key: Key pointing to input audio files in the manifest + target_key: Key pointing to target audio files in manifest + audio_duration: Optional duration of each item returned by __getitem__. + If `None`, complete audio will be loaded. + If set, a random subsegment will be loaded synchronously from + target and audio, i.e., with the same start and end point. + random_offset: If `True`, offset will be randomized when loading a subsegment + from a file. + max_duration: If audio exceeds this length, do not include in dataset. + min_duration: If audio is less than this length, do not include in dataset. + max_utts: Limit number of utterances. + input_channel_selector: Optional, select subset of channels from each input audio file. + If `None`, all channels will be loaded. + target_channel_selector: Optional, select subset of channels from each input audio file. + If `None`, all channels will be loaded. + """ + + def __init__( + self, + manifest_filepath: str, + sample_rate: int, + input_key: str, + target_key: str, + audio_duration: Optional[float] = None, + random_offset: bool = False, + max_duration: Optional[float] = None, + min_duration: Optional[float] = None, + max_utts: Optional[int] = None, + input_channel_selector: Optional[int] = None, + target_channel_selector: Optional[int] = None, + ): + audio_to_manifest_key = { + 'input_signal': input_key, + 'target_signal': target_key, + } + + collection = collections.AudioCollection( + manifest_files=manifest_filepath, + audio_to_manifest_key=audio_to_manifest_key, + min_duration=min_duration, + max_duration=max_duration, + max_number=max_utts, + ) + + audio_processor = ASRAudioProcessor(sample_rate=sample_rate, random_offset=random_offset,) + audio_processor.sync_setup = SignalSetup( + signals=['input_signal', 'target_signal'], + duration=audio_duration, + channel_selectors=[input_channel_selector, target_channel_selector], + ) + + super().__init__(collection=collection, audio_processor=audio_processor, output_type=AudioToTargetExample) + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + + Returns: + Ordered dictionary in the following form: + ``` + { + 'input_signal': batched single- or multi-channel format, + 'input_length': batched original length of each input signal + 'target_signal': batched single- or multi-channel format, + 'target_length': batched original length of each target signal + } + ``` + """ + sc_audio_type = NeuralType(('B', 'T'), AudioSignal()) + mc_audio_type = NeuralType(('B', 'C', 'T'), AudioSignal()) + + return OrderedDict( + input_signal=sc_audio_type if self.num_channels('input_signal') == 1 else mc_audio_type, + input_length=NeuralType(('B',), LengthsType()), + target_signal=sc_audio_type if self.num_channels('target_signal') == 1 else mc_audio_type, + target_length=NeuralType(('B',), LengthsType()), + ) + + +AudioToTargetWithReferenceExample = namedtuple( + typename='AudioToTargetWithReferenceExample', + field_names='input_signal input_length target_signal target_length reference_signal reference_length', +) + + +class AudioToTargetWithReferenceDataset(BaseAudioDataset): + """A dataset for audio-to-audio tasks where the goal is to use + an input signal to recover the corresponding target signal and an + additional reference signal is available. + + This can be used, for example, when a reference signal is + available from + - enrollment utterance for the target signal + - echo reference from playback + - reference from another sensor that correlates with the target signal + + Each line of the manifest file is expected to have the following format + ``` + { + 'input_key': 'path/to/input.wav', + 'target_key': 'path/to/path_to_target.wav', + 'reference_key': 'path/to/path_to_reference.wav', + 'duration': duration_of_input, + } + ``` + + Keys for input, target and reference signals can be configured in the constructor. + + Args: + manifest_filepath: Path to manifest file in a format described above. + sample_rate: Sample rate for loaded audio signals. + input_key: Key pointing to input audio files in the manifest + target_key: Key pointing to target audio files in manifest + reference_key: Key pointing to reference audio files in manifest + audio_duration: Optional duration of each item returned by __getitem__. + If `None`, complete audio will be loaded. + If set, a random subsegment will be loaded synchronously from + target and audio, i.e., with the same start and end point. + random_offset: If `True`, offset will be randomized when loading a subsegment + from a file. + max_duration: If audio exceeds this length, do not include in dataset. + min_duration: If audio is less than this length, do not include in dataset. + max_utts: Limit number of utterances. + input_channel_selector: Optional, select subset of channels from each input audio file. + If `None`, all channels will be loaded. + target_channel_selector: Optional, select subset of channels from each input audio file. + If `None`, all channels will be loaded. + reference_channel_selector: Optional, select subset of channels from each input audio file. + If `None`, all channels will be loaded. + reference_is_synchronized: If True, it is assumed that the reference signal is synchronized + with the input signal, so the same subsegment will be loaded as for + input and target. If False, reference signal will be loaded independently + from input and target. + reference_duration: Optional, can be used to set a fixed duration of the reference utterance. If `None`, + complete audio file will be loaded. + """ + + def __init__( + self, + manifest_filepath: str, + sample_rate: int, + input_key: str, + target_key: str, + reference_key: str, + audio_duration: Optional[float] = None, + random_offset: bool = False, + max_duration: Optional[float] = None, + min_duration: Optional[float] = None, + max_utts: Optional[int] = None, + input_channel_selector: Optional[int] = None, + target_channel_selector: Optional[int] = None, + reference_channel_selector: Optional[int] = None, + reference_is_synchronized: bool = True, + reference_duration: Optional[float] = None, + ): + audio_to_manifest_key = { + 'input_signal': input_key, + 'target_signal': target_key, + 'reference_signal': reference_key, + } + + collection = collections.AudioCollection( + manifest_files=manifest_filepath, + audio_to_manifest_key=audio_to_manifest_key, + min_duration=min_duration, + max_duration=max_duration, + max_number=max_utts, + ) + + audio_processor = ASRAudioProcessor(sample_rate=sample_rate, random_offset=random_offset,) + + if reference_is_synchronized: + audio_processor.sync_setup = SignalSetup( + signals=['input_signal', 'target_signal', 'reference_signal'], + duration=audio_duration, + channel_selectors=[input_channel_selector, target_channel_selector, reference_channel_selector], + ) + else: + audio_processor.sync_setup = SignalSetup( + signals=['input_signal', 'target_signal'], + duration=audio_duration, + channel_selectors=[input_channel_selector, target_channel_selector], + ) + audio_processor.async_setup = SignalSetup( + signals=['reference_signal'], + duration=[reference_duration], + channel_selectors=[reference_channel_selector], + ) + + super().__init__( + collection=collection, audio_processor=audio_processor, output_type=AudioToTargetWithReferenceExample + ) + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + + Returns: + Ordered dictionary in the following form: + ``` + { + 'input_signal': batched single- or multi-channel format, + 'input_length': batched original length of each input signal + 'target_signal': batched single- or multi-channel format, + 'target_length': batched original length of each target signal + 'reference_signal': single- or multi-channel format, + 'reference_length': original length of each reference signal + } + ``` + """ + sc_audio_type = NeuralType(('B', 'T'), AudioSignal()) + mc_audio_type = NeuralType(('B', 'C', 'T'), AudioSignal()) + + return OrderedDict( + input_signal=sc_audio_type if self.num_channels('input_signal') == 1 else mc_audio_type, + input_length=NeuralType(('B',), LengthsType()), + target_signal=sc_audio_type if self.num_channels('target_signal') == 1 else mc_audio_type, + target_length=NeuralType(('B',), LengthsType()), + reference_signal=sc_audio_type if self.num_channels('reference_signal') == 1 else mc_audio_type, + reference_length=NeuralType(('B',), LengthsType()), + ) + + +AudioToTargetWithEmbeddingExample = namedtuple( + typename='AudioToTargetWithEmbeddingExample', + field_names='input_signal input_length target_signal target_length embedding_vector embedding_length', +) + + +class AudioToTargetWithEmbeddingDataset(BaseAudioDataset): + """A dataset for audio-to-audio tasks where the goal is to use + an input signal to recover the corresponding target signal and an + additional embedding signal. It is assumed that the embedding + is in a form of a vector. + + Each line of the manifest file is expected to have the following format + ``` + { + input_key: 'path/to/input.wav', + target_key: 'path/to/path_to_target.wav', + embedding_key: 'path/to/path_to_reference.npy', + 'duration': duration_of_input, + } + ``` + + Keys for input, target and embedding signals can be configured in the constructor. + + Args: + manifest_filepath: Path to manifest file in a format described above. + sample_rate: Sample rate for loaded audio signals. + input_key: Key pointing to input audio files in the manifest + target_key: Key pointing to target audio files in manifest + embedding_key: Key pointing to embedding files in manifest + audio_duration: Optional duration of each item returned by __getitem__. + If `None`, complete audio will be loaded. + If set, a random subsegment will be loaded synchronously from + target and audio, i.e., with the same start and end point. + random_offset: If `True`, offset will be randomized when loading a subsegment + from a file. + max_duration: If audio exceeds this length, do not include in dataset. + min_duration: If audio is less than this length, do not include in dataset. + max_utts: Limit number of utterances. + input_channel_selector: Optional, select subset of channels from each input audio file. + If `None`, all channels will be loaded. + target_channel_selector: Optional, select subset of channels from each input audio file. + If `None`, all channels will be loaded. + """ + + def __init__( + self, + manifest_filepath: str, + sample_rate: int, + input_key: str, + target_key: str, + embedding_key: str, + audio_duration: Optional[float] = None, + random_offset: bool = False, + max_duration: Optional[float] = None, + min_duration: Optional[float] = None, + max_utts: Optional[int] = None, + input_channel_selector: Optional[int] = None, + target_channel_selector: Optional[int] = None, + ): + audio_to_manifest_key = { + 'input_signal': input_key, + 'target_signal': target_key, + 'embedding_vector': embedding_key, + } + + collection = collections.AudioCollection( + manifest_files=manifest_filepath, + audio_to_manifest_key=audio_to_manifest_key, + min_duration=min_duration, + max_duration=max_duration, + max_number=max_utts, + ) + + audio_processor = ASRAudioProcessor(sample_rate=sample_rate, random_offset=random_offset,) + audio_processor.sync_setup = SignalSetup( + signals=['input_signal', 'target_signal'], + duration=audio_duration, + channel_selectors=[input_channel_selector, target_channel_selector], + ) + audio_processor.embedding_setup = SignalSetup(signals=['embedding_vector']) + + super().__init__( + collection=collection, audio_processor=audio_processor, output_type=AudioToTargetWithEmbeddingExample + ) + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + + Returns: + Ordered dictionary in the following form: + ``` + { + 'input_signal': batched single- or multi-channel format, + 'input_length': batched original length of each input signal + 'target_signal': batched single- or multi-channel format, + 'target_length': batched original length of each target signal + 'embedding_vector': batched embedded vector format, + 'embedding_length': batched original length of each embedding vector + } + ``` + """ + sc_audio_type = NeuralType(('B', 'T'), AudioSignal()) + mc_audio_type = NeuralType(('B', 'C', 'T'), AudioSignal()) + + return OrderedDict( + input_signal=sc_audio_type if self.num_channels('input_signal') == 1 else mc_audio_type, + input_length=NeuralType(('B',), LengthsType()), + target_signal=sc_audio_type if self.num_channels('target_signal') == 1 else mc_audio_type, + target_length=NeuralType(('B',), LengthsType()), + embedding_vector=NeuralType(('B', 'D'), EncodedRepresentation()), + embedding_length=NeuralType(('B',), LengthsType()), + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_audio_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_audio_dataset.py new file mode 100644 index 0000000..b296d64 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_audio_dataset.py @@ -0,0 +1,95 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.data import audio_to_audio + + +def get_audio_to_target_dataset(config: dict) -> audio_to_audio.AudioToTargetDataset: + """Instantiates an audio-to-audio dataset. + + Args: + config: Config of AudioToTargetDataset. + + Returns: + An instance of AudioToTargetDataset + """ + dataset = audio_to_audio.AudioToTargetDataset( + manifest_filepath=config['manifest_filepath'], + sample_rate=config['sample_rate'], + input_key=config['input_key'], + target_key=config['target_key'], + audio_duration=config.get('audio_duration', None), + random_offset=config.get('random_offset', False), + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + max_utts=config.get('max_utts', 0), + input_channel_selector=config.get('input_channel_selector', None), + target_channel_selector=config.get('target_channel_selector', None), + ) + return dataset + + +def get_audio_to_target_with_reference_dataset(config: dict) -> audio_to_audio.AudioToTargetWithReferenceDataset: + """Instantiates an audio-to-audio dataset. + + Args: + config: Config of AudioToTargetWithReferenceDataset. + + Returns: + An instance of AudioToTargetWithReferenceDataset + """ + dataset = audio_to_audio.AudioToTargetWithReferenceDataset( + manifest_filepath=config['manifest_filepath'], + sample_rate=config['sample_rate'], + input_key=config['input_key'], + target_key=config['target_key'], + reference_key=config['reference_key'], + audio_duration=config.get('audio_duration', None), + random_offset=config.get('random_offset', False), + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + max_utts=config.get('max_utts', 0), + input_channel_selector=config.get('input_channel_selector', None), + target_channel_selector=config.get('target_channel_selector', None), + reference_channel_selector=config.get('reference_channel_selector', None), + reference_is_synchronized=config.get('reference_is_synchronized', True), + reference_duration=config.get('reference_duration', None), + ) + return dataset + + +def get_audio_to_target_with_embedding_dataset(config: dict) -> audio_to_audio.AudioToTargetWithEmbeddingDataset: + """Instantiates an audio-to-audio dataset. + + Args: + config: Config of AudioToTargetWithEmbeddingDataset. + + Returns: + An instance of AudioToTargetWithEmbeddingDataset + """ + dataset = audio_to_audio.AudioToTargetWithEmbeddingDataset( + manifest_filepath=config['manifest_filepath'], + sample_rate=config['sample_rate'], + input_key=config['input_key'], + target_key=config['target_key'], + embedding_key=config['embedding_key'], + audio_duration=config.get('audio_duration', None), + random_offset=config.get('random_offset', False), + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + max_utts=config.get('max_utts', 0), + input_channel_selector=config.get('input_channel_selector', None), + target_channel_selector=config.get('target_channel_selector', None), + ) + return dataset diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_ctm_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_ctm_dataset.py new file mode 100644 index 0000000..5450305 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_ctm_dataset.py @@ -0,0 +1,95 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +from dataclasses import dataclass +from pathlib import Path +from typing import Any, List, Tuple + +from nemo.collections.asr.data.audio_to_text_dataset import ASRPredictionWriter +from nemo.utils import logging + + +@dataclass +class FrameCtmUnit: + """A container class for one CTM unit with start and length countable in frames. + """ + + label: str + start_frame: int + length: int + probability: float + + def __repr__(self) -> str: + return f"{self.label}\t({self.probability:1.3f}): [{self.start_frame:6d}, {self.length:6d}]" + + @property + def end_frame(self): + return self.start_frame + self.length + + def to_ctm_str(self, time_per_frame: int) -> str: + """Represents the data as part of the CTM line. + + The CTM line format is + + This method prepares the last four entities.""" + return f"{self.start_frame * time_per_frame :.3f} {self.length * time_per_frame :.3f} {self.label} {self.probability :1.3f}" + + +class ASRCTMPredictionWriter(ASRPredictionWriter): + def __init__(self, dataset, output_file: str, output_ctm_dir: str, time_per_frame: float): + super().__init__(dataset, output_file) + self.output_ctm_dir = output_ctm_dir + self.time_per_frame = time_per_frame + os.makedirs(self.output_ctm_dir, exist_ok=True) + + def write_ctm(self, name, filepath, frameCtmUnits): + with open(filepath, "tw", encoding="utf-8") as f: + for unit in frameCtmUnits: + f.write(f"{name} 1 {unit.to_ctm_str(self.time_per_frame)}\n") + + def write_on_batch_end( + self, + trainer, + pl_module: 'LightningModule', + prediction: Tuple[int, List[FrameCtmUnit]], + batch_indices: List[int], + batch: Any, + batch_idx: int, + dataloader_idx: int, + ): + for sample_id, units in prediction: + sample = self.dataset.get_manifest_sample(sample_id) + with_ctm = True + if len(units) == 0: + logging.warning( + f"""Do not producing CTM output for item `{sample.audio_file}`. + Check if text is empty or if duration is too short: `{sample.text_raw}`, {sample.duration}""" + ) + with_ctm = False + item = {} + item["audio_filepath"] = sample.audio_file + item["duration"] = sample.duration + item["text"] = sample.text_raw + if with_ctm: + utt_name = Path(sample.audio_file).stem + ctm_filepath = os.path.join(self.output_ctm_dir, utt_name) + ".ctm" + self.write_ctm(utt_name, ctm_filepath, units) + item["ctm_filepath"] = ctm_filepath + else: + item["ctm_filepath"] = "" + self.outf.write(json.dumps(item) + "\n") + self.samples_num += 1 + return diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_diar_label.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_diar_label.py new file mode 100644 index 0000000..a1cb6d0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_diar_label.py @@ -0,0 +1,853 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from collections import OrderedDict +from statistics import mode +from typing import Dict, Optional + +import torch + +from nemo.collections.asr.parts.utils.offline_clustering import get_argmin_mat +from nemo.collections.asr.parts.utils.speaker_utils import convert_rttm_line, prepare_split_data +from nemo.collections.common.parts.preprocessing.collections import DiarizationSpeechLabel +from nemo.core.classes import Dataset +from nemo.core.neural_types import AudioSignal, EncodedRepresentation, LengthsType, NeuralType, ProbsType + + +def get_scale_mapping_list(uniq_timestamps): + """ + Call get_argmin_mat function to find the index of the non-base-scale segment that is closest to the + given base-scale segment. For each scale and each segment, a base-scale segment is assigned. + + Args: + uniq_timestamps: (dict) + The dictionary containing embeddings, timestamps and multiscale weights. + If uniq_timestamps contains only one scale, single scale diarization is performed. + + Returns: + scale_mapping_argmat (torch.tensor): + + The element at the m-th row and the n-th column of the scale mapping matrix indicates the (m+1)-th scale + segment index which has the closest center distance with (n+1)-th segment in the base scale. + + - Example: + `scale_mapping_argmat[2][101] = 85` + + In the above example, the code snippet means that 86-th segment in the 3rd scale (python index is 2) is + mapped to the 102-th segment in the base scale. Thus, the longer segments bound to have more repeating + numbers since multiple base scale segments (since the base scale has the shortest length) fall into the + range of the longer segments. At the same time, each row contains N numbers of indices where N is number + of segments in the base-scale (i.e., the finest scale). + """ + timestamps_in_scales = [] + for key, val in uniq_timestamps['scale_dict'].items(): + timestamps_in_scales.append(torch.tensor(val['time_stamps'])) + session_scale_mapping_list = get_argmin_mat(timestamps_in_scales) + scale_mapping_argmat = [[] for _ in range(len(uniq_timestamps['scale_dict'].keys()))] + for scale_idx in range(len(session_scale_mapping_list)): + scale_mapping_argmat[scale_idx] = session_scale_mapping_list[scale_idx] + scale_mapping_argmat = torch.stack(scale_mapping_argmat) + return scale_mapping_argmat + + +def extract_seg_info_from_rttm(uniq_id, rttm_lines, mapping_dict=None, target_spks=None): + """ + Get RTTM lines containing speaker labels, start time and end time. target_spks contains two targeted + speaker indices for creating groundtruth label files. Only speakers in target_spks variable will be + included in the output lists. + + Args: + uniq_id (str): + Unique file ID that refers to an input audio file and corresponding RTTM (Annotation) file. + rttm_lines (list): + List containing RTTM lines in str format. + mapping_dict (dict): + Mapping between the estimated speakers and the speakers in the ground-truth annotation. + `mapping_dict` variable is only provided when the inference mode is running in sequence-eval mode. + Sequence eval mode uses the mapping between the estimated speakers and the speakers in ground-truth annotation. + Returns: + rttm_tup (tuple): + Tuple containing lists of start time, end time and speaker labels. + + """ + stt_list, end_list, speaker_list, pairwise_infer_spks = [], [], [], [] + if target_spks: + inv_map = {v: k for k, v in mapping_dict.items()} + for spk_idx in target_spks: + spk_str = f'speaker_{spk_idx}' + if spk_str in inv_map: + pairwise_infer_spks.append(inv_map[spk_str]) + for rttm_line in rttm_lines: + start, end, speaker = convert_rttm_line(rttm_line) + if target_spks is None or speaker in pairwise_infer_spks: + end_list.append(end) + stt_list.append(start) + speaker_list.append(speaker) + rttm_tup = (stt_list, end_list, speaker_list) + return rttm_tup + + +def assign_frame_level_spk_vector(rttm_timestamps, round_digits, frame_per_sec, target_spks, min_spks=2): + """ + Create a multi-dimensional vector sequence containing speaker timestamp information in RTTM. + The unit-length is the frame shift length of the acoustic feature. The feature-level annotations + `fr_level_target` will later be converted to base-segment level diarization label. + + Args: + rttm_timestamps (list): + List containing start and end time for each speaker segment label. + stt_list, end_list and speaker_list are contained. + frame_per_sec (int): + Number of feature frames per second. This quantity is determined by window_stride variable in preprocessing module. + target_spks (tuple): + Speaker indices that are generated from combinations. If there are only one or two speakers, + only a single target_spks variable is generated. + + Returns: + fr_level_target (torch.tensor): + Tensor containing label for each feature level frame. + """ + stt_list, end_list, speaker_list = rttm_timestamps + if len(speaker_list) == 0: + return None + else: + sorted_speakers = sorted(list(set(speaker_list))) + total_fr_len = int(max(end_list) * (10 ** round_digits)) + spk_num = max(len(sorted_speakers), min_spks) + speaker_mapping_dict = {rttm_key: x_int for x_int, rttm_key in enumerate(sorted_speakers)} + fr_level_target = torch.zeros(total_fr_len, spk_num) + + # If RTTM is not provided, then there is no speaker mapping dict in target_spks. + # Thus, return a zero-filled tensor as a placeholder. + for count, (stt, end, spk_rttm_key) in enumerate(zip(stt_list, end_list, speaker_list)): + stt, end = round(stt, round_digits), round(end, round_digits) + spk = speaker_mapping_dict[spk_rttm_key] + stt_fr, end_fr = int(round(stt, 2) * frame_per_sec), int(round(end, round_digits) * frame_per_sec) + fr_level_target[stt_fr:end_fr, spk] = 1 + return fr_level_target + + +class _AudioMSDDTrainDataset(Dataset): + """ + Dataset class that loads a json file containing paths to audio files, + RTTM files and number of speakers. This Dataset class is designed for + training or fine-tuning speaker embedding extractor and diarization decoder + at the same time. + + Example: + {"audio_filepath": "/path/to/audio_0.wav", "num_speakers": 2, + "rttm_filepath": "/path/to/diar_label_0.rttm} + ... + {"audio_filepath": "/path/to/audio_n.wav", "num_speakers": 2, + "rttm_filepath": "/path/to/diar_label_n.rttm} + + Args: + manifest_filepath (str): + Path to input manifest json files. + multiscale_args_dict (dict): + Dictionary containing the parameters for multiscale segmentation and clustering. + emb_dir (str): + Path to a temporary folder where segmentation information for embedding extraction is saved. + soft_label_thres (float): + Threshold that determines the label of each segment based on RTTM file information. + featurizer: + Featurizer instance for generating features from the raw waveform. + window_stride (float): + Window stride for acoustic feature. This value is used for calculating the numbers of feature-level frames. + emb_batch_size (int): + Number of embedding vectors that are trained with attached computational graphs. + pairwise_infer (bool): + This variable should be True if dataloader is created for an inference task. + random_flip (bool): + If True, the two labels and input signals are randomly flipped per every epoch while training. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports.""" + output_types = { + "features": NeuralType(('B', 'T'), AudioSignal()), + "feature_length": NeuralType(('B'), LengthsType()), + "ms_seg_timestamps": NeuralType(('B', 'C', 'T', 'D'), LengthsType()), + "ms_seg_counts": NeuralType(('B', 'C'), LengthsType()), + "clus_label_index": NeuralType(('B', 'T'), LengthsType()), + "scale_mapping": NeuralType(('B', 'C', 'T'), LengthsType()), + "targets": NeuralType(('B', 'T', 'C'), ProbsType()), + } + + return output_types + + def __init__( + self, + *, + manifest_filepath: str, + multiscale_args_dict: str, + emb_dir: str, + soft_label_thres: float, + featurizer, + window_stride, + emb_batch_size, + pairwise_infer: bool, + random_flip: bool = True, + global_rank: int = 0, + ): + super().__init__() + self.collection = DiarizationSpeechLabel( + manifests_files=manifest_filepath.split(','), + emb_dict=None, + clus_label_dict=None, + pairwise_infer=pairwise_infer, + ) + self.featurizer = featurizer + self.multiscale_args_dict = multiscale_args_dict + self.emb_dir = emb_dir + self.round_digits = 2 + self.decim = 10 ** self.round_digits + self.soft_label_thres = soft_label_thres + self.pairwise_infer = pairwise_infer + self.max_spks = 2 + self.frame_per_sec = int(1 / window_stride) + self.emb_batch_size = emb_batch_size + self.random_flip = random_flip + self.global_rank = global_rank + self.manifest_filepath = manifest_filepath + self.multiscale_timestamp_dict = prepare_split_data( + self.manifest_filepath, self.emb_dir, self.multiscale_args_dict, self.global_rank, + ) + + def __len__(self): + return len(self.collection) + + def assign_labels_to_longer_segs(self, uniq_id, base_scale_clus_label): + """ + Assign the generated speaker labels from the base scale (the finest scale) to the longer scales. + This process is needed to get the cluster labels for each scale. The cluster labels are needed to + calculate the cluster-average speaker embedding for each scale. + + Args: + uniq_id (str): + Unique sample ID for training. + base_scale_clus_label (torch.tensor): + Tensor variable containing the speaker labels for the base-scale segments. + + Returns: + per_scale_clus_label (torch.tensor): + Tensor variable containing the speaker labels for each segment in each scale. + Note that the total length of the speaker label sequence differs over scale since + each scale has a different number of segments for the same session. + + scale_mapping (torch.tensor): + Matrix containing the segment indices of each scale. scale_mapping is necessary for reshaping the + multiscale embeddings to form an input matrix for the MSDD model. + """ + per_scale_clus_label = [] + self.scale_n = len(self.multiscale_timestamp_dict[uniq_id]['scale_dict']) + uniq_scale_mapping = get_scale_mapping_list(self.multiscale_timestamp_dict[uniq_id]) + for scale_index in range(self.scale_n): + new_clus_label = [] + scale_seq_len = len(self.multiscale_timestamp_dict[uniq_id]["scale_dict"][scale_index]["time_stamps"]) + for seg_idx in range(scale_seq_len): + if seg_idx in uniq_scale_mapping[scale_index]: + seg_clus_label = mode(base_scale_clus_label[uniq_scale_mapping[scale_index] == seg_idx]) + else: + seg_clus_label = 0 if len(new_clus_label) == 0 else new_clus_label[-1] + new_clus_label.append(seg_clus_label) + per_scale_clus_label.extend(new_clus_label) + per_scale_clus_label = torch.tensor(per_scale_clus_label) + return per_scale_clus_label, uniq_scale_mapping + + def get_diar_target_labels(self, uniq_id, sample, fr_level_target): + """ + Convert frame-level diarization target variable into segment-level target variable. Since the granularity is reduced + from frame level (10ms) to segment level (100ms~500ms), we need a threshold value, `soft_label_thres`, which determines + the label of each segment based on the overlap between a segment range (start and end time) and the frame-level target variable. + + Args: + uniq_id (str): + Unique file ID that refers to an input audio file and corresponding RTTM (Annotation) file. + sample: + `DiarizationSpeechLabel` instance containing sample information such as audio filepath and RTTM filepath. + fr_level_target (torch.tensor): + Tensor containing label for each feature-level frame. + + Returns: + seg_target (torch.tensor): + Tensor containing binary speaker labels for base-scale segments. + base_clus_label (torch.tensor): + Representative speaker label for each segment. This variable only has one speaker label for each base-scale segment. + -1 means that there is no corresponding speaker in the target_spks tuple. + """ + seg_target_list, base_clus_label = [], [] + self.scale_n = len(self.multiscale_timestamp_dict[uniq_id]['scale_dict']) + subseg_time_stamp_list = self.multiscale_timestamp_dict[uniq_id]["scale_dict"][self.scale_n - 1]["time_stamps"] + for (seg_stt, seg_end) in subseg_time_stamp_list: + seg_stt_fr, seg_end_fr = int(seg_stt * self.frame_per_sec), int(seg_end * self.frame_per_sec) + soft_label_vec_sess = torch.sum(fr_level_target[seg_stt_fr:seg_end_fr, :], axis=0) / ( + seg_end_fr - seg_stt_fr + ) + label_int_sess = torch.argmax(soft_label_vec_sess) + soft_label_vec = soft_label_vec_sess.unsqueeze(0)[:, sample.target_spks].squeeze() + if label_int_sess in sample.target_spks and torch.sum(soft_label_vec_sess) > 0: + label_int = sample.target_spks.index(label_int_sess) + else: + label_int = -1 + label_vec = (soft_label_vec > self.soft_label_thres).float() + seg_target_list.append(label_vec.detach()) + base_clus_label.append(label_int) + seg_target = torch.stack(seg_target_list) + base_clus_label = torch.tensor(base_clus_label) + return seg_target, base_clus_label + + def parse_rttm_for_ms_targets(self, sample): + """ + Generate target tensor variable by extracting groundtruth diarization labels from an RTTM file. + This function converts (start, end, speaker_id) format into base-scale (the finest scale) segment level + diarization label in a matrix form. + + Example of seg_target: + [[0., 1.], [0., 1.], [1., 1.], [1., 0.], [1., 0.], ..., [0., 1.]] + + Args: + sample: + `DiarizationSpeechLabel` instance containing sample information such as audio filepath and RTTM filepath. + target_spks (tuple): + Speaker indices that are generated from combinations. If there are only one or two speakers, + only a single target_spks tuple is generated. + + Returns: + clus_label_index (torch.tensor): + Groundtruth clustering label (cluster index for each segment) from RTTM files for training purpose. + seg_target (torch.tensor): + Tensor variable containing hard-labels of speaker activity in each base-scale segment. + scale_mapping (torch.tensor): + Matrix containing the segment indices of each scale. scale_mapping is necessary for reshaping the + multiscale embeddings to form an input matrix for the MSDD model. + + """ + rttm_lines = open(sample.rttm_file).readlines() + uniq_id = self.get_uniq_id_with_range(sample) + rttm_timestamps = extract_seg_info_from_rttm(uniq_id, rttm_lines) + fr_level_target = assign_frame_level_spk_vector( + rttm_timestamps, self.round_digits, self.frame_per_sec, target_spks=sample.target_spks + ) + seg_target, base_clus_label = self.get_diar_target_labels(uniq_id, sample, fr_level_target) + clus_label_index, scale_mapping = self.assign_labels_to_longer_segs(uniq_id, base_clus_label) + return clus_label_index, seg_target, scale_mapping + + def get_uniq_id_with_range(self, sample, deci=3): + """ + Generate unique training sample ID from unique file ID, offset and duration. The start-end time added + unique ID is required for identifying the sample since multiple short audio samples are generated from a single + audio file. The start time and end time of the audio stream uses millisecond units if `deci=3`. + + Args: + sample: + `DiarizationSpeechLabel` instance from collections. + + Returns: + uniq_id (str): + Unique sample ID which includes start and end time of the audio stream. + Example: abc1001_3122_6458 + + """ + bare_uniq_id = os.path.splitext(os.path.basename(sample.rttm_file))[0] + offset = str(int(round(sample.offset, deci) * pow(10, deci))) + endtime = str(int(round(sample.offset + sample.duration, deci) * pow(10, deci))) + uniq_id = f"{bare_uniq_id}_{offset}_{endtime}" + return uniq_id + + def get_ms_seg_timestamps(self, sample): + """ + Get start and end time of segments in each scale. + + Args: + sample: + `DiarizationSpeechLabel` instance from preprocessing.collections + Returns: + ms_seg_timestamps (torch.tensor): + Tensor containing Multiscale segment timestamps. + ms_seg_counts (torch.tensor): + Number of segments for each scale. This information is used for reshaping embedding batch + during forward propagation. + """ + uniq_id = self.get_uniq_id_with_range(sample) + ms_seg_timestamps_list = [] + max_seq_len = len(self.multiscale_timestamp_dict[uniq_id]["scale_dict"][self.scale_n - 1]["time_stamps"]) + ms_seg_counts = [0 for _ in range(self.scale_n)] + for scale_idx in range(self.scale_n): + scale_ts_list = [] + for k, (seg_stt, seg_end) in enumerate( + self.multiscale_timestamp_dict[uniq_id]["scale_dict"][scale_idx]["time_stamps"] + ): + stt, end = ( + int((seg_stt - sample.offset) * self.frame_per_sec), + int((seg_end - sample.offset) * self.frame_per_sec), + ) + scale_ts_list.append(torch.tensor([stt, end]).detach()) + ms_seg_counts[scale_idx] = len( + self.multiscale_timestamp_dict[uniq_id]["scale_dict"][scale_idx]["time_stamps"] + ) + scale_ts = torch.stack(scale_ts_list) + scale_ts_padded = torch.cat([scale_ts, torch.zeros(max_seq_len - len(scale_ts_list), 2)], dim=0) + ms_seg_timestamps_list.append(scale_ts_padded.detach()) + ms_seg_timestamps = torch.stack(ms_seg_timestamps_list) + ms_seg_counts = torch.tensor(ms_seg_counts) + return ms_seg_timestamps, ms_seg_counts + + def __getitem__(self, index): + sample = self.collection[index] + if sample.offset is None: + sample.offset = 0 + clus_label_index, targets, scale_mapping = self.parse_rttm_for_ms_targets(sample) + features = self.featurizer.process(sample.audio_file, offset=sample.offset, duration=sample.duration) + feature_length = torch.tensor(features.shape[0]).long() + ms_seg_timestamps, ms_seg_counts = self.get_ms_seg_timestamps(sample) + if self.random_flip: + torch.manual_seed(index) + flip = torch.cat([torch.randperm(self.max_spks), torch.tensor(-1).unsqueeze(0)]) + clus_label_index, targets = flip[clus_label_index], targets[:, flip[: self.max_spks]] + return features, feature_length, ms_seg_timestamps, ms_seg_counts, clus_label_index, scale_mapping, targets + + +class _AudioMSDDInferDataset(Dataset): + """ + Dataset class that loads a json file containing paths to audio files, + RTTM files and number of speakers. This Dataset class is built for diarization inference and + evaluation. Speaker embedding sequences, segment timestamps, cluster-average speaker embeddings + are loaded from memory and fed into the dataloader. + + Example: + {"audio_filepath": "/path/to/audio_0.wav", "num_speakers": 2, + "rttm_filepath": "/path/to/diar_label_0.rttm} + ... + {"audio_filepath": "/path/to/audio_n.wav", "num_speakers": 2, + "rttm_filepath": "/path/to/diar_label_n.rttm} + + Args: + manifest_filepath (str): + Path to input manifest json files. + emb_dict (dict): + Dictionary containing cluster-average embeddings and speaker mapping information. + emb_seq (dict): + Dictionary containing multiscale speaker embedding sequence, scale mapping and corresponding segment timestamps. + clus_label_dict (dict): + Subsegment-level (from base-scale) speaker labels from clustering results. + soft_label_thres (float): + A threshold that determines the label of each segment based on RTTM file information. + featurizer: + Featurizer instance for generating features from raw waveform. + seq_eval_mode (bool): + If True, F1 score will be calculated for each speaker pair during inference mode. + window_stride (float): + Window stride for acoustic feature. This value is used for calculating the numbers of feature-level frames. + use_single_scale_clus (bool): + Use only one scale for clustering instead of using multiple scales of embeddings for clustering. + pairwise_infer (bool): + This variable should be True if dataloader is created for an inference task. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports.""" + output_types = OrderedDict( + { + "ms_emb_seq": NeuralType(('B', 'T', 'C', 'D'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + "ms_avg_embs": NeuralType(('B', 'C', 'D', 'C'), EncodedRepresentation()), + "targets": NeuralType(('B', 'T', 'C'), ProbsType()), + } + ) + return output_types + + def __init__( + self, + *, + manifest_filepath: str, + emb_dict: Dict, + emb_seq: Dict, + clus_label_dict: Dict, + soft_label_thres: float, + seq_eval_mode: bool, + window_stride: float, + use_single_scale_clus: bool, + pairwise_infer: bool, + ): + super().__init__() + self.collection = DiarizationSpeechLabel( + manifests_files=manifest_filepath.split(','), + emb_dict=emb_dict, + clus_label_dict=clus_label_dict, + seq_eval_mode=seq_eval_mode, + pairwise_infer=pairwise_infer, + ) + self.emb_dict = emb_dict + self.emb_seq = emb_seq + self.clus_label_dict = clus_label_dict + self.round_digits = 2 + self.decim = 10 ** self.round_digits + self.frame_per_sec = int(1 / window_stride) + self.soft_label_thres = soft_label_thres + self.pairwise_infer = pairwise_infer + self.max_spks = 2 + self.use_single_scale_clus = use_single_scale_clus + self.seq_eval_mode = seq_eval_mode + + def __len__(self): + return len(self.collection) + + def parse_rttm_multiscale(self, sample): + """ + Generate target tensor variable by extracting groundtruth diarization labels from an RTTM file. + This function is only used when ``self.seq_eval_mode=True`` and RTTM files are provided. This function converts + (start, end, speaker_id) format into base-scale (the finest scale) segment level diarization label in a matrix + form to create target matrix. + + Args: + sample: + DiarizationSpeechLabel instance containing sample information such as audio filepath and RTTM filepath. + target_spks (tuple): + Two Indices of targeted speakers for evaluation. + Example of target_spks: (2, 3) + Returns: + seg_target (torch.tensor): + Tensor variable containing hard-labels of speaker activity in each base-scale segment. + """ + if sample.rttm_file is None: + raise ValueError(f"RTTM file is not provided for this sample {sample}") + rttm_lines = open(sample.rttm_file).readlines() + uniq_id = os.path.splitext(os.path.basename(sample.rttm_file))[0] + mapping_dict = self.emb_dict[max(self.emb_dict.keys())][uniq_id]['mapping'] + rttm_timestamps = extract_seg_info_from_rttm(uniq_id, rttm_lines, mapping_dict, sample.target_spks) + fr_level_target = assign_frame_level_spk_vector( + rttm_timestamps, self.round_digits, self.frame_per_sec, sample.target_spks + ) + seg_target = self.get_diar_target_labels_from_fr_target(uniq_id, fr_level_target) + return seg_target + + def get_diar_target_labels_from_fr_target(self, uniq_id, fr_level_target): + """ + Generate base-scale level binary diarization label from frame-level target matrix. For the given frame-level + speaker target matrix fr_level_target, we count the number of frames that belong to each speaker and calculate + ratios for each speaker into the `soft_label_vec` variable. Finally, `soft_label_vec` variable is compared with `soft_label_thres` + to determine whether a label vector should contain 0 or 1 for each speaker bin. Note that seg_target variable has + dimension of (number of base-scale segments x 2) dimension. + + Example of seg_target: + [[0., 1.], [0., 1.], [1., 1.], [1., 0.], [1., 0.], ..., [0., 1.]] + + Args: + uniq_id (str): + Unique file ID that refers to an input audio file and corresponding RTTM (Annotation) file. + fr_level_target (torch.tensor): + frame-level binary speaker annotation (1: exist 0: non-exist) generated from RTTM file. + + Returns: + seg_target (torch.tensor): + Tensor variable containing binary hard-labels of speaker activity in each base-scale segment. + + """ + if fr_level_target is None: + return None + else: + seg_target_list = [] + for (seg_stt, seg_end, label_int) in self.clus_label_dict[uniq_id]: + seg_stt_fr, seg_end_fr = int(seg_stt * self.frame_per_sec), int(seg_end * self.frame_per_sec) + soft_label_vec = torch.sum(fr_level_target[seg_stt_fr:seg_end_fr, :], axis=0) / ( + seg_end_fr - seg_stt_fr + ) + label_vec = (soft_label_vec > self.soft_label_thres).int() + seg_target_list.append(label_vec) + seg_target = torch.stack(seg_target_list) + return seg_target + + def __getitem__(self, index): + sample = self.collection[index] + if sample.offset is None: + sample.offset = 0 + + uniq_id = os.path.splitext(os.path.basename(sample.audio_file))[0] + scale_n = len(self.emb_dict.keys()) + _avg_embs = torch.stack([self.emb_dict[scale_index][uniq_id]['avg_embs'] for scale_index in range(scale_n)]) + + if self.pairwise_infer: + avg_embs = _avg_embs[:, :, self.collection[index].target_spks] + else: + avg_embs = _avg_embs + + if avg_embs.shape[2] > self.max_spks: + raise ValueError( + f" avg_embs.shape[2] {avg_embs.shape[2]} should be less than or equal to self.max_num_speakers {self.max_spks}" + ) + + feats = [] + for scale_index in range(scale_n): + repeat_mat = self.emb_seq["session_scale_mapping"][uniq_id][scale_index] + feats.append(self.emb_seq[scale_index][uniq_id][repeat_mat, :]) + feats_out = torch.stack(feats).permute(1, 0, 2) + feats_len = feats_out.shape[0] + + if self.seq_eval_mode: + targets = self.parse_rttm_multiscale(sample) + else: + targets = torch.zeros(feats_len, 2).float() + + return feats_out, feats_len, targets, avg_embs + + +def _msdd_train_collate_fn(self, batch): + """ + Collate batch of variables that are needed for raw waveform to diarization label training. + The following variables are included in training/validation batch: + + Args: + batch (tuple): + Batch tuple containing the variables for the diarization training. + Returns: + features (torch.tensor): + Raw waveform samples (time series) loaded from the audio_filepath in the input manifest file. + feature lengths (time series sample length): + A list of lengths of the raw waveform samples. + ms_seg_timestamps (torch.tensor): + Matrix containing the start time and end time (timestamps) for each segment and each scale. + ms_seg_timestamps is needed for extracting acoustic features from raw waveforms. + ms_seg_counts (torch.tensor): + Matrix containing The number of segments for each scale. ms_seg_counts is necessary for reshaping + the input matrix for the MSDD model. + clus_label_index (torch.tensor): + Groundtruth Clustering label (cluster index for each segment) from RTTM files for training purpose. + clus_label_index is necessary for calculating cluster-average embedding. + scale_mapping (torch.tensor): + Matrix containing the segment indices of each scale. scale_mapping is necessary for reshaping the + multiscale embeddings to form an input matrix for the MSDD model. + targets (torch.tensor): + Groundtruth Speaker label for the given input embedding sequence. + """ + packed_batch = list(zip(*batch)) + features, feature_length, ms_seg_timestamps, ms_seg_counts, clus_label_index, scale_mapping, targets = packed_batch + features_list, feature_length_list = [], [] + ms_seg_timestamps_list, ms_seg_counts_list, scale_clus_label_list, scale_mapping_list, targets_list = ( + [], + [], + [], + [], + [], + ) + + max_raw_feat_len = max([x.shape[0] for x in features]) + max_target_len = max([x.shape[0] for x in targets]) + max_total_seg_len = max([x.shape[0] for x in clus_label_index]) + + for feat, feat_len, ms_seg_ts, ms_seg_ct, scale_clus, scl_map, tgt in batch: + seq_len = tgt.shape[0] + pad_feat = (0, max_raw_feat_len - feat_len) + pad_tgt = (0, 0, 0, max_target_len - seq_len) + pad_sm = (0, max_target_len - seq_len) + pad_ts = (0, 0, 0, max_target_len - seq_len) + pad_sc = (0, max_total_seg_len - scale_clus.shape[0]) + padded_feat = torch.nn.functional.pad(feat, pad_feat) + padded_tgt = torch.nn.functional.pad(tgt, pad_tgt) + padded_sm = torch.nn.functional.pad(scl_map, pad_sm) + padded_ms_seg_ts = torch.nn.functional.pad(ms_seg_ts, pad_ts) + padded_scale_clus = torch.nn.functional.pad(scale_clus, pad_sc) + + features_list.append(padded_feat) + feature_length_list.append(feat_len.clone().detach()) + ms_seg_timestamps_list.append(padded_ms_seg_ts) + ms_seg_counts_list.append(ms_seg_ct.clone().detach()) + scale_clus_label_list.append(padded_scale_clus) + scale_mapping_list.append(padded_sm) + targets_list.append(padded_tgt) + + features = torch.stack(features_list) + feature_length = torch.stack(feature_length_list) + ms_seg_timestamps = torch.stack(ms_seg_timestamps_list) + clus_label_index = torch.stack(scale_clus_label_list) + ms_seg_counts = torch.stack(ms_seg_counts_list) + scale_mapping = torch.stack(scale_mapping_list) + targets = torch.stack(targets_list) + return features, feature_length, ms_seg_timestamps, ms_seg_counts, clus_label_index, scale_mapping, targets + + +def _msdd_infer_collate_fn(self, batch): + """ + Collate batch of feats (speaker embeddings), feature lengths, target label sequences and cluster-average embeddings. + + Args: + batch (tuple): + Batch tuple containing feats, feats_len, targets and ms_avg_embs. + Returns: + feats (torch.tensor): + Collated speaker embedding with unified length. + feats_len (torch.tensor): + The actual length of each embedding sequence without zero padding. + targets (torch.tensor): + Groundtruth Speaker label for the given input embedding sequence. + ms_avg_embs (torch.tensor): + Cluster-average speaker embedding vectors. + """ + + packed_batch = list(zip(*batch)) + feats, feats_len, targets, ms_avg_embs = packed_batch + feats_list, flen_list, targets_list, ms_avg_embs_list = [], [], [], [] + max_audio_len = max(feats_len) + max_target_len = max([x.shape[0] for x in targets]) + + for feature, feat_len, target, ivector in batch: + flen_list.append(feat_len) + ms_avg_embs_list.append(ivector) + if feat_len < max_audio_len: + pad_a = (0, 0, 0, 0, 0, max_audio_len - feat_len) + pad_t = (0, 0, 0, max_target_len - target.shape[0]) + padded_feature = torch.nn.functional.pad(feature, pad_a) + padded_target = torch.nn.functional.pad(target, pad_t) + feats_list.append(padded_feature) + targets_list.append(padded_target) + else: + targets_list.append(target.clone().detach()) + feats_list.append(feature.clone().detach()) + + feats = torch.stack(feats_list) + feats_len = torch.tensor(flen_list) + targets = torch.stack(targets_list) + ms_avg_embs = torch.stack(ms_avg_embs_list) + return feats, feats_len, targets, ms_avg_embs + + +class AudioToSpeechMSDDTrainDataset(_AudioMSDDTrainDataset): + """ + Dataset class that loads a json file containing paths to audio files, + rttm files and number of speakers. This Dataset class is designed for + training or fine-tuning speaker embedding extractor and diarization decoder + at the same time. + + Example: + {"audio_filepath": "/path/to/audio_0.wav", "num_speakers": 2, + "rttm_filepath": "/path/to/diar_label_0.rttm} + ... + {"audio_filepath": "/path/to/audio_n.wav", "num_speakers": 2, + "rttm_filepath": "/path/to/diar_label_n.rttm} + + Args: + manifest_filepath (str): + Path to input manifest json files. + multiscale_args_dict (dict): + Dictionary containing the parameters for multiscale segmentation and clustering. + emb_dir (str): + Path to a temporary folder where segmentation information for embedding extraction is saved. + soft_label_thres (float): + A threshold that determines the label of each segment based on RTTM file information. + featurizer: + Featurizer instance for generating features from the raw waveform. + window_stride (float): + Window stride for acoustic feature. This value is used for calculating the numbers of feature-level frames. + emb_batch_size (int): + Number of embedding vectors that are trained with attached computational graphs. + pairwise_infer (bool): + This variable should be True if dataloader is created for an inference task. + """ + + def __init__( + self, + *, + manifest_filepath: str, + multiscale_args_dict: Dict, + emb_dir: str, + soft_label_thres: float, + featurizer, + window_stride, + emb_batch_size, + pairwise_infer: bool, + global_rank: int, + ): + super().__init__( + manifest_filepath=manifest_filepath, + multiscale_args_dict=multiscale_args_dict, + emb_dir=emb_dir, + soft_label_thres=soft_label_thres, + featurizer=featurizer, + window_stride=window_stride, + emb_batch_size=emb_batch_size, + pairwise_infer=pairwise_infer, + global_rank=global_rank, + ) + + def msdd_train_collate_fn(self, batch): + return _msdd_train_collate_fn(self, batch) + + +class AudioToSpeechMSDDInferDataset(_AudioMSDDInferDataset): + """ + Dataset class that loads a json file containing paths to audio files, + rttm files and number of speakers. The created labels are used for diarization inference. + + Example: + {"audio_filepath": "/path/to/audio_0.wav", "num_speakers": 2, + "rttm_filepath": "/path/to/diar_label_0.rttm} + ... + {"audio_filepath": "/path/to/audio_n.wav", "num_speakers": 2, + "rttm_filepath": "/path/to/diar_label_n.rttm} + + Args: + manifest_filepath (str): + Path to input manifest json files. + emb_dict (dict): + Dictionary containing cluster-average embeddings and speaker mapping information. + emb_seq (dict): + Dictionary containing multiscale speaker embedding sequence, scale mapping and corresponding segment timestamps. + clus_label_dict (dict): + Subsegment-level (from base-scale) speaker labels from clustering results. + soft_label_thres (float): + Threshold that determines speaker labels of segments depending on the overlap with groundtruth speaker timestamps. + featurizer: + Featurizer instance for generating features from raw waveform. + use_single_scale_clus (bool): + Use only one scale for clustering instead of using multiple scales of embeddings for clustering. + seq_eval_mode (bool): + If True, F1 score will be calculated for each speaker pair during inference mode. + window_stride (float): + Window stride for acoustic feature. This value is used for calculating the numbers of feature-level frames. + pairwise_infer (bool): + If True, this Dataset class operates in inference mode. In inference mode, a set of speakers in the input audio + is split into multiple pairs of speakers and speaker tuples (e.g. 3 speakers: [(0,1), (1,2), (0,2)]) and then + fed into the MSDD to merge the individual results. + """ + + def __init__( + self, + *, + manifest_filepath: str, + emb_dict: Dict, + emb_seq: Dict, + clus_label_dict: Dict, + soft_label_thres: float, + use_single_scale_clus: bool, + seq_eval_mode: bool, + window_stride: float, + pairwise_infer: bool, + ): + super().__init__( + manifest_filepath=manifest_filepath, + emb_dict=emb_dict, + emb_seq=emb_seq, + clus_label_dict=clus_label_dict, + soft_label_thres=soft_label_thres, + use_single_scale_clus=use_single_scale_clus, + window_stride=window_stride, + seq_eval_mode=seq_eval_mode, + pairwise_infer=pairwise_infer, + ) + + def msdd_infer_collate_fn(self, batch): + return _msdd_infer_collate_fn(self, batch) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_label.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_label.py new file mode 100644 index 0000000..4ff27f9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_label.py @@ -0,0 +1,1289 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import io +import os +from typing import Dict, List, Optional, Union + +import torch +import webdataset as wds + +from nemo.collections.asr.data.audio_to_text import cache_datastore_manifests, expand_sharded_filepaths +from nemo.collections.asr.parts.preprocessing.features import WaveformFeaturizer +from nemo.collections.asr.parts.preprocessing.segment import available_formats as valid_sf_formats +from nemo.collections.common.parts.preprocessing import collections +from nemo.core.classes import Dataset, IterableDataset +from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, NeuralType, RegressionValuesType +from nemo.utils import logging +from nemo.utils.distributed import webdataset_split_by_workers + +# List of valid file formats (prioritized by order of importance) +VALID_FILE_FORMATS = ';'.join(['wav', 'mp3', 'flac', 'opus'] + [fmt.lower() for fmt in valid_sf_formats.keys()]) + + +def repeat_signal(signal: torch.Tensor, sig_len: int, required_length: int) -> torch.Tensor: + """repeat signal to make short signal to have required_length + Args: + signal (Tensor): input signal + sig_len (int): length of input signal + required_length (int): length of generated signal + Returns: + signal (Tensor): generated signal of required_length by repeating itself. + """ + sub: torch.Tensor = torch.tensor([]) + repeat = int(required_length // sig_len) + rem = int(required_length % sig_len) + sub: torch.Tensor = torch.tensor([]) + rep_sig: torch.Tensor = torch.cat(repeat * [signal]) + if rem > 0: + sub = signal[-rem:] + signal = torch.cat((rep_sig, sub)) + else: + signal = rep_sig + return signal + + +def normalize(signal): + """normalize signal + Args: + signal(FloatTensor): signal to be normalized. + """ + signal_minusmean = signal - signal.mean() + return signal_minusmean / signal_minusmean.abs().max() + + +def count_occurence(manifest_file_id): + """Count number of wav files in Dict manifest_file_id. Use for _TarredAudioToLabelDataset. + Args: + manifest_file_id (Dict): Dict of files and their corresponding id. {'A-sub0' : 1, ..., 'S-sub10':100} + Returns: + count (Dict): Dict of wav files {'A' : 2, ..., 'S':10} + """ + count = dict() + for i in manifest_file_id: + audio_filename = i.split("-sub")[0] + count[audio_filename] = count.get(audio_filename, 0) + 1 + return count + + +def _speech_collate_fn(batch, pad_id): + """collate batch of audio sig, audio len, tokens, tokens len + Args: + batch (Optional[FloatTensor], Optional[LongTensor], LongTensor, + LongTensor): A tuple of tuples of signal, signal lengths, + encoded tokens, and encoded tokens length. This collate func + assumes the signals are 1d torch tensors (i.e. mono audio). + """ + _, audio_lengths, _, tokens_lengths = zip(*batch) + max_audio_len = 0 + has_audio = audio_lengths[0] is not None + if has_audio: + max_audio_len = max(audio_lengths).item() + max_tokens_len = max(tokens_lengths).item() + + audio_signal, tokens = [], [] + for sig, sig_len, tokens_i, tokens_i_len in batch: + if has_audio: + sig_len = sig_len.item() + if sig_len < max_audio_len: + pad = (0, max_audio_len - sig_len) + sig = torch.nn.functional.pad(sig, pad) + audio_signal.append(sig) + tokens_i_len = tokens_i_len.item() + if tokens_i_len < max_tokens_len: + pad = (0, max_tokens_len - tokens_i_len) + tokens_i = torch.nn.functional.pad(tokens_i, pad, value=pad_id) + tokens.append(tokens_i) + + if has_audio: + audio_signal = torch.stack(audio_signal) + audio_lengths = torch.stack(audio_lengths) + else: + audio_signal, audio_lengths = None, None + tokens = torch.stack(tokens) + tokens_lengths = torch.stack(tokens_lengths) + + return audio_signal, audio_lengths, tokens, tokens_lengths + + +def _fixed_seq_collate_fn(self, batch): + """collate batch of audio sig, audio len, tokens, tokens len + Args: + batch (Optional[FloatTensor], Optional[LongTensor], LongTensor, + LongTensor): A tuple of tuples of signal, signal lengths, + encoded tokens, and encoded tokens length. This collate func + assumes the signals are 1d torch tensors (i.e. mono audio). + """ + _, audio_lengths, _, tokens_lengths = zip(*batch) + + has_audio = audio_lengths[0] is not None + fixed_length = int(max(audio_lengths)) + + audio_signal, tokens, new_audio_lengths = [], [], [] + for sig, sig_len, tokens_i, _ in batch: + if has_audio: + sig_len = sig_len.item() + chunck_len = sig_len - fixed_length + + if chunck_len < 0: + repeat = fixed_length // sig_len + rem = fixed_length % sig_len + sub = sig[-rem:] if rem > 0 else torch.tensor([]) + rep_sig = torch.cat(repeat * [sig]) + sig = torch.cat((rep_sig, sub)) + new_audio_lengths.append(torch.tensor(fixed_length)) + + audio_signal.append(sig) + + tokens.append(tokens_i) + + if has_audio: + audio_signal = torch.stack(audio_signal) + audio_lengths = torch.stack(new_audio_lengths) + else: + audio_signal, audio_lengths = None, None + tokens = torch.stack(tokens) + tokens_lengths = torch.stack(tokens_lengths) + + return audio_signal, audio_lengths, tokens, tokens_lengths + + +def _vad_frame_seq_collate_fn(self, batch): + """collate batch of audio sig, audio len, tokens, tokens len + Args: + batch (Optional[FloatTensor], Optional[LongTensor], LongTensor, + LongTensor): A tuple of tuples of signal, signal lengths, + encoded tokens, and encoded tokens length. This collate func + assumes the signals are 1d torch tensors (i.e. mono audio). + batch size equals to 1. + """ + slice_length = int(self.featurizer.sample_rate * self.window_length_in_sec) + _, audio_lengths, _, tokens_lengths = zip(*batch) + slice_length = int(min(slice_length, max(audio_lengths))) + shift = int(self.featurizer.sample_rate * self.shift_length_in_sec) + has_audio = audio_lengths[0] is not None + + audio_signal, num_slices, tokens, audio_lengths = [], [], [], [] + + append_len_start = slice_length // 2 + append_len_end = slice_length - slice_length // 2 + for sig, sig_len, tokens_i, _ in batch: + if self.normalize_audio: + sig = normalize(sig) + start = torch.zeros(append_len_start) + end = torch.zeros(append_len_end) + sig = torch.cat((start, sig, end)) + sig_len += slice_length + + if has_audio: + slices = torch.div(sig_len - slice_length, shift, rounding_mode='trunc') + for slice_id in range(slices): + start_idx = slice_id * shift + end_idx = start_idx + slice_length + signal = sig[start_idx:end_idx] + audio_signal.append(signal) + + num_slices.append(slices) + tokens.extend([tokens_i] * slices) + audio_lengths.extend([slice_length] * slices) + + if has_audio: + audio_signal = torch.stack(audio_signal) + audio_lengths = torch.tensor(audio_lengths) + else: + audio_signal, audio_lengths = None, None + + tokens = torch.stack(tokens) + tokens_lengths = torch.tensor(num_slices) + return audio_signal, audio_lengths, tokens, tokens_lengths + + +class _AudioLabelDataset(Dataset): + """ + Dataset that loads tensors via a json file containing paths to audio files, + labels, and durations and offsets(in seconds). Each new line is a + different sample. Example below: + and their target labels. JSON files should be of the following format:: + {"audio_filepath": "/path/to/audio_wav_0.wav", "duration": time_in_sec_0, "label": \ +target_label_0, "offset": offset_in_sec_0} + ... + {"audio_filepath": "/path/to/audio_wav_n.wav", "duration": time_in_sec_n, "label": \ +target_label_n, "offset": offset_in_sec_n} + Args: + manifest_filepath (Union[str, List[str]]): Dataset parameter. Path to JSON containing data. + labels (list): Dataset parameter. List of target classes that can be output by the speaker recognition model. + featurizer + min_duration (float): Dataset parameter. All training files which have a duration less than min_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to 0.1. + max_duration (float): Dataset parameter. + All training files which have a duration more than max_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to None. + trim (bool): Whether to use trim silence from beginning and end of audio signal using librosa.effects.trim(). + Defaults to False. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + + output_types = { + 'audio_signal': NeuralType( + ('B', 'T'), + AudioSignal(freq=self._sample_rate) + if self is not None and hasattr(self, '_sample_rate') + else AudioSignal(), + ), + 'a_sig_length': NeuralType(tuple('B'), LengthsType()), + } + + if self.is_regression_task: + output_types.update( + { + 'targets': NeuralType(tuple('B'), RegressionValuesType()), + 'targets_length': NeuralType(tuple('B'), LengthsType()), + } + ) + else: + + output_types.update( + {'label': NeuralType(tuple('B'), LabelsType()), 'label_length': NeuralType(tuple('B'), LengthsType()),} + ) + + return output_types + + def __init__( + self, + *, + manifest_filepath: Union[str, List[str]], + labels: List[str], + featurizer, + min_duration: Optional[float] = 0.1, + max_duration: Optional[float] = None, + trim: bool = False, + is_regression_task: bool = False, + cal_labels_occurrence: Optional[bool] = False, + ): + super().__init__() + if isinstance(manifest_filepath, str): + manifest_filepath = manifest_filepath.split(',') + cache_datastore_manifests(manifest_filepaths=manifest_filepath, cache_audio=True) + self.collection = collections.ASRSpeechLabel( + manifests_files=manifest_filepath, + min_duration=min_duration, + max_duration=max_duration, + is_regression_task=is_regression_task, + cal_labels_occurrence=cal_labels_occurrence, + ) + + self.featurizer = featurizer + self.trim = trim + self.is_regression_task = is_regression_task + + if not is_regression_task: + self.labels = labels if labels else self.collection.uniq_labels + self.num_classes = len(self.labels) if self.labels is not None else 1 + self.label2id, self.id2label = {}, {} + self.id2occurrence, self.labels_occurrence = {}, [] + + for label_id, label in enumerate(self.labels): + self.label2id[label] = label_id + self.id2label[label_id] = label + if cal_labels_occurrence: + self.id2occurrence[label_id] = self.collection.labels_occurrence[label] + + if cal_labels_occurrence: + self.labels_occurrence = [self.id2occurrence[k] for k in sorted(self.id2occurrence)] + + for idx in range(len(self.labels[:5])): + logging.debug(" label id {} and its mapped label {}".format(idx, self.id2label[idx])) + + else: + self.labels = [] + self.num_classes = 1 + + def __len__(self): + return len(self.collection) + + def __getitem__(self, index): + sample = self.collection[index] + + offset = sample.offset + + if offset is None: + offset = 0 + + features = self.featurizer.process(sample.audio_file, offset=offset, duration=sample.duration, trim=self.trim) + f, fl = features, torch.tensor(features.shape[0]).long() + + if not self.is_regression_task: + t = torch.tensor(self.label2id[sample.label]).long() + else: + t = torch.tensor(sample.label).float() + + tl = torch.tensor(1).long() # For compatibility with collate_fn used later + + return f, fl, t, tl + + +# Ported from https://github.com/NVIDIA/OpenSeq2Seq/blob/master/open_seq2seq/data/speech2text/speech_commands.py +class AudioToClassificationLabelDataset(_AudioLabelDataset): + """ + Dataset that loads tensors via a json file containing paths to audio + files, command class, and durations (in seconds). Each new line is a + different sample. Example below: + {"audio_filepath": "/path/to/audio_wav_0.wav", "duration": time_in_sec_0, "label": \ + target_label_0, "offset": offset_in_sec_0} + ... + {"audio_filepath": "/path/to/audio_wav_n.wav", "duration": time_in_sec_n, "label": \ + target_label_n, "offset": offset_in_sec_n} + Args: + manifest_filepath (Union[str, List[str]]): Path to manifest json as described above. Can + be comma-separated paths. + labels (Optional[list]): String containing all the possible labels to map to + if None then automatically picks from ASRSpeechLabel collection. + featurizer: Initialized featurizer class that converts paths of + audio to feature tensors + max_duration: If audio exceeds this length, do not include in dataset + min_duration: If audio is less than this length, do not include + in dataset + trim: Boolean flag whether to trim the audio + """ + + def _collate_fn(self, batch): + return _speech_collate_fn(batch, pad_id=0) + + +class AudioToSpeechLabelDataset(_AudioLabelDataset): + """ + Dataset that loads tensors via a json file containing paths to audio + files, command class, and durations (in seconds). Each new line is a + different sample. Example below: + {"audio_filepath": "/path/to/audio_wav_0.wav", "duration": time_in_sec_0, "label": \ + target_label_0, "offset": offset_in_sec_0} + ... + {"audio_filepath": "/path/to/audio_wav_n.wav", "duration": time_in_sec_n, "label": \ + target_label_n, "offset": offset_in_sec_n} + Args: + manifest_filepath (Union[str, List[str]]): Path to manifest json as described above. Can + be comma-separated paths. + labels (Optional[list]): String containing all the possible labels to map to + if None then automatically picks from ASRSpeechLabel collection. + min_duration (float): Dataset parameter. + All training files which have a duration less than min_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to 0.1. + max_duration (float): Dataset parameter. + All training files which have a duration more than max_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to None. + trim (bool): Whether to use trim silence from beginning and end + of audio signal using librosa.effects.trim(). + Defaults to False. + window_length_in_sec (float): length of window/slice (in seconds) + Use this for speaker recognition and VAD tasks. + shift_length_in_sec (float): amount of shift of window for generating the frame for VAD task in a batch + Use this for VAD task during inference. + normalize_audio (bool): Whether to normalize audio signal. + Defaults to False. + is_regression_task (bool): Whether the dataset is for a regression task instead of classification. + Defaults to False. + cal_labels_occurrence (bool): Whether to calculate occurrence of labels + Defaults to False. + """ + + def __init__( + self, + *, + manifest_filepath: Union[str, List[str]], + labels: List[str], + featurizer, + min_duration: Optional[float] = 0.1, + max_duration: Optional[float] = None, + trim: bool = False, + window_length_in_sec: Optional[float] = 8, + shift_length_in_sec: Optional[float] = 1, + normalize_audio: bool = False, + is_regression_task: bool = False, + cal_labels_occurrence: Optional[bool] = False, + ): + self.window_length_in_sec = window_length_in_sec + self.shift_length_in_sec = shift_length_in_sec + self.normalize_audio = normalize_audio + + logging.debug("Window/slice length considered for collate func is {}".format(self.window_length_in_sec)) + logging.debug("Shift length considered for collate func is {}".format(self.shift_length_in_sec)) + + super().__init__( + manifest_filepath=manifest_filepath, + labels=labels, + featurizer=featurizer, + min_duration=min_duration, + max_duration=max_duration, + trim=trim, + is_regression_task=is_regression_task, + cal_labels_occurrence=cal_labels_occurrence, + ) + + def fixed_seq_collate_fn(self, batch): + return _fixed_seq_collate_fn(self, batch) + + def vad_frame_seq_collate_fn(self, batch): + return _vad_frame_seq_collate_fn(self, batch) + + +class _TarredAudioLabelDataset(IterableDataset): + """ + A similar Dataset to the AudioLabelDataSet, but which loads tarred audio files. + + Accepts a single comma-separated JSON manifest file (in the same style as for the AudioToSpeechLabelDataset), + as well as the path(s) to the tarball(s) containing the wav files. Each line of the manifest should + contain the information for one audio file, including at least the label and name of the audio + file within the tarball. + + Valid formats for the audio_tar_filepaths argument include: + (1) a single string that can be brace-expanded, e.g. 'path/to/audio.tar' or 'path/to/audio_{1..100}.tar.gz', or + (2) a list of file paths that will not be brace-expanded, e.g. ['audio_1.tar', 'audio_2.tar', ...]. + + Note: For brace expansion in (1), there may be cases where `{x..y}` syntax cannot be used due to shell interference. + This occurs most commonly inside SLURM scripts. Therefore we provide a few equivalent replacements. + Supported opening braces - { <=> (, [, < and the special tag _OP_. + Supported closing braces - } <=> ), ], > and the special tag _CL_. + For SLURM based tasks, we suggest the use of the special tags for ease of use. + + See the documentation for more information about accepted data and input formats. + + If using multiple processes the number of shards should be divisible by the number of workers to ensure an + even split among workers. If it is not divisible, logging will give a warning but training will proceed. + In addition, if using mutiprocessing, each shard MUST HAVE THE SAME NUMBER OF ENTRIES after filtering + is applied. We currently do not check for this, but your program may hang if the shards are uneven! + + Notice that a few arguments are different from the AudioLabelDataSet; for example, shuffle (bool) has been + replaced by shuffle_n (int). + + Additionally, please note that the len() of this DataLayer is assumed to be the length of the manifest + after filtering. An incorrect manifest length may lead to some DataLoader issues down the line. + + Args: + audio_tar_filepaths: Either a list of audio tarball filepaths, or a + string (can be brace-expandable). + manifest_filepath (str): Path to the manifest. + labels (list): Dataset parameter. + List of target classes that can be output by the speaker recognition model. + featurizer + shuffle_n (int): How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + Defaults to 0. + min_duration (float): Dataset parameter. + All training files which have a duration less than min_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to 0.1. + max_duration (float): Dataset parameter. + All training files which have a duration more than max_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to None. + trim(bool): Whether to use trim silence from beginning and end + of audio signal using librosa.effects.trim(). + Defaults to False. + window_length_in_sec (float): length of slice/window (in seconds) # Pass this only for speaker recognition and VAD task + shift_length_in_sec (float): amount of shift of window for generating the frame for VAD task. in a batch # Pass this only for VAD task during inference. + normalize_audio (bool): Whether to normalize audio signal. Defaults to False. + shard_strategy (str): Tarred dataset shard distribution strategy chosen as a str value during ddp. + - `scatter`: The default shard strategy applied by WebDataset, where each node gets + a unique set of shards, which are permanently pre-allocated and never changed at runtime. + - `replicate`: Optional shard strategy, where each node gets all of the set of shards + available in the tarred dataset, which are permanently pre-allocated and never changed at runtime. + The benefit of replication is that it allows each node to sample data points from the entire + dataset independently of other nodes, and reduces dependence on the value of `shuffle_n`. + + .. warning:: + Replicated strategy allows every node to sample the entire set of available tarfiles, + and therefore more than one node may sample the same tarfile, and even sample the same + data points! As such, there is no assured guarantee that all samples in the dataset will be + sampled at least once during 1 epoch. Scattered strategy, on the other hand, on specific + occasions (when the number of shards is not divisible with ``world_size``), will not sample + the entire dataset. For these reasons it is not advisable to use tarred datasets as validation + or test datasets. + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 0. + is_regression_task (bool): Whether it is a regression task. Defualts to False. + """ + + def __init__( + self, + *, + audio_tar_filepaths: Union[str, List[str]], + manifest_filepath: Union[str, List[str]], + labels: List[str], + featurizer, + shuffle_n: int = 0, + min_duration: Optional[float] = 0.1, + max_duration: Optional[float] = None, + trim: bool = False, + shard_strategy: str = "scatter", + global_rank: int = 0, + world_size: int = 0, + is_regression_task: bool = False, + ): + cache_datastore_manifests(manifest_filepaths=manifest_filepath) + self.collection = collections.ASRSpeechLabel( + manifests_files=manifest_filepath, + min_duration=min_duration, + max_duration=max_duration, + index_by_file_id=True, # Must set this so the manifest lines can be indexed by file ID + ) + + self.file_occurence = count_occurence(self.collection.mapping) + + self.featurizer = featurizer + self.trim = trim + + self.labels = labels if labels else self.collection.uniq_labels + self.num_classes = len(self.labels) + + self.label2id, self.id2label = {}, {} + for label_id, label in enumerate(self.labels): + self.label2id[label] = label_id + self.id2label[label_id] = label + + for idx in range(len(self.labels[:5])): + logging.debug(" label id {} and its mapped label {}".format(idx, self.id2label[idx])) + + audio_tar_filepaths = expand_sharded_filepaths( + sharded_filepaths=audio_tar_filepaths, + shard_strategy=shard_strategy, + world_size=world_size, + global_rank=global_rank, + ) + # Put together WebDataset + self._dataset = wds.DataPipeline( + wds.SimpleShardList(urls=audio_tar_filepaths), + webdataset_split_by_workers, + wds.shuffle(shuffle_n), + wds.tarfile_to_samples(), + wds.rename(audio=VALID_FILE_FORMATS, key='__key__'), + wds.to_tuple('audio', 'key'), + self._filter, + wds.map(self._build_sample), + ) + + def _filter(self, iterator): + """This function is used to remove samples that have been filtered out by ASRSpeechLabel already. + Otherwise, we would get a KeyError as _build_sample attempts to find the manifest entry for a sample + that was filtered out (e.g. for duration). + Note that if using multi-GPU training, filtering may lead to an imbalance in samples in each shard, + which may make your code hang as one process will finish before the other. + """ + + class TarredAudioFilter: + def __init__(self, collection, file_occurence): + self.iterator = iterator + self.collection = collection + self.file_occurence = file_occurence + self._iterable = self._internal_generator() + + def __iter__(self): + self._iterable = self._internal_generator() + return self + + def __next__(self): + try: + values = next(self._iterable) + except StopIteration: + # reset generator + self._iterable = self._internal_generator() + values = next(self._iterable) + + return values + + def _internal_generator(self): + """ + WebDataset requires an Iterator, but we require an iterable that yields 1-or-more + values per value inside self.iterator. + + Therefore wrap the iterator with a generator function that will yield 1-or-more + values per sample in the iterator. + """ + for _, tup in enumerate(self.iterator): + audio_bytes, audio_filename = tup + + file_id, _ = os.path.splitext(os.path.basename(audio_filename)) + if audio_filename in self.file_occurence: + for j in range(0, self.file_occurence[file_id]): + if j == 0: + audio_filename = file_id + else: + audio_filename = file_id + "-sub" + str(j) + yield audio_bytes, audio_filename + + return TarredAudioFilter(self.collection, self.file_occurence) + + def _build_sample(self, tup): + """Builds the training sample by combining the data from the WebDataset with the manifest info. + """ + audio_bytes, audio_filename = tup + # Grab manifest entry from self.collection + file_id, _ = os.path.splitext(os.path.basename(audio_filename)) + + manifest_idx = self.collection.mapping[file_id] + manifest_entry = self.collection[manifest_idx] + + offset = manifest_entry.offset + if offset is None: + offset = 0 + + # Convert audio bytes to IO stream for processing (for SoundFile to read) + audio_filestream = io.BytesIO(audio_bytes) + features = self.featurizer.process( + audio_filestream, offset=offset, duration=manifest_entry.duration, trim=self.trim, + ) + + audio_filestream.close() + + # Audio features + f, fl = features, torch.tensor(features.shape[0]).long() + + t = self.label2id[manifest_entry.label] + tl = 1 # For compatibility with collate_fn used later + + return f, fl, torch.tensor(t).long(), torch.tensor(tl).long() + + def __iter__(self): + return self._dataset.__iter__() + + def __len__(self): + return len(self.collection) + + +class TarredAudioToClassificationLabelDataset(_TarredAudioLabelDataset): + """ + A similar Dataset to the AudioToClassificationLabelDataset, but which loads tarred audio files. + + Accepts a single comma-separated JSON manifest file (in the same style as for the AudioToClassificationLabelDataset), + as well as the path(s) to the tarball(s) containing the wav files. Each line of the manifest should + contain the information for one audio file, including at least the transcript and name of the audio + file within the tarball. + + Valid formats for the audio_tar_filepaths argument include: + (1) a single string that can be brace-expanded, e.g. 'path/to/audio.tar' or 'path/to/audio_{1..100}.tar.gz', or + (2) a list of file paths that will not be brace-expanded, e.g. ['audio_1.tar', 'audio_2.tar', ...]. + + See the WebDataset documentation for more information about accepted data and input formats. + + If using multiple processes the number of shards should be divisible by the number of workers to ensure an + even split among workers. If it is not divisible, logging will give a warning but training will proceed. + In addition, if using mutiprocessing, each shard MUST HAVE THE SAME NUMBER OF ENTRIES after filtering + is applied. We currently do not check for this, but your program may hang if the shards are uneven! + + Notice that a few arguments are different from the AudioToBPEDataset; for example, shuffle (bool) has been + replaced by shuffle_n (int). + + Additionally, please note that the len() of this DataLayer is assumed to be the length of the manifest + after filtering. An incorrect manifest length may lead to some DataLoader issues down the line. + + Args: + audio_tar_filepaths: Either a list of audio tarball filepaths, or a + string (can be brace-expandable). + manifest_filepath (str): Path to the manifest. + labels (list): Dataset parameter. + List of target classes that can be output by the speaker recognition model. + featurizer + shuffle_n (int): How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + Defaults to 0. + min_duration (float): Dataset parameter. + All training files which have a duration less than min_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to 0.1. + max_duration (float): Dataset parameter. + All training files which have a duration more than max_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to None. + trim(bool): Whether to use trim silence from beginning and end + of audio signal using librosa.effects.trim(). + Defaults to False. + shard_strategy (str): Tarred dataset shard distribution strategy chosen as a str value during ddp. + - `scatter`: The default shard strategy applied by WebDataset, where each node gets + a unique set of shards, which are permanently pre-allocated and never changed at runtime. + - `replicate`: Optional shard strategy, where each node gets all of the set of shards + available in the tarred dataset, which are permanently pre-allocated and never changed at runtime. + The benefit of replication is that it allows each node to sample data points from the entire + dataset independently of other nodes, and reduces dependence on value of `shuffle_n`. + + .. warning:: + Replicated strategy allows every node to sample the entire set of available tarfiles, + and therefore more than one node may sample the same tarfile, and even sample the same + data points! As such, there is no assured guarantee that all samples in the dataset will be + sampled at least once during 1 epoch. Scattered strategy, on the other hand, on specific + occasions (when the number of shards is not divisible with ``world_size``), will not sample + the entire dataset. For these reasons it is not advisable to use tarred datasets as validation + or test datasets. + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 0. + is_regression_task (bool): Whether it is a regression task. Defualts to False. + """ + + def _collate_fn(self, batch): + return _speech_collate_fn(batch, pad_id=0) + + +class TarredAudioToSpeechLabelDataset(_TarredAudioLabelDataset): + """ + A similar Dataset to the AudioToSpeechLabelDataset, but which loads tarred audio files. + + Accepts a single comma-separated JSON manifest file (in the same style as for the AudioToSpeechLabelDataset), + as well as the path(s) to the tarball(s) containing the wav files. Each line of the manifest should + contain the information for one audio file, including at least the transcript and name of the audio + file within the tarball. + + Valid formats for the audio_tar_filepaths argument include: + (1) a single string that can be brace-expanded, e.g. 'path/to/audio.tar' or 'path/to/audio_{1..100}.tar.gz', or + (2) a list of file paths that will not be brace-expanded, e.g. ['audio_1.tar', 'audio_2.tar', ...]. + + See the WebDataset documentation for more information about accepted data and input formats. + + If using multiple processes the number of shards should be divisible by the number of workers to ensure an + even split among workers. If it is not divisible, logging will give a warning but training will proceed. + In addition, if using mutiprocessing, each shard MUST HAVE THE SAME NUMBER OF ENTRIES after filtering + is applied. We currently do not check for this, but your program may hang if the shards are uneven! + + Notice that a few arguments are different from the AudioToBPEDataset; for example, shuffle (bool) has been + replaced by shuffle_n (int). + + Additionally, please note that the len() of this DataLayer is assumed to be the length of the manifest + after filtering. An incorrect manifest length may lead to some DataLoader issues down the line. + + Args: + audio_tar_filepaths: Either a list of audio tarball filepaths, or a + string (can be brace-expandable). + manifest_filepath (str): Path to the manifest. + labels (list): Dataset parameter. + List of target classes that can be output by the speaker recognition model. + featurizer + shuffle_n (int): How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + Defaults to 0. + min_duration (float): Dataset parameter. + All training files which have a duration less than min_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to 0.1. + max_duration (float): Dataset parameter. + All training files which have a duration more than max_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to None. + trim(bool): Whether to use trim silence from beginning and end + of audio signal using librosa.effects.trim(). + Defaults to False. + window_length_in_sec (float): time length of window/slice (in seconds) # Pass this only for speaker recognition and VAD task + shift_length_in_sec (float): amount of shift of window for generating the frame for VAD task. in a batch # Pass this only for VAD task during inference. + normalize_audio (bool): Whether to normalize audio signal. Defaults to False. + shard_strategy (str): Tarred dataset shard distribution strategy chosen as a str value during ddp. + - `scatter`: The default shard strategy applied by WebDataset, where each node gets + a unique set of shards, which are permanently pre-allocated and never changed at runtime. + - `replicate`: Optional shard strategy, where each node gets all of the set of shards + available in the tarred dataset, which are permanently pre-allocated and never changed at runtime. + The benefit of replication is that it allows each node to sample data points from the entire + dataset independently of other nodes, and reduces dependence on value of `shuffle_n`. + + .. warning:: + Replicated strategy allows every node to sample the entire set of available tarfiles, + and therefore more than one node may sample the same tarfile, and even sample the same + data points! As such, there is no assured guarantee that all samples in the dataset will be + sampled at least once during 1 epoch. Scattered strategy, on the other hand, on specific + occasions (when the number of shards is not divisible with ``world_size``), will not sample + the entire dataset. For these reasons it is not advisable to use tarred datasets as validation + or test datasets. + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 0. + """ + + def __init__( + self, + *, + audio_tar_filepaths: Union[str, List[str]], + manifest_filepath: Union[str, List[str]], + labels: List[str], + featurizer, + shuffle_n: int = 0, + min_duration: Optional[float] = 0.1, + max_duration: Optional[float] = None, + trim: bool = False, + window_length_in_sec: Optional[float] = 8, + shift_length_in_sec: Optional[float] = 1, + normalize_audio: bool = False, + shard_strategy: str = "scatter", + global_rank: int = 0, + world_size: int = 0, + ): + logging.info("Window/slice length considered for collate func is {}".format(window_length_in_sec)) + logging.info("Shift length considered for collate func is {}".format(shift_length_in_sec)) + self.window_length_in_sec = window_length_in_sec + self.shift_length_in_sec = shift_length_in_sec + self.normalize_audio = normalize_audio + + super().__init__( + audio_tar_filepaths=audio_tar_filepaths, + manifest_filepath=manifest_filepath, + labels=labels, + featurizer=featurizer, + shuffle_n=shuffle_n, + min_duration=min_duration, + max_duration=max_duration, + trim=trim, + shard_strategy=shard_strategy, + global_rank=global_rank, + world_size=world_size, + ) + + def fixed_seq_collate_fn(self, batch): + return _fixed_seq_collate_fn(self, batch) + + def sliced_seq_collate_fn(self, batch): + raise NotImplementedError + + def vad_frame_seq_collate_fn(self, batch): + return _vad_frame_seq_collate_fn(self, batch) + + +class AudioToMultiLabelDataset(Dataset): + """ + Dataset that loads a json file containing paths to audio files, durations (in seconds), and a sequence of labels. + Each new line is a different sample. Example below: + {"audio_filepath": "/path/to/audio_wav_0.wav", "duration": time_in_sec_0, "label": \ + "0 1 1 0 1", "offset": offset_in_sec_0} + ... + {"audio_filepath": "/path/to/audio_wav_n.wav", "duration": time_in_sec_n, "label": \ + "0 1 0 0 1", "offset": offset_in_sec_n} + Args: + manifest_filepath (Union[str, List[str]]): Path to manifest json as described above. Can + be comma-separated paths. + labels (Optional[list]): String containing all the possible labels to map to + if None then automatically picks from ASRSpeechLabel collection. + min_duration (float): Dataset parameter. + All training files which have a duration less than min_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to 0.1. + max_duration (float): Dataset parameter. + All training files which have a duration more than max_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to None. + trim (bool): Whether to use trim silence from beginning and end + of audio signal using librosa.effects.trim(). + Defaults to False. + window_length_in_sec (float): length of window/slice (in seconds) + Use this for speaker recognition and VAD tasks. + shift_length_in_sec (float): amount of shift of window for generating the frame for VAD task in a batch + Use this for VAD task during inference. + normalize_audio (bool): Whether to normalize audio signal. + Defaults to False. + is_regression_task (bool): Whether the dataset is for a regression task instead of classification. + Defaults to False. + cal_labels_occurrence (bool): Whether to calculate occurrence of labels + Defaults to False. + delimiter (Optional[str]): Delimiter to use when splitting the label string, default to None. + normalize_audio_db (Optional[float]): normalize audio signal to a target db, default to None. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + + output_types = { + 'audio_signal': NeuralType( + ('B', 'T'), + AudioSignal(freq=self._sample_rate) + if self is not None and hasattr(self, '_sample_rate') + else AudioSignal(), + ), + 'a_sig_length': NeuralType(tuple('B'), LengthsType()), + } + + if self.is_regression_task: + output_types.update( + { + 'targets': NeuralType(tuple('B, T'), RegressionValuesType()), + 'targets_length': NeuralType(tuple('B'), LengthsType()), + } + ) + else: + output_types.update( + {'label': NeuralType(('B', 'T'), LabelsType()), 'label_length': NeuralType(tuple('B'), LengthsType()),} + ) + + return output_types + + def __init__( + self, + *, + manifest_filepath: Union[str, List[str]], + sample_rate: int, + labels: Optional[List[str]] = None, + int_values: bool = False, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + min_duration: Optional[float] = 0.1, + max_duration: Optional[float] = None, + trim_silence: bool = False, + is_regression_task: bool = False, + cal_labels_occurrence: Optional[bool] = False, + delimiter: Optional[str] = None, + normalize_audio_db: Optional[float] = None, + ): + super().__init__() + if isinstance(manifest_filepath, str): + manifest_filepath = manifest_filepath.split(',') + + self.delimiter = delimiter + self.normalize_audio_db = normalize_audio_db + + self.collection = collections.ASRSpeechLabel( + manifests_files=manifest_filepath, + min_duration=min_duration, + max_duration=max_duration, + is_regression_task=is_regression_task, + cal_labels_occurrence=cal_labels_occurrence, + delimiter=delimiter, + ) + + self.featurizer = WaveformFeaturizer(sample_rate=sample_rate, int_values=int_values, augmentor=augmentor) + self.trim = trim_silence + self.is_regression_task = is_regression_task + self.id2occurrence = {} + self.labels_occurrence = None + + if not is_regression_task: + self.labels = labels if labels else self._get_label_set() + self.num_classes = len(self.labels) if self.labels is not None else 1 + self.label2id, self.id2label = {}, {} + for label_id, label in enumerate(self.labels): + self.label2id[label] = label_id + self.id2label[label_id] = label + if cal_labels_occurrence: + self.id2occurrence[label_id] = self.collection.labels_occurrence[label] + self.labels_occurrence.append(self.id2occurrence[label_id]) + + for idx in range(len(self.labels[:5])): + logging.debug(" label id {} and its mapped label {}".format(idx, self.id2label[idx])) + else: + self.labels = [] + self.num_classes = 1 + + def _get_label_set(self): + labels = [] + for sample in self.collection: + label_str = sample.label + if label_str: + label_str_list = label_str.split(self.delimiter) if self.delimiter else label_str.split() + labels.extend(label_str_list) + return sorted(set(labels)) + + def _label_str_to_tensor(self, label_str: str): + labels = label_str.split(self.delimiter) if self.delimiter else label_str.split() + + if self.is_regression_task: + labels = [float(s) for s in labels] + labels = torch.tensor(labels).float() + else: + labels = [self.label2id[s] for s in labels] + labels = torch.tensor(labels).long() + return labels + + def __len__(self): + return len(self.collection) + + def __getitem__(self, index): + sample = self.collection[index] + + offset = sample.offset + + if offset is None: + offset = 0 + + features = self.featurizer.process( + sample.audio_file, + offset=offset, + duration=sample.duration, + trim=self.trim, + normalize_db=self.normalize_audio_db, + ) + + f, fl = features, torch.tensor(features.size(0)).long() + + t = self._label_str_to_tensor(sample.label) + + tl = torch.tensor(t.size(0)).long() + + return f, fl, t, tl + + def _collate_fn(self, batch): + return _speech_collate_fn(batch, pad_id=0) + + +class TarredAudioToMultiLabelDataset(IterableDataset): + """ + A similar Dataset to the AudioToMultiLabelDataset, but which loads tarred audio files. + + Accepts a single comma-separated JSON manifest file (in the same style as for the AudioToSpeechLabelDataset), + as well as the path(s) to the tarball(s) containing the wav files. Each line of the manifest should + contain the information for one audio file, including at least the transcript and name of the audio + file within the tarball. + + Valid formats for the audio_tar_filepaths argument include: + (1) a single string that can be brace-expanded, e.g. 'path/to/audio.tar' or 'path/to/audio_{1..100}.tar.gz', or + (2) a list of file paths that will not be brace-expanded, e.g. ['audio_1.tar', 'audio_2.tar', ...]. + + See the WebDataset documentation for more information about accepted data and input formats. + + If using multiple processes the number of shards should be divisible by the number of workers to ensure an + even split among workers. If it is not divisible, logging will give a warning but training will proceed. + In addition, if using mutiprocessing, each shard MUST HAVE THE SAME NUMBER OF ENTRIES after filtering + is applied. We currently do not check for this, but your program may hang if the shards are uneven! + + Notice that a few arguments are different from the AudioToBPEDataset; for example, shuffle (bool) has been + replaced by shuffle_n (int). + + Additionally, please note that the len() of this DataLayer is assumed to be the length of the manifest + after filtering. An incorrect manifest length may lead to some DataLoader issues down the line. + + Args: + audio_tar_filepaths: Either a list of audio tarball filepaths, or a + string (can be brace-expandable). + manifest_filepath (str): Path to the manifest. + labels (list): Dataset parameter. + List of target classes that can be output by the speaker recognition model. + shuffle_n (int): How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + Defaults to 0. + min_duration (float): Dataset parameter. + All training files which have a duration less than min_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to 0.1. + max_duration (float): Dataset parameter. + All training files which have a duration more than max_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to None. + trim(bool): Whether to use trim silence from beginning and end + of audio signal using librosa.effects.trim(). + Defaults to False. + window_length_in_sec (float): time length of window/slice (in seconds) # Pass this only for speaker recognition and VAD task + shift_length_in_sec (float): amount of shift of window for generating the frame for VAD task. in a batch # Pass this only for VAD task during inference. + normalize_audio (bool): Whether to normalize audio signal. Defaults to False. + shard_strategy (str): Tarred dataset shard distribution strategy chosen as a str value during ddp. + - `scatter`: The default shard strategy applied by WebDataset, where each node gets + a unique set of shards, which are permanently pre-allocated and never changed at runtime. + - `replicate`: Optional shard strategy, where each node gets all of the set of shards + available in the tarred dataset, which are permanently pre-allocated and never changed at runtime. + The benefit of replication is that it allows each node to sample data points from the entire + dataset independently of other nodes, and reduces dependence on value of `shuffle_n`. + + .. warning:: + Replicated strategy allows every node to sample the entire set of available tarfiles, + and therefore more than one node may sample the same tarfile, and even sample the same + data points! As such, there is no assured guarantee that all samples in the dataset will be + sampled at least once during 1 epoch. Scattered strategy, on the other hand, on specific + occasions (when the number of shards is not divisible with ``world_size``), will not sample + the entire dataset. For these reasons it is not advisable to use tarred datasets as validation + or test datasets. + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 0. + delimiter (Optional[str]): Delimiter to use when splitting the label string, default to None. + normalize_audio_db (Optional[float]): normalize audio signal to a target db, default to None. + """ + + def __init__( + self, + *, + audio_tar_filepaths: Union[str, List[str]], + manifest_filepath: Union[str, List[str]], + sample_rate: int, + labels: Optional[List[str]] = None, + shuffle_n: int = 0, + int_values: bool = False, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + min_duration: Optional[float] = 0.1, + max_duration: Optional[float] = None, + trim_silence: bool = False, + is_regression_task: bool = False, + shard_strategy: str = "scatter", + global_rank: int = 0, + world_size: int = 0, + delimiter: Optional[str] = None, + normalize_audio_db: Optional[float] = None, + ): + super().__init__() + if isinstance(manifest_filepath, str): + manifest_filepath = manifest_filepath.split(',') + + self.trim = trim_silence + self.is_regression_task = is_regression_task + self.delimiter = delimiter + self.normalize_audio_db = normalize_audio_db + + self.collection = collections.ASRSpeechLabel( + manifests_files=manifest_filepath, + min_duration=min_duration, + max_duration=max_duration, + is_regression_task=is_regression_task, + index_by_file_id=True, + ) + self.file_occurence = count_occurence(self.collection.mapping) + + self.featurizer = WaveformFeaturizer(sample_rate=sample_rate, int_values=int_values, augmentor=augmentor) + + if not is_regression_task: + self.labels = labels if labels else self._get_label_set() + self.num_classes = len(self.labels) if self.labels is not None else 1 + self.label2id, self.id2label = {}, {} + for label_id, label in enumerate(self.labels): + self.label2id[label] = label_id + self.id2label[label_id] = label + for idx in range(len(self.labels[:5])): + logging.debug(" label id {} and its mapped label {}".format(idx, self.id2label[idx])) + else: + self.labels = [] + self.num_classes = 1 + + audio_tar_filepaths = expand_sharded_filepaths( + sharded_filepaths=audio_tar_filepaths, + shard_strategy=shard_strategy, + world_size=world_size, + global_rank=global_rank, + ) + # Put together WebDataset + self._dataset = wds.DataPipeline( + wds.SimpleShardList(urls=audio_tar_filepaths), + webdataset_split_by_workers, + wds.shuffle(shuffle_n), + wds.tarfile_to_samples(), + wds.rename(audio=VALID_FILE_FORMATS, key='__key__'), + wds.to_tuple('audio', 'key'), + self._filter, + wds.map(self._build_sample), + ) + + def _get_label_set(self): + labels = [] + for sample in self.collection: + label_str = sample.label + if label_str: + label_str_list = label_str.split(self.delimiter) if self.delimiter else label_str.split() + labels.extend(label_str_list) + return sorted(set(labels)) + + def _label_str_to_tensor(self, label_str: str): + labels = label_str.split(self.delimiter) if self.delimiter else label_str.split() + + if self.is_regression_task: + labels = [float(s) for s in labels] + labels = torch.tensor(labels).float() + else: + labels = [self.label2id[s] for s in labels] + labels = torch.tensor(labels).long() + return labels + + def _filter(self, iterator): + """This function is used to remove samples that have been filtered out by ASRSpeechLabel already. + Otherwise, we would get a KeyError as _build_sample attempts to find the manifest entry for a sample + that was filtered out (e.g. for duration). + Note that if using multi-GPU training, filtering may lead to an imbalance in samples in each shard, + which may make your code hang as one process will finish before the other. + """ + + class TarredAudioFilter: + def __init__(self, collection, file_occurence): + self.iterator = iterator + self.collection = collection + self.file_occurence = file_occurence + self._iterable = self._internal_generator() + + def __iter__(self): + self._iterable = self._internal_generator() + return self + + def __next__(self): + try: + values = next(self._iterable) + except StopIteration: + # reset generator + self._iterable = self._internal_generator() + values = next(self._iterable) + + return values + + def _internal_generator(self): + """ + WebDataset requires an Iterator, but we require an iterable that yields 1-or-more + values per value inside self.iterator. + + Therefore wrap the iterator with a generator function that will yield 1-or-more + values per sample in the iterator. + """ + for _, tup in enumerate(self.iterator): + audio_bytes, audio_filename = tup + + file_id, _ = os.path.splitext(os.path.basename(audio_filename)) + if audio_filename in self.file_occurence: + for j in range(0, self.file_occurence[file_id]): + if j == 0: + audio_filename = file_id + else: + audio_filename = file_id + "-sub" + str(j) + yield audio_bytes, audio_filename + + return TarredAudioFilter(self.collection, self.file_occurence) + + def _build_sample(self, tup): + """Builds the training sample by combining the data from the WebDataset with the manifest info. + """ + audio_bytes, audio_filename = tup + # Grab manifest entry from self.collection + file_id, _ = os.path.splitext(os.path.basename(audio_filename)) + + manifest_idx = self.collection.mapping[file_id] + manifest_entry = self.collection[manifest_idx] + + offset = manifest_entry.offset + if offset is None: + offset = 0 + + # Convert audio bytes to IO stream for processing (for SoundFile to read) + audio_filestream = io.BytesIO(audio_bytes) + features = self.featurizer.process( + audio_filestream, + offset=offset, + duration=manifest_entry.duration, + trim=self.trim, + normalize_db=self.normalize_audio_db, + ) + + audio_filestream.close() + + # Audio features + f, fl = features, torch.tensor(features.shape[0]).long() + + t = self._label_str_to_tensor(manifest_entry.label) + + tl = torch.tensor(t.size(0)).long() + + return f, fl, t, tl + + def __iter__(self): + return self._dataset.__iter__() + + def __len__(self): + return len(self.collection) + + def _collate_fn(self, batch): + return _speech_collate_fn(batch, pad_id=0) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_label_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_label_dataset.py new file mode 100644 index 0000000..dcead6d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_label_dataset.py @@ -0,0 +1,304 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import copy + +from omegaconf import DictConfig + +from nemo.collections.asr.data import audio_to_label +from nemo.collections.asr.data.audio_to_text_dataset import convert_to_config_list, get_chain_dataset +from nemo.collections.asr.parts.preprocessing.perturb import process_augmentations +from nemo.collections.common.data.dataset import ConcatDataset + + +def get_classification_label_dataset(featurizer, config: dict) -> audio_to_label.AudioToClassificationLabelDataset: + """ + Instantiates a Classification AudioLabelDataset. + + Args: + config: Config of the AudioToClassificationLabelDataset. + + Returns: + An instance of AudioToClassificationLabelDataset. + """ + dataset = audio_to_label.AudioToClassificationLabelDataset( + manifest_filepath=config['manifest_filepath'], + labels=config['labels'], + featurizer=featurizer, + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + trim=config.get('trim_silence', False), + is_regression_task=config.get('is_regression_task', False), + cal_labels_occurrence=config.get('cal_labels_occurrence', False), + ) + return dataset + + +def get_speech_label_dataset(featurizer, config: dict) -> audio_to_label.AudioToSpeechLabelDataset: + """ + Instantiates a Speech Label (e.g. VAD, speaker recognition) AudioLabelDataset. + + Args: + config: Config of the AudioToSpeechLabelDataSet. + + Returns: + An instance of AudioToSpeechLabelDataset. + """ + dataset = audio_to_label.AudioToSpeechLabelDataset( + manifest_filepath=config['manifest_filepath'], + labels=config['labels'], + featurizer=featurizer, + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + trim=config.get('trim_silence', False), + window_length_in_sec=config.get('window_length_in_sec', 0.31), + shift_length_in_sec=config.get('shift_length_in_sec', 0.01), + normalize_audio=config.get('normalize_audio', False), + cal_labels_occurrence=config.get('cal_labels_occurrence', False), + ) + return dataset + + +def get_tarred_classification_label_dataset( + featurizer, config: dict, shuffle_n: int, global_rank: int, world_size: int +) -> audio_to_label.TarredAudioToClassificationLabelDataset: + """ + Instantiates a Classification TarredAudioLabelDataset. + + Args: + config: Config of the TarredAudioToClassificationLabelDataset. + shuffle_n: How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + global_rank: Global rank of this device. + world_size: Global world size in the training method. + + Returns: + An instance of TarredAudioToClassificationLabelDataset. + """ + tarred_audio_filepaths = config['tarred_audio_filepaths'] + manifest_filepaths = config['manifest_filepath'] + datasets = [] + tarred_audio_filepaths = convert_to_config_list(tarred_audio_filepaths) + manifest_filepaths = convert_to_config_list(manifest_filepaths) + + bucketing_weights = config.get('bucketing_weights', None) # For upsampling buckets + if bucketing_weights: + for idx, weight in enumerate(bucketing_weights): + if not isinstance(weight, int) or weight <= 0: + raise ValueError(f"bucket weights must be positive integers") + + if len(manifest_filepaths) != len(tarred_audio_filepaths): + raise ValueError( + f"manifest_filepaths (length={len(manifest_filepaths)}) and tarred_audio_filepaths (length={len(tarred_audio_filepaths)}) need to have the same number of buckets." + ) + + for dataset_idx, (tarred_audio_filepath, manifest_filepath) in enumerate( + zip(tarred_audio_filepaths, manifest_filepaths) + ): + if len(tarred_audio_filepath) == 1: + tarred_audio_filepath = tarred_audio_filepath[0] + dataset = audio_to_label.TarredAudioToClassificationLabelDataset( + audio_tar_filepaths=tarred_audio_filepath, + manifest_filepath=manifest_filepath, + labels=config['labels'], + featurizer=featurizer, + shuffle_n=shuffle_n, + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + trim=config.get('trim_silence', False), + shard_strategy=config.get('tarred_shard_strategy', 'scatter'), + global_rank=global_rank, + world_size=world_size, + is_regression_task=config.get('is_regression_task', False), + ) + + if bucketing_weights: + [datasets.append(dataset) for _ in range(bucketing_weights[dataset_idx])] + else: + datasets.append(dataset) + + return get_chain_dataset(datasets=datasets, ds_config=config, rank=global_rank) + + +def get_concat_tarred_speech_label_dataset( + featurizer, config: dict, shuffle_n: int, global_rank: int, world_size: int, +): + tarred_audio_filepaths = config['tarred_audio_filepaths'] + manifest_filepaths = config['manifest_filepath'] + datasets = [] + for dataset_idx, (tarred_audio_filepath, manifest_filepath) in enumerate( + zip(tarred_audio_filepaths, manifest_filepaths) + ): + conf = copy.deepcopy(config) + conf['manifest_filepath'] = manifest_filepath + conf['tarred_audio_filepaths'] = tarred_audio_filepath + dataset = get_tarred_speech_label_dataset( + config=conf, featurizer=featurizer, shuffle_n=shuffle_n, global_rank=global_rank, world_size=world_size, + ) + datasets.append(dataset) + + dataset = ConcatDataset( + datasets, + sampling_technique=config.get('concat_sampling_technique', 'temperature'), + sampling_temperature=config.get('concat_sampling_temperature', 5), + sampling_probabilities=config.get('concat_sampling_probabilities', None), + global_rank=global_rank, + world_size=world_size, + shuffle=config['shuffle'], + ) + return dataset + + +def get_tarred_speech_label_dataset( + featurizer, config: dict, shuffle_n: int, global_rank: int, world_size: int, +) -> audio_to_label.TarredAudioToSpeechLabelDataset: + """ + InInstantiates a Speech Label (e.g. VAD, speaker recognition) TarredAudioLabelDataset. + + Args: + config: Config of the TarredAudioToSpeechLabelDataset. + shuffle_n: How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + global_rank: Global rank of this device. + world_size: Global world size in the training method. + + Returns: + An instance of TarredAudioToSpeechLabelDataset. + """ + tarred_audio_filepaths = config['tarred_audio_filepaths'] + manifest_filepaths = config['manifest_filepath'] + datasets = [] + tarred_audio_filepaths = convert_to_config_list(tarred_audio_filepaths) + manifest_filepaths = convert_to_config_list(manifest_filepaths) + + bucketing_weights = config.get('bucketing_weights', None) # For upsampling buckets + if bucketing_weights: + for idx, weight in enumerate(bucketing_weights): + if not isinstance(weight, int) or weight <= 0: + raise ValueError(f"bucket weights must be positive integers") + + if len(manifest_filepaths) != len(tarred_audio_filepaths): + raise ValueError( + f"manifest_filepaths (length={len(manifest_filepaths)}) and tarred_audio_filepaths (length={len(tarred_audio_filepaths)}) need to have the same number of buckets." + ) + + for dataset_idx, (tarred_audio_filepath, manifest_filepath) in enumerate( + zip(tarred_audio_filepaths, manifest_filepaths) + ): + if len(tarred_audio_filepath) == 1: + tarred_audio_filepath = tarred_audio_filepath[0] + dataset = audio_to_label.TarredAudioToSpeechLabelDataset( + audio_tar_filepaths=tarred_audio_filepath, + manifest_filepath=manifest_filepath, + labels=config['labels'], + featurizer=featurizer, + shuffle_n=shuffle_n, + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + trim=config.get('trim_silence', False), + window_length_in_sec=config.get('window_length_in_sec', 8), + shift_length_in_sec=config.get('shift_length_in_sec', 0.075), + normalize_audio=config.get('normalize_audio', False), + shard_strategy=config.get('tarred_shard_strategy', 'scatter'), + global_rank=global_rank, + world_size=world_size, + ) + + if bucketing_weights: + [datasets.append(dataset) for _ in range(bucketing_weights[dataset_idx])] + else: + datasets.append(dataset) + + return get_chain_dataset(datasets=datasets, ds_config=config, rank=global_rank) + + +def get_audio_multi_label_dataset(cfg: DictConfig) -> audio_to_label.AudioToMultiLabelDataset: + if "augmentor" in cfg: + augmentor = process_augmentations(cfg.augmentor) + else: + augmentor = None + + dataset = audio_to_label.AudioToMultiLabelDataset( + manifest_filepath=cfg.get("manifest_filepath"), + sample_rate=cfg.get("sample_rate"), + labels=cfg.get("labels", None), + int_values=cfg.get("int_values", False), + augmentor=augmentor, + min_duration=cfg.get("min_duration", None), + max_duration=cfg.get("max_duration", None), + trim_silence=cfg.get("trim_silence", False), + is_regression_task=cfg.get("is_regression_task", False), + cal_labels_occurrence=cfg.get("cal_labels_occurrence", False), + delimiter=cfg.get("delimiter", None), + normalize_audio_db=cfg.get("normalize_audio_db", None), + ) + return dataset + + +def get_tarred_audio_multi_label_dataset( + cfg: DictConfig, shuffle_n: int, global_rank: int, world_size: int +) -> audio_to_label.TarredAudioToMultiLabelDataset: + + if "augmentor" in cfg: + augmentor = process_augmentations(cfg.augmentor) + else: + augmentor = None + + tarred_audio_filepaths = cfg['tarred_audio_filepaths'] + manifest_filepaths = cfg['manifest_filepath'] + datasets = [] + tarred_audio_filepaths = convert_to_config_list(tarred_audio_filepaths) + manifest_filepaths = convert_to_config_list(manifest_filepaths) + + bucketing_weights = cfg.get('bucketing_weights', None) # For upsampling buckets + if bucketing_weights: + for idx, weight in enumerate(bucketing_weights): + if not isinstance(weight, int) or weight <= 0: + raise ValueError(f"bucket weights must be positive integers") + + if len(manifest_filepaths) != len(tarred_audio_filepaths): + raise ValueError( + f"manifest_filepaths (length={len(manifest_filepaths)}) and tarred_audio_filepaths (length={len(tarred_audio_filepaths)}) need to have the same number of buckets." + ) + + for dataset_idx, (tarred_audio_filepath, manifest_filepath) in enumerate( + zip(tarred_audio_filepaths, manifest_filepaths) + ): + if len(tarred_audio_filepath) == 1: + tarred_audio_filepath = tarred_audio_filepath[0] + + dataset = audio_to_label.TarredAudioToMultiLabelDataset( + audio_tar_filepaths=tarred_audio_filepath, + manifest_filepath=manifest_filepath, + sample_rate=cfg["sample_rate"], + labels=cfg['labels'], + shuffle_n=shuffle_n, + int_values=cfg.get("int_values", False), + augmentor=augmentor, + min_duration=cfg.get('min_duration', None), + max_duration=cfg.get('max_duration', None), + trim_silence=cfg.get('trim_silence', False), + is_regression_task=cfg.get('is_regression_task', False), + delimiter=cfg.get("delimiter", None), + shard_strategy=cfg.get('tarred_shard_strategy', 'scatter'), + global_rank=global_rank, + world_size=world_size, + normalize_audio_db=cfg.get("normalize_audio_db", None), + ) + + if bucketing_weights: + [datasets.append(dataset) for _ in range(bucketing_weights[dataset_idx])] + else: + datasets.append(dataset) + + return get_chain_dataset(datasets=datasets, ds_config=cfg, rank=global_rank) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text.py new file mode 100644 index 0000000..00c1510 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text.py @@ -0,0 +1,1375 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import io +import json +import math +import multiprocessing +import os +from collections.abc import Iterable as IterableABC +from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union + +import braceexpand +import numpy as np +import torch +import webdataset as wds +from torch.utils.data import ChainDataset +from tqdm import tqdm + +from nemo.collections.asr.parts.preprocessing.features import WaveformFeaturizer +from nemo.collections.asr.parts.preprocessing.segment import available_formats as valid_sf_formats +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.common import tokenizers +from nemo.collections.common.parts.preprocessing import collections, parsers +from nemo.core.classes import Dataset, IterableDataset +from nemo.core.neural_types import * +from nemo.utils import logging +from nemo.utils.data_utils import ( + DataStoreObject, + datastore_object_get, + datastore_path_to_webdataset_url, + is_datastore_cache_shared, + is_datastore_path, + is_tarred_path, +) +from nemo.utils.distributed import webdataset_split_by_workers +from nemo.utils.get_rank import is_global_rank_zero + +__all__ = [ + 'AudioToCharDataset', + 'AudioToBPEDataset', + 'TarredAudioToCharDataset', + 'TarredAudioToBPEDataset', +] + +VALID_FILE_FORMATS = ';'.join(['wav', 'mp3', 'flac', 'opus'] + [fmt.lower() for fmt in valid_sf_formats.keys()]) + + +def _speech_collate_fn(batch, pad_id): + """collate batch of audio sig, audio len, tokens, tokens len + Args: + batch (Optional[FloatTensor], Optional[LongTensor], LongTensor, + LongTensor): A tuple of tuples of signal, signal lengths, + encoded tokens, and encoded tokens length. This collate func + assumes the signals are 1d torch tensors (i.e. mono audio). + """ + packed_batch = list(zip(*batch)) + if len(packed_batch) == 5: + _, audio_lengths, _, tokens_lengths, sample_ids = packed_batch + elif len(packed_batch) == 4: + sample_ids = None + _, audio_lengths, _, tokens_lengths = packed_batch + else: + raise ValueError("Expects 4 or 5 tensors in the batch!") + max_audio_len = 0 + has_audio = audio_lengths[0] is not None + if has_audio: + max_audio_len = max(audio_lengths).item() + max_tokens_len = max(tokens_lengths).item() + + audio_signal, tokens = [], [] + for b in batch: + if len(b) == 5: + sig, sig_len, tokens_i, tokens_i_len, _ = b + else: + sig, sig_len, tokens_i, tokens_i_len = b + if has_audio: + sig_len = sig_len.item() + if sig_len < max_audio_len: + pad = (0, max_audio_len - sig_len) + sig = torch.nn.functional.pad(sig, pad) + audio_signal.append(sig) + tokens_i_len = tokens_i_len.item() + if tokens_i_len < max_tokens_len: + pad = (0, max_tokens_len - tokens_i_len) + tokens_i = torch.nn.functional.pad(tokens_i, pad, value=pad_id) + tokens.append(tokens_i) + + if has_audio: + audio_signal = torch.stack(audio_signal) + audio_lengths = torch.stack(audio_lengths) + else: + audio_signal, audio_lengths = None, None + tokens = torch.stack(tokens) + tokens_lengths = torch.stack(tokens_lengths) + if sample_ids is None: + return audio_signal, audio_lengths, tokens, tokens_lengths + else: + sample_ids = torch.tensor(sample_ids, dtype=torch.int32) + return audio_signal, audio_lengths, tokens, tokens_lengths, sample_ids + + +class ASRManifestProcessor: + """ + Class that processes a manifest json file containing paths to audio files, transcripts, and durations (in seconds). + Each new line is a different sample. Example below: + {"audio_filepath": "/path/to/audio.wav", "text_filepath": "/path/to/audio.txt", "duration": 23.147} + ... + {"audio_filepath": "/path/to/audio.wav", "text": "the transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + Args: + manifest_filepath: Path to manifest json as described above. Can be comma-separated paths. + parser: Str for a language specific preprocessor or a callable. + max_duration: If audio exceeds this length, do not include in dataset. + min_duration: If audio is less than this length, do not include in dataset. + max_utts: Limit number of utterances. + bos_id: Id of beginning of sequence symbol to append if not None. + eos_id: Id of end of sequence symbol to append if not None. + pad_id: Id of pad symbol. Defaults to 0. + """ + + def __init__( + self, + manifest_filepath: str, + parser: Union[str, Callable], + max_duration: Optional[float] = None, + min_duration: Optional[float] = None, + max_utts: int = 0, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + index_by_file_id: bool = False, + ): + self.parser = parser + + self.collection = collections.ASRAudioText( + manifests_files=manifest_filepath, + parser=parser, + min_duration=min_duration, + max_duration=max_duration, + max_number=max_utts, + index_by_file_id=index_by_file_id, + ) + + self.eos_id = eos_id + self.bos_id = bos_id + self.pad_id = pad_id + + def process_text_by_id(self, index: int) -> Tuple[List[int], int]: + sample = self.collection[index] + return self.process_text_by_sample(sample) + + def process_text_by_file_id(self, file_id: str) -> Tuple[List[int], int]: + manifest_idx = self.collection.mapping[file_id][0] + sample = self.collection[manifest_idx] + return self.process_text_by_sample(sample) + + def process_text_by_sample(self, sample: collections.ASRAudioText.OUTPUT_TYPE) -> Tuple[List[int], int]: + t, tl = sample.text_tokens, len(sample.text_tokens) + + if self.bos_id is not None: + t = [self.bos_id] + t + tl += 1 + if self.eos_id is not None: + t = t + [self.eos_id] + tl += 1 + + return t, tl + + +def expand_sharded_filepaths(sharded_filepaths, shard_strategy: str, world_size: int, global_rank: int): + valid_shard_strategies = ['scatter', 'replicate'] + if shard_strategy not in valid_shard_strategies: + raise ValueError(f"`shard_strategy` must be one of {valid_shard_strategies}") + + if isinstance(sharded_filepaths, str): + # Replace '(' and '[' with '{' + brace_keys_open = ['(', '[', '<', '_OP_'] + for bkey in brace_keys_open: + if bkey in sharded_filepaths: + sharded_filepaths = sharded_filepaths.replace(bkey, "{") + + # Replace ')' and ']' with '}' + brace_keys_close = [')', ']', '>', '_CL_'] + for bkey in brace_keys_close: + if bkey in sharded_filepaths: + sharded_filepaths = sharded_filepaths.replace(bkey, "}") + + if isinstance(sharded_filepaths, str): + # Brace expand, set escape=False for Windows compatibility + sharded_filepaths = list(braceexpand.braceexpand(sharded_filepaths, escape=False)) + + # Expand store paths into WebDataset URLs + sharded_filepaths = [ + datastore_path_to_webdataset_url(p) if is_datastore_path(p) and is_tarred_path(p) else p + for p in sharded_filepaths + ] + + # Check for distributed and partition shards accordingly + if world_size > 1: + if shard_strategy == 'scatter': + logging.info("All tarred dataset shards will be scattered evenly across all nodes.") + + if len(sharded_filepaths) % world_size != 0: + logging.warning( + f"Number of shards in tarred dataset ({len(sharded_filepaths)}) is not divisible " + f"by number of distributed workers ({world_size})." + ) + + begin_idx = (len(sharded_filepaths) // world_size) * global_rank + end_idx = begin_idx + len(sharded_filepaths) // world_size + sharded_filepaths = sharded_filepaths[begin_idx:end_idx] + logging.info( + "Partitioning tarred dataset: process (%d) taking shards [%d, %d)", global_rank, begin_idx, end_idx + ) + + elif shard_strategy == 'replicate': + logging.info("All tarred dataset shards will be replicated across all nodes.") + else: + raise ValueError(f"Invalid shard strategy ! Allowed values are : {valid_shard_strategies}") + + return sharded_filepaths + + +def cache_datastore_manifests( + manifest_filepaths: Union[str, List[str]], + cache_audio: bool = False, + shared_cache: Optional[bool] = None, + num_workers: Optional[int] = None, + max_num_workers: int = 20, +): + """Cache manifests and audio from an object store. + It is assumed that remote manifests are using relative paths. + + Args: + manifest_filepaths: list of paths to manifest files (list of strings or a string with `,` as separator) + cache_audio: If True, audio from manifest will also be cached + shared_cache: Optional, True if cache is shared across all nodes + num_workers: Optional, number of workers to be used for download + max_num_workers: max number of workers to be used for download, used when setting num_workers automatically + """ + if isinstance(manifest_filepaths, str): + manifest_filepaths = manifest_filepaths.split(',') + + num_datastore_manifests = sum([is_datastore_path(f) for f in manifest_filepaths]) + + if num_datastore_manifests > 0: + # Local utility function + def cache_data(manifest_filepaths, cache_audio, num_workers, max_num_workers): + """Cache manifests and audio data from object store. + """ + # Determine the number of workers to use + if num_workers is None: + num_workers = os.cpu_count() - 1 + num_workers = min(num_workers, max_num_workers) + + # Process each manifest file + for manifest_file in manifest_filepaths: + # If manifest is on a data store, then cache it. + # Otherwise, nothing to do. + if is_datastore_path(manifest_file): + logging.info('Cache manifest file: %s', manifest_file) + cached_manifest_file = DataStoreObject(manifest_file).get() + logging.info('Cached at: %s', str(cached_manifest_file)) + + if cache_audio: + # Each audio file from manifest will be cached. + logging.info('Cache audio from manifest file: %s', manifest_file) + # Assumes that manifest is using relative paths + manifest_dir = os.path.dirname(manifest_file) + # Prepare all store objects + audio_objects = [] + with open(cached_manifest_file, 'r') as f: + for line in f: + item = json.loads(line) + store_path = os.path.join(manifest_dir, item['audio_filepath']) + audio_objects.append(DataStoreObject(store_path=store_path)) + + if num_workers is not None and num_workers > 1: + logging.debug('Using multiprocessing with num_workers: %d.', num_workers) + with multiprocessing.Pool(processes=num_workers) as p: + result = list( + tqdm(p.imap(datastore_object_get, audio_objects), total=len(audio_objects)) + ) + else: + logging.debug('Using a single process.') + result = [] + for audio_object in tqdm(audio_objects): + result.append(audio_object.get() is not None) + + if not all(result): + raise RuntimeError('Some files not downloaded successfully') + logging.info('Caching complete') + + else: + # Nothing to do here + logging.debug('Manifest is not on a data store: %s', manifest_file) + + if torch.distributed.is_available() and torch.distributed.is_initialized(): + logging.debug('Distributed environment is available and initialized.') + + # Handle distributed environment + if shared_cache is None: + shared_cache = is_datastore_cache_shared() + + if shared_cache: + logging.debug('Cache is shared among nodes, cache data on global rank zero.') + is_rank_zero = is_global_rank_zero() + else: + logging.debug('Cache is not shared among nodes, cache data on local rank zero.') + local_rank = int(os.environ.get("LOCAL_RANK", 0)) + is_rank_zero = local_rank == 0 + + if is_rank_zero: + logging.info('Cache data from %s rank 0', 'global' if shared_cache else 'local') + cache_data( + manifest_filepaths=manifest_filepaths, + cache_audio=cache_audio, + num_workers=num_workers, + max_num_workers=max_num_workers, + ) + logging.debug('Reached barrier') + torch.distributed.barrier() + + elif is_global_rank_zero(): + # Handle non-distributed environment, e.g., if running on a single GPU + logging.warning( + 'Torch distributed is not initialized and caching may be prone to data race conditions. ' + 'Now caching data from global rank 0. If there are other ranks and they pass this ' + 'before rank 0, errors might result.' + ) + cache_data( + manifest_filepaths=manifest_filepaths, + cache_audio=cache_audio, + num_workers=num_workers, + max_num_workers=max_num_workers, + ) + else: + raise RuntimeError( + 'Torch distributed is not initialized and caching on nodes other than global rank zero is disabled ' + 'to avoid race condition between different ranks. To ensure distributed environment is ' + 'initialized, please update data config to use `defer_setup = True`.' + ) + + +"""Optionally expand / shard the list of manifests + This is made to use the same notation as the sharded audio files + + Args: + manifest_filepaths: list of manifest files (the sharded notation) + shard_strategy: scatter or replicate (scatter by default) + shard_manifests: bool, if False, no sharding / manifest filepath expansion will be attempted + global_rank: int, the rank of this worker + world_size: int, total number of workers +""" + + +def shard_manifests_if_needed( + manifest_filepaths: Union[str, List[str]], + shard_strategy: str, + shard_manifests: bool, + global_rank: int, + world_size: int, +): + if shard_manifests: + if not torch.distributed.is_available(): + logging.warning("Not running in torch.distributed mode. Manifest sharding not available") + return manifest_filepaths + + if not torch.distributed.is_initialized(): + logging.warning( + 'Manifest sharding was requested but torch.distributed is not initialized ' + 'Did you intend to set the defer_setup flag?' + ) + return manifest_filepaths + + manifest_filepaths = expand_sharded_filepaths( + sharded_filepaths=manifest_filepaths, + shard_strategy=shard_strategy, + world_size=world_size, + global_rank=global_rank, + ) + + return manifest_filepaths + + +class _AudioTextDataset(Dataset): + """ + Dataset that loads tensors via a json file containing paths to audio files, transcripts, and durations (in seconds). + Each new line is a different sample. Example below: + {"audio_filepath": "/path/to/audio.wav", "text_filepath": "/path/to/audio.txt", "duration": 23.147} + ... + {"audio_filepath": "/path/to/audio.wav", "text": "the transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + Args: + manifest_filepath: Path to manifest json as described above. Can be comma-separated paths. + parser: Str for a language specific preprocessor or a callable. + sample_rate (int): Sample rate to resample loaded audio to + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): An AudioAugmentor object used to augment loaded + audio + max_duration: If audio exceeds this length, do not include in dataset + min_duration: If audio is less than this length, do not include in dataset + max_utts: Limit number of utterances + trim: whether or not to trim silence. Defaults to False + bos_id: Id of beginning of sequence symbol to append if not None + eos_id: Id of end of sequence symbol to append if not None + pad_id: Id of pad symbol. Defaults to 0 + return_sample_id (bool): whether to return the sample_id as a part of each sample + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'audio_signal': NeuralType(('B', 'T'), AudioSignal()), + 'a_sig_length': NeuralType(tuple('B'), LengthsType()), + 'transcripts': NeuralType(('B', 'T'), LabelsType()), + 'transcript_length': NeuralType(tuple('B'), LengthsType()), + 'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def __init__( + self, + manifest_filepath: str, + parser: Union[str, Callable], + sample_rate: int, + int_values: bool = False, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + max_duration: Optional[int] = None, + min_duration: Optional[int] = None, + max_utts: int = 0, + trim: bool = False, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + ): + if type(manifest_filepath) == str: + manifest_filepath = manifest_filepath.split(",") + + # If necessary, cache manifests and audio from object store + cache_datastore_manifests(manifest_filepaths=manifest_filepath, cache_audio=True) + + self.manifest_processor = ASRManifestProcessor( + manifest_filepath=manifest_filepath, + parser=parser, + max_duration=max_duration, + min_duration=min_duration, + max_utts=max_utts, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + ) + self.featurizer = WaveformFeaturizer(sample_rate=sample_rate, int_values=int_values, augmentor=augmentor) + self.trim = trim + self.return_sample_id = return_sample_id + self.channel_selector = channel_selector + + def get_manifest_sample(self, sample_id): + return self.manifest_processor.collection[sample_id] + + def __getitem__(self, index): + if isinstance(index, IterableABC): + return [self._process_sample(_index) for _index in index] + else: + return self._process_sample(index) + + def _process_sample(self, index): + sample = self.manifest_processor.collection[index] + offset = sample.offset + + if offset is None: + offset = 0 + + features = self.featurizer.process( + sample.audio_file, + offset=offset, + duration=sample.duration, + trim=self.trim, + orig_sr=sample.orig_sr, + channel_selector=self.channel_selector, + ) + f, fl = features, torch.tensor(features.shape[0]).long() + + t, tl = self.manifest_processor.process_text_by_sample(sample=sample) + + if self.return_sample_id: + output = f, fl, torch.tensor(t).long(), torch.tensor(tl).long(), index + else: + output = f, fl, torch.tensor(t).long(), torch.tensor(tl).long() + + return output + + def __len__(self): + return len(self.manifest_processor.collection) + + def _collate_fn(self, batch): + return _speech_collate_fn(batch, pad_id=self.manifest_processor.pad_id) + + +class AudioToCharDataset(_AudioTextDataset): + """ + Dataset that loads tensors via a json file containing paths to audio + files, transcripts, and durations (in seconds). Each new line is a + different sample. Example below: + {"audio_filepath": "/path/to/audio.wav", "text_filepath": + "/path/to/audio.txt", "duration": 23.147} + ... + {"audio_filepath": "/path/to/audio.wav", "text": "the + transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + + Args: + manifest_filepath: Path to manifest json as described above. Can + be comma-separated paths. + labels: String containing all the possible characters to map to + sample_rate (int): Sample rate to resample loaded audio to + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): An AudioAugmentor + object used to augment loaded audio + max_duration: If audio exceeds this length, do not include in dataset + min_duration: If audio is less than this length, do not include + in dataset + max_utts: Limit number of utterances + blank_index: blank character index, default = -1 + unk_index: unk_character index, default = -1 + normalize: whether to normalize transcript text (default): True + bos_id: Id of beginning of sequence symbol to append if not None + eos_id: Id of end of sequence symbol to append if not None + return_sample_id (bool): whether to return the sample_id as a part of each sample + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'audio_signal': NeuralType(('B', 'T'), AudioSignal()), + 'a_sig_length': NeuralType(tuple('B'), LengthsType()), + 'transcripts': NeuralType(('B', 'T'), LabelsType()), + 'transcript_length': NeuralType(tuple('B'), LengthsType()), + 'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def __init__( + self, + manifest_filepath: str, + labels: Union[str, List[str]], + sample_rate: int, + int_values: bool = False, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + max_duration: Optional[float] = None, + min_duration: Optional[float] = None, + max_utts: int = 0, + blank_index: int = -1, + unk_index: int = -1, + normalize: bool = True, + trim: bool = False, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + parser: Union[str, Callable] = 'en', + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + ): + self.labels = labels + + parser = parsers.make_parser( + labels=labels, name=parser, unk_id=unk_index, blank_id=blank_index, do_normalize=normalize + ) + + super().__init__( + manifest_filepath=manifest_filepath, + parser=parser, + sample_rate=sample_rate, + int_values=int_values, + augmentor=augmentor, + max_duration=max_duration, + min_duration=min_duration, + max_utts=max_utts, + trim=trim, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + return_sample_id=return_sample_id, + channel_selector=channel_selector, + ) + + +class AudioToBPEDataset(_AudioTextDataset): + """ + Dataset that loads tensors via a json file containing paths to audio + files, transcripts, and durations (in seconds). Each new line is a + different sample. Example below: + {"audio_filepath": "/path/to/audio.wav", "text_filepath": + "/path/to/audio.txt", "duration": 23.147} + ... + {"audio_filepath": "/path/to/audio.wav", "text": "the + transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + + In practice, the dataset and manifest used for character encoding and byte pair encoding + are exactly the same. The only difference lies in how the dataset tokenizes the text in + the manifest. + + Args: + manifest_filepath: Path to manifest json as described above. Can + be comma-separated paths. + tokenizer: A subclass of the Tokenizer wrapper found in the common collection, + nemo.collections.common.tokenizers.TokenizerSpec. ASR Models support a subset of + all available tokenizers. + sample_rate (int): Sample rate to resample loaded audio to + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): An AudioAugmentor + object used to augment loaded audio + max_duration: If audio exceeds this length, do not include in dataset + min_duration: If audio is less than this length, do not include + in dataset + max_utts: Limit number of utterances + trim: Whether to trim silence segments + use_start_end_token: Boolean which dictates whether to add [BOS] and [EOS] + tokens to beginning and ending of speech respectively. + return_sample_id (bool): whether to return the sample_id as a part of each sample + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'audio_signal': NeuralType(('B', 'T'), AudioSignal()), + 'a_sig_length': NeuralType(tuple('B'), LengthsType()), + 'transcripts': NeuralType(('B', 'T'), LabelsType()), + 'transcript_length': NeuralType(tuple('B'), LengthsType()), + 'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def __init__( + self, + manifest_filepath: str, + tokenizer: 'nemo.collections.common.tokenizers.TokenizerSpec', + sample_rate: int, + int_values: bool = False, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + max_duration: Optional[int] = None, + min_duration: Optional[int] = None, + max_utts: int = 0, + trim: bool = False, + use_start_end_token: bool = True, + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + ): + if use_start_end_token and hasattr(tokenizer, "bos_id") and tokenizer.bos_id > 0: + bos_id = tokenizer.bos_id + else: + bos_id = None + + if use_start_end_token and hasattr(tokenizer, "eos_id") and tokenizer.eos_id > 0: + eos_id = tokenizer.eos_id + else: + eos_id = None + + if hasattr(tokenizer, "pad_id") and tokenizer.pad_id > 0: + pad_id = tokenizer.pad_id + else: + pad_id = 0 + + class TokenizerWrapper: + def __init__(self, tokenizer): + if isinstance(tokenizer, tokenizers.aggregate_tokenizer.AggregateTokenizer): + self.is_aggregate = True + else: + self.is_aggregate = False + self._tokenizer = tokenizer + + def __call__(self, *args): + if isinstance(args[0], List) and self.is_aggregate: + t = [] + for span in args[0]: + t.extend(self._tokenizer.text_to_ids(span['str'], span['lang'])) + return t + + t = self._tokenizer.text_to_ids(*args) + return t + + super().__init__( + manifest_filepath=manifest_filepath, + parser=TokenizerWrapper(tokenizer), + sample_rate=sample_rate, + int_values=int_values, + augmentor=augmentor, + max_duration=max_duration, + min_duration=min_duration, + max_utts=max_utts, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + trim=trim, + return_sample_id=return_sample_id, + channel_selector=channel_selector, + ) + + +class _TarredAudioToTextDataset(IterableDataset): + """ + A similar Dataset to the AudioToCharDataset/AudioToBPEDataset, but which loads tarred audio files. + + Accepts a single comma-separated JSON manifest file (in the same style as for the AudioToCharDataset/AudioToBPEDataset), + as well as the path(s) to the tarball(s) containing the wav files. Each line of the manifest should + contain the information for one audio file, including at least the transcript and name of the audio + file within the tarball. + + Valid formats for the audio_tar_filepaths argument include: + (1) a single string that can be brace-expanded, e.g. 'path/to/audio.tar' or 'path/to/audio_{1..100}.tar.gz', or + (2) a list of file paths that will not be brace-expanded, e.g. ['audio_1.tar', 'audio_2.tar', ...]. + + Note: For brace expansion in (1), there may be cases where `{x..y}` syntax cannot be used due to shell interference. + This occurs most commonly inside SLURM scripts. Therefore we provide a few equivalent replacements. + Supported opening braces - { <=> (, [, < and the special tag _OP_. + Supported closing braces - } <=> ), ], > and the special tag _CL_. + For SLURM based tasks, we suggest the use of the special tags for ease of use. + + See the WebDataset documentation for more information about accepted data and input formats. + + If using multiple workers the number of shards should be divisible by world_size to ensure an + even split among workers. If it is not divisible, logging will give a warning but training will proceed. + In addition, if using mutiprocessing, each shard MUST HAVE THE SAME NUMBER OF ENTRIES after filtering + is applied. We currently do not check for this, but your program may hang if the shards are uneven! + + Notice that a few arguments are different from the AudioToCharDataset; for example, shuffle (bool) has been + replaced by shuffle_n (int). + + Additionally, please note that the len() of this DataLayer is assumed to be the length of the manifest + after filtering. An incorrect manifest length may lead to some DataLoader issues down the line. + + Args: + audio_tar_filepaths: Either a list of audio tarball filepaths, or a + string (can be brace-expandable). + manifest_filepath (str): Path to the manifest. + parser (callable): A callable which is used to pre-process the text output. + sample_rate (int): Sample rate to resample loaded audio to + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): An AudioAugmentor + object used to augment loaded audio + shuffle_n (int): How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + Defaults to 0. + min_duration (float): Dataset parameter. + All training files which have a duration less than min_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to 0.1. + max_duration (float): Dataset parameter. + All training files which have a duration more than max_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to None. + blank_index (int): Blank character index, defaults to -1. + unk_index (int): Unknown character index, defaults to -1. + normalize (bool): Dataset parameter. + Whether to use automatic text cleaning. + It is highly recommended to manually clean text for best results. + Defaults to True. + trim (bool): Whether to use trim silence from beginning and end + of audio signal using librosa.effects.trim(). + Defaults to False. + bos_id (id): Dataset parameter. + Beginning of string symbol id used for seq2seq models. + Defaults to None. + eos_id (id): Dataset parameter. + End of string symbol id used for seq2seq models. + Defaults to None. + pad_id (id): Token used to pad when collating samples in batches. + If this is None, pads using 0s. + Defaults to None. + shard_strategy (str): Tarred dataset shard distribution strategy chosen as a str value during ddp. + - `scatter`: The default shard strategy applied by WebDataset, where each node gets + a unique set of shards, which are permanently pre-allocated and never changed at runtime. + - `replicate`: Optional shard strategy, where each node gets all of the set of shards + available in the tarred dataset, which are permanently pre-allocated and never changed at runtime. + The benefit of replication is that it allows each node to sample data points from the entire + dataset independently of other nodes, and reduces dependence on value of `shuffle_n`. + + .. warning:: + Replicated strategy allows every node to sample the entire set of available tarfiles, + and therefore more than one node may sample the same tarfile, and even sample the same + data points! As such, there is no assured guarantee that all samples in the dataset will be + sampled at least once during 1 epoch. Scattered strategy, on the other hand, on specific + occasions (when the number of shards is not divisible with ``world_size``), will not sample + the entire dataset. For these reasons it is not advisable to use tarred datasets as validation + or test datasets. + shard_manifests (bool): Whether or not to try / shard manifests. Defaults to False. + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 0. + return_sample_id (bool): whether to return the sample_id as a part of each sample + """ + + def __init__( + self, + audio_tar_filepaths: Union[str, List[str]], + manifest_filepath: str, + parser: Callable, + sample_rate: int, + int_values: bool = False, + augmentor: Optional['nemo.collections.asr.parts.perturb.AudioAugmentor'] = None, + shuffle_n: int = 0, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + trim: bool = False, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + shard_strategy: str = "scatter", + shard_manifests: bool = False, + global_rank: int = 0, + world_size: int = 0, + return_sample_id: bool = False, + ): + self.shard_manifests = shard_manifests + + # Shard manifests if necessary and possible and then expand the paths + manifest_filepath = shard_manifests_if_needed( + shard_manifests=shard_manifests, + shard_strategy=shard_strategy, + manifest_filepaths=manifest_filepath, + world_size=world_size, + global_rank=global_rank, + ) + + # If necessary, cache manifests from object store + cache_datastore_manifests(manifest_filepaths=manifest_filepath) + + self.manifest_processor = ASRManifestProcessor( + manifest_filepath=manifest_filepath, + parser=parser, + max_duration=max_duration, + min_duration=min_duration, + max_utts=0, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + index_by_file_id=True, # Must set this so the manifest lines can be indexed by file ID + ) + + self.len = self._compute_len() + + self.featurizer = WaveformFeaturizer(sample_rate=sample_rate, int_values=int_values, augmentor=augmentor) + self.trim = trim + self.eos_id = eos_id + self.bos_id = bos_id + self.pad_id = pad_id + self.return_sample_id = return_sample_id + + audio_tar_filepaths = expand_sharded_filepaths( + sharded_filepaths=audio_tar_filepaths, + shard_strategy=shard_strategy, + world_size=world_size, + global_rank=global_rank, + ) + + # Put together WebDataset pipeline + self._dataset = wds.DataPipeline( + wds.SimpleShardList(urls=audio_tar_filepaths), + webdataset_split_by_workers, + wds.shuffle(shuffle_n), + wds.tarfile_to_samples(), + wds.rename(audio=VALID_FILE_FORMATS, key='__key__'), + wds.to_tuple('audio', 'key'), + self._filter, + self._loop_offsets, + wds.map(self._build_sample), + ) + + def _filter(self, iterator): + """This function is used to remove samples that have been filtered out by ASRAudioText already. + Otherwise, we would get a KeyError as _build_sample attempts to find the manifest entry for a sample + that was filtered out (e.g. for duration). + Note that if using multi-GPU training, filtering may lead to an imbalance in samples in each shard, + which may make your code hang as one process will finish before the other. + """ + + class TarredAudioFilter: + def __init__(self, collection): + self.iterator = iterator + self.collection = collection + + def __iter__(self): + return self + + def __next__(self): + while True: + audio_bytes, audio_filename = next(self.iterator) + file_id, _ = os.path.splitext(os.path.basename(audio_filename)) + if file_id in self.collection.mapping: + return audio_bytes, audio_filename + + return TarredAudioFilter(self.manifest_processor.collection) + + def _loop_offsets(self, iterator): + """This function is used to iterate through utterances with different offsets for each file. + """ + + class TarredAudioLoopOffsets: + def __init__(self, collection): + self.iterator = iterator + self.collection = collection + self.current_fn = None + self.current_bytes = None + self.offset_id = 0 + + def __iter__(self): + return self + + def __next__(self): + if self.current_fn is None: + self.current_bytes, self.current_fn = next(self.iterator) + self.offset_id = 0 + else: + offset_list = self.collection.mapping[self.current_fn] + if len(offset_list) == self.offset_id + 1: + self.current_bytes, self.current_fn = next(self.iterator) + self.offset_id = 0 + else: + self.offset_id += 1 + + return self.current_bytes, self.current_fn, self.offset_id + + return TarredAudioLoopOffsets(self.manifest_processor.collection) + + def _collate_fn(self, batch): + return _speech_collate_fn(batch, self.pad_id) + + def _build_sample(self, tup): + """Builds the training sample by combining the data from the WebDataset with the manifest info. + """ + audio_bytes, audio_filename, offset_id = tup + + # Grab manifest entry from self.manifest_preprocessor.collection + file_id, _ = os.path.splitext(os.path.basename(audio_filename)) + + manifest_idx = self.manifest_processor.collection.mapping[file_id][offset_id] + manifest_entry = self.manifest_processor.collection[manifest_idx] + + offset = manifest_entry.offset + if offset is None: + offset = 0 + + # Convert audio bytes to IO stream for processing (for SoundFile to read) + audio_filestream = io.BytesIO(audio_bytes) + features = self.featurizer.process( + audio_filestream, + offset=offset, + duration=manifest_entry.duration, + trim=self.trim, + orig_sr=manifest_entry.orig_sr, + ) + audio_filestream.close() + + # Audio features + f, fl = features, torch.tensor(features.shape[0]).long() + + # Text features + t, tl = manifest_entry.text_tokens, len(manifest_entry.text_tokens) + + self.manifest_processor.process_text_by_sample(sample=manifest_entry) + + if self.bos_id is not None: + t = [self.bos_id] + t + tl += 1 + if self.eos_id is not None: + t = t + [self.eos_id] + tl += 1 + + if self.return_sample_id: + return f, fl, torch.tensor(t).long(), torch.tensor(tl).long(), manifest_idx + else: + return f, fl, torch.tensor(t).long(), torch.tensor(tl).long() + + def get_manifest_sample(self, sample_id): + return self.manifest_processor.collection[sample_id] + + def __iter__(self): + return self._dataset.__iter__() + + def _compute_len(self): + if self.shard_manifests and torch.distributed.is_available() and torch.distributed.is_initialized(): + my_len = torch.tensor(len(self.manifest_processor.collection), dtype=torch.int32).cuda() + torch.distributed.all_reduce(my_len) + my_len = my_len.int() + logging.info(f'Sharded manifests: Total length: {my_len}') + else: + my_len = len(self.manifest_processor.collection) + + return my_len + + def __len__(self): + return self.len + + +class TarredAudioToCharDataset(_TarredAudioToTextDataset): + """ + A similar Dataset to the AudioToCharDataset, but which loads tarred audio files. + + Accepts a single comma-separated JSON manifest file (in the same style as for the AudioToCharDataset), + as well as the path(s) to the tarball(s) containing the wav files. Each line of the manifest should + contain the information for one audio file, including at least the transcript and name of the audio + file within the tarball. + + Valid formats for the audio_tar_filepaths argument include: + (1) a single string that can be brace-expanded, e.g. 'path/to/audio.tar' or 'path/to/audio_{1..100}.tar.gz', or + (2) a list of file paths that will not be brace-expanded, e.g. ['audio_1.tar', 'audio_2.tar', ...]. + + See the WebDataset documentation for more information about accepted data and input formats. + + If using multiple workers the number of shards should be divisible by world_size to ensure an + even split among workers. If it is not divisible, logging will give a warning but training will proceed. + In addition, if using mutiprocessing, each shard MUST HAVE THE SAME NUMBER OF ENTRIES after filtering + is applied. We currently do not check for this, but your program may hang if the shards are uneven! + + Notice that a few arguments are different from the AudioToCharDataset; for example, shuffle (bool) has been + replaced by shuffle_n (int). + + Additionally, please note that the len() of this DataLayer is assumed to be the length of the manifest + after filtering. An incorrect manifest length may lead to some DataLoader issues down the line. + + Args: + audio_tar_filepaths: Either a list of audio tarball filepaths, or a + string (can be brace-expandable). + manifest_filepath (str): Path to the manifest. + labels (list): List of characters that can be output by the ASR model. + For Jasper, this is the 28 character set {a-z '}. The CTC blank + symbol is automatically added later for models using ctc. + sample_rate (int): Sample rate to resample loaded audio to + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): An AudioAugmentor + object used to augment loaded audio + shuffle_n (int): How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + Defaults to 0. + min_duration (float): Dataset parameter. + All training files which have a duration less than min_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to 0.1. + max_duration (float): Dataset parameter. + All training files which have a duration more than max_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to None. + blank_index (int): Blank character index, defaults to -1. + unk_index (int): Unknown character index, defaults to -1. + normalize (bool): Dataset parameter. + Whether to use automatic text cleaning. + It is highly recommended to manually clean text for best results. + Defaults to True. + trim (bool): Whether to use trim silence from beginning and end + of audio signal using librosa.effects.trim(). + Defaults to False. + bos_id (id): Dataset parameter. + Beginning of string symbol id used for seq2seq models. + Defaults to None. + eos_id (id): Dataset parameter. + End of string symbol id used for seq2seq models. + Defaults to None. + pad_id (id): Token used to pad when collating samples in batches. + If this is None, pads using 0s. + Defaults to None. + shard_strategy (str): Tarred dataset shard distribution strategy chosen as a str value during ddp. + + - `scatter`: The default shard strategy applied by WebDataset, where each node gets + a unique set of shards, which are permanently pre-allocated and never changed at runtime. + - `replicate`: Optional shard strategy, where each node gets all of the set of shards + available in the tarred dataset, which are permanently pre-allocated and never changed at runtime. + The benefit of replication is that it allows each node to sample data points from the entire + dataset independently of other nodes, and reduces dependence on value of `shuffle_n`. + + .. warning:: + + Replicated strategy allows every node to sample the entire set of available tarfiles, + and therefore more than one node may sample the same tarfile, and even sample the same + data points! As such, there is no assured guarantee that all samples in the dataset will be + sampled at least once during 1 epoch. Scattered strategy, on the other hand, on specific + occasions (when the number of shards is not divisible with ``world_size``), will not sample + the entire dataset. For these reasons it is not advisable to use tarred datasets as validation + or test datasets. + + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 0. + return_sample_id (bool): whether to return the sample_id as a part of each sample + """ + + def __init__( + self, + audio_tar_filepaths: Union[str, List[str]], + manifest_filepath: str, + labels: List[str], + sample_rate: int, + int_values: bool = False, + augmentor: Optional['nemo.collections.asr.parts.perturb.AudioAugmentor'] = None, + shuffle_n: int = 0, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + blank_index: int = -1, + unk_index: int = -1, + normalize: bool = True, + trim: bool = False, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + parser: Optional[str] = 'en', + pad_id: int = 0, + shard_strategy: str = "scatter", + shard_manifests: bool = False, + global_rank: int = 0, + world_size: int = 0, + return_sample_id: bool = False, + ): + self.labels = labels + + parser = parsers.make_parser( + labels=labels, name=parser, unk_id=unk_index, blank_id=blank_index, do_normalize=normalize + ) + + super().__init__( + audio_tar_filepaths=audio_tar_filepaths, + manifest_filepath=manifest_filepath, + parser=parser, + sample_rate=sample_rate, + int_values=int_values, + augmentor=augmentor, + shuffle_n=shuffle_n, + min_duration=min_duration, + max_duration=max_duration, + trim=trim, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + shard_strategy=shard_strategy, + shard_manifests=shard_manifests, + global_rank=global_rank, + world_size=world_size, + return_sample_id=return_sample_id, + ) + + +class TarredAudioToBPEDataset(_TarredAudioToTextDataset): + """ + A similar Dataset to the AudioToBPEDataset, but which loads tarred audio files. + + Accepts a single comma-separated JSON manifest file (in the same style as for the AudioToBPEDataset), + as well as the path(s) to the tarball(s) containing the wav files. Each line of the manifest should + contain the information for one audio file, including at least the transcript and name of the audio + file within the tarball. + + Valid formats for the audio_tar_filepaths argument include: + (1) a single string that can be brace-expanded, e.g. 'path/to/audio.tar' or 'path/to/audio_{1..100}.tar.gz', or + (2) a list of file paths that will not be brace-expanded, e.g. ['audio_1.tar', 'audio_2.tar', ...]. + + See the WebDataset documentation for more information about accepted data and input formats. + + If using multiple workers the number of shards should be divisible by world_size to ensure an + even split among workers. If it is not divisible, logging will give a warning but training will proceed. + In addition, if using mutiprocessing, each shard MUST HAVE THE SAME NUMBER OF ENTRIES after filtering + is applied. We currently do not check for this, but your program may hang if the shards are uneven! + + Notice that a few arguments are different from the AudioToBPEDataset; for example, shuffle (bool) has been + replaced by shuffle_n (int). + + Additionally, please note that the len() of this DataLayer is assumed to be the length of the manifest + after filtering. An incorrect manifest length may lead to some DataLoader issues down the line. + + Args: + audio_tar_filepaths: Either a list of audio tarball filepaths, or a + string (can be brace-expandable). + manifest_filepath (str): Path to the manifest. + tokenizer (TokenizerSpec): Either a Word Piece Encoding tokenizer (BERT), + or a Sentence Piece Encoding tokenizer (BPE). The CTC blank + symbol is automatically added later for models using ctc. + sample_rate (int): Sample rate to resample loaded audio to + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): An AudioAugmentor + object used to augment loaded audio + shuffle_n (int): How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + Defaults to 0. + min_duration (float): Dataset parameter. + All training files which have a duration less than min_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to 0.1. + max_duration (float): Dataset parameter. + All training files which have a duration more than max_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to None. + trim (bool): Whether to use trim silence from beginning and end + of audio signal using librosa.effects.trim(). + Defaults to False. + use_start_end_token: Boolean which dictates whether to add [BOS] and [EOS] + tokens to beginning and ending of speech respectively. + pad_id (id): Token used to pad when collating samples in batches. + If this is None, pads using 0s. + Defaults to None. + shard_strategy (str): Tarred dataset shard distribution strategy chosen as a str value during ddp. + + - `scatter`: The default shard strategy applied by WebDataset, where each node gets + a unique set of shards, which are permanently pre-allocated and never changed at runtime. + - `replicate`: Optional shard strategy, where each node gets all of the set of shards + available in the tarred dataset, which are permanently pre-allocated and never changed at runtime. + The benefit of replication is that it allows each node to sample data points from the entire + dataset independently of other nodes, and reduces dependence on value of `shuffle_n`. + + .. warning:: + + Replicated strategy allows every node to sample the entire set of available tarfiles, + and therefore more than one node may sample the same tarfile, and even sample the same + data points! As such, there is no assured guarantee that all samples in the dataset will be + sampled at least once during 1 epoch. Scattered strategy, on the other hand, on specific + occasions (when the number of shards is not divisible with ``world_size``), will not sample + the entire dataset. For these reasons it is not advisable to use tarred datasets as validation + or test datasets. + + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 0. + return_sample_id (bool): whether to return the sample_id as a part of each sample + """ + + def __init__( + self, + audio_tar_filepaths: Union[str, List[str]], + manifest_filepath: str, + tokenizer: 'nemo.collections.common.tokenizers.TokenizerSpec', + sample_rate: int, + int_values: bool = False, + augmentor: Optional['nemo.collections.asr.parts.perturb.AudioAugmentor'] = None, + shuffle_n: int = 0, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + trim: bool = False, + use_start_end_token: bool = True, + shard_strategy: str = "scatter", + shard_manifests: bool = False, + global_rank: int = 0, + world_size: int = 0, + return_sample_id: bool = False, + ): + if use_start_end_token and hasattr(tokenizer, "bos_id") and tokenizer.bos_id > 0: + bos_id = tokenizer.bos_id + else: + bos_id = None + + if use_start_end_token and hasattr(tokenizer, "eos_id") and tokenizer.eos_id > 0: + eos_id = tokenizer.eos_id + else: + eos_id = None + + if hasattr(tokenizer, "pad_id") and tokenizer.pad_id > 0: + pad_id = tokenizer.pad_id + else: + pad_id = 0 + + class TokenizerWrapper: + def __init__(self, tokenizer): + if isinstance(tokenizer, tokenizers.aggregate_tokenizer.AggregateTokenizer): + self.is_aggregate = True + else: + self.is_aggregate = False + self._tokenizer = tokenizer + + def __call__(self, *args): + if isinstance(args[0], List) and self.is_aggregate: + t = [] + for span in args[0]: + t.extend(self._tokenizer.text_to_ids(span['str'], span['lang'])) + return t + + t = self._tokenizer.text_to_ids(*args) + return t + + super().__init__( + audio_tar_filepaths=audio_tar_filepaths, + manifest_filepath=manifest_filepath, + parser=TokenizerWrapper(tokenizer), + sample_rate=sample_rate, + int_values=int_values, + augmentor=augmentor, + shuffle_n=shuffle_n, + min_duration=min_duration, + max_duration=max_duration, + trim=trim, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + shard_strategy=shard_strategy, + shard_manifests=shard_manifests, + global_rank=global_rank, + world_size=world_size, + return_sample_id=return_sample_id, + ) + + +class BucketingDataset(IterableDataset): + """ + A Dataset which wraps another IterableDataset and adopts it for bucketing + Args: + dataset (IterableDataset): The IterableDataset to get wrapped + bucketing_batch_size (int): Number of samples to build a batch + """ + + def __init__( + self, dataset: IterableDataset, bucketing_batch_size: int, + ): + self.wrapped_dataset = dataset + self.bucketing_batch_size = bucketing_batch_size + super().__init__() + + def _collate_fn(self, batch): + return _speech_collate_fn(batch[0], self.wrapped_dataset.pad_id) + + def __iter__(self): + return BucketingIterator( + wrapped_ds=self.wrapped_dataset._dataset, bucketing_batch_size=self.bucketing_batch_size + ).__iter__() + + def __len__(self): + return int(math.ceil(len(self.wrapped_dataset) / float(self.bucketing_batch_size))) + + +class BucketingIterator: + def __init__(self, wrapped_ds, bucketing_batch_size): + self.wrapped_ds = wrapped_ds + self.wrapped_iter = None + self.bucketing_batch_size = bucketing_batch_size + + def __iter__(self): + self.wrapped_iter = iter(self.wrapped_ds) + return self + + def __next__(self): + batches = [] + for idx in range(self.bucketing_batch_size): + try: + sample = next(self.wrapped_iter) + except StopIteration: + break + batches.append(sample) + if len(batches) == 0: + raise StopIteration + return batches + + +class RandomizedChainDataset(ChainDataset): + def __init__(self, datasets: Iterable[Dataset], rnd_seed=0) -> None: + super(RandomizedChainDataset, self).__init__(list(datasets)) + self.rnd_gen = np.random.RandomState(rnd_seed) + + def __iter__(self): + shuffled_order = self.rnd_gen.permutation(len(self.datasets)) + for dataset_idx in shuffled_order: + d = self.datasets[dataset_idx] + assert isinstance(d, IterableDataset), "ChainDataset only supports IterableDataset" + for idx, x in enumerate(d): + yield x + # in case d is an infinite dataset, we want to break the loop + # so that the other datasets get a chance to yield too + if idx >= len(d) - 1: + break diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text_dali.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text_dali.py new file mode 100644 index 0000000..77bd711 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text_dali.py @@ -0,0 +1,772 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import operator +import os.path +import time +from collections.abc import Iterator +from typing import Callable, List, Optional, Union + +import torch +from omegaconf import DictConfig + +from nemo.collections.asr.data.audio_to_text import ASRManifestProcessor, expand_sharded_filepaths +from nemo.collections.common.parts.preprocessing import parsers +from nemo.utils import logging, model_utils + +try: + import nvidia.dali as dali + from nvidia.dali.pipeline import Pipeline + from nvidia.dali.plugin.pytorch import DALIGenericIterator as DALIPytorchIterator + from nvidia.dali.plugin.pytorch import LastBatchPolicy as LastBatchPolicy + + HAVE_DALI = True +except (ImportError, ModuleNotFoundError): + HAVE_DALI = False + +__all__ = [ + 'AudioToCharDALIDataset', + 'AudioToBPEDALIDataset', +] + +""" +Below minimum version is required to access the "read_idxs" argument in +dali.fn.readers.nemo_asr +""" +__DALI_MINIMUM_VERSION__ = "1.11" + +DALI_INSTALLATION_MESSAGE = ( + "Could not import `nvidia.dali`.\n" + "Please install DALI by following the steps provided here - \n" + "https://docs.nvidia.com/deeplearning/dali/user-guide/docs/installation.html" +) + + +def is_dali_supported(min_version: str, verbose: bool = False) -> bool: + """ + Checks if DALI in installed, and version is >= min_verion. + + Args: + min_version: A semver str that is the minimum requirement. + verbose: Whether to log the installation instructions if DALI is not found. + + Returns: + bool - whether DALI could be imported or not. + """ + module_available, _ = model_utils.check_lib_version( + 'nvidia.dali', checked_version=min_version, operator=operator.ge + ) + + # If DALI is not installed + if module_available is None: + if verbose: + logging.info(DALI_INSTALLATION_MESSAGE) + + return False + + return module_available + + +class DALIOutputs(object): + def __init__(self, out_dict): + self._has_processed_signal = 'processed_signal' in out_dict and 'processed_signal_len' in out_dict + if not self._has_processed_signal: + assert 'audio' in out_dict and 'audio_len' in out_dict + assert 'transcript' in out_dict and 'transcript_len' in out_dict + if self._has_processed_signal: + self._outs = ( + out_dict['processed_signal'], + out_dict['processed_signal_len'].reshape(-1), + out_dict['transcript'], + out_dict['transcript_len'].reshape(-1), + ) + else: + self._outs = ( + out_dict['audio'], + out_dict['audio_len'].reshape(-1), + out_dict['transcript'], + out_dict['transcript_len'].reshape(-1), + ) + + @property + def has_processed_signal(self): + return self._has_processed_signal + + def __getitem__(self, key): + return self._outs[key] + + def __len__(self): + return len(self._outs) + + +class _AudioTextDALIDataset(Iterator): + """ + NVIDIA DALI pipeline that loads tensors via one or more manifest files where each line containing a sample descriptor in JSON, + including audio files, transcripts, and durations (in seconds). + Here's an example: + {"audio_filepath": "/path/to/audio.wav", "text_filepath": "/path/to/audio.txt", "duration": 23.147} + ... + {"audio_filepath": "/path/to/audio.wav", "text": "the transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + + Args: + manifest_filepath: Path to manifest file with the format described above. Can be comma-separated paths. + device (str): Determines the device type to be used for preprocessing. Allowed values are: 'cpu', 'gpu'. + batch_size (int): Number of samples in a batch. + parser (str, callable): A str for an inbuilt parser, or a callable with signature f(str) -> List[int]. + sample_rate (int): Sample rate to resample loaded audio to. + num_threads (int): Number of CPU processing threads to be created by the DALI pipeline. + max_duration (float): Determines the maximum allowed duration, in seconds, of the loaded audio files. + min_duration (float): Determines the minimum allowed duration, in seconds, of the loaded audio files. + bos_id (int): Id of beginning of sequence symbol to append if not None + eos_id (int): Id of end of sequence symbol to append if not None + pad_id (int): Id used to pad the input. Defaults to 0 if not provided. + trim (bool): If True, it will extract the nonsilent region of the loaded audio signal. + shuffle (bool): If set to True, the dataset will shuffled after loading. + drop_last (bool): If set to True, the last batch will be dropped if incomplete. This will be the case when the shard size is not divisible by the batch size. + If set to False and the size of dataset is not divisible by the batch size, then the last batch will be smaller. + device_id (int): Index of the GPU to be used (local_rank). Only applicable when device == 'gpu'. Defaults to 0. + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 1. + preprocessor_cfg (DictConfig): Preprocessor configuration. Supports AudioToMelSpectrogramPreprocessor and AudioToMFCCPreprocessor. + return_sample_id (bool): whether to return the sample_id as a part of each sample (not supported yet). + """ + + def __init__( + self, + manifest_filepath: str, + device: str, + batch_size: int, + parser: Union[str, Callable], + audio_tar_filepaths: Optional[Union[str, List[str]]] = None, + audio_tar_index_filepaths: Optional[Union[str, List[str]]] = None, + sample_rate: int = 16000, + num_threads: int = 4, + max_duration: float = 0.0, + min_duration: float = 0.0, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + trim: bool = False, + shuffle: bool = False, + drop_last: bool = False, + shard_strategy: str = "scatter", + device_id: int = 0, + global_rank: int = 0, + world_size: int = 1, + preprocessor_cfg: DictConfig = None, + return_sample_id: bool = False, + ): + self.drop_last = drop_last # used by lr_scheduler + if return_sample_id: + raise ValueError( + "Currently DALI data layers don't support returning the sample_id and return_sample_id can not be enabled." + ) + self.return_sample_id = return_sample_id + + if not HAVE_DALI: + raise ModuleNotFoundError( + f"{self} requires NVIDIA DALI to be installed. " + f"See: https://docs.nvidia.com/deeplearning/dali/user-guide/docs/installation.html#id1" + ) + + if device not in ('cpu', 'gpu'): + raise ValueError( + f"{self} received an unexpected device argument {device}. Supported values are: 'cpu', 'gpu'" + ) + + device_id = device_id if device == 'gpu' else None + + self.batch_size = batch_size # Used by NeMo + + self.device = device + self.device_id = device_id + + if world_size > 1: + self.shard_id = global_rank + self.num_shards = world_size + else: + self.shard_id = None + self.num_shards = None + + self.eos_id = eos_id + self.bos_id = bos_id + self.sample_rate = sample_rate + + self.pipe = Pipeline( + batch_size=batch_size, + num_threads=num_threads, + device_id=self.device_id, + exec_async=True, + exec_pipelined=True, + ) + + has_preprocessor = preprocessor_cfg is not None + if has_preprocessor: + if preprocessor_cfg._target_ == "nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor": + feature_type = "mel_spectrogram" + elif preprocessor_cfg._target_ == "nemo.collections.asr.modules.AudioToMFCCPreprocessor": + feature_type = "mfcc" + else: + raise ValueError( + f"{self} received an unexpected preprocessor configuration: {preprocessor_cfg._target_}." + f" Supported preprocessors are: AudioToMelSpectrogramPreprocessor, AudioToMFCCPreprocessor" + ) + + # Default values taken from AudioToMelSpectrogramPreprocessor + params = preprocessor_cfg + self.dither = params['dither'] if 'dither' in params else 0.0 + self.preemph = params['preemph'] if 'preemph' in params else 0.97 + self.window_size_sec = params['window_size'] if 'window_size' in params else 0.02 + self.window_stride_sec = params['window_stride'] if 'window_stride' in params else 0.01 + self.sample_rate = params['sample_rate'] if 'sample_rate' in params else sample_rate + self.window_size = int(self.window_size_sec * self.sample_rate) + self.window_stride = int(self.window_stride_sec * self.sample_rate) + + normalize = params['normalize'] if 'normalize' in params else 'per_feature' + if normalize == 'per_feature': # Each freq channel independently + self.normalization_axes = (1,) + elif normalize == 'all_features': + self.normalization_axes = (0, 1) + else: + raise ValueError( + f"{self} received {normalize} for the normalize parameter." + f" It must be either 'per_feature' or 'all_features'." + ) + + self.window = None + window_name = params['window'] if 'window' in params else 'hann' + torch_windows = { + 'hann': torch.hann_window, + 'hamming': torch.hamming_window, + 'blackman': torch.blackman_window, + 'bartlett': torch.bartlett_window, + 'none': None, + } + + if window_name == 'ones': + window_tensor = torch.ones(self.window_size) + else: + try: + window_fn = torch_windows.get(window_name, None) + except: + raise ValueError( + f"{self} received '{window_name}' for the window parameter." + f" It must be one of: ('hann', 'ones', 'hamming', 'blackman', 'bartlett', None)." + f" None is equivalent to 'hann'." + ) + window_tensor = window_fn(self.window_size, periodic=False) if window_fn else None + self.window = window_tensor.numpy().tolist() if window_tensor is not None else None + + self.n_fft = params['n_fft'] if 'n_fft' in params else 2 ** math.ceil(math.log2(self.window_size)) + self.n_mels = params['n_mels'] if 'n_mels' in params else 64 + self.n_mfcc = params['n_mfcc'] if 'n_mfcc' in params else 64 + + features = params['features'] if 'features' in params else 0 + if features > 0: + if feature_type == 'mel_spectrogram': + self.n_mels = features + elif feature_type == 'mfcc': + self.n_mfcc = features + + # TODO Implement frame splicing + if 'frame_splicing' in params: + assert params['frame_splicing'] == 1, "Frame splicing is not implemented" + + self.freq_low = params['lowfreq'] if 'lowfreq' in params else 0.0 + self.freq_high = params['highfreq'] if 'highfreq' in params else self.sample_rate / 2.0 + self.log_features = params['log'] if 'log' in params else True + + # We want to avoid taking the log of zero + # There are two options: either adding or clamping to a small value + + self.log_zero_guard_type = params['log_zero_guard_type'] if 'log_zero_guard_type' in params else 'add' + if self.log_zero_guard_type not in ["add", "clamp"]: + raise ValueError( + f"{self} received {self.log_zero_guard_type} for the " + f"log_zero_guard_type parameter. It must be either 'add' or " + f"'clamp'." + ) + + self.log_zero_guard_value = ( + params['log_zero_guard_value'] if 'log_zero_guard_value' in params else 2 ** -24 + ) + if isinstance(self.log_zero_guard_value, str): + if self.log_zero_guard_value == "tiny": + self.log_zero_guard_value = torch.finfo(torch.float32).tiny + elif self.log_zero_guard_value == "eps": + self.log_zero_guard_value = torch.finfo(torch.float32).eps + else: + raise ValueError( + f"{self} received {self.log_zero_guard_value} for the log_zero_guard_type parameter." + f"It must be either a number, 'tiny', or 'eps'" + ) + + self.mag_power = params['mag_power'] if 'mag_power' in params else 2 + if self.mag_power != 1.0 and self.mag_power != 2.0: + raise ValueError( + f"{self} received {self.mag_power} for the mag_power parameter." f" It must be either 1.0 or 2.0." + ) + + self.pad_to = max(params['pad_to'], 1) if 'pad_to' in params else 16 + self.pad_value = params['pad_value'] if 'pad_value' in params else 0.0 + + with self.pipe: + if audio_tar_filepaths is None and audio_tar_index_filepaths is None: + audio, indices = dali.fn.readers.nemo_asr( + name="Reader", + manifest_filepaths=manifest_filepath.split(','), + dtype=dali.types.FLOAT, + downmix=True, + sample_rate=float(self.sample_rate), + min_duration=min_duration, + max_duration=max_duration, + read_sample_rate=False, + read_text=False, + read_idxs=True, + random_shuffle=shuffle, + shard_id=self.shard_id, + num_shards=self.num_shards, + pad_last_batch=True, + ) + + self.is_tarred_dataset = False + + elif audio_tar_filepaths is not None and audio_tar_index_filepaths is not None: + audio_tar_filepaths = expand_sharded_filepaths( + audio_tar_filepaths, shard_strategy=shard_strategy, world_size=world_size, global_rank=global_rank + ) + audio_tar_index_filepaths = expand_sharded_filepaths( + audio_tar_index_filepaths, + shard_strategy=shard_strategy, + world_size=world_size, + global_rank=global_rank, + ) + + if len(audio_tar_filepaths) != len(audio_tar_index_filepaths) and len(audio_tar_index_filepaths) != 0: + raise ValueError( + f"Number of filepaths provided for `audio_tar_filepaths` must match " + f"`audio_tar_index_filepaths`. Got {len(audio_tar_filepaths)} audio_tar_filepaths and " + f"{len(audio_tar_index_filepaths)} audio_tar_index_filepaths." + ) + + tar_file = dali.fn.readers.webdataset( + paths=audio_tar_filepaths, + index_paths=audio_tar_index_filepaths, + name="Reader", + ext=["wav"], + missing_component_behavior="error", + random_shuffle=shuffle, + shard_id=self.shard_id, + num_shards=self.num_shards, + pad_last_batch=True, + ) + audio, _ = dali.fn.decoders.audio( + tar_file, dtype=dali.types.FLOAT, downmix=True, sample_rate=float(self.sample_rate), + ) + indices = dali.fn.get_property(tar_file, key="source_info") + indices = dali.fn.pad(indices) + + self.is_tarred_dataset = True + + else: + raise RuntimeError( + "When using DALI datasets, either `audio_tar_filepaths` " + "and `audio_tar_index_filepaths` should either both be None (sequential dataset)" + "or provided (tarred dataset)." + ) + + # Extract nonsilent region, if necessary + if trim: + # Need to extract non-silent region before moving to the GPU + roi_start, roi_len = dali.fn.nonsilent_region(audio, cutoff_db=-60) + audio = audio.gpu() if self.device == 'gpu' else audio + audio = dali.fn.slice( + audio, roi_start, roi_len, normalized_anchor=False, normalized_shape=False, axes=[0] + ) + else: + audio = audio.gpu() if self.device == 'gpu' else audio + + if not has_preprocessor: + # No preprocessing, the output is the audio signal + audio_len = dali.fn.shapes(dali.fn.reshape(audio, shape=[-1])) + audio = dali.fn.pad(audio) + self.pipe.set_outputs(audio, audio_len, indices) + else: + # Additive gaussian noise (dither) + if self.dither > 0.0: + gaussian_noise = dali.fn.random.normal(audio) + audio = audio + self.dither * gaussian_noise + + # Preemphasis filter + if self.preemph > 0.0: + audio = dali.fn.preemphasis_filter(audio, preemph_coeff=self.preemph, border='zero') + + # Power spectrogram + spec = dali.fn.spectrogram( + audio, + nfft=self.n_fft, + window_length=self.window_size, + window_step=self.window_stride, + window_fn=self.window, + ) + + if feature_type == 'mel_spectrogram' or feature_type == 'mfcc': + # Spectrogram to Mel Spectrogram + spec = dali.fn.mel_filter_bank( + spec, + sample_rate=self.sample_rate, + nfilter=self.n_mels, + normalize=True, + freq_low=self.freq_low, + freq_high=self.freq_high, + ) + # Mel Spectrogram to MFCC + if feature_type == 'mfcc': + spec = dali.fn.mfcc(spec, n_mfcc=self.n_mfcc) + + # Logarithm + if self.log_zero_guard_type == 'add': + spec = spec + self.log_zero_guard_value + + spec = dali.fn.to_decibels( + spec, multiplier=math.log(10), reference=1.0, cutoff_db=math.log(self.log_zero_guard_value) + ) + + # Normalization + spec = dali.fn.normalize(spec, axes=self.normalization_axes, epsilon=1e-5 ** 2, ddof=1) + + # Extracting the length of the spectrogram + spec_len = dali.fn.slice(dali.fn.shapes(spec), 1, 1, axes=(0,)) + + # Pads feature dimension to be a multiple of `pad_to` and the temporal dimension to be as big as the largest sample (shape -1) + spec = dali.fn.pad(spec, fill_value=self.pad_value, axes=(0, 1), align=(self.pad_to, 1), shape=(1, -1)) + self.pipe.set_outputs(spec, spec_len, indices) + + x = time.time() + # Building DALI pipeline + self.pipe.build() + y = time.time() + + logging.info(f"Time for pipe.build() : {(y - x)} seconds") + + if has_preprocessor: + output_names = ['processed_signal', 'processed_signal_len', 'manifest_indices'] + else: + output_names = ['audio', 'audio_len', 'manifest_indices'] + + x = time.time() + last_batch_policy = LastBatchPolicy.DROP if drop_last else LastBatchPolicy.PARTIAL + self._iter = DALIPytorchIterator( + [self.pipe], + output_map=output_names, + reader_name="Reader", + last_batch_policy=last_batch_policy, + dynamic_shape=True, + auto_reset=True, + ) + y = time.time() + logging.info(f"Time for DALIPytorchIterator to initialize : {(y - x)} seconds") + + # TODO come up with a better solution + class DummyDataset: + def __init__(self, parent): + self.parent = parent + + def __len__(self): + return self.parent.size + + self.dataset = DummyDataset(self) # Used by NeMo + + x = time.time() + self.manifest_processor = ASRManifestProcessor( + manifest_filepath=manifest_filepath, + parser=parser, + max_duration=max_duration, + min_duration=min_duration, + max_utts=0, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + index_by_file_id=self.is_tarred_dataset, + ) + y = time.time() + logging.info(f"Time to build nemo manifest processor - {(y - x)} seconds") + + def reset(self): + self._iter.reset() + + def __iter__(self): + return self + + def next(self): + return self.__next__() + + @property + def size(self): + return self._iter.size + + def __len__(self): + return len(self._iter) + + def __next__(self): + outputs = self._iter.next() + assert len(outputs) == 1 + dali_out = outputs[0] + manifest_indices = dali_out['manifest_indices'].numpy() + + out = {} + out_names = ['processed_signal', 'processed_signal_len', 'audio', 'audio_len'] + for out_name in out_names: + if out_name in dali_out: + out[out_name] = dali_out[out_name].detach().clone() + + text_tokens = [] + text_tokens_len = [] + max_len = 0 + batch_size = manifest_indices.shape[0] + for i, manifest_index in enumerate(manifest_indices): + + if not self.is_tarred_dataset: + # Loose-file dataset. Index is integer based. + manifest_index = manifest_index[0] + text, text_length = self.manifest_processor.process_text_by_id(manifest_index) + else: + # Tarred-file dataset. Index is filename based. + resolved_manifest_indices = manifest_index.tobytes().decode().split(":") + resolved_manifest_index = resolved_manifest_indices[2] # we require just the filename segment + resolved_manifest_index = os.path.splitext(resolved_manifest_index)[0] # we dont need file extension + text, text_length = self.manifest_processor.process_text_by_file_id(resolved_manifest_index) + + text_tokens_len.append(text_length) + text_tokens.append(text) + if text_length > max_len: + max_len = text_length + + transcript_out = torch.full([batch_size, max_len], fill_value=self.manifest_processor.pad_id, dtype=torch.long) + for i, n in enumerate(text_tokens_len): + transcript_out[i, :n] = torch.tensor(text_tokens[i], dtype=torch.long) + transcript_len_out = torch.tensor(text_tokens_len, dtype=torch.long) + + out['transcript'] = transcript_out + out['transcript_len'] = transcript_len_out + return DALIOutputs(out) + + +class AudioToCharDALIDataset(_AudioTextDALIDataset): + """ + Character based NVIDIA DALI pipeline that loads tensors via one or more manifest files where each line containing a + sample descriptor in JSON, including audio files, transcripts, and durations (in seconds). + Here's an example: + {"audio_filepath": "/path/to/audio.wav", "text_filepath": "/path/to/audio.txt", "duration": 23.147} + ... + {"audio_filepath": "/path/to/audio.wav", "text": "the transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + + Args: + manifest_filepath: Path to manifest file with the format described above. Can be comma-separated paths. + device (str): Determines the device type to be used for preprocessing. Allowed values are: 'cpu', 'gpu'. + batch_size (int): Number of samples in a batch. + labels (List[str]): String containing all the possible characters to map to. + sample_rate (int): Sample rate to resample loaded audio to. + num_threads (int): Number of CPU processing threads to be created by the DALI pipeline. + max_duration (float): Determines the maximum allowed duration, in seconds, of the loaded audio files. + min_duration (float): Determines the minimum allowed duration, in seconds, of the loaded audio files. + blank_index (int): blank character index, default = -1 + unk_index (int): unk_character index, default = -1 + normalize (bool): whether to normalize transcript text (default): True + bos_id (int): Id of beginning of sequence symbol to append if not None + eos_id (int): Id of end of sequence symbol to append if not None + pad_id (int): Id used to pad the input. Defaults to 0 if not provided. + trim (bool): If True, it will extract the nonsilent region of the loaded audio signal. + shuffle (bool): If set to True, the dataset will shuffled after loading. + drop_last (bool): If set to True, the last batch will be dropped if incomplete. This will be the case when the shard size is not divisible by the batch size. + If set to False and the size of dataset is not divisible by the batch size, then the last batch will be smaller. + parser (str, callable): A str for an inbuilt parser, or a callable with signature f(str) -> List[int]. + device_id (int): Index of the GPU to be used (local_rank). Only applicable when device == 'gpu'. Defaults to 0. + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 1. + preprocessor_cfg (DictConfig): Preprocessor configuration. Supports AudioToMelSpectrogramPreprocessor and AudioToMFCCPreprocessor. + return_sample_id (bool): whether to return the sample_id as a part of each sample (not supported yet). + """ + + def __init__( + self, + manifest_filepath: str, + device: str, + batch_size: int, + labels: Union[str, List[str]], + sample_rate: int = 16000, + audio_tar_filepaths: Optional[Union[str, List[str]]] = None, + audio_tar_index_filepaths: Optional[Union[str, List[str]]] = None, + num_threads: int = 4, + max_duration: float = 0.0, + min_duration: float = 0.0, + blank_index: int = -1, + unk_index: int = -1, + normalize: bool = True, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + trim: bool = False, + shuffle: bool = False, + drop_last: bool = False, + parser: Union[str, Callable] = 'en', + shard_strategy: str = "scatter", + device_id: int = 0, + global_rank: int = 0, + world_size: int = 1, + preprocessor_cfg: DictConfig = None, + return_sample_id: bool = False, + ): + self.labels = labels + + parser = parsers.make_parser( + labels=labels, name=parser, unk_id=unk_index, blank_id=blank_index, do_normalize=normalize + ) + + super().__init__( + manifest_filepath=manifest_filepath, + device=device, + batch_size=batch_size, + audio_tar_filepaths=audio_tar_filepaths, + audio_tar_index_filepaths=audio_tar_index_filepaths, + sample_rate=sample_rate, + num_threads=num_threads, + max_duration=max_duration, + min_duration=min_duration, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + trim=trim, + shuffle=shuffle, + drop_last=drop_last, + parser=parser, + shard_strategy=shard_strategy, + device_id=device_id, + global_rank=global_rank, + world_size=world_size, + preprocessor_cfg=preprocessor_cfg, + return_sample_id=return_sample_id, + ) + + +class AudioToBPEDALIDataset(_AudioTextDALIDataset): + """ + Subword based NVIDIA DALI pipeline that loads tensors via one or more manifest files where each line containing a + sample descriptor in JSON, including audio files, transcripts, and durations (in seconds). + Here's an example: + {"audio_filepath": "/path/to/audio.wav", "text_filepath": "/path/to/audio.txt", "duration": 23.147} + ... + {"audio_filepath": "/path/to/audio.wav", "text": "the transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + + Args: + manifest_filepath: Path to manifest file with the format described above. Can be comma-separated paths. + tokenizer (TokenizerSpec): A TokenizerSpec implementation that wraps a tokenization implementation. + device (str): Determines the device type to be used for preprocessing. Allowed values are: 'cpu', 'gpu'. + batch_size (int): Number of samples in a batch. + sample_rate (int): Sample rate to resample loaded audio to. + num_threads (int): Number of CPU processing threads to be created by the DALI pipeline. + max_duration (float): Determines the maximum allowed duration, in seconds, of the loaded audio files. + min_duration (float): Determines the minimum allowed duration, in seconds, of the loaded audio files. + bos_id (int): Id of beginning of sequence symbol to append if not None. Injected from the tokenizer. + eos_id (int): Id of end of sequence symbol to append if not None. Injected from the tokenizer. + pad_id (int): Id used to pad the input. Defaults to 0 if not provided. Injected from the tokenizer. + trim (bool): If True, it will extract the nonsilent region of the loaded audio signal. + shuffle (bool): If set to True, the dataset will shuffled after loading. + drop_last (bool): If set to True, the last batch will be dropped if incomplete. This will be the case when the shard size is not divisible by the batch size. + If set to False and the size of dataset is not divisible by the batch size, then the last batch will be smaller. + + device_id (int): Index of the GPU to be used (local_rank). Only applicable when device == 'gpu'. Defaults to 0. + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 1. + preprocessor_cfg (DictConfig): Preprocessor configuration. Supports AudioToMelSpectrogramPreprocessor and AudioToMFCCPreprocessor. + use_start_end_token (bool): Boolean which dictates whether to add [BOS] and [EOS] tokens to beginning and + ending of speech respectively. + return_sample_id (bool): whether to return the sample_id as a part of each sample (not supported yet). + """ + + def __init__( + self, + manifest_filepath: str, + tokenizer: 'nemo.collections.common.tokenizers.TokenizerSpec', + device: str, + batch_size: int, + sample_rate: int = 16000, + audio_tar_filepaths: Optional[Union[str, List[str]]] = None, + audio_tar_index_filepaths: Optional[Union[str, List[str]]] = None, + num_threads: int = 4, + max_duration: float = 0.0, + min_duration: float = 0.0, + trim: bool = False, + shuffle: bool = False, + drop_last: bool = False, + shard_strategy: str = "scatter", + device_id: int = 0, + global_rank: int = 0, + world_size: int = 1, + preprocessor_cfg: DictConfig = None, + use_start_end_token: bool = True, + return_sample_id: bool = False, + ): + + if use_start_end_token and hasattr(tokenizer, 'bos_token'): + bos_id = tokenizer.bos_id + else: + bos_id = None + + if use_start_end_token and hasattr(tokenizer, 'eos_token'): + eos_id = tokenizer.eos_id + else: + eos_id = None + + if hasattr(tokenizer, 'pad_token'): + pad_id = tokenizer.pad_id + else: + pad_id = 0 + + class TokenizerWrapper: + def __init__(self, tokenizer): + self._tokenizer = tokenizer + + def __call__(self, text): + t = self._tokenizer.text_to_ids(text) + return t + + super().__init__( + manifest_filepath=manifest_filepath, + device=device, + batch_size=batch_size, + sample_rate=sample_rate, + audio_tar_filepaths=audio_tar_filepaths, + audio_tar_index_filepaths=audio_tar_index_filepaths, + num_threads=num_threads, + max_duration=max_duration, + min_duration=min_duration, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + trim=trim, + shuffle=shuffle, + drop_last=drop_last, + parser=TokenizerWrapper(tokenizer), + shard_strategy=shard_strategy, + device_id=device_id, + global_rank=global_rank, + world_size=world_size, + preprocessor_cfg=preprocessor_cfg, + return_sample_id=return_sample_id, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text_dataset.py new file mode 100644 index 0000000..7ad6560 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text_dataset.py @@ -0,0 +1,964 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import json +import random +from math import isclose +from typing import Any, List, Optional, Union + +import torch +from omegaconf import DictConfig, OmegaConf, open_dict +from omegaconf.listconfig import ListConfig +from pytorch_lightning.callbacks import BasePredictionWriter +from torch.utils.data import ChainDataset + +from nemo.collections.asr.data import audio_to_text, audio_to_text_dali +from nemo.collections.asr.data.huggingface.hf_audio_to_text_dataset import ( + get_hf_audio_to_text_bpe_dataset, + get_hf_audio_to_text_char_dataset, +) +from nemo.collections.asr.parts.preprocessing.perturb import process_augmentations +from nemo.collections.common.data.dataset import CodeSwitchedDataset, ConcatDataset +from nemo.utils import logging + + +def inject_dataloader_value_from_model_config(model_cfg: dict, dataloader_cfg: DictConfig, key: str): + """ + Extracts the label set provided at the top level of the model, and propagates it to the dataloader + config. + + Args: + model_cfg: A DictConfig representing the model's config. + dataloader_cfg: A DictConfig representing the individual data loader + key: A str value representing a key in the model_cfg whose value will be propagated to the + dataloader config. + """ + if key not in model_cfg: + logging.info( + f"Model level config does not contain `{key}`, please explicitly provide `{key}` to the dataloaders." + ) + return + + if not isinstance(dataloader_cfg, DictConfig): + dataloader_cfg = DictConfig(dataloader_cfg) + + # If key exists in the data loader config (either set explicitly or as a placeholder (via None)) + if key in dataloader_cfg: + # Dataloader `labels` is provided and is non-null + if dataloader_cfg[key] is not None and model_cfg[key] != dataloader_cfg[key]: + # Model level `labels` dont match Dataloader level `labels` + logging.warning( + f'`{key}` is explicitly provided to the data loader, and is different from ' + f'the `{key}` provided at the model level config.\n' + f'If this is incorrect, please set the dataloader\'s `{key}` to None.' + ) + + else: + # Dataloader `key` is None or values match + # Propagate from model level `key` (even if they match) + with open_dict(dataloader_cfg): + dataloader_cfg[key] = model_cfg[key] + + else: + # If key key doesnt even exist in dataloader_cfg, inject it explicitly + with open_dict(dataloader_cfg): + dataloader_cfg[key] = model_cfg[key] + + +def get_concat_char_dataset( + config: dict, global_rank: int, world_size: int, augmentor: Optional['AudioAugmentor'] = None +) -> ConcatDataset: + """ + Instantiates an instance of ConcatDataset containing one or more intances of + Character Encoding based AudioToCharDataset. + + Args: + config: Config of the AudioToCharDataset. + global_rank: Global rank of this device. + world_size: Global world size in the training method. + augmentor: Optional AudioAugmentor object for augmentations on audio data. + + Returns: + An instance of ConcatDataset containing one or more instances of AudioToCharDataset. + """ + if 'labels' not in config: + logging.warning(f"dataset does not have explicitly defined labels") + + manifest_filepaths = config['manifest_filepath'] + datasets = [] + + # needed to support validation Concat Datasets that arrive here as + # [[dataset1,dataset2]] otherwise ModelPT would interfere + if len(manifest_filepaths) == 1 and not isinstance(manifest_filepaths[0], str): + logging.info(f"removing an extra nesting level from {manifest_filepaths}") + manifest_filepaths = config['manifest_filepath'][0] + + for manifest_filepath in manifest_filepaths: + conf = copy.deepcopy(config) + conf['manifest_filepath'] = manifest_filepath + + dataset = get_char_dataset(config=conf, augmentor=augmentor) + datasets.append(dataset) + + dataset = ConcatDataset( + datasets, + sampling_technique=config.get('concat_sampling_technique', 'temperature'), + sampling_temperature=config.get('concat_sampling_temperature', 5), + sampling_scale=config.get('concat_sampling_scale', 1), + sampling_probabilities=config.get('concat_sampling_probabilities', None), + shuffle=config.get('concat_shuffle', True), + seed=config.get('concat_sampling_seed', None), + global_rank=global_rank, + world_size=world_size, + ) + return dataset + + +def get_char_dataset(config: dict, augmentor: Optional['AudioAugmentor'] = None) -> audio_to_text.AudioToCharDataset: + """ + Instantiates a Character Encoding based AudioToCharDataset. + + Args: + config: Config of the AudioToCharDataset. + augmentor: Optional AudioAugmentor object for augmentations on audio data. + + Returns: + An instance of AudioToCharDataset. + """ + if 'labels' not in config: + logging.warning(f"dataset does not have explicitly defined labels") + + dataset = audio_to_text.AudioToCharDataset( + manifest_filepath=config['manifest_filepath'], + labels=config.get('labels', None), + sample_rate=config['sample_rate'], + int_values=config.get('int_values', False), + augmentor=augmentor, + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + max_utts=config.get('max_utts', 0), + blank_index=config.get('blank_index', -1), + unk_index=config.get('unk_index', -1), + normalize=config.get('normalize_transcripts', False), + trim=config.get('trim_silence', False), + parser=config.get('parser', 'en'), + return_sample_id=config.get('return_sample_id', False), + channel_selector=config.get('channel_selector', None), + ) + return dataset + + +def get_concat_bpe_dataset( + config: dict, + tokenizer: 'TokenizerSpec', + global_rank: int, + world_size: int, + augmentor: Optional['AudioAugmentor'] = None, +) -> ConcatDataset: + """ + Instantiates a ContactDataset based on several Byte Pair Encoding / Word Piece Encoding based AudioToBPEDatasets. + + Args: + config: Config of the AudioToBPEDataset. + tokenizer: An instance of a TokenizerSpec object. + global_rank: Global rank of this device. + world_size: Global world size in the training method. + augmentor: Optional AudioAugmentor object for augmentations on audio data. + + Returns: + An instance of ConcatDataset containing several instances of AudioToBPEDataset. + """ + manifest_filepaths = config['manifest_filepath'] + datasets = [] + + # needed to support validation Concat Datasets that arrive here as + # [[dataset1,dataset2]] otherwise ModelPT would interfere + if len(manifest_filepaths) == 1 and not isinstance(manifest_filepaths[0], str): + logging.info(f"removing an extra nesting level from {manifest_filepaths}") + manifest_filepaths = config['manifest_filepath'][0] + + for manifest_filepath in manifest_filepaths: + conf = copy.deepcopy(config) + conf['manifest_filepath'] = manifest_filepath + dataset = get_bpe_dataset(config=conf, tokenizer=tokenizer, augmentor=augmentor) + datasets.append(dataset) + + dataset = ConcatDataset( + datasets, + sampling_technique=config.get('concat_sampling_technique', 'temperature'), + sampling_temperature=config.get('concat_sampling_temperature', 5), + sampling_scale=config.get('concat_sampling_scale', 1), + sampling_probabilities=config.get('concat_sampling_probabilities', None), + shuffle=config.get('concat_shuffle', True), + seed=config.get('concat_sampling_seed', None), + global_rank=global_rank, + world_size=world_size, + ) + return dataset + + +def get_bpe_dataset( + config: dict, tokenizer: 'TokenizerSpec', augmentor: Optional['AudioAugmentor'] = None +) -> audio_to_text.AudioToBPEDataset: + """ + Instantiates a Byte Pair Encoding / Word Piece Encoding based AudioToBPEDataset. + + Args: + config: Config of the AudioToBPEDataset. + tokenizer: An instance of a TokenizerSpec object. + augmentor: Optional AudioAugmentor object for augmentations on audio data. + + Returns: + An instance of AudioToBPEDataset. + """ + dataset = audio_to_text.AudioToBPEDataset( + manifest_filepath=config['manifest_filepath'], + tokenizer=tokenizer, + sample_rate=config['sample_rate'], + int_values=config.get('int_values', False), + augmentor=augmentor, + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + max_utts=config.get('max_utts', 0), + trim=config.get('trim_silence', False), + use_start_end_token=config.get('use_start_end_token', True), + return_sample_id=config.get('return_sample_id', False), + channel_selector=config.get('channel_selector', None), + ) + return dataset + + +def get_concat_tarred_dataset( + config: dict, + shuffle_n: int, + global_rank: int, + world_size: int, + tokenizer: Optional['TokenizerSpec'] = None, + augmentor: Optional['AudioAugmentor'] = None, +) -> ConcatDataset: + """ + Instantiates a ConcatDataset containing multiple Word Piece/BPE Encoding based TarredAudioToBPEDataset or a char based TarredAudioToCharDataset. + + Args: + config: Config of the TarredAudioToBPEDataset or TarredAudioToCharDataset. + shuffle_n: How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + tokenizer: An instance of a TokenizerSpec object if BPE dataset is needed. + global_rank: Global rank of this device. + world_size: Global world size in the training method. + Passsing None would return a char-based dataset. + augmentor: Optional AudioAugmentor object for augmentations on audio data. + + Returns: + An instance of ConcatDataset containing one or more TarredAudioToBPEDatasets or TarredAudioToCharDatasets. + """ + + tarred_audio_filepaths = config['tarred_audio_filepaths'] + manifest_filepaths = config['manifest_filepath'] + datasets = [] + for dataset_idx, (tarred_audio_filepath, manifest_filepath) in enumerate( + zip(tarred_audio_filepaths, manifest_filepaths) + ): + conf = copy.deepcopy(config) + conf['manifest_filepath'] = manifest_filepath + conf['tarred_audio_filepaths'] = tarred_audio_filepath + dataset = get_tarred_dataset( + config=conf, + tokenizer=tokenizer, + shuffle_n=shuffle_n, + global_rank=global_rank, + world_size=world_size, + augmentor=augmentor, + ) + datasets.append(dataset) + + dataset = ConcatDataset( + datasets, + sampling_technique=config.get('concat_sampling_technique', 'temperature'), + sampling_temperature=config.get('concat_sampling_temperature', 5), + sampling_scale=config.get('concat_sampling_scale', 1), + sampling_probabilities=config.get('concat_sampling_probabilities', None), + shuffle=config.get('concat_shuffle', True), + seed=config.get('concat_sampling_seed', None), + global_rank=global_rank, + world_size=world_size, + ) + return dataset + + +def get_tarred_dataset( + config: dict, + shuffle_n: int, + global_rank: int, + world_size: int, + tokenizer: Optional['TokenizerSpec'] = None, + augmentor: Optional['AudioAugmentor'] = None, +) -> Union[audio_to_text.TarredAudioToBPEDataset, audio_to_text.TarredAudioToCharDataset]: + """ + Instantiates a Word Piece/BPE Encoding based TarredAudioToBPEDataset or a char based TarredAudioToCharDataset. + + Args: + config: Config of the TarredAudioToBPEDataset or TarredAudioToCharDataset. + shuffle_n: How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + tokenizer: An instance of a TokenizerSpec object if BPE dataset is needed. + global_rank: Global rank of this device. + world_size: Global world size in the training method. + Passsing None would return a char-based dataset. + augmentor: Optional AudioAugmentor object for augmentations on audio data. + + Returns: + An instance of TarredAudioToBPEDataset or TarredAudioToCharDataset. + """ + tarred_audio_filepaths = config['tarred_audio_filepaths'] + manifest_filepaths = config['manifest_filepath'] + datasets = [] + tarred_audio_filepaths = convert_to_config_list(tarred_audio_filepaths) + manifest_filepaths = convert_to_config_list(manifest_filepaths) + + bucketing_weights = config.get('bucketing_weights', None) # For upsampling buckets + if bucketing_weights: + for idx, weight in enumerate(bucketing_weights): + if not isinstance(weight, int) or weight <= 0: + raise ValueError(f"bucket weights must be positive integers") + + if len(manifest_filepaths) != len(tarred_audio_filepaths): + raise ValueError( + f"manifest_filepaths (length={len(manifest_filepaths)}) and tarred_audio_filepaths (length={len(tarred_audio_filepaths)}) need to have the same number of buckets." + ) + + if 'labels' not in config: + logging.warning(f"dataset does not have explicitly defined labels") + + if 'max_utts' in config: + raise ValueError('"max_utts" parameter is not supported for tarred datasets') + + for dataset_idx, (tarred_audio_filepath, manifest_filepath) in enumerate( + zip(tarred_audio_filepaths, manifest_filepaths) + ): + if len(tarred_audio_filepath) == 1: + tarred_audio_filepath = tarred_audio_filepath[0] + if len(manifest_filepath) == 1: + manifest_filepath = manifest_filepath[0] + + if tokenizer is None: + dataset = audio_to_text.TarredAudioToCharDataset( + audio_tar_filepaths=tarred_audio_filepath, + manifest_filepath=manifest_filepath, + labels=config.get('labels', None), + sample_rate=config['sample_rate'], + int_values=config.get('int_values', False), + augmentor=augmentor, + shuffle_n=shuffle_n, + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + blank_index=config.get('blank_index', -1), + unk_index=config.get('unk_index', -1), + normalize=config.get('normalize_transcripts', False), + trim=config.get('trim_silence', False), + parser=config.get('parser', 'en'), + shard_strategy=config.get('tarred_shard_strategy', 'scatter'), + shard_manifests=config.get('shard_manifests', False), + global_rank=global_rank, + world_size=world_size, + return_sample_id=config.get('return_sample_id', False), + ) + else: + dataset = audio_to_text.TarredAudioToBPEDataset( + audio_tar_filepaths=tarred_audio_filepath, + manifest_filepath=manifest_filepath, + tokenizer=tokenizer, + sample_rate=config['sample_rate'], + int_values=config.get('int_values', False), + augmentor=augmentor, + shuffle_n=shuffle_n, + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + trim=config.get('trim_silence', False), + use_start_end_token=config.get('use_start_end_token', True), + shard_strategy=config.get('tarred_shard_strategy', 'scatter'), + shard_manifests=config.get('shard_manifests', False), + global_rank=global_rank, + world_size=world_size, + return_sample_id=config.get('return_sample_id', False), + ) + if bucketing_weights: + [datasets.append(dataset) for _ in range(bucketing_weights[dataset_idx])] + else: + datasets.append(dataset) + + return get_chain_dataset(datasets=datasets, ds_config=config, rank=global_rank) + + +def get_code_switched_dataset( + config: dict, + shuffle_n: int, + global_rank: int, + world_size: int, + tokenizer: Optional['TokenizerSpec'] = None, + augmentor: Optional['AudioAugmentor'] = None, +) -> CodeSwitchedDataset: + + if 'manifest_filepath' not in config: + raise ValueError("`manifest_filepath` must be provided in the dataset config if `is_code_switched=True`") + if 'code_switched' not in config: + raise ValueError("`code_switched` param group must be in the dataset config if `is_code_switched=True`") + + manifest_filepaths = config['manifest_filepath'] + tarred_audio_filepaths = config.get('tarred_audio_filepaths', None) + + cs_config = OmegaConf.to_container(config['code_switched']) + + # needed to support validation Datasets that arrive here as + # [[dataset1,dataset2]] otherwise ModelPT would interfere + if len(manifest_filepaths) == 1 and not isinstance(manifest_filepaths[0], str): + manifest_filepaths = config['manifest_filepath'][0] + if tarred_audio_filepaths is None: + tarred_audio_filepaths = [None] * len(manifest_filepaths) + + if len(manifest_filepaths) != len(tarred_audio_filepaths): + raise ValueError( + f"manifest_filepaths (length={len(manifest_filepaths)}) and tarred_audio_filepaths (length={len(tarred_audio_filepaths)}) need to have the same number of items." + ) + + datasets = [] + for dataset_idx, (tarred_audio_filepath, manifest_filepath) in enumerate( + zip(tarred_audio_filepaths, manifest_filepaths) + ): + conf = copy.deepcopy(config) + conf['manifest_filepath'] = manifest_filepath + with open_dict(conf): + conf['tarred_audio_filepaths'] = tarred_audio_filepath + if tarred_audio_filepath is None or len(tarred_audio_filepath) == 0: + if tokenizer is None: + dataset = get_char_dataset(config=conf, augmentor=None) + else: + dataset = get_bpe_dataset(config=conf, tokenizer=tokenizer, augmentor=None) + else: + dataset = get_tarred_dataset( + config=conf, + tokenizer=tokenizer, + shuffle_n=shuffle_n, + global_rank=global_rank, + world_size=world_size, + augmentor=None, + ) + datasets.append(dataset) + + config = OmegaConf.to_container(config) + + dataset = CodeSwitchedDataset( + datasets, + shuffle=cs_config.get('shuffle', True), + min_duration=cs_config.get('min_duration', 4), + max_duration=cs_config.get('max_duration', 20), + min_monolingual=cs_config.get('min_monolingual', 0.3), + lang_probs=cs_config.get('probs', None), + db_norm=cs_config.get('db_norm', -25.0), + pause_start=cs_config.get('pause_start', 0), + pause_join=cs_config.get('pause_join', 0), + pause_end=cs_config.get('pause_end', 0), + sampling_scales=cs_config.get('sampling_scales', None), + seed=cs_config.get('seed', None), + global_rank=global_rank, + world_size=world_size, + pure_random=cs_config.get('pure_random', False), + force_monochannel=cs_config.get('force_monochannel', True), + infinity_mode=cs_config.get('infinity_mode', False), + sample_rate=config['sample_rate'], + augmentor=augmentor, + ) + + return dataset + + +def get_dali_char_dataset( + config: dict, + shuffle: bool, + device_id: int, + global_rank: int, + world_size: int, + preprocessor_cfg: Optional[DictConfig] = None, +) -> audio_to_text_dali.AudioToCharDALIDataset: + """ + Instantiates a Character Encoding based AudioToCharDALIDataset. + + Args: + config: Config of the AudioToCharDALIDataset. + shuffle: Bool flag whether to shuffle the dataset. + device_id: Index of the GPU to be used (local_rank). Only applicable when device == 'gpu'. Defaults to 0. + global_rank: Global rank of this device. + world_size: Global world size in the training method. + augmentor: Optional AudioAugmentor object for augmentations on audio data. + preprocessor_cfg: Preprocessor configuration. Supports AudioToMelSpectrogramPreprocessor and AudioToMFCCPreprocessor. + + Returns: + An instance of AudioToCharDALIDataset. + """ + device = 'gpu' if torch.cuda.is_available() else 'cpu' + dataset = audio_to_text_dali.AudioToCharDALIDataset( + manifest_filepath=config['manifest_filepath'], + device=device, + batch_size=config['batch_size'], + labels=config['labels'], + sample_rate=config['sample_rate'], + audio_tar_filepaths=config.get('tarred_audio_filepaths', None), + audio_tar_index_filepaths=config.get('tarred_audio_index_filepaths', None), + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + blank_index=config.get('blank_index', -1), + unk_index=config.get('unk_index', -1), + normalize=config.get('normalize_transcripts', False), + trim=config.get('trim_silence', False), + parser=config.get('parser', 'en'), + shuffle=shuffle, + shard_strategy=config.get('tarred_shard_strategy', 'scatter'), + device_id=device_id, + global_rank=global_rank, + world_size=world_size, + preprocessor_cfg=preprocessor_cfg, + return_sample_id=config.get('return_sample_id', False), + ) + return dataset + + +def get_dali_bpe_dataset( + config: dict, + tokenizer, + shuffle: bool, + device_id: int, + global_rank: int, + world_size: int, + preprocessor_cfg: Optional[DictConfig] = None, +) -> audio_to_text_dali.AudioToCharDALIDataset: + """ + Instantiates a Subword Encoding based AudioToBPEDALIDataset. + + Args: + config: Config of the AudioToBPEDALIDataset. + tokenizer: An implementation of NeMo TokenizerSpec. + shuffle: Bool flag whether to shuffle the dataset. + device_id: Index of the GPU to be used (local_rank). Only applicable when device == 'gpu'. Defaults to 0. + global_rank: Global rank of this device. + world_size: Global world size in the training method. + preprocessor_cfg: Preprocessor configuration. Supports AudioToMelSpectrogramPreprocessor and AudioToMFCCPreprocessor. + + Returns: + An instance of AudioToCharDALIDataset. + """ + device = 'gpu' if torch.cuda.is_available() else 'cpu' + dataset = audio_to_text_dali.AudioToBPEDALIDataset( + manifest_filepath=config['manifest_filepath'], + tokenizer=tokenizer, + device=device, + batch_size=config['batch_size'], + sample_rate=config['sample_rate'], + audio_tar_filepaths=config.get('tarred_audio_filepaths', None), + audio_tar_index_filepaths=config.get('tarred_audio_index_filepaths', None), + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + trim=config.get('trim_silence', False), + use_start_end_token=config.get('use_start_end_token', True), + shuffle=shuffle, + shard_strategy=config.get('tarred_shard_strategy', 'scatter'), + device_id=device_id, + global_rank=global_rank, + world_size=world_size, + preprocessor_cfg=preprocessor_cfg, + return_sample_id=config.get('return_sample_id', False), + ) + return dataset + + +def get_audio_to_text_char_dataset_from_config( + config, local_rank: int, global_rank: int, world_size: int, preprocessor_cfg: Optional[DictConfig] = None +): + """ + Construct Audio-To-Text Char dataset from a config. + Args: + config: dataset config + local_rank: model local rank + global_rank: model global rand + world_size: world size + preprocessor_cfg: preprocessor config, for DALI dataset + + Returns: + constructed dataset or None if dataset config is invalid or nothing to load + """ + if 'augmentor' in config: + augmentor = process_augmentations(config['augmentor'], global_rank=global_rank, world_size=world_size) + else: + augmentor = None + + if 'hf_data_cfg' in config: + return get_hf_audio_to_text_char_dataset( + config=config, global_rank=global_rank, world_size=world_size, augmentor=augmentor + ) + + is_concat = config.get('is_concat', False) + if is_concat: + if 'concat_sampling_technique' in config and config['concat_sampling_technique'] is None: + logging.warning( + f"Concat dataset requires `concat_sampling_technique` but it was not provided. Config: {config}" + ) + return None + if config['concat_sampling_technique'] == 'random': + if not 'concat_sampling_probabilities' in config: + logging.warning(f"Concat dataset requires `concat_sampling_probabilities` list. Config: {config}") + return None + else: + if not isclose(sum(config['concat_sampling_probabilities']), 1, abs_tol=1e-6): + logging.warning(f"`concat_sampling_probabilities` need to sum to 1. Config: {config}") + return None + + shuffle = config['shuffle'] + device = 'gpu' if torch.cuda.is_available() else 'cpu' + if config.get('use_dali', False): + device_id = local_rank if device == 'gpu' else None + dataset = get_dali_char_dataset( + config=config, + shuffle=shuffle, + device_id=device_id, + global_rank=global_rank, + world_size=world_size, + preprocessor_cfg=preprocessor_cfg, + ) + return dataset + + # Instantiate a code-switched dataset if config is present + if config.get('is_code_switched', False): + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") + return None + if not ('code_switched' in config and config['code_switched'] is not None): + logging.warning( + f"Code switched dataset requires `*_ds.code_switched.*` dict but it was not provided. Config: {config}" + ) + return None + if ( + ('probs' in config['code_switched']) + and (config['code_switched']['probs'] is not None) + and (not isclose(sum(config['code_switched']['probs']), 1, abs_tol=1e-6)) + ): + logging.warning(f"`.code_switched.probs` need to sum to 1. Config: {config['code_switched']}") + return None + + shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 + dataset = get_code_switched_dataset( + config=config, + shuffle_n=shuffle_n, + global_rank=global_rank, + world_size=world_size, + tokenizer=None, + augmentor=augmentor, + ) + # Instantiate tarred dataset loader or normal dataset loader + elif config.get('is_tarred', False): + if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or ( + 'manifest_filepath' in config and config['manifest_filepath'] is None + ): + logging.warning( + "Could not load dataset as `manifest_filepath` was None or " + f"`tarred_audio_filepaths` is None. Provided config : {config}" + ) + return None + + shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 + if is_concat: + dataset = get_concat_tarred_dataset( + config=config, + shuffle_n=shuffle_n, + global_rank=global_rank, + world_size=world_size, + augmentor=augmentor, + ) + else: + dataset = get_tarred_dataset( + config=config, + shuffle_n=shuffle_n, + global_rank=global_rank, + world_size=world_size, + augmentor=augmentor, + ) + else: + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") + return None + if is_concat: + dataset = get_concat_char_dataset( + config=config, global_rank=global_rank, world_size=world_size, augmentor=augmentor + ) + else: + dataset = get_char_dataset(config=config, augmentor=augmentor) + return dataset + + +def get_audio_to_text_bpe_dataset_from_config( + config, + local_rank: int, + global_rank: int, + world_size: int, + tokenizer, + preprocessor_cfg: Optional[DictConfig] = None, +): + """ + Construct Audio-To-Text BPE dataset from a config. + Args: + config: BPE dataset config + local_rank: model local rank + global_rank: model global rand + world_size: world size + tokenizer: BPE tokenizer + preprocessor_cfg: preprocessor config, for DALI BPE dataset + + Returns: + constructed dataset or None if dataset config is invalid or nothing to load + """ + if 'augmentor' in config: + augmentor = process_augmentations(config['augmentor'], global_rank=global_rank, world_size=world_size) + else: + augmentor = None + + if 'hf_data_cfg' in config: + return get_hf_audio_to_text_bpe_dataset( + config=config, global_rank=global_rank, world_size=world_size, tokenizer=tokenizer, augmentor=augmentor + ) + + is_concat = config.get('is_concat', False) + if is_concat: + if 'concat_sampling_technique' in config and config['concat_sampling_technique'] is None: + logging.warning( + f"Concat dataset requires `concat_sampling_technique` but it was not provided. Config: {config}" + ) + return None + + if config['concat_sampling_technique'] == 'random': + if not 'concat_sampling_probabilities' in config: + logging.warning(f"Concat dataset requires `concat_sampling_probabilities` list. Config: {config}") + return None + else: + if not isclose(sum(config['concat_sampling_probabilities']), 1, abs_tol=1e-6): + logging.warning(f"`concat_sampling_probabilities` need to sum to 1. Config: {config}") + return None + + shuffle = config['shuffle'] + device = 'gpu' if torch.cuda.is_available() else 'cpu' + if config.get('use_dali', False): + device_id = local_rank if device == 'gpu' else None + dataset = get_dali_bpe_dataset( + config=config, + tokenizer=tokenizer, + shuffle=shuffle, + device_id=device_id, + global_rank=global_rank, + world_size=world_size, + preprocessor_cfg=preprocessor_cfg, + ) + return dataset + + # Instantiate a code-switched dataset if config is present + if config.get('is_code_switched', False): + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") + return None + if not ('code_switched' in config and config['code_switched'] is not None): + logging.warning( + f"Code switched dataset requires `*_ds.code_switched.*` dict but it was not provided. Config: {config}" + ) + return None + if ( + ('probs' in config['code_switched']) + and (config['code_switched']['probs'] is not None) + and (not isclose(sum(config['code_switched']['probs']), 1, abs_tol=1e-6)) + ): + logging.warning(f"`.code_switched.probs` need to sum to 1. Config: {config['code_switched']}") + return None + + shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 + dataset = get_code_switched_dataset( + config=config, + shuffle_n=shuffle_n, + global_rank=global_rank, + world_size=world_size, + tokenizer=tokenizer, + augmentor=augmentor, + ) + # Instantiate tarred dataset loader or normal dataset loader + elif config.get('is_tarred', False): + if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or ( + 'manifest_filepath' in config and config['manifest_filepath'] is None + ): + logging.warning( + "Could not load dataset as `manifest_filepath` was None or " + f"`tarred_audio_filepaths` is None. Provided config : {config}" + ) + return None + + shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 + if is_concat: + dataset = get_concat_tarred_dataset( + config=config, + tokenizer=tokenizer, + shuffle_n=shuffle_n, + global_rank=global_rank, + world_size=world_size, + augmentor=augmentor, + ) + else: + dataset = get_tarred_dataset( + config=config, + tokenizer=tokenizer, + shuffle_n=shuffle_n, + global_rank=global_rank, + world_size=world_size, + augmentor=augmentor, + ) + else: + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") + return None + if is_concat: + dataset = get_concat_bpe_dataset( + config=config, + global_rank=global_rank, + world_size=world_size, + tokenizer=tokenizer, + augmentor=augmentor, + ) + else: + dataset = get_bpe_dataset(config=config, tokenizer=tokenizer, augmentor=augmentor) + return dataset + + +class ASRPredictionWriter(BasePredictionWriter): + def __init__(self, dataset, output_file: str): + super().__init__(write_interval="batch") + self.outf = open(output_file, 'w', encoding='utf-8') + self.dataset = dataset + self.samples_num = 0 + + def write_on_batch_end( + self, + trainer, + pl_module: 'LightningModule', + prediction: Any, + batch_indices: List[int], + batch: Any, + batch_idx: int, + dataloader_idx: int, + ): + for sample_id, transcribed_text in prediction: + item = {} + sample = self.dataset.get_manifest_sample(sample_id) + item["audio_filepath"] = sample.audio_file + item["offset"] = sample.offset + item["duration"] = sample.duration + item["text"] = sample.text_raw + item["pred_text"] = transcribed_text + self.outf.write(json.dumps(item) + "\n") + self.samples_num += 1 + return + + def close_output_file(self): + self.outf.close() + return self.samples_num + + +def convert_to_config_list(initial_list): + if type(initial_list) is str: + initial_list = initial_list.split(",") + if initial_list is None or initial_list == []: + raise ValueError("manifest_filepaths and tarred_audio_filepaths must not be empty.") + if not isinstance(initial_list, ListConfig): + initial_list = ListConfig([initial_list]) + + for list_idx, list_val in enumerate(initial_list): + if type(list_val) != type(initial_list[0]): + raise ValueError( + "manifest_filepaths and tarred_audio_filepaths need to be a list of lists for bucketing or just a list of strings" + ) + if type(initial_list[0]) is not ListConfig: + initial_list = ListConfig([initial_list]) + return initial_list + + +def get_chain_dataset(datasets, ds_config, rank=0): + if len(datasets) > 1: + if ds_config.get('bucketing_batch_size', None) is not None: + bucketing_batch_sizes = calc_bucketing_batch_sizes(ds_config, len(datasets)) + logging.info( + f"Batch bucketing is enabled for {len(datasets)} buckets with adaptive batch sizes of {bucketing_batch_sizes}!" + ) + for idx, dataset in enumerate(datasets): + datasets[idx] = audio_to_text.BucketingDataset( + dataset=dataset, bucketing_batch_size=bucketing_batch_sizes[idx] + ) + else: + logging.info( + f"Batch bucketing is enabled for {len(datasets)} buckets with fixed batch size of {ds_config['batch_size']}!" + ) + + if len(datasets) == 1: + return datasets[0] + bucketing_strategy = ds_config.get('bucketing_strategy', 'synced_randomized') + if bucketing_strategy == 'fixed_order': + return ChainDataset(datasets) + elif bucketing_strategy == 'synced_randomized': + return audio_to_text.RandomizedChainDataset(datasets=datasets, rnd_seed=0) + elif bucketing_strategy == 'fully_randomized': + return audio_to_text.RandomizedChainDataset(datasets=datasets, rnd_seed=random.randint(0, 30000) + rank) + else: + raise ValueError( + f'bucketing_strategy={bucketing_strategy} is not supported! Supported strategies are [fixed_order, fully_randomized, synced_randomized].' + ) + + +def calc_bucketing_batch_sizes(ds_config, datasets_len): + bucketing_batch_size = ds_config['bucketing_batch_size'] + bucketing_weights = ds_config.get('bucketing_weights', None) # To adjust for upsampled buckets + + bucketing_batch_sizes = [] + + if ds_config['batch_size'] != 1: + raise ValueError( + f"batch_size should be set to one when bucketing_batch_size is set and adaptive bucketing is enabled (batch_size={ds_config['batch_size']}!" + ) + if type(bucketing_batch_size) == int: # linear scaling + if bucketing_weights: # Want same batchsize for the same duplicated bucket + for idx, weight in enumerate(bucketing_weights): + scale_factor = datasets_len - idx + [bucketing_batch_sizes.append(scale_factor * bucketing_batch_size) for _ in range(weight)] + else: + for idx in range(datasets_len): + scale_factor = datasets_len - idx + bucketing_batch_sizes.append(scale_factor * bucketing_batch_size) + elif isinstance(bucketing_batch_size, ListConfig) or isinstance( + bucketing_batch_size, list + ): # assigned bucket sizes + if bucketing_weights: # Want same batchsize for same duplicated bucket + for idx, weight in enumerate(bucketing_weights): + [bucketing_batch_sizes.append(bucketing_batch_size[idx]) for _ in range(weight)] + else: + bucketing_batch_sizes = bucketing_batch_size + else: + raise ValueError( + f"bucketing_batch_size should be an integer or a list (bucketing_batch_size={bucketing_batch_size})!" + ) + + if len(bucketing_batch_sizes) != datasets_len: + raise ValueError( + f"batch_size should have the same length as the number of buckets ({len(bucketing_batch_sizes)}!={datasets_len}) " + ) + return bucketing_batch_sizes diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text_lhotse.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text_lhotse.py new file mode 100644 index 0000000..21ceced --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text_lhotse.py @@ -0,0 +1,84 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, Optional, Tuple + +import torch.utils.data +from lhotse.dataset import AudioSamples +from lhotse.dataset.collation import collate_vectors + +from nemo.collections.common.tokenizers.aggregate_tokenizer import AggregateTokenizer +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, NeuralType + + +class LhotseSpeechToTextBpeDataset(torch.utils.data.Dataset): + """ + This dataset is based on BPE datasets from audio_to_text.py. + Unlike native NeMo datasets, Lhotse dataset defines only the mapping from + a CutSet (meta-data) to a mini-batch with PyTorch tensors. + Specifically, it performs tokenization, I/O, augmentation, and feature extraction (if any). + Managing data, sampling, de-duplication across workers/nodes etc. is all handled + by Lhotse samplers instead. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return { + 'audio_signal': NeuralType(('B', 'T'), AudioSignal()), + 'a_sig_length': NeuralType(tuple('B'), LengthsType()), + 'transcripts': NeuralType(('B', 'T'), LabelsType()), + 'transcript_length': NeuralType(tuple('B'), LengthsType()), + 'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def __init__(self, tokenizer): + super().__init__() + self.tokenizer = TokenizerWrapper(tokenizer) + self.load_audio = AudioSamples(fault_tolerant=True) + + def __getitem__(self, cuts) -> Tuple[torch.Tensor, ...]: + audio, audio_lens, cuts = self.load_audio(cuts) + tokens = [torch.as_tensor(self.tokenizer(c.supervisions[0].text, c.supervisions[0].language)) for c in cuts] + token_lens = torch.tensor([t.size(0) for t in tokens], dtype=torch.long) + tokens = collate_vectors(tokens, padding_value=0) + return audio, audio_lens, tokens, token_lens + + +class TokenizerWrapper: + """ + Provide a unified interface for NeMo Tokenizer, AggregateTokenizer, and (char) Parser. + """ + + def __init__(self, tokenizer): + self._tokenizer = tokenizer + if isinstance(tokenizer, AggregateTokenizer): + self._impl = self._call_agg_tokenizer + elif isinstance(tokenizer, TokenizerSpec): + self._impl = self._call_tokenizer + else: + self._impl = self._call_parser + + def __call__(self, text: str, lang: str | None = None): + return self._impl(text, lang) + + def _call_agg_tokenizer(self, text: str, lang: str | None = None): + assert lang is not None, "Expected 'lang' to be set for AggregateTokenizer." + return self._tokenizer.text_to_ids(text, lang) + + def _call_tokenizer(self, text: str, lang: str | None = None): + return self._tokenizer.text_to_ids(text) + + def _call_parser(self, text: str, lang: str | None = None): + return self._tokenizer(text) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text_lhotse_prompted.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text_lhotse_prompted.py new file mode 100644 index 0000000..000b1a8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/audio_to_text_lhotse_prompted.py @@ -0,0 +1,248 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Callable, Sequence + +import omegaconf +import torch.utils.data +from lhotse import CutSet +from lhotse.cut import MixedCut, MonoCut +from lhotse.dataset import AudioSamples +from lhotse.dataset.collation import collate_vectors + +from nemo.collections.asr.data.audio_to_text_lhotse import TokenizerWrapper +from nemo.collections.common.tokenizers import CanaryTokenizer, TokenizerSpec + + +class PromptedAudioToTextLhotseDataset(torch.utils.data.Dataset): + """ + This dataset is based on :class:`~nemo.collections.asr.data.audio_to_text_lhotse.LhotseSpeechToTextBpeDataset`. + It is a Lhotse-style dataset that converts a mini-batch of Cuts into tensors. + The main difference from ``LhotseSpeechToTextBpeDataset`` is that we introduce + a special prompt format for multitask encoder-decoder models. + + To perform the prompt formatting, we accept a ``prompt_format_fn``. + It's expected to accept: + * a ``CutSet`` which it will internally iterate over for utterances, and + * a ``TokenizerWrapper`` object that will be internally used to tokenize the utterances + + Tokenized utterances will be extended with special prompt tokens according to ``prompt_format_fn`` logic. + We support cuts with multiple supervision segments -- their tokenized texts will be concatenated before we add the prompt tokens. + This is useful, for example, in code-switched scenarios where each segment is spoken in a different language. + """ + + def __init__( + self, + tokenizer: TokenizerSpec, + prompt_format_fn: Callable[[CutSet, TokenizerWrapper, bool], Sequence[Sequence[int]]], + inference: bool = False, + ): + super().__init__() + self.tokenizer = TokenizerWrapper(tokenizer) + self.load_audio = AudioSamples(fault_tolerant=True) + self.padding_value = self.tokenizer._tokenizer.pad_id + self.prompt_format_fn = prompt_format_fn + self.inference = inference + + def __getitem__(self, cuts: CutSet) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + audio, audio_lens, cuts = self.load_audio(cuts) + + tokens, prompt_tokens = self.prompt_format_fn(cuts, self.tokenizer, inference=self.inference) + + tokens = [torch.as_tensor(t) for t in tokens] + token_lens = torch.tensor([t.size(0) for t in tokens], dtype=torch.long) + tokens = collate_vectors(tokens, padding_value=self.padding_value) + + if self.inference: + prompt_tokens = [torch.as_tensor(t) for t in prompt_tokens] + prompt_token_lens = torch.tensor([t.size(0) for t in prompt_tokens], dtype=torch.long) + prompt_tokens = collate_vectors(prompt_tokens, padding_value=self.padding_value) + else: + prompt_tokens = None + prompt_token_lens = None + + return audio, audio_lens, tokens, token_lens, prompt_tokens, prompt_token_lens + + +# Mapping from a string name to a known prompt formatter function. +PROMPT_FORMAT_FNS = {} + + +def registered_prompt_format_fn(prompt_fn: Callable[[CutSet, TokenizerWrapper, bool], Sequence[Sequence[int]]]): + """ + Decorator for registering prompt functions under a name. + + Example:: + + >>> @registered_prompt_format_fn + ... def my_prompt(cuts, tokenizer): + ... pass + ... + ... prompt_fn = get_prompt_format_fn("my_prompt") + """ + global PROMPT_FORMAT_FNS + + PROMPT_FORMAT_FNS[prompt_fn.__name__] = prompt_fn + return prompt_fn + + +def get_prompt_format_fn(name: str) -> Callable[[CutSet, TokenizerWrapper, bool], Sequence[Sequence[int]]]: + if name not in PROMPT_FORMAT_FNS: + raise ValueError( + f"Unknown prompt format function name: {name} " f"(must be one of: {list(PROMPT_FORMAT_FNS.keys())}" + ) + return PROMPT_FORMAT_FNS[name] + + +@registered_prompt_format_fn +def canary(cuts: CutSet, tokenizer: TokenizerWrapper, inference: bool = False) -> Sequence[Sequence[int]]: + """ + Prepend and append control tokens to the token sequence as per Canary format. + + We use the following special tokens: + * <|startoftranscript|> + * <|transcribe|> + * <|translate|> + * <|nopnc|> + * <|pnc|> + * <|endoftext|> + * <|LANG|> - for each supported language. + * <|nospeech|> + + The prompt format syntax is as follows: + + <|startoftranscript|> [ <|nospeech|> | <|LANG|> [ <|transcribe|> | <|translate|> ] <|LANG|> [ <|pnc|> | <|nopnc|> ] TEXT <|endoftext|> ] + + Where expression ``[ a | b ]`` denotes expression ``a`` or expression ``b``, and can be nested. + Note that ``<|LANG|>`` appears twice: the first occurrence is for the "source" language + (i.e., spoken language in the recording) and the second occurrence is for the "target" language + (i.e., the language in which we are going to output the text). + """ + + assert isinstance( + tokenizer._tokenizer, CanaryTokenizer + ), "To use 'canary' prompt format, you must use the CanaryTokenizer." + tokenizer = tokenizer._tokenizer + + tokens, prompts = [], [] + for cut in cuts: + if isinstance(cut, MixedCut): + cut = cut._first_non_padding_cut + assert isinstance(cut, MonoCut), "Expected MonoCut." + + # first, validate the utterance + missing_keys = [k for k in ("source_lang", "target_lang", "taskname", "pnc") if k not in cut.custom] + if missing_keys: + raise RuntimeError( + f"We found cut with ID {cut.id} that is missing the following keys: {missing_keys}" + f"Please ensure that every utterance in the input manifests contains these keys." + ) + + # Actual tokenization. If a cut has multiple supervisions, we'll stitch their tokenized texts together. + texts = [sup.text for sup in cut.supervisions] + langs = [sup.language for sup in cut.supervisions] + taskname = cut.custom['taskname'] + pnc = cut.custom['pnc'] + source_lang = cut.custom['source_lang'] + target_lang = cut.custom['target_lang'] + + tokens.append(canary_prompt(tokenizer, texts, langs, source_lang, target_lang, taskname, pnc)) + if inference: + prompts.append(canary_prompt(tokenizer, None, None, source_lang, target_lang, taskname, pnc)) + return tokens, prompts + + +def canary_prompt( + tokenizer: CanaryTokenizer, + text: str | list[str] | None, + language: str | list[str] | None, + source_language: str, + target_language: str, + taskname: str, + pnc: str, +) -> list[int]: + if isinstance(text, str): + text = [text] + if isinstance(language, str): + language = [language] + + if text is not None: + try: + tokens = sum((tokenizer.text_to_ids(text_, lang_) for text_, lang_ in zip(text, language)), start=[]) + except omegaconf.errors.KeyValidationError as e: + raise ProbablyIncorrectLanguageKeyError( + "We couldn't select the right tokenizer, which could be due to issues with reading " + "the language from the manifest. " + "If you're training, try setting lang_field='' to a different value (probably 'target_lang' or 'lang'). " + "If you're using model.transcribe() directly, please use override_config kwarg to set this. " + "If you're using transcribe_speech.py, use option gt_lang_attr_name='...' " + ) from e + else: + tokens = None # create prompt for inference + + # bos + prompted_tokens = [tokenizer.bos_id] + + if tokens is not None and len(tokens) == 0: + # no speech token + prompted_tokens.append(tokenizer.nospeech_id) + else: + # first, validate the utterance + if source_language is None or target_language is None or taskname is None or pnc is None: + raise RuntimeError( + f"Missing keys provided to prompt: " + f"source_langauge={source_language},\n" + f"target_language={target_language},\n" + f"taskname={taskname},\n" + f"pnc={pnc}\n" + f"Please ensure that every utterance in the input manifests contains these keys." + ) + + # src_lang_id/no_speech + src_lang_id = tokenizer.spl_token_to_id(source_language) + prompted_tokens.append(src_lang_id) + + # task + task = taskname + if task == 'asr' or task == "transcribe": + prompted_tokens.append(tokenizer.spl_token_to_id("transcribe")) + elif task == 's2t_translation' or task == 'ast' or task == "translate": + prompted_tokens.append(tokenizer.spl_token_to_id("translate")) + else: + raise ValueError(f"Unknown task: {task}") + + # tgt_lang_id + tgt_lang_id = tokenizer.spl_token_to_id(target_language) + prompted_tokens.append(tgt_lang_id) + + # PnC + pnc = f"{pnc}".lower().strip() # to account for bool or str + if pnc in {'yes', 'true'}: + prompted_tokens.append(tokenizer.spl_token_to_id("pnc")) + elif pnc in {'no', 'false'}: + prompted_tokens.append(tokenizer.spl_token_to_id("nopnc")) + else: + raise ValueError(f"Unknown value for key 'pnc': {pnc}") + + # text (only in training) + if tokens is not None: + prompted_tokens.extend(tokens) + + # eos (only in training) + if tokens is not None: + prompted_tokens.append(tokenizer.eos_id) + return prompted_tokens + + +class ProbablyIncorrectLanguageKeyError(RuntimeError): + pass diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/data_simulation.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/data_simulation.py new file mode 100644 index 0000000..5bbdcdf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/data_simulation.py @@ -0,0 +1,4037 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import concurrent +import itertools +import multiprocessing +import os +import random +import warnings +from typing import Dict, Iterable, List, Optional, Tuple, Union + +import h5py +import librosa +import matplotlib.pyplot as plt +import numpy as np +import soundfile as sf +import torch +from numpy.random import default_rng +from omegaconf import DictConfig, OmegaConf +from scipy.signal import convolve +from scipy.signal.windows import cosine, hamming, hann +from scipy.spatial.transform import Rotation +from tqdm import tqdm + +from nemo.collections.asr.parts.preprocessing.perturb import process_augmentations +from nemo.collections.asr.parts.preprocessing.segment import AudioSegment +from nemo.collections.asr.parts.utils.audio_utils import db2mag, generate_approximate_noise_field, mag2db, pow2db, rms +from nemo.collections.asr.parts.utils.data_simulation_utils import ( + DataAnnotator, + SpeechSampler, + build_speaker_samples_map, + get_background_noise, + get_cleaned_base_path, + get_random_offset_index, + get_speaker_ids, + get_speaker_samples, + get_split_points_in_alignments, + load_speaker_sample, + normalize_audio, + per_speaker_normalize, + perturb_audio, + read_audio_from_buffer, + read_noise_manifest, +) +from nemo.collections.asr.parts.utils.manifest_utils import read_manifest, write_manifest +from nemo.collections.asr.parts.utils.speaker_utils import get_overlap_range, is_overlap, merge_float_intervals +from nemo.utils import logging + +try: + import pyroomacoustics as pra + from pyroomacoustics.directivities import CardioidFamily, DirectionVector, DirectivityPattern + + PRA = True +except ImportError: + PRA = False +try: + from gpuRIR import att2t_SabineEstimator, beta_SabineEstimation, simulateRIR, t2n + + GPURIR = True +except ImportError: + GPURIR = False + + +class MultiSpeakerSimulator(object): + """ + Multispeaker Audio Session Simulator - Simulates multispeaker audio sessions using single-speaker audio files and + corresponding word alignments. + + Change Log: + v1.0: Dec 2022 + - First working verison, supports multispeaker simulation with overlaps, silence and RIR + v1.0.1: Feb 2023 + - Multi-GPU support for speed up + - Faster random sampling routine + - Fixed sentence duration bug + - Silence and overlap length sampling algorithms are updated to guarantee `mean_silence` approximation + v1.0.2: March 2023 + - Added support for segment-level gain perturbation and session-level white-noise perturbation + - Modified speaker sampling mechanism to include as many speakers as possible in each data-generation run + - Added chunking mechanism to avoid freezing in multiprocessing processes + + v1.1.0 March 2023 + - Faster audio-file loading with maximum audio duration parameter + - Re-organized MultiSpeakerSimulator class and moved util functions to util files. + v1.1.1 March 2023 + - Changed `silence_mean` to use exactly the same sampling equation as `overlap_mean`. + + + Args: + cfg: OmegaConf configuration loaded from yaml file. + + Parameters: + manifest_filepath (str): Manifest file with paths to single speaker audio files + sr (int): Sampling rate of the input audio files from the manifest + random_seed (int): Seed to random number generator + + session_config: + num_speakers (int): Number of unique speakers per multispeaker audio session + num_sessions (int): Number of sessions to simulate + session_length (int): Length of each simulated multispeaker audio session (seconds). Short sessions + (e.g. ~240 seconds) tend to fall short of the expected overlap-ratio and silence-ratio. + + session_params: + max_audio_read_sec (int): The maximum audio length in second when loading an audio file. + The bigger the number, the slower the reading speed. Should be greater than 2.5 second. + sentence_length_params (list): k,p values for a negative_binomial distribution which is sampled to get the + sentence length (in number of words) + dominance_var (float): Variance in speaker dominance (where each speaker's dominance is sampled from a normal + distribution centered on 1/`num_speakers`, and then the dominance values are together + normalized to 1) + min_dominance (float): Minimum percentage of speaking time per speaker (note that this can cause the dominance of + the other speakers to be slightly reduced) + turn_prob (float): Probability of switching speakers after each utterance + + mean_silence (float): Mean proportion of silence to speaking time in the audio session. Should be in range [0, 1). + mean_silence_var (float): Variance for mean silence in all audio sessions. + This value should be 0 <= mean_silence_var < mean_silence * (1 - mean_silence). + per_silence_var (float): Variance for each silence in an audio session, set large values (e.g., 20) for de-correlation. + per_silence_min (float): Minimum duration for each silence, default to 0. + per_silence_max (float): Maximum duration for each silence, default to -1 for no maximum. + mean_overlap (float): Mean proportion of overlap in the overall non-silence duration. Should be in range [0, 1) and + recommend [0, 0.15] range for accurate results. + mean_overlap_var (float): Variance for mean overlap in all audio sessions. + This value should be 0 <= mean_overlap_var < mean_overlap * (1 - mean_overlap). + per_overlap_var (float): Variance for per overlap in each session, set large values to de-correlate silence lengths + with the latest speech segment lengths + per_overlap_min (float): Minimum per overlap duration in seconds + per_overlap_max (float): Maximum per overlap duration in seconds, set -1 for no maximum + start_window (bool): Whether to window the start of sentences to smooth the audio signal (and remove silence at + the start of the clip) + window_type (str): Type of windowing used when segmenting utterances ("hamming", "hann", "cosine") + window_size (float): Length of window at the start or the end of segmented utterance (seconds) + start_buffer (float): Buffer of silence before the start of the sentence (to avoid cutting off speech or starting + abruptly) + split_buffer (float): Split RTTM labels if greater than twice this amount of silence (to avoid long gaps between + utterances as being labelled as speech) + release_buffer (float): Buffer before window at end of sentence (to avoid cutting off speech or ending abruptly) + normalize (bool): Normalize speaker volumes + normalization_type (str): Normalizing speakers ("equal" - same volume per speaker, "var" - variable volume per + speaker) + normalization_var (str): Variance in speaker volume (sample from standard deviation centered at 1) + min_volume (float): Minimum speaker volume (only used when variable normalization is used) + max_volume (float): Maximum speaker volume (only used when variable normalization is used) + end_buffer (float): Buffer at the end of the session to leave blank + + outputs: + output_dir (str): Output directory for audio sessions and corresponding label files + output_filename (str): Output filename for the wav and RTTM files + overwrite_output (bool): If true, delete the output directory if it exists + output_precision (int): Number of decimal places in output files + + background_noise: + add_bg (bool): Add ambient background noise if true + background_manifest (str): Path to background noise manifest file + snr (int): SNR for background noise (using average speaker power), set `snr_min` and `snr_max` values to enable random SNR + snr_min (int): Min random SNR for background noise (using average speaker power), set `null` to use fixed SNR + snr_max (int): Max random SNR for background noise (using average speaker power), set `null` to use fixed SNR + + segment_augmentor: + add_seg_aug (bool): Set True to enable augmentation on each speech segment (Default: False) + segmentor: + gain: + prob (float): Probability range (uniform distribution) gain augmentation for individual segment + min_gain_dbfs (float): minimum gain in terms of dB + max_gain_dbfs (float): maximum gain in terms of dB + + session_augmentor: + add_sess_aug: (bool) set True to enable audio augmentation on the whole session (Default: False) + segmentor: + white_noise: + prob (float): Probability of adding white noise (Default: 1.0) + min_level (float): minimum gain in terms of dB + max_level (float): maximum gain in terms of dB + + speaker_enforcement: + enforce_num_speakers (bool): Enforce that all requested speakers are present in the output wav file + enforce_time (list): Percentage of the way through the audio session that enforcement mode is triggered (sampled + between time 1 and 2) + + segment_manifest: (parameters for regenerating the segment manifest file) + window (float): Window length for segmentation + shift (float): Shift length for segmentation + step_count (int): Number of the unit segments you want to create per utterance + deci (int): Rounding decimals for segment manifest file + """ + + def __init__(self, cfg): + self._params = cfg + self.annotator = DataAnnotator(cfg) + self.sampler = SpeechSampler(cfg) + # internal params + self._manifest = read_manifest(self._params.data_simulator.manifest_filepath) + self._speaker_samples = build_speaker_samples_map(self._manifest) + self._noise_samples = [] + self._sentence = None + self._text = "" + self._words = [] + self._alignments = [] + # minimum number of alignments for a manifest to be considered valid + self._min_alignment_count = 2 + self._merged_speech_intervals = [] + # keep track of furthest sample per speaker to avoid overlapping same speaker + self._furthest_sample = [0 for n in range(self._params.data_simulator.session_config.num_speakers)] + # use to ensure overlap percentage is correct + self._missing_overlap = 0 + # creating manifests during online data simulation + self.base_manifest_filepath = None + self.segment_manifest_filepath = None + self._max_audio_read_sec = self._params.data_simulator.session_params.max_audio_read_sec + self._turn_prob_min = self._params.data_simulator.session_params.get("turn_prob_min", 0.5) + # variable speaker volume + self._volume = None + self._speaker_ids = None + self._device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + self._audio_read_buffer_dict = {} + self.add_missing_overlap = self._params.data_simulator.session_params.get("add_missing_overlap", False) + + if ( + self._params.data_simulator.segment_augmentor.get("augmentor", None) + and self._params.data_simulator.segment_augmentor.add_seg_aug + ): + self.segment_augmentor = process_augmentations( + augmenter=self._params.data_simulator.segment_augmentor.augmentor + ) + else: + self.segment_augmentor = None + + if ( + self._params.data_simulator.session_augmentor.get("augmentor", None) + and self._params.data_simulator.session_augmentor.add_sess_aug + ): + self.session_augmentor = process_augmentations( + augmenter=self._params.data_simulator.session_augmentor.augmentor + ) + else: + self.session_augmentor = None + + # Error check the input arguments for simulation + self._check_args() + + # Initialize speaker permutations to maximize the number of speakers in the created dataset + self._permutated_speaker_inds = self._init_speaker_permutations( + num_sess=self._params.data_simulator.session_config.num_sessions, + num_speakers=self._params.data_simulator.session_config.num_speakers, + all_speaker_ids=self._speaker_samples.keys(), + random_seed=self._params.data_simulator.random_seed, + ) + + # Intialize multiprocessing related variables + self.num_workers = self._params.get("num_workers", 1) + self.multiprocessing_chunksize = self._params.data_simulator.get('multiprocessing_chunksize', 10000) + self.chunk_count = self._init_chunk_count() + + def _init_speaker_permutations(self, num_sess: int, num_speakers: int, all_speaker_ids: List, random_seed: int): + """ + Initialize the speaker permutations for the number of speakers in the session. + When generating the simulated sessions, we want to include as many speakers as possible. + This function generates a set of permutations that can be used to sweep all speakers in + the source dataset to make sure we maximize the total number of speakers included in + the simulated sessions. + + Args: + num_sess (int): Number of sessions to generate + num_speakers (int): Number of speakers in each session + all_speaker_ids (list): List of all speaker IDs + + Returns: + permuted_inds (np.array): + Array of permuted speaker indices to use for each session + Dimensions: (num_sess, num_speakers) + """ + np.random.seed(random_seed) + all_speaker_id_counts = len(list(all_speaker_ids)) + + # Calculate how many permutations are needed + perm_set_count = int(np.ceil(num_speakers * num_sess / all_speaker_id_counts)) + + target_count = num_speakers * num_sess + for count in range(perm_set_count): + if target_count < all_speaker_id_counts: + seq_len = target_count + else: + seq_len = all_speaker_id_counts + if seq_len <= 0: + raise ValueError(f"seq_len is {seq_len} at count {count} and should be greater than 0") + + if count == 0: + permuted_inds = np.random.permutation(len(all_speaker_ids))[:seq_len] + else: + permuted_inds = np.hstack((permuted_inds, np.random.permutation(len(all_speaker_ids))[:seq_len])) + target_count -= seq_len + + logging.info(f"Total {all_speaker_id_counts} speakers in the source dataset.") + logging.info(f"Initialized speaker permutations for {num_sess} sessions with {num_speakers} speakers each.") + return permuted_inds.reshape(num_sess, num_speakers) + + def _init_chunk_count(self): + """ + Initialize the chunk count for multi-processing to prevent over-flow of job counts. + The multi-processing pipeline can freeze if there are more than approximately 10,000 jobs + in the pipeline at the same time. + """ + return int(np.ceil(self._params.data_simulator.session_config.num_sessions / self.multiprocessing_chunksize)) + + def _check_args(self): + """ + Checks YAML arguments to ensure they are within valid ranges. + """ + if self._params.data_simulator.session_config.num_speakers < 1: + raise Exception("At least one speaker is required for making audio sessions (num_speakers < 1)") + if ( + self._params.data_simulator.session_params.turn_prob < 0 + or self._params.data_simulator.session_params.turn_prob > 1 + ): + raise Exception("Turn probability is outside of [0,1]") + if ( + self._params.data_simulator.session_params.turn_prob < 0 + or self._params.data_simulator.session_params.turn_prob > 1 + ): + raise Exception("Turn probability is outside of [0,1]") + elif ( + self._params.data_simulator.session_params.turn_prob < self._turn_prob_min + and self._params.data_simulator.speaker_enforcement.enforce_num_speakers == True + ): + logging.warning( + "Turn probability is less than {self._turn_prob_min} while enforce_num_speakers=True, which may result in excessive session lengths. Forcing turn_prob to 0.5." + ) + self._params.data_simulator.session_params.turn_prob = self._turn_prob_min + if self._params.data_simulator.session_params.max_audio_read_sec < 2.5: + raise Exception("Max audio read time must be greater than 2.5 seconds") + + if self._params.data_simulator.session_params.sentence_length_params[0] <= 0: + raise Exception( + "k (number of success until the exp. ends) in Sentence length parameter value must be a positive number" + ) + + if not (0 < self._params.data_simulator.session_params.sentence_length_params[1] <= 1): + raise Exception("p (success probability) value in sentence length parameter must be in range (0,1]") + + if ( + self._params.data_simulator.session_params.mean_overlap < 0 + or self._params.data_simulator.session_params.mean_overlap > 1 + ): + raise Exception("Mean overlap is outside of [0,1]") + if ( + self._params.data_simulator.session_params.mean_silence < 0 + or self._params.data_simulator.session_params.mean_silence > 1 + ): + raise Exception("Mean silence is outside of [0,1]") + if self._params.data_simulator.session_params.mean_silence_var < 0: + raise Exception("Mean silence variance is not below 0") + if ( + self._params.data_simulator.session_params.mean_silence > 0 + and self._params.data_simulator.session_params.mean_silence_var + >= self._params.data_simulator.session_params.mean_silence + * (1 - self._params.data_simulator.session_params.mean_silence) + ): + raise Exception("Mean silence variance should be lower than mean_silence * (1-mean_silence)") + if self._params.data_simulator.session_params.per_silence_var < 0: + raise Exception("Per silence variance is below 0") + + if self._params.data_simulator.session_params.mean_overlap_var < 0: + raise Exception("Mean overlap variance is not larger than 0") + if ( + self._params.data_simulator.session_params.mean_overlap > 0 + and self._params.data_simulator.session_params.mean_overlap_var + >= self._params.data_simulator.session_params.mean_overlap + * (1 - self._params.data_simulator.session_params.mean_overlap) + ): + raise Exception("Mean overlap variance should be lower than mean_overlap * (1-mean_overlap)") + if self._params.data_simulator.session_params.per_overlap_var < 0: + raise Exception("Per overlap variance is not larger than 0") + + if ( + self._params.data_simulator.session_params.min_dominance < 0 + or self._params.data_simulator.session_params.min_dominance > 1 + ): + raise Exception("Minimum dominance is outside of [0,1]") + if ( + self._params.data_simulator.speaker_enforcement.enforce_time[0] < 0 + or self._params.data_simulator.speaker_enforcement.enforce_time[0] > 1 + ): + raise Exception("Speaker enforcement start is outside of [0,1]") + if ( + self._params.data_simulator.speaker_enforcement.enforce_time[1] < 0 + or self._params.data_simulator.speaker_enforcement.enforce_time[1] > 1 + ): + raise Exception("Speaker enforcement end is outside of [0,1]") + + if ( + self._params.data_simulator.session_params.min_dominance + * self._params.data_simulator.session_config.num_speakers + > 1 + ): + raise Exception("Number of speakers times minimum dominance is greater than 1") + + if ( + self._params.data_simulator.session_params.window_type not in ['hamming', 'hann', 'cosine'] + and self._params.data_simulator.session_params.window_type is not None + ): + raise Exception("Incorrect window type provided") + + if len(self._manifest) == 0: + raise Exception("Manifest file is empty. Check that the source path is correct.") + + def clean_up(self): + """ + Clear the system memory. Cache data for audio files and alignments are removed. + """ + self._sentence = None + self._words = [] + self._alignments = [] + self._audio_read_buffer_dict = {} + torch.cuda.empty_cache() + + def _get_speaker_dominance(self) -> List[float]: + """ + Get the dominance value for each speaker, accounting for the dominance variance and + the minimum per-speaker dominance. + + Returns: + dominance (list): Per-speaker dominance + """ + dominance_mean = 1.0 / self._params.data_simulator.session_config.num_speakers + dominance = np.random.normal( + loc=dominance_mean, + scale=self._params.data_simulator.session_params.dominance_var, + size=self._params.data_simulator.session_config.num_speakers, + ) + dominance = np.clip(dominance, a_min=0, a_max=np.inf) + # normalize while maintaining minimum dominance + total = np.sum(dominance) + if total == 0: + for i in range(len(dominance)): + dominance[i] += self._params.data_simulator.session_params.min_dominance + # scale accounting for min_dominance which has to be added after + dominance = (dominance / total) * ( + 1 + - self._params.data_simulator.session_params.min_dominance + * self._params.data_simulator.session_config.num_speakers + ) + for i in range(len(dominance)): + dominance[i] += self._params.data_simulator.session_params.min_dominance + if ( + i > 0 + ): # dominance values are cumulative to make it easy to select the speaker using a random value in [0,1] + dominance[i] = dominance[i] + dominance[i - 1] + return dominance + + def _increase_speaker_dominance( + self, base_speaker_dominance: List[float], factor: int + ) -> Tuple[List[float], bool]: + """ + Increase speaker dominance for unrepresented speakers (used only in enforce mode). + Increases the dominance for these speakers by the input factor (and then re-normalizes the probabilities to 1). + + Args: + base_speaker_dominance (list): Dominance values for each speaker. + factor (int): Factor to increase dominance of unrepresented speakers by. + Returns: + dominance (list): Per-speaker dominance + enforce (bool): Whether to keep enforce mode turned on + """ + increase_percent = [] + for i in range(self._params.data_simulator.session_config.num_speakers): + if self._furthest_sample[i] == 0: + increase_percent.append(i) + # ramp up enforce counter until speaker is sampled, then reset once all speakers have spoken + if len(increase_percent) > 0: + # extract original per-speaker probabilities + dominance = np.copy(base_speaker_dominance) + for i in range(len(dominance) - 1, 0, -1): + dominance[i] = dominance[i] - dominance[i - 1] + # increase specified speakers by the desired factor + for i in increase_percent: + dominance[i] = dominance[i] * factor + # renormalize + dominance = dominance / np.sum(dominance) + for i in range(1, len(dominance)): + dominance[i] = dominance[i] + dominance[i - 1] + enforce = True + else: # no unrepresented speakers, so enforce mode can be turned off + dominance = base_speaker_dominance + enforce = False + return dominance, enforce + + def _set_speaker_volume(self): + """ + Set the volume for each speaker (either equal volume or variable speaker volume). + """ + if self._params.data_simulator.session_params.normalization_type == 'equal': + self._volume = np.ones(self._params.data_simulator.session_config.num_speakers) + elif self._params.data_simulator.session_params.normalization_type == 'variable': + self._volume = np.random.normal( + loc=1.0, + scale=self._params.data_simulator.session_params.normalization_var, + size=self._params.data_simulator.session_config.num_speakers, + ) + self._volume = np.clip( + np.array(self._volume), + a_min=self._params.data_simulator.session_params.min_volume, + a_max=self._params.data_simulator.session_params.max_volume, + ).tolist() + + def _get_next_speaker(self, prev_speaker: int, dominance: List[float]) -> int: + """ + Get the next speaker (accounting for turn probability and dominance distribution). + + Args: + prev_speaker (int): Previous speaker turn. + dominance (list): Dominance values for each speaker. + Returns: + prev_speaker/speaker_turn (int): Speaker turn + """ + if self._params.data_simulator.session_config.num_speakers == 1: + prev_speaker = 0 if prev_speaker is None else prev_speaker + return prev_speaker + else: + if ( + np.random.uniform(0, 1) > self._params.data_simulator.session_params.turn_prob + and prev_speaker is not None + ): + return prev_speaker + else: + speaker_turn = prev_speaker + while speaker_turn == prev_speaker: # ensure another speaker goes next + rand = np.random.uniform(0, 1) + speaker_turn = 0 + while rand > dominance[speaker_turn]: + speaker_turn += 1 + return speaker_turn + + def _get_window(self, window_amount: int, start: bool = False): + """ + Get window curve to alleviate abrupt change of time-series signal when segmenting audio samples. + + Args: + window_amount (int): Window length (in terms of number of samples). + start (bool): If true, return the first half of the window. + + Returns: + window (tensor): Half window (either first half or second half) + """ + if self._params.data_simulator.session_params.window_type == 'hamming': + window = hamming(window_amount * 2) + elif self._params.data_simulator.session_params.window_type == 'hann': + window = hann(window_amount * 2) + elif self._params.data_simulator.session_params.window_type == 'cosine': + window = cosine(window_amount * 2) + else: + raise Exception("Incorrect window type provided") + + window = torch.from_numpy(window).to(self._device) + + # return the first half or second half of the window + if start: + return window[:window_amount] + else: + return window[window_amount:] + + def _get_start_buffer_and_window(self, first_alignment: int) -> Tuple[int, int]: + """ + Get the start cutoff and window length for smoothing the start of the sentence. + + Args: + first_alignment (int): Start of the first word (in terms of number of samples). + Returns: + start_cutoff (int): Amount into the audio clip to start + window_amount (int): Window length + """ + window_amount = int(self._params.data_simulator.session_params.window_size * self._params.data_simulator.sr) + start_buffer = int(self._params.data_simulator.session_params.start_buffer * self._params.data_simulator.sr) + + if first_alignment < start_buffer: + window_amount = 0 + start_cutoff = 0 + elif first_alignment < start_buffer + window_amount: + window_amount = first_alignment - start_buffer + start_cutoff = 0 + else: + start_cutoff = first_alignment - start_buffer - window_amount + + return start_cutoff, window_amount + + def _get_end_buffer_and_window( + self, current_sample_cursor: int, remaining_dur_samples: int, remaining_len_audio_file: int + ) -> Tuple[int, int]: + """ + Get the end buffer and window length for smoothing the end of the sentence. + + Args: + current_sample_cursor (int): Current location in the target file (in terms of number of samples). + remaining_dur_samples (int): Remaining duration in the target file (in terms of number of samples). + remaining_len_audio_file (int): Length remaining in audio file (in terms of number of samples). + Returns: + release_buffer (int): Amount after the end of the last alignment to include + window_amount (int): Window length + """ + window_amount = int(self._params.data_simulator.session_params.window_size * self._params.data_simulator.sr) + release_buffer = int( + self._params.data_simulator.session_params.release_buffer * self._params.data_simulator.sr + ) + + if current_sample_cursor + release_buffer > remaining_dur_samples: + release_buffer = remaining_dur_samples - current_sample_cursor + window_amount = 0 + elif current_sample_cursor + window_amount + release_buffer > remaining_dur_samples: + window_amount = remaining_dur_samples - current_sample_cursor - release_buffer + + if remaining_len_audio_file < release_buffer: + release_buffer = remaining_len_audio_file + window_amount = 0 + elif remaining_len_audio_file < release_buffer + window_amount: + window_amount = remaining_len_audio_file - release_buffer + + return release_buffer, window_amount + + def _check_missing_speakers(self, num_missing: int = 0): + """ + Check if any speakers were not included in the clip and display a warning. + + Args: + num_missing (int): Number of missing speakers. + """ + for k in range(len(self._furthest_sample)): + if self._furthest_sample[k] == 0: + num_missing += 1 + if num_missing != 0: + warnings.warn( + f"{self._params.data_simulator.session_config.num_speakers - num_missing}" + f"speakers were included in the clip instead of the requested amount of " + f"{self._params.data_simulator.session_config.num_speakers}" + ) + + def _add_file( + self, + audio_manifest: dict, + audio_file, + sentence_word_count: int, + max_word_count_in_sentence: int, + max_samples_in_sentence: int, + random_offset: bool = False, + ) -> Tuple[int, torch.Tensor]: + """ + Add audio file to current sentence (up to the desired number of words). + Uses the alignments to segment the audio file. + NOTE: 0 index is always silence in `audio_manifest['words']`, so we choose `offset_idx=1` as the first word + + Args: + audio_manifest (dict): Line from manifest file for current audio file + audio_file (tensor): Current loaded audio file + sentence_word_count (int): Running count for number of words in sentence + max_word_count_in_sentence (int): Maximum count for number of words in sentence + max_samples_in_sentence (int): Maximum length for sentence in terms of samples + + Returns: + sentence_word_count+current_word_count (int): Running word count + len(self._sentence) (tensor): Current length of the audio file + """ + # In general, random offset is not needed since random silence index has already been chosen + if random_offset: + offset_idx = np.random.randint(low=1, high=len(audio_manifest['words'])) + else: + offset_idx = 1 + + first_alignment = int(audio_manifest['alignments'][offset_idx - 1] * self._params.data_simulator.sr) + start_cutoff, start_window_amount = self._get_start_buffer_and_window(first_alignment) + if not self._params.data_simulator.session_params.start_window: # cut off the start of the sentence + start_window_amount = 0 + + # Ensure the desired number of words are added and the length of the output session isn't exceeded + sentence_samples = len(self._sentence) + + remaining_dur_samples = max_samples_in_sentence - sentence_samples + remaining_duration = max_word_count_in_sentence - sentence_word_count + prev_dur_samples, dur_samples, curr_dur_samples = 0, 0, 0 + current_word_count = 0 + word_idx = offset_idx + silence_count = 1 + while ( + current_word_count < remaining_duration + and dur_samples < remaining_dur_samples + and word_idx < len(audio_manifest['words']) + ): + dur_samples = int(audio_manifest['alignments'][word_idx] * self._params.data_simulator.sr) - start_cutoff + + # check the length of the generated sentence in terms of sample count (int). + if curr_dur_samples + dur_samples > remaining_dur_samples: + # if the upcoming loop will exceed the remaining sample count, break out of the loop. + break + + word = audio_manifest['words'][word_idx] + + if silence_count > 0 and word == "": + break + + self._words.append(word) + self._alignments.append( + float(sentence_samples * 1.0 / self._params.data_simulator.sr) + - float(start_cutoff * 1.0 / self._params.data_simulator.sr) + + audio_manifest['alignments'][word_idx] + ) + + if word == "": + word_idx += 1 + silence_count += 1 + continue + elif self._text == "": + self._text += word + else: + self._text += " " + word + + word_idx += 1 + current_word_count += 1 + prev_dur_samples = dur_samples + curr_dur_samples += dur_samples + + # add audio clip up to the final alignment + if self._params.data_simulator.session_params.window_type is not None: # cut off the start of the sentence + if start_window_amount > 0: # include window + window = self._get_window(start_window_amount, start=True) + self._sentence = self._sentence.to(self._device) + self._sentence = torch.cat( + ( + self._sentence, + torch.multiply(audio_file[start_cutoff : start_cutoff + start_window_amount], window), + ), + 0, + ) + self._sentence = torch.cat( + (self._sentence, audio_file[start_cutoff + start_window_amount : start_cutoff + prev_dur_samples],), 0, + ).to(self._device) + + else: + self._sentence = torch.cat( + (self._sentence, audio_file[start_cutoff : start_cutoff + prev_dur_samples]), 0 + ).to(self._device) + + # windowing at the end of the sentence + if ( + word_idx < len(audio_manifest['words']) + ) and self._params.data_simulator.session_params.window_type is not None: + release_buffer, end_window_amount = self._get_end_buffer_and_window( + prev_dur_samples, remaining_dur_samples, len(audio_file[start_cutoff + prev_dur_samples :]), + ) + self._sentence = torch.cat( + ( + self._sentence, + audio_file[start_cutoff + prev_dur_samples : start_cutoff + prev_dur_samples + release_buffer], + ), + 0, + ).to(self._device) + + if end_window_amount > 0: # include window + window = self._get_window(end_window_amount, start=False) + sig_start = start_cutoff + prev_dur_samples + release_buffer + sig_end = start_cutoff + prev_dur_samples + release_buffer + end_window_amount + windowed_audio_file = torch.multiply(audio_file[sig_start:sig_end], window) + self._sentence = torch.cat((self._sentence, windowed_audio_file), 0).to(self._device) + + del audio_file + return sentence_word_count + current_word_count, len(self._sentence) + + def _build_sentence( + self, + speaker_turn: int, + speaker_ids: List[str], + speaker_wav_align_map: Dict[str, list], + max_samples_in_sentence: int, + ): + """ + Build a new sentence by attaching utterance samples together until the sentence has reached a desired length. + While generating the sentence, alignment information is used to segment the audio. + + Args: + speaker_turn (int): Current speaker turn. + speaker_ids (list): LibriSpeech speaker IDs for each speaker in the current session. + speaker_wav_align_map (dict): Dictionary containing speaker IDs and their corresponding wav filepath and alignments. + max_samples_in_sentence (int): Maximum length for sentence in terms of samples + """ + # select speaker length + sl = ( + np.random.negative_binomial( + self._params.data_simulator.session_params.sentence_length_params[0], + self._params.data_simulator.session_params.sentence_length_params[1], + ) + + 1 + ) + + # initialize sentence, text, words, alignments + self._sentence = torch.zeros(0, dtype=torch.float64, device=self._device) + self._text = "" + self._words, self._alignments = [], [] + sentence_word_count, sentence_samples = 0, 0 + + # build sentence + while sentence_word_count < sl and sentence_samples < max_samples_in_sentence: + audio_manifest = load_speaker_sample( + speaker_wav_align_map=speaker_wav_align_map, + speaker_ids=speaker_ids, + speaker_turn=speaker_turn, + min_alignment_count=self._min_alignment_count, + ) + + offset_index = get_random_offset_index( + audio_manifest=audio_manifest, + audio_read_buffer_dict=self._audio_read_buffer_dict, + offset_min=0, + max_audio_read_sec=self._max_audio_read_sec, + min_alignment_count=self._min_alignment_count, + ) + + audio_file, sr, audio_manifest = read_audio_from_buffer( + audio_manifest=audio_manifest, + buffer_dict=self._audio_read_buffer_dict, + offset_index=offset_index, + device=self._device, + max_audio_read_sec=self._max_audio_read_sec, + min_alignment_count=self._min_alignment_count, + read_subset=True, + ) + + # Step 6-2: Add optional perturbations to the specific audio segment (i.e. to `self._sentnece`) + if self._params.data_simulator.segment_augmentor.add_seg_aug: + audio_file = perturb_audio(audio_file, sr, self.segment_augmentor, device=self._device) + + sentence_word_count, sentence_samples = self._add_file( + audio_manifest, audio_file, sentence_word_count, sl, max_samples_in_sentence + ) + + # per-speaker normalization (accounting for active speaker time) + if self._params.data_simulator.session_params.normalize and torch.max(torch.abs(self._sentence)) > 0: + splits = get_split_points_in_alignments( + words=self._words, + alignments=self._alignments, + split_buffer=self._params.data_simulator.session_params.split_buffer, + sr=self._params.data_simulator.sr, + sentence_audio_len=len(self._sentence), + ) + self._sentence = per_speaker_normalize( + sentence_audio=self._sentence, + splits=splits, + speaker_turn=speaker_turn, + volume=self._volume, + device=self._device, + ) + + def _add_silence_or_overlap( + self, + speaker_turn: int, + prev_speaker: int, + start: int, + length: int, + session_len_samples: int, + prev_len_samples: int, + enforce: bool, + ) -> int: + """ + Returns new overlapped (or shifted) start position after inserting overlap or silence. + + Args: + speaker_turn (int): The integer index of the current speaker turn. + prev_speaker (int): The integer index of the previous speaker turn. + start (int): Current start of the audio file being inserted. + length (int): Length of the audio file being inserted. + session_len_samples (int): Maximum length of the session in terms of number of samples + prev_len_samples (int): Length of previous sentence (in terms of number of samples) + enforce (bool): Whether speaker enforcement mode is being used + Returns: + new_start (int): New starting position in the session accounting for overlap or silence + """ + running_len_samples = start + length + # `length` is the length of the current sentence to be added, so not included in self.sampler.running_speech_len_samples + non_silence_len_samples = self.sampler.running_speech_len_samples + length + + # compare silence and overlap ratios + add_overlap = self.sampler.silence_vs_overlap_selector(running_len_samples, non_silence_len_samples) + + # choose overlap if this speaker is not the same as the previous speaker and add_overlap is True. + if prev_speaker != speaker_turn and prev_speaker is not None and add_overlap: + desired_overlap_amount = self.sampler.sample_from_overlap_model(non_silence_len_samples) + new_start = start - desired_overlap_amount + + # avoid overlap at start of clip + if new_start < 0: + desired_overlap_amount -= 0 - new_start + self._missing_overlap += 0 - new_start + new_start = 0 + + # if same speaker ends up overlapping from any previous clip, pad with silence instead + if new_start < self._furthest_sample[speaker_turn]: + desired_overlap_amount -= self._furthest_sample[speaker_turn] - new_start + self._missing_overlap += self._furthest_sample[speaker_turn] - new_start + new_start = self._furthest_sample[speaker_turn] + + prev_start = start - prev_len_samples + prev_end = start + new_end = new_start + length + + # check overlap amount to calculate the actual amount of generated overlaps + overlap_amount = 0 + if is_overlap([prev_start, prev_end], [new_start, new_end]): + overlap_range = get_overlap_range([prev_start, prev_end], [new_start, new_end]) + overlap_amount = max(overlap_range[1] - overlap_range[0], 0) + + if overlap_amount < desired_overlap_amount: + self._missing_overlap += desired_overlap_amount - overlap_amount + self.sampler.running_overlap_len_samples += overlap_amount + + # if we are not adding overlap, add silence + else: + silence_amount = self.sampler.sample_from_silence_model(running_len_samples) + if start + length + silence_amount > session_len_samples and not enforce: + new_start = max(session_len_samples - length, start) + else: + new_start = start + silence_amount + return new_start + + def _get_session_meta_data(self, array: np.ndarray, snr: float) -> dict: + """ + Get meta data for the current session. + + Args: + array (np.ndarray): audio array + snr (float): signal-to-noise ratio + + Returns: + dict: meta data + """ + meta_data = { + "duration": array.shape[0] / self._params.data_simulator.sr, + "silence_mean": self.sampler.sess_silence_mean, + "overlap_mean": self.sampler.sess_overlap_mean, + "bg_snr": snr, + "speaker_ids": self._speaker_ids, + "speaker_volumes": list(self._volume), + } + return meta_data + + def _get_session_silence_from_rttm(self, rttm_list: List[str], running_len_samples: int): + """ + Calculate the total speech and silence duration in the current session using RTTM file. + + Args: + rttm_list (list): + List of RTTM timestamps + running_len_samples (int): + Total number of samples generated so far in the current session + + Returns: + sess_speech_len_rttm (int): + The total number of speech samples in the current session + sess_silence_len_rttm (int): + The total number of silence samples in the current session + """ + all_sample_list = [] + for x_raw in rttm_list: + x = [token for token in x_raw.split()] + all_sample_list.append([float(x[0]), float(x[1])]) + + self._merged_speech_intervals = merge_float_intervals(all_sample_list) + total_speech_in_secs = sum([x[1] - x[0] for x in self._merged_speech_intervals]) + total_silence_in_secs = running_len_samples / self._params.data_simulator.sr - total_speech_in_secs + sess_speech_len = int(total_speech_in_secs * self._params.data_simulator.sr) + sess_silence_len = int(total_silence_in_secs * self._params.data_simulator.sr) + return sess_speech_len, sess_silence_len + + def _add_sentence_to_array( + self, start: int, length: int, array: torch.Tensor, is_speech: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, int]: + """ + Add a sentence to the session array containing time-series signal. + + Args: + start (int): Starting position in the session + length (int): Length of the sentence + array (torch.Tensor): Session array + is_speech (torch.Tensor): Session array containing speech/non-speech labels + + Returns: + array (torch.Tensor): Session array in torch.Tensor format + is_speech (torch.Tensor): Session array containing speech/non-speech labels in torch.Tensor format + """ + end = start + length + if end > len(array): # only occurs in enforce mode + array = torch.nn.functional.pad(array, (0, end - len(array))) + is_speech = torch.nn.functional.pad(is_speech, (0, end - len(is_speech))) + array[start:end] += self._sentence + is_speech[start:end] = 1 + return array, is_speech, end + + def _generate_session( + self, + idx: int, + basepath: str, + filename: str, + speaker_ids: List[str], + speaker_wav_align_map: Dict[str, list], + noise_samples: list, + device: torch.device, + enforce_counter: int = 2, + ): + """ + _generate_session function without RIR simulation. + Generate a multispeaker audio session and corresponding label files. + + Args: + idx (int): Index for current session (out of total number of sessions). + basepath (str): Path to output directory. + filename (str): Filename for output files. + speaker_ids (list): List of speaker IDs that will be used in this session. + speaker_wav_align_map (dict): Dictionary containing speaker IDs and their corresponding wav filepath and alignments. + noise_samples (list): List of randomly sampled noise source files that will be used for generating this session. + device (torch.device): Device to use for generating this session. + enforce_counter (int): In enforcement mode, dominance is increased by a factor of enforce_counter for unrepresented speakers + """ + random_seed = self._params.data_simulator.random_seed + np.random.seed(random_seed + idx) + + self._device = device + speaker_dominance = self._get_speaker_dominance() # randomly determine speaker dominance + base_speaker_dominance = np.copy(speaker_dominance) + self._set_speaker_volume() + + running_len_samples, prev_len_samples = 0, 0 + prev_speaker = None + self.annotator.init_annotation_lists() + self._noise_samples = noise_samples + self._furthest_sample = [0 for n in range(self._params.data_simulator.session_config.num_speakers)] + self._missing_silence = 0 + + # hold enforce until all speakers have spoken + enforce_time = np.random.uniform( + self._params.data_simulator.speaker_enforcement.enforce_time[0], + self._params.data_simulator.speaker_enforcement.enforce_time[1], + ) + enforce = self._params.data_simulator.speaker_enforcement.enforce_num_speakers + + session_len_samples = int( + (self._params.data_simulator.session_config.session_length * self._params.data_simulator.sr) + ) + array = torch.zeros(session_len_samples).to(self._device) + is_speech = torch.zeros(session_len_samples).to(self._device) + + self.sampler.get_session_silence_mean() + self.sampler.get_session_overlap_mean() + + while running_len_samples < session_len_samples or enforce: + # Step 1: Prepare parameters for sentence generation + # Enforce speakers depending on running length + if running_len_samples > enforce_time * session_len_samples and enforce: + speaker_dominance, enforce = self._increase_speaker_dominance(base_speaker_dominance, enforce_counter) + if enforce: + enforce_counter += 1 + + # Step 2: Select a speaker + speaker_turn = self._get_next_speaker(prev_speaker, speaker_dominance) + + # Calculate parameters for building a sentence (only add if remaining length > specific time) + max_samples_in_sentence = session_len_samples - running_len_samples + if enforce: + max_samples_in_sentence = float('inf') + elif ( + max_samples_in_sentence + < self._params.data_simulator.session_params.end_buffer * self._params.data_simulator.sr + ): + break + + # Step 3: Generate a sentence + self._build_sentence(speaker_turn, speaker_ids, speaker_wav_align_map, max_samples_in_sentence) + length = len(self._sentence) + + # Step 4: Generate a timestamp for either silence or overlap + start = self._add_silence_or_overlap( + speaker_turn=speaker_turn, + prev_speaker=prev_speaker, + start=running_len_samples, + length=length, + session_len_samples=session_len_samples, + prev_len_samples=prev_len_samples, + enforce=enforce, + ) + # step 5: add sentence to array + array, is_speech, end = self._add_sentence_to_array( + start=start, length=length, array=array, is_speech=is_speech, + ) + + # Step 6: Build entries for output files + new_rttm_entries = self.annotator.create_new_rttm_entry( + words=self._words, + alignments=self._alignments, + start=start / self._params.data_simulator.sr, + end=end / self._params.data_simulator.sr, + speaker_id=speaker_ids[speaker_turn], + ) + + self.annotator.annote_lists['rttm'].extend(new_rttm_entries) + + new_json_entry = self.annotator.create_new_json_entry( + text=self._text, + wav_filename=os.path.join(basepath, filename + '.wav'), + start=start / self._params.data_simulator.sr, + length=length / self._params.data_simulator.sr, + speaker_id=speaker_ids[speaker_turn], + rttm_filepath=os.path.join(basepath, filename + '.rttm'), + ctm_filepath=os.path.join(basepath, filename + '.ctm'), + ) + self.annotator.annote_lists['json'].append(new_json_entry) + + new_ctm_entries = self.annotator.create_new_ctm_entry( + words=self._words, + alignments=self._alignments, + session_name=filename, + speaker_id=speaker_ids[speaker_turn], + start=int(start / self._params.data_simulator.sr), + ) + + self.annotator.annote_lists['ctm'].extend(new_ctm_entries) + + running_len_samples = np.maximum(running_len_samples, end) + ( + self.sampler.running_speech_len_samples, + self.sampler.running_silence_len_samples, + ) = self._get_session_silence_from_rttm( + rttm_list=self.annotator.annote_lists['rttm'], running_len_samples=running_len_samples + ) + + self._furthest_sample[speaker_turn] = running_len_samples + prev_speaker = speaker_turn + prev_len_samples = length + + # Step 7-1: Add optional perturbations to the whole session, such as white noise. + if self._params.data_simulator.session_augmentor.add_sess_aug: + # NOTE: This perturbation is not reflected in the session SNR in meta dictionary. + array = perturb_audio(array, self._params.data_simulator.sr, self.session_augmentor, device=array.device) + + # Step 7-2: Additive background noise from noise manifest files + if self._params.data_simulator.background_noise.add_bg: + if len(self._noise_samples) > 0: + avg_power_array = torch.mean(array[is_speech == 1] ** 2) + bg, snr = get_background_noise( + len_array=len(array), + power_array=avg_power_array, + noise_samples=self._noise_samples, + audio_read_buffer_dict=self._audio_read_buffer_dict, + snr_min=self._params.data_simulator.background_noise.snr_min, + snr_max=self._params.data_simulator.background_noise.snr_max, + background_noise_snr=self._params.data_simulator.background_noise.snr, + seed=(random_seed + idx), + device=self._device, + ) + array += bg + else: + raise ValueError('No background noise samples found in self._noise_samples.') + else: + snr = "N/A" + + # Step 7: Normalize and write to disk + array = normalize_audio(array) + + if torch.is_tensor(array): + array = array.cpu().numpy() + sf.write(os.path.join(basepath, filename + '.wav'), array, self._params.data_simulator.sr) + + self.annotator.write_annotation_files( + basepath=basepath, filename=filename, meta_data=self._get_session_meta_data(array=array, snr=snr), + ) + + # Step 8: Clean up memory + del array + self.clean_up() + return basepath, filename + + def generate_sessions(self, random_seed: int = None): + """ + Generate several multispeaker audio sessions and corresponding list files. + + Args: + random_seed (int): random seed for reproducibility + """ + logging.info(f"Generating Diarization Sessions") + if random_seed is None: + random_seed = self._params.data_simulator.random_seed + np.random.seed(random_seed) + + output_dir = self._params.data_simulator.outputs.output_dir + + basepath = get_cleaned_base_path( + output_dir, overwrite_output=self._params.data_simulator.outputs.overwrite_output + ) + OmegaConf.save(self._params, os.path.join(output_dir, "params.yaml")) + + tp = concurrent.futures.ProcessPoolExecutor(max_workers=self.num_workers) + futures = [] + + num_sessions = self._params.data_simulator.session_config.num_sessions + source_noise_manifest = read_noise_manifest( + add_bg=self._params.data_simulator.background_noise.add_bg, + background_manifest=self._params.data_simulator.background_noise.background_manifest, + ) + queue = [] + + # add radomly sampled arguments to a list(queue) for multiprocessing + for sess_idx in range(num_sessions): + filename = self._params.data_simulator.outputs.output_filename + f"_{sess_idx}" + speaker_ids = get_speaker_ids( + sess_idx=sess_idx, + speaker_samples=self._speaker_samples, + permutated_speaker_inds=self._permutated_speaker_inds, + ) + speaker_wav_align_map = get_speaker_samples(speaker_ids=speaker_ids, speaker_samples=self._speaker_samples) + noise_samples = self.sampler.sample_noise_manifest(noise_manifest=source_noise_manifest) + + if torch.cuda.is_available(): + device = torch.device(f"cuda:{sess_idx % torch.cuda.device_count()}") + else: + device = self._device + queue.append((sess_idx, basepath, filename, speaker_ids, speaker_wav_align_map, noise_samples, device)) + + # for multiprocessing speed, we avoid loading potentially huge manifest list and speaker sample files into each process. + if self.num_workers > 1: + self._manifest = None + self._speaker_samples = None + + # Chunk the sessions into smaller chunks for very large number of sessions (10K+ sessions) + for chunk_idx in range(self.chunk_count): + futures = [] + stt_idx, end_idx = ( + chunk_idx * self.multiprocessing_chunksize, + min((chunk_idx + 1) * self.multiprocessing_chunksize, num_sessions), + ) + for sess_idx in range(stt_idx, end_idx): + self._furthest_sample = [0 for n in range(self._params.data_simulator.session_config.num_speakers)] + self._audio_read_buffer_dict = {} + if self.num_workers > 1: + futures.append(tp.submit(self._generate_session, *queue[sess_idx])) + else: + futures.append(queue[sess_idx]) + + if self.num_workers > 1: + generator = concurrent.futures.as_completed(futures) + else: + generator = futures + + for future in tqdm( + generator, + desc=f"[{chunk_idx+1}/{self.chunk_count}] Waiting jobs from {stt_idx+1: 2} to {end_idx: 2}", + unit="jobs", + total=len(futures), + ): + if self.num_workers > 1: + basepath, filename = future.result() + else: + self._noise_samples = self.sampler.sample_noise_manifest(noise_manifest=source_noise_manifest,) + basepath, filename = self._generate_session(*future) + + self.annotator.add_to_filename_lists(basepath=basepath, filename=filename) + + # throw warning if number of speakers is less than requested + self._check_missing_speakers() + + tp.shutdown() + self.annotator.write_filelist_files(basepath=basepath) + logging.info(f"Data simulation has been completed, results saved at: {basepath}") + + +class RIRMultiSpeakerSimulator(MultiSpeakerSimulator): + """ + RIR Augmented Multispeaker Audio Session Simulator - simulates multispeaker audio sessions using single-speaker + audio files and corresponding word alignments, as well as simulated RIRs for augmentation. + + Args: + cfg: OmegaConf configuration loaded from yaml file. + + Parameters (in addition to the base MultiSpeakerSimulator parameters): + rir_generation: + use_rir (bool): Whether to generate synthetic RIR + toolkit (str): Which toolkit to use ("pyroomacoustics", "gpuRIR") + room_config: + room_sz (list): Size of the shoebox room environment (1d array for specific, 2d array for random range to be + sampled from) + pos_src (list): Positions of the speakers in the simulated room environment (2d array for specific, 3d array + for random ranges to be sampled from) + noise_src_pos (list): Position in room for the ambient background noise source + mic_config: + num_channels (int): Number of output audio channels + pos_rcv (list): Microphone positions in the simulated room environment (1d/2d array for specific, 2d/3d array + for range assuming num_channels is 1/2+) + orV_rcv (list or null): Microphone orientations (needed for non-omnidirectional microphones) + mic_pattern (str): Microphone type ("omni" - omnidirectional) - currently only omnidirectional microphones are + supported for pyroomacoustics + absorbtion_params: (Note that only `T60` is used for pyroomacoustics simulations) + abs_weights (list): Absorption coefficient ratios for each surface + T60 (float): Room reverberation time (`T60` is the time it takes for the RIR to decay by 60DB) + att_diff (float): Starting attenuation (if this is different than att_max, the diffuse reverberation model is + used by gpuRIR) + att_max (float): End attenuation when using the diffuse reverberation model (gpuRIR) + """ + + def __init__(self, cfg): + super().__init__(cfg) + self._check_args_rir() + + def _check_args_rir(self): + """ + Checks RIR YAML arguments to ensure they are within valid ranges + """ + + if not (self._params.data_simulator.rir_generation.toolkit in ['pyroomacoustics', 'gpuRIR']): + raise Exception("Toolkit must be pyroomacoustics or gpuRIR") + if self._params.data_simulator.rir_generation.toolkit == 'pyroomacoustics' and not PRA: + raise ImportError("pyroomacoustics should be installed to run this simulator with RIR augmentation") + + if self._params.data_simulator.rir_generation.toolkit == 'gpuRIR' and not GPURIR: + raise ImportError("gpuRIR should be installed to run this simulator with RIR augmentation") + + if len(self._params.data_simulator.rir_generation.room_config.room_sz) != 3: + raise Exception("Incorrect room dimensions provided") + if self._params.data_simulator.rir_generation.mic_config.num_channels == 0: + raise Exception("Number of channels should be greater or equal to 1") + if len(self._params.data_simulator.rir_generation.room_config.pos_src) < 2: + raise Exception("Less than 2 provided source positions") + for sublist in self._params.data_simulator.rir_generation.room_config.pos_src: + if len(sublist) != 3: + raise Exception("Three coordinates must be provided for sources positions") + if len(self._params.data_simulator.rir_generation.mic_config.pos_rcv) == 0: + raise Exception("No provided mic positions") + for sublist in self._params.data_simulator.rir_generation.room_config.pos_src: + if len(sublist) != 3: + raise Exception("Three coordinates must be provided for mic positions") + + if self._params.data_simulator.session_config.num_speakers != len( + self._params.data_simulator.rir_generation.room_config.pos_src + ): + raise Exception("Number of speakers is not equal to the number of provided source positions") + if self._params.data_simulator.rir_generation.mic_config.num_channels != len( + self._params.data_simulator.rir_generation.mic_config.pos_rcv + ): + raise Exception("Number of channels is not equal to the number of provided microphone positions") + + if ( + not self._params.data_simulator.rir_generation.mic_config.orV_rcv + and self._params.data_simulator.rir_generation.mic_config.mic_pattern != 'omni' + ): + raise Exception("Microphone orientations must be provided if mic_pattern != omni") + if self._params.data_simulator.rir_generation.mic_config.orV_rcv is not None: + if len(self._params.data_simulator.rir_generation.mic_config.orV_rcv) != len( + self._params.data_simulator.rir_generation.mic_config.pos_rcv + ): + raise Exception("A different number of microphone orientations and microphone positions were provided") + for sublist in self._params.data_simulator.rir_generation.mic_config.orV_rcv: + if len(sublist) != 3: + raise Exception("Three coordinates must be provided for orientations") + + def _generate_rir_gpuRIR(self): + """ + Create simulated RIR using the gpuRIR library + + Returns: + RIR (tensor): Generated RIR + RIR_pad (int): Length of padding added when convolving the RIR with an audio file + """ + room_sz_tmp = np.array(self._params.data_simulator.rir_generation.room_config.room_sz) + if room_sz_tmp.ndim == 2: # randomize + room_sz = np.zeros(room_sz_tmp.shape[0]) + for i in range(room_sz_tmp.shape[0]): + room_sz[i] = np.random.uniform(room_sz_tmp[i, 0], room_sz_tmp[i, 1]) + else: + room_sz = room_sz_tmp + + pos_src_tmp = np.array(self._params.data_simulator.rir_generation.room_config.pos_src) + if pos_src_tmp.ndim == 3: # randomize + pos_src = np.zeros((pos_src_tmp.shape[0], pos_src_tmp.shape[1])) + for i in range(pos_src_tmp.shape[0]): + for j in range(pos_src_tmp.shape[1]): + pos_src[i] = np.random.uniform(pos_src_tmp[i, j, 0], pos_src_tmp[i, j, 1]) + else: + pos_src = pos_src_tmp + + if self._params.data_simulator.background_noise.add_bg: + pos_src = np.vstack((pos_src, self._params.data_simulator.rir_generation.room_config.noise_src_pos)) + + mic_pos_tmp = np.array(self._params.data_simulator.rir_generation.mic_config.pos_rcv) + if mic_pos_tmp.ndim == 3: # randomize + mic_pos = np.zeros((mic_pos_tmp.shape[0], mic_pos_tmp.shape[1])) + for i in range(mic_pos_tmp.shape[0]): + for j in range(mic_pos_tmp.shape[1]): + mic_pos[i] = np.random.uniform(mic_pos_tmp[i, j, 0], mic_pos_tmp[i, j, 1]) + else: + mic_pos = mic_pos_tmp + + orV_rcv = self._params.data_simulator.rir_generation.mic_config.orV_rcv + if orV_rcv: # not needed for omni mics + orV_rcv = np.array(orV_rcv) + mic_pattern = self._params.data_simulator.rir_generation.mic_config.mic_pattern + abs_weights = self._params.data_simulator.rir_generation.absorbtion_params.abs_weights + T60 = self._params.data_simulator.rir_generation.absorbtion_params.T60 + att_diff = self._params.data_simulator.rir_generation.absorbtion_params.att_diff + att_max = self._params.data_simulator.rir_generation.absorbtion_params.att_max + sr = self._params.data_simulator.sr + + beta = beta_SabineEstimation(room_sz, T60, abs_weights=abs_weights) # Reflection coefficients + Tdiff = att2t_SabineEstimator(att_diff, T60) # Time to start the diffuse reverberation model [s] + Tmax = att2t_SabineEstimator(att_max, T60) # Time to stop the simulation [s] + nb_img = t2n(Tdiff, room_sz) # Number of image sources in each dimension + RIR = simulateRIR( + room_sz, beta, pos_src, mic_pos, nb_img, Tmax, sr, Tdiff=Tdiff, orV_rcv=orV_rcv, mic_pattern=mic_pattern + ) + RIR_pad = RIR.shape[2] - 1 + return RIR, RIR_pad + + def _generate_rir_pyroomacoustics(self) -> Tuple[torch.Tensor, int]: + """ + Create simulated RIR using the pyroomacoustics library + + Returns: + RIR (tensor): Generated RIR + RIR_pad (int): Length of padding added when convolving the RIR with an audio file + """ + + rt60 = self._params.data_simulator.rir_generation.absorbtion_params.T60 # The desired reverberation time + sr = self._params.data_simulator.sr + + room_sz_tmp = np.array(self._params.data_simulator.rir_generation.room_config.room_sz) + if room_sz_tmp.ndim == 2: # randomize + room_sz = np.zeros(room_sz_tmp.shape[0]) + for i in range(room_sz_tmp.shape[0]): + room_sz[i] = np.random.uniform(room_sz_tmp[i, 0], room_sz_tmp[i, 1]) + else: + room_sz = room_sz_tmp + + pos_src_tmp = np.array(self._params.data_simulator.rir_generation.room_config.pos_src) + if pos_src_tmp.ndim == 3: # randomize + pos_src = np.zeros((pos_src_tmp.shape[0], pos_src_tmp.shape[1])) + for i in range(pos_src_tmp.shape[0]): + for j in range(pos_src_tmp.shape[1]): + pos_src[i] = np.random.uniform(pos_src_tmp[i, j, 0], pos_src_tmp[i, j, 1]) + else: + pos_src = pos_src_tmp + + # We invert Sabine's formula to obtain the parameters for the ISM simulator + e_absorption, max_order = pra.inverse_sabine(rt60, room_sz) + room = pra.ShoeBox(room_sz, fs=sr, materials=pra.Material(e_absorption), max_order=max_order) + + if self._params.data_simulator.background_noise.add_bg: + pos_src = np.vstack((pos_src, self._params.data_simulator.rir_generation.room_config.noise_src_pos)) + for pos in pos_src: + room.add_source(pos) + + # currently only supports omnidirectional microphones + mic_pattern = self._params.data_simulator.rir_generation.mic_config.mic_pattern + if self._params.data_simulator.rir_generation.mic_config.mic_pattern == 'omni': + mic_pattern = DirectivityPattern.OMNI + dir_vec = DirectionVector(azimuth=0, colatitude=90, degrees=True) + dir_obj = CardioidFamily(orientation=dir_vec, pattern_enum=mic_pattern,) + + mic_pos_tmp = np.array(self._params.data_simulator.rir_generation.mic_config.pos_rcv) + if mic_pos_tmp.ndim == 3: # randomize + mic_pos = np.zeros((mic_pos_tmp.shape[0], mic_pos_tmp.shape[1])) + for i in range(mic_pos_tmp.shape[0]): + for j in range(mic_pos_tmp.shape[1]): + mic_pos[i] = np.random.uniform(mic_pos_tmp[i, j, 0], mic_pos_tmp[i, j, 1]) + else: + mic_pos = mic_pos_tmp + + room.add_microphone_array(mic_pos.T, directivity=dir_obj) + + room.compute_rir() + rir_pad = 0 + for channel in room.rir: + for pos in channel: + if pos.shape[0] - 1 > rir_pad: + rir_pad = pos.shape[0] - 1 + return room.rir, rir_pad + + def _convolve_rir(self, input, speaker_turn: int, RIR: torch.Tensor) -> Tuple[list, int]: + """ + Augment one sentence (or background noise segment) using a synthetic RIR. + + Args: + input (torch.tensor): Input audio. + speaker_turn (int): Current speaker turn. + RIR (torch.tensor): Room Impulse Response. + Returns: + output_sound (list): List of tensors containing augmented audio + length (int): Length of output audio channels (or of the longest if they have different lengths) + """ + output_sound = [] + length = 0 + for channel in range(self._params.data_simulator.rir_generation.mic_config.num_channels): + if self._params.data_simulator.rir_generation.toolkit == 'gpuRIR': + out_channel = convolve(input, RIR[speaker_turn, channel, : len(input)]).tolist() + elif self._params.data_simulator.rir_generation.toolkit == 'pyroomacoustics': + out_channel = convolve(input, RIR[channel][speaker_turn][: len(input)]).tolist() + if len(out_channel) > length: + length = len(out_channel) + output_sound.append(torch.tensor(out_channel)) + return output_sound, length + + def _generate_session( + self, + idx: int, + basepath: str, + filename: str, + speaker_ids: list, + speaker_wav_align_map: dict, + noise_samples: list, + device: torch.device, + enforce_counter: int = 2, + ): + """ + Generate a multispeaker audio session and corresponding label files. + + Args: + idx (int): Index for current session (out of total number of sessions). + basepath (str): Path to output directory. + filename (str): Filename for output files. + speaker_ids (list): List of speaker IDs that will be used in this session. + speaker_wav_align_map (dict): Dictionary containing speaker IDs and their corresponding wav filepath and alignments. + noise_samples (list): List of randomly sampled noise source files that will be used for generating this session. + device (torch.device): Device to use for generating this session. + enforce_counter (int): In enforcement mode, dominance is increased by a factor of enforce_counter for unrepresented speakers + """ + random_seed = self._params.data_simulator.random_seed + np.random.seed(random_seed + idx) + + self._device = device + speaker_dominance = self._get_speaker_dominance() # randomly determine speaker dominance + base_speaker_dominance = np.copy(speaker_dominance) + self._set_speaker_volume() + + running_len_samples, prev_len_samples = 0, 0 # starting point for each sentence + prev_speaker = None + self.annotator.init_annotation_lists() + self._noise_samples = noise_samples + self._furthest_sample = [0 for n in range(self._params.data_simulator.session_config.num_speakers)] + + # Room Impulse Response Generation (performed once per batch of sessions) + if self._params.data_simulator.rir_generation.toolkit == 'gpuRIR': + RIR, RIR_pad = self._generate_rir_gpuRIR() + elif self._params.data_simulator.rir_generation.toolkit == 'pyroomacoustics': + RIR, RIR_pad = self._generate_rir_pyroomacoustics() + else: + raise Exception("Toolkit must be pyroomacoustics or gpuRIR") + + # hold enforce until all speakers have spoken + enforce_time = np.random.uniform( + self._params.data_simulator.speaker_enforcement.enforce_time[0], + self._params.data_simulator.speaker_enforcement.enforce_time[1], + ) + enforce = self._params.data_simulator.speaker_enforcement.enforce_num_speakers + + session_len_samples = int( + (self._params.data_simulator.session_config.session_length * self._params.data_simulator.sr) + ) + array = torch.zeros((session_len_samples, self._params.data_simulator.rir_generation.mic_config.num_channels)) + is_speech = torch.zeros(session_len_samples) + + while running_len_samples < session_len_samples or enforce: + # Step 1: Prepare parameters for sentence generation + # Enforce speakers depending on running length + if running_len_samples > enforce_time * session_len_samples and enforce: + speaker_dominance, enforce = self._increase_speaker_dominance(base_speaker_dominance, enforce_counter) + if enforce: + enforce_counter += 1 + + # Step 2: Select a speaker + speaker_turn = self._get_next_speaker(prev_speaker, speaker_dominance) + + # Calculate parameters for building a sentence (only add if remaining length > specific time) + max_samples_in_sentence = ( + session_len_samples - running_len_samples - RIR_pad + ) # sentence will be RIR_len - 1 longer than the audio was pre-augmentation + if enforce: + max_samples_in_sentence = float('inf') + elif ( + max_samples_in_sentence + < self._params.data_simulator.session_params.end_buffer * self._params.data_simulator.sr + ): + break + + # Step 3: Generate a sentence + self._build_sentence(speaker_turn, speaker_ids, speaker_wav_align_map, max_samples_in_sentence) + augmented_sentence, length = self._convolve_rir(self._sentence, speaker_turn, RIR) + + # Step 4: Generate a time-stamp for either silence or overlap + start = self._add_silence_or_overlap( + speaker_turn=speaker_turn, + prev_speaker=prev_speaker, + start=running_len_samples, + length=length, + session_len_samples=session_len_samples, + prev_len_samples=prev_len_samples, + enforce=enforce, + ) + # step 5: add sentence to array + end = start + length + if end > len(array): + array = torch.nn.functional.pad(array, (0, 0, 0, end - len(array))) + is_speech = torch.nn.functional.pad(is_speech, (0, end - len(is_speech))) + is_speech[start:end] = 1 + + for channel in range(self._params.data_simulator.rir_generation.mic_config.num_channels): + len_ch = len(augmented_sentence[channel]) # accounts for how channels are slightly different lengths + array[start : start + len_ch, channel] += augmented_sentence[channel] + + # Step 6: Build entries for output files + new_rttm_entries = self.annotator.create_new_rttm_entry( + self._words, + self._alignments, + start / self._params.data_simulator.sr, + end / self._params.data_simulator.sr, + speaker_ids[speaker_turn], + ) + + self.annotator.annote_lists['rttm'].extend(new_rttm_entries) + + new_json_entry = self.annotator.create_new_json_entry( + self._text, + os.path.join(basepath, filename + '.wav'), + start / self._params.data_simulator.sr, + length / self._params.data_simulator.sr, + speaker_ids[speaker_turn], + os.path.join(basepath, filename + '.rttm'), + os.path.join(basepath, filename + '.ctm'), + ) + self.annotator.annote_lists['json'].append(new_json_entry) + + new_ctm_entries = self.annotator.create_new_ctm_entry( + filename, speaker_ids[speaker_turn], start / self._params.data_simulator.sr + ) + self.annotator.annote_lists['ctm'].extend(new_ctm_entries) + + running_len_samples = np.maximum(running_len_samples, end) + self._furthest_sample[speaker_turn] = running_len_samples + prev_speaker = speaker_turn + prev_len_samples = length + + # Step 7-1: Add optional perturbations to the whole session, such as white noise. + if self._params.data_simulator.session_augmentor.add_sess_aug: + # NOTE: This perturbation is not reflected in the session SNR in meta dictionary. + array = perturb_audio(array, self._params.data_simulator.sr, self.session_augmentor) + + # Step 7-2: Additive background noise from noise manifest files + if self._params.data_simulator.background_noise.add_bg: + if len(self._noise_samples) > 0: + avg_power_array = torch.mean(array[is_speech == 1] ** 2) + bg, snr = get_background_noise( + len_array=len(array), + power_array=avg_power_array, + noise_samples=self._noise_samples, + audio_read_buffer_dict=self._audio_read_buffer_dict, + snr_min=self._params.data_simulator.background_noise.snr_min, + snr_max=self._params.data_simulator.background_noise.snr_max, + background_noise_snr=self._params.data_simulator.background_noise.snr, + seed=(random_seed + idx), + device=self._device, + ) + array += bg + length = array.shape[0] + bg, snr = self._get_background(length, avg_power_array) + augmented_bg, _ = self._convolve_rir(bg, -1, RIR) + for channel in range(self._params.data_simulator.rir_generation.mic_config.num_channels): + array[:, channel] += augmented_bg[channel][:length] + else: + snr = "N/A" + + # Step 7: Normalize and write to disk + array = normalize_audio(array) + + if torch.is_tensor(array): + array = array.cpu().numpy() + sf.write(os.path.join(basepath, filename + '.wav'), array, self._params.data_simulator.sr) + + self.annotator.write_annotation_files( + basepath=basepath, filename=filename, meta_data=self._get_session_meta_data(array=array, snr=snr), + ) + + del array + self.clean_up() + return basepath, filename + + +def check_angle(key: str, val: Union[float, Iterable[float]]) -> bool: + """Check if the angle value is within the expected range. Input + values are in degrees. + + Note: + azimuth: angle between a projection on the horizontal (xy) plane and + positive x axis. Increases counter-clockwise. Range: [-180, 180]. + elevation: angle between a vector an its projection on the horizontal (xy) plane. + Positive above, negative below, i.e., north=+90, south=-90. Range: [-90, 90] + yaw: rotation around the z axis. Defined accoding to right-hand rule. + Range: [-180, 180] + pitch: rotation around the yʹ axis. Defined accoding to right-hand rule. + Range: [-90, 90] + roll: rotation around the xʺ axis. Defined accoding to right-hand rule. + Range: [-180, 180] + + Args: + key: angle type + val: values in degrees + + Returns: + True if all values are within the expected range. + """ + if np.isscalar(val): + min_val = max_val = val + else: + min_val = min(val) + max_val = max(val) + + if key == 'azimuth' and -180 <= min_val <= max_val <= 180: + return True + if key == 'elevation' and -90 <= min_val <= max_val <= 90: + return True + if key == 'yaw' and -180 <= min_val <= max_val <= 180: + return True + if key == 'pitch' and -90 <= min_val <= max_val <= 90: + return True + if key == 'roll' and -180 <= min_val <= max_val <= 180: + return True + + raise ValueError(f'Invalid value for angle {key} = {val}') + + +def wrap_to_180(angle: float) -> float: + """Wrap an angle to range ±180 degrees. + + Args: + angle: angle in degrees + + Returns: + Angle in degrees wrapped to ±180 degrees. + """ + return angle - np.floor(angle / 360 + 1 / 2) * 360 + + +class ArrayGeometry(object): + """A class to simplify handling of array geometry. + + Supports translation and rotation of the array and calculation of + spherical coordinates of a given point relative to the internal + coordinate system of the array. + + Args: + mic_positions: 3D coordinates, with shape (num_mics, 3) + center: optional position of the center of the array. Defaults to the average of the coordinates. + internal_cs: internal coordinate system for the array relative to the global coordinate system. + Defaults to (x, y, z), and is rotated with the array. + """ + + def __init__( + self, + mic_positions: Union[np.ndarray, List], + center: Optional[np.ndarray] = None, + internal_cs: Optional[np.ndarray] = None, + ): + if isinstance(mic_positions, Iterable): + mic_positions = np.array(mic_positions) + + if not mic_positions.ndim == 2: + raise ValueError( + f'Expecting a 2D array specifying mic positions, but received {mic_positions.ndim}-dim array' + ) + + if not mic_positions.shape[1] == 3: + raise ValueError(f'Expecting 3D positions, but received {mic_positions.shape[1]}-dim positions') + + mic_positions_center = np.mean(mic_positions, axis=0) + self.centered_positions = mic_positions - mic_positions_center + self.center = mic_positions_center if center is None else center + + # Internal coordinate system + if internal_cs is None: + # Initially aligned with the global + self.internal_cs = np.eye(3) + else: + self.internal_cs = internal_cs + + @property + def num_mics(self): + """Return the number of microphones for the current array. + """ + return self.centered_positions.shape[0] + + @property + def positions(self): + """Absolute positions of the microphones. + """ + return self.centered_positions + self.center + + @property + def internal_positions(self): + """Positions in the internal coordinate system. + """ + return np.matmul(self.centered_positions, self.internal_cs.T) + + @property + def radius(self): + """Radius of the array, relative to the center. + """ + return max(np.linalg.norm(self.centered_positions, axis=1)) + + @staticmethod + def get_rotation(yaw: float = 0, pitch: float = 0, roll: float = 0) -> Rotation: + """Get a Rotation object for given angles. + + All angles are defined according to the right-hand rule. + + Args: + yaw: rotation around the z axis + pitch: rotation around the yʹ axis + roll: rotation around the xʺ axis + + Returns: + A rotation object constructed using the provided angles. + """ + check_angle('yaw', yaw) + check_angle('pitch', pitch) + check_angle('roll', roll) + + return Rotation.from_euler('ZYX', [yaw, pitch, roll], degrees=True) + + def translate(self, to: np.ndarray): + """Translate the array center to a new point. + + Translation does not change the centered positions or the internal coordinate system. + + Args: + to: 3D point, shape (3,) + """ + self.center = to + + def rotate(self, yaw: float = 0, pitch: float = 0, roll: float = 0): + """Apply rotation on the mic array. + + This rotates the centered microphone positions and the internal + coordinate system, it doesn't change the center of the array. + + All angles are defined according to the right-hand rule. + For example, this means that a positive pitch will result in a rotation from z + to x axis, which will result in a reduced elevation with respect to the global + horizontal plane. + + Args: + yaw: rotation around the z axis + pitch: rotation around the yʹ axis + roll: rotation around the xʺ axis + """ + # construct rotation using TB angles + rotation = self.get_rotation(yaw=yaw, pitch=pitch, roll=roll) + + # rotate centered positions + self.centered_positions = rotation.apply(self.centered_positions) + + # apply the same transformation on the internal coordinate system + self.internal_cs = rotation.apply(self.internal_cs) + + def new_rotated_array(self, yaw: float = 0, pitch: float = 0, roll: float = 0): + """Create a new array by rotating this array. + + Args: + yaw: rotation around the z axis + pitch: rotation around the yʹ axis + roll: rotation around the xʺ axis + + Returns: + A new ArrayGeometry object constructed using the provided angles. + """ + new_array = ArrayGeometry(mic_positions=self.positions, center=self.center, internal_cs=self.internal_cs) + new_array.rotate(yaw=yaw, pitch=pitch, roll=roll) + return new_array + + def spherical_relative_to_array( + self, point: np.ndarray, use_internal_cs: bool = True + ) -> Tuple[float, float, float]: + """Return spherical coordinates of a point relative to the internal coordinate system. + + Args: + point: 3D coordinate, shape (3,) + use_internal_cs: Calculate position relative to the internal coordinate system. + If `False`, the positions will be calculated relative to the + external coordinate system centered at `self.center`. + + Returns: + A tuple (distance, azimuth, elevation) relative to the mic array. + """ + rel_position = point - self.center + distance = np.linalg.norm(rel_position) + + if use_internal_cs: + # transform from the absolute coordinate system to the internal coordinate system + rel_position = np.matmul(self.internal_cs, rel_position) + + # get azimuth + azimuth = np.arctan2(rel_position[1], rel_position[0]) / np.pi * 180 + # get elevation + elevation = np.arcsin(rel_position[2] / distance) / np.pi * 180 + + return distance, azimuth, elevation + + def __str__(self): + with np.printoptions(precision=3, suppress=True): + desc = f"{type(self)}:\ncenter =\n{self.center}\ncentered positions =\n{self.centered_positions}\nradius = \n{self.radius:.3}\nabsolute positions =\n{self.positions}\ninternal coordinate system =\n{self.internal_cs}\n\n" + return desc + + def plot(self, elev=30, azim=-55, mic_size=25): + """Plot microphone positions. + + Args: + elev: elevation for the view of the plot + azim: azimuth for the view of the plot + mic_size: size of the microphone marker in the plot + """ + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + # show mic positions + for m in range(self.num_mics): + # show mic + ax.scatter( + self.positions[m, 0], + self.positions[m, 1], + self.positions[m, 2], + marker='o', + c='black', + s=mic_size, + depthshade=False, + ) + # add label + ax.text(self.positions[m, 0], self.positions[m, 1], self.positions[m, 2], str(m), c='red', zorder=10) + + # show the internal coordinate system + ax.quiver( + self.center[0], + self.center[1], + self.center[2], + self.internal_cs[:, 0], + self.internal_cs[:, 1], + self.internal_cs[:, 2], + length=self.radius, + label='internal cs', + normalize=False, + linestyle=':', + linewidth=1.0, + ) + for dim, label in enumerate(['x′', 'y′', 'z′']): + label_pos = self.center + self.radius * self.internal_cs[dim] + ax.text(label_pos[0], label_pos[1], label_pos[2], label, tuple(self.internal_cs[dim]), c='blue') + try: + # Unfortunately, equal aspect ratio has been added very recently to Axes3D + ax.set_aspect('equal') + except NotImplementedError: + logging.warning('Equal aspect ratio not supported by Axes3D') + # Set view + ax.view_init(elev=elev, azim=azim) + # Set reasonable limits for all axes, even for the case of an unequal aspect ratio + ax.set_xlim([self.center[0] - self.radius, self.center[0] + self.radius]) + ax.set_ylim([self.center[1] - self.radius, self.center[1] + self.radius]) + ax.set_zlim([self.center[2] - self.radius, self.center[2] + self.radius]) + + ax.set_xlabel('x/m') + ax.set_ylabel('y/m') + ax.set_zlabel('z/m') + ax.set_title('Microphone positions') + ax.legend() + plt.show() + + +def convert_placement_to_range( + placement: dict, room_dim: Iterable[float], object_radius: float = 0 +) -> List[List[float]]: + """Given a placement dictionary, return ranges for each dimension. + + Args: + placement: dictionary containing x, y, height, and min_to_wall + room_dim: dimensions of the room, shape (3,) + object_radius: radius of the object to be placed + + Returns + List with a range of values for each dimensions. + """ + if not np.all(np.array(room_dim) > 0): + raise ValueError(f'Room dimensions must be positive: {room_dim}') + + if object_radius < 0: + raise ValueError(f'Object radius must be non-negative: {object_radius}') + + placement_range = [None] * 3 + min_to_wall = placement.get('min_to_wall', 0) + + if min_to_wall < 0: + raise ValueError(f'Min distance to wall must be positive: {min_to_wall}') + + for idx, key in enumerate(['x', 'y', 'height']): + # Room dimension + dim = room_dim[idx] + # Construct the range + val = placement.get(key) + if val is None: + # No constrained specified on the coordinate of the mic center + min_val, max_val = 0, dim + elif np.isscalar(val): + min_val = max_val = val + else: + if len(val) != 2: + raise ValueError(f'Invalid value for placement for dim {idx}/{key}: {str(placement)}') + min_val, max_val = val + + # Make sure the array is not too close to a wall + min_val = max(min_val, min_to_wall + object_radius) + max_val = min(max_val, dim - min_to_wall - object_radius) + + if min_val > max_val or min(min_val, max_val) < 0: + raise ValueError(f'Invalid range dim {idx}/{key}: min={min_val}, max={max_val}') + + placement_range[idx] = [min_val, max_val] + + return placement_range + + +class RIRCorpusGenerator(object): + """Creates a corpus of RIRs based on a defined configuration of rooms and microphone array. + + RIRs are generated using `generate` method. + """ + + def __init__(self, cfg: DictConfig): + """ + Args: + cfg: dictionary with parameters of the simulation + """ + logging.info("Initialize RIRCorpusGenerator") + self._cfg = cfg + self.check_cfg() + + @property + def cfg(self): + """Property holding the internal config of the object. + + Note: + Changes to this config are not reflected in the state of the object. + Please create a new model with the updated config. + """ + return self._cfg + + @property + def sample_rate(self): + return self._cfg.sample_rate + + @cfg.setter + def cfg(self, cfg): + """Property holding the internal config of the object. + + Note: + Changes to this config are not reflected in the state of the object. + Please create a new model with the updated config. + """ + self._cfg = cfg + + def check_cfg(self): + """ + Checks provided configuration to ensure it has the minimal required + configuration the values are in a reasonable range. + """ + # sample rate + sample_rate = self.cfg.get('sample_rate') + if sample_rate is None: + raise ValueError('Sample rate not provided.') + elif sample_rate < 0: + raise ValueError(f'Sample rate must to be positive: {sample_rate}') + + # room configuration + room_cfg = self.cfg.get('room') + if room_cfg is None: + raise ValueError('Room configuration not provided') + + if room_cfg.get('num') is None: + raise ValueError('Number of rooms per subset not provided') + + if room_cfg.get('dim') is None: + raise ValueError('Room dimensions not provided') + + for idx, key in enumerate(['width', 'length', 'height']): + dim = room_cfg.dim.get(key) + + if dim is None: + # not provided + raise ValueError(f'Room {key} needs to be a scalar or a range, currently it is None') + elif np.isscalar(dim) and dim <= 0: + # fixed dimension + raise ValueError(f'A fixed dimension must be positive for {key}: {dim}') + elif len(dim) != 2 or not 0 < dim[0] < dim[1]: + # not a valid range + raise ValueError(f'Range must be specified with two positive increasing elements for {key}: {dim}') + + rt60 = room_cfg.get('rt60') + if rt60 is None: + # not provided + raise ValueError(f'RT60 needs to be a scalar or a range, currently it is None') + elif np.isscalar(rt60) and rt60 <= 0: + # fixed dimension + raise ValueError(f'RT60 must be positive: {rt60}') + elif len(rt60) != 2 or not 0 < rt60[0] < rt60[1]: + # not a valid range + raise ValueError(f'RT60 range must be specified with two positive increasing elements: {rt60}') + + # mic array + mic_cfg = self.cfg.get('mic_array') + if mic_cfg is None: + raise ValueError('Mic configuration not provided') + + if mic_cfg.get('positions') == 'random': + # Only num_mics and placement are required + mic_cfg_keys = ['num_mics', 'placement'] + else: + mic_cfg_keys = ['positions', 'placement', 'orientation'] + + for key in mic_cfg_keys: + if key not in mic_cfg: + raise ValueError(f'Mic array {key} not provided') + + # source + source_cfg = self.cfg.get('source') + if source_cfg is None: + raise ValueError('Source configuration not provided') + + if source_cfg.get('num') is None: + raise ValueError('Number of sources per room not provided') + elif source_cfg.num <= 0: + raise ValueError(f'Number of sources must be positive: {source_cfg.num}') + + if 'placement' not in source_cfg: + raise ValueError('Source placement dictionary not provided') + + # anechoic + if self.cfg.get('anechoic') is None: + raise ValueError(f'Anechoic configuratio not provided.') + + def generate_room_params(self) -> dict: + """Generate randomized room parameters based on the provided + configuration. + """ + # Prepare room sim parameters + if not PRA: + raise ImportError('pyroomacoustics is required for room simulation') + + room_cfg = self.cfg.room + + # Prepare rt60 + if room_cfg.rt60 is None: + raise ValueError(f'Room RT60 needs to be a scalar or a range, currently it is None') + + if np.isscalar(room_cfg.rt60): + assert room_cfg.rt60 > 0, f'RT60 should be positive: {room_cfg.rt60}' + rt60 = room_cfg.rt60 + elif len(room_cfg.rt60) == 2: + assert ( + 0 < room_cfg.rt60[0] <= room_cfg.rt60[1] + ), f'Expecting two non-decreasing values for RT60, received {room_cfg.rt60}' + rt60 = self.random.uniform(low=room_cfg.rt60[0], high=room_cfg.rt60[1]) + else: + raise ValueError(f'Unexpected value for RT60: {room_cfg.rt60}') + + # Generate a room with random dimensions + num_retries = self.cfg.get('num_retries', 20) + + for n in range(num_retries): + + # width, length, height + room_dim = np.zeros(3) + + # prepare dimensions + for idx, key in enumerate(['width', 'length', 'height']): + # get configured dimension + dim = room_cfg.dim[key] + + # set a value + if dim is None: + raise ValueError(f'Room {key} needs to be a scalar or a range, currently it is None') + elif np.isscalar(dim): + assert dim > 0, f'Dimension should be positive for {key}: {dim}' + room_dim[idx] = dim + elif len(dim) == 2: + assert 0 < dim[0] <= dim[1], f'Expecting two non-decreasing values for {key}, received {dim}' + # Reduce dimension if the previous attempt failed + room_dim[idx] = self.random.uniform(low=dim[0], high=dim[1] - n * (dim[1] - dim[0]) / num_retries) + else: + raise ValueError(f'Unexpected value for {key}: {dim}') + + try: + # Get parameters from size and RT60 + room_absorption, room_max_order = pra.inverse_sabine(rt60, room_dim) + break + except Exception as e: + logging.debug('Inverse sabine failed: %s', str(e)) + # Inverse sabine may fail if the room is too large for the selected RT60. + # Try again by generate a smaller room. + room_absorption = room_max_order = None + continue + + if room_absorption is None or room_max_order is None: + raise RuntimeError(f'Evaluation of parameters failed for RT60 {rt60}s and room size {room_dim}.') + + # Return the required values + room_params = { + 'dim': room_dim, + 'absorption': room_absorption, + 'max_order': room_max_order, + 'rt60_theoretical': rt60, + 'anechoic_absorption': self.cfg.anechoic.absorption, + 'anechoic_max_order': self.cfg.anechoic.max_order, + 'sample_rate': self.cfg.sample_rate, + } + return room_params + + def generate_array(self, room_dim: Iterable[float]) -> ArrayGeometry: + """Generate array placement for the current room and config. + + Args: + room_dim: dimensions of the room, [width, length, height] + + Returns: + Randomly placed microphone array. + """ + mic_cfg = self.cfg.mic_array + + if mic_cfg.positions == 'random': + # Create a radom set of microphones + num_mics = mic_cfg.num_mics + mic_positions = [] + + # Each microphone is placed individually + placement_range = convert_placement_to_range( + placement=mic_cfg.placement, room_dim=room_dim, object_radius=0 + ) + + # Randomize mic placement + for m in range(num_mics): + position_m = [None] * 3 + for idx in range(3): + position_m[idx] = self.random.uniform(low=placement_range[idx][0], high=placement_range[idx][1]) + mic_positions.append(position_m) + + mic_array = ArrayGeometry(mic_positions) + + else: + mic_array = ArrayGeometry(mic_cfg.positions) + + # Randomize center placement + center = np.zeros(3) + placement_range = convert_placement_to_range( + placement=mic_cfg.placement, room_dim=room_dim, object_radius=mic_array.radius + ) + + for idx in range(len(center)): + center[idx] = self.random.uniform(low=placement_range[idx][0], high=placement_range[idx][1]) + + # Place the array at the configured center point + mic_array.translate(to=center) + + # Randomize orientation + orientation = dict() + for key in ['yaw', 'roll', 'pitch']: + # angle for current orientation + angle = mic_cfg.orientation[key] + + if angle is None: + raise ValueError(f'Mic array {key} should be a scalar or a range, currently it is set to None.') + + # check it's within the expected range + check_angle(key, angle) + + if np.isscalar(angle): + orientation[key] = angle + elif len(angle) == 2: + assert angle[0] <= angle[1], f"Expecting two non-decreasing values for {key}, received {angle}" + # generate integer values, for easier bucketing, if necessary + orientation[key] = self.random.uniform(low=angle[0], high=angle[1]) + else: + raise ValueError(f'Unexpected value for orientation {key}: {angle}') + + # Rotate the array to match the selected orientation + mic_array.rotate(**orientation) + + return mic_array + + def generate_source_position(self, room_dim: Iterable[float]) -> List[List[float]]: + """Generate position for all sources in a room. + + Args: + room_dim: dimensions of a 3D shoebox room + + Returns: + List of source positions, with each position characterized with a 3D coordinate + """ + source_cfg = self.cfg.source + placement_range = convert_placement_to_range(placement=source_cfg.placement, room_dim=room_dim) + source_position = [] + + for n in range(source_cfg.num): + # generate a random point withing the range + s_pos = [None] * 3 + for idx in range(len(s_pos)): + s_pos[idx] = self.random.uniform(low=placement_range[idx][0], high=placement_range[idx][1]) + source_position.append(s_pos) + + return source_position + + def generate(self): + """Generate RIR corpus. + + This method will prepare randomized examples based on the current configuration, + run room simulations and save results to output_dir. + """ + logging.info("Generate RIR corpus") + + # Initialize + self.random = default_rng(seed=self.cfg.random_seed) + + # Prepare output dir + output_dir = self.cfg.output_dir + if output_dir.endswith('.yaml'): + output_dir = output_dir[:-5] + + # Create absolute path + logging.info('Output dir set to: %s', output_dir) + + # Generate all cases + for subset, num_rooms in self.cfg.room.num.items(): + + output_dir_subset = os.path.join(output_dir, subset) + examples = [] + + if not os.path.exists(output_dir_subset): + logging.info('Creating output directory: %s', output_dir_subset) + os.makedirs(output_dir_subset) + elif os.path.isdir(output_dir_subset) and len(os.listdir(output_dir_subset)) > 0: + raise RuntimeError(f'Output directory {output_dir_subset} is not empty.') + + # Generate examples + for n_room in range(num_rooms): + + # room info + room_params = self.generate_room_params() + + # array placement + mic_array = self.generate_array(room_params['dim']) + + # source placement + source_position = self.generate_source_position(room_params['dim']) + + # file name for the file + room_filepath = os.path.join(output_dir_subset, f'{subset}_room_{n_room:06d}.h5') + + # prepare example + example = { + 'room_params': room_params, + 'mic_array': mic_array, + 'source_position': source_position, + 'room_filepath': room_filepath, + } + examples.append(example) + + # Simulation + if (num_workers := self.cfg.get('num_workers')) is None: + num_workers = os.cpu_count() - 1 + + if num_workers > 1: + logging.info(f'Simulate using {num_workers} workers') + with multiprocessing.Pool(processes=num_workers) as pool: + metadata = list(tqdm(pool.imap(simulate_room_kwargs, examples), total=len(examples))) + + else: + logging.info('Simulate using a single worker') + metadata = [] + for example in tqdm(examples, total=len(examples)): + metadata.append(simulate_room(**example)) + + # Save manifest + manifest_filepath = os.path.join(output_dir, f'{subset}_manifest.json') + + if os.path.exists(manifest_filepath) and os.path.isfile(manifest_filepath): + raise RuntimeError(f'Manifest config file exists: {manifest_filepath}') + + # Make all paths in the manifest relative to the output dir + for data in metadata: + data['room_filepath'] = os.path.relpath(data['room_filepath'], start=output_dir) + + write_manifest(manifest_filepath, metadata) + + # Generate plots with information about generated data + plot_filepath = os.path.join(output_dir, f'{subset}_info.png') + + if os.path.exists(plot_filepath) and os.path.isfile(plot_filepath): + raise RuntimeError(f'Plot file exists: {plot_filepath}') + + plot_rir_manifest_info(manifest_filepath, plot_filepath=plot_filepath) + + # Save used configuration for reference + config_filepath = os.path.join(output_dir, 'config.yaml') + if os.path.exists(config_filepath) and os.path.isfile(config_filepath): + raise RuntimeError(f'Output config file exists: {config_filepath}') + + OmegaConf.save(self.cfg, config_filepath, resolve=True) + + +def simulate_room_kwargs(kwargs: dict) -> dict: + """Wrapper around `simulate_room` to handle kwargs. + + `pool.map(simulate_room_kwargs, examples)` would be + equivalent to `pool.starstarmap(simulate_room, examples)` + if `starstarmap` would exist. + + Args: + kwargs: kwargs that are forwarded to `simulate_room` + + Returns: + Dictionary with metadata, see `simulate_room` + """ + return simulate_room(**kwargs) + + +def simulate_room( + room_params: dict, mic_array: ArrayGeometry, source_position: Iterable[Iterable[float]], room_filepath: str, +) -> dict: + """Simulate room + + Args: + room_params: parameters of the room to be simulated + mic_array: defines positions of the microphones + source_positions: positions for all sources to be simulated + room_filepath: results are saved to this path + + Returns: + Dictionary with metadata based on simulation setup + and simulation results. Used to create the corresponding + manifest file. + """ + # room with the selected parameters + room_sim = pra.ShoeBox( + room_params['dim'], + fs=room_params['sample_rate'], + materials=pra.Material(room_params['absorption']), + max_order=room_params['max_order'], + ) + + # same geometry for generating anechoic responses + room_anechoic = pra.ShoeBox( + room_params['dim'], + fs=room_params['sample_rate'], + materials=pra.Material(room_params['anechoic_absorption']), + max_order=room_params['anechoic_max_order'], + ) + + # Compute RIRs + for room in [room_sim, room_anechoic]: + # place the array + room.add_microphone_array(mic_array.positions.T) + + # place the sources + for s_pos in source_position: + room.add_source(s_pos) + + # generate RIRs + room.compute_rir() + + # Get metadata for sources + source_distance = [] + source_azimuth = [] + source_elevation = [] + for s_pos in source_position: + distance, azimuth, elevation = mic_array.spherical_relative_to_array(s_pos) + source_distance.append(distance) + source_azimuth.append(azimuth) + source_elevation.append(elevation) + + # RIRs + rir_dataset = { + 'rir': convert_rir_to_multichannel(room_sim.rir), + 'anechoic': convert_rir_to_multichannel(room_anechoic.rir), + } + + # Prepare metadata dict and return + metadata = { + 'room_filepath': room_filepath, + 'sample_rate': room_params['sample_rate'], + 'dim': room_params['dim'], + 'rir_absorption': room_params['absorption'], + 'rir_max_order': room_params['max_order'], + 'rir_rt60_theory': room_sim.rt60_theory(), + 'rir_rt60_measured': room_sim.measure_rt60().mean(axis=0), # average across mics for each source + 'anechoic_rt60_theory': room_anechoic.rt60_theory(), + 'anechoic_rt60_measured': room_anechoic.measure_rt60().mean(axis=0), # average across mics for each source + 'anechoic_absorption': room_params['anechoic_absorption'], + 'anechoic_max_order': room_params['anechoic_max_order'], + 'mic_positions': mic_array.positions, + 'mic_center': mic_array.center, + 'source_position': source_position, + 'source_distance': source_distance, + 'source_azimuth': source_azimuth, + 'source_elevation': source_elevation, + 'num_sources': len(source_position), + } + + # Save simulated RIR + save_rir_simulation(room_filepath, rir_dataset, metadata) + + return convert_numpy_to_serializable(metadata) + + +def save_rir_simulation(filepath: str, rir_dataset: Dict[str, List[np.array]], metadata: dict): + """Save simulated RIRs and metadata. + + Args: + filepath: Path to the file where the data will be saved. + rir_dataset: Dictionary with RIR data. Each item is a set of multi-channel RIRs. + metadata: Dictionary with related metadata. + """ + if os.path.exists(filepath): + raise RuntimeError(f'Output file exists: {room_filepath}') + + num_sources = metadata['num_sources'] + + with h5py.File(filepath, 'w') as h5f: + # Save RIRs, each RIR set in a separate group + for rir_key, rir_value in rir_dataset.items(): + if len(rir_value) != num_sources: + raise ValueError( + f'Each RIR dataset should have exactly {num_sources} elements. Current RIR {key} has {len(rir_value)} elements' + ) + + rir_group = h5f.create_group(rir_key) + + # RIRs for different sources are saved under [group]['idx'] + for idx, rir in enumerate(rir_value): + rir_group.create_dataset(f'{idx}', data=rir_value[idx]) + + # Save metadata + metadata_group = h5f.create_group('metadata') + for key, value in metadata.items(): + metadata_group.create_dataset(key, data=value) + + +def load_rir_simulation(filepath: str, source: int = 0, rir_key: str = 'rir') -> Tuple[np.ndarray, float]: + """Load simulated RIRs and metadata. + + Args: + filepath: Path to simulated RIR data + source: Index of a source. + rir_key: String to denote which RIR to load, if there are multiple available. + + Returns: + Multichannel RIR as ndarray with shape (num_samples, num_channels) and scalar sample rate. + """ + with h5py.File(filepath, 'r') as h5f: + # Load RIR + rir = h5f[rir_key][f'{source}'][:] + + # Load metadata + sample_rate = h5f['metadata']['sample_rate'][()] + + return rir, sample_rate + + +def convert_numpy_to_serializable(data: Union[dict, float, np.ndarray]) -> Union[dict, float, np.ndarray]: + """Convert all numpy estries to list. + Can be used to preprocess data before writing to a JSON file. + + Args: + data: Dictionary, array or scalar. + + Returns: + The same structure, but converted to list if + the input is np.ndarray, so `data` can be seralized. + """ + if isinstance(data, dict): + for key, val in data.items(): + data[key] = convert_numpy_to_serializable(val) + elif isinstance(data, list): + data = [convert_numpy_to_serializable(d) for d in data] + elif isinstance(data, np.ndarray): + data = data.tolist() + elif isinstance(data, np.integer): + data = int(data) + elif isinstance(data, np.floating): + data = float(data) + elif isinstance(data, np.generic): + data = data.item() + + return data + + +def convert_rir_to_multichannel(rir: List[List[np.ndarray]]) -> List[np.ndarray]: + """Convert RIR to a list of arrays. + + Args: + rir: list of lists, each element is a single-channel RIR + + Returns: + List of multichannel RIRs + """ + num_mics = len(rir) + num_sources = len(rir[0]) + + mc_rir = [None] * num_sources + + for n_source in range(num_sources): + rir_len = [len(rir[m][n_source]) for m in range(num_mics)] + max_len = max(rir_len) + mc_rir[n_source] = np.zeros((max_len, num_mics)) + for n_mic, len_mic in enumerate(rir_len): + mc_rir[n_source][:len_mic, n_mic] = rir[n_mic][n_source] + + return mc_rir + + +def plot_rir_manifest_info(filepath: str, plot_filepath: str = None): + """Plot distribution of parameters from manifest file. + + Args: + filepath: path to a RIR corpus manifest file + plot_filepath: path to save the plot at + """ + metadata = read_manifest(filepath) + + # source placement + source_distance = [] + source_azimuth = [] + source_elevation = [] + source_height = [] + + # room config + rir_rt60_theory = [] + rir_rt60_measured = [] + anechoic_rt60_theory = [] + anechoic_rt60_measured = [] + + # get the required data + for data in metadata: + # source config + source_distance += data['source_distance'] + source_azimuth += data['source_azimuth'] + source_elevation += data['source_elevation'] + source_height += [s_pos[2] for s_pos in data['source_position']] + + # room config + rir_rt60_theory.append(data['rir_rt60_theory']) + rir_rt60_measured += data['rir_rt60_measured'] + anechoic_rt60_theory.append(data['anechoic_rt60_theory']) + anechoic_rt60_measured += data['anechoic_rt60_measured'] + + # plot + plt.figure(figsize=(12, 6)) + + plt.subplot(2, 4, 1) + plt.hist(source_distance, label='distance') + plt.xlabel('distance / m') + plt.ylabel('# examples') + plt.title('Source-to-array center distance') + + plt.subplot(2, 4, 2) + plt.hist(source_azimuth, label='azimuth') + plt.xlabel('azimuth / deg') + plt.ylabel('# examples') + plt.title('Source-to-array center azimuth') + + plt.subplot(2, 4, 3) + plt.hist(source_elevation, label='elevation') + plt.xlabel('elevation / deg') + plt.ylabel('# examples') + plt.title('Source-to-array center elevation') + + plt.subplot(2, 4, 4) + plt.hist(source_height, label='source height') + plt.xlabel('height / m') + plt.ylabel('# examples') + plt.title('Source height') + + plt.subplot(2, 4, 5) + plt.hist(rir_rt60_theory, label='theory') + plt.xlabel('RT60 / s') + plt.ylabel('# examples') + plt.title('RT60 theory') + + plt.subplot(2, 4, 6) + plt.hist(rir_rt60_measured, label='measured') + plt.xlabel('RT60 / s') + plt.ylabel('# examples') + plt.title('RT60 measured') + + plt.subplot(2, 4, 7) + plt.hist(anechoic_rt60_theory, label='theory') + plt.xlabel('RT60 / s') + plt.ylabel('# examples') + plt.title('RT60 theory (anechoic)') + + plt.subplot(2, 4, 8) + plt.hist(anechoic_rt60_measured, label='measured') + plt.xlabel('RT60 / s') + plt.ylabel('# examples') + plt.title('RT60 measured (anechoic)') + + for n in range(8): + plt.subplot(2, 4, n + 1) + plt.grid() + plt.legend(loc='lower left') + + plt.tight_layout() + + if plot_filepath is not None: + plt.savefig(plot_filepath) + plt.close() + logging.info('Plot saved at %s', plot_filepath) + + +class RIRMixGenerator(object): + """Creates a dataset of mixed signals at the microphone + by combining target speech, background noise and interference. + + Correspnding signals are are generated and saved + using the `generate` method. + + Input configuration is expexted to have the following structure + ``` + sample_rate: sample rate used for simulation + room: + subset: manifest for RIR data + target: + subset: manifest for target source data + noise: + subset: manifest for noise data + interference: + subset: manifest for interference data + interference_probability: probability that interference is present + max_num_interferers: max number of interferers, randomly selected between 0 and max + mix: + subset: + num: number of examples to generate + rsnr: range of RSNR + rsir: range of RSIR + ref_mic: reference microphone + ref_mic_rms: desired RMS at ref_mic + ``` + """ + + def __init__(self, cfg: DictConfig): + """ + Instantiate a RIRMixGenerator object. + + Args: + cfg: generator configuration defining data for room, + target signal, noise, interference and mixture + """ + logging.info("Initialize RIRMixGenerator") + self._cfg = cfg + self.check_cfg() + + self.subsets = self.cfg.room.keys() + logging.info('Initialized with %d subsets: %s', len(self.subsets), str(self.subsets)) + + # load manifests + self.metadata = dict() + for subset in self.subsets: + subset_data = dict() + + logging.info('Loading data for %s', subset) + for key in ['room', 'target', 'noise', 'interference']: + try: + subset_data[key] = read_manifest(self.cfg[key][subset]) + logging.info('\t%-*s: \t%d files', 15, key, len(subset_data[key])) + except Exception as e: + subset_data[key] = None + logging.info('\t%-*s: \t0 files', 15, key) + logging.warning('\t\tManifest data not loaded. Exception: %s', str(e)) + + self.metadata[subset] = subset_data + + logging.info('Loaded all manifests') + + self.num_retries = self.cfg.get('num_retries', 5) + + @property + def cfg(self): + """Property holding the internal config of the object. + + Note: + Changes to this config are not reflected in the state of the object. + Please create a new model with the updated config. + """ + return self._cfg + + @property + def sample_rate(self): + return self._cfg.sample_rate + + @cfg.setter + def cfg(self, cfg): + """Property holding the internal config of the object. + + Note: + Changes to this config are not reflected in the state of the object. + Please create a new model with the updated config. + """ + self._cfg = cfg + + def check_cfg(self): + """ + Checks provided configuration to ensure it has the minimal required + configuration the values are in a reasonable range. + """ + # sample rate + sample_rate = self.cfg.get('sample_rate') + if sample_rate is None: + raise ValueError('Sample rate not provided.') + elif sample_rate < 0: + raise ValueError(f'Sample rate must be positive: {sample_rate}') + + # room configuration + room_cfg = self.cfg.get('room') + if not room_cfg: + raise ValueError( + 'Room configuration not provided. Expecting RIR manifests in format {subset: path_to_manifest}' + ) + + # target configuration + target_cfg = self.cfg.get('target') + if not target_cfg: + raise ValueError( + 'Target configuration not provided. Expecting audio manifests in format {subset: path_to_manifest}' + ) + + for key in ['azimuth', 'elevation', 'distance']: + value = target_cfg.get(key) + + if value is None or np.isscalar(value): + # no constraint or a fixed dimension is ok + pass + elif len(value) != 2 or not value[0] < value[1]: + # not a valid range + raise ValueError(f'Range must be specified with two positive increasing elements for {key}: {value}') + + # noise configuration + noise_cfg = self.cfg.get('noise') + if not noise_cfg: + raise ValueError( + 'Noise configuration not provided. Expecting audio manifests in format {subset: path_to_manifest}' + ) + + # interference configuration + interference_cfg = self.cfg.get('interference') + if not interference_cfg: + logging.info('Interference configuration not provided.') + else: + interference_probability = interference_cfg.get('interference_probability', 0) + max_num_interferers = interference_cfg.get('max_num_interferers', 0) + min_azimuth_to_target = interference_cfg.get('min_azimuth_to_target', 0) + if interference_probability is not None: + if interference_probability < 0: + raise ValueError( + f'Interference probability must be non-negative. Current value: {interference_prob}' + ) + elif interference_probability > 0: + assert ( + max_num_interferers is not None and max_num_interferers > 0 + ), f'Max number of interferers must be positive. Current value: {max_num_interferers}' + assert ( + min_azimuth_to_target is not None and min_azimuth_to_target >= 0 + ), f'Min azimuth to target must be non-negative' + + # mix configuration + mix_cfg = self.cfg.get('mix') + if not mix_cfg: + raise ValueError('Mix configuration not provided. Expecting configuration for each subset.') + if 'ref_mic' not in mix_cfg: + raise ValueError('Reference microphone not defined.') + if 'ref_mic_rms' not in mix_cfg: + raise ValueError('Reference microphone RMS not defined.') + + def generate_target(self, subset: str) -> dict: + """ + Prepare a dictionary with target configuration. + + The output dictionary contains the following information + ``` + room_index: index of the selected room from the RIR corpus + room_filepath: path to the room simulation file + source: index of the selected source for the target + rt60: reverberation time of the selected room + num_mics: number of microphones + azimuth: azimuth of the target source, relative to the microphone array + elevation: elevation of the target source, relative to the microphone array + distance: distance of the target source, relative to the microphone array + audio_filepath: path to the audio file for the target source + text: text for the target source audio signal, if available + duration: duration of the target source audio signal + ``` + + Args: + subset: string denoting a subset which will be used to selected target + audio and room parameters. + + Returns: + Dictionary with target configuration, including room, source index, and audio information. + """ + # Utility function + def select_target_source(room_metadata, room_indices): + """Find a room and a source that satisfies the constraints. + """ + for room_index in room_indices: + # Select room + room_data = room_metadata[room_index] + + # Candidate sources + sources = self.random.choice(room_data['num_sources'], size=self.num_retries, replace=False) + + # Select target source in this room + for source in sources: + # Check constraints + constraints_met = [] + for constraint in ['azimuth', 'elevation', 'distance']: + if self.cfg.target.get(constraint) is not None: + # Check that the selected source is in the range + source_value = room_data[f'source_{constraint}'][source] + if self.cfg.target[constraint][0] <= source_value <= self.cfg.target[constraint][1]: + constraints_met.append(True) + else: + constraints_met.append(False) + # No need to check the remaining constraints + break + + # Check if a feasible source is found + if all(constraints_met): + # A feasible source has been found + return source, room_index + + return None, None + + # Prepare room & source position + room_metadata = self.metadata[subset]['room'] + room_indices = self.random.choice(len(room_metadata), size=self.num_retries, replace=False) + source, room_index = select_target_source(room_metadata, room_indices) + + if source is None: + raise RuntimeError(f'Could not find a feasible source given target constraints {self.cfg.target}') + + room_data = room_metadata[room_index] + + # Optional: select subset of channels + num_available_mics = len(room_data['mic_positions']) + if 'mic_array' in self.cfg: + num_mics = self.cfg.mic_array['num_mics'] + mic_selection = self.cfg.mic_array['selection'] + + if mic_selection == 'random': + logging.debug('Randomly selecting %d mics', num_mics) + selected_mics = self.random.choice(num_available_mics, size=num_mics, replace=False) + elif isinstance(mic_selection, Iterable): + logging.debug('Using explicitly selected mics: %s', str(mic_selection)) + assert ( + 0 <= min(mic_selection) < num_available_mics + ), f'Expecting mic_selection in range [0,{num_available_mics}), current value: {mic_selection}' + selected_mics = np.array(mic_selection) + else: + raise ValueError(f'Unexpected value for mic_selection: {mic_selection}') + else: + logging.debug('Using all %d available mics', num_available_mics) + num_mics = num_available_mics + selected_mics = np.arange(num_mics) + + # Double-check the number of mics is as expected + assert ( + len(selected_mics) == num_mics + ), f'Expecting {num_mics} mics, but received {len(selected_mics)} mics: {selected_mics}' + logging.debug('Selected mics: %s', str(selected_mics)) + + # Calculate distance from the source to each microphone + mic_positions = np.array(room_data['mic_positions'])[selected_mics] + source_position = np.array(room_data['source_position'][source]) + distance_source_to_mic = np.linalg.norm(mic_positions - source_position, axis=1) + + # Handle relative paths + room_filepath = room_data['room_filepath'] + if not os.path.isabs(room_filepath): + manifest_dir = os.path.dirname(self.cfg.room[subset]) + room_filepath = os.path.join(manifest_dir, room_filepath) + + target_cfg = { + 'room_index': int(room_index), + 'room_filepath': room_filepath, + 'source': source, + 'rt60': room_data['rir_rt60_measured'][source], + 'selected_mics': selected_mics.tolist(), + # Positions + 'source_position': source_position.tolist(), + 'mic_positions': mic_positions.tolist(), + # Relative to center of the array + 'azimuth': room_data['source_azimuth'][source], + 'elevation': room_data['source_elevation'][source], + 'distance': room_data['source_distance'][source], + # Relative to mics + 'distance_source_to_mic': distance_source_to_mic, + } + + return target_cfg + + def generate_interference(self, subset: str, target_cfg: dict) -> List[dict]: + """ + Prepare a list of dictionaries with interference configuration. + + Args: + subset: string denoting a subset which will be used to select interference audio. + target_cfg: dictionary with target configuration. This is used to determine + the minimal required duration for the noise signal. + + Returns: + List of dictionary with interference configuration, including source index and audio information + for one or more interference sources. + """ + if (interference_metadata := self.metadata[subset]['interference']) is None: + # No interference to be configured + return None + + # Configure interfering sources + max_num_sources = self.cfg.interference.get('max_num_interferers', 0) + interference_probability = self.cfg.interference.get('interference_probability', 0) + + if ( + max_num_sources >= 1 + and interference_probability > 0 + and self.random.uniform(low=0.0, high=1.0) < interference_probability + ): + # interference present + num_interferers = self.random.integers(low=1, high=max_num_sources + 1) + else: + # interference not present + return None + + # Room setup: same room as target + room_index = target_cfg['room_index'] + room_data = self.metadata[subset]['room'][room_index] + feasible_sources = list(range(room_data['num_sources'])) + # target source is not eligible + feasible_sources.remove(target_cfg['source']) + + # Constraints for interfering sources + min_azimuth_to_target = self.cfg.interference.get('min_azimuth_to_target', 0) + + # Prepare interference configuration + interference_cfg = [] + for n in range(num_interferers): + + # Select a source + source = None + while len(feasible_sources) > 0 and source is None: + + # Select a potential source for the target + source = self.random.choice(feasible_sources) + feasible_sources.remove(source) + + # Check azimuth separation + if min_azimuth_to_target > 0: + source_azimuth = room_data['source_azimuth'][source] + azimuth_diff = wrap_to_180(source_azimuth - target_cfg['azimuth']) + if abs(azimuth_diff) < min_azimuth_to_target: + # Try again + source = None + continue + + if source is None: + logging.warning('Could not select a feasible interference source %d of %s', n, num_interferers) + + # Return what we have for now or None + return interference_cfg if interference_cfg else None + + # Current source setup + interfering_source = { + 'source': source, + 'selected_mics': target_cfg['selected_mics'], + 'position': room_data['source_position'][source], + 'azimuth': room_data['source_azimuth'][source], + 'elevation': room_data['source_elevation'][source], + 'distance': room_data['source_distance'][source], + } + + # Done with interference for this source + interference_cfg.append(interfering_source) + + return interference_cfg + + def generate_mix(self, subset: str, target_cfg: dict) -> dict: + """Generate scaling parameters for mixing + the target speech at the microphone, background noise + and interference signal at the microphone. + + The output dictionary contains the following information + ``` + rsnr: reverberant signal-to-noise ratio + rsir: reverberant signal-to-interference ratio + ref_mic: reference microphone for calculating the metrics + ref_mic_rms: RMS of the signal at the reference microphone + ``` + + Args: + subset: string denoting the subset of configuration + target_cfg: dictionary with target configuration + + Returns: + Dictionary containing configured RSNR, RSIR, ref_mic + and RMS on ref_mic. + """ + mix_cfg = dict() + + for key in ['rsnr', 'rsir', 'ref_mic', 'ref_mic_rms', 'min_duration']: + if key in self.cfg.mix[subset]: + # Take the value from subset config + value = self.cfg.mix[subset].get(key) + else: + # Take the global value + value = self.cfg.mix.get(key) + + if value is None: + mix_cfg[key] = None + elif np.isscalar(value): + mix_cfg[key] = value + elif len(value) == 2: + # Select from the given range, including the upper bound + mix_cfg[key] = self.random.integers(low=value[0], high=value[1] + 1) + else: + # Select one of the multiple values + mix_cfg[key] = self.random.choice(value) + + if mix_cfg['ref_mic'] == 'closest': + # Select the closest mic as the reference + mix_cfg['ref_mic'] = np.argmin(target_cfg['distance_source_to_mic']) + + # Configuration for saving individual components + mix_cfg['save'] = OmegaConf.to_object(self.cfg.mix['save']) if 'save' in self.cfg.mix else {} + + return mix_cfg + + def generate(self): + """Generate a corpus of microphone signals by mixing target, background noise + and interference signals. + + This method will prepare randomized examples based on the current configuration, + run simulations and save results to output_dir. + """ + logging.info('Generate mixed signals') + + # Initialize + self.random = default_rng(seed=self.cfg.random_seed) + + # Prepare output dir + output_dir = self.cfg.output_dir + if output_dir.endswith('.yaml'): + output_dir = output_dir[:-5] + + # Create absolute path + logging.info('Output dir set to: %s', output_dir) + + # Generate all cases + for subset in self.subsets: + + output_dir_subset = os.path.join(output_dir, subset) + examples = [] + + if not os.path.exists(output_dir_subset): + logging.info('Creating output directory: %s', output_dir_subset) + os.makedirs(output_dir_subset) + elif os.path.isdir(output_dir_subset) and len(os.listdir(output_dir_subset)) > 0: + raise RuntimeError(f'Output directory {output_dir_subset} is not empty.') + + num_examples = self.cfg.mix[subset].num + logging.info('Preparing %d examples for subset %s', num_examples, subset) + + # Generate examples + for n_example in tqdm(range(num_examples), total=num_examples, desc=f'Preparing {subset}'): + # prepare configuration + target_cfg = self.generate_target(subset) + interference_cfg = self.generate_interference(subset, target_cfg) + mix_cfg = self.generate_mix(subset, target_cfg) + + # base file name + base_output_filepath = os.path.join(output_dir_subset, f'{subset}_example_{n_example:09d}') + + # prepare example + example = { + 'sample_rate': self.sample_rate, + 'target_cfg': target_cfg, + 'interference_cfg': interference_cfg, + 'mix_cfg': mix_cfg, + 'base_output_filepath': base_output_filepath, + } + + examples.append(example) + + # Audio data + audio_metadata = { + 'target': self.metadata[subset]['target'], + 'target_dir': os.path.dirname(self.cfg.target[subset]), # manifest_dir + 'noise': self.metadata[subset]['noise'], + 'noise_dir': os.path.dirname(self.cfg.noise[subset]), # manifest_dir + } + + if interference_cfg is not None: + audio_metadata.update( + { + 'interference': self.metadata[subset]['interference'], + 'interference_dir': os.path.dirname(self.cfg.interference[subset]), # manifest_dir + } + ) + + # Simulation + if (num_workers := self.cfg.get('num_workers')) is None: + num_workers = os.cpu_count() - 1 + + if num_workers is not None and num_workers > 1: + logging.info(f'Simulate using {num_workers} workers') + examples_and_audio_metadata = zip(examples, itertools.repeat(audio_metadata, len(examples))) + with multiprocessing.Pool(processes=num_workers) as pool: + metadata = list( + tqdm( + pool.imap(simulate_room_mix_helper, examples_and_audio_metadata), + total=len(examples), + desc=f'Simulating {subset}', + ) + ) + else: + logging.info('Simulate using a single worker') + metadata = [] + for example in tqdm(examples, total=len(examples), desc=f'Simulating {subset}'): + metadata.append(simulate_room_mix(**example, audio_metadata=audio_metadata)) + + # Save manifest + manifest_filepath = os.path.join(output_dir, f'{os.path.basename(output_dir)}_{subset}.json') + + if os.path.exists(manifest_filepath) and os.path.isfile(manifest_filepath): + raise RuntimeError(f'Manifest config file exists: {manifest_filepath}') + + # Make all paths in the manifest relative to the output dir + for data in tqdm(metadata, total=len(metadata), desc=f'Making filepaths relative {subset}'): + for key, val in data.items(): + if key.endswith('_filepath') and val is not None: + data[key] = os.path.relpath(val, start=output_dir) + + write_manifest(manifest_filepath, metadata) + + # Generate plots with information about generated data + plot_filepath = os.path.join(output_dir, f'{os.path.basename(output_dir)}_{subset}_info.png') + + if os.path.exists(plot_filepath) and os.path.isfile(plot_filepath): + raise RuntimeError(f'Plot file exists: {plot_filepath}') + + plot_mix_manifest_info(manifest_filepath, plot_filepath=plot_filepath) + + # Save used configuration for reference + config_filepath = os.path.join(output_dir, 'config.yaml') + if os.path.exists(config_filepath) and os.path.isfile(config_filepath): + raise RuntimeError(f'Output config file exists: {config_filepath}') + + OmegaConf.save(self.cfg, config_filepath, resolve=True) + + +def convolve_rir(signal: np.ndarray, rir: np.ndarray) -> np.ndarray: + """Convolve signal with a possibly multichannel IR in rir, i.e., + calculate the following for each channel m: + + signal_m = rir_m \ast signal + + Args: + signal: single-channel signal (samples,) + rir: single- or multi-channel IR, (samples,) or (samples, channels) + + Returns: + out: same length as signal, same number of channels as rir, shape (samples, channels) + """ + num_samples = len(signal) + if rir.ndim == 1: + # convolve and trim to length + out = convolve(signal, rir)[:num_samples] + elif rir.ndim == 2: + num_channels = rir.shape[1] + out = np.zeros((num_samples, num_channels)) + for m in range(num_channels): + out[:, m] = convolve(signal, rir[:, m])[:num_samples] + + else: + raise RuntimeError(f'RIR with {rir.ndim} not supported') + + return out + + +def calculate_drr(rir: np.ndarray, sample_rate: float, n_direct: List[int], n_0_ms=2.5) -> List[float]: + """Calculate direct-to-reverberant ratio (DRR) from the measured RIR. + + Calculation is done as in eq. (3) from [1]. + + Args: + rir: room impulse response, shape (num_samples, num_channels) + sample_rate: sample rate for the impulse response + n_direct: direct path delay + n_0_ms: window around n_direct for calculating the direct path energy + + Returns: + Calculated DRR for each channel of the input RIR. + + References: + [1] Eaton et al, The ACE challenge: Corpus description and performance evaluation, WASPAA 2015 + """ + # Define a window around the direct path delay + n_0 = int(n_0_ms * sample_rate / 1000) + + len_rir, num_channels = rir.shape + drr = [None] * num_channels + for m in range(num_channels): + + # Window around the direct path + dir_start = max(n_direct[m] - n_0, 0) + dir_end = n_direct[m] + n_0 + + # Power of the direct component + pow_dir = np.sum(np.abs(rir[dir_start:dir_end, m]) ** 2) / len_rir + + # Power of the reverberant component + pow_reverberant = (np.sum(np.abs(rir[0:dir_start, m]) ** 2) + np.sum(np.abs(rir[dir_end:, m]) ** 2)) / len_rir + + # DRR in dB + drr[m] = pow2db(pow_dir / pow_reverberant) + + return drr + + +def normalize_max(x: np.ndarray, max_db: float = 0, eps: float = 1e-16) -> np.ndarray: + """Normalize max input value to max_db full scale (±1). + + Args: + x: input signal + max_db: desired max magnitude compared to full scale + eps: small regularization constant + + Returns: + Normalized signal with max absolute value max_db. + """ + max_val = db2mag(max_db) + return max_val * x / (np.max(np.abs(x)) + eps) + + +def simultaneously_active_rms( + x: np.ndarray, + y: np.ndarray, + sample_rate: float, + rms_threshold_db: float = -60, + window_len_ms: float = 200, + min_active_duration: float = 0.5, +) -> Tuple[float, float]: + """Calculate RMS over segments where both input signals are active. + + Args: + x: first input signal + y: second input signal + sample_rate: sample rate for input signals in Hz + rms_threshold_db: threshold for determining activity of the signal, relative + to max absolute value + window_len_ms: window length in milliseconds, used for calculating segmental RMS + min_active_duration: minimal duration of the active segments + + Returns: + RMS value over active segments for x and y. + """ + if len(x) != len(y): + raise RuntimeError(f'Expecting signals of same length: len(x)={len(x)}, len(y)={len(y)}') + window_len = int(window_len_ms * sample_rate / 1000) + rms_threshold = db2mag(rms_threshold_db) # linear scale + + x_normalized = normalize_max(x) + y_normalized = normalize_max(y) + + x_active_power = y_active_power = active_len = 0 + for start in range(0, len(x) - window_len, window_len): + window = slice(start, start + window_len) + + # check activity on the scaled signal + x_window_rms = rms(x_normalized[window]) + y_window_rms = rms(y_normalized[window]) + + if x_window_rms > rms_threshold and y_window_rms > rms_threshold: + # sum the power of the original non-scaled signal + x_active_power += np.sum(np.abs(x[window]) ** 2) + y_active_power += np.sum(np.abs(y[window]) ** 2) + active_len += window_len + + if active_len < int(min_active_duration * sample_rate): + raise RuntimeError( + f'Signals are simultaneously active less than {min_active_duration} s: only {active_len/sample_rate} s' + ) + + # normalize + x_active_power /= active_len + y_active_power /= active_len + + return np.sqrt(x_active_power), np.sqrt(y_active_power) + + +def scaled_disturbance( + signal: np.ndarray, + disturbance: np.ndarray, + sdr: float, + sample_rate: float = None, + ref_channel: int = 0, + eps: float = 1e-16, +) -> np.ndarray: + """ + Args: + signal: numpy array, shape (num_samples, num_channels) + disturbance: numpy array, same shape as signal + sdr: desired signal-to-disturbance ration + sample_rate: sample rate of the input signals + ref_channel: ref mic used to calculate RMS + eps: regularization constant + + Returns: + Scaled disturbance, so that signal-to-disturbance ratio at ref_channel + is approximately equal to input SDR during simultaneously active + segment of signal and disturbance. + """ + if signal.shape != disturbance.shape: + raise ValueError(f'Signal and disturbance shapes do not match: {signal.shape} != {disturbance.shape}') + + # set scaling based on RMS at ref_mic + signal_rms, disturbance_rms = simultaneously_active_rms( + signal[:, ref_channel], disturbance[:, ref_channel], sample_rate=sample_rate + ) + disturbance_gain = db2mag(-sdr) * signal_rms / (disturbance_rms + eps) + # scale disturbance + scaled_disturbance = disturbance_gain * disturbance + return scaled_disturbance + + +def prepare_source_signal( + signal_type: str, + sample_rate: int, + audio_data: List[dict], + audio_dir: Optional[str] = None, + min_duration: Optional[int] = None, + ref_signal: Optional[np.ndarray] = None, + mic_positions: Optional[np.ndarray] = None, + num_retries: int = 10, +) -> tuple: + """Prepare an audio signal for a source. + + Args: + signal_type: 'point' or 'diffuse' + sample_rate: Sampling rate for the signal + audio_data: List of audio items, each is a dictionary with audio_filepath, duration, offset and optionally text + audio_dir: Base directory for resolving paths, e.g., manifest basedir + min_duration: Minimal duration to be loaded if ref_signal is not provided, in seconds + ref_signal: Optional, used to determine the length of the signal + mic_positions: Optional, used to prepare approximately diffuse signal + num_retries: Number of retries when selecting the source files + + Returns: + (audio_signal, metadata), where audio_signal is an ndarray and metadata is a dictionary + with audio filepaths, durations and offsets + """ + if not signal_type in ['point', 'diffuse']: + raise ValueError(f'Unexpected signal type {signal_type}.') + + if audio_data is None: + # No data to load + return None + + metadata = {} + + if ref_signal is None: + audio_signal = None + # load at least one sample if min_duration is not provided + samples_to_load = int(min_duration * sample_rate) if min_duration is not None else 1 + source_signals_metadata = {'audio_filepath': [], 'duration': [], 'offset': [], 'text': []} + + while samples_to_load > 0: + # Select a random item and load the audio + item = random.choice(audio_data) + + audio_filepath = item['audio_filepath'] + if not os.path.isabs(audio_filepath) and audio_dir is not None: + audio_filepath = os.path.join(audio_dir, audio_filepath) + + # Load audio + check_min_sample_rate(audio_filepath, sample_rate) + audio_segment = AudioSegment.from_file( + audio_file=audio_filepath, + target_sr=sample_rate, + duration=item['duration'], + offset=item.get('offset', 0), + ) + + if signal_type == 'point': + if audio_segment.num_channels > 1: + raise RuntimeError( + f'Expecting single-channel source signal, but received {audio_segment.num_channels}. File: {audio_filepath}' + ) + else: + raise ValueError(f'Unexpected signal type {signal_type}.') + + source_signals_metadata['audio_filepath'].append(audio_filepath) + source_signals_metadata['duration'].append(item['duration']) + source_signals_metadata['duration'].append(item.get('offset', 0)) + source_signals_metadata['text'].append(item.get('text')) + + # not perfect, since different files may have different distributions + segment_samples = normalize_max(audio_segment.samples) + # concatenate + audio_signal = ( + np.concatenate((audio_signal, segment_samples)) if audio_signal is not None else segment_samples + ) + # remaining samples + samples_to_load -= len(segment_samples) + + # Finally, we need only the metadata for the complete signal + metadata = { + 'duration': sum(source_signals_metadata['duration']), + 'offset': 0, + } + + # Add text only if all source signals have text + if all([isinstance(tt, str) for tt in source_signals_metadata['text']]): + metadata['text'] = ' '.join(source_signals_metadata['text']) + else: + # Load a signal with total_len samples and ensure it has enough simultaneous activity/overlap with ref_signal + # Concatenate multiple files if necessary + total_len = len(ref_signal) + + for n in range(num_retries): + + audio_signal = None + source_signals_metadata = {'audio_filepath': [], 'duration': [], 'offset': []} + + if signal_type == 'point': + samples_to_load = total_len + elif signal_type == 'diffuse': + # Load longer signal so it can be reshaped into (samples, mics) and + # used to generate approximately diffuse noise field + num_mics = len(mic_positions) + samples_to_load = num_mics * total_len + + while samples_to_load > 0: + # Select an audio file + item = random.choice(audio_data) + + audio_filepath = item['audio_filepath'] + if not os.path.isabs(audio_filepath) and audio_dir is not None: + audio_filepath = os.path.join(audio_dir, audio_filepath) + + # Load audio signal + check_min_sample_rate(audio_filepath, sample_rate) + + if (max_offset := item['duration'] - np.ceil(samples_to_load / sample_rate)) > 0: + # Load with a random offset if the example is longer than samples_to_load + offset = random.uniform(0, max_offset) + duration = -1 + else: + # Load the whole file + offset, duration = 0, item['duration'] + audio_segment = AudioSegment.from_file( + audio_file=audio_filepath, target_sr=sample_rate, duration=duration, offset=offset + ) + + # Prepare a single-channel signal + if audio_segment.num_channels == 1: + # Take all samples + segment_samples = audio_segment.samples + else: + # Take a random channel + selected_channel = random.choice(range(audio_segment.num_channels)) + segment_samples = audio_segment.samples[:, selected_channel] + + source_signals_metadata['audio_filepath'].append(audio_filepath) + source_signals_metadata['duration'].append(len(segment_samples) / sample_rate) + source_signals_metadata['offset'].append(offset) + + # not perfect, since different files may have different distributions + segment_samples = normalize_max(segment_samples) + # concatenate + audio_signal = ( + np.concatenate((audio_signal, segment_samples)) if audio_signal is not None else segment_samples + ) + # remaining samples + samples_to_load -= len(segment_samples) + + if signal_type == 'diffuse' and num_mics > 1: + try: + # Trim and reshape to num_mics to prepare num_mics source signals + audio_signal = audio_signal[: num_mics * total_len].reshape(num_mics, -1).T + + # Make spherically diffuse noise + audio_signal = generate_approximate_noise_field( + mic_positions=np.array(mic_positions), noise_signal=audio_signal, sample_rate=sample_rate + ) + except Exception as e: + logging.info('Failed to generate approximate noise field: %s', str(e)) + logging.info('Try again.') + # Try again + audio_signal, source_signals_metadata = None, {} + continue + + # Trim to length + audio_signal = audio_signal[:total_len, ...] + + # Include the channel dimension if the reference includes it + if ref_signal.ndim == 2 and audio_signal.ndim == 1: + audio_signal = audio_signal[:, None] + + try: + # Signal and ref_signal should be simultaneously active + simultaneously_active_rms(ref_signal, audio_signal, sample_rate=sample_rate) + # We have enough overlap + break + except Exception as e: + # Signal and ref_signal are not overlapping, try again + logging.info('Exception: %s', str(e)) + logging.info('Signals are not overlapping, try again.') + audio_signal, source_signals_metadata = None, {} + continue + + if audio_signal is None: + logging.warning('Audio signal not set: %s.', signal_type) + + metadata['source_signals'] = source_signals_metadata + + return audio_signal, metadata + + +def check_min_sample_rate(filepath: str, sample_rate: float): + """Make sure the file's sample rate is at least sample_rate. + This will make sure that we have only downsampling if loading + this file, while upsampling is not permitted. + + Args: + filepath: path to a file + sample_rate: desired sample rate + """ + file_sample_rate = librosa.get_samplerate(path=filepath) + if file_sample_rate < sample_rate: + raise RuntimeError( + f'Sample rate ({file_sample_rate}) is lower than the desired sample rate ({sample_rate}). File: {filepath}.' + ) + + +def simulate_room_mix( + sample_rate: int, + target_cfg: dict, + interference_cfg: dict, + mix_cfg: dict, + audio_metadata: dict, + base_output_filepath: str, + max_amplitude: float = 0.999, + eps: float = 1e-16, +) -> dict: + """Simulate mixture signal at the microphone, including target, noise and + interference signals and mixed at specific RSNR and RSIR. + + Args: + sample_rate: Sample rate for all signals + target_cfg: Dictionary with configuration of the target. Includes + room_filepath, source index, audio_filepath, duration + noise_cfg: List of dictionaries, where each item includes audio_filepath, + offset and duration. + interference_cfg: List of dictionaries, where each item contains source + index + mix_cfg: Dictionary with the mixture configuration. Includes RSNR, RSIR, + ref_mic and ref_mic_rms. + audio_metadata: Dictionary with a list of files for target, noise and interference + base_output_filepath: All output audio files will be saved with this prefix by + adding a diffierent suffix for each component, e.g., _mic.wav. + max_amplitude: Maximum amplitude of the mic signal, used to prevent clipping. + eps: Small regularization constant. + + Returns: + Dictionary with metadata based on the mixture setup and + simulation results. This corresponds to a line of the + output manifest file. + """ + # Local utilities + def load_rir( + room_filepath: str, source: int, selected_mics: list, sample_rate: float, rir_key: str = 'rir' + ) -> np.ndarray: + """Load a RIR and check that the sample rate is matching the desired sample rate + + Args: + room_filepath: Path to a room simulation in an h5 file + source: Index of the desired source + sample_rate: Sample rate of the simulation + rir_key: Key of the RIR to load from the simulation. + + Returns: + Numpy array with shape (num_samples, num_channels) + """ + rir, rir_sample_rate = load_rir_simulation(room_filepath, source=source, rir_key=rir_key) + if rir_sample_rate != sample_rate: + raise RuntimeError( + f'RIR sample rate ({sample_rate}) is not matching the expected sample rate ({sample_rate}). File: {room_filepath}' + ) + return rir[:, selected_mics] + + def get_early_rir( + rir: np.ndarray, rir_anechoic: np.ndarray, sample_rate: int, early_duration: float = 0.050 + ) -> np.ndarray: + """Return only the early part of the RIR. + """ + early_len = int(early_duration * sample_rate) + direct_path_delay = np.min(np.argmax(rir_anechoic, axis=0)) + rir_early = rir.copy() + rir_early[direct_path_delay + early_len :, :] = 0 + return rir_early + + def save_audio( + base_path: str, + tag: str, + audio_signal: Optional[np.ndarray], + sample_rate: int, + save: str = 'all', + ref_mic: Optional[int] = None, + format: str = 'wav', + subtype: str = 'float', + ): + """Save audio signal and return filepath. + """ + if (audio_signal is None) or (not save): + return None + + if save == 'ref_mic': + # save only ref_mic + audio_signal = audio_signal[:, ref_mic] + + audio_filepath = base_path + f'_{tag}.{format}' + sf.write(audio_filepath, audio_signal, sample_rate, subtype) + + return audio_filepath + + # Target RIRs + target_rir = load_rir( + target_cfg['room_filepath'], + source=target_cfg['source'], + selected_mics=target_cfg['selected_mics'], + sample_rate=sample_rate, + ) + target_rir_anechoic = load_rir( + target_cfg['room_filepath'], + source=target_cfg['source'], + sample_rate=sample_rate, + selected_mics=target_cfg['selected_mics'], + rir_key='anechoic', + ) + target_rir_early = get_early_rir(rir=target_rir, rir_anechoic=target_rir_anechoic, sample_rate=sample_rate) + + # Target signals + target_signal, target_metadata = prepare_source_signal( + signal_type='point', + sample_rate=sample_rate, + audio_data=audio_metadata['target'], + audio_dir=audio_metadata['target_dir'], + min_duration=mix_cfg['min_duration'], + ) + source_signals_metadata = {'target': target_metadata['source_signals']} + + # Convolve target + target_reverberant = convolve_rir(target_signal, target_rir) + target_anechoic = convolve_rir(target_signal, target_rir_anechoic) + target_early = convolve_rir(target_signal, target_rir_early) + + # Prepare noise signal + noise, noise_metadata = prepare_source_signal( + signal_type='diffuse', + sample_rate=sample_rate, + mic_positions=target_cfg['mic_positions'], + audio_data=audio_metadata['noise'], + audio_dir=audio_metadata['noise_dir'], + ref_signal=target_reverberant, + ) + source_signals_metadata['noise'] = noise_metadata['source_signals'] + + # Prepare interference signal + if interference_cfg is None: + interference = None + else: + # Load interference signals + interference = 0 + source_signals_metadata['interference'] = [] + for i_cfg in interference_cfg: + # Load single-channel signal for directional interference + i_signal, i_metadata = prepare_source_signal( + signal_type='point', + sample_rate=sample_rate, + audio_data=audio_metadata['interference'], + audio_dir=audio_metadata['interference_dir'], + ref_signal=target_signal, + ) + source_signals_metadata['interference'].append(i_metadata['source_signals']) + # Load RIR from the same room as the target, but a difference source + i_rir = load_rir( + target_cfg['room_filepath'], + source=i_cfg['source'], + selected_mics=i_cfg['selected_mics'], + sample_rate=sample_rate, + ) + # Convolve interference + i_reverberant = convolve_rir(i_signal, i_rir) + # Sum + interference += i_reverberant + + # Scale and add components of the signal + mic = target_reverberant.copy() + + if noise is not None: + noise = scaled_disturbance( + signal=target_reverberant, + disturbance=noise, + sdr=mix_cfg['rsnr'], + sample_rate=sample_rate, + ref_channel=mix_cfg['ref_mic'], + ) + # Update mic signal + mic += noise + + if interference is not None: + interference = scaled_disturbance( + signal=target_reverberant, + disturbance=interference, + sdr=mix_cfg['rsir'], + sample_rate=sample_rate, + ref_channel=mix_cfg['ref_mic'], + ) + # Update mic signal + mic += interference + + # Set the final mic signal level + mic_rms = rms(mic[:, mix_cfg['ref_mic']]) + global_gain = db2mag(mix_cfg['ref_mic_rms']) / (mic_rms + eps) + mic_max = np.max(np.abs(mic)) + if (clipped_max := mic_max * global_gain) > max_amplitude: + # Downscale the global gain to prevent clipping + adjust ref_mic_rms accordingly + clipping_prevention_gain = max_amplitude / clipped_max + global_gain *= clipping_prevention_gain + mix_cfg['ref_mic_rms'] += mag2db(clipping_prevention_gain) + + logging.debug( + 'Clipping prevented for example %s (protection gain: %.2f dB)', + base_output_filepath, + mag2db(clipping_prevention_gain), + ) + + # save signals + signals = { + 'mic': mic, + 'target_reverberant': target_reverberant, + 'target_anechoic': target_anechoic, + 'target_early': target_early, + 'noise': noise, + 'interference': interference, + } + + metadata = {} + + for tag, signal in signals.items(): + + if signal is not None: + # scale all signal components with the global gain + signal = global_gain * signal + + audio_filepath = save_audio( + base_path=base_output_filepath, + tag=tag, + audio_signal=signal, + sample_rate=sample_rate, + save=mix_cfg['save'].get(tag, 'all'), + ref_mic=mix_cfg['ref_mic'], + format=mix_cfg['save'].get('format', 'wav'), + subtype=mix_cfg['save'].get('subtype', 'float'), + ) + + if tag == 'mic': + metadata['audio_filepath'] = audio_filepath + else: + metadata[tag + '_filepath'] = audio_filepath + + # Add metadata + metadata.update( + { + 'text': target_metadata.get('text'), + 'duration': target_metadata['duration'], + 'target_cfg': target_cfg, + 'interference_cfg': interference_cfg, + 'mix_cfg': mix_cfg, + 'ref_channel': mix_cfg.get('ref_mic'), + 'rt60': target_cfg.get('rt60'), + 'drr': calculate_drr(target_rir, sample_rate, n_direct=np.argmax(target_rir_anechoic, axis=0)), + 'rsnr': None if noise is None else mix_cfg['rsnr'], + 'rsir': None if interference is None else mix_cfg['rsir'], + 'source_signals': source_signals_metadata, + } + ) + + return convert_numpy_to_serializable(metadata) + + +def simulate_room_mix_helper(example_and_audio_metadata: tuple) -> dict: + """Wrapper around `simulate_room_mix` for pool.imap. + + Args: + args: example and audio_metadata that are forwarded to `simulate_room_mix` + + Returns: + Dictionary with metadata, see `simulate_room_mix` + """ + example, audio_metadata = example_and_audio_metadata + return simulate_room_mix(**example, audio_metadata=audio_metadata) + + +def plot_mix_manifest_info(filepath: str, plot_filepath: str = None): + """Plot distribution of parameters from the manifest file. + + Args: + filepath: path to a RIR corpus manifest file + plot_filepath: path to save the plot at + """ + metadata = read_manifest(filepath) + + # target info + target_distance = [] + target_azimuth = [] + target_elevation = [] + target_duration = [] + + # room config + rt60 = [] + drr = [] + + # noise + rsnr = [] + rsir = [] + + # get the required data + for data in metadata: + # target info + target_distance.append(data['target_cfg']['distance']) + target_azimuth.append(data['target_cfg']['azimuth']) + target_elevation.append(data['target_cfg']['elevation']) + target_duration.append(data['duration']) + + # room config + rt60.append(data['rt60']) + drr += data['drr'] # average DRR across all mics + + # noise + if data['rsnr'] is not None: + rsnr.append(data['rsnr']) + + if data['rsir'] is not None: + rsir.append(data['rsir']) + + # plot + plt.figure(figsize=(12, 6)) + + plt.subplot(2, 4, 1) + plt.hist(target_distance, label='distance') + plt.xlabel('distance / m') + plt.ylabel('# examples') + plt.title('Target-to-array distance') + + plt.subplot(2, 4, 2) + plt.hist(target_azimuth, label='azimuth') + plt.xlabel('azimuth / deg') + plt.ylabel('# examples') + plt.title('Target-to-array azimuth') + + plt.subplot(2, 4, 3) + plt.hist(target_elevation, label='elevation') + plt.xlabel('elevation / deg') + plt.ylabel('# examples') + plt.title('Target-to-array elevation') + + plt.subplot(2, 4, 4) + plt.hist(target_duration, label='duration') + plt.xlabel('time / s') + plt.ylabel('# examples') + plt.title('Target duration') + + plt.subplot(2, 4, 5) + plt.hist(rt60, label='RT60') + plt.xlabel('RT60 / s') + plt.ylabel('# examples') + plt.title('RT60') + + plt.subplot(2, 4, 6) + plt.hist(drr, label='DRR') + plt.xlabel('DRR / dB') + plt.ylabel('# examples') + plt.title('DRR [avg over mics]') + + if len(rsnr) > 0: + plt.subplot(2, 4, 7) + plt.hist(rsnr, label='RSNR') + plt.xlabel('RSNR / dB') + plt.ylabel('# examples') + plt.title(f'RSNR [{100 * len(rsnr) / len(rt60):.0f}% ex]') + + if len(rsir): + plt.subplot(2, 4, 8) + plt.hist(rsir, label='RSIR') + plt.xlabel('RSIR / dB') + plt.ylabel('# examples') + plt.title(f'RSIR [{100 * len(rsir) / len(rt60):.0f}% ex]') + + for n in range(8): + plt.subplot(2, 4, n + 1) + plt.grid() + plt.legend(loc='lower left') + + plt.tight_layout() + + if plot_filepath is not None: + plt.savefig(plot_filepath) + plt.close() + logging.info('Plot saved at %s', plot_filepath) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/feature_to_label.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/feature_to_label.py new file mode 100644 index 0000000..058d015 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/feature_to_label.py @@ -0,0 +1,497 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Dict, List, Optional + +import torch + +from nemo.collections.asr.parts.preprocessing.feature_loader import ExternalFeatureLoader +from nemo.collections.common.parts.preprocessing import collections +from nemo.core.classes import Dataset +from nemo.core.neural_types import AcousticEncodedRepresentation, LabelsType, LengthsType, NeuralType +from nemo.utils import logging + + +def _feature_collate_fn(batch): + """collate batch of feat sig, feat len, labels, labels len, assuming all features have the same shape. + Args: + batch (FloatTensor, LongTensor, LongTensor, LongTensor): A tuple of tuples of feature, feature lengths, + encoded labels, and encoded labels length. + """ + packed_batch = list(zip(*batch)) + if len(packed_batch) == 5: + _, feat_lengths, _, labels_lengths, sample_ids = packed_batch + elif len(packed_batch) == 4: + sample_ids = None + _, feat_lengths, _, labels_lengths = packed_batch + else: + raise ValueError("Expects 4 or 5 tensors in the batch!") + + features, labels = [], [] + for b in batch: + feat_i, labels_i = b[0], b[2] + features.append(feat_i) + labels.append(labels_i) + + features = torch.stack(features) + feat_lengths = torch.stack(feat_lengths) + + labels = torch.stack(labels) + labels_lengths = torch.stack(labels_lengths) + + if sample_ids is None: + return features, feat_lengths, labels, labels_lengths + else: + sample_ids = torch.tensor(sample_ids, dtype=torch.int32) + return features, feat_lengths, labels, labels_lengths, sample_ids + + +def _audio_feature_collate_fn(batch, feat_pad_val, label_pad_id): + """collate batch of audio feature, audio len, labels, labels len + Args: + batch (Optional[FloatTensor], Optional[LongTensor], LongTensor, + LongTensor): A tuple of tuples of feature, feature lengths, + labels, and label lengths. This collate func assumes the + features are torch tensors of Log-Melspectrogram (i.e. [N_MEL, T]). + """ + packed_batch = list(zip(*batch)) + if len(packed_batch) == 5: + _, feat_lengths, _, labels_lengths, sample_ids = packed_batch + elif len(packed_batch) == 4: + sample_ids = None + _, feat_lengths, _, labels_lengths = packed_batch + else: + raise ValueError("Expects 4 or 5 tensors in the batch!") + max_feat_len = 0 + has_feat = feat_lengths[0] is not None + if has_feat: + max_feat_len = max(feat_lengths).item() + max_labels_len = max(labels_lengths).item() + + features, labels = [], [] + for b in batch: + feat_i, feat_i_len, label_i, label_i_len = b[0], b[1], b[2], b[3] + + if has_feat: + feat_i_len = feat_i_len.item() + if feat_i_len < max_feat_len: + pad = (0, max_feat_len - feat_i_len) + feat_i = torch.nn.functional.pad(feat_i, pad, value=feat_pad_val) + features.append(feat_i) + + label_i_len = label_i_len.item() + if label_i_len < max_labels_len: + pad = (0, max_labels_len - label_i_len) + label_i = torch.nn.functional.pad(label_i, pad, value=label_pad_id) + labels.append(label_i) + + if has_feat: + features = torch.stack(features) + feature_lengths = torch.stack(feat_lengths) + else: + features, feat_lengths = None, None + labels = torch.stack(labels) + labels_lengths = torch.stack(labels_lengths) + + if sample_ids is None: + return features, feature_lengths, labels, labels_lengths + else: + sample_ids = torch.tensor(sample_ids, dtype=torch.int32) + return features, feature_lengths, labels, labels_lengths, sample_ids + + +def _vad_feature_segment_collate_fn(batch, window_length_in_sec, shift_length_in_sec, frame_unit_in_sec): + """collate batch of audio features, features len, tokens, tokens len + Args: + batch (Optional[FloatTensor], Optional[LongTensor], LongTensor, + LongTensor): A tuple of tuples of signal, signal lengths, + encoded tokens, and encoded tokens length. This collate func + assumes the signals are 1d torch tensors (i.e. mono audio). + batch size equals to 1. + """ + slice_length = int(window_length_in_sec / frame_unit_in_sec) + audio_features, feat_lengths, _, tokens_lengths = zip(*batch) + + slice_length = int(min(slice_length, max(feat_lengths))) + shift = int(shift_length_in_sec / frame_unit_in_sec) + has_audio = feat_lengths[0] is not None + + f_dim = audio_features[0].shape[0] + audio_features, num_slices, tokens, feat_lengths = [], [], [], [] + append_len_start = torch.div(slice_length, 2, rounding_mode='trunc') + append_len_end = slice_length - torch.div(slice_length, 2, rounding_mode='trunc') + for feat_i, feat_i_len, tokens_i, _ in batch: + start = torch.zeros(f_dim, append_len_start) + end = torch.zeros(f_dim, append_len_end) + feat_i = torch.cat((start, feat_i, end), dim=1) + feat_i_len += slice_length + + if has_audio: + slices = max(1, torch.div(feat_i_len - slice_length, shift, rounding_mode='trunc')) + + for slice_id in range(slices): + start_idx = slice_id * shift + end_idx = start_idx + slice_length + feat_slice = feat_i[:, start_idx:end_idx] + audio_features.append(feat_slice) + + num_slices.append(slices) + tokens.extend([tokens_i] * slices) + feat_lengths.extend([slice_length] * slices) + + if has_audio: + audio_features = torch.stack(audio_features) + feat_lengths = torch.tensor(feat_lengths) + else: + audio_features, feat_lengths = None, None + + tokens = torch.stack(tokens) + tokens_lengths = torch.tensor(num_slices) + return audio_features, feat_lengths, tokens, tokens_lengths + + +class _FeatureSeqSpeakerLabelDataset(Dataset): + """ + Dataset that loads tensors via a json file containing paths to feature files, sequences of labels. + Each new line is a different sample. Example below: + and their target labels. JSON files should be of the following format: + {"feature_filepath": "/path/to/feature_0.p", "seq_label": speakerA speakerB SpeakerA ....} \ + ... + {"feature_filepath": "/path/to/feature_n.p", "seq_label": target_seq_label_n} + target_seq_label_n is the string of sequence of speaker label, separated by space. + + Args: + manifest_filepath (str): Dataset parameter. Path to JSON containing data. + labels (Optional[list]): Dataset parameter. List of unique labels collected from all samples. + feature_loader : Dataset parameter. Feature loader to load (external) feature. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + # TODO output type for external features + output_types = { + 'external_feat': NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + 'feat_length': NeuralType(tuple('B'), LengthsType()), + } + + if self.is_speaker_emb: + output_types.update( + { + 'embs': NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + 'embs_length': NeuralType(tuple('B'), LengthsType()), + 'label': NeuralType(('B', 'T'), LabelsType()), + 'label_length': NeuralType(tuple('B'), LengthsType()), + } + ) + else: + output_types.update( + {'label': NeuralType(('B', 'T'), LabelsType()), 'label_length': NeuralType(tuple('B'), LengthsType()),} + ) + + return output_types + + def __init__( + self, *, manifest_filepath: str, labels: List[str], feature_loader, is_speaker_emb: bool = False, + ): + super().__init__() + self.collection = collections.ASRFeatureSequenceLabel(manifests_files=manifest_filepath.split(','),) + + self.feature_loader = feature_loader + self.labels = labels if labels else self.collection.uniq_labels + self.is_speaker_emb = is_speaker_emb + + self.label2id, self.id2label = {}, {} + for label_id, label in enumerate(self.labels): + self.label2id[label] = label_id + self.id2label[label_id] = label + + for idx in range(len(self.labels[:5])): + logging.debug(" label id {} and its mapped label {}".format(idx, self.id2label[idx])) + + def __len__(self): + return len(self.collection) + + def __getitem__(self, index): + sample = self.collection[index] + + features = self.feature_loader.process(sample.feature_file) + f, fl = features, torch.tensor(features.shape[0]).long() + + t = torch.tensor(sample.seq_label).float() + tl = torch.tensor(len(sample.seq_label)).long() + + return f, fl, t, tl + + +class FeatureToSeqSpeakerLabelDataset(_FeatureSeqSpeakerLabelDataset): + """ + Dataset that loads tensors via a json file containing paths to feature + files and sequence of speakers. Each new line is a + different sample. Example below: + {"feature_filepath": "/path/to/feature_0.p", "seq_label": speakerA speakerB SpeakerA ....} \ + ... + {"feature_filepath": "/path/to/feature_n.p", "seq_label": target_seq_label_n} + target_seq_label_n is the string of sequence of speaker label, separated by space. + + Args: + manifest_filepath (str): Path to manifest json as described above. Canbe comma-separated paths. + labels (Optional[list]): String containing all the possible labels to map to + if None then automatically picks from ASRFeatureSequenceLabel collection. + feature_loader, Feature load to loader (external) feature. + + """ + + def _collate_fn(self, batch): + return _feature_collate_fn(batch) + + +class FeatureToLabelDataset(Dataset): + """ + Dataset that loads tensors via a json file containing paths to feature files and their labels. + Each new line is a different sample. Example below: + and their target labels. JSON files should be of the following format: + {"feature_filepath": "/path/to/audio_feature.pt", "label": "1"} + ... + {"feature_filepath": "/path/to/audio_feature.pt", "label": "0"} + Args: + manifest_filepath (str): Path to JSON containing data. + labels (Optional[list]): List of unique labels collected from all samples. + augmentor (Optional): feature augmentation + window_length_in_sec (float): Window length in seconds. + shift_length_in_sec (float): Shift length in seconds. + is_regression_task (bool): if True, the labels are treated as for a regression task. + cal_labels_occurrence (bool): if True, the labels occurrence will be calculated. + zero_spec_db_val (float): Value to replace non-speech signals in log-melspectrogram. + min_duration (float): Minimum duration of the audio file in seconds. + max_duration (float): Maximum duration of the audio file in seconds. + """ + + ZERO_LEVEL_SPEC_DB_VAL = -16.635 # Log-Melspectrogram value for zero signal + FRAME_UNIT_TIME_SECS = 0.01 + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + output_types = { + 'audio_feat': NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + 'feat_length': NeuralType(tuple('B'), LengthsType()), + 'labels': NeuralType(('B'), LabelsType()), + 'labels_length': NeuralType(tuple('B'), LengthsType()), + } + + return output_types + + def __init__( + self, + *, + manifest_filepath: str, + labels: List[str] = None, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + window_length_in_sec: float = 0.63, + shift_length_in_sec: float = 0.01, + is_regression_task: bool = False, + cal_labels_occurrence: Optional[bool] = False, + zero_spec_db_val: float = -16.635, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + ): + super().__init__() + self.window_length_in_sec = window_length_in_sec + self.shift_length_in_sec = shift_length_in_sec + self.zero_spec_db_val = zero_spec_db_val + + if isinstance(manifest_filepath, str): + manifest_filepath = manifest_filepath.split(',') + + self.collection = collections.ASRFeatureLabel( + manifests_files=manifest_filepath, + is_regression_task=is_regression_task, + cal_labels_occurrence=cal_labels_occurrence, + min_duration=min_duration, + max_duration=max_duration, + ) + + self.feature_loader = ExternalFeatureLoader(augmentor=augmentor) + self.labels = labels if labels else self.collection.uniq_labels + + self.is_regression_task = is_regression_task + + if not is_regression_task: + self.labels = labels if labels else self.collection.uniq_labels + self.num_classes = len(self.labels) if self.labels is not None else 1 + self.label2id, self.id2label = {}, {} + self.id2occurrence, self.labels_occurrence = {}, [] + + for label_id, label in enumerate(self.labels): + self.label2id[label] = label_id + self.id2label[label_id] = label + if cal_labels_occurrence: + self.id2occurrence[label_id] = self.collection.labels_occurrence[label] + + if cal_labels_occurrence: + self.labels_occurrence = [self.id2occurrence[k] for k in sorted(self.id2occurrence)] + + for idx in range(len(self.labels[:5])): + logging.debug(" label id {} and its mapped label {}".format(idx, self.id2label[idx])) + else: + self.labels = [] + self.num_classes = 1 + + def __len__(self): + return len(self.collection) + + def __getitem__(self, index): + sample = self.collection[index] + + features = self.feature_loader.process(sample.feature_file) + f, fl = features, torch.tensor(features.shape[1]).long() + + t = torch.tensor(self.label2id[sample.label]) + tl = torch.tensor(1).long() + + return f, fl, t, tl + + def _collate_fn(self, batch): + return _audio_feature_collate_fn(batch, self.zero_spec_db_val, 0) + + def _vad_segment_collate_fn(self, batch): + return _vad_feature_segment_collate_fn( + batch, self.window_length_in_sec, self.shift_length_in_sec, self.FRAME_UNIT_TIME_SECS + ) + + +class FeatureToMultiLabelDataset(Dataset): + """ + Dataset that loads tensors via a json file containing paths to feature files and their labels. + Each new line is a different sample. Example below: + and their target labels. JSON files should be of the following format: + {"feature_filepath": "/path/to/audio_feature.pt", "label": "1 1 0 0 1"} + ... + {"feature_filepath": "/path/to/audio_feature.pt", "label": "0 1 0 0"} + Args: + manifest_filepath (str): Path to JSON containing data. + labels (Optional[list]): List of unique labels collected from all samples. + augmentor (Optional): feature augmentation + delimiter (str): delimiter to split the labels. + is_regression_task (bool): if True, the labels are treated as for a regression task. + cal_labels_occurrence (bool): if True, the labels occurrence will be calculated. + zero_spec_db_val (float): Value to replace non-speech signals in log-melspectrogram. + min_duration (float): Minimum duration of the audio file in seconds. + max_duration (float): Maximum duration of the audio file in seconds. + """ + + ZERO_LEVEL_SPEC_DB_VAL = -16.635 # Log-Melspectrogram value for zero signal + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + output_types = { + 'audio_feat': NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + 'feat_length': NeuralType(tuple('B'), LengthsType()), + 'labels': NeuralType(('B', 'T'), LabelsType()), + 'labels_length': NeuralType(tuple('B'), LengthsType()), + } + + return output_types + + def __init__( + self, + *, + manifest_filepath: str, + labels: List[str] = None, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + delimiter: Optional[str] = None, + is_regression_task: bool = False, + cal_labels_occurrence: Optional[bool] = False, + zero_spec_db_val: float = -16.635, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + ): + super().__init__() + self.delimiter = delimiter + self.zero_spec_db_val = zero_spec_db_val + + if isinstance(manifest_filepath, str): + manifest_filepath = manifest_filepath.split(',') + + self.collection = collections.ASRFeatureLabel( + manifests_files=manifest_filepath, + is_regression_task=is_regression_task, + cal_labels_occurrence=cal_labels_occurrence, + delimiter=delimiter, + min_duration=min_duration, + max_duration=max_duration, + ) + + self.is_regression_task = is_regression_task + self.feature_loader = ExternalFeatureLoader(augmentor=augmentor) + self.labels = labels if labels else self.collection.uniq_labels + + self.label2id, self.id2label = {}, {} + if not is_regression_task: + self.labels = labels if labels else self._get_label_set() + self.num_classes = len(self.labels) if self.labels is not None else 1 + self.label2id, self.id2label = {}, {} + for label_id, label in enumerate(self.labels): + self.label2id[label] = label_id + self.id2label[label_id] = label + if cal_labels_occurrence: + self.id2occurrence[label_id] = self.collection.labels_occurrence[label] + self.labels_occurrence.append(self.id2occurrence[label_id]) + + for idx in range(len(self.labels[:5])): + logging.debug(" label id {} and its mapped label {}".format(idx, self.id2label[idx])) + else: + self.labels = [] + self.num_classes = 1 + + def _get_label_set(self): + labels = [] + for sample in self.collection: + label_str = sample.label + if label_str: + label_str_list = label_str.split(self.delimiter) if self.delimiter else label_str.split() + labels.extend(label_str_list) + return sorted(set(labels)) + + def _label_str_to_tensor(self, label_str: str): + labels = label_str.split(self.delimiter) if self.delimiter else label_str.split() + + if self.is_regression_task: + labels = [float(s) for s in labels] + labels = torch.tensor(labels).float() + else: + labels = [self.label2id[s] for s in labels] + labels = torch.tensor(labels).long() + return labels + + def __len__(self): + return len(self.collection) + + def __getitem__(self, index): + sample = self.collection[index] + + features = self.feature_loader.process(sample.feature_file) + f, fl = features, torch.tensor(features.shape[1]).long() + + t = self._label_str_to_tensor(sample.label) + tl = torch.tensor(t.size(0)).long() + + return f, fl, t, tl + + def _collate_fn(self, batch): + return _audio_feature_collate_fn(batch, self.zero_spec_db_val, 0) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/feature_to_label_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/feature_to_label_dataset.py new file mode 100644 index 0000000..08803f4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/feature_to_label_dataset.py @@ -0,0 +1,68 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Optional + +from nemo.collections.asr.data import feature_to_label + + +def get_feature_seq_speakerlabel_dataset( + feature_loader, config: dict +) -> feature_to_label.FeatureToSeqSpeakerLabelDataset: + """ + Instantiates a FeatureSeqSpeakerLabelDataset. + Args: + config: Config of the FeatureToSeqSpeakerLabelDataset. + + Returns: + An instance of FeatureToSeqSpeakerLabelDataset. + """ + dataset = feature_to_label.FeatureToSeqSpeakerLabelDataset( + manifest_filepath=config['manifest_filepath'], labels=config['labels'], feature_loader=feature_loader, + ) + return dataset + + +def get_feature_label_dataset( + config: dict, augmentor: Optional['FeatureAugmentor'] = None +) -> feature_to_label.FeatureToLabelDataset: + dataset = feature_to_label.FeatureToLabelDataset( + manifest_filepath=config['manifest_filepath'], + labels=config['labels'], + augmentor=augmentor, + window_length_in_sec=config.get("window_length_in_sec", 0.63), + shift_length_in_sec=config.get("shift_length_in_sec", 0.08), + is_regression_task=config.get("is_regression_task", False), + cal_labels_occurrence=config.get("cal_labels_occurrence", False), + zero_spec_db_val=config.get("zero_spec_db_val", -16.635), + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + ) + return dataset + + +def get_feature_multi_label_dataset( + config: dict, augmentor: Optional['FeatureAugmentor'] = None +) -> feature_to_label.FeatureToMultiLabelDataset: + dataset = feature_to_label.FeatureToMultiLabelDataset( + manifest_filepath=config['manifest_filepath'], + labels=config['labels'], + augmentor=augmentor, + delimiter=config.get('delimiter', None), + is_regression_task=config.get("is_regression_task", False), + cal_labels_occurrence=config.get("cal_labels_occurrence", False), + zero_spec_db_val=config.get("zero_spec_db_val", -16.635), + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + ) + return dataset diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/feature_to_text.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/feature_to_text.py new file mode 100644 index 0000000..a7e2950 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/feature_to_text.py @@ -0,0 +1,488 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Callable, Dict, List, Optional, Tuple, Union + +import torch + +from nemo.collections.asr.data.feature_to_label import _audio_feature_collate_fn +from nemo.collections.asr.parts.preprocessing.feature_loader import ExternalFeatureLoader +from nemo.collections.asr.parts.preprocessing.features import normalize_batch +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.asr.parts.utils.vad_utils import load_speech_segments_from_rttm +from nemo.collections.common import tokenizers +from nemo.collections.common.parts.preprocessing import collections, parsers +from nemo.core.classes import Dataset +from nemo.core.neural_types import AcousticEncodedRepresentation, LabelsType, LengthsType, NeuralType + + +class ASRFeatureManifestProcessor: + def __init__( + self, + manifest_filepath: str, + parser: Union[str, Callable], + max_duration: Optional[float] = None, + min_duration: Optional[float] = None, + max_utts: int = 0, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + index_by_file_id: bool = False, + ): + self.parser = parser + self.collection = collections.ASRFeatureText( + manifests_files=manifest_filepath, + parser=parser, + min_duration=min_duration, + max_duration=max_duration, + max_number=max_utts, + index_by_file_id=index_by_file_id, + ) + + self.eos_id = eos_id + self.bos_id = bos_id + self.pad_id = pad_id + + def process_text_by_id(self, index: int) -> Tuple[List[int], int]: + sample = self.collection[index] + return self.process_text_by_sample(sample) + + def process_text_by_file_id(self, file_id: str) -> Tuple[List[int], int]: + manifest_idx = self.collection.mapping[file_id][0] + sample = self.collection[manifest_idx] + return self.process_text_by_sample(sample) + + def process_text_by_sample(self, sample: collections.ASRAudioText.OUTPUT_TYPE) -> Tuple[List[int], int]: + t, tl = sample.text_tokens, len(sample.text_tokens) + + if self.bos_id is not None: + t = [self.bos_id] + t + tl += 1 + if self.eos_id is not None: + t = t + [self.eos_id] + tl += 1 + + return t, tl + + +class _FeatureTextDataset(Dataset): + """ + Dataset that loads tensors via a json file containing paths to audio feature files, transcripts, + durations (in seconds) and optional RTTM files. Each new line is a different sample. Example below: + {"feature_filepath": "/path/to/audio_feature.pt", "text_filepath": "/path/to/audio.txt", + "rttm_filepath": "/path/to/audio_rttm.rttm", "duration": 23.147} + ... + {"feature_filepath": "/path/to/audio_feature.pt", "text": "the transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + Args: + manifest_filepath (str): Path to manifest json as described above. Can be comma-separated paths. + parser: Str for a language specific preprocessor or a callable. + normalize (bool): whether and where to normalize feature, must be one of [None, "post_norm", "pre_norm"] + normalize_type (Union[str, dict]): how to normalize feature, see `nemo.collections.asr.parts.preprocessing.features.normalize_batch` + use_rttm (bool): whether to use RTTM files if there is any, default to False + rttm_mode (str): how to use RTTM files, must be one of ['mask', 'drop'], default to 'mask' + feat_min_len (int): minimum length of feature when rttm_mode=deop, default to 4. + feat_mask_val (Optional[float]): value used to mask features with RTTM files, default to None to use zero mel-spectralgram + frame_unit_time_secs (float): time in seconds for each frame + sample_rate (int): Sample rate to resample loaded audio to + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): An AudioAugmentor object used to augment loaded audio + max_duration (float): If audio exceeds this length, do not include in dataset + min_duration (float): If audio is less than this length, do not include in dataset + max_utts (int): Limit number of utterances + trim (bool): whether or not to trim silence. Defaults to False + bos_id (int): Id of beginning of sequence symbol to append if not None + eos_id (int): Id of end of sequence symbol to append if not None + pad_id (int): Id of pad symbol. Defaults to 0 + return_sample_id (bool): whether to return the sample_id as a part of each sample + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + """ + + ZERO_LEVEL_SPEC_DB_VAL = -16.635 # Log-Melspectrogram value for zero signal + NORM_MODES = ["pre_norm", "post_norm"] + RTTM_MODES = ["mask", "drop"] + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'features': NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + 'feature_length': NeuralType(tuple('B'), LengthsType()), + 'transcripts': NeuralType(('B', 'T'), LabelsType()), + 'transcript_length': NeuralType(tuple('B'), LengthsType()), + 'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def __init__( + self, + manifest_filepath: str, + parser: Union[str, Callable], + normalize: Optional[str] = "post_norm", + normalize_type: Union[str, dict] = "per_feature", + use_rttm: bool = False, + rttm_mode: str = "mask", + feat_min_len: int = 4, + feat_mask_val: Optional[float] = None, + frame_unit_time_secs: float = 0.01, + sample_rate: Optional[int] = 16000, + augmentor: 'nemo.collections.asr.parts.perturb.FeatureAugmentor' = None, + max_duration: Optional[int] = None, + min_duration: Optional[int] = None, + max_utts: int = 0, + trim: bool = False, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + ): + if type(manifest_filepath) == str: + manifest_filepath = manifest_filepath.split(",") + + self.sample_rate = sample_rate + self.normalize = normalize + self.normalize_type = normalize_type + self.use_rttm = use_rttm + self.rttm_mode = rttm_mode + if self.use_rttm and self.rttm_mode not in self.RTTM_MODES: + raise ValueError(f"`rttm_mode` must be one of {self.RTTM_MODES}, got `{rttm_mode}` instead") + + self.feat_min_len = feat_min_len + if feat_mask_val is not None: + self.feat_mask_val = feat_mask_val + elif normalize == "pre_norm": + self.feat_mask_val = 0.0 # similar to SpectralAugmentation + else: + self.feat_mask_val = self.ZERO_LEVEL_SPEC_DB_VAL + + if normalize is not None and normalize not in self.NORM_MODES: + raise ValueError(f"`normalize` must be one of {self.NORM_MODES}, got `{normalize}` instead") + + self.frame_unit_time_secs = frame_unit_time_secs + + self.manifest_processor = ASRFeatureManifestProcessor( + manifest_filepath=manifest_filepath, + parser=parser, + max_duration=max_duration, + min_duration=min_duration, + max_utts=max_utts, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + ) + self.featurizer = ExternalFeatureLoader(augmentor=augmentor) + self.trim = trim + self.return_sample_id = return_sample_id + self.channel_selector = channel_selector + + def get_manifest_sample(self, sample_id): + return self.manifest_processor.collection[sample_id] + + def __getitem__(self, index): + sample = self.manifest_processor.collection[index] + offset = sample.offset + + if offset is None: + offset = 0 + + features = self.featurizer.process(sample.feature_file) + + f, fl = features, torch.tensor(features.shape[1]).long() + + t, tl = self.manifest_processor.process_text_by_sample(sample=sample) + + # Feature normalization + if self.normalize is None: + if self.use_rttm and sample.rttm_file: + f = self.process_features_with_rttm(f, offset, sample.rttm_file, self.feat_mask_val) + elif self.normalize == "post_norm": + # (Optional) Masking based on RTTM file + if self.use_rttm and sample.rttm_file: + f = self.process_features_with_rttm(f, offset, sample.rttm_file, self.feat_mask_val) + + f = self.normalize_feature(f) + else: # pre-norm + f = self.normalize_feature(f) + # (Optional) Masking based on RTTM file + if self.use_rttm and sample.rttm_file: + f = self.process_features_with_rttm(f, offset, sample.rttm_file, self.feat_mask_val) + + if self.return_sample_id: + output = f, fl, torch.tensor(t).long(), torch.tensor(tl).long(), index + else: + output = f, fl, torch.tensor(t).long(), torch.tensor(tl).long() + + return output + + def process_features_with_rttm(self, features, offset, rttm_file, mask_val): + segments = load_speech_segments_from_rttm(rttm_file) + new_features = features.clone() + sid, fid = 0, 0 + for i in range(features.size(1)): + t = offset + i * self.frame_unit_time_secs + while sid < len(segments) - 1 and segments[sid][1] < t: + sid += 1 + if segments[sid][1] == 0 or t < segments[sid][0] or t > segments[sid][1]: + # not in speech segment + if self.rttm_mode == "drop": + # drop the frame + continue + else: + # mask the frame with specified value + new_features[:, i] = mask_val + fid += 1 + else: + # in speech segment + new_features[:, fid] = features[:, i] + fid += 1 + + if fid < self.feat_min_len and self.rttm_mode == "drop": + new_features[:, : self.feat_min_len] = mask_val + return new_features[:, : self.feat_min_len] + return new_features[:, :fid] + + def __len__(self): + return len(self.manifest_processor.collection) + + def _collate_fn(self, batch): + return _audio_feature_collate_fn( + batch, feat_pad_val=self.feat_mask_val, label_pad_id=self.manifest_processor.pad_id + ) + + def normalize_feature(self, feat): + """ + Args: + feat: feature tensor of shape [M, T] + """ + feat = feat.unsqueeze(0) # add batch dim + feat, _, _ = normalize_batch(feat, torch.tensor([feat.size(-1)]), self.normalize_type) + return feat.squeeze(0) # delete batch dim + + +class FeatureToCharDataset(_FeatureTextDataset): + """ + Dataset that loads tensors via a json file containing paths to audio feature + files, transcripts, durations (in seconds) and optional RTTM files. Each new line is a + different sample. Example below: + {"feature_filepath": "/path/to/audio_feature.pt", "text_filepath": + "/path/to/audio.txt", "duration": 23.147, "rttm_filepath": "/path/to/audio_rttm.rttm",} + ... + {"feature_filepath": "/path/to/audio_feature.pt", "text": "the + transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + + Args: + manifest_filepath (str): Path to manifest json as described above. Can + be comma-separated paths. + labels (str): String containing all the possible characters to map to + normalize (str): how to normalize feature, must be one of [None, "post_norm", "pre_norm"] + normalize_type (Union[str, dict]): how to normalize feature, see `nemo.collections.asr.parts.preprocessing.features.normalize_batch` + use_rttm (bool): whether to use RTTM files if there is any, default to False + rttm_mode (str): how to use RTTM files, must be one of ['mask', 'drop'], default to 'mask' + feat_min_len (int): minimum length of feature, default to 4 + feat_mask_val (Optional[float]): value used to mask features with RTTM files, default to None to use zero mel-spectralgram + frame_unit_time_secs: time in seconds for each frame + sample_rate (int): Sample rate to resample loaded audio to + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): An AudioAugmentor + object used to augment loaded audio + max_duration: If audio exceeds this length, do not include in dataset + min_duration: If audio is less than this length, do not include + in dataset + max_utts: Limit number of utterances + blank_index: blank character index, default = -1 + unk_index: unk_character index, default = -1 + bos_id: Id of beginning of sequence symbol to append if not None + eos_id: Id of end of sequence symbol to append if not None + return_sample_id (bool): whether to return the sample_id as a part of each sample + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + """ + + def __init__( + self, + manifest_filepath: str, + labels: Union[str, List[str]], + normalize: Optional[str] = "post_norm", + normalize_type: Union[str, dict] = "per_feature", + use_rttm: bool = False, + rttm_mode: str = "mask", + feat_min_len: int = 4, + feat_mask_val: Optional[float] = None, + frame_unit_time_secs: float = 0.01, + sample_rate: Optional[int] = 16000, + augmentor: 'nemo.collections.asr.parts.perturb.FeatureAugmentor' = None, + max_duration: Optional[int] = None, + min_duration: Optional[int] = None, + max_utts: int = 0, + blank_index: int = -1, + unk_index: int = -1, + trim: bool = False, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + parser: Union[str, Callable] = 'en', + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + ): + self.labels = labels + + parser = parsers.make_parser( + labels=labels, name=parser, unk_id=unk_index, blank_id=blank_index, do_normalize=normalize + ) + + super().__init__( + manifest_filepath=manifest_filepath, + parser=parser, + normalize=normalize, + normalize_type=normalize_type, + use_rttm=use_rttm, + rttm_mode=rttm_mode, + feat_min_len=feat_min_len, + feat_mask_val=feat_mask_val, + frame_unit_time_secs=frame_unit_time_secs, + sample_rate=sample_rate, + augmentor=augmentor, + max_duration=max_duration, + min_duration=min_duration, + max_utts=max_utts, + trim=trim, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + return_sample_id=return_sample_id, + channel_selector=channel_selector, + ) + + +class FeatureToBPEDataset(_FeatureTextDataset): + """ + Dataset that loads tensors via a json file containing paths to audio feature + files, transcripts, durations (in seconds) and optional RTTM files. Each new line is a different sample. + Example below: + {"audio_filepath": "/path/to/audio.wav", "text_filepath": + "/path/to/audio.txt", "duration": 23.147, "rttm_filepath": "/path/to/audio_rttm.rttm",} + ... + {"audio_filepath": "/path/to/audio.wav", "text": "the + transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + + In practice, the dataset and manifest used for character encoding and byte pair encoding + are exactly the same. The only difference lies in how the dataset tokenizes the text in + the manifest. + + Args: + manifest_filepath (str): Path to manifest json as described above. Can + be comma-separated paths. + tokenizer: A subclass of the Tokenizer wrapper found in the common collection, + nemo.collections.common.tokenizers.TokenizerSpec. ASR Models support a subset of + all available tokenizers. + normalize (str): how to normalize feature, must be one of [None, "post_norm", "pre_norm"] + normalize_type (Union[str, dict]): how to normalize feature, see `nemo.collections.asr.parts.preprocessing.features.normalize_batch` + use_rttm (bool): whether to use RTTM files if there is any, default to False + rttm_mode (str): how to use RTTM files, must be one of ['mask', 'drop'], default to 'mask' + feat_min_len (int): minimum length of feature, default to 4 + feat_mask_val (Optional[float]): value used to mask features with RTTM files, default to None to use zero mel-spectralgram + frame_unit_time_secs: time in seconds for each frame + sample_rate (int): Sample rate to resample loaded audio to + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): An AudioAugmentor + object used to augment loaded audio + max_duration: If audio exceeds this length, do not include in dataset + min_duration: If audio is less than this length, do not include + in dataset + max_utts: Limit number of utterances + trim: Whether to trim silence segments + use_start_end_token: Boolean which dictates whether to add [BOS] and [EOS] + tokens to beginning and ending of speech respectively. + return_sample_id (bool): whether to return the sample_id as a part of each sample + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + """ + + def __init__( + self, + manifest_filepath: str, + tokenizer: 'nemo.collections.common.tokenizers.TokenizerSpec', + normalize: Optional[str] = "post_norm", + normalize_type: Union[str, dict] = "per_feature", + use_rttm: bool = False, + rttm_mode: str = "mask", + feat_min_len: int = 4, + feat_mask_val: Optional[float] = None, + frame_unit_time_secs: float = 0.01, + sample_rate: Optional[int] = 16000, + augmentor: 'nemo.collections.asr.parts.perturb.FeatureAugmentor' = None, + max_duration: Optional[int] = None, + min_duration: Optional[int] = None, + max_utts: int = 0, + use_start_end_token: bool = True, + trim: bool = False, + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + ): + if use_start_end_token and hasattr(tokenizer, "bos_id") and tokenizer.bos_id > 0: + bos_id = tokenizer.bos_id + else: + bos_id = None + + if use_start_end_token and hasattr(tokenizer, "eos_id") and tokenizer.eos_id > 0: + eos_id = tokenizer.eos_id + else: + eos_id = None + + if hasattr(tokenizer, "pad_id") and tokenizer.pad_id > 0: + pad_id = tokenizer.pad_id + else: + pad_id = 0 + + class TokenizerWrapper: + def __init__(self, tokenizer): + if isinstance(tokenizer, tokenizers.aggregate_tokenizer.AggregateTokenizer): + self.is_aggregate = True + else: + self.is_aggregate = False + self._tokenizer = tokenizer + + def __call__(self, *args): + if isinstance(args[0], List) and self.is_aggregate: + t = [] + for span in args[0]: + t.extend(self._tokenizer.text_to_ids(span['str'], span['lang'])) + return t + + t = self._tokenizer.text_to_ids(*args) + return t + + super().__init__( + manifest_filepath=manifest_filepath, + parser=TokenizerWrapper(tokenizer), + normalize=normalize, + normalize_type=normalize_type, + use_rttm=use_rttm, + rttm_mode=rttm_mode, + feat_min_len=feat_min_len, + feat_mask_val=feat_mask_val, + frame_unit_time_secs=frame_unit_time_secs, + sample_rate=sample_rate, + augmentor=augmentor, + max_duration=max_duration, + min_duration=min_duration, + max_utts=max_utts, + trim=trim, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + return_sample_id=return_sample_id, + channel_selector=channel_selector, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/feature_to_text_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/feature_to_text_dataset.py new file mode 100644 index 0000000..6bc03bc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/feature_to_text_dataset.py @@ -0,0 +1,94 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional + +from nemo.collections.asr.data.feature_to_text import FeatureToBPEDataset, FeatureToCharDataset +from nemo.utils import logging + + +def get_char_dataset(config: dict, augmentor: Optional['FeatureAugmentor'] = None) -> FeatureToCharDataset: + """ + Instantiates a Character Encoding based FeatureToCharDataset. + + Args: + config: Config of the FeatureToCharDataset. + augmentor: Optional AudioAugmentor object for augmentations on audio data. + + Returns: + An instance of FeatureToCharDataset. + """ + if 'labels' not in config: + logging.warning(f"dataset does not have explicitly defined labels") + + dataset = FeatureToCharDataset( + manifest_filepath=config['manifest_filepath'], + labels=config.get('labels', None), + normalize=config.get('normalize', 'post_norm'), + normalize_type=config.get('normalize_type', 'per_feature'), + use_rttm=config.get('use_rttm', False), + rttm_mode=config.get('rttm_mode', 'mask'), + feat_min_len=config.get('feat_min_len', 4), + feat_mask_val=config.get('feat_mask_val', None), + frame_unit_time_secs=config.get('frame_unit_time_secs', 0.01), + sample_rate=config.get('sample_rate', 16000), + augmentor=augmentor, + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + max_utts=config.get('max_utts', 0), + blank_index=config.get('blank_index', -1), + unk_index=config.get('unk_index', -1), + trim=config.get('trim_silence', False), + parser=config.get('parser', 'en'), + return_sample_id=config.get('return_sample_id', False), + channel_selector=config.get('channel_selector', None), + ) + return dataset + + +def get_bpe_dataset( + config: dict, tokenizer: 'TokenizerSpec', augmentor: Optional['FeatureAugmentor'] = None +) -> FeatureToBPEDataset: + """ + Instantiates a Byte Pair Encoding / Word Piece Encoding based FeatureoToBPEDataset. + + Args: + config: Config of the FeatureToBPEDataset. + tokenizer: An instance of a TokenizerSpec object. + augmentor: Optional FeatureAugmentor object for augmentations on audio features. + + Returns: + An instance of FeatureToBPEDataset. + """ + dataset = FeatureToBPEDataset( + manifest_filepath=config['manifest_filepath'], + tokenizer=tokenizer, + normalize=config.get('normalize', 'post_norm'), + normalize_type=config.get('normalize_type', 'per_feature'), + use_rttm=config.get('use_rttm', False), + rttm_mode=config.get('rttm_mode', 'mask'), + feat_min_len=config.get('feat_min_len', 4), + feat_mask_val=config.get('feat_mask_val', None), + frame_unit_time_secs=config.get('frame_unit_time_secs', 0.01), + sample_rate=config.get('sample_rate', 16000), + augmentor=augmentor, + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + max_utts=config.get('max_utts', 0), + trim=config.get('trim_silence', False), + use_start_end_token=config.get('use_start_end_token', True), + return_sample_id=config.get('return_sample_id', False), + channel_selector=config.get('channel_selector', None), + ) + return dataset diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/huggingface/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/huggingface/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/huggingface/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/huggingface/hf_audio_to_text.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/huggingface/hf_audio_to_text.py new file mode 100644 index 0000000..f0a3f83 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/huggingface/hf_audio_to_text.py @@ -0,0 +1,699 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Callable, Dict, List, Optional, Tuple, Union + +import datasets as hf_datasets +import torch +from datasets import concatenate_datasets +from datasets.distributed import split_dataset_by_node +from omegaconf import DictConfig, ListConfig, open_dict + +from nemo.collections.asr.data.audio_to_text import _speech_collate_fn +from nemo.collections.asr.parts.preprocessing.perturb import AudioAugmentor +from nemo.collections.asr.parts.preprocessing.segment import AudioSegment +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.common import tokenizers +from nemo.collections.common.parts.preprocessing import parsers +from nemo.core.classes import Dataset, IterableDataset +from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, NeuralType +from nemo.utils import logging + + +class HFTextProcessor: + """ + Text processor for huggingface datasets, mimicing the behavior of + `nemo.collections.asr.data.audio_to_text.ASRManifestProcessor`. + Basic text cleaning is also supported. + Args: + parser: Str for a language specific preprocessor or a callable. + bos_id: BOS token id to add to the beginning of the transcript. + eos_id: EOS token id to add to the end of the transcript. + pad_id: PAD token id to pad transcripts to the same length. + normalize_text: If true, normalizes text in HFTextProcessor + symbols_to_keep: If not None, only keeps symbols in this list when normalizing text + """ + + def __init__( + self, + parser: Union[str, Callable], + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + normalize_text: bool = False, + symbols_to_keep: Optional[str | List[str]] = None, + ): + self.parser = parser + self.eos_id = eos_id + self.bos_id = bos_id + self.pad_id = pad_id + self.normalize_text = normalize_text + self.symbols_to_keep = [x for x in symbols_to_keep] if symbols_to_keep is not None else [] + + def process_text(self, text: str, lang: Optional[str] = None) -> List[int]: + + if self.normalize_text: + text = text.lower() + # only keep alphanumeric characters, spaces and symbols defined in self.symbols_to_keep + text = ''.join([c for c in text if c.isalnum() or c.isspace() or c in self.symbols_to_keep]) + + if hasattr(self.parser, "is_aggregate") and self.parser.is_aggregate and isinstance(text, str): + if lang is not None: + text_tokens = self.parser(text, lang) + # for future use if want to add language bypass to audio_to_text classes + # elif hasattr(parser, "lang") and parser.lang is not None: + # text_tokens = parser(text, parser.lang) + else: + raise ValueError("lang required in manifest when using aggregate tokenizers") + else: + text_tokens = self.parser(text) + text_tokens_length = len(text_tokens) + if self.bos_id is not None: + text_tokens = [self.bos_id] + text_tokens + text_tokens_length += 1 + if self.eos_id is not None: + text_tokens = text_tokens + [self.eos_id] + text_tokens_length += 1 + return text_tokens, text_tokens_length + + +def get_nested_dict_value(dictionary: dict, key: str): + """ + the key should be a string of nested keys separated by `.`, e.g. `key1.key2.key3`, + then the returned value will be `dictionary[key1][key2][key3]` + """ + nested_keys = key.split(".") + result = dictionary + for k in nested_keys: + if k not in result: + raise KeyError( + f"Key `{key}` not found in [{result.keys()}], target is {nested_keys}, input is {dictionary}" + ) + result = result[k] + return result + + +class _HFAudioTextDataset(Dataset): + """ + A Dataset wrapper that loads from HuggingFace datasets and converts to NeMo compatible format. + Args: + audio_key: key to access audio data from the dataset + text_key: key to access text data from the dataset + sample_rate_key: key to access sample rate data from the dataset + hf_data_cfg: HuggingFace dataset config, all params in this config will be passed to `hf_datasets.load_dataset` + parser: Str for a language specific preprocessor or a callable. + augmentor: An instance of `nemo.collections.asr.parts.perturb.AudioAugmentor` to apply on audio. + trim: If true, trims silence using `nemo.collections.asr.parts.preprocessing.segment.AudioSegment` + bos_id: BOS token id to add to the beginning of the transcript. + eos_id: EOS token id to add to the end of the transcript. + pad_id: PAD token id to pad transcripts to the same length. + return_sample_id: If true, returns sample id from the dataset. + channel_selector: ChannelSelectorType, which channel(s) to use for audio. + normalize_db: Target RMS value for audio normalization. + ref_channel: Reference channel for normalization. + id_key: key to access sample id from the dataset + normalize_text: If true, normalizes text in HFTextProcessor + symbols_to_keep: If not None, only keeps symbols in this list when normalizing text + """ + + def __init__( + self, + audio_key: str, + text_key: str, + sample_rate_key: str, + hf_data_cfg: Union[DictConfig, ListConfig], + parser: Union[str, Callable], + sample_rate: int, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + trim: bool = False, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + normalize_db: Optional[float] = None, + ref_channel: Optional[int] = None, + id_key: Optional[str] = None, + normalize_text: bool = False, + symbols_to_keep: Optional[str] = None, + ) -> None: + super().__init__() + self.audio_key = audio_key + self.text_key = text_key + self.sample_rate_key = sample_rate_key + self.id_key = id_key + self.sample_rate = sample_rate + self.augmentor = augmentor if augmentor is not None else AudioAugmentor() + self.trim = trim + self.return_sample_id = return_sample_id + self.channel_selector = channel_selector + self.normalize_db = normalize_db + self.ref_channel = ref_channel + + self.text_processor = HFTextProcessor(parser, bos_id, eos_id, pad_id, normalize_text, symbols_to_keep) + + data_config_list = [hf_data_cfg] if isinstance(hf_data_cfg, DictConfig) else hf_data_cfg + dataset_list = [] + for data_cfg in data_config_list: + with open_dict(data_cfg): + if "streaming" in data_cfg and data_cfg.streaming: + logging.warning( + "streaming must be False for random access dataset, but you use streaming=True. Forcing streaming=False" + ) + data_cfg.streaming = False + logging.info(f"Loading HuggingFace Dataset with cfg: {data_cfg}") + dataset_list.append(hf_datasets.load_dataset(**data_cfg)) + logging.info(f"Dataset loaded with {len(dataset_list[-1])} samples") + self.dataset = concatenate_datasets(dataset_list) + + logging.info(f"Total number of samples loaded: {len(self.dataset)}") + + def __len__(self): + return len(self.dataset) + + def __getitem__(self, index) -> Tuple: + item = self.dataset[index] + + audio_array = get_nested_dict_value(item, self.audio_key) + origin_sr = get_nested_dict_value(item, self.sample_rate_key) + audio_segment = AudioSegment( + samples=audio_array, + sample_rate=origin_sr, + target_sr=self.sample_rate, + trim=self.trim, + channel_selector=self.channel_selector, + normalize_db=self.normalize_db, + ref_channel=self.ref_channel, + ) + self.augmentor.perturb(audio_segment) + f = torch.tensor(audio_segment.samples, dtype=torch.float) + fl = torch.tensor(f.shape[0], dtype=torch.long) + + text = get_nested_dict_value(item, self.text_key) + t, tl = self.text_processor.process_text(text) + + index = get_nested_dict_value(item, self.id_key) if self.id_key else index + if self.return_sample_id: + output = f, fl, torch.tensor(t).long(), torch.tensor(tl).long(), index + else: + output = f, fl, torch.tensor(t).long(), torch.tensor(tl).long() + + return output + + def _collate_fn(self, batch): + return _speech_collate_fn(batch, pad_id=self.text_processor.pad_id) + + +class HFAudioToCharDataset(_HFAudioTextDataset): + """ + Wrapper class for loading HuggingFace dataset for a char-based ASR model + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'audio_signal': NeuralType(('B', 'T'), AudioSignal()), + 'a_sig_length': NeuralType(tuple('B'), LengthsType()), + 'transcripts': NeuralType(('B', 'T'), LabelsType()), + 'transcript_length': NeuralType(tuple('B'), LengthsType()), + 'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def __init__( + self, + audio_key: str, + text_key: str, + sample_rate_key: str, + hf_data_cfg: DictConfig, + labels: List[str], + sample_rate: int, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + trim: bool = False, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + normalize_db: Optional[float] = None, + ref_channel: Optional[int] = None, + parser: Union[str, Callable] = 'en', + blank_index: int = -1, + unk_index: int = -1, + normalize: bool = True, + id_key: Optional[str] = None, + normalize_text: bool = False, + symbols_to_keep: Optional[str] = None, + ): + self.labels = labels + + parser = parsers.make_parser( + labels=labels, name=parser, unk_id=unk_index, blank_id=blank_index, do_normalize=normalize + ) + + super().__init__( + audio_key=audio_key, + text_key=text_key, + sample_rate_key=sample_rate_key, + hf_data_cfg=hf_data_cfg, + parser=parser, + sample_rate=sample_rate, + augmentor=augmentor, + trim=trim, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + return_sample_id=return_sample_id, + channel_selector=channel_selector, + normalize_db=normalize_db, + ref_channel=ref_channel, + id_key=id_key, + normalize_text=normalize_text, + symbols_to_keep=symbols_to_keep, + ) + + +class HFAudioToBPEDataset(_HFAudioTextDataset): + """ + Wrapper class for loading a HuggingFace dataset for a BPE-based ASR model + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'audio_signal': NeuralType(('B', 'T'), AudioSignal()), + 'a_sig_length': NeuralType(tuple('B'), LengthsType()), + 'transcripts': NeuralType(('B', 'T'), LabelsType()), + 'transcript_length': NeuralType(tuple('B'), LengthsType()), + 'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def __init__( + self, + audio_key: str, + text_key: str, + sample_rate_key: str, + hf_data_cfg: DictConfig, + tokenizer: 'nemo.collections.common.tokenizers.TokenizerSpec', + sample_rate: int, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + trim: bool = False, + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + normalize_db: Optional[float] = None, + ref_channel: Optional[int] = None, + use_start_end_token: bool = True, + id_key: Optional[str] = None, + normalize_text: bool = False, + symbols_to_keep: Optional[str] = None, + ): + if use_start_end_token and hasattr(tokenizer, "bos_id") and tokenizer.bos_id > 0: + bos_id = tokenizer.bos_id + else: + bos_id = None + + if use_start_end_token and hasattr(tokenizer, "eos_id") and tokenizer.eos_id > 0: + eos_id = tokenizer.eos_id + else: + eos_id = None + + if hasattr(tokenizer, "pad_id") and tokenizer.pad_id > 0: + pad_id = tokenizer.pad_id + else: + pad_id = 0 + + class TokenizerWrapper: + def __init__(self, tokenizer): + if isinstance(tokenizer, tokenizers.aggregate_tokenizer.AggregateTokenizer): + self.is_aggregate = True + else: + self.is_aggregate = False + self._tokenizer = tokenizer + + def __call__(self, *args): + if isinstance(args[0], List) and self.is_aggregate: + t = [] + for span in args[0]: + t.extend(self._tokenizer.text_to_ids(span['str'], span['lang'])) + return t + + t = self._tokenizer.text_to_ids(*args) + return t + + super().__init__( + audio_key=audio_key, + text_key=text_key, + sample_rate_key=sample_rate_key, + hf_data_cfg=hf_data_cfg, + parser=TokenizerWrapper(tokenizer), + sample_rate=sample_rate, + augmentor=augmentor, + trim=trim, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + return_sample_id=return_sample_id, + channel_selector=channel_selector, + normalize_db=normalize_db, + ref_channel=ref_channel, + id_key=id_key, + normalize_text=normalize_text, + symbols_to_keep=symbols_to_keep, + ) + + +class _HFIterableAudioTextDataset(IterableDataset): + """ + Wrapper class for loading HuggingFace IterableDataset and converts to NeMo compatible format. + Args: + audio_key: key to access audio data from the dataset + text_key: key to access text data from the dataset + sample_rate_key: key to access sample rate data from the dataset + hf_data_cfg: HuggingFace dataset config, all params in this config will be passed to `hf_datasets.load_dataset` + parser: Str for a language specific preprocessor or a callable. + augmentor: An instance of `nemo.collections.asr.parts.perturb.AudioAugmentor` to apply on audio. + trim: If true, trims silence using `nemo.collections.asr.parts.preprocessing.segment.AudioSegment` + bos_id: BOS token id to add to the beginning of the transcript. + eos_id: EOS token id to add to the end of the transcript. + pad_id: PAD token id to pad transcripts to the same length. + return_sample_id: If true, returns sample id from the dataset. + channel_selector: ChannelSelectorType, which channel(s) to use for audio. + normalize_db: Target RMS value for audio normalization. + ref_channel: Reference channel for normalization. + id_key: key to access sample id from the dataset + global_rank: global rank of the current worker + world_size: total number of workers + shuffle_n: buffer size for shuffling + shuffle_seed: seed for shuffling + normalize_text: If true, normalizes text in HFTextProcessor + symbols_to_keep: If not None, only keeps symbols in this list when normalizing text + """ + + def __init__( + self, + audio_key: str, + text_key: str, + sample_rate_key: str, + hf_data_cfg: Union[DictConfig, ListConfig], + parser: Union[str, Callable], + sample_rate: int, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + trim: bool = False, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + normalize_db: Optional[float] = None, + ref_channel: Optional[int] = None, + id_key: Optional[str] = None, + global_rank: int = 0, + world_size: int = 0, + shuffle_n: int = 0, + shuffle_seed: Optional[int] = None, + normalize_text: bool = False, + symbols_to_keep: Optional[str] = None, + ) -> None: + super().__init__() + + if return_sample_id and id_key is None: + raise ValueError("return_sample_id is True, but id_key is None") + + self.audio_key = audio_key + self.text_key = text_key + self.sample_rate_key = sample_rate_key + self.id_key = id_key + self.sample_rate = sample_rate + self.augmentor = augmentor if augmentor is not None else AudioAugmentor() + self.trim = trim + self.return_sample_id = return_sample_id + self.channel_selector = channel_selector + self.normalize_db = normalize_db + self.ref_channel = ref_channel + + self.text_processor = HFTextProcessor(parser, bos_id, eos_id, pad_id, normalize_text, symbols_to_keep) + + data_config_list = [hf_data_cfg] if isinstance(hf_data_cfg, DictConfig) else hf_data_cfg + dataset_list = [] + for data_cfg in data_config_list: + with open_dict(data_cfg): + if "streaming" in data_cfg and not data_cfg.streaming: + logging.warning( + "streaming must be True for streaming dataset, but you use streaming=False. Forcing streaming=True" + ) + # streaming must be True for iterable dataset + data_cfg.streaming = True + logging.info(f"Streaming HuggingFace IterableDataset with cfg: {data_cfg}") + dataset_list.append(hf_datasets.load_dataset(**data_cfg)) + + self.dataset = concatenate_datasets(dataset_list) + logging.info(f"Total number of samples cannot be extracted from HF streaming dataset") + + if shuffle_n > 0: + self.dataset = self.dataset.shuffle(seed=shuffle_seed, buffer_size=shuffle_n) + + self.dataset = split_dataset_by_node(self.dataset, global_rank, world_size) + self.dataset = self.dataset.map(self._build_sample) + + def __len__(self): + raise NotImplementedError( + f"len() is not supported for {self.__class__.__name__}. Please set `trainer.max_steps` to explicitly set the number of steps to train for." + ) + + def __iter__(self): + return self.dataset.__iter__() + + def _collate_fn(self, batch): + a_signal = [b['audio_signal'] for b in batch] + a_sig_length = [b['a_sig_length'] for b in batch] + transcripts = [b['transcripts'] for b in batch] + transcript_length = [b['transcript_length'] for b in batch] + if self.return_sample_id: + sample_id = [b['sample_id'] for b in batch] + batch_list = list(zip(a_signal, a_sig_length, transcripts, transcript_length, sample_id)) + else: + batch_list = list(zip(a_signal, a_sig_length, transcripts, transcript_length)) + + return _speech_collate_fn(batch_list, pad_id=self.text_processor.pad_id) + + def _build_sample(self, sample): + audio_array = get_nested_dict_value(sample, self.audio_key) + origin_sr = get_nested_dict_value(sample, self.sample_rate_key) + audio_segment = AudioSegment( + samples=audio_array, + sample_rate=origin_sr, + target_sr=self.sample_rate, + trim=self.trim, + channel_selector=self.channel_selector, + normalize_db=self.normalize_db, + ref_channel=self.ref_channel, + ) + self.augmentor.perturb(audio_segment) + f = torch.tensor(audio_segment.samples, dtype=torch.float) + fl = torch.tensor(f.shape[0], dtype=torch.long) + + text = get_nested_dict_value(sample, self.text_key) + t, tl = self.text_processor.process_text(text) + + output = { + 'audio_signal': f, + 'a_sig_length': fl, + 'transcripts': torch.tensor(t).long(), + 'transcript_length': torch.tensor(tl).long(), + } + + if self.return_sample_id: + output['sample_id'] = get_nested_dict_value(sample, self.id_key) + return output + + +class HFIterableAudioToCharDataset(_HFIterableAudioTextDataset): + """ + Wrapper class for loading HuggingFace IterableDataset for a char-based ASR model + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'audio_signal': NeuralType(('B', 'T'), AudioSignal()), + 'a_sig_length': NeuralType(tuple('B'), LengthsType()), + 'transcripts': NeuralType(('B', 'T'), LabelsType()), + 'transcript_length': NeuralType(tuple('B'), LengthsType()), + 'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def __init__( + self, + labels: List[str], + audio_key: str, + text_key: str, + sample_rate_key: str, + hf_data_cfg: DictConfig, + sample_rate: int, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + trim: bool = False, + bos_id: int | None = None, + eos_id: int | None = None, + pad_id: int = 0, + return_sample_id: bool = False, + id_key: str | None = None, + channel_selector: ChannelSelectorType | None = None, + normalize_db: float | None = None, + ref_channel: int | None = None, + global_rank: int = 0, + world_size: int = 0, + shuffle_n: int = 0, + shuffle_seed: Optional[int] = None, + parser: Union[str, Callable] = 'en', + blank_index: int = -1, + unk_index: int = -1, + normalize: bool = True, + normalize_text: bool = False, + symbols_to_keep: Optional[str] = None, + ) -> None: + self.labels = labels + + parser = parsers.make_parser( + labels=labels, name=parser, unk_id=unk_index, blank_id=blank_index, do_normalize=normalize + ) + + super().__init__( + audio_key=audio_key, + text_key=text_key, + sample_rate_key=sample_rate_key, + hf_data_cfg=hf_data_cfg, + parser=parser, + sample_rate=sample_rate, + augmentor=augmentor, + trim=trim, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + return_sample_id=return_sample_id, + id_key=id_key, + channel_selector=channel_selector, + normalize_db=normalize_db, + ref_channel=ref_channel, + global_rank=global_rank, + world_size=world_size, + shuffle_n=shuffle_n, + shuffle_seed=shuffle_seed, + normalize_text=normalize_text, + symbols_to_keep=symbols_to_keep, + ) + + +class HFIterableAudioToBPEDataset(_HFIterableAudioTextDataset): + """ + Wrapper class for loading HuggingFace IterableDataset for a BPE-based ASR model + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'audio_signal': NeuralType(('B', 'T'), AudioSignal()), + 'a_sig_length': NeuralType(tuple('B'), LengthsType()), + 'transcripts': NeuralType(('B', 'T'), LabelsType()), + 'transcript_length': NeuralType(tuple('B'), LengthsType()), + 'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def __init__( + self, + audio_key: str, + text_key: str, + sample_rate_key: str, + hf_data_cfg: DictConfig, + tokenizer: 'nemo.collections.common.tokenizers.TokenizerSpec', + sample_rate: int, + augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, + trim: bool = False, + return_sample_id: bool = False, + id_key: str | None = None, + channel_selector: ChannelSelectorType | None = None, + normalize_db: float | None = None, + ref_channel: int | None = None, + global_rank: int = 0, + world_size: int = 0, + shuffle_n: int = 0, + shuffle_seed: Optional[int] = None, + use_start_end_token: bool = True, + normalize_text: bool = False, + symbols_to_keep: Optional[str] = None, + ) -> None: + + if use_start_end_token and hasattr(tokenizer, "bos_id") and tokenizer.bos_id > 0: + bos_id = tokenizer.bos_id + else: + bos_id = None + + if use_start_end_token and hasattr(tokenizer, "eos_id") and tokenizer.eos_id > 0: + eos_id = tokenizer.eos_id + else: + eos_id = None + + if hasattr(tokenizer, "pad_id") and tokenizer.pad_id > 0: + pad_id = tokenizer.pad_id + else: + pad_id = 0 + + class TokenizerWrapper: + def __init__(self, tokenizer): + if isinstance(tokenizer, tokenizers.aggregate_tokenizer.AggregateTokenizer): + self.is_aggregate = True + else: + self.is_aggregate = False + self._tokenizer = tokenizer + + def __call__(self, *args): + if isinstance(args[0], List) and self.is_aggregate: + t = [] + for span in args[0]: + t.extend(self._tokenizer.text_to_ids(span['str'], span['lang'])) + return t + + t = self._tokenizer.text_to_ids(*args) + return t + + super().__init__( + audio_key=audio_key, + text_key=text_key, + sample_rate_key=sample_rate_key, + hf_data_cfg=hf_data_cfg, + parser=TokenizerWrapper(tokenizer), + sample_rate=sample_rate, + augmentor=augmentor, + trim=trim, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + return_sample_id=return_sample_id, + id_key=id_key, + channel_selector=channel_selector, + normalize_db=normalize_db, + ref_channel=ref_channel, + global_rank=global_rank, + world_size=world_size, + shuffle_n=shuffle_n, + shuffle_seed=shuffle_seed, + normalize_text=normalize_text, + symbols_to_keep=symbols_to_keep, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/huggingface/hf_audio_to_text_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/huggingface/hf_audio_to_text_dataset.py new file mode 100644 index 0000000..0b36d58 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/huggingface/hf_audio_to_text_dataset.py @@ -0,0 +1,132 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from omegaconf import DictConfig + +from nemo.collections.asr.data.huggingface.hf_audio_to_text import ( + HFAudioToBPEDataset, + HFAudioToCharDataset, + HFIterableAudioToBPEDataset, + HFIterableAudioToCharDataset, +) + + +def get_hf_audio_to_text_bpe_dataset( + config: DictConfig, global_rank: int, world_size: int, tokenizer, augmentor=None, +): + if "streaming" in config and config["streaming"]: + dataset = HFIterableAudioToBPEDataset( + audio_key=config.get('audio_key', 'audio.array'), + text_key=config["text_key"], + sample_rate_key=config.get('sample_rate_key', 'audio.sampling_rate'), + tokenizer=tokenizer, + hf_data_cfg=config["hf_data_cfg"], + sample_rate=config["sample_rate"], + augmentor=augmentor, + trim=config.get('trim_silence', False), + return_sample_id=config.get('return_sample_id', False), + id_key=config.get("id_key", None), + channel_selector=config.get('channel_selector', None), + normalize_db=config.get('normalize_db', None), + ref_channel=config.get('ref_channel', None), + global_rank=global_rank, + world_size=world_size, + shuffle_n=config.get("shuffle_n", 2048), + shuffle_seed=config.get("shuffle_seed", None), + use_start_end_token=config.get('use_start_end_token', True), + normalize_text=config.get('normalize_text', False), + symbols_to_keep=config.get('symbols_to_keep', None), + ) + else: + dataset = HFAudioToBPEDataset( + audio_key=config.get('audio_key', 'audio.array'), + text_key=config["text_key"], + sample_rate_key=config.get('sample_rate_key', 'audio.sampling_rate'), + tokenizer=tokenizer, + hf_data_cfg=config["hf_data_cfg"], + sample_rate=config["sample_rate"], + augmentor=augmentor, + trim=config.get('trim_silence', False), + return_sample_id=config.get('return_sample_id', False), + id_key=config.get("id_key", None), + channel_selector=config.get('channel_selector', None), + normalize_db=config.get('normalize_db', None), + ref_channel=config.get('ref_channel', None), + use_start_end_token=config.get('use_start_end_token', True), + normalize_text=config.get('normalize_text', False), + symbols_to_keep=config.get('symbols_to_keep', None), + ) + + return dataset + + +def get_hf_audio_to_text_char_dataset( + config: DictConfig, global_rank: int, world_size: int, augmentor=None, +): + if "streaming" in config and config["streaming"]: + dataset = HFIterableAudioToCharDataset( + labels=config["labels"], + audio_key=config.get('audio_key', 'audio.array'), + text_key=config["text_key"], + sample_rate_key=config.get('sample_rate_key', 'audio.sampling_rate'), + hf_data_cfg=config["hf_data_cfg"], + sample_rate=config["sample_rate"], + augmentor=augmentor, + trim=config.get('trim_silence', False), + return_sample_id=config.get('return_sample_id', False), + id_key=config.get("id_key", None), + channel_selector=config.get('channel_selector', None), + normalize_db=config.get('normalize_db', None), + ref_channel=config.get('ref_channel', None), + global_rank=global_rank, + world_size=world_size, + shuffle_n=config.get("shuffle_n", 2048), + shuffle_seed=config.get("shuffle_seed", None), + parser=config.get("parser", "en"), + blank_index=config.get("blank_index", -1), + unk_index=config.get("unk_index", -1), + normalize=config.get("normalize", False), + normalize_text=config.get('normalize_text', False), + symbols_to_keep=config.get('symbols_to_keep', None), + pad_id=config.get('pad_id', 0), + bos_id=config.get('bos_id', None), + eos_id=config.get('eos_id', None), + ) + else: + dataset = HFAudioToCharDataset( + labels=config["labels"], + audio_key=config.get('audio_key', 'audio.array'), + text_key=config["text_key"], + sample_rate_key=config.get('sample_rate_key', 'audio.sampling_rate'), + hf_data_cfg=config["hf_data_cfg"], + sample_rate=config["sample_rate"], + augmentor=augmentor, + trim=config.get('trim_silence', False), + bos_id=config.get('bos_id', None), + eos_id=config.get('eos_id', None), + pad_id=config.get('pad_id', 0), + return_sample_id=config.get('return_sample_id', False), + id_key=config.get("id_key", None), + channel_selector=config.get('channel_selector', None), + normalize_db=config.get('normalize_db', None), + ref_channel=config.get('ref_channel', None), + parser=config.get("parser", "en"), + blank_index=config.get("blank_index", -1), + unk_index=config.get("unk_index", -1), + normalize=config.get("normalize", False), + normalize_text=config.get('normalize_text', False), + symbols_to_keep=config.get('symbols_to_keep', None), + ) + + return dataset diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/text_to_text.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/text_to_text.py new file mode 100644 index 0000000..88b417e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/data/text_to_text.py @@ -0,0 +1,482 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import concurrent.futures +import copy +import gc +import json +import math +import random +from pathlib import Path +from typing import Any, Callable, Dict, Iterable, List, NamedTuple, Optional, Set, Union + +import numpy as np +import torch +import torch.utils.data +from torch.nn.utils.rnn import pad_sequence +from tqdm.auto import tqdm + +from nemo.collections.asr.data.audio_to_text import _speech_collate_fn +from nemo.collections.common.tokenizers import TokenizerSpec +from nemo.core.classes import Dataset, IterableDataset +from nemo.utils import logging + +try: + from nemo_text_processing.text_normalization.normalize import Normalizer +except Exception as e: + pass # Normalizer imported only for annotation purposes, error can be ignored + +AnyPath = Union[Path, str] + + +class TextToTextItem(NamedTuple): + tts_text: torch.Tensor # normalized and tokenized text for TTS + transcript: torch.Tensor # tokenized text for ASR + speaker: int # speaker id for multi-speaker TTS + + +class TextToTextBatch(NamedTuple): + tts_texts: torch.Tensor # tokenized texts for tts + tts_text_lengths: torch.Tensor + transcripts: torch.Tensor # tokenized texts for ASR + transcript_lengths: torch.Tensor + speakers: torch.Tensor # speaker ids for multi-speaker TTS + + @staticmethod + def collate_fn(batch: List[TextToTextItem], asr_pad_id: int, tts_text_pad_id: int) -> TextToTextBatch: + return TextToTextBatch( + tts_texts=pad_sequence([item.tts_text for item in batch], batch_first=True, padding_value=tts_text_pad_id), + tts_text_lengths=torch.tensor([item.tts_text.shape[0] for item in batch]).long(), + transcripts=pad_sequence([item.transcript for item in batch], batch_first=True, padding_value=asr_pad_id), + transcript_lengths=torch.tensor([item.transcript.shape[0] for item in batch]).long(), + speakers=torch.tensor([item.speaker for item in batch]).long(), + ) + + +class TextOrAudioToTextBatch(NamedTuple): + audio_signals: torch.Tensor + audio_signal_lengths: torch.Tensor + tts_texts: torch.Tensor + tts_text_lengths: torch.Tensor + speakers: torch.Tensor + transcripts: torch.Tensor + transcript_lengths: torch.Tensor + + @staticmethod + def collate_fn( + batch: List[Union[TextToTextItem, tuple]], tts_text_pad_id: int, asr_pad_id: int + ) -> Union[TextToTextBatch, TextOrAudioToTextBatch, tuple]: + """ + Collate function for dataloader + Can accept mixed batch of text-to-text items and audio-text items (typical for ASR) + """ + text_items: List[TextToTextItem] = [item for item in batch if isinstance(item, TextToTextItem)] + if not text_items: + # pure audio-text batch + return _speech_collate_fn(batch=batch, pad_id=asr_pad_id) + + asr_items = [item for item in batch if not isinstance(item, TextToTextItem)] + + if not asr_items: + # pure text-to-text batch + return TextToTextBatch.collate_fn(batch=text_items, asr_pad_id=asr_pad_id, tts_text_pad_id=tts_text_pad_id) + + # mixed batch + + # each asr item is a tuple: + # audio_signal (0), audio_length (1), transcript (2), transcript_length (3), sample_id (4, optional) + audio_signals = pad_sequence([item[0] for item in asr_items], batch_first=True, padding_value=0.0) + audio_signal_lengths = torch.tensor([item[1] for item in asr_items]).long() + + tts_texts = pad_sequence( + [item.tts_text for item in text_items], batch_first=True, padding_value=tts_text_pad_id + ) + tts_text_lengths = torch.tensor([item.tts_text.shape[0] for item in text_items]).long() + speakers = torch.tensor([item.speaker for item in text_items]).long() + + transcripts = pad_sequence( + [item.transcript for item in text_items] + [item[2] for item in asr_items], + batch_first=True, + padding_value=asr_pad_id, + ) + transcript_lengths = torch.tensor( + [item.transcript.shape[0] for item in text_items] + [item[3] for item in asr_items] + ).long() + + return TextOrAudioToTextBatch( + audio_signals=audio_signals, + audio_signal_lengths=audio_signal_lengths, + tts_texts=tts_texts, + tts_text_lengths=tts_text_lengths, + speakers=speakers, + transcripts=transcripts, + transcript_lengths=transcript_lengths, + ) + + +def _asr_text_to_tokens(text: str) -> np.ndarray: + """ + Helper function for asr tokenization with multiprocessing pool only. + Must be defined on the top level. + Expects asr_tokenizer_global, asr_bos_id_global, asr_eos_id_global to exist in the current pool process + """ + ids = asr_tokenizer_global.text_to_ids(text) + if asr_bos_id_global is not None: + ids = [asr_bos_id_global] + ids + if asr_eos_id_global is not None: + ids.append(asr_eos_id_global) + return np.asarray(ids) + + +def _tts_text_to_tokens(text: str) -> np.ndarray: + """ + Helper function for asr tokenization with multiprocessing pool only. + Must be defined on the top level. + Expects tts_tokenizer_global to exist in the current pool process + """ + return np.asarray(tts_tokenizer_global(text)) + + +def _iterate_manifest(filepath: AnyPath) -> Iterable[Dict[str, Any]]: + """ + Helper function to iterate manifest + """ + with open(filepath, "r", encoding="utf-8") as f: + for line in f: + record = json.loads(line) + yield record + + +class TextToTextDatasetBase: + """ + Base class for loading text-to-text manifests + Map-style and Iterable datasets should inherit this class + """ + + asr_pad_id: int + tts_text_pad_id: int + asr_bos_id: Optional[int] = None + asr_eos_id: Optional[int] = None + data: List[Dict[str, Any]] + + def __init__( + self, + manifest_filepath: Union[AnyPath, List[AnyPath]], + speakers_filepath: Union[AnyPath, List[AnyPath]], + asr_tokenizer: TokenizerSpec, + asr_use_start_end_token: bool, + tts_parser: Callable, + tts_text_pad_id: int, + tts_text_normalizer: "Normalizer", + tts_text_normalizer_call_kwargs: Dict, + min_words: int = 1, + max_words: int = 1_000_000, + tokenizer_workers: int = 1, + num_parts: int = 1, + current_part_index: int = 0, + ): + super().__init__() + # ASR tokenizer setup + if asr_use_start_end_token and hasattr(asr_tokenizer, 'bos_token'): + self.asr_bos_id = asr_tokenizer.bos_id + + if asr_use_start_end_token and hasattr(asr_tokenizer, 'eos_token'): + self.asr_eos_id = asr_tokenizer.eos_id + + if hasattr(asr_tokenizer, 'pad_token'): + self.asr_pad_id = asr_tokenizer.pad_id + else: + self.asr_pad_id = 0 + + self.asr_tokenizer = asr_tokenizer + + # TTS tokenizer setup + self.tts_parser = tts_parser + self.tts_normalizer = tts_text_normalizer + self.tts_normalizer_kwargs = tts_text_normalizer_call_kwargs + self.tts_text_pad_id = tts_text_pad_id + + # Load speakers + if isinstance(speakers_filepath, str): + speakers_filepath = speakers_filepath.split(",") + elif isinstance(speakers_filepath, Path): + speakers_filepath = [speakers_filepath] + speakers: Set[int] = set() + for filepath in speakers_filepath: + with open(Path(filepath).expanduser(), "r") as f: + speakers.update(map(int, f.read().split())) + self.speakers = np.asarray(sorted(speakers)) + logging.info(f"Loaded {len(self.speakers)} speakers") + + # Load manifest + if isinstance(manifest_filepath, str): + manifest_filepath = manifest_filepath.split(",") + elif isinstance(manifest_filepath, Path): + manifest_filepath = [manifest_filepath] + self.manifest_paths = [Path(filepath) for filepath in manifest_filepath] + + num_skipped_words = 0 + num_skipped_utterances = 0 + asr_texts = [] + tts_texts = [] + need_normalization = False + + for manifest_path in self.manifest_paths: + for tmp_item in tqdm(_iterate_manifest(manifest_path)): + text = tmp_item["text"] + num_words = len(text.split()) + # skip if number of works not in desired range + # TODO: maybe it would be valuable to sample sub-utterances from long utterances + if not (min_words <= num_words <= max_words): + num_skipped_words += num_words + num_skipped_utterances += 1 + continue + asr_texts.append(tmp_item["text"]) + if "tts_text_normalized" in tmp_item: + tts_texts.append(tmp_item["tts_text_normalized"]) + else: + tts_texts.append(tmp_item["tts_text"]) + need_normalization = True + + if need_normalization: + logging.warning("TTS normalization is extremely slow! It is recommended to normalize TTS text") + + if num_skipped_utterances: + logging.warning(f"Skipped {num_skipped_utterances} utterances " f"with {num_skipped_words}") + + num_utterances = len(asr_texts) + # preprocessing is very costly, if we need only part - remove unnecessary utterances + if num_parts > 1: + # NB: floor division, full dataset can contain fewer utterances than original, like in tarred dataset + num_utterances_part = num_utterances // num_parts + start = num_utterances_part * current_part_index + end = start + num_utterances_part + logging.info( + f"Taking part of the dataset: {current_part_index} index, total {num_parts} from {start} to {end}" + ) + asr_texts = asr_texts[start:end] + tts_texts = tts_texts[start:end] + num_utterances = num_utterances_part + + self.data = [dict() for _ in range(num_utterances)] + + if len(asr_texts) == 0: + # no data was loaded + logging.warning("Text-to-text dataset is empty") + return + + if tokenizer_workers == 1: + logging.warning( + "Preprocessing large text with tokenizer_workers=1 may be slow with TTS tokenizer. " + "Prefer tokenizer_workers=(num_cpu_cores/num_gpus_per_node)" + ) + for i, tokenized_text in enumerate( + tqdm((self._asr_text_to_tokens(text) for text in asr_texts), total=len(asr_texts)) + ): + self.data[i]["asr_text_tokens"] = tokenized_text + else: + # Multiprocessing hack: use global variables for every process (not really global in program context) + def _init_asr_tokenize_process(tokenizer, bos_id, eos_id): + global asr_tokenizer_global, asr_bos_id_global, asr_eos_id_global # process-global + # deepcopy to avoid serialization of parent models + asr_tokenizer_global = copy.deepcopy(tokenizer) + asr_bos_id_global = copy.deepcopy(bos_id) + asr_eos_id_global = copy.deepcopy(eos_id) + + with concurrent.futures.ProcessPoolExecutor( + initializer=_init_asr_tokenize_process, + initargs=(asr_tokenizer, self.asr_bos_id, self.asr_eos_id), + max_workers=tokenizer_workers, + ) as pool: + # chunk size for pool map is empirically chosen as a trade-off between speed and responsiveness + for i, tokenized_text in enumerate( + tqdm(pool.map(_asr_text_to_tokens, asr_texts, chunksize=1000), total=len(asr_texts)) + ): + self.data[i]["asr_text_tokens"] = tokenized_text + # force free memory + del asr_texts + gc.collect() + + if tokenizer_workers == 1: + logging.warning( + "Preprocessing large text with tokenizer_workers=1 may be slow with TTS tokenizer. " + "Prefer tokenizer_workers=(num_cpu_cores/num_gpus_per_node)" + ) + for i, tokenized_text in enumerate( + tqdm( + (self._tts_text_to_tokens(text, normalize=need_normalization) for text in tts_texts), + total=len(tts_texts), + ) + ): + self.data[i]["tts_text_tokens"] = tokenized_text + else: + if need_normalization: + # TODO: implement, if we really need normalization inplace + raise NotImplementedError( + "Normalization with tokenizer_workers > 1 is not implemented. " + "It is not recommended to use normalization on the fly at all, since it's extremely slow" + ) + + def _init_tts_tokenize_process(tokenizer): + global tts_tokenizer_global # process-global + tts_tokenizer_global = copy.deepcopy(tokenizer) + + with concurrent.futures.ProcessPoolExecutor( + initializer=_init_tts_tokenize_process, initargs=(tts_parser,), max_workers=tokenizer_workers, + ) as pool: + # chunk size for pool map is empirically chosen as a trade-off between speed and responsiveness + for i, tokenized_text in enumerate( + tqdm(pool.map(_tts_text_to_tokens, tts_texts, chunksize=1000), total=len(tts_texts)) + ): + self.data[i]["tts_text_tokens"] = tokenized_text + # force free memory + del tts_texts + gc.collect() + + def _asr_text_to_tokens(self, text: str) -> np.ndarray: + ids = self.asr_tokenizer.text_to_ids(text) + if self.asr_bos_id is not None: + ids = [self.asr_bos_id] + ids + if self.asr_eos_id is not None: + ids.append(self.asr_eos_id) + return np.asarray(ids) + + def _tts_text_to_tokens(self, text: str, normalize=True) -> np.ndarray: + if normalize: + text = self.tts_normalizer.normalize(text, **self.tts_normalizer_kwargs) + tokens = self.tts_parser(text) + return np.asarray(tokens) + + def __getitem__(self, index): + item = self.data[index] + return TextToTextItem( + transcript=torch.from_numpy(item["asr_text_tokens"]).long(), + tts_text=torch.from_numpy(item["tts_text_tokens"]).long(), + speaker=random.choice(self.speakers), + ) + + def __len__(self): + return len(self.data) + + +class TextToTextDataset(TextToTextDatasetBase, Dataset): + """Text-to-Text Map-style Dataset for hybrid ASR-TTS models""" + + def __init__( + self, + manifest_filepath: Union[AnyPath, List[AnyPath]], + speakers_filepath: Union[AnyPath, List[AnyPath]], + asr_tokenizer: TokenizerSpec, + asr_use_start_end_token: bool, + tts_parser: Callable, + tts_text_pad_id: int, + tts_text_normalizer: "Normalizer", + tts_text_normalizer_call_kwargs: Dict, + min_words: int = 1, + max_words: int = 1_000_000, + tokenizer_workers: int = 1, + ): + super().__init__( + manifest_filepath=manifest_filepath, + speakers_filepath=speakers_filepath, + asr_tokenizer=asr_tokenizer, + asr_use_start_end_token=asr_use_start_end_token, + tts_parser=tts_parser, + tts_text_pad_id=tts_text_pad_id, + tts_text_normalizer=tts_text_normalizer, + tts_text_normalizer_call_kwargs=tts_text_normalizer_call_kwargs, + min_words=min_words, + max_words=max_words, + tokenizer_workers=tokenizer_workers, + num_parts=1, + ) + + def collate_fn( + self, batch: List[Union[TextToTextItem, tuple]] + ) -> Union[TextToTextBatch, TextOrAudioToTextBatch, tuple]: + """ + Collate function for dataloader + Can accept mixed batch of text-to-text items and audio-text items (typical for ASR) + """ + return TextOrAudioToTextBatch.collate_fn( + batch=batch, asr_pad_id=self.asr_pad_id, tts_text_pad_id=self.tts_text_pad_id + ) + + +class TextToTextIterableDataset(TextToTextDatasetBase, IterableDataset): + """ + Text-to-Text Iterable Dataset for hybrid ASR-TTS models + Only part necessary for current process should be loaded and stored + """ + + def __init__( + self, + manifest_filepath: Union[AnyPath, List[AnyPath]], + speakers_filepath: Union[AnyPath, List[AnyPath]], + asr_tokenizer: TokenizerSpec, + asr_use_start_end_token: bool, + tts_parser: Callable, + tts_text_pad_id: int, + tts_text_normalizer: "Normalizer", + tts_text_normalizer_call_kwargs: Dict, + min_words: int = 1, + max_words: int = 1_000_000, + tokenizer_workers: int = 1, + num_parts: int = 1, + current_part_index: int = 0, + ): + super().__init__( + manifest_filepath=manifest_filepath, + speakers_filepath=speakers_filepath, + asr_tokenizer=asr_tokenizer, + asr_use_start_end_token=asr_use_start_end_token, + tts_parser=tts_parser, + tts_text_pad_id=tts_text_pad_id, + tts_text_normalizer=tts_text_normalizer, + tts_text_normalizer_call_kwargs=tts_text_normalizer_call_kwargs, + min_words=min_words, + max_words=max_words, + tokenizer_workers=tokenizer_workers, + num_parts=num_parts, + current_part_index=current_part_index, + ) + + def __iter__(self): + # Implementation based on docs: https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset + worker_info = torch.utils.data.get_worker_info() + if worker_info is None: # single-process data loading, return the full iterator + start = 0 + end = len(self) + else: # in a worker process + # split workload + per_worker = int(math.ceil(len(self) / float(worker_info.num_workers))) + worker_id = worker_info.id + start = worker_id * per_worker + end = min(start + per_worker, len(self)) + indices = np.arange(start, end) + np.random.shuffle(indices) + return map(self.__getitem__, indices) + + def collate_fn( + self, batch: List[Union[TextToTextItem, tuple]] + ) -> Union[TextToTextBatch, TextOrAudioToTextBatch, tuple]: + """ + Collate function for dataloader + Can accept mixed batch of text-to-text items and audio-text items (typical for ASR) + """ + return TextOrAudioToTextBatch.collate_fn( + batch=batch, asr_pad_id=self.asr_pad_id, tts_text_pad_id=self.tts_text_pad_id + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/__init__.py new file mode 100644 index 0000000..3e50cea --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/__init__.py @@ -0,0 +1,22 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.losses.angularloss import AngularSoftmaxLoss +from nemo.collections.asr.losses.audio_losses import SDRLoss +from nemo.collections.asr.losses.ctc import CTCLoss +from nemo.collections.asr.losses.lattice_losses import LatticeLoss +from nemo.collections.asr.losses.ssl_losses.contrastive import ContrastiveLoss +from nemo.collections.asr.losses.ssl_losses.ctc import CTCLossForSSL +from nemo.collections.asr.losses.ssl_losses.mlm import MLMLoss +from nemo.collections.asr.losses.ssl_losses.rnnt import RNNTLossForSSL diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/angularloss.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/angularloss.py new file mode 100644 index 0000000..e2aee9b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/angularloss.py @@ -0,0 +1,68 @@ +# ! /usr/bin/python +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + +from nemo.core.classes import Loss, Typing, typecheck +from nemo.core.neural_types import LabelsType, LogitsType, LossType, NeuralType + +__all__ = ['AngularSoftmaxLoss'] + + +class AngularSoftmaxLoss(Loss, Typing): + """ + Computes ArcFace Angular softmax angle loss + reference: https://openaccess.thecvf.com/content_CVPR_2019/papers/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.pdf + args: + scale: scale value for cosine angle + margin: margin value added to cosine angle + """ + + @property + def input_types(self): + """Input types definitions for AnguarLoss. + """ + return { + "logits": NeuralType(('B', 'D'), LogitsType()), + "labels": NeuralType(('B',), LabelsType()), + } + + @property + def output_types(self): + """Output types definitions for AngularLoss. + loss: + NeuralType(None) + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__(self, scale=20.0, margin=1.35): + super().__init__() + + self.eps = 1e-7 + self.scale = scale + self.margin = margin + + @typecheck() + def forward(self, logits, labels): + numerator = self.scale * torch.cos( + torch.acos(torch.clamp(torch.diagonal(logits.transpose(0, 1)[labels]), -1.0 + self.eps, 1 - self.eps)) + + self.margin + ) + excl = torch.cat( + [torch.cat((logits[i, :y], logits[i, y + 1 :])).unsqueeze(0) for i, y in enumerate(labels)], dim=0 + ) + denominator = torch.exp(numerator) + torch.sum(torch.exp(self.scale * excl), dim=1) + L = numerator - torch.log(denominator) + return -torch.mean(L) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/audio_losses.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/audio_losses.py new file mode 100644 index 0000000..62ce4a9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/audio_losses.py @@ -0,0 +1,412 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import List, Optional + +import numpy as np +import torch + +from nemo.collections.asr.parts.preprocessing.features import make_seq_mask_like +from nemo.collections.asr.parts.utils.audio_utils import toeplitz +from nemo.core.classes import Loss, Typing, typecheck +from nemo.core.neural_types import AudioSignal, LengthsType, LossType, MaskType, NeuralType +from nemo.utils import logging + +__all__ = ['SDRLoss'] + + +def temporal_mean( + input: torch.Tensor, + input_length: Optional[torch.Tensor] = None, + mask: Optional[torch.Tensor] = None, + keepdim: bool = False, + eps: float = 1e-10, +) -> torch.Tensor: + """Calculate mean along temporal dimension with optionally + averaging only over valid samples (based on the input length). + + Args: + input: Batch of signals, shape (B, C, T) + input_length: Optional, length of each example in the batch, shape (B,) + mask: Optional, temporal mask for each example in the batch, shape (B, T) + keepdim: Whether to keep the temporal dimension + eps: Regularization to avoid division by zero + + Returns: + (B, C, 1) if keepdim=True, otherwise (B, C) + """ + if input_length is not None: + if mask is not None: + raise RuntimeError( + 'Argument `input_length` is mutually exclusive with `mask`. Both cannot be used at the same time.' + ) + # Construct a binary mask + mask = make_seq_mask_like(lengths=input_length, like=input, time_dim=-1, valid_ones=True).squeeze(1) + + if mask is None: + # No length information, assume all samples are valid + mean = torch.mean(input, dim=-1, keepdim=keepdim) + else: + # Average using temporal mask + mean = mask.unsqueeze(1) * input + mean = torch.sum(mean, axis=-1, keepdim=keepdim) + normalization = torch.sum(mask, axis=-1, keepdim=keepdim) + mean = mean / (normalization.unsqueeze(1) + eps) + + return mean + + +def scale_invariant_target( + estimate: torch.Tensor, + target: torch.Tensor, + input_length: Optional[torch.Tensor] = None, + mask: Optional[torch.Tensor] = None, + eps: float = 1e-10, +) -> torch.Tensor: + """Calculate optimal scale-invariant target. + Assumes time dimension is the last dimension in the array. + + Calculate scaled target obtained by solving + + min_scale || scale * target - estimate ||^2 + + for each example in batch and each channel (b, c). + + Args: + estimate: tensor, shape (B, C, T) + target: tensor, shape (B, C, T) + input_length: optional, length of valid samples, shape (B,) + mask: optional, mask for input samples, shape (B, T) + eps: regularization constant + + Returns: + Scaled target, shape (B, C, T) + """ + if input_length is not None: + if mask is not None: + raise RuntimeError( + 'Argument `input_length` is mutually exclusive with `mask`. Both cannot be used at the same time.' + ) + + # Construct a binary mask + mask = make_seq_mask_like(lengths=input_length, like=estimate, time_dim=-1, valid_ones=True).squeeze(1) + + estimate_dot_target = temporal_mean(estimate * target, mask=mask, keepdim=True, eps=eps) + target_pow = temporal_mean(torch.abs(target) ** 2, mask=mask, keepdim=True, eps=eps) + scale = estimate_dot_target / (target_pow + eps) + target_scaled = scale * target + + # Mask to keep only the valid samples + if mask is not None: + target_scaled = mask.unsqueeze(1) * target_scaled + + return target_scaled + + +def convolution_invariant_target( + estimate: torch.Tensor, + target: torch.Tensor, + input_length: Optional[torch.Tensor] = None, + mask: Optional[torch.Tensor] = None, + filter_length: int = 512, + diag_reg: float = 1e-6, + eps: float = 1e-8, +) -> torch.Tensor: + """Calculate optimal convolution-invariant target for a given estimate. + Assumes time dimension is the last dimension in the array. + + Calculate target filtered with a linear f obtained by solving + + min_filter || conv(filter, target) - estimate ||^2 + + for each example in batch and each channel (b, c). + + Args: + estimate: tensor, shape (B, C, T) + target: tensor, shape (B, C, T) + input_length: optional, length of valid samples, shape (B,) + mask: optional, mask for input samples, shape (B, T) + filter_length: length of the (convolutional) filter for target + diag_reg: relative diagonal regularization for the linear system + eps: absolute regularization for the diagonal + + Returns: + Filtered target, shape (B, C, T) + + Reference: + C. Boeddeker et al., Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation, 2021 + """ + if input_length is not None: + if mask is not None: + raise RuntimeError( + 'Argument `input_length` is mutually exclusive with `mask`. Both cannot be used at the same time.' + ) + + if torch.min(input_length) < filter_length: + logging.warning( + 'Current min input_length (%d) is smaller than filter_length (%d). This will result in a singular linear system.', + torch.min(input_length), + filter_length, + ) + + # Construct a binary mask + mask = make_seq_mask_like(lengths=input_length, like=estimate, time_dim=-1, valid_ones=True).squeeze(1) + + # Apply a mask, if available + if mask is not None: + estimate = mask.unsqueeze(1) * estimate + target = mask.unsqueeze(1) * target + + # Calculate filtered target + input_shape = estimate.shape + estimate = estimate.view(-1, input_shape[-1]) + target = target.view(-1, input_shape[-1]) + + n_fft = 2 ** math.ceil(math.log2(2 * input_shape[-1] - 1)) + + T = torch.fft.rfft(target, n=n_fft) + E = torch.fft.rfft(estimate, n=n_fft) + + # Target autocorrelation + tt_corr = torch.fft.irfft(torch.abs(T) ** 2, n=n_fft) + # Target-estimate crosscorrelation + te_corr = torch.fft.irfft(T.conj() * E, n=n_fft) + + # Use only filter_length + tt_corr = tt_corr[..., :filter_length] + te_corr = te_corr[..., :filter_length] + + # Diagonal regularization + if diag_reg is not None: + tt_corr[..., 0] += diag_reg * tt_corr[..., 0] + eps + + # Construct the Toeplitz system matrix + TT = toeplitz(tt_corr) + + # Solve the linear system for the optimal filter + filt = torch.linalg.solve(TT, te_corr) + + # Calculate filtered target + T_filt = T * torch.fft.rfft(filt, n=n_fft) + target_filt = torch.fft.irfft(T_filt, n=n_fft) + + # Reshape to the original format + target_filt = target_filt[..., : input_shape[-1]].view(*input_shape) + + # Mask to keep only the valid samples + if mask is not None: + target_filt = mask.unsqueeze(1) * target_filt + + return target_filt + + +def calculate_sdr_batch( + estimate: torch.Tensor, + target: torch.Tensor, + input_length: Optional[torch.Tensor] = None, + mask: Optional[torch.Tensor] = None, + scale_invariant: bool = False, + convolution_invariant: bool = False, + convolution_filter_length: Optional[int] = 512, + remove_mean: bool = True, + sdr_max: Optional[float] = None, + eps: float = 1e-8, +) -> torch.Tensor: + """Calculate signal-to-distortion ratio per channel. + + SDR = 10 * log10( ||t||_2^2 / (||e-t||_2^2 + alpha * ||t||^2) + + where + alpha = 10^(-sdr_max/10) + + Optionally, use scale- or convolution- invariant target signal. + + Args: + estimate: estimated signal, shape (B, C, T) + target: target signal, shape (B, C, T) + input_length: Optional, length of valid samples, shape (B,) + mask: Optional, temporal mask, shape (B, T) + scale_invariant: Use scale invariant SDR + convolution_invariant: Use convolution invariant SDR + convolution_filter_length: Filter length for convolution invariant SDR + remove_mean: If True, mean will be removed before calculating SDR + eps: Small regularization constant + + Returns: + SDR in dB for each channel, shape (B, C) + """ + if scale_invariant and convolution_invariant: + raise ValueError(f'Arguments scale_invariant and convolution_invariant cannot be used simultaneously.') + + assert ( + estimate.shape == target.shape + ), f'Estimate shape ({estimate.shape}) not matching target shape ({target.shape})' + + if input_length is not None: + if mask is not None: + raise RuntimeError( + 'Argument `input_length` is mutually exclusive with `mask`. Both cannot be used at the same time.' + ) + + # Construct a binary mask + mask = make_seq_mask_like(lengths=input_length, like=estimate, time_dim=-1, valid_ones=True).squeeze(1) + + if remove_mean: + estimate = estimate - temporal_mean(estimate, mask=mask, keepdim=True, eps=eps) + target = target - temporal_mean(target, mask=mask, keepdim=True, eps=eps) + + if scale_invariant or (convolution_invariant and convolution_filter_length == 1): + target = scale_invariant_target(estimate=estimate, target=target, mask=mask, eps=eps) + elif convolution_invariant: + target = convolution_invariant_target( + estimate=estimate, target=target, mask=mask, filter_length=convolution_filter_length, eps=eps, + ) + + distortion = estimate - target + + target_pow = temporal_mean(torch.abs(target) ** 2, mask=mask, eps=eps) + distortion_pow = temporal_mean(torch.abs(distortion) ** 2, mask=mask, eps=eps) + + if sdr_max is not None: + distortion_pow = distortion_pow + 10 ** (-sdr_max / 10) * target_pow + + sdr = target_pow / (distortion_pow + eps) + sdr = 10 * torch.log10(sdr + eps) + + return sdr + + +class SDRLoss(Loss, Typing): + """ + Computes signal-to-distortion ratio (SDR) loss with weighted average across channels. + + Args: + weight: weight for SDR of each output channel, used for averaging the loss across channels. Defaults to `None` (averaging). + reduction: batch reduction. Defaults to `mean` over the batch. + scale_invariant: If `True`, use scale-invariant SDR. Defaults to `False`. + remove_mean: Remove mean before calculating the loss. Defaults to `True`. + sdr_max: Soft thresholding of the loss to SDR_max. + eps: Small value for regularization. + """ + + def __init__( + self, + weight: Optional[List[float]] = None, + reduction: str = 'mean', + scale_invariant: bool = False, + convolution_invariant: bool = False, + convolution_filter_length: Optional[int] = 512, + remove_mean: bool = True, + sdr_max: Optional[float] = None, + eps: float = 1e-8, + ): + super().__init__() + + # SDR weight buffer + if weight is not None: + if any([w <= 0 for w in weight]): + raise ValueError(f'Weight must be positive! Current value: {weight}') + elif not np.isclose(sum(weight), 1, atol=1e-6): + raise ValueError(f'Weight should add to one, current weight: {weight}') + weight = torch.tensor(weight).reshape(1, -1) + logging.info(f'Channel weight set to %s', weight) + self.register_buffer('weight', weight) + self.weight: Optional[Tensor] + + # Batch reduction + self.reduction = reduction + if reduction == 'mean': + self.reduce = torch.mean + else: + raise ValueError(f'Unexpected reduction mode {reduction}.') + + # SDR calculation setup + if scale_invariant and convolution_invariant: + raise ValueError( + f'{self.__class__.__name__}: arguments scale_invariant and convolution_invariant cannot be used simultaneously.' + ) + self.scale_invariant = scale_invariant + self.convolution_invariant = convolution_invariant + self.convolution_filter_length = convolution_filter_length + self.remove_mean = remove_mean + self.sdr_max = sdr_max + self.eps = eps + + @property + def input_types(self): + """Input types definitions for SDRLoss. + """ + signal_shape = ('B', 'C', 'T') + return { + "estimate": NeuralType(signal_shape, AudioSignal()), + "target": NeuralType(signal_shape, AudioSignal()), + "input_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "mask": NeuralType(('B', 'T'), MaskType(), optional=True), + } + + @property + def output_types(self): + """Output types definitions for SDRLoss. + loss: + NeuralType(None) + """ + return {"loss": NeuralType(elements_type=LossType())} + + @typecheck() + def forward( + self, + estimate: torch.Tensor, + target: torch.Tensor, + input_length: Optional[torch.Tensor] = None, + mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """For input batch of multi-channel signals, calculate SDR between estimate and target for each channel, + perform averaging across channels (weighting optional), and apply reduction across the batch. + + Args: + estimate: Batch of signals, shape (B, T, C) + target: Batch of signals, shape (B, T, C) + input_length: Batch of lengths, shape (B,) + mask: Batch of temporal masks, shape (B, T) + + Returns: + Scalar loss. + """ + + sdr = calculate_sdr_batch( + estimate=estimate, + target=target, + input_length=input_length, + mask=mask, + scale_invariant=self.scale_invariant, + convolution_invariant=self.convolution_invariant, + convolution_filter_length=self.convolution_filter_length, + remove_mean=self.remove_mean, + sdr_max=self.sdr_max, + eps=self.eps, + ) + + # channel averaging + if self.weight is None: + sdr = torch.mean(sdr, dim=1) + else: + # weighting across channels + sdr = sdr * self.weight + sdr = torch.sum(sdr, dim=1) + + # reduction + sdr = self.reduce(sdr) + + return -sdr diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/bce_loss.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/bce_loss.py new file mode 100644 index 0000000..30e31b8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/bce_loss.py @@ -0,0 +1,73 @@ +# ! /usr/bin/python +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + +from nemo.core.classes import Loss, Typing, typecheck +from nemo.core.neural_types import LabelsType, LengthsType, LossType, NeuralType, ProbsType + +__all__ = ['BCELoss'] + + +class BCELoss(Loss, Typing): + """ + Computes Binary Cross Entropy (BCE) loss. The BCELoss class expects output from Sigmoid function. + """ + + @property + def input_types(self): + """Input types definitions for AnguarLoss. + """ + return { + "probs": NeuralType(('B', 'T', 'C'), ProbsType()), + 'labels': NeuralType(('B', 'T', 'C'), LabelsType()), + "signal_lengths": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """ + Output types definitions for binary cross entropy loss. Weights for labels can be set using weight variables. + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__(self, reduction='sum', alpha=1.0, weight=torch.tensor([0.5, 0.5])): + super().__init__() + self.reduction = reduction + self.loss_weight = weight + self.loss_f = torch.nn.BCELoss(weight=self.loss_weight, reduction=self.reduction) + + @typecheck() + def forward(self, probs, labels, signal_lengths): + """ + Calculate binary cross entropy loss based on probs, labels and signal_lengths variables. + + Args: + probs (torch.tensor) + Predicted probability value which ranges from 0 to 1. Sigmoid output is expected. + labels (torch.tensor) + Groundtruth label for the predicted samples. + signal_lengths (torch.tensor): + The actual length of the sequence without zero-padding. + + Returns: + loss (NeuralType) + Binary cross entropy loss value. + """ + probs_list = [probs[k, : signal_lengths[k], :] for k in range(probs.shape[0])] + targets_list = [labels[k, : signal_lengths[k], :] for k in range(labels.shape[0])] + probs = torch.cat(probs_list, dim=0) + labels = torch.cat(targets_list, dim=0) + return self.loss_f(probs, labels) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ctc.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ctc.py new file mode 100644 index 0000000..8a1f724 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ctc.py @@ -0,0 +1,82 @@ +# ! /usr/bin/python +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from torch import nn + +from nemo.core.classes import Serialization, Typing, typecheck +from nemo.core.neural_types import LabelsType, LengthsType, LogprobsType, LossType, NeuralType + +__all__ = ['CTCLoss'] + + +class CTCLoss(nn.CTCLoss, Serialization, Typing): + @property + def input_types(self): + """Input types definitions for CTCLoss. + """ + return { + "log_probs": NeuralType(('B', 'T', 'D'), LogprobsType()), + "targets": NeuralType(('B', 'T'), LabelsType()), + "input_lengths": NeuralType(tuple('B'), LengthsType()), + "target_lengths": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Output types definitions for CTCLoss. + loss: + NeuralType(None) + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__(self, num_classes, zero_infinity=False, reduction='mean_batch'): + self._blank = num_classes + # Don't forget to properly call base constructor + if reduction not in ['none', 'mean', 'sum', 'mean_batch', 'mean_volume']: + raise ValueError('`reduction` must be one of [mean, sum, mean_batch, mean_volume]') + + self.config_reduction = reduction + if reduction == 'mean_batch' or reduction == 'mean_volume': + ctc_reduction = 'none' + self._apply_reduction = True + elif reduction in ['sum', 'mean', 'none']: + ctc_reduction = reduction + self._apply_reduction = False + super().__init__(blank=self._blank, reduction=ctc_reduction, zero_infinity=zero_infinity) + + def reduce(self, losses, target_lengths): + if self.config_reduction == 'mean_batch': + losses = losses.mean() # global batch size average + elif self.config_reduction == 'mean_volume': + losses = losses.sum() / target_lengths.sum() # same as above but longer samples weigh more + + return losses + + @typecheck() + def forward(self, log_probs, targets, input_lengths, target_lengths): + # override forward implementation + # custom logic, if necessary + input_lengths = input_lengths.long() + target_lengths = target_lengths.long() + targets = targets.long() + # here we transpose because we expect [B, T, D] while PyTorch assumes [T, B, D] + log_probs = log_probs.transpose(1, 0) + loss = super().forward( + log_probs=log_probs, targets=targets, input_lengths=input_lengths, target_lengths=target_lengths + ) + if self._apply_reduction: + loss = self.reduce(loss, target_lengths) + return loss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/lattice_losses.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/lattice_losses.py new file mode 100644 index 0000000..7dae44b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/lattice_losses.py @@ -0,0 +1,184 @@ +# ! /usr/bin/python +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional + +import torch +from omegaconf import DictConfig + +from nemo.core.classes import Loss, typecheck +from nemo.core.neural_types import LabelsType, LengthsType, LogprobsType, LossType, NeuralType + + +class LatticeLoss(Loss): + """Family of loss functions based on various lattice scores. + + Note: + Requires k2 v1.14 or later to be installed to use this loss function. + + Losses can be selected via the config, and optionally be passed keyword arguments as follows. + + Examples: + .. code-block:: yaml + + model: # Model config + ... + graph_module_cfg: # Config for graph modules, e.g. LatticeLoss + criterion_type: "map" + loss_type: "mmi" + split_batch_size: 0 + backend_cfg: + topo_type: "default" # other options: "compact", "shared_blank", "minimal" + topo_with_self_loops: true + token_lm: # must be provided for criterion_type: "map" + + Args: + num_classes: Number of target classes for the decoder network to predict. + (Excluding the blank token). + + reduction: Type of reduction to perform on loss. Possible values are `mean_batch`, `mean`, `sum`, or None. + None will return a torch vector comprising the individual loss values of the batch. + + backend: Which backend to use for loss calculation. Currently only `k2` is supported. + + criterion_type: Type of criterion to use. Choices: `ml` and `map`, + with `ml` standing for Maximum Likelihood and `map` for Maximum A Posteriori Probability. + + loss_type: Type of the loss function to use. Choices: `ctc` and `rnnt` for `ml`, and `mmi` for `map`. + + split_batch_size: Local batch size. Used for memory consumption reduction at the cost of speed performance. + Effective if complies 0 < split_batch_size < batch_size. + + graph_module_cfg: Optional Dict of (str, value) pairs that are passed to the backend loss function. + """ + + @property + def input_types(self): + """Input types definitions for LatticeLoss. + """ + return { + "log_probs": NeuralType(("B", "T", "D") if self._3d_input else ("B", "T", "T", "D"), LogprobsType()), + "targets": NeuralType(("B", "T"), LabelsType()), + "input_lengths": NeuralType(tuple("B"), LengthsType()), + "target_lengths": NeuralType(tuple("B"), LengthsType()), + } + + @property + def output_types(self): + """Output types definitions for LatticeLoss. + loss: + NeuralType(None) + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__( + self, + num_classes: int, + reduction: str = "mean_batch", + backend: str = "k2", + criterion_type: str = "ml", + loss_type: str = "ctc", + split_batch_size: int = 0, + graph_module_cfg: Optional[DictConfig] = None, + ): + super().__init__() + self._blank = num_classes + self.split_batch_size = split_batch_size + inner_reduction = None + if reduction == "mean_batch": + inner_reduction = "none" + self._apply_batch_mean = True + elif reduction in ["sum", "mean", "none"]: + inner_reduction = reduction + self._apply_batch_mean = False + + # we assume that self._blank + 1 == num_classes + if backend == "k2": + if criterion_type == "ml": + if loss_type == "ctc": + from nemo.collections.asr.parts.k2.ml_loss import CtcLoss as K2Loss + elif loss_type == "rnnt": + from nemo.collections.asr.parts.k2.ml_loss import RnntLoss as K2Loss + else: + raise ValueError(f"Unsupported `loss_type`: {loss_type}.") + elif criterion_type == "map": + if loss_type == "ctc": + from nemo.collections.asr.parts.k2.map_loss import CtcMmiLoss as K2Loss + else: + raise ValueError(f"Unsupported `loss_type`: {loss_type}.") + else: + raise ValueError(f"Unsupported `criterion_type`: {criterion_type}.") + + self._loss = K2Loss( + num_classes=self._blank + 1, blank=self._blank, reduction=inner_reduction, cfg=graph_module_cfg, + ) + elif backend == "gtn": + raise NotImplementedError(f"Backend {backend} is not supported.") + else: + raise ValueError(f"Invalid value of `backend`: {backend}.") + + self.criterion_type = criterion_type + self.loss_type = loss_type + self._3d_input = self.loss_type != "rnnt" + + if self.split_batch_size > 0: + # don't need to guard grad_utils + from nemo.collections.asr.parts.k2.grad_utils import PartialGrad + + self._partial_loss = PartialGrad(self._loss) + + def update_graph(self, graph): + """Updates graph of the backend loss function. + """ + if self.criterion_type != "ml": + self._loss.update_graph(graph) + + @typecheck() + def forward(self, log_probs, targets, input_lengths, target_lengths): + # override forward implementation + # custom logic, if necessary + + assert not (torch.isnan(log_probs).any() or torch.isinf(log_probs).any()) + + log_probs = log_probs.float() + input_lengths = input_lengths.long() + target_lengths = target_lengths.long() + targets = targets.long() + batch_size = log_probs.shape[0] + if self.split_batch_size > 0 and self.split_batch_size <= batch_size: + loss_list = [] + for batch_idx in range(0, batch_size, self.split_batch_size): + begin = batch_idx + end = min(begin + self.split_batch_size, batch_size) + input_lengths_part = input_lengths[begin:end] + log_probs_part = log_probs[begin:end, : input_lengths_part.max()] + target_lengths_part = target_lengths[begin:end] + targets_part = targets[begin:end, : target_lengths_part.max()] + loss_part, _ = ( + self._partial_loss(log_probs_part, targets_part, input_lengths_part, target_lengths_part) + if log_probs_part.requires_grad + else self._loss(log_probs_part, targets_part, input_lengths_part, target_lengths_part) + ) + del log_probs_part, targets_part, input_lengths_part, target_lengths_part + loss_list.append(loss_part) + loss = torch.cat(loss_list, 0) + else: + loss, _ = self._loss( + log_probs=log_probs, targets=targets, input_lengths=input_lengths, target_lengths=target_lengths, + ) + if self._apply_batch_mean: + # torch.mean gives nan if loss is empty + loss = torch.mean(loss) if loss.nelement() > 0 else torch.sum(loss) + return loss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/rnnt.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/rnnt.py new file mode 100644 index 0000000..894be63 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/rnnt.py @@ -0,0 +1,508 @@ +# ! /usr/bin/python +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright 2018-2019, Mingkun Huang +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect +import operator +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Optional, Set + +import torch +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.asr.losses.rnnt_pytorch import MultiblankRNNTLossPytorch, RNNTLossPytorch, TDTLossPytorch +from nemo.core.classes import Loss, typecheck +from nemo.core.neural_types import LabelsType, LengthsType, LogprobsType, LossType, NeuralType +from nemo.core.utils import numba_utils +from nemo.core.utils.k2_utils import K2_INSTALLATION_MESSAGE +from nemo.core.utils.numba_utils import NUMBA_INSTALLATION_MESSAGE +from nemo.utils import logging, logging_mode, model_utils + +try: + import warprnnt_pytorch as warprnnt + + WARP_RNNT_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + WARP_RNNT_AVAILABLE = False + +try: + from nemo.collections.asr.parts.numba.rnnt_loss import MultiblankRNNTLossNumba, RNNTLossNumba, TDTLossNumba + + NUMBA_RNNT_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + NUMBA_RNNT_AVAILABLE = False + +try: + from nemo.collections.asr.parts.k2.graph_transducer import GraphRnntLoss + from nemo.collections.asr.parts.k2.w_transducer import GraphWTransducerLoss + + K2_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + K2_AVAILABLE = False + +WARP_RNNT_INSTALLATION_MESSAGE = ( + "Could not import `warprnnt_pytorch`.\n" + "Please visit https://github.com/HawkAaron/warp-transducer " + "and follow the steps in the readme to build and install the " + "pytorch bindings for RNNT Loss, or use the provided docker " + "container that supports RNN-T loss." +) + + +@dataclass +class RNNTLossConfig: + loss_name: str + lib_name: str + is_available: bool = False + installation_msg: str = "" + min_version: Optional[str] = None + force_float32: bool = True # default True for now for all losses except graph-based + + +# Resolved list of available RNNT losses +RNNT_LOSS_RESOLVER = { + "warprnnt": RNNTLossConfig( + loss_name="warprnnt", + lib_name="warprnnt_pytorch", + is_available=WARP_RNNT_AVAILABLE, + installation_msg=WARP_RNNT_INSTALLATION_MESSAGE, + force_float32=True, + ), + "warprnnt_numba": RNNTLossConfig( + loss_name="warprnnt_numba", + lib_name="numba", + min_version='0.53.0', + is_available=NUMBA_RNNT_AVAILABLE, + installation_msg=NUMBA_INSTALLATION_MESSAGE, + force_float32=False, # This is only temporarily false, will be dynamically updated during resolution + ), + "pytorch": RNNTLossConfig( + loss_name="pytorch", + lib_name="torch", + min_version='0.0', + is_available=True, + installation_msg="Pure Pytorch implementation of RNN-T loss. Slow and for debugging purposes only.", + force_float32=True, + ), + "multiblank_rnnt": RNNTLossConfig( + loss_name="multiblank_rnnt", + lib_name="numba", + min_version='0.53.0', + is_available=NUMBA_RNNT_AVAILABLE, + installation_msg=NUMBA_INSTALLATION_MESSAGE, + force_float32=True, + ), + "multiblank_rnnt_pytorch": RNNTLossConfig( + loss_name="pytorch", + lib_name="torch", + min_version='0.0', + is_available=True, + installation_msg="Pure Pytorch implementation of Multiblank RNN-T loss. Slow and for debugging purposes only.", + force_float32=True, + ), + "graph_w_transducer": RNNTLossConfig( + loss_name="graph_w_transducer", + lib_name="k2", + is_available=K2_AVAILABLE, + installation_msg=K2_INSTALLATION_MESSAGE, + force_float32=False, + ), + "graph_rnnt": RNNTLossConfig( + loss_name="graph_rnnt", + lib_name="k2", + is_available=K2_AVAILABLE, + installation_msg=K2_INSTALLATION_MESSAGE, + force_float32=False, + ), + "tdt": RNNTLossConfig( + loss_name="tdt", + lib_name="numba", + min_version='0.53.0', + is_available=NUMBA_RNNT_AVAILABLE, + installation_msg=NUMBA_INSTALLATION_MESSAGE, + ), + "tdt_pytorch": RNNTLossConfig( + loss_name="tdt_pytorch", + lib_name="torch", + min_version='0.0', + is_available=True, + installation_msg="Pure Pytorch implementation of TDT loss. Slow and for debugging purposes only.", + ), +} + +RNNT_LOSS_RESOLVER['default'] = RNNT_LOSS_RESOLVER['warprnnt_numba'] + + +def _warn_unused_additional_kwargs(loss_name, kwargs): + if len(kwargs) > 0: + logging.warning( + f"Loss function `{loss_name}` was provided with following additional kwargs,\n" + f"however they were ignored as it is unused.\n" + f"{kwargs}" + ) + + +def _clean_kwargs( + loss_name: str, kwargs: Optional[Dict[str, Any]], init_method: Callable, ignore_params: Optional[Set[str]] = None +) -> Dict[str, Any]: + """ + Cleans kwargs for the given loss function. Warn if there are unused kwargs. + + Args: + loss_name: name of the loss function + kwargs: kwargs to clean + init_method: LossClass.__init__ method + ignore_params: set of argument names for init_method to ignore + + Returns: + only used kwargs for the given `init_method` + """ + if not kwargs: + return {} + init_params = set(inspect.signature(init_method).parameters.keys()) - {"self"} + if ignore_params is not None: + init_params -= ignore_params + unused_kwargs = dict() + used_kwargs = dict() + for key, value in kwargs.items(): + if key not in init_params: + unused_kwargs[key] = value + else: + used_kwargs[key] = value + if len(unused_kwargs) > 0: + _warn_unused_additional_kwargs(loss_name, unused_kwargs) + return used_kwargs + + +def resolve_rnnt_default_loss_name() -> str: + return RNNT_LOSS_RESOLVER['default'].loss_name + + +def resolve_rnnt_loss(loss_name: str, blank_idx: int, loss_kwargs: dict = None) -> torch.nn.Module: + loss_function_names = list(RNNT_LOSS_RESOLVER.keys()) + + if loss_name not in loss_function_names: + raise ValueError( + f"Provided `loss_name` {loss_name} not in list of available RNNT losses \n" f"{loss_function_names}" + ) + + all_available_losses = {name: config for name, config in RNNT_LOSS_RESOLVER.items() if config.is_available} + + loss_config = RNNT_LOSS_RESOLVER[loss_name] # type: RNNTLossConfig + + # Re-raise import error with installation message + if not loss_config.is_available: + msg = ( + f"Installed RNNT losses are : {list(all_available_losses.keys())}.\n" + f"****************************************************************\n" + f"To install the selected loss function, please follow the steps below:\n" + f"{loss_config.installation_msg}" + ) + raise ImportError(msg) + + # Library version check + if loss_config.min_version is not None: + ver_matched, msg = model_utils.check_lib_version( + loss_config.lib_name, checked_version=loss_config.min_version, operator=operator.ge + ) + + if ver_matched is False: + msg = ( + f"{msg}\n" + f"****************************************************************\n" + f"To update the selected loss function, please follow the steps below:\n" + f"{loss_config.installation_msg}" + ) + raise RuntimeError(msg) + + # Resolve loss functions sequentially + loss_kwargs = {} if loss_kwargs is None else loss_kwargs + + if isinstance(loss_kwargs, DictConfig): + loss_kwargs = OmegaConf.to_container(loss_kwargs, resolve=True) + + # Get actual loss name for `default` + if loss_name == 'default': + loss_name = loss_config.loss_name + + """ + Resolve RNNT loss functions + """ + if loss_name == 'warprnnt': + loss_func = warprnnt.RNNTLoss(blank=blank_idx, reduction='none') + _warn_unused_additional_kwargs(loss_name, loss_kwargs) + + elif loss_name == 'warprnnt_numba': + # Update loss config's forced float32 flag if set to None + loss_config.force_float32 = not numba_utils.is_numba_cuda_fp16_supported() + + fastemit_lambda = loss_kwargs.pop('fastemit_lambda', 0.0) + clamp = loss_kwargs.pop('clamp', -1.0) + loss_func = RNNTLossNumba(blank=blank_idx, reduction='none', fastemit_lambda=fastemit_lambda, clamp=clamp) + _warn_unused_additional_kwargs(loss_name, loss_kwargs) + + elif loss_name == 'pytorch': + loss_func = RNNTLossPytorch(blank=blank_idx, reduction='none') + _warn_unused_additional_kwargs(loss_name, loss_kwargs) + + elif loss_name == 'multiblank_rnnt': + fastemit_lambda = loss_kwargs.pop('fastemit_lambda', 0.0) + clamp = loss_kwargs.pop('clamp', -1.0) + big_blank_durations = loss_kwargs.pop('big_blank_durations', None) + sigma = loss_kwargs.pop('sigma', 0.0) + loss_func = MultiblankRNNTLossNumba( + blank=blank_idx, + big_blank_durations=big_blank_durations, + reduction='none', + fastemit_lambda=fastemit_lambda, + clamp=clamp, + sigma=sigma, + ) + _warn_unused_additional_kwargs(loss_name, loss_kwargs) + + elif loss_name == 'multiblank_rnnt_pytorch': + big_blank_durations = loss_kwargs.pop('big_blank_durations', None) + sigma = loss_kwargs.pop('sigma', 0.0) + loss_func = MultiblankRNNTLossPytorch( + blank=blank_idx, big_blank_durations=big_blank_durations, reduction='none', sigma=sigma + ) + _warn_unused_additional_kwargs(loss_name, loss_kwargs) + + elif loss_name == 'tdt': + fastemit_lambda = loss_kwargs.pop('fastemit_lambda', 0.0) + clamp = loss_kwargs.pop('clamp', -1.0) + durations = loss_kwargs.pop('durations', None) + sigma = loss_kwargs.pop('sigma', 0.0) + omega = loss_kwargs.pop('omega', 0.0) + loss_func = TDTLossNumba( + blank=blank_idx, + durations=durations, + reduction='none', + fastemit_lambda=fastemit_lambda, + clamp=clamp, + sigma=sigma, + omega=omega, + ) + _warn_unused_additional_kwargs(loss_name, loss_kwargs) + + elif loss_name == 'tdt_pytorch': + durations = loss_kwargs.pop('durations', None) + sigma = loss_kwargs.pop('sigma', 0.0) + loss_func = TDTLossPytorch(blank=blank_idx, durations=durations, reduction='none', sigma=sigma) + _warn_unused_additional_kwargs(loss_name, loss_kwargs) + + elif loss_name == "graph_rnnt": + loss_kwargs = _clean_kwargs(loss_name, loss_kwargs, GraphRnntLoss.__init__, ignore_params={"blank"}) + loss_func = GraphRnntLoss(blank=blank_idx, **loss_kwargs) + elif loss_name == "graph_w_transducer": + loss_kwargs = _clean_kwargs(loss_name, loss_kwargs, GraphWTransducerLoss.__init__, ignore_params={"blank"}) + loss_func = GraphWTransducerLoss(blank=blank_idx, **loss_kwargs) + else: + raise ValueError( + f"Invalid value of `loss_name`: {loss_name}. Allowed loss names are :" f"{loss_function_names}" + ) + + return loss_func + + +class RNNTLoss(Loss): + @property + def input_types(self): + """Input types definitions for CTCLoss. + """ + return { + "log_probs": NeuralType(('B', 'T', 'T', 'D'), LogprobsType()), + "targets": NeuralType(('B', 'T'), LabelsType()), + "input_lengths": NeuralType(tuple('B'), LengthsType()), + "target_lengths": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Output types definitions for CTCLoss. + loss: + NeuralType(None) + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__(self, num_classes, reduction: str = 'mean_batch', loss_name: str = "default", loss_kwargs=None): + """ + RNN-T Loss function based on https://github.com/HawkAaron/warp-transducer. + Optionally, can utilize a numba implementation of the same loss without having to compile the loss, + albiet there is a small speed penalty for JIT numba compile. + + Note: + Requires Numba 0.53.0 or later to be installed to use this loss function. + + Losses can be selected via the config, and optionally be passed keyword arguments as follows. + + Examples: + .. code-block:: yaml + + model: # RNNT Model config + ... + loss: + loss_name: "warprnnt_numba" + warprnnt_numba_kwargs: + fastemit_lambda: 0.0 + + Warning: + In the case that GPU memory is exhausted in order to compute RNNTLoss, it might cause + a core dump at the cuda level with the following error message. + + ``` + ... + costs = costs.to(acts.device) + RuntimeError: CUDA error: an illegal memory access was encountered + terminate called after throwing an instance of 'c10::Error' + ``` + + Please kill all remaining python processes after this point, and use a smaller batch size + for train, validation and test sets so that CUDA memory is not exhausted. + + Args: + num_classes: Number of target classes for the joint network to predict. + In all cases (conventional RNNT, multi-blank RNNT, and TDT model), this equals the token-id + for the standard "blank" symbol. In particular, say V is the number of non-blank tokens in + the vocabulary, then in the case of, + standard RNNT: num_classes = V + multiblank RNNT: num_classes = V + number-big-blanks (since we store big-blanks before + standard blank, and the standard blank is the last symbol in the vocab) + TDT: num_classes = V. Note, V here does not include any of the "duration outputs". + + reduction: Type of reduction to perform on loss. Possible values are + `mean_batch`, 'mean_volume`, `mean`, `sum` or None. + `None` will return a torch vector comprising the individual loss values of the batch. + `mean_batch` will average the losses in the batch + `mean` will divide each loss by the target length and then average + `mean_volume` will add up all the losses and divide by sum of target lengths + + loss_name: String that is resolved into an RNNT loss function. Available list of losses + is ininitialized in `RNNT_LOSS_RESOLVER` dictionary. + + loss_kwargs: Optional Dict of (str, value) pairs that are passed to the instantiated loss + function. + """ + super(RNNTLoss, self).__init__() + + if reduction not in [None, 'mean', 'sum', 'mean_batch', 'mean_volume']: + raise ValueError('`reduction` must be one of [mean, sum, mean_batch, mean_volume]') + + self._blank = num_classes + self.reduction = reduction + self._loss = resolve_rnnt_loss(loss_name, blank_idx=self._blank, loss_kwargs=loss_kwargs) + self._force_float32 = RNNT_LOSS_RESOLVER[loss_name].force_float32 + self._fp16_compat_checked = False + + def reduce(self, losses, target_lengths): + + if isinstance(losses, List): + losses = torch.cat(losses, 0) + target_lengths = torch.cat(target_lengths, 0) + + if self.reduction == 'mean_batch': + losses = losses.mean() # global batch size average + elif self.reduction == 'mean': + losses = torch.div(losses, target_lengths).mean() + elif self.reduction == 'sum': + losses = losses.sum() + elif self.reduction == 'mean_volume': + losses = losses.sum() / target_lengths.sum() # same as above but longer samples weigh more + + return losses + + @typecheck() + def forward(self, log_probs, targets, input_lengths, target_lengths): + # Cast to int 64 + targets = targets.long() + input_lengths = input_lengths.long() + target_lengths = target_lengths.long() + + max_logit_len = input_lengths.max() + max_targets_len = target_lengths.max() + + # Force cast joint to float32 + if not self._force_float32 and numba_utils.is_numba_cuda_fp16_supported(): + # Execute the kernel in fp16 + pass + elif self._force_float32 and log_probs.dtype != torch.float32: + # Log just once if fp16 tensor was passed and fp16 Numba CUDA loss could not be used. + if log_probs.dtype == torch.float16 and not self._fp16_compat_checked: + _, reason = numba_utils.is_numba_cuda_fp16_supported(return_reason=True) + logging.warning( + f"Provided RNNT Joint tensor is of dtype {log_probs.dtype}, but RNNT loss could not be calculated " + f"in fp16 due to following reason stated below. Loss will be calculated in fp32. \n\n" + f"{reason}", + mode=logging_mode.ONCE, + ) + self._fp16_compat_checked = True + + # Upcast the activation tensor and compute loss and grads in fp32 + logits_orig = log_probs + log_probs = log_probs.float() + del logits_orig # save memory *before* computing the loss + + # Ensure that shape mismatch does not occur due to padding + # Due to padding and subsequent downsampling, it may be possible that + # max sequence length computed does not match the actual max sequence length + # of the log_probs tensor, therefore we increment the input_lengths by the difference. + # This difference is generally small. + if log_probs.shape[1] != max_logit_len: + log_probs = log_probs.narrow(dim=1, start=0, length=max_logit_len).contiguous() + + # Reduce transcript length to correct alignment if additional padding was applied. + # Transcript: [B, L] -> [B, L']; If L' < L + if not targets.is_contiguous(): + targets = targets.contiguous() + + if targets.shape[1] != max_targets_len: + targets = targets.narrow(dim=1, start=0, length=max_targets_len).contiguous() + + # Temporarily override loss reduction + loss_reduction = self._loss.reduction + self._loss.reduction = None + + # Compute RNNT loss + loss = self._loss(acts=log_probs, labels=targets, act_lens=input_lengths, label_lens=target_lengths) + + # Loss reduction can be dynamic, so reset it after call + self._loss.reduction = loss_reduction + + # reduce here using our own reduction function + if self.reduction is not None: + loss = self.reduce(loss, target_lengths) + + # del new variables that may have been created + del ( + log_probs, + targets, + input_lengths, + target_lengths, + ) + + return loss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/rnnt_pytorch.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/rnnt_pytorch.py new file mode 100644 index 0000000..c8eee90 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/rnnt_pytorch.py @@ -0,0 +1,374 @@ +# ! /usr/bin/python +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List + +import torch + +from nemo.core.classes import Loss +from nemo.core.neural_types import LabelsType, LengthsType, LogprobsType, LossType, NeuralType + + +class RNNTLossPytorch(Loss): + @property + def input_types(self): + """Input types definitions for CTCLoss. + """ + return { + "acts": NeuralType(('B', 'T', 'T', 'D'), LogprobsType()), + "labels": NeuralType(('B', 'T'), LabelsType()), + "act_lens": NeuralType(tuple('B'), LengthsType()), + "label_lens": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Output types definitions for CTCLoss. + loss: + NeuralType(None) + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__(self, blank, reduction): + super().__init__() + self.blank = blank + self.reduction = reduction + + def forward(self, acts, labels, act_lens, label_lens): + # CPU patch for FP16 + if not acts.is_cuda and acts.dtype == torch.float16: + acts = acts.float() + + acts = torch.log_softmax(acts, -1) + + forward_logprob = self.compute_forward_prob(acts, labels, act_lens, label_lens) + losses = -forward_logprob + if self.reduction == 'mean_batch': + losses = losses.mean() # global batch size average + elif self.reduction == 'mean': + losses = torch.div(losses, label_lens).mean() + elif self.reduction == 'sum': + losses = losses.sum() + elif self.reduction == 'mean_volume': + losses = losses.sum() / label_lens.sum() # same as above but longer samples weigh more + + return losses + + def compute_forward_prob(self, acts, labels, act_lens, label_lens): + B, T, U, _ = acts.shape + + log_alpha = torch.zeros(B, T, U) + log_alpha = log_alpha.to(acts.device) + + for t in range(T): + for u in range(U): + if u == 0: + if t == 0: + # this is the base case: (t=0, u=0) with log-alpha = 0. + log_alpha[:, t, u] = 0.0 + else: + # this is case for (t = 0, u > 0), reached by (t, u - 1) + # emitting a blank symbol. + log_alpha[:, t, u] = log_alpha[:, t - 1, u] + acts[:, t - 1, 0, self.blank] + else: + if t == 0: + # in case of (u > 0, t = 0), this is only reached from + # (t, u - 1) with a label emission. + gathered = torch.gather( + acts[:, t, u - 1], dim=1, index=labels[:, u - 1].view(-1, 1).type(torch.int64) + ).reshape(-1) + log_alpha[:, t, u] = log_alpha[:, t, u - 1] + gathered.to(log_alpha.device) + else: + # here both t and u are > 0, this state is reachable + # with two possibilities: (t - 1, u) with a blank emission + # or (t, u - 1) with a label emission. + log_alpha[:, t, u] = torch.logsumexp( + torch.stack( + [ + log_alpha[:, t - 1, u] + acts[:, t - 1, u, self.blank], + log_alpha[:, t, u - 1] + + torch.gather( + acts[:, t, u - 1], dim=1, index=labels[:, u - 1].view(-1, 1).type(torch.int64) + ).reshape(-1), + ] + ), + dim=0, + ) + + log_probs = [] + for b in range(B): + # here we need to add the final blank emission weights. + to_append = ( + log_alpha[b, act_lens[b] - 1, label_lens[b]] + acts[b, act_lens[b] - 1, label_lens[b], self.blank] + ) + log_probs.append(to_append) + log_prob = torch.stack(log_probs) + + return log_prob + + +class TDTLossPytorch(Loss): + """ + Pure Python implementation of TDT loss (https://arxiv.org/pdf/2304.06795.pdf) + """ + + @property + def input_types(self): + """Input types definitions for CTCLoss. + """ + return { + "acts": NeuralType(('B', 'T', 'T', 'D'), LogprobsType()), + "labels": NeuralType(('B', 'T'), LabelsType()), + "act_lens": NeuralType(tuple('B'), LengthsType()), + "label_lens": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Output types definitions for CTCLoss. + loss: + NeuralType(None) + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__(self, blank: int, durations: List[int] = [], reduction: str = 'sum', sigma: float = 0.0): + super().__init__() + self.blank = blank + self.durations = durations + self.n_durations = len(durations) + self.reduction = reduction + self.sigma = sigma + + def forward(self, acts, labels, act_lens, label_lens): + label_acts = acts[:, :, :, : -self.n_durations] + duration_acts = acts[:, :, :, -self.n_durations :] + + # the - self.sigma here is for logit-undernormalization. Check the paper for details. + label_acts = torch.log_softmax(label_acts, -1) - self.sigma + + duration_acts = torch.log_softmax(duration_acts, -1) + + forward_logprob, _ = self.compute_forward_prob(label_acts, duration_acts, labels, act_lens, label_lens) + losses = -forward_logprob + if self.reduction == 'mean_batch': + losses = losses.mean() # global batch size average + elif self.reduction == 'mean': + losses = torch.div(losses, label_lens).mean() + elif self.reduction == 'sum': + losses = losses.sum() + elif self.reduction == 'mean_volume': + losses = losses.sum() / label_lens.sum() # same as above but longer samples weigh more + + return losses + + def logsumexp(self, a, b): + ret = torch.logsumexp(torch.stack([a, b]), dim=0) + return ret + + def compute_forward_prob(self, acts, duration_acts, labels, act_lens, label_lens): + """This function implements Equation 7 in the TDT paper https://arxiv.org/pdf/2304.06795.pdf, + Simply put, for each alpha(t, u), it sums over the contribution from all incoming blank arcs and non-blank arcs. + """ + B, T, U, _ = acts.shape + + log_alpha = torch.zeros(B, T, U) + log_alpha = log_alpha.cuda() + for b in range(B): + for t in range(T): + for u in range(U): + if u == 0: + if t == 0: + # both t and u are 0, this is the base case for alphas. + log_alpha[b, t, u] = 0.0 + else: + # u = 0 and t != 0: only considers blank emissions. + log_alpha[b, t, u] = -1000.0 + for n, l in enumerate(self.durations): + if ( + t - l >= 0 and l > 0 + ): # checking conditions for blank emission, l has to be at least 1 + tmp = ( + log_alpha[b, t - l, u] + + acts[b, t - l, u, self.blank] + + duration_acts[b, t - l, u, n] + ) + log_alpha[b, t, u] = self.logsumexp(tmp, 1.0 * log_alpha[b, t, u]) + + else: + # u != 0 here, need to consider both blanks and non-blanks. + log_alpha[b, t, u] = -1000.0 + for n, l in enumerate(self.durations): + if t - l >= 0: + if l > 0: # for blank emissions. Need to ensure index is not out-of-bound. + tmp = ( + log_alpha[b, t - l, u] + + acts[b, t - l, u, self.blank] + + duration_acts[b, t - l, u, n] + ) + log_alpha[b, t, u] = self.logsumexp(tmp, 1.0 * log_alpha[b, t, u]) + + # non-blank emissions. + tmp = ( + log_alpha[b, t - l, u - 1] + + acts[b, t - l, u - 1, labels[b, u - 1]] + + duration_acts[b, t - l, u - 1, n] + ) + log_alpha[b, t, u] = self.logsumexp(tmp, 1.0 * log_alpha[b, t, u]) + + log_probs = [] + for b in range(B): + tt = torch.Tensor([-1000.0]).cuda()[0] + + # need to loop over all possible ways that blank with different durations contributes to the final loss. + for n, l in enumerate(self.durations): + if act_lens[b] - l >= 0 and l > 0: + bb = ( + log_alpha[b, act_lens[b] - l, label_lens[b]] + + acts[b, act_lens[b] - l, label_lens[b], self.blank] + + duration_acts[b, act_lens[b] - l, label_lens[b], n] + ) + + tt = self.logsumexp(bb, 1.0 * tt) + + log_probs.append(tt) + + log_prob = torch.stack(log_probs) + + return log_prob, log_alpha + + +class MultiblankRNNTLossPytorch(Loss): + """ + Pure Python implementation of multi-blank transducer loss (https://arxiv.org/pdf/2211.03541.pdf) + """ + + @property + def input_types(self): + """Input types definitions for CTCLoss. + """ + return { + "acts": NeuralType(('B', 'T', 'T', 'D'), LogprobsType()), + "labels": NeuralType(('B', 'T'), LabelsType()), + "act_lens": NeuralType(tuple('B'), LengthsType()), + "label_lens": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Output types definitions for CTCLoss. + loss: + NeuralType(None) + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__(self, blank, big_blank_durations, reduction: str = "sum", sigma: float = 0.0): + super().__init__() + self.blank = blank + self.big_blank_durations = big_blank_durations + self.reduction = reduction + self.sigma = sigma + + def forward(self, acts, labels, act_lens, label_lens): + acts = torch.log_softmax(acts, -1) - self.sigma + forward_logprob, _ = self.compute_forward_prob(acts, labels, act_lens, label_lens) + + losses = -forward_logprob + if self.reduction == 'mean_batch': + losses = losses.mean() # global batch size average + elif self.reduction == 'mean': + losses = torch.div(losses, label_lens).mean() + elif self.reduction == 'sum': + losses = losses.sum() + elif self.reduction == 'mean_volume': + losses = losses.sum() / label_lens.sum() # same as above but longer samples weigh more + + return losses + + def compute_forward_prob(self, acts, labels, act_lens, label_lens): + B, T, U, _ = acts.shape + + log_alpha = torch.zeros(B, T, U, device=acts.device) + for t in range(T): + for u in range(U): + if u == 0: + if t == 0: + # this is the base case: (t=0, u=0) with log-alpha = 0. + log_alpha[:, t, u] = 0.0 + else: + # this is case for (t = 0, u > 0), reached by (t, u - d) + # emitting a blank symbol of duration d. + log_alpha[:, t, u] = log_alpha[:, t - 1, u] + acts[:, t - 1, 0, self.blank] + for i, d in enumerate(self.big_blank_durations): + if t >= d: + tt = log_alpha[:, t - d, u] + acts[:, t - d, 0, self.blank - 1 - i] + log_alpha[:, t, u] = torch.logsumexp( + torch.stack([1.0 * log_alpha[:, t, u], tt]), dim=0 + ) + + else: + if t == 0: + # in case of (u > 0, t = 0), this is only reached from + # (t, u - 1) with a label emission. + gathered = torch.gather( + acts[:, t, u - 1], dim=1, index=labels[:, u - 1].view(-1, 1).type(torch.int64) + ).reshape(-1) + log_alpha[:, t, u] = log_alpha[:, t, u - 1] + gathered + else: + # here both t and u are > 0, this state is reachable + # with two possibilities: (t - d, u) with emission of + # blank with duration d, or (t, u - 1) with a label emission. + + # first we take care of the standard blank. + log_alpha[:, t, u] = torch.logsumexp( + torch.stack( + [ + log_alpha[:, t - 1, u] + acts[:, t - 1, u, self.blank], + log_alpha[:, t, u - 1] + + torch.gather( + acts[:, t, u - 1], dim=1, index=labels[:, u - 1].view(-1, 1).type(torch.int64) + ).reshape(-1), + ] + ), + dim=0, + ) + + # now we go over all big blanks. They need to be considered if current t >= blank duration d. + for i, d in enumerate(self.big_blank_durations): + if t >= d: + tt = log_alpha[:, t - d, u] + acts[:, t - d, u, self.blank - 1 - i] + log_alpha[:, t, u] = torch.logsumexp( + torch.stack([1.0 * log_alpha[:, t, u], tt]), dim=0 + ) + + log_probs = [] + for b in range(B): + # here we need to add the final blank emission weights, which needs + # to consider all possible blank durations. + to_append = ( + log_alpha[b, act_lens[b] - 1, label_lens[b]] + acts[b, act_lens[b] - 1, label_lens[b], self.blank] + ) + + for i, d in enumerate(self.big_blank_durations): + if act_lens[b] >= d: + tt = ( + log_alpha[b, act_lens[b] - d, label_lens[b]] + + acts[b, act_lens[b] - d, label_lens[b], self.blank - 1 - i] + ) + to_append = torch.logsumexp(torch.stack([1.0 * to_append, tt]), dim=0) + + log_probs.append(to_append) + log_prob = torch.stack(log_probs) + + return log_prob, log_alpha diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/__init__.py new file mode 100644 index 0000000..2497903 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.losses.ssl_losses.contrastive import ContrastiveLoss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/contrastive.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/contrastive.py new file mode 100644 index 0000000..bab6919 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/contrastive.py @@ -0,0 +1,297 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn.functional as F +from torch import nn + +from nemo.core import Loss, typecheck +from nemo.core.neural_types import AcousticEncodedRepresentation, LengthsType, LossType, NeuralType, SpectrogramType + +__all__ = ["ContrastiveLoss"] + + +class ContrastiveLoss(Loss): + @property + def input_types(self): + """Input types definitions for Contrastive. + """ + return { + "spectrograms": NeuralType(("B", "D", "T"), SpectrogramType()), + "spec_masks": NeuralType(("B", "D", "T"), SpectrogramType()), + "decoder_outputs": NeuralType(("B", "T", "D"), AcousticEncodedRepresentation()), + "decoder_lengths": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self): + """Output types definitions for Contrastive. + loss: + NeuralType(None) + """ + return {"loss": NeuralType(elements_type=LossType())} + + @property + def needs_labels(self): + return False + + def __init__( + self, + in_dim: int, + proj_dim: int = 128, + combine_time_steps: int = 1, + num_negatives: int = 100, + quantized_targets: bool = False, + codebook_size: int = 320, + prob_ppl_weight: float = 0.1, + logit_temp: float = 0.1, + reduce: str = "sum", + sample_from_same_utterance_only: bool = True, + sample_from_non_masked: bool = False, + sample_from_codebook: bool = False, + group_loss: bool = False, + num_groups: int = 2, + quantizer_temp_start: float = 2, + quantizer_temp_min: float = 0.5, + quantizer_temp_decay: float = 0.999995, + mask_threshold: float = 0.8, + store_ids: bool = True, + reduce_ids: bool = False, + multiplier: float = 16.0, + ): + """ + Loss function representing the contrastive task of identifying the true latent speech representation of + the masked spectrogram steps from a set of sampled distractors. + + Args: + in_dim: Number of spectrogram channels. + proj_dim: Number of channels in the model outputs. + combine_time_steps: How many time steps should be combined into a single representation. + num_negatives: Number of sampled negatives for each target. + quantized_targets: Bool that determines if the targets should be quantized. + codebook_size: Number of vectors in the codebook per group. + prob_ppl_weight: Float multiplier on the perplexity loss for target quantization. + logit_temp: Float temperature for normalizing logits. + reduce: String representing the type of reduction used for cross entropy. + sample_from_same_utterance_only: Bool that determines if negatives should be sampled only from same utterance. + sample_from_non_masked: Bool that determines if negatives should be sampled from non-masked steps of the spectrogram. + sample_from_codebook: Bool that determines if negatives should be sampled from entire codebook. + group_loss: Bool that determines if loss should be computed separately for each group in the quantizer codebook. + num_groups: Number of groups in the quantizer codebook. + quantizer_temp_start: Starting temperature in quantizer. + quantizer_temp_min: Minimum temperature in quantizer. + quantizer_temp_decay: Decay rate of quantizer temperature per global step. + mask_threshold: Float threshold for determining if a time step of the spectrogram is masked based on percent of masked channels. + store_ids: Bool that determines if the quantizer ids will be stored to be potentially used by other losses. + reduce_ids: Bool that determines if we convert any sequence of consecutive equivalent ids to a single occurence of that id. + multiplier: Float multipler on final loss + """ + + super().__init__() + quantizer_temp = (quantizer_temp_start, quantizer_temp_min, quantizer_temp_decay) + self.quantized_targets = quantized_targets + self.num_negatives = num_negatives + self.prob_ppl_weight = prob_ppl_weight + if self.quantized_targets: + quantizer_cfg = { + "_target_": "nemo.collections.asr.parts.submodules.ssl_quantizers.GumbelVectorQuantizer", + "dim": in_dim * combine_time_steps, + "vq_dim": proj_dim, + "num_vars": codebook_size, + "groups": num_groups, + "temp": quantizer_temp, + "combine_groups": True, + "time_first": True, + } + self.quantizer = ContrastiveLoss.from_config_dict(quantizer_cfg) + self.prob_ppl_weight = prob_ppl_weight + self.logit_temp = logit_temp + self.reduce = reduce + self.combine_time_steps = combine_time_steps + self.sample_from_same_utterance_only = sample_from_same_utterance_only + self.sample_from_non_masked = sample_from_non_masked + self.sample_from_codebook = sample_from_codebook + self.group_loss = group_loss + self.mask_threshold = mask_threshold + self.multiplier = multiplier + + self.store_ids = store_ids + self.reduce_ids = reduce_ids + + if not self.quantized_targets: + self.target_proj = nn.Linear(in_dim * combine_time_steps, proj_dim) + + def sample_negatives(self, y, num): + # y - T'xBxC or T'xC + + high = y.shape[0] + neg_idxs = torch.multinomial(torch.ones((num, high), device=y.device), self.num_negatives) + + negs = y[neg_idxs.view(-1)] + negs = negs.view((num, self.num_negatives) + y.shape[1:]) + negs = negs.transpose(0, 1) + # negs - NxT'xBxC or NxT'xC + + return negs, neg_idxs + + @typecheck() + def forward(self, spectrograms, spec_masks, decoder_outputs, decoder_lengths=None): + spec_in = spectrograms.transpose(-2, -1) + masks = spec_masks.transpose(-2, -1) + targets = spec_in + # BxTxC + + targets = targets.reshape(targets.shape[0], targets.shape[1] // self.combine_time_steps, -1) + masks = masks.reshape(targets.shape[0], targets.shape[1], -1) + + if self.quantized_targets: + if self.store_ids: + # store ids for use by other losses + targets, prob_ppl_loss, cur_codebook_temp, self.target_ids = self.quantizer(targets, return_ids=True) + + if self.reduce_ids: + # reduce consecutive equivalent ids to a single occurence + _, indices = torch.unique_consecutive(self.target_ids, return_inverse=True) + indices -= indices.min(dim=1, keepdims=True)[0] + reduced_ids = torch.zeros_like(self.target_ids) + reduced_ids = reduced_ids.scatter_(1, indices, self.target_ids) + reduced_lens = indices.max(dim=-1)[0] + 1 + + self.target_ids = reduced_ids.narrow(1, 0, reduced_lens.max()) + self.target_lengths = reduced_lens + + else: + self.target_lengths = None + + else: + targets, prob_ppl_loss, cur_codebook_temp = self.quantizer(targets) + else: + targets = self.target_proj(targets) + + if self.sample_from_same_utterance_only: + bs = decoder_outputs.shape[0] + masks = masks.mean(-1) > self.mask_threshold + out_masked_only = decoder_outputs[masks] + targets_masked_only = targets[masks] + out_masked_only = out_masked_only.reshape(bs, -1, out_masked_only.shape[-1]) + targets_masked_only = targets_masked_only.reshape(bs, -1, targets_masked_only.shape[-1]) + + # BxT'xC + # number of masked time steps to predict (T') + # -> T'xBxC + + out_masked_only = out_masked_only.transpose(0, 1) + targets_masked_only = targets_masked_only.transpose(0, 1) + # -> T'xBxC + + if self.sample_from_non_masked: + # sample from all steps in utterance + negatives, _ = self.sample_negatives( + targets.transpose(0, 1), targets_masked_only.size(0), # TxBxC # T' + ) + else: + # only sample from masked steps in utterance + negatives, _ = self.sample_negatives(targets_masked_only, targets_masked_only.size(0)) # T'xBxC # T' + # NxT'xBxC + + out_masked_only = out_masked_only.reshape(-1, out_masked_only.shape[-1]) + targets_masked_only = targets_masked_only.reshape(-1, targets_masked_only.shape[-1]) + negatives = negatives.reshape(self.num_negatives, -1, negatives.shape[-1]) + + # T'BxC and NxT'BxC + + else: + masks = masks.mean(-1) > self.mask_threshold + out_masked_only = decoder_outputs[masks] + targets_masked_only = targets[masks] + + # T'xC + # number of masked time steps to predict (T') + + if self.group_loss: + num_groups = self.quantizer.groups + negatives = self.quantizer.vars.reshape(num_groups, self.quantizer.num_vars, -1) + # GxNx(C//G) + negatives = negatives.transpose(0, 1) + # NxGx(C//G) + negatives = negatives.unsqueeze(1).expand(-1, out_masked_only.shape[0], -1, -1) + # NxT'xGx(C//G) + negatives = negatives.reshape(negatives.shape[0], -1, negatives.shape[-1]) + # NxT'Gx(C//G) + + out_masked_only = out_masked_only.reshape(-1, out_masked_only.shape[-1] // num_groups) + targets_masked_only = targets_masked_only.reshape(-1, targets_masked_only.shape[-1] // num_groups) + # T'Gx(C//G) + elif self.sample_from_codebook: + # sample from the full codebook + negatives = self.quantizer.sample_from_codebook(self.num_negatives, targets_masked_only.size(0)) + elif self.sample_from_non_masked: + # sample from all steps in batch + negatives, _ = self.sample_negatives( + targets.reshape(targets.shape[0] * targets.shape[1], -1), targets_masked_only.size(0), # BTxC + ) # T' + else: + # only sample from masked steps + negatives, _ = self.sample_negatives(targets_masked_only, targets_masked_only.size(0)) # T'xC # T' + # NxT'xC + + # Calculate similarity between outputs and all targets + similarity_scores = self._calculate_similarity(out_masked_only, negatives, targets_masked_only) + # (1+N)xT' + # cosine similarity of outs with targets + N negatives + + # Create targets of size T + similarity_targets = decoder_outputs.new_zeros(similarity_scores.size(1), dtype=torch.long) + # T' + # targets are 0, since it's the first, followed by N sampled negatives + + # Transpose similarity scores to TxF for loss + similarity_scores = similarity_scores.transpose(0, 1) + # T'x(1+N) + + loss = F.cross_entropy(similarity_scores, similarity_targets, reduction=self.reduce) + + sample_size = similarity_targets.numel() + + if self.prob_ppl_weight != 0 and self.quantized_targets: + prob_ppl_loss = self.prob_ppl_weight * prob_ppl_loss * sample_size + loss += prob_ppl_loss + + if not isinstance(loss, torch.Tensor): + loss = torch.Tensor([0]).to(device=decoder_outputs.device) + + batch_size = spectrograms.shape[0] + loss *= self.multiplier / batch_size + + return loss + + def _calculate_similarity(self, logits, negatives, targets): + neg_is_pos = (targets == negatives).all(-1) + # NxT' - true where the negative is actually the positive + targets = targets.unsqueeze(0) + # 1xT'xC + targets = torch.cat([targets, negatives], dim=0) + # (1+N)xT'XC + logits = torch.cosine_similarity( + logits.float().unsqueeze(0).expand(targets.shape[0], -1, -1), targets.float(), dim=-1 + ).type_as(logits) + # (1+N)xT' + logits /= self.logit_temp + if neg_is_pos.any(): + logits[1:][neg_is_pos] = float("-inf") + return logits + + def set_num_updates(self, num_updates): + if self.quantized_targets: + self.quantizer.set_num_updates(num_updates) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/ctc.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/ctc.py new file mode 100644 index 0000000..e71d60a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/ctc.py @@ -0,0 +1,57 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.losses import CTCLoss +from nemo.core import Loss, typecheck +from nemo.core.neural_types import LabelsType, LengthsType, LossType, NeuralType, SpectrogramType, VoidType + +__all__ = ["CTCLossForSSL"] + + +class CTCLossForSSL(Loss): + @property + def input_types(self): + """Input types definitions for Contrastive. + """ + return { + "spec_masks": NeuralType(("B", "D", "T"), SpectrogramType()), + "decoder_outputs": NeuralType(("B", "T", "D"), VoidType()), + "targets": NeuralType(('B', 'T'), LabelsType()), + "decoder_lengths": NeuralType(tuple('B'), LengthsType(), optional=True), + "target_lengths": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self): + """Output types definitions for Contrastive. + loss: + NeuralType(None) + """ + return {"loss": NeuralType(elements_type=LossType())} + + @property + def needs_labels(self): + return True + + def __init__(self, num_classes, zero_infinity=True, reduction='mean_batch'): + super().__init__() + self.loss = CTCLoss(num_classes=num_classes, reduction=reduction, zero_infinity=zero_infinity) + + @typecheck() + def forward(self, spec_masks, decoder_outputs, targets, decoder_lengths=None, target_lengths=None): + loss = self.loss( + log_probs=decoder_outputs, targets=targets, input_lengths=decoder_lengths, target_lengths=target_lengths + ) + + return loss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/mlm.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/mlm.py new file mode 100644 index 0000000..89de01d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/mlm.py @@ -0,0 +1,75 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn.functional as F +from torch import nn + +from nemo.core import Loss, typecheck +from nemo.core.neural_types import LabelsType, LengthsType, LogprobsType, LossType, NeuralType, SpectrogramType + +__all__ = ["MLMLoss"] + + +class MLMLoss(Loss): + @property + def input_types(self): + """Input types definitions for Contrastive. + """ + return { + "spec_masks": NeuralType(("B", "D", "T"), SpectrogramType()), + "decoder_outputs": NeuralType(("B", "T", "D"), LogprobsType()), + "targets": NeuralType(('B', 'T'), LabelsType()), + "decoder_lengths": NeuralType(tuple('B'), LengthsType(), optional=True), + "target_lengths": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self): + """Output types definitions for Contrastive. + loss: + NeuralType(None) + """ + return {"loss": NeuralType(elements_type=LossType())} + + @property + def needs_labels(self): + return True + + def __init__( + self, combine_time_steps: int = 1, mask_threshold: float = 0.8, + ): + super().__init__() + self.nll_loss = nn.NLLLoss() + self.combine_time_steps = combine_time_steps + self.mask_threshold = mask_threshold + + @typecheck() + def forward(self, spec_masks, decoder_outputs, targets, decoder_lengths=None, target_lengths=None): + + # outputs are log_probs + masks = spec_masks.transpose(-2, -1) + # BxTxC + + masks = masks.reshape(masks.shape[0], masks.shape[1] // self.combine_time_steps, -1) + masks = masks.mean(-1) > self.mask_threshold + + out_masked_only = decoder_outputs[masks] + targets = F.pad(targets, (0, masks.shape[-1] - targets.shape[-1])) + targets_masked_only = targets[masks] + + loss = self.nll_loss(out_masked_only, targets_masked_only) + loss = torch.mean(loss) + + return loss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/rnnt.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/rnnt.py new file mode 100644 index 0000000..0336063 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/losses/ssl_losses/rnnt.py @@ -0,0 +1,58 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.losses.rnnt import RNNTLoss +from nemo.core import Loss, typecheck +from nemo.core.neural_types import LabelsType, LengthsType, LogprobsType, LossType, NeuralType, SpectrogramType + +__all__ = ["RNNTLossForSSL"] + + +class RNNTLossForSSL(Loss): + @property + def input_types(self): + """Input types definitions for Contrastive. + """ + return { + "spec_masks": NeuralType(("B", "D", "T"), SpectrogramType()), + "decoder_outputs": NeuralType(('B', 'T', 'T', 'D'), LogprobsType()), + "targets": NeuralType(('B', 'T'), LabelsType()), + "decoder_lengths": NeuralType(tuple('B'), LengthsType(), optional=True), + "target_lengths": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self): + """Output types definitions for Contrastive. + loss: + NeuralType(None) + """ + return {"loss": NeuralType(elements_type=LossType())} + + @property + def needs_labels(self): + return True + + def __init__(self, num_classes): + super().__init__() + self.loss = RNNTLoss(num_classes=num_classes) + + @typecheck() + def forward(self, spec_masks, decoder_outputs, targets, decoder_lengths=None, target_lengths=None): + + loss = self.loss( + log_probs=decoder_outputs, targets=targets, input_lengths=decoder_lengths, target_lengths=target_lengths + ) + + return loss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/__init__.py new file mode 100644 index 0000000..843d58c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.metrics.bleu import BLEU +from nemo.collections.asr.metrics.wer import WER diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/audio.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/audio.py new file mode 100644 index 0000000..5e8c291 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/audio.py @@ -0,0 +1,195 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Callable, Iterable, List, Optional, Tuple + +import torch +from torchmetrics import Metric +from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality +from torchmetrics.audio.pit import PermutationInvariantTraining +from torchmetrics.audio.sdr import ScaleInvariantSignalDistortionRatio, SignalDistortionRatio +from torchmetrics.audio.snr import ScaleInvariantSignalNoiseRatio, SignalNoiseRatio +from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility + +from nemo.utils import logging + +__all__ = ['AudioMetricWrapper'] + +__VERIFIED_METRICS__ = [ + PermutationInvariantTraining, + ScaleInvariantSignalDistortionRatio, + SignalDistortionRatio, + ScaleInvariantSignalNoiseRatio, + SignalNoiseRatio, + PerceptualEvaluationSpeechQuality, + ShortTimeObjectiveIntelligibility, +] + + +class AudioMetricWrapper(Metric): + """A wrapper around an audio metric enabling selection of a specific channel + and handling of examples in a batch with varying valid input length. + + Note: + This class assumes that the underlying metric uses averaging to calculate the + value over a batch. This assumption is only used by `forward` and does not + impact other methods, such as `update` and `compute`. + + Args: + metric: base metric that should be wrapped. It is assumed that calculation + of the metric over a batch is done by averaging. + channel: Optional, for selecting a channel from `preds` and `target` signals. + If None, all channels are used. + metric_using_batch_averaging: Optional, used to denote that the base metric + is using averaging to calculate the metric value + for a batch. + """ + + full_state_update: bool = False + + def __init__( + self, metric: Metric, channel: Optional[int] = None, metric_using_batch_averaging: Optional[bool] = None + ): + super().__init__() + if not isinstance(metric, Metric): + raise ValueError(f"Expected argument `metric` to be an instance of `torchmetrics.Metric` but got {metric}") + + if not metric_using_batch_averaging and type(metric) not in __VERIFIED_METRICS__: + raise ValueError( + f'Metric {metric} is not in verified metrics. {self.__class__.__name__} assumes reduction over batch is calculated using averaging. \n' + 'This should not affect the final results, but values for a single batch obtained using `forward` may be inaccurate if using `input_length`. \n' + 'To suppress this message, please confirm the used metric is using batch averaging and set "metric_using_batch_averaging = True"' + ) + + self._metric = metric + self._channel = channel + logging.debug('Setup metric %s, channel %s', metric, str(channel)) + + def _select_channel(self, preds: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """Select a single channel from input signals. + + Args: + preds: tensor with shape (B, C, T) + target: tensor with shape (B, C, T) + + Returns: + Original tensors if self.channel is None, shape (B, C, T). + A single channel from input tensors if self.channel is set, shape (B, T) + """ + if self._channel is None: + return preds, target + else: + return preds[:, self._channel, ...], target[:, self._channel, ...] + + @staticmethod + def _trim_inputs( + preds: torch.Tensor, target: torch.Tensor, input_length: torch.Tensor + ) -> Iterable[Tuple[torch.Tensor, torch.Tensor]]: + """Trim input tensors to input_length samples. + + Args: + preds: tensor with shape (B, C, T) + target: tensor with shape (B, C, T) + + Returns: + An iterable with tuples of (preds, target) with + the correct length. + """ + # Each example has a different length + for b_idx, b_len in enumerate(input_length): + b_preds = preds[b_idx, ..., :b_len] + b_target = target[b_idx, ..., :b_len] + + yield b_preds, b_target + + @staticmethod + def _batch_reduction(batch_values: List[torch.Tensor]) -> torch.Tensor: + """Reduce metric values for each example in a batch to a single + value for the whole batch. + + Args: + batch_values: list of metric values for each example in a batch + + Returns: + Average metric value over the batch. + """ + return sum(batch_values) / len(batch_values) + + def update(self, preds: torch.Tensor, target: torch.Tensor, input_length: Optional[torch.Tensor] = None) -> None: + """Update the underlying metric by taking into account channel selector and input length. + + Args: + preds: tensor with predictions, shape (B, C, T) + target: tensor with target signals, shape (B, C, T) + input_length: Optional, input tensor with length (in samples) of each signal in the batch, shape (B,). + If not provided, it is assumed that all samples are valid. + """ + preds, target = self._select_channel(preds=preds, target=target) + + if input_length is None: + self._metric.update(preds=preds, target=target) + else: + # Each example in this batch has a different length + for b_preds, b_target in self._trim_inputs(preds=preds, target=target, input_length=input_length): + self._metric.update(preds=b_preds, target=b_target) + + def compute(self) -> torch.Tensor: + """Compute the underlying metric. + """ + return self._metric.compute() + + def forward( + self, preds: torch.Tensor, target: torch.Tensor, input_length: Optional[torch.Tensor] = None + ) -> torch.Tensor: + """Call underlying forward method to add the batch statistics to the accumulated metric state + and return the result for the current batch. + + Args: + preds: tensor with predictions, shape (B, C, T) + target: tensor with target signals, shape (B, C, T) + input_length: Optional, input tensor with length (in samples) of each signal in the batch, shape (B,). + If not provided, it is assumed that all samples are valid. + + Returns: + Underlying metric averaged on the current batch. + """ + preds, target = self._select_channel(preds=preds, target=target) + + if input_length is None: + return self._metric(preds=preds, target=target) + else: + # Each example in this batch has a different length + batch_values = [] + for b_preds, b_target in self._trim_inputs(preds=preds, target=target, input_length=input_length): + batch_values.append(self._metric(preds=b_preds, target=b_target)) + # Average over the batch + return self._batch_reduction(batch_values) + + def reset(self) -> None: + """Reset the underlying metric. + """ + self._metric.reset() + + def __repr__(self) -> str: + """Return string representation of the object. + """ + _op_metric = f"(metric: {repr(self._metric)}, channel: {self._channel})" + repr_str = self.__class__.__name__ + _op_metric + + return repr_str + + def _wrap_compute(self, compute: Callable) -> Callable: + """Overwrite to do nothing, as in CompositionalMetric. + """ + return compute diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/bleu.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/bleu.py new file mode 100644 index 0000000..011e3ef --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/bleu.py @@ -0,0 +1,212 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Literal, Optional, Sequence, Union + +import torch +from torchmetrics.functional.text.bleu import _bleu_score_compute +from torchmetrics.text import SacreBLEUScore + +from nemo.collections.asr.parts.submodules.ctc_decoding import AbstractCTCDecoding +from nemo.collections.asr.parts.submodules.multitask_decoding import AbstractMultiTaskDecoding +from nemo.collections.asr.parts.submodules.rnnt_decoding import AbstractRNNTDecoding +from nemo.utils import logging + +__all__ = ['BLEU'] + + +def move_dimension_to_the_front(tensor, dim_index): + all_dims = list(range(tensor.ndim)) + return tensor.permute(*([dim_index] + all_dims[:dim_index] + all_dims[dim_index + 1 :])) + + +# TODO: Add documentation +class BLEU(SacreBLEUScore): + """ + This metric computes numerator, denominator, hypotheses lengths, and target lengths for Overall Bilingual Evaluation Understudy (BLEU) + between prediction and reference texts. When doing distributed training/evaluation the result of + ``res=BLEU.(predictions, predictions_lengths, targets, target_lengths)`` + calls will be all-reduced between all workers using SUM operations. + + If used with PytorchLightning LightningModule, include bleu_num bleur_den, bleu_pred_len, and bleu_target_len values inside + validation_step results. Then aggregate (sum) then at the end of validation epoch to correctly compute validation BLEUR. + + Example: + def validation_step(self, batch, batch_idx): + ... + bleu_values = self.bleu(predictions, predictions_len, transcript, transcript_len) + self.val_outputs = {'val_loss': loss_value, **bleu_values} + return self.val_outputs + + def on_validation_epoch_end(self): + ... + bleu_num = torch.stack([x['val_wer_num'] for x in self.val_outputs]).sum() + bleu_denom = torch.stack([x['val_wer_denom'] for x in self.val_outputs]).sum() + bleu_num = torch.stack([x[f"val_bleu_num"] for x in outputs]).sum(dim=0) + bleu_denom = torch.stack([x[f"val_bleu_denom"] for x in outputs]).sum(dim=0) + + val_bleu = {"val_bleu": self.bleu._compute_bleu(bleu_pred_len, bleu_target_len, bleu_num, bleu_denom)} + tensorboard_logs.update(val_bleu) + + self.val_outputs.clear() # free memory + return {'val_loss': val_loss_mean, 'log': tensorboard_logs} + + Args: + decoding: An instance of CTCDecoding, RNNTDecoding, or MultiTaskDecoding. + tokenize: Desired tokenizer for BLEU evaluation. (Depending on language, this will drastically affect BLEU score.) + n_gram: Maximum number of n_grams to compute BLEU values over. Max: 4. + lowercase: Whether to lowercase all inputs. + weights: List of float values to weight each n_gram score. + log_prediction: Whether to log a single decoded sample per call. + batch_dim_index: Index corresponding to batch dimension. (For RNNT.) + dist_dync_on_step: Whether to perform reduction on forward pass of metric. + + Returns: + res: a tuple of 3 zero dimensional float32 ``torch.Tensor` objects: a WER score, a sum of Levenstein's + distances for all prediction - reference pairs, total number of words in all references. + """ + + full_state_update: bool = True + + def __init__( + self, + decoding: Union[AbstractCTCDecoding, AbstractRNNTDecoding, AbstractMultiTaskDecoding], + tokenize: Literal["none", "13a", "zh", "intl", "char"] = "13a", + n_gram: int = 4, + lowercase: bool = False, + weights: Optional[Sequence[float]] = None, + smooth: bool = False, + log_prediction=True, + batch_dim_index=0, + dist_sync_on_step=False, + ): + super().__init__( + tokenize=tokenize, + n_gram=n_gram, + lowercase=lowercase, + weights=weights, + smooth=smooth, + dist_sync_on_step=dist_sync_on_step, + ) + self.has_spl_tokens = False + self.decoding = decoding + self.decode = None + if isinstance(self.decoding, AbstractRNNTDecoding): + self.decode = lambda predictions, predictions_lengths, predictions_mask, input_ids, targets: self.decoding.rnnt_decoder_predictions_tensor( + encoder_output=predictions, encoded_lengths=predictions_lengths + ) + elif isinstance(self.decoding, AbstractCTCDecoding): + self.decode = lambda predictions, predictions_lengths, predictions_mask, input_ids, targets: self.decoding.ctc_decoder_predictions_tensor( + decoder_outputs=predictions, + decoder_lengths=predictions_lengths, + fold_consecutive=self.fold_consecutive, + ) + elif isinstance(self.decoding, AbstractMultiTaskDecoding): + self.has_spl_tokens = True + self.decode = lambda predictions, prediction_lengths, predictions_mask, input_ids, targets: self.decoding.decode_predictions_tensor( + encoder_hidden_states=predictions, + encoder_input_mask=predictions_mask, + decoder_input_ids=input_ids, + return_hypotheses=False, + ) + else: + raise TypeError(f"WER metric does not support decoding of type {type(self.decoding)}") + + self.tokenize = tokenize + self.log_prediction = log_prediction + self.batch_dim_index = batch_dim_index + + def update( + self, + predictions: torch.Tensor, + predictions_lengths: torch.Tensor, + targets: torch.Tensor, + targets_lengths: torch.Tensor, + predictions_mask: Optional[torch.Tensor] = None, + input_ids: Optional[torch.Tensor] = None, + ): + """ + Updates metric state. + Args: + predictions: an integer torch.Tensor of shape ``[Batch, Time, {Vocabulary}]`` (if ``batch_dim_index == 0``) or + ``[Time, Batch]`` (if ``batch_dim_index == 1``) + predictions_lengths: an integer torch.Tensor of shape ``[Batch]`` + targets: an integer torch.Tensor of shape ``[Batch, Time]`` (if ``batch_dim_index == 0``) or + ``[Time, Batch]`` (if ``batch_dim_index == 1``) + target_lengths: an integer torch.Tensor of shape ``[Batch]`` + predictions_mask: a bool torch.Tensor of shape ``[Batch, Time]`` (if ``batch_dim_index == 0``) or + ``[Time, Batch]`` (if ``batch_dim_index == 1``). Required for MultiTaskDecoding. + input_ids: an int torch.Tensor of shape ``[Batch, Time]`` (if ``batch_dim_index == 0``) or + ``[Time, Batch]`` (if ``batch_dim_index == 1``). Required for MultiTaskDecoding. + """ + references = [] + with torch.no_grad(): + tgt_lenths_cpu_tensor = targets_lengths.long().cpu() + targets_cpu_tensor = targets.long().cpu() + # check batch_dim_index is first dim + if self.batch_dim_index != 0: + targets_cpu_tensor = move_dimension_to_the_front(targets_cpu_tensor, self.batch_dim_index) + # iterate over batch + for ind in range(targets_cpu_tensor.shape[0]): + tgt_len = tgt_lenths_cpu_tensor[ind].item() + target = targets_cpu_tensor[ind][:tgt_len].numpy().tolist() + reference = self.decoding.decode_tokens_to_str(target) + references.append(reference) + hypotheses, _ = self.decode(predictions, predictions_lengths, predictions_mask, input_ids, targets) + + if self.has_spl_tokens: + hypotheses = [self.decoding.strip_special_tokens(hyp) for hyp in hypotheses] + references = [self.decoding.strip_special_tokens(ref) for ref in references] + + if self.log_prediction: + logging.info(f"\n") + logging.info(f"reference:{references[0]}") + logging.info(f"predicted:{hypotheses[0]}") + + super().update(hypotheses, [references]) # Note: [references] since BLEU allows multiple references. + + def compute(self, return_all_metrics=True, prefix="", suffix=""): + """ + Returns BLEU values and component metrics. + + Args: + return_all_metrics: bool flag. On True, BLEU and composite metrics returned. If False, returns + only BLEU. Default: True. + prefix: str to prepend to metric value keys. + suffix: str to append to metric value keys. + + Returns: + Dict: key-value pairs of BLEU metrics and values. Keys are prepended and appended with prefix + and suffix flags, respectively. + """ + bleu = super().compute() + if return_all_metrics: + return { + f"{prefix}bleu{suffix}": bleu, + f"{prefix}bleu_pred_len{suffix}": self.preds_len.detach().float(), + f"{prefix}bleu_target_len{suffix}": self.target_len.detach().float(), + f"{prefix}bleu_num{suffix}": self.numerator.detach().float(), + f"{prefix}bleu_denom{suffix}": self.denominator.detach().float(), + } + return { + f"{prefix}bleu{suffix}": bleu, + } + + # Adding wrapper to avoid imports and extra variables over the namespace + def _compute_bleu( + self, predictions_lengths, targets_lengths, numerator, denominator, + ): + return _bleu_score_compute( + predictions_lengths, targets_lengths, numerator, denominator, self.n_gram, self.weights, self.smooth + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/der.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/der.py new file mode 100644 index 0000000..fc5cded --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/der.py @@ -0,0 +1,427 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import itertools +from itertools import permutations +from typing import Dict, List, Optional, Tuple + +import numpy as np +import torch +from pyannote.core import Segment, Timeline +from pyannote.metrics.diarization import DiarizationErrorRate + +from nemo.collections.asr.metrics.wer import word_error_rate +from nemo.collections.asr.parts.utils.optimization_utils import linear_sum_assignment + +from nemo.utils import logging + +__all__ = [ + 'score_labels', + 'calculate_session_cpWER', + 'calculate_session_cpWER_bruteforce', + 'concat_perm_word_error_rate', +] + + +def get_partial_ref_labels(pred_labels: List[str], ref_labels: List[str]) -> List[str]: + """ + For evaluation of online diarization performance, generate partial reference labels + from the last prediction time. + + Args: + pred_labels (list[str]): list of partial prediction labels + ref_labels (list[str]): list of full reference labels + + Returns: + ref_labels_out (list[str]): list of partial reference labels + """ + # If there is no reference, return empty list + if len(ref_labels) == 0: + return [] + + # If there is no prediction, set the last prediction time to 0 + if len(pred_labels) == 0: + last_pred_time = 0 + else: + # The lastest prediction time in the prediction labels + last_pred_time = max([float(labels.split()[1]) for labels in pred_labels]) + ref_labels_out = [] + for label in ref_labels: + start, end, speaker = label.split() + start, end = float(start), float(end) + # If the current [start, end] interval extends beyond the end of hypothesis time stamps + if start < last_pred_time: + end_time = min(end, last_pred_time) + label = f"{start} {end_time} {speaker}" + ref_labels_out.append(label) + # Other cases where the current [start, end] interval is before the last prediction time + elif end < last_pred_time: + ref_labels_out.append(label) + return ref_labels_out + + +def get_online_DER_stats( + DER: float, + CER: float, + FA: float, + MISS: float, + diar_eval_count: int, + der_stat_dict: Dict[str, float], + deci: int = 3, +) -> Tuple[Dict[str, float], Dict[str, float]]: + """ + For evaluation of online diarization performance, add cumulative, average, and maximum DER/CER. + + Args: + DER (float): Diarization Error Rate from the start to the current point + CER (float): Confusion Error Rate from the start to the current point + FA (float): False Alarm from the start to the current point + MISS (float): Miss rate from the start to the current point + diar_eval_count (int): Number of evaluation sessions + der_stat_dict (dict): Dictionary containing cumulative, average, and maximum DER/CER + deci (int): Number of decimal places to round + + Returns: + der_dict (dict): Dictionary containing DER, CER, FA, and MISS + der_stat_dict (dict): Dictionary containing cumulative, average, and maximum DER/CER + """ + der_dict = { + "DER": round(100 * DER, deci), + "CER": round(100 * CER, deci), + "FA": round(100 * FA, deci), + "MISS": round(100 * MISS, deci), + } + der_stat_dict['cum_DER'] += DER + der_stat_dict['cum_CER'] += CER + der_stat_dict['avg_DER'] = round(100 * der_stat_dict['cum_DER'] / diar_eval_count, deci) + der_stat_dict['avg_CER'] = round(100 * der_stat_dict['cum_CER'] / diar_eval_count, deci) + der_stat_dict['max_DER'] = round(max(der_dict['DER'], der_stat_dict['max_DER']), deci) + der_stat_dict['max_CER'] = round(max(der_dict['CER'], der_stat_dict['max_CER']), deci) + return der_dict, der_stat_dict + + +def uem_timeline_from_file(uem_file, uniq_name=''): + """ + Generate pyannote timeline segments for uem file + + file format + UNIQ_SPEAKER_ID CHANNEL START_TIME END_TIME + """ + timeline = Timeline(uri=uniq_name) + with open(uem_file, 'r') as f: + lines = f.readlines() + for line in lines: + line = line.strip() + speaker_id, channel, start_time, end_time = line.split() + timeline.add(Segment(float(start_time), float(end_time))) + + return timeline + + +def score_labels( + AUDIO_RTTM_MAP, all_reference, all_hypothesis, collar=0.25, ignore_overlap=True, verbose: bool = True +) -> Optional[Tuple[DiarizationErrorRate, Dict]]: + """ + Calculate DER, CER, FA and MISS rate from hypotheses and references. Hypothesis results are + coming from Pyannote-formatted speaker diarization results and References are coming from + Pyannote-formatted RTTM data. + + + Args: + AUDIO_RTTM_MAP (dict): Dictionary containing information provided from manifestpath + all_reference (list[uniq_name,Annotation]): reference annotations for score calculation + all_hypothesis (list[uniq_name,Annotation]): hypothesis annotations for score calculation + verbose (bool): Warns if RTTM file is not found. + + Returns: + metric (pyannote.DiarizationErrorRate): Pyannote Diarization Error Rate metric object. This object contains detailed scores of each audiofile. + mapping (dict): Mapping dict containing the mapping speaker label for each audio input + + < Caveat > + Unlike md-eval.pl, "no score" collar in pyannote.metrics is the maximum length of + "no score" collar from left to right. Therefore, if 0.25s is applied for "no score" + collar in md-eval.pl, 0.5s should be applied for pyannote.metrics. + """ + metric = None + if len(all_reference) == len(all_hypothesis): + metric = DiarizationErrorRate(collar=2 * collar, skip_overlap=ignore_overlap) + + mapping_dict = {} + for (reference, hypothesis) in zip(all_reference, all_hypothesis): + ref_key, ref_labels = reference + _, hyp_labels = hypothesis + uem = AUDIO_RTTM_MAP[ref_key].get('uem_filepath', None) + if uem is not None: + uem = uem_timeline_from_file(uem_file=uem, uniq_name=ref_key) + metric(ref_labels, hyp_labels, uem=uem, detailed=True) + mapping_dict[ref_key] = metric.optimal_mapping(ref_labels, hyp_labels) + + DER = abs(metric) + CER = metric['confusion'] / metric['total'] + FA = metric['false alarm'] / metric['total'] + MISS = metric['missed detection'] / metric['total'] + itemized_errors = (DER, CER, FA, MISS) + + logging.info( + "Cumulative Results for collar {} sec and ignore_overlap {}: \n FA: {:.4f}\t MISS {:.4f}\t \ + Diarization ER: {:.4f}\t, Confusion ER:{:.4f}".format( + collar, ignore_overlap, FA, MISS, DER, CER + ) + ) + + return metric, mapping_dict, itemized_errors + elif verbose: + logging.warning( + "Check if each ground truth RTTMs were present in the provided manifest file. Skipping calculation of Diariazation Error Rate" + ) + return None + + +def evaluate_der(audio_rttm_map_dict, all_reference, all_hypothesis, diar_eval_mode='all'): + """ + Evaluate with a selected diarization evaluation scheme + + AUDIO_RTTM_MAP (dict): + Dictionary containing information provided from manifestpath + all_reference (list[uniq_name,annotation]): + reference annotations for score calculation + all_hypothesis (list[uniq_name,annotation]): + hypothesis annotations for score calculation + diar_eval_mode (str): + Diarization evaluation modes + + diar_eval_mode == "full": + DIHARD challenge style evaluation, the most strict way of evaluating diarization + (collar, ignore_overlap) = (0.0, False) + diar_eval_mode == "fair": + Evaluation setup used in VoxSRC challenge + (collar, ignore_overlap) = (0.25, False) + diar_eval_mode == "forgiving": + Traditional evaluation setup + (collar, ignore_overlap) = (0.25, True) + diar_eval_mode == "all": + Compute all three modes (default) + """ + eval_settings = [] + if diar_eval_mode == "full": + eval_settings = [(0.0, False)] + elif diar_eval_mode == "fair": + eval_settings = [(0.25, False)] + elif diar_eval_mode == "forgiving": + eval_settings = [(0.25, True)] + elif diar_eval_mode == "all": + eval_settings = [(0.0, False), (0.25, False), (0.25, True)] + else: + raise ValueError("`diar_eval_mode` variable contains an unsupported value") + + for collar, ignore_overlap in eval_settings: + diar_score = score_labels( + AUDIO_RTTM_MAP=audio_rttm_map_dict, + all_reference=all_reference, + all_hypothesis=all_hypothesis, + collar=collar, + ignore_overlap=ignore_overlap, + ) + return diar_score + + +def calculate_session_cpWER_bruteforce(spk_hypothesis: List[str], spk_reference: List[str]) -> Tuple[float, str, str]: + """ + Calculate cpWER with actual permutations in brute-force way when LSA algorithm cannot deliver the correct result. + + Args: + spk_hypothesis (list): + List containing the hypothesis transcript for each speaker. A list containing the sequence + of words is assigned for each speaker. + + Example: + >>> spk_hypothesis = ["hey how are you we that's nice", "i'm good yes hi is your sister"] + + spk_reference (list): + List containing the reference transcript for each speaker. A list containing the sequence + of words is assigned for each speaker. + + Example: + >>> spk_reference = ["hi how are you well that's nice", "i'm good yeah how is your sister"] + + Returns: + cpWER (float): + cpWER value for the given session. + min_perm_hyp_trans (str): + Hypothesis transcript containing the permutation that minimizes WER. Words are separated by spaces. + ref_trans (str): + Reference transcript in an arbitrary permutation. Words are separated by spaces. + """ + p_wer_list, permed_hyp_lists = [], [] + ref_word_list = [] + + # Concatenate the hypothesis transcripts into a list + for spk_id, word_list in enumerate(spk_reference): + ref_word_list.append(word_list) + ref_trans = " ".join(ref_word_list) + + # Calculate WER for every permutation + for hyp_word_list in permutations(spk_hypothesis): + hyp_trans = " ".join(hyp_word_list) + permed_hyp_lists.append(hyp_trans) + + # Calculate a WER value of the permuted and concatenated transcripts + p_wer = word_error_rate(hypotheses=[hyp_trans], references=[ref_trans]) + p_wer_list.append(p_wer) + + # Find the lowest WER and its hypothesis transcript + argmin_idx = np.argmin(p_wer_list) + min_perm_hyp_trans = permed_hyp_lists[argmin_idx] + cpWER = p_wer_list[argmin_idx] + return cpWER, min_perm_hyp_trans, ref_trans + + +def calculate_session_cpWER( + spk_hypothesis: List[str], spk_reference: List[str], use_lsa_only: bool = False +) -> Tuple[float, str, str]: + """ + Calculate a session-level concatenated minimum-permutation word error rate (cpWER) value. cpWER is + a scoring method that can evaluate speaker diarization and speech recognition performance at the same time. + cpWER is calculated by going through the following steps. + + 1. Concatenate all utterances of each speaker for both reference and hypothesis files. + 2. Compute the WER between the reference and all possible speaker permutations of the hypothesis. + 3. Pick the lowest WER among them (this is assumed to be the best permutation: `min_perm_hyp_trans`). + + cpWER was proposed in the following article: + CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings + https://arxiv.org/pdf/2004.09249.pdf + + Implementation: + - Brute force permutation method for calculating cpWER has a time complexity of `O(n!)`. + - To reduce the computational burden, linear sum assignment (LSA) algorithm is applied + (also known as Hungarian algorithm) to find the permutation that leads to the lowest WER. + - In this implementation, instead of calculating all WER values for all permutation of hypotheses, + we only calculate WER values of (estimated number of speakers) x (reference number of speakers) + combinations with `O(n^2)`) time complexity and then select the permutation that yields the lowest + WER based on LSA algorithm. + - LSA algorithm has `O(n^3)` time complexity in the worst case. + - We cannot use LSA algorithm to find the best permutation when there are more hypothesis speakers + than reference speakers. In this case, we use the brute-force permutation method instead. + + Example: + >>> transcript_A = ['a', 'b', 'c', 'd', 'e', 'f'] # 6 speakers + >>> transcript_B = ['a c b d', 'e f'] # 2 speakers + + [case1] hypothesis is transcript_A, reference is transcript_B + [case2] hypothesis is transcript_B, reference is transcript_A + + LSA algorithm based cpWER is: + [case1] 4/6 (4 deletion) + [case2] 2/6 (2 substitution) + brute force permutation based cpWER is: + [case1] 0 + [case2] 2/6 (2 substitution) + + Args: + spk_hypothesis (list): + List containing the hypothesis transcript for each speaker. A list containing the sequence + of words is assigned for each speaker. + + Example: + >>> spk_hypothesis = ["hey how are you we that's nice", "i'm good yes hi is your sister"] + + spk_reference (list): + List containing the reference transcript for each speaker. A list containing the sequence + of words is assigned for each speaker. + + Example: + >>> spk_reference = ["hi how are you well that's nice", "i'm good yeah how is your sister"] + + Returns: + cpWER (float): + cpWER value for the given session. + min_perm_hyp_trans (str): + Hypothesis transcript containing the permutation that minimizes WER. Words are separated by spaces. + ref_trans (str): + Reference transcript in an arbitrary permutation. Words are separated by spaces. + """ + # Get all pairs of (estimated num of spks) x (reference num of spks) combinations + hyp_ref_pair = [spk_hypothesis, spk_reference] + all_pairs = list(itertools.product(*hyp_ref_pair)) + + num_hyp_spks, num_ref_spks = len(spk_hypothesis), len(spk_reference) + + if not use_lsa_only and num_ref_spks < num_hyp_spks: + # Brute force algorithm when there are more speakers in the hypothesis + cpWER, min_perm_hyp_trans, ref_trans = calculate_session_cpWER_bruteforce(spk_hypothesis, spk_reference) + else: + # Calculate WER for each speaker in hypothesis with reference + # There are (number of hyp speakers) x (number of ref speakers) combinations + lsa_wer_list = [] + for (spk_hyp_trans, spk_ref_trans) in all_pairs: + spk_wer = word_error_rate(hypotheses=[spk_hyp_trans], references=[spk_ref_trans]) + lsa_wer_list.append(spk_wer) + + # Make a cost matrix and calculate a linear sum assignment on the cost matrix. + # Row is hypothesis index and column is reference index + cost_wer = torch.tensor(lsa_wer_list).reshape([len(spk_hypothesis), len(spk_reference)]) + row_hyp_ind, col_ref_ind = linear_sum_assignment(cost_wer) + + # In case where hypothesis has more speakers, add words from residual speakers + hyp_permed = [spk_hypothesis[k] for k in np.argsort(col_ref_ind)] + min_perm_hyp_trans = " ".join(hyp_permed) + + # Concatenate the reference transcripts into a string variable + ref_trans = " ".join(spk_reference) + + # Calculate a WER value from the permutation that yields the lowest WER. + cpWER = word_error_rate(hypotheses=[min_perm_hyp_trans], references=[ref_trans]) + + return cpWER, min_perm_hyp_trans, ref_trans + + +def concat_perm_word_error_rate( + spk_hypotheses: List[List[str]], spk_references: List[List[str]] +) -> Tuple[List[float], List[str], List[str]]: + """ + Launcher function for `calculate_session_cpWER`. Calculate session-level cpWER and average cpWER. + For detailed information about cpWER, see docstrings of `calculate_session_cpWER` function. + + As opposed to `cpWER`, `WER` is the regular WER value where the hypothesis transcript contains + words in temporal order regardless of the speakers. `WER` value can be different from cpWER value, + depending on the speaker diarization results. + + Args: + spk_hypotheses (list): + List containing the lists of speaker-separated hypothesis transcripts. + spk_references (list): + List containing the lists of speaker-separated reference transcripts. + + Returns: + cpWER (float): + List containing cpWER values for each session + min_perm_hyp_trans (list): + List containing transcripts that lead to the minimum WER in string format + ref_trans (list): + List containing concatenated reference transcripts + """ + if len(spk_hypotheses) != len(spk_references): + raise ValueError( + "In concatenated-minimum permutation word error rate calculation, " + "hypotheses and reference lists must have the same number of elements. But got arguments:" + f"{len(spk_hypotheses)} and {len(spk_references)} correspondingly" + ) + cpWER_values, hyps_spk, refs_spk = [], [], [] + for (spk_hypothesis, spk_reference) in zip(spk_hypotheses, spk_references): + cpWER, min_hypothesis, concat_reference = calculate_session_cpWER(spk_hypothesis, spk_reference) + cpWER_values.append(cpWER) + hyps_spk.append(min_hypothesis) + refs_spk.append(concat_reference) + return cpWER_values, hyps_spk, refs_spk diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/multi_binary_acc.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/multi_binary_acc.py new file mode 100644 index 0000000..8cc21c5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/multi_binary_acc.py @@ -0,0 +1,112 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + +import torch +from torchmetrics import Metric + +__all__ = ['MultiBinaryAccuracy'] + + +class MultiBinaryAccuracy(Metric): + """ + This metric computes accuracies that are needed to evaluate multiple binary outputs. + For example, if a model returns a set of multiple sigmoid outputs per each sample or at each time step, + F1 score can be calculated to monitor Type 1 error and Type 2 error together. + + Example: + def validation_step(self, batch, batch_idx): + ... + signals, signal_lengths, targets = batch + preds, _ = self.forward(input_signal=signals, + signal_lengths=signal_lengths, + targets=targets) + loss = self.loss(logits=preds, labels=targets) + self._accuracy_valid(preds, targets, signal_lengths) + f1_acc = self._accuracy.compute() + self.val_outputs = {'val_loss': loss, 'val_f1_acc': f1_acc} + return self.val_outputs + + def on_validation_epoch_end(self): + ... + val_loss_mean = torch.stack([x['val_loss'] for x in self.val_outputs]).mean() + correct_counts = torch.stack([x['val_correct_counts'] for x in self.val_outputs]).sum(axis=0) + total_counts = torch.stack([x['val_total_counts'] for x in self.val_outputs]).sum(axis=0) + + self._accuracy_valid.correct_counts_k = correct_counts + self._accuracy_valid.total_counts_k = total_counts + f1_acc = self._accuracy_valid.compute() + self._accuracy_valid.reset() + + self.log('val_loss', val_loss_mean) + self.log('val_f1_acc', f1_acc) + self.val_outputs.clear() # free memory + return {'val_loss': val_loss_mean, 'val_f1_acc': f1_acc} + + Args: + preds (torch.Tensor): + Predicted values which should be in range of [0, 1]. + targets (torch.Tensor): + Target values which should be in range of [0, 1]. + signal_lengths (torch.Tensor): + Length of each sequence in the batch input. signal_lengths values are used to + filter out zero-padded parts in each sequence. + + Returns: + f1_score (torch.Tensor): + F1 score calculated from the predicted value and binarized target values. + """ + + full_state_update = False + + def __init__(self, dist_sync_on_step=False): + super().__init__(dist_sync_on_step=dist_sync_on_step) + self.total_correct_counts = 0 + self.total_sample_counts = 0 + self.true_positive_count = 0 + self.false_positive_count = 0 + self.false_negative_count = 0 + + def update(self, preds: torch.Tensor, targets: torch.Tensor, signal_lengths: torch.Tensor) -> torch.Tensor: + with torch.no_grad(): + preds_list = [preds[k, : signal_lengths[k], :] for k in range(preds.shape[0])] + targets_list = [targets[k, : signal_lengths[k], :] for k in range(targets.shape[0])] + self.preds = torch.cat(preds_list, dim=0) + self.targets = torch.cat(targets_list, dim=0) + + self.true = self.preds.round().bool() == self.targets.round().bool() + self.false = self.preds.round().bool() != self.targets.round().bool() + self.positive = self.preds.round().bool() == 1 + self.negative = self.preds.round().bool() == 0 + + self.positive_count = torch.sum(self.preds.round().bool() == True) + self.true_positive_count += torch.sum(torch.logical_and(self.true, self.positive)) + self.false_positive_count += torch.sum(torch.logical_and(self.false, self.positive)) + self.false_negative_count += torch.sum(torch.logical_and(self.false, self.negative)) + + self.total_correct_counts += torch.sum(self.preds.round().bool() == self.targets.round().bool()) + self.total_sample_counts += torch.prod(torch.tensor(self.targets.shape)) + + def compute(self): + """ + Compute F1 score from the accumulated values. Return -1 if the F1 score is NaN. + """ + self.precision = self.true_positive_count / (self.true_positive_count + self.false_positive_count) + self.recall = self.true_positive_count / (self.true_positive_count + self.false_negative_count) + self.f1_score = 2 * self.precision * self.recall / (self.precision + self.recall) + if torch.isnan(self.f1_score): + logging.warn("self.f1_score contains NaN value. Returning -1 instead of NaN value.") + self.f1_score = -1 + return self.f1_score diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/wer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/wer.py new file mode 100644 index 0000000..1cb4cf0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/metrics/wer.py @@ -0,0 +1,355 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Optional, Tuple, Union + +import editdistance +import jiwer +import torch +from torchmetrics import Metric + +from nemo.collections.asr.parts.submodules.ctc_decoding import AbstractCTCDecoding +from nemo.collections.asr.parts.submodules.multitask_decoding import AbstractMultiTaskDecoding +from nemo.collections.asr.parts.submodules.rnnt_decoding import AbstractRNNTDecoding +from nemo.utils import logging + +__all__ = ['word_error_rate', 'word_error_rate_detail', 'WER'] + + +def move_dimension_to_the_front(tensor, dim_index): + all_dims = list(range(tensor.ndim)) + return tensor.permute(*([dim_index] + all_dims[:dim_index] + all_dims[dim_index + 1 :])) + + +def word_error_rate(hypotheses: List[str], references: List[str], use_cer=False) -> float: + """ + Computes Average Word Error rate between two texts represented as + corresponding lists of string. + + Hypotheses and references must have same length. + + Args: + hypotheses (list): list of hypotheses + references(list) : list of references + use_cer (bool): set True to enable cer + + Returns: + wer (float): average word error rate + """ + scores = 0 + words = 0 + if len(hypotheses) != len(references): + raise ValueError( + "In word error rate calculation, hypotheses and reference" + " lists must have the same number of elements. But I got:" + "{0} and {1} correspondingly".format(len(hypotheses), len(references)) + ) + for h, r in zip(hypotheses, references): + if use_cer: + h_list = list(h) + r_list = list(r) + else: + h_list = h.split() + r_list = r.split() + words += len(r_list) + # May deprecate using editdistance in future release for here and rest of codebase + # once we confirm jiwer is reliable. + scores += editdistance.eval(h_list, r_list) + if words != 0: + wer = 1.0 * scores / words + else: + wer = float('inf') + return wer + + +def word_error_rate_detail( + hypotheses: List[str], references: List[str], use_cer=False +) -> Tuple[float, int, float, float, float]: + """ + Computes Average Word Error Rate with details (insertion rate, deletion rate, substitution rate) + between two texts represented as corresponding lists of string. + + Hypotheses and references must have same length. + + Args: + hypotheses (list): list of hypotheses + references(list) : list of references + use_cer (bool): set True to enable cer + + Returns: + wer (float): average word error rate + words (int): Total number of words/charactors of given reference texts + ins_rate (float): average insertion error rate + del_rate (float): average deletion error rate + sub_rate (float): average substitution error rate + """ + scores = 0 + words = 0 + ops_count = {'substitutions': 0, 'insertions': 0, 'deletions': 0} + + if len(hypotheses) != len(references): + raise ValueError( + "In word error rate calculation, hypotheses and reference" + " lists must have the same number of elements. But I got:" + "{0} and {1} correspondingly".format(len(hypotheses), len(references)) + ) + + for h, r in zip(hypotheses, references): + if use_cer: + h_list = list(h) + r_list = list(r) + else: + h_list = h.split() + r_list = r.split() + + # To get rid of the issue that jiwer does not allow empty string + if len(r_list) == 0: + if len(h_list) != 0: + errors = len(h_list) + ops_count['insertions'] += errors + else: + errors = 0 + else: + if use_cer: + measures = jiwer.cer(r, h, return_dict=True) + else: + measures = jiwer.compute_measures(r, h) + + errors = measures['insertions'] + measures['deletions'] + measures['substitutions'] + ops_count['insertions'] += measures['insertions'] + ops_count['deletions'] += measures['deletions'] + ops_count['substitutions'] += measures['substitutions'] + + scores += errors + words += len(r_list) + + if words != 0: + wer = 1.0 * scores / words + ins_rate = 1.0 * ops_count['insertions'] / words + del_rate = 1.0 * ops_count['deletions'] / words + sub_rate = 1.0 * ops_count['substitutions'] / words + else: + wer, ins_rate, del_rate, sub_rate = float('inf'), float('inf'), float('inf'), float('inf') + + return wer, words, ins_rate, del_rate, sub_rate + + +def word_error_rate_per_utt(hypotheses: List[str], references: List[str], use_cer=False) -> Tuple[List[float], float]: + """ + Computes Word Error Rate per utterance and the average WER + between two texts represented as corresponding lists of string. + + Hypotheses and references must have same length. + + Args: + hypotheses (list): list of hypotheses + references(list) : list of references + use_cer (bool): set True to enable cer + + Returns: + wer_per_utt (List[float]): word error rate per utterance + avg_wer (float): average word error rate + """ + scores = 0 + words = 0 + wer_per_utt = [] + + if len(hypotheses) != len(references): + raise ValueError( + "In word error rate calculation, hypotheses and reference" + " lists must have the same number of elements. But I got:" + "{0} and {1} correspondingly".format(len(hypotheses), len(references)) + ) + + for h, r in zip(hypotheses, references): + if use_cer: + h_list = list(h) + r_list = list(r) + else: + h_list = h.split() + r_list = r.split() + + # To get rid of the issue that jiwer does not allow empty string + if len(r_list) == 0: + if len(h_list) != 0: + errors = len(h_list) + wer_per_utt.append(float('inf')) + else: + if use_cer: + measures = jiwer.cer(r, h, return_dict=True) + er = measures['cer'] + else: + measures = jiwer.compute_measures(r, h) + er = measures['wer'] + + errors = measures['insertions'] + measures['deletions'] + measures['substitutions'] + wer_per_utt.append(er) + + scores += errors + words += len(r_list) + + if words != 0: + avg_wer = 1.0 * scores / words + else: + avg_wer = float('inf') + + return wer_per_utt, avg_wer + + +class WER(Metric): + """ + This metric computes numerator and denominator for Overall Word Error Rate (WER) between prediction and reference + texts. When doing distributed training/evaluation the result of ``res=WER(predictions, predictions_lengths, targets, target_lengths)`` + calls will be all-reduced between all workers using SUM operations. Here ``res`` contains three numbers + ``res=[wer, total_levenstein_distance, total_number_of_words]``. + + If used with PytorchLightning LightningModule, include wer_numerator and wer_denominators inside validation_step + results. Then aggregate (sum) then at the end of validation epoch to correctly compute validation WER. + + Example: + def validation_step(self, batch, batch_idx): + ... + wer_num, wer_denom = self.__wer(predictions, predictions_len, transcript, transcript_len) + self.val_outputs = {'val_loss': loss_value, 'val_wer_num': wer_num, 'val_wer_denom': wer_denom} + return self.val_outputs + + def on_validation_epoch_end(self): + ... + wer_num = torch.stack([x['val_wer_num'] for x in self.val_outputs]).sum() + wer_denom = torch.stack([x['val_wer_denom'] for x in self.val_outputs]).sum() + tensorboard_logs = {'validation_loss': val_loss_mean, 'validation_avg_wer': wer_num / wer_denom} + self.val_outputs.clear() # free memory + return {'val_loss': val_loss_mean, 'log': tensorboard_logs} + + Args: + decoding: An instance of CTCDecoding or RNNTDecoding. + use_cer: Whether to use Character Error Rate instead of Word Error Rate. + log_prediction: Whether to log a single decoded sample per call. + batch_dim_index: Index corresponding to batch dimension. (For RNNT.) + dist_dync_on_step: Whether to perform reduction on forward pass of metric. + + Returns: + res: a tuple of 3 zero dimensional float32 ``torch.Tensor` objects: a WER score, a sum of Levenstein's + distances for all prediction - reference pairs, total number of words in all references. + """ + + full_state_update: bool = True + + def __init__( + self, + decoding: Union[AbstractCTCDecoding, AbstractRNNTDecoding, AbstractMultiTaskDecoding], + use_cer=False, + log_prediction=True, + fold_consecutive=True, + batch_dim_index=0, + dist_sync_on_step=False, + ): + super().__init__(dist_sync_on_step=dist_sync_on_step) + + self.decoding = decoding + self.use_cer = use_cer + self.log_prediction = log_prediction + self.fold_consecutive = fold_consecutive + self.batch_dim_index = batch_dim_index + + self.has_spl_tokens = False + self.decode = None + if isinstance(self.decoding, AbstractRNNTDecoding): + self.decode = lambda predictions, predictions_lengths, predictions_mask, input_ids, targets: self.decoding.rnnt_decoder_predictions_tensor( + encoder_output=predictions, encoded_lengths=predictions_lengths + ) + elif isinstance(self.decoding, AbstractCTCDecoding): + self.decode = lambda predictions, predictions_lengths, predictions_mask, input_ids, targets: self.decoding.ctc_decoder_predictions_tensor( + decoder_outputs=predictions, + decoder_lengths=predictions_lengths, + fold_consecutive=self.fold_consecutive, + ) + elif isinstance(self.decoding, AbstractMultiTaskDecoding): + self.has_spl_tokens = True + self.decode = lambda predictions, prediction_lengths, predictions_mask, input_ids, targets: self.decoding.decode_predictions_tensor( + encoder_hidden_states=predictions, + encoder_input_mask=predictions_mask, + decoder_input_ids=input_ids, + return_hypotheses=False, + ) + else: + raise TypeError(f"WER metric does not support decoding of type {type(self.decoding)}") + + self.add_state("scores", default=torch.tensor(0), dist_reduce_fx='sum', persistent=False) + self.add_state("words", default=torch.tensor(0), dist_reduce_fx='sum', persistent=False) + + def update( + self, + predictions: torch.Tensor, + predictions_lengths: torch.Tensor, + targets: torch.Tensor, + targets_lengths: torch.Tensor, + predictions_mask: Optional[torch.Tensor] = None, + input_ids: Optional[torch.Tensor] = None, + ): + """ + Updates metric state. + Args: + predictions: an integer torch.Tensor of shape ``[Batch, Time, {Vocabulary}]`` (if ``batch_dim_index == 0``) or + ``[Time, Batch]`` (if ``batch_dim_index == 1``) + prediction_lengths: an integer torch.Tensor of shape ``[Batch]`` + targets: an integer torch.Tensor of shape ``[Batch, Time]`` (if ``batch_dim_index == 0``) or + ``[Time, Batch]`` (if ``batch_dim_index == 1``) + target_lengths: an integer torch.Tensor of shape ``[Batch]`` + predictions_lengths: an integer torch.Tensor of shape ``[Batch]`` + """ + words = 0 + scores = 0 + references = [] + with torch.no_grad(): + tgt_lenths_cpu_tensor = targets_lengths.long().cpu() + targets_cpu_tensor = targets.long().cpu() + # check batch_dim_index is first dim + if self.batch_dim_index != 0: + targets_cpu_tensor = move_dimension_to_the_front(targets_cpu_tensor, self.batch_dim_index) + # iterate over batch + for ind in range(targets_cpu_tensor.shape[0]): + tgt_len = tgt_lenths_cpu_tensor[ind].item() + target = targets_cpu_tensor[ind][:tgt_len].numpy().tolist() + reference = self.decoding.decode_tokens_to_str(target) + references.append(reference) + hypotheses, _ = self.decode(predictions, predictions_lengths, predictions_mask, input_ids, targets) + + if self.has_spl_tokens: + hypotheses = [self.decoding.strip_special_tokens(hyp) for hyp in hypotheses] + references = [self.decoding.strip_special_tokens(ref) for ref in references] + + if self.log_prediction: + logging.info(f"\n") + logging.info(f"reference:{references[0]}") + logging.info(f"predicted:{hypotheses[0]}") + + for h, r in zip(hypotheses, references): + if self.use_cer: + h_list = list(h) + r_list = list(r) + else: + h_list = h.split() + r_list = r.split() + words += len(r_list) + # Compute Levenstein's distance + scores += editdistance.eval(h_list, r_list) + + self.scores = torch.tensor(scores, device=self.scores.device, dtype=self.scores.dtype) + self.words = torch.tensor(words, device=self.words.device, dtype=self.words.dtype) + + def compute(self): + scores = self.scores.detach().float() + words = self.words.detach().float() + return scores / words, scores, words diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/__init__.py new file mode 100644 index 0000000..019c57f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/__init__.py @@ -0,0 +1,41 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.models.aed_multitask_models import EncDecMultiTaskModel +from nemo.collections.asr.models.asr_model import ASRModel +from nemo.collections.asr.models.audio_to_audio_model import AudioToAudioModel +from nemo.collections.asr.models.classification_models import ( + ClassificationInferConfig, + EncDecClassificationModel, + EncDecFrameClassificationModel, +) +from nemo.collections.asr.models.clustering_diarizer import ClusteringDiarizer +from nemo.collections.asr.models.ctc_bpe_models import EncDecCTCModelBPE +from nemo.collections.asr.models.ctc_models import EncDecCTCModel +from nemo.collections.asr.models.enhancement_models import EncMaskDecAudioToAudioModel +from nemo.collections.asr.models.hybrid_rnnt_ctc_bpe_models import EncDecHybridRNNTCTCBPEModel +from nemo.collections.asr.models.hybrid_rnnt_ctc_models import EncDecHybridRNNTCTCModel +from nemo.collections.asr.models.k2_sequence_models import ( + EncDecK2RnntSeqModel, + EncDecK2RnntSeqModelBPE, + EncDecK2SeqModel, + EncDecK2SeqModelBPE, +) +from nemo.collections.asr.models.label_models import EncDecSpeakerLabelModel +from nemo.collections.asr.models.msdd_models import EncDecDiarLabelModel, NeuralDiarizer +from nemo.collections.asr.models.rnnt_bpe_models import EncDecRNNTBPEModel +from nemo.collections.asr.models.rnnt_models import EncDecRNNTModel +from nemo.collections.asr.models.slu_models import SLUIntentSlotBPEModel +from nemo.collections.asr.models.ssl_models import SpeechEncDecSelfSupervisedModel +from nemo.collections.asr.models.transformer_bpe_models import EncDecTransfModelBPE diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/aed_multitask_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/aed_multitask_models.py new file mode 100644 index 0000000..5cda453 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/aed_multitask_models.py @@ -0,0 +1,976 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from dataclasses import dataclass, field +from math import ceil +from typing import Any, Dict, List, Optional, Union + +import numpy as np +import torch +from omegaconf import DictConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer + +from nemo.collections.asr.data.audio_to_text_lhotse_prompted import ( + PromptedAudioToTextLhotseDataset, + get_prompt_format_fn, +) +from nemo.collections.asr.metrics import BLEU, WER +from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel +from nemo.collections.asr.parts.mixins import ASRBPEMixin, ASRTranscriptionMixin +from nemo.collections.asr.parts.mixins.transcription import ( + GenericTranscriptionType, + InternalTranscribeConfig, + TranscribeConfig, +) +from nemo.collections.asr.parts.submodules.multitask_decoding import MultiTaskDecoding, MultiTaskDecodingConfig +from nemo.collections.asr.parts.submodules.token_classifier import TokenClassifier +from nemo.collections.asr.parts.utils import manifest_utils +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis +from nemo.collections.common import tokenizers +from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config +from nemo.collections.common.metrics import GlobalAverageLossMetric +from nemo.collections.common.parts import transformer_weights_init +from nemo.collections.common.parts.preprocessing.manifest import get_full_path +from nemo.core.classes.common import typecheck +from nemo.core.neural_types import ( + AudioSignal, + ChannelType, + LabelsType, + LengthsType, + LogprobsType, + MaskType, + NeuralType, + SpectrogramType, +) +from nemo.utils import logging, model_utils + +__all__ = ['EncDecMultiTaskModel'] + + +def lens_to_mask(lens, max_length): + batch_size = lens.shape[0] + mask = torch.arange(max_length).repeat(batch_size, 1).to(lens.device) < lens[:, None] + return mask + + +def _config_check(cfg): + if 'tokenizer' not in cfg: + raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !") + # Assert config has "prompt_format" + if "prompt_format" not in cfg: + raise ValueError("`cfg` must have `prompt_format` config to create a multi task model !") + # Assert config has `model_defaults` + if 'model_defaults' not in cfg: + raise ValueError("`cfg` must have `model_defaults` config to create a model !") + if "asr_enc_hidden" not in cfg.model_defaults: + raise ValueError("`cfg.model_defaults` must have `asr_enc_hidden` key !") + if "lm_enc_hidden" not in cfg.model_defaults: + raise ValueError("`cfg.model_defaults` must have `lm_enc_hidden` key !") + if "lm_dec_hidden" not in cfg.model_defaults: + raise ValueError("`cfg.model_defaults` must have `lm_dec_hidden` key !") + + +@dataclass +class MultiTaskTranscriptionInternalConfig(InternalTranscribeConfig): + """ + Configuration for Multi Task Transcription + """ + + manifest_filepath: Optional[str] = None + primary_language: Optional[str] = None + + +@dataclass +class MultiTaskTranscriptionConfig(TranscribeConfig): + """ + Configuration for Multi Task Transcription + """ + + task: Optional[str] = None + pnc: Optional[bool] = None + source_lang: Optional[str] = None + target_lang: Optional[str] = None + text_field: str = "answer" + lang_field: str = "target_lang" + + _internal: Optional[MultiTaskTranscriptionInternalConfig] = field( + default_factory=lambda: MultiTaskTranscriptionInternalConfig() + ) + + def __post_init__(self): + required_fields = ['task', 'pnc', 'source_lang', 'target_lang', 'text_field', 'lang_field'] + for field in required_fields: + if not hasattr(self, field): + raise ValueError(f"`{field}` must be present in the transcription config: {self}") + + +class EncDecMultiTaskModel(ASRModel, ExportableEncDecModel, ASRBPEMixin, ASRTranscriptionMixin): + """Base class for AED multi-task models""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + + # Convert to Hydra 1.0 compatible DictConfig + cfg = model_utils.convert_model_config_to_dict_config(cfg) + cfg = model_utils.maybe_update_config_version(cfg) + _config_check(cfg) + + self.prompt_format = cfg.prompt_format + self.sample_rate = cfg.sample_rate + self._setup_tokenizer(cfg.tokenizer) + + super().__init__(cfg=cfg, trainer=trainer) + + # Setup audio preprocessor + self.preprocessor = EncDecMultiTaskModel.from_config_dict(self.cfg.preprocessor) + # Setup audio encoder + self.encoder = EncDecMultiTaskModel.from_config_dict(self.cfg.encoder) + + # Add projection layer if encoder and decoder differ in hidden size + asr_enc_hidden_size = self.cfg.model_defaults.asr_enc_hidden + decoder_hidden_size = self.cfg.model_defaults.lm_dec_hidden + if asr_enc_hidden_size != decoder_hidden_size: + self.encoder_decoder_proj = torch.nn.Linear(asr_enc_hidden_size, decoder_hidden_size) + else: + self.encoder_decoder_proj = torch.nn.Identity() + + transf_encoder_cfg_dict = self.cfg.get('transf_encoder', None) + + # Whether to add Transformer Encoder block between Conformer and Transformer Decoder + self.use_transf_encoder = False + if transf_encoder_cfg_dict is not None and transf_encoder_cfg_dict['num_layers'] > 0: + self.use_transf_encoder = True + + self.transf_encoder = EncDecMultiTaskModel.from_config_dict(transf_encoder_cfg_dict) + + # Initialize weights + std_init_range = 1 / self.cfg.model_defaults.lm_enc_hidden ** 0.5 + self.transf_encoder.apply(lambda module: transformer_weights_init(module, std_init_range)) + + transf_decoder_cfg_dict = cfg.transf_decoder + + # Transformer decoder + vocab_size = 8 * ceil(self.tokenizer.vocab_size / 8) + + # Auto inject vocab size for `get_transformer` + with open_dict(transf_decoder_cfg_dict): + if 'config_dict' in transf_decoder_cfg_dict: + transf_decoder_cfg_dict['config_dict']['vocab_size'] = vocab_size + + self.transf_decoder = EncDecMultiTaskModel.from_config_dict(transf_decoder_cfg_dict) + + # Setup token classifier + with open_dict(self.cfg.head): + self.cfg.head.num_classes = vocab_size + + self.log_softmax = EncDecMultiTaskModel.from_config_dict(self.cfg.head) + + # Weight tying - if using TokenClassifier only + if isinstance(self.log_softmax, TokenClassifier): + self.log_softmax.mlp.layer0.weight = self.transf_decoder.embedding.token_embedding.weight + + # Initialize weights + std_init_range = 1 / self.cfg.model_defaults.lm_dec_hidden ** 0.5 + self.transf_decoder.apply(lambda module: transformer_weights_init(module, std_init_range)) + self.log_softmax.apply(lambda module: transformer_weights_init(module, std_init_range)) + + # Setup decoding objects + decoding_cfg = self.cfg.get('decoding', None) + + # In case decoding config not found, use default config + if decoding_cfg is None: + decoding_cfg = OmegaConf.structured(MultiTaskDecodingConfig) + with open_dict(self.cfg): + self.cfg.decoding = decoding_cfg + + self.decoding = MultiTaskDecoding( + decoding_cfg=self.cfg.decoding, + transformer_decoder=self.transf_decoder, + log_softmax_module=self.log_softmax, + tokenizer=self.tokenizer, + ) + + # Define autoregressive CE loss + with open_dict(self.cfg.loss): + self.cfg.loss.pad_id = self.tokenizer.pad_id + + self.loss = EncDecMultiTaskModel.from_config_dict(self.cfg.loss) + + if hasattr(self.cfg, 'spec_augment') and self.cfg.spec_augment is not None: + self.spec_augmentation = EncDecMultiTaskModel.from_config_dict(self.cfg.spec_augment) + else: + self.spec_augmentation = None + + self.val_loss = GlobalAverageLossMetric(dist_sync_on_step=False, take_avg_loss=True) + + # TODO: PytorchMetrics lets you join two metrics together to save compute. But need to make wer and bleu have same outputs first + self.wer = WER(self.decoding, log_prediction=self.cfg.get("log_prediction")) + self.bleu = BLEU( + self.decoding, tokenize=self.cfg.get('bleu_tokenizer', "13a"), log_prediction=False + ) # Wer is handling logging + + def change_decoding_strategy(self, decoding_cfg: DictConfig): + """ + Changes decoding strategy used during Multi Task decoding process. + + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + """ + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(MultiTaskDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = MultiTaskDecoding( + decoding_cfg=decoding_cfg, + transformer_decoder=self.transf_decoder, + log_softmax_module=self.log_softmax, + tokenizer=self.tokenizer, + ) + + # Update config + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}") + + def change_vocabulary( + self, + new_tokenizer_dir: Union[str, DictConfig], + new_tokenizer_type: str, + decoding_cfg: Optional[DictConfig] = None, + prompt_format: Optional[str] = None, + ): + """ + Changes vocabulary used during AED decoding process. Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_tokenizer_dir: Directory path to tokenizer or a config for a new tokenizer (if the tokenizer type is `agg`) + new_tokenizer_type: Type of tokenizer. Can be either `agg`, `bpe` or `wpe`. + decoding_cfg: A config for the decoding, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + prompt_format: A string alias of the object that represents the prompt structure. + If not None, it will be used to update the prompt format. + """ + if isinstance(new_tokenizer_dir, (dict, DictConfig)): + if new_tokenizer_type == 'agg': + if not isinstance(new_tokenizer_dir, DictConfig): + new_tokenizer_dir = OmegaConf.create(new_tokenizer_dir) + + new_tokenizer_cfg = new_tokenizer_dir + else: + raise ValueError( + f'New tokenizer dir should be a string unless the tokenizer is `agg`, but this tokenizer type is: {new_tokenizer_type}' + ) + else: + new_tokenizer_cfg = None + + if new_tokenizer_cfg is not None: + tokenizer_cfg = new_tokenizer_cfg + else: + if not os.path.isdir(new_tokenizer_dir): + raise NotADirectoryError( + f'New tokenizer dir must be non-empty path to a directory. But instead got: {new_tokenizer_dir}' + ) + + if new_tokenizer_type.lower() not in ('bpe', 'wpe'): + raise ValueError(f'New tokenizer type must be either `bpe` or `wpe`') + + tokenizer_cfg = OmegaConf.create({'dir': new_tokenizer_dir, 'type': new_tokenizer_type}) + + if prompt_format is None: + prompt_format = self.cfg.prompt_format + + # Setup the tokenizer + self._setup_tokenizer(tokenizer_cfg) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + # Setup Decoder + transf_decoder_cfg_dict = self.transf_decoder.to_config_dict() + + vocab_size = 8 * ceil(self.tokenizer.vocab_size / 8) + + # Auto inject vocab size for `get_transformer` + with open_dict(transf_decoder_cfg_dict): + if 'config_dict' in transf_decoder_cfg_dict: + transf_decoder_cfg_dict['config_dict']['vocab_size'] = vocab_size + + original_decoder_state_dict = self.transf_decoder.state_dict() + self.transf_decoder = EncDecMultiTaskModel.from_config_dict(transf_decoder_cfg_dict) + + # Partially load the original state dict into the new decoder + decoder_state_dict = self.transf_decoder.state_dict() + for og_key, og_value in original_decoder_state_dict.items(): + if og_key in decoder_state_dict and og_value.shape == decoder_state_dict[og_key].shape: + decoder_state_dict[og_key] = og_value + else: + logging.warning( + f"Skipping key `{og_key}` in the `transf_decoder` module from original state dict due " + f"to shape mismatch after change in vocabulary.\n" + f"Original shape: {og_value.shape}, New shape: {decoder_state_dict[og_key].shape}" + ) + + self.transf_decoder.load_state_dict(decoder_state_dict) + + # Setup token classifier + with open_dict(self.cfg.head): + self.cfg.head.num_classes = vocab_size + + del self.log_softmax + self.log_softmax = EncDecMultiTaskModel.from_config_dict(self.cfg.head) + + # Weight tying - if using TokenClassifier only + if isinstance(self.log_softmax, TokenClassifier): + self.log_softmax.mlp.layer0.weight = self.transf_decoder.embedding.token_embedding.weight + + # Initialize weights of token classifier + std_init_range = 1 / self.cfg.model_defaults.lm_dec_hidden ** 0.5 + self.log_softmax.apply(lambda module: transformer_weights_init(module, std_init_range)) + + # Setup Decoding class + if decoding_cfg is None: + # Assume same decoding config as before + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(MultiTaskDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + del self.decoding + self.decoding = MultiTaskDecoding( + decoding_cfg=decoding_cfg, + transformer_decoder=self.transf_decoder, + log_softmax_module=self.log_softmax, + tokenizer=self.tokenizer, + ) + + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + # Setup loss + with open_dict(self.cfg.loss): + self.cfg.loss.pad_id = self.tokenizer.pad_id + + del self.loss + self.loss = EncDecMultiTaskModel.from_config_dict(self.cfg.loss) + + # Update config + with open_dict(self.cfg): + self.cfg.prompt_format = prompt_format + + logging.info(f"Changed decoder to output to {vocabulary} vocabulary.") + + @torch.no_grad() + def transcribe( + self, + audio: Union[List[str], str], + batch_size: int = 4, + return_hypotheses: bool = False, + task: Optional[str] = None, + pnc: Optional[bool] = None, + source_lang: Optional[str] = None, + target_lang: Optional[str] = None, + num_workers: int = 0, + channel_selector: Optional[ChannelSelectorType] = None, + augmentor: DictConfig = None, + verbose: bool = True, + override_config: Optional[MultiTaskTranscriptionConfig] = None, + ) -> Union[List[str], List[Hypothesis]]: + """ + Uses greedy decoding to transcribe audio files. Use this method for debugging and prototyping. + Args: + audio: (a list) of paths to audio files. \ + Recommended length per file is between 5 and 25 seconds. \ + But it is possible to pass a few hours long file if enough GPU memory is available. + batch_size: (int) batch size to use during inference. + Bigger will result in better throughput performance but would use more memory. + return_hypotheses: (bool) Either return hypotheses or text + With hypotheses can do some postprocessing like getting timestamp or rescoring + task: (str) task name. Defaults to `asr`. + pnc: (bool) whether to apply punctuation and capitalization or not. Defaults to True. + source_lang: (str) source language. Defaults to `en`. + target_lang: (str) target language. Defaults to `en`. + num_workers: (int) number of workers for DataLoader + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. + augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied. + verbose: (bool) whether to display tqdm progress bar + override_config: (Optional[MultiTaskTranscriptionConfig]) A config to override the default config. + + Returns: + A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files + """ + if override_config is None: + trcfg = MultiTaskTranscriptionConfig( + batch_size=batch_size, + return_hypotheses=return_hypotheses, + num_workers=num_workers, + channel_selector=channel_selector, + augmentor=augmentor, + verbose=verbose, + task=task, + pnc=pnc, + source_lang=source_lang, + target_lang=target_lang, + ) + else: + if not isinstance(override_config, MultiTaskTranscriptionConfig): + raise ValueError( + f"override_config must be of type {MultiTaskTranscriptionConfig}, " + f"but got {type(override_config)}" + ) + trcfg = override_config + + return super().transcribe(audio=audio, override_config=trcfg) + + def _setup_dataloader_from_config(self, config: Optional[Dict], inference: bool = False): + assert config.get("use_lhotse", False), ( + "Multi-task model only supports dataloading with Lhotse. " + "Please set config.{train,validation,test}_ds.use_lhotse=True" + ) + return get_lhotse_dataloader_from_config( + config, + global_rank=self.global_rank, + world_size=self.world_size, + dataset=PromptedAudioToTextLhotseDataset( + tokenizer=self.tokenizer, + prompt_format_fn=get_prompt_format_fn(self.prompt_format), + inference=inference, + ), + ) + + def setup_training_data(self, train_data_config: Optional[DictConfig]): + + # create audio-only data loader + self._update_dataset_config(dataset_name='train', config=train_data_config) + self._train_dl = self._setup_dataloader_from_config(config=train_data_config) + + # Need to set this because if using an IterableDataset, the length of the + # dataloader is the total number of samples rather than the number of batches, + # and this messes up the tqdm progress bar. So we set the number of steps manually + # (to the correct number) to fix this. + if 'is_tarred' in train_data_config and train_data_config['is_tarred']: + # We also need to check if limit_train_batches is already set. + # If it's an int, we assume that the user has set it to something sane, + # i.e. <= # training batches, and don't change it. Otherwise, adjust + # batches accordingly if it's a float (including 1.0). + if self._trainer is not None and isinstance(self._trainer.limit_train_batches, float): + self._trainer.limit_train_batches = int( + self._trainer.limit_train_batches + * ceil((len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size']) + ) + elif self._trainer is None: + logging.warning( + "Model Trainer was not set before constructing the dataset, incorrect number of " + "training batches will be used. Please set the trainer and rebuild the dataset." + ) + + def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the validation data loader via a Dict-like object. + Args: + val_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text_lhotse_prompted.PromptedAudioToTextLhotseDataset` + """ + if 'shuffle' not in val_data_config: + val_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='validation', config=val_data_config) + self._validation_dl = self._setup_dataloader_from_config(config=val_data_config, inference=True) + + def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the test data loader via a Dict-like object. + Args: + test_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text_lhotse_prompted.PromptedAudioToTextLhotseDataset` + """ + if 'shuffle' not in test_data_config: + test_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='test', config=test_data_config) + self._test_dl = self._setup_dataloader_from_config(config=test_data_config, inference=True) + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + if hasattr(self.preprocessor, '_sample_rate'): + input_signal_eltype = AudioSignal(freq=self.preprocessor._sample_rate) + else: + input_signal_eltype = AudioSignal() + return { + "input_signal": NeuralType(('B', 'T'), input_signal_eltype, optional=True), + "input_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "processed_signal": NeuralType(('B', 'D', 'T'), SpectrogramType(), optional=True), + "processed_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "transcript": NeuralType(('B', 'T'), LabelsType(), optional=True), + "transcript_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "prompt": NeuralType(('B', 'T'), LabelsType(), optional=True), + "prompt_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "sample_id": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return { + "transf_log_probs": NeuralType(('B', 'T', 'D'), LogprobsType()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + "encoder_states": NeuralType(('B', 'T', 'D'), ChannelType()), + "encoder_mask": NeuralType(('B', 'T'), MaskType()), + } + + @typecheck() + def forward( + self, + input_signal=None, + input_signal_length=None, + processed_signal=None, + processed_signal_length=None, + transcript=None, + transcript_length=None, + ): + """ + Forward pass of the model. + Args: + input_signal: Tensor that represents a batch of raw audio signals, + of shape [B, T]. T here represents timesteps, with 1 second of audio represented as + `self.sample_rate` number of floating point values. + input_signal_length: Vector of length B, that contains the individual lengths of the audio + sequences. + processed_signal: Tensor that represents a batch of processed audio signals, + of shape (B, D, T). + processed_signal_length: Vector of length B, that contains the individual lengths of the + processed audio sequences. + # TODO: Add support for `transcript` and `transcript_length` in the docstring + + Returns: + A tuple of 3 elements - + 1) The log probabilities tensor of shape [B, T, D]. + 2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. + 3) The greedy token predictions of the model of shape [B, T] (via argmax) + """ + has_input_signal = input_signal is not None and input_signal_length is not None + has_processed_signal = processed_signal is not None and processed_signal_length is not None + if (has_input_signal ^ has_processed_signal) == False: + raise ValueError( + f"{self} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive " + " with ``processed_signal`` and ``processed_signal_len`` arguments." + ) + + if not has_processed_signal: + processed_signal, processed_signal_length = self.preprocessor( + input_signal=input_signal, length=input_signal_length + ) + + if self.spec_augmentation is not None and self.training: + processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length) + + encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length) + + enc_states = encoded.permute(0, 2, 1) + enc_states = self.encoder_decoder_proj(enc_states) + enc_mask = lens_to_mask(encoded_len, enc_states.shape[1]).to(enc_states.dtype) + if self.use_transf_encoder: + enc_states = self.transf_encoder(encoder_states=enc_states, encoder_mask=enc_mask) + + transf_log_probs = None + if transcript is not None: + dec_mask = lens_to_mask(transcript_length, transcript.shape[1]).to(transcript.dtype) + dec_states = self.transf_decoder( + input_ids=transcript, decoder_mask=dec_mask, encoder_embeddings=enc_states, encoder_mask=enc_mask + ) + transf_log_probs = self.log_softmax(hidden_states=dec_states) + + return transf_log_probs, encoded_len, enc_states, enc_mask + + # PTL-specific methods + def training_step(self, batch, batch_nb): + + if batch is None: + return torch.tensor([0.0]) + + # During training prompt and prompt_len are null, ignore. + signal, signal_len, transcript, transcript_len, prompt, prompt_len = batch + input_ids, labels = transcript[:, :-1], transcript[:, 1:] + + transf_log_probs, encoded_len, enc_states, enc_mask = self.forward( + input_signal=signal, + input_signal_length=signal_len, + transcript=input_ids, + transcript_length=transcript_len, + ) + + audio_loss = self.loss(log_probs=transf_log_probs, labels=labels) + + tensorboard_logs = { + 'train_loss': audio_loss, + 'learning_rate': self._optimizer.param_groups[0]['lr'], + } + + return {'loss': audio_loss, 'log': tensorboard_logs} + + def validation_pass(self, batch, batch_idx, dataloader_idx=0, eval_mode="val"): + # During inference, dataloader passes pure prompt without transcript text. + signal, signal_len, transcript, transcript_len, prompt, prompt_len = batch + input_ids, labels = transcript[:, :-1], transcript[:, 1:] + + transf_log_probs, encoded_len, enc_states, enc_mask = self.forward( + input_signal=signal, + input_signal_length=signal_len, + transcript=input_ids, + transcript_length=transcript_len, + ) + + transf_loss = self.loss(log_probs=transf_log_probs, labels=labels) + self.val_loss(loss=transf_loss, num_measurements=transf_log_probs.shape[0] * transf_log_probs.shape[1]) + output_dict = { + f'{eval_mode}_loss': transf_loss, + } + + self.wer.update( + predictions=enc_states, + predictions_lengths=encoded_len, + targets=transcript, + targets_lengths=transcript_len, + predictions_mask=enc_mask, + input_ids=prompt, + ) + wer, wer_num, wer_denom = self.wer.compute() + output_dict.update({"val_wer": wer, "val_wer_num": wer_num, "val_wer_denom": wer_denom}) + self.wer.reset() + + self.bleu.update( + predictions=enc_states, + predictions_lengths=encoded_len, + targets=transcript, + targets_lengths=transcript_len, + predictions_mask=enc_mask, + input_ids=prompt, + ) + bleu_metrics = self.bleu.compute(prefix=f"{eval_mode}_") + output_dict.update(bleu_metrics) + self.bleu.reset() + + return output_dict + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + metrics = self.validation_pass(batch, batch_idx, dataloader_idx, eval_mode="val") + if type(self.trainer.val_dataloaders) == list and len(self.trainer.val_dataloaders) > 1: + self.validation_step_outputs[dataloader_idx].append(metrics) + else: + self.validation_step_outputs.append(metrics) + return metrics + + def test_step(self, batch, batch_idx, dataloader_idx=0): + metrics = self.validation_pass(batch, batch_idx, dataloader_idx, eval_mode="test") + if type(self.trainer.val_dataloaders) == list and len(self.trainer.val_dataloaders) > 1: + self.validation_step_outputs[dataloader_idx].append(metrics) + else: + self.validation_step_outputs.append(metrics) + return metrics + + def test_dataloader(self): + if self._test_dl is not None: + return self._test_dl + + """ Transcription methods """ + + def _transcribe_on_begin(self, audio, trcfg: MultiTaskTranscriptionConfig): + """ + Transcription setup method. + Args: + audio: A list of paths to audio files or a path to a manifest file. + trcfg: A config for the transcription, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + """ + super()._transcribe_on_begin(audio, trcfg) + + # Switch model to evaluation mode + self.transf_decoder.freeze() + + if isinstance(audio, list): + logging.debug(f"Found 'audio' to be a list of {len(audio)} items.") + logging.debug(f"Assuming each item in 'audio' is a path to audio file.") + + if isinstance(self.tokenizer, tokenizers.AggregateTokenizer): + if hasattr(trcfg, '_internal') and hasattr(trcfg._internal, 'primary_language'): + trcfg._internal.primary_language = self.tokenizer.langs[0] + logging.debug(f"Transcribing with default setting of {trcfg._internal.primary_language}.") + + elif isinstance(audio, str): + logging.debug(f"Found 'audio' to be a string. Assuming it is a path to manifest file.") + assert os.path.exists(audio), f"File {audio} doesn't exist" + # assert audio.endswith('.json') or audio.endswith('.jsonl'), f"File {audio} must be a json or jsonl file" + + # load json lines + manifest_path = audio # need to save this as we are overwriting paths2audio_files in nextline + if audio.endswith('.json') or audio.endswith('.jsonl'): + if hasattr(trcfg, '_internal') and hasattr(trcfg._internal, 'manifest_path'): + trcfg._internal.manifest_filepath = manifest_path + + elif isinstance(audio, (np.ndarray, torch.Tensor)): + raise NotImplementedError("Transcribing from numpy or torch tensors is not supported yet.") + + def _transcribe_input_manifest_processing( + self, audio_files: List[str], temp_dir: str, trcfg: MultiTaskTranscriptionConfig + ) -> Dict[str, Any]: + """ + Internal function to process the input audio filepaths and return a config dict for the dataloader. + This implementation adds support for dictionaries as manifest items. + + Args: + audio_files: A list of string filepaths for audio files, or a single string filepath for a manifest file. + temp_dir: A temporary directory to store intermediate files. + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + + Returns: + A config dict that is used to setup the dataloader for transcription. + """ + manifest_filepath = None + if len(audio_files) == 1 and isinstance(audio_files[0], str): + # Check if manifest file is provided + if ( + hasattr(trcfg._internal, 'manifest_filepath') + and getattr(trcfg._internal, 'manifest_filepath') is not None + ): + manifest_filepath = trcfg._internal.manifest_filepath + + elif audio_files[0].endswith('.json') or audio_files[0].endswith('.jsonl'): + # Assume it is a path to a manifest file + manifest_filepath = audio_files[0] + + if manifest_filepath is not None: + audio_files = manifest_utils.read_manifest(audio_files[0]) + + audio_files = self._may_be_make_dict_and_fix_paths(audio_files, manifest_filepath, trcfg) + + return super()._transcribe_input_manifest_processing(audio_files, temp_dir, trcfg) + + def _transcribe_forward(self, batch: Any, trcfg: MultiTaskTranscriptionConfig): + """ + Internal function to perform the model's custom forward pass to return outputs that are processed by + `_transcribe_output_processing()`. + This function is called by `transcribe()` and `transcribe_generator()` to perform the model's forward pass. + + Args: + batch: A batch of input data from the data loader that is used to perform the model's forward pass. + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + + Returns: + The model's outputs that are processed by `_transcribe_output_processing()`. + """ + log_probs, encoded_len, enc_states, enc_mask = self.forward( + input_signal=batch[0], input_signal_length=batch[1] + ) + decoder_input_ids = batch[-2].to(trcfg._internal.device) + output = dict( + log_probs=log_probs, + encoded_lengths=encoded_len, + encoder_states=enc_states, + encoder_mask=enc_mask, + decoder_input_ids=decoder_input_ids, + ) + return output + + def _transcribe_output_processing(self, outputs, trcfg: MultiTaskTranscriptionConfig) -> GenericTranscriptionType: + """ + Internal function to process the model's outputs to return the results to the user. This function is called by + `transcribe()` and `transcribe_generator()` to process the model's outputs. + + Args: + outputs: The model's outputs that are processed by `_transcribe_forward()`. + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + + Returns: + The output can be a list of + objects, list of list of objects, tuple of objects, tuple of list of objects, or a dict of list of objects. + Its type is defined in `TranscriptionReturnType`. + """ + log_probs = outputs.pop('log_probs') + encoded_len = outputs.pop('encoded_lengths') + enc_states = outputs.pop('encoder_states') + enc_mask = outputs.pop('encoder_mask') + decoder_input_ids = outputs.pop('decoder_input_ids') + + del log_probs, encoded_len + + best_hypotheses, all_hypotheses = self.decoding.decode_predictions_tensor( + encoder_hidden_states=enc_states, + encoder_input_mask=enc_mask, + decoder_input_ids=decoder_input_ids, + return_hypotheses=trcfg.return_hypotheses, + ) + + if trcfg.return_hypotheses: + for hyp in best_hypotheses: + hyp.text = self.decoding.strip_special_tokens(hyp.text) + if all_hypotheses is not None: + for i in range(len(all_hypotheses)): + for j in range(len(all_hypotheses[i])): + all_hypotheses[i][j].text = self.decoding.strip_special_tokens(all_hypotheses[i][j].text) + else: + best_hypotheses = [self.decoding.strip_special_tokens(text) for text in best_hypotheses] + if all_hypotheses is not None: + for i in range(len(all_hypotheses)): + all_hypotheses[i] = [self.decoding.strip_special_tokens(text) for text in all_hypotheses[i]] + + del enc_states, enc_mask, decoder_input_ids + if all_hypotheses is None: + return best_hypotheses + return best_hypotheses, all_hypotheses + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided audio file. + Args: + config: A python dictionary which contains the following keys: + paths2audio_files: (a list) of paths to audio files. The files should be relatively short fragments. \ + Recommended length per file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the audio manifest is temporarily + stored. + Returns: + A pytorch DataLoader for the given audio file(s). + """ + batch_size = min(config['batch_size'], len(config['paths2audio_files'])) + dl_config = { + 'manifest_filepath': os.path.join(config['temp_dir'], 'manifest.json'), + 'sample_rate': self.preprocessor._sample_rate, + 'batch_size': batch_size, + 'trim_silence': False, + 'shuffle': False, + 'num_workers': min(batch_size, os.cpu_count() - 1), + 'pin_memory': True, + 'use_lhotse': True, + 'use_bucketing': False, + 'drop_last': False, + 'text_field': config.get('text_field', 'answer'), + 'lang_field': config.get('lang_field', 'target_lang'), + } + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config), inference=True) + return temporary_datalayer + + def _transcribe_on_end(self, trcfg: MultiTaskTranscriptionConfig): + """ + Internal function to teardown the model after transcription. Perform all teardown and post-checks here. + + Args: + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + """ + super()._transcribe_on_end(trcfg) + + self.transf_decoder.unfreeze() + + def _may_be_make_dict_and_fix_paths(self, json_items, manifest_path, trcfg: MultiTaskTranscriptionConfig): + """ + Utility method to convert a list of strings to a list of dictionaries. + + Args: + json_items: A list of strings or dictionaries. + manifest_path: A path to a manifest file. + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + + Returns: + A list of dictionaries with the audio file paths fixed. + """ + out_json_items = [] + for item in json_items: + if isinstance(item, str): + # assume it is a path to audio file + entry = { + 'audio_filepath': item, + 'duration': 100000, + 'source_lang': 'en' if trcfg.source_lang is None else trcfg.source_lang, + 'taskname': 'asr' if trcfg.task is None else trcfg.task, + 'target_lang': 'en' if trcfg.target_lang is None else trcfg.target_lang, + 'pnc': 'yes' if trcfg.pnc is None else 'yes' if trcfg.pnc else 'no', + trcfg.text_field: 'nothing', + } + elif isinstance(item, dict): + entry = item + entry['audio_filepath'] = get_full_path(entry['audio_filepath'], manifest_file=manifest_path) + + if 'source_lang' not in entry: + entry['source_lang'] = 'en' if trcfg.source_lang is None else trcfg.source_lang + if 'taskname' not in entry: + entry['taskname'] = 'asr' if trcfg.task is None else trcfg.task + if 'target_lang' not in entry: + entry['target_lang'] = 'en' if trcfg.target_lang is None else trcfg.target_lang + if 'pnc' not in entry: + entry['pnc'] = 'yes' if trcfg.pnc is None else 'yes' if trcfg.pnc else 'no' + if trcfg.text_field not in entry: + entry[trcfg.text_field] = 'nothing' + else: + raise ValueError(f"Expected str or dict, got {type(item)}") + out_json_items.append(entry) + return out_json_items + + @classmethod + def get_transcribe_config(cls) -> MultiTaskTranscriptionConfig: + """ + Utility method that returns the default config for transcribe() function. + + Returns: + A dataclass + """ + return MultiTaskTranscriptionConfig() + + def predict_step(self, batch, batch_idx=0, dataloader_idx=0, has_processed_signal=False): + signal, signal_len, _, _, prompt, prompt_len = batch + + processed_signal = None + processed_signal_length = None + if has_processed_signal: + processed_signal = signal + processed_signal_length = signal_len + signal = None + signal_len = None + + transf_log_probs, encoded_len, enc_states, enc_mask = self.forward( + input_signal=signal, + input_signal_length=signal_len, + processed_signal=processed_signal, + processed_signal_length=processed_signal_length, + transcript=prompt, + transcript_length=prompt_len, + ) + + text = self.decoding.decode_predictions_tensor( + encoder_hidden_states=enc_states, + encoder_input_mask=enc_mask, + decoder_input_ids=prompt, + return_hypotheses=False, + )[0] + + text = [self.decoding.strip_special_tokens(t) for t in text] + return text diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/asr_model.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/asr_model.py new file mode 100644 index 0000000..4420318 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/asr_model.py @@ -0,0 +1,254 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +from abc import ABC, abstractmethod +from typing import List + +import torch + +from nemo.core.classes import ModelPT +from nemo.core.classes.common import PretrainedModelInfo +from nemo.core.classes.exportable import Exportable +from nemo.core.classes.mixins import AccessMixin +from nemo.core.utils.neural_type_utils import get_io_names +from nemo.utils import logging, model_utils +from nemo.utils.cast_utils import cast_all + +__all__ = ['ASRModel'] + + +class ASRModel(ModelPT, ABC): + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + val_loss = {} + tensorboard_logs = {} + + if 'val_loss' in outputs[0]: + val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean() + val_loss = {'val_loss': val_loss_mean} + + tensorboard_logs.update(val_loss) + + if "val_wer_num" in outputs[0]: + wer_num = torch.stack([x['val_wer_num'] for x in outputs]).sum() + wer_denom = torch.stack([x['val_wer_denom'] for x in outputs]).sum() + val_wer = {'val_wer': wer_num / wer_denom} + + tensorboard_logs.update(val_wer) + + if "val_bleu_num" in outputs[0]: + bleu_pred_len = torch.stack([x[f"val_bleu_pred_len"] for x in outputs]).sum() + bleu_target_len = torch.stack([x[f"val_bleu_target_len"] for x in outputs]).sum() + bleu_num = torch.stack([x[f"val_bleu_num"] for x in outputs]).sum(dim=0) + bleu_denom = torch.stack([x[f"val_bleu_denom"] for x in outputs]).sum(dim=0) + val_bleu = {"val_bleu": self.bleu._compute_bleu(bleu_pred_len, bleu_target_len, bleu_num, bleu_denom)} + + tensorboard_logs.update(val_bleu) + + return {**val_loss, 'log': tensorboard_logs} + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + val_loss = {} + tensorboard_logs = {} + + if 'test_loss' in outputs[0]: + val_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean() + val_loss = {'test_loss': val_loss_mean} + + tensorboard_logs.update(val_loss) + + if "test_wer_num" in outputs[0]: + wer_num = torch.stack([x['test_wer_num'] for x in outputs]).sum() + wer_denom = torch.stack([x['test_wer_denom'] for x in outputs]).sum() + val_wer = {'test_wer': wer_num / wer_denom} + + tensorboard_logs.update(val_wer) + + if "test_bleu_num" in outputs[0]: + bleu_pred_len = torch.stack([x[f"test_bleu_pred_len"] for x in outputs]).sum() + bleu_target_len = torch.stack([x[f"test_bleu_target_len"] for x in outputs]).sum() + bleu_num = torch.stack([x[f"test_bleu_num"] for x in outputs]).sum() + bleu_denom = torch.stack([x[f"test_bleu_denom"] for x in outputs]).sum() + val_bleu = {"test_bleu": self.wer._compute_bleu(bleu_pred_len, bleu_target_len, bleu_num, bleu_denom)} + + tensorboard_logs.update(val_bleu) + + return {**val_loss, 'log': tensorboard_logs} + + @classmethod + def list_available_models(cls) -> 'List[PretrainedModelInfo]': + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + Returns: + List of available pre-trained models. + """ + # recursively walk the subclasses to generate pretrained model info + list_of_models = model_utils.resolve_subclass_pretrained_model_info(cls) + return list_of_models + + def add_auxiliary_losses(self, loss: torch.Tensor, reset_registry: bool = False) -> torch.Tensor: + """ + Utility method to enable calculation of auxiliary losses for ASR training. + + Args: + loss: The output loss value prior to addition with auxiliary losses. + reset_registry: Bool, whether to reset the AccessMixin registry after adding auxiliary losses. + + Returns: + Loss tensor used for back propagation. + """ + # Add adapter auxiliary losses, if registered + if AccessMixin.is_access_enabled(self.model_guid): + registry = AccessMixin.get_module_registry(self) + log_dict = {} + + for loss_key, loss_registry in registry.items(): + # Add auxiliary loss to total loss + if 'adapter_loss' in loss_registry: + loss_list = loss_registry['adapter_loss'] + loss_value = sum(loss_list) + loss += loss_value + + # Log current loss name and value + keys = loss_key.split(".") + key = "/".join(keys) + key = "adapter_loss/" + key + log_dict[key] = loss_value.detach() + + if len(log_dict) > 0: + self.log_dict(log_dict) + + if reset_registry: + AccessMixin.reset_registry(self) + + # return total loss + return loss + + def setup_optimization_flags(self): + """ + Utility method that must be explicitly called by the subclass in order to support optional optimization flags. + This method is the only valid place to access self.cfg prior to DDP training occurs. + + The subclass may chose not to support this method, therefore all variables here must be checked via hasattr() + """ + # Skip update if nan/inf grads appear on any rank. + self._skip_nan_grad = False + if "skip_nan_grad" in self._cfg and self._cfg["skip_nan_grad"]: + self._skip_nan_grad = self._cfg["skip_nan_grad"] + + def on_after_backward(self): + """ + zero-out the gradients which any of them is NAN or INF + """ + super().on_after_backward() + + if hasattr(self, '_skip_nan_grad') and self._skip_nan_grad: + device = next(self.parameters()).device + valid_gradients = torch.tensor([1], device=device, dtype=torch.float32) + + # valid_gradients = True + for param_name, param in self.named_parameters(): + if param.grad is not None: + is_not_nan_or_inf = not (torch.isnan(param.grad).any() or torch.isinf(param.grad).any()) + if not is_not_nan_or_inf: + valid_gradients = valid_gradients * 0 + break + + if torch.distributed.is_initialized(): + torch.distributed.all_reduce(valid_gradients, op=torch.distributed.ReduceOp.MIN) + + if valid_gradients < 1: + logging.warning(f'detected inf or nan values in gradients! Setting gradients to zero.') + self.zero_grad() + + +class ExportableEncDecModel(Exportable): + """ + Simple utiliy mix-in to export models that consist of encoder/decoder pair + plus pre/post processor, but have to be exported as encoder/decoder pair only + (covers most ASR classes) + """ + + @property + def input_module(self): + return self.encoder + + @property + def output_module(self): + return self.decoder + + @property + def output_names(self): + otypes = self.output_module.output_types + if getattr(self.input_module, 'export_cache_support', False): + in_types = self.input_module.output_types + otypes = {n: t for (n, t) in list(otypes.items())[:1]} + for (n, t) in list(in_types.items())[1:]: + otypes[n] = t + return get_io_names(otypes, self.disabled_deployment_output_names) + + def forward_for_export( + self, input, length=None, cache_last_channel=None, cache_last_time=None, cache_last_channel_len=None + ): + """ + This forward is used when we need to export the model to ONNX format. + Inputs cache_last_channel and cache_last_time are needed to be passed for exporting streaming models. + Args: + input: Tensor that represents a batch of raw audio signals, + of shape [B, T]. T here represents timesteps. + length: Vector of length B, that contains the individual lengths of the audio sequences. + cache_last_channel: Tensor of shape [N, B, T, H] which contains the cache for last channel layers + cache_last_time: Tensor of shape [N, B, H, T] which contains the cache for last time layers + N is the number of such layers which need caching, B is batch size, H is the hidden size of activations, + and T is the length of the cache + + Returns: + the output of the model + """ + enc_fun = getattr(self.input_module, 'forward_for_export', self.input_module.forward) + if cache_last_channel is None: + encoder_output = enc_fun(audio_signal=input, length=length) + if isinstance(encoder_output, tuple): + encoder_output = encoder_output[0] + else: + encoder_output, length, cache_last_channel, cache_last_time, cache_last_channel_len = enc_fun( + audio_signal=input, + length=length, + cache_last_channel=cache_last_channel, + cache_last_time=cache_last_time, + cache_last_channel_len=cache_last_channel_len, + ) + + dec_fun = getattr(self.output_module, 'forward_for_export', self.output_module.forward) + ret = dec_fun(encoder_output=encoder_output) + if isinstance(ret, tuple): + ret = ret[0] + if cache_last_channel is not None: + ret = (ret, length, cache_last_channel, cache_last_time, cache_last_channel_len) + return cast_all(ret, from_dtype=torch.float16, to_dtype=torch.float32) + + @property + def disabled_deployment_input_names(self): + return self.encoder.disabled_deployment_input_names + + @property + def disabled_deployment_output_names(self): + return self.encoder.disabled_deployment_output_names + + def set_export_config(self, args): + if 'cache_support' in args: + enable = bool(args['cache_support']) + self.encoder.export_cache_support = enable + logging.info(f"Caching support enabled: {enable}") + self.encoder.setup_streaming_params() + super().set_export_config(args) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/audio_to_audio_model.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/audio_to_audio_model.py new file mode 100644 index 0000000..4936484 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/audio_to_audio_model.py @@ -0,0 +1,225 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC, abstractmethod +from typing import List, Union + +import hydra +import torch +from omegaconf import DictConfig, OmegaConf +from pytorch_lightning import Trainer + +from nemo.collections.asr.metrics.audio import AudioMetricWrapper +from nemo.core.classes import ModelPT +from nemo.utils import logging, model_utils + +__all__ = ['AudioToAudioModel'] + + +class AudioToAudioModel(ModelPT, ABC): + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + super().__init__(cfg=cfg, trainer=trainer) + + self._setup_loss() + + def _setup_loss(self): + """Setup loss for this model. + """ + self.loss = AudioToAudioModel.from_config_dict(self._cfg.loss) + + def _get_num_dataloaders(self, tag: str = 'val'): + if tag == 'val': + num_dataloaders = len(self._validation_dl) if isinstance(self._validation_dl, List) else 1 + elif tag == 'test': + num_dataloaders = len(self._test_dl) if isinstance(self._test_dl, List) else 1 + else: + raise ValueError(f'Unexpected tag {tag}.') + + return num_dataloaders + + def _setup_metrics(self, tag: str = 'val'): + """Setup metrics for this model for all available dataloaders. + + When using multiple DataLoaders, it is recommended to initialize separate modular + metric instances for each DataLoader and use them separately. + + Reference: + - https://torchmetrics.readthedocs.io/en/stable/pages/lightning.html#common-pitfalls + """ + # Number of currently configured dataloaders + num_dataloaders = self._get_num_dataloaders(tag) + logging.debug('Found %d dataloaders for %s', num_dataloaders, tag) + + if hasattr(self, 'metrics'): + if tag in self.metrics and len(self.metrics[tag]) == num_dataloaders: + # Exact number of metrics have already been configured, nothing else to do + logging.debug('Found %d metrics for tag %s, not necesary to initialize again', num_dataloaders, tag) + return + + if self.cfg.get('metrics') is None: + # Metrics are not available in the configuration, nothing to do + logging.debug('No metrics configured in model.metrics') + return + + if (metrics_cfg := self.cfg['metrics'].get(tag)) is None: + # Metrics configuration is not available in the configuration, nothing to do + logging.debug('No metrics configured for %s in model.metrics', tag) + return + + if 'loss' in metrics_cfg: + raise ValueError( + f'Loss is automatically included in the metrics, it should not be specified in model.metrics.{tag}.' + ) + + # Initialize metrics + if not hasattr(self, 'metrics'): + self.metrics = torch.nn.ModuleDict() + + # Setup metrics for each dataloader + self.metrics[tag] = torch.nn.ModuleList() + for dataloader_idx in range(num_dataloaders): + metrics_dataloader_idx = {} + for name, cfg in metrics_cfg.items(): + logging.debug('Initialize %s for dataloader_idx %s', name, dataloader_idx) + cfg_dict = OmegaConf.to_container(cfg) + cfg_channel = cfg_dict.pop('channel', None) + cfg_batch_averaging = cfg_dict.pop('metric_using_batch_averaging', None) + metrics_dataloader_idx[name] = AudioMetricWrapper( + metric=hydra.utils.instantiate(cfg_dict), + channel=cfg_channel, + metric_using_batch_averaging=cfg_batch_averaging, + ) + + metrics_dataloader_idx = torch.nn.ModuleDict(metrics_dataloader_idx) + self.metrics[tag].append(metrics_dataloader_idx.to(self.device)) + + logging.info( + 'Setup metrics for %s, dataloader %d: %s', tag, dataloader_idx, ', '.join(metrics_dataloader_idx) + ) + + @abstractmethod + def evaluation_step(self, batch, batch_idx, dataloader_idx: int = 0, tag: str = 'val'): + pass + + def on_validation_start(self): + self._setup_metrics('val') + return super().on_validation_start() + + def on_test_start(self): + self._setup_metrics('test') + return super().on_test_start() + + def validation_step(self, batch, batch_idx, dataloader_idx: int = 0): + output_dict = self.evaluation_step(batch, batch_idx, dataloader_idx, 'val') + if isinstance(self.trainer.val_dataloaders, (list, tuple)) and len(self.trainer.val_dataloaders) > 1: + self.validation_step_outputs[dataloader_idx].append(output_dict) + else: + self.validation_step_outputs.append(output_dict) + return output_dict + + def test_step(self, batch, batch_idx, dataloader_idx=0): + output_dict = self.evaluation_step(batch, batch_idx, dataloader_idx, 'test') + if isinstance(self.trainer.test_dataloaders, (list, tuple)) and len(self.trainer.test_dataloaders) > 1: + self.test_step_outputs[dataloader_idx].append(output_dict) + else: + self.test_step_outputs.append(output_dict) + return output_dict + + def multi_evaluation_epoch_end(self, outputs, dataloader_idx: int = 0, tag: str = 'val'): + # Handle loss + loss_mean = torch.stack([x[f'{tag}_loss'] for x in outputs]).mean() + tensorboard_logs = {f'{tag}_loss': loss_mean} + + # Handle metrics for this tag and dataloader_idx + if hasattr(self, 'metrics') and tag in self.metrics: + for name, metric in self.metrics[tag][dataloader_idx].items(): + # Compute & reset the metric + value = metric.compute() + metric.reset() + # Store for logs + tensorboard_logs[f'{tag}_{name}'] = value + + return {f'{tag}_loss': loss_mean, 'log': tensorboard_logs} + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + return self.multi_evaluation_epoch_end(outputs, dataloader_idx, 'val') + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + return self.multi_evaluation_epoch_end(outputs, dataloader_idx, 'test') + + @abstractmethod + def process( + self, paths2audio_files: List[str], output_dir: str, batch_size: int = 4 + ) -> List[Union[str, List[str]]]: + """ + Takes paths to audio files and returns a list of paths to processed + audios. + + Args: + paths2audio_files: paths to audio files to be processed + output_dir: directory to save processed files + batch_size: batch size for inference + + Returns: + Paths to processed audio signals. + """ + pass + + @classmethod + def list_available_models(cls) -> 'List[PretrainedModelInfo]': + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + Returns: + List of available pre-trained models. + """ + # recursively walk the subclasses to generate pretrained model info + list_of_models = model_utils.resolve_subclass_pretrained_model_info(cls) + return list_of_models + + def setup_optimization_flags(self): + """ + Utility method that must be explicitly called by the subclass in order to support optional optimization flags. + This method is the only valid place to access self.cfg prior to DDP training occurs. + + The subclass may chose not to support this method, therefore all variables here must be checked via hasattr() + """ + # Skip update if nan/inf grads appear on any rank. + self._skip_nan_grad = False + if "skip_nan_grad" in self._cfg and self._cfg["skip_nan_grad"]: + self._skip_nan_grad = self._cfg["skip_nan_grad"] + + def on_after_backward(self): + """ + zero-out the gradients which any of them is NAN or INF + """ + super().on_after_backward() + + if hasattr(self, '_skip_nan_grad') and self._skip_nan_grad: + device = next(self.parameters()).device + valid_gradients = torch.tensor([1], device=device, dtype=torch.float32) + + # valid_gradients = True + for param_name, param in self.named_parameters(): + if param.grad is not None: + is_not_nan_or_inf = not (torch.isnan(param.grad).any() or torch.isinf(param.grad).any()) + if not is_not_nan_or_inf: + valid_gradients = valid_gradients * 0 + break + + if torch.distributed.is_initialized(): + torch.distributed.all_reduce(valid_gradients, op=torch.distributed.ReduceOp.MIN) + + if valid_gradients < 1: + logging.warning(f'detected inf or nan values in gradients! Setting gradients to zero.') + self.zero_grad() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/classification_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/classification_models.py new file mode 100644 index 0000000..c1294de --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/classification_models.py @@ -0,0 +1,1248 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import json +import os +import tempfile +from abc import abstractmethod +from dataclasses import dataclass, field +from math import ceil, floor +from typing import Any, Dict, List, Optional, Union + +import torch +from omegaconf import DictConfig, ListConfig, OmegaConf +from pytorch_lightning import Trainer +from torchmetrics import Accuracy +from torchmetrics.regression import MeanAbsoluteError, MeanSquaredError + +from nemo.collections.asr.data import audio_to_label_dataset, feature_to_label_dataset +from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel +from nemo.collections.asr.parts.mixins import TranscriptionMixin, TranscriptionReturnType +from nemo.collections.asr.parts.mixins.transcription import InternalTranscribeConfig +from nemo.collections.asr.parts.preprocessing.features import WaveformFeaturizer +from nemo.collections.asr.parts.preprocessing.perturb import process_augmentations +from nemo.collections.common.losses import CrossEntropyLoss, MSELoss +from nemo.collections.common.metrics import TopKClassificationAccuracy +from nemo.core.classes.common import PretrainedModelInfo, typecheck +from nemo.core.neural_types import * +from nemo.utils import logging, model_utils +from nemo.utils.cast_utils import cast_all + +__all__ = ['EncDecClassificationModel', 'EncDecRegressionModel'] + + +@dataclass +class ClassificationInferConfig: + batch_size: int = 4 + logprobs: bool = False + + _internal: InternalTranscribeConfig = field(default_factory=lambda: InternalTranscribeConfig()) + + +@dataclass +class RegressionInferConfig: + batch_size: int = 4 + logprobs: bool = True + + _internal: InternalTranscribeConfig = field(default_factory=lambda: InternalTranscribeConfig()) + + +class _EncDecBaseModel(ASRModel, ExportableEncDecModel, TranscriptionMixin): + """Encoder decoder Classification models.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + # Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable + # Global_rank and local_rank is set by LightningModule in Lightning 1.2.0 + self.world_size = 1 + if trainer is not None: + self.world_size = trainer.num_nodes * trainer.num_devices + + # Convert config to a DictConfig + cfg = model_utils.convert_model_config_to_dict_config(cfg) + + # Convert config to support Hydra 1.0+ instantiation + cfg = model_utils.maybe_update_config_version(cfg) + + self.is_regression_task = cfg.get('is_regression_task', False) + # Change labels if needed + self._update_decoder_config(cfg.labels, cfg.decoder) + super().__init__(cfg=cfg, trainer=trainer) + + if hasattr(self._cfg, 'spec_augment') and self._cfg.spec_augment is not None: + self.spec_augmentation = ASRModel.from_config_dict(self._cfg.spec_augment) + else: + self.spec_augmentation = None + if hasattr(self._cfg, 'crop_or_pad_augment') and self._cfg.crop_or_pad_augment is not None: + self.crop_or_pad = ASRModel.from_config_dict(self._cfg.crop_or_pad_augment) + else: + self.crop_or_pad = None + + self.preprocessor = self._setup_preprocessor() + self.encoder = self._setup_encoder() + self.decoder = self._setup_decoder() + self.loss = self._setup_loss() + self._setup_metrics() + + @abstractmethod + def _setup_preprocessor(self): + """ + Setup preprocessor for audio data + Returns: Preprocessor + + """ + pass + + @abstractmethod + def _setup_encoder(self): + """ + Setup encoder for the Encoder-Decoder network + Returns: Encoder + """ + pass + + @abstractmethod + def _setup_decoder(self): + """ + Setup decoder for the Encoder-Decoder network + Returns: Decoder + """ + pass + + @abstractmethod + def _setup_loss(self): + """ + Setup loss function for training + Returns: Loss function + + """ + pass + + @abstractmethod + def _setup_metrics(self): + """ + Setup metrics to be tracked in addition to loss + Returns: void + + """ + pass + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + if hasattr(self.preprocessor, '_sample_rate'): + audio_eltype = AudioSignal(freq=self.preprocessor._sample_rate) + else: + audio_eltype = AudioSignal() + return { + "input_signal": NeuralType(('B', 'T'), audio_eltype, optional=True), + "input_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "processed_signal": NeuralType(('B', 'D', 'T'), SpectrogramType(), optional=True), + "processed_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + @abstractmethod + def output_types(self) -> Optional[Dict[str, NeuralType]]: + pass + + def forward( + self, input_signal=None, input_signal_length=None, processed_signal=None, processed_signal_length=None + ): + has_input_signal = input_signal is not None and input_signal_length is not None + has_processed_signal = processed_signal is not None and processed_signal_length is not None + if (has_input_signal ^ has_processed_signal) == False: + raise ValueError( + f"{self} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive " + " with ``processed_signal`` and ``processed_signal_length`` arguments." + ) + + if not has_processed_signal: + processed_signal, processed_signal_length = self.preprocessor( + input_signal=input_signal, length=input_signal_length, + ) + # Crop or pad is always applied + if self.crop_or_pad is not None: + processed_signal, processed_signal_length = self.crop_or_pad( + input_signal=processed_signal, length=processed_signal_length + ) + # Spec augment is not applied during evaluation/testing + if self.spec_augmentation is not None and self.training: + processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length) + encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length) + logits = self.decoder(encoder_output=encoded) + return logits + + def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): + if 'shuffle' not in train_data_config: + train_data_config['shuffle'] = True + # preserve config + self._update_dataset_config(dataset_name='train', config=train_data_config) + + self._train_dl = self._setup_dataloader_from_config(config=DictConfig(train_data_config)) + + # Need to set this because if using an IterableDataset, the length of the dataloader is the total number + # of samples rather than the number of batches, and this messes up the tqdm progress bar. + # So we set the number of steps manually (to the correct number) to fix this. + if ( + self._train_dl is not None + and hasattr(self._train_dl, 'dataset') + and isinstance(self._train_dl.dataset, torch.utils.data.IterableDataset) + ): + # We also need to check if limit_train_batches is already set. + # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches, + # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0). + if isinstance(self._trainer.limit_train_batches, float): + self._trainer.limit_train_batches = int( + self._trainer.limit_train_batches + * ceil((len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size']) + ) + + def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): + if 'shuffle' not in val_data_config: + val_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='validation', config=val_data_config) + + self._validation_dl = self._setup_dataloader_from_config(config=DictConfig(val_data_config)) + + def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]], use_feat: bool = False): + if 'shuffle' not in test_data_config: + test_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='test', config=test_data_config) + + if use_feat and hasattr(self, '_setup_feature_label_dataloader'): + self._test_dl = self._setup_feature_label_dataloader(config=DictConfig(test_data_config)) + else: + self._test_dl = self._setup_dataloader_from_config(config=DictConfig(test_data_config)) + + def test_dataloader(self): + if self._test_dl is not None: + return self._test_dl + + def _setup_dataloader_from_config(self, config: DictConfig): + + OmegaConf.set_struct(config, False) + config.is_regression_task = self.is_regression_task + OmegaConf.set_struct(config, True) + + if 'augmentor' in config: + augmentor = process_augmentations(config['augmentor']) + else: + augmentor = None + + featurizer = WaveformFeaturizer( + sample_rate=config['sample_rate'], int_values=config.get('int_values', False), augmentor=augmentor + ) + shuffle = config['shuffle'] + + # Instantiate tarred dataset loader or normal dataset loader + if config.get('is_tarred', False): + if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or ( + 'manifest_filepath' in config and config['manifest_filepath'] is None + ): + logging.warning( + "Could not load dataset as `manifest_filepath` is None or " + f"`tarred_audio_filepaths` is None. Provided config : {config}" + ) + return None + + if 'vad_stream' in config and config['vad_stream']: + logging.warning("VAD inference does not support tarred dataset now") + return None + + shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 + dataset = audio_to_label_dataset.get_tarred_classification_label_dataset( + featurizer=featurizer, + config=config, + shuffle_n=shuffle_n, + global_rank=self.global_rank, + world_size=self.world_size, + ) + shuffle = False + batch_size = config['batch_size'] + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + elif hasattr(dataset.datasets[0], 'collate_fn'): + # support datasets that are lists of entries + collate_fn = dataset.datasets[0].collate_fn + else: + # support datasets that are lists of lists + collate_fn = dataset.datasets[0].datasets[0].collate_fn + + else: + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` is None. Provided config : {config}") + return None + + if 'vad_stream' in config and config['vad_stream']: + logging.info("Perform streaming frame-level VAD") + dataset = audio_to_label_dataset.get_speech_label_dataset(featurizer=featurizer, config=config) + batch_size = 1 + collate_fn = dataset.vad_frame_seq_collate_fn + else: + dataset = audio_to_label_dataset.get_classification_label_dataset(featurizer=featurizer, config=config) + batch_size = config['batch_size'] + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + elif hasattr(dataset.datasets[0], 'collate_fn'): + # support datasets that are lists of entries + collate_fn = dataset.datasets[0].collate_fn + else: + # support datasets that are lists of lists + collate_fn = dataset.datasets[0].datasets[0].collate_fn + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=batch_size, + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def _setup_feature_label_dataloader(self, config: DictConfig) -> torch.utils.data.DataLoader: + """ + setup dataloader for VAD inference with audio features as input + """ + + OmegaConf.set_struct(config, False) + config.is_regression_task = self.is_regression_task + OmegaConf.set_struct(config, True) + + if 'augmentor' in config: + augmentor = process_augmentations(config['augmentor']) + else: + augmentor = None + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` is None. Provided config : {config}") + return None + + dataset = feature_to_label_dataset.get_feature_label_dataset(config=config, augmentor=augmentor) + if 'vad_stream' in config and config['vad_stream']: + collate_func = dataset._vad_segment_collate_fn + batch_size = 1 + shuffle = False + else: + collate_func = dataset._collate_fn + batch_size = config['batch_size'] + shuffle = config['shuffle'] + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=batch_size, + collate_fn=collate_func, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + @torch.no_grad() + def transcribe( + self, + audio: List[str], + batch_size: int = 4, + logprobs=None, + override_config: Optional[ClassificationInferConfig] | Optional[RegressionInferConfig] = None, + ) -> TranscriptionReturnType: + """ + Generate class labels for provided audio files. Use this method for debugging and prototyping. + + Args: + audio: (a single or list) of paths to audio files or a np.ndarray audio sample. \ + Recommended length per file is approximately 1 second. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + logprobs: (bool) pass True to get log probabilities instead of class labels. + override_config: (Optional) ClassificationInferConfig to use for this inference call. + If None, will use the default config. + + Returns: + + A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files + """ + if logprobs is None: + logprobs = self.is_regression_task + + if override_config is None: + if not self.is_regression_task: + trcfg = ClassificationInferConfig(batch_size=batch_size, logprobs=logprobs) + else: + trcfg = RegressionInferConfig(batch_size=batch_size, logprobs=logprobs) + else: + if not isinstance(override_config, ClassificationInferConfig) and not isinstance( + override_config, RegressionInferConfig + ): + raise ValueError( + f"override_config must be of type {ClassificationInferConfig}, " f"but got {type(override_config)}" + ) + trcfg = override_config + + return super().transcribe(audio=audio, override_config=trcfg) + + """ Transcription related methods """ + + def _transcribe_input_manifest_processing( + self, audio_files: List[str], temp_dir: str, trcfg: ClassificationInferConfig + ): + with open(os.path.join(temp_dir, 'manifest.json'), 'w', encoding='utf-8') as fp: + for audio_file in audio_files: + label = 0.0 if self.is_regression_task else self.cfg.labels[0] + entry = {'audio_filepath': audio_file, 'duration': 100000.0, 'label': label} + fp.write(json.dumps(entry) + '\n') + + config = {'paths2audio_files': audio_files, 'batch_size': trcfg.batch_size, 'temp_dir': temp_dir} + return config + + def _transcribe_forward(self, batch: Any, trcfg: ClassificationInferConfig): + logits = self.forward(input_signal=batch[0], input_signal_length=batch[1]) + output = dict(logits=logits) + return output + + def _transcribe_output_processing( + self, outputs, trcfg: ClassificationInferConfig + ) -> Union[List[str], List[torch.Tensor]]: + logits = outputs.pop('logits') + labels = [] + + if trcfg.logprobs: + # dump log probs per file + for idx in range(logits.shape[0]): + lg = logits[idx] + labels.append(lg.cpu().numpy()) + else: + labels_k = [] + top_ks = self._accuracy.top_k + for top_k_i in top_ks: + # replace top k value with current top k + self._accuracy.top_k = top_k_i + labels_k_i = self._accuracy.top_k_predicted_labels(logits) + labels_k_i = labels_k_i.cpu() + labels_k.append(labels_k_i) + + # convenience: if only one top_k, pop out the nested list + if len(top_ks) == 1: + labels_k = labels_k[0] + + labels += labels_k + # reset top k to orignal value + self._accuracy.top_k = top_ks + + return labels + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided audio file. + + Args: + config: A python dictionary which contains the following keys: + + Returns: + A pytorch DataLoader for the given audio file(s). + """ + dl_config = { + 'manifest_filepath': os.path.join(config['temp_dir'], 'manifest.json'), + 'sample_rate': self.preprocessor._sample_rate, + 'labels': self.cfg.labels, + 'batch_size': min(config['batch_size'], len(config['paths2audio_files'])), + 'trim_silence': False, + 'shuffle': False, + } + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer + + @abstractmethod + def _update_decoder_config(self, labels, cfg): + pass + + @classmethod + def get_transcribe_config(cls) -> ClassificationInferConfig: + """ + Utility method that returns the default config for transcribe() function. + Returns: + A dataclass + """ + return ClassificationInferConfig() + + +class EncDecClassificationModel(_EncDecBaseModel): + """Encoder decoder Classification models.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + + if cfg.get("is_regression_task", False): + raise ValueError(f"EndDecClassificationModel requires the flag is_regression_task to be set as false") + + super().__init__(cfg=cfg, trainer=trainer) + + def _setup_preprocessor(self): + return EncDecClassificationModel.from_config_dict(self._cfg.preprocessor) + + def _setup_encoder(self): + return EncDecClassificationModel.from_config_dict(self._cfg.encoder) + + def _setup_decoder(self): + return EncDecClassificationModel.from_config_dict(self._cfg.decoder) + + def _setup_loss(self): + return CrossEntropyLoss() + + def _setup_metrics(self): + self._accuracy = TopKClassificationAccuracy(dist_sync_on_step=True) + + @classmethod + def list_available_models(cls) -> Optional[List[PretrainedModelInfo]]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + model = PretrainedModelInfo( + pretrained_model_name="vad_multilingual_marblenet", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_multilingual_marblenet/versions/1.10.0/files/vad_multilingual_marblenet.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="vad_telephony_marblenet", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_telephony_marblenet", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_telephony_marblenet/versions/1.0.0rc1/files/vad_telephony_marblenet.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="vad_marblenet", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_marblenet", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_marblenet/versions/1.0.0rc1/files/vad_marblenet.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="commandrecognition_en_matchboxnet3x1x64_v1", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v1", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x1x64_v1/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x1x64_v1.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="commandrecognition_en_matchboxnet3x2x64_v1", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x2x64_v1", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x2x64_v1/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x2x64_v1.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="commandrecognition_en_matchboxnet3x1x64_v2", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v2", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x1x64_v2/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x1x64_v2.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="commandrecognition_en_matchboxnet3x2x64_v2", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x2x64_v2", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x2x64_v2/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x2x64_v2.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="commandrecognition_en_matchboxnet3x1x64_v2_subset_task", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v2_subset_task", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x1x64_v2_subset_task/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x1x64_v2_subset_task.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="commandrecognition_en_matchboxnet3x2x64_v2_subset_task", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x2x64_v2_subset_task", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x2x64_v2_subset_task/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x2x64_v2_subset_task.nemo", + ) + results.append(model) + return results + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return {"outputs": NeuralType(('B', 'D'), LogitsType())} + + # PTL-specific methods + def training_step(self, batch, batch_nb): + audio_signal, audio_signal_len, labels, labels_len = batch + logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + loss_value = self.loss(logits=logits, labels=labels) + + self.log('train_loss', loss_value) + self.log('learning_rate', self._optimizer.param_groups[0]['lr']) + self.log('global_step', self.trainer.global_step) + + self._accuracy(logits=logits, labels=labels) + topk_scores = self._accuracy.compute() + self._accuracy.reset() + + for top_k, score in zip(self._accuracy.top_k, topk_scores): + self.log('training_batch_accuracy_top_{}'.format(top_k), score) + + return { + 'loss': loss_value, + } + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + audio_signal, audio_signal_len, labels, labels_len = batch + logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + loss_value = self.loss(logits=logits, labels=labels) + acc = self._accuracy(logits=logits, labels=labels) + correct_counts, total_counts = self._accuracy.correct_counts_k, self._accuracy.total_counts_k + loss = { + 'val_loss': loss_value, + 'val_correct_counts': correct_counts, + 'val_total_counts': total_counts, + 'val_acc': acc, + } + if type(self.trainer.val_dataloaders) == list and len(self.trainer.val_dataloaders) > 1: + self.validation_step_outputs[dataloader_idx].append(loss) + else: + self.validation_step_outputs.append(loss) + return loss + + def test_step(self, batch, batch_idx, dataloader_idx=0): + audio_signal, audio_signal_len, labels, labels_len = batch + logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + loss_value = self.loss(logits=logits, labels=labels) + acc = self._accuracy(logits=logits, labels=labels) + correct_counts, total_counts = self._accuracy.correct_counts_k, self._accuracy.total_counts_k + loss = { + 'test_loss': loss_value, + 'test_correct_counts': correct_counts, + 'test_total_counts': total_counts, + 'test_acc': acc, + } + if type(self.trainer.test_dataloaders) == list and len(self.trainer.test_dataloaders) > 1: + self.test_step_outputs[dataloader_idx].append(loss) + else: + self.test_step_outputs.append(loss) + return loss + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean() + correct_counts = torch.stack([x['val_correct_counts'] for x in outputs]).sum(axis=0) + total_counts = torch.stack([x['val_total_counts'] for x in outputs]).sum(axis=0) + + self._accuracy.correct_counts_k = correct_counts + self._accuracy.total_counts_k = total_counts + topk_scores = self._accuracy.compute() + self._accuracy.reset() + + tensorboard_log = {'val_loss': val_loss_mean} + for top_k, score in zip(self._accuracy.top_k, topk_scores): + tensorboard_log['val_epoch_top@{}'.format(top_k)] = score + + return {'log': tensorboard_log} + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean() + correct_counts = torch.stack([x['test_correct_counts'].unsqueeze(0) for x in outputs]).sum(axis=0) + total_counts = torch.stack([x['test_total_counts'].unsqueeze(0) for x in outputs]).sum(axis=0) + + self._accuracy.correct_counts_k = correct_counts + self._accuracy.total_counts_k = total_counts + topk_scores = self._accuracy.compute() + self._accuracy.reset() + + tensorboard_log = {'test_loss': test_loss_mean} + for top_k, score in zip(self._accuracy.top_k, topk_scores): + tensorboard_log['test_epoch_top@{}'.format(top_k)] = score + + return {'log': tensorboard_log} + + @typecheck() + def forward( + self, input_signal=None, input_signal_length=None, processed_signal=None, processed_signal_length=None + ): + logits = super().forward( + input_signal=input_signal, + input_signal_length=input_signal_length, + processed_signal=processed_signal, + processed_signal_length=processed_signal_length, + ) + return logits + + def change_labels(self, new_labels: List[str]): + """ + Changes labels used by the decoder model. Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on a data in another dataset. + + If new_labels == self.decoder.vocabulary then nothing will be changed. + + Args: + + new_labels: list with new labels. Must contain at least 2 elements. Typically, \ + this is set of labels for the dataset. + + Returns: None + + """ + if new_labels is not None and not isinstance(new_labels, ListConfig): + new_labels = ListConfig(new_labels) + + if self._cfg.labels == new_labels: + logging.warning( + f"Old labels ({self._cfg.labels}) and new labels ({new_labels}) match. Not changing anything" + ) + else: + if new_labels is None or len(new_labels) == 0: + raise ValueError(f'New labels must be non-empty list of labels. But I got: {new_labels}') + + # Update config + self._cfg.labels = new_labels + + decoder_config = self.decoder.to_config_dict() + new_decoder_config = copy.deepcopy(decoder_config) + self._update_decoder_config(new_labels, new_decoder_config) + del self.decoder + self.decoder = EncDecClassificationModel.from_config_dict(new_decoder_config) + + OmegaConf.set_struct(self._cfg.decoder, False) + self._cfg.decoder = new_decoder_config + OmegaConf.set_struct(self._cfg.decoder, True) + + if 'train_ds' in self._cfg and self._cfg.train_ds is not None: + self._cfg.train_ds.labels = new_labels + + if 'validation_ds' in self._cfg and self._cfg.validation_ds is not None: + self._cfg.validation_ds.labels = new_labels + + if 'test_ds' in self._cfg and self._cfg.test_ds is not None: + self._cfg.test_ds.labels = new_labels + + logging.info(f"Changed decoder output to {self.decoder.num_classes} labels.") + + def _update_decoder_config(self, labels, cfg): + """ + Update the number of classes in the decoder based on labels provided. + + Args: + labels: The current labels of the model + cfg: The config of the decoder which will be updated. + """ + OmegaConf.set_struct(cfg, False) + + if 'params' in cfg: + cfg.params.num_classes = len(labels) + else: + cfg.num_classes = len(labels) + + OmegaConf.set_struct(cfg, True) + + +class EncDecRegressionModel(_EncDecBaseModel): + """Encoder decoder class for speech regression models. + Model class creates training, validation methods for setting up data + performing model forward pass. + """ + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + Returns: + List of available pre-trained models. + """ + result = [] + + return result + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + if not cfg.get('is_regression_task', False): + raise ValueError(f"EndDecRegressionModel requires the flag is_regression_task to be set as true") + super().__init__(cfg=cfg, trainer=trainer) + + def _setup_preprocessor(self): + return EncDecRegressionModel.from_config_dict(self._cfg.preprocessor) + + def _setup_encoder(self): + return EncDecRegressionModel.from_config_dict(self._cfg.encoder) + + def _setup_decoder(self): + return EncDecRegressionModel.from_config_dict(self._cfg.decoder) + + def _setup_loss(self): + return MSELoss() + + def _setup_metrics(self): + self._mse = MeanSquaredError() + self._mae = MeanAbsoluteError() + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return {"preds": NeuralType(tuple('B'), RegressionValuesType())} + + @typecheck() + def forward(self, input_signal, input_signal_length): + logits = super().forward(input_signal=input_signal, input_signal_length=input_signal_length) + return logits.view(-1) + + # PTL-specific methods + def training_step(self, batch, batch_idx): + audio_signal, audio_signal_len, targets, targets_len = batch + logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + loss = self.loss(preds=logits, labels=targets) + train_mse = self._mse(preds=logits, target=targets) + train_mae = self._mae(preds=logits, target=targets) + + self.log_dict( + { + 'train_loss': loss, + 'train_mse': train_mse, + 'train_mae': train_mae, + 'learning_rate': self._optimizer.param_groups[0]['lr'], + }, + ) + + return {'loss': loss} + + def validation_step(self, batch, batch_idx, dataloader_idx: int = 0): + audio_signal, audio_signal_len, targets, targets_len = batch + logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + loss_value = self.loss(preds=logits, labels=targets) + val_mse = self._mse(preds=logits, target=targets) + val_mae = self._mae(preds=logits, target=targets) + + return {'val_loss': loss_value, 'val_mse': val_mse, 'val_mae': val_mae} + + def test_step(self, batch, batch_idx, dataloader_idx: int = 0): + logs = self.validation_step(batch, batch_idx, dataloader_idx) + + return {'test_loss': logs['val_loss'], 'test_mse': logs['test_mse'], 'test_mae': logs['val_mae']} + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean() + val_mse = self._mse.compute() + self._mse.reset() + val_mae = self._mae.compute() + self._mae.reset() + + tensorboard_logs = {'val_loss': val_loss_mean, 'val_mse': val_mse, 'val_mae': val_mae} + + return {'val_loss': val_loss_mean, 'val_mse': val_mse, 'val_mae': val_mae, 'log': tensorboard_logs} + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean() + test_mse = self._mse.compute() + self._mse.reset() + test_mae = self._mae.compute() + self._mae.reset() + + tensorboard_logs = {'test_loss': test_loss_mean, 'test_mse': test_mse, 'test_mae': test_mae} + + return {'test_loss': test_loss_mean, 'test_mse': test_mse, 'test_mae': test_mae, 'log': tensorboard_logs} + + @torch.no_grad() + def transcribe( + self, audio: List[str], batch_size: int = 4, override_config: Optional[RegressionInferConfig] = None + ) -> List[float]: + """ + Generate class labels for provided audio files. Use this method for debugging and prototyping. + + Args: + paths2audio_files: (a list) of paths to audio files. \ + Recommended length per file is approximately 1 second. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + + Returns: + + A list of predictions in the same order as paths2audio_files + """ + if override_config is None: + trcfg = RegressionInferConfig(batch_size=batch_size, logprobs=True) + else: + if not isinstance(override_config, RegressionInferConfig): + raise ValueError( + f"override_config must be of type {RegressionInferConfig}, " f"but got {type(override_config)}" + ) + trcfg = override_config + + predictions = super().transcribe(audio, override_config=trcfg) + return [float(pred) for pred in predictions] + + def _update_decoder_config(self, labels, cfg): + + OmegaConf.set_struct(cfg, False) + + if 'params' in cfg: + cfg.params.num_classes = 1 + else: + cfg.num_classes = 1 + + OmegaConf.set_struct(cfg, True) + + +class EncDecFrameClassificationModel(EncDecClassificationModel): + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return {"outputs": NeuralType(('B', 'T', 'C'), LogitsType())} + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + self.num_classes = len(cfg.labels) + self.eval_loop_cnt = 0 + self.ratio_threshold = cfg.get('ratio_threshold', 0.2) + super().__init__(cfg=cfg, trainer=trainer) + self.decoder.output_types = self.output_types + self.decoder.output_types_for_export = self.output_types + + @classmethod + def list_available_models(cls) -> Optional[List[PretrainedModelInfo]]: + results = [] + model = PretrainedModelInfo( + pretrained_model_name="vad_multilingual_frame_marblenet", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_frame_marblenet", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_multilingual_frame_marblenet/versions/1.20.0/files/vad_multilingual_frame_marblenet.nemo", + ) + results.append(model) + return results + + def _setup_metrics(self): + self._accuracy = TopKClassificationAccuracy(dist_sync_on_step=True) + self._macro_accuracy = Accuracy(num_classes=self.num_classes, average='macro', task="multiclass") + + def _setup_loss(self): + if "loss" in self.cfg: + weight = self.cfg.loss.get("weight", None) + if weight in [None, "none", "None"]: + weight = [1.0] * self.num_classes + logging.info(f"Using cross-entropy with weights: {weight}") + else: + weight = [1.0] * self.num_classes + return CrossEntropyLoss(logits_ndim=3, weight=weight) + + def _setup_dataloader_from_config(self, config: DictConfig): + OmegaConf.set_struct(config, False) + config.is_regression_task = self.is_regression_task + OmegaConf.set_struct(config, True) + shuffle = config.get('shuffle', False) + + if config.get('is_tarred', False): + if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or ( + 'manifest_filepath' in config and config['manifest_filepath'] is None + ): + raise ValueError( + "Could not load dataset as `manifest_filepath` is None or " + f"`tarred_audio_filepaths` is None. Provided cfg : {config}" + ) + + shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 + dataset = audio_to_label_dataset.get_tarred_audio_multi_label_dataset( + cfg=config, shuffle_n=shuffle_n, global_rank=self.global_rank, world_size=self.world_size, + ) + shuffle = False + if hasattr(dataset, 'collate_fn'): + collate_func = dataset.collate_fn + else: + collate_func = dataset.datasets[0].collate_fn + else: + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + raise ValueError(f"Could not load dataset as `manifest_filepath` is None. Provided cfg : {config}") + dataset = audio_to_label_dataset.get_audio_multi_label_dataset(config) + collate_func = dataset.collate_fn + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config.get("batch_size", 1), + collate_fn=collate_func, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def _setup_feature_label_dataloader(self, config: DictConfig) -> torch.utils.data.DataLoader: + """ + setup dataloader for VAD inference with audio features as input + """ + + OmegaConf.set_struct(config, False) + config.is_regression_task = self.is_regression_task + OmegaConf.set_struct(config, True) + + if 'augmentor' in config: + augmentor = process_augmentations(config['augmentor']) + else: + augmentor = None + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` is None. Provided config : {config}") + return None + + dataset = feature_to_label_dataset.get_feature_multi_label_dataset(config=config, augmentor=augmentor) + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config.get("batch_size", 1), + collate_fn=dataset.collate_fn, + drop_last=config.get('drop_last', False), + shuffle=config.get('shuffle', False), + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def get_label_masks(self, labels, labels_len): + mask = torch.arange(labels.size(1))[None, :].to(labels.device) < labels_len[:, None] + return mask.to(labels.device, dtype=bool) + + @typecheck() + def forward( + self, input_signal=None, input_signal_length=None, processed_signal=None, processed_signal_length=None + ): + has_input_signal = input_signal is not None and input_signal_length is not None + has_processed_signal = processed_signal is not None and processed_signal_length is not None + if (has_input_signal ^ has_processed_signal) == False: + raise ValueError( + f"{self} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive " + " with ``processed_signal`` and ``processed_signal_length`` arguments." + ) + + if not has_processed_signal: + processed_signal, processed_signal_length = self.preprocessor( + input_signal=input_signal, length=input_signal_length, + ) + + # Crop or pad is always applied + if self.crop_or_pad is not None: + processed_signal, processed_signal_length = self.crop_or_pad( + input_signal=processed_signal, length=processed_signal_length + ) + # Spec augment is not applied during evaluation/testing + if self.spec_augmentation is not None and self.training: + processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length) + encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length) + logits = self.decoder(encoded.transpose(1, 2)) + return logits + + # PTL-specific methods + def training_step(self, batch, batch_idx): + audio_signal, audio_signal_len, labels, labels_len = batch + logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + labels, labels_len = self.reshape_labels(logits, labels, audio_signal_len, labels_len) + masks = self.get_label_masks(labels, labels_len) + + loss_value = self.loss(logits=logits, labels=labels, loss_mask=masks) + + tensorboard_logs = { + 'train_loss': loss_value, + 'learning_rate': self._optimizer.param_groups[0]['lr'], + 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), + } + + metric_logits, metric_labels = self.get_metric_logits_labels(logits, labels, masks) + self._accuracy(logits=metric_logits, labels=metric_labels) + topk_scores = self._accuracy.compute() + self._accuracy.reset() + + for top_k, score in zip(self._accuracy.top_k, topk_scores): + tensorboard_logs[f'training_batch_accuracy_top@{top_k}'] = score + + return {'loss': loss_value, 'log': tensorboard_logs} + + def validation_step(self, batch, batch_idx, dataloader_idx: int = 0, tag: str = 'val'): + audio_signal, audio_signal_len, labels, labels_len = batch + logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + labels, labels_len = self.reshape_labels(logits, labels, audio_signal_len, labels_len) + masks = self.get_label_masks(labels, labels_len) + + loss_value = self.loss(logits=logits, labels=labels, loss_mask=masks) + + metric_logits, metric_labels = self.get_metric_logits_labels(logits, labels, masks) + + acc = self._accuracy(logits=metric_logits, labels=metric_labels) + correct_counts, total_counts = self._accuracy.correct_counts_k, self._accuracy.total_counts_k + + self._macro_accuracy.update(preds=metric_logits, target=metric_labels) + stats = self._macro_accuracy._final_state() + + return { + f'{tag}_loss': loss_value, + f'{tag}_correct_counts': correct_counts, + f'{tag}_total_counts': total_counts, + f'{tag}_acc_micro': acc, + f'{tag}_acc_stats': stats, + } + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0, tag: str = 'val'): + val_loss_mean = torch.stack([x[f'{tag}_loss'] for x in outputs]).mean() + correct_counts = torch.stack([x[f'{tag}_correct_counts'] for x in outputs]).sum(axis=0) + total_counts = torch.stack([x[f'{tag}_total_counts'] for x in outputs]).sum(axis=0) + + self._accuracy.correct_counts_k = correct_counts + self._accuracy.total_counts_k = total_counts + topk_scores = self._accuracy.compute() + + self._macro_accuracy.tp = torch.stack([x[f'{tag}_acc_stats'][0] for x in outputs]).sum(axis=0) + self._macro_accuracy.fp = torch.stack([x[f'{tag}_acc_stats'][1] for x in outputs]).sum(axis=0) + self._macro_accuracy.tn = torch.stack([x[f'{tag}_acc_stats'][2] for x in outputs]).sum(axis=0) + self._macro_accuracy.fn = torch.stack([x[f'{tag}_acc_stats'][3] for x in outputs]).sum(axis=0) + macro_accuracy_score = self._macro_accuracy.compute() + + self._accuracy.reset() + self._macro_accuracy.reset() + + tensorboard_log = { + f'{tag}_loss': val_loss_mean, + f'{tag}_acc_macro': macro_accuracy_score, + } + + for top_k, score in zip(self._accuracy.top_k, topk_scores): + tensorboard_log[f'{tag}_acc_micro_top@{top_k}'] = score + + self.log_dict(tensorboard_log, sync_dist=True) + return tensorboard_log + + def test_step(self, batch, batch_idx, dataloader_idx=0): + return self.validation_step(batch, batch_idx, dataloader_idx, tag='test') + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + return self.multi_validation_epoch_end(outputs, dataloader_idx, tag='test') + + def reshape_labels(self, logits, labels, logits_len, labels_len): + """ + Reshape labels to match logits shape. For example, each label is expected to cover a 40ms frame, while each frme prediction from the + model covers 20ms. If labels are shorter than logits, labels are repeated, otherwise labels are folded and argmax is applied to obtain + the label of each frame. When lengths of labels and logits are not factors of each other, labels are truncated or padded with zeros. + The ratio_threshold=0.2 is used to determine whether to pad or truncate labels, where the value 0.2 is not important as in real cases the ratio + is very close to either ceil(ratio) or floor(ratio). We use 0.2 here for easier unit-testing. This implementation does not allow frame length + and label length that are not multiples of each other. + Args: + logits: logits tensor with shape [B, T1, C] + labels: labels tensor with shape [B, T2] + logits_len: logits length tensor with shape [B] + labels_len: labels length tensor with shape [B] + Returns: + labels: labels tensor with shape [B, T1] + labels_len: labels length tensor with shape [B] + """ + logits_max_len = logits.size(1) + labels_max_len = labels.size(1) + batch_size = logits.size(0) + if logits_max_len < labels_max_len: + ratio = labels_max_len // logits_max_len + res = labels_max_len % logits_max_len + if ceil(ratio) - ratio < self.ratio_threshold: # e.g., ratio is 1.99 + # pad labels with zeros until labels_max_len is a multiple of logits_max_len + labels = labels.cpu().tolist() + if len(labels) % ceil(ratio) != 0: + labels += [0] * (ceil(ratio) - len(labels) % ceil(ratio)) + labels = torch.tensor(labels).long().to(logits.device) + labels = labels.view(-1, ceil(ratio)).amax(1) + return self.reshape_labels(logits, labels, logits_len, labels_len) + else: + # truncate additional labels until labels_max_len is a multiple of logits_max_len + if res > 0: + labels = labels[:, :-res] + mask = labels_len > (labels_max_len - res) + labels_len = labels_len - mask * (labels_len - (labels_max_len - res)) + labels = labels.view(batch_size, ratio, -1).amax(1) + labels_len = torch.div(labels_len, ratio, rounding_mode="floor") + labels_len = torch.min(torch.cat([logits_len[:, None], labels_len[:, None]], dim=1), dim=1)[0] + return labels.contiguous(), labels_len.contiguous() + elif logits_max_len > labels_max_len: + ratio = logits_max_len / labels_max_len + res = logits_max_len % labels_max_len + if ceil(ratio) - ratio < self.ratio_threshold: # e.g., ratio is 1.99 + # repeat labels for ceil(ratio) times, and DROP additional labels based on logits_max_len + labels = labels.repeat_interleave(ceil(ratio), dim=1).long() + labels = labels[:, :logits_max_len] + labels_len = labels_len * ceil(ratio) + mask = labels_len > logits_max_len + labels_len = labels_len - mask * (labels_len - logits_max_len) + else: # e.g., ratio is 2.01 + # repeat labels for floor(ratio) times, and ADD padding labels based on logits_max_len + labels = labels.repeat_interleave(floor(ratio), dim=1).long() + labels_len = labels_len * floor(ratio) + if res > 0: + labels = torch.cat([labels, labels[:, -res:]], dim=1) + # no need to update `labels_len` since we ignore additional "res" padded labels + labels_len = torch.min(torch.cat([logits_len[:, None], labels_len[:, None]], dim=1), dim=1)[0] + return labels.contiguous(), labels_len.contiguous() + else: + labels_len = torch.min(torch.cat([logits_len[:, None], labels_len[:, None]], dim=1), dim=1)[0] + return labels, labels_len + + def get_metric_logits_labels(self, logits, labels, masks): + """ + Computes valid logits and labels for metric computation. + Args: + logits: tensor of shape [B, T, C] + labels: tensor of shape [B, T] + masks: tensor of shape [B, T] + Returns: + logits of shape [N, C] + labels of shape [N,] + """ + C = logits.size(2) + logits = logits.view(-1, C) # [BxT, C] + labels = labels.view(-1).contiguous() # [BxT,] + masks = masks.view(-1) # [BxT,] + idx = masks.nonzero() # [BxT, 1] + + logits = logits.gather(dim=0, index=idx.repeat(1, 2)) + labels = labels.gather(dim=0, index=idx.view(-1)) + + return logits, labels + + def forward_for_export( + self, input, length=None, cache_last_channel=None, cache_last_time=None, cache_last_channel_len=None + ): + """ + This forward is used when we need to export the model to ONNX format. + Inputs cache_last_channel and cache_last_time are needed to be passed for exporting streaming models. + Args: + input: Tensor that represents a batch of raw audio signals, + of shape [B, T]. T here represents timesteps. + length: Vector of length B, that contains the individual lengths of the audio sequences. + cache_last_channel: Tensor of shape [N, B, T, H] which contains the cache for last channel layers + cache_last_time: Tensor of shape [N, B, H, T] which contains the cache for last time layers + N is the number of such layers which need caching, B is batch size, H is the hidden size of activations, + and T is the length of the cache + + Returns: + the output of the model + """ + enc_fun = getattr(self.input_module, 'forward_for_export', self.input_module.forward) + if cache_last_channel is None: + encoder_output = enc_fun(audio_signal=input, length=length) + if isinstance(encoder_output, tuple): + encoder_output = encoder_output[0] + else: + encoder_output, length, cache_last_channel, cache_last_time, cache_last_channel_len = enc_fun( + audio_signal=input, + length=length, + cache_last_channel=cache_last_channel, + cache_last_time=cache_last_time, + cache_last_channel_len=cache_last_channel_len, + ) + + dec_fun = getattr(self.output_module, 'forward_for_export', self.output_module.forward) + ret = dec_fun(hidden_states=encoder_output.transpose(1, 2)) + if isinstance(ret, tuple): + ret = ret[0] + if cache_last_channel is not None: + ret = (ret, length, cache_last_channel, cache_last_time, cache_last_channel_len) + return cast_all(ret, from_dtype=torch.float16, to_dtype=torch.float32) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/clustering_diarizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/clustering_diarizer.py new file mode 100644 index 0000000..533f276 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/clustering_diarizer.py @@ -0,0 +1,559 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import pickle as pkl +import shutil +import tarfile +import tempfile +from copy import deepcopy +from typing import Any, List, Optional, Union + +import torch +from omegaconf import DictConfig, OmegaConf +from pytorch_lightning.utilities import rank_zero_only +from tqdm import tqdm + +from nemo.collections.asr.metrics.der import score_labels +from nemo.collections.asr.models.classification_models import EncDecClassificationModel +from nemo.collections.asr.models.label_models import EncDecSpeakerLabelModel +from nemo.collections.asr.parts.mixins.mixins import DiarizationMixin +from nemo.collections.asr.parts.utils.speaker_utils import ( + audio_rttm_map, + get_embs_and_timestamps, + get_uniqname_from_filepath, + parse_scale_configs, + perform_clustering, + segments_manifest_to_subsegments_manifest, + validate_vad_manifest, + write_rttm2manifest, +) +from nemo.collections.asr.parts.utils.vad_utils import ( + generate_overlap_vad_seq, + generate_vad_segment_table, + get_vad_stream_status, + prepare_manifest, +) +from nemo.core.classes import Model +from nemo.utils import logging, model_utils + +try: + from torch.cuda.amp import autocast +except ImportError: + from contextlib import contextmanager + + @contextmanager + def autocast(enabled=None): + yield + + +__all__ = ['ClusteringDiarizer'] + +_MODEL_CONFIG_YAML = "model_config.yaml" +_VAD_MODEL = "vad_model.nemo" +_SPEAKER_MODEL = "speaker_model.nemo" + + +def get_available_model_names(class_name): + "lists available pretrained model names from NGC" + available_models = class_name.list_available_models() + return list(map(lambda x: x.pretrained_model_name, available_models)) + + +class ClusteringDiarizer(torch.nn.Module, Model, DiarizationMixin): + """ + Inference model Class for offline speaker diarization. + This class handles required functionality for diarization : Speech Activity Detection, Segmentation, + Extract Embeddings, Clustering, Resegmentation and Scoring. + All the parameters are passed through config file + """ + + def __init__(self, cfg: Union[DictConfig, Any], speaker_model=None): + super().__init__() + if isinstance(cfg, DictConfig): + cfg = model_utils.convert_model_config_to_dict_config(cfg) + # Convert config to support Hydra 1.0+ instantiation + cfg = model_utils.maybe_update_config_version(cfg) + self._cfg = cfg + + # Diarizer set up + self._diarizer_params = self._cfg.diarizer + + # init vad model + self.has_vad_model = False + if not self._diarizer_params.oracle_vad: + if self._cfg.diarizer.vad.model_path is not None: + self._vad_params = self._cfg.diarizer.vad.parameters + self._init_vad_model() + + # init speaker model + self.multiscale_embeddings_and_timestamps = {} + self._init_speaker_model(speaker_model) + self._speaker_params = self._cfg.diarizer.speaker_embeddings.parameters + + # Clustering params + self._cluster_params = self._diarizer_params.clustering.parameters + + @classmethod + def list_available_models(cls): + pass + + def _init_vad_model(self): + """ + Initialize VAD model with model name or path passed through config + """ + model_path = self._cfg.diarizer.vad.model_path + if model_path.endswith('.nemo'): + self._vad_model = EncDecClassificationModel.restore_from(model_path, map_location=self._cfg.device) + logging.info("VAD model loaded locally from {}".format(model_path)) + else: + if model_path not in get_available_model_names(EncDecClassificationModel): + logging.warning( + "requested {} model name not available in pretrained models, instead".format(model_path) + ) + model_path = "vad_telephony_marblenet" + logging.info("Loading pretrained {} model from NGC".format(model_path)) + self._vad_model = EncDecClassificationModel.from_pretrained( + model_name=model_path, map_location=self._cfg.device + ) + self._vad_window_length_in_sec = self._vad_params.window_length_in_sec + self._vad_shift_length_in_sec = self._vad_params.shift_length_in_sec + self.has_vad_model = True + + def _init_speaker_model(self, speaker_model=None): + """ + Initialize speaker embedding model with model name or path passed through config + """ + if speaker_model is not None: + self._speaker_model = speaker_model + else: + model_path = self._cfg.diarizer.speaker_embeddings.model_path + if model_path is not None and model_path.endswith('.nemo'): + self._speaker_model = EncDecSpeakerLabelModel.restore_from(model_path, map_location=self._cfg.device) + logging.info("Speaker Model restored locally from {}".format(model_path)) + elif model_path.endswith('.ckpt'): + self._speaker_model = EncDecSpeakerLabelModel.load_from_checkpoint( + model_path, map_location=self._cfg.device + ) + logging.info("Speaker Model restored locally from {}".format(model_path)) + else: + if model_path not in get_available_model_names(EncDecSpeakerLabelModel): + logging.warning( + "requested {} model name not available in pretrained models, instead".format(model_path) + ) + model_path = "ecapa_tdnn" + logging.info("Loading pretrained {} model from NGC".format(model_path)) + self._speaker_model = EncDecSpeakerLabelModel.from_pretrained( + model_name=model_path, map_location=self._cfg.device + ) + + self.multiscale_args_dict = parse_scale_configs( + self._diarizer_params.speaker_embeddings.parameters.window_length_in_sec, + self._diarizer_params.speaker_embeddings.parameters.shift_length_in_sec, + self._diarizer_params.speaker_embeddings.parameters.multiscale_weights, + ) + + def _setup_vad_test_data(self, manifest_vad_input): + vad_dl_config = { + 'manifest_filepath': manifest_vad_input, + 'sample_rate': self._cfg.sample_rate, + 'batch_size': self._cfg.get('batch_size'), + 'vad_stream': True, + 'labels': ['infer',], + 'window_length_in_sec': self._vad_window_length_in_sec, + 'shift_length_in_sec': self._vad_shift_length_in_sec, + 'trim_silence': False, + 'num_workers': self._cfg.num_workers, + } + self._vad_model.setup_test_data(test_data_config=vad_dl_config) + + def _setup_spkr_test_data(self, manifest_file): + spk_dl_config = { + 'manifest_filepath': manifest_file, + 'sample_rate': self._cfg.sample_rate, + 'batch_size': self._cfg.get('batch_size'), + 'trim_silence': False, + 'labels': None, + 'num_workers': self._cfg.num_workers, + } + self._speaker_model.setup_test_data(spk_dl_config) + + def _run_vad(self, manifest_file): + """ + Run voice activity detection. + Get log probability of voice activity detection and smoothes using the post processing parameters. + Using generated frame level predictions generated manifest file for later speaker embedding extraction. + input: + manifest_file (str) : Manifest file containing path to audio file and label as infer + + """ + + shutil.rmtree(self._vad_dir, ignore_errors=True) + os.makedirs(self._vad_dir) + + self._vad_model.eval() + + time_unit = int(self._vad_window_length_in_sec / self._vad_shift_length_in_sec) + trunc = int(time_unit / 2) + trunc_l = time_unit - trunc + all_len = 0 + data = [] + for line in open(manifest_file, 'r', encoding='utf-8'): + file = json.loads(line)['audio_filepath'] + data.append(get_uniqname_from_filepath(file)) + + status = get_vad_stream_status(data) + for i, test_batch in enumerate( + tqdm(self._vad_model.test_dataloader(), desc='vad', leave=True, disable=not self.verbose) + ): + test_batch = [x.to(self._vad_model.device) for x in test_batch] + with autocast(): + log_probs = self._vad_model(input_signal=test_batch[0], input_signal_length=test_batch[1]) + probs = torch.softmax(log_probs, dim=-1) + pred = probs[:, 1] + if status[i] == 'start': + to_save = pred[:-trunc] + elif status[i] == 'next': + to_save = pred[trunc:-trunc_l] + elif status[i] == 'end': + to_save = pred[trunc_l:] + else: + to_save = pred + all_len += len(to_save) + outpath = os.path.join(self._vad_dir, data[i] + ".frame") + with open(outpath, "a", encoding='utf-8') as fout: + for f in range(len(to_save)): + fout.write('{0:0.4f}\n'.format(to_save[f])) + del test_batch + if status[i] == 'end' or status[i] == 'single': + all_len = 0 + + if not self._vad_params.smoothing: + # Shift the window by 10ms to generate the frame and use the prediction of the window to represent the label for the frame; + self.vad_pred_dir = self._vad_dir + frame_length_in_sec = self._vad_shift_length_in_sec + else: + # Generate predictions with overlapping input segments. Then a smoothing filter is applied to decide the label for a frame spanned by multiple segments. + # smoothing_method would be either in majority vote (median) or average (mean) + logging.info("Generating predictions with overlapping input segments") + smoothing_pred_dir = generate_overlap_vad_seq( + frame_pred_dir=self._vad_dir, + smoothing_method=self._vad_params.smoothing, + overlap=self._vad_params.overlap, + window_length_in_sec=self._vad_window_length_in_sec, + shift_length_in_sec=self._vad_shift_length_in_sec, + num_workers=self._cfg.num_workers, + ) + self.vad_pred_dir = smoothing_pred_dir + frame_length_in_sec = 0.01 + + logging.info("Converting frame level prediction to speech/no-speech segment in start and end times format.") + + vad_params = self._vad_params if isinstance(self._vad_params, (DictConfig, dict)) else self._vad_params.dict() + table_out_dir = generate_vad_segment_table( + vad_pred_dir=self.vad_pred_dir, + postprocessing_params=vad_params, + frame_length_in_sec=frame_length_in_sec, + num_workers=self._cfg.num_workers, + out_dir=self._vad_dir, + ) + + AUDIO_VAD_RTTM_MAP = {} + for key in self.AUDIO_RTTM_MAP: + if os.path.exists(os.path.join(table_out_dir, key + ".txt")): + AUDIO_VAD_RTTM_MAP[key] = deepcopy(self.AUDIO_RTTM_MAP[key]) + AUDIO_VAD_RTTM_MAP[key]['rttm_filepath'] = os.path.join(table_out_dir, key + ".txt") + else: + logging.warning(f"no vad file found for {key} due to zero or negative duration") + + write_rttm2manifest(AUDIO_VAD_RTTM_MAP, self._vad_out_file) + self._speaker_manifest_path = self._vad_out_file + + def _run_segmentation(self, window: float, shift: float, scale_tag: str = ''): + + self.subsegments_manifest_path = os.path.join(self._speaker_dir, f'subsegments{scale_tag}.json') + logging.info( + f"Subsegmentation for embedding extraction:{scale_tag.replace('_',' ')}, {self.subsegments_manifest_path}" + ) + self.subsegments_manifest_path = segments_manifest_to_subsegments_manifest( + segments_manifest_file=self._speaker_manifest_path, + subsegments_manifest_file=self.subsegments_manifest_path, + window=window, + shift=shift, + ) + return None + + def _perform_speech_activity_detection(self): + """ + Checks for type of speech activity detection from config. Choices are NeMo VAD, + external vad manifest and oracle VAD (generates speech activity labels from provided RTTM files) + """ + if self.has_vad_model: + self._auto_split = True + self._split_duration = 50 + manifest_vad_input = self._diarizer_params.manifest_filepath + + if self._auto_split: + logging.info("Split long audio file to avoid CUDA memory issue") + logging.debug("Try smaller split_duration if you still have CUDA memory issue") + config = { + 'input': manifest_vad_input, + 'window_length_in_sec': self._vad_window_length_in_sec, + 'split_duration': self._split_duration, + 'num_workers': self._cfg.num_workers, + 'out_dir': self._diarizer_params.out_dir, + } + manifest_vad_input = prepare_manifest(config) + else: + logging.warning( + "If you encounter CUDA memory issue, try splitting manifest entry by split_duration to avoid it." + ) + + self._setup_vad_test_data(manifest_vad_input) + self._run_vad(manifest_vad_input) + + elif self._diarizer_params.vad.external_vad_manifest is not None: + self._speaker_manifest_path = self._diarizer_params.vad.external_vad_manifest + elif self._diarizer_params.oracle_vad: + self._speaker_manifest_path = os.path.join(self._speaker_dir, 'oracle_vad_manifest.json') + self._speaker_manifest_path = write_rttm2manifest(self.AUDIO_RTTM_MAP, self._speaker_manifest_path) + else: + raise ValueError( + "Only one of diarizer.oracle_vad, vad.model_path or vad.external_vad_manifest must be passed from config" + ) + validate_vad_manifest(self.AUDIO_RTTM_MAP, vad_manifest=self._speaker_manifest_path) + + def _extract_embeddings(self, manifest_file: str, scale_idx: int, num_scales: int): + """ + This method extracts speaker embeddings from segments passed through manifest_file + Optionally you may save the intermediate speaker embeddings for debugging or any use. + """ + logging.info("Extracting embeddings for Diarization") + self._setup_spkr_test_data(manifest_file) + self.embeddings = {} + self._speaker_model.eval() + self.time_stamps = {} + + all_embs = torch.empty([0]) + for test_batch in tqdm( + self._speaker_model.test_dataloader(), + desc=f'[{scale_idx+1}/{num_scales}] extract embeddings', + leave=True, + disable=not self.verbose, + ): + test_batch = [x.to(self._speaker_model.device) for x in test_batch] + audio_signal, audio_signal_len, labels, slices = test_batch + with autocast(): + _, embs = self._speaker_model.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + emb_shape = embs.shape[-1] + embs = embs.view(-1, emb_shape) + all_embs = torch.cat((all_embs, embs.cpu().detach()), dim=0) + del test_batch + + with open(manifest_file, 'r', encoding='utf-8') as manifest: + for i, line in enumerate(manifest.readlines()): + line = line.strip() + dic = json.loads(line) + uniq_name = get_uniqname_from_filepath(dic['audio_filepath']) + if uniq_name in self.embeddings: + self.embeddings[uniq_name] = torch.cat((self.embeddings[uniq_name], all_embs[i].view(1, -1))) + else: + self.embeddings[uniq_name] = all_embs[i].view(1, -1) + if uniq_name not in self.time_stamps: + self.time_stamps[uniq_name] = [] + start = dic['offset'] + end = start + dic['duration'] + self.time_stamps[uniq_name].append([start, end]) + + if self._speaker_params.save_embeddings: + embedding_dir = os.path.join(self._speaker_dir, 'embeddings') + if not os.path.exists(embedding_dir): + os.makedirs(embedding_dir, exist_ok=True) + + prefix = get_uniqname_from_filepath(manifest_file) + name = os.path.join(embedding_dir, prefix) + self._embeddings_file = name + f'_embeddings.pkl' + pkl.dump(self.embeddings, open(self._embeddings_file, 'wb')) + logging.info("Saved embedding files to {}".format(embedding_dir)) + + def path2audio_files_to_manifest(self, paths2audio_files, manifest_filepath): + with open(manifest_filepath, 'w', encoding='utf-8') as fp: + for audio_file in paths2audio_files: + audio_file = audio_file.strip() + entry = {'audio_filepath': audio_file, 'offset': 0.0, 'duration': None, 'text': '-', 'label': 'infer'} + fp.write(json.dumps(entry) + '\n') + + def diarize(self, paths2audio_files: List[str] = None, batch_size: int = 0): + """ + Diarize files provided through paths2audio_files or manifest file + input: + paths2audio_files (List[str]): list of paths to file containing audio file + batch_size (int): batch_size considered for extraction of speaker embeddings and VAD computation + """ + + self._out_dir = self._diarizer_params.out_dir + + self._speaker_dir = os.path.join(self._diarizer_params.out_dir, 'speaker_outputs') + + if os.path.exists(self._speaker_dir): + logging.warning("Deleting previous clustering diarizer outputs.") + shutil.rmtree(self._speaker_dir, ignore_errors=True) + os.makedirs(self._speaker_dir) + + if not os.path.exists(self._out_dir): + os.mkdir(self._out_dir) + + self._vad_dir = os.path.join(self._out_dir, 'vad_outputs') + self._vad_out_file = os.path.join(self._vad_dir, "vad_out.json") + + if batch_size: + self._cfg.batch_size = batch_size + + if paths2audio_files: + if type(paths2audio_files) is list: + self._diarizer_params.manifest_filepath = os.path.join(self._out_dir, 'paths2audio_filepath.json') + self.path2audio_files_to_manifest(paths2audio_files, self._diarizer_params.manifest_filepath) + else: + raise ValueError("paths2audio_files must be of type list of paths to file containing audio file") + + self.AUDIO_RTTM_MAP = audio_rttm_map(self._diarizer_params.manifest_filepath) + + out_rttm_dir = os.path.join(self._out_dir, 'pred_rttms') + os.makedirs(out_rttm_dir, exist_ok=True) + + # Speech Activity Detection + self._perform_speech_activity_detection() + + # Segmentation + scales = self.multiscale_args_dict['scale_dict'].items() + for scale_idx, (window, shift) in scales: + + # Segmentation for the current scale (scale_idx) + self._run_segmentation(window, shift, scale_tag=f'_scale{scale_idx}') + + # Embedding Extraction for the current scale (scale_idx) + self._extract_embeddings(self.subsegments_manifest_path, scale_idx, len(scales)) + + self.multiscale_embeddings_and_timestamps[scale_idx] = [self.embeddings, self.time_stamps] + + embs_and_timestamps = get_embs_and_timestamps( + self.multiscale_embeddings_and_timestamps, self.multiscale_args_dict + ) + + # Clustering + all_reference, all_hypothesis = perform_clustering( + embs_and_timestamps=embs_and_timestamps, + AUDIO_RTTM_MAP=self.AUDIO_RTTM_MAP, + out_rttm_dir=out_rttm_dir, + clustering_params=self._cluster_params, + device=self._speaker_model.device, + verbose=self.verbose, + ) + logging.info("Outputs are saved in {} directory".format(os.path.abspath(self._diarizer_params.out_dir))) + + # Scoring + return score_labels( + self.AUDIO_RTTM_MAP, + all_reference, + all_hypothesis, + collar=self._diarizer_params.collar, + ignore_overlap=self._diarizer_params.ignore_overlap, + verbose=self.verbose, + ) + + @staticmethod + def __make_nemo_file_from_folder(filename, source_dir): + with tarfile.open(filename, "w:gz") as tar: + tar.add(source_dir, arcname="./") + + @rank_zero_only + def save_to(self, save_path: str): + """ + Saves model instance (weights and configuration) into EFF archive or . + You can use "restore_from" method to fully restore instance from .nemo file. + + .nemo file is an archive (tar.gz) with the following: + model_config.yaml - model configuration in .yaml format. You can deserialize this into cfg argument for model's constructor + model_wights.chpt - model checkpoint + + Args: + save_path: Path to .nemo file where model instance should be saved + """ + + # TODO: Why does this override the main save_to? + + with tempfile.TemporaryDirectory() as tmpdir: + config_yaml = os.path.join(tmpdir, _MODEL_CONFIG_YAML) + spkr_model = os.path.join(tmpdir, _SPEAKER_MODEL) + + self.to_config_file(path2yaml_file=config_yaml) + if self.has_vad_model: + vad_model = os.path.join(tmpdir, _VAD_MODEL) + self._vad_model.save_to(vad_model) + self._speaker_model.save_to(spkr_model) + self.__make_nemo_file_from_folder(filename=save_path, source_dir=tmpdir) + + @staticmethod + def __unpack_nemo_file(path2file: str, out_folder: str) -> str: + if not os.path.exists(path2file): + raise FileNotFoundError(f"{path2file} does not exist") + tar = tarfile.open(path2file, "r:gz") + tar.extractall(path=out_folder) + tar.close() + return out_folder + + @classmethod + def restore_from( + cls, + restore_path: str, + override_config_path: Optional[str] = None, + map_location: Optional[torch.device] = None, + strict: bool = False, + ): + # Get path where the command is executed - the artifacts will be "retrieved" there + # (original .nemo behavior) + cwd = os.getcwd() + + with tempfile.TemporaryDirectory() as tmpdir: + try: + cls.__unpack_nemo_file(path2file=restore_path, out_folder=tmpdir) + os.chdir(tmpdir) + if override_config_path is None: + config_yaml = os.path.join(tmpdir, _MODEL_CONFIG_YAML) + else: + config_yaml = override_config_path + conf = OmegaConf.load(config_yaml) + if os.path.exists(os.path.join(tmpdir, _VAD_MODEL)): + conf.diarizer.vad.model_path = os.path.join(tmpdir, _VAD_MODEL) + else: + logging.info( + f'Model {cls.__name__} does not contain a VAD model. A VAD model or manifest file with' + f'speech segments need for diarization with this model' + ) + + conf.diarizer.speaker_embeddings.model_path = os.path.join(tmpdir, _SPEAKER_MODEL) + conf.restore_map_location = map_location + OmegaConf.set_struct(conf, True) + instance = cls(cfg=conf) + + logging.info(f'Model {cls.__name__} was successfully restored from {restore_path}.') + finally: + os.chdir(cwd) + + return instance + + @property + def verbose(self) -> bool: + return self._cfg.verbose diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/confidence_ensemble.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/confidence_ensemble.py new file mode 100644 index 0000000..dcbb0a0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/confidence_ensemble.py @@ -0,0 +1,323 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Dict, List, Optional, Union + +import joblib +import numpy as np +import torch +from omegaconf import DictConfig, open_dict +from pytorch_lightning import Trainer + +from nemo.collections.asr.models.asr_model import ASRModel +from nemo.collections.asr.models.hybrid_rnnt_ctc_models import EncDecHybridRNNTCTCModel +from nemo.collections.asr.parts.utils.asr_confidence_utils import ( + ConfidenceConfig, + ConfidenceMethodConfig, + get_confidence_aggregation_bank, + get_confidence_measure_bank, +) +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis +from nemo.core.classes import ModelPT +from nemo.utils import model_utils + + +# frozen is required to allow hashing of this class and use it +# as a dictionary key when running confidence tuning +@dataclass(frozen=True) +class ConfidenceSpec: + exclude_blank: bool + aggregation: str + confidence_type: str + alpha: float + + def to_confidence_config(self) -> ConfidenceConfig: + """Converts confidence spec to the confidence config. + + Internally, the tuning procedure uses this "spec" objects as they + are more aligned with how things are implemented. But when it's time + to save the models or call transcribe, we need to use the proper + object of type ``ConfidenceConfig``. + """ + if self.confidence_type == 'max_prob': + name = 'max_prob' + entropy_type = 'tsallis' # can be any + entropy_norm = 'lin' # can be any + else: + name, entropy_type, entropy_norm = self.confidence_type.split("_") + return ConfidenceConfig( + exclude_blank=self.exclude_blank, + aggregation=self.aggregation, + method_cfg=ConfidenceMethodConfig( + name=name, entropy_type=entropy_type, alpha=self.alpha, entropy_norm=entropy_norm, + ), + ) + + +def get_filtered_logprobs(hypothesis: Hypothesis, exclude_blank: bool) -> torch.Tensor: + """Returns logprobs from the hypothesis object with optional blanks filter. + + This function supports both CTC and Transducer hypotheses. Will place the + logprobs on GPU if it's available. + + Args: + hypothesis: generated hypothesis as returned from the transcribe + method of the ASR model. + exclude_blank: whether to filter out all ```` tokens. + + Returns: + torch.Tensor: of shape [S, V], where S is (filtered) sequence length and + V is the vocabulary size. + """ + if isinstance(hypothesis.alignments, list): # Transducer + filtered_logprobs = [] + for alignment in hypothesis.alignments: + for align_elem in alignment: + if not exclude_blank: + filtered_logprobs.append(align_elem[0]) + elif align_elem[1].item() != align_elem[0].shape[-1] - 1: + filtered_logprobs.append(align_elem[0]) + if not filtered_logprobs: # for the edge-case of all blanks + filtered_logprobs.append(align_elem[0]) + filtered_logprobs = torch.stack(filtered_logprobs) + if torch.cuda.is_available(): # by default logprobs are placed on cpu in nemo + filtered_logprobs = filtered_logprobs.cuda() + else: # CTC + logprobs = hypothesis.y_sequence + if torch.cuda.is_available(): # by default logprobs are placed on cpu in nemo + logprobs = logprobs.cuda() + if exclude_blank: # filtering blanks + labels = logprobs.argmax(dim=-1) + filtered_logprobs = logprobs[labels != logprobs.shape[1] - 1] + if filtered_logprobs.shape[0] == 0: # for the edge-case of all blanks + filtered_logprobs = logprobs[:1] + else: + filtered_logprobs = logprobs + + # need to make sure logprobs are always normalized, so checking if they sum up to 1 + if not torch.allclose(filtered_logprobs[0].exp().sum(), torch.tensor(1.0)): + filtered_logprobs = torch.log_softmax(filtered_logprobs, dim=1) + + return filtered_logprobs + + +def compute_confidence(hypothesis: Hypothesis, confidence_cfg: ConfidenceConfig) -> float: + """Computes confidence score of the full utterance from a given hypothesis. + + This is essentially a re-implementation of the built-in confidence + computation in NeMo. The difference is that we aggregate full-utterance + scores, while core functionality only supports word and token level + aggregations. + + Args: + hypothesis: generated hypothesis as returned from the transcribe + method of the ASR model. + confidence_cfg: confidence config specifying what kind of + method/aggregation should be used. + + Returns: + float: confidence score. + + """ + filtered_logprobs = get_filtered_logprobs(hypothesis, confidence_cfg.exclude_blank) + vocab_size = filtered_logprobs.shape[1] + aggr_func = get_confidence_aggregation_bank()[confidence_cfg.aggregation] + if confidence_cfg.method_cfg.name == "max_prob": + conf_type = "max_prob" + alpha = 1.0 + else: + conf_type = f"entropy_{confidence_cfg.method_cfg.entropy_type}_{confidence_cfg.method_cfg.entropy_norm}" + alpha = confidence_cfg.method_cfg.alpha + conf_func = get_confidence_measure_bank()[conf_type] + + conf_value = aggr_func(conf_func(filtered_logprobs, v=vocab_size, t=alpha)).cpu().item() + + return conf_value + + +class ConfidenceEnsembleModel(ModelPT): + """Implementation of the confidence ensemble model. + + See https://arxiv.org/abs/2306.15824 for details. + + .. note:: + Currently this class only support `transcribe` method as it requires + full-utterance confidence scores to operate. + """ + + def __init__( + self, cfg: DictConfig, trainer: 'Trainer' = None, + ): + super().__init__(cfg=cfg, trainer=trainer) + + # either we load all models from ``load_models`` cfg parameter + # or all of them are specified in the config as modelX alongside the num_models key + # + # ideally, we'd like to directly store all models in a list, but that + # is not currently supported by the submodule logic + # so to access all the models, we do something like + # + # for model_idx in range(self.num_models): + # model = getattr(self, f"model{model_idx}") + + if 'num_models' in self.cfg: + self.num_models = self.cfg.num_models + for idx in range(self.num_models): + cfg_field = f"model{idx}" + model_cfg = self.cfg[cfg_field] + model_class = model_utils.import_class_by_path(model_cfg['target']) + self.register_nemo_submodule( + name=cfg_field, config_field=cfg_field, model=model_class(model_cfg, trainer=trainer), + ) + else: + self.num_models = len(cfg.load_models) + with open_dict(self.cfg): + self.cfg.num_models = self.num_models + for idx, model in enumerate(cfg.load_models): + cfg_field = f"model{idx}" + if model.endswith(".nemo"): + self.register_nemo_submodule( + name=cfg_field, + config_field=cfg_field, + model=ASRModel.restore_from(model, trainer=trainer, map_location="cpu"), + ) + else: + self.register_nemo_submodule( + cfg_field, config_field=cfg_field, model=ASRModel.from_pretrained(model, map_location="cpu"), + ) + + # registering model selection block - this is expected to be a joblib-saved + # pretrained sklearn pipeline containing standardization + logistic regression + # trained to predict "most-confident" model index from the confidence scores of all models + model_selection_block_path = self.register_artifact("model_selection_block", cfg.model_selection_block) + self.model_selection_block = joblib.load(model_selection_block_path) + self.confidence_cfg = ConfidenceConfig(**self.cfg.confidence) + + # making sure each model has correct temperature setting in the decoder strategy + for model_idx in range(self.num_models): + model = getattr(self, f"model{model_idx}") + # for now we assume users are direclty responsible for matching + # decoder type when building ensemble with inference type + # TODO: add automatic checks for errors + if isinstance(model, EncDecHybridRNNTCTCModel): + self.update_decoding_parameters(model.cfg.decoding) + model.change_decoding_strategy(model.cfg.decoding, decoder_type="rnnt") + self.update_decoding_parameters(model.cfg.aux_ctc.decoding) + model.change_decoding_strategy(model.cfg.aux_ctc.decoding, decoder_type="ctc") + else: + self.update_decoding_parameters(model.cfg.decoding) + model.change_decoding_strategy(model.cfg.decoding) + + def update_decoding_parameters(self, decoding_cfg: DictConfig): + """Updating temperature/preserve_alignment parameters of the config.""" + with open_dict(decoding_cfg): + decoding_cfg.temperature = self.cfg.temperature + decoding_cfg.preserve_alignments = True + + def setup_training_data(self, train_data_config: Union[DictConfig, Dict]): + """Pass-through to the ensemble models. + + Note that training is not actually supported for this class! + """ + for model_idx in range(self.num_models): + getattr(self, f"model{model_idx}").setup_training_data(train_data_config) + + def setup_validation_data(self, val_data_config: Union[DictConfig, Dict]): + """Pass-through to the ensemble models.""" + for model_idx in range(self.num_models): + getattr(self, f"model{model_idx}").setup_validation_data(val_data_config) + + def change_attention_model( + self, self_attention_model: str = None, att_context_size: List[int] = None, update_config: bool = True + ): + """Pass-through to the ensemble models.""" + for model_idx in range(self.num_models): + getattr(self, f"model{model_idx}").change_attention_model( + self_attention_model, att_context_size, update_config + ) + + def change_decoding_strategy(self, decoding_cfg: Optional[DictConfig] = None, decoder_type: str = None): + """Pass-through to the ensemble models. + + The only change here is that we always require expected temperature + to be set as well as ``decoding_cfg.preserve_alignments = True`` + """ + self.update_decoding_parameters(decoding_cfg) + for model_idx in range(self.num_models): + model = getattr(self, f"model{model_idx}") + if isinstance(model, EncDecHybridRNNTCTCModel): + model.change_decoding_strategy(decoding_cfg, decoder_type=decoder_type) + else: + model.change_decoding_strategy(decoding_cfg) + + @torch.no_grad() + def transcribe( + self, + paths2audio_files: List[str], + batch_size: int = 4, + return_hypotheses: bool = False, + num_workers: int = 0, + channel_selector: Optional[ChannelSelectorType] = None, + augmentor: DictConfig = None, + verbose: bool = True, + **kwargs, # any other model specific parameters are passed directly + ) -> List[str]: + """Confidence-ensemble transcribe method. + + Consists of the following steps: + + 1. Run all models (TODO: in parallel) + 2. Compute confidence for each model + 3. Use logistic regression to pick the "most confident" model + 4. Return the output of that model + """ + confidences = [] + all_transcriptions = [] + # always requiring to return hypothesis + # TODO: make sure to return text only if was False originally + return_hypotheses = True + for model_idx in range(self.num_models): + model = getattr(self, f"model{model_idx}") + transcriptions = model.transcribe( + paths2audio_files=paths2audio_files, + batch_size=batch_size, + return_hypotheses=return_hypotheses, + num_workers=num_workers, + channel_selector=channel_selector, + augmentor=augmentor, + verbose=verbose, + **kwargs, + ) + if isinstance(transcriptions, tuple): # transducers return a tuple + transcriptions = transcriptions[0] + + model_confidences = [] + for transcription in transcriptions: + model_confidences.append(compute_confidence(transcription, self.confidence_cfg)) + confidences.append(model_confidences) + all_transcriptions.append(transcriptions) + + # transposing with zip(*list) + features = np.array(list(zip(*confidences))) + model_indices = self.model_selection_block.predict(features) + final_transcriptions = [] + for transcrption_idx in range(len(all_transcriptions[0])): + final_transcriptions.append(all_transcriptions[model_indices[transcrption_idx]][transcrption_idx]) + + return final_transcriptions + + def list_available_models(self): + return [] diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/__init__.py new file mode 100644 index 0000000..b25f111 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/__init__.py @@ -0,0 +1,48 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.models.configs.asr_models_config import ( + ASRDatasetConfig, + CacheAwareStreamingConfig, + EncDecCTCConfig, + EncDecCTCModelConfig, +) +from nemo.collections.asr.models.configs.classification_models_config import ( + EncDecClassificationConfig, + EncDecClassificationDatasetConfig, + EncDecClassificationModelConfig, +) +from nemo.collections.asr.models.configs.diarizer_config import NeuralDiarizerInferenceConfig +from nemo.collections.asr.models.configs.matchboxnet_config import ( + EncDecClassificationModelConfigBuilder, + MatchboxNetModelConfig, + MatchboxNetVADModelConfig, +) +from nemo.collections.asr.models.configs.quartznet_config import ( + EncDecCTCModelConfigBuilder, + JasperModelConfig, + QuartzNetModelConfig, +) +from nemo.collections.asr.modules.audio_preprocessing import ( + AudioToMelSpectrogramPreprocessorConfig, + AudioToMFCCPreprocessorConfig, + CropOrPadSpectrogramAugmentationConfig, + SpectrogramAugmentationConfig, +) +from nemo.collections.asr.modules.conv_asr import ( + ConvASRDecoderClassificationConfig, + ConvASRDecoderConfig, + ConvASREncoderConfig, + JasperEncoderConfig, +) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/aligner_config.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/aligner_config.py new file mode 100644 index 0000000..cf2cdd1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/aligner_config.py @@ -0,0 +1,44 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from nemo.collections.asr.parts.k2.classes import GraphModuleConfig + + +@dataclass +class AlignerCTCConfig: + prob_suppress_index: int = -1 + prob_suppress_value: float = 1.0 + + +@dataclass +class AlignerRNNTConfig: + predictor_window_size: int = 0 + predictor_step_size: int = 1 + + +@dataclass +class AlignerWrapperModelConfig: + alignment_type: str = "forced" + word_output: bool = True + cpu_decoding: bool = False + decode_batch_size: int = 0 + ctc_cfg: AlignerCTCConfig = field(default_factory=lambda: AlignerCTCConfig()) + rnnt_cfg: AlignerRNNTConfig = field(default_factory=lambda: AlignerRNNTConfig()) + + +@dataclass +class K2AlignerWrapperModelConfig(AlignerWrapperModelConfig): + decoder_module_cfg: GraphModuleConfig = field(default_factory=lambda: GraphModuleConfig()) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/asr_models_config.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/asr_models_config.py new file mode 100644 index 0000000..397c13f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/asr_models_config.py @@ -0,0 +1,119 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional + +from omegaconf import MISSING + +import nemo.core.classes.dataset +from nemo.collections.asr.modules.audio_preprocessing import ( + AudioToMelSpectrogramPreprocessorConfig, + SpectrogramAugmentationConfig, +) +from nemo.collections.asr.modules.conv_asr import ConvASRDecoderConfig, ConvASREncoderConfig +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecodingConfig +from nemo.core.config import modelPT as model_cfg + + +@dataclass +class ASRDatasetConfig(nemo.core.classes.dataset.DatasetConfig): + manifest_filepath: Optional[Any] = None + sample_rate: int = MISSING + labels: List[str] = MISSING + trim_silence: bool = False + + # Tarred dataset support + is_tarred: bool = False + tarred_audio_filepaths: Optional[Any] = None + tarred_shard_strategy: str = "scatter" + shard_manifests: bool = False + shuffle_n: int = 0 + + # Optional + int_values: Optional[int] = None + augmentor: Optional[Dict[str, Any]] = None + max_duration: Optional[float] = None + min_duration: Optional[float] = None + max_utts: int = 0 + blank_index: int = -1 + unk_index: int = -1 + normalize: bool = False + trim: bool = True + parser: Optional[str] = 'en' + eos_id: Optional[int] = None + bos_id: Optional[int] = None + pad_id: int = 0 + use_start_end_token: bool = False + return_sample_id: Optional[bool] = False + + # bucketing params + bucketing_strategy: str = "synced_randomized" + bucketing_batch_size: Optional[Any] = None + bucketing_weights: Optional[List[int]] = None + + +@dataclass +class EncDecCTCConfig(model_cfg.ModelConfig): + # Model global arguments + sample_rate: int = 16000 + repeat: int = 1 + dropout: float = 0.0 + separable: bool = False + labels: List[str] = MISSING + + # Dataset configs + train_ds: ASRDatasetConfig = field(default_factory=lambda: ASRDatasetConfig(manifest_filepath=None, shuffle=True)) + validation_ds: ASRDatasetConfig = field( + default_factory=lambda: ASRDatasetConfig(manifest_filepath=None, shuffle=False) + ) + test_ds: ASRDatasetConfig = field(default_factory=lambda: ASRDatasetConfig(manifest_filepath=None, shuffle=False)) + + # Optimizer / Scheduler config + optim: Optional[model_cfg.OptimConfig] = field( + default_factory=lambda: model_cfg.OptimConfig(sched=model_cfg.SchedConfig()) + ) + + # Model component configs + preprocessor: AudioToMelSpectrogramPreprocessorConfig = field( + default_factory=lambda: AudioToMelSpectrogramPreprocessorConfig() + ) + spec_augment: Optional[SpectrogramAugmentationConfig] = field( + default_factory=lambda: SpectrogramAugmentationConfig() + ) + encoder: ConvASREncoderConfig = field(default_factory=lambda: ConvASREncoderConfig()) + decoder: ConvASRDecoderConfig = field(default_factory=lambda: ConvASRDecoderConfig()) + decoding: CTCDecodingConfig = field(default_factory=lambda: CTCDecodingConfig()) + + +@dataclass +class EncDecCTCModelConfig(model_cfg.NemoConfig): + model: EncDecCTCConfig = field(default_factory=lambda: EncDecCTCConfig()) + + +@dataclass +class CacheAwareStreamingConfig: + chunk_size: int = 0 # the size of each chunk at each step, it can be a list of two integers to specify different chunk sizes for the first step and others + shift_size: int = 0 # the size of the shift in each step, it can be a list of two integers to specify different shift sizes for the first step and others + + cache_drop_size: int = 0 # the number of steps to drop from the cache + last_channel_cache_size: int = 0 # the size of the needed cache for last channel layers + + valid_out_len: int = 0 # the number of the steps in the final output which are valid (have the same value as in the offline mode) + + pre_encode_cache_size: int = 0 # the size of the needed cache for the pre-encoding part of the model to avoid caching inside the pre-encoding layers + drop_extra_pre_encoded: int = 0 # the number of steps to get dropped after the pre-encoding layer + + last_channel_num: int = 0 # number of the last channel layers (like MHA layers) which need caching in the model + last_time_num: int = 0 # number of the last time layers (like convolutions) which need caching in the model diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/classification_models_config.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/classification_models_config.py new file mode 100644 index 0000000..33408f5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/classification_models_config.py @@ -0,0 +1,111 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional + +from omegaconf import MISSING + +import nemo.core.classes.dataset +from nemo.collections.asr.modules.audio_preprocessing import ( + AudioToMFCCPreprocessorConfig, + CropOrPadSpectrogramAugmentationConfig, + SpectrogramAugmentationConfig, +) +from nemo.collections.asr.modules.conv_asr import ConvASRDecoderClassificationConfig, ConvASREncoderConfig +from nemo.core.config import modelPT as model_cfg + + +@dataclass +class EncDecClassificationDatasetConfig(nemo.core.classes.dataset.DatasetConfig): + manifest_filepath: Optional[str] = None + sample_rate: int = MISSING + labels: List[str] = MISSING + trim_silence: bool = False + + # Tarred dataset support + is_tarred: bool = False + tarred_audio_filepaths: Optional[str] = None + tarred_shard_strategy: str = "scatter" + shuffle_n: int = 0 + + # Optional + int_values: Optional[int] = None + augmentor: Optional[Dict[str, Any]] = None + max_duration: Optional[float] = None + min_duration: Optional[float] = None + cal_labels_occurrence: Optional[bool] = False + + # VAD Optional + vad_stream: Optional[bool] = None + window_length_in_sec: float = 0.31 + shift_length_in_sec: float = 0.01 + normalize_audio: bool = False + is_regression_task: bool = False + + # bucketing params + bucketing_strategy: str = "synced_randomized" + bucketing_batch_size: Optional[Any] = None + bucketing_weights: Optional[List[int]] = None + + +@dataclass +class EncDecClassificationConfig(model_cfg.ModelConfig): + # Model global arguments + sample_rate: int = 16000 + repeat: int = 1 + dropout: float = 0.0 + separable: bool = True + kernel_size_factor: float = 1.0 + labels: List[str] = MISSING + timesteps: int = MISSING + + # Dataset configs + train_ds: EncDecClassificationDatasetConfig = field( + default_factory=lambda: EncDecClassificationDatasetConfig( + manifest_filepath=None, shuffle=True, trim_silence=False + ) + ) + validation_ds: EncDecClassificationDatasetConfig = field( + default_factory=lambda: EncDecClassificationDatasetConfig(manifest_filepath=None, shuffle=False) + ) + test_ds: EncDecClassificationDatasetConfig = field( + default_factory=lambda: EncDecClassificationDatasetConfig(manifest_filepath=None, shuffle=False) + ) + + # Optimizer / Scheduler config + optim: Optional[model_cfg.OptimConfig] = field( + default_factory=lambda: model_cfg.OptimConfig(sched=model_cfg.SchedConfig()) + ) + + # Model component configs + preprocessor: AudioToMFCCPreprocessorConfig = field(default_factory=lambda: AudioToMFCCPreprocessorConfig()) + spec_augment: Optional[SpectrogramAugmentationConfig] = field( + default_factory=lambda: SpectrogramAugmentationConfig() + ) + crop_or_pad_augment: Optional[CropOrPadSpectrogramAugmentationConfig] = field( + default_factory=lambda: CropOrPadSpectrogramAugmentationConfig(audio_length=-1) + ) + + encoder: ConvASREncoderConfig = field(default_factory=lambda: ConvASREncoderConfig()) + decoder: ConvASRDecoderClassificationConfig = field(default_factory=lambda: ConvASRDecoderClassificationConfig()) + + def __post_init__(self): + if self.crop_or_pad_augment is not None: + self.crop_or_pad_augment.audio_length = self.timesteps + + +@dataclass +class EncDecClassificationModelConfig(model_cfg.NemoConfig): + model: EncDecClassificationConfig = field(default_factory=lambda: EncDecClassificationConfig()) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/diarizer_config.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/diarizer_config.py new file mode 100644 index 0000000..0745a6f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/diarizer_config.py @@ -0,0 +1,204 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import asdict, dataclass, field +from typing import Any, Dict, Optional, Tuple, Union + + +@dataclass +class DiarizerComponentConfig: + """Dataclass to imitate HydraConfig dict when accessing parameters.""" + + def get(self, name: str, default: Optional[Any] = None): + return getattr(self, name, default) + + def __iter__(self): + for key in asdict(self): + yield key + + def dict(self) -> Dict: + return asdict(self) + + +@dataclass +class ASRDiarizerCTCDecoderParams: + pretrained_language_model: Optional[str] = None # KenLM model file: .arpa model file or .bin binary file. + beam_width: int = 32 + alpha: float = 0.5 + beta: float = 2.5 + + +@dataclass +class ASRRealigningLMParams: + # Provide a KenLM language model in .arpa format. + arpa_language_model: Optional[str] = None + # Min number of words for the left context. + min_number_of_words: int = 3 + # Max number of words for the right context. + max_number_of_words: int = 10 + # The threshold for the difference between two log probability values from two hypotheses. + logprob_diff_threshold: float = 1.2 + + +@dataclass +class ASRDiarizerParams(DiarizerComponentConfig): + # if True, speech segmentation for diarization is based on word-timestamps from ASR inference. + asr_based_vad: bool = False + # Threshold (in sec) that caps the gap between two words when generating VAD timestamps using ASR based VAD. + asr_based_vad_threshold: float = 1.0 + # Batch size can be dependent on each ASR model. Default batch sizes are applied if set to null. + asr_batch_size: Optional[int] = None + # Native decoder delay. null is recommended to use the default values for each ASR model. + decoder_delay_in_sec: Optional[float] = None + # Offset to set a reference point from the start of the word. Recommended range of values is [-0.05 0.2]. + word_ts_anchor_offset: Optional[float] = None + # Select which part of the word timestamp we want to use. The options are: 'start', 'end', 'mid'. + word_ts_anchor_pos: str = "start" + # Fix the word timestamp using VAD output. You must provide a VAD model to use this feature. + fix_word_ts_with_VAD: bool = False + # If True, use colored text to distinguish speakers in the output transcript. + colored_text: bool = False + # If True, the start and end time of each speaker turn is printed in the output transcript. + print_time: bool = True + # If True, the output transcript breaks the line to fix the line width (default is 90 chars) + break_lines: bool = False + + +@dataclass +class ASRDiarizerConfig(DiarizerComponentConfig): + model_path: Optional[str] = "stt_en_conformer_ctc_large" + parameters: ASRDiarizerParams = field(default_factory=lambda: ASRDiarizerParams()) + ctc_decoder_parameters: ASRDiarizerCTCDecoderParams = field(default_factory=lambda: ASRDiarizerCTCDecoderParams()) + realigning_lm_parameters: ASRRealigningLMParams = field(default_factory=lambda: ASRRealigningLMParams()) + + +@dataclass +class VADParams(DiarizerComponentConfig): + window_length_in_sec: float = 0.15 # Window length in sec for VAD context input + shift_length_in_sec: float = 0.01 # Shift length in sec for generate frame level VAD prediction + smoothing: Union[str, bool] = "median" # False or type of smoothing method (eg: median) + overlap: float = 0.5 # Overlap ratio for overlapped mean/median smoothing filter + onset: float = 0.1 # Onset threshold for detecting the beginning and end of a speech + offset: float = 0.1 # Offset threshold for detecting the end of a speech + pad_onset: float = 0.1 # Adding durations before each speech segment + pad_offset: float = 0 # Adding durations after each speech segment + min_duration_on: float = 0 # Threshold for small non_speech deletion + min_duration_off: float = 0.2 # Threshold for short speech segment deletion + filter_speech_first: bool = True + + +@dataclass +class VADConfig(DiarizerComponentConfig): + model_path: str = "vad_multilingual_marblenet" # .nemo local model path or pretrained VAD model name + external_vad_manifest: Optional[str] = None + parameters: VADParams = field(default_factory=lambda: VADParams()) + + +@dataclass +class SpeakerEmbeddingsParams(DiarizerComponentConfig): + # Window length(s) in sec (floating-point number). either a number or a list. ex) 1.5 or [1.5,1.0,0.5] + window_length_in_sec: Tuple[float] = (1.5, 1.25, 1.0, 0.75, 0.5) + # Shift length(s) in sec (floating-point number). either a number or a list. ex) 0.75 or [0.75,0.5,0.25] + shift_length_in_sec: Tuple[float] = (0.75, 0.625, 0.5, 0.375, 0.25) + # Weight for each scale. None (for single scale) or list with window/shift scale count. ex) [0.33,0.33,0.33] + multiscale_weights: Tuple[float] = (1, 1, 1, 1, 1) + # save speaker embeddings in pickle format. True if clustering result is used for other models, such as MSDD. + save_embeddings: bool = True + + +@dataclass +class SpeakerEmbeddingsConfig(DiarizerComponentConfig): + # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet) + model_path: Optional[str] = None + parameters: SpeakerEmbeddingsParams = field(default_factory=lambda: SpeakerEmbeddingsParams()) + + +@dataclass +class ClusteringParams(DiarizerComponentConfig): + # If True, use num of speakers value provided in manifest file. + oracle_num_speakers: bool = False + # Max number of speakers for each recording. If an oracle number of speakers is passed, this value is ignored. + max_num_speakers: int = 8 + # If the number of segments is lower than this number, enhanced speaker counting is activated. + enhanced_count_thres: int = 80 + # Determines the range of p-value search: 0 < p <= max_rp_threshold. + max_rp_threshold: float = 0.25 + # The higher the number, the more values will be examined with more time. + sparse_search_volume: int = 30 + # If True, take a majority vote on multiple p-values to estimate the number of speakers. + maj_vote_spk_count: bool = False + + +@dataclass +class ClusteringConfig(DiarizerComponentConfig): + parameters: ClusteringParams = field(default_factory=lambda: ClusteringParams()) + + +@dataclass +class MSDDParams(DiarizerComponentConfig): + # If True, use speaker embedding model in checkpoint, else provided speaker embedding model in config will be used. + use_speaker_model_from_ckpt: bool = True + # Batch size for MSDD inference. + infer_batch_size: int = 25 + # Sigmoid threshold for generating binarized speaker labels. The smaller the more generous on detecting overlaps. + sigmoid_threshold: Tuple[float] = (0.7,) + # If True, use oracle number of speaker and evaluate F1 score for the given speaker sequences. Default is False. + seq_eval_mode: bool = False + # If True, break the input audio clip to short sequences and calculate cluster average embeddings for inference. + split_infer: bool = True + # The length of split short sequence when split_infer is True. + diar_window_length: int = 50 + # If the estimated number of speakers are larger than this number, overlap speech is not estimated. + overlap_infer_spk_limit: int = 5 + + +@dataclass +class MSDDConfig(DiarizerComponentConfig): + model_path: Optional[str] = "diar_msdd_telephonic" + parameters: MSDDParams = field(default_factory=lambda: MSDDParams()) + + +@dataclass +class DiarizerConfig(DiarizerComponentConfig): + manifest_filepath: Optional[str] = None + out_dir: Optional[str] = None + oracle_vad: bool = False # If True, uses RTTM files provided in the manifest file to get VAD timestamps + collar: float = 0.25 # Collar value for scoring + ignore_overlap: bool = True # Consider or ignore overlap segments while scoring + vad: VADConfig = field(default_factory=lambda: VADConfig()) + speaker_embeddings: SpeakerEmbeddingsConfig = field(default_factory=lambda: SpeakerEmbeddingsConfig()) + clustering: ClusteringConfig = field(default_factory=lambda: ClusteringConfig()) + msdd_model: MSDDConfig = field(default_factory=lambda: MSDDConfig()) + asr: ASRDiarizerConfig = field(default_factory=lambda: ASRDiarizerConfig()) + + +@dataclass +class NeuralDiarizerInferenceConfig(DiarizerComponentConfig): + diarizer: DiarizerConfig = field(default_factory=lambda: DiarizerConfig()) + device: str = "cpu" + verbose: bool = False + batch_size: int = 64 + num_workers: int = 1 + sample_rate: int = 16000 + name: str = "" + + @classmethod + def init_config(cls, diar_model_path: str, vad_model_path: str, map_location: str, verbose: bool): + return NeuralDiarizerInferenceConfig( + DiarizerConfig( + vad=VADConfig(model_path=vad_model_path), msdd_model=MSDDConfig(model_path=diar_model_path), + ), + device=map_location, + verbose=verbose, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/k2_sequence_models_config.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/k2_sequence_models_config.py new file mode 100644 index 0000000..53ed3e1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/k2_sequence_models_config.py @@ -0,0 +1,39 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from nemo.collections.asr.models.configs.asr_models_config import EncDecCTCConfig +from nemo.collections.asr.parts.k2.classes import GraphModuleConfig as BackendConfig +from nemo.core.config.modelPT import NemoConfig + + +@dataclass +class GraphModuleConfig: + criterion_type: str = "ml" + loss_type: str = "ctc" + split_batch_size: int = 0 + dec_type: str = "topo" + transcribe_training: bool = True + backend_cfg: BackendConfig = field(default_factory=lambda: BackendConfig()) + + +@dataclass +class EncDecK2SeqConfig(EncDecCTCConfig): + graph_module_cfg: GraphModuleConfig = field(default_factory=lambda: GraphModuleConfig()) + + +@dataclass +class EncDecK2SeqModelConfig(NemoConfig): + model: EncDecK2SeqConfig = field(default_factory=lambda: EncDecK2SeqConfig()) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/matchboxnet_config.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/matchboxnet_config.py new file mode 100644 index 0000000..52ec4c3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/matchboxnet_config.py @@ -0,0 +1,261 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field +from typing import Any, Callable, List, Optional + +from omegaconf import MISSING + +from nemo.collections.asr.models.configs import classification_models_config as clf_cfg +from nemo.collections.asr.modules.audio_preprocessing import ( + AudioToMFCCPreprocessorConfig, + CropOrPadSpectrogramAugmentationConfig, + SpectrogramAugmentationConfig, +) +from nemo.collections.asr.modules.conv_asr import ( + ConvASRDecoderClassificationConfig, + ConvASREncoderConfig, + JasperEncoderConfig, +) +from nemo.core.config import modelPT as model_cfg + + +# fmt: off +def matchboxnet_3x1x64(): + config = [ + JasperEncoderConfig(filters=128, repeat=1, kernel=[11], stride=[1], dilation=[1], dropout=0.0, + residual=False, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=64, repeat=1, kernel=[13], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=64, repeat=1, kernel=[15], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=64, repeat=1, kernel=[17], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=128, repeat=1, kernel=[29], stride=[1], dilation=[2], dropout=0.0, + residual=False, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=128, repeat=1, kernel=[1], stride=[1], dilation=[1], dropout=0.0, + residual=False, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False) + ] + return config + + +def matchboxnet_3x1x64_vad(): + config = [ + JasperEncoderConfig(filters=128, repeat=1, kernel=[11], stride=[1], dilation=[1], dropout=0.0, + residual=False, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=64, repeat=1, kernel=[13], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=64, repeat=1, kernel=[15], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=64, repeat=1, kernel=[17], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=128, repeat=1, kernel=[29], stride=[1], dilation=[2], dropout=0.0, + residual=False, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=128, repeat=1, kernel=[1], stride=[1], dilation=[1], dropout=0.0, + residual=False, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False) + ] + return config + + +# fmt: on + + +@dataclass +class MatchboxNetModelConfig(clf_cfg.EncDecClassificationConfig): + # Model global arguments + sample_rate: int = 16000 + repeat: int = 1 + dropout: float = 0.0 + separable: bool = True + kernel_size_factor: float = 1.0 + timesteps: int = 128 + labels: List[str] = MISSING + + # Dataset configs + train_ds: clf_cfg.EncDecClassificationDatasetConfig = field( + default_factory=lambda: clf_cfg.EncDecClassificationDatasetConfig( + manifest_filepath=None, shuffle=True, trim_silence=False + ) + ) + validation_ds: clf_cfg.EncDecClassificationDatasetConfig = field( + default_factory=lambda: clf_cfg.EncDecClassificationDatasetConfig(manifest_filepath=None, shuffle=False) + ) + test_ds: clf_cfg.EncDecClassificationDatasetConfig = field( + default_factory=lambda: clf_cfg.EncDecClassificationDatasetConfig(manifest_filepath=None, shuffle=False) + ) + + # Optimizer / Scheduler config + optim: Optional[model_cfg.OptimConfig] = field( + default_factory=lambda: model_cfg.OptimConfig(sched=model_cfg.SchedConfig()) + ) + + # Model general component configs + preprocessor: AudioToMFCCPreprocessorConfig = field( + default_factory=lambda: AudioToMFCCPreprocessorConfig(window_size=0.025) + ) + spec_augment: Optional[SpectrogramAugmentationConfig] = field( + default_factory=lambda: SpectrogramAugmentationConfig( + freq_masks=2, time_masks=2, freq_width=15, time_width=25, rect_masks=5, rect_time=25, rect_freq=15 + ) + ) + crop_or_pad_augment: Optional[CropOrPadSpectrogramAugmentationConfig] = field( + default_factory=lambda: CropOrPadSpectrogramAugmentationConfig(audio_length=128) + ) + + encoder: ConvASREncoderConfig = field(default_factory=lambda: ConvASREncoderConfig(activation="relu")) + decoder: ConvASRDecoderClassificationConfig = field(default_factory=lambda: ConvASRDecoderClassificationConfig()) + + +@dataclass +class MatchboxNetVADModelConfig(MatchboxNetModelConfig): + timesteps: int = 64 + labels: List[str] = field(default_factory=lambda: ['background', 'speech']) + + crop_or_pad_augment: Optional[CropOrPadSpectrogramAugmentationConfig] = None + + +class EncDecClassificationModelConfigBuilder(model_cfg.ModelConfigBuilder): + VALID_CONFIGS = ['matchboxnet_3x1x64', 'matchboxnet_3x1x64_vad'] + + def __init__(self, name: str = 'matchboxnet_3x1x64', encoder_cfg_func: Optional[Callable[[], List[Any]]] = None): + if name not in EncDecClassificationModelConfigBuilder.VALID_CONFIGS: + raise ValueError("`name` must be one of : \n" f"{EncDecClassificationModelConfigBuilder.VALID_CONFIGS}") + + self.name = name + + if 'matchboxnet_3x1x64_vad' in name: + if encoder_cfg_func is None: + encoder_cfg_func = matchboxnet_3x1x64_vad + + model_cfg = MatchboxNetVADModelConfig( + repeat=1, + separable=True, + encoder=ConvASREncoderConfig(jasper=encoder_cfg_func(), activation="relu"), + decoder=ConvASRDecoderClassificationConfig(), + ) + + elif 'matchboxnet_3x1x64' in name: + if encoder_cfg_func is None: + encoder_cfg_func = matchboxnet_3x1x64 + + model_cfg = MatchboxNetModelConfig( + repeat=1, + separable=False, + spec_augment=SpectrogramAugmentationConfig(rect_masks=5, rect_freq=50, rect_time=120), + encoder=ConvASREncoderConfig(jasper=encoder_cfg_func(), activation="relu"), + decoder=ConvASRDecoderClassificationConfig(), + ) + + else: + raise ValueError(f"Invalid config name submitted to {self.__class__.__name__}") + + super(EncDecClassificationModelConfigBuilder, self).__init__(model_cfg) + self.model_cfg: clf_cfg.EncDecClassificationConfig = model_cfg # enable type hinting + + def set_labels(self, labels: List[str]): + self.model_cfg.labels = labels + + def set_separable(self, separable: bool): + self.model_cfg.separable = separable + + def set_repeat(self, repeat: int): + self.model_cfg.repeat = repeat + + def set_sample_rate(self, sample_rate: int): + self.model_cfg.sample_rate = sample_rate + + def set_dropout(self, dropout: float = 0.0): + self.model_cfg.dropout = dropout + + def set_timesteps(self, timesteps: int): + self.model_cfg.timesteps = timesteps + + def set_is_regression_task(self, is_regression_task: bool): + self.model_cfg.is_regression_task = is_regression_task + + # Note: Autocomplete for users wont work without these overrides + # But practically it is not needed since python will infer at runtime + + # def set_train_ds(self, cfg: Optional[clf_cfg.EncDecClassificationDatasetConfig] = None): + # super().set_train_ds(cfg) + # + # def set_validation_ds(self, cfg: Optional[clf_cfg.EncDecClassificationDatasetConfig] = None): + # super().set_validation_ds(cfg) + # + # def set_test_ds(self, cfg: Optional[clf_cfg.EncDecClassificationDatasetConfig] = None): + # super().set_test_ds(cfg) + + def _finalize_cfg(self): + # propagate labels + self.model_cfg.train_ds.labels = self.model_cfg.labels + self.model_cfg.validation_ds.labels = self.model_cfg.labels + self.model_cfg.test_ds.labels = self.model_cfg.labels + self.model_cfg.decoder.vocabulary = self.model_cfg.labels + + # propagate num classes + self.model_cfg.decoder.num_classes = len(self.model_cfg.labels) + + # propagate sample rate + self.model_cfg.sample_rate = self.model_cfg.sample_rate + self.model_cfg.preprocessor.sample_rate = self.model_cfg.sample_rate + self.model_cfg.train_ds.sample_rate = self.model_cfg.sample_rate + self.model_cfg.validation_ds.sample_rate = self.model_cfg.sample_rate + self.model_cfg.test_ds.sample_rate = self.model_cfg.sample_rate + + # propagate filters + self.model_cfg.encoder.feat_in = self.model_cfg.preprocessor.features + self.model_cfg.decoder.feat_in = self.model_cfg.encoder.jasper[-1].filters + + # propagate timeteps + if self.model_cfg.crop_or_pad_augment is not None: + self.model_cfg.crop_or_pad_augment.audio_length = self.model_cfg.timesteps + + # propagate separable + for layer in self.model_cfg.encoder.jasper[:-1]: # type: JasperEncoderConfig + layer.separable = self.model_cfg.separable + + # propagate repeat + for layer in self.model_cfg.encoder.jasper[1:-2]: # type: JasperEncoderConfig + layer.repeat = self.model_cfg.repeat + + # propagate dropout + for layer in self.model_cfg.encoder.jasper: # type: JasperEncoderConfig + layer.dropout = self.model_cfg.dropout + + def build(self) -> clf_cfg.EncDecClassificationConfig: + return super().build() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/quartznet_config.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/quartznet_config.py new file mode 100644 index 0000000..93412b0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/configs/quartznet_config.py @@ -0,0 +1,316 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field +from typing import Any, Callable, List, Optional + +from omegaconf import MISSING + +from nemo.collections.asr.models.configs import asr_models_config as ctc_cfg +from nemo.collections.asr.modules.audio_preprocessing import ( + AudioToMelSpectrogramPreprocessorConfig, + SpectrogramAugmentationConfig, +) +from nemo.collections.asr.modules.conv_asr import ConvASRDecoderConfig, ConvASREncoderConfig, JasperEncoderConfig +from nemo.core.config import modelPT as model_cfg + + +# fmt: off +def qn_15x5(): + config = [ + JasperEncoderConfig(filters=256, repeat=1, kernel=[33], stride=[2], dilation=[1], dropout=0.0, + residual=False, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=256, repeat=5, kernel=[33], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=256, repeat=5, kernel=[33], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=256, repeat=5, kernel=[33], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=256, repeat=5, kernel=[39], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=256, repeat=5, kernel=[39], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=256, repeat=5, kernel=[39], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=512, repeat=5, kernel=[51], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=512, repeat=5, kernel=[51], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=512, repeat=5, kernel=[51], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=512, repeat=5, kernel=[63], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=512, repeat=5, kernel=[63], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=512, repeat=5, kernel=[63], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=512, repeat=5, kernel=[75], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=512, repeat=5, kernel=[75], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=512, repeat=5, kernel=[75], stride=[1], dilation=[1], dropout=0.0, + residual=True, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=512, repeat=1, kernel=[87], stride=[1], dilation=[2], dropout=0.0, + residual=False, groups=1, separable=True, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=1024, repeat=1, kernel=[1], stride=[1], dilation=[1], dropout=0.0, + residual=False, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False) + ] + return config + + +def jasper_10x5_dr(): + config = [ + JasperEncoderConfig(filters=256, repeat=1, kernel=[11], stride=[2], dilation=[1], dropout=0.2, + residual=False, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=256, repeat=5, kernel=[11], stride=[1], dilation=[1], dropout=0.2, + residual=True, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=True, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=256, repeat=5, kernel=[11], stride=[1], dilation=[1], dropout=0.2, + residual=True, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=True, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=384, repeat=5, kernel=[13], stride=[1], dilation=[1], dropout=0.2, + residual=True, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=True, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=384, repeat=5, kernel=[13], stride=[1], dilation=[1], dropout=0.2, + residual=True, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=True, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=512, repeat=5, kernel=[17], stride=[1], dilation=[1], dropout=0.2, + residual=True, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=True, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=512, repeat=5, kernel=[17], stride=[1], dilation=[1], dropout=0.2, + residual=True, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=True, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=640, repeat=5, kernel=[21], stride=[1], dilation=[1], dropout=0.3, + residual=True, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=True, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=640, repeat=5, kernel=[21], stride=[1], dilation=[1], dropout=0.3, + residual=True, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=True, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=768, repeat=5, kernel=[25], stride=[1], dilation=[1], dropout=0.3, + residual=True, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=True, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=768, repeat=5, kernel=[25], stride=[1], dilation=[1], dropout=0.3, + residual=True, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=True, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=896, repeat=1, kernel=[29], stride=[1], dilation=[2], dropout=0.4, + residual=False, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False), + JasperEncoderConfig(filters=1024, repeat=1, kernel=[1], stride=[1], dilation=[1], dropout=0.4, + residual=False, groups=1, separable=False, heads=-1, residual_mode='add', + residual_dense=False, se=False, se_reduction_ratio=8, se_context_size=-1, + se_interpolation_mode='nearest', kernel_size_factor=1.0, stride_last=False) + ] + return config +# fmt: on + + +@dataclass +class JasperModelConfig(ctc_cfg.EncDecCTCConfig): + # Model global arguments + sample_rate: int = 16000 + repeat: int = 1 + dropout: float = 0.0 + separable: bool = False + labels: List[str] = MISSING + + # Dataset configs + train_ds: ctc_cfg.ASRDatasetConfig = field( + default_factory=lambda: ctc_cfg.ASRDatasetConfig(manifest_filepath=None, shuffle=True, trim_silence=True) + ) + validation_ds: ctc_cfg.ASRDatasetConfig = field( + default_factory=lambda: ctc_cfg.ASRDatasetConfig(manifest_filepath=None, shuffle=False) + ) + test_ds: ctc_cfg.ASRDatasetConfig = field( + default_factory=lambda: ctc_cfg.ASRDatasetConfig(manifest_filepath=None, shuffle=False) + ) + + # Optimizer / Scheduler config + optim: Optional[model_cfg.OptimConfig] = field( + default_factory=lambda: model_cfg.OptimConfig(sched=model_cfg.SchedConfig()) + ) + + # Model general component configs + preprocessor: AudioToMelSpectrogramPreprocessorConfig = field( + default_factory=lambda: AudioToMelSpectrogramPreprocessorConfig() + ) + spec_augment: Optional[SpectrogramAugmentationConfig] = field( + default_factory=lambda: SpectrogramAugmentationConfig() + ) + encoder: ConvASREncoderConfig = field(default_factory=lambda: ConvASREncoderConfig(activation="relu")) + decoder: ConvASRDecoderConfig = field(default_factory=lambda: ConvASRDecoderConfig()) + + +@dataclass +class QuartzNetModelConfig(JasperModelConfig): + separable: bool = True + + +class EncDecCTCModelConfigBuilder(model_cfg.ModelConfigBuilder): + VALID_CONFIGS = ['quartznet_15x5', 'quartznet_15x5_zh', 'jasper_10x5dr'] + + def __init__(self, name: str = 'quartznet_15x5', encoder_cfg_func: Optional[Callable[[], List[Any]]] = None): + if name not in EncDecCTCModelConfigBuilder.VALID_CONFIGS: + raise ValueError("`name` must be one of : \n" f"{EncDecCTCModelConfigBuilder.VALID_CONFIGS}") + + self.name = name + + if 'quartznet_15x5' in name: + if encoder_cfg_func is None: + encoder_cfg_func = qn_15x5 + + model_cfg = QuartzNetModelConfig( + repeat=5, + separable=True, + spec_augment=SpectrogramAugmentationConfig(rect_masks=5, rect_freq=50, rect_time=120), + encoder=ConvASREncoderConfig(jasper=encoder_cfg_func(), activation="relu"), + decoder=ConvASRDecoderConfig(), + ) + + elif 'jasper_10x5' in name: + if encoder_cfg_func is None: + encoder_cfg_func = jasper_10x5_dr + + model_cfg = JasperModelConfig( + repeat=5, + separable=False, + spec_augment=SpectrogramAugmentationConfig(rect_masks=5, rect_freq=50, rect_time=120), + encoder=ConvASREncoderConfig(jasper=encoder_cfg_func(), activation="relu"), + decoder=ConvASRDecoderConfig(), + ) + + else: + raise ValueError(f"Invalid config name submitted to {self.__class__.__name__}") + + super(EncDecCTCModelConfigBuilder, self).__init__(model_cfg) + self.model_cfg: ctc_cfg.EncDecCTCConfig = model_cfg # enable type hinting + + if 'zh' in name: + self.set_dataset_normalize(normalize=False) + + def set_labels(self, labels: List[str]): + self.model_cfg.labels = labels + + def set_separable(self, separable: bool): + self.model_cfg.separable = separable + + def set_repeat(self, repeat: int): + self.model_cfg.repeat = repeat + + def set_sample_rate(self, sample_rate: int): + self.model_cfg.sample_rate = sample_rate + + def set_dropout(self, dropout: float = 0.0): + self.model_cfg.dropout = dropout + + def set_dataset_normalize(self, normalize: bool): + self.model_cfg.train_ds.normalize = normalize + self.model_cfg.validation_ds.normalize = normalize + self.model_cfg.test_ds.normalize = normalize + + # Note: Autocomplete for users wont work without these overrides + # But practically it is not needed since python will infer at runtime + + # def set_train_ds(self, cfg: Optional[ctc_cfg.ASRDatasetConfig] = None): + # super().set_train_ds(cfg) + # + # def set_validation_ds(self, cfg: Optional[ctc_cfg.ASRDatasetConfig] = None): + # super().set_validation_ds(cfg) + # + # def set_test_ds(self, cfg: Optional[ctc_cfg.ASRDatasetConfig] = None): + # super().set_test_ds(cfg) + + def _finalize_cfg(self): + # propagate labels + self.model_cfg.train_ds.labels = self.model_cfg.labels + self.model_cfg.validation_ds.labels = self.model_cfg.labels + self.model_cfg.test_ds.labels = self.model_cfg.labels + self.model_cfg.decoder.vocabulary = self.model_cfg.labels + + # propagate num classes + self.model_cfg.decoder.num_classes = len(self.model_cfg.labels) + + # propagate sample rate + self.model_cfg.sample_rate = self.model_cfg.sample_rate + self.model_cfg.preprocessor.sample_rate = self.model_cfg.sample_rate + self.model_cfg.train_ds.sample_rate = self.model_cfg.sample_rate + self.model_cfg.validation_ds.sample_rate = self.model_cfg.sample_rate + self.model_cfg.test_ds.sample_rate = self.model_cfg.sample_rate + + # propagate filters + self.model_cfg.encoder.feat_in = self.model_cfg.preprocessor.features + self.model_cfg.decoder.feat_in = self.model_cfg.encoder.jasper[-1].filters + + # propagate separable + for layer in self.model_cfg.encoder.jasper[:-1]: # type: JasperEncoderConfig + layer.separable = self.model_cfg.separable + + # propagate repeat + for layer in self.model_cfg.encoder.jasper[1:-2]: # type: JasperEncoderConfig + layer.repeat = self.model_cfg.repeat + + # propagate dropout + for layer in self.model_cfg.encoder.jasper: # type: JasperEncoderConfig + layer.dropout = self.model_cfg.dropout + + def build(self) -> ctc_cfg.EncDecCTCConfig: + return super().build() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/ctc_bpe_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/ctc_bpe_models.py new file mode 100644 index 0000000..f861a97 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/ctc_bpe_models.py @@ -0,0 +1,658 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import os +from typing import Dict, List, Optional, Union + +import torch +from omegaconf import DictConfig, ListConfig, OmegaConf, open_dict + +from nemo.collections.asr.data import audio_to_text_dataset +from nemo.collections.asr.data.audio_to_text import _AudioTextDataset +from nemo.collections.asr.data.audio_to_text_dali import AudioToBPEDALIDataset +from nemo.collections.asr.data.audio_to_text_lhotse import LhotseSpeechToTextBpeDataset +from nemo.collections.asr.losses.ctc import CTCLoss +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.models.ctc_models import EncDecCTCModel +from nemo.collections.asr.parts.mixins import ASRBPEMixin +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCBPEDecoding, CTCBPEDecodingConfig +from nemo.collections.asr.parts.utils.asr_batching import get_semi_sorted_batch_sampler +from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config +from nemo.core.classes.common import PretrainedModelInfo +from nemo.utils import logging, model_utils + +__all__ = ['EncDecCTCModelBPE'] + + +class EncDecCTCModelBPE(EncDecCTCModel, ASRBPEMixin): + """Encoder decoder CTC-based models with Byte Pair Encoding.""" + + def __init__(self, cfg: DictConfig, trainer=None): + # Convert to Hydra 1.0 compatible DictConfig + cfg = model_utils.convert_model_config_to_dict_config(cfg) + cfg = model_utils.maybe_update_config_version(cfg) + + if 'tokenizer' not in cfg: + raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !") + + # Setup the tokenizer + self._setup_tokenizer(cfg.tokenizer) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + # Set the new vocabulary + with open_dict(cfg): + # sidestepping the potential overlapping tokens issue in aggregate tokenizers + if self.tokenizer_type == "agg": + cfg.decoder.vocabulary = ListConfig(vocabulary) + else: + cfg.decoder.vocabulary = ListConfig(list(vocabulary.keys())) + + # Override number of classes if placeholder provided + num_classes = cfg.decoder["num_classes"] + + if num_classes < 1: + logging.info( + "\nReplacing placeholder number of classes ({}) with actual number of classes - {}".format( + num_classes, len(vocabulary) + ) + ) + cfg.decoder["num_classes"] = len(vocabulary) + + super().__init__(cfg=cfg, trainer=trainer) + + # Setup decoding objects + decoding_cfg = self.cfg.get('decoding', None) + + # In case decoding config not found, use default config + if decoding_cfg is None: + decoding_cfg = OmegaConf.structured(CTCBPEDecodingConfig) + with open_dict(self.cfg): + self.cfg.decoding = decoding_cfg + + self.decoding = CTCBPEDecoding(self.cfg.decoding, tokenizer=self.tokenizer) + + # Setup metric with decoding strategy + self.wer = WER( + decoding=self.decoding, + use_cer=self._cfg.get('use_cer', False), + dist_sync_on_step=True, + log_prediction=self._cfg.get("log_prediction", False), + ) + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + if config.get("use_lhotse"): + return get_lhotse_dataloader_from_config( + config, + global_rank=self.global_rank, + world_size=self.world_size, + dataset=LhotseSpeechToTextBpeDataset(tokenizer=self.tokenizer), + ) + + dataset = audio_to_text_dataset.get_audio_to_text_bpe_dataset_from_config( + config=config, + local_rank=self.local_rank, + global_rank=self.global_rank, + world_size=self.world_size, + tokenizer=self.tokenizer, + preprocessor_cfg=self.cfg.get("preprocessor", None), + ) + + if dataset is None: + return None + + if isinstance(dataset, AudioToBPEDALIDataset): + # DALI Dataset implements dataloader interface + return dataset + + shuffle = config['shuffle'] + if isinstance(dataset, torch.utils.data.IterableDataset): + shuffle = False + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + elif hasattr(dataset.datasets[0], 'collate_fn'): + # support datasets that are lists of entries + collate_fn = dataset.datasets[0].collate_fn + else: + # support datasets that are lists of lists + collate_fn = dataset.datasets[0].datasets[0].collate_fn + + batch_sampler = None + if config.get('use_semi_sorted_batching', False): + if not isinstance(dataset, _AudioTextDataset): + raise RuntimeError( + "Semi Sorted Batch sampler can be used with AudioToCharDataset or AudioToBPEDataset " + f"but found dataset of type {type(dataset)}" + ) + # set batch_size and batch_sampler to None to disable automatic batching + batch_sampler = get_semi_sorted_batch_sampler(self, dataset, config) + config['batch_size'] = None + config['drop_last'] = False + shuffle = False + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + sampler=batch_sampler, + batch_sampler=None, + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided audio file. + + Args: + config: A python dictionary which contains the following keys: + paths2audio_files: (a list) of paths to audio files. The files should be relatively short fragments. \ + Recommended length per file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the audio manifest is temporarily + stored. + num_workers: (int) number of workers. Depends of the batch_size and machine. \ + 0 - only the main process will load batches, 1 - one worker (not main process) + + Returns: + A pytorch DataLoader for the given audio file(s). + """ + + if 'manifest_filepath' in config: + manifest_filepath = config['manifest_filepath'] + batch_size = config['batch_size'] + else: + manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json') + batch_size = min(config['batch_size'], len(config['paths2audio_files'])) + + dl_config = { + 'manifest_filepath': manifest_filepath, + 'sample_rate': self.preprocessor._sample_rate, + 'batch_size': batch_size, + 'shuffle': False, + 'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)), + 'pin_memory': True, + 'channel_selector': config.get('channel_selector', None), + 'use_start_end_token': self.cfg.validation_ds.get('use_start_end_token', False), + } + + if config.get("augmentor"): + dl_config['augmentor'] = config.get("augmentor") + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer + + def change_vocabulary( + self, + new_tokenizer_dir: Union[str, DictConfig], + new_tokenizer_type: str, + decoding_cfg: Optional[DictConfig] = None, + ): + """ + Changes vocabulary of the tokenizer used during CTC decoding process. + Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_tokenizer_dir: Directory path to tokenizer or a config for a new tokenizer (if the tokenizer type is `agg`) + new_tokenizer_type: Either `agg`, `bpe` or `wpe`. `bpe` is used for SentencePiece tokenizers, + whereas `wpe` is used for `BertTokenizer`. + new_tokenizer_cfg: A config for the new tokenizer. if provided, pre-empts the dir and type + + Returns: None + + """ + if isinstance(new_tokenizer_dir, DictConfig): + if new_tokenizer_type == 'agg': + new_tokenizer_cfg = new_tokenizer_dir + else: + raise ValueError( + f'New tokenizer dir should be a string unless the tokenizer is `agg`, but this tokenizer type is: {new_tokenizer_type}' + ) + else: + new_tokenizer_cfg = None + + if new_tokenizer_cfg is not None: + tokenizer_cfg = new_tokenizer_cfg + else: + if not os.path.isdir(new_tokenizer_dir): + raise NotADirectoryError( + f'New tokenizer dir must be non-empty path to a directory. But I got: {new_tokenizer_dir}' + f"New tokenizer dir must be non-empty path to a directory. But I got: {new_tokenizer_dir}" + ) + + if new_tokenizer_type.lower() not in ('bpe', 'wpe'): + raise ValueError(f'New tokenizer type must be either `bpe` or `wpe`') + + tokenizer_cfg = OmegaConf.create({'dir': new_tokenizer_dir, 'type': new_tokenizer_type}) + + # Setup the tokenizer + self._setup_tokenizer(tokenizer_cfg) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + # Set the new vocabulary + decoder_config = copy.deepcopy(self.decoder.to_config_dict()) + # sidestepping the potential overlapping tokens issue in aggregate tokenizers + if self.tokenizer_type == "agg": + decoder_config.vocabulary = ListConfig(vocabulary) + else: + decoder_config.vocabulary = ListConfig(list(vocabulary.keys())) + + decoder_num_classes = decoder_config['num_classes'] + + # Override number of classes if placeholder provided + logging.info( + "\nReplacing old number of classes ({}) with new number of classes - {}".format( + decoder_num_classes, len(vocabulary) + ) + ) + + decoder_config['num_classes'] = len(vocabulary) + + del self.decoder + self.decoder = EncDecCTCModelBPE.from_config_dict(decoder_config) + del self.loss + self.loss = CTCLoss( + num_classes=self.decoder.num_classes_with_blank - 1, + zero_infinity=True, + reduction=self._cfg.get("ctc_reduction", "mean_batch"), + ) + + if decoding_cfg is None: + # Assume same decoding config as before + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(CTCBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = CTCBPEDecoding(decoding_cfg=decoding_cfg, tokenizer=self.tokenizer) + + self.wer = WER( + decoding=self.decoding, + use_cer=self._cfg.get('use_cer', False), + log_prediction=self._cfg.get("log_prediction", False), + dist_sync_on_step=True, + ) + + # Update config + with open_dict(self.cfg.decoder): + self._cfg.decoder = decoder_config + + with open_dict(self.cfg.decoding): + self._cfg.decoding = decoding_cfg + + logging.info(f"Changed tokenizer to {self.decoder.vocabulary} vocabulary.") + + def change_decoding_strategy(self, decoding_cfg: DictConfig): + """ + Changes decoding strategy used during CTC decoding process. + + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + """ + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(CTCBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = CTCBPEDecoding(decoding_cfg=decoding_cfg, tokenizer=self.tokenizer,) + + self.wer = WER( + decoding=self.decoding, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + self.decoder.temperature = decoding_cfg.get('temperature', 1.0) + + # Update config + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}") + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_citrinet_256", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_256", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_256/versions/1.0.0rc1/files/stt_en_citrinet_256.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_citrinet_512", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_512", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_512/versions/1.0.0rc1/files/stt_en_citrinet_512.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_citrinet_1024", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_1024", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_1024/versions/1.0.0rc1/files/stt_en_citrinet_1024.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_citrinet_256_gamma_0_25", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_256_gamma_0_25", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_256_gamma_0_25/versions/1.0.0/files/stt_en_citrinet_256_gamma_0_25.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_citrinet_512_gamma_0_25", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_512_gamma_0_25", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_512_gamma_0_25/versions/1.0.0/files/stt_en_citrinet_512_gamma_0_25.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_citrinet_1024_gamma_0_25", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_1024_gamma_0_25", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_en_citrinet_1024_gamma_0_25.nemo", + ) + + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_es_citrinet_512", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_citrinet_512", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_citrinet_512/versions/1.0.0/files/stt_es_citrinet_512.nemo", + ) + + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_de_citrinet_1024", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_citrinet_1024", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_citrinet_1024/versions/1.5.0/files/stt_de_citrinet_1024.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_fr_citrinet_1024_gamma_0_25", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_citrinet_1024_gamma_0_25", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_citrinet_1024_gamma_0_25/versions/1.5/files/stt_fr_citrinet_1024_gamma_0_25.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_fr_no_hyphen_citrinet_1024_gamma_0_25", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_citrinet_1024_gamma_0_25", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_citrinet_1024_gamma_0_25/versions/1.5/files/stt_fr_no_hyphen_citrinet_1024_gamma_0_25.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_es_citrinet_1024_gamma_0_25", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_citrinet_1024_gamma_0_25", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_citrinet_1024_gamma_0_25/versions/1.8.0/files/stt_es_citrinet_1024_gamma_0_25.nemo", + ) + + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_ctc_small", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_small", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_small/versions/1.6.0/files/stt_en_conformer_ctc_small.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_ctc_medium", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_medium", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_medium/versions/1.6.0/files/stt_en_conformer_ctc_medium.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_ctc_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_large/versions/1.10.0/files/stt_en_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_ctc_xlarge", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_xlarge", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_xlarge/versions/1.10.0/files/stt_en_conformer_ctc_xlarge.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_squeezeformer_ctc_xsmall_ls", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_squeezeformer_ctc_xsmall_ls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_squeezeformer_ctc_xsmall_ls/versions/1.13.0/files/stt_en_squeezeformer_ctc_xsmall_ls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_squeezeformer_ctc_small_ls", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_squeezeformer_ctc_small_ls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_squeezeformer_ctc_small_ls/versions/1.13.0/files/stt_en_squeezeformer_ctc_small_ls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_squeezeformer_ctc_small_medium_ls", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_squeezeformer_ctc_small_medium_ls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_squeezeformer_ctc_small_medium_ls/versions/1.13.0/files/stt_en_squeezeformer_ctc_small_medium_ls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_squeezeformer_ctc_medium_ls", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_squeezeformer_ctc_medium_ls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_squeezeformer_ctc_medium_ls/versions/1.13.0/files/stt_en_squeezeformer_ctc_medium_ls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_squeezeformer_ctc_medium_large_ls", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_squeezeformer_ctc_medium_large_ls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_squeezeformer_ctc_medium_large_ls/versions/1.13.0/files/stt_en_squeezeformer_ctc_medium_large_ls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_squeezeformer_ctc_large_ls", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_squeezeformer_ctc_large_ls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_squeezeformer_ctc_large_ls/versions/1.13.0/files/stt_en_squeezeformer_ctc_large_ls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_ctc_small_ls", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_small_ls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_small_ls/versions/1.0.0/files/stt_en_conformer_ctc_small_ls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_ctc_medium_ls", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_medium_ls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_medium_ls/versions/1.0.0/files/stt_en_conformer_ctc_medium_ls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_ctc_large_ls", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large_ls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_large_ls/versions/1.0.0/files/stt_en_conformer_ctc_large_ls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_fr_conformer_ctc_large", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_conformer_ctc_large/versions/1.5.1/files/stt_fr_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_fr_no_hyphen_conformer_ctc_large", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_conformer_ctc_large/versions/1.5.1/files/stt_fr_no_hyphen_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_de_conformer_ctc_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_conformer_ctc_large/versions/1.5.0/files/stt_de_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_es_conformer_ctc_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_conformer_ctc_large/versions/1.8.0/files/stt_es_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_hi_conformer_ctc_medium", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_hi_conformer_ctc_medium", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_hi_conformer_ctc_medium/versions/1.6.0/files/stt_hi_conformer_ctc_medium.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_mr_conformer_ctc_medium", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_mr_conformer_ctc_medium", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_mr_conformer_ctc_medium/versions/1.6.0/files/stt_mr_conformer_ctc_medium.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_enes_conformer_ctc_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_enes_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_enes_conformer_ctc_large/versions/1.0.0/files/stt_enes_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_ca_conformer_ctc_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ca_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ca_conformer_ctc_large/versions/1.11.0/files/stt_ca_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_rw_conformer_ctc_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_rw_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_rw_conformer_ctc_large/versions/1.11.0/files/stt_rw_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_enes_conformer_ctc_large_codesw", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_enes_conformer_ctc_large_codesw", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_enes_conformer_ctc_large_codesw/versions/1.0.0/files/stt_enes_conformer_ctc_large_codesw.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_be_conformer_ctc_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_be_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_be_conformer_ctc_large/versions/1.12.0/files/stt_be_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_hr_conformer_ctc_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_hr_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_hr_conformer_ctc_large/versions/1.11.0/files/stt_hr_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_it_conformer_ctc_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_it_conformer_ctc_large/versions/1.13.0/files/stt_it_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_ru_conformer_ctc_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ru_conformer_ctc_large/versions/1.13.0/files/stt_ru_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_eo_conformer_ctc_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_eo_conformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_eo_conformer_ctc_large/versions/1.14.0/files/stt_eo_conformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_ctc_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_ctc_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_ctc_large/versions/1.0.0/files/stt_en_fastconformer_ctc_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_ctc_large_ls", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_ctc_large_ls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_ctc_large_ls/versions/1.0.0/files/stt_en_fastconformer_ctc_large_ls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_ctc_xlarge", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_ctc_xlarge", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_ctc_xlarge/versions/1.20.0/files/stt_en_fastconformer_ctc_xlarge.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_ctc_xxlarge", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_ctc_xxlarge", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_ctc_xxlarge/versions/1.20.1/files/stt_en_fastconformer_ctc_xxlarge.nemo", + ) + results.append(model) + + return results diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/ctc_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/ctc_models.py new file mode 100644 index 0000000..4df02b1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/ctc_models.py @@ -0,0 +1,869 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import copy +import json +import os +import tempfile +from math import ceil +from typing import Any, Dict, List, Optional, Union + +import numpy as np +import torch +from omegaconf import DictConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer +from tqdm.auto import tqdm + +from nemo.collections.asr.data import audio_to_text_dataset +from nemo.collections.asr.data.audio_to_text import _AudioTextDataset +from nemo.collections.asr.data.audio_to_text_dali import AudioToCharDALIDataset, DALIOutputs +from nemo.collections.asr.data.audio_to_text_lhotse import LhotseSpeechToTextBpeDataset +from nemo.collections.asr.losses.ctc import CTCLoss +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel +from nemo.collections.asr.parts.mixins import ASRModuleMixin, ASRTranscriptionMixin, InterCTCMixin, TranscribeConfig +from nemo.collections.asr.parts.mixins.transcription import GenericTranscriptionType, TranscriptionReturnType +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecoding, CTCDecodingConfig +from nemo.collections.asr.parts.utils.asr_batching import get_semi_sorted_batch_sampler +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config +from nemo.collections.common.parts.preprocessing.parsers import make_parser +from nemo.core.classes.common import PretrainedModelInfo, typecheck +from nemo.core.classes.mixins import AccessMixin +from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, LogprobsType, NeuralType, SpectrogramType +from nemo.utils import logging + +__all__ = ['EncDecCTCModel'] + + +class EncDecCTCModel(ASRModel, ExportableEncDecModel, ASRModuleMixin, InterCTCMixin, ASRTranscriptionMixin): + """Base class for encoder decoder CTC-based models.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + # Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable + # Global_rank and local_rank is set by LightningModule in Lightning 1.2.0 + self.world_size = 1 + if trainer is not None: + self.world_size = trainer.world_size + + super().__init__(cfg=cfg, trainer=trainer) + self.preprocessor = EncDecCTCModel.from_config_dict(self._cfg.preprocessor) + self.encoder = EncDecCTCModel.from_config_dict(self._cfg.encoder) + + with open_dict(self._cfg): + if "feat_in" not in self._cfg.decoder or ( + not self._cfg.decoder.feat_in and hasattr(self.encoder, '_feat_out') + ): + self._cfg.decoder.feat_in = self.encoder._feat_out + if "feat_in" not in self._cfg.decoder or not self._cfg.decoder.feat_in: + raise ValueError("param feat_in of the decoder's config is not set!") + + if self.cfg.decoder.num_classes < 1 and self.cfg.decoder.vocabulary is not None: + logging.info( + "\nReplacing placeholder number of classes ({}) with actual number of classes - {}".format( + self.cfg.decoder.num_classes, len(self.cfg.decoder.vocabulary) + ) + ) + cfg.decoder["num_classes"] = len(self.cfg.decoder.vocabulary) + + self.decoder = EncDecCTCModel.from_config_dict(self._cfg.decoder) + + self.loss = CTCLoss( + num_classes=self.decoder.num_classes_with_blank - 1, + zero_infinity=True, + reduction=self._cfg.get("ctc_reduction", "mean_batch"), + ) + + if hasattr(self._cfg, 'spec_augment') and self._cfg.spec_augment is not None: + self.spec_augmentation = EncDecCTCModel.from_config_dict(self._cfg.spec_augment) + else: + self.spec_augmentation = None + + # Setup decoding objects + decoding_cfg = self.cfg.get('decoding', None) + + # In case decoding config not found, use default config + if decoding_cfg is None: + decoding_cfg = OmegaConf.structured(CTCDecodingConfig) + with open_dict(self.cfg): + self.cfg.decoding = decoding_cfg + + self.decoding = CTCDecoding(self.cfg.decoding, vocabulary=OmegaConf.to_container(self.decoder.vocabulary)) + + # Setup metric with decoding strategy + self.wer = WER( + decoding=self.decoding, + use_cer=self._cfg.get('use_cer', False), + dist_sync_on_step=True, + log_prediction=self._cfg.get("log_prediction", False), + ) + + # Setup optional Optimization flags + self.setup_optimization_flags() + + # setting up interCTC loss (from InterCTCMixin) + self.setup_interctc(decoder_name='decoder', loss_name='loss', wer_name='wer') + + # Adapter modules setup (from ASRAdapterModelMixin) + self.setup_adapters() + + def transcribe( + self, + audio: Union[str, List[str], torch.Tensor, np.ndarray], + batch_size: int = 4, + return_hypotheses: bool = False, + num_workers: int = 0, + channel_selector: Optional[ChannelSelectorType] = None, + augmentor: DictConfig = None, + verbose: bool = True, + override_config: Optional[TranscribeConfig] = None, + ) -> TranscriptionReturnType: + """ + If modify this function, please remember update transcribe_partial_audio() in + nemo/collections/asr/parts/utils/trancribe_utils.py + + Uses greedy decoding to transcribe audio files. Use this method for debugging and prototyping. + + Args: + audio: (a single or list) of paths to audio files or a np.ndarray audio array. \ + Recommended length per file is between 5 and 25 seconds. \ + But it is possible to pass a few hours long file if enough GPU memory is available. + batch_size: (int) batch size to use during inference. + Bigger will result in better throughput performance but would use more memory. + return_hypotheses: (bool) Either return hypotheses or text + With hypotheses can do some postprocessing like getting timestamp or rescoring + num_workers: (int) number of workers for DataLoader + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. + augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied. + verbose: (bool) whether to display tqdm progress bar + override_config: (Optional[TranscribeConfig]) override transcription config pre-defined by the user. + **Note**: All other arguments in the function will be ignored if override_config is passed. + You should call this argument as `model.transcribe(audio, override_config=TranscribeConfig(...))`. + + Returns: + A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files + """ + return super().transcribe( + audio=audio, + batch_size=batch_size, + return_hypotheses=return_hypotheses, + num_workers=num_workers, + channel_selector=channel_selector, + augmentor=augmentor, + verbose=verbose, + override_config=override_config, + ) + + def change_vocabulary(self, new_vocabulary: List[str], decoding_cfg: Optional[DictConfig] = None): + """ + Changes vocabulary used during CTC decoding process. Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + If new_vocabulary == self.decoder.vocabulary then nothing will be changed. + + Args: + + new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \ + this is target alphabet. + + Returns: None + + """ + if self.decoder.vocabulary == new_vocabulary: + logging.warning(f"Old {self.decoder.vocabulary} and new {new_vocabulary} match. Not changing anything.") + else: + if new_vocabulary is None or len(new_vocabulary) == 0: + raise ValueError(f'New vocabulary must be non-empty list of chars. But I got: {new_vocabulary}') + decoder_config = self.decoder.to_config_dict() + new_decoder_config = copy.deepcopy(decoder_config) + new_decoder_config['vocabulary'] = new_vocabulary + new_decoder_config['num_classes'] = len(new_vocabulary) + + del self.decoder + self.decoder = EncDecCTCModel.from_config_dict(new_decoder_config) + del self.loss + self.loss = CTCLoss( + num_classes=self.decoder.num_classes_with_blank - 1, + zero_infinity=True, + reduction=self._cfg.get("ctc_reduction", "mean_batch"), + ) + + if decoding_cfg is None: + # Assume same decoding config as before + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(CTCDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = CTCDecoding( + decoding_cfg=decoding_cfg, vocabulary=OmegaConf.to_container(self.decoder.vocabulary) + ) + + self.wer = WER( + decoding=self.decoding, + use_cer=self._cfg.get('use_cer', False), + dist_sync_on_step=True, + log_prediction=self._cfg.get("log_prediction", False), + ) + + # Update config + with open_dict(self.cfg.decoder): + self._cfg.decoder = new_decoder_config + + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + ds_keys = ['train_ds', 'validation_ds', 'test_ds'] + for key in ds_keys: + if key in self.cfg: + with open_dict(self.cfg[key]): + self.cfg[key]['labels'] = OmegaConf.create(new_vocabulary) + + logging.info(f"Changed decoder to output to {self.decoder.vocabulary} vocabulary.") + + def change_decoding_strategy(self, decoding_cfg: DictConfig): + """ + Changes decoding strategy used during CTC decoding process. + + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + """ + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(CTCDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = CTCDecoding( + decoding_cfg=decoding_cfg, vocabulary=OmegaConf.to_container(self.decoder.vocabulary) + ) + + self.wer = WER( + decoding=self.decoding, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + self.decoder.temperature = decoding_cfg.get('temperature', 1.0) + + # Update config + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}") + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + # Automatically inject args from model config to dataloader config + audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='sample_rate') + audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='labels') + + if config.get("use_lhotse"): + return get_lhotse_dataloader_from_config( + config, + global_rank=self.global_rank, + world_size=self.world_size, + dataset=LhotseSpeechToTextBpeDataset( + tokenizer=make_parser( + labels=config.get('labels', None), + name=config.get('parser', 'en'), + unk_id=config.get('unk_index', -1), + blank_id=config.get('blank_index', -1), + do_normalize=config.get('normalize_transcripts', False), + ), + ), + ) + + dataset = audio_to_text_dataset.get_audio_to_text_char_dataset_from_config( + config=config, + local_rank=self.local_rank, + global_rank=self.global_rank, + world_size=self.world_size, + preprocessor_cfg=self._cfg.get("preprocessor", None), + ) + + if dataset is None: + return None + + if isinstance(dataset, AudioToCharDALIDataset): + # DALI Dataset implements dataloader interface + return dataset + + shuffle = config['shuffle'] + if isinstance(dataset, torch.utils.data.IterableDataset): + shuffle = False + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + elif hasattr(dataset.datasets[0], 'collate_fn'): + # support datasets that are lists of entries + collate_fn = dataset.datasets[0].collate_fn + else: + # support datasets that are lists of lists + collate_fn = dataset.datasets[0].datasets[0].collate_fn + + batch_sampler = None + if config.get('use_semi_sorted_batching', False): + if not isinstance(dataset, _AudioTextDataset): + raise RuntimeError( + "Semi Sorted Batch sampler can be used with AudioToCharDataset or AudioToBPEDataset " + f"but found dataset of type {type(dataset)}" + ) + # set batch_size and batch_sampler to None to disable automatic batching + batch_sampler = get_semi_sorted_batch_sampler(self, dataset, config) + config['batch_size'] = None + config['drop_last'] = False + shuffle = False + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + sampler=batch_sampler, + batch_sampler=None, + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the training data loader via a Dict-like object. + + Args: + train_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in train_data_config: + train_data_config['shuffle'] = True + + # preserve config + self._update_dataset_config(dataset_name='train', config=train_data_config) + + self._train_dl = self._setup_dataloader_from_config(config=train_data_config) + + # Need to set this because if using an IterableDataset, the length of the dataloader is the total number + # of samples rather than the number of batches, and this messes up the tqdm progress bar. + # So we set the number of steps manually (to the correct number) to fix this. + if ( + self._train_dl is not None + and hasattr(self._train_dl, 'dataset') + and isinstance(self._train_dl.dataset, torch.utils.data.IterableDataset) + ): + # We also need to check if limit_train_batches is already set. + # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches, + # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0). + if self._trainer is not None and isinstance(self._trainer.limit_train_batches, float): + self._trainer.limit_train_batches = int( + self._trainer.limit_train_batches + * ceil((len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size']) + ) + elif self._trainer is None: + logging.warning( + "Model Trainer was not set before constructing the dataset, incorrect number of " + "training batches will be used. Please set the trainer and rebuild the dataset." + ) + + def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the validation data loader via a Dict-like object. + + Args: + val_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in val_data_config: + val_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='validation', config=val_data_config) + + self._validation_dl = self._setup_dataloader_from_config(config=val_data_config) + + def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the test data loader via a Dict-like object. + + Args: + test_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in test_data_config: + test_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='test', config=test_data_config) + + self._test_dl = self._setup_dataloader_from_config(config=test_data_config) + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + if hasattr(self.preprocessor, '_sample_rate'): + input_signal_eltype = AudioSignal(freq=self.preprocessor._sample_rate) + else: + input_signal_eltype = AudioSignal() + return { + "input_signal": NeuralType(('B', 'T'), input_signal_eltype, optional=True), + "input_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "processed_signal": NeuralType(('B', 'D', 'T'), SpectrogramType(), optional=True), + "processed_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "sample_id": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return { + "outputs": NeuralType(('B', 'T', 'D'), LogprobsType()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + "greedy_predictions": NeuralType(('B', 'T'), LabelsType()), + } + + @typecheck() + def forward( + self, input_signal=None, input_signal_length=None, processed_signal=None, processed_signal_length=None + ): + """ + Forward pass of the model. + + Args: + input_signal: Tensor that represents a batch of raw audio signals, + of shape [B, T]. T here represents timesteps, with 1 second of audio represented as + `self.sample_rate` number of floating point values. + input_signal_length: Vector of length B, that contains the individual lengths of the audio + sequences. + processed_signal: Tensor that represents a batch of processed audio signals, + of shape (B, D, T) that has undergone processing via some DALI preprocessor. + processed_signal_length: Vector of length B, that contains the individual lengths of the + processed audio sequences. + + Returns: + A tuple of 3 elements - + 1) The log probabilities tensor of shape [B, T, D]. + 2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. + 3) The greedy token predictions of the model of shape [B, T] (via argmax) + """ + has_input_signal = input_signal is not None and input_signal_length is not None + has_processed_signal = processed_signal is not None and processed_signal_length is not None + if (has_input_signal ^ has_processed_signal) == False: + raise ValueError( + f"{self} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive " + " with ``processed_signal`` and ``processed_signal_len`` arguments." + ) + + if not has_processed_signal: + processed_signal, processed_signal_length = self.preprocessor( + input_signal=input_signal, length=input_signal_length, + ) + + if self.spec_augmentation is not None and self.training: + processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length) + + encoder_output = self.encoder(audio_signal=processed_signal, length=processed_signal_length) + encoded = encoder_output[0] + encoded_len = encoder_output[1] + log_probs = self.decoder(encoder_output=encoded) + greedy_predictions = log_probs.argmax(dim=-1, keepdim=False) + + return ( + log_probs, + encoded_len, + greedy_predictions, + ) + + # PTL-specific methods + def training_step(self, batch, batch_nb): + # Reset access registry + if AccessMixin.is_access_enabled(self.model_guid): + AccessMixin.reset_registry(self) + + if self.is_interctc_enabled(): + AccessMixin.set_access_enabled(access_enabled=True, guid=self.model_guid) + + signal, signal_len, transcript, transcript_len = batch + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + log_probs, encoded_len, predictions = self.forward( + processed_signal=signal, processed_signal_length=signal_len + ) + else: + log_probs, encoded_len, predictions = self.forward(input_signal=signal, input_signal_length=signal_len) + + if hasattr(self, '_trainer') and self._trainer is not None: + log_every_n_steps = self._trainer.log_every_n_steps + else: + log_every_n_steps = 1 + + loss_value = self.loss( + log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len + ) + + # Add auxiliary losses, if registered + loss_value = self.add_auxiliary_losses(loss_value) + # only computing WER when requested in the logs (same as done for final-layer WER below) + loss_value, tensorboard_logs = self.add_interctc_losses( + loss_value, transcript, transcript_len, compute_wer=((batch_nb + 1) % log_every_n_steps == 0) + ) + + # Reset access registry + if AccessMixin.is_access_enabled(self.model_guid): + AccessMixin.reset_registry(self) + + tensorboard_logs.update( + { + 'train_loss': loss_value, + 'learning_rate': self._optimizer.param_groups[0]['lr'], + 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), + } + ) + + if (batch_nb + 1) % log_every_n_steps == 0: + self.wer.update( + predictions=log_probs, + targets=transcript, + targets_lengths=transcript_len, + predictions_lengths=encoded_len, + ) + wer, _, _ = self.wer.compute() + self.wer.reset() + tensorboard_logs.update({'training_batch_wer': wer}) + + return {'loss': loss_value, 'log': tensorboard_logs} + + def predict_step(self, batch, batch_idx, dataloader_idx=0): + signal, signal_len, transcript, transcript_len, sample_id = batch + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + log_probs, encoded_len, predictions = self.forward( + processed_signal=signal, processed_signal_length=signal_len + ) + else: + log_probs, encoded_len, predictions = self.forward(input_signal=signal, input_signal_length=signal_len) + + transcribed_texts, _ = self.wer.decoding.ctc_decoder_predictions_tensor( + decoder_outputs=log_probs, decoder_lengths=encoded_len, return_hypotheses=False, + ) + + sample_id = sample_id.cpu().detach().numpy() + return list(zip(sample_id, transcribed_texts)) + + def validation_pass(self, batch, batch_idx, dataloader_idx=0): + if self.is_interctc_enabled(): + AccessMixin.set_access_enabled(access_enabled=True, guid=self.model_guid) + + signal, signal_len, transcript, transcript_len = batch + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + log_probs, encoded_len, predictions = self.forward( + processed_signal=signal, processed_signal_length=signal_len + ) + else: + log_probs, encoded_len, predictions = self.forward(input_signal=signal, input_signal_length=signal_len) + + loss_value = self.loss( + log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len + ) + loss_value, metrics = self.add_interctc_losses( + loss_value, transcript, transcript_len, compute_wer=True, log_wer_num_denom=True, log_prefix="val_", + ) + + self.wer.update( + predictions=log_probs, targets=transcript, targets_lengths=transcript_len, predictions_lengths=encoded_len, + ) + wer, wer_num, wer_denom = self.wer.compute() + self.wer.reset() + metrics.update({'val_loss': loss_value, 'val_wer_num': wer_num, 'val_wer_denom': wer_denom, 'val_wer': wer}) + + self.log('global_step', torch.tensor(self.trainer.global_step, dtype=torch.float32)) + + # Reset access registry + if AccessMixin.is_access_enabled(self.model_guid): + AccessMixin.reset_registry(self) + + return metrics + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + metrics = self.validation_pass(batch, batch_idx, dataloader_idx) + if type(self.trainer.val_dataloaders) == list and len(self.trainer.val_dataloaders) > 1: + self.validation_step_outputs[dataloader_idx].append(metrics) + else: + self.validation_step_outputs.append(metrics) + return metrics + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + metrics = super().multi_validation_epoch_end(outputs, dataloader_idx) + self.finalize_interctc_metrics(metrics, outputs, prefix="val_") + return metrics + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + metrics = super().multi_test_epoch_end(outputs, dataloader_idx) + self.finalize_interctc_metrics(metrics, outputs, prefix="test_") + return metrics + + def test_step(self, batch, batch_idx, dataloader_idx=0): + logs = self.validation_pass(batch, batch_idx, dataloader_idx=dataloader_idx) + test_logs = {name.replace("val_", "test_"): value for name, value in logs.items()} + if type(self.trainer.test_dataloaders) == list and len(self.trainer.test_dataloaders) > 1: + self.test_step_outputs[dataloader_idx].append(test_logs) + else: + self.test_step_outputs.append(test_logs) + return test_logs + + def test_dataloader(self): + if self._test_dl is not None: + return self._test_dl + + """ Transcription related methods """ + + def _transcribe_on_begin(self, audio, trcfg: TranscribeConfig): + super()._transcribe_on_begin(audio, trcfg) + + # Freeze the encoder and decoure_exder modules + self.encoder.freeze() + self.decoder.freeze() + + def _transcribe_on_end(self, trcfg: TranscribeConfig): + super()._transcribe_on_end(trcfg) + + # Unfreeze the encoder and decoder modules + self.encoder.unfreeze() + self.decoder.unfreeze() + + def _transcribe_forward(self, batch: Any, trcfg: TranscribeConfig): + logits, logits_len, greedy_predictions = self.forward(input_signal=batch[0], input_signal_length=batch[1]) + output = dict(logits=logits, logits_len=logits_len) + del greedy_predictions + return output + + def _transcribe_output_processing(self, outputs, trcfg: TranscribeConfig) -> GenericTranscriptionType: + logits = outputs.pop('logits') + logits_len = outputs.pop('logits_len') + + current_hypotheses, all_hyp = self.decoding.ctc_decoder_predictions_tensor( + logits, decoder_lengths=logits_len, return_hypotheses=trcfg.return_hypotheses, + ) + if trcfg.return_hypotheses: + if logits.is_cuda: + # See comment in + # ctc_greedy_decoding.py::GreedyCTCInfer::forward() to + # understand this idiom. + logits_cpu = torch.empty(logits.shape, dtype=logits.dtype, device=torch.device("cpu"), pin_memory=True) + logits_cpu.copy_(logits, non_blocking=True) + else: + logits_cpu = logits + logits_len = logits_len.cpu() + # dump log probs per file + for idx in range(logits_cpu.shape[0]): + current_hypotheses[idx].y_sequence = logits_cpu[idx][: logits_len[idx]] + if current_hypotheses[idx].alignments is None: + current_hypotheses[idx].alignments = current_hypotheses[idx].y_sequence + del logits_cpu + + # cleanup memory + del logits, logits_len + + hypotheses = [] + if all_hyp is None: + hypotheses += current_hypotheses + else: + hypotheses += all_hyp + + return hypotheses + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided audio file. + + Args: + config: A python dictionary which contains the following keys: + paths2audio_files: (a list) of paths to audio files. The files should be relatively short fragments. \ + Recommended length per file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the audio manifest is temporarily + stored. + num_workers: (int) number of workers. Depends of the batch_size and machine. \ + 0 - only the main process will load batches, 1 - one worker (not main process) + + Returns: + A pytorch DataLoader for the given audio file(s). + """ + if 'manifest_filepath' in config: + manifest_filepath = config['manifest_filepath'] + batch_size = config['batch_size'] + else: + manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json') + batch_size = min(config['batch_size'], len(config['paths2audio_files'])) + + dl_config = { + 'manifest_filepath': manifest_filepath, + 'sample_rate': self.preprocessor._sample_rate, + 'labels': OmegaConf.to_container(self.decoder.vocabulary), + 'batch_size': batch_size, + 'trim_silence': False, + 'shuffle': False, + 'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)), + 'pin_memory': True, + 'channel_selector': config.get('channel_selector', None), + } + if config.get("augmentor"): + dl_config['augmentor'] = config.get("augmentor") + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + model = PretrainedModelInfo( + pretrained_model_name="QuartzNet15x5Base-En", + description="QuartzNet15x5 model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1 for 600 epochs. The model achieves a WER of 3.79% on LibriSpeech dev-clean, and a WER of 10.05% on dev-other. Please visit https://ngc.nvidia.com/catalog/models/nvidia:nemospeechmodels for further details.", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/QuartzNet15x5Base-En.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_quartznet15x5", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_quartznet15x5", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_quartznet15x5/versions/1.0.0rc1/files/stt_en_quartznet15x5.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_jasper10x5dr", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_jasper10x5dr", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_jasper10x5dr/versions/1.0.0rc1/files/stt_en_jasper10x5dr.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_ca_quartznet15x5", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ca_quartznet15x5", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ca_quartznet15x5/versions/1.0.0rc1/files/stt_ca_quartznet15x5.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_it_quartznet15x5", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_quartznet15x5", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_it_quartznet15x5/versions/1.0.0rc1/files/stt_it_quartznet15x5.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_fr_quartznet15x5", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_quartznet15x5", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_quartznet15x5/versions/1.0.0rc1/files/stt_fr_quartznet15x5.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_es_quartznet15x5", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_quartznet15x5", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_quartznet15x5/versions/1.0.0rc1/files/stt_es_quartznet15x5.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_de_quartznet15x5", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_quartznet15x5", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_quartznet15x5/versions/1.0.0rc1/files/stt_de_quartznet15x5.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_pl_quartznet15x5", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_pl_quartznet15x5", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_pl_quartznet15x5/versions/1.0.0rc1/files/stt_pl_quartznet15x5.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_ru_quartznet15x5", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_quartznet15x5", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ru_quartznet15x5/versions/1.0.0rc1/files/stt_ru_quartznet15x5.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_zh_citrinet_512", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_512", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_512/versions/1.0.0rc1/files/stt_zh_citrinet_512.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_zh_citrinet_1024_gamma_0_25", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_1024_gamma_0_25", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_zh_citrinet_1024_gamma_0_25.nemo", + ) + + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_zh_citrinet_1024_gamma_0_25", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_1024_gamma_0_25", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_zh_citrinet_1024_gamma_0_25.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="asr_talknet_aligner", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:asr_talknet_aligner", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/asr_talknet_aligner/versions/1.0.0rc1/files/qn5x5_libri_tts_phonemes.nemo", + ) + results.append(model) + + return results + + @property + def wer(self): + return self._wer + + @wer.setter + def wer(self, wer): + self._wer = wer diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/enhancement_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/enhancement_models.py new file mode 100644 index 0000000..7cc5c3d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/enhancement_models.py @@ -0,0 +1,466 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import json +import os +import tempfile +from typing import Dict, List, Optional, Union + +import librosa +import soundfile as sf +import torch +from omegaconf import DictConfig +from pytorch_lightning import Trainer +from tqdm import tqdm + +from nemo.collections.asr.data import audio_to_audio_dataset +from nemo.collections.asr.data.audio_to_text_dataset import inject_dataloader_value_from_model_config +from nemo.collections.asr.models.audio_to_audio_model import AudioToAudioModel +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.core.classes.common import PretrainedModelInfo, typecheck +from nemo.core.neural_types import AudioSignal, LengthsType, NeuralType +from nemo.utils import logging + +__all__ = ['EncMaskDecAudioToAudioModel'] + + +class EncMaskDecAudioToAudioModel(AudioToAudioModel): + """Class for encoder-mask-decoder audio processing models. + + The model consists of the following blocks: + - encoder: transforms input multi-channel audio signal into an encoded representation (analysis transform) + - mask_estimator: estimates a mask used by signal processor + - mask_processor: mask-based signal processor, combines the encoded input and the estimated mask + - decoder: transforms processor output into the time domain (synthesis transform) + """ + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + # Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable + # Global_rank and local_rank is set by LightningModule in Lightning 1.2.0 + self.world_size = 1 + if trainer is not None: + self.world_size = trainer.world_size + + super().__init__(cfg=cfg, trainer=trainer) + self.sample_rate = self._cfg.sample_rate + + # Setup processing modules + self.encoder = EncMaskDecAudioToAudioModel.from_config_dict(self._cfg.encoder) + self.mask_estimator = EncMaskDecAudioToAudioModel.from_config_dict(self._cfg.mask_estimator) + self.mask_processor = EncMaskDecAudioToAudioModel.from_config_dict(self._cfg.mask_processor) + self.decoder = EncMaskDecAudioToAudioModel.from_config_dict(self._cfg.decoder) + + if 'mixture_consistency' in self._cfg: + logging.debug('Using mixture consistency') + self.mixture_consistency = EncMaskDecAudioToAudioModel.from_config_dict(self._cfg.mixture_consistency) + else: + logging.debug('Mixture consistency not used') + self.mixture_consistency = None + + # Future enhancement: + # If subclasses need to modify the config before calling super() + # Check ASRBPE* classes do with their mixin + + # Setup augmentation + if hasattr(self.cfg, 'channel_augment') and self.cfg.channel_augment is not None: + logging.debug('Using channel augmentation') + self.channel_augmentation = EncMaskDecAudioToAudioModel.from_config_dict(self.cfg.channel_augment) + else: + logging.debug('Channel augmentation not used') + self.channel_augmentation = None + + # Setup optional Optimization flags + self.setup_optimization_flags() + + @torch.no_grad() + def process( + self, + paths2audio_files: List[str], + output_dir: str, + batch_size: int = 1, + num_workers: Optional[int] = None, + input_channel_selector: Optional[ChannelSelectorType] = None, + ) -> List[str]: + """ + Process audio files provided in paths2audio_files. + Processed signals will be saved in output_dir. + + Args: + paths2audio_files: (a list) of paths to audio files. \ + Recommended length per file is between 5 and 25 seconds. \ + But it is possible to pass a few hours long file if enough GPU memory is available. + output_dir: + batch_size: (int) batch size to use during inference. + Bigger will result in better throughput performance but would use more memory. + num_workers: Number of workers for the dataloader + input_channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. + + Returns: + """ + if paths2audio_files is None or len(paths2audio_files) == 0: + return {} + + if num_workers is None: + num_workers = min(batch_size, os.cpu_count() - 1) + + # Output + paths2processed_files = [] + + # Model's mode and device + mode = self.training + device = next(self.parameters()).device + + try: + # Switch model to evaluation mode + self.eval() + # Freeze weights + self.freeze() + + logging_level = logging.get_verbosity() + logging.set_verbosity(logging.WARNING) + + # Processing + with tempfile.TemporaryDirectory() as tmpdir: + # Save temporary manifest + temporary_manifest_filepath = os.path.join(tmpdir, 'manifest.json') + with open(temporary_manifest_filepath, 'w', encoding='utf-8') as fp: + for audio_file in paths2audio_files: + entry = {'input_filepath': audio_file, 'duration': librosa.get_duration(path=audio_file)} + fp.write(json.dumps(entry) + '\n') + + config = { + 'manifest_filepath': temporary_manifest_filepath, + 'input_key': 'input_filepath', + 'input_channel_selector': input_channel_selector, + 'batch_size': min(batch_size, len(paths2audio_files)), + 'num_workers': num_workers, + } + + # Create output dir if necessary + if not os.path.isdir(output_dir): + os.makedirs(output_dir) + + # DataLoader for the input files + temporary_dataloader = self._setup_process_dataloader(config) + + # Indexing of the original files, used to form the output file name + file_idx = 0 + + # Process batches + for test_batch in tqdm(temporary_dataloader, desc="Processing"): + input_signal = test_batch[0] + input_length = test_batch[1] + + # Expand channel dimension, if necessary + # For consistency, the model uses multi-channel format, even if the channel dimension is 1 + if input_signal.ndim == 2: + input_signal = input_signal.unsqueeze(1) + + processed_batch, _ = self.forward( + input_signal=input_signal.to(device), input_length=input_length.to(device) + ) + + for example_idx in range(processed_batch.size(0)): + # This assumes the data loader is not shuffling files + file_name = os.path.basename(paths2audio_files[file_idx]) + # Prepare output file + output_file = os.path.join(output_dir, f'processed_{file_name}') + # Crop the output signal to the actual length + output_signal = processed_batch[example_idx, :, : input_length[example_idx]].cpu().numpy() + # Write audio + sf.write(output_file, output_signal.T, self.sample_rate, 'float') + # Update the file counter + file_idx += 1 + # Save processed file + paths2processed_files.append(output_file) + + del test_batch + del processed_batch + + finally: + # set mode back to its original value + self.train(mode=mode) + if mode is True: + self.unfreeze() + logging.set_verbosity(logging_level) + + return paths2processed_files + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + + is_concat = config.get('is_concat', False) + if is_concat: + raise NotImplementedError('Concat not implemented') + + # TODO: Consider moving `inject` from `audio_to_text_dataset` to a utility module? + # Automatically inject args from model config to dataloader config + inject_dataloader_value_from_model_config(self.cfg, config, key='sample_rate') + + # Instantiate tarred dataset loader or normal dataset loader + if config.get('is_tarred', False): + raise NotImplementedError('Tarred datasets not supported') + + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") + return None + + dataset = audio_to_audio_dataset.get_audio_to_target_dataset(config=config) + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + elif hasattr(dataset.datasets[0], 'collate_fn'): + # support datasets that are lists of entries + collate_fn = dataset.datasets[0].collate_fn + else: + # support datasets that are lists of lists + collate_fn = dataset.datasets[0].datasets[0].collate_fn + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=config['shuffle'], + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the training data loader via a Dict-like object. + + Args: + train_data_config: A config that contains the information regarding construction + of a training dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_audio.AudioToTargetDataset` + """ + if 'shuffle' not in train_data_config: + train_data_config['shuffle'] = True + + # preserve config + self._update_dataset_config(dataset_name='train', config=train_data_config) + + self._train_dl = self._setup_dataloader_from_config(config=train_data_config) + + if 'is_tarred' in train_data_config and train_data_config['is_tarred']: + raise NotImplementedError('Tarred datasets not supported') + + def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the validation data loader via a Dict-like object. + + Args: + val_data_config: A config that contains the information regarding construction + of a validation dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_audio.AudioToTargetDataset` + """ + if 'shuffle' not in val_data_config: + val_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='validation', config=val_data_config) + + self._validation_dl = self._setup_dataloader_from_config(config=val_data_config) + + def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the test data loader via a Dict-like object. + + Args: + test_data_config: A config that contains the information regarding construction + of a test dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_audio.AudioToTargetDataset` + """ + if 'shuffle' not in test_data_config: + test_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='test', config=test_data_config) + + self._test_dl = self._setup_dataloader_from_config(config=test_data_config) + + def _setup_process_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """Prepare a dataloader for processing files. + + Args: + config: A python dictionary which contains the following keys: + manifest_filepath: path to a manifest file + input_key: key with audio filepaths in the manifest + input_channel_selector: Optional, used to select a subset of channels from input audio files + batch_size: batch size for the dataloader + num_workers: number of workers for the dataloader + + Returns: + A pytorch DataLoader for the given manifest filepath. + """ + dl_config = { + 'manifest_filepath': config['manifest_filepath'], + 'sample_rate': self.sample_rate, + 'input_key': config['input_key'], + 'input_channel_selector': config.get('input_channel_selector', None), + 'target_key': None, + 'target_channel_selector': None, + 'batch_size': config['batch_size'], + 'shuffle': False, + 'num_workers': config.get('num_workers', min(config['batch_size'], os.cpu_count() - 1)), + 'pin_memory': True, + } + + temporary_dataloader = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_dataloader + + @property + def input_types(self) -> Dict[str, NeuralType]: + return { + "input_signal": NeuralType( + ('B', 'C', 'T'), AudioSignal(freq=self.sample_rate) + ), # multi-channel format, channel dimension can be 1 for single-channel audio + "input_length": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + return { + "output_signal": NeuralType( + ('B', 'C', 'T'), AudioSignal(freq=self.sample_rate) + ), # multi-channel format, channel dimension can be 1 for single-channel audio + "output_length": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def match_batch_length(self, input: torch.Tensor, batch_length: int): + """Trim or pad the output to match the batch length. + + Args: + input: tensor with shape (B, C, T) + batch_length: int + + Returns: + Tensor with shape (B, C, T), where T matches the + batch length. + """ + input_length = input.size(-1) + pad_length = batch_length - input_length + pad = (0, pad_length) + # pad with zeros or crop + return torch.nn.functional.pad(input, pad, 'constant', 0) + + @typecheck() + def forward(self, input_signal, input_length=None): + """ + Forward pass of the model. + + Args: + input_signal: Tensor that represents a batch of raw audio signals, + of shape [B, T] or [B, T, C]. T here represents timesteps, with 1 second of audio represented as + `self.sample_rate` number of floating point values. + input_signal_length: Vector of length B, that contains the individual lengths of the audio + sequences. + + Returns: + """ + batch_length = input_signal.size(-1) + + # Encoder + encoded, encoded_length = self.encoder(input=input_signal, input_length=input_length) + + # Mask estimator + mask, _ = self.mask_estimator(input=encoded, input_length=encoded_length) + + # Mask-based processor in the encoded domain + processed, processed_length = self.mask_processor(input=encoded, input_length=encoded_length, mask=mask) + + # Mixture consistency + if self.mixture_consistency is not None: + processed = self.mixture_consistency(mixture=encoded, estimate=processed) + + # Decoder + processed, processed_length = self.decoder(input=processed, input_length=processed_length) + + # Trim or pad the estimated signal to match input length + processed = self.match_batch_length(input=processed, batch_length=batch_length) + return processed, processed_length + + # PTL-specific methods + def training_step(self, batch, batch_idx): + input_signal, input_length, target_signal, target_length = batch + + # Expand channel dimension, if necessary + # For consistency, the model uses multi-channel format, even if the channel dimension is 1 + if input_signal.ndim == 2: + input_signal = input_signal.unsqueeze(1) + if target_signal.ndim == 2: + target_signal = target_signal.unsqueeze(1) + + # Apply channel augmentation + if self.training and self.channel_augmentation is not None: + input_signal = self.channel_augmentation(input=input_signal) + + # Process input + processed_signal, _ = self.forward(input_signal=input_signal, input_length=input_length) + + # Calculate the loss + loss = self.loss(estimate=processed_signal, target=target_signal, input_length=input_length) + + # Logs + self.log('train_loss', loss) + self.log('learning_rate', self._optimizer.param_groups[0]['lr']) + self.log('global_step', torch.tensor(self.trainer.global_step, dtype=torch.float32)) + + # Return loss + return loss + + def evaluation_step(self, batch, batch_idx, dataloader_idx: int = 0, tag: str = 'val'): + input_signal, input_length, target_signal, target_length = batch + + # Expand channel dimension, if necessary + # For consistency, the model uses multi-channel format, even if the channel dimension is 1 + if input_signal.ndim == 2: + input_signal = input_signal.unsqueeze(1) + if target_signal.ndim == 2: + target_signal = target_signal.unsqueeze(1) + + # Process input + processed_signal, _ = self.forward(input_signal=input_signal, input_length=input_length) + + # Calculate the loss + loss = self.loss(estimate=processed_signal, target=target_signal, input_length=input_length) + + # Update metrics + if hasattr(self, 'metrics') and tag in self.metrics: + # Update metrics for this (tag, dataloader_idx) + for name, metric in self.metrics[tag][dataloader_idx].items(): + metric.update(preds=processed_signal, target=target_signal, input_length=input_length) + + # Log global step + self.log('global_step', torch.tensor(self.trainer.global_step, dtype=torch.float32)) + + # Return loss + return {f'{tag}_loss': loss} + + @classmethod + def list_available_models(cls) -> Optional[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + return results diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/hybrid_asr_tts_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/hybrid_asr_tts_models.py new file mode 100644 index 0000000..628395e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/hybrid_asr_tts_models.py @@ -0,0 +1,601 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import itertools +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple, Union, cast + +import torch +from omegaconf import MISSING, DictConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer +from torch.nn.utils.rnn import pad_sequence + +from nemo.collections.asr.data.audio_to_text_dali import DALIOutputs +from nemo.collections.asr.data.audio_to_text_dataset import get_audio_to_text_bpe_dataset_from_config +from nemo.collections.asr.data.text_to_text import ( + TextOrAudioToTextBatch, + TextToTextBatch, + TextToTextDataset, + TextToTextIterableDataset, +) +from nemo.collections.asr.models.asr_model import ASRModel +from nemo.collections.asr.models.ctc_bpe_models import EncDecCTCModelBPE +from nemo.collections.asr.models.hybrid_rnnt_ctc_bpe_models import EncDecHybridRNNTCTCBPEModel +from nemo.collections.asr.models.rnnt_bpe_models import EncDecRNNTBPEModel +from nemo.collections.asr.modules.conformer_encoder import ConformerEncoder +from nemo.collections.asr.parts.preprocessing.features import clean_spectrogram_batch, normalize_batch +from nemo.collections.asr.parts.submodules.batchnorm import replace_bn_with_fused_bn_all +from nemo.collections.common.data import ConcatDataset, ConcatMapDataset +from nemo.collections.tts.models import FastPitchModel, SpectrogramEnhancerModel +from nemo.core.classes import Dataset, typecheck +from nemo.core.classes.common import PretrainedModelInfo +from nemo.utils import logging +from nemo.utils.enum import PrettyStrEnum +from nemo.utils.exceptions import NeMoBaseException + + +def _fuse_bn_in_conformer(asr_model: ASRModel): + """ + Replace BatchNorm with Fused BatchNorm in Conformer and fixes model config inplace + Expected `encoder` model to exist and be of type ConformerEncoder + """ + logging.info("Replacing BatchNorm with Fused BatchNorm") + if not hasattr(asr_model, "encoder"): + raise NotImplementedError("No encoder found in ASR Model, replacement not supported") + if not isinstance(asr_model.encoder, ConformerEncoder): + raise NotImplementedError(f"Unsupported encoder type: {type(asr_model.encoder)}") + replace_bn_with_fused_bn_all(asr_model.encoder) + if "conv_norm_type" not in asr_model.cfg.encoder: + # old CTC models from NGC don't have such param + logging.warning("conv_norm_type not in encoder config, adding parameter") + with open_dict(asr_model.cfg): + asr_model.cfg.encoder.conv_norm_type = "fused_batch_norm" + else: + asr_model.cfg.encoder.conv_norm_type = "fused_batch_norm" + + +@dataclass +class TextDataConfig: + """ + Text dataset subconfig for text-only dataset + """ + + manifest_filepath: Any = MISSING # actual Union[str, List[str]], but this type is not supported by OmegaConf + speakers_filepath: Any = MISSING + min_words: int = 1 + max_words: int = 45 # 45 - recommended value, ~16.7 sec for LibriSpeech + tokenizer_workers: int = 1 + asr_tts_sampling_technique: Optional[str] = None + asr_tts_sampling_temperature: Optional[int] = None + asr_tts_sampling_probabilities: Optional[List[float]] = None + + +class ASRWithTTSModel(ASRModel): + """ + Hybrid ASR-TTS model: a transparent wrapper for ASR model + with frozen text-to-spectrogram pretrained model, which allows to use text-only data for training/finetuning + Text-only data can be mixed with audio-text pairs + """ + + asr_model: Union[EncDecRNNTBPEModel, EncDecCTCModelBPE, EncDecHybridRNNTCTCBPEModel] + tts_model: FastPitchModel + enhancer_model: Optional[SpectrogramEnhancerModel] + + class ASRModelTypes(PrettyStrEnum): + """ + Supported ASR types, needed for training from scratch + """ + + RNNT_BPE = "rnnt_bpe" + CTC_BPE = "ctc_bpe" + HYBRID_RNNT_CTC_BPE = "hybrid_rnnt_ctc_bpe" + + @classmethod + def from_asr_model(cls, model: Any): + if isinstance(model, EncDecRNNTBPEModel): + return cls.RNNT_BPE + if isinstance(model, EncDecCTCModelBPE): + return cls.CTC_BPE + if isinstance(model, EncDecHybridRNNTCTCBPEModel): + return cls.HYBRID_RNNT_CTC_BPE + raise ValueError(f"Unsupported model type: {type(model)}") + + def get_asr_cls(self): + if self == self.RNNT_BPE: + return EncDecRNNTBPEModel + if self == self.CTC_BPE: + return EncDecCTCModelBPE + if self == self.HYBRID_RNNT_CTC_BPE: + return EncDecHybridRNNTCTCBPEModel + raise NotImplementedError(f"Not implemented for value {self.value}") + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + return [] + + @classmethod + def _check_config(cls, cfg: DictConfig): + """ + Check that all required fields are present in config + Structured configs are not compatible with model serialization, so we check fields manually + """ + expected_fields = [ + # asr + "asr_model", + "asr_model_path", + "asr_model_fuse_bn", + "asr_model_type", + # tts + "tts_model", + "tts_model_path", + # enhancer + "enhancer_model_path", + "enhancer_model", + ] + for field in expected_fields: + if field not in cfg: + raise NeMoBaseException(f"Field {field} is required in config (possibly should be None/null)") + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + self._full_init_guard = False + + self._check_config(cfg) # check all required keys are in config + + # setup datasets and optimizer after model is fully initialized + # since it's done automatically, remove options from config + cfg = copy.deepcopy(cfg) # copy to avoid modifying original config + with open_dict(cfg): + train_ds_cfg = cfg.pop("train_ds", None) + validation_ds_cfg = cfg.pop("validation_ds", None) + test_ds_cfg = cfg.pop("test_ds", None) + optim_cfg = cfg.pop("optim", None) + + super().__init__(cfg, trainer=trainer) + + # tts model + if cfg.tts_model is not None: + self.register_nemo_submodule("tts_model", config_field="tts_model", model=FastPitchModel(cfg.tts_model)) + else: + if cfg.tts_model_path is None: + raise NeMoBaseException("Either tts_model or tts_model_path should be provided") + self.register_nemo_submodule( + "tts_model", + config_field="tts_model", + model=FastPitchModel.restore_from(f"{cfg.tts_model_path}", map_location=torch.device("cpu")), + ) + self.tts_model.freeze() # tts model should be always frozen + + if cfg.asr_model is not None: + self.asr_model_type = self.ASRModelTypes(cfg.asr_model_type) # convert to enum + self.register_nemo_submodule( + "asr_model", config_field="asr_model", model=self.asr_model_type.get_asr_cls()(cfg.asr_model) + ) + else: + if cfg.asr_model_path is None: + raise NeMoBaseException("Either asr_model or asr_model_path should be provided") + self.register_nemo_submodule( + "asr_model", + config_field="asr_model", + model=ASRModel.restore_from(f"{cfg.asr_model_path}", map_location=torch.device("cpu")), + ) + self.asr_model_type = self.ASRModelTypes.from_asr_model(self.asr_model) + self.cfg.asr_model_type = f"{self.asr_model_type}" # save to config + + # replace BatchNorm with FusedBatchNorm + if cfg.asr_model_fuse_bn: + _fuse_bn_in_conformer(self.asr_model) + self.cfg.asr_model_fuse_bn = False # no need to fuse anymore + + if cfg.enhancer_model is not None: + self.register_nemo_submodule( + "enhancer_model", config_field="enhancer_model", model=SpectrogramEnhancerModel(cfg.enhancer_model) + ) + elif cfg.enhancer_model_path is not None: + self.register_nemo_submodule( + "enhancer_model", + config_field="enhancer_model", + model=SpectrogramEnhancerModel.restore_from(cfg.enhancer_model_path, map_location=torch.device("cpu")), + ) + else: + self.enhancer_model = None + + self._full_init_guard = True + + # initialize optimizer and datasets, asr/tts models are initialized here + if optim_cfg: + with open_dict(self.cfg): + self.cfg.optim = optim_cfg + self.setup_optimization(optim_config=optim_cfg) + if train_ds_cfg: + with open_dict(self.cfg): + self.cfg.train_ds = train_ds_cfg + self.setup_training_data(train_data_config=train_ds_cfg) + if validation_ds_cfg: + with open_dict(self.cfg): + self.cfg.validation_ds = validation_ds_cfg + self.setup_multiple_validation_data(val_data_config=validation_ds_cfg) + if test_ds_cfg: + with open_dict(self.cfg): + self.cfg.test_ds = test_ds_cfg + self.setup_test_data(test_data_config=test_ds_cfg) + + @classmethod + def from_asr_config( + cls, + asr_cfg: DictConfig, + asr_model_type: Union[str, ASRModelTypes], + tts_model_path: Union[str, Path], + enhancer_model_path: Optional[Union[str, Path]] = None, + trainer: Trainer = None, + ): + """ + Method to construct model from ASR config for training from scratch + """ + model_type = cls.ASRModelTypes(asr_model_type) + cfg = DictConfig( + dict( + asr_model_path=None, + asr_model=None, + asr_model_type=f"{model_type}", + asr_model_fuse_bn=False, # for training from scratch always should be False + tts_model_path=f"{tts_model_path}", + tts_model=None, + enhancer_model_path=f"{enhancer_model_path}" if enhancer_model_path is not None else None, + enhancer_model=None, + train_ds=None, + validation_ds=None, + test_ds=None, + optim=None, + ) + ) + + asr_cfg = copy.deepcopy(asr_cfg) # copy not to affect original config + with open_dict(asr_cfg): + for subconfig_path in ["train_ds", "validation_ds", "test_ds", "optim"]: + if subconfig_path in asr_cfg: + cfg[subconfig_path] = asr_cfg.pop(subconfig_path) + cfg.asr_model = asr_cfg + return cls(cfg=cfg, trainer=trainer) + + @classmethod + def from_pretrained_models( + cls, + asr_model_path: Union[str, Path], + tts_model_path: Union[str, Path], + enhancer_model_path: Optional[Union[str, Path]] = None, + asr_model_fuse_bn: bool = False, + cfg: Optional[DictConfig] = None, + trainer: Optional[Trainer] = None, + ): + """ + Load model from pretrained ASR and TTS models + Args: + asr_model_path: path to .nemo ASR model checkpoint + tts_model_path: path to .nemo TTS model checkpoint + enhancer_model_path: path to .nemo enhancer model checkpoint + asr_model_fuse_bn: automatically fuse batchnorm layers in ASR model + cfg: optional config for hybrid model + trainer: Pytorch-Lightning trainer + + Returns: + ASRWithTTSModel instance + """ + if cfg is None: + cfg = DictConfig( + dict( + asr_model_path=f"{asr_model_path}", + asr_model=None, + tts_model_path=f"{tts_model_path}", + tts_model=None, + enhancer_model_path=f"{enhancer_model_path}" if enhancer_model_path is not None else None, + enhancer_model=None, + asr_model_type=None, + asr_model_fuse_bn=asr_model_fuse_bn, + train_ds=None, + validation_ds=None, + test_ds=None, + optim=None, + ) + ) + else: + cfg = copy.deepcopy(cfg) # copy to avoid modifying original config + cfg.tts_model_path = f"{tts_model_path}" + cfg.asr_model_path = f"{asr_model_path}" + cfg.enhancer_model_path = f"{enhancer_model_path}" if enhancer_model_path is not None else None + return ASRWithTTSModel(cfg, trainer=trainer) + + def __setattr__(self, name, value): + # pytorch-lightning magic, allows to call *_step on asr_model + if name == "_current_fx_name" and self._full_init_guard: + self.asr_model._current_fx_name = value # need to make logging inside asr_model work + return super().__setattr__(name, value) + + def setup_optimization( + self, optim_config: Optional[Union[DictConfig, Dict]] = None, optim_kwargs: Optional[Dict[str, Any]] = None, + ): + """ + Setup optimizer and scheduler. Ensure tts model is frozen. + Add optimizer and scheduler to asr model, to allow `train_step` on ASR model + """ + self.tts_model.freeze() + optimizer, scheduler = super().setup_optimization(optim_config=optim_config, optim_kwargs=optim_kwargs) + # set ASR model optimizer/scheduler to allow training_step on asr_model + self.asr_model._optimizer = optimizer + self.asr_model._scheduler = scheduler + return optimizer, scheduler + + def setup_validation_data(self, val_data_config: Union[DictConfig, Dict]): + """Setup validation data for ASR model""" + return self.asr_model.setup_validation_data(val_data_config) + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + """Validation epoch end hook for ASR model""" + return self.asr_model.multi_validation_epoch_end(outputs=outputs, dataloader_idx=dataloader_idx) + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + """Test epoch end hook for ASR model""" + return self.asr_model.multi_test_epoch_end(outputs=outputs, dataloader_idx=dataloader_idx) + + def transcribe(self, audio: List[str], batch_size: int = 4, verbose: bool = True) -> List[str]: + """Transcribe audio data using ASR model""" + return self.asr_model.transcribe(audio=audio, batch_size=batch_size, verbose=verbose) + + def setup_multiple_validation_data(self, val_data_config: Union[DictConfig, Dict]): + """Setup multiple validation data for ASR model""" + self.asr_model.setup_multiple_validation_data(val_data_config) + + def setup_test_data(self, test_data_config: Union[DictConfig, Dict]): + """Setup test data for ASR model""" + self.asr_model.setup_test_data(test_data_config) + + def setup_multiple_test_data(self, test_data_config: Union[DictConfig, Dict]): + """Setup multiple test data for ASR Model""" + return self.asr_model.setup_multiple_test_data(test_data_config) + + def save_asr_model_to(self, save_path: str): + """Save ASR model separately""" + return self.asr_model.save_to(save_path=save_path) + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + """Validation step, forward to ASR model""" + loss = self.asr_model.validation_step(batch=batch, batch_idx=batch_idx, dataloader_idx=dataloader_idx) + if type(self.trainer.val_dataloaders) == list and len(self.trainer.val_dataloaders) > 1: + self.validation_step_outputs[dataloader_idx].append(loss) + else: + self.validation_step_outputs.append(loss) + return loss + + def on_validation_epoch_end(self) -> Optional[Dict[str, Dict[str, torch.Tensor]]]: + """Validation epoch end hook, forward to ASR model""" + return self.asr_model.on_validation_epoch_end() + + def on_test_epoch_end(self) -> Optional[Dict[str, Dict[str, torch.Tensor]]]: + """Test epoch end hook, forward to ASR model""" + return self.asr_model.on_test_epoch_end() + + def val_dataloader(self): + """Get valudation dataloader from ASR model""" + return self.asr_model.val_dataloader() + + def unfreeze(self) -> None: + """Unfreeze the ASR model, keep TTS model frozen.""" + super().unfreeze() + self.tts_model.freeze() # tts model should be always frozen + + def on_fit_start(self): + """Call asr_model on_fit_start hook, ensure TTS model is frozen""" + self.asr_model.on_fit_start() + self.tts_model.freeze() + + def train(self, mode: bool = True): + """Train mode, ensure TTS model is frozen""" + super().train(mode) + self.tts_model.eval() + return self + + def _get_tts_spectrogram( + self, tts_texts: torch.Tensor, speakers: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Get TTS spectrogram from text and speaker ids""" + with torch.no_grad(): + spectrogram, spectrogram_len, *_ = self.tts_model(text=tts_texts, durs=None, pitch=None, speaker=speakers) + if self.enhancer_model is not None: + # apply enhancer + with typecheck.disable_checks(): + # spectrogram_len are of TokenDurationType, enhancer requires LengthsType + # TODO: fix FastPitch model to return LengthsType + spectrogram = self.enhancer_model.forward(input_spectrograms=spectrogram, lengths=spectrogram_len) + spectrogram, *_ = normalize_batch(spectrogram, spectrogram_len, self.asr_model.cfg.preprocessor.normalize) + return spectrogram, spectrogram_len + + def _get_batch_spect(self, batch: Union[TextToTextBatch, TextOrAudioToTextBatch, tuple]): + """Get batch with spectrograms from text-only, audio-text or mixed batch data""" + if isinstance(batch, TextToTextBatch): + spectrogram, spectrogram_len = self._get_tts_spectrogram(batch.tts_texts, batch.speakers) + transcript = batch.transcripts + transcript_len = batch.transcript_lengths + elif isinstance(batch, TextOrAudioToTextBatch): + tts_spectrogram, tts_spectrogram_len = self._get_tts_spectrogram(batch.tts_texts, batch.speakers) + asr_spectrogram, asr_spectrogram_len = self.asr_model.preprocessor( + input_signal=batch.audio_signals, length=batch.audio_signal_lengths, + ) + + spectrogram = pad_sequence( + [ + x.squeeze(0) + for x in itertools.chain( + torch.tensor_split(tts_spectrogram.transpose(1, 2), tts_spectrogram.size(0)), + torch.tensor_split(asr_spectrogram.transpose(1, 2), asr_spectrogram.size(0)), + ) + ], + batch_first=True, + padding_value=0.0, + ).transpose(1, 2) + spectrogram_len = torch.cat([tts_spectrogram_len, asr_spectrogram_len], dim=0) + + transcript = batch.transcripts + transcript_len = batch.transcript_lengths + else: + audio_signal, audio_signal_len, transcript, transcript_len, *_ = batch # audio batch: 4 or 5 elements + spectrogram, spectrogram_len = self.asr_model.preprocessor( + input_signal=audio_signal, length=audio_signal_len + ) + spectrogram = clean_spectrogram_batch(spectrogram, spectrogram_len) + return spectrogram.detach(), spectrogram_len.detach(), transcript, transcript_len + + def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): + """ + Setup training data from config: text-only, audio-text or mixed data. + """ + if train_data_config is None: + logging.warning("No training data") + return + + self._update_dataset_config(dataset_name='train', config=train_data_config) + asr_dataset = get_audio_to_text_bpe_dataset_from_config( + train_data_config, + local_rank=self.local_rank, + global_rank=self.global_rank, + world_size=self.world_size, + tokenizer=self.asr_model.tokenizer, + preprocessor_cfg=self.asr_model.cfg.get("preprocessor", None), + ) + + dataset_iterable = True + if asr_dataset is not None and isinstance(asr_dataset, Dataset): + # asr_dataset is map-style, for mixing datasets use map-style text-to-text dataset + dataset_iterable = False + if train_data_config.get("text_data") is not None: + tts_dataset = self._setup_text_dataset_from_config(train_data_config, iterable=dataset_iterable) + else: + tts_dataset = None + + if tts_dataset and asr_dataset: + text_data_config: TextDataConfig = cast( + TextDataConfig, OmegaConf.merge(OmegaConf.structured(TextDataConfig), train_data_config.text_data) + ) + concat_kwargs = dict() + if text_data_config.asr_tts_sampling_technique is not None: + concat_kwargs["sampling_technique"] = text_data_config.asr_tts_sampling_technique + if text_data_config.asr_tts_sampling_temperature is not None: + concat_kwargs["sampling_temperature"] = text_data_config.asr_tts_sampling_temperature + if text_data_config.asr_tts_sampling_probabilities: + concat_kwargs["sampling_probabilities"] = text_data_config.asr_tts_sampling_probabilities + + if dataset_iterable: + dataset = ConcatDataset(datasets=[asr_dataset, tts_dataset], **concat_kwargs) + else: + dataset = ConcatMapDataset(datasets=[asr_dataset, tts_dataset], **concat_kwargs) + else: + dataset = tts_dataset or asr_dataset + + if dataset is None: + return + + if tts_dataset: + collate_fn = tts_dataset.collate_fn + else: + if hasattr(asr_dataset, 'collate_fn'): + collate_fn = asr_dataset.collate_fn + elif hasattr(asr_dataset.datasets[0], 'collate_fn'): + # support datasets that are lists of entries + collate_fn = asr_dataset.datasets[0].collate_fn + else: + # support datasets that are lists of lists + collate_fn = asr_dataset.datasets[0].datasets[0].collate_fn + + shuffle = train_data_config.get("shuffle", True) and not dataset_iterable + self._train_dl = torch.utils.data.DataLoader( + dataset=dataset, + batch_size=train_data_config['batch_size'], + collate_fn=collate_fn, + drop_last=train_data_config.get('drop_last', False), + shuffle=shuffle, + num_workers=train_data_config.get('num_workers', 0), + pin_memory=train_data_config.get('pin_memory', False), + ) + + def _setup_text_dataset_from_config( + self, train_data_config: DictConfig, iterable=True + ) -> Union[TextToTextDataset, TextToTextIterableDataset]: + """ + Construct text-to-text (text-only) dataset from config. + + Args: + train_data_config: config + iterable: construct iterable-style datasset if True, otherwise map-style + + Returns: + text-to-text dataset of TextToTextDataset or TextToTextIterableDataset type + """ + text_data_config: TextDataConfig = cast( + TextDataConfig, OmegaConf.merge(OmegaConf.structured(TextDataConfig), train_data_config.text_data) + ) + if iterable: + textonly_ds = TextToTextIterableDataset( + manifest_filepath=text_data_config.manifest_filepath, + speakers_filepath=text_data_config.speakers_filepath, + asr_tokenizer=self.asr_model.tokenizer, + asr_use_start_end_token=train_data_config.get("use_start_end_token", False), + tts_parser=self.tts_model.parser, + tts_text_pad_id=self.tts_model.vocab.pad, + tts_text_normalizer=self.tts_model.normalizer, + tts_text_normalizer_call_kwargs=self.tts_model.text_normalizer_call_kwargs, + min_words=text_data_config.min_words, + max_words=text_data_config.max_words, + tokenizer_workers=text_data_config.tokenizer_workers, + num_parts=self.world_size, + current_part_index=self.global_rank, + ) + else: + textonly_ds = TextToTextDataset( + manifest_filepath=text_data_config.manifest_filepath, + speakers_filepath=text_data_config.speakers_filepath, + asr_tokenizer=self.asr_model.tokenizer, + asr_use_start_end_token=train_data_config.get("use_start_end_token", False), + tts_parser=self.tts_model.parser, + tts_text_pad_id=self.tts_model.vocab.pad, + tts_text_normalizer=self.tts_model.normalizer, + tts_text_normalizer_call_kwargs=self.tts_model.text_normalizer_call_kwargs, + min_words=text_data_config.min_words, + max_words=text_data_config.max_words, + tokenizer_workers=text_data_config.tokenizer_workers, + ) + return textonly_ds + + def training_step(self, batch: Union[TextOrAudioToTextBatch, TextToTextBatch, DALIOutputs, tuple], batch_nb: int): + """ + Training step for ASR-TTS model. + - construct spectrogram for the batch (from text - using TTS model, from audio - using ASR preprocessor) + - call training_step on ASR model + """ + assert not self.tts_model.training + if isinstance(batch, DALIOutputs): + return self.asr_model.training_step(batch=batch, batch_nb=batch_nb) + with torch.no_grad(): + spectrogram, spectrogram_len, transcript, transcript_len = self._get_batch_spect(batch) + # TODO: maybe support precomputed without DALIOutputs + return self.asr_model.training_step( + batch=DALIOutputs( + dict( + processed_signal=spectrogram, + processed_signal_len=spectrogram_len, + transcript=transcript, + transcript_len=transcript_len, + ) + ), + batch_nb=batch_nb, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py new file mode 100644 index 0000000..39375f0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py @@ -0,0 +1,616 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import os +from typing import Dict, List, Optional, Union + +import torch +from omegaconf import DictConfig, ListConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer + +from nemo.collections.asr.data import audio_to_text_dataset +from nemo.collections.asr.data.audio_to_text import _AudioTextDataset +from nemo.collections.asr.data.audio_to_text_dali import AudioToBPEDALIDataset +from nemo.collections.asr.data.audio_to_text_lhotse import LhotseSpeechToTextBpeDataset +from nemo.collections.asr.losses.ctc import CTCLoss +from nemo.collections.asr.losses.rnnt import RNNTLoss +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.models.hybrid_rnnt_ctc_models import EncDecHybridRNNTCTCModel +from nemo.collections.asr.parts.mixins import ASRBPEMixin +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCBPEDecoding, CTCBPEDecodingConfig +from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTBPEDecoding, RNNTBPEDecodingConfig +from nemo.collections.asr.parts.utils.asr_batching import get_semi_sorted_batch_sampler +from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config +from nemo.core.classes.common import PretrainedModelInfo +from nemo.utils import logging, model_utils + + +class EncDecHybridRNNTCTCBPEModel(EncDecHybridRNNTCTCModel, ASRBPEMixin): + """Base class for encoder decoder RNNT-based models with auxiliary CTC decoder/loss and subword tokenization.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + # Convert to Hydra 1.0 compatible DictConfig + cfg = model_utils.convert_model_config_to_dict_config(cfg) + cfg = model_utils.maybe_update_config_version(cfg) + + # Tokenizer is necessary for this model + if 'tokenizer' not in cfg: + raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !") + + if not isinstance(cfg, DictConfig): + cfg = OmegaConf.create(cfg) + + # Setup the tokenizer + self._setup_tokenizer(cfg.tokenizer) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + # Set the new vocabulary + with open_dict(cfg): + cfg.labels = ListConfig(list(vocabulary)) + + with open_dict(cfg.decoder): + cfg.decoder.vocab_size = len(vocabulary) + + with open_dict(cfg.joint): + cfg.joint.num_classes = len(vocabulary) + cfg.joint.vocabulary = ListConfig(list(vocabulary)) + cfg.joint.jointnet.encoder_hidden = cfg.model_defaults.enc_hidden + cfg.joint.jointnet.pred_hidden = cfg.model_defaults.pred_hidden + + # setup auxiliary CTC decoder + if 'aux_ctc' not in cfg: + raise ValueError( + "The config need to have a section for the CTC decoder named as aux_ctc for Hybrid models." + ) + + with open_dict(cfg): + if self.tokenizer_type == "agg": + cfg.aux_ctc.decoder.vocabulary = ListConfig(vocabulary) + else: + cfg.aux_ctc.decoder.vocabulary = ListConfig(list(vocabulary.keys())) + + if cfg.aux_ctc.decoder["num_classes"] < 1: + logging.info( + "\nReplacing placholder number of classes ({}) with actual number of classes - {}".format( + cfg.aux_ctc.decoder["num_classes"], len(vocabulary) + ) + ) + cfg.aux_ctc.decoder["num_classes"] = len(vocabulary) + + super().__init__(cfg=cfg, trainer=trainer) + + self.cfg.decoding = self.set_decoding_type_according_to_loss(self.cfg.decoding) + # Setup decoding object + self.decoding = RNNTBPEDecoding( + decoding_cfg=self.cfg.decoding, decoder=self.decoder, joint=self.joint, tokenizer=self.tokenizer, + ) + + # Setup wer object + self.wer = WER( + decoding=self.decoding, + batch_dim_index=0, + use_cer=self.cfg.get('use_cer', False), + log_prediction=self.cfg.get('log_prediction', True), + dist_sync_on_step=True, + ) + + # Setup fused Joint step if flag is set + if self.joint.fuse_loss_wer: + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Setup CTC decoding + ctc_decoding_cfg = self.cfg.aux_ctc.get('decoding', None) + if ctc_decoding_cfg is None: + ctc_decoding_cfg = OmegaConf.structured(CTCBPEDecodingConfig) + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoding = ctc_decoding_cfg + self.ctc_decoding = CTCBPEDecoding(self.cfg.aux_ctc.decoding, tokenizer=self.tokenizer) + + # Setup CTC WER + self.ctc_wer = WER( + decoding=self.ctc_decoding, + use_cer=self.cfg.aux_ctc.get('use_cer', False), + dist_sync_on_step=True, + log_prediction=self.cfg.get("log_prediction", False), + ) + + # setting the RNNT decoder as the default one + self.cur_decoder = "rnnt" + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + + if config.get("use_lhotse"): + return get_lhotse_dataloader_from_config( + config, + global_rank=self.global_rank, + world_size=self.world_size, + dataset=LhotseSpeechToTextBpeDataset(tokenizer=self.tokenizer,), + ) + + dataset = audio_to_text_dataset.get_audio_to_text_bpe_dataset_from_config( + config=config, + local_rank=self.local_rank, + global_rank=self.global_rank, + world_size=self.world_size, + tokenizer=self.tokenizer, + preprocessor_cfg=self.cfg.get("preprocessor", None), + ) + + if dataset is None: + return None + + if isinstance(dataset, AudioToBPEDALIDataset): + # DALI Dataset implements dataloader interface + return dataset + + shuffle = config['shuffle'] + if isinstance(dataset, torch.utils.data.IterableDataset): + shuffle = False + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + elif hasattr(dataset.datasets[0], 'collate_fn'): + # support datasets that are lists of entries + collate_fn = dataset.datasets[0].collate_fn + else: + # support datasets that are lists of lists + collate_fn = dataset.datasets[0].datasets[0].collate_fn + + batch_sampler = None + if config.get('use_semi_sorted_batching', False): + if not isinstance(dataset, _AudioTextDataset): + raise RuntimeError( + "Semi Sorted Batch sampler can be used with AudioToCharDataset or AudioToBPEDataset " + f"but found dataset of type {type(dataset)}" + ) + # set batch_size and batch_sampler to None to disable automatic batching + batch_sampler = get_semi_sorted_batch_sampler(self, dataset, config) + config['batch_size'] = None + config['drop_last'] = False + shuffle = False + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + sampler=batch_sampler, + batch_sampler=None, + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided audio file. + + Args: + config: A python dictionary which contains the following keys: + paths2audio_files: (a list) of paths to audio files. The files should be relatively short fragments. \ + Recommended length per file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the audio manifest is temporarily + stored. + num_workers: (int) number of workers. Depends of the batch_size and machine. \ + 0 - only the main process will load batches, 1 - one worker (not main process) + + Returns: + A pytorch DataLoader for the given audio file(s). + """ + + if 'manifest_filepath' in config: + manifest_filepath = config['manifest_filepath'] + batch_size = config['batch_size'] + else: + manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json') + batch_size = min(config['batch_size'], len(config['paths2audio_files'])) + + dl_config = { + 'manifest_filepath': manifest_filepath, + 'sample_rate': self.preprocessor._sample_rate, + 'batch_size': batch_size, + 'shuffle': False, + 'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)), + 'pin_memory': True, + 'channel_selector': config.get('channel_selector', None), + 'use_start_end_token': self.cfg.validation_ds.get('use_start_end_token', False), + } + + if config.get("augmentor"): + dl_config['augmentor'] = config.get("augmentor") + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer + + def change_vocabulary( + self, + new_tokenizer_dir: Union[str, DictConfig], + new_tokenizer_type: str, + decoding_cfg: Optional[DictConfig] = None, + ctc_decoding_cfg: Optional[DictConfig] = None, + ): + """ + Changes vocabulary used during RNNT decoding process. Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_tokenizer_dir: Directory path to tokenizer or a config for a new tokenizer (if the tokenizer type is `agg`) + new_tokenizer_type: Type of tokenizer. Can be either `agg`, `bpe` or `wpe`. + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + ctc_decoding_cfg: A config for auxiliary CTC decoding, which is optional and can be used to change the decoding type. + + Returns: None + + """ + if isinstance(new_tokenizer_dir, DictConfig): + if new_tokenizer_type == 'agg': + new_tokenizer_cfg = new_tokenizer_dir + else: + raise ValueError( + f'New tokenizer dir should be a string unless the tokenizer is `agg`, but this tokenizer type is: {new_tokenizer_type}' + ) + else: + new_tokenizer_cfg = None + + if new_tokenizer_cfg is not None: + tokenizer_cfg = new_tokenizer_cfg + else: + if not os.path.isdir(new_tokenizer_dir): + raise NotADirectoryError( + f'New tokenizer dir must be non-empty path to a directory. But I got: {new_tokenizer_dir}' + ) + + if new_tokenizer_type.lower() not in ('bpe', 'wpe'): + raise ValueError(f'New tokenizer type must be either `bpe` or `wpe`') + + tokenizer_cfg = OmegaConf.create({'dir': new_tokenizer_dir, 'type': new_tokenizer_type}) + + # Setup the tokenizer + self._setup_tokenizer(tokenizer_cfg) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + joint_config = self.joint.to_config_dict() + new_joint_config = copy.deepcopy(joint_config) + if self.tokenizer_type == "agg": + new_joint_config["vocabulary"] = ListConfig(vocabulary) + else: + new_joint_config["vocabulary"] = ListConfig(list(vocabulary.keys())) + + new_joint_config['num_classes'] = len(vocabulary) + del self.joint + self.joint = EncDecHybridRNNTCTCBPEModel.from_config_dict(new_joint_config) + + decoder_config = self.decoder.to_config_dict() + new_decoder_config = copy.deepcopy(decoder_config) + new_decoder_config.vocab_size = len(vocabulary) + del self.decoder + self.decoder = EncDecHybridRNNTCTCBPEModel.from_config_dict(new_decoder_config) + + del self.loss + self.loss = RNNTLoss(num_classes=self.joint.num_classes_with_blank - 1) + + if decoding_cfg is None: + # Assume same decoding config as before + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(RNNTBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + decoding_cfg = self.set_decoding_type_according_to_loss(decoding_cfg) + + self.decoding = RNNTBPEDecoding( + decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, tokenizer=self.tokenizer, + ) + + self.wer = WER( + decoding=self.decoding, + batch_dim_index=self.wer.batch_dim_index, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Setup fused Joint step + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Update config + with open_dict(self.cfg.joint): + self.cfg.joint = new_joint_config + + with open_dict(self.cfg.decoder): + self.cfg.decoder = new_decoder_config + + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed tokenizer of the RNNT decoder to {self.joint.vocabulary} vocabulary.") + + # set up the new tokenizer for the CTC decoder + if hasattr(self, 'ctc_decoder'): + ctc_decoder_config = copy.deepcopy(self.ctc_decoder.to_config_dict()) + # sidestepping the potential overlapping tokens issue in aggregate tokenizers + if self.tokenizer_type == "agg": + ctc_decoder_config.vocabulary = ListConfig(vocabulary) + else: + ctc_decoder_config.vocabulary = ListConfig(list(vocabulary.keys())) + + decoder_num_classes = ctc_decoder_config['num_classes'] + # Override number of classes if placeholder provided + logging.info( + "\nReplacing old number of classes ({}) with new number of classes - {}".format( + decoder_num_classes, len(vocabulary) + ) + ) + ctc_decoder_config['num_classes'] = len(vocabulary) + + del self.ctc_decoder + self.ctc_decoder = EncDecHybridRNNTCTCBPEModel.from_config_dict(ctc_decoder_config) + del self.ctc_loss + self.ctc_loss = CTCLoss( + num_classes=self.ctc_decoder.num_classes_with_blank - 1, + zero_infinity=True, + reduction=self.cfg.aux_ctc.get("ctc_reduction", "mean_batch"), + ) + + if ctc_decoding_cfg is None: + # Assume same decoding config as before + ctc_decoding_cfg = self.cfg.aux_ctc.decoding + + # Assert the decoding config with all hyper parameters + ctc_decoding_cls = OmegaConf.structured(CTCBPEDecodingConfig) + ctc_decoding_cls = OmegaConf.create(OmegaConf.to_container(ctc_decoding_cls)) + ctc_decoding_cfg = OmegaConf.merge(ctc_decoding_cls, ctc_decoding_cfg) + + self.ctc_decoding = CTCBPEDecoding(decoding_cfg=ctc_decoding_cfg, tokenizer=self.tokenizer) + + self.ctc_wer = WER( + decoding=self.ctc_decoding, + use_cer=self.cfg.aux_ctc.get('use_cer', False), + log_prediction=self.cfg.get("log_prediction", False), + dist_sync_on_step=True, + ) + + # Update config + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoder = ctc_decoder_config + + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoding = ctc_decoding_cfg + + logging.info(f"Changed tokenizer of the CTC decoder to {self.ctc_decoder.vocabulary} vocabulary.") + + def change_decoding_strategy(self, decoding_cfg: DictConfig = None, decoder_type: str = None): + """ + Changes decoding strategy used during RNNT decoding process. + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + decoder_type: (str) Can be set to 'rnnt' or 'ctc' to switch between appropriate decoder in a + model having both RNN-T and CTC decoders. Defaults to None, in which case RNN-T decoder is + used. If set to 'ctc', it raises error if 'ctc_decoder' is not an attribute of the model. + """ + if decoder_type is None or decoder_type == 'rnnt': + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(RNNTBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + decoding_cfg = self.set_decoding_type_according_to_loss(decoding_cfg) + + self.decoding = RNNTBPEDecoding( + decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, tokenizer=self.tokenizer, + ) + + self.wer = WER( + decoding=self.decoding, + batch_dim_index=self.wer.batch_dim_index, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Setup fused Joint step + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + self.joint.temperature = decoding_cfg.get('temperature', 1.0) + + # Update config + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + self.cur_decoder = "rnnt" + logging.info(f"Changed decoding strategy of the RNNT decoder to \n{OmegaConf.to_yaml(self.cfg.decoding)}") + + elif decoder_type == 'ctc': + if not hasattr(self, 'ctc_decoding'): + raise ValueError("The model does not have the ctc_decoding module and does not support ctc decoding.") + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.aux_ctc.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(CTCBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.ctc_decoding = CTCBPEDecoding(decoding_cfg=decoding_cfg, tokenizer=self.tokenizer) + + self.ctc_wer = WER( + decoding=self.ctc_decoding, + use_cer=self.ctc_wer.use_cer, + log_prediction=self.ctc_wer.log_prediction, + dist_sync_on_step=True, + ) + + self.ctc_decoder.temperature = decoding_cfg.get('temperature', 1.0) + + # Update config + with open_dict(self.cfg.aux_ctc.decoding): + self.cfg.aux_ctc.decoding = decoding_cfg + + self.cur_decoder = "ctc" + logging.info( + f"Changed decoding strategy of the CTC decoder to \n{OmegaConf.to_yaml(self.cfg.aux_ctc.decoding)}" + ) + else: + raise ValueError(f"decoder_type={decoder_type} is not supported. Supported values: [ctc,rnnt]") + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_hybrid_large_pc", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_hybrid_large_pc", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_hybrid_large_pc/versions/1.21.0/files/stt_en_fastconformer_hybrid_large_pc.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_de_fastconformer_hybrid_large_pc", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_fastconformer_hybrid_large_pc", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_fastconformer_hybrid_large_pc/versions/1.21.0/files/stt_de_fastconformer_hybrid_large_pc.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_it_fastconformer_hybrid_large_pc", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_fastconformer_hybrid_large_pc", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_it_fastconformer_hybrid_large_pc/versions/1.20.0/files/stt_it_fastconformer_hybrid_large_pc.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_es_fastconformer_hybrid_large_pc", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_fastconformer_hybrid_large_pc", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_fastconformer_hybrid_large_pc/versions/1.21.0/files/stt_es_fastconformer_hybrid_large_pc.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_hr_fastconformer_hybrid_large_pc", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_hr_fastconformer_hybrid_large_pc", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_hr_fastconformer_hybrid_large_pc/versions/1.21.0/files/FastConformer-Hybrid-Transducer-CTC-BPE-v256-averaged.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_ua_fastconformer_hybrid_large_pc", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ua_fastconformer_hybrid_large_pc", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ua_fastconformer_hybrid_large_pc/versions/1.21.0/files/stt_ua_fastconformer_hybrid_large_pc.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_pl_fastconformer_hybrid_large_pc", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_pl_fastconformer_hybrid_large_pc", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_pl_fastconformer_hybrid_large_pc/versions/1.21.0/files/stt_pl_fastconformer_hybrid_large_pc.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_by_fastconformer_hybrid_large_pc", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_by_fastconformer_hybrid_large_pc", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_by_fastconformer_hybrid_large_pc/versions/1.21.0/files/stt_by_fastconformer_hybrid_large_pc.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_ru_fastconformer_hybrid_large_pc", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_fastconformer_hybrid_large_pc", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ru_fastconformer_hybrid_large_pc/versions/1.21.0/files/stt_ru_fastconformer_hybrid_large_pc.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_fr_fastconformer_hybrid_large_pc", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_fastconformer_hybrid_large_pc", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_fastconformer_hybrid_large_pc/versions/1.21.0/files/stt_fr_fastconformer_hybrid_large_pc.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_multilingual_fastconformer_hybrid_large_pc_blend_eu", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_multilingual_fastconformer_hybrid_large_pc_blend_eu", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_multilingual_fastconformer_hybrid_large_pc_blend_eu/versions/1.21.0/files/stt_multilingual_fastconformer_hybrid_large_pc_blend_eu.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_multilingual_fastconformer_hybrid_large_pc", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_multilingual_fastconformer_hybrid_large_pc", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_multilingual_fastconformer_hybrid_large_pc/versions/1.21.0/files/stt_multilingual_fastconformer_hybrid_large_pc.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_hybrid_large_streaming_80ms", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_hybrid_large_streaming_80ms", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_hybrid_large_streaming_80ms/versions/1.20.0/files/stt_en_fastconformer_hybrid_large_streaming_80ms.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_hybrid_large_streaming_480ms", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_hybrid_large_streaming_480ms", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_hybrid_large_streaming_480ms/versions/1.20.0/files/stt_en_fastconformer_hybrid_large_streaming_480ms.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_hybrid_large_streaming_1040ms", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_hybrid_large_streaming_1040ms", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_hybrid_large_streaming_1040ms/versions/1.20.0/files/stt_en_fastconformer_hybrid_large_streaming_1040ms.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_hybrid_large_streaming_multi", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_hybrid_large_streaming_multi", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_hybrid_large_streaming_multi/versions/1.20.0/files/stt_en_fastconformer_hybrid_large_streaming_multi.nemo", + ) + results.append(model) + + return results diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/hybrid_rnnt_ctc_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/hybrid_rnnt_ctc_models.py new file mode 100644 index 0000000..3eaab99 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/hybrid_rnnt_ctc_models.py @@ -0,0 +1,664 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import json +import os +import tempfile +from typing import Any, List, Optional, Tuple + +import torch +from omegaconf import DictConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer +from tqdm.auto import tqdm + +from nemo.collections.asr.data.audio_to_text_dali import DALIOutputs +from nemo.collections.asr.losses.ctc import CTCLoss +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.models.rnnt_models import EncDecRNNTModel +from nemo.collections.asr.parts.mixins import ASRBPEMixin, InterCTCMixin, TranscribeConfig +from nemo.collections.asr.parts.mixins.transcription import TranscriptionReturnType +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecoding, CTCDecodingConfig +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.core.classes.common import PretrainedModelInfo +from nemo.core.classes.mixins import AccessMixin +from nemo.utils import logging, model_utils + + +class EncDecHybridRNNTCTCModel(EncDecRNNTModel, ASRBPEMixin, InterCTCMixin): + """Base class for hybrid RNNT/CTC models.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + cfg = model_utils.convert_model_config_to_dict_config(cfg) + cfg = model_utils.maybe_update_config_version(cfg) + super().__init__(cfg=cfg, trainer=trainer) + + if 'aux_ctc' not in self.cfg: + raise ValueError( + "The config need to have a section for the CTC decoder named as aux_ctc for Hybrid models." + ) + with open_dict(self.cfg.aux_ctc): + if "feat_in" not in self.cfg.aux_ctc.decoder or ( + not self.cfg.aux_ctc.decoder.feat_in and hasattr(self.encoder, '_feat_out') + ): + self.cfg.aux_ctc.decoder.feat_in = self.encoder._feat_out + if "feat_in" not in self.cfg.aux_ctc.decoder or not self.cfg.aux_ctc.decoder.feat_in: + raise ValueError("param feat_in of the decoder's config is not set!") + + if self.cfg.aux_ctc.decoder.num_classes < 1 and self.cfg.aux_ctc.decoder.vocabulary is not None: + logging.info( + "\nReplacing placeholder number of classes ({}) with actual number of classes - {}".format( + self.cfg.aux_ctc.decoder.num_classes, len(self.cfg.aux_ctc.decoder.vocabulary) + ) + ) + self.cfg.aux_ctc.decoder["num_classes"] = len(self.cfg.aux_ctc.decoder.vocabulary) + + self.ctc_decoder = EncDecRNNTModel.from_config_dict(self.cfg.aux_ctc.decoder) + self.ctc_loss_weight = self.cfg.aux_ctc.get("ctc_loss_weight", 0.5) + + self.ctc_loss = CTCLoss( + num_classes=self.ctc_decoder.num_classes_with_blank - 1, + zero_infinity=True, + reduction=self.cfg.aux_ctc.get("ctc_reduction", "mean_batch"), + ) + + ctc_decoding_cfg = self.cfg.aux_ctc.get('decoding', None) + if ctc_decoding_cfg is None: + ctc_decoding_cfg = OmegaConf.structured(CTCDecodingConfig) + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoding = ctc_decoding_cfg + + self.ctc_decoding = CTCDecoding(self.cfg.aux_ctc.decoding, vocabulary=self.ctc_decoder.vocabulary) + self.ctc_wer = WER( + decoding=self.ctc_decoding, + use_cer=self.cfg.aux_ctc.get('use_cer', False), + dist_sync_on_step=True, + log_prediction=self.cfg.get("log_prediction", False), + ) + + # setting the RNNT decoder as the default one + self.cur_decoder = "rnnt" + + # setting up interCTC loss (from InterCTCMixin) + self.setup_interctc(decoder_name='ctc_decoder', loss_name='ctc_loss', wer_name='ctc_wer') + + @torch.no_grad() + def transcribe( + self, + audio: List[str], + batch_size: int = 4, + return_hypotheses: bool = False, + partial_hypothesis: Optional[List['Hypothesis']] = None, + num_workers: int = 0, + channel_selector: Optional[ChannelSelectorType] = None, + augmentor: DictConfig = None, + verbose: bool = True, + override_config: Optional[TranscribeConfig] = None, + ) -> TranscriptionReturnType: + """ + Uses greedy decoding to transcribe audio files. Use this method for debugging and prototyping. + + Args: + + audio: (a list) of paths to audio files. \ + Recommended length per file is between 5 and 25 seconds. \ + But it is possible to pass a few hours long file if enough GPU memory is available. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + return_hypotheses: (bool) Either return hypotheses or text + With hypotheses can do some postprocessing like getting timestamp or rescoring + num_workers: (int) number of workers for DataLoader + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied. + verbose: (bool) whether to display tqdm progress bar + logprobs: (bool) whether to return ctc logits insted of hypotheses + + Returns: + Returns a tuple of 2 items - + * A list of greedy transcript texts / Hypothesis + * An optional list of beam search transcript texts / Hypothesis / NBestHypothesis. + """ + if self.cur_decoder not in ["ctc", "rnnt"]: + raise ValueError( + f"{self.cur_decoder} is not supported for cur_decoder. Supported values are ['ctc', 'rnnt']" + ) + + return super().transcribe( + audio=audio, + batch_size=batch_size, + return_hypotheses=return_hypotheses, + partial_hypothesis=partial_hypothesis, + num_workers=num_workers, + channel_selector=channel_selector, + augmentor=augmentor, + verbose=verbose, + override_config=override_config, + ) + + def _transcribe_on_begin(self, audio, trcfg: TranscribeConfig): + super()._transcribe_on_begin(audio, trcfg) + + if hasattr(self, 'ctc_decoder'): + self.ctc_decoder.freeze() + + def _transcribe_on_end(self, trcfg: TranscribeConfig): + super()._transcribe_on_end(trcfg) + + if hasattr(self, 'ctc_decoder'): + self.ctc_decoder.unfreeze() + + def _transcribe_forward(self, batch: Any, trcfg: TranscribeConfig): + if self.cur_decoder == "rnnt": + return super()._transcribe_forward(batch, trcfg) + + # CTC Path + encoded, encoded_len = self.forward(input_signal=batch[0], input_signal_length=batch[1]) + + logits = self.ctc_decoder(encoder_output=encoded) + output = dict(logits=logits, encoded_len=encoded_len) + + del encoded + return output + + def _transcribe_output_processing( + self, outputs, trcfg: TranscribeConfig + ) -> Tuple[List['Hypothesis'], List['Hypothesis']]: + if self.cur_decoder == "rnnt": + return super()._transcribe_output_processing(outputs, trcfg) + + # CTC Path + logits = outputs.pop('logits') + encoded_len = outputs.pop('encoded_len') + + best_hyp, all_hyp = self.ctc_decoding.ctc_decoder_predictions_tensor( + logits, encoded_len, return_hypotheses=trcfg.return_hypotheses, + ) + logits = logits.cpu() + + if trcfg.return_hypotheses: + # dump log probs per file + for idx in range(logits.shape[0]): + best_hyp[idx].y_sequence = logits[idx][: encoded_len[idx]] + if best_hyp[idx].alignments is None: + best_hyp[idx].alignments = best_hyp[idx].y_sequence + + # DEPRECATED? + # if logprobs: + # for logit, elen in zip(logits, encoded_len): + # logits_list.append(logit[:elen]) + + del logits, encoded_len + + hypotheses = [] + all_hypotheses = [] + + hypotheses += best_hyp + if all_hyp is not None: + all_hypotheses += all_hyp + else: + all_hypotheses += best_hyp + + return (hypotheses, all_hypotheses) + + def change_vocabulary( + self, + new_vocabulary: List[str], + decoding_cfg: Optional[DictConfig] = None, + ctc_decoding_cfg: Optional[DictConfig] = None, + ): + """ + Changes vocabulary used during RNNT decoding process. Use this method when fine-tuning a pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \ + this is target alphabet. + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + ctc_decoding_cfg: A config for CTC decoding, which is optional and can be used to change decoding type. + + Returns: None + + """ + super().change_vocabulary(new_vocabulary=new_vocabulary, decoding_cfg=decoding_cfg) + + # set up the new tokenizer for the CTC decoder + if hasattr(self, 'ctc_decoder'): + if self.ctc_decoder.vocabulary == new_vocabulary: + logging.warning( + f"Old {self.ctc_decoder.vocabulary} and new {new_vocabulary} match. Not changing anything." + ) + else: + if new_vocabulary is None or len(new_vocabulary) == 0: + raise ValueError(f'New vocabulary must be non-empty list of chars. But I got: {new_vocabulary}') + decoder_config = self.ctc_decoder.to_config_dict() + new_decoder_config = copy.deepcopy(decoder_config) + new_decoder_config['vocabulary'] = new_vocabulary + new_decoder_config['num_classes'] = len(new_vocabulary) + + del self.ctc_decoder + self.ctc_decoder = EncDecHybridRNNTCTCModel.from_config_dict(new_decoder_config) + del self.ctc_loss + self.ctc_loss = CTCLoss( + num_classes=self.ctc_decoder.num_classes_with_blank - 1, + zero_infinity=True, + reduction=self.cfg.aux_ctc.get("ctc_reduction", "mean_batch"), + ) + + if ctc_decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `ctc_decoding_cfg` passed when changing decoding strategy, using internal config") + ctc_decoding_cfg = self.cfg.aux_ctc.decoding + + # Assert the decoding config with all hyper parameters + ctc_decoding_cls = OmegaConf.structured(CTCDecodingConfig) + ctc_decoding_cls = OmegaConf.create(OmegaConf.to_container(ctc_decoding_cls)) + ctc_decoding_cfg = OmegaConf.merge(ctc_decoding_cls, ctc_decoding_cfg) + + self.ctc_decoding = CTCDecoding(decoding_cfg=ctc_decoding_cfg, vocabulary=self.ctc_decoder.vocabulary) + + self.ctc_wer = WER( + decoding=self.ctc_decoding, + use_cer=self.ctc_wer.use_cer, + log_prediction=self.ctc_wer.log_prediction, + dist_sync_on_step=True, + ) + + # Update config + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoding = ctc_decoding_cfg + + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoder = new_decoder_config + + ds_keys = ['train_ds', 'validation_ds', 'test_ds'] + for key in ds_keys: + if key in self.cfg: + with open_dict(self.cfg[key]): + self.cfg[key]['labels'] = OmegaConf.create(new_vocabulary) + + logging.info(f"Changed the tokenizer of the CTC decoder to {self.ctc_decoder.vocabulary} vocabulary.") + + def change_decoding_strategy(self, decoding_cfg: DictConfig = None, decoder_type: str = None): + """ + Changes decoding strategy used during RNNT decoding process. + + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + decoder_type: (str) Can be set to 'rnnt' or 'ctc' to switch between appropriate decoder in a + model having RNN-T and CTC decoders. Defaults to None, in which case RNN-T decoder is + used. If set to 'ctc', it raises error if 'ctc_decoder' is not an attribute of the model. + """ + if decoder_type is None or decoder_type == 'rnnt': + self.cur_decoder = "rnnt" + return super().change_decoding_strategy(decoding_cfg=decoding_cfg) + + assert decoder_type == 'ctc' and hasattr(self, 'ctc_decoder') + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.aux_ctc.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(CTCDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.ctc_decoding = CTCDecoding(decoding_cfg=decoding_cfg, vocabulary=self.ctc_decoder.vocabulary) + + self.ctc_wer = WER( + decoding=self.ctc_decoding, + use_cer=self.ctc_wer.use_cer, + log_prediction=self.ctc_wer.log_prediction, + dist_sync_on_step=True, + ) + + self.ctc_decoder.temperature = decoding_cfg.get('temperature', 1.0) + + # Update config + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoding = decoding_cfg + + self.cur_decoder = "ctc" + logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.aux_ctc.decoding)}") + + # PTL-specific methods + def training_step(self, batch, batch_nb): + # Reset access registry + if AccessMixin.is_access_enabled(self.model_guid): + AccessMixin.reset_registry(self) + + if self.is_interctc_enabled(): + AccessMixin.set_access_enabled(access_enabled=True, guid=self.model_guid) + + signal, signal_len, transcript, transcript_len = batch + + # forward() only performs encoder forward + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + encoded, encoded_len = self.forward(processed_signal=signal, processed_signal_length=signal_len) + else: + encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) + del signal + + # During training, loss must be computed, so decoder forward is necessary + decoder, target_length, states = self.decoder(targets=transcript, target_length=transcript_len) + + if hasattr(self, '_trainer') and self._trainer is not None: + log_every_n_steps = self._trainer.log_every_n_steps + sample_id = self._trainer.global_step + else: + log_every_n_steps = 1 + sample_id = batch_nb + + if (sample_id + 1) % log_every_n_steps == 0: + compute_wer = True + else: + compute_wer = False + + # If fused Joint-Loss-WER is not used + if not self.joint.fuse_loss_wer: + # Compute full joint and loss + joint = self.joint(encoder_outputs=encoded, decoder_outputs=decoder) + loss_value = self.loss( + log_probs=joint, targets=transcript, input_lengths=encoded_len, target_lengths=target_length + ) + + # Add auxiliary losses, if registered + loss_value = self.add_auxiliary_losses(loss_value) + + tensorboard_logs = { + 'learning_rate': self._optimizer.param_groups[0]['lr'], + 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), + } + + if compute_wer: + self.wer.update( + predictions=encoded, + predictions_lengths=encoded_len, + targets=transcript, + targets_lengths=transcript_len, + ) + _, scores, words = self.wer.compute() + self.wer.reset() + tensorboard_logs.update({'training_batch_wer': scores.float() / words}) + + else: # If fused Joint-Loss-WER is used + # Fused joint step + loss_value, wer, _, _ = self.joint( + encoder_outputs=encoded, + decoder_outputs=decoder, + encoder_lengths=encoded_len, + transcripts=transcript, + transcript_lengths=transcript_len, + compute_wer=compute_wer, + ) + + # Add auxiliary losses, if registered + loss_value = self.add_auxiliary_losses(loss_value) + + tensorboard_logs = { + 'learning_rate': self._optimizer.param_groups[0]['lr'], + 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), + } + + if compute_wer: + tensorboard_logs.update({'training_batch_wer': wer}) + + if self.ctc_loss_weight > 0: + log_probs = self.ctc_decoder(encoder_output=encoded) + ctc_loss = self.ctc_loss( + log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len + ) + tensorboard_logs['train_rnnt_loss'] = loss_value + tensorboard_logs['train_ctc_loss'] = ctc_loss + loss_value = (1 - self.ctc_loss_weight) * loss_value + self.ctc_loss_weight * ctc_loss + if compute_wer: + self.ctc_wer.update( + predictions=log_probs, + targets=transcript, + targets_lengths=transcript_len, + predictions_lengths=encoded_len, + ) + ctc_wer, _, _ = self.ctc_wer.compute() + self.ctc_wer.reset() + tensorboard_logs.update({'training_batch_wer_ctc': ctc_wer}) + + # note that we want to apply interctc independent of whether main ctc + # loss is used or not (to allow rnnt + interctc training). + # assuming ``ctc_loss_weight=0.3`` and interctc is applied to a single + # layer with weight of ``0.1``, the total loss will be + # ``loss = 0.9 * (0.3 * ctc_loss + 0.7 * rnnt_loss) + 0.1 * interctc_loss`` + loss_value, additional_logs = self.add_interctc_losses( + loss_value, transcript, transcript_len, compute_wer=compute_wer + ) + tensorboard_logs.update(additional_logs) + tensorboard_logs['train_loss'] = loss_value + # Reset access registry + if AccessMixin.is_access_enabled(self.model_guid): + AccessMixin.reset_registry(self) + + # Log items + self.log_dict(tensorboard_logs) + + # Preserve batch acoustic model T and language model U parameters if normalizing + if self._optim_normalize_joint_txu: + self._optim_normalize_txu = [encoded_len.max(), transcript_len.max()] + + return {'loss': loss_value} + + def predict_step(self, batch, batch_idx, dataloader_idx=0): + # TODO: add support for CTC decoding + signal, signal_len, transcript, transcript_len, sample_id = batch + + # forward() only performs encoder forward + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + encoded, encoded_len = self.forward(processed_signal=signal, processed_signal_length=signal_len) + else: + encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) + del signal + + best_hyp_text, all_hyp_text = self.decoding.rnnt_decoder_predictions_tensor( + encoder_output=encoded, encoded_lengths=encoded_len, return_hypotheses=False + ) + + sample_id = sample_id.cpu().detach().numpy() + return list(zip(sample_id, best_hyp_text)) + + def validation_pass(self, batch, batch_idx, dataloader_idx): + if self.is_interctc_enabled(): + AccessMixin.set_access_enabled(access_enabled=True, guid=self.model_guid) + + signal, signal_len, transcript, transcript_len = batch + + # forward() only performs encoder forward + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + encoded, encoded_len = self.forward(processed_signal=signal, processed_signal_length=signal_len) + else: + encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) + del signal + + tensorboard_logs = {} + loss_value = None + + # If experimental fused Joint-Loss-WER is not used + if not self.joint.fuse_loss_wer: + if self.compute_eval_loss: + decoder, target_length, states = self.decoder(targets=transcript, target_length=transcript_len) + joint = self.joint(encoder_outputs=encoded, decoder_outputs=decoder) + + loss_value = self.loss( + log_probs=joint, targets=transcript, input_lengths=encoded_len, target_lengths=target_length + ) + tensorboard_logs['val_loss'] = loss_value + + self.wer.update( + predictions=encoded, + predictions_lengths=encoded_len, + targets=transcript, + targets_lengths=transcript_len, + ) + wer, wer_num, wer_denom = self.wer.compute() + self.wer.reset() + + tensorboard_logs['val_wer_num'] = wer_num + tensorboard_logs['val_wer_denom'] = wer_denom + tensorboard_logs['val_wer'] = wer + + else: + # If experimental fused Joint-Loss-WER is used + compute_wer = True + + if self.compute_eval_loss: + decoded, target_len, states = self.decoder(targets=transcript, target_length=transcript_len) + else: + decoded = None + target_len = transcript_len + + # Fused joint step + loss_value, wer, wer_num, wer_denom = self.joint( + encoder_outputs=encoded, + decoder_outputs=decoded, + encoder_lengths=encoded_len, + transcripts=transcript, + transcript_lengths=target_len, + compute_wer=compute_wer, + ) + if loss_value is not None: + tensorboard_logs['val_loss'] = loss_value + + tensorboard_logs['val_wer_num'] = wer_num + tensorboard_logs['val_wer_denom'] = wer_denom + tensorboard_logs['val_wer'] = wer + + log_probs = self.ctc_decoder(encoder_output=encoded) + if self.compute_eval_loss: + ctc_loss = self.ctc_loss( + log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len + ) + tensorboard_logs['val_ctc_loss'] = ctc_loss + tensorboard_logs['val_rnnt_loss'] = loss_value + loss_value = (1 - self.ctc_loss_weight) * loss_value + self.ctc_loss_weight * ctc_loss + tensorboard_logs['val_loss'] = loss_value + self.ctc_wer.update( + predictions=log_probs, targets=transcript, targets_lengths=transcript_len, predictions_lengths=encoded_len, + ) + ctc_wer, ctc_wer_num, ctc_wer_denom = self.ctc_wer.compute() + self.ctc_wer.reset() + tensorboard_logs['val_wer_num_ctc'] = ctc_wer_num + tensorboard_logs['val_wer_denom_ctc'] = ctc_wer_denom + tensorboard_logs['val_wer_ctc'] = ctc_wer + + self.log('global_step', torch.tensor(self.trainer.global_step, dtype=torch.float32)) + + loss_value, additional_logs = self.add_interctc_losses( + loss_value, + transcript, + transcript_len, + compute_wer=True, + compute_loss=self.compute_eval_loss, + log_wer_num_denom=True, + log_prefix="val_", + ) + if self.compute_eval_loss: + # overriding total loss value. Note that the previous + # rnnt + ctc loss is available in metrics as "val_final_loss" now + tensorboard_logs['val_loss'] = loss_value + tensorboard_logs.update(additional_logs) + # Reset access registry + if AccessMixin.is_access_enabled(self.model_guid): + AccessMixin.reset_registry(self) + + return tensorboard_logs + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + tensorboard_logs = self.validation_pass(batch, batch_idx, dataloader_idx) + if type(self.trainer.val_dataloaders) == list and len(self.trainer.val_dataloaders) > 1: + self.validation_step_outputs[dataloader_idx].append(tensorboard_logs) + else: + self.validation_step_outputs.append(tensorboard_logs) + + return tensorboard_logs + + def test_step(self, batch, batch_idx, dataloader_idx=0): + logs = self.validation_pass(batch, batch_idx, dataloader_idx=dataloader_idx) + test_logs = {name.replace("val_", "test_"): value for name, value in logs.items()} + if type(self.trainer.test_dataloaders) == list and len(self.trainer.test_dataloaders) > 1: + self.test_step_outputs[dataloader_idx].append(test_logs) + else: + self.test_step_outputs.append(test_logs) + return test_logs + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + if self.compute_eval_loss: + val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean() + val_loss_log = {'val_loss': val_loss_mean} + else: + val_loss_log = {} + wer_num = torch.stack([x['val_wer_num'] for x in outputs]).sum() + wer_denom = torch.stack([x['val_wer_denom'] for x in outputs]).sum() + tensorboard_logs = {**val_loss_log, 'val_wer': wer_num.float() / wer_denom} + if self.ctc_loss_weight > 0: + ctc_wer_num = torch.stack([x['val_wer_num_ctc'] for x in outputs]).sum() + ctc_wer_denom = torch.stack([x['val_wer_denom_ctc'] for x in outputs]).sum() + tensorboard_logs['val_wer_ctc'] = ctc_wer_num.float() / ctc_wer_denom + metrics = {**val_loss_log, 'log': tensorboard_logs} + self.finalize_interctc_metrics(metrics, outputs, prefix="val_") + return metrics + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + if self.compute_eval_loss: + test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean() + test_loss_log = {'test_loss': test_loss_mean} + else: + test_loss_log = {} + wer_num = torch.stack([x['test_wer_num'] for x in outputs]).sum() + wer_denom = torch.stack([x['test_wer_denom'] for x in outputs]).sum() + tensorboard_logs = {**test_loss_log, 'test_wer': wer_num.float() / wer_denom} + + if self.ctc_loss_weight > 0: + ctc_wer_num = torch.stack([x['test_wer_num_ctc'] for x in outputs]).sum() + ctc_wer_denom = torch.stack([x['test_wer_denom_ctc'] for x in outputs]).sum() + tensorboard_logs['test_wer_ctc'] = ctc_wer_num.float() / ctc_wer_denom + + metrics = {**test_loss_log, 'log': tensorboard_logs} + self.finalize_interctc_metrics(metrics, outputs, prefix="test_") + return metrics + + # EncDecRNNTModel is exported in 2 parts + def list_export_subnets(self): + if self.cur_decoder == 'rnnt': + return ['encoder', 'decoder_joint'] + else: + return ['self'] + + @property + def output_module(self): + if self.cur_decoder == 'rnnt': + return self.decoder + else: + return self.ctc_decoder + + @classmethod + def list_available_models(cls) -> Optional[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + return results diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/k2_aligner_model.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/k2_aligner_model.py new file mode 100644 index 0000000..54d342d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/k2_aligner_model.py @@ -0,0 +1,616 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import os +from typing import Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +from omegaconf import DictConfig, OmegaConf, open_dict +from tqdm.auto import tqdm + +from nemo.collections.asr.data.audio_to_ctm_dataset import FrameCtmUnit +from nemo.collections.asr.data.audio_to_text_dali import DALIOutputs +from nemo.collections.asr.models.asr_model import ASRModel +from nemo.utils import logging + + +class AlignerWrapperModel(ASRModel): + """ASR model wrapper to perform alignment building. + Functionality is limited to the components needed to build an alignment.""" + + def __init__(self, model: ASRModel, cfg: DictConfig): + model_cfg = model.cfg + for ds in ("train_ds", "validation_ds", "test_ds"): + if ds in model_cfg: + model_cfg[ds] = None + super().__init__(cfg=model_cfg, trainer=model.trainer) + self._model = model + self.alignment_type = cfg.get("alignment_type", "forced") + self.word_output = cfg.get("word_output", True) + self.cpu_decoding = cfg.get("cpu_decoding", False) + self.decode_batch_size = cfg.get("decode_batch_size", 0) + + # list possible alignment types here for future work + if self.alignment_type == "forced": + pass + elif self.alignment_type == "argmax": + pass + elif self.alignment_type == "loose": + raise NotImplementedError(f"alignment_type=`{self.alignment_type}` is not supported at the moment.") + elif self.alignment_type == "rnnt_decoding_aux": + raise NotImplementedError(f"alignment_type=`{self.alignment_type}` is not supported at the moment.") + else: + raise RuntimeError(f"Unsupported alignment type: {self.alignment_type}") + + self._init_model_specific(cfg) + + def _init_ctc_alignment_specific(self, cfg: DictConfig): + """Part of __init__ intended to initialize attributes specific to the alignment type for CTC models. + + This method is not supposed to be called outside of __init__. + """ + # do nothing for regular CTC with `argmax` alignment type + if self.alignment_type == "argmax" and not hasattr(self._model, "use_graph_lm"): + return + + from nemo.collections.asr.modules.graph_decoder import ViterbiDecoderWithGraph + + if self.alignment_type == "forced": + if hasattr(self._model, "use_graph_lm"): + if self._model.use_graph_lm: + self.graph_decoder = self._model.transcribe_decoder + self._model.use_graph_lm = False + else: + self.graph_decoder = ViterbiDecoderWithGraph( + num_classes=self.blank_id, backend="k2", dec_type="topo", return_type="1best" + ) + # override split_batch_size + self.graph_decoder.split_batch_size = self.decode_batch_size + else: + self.graph_decoder = ViterbiDecoderWithGraph( + num_classes=self.blank_id, split_batch_size=self.decode_batch_size, + ) + # override decoder args if a config is provided + decoder_module_cfg = cfg.get("decoder_module_cfg", None) + if decoder_module_cfg is not None: + self.graph_decoder._decoder.intersect_pruned = decoder_module_cfg.get("intersect_pruned") + self.graph_decoder._decoder.intersect_conf = decoder_module_cfg.get("intersect_conf") + return + + if self.alignment_type == "argmax": + # we use transcribe_decoder to get topology-independent output + if not self._model.use_graph_lm: + self._model.transcribe_decoder = ViterbiDecoderWithGraph( + num_classes=self.blank_id, backend="k2", dec_type="topo", return_type="1best" + ) + # override decoder args + self._model.transcribe_decoder.return_ilabels = False + self._model.transcribe_decoder.output_aligned = True + self._model.transcribe_decoder.split_batch_size = self.decode_batch_size + self._model.use_graph_lm = False + return + + def _init_rnnt_alignment_specific(self, cfg: DictConfig): + """Part of __init__ intended to initialize attributes specific to the alignment type for RNNT models. + + This method is not supposed to be called outside of __init__. + """ + if self.alignment_type == "argmax": + return + + from nemo.collections.asr.modules.graph_decoder import ViterbiDecoderWithGraph + + if self.alignment_type == "forced": + self.predictor_window_size = cfg.rnnt_cfg.get("predictor_window_size", 0) + self.predictor_step_size = cfg.rnnt_cfg.get("predictor_step_size", 0) + + from nemo.collections.asr.parts.k2.utils import apply_rnnt_prune_ranges, get_uniform_rnnt_prune_ranges + + self.prepare_pruned_outputs = lambda encoder_outputs, encoded_len, decoder_outputs, transcript_len: apply_rnnt_prune_ranges( + encoder_outputs, + decoder_outputs, + get_uniform_rnnt_prune_ranges( + encoded_len, + transcript_len, + self.predictor_window_size + 1, + self.predictor_step_size, + encoder_outputs.size(1), + ).to(device=encoder_outputs.device), + ) + + from nemo.collections.asr.parts.k2.classes import GraphModuleConfig + + self.graph_decoder = ViterbiDecoderWithGraph( + num_classes=self.blank_id, + backend="k2", + dec_type="topo_rnnt_ali", + split_batch_size=self.decode_batch_size, + graph_module_cfg=OmegaConf.structured( + GraphModuleConfig( + topo_type="minimal", + predictor_window_size=self.predictor_window_size, + predictor_step_size=self.predictor_step_size, + ) + ), + ) + # override decoder args if a config is provided + decoder_module_cfg = cfg.get("decoder_module_cfg", None) + if decoder_module_cfg is not None: + self.graph_decoder._decoder.intersect_pruned = decoder_module_cfg.get("intersect_pruned") + self.graph_decoder._decoder.intersect_conf = decoder_module_cfg.get("intersect_conf") + return + + def _init_model_specific(self, cfg: DictConfig): + """Part of __init__ intended to initialize attributes specific to the model type. + + This method is not supposed to be called outside of __init__. + """ + from nemo.collections.asr.models.ctc_models import EncDecCTCModel + + if isinstance(self._model, EncDecCTCModel): + self.model_type = "ctc" + self.blank_id = self._model.decoder.num_classes_with_blank - 1 + self._predict_impl = self._predict_impl_ctc + + prob_suppress_index = cfg.ctc_cfg.get("prob_suppress_index", -1) + prob_suppress_value = cfg.ctc_cfg.get("prob_suppress_value", 1.0) + if prob_suppress_value > 1 or prob_suppress_value <= 0: + raise ValueError(f"Suppression value has to be in (0,1]: {prob_suppress_value}") + if prob_suppress_index < -(self.blank_id + 1) or prob_suppress_index > self.blank_id: + raise ValueError( + f"Suppression index for the provided model has to be in [{-self.blank_id+1},{self.blank_id}]: {prob_suppress_index}" + ) + self.prob_suppress_index = ( + self._model.decoder.num_classes_with_blank + prob_suppress_index + if prob_suppress_index < 0 + else prob_suppress_index + ) + self.prob_suppress_value = prob_suppress_value + + self._init_ctc_alignment_specific(cfg) + return + + from nemo.collections.asr.models.rnnt_models import EncDecRNNTModel + + if isinstance(self._model, EncDecRNNTModel): + self.model_type = "rnnt" + self.blank_id = self._model.joint.num_classes_with_blank - 1 + self.log_softmax = None if self._model.joint.log_softmax is None else not self._model.joint.log_softmax + self._predict_impl = self._predict_impl_rnnt + + decoding_config = copy.deepcopy(self._model.cfg.decoding) + decoding_config.strategy = "greedy_batch" + with open_dict(decoding_config): + decoding_config.preserve_alignments = True + decoding_config.fused_batch_size = -1 + self._model.change_decoding_strategy(decoding_config) + self._init_rnnt_alignment_specific(cfg) + return + + raise RuntimeError(f"Unsupported model type: {type(self._model)}") + + def _rnnt_joint_pruned( + self, + encoder_outputs: torch.Tensor, + encoded_len: torch.Tensor, + decoder_outputs: torch.Tensor, + transcript_len: torch.Tensor, + ) -> torch.Tensor: + """A variant of the RNNT Joiner tensor calculation with pruned Encoder and Predictor sum. + Only the uniform pruning is supported at the moment. + """ + encoder_outputs = self._model.joint.enc(encoder_outputs.transpose(1, 2)) # (B, T, H) + decoder_outputs = self._model.joint.pred(decoder_outputs.transpose(1, 2)) # (B, U, H) + + encoder_outputs_pruned, decoder_outputs_pruned = self.prepare_pruned_outputs( + encoder_outputs, encoded_len, decoder_outputs, transcript_len + ) + res = self._model.joint.joint_net(encoder_outputs_pruned + decoder_outputs_pruned) + # copied from model.joint.joint(...) + if self._model.joint.log_softmax is None: + if not res.is_cuda: + res = res.log_softmax(dim=-1) + else: + if self._model.joint.log_softmax: + res = res.log_softmax(dim=-1) + return res + + def _apply_prob_suppress(self, log_probs: torch.Tensor) -> torch.Tensor: + """Multiplies probability of an element with index self.prob_suppress_index by self.prob_suppress_value times + with stochasticity preservation of the log_probs tensor. + + Often used to suppress probability of the output of a CTC model. + + Example: + For + - log_probs = torch.log(torch.tensor([0.015, 0.085, 0.9])) + - self.prob_suppress_index = -1 + - self.prob_suppress_value = 0.5 + the result of _apply_prob_suppress(log_probs) is + - torch.log(torch.tensor([0.0825, 0.4675, 0.45])) + """ + exp_probs = (log_probs).exp() + x = exp_probs[:, :, self.prob_suppress_index] + # we cannot do y=1-x because exp_probs can be not stochastic due to numerical limitations + y = torch.cat( + [exp_probs[:, :, : self.prob_suppress_index], exp_probs[:, :, self.prob_suppress_index + 1 :]], 2 + ).sum(-1) + b1 = torch.full((exp_probs.shape[0], exp_probs.shape[1], 1), self.prob_suppress_value, device=log_probs.device) + b2 = ((1 - self.prob_suppress_value * x) / y).unsqueeze(2).repeat(1, 1, exp_probs.shape[-1] - 1) + return ( + exp_probs * torch.cat([b2[:, :, : self.prob_suppress_index], b1, b2[:, :, self.prob_suppress_index :]], 2) + ).log() + + def _prepare_ctc_argmax_predictions( + self, log_probs: torch.Tensor, encoded_len: torch.Tensor + ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + """Obtains argmax predictions with corresponding probabilities. + Replaces consecutive repeated indices in the argmax predictions with the index. + """ + if hasattr(self._model, "transcribe_decoder"): + predictions, _, probs = self.transcribe_decoder.forward(log_probs=log_probs, log_probs_length=encoded_len) + else: + greedy_predictions = log_probs.argmax(dim=-1, keepdim=False) + probs_tensor, _ = log_probs.exp().max(dim=-1, keepdim=False) + predictions, probs = [], [] + for i in range(log_probs.shape[0]): + utt_len = encoded_len[i] + probs.append(probs_tensor[i, :utt_len]) + pred_candidate = greedy_predictions[i, :utt_len].cpu() + # replace consecutive tokens with + previous = self.blank_id + for j in range(utt_len): + p = pred_candidate[j] + if p == previous and previous != self.blank_id: + pred_candidate[j] = self.blank_id + previous = p + predictions.append(pred_candidate.to(device=greedy_predictions.device)) + return predictions, probs + + def _predict_impl_rnnt_argmax( + self, + encoded: torch.Tensor, + encoded_len: torch.Tensor, + transcript: torch.Tensor, + transcript_len: torch.Tensor, + sample_id: torch.Tensor, + ) -> List[Tuple[int, 'FrameCtmUnit']]: + """Builds time alignment of an encoded sequence. + This method assumes that the RNNT model is used and the alignment type is `argmax`. + + It produces a list of sample ids and fours: (label, start_frame, length, probability), called FrameCtmUnit. + """ + hypotheses = self._model.decoding.rnnt_decoder_predictions_tensor( + encoded, encoded_len, return_hypotheses=True + )[0] + results = [] + for s_id, hypothesis in zip(sample_id, hypotheses): + pred_ids = hypothesis.y_sequence.tolist() + tokens = self._model.decoding.decode_ids_to_tokens(pred_ids) + token_begin = hypothesis.timestep + token_len = [j - i for i, j in zip(token_begin, token_begin[1:] + [len(hypothesis.alignments)])] + # we have no token probabilities for the argmax rnnt setup + token_prob = [1.0] * len(tokens) + if self.word_output: + words = [w for w in self._model.decoding.decode_tokens_to_str(pred_ids).split(" ") if w != ""] + words, word_begin, word_len, word_prob = ( + self._process_tokens_to_words(tokens, token_begin, token_len, token_prob, words) + if hasattr(self._model, "tokenizer") + else self._process_char_with_space_to_words(tokens, token_begin, token_len, token_prob, words) + ) + results.append( + (s_id, [FrameCtmUnit(t, b, l, p) for t, b, l, p in zip(words, word_begin, word_len, word_prob)]) + ) + else: + results.append( + ( + s_id, + [FrameCtmUnit(t, b, l, p) for t, b, l, p in zip(tokens, token_begin, token_len, token_prob)], + ) + ) + return results + + def _process_tokens_to_words( + self, + tokens: List[str], + token_begin: List[int], + token_len: List[int], + token_prob: List[float], + words: List[str], + ) -> Tuple[List[str], List[int], List[int], List[float]]: + """Transforms alignment information from token level to word level. + + Used when self._model.tokenizer is present. + """ + # suppose that there are no whitespaces + assert len(self._model.tokenizer.text_to_tokens(words[0])) == len( + self._model.tokenizer.text_to_tokens(words[0] + " ") + ) + word_begin, word_len, word_prob = [], [], [] + token_len_nonzero = [(t_l if t_l > 0 else 1) for t_l in token_len] + i = 0 + for word in words: + loc_tokens = self._model.tokenizer.text_to_tokens(word) + step = len(loc_tokens) + # we assume that an empty word consists of only one token + # drop current token + if step == 0: + token_begin[i + 1] = token_begin[i] + token_len[i + 1] += token_len[i] + token_len_nonzero[i + 1] += token_len_nonzero[i] + del tokens[i], token_begin[i], token_len[i], token_len_nonzero[i], token_prob[i] + continue + # fix tokenization + if step == 2 and loc_tokens[-1] == "??": + step -= 1 + j = i + step + word_begin.append(token_begin[i]) + word_len.append(sum(token_len[i:j])) + denominator = sum(token_len_nonzero[i:j]) + word_prob.append(sum(token_prob[k] * token_len_nonzero[k] for k in range(i, j)) / denominator) + i = j + return words, word_begin, word_len, word_prob + + def _process_char_with_space_to_words( + self, + tokens: List[str], + token_begin: List[int], + token_len: List[int], + token_prob: List[float], + words: List[str], + ) -> Tuple[List[str], List[int], List[int], List[float]]: + """Transforms alignment information from character level to word level. + This method includes separator (typically the space) information in the results. + + Used with character-based models (no self._model.tokenizer). + """ + # suppose that there are no whitespaces anywhere except between words + space_idx = (np.array(tokens) == " ").nonzero()[0].tolist() + assert len(words) == len(space_idx) + 1 + token_len_nonzero = [(t_l if t_l > 0 else 1) for t_l in token_len] + if len(space_idx) == 0: + word_begin = [token_begin[0]] + word_len = [sum(token_len)] + denominator = sum(token_len_nonzero) + word_prob = [sum(t_p * t_l for t_p, t_l in zip(token_prob, token_len_nonzero)) / denominator] + else: + space_word = "[SEP]" + word_begin = [token_begin[0]] + word_len = [sum(token_len[: space_idx[0]])] + denominator = sum(token_len_nonzero[: space_idx[0]]) + word_prob = [sum(token_prob[k] * token_len_nonzero[k] for k in range(space_idx[0])) / denominator] + words_with_space = [words[0]] + for word, i, j in zip(words[1:], space_idx, space_idx[1:] + [len(tokens)]): + # append space + word_begin.append(token_begin[i]) + word_len.append(token_len[i]) + word_prob.append(token_prob[i]) + words_with_space.append(space_word) + # append next word + word_begin.append(token_begin[i + 1]) + word_len.append(sum(token_len[i + 1 : j])) + denominator = sum(token_len_nonzero[i + 1 : j]) + word_prob.append(sum(token_prob[k] * token_len_nonzero[k] for k in range(i + 1, j)) / denominator) + words_with_space.append(word) + words = words_with_space + return words, word_begin, word_len, word_prob + + def _results_to_ctmUnits( + self, s_id: int, pred: torch.Tensor, prob: torch.Tensor + ) -> Tuple[int, List['FrameCtmUnit']]: + """Transforms predictions with probabilities to a list of FrameCtmUnit objects, + containing frame-level alignment information (label, start, duration, probability), for a given sample id. + + Alignment information can be either token-based (char, wordpiece, ...) or word-based. + """ + if len(pred) == 0: + return (s_id, []) + + non_blank_idx = (pred != self.blank_id).nonzero(as_tuple=True)[0].cpu() + pred_ids = pred[non_blank_idx].tolist() + prob_list = prob.tolist() + if self.model_type == "rnnt": + wer_module = self._model.decoding + # for rnnt forced alignment we always have num_blanks == num_frames, + # thus len(pred) == num_frames + num_non_blanks + token_begin = non_blank_idx - torch.arange(len(non_blank_idx)) + token_end = torch.cat((token_begin[1:], torch.tensor([len(pred) - len(non_blank_idx)]))) + else: + wer_module = self._model._wer + token_begin = non_blank_idx + token_end = torch.cat((token_begin[1:], torch.tensor([len(pred)]))) + tokens = wer_module.decode_ids_to_tokens(pred_ids) + token_len = (token_end - token_begin).tolist() + token_begin = token_begin.tolist() + token_prob = [ + sum(prob_list[i:j]) / (j - i) + for i, j in zip(non_blank_idx.tolist(), non_blank_idx[1:].tolist() + [len(pred)]) + ] + if self.word_output: + words = wer_module.decode_tokens_to_str(pred_ids).split(" ") + words, word_begin, word_len, word_prob = ( + self._process_tokens_to_words(tokens, token_begin, token_len, token_prob, words) + if hasattr(self._model, "tokenizer") + else self._process_char_with_space_to_words(tokens, token_begin, token_len, token_prob, words) + ) + return s_id, [FrameCtmUnit(t, b, l, p) for t, b, l, p in zip(words, word_begin, word_len, word_prob)] + return s_id, [FrameCtmUnit(t, b, l, p) for t, b, l, p in zip(tokens, token_begin, token_len, token_prob)] + + def _predict_impl_ctc( + self, + encoded: torch.Tensor, + encoded_len: torch.Tensor, + transcript: torch.Tensor, + transcript_len: torch.Tensor, + sample_id: torch.Tensor, + ) -> List[Tuple[int, 'FrameCtmUnit']]: + """Builds time alignment of an encoded sequence. + This method assumes that the CTC model is used. + + It produces a list of sample ids and fours: (label, start_frame, length, probability), called FrameCtmUnit. + """ + log_probs = encoded + + if self.prob_suppress_value != 1.0: + log_probs = self._apply_prob_suppress(log_probs) + + if self.alignment_type == "argmax": + predictions, probs = self._prepare_ctc_argmax_predictions(log_probs, encoded_len) + elif self.alignment_type == "forced": + if self.cpu_decoding: + log_probs, encoded_len, transcript, transcript_len = ( + log_probs.cpu(), + encoded_len.cpu(), + transcript.cpu(), + transcript_len.cpu(), + ) + predictions, probs = self.graph_decoder.align(log_probs, encoded_len, transcript, transcript_len) + else: + raise NotImplementedError() + + return [ + self._results_to_ctmUnits(s_id, pred, prob) + for s_id, pred, prob in zip(sample_id.tolist(), predictions, probs) + ] + + def _predict_impl_rnnt( + self, + encoded: torch.Tensor, + encoded_len: torch.Tensor, + transcript: torch.Tensor, + transcript_len: torch.Tensor, + sample_id: torch.Tensor, + ) -> List[Tuple[int, 'FrameCtmUnit']]: + """Builds time alignment of an encoded sequence. + This method assumes that the RNNT model is used. + + It produces a list of sample ids and fours: (label, start_frame, length, probability), called FrameCtmUnit. + """ + if self.alignment_type == "argmax": + return self._predict_impl_rnnt_argmax(encoded, encoded_len, transcript, transcript_len, sample_id) + elif self.alignment_type == "forced": + decoded = self._model.decoder(targets=transcript, target_length=transcript_len)[0] + log_probs = ( + self._rnnt_joint_pruned(encoded, encoded_len, decoded, transcript_len) + if self.predictor_window_size > 0 and self.predictor_window_size < transcript_len.max() + else self._model.joint(encoder_outputs=encoded, decoder_outputs=decoded) + ) + apply_log_softmax = True if self.log_softmax is None and encoded.is_cuda else self.log_softmax + if apply_log_softmax: + log_probs = log_probs.log_softmax(dim=-1) + if self.cpu_decoding: + log_probs, encoded_len, transcript, transcript_len = ( + log_probs.cpu(), + encoded_len.cpu(), + transcript.cpu(), + transcript_len.cpu(), + ) + predictions, probs = self.graph_decoder.align(log_probs, encoded_len, transcript, transcript_len) + return [ + self._results_to_ctmUnits(s_id, pred, prob) + for s_id, pred, prob in zip(sample_id.tolist(), predictions, probs) + ] + else: + raise NotImplementedError() + + @torch.no_grad() + def predict_step(self, batch, batch_idx, dataloader_idx=0) -> List[Tuple[int, 'FrameCtmUnit']]: + signal, signal_len, transcript, transcript_len, sample_id = batch + + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + encoded, encoded_len = self._model.forward(processed_signal=signal, processed_signal_length=signal_len)[:2] + else: + encoded, encoded_len = self._model.forward(input_signal=signal, input_signal_length=signal_len)[:2] + + return self._predict_impl(encoded, encoded_len, transcript, transcript_len, sample_id) + + @torch.no_grad() + def transcribe( + self, manifest: List[str], batch_size: int = 4, num_workers: int = None, verbose: bool = True, + ) -> List['FrameCtmUnit']: + """ + Does alignment. Use this method for debugging and prototyping. + + Args: + + manifest: path to dataset JSON manifest file (in NeMo format). \ + Recommended length per audio file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + num_workers: (int) number of workers for DataLoader + verbose: (bool) whether to display tqdm progress bar + + Returns: + A list of four: (label, start_frame, length, probability), called FrameCtmUnit, \ + in the same order as in the manifest. + """ + hypotheses = [] + # Model's mode and device + mode = self._model.training + device = next(self._model.parameters()).device + dither_value = self._model.preprocessor.featurizer.dither + pad_to_value = self._model.preprocessor.featurizer.pad_to + + if num_workers is None: + num_workers = min(batch_size, os.cpu_count() - 1) + + try: + self._model.preprocessor.featurizer.dither = 0.0 + self._model.preprocessor.featurizer.pad_to = 0 + + # Switch model to evaluation mode + self._model.eval() + # Freeze the encoder and decoder modules + self._model.encoder.freeze() + self._model.decoder.freeze() + if hasattr(self._model, "joint"): + self._model.joint.freeze() + logging_level = logging.get_verbosity() + logging.set_verbosity(logging.WARNING) + + config = { + 'manifest_filepath': manifest, + 'batch_size': batch_size, + 'num_workers': num_workers, + } + temporary_datalayer = self._model._setup_transcribe_dataloader(config) + for test_batch in tqdm(temporary_datalayer, desc="Aligning", disable=not verbose): + test_batch[0] = test_batch[0].to(device) + test_batch[1] = test_batch[1].to(device) + hypotheses += [unit for i, unit in self.predict_step(test_batch, 0)] + del test_batch + finally: + # set mode back to its original value + self._model.train(mode=mode) + self._model.preprocessor.featurizer.dither = dither_value + self._model.preprocessor.featurizer.pad_to = pad_to_value + + logging.set_verbosity(logging_level) + if mode is True: + self._model.encoder.unfreeze() + self._model.decoder.unfreeze() + if hasattr(self._model, "joint"): + self._model.joint.unfreeze() + return hypotheses + + def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): + raise RuntimeError("This module cannot be used in training.") + + def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): + raise RuntimeError("This module cannot be used in validation.") + + def setup_test_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): + raise RuntimeError("This module cannot be used in testing.") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/k2_sequence_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/k2_sequence_models.py new file mode 100644 index 0000000..087e9e4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/k2_sequence_models.py @@ -0,0 +1,298 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Optional + +from omegaconf import DictConfig +from pytorch_lightning import Trainer + +from nemo.collections.asr.models.ctc_bpe_models import EncDecCTCModelBPE +from nemo.collections.asr.models.ctc_models import EncDecCTCModel +from nemo.collections.asr.models.rnnt_bpe_models import EncDecRNNTBPEModel +from nemo.collections.asr.models.rnnt_models import EncDecRNNTModel +from nemo.collections.asr.parts.k2.classes import ASRK2Mixin +from nemo.core.classes.common import PretrainedModelInfo, typecheck +from nemo.utils import logging + + +class EncDecK2SeqModel(EncDecCTCModel, ASRK2Mixin): + """Encoder decoder models with various lattice losses.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + loss_type = cfg.graph_module_cfg.get("loss_type", "ctc") + if loss_type != "ctc" and loss_type != "mmi": + raise ValueError(f"Class {self.__class__.__name__} does not support `loss_type`={loss_type}") + super().__init__(cfg=cfg, trainer=trainer) + self._init_k2() + + @classmethod + def list_available_models(cls) -> Optional[List[PretrainedModelInfo]]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + pass + + def change_vocabulary(self, new_vocabulary: List[str]): + """ + Changes vocabulary used during CTC decoding process. Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + If new_vocabulary == self.decoder.vocabulary then nothing will be changed. + + Args: + new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \ + this is target alphabet. + + Returns: None + + """ + super().change_vocabulary(new_vocabulary) + + if self.use_graph_lm: + self.token_lm = None + logging.warning( + f"""With .change_vocabulary() call for a model with criterion_type=`{self.loss.criterion_type}`, + a new token_lm has to be set manually: call .update_k2_modules(new_cfg) + or update .graph_module_cfg.backend_cfg.token_lm before calling this method.""" + ) + + self.update_k2_modules(self.graph_module_cfg) + + @typecheck() + def forward( + self, input_signal=None, input_signal_length=None, processed_signal=None, processed_signal_length=None, + ): + """ + Forward pass of the model. + + Args: + input_signal: Tensor that represents a batch of raw audio signals, + of shape [B, T]. T here represents timesteps, with 1 second of audio represented as + `self.sample_rate` number of floating point values. + input_signal_length: Vector of length B, that contains the individual lengths of the audio + sequences. + processed_signal: Tensor that represents a batch of processed audio signals, + of shape (B, D, T) that has undergone processing via some DALI preprocessor. + processed_signal_length: Vector of length B, that contains the individual lengths of the + processed audio sequences. + + Returns: + A tuple of 3 elements - + 1) The log probabilities tensor of shape [B, T, D]. + 2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. + 3) The greedy token predictions of the model of shape [B, T] (via argmax) + """ + log_probs, encoded_len, greedy_predictions = super().forward( + input_signal=input_signal, + input_signal_length=input_signal_length, + processed_signal=processed_signal, + processed_signal_length=processed_signal_length, + ) + return self._forward_k2_post_processing( + log_probs=log_probs, encoded_length=encoded_len, greedy_predictions=greedy_predictions + ) + + +class EncDecK2SeqModelBPE(EncDecCTCModelBPE, ASRK2Mixin): + """Encoder decoder models with Byte Pair Encoding and various lattice losses.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + loss_type = cfg.graph_module_cfg.get("loss_type", "ctc") + if loss_type != "ctc" and loss_type != "mmi": + raise ValueError(f"Class {self.__class__.__name__} does not support `loss_type`={loss_type}") + super().__init__(cfg=cfg, trainer=trainer) + self._init_k2() + + @classmethod + def list_available_models(cls) -> Optional[List[PretrainedModelInfo]]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + pass + + def change_vocabulary(self, new_tokenizer_dir: str, new_tokenizer_type: str): + """ + Changes vocabulary of the tokenizer used during CTC decoding process. + Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_tokenizer_dir: Path to the new tokenizer directory. + new_tokenizer_type: Either `bpe` or `wpe`. `bpe` is used for SentencePiece tokenizers, + whereas `wpe` is used for `BertTokenizer`. + + Returns: None + + """ + super().change_vocabulary(new_tokenizer_dir, new_tokenizer_type) + + if self.use_graph_lm: + self.token_lm = None + logging.warning( + f"""With .change_vocabulary() call for a model with criterion_type=`{self.loss.criterion_type}`, + a new token_lm has to be set manually: call .update_k2_modules(new_cfg) + or update .graph_module_cfg.backend_cfg.token_lm before calling this method.""" + ) + + self.update_k2_modules(self.graph_module_cfg) + + @typecheck() + def forward( + self, input_signal=None, input_signal_length=None, processed_signal=None, processed_signal_length=None, + ): + """ + Forward pass of the model. + + Args: + input_signal: Tensor that represents a batch of raw audio signals, + of shape [B, T]. T here represents timesteps, with 1 second of audio represented as + `self.sample_rate` number of floating point values. + input_signal_length: Vector of length B, that contains the individual lengths of the audio + sequences. + processed_signal: Tensor that represents a batch of processed audio signals, + of shape (B, D, T) that has undergone processing via some DALI preprocessor. + processed_signal_length: Vector of length B, that contains the individual lengths of the + processed audio sequences. + + Returns: + A tuple of 3 elements - + 1) The log probabilities tensor of shape [B, T, D]. + 2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. + 3) The greedy token predictions of the model of shape [B, T] (via argmax) + """ + log_probs, encoded_len, greedy_predictions = super().forward( + input_signal=input_signal, + input_signal_length=input_signal_length, + processed_signal=processed_signal, + processed_signal_length=processed_signal_length, + ) + return self._forward_k2_post_processing( + log_probs=log_probs, encoded_length=encoded_len, greedy_predictions=greedy_predictions + ) + + +class EncDecK2RnntSeqModel(EncDecRNNTModel, ASRK2Mixin): + """Encoder decoder models with various lattice losses.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + loss_type = cfg.graph_module_cfg.get("loss_type", "rnnt") + criterion_type = cfg.graph_module_cfg.get("criterion_type", "ml") + if loss_type != "rnnt" or criterion_type != "ml": + raise ValueError( + f"""Class {self.__class__.__name__} does not support + `criterion_type`={criterion_type} with `loss_type`={loss_type}""" + ) + super().__init__(cfg=cfg, trainer=trainer) + self._init_k2() + + @classmethod + def list_available_models(cls) -> Optional[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + pass + + def change_vocabulary(self, new_vocabulary: List[str]): + """ + Changes vocabulary used during CTC decoding process. Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + If new_vocabulary == self.decoder.vocabulary then nothing will be changed. + + Args: + new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \ + this is target alphabet. + + Returns: None + + """ + super().change_vocabulary(new_vocabulary) + + if self.use_graph_lm: + self.token_lm = None + logging.warning( + f"""With .change_vocabulary() call for a model with criterion_type=`{self.loss.criterion_type}`, + a new token_lm has to be set manually: call .update_k2_modules(new_cfg) + or update .graph_module_cfg.backend_cfg.token_lm before calling this method.""" + ) + + self.update_k2_modules(self.graph_module_cfg) + + +class EncDecK2RnntSeqModelBPE(EncDecRNNTBPEModel, ASRK2Mixin): + """Encoder decoder models with Byte Pair Encoding and various lattice losses.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + loss_type = cfg.graph_module_cfg.get("loss_type", "rnnt") + criterion_type = cfg.graph_module_cfg.get("criterion_type", "ml") + if loss_type != "rnnt" or criterion_type != "ml": + raise ValueError( + f"""Class {self.__class__.__name__} does not support + `criterion_type`={criterion_type} with `loss_type`={loss_type}""" + ) + super().__init__(cfg=cfg, trainer=trainer) + self._init_k2() + + @classmethod + def list_available_models(cls) -> Optional[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + pass + + def change_vocabulary(self, new_tokenizer_dir: str, new_tokenizer_type: str): + """ + Changes vocabulary of the tokenizer used during CTC decoding process. + Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_tokenizer_dir: Path to the new tokenizer directory. + new_tokenizer_type: Either `bpe` or `wpe`. `bpe` is used for SentencePiece tokenizers, + whereas `wpe` is used for `BertTokenizer`. + + Returns: None + + """ + super().change_vocabulary(new_tokenizer_dir, new_tokenizer_type) + + if self.use_graph_lm: + self.token_lm = None + logging.warning( + f"""With .change_vocabulary() call for a model with criterion_type=`{self.loss.criterion_type}`, + a new token_lm has to be set manually: call .update_k2_modules(new_cfg) + or update .graph_module_cfg.backend_cfg.token_lm before calling this method.""" + ) + + self.update_k2_modules(self.graph_module_cfg) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/label_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/label_models.py new file mode 100644 index 0000000..23ab546 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/label_models.py @@ -0,0 +1,655 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import copy +import itertools +from collections import Counter +from math import ceil +from typing import Dict, List, Optional, Union + +import librosa +import numpy as np +import soundfile as sf +import torch +from hydra.utils import instantiate +from omegaconf import DictConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer +from torchmetrics import Accuracy +from tqdm import tqdm + +from nemo.collections.asr.data.audio_to_label import AudioToSpeechLabelDataset, cache_datastore_manifests +from nemo.collections.asr.data.audio_to_label_dataset import ( + get_concat_tarred_speech_label_dataset, + get_tarred_speech_label_dataset, +) +from nemo.collections.asr.data.audio_to_text_dataset import convert_to_config_list +from nemo.collections.asr.models.asr_model import ExportableEncDecModel +from nemo.collections.asr.parts.preprocessing.features import WaveformFeaturizer +from nemo.collections.asr.parts.preprocessing.perturb import process_augmentations +from nemo.collections.common.metrics import TopKClassificationAccuracy +from nemo.collections.common.parts.preprocessing.collections import ASRSpeechLabel +from nemo.core.classes import ModelPT +from nemo.core.classes.common import PretrainedModelInfo, typecheck +from nemo.core.neural_types import * +from nemo.utils import logging + +__all__ = ['EncDecSpeakerLabelModel'] + + +class EncDecSpeakerLabelModel(ModelPT, ExportableEncDecModel): + """ + Encoder decoder class for speaker label models. + Model class creates training, validation methods for setting up data + performing model forward pass. + Expects config dict for + + * preprocessor + + * Jasper/Quartznet Encoder + + * Speaker Decoder + """ + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + Returns: + List of available pre-trained models. + """ + result = [] + + model = PretrainedModelInfo( + pretrained_model_name="speakerverification_speakernet", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/speakerverification_speakernet/versions/1.16.0/files/speakerverification_speakernet.nemo", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:speakerverification_speakernet", + ) + result.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="ecapa_tdnn", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/ecapa_tdnn/versions/1.16.0/files/ecapa_tdnn.nemo", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:ecapa_tdnn", + ) + result.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="titanet_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/titanet_large/versions/v1/files/titanet-l.nemo", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/titanet_large", + ) + result.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="langid_ambernet", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/langid_ambernet/versions/1.12.0/files/ambernet.nemo", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/langid_ambernet", + ) + result.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="titanet_small", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:titanet_small", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/titanet_small/versions/1.19.0/files/titanet-s.nemo", + ) + result.append(model) + + return result + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + self.world_size = 1 + self.cal_labels_occurrence_train = False + self.labels_occurrence = None + self.labels = None + + num_classes = cfg.decoder.num_classes + + if 'loss' in cfg: + if 'weight' in cfg.loss: + if cfg.loss.weight == 'auto': + weight = num_classes * [1] + self.cal_labels_occurrence_train = True + else: + weight = cfg.loss.weight + else: + weight = None # weight is None for angular loss and CE loss if it's not specified. + + if trainer is not None: + self.world_size = trainer.num_nodes * trainer.num_devices + + super().__init__(cfg=cfg, trainer=trainer) + + if self.labels_occurrence: + # Goal is to give more weight to the classes with less samples so as to match the ones with the higher frequencies + weight = [sum(self.labels_occurrence) / (len(self.labels_occurrence) * i) for i in self.labels_occurrence] + + if 'loss' in cfg: + cfg_eval_loss = copy.deepcopy(cfg.loss) + + if 'angular' in cfg.loss._target_: + OmegaConf.set_struct(cfg, True) + with open_dict(cfg): + cfg.decoder.angular = True + + if 'weight' in cfg.loss: + cfg.loss.weight = weight + cfg_eval_loss.weight = None + + # May need a general check for arguments of loss + self.loss = instantiate(cfg.loss) + self.eval_loss = instantiate(cfg_eval_loss) + + else: + tmp_loss_cfg = OmegaConf.create( + {"_target_": "nemo.collections.common.losses.cross_entropy.CrossEntropyLoss"} + ) + + self.loss = instantiate(tmp_loss_cfg) + self.eval_loss = instantiate(tmp_loss_cfg) + + self._accuracy = TopKClassificationAccuracy(top_k=[1]) + + self.preprocessor = EncDecSpeakerLabelModel.from_config_dict(cfg.preprocessor) + self.encoder = EncDecSpeakerLabelModel.from_config_dict(cfg.encoder) + self.decoder = EncDecSpeakerLabelModel.from_config_dict(cfg.decoder) + + self._macro_accuracy = Accuracy(num_classes=num_classes, top_k=1, average='macro', task='multiclass') + + if hasattr(self._cfg, 'spec_augment') and self._cfg.spec_augment is not None: + self.spec_augmentation = EncDecSpeakerLabelModel.from_config_dict(self._cfg.spec_augment) + else: + self.spec_augmentation = None + + @staticmethod + def extract_labels(data_layer_config): + labels = set() + manifest_filepath = data_layer_config.get('manifest_filepath', None) + if manifest_filepath is None: + logging.warning("No manifest_filepath was provided, no labels got extracted!") + return None + manifest_filepaths = convert_to_config_list(data_layer_config['manifest_filepath']) + + for manifest_filepath in itertools.chain.from_iterable(manifest_filepaths): + cache_datastore_manifests(manifest_filepaths=manifest_filepath) + collection = ASRSpeechLabel( + manifests_files=manifest_filepath, + min_duration=data_layer_config.get("min_duration", None), + max_duration=data_layer_config.get("max_duration", None), + index_by_file_id=True, + ) + labels.update(collection.uniq_labels) + labels = list(sorted(labels)) + logging.warning(f"Total number of {len(labels)} found in all the manifest files.") + return labels + + def __setup_dataloader_from_config(self, config: Optional[Dict]): + if 'augmentor' in config: + augmentor = process_augmentations(config['augmentor']) + else: + augmentor = None + + featurizer = WaveformFeaturizer( + sample_rate=config['sample_rate'], int_values=config.get('int_values', False), augmentor=augmentor + ) + shuffle = config.get('shuffle', False) + if config.get('is_tarred', False): + if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or ( + 'manifest_filepath' in config and config['manifest_filepath'] is None + ): + logging.warning( + "Could not load dataset as `manifest_filepath` was None or " + f"`tarred_audio_filepaths` is None. Provided config : {config}" + ) + return None + + shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 + if config.get("is_concat", False): + dataset = get_concat_tarred_speech_label_dataset( + featurizer=featurizer, + config=config, + shuffle_n=shuffle_n, + global_rank=self.global_rank, + world_size=self.world_size, + ) + else: + dataset = get_tarred_speech_label_dataset( + featurizer=featurizer, + config=config, + shuffle_n=shuffle_n, + global_rank=self.global_rank, + world_size=self.world_size, + ) + shuffle = False + else: + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") + return None + + dataset = AudioToSpeechLabelDataset( + manifest_filepath=config['manifest_filepath'], + labels=config['labels'], + featurizer=featurizer, + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + trim=config.get('trim_silence', False), + normalize_audio=config.get('normalize_audio', False), + cal_labels_occurrence=config.get('cal_labels_occurrence', False), + ) + if dataset.labels_occurrence: + self.labels_occurrence = dataset.labels_occurrence + + if hasattr(dataset, 'fixed_seq_collate_fn'): + collate_fn = dataset.fixed_seq_collate_fn + else: + collate_fn = dataset.datasets[0].fixed_seq_collate_fn + + batch_size = config['batch_size'] + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=batch_size, + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def setup_training_data(self, train_data_layer_config: Optional[Union[DictConfig, Dict]]): + if self.cal_labels_occurrence_train: + # Calculate labels occurence for weighed CE loss for train set if weight equals 'auto' + # Note in this case, the cal_labels_occurrence in val_data_layer_config and test_data_layer_params need to be stay as False + OmegaConf.set_struct(train_data_layer_config, True) + with open_dict(train_data_layer_config): + train_data_layer_config['cal_labels_occurrence'] = True + + self.labels = self.extract_labels(train_data_layer_config) + train_data_layer_config['labels'] = self.labels + if 'shuffle' not in train_data_layer_config: + train_data_layer_config['shuffle'] = True + self._train_dl = self.__setup_dataloader_from_config(config=train_data_layer_config) + # Need to set this because if using an IterableDataset, the length of the dataloader is the total number + # of samples rather than the number of batches, and this messes up the tqdm progress bar. + # So we set the number of steps manually (to the correct number) to fix this. + if ( + self._train_dl is not None + and hasattr(self._train_dl, 'dataset') + and isinstance(self._train_dl.dataset, torch.utils.data.IterableDataset) + ): + # We also need to check if limit_train_batches is already set. + # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches, + # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0). + if self._trainer is not None and isinstance(self._trainer.limit_train_batches, float): + self._trainer.limit_train_batches = int( + self._trainer.limit_train_batches + * ceil((len(self._train_dl.dataset) / self.world_size) / train_data_layer_config['batch_size']) + ) + elif self._trainer is None: + logging.warning( + "Model Trainer was not set before constructing the dataset, incorrect number of " + "training batches will be used. Please set the trainer and rebuild the dataset." + ) + + def setup_validation_data(self, val_data_layer_config: Optional[Union[DictConfig, Dict]]): + val_data_layer_config['labels'] = self.labels + self._validation_dl = self.__setup_dataloader_from_config(config=val_data_layer_config) + + def setup_test_data(self, test_data_layer_params: Optional[Union[DictConfig, Dict]]): + if hasattr(self, 'dataset'): + test_data_layer_params['labels'] = self.labels + + self.embedding_dir = test_data_layer_params.get('embedding_dir', './') + self._test_dl = self.__setup_dataloader_from_config(config=test_data_layer_params) + self.test_manifest = test_data_layer_params.get('manifest_filepath', None) + + def test_dataloader(self): + if self._test_dl is not None: + return self._test_dl + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + if hasattr(self.preprocessor, '_sample_rate'): + audio_eltype = AudioSignal(freq=self.preprocessor._sample_rate) + else: + audio_eltype = AudioSignal() + return { + "input_signal": NeuralType(('B', 'T'), audio_eltype), + "input_signal_length": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return { + "logits": NeuralType(('B', 'D'), LogitsType()), + "embs": NeuralType(('B', 'D'), AcousticEncodedRepresentation()), + } + + def forward_for_export(self, processed_signal, processed_signal_len): + encoded, length = self.encoder(audio_signal=processed_signal, length=processed_signal_len) + logits, embs = self.decoder(encoder_output=encoded, length=length) + return logits, embs + + @typecheck() + def forward(self, input_signal, input_signal_length): + processed_signal, processed_signal_len = self.preprocessor( + input_signal=input_signal, length=input_signal_length, + ) + + if self.spec_augmentation is not None and self.training: + processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_len) + + encoded, length = self.encoder(audio_signal=processed_signal, length=processed_signal_len) + logits, embs = self.decoder(encoder_output=encoded, length=length) + return logits, embs + + # PTL-specific methods + def training_step(self, batch, batch_idx): + audio_signal, audio_signal_len, labels, _ = batch + logits, _ = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + loss = self.loss(logits=logits, labels=labels) + + self.log('loss', loss) + self.log('learning_rate', self._optimizer.param_groups[0]['lr']) + self.log('global_step', self.trainer.global_step) + + self._accuracy(logits=logits, labels=labels) + top_k = self._accuracy.compute() + self._accuracy.reset() + for i, top_i in enumerate(top_k): + self.log(f'training_batch_accuracy_top_{i}', top_i) + + return {'loss': loss} + + def evaluation_step(self, batch, batch_idx, dataloader_idx: int = 0, tag: str = 'val'): + audio_signal, audio_signal_len, labels, _ = batch + logits, _ = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + loss_value = self.eval_loss(logits=logits, labels=labels) + acc_top_k = self._accuracy(logits=logits, labels=labels) + correct_counts, total_counts = self._accuracy.correct_counts_k, self._accuracy.total_counts_k + self._macro_accuracy.update(preds=logits, target=labels) + stats = self._macro_accuracy._final_state() + + output = { + f'{tag}_loss': loss_value, + f'{tag}_correct_counts': correct_counts, + f'{tag}_total_counts': total_counts, + f'{tag}_acc_micro_top_k': acc_top_k, + f'{tag}_acc_macro_stats': stats, + } + if tag == 'val': + if isinstance(self.trainer.val_dataloaders, (list, tuple)) and len(self.trainer.val_dataloaders) > 1: + self.validation_step_outputs[dataloader_idx].append(output) + else: + self.validation_step_outputs.append(output) + else: + if isinstance(self.trainer.test_dataloaders, (list, tuple)) and len(self.trainer.test_dataloaders) > 1: + self.test_step_outputs[dataloader_idx].append(output) + else: + self.test_step_outputs.append(output) + + return output + + def multi_evaluation_epoch_end(self, outputs, dataloader_idx: int = 0, tag: str = 'val'): + loss_mean = torch.stack([x[f'{tag}_loss'] for x in outputs]).mean() + correct_counts = torch.stack([x[f'{tag}_correct_counts'] for x in outputs]).sum(axis=0) + total_counts = torch.stack([x[f'{tag}_total_counts'] for x in outputs]).sum(axis=0) + + self._accuracy.correct_counts_k = correct_counts + self._accuracy.total_counts_k = total_counts + topk_scores = self._accuracy.compute() + + self._macro_accuracy.tp = torch.stack([x[f'{tag}_acc_macro_stats'][0] for x in outputs]).sum(axis=0) + self._macro_accuracy.fp = torch.stack([x[f'{tag}_acc_macro_stats'][1] for x in outputs]).sum(axis=0) + self._macro_accuracy.tn = torch.stack([x[f'{tag}_acc_macro_stats'][2] for x in outputs]).sum(axis=0) + self._macro_accuracy.fn = torch.stack([x[f'{tag}_acc_macro_stats'][3] for x in outputs]).sum(axis=0) + macro_accuracy_score = self._macro_accuracy.compute() + + self._accuracy.reset() + self._macro_accuracy.reset() + + self.log(f'{tag}_loss', loss_mean, sync_dist=True) + for top_k, score in zip(self._accuracy.top_k, topk_scores): + self.log(f'{tag}_acc_micro_top_{top_k}', score, sync_dist=True) + self.log(f'{tag}_acc_macro', macro_accuracy_score, sync_dist=True) + + return { + f'{tag}_loss': loss_mean, + f'{tag}_acc_micro_top_k': topk_scores, + f'{tag}_acc_macro': macro_accuracy_score, + } + + def validation_step(self, batch, batch_idx, dataloader_idx: int = 0): + return self.evaluation_step(batch, batch_idx, dataloader_idx, 'val') + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + return self.multi_evaluation_epoch_end(outputs, dataloader_idx, 'val') + + def test_step(self, batch, batch_idx, dataloader_idx: int = 0): + return self.evaluation_step(batch, batch_idx, dataloader_idx, 'test') + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + return self.multi_evaluation_epoch_end(outputs, dataloader_idx, 'test') + + @torch.no_grad() + def infer_file(self, path2audio_file): + """ + Args: + path2audio_file: path to an audio wav file + + Returns: + emb: speaker embeddings (Audio representations) + logits: logits corresponding of final layer + """ + audio, sr = sf.read(path2audio_file) + target_sr = self._cfg.train_ds.get('sample_rate', 16000) + if sr != target_sr: + audio = librosa.core.resample(audio, orig_sr=sr, target_sr=target_sr) + audio_length = audio.shape[0] + device = self.device + audio = np.array([audio]) + audio_signal, audio_signal_len = ( + torch.tensor(audio, device=device, dtype=torch.float32), + torch.tensor([audio_length], device=device), + ) + mode = self.training + self.freeze() + + logits, emb = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + + self.train(mode=mode) + if mode is True: + self.unfreeze() + del audio_signal, audio_signal_len + return emb, logits + + @torch.no_grad() + def infer_segment(self, segment): + """ + Args: + segment: segment of audio file + + Returns: + emb: speaker embeddings (Audio representations) + logits: logits corresponding of final layer + """ + segment_length = segment.shape[0] + + device = self.device + audio = np.array([segment]) + audio_signal, audio_signal_len = ( + torch.tensor(audio, device=device, dtype=torch.float32), + torch.tensor([segment_length], device=device), + ) + mode = self.training + self.freeze() + + logits, emb = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + + self.train(mode=mode) + if mode is True: + self.unfreeze() + del audio_signal, audio_signal_len + return emb, logits + + def get_label( + self, path2audio_file: str, segment_duration: float = np.inf, num_segments: int = 1, random_seed: int = None + ): + """ + Returns label of path2audio_file from classes the model was trained on. + Args: + path2audio_file (str): Path to audio wav file. + segment_duration (float): Random sample duration in seconds. + num_segments (int): Number of segments of file to use for majority vote. + random_seed (int): Seed for generating the starting position of the segment. + + Returns: + label: label corresponding to the trained model + """ + audio, sr = sf.read(path2audio_file) + target_sr = self._cfg.train_ds.get('sample_rate', 16000) + if sr != target_sr: + audio = librosa.core.resample(audio, orig_sr=sr, target_sr=target_sr) + audio_length = audio.shape[0] + + duration = target_sr * segment_duration + if duration > audio_length: + duration = audio_length + + label_id_list = [] + np.random.seed(random_seed) + starts = np.random.randint(0, audio_length - duration + 1, size=num_segments) + for start in starts: + audio = audio[start : start + duration] + + _, logits = self.infer_segment(audio) + label_id = logits.argmax(axis=1) + label_id_list.append(int(label_id[0])) + + m_label_id = Counter(label_id_list).most_common(1)[0][0] + + trained_labels = self._cfg['train_ds'].get('labels', None) + if trained_labels is not None: + trained_labels = list(trained_labels) + label = trained_labels[m_label_id] + else: + logging.info("labels are not saved to model, hence only outputting the label id index") + label = m_label_id + + return label + + def get_embedding(self, path2audio_file): + """ + Returns the speaker embeddings for a provided audio file. + + Args: + path2audio_file: path to an audio wav file + + Returns: + emb: speaker embeddings (Audio representations) + """ + + emb, _ = self.infer_file(path2audio_file=path2audio_file) + + return emb + + @torch.no_grad() + def verify_speakers(self, path2audio_file1, path2audio_file2, threshold=0.7): + """ + Verify if two audio files are from the same speaker or not. + + Args: + path2audio_file1: path to audio wav file of speaker 1 + path2audio_file2: path to audio wav file of speaker 2 + threshold: cosine similarity score used as a threshold to distinguish two embeddings (default = 0.7) + + Returns: + True if both audio files are from same speaker, False otherwise + """ + embs1 = self.get_embedding(path2audio_file1).squeeze() + embs2 = self.get_embedding(path2audio_file2).squeeze() + # Length Normalize + X = embs1 / torch.linalg.norm(embs1) + Y = embs2 / torch.linalg.norm(embs2) + # Score + similarity_score = torch.dot(X, Y) / ((torch.dot(X, X) * torch.dot(Y, Y)) ** 0.5) + similarity_score = (similarity_score + 1) / 2 + # Decision + if similarity_score >= threshold: + logging.info(" two audio files are from same speaker") + return True + else: + logging.info(" two audio files are from different speakers") + return False + + @torch.no_grad() + def batch_inference(self, manifest_filepath, batch_size=32, sample_rate=16000, device='cuda'): + """ + Perform batch inference on EncDecSpeakerLabelModel. + To perform inference on single audio file, once can use infer_model, get_label or get_embedding + + To map predicted labels, one can do + `arg_values = logits.argmax(axis=1)` + `pred_labels = list(map(lambda t : trained_labels[t], arg_values))` + + Args: + manifest_filepath: Path to manifest file + batch_size: batch size to perform batch inference + sample_rate: sample rate of audio files in manifest file + device: compute device to perform operations. + + Returns: + The variables below all follow the audio file order in the manifest file. + embs: embeddings of files provided in manifest file + logits: logits of final layer of EncDecSpeakerLabel Model + gt_labels: labels from manifest file (needed for speaker enrollment and testing) + trained_labels: Classification labels sorted in the order that they are mapped by the trained model + + """ + mode = self.training + self.freeze() + self.eval() + self.to(device) + trained_labels = self._cfg['train_ds']['labels'] + if trained_labels is not None: + trained_labels = list(trained_labels) + + featurizer = WaveformFeaturizer(sample_rate=sample_rate) + + dataset = AudioToSpeechLabelDataset(manifest_filepath=manifest_filepath, labels=None, featurizer=featurizer) + + dataloader = torch.utils.data.DataLoader( + dataset=dataset, batch_size=batch_size, collate_fn=dataset.fixed_seq_collate_fn, + ) + + logits = [] + embs = [] + gt_labels = [] + + for test_batch in tqdm(dataloader): + if device == 'cuda': + test_batch = [x.to(device) for x in test_batch] + audio_signal, audio_signal_len, labels, _ = test_batch + logit, emb = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len) + + logits.extend(logit.cpu().numpy()) + gt_labels.extend(labels.cpu().numpy()) + embs.extend(emb.cpu().numpy()) + + gt_labels = list(map(lambda t: dataset.id2label[t], gt_labels)) + + self.train(mode=mode) + if mode is True: + self.unfreeze() + + logits, embs, gt_labels = np.asarray(logits), np.asarray(embs), np.asarray(gt_labels) + + return embs, logits, gt_labels, trained_labels diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/msdd_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/msdd_models.py new file mode 100644 index 0000000..d96bafd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/msdd_models.py @@ -0,0 +1,1545 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import json +import os +import pickle as pkl +import tempfile +from collections import OrderedDict +from pathlib import Path +from statistics import mode +from typing import Any, Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +from hydra.utils import instantiate +from omegaconf import DictConfig, open_dict +from pyannote.core import Annotation +from pyannote.metrics.diarization import DiarizationErrorRate +from pytorch_lightning import LightningModule, Trainer +from pytorch_lightning.utilities import rank_zero_only +from tqdm import tqdm + +from nemo.collections.asr.data.audio_to_diar_label import AudioToSpeechMSDDInferDataset, AudioToSpeechMSDDTrainDataset +from nemo.collections.asr.metrics.der import score_labels +from nemo.collections.asr.metrics.multi_binary_acc import MultiBinaryAccuracy +from nemo.collections.asr.models import ClusteringDiarizer +from nemo.collections.asr.models.asr_model import ExportableEncDecModel +from nemo.collections.asr.models.clustering_diarizer import ( + _MODEL_CONFIG_YAML, + _SPEAKER_MODEL, + _VAD_MODEL, + get_available_model_names, +) +from nemo.collections.asr.models.configs.diarizer_config import NeuralDiarizerInferenceConfig +from nemo.collections.asr.models.label_models import EncDecSpeakerLabelModel +from nemo.collections.asr.parts.preprocessing.features import WaveformFeaturizer +from nemo.collections.asr.parts.utils.speaker_utils import ( + audio_rttm_map, + get_embs_and_timestamps, + get_id_tup_dict, + get_scale_mapping_argmat, + get_uniq_id_list_from_manifest, + labels_to_pyannote_object, + make_rttm_with_overlap, + parse_scale_configs, + rttm_to_labels, +) +from nemo.core.classes import ModelPT +from nemo.core.classes.common import PretrainedModelInfo, typecheck +from nemo.core.neural_types import AudioSignal, LengthsType, NeuralType +from nemo.core.neural_types.elements import ProbsType +from nemo.utils import logging + +try: + from torch.cuda.amp import autocast +except ImportError: + from contextlib import contextmanager + + @contextmanager + def autocast(enabled=None): + yield + + +__all__ = ['EncDecDiarLabelModel', 'ClusterEmbedding', 'NeuralDiarizer'] + + +class EncDecDiarLabelModel(ModelPT, ExportableEncDecModel): + """ + Encoder decoder class for multiscale diarization decoder (MSDD). Model class creates training, validation methods for setting + up data performing model forward pass. + + This model class expects config dict for: + * preprocessor + * msdd_model + * speaker_model + """ + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + result = [] + + model = PretrainedModelInfo( + pretrained_model_name="diar_msdd_telephonic", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/diar_msdd_telephonic/versions/1.0.1/files/diar_msdd_telephonic.nemo", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:diar_msdd_telephonic", + ) + result.append(model) + return result + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + """ + Initialize an MSDD model and the specified speaker embedding model. In this init function, training and validation datasets are prepared. + """ + self._trainer = trainer if trainer else None + self.cfg_msdd_model = cfg + + if self._trainer: + self._init_segmentation_info() + self.world_size = trainer.num_nodes * trainer.num_devices + self.emb_batch_size = self.cfg_msdd_model.emb_batch_size + self.pairwise_infer = False + else: + self.world_size = 1 + self.pairwise_infer = True + super().__init__(cfg=self.cfg_msdd_model, trainer=trainer) + + window_length_in_sec = self.cfg_msdd_model.diarizer.speaker_embeddings.parameters.window_length_in_sec + if isinstance(window_length_in_sec, int) or len(window_length_in_sec) <= 1: + raise ValueError("window_length_in_sec should be a list containing multiple segment (window) lengths") + else: + self.cfg_msdd_model.scale_n = len(window_length_in_sec) + self.cfg_msdd_model.msdd_module.scale_n = self.cfg_msdd_model.scale_n + self.scale_n = self.cfg_msdd_model.scale_n + + self.preprocessor = EncDecSpeakerLabelModel.from_config_dict(self.cfg_msdd_model.preprocessor) + self.frame_per_sec = int(1 / self.preprocessor._cfg.window_stride) + self.msdd = EncDecDiarLabelModel.from_config_dict(self.cfg_msdd_model.msdd_module) + + if trainer is not None: + self._init_speaker_model() + self.add_speaker_model_config(cfg) + else: + self.msdd._speaker_model = EncDecSpeakerLabelModel.from_config_dict(cfg.speaker_model_cfg) + + # Call `self.save_hyperparameters` in modelPT.py again since cfg should contain speaker model's config. + self.save_hyperparameters("cfg") + + self.loss = instantiate(self.cfg_msdd_model.loss) + self._accuracy_test = MultiBinaryAccuracy() + self._accuracy_train = MultiBinaryAccuracy() + self._accuracy_valid = MultiBinaryAccuracy() + + def add_speaker_model_config(self, cfg): + """ + Add config dictionary of the speaker model to the model's config dictionary. This is required to + save and load speaker model with MSDD model. + + Args: + cfg (DictConfig): DictConfig type variable that conatains hyperparameters of MSDD model. + """ + with open_dict(cfg): + cfg_cp = copy.copy(self.msdd._speaker_model.cfg) + cfg.speaker_model_cfg = cfg_cp + del cfg.speaker_model_cfg.train_ds + del cfg.speaker_model_cfg.validation_ds + + def _init_segmentation_info(self): + """Initialize segmentation settings: window, shift and multiscale weights. + """ + self._diarizer_params = self.cfg_msdd_model.diarizer + self.multiscale_args_dict = parse_scale_configs( + self._diarizer_params.speaker_embeddings.parameters.window_length_in_sec, + self._diarizer_params.speaker_embeddings.parameters.shift_length_in_sec, + self._diarizer_params.speaker_embeddings.parameters.multiscale_weights, + ) + + def _init_speaker_model(self): + """ + Initialize speaker embedding model with model name or path passed through config. Note that speaker embedding model is loaded to + `self.msdd` to enable multi-gpu and multi-node training. In addition, speaker embedding model is also saved with msdd model when + `.ckpt` files are saved. + """ + model_path = self.cfg_msdd_model.diarizer.speaker_embeddings.model_path + self._diarizer_params = self.cfg_msdd_model.diarizer + + if not torch.cuda.is_available(): + rank_id = torch.device('cpu') + elif self._trainer: + rank_id = torch.device(self._trainer.global_rank) + else: + rank_id = None + + if model_path is not None and model_path.endswith('.nemo'): + self.msdd._speaker_model = EncDecSpeakerLabelModel.restore_from(model_path, map_location=rank_id) + logging.info("Speaker Model restored locally from {}".format(model_path)) + elif model_path.endswith('.ckpt'): + self._speaker_model = EncDecSpeakerLabelModel.load_from_checkpoint(model_path, map_location=rank_id) + logging.info("Speaker Model restored locally from {}".format(model_path)) + else: + if model_path not in get_available_model_names(EncDecSpeakerLabelModel): + logging.warning( + "requested {} model name not available in pretrained models, instead".format(model_path) + ) + model_path = "titanet_large" + logging.info("Loading pretrained {} model from NGC".format(model_path)) + self.msdd._speaker_model = EncDecSpeakerLabelModel.from_pretrained( + model_name=model_path, map_location=rank_id + ) + self._speaker_params = self.cfg_msdd_model.diarizer.speaker_embeddings.parameters + + def __setup_dataloader_from_config(self, config): + featurizer = WaveformFeaturizer( + sample_rate=config['sample_rate'], int_values=config.get('int_values', False), augmentor=None + ) + + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") + return None + dataset = AudioToSpeechMSDDTrainDataset( + manifest_filepath=config.manifest_filepath, + emb_dir=config.emb_dir, + multiscale_args_dict=self.multiscale_args_dict, + soft_label_thres=config.soft_label_thres, + featurizer=featurizer, + window_stride=self.cfg_msdd_model.preprocessor.window_stride, + emb_batch_size=config.emb_batch_size, + pairwise_infer=False, + global_rank=self._trainer.global_rank, + ) + + self.data_collection = dataset.collection + collate_ds = dataset + collate_fn = collate_ds.msdd_train_collate_fn + batch_size = config['batch_size'] + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=batch_size, + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=False, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def __setup_dataloader_from_config_infer( + self, config: DictConfig, emb_dict: dict, emb_seq: dict, clus_label_dict: dict, pairwise_infer=False + ): + shuffle = config.get('shuffle', False) + + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") + return None + + dataset = AudioToSpeechMSDDInferDataset( + manifest_filepath=config['manifest_filepath'], + emb_dict=emb_dict, + clus_label_dict=clus_label_dict, + emb_seq=emb_seq, + soft_label_thres=config.soft_label_thres, + seq_eval_mode=config.seq_eval_mode, + window_stride=self._cfg.preprocessor.window_stride, + use_single_scale_clus=False, + pairwise_infer=pairwise_infer, + ) + self.data_collection = dataset.collection + collate_ds = dataset + collate_fn = collate_ds.msdd_infer_collate_fn + batch_size = config['batch_size'] + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=batch_size, + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): + self._train_dl = self.__setup_dataloader_from_config(config=train_data_config,) + + def setup_validation_data(self, val_data_layer_config: Optional[Union[DictConfig, Dict]]): + self._validation_dl = self.__setup_dataloader_from_config(config=val_data_layer_config,) + + def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]): + if self.pairwise_infer: + self._test_dl = self.__setup_dataloader_from_config_infer( + config=test_data_config, + emb_dict=self.emb_sess_test_dict, + emb_seq=self.emb_seq_test, + clus_label_dict=self.clus_test_label_dict, + pairwise_infer=self.pairwise_infer, + ) + + def setup_multiple_test_data(self, test_data_config): + """ + MSDD does not use multiple_test_data template. This function is a placeholder for preventing error. + """ + return None + + def test_dataloader(self): + if self._test_dl is not None: + return self._test_dl + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + if hasattr(self.preprocessor, '_sample_rate'): + audio_eltype = AudioSignal(freq=self.preprocessor._sample_rate) + else: + audio_eltype = AudioSignal() + return { + "features": NeuralType(('B', 'T'), audio_eltype), + "feature_length": NeuralType(('B',), LengthsType()), + "ms_seg_timestamps": NeuralType(('B', 'C', 'T', 'D'), LengthsType()), + "ms_seg_counts": NeuralType(('B', 'C'), LengthsType()), + "clus_label_index": NeuralType(('B', 'T'), LengthsType()), + "scale_mapping": NeuralType(('B', 'C', 'T'), LengthsType()), + "targets": NeuralType(('B', 'T', 'C'), ProbsType()), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + return OrderedDict( + { + "probs": NeuralType(('B', 'T', 'C'), ProbsType()), + "scale_weights": NeuralType(('B', 'T', 'C', 'D'), ProbsType()), + } + ) + + def get_ms_emb_seq( + self, embs: torch.Tensor, scale_mapping: torch.Tensor, ms_seg_counts: torch.Tensor + ) -> torch.Tensor: + """ + Reshape the given tensor and organize the embedding sequence based on the original sequence counts. + Repeat the embeddings according to the scale_mapping information so that the final embedding sequence has + the identical length for all scales. + + Args: + embs (Tensor): + Merged embeddings without zero-padding in the batch. See `ms_seg_counts` for details. + Shape: (Total number of segments in the batch, emb_dim) + scale_mapping (Tensor): + The element at the m-th row and the n-th column of the scale mapping matrix indicates the (m+1)-th scale + segment index which has the closest center distance with (n+1)-th segment in the base scale. + Example: + scale_mapping_argmat[2][101] = 85 + In the above example, it means that 86-th segment in the 3rd scale (python index is 2) is mapped with + 102-th segment in the base scale. Thus, the longer segments bound to have more repeating numbers since + multiple base scale segments (since the base scale has the shortest length) fall into the range of the + longer segments. At the same time, each row contains N numbers of indices where N is number of + segments in the base-scale (i.e., the finest scale). + Shape: (batch_size, scale_n, self.diar_window_length) + ms_seg_counts (Tensor): + Cumulative sum of the number of segments in each scale. This information is needed to reconstruct + the multi-scale input matrix during forward propagating. + + Example: `batch_size=3, scale_n=6, emb_dim=192` + ms_seg_counts = + [[8, 9, 12, 16, 25, 51], + [11, 13, 14, 17, 25, 51], + [ 9, 9, 11, 16, 23, 50]] + + In this function, `ms_seg_counts` is used to get the actual length of each embedding sequence without + zero-padding. + + Returns: + ms_emb_seq (Tensor): + Multi-scale embedding sequence that is mapped, matched and repeated. The longer scales are less repeated, + while shorter scales are more frequently repeated following the scale mapping tensor. + """ + scale_n, batch_size = scale_mapping[0].shape[0], scale_mapping.shape[0] + split_emb_tup = torch.split(embs, ms_seg_counts.view(-1).tolist(), dim=0) + batch_emb_list = [split_emb_tup[i : i + scale_n] for i in range(0, len(split_emb_tup), scale_n)] + ms_emb_seq_list = [] + for batch_idx in range(batch_size): + feats_list = [] + for scale_index in range(scale_n): + repeat_mat = scale_mapping[batch_idx][scale_index] + feats_list.append(batch_emb_list[batch_idx][scale_index][repeat_mat, :]) + repp = torch.stack(feats_list).permute(1, 0, 2) + ms_emb_seq_list.append(repp) + ms_emb_seq = torch.stack(ms_emb_seq_list) + return ms_emb_seq + + @torch.no_grad() + def get_cluster_avg_embs_model( + self, embs: torch.Tensor, clus_label_index: torch.Tensor, ms_seg_counts: torch.Tensor, scale_mapping + ) -> torch.Tensor: + """ + Calculate the cluster-average speaker embedding based on the ground-truth speaker labels (i.e., cluster labels). + + Args: + embs (Tensor): + Merged embeddings without zero-padding in the batch. See `ms_seg_counts` for details. + Shape: (Total number of segments in the batch, emb_dim) + clus_label_index (Tensor): + Merged ground-truth cluster labels from all scales with zero-padding. Each scale's index can be + retrieved by using segment index in `ms_seg_counts`. + Shape: (batch_size, maximum total segment count among the samples in the batch) + ms_seg_counts (Tensor): + Cumulative sum of the number of segments in each scale. This information is needed to reconstruct + multi-scale input tensors during forward propagating. + + Example: `batch_size=3, scale_n=6, emb_dim=192` + ms_seg_counts = + [[8, 9, 12, 16, 25, 51], + [11, 13, 14, 17, 25, 51], + [ 9, 9, 11, 16, 23, 50]] + Counts of merged segments: (121, 131, 118) + embs has shape of (370, 192) + clus_label_index has shape of (3, 131) + + Shape: (batch_size, scale_n) + + Returns: + ms_avg_embs (Tensor): + Multi-scale cluster-average speaker embedding vectors. These embedding vectors are used as reference for + each speaker to predict the speaker label for the given multi-scale embedding sequences. + Shape: (batch_size, scale_n, emb_dim, self.num_spks_per_model) + """ + scale_n, batch_size = scale_mapping[0].shape[0], scale_mapping.shape[0] + split_emb_tup = torch.split(embs, ms_seg_counts.view(-1).tolist(), dim=0) + batch_emb_list = [split_emb_tup[i : i + scale_n] for i in range(0, len(split_emb_tup), scale_n)] + ms_avg_embs_list = [] + for batch_idx in range(batch_size): + oracle_clus_idx = clus_label_index[batch_idx] + max_seq_len = sum(ms_seg_counts[batch_idx]) + clus_label_index_batch = torch.split(oracle_clus_idx[:max_seq_len], ms_seg_counts[batch_idx].tolist()) + session_avg_emb_set_list = [] + for scale_index in range(scale_n): + spk_set_list = [] + for idx in range(self.cfg_msdd_model.max_num_of_spks): + _where = (clus_label_index_batch[scale_index] == idx).clone().detach() + if not torch.any(_where): + avg_emb = torch.zeros(self.msdd._speaker_model._cfg.decoder.emb_sizes).to(embs.device) + else: + avg_emb = torch.mean(batch_emb_list[batch_idx][scale_index][_where], dim=0) + spk_set_list.append(avg_emb) + session_avg_emb_set_list.append(torch.stack(spk_set_list)) + session_avg_emb_set = torch.stack(session_avg_emb_set_list) + ms_avg_embs_list.append(session_avg_emb_set) + + ms_avg_embs = torch.stack(ms_avg_embs_list).permute(0, 1, 3, 2) + ms_avg_embs = ms_avg_embs.float().detach().to(embs.device) + assert ( + not ms_avg_embs.requires_grad + ), "ms_avg_embs.requires_grad = True. ms_avg_embs should be detached from the torch graph." + return ms_avg_embs + + @torch.no_grad() + def get_ms_mel_feat( + self, + processed_signal: torch.Tensor, + processed_signal_len: torch.Tensor, + ms_seg_timestamps: torch.Tensor, + ms_seg_counts: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + """ + Load acoustic feature from audio segments for each scale and save it into a torch.tensor matrix. + In addition, create variables containing the information of the multiscale subsegmentation information. + + Note: `self.emb_batch_size` determines the number of embedding tensors attached to the computational graph. + If `self.emb_batch_size` is greater than 0, speaker embedding models are simultaneosly trained. Due to the + constrant of GPU memory size, only a subset of embedding tensors can be attached to the computational graph. + By default, the graph-attached embeddings are selected randomly by `torch.randperm`. Default value of + `self.emb_batch_size` is 0. + + Args: + processed_signal (Tensor): + Zero-padded Feature input. + Shape: (batch_size, feat_dim, the longest feature sequence length) + processed_signal_len (Tensor): + The actual legnth of feature input without zero-padding. + Shape: (batch_size,) + ms_seg_timestamps (Tensor): + Timestamps of the base-scale segments. + Shape: (batch_size, scale_n, number of base-scale segments, self.num_spks_per_model) + ms_seg_counts (Tensor): + Cumulative sum of the number of segments in each scale. This information is needed to reconstruct + the multi-scale input matrix during forward propagating. + Shape: (batch_size, scale_n) + + Returns: + ms_mel_feat (Tensor): + Feature input stream split into the same length. + Shape: (total number of segments, feat_dim, self.frame_per_sec * the-longest-scale-length) + ms_mel_feat_len (Tensor): + The actual length of feature without zero-padding. + Shape: (total number of segments,) + seq_len (Tensor): + The length of the input embedding sequences. + Shape: (total number of segments,) + detach_ids (tuple): + Tuple containing both detached embeding indices and attached embedding indices + """ + device = processed_signal.device + _emb_batch_size = min(self.emb_batch_size, ms_seg_counts.sum().item()) + feat_dim = self.preprocessor._cfg.features + max_sample_count = int(self.multiscale_args_dict["scale_dict"][0][0] * self.frame_per_sec) + ms_mel_feat_len_list, sequence_lengths_list, ms_mel_feat_list = [], [], [] + total_seg_count = torch.sum(ms_seg_counts) + + batch_size = processed_signal.shape[0] + for batch_idx in range(batch_size): + for scale_idx in range(self.scale_n): + scale_seg_num = ms_seg_counts[batch_idx][scale_idx] + for k, (stt, end) in enumerate(ms_seg_timestamps[batch_idx][scale_idx][:scale_seg_num]): + stt, end = int(stt.detach().item()), int(end.detach().item()) + end = min(end, stt + max_sample_count) + _features = torch.zeros(feat_dim, max_sample_count).to(torch.float32).to(device) + _features[:, : (end - stt)] = processed_signal[batch_idx][:, stt:end] + ms_mel_feat_list.append(_features) + ms_mel_feat_len_list.append(end - stt) + sequence_lengths_list.append(ms_seg_counts[batch_idx][-1]) + ms_mel_feat = torch.stack(ms_mel_feat_list).to(device) + ms_mel_feat_len = torch.tensor(ms_mel_feat_len_list).to(device) + seq_len = torch.tensor(sequence_lengths_list).to(device) + + if _emb_batch_size == 0: + attached, _emb_batch_size = torch.tensor([]), 0 + detached = torch.arange(total_seg_count) + else: + torch.manual_seed(self._trainer.current_epoch) + attached = torch.randperm(total_seg_count)[:_emb_batch_size] + detached = torch.randperm(total_seg_count)[_emb_batch_size:] + detach_ids = (attached, detached) + return ms_mel_feat, ms_mel_feat_len, seq_len, detach_ids + + def forward_infer(self, input_signal, input_signal_length, emb_vectors, targets): + """ + Wrapper function for inference case. + """ + preds, scale_weights = self.msdd( + ms_emb_seq=input_signal, length=input_signal_length, ms_avg_embs=emb_vectors, targets=targets + ) + return preds, scale_weights + + @typecheck() + def forward( + self, features, feature_length, ms_seg_timestamps, ms_seg_counts, clus_label_index, scale_mapping, targets + ): + processed_signal, processed_signal_len = self.msdd._speaker_model.preprocessor( + input_signal=features, length=feature_length + ) + audio_signal, audio_signal_len, sequence_lengths, detach_ids = self.get_ms_mel_feat( + processed_signal, processed_signal_len, ms_seg_timestamps, ms_seg_counts + ) + + # For detached embeddings + with torch.no_grad(): + self.msdd._speaker_model.eval() + logits, embs_d = self.msdd._speaker_model.forward_for_export( + processed_signal=audio_signal[detach_ids[1]], processed_signal_len=audio_signal_len[detach_ids[1]] + ) + embs = torch.zeros(audio_signal.shape[0], embs_d.shape[1]).to(embs_d.device) + embs[detach_ids[1], :] = embs_d.detach() + + # For attached embeddings + self.msdd._speaker_model.train() + if len(detach_ids[0]) > 1: + logits, embs_a = self.msdd._speaker_model.forward_for_export( + processed_signal=audio_signal[detach_ids[0]], processed_signal_len=audio_signal_len[detach_ids[0]] + ) + embs[detach_ids[0], :] = embs_a + + ms_emb_seq = self.get_ms_emb_seq(embs, scale_mapping, ms_seg_counts) + ms_avg_embs = self.get_cluster_avg_embs_model(embs, clus_label_index, ms_seg_counts, scale_mapping) + preds, scale_weights = self.msdd( + ms_emb_seq=ms_emb_seq, length=sequence_lengths, ms_avg_embs=ms_avg_embs, targets=targets + ) + return preds, scale_weights + + def training_step(self, batch: list, batch_idx: int): + features, feature_length, ms_seg_timestamps, ms_seg_counts, clus_label_index, scale_mapping, targets = batch + sequence_lengths = torch.tensor([x[-1] for x in ms_seg_counts.detach()]) + preds, _ = self.forward( + features=features, + feature_length=feature_length, + ms_seg_timestamps=ms_seg_timestamps, + ms_seg_counts=ms_seg_counts, + clus_label_index=clus_label_index, + scale_mapping=scale_mapping, + targets=targets, + ) + loss = self.loss(probs=preds, labels=targets, signal_lengths=sequence_lengths) + self._accuracy_train(preds, targets, sequence_lengths) + torch.cuda.empty_cache() + f1_acc = self._accuracy_train.compute() + self.log('loss', loss, sync_dist=True) + self.log('learning_rate', self._optimizer.param_groups[0]['lr'], sync_dist=True) + self.log('train_f1_acc', f1_acc, sync_dist=True) + self._accuracy_train.reset() + return {'loss': loss} + + def validation_step(self, batch: list, batch_idx: int, dataloader_idx: int = 0): + features, feature_length, ms_seg_timestamps, ms_seg_counts, clus_label_index, scale_mapping, targets = batch + sequence_lengths = torch.tensor([x[-1] for x in ms_seg_counts]) + preds, _ = self.forward( + features=features, + feature_length=feature_length, + ms_seg_timestamps=ms_seg_timestamps, + ms_seg_counts=ms_seg_counts, + clus_label_index=clus_label_index, + scale_mapping=scale_mapping, + targets=targets, + ) + loss = self.loss(probs=preds, labels=targets, signal_lengths=sequence_lengths) + self._accuracy_valid(preds, targets, sequence_lengths) + f1_acc = self._accuracy_valid.compute() + self.log('val_loss', loss, sync_dist=True) + self.log('val_f1_acc', f1_acc, sync_dist=True) + return { + 'val_loss': loss, + 'val_f1_acc': f1_acc, + } + + def multi_validation_epoch_end(self, outputs: list, dataloader_idx: int = 0): + val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean() + f1_acc = self._accuracy_valid.compute() + self._accuracy_valid.reset() + + self.log('val_loss', val_loss_mean, sync_dist=True) + self.log('val_f1_acc', f1_acc, sync_dist=True) + return { + 'val_loss': val_loss_mean, + 'val_f1_acc': f1_acc, + } + + def multi_test_epoch_end(self, outputs: List[Dict[str, torch.Tensor]], dataloader_idx: int = 0): + test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean() + f1_acc = self._accuracy_test.compute() + self._accuracy_test.reset() + self.log('test_f1_acc', f1_acc, sync_dist=True) + return { + 'test_loss': test_loss_mean, + 'test_f1_acc': f1_acc, + } + + def compute_accuracies(self): + """ + Calculate F1 score and accuracy of the predicted sigmoid values. + + Returns: + f1_score (float): F1 score of the estimated diarized speaker label sequences. + simple_acc (float): Accuracy of predicted speaker labels: (total # of correct labels)/(total # of sigmoid values) + """ + f1_score = self._accuracy_test.compute() + num_correct = torch.sum(self._accuracy_test.true.bool()) + total_count = torch.prod(torch.tensor(self._accuracy_test.targets.shape)) + simple_acc = num_correct / total_count + return f1_score, simple_acc + + +class ClusterEmbedding(torch.nn.Module): + """ + This class is built for calculating cluster-average embeddings, segmentation and load/save of the estimated cluster labels. + The methods in this class is used for the inference of MSDD models. + + Args: + cfg_diar_infer (DictConfig): + Config dictionary from diarization inference YAML file + cfg_msdd_model (DictConfig): + Config dictionary from MSDD model checkpoint file + + Class Variables: + self.cfg_diar_infer (DictConfig): + Config dictionary from diarization inference YAML file + cfg_msdd_model (DictConfig): + Config dictionary from MSDD model checkpoint file + self._speaker_model (class `EncDecSpeakerLabelModel`): + This is a placeholder for class instance of `EncDecSpeakerLabelModel` + self.scale_window_length_list (list): + List containing the window lengths (i.e., scale length) of each scale. + self.scale_n (int): + Number of scales for multi-scale clustering diarizer + self.base_scale_index (int): + The index of the base-scale which is the shortest scale among the given multiple scales + """ + + def __init__( + self, cfg_diar_infer: DictConfig, cfg_msdd_model: DictConfig, speaker_model: Optional[EncDecSpeakerLabelModel] + ): + super().__init__() + self.cfg_diar_infer = cfg_diar_infer + self._cfg_msdd = cfg_msdd_model + self._speaker_model = speaker_model + self.scale_window_length_list = list( + self.cfg_diar_infer.diarizer.speaker_embeddings.parameters.window_length_in_sec + ) + self.scale_n = len(self.scale_window_length_list) + self.base_scale_index = len(self.scale_window_length_list) - 1 + self.clus_diar_model = ClusteringDiarizer(cfg=self.cfg_diar_infer, speaker_model=self._speaker_model) + + def prepare_cluster_embs_infer(self): + """ + Launch clustering diarizer to prepare embedding vectors and clustering results. + """ + self.max_num_speakers = self.cfg_diar_infer.diarizer.clustering.parameters.max_num_speakers + self.emb_sess_test_dict, self.emb_seq_test, self.clus_test_label_dict, _ = self.run_clustering_diarizer( + self._cfg_msdd.test_ds.manifest_filepath, self._cfg_msdd.test_ds.emb_dir + ) + + def assign_labels_to_longer_segs(self, base_clus_label_dict: Dict, session_scale_mapping_dict: Dict): + """ + In multi-scale speaker diarization system, clustering result is solely based on the base-scale (the shortest scale). + To calculate cluster-average speaker embeddings for each scale that are longer than the base-scale, this function assigns + clustering results for the base-scale to the longer scales by measuring the distance between subsegment timestamps in the + base-scale and non-base-scales. + + Args: + base_clus_label_dict (dict): + Dictionary containing clustering results for base-scale segments. Indexed by `uniq_id` string. + session_scale_mapping_dict (dict): + Dictionary containing multiscale mapping information for each session. Indexed by `uniq_id` string. + + Returns: + all_scale_clus_label_dict (dict): + Dictionary containing clustering labels of all scales. Indexed by scale_index in integer format. + + """ + all_scale_clus_label_dict = {scale_index: {} for scale_index in range(self.scale_n)} + for uniq_id, uniq_scale_mapping_dict in session_scale_mapping_dict.items(): + base_scale_clus_label = np.array([x[-1] for x in base_clus_label_dict[uniq_id]]) + all_scale_clus_label_dict[self.base_scale_index][uniq_id] = base_scale_clus_label + for scale_index in range(self.scale_n - 1): + new_clus_label = [] + assert ( + uniq_scale_mapping_dict[scale_index].shape[0] == base_scale_clus_label.shape[0] + ), "The number of base scale labels does not match the segment numbers in uniq_scale_mapping_dict" + max_index = max(uniq_scale_mapping_dict[scale_index]) + for seg_idx in range(max_index + 1): + if seg_idx in uniq_scale_mapping_dict[scale_index]: + seg_clus_label = mode(base_scale_clus_label[uniq_scale_mapping_dict[scale_index] == seg_idx]) + else: + seg_clus_label = 0 if len(new_clus_label) == 0 else new_clus_label[-1] + new_clus_label.append(seg_clus_label) + all_scale_clus_label_dict[scale_index][uniq_id] = new_clus_label + return all_scale_clus_label_dict + + def get_base_clus_label_dict(self, clus_labels: List[str], emb_scale_seq_dict: Dict[int, dict]): + """ + Retrieve base scale clustering labels from `emb_scale_seq_dict`. + + Args: + clus_labels (list): + List containing cluster results generated by clustering diarizer. + emb_scale_seq_dict (dict): + Dictionary containing multiscale embedding input sequences. + Returns: + base_clus_label_dict (dict): + Dictionary containing start and end of base scale segments and its cluster label. Indexed by `uniq_id`. + emb_dim (int): + Embedding dimension in integer. + """ + base_clus_label_dict = {key: [] for key in emb_scale_seq_dict[self.base_scale_index].keys()} + for line in clus_labels: + uniq_id = line.split()[0] + label = int(line.split()[-1].split('_')[-1]) + stt, end = [round(float(x), 2) for x in line.split()[1:3]] + base_clus_label_dict[uniq_id].append([stt, end, label]) + emb_dim = emb_scale_seq_dict[0][uniq_id][0].shape[0] + return base_clus_label_dict, emb_dim + + def get_cluster_avg_embs( + self, emb_scale_seq_dict: Dict, clus_labels: List, speaker_mapping_dict: Dict, session_scale_mapping_dict: Dict + ): + """ + MSDD requires cluster-average speaker embedding vectors for each scale. This function calculates an average embedding vector for each cluster (speaker) + and each scale. + + Args: + emb_scale_seq_dict (dict): + Dictionary containing embedding sequence for each scale. Keys are scale index in integer. + clus_labels (list): + Clustering results from clustering diarizer including all the sessions provided in input manifest files. + speaker_mapping_dict (dict): + Speaker mapping dictionary in case RTTM files are provided. This is mapping between integer based speaker index and + speaker ID tokens in RTTM files. + Example: + {'en_0638': {'speaker_0': 'en_0638_A', 'speaker_1': 'en_0638_B'}, + 'en_4065': {'speaker_0': 'en_4065_B', 'speaker_1': 'en_4065_A'}, ...,} + session_scale_mapping_dict (dict): + Dictionary containing multiscale mapping information for each session. Indexed by `uniq_id` string. + + Returns: + emb_sess_avg_dict (dict): + Dictionary containing speaker mapping information and cluster-average speaker embedding vector. + Each session-level dictionary is indexed by scale index in integer. + output_clus_label_dict (dict): + Subegmentation timestamps in float type and Clustering result in integer type. Indexed by `uniq_id` keys. + """ + self.scale_n = len(emb_scale_seq_dict.keys()) + emb_sess_avg_dict = { + scale_index: {key: [] for key in emb_scale_seq_dict[self.scale_n - 1].keys()} + for scale_index in emb_scale_seq_dict.keys() + } + output_clus_label_dict, emb_dim = self.get_base_clus_label_dict(clus_labels, emb_scale_seq_dict) + all_scale_clus_label_dict = self.assign_labels_to_longer_segs( + output_clus_label_dict, session_scale_mapping_dict + ) + for scale_index in emb_scale_seq_dict.keys(): + for uniq_id, _emb_tensor in emb_scale_seq_dict[scale_index].items(): + if type(_emb_tensor) == list: + emb_tensor = torch.tensor(np.array(_emb_tensor)) + else: + emb_tensor = _emb_tensor + clus_label_list = all_scale_clus_label_dict[scale_index][uniq_id] + spk_set = set(clus_label_list) + + # Create a label array which identifies clustering result for each segment. + label_array = torch.Tensor(clus_label_list) + avg_embs = torch.zeros(emb_dim, self.max_num_speakers) + for spk_idx in spk_set: + selected_embs = emb_tensor[label_array == spk_idx] + avg_embs[:, spk_idx] = torch.mean(selected_embs, dim=0) + + if speaker_mapping_dict is not None: + inv_map = {clus_key: rttm_key for rttm_key, clus_key in speaker_mapping_dict[uniq_id].items()} + else: + inv_map = None + + emb_sess_avg_dict[scale_index][uniq_id] = {'mapping': inv_map, 'avg_embs': avg_embs} + return emb_sess_avg_dict, output_clus_label_dict + + def run_clustering_diarizer(self, manifest_filepath: str, emb_dir: str): + """ + If no pre-existing data is provided, run clustering diarizer from scratch. This will create scale-wise speaker embedding + sequence, cluster-average embeddings, scale mapping and base scale clustering labels. Note that speaker embedding `state_dict` + is loaded from the `state_dict` in the provided MSDD checkpoint. + + Args: + manifest_filepath (str): + Input manifest file for creating audio-to-RTTM mapping. + emb_dir (str): + Output directory where embedding files and timestamp files are saved. + + Returns: + emb_sess_avg_dict (dict): + Dictionary containing cluster-average embeddings for each session. + emb_scale_seq_dict (dict): + Dictionary containing embedding tensors which are indexed by scale numbers. + base_clus_label_dict (dict): + Dictionary containing clustering results. Clustering results are cluster labels for the base scale segments. + """ + self.cfg_diar_infer.diarizer.manifest_filepath = manifest_filepath + self.cfg_diar_infer.diarizer.out_dir = emb_dir + + # Run ClusteringDiarizer which includes system VAD or oracle VAD. + self._out_dir = self.clus_diar_model._diarizer_params.out_dir + self.out_rttm_dir = os.path.join(self._out_dir, 'pred_rttms') + os.makedirs(self.out_rttm_dir, exist_ok=True) + + self.clus_diar_model._cluster_params = self.cfg_diar_infer.diarizer.clustering.parameters + self.clus_diar_model.multiscale_args_dict[ + "multiscale_weights" + ] = self.cfg_diar_infer.diarizer.speaker_embeddings.parameters.multiscale_weights + self.clus_diar_model._diarizer_params.speaker_embeddings.parameters = ( + self.cfg_diar_infer.diarizer.speaker_embeddings.parameters + ) + cluster_params = self.clus_diar_model._cluster_params + cluster_params = dict(cluster_params) if isinstance(cluster_params, DictConfig) else cluster_params.dict() + clustering_params_str = json.dumps(cluster_params, indent=4) + + logging.info(f"Multiscale Weights: {self.clus_diar_model.multiscale_args_dict['multiscale_weights']}") + logging.info(f"Clustering Parameters: {clustering_params_str}") + scores = self.clus_diar_model.diarize(batch_size=self.cfg_diar_infer.batch_size) + + # If RTTM (ground-truth diarization annotation) files do not exist, scores is None. + if scores is not None: + metric, speaker_mapping_dict, _ = scores + else: + metric, speaker_mapping_dict = None, None + + # Get the mapping between segments in different scales. + self._embs_and_timestamps = get_embs_and_timestamps( + self.clus_diar_model.multiscale_embeddings_and_timestamps, self.clus_diar_model.multiscale_args_dict + ) + session_scale_mapping_dict = self.get_scale_map(self._embs_and_timestamps) + emb_scale_seq_dict = self.load_emb_scale_seq_dict(emb_dir) + clus_labels = self.load_clustering_labels(emb_dir) + emb_sess_avg_dict, base_clus_label_dict = self.get_cluster_avg_embs( + emb_scale_seq_dict, clus_labels, speaker_mapping_dict, session_scale_mapping_dict + ) + emb_scale_seq_dict['session_scale_mapping'] = session_scale_mapping_dict + return emb_sess_avg_dict, emb_scale_seq_dict, base_clus_label_dict, metric + + def get_scale_map(self, embs_and_timestamps): + """ + Save multiscale mapping data into dictionary format. + + Args: + embs_and_timestamps (dict): + Dictionary containing embedding tensors and timestamp tensors. Indexed by `uniq_id` string. + Returns: + session_scale_mapping_dict (dict): + Dictionary containing multiscale mapping information for each session. Indexed by `uniq_id` string. + """ + session_scale_mapping_dict = {} + for uniq_id, uniq_embs_and_timestamps in embs_and_timestamps.items(): + scale_mapping_dict = get_scale_mapping_argmat(uniq_embs_and_timestamps) + session_scale_mapping_dict[uniq_id] = scale_mapping_dict + return session_scale_mapping_dict + + def check_clustering_labels(self, out_dir): + """ + Check whether the laoded clustering label file is including clustering results for all sessions. + This function is used for inference mode of MSDD. + + Args: + out_dir (str): + Path to the directory where clustering result files are saved. + Returns: + file_exists (bool): + Boolean that indicates whether clustering result file exists. + clus_label_path (str): + Path to the clustering label output file. + """ + clus_label_path = os.path.join( + out_dir, 'speaker_outputs', f'subsegments_scale{self.base_scale_index}_cluster.label' + ) + file_exists = os.path.exists(clus_label_path) + if not file_exists: + logging.info(f"Clustering label file {clus_label_path} does not exist.") + return file_exists, clus_label_path + + def load_clustering_labels(self, out_dir): + """ + Load clustering labels generated by clustering diarizer. This function is used for inference mode of MSDD. + + Args: + out_dir (str): + Path to the directory where clustering result files are saved. + Returns: + emb_scale_seq_dict (dict): + List containing clustering results in string format. + """ + file_exists, clus_label_path = self.check_clustering_labels(out_dir) + logging.info(f"Loading cluster label file from {clus_label_path}") + with open(clus_label_path) as f: + clus_labels = f.readlines() + return clus_labels + + def load_emb_scale_seq_dict(self, out_dir): + """ + Load saved embeddings generated by clustering diarizer. This function is used for inference mode of MSDD. + + Args: + out_dir (str): + Path to the directory where embedding pickle files are saved. + Returns: + emb_scale_seq_dict (dict): + Dictionary containing embedding tensors which are indexed by scale numbers. + """ + window_len_list = list(self.cfg_diar_infer.diarizer.speaker_embeddings.parameters.window_length_in_sec) + emb_scale_seq_dict = {scale_index: None for scale_index in range(len(window_len_list))} + for scale_index in range(len(window_len_list)): + pickle_path = os.path.join( + out_dir, 'speaker_outputs', 'embeddings', f'subsegments_scale{scale_index}_embeddings.pkl' + ) + logging.info(f"Loading embedding pickle file of scale:{scale_index} at {pickle_path}") + with open(pickle_path, "rb") as input_file: + emb_dict = pkl.load(input_file) + for key, val in emb_dict.items(): + emb_dict[key] = val + emb_scale_seq_dict[scale_index] = emb_dict + return emb_scale_seq_dict + + +class NeuralDiarizer(LightningModule): + """ + Class for inference based on multiscale diarization decoder (MSDD). MSDD requires initializing clustering results from + clustering diarizer. Overlap-aware diarizer requires separate RTTM generation and evaluation modules to check the effect of + overlap detection in speaker diarization. + """ + + def __init__(self, cfg: Union[DictConfig, NeuralDiarizerInferenceConfig]): + super().__init__() + self._cfg = cfg + + # Parameter settings for MSDD model + self.use_speaker_model_from_ckpt = cfg.diarizer.msdd_model.parameters.get('use_speaker_model_from_ckpt', True) + self.use_clus_as_main = cfg.diarizer.msdd_model.parameters.get('use_clus_as_main', False) + self.max_overlap_spks = cfg.diarizer.msdd_model.parameters.get('max_overlap_spks', 2) + self.num_spks_per_model = cfg.diarizer.msdd_model.parameters.get('num_spks_per_model', 2) + self.use_adaptive_thres = cfg.diarizer.msdd_model.parameters.get('use_adaptive_thres', True) + self.max_pred_length = cfg.diarizer.msdd_model.parameters.get('max_pred_length', 0) + self.diar_eval_settings = cfg.diarizer.msdd_model.parameters.get( + 'diar_eval_settings', [(0.25, True), (0.25, False), (0.0, False)] + ) + + self._init_msdd_model(cfg) + self.diar_window_length = cfg.diarizer.msdd_model.parameters.diar_window_length + self.msdd_model.cfg = self.transfer_diar_params_to_model_params(self.msdd_model, cfg) + + # Initialize clustering and embedding preparation instance (as a diarization encoder). + self.clustering_embedding = ClusterEmbedding( + cfg_diar_infer=cfg, cfg_msdd_model=self.msdd_model.cfg, speaker_model=self._speaker_model + ) + + # Parameters for creating diarization results from MSDD outputs. + self.clustering_max_spks = self.msdd_model._cfg.max_num_of_spks + self.overlap_infer_spk_limit = cfg.diarizer.msdd_model.parameters.get( + 'overlap_infer_spk_limit', self.clustering_max_spks + ) + + def transfer_diar_params_to_model_params(self, msdd_model, cfg): + """ + Transfer the parameters that are needed for MSDD inference from the diarization inference config files + to MSDD model config `msdd_model.cfg`. + """ + msdd_model.cfg.diarizer.out_dir = cfg.diarizer.out_dir + msdd_model.cfg.test_ds.manifest_filepath = cfg.diarizer.manifest_filepath + msdd_model.cfg.test_ds.emb_dir = cfg.diarizer.out_dir + msdd_model.cfg.test_ds.batch_size = cfg.diarizer.msdd_model.parameters.infer_batch_size + msdd_model.cfg.test_ds.seq_eval_mode = cfg.diarizer.msdd_model.parameters.seq_eval_mode + msdd_model._cfg.max_num_of_spks = cfg.diarizer.clustering.parameters.max_num_speakers + return msdd_model.cfg + + @rank_zero_only + def save_to(self, save_path: str): + """ + Saves model instances (weights and configuration) into EFF archive. + You can use "restore_from" method to fully restore instance from .nemo file. + + .nemo file is an archive (tar.gz) with the following: + model_config.yaml - model configuration in .yaml format. You can deserialize this into cfg argument for model's constructor + model_wights.chpt - model checkpoint + + Args: + save_path: Path to .nemo file where model instance should be saved + """ + self.clus_diar = self.clustering_embedding.clus_diar_model + _NEURAL_DIAR_MODEL = "msdd_model.nemo" + + with tempfile.TemporaryDirectory() as tmpdir: + config_yaml = os.path.join(tmpdir, _MODEL_CONFIG_YAML) + spkr_model = os.path.join(tmpdir, _SPEAKER_MODEL) + neural_diar_model = os.path.join(tmpdir, _NEURAL_DIAR_MODEL) + + self.clus_diar.to_config_file(path2yaml_file=config_yaml) + if self.clus_diar.has_vad_model: + vad_model = os.path.join(tmpdir, _VAD_MODEL) + self.clus_diar._vad_model.save_to(vad_model) + self.clus_diar._speaker_model.save_to(spkr_model) + self.msdd_model.save_to(neural_diar_model) + self.clus_diar.__make_nemo_file_from_folder(filename=save_path, source_dir=tmpdir) + + def extract_standalone_speaker_model(self, prefix: str = 'msdd._speaker_model.') -> EncDecSpeakerLabelModel: + """ + MSDD model file contains speaker embedding model and MSDD model. This function extracts standalone speaker model and save it to + `self.spk_emb_state_dict` to be loaded separately for clustering diarizer. + + Args: + ext (str): + File-name extension of the provided model path. + Returns: + standalone_model_path (str): + Path to the extracted standalone model without speaker embedding extractor model. + """ + model_state_dict = self.msdd_model.state_dict() + spk_emb_module_names = [] + for name in model_state_dict.keys(): + if prefix in name: + spk_emb_module_names.append(name) + + spk_emb_state_dict = {} + for name in spk_emb_module_names: + org_name = name.replace(prefix, '') + spk_emb_state_dict[org_name] = model_state_dict[name] + + _speaker_model = EncDecSpeakerLabelModel.from_config_dict(self.msdd_model.cfg.speaker_model_cfg) + _speaker_model.load_state_dict(spk_emb_state_dict) + return _speaker_model + + def _init_msdd_model(self, cfg: Union[DictConfig, NeuralDiarizerInferenceConfig]): + + """ + Initialized MSDD model with the provided config. Load either from `.nemo` file or `.ckpt` checkpoint files. + """ + model_path = cfg.diarizer.msdd_model.model_path + if model_path.endswith('.nemo'): + logging.info(f"Using local nemo file from {model_path}") + self.msdd_model = EncDecDiarLabelModel.restore_from(restore_path=model_path, map_location=cfg.device) + elif model_path.endswith('.ckpt'): + logging.info(f"Using local checkpoint from {model_path}") + self.msdd_model = EncDecDiarLabelModel.load_from_checkpoint( + checkpoint_path=model_path, map_location=cfg.device + ) + else: + if model_path not in get_available_model_names(EncDecDiarLabelModel): + logging.warning(f"requested {model_path} model name not available in pretrained models, instead") + logging.info("Loading pretrained {} model from NGC".format(model_path)) + self.msdd_model = EncDecDiarLabelModel.from_pretrained(model_name=model_path, map_location=cfg.device) + # Load speaker embedding model state_dict which is loaded from the MSDD checkpoint. + if self.use_speaker_model_from_ckpt: + self._speaker_model = self.extract_standalone_speaker_model() + else: + self._speaker_model = None + + def get_pred_mat(self, data_list: List[Union[Tuple[int], List[torch.Tensor]]]) -> torch.Tensor: + """ + This module puts together the pairwise, two-speaker, predicted results to form a finalized matrix that has dimension of + `(total_len, n_est_spks)`. The pairwise results are evenutally averaged. For example, in 4 speaker case (speaker 1, 2, 3, 4), + the sum of the pairwise results (1, 2), (1, 3), (1, 4) are then divided by 3 to take average of the sigmoid values. + + Args: + data_list (list): + List containing data points from `test_data_collection` variable. `data_list` has sublists `data` as follows: + data[0]: `target_spks` tuple + Examples: (0, 1, 2) + data[1]: Tensor containing estimaged sigmoid values. + [[0.0264, 0.9995], + [0.0112, 1.0000], + ..., + [1.0000, 0.0512]] + + Returns: + sum_pred (Tensor): + Tensor containing the averaged sigmoid values for each speaker. + """ + all_tups = tuple() + for data in data_list: + all_tups += data[0] + n_est_spks = len(set(all_tups)) + digit_map = dict(zip(sorted(set(all_tups)), range(n_est_spks))) + total_len = max([sess[1].shape[1] for sess in data_list]) + sum_pred = torch.zeros(total_len, n_est_spks) + for (_dim_tup, pred_mat) in data_list: + dim_tup = [digit_map[x] for x in _dim_tup] + if len(pred_mat.shape) == 3: + pred_mat = pred_mat.squeeze(0) + if n_est_spks <= self.num_spks_per_model: + sum_pred = pred_mat + else: + _end = pred_mat.shape[0] + sum_pred[:_end, dim_tup] += pred_mat.cpu().float() + sum_pred = sum_pred / (n_est_spks - 1) + return sum_pred + + def get_integrated_preds_list( + self, uniq_id_list: List[str], test_data_collection: List[Any], preds_list: List[torch.Tensor] + ) -> List[torch.Tensor]: + """ + Merge multiple sequence inference outputs into a session level result. + + Args: + uniq_id_list (list): + List containing `uniq_id` values. + test_data_collection (collections.DiarizationLabelEntity): + Class instance that is containing session information such as targeted speaker indices, audio filepaths and RTTM filepaths. + preds_list (list): + List containing tensors filled with sigmoid values. + + Returns: + output_list (list): + List containing session-level estimated prediction matrix. + """ + session_dict = get_id_tup_dict(uniq_id_list, test_data_collection, preds_list) + output_dict = {uniq_id: [] for uniq_id in uniq_id_list} + for uniq_id, data_list in session_dict.items(): + sum_pred = self.get_pred_mat(data_list) + output_dict[uniq_id] = sum_pred.unsqueeze(0) + output_list = [output_dict[uniq_id] for uniq_id in uniq_id_list] + return output_list + + def get_emb_clus_infer(self, cluster_embeddings): + """Assign dictionaries containing the clustering results from the class instance `cluster_embeddings`. + """ + self.msdd_model.emb_sess_test_dict = cluster_embeddings.emb_sess_test_dict + self.msdd_model.clus_test_label_dict = cluster_embeddings.clus_test_label_dict + self.msdd_model.emb_seq_test = cluster_embeddings.emb_seq_test + + @torch.no_grad() + def diarize(self) -> Optional[List[Optional[List[Tuple[DiarizationErrorRate, Dict]]]]]: + """ + Launch diarization pipeline which starts from VAD (or a oracle VAD stamp generation), initialization clustering and multiscale diarization decoder (MSDD). + Note that the result of MSDD can include multiple speakers at the same time. Therefore, RTTM output of MSDD needs to be based on `make_rttm_with_overlap()` + function that can generate overlapping timestamps. `self.run_overlap_aware_eval()` function performs DER evaluation. + """ + self.clustering_embedding.prepare_cluster_embs_infer() + self.msdd_model.pairwise_infer = True + self.get_emb_clus_infer(self.clustering_embedding) + preds_list, targets_list, signal_lengths_list = self.run_pairwise_diarization() + thresholds = list(self._cfg.diarizer.msdd_model.parameters.sigmoid_threshold) + return [self.run_overlap_aware_eval(preds_list, threshold) for threshold in thresholds] + + def get_range_average( + self, signals: torch.Tensor, emb_vectors: torch.Tensor, diar_window_index: int, test_data_collection: List[Any] + ) -> Tuple[torch.Tensor, torch.Tensor, int]: + """ + This function is only used when `split_infer=True`. This module calculates cluster-average embeddings for the given short range. + The range length is set by `self.diar_window_length`, and each cluster-average is only calculated for the specified range. + Note that if the specified range does not contain some speakers (e.g. the range contains speaker 1, 3) compared to the global speaker sets + (e.g. speaker 1, 2, 3, 4) then the missing speakers (e.g. speakers 2, 4) are assigned with zero-filled cluster-average speaker embedding. + + Args: + signals (Tensor): + Zero-padded Input multi-scale embedding sequences. + Shape: (length, scale_n, emb_vectors, emb_dim) + emb_vectors (Tensor): + Cluster-average multi-scale embedding vectors. + Shape: (length, scale_n, emb_vectors, emb_dim) + diar_window_index (int): + Index of split diarization wondows. + test_data_collection (collections.DiarizationLabelEntity) + Class instance that is containing session information such as targeted speaker indices, audio filepath and RTTM filepath. + + Returns: + return emb_vectors_split (Tensor): + Cluster-average speaker embedding vectors for each scale. + emb_seq (Tensor): + Zero-padded multi-scale embedding sequences. + seq_len (int): + Length of the sequence determined by `self.diar_window_length` variable. + """ + emb_vectors_split = torch.zeros_like(emb_vectors) + uniq_id = os.path.splitext(os.path.basename(test_data_collection.audio_file))[0] + clus_label_tensor = torch.tensor([x[-1] for x in self.msdd_model.clus_test_label_dict[uniq_id]]) + for spk_idx in range(len(test_data_collection.target_spks)): + stt, end = ( + diar_window_index * self.diar_window_length, + min((diar_window_index + 1) * self.diar_window_length, clus_label_tensor.shape[0]), + ) + seq_len = end - stt + if stt < clus_label_tensor.shape[0]: + target_clus_label_tensor = clus_label_tensor[stt:end] + emb_seq, seg_length = ( + signals[stt:end, :, :], + min( + self.diar_window_length, + clus_label_tensor.shape[0] - diar_window_index * self.diar_window_length, + ), + ) + target_clus_label_bool = target_clus_label_tensor == test_data_collection.target_spks[spk_idx] + + # There are cases where there is no corresponding speaker in split range, so any(target_clus_label_bool) could be False. + if any(target_clus_label_bool): + emb_vectors_split[:, :, spk_idx] = torch.mean(emb_seq[target_clus_label_bool], dim=0) + + # In case when the loop reaches the end of the sequence + if seq_len < self.diar_window_length: + emb_seq = torch.cat( + [ + emb_seq, + torch.zeros(self.diar_window_length - seq_len, emb_seq.shape[1], emb_seq.shape[2]).to( + signals.device + ), + ], + dim=0, + ) + else: + emb_seq = torch.zeros(self.diar_window_length, emb_vectors.shape[0], emb_vectors.shape[1]).to( + signals.device + ) + seq_len = 0 + return emb_vectors_split, emb_seq, seq_len + + def get_range_clus_avg_emb( + self, test_batch: List[torch.Tensor], _test_data_collection: List[Any], device: torch.device('cpu') + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + This function is only used when `get_range_average` function is called. This module calculates cluster-average embeddings for + the given short range. The range length is set by `self.diar_window_length`, and each cluster-average is only calculated for the specified range. + + Args: + test_batch: (list) + List containing embedding sequences, length of embedding sequences, ground truth labels (if exists) and initializing embedding vectors. + test_data_collection: (list) + List containing test-set dataloader contents. test_data_collection includes wav file path, RTTM file path, clustered speaker indices. + + Returns: + sess_emb_vectors (Tensor): + Tensor of cluster-average speaker embedding vectors. + Shape: (batch_size, scale_n, emb_dim, 2*num_of_spks) + sess_emb_seq (Tensor): + Tensor of input multi-scale embedding sequences. + Shape: (batch_size, length, scale_n, emb_dim) + sess_sig_lengths (Tensor): + Tensor of the actucal sequence length without zero-padding. + Shape: (batch_size) + """ + _signals, signal_lengths, _targets, _emb_vectors = test_batch + sess_emb_vectors, sess_emb_seq, sess_sig_lengths = [], [], [] + split_count = torch.ceil(torch.tensor(_signals.shape[1] / self.diar_window_length)).int() + self.max_pred_length = max(self.max_pred_length, self.diar_window_length * split_count) + for k in range(_signals.shape[0]): + signals, emb_vectors, test_data_collection = _signals[k], _emb_vectors[k], _test_data_collection[k] + for diar_window_index in range(split_count): + emb_vectors_split, emb_seq, seq_len = self.get_range_average( + signals, emb_vectors, diar_window_index, test_data_collection + ) + sess_emb_vectors.append(emb_vectors_split) + sess_emb_seq.append(emb_seq) + sess_sig_lengths.append(seq_len) + sess_emb_vectors = torch.stack(sess_emb_vectors).to(device) + sess_emb_seq = torch.stack(sess_emb_seq).to(device) + sess_sig_lengths = torch.tensor(sess_sig_lengths).to(device) + return sess_emb_vectors, sess_emb_seq, sess_sig_lengths + + def diar_infer( + self, test_batch: List[torch.Tensor], test_data_collection: List[Any] + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Launch forward_infer() function by feeding the session-wise embedding sequences to get pairwise speaker prediction values. + If split_infer is True, the input audio clips are broken into short sequences then cluster average embeddings are calculated + for inference. Split-infer might result in an improved results if calculating clustering average on the shorter tim-espan can + help speaker assignment. + + Args: + test_batch: (list) + List containing embedding sequences, length of embedding sequences, ground truth labels (if exists) and initializing embedding vectors. + test_data_collection: (list) + List containing test-set dataloader contents. test_data_collection includes wav file path, RTTM file path, clustered speaker indices. + + Returns: + preds (Tensor): + Tensor containing predicted values which are generated from MSDD model. + targets (Tensor): + Tensor containing binary ground-truth values. + signal_lengths (Tensor): + The actual Session length (number of steps = number of base-scale segments) without zero padding. + """ + signals, signal_lengths, _targets, emb_vectors = test_batch + if self._cfg.diarizer.msdd_model.parameters.split_infer: + split_count = torch.ceil(torch.tensor(signals.shape[1] / self.diar_window_length)).int() + sess_emb_vectors, sess_emb_seq, sess_sig_lengths = self.get_range_clus_avg_emb( + test_batch, test_data_collection, device=self.msdd_model.device + ) + with autocast(): + _preds, scale_weights = self.msdd_model.forward_infer( + input_signal=sess_emb_seq, + input_signal_length=sess_sig_lengths, + emb_vectors=sess_emb_vectors, + targets=None, + ) + _preds = _preds.reshape(len(signal_lengths), split_count * self.diar_window_length, -1) + _preds = _preds[:, : signals.shape[1], :] + else: + with autocast(): + _preds, scale_weights = self.msdd_model.forward_infer( + input_signal=signals, input_signal_length=signal_lengths, emb_vectors=emb_vectors, targets=None + ) + self.max_pred_length = max(_preds.shape[1], self.max_pred_length) + preds = torch.zeros(_preds.shape[0], self.max_pred_length, _preds.shape[2]) + targets = torch.zeros(_preds.shape[0], self.max_pred_length, _preds.shape[2]) + preds[:, : _preds.shape[1], :] = _preds + return preds, targets, signal_lengths + + @torch.no_grad() + def run_pairwise_diarization(self) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[torch.Tensor]]: + """ + Setup the parameters needed for batch inference and run batch inference. Note that each sample is pairwise speaker input. + The pairwise inference results are reconstructed to make session-wise prediction results. + + Returns: + integrated_preds_list: (list) + List containing the session-wise speaker predictions in torch.tensor format. + targets_list: (list) + List containing the ground-truth labels in matrix format filled with 0 or 1. + signal_lengths_list: (list) + List containing the actual length of each sequence in session. + """ + self.out_rttm_dir = self.clustering_embedding.out_rttm_dir + self.msdd_model.setup_test_data(self.msdd_model.cfg.test_ds) + self.msdd_model.eval() + cumul_sample_count = [0] + preds_list, targets_list, signal_lengths_list = [], [], [] + uniq_id_list = get_uniq_id_list_from_manifest(self.msdd_model.cfg.test_ds.manifest_filepath) + test_data_collection = [d for d in self.msdd_model.data_collection] + for sidx, test_batch in enumerate(tqdm(self.msdd_model.test_dataloader())): + signals, signal_lengths, _targets, emb_vectors = test_batch + cumul_sample_count.append(cumul_sample_count[-1] + signal_lengths.shape[0]) + preds, targets, signal_lengths = self.diar_infer( + test_batch, test_data_collection[cumul_sample_count[-2] : cumul_sample_count[-1]] + ) + if self._cfg.diarizer.msdd_model.parameters.seq_eval_mode: + self.msdd_model._accuracy_test(preds, targets, signal_lengths) + + preds_list.extend(list(torch.split(preds, 1))) + targets_list.extend(list(torch.split(targets, 1))) + signal_lengths_list.extend(list(torch.split(signal_lengths, 1))) + + if self._cfg.diarizer.msdd_model.parameters.seq_eval_mode: + f1_score, simple_acc = self.msdd_model.compute_accuracies() + logging.info(f"Test Inference F1 score. {f1_score:.4f}, simple Acc. {simple_acc:.4f}") + integrated_preds_list = self.get_integrated_preds_list(uniq_id_list, test_data_collection, preds_list) + return integrated_preds_list, targets_list, signal_lengths_list + + def run_overlap_aware_eval( + self, preds_list: List[torch.Tensor], threshold: float + ) -> List[Optional[Tuple[DiarizationErrorRate, Dict]]]: + """ + Based on the predicted sigmoid values, render RTTM files then evaluate the overlap-aware diarization results. + + Args: + preds_list: (list) + List containing predicted pairwise speaker labels. + threshold: (float) + A floating-point threshold value that determines overlapped speech detection. + - If threshold is 1.0, no overlap speech is detected and only detect major speaker. + - If threshold is 0.0, all speakers are considered active at any time step. + """ + logging.info( + f" [Threshold: {threshold:.4f}] [use_clus_as_main={self.use_clus_as_main}] [diar_window={self.diar_window_length}]" + ) + outputs = [] + manifest_filepath = self.msdd_model.cfg.test_ds.manifest_filepath + rttm_map = audio_rttm_map(manifest_filepath) + for k, (collar, ignore_overlap) in enumerate(self.diar_eval_settings): + all_reference, all_hypothesis = make_rttm_with_overlap( + manifest_filepath, + self.msdd_model.clus_test_label_dict, + preds_list, + threshold=threshold, + infer_overlap=True, + use_clus_as_main=self.use_clus_as_main, + overlap_infer_spk_limit=self.overlap_infer_spk_limit, + use_adaptive_thres=self.use_adaptive_thres, + max_overlap_spks=self.max_overlap_spks, + out_rttm_dir=self.out_rttm_dir, + ) + output = score_labels( + rttm_map, + all_reference, + all_hypothesis, + collar=collar, + ignore_overlap=ignore_overlap, + verbose=self._cfg.verbose, + ) + outputs.append(output) + logging.info(f" \n") + return outputs + + @classmethod + def from_pretrained( + cls, + model_name: str, + vad_model_name: str = 'vad_multilingual_marblenet', + map_location: Optional[str] = None, + verbose: bool = False, + ): + """ + Instantiate a `NeuralDiarizer` to run Speaker Diarization. + + Args: + model_name (str): Path/Name of the neural diarization model to load. + vad_model_name (str): Path/Name of the voice activity detection (VAD) model to load. + map_location (str): Optional str to map the instantiated model to a device (cpu, cuda). + By default, (None), it will select a GPU if available, falling back to CPU otherwise. + verbose (bool): Enable verbose logging when loading models/running diarization. + Returns: + `NeuralDiarizer` + """ + logging.setLevel(logging.INFO if verbose else logging.WARNING) + cfg = NeuralDiarizerInferenceConfig.init_config( + diar_model_path=model_name, vad_model_path=vad_model_name, map_location=map_location, verbose=verbose, + ) + return cls(cfg) + + def __call__( + self, + audio_filepath: str, + batch_size: int = 64, + num_workers: int = 1, + max_speakers: Optional[int] = None, + num_speakers: Optional[int] = None, + out_dir: Optional[str] = None, + verbose: bool = False, + ) -> Union[Annotation, List[Annotation]]: + """ + Run the `NeuralDiarizer` inference pipeline. + + Args: + audio_filepath (str, list): Audio path to run speaker diarization on. + max_speakers (int): If known, the max number of speakers in the file(s). + num_speakers (int): If known, the exact number of speakers in the file(s). + batch_size (int): Batch size when running inference. + num_workers (int): Number of workers to use in data-loading. + out_dir (str): Path to store intermediate files during inference (default temp directory). + Returns: + `pyannote.Annotation` for each audio path, containing speaker labels and segment timestamps. + """ + if out_dir: + os.makedirs(out_dir, exist_ok=True) + with tempfile.TemporaryDirectory(dir=out_dir) as tmpdir: + manifest_path = os.path.join(tmpdir, 'manifest.json') + meta = [ + { + 'audio_filepath': audio_filepath, + 'offset': 0, + 'duration': None, + 'label': 'infer', + 'text': '-', + 'num_speakers': num_speakers, + 'rttm_filepath': None, + 'uem_filepath': None, + } + ] + + with open(manifest_path, 'w') as f: + f.write('\n'.join(json.dumps(x) for x in meta)) + + self._initialize_configs( + manifest_path=manifest_path, + max_speakers=max_speakers, + num_speakers=num_speakers, + tmpdir=tmpdir, + batch_size=batch_size, + num_workers=num_workers, + verbose=verbose, + ) + + self.msdd_model.cfg.test_ds.manifest_filepath = manifest_path + self.diarize() + + pred_labels_clus = rttm_to_labels(f'{tmpdir}/pred_rttms/{Path(audio_filepath).stem}.rttm') + return labels_to_pyannote_object(pred_labels_clus) + + def _initialize_configs( + self, + manifest_path: str, + max_speakers: Optional[int], + num_speakers: Optional[int], + tmpdir: tempfile.TemporaryDirectory, + batch_size: int, + num_workers: int, + verbose: bool, + ) -> None: + self._cfg.batch_size = batch_size + self._cfg.num_workers = num_workers + self._cfg.diarizer.manifest_filepath = manifest_path + self._cfg.diarizer.out_dir = tmpdir + self._cfg.verbose = verbose + self._cfg.diarizer.clustering.parameters.oracle_num_speakers = num_speakers is not None + if max_speakers: + self._cfg.diarizer.clustering.parameters.max_num_speakers = max_speakers + self.transfer_diar_params_to_model_params(self.msdd_model, self._cfg) + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + return EncDecDiarLabelModel.list_available_models() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/online_diarizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/online_diarizer.py new file mode 100644 index 0000000..7074b92 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/online_diarizer.py @@ -0,0 +1,579 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import time +from copy import deepcopy +from typing import Dict + +import torch +from omegaconf import DictConfig + +from nemo.collections.asr.models import ClusteringDiarizer +from nemo.collections.asr.parts.utils.offline_clustering import get_scale_interpolated_embs, split_input_data +from nemo.collections.asr.parts.utils.online_clustering import OnlineSpeakerClustering +from nemo.collections.asr.parts.utils.speaker_utils import ( + OnlineSegmentor, + audio_rttm_map, + generate_cluster_labels, + get_embs_and_timestamps, +) +from nemo.utils import logging, model_utils + +__all__ = ['OnlineClusteringDiarizer'] + + +def timeit(method): + """ + Monitor elapsed time of the corresponding function displaying the method name. + + Args: + method: function that is being measured + + Return: + `timed` function for measuring the elapsed time + """ + + def timed(*args, **kwargs): + ts = time.time() + result = method(*args, **kwargs) + te = time.time() + if 'log_time' in kwargs: + name = kwargs.get('log_name', method.__name__.upper()) + kwargs['log_time'][name] = int((te - ts) * 1000) + else: + logging.info('%2.2fms %r' % ((te - ts) * 1000, method.__name__)) + return result + + return timed + + +class OnlineClusteringDiarizer(ClusteringDiarizer): + """ + A class that enables online (streaming) clustering based diarization. + + - The instance created from `OnlineClusteringDiarizer` sets aside a certain amount of memory + to provide the upcoming inference with history information + + - There are two major modules involved: `OnlineSegmentor` and `OnlineSpeakerClustering`. + OnlineSegmentor: Take the VAD-timestamps and generate segments for each scale + OnlineSpeakerClustering: Update the entire speaker labels of the given online session + while updating the speaker labels of the streaming inputs. + + - The overall diarization process is done by calling `diarize_step` function. + `diarize_step` function goes through the following steps: + (1) Segmentation (`OnlineSegmentor` class) + (2) Embedding extraction (`_extract_online_embeddings` function call) + (3) Online speaker counting and speaker clustering (`OnlineClusteringDiarizer` class) + (4) Label generation (`generate_cluster_labels` function call) + """ + + def __init__(self, cfg: DictConfig): + super().__init__(cfg) + self.cfg = model_utils.convert_model_config_to_dict_config(cfg) + self._cfg_diarizer = self.cfg.diarizer + self.base_scale_index = max(self.multiscale_args_dict['scale_dict'].keys()) + + self.uniq_id = self._cfg_diarizer.get('uniq_id', None) + self.decimals = self._cfg_diarizer.get('decimals', 2) + self.AUDIO_RTTM_MAP = audio_rttm_map(self.cfg.diarizer.manifest_filepath) + self.sample_rate = self.cfg.sample_rate + torch.manual_seed(0) + + self._out_dir = self._cfg_diarizer.out_dir + if not os.path.exists(self._out_dir): + os.mkdir(self._out_dir) + + if torch.cuda.is_available(): + self.cuda = True + self.device = torch.device("cuda") + else: + self.cuda = False + self.device = torch.device("cpu") + + self.reset() + + # Set speaker embedding model in eval mode + self._speaker_model.eval() + + def _init_online_clustering_module(self, clustering_params): + """ + Initialize online speaker clustering module + + Attributes: + online_clus (OnlineSpeakerClustering): + Online clustering diarizer class instance + history_n (int): + History buffer size for saving history of speaker label inference + Total number of embedding vectors saved in the buffer that is kept till the end of the session + current_n (int): + Current buffer (FIFO queue) size for calculating the speaker label inference + Total number of embedding vectors saved in the FIFO queue for clustering inference + """ + self.online_clus = OnlineSpeakerClustering( + max_num_speakers=clustering_params.max_num_speakers, + max_rp_threshold=clustering_params.max_rp_threshold, + sparse_search_volume=clustering_params.sparse_search_volume, + history_buffer_size=clustering_params.history_buffer_size, + current_buffer_size=clustering_params.current_buffer_size, + cuda=self.cuda, + ) + self.history_n = clustering_params.history_buffer_size + self.current_n = clustering_params.current_buffer_size + + self.max_num_speakers = self.online_clus.max_num_speakers + + def _init_online_segmentor_module(self, sample_rate): + """ + Initialize an online segmentor module + + Attributes: + online_segmentor (OnlineSegmentor): + online segmentation module that generates short speech segments from the VAD input + """ + self.online_segmentor = OnlineSegmentor(sample_rate) + + def _init_memory_buffer(self): + """ + Variables are kept in memory for future updates + + Attributes: + memory_margin (int): + The number of embeddings saved in the memory buffer. + This memory margin is dependent on the base scale length: margin = (buffer_length)/(base scale shift) + memory margin is automatically calculated to have minimal memory usage + memory_segment_ranges (dict): + The segment range information kept in the memory buffer + memory_segment_indexes (dict): + The segment indexes kept in the memory buffer + memory_cluster_labels (Tensor): + The cluster labels inferred in the previous diarization steps + """ + self.memory_margin = 0 + self.memory_segment_ranges = {key: [] for key in self.multiscale_args_dict['scale_dict'].keys()} + self.memory_segment_indexes = {key: [] for key in self.multiscale_args_dict['scale_dict'].keys()} + self.memory_cluster_labels = torch.tensor([]) + + def _init_temporal_major_voting_module(self, clustering_params): + """ + Variables needed for taking majority votes for speaker labels + + Attributes: + use_temporal_label_major_vote (bool): + Boolean for whether to use temporal majority voting + temporal_label_major_vote_buffer_size (int): + buffer size for majority voting + base_scale_label_dict (dict): + Dictionary containing multiple speaker labels for major voting + Speaker labels from multiple steps are saved for each segment index. + """ + self.use_temporal_label_major_vote = clustering_params.get('use_temporal_label_major_vote', False) + self.temporal_label_major_vote_buffer_size = clustering_params.get('temporal_label_major_vote_buffer_size', 1) + self.base_scale_label_dict = {} + + def _init_segment_variables(self): + """ + Initialize segment variables for each scale. + Note that we have `uniq_id` variable in case where multiple sessions are handled. + """ + self.emb_vectors = {} + self.time_stamps = {} + self.segment_range_ts = {} + self.segment_raw_audio = {} + self.segment_indexes = {} + + for scale_idx in self.multiscale_args_dict['scale_dict'].keys(): + self.multiscale_embeddings_and_timestamps[scale_idx] = [None, None] + self.emb_vectors[scale_idx] = torch.tensor([]) + self.time_stamps[scale_idx] = [] + self.segment_range_ts[scale_idx] = [] + self.segment_raw_audio[scale_idx] = [] + self.segment_indexes[scale_idx] = [] + + def _init_buffer_frame_timestamps(self): + """ + Timing variables transferred from OnlineDiarWithASR class. + Buffer is window region where input signal is kept for ASR. + Frame is window region where the actual inference ASR decoded results are updated + + Example: + buffer_len = 5.0 + frame_len = 1.0 + + |___Buffer___[___________]____________| + |____________[ Frame ]____________| + + | <- buffer_start + |____________| <- frame_start + |_____________________________________| <- buffer_end + + buffer_start = 12.0 + buffer_end = 17.0 + frame_start = 14.0 + + These timestamps and index variables are updated by OnlineDiarWithASR. + + Attributes: + frame_index (int): + Integer index of frame window + frame_start (float): + The start of the frame window + buffer_start (float): + The start of the buffer window + buffer_end (float): + The end of the buffer + """ + self.frame_index = 0 + self.frame_start = 0.0 + self.buffer_start = 0.0 + self.buffer_end = 0.0 + + def _transfer_timestamps_to_segmentor(self): + """ + Pass the timing information from streaming ASR buffers. + """ + self.online_segmentor.frame_start = self.frame_start + self.online_segmentor.buffer_start = self.buffer_start + self.online_segmentor.buffer_end = self.buffer_end + + def reset(self): + """ + Reset all the necessary variables and initialize classes. + + Attributes: + n_embed_seg_len (int): + Number of segments needed for 1 second of input time-series signal + """ + self.n_embed_seg_len = int( + self.sample_rate * self.multiscale_args_dict['scale_dict'][self.base_scale_index][0] + ) + self._init_segment_variables() + self._init_online_clustering_module(self._cfg_diarizer.clustering.parameters) + self._init_online_segmentor_module(self.cfg.sample_rate) + self._init_memory_buffer() + self._init_temporal_major_voting_module(self._cfg_diarizer.clustering.parameters) + self._init_buffer_frame_timestamps() + + def _clear_memory(self, scale_idx: int): + """ + Calculate how many segments should be removed from memory (`memory_margin`) and + save the necessary information. + `keep_range` determines how many segments and their corresponding embedding, raw audio, + timestamps in the memory of the online diarizer instance. + + Args: + scale_idx (int): + Scale index in integer type + """ + base_scale_shift = self.multiscale_args_dict['scale_dict'][self.base_scale_index][1] + self.memory_margin = int((self.buffer_end - self.buffer_start) / base_scale_shift) + + scale_buffer_size = int( + len(set(self.scale_mapping_dict[scale_idx].tolist())) + / len(set(self.scale_mapping_dict[self.base_scale_index].tolist())) + * (self.history_n + self.current_n) + ) + keep_range = scale_buffer_size + self.memory_margin + self.emb_vectors[scale_idx] = self.emb_vectors[scale_idx][-keep_range:] + self.segment_raw_audio[scale_idx] = self.segment_raw_audio[scale_idx][-keep_range:] + self.segment_range_ts[scale_idx] = self.segment_range_ts[scale_idx][-keep_range:] + self.segment_indexes[scale_idx] = self.segment_indexes[scale_idx][-keep_range:] + + @timeit + def _temporal_label_major_vote(self) -> torch.Tensor: + """ + Take a majority voting for every segment on temporal steps. This feature significantly reduces the error coming + from unstable speaker counting in the beginning of sessions. + + Returns: + maj_vote_labels (list): + List containing the major-voted speaker labels on temporal domain + """ + maj_vote_labels = [] + for seg_idx in self.memory_segment_indexes[self.base_scale_index]: + if seg_idx not in self.base_scale_label_dict: + self.base_scale_label_dict[seg_idx] = [self.memory_cluster_labels[seg_idx]] + else: + while len(self.base_scale_label_dict[seg_idx]) > self.temporal_label_major_vote_buffer_size: + self.base_scale_label_dict[seg_idx].pop(0) + self.base_scale_label_dict[seg_idx].append(self.memory_cluster_labels[seg_idx]) + + maj_vote_labels.append(torch.mode(torch.tensor(self.base_scale_label_dict[seg_idx]))[0].item()) + return maj_vote_labels + + def save_history_data(self, scale_idx: int, total_cluster_labels: torch.Tensor, is_online: bool) -> torch.Tensor: + """ + Save the temporary input to the class memory buffer. + + - Clustering is done for (hist_N + curr_N) number of embeddings. + - Thus, we need to remove the clustering results on the embedding memory. + - If self.diar.history_buffer_seg_end is not None, that indicates streaming diarization system + is starting to save embeddings to its memory. Thus, the new incoming clustering label should be separated. + - If `is_online = True`, old embeddings outside the window are removed to save GPU memory. + + Args: + scale_idx (int): + Scale index in integer + total_cluster_labels (Tensor): + The speaker labels from the beginning of the session to the current position + is_online (bool) + Boolean variable that indicates whether the system is currently in online mode or not + + Returns: + cluster_label_hyp (Tensor): + Majority voted speaker labels over multiple inferences + """ + total_cluster_labels = total_cluster_labels.tolist() + + if not is_online: + self.memory_segment_ranges[scale_idx] = deepcopy(self.segment_range_ts[scale_idx]) + self.memory_segment_indexes[scale_idx] = deepcopy(self.segment_indexes[scale_idx]) + if scale_idx == self.base_scale_index: + self.memory_cluster_labels = deepcopy(total_cluster_labels) + + # Only if there are newly obtained embeddings, update ranges and embeddings. + elif self.segment_indexes[scale_idx][-1] > self.memory_segment_indexes[scale_idx][-1]: + # Get the global index of the first segment we want to keep in the buffer + global_stt_idx = max(max(self.memory_segment_indexes[scale_idx]) - self.memory_margin, 0) + + # Convert global index global_stt_idx to buffer index buffer_stt_idx + segment_indexes_mat = torch.tensor(self.segment_indexes[scale_idx]) + buffer_stt_idx = torch.where(segment_indexes_mat == global_stt_idx)[0][0] + self.memory_segment_ranges[scale_idx][global_stt_idx:] = deepcopy( + self.segment_range_ts[scale_idx][buffer_stt_idx:] + ) + self.memory_segment_indexes[scale_idx][global_stt_idx:] = deepcopy( + self.segment_indexes[scale_idx][buffer_stt_idx:] + ) + if scale_idx == self.base_scale_index: + self.memory_cluster_labels[global_stt_idx:] = deepcopy(total_cluster_labels[global_stt_idx:]) + if len(self.memory_cluster_labels) != len(self.memory_segment_ranges[scale_idx]): + raise ValueError( + "self.memory_cluster_labels and self.memory_segment_ranges should always have the same length, " + f"but they have {len(self.memory_cluster_labels)} and {len(self.memory_segment_ranges[scale_idx])}." + ) + + # Remove unnecessary old values + self._clear_memory(scale_idx) + + if not ( + len(self.emb_vectors[scale_idx]) + == len(self.segment_raw_audio[scale_idx]) + == len(self.segment_indexes[scale_idx]) + == len(self.segment_range_ts[scale_idx]) + ): + raise ValueError( + "self.emb_vectors, self.segment_raw_audio, self.segment_indexes, and self.segment_range_ts " + "should always have the same length, " + f"but they have {len(self.emb_vectors[scale_idx])}, {len(self.segment_raw_audio[scale_idx])}, " + f"{len(self.segment_indexes[scale_idx])}, and {len(self.segment_range_ts[scale_idx])}, respectively." + ) + + if self.use_temporal_label_major_vote: + cluster_label_hyp = self._temporal_label_major_vote() + else: + cluster_label_hyp = self.memory_cluster_labels + return cluster_label_hyp + + @timeit + @torch.no_grad() + def _run_embedding_extractor(self, audio_signal: torch.Tensor) -> torch.Tensor: + """ + Call `forward` function of the speaker embedding model. + + Args: + audio_signal (Tensor): + Torch tensor containing time-series signal + + Returns: + Speaker embedding vectors for the given time-series input `audio_signal`. + """ + audio_signal = torch.stack(audio_signal).float().to(self.device) + audio_signal_lens = torch.tensor([self.n_embed_seg_len for k in range(audio_signal.shape[0])]).to(self.device) + _, torch_embs = self._speaker_model.forward(input_signal=audio_signal, input_signal_length=audio_signal_lens) + return torch_embs + + @timeit + def _extract_online_embeddings( + self, audio_signal: torch.Tensor, segment_ranges: torch.Tensor, embeddings + ) -> torch.Tensor: + """ + Incrementally extract speaker embeddings based on `audio_signal` and `segment_ranges` variables. + Unlike offline speaker diarization, speaker embedding and subsegment ranges are not saved to disk. + Measures the mismatch between `segment_ranges` and `embeddings` then extract the necessary amount of + speaker embeddings. + + Args: + audio_signal (Tensor): + Torch tensor containing time-series audio signal + embeddings (Tensor): + Previously existing Torch tensor containing speaker embedding vector + segment_ranges(Tensor): + Torch tensor containing the start and end of each segment + + Returns: + embeddings (Tensor): + Concatenated speaker embedding vectors that match segment range information in `segment_ranges`. + """ + stt_idx = 0 if embeddings is None else embeddings.shape[0] + end_idx = len(segment_ranges) + + if end_idx > stt_idx: + torch_embs = self._run_embedding_extractor(audio_signal[stt_idx:end_idx]) + if embeddings is None or embeddings.shape[0] == 0: + embeddings = torch_embs + else: + embeddings = torch.vstack((embeddings[:stt_idx, :], torch_embs)) + + elif end_idx < stt_idx: + embeddings = embeddings[: len(segment_ranges)] + + if len(segment_ranges) != embeddings.shape[0]: + raise ValueError("Segment ranges and embeddings shapes do not match.") + return embeddings + + @timeit + def _perform_online_clustering( + self, uniq_embs_and_timestamps: Dict[str, torch.Tensor], cuda=False, + ) -> torch.Tensor: + """ + Launch online clustering for `uniq_embs_and_timestamps` input variable. + + Args: + uniq_embs_and_timestamps (dict): + Dictionary containing embeddings, timestamps and multiscale weights. + If uniq_embs_and_timestamps contains only one scale, single scale diarization + is performed. + cuda (bool): + Boolean indicator for cuda usages + """ + device = torch.device("cuda") if cuda else torch.device("cpu") + + # Get base-scale (the highest index) information from uniq_embs_and_timestamps. + embeddings_in_scales, timestamps_in_scales = split_input_data( + embeddings_in_scales=uniq_embs_and_timestamps['embeddings'], + timestamps_in_scales=uniq_embs_and_timestamps['timestamps'], + multiscale_segment_counts=uniq_embs_and_timestamps['multiscale_segment_counts'], + ) + + curr_emb, self.scale_mapping_dict = get_scale_interpolated_embs( + multiscale_weights=uniq_embs_and_timestamps['multiscale_weights'], + embeddings_in_scales=embeddings_in_scales, + timestamps_in_scales=timestamps_in_scales, + device=device, + ) + + base_segment_indexes = torch.tensor(self.segment_indexes[self.base_scale_index]).to(curr_emb.device) + merged_clus_labels = self.online_clus.forward_infer( + curr_emb=curr_emb, base_segment_indexes=base_segment_indexes, frame_index=self.frame_index, cuda=cuda, + ) + # Update history data + for scale_idx, (window, shift) in self.multiscale_args_dict['scale_dict'].items(): + cluster_label_hyp = self.save_history_data(scale_idx, merged_clus_labels, self.online_clus.is_online) + + return cluster_label_hyp + + def _get_interim_output(self) -> torch.Tensor: + """ + In case buffer is not filled or there is no speech activity in the input, generate temporary output. + + Returns: + diar_hyp (Tensor): Speaker labels based on the previously saved segments and speaker labels + """ + if len(self.memory_cluster_labels) == 0 or self.buffer_start < 0: + diar_hyp, _ = generate_cluster_labels([[0.0, self.total_buffer_in_secs]], [0]) + else: + diar_hyp, _ = generate_cluster_labels( + self.memory_segment_ranges[self.base_scale_index], self.memory_cluster_labels + ) + return diar_hyp + + @timeit + def diarize_step(self, audio_buffer: torch.Tensor, vad_timestamps: torch.Tensor) -> torch.Tensor: + """ + A function for a unit diarization step. Each diarization step goes through the following steps: + + 1. Segmentation: + Using `OnlineSegmentor` class, call `run_online_segmentation` method to get the segments. + 2. Embedding Extraction: + Extract multiscale embeddings from the extracted speech segments. + 3. Online Clustering & Counting + Perform online speaker clustering by using `OnlineSpeakerClustering` class. + 4. Generate speaker labels: + Generate start and end timestamps of speaker labels based on the diarization results. + + c.f.) Also see method `diarize` in `ClusteringDiarizer` class. + + Args: + audio_buffer (Tensor): + Tensor variable containing the time series signal at the current frame + Dimensions: (Number of audio time-series samples) x 1 + vad_timestamps (Tensor): + List containing VAD timestamps. + Dimensions: (Number of segments) x 2 + Example: + >>> vad_timestamps = torch.Tensor([[0.05, 2.52], [3.12, 6.85]]) + + Returns: + diar_hyp (Tensor): + Speaker label hypothesis from the start of the session to the current position + """ + self._transfer_timestamps_to_segmentor() + + # In case buffer is not filled or there is no speech activity in the input + if self.buffer_start < 0 or len(vad_timestamps) == 0: + return self._get_interim_output() + + # Segmentation: (c.f. see `diarize` function in ClusteringDiarizer class) + for scale_idx, (window, shift) in self.multiscale_args_dict['scale_dict'].items(): + + # Step 1: Get subsegments for embedding extraction. + audio_sigs, segment_ranges, range_inds = self.online_segmentor.run_online_segmentation( + audio_buffer=audio_buffer, + vad_timestamps=vad_timestamps, + segment_raw_audio=self.segment_raw_audio[scale_idx], + segment_range_ts=self.segment_range_ts[scale_idx], + segment_indexes=self.segment_indexes[scale_idx], + window=window, + shift=shift, + ) + self.segment_raw_audio[scale_idx] = audio_sigs + self.segment_range_ts[scale_idx] = segment_ranges + self.segment_indexes[scale_idx] = range_inds + + # Step 2-1: Extract speaker embeddings from the extracted subsegment timestamps. + embeddings = self._extract_online_embeddings( + audio_signal=self.segment_raw_audio[scale_idx], + segment_ranges=self.segment_range_ts[scale_idx], + embeddings=self.emb_vectors[scale_idx], + ) + + # Step 2-2:Save the embeddings and segmentation timestamps in memory + self.emb_vectors[scale_idx] = embeddings + + self.multiscale_embeddings_and_timestamps[scale_idx] = [ + {self.uniq_id: embeddings}, + {self.uniq_id: segment_ranges}, + ] + + embs_and_timestamps = get_embs_and_timestamps( + self.multiscale_embeddings_and_timestamps, self.multiscale_args_dict + ) + + # Step 3 - Clustering: Perform an online version of clustering algorithm + cluster_label_hyp = self._perform_online_clustering(embs_and_timestamps[self.uniq_id], cuda=self.cuda,) + + # Step 4: Generate RTTM style diarization labels from segment ranges and cluster labels + diar_hyp, _ = generate_cluster_labels(self.memory_segment_ranges[self.base_scale_index], cluster_label_hyp) + return diar_hyp diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/rnnt_bpe_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/rnnt_bpe_models.py new file mode 100644 index 0000000..bb4e7f7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/rnnt_bpe_models.py @@ -0,0 +1,595 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import os +from typing import Dict, List, Optional, Union + +import torch +from omegaconf import DictConfig, ListConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer + +from nemo.collections.asr.data import audio_to_text_dataset +from nemo.collections.asr.data.audio_to_text import _AudioTextDataset +from nemo.collections.asr.data.audio_to_text_dali import AudioToBPEDALIDataset +from nemo.collections.asr.data.audio_to_text_lhotse import LhotseSpeechToTextBpeDataset +from nemo.collections.asr.losses.rnnt import RNNTLoss +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.models.rnnt_models import EncDecRNNTModel +from nemo.collections.asr.parts.mixins import ASRBPEMixin +from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTBPEDecoding, RNNTBPEDecodingConfig +from nemo.collections.asr.parts.utils.asr_batching import get_semi_sorted_batch_sampler +from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config +from nemo.core.classes.common import PretrainedModelInfo +from nemo.utils import logging, model_utils + + +class EncDecRNNTBPEModel(EncDecRNNTModel, ASRBPEMixin): + """Base class for encoder decoder RNNT-based models with subword tokenization.""" + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_contextnet_256", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_contextnet_256", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_contextnet_256/versions/1.6.0/files/stt_en_contextnet_256.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_contextnet_512", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_contextnet_512", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_contextnet_512/versions/1.6.0/files/stt_en_contextnet_512.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_contextnet_1024", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_contextnet_1024", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_contextnet_1024/versions/1.9.0/files/stt_en_contextnet_1024.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_contextnet_256_mls", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_contextnet_256_mls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_contextnet_256_mls/versions/1.0.0/files/stt_en_contextnet_256_mls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_contextnet_512_mls", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_contextnet_512_mls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_contextnet_512_mls/versions/1.0.0/files/stt_en_contextnet_512_mls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_contextnet_1024_mls", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_contextnet_1024_mls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_contextnet_1024_mls/versions/1.0.0/files/stt_en_contextnet_1024_mls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_transducer_small", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_small", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_small/versions/1.6.0/files/stt_en_conformer_transducer_small.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_transducer_medium", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_medium", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_medium/versions/1.6.0/files/stt_en_conformer_transducer_medium.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_large/versions/1.10.0/files/stt_en_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_transducer_large_ls", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_large_ls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_large_ls/versions/1.8.0/files/stt_en_conformer_transducer_large_ls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_transducer_xlarge", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_xlarge", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_xlarge/versions/1.10.0/files/stt_en_conformer_transducer_xlarge.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_conformer_transducer_xxlarge", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_xxlarge", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_xxlarge/versions/1.8.0/files/stt_en_conformer_transducer_xxlarge.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_de_contextnet_1024", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_contextnet_1024", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_contextnet_1024/versions/1.4.0/files/stt_de_contextnet_1024.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_fr_contextnet_1024", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_contextnet_1024", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_contextnet_1024/versions/1.5/files/stt_fr_contextnet_1024.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_es_contextnet_1024", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_contextnet_1024", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_contextnet_1024/versions/1.8.0/files/stt_es_contextnet_1024.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_de_conformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_conformer_transducer_large/versions/1.5.0/files/stt_de_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_fr_conformer_transducer_large", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_conformer_transducer_large/versions/1.5/files/stt_fr_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_es_conformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_conformer_transducer_large/versions/1.8.0/files/stt_es_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_enes_conformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_enes_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_enes_conformer_transducer_large/versions/1.0.0/files/stt_enes_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_enes_contextnet_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_enes_contextnet_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_enes_contextnet_large/versions/1.0.0/files/stt_enes_contextnet_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_ca_conformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ca_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ca_conformer_transducer_large/versions/1.11.0/files/stt_ca_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_rw_conformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_rw_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_rw_conformer_transducer_large/versions/1.11.0/files/stt_rw_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_enes_conformer_transducer_large_codesw", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_enes_conformer_transducer_large_codesw", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_enes_conformer_transducer_large_codesw/versions/1.0.0/files/stt_enes_conformer_transducer_large_codesw.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_kab_conformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_kab_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_kab_conformer_transducer_large/versions/1.12.0/files/stt_kab_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_be_conformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_be_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_be_conformer_transducer_large/versions/1.12.0/files/stt_be_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_hr_conformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_hr_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_hr_conformer_transducer_large/versions/1.11.0/files/stt_hr_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_it_conformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_it_conformer_transducer_large/versions/1.13.0/files/stt_it_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_ru_conformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ru_conformer_transducer_large/versions/1.13.0/files/stt_ru_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_eo_conformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_eo_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_eo_conformer_transducer_large/versions/1.14.0/files/stt_eo_conformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_transducer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_transducer_large/versions/1.0.0/files/stt_en_fastconformer_transducer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_transducer_large_ls", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_transducer_large_ls", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_transducer_large_ls/versions/1.0.0/files/stt_en_fastconformer_transducer_large_ls.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_transducer_xlarge", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_transducer_xlarge", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_transducer_xlarge/versions/1.20.1/files/stt_en_fastconformer_transducer_xlarge.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="stt_en_fastconformer_transducer_xxlarge", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_transducer_xxlarge", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_transducer_xxlarge/versions/1.20.1/files/stt_en_fastconformer_transducer_xxlarge.nemo", + ) + results.append(model) + + return results + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + # Convert to Hydra 1.0 compatible DictConfig + cfg = model_utils.convert_model_config_to_dict_config(cfg) + cfg = model_utils.maybe_update_config_version(cfg) + + # Tokenizer is necessary for this model + if 'tokenizer' not in cfg: + raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !") + + if not isinstance(cfg, DictConfig): + cfg = OmegaConf.create(cfg) + + # Setup the tokenizer + self._setup_tokenizer(cfg.tokenizer) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + # Set the new vocabulary + with open_dict(cfg): + cfg.labels = ListConfig(list(vocabulary)) + + with open_dict(cfg.decoder): + cfg.decoder.vocab_size = len(vocabulary) + + with open_dict(cfg.joint): + cfg.joint.num_classes = len(vocabulary) + cfg.joint.vocabulary = ListConfig(list(vocabulary)) + cfg.joint.jointnet.encoder_hidden = cfg.model_defaults.enc_hidden + cfg.joint.jointnet.pred_hidden = cfg.model_defaults.pred_hidden + + super().__init__(cfg=cfg, trainer=trainer) + + self.cfg.decoding = self.set_decoding_type_according_to_loss(self.cfg.decoding) + # Setup decoding object + self.decoding = RNNTBPEDecoding( + decoding_cfg=self.cfg.decoding, decoder=self.decoder, joint=self.joint, tokenizer=self.tokenizer, + ) + + # Setup wer object + self.wer = WER( + decoding=self.decoding, + batch_dim_index=0, + use_cer=self._cfg.get('use_cer', False), + log_prediction=self._cfg.get('log_prediction', True), + dist_sync_on_step=True, + ) + + # Setup fused Joint step if flag is set + if self.joint.fuse_loss_wer: + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + def change_vocabulary( + self, + new_tokenizer_dir: Union[str, DictConfig], + new_tokenizer_type: str, + decoding_cfg: Optional[DictConfig] = None, + ): + """ + Changes vocabulary used during RNNT decoding process. Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_tokenizer_dir: Directory path to tokenizer or a config for a new tokenizer (if the tokenizer type is `agg`) + new_tokenizer_type: Type of tokenizer. Can be either `agg`, `bpe` or `wpe`. + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + + Returns: None + + """ + if isinstance(new_tokenizer_dir, DictConfig): + if new_tokenizer_type == 'agg': + new_tokenizer_cfg = new_tokenizer_dir + else: + raise ValueError( + f'New tokenizer dir should be a string unless the tokenizer is `agg`, but this tokenizer type is: {new_tokenizer_type}' + ) + else: + new_tokenizer_cfg = None + + if new_tokenizer_cfg is not None: + tokenizer_cfg = new_tokenizer_cfg + else: + if not os.path.isdir(new_tokenizer_dir): + raise NotADirectoryError( + f'New tokenizer dir must be non-empty path to a directory. But I got: {new_tokenizer_dir}' + ) + + if new_tokenizer_type.lower() not in ('bpe', 'wpe'): + raise ValueError(f'New tokenizer type must be either `bpe` or `wpe`') + + tokenizer_cfg = OmegaConf.create({'dir': new_tokenizer_dir, 'type': new_tokenizer_type}) + + # Setup the tokenizer + self._setup_tokenizer(tokenizer_cfg) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + joint_config = self.joint.to_config_dict() + new_joint_config = copy.deepcopy(joint_config) + if self.tokenizer_type == "agg": + new_joint_config["vocabulary"] = ListConfig(vocabulary) + else: + new_joint_config["vocabulary"] = ListConfig(list(vocabulary.keys())) + + new_joint_config['num_classes'] = len(vocabulary) + del self.joint + self.joint = EncDecRNNTBPEModel.from_config_dict(new_joint_config) + + decoder_config = self.decoder.to_config_dict() + new_decoder_config = copy.deepcopy(decoder_config) + new_decoder_config.vocab_size = len(vocabulary) + del self.decoder + self.decoder = EncDecRNNTBPEModel.from_config_dict(new_decoder_config) + + del self.loss + self.loss = RNNTLoss(num_classes=self.joint.num_classes_with_blank - 1) + + if decoding_cfg is None: + # Assume same decoding config as before + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(RNNTBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + decoding_cfg = self.set_decoding_type_according_to_loss(decoding_cfg) + + self.decoding = RNNTBPEDecoding( + decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, tokenizer=self.tokenizer, + ) + + self.wer = WER( + decoding=self.decoding, + batch_dim_index=self.wer.batch_dim_index, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Setup fused Joint step + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Update config + with open_dict(self.cfg.joint): + self.cfg.joint = new_joint_config + + with open_dict(self.cfg.decoder): + self.cfg.decoder = new_decoder_config + + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed decoder to output to {self.joint.vocabulary} vocabulary.") + + def change_decoding_strategy(self, decoding_cfg: DictConfig): + """ + Changes decoding strategy used during RNNT decoding process. + + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + """ + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(RNNTBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + decoding_cfg = self.set_decoding_type_according_to_loss(decoding_cfg) + + self.decoding = RNNTBPEDecoding( + decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, tokenizer=self.tokenizer, + ) + + self.wer = WER( + decoding=self.decoding, + batch_dim_index=self.wer.batch_dim_index, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Setup fused Joint step + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + self.joint.temperature = decoding_cfg.get('temperature', 1.0) + + # Update config + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}") + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + if config.get("use_lhotse"): + return get_lhotse_dataloader_from_config( + config, + global_rank=self.global_rank, + world_size=self.world_size, + dataset=LhotseSpeechToTextBpeDataset(tokenizer=self.tokenizer,), + ) + + dataset = audio_to_text_dataset.get_audio_to_text_bpe_dataset_from_config( + config=config, + local_rank=self.local_rank, + global_rank=self.global_rank, + world_size=self.world_size, + tokenizer=self.tokenizer, + preprocessor_cfg=self.cfg.get("preprocessor", None), + ) + + if dataset is None: + return None + + if isinstance(dataset, AudioToBPEDALIDataset): + # DALI Dataset implements dataloader interface + return dataset + + shuffle = config['shuffle'] + if isinstance(dataset, torch.utils.data.IterableDataset): + shuffle = False + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + elif hasattr(dataset.datasets[0], 'collate_fn'): + # support datasets that are lists of entries + collate_fn = dataset.datasets[0].collate_fn + else: + # support datasets that are lists of lists + collate_fn = dataset.datasets[0].datasets[0].collate_fn + + batch_sampler = None + if config.get('use_semi_sorted_batching', False): + if not isinstance(dataset, _AudioTextDataset): + raise RuntimeError( + "Semi Sorted Batch sampler can be used with AudioToCharDataset or AudioToBPEDataset " + f"but found dataset of type {type(dataset)}" + ) + # set batch_size and batch_sampler to None to disable automatic batching + batch_sampler = get_semi_sorted_batch_sampler(self, dataset, config) + config['batch_size'] = None + config['drop_last'] = False + shuffle = False + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + sampler=batch_sampler, + batch_sampler=None, + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided audio file. + + Args: + config: A python dictionary which contains the following keys: + paths2audio_files: (a list) of paths to audio files. The files should be relatively short fragments. \ + Recommended length per file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the audio manifest is temporarily + stored. + + Returns: + A pytorch DataLoader for the given audio file(s). + """ + if 'manifest_filepath' in config: + manifest_filepath = config['manifest_filepath'] + batch_size = config['batch_size'] + else: + manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json') + batch_size = min(config['batch_size'], len(config['paths2audio_files'])) + + dl_config = { + 'manifest_filepath': manifest_filepath, + 'sample_rate': self.preprocessor._sample_rate, + 'batch_size': batch_size, + 'shuffle': False, + 'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)), + 'pin_memory': True, + 'channel_selector': config.get('channel_selector', None), + 'use_start_end_token': self.cfg.validation_ds.get('use_start_end_token', False), + } + + if config.get("augmentor"): + dl_config['augmentor'] = config.get("augmentor") + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/rnnt_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/rnnt_models.py new file mode 100644 index 0000000..386f2a9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/rnnt_models.py @@ -0,0 +1,1044 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import json +import os +import tempfile +from math import ceil +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +from omegaconf import DictConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer +from tqdm.auto import tqdm + +from nemo.collections.asr.data import audio_to_text_dataset +from nemo.collections.asr.data.audio_to_text import _AudioTextDataset +from nemo.collections.asr.data.audio_to_text_dali import AudioToCharDALIDataset, DALIOutputs +from nemo.collections.asr.data.audio_to_text_lhotse import LhotseSpeechToTextBpeDataset +from nemo.collections.asr.losses.rnnt import RNNTLoss, resolve_rnnt_default_loss_name +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel +from nemo.collections.asr.modules.rnnt import RNNTDecoderJoint +from nemo.collections.asr.parts.mixins import ( + ASRModuleMixin, + ASRTranscriptionMixin, + TranscribeConfig, + TranscriptionReturnType, +) +from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTDecoding, RNNTDecodingConfig +from nemo.collections.asr.parts.utils.asr_batching import get_semi_sorted_batch_sampler +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config +from nemo.collections.common.parts.preprocessing.parsers import make_parser +from nemo.core.classes.common import PretrainedModelInfo, typecheck +from nemo.core.classes.mixins import AccessMixin +from nemo.core.neural_types import AcousticEncodedRepresentation, AudioSignal, LengthsType, NeuralType, SpectrogramType +from nemo.utils import logging + + +class EncDecRNNTModel(ASRModel, ASRModuleMixin, ExportableEncDecModel, ASRTranscriptionMixin): + """Base class for encoder decoder RNNT-based models.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + # Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable + # Global_rank and local_rank is set by LightningModule in Lightning 1.2.0 + self.world_size = 1 + if trainer is not None: + self.world_size = trainer.world_size + + super().__init__(cfg=cfg, trainer=trainer) + + # Initialize components + self.preprocessor = EncDecRNNTModel.from_config_dict(self.cfg.preprocessor) + self.encoder = EncDecRNNTModel.from_config_dict(self.cfg.encoder) + + # Update config values required by components dynamically + with open_dict(self.cfg.decoder): + self.cfg.decoder.vocab_size = len(self.cfg.labels) + + with open_dict(self.cfg.joint): + self.cfg.joint.num_classes = len(self.cfg.labels) + self.cfg.joint.vocabulary = self.cfg.labels + self.cfg.joint.jointnet.encoder_hidden = self.cfg.model_defaults.enc_hidden + self.cfg.joint.jointnet.pred_hidden = self.cfg.model_defaults.pred_hidden + + self.decoder = EncDecRNNTModel.from_config_dict(self.cfg.decoder) + self.joint = EncDecRNNTModel.from_config_dict(self.cfg.joint) + + # Setup RNNT Loss + loss_name, loss_kwargs = self.extract_rnnt_loss_cfg(self.cfg.get("loss", None)) + + num_classes = self.joint.num_classes_with_blank - 1 # for standard RNNT and multi-blank + + if loss_name == 'tdt': + num_classes = num_classes - self.joint.num_extra_outputs + + self.loss = RNNTLoss( + num_classes=num_classes, + loss_name=loss_name, + loss_kwargs=loss_kwargs, + reduction=self.cfg.get("rnnt_reduction", "mean_batch"), + ) + + if hasattr(self.cfg, 'spec_augment') and self._cfg.spec_augment is not None: + self.spec_augmentation = EncDecRNNTModel.from_config_dict(self.cfg.spec_augment) + else: + self.spec_augmentation = None + + self.cfg.decoding = self.set_decoding_type_according_to_loss(self.cfg.decoding) + # Setup decoding objects + self.decoding = RNNTDecoding( + decoding_cfg=self.cfg.decoding, decoder=self.decoder, joint=self.joint, vocabulary=self.joint.vocabulary, + ) + # Setup WER calculation + self.wer = WER( + decoding=self.decoding, + batch_dim_index=0, + use_cer=self._cfg.get('use_cer', False), + log_prediction=self._cfg.get('log_prediction', True), + dist_sync_on_step=True, + ) + + # Whether to compute loss during evaluation + if 'compute_eval_loss' in self.cfg: + self.compute_eval_loss = self.cfg.compute_eval_loss + else: + self.compute_eval_loss = True + + # Setup fused Joint step if flag is set + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Setup optimization normalization (if provided in config) + self.setup_optim_normalization() + + # Setup optional Optimization flags + self.setup_optimization_flags() + + # Setup encoder adapters (from ASRAdapterModelMixin) + self.setup_adapters() + + def setup_optim_normalization(self): + """ + Helper method to setup normalization of certain parts of the model prior to the optimization step. + + Supported pre-optimization normalizations are as follows: + + .. code-block:: yaml + + # Variation Noise injection + model: + variational_noise: + std: 0.0 + start_step: 0 + + # Joint - Length normalization + model: + normalize_joint_txu: false + + # Encoder Network - gradient normalization + model: + normalize_encoder_norm: false + + # Decoder / Prediction Network - gradient normalization + model: + normalize_decoder_norm: false + + # Joint - gradient normalization + model: + normalize_joint_norm: false + """ + # setting up the variational noise for the decoder + if hasattr(self.cfg, 'variational_noise'): + self._optim_variational_noise_std = self.cfg['variational_noise'].get('std', 0) + self._optim_variational_noise_start = self.cfg['variational_noise'].get('start_step', 0) + else: + self._optim_variational_noise_std = 0 + self._optim_variational_noise_start = 0 + + # Setup normalized gradients for model joint by T x U scaling factor (joint length normalization) + self._optim_normalize_joint_txu = self.cfg.get('normalize_joint_txu', False) + self._optim_normalize_txu = None + + # Setup normalized encoder norm for model + self._optim_normalize_encoder_norm = self.cfg.get('normalize_encoder_norm', False) + + # Setup normalized decoder norm for model + self._optim_normalize_decoder_norm = self.cfg.get('normalize_decoder_norm', False) + + # Setup normalized joint norm for model + self._optim_normalize_joint_norm = self.cfg.get('normalize_joint_norm', False) + + def extract_rnnt_loss_cfg(self, cfg: Optional[DictConfig]): + """ + Helper method to extract the rnnt loss name, and potentially its kwargs + to be passed. + + Args: + cfg: Should contain `loss_name` as a string which is resolved to a RNNT loss name. + If the default should be used, then `default` can be used. + Optionally, one can pass additional kwargs to the loss function. The subdict + should have a keyname as follows : `{loss_name}_kwargs`. + + Note that whichever loss_name is selected, that corresponding kwargs will be + selected. For the "default" case, the "{resolved_default}_kwargs" will be used. + + Examples: + .. code-block:: yaml + + loss_name: "default" + warprnnt_numba_kwargs: + kwargs2: some_other_val + + Returns: + A tuple, the resolved loss name as well as its kwargs (if found). + """ + if cfg is None: + cfg = DictConfig({}) + + loss_name = cfg.get("loss_name", "default") + + if loss_name == "default": + loss_name = resolve_rnnt_default_loss_name() + + loss_kwargs = cfg.get(f"{loss_name}_kwargs", None) + + logging.info(f"Using RNNT Loss : {loss_name}\n" f"Loss {loss_name}_kwargs: {loss_kwargs}") + + return loss_name, loss_kwargs + + def set_decoding_type_according_to_loss(self, decoding_cfg): + loss_name, loss_kwargs = self.extract_rnnt_loss_cfg(self.cfg.get("loss", None)) + + if loss_name == 'tdt': + decoding_cfg.durations = loss_kwargs.durations + elif loss_name == 'multiblank_rnnt': + decoding_cfg.big_blank_durations = loss_kwargs.big_blank_durations + + return decoding_cfg + + @torch.no_grad() + def transcribe( + self, + audio: List[str], + batch_size: int = 4, + return_hypotheses: bool = False, + partial_hypothesis: Optional[List['Hypothesis']] = None, + num_workers: int = 0, + channel_selector: Optional[ChannelSelectorType] = None, + augmentor: DictConfig = None, + verbose: bool = True, + override_config: Optional[TranscribeConfig] = None, + ) -> TranscriptionReturnType: + """ + Uses greedy decoding to transcribe audio files. Use this method for debugging and prototyping. + + Args: + audio: (a list) of paths to audio files. \ + Recommended length per file is between 5 and 25 seconds. \ + But it is possible to pass a few hours long file if enough GPU memory is available. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + return_hypotheses: (bool) Either return hypotheses or text + With hypotheses can do some postprocessing like getting timestamp or rescoring + partial_hypothesis: Optional[List['Hypothesis']] - A list of partial hypotheses to be used during rnnt + decoding. This is useful for streaming rnnt decoding. If this is not None, then the length of this + list should be equal to the length of the audio list. + num_workers: (int) number of workers for DataLoader + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied. + verbose: (bool) whether to display tqdm progress bar + override_config: (Optional[TranscribeConfig]) override transcription config pre-defined by the user. + **Note**: All other arguments in the function will be ignored if override_config is passed. + You should call this argument as `model.transcribe(audio, override_config=TranscribeConfig(...))`. + + Returns: + Returns a tuple of 2 items - + * A list of greedy transcript texts / Hypothesis + * An optional list of beam search transcript texts / Hypothesis / NBestHypothesis. + """ + return super().transcribe( + audio=audio, + batch_size=batch_size, + return_hypotheses=return_hypotheses, + num_workers=num_workers, + channel_selector=channel_selector, + augmentor=augmentor, + verbose=verbose, + override_config=override_config, + # Additional arguments + partial_hypothesis=partial_hypothesis, + ) + + def change_vocabulary(self, new_vocabulary: List[str], decoding_cfg: Optional[DictConfig] = None): + """ + Changes vocabulary used during RNNT decoding process. Use this method when fine-tuning a pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \ + this is target alphabet. + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + + Returns: None + + """ + if self.joint.vocabulary == new_vocabulary: + logging.warning(f"Old {self.joint.vocabulary} and new {new_vocabulary} match. Not changing anything.") + else: + if new_vocabulary is None or len(new_vocabulary) == 0: + raise ValueError(f'New vocabulary must be non-empty list of chars. But I got: {new_vocabulary}') + + joint_config = self.joint.to_config_dict() + new_joint_config = copy.deepcopy(joint_config) + new_joint_config['vocabulary'] = new_vocabulary + new_joint_config['num_classes'] = len(new_vocabulary) + del self.joint + self.joint = EncDecRNNTModel.from_config_dict(new_joint_config) + + decoder_config = self.decoder.to_config_dict() + new_decoder_config = copy.deepcopy(decoder_config) + new_decoder_config.vocab_size = len(new_vocabulary) + del self.decoder + self.decoder = EncDecRNNTModel.from_config_dict(new_decoder_config) + + del self.loss + loss_name, loss_kwargs = self.extract_rnnt_loss_cfg(self.cfg.get('loss', None)) + self.loss = RNNTLoss( + num_classes=self.joint.num_classes_with_blank - 1, loss_name=loss_name, loss_kwargs=loss_kwargs + ) + + if decoding_cfg is None: + # Assume same decoding config as before + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(RNNTDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + decoding_cfg = self.set_decoding_type_according_to_loss(decoding_cfg) + + self.decoding = RNNTDecoding( + decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, vocabulary=self.joint.vocabulary, + ) + + self.wer = WER( + decoding=self.decoding, + batch_dim_index=self.wer.batch_dim_index, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Setup fused Joint step + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Update config + with open_dict(self.cfg.joint): + self.cfg.joint = new_joint_config + + with open_dict(self.cfg.decoder): + self.cfg.decoder = new_decoder_config + + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + ds_keys = ['train_ds', 'validation_ds', 'test_ds'] + for key in ds_keys: + if key in self.cfg: + with open_dict(self.cfg[key]): + self.cfg[key]['labels'] = OmegaConf.create(new_vocabulary) + + logging.info(f"Changed decoder to output to {self.joint.vocabulary} vocabulary.") + + def change_decoding_strategy(self, decoding_cfg: DictConfig): + """ + Changes decoding strategy used during RNNT decoding process. + + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + """ + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(RNNTDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + decoding_cfg = self.set_decoding_type_according_to_loss(decoding_cfg) + + self.decoding = RNNTDecoding( + decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, vocabulary=self.joint.vocabulary, + ) + + self.wer = WER( + decoding=self.decoding, + batch_dim_index=self.wer.batch_dim_index, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Setup fused Joint step + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + self.joint.temperature = decoding_cfg.get('temperature', 1.0) + + # Update config + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}") + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + # Automatically inject args from model config to dataloader config + audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='sample_rate') + audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='labels') + + if config.get("use_lhotse"): + return get_lhotse_dataloader_from_config( + config, + global_rank=self.global_rank, + world_size=self.world_size, + dataset=LhotseSpeechToTextBpeDataset( + tokenizer=make_parser( + labels=config.get('labels', None), + name=config.get('parser', 'en'), + unk_id=config.get('unk_index', -1), + blank_id=config.get('blank_index', -1), + do_normalize=config.get('normalize_transcripts', False), + ), + ), + ) + + dataset = audio_to_text_dataset.get_audio_to_text_char_dataset_from_config( + config=config, + local_rank=self.local_rank, + global_rank=self.global_rank, + world_size=self.world_size, + preprocessor_cfg=self._cfg.get("preprocessor", None), + ) + + if dataset is None: + return None + + if isinstance(dataset, AudioToCharDALIDataset): + # DALI Dataset implements dataloader interface + return dataset + + shuffle = config['shuffle'] + if isinstance(dataset, torch.utils.data.IterableDataset): + shuffle = False + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + elif hasattr(dataset.datasets[0], 'collate_fn'): + # support datasets that are lists of entries + collate_fn = dataset.datasets[0].collate_fn + else: + # support datasets that are lists of lists + collate_fn = dataset.datasets[0].datasets[0].collate_fn + + batch_sampler = None + if config.get('use_semi_sorted_batching', False): + if not isinstance(dataset, _AudioTextDataset): + raise RuntimeError( + "Semi Sorted Batch sampler can be used with AudioToCharDataset or AudioToBPEDataset " + f"but found dataset of type {type(dataset)}" + ) + # set batch_size and batch_sampler to None to disable automatic batching + batch_sampler = get_semi_sorted_batch_sampler(self, dataset, config) + config['batch_size'] = None + config['drop_last'] = False + shuffle = False + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + sampler=batch_sampler, + batch_sampler=None, + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the training data loader via a Dict-like object. + + Args: + train_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in train_data_config: + train_data_config['shuffle'] = True + + # preserve config + self._update_dataset_config(dataset_name='train', config=train_data_config) + + self._train_dl = self._setup_dataloader_from_config(config=train_data_config) + + # Need to set this because if using an IterableDataset, the length of the dataloader is the total number + # of samples rather than the number of batches, and this messes up the tqdm progress bar. + # So we set the number of steps manually (to the correct number) to fix this. + + if ( + self._train_dl is not None + and hasattr(self._train_dl, 'dataset') + and isinstance(self._train_dl.dataset, torch.utils.data.IterableDataset) + ): + # We also need to check if limit_train_batches is already set. + # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches, + # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0). + if self._trainer is not None and isinstance(self._trainer.limit_train_batches, float): + self._trainer.limit_train_batches = int( + self._trainer.limit_train_batches + * ceil((len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size']) + ) + elif self._trainer is None: + logging.warning( + "Model Trainer was not set before constructing the dataset, incorrect number of " + "training batches will be used. Please set the trainer and rebuild the dataset." + ) + + def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the validation data loader via a Dict-like object. + + Args: + val_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in val_data_config: + val_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='validation', config=val_data_config) + + self._validation_dl = self._setup_dataloader_from_config(config=val_data_config) + + def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the test data loader via a Dict-like object. + + Args: + test_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in test_data_config: + test_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='test', config=test_data_config) + + self._test_dl = self._setup_dataloader_from_config(config=test_data_config) + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + if hasattr(self.preprocessor, '_sample_rate'): + input_signal_eltype = AudioSignal(freq=self.preprocessor._sample_rate) + else: + input_signal_eltype = AudioSignal() + + return { + "input_signal": NeuralType(('B', 'T'), input_signal_eltype, optional=True), + "input_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "processed_signal": NeuralType(('B', 'D', 'T'), SpectrogramType(), optional=True), + "processed_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return { + "outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + } + + @typecheck() + def forward( + self, input_signal=None, input_signal_length=None, processed_signal=None, processed_signal_length=None + ): + """ + Forward pass of the model. Note that for RNNT Models, the forward pass of the model is a 3 step process, + and this method only performs the first step - forward of the acoustic model. + + Please refer to the `training_step` in order to see the full `forward` step for training - which + performs the forward of the acoustic model, the prediction network and then the joint network. + Finally, it computes the loss and possibly compute the detokenized text via the `decoding` step. + + Please refer to the `validation_step` in order to see the full `forward` step for inference - which + performs the forward of the acoustic model, the prediction network and then the joint network. + Finally, it computes the decoded tokens via the `decoding` step and possibly compute the batch metrics. + + Args: + input_signal: Tensor that represents a batch of raw audio signals, + of shape [B, T]. T here represents timesteps, with 1 second of audio represented as + `self.sample_rate` number of floating point values. + input_signal_length: Vector of length B, that contains the individual lengths of the audio + sequences. + processed_signal: Tensor that represents a batch of processed audio signals, + of shape (B, D, T) that has undergone processing via some DALI preprocessor. + processed_signal_length: Vector of length B, that contains the individual lengths of the + processed audio sequences. + + Returns: + A tuple of 2 elements - + 1) The log probabilities tensor of shape [B, T, D]. + 2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. + """ + has_input_signal = input_signal is not None and input_signal_length is not None + has_processed_signal = processed_signal is not None and processed_signal_length is not None + if (has_input_signal ^ has_processed_signal) is False: + raise ValueError( + f"{self} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive " + " with ``processed_signal`` and ``processed_signal_len`` arguments." + ) + + if not has_processed_signal: + processed_signal, processed_signal_length = self.preprocessor( + input_signal=input_signal, length=input_signal_length, + ) + + # Spec augment is not applied during evaluation/testing + if self.spec_augmentation is not None and self.training: + processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length) + + encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length) + return encoded, encoded_len + + # PTL-specific methods + def training_step(self, batch, batch_nb): + # Reset access registry + if AccessMixin.is_access_enabled(self.model_guid): + AccessMixin.reset_registry(self) + + signal, signal_len, transcript, transcript_len = batch + + # forward() only performs encoder forward + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + encoded, encoded_len = self.forward(processed_signal=signal, processed_signal_length=signal_len) + else: + encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) + del signal + + # During training, loss must be computed, so decoder forward is necessary + decoder, target_length, states = self.decoder(targets=transcript, target_length=transcript_len) + + if hasattr(self, '_trainer') and self._trainer is not None: + log_every_n_steps = self._trainer.log_every_n_steps + sample_id = self._trainer.global_step + else: + log_every_n_steps = 1 + sample_id = batch_nb + + # If experimental fused Joint-Loss-WER is not used + if not self.joint.fuse_loss_wer: + # Compute full joint and loss + joint = self.joint(encoder_outputs=encoded, decoder_outputs=decoder) + loss_value = self.loss( + log_probs=joint, targets=transcript, input_lengths=encoded_len, target_lengths=target_length + ) + + # Add auxiliary losses, if registered + loss_value = self.add_auxiliary_losses(loss_value) + + # Reset access registry + if AccessMixin.is_access_enabled(self.model_guid): + AccessMixin.reset_registry(self) + + tensorboard_logs = { + 'train_loss': loss_value, + 'learning_rate': self._optimizer.param_groups[0]['lr'], + 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), + } + + if (sample_id + 1) % log_every_n_steps == 0: + self.wer.update( + predictions=encoded, + predictions_lengths=encoded_len, + targets=transcript, + targets_lengths=transcript_len, + ) + _, scores, words = self.wer.compute() + self.wer.reset() + tensorboard_logs.update({'training_batch_wer': scores.float() / words}) + + else: + # If experimental fused Joint-Loss-WER is used + if (sample_id + 1) % log_every_n_steps == 0: + compute_wer = True + else: + compute_wer = False + + # Fused joint step + loss_value, wer, _, _ = self.joint( + encoder_outputs=encoded, + decoder_outputs=decoder, + encoder_lengths=encoded_len, + transcripts=transcript, + transcript_lengths=transcript_len, + compute_wer=compute_wer, + ) + + # Add auxiliary losses, if registered + loss_value = self.add_auxiliary_losses(loss_value) + + # Reset access registry + if AccessMixin.is_access_enabled(self.model_guid): + AccessMixin.reset_registry(self) + + tensorboard_logs = { + 'train_loss': loss_value, + 'learning_rate': self._optimizer.param_groups[0]['lr'], + 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), + } + + if compute_wer: + tensorboard_logs.update({'training_batch_wer': wer}) + + # Log items + self.log_dict(tensorboard_logs) + + # Preserve batch acoustic model T and language model U parameters if normalizing + if self._optim_normalize_joint_txu: + self._optim_normalize_txu = [encoded_len.max(), transcript_len.max()] + + return {'loss': loss_value} + + def predict_step(self, batch, batch_idx, dataloader_idx=0): + signal, signal_len, transcript, transcript_len, sample_id = batch + + # forward() only performs encoder forward + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + encoded, encoded_len = self.forward(processed_signal=signal, processed_signal_length=signal_len) + else: + encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) + del signal + + best_hyp_text, all_hyp_text = self.decoding.rnnt_decoder_predictions_tensor( + encoder_output=encoded, encoded_lengths=encoded_len, return_hypotheses=False + ) + + sample_id = sample_id.cpu().detach().numpy() + return list(zip(sample_id, best_hyp_text)) + + def validation_pass(self, batch, batch_idx, dataloader_idx=0): + signal, signal_len, transcript, transcript_len = batch + + # forward() only performs encoder forward + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + encoded, encoded_len = self.forward(processed_signal=signal, processed_signal_length=signal_len) + else: + encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) + del signal + + tensorboard_logs = {} + + # If experimental fused Joint-Loss-WER is not used + if not self.joint.fuse_loss_wer: + if self.compute_eval_loss: + decoder, target_length, states = self.decoder(targets=transcript, target_length=transcript_len) + joint = self.joint(encoder_outputs=encoded, decoder_outputs=decoder) + + loss_value = self.loss( + log_probs=joint, targets=transcript, input_lengths=encoded_len, target_lengths=target_length + ) + + tensorboard_logs['val_loss'] = loss_value + + self.wer.update( + predictions=encoded, + predictions_lengths=encoded_len, + targets=transcript, + targets_lengths=transcript_len, + ) + wer, wer_num, wer_denom = self.wer.compute() + self.wer.reset() + + tensorboard_logs['val_wer_num'] = wer_num + tensorboard_logs['val_wer_denom'] = wer_denom + tensorboard_logs['val_wer'] = wer + + else: + # If experimental fused Joint-Loss-WER is used + compute_wer = True + + if self.compute_eval_loss: + decoded, target_len, states = self.decoder(targets=transcript, target_length=transcript_len) + else: + decoded = None + target_len = transcript_len + + # Fused joint step + loss_value, wer, wer_num, wer_denom = self.joint( + encoder_outputs=encoded, + decoder_outputs=decoded, + encoder_lengths=encoded_len, + transcripts=transcript, + transcript_lengths=target_len, + compute_wer=compute_wer, + ) + + if loss_value is not None: + tensorboard_logs['val_loss'] = loss_value + + tensorboard_logs['val_wer_num'] = wer_num + tensorboard_logs['val_wer_denom'] = wer_denom + tensorboard_logs['val_wer'] = wer + + self.log('global_step', torch.tensor(self.trainer.global_step, dtype=torch.float32)) + + return tensorboard_logs + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + metrics = self.validation_pass(batch, batch_idx, dataloader_idx) + if type(self.trainer.val_dataloaders) == list and len(self.trainer.val_dataloaders) > 1: + self.validation_step_outputs[dataloader_idx].append(metrics) + else: + self.validation_step_outputs.append(metrics) + return metrics + + def test_step(self, batch, batch_idx, dataloader_idx=0): + logs = self.validation_pass(batch, batch_idx, dataloader_idx=dataloader_idx) + test_logs = {name.replace("val_", "test_"): value for name, value in logs.items()} + if type(self.trainer.test_dataloaders) == list and len(self.trainer.test_dataloaders) > 1: + self.test_step_outputs[dataloader_idx].append(test_logs) + else: + self.test_step_outputs.append(test_logs) + return test_logs + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + if self.compute_eval_loss: + val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean() + val_loss_log = {'val_loss': val_loss_mean} + else: + val_loss_log = {} + wer_num = torch.stack([x['val_wer_num'] for x in outputs]).sum() + wer_denom = torch.stack([x['val_wer_denom'] for x in outputs]).sum() + tensorboard_logs = {**val_loss_log, 'val_wer': wer_num.float() / wer_denom} + return {**val_loss_log, 'log': tensorboard_logs} + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + if self.compute_eval_loss: + test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean() + test_loss_log = {'test_loss': test_loss_mean} + else: + test_loss_log = {} + wer_num = torch.stack([x['test_wer_num'] for x in outputs]).sum() + wer_denom = torch.stack([x['test_wer_denom'] for x in outputs]).sum() + tensorboard_logs = {**test_loss_log, 'test_wer': wer_num.float() / wer_denom} + return {**test_loss_log, 'log': tensorboard_logs} + + """ Transcription related methods """ + + def _transcribe_forward(self, batch: Any, trcfg: TranscribeConfig): + encoded, encoded_len = self.forward(input_signal=batch[0], input_signal_length=batch[1]) + output = dict(encoded=encoded, encoded_len=encoded_len) + return output + + def _transcribe_output_processing( + self, outputs, trcfg: TranscribeConfig + ) -> Tuple[List['Hypothesis'], List['Hypothesis']]: + encoded = outputs.pop('encoded') + encoded_len = outputs.pop('encoded_len') + + best_hyp, all_hyp = self.decoding.rnnt_decoder_predictions_tensor( + encoded, + encoded_len, + return_hypotheses=trcfg.return_hypotheses, + partial_hypotheses=trcfg.partial_hypothesis, + ) + + # cleanup memory + del encoded, encoded_len + + hypotheses = [] + all_hypotheses = [] + + hypotheses += best_hyp + if all_hyp is not None: + all_hypotheses += all_hyp + else: + all_hypotheses += best_hyp + + return (hypotheses, all_hypotheses) + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided audio file. + + Args: + config: A python dictionary which contains the following keys: + paths2audio_files: (a list) of paths to audio files. The files should be relatively short fragments. \ + Recommended length per file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the audio manifest is temporarily + stored. + + Returns: + A pytorch DataLoader for the given audio file(s). + """ + if 'manifest_filepath' in config: + manifest_filepath = config['manifest_filepath'] + batch_size = config['batch_size'] + else: + manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json') + batch_size = min(config['batch_size'], len(config['paths2audio_files'])) + + dl_config = { + 'manifest_filepath': manifest_filepath, + 'sample_rate': self.preprocessor._sample_rate, + 'labels': self.joint.vocabulary, + 'batch_size': batch_size, + 'trim_silence': False, + 'shuffle': False, + 'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)), + 'pin_memory': True, + } + + if config.get("augmentor"): + dl_config['augmentor'] = config.get("augmentor") + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer + + def on_after_backward(self): + super().on_after_backward() + if self._optim_variational_noise_std > 0 and self.global_step >= self._optim_variational_noise_start: + for param_name, param in self.decoder.named_parameters(): + if param.grad is not None: + noise = torch.normal( + mean=0.0, + std=self._optim_variational_noise_std, + size=param.size(), + device=param.device, + dtype=param.dtype, + ) + param.grad.data.add_(noise) + + if self._optim_normalize_joint_txu: + T, U = self._optim_normalize_txu + if T is not None and U is not None: + for param_name, param in self.encoder.named_parameters(): + if param.grad is not None: + param.grad.data.div_(U) + + for param_name, param in self.decoder.named_parameters(): + if param.grad is not None: + param.grad.data.div_(T) + + if self._optim_normalize_encoder_norm: + for param_name, param in self.encoder.named_parameters(): + if param.grad is not None: + norm = param.grad.norm() + param.grad.data.div_(norm) + + if self._optim_normalize_decoder_norm: + for param_name, param in self.decoder.named_parameters(): + if param.grad is not None: + norm = param.grad.norm() + param.grad.data.div_(norm) + + if self._optim_normalize_joint_norm: + for param_name, param in self.joint.named_parameters(): + if param.grad is not None: + norm = param.grad.norm() + param.grad.data.div_(norm) + + # EncDecRNNTModel is exported in 2 parts + def list_export_subnets(self): + return ['encoder', 'decoder_joint'] + + # for export + @property + def decoder_joint(self): + return RNNTDecoderJoint(self.decoder, self.joint) + + def set_export_config(self, args): + if 'decoder_type' in args: + if hasattr(self, 'change_decoding_strategy'): + self.change_decoding_strategy(decoder_type=args['decoder_type']) + else: + raise Exception("Model does not have decoder type option") + super().set_export_config(args) + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + model = PretrainedModelInfo( + pretrained_model_name="stt_zh_conformer_transducer_large", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_zh_conformer_transducer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_conformer_transducer_large/versions/1.8.0/files/stt_zh_conformer_transducer_large.nemo", + ) + results.append(model) + + return results + + @property + def wer(self): + return self._wer + + @wer.setter + def wer(self, wer): + self._wer = wer diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/slu_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/slu_models.py new file mode 100644 index 0000000..1303bbf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/slu_models.py @@ -0,0 +1,629 @@ +# ! /usr/bin/python +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import tempfile +from math import ceil +from typing import Any, Dict, List, Optional, Union + +import torch +from omegaconf import DictConfig, OmegaConf, open_dict +from tqdm.auto import tqdm + +from nemo.collections.asr.data import audio_to_text_dataset +from nemo.collections.asr.data.audio_to_text_dali import DALIOutputs +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel +from nemo.collections.asr.parts.mixins import ( + ASRBPEMixin, + ASRModuleMixin, + ASRTranscriptionMixin, + TranscribeConfig, + TranscriptionReturnType, +) +from nemo.collections.asr.parts.preprocessing.perturb import process_augmentations +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCBPEDecoding, CTCBPEDecodingConfig +from nemo.collections.asr.parts.utils.slu_utils import SequenceGenerator, SequenceGeneratorConfig, get_seq_mask +from nemo.collections.common.losses import SmoothedNLLLoss +from nemo.core.classes.common import PretrainedModelInfo, typecheck +from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, LogprobsType, NeuralType, SpectrogramType +from nemo.utils import logging, model_utils + +__all__ = ["SLUIntentSlotBPEModel"] + + +class SLUIntentSlotBPEModel(ASRModel, ExportableEncDecModel, ASRModuleMixin, ASRBPEMixin, ASRTranscriptionMixin): + """Model for end-to-end speech intent classification and slot filling, which is formulated as a speech-to-sequence task""" + + def __init__(self, cfg: DictConfig, trainer=None): + # Convert to Hydra 1.0 compatible DictConfig + cfg = model_utils.convert_model_config_to_dict_config(cfg) + cfg = model_utils.maybe_update_config_version(cfg) + + if 'tokenizer' not in cfg: + raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !") + + # Setup the tokenizer + self._setup_tokenizer(cfg.tokenizer) + + super().__init__(cfg=cfg, trainer=trainer) + + self.preprocessor = self.from_config_dict(self.cfg.preprocessor) + self.encoder = self.from_config_dict(self.cfg.encoder) + self.decoder = self.from_config_dict(self.cfg.decoder) + + if hasattr(self._cfg, 'spec_augment') and self._cfg.spec_augment is not None: + self.spec_augmentation = self.from_config_dict(self._cfg.spec_augment) + else: + self.spec_augmentation = None + + # Setup optional Optimization flags + self.setup_optimization_flags() + + # Adapter modules setup (from ASRAdapterModelMixin) + self.setup_adapters() + + self.vocabulary = self.tokenizer.tokenizer.get_vocab() + vocab_size = len(self.vocabulary) + + # Create embedding layer + self.cfg.embedding["vocab_size"] = vocab_size + self.embedding = self.from_config_dict(self.cfg.embedding) + + # Create token classifier + self.cfg.classifier["num_classes"] = vocab_size + self.classifier = self.from_config_dict(self.cfg.classifier) + + self.loss = SmoothedNLLLoss(label_smoothing=self.cfg.loss.label_smoothing) + + self.sequence_generator = SequenceGenerator( + cfg=self.cfg.sequence_generator, + embedding=self.embedding, + decoder=self.decoder, + log_softmax=self.classifier, + tokenizer=self.tokenizer, + ) + + # Setup decoding objects + decoding_cfg = self.cfg.get('decoding', None) + + # In case decoding config not found, use default config + if decoding_cfg is None: + decoding_cfg = OmegaConf.structured(CTCBPEDecodingConfig) + with open_dict(self.cfg): + self.cfg.decoding = decoding_cfg + + self.decoding = CTCBPEDecoding(self.cfg.decoding, tokenizer=self.tokenizer) + + # Setup metric with decoding strategy + self.wer = WER( + decoding=self.decoding, + use_cer=self._cfg.get('use_cer', False), + dist_sync_on_step=True, + log_prediction=self._cfg.get("log_prediction", False), + fold_consecutive=False, + ) + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + if hasattr(self.preprocessor, '_sample_rate'): + input_signal_eltype = AudioSignal(freq=self.preprocessor._sample_rate) + else: + input_signal_eltype = AudioSignal() + return { + "input_signal": NeuralType(('B', 'T'), input_signal_eltype, optional=True), + "input_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "target_semantics": NeuralType(('B', 'T'), input_signal_eltype, optional=True), + "target_semantics_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "processed_signal": NeuralType(('B', 'D', 'T'), SpectrogramType(), optional=True), + "processed_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "sample_id": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return { + "log_probs": NeuralType(('B', 'T', 'D'), LogprobsType(), optional=True), + "lengths": NeuralType(tuple('B'), LengthsType(), optional=True), + "greedy_predictions": NeuralType(('B', 'T'), LabelsType(), optional=True), + } + + def set_decoding_strategy(self, cfg: SequenceGeneratorConfig): + cfg.max_sequence_length = self.sequence_generator.generator.max_seq_length + self.sequence_generator = SequenceGenerator(cfg, self.embedding, self.decoder, self.classifier, self.tokenizer) + + @typecheck() + def forward( + self, + input_signal=None, + input_signal_length=None, + target_semantics=None, + target_semantics_length=None, + processed_signal=None, + processed_signal_length=None, + ): + """ + Forward pass of the model. + + Params: + input_signal: Tensor that represents a batch of raw audio signals, of shape [B, T]. T here represents + timesteps, with 1 second of audio represented as `self.sample_rate` number of floating point values. + + input_signal_length: Vector of length B, that contains the individual lengths of the audio sequences. + + target_semantics: Tensor that represents a batch of semantic tokens, of shape [B, L]. + + target_semantics_length: Vector of length B, that contains the individual lengths of the semantic sequences. + + processed_signal: Tensor that represents a batch of processed audio signals, of shape (B, D, T) that has + undergone processing via some DALI preprocessor. + + processed_signal_length: Vector of length B, that contains the individual lengths of the processed audio + sequences. + + Returns: + A tuple of 3 elements - + 1) The log probabilities tensor of shape [B, T, D]. + 2) The lengths of the output sequence after decoder, of shape [B]. + 3) The token predictions of the model of shape [B, T]. + """ + has_input_signal = input_signal is not None and input_signal_length is not None + has_processed_signal = processed_signal is not None and processed_signal_length is not None + if (has_input_signal ^ has_processed_signal) == False: + raise ValueError( + f"{self} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive " + " with ``processed_signal`` and ``processed_signal_len`` arguments." + ) + + if not has_processed_signal: + processed_signal, processed_signal_length = self.preprocessor( + input_signal=input_signal, length=input_signal_length, + ) + + if self.spec_augmentation is not None and self.training: + processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length) + + encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length) + encoded = encoded.transpose(1, 2) # BxDxT -> BxTxD + encoded_mask = get_seq_mask(encoded, encoded_len) + + if target_semantics is None: # in inference-only mode + predictions = self.sequence_generator(encoded, encoded_mask) + return None, None, predictions + + bos_semantics_tokens = target_semantics[:, :-1] + bos_semantics = self.embedding(bos_semantics_tokens) + bos_semantics_mask = get_seq_mask(bos_semantics, target_semantics_length - 1) + + decoded = self.decoder( + encoder_states=encoded, + encoder_mask=encoded_mask, + decoder_states=bos_semantics, + decoder_mask=bos_semantics_mask, + ) + log_probs = self.classifier(decoded) + + predictions = log_probs.argmax(dim=-1, keepdim=False) + + pred_len = self.sequence_generator.get_seq_length(predictions) + return log_probs, pred_len, predictions + + # PTL-specific methods + def training_step(self, batch, batch_nb): + if len(batch) == 4: + signal, signal_len, semantics, semantics_len = batch + else: + signal, signal_len, semantics, semantics_len, sample_id = batch + + log_probs, pred_len, predictions = self.forward( + input_signal=signal, + input_signal_length=signal_len, + target_semantics=semantics, + target_semantics_length=semantics_len, + ) + + eos_semantics = semantics[:, 1:] + eos_semantics_len = semantics_len - 1 # subtract 1 for eos tokens + + loss_value = self.loss(log_probs=log_probs, labels=eos_semantics, lengths=eos_semantics_len) + + tensorboard_logs = {'train_loss': loss_value.item()} + if len(self._optimizer.param_groups) == 1: + tensorboard_logs['learning_rate'] = self._optimizer.param_groups[0]['lr'] + else: + for i, group in enumerate(self._optimizer.param_groups): + tensorboard_logs[f'learning_rate_g{i}'] = group['lr'] + + if hasattr(self, '_trainer') and self._trainer is not None: + log_every_n_steps = self._trainer.log_every_n_steps + else: + log_every_n_steps = 1 + + if (batch_nb + 1) % log_every_n_steps == 0: + self.wer.update( + predictions=predictions, + targets=eos_semantics, + predictions_lengths=pred_len, + targets_lengths=eos_semantics_len, + ) + wer, _, _ = self.wer.compute() + self.wer.reset() + tensorboard_logs.update({'training_batch_wer': wer}) + + return {'loss': loss_value, 'log': tensorboard_logs} + + def predict( + self, input_signal, input_signal_length, processed_signal=None, processed_signal_length=None, dataloader_idx=0 + ) -> List[str]: + has_input_signal = input_signal is not None and input_signal_length is not None + has_processed_signal = processed_signal is not None and processed_signal_length is not None + if (has_input_signal ^ has_processed_signal) == False: + raise ValueError( + f"{self} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive " + " with ``processed_signal`` and ``processed_signal_len`` arguments." + ) + + if not has_processed_signal: + processed_signal, processed_signal_length = self.preprocessor( + input_signal=input_signal, length=input_signal_length, + ) + + if self.spec_augmentation is not None and self.training: + processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length) + + encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length) + encoded = encoded.transpose(1, 2) # BxDxT -> BxTxD + encoded_mask = get_seq_mask(encoded, encoded_len) + + pred_tokens = self.sequence_generator(encoded, encoded_mask) + predictions = self.sequence_generator.decode_semantics_from_tokens(pred_tokens) + return predictions + + def validation_pass(self, batch, batch_idx, dataloader_idx=0): + if len(batch) == 4: + signal, signal_len, semantics, semantics_len = batch + else: + signal, signal_len, semantics, semantics_len, sample_id = batch + + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + log_probs, pred_len, predictions = self.forward( + processed_signal=signal, + processed_signal_length=signal_len, + target_semantics=semantics, + target_semantics_length=semantics_len, + ) + else: + log_probs, pred_len, predictions = self.forward( + input_signal=signal, + input_signal_length=signal_len, + target_semantics=semantics, + target_semantics_length=semantics_len, + ) + + eos_semantics = semantics[:, 1:] + eos_semantics_len = semantics_len - 1 # subtract 1 for bos&eos tokens + + loss_value = self.loss(log_probs=log_probs, labels=eos_semantics, lengths=eos_semantics_len) + + self.wer.update( + predictions=predictions, + targets=eos_semantics, + predictions_lengths=pred_len, + targets_lengths=eos_semantics_len, + ) + wer, wer_num, wer_denom = self.wer.compute() + self.wer.reset() + + return { + 'val_loss': loss_value, + 'val_wer_num': wer_num, + 'val_wer_denom': wer_denom, + 'val_wer': wer, + } + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + metrics = self.validation_pass(batch, batch_idx, dataloader_idx) + if type(self.trainer.val_dataloaders) == list and len(self.trainer.val_dataloaders) > 1: + self.validation_step_outputs[dataloader_idx].append(metrics) + else: + self.validation_step_outputs.append(metrics) + return metrics + + def test_step(self, batch, batch_idx, dataloader_idx=0): + logs = self.validation_pass(batch, batch_idx, dataloader_idx=dataloader_idx) + test_logs = {name.replace("val_", "test_"): value for name, value in logs.items()} + if type(self.trainer.test_dataloaders) == list and len(self.trainer.test_dataloaders) > 1: + self.test_step_outputs[dataloader_idx].append(test_logs) + else: + self.test_step_outputs.append(test_logs) + return test_logs + + def test_dataloader(self): + if self._test_dl is None: + # None dataloader no longer supported in PTL2.0 + self._test_dl = [] + + return self._test_dl + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + if 'augmentor' in config: + augmentor = process_augmentations(config['augmentor']) + else: + augmentor = None + + shuffle = config['shuffle'] + device = 'gpu' if torch.cuda.is_available() else 'cpu' + if config.get('use_dali', False): + device_id = self.local_rank if device == 'gpu' else None + dataset = audio_to_text_dataset.get_dali_bpe_dataset( + config=config, + tokenizer=self.tokenizer, + shuffle=shuffle, + device_id=device_id, + global_rank=self.global_rank, + world_size=self.world_size, + preprocessor_cfg=self._cfg.preprocessor, + ) + return dataset + + # Instantiate tarred dataset loader or normal dataset loader + if config.get('is_tarred', False): + if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or ( + 'manifest_filepath' in config and config['manifest_filepath'] is None + ): + logging.warning( + "Could not load dataset as `manifest_filepath` was None or " + f"`tarred_audio_filepaths` is None. Provided config : {config}" + ) + return None + + shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 + dataset = audio_to_text_dataset.get_tarred_dataset( + config=config, + tokenizer=self.tokenizer, + shuffle_n=shuffle_n, + global_rank=self.global_rank, + world_size=self.world_size, + augmentor=augmentor, + ) + shuffle = False + else: + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") + return None + + dataset = audio_to_text_dataset.get_bpe_dataset( + config=config, tokenizer=self.tokenizer, augmentor=augmentor + ) + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + elif hasattr(dataset.datasets[0], 'collate_fn'): + # support datasets that are lists of entries + collate_fn = dataset.datasets[0].collate_fn + else: + # support datasets that are lists of lists + collate_fn = dataset.datasets[0].datasets[0].collate_fn + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the training data loader via a Dict-like object. + + Args: + train_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in train_data_config: + train_data_config['shuffle'] = True + + # preserve config + self._update_dataset_config(dataset_name='train', config=train_data_config) + + self._train_dl = self._setup_dataloader_from_config(config=train_data_config) + + # Need to set this because if using an IterableDataset, the length of the dataloader is the total number + # of samples rather than the number of batches, and this messes up the tqdm progress bar. + # So we set the number of steps manually (to the correct number) to fix this. + if ( + self._train_dl is not None + and hasattr(self._train_dl, 'dataset') + and isinstance(self._train_dl.dataset, torch.utils.data.IterableDataset) + ): + # We also need to check if limit_train_batches is already set. + # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches, + # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0). + if self._trainer is not None and isinstance(self._trainer.limit_train_batches, float): + self._trainer.limit_train_batches = int( + self._trainer.limit_train_batches + * ceil((len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size']) + ) + elif self._trainer is None: + logging.warning( + "Model Trainer was not set before constructing the dataset, incorrect number of " + "training batches will be used. Please set the trainer and rebuild the dataset." + ) + + def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the validation data loader via a Dict-like object. + + Args: + val_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in val_data_config: + val_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='validation', config=val_data_config) + + self._validation_dl = self._setup_dataloader_from_config(config=val_data_config) + + def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the test data loader via a Dict-like object. + + Args: + test_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in test_data_config: + test_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='test', config=test_data_config) + + self._test_dl = self._setup_dataloader_from_config(config=test_data_config) + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided audio file. + + Args: + config: A python dictionary which contains the following keys: + paths2audio_files: (a list) of paths to audio files. The files should be relatively short fragments. \ + Recommended length per file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the audio manifest is temporarily + stored. + num_workers: (int) number of workers. Depends of the batch_size and machine. \ + 0 - only the main process will load batches, 1 - one worker (not main process) + + Returns: + A pytorch DataLoader for the given audio file(s). + """ + + if 'manifest_filepath' in config: + manifest_filepath = config['manifest_filepath'] + batch_size = config['batch_size'] + else: + manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json') + batch_size = min(config['batch_size'], len(config['paths2audio_files'])) + + dl_config = { + 'manifest_filepath': manifest_filepath, + 'sample_rate': self.preprocessor._sample_rate, + 'batch_size': batch_size, + 'shuffle': False, + 'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)), + 'pin_memory': True, + 'use_start_end_token': self.cfg.validation_ds.get('use_start_end_token', False), + } + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer + + @torch.no_grad() + def transcribe( + self, + audio: List[str], + batch_size: int = 4, + return_hypotheses: bool = False, + num_workers: int = 0, + verbose: bool = True, + ) -> TranscriptionReturnType: + """ + Uses greedy decoding to transcribe audio files into SLU semantics. + Use this method for debugging and prototyping. + + Args: + audio: (a list) of paths to audio files. \ + Recommended length per file is between 5 and 25 seconds. \ + But it is possible to pass a few hours long file if enough GPU memory is available. + batch_size: (int) batch size to use during inference. + Bigger will result in better throughput performance but would use more memory. + return_hypotheses: (bool) Either return hypotheses or text + With hypotheses can do some postprocessing like getting timestamp or rescoring + num_workers: (int) number of workers for DataLoader + verbose: (bool) whether to display tqdm progress bar + + Returns: + A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files + """ + return super().transcribe( + audio=audio, + batch_size=batch_size, + return_hypotheses=return_hypotheses, + num_workers=num_workers, + verbose=verbose, + ) + + """ Transcription related methods """ + + def _transcribe_forward(self, batch: Any, trcfg: TranscribeConfig): + predictions = self.predict(input_signal=batch[0], input_signal_length=batch[1]) + output = {'predictions': predictions} + return output + + def _transcribe_output_processing(self, outputs, trcfg: TranscribeConfig) -> List[str]: + hypotheses = outputs.pop('predictions') + return hypotheses + + @classmethod + def list_available_models(cls) -> Optional[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + model = PretrainedModelInfo( + pretrained_model_name="slu_conformer_transformer_large_slurp", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:slu_conformer_transformer_large_slurp", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/slu_conformer_transformer_large_slurp/versions/1.13.0/files/slu_conformer_transformer_large_slurp.nemo", + ) + results.append(model) + + @property + def wer(self): + return self._wer + + @wer.setter + def wer(self, wer): + self._wer = wer diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/ssl_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/ssl_models.py new file mode 100644 index 0000000..787c91e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/ssl_models.py @@ -0,0 +1,591 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from math import ceil +from typing import Dict, List, Optional, Union + +import torch +import torch.nn as nn +from omegaconf import DictConfig +from pytorch_lightning import Trainer + +from nemo.collections.asr.data import audio_to_text_dataset +from nemo.collections.asr.data.audio_to_text_dali import DALIOutputs +from nemo.collections.asr.data.audio_to_text_lhotse import LhotseSpeechToTextBpeDataset +from nemo.collections.asr.parts.mixins import ASRModuleMixin +from nemo.collections.asr.parts.preprocessing.perturb import process_augmentations +from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config +from nemo.collections.common.parts.preprocessing.parsers import make_parser +from nemo.core.classes import ModelPT +from nemo.core.classes.common import PretrainedModelInfo, typecheck +from nemo.core.classes.mixins import AccessMixin, set_access_cfg +from nemo.core.neural_types import ( + AcousticEncodedRepresentation, + AudioSignal, + LabelsType, + LengthsType, + NeuralType, + SpectrogramType, +) +from nemo.utils import logging + +__all__ = ['SpeechEncDecSelfSupervisedModel'] + + +class SpeechEncDecSelfSupervisedModel(ModelPT, ASRModuleMixin, AccessMixin): + """Base class for encoder-decoder models used for self-supervised encoder pre-training""" + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + model = PretrainedModelInfo( + pretrained_model_name="ssl_en_conformer_large", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:ssl_en_conformer_large", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/ssl_en_conformer_large/versions/1.10.1/files/ssl_en_conformer_large.nemo", + ) + results.append(model) + + model = PretrainedModelInfo( + pretrained_model_name="ssl_en_conformer_xlarge", + description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:ssl_en_conformer_xlarge", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/ssl_en_conformer_xlarge/versions/1.10.0/files/ssl_en_conformer_xlarge.nemo", + ) + results.append(model) + + return results + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + # Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable + # Global_rank and local_rank is set by LightningModule in Lightning 1.2.0 + self.world_size = 1 + if trainer is not None: + self.world_size = trainer.world_size + + super().__init__(cfg=cfg, trainer=trainer) + self.preprocessor = SpeechEncDecSelfSupervisedModel.from_config_dict(self._cfg.preprocessor) + self.encoder = SpeechEncDecSelfSupervisedModel.from_config_dict(self._cfg.encoder) + + self.decoder_losses = None + + if "loss_list" in self._cfg: + + self.decoder_losses = {} + self.loss_alphas = {} + self.start_step = {} + self.output_from_layer = {} + self.transpose_encoded = {} + self.targets_from_loss = {} + self.decoder_losses_active = {} + # need to be separate for moduledict + + for decoder_loss_name, decoder_loss_cfg in self._cfg.loss_list.items(): + if not decoder_loss_cfg.get("is_active", True): # active by default + continue + + new_decoder_loss = { + 'decoder': SpeechEncDecSelfSupervisedModel.from_config_dict(decoder_loss_cfg.decoder), + 'loss': SpeechEncDecSelfSupervisedModel.from_config_dict(decoder_loss_cfg.loss), + } + new_decoder_loss = nn.ModuleDict(new_decoder_loss) + self.decoder_losses[decoder_loss_name] = new_decoder_loss + self.loss_alphas[decoder_loss_name] = decoder_loss_cfg.get("loss_alpha", 1.0) + self.output_from_layer[decoder_loss_name] = decoder_loss_cfg.get("output_from_layer", None) + self.targets_from_loss[decoder_loss_name] = decoder_loss_cfg.get("targets_from_loss", None) + self.start_step[decoder_loss_name] = decoder_loss_cfg.get("start_step", 0) + self.transpose_encoded[decoder_loss_name] = decoder_loss_cfg.get("transpose_encoded", False) + self.decoder_losses_active[decoder_loss_name] = True + + self.decoder_losses = nn.ModuleDict(self.decoder_losses) + + else: + self.decoder_ssl = SpeechEncDecSelfSupervisedModel.from_config_dict(self._cfg.decoder) + self.loss = SpeechEncDecSelfSupervisedModel.from_config_dict(self._cfg.loss) + + self.spec_augmentation = SpeechEncDecSelfSupervisedModel.from_config_dict(self._cfg.spec_augment) + + # dropout for features/spectrograms (applied before masking) + self.dropout_features = ( + torch.nn.Dropout(self._cfg.dropout_features) if "dropout_features" in self._cfg else None + ) + + # dropout for targets (applied before quantization) + self.dropout_features_q = ( + torch.nn.Dropout(self._cfg.dropout_features_q) if "dropout_features_q" in self._cfg else None + ) + + # Feature penalty for preprocessor encodings (for Wav2Vec training) + if "feature_penalty" in self._cfg: + self.feat_pen, self.pen_factor = 0.0, self._cfg.feature_penalty + else: + self.feat_pen, self.pen_factor = None, None + + if "access" in self._cfg: + set_access_cfg(self._cfg.access, self.model_guid) + + self.apply_masking = True + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + if 'augmentor' in config: + augmentor = process_augmentations(config['augmentor']) + else: + augmentor = None + + # Automatically inject args from model config to dataloader config + audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='sample_rate') + + if config.get("use_lhotse"): + return get_lhotse_dataloader_from_config( + config, + global_rank=self.global_rank, + world_size=self.world_size, + dataset=LhotseSpeechToTextBpeDataset( + tokenizer=make_parser( + labels=config.get('labels', None), + name=config.get('parser', 'en'), + unk_id=config.get('unk_index', -1), + blank_id=config.get('blank_index', -1), + do_normalize=config.get('normalize_transcripts', False), + ), + ), + ) + + shuffle = config['shuffle'] + device = 'gpu' if torch.cuda.is_available() else 'cpu' + if config.get('use_dali', False): + device_id = self.local_rank if device == 'gpu' else None + dataset = audio_to_text_dataset.get_dali_char_dataset( + config=config, + shuffle=shuffle, + device_id=device_id, + global_rank=self.global_rank, + world_size=self.world_size, + preprocessor_cfg=self._cfg.preprocessor, + ) + return dataset + + # Instantiate tarred dataset loader or normal dataset loader + if config.get('is_tarred', False): + if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or ( + 'manifest_filepath' in config and config['manifest_filepath'] is None + ): + logging.warning( + "Could not load dataset as `manifest_filepath` was None or " + f"`tarred_audio_filepaths` is None. Provided config : {config}" + ) + return None + + shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 + dataset = audio_to_text_dataset.get_tarred_dataset( + config=config, + shuffle_n=shuffle_n, + global_rank=self.global_rank, + world_size=self.world_size, + augmentor=augmentor, + ) + shuffle = False + else: + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") + return None + + dataset = audio_to_text_dataset.get_char_dataset(config=config, augmentor=augmentor) + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + elif hasattr(dataset.datasets[0], 'collate_fn'): + # support datasets that are lists of entries + collate_fn = dataset.datasets[0].collate_fn + else: + # support datasets that are lists of lists + collate_fn = dataset.datasets[0].datasets[0].collate_fn + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the training data loader via a Dict-like object. + + Args: + train_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in train_data_config: + train_data_config['shuffle'] = True + + # preserve config + self._update_dataset_config(dataset_name='train', config=train_data_config) + + self._train_dl = self._setup_dataloader_from_config(config=train_data_config) + + # Need to set this because if using an IterableDataset, the length of the dataloader is the total number + # of samples rather than the number of batches, and this messes up the tqdm progress bar. + # So we set the number of steps manually (to the correct number) to fix this. + if ( + self._train_dl is not None + and hasattr(self._train_dl, 'dataset') + and isinstance(self._train_dl.dataset, torch.utils.data.IterableDataset) + ): + # We also need to check if limit_train_batches is already set. + # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches, + # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0). + if isinstance(self._trainer.limit_train_batches, float): + self._trainer.limit_train_batches = int( + self._trainer.limit_train_batches + * ceil((len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size']) + ) + + def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the validation data loader via a Dict-like object. + + Args: + val_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in val_data_config: + val_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='validation', config=val_data_config) + + self._validation_dl = self._setup_dataloader_from_config(config=val_data_config) + + # Need to set this because if using an IterableDataset, the length of the dataloader is the total number + # of samples rather than the number of batches, and this messes up the tqdm progress bar. + # So we set the number of steps manually (to the correct number) to fix this. + if ( + self._validation_dl is not None + and hasattr(self._validation_dl, 'dataset') + and isinstance(self._validation_dl.dataset, torch.utils.data.IterableDataset) + ): + # We also need to check if limit_train_batches is already set. + # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches, + # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0). + if isinstance(self._trainer.limit_val_batches, float): + self._trainer.limit_val_batches = int( + self._trainer.limit_val_batches + * ceil((len(self._validation_dl.dataset) / self.world_size) / val_data_config['batch_size']) + ) + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + if hasattr(self.preprocessor, '_sample_rate'): + input_signal_eltype = AudioSignal(freq=self.preprocessor._sample_rate) + else: + input_signal_eltype = AudioSignal() + return { + "input_signal": NeuralType(('B', 'T'), input_signal_eltype, optional=True), + "input_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "processed_signal": NeuralType(('B', 'D', 'T'), SpectrogramType(), optional=True), + "processed_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "targets": NeuralType(('B', 'T'), LabelsType(), optional=True), + "target_lengths": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return { + "spectrograms": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "spec_masks": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "encoded": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "encoded_len": NeuralType(tuple('B'), LengthsType()), + } + + @typecheck() + def forward( + self, input_signal=None, input_signal_length=None, processed_signal=None, processed_signal_length=None, + ): + """ + Forward pass of the model. + + Args: + input_signal: Tensor that represents a batch of raw audio signals, + of shape [B, T]. T here represents timesteps, with 1 second of audio represented as + `self.sample_rate` number of floating point values. + input_signal_length: Vector of length B, that contains the individual lengths of the audio + sequences. + processed_signal: Tensor that represents a batch of processed audio signals, + of shape (B, D, T) that has undergone processing via some DALI preprocessor. + processed_signal_length: Vector of length B, that contains the individual lengths of the + processed audio sequences. + + Returns: + A tuple of 4 elements - + 1) Processed spectrograms of shape [B, D, T]. + 2) Masks applied to spectrograms of shape [B, D, T]. + 3) The encoded features tensor of shape [B, D, T]. + 2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. + """ + # Reset access registry + if self.is_access_enabled(self.model_guid): + self.reset_registry() + + # Check for special flag for validation step + if hasattr(self, '_in_validation_step'): + in_validation_step = self._in_validation_step + else: + in_validation_step = False + + # reset module registry from AccessMixin + if ( + (self.training or in_validation_step) + and self.decoder_losses is not None + and self.output_from_layer is not None + and len(self.output_from_layer) > 0 + ): + layer_names = list(self.output_from_layer.values()) + register_layer = any([name is not None for name in layer_names]) + + if register_layer: + self.access_cfg['save_encoder_tensors'] = True + self.set_access_enabled(access_enabled=True, guid=self.model_guid) + + has_input_signal = input_signal is not None and input_signal_length is not None + has_processed_signal = processed_signal is not None and processed_signal_length is not None + if (has_input_signal ^ has_processed_signal) == False: + raise ValueError( + f"{self} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive " + " with ``processed_signal`` and ``processed_signal_len`` arguments." + ) + + if not has_processed_signal: + processed_signal, processed_signal_length = self.preprocessor( + input_signal=input_signal, length=input_signal_length, + ) + + if self.pen_factor: + self.feat_pen = processed_signal.float().pow(2).mean() * self.pen_factor + spectrograms = processed_signal.detach().clone() + + if self.dropout_features: + processed_signal = self.dropout_features(processed_signal) + if self.dropout_features_q: + spectrograms = self.dropout_features_q(spectrograms) + + if self.apply_masking: + processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length) + + masked_spectrograms = processed_signal.detach() + spec_masks = torch.logical_and(masked_spectrograms < 1e-5, masked_spectrograms > -1e-5).float() + for idx, proc_len in enumerate(processed_signal_length): + spec_masks[idx, :, proc_len:] = 0.0 + + encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length) + + return spectrograms, spec_masks, encoded, encoded_len + + def decoder_loss_step(self, spectrograms, spec_masks, encoded, encoded_len, targets=None, target_lengths=None): + """ + Forward pass through all decoders and calculate corresponding losses. + Args: + spectrograms: Processed spectrograms of shape [B, D, T]. + spec_masks: Masks applied to spectrograms of shape [B, D, T]. + encoded: The encoded features tensor of shape [B, D, T]. + encoded_len: The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. + targets: Optional target labels of shape [B, T] + target_lengths: Optional target label lengths of shape [B] + + Returns: + A tuple of 2 elements - + 1) Total sum of losses weighted by corresponding loss_alphas + 2) Dictionary of unweighted losses + """ + loss_val_dict = {} + + if self.decoder_losses is None: + if hasattr(self.decoder_ssl, "needs_labels") and self.decoder_ssl.needs_labels: + outputs = self.decoder_ssl(encoder_output=encoded, targets=targets, target_lengths=target_lengths) + else: + outputs = self.decoder_ssl(encoder_output=encoded) + if self.loss.needs_labels: + loss_value = self.loss( + spec_masks=spec_masks, + decoder_outputs=outputs, + targets=targets, + decoder_lengths=encoded_len, + target_lengths=target_lengths, + ) + else: + loss_value = self.loss(spectrograms=spectrograms, spec_masks=spec_masks, decoder_outputs=outputs) + else: + + loss_value = encoded.new_zeros(1) + outputs = {} + registry = self.get_module_registry(self.encoder) + + for dec_loss_name, dec_loss in self.decoder_losses.items(): + # loop through decoders and corresponding losses + if not self.decoder_losses_active[dec_loss_name]: + continue + + if self.output_from_layer[dec_loss_name] is None: + dec_input = encoded + else: + # extract output from specified layer using AccessMixin registry + dec_input = registry[self.output_from_layer[dec_loss_name]]['encoder'][-1] + if self.transpose_encoded[dec_loss_name]: + dec_input = dec_input.transpose(-2, -1) + + if self.targets_from_loss[dec_loss_name] is not None: + # extract targets from specified loss + target_loss = self.targets_from_loss[dec_loss_name] + targets = self.decoder_losses[target_loss]['loss'].target_ids + target_lengths = self.decoder_losses[target_loss]['loss'].target_lengths + if target_lengths is None: + target_lengths = encoded_len + + if hasattr(dec_loss['decoder'], "needs_labels") and dec_loss['decoder'].needs_labels: + # if we are using a decoder which needs labels, provide them + outputs[dec_loss_name] = dec_loss['decoder']( + encoder_output=dec_input, targets=targets, target_lengths=target_lengths + ) + else: + outputs[dec_loss_name] = dec_loss['decoder'](encoder_output=dec_input) + + current_loss = dec_loss['loss'] + if current_loss.needs_labels: + # if we are using a loss which needs labels, provide them + current_loss_value = current_loss( + spec_masks=spec_masks, + decoder_outputs=outputs[dec_loss_name], + targets=targets, + decoder_lengths=encoded_len, + target_lengths=target_lengths, + ) + else: + current_loss_value = current_loss( + spectrograms=spectrograms, + spec_masks=spec_masks, + decoder_outputs=outputs[dec_loss_name], + decoder_lengths=encoded_len, + ) + loss_value = loss_value + current_loss_value * self.loss_alphas[dec_loss_name] + loss_val_dict[dec_loss_name] = current_loss_value + + return loss_value, loss_val_dict + + # PTL-specific methods + def training_step(self, batch, batch_nb): + signal, signal_len, targets, target_lengths = batch + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + spectrograms, spec_masks, encoded, encoded_len = self.forward( + processed_signal=signal, processed_signal_length=signal_len, + ) + else: + spectrograms, spec_masks, encoded, encoded_len = self.forward( + input_signal=signal, input_signal_length=signal_len, + ) + + if self.decoder_losses is not None: + for dec_loss_name, dec_loss in self.decoder_losses.items(): + self.decoder_losses_active[dec_loss_name] = self.trainer.global_step >= self.start_step[dec_loss_name] + loss = dec_loss['loss'] + if hasattr(loss, "set_num_updates"): + loss.set_num_updates(self.trainer.global_step) + else: + if hasattr(self.loss, "set_num_updates"): + self.loss.set_num_updates(self.trainer.global_step) + + loss_value, loss_val_dict = self.decoder_loss_step( + spectrograms, spec_masks, encoded, encoded_len, targets, target_lengths + ) + + tensorboard_logs = { + 'learning_rate': self._optimizer.param_groups[0]['lr'], + 'global_step': self.trainer.global_step, + } + + for loss_name, loss_val in loss_val_dict.items(): + tensorboard_logs['train_' + loss_name] = loss_val + + if self.feat_pen: + loss_value += self.feat_pen + + # Reset access registry + self.reset_registry() + + return {'loss': loss_value, 'log': tensorboard_logs} + + def validation_pass(self, batch, batch_idx, dataloader_idx=0): + # Set flag to register tensors + self._in_validation_step = True + + signal, signal_len, targets, target_lengths = batch + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + spectrograms, spec_masks, encoded, encoded_len = self.forward( + processed_signal=signal, processed_signal_length=signal_len, + ) + else: + spectrograms, spec_masks, encoded, encoded_len = self.forward( + input_signal=signal, input_signal_length=signal_len, + ) + + if self.decoder_losses is not None: + for dec_loss_name, dec_loss in self.decoder_losses.items(): + self.decoder_losses_active[dec_loss_name] = self.trainer.global_step >= self.start_step[dec_loss_name] + + loss_value, _ = self.decoder_loss_step(spectrograms, spec_masks, encoded, encoded_len, targets, target_lengths) + + if self.feat_pen: + loss_value += self.feat_pen + + # reset access registry + self.reset_registry() + del self._in_validation_step + + metrics = {'val_loss': loss_value} + + return metrics + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + metrics = self.validation_pass(batch, batch_idx, dataloader_idx) + if type(self.trainer.val_dataloaders) == list and len(self.trainer.val_dataloaders) > 1: + self.validation_step_outputs[dataloader_idx].append(metrics) + else: + self.validation_step_outputs.append(metrics) + return metrics + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean() + tensorboard_logs = {'val_loss': val_loss_mean} + return {'val_loss': val_loss_mean, 'log': tensorboard_logs} diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/transformer_bpe_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/transformer_bpe_models.py new file mode 100644 index 0000000..21a5f34 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/models/transformer_bpe_models.py @@ -0,0 +1,632 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import itertools +import json +import os +import tempfile +from math import ceil +from typing import Any, Dict, List, Optional, Union + +import editdistance +import torch +import torch.distributed as dist +from omegaconf import DictConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer +from torchmetrics.text import SacreBLEUScore +from tqdm.auto import tqdm + +from nemo.collections.asr.data import audio_to_text_dataset +from nemo.collections.asr.data.audio_to_text_dali import DALIOutputs +from nemo.collections.asr.data.audio_to_text_lhotse import LhotseSpeechToTextBpeDataset +from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel +from nemo.collections.asr.modules.transformer import ( + BeamSearchSequenceGenerator, + TransformerEncoder, + get_nemo_transformer, +) +from nemo.collections.asr.parts.mixins import ASRBPEMixin, ASRTranscriptionMixin, TranscribeConfig +from nemo.collections.asr.parts.submodules.token_classifier import TokenClassifier +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis +from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config +from nemo.collections.common.losses import SmoothedCrossEntropyLoss +from nemo.collections.common.metrics import GlobalAverageLossMetric +from nemo.collections.common.parts import transformer_weights_init +from nemo.core.classes.common import typecheck +from nemo.core.neural_types import ( + AudioSignal, + ChannelType, + LabelsType, + LengthsType, + LogprobsType, + MaskType, + NeuralType, + SpectrogramType, +) +from nemo.utils import logging + +__all__ = ['EncDecTransfModelBPE'] + + +def lens_to_mask(lens, max_length): + batch_size = lens.shape[0] + mask = torch.arange(max_length).repeat(batch_size, 1).to(lens.device) < lens[:, None] + return mask + + +class EncDecTransfModelBPE(ASRModel, ExportableEncDecModel, ASRBPEMixin, ASRTranscriptionMixin): + """Base class for encoder decoder CTC-based models.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + + if 'tokenizer' not in cfg: + raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !") + + # Setup the tokenizer + self._setup_tokenizer(cfg.tokenizer) + + super().__init__(cfg=cfg, trainer=trainer) + + # Setup audio preprocessor + self.preprocessor = EncDecTransfModelBPE.from_config_dict(self.cfg.preprocessor) + + # Setup audio encoder + self.encoder = EncDecTransfModelBPE.from_config_dict(self.cfg.encoder) + + # Add projection layer if encoder and decoder differ in hidden size + if self.cfg.encoder['d_model'] != self.cfg.transf_decoder['hidden_size']: + self.adapter = torch.nn.Linear(self.cfg.encoder['d_model'], self.cfg.transf_decoder['hidden_size']) + else: + self.adapter = torch.nn.Identity() + + transf_encoder_cfg_dict = OmegaConf.to_container(cfg.get('transf_encoder')) + + # Whether to add Transformer Encoder block between Conformer and Transformer Decoder + self.use_transf_encoder = False + if transf_encoder_cfg_dict['num_layers'] > 0: + self.use_transf_encoder = True + + self.transf_encoder = TransformerEncoder( + num_layers=transf_encoder_cfg_dict['num_layers'], + hidden_size=transf_encoder_cfg_dict['hidden_size'], + inner_size=transf_encoder_cfg_dict['inner_size'], + mask_future=False, + num_attention_heads=transf_encoder_cfg_dict['num_attention_heads'], + attn_score_dropout=transf_encoder_cfg_dict['attn_score_dropout'], + attn_layer_dropout=transf_encoder_cfg_dict['attn_layer_dropout'], + ffn_dropout=transf_encoder_cfg_dict['ffn_dropout'], + pre_ln=transf_encoder_cfg_dict.get('pre_ln', True), + pre_ln_final_layer_norm=transf_encoder_cfg_dict.get('pre_ln_final_layer_norm', True), + ) + std_init_range = 1 / transf_encoder_cfg_dict['hidden_size'] ** 0.5 + self.transf_encoder.apply(lambda module: transformer_weights_init(module, std_init_range)) + + transf_decoder_cfg_dict = OmegaConf.to_container(cfg.get('transf_decoder')) + + # Transformer decoder + vocab_size = 8 * ceil(self.tokenizer.vocab_size / 8) + transf_decoder_cfg_dict['vocab_size'] = vocab_size + library = transf_decoder_cfg_dict.pop('library', 'nemo') + if library != 'nemo': + raise ValueError(f"Currently only 'nemo' library is supported for Transformer decoder. Got {library}") + model_name = transf_decoder_cfg_dict.pop('model_name', None) + pretrained = transf_decoder_cfg_dict.pop('pretrained', False) + self.transf_decoder = get_nemo_transformer( + model_name=model_name, + pretrained=pretrained, + config_dict=transf_decoder_cfg_dict, + encoder=False, + pre_ln_final_layer_norm=transf_decoder_cfg_dict.get("pre_ln_final_layer_norm", False), + ) + + self.log_softmax = TokenClassifier( + hidden_size=self.transf_decoder.hidden_size, + num_classes=vocab_size, + activation=self.cfg.head.activation, + log_softmax=self.cfg.head.log_softmax, + dropout=self.cfg.head.dropout, + use_transformer_init=self.cfg.head.use_transformer_init, + num_layers=self.cfg.head.num_layers, + ) + self.log_softmax.mlp.layer0.weight = self.transf_decoder.embedding.token_embedding.weight + std_init_range = 1 / self.transf_decoder.hidden_size ** 0.5 + self.transf_decoder.apply(lambda module: transformer_weights_init(module, std_init_range)) + self.log_softmax.apply(lambda module: transformer_weights_init(module, std_init_range)) + + # Beam Search decoding + self.beam_search = BeamSearchSequenceGenerator( + embedding=self.transf_decoder.embedding, + decoder=self.transf_decoder.decoder, + log_softmax=self.log_softmax, + max_sequence_length=self.transf_decoder.max_sequence_length, + beam_size=self.cfg.beam_search.beam_size, + bos=self.tokenizer.bos_id, + pad=self.tokenizer.pad_id, + eos=self.tokenizer.eos_id, + len_pen=self.cfg.beam_search.len_pen, + max_delta_length=self.cfg.beam_search.max_generation_delta, + ) + + # Define autoregressive CE loss + self.transf_loss = SmoothedCrossEntropyLoss( + pad_id=self.tokenizer.pad_id, label_smoothing=self.cfg.label_smoothing + ) + + if hasattr(self.cfg, 'spec_augment') and self.cfg.spec_augment is not None: + self.spec_augmentation = EncDecTransfModelBPE.from_config_dict(self.cfg.spec_augment) + else: + self.spec_augmentation = None + + self.val_loss = GlobalAverageLossMetric(dist_sync_on_step=False, take_avg_loss=True) + + @torch.no_grad() + def transcribe( + self, + audio: List[str], + batch_size: int = 4, + return_hypotheses: bool = False, + num_workers: int = 0, + channel_selector: Optional[ChannelSelectorType] = None, + augmentor: DictConfig = None, + verbose: bool = True, + ) -> Union[List[str], List[Hypothesis]]: + """ + Uses greedy decoding to transcribe audio files. Use this method for debugging and prototyping. + Args: + audio: (a list) of paths to audio files. \ + Recommended length per file is between 5 and 25 seconds. \ + But it is possible to pass a few hours long file if enough GPU memory is available. + batch_size: (int) batch size to use during inference. + Bigger will result in better throughput performance but would use more memory. + return_hypotheses: (bool) Either return hypotheses or text + With hypotheses can do some postprocessing like getting timestamp or rescoring + num_workers: (int) number of workers for DataLoader + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. + augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied. + verbose: (bool) whether to display tqdm progress bar + Returns: + A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files + """ + return super().transcribe( + audio=audio, + batch_size=batch_size, + return_hypotheses=return_hypotheses, + num_workers=num_workers, + channel_selector=channel_selector, + augmentor=augmentor, + verbose=verbose, + ) + + def _update_default_values(self, config: DictConfig): + if self.training: # don't do anything for training + return config + with open_dict(config): + for k, v in self.cfg.train_ds.items(): + if k not in config: + config[k] = v + return config + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + if config.get("use_lhotse"): + config = self._update_default_values(config) + return get_lhotse_dataloader_from_config( + config, + global_rank=self.global_rank, + world_size=self.world_size, + dataset=LhotseSpeechToTextBpeDataset(tokenizer=self.tokenizer,), + ) + + dataset = audio_to_text_dataset.get_audio_to_text_bpe_dataset_from_config( + config=config, + local_rank=self.local_rank, + global_rank=self.global_rank, + world_size=self.world_size, + tokenizer=self.tokenizer, + preprocessor_cfg=self.cfg.get("preprocessor", None), + ) + + if dataset is None: + return None + + shuffle = config['shuffle'] + if config.get('is_tarred', False): + shuffle = False + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + else: + collate_fn = dataset.datasets[0].collate_fn + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def setup_training_data(self, train_data_config: Optional[DictConfig]): + + # create audio-only data loader + self._update_dataset_config(dataset_name='train', config=train_data_config) + self._train_dl = self._setup_dataloader_from_config(config=train_data_config) + + # Need to set this because if using an IterableDataset, the length of the + # dataloader is the total number of samples rather than the number of batches, + # and this messes up the tqdm progress bar. So we set the number of steps manually + # (to the correct number) to fix this. + if 'is_tarred' in train_data_config and train_data_config['is_tarred']: + # We also need to check if limit_train_batches is already set. + # If it's an int, we assume that the user has set it to something sane, + # i.e. <= # training batches, and don't change it. Otherwise, adjust + # batches accordingly if it's a float (including 1.0). + if self._trainer is not None and isinstance(self._trainer.limit_train_batches, float): + self._trainer.limit_train_batches = int( + self._trainer.limit_train_batches + * ceil((len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size']) + ) + elif self._trainer is None: + logging.warning( + "Model Trainer was not set before constructing the dataset, incorrect number of " + "training batches will be used. Please set the trainer and rebuild the dataset." + ) + + def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the validation data loader via a Dict-like object. + Args: + val_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in val_data_config: + val_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='validation', config=val_data_config) + self._validation_dl = self._setup_dataloader_from_config(config=val_data_config) + + def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the test data loader via a Dict-like object. + Args: + test_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + Supported Datasets: + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.AudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToCharDataset` + - :class:`~nemo.collections.asr.data.audio_to_text.TarredAudioToBPEDataset` + - :class:`~nemo.collections.asr.data.audio_to_text_dali.AudioToCharDALIDataset` + """ + if 'shuffle' not in test_data_config: + test_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='test', config=test_data_config) + self._test_dl = self._setup_dataloader_from_config(config=test_data_config) + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + if hasattr(self.preprocessor, '_sample_rate'): + input_signal_eltype = AudioSignal(freq=self.preprocessor._sample_rate) + else: + input_signal_eltype = AudioSignal() + return { + "input_signal": NeuralType(('B', 'T'), input_signal_eltype, optional=True), + "input_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "processed_signal": NeuralType(('B', 'D', 'T'), SpectrogramType(), optional=True), + "processed_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "transcript": NeuralType(('B', 'T'), LabelsType(), optional=True), + "transcript_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "sample_id": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return { + "transf_log_probs": NeuralType(('B', 'T', 'D'), LogprobsType()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + "encoder_states": NeuralType(('B', 'T', 'D'), ChannelType()), + "encoder_mask": NeuralType(('B', 'T'), MaskType()), + } + + @typecheck() + def forward( + self, + input_signal=None, + input_signal_length=None, + processed_signal=None, + processed_signal_length=None, + transcript=None, + transcript_length=None, + ): + """ + Forward pass of the model. + Args: + input_signal: Tensor that represents a batch of raw audio signals, + of shape [B, T]. T here represents timesteps, with 1 second of audio represented as + `self.sample_rate` number of floating point values. + input_signal_length: Vector of length B, that contains the individual lengths of the audio + sequences. + processed_signal: Tensor that represents a batch of processed audio signals, + of shape (B, D, T) that has undergone processing via some DALI preprocessor. + processed_signal_length: Vector of length B, that contains the individual lengths of the + processed audio sequences. + Returns: + A tuple of 3 elements - + 1) The log probabilities tensor of shape [B, T, D]. + 2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. + 3) The greedy token predictions of the model of shape [B, T] (via argmax) + """ + has_input_signal = input_signal is not None and input_signal_length is not None + has_processed_signal = processed_signal is not None and processed_signal_length is not None + if (has_input_signal ^ has_processed_signal) == False: + raise ValueError( + f"{self} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive " + " with ``processed_signal`` and ``processed_signal_len`` arguments." + ) + + if not has_processed_signal: + processed_signal, processed_signal_length = self.preprocessor( + input_signal=input_signal, length=input_signal_length + ) + + if self.spec_augmentation is not None and self.training: + processed_signal = self.spec_augmentation(input_spec=processed_signal, length=processed_signal_length) + + encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_length) + + enc_states = encoded.permute(0, 2, 1) + enc_states = self.adapter(enc_states) + enc_mask = lens_to_mask(encoded_len, enc_states.shape[1]).to(enc_states.dtype) + if self.use_transf_encoder: + enc_states = self.transf_encoder(encoder_states=enc_states, encoder_mask=enc_mask) + + transf_log_probs = None + if transcript is not None: + dec_mask = lens_to_mask(transcript_length, transcript.shape[1]).to(transcript.dtype) + dec_states = self.transf_decoder( + input_ids=transcript, decoder_mask=dec_mask, encoder_embeddings=enc_states, encoder_mask=enc_mask + ) + transf_log_probs = self.log_softmax(hidden_states=dec_states) + + return transf_log_probs, encoded_len, enc_states, enc_mask + + def compute_audio_loss(self, batch): + + if batch is None: + return 0 + + signal, signal_len, transcript, transcript_len = batch + input_ids, labels = transcript[:, :-1], transcript[:, 1:] + + transf_log_probs, encoded_len, enc_states, enc_mask = self.forward( + input_signal=signal, + input_signal_length=signal_len, + transcript=input_ids, + transcript_length=transcript_len, + ) + + transf_loss = self.transf_loss(log_probs=transf_log_probs, labels=labels) + + return transf_loss + + # PTL-specific methods + def training_step(self, batch, batch_nb): + + audio_loss = self.compute_audio_loss(batch) + + tensorboard_logs = { + 'train_loss': audio_loss, + 'learning_rate': self._optimizer.param_groups[0]['lr'], + } + + return {'loss': audio_loss, 'log': tensorboard_logs} + + def validation_step(self, batch, batch_idx, dataloader_idx=0, eval_mode="val"): + signal, signal_len, transcript, transcript_len = batch + input_ids, labels = transcript[:, :-1], transcript[:, 1:] + + if isinstance(batch, DALIOutputs) and batch.has_processed_signal: + transf_log_probs, encoded_len, enc_states, enc_mask = self.forward( + processed_signal=signal, + processed_signal_length=signal_len, + transcript=input_ids, + transcript_length=transcript_len, + ) + else: + transf_log_probs, encoded_len, enc_states, enc_mask = self.forward( + input_signal=signal, + input_signal_length=signal_len, + transcript=input_ids, + transcript_length=transcript_len, + ) + + beam_hypotheses = self.beam_search( + encoder_hidden_states=enc_states, encoder_input_mask=enc_mask, return_beam_scores=False + ) + transf_loss = self.transf_loss(log_probs=transf_log_probs, labels=labels) + + ground_truths = [self.tokenizer.ids_to_text(sent) for sent in transcript.detach().cpu().tolist()] + translations = [self.tokenizer.ids_to_text(sent) for sent in beam_hypotheses.detach().cpu().tolist()] + + self.val_loss(loss=transf_loss, num_measurements=transf_log_probs.shape[0] * transf_log_probs.shape[1]) + + output_dict = {f'{eval_mode}_loss': transf_loss, 'translations': translations, 'ground_truths': ground_truths} + + self.validation_step_outputs.append(output_dict) + + return output_dict + + def test_step(self, batch, batch_idx, dataloader_idx=0): + return self.validation_step(batch, batch_idx, dataloader_idx, eval_mode="test") + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0, eval_mode: str = "val"): + """ + Called at the end of validation to aggregate outputs. + :param outputs: list of individual outputs of each validation step. + """ + if not outputs: + return + + if isinstance(outputs[0], dict): + outputs = [outputs] + + for output in outputs: + eval_loss = getattr(self, 'val_loss').compute() + translations = list(itertools.chain(*[x['translations'] for x in output])) + ground_truths = list(itertools.chain(*[x['ground_truths'] for x in output])) + + # Gather translations and ground truths from all workers + tr_and_gt = [None for _ in range(self.world_size)] + # we also need to drop pairs where ground truth is an empty string + if self.world_size > 1: + dist.all_gather_object( + tr_and_gt, [(t, g) for (t, g) in zip(translations, ground_truths) if g.strip() != ''] + ) + else: + tr_and_gt[0] = [(t, g) for (t, g) in zip(translations, ground_truths) if g.strip() != ''] + + if self.global_rank == 0: + _translations = [] + _ground_truths = [] + for rank in range(0, self.world_size): + _translations += [t for (t, g) in tr_and_gt[rank]] + _ground_truths += [g for (t, g) in tr_and_gt[rank]] + + sacre_bleu = SacreBLEUScore()(_translations, [[x] for x in _ground_truths]).item() + sb_score = sacre_bleu * self.world_size + + wer_scores, wer_words = 0, 0 + for h, r in zip(_translations, _ground_truths): + wer_words += len(r.split()) + wer_scores += editdistance.eval(h.split(), r.split()) + wer_score = 1.0 * wer_scores * self.world_size / wer_words + + else: + sb_score = 0.0 + wer_score = 0.0 + + self.log(f"{eval_mode}_loss", eval_loss, sync_dist=True) + self.log(f"{eval_mode}_sacreBLEU", sb_score, sync_dist=True) + self.log(f"{eval_mode}_WER", wer_score, sync_dist=True) + self.val_loss.reset() + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + return self.multi_validation_epoch_end(outputs, dataloader_idx, eval_mode="test") + + def test_dataloader(self): + if self._test_dl is not None: + return self._test_dl + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided audio file. + Args: + config: A python dictionary which contains the following keys: + paths2audio_files: (a list) of paths to audio files. The files should be relatively short fragments. \ + Recommended length per file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the audio manifest is temporarily + stored. + Returns: + A pytorch DataLoader for the given audio file(s). + """ + batch_size = min(config['batch_size'], len(config['paths2audio_files'])) + dl_config = { + 'manifest_filepath': os.path.join(config['temp_dir'], 'manifest.json'), + 'sample_rate': self.preprocessor._sample_rate, + 'batch_size': batch_size, + 'trim_silence': False, + 'shuffle': False, + 'num_workers': min(batch_size, os.cpu_count() - 1), + 'pin_memory': True, + } + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer + + """ Transcription related methods """ + + def _transcribe_on_begin(self, audio, trcfg: TranscribeConfig): + super()._transcribe_on_begin(audio, trcfg) + + # Freeze the encoder and decoder modules + self.transf_decoder.freeze() + + def _transcribe_forward(self, batch: Any, trcfg: TranscribeConfig): + log_probs, encoded_len, enc_states, enc_mask = self.forward( + input_signal=batch[0], input_signal_length=batch[1] + ) + output = dict(log_probs=log_probs, encoded_len=encoded_len, enc_states=enc_states, enc_mask=enc_mask) + return output + + def _transcribe_output_processing(self, outputs, trcfg: TranscribeConfig) -> List[str]: + log_probs = outputs.pop('log_probs') + encoded_len = outputs.pop('encoded_len') + enc_states = outputs.pop('enc_states') + enc_mask = outputs.pop('enc_mask') + + # TODO(@AlexGrinch): add support for returning logprobs from return_hypotheses=True + del log_probs + + beam_hypotheses = ( + # TODO(@titu1994): maybe set return_beam_scores to True if theres no perf difference + self.beam_search(encoder_hidden_states=enc_states, encoder_input_mask=enc_mask, return_beam_scores=False) + .detach() + .cpu() + .numpy() + ) + + beam_hypotheses_out = [self.tokenizer.ids_to_text(hyp) for hyp in beam_hypotheses] + del enc_states, enc_mask, encoded_len + + if trcfg.return_hypotheses: + # TODO: add support for returning logprobs from return_hypotheses=True @AlexGrinch + # dump log probs per file + # for idx in range(logits.shape[0]): + # current_hypotheses[idx].y_sequence = logits[idx][: logits_len[idx]] + hypotheses = [] + for idx, hyp in enumerate(beam_hypotheses): + hypotheses.append( + Hypothesis( + score=0.0, + y_sequence=beam_hypotheses[idx], + text=beam_hypotheses_out[idx], + length=len(beam_hypotheses[idx]), + ) + ) + + # Replace output with Hypothesis list + beam_hypotheses_out = hypotheses + + del beam_hypotheses + + return beam_hypotheses_out + + def _transcribe_on_end(self, trcfg: TranscribeConfig): + super()._transcribe_on_end(trcfg) + + # Unfreeze the encoder and decoder modules + self.transf_decoder.unfreeze() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/__init__.py new file mode 100644 index 0000000..0265d9e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/__init__.py @@ -0,0 +1,54 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.modules.audio_modules import ( + MaskBasedBeamformer, + MaskEstimatorFlexChannels, + MaskEstimatorRNN, + MaskReferenceChannel, +) +from nemo.collections.asr.modules.audio_preprocessing import ( + AudioToMelSpectrogramPreprocessor, + AudioToMFCCPreprocessor, + AudioToSpectrogram, + CropOrPadSpectrogramAugmentation, + MaskedPatchAugmentation, + SpectrogramAugmentation, + SpectrogramToAudio, +) +from nemo.collections.asr.modules.beam_search_decoder import BeamSearchDecoderWithLM +from nemo.collections.asr.modules.conformer_encoder import ConformerEncoder, ConformerEncoderAdapter +from nemo.collections.asr.modules.conv_asr import ( + ConvASRDecoder, + ConvASRDecoderClassification, + ConvASRDecoderReconstruction, + ConvASREncoder, + ConvASREncoderAdapter, + ECAPAEncoder, + ParallelConvASREncoder, + SpeakerDecoder, +) +from nemo.collections.asr.modules.graph_decoder import ViterbiDecoderWithGraph +from nemo.collections.asr.modules.hybrid_autoregressive_transducer import HATJoint +from nemo.collections.asr.modules.lstm_decoder import LSTMDecoder +from nemo.collections.asr.modules.msdd_diarizer import MSDD_module +from nemo.collections.asr.modules.rnn_encoder import RNNEncoder +from nemo.collections.asr.modules.rnnt import ( + RNNTDecoder, + RNNTDecoderJointSSL, + RNNTJoint, + SampledRNNTJoint, + StatelessTransducerDecoder, +) +from nemo.collections.asr.modules.squeezeformer_encoder import SqueezeformerEncoder, SqueezeformerEncoderAdapter diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/audio_modules.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/audio_modules.py new file mode 100644 index 0000000..82cfbef --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/audio_modules.py @@ -0,0 +1,1685 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, List, Optional, Tuple + +import numpy as np +import torch + +from nemo.collections.asr.losses.audio_losses import temporal_mean +from nemo.collections.asr.modules.conformer_encoder import ConformerEncoder +from nemo.collections.asr.parts.preprocessing.features import make_seq_mask_like +from nemo.collections.asr.parts.submodules.multichannel_modules import ( + ChannelAttentionPool, + ChannelAveragePool, + ParametricMultichannelWienerFilter, + TransformAttendConcatenate, + TransformAverageConcatenate, +) +from nemo.collections.asr.parts.utils.audio_utils import db2mag, wrap_to_pi +from nemo.core.classes import NeuralModule, typecheck +from nemo.core.neural_types import FloatType, LengthsType, NeuralType, SpectrogramType +from nemo.utils import logging +from nemo.utils.decorators import experimental + +__all__ = [ + 'MaskEstimatorRNN', + 'MaskEstimatorFlexChannels', + 'MaskReferenceChannel', + 'MaskBasedBeamformer', + 'MaskBasedDereverbWPE', +] + + +class SpectrogramToMultichannelFeatures(NeuralModule): + """Convert a complex-valued multi-channel spectrogram to + multichannel features. + + Args: + num_subbands: Expected number of subbands in the input signal + num_input_channels: Optional, provides the number of channels + of the input signal. Used to infer the number + of output channels. + mag_reduction: Reduction across channels. Default `None`, will calculate + magnitude of each channel. + mag_power: Optional, apply power on the magnitude. + use_ipd: Use inter-channel phase difference (IPD). + mag_normalization: Normalization for magnitude features + ipd_normalization: Normalization for IPD features + eps: Small regularization constant. + """ + + def __init__( + self, + num_subbands: int, + num_input_channels: Optional[int] = None, + mag_reduction: Optional[str] = None, + mag_power: Optional[float] = None, + use_ipd: bool = False, + mag_normalization: Optional[str] = None, + ipd_normalization: Optional[str] = None, + eps: float = 1e-8, + ): + super().__init__() + self.mag_reduction = mag_reduction + self.mag_power = mag_power + self.use_ipd = use_ipd + + if mag_normalization not in [None, 'mean', 'mean_var']: + raise NotImplementedError(f'Unknown magnitude normalization {mag_normalization}') + self.mag_normalization = mag_normalization + + if ipd_normalization not in [None, 'mean', 'mean_var']: + raise NotImplementedError(f'Unknown ipd normalization {ipd_normalization}') + self.ipd_normalization = ipd_normalization + + if self.use_ipd: + self._num_features = 2 * num_subbands + self._num_channels = num_input_channels + else: + self._num_features = num_subbands + self._num_channels = num_input_channels if self.mag_reduction is None else 1 + + self.eps = eps + + logging.debug('Initialized %s with', self.__class__.__name__) + logging.debug('\tnum_subbands: %d', num_subbands) + logging.debug('\tmag_reduction: %s', self.mag_reduction) + logging.debug('\tmag_power: %s', self.mag_power) + logging.debug('\tuse_ipd: %s', self.use_ipd) + logging.debug('\tmag_normalization: %s', self.mag_normalization) + logging.debug('\tipd_normalization: %s', self.ipd_normalization) + logging.debug('\teps: %f', self.eps) + logging.debug('\t_num_features: %s', self._num_features) + logging.debug('\t_num_channels: %s', self._num_channels) + + @property + def input_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "input": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "input_length": NeuralType(('B',), LengthsType()), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "output": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "output_length": NeuralType(('B',), LengthsType()), + } + + @property + def num_features(self) -> int: + """Configured number of features + """ + return self._num_features + + @property + def num_channels(self) -> int: + """Configured number of channels + """ + if self._num_channels is not None: + return self._num_channels + else: + raise ValueError( + 'Num channels is not configured. To configure this, `num_input_channels` ' + 'must be provided when constructing the object.' + ) + + @staticmethod + def get_mean_time_channel(input: torch.Tensor, input_length: Optional[torch.Tensor] = None) -> torch.Tensor: + """Calculate mean across time and channel dimensions. + + Args: + input: tensor with shape (B, C, F, T) + input_length: tensor with shape (B,) + + Returns: + Mean of `input` calculated across time and channel dimension + with shape (B, 1, F, 1) + """ + assert input.ndim == 4, f'Expected input to have 4 dimensions, got {input.ndim}' + + if input_length is None: + mean = torch.mean(input, dim=(-1, -3), keepdim=True) + else: + # temporal mean + mean = temporal_mean(input, input_length, keepdim=True) + # channel mean + mean = torch.mean(mean, dim=-3, keepdim=True) + + return mean + + @classmethod + def get_mean_std_time_channel( + cls, input: torch.Tensor, input_length: Optional[torch.Tensor] = None, eps: float = 1e-10 + ) -> torch.Tensor: + """Calculate mean and standard deviation across time and channel dimensions. + + Args: + input: tensor with shape (B, C, F, T) + input_length: tensor with shape (B,) + + Returns: + Mean and standard deviation of the `input` calculated across time and + channel dimension, each with shape (B, 1, F, 1). + """ + assert input.ndim == 4, f'Expected input to have 4 dimensions, got {input.ndim}' + + if input_length is None: + std, mean = torch.std_mean(input, dim=(-1, -3), unbiased=False, keepdim=True) + else: + mean = cls.get_mean_time_channel(input, input_length) + std = (input - mean).pow(2) + # temporal mean + std = temporal_mean(std, input_length, keepdim=True) + # channel mean + std = torch.mean(std, dim=-3, keepdim=True) + # final value + std = torch.sqrt(std.clamp(eps)) + + return mean, std + + @typecheck( + input_types={ + 'input': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + 'input_length': NeuralType(tuple('B'), LengthsType()), + }, + output_types={'output': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),}, + ) + def normalize_mean(self, input: torch.Tensor, input_length: torch.Tensor) -> torch.Tensor: + """Mean normalization for the input tensor. + + Args: + input: input tensor + input_length: valid length for each example + + Returns: + Mean normalized input. + """ + mean = self.get_mean_time_channel(input=input, input_length=input_length) + output = input - mean + return output + + @typecheck( + input_types={ + 'input': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + 'input_length': NeuralType(tuple('B'), LengthsType()), + }, + output_types={'output': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),}, + ) + def normalize_mean_var(self, input: torch.Tensor, input_length: torch.Tensor) -> torch.Tensor: + """Mean and variance normalization for the input tensor. + + Args: + input: input tensor + input_length: valid length for each example + + Returns: + Mean and variance normalized input. + """ + mean, std = self.get_mean_std_time_channel(input=input, input_length=input_length, eps=self.eps) + output = (input - mean) / std + return output + + @typecheck() + def forward(self, input: torch.Tensor, input_length: torch.Tensor) -> torch.Tensor: + """Convert input batch of C-channel spectrograms into + a batch of time-frequency features with dimension num_feat. + The output number of channels may be the same as input, or + reduced to 1, e.g., if averaging over magnitude and not appending individual IPDs. + + Args: + input: Spectrogram for C channels with F subbands and N time frames, (B, C, F, N) + input_length: Length of valid entries along the time dimension, shape (B,) + + Returns: + num_feat_channels channels with num_feat features, shape (B, num_feat_channels, num_feat, N) + """ + # Magnitude spectrum + if self.mag_reduction is None: + mag = torch.abs(input) + elif self.mag_reduction == 'abs_mean': + mag = torch.abs(torch.mean(input, axis=1, keepdim=True)) + elif self.mag_reduction == 'mean_abs': + mag = torch.mean(torch.abs(input), axis=1, keepdim=True) + elif self.mag_reduction == 'rms': + mag = torch.sqrt(torch.mean(torch.abs(input) ** 2, axis=1, keepdim=True)) + else: + raise ValueError(f'Unexpected magnitude reduction {self.mag_reduction}') + + if self.mag_power is not None: + mag = torch.pow(mag, self.mag_power) + + if self.mag_normalization == 'mean': + # normalize mean across channels and time steps + mag = self.normalize_mean(input=mag, input_length=input_length) + elif self.mag_normalization == 'mean_var': + mag = self.normalize_mean_var(input=mag, input_length=input_length) + + features = mag + + if self.use_ipd: + # Calculate IPD relative to the average spec + spec_mean = torch.mean(input, axis=1, keepdim=True) # channel average + ipd = torch.angle(input) - torch.angle(spec_mean) + # Modulo to [-pi, pi] + ipd = wrap_to_pi(ipd) + + if self.ipd_normalization == 'mean': + # normalize mean across channels and time steps + # mean across time + ipd = self.normalize_mean(input=ipd, input_length=input_length) + elif self.ipd_normalization == 'mean_var': + ipd = self.normalize_mean_var(input=ipd, input_length=input_length) + + # Concatenate to existing features + features = torch.cat([features.expand(ipd.shape), ipd], axis=2) + + if self._num_channels is not None and features.size(1) != self._num_channels: + raise RuntimeError( + f'Number of channels in features {features.size(1)} is different than the configured number of channels {self._num_channels}' + ) + + return features, input_length + + +class MaskEstimatorRNN(NeuralModule): + """Estimate `num_outputs` masks from the input spectrogram + using stacked RNNs and projections. + + The module is structured as follows: + input --> spatial features --> input projection --> + --> stacked RNNs --> output projection for each output --> sigmoid + + Reference: + Multi-microphone neural speech separation for far-field multi-talker + speech recognition (https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8462081) + + Args: + num_outputs: Number of output masks to estimate + num_subbands: Number of subbands of the input spectrogram + num_features: Number of features after the input projections + num_layers: Number of RNN layers + num_hidden_features: Number of hidden features in RNN layers + num_input_channels: Number of input channels + dropout: If non-zero, introduces dropout on the outputs of each RNN layer except the last layer, with dropout + probability equal to `dropout`. Default: 0 + bidirectional: If `True`, use bidirectional RNN. + rnn_type: Type of RNN, either `lstm` or `gru`. Default: `lstm` + mag_reduction: Channel-wise reduction for magnitude features + use_ipd: Use inter-channel phase difference (IPD) features + """ + + def __init__( + self, + num_outputs: int, + num_subbands: int, + num_features: int = 1024, + num_layers: int = 3, + num_hidden_features: Optional[int] = None, + num_input_channels: Optional[int] = None, + dropout: float = 0, + bidirectional=True, + rnn_type: str = 'lstm', + mag_reduction: str = 'rms', + use_ipd: bool = None, + ): + super().__init__() + if num_hidden_features is None: + num_hidden_features = num_features + + self.features = SpectrogramToMultichannelFeatures( + num_subbands=num_subbands, + num_input_channels=num_input_channels, + mag_reduction=mag_reduction, + use_ipd=use_ipd, + ) + + self.input_projection = torch.nn.Linear( + in_features=self.features.num_features * self.features.num_channels, out_features=num_features + ) + + if rnn_type == 'lstm': + self.rnn = torch.nn.LSTM( + input_size=num_features, + hidden_size=num_hidden_features, + num_layers=num_layers, + batch_first=True, + dropout=dropout, + bidirectional=bidirectional, + ) + elif rnn_type == 'gru': + self.rnn = torch.nn.GRU( + input_size=num_features, + hidden_size=num_hidden_features, + num_layers=num_layers, + batch_first=True, + dropout=dropout, + bidirectional=bidirectional, + ) + else: + raise ValueError(f'Unknown rnn_type: {rnn_type}') + + self.fc = torch.nn.Linear( + in_features=2 * num_features if bidirectional else num_features, out_features=num_features + ) + self.norm = torch.nn.LayerNorm(num_features) + + # Each output shares the RNN and has a separate projection + self.output_projections = torch.nn.ModuleList( + [torch.nn.Linear(in_features=num_features, out_features=num_subbands) for _ in range(num_outputs)] + ) + self.output_nonlinearity = torch.nn.Sigmoid() + + @property + def input_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "input": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "input_length": NeuralType(('B',), LengthsType()), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "output": NeuralType(('B', 'C', 'D', 'T'), FloatType()), + "output_length": NeuralType(('B',), LengthsType()), + } + + @typecheck() + def forward(self, input: torch.Tensor, input_length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """Estimate `num_outputs` masks from the input spectrogram. + + Args: + input: C-channel input, shape (B, C, F, N) + input_length: Length of valid entries along the time dimension, shape (B,) + + Returns: + Returns `num_outputs` masks in a tensor, shape (B, num_outputs, F, N), + and output length with shape (B,) + """ + input, _ = self.features(input=input, input_length=input_length) + B, num_feature_channels, num_features, N = input.shape + + # (B, num_feat_channels, num_feat, N) -> (B, N, num_feat_channels, num_feat) + input = input.permute(0, 3, 1, 2) + + # (B, N, num_feat_channels, num_feat) -> (B, N, num_feat_channels * num_features) + input = input.view(B, N, -1) + + # Apply projection on num_feat + input = self.input_projection(input) + + # Apply RNN on the input sequence + input_packed = torch.nn.utils.rnn.pack_padded_sequence( + input, input_length.cpu(), batch_first=True, enforce_sorted=False + ).to(input.device) + self.rnn.flatten_parameters() + input_packed, _ = self.rnn(input_packed) + output, output_length = torch.nn.utils.rnn.pad_packed_sequence(input_packed, batch_first=True) + output_length = output_length.to(input.device) + + # Layer normalization and skip connection + output = self.norm(self.fc(output)) + input + + # Create `num_outputs` masks + masks = [] + for output_projection in self.output_projections: + # Output projection + mask = output_projection(output) + mask = self.output_nonlinearity(mask) + + # Back to the original format + # (B, N, F) -> (B, F, N) + mask = mask.transpose(2, 1) + + # Append to the output + masks.append(mask) + + # Stack along channel dimension to get (B, M, F, N) + masks = torch.stack(masks, axis=1) + + # Mask frames beyond output length + length_mask: torch.Tensor = make_seq_mask_like( + lengths=output_length, like=masks, time_dim=-1, valid_ones=False + ) + masks = masks.masked_fill(length_mask, 0.0) + + return masks, output_length + + +class MaskEstimatorFlexChannels(NeuralModule): + """Estimate `num_outputs` masks from the input spectrogram + using stacked channel-wise and temporal layers. + + This model is using interlaved channel blocks and temporal blocks, and + it can process arbitrary number of input channels. + Default channel block is the transform-average-concatenate layer. + Default temporal block is the Conformer encoder. + Reduction from multichannel signal to single-channel signal is performed + after `channel_reduction_position` blocks. Only temporal blocks are used afterwards. + After the sequence of blocks, the output mask is computed using an additional + output temporal layer and a nonlinearity. + + References: + - Yoshioka et al, VarArray: Array-Geometry-Agnostic Continuous Speech Separation, 2022 + - Jukić et al, Flexible multichannel speech enhancement for noise-robust frontend, 2023 + + Args: + num_outputs: Number of output masks. + num_subbands: Number of subbands on the input spectrogram. + num_blocks: Number of blocks in the model. + channel_reduction_position: After this block, the signal will be reduced across channels. + channel_reduction_type: Reduction across channels: 'average' or 'attention' + channel_block_type: Block for channel processing: 'transform_average_concatenate' or 'transform_attend_concatenate' + temporal_block_type: Block for temporal processing: 'conformer_encoder' + temporal_block_num_layers: Number of layers for the temporal block + temporal_block_num_heads: Number of heads for the temporal block + temporal_block_dimension: The hidden size of the model + temporal_block_self_attention_model: Self attention model for the temporal block + temporal_block_att_context_size: Attention context size for the temporal block + mag_reduction: Channel-wise reduction for magnitude features + mag_power: Power to apply on magnitude features + use_ipd: Use inter-channel phase difference (IPD) features + mag_normalization: Normalize using mean ('mean') or mean and variance ('mean_var') + ipd_normalization: Normalize using mean ('mean') or mean and variance ('mean_var') + """ + + def __init__( + self, + num_outputs: int, + num_subbands: int, + num_blocks: int, + channel_reduction_position: int = -1, # if 0, apply before block 0, if -1 apply at the end + channel_reduction_type: str = 'attention', + channel_block_type: str = 'transform_attend_concatenate', + temporal_block_type: str = 'conformer_encoder', + temporal_block_num_layers: int = 5, + temporal_block_num_heads: int = 4, + temporal_block_dimension: int = 128, + temporal_block_self_attention_model: str = 'rel_pos', + temporal_block_att_context_size: Optional[List[int]] = None, + num_input_channels: Optional[int] = None, + mag_reduction: str = 'abs_mean', + mag_power: Optional[float] = None, + use_ipd: bool = True, + mag_normalization: Optional[str] = None, + ipd_normalization: Optional[str] = None, + ): + super().__init__() + + self.features = SpectrogramToMultichannelFeatures( + num_subbands=num_subbands, + num_input_channels=num_input_channels, + mag_reduction=mag_reduction, + mag_power=mag_power, + use_ipd=use_ipd, + mag_normalization=mag_normalization, + ipd_normalization=ipd_normalization, + ) + self.num_blocks = num_blocks + logging.debug('Total number of blocks: %d', self.num_blocks) + + # Channel reduction + if channel_reduction_position == -1: + # Apply reduction after the last layer + channel_reduction_position = num_blocks + + if channel_reduction_position > num_blocks: + raise ValueError( + f'Channel reduction position {channel_reduction_position} exceeds the number of blocks {num_blocks}' + ) + self.channel_reduction_position = channel_reduction_position + logging.debug('Channel reduction will be applied before block %d', self.channel_reduction_position) + + # Prepare processing blocks + self.channel_blocks = torch.nn.ModuleList() + self.temporal_blocks = torch.nn.ModuleList() + + for n in range(num_blocks): + logging.debug('Prepare block %d', n) + + # Setup channel block + if n < channel_reduction_position: + # Number of input features is either the number of input channels or the number of temporal block features + channel_in_features = self.features.num_features if n == 0 else temporal_block_dimension + logging.debug( + 'Setup channel block %s with %d input features and %d output features', + channel_block_type, + channel_in_features, + temporal_block_dimension, + ) + + # Instantiante the channel block + if channel_block_type == 'transform_average_concatenate': + channel_block = TransformAverageConcatenate( + in_features=channel_in_features, out_features=temporal_block_dimension + ) + elif channel_block_type == 'transform_attend_concatenate': + channel_block = TransformAttendConcatenate( + in_features=channel_in_features, out_features=temporal_block_dimension + ) + else: + raise ValueError(f'Unknown channel layer type: {channel_block_type}') + self.channel_blocks.append(channel_block) + + # Setup temporal block + temporal_in_features = ( + self.features.num_features if n == self.channel_reduction_position == 0 else temporal_block_dimension + ) + logging.debug('Setup temporal block %s', temporal_block_type) + if temporal_block_type == 'conformer_encoder': + temporal_block = ConformerEncoder( + feat_in=temporal_in_features, + n_layers=temporal_block_num_layers, + d_model=temporal_block_dimension, + subsampling_factor=1, + self_attention_model=temporal_block_self_attention_model, + att_context_size=temporal_block_att_context_size, + n_heads=temporal_block_num_heads, + ) + else: + raise ValueError(f'Unknown temporal block {temporal_block}.') + + self.temporal_blocks.append(temporal_block) + + logging.debug('Setup channel reduction %s', channel_reduction_type) + if channel_reduction_type == 'average': + # Mean across channel dimension + self.channel_reduction = ChannelAveragePool() + elif channel_reduction_type == 'attention': + # Number of input features is either the number of input channels or the number of temporal block features + channel_reduction_in_features = ( + self.features.num_features if self.channel_reduction_position == 0 else temporal_block_dimension + ) + # Attention across channel dimension + self.channel_reduction = ChannelAttentionPool(in_features=channel_reduction_in_features) + else: + raise ValueError(f'Unknown channel reduction type: {channel_reduction_type}') + + logging.debug('Setup %d output layers', num_outputs) + self.output_layers = torch.nn.ModuleList( + [ + ConformerEncoder( + feat_in=temporal_block_dimension, + n_layers=1, + d_model=temporal_block_dimension, + feat_out=num_subbands, + subsampling_factor=1, + self_attention_model=temporal_block_self_attention_model, + att_context_size=temporal_block_att_context_size, + n_heads=temporal_block_num_heads, + ) + for _ in range(num_outputs) + ] + ) + + # Output nonlinearity + self.output_nonlinearity = torch.nn.Sigmoid() + + @property + def input_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "input": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "input_length": NeuralType(('B',), LengthsType()), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "output": NeuralType(('B', 'C', 'D', 'T'), FloatType()), + "output_length": NeuralType(('B',), LengthsType()), + } + + @typecheck() + def forward(self, input: torch.Tensor, input_length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """Estimate `num_outputs` masks from the input spectrogram. + """ + # get input features from a complex-valued spectrogram, (B, C, F, T) + output, output_length = self.features(input=input, input_length=input_length) + + # batch and num channels + B, M = input.size(0), input.size(1) + + # process all blocks + for n in range(self.num_blocks): + if n < self.channel_reduction_position: + # apply multichannel block + output = self.channel_blocks[n](input=output) + # change to a single-stream format + F, T = output.size(-2), output.size(-1) + # (B, M, F, T) -> (B * M, F, T) + output = output.reshape(-1, F, T) + if M > 1: + # adjust the lengths accordingly + output_length = output_length.repeat_interleave(M) + + elif n == self.channel_reduction_position: + # apply channel reduction + # (B, M, F, T) -> (B, F, T) + output = self.channel_reduction(input=output) + + # apply temporal model on each channel independently + with typecheck.disable_checks(): + # output is AcousticEncodedRepresentation, conformer encoder requires SpectrogramType + output, output_length = self.temporal_blocks[n](audio_signal=output, length=output_length) + + # if channel reduction has not been applied yet, go back to multichannel layout + if n < self.channel_reduction_position: + # back to multi-channel format with possibly a different number of features + T = output.size(-1) + # (B * M, F, T) -> (B, M, F, T) + output = output.reshape(B, M, -1, T) + if M > 1: + # convert lengths from single-stream format to original multichannel + output_length = output_length[0:-1:M] + + if self.channel_reduction_position == self.num_blocks: + # apply channel reduction after the last layer + # (B, M, F, T) -> (B, F, T) + output = self.channel_reduction(input=output) + + # final mask for each output + masks = [] + for output_layer in self.output_layers: + # calculate mask + with typecheck.disable_checks(): + # output is AcousticEncodedRepresentation, conformer encoder requires SpectrogramType + mask, mask_length = output_layer(audio_signal=output, length=output_length) + mask = self.output_nonlinearity(mask) + # append to all masks + masks.append(mask) + + # stack masks along channel dimensions + masks = torch.stack(masks, dim=1) + + return masks, mask_length + + +class MaskEstimatorGSS(NeuralModule): + """Estimate masks using guided source separation with a complex + angular Central Gaussian Mixture Model (cACGMM) [1]. + + This module corresponds to `GSS` in Fig. 2 in [2]. + + Notation is approximately following [1], where `gamma` denotes + the time-frequency mask, `alpha` denotes the mixture weights, + and `BM` denotes the shape matrix. Additionally, the provided + source activity is denoted as `activity`. + + Args: + num_iterations: Number of iterations for the EM algorithm + eps: Small value for regularization + dtype: Data type for internal computations (default `torch.cdouble`) + + References: + [1] Ito et al., Complex Angular Central Gaussian Mixture Model for Directional Statistics in Mask-Based Microphone Array Signal Processing, 2016 + [2] Boeddeker et al., Front-End Processing for the CHiME-5 Dinner Party Scenario, 2018 + """ + + def __init__(self, num_iterations: int = 3, eps: float = 1e-8, dtype: torch.dtype = torch.cdouble): + super().__init__() + + if num_iterations <= 0: + raise ValueError(f'Number of iterations must be positive, got {num_iterations}') + + # number of iterations for the EM algorithm + self.num_iterations = num_iterations + + if eps <= 0: + raise ValueError(f'eps must be positive, got {eps}') + + # small regularization constant + self.eps = eps + + # internal calculations + if dtype not in [torch.cfloat, torch.cdouble]: + raise ValueError(f'Unsupported dtype {dtype}, expecting cfloat or cdouble') + self.dtype = dtype + + logging.debug('Initialized %s', self.__class__.__name__) + logging.debug('\tnum_iterations: %s', self.num_iterations) + logging.debug('\teps: %g', self.eps) + logging.debug('\tdtype: %s', self.dtype) + + def normalize(self, x: torch.Tensor, dim: int = 1) -> torch.Tensor: + """Normalize input to have a unit L2-norm across `dim`. + By default, normalizes across the input channels. + + Args: + x: C-channel input signal, shape (B, C, F, T) + dim: Dimension for normalization, defaults to -3 to normalize over channels + + Returns: + Normalized signal, shape (B, C, F, T) + """ + norm_x = torch.linalg.vector_norm(x, ord=2, dim=dim, keepdim=True) + x = x / (norm_x + self.eps) + return x + + @typecheck( + input_types={ + 'alpha': NeuralType(('B', 'C', 'D')), + 'activity': NeuralType(('B', 'C', 'T')), + 'log_pdf': NeuralType(('B', 'C', 'D', 'T')), + }, + output_types={'gamma': NeuralType(('B', 'C', 'D', 'T')),}, + ) + def update_masks(self, alpha: torch.Tensor, activity: torch.Tensor, log_pdf: torch.Tensor) -> torch.Tensor: + """Update masks for the cACGMM. + + Args: + alpha: component weights, shape (B, num_outputs, F) + activity: temporal activity for the components, shape (B, num_outputs, T) + log_pdf: logarithm of the PDF, shape (B, num_outputs, F, T) + + Returns: + Masks for the components of the model, shape (B, num_outputs, F, T) + """ + # (B, num_outputs, F) + # normalize across outputs in the log domain + log_gamma = log_pdf - torch.max(log_pdf, axis=-3, keepdim=True)[0] + + gamma = torch.exp(log_gamma) + + # calculate the mask using weight, pdf and source activity + gamma = alpha[..., None] * gamma * activity[..., None, :] + + # normalize across components/output channels + gamma = gamma / (torch.sum(gamma, dim=-3, keepdim=True) + self.eps) + + return gamma + + @typecheck( + input_types={'gamma': NeuralType(('B', 'C', 'D', 'T')),}, output_types={'alpha': NeuralType(('B', 'C', 'D')),}, + ) + def update_weights(self, gamma: torch.Tensor) -> torch.Tensor: + """Update weights for the individual components + in the mixture model. + + Args: + gamma: masks, shape (B, num_outputs, F, T) + + Returns: + Component weights, shape (B, num_outputs, F) + """ + alpha = torch.mean(gamma, dim=-1) + return alpha + + @typecheck( + input_types={ + 'z': NeuralType(('B', 'C', 'D', 'T')), + 'gamma': NeuralType(('B', 'C', 'D', 'T')), + 'zH_invBM_z': NeuralType(('B', 'C', 'D', 'T')), + }, + output_types={'log_pdf': NeuralType(('B', 'C', 'D', 'T')), 'zH_invBM_z': NeuralType(('B', 'C', 'D', 'T')),}, + ) + def update_pdf( + self, z: torch.Tensor, gamma: torch.Tensor, zH_invBM_z: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Update PDF of the cACGMM. + + Args: + z: directional statistics, shape (B, num_inputs, F, T) + gamma: masks, shape (B, num_outputs, F, T) + zH_invBM_z: energy weighted by shape matrices, shape (B, num_outputs, F, T) + + Returns: + Logarithm of the PDF, shape (B, num_outputs, F, T), the energy term, shape (B, num_outputs, F, T) + """ + num_inputs = z.size(-3) + + # shape (B, num_outputs, F, T) + scale = gamma / (zH_invBM_z + self.eps) + + # scale outer product and sum over time + # shape (B, num_outputs, F, num_inputs, num_inputs) + BM = num_inputs * torch.einsum('bmft,bift,bjft->bmfij', scale.to(z.dtype), z, z.conj()) + + # normalize across time + denom = torch.sum(gamma, dim=-1) + BM = BM / (denom[..., None, None] + self.eps) + + # make sure the matrix is Hermitian + BM = (BM + BM.conj().transpose(-1, -2)) / 2 + + # use eigenvalue decomposition to calculate the log determinant + # and the inverse-weighted energy term + L, Q = torch.linalg.eigh(BM) + + # BM is positive definite, so all eigenvalues should be positive + # However, small negative values may occur due to a limited precision + L = torch.clamp(L.real, min=self.eps) + + # PDF is invariant to scaling of the shape matrix [1], so + # eignevalues can be normalized (across num_inputs) + L = L / (torch.max(L, axis=-1, keepdim=True)[0] + self.eps) + + # small regularization to avoid numerical issues + L = L + self.eps + + # calculate the log determinant using the eigenvalues + log_detBM = torch.sum(torch.log(L), dim=-1) + + # calculate the energy term using the inverse eigenvalues + # NOTE: keeping an alternative implementation for reference (slower) + # zH_invBM_z = torch.einsum('bift,bmfij,bmfj,bmfkj,bkft->bmft', z.conj(), Q, (1 / L).to(Q.dtype), Q.conj(), z) + # zH_invBM_z = zH_invBM_z.abs() + self.eps # small regularization + + # calc sqrt(L) * Q^H * z + zH_invBM_z = torch.einsum('bmfj,bmfkj,bkft->bmftj', (1 / L.sqrt()).to(Q.dtype), Q.conj(), z) + # calc squared norm + zH_invBM_z = zH_invBM_z.abs().pow(2).sum(-1) + # small regularization + zH_invBM_z = zH_invBM_z + self.eps + + # final log PDF + log_pdf = -num_inputs * torch.log(zH_invBM_z) - log_detBM[..., None] + + return log_pdf, zH_invBM_z + + @property + def input_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "input": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "activity": NeuralType(('B', 'C', 'T')), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "gamma": NeuralType(('B', 'C', 'D', 'T')), + } + + @typecheck() + def forward(self, input: torch.Tensor, activity: torch.Tensor) -> torch.Tensor: + """Apply GSS to estimate the time-frequency masks for each output source. + + Args: + input: batched C-channel input signal, shape (B, num_inputs, F, T) + activity: batched frame-wise activity for each output source, shape (B, num_outputs, T) + + Returns: + Masks for the components of the model, shape (B, num_outputs, F, T) + """ + B, num_inputs, F, T = input.shape + num_outputs = activity.size(1) + + if activity.size(0) != B: + raise ValueError(f'Batch dimension mismatch: activity {activity.shape} vs input {input.shape}') + + if activity.size(-1) != T: + raise ValueError(f'Time dimension mismatch: activity {activity.shape} vs input {input.shape}') + + if num_outputs == 1: + raise ValueError(f'Expecting multiple outputs, got {num_outputs}') + + with torch.cuda.amp.autocast(enabled=False): + input = input.to(dtype=self.dtype) + + assert input.is_complex(), f'Expecting complex input, got {input.dtype}' + + # convert input to directional statistics by normalizing across channels + z = self.normalize(input, dim=-3) + + # initialize masks + gamma = torch.clamp(activity, min=self.eps) + # normalize across channels + gamma = gamma / torch.sum(gamma, dim=-2, keepdim=True) + # expand to input shape + gamma = gamma.unsqueeze(2).expand(-1, -1, F, -1) + + # initialize the energy term + zH_invBM_z = torch.ones(B, num_outputs, F, T, dtype=input.dtype, device=input.device) + + # EM iterations + for it in range(self.num_iterations): + alpha = self.update_weights(gamma=gamma) + log_pdf, zH_invBM_z = self.update_pdf(z=z, gamma=gamma, zH_invBM_z=zH_invBM_z) + gamma = self.update_masks(alpha=alpha, activity=activity, log_pdf=log_pdf) + + if torch.any(torch.isnan(gamma)): + raise RuntimeError(f'gamma contains NaNs: {gamma}') + + return gamma + + +class MaskReferenceChannel(NeuralModule): + """A simple mask processor which applies mask + on ref_channel of the input signal. + + Args: + ref_channel: Index of the reference channel. + mask_min_db: Threshold mask to a minimal value before applying it, defaults to -200dB + mask_max_db: Threshold mask to a maximal value before applying it, defaults to 0dB + """ + + def __init__(self, ref_channel: int = 0, mask_min_db: float = -200, mask_max_db: float = 0): + super().__init__() + self.ref_channel = ref_channel + # Mask thresholding + self.mask_min = db2mag(mask_min_db) + self.mask_max = db2mag(mask_max_db) + + logging.debug('Initialized %s with', self.__class__.__name__) + logging.debug('\tref_channel: %d', self.ref_channel) + logging.debug('\tmask_min: %f', self.mask_min) + logging.debug('\tmask_max: %f', self.mask_max) + + @property + def input_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "input": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "input_length": NeuralType(('B',), LengthsType()), + "mask": NeuralType(('B', 'C', 'D', 'T'), FloatType()), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "output": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "output_length": NeuralType(('B',), LengthsType()), + } + + @typecheck() + def forward( + self, input: torch.Tensor, input_length: torch.Tensor, mask: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Apply mask on `ref_channel` of the input signal. + This can be used to generate multi-channel output. + If `mask` has `M` channels, the output will have `M` channels as well. + + Args: + input: Input signal complex-valued spectrogram, shape (B, C, F, N) + input_length: Length of valid entries along the time dimension, shape (B,) + mask: Mask for M outputs, shape (B, M, F, N) + + Returns: + M-channel output complex-valed spectrogram with shape (B, M, F, N) + """ + # Apply thresholds + mask = torch.clamp(mask, min=self.mask_min, max=self.mask_max) + + # Apply each output mask on the ref channel + output = mask * input[:, self.ref_channel : self.ref_channel + 1, ...] + return output, input_length + + +class MaskBasedBeamformer(NeuralModule): + """Multi-channel processor using masks to estimate signal statistics. + + Args: + filter_type: string denoting the type of the filter. Defaults to `mvdr` + filter_beta: Parameter of the parameteric multichannel Wiener filter + filter_rank: Parameter of the parametric multichannel Wiener filter + filter_postfilter: Optional, postprocessing of the filter + ref_channel: Optional, reference channel. If None, it will be estimated automatically + ref_hard: If true, hard (one-hot) reference. If false, a soft reference + ref_hard_use_grad: If true, use straight-through gradient when using the hard reference + ref_subband_weighting: If true, use subband weighting when estimating reference channel + num_subbands: Optional, used to determine the parameter size for reference estimation + mask_min_db: Threshold mask to a minimal value before applying it, defaults to -200dB + mask_max_db: Threshold mask to a maximal value before applying it, defaults to 0dB + diag_reg: Optional, diagonal regularization for the multichannel filter + eps: Small regularization constant to avoid division by zero + """ + + def __init__( + self, + filter_type: str = 'mvdr_souden', + filter_beta: float = 0.0, + filter_rank: str = 'one', + filter_postfilter: Optional[str] = None, + ref_channel: Optional[int] = 0, + ref_hard: bool = True, + ref_hard_use_grad: bool = False, + ref_subband_weighting: bool = False, + num_subbands: Optional[int] = None, + mask_min_db: float = -200, + mask_max_db: float = 0, + postmask_min_db: float = 0, + postmask_max_db: float = 0, + diag_reg: Optional[float] = 1e-6, + eps: float = 1e-8, + ): + super().__init__() + if filter_type not in ['pmwf', 'mvdr_souden']: + raise ValueError(f'Unknown filter type {filter_type}') + + self.filter_type = filter_type + if self.filter_type == 'mvdr_souden' and filter_beta != 0: + logging.warning( + 'Using filter type %s: beta will be automatically set to zero (current beta %f) and rank to one (current rank %s).', + self.filter_type, + filter_beta, + filter_rank, + ) + filter_beta = 0.0 + filter_rank = 'one' + # Prepare filter + self.filter = ParametricMultichannelWienerFilter( + beta=filter_beta, + rank=filter_rank, + postfilter=filter_postfilter, + ref_channel=ref_channel, + ref_hard=ref_hard, + ref_hard_use_grad=ref_hard_use_grad, + ref_subband_weighting=ref_subband_weighting, + num_subbands=num_subbands, + diag_reg=diag_reg, + eps=eps, + ) + # Mask thresholding + if mask_min_db >= mask_max_db: + raise ValueError( + f'Lower bound for the mask {mask_min_db}dB must be smaller than the upper bound {mask_max_db}dB' + ) + self.mask_min = db2mag(mask_min_db) + self.mask_max = db2mag(mask_max_db) + # Postmask thresholding + if postmask_min_db > postmask_max_db: + raise ValueError( + f'Lower bound for the postmask {postmask_min_db}dB must be smaller or equal to the upper bound {postmask_max_db}dB' + ) + self.postmask_min = db2mag(postmask_min_db) + self.postmask_max = db2mag(postmask_max_db) + + logging.debug('Initialized %s', self.__class__.__name__) + logging.debug('\tfilter_type: %s', self.filter_type) + logging.debug('\tmask_min: %e', self.mask_min) + logging.debug('\tmask_max: %e', self.mask_max) + logging.debug('\tpostmask_min: %e', self.postmask_min) + logging.debug('\tpostmask_max: %e', self.postmask_max) + + @property + def input_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "input": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "mask": NeuralType(('B', 'C', 'D', 'T'), FloatType()), + "mask_undesired": NeuralType(('B', 'C', 'D', 'T'), FloatType(), optional=True), + "input_length": NeuralType(('B',), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "output": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "output_length": NeuralType(('B',), LengthsType(), optional=True), + } + + @typecheck() + def forward( + self, + input: torch.Tensor, + mask: torch.Tensor, + mask_undesired: Optional[torch.Tensor] = None, + input_length: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """Apply a mask-based beamformer to the input spectrogram. + This can be used to generate multi-channel output. + If `mask` has multiple channels, a multichannel filter is created for each mask, + and the output is concatenation of individual outputs along the channel dimension. + The total number of outputs is `num_masks * M`, where `M` is the number of channels + at the filter output. + + Args: + input: Input signal complex-valued spectrogram, shape (B, C, F, N) + mask: Mask for M output signals, shape (B, num_masks, F, N) + input_length: Length of valid entries along the time dimension, shape (B,) + + Returns: + Multichannel output signal complex-valued spectrogram, shape (B, num_masks * M, F, N) + """ + # Length mask + if input_length is not None: + length_mask: torch.Tensor = make_seq_mask_like( + lengths=input_length, like=mask[:, 0, ...], time_dim=-1, valid_ones=False + ) + + # Use each mask to generate an output + output, num_masks = [], mask.size(1) + for m in range(num_masks): + # Desired signal mask + mask_d = mask[:, m, ...] + # Undesired signal mask + if mask_undesired is not None: + mask_u = mask_undesired[:, m, ...] + elif num_masks == 1: + # If a single mask is estimated, use the complement + mask_u = 1 - mask_d + else: + # Use sum of all other sources + mask_u = torch.sum(mask, dim=1) - mask_d + + # Threshold masks + mask_d = torch.clamp(mask_d, min=self.mask_min, max=self.mask_max) + mask_u = torch.clamp(mask_u, min=self.mask_min, max=self.mask_max) + + if input_length is not None: + mask_d = mask_d.masked_fill(length_mask, 0.0) + mask_u = mask_u.masked_fill(length_mask, 0.0) + + # Apply filter + output_m = self.filter(input=input, mask_s=mask_d, mask_n=mask_u) + + # Optional: apply a postmask with min and max thresholds + if self.postmask_min < self.postmask_max: + postmask_m = torch.clamp(mask[:, m, ...], min=self.postmask_min, max=self.postmask_max) + output_m = output_m * postmask_m.unsqueeze(1) + + # Save the current output (B, M, F, T) + output.append(output_m) + + # Combine outputs along the channel dimension + # Each output is (B, M, F, T) + output = torch.concatenate(output, axis=1) + + # Apply masking + if input_length is not None: + output = output.masked_fill(length_mask[:, None, ...], 0.0) + + return output, input_length + + +class WPEFilter(NeuralModule): + """A weighted prediction error filter. + Given input signal, and expected power of the desired signal, this + class estimates a multiple-input multiple-output prediction filter + and returns the filtered signal. Currently, estimation of statistics + and processing is performed in batch mode. + + Args: + filter_length: Length of the prediction filter in frames, per channel + prediction_delay: Prediction delay in frames + diag_reg: Diagonal regularization for the correlation matrix Q, applied as diag_reg * trace(Q) + eps + eps: Small positive constant for regularization + + References: + - Yoshioka and Nakatani, Generalization of Multi-Channel Linear Prediction + Methods for Blind MIMO Impulse Response Shortening, 2012 + - Jukić et al, Group sparsity for MIMO speech dereverberation, 2015 + """ + + def __init__(self, filter_length: int, prediction_delay: int, diag_reg: Optional[float] = 1e-6, eps: float = 1e-8): + super().__init__() + self.filter_length = filter_length + self.prediction_delay = prediction_delay + self.diag_reg = diag_reg + self.eps = eps + + logging.debug('Initialized %s', self.__class__.__name__) + logging.debug('\tfilter_length: %d', self.filter_length) + logging.debug('\tprediction_delay: %d', self.prediction_delay) + logging.debug('\tdiag_reg: %g', self.diag_reg) + logging.debug('\teps: %g', self.eps) + + @property + def input_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "input": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "power": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "input_length": NeuralType(('B',), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "output": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "output_length": NeuralType(('B',), LengthsType(), optional=True), + } + + @typecheck() + def forward( + self, input: torch.Tensor, power: torch.Tensor, input_length: Optional[torch.Tensor] = None + ) -> torch.Tensor: + """Given input and the predicted power for the desired signal, estimate + the WPE filter and return the processed signal. + + Args: + input: Input signal, shape (B, C, F, N) + power: Predicted power of the desired signal, shape (B, C, F, N) + input_length: Optional, length of valid frames in `input`. Defaults to `None` + + Returns: + Tuple of (processed_signal, output_length). Processed signal has the same + shape as the input signal (B, C, F, N), and the output length is the same + as the input length. + """ + # Temporal weighting: average power over channels, output shape (B, F, N) + weight = torch.mean(power, dim=1) + # Use inverse power as the weight + weight = 1 / (weight + self.eps) + + # Multi-channel convolution matrix for each subband + tilde_input = self.convtensor(input, filter_length=self.filter_length, delay=self.prediction_delay) + + # Estimate correlation matrices + Q, R = self.estimate_correlations( + input=input, weight=weight, tilde_input=tilde_input, input_length=input_length + ) + + # Estimate prediction filter + G = self.estimate_filter(Q=Q, R=R) + + # Apply prediction filter + undesired_signal = self.apply_filter(filter=G, tilde_input=tilde_input) + + # Dereverberation + desired_signal = input - undesired_signal + + if input_length is not None: + # Mask padded frames + length_mask: torch.Tensor = make_seq_mask_like( + lengths=input_length, like=desired_signal, time_dim=-1, valid_ones=False + ) + desired_signal = desired_signal.masked_fill(length_mask, 0.0) + + return desired_signal, input_length + + @classmethod + def convtensor( + cls, x: torch.Tensor, filter_length: int, delay: int = 0, n_steps: Optional[int] = None + ) -> torch.Tensor: + """Create a tensor equivalent of convmtx_mc for each example in the batch. + The input signal tensor `x` has shape (B, C, F, N). + Convtensor returns a view of the input signal `x`. + + Note: We avoid reshaping the output to collapse channels and filter taps into + a single dimension, e.g., (B, F, N, -1). In this way, the output is a view of the input, + while an additional reshape would result in a contiguous array and more memory use. + + Args: + x: input tensor, shape (B, C, F, N) + filter_length: length of the filter, determines the shape of the convolution tensor + delay: delay to add to the input signal `x` before constructing the convolution tensor + n_steps: Optional, number of time steps to keep in the out. Defaults to the number of + time steps in the input tensor. + + Returns: + Return a convolutional tensor with shape (B, C, F, n_steps, filter_length) + """ + if x.ndim != 4: + raise RuntimeError(f'Expecting a 4-D input. Received input with shape {x.shape}') + + B, C, F, N = x.shape + + if n_steps is None: + # Keep the same length as the input signal + n_steps = N + + # Pad temporal dimension + x = torch.nn.functional.pad(x, (filter_length - 1 + delay, 0)) + + # Build Toeplitz-like matrix view by unfolding across time + tilde_X = x.unfold(-1, filter_length, 1) + + # Trim to the set number of time steps + tilde_X = tilde_X[:, :, :, :n_steps, :] + + return tilde_X + + @classmethod + def permute_convtensor(cls, x: torch.Tensor) -> torch.Tensor: + """Reshape and permute columns to convert the result of + convtensor to be equal to convmtx_mc. This is used for verification + purposes and it is not required to use the filter. + + Args: + x: output of self.convtensor, shape (B, C, F, N, filter_length) + + Returns: + Output has shape (B, F, N, C*filter_length) that corresponds to + the layout of convmtx_mc. + """ + B, C, F, N, filter_length = x.shape + + # .view will not work, so a copy will have to be created with .reshape + # That will result in more memory use, since we don't use a view of the original + # multi-channel signal + x = x.permute(0, 2, 3, 1, 4) + x = x.reshape(B, F, N, C * filter_length) + + permute = [] + for m in range(C): + permute[m * filter_length : (m + 1) * filter_length] = m * filter_length + np.flip( + np.arange(filter_length) + ) + return x[..., permute] + + def estimate_correlations( + self, + input: torch.Tensor, + weight: torch.Tensor, + tilde_input: torch.Tensor, + input_length: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor]: + """ + Args: + input: Input signal, shape (B, C, F, N) + weight: Time-frequency weight, shape (B, F, N) + tilde_input: Multi-channel convolution tensor, shape (B, C, F, N, filter_length) + input_length: Length of each input example, shape (B) + + Returns: + Returns a tuple of correlation matrices for each batch. + + Let `X` denote the input signal in a single subband, + `tilde{X}` the corresponding multi-channel correlation matrix, + and `w` the vector of weights. + + The first output is + Q = tilde{X}^H * diag(w) * tilde{X} (1) + for each (b, f). + The matrix calculated in (1) has shape (C * filter_length, C * filter_length) + The output is returned in a tensor with shape (B, F, C, filter_length, C, filter_length). + + The second output is + R = tilde{X}^H * diag(w) * X (2) + for each (b, f). + The matrix calculated in (2) has shape (C * filter_length, C) + The output is returned in a tensor with shape (B, F, C, filter_length, C). The last + dimension corresponds to output channels. + """ + if input_length is not None: + # Take only valid samples into account + length_mask: torch.Tensor = make_seq_mask_like( + lengths=input_length, like=weight, time_dim=-1, valid_ones=False + ) + weight = weight.masked_fill(length_mask, 0.0) + + # Calculate (1) + # result: (B, F, C, filter_length, C, filter_length) + Q = torch.einsum('bjfik,bmfin->bfjkmn', tilde_input.conj(), weight[:, None, :, :, None] * tilde_input) + + # Calculate (2) + # result: (B, F, C, filter_length, C) + R = torch.einsum('bjfik,bmfi->bfjkm', tilde_input.conj(), weight[:, None, :, :] * input) + + return Q, R + + def estimate_filter(self, Q: torch.Tensor, R: torch.Tensor) -> torch.Tensor: + """Estimate the MIMO prediction filter as + G(b,f) = Q(b,f) \ R(b,f) + for each subband in each example in the batch (b, f). + + Args: + Q: shape (B, F, C, filter_length, C, filter_length) + R: shape (B, F, C, filter_length, C) + + Returns: + Complex-valued prediction filter, shape (B, C, F, C, filter_length) + """ + B, F, C, filter_length, _, _ = Q.shape + assert ( + filter_length == self.filter_length + ), f'Shape of Q {Q.shape} is not matching filter length {self.filter_length}' + + # Reshape to analytical dimensions for each (b, f) + Q = Q.reshape(B, F, C * self.filter_length, C * filter_length) + R = R.reshape(B, F, C * self.filter_length, C) + + # Diagonal regularization + if self.diag_reg: + # Regularization: diag_reg * trace(Q) + eps + diag_reg = self.diag_reg * torch.diagonal(Q, dim1=-2, dim2=-1).sum(-1).real + self.eps + # Apply regularization on Q + Q = Q + torch.diag_embed(diag_reg.unsqueeze(-1) * torch.ones(Q.shape[-1], device=Q.device)) + + # Solve for the filter + G = torch.linalg.solve(Q, R) + + # Reshape to desired representation: (B, F, input channels, filter_length, output channels) + G = G.reshape(B, F, C, filter_length, C) + # Move output channels to front: (B, output channels, F, input channels, filter_length) + G = G.permute(0, 4, 1, 2, 3) + + return G + + def apply_filter( + self, filter: torch.Tensor, input: Optional[torch.Tensor] = None, tilde_input: Optional[torch.Tensor] = None + ) -> torch.Tensor: + """Apply a prediction filter `filter` on the input `input` as + + output(b,f) = tilde{input(b,f)} * filter(b,f) + + If available, directly use the convolution matrix `tilde_input`. + + Args: + input: Input signal, shape (B, C, F, N) + tilde_input: Convolution matrix for the input signal, shape (B, C, F, N, filter_length) + filter: Prediction filter, shape (B, C, F, C, filter_length) + + Returns: + Multi-channel signal obtained by applying the prediction filter on + the input signal, same shape as input (B, C, F, N) + """ + if input is None and tilde_input is None: + raise RuntimeError(f'Both inputs cannot be None simultaneously.') + if input is not None and tilde_input is not None: + raise RuntimeError(f'Both inputs cannot be provided simultaneously.') + + if tilde_input is None: + tilde_input = self.convtensor(input, filter_length=self.filter_length, delay=self.prediction_delay) + + # For each (batch, output channel, f, time step), sum across (input channel, filter tap) + output = torch.einsum('bjfik,bmfjk->bmfi', tilde_input, filter) + + return output + + +class MaskBasedDereverbWPE(NeuralModule): + """Multi-channel linear prediction-based dereverberation using + weighted prediction error for filter estimation. + + An optional mask to estimate the signal power can be provided. + If a time-frequency mask is not provided, the algorithm corresponds + to the conventional WPE algorithm. + + Args: + filter_length: Length of the convolutional filter for each channel in frames. + prediction_delay: Delay of the input signal for multi-channel linear prediction in frames. + num_iterations: Number of iterations for reweighting + mask_min_db: Threshold mask to a minimal value before applying it, defaults to -200dB + mask_max_db: Threshold mask to a minimal value before applying it, defaults to 0dB + diag_reg: Diagonal regularization for WPE + eps: Small regularization constant + dtype: Data type for internal computations + + References: + - Kinoshita et al, Neural network-based spectrum estimation for online WPE dereverberation, 2017 + - Yoshioka and Nakatani, Generalization of Multi-Channel Linear Prediction Methods for Blind MIMO Impulse Response Shortening, 2012 + """ + + def __init__( + self, + filter_length: int, + prediction_delay: int, + num_iterations: int = 1, + mask_min_db: float = -200, + mask_max_db: float = 0, + diag_reg: Optional[float] = 1e-6, + eps: float = 1e-8, + dtype: torch.dtype = torch.cdouble, + ): + super().__init__() + # Filter setup + self.filter = WPEFilter( + filter_length=filter_length, prediction_delay=prediction_delay, diag_reg=diag_reg, eps=eps + ) + self.num_iterations = num_iterations + # Mask thresholding + self.mask_min = db2mag(mask_min_db) + self.mask_max = db2mag(mask_max_db) + # Internal calculations + if dtype not in [torch.cfloat, torch.cdouble]: + raise ValueError(f'Unsupported dtype {dtype}, expecting torch.cfloat or torch.cdouble') + self.dtype = dtype + + logging.debug('Initialized %s', self.__class__.__name__) + logging.debug('\tnum_iterations: %s', self.num_iterations) + logging.debug('\tmask_min: %g', self.mask_min) + logging.debug('\tmask_max: %g', self.mask_max) + logging.debug('\tdtype: %s', self.dtype) + + @property + def input_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "input": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "input_length": NeuralType(('B',), LengthsType(), optional=True), + "mask": NeuralType(('B', 'C', 'D', 'T'), FloatType(), optional=True), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "output": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "output_length": NeuralType(('B',), LengthsType(), optional=True), + } + + @typecheck() + def forward( + self, input: torch.Tensor, input_length: Optional[torch.Tensor] = None, mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + """Given an input signal `input`, apply the WPE dereverberation algoritm. + + Args: + input: C-channel complex-valued spectrogram, shape (B, C, F, T) + input_length: Optional length for each signal in the batch, shape (B,) + mask: Optional mask, shape (B, 1, F, N) or (B, C, F, T) + + Returns: + Processed tensor with the same number of channels as the input, + shape (B, C, F, T). + """ + io_dtype = input.dtype + + with torch.cuda.amp.autocast(enabled=False): + output = input.to(dtype=self.dtype) + + if not output.is_complex(): + raise RuntimeError(f'Expecting complex input, got {output.dtype}') + + for i in range(self.num_iterations): + magnitude = torch.abs(output) + if i == 0 and mask is not None: + # Apply thresholds + mask = torch.clamp(mask, min=self.mask_min, max=self.mask_max) + # Mask magnitude + magnitude = mask * magnitude + # Calculate power + power = magnitude ** 2 + # Apply filter + output, output_length = self.filter(input=output, input_length=input_length, power=power) + + return output.to(io_dtype), output_length + + +class MixtureConsistencyProjection(NeuralModule): + """Ensure estimated sources are consistent with the input mixture. + Note that the input mixture is assume to be a single-channel signal. + + Args: + weighting: Optional weighting mode for the consistency constraint. + If `None`, use uniform weighting. If `power`, use the power of the + estimated source as the weight. + eps: Small positive value for regularization + + Reference: + Wisdom et al, Differentiable consistency constraints for improved deep speech enhancement, 2018 + """ + + def __init__(self, weighting: Optional[str] = None, eps: float = 1e-8): + super().__init__() + self.weighting = weighting + self.eps = eps + + if self.weighting not in [None, 'power']: + raise NotImplementedError(f'Weighting mode {self.weighting} not implemented') + + @property + def input_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "mixture": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "estimate": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "output": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + } + + @typecheck() + def forward(self, mixture: torch.Tensor, estimate: torch.Tensor) -> torch.Tensor: + """Enforce mixture consistency on the estimated sources. + Args: + mixture: Single-channel mixture, shape (B, 1, F, N) + estimate: M estimated sources, shape (B, M, F, N) + + Returns: + Source estimates consistent with the mixture, shape (B, M, F, N) + """ + # number of sources + M = estimate.size(-3) + # estimated mixture based on the estimated sources + estimated_mixture = torch.sum(estimate, dim=-3, keepdim=True) + + # weighting + if self.weighting is None: + weight = 1 / M + elif self.weighting == 'power': + weight = estimate.abs().pow(2) + weight = weight / (weight.sum(dim=-3, keepdim=True) + self.eps) + else: + raise NotImplementedError(f'Weighting mode {self.weighting} not implemented') + + # consistent estimate + consistent_estimate = estimate + weight * (mixture - estimated_mixture) + + return consistent_estimate diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/audio_preprocessing.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/audio_preprocessing.py new file mode 100644 index 0000000..cc53124 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/audio_preprocessing.py @@ -0,0 +1,986 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import random +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import Any, Dict, Optional, Tuple + +import torch +from packaging import version + +from nemo.collections.asr.parts.numba.spec_augment import SpecAugmentNumba, spec_augment_launch_heuristics +from nemo.collections.asr.parts.preprocessing.features import ( + FilterbankFeatures, + FilterbankFeaturesTA, + make_seq_mask_like, +) +from nemo.collections.asr.parts.submodules.spectr_augment import SpecAugment, SpecCutout +from nemo.core.classes import Exportable, NeuralModule, typecheck +from nemo.core.neural_types import ( + AudioSignal, + LengthsType, + MelSpectrogramType, + MFCCSpectrogramType, + NeuralType, + SpectrogramType, +) +from nemo.core.utils import numba_utils +from nemo.core.utils.numba_utils import __NUMBA_MINIMUM_VERSION__ +from nemo.utils import logging + +try: + import torchaudio + import torchaudio.functional + import torchaudio.transforms + + TORCHAUDIO_VERSION = version.parse(torchaudio.__version__) + TORCHAUDIO_VERSION_MIN = version.parse('0.5') + + HAVE_TORCHAUDIO = True +except ModuleNotFoundError: + HAVE_TORCHAUDIO = False + +__all__ = [ + 'AudioToMelSpectrogramPreprocessor', + 'AudioToSpectrogram', + 'SpectrogramToAudio', + 'AudioToMFCCPreprocessor', + 'SpectrogramAugmentation', + 'MaskedPatchAugmentation', + 'CropOrPadSpectrogramAugmentation', +] + + +class AudioPreprocessor(NeuralModule, ABC): + """ + An interface for Neural Modules that performs audio pre-processing, + transforming the wav files to features. + """ + + def __init__(self, win_length, hop_length): + super().__init__() + + self.win_length = win_length + self.hop_length = hop_length + + self.torch_windows = { + 'hann': torch.hann_window, + 'hamming': torch.hamming_window, + 'blackman': torch.blackman_window, + 'bartlett': torch.bartlett_window, + 'ones': torch.ones, + None: torch.ones, + } + + @typecheck() + @torch.no_grad() + def forward(self, input_signal, length): + processed_signal, processed_length = self.get_features(input_signal, length) + + return processed_signal, processed_length + + @abstractmethod + def get_features(self, input_signal, length): + # Called by forward(). Subclasses should implement this. + pass + + +class AudioToMelSpectrogramPreprocessor(AudioPreprocessor, Exportable): + """Featurizer module that converts wavs to mel spectrograms. + + Args: + sample_rate (int): Sample rate of the input audio data. + Defaults to 16000 + window_size (float): Size of window for fft in seconds + Defaults to 0.02 + window_stride (float): Stride of window for fft in seconds + Defaults to 0.01 + n_window_size (int): Size of window for fft in samples + Defaults to None. Use one of window_size or n_window_size. + n_window_stride (int): Stride of window for fft in samples + Defaults to None. Use one of window_stride or n_window_stride. + window (str): Windowing function for fft. can be one of ['hann', + 'hamming', 'blackman', 'bartlett'] + Defaults to "hann" + normalize (str): Can be one of ['per_feature', 'all_features']; all + other options disable feature normalization. 'all_features' + normalizes the entire spectrogram to be mean 0 with std 1. + 'pre_features' normalizes per channel / freq instead. + Defaults to "per_feature" + n_fft (int): Length of FT window. If None, it uses the smallest power + of 2 that is larger than n_window_size. + Defaults to None + preemph (float): Amount of pre emphasis to add to audio. Can be + disabled by passing None. + Defaults to 0.97 + features (int): Number of mel spectrogram freq bins to output. + Defaults to 64 + lowfreq (int): Lower bound on mel basis in Hz. + Defaults to 0 + highfreq (int): Lower bound on mel basis in Hz. + Defaults to None + log (bool): Log features. + Defaults to True + log_zero_guard_type(str): Need to avoid taking the log of zero. There + are two options: "add" or "clamp". + Defaults to "add". + log_zero_guard_value(float, or str): Add or clamp requires the number + to add with or clamp to. log_zero_guard_value can either be a float + or "tiny" or "eps". torch.finfo is used if "tiny" or "eps" is + passed. + Defaults to 2**-24. + dither (float): Amount of white-noise dithering. + Defaults to 1e-5 + pad_to (int): Ensures that the output size of the time dimension is + a multiple of pad_to. + Defaults to 16 + frame_splicing (int): Defaults to 1 + exact_pad (bool): If True, sets stft center to False and adds padding, such that num_frames = audio_length + // hop_length. Defaults to False. + pad_value (float): The value that shorter mels are padded with. + Defaults to 0 + mag_power (float): The power that the linear spectrogram is raised to + prior to multiplication with mel basis. + Defaults to 2 for a power spec + rng : Random number generator + nb_augmentation_prob (float) : Probability with which narrowband augmentation would be applied to + samples in the batch. + Defaults to 0.0 + nb_max_freq (int) : Frequency above which all frequencies will be masked for narrowband augmentation. + Defaults to 4000 + use_torchaudio: Whether to use the `torchaudio` implementation. + mel_norm: Normalization used for mel filterbank weights. + Defaults to 'slaney' (area normalization) + stft_exact_pad: Deprecated argument, kept for compatibility with older checkpoints. + stft_conv: Deprecated argument, kept for compatibility with older checkpoints. + """ + + def save_to(self, save_path: str): + pass + + @classmethod + def restore_from(cls, restore_path: str): + pass + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "input_signal": NeuralType(('B', 'T'), AudioSignal(freq=self._sample_rate)), + "length": NeuralType( + tuple('B'), LengthsType() + ), # Please note that length should be in samples not seconds. + } + + @property + def output_types(self): + """Returns definitions of module output ports. + + processed_signal: + 0: AxisType(BatchTag) + 1: AxisType(MelSpectrogramSignalTag) + 2: AxisType(ProcessedTimeTag) + processed_length: + 0: AxisType(BatchTag) + """ + return { + "processed_signal": NeuralType(('B', 'D', 'T'), MelSpectrogramType()), + "processed_length": NeuralType(tuple('B'), LengthsType()), + } + + def __init__( + self, + sample_rate=16000, + window_size=0.02, + window_stride=0.01, + n_window_size=None, + n_window_stride=None, + window="hann", + normalize="per_feature", + n_fft=None, + preemph=0.97, + features=64, + lowfreq=0, + highfreq=None, + log=True, + log_zero_guard_type="add", + log_zero_guard_value=2 ** -24, + dither=1e-5, + pad_to=16, + frame_splicing=1, + exact_pad=False, + pad_value=0, + mag_power=2.0, + rng=None, + nb_augmentation_prob=0.0, + nb_max_freq=4000, + use_torchaudio: bool = False, + mel_norm="slaney", + stft_exact_pad=False, # Deprecated arguments; kept for config compatibility + stft_conv=False, # Deprecated arguments; kept for config compatibility + ): + super().__init__(n_window_size, n_window_stride) + + self._sample_rate = sample_rate + if window_size and n_window_size: + raise ValueError(f"{self} received both window_size and " f"n_window_size. Only one should be specified.") + if window_stride and n_window_stride: + raise ValueError( + f"{self} received both window_stride and " f"n_window_stride. Only one should be specified." + ) + if window_size: + n_window_size = int(window_size * self._sample_rate) + if window_stride: + n_window_stride = int(window_stride * self._sample_rate) + + # Given the long and similar argument list, point to the class and instantiate it by reference + if not use_torchaudio: + featurizer_class = FilterbankFeatures + else: + featurizer_class = FilterbankFeaturesTA + self.featurizer = featurizer_class( + sample_rate=self._sample_rate, + n_window_size=n_window_size, + n_window_stride=n_window_stride, + window=window, + normalize=normalize, + n_fft=n_fft, + preemph=preemph, + nfilt=features, + lowfreq=lowfreq, + highfreq=highfreq, + log=log, + log_zero_guard_type=log_zero_guard_type, + log_zero_guard_value=log_zero_guard_value, + dither=dither, + pad_to=pad_to, + frame_splicing=frame_splicing, + exact_pad=exact_pad, + pad_value=pad_value, + mag_power=mag_power, + rng=rng, + nb_augmentation_prob=nb_augmentation_prob, + nb_max_freq=nb_max_freq, + mel_norm=mel_norm, + stft_exact_pad=stft_exact_pad, # Deprecated arguments; kept for config compatibility + stft_conv=stft_conv, # Deprecated arguments; kept for config compatibility + ) + + def input_example(self, max_batch: int = 8, max_dim: int = 32000, min_length: int = 200): + batch_size = torch.randint(low=1, high=max_batch, size=[1]).item() + max_length = torch.randint(low=min_length, high=max_dim, size=[1]).item() + signals = torch.rand(size=[batch_size, max_length]) * 2 - 1 + lengths = torch.randint(low=min_length, high=max_dim, size=[batch_size]) + lengths[0] = max_length + return signals, lengths + + def get_features(self, input_signal, length): + return self.featurizer(input_signal, length) + + @property + def filter_banks(self): + return self.featurizer.filter_banks + + +class AudioToMFCCPreprocessor(AudioPreprocessor): + """Preprocessor that converts wavs to MFCCs. + Uses torchaudio.transforms.MFCC. + + Args: + sample_rate: The sample rate of the audio. + Defaults to 16000. + window_size: Size of window for fft in seconds. Used to calculate the + win_length arg for mel spectrogram. + Defaults to 0.02 + window_stride: Stride of window for fft in seconds. Used to caculate + the hop_length arg for mel spect. + Defaults to 0.01 + n_window_size: Size of window for fft in samples + Defaults to None. Use one of window_size or n_window_size. + n_window_stride: Stride of window for fft in samples + Defaults to None. Use one of window_stride or n_window_stride. + window: Windowing function for fft. can be one of ['hann', + 'hamming', 'blackman', 'bartlett', 'none', 'null']. + Defaults to 'hann' + n_fft: Length of FT window. If None, it uses the smallest power of 2 + that is larger than n_window_size. + Defaults to None + lowfreq (int): Lower bound on mel basis in Hz. + Defaults to 0 + highfreq (int): Lower bound on mel basis in Hz. + Defaults to None + n_mels: Number of mel filterbanks. + Defaults to 64 + n_mfcc: Number of coefficients to retain + Defaults to 64 + dct_type: Type of discrete cosine transform to use + norm: Type of norm to use + log: Whether to use log-mel spectrograms instead of db-scaled. + Defaults to True. + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "input_signal": NeuralType(('B', 'T'), AudioSignal(freq=self._sample_rate)), + "length": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return { + "processed_signal": NeuralType(('B', 'D', 'T'), MFCCSpectrogramType()), + "processed_length": NeuralType(tuple('B'), LengthsType()), + } + + def save_to(self, save_path: str): + pass + + @classmethod + def restore_from(cls, restore_path: str): + pass + + def __init__( + self, + sample_rate=16000, + window_size=0.02, + window_stride=0.01, + n_window_size=None, + n_window_stride=None, + window='hann', + n_fft=None, + lowfreq=0.0, + highfreq=None, + n_mels=64, + n_mfcc=64, + dct_type=2, + norm='ortho', + log=True, + ): + self._sample_rate = sample_rate + if not HAVE_TORCHAUDIO: + logging.error('Could not import torchaudio. Some features might not work.') + + raise ModuleNotFoundError( + "torchaudio is not installed but is necessary for " + "AudioToMFCCPreprocessor. We recommend you try " + "building it from source for the PyTorch version you have." + ) + if window_size and n_window_size: + raise ValueError(f"{self} received both window_size and " f"n_window_size. Only one should be specified.") + if window_stride and n_window_stride: + raise ValueError( + f"{self} received both window_stride and " f"n_window_stride. Only one should be specified." + ) + # Get win_length (n_window_size) and hop_length (n_window_stride) + if window_size: + n_window_size = int(window_size * self._sample_rate) + if window_stride: + n_window_stride = int(window_stride * self._sample_rate) + + super().__init__(n_window_size, n_window_stride) + + mel_kwargs = {} + + mel_kwargs['f_min'] = lowfreq + mel_kwargs['f_max'] = highfreq + mel_kwargs['n_mels'] = n_mels + + mel_kwargs['n_fft'] = n_fft or 2 ** math.ceil(math.log2(n_window_size)) + + mel_kwargs['win_length'] = n_window_size + mel_kwargs['hop_length'] = n_window_stride + + # Set window_fn. None defaults to torch.ones. + window_fn = self.torch_windows.get(window, None) + if window_fn is None: + raise ValueError( + f"Window argument for AudioProcessor is invalid: {window}." + f"For no window function, use 'ones' or None." + ) + mel_kwargs['window_fn'] = window_fn + + # Use torchaudio's implementation of MFCCs as featurizer + self.featurizer = torchaudio.transforms.MFCC( + sample_rate=self._sample_rate, + n_mfcc=n_mfcc, + dct_type=dct_type, + norm=norm, + log_mels=log, + melkwargs=mel_kwargs, + ) + + def get_features(self, input_signal, length): + features = self.featurizer(input_signal) + seq_len = torch.ceil(length.to(torch.float32) / self.hop_length).to(dtype=torch.long) + return features, seq_len + + +class SpectrogramAugmentation(NeuralModule): + """ + Performs time and freq cuts in one of two ways. + SpecAugment zeroes out vertical and horizontal sections as described in + SpecAugment (https://arxiv.org/abs/1904.08779). Arguments for use with + SpecAugment are `freq_masks`, `time_masks`, `freq_width`, and `time_width`. + SpecCutout zeroes out rectangulars as described in Cutout + (https://arxiv.org/abs/1708.04552). Arguments for use with Cutout are + `rect_masks`, `rect_freq`, and `rect_time`. + + Args: + freq_masks (int): how many frequency segments should be cut. + Defaults to 0. + time_masks (int): how many time segments should be cut + Defaults to 0. + freq_width (int): maximum number of frequencies to be cut in one + segment. + Defaults to 10. + time_width (int): maximum number of time steps to be cut in one + segment + Defaults to 10. + rect_masks (int): how many rectangular masks should be cut + Defaults to 0. + rect_freq (int): maximum size of cut rectangles along the frequency + dimension + Defaults to 5. + rect_time (int): maximum size of cut rectangles along the time + dimension + Defaults to 25. + """ + + @property + def input_types(self): + """Returns definitions of module input types + """ + return { + "input_spec": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Returns definitions of module output types + """ + return {"augmented_spec": NeuralType(('B', 'D', 'T'), SpectrogramType())} + + def __init__( + self, + freq_masks=0, + time_masks=0, + freq_width=10, + time_width=10, + rect_masks=0, + rect_time=5, + rect_freq=20, + rng=None, + mask_value=0.0, + use_numba_spec_augment: bool = True, + ): + super().__init__() + + if rect_masks > 0: + self.spec_cutout = SpecCutout(rect_masks=rect_masks, rect_time=rect_time, rect_freq=rect_freq, rng=rng,) + # self.spec_cutout.to(self._device) + else: + self.spec_cutout = lambda input_spec: input_spec + if freq_masks + time_masks > 0: + self.spec_augment = SpecAugment( + freq_masks=freq_masks, + time_masks=time_masks, + freq_width=freq_width, + time_width=time_width, + rng=rng, + mask_value=mask_value, + ) + else: + self.spec_augment = lambda input_spec, length: input_spec + + # Check if numba is supported, and use a Numba kernel if it is + if use_numba_spec_augment and numba_utils.numba_cuda_is_supported(__NUMBA_MINIMUM_VERSION__): + logging.info('Numba CUDA SpecAugment kernel is being used') + self.spec_augment_numba = SpecAugmentNumba( + freq_masks=freq_masks, + time_masks=time_masks, + freq_width=freq_width, + time_width=time_width, + rng=rng, + mask_value=mask_value, + ) + else: + self.spec_augment_numba = None + + @typecheck() + def forward(self, input_spec, length): + augmented_spec = self.spec_cutout(input_spec=input_spec) + + # To run the Numba kernel, correct numba version is required as well as + # tensor must be on GPU and length must be provided + if self.spec_augment_numba is not None and spec_augment_launch_heuristics(augmented_spec, length): + augmented_spec = self.spec_augment_numba(input_spec=augmented_spec, length=length) + else: + augmented_spec = self.spec_augment(input_spec=augmented_spec, length=length) + return augmented_spec + + +class MaskedPatchAugmentation(NeuralModule): + """ + Zeroes out fixed size time patches of the spectrogram. + All samples in batch are guaranteed to have the same amount of masked time steps. + Optionally also performs frequency masking in the same way as SpecAugment. + Args: + patch_size (int): up to how many time steps does one patch consist of. + Defaults to 48. + mask_patches (float): how many patches should be masked in each sample. + if >= 1., interpreted as number of patches (after converting to int) + if <1., interpreted as fraction of total tokens to be masked (number of patches is rounded up) + Defaults to 10. + freq_masks (int): how many frequency segments should be cut. + Defaults to 0. + freq_width (int): maximum number of frequencies to be cut in a segment. + Defaults to 0. + """ + + @property + def input_types(self): + """Returns definitions of module input types + """ + return { + "input_spec": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Returns definitions of module output types + """ + return {"augmented_spec": NeuralType(('B', 'D', 'T'), SpectrogramType())} + + def __init__( + self, patch_size: int = 48, mask_patches: float = 10.0, freq_masks: int = 0, freq_width: int = 0, + ): + super().__init__() + self.patch_size = patch_size + if mask_patches >= 1: + self.mask_patches = int(mask_patches) + elif mask_patches >= 0: + self._mask_fraction = mask_patches + self.mask_patches = None + else: + raise ValueError('mask_patches cannot be negative') + + if freq_masks > 0: + self.spec_augment = SpecAugment(freq_masks=freq_masks, time_masks=0, freq_width=freq_width, time_width=0,) + else: + self.spec_augment = None + + @typecheck() + def forward(self, input_spec, length): + augmented_spec = input_spec + + min_len = torch.min(length) + + if self.mask_patches is None: + # masking specified as fraction + len_fraction = int(min_len * self._mask_fraction) + mask_patches = len_fraction // self.patch_size + int(len_fraction % self.patch_size != 0) + else: + mask_patches = self.mask_patches + + if min_len < self.patch_size * mask_patches: + mask_patches = min_len // self.patch_size + + for idx in range(input_spec.shape[0]): + cur_len = length[idx] + patches = range(cur_len // self.patch_size) + masked_patches = random.sample(patches, mask_patches) + + for mp in masked_patches: + augmented_spec[idx, :, mp * self.patch_size : (mp + 1) * self.patch_size] = 0.0 + + if self.spec_augment is not None: + augmented_spec = self.spec_augment(input_spec=augmented_spec, length=length) + + return augmented_spec + + +class CropOrPadSpectrogramAugmentation(NeuralModule): + """ + Pad or Crop the incoming Spectrogram to a certain shape. + + Args: + audio_length (int): the final number of timesteps that is required. + The signal will be either padded or cropped temporally to this + size. + """ + + def __init__(self, audio_length): + super(CropOrPadSpectrogramAugmentation, self).__init__() + self.audio_length = audio_length + + if self.audio_length < 0: + raise ValueError( + 'audio_length must be non-negative. If using a dataclass with OmegaConf, ' + 'please call OmegaConf.to_object(cfg) to call appropriate __post_init__ methods.' + ) + + @typecheck() + @torch.no_grad() + def forward(self, input_signal, length): + image = input_signal + num_images = image.shape[0] + + audio_length = self.audio_length + image_len = image.shape[-1] + + # Crop long signal + if image_len > audio_length: # randomly slice + cutout_images = [] + offset = torch.randint(low=0, high=image_len - audio_length + 1, size=[num_images]) + + for idx, offset in enumerate(offset): + cutout_images.append(image[idx : idx + 1, :, offset : offset + audio_length]) + + image = torch.cat(cutout_images, dim=0) + del cutout_images + + else: # symmetrically pad short signal with zeros + pad_left = (audio_length - image_len) // 2 + pad_right = (audio_length - image_len) // 2 + + if (audio_length - image_len) % 2 == 1: + pad_right += 1 + + image = torch.nn.functional.pad(image, [pad_left, pad_right], mode="constant", value=0) + + # Replace dynamic length sequences with static number of timesteps + length = (length * 0) + audio_length + + return image, length + + @property + def input_types(self): + """Returns definitions of module output ports. + """ + return { + "input_signal": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return { + "processed_signal": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "processed_length": NeuralType(tuple('B'), LengthsType()), + } + + def save_to(self, save_path: str): + pass + + @classmethod + def restore_from(cls, restore_path: str): + pass + + +class AudioToSpectrogram(NeuralModule): + """Transform a batch of input multi-channel signals into a batch of + STFT-based spectrograms. + + Args: + fft_length: length of FFT + hop_length: length of hops/shifts of the sliding window + power: exponent for magnitude spectrogram. Default `None` will + return a complex-valued spectrogram + """ + + def __init__(self, fft_length: int, hop_length: int, power: Optional[float] = None): + if not HAVE_TORCHAUDIO: + logging.error('Could not import torchaudio. Some features might not work.') + + raise ModuleNotFoundError( + f"torchaudio is not installed but is necessary to instantiate a {self.__class__.__name__}" + ) + + super().__init__() + + # For now, assume FFT length is divisible by two + if fft_length % 2 != 0: + raise ValueError(f'fft_length = {fft_length} must be divisible by 2') + + self.stft = torchaudio.transforms.Spectrogram( + n_fft=fft_length, hop_length=hop_length, power=power, pad_mode='constant' + ) + + # number of subbands + self.F = fft_length // 2 + 1 + + @property + def num_subbands(self) -> int: + return self.F + + @property + def input_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "input": NeuralType(('B', 'C', 'T'), AudioSignal()), + "input_length": NeuralType(('B',), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "output": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "output_length": NeuralType(('B',), LengthsType()), + } + + @typecheck() + def forward( + self, input: torch.Tensor, input_length: Optional[torch.Tensor] = None + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Convert a batch of C-channel input signals + into a batch of complex-valued spectrograms. + + Args: + input: Time-domain input signal with C channels, shape (B, C, T) + input_length: Length of valid entries along the time dimension, shape (B,) + + Returns: + Output spectrogram with F subbands and N time frames, shape (B, C, F, N) + and output length with shape (B,). + """ + B, T = input.size(0), input.size(-1) + input = input.view(B, -1, T) + + # STFT output (B, C, F, N) + with torch.cuda.amp.autocast(enabled=False): + output = self.stft(input.float()) + + if input_length is not None: + # Mask padded frames + output_length = self.get_output_length(input_length=input_length) + + length_mask: torch.Tensor = make_seq_mask_like( + lengths=output_length, like=output, time_dim=-1, valid_ones=False + ) + output = output.masked_fill(length_mask, 0.0) + else: + # Assume all frames are valid for all examples in the batch + output_length = output.size(-1) * torch.ones(B, device=output.device).long() + + return output, output_length + + def get_output_length(self, input_length: torch.Tensor) -> torch.Tensor: + """Get length of valid frames for the output. + + Args: + input_length: number of valid samples, shape (B,) + + Returns: + Number of valid frames, shape (B,) + """ + output_length = input_length.div(self.stft.hop_length, rounding_mode='floor').add(1).long() + return output_length + + +class SpectrogramToAudio(NeuralModule): + """Transform a batch of input multi-channel spectrograms into a batch of + time-domain multi-channel signals. + + Args: + fft_length: length of FFT + hop_length: length of hops/shifts of the sliding window + power: exponent for magnitude spectrogram. Default `None` will + return a complex-valued spectrogram + """ + + def __init__(self, fft_length: int, hop_length: int): + if not HAVE_TORCHAUDIO: + logging.error('Could not import torchaudio. Some features might not work.') + + raise ModuleNotFoundError( + f"torchaudio is not installed but is necessary to instantiate a {self.__class__.__name__}" + ) + + super().__init__() + + # For now, assume FFT length is divisible by two + if fft_length % 2 != 0: + raise ValueError(f'fft_length = {fft_length} must be divisible by 2') + + self.istft = torchaudio.transforms.InverseSpectrogram( + n_fft=fft_length, hop_length=hop_length, pad_mode='constant' + ) + + self.F = fft_length // 2 + 1 + + @property + def num_subbands(self) -> int: + return self.F + + @property + def input_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "input": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + "input_length": NeuralType(('B',), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Dict[str, NeuralType]: + """Returns definitions of module output ports. + """ + return { + "output": NeuralType(('B', 'C', 'T'), AudioSignal()), + "output_length": NeuralType(('B',), LengthsType()), + } + + @typecheck() + def forward(self, input: torch.Tensor, input_length: Optional[torch.Tensor] = None) -> torch.Tensor: + """Convert input complex-valued spectrogram to a time-domain + signal. Multi-channel IO is supported. + + Args: + input: Input spectrogram for C channels, shape (B, C, F, N) + input_length: Length of valid entries along the time dimension, shape (B,) + + Returns: + Time-domain signal with T time-domain samples and C channels, (B, C, T) + and output length with shape (B,). + """ + B, F, N = input.size(0), input.size(-2), input.size(-1) + assert F == self.F, f'Number of subbands F={F} not matching self.F={self.F}' + input = input.view(B, -1, F, N) + + # iSTFT output (B, C, T) + with torch.cuda.amp.autocast(enabled=False): + output = self.istft(input.cfloat()) + + if input_length is not None: + # Mask padded samples + output_length = self.get_output_length(input_length=input_length) + + length_mask: torch.Tensor = make_seq_mask_like( + lengths=output_length, like=output, time_dim=-1, valid_ones=False + ) + output = output.masked_fill(length_mask, 0.0) + else: + # Assume all frames are valid for all examples in the batch + output_length = output.size(-1) * torch.ones(B, device=output.device).long() + + return output, output_length + + def get_output_length(self, input_length: torch.Tensor) -> torch.Tensor: + """Get length of valid samples for the output. + + Args: + input_length: number of valid frames, shape (B,) + + Returns: + Number of valid samples, shape (B,) + """ + output_length = input_length.sub(1).mul(self.istft.hop_length).long() + return output_length + + +@dataclass +class AudioToMelSpectrogramPreprocessorConfig: + _target_: str = "nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor" + sample_rate: int = 16000 + window_size: float = 0.02 + window_stride: float = 0.01 + n_window_size: Optional[int] = None + n_window_stride: Optional[int] = None + window: str = "hann" + normalize: str = "per_feature" + n_fft: Optional[int] = None + preemph: float = 0.97 + features: int = 64 + lowfreq: int = 0 + highfreq: Optional[int] = None + log: bool = True + log_zero_guard_type: str = "add" + log_zero_guard_value: float = 2 ** -24 + dither: float = 1e-5 + pad_to: int = 16 + frame_splicing: int = 1 + exact_pad: bool = False + pad_value: int = 0 + mag_power: float = 2.0 + rng: Optional[str] = None + nb_augmentation_prob: float = 0.0 + nb_max_freq: int = 4000 + use_torchaudio: bool = False + mel_norm: str = "slaney" + stft_exact_pad: bool = False # Deprecated argument, kept for compatibility with older checkpoints. + stft_conv: bool = False # Deprecated argument, kept for compatibility with older checkpoints. + + +@dataclass +class AudioToMFCCPreprocessorConfig: + _target_: str = 'nemo.collections.asr.modules.AudioToMFCCPreprocessor' + sample_rate: int = 16000 + window_size: float = 0.02 + window_stride: float = 0.01 + n_window_size: Optional[int] = None + n_window_stride: Optional[int] = None + window: str = 'hann' + n_fft: Optional[int] = None + lowfreq: Optional[float] = 0.0 + highfreq: Optional[float] = None + n_mels: int = 64 + n_mfcc: int = 64 + dct_type: int = 2 + norm: str = 'ortho' + log: bool = True + + +@dataclass +class SpectrogramAugmentationConfig: + _target_: str = "nemo.collections.asr.modules.SpectrogramAugmentation" + freq_masks: int = 0 + time_masks: int = 0 + freq_width: int = 0 + time_width: Optional[Any] = 0 + rect_masks: int = 0 + rect_time: int = 0 + rect_freq: int = 0 + mask_value: float = 0 + rng: Optional[Any] = None # random.Random() type + use_numba_spec_augment: bool = True + + +@dataclass +class CropOrPadSpectrogramAugmentationConfig: + audio_length: int + _target_: str = "nemo.collections.asr.modules.CropOrPadSpectrogramAugmentation" + + +@dataclass +class MaskedPatchAugmentationConfig: + patch_size: int = 48 + mask_patches: float = 10.0 + freq_masks: int = 0 + freq_width: int = 0 + _target_: str = "nemo.collections.asr.modules.MaskedPatchAugmentation" diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/beam_search_decoder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/beam_search_decoder.py new file mode 100644 index 0000000..b39804a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/beam_search_decoder.py @@ -0,0 +1,103 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + +from nemo.core.classes import NeuralModule, typecheck +from nemo.core.neural_types import LengthsType, LogprobsType, NeuralType, PredictionsType + + +class BeamSearchDecoderWithLM(NeuralModule): + """Neural Module that does CTC beam search with a N-gram language model. + It takes a batch of log_probabilities. Note the bigger the batch, the + better as processing is parallelized. Outputs a list of size batch_size. + Each element in the list is a list of size beam_search, and each element + in that list is a tuple of (final_log_prob, hyp_string). + Args: + vocab (list): List of characters that can be output by the ASR model. For English, this is the 28 character set + {a-z '}. The CTC blank symbol is automatically added. + beam_width (int): Size of beams to keep and expand upon. Larger beams result in more accurate but slower + predictions + alpha (float): The amount of importance to place on the N-gram language model. Larger alpha means more + importance on the LM and less importance on the acoustic model. + beta (float): A penalty term given to longer word sequences. Larger beta will result in shorter sequences. + lm_path (str): Path to N-gram language model + num_cpus (int): Number of CPUs to use + cutoff_prob (float): Cutoff probability in vocabulary pruning, default 1.0, no pruning + cutoff_top_n (int): Cutoff number in pruning, only top cutoff_top_n characters with highest probs in + vocabulary will be used in beam search, default 40. + input_tensor (bool): Set to True if you intend to pass PyTorch Tensors, set to False if you intend to pass + NumPy arrays. + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "log_probs": NeuralType(('B', 'T', 'D'), LogprobsType()), + "log_probs_length": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"predictions": NeuralType(('B', 'T'), PredictionsType())} + + def __init__( + self, vocab, beam_width, alpha, beta, lm_path, num_cpus, cutoff_prob=1.0, cutoff_top_n=40, input_tensor=False + ): + + try: + from ctc_decoders import Scorer, ctc_beam_search_decoder_batch + except ModuleNotFoundError: + raise ModuleNotFoundError( + "BeamSearchDecoderWithLM requires the installation of ctc_decoders " + "from scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh" + ) + + super().__init__() + + if lm_path is not None: + self.scorer = Scorer(alpha, beta, model_path=lm_path, vocabulary=vocab) + else: + self.scorer = None + self.beam_search_func = ctc_beam_search_decoder_batch + self.vocab = vocab + self.beam_width = beam_width + self.num_cpus = num_cpus + self.cutoff_prob = cutoff_prob + self.cutoff_top_n = cutoff_top_n + self.input_tensor = input_tensor + + @typecheck(ignore_collections=True) + @torch.no_grad() + def forward(self, log_probs, log_probs_length): + probs_list = log_probs + if self.input_tensor: + probs = torch.exp(log_probs) + probs_list = [] + for i, prob in enumerate(probs): + probs_list.append(prob[: log_probs_length[i], :]) + res = self.beam_search_func( + probs_list, + self.vocab, + beam_size=self.beam_width, + num_processes=self.num_cpus, + ext_scoring_func=self.scorer, + cutoff_prob=self.cutoff_prob, + cutoff_top_n=self.cutoff_top_n, + ) + return res diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/conformer_encoder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/conformer_encoder.py new file mode 100644 index 0000000..b9642b3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/conformer_encoder.py @@ -0,0 +1,1137 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import random +from collections import OrderedDict +from dataclasses import dataclass +from typing import List, Optional, Set + +import torch +import torch.distributed +import torch.nn as nn +from omegaconf import DictConfig, ListConfig, open_dict + +from nemo.collections.asr.models.configs import CacheAwareStreamingConfig +from nemo.collections.asr.parts.mixins.streaming import StreamingEncoder +from nemo.collections.asr.parts.submodules.causal_convs import CausalConv1D +from nemo.collections.asr.parts.submodules.conformer_modules import ConformerLayer +from nemo.collections.asr.parts.submodules.multi_head_attention import ( + LocalAttRelPositionalEncoding, + MultiHeadAttention, + PositionalEncoding, + RelPositionalEncoding, + RelPositionMultiHeadAttention, + RelPositionMultiHeadAttentionLongformer, +) +from nemo.collections.asr.parts.submodules.subsampling import ( + ConvSubsampling, + StackingSubsampling, + SubsamplingReductionModule, +) +from nemo.collections.asr.parts.utils import adapter_utils +from nemo.collections.asr.parts.utils.regularization_utils import compute_stochastic_depth_drop_probs +from nemo.core.classes.common import typecheck +from nemo.core.classes.exportable import Exportable +from nemo.core.classes.mixins import AccessMixin, adapter_mixins +from nemo.core.classes.module import NeuralModule +from nemo.core.neural_types import AcousticEncodedRepresentation, ChannelType, LengthsType, NeuralType, SpectrogramType +from nemo.utils import logging + +__all__ = ['ConformerEncoder'] + + +class ConformerEncoder(NeuralModule, StreamingEncoder, Exportable, AccessMixin): + """ + The encoder for ASR model of Conformer. + Based on this paper: + 'Conformer: Convolution-augmented Transformer for Speech Recognition' by Anmol Gulati et al. + https://arxiv.org/abs/2005.08100 + + Args: + feat_in (int): the size of feature channels + n_layers (int): number of layers of ConformerBlock + d_model (int): the hidden size of the model + feat_out (int): the size of the output features + Defaults to -1 (means feat_out is d_model) + subsampling (str): the method of subsampling, choices=['vggnet', 'striding', 'dw-striding', 'stacking', 'stacking_norm'] + Defaults to striding. + subsampling_factor (int): the subsampling factor which should be power of 2 + Defaults to 4. + subsampling_conv_chunking_factor(int): optionally, force chunk inputs (helpful for large inputs) + Should be power of 2, 1 (auto-chunking, default), or -1 (no chunking) + subsampling_conv_channels (int): the size of the convolutions in the subsampling module + Defaults to -1 which would set it to d_model. + reduction (str, Optional): the method of reduction, choices=['pooling', 'striding']. If no value + is passed, then no reduction is performed and the models runs with the original 4x subsampling. + reduction_position (int, Optional): the index of the layer to apply reduction. If -1, apply reduction + at the end. + reduction_factor (int): the reduction factor which should be either 1 or a power of 2 + Defaults to 1. + ff_expansion_factor (int): the expansion factor in feed forward layers + Defaults to 4. + self_attention_model (str): type of the attention layer and positional encoding + + 'rel_pos': + relative positional embedding and Transformer-XL + + 'rel_pos_local_attn': + relative positional embedding and Transformer-XL with local attention using + overlapping chunks. Attention context is determined by att_context_size parameter. + + 'abs_pos': + absolute positional embedding and Transformer + + Default is rel_pos. + pos_emb_max_len (int): the maximum length of positional embeddings + Defaults to 5000 + n_heads (int): number of heads in multi-headed attention layers + Defaults to 4. + att_context_size (List[Union[List[int],int]]): specifies the context sizes on each side. Each context size should be a list of two integers like [100,100]. + A list of context sizes like [[100,100],[100,50]] can also be passed. -1 means unlimited context. + Defaults to [-1,-1] + att_context_probs (List[float]): a list of probabilities of each one of the att_context_size when a list of them is passed. If not specified, uniform distribution is being used. + Defaults to None + att_context_style (str): 'regular' or 'chunked_limited'. + Defaults to 'regular' + xscaling (bool): enables scaling the inputs to the multi-headed attention layers by sqrt(d_model) + Defaults to True. + untie_biases (bool): whether to not share (untie) the bias weights between layers of Transformer-XL + Defaults to True. + conv_kernel_size (int): the size of the convolutions in the convolutional modules + Defaults to 31. + conv_norm_type (str): the type of the normalization in the convolutional modules + Defaults to 'batch_norm'. + conv_context_size (list): it can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size. + None means [(conv_kernel_size-1)//2, (conv_kernel_size-1)//2], and 'causal' means [(conv_kernel_size-1), 0]. + Defaults to None. + conv_dual_mode (bool): specifies if convolution should be dual mode when dual_offline mode is being used. When enables, the left half of the convolution kernel would get masked in streaming cases. + Defaults to False + dropout (float): the dropout rate used in all layers except the attention layers + Defaults to 0.1. + dropout_pre_encoder (float): the dropout rate used before the encoder + Defaults to 0.1. + dropout_emb (float): the dropout rate used for the positional embeddings + Defaults to 0.1. + dropout_att (float): the dropout rate used for the attention layer + Defaults to 0.0. + stochastic_depth_drop_prob (float): if non-zero, will randomly drop + layers during training. The higher this value, the more often layers + are dropped. Defaults to 0.0. + stochastic_depth_mode (str): can be either "linear" or "uniform". If + set to "uniform", all layers have the same probability of drop. If + set to "linear", the drop probability grows linearly from 0 for the + first layer to the desired value for the final layer. Defaults to + "linear". + stochastic_depth_start_layer (int): starting layer for stochastic depth. + All layers before this will never be dropped. Note that drop + probability will be adjusted accordingly if mode is "linear" when + start layer is > 1. Defaults to 1. + global_tokens (int): number of tokens to be used for global attention. + Only relevant if self_attention_model is 'rel_pos_local_attn'. + Defaults to 0. + global_tokens_spacing (int): how far apart the global tokens are + Defaults to 1. + global_attn_separate (bool): whether the q, k, v layers used for global tokens should be separate. + Defaults to False. + + """ + + def input_example(self, max_batch=1, max_dim=256): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + dev = next(self.parameters()).device + if self.export_cache_support: + window_size = max_dim + if self.streaming_cfg is not None: + if isinstance(self.streaming_cfg.chunk_size, list): + chunk_size = self.streaming_cfg.chunk_size[1] + else: + chunk_size = self.streaming_cfg.chunk_size + if isinstance(self.streaming_cfg.pre_encode_cache_size, list): + pre_encode_cache_size = self.streaming_cfg.pre_encode_cache_size[1] + else: + pre_encode_cache_size = self.streaming_cfg.pre_encode_cache_size + window_size = chunk_size + pre_encode_cache_size + input_example = torch.randn(max_batch, self._feat_in, window_size, device=dev) + input_example_length = torch.randint( + window_size // 4, window_size, (max_batch,), device=dev, dtype=torch.int64 + ) + cache_last_channel, cache_last_time, cache_last_channel_len = self.get_initial_cache_state( + batch_size=max_batch, device=dev, max_dim=max_dim + ) + all_input_example = tuple( + [ + input_example, + input_example_length, + cache_last_channel.transpose(0, 1), + cache_last_time.transpose(0, 1), + cache_last_channel_len, + ] + ) + else: + input_example = torch.randn(max_batch, self._feat_in, max_dim, device=dev) + input_example_length = torch.randint(max_dim // 4, max_dim, (max_batch,), device=dev, dtype=torch.int64) + all_input_example = tuple([input_example, input_example_length]) + + return all_input_example + + @property + def input_types(self): + """Returns definitions of module input ports.""" + return OrderedDict( + { + "audio_signal": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + "cache_last_channel": NeuralType(('D', 'B', 'T', 'D'), ChannelType(), optional=True), + "cache_last_time": NeuralType(('D', 'B', 'D', 'T'), ChannelType(), optional=True), + "cache_last_channel_len": NeuralType(tuple('B'), LengthsType(), optional=True), + } + ) + + @property + def input_types_for_export(self): + """Returns definitions of module input ports.""" + return OrderedDict( + { + "audio_signal": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + "cache_last_channel": NeuralType(('B', 'D', 'T', 'D'), ChannelType(), optional=True), + "cache_last_time": NeuralType(('B', 'D', 'D', 'T'), ChannelType(), optional=True), + "cache_last_channel_len": NeuralType(tuple('B'), LengthsType(), optional=True), + } + ) + + @property + def output_types(self): + """Returns definitions of module output ports.""" + return OrderedDict( + { + "outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + "cache_last_channel_next": NeuralType(('D', 'B', 'T', 'D'), ChannelType(), optional=True), + "cache_last_time_next": NeuralType(('D', 'B', 'D', 'T'), ChannelType(), optional=True), + "cache_last_channel_next_len": NeuralType(tuple('B'), LengthsType(), optional=True), + } + ) + + @property + def output_types_for_export(self): + """Returns definitions of module output ports.""" + return OrderedDict( + { + "outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + "cache_last_channel_next": NeuralType(('B', 'D', 'T', 'D'), ChannelType(), optional=True), + "cache_last_time_next": NeuralType(('B', 'D', 'D', 'T'), ChannelType(), optional=True), + "cache_last_channel_next_len": NeuralType(tuple('B'), LengthsType(), optional=True), + } + ) + + @property + def disabled_deployment_input_names(self): + if not self.export_cache_support: + return set(["cache_last_channel", "cache_last_time", "cache_last_channel_len"]) + else: + return set() + + @property + def disabled_deployment_output_names(self): + if not self.export_cache_support: + return set(["cache_last_channel_next", "cache_last_time_next", "cache_last_channel_next_len"]) + else: + return set() + + def __init__( + self, + feat_in, + n_layers, + d_model, + feat_out=-1, + causal_downsampling=False, + subsampling='striding', + subsampling_factor=4, + subsampling_conv_chunking_factor=1, + subsampling_conv_channels=-1, + reduction=None, + reduction_position=None, + reduction_factor=1, + ff_expansion_factor=4, + self_attention_model='rel_pos', + n_heads=4, + att_context_size=None, + att_context_probs=None, + att_context_style='regular', + xscaling=True, + untie_biases=True, + pos_emb_max_len=5000, + conv_kernel_size=31, + conv_norm_type='batch_norm', + conv_context_size=None, + dropout=0.1, + dropout_pre_encoder=0.1, + dropout_emb=0.1, + dropout_att=0.0, + stochastic_depth_drop_prob: float = 0.0, + stochastic_depth_mode: str = "linear", + stochastic_depth_start_layer: int = 1, + global_tokens: int = 0, + global_tokens_spacing: int = 1, + global_attn_separate: bool = False, + ): + super().__init__() + d_ff = d_model * ff_expansion_factor + self.d_model = d_model + self.n_layers = n_layers + self._feat_in = feat_in + self.att_context_style = att_context_style + self.subsampling_factor = subsampling_factor + self.subsampling_conv_chunking_factor = subsampling_conv_chunking_factor + + self.self_attention_model = self_attention_model + self.global_tokens = global_tokens + self.global_attn_separate = global_attn_separate + self.global_tokens_spacing = global_tokens_spacing + + # Setting up the att_context_size + ( + self.att_context_size_all, + self.att_context_size, + self.att_context_probs, + self.conv_context_size, + ) = self._calc_context_sizes( + att_context_style=att_context_style, + att_context_size=att_context_size, + att_context_probs=att_context_probs, + conv_context_size=conv_context_size, + conv_kernel_size=conv_kernel_size, + ) + + if xscaling: + self.xscale = math.sqrt(d_model) + else: + self.xscale = None + + # Subsampling + if subsampling_conv_channels == -1: + subsampling_conv_channels = d_model + if subsampling and subsampling_factor > 1: + if subsampling in ['stacking', 'stacking_norm']: + # stacking_norm has an extra layer norm after stacking comparing to stacking + self.pre_encode = StackingSubsampling( + subsampling_factor=subsampling_factor, + feat_in=feat_in, + feat_out=d_model, + norm=True if subsampling == 'stacking_norm' else False, + ) + else: + self.pre_encode = ConvSubsampling( + subsampling=subsampling, + subsampling_factor=subsampling_factor, + feat_in=feat_in, + feat_out=d_model, + conv_channels=subsampling_conv_channels, + subsampling_conv_chunking_factor=subsampling_conv_chunking_factor, + activation=nn.ReLU(True), + is_causal=causal_downsampling, + ) + else: + self.pre_encode = nn.Linear(feat_in, d_model) + + # Reduction + if reduction and reduction_factor > 1: + assert reduction_position >= -1 and reduction_position < n_layers + self.reduction_subsampling = SubsamplingReductionModule( + reduction=reduction, d_model=d_model, reduction_factor=reduction_factor, + ) + self.reduction_position = reduction_position + else: + self.reduction_subsampling = None + self.reduction_position = None + + self._feat_out = d_model + + # Biases for relative positional encoding + if not untie_biases and self_attention_model == "rel_pos": + d_head = d_model // n_heads + pos_bias_u = nn.Parameter(torch.Tensor(n_heads, d_head)) + pos_bias_v = nn.Parameter(torch.Tensor(n_heads, d_head)) + nn.init.zeros_(pos_bias_u) + nn.init.zeros_(pos_bias_v) + else: + pos_bias_u = None + pos_bias_v = None + + # Positional encodings + self.pos_emb_max_len = pos_emb_max_len + if self_attention_model == "rel_pos": + self.pos_enc = RelPositionalEncoding( + d_model=d_model, + dropout_rate=dropout_pre_encoder, + max_len=pos_emb_max_len, + xscale=self.xscale, + dropout_rate_emb=dropout_emb, + ) + elif self_attention_model == 'rel_pos_local_attn': + if max(att_context_size) <= 0: + raise ValueError("When using local attention, context size must be set > 0") + self.pos_enc = LocalAttRelPositionalEncoding( + att_context_size=att_context_size, + d_model=d_model, + dropout_rate=dropout, + max_len=pos_emb_max_len, + xscale=self.xscale, + dropout_rate_emb=dropout_emb, + ) + elif self_attention_model == "abs_pos": + pos_bias_u = None + pos_bias_v = None + self.pos_enc = PositionalEncoding( + d_model=d_model, dropout_rate=dropout_pre_encoder, max_len=pos_emb_max_len, xscale=self.xscale + ) + else: + raise ValueError(f"Not valid self_attention_model: '{self_attention_model}'!") + + self.layers = nn.ModuleList() + for i in range(n_layers): + layer = ConformerLayer( + d_model=d_model, + d_ff=d_ff, + self_attention_model=self_attention_model, + global_tokens=global_tokens, + global_tokens_spacing=global_tokens_spacing, + global_attn_separate=global_attn_separate, + n_heads=n_heads, + conv_kernel_size=conv_kernel_size, + conv_norm_type=conv_norm_type, + conv_context_size=self.conv_context_size, + dropout=dropout, + dropout_att=dropout_att, + pos_bias_u=pos_bias_u, + pos_bias_v=pos_bias_v, + att_context_size=self.att_context_size, + ) + self.layers.append(layer) + + if feat_out > 0 and feat_out != self._feat_out: + self.out_proj = nn.Linear(self._feat_out, feat_out) + self._feat_out = feat_out + else: + self.out_proj = None + self._feat_out = d_model + self.set_max_audio_length(self.pos_emb_max_len) + self.use_pad_mask = True + + self.setup_streaming_params() + self.export_cache_support = False + + self.layer_drop_probs = compute_stochastic_depth_drop_probs( + len(self.layers), stochastic_depth_drop_prob, stochastic_depth_mode, stochastic_depth_start_layer + ) + # will be set in self.forward() if defined in AccessMixin config + self.interctc_capture_at_layers = None + + def forward_for_export( + self, audio_signal, length, cache_last_channel=None, cache_last_time=None, cache_last_channel_len=None + ): + if cache_last_channel is not None: + cache_last_channel = cache_last_channel.transpose(0, 1) + cache_last_time = cache_last_time.transpose(0, 1) + + rets = self.forward_internal( + audio_signal, + length, + cache_last_channel=cache_last_channel, + cache_last_time=cache_last_time, + cache_last_channel_len=cache_last_channel_len, + ) + rets = self.streaming_post_process(rets, keep_all_outputs=False) + if len(rets) == 2: + return rets + elif rets[2] is None and rets[3] is None and rets[4] is None: + return (rets[0], rets[1]) + else: + return ( + rets[0], + rets[1], + rets[2].transpose(0, 1), + rets[3].transpose(0, 1), + rets[4], + ) + + def streaming_post_process(self, rets, keep_all_outputs=True): + if len(rets) == 2: + return rets[0], rets[1], None, None, None + + (encoded, encoded_len, cache_last_channel_next, cache_last_time_next, cache_last_channel_next_len) = rets + + if cache_last_channel_next is not None and self.streaming_cfg.last_channel_cache_size >= 0: + if self.streaming_cfg.last_channel_cache_size > 0: + cache_last_channel_next = cache_last_channel_next[ + :, :, -self.streaming_cfg.last_channel_cache_size :, : + ] + + if self.streaming_cfg.valid_out_len > 0 and (not keep_all_outputs or self.att_context_style == "regular"): + encoded = encoded[:, :, : self.streaming_cfg.valid_out_len] + encoded_len = torch.clamp(encoded_len, max=self.streaming_cfg.valid_out_len) + + return (encoded, encoded_len, cache_last_channel_next, cache_last_time_next, cache_last_channel_next_len) + + @typecheck() + def forward( + self, audio_signal, length, cache_last_channel=None, cache_last_time=None, cache_last_channel_len=None + ): + return self.forward_internal( + audio_signal, + length, + cache_last_channel=cache_last_channel, + cache_last_time=cache_last_time, + cache_last_channel_len=cache_last_channel_len, + ) + + def forward_internal( + self, audio_signal, length, cache_last_channel=None, cache_last_time=None, cache_last_channel_len=None + ): + self.update_max_seq_length(seq_length=audio_signal.size(2), device=audio_signal.device) + + if length is None: + length = audio_signal.new_full( + (audio_signal.size(0),), audio_signal.size(-1), dtype=torch.int64, device=audio_signal.device + ) + + # select a random att_context_size with the distribution specified by att_context_probs during training + # for non-validation cases like test, validation or inference, it uses the first mode in self.att_context_size + if self.training and len(self.att_context_size_all) > 1: + cur_att_context_size = random.choices(self.att_context_size_all, weights=self.att_context_probs)[0] + else: + cur_att_context_size = self.att_context_size + + audio_signal = torch.transpose(audio_signal, 1, 2) + + if isinstance(self.pre_encode, nn.Linear): + audio_signal = self.pre_encode(audio_signal) + else: + audio_signal, length = self.pre_encode(x=audio_signal, lengths=length) + length = length.to(torch.int64) + # self.streaming_cfg is set by setup_streaming_cfg(), called in the init + if self.streaming_cfg.drop_extra_pre_encoded > 0 and cache_last_channel is not None: + audio_signal = audio_signal[:, self.streaming_cfg.drop_extra_pre_encoded :, :] + length = (length - self.streaming_cfg.drop_extra_pre_encoded).clamp(min=0) + + if self.reduction_position is not None and cache_last_channel is not None: + raise ValueError("Caching with reduction feature is not supported yet!") + + max_audio_length = audio_signal.size(1) + if cache_last_channel is not None: + cache_len = self.streaming_cfg.last_channel_cache_size + cache_keep_size = max_audio_length - self.streaming_cfg.cache_drop_size + max_audio_length = max_audio_length + cache_len + padding_length = length + cache_len + offset = torch.neg(cache_last_channel_len) + cache_len + else: + padding_length = length + cache_last_channel_next = None + cache_len = 0 + offset = None + + audio_signal, pos_emb = self.pos_enc(x=audio_signal, cache_len=cache_len) + + # Create the self-attention and padding masks + pad_mask, att_mask = self._create_masks( + att_context_size=cur_att_context_size, + padding_length=padding_length, + max_audio_length=max_audio_length, + offset=offset, + device=audio_signal.device, + ) + + if cache_last_channel is not None: + pad_mask = pad_mask[:, cache_len:] + if att_mask is not None: + att_mask = att_mask[:, cache_len:] + # Convert caches from the tensor to list + cache_last_time_next = [] + cache_last_channel_next = [] + + for lth, (drop_prob, layer) in enumerate(zip(self.layer_drop_probs, self.layers)): + original_signal = audio_signal + if cache_last_channel is not None: + cache_last_channel_cur = cache_last_channel[lth] + cache_last_time_cur = cache_last_time[lth] + else: + cache_last_channel_cur = None + cache_last_time_cur = None + audio_signal = layer( + x=audio_signal, + att_mask=att_mask, + pos_emb=pos_emb, + pad_mask=pad_mask, + cache_last_channel=cache_last_channel_cur, + cache_last_time=cache_last_time_cur, + ) + + if cache_last_channel_cur is not None: + (audio_signal, cache_last_channel_cur, cache_last_time_cur) = audio_signal + cache_last_channel_next.append(cache_last_channel_cur) + cache_last_time_next.append(cache_last_time_cur) + + # applying stochastic depth logic from https://arxiv.org/abs/2102.03216 + if self.training and drop_prob > 0.0: + should_drop = torch.rand(1) < drop_prob + # adjusting to match expectation + if should_drop: + # that's not efficient, but it's hard to implement distributed + # version of dropping layers without deadlock or random seed meddling + # so multiplying the signal by 0 to ensure all weights get gradients + audio_signal = audio_signal * 0.0 + original_signal + else: + # not doing this operation if drop prob is 0 as it's identity in that case + audio_signal = (audio_signal - original_signal) / (1.0 - drop_prob) + original_signal + + if self.reduction_position == lth: + audio_signal, length = self.reduction_subsampling(x=audio_signal, lengths=length) + max_audio_length = audio_signal.size(1) + # Don't update the audio_signal here because then it will again scale the audio_signal + # and cause an increase in the WER + _, pos_emb = self.pos_enc(x=audio_signal, cache_len=cache_len) + pad_mask, att_mask = self._create_masks( + att_context_size=cur_att_context_size, + padding_length=length, + max_audio_length=max_audio_length, + offset=offset, + device=audio_signal.device, + ) + + # saving tensors if required for interctc loss + if self.is_access_enabled(getattr(self, "model_guid", None)): + if self.interctc_capture_at_layers is None: + self.interctc_capture_at_layers = self.access_cfg.get('interctc', {}).get('capture_layers', []) + if lth in self.interctc_capture_at_layers: + lth_audio_signal = audio_signal + if self.out_proj is not None: + lth_audio_signal = self.out_proj(audio_signal) + # shape is the same as the shape of audio_signal output, i.e. [B, D, T] + self.register_accessible_tensor( + name=f'interctc/layer_output_{lth}', tensor=torch.transpose(lth_audio_signal, 1, 2) + ) + self.register_accessible_tensor(name=f'interctc/layer_length_{lth}', tensor=length) + + if self.out_proj is not None: + audio_signal = self.out_proj(audio_signal) + + # Reduction + if self.reduction_position == -1: + audio_signal, length = self.reduction_subsampling(x=audio_signal, lengths=length) + + audio_signal = torch.transpose(audio_signal, 1, 2) + length = length.to(dtype=torch.int64) + + if cache_last_channel is not None: + cache_last_channel_next = torch.stack(cache_last_channel_next, dim=0) + cache_last_time_next = torch.stack(cache_last_time_next, dim=0) + return ( + audio_signal, + length, + cache_last_channel_next, + cache_last_time_next, + torch.clamp(cache_last_channel_len + cache_keep_size, max=cache_len), + ) + else: + return audio_signal, length + + def update_max_seq_length(self, seq_length: int, device): + # Find global max audio length across all nodes + if torch.distributed.is_initialized(): + global_max_len = torch.tensor([seq_length], dtype=torch.float32, device=device) + + # Update across all ranks in the distributed system + torch.distributed.all_reduce(global_max_len, op=torch.distributed.ReduceOp.MAX) + + seq_length = global_max_len.int().item() + + if seq_length > self.max_audio_length: + self.set_max_audio_length(seq_length) + + def set_max_audio_length(self, max_audio_length): + """ + Sets maximum input length. + Pre-calculates internal seq_range mask. + """ + self.max_audio_length = max_audio_length + device = next(self.parameters()).device + self.pos_enc.extend_pe(max_audio_length, device) + + def _create_masks(self, att_context_size, padding_length, max_audio_length, offset, device): + if self.self_attention_model != "rel_pos_local_attn": + att_mask = torch.ones(1, max_audio_length, max_audio_length, dtype=torch.bool, device=device) + + if self.att_context_style == "regular": + if att_context_size[0] >= 0: + att_mask = att_mask.triu(diagonal=-att_context_size[0]) + if att_context_size[1] >= 0: + att_mask = att_mask.tril(diagonal=att_context_size[1]) + elif self.att_context_style == "chunked_limited": + # When right context is unlimited, just the left side of the masking need to get updated + if att_context_size[1] == -1: + if att_context_size[0] >= 0: + att_mask = att_mask.triu(diagonal=-att_context_size[0]) + else: + chunk_size = att_context_size[1] + 1 + # left_chunks_num specifies the number of chunks to be visible by each chunk on the left side + if att_context_size[0] >= 0: + left_chunks_num = att_context_size[0] // chunk_size + else: + left_chunks_num = 10000 + + chunk_idx = torch.arange(0, max_audio_length, dtype=torch.int, device=att_mask.device) + chunk_idx = torch.div(chunk_idx, chunk_size, rounding_mode="trunc") + diff_chunks = chunk_idx.unsqueeze(1) - chunk_idx.unsqueeze(0) + chunked_limited_mask = torch.logical_and( + torch.le(diff_chunks, left_chunks_num), torch.ge(diff_chunks, 0) + ) + att_mask = torch.logical_and(att_mask, chunked_limited_mask.unsqueeze(0)) + else: + att_mask = None + + # pad_mask is the masking to be used to ignore paddings + pad_mask = torch.arange(0, max_audio_length, device=device).expand( + padding_length.size(0), -1 + ) < padding_length.unsqueeze(-1) + + if offset is not None: + pad_mask_off = torch.arange(0, max_audio_length, device=device).expand( + padding_length.size(0), -1 + ) >= offset.unsqueeze(-1) + pad_mask = pad_mask_off.logical_and(pad_mask) + + if att_mask is not None: + # pad_mask_for_att_mask is the mask which helps to ignore paddings + pad_mask_for_att_mask = pad_mask.unsqueeze(1).repeat([1, max_audio_length, 1]) + pad_mask_for_att_mask = torch.logical_and(pad_mask_for_att_mask, pad_mask_for_att_mask.transpose(1, 2)) + # att_mask is the masking to be used by the MHA layers to ignore the tokens not supposed to be visible + att_mask = att_mask[:, :max_audio_length, :max_audio_length] + # paddings should also get ignored, so pad_mask_for_att_mask is used to ignore their corresponding scores + att_mask = torch.logical_and(pad_mask_for_att_mask, att_mask.to(pad_mask_for_att_mask.device)) + att_mask = ~att_mask + + pad_mask = ~pad_mask + return pad_mask, att_mask + + def enable_pad_mask(self, on=True): + # On inference, user may choose to disable pad mask + mask = self.use_pad_mask + self.use_pad_mask = on + return mask + + def _calc_context_sizes( + self, att_context_size, att_context_probs, att_context_style, conv_context_size, conv_kernel_size + ): + # convert att_context_size to a standard list of lists + if att_context_size: + att_context_size_all = list(att_context_size) + if isinstance(att_context_size_all[0], int): + att_context_size_all = [att_context_size_all] + for i, att_cs in enumerate(att_context_size_all): + if isinstance(att_cs, ListConfig): + att_context_size_all[i] = list(att_cs) + if att_context_style == "chunked_limited": + if att_cs[0] > 0 and att_cs[0] % (att_cs[1] + 1) > 0: + raise ValueError(f"att_context_size[{i}][0] % (att_context_size[{i}][1] + 1) should be zero!") + if att_cs[1] < 0 and len(att_context_size_all) <= 1: + raise ValueError( + f"Right context (att_context_size[{i}][1]) can not be unlimited for chunked_limited style!" + ) + else: + att_context_size_all = [[-1, -1]] + + if att_context_probs: + if len(att_context_probs) != len(att_context_size_all): + raise ValueError("The size of the att_context_probs should be the same as att_context_size.") + att_context_probs = list(att_context_probs) + if sum(att_context_probs) != 1: + raise ValueError( + "The sum of numbers in att_context_probs should be equal to one to be a distribution." + ) + else: + att_context_probs = [1.0 / len(att_context_size_all)] * len(att_context_size_all) + + if conv_context_size is not None: + if isinstance(conv_context_size, ListConfig): + conv_context_size = list(conv_context_size) + if not isinstance(conv_context_size, list) and not isinstance(conv_context_size, str): + raise ValueError( + f"Invalid conv_context_size! It should be the string 'causal' or a list of two integers." + ) + if conv_context_size == "causal": + conv_context_size = [conv_kernel_size - 1, 0] + else: + if conv_context_size[0] + conv_context_size[1] + 1 != conv_kernel_size: + raise ValueError(f"Invalid conv_context_size: {self.conv_context_size}!") + else: + conv_context_size = [(conv_kernel_size - 1) // 2, (conv_kernel_size - 1) // 2] + return att_context_size_all, att_context_size_all[0], att_context_probs, conv_context_size + + def set_default_att_context_size(self, att_context_size): + if att_context_size not in self.att_context_size_all: + logging.warning( + f"att_context_size={att_context_size} is not among the list of the supported look-aheads: {self.att_context_size_all}" + ) + if att_context_size is not None: + self.att_context_size = att_context_size + + self.setup_streaming_params() + + def setup_streaming_params( + self, + chunk_size: int = None, + shift_size: int = None, + left_chunks: int = None, + att_context_size: list = None, + max_context: int = 10000, + ): + """ + This function sets the needed values and parameters to perform streaming. The configuration would be stored in self.streaming_cfg. + The streaming configuration is needed to simulate streaming inference. + + Args: + chunk_size (int): overrides the chunk size + shift_size (int): overrides the shift size for chunks + left_chunks (int): overrides the number of left chunks visible to each chunk + max_context (int): the value used for the cache size of last_channel layers if left context is set to infinity (-1) + Defaults to -1 (means feat_out is d_model) + """ + streaming_cfg = CacheAwareStreamingConfig() + + # When att_context_size is not specified, it uses the default_att_context_size + if att_context_size is None: + att_context_size = self.att_context_size + + if chunk_size is not None: + if chunk_size < 1: + raise ValueError("chunk_size needs to be a number larger or equal to one.") + lookahead_steps = chunk_size - 1 + streaming_cfg.cache_drop_size = chunk_size - shift_size + elif self.att_context_style == "chunked_limited": + lookahead_steps = att_context_size[1] + streaming_cfg.cache_drop_size = 0 + elif self.att_context_style == "regular": + lookahead_steps = att_context_size[1] * self.n_layers + self.conv_context_size[1] * self.n_layers + streaming_cfg.cache_drop_size = lookahead_steps + else: + streaming_cfg.cache_drop_size = 0 + lookahead_steps = None + + if chunk_size is None: + streaming_cfg.last_channel_cache_size = att_context_size[0] if att_context_size[0] >= 0 else max_context + else: + if left_chunks is None: + raise ValueError("left_chunks can not be None when chunk_size is set.") + streaming_cfg.last_channel_cache_size = left_chunks * chunk_size + + if hasattr(self.pre_encode, "get_sampling_frames"): + sampling_frames = self.pre_encode.get_sampling_frames() + else: + sampling_frames = 0 + + if isinstance(sampling_frames, list): + streaming_cfg.chunk_size = [ + sampling_frames[0] + self.subsampling_factor * lookahead_steps, + sampling_frames[1] + self.subsampling_factor * lookahead_steps, + ] + else: + streaming_cfg.chunk_size = sampling_frames * (1 + lookahead_steps) + + if isinstance(sampling_frames, list): + streaming_cfg.shift_size = [ + sampling_frames[0] + sampling_frames[1] * (lookahead_steps - streaming_cfg.cache_drop_size), + sampling_frames[1] + sampling_frames[1] * (lookahead_steps - streaming_cfg.cache_drop_size), + ] + else: + streaming_cfg.shift_size = sampling_frames * (1 + lookahead_steps - streaming_cfg.cache_drop_size) + + if isinstance(streaming_cfg.shift_size, list): + streaming_cfg.valid_out_len = ( + streaming_cfg.shift_size[1] - sampling_frames[1] + ) // self.subsampling_factor + 1 + else: + streaming_cfg.valid_out_len = streaming_cfg.shift_size // self.subsampling_factor + + if hasattr(self.pre_encode, "get_streaming_cache_size"): + streaming_cfg.pre_encode_cache_size = self.pre_encode.get_streaming_cache_size() + else: + streaming_cfg.pre_encode_cache_size = 0 + + if isinstance(streaming_cfg.pre_encode_cache_size, list): + if streaming_cfg.pre_encode_cache_size[1] >= 1: + streaming_cfg.drop_extra_pre_encoded = ( + 1 + (streaming_cfg.pre_encode_cache_size[1] - 1) // self.subsampling_factor + ) + else: + streaming_cfg.drop_extra_pre_encoded = 0 + else: + streaming_cfg.drop_extra_pre_encoded = streaming_cfg.pre_encode_cache_size // self.subsampling_factor + + for m in self.layers.modules(): + if hasattr(m, "_max_cache_len"): + if isinstance(m, MultiHeadAttention): + m.cache_drop_size = streaming_cfg.cache_drop_size + if isinstance(m, CausalConv1D): + m.cache_drop_size = streaming_cfg.cache_drop_size + + self.streaming_cfg = streaming_cfg + + def get_initial_cache_state(self, batch_size=1, dtype=torch.float32, device=None, max_dim=0): + if device is None: + device = next(self.parameters()).device + if max_dim > 0: + create_tensor = torch.randn + else: + create_tensor = torch.zeros + last_time_cache_size = self.conv_context_size[0] + cache_last_channel = create_tensor( + (len(self.layers), batch_size, self.streaming_cfg.last_channel_cache_size, self.d_model,), + device=device, + dtype=dtype, + ) + cache_last_time = create_tensor( + (len(self.layers), batch_size, self.d_model, last_time_cache_size), device=device, dtype=dtype, + ) + if max_dim > 0: + cache_last_channel_len = torch.randint( + 0, + min(max_dim, self.streaming_cfg.last_channel_cache_size), + (batch_size,), + device=device, + dtype=torch.int64, + ) + for i in range(batch_size): + cache_last_channel[:, i, cache_last_channel_len[i] :, :] = 0 + # what is the right rule to zero out cache_last_time? + if cache_last_channel_len[i] == 0: + cache_last_time[:, i, :, :] = 0 + else: + cache_last_channel_len = torch.zeros(batch_size, device=device, dtype=torch.int64) + return cache_last_channel, cache_last_time, cache_last_channel_len + + def change_attention_model( + self, + self_attention_model: str = None, + att_context_size: List[int] = None, + update_config: bool = True, + device: torch.device = None, + ): + + """ + Update the self_attention_model which changes the positional encoding and attention layers. + + Args: + self_attention_model (str): type of the attention layer and positional encoding + + 'rel_pos': + relative positional embedding and Transformer-XL + + 'rel_pos_local_attn': + relative positional embedding and Transformer-XL with local attention using + overlapping windows. Attention context is determined by att_context_size parameter. + + 'abs_pos': + absolute positional embedding and Transformer + + If None is provided, the self_attention_model isn't changed. Defaults to None. + att_context_size (List[int]): List of 2 ints corresponding to left and right attention context sizes, + or None to keep as it is. Defaults to None. + update_config (bool): Whether to update the config or not with the new attention model. + Defaults to True. + device (torch.device): If provided, new layers will be moved to the device. + Defaults to None. + """ + + if att_context_size: + att_context_size = list(att_context_size) + else: + att_context_size = self.att_context_size + + if self_attention_model is None: + self_attention_model = self.self_attention_model + + if self_attention_model == 'rel_pos_local_attn' and max(att_context_size) <= 0: + raise ValueError("When using local attention, context size must be set > 0") + + if self_attention_model == "rel_pos": + new_pos_enc = RelPositionalEncoding( + d_model=self._cfg.d_model, + dropout_rate=self._cfg.dropout, + max_len=self._cfg.pos_emb_max_len, + xscale=self.xscale, + dropout_rate_emb=self._cfg.dropout_emb, + ) + elif self_attention_model == 'rel_pos_local_attn': + new_pos_enc = LocalAttRelPositionalEncoding( + att_context_size=att_context_size, + d_model=self._cfg.d_model, + dropout_rate=self._cfg.dropout, + max_len=self._cfg.pos_emb_max_len, + xscale=self.xscale, + dropout_rate_emb=self._cfg.dropout_emb, + ) + elif self_attention_model == "abs_pos": + new_pos_enc = PositionalEncoding( + d_model=self._cfg.d_model, + dropout_rate=self._cfg.dropout, + max_len=self._cfg.pos_emb_max_len, + xscale=self.xscale, + ) + else: + raise ValueError(f"Not valid self_attention_model: '{self_attention_model}'!") + + if device is not None: + new_pos_enc = new_pos_enc.to(device=device) + del self.pos_enc + self.pos_enc = new_pos_enc + self.self_attention_model = self_attention_model + self.att_context_size = att_context_size + self.set_max_audio_length(self.pos_emb_max_len) + + for name, m in self.named_modules(): + if type(m) == ConformerLayer: + if self_attention_model == 'rel_pos': + new_attn = RelPositionMultiHeadAttention( + n_head=self._cfg.n_heads, + n_feat=self._cfg.d_model, + dropout_rate=self._cfg.dropout_att, + max_cache_len=att_context_size[0], + pos_bias_u=None, + pos_bias_v=None, + ) + elif self_attention_model == 'rel_pos_local_attn': + new_attn = RelPositionMultiHeadAttentionLongformer( + n_head=self._cfg.n_heads, + n_feat=self._cfg.d_model, + dropout_rate=self._cfg.dropout_att, + max_cache_len=att_context_size[0], + att_context_size=att_context_size, + pos_bias_u=None, + pos_bias_v=None, + ) + elif self_attention_model == 'abs_pos': + new_attn = MultiHeadAttention( + n_head=self._cfg.n_heads, + n_feat=self._cfg.d_model, + dropout_rate=self._cfg.dropout_att, + max_cache_len=att_context_size[0], + ) + else: + raise ValueError( + f"'{self_attention_model}' is not not a valid value for 'self_attention_model', " + f"valid values can be from ['rel_pos', 'rel_pos_local_attn', 'abs_pos']" + ) + if device is not None: + new_attn = new_attn.to(device=device) + new_attn.load_state_dict(m.self_attn.state_dict(), strict=False) + del m.self_attn + m.self_attn = new_attn + m.self_attention_model = self_attention_model + + if update_config: + with open_dict(self._cfg): + self._cfg.self_attention_model = self_attention_model + self._cfg.att_context_size = att_context_size + + def change_subsampling_conv_chunking_factor(self, subsampling_conv_chunking_factor: int): + """ + Update the conv_chunking_factor (int) + Default is 1 (auto) + Set it to -1 (disabled) or to a specific value (power of 2) if you OOM in the conv subsampling layers + + + Args: + subsampling_conv_chunking_factor (int) + """ + + if not hasattr(self.pre_encode, "change_subsampling_conv_chunking_factor"): + logging.info("Model pre_encoder doesn't have a change_subsampling_conv_chunking_factor method ") + return + + self.pre_encode.change_subsampling_conv_chunking_factor( + subsampling_conv_chunking_factor=subsampling_conv_chunking_factor + ) + + +class ConformerEncoderAdapter(ConformerEncoder, adapter_mixins.AdapterModuleMixin): + + # Higher level forwarding + def add_adapter(self, name: str, cfg: dict): + cfg = self._update_adapter_cfg_input_dim(cfg) + for conformer_layer in self.layers: # type: adapter_mixins.AdapterModuleMixin + conformer_layer.add_adapter(name, cfg) + + def is_adapter_available(self) -> bool: + return any([conformer_layer.is_adapter_available() for conformer_layer in self.layers]) + + def set_enabled_adapters(self, name: Optional[str] = None, enabled: bool = True): + for conformer_layer in self.layers: # type: adapter_mixins.AdapterModuleMixin + conformer_layer.set_enabled_adapters(name=name, enabled=enabled) + + def get_enabled_adapters(self) -> List[str]: + names = set([]) + for conformer_layer in self.layers: # type: adapter_mixins.AdapterModuleMixin + names.update(conformer_layer.get_enabled_adapters()) + + names = sorted(list(names)) + return names + + def _update_adapter_cfg_input_dim(self, cfg: DictConfig): + cfg = adapter_utils.update_adapter_cfg_input_dim(self, cfg, module_dim=self.d_model) + return cfg + + def get_accepted_adapter_types(self,) -> Set[type]: + types = super().get_accepted_adapter_types() + + if len(types) == 0: + self.set_accepted_adapter_types( + [ + adapter_utils.LINEAR_ADAPTER_CLASSPATH, + adapter_utils.MHA_ADAPTER_CLASSPATH, + adapter_utils.RELMHA_ADAPTER_CLASSPATH, + ] + ) + types = self.get_accepted_adapter_types() + return types + + +""" +Register any additional information +""" +if adapter_mixins.get_registered_adapter(ConformerEncoder) is None: + adapter_mixins.register_adapter(base_class=ConformerEncoder, adapter_class=ConformerEncoderAdapter) + + +@dataclass +class ConformerChangeConfig: + # Change self_attention_model for Conformer + # Options: + # 'rel_pos': relative positional embedding and Transformer-XL + # 'rel_pos_local_attn': relative positional embedding and Transformer-XL with local attention using + # overlapping chunks. Attention context is determined by att_context_size parameter. + # 'abs_pos': absolute positional embedding and Transformer + # If None is provided, self_attention_model is not changed. + self_attention_model: Optional[str] = None + + # Change the attention context size by providing 2 integers, + # corresponding to left and right context, or -1 for full context. + # If None is provided, the attention context size isn't changed. + att_context_size: Optional[List[int]] = None diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/conv_asr.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/conv_asr.py new file mode 100644 index 0000000..03b94ae --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/conv_asr.py @@ -0,0 +1,994 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from collections import OrderedDict +from dataclasses import dataclass, field +from typing import List, Optional, Set, Union + +import torch +import torch.distributed +import torch.nn as nn +import torch.nn.functional as F +from omegaconf import MISSING, DictConfig, ListConfig, OmegaConf + +from nemo.collections.asr.parts.submodules.jasper import ( + JasperBlock, + MaskedConv1d, + ParallelBlock, + SqueezeExcite, + init_weights, + jasper_activations, +) +from nemo.collections.asr.parts.submodules.tdnn_attention import ( + AttentivePoolLayer, + StatsPoolLayer, + TDNNModule, + TDNNSEModule, +) +from nemo.collections.asr.parts.utils import adapter_utils +from nemo.core.classes.common import typecheck +from nemo.core.classes.exportable import Exportable +from nemo.core.classes.mixins import AccessMixin, adapter_mixins +from nemo.core.classes.module import NeuralModule +from nemo.core.neural_types import ( + AcousticEncodedRepresentation, + LengthsType, + LogitsType, + LogprobsType, + NeuralType, + SpectrogramType, +) +from nemo.utils import logging + +__all__ = ['ConvASRDecoder', 'ConvASREncoder', 'ConvASRDecoderClassification'] + + +class ConvASREncoder(NeuralModule, Exportable, AccessMixin): + """ + Convolutional encoder for ASR models. With this class you can implement JasperNet and QuartzNet models. + + Based on these papers: + https://arxiv.org/pdf/1904.03288.pdf + https://arxiv.org/pdf/1910.10261.pdf + """ + + def _prepare_for_export(self, **kwargs): + m_count = 0 + for name, m in self.named_modules(): + if isinstance(m, MaskedConv1d): + m.use_mask = False + m_count += 1 + + Exportable._prepare_for_export(self, **kwargs) + logging.warning(f"Turned off {m_count} masked convolutions") + + def input_example(self, max_batch=1, max_dim=8192): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + device = next(self.parameters()).device + input_example = torch.randn(max_batch, self._feat_in, max_dim, device=device) + lens = torch.full(size=(input_example.shape[0],), fill_value=max_dim, device=device) + return tuple([input_example, lens]) + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return OrderedDict( + { + "audio_signal": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + ) + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return OrderedDict( + { + "outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + } + ) + + def __init__( + self, + jasper, + activation: str, + feat_in: int, + normalization_mode: str = "batch", + residual_mode: str = "add", + norm_groups: int = -1, + conv_mask: bool = True, + frame_splicing: int = 1, + init_mode: Optional[str] = 'xavier_uniform', + quantize: bool = False, + ): + super().__init__() + if isinstance(jasper, ListConfig): + jasper = OmegaConf.to_container(jasper) + + activation = jasper_activations[activation]() + + # If the activation can be executed in place, do so. + if hasattr(activation, 'inplace'): + activation.inplace = True + + feat_in = feat_in * frame_splicing + + self._feat_in = feat_in + + residual_panes = [] + encoder_layers = [] + self.dense_residual = False + for layer_idx, lcfg in enumerate(jasper): + dense_res = [] + if lcfg.get('residual_dense', False): + residual_panes.append(feat_in) + dense_res = residual_panes + self.dense_residual = True + groups = lcfg.get('groups', 1) + separable = lcfg.get('separable', False) + heads = lcfg.get('heads', -1) + residual_mode = lcfg.get('residual_mode', residual_mode) + se = lcfg.get('se', False) + se_reduction_ratio = lcfg.get('se_reduction_ratio', 8) + se_context_window = lcfg.get('se_context_size', -1) + se_interpolation_mode = lcfg.get('se_interpolation_mode', 'nearest') + kernel_size_factor = lcfg.get('kernel_size_factor', 1.0) + stride_last = lcfg.get('stride_last', False) + future_context = lcfg.get('future_context', -1) + encoder_layers.append( + JasperBlock( + feat_in, + lcfg['filters'], + repeat=lcfg['repeat'], + kernel_size=lcfg['kernel'], + stride=lcfg['stride'], + dilation=lcfg['dilation'], + dropout=lcfg['dropout'], + residual=lcfg['residual'], + groups=groups, + separable=separable, + heads=heads, + residual_mode=residual_mode, + normalization=normalization_mode, + norm_groups=norm_groups, + activation=activation, + residual_panes=dense_res, + conv_mask=conv_mask, + se=se, + se_reduction_ratio=se_reduction_ratio, + se_context_window=se_context_window, + se_interpolation_mode=se_interpolation_mode, + kernel_size_factor=kernel_size_factor, + stride_last=stride_last, + future_context=future_context, + quantize=quantize, + layer_idx=layer_idx, + ) + ) + feat_in = lcfg['filters'] + + self._feat_out = feat_in + + self.encoder = torch.nn.Sequential(*encoder_layers) + self.apply(lambda x: init_weights(x, mode=init_mode)) + + self.max_audio_length = 0 + + @typecheck() + def forward(self, audio_signal, length): + self.update_max_sequence_length(seq_length=audio_signal.size(2), device=audio_signal.device) + s_input, length = self.encoder(([audio_signal], length)) + if length is None: + return s_input[-1] + + return s_input[-1], length + + def update_max_sequence_length(self, seq_length: int, device): + # Find global max audio length across all nodes + if torch.distributed.is_initialized(): + global_max_len = torch.tensor([seq_length], dtype=torch.float32, device=device) + + # Update across all ranks in the distributed system + torch.distributed.all_reduce(global_max_len, op=torch.distributed.ReduceOp.MAX) + + seq_length = global_max_len.int().item() + + if seq_length > self.max_audio_length: + if seq_length < 5000: + seq_length = seq_length * 2 + elif seq_length < 10000: + seq_length = seq_length * 1.5 + self.max_audio_length = seq_length + + device = next(self.parameters()).device + seq_range = torch.arange(0, self.max_audio_length, device=device) + if hasattr(self, 'seq_range'): + self.seq_range = seq_range + else: + self.register_buffer('seq_range', seq_range, persistent=False) + + # Update all submodules + for name, m in self.named_modules(): + if isinstance(m, MaskedConv1d): + m.update_masked_length(self.max_audio_length, seq_range=self.seq_range) + elif isinstance(m, SqueezeExcite): + m.set_max_len(self.max_audio_length, seq_range=self.seq_range) + + +class ParallelConvASREncoder(NeuralModule, Exportable): + """ + Convolutional encoder for ASR models with parallel blocks. CarneliNet can be implemented with this class. + """ + + def _prepare_for_export(self): + m_count = 0 + for m in self.modules(): + if isinstance(m, MaskedConv1d): + m.use_mask = False + m_count += 1 + logging.warning(f"Turned off {m_count} masked convolutions") + + def input_example(self, max_batch=1, max_dim=256): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + input_example = torch.randn(max_batch, self._feat_in, max_dim).to(next(self.parameters()).device) + return tuple([input_example]) + + @property + def disabled_deployment_input_names(self): + """Implement this method to return a set of input names disabled for export""" + return set(["length"]) + + @property + def disabled_deployment_output_names(self): + """Implement this method to return a set of output names disabled for export""" + return set(["encoded_lengths"]) + + def save_to(self, save_path: str): + pass + + @classmethod + def restore_from(cls, restore_path: str): + pass + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return OrderedDict( + { + "audio_signal": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + ) + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return OrderedDict( + { + "outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + } + ) + + def __init__( + self, + jasper, + activation: str, + feat_in: int, + normalization_mode: str = "batch", + residual_mode: str = "add", + norm_groups: int = -1, + conv_mask: bool = True, + frame_splicing: int = 1, + init_mode: Optional[str] = 'xavier_uniform', + aggregation_mode: Optional[str] = None, + quantize: bool = False, + ): + super().__init__() + if isinstance(jasper, ListConfig): + jasper = OmegaConf.to_container(jasper) + + activation = jasper_activations[activation]() + feat_in = feat_in * frame_splicing + + self._feat_in = feat_in + + residual_panes = [] + encoder_layers = [] + self.dense_residual = False + for lcfg in jasper: + dense_res = [] + if lcfg.get('residual_dense', False): + residual_panes.append(feat_in) + dense_res = residual_panes + self.dense_residual = True + groups = lcfg.get('groups', 1) + separable = lcfg.get('separable', False) + heads = lcfg.get('heads', -1) + residual_mode = lcfg.get('residual_mode', residual_mode) + se = lcfg.get('se', False) + se_reduction_ratio = lcfg.get('se_reduction_ratio', 8) + se_context_window = lcfg.get('se_context_size', -1) + se_interpolation_mode = lcfg.get('se_interpolation_mode', 'nearest') + kernel_size_factor = lcfg.get('kernel_size_factor', 1.0) + stride_last = lcfg.get('stride_last', False) + aggregation_mode = lcfg.get('aggregation_mode', 'sum') + block_dropout = lcfg.get('block_dropout', 0.0) + parallel_residual_mode = lcfg.get('parallel_residual_mode', 'sum') + + parallel_blocks = [] + for kernel_size in lcfg['kernel']: + parallel_blocks.append( + JasperBlock( + feat_in, + lcfg['filters'], + repeat=lcfg['repeat'], + kernel_size=[kernel_size], + stride=lcfg['stride'], + dilation=lcfg['dilation'], + dropout=lcfg['dropout'], + residual=lcfg['residual'], + groups=groups, + separable=separable, + heads=heads, + residual_mode=residual_mode, + normalization=normalization_mode, + norm_groups=norm_groups, + activation=activation, + residual_panes=dense_res, + conv_mask=conv_mask, + se=se, + se_reduction_ratio=se_reduction_ratio, + se_context_window=se_context_window, + se_interpolation_mode=se_interpolation_mode, + kernel_size_factor=kernel_size_factor, + stride_last=stride_last, + quantize=quantize, + ) + ) + if len(parallel_blocks) == 1: + encoder_layers.append(parallel_blocks[0]) + else: + encoder_layers.append( + ParallelBlock( + parallel_blocks, + aggregation_mode=aggregation_mode, + block_dropout_prob=block_dropout, + residual_mode=parallel_residual_mode, + in_filters=feat_in, + out_filters=lcfg['filters'], + ) + ) + feat_in = lcfg['filters'] + + self._feat_out = feat_in + + self.encoder = torch.nn.Sequential(*encoder_layers) + self.apply(lambda x: init_weights(x, mode=init_mode)) + + @typecheck() + def forward(self, audio_signal, length=None): + s_input, length = self.encoder(([audio_signal], length)) + if length is None: + return s_input[-1] + + return s_input[-1], length + + +class ConvASRDecoder(NeuralModule, Exportable, adapter_mixins.AdapterModuleMixin): + """Simple ASR Decoder for use with CTC-based models such as JasperNet and QuartzNet + + Based on these papers: + https://arxiv.org/pdf/1904.03288.pdf + https://arxiv.org/pdf/1910.10261.pdf + https://arxiv.org/pdf/2005.04290.pdf + """ + + @property + def input_types(self): + return OrderedDict({"encoder_output": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation())}) + + @property + def output_types(self): + return OrderedDict({"logprobs": NeuralType(('B', 'T', 'D'), LogprobsType())}) + + def __init__(self, feat_in, num_classes, init_mode="xavier_uniform", vocabulary=None): + super().__init__() + + if vocabulary is None and num_classes < 0: + raise ValueError( + f"Neither of the vocabulary and num_classes are set! At least one of them need to be set." + ) + + if num_classes <= 0: + num_classes = len(vocabulary) + logging.info(f"num_classes of ConvASRDecoder is set to the size of the vocabulary: {num_classes}.") + + if vocabulary is not None: + if num_classes != len(vocabulary): + raise ValueError( + f"If vocabulary is specified, it's length should be equal to the num_classes. Instead got: num_classes={num_classes} and len(vocabulary)={len(vocabulary)}" + ) + self.__vocabulary = vocabulary + self._feat_in = feat_in + # Add 1 for blank char + self._num_classes = num_classes + 1 + + self.decoder_layers = torch.nn.Sequential( + torch.nn.Conv1d(self._feat_in, self._num_classes, kernel_size=1, bias=True) + ) + self.apply(lambda x: init_weights(x, mode=init_mode)) + + accepted_adapters = [adapter_utils.LINEAR_ADAPTER_CLASSPATH] + self.set_accepted_adapter_types(accepted_adapters) + + # to change, requires running ``model.temperature = T`` explicitly + self.temperature = 1.0 + + @typecheck() + def forward(self, encoder_output): + # Adapter module forward step + if self.is_adapter_available(): + encoder_output = encoder_output.transpose(1, 2) # [B, T, C] + encoder_output = self.forward_enabled_adapters(encoder_output) + encoder_output = encoder_output.transpose(1, 2) # [B, C, T] + + if self.temperature != 1.0: + return torch.nn.functional.log_softmax( + self.decoder_layers(encoder_output).transpose(1, 2) / self.temperature, dim=-1 + ) + return torch.nn.functional.log_softmax(self.decoder_layers(encoder_output).transpose(1, 2), dim=-1) + + def input_example(self, max_batch=1, max_dim=256): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + input_example = torch.randn(max_batch, self._feat_in, max_dim).to(next(self.parameters()).device) + return tuple([input_example]) + + def _prepare_for_export(self, **kwargs): + m_count = 0 + for m in self.modules(): + if type(m).__name__ == "MaskedConv1d": + m.use_mask = False + m_count += 1 + if m_count > 0: + logging.warning(f"Turned off {m_count} masked convolutions") + Exportable._prepare_for_export(self, **kwargs) + + # Adapter method overrides + def add_adapter(self, name: str, cfg: DictConfig): + # Update the config with correct input dim + cfg = self._update_adapter_cfg_input_dim(cfg) + # Add the adapter + super().add_adapter(name=name, cfg=cfg) + + def _update_adapter_cfg_input_dim(self, cfg: DictConfig): + cfg = adapter_utils.update_adapter_cfg_input_dim(self, cfg, module_dim=self._feat_in) + return cfg + + @property + def vocabulary(self): + return self.__vocabulary + + @property + def num_classes_with_blank(self): + return self._num_classes + + +class ConvASRDecoderReconstruction(NeuralModule, Exportable): + """ASR Decoder for reconstructing masked regions of spectrogram + """ + + @property + def input_types(self): + return OrderedDict({"encoder_output": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation())}) + + @property + def output_types(self): + if self.apply_softmax: + return OrderedDict({"out": NeuralType(('B', 'T', 'D'), LogprobsType())}) + else: + return OrderedDict({"out": NeuralType(('B', 'T', 'D'), AcousticEncodedRepresentation())}) + + def __init__( + self, + feat_in, + feat_out, + feat_hidden, + stride_layers=0, + non_stride_layers=0, + kernel_size=11, + init_mode="xavier_uniform", + activation="relu", + stride_transpose=True, + apply_softmax=False, + ): + super().__init__() + + if ((stride_layers + non_stride_layers) > 0) and (kernel_size < 3 or kernel_size % 2 == 0): + raise ValueError("Kernel size in this decoder needs to be >= 3 and odd when using at least 1 conv layer.") + + activation = jasper_activations[activation]() + + self.feat_in = feat_in + self.feat_out = feat_out + self.feat_hidden = feat_hidden + + self.decoder_layers = [nn.Conv1d(self.feat_in, self.feat_hidden, kernel_size=1, bias=True)] + for i in range(stride_layers): + self.decoder_layers.append(activation) + if stride_transpose: + self.decoder_layers.append( + nn.ConvTranspose1d( + self.feat_hidden, + self.feat_hidden, + kernel_size, + stride=2, + padding=(kernel_size - 3) // 2 + 1, + output_padding=1, + bias=True, + groups=self.feat_hidden, + ) + ) + else: + self.decoder_layers.append( + nn.Conv1d( + self.feat_hidden, + self.feat_hidden, + kernel_size, + stride=2, + padding=(kernel_size - 1) // 2, + bias=True, + groups=self.feat_hidden, + ) + ) + self.decoder_layers.append(nn.Conv1d(self.feat_hidden, self.feat_hidden, kernel_size=1, bias=True)) + self.decoder_layers.append(nn.BatchNorm1d(self.feat_hidden, eps=1e-3, momentum=0.1)) + for i in range(non_stride_layers): + self.decoder_layers.append(activation) + self.decoder_layers.append( + nn.Conv1d( + self.feat_hidden, + self.feat_hidden, + kernel_size, + bias=True, + groups=self.feat_hidden, + padding=kernel_size // 2, + ) + ) + self.decoder_layers.append(nn.Conv1d(self.feat_hidden, self.feat_hidden, kernel_size=1, bias=True)) + self.decoder_layers.append(nn.BatchNorm1d(self.feat_hidden, eps=1e-3, momentum=0.1)) + + self.decoder_layers.append(activation) + self.decoder_layers.append(nn.Conv1d(self.feat_hidden, self.feat_out, kernel_size=1, bias=True)) + + self.decoder_layers = nn.Sequential(*self.decoder_layers) + self.apply_softmax = apply_softmax + + self.apply(lambda x: init_weights(x, mode=init_mode)) + + @typecheck() + def forward(self, encoder_output): + out = self.decoder_layers(encoder_output).transpose(-2, -1) + if self.apply_softmax: + out = torch.nn.functional.log_softmax(out, dim=-1) + return out + + def input_example(self, max_batch=1, max_dim=256): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + input_example = torch.randn(max_batch, self._feat_in, max_dim).to(next(self.parameters()).device) + return tuple([input_example]) + + def _prepare_for_export(self, **kwargs): + m_count = 0 + for m in self.modules(): + if type(m).__name__ == "MaskedConv1d": + m.use_mask = False + m_count += 1 + if m_count > 0: + logging.warning(f"Turned off {m_count} masked convolutions") + Exportable._prepare_for_export(self, **kwargs) + + +class ConvASRDecoderClassification(NeuralModule, Exportable): + """Simple ASR Decoder for use with classification models such as JasperNet and QuartzNet + + Based on these papers: + https://arxiv.org/pdf/2005.04290.pdf + """ + + def input_example(self, max_batch=1, max_dim=256): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + input_example = torch.randn(max_batch, self._feat_in, max_dim).to(next(self.parameters()).device) + return tuple([input_example]) + + @property + def input_types(self): + return OrderedDict({"encoder_output": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation())}) + + @property + def output_types(self): + return OrderedDict({"logits": NeuralType(('B', 'D'), LogitsType())}) + + def __init__( + self, + feat_in: int, + num_classes: int, + init_mode: Optional[str] = "xavier_uniform", + return_logits: bool = True, + pooling_type='avg', + ): + super().__init__() + + self._feat_in = feat_in + self._return_logits = return_logits + self._num_classes = num_classes + + if pooling_type == 'avg': + self.pooling = torch.nn.AdaptiveAvgPool1d(1) + elif pooling_type == 'max': + self.pooling = torch.nn.AdaptiveMaxPool1d(1) + else: + raise ValueError('Pooling type chosen is not valid. Must be either `avg` or `max`') + + self.decoder_layers = torch.nn.Sequential(torch.nn.Linear(self._feat_in, self._num_classes, bias=True)) + self.apply(lambda x: init_weights(x, mode=init_mode)) + + @typecheck() + def forward(self, encoder_output): + batch, in_channels, timesteps = encoder_output.size() + + encoder_output = self.pooling(encoder_output).view(batch, in_channels) # [B, C] + logits = self.decoder_layers(encoder_output) # [B, num_classes] + + if self._return_logits: + return logits + + return torch.nn.functional.softmax(logits, dim=-1) + + @property + def num_classes(self): + return self._num_classes + + +class ECAPAEncoder(NeuralModule, Exportable): + """ + Modified ECAPA Encoder layer without Res2Net module for faster training and inference which achieves + better numbers on speaker diarization tasks + Reference: ECAPA-TDNN Embeddings for Speaker Diarization (https://arxiv.org/pdf/2104.01466.pdf) + + input: + feat_in: input feature shape (mel spec feature shape) + filters: list of filter shapes for SE_TDNN modules + kernel_sizes: list of kernel shapes for SE_TDNN modules + dilations: list of dilations for group conv se layer + scale: scale value to group wider conv channels (deafult:8) + + output: + outputs : encoded output + output_length: masked output lengths + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return OrderedDict( + { + "audio_signal": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + ) + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return OrderedDict( + { + "outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + } + ) + + def __init__( + self, + feat_in: int, + filters: list, + kernel_sizes: list, + dilations: list, + scale: int = 8, + init_mode: str = 'xavier_uniform', + ): + super().__init__() + self.layers = nn.ModuleList() + self.layers.append(TDNNModule(feat_in, filters[0], kernel_size=kernel_sizes[0], dilation=dilations[0])) + + for i in range(len(filters) - 2): + self.layers.append( + TDNNSEModule( + filters[i], + filters[i + 1], + group_scale=scale, + se_channels=128, + kernel_size=kernel_sizes[i + 1], + dilation=dilations[i + 1], + ) + ) + self.feature_agg = TDNNModule(filters[-1], filters[-1], kernel_sizes[-1], dilations[-1]) + self.apply(lambda x: init_weights(x, mode=init_mode)) + + def forward(self, audio_signal, length=None): + x = audio_signal + outputs = [] + + for layer in self.layers: + x = layer(x, length=length) + outputs.append(x) + + x = torch.cat(outputs[1:], dim=1) + x = self.feature_agg(x) + return x, length + + +class SpeakerDecoder(NeuralModule, Exportable): + """ + Speaker Decoder creates the final neural layers that maps from the outputs + of Jasper Encoder to the embedding layer followed by speaker based softmax loss. + + Args: + feat_in (int): Number of channels being input to this module + num_classes (int): Number of unique speakers in dataset + emb_sizes (list) : shapes of intermediate embedding layers (we consider speaker embbeddings from 1st of this layers) + Defaults to [1024,1024] + pool_mode (str) : Pooling strategy type. options are 'xvector','tap', 'attention' + Defaults to 'xvector (mean and variance)' + tap (temporal average pooling: just mean) + attention (attention based pooling) + init_mode (str): Describes how neural network parameters are + initialized. Options are ['xavier_uniform', 'xavier_normal', + 'kaiming_uniform','kaiming_normal']. + Defaults to "xavier_uniform". + """ + + def input_example(self, max_batch=1, max_dim=256): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + input_example = torch.randn(max_batch, self.input_feat_in, max_dim).to(next(self.parameters()).device) + return tuple([input_example]) + + @property + def input_types(self): + return OrderedDict( + { + "encoder_output": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "length": NeuralType(('B',), LengthsType(), optional=True), + } + ) + + @property + def output_types(self): + return OrderedDict( + { + "logits": NeuralType(('B', 'D'), LogitsType()), + "embs": NeuralType(('B', 'D'), AcousticEncodedRepresentation()), + } + ) + + def __init__( + self, + feat_in: int, + num_classes: int, + emb_sizes: Optional[Union[int, list]] = 256, + pool_mode: str = 'xvector', + angular: bool = False, + attention_channels: int = 128, + init_mode: str = "xavier_uniform", + ): + super().__init__() + self.angular = angular + self.emb_id = 2 + bias = False if self.angular else True + emb_sizes = [emb_sizes] if type(emb_sizes) is int else emb_sizes + + self._num_classes = num_classes + self.pool_mode = pool_mode.lower() + if self.pool_mode == 'xvector' or self.pool_mode == 'tap': + self._pooling = StatsPoolLayer(feat_in=feat_in, pool_mode=self.pool_mode) + affine_type = 'linear' + elif self.pool_mode == 'attention': + self._pooling = AttentivePoolLayer(inp_filters=feat_in, attention_channels=attention_channels) + affine_type = 'conv' + + shapes = [self._pooling.feat_in] + for size in emb_sizes: + shapes.append(int(size)) + + emb_layers = [] + for shape_in, shape_out in zip(shapes[:-1], shapes[1:]): + layer = self.affine_layer(shape_in, shape_out, learn_mean=False, affine_type=affine_type) + emb_layers.append(layer) + + self.emb_layers = nn.ModuleList(emb_layers) + + self.final = nn.Linear(shapes[-1], self._num_classes, bias=bias) + + self.apply(lambda x: init_weights(x, mode=init_mode)) + + def affine_layer( + self, inp_shape, out_shape, learn_mean=True, affine_type='conv', + ): + if affine_type == 'conv': + layer = nn.Sequential( + nn.BatchNorm1d(inp_shape, affine=True, track_running_stats=True), + nn.Conv1d(inp_shape, out_shape, kernel_size=1), + ) + + else: + layer = nn.Sequential( + nn.Linear(inp_shape, out_shape), + nn.BatchNorm1d(out_shape, affine=learn_mean, track_running_stats=True), + nn.ReLU(), + ) + + return layer + + @typecheck() + def forward(self, encoder_output, length=None): + pool = self._pooling(encoder_output, length) + embs = [] + + for layer in self.emb_layers: + pool, emb = layer(pool), layer[: self.emb_id](pool) + embs.append(emb) + + pool = pool.squeeze(-1) + if self.angular: + for W in self.final.parameters(): + W = F.normalize(W, p=2, dim=1) + pool = F.normalize(pool, p=2, dim=1) + + out = self.final(pool) + + return out, embs[-1].squeeze(-1) + + +class ConvASREncoderAdapter(ConvASREncoder, adapter_mixins.AdapterModuleMixin): + + # Higher level forwarding + def add_adapter(self, name: str, cfg: dict): + for jasper_block in self.encoder: # type: adapter_mixins.AdapterModuleMixin + cfg = self._update_adapter_cfg_input_dim(jasper_block, cfg) + + jasper_block.set_accepted_adapter_types(self.get_accepted_adapter_types()) + jasper_block.add_adapter(name, cfg) + + def is_adapter_available(self) -> bool: + return any([jasper_block.is_adapter_available() for jasper_block in self.encoder]) + + def set_enabled_adapters(self, name: Optional[str] = None, enabled: bool = True): + for jasper_block in self.encoder: # type: adapter_mixins.AdapterModuleMixin + jasper_block.set_enabled_adapters(name=name, enabled=enabled) + + def get_enabled_adapters(self) -> List[str]: + names = set([]) + for jasper_block in self.encoder: # type: adapter_mixins.AdapterModuleMixin + names.update(jasper_block.get_enabled_adapters()) + + names = sorted(list(names)) + return names + + def _update_adapter_cfg_input_dim(self, block: JasperBlock, cfg): + cfg = adapter_utils.update_adapter_cfg_input_dim(self, cfg, module_dim=block.planes) + return cfg + + def get_accepted_adapter_types(self,) -> Set[type]: + types = super().get_accepted_adapter_types() + + if len(types) == 0: + self.set_accepted_adapter_types( + [adapter_utils.LINEAR_ADAPTER_CLASSPATH,] + ) + types = self.get_accepted_adapter_types() + return types + + +@dataclass +class JasperEncoderConfig: + filters: int = MISSING + repeat: int = MISSING + kernel: List[int] = MISSING + stride: List[int] = MISSING + dilation: List[int] = MISSING + dropout: float = MISSING + residual: bool = MISSING + + # Optional arguments + groups: int = 1 + separable: bool = False + heads: int = -1 + residual_mode: str = "add" + residual_dense: bool = False + se: bool = False + se_reduction_ratio: int = 8 + se_context_size: int = -1 + se_interpolation_mode: str = 'nearest' + kernel_size_factor: float = 1.0 + stride_last: bool = False + + +@dataclass +class ConvASREncoderConfig: + _target_: str = 'nemo.collections.asr.modules.ConvASREncoder' + jasper: Optional[List[JasperEncoderConfig]] = field(default_factory=list) + activation: str = MISSING + feat_in: int = MISSING + normalization_mode: str = "batch" + residual_mode: str = "add" + norm_groups: int = -1 + conv_mask: bool = True + frame_splicing: int = 1 + init_mode: Optional[str] = "xavier_uniform" + + +@dataclass +class ConvASRDecoderConfig: + _target_: str = 'nemo.collections.asr.modules.ConvASRDecoder' + feat_in: int = MISSING + num_classes: int = MISSING + init_mode: Optional[str] = "xavier_uniform" + vocabulary: Optional[List[str]] = field(default_factory=list) + + +@dataclass +class ConvASRDecoderClassificationConfig: + _target_: str = 'nemo.collections.asr.modules.ConvASRDecoderClassification' + feat_in: int = MISSING + num_classes: int = MISSING + init_mode: Optional[str] = "xavier_uniform" + return_logits: bool = True + pooling_type: str = 'avg' + + +""" +Register any additional information +""" +if adapter_mixins.get_registered_adapter(ConvASREncoder) is None: + adapter_mixins.register_adapter(base_class=ConvASREncoder, adapter_class=ConvASREncoderAdapter) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/flashlight_decoder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/flashlight_decoder.py new file mode 100644 index 0000000..05a111e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/flashlight_decoder.py @@ -0,0 +1,290 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import itertools +import math +from typing import Iterable, List, Optional, Tuple, Union + +import numpy as np +import torch + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.core.classes import NeuralModule, typecheck +from nemo.core.neural_types import LengthsType, LogprobsType, NeuralType, PredictionsType + + +class _TokensWrapper: + def __init__(self, vocabulary: List[str], tokenizer: TokenizerSpec): + self.vocabulary = vocabulary + self.tokenizer = tokenizer + + if tokenizer is None: + self.reverse_map = {self.vocabulary[i]: i for i in range(len(self.vocabulary))} + + self.vocab_len = len(self.vocabulary) + + if (self.tokenizer is not None) and hasattr(self.tokenizer, 'unk_id') and self.tokenizer.unk_id is not None: + self.unknown_id = self.tokenizer.unk_id + elif ' ' in self.vocabulary: + self.unknown_id = self.token_to_id(' ') + elif '' in self.vocabulary: + self.unknown_id = self.token_to_id('') + else: + self.unknown_id = -1 + + @property + def blank(self): + return self.vocab_len + + @property + def unk_id(self): + return self.unknown_id + + @property + def vocab(self): + return self.vocabulary + + @property + def vocab_size(self): + # the +1 is because we add the blank id + return self.vocab_len + 1 + + def token_to_id(self, token: str): + if token == self.blank: + return -1 + + if self.tokenizer is not None: + return self.tokenizer.token_to_id(token) + else: + return self.reverse_map[token] + + def text_to_tokens(self, text: str): + if self.tokenizer is not None: + return self.tokenizer.text_to_tokens(text) + else: + return list(text) + + +class FlashLightKenLMBeamSearchDecoder(NeuralModule): + ''' + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "log_probs": NeuralType(('B', 'T', 'D'), LogprobsType()), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"hypos": NeuralType(('B'), PredictionsType())} + ''' + + def __init__( + self, + lm_path: str, + vocabulary: List[str], + tokenizer: Optional[TokenizerSpec] = None, + lexicon_path: Optional[str] = None, + boost_path: Optional[str] = None, + beam_size: int = 32, + beam_size_token: int = 32, + beam_threshold: float = 25.0, + lm_weight: float = 2.0, + word_score: float = -1.0, + unk_weight: float = -math.inf, + sil_weight: float = 0.0, + ): + + try: + from flashlight.lib.text.decoder import ( + LM, + CriterionType, + KenLM, + LexiconDecoder, + LexiconDecoderOptions, + SmearingMode, + Trie, + ) + from flashlight.lib.text.dictionary import create_word_dict, load_words + except ModuleNotFoundError: + raise ModuleNotFoundError( + "FlashLightKenLMBeamSearchDecoder requires the installation of flashlight python bindings " + "from https://github.com/flashlight/text. Please follow the build instructions there." + ) + + super().__init__() + + self.criterion_type = CriterionType.CTC + self.tokenizer_wrapper = _TokensWrapper(vocabulary, tokenizer) + self.vocab_size = self.tokenizer_wrapper.vocab_size + self.blank = self.tokenizer_wrapper.blank + self.silence = self.tokenizer_wrapper.unk_id + + if lexicon_path is not None: + self.lexicon = load_words(lexicon_path) + self.word_dict = create_word_dict(self.lexicon) + self.unk_word = self.word_dict.get_index("") + + # loads in the boosted words if given via a file + if boost_path is not None: + with open(boost_path, 'r', encoding='utf_8') as fr: + boost_words = [line.strip().split('\t') for line in fr] + boost_words = {w[0]: w[1] for w in boost_words} + else: + boost_words = {} + + # add OOV boosted words to word_dict so it gets picked up in LM obj creation + for word in boost_words.keys(): + if word not in self.lexicon: + self.word_dict.add_entry(word) + + # loads in the kenlm binary and combines in with the dictionary object from the lexicon + # this gives a mapping between each entry in the kenlm binary and its mapping to whatever + # numeraire is used by the AM, which is explicitly mapped via the lexicon + # this information is ued to build a vocabulary trie for decoding + self.lm = KenLM(lm_path, self.word_dict) + self.trie = Trie(self.vocab_size, self.silence) + + start_state = self.lm.start(False) + for i, (word, spellings) in enumerate(self.lexicon.items()): + word_idx = self.word_dict.get_index(word) + _, score = self.lm.score(start_state, word_idx) + for spelling in spellings: + spelling_idxs = [self.tokenizer_wrapper.token_to_id(token) for token in spelling] + if self.tokenizer_wrapper.unk_id in spelling_idxs: + print(f'tokenizer has unknown id for word[ {word} ] {spelling} {spelling_idxs}', flush=True) + continue + self.trie.insert( + spelling_idxs, word_idx, score if word not in boost_words else float(boost_words[word]) + ) + # handle OOV boosted words + for word, boost in boost_words.items(): + if word not in self.lexicon: + word_idx = self.word_dict.get_index(word) + spelling = self.tokenizer_wrapper.text_to_tokens(word) + spelling_idxs = [self.tokenizer_wrapper.token_to_id(token) for token in spelling] + if self.tokenizer_wrapper.unk_id in spelling_idxs: + print(f'tokenizer has unknown id for word[ {word} ] {spelling} {spelling_idxs}', flush=True) + continue + self.trie.insert(spelling_idxs, word_idx, float(boost)) + self.trie.smear(SmearingMode.MAX) + + self.decoder_opts = LexiconDecoderOptions( + beam_size=beam_size, + beam_size_token=int(beam_size_token), + beam_threshold=beam_threshold, + lm_weight=lm_weight, + word_score=word_score, + unk_score=unk_weight, + sil_score=sil_weight, + log_add=False, + criterion_type=self.criterion_type, + ) + + self.decoder = LexiconDecoder( + self.decoder_opts, self.trie, self.lm, self.silence, self.blank, self.unk_word, [], False, + ) + else: + from flashlight.lib.text.decoder import LexiconFreeDecoder, LexiconFreeDecoderOptions + + d = { + w: [[w]] + for w in self.tokenizer_wrapper.vocab + ([] if '' in self.tokenizer_wrapper.vocab else ['']) + } + self.word_dict = create_word_dict(d) + self.lm = KenLM(lm_path, self.word_dict) + self.decoder_opts = LexiconFreeDecoderOptions( + beam_size=beam_size, + beam_size_token=int(beam_size_token), + beam_threshold=beam_threshold, + lm_weight=lm_weight, + sil_score=sil_weight, + log_add=False, + criterion_type=self.criterion_type, + ) + self.decoder = LexiconFreeDecoder(self.decoder_opts, self.lm, self.silence, self.blank, []) + + def _get_tokens(self, idxs: List[int]): + """Normalize tokens by handling CTC blank, ASG replabels, etc.""" + + idxs = (g[0] for g in itertools.groupby(idxs)) + if self.silence < 0: + idxs = filter(lambda x: x != self.blank and x != self.silence, idxs) + else: + idxs = filter(lambda x: x != self.blank, idxs) + idxs = list(idxs) + if idxs[0] == self.silence: + idxs = idxs[1:] + if idxs[-1] == self.silence: + idxs = idxs[:-1] + + return torch.LongTensor(idxs) + + def _get_timesteps(self, token_idxs: List[int]): + """Returns frame numbers corresponding to every non-blank token. + Parameters + ---------- + token_idxs : List[int] + IDs of decoded tokens. + Returns + ------- + List[int] + Frame numbers corresponding to every non-blank token. + """ + + timesteps = [] + for i, token_idx in enumerate(token_idxs): + if token_idx == self.blank: + continue + if i == 0 or token_idx != token_idxs[i - 1]: + timesteps.append(i) + + return timesteps + + # @typecheck(ignore_collections=True) + @torch.no_grad() + def forward(self, log_probs: Union[np.ndarray, torch.Tensor]): + if isinstance(log_probs, np.ndarray): + log_probs = torch.from_numpy(log_probs).float() + if log_probs.dim() == 2: + log_probs = log_probs.unsqueeze(0) + + emissions = log_probs.cpu().contiguous() + + B, T, N = emissions.size() + hypos = [] + # we iterate over the batch dimension of our input tensor log probabilities + for b in range(B): + # the flashlight C++ expects a C style pointer, so the memory address + # which is what we obtain here. Then we pass it to pybinding method which + # is bound to the underlying C++ code + emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0) + results = self.decoder.decode(emissions_ptr, T, N) + + hypos.append( + [ + { + "tokens": self._get_tokens(result.tokens), + "score": result.score, + "timesteps": self._get_timesteps(result.tokens), + "words": [self.word_dict.get_entry(x) for x in result.words if x >= 0], + } + for result in results + ] + ) + + return hypos diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/graph_decoder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/graph_decoder.py new file mode 100644 index 0000000..d66dbd7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/graph_decoder.py @@ -0,0 +1,214 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional + +import torch +from omegaconf import DictConfig + +from nemo.core.classes import NeuralModule +from nemo.core.neural_types import LengthsType, LogprobsType, NeuralType, PredictionsType + + +class ViterbiDecoderWithGraph(NeuralModule): + """Viterbi Decoder with WFSA (Weighted Finite State Automaton) graphs. + + Note: + Requires k2 v1.14 or later to be installed to use this module. + + Decoder can be set up via the config, and optionally be passed keyword arguments as follows. + + Examples: + .. code-block:: yaml + + model: # Model config + ... + graph_module_cfg: # Config for graph modules, e.g. ViterbiDecoderWithGraph + split_batch_size: 0 + backend_cfg: + topo_type: "default" # other options: "compact", "shared_blank", "minimal" + topo_with_self_loops: true + token_lm: # must be provided for criterion_type: "map" + + Args: + num_classes: Number of target classes for the decoder network to predict. + (Excluding the blank token). + + backend: Which backend to use for decoding. Currently only `k2` is supported. + + dec_type: Type of decoding graph to use. Choices: `topo` and `token_lm`, + with `topo` standing for the loss topology graph only + and `token_lm` for the topology composed with a token_lm graph. + + return_type: Type of output. Choices: `1best` and `lattice`. + `1best` is represented as a list of 1D tensors. + `lattice` can be of type corresponding to the backend (e.g. k2.Fsa). + + return_ilabels: For return_type=`1best`. + Whether to return input labels of a lattice (otherwise output labels). + + output_aligned: For return_type=`1best`. + Whether the tensors length will correspond to log_probs_length + and the labels will be aligned to the frames of emission + (otherwise there will be only the necessary labels). + + split_batch_size: Local batch size. Used for memory consumption reduction at the cost of speed performance. + Effective if complies 0 < split_batch_size < batch_size. + + graph_module_cfg: Optional Dict of (str, value) pairs that are passed to the backend graph decoder. + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "log_probs": NeuralType(("B", "T", "D") if self._3d_input else ("B", "T", "T", "D"), LogprobsType()), + "input_lengths": NeuralType(tuple("B"), LengthsType()), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"predictions": NeuralType(("B", "T"), PredictionsType())} + + def __init__( + self, + num_classes, + backend: str = "k2", + dec_type: str = "topo", + return_type: str = "1best", + return_ilabels: bool = True, + output_aligned: bool = True, + split_batch_size: int = 0, + graph_module_cfg: Optional[DictConfig] = None, + ): + self._blank = num_classes + self.return_ilabels = return_ilabels + self.output_aligned = output_aligned + self.split_batch_size = split_batch_size + self.dec_type = dec_type + + if return_type == "1best": + self.return_lattices = False + elif return_type == "lattice": + self.return_lattices = True + elif return_type == "nbest": + raise NotImplementedError(f"return_type {return_type} is not supported at the moment") + else: + raise ValueError(f"Unsupported return_type: {return_type}") + + # we assume that self._blank + 1 == num_classes + if backend == "k2": + if self.dec_type == "topo": + from nemo.collections.asr.parts.k2.graph_decoders import CtcDecoder as Decoder + elif self.dec_type == "topo_rnnt_ali": + from nemo.collections.asr.parts.k2.graph_decoders import RnntAligner as Decoder + elif self.dec_type == "token_lm": + from nemo.collections.asr.parts.k2.graph_decoders import TokenLMDecoder as Decoder + elif self.dec_type == "loose_ali": + raise NotImplementedError() + elif self.dec_type == "tlg": + raise NotImplementedError(f"dec_type {self.dec_type} is not supported at the moment") + else: + raise ValueError(f"Unsupported dec_type: {self.dec_type}") + + self._decoder = Decoder(num_classes=self._blank + 1, blank=self._blank, cfg=graph_module_cfg) + elif backend == "gtn": + raise NotImplementedError("gtn-backed decoding is not implemented") + + self._3d_input = self.dec_type != "topo_rnnt" + super().__init__() + + def update_graph(self, graph): + """Updates graph of the backend graph decoder. + """ + self._decoder.update_graph(graph) + + def _forward_impl(self, log_probs, log_probs_length, targets=None, target_length=None): + if targets is None and target_length is not None or targets is not None and target_length is None: + raise RuntimeError( + f"Both targets and target_length have to be None or not None: {targets}, {target_length}" + ) + # do not use self.return_lattices for now + if targets is None: + align = False + decode_func = lambda a, b: self._decoder.decode( + a, b, return_lattices=False, return_ilabels=self.return_ilabels, output_aligned=self.output_aligned + ) + else: + align = True + decode_func = lambda a, b, c, d: self._decoder.align( + a, b, c, d, return_lattices=False, return_ilabels=False, output_aligned=True + ) + batch_size = log_probs.shape[0] + if self.split_batch_size > 0 and self.split_batch_size <= batch_size: + predictions = [] + probs = [] + for batch_idx in range(0, batch_size, self.split_batch_size): + begin = batch_idx + end = min(begin + self.split_batch_size, batch_size) + log_probs_length_part = log_probs_length[begin:end] + log_probs_part = log_probs[begin:end, : log_probs_length_part.max()] + if align: + target_length_part = target_length[begin:end] + targets_part = targets[begin:end, : target_length_part.max()] + predictions_part, probs_part = decode_func( + log_probs_part, log_probs_length_part, targets_part, target_length_part + ) + del targets_part, target_length_part + else: + predictions_part, probs_part = decode_func(log_probs_part, log_probs_length_part) + del log_probs_part, log_probs_length_part + predictions += predictions_part + probs += probs_part + else: + predictions, probs = ( + decode_func(log_probs, log_probs_length, targets, target_length) + if align + else decode_func(log_probs, log_probs_length) + ) + assert len(predictions) == len(probs) + return predictions, probs + + @torch.no_grad() + def forward(self, log_probs, log_probs_length): + if self.dec_type == "looseali": + raise RuntimeError(f"Decoder with dec_type=`{self.dec_type}` is not intended for regular decoding.") + predictions, probs = self._forward_impl(log_probs, log_probs_length) + lengths = torch.tensor([len(pred) for pred in predictions], device=predictions[0].device) + predictions_tensor = torch.full((len(predictions), lengths.max()), self._blank).to( + device=predictions[0].device + ) + probs_tensor = torch.full((len(probs), lengths.max()), 1.0).to(device=predictions[0].device) + for i, (pred, prob) in enumerate(zip(predictions, probs)): + predictions_tensor[i, : lengths[i]] = pred + probs_tensor[i, : lengths[i]] = prob + return predictions_tensor, lengths, probs_tensor + + @torch.no_grad() + def align(self, log_probs, log_probs_length, targets, target_length): + len_enough = (log_probs_length >= target_length) & (target_length > 0) + if torch.all(len_enough) or self.dec_type == "looseali": + results = self._forward_impl(log_probs, log_probs_length, targets, target_length) + else: + results = self._forward_impl( + log_probs[len_enough], log_probs_length[len_enough], targets[len_enough], target_length[len_enough] + ) + for i, computed in enumerate(len_enough): + if not computed: + results[0].insert(i, torch.empty(0, dtype=torch.int32)) + results[1].insert(i, torch.empty(0, dtype=torch.float)) + return results diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/hybrid_autoregressive_transducer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/hybrid_autoregressive_transducer.py new file mode 100644 index 0000000..8fac6a0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/hybrid_autoregressive_transducer.py @@ -0,0 +1,239 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright 2017 Johns Hopkins University (Shinji Watanabe) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch + +from nemo.collections.asr.modules import rnnt +from nemo.collections.asr.parts.utils.rnnt_utils import HATJointOutput + +from nemo.utils import logging + + +class HATJoint(rnnt.RNNTJoint): + """A Hybrid Autoregressive Transducer Joint Network (HAT Joint Network). + A HAT Joint network, comprised of a feedforward model. + + Args: + jointnet: A dict-like object which contains the following key-value pairs. + encoder_hidden: int specifying the hidden dimension of the encoder net. + pred_hidden: int specifying the hidden dimension of the prediction net. + joint_hidden: int specifying the hidden dimension of the joint net + activation: Activation function used in the joint step. Can be one of + ['relu', 'tanh', 'sigmoid']. + + Optionally, it may also contain the following: + dropout: float, set to 0.0 by default. Optional dropout applied at the end of the joint net. + + num_classes: int, specifying the vocabulary size that the joint network must predict, + excluding the HAT blank token. + + vocabulary: Optional list of strings/tokens that comprise the vocabulary of the joint network. + Unused and kept only for easy access for character based encoding HAT models. + + log_softmax: Optional bool, set to None by default. If set as None, will compute the log_softmax() + based on the value provided. + + preserve_memory: Optional bool, set to False by default. If the model crashes due to the memory + intensive joint step, one might try this flag to empty the tensor cache in pytorch. + + Warning: This will make the forward-backward pass much slower than normal. + It also might not fix the OOM if the GPU simply does not have enough memory to compute the joint. + + fuse_loss_wer: Optional bool, set to False by default. + + Fuses the joint forward, loss forward and + wer forward steps. In doing so, it trades of speed for memory conservation by creating sub-batches + of the provided batch of inputs, and performs Joint forward, loss forward and wer forward (optional), + all on sub-batches, then collates results to be exactly equal to results from the entire batch. + + When this flag is set, prior to calling forward, the fields `loss` and `wer` (either one) *must* + be set using the `HATJoint.set_loss()` or `HATJoint.set_wer()` methods. + + Further, when this flag is set, the following argument `fused_batch_size` *must* be provided + as a non negative integer. This value refers to the size of the sub-batch. + + When the flag is set, the input and output signature of `forward()` of this method changes. + Input - in addition to `encoder_outputs` (mandatory argument), the following arguments can be provided. + - decoder_outputs (optional). Required if loss computation is required. + - encoder_lengths (required) + - transcripts (optional). Required for wer calculation. + - transcript_lengths (optional). Required for wer calculation. + - compute_wer (bool, default false). Whether to compute WER or not for the fused batch. + + Output - instead of the usual `joint` log prob tensor, the following results can be returned. + - loss (optional). Returned if decoder_outputs, transcripts and transript_lengths are not None. + - wer_numerator + wer_denominator (optional). Returned if transcripts, transcripts_lengths are provided + and compute_wer is set. + + fused_batch_size: Optional int, required if `fuse_loss_wer` flag is set. Determines the size of the + sub-batches. Should be any value below the actual batch size per GPU. + """ + + def __init__( + self, + jointnet: Dict[str, Any], + num_classes: int, + num_extra_outputs: int = 0, + vocabulary: Optional[List] = None, + log_softmax: Optional[bool] = None, + preserve_memory: bool = False, + fuse_loss_wer: bool = False, + fused_batch_size: Optional[int] = None, + experimental_fuse_loss_wer: Any = None, + ): + super().__init__( + jointnet=jointnet, + num_classes=num_classes, + num_extra_outputs=num_extra_outputs, + vocabulary=vocabulary, + log_softmax=log_softmax, + preserve_memory=preserve_memory, + fuse_loss_wer=fuse_loss_wer, + fused_batch_size=fused_batch_size, + experimental_fuse_loss_wer=experimental_fuse_loss_wer, + ) + + self.pred, self.enc, self.joint_net, self.blank_pred = self._joint_hat_net_modules( + num_classes=self._vocab_size, # non blank symbol + pred_n_hidden=self.pred_hidden, + enc_n_hidden=self.encoder_hidden, + joint_n_hidden=self.joint_hidden, + activation=self.activation, + dropout=jointnet.get('dropout', 0.0), + ) + self._return_hat_ilm = False + + @property + def return_hat_ilm(self): + return self._return_hat_ilm + + @return_hat_ilm.setter + def return_hat_ilm(self, hat_subtract_ilm): + self._return_hat_ilm = hat_subtract_ilm + + def joint_after_projection(self, f: torch.Tensor, g: torch.Tensor) -> Union[torch.Tensor, HATJointOutput]: + """ + Compute the joint step of the network after Encoder/Decoder projection. + + Here, + B = Batch size + T = Acoustic model timesteps + U = Target sequence length + H1, H2 = Hidden dimensions of the Encoder / Decoder respectively + H = Hidden dimension of the Joint hidden step. + V = Vocabulary size of the Decoder (excluding the HAT blank token). + + NOTE: + The implementation of this model is slightly modified from the original paper. + The original paper proposes the following steps : + (enc, dec) -> Expand + Concat + Sum [B, T, U, H1+H2] -> Forward through joint hidden [B, T, U, H] -- *1 + *1 -> Forward through joint final [B, T, U, V + 1]. + + We instead split the joint hidden into joint_hidden_enc and joint_hidden_dec and act as follows: + enc -> Forward through joint_hidden_enc -> Expand [B, T, 1, H] -- *1 + dec -> Forward through joint_hidden_dec -> Expand [B, 1, U, H] -- *2 + (*1, *2) -> Sum [B, T, U, H] -> Forward through joint final [B, T, U, V + 1]. + + Args: + f: Output of the Encoder model. A torch.Tensor of shape [B, T, H1] + g: Output of the Decoder model. A torch.Tensor of shape [B, U, H2] + + Returns: + Log softmaxed tensor of shape (B, T, U, V + 1). + Internal LM probability (B, 1, U, V) -- in case of return_ilm==True. + """ + f = f.unsqueeze(dim=2) # (B, T, 1, H) + g = g.unsqueeze(dim=1) # (B, 1, U, H) + inp = f + g # [B, T, U, H] + + del f + + # Forward adapter modules on joint hidden + if self.is_adapter_available(): + inp = self.forward_enabled_adapters(inp) + + blank_logprob = self.blank_pred(inp) # [B, T, U, 1] + label_logit = self.joint_net(inp) # [B, T, U, V] + + del inp + + label_logprob = label_logit.log_softmax(dim=-1) + scale_prob = torch.clamp(1 - torch.exp(blank_logprob), min=1e-6) + label_logprob_scaled = torch.log(scale_prob) + label_logprob # [B, T, U, V] + + res = torch.cat((label_logprob_scaled, blank_logprob), dim=-1).contiguous() # [B, T, U, V+1] + + if self.return_hat_ilm: + ilm_logprobs = self.joint_net(g).log_softmax(dim=-1) # [B, 1, U, V] + res = HATJointOutput(hat_logprobs=res, ilm_logprobs=ilm_logprobs) + + del g, blank_logprob, label_logprob, label_logit, scale_prob, label_logprob_scaled + + if self.preserve_memory: + torch.cuda.empty_cache() + + return res + + def _joint_hat_net_modules(self, num_classes, pred_n_hidden, enc_n_hidden, joint_n_hidden, activation, dropout): + """ + Prepare the trainable modules of the Joint Network + + Args: + num_classes: Number of output classes (vocab size) excluding the HAT blank token. + pred_n_hidden: Hidden size of the prediction network. + enc_n_hidden: Hidden size of the encoder network. + joint_n_hidden: Hidden size of the joint network. + activation: Activation of the joint. Can be one of [relu, tanh, sigmoid] + dropout: Dropout value to apply to joint. + """ + pred = torch.nn.Linear(pred_n_hidden, joint_n_hidden) + enc = torch.nn.Linear(enc_n_hidden, joint_n_hidden) + blank_pred = torch.nn.Sequential( + torch.nn.Tanh(), torch.nn.Dropout(p=dropout), torch.nn.Linear(joint_n_hidden, 1), torch.nn.LogSigmoid() + ) + + if activation not in ['relu', 'sigmoid', 'tanh']: + raise ValueError("Unsupported activation for joint step - please pass one of " "[relu, sigmoid, tanh]") + + activation = activation.lower() + + if activation == 'relu': + activation = torch.nn.ReLU(inplace=True) + elif activation == 'sigmoid': + activation = torch.nn.Sigmoid() + elif activation == 'tanh': + activation = torch.nn.Tanh() + + layers = ( + [activation] + + ([torch.nn.Dropout(p=dropout)] if dropout else []) + + [torch.nn.Linear(joint_n_hidden, num_classes)] + ) + return pred, enc, torch.nn.Sequential(*layers), blank_pred diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/lstm_decoder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/lstm_decoder.py new file mode 100644 index 0000000..9bb60e2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/lstm_decoder.py @@ -0,0 +1,94 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections import OrderedDict + +import torch +import torch.nn as nn + +from nemo.core.classes.common import typecheck +from nemo.core.classes.exportable import Exportable +from nemo.core.classes.module import NeuralModule +from nemo.core.neural_types import AcousticEncodedRepresentation, LogprobsType, NeuralType + +__all__ = ['LSTMDecoder'] + + +class LSTMDecoder(NeuralModule, Exportable): + """ + Simple LSTM Decoder for ASR models + Args: + feat_in (int): size of the input features + num_classes (int): the size of the vocabulary + lstm_hidden_size (int): hidden size of the LSTM layers + vocabulary (vocab): The vocabulary + bidirectional (bool): default is False. Whether LSTMs are bidirectional or not + num_layers (int): default is 1. Number of LSTM layers stacked + """ + + @property + def input_types(self): + return OrderedDict({"encoder_output": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation())}) + + @property + def output_types(self): + return OrderedDict({"logprobs": NeuralType(('B', 'T', 'D'), LogprobsType())}) + + def __init__(self, feat_in, num_classes, lstm_hidden_size, vocabulary=None, bidirectional=False, num_layers=1): + super().__init__() + + if vocabulary is not None: + if num_classes != len(vocabulary): + raise ValueError( + f"If vocabulary is specified, it's length should be equal to the num_classes. " + f"Instead got: num_classes={num_classes} and len(vocabulary)={len(vocabulary)}" + ) + self.__vocabulary = vocabulary + self._feat_in = feat_in + # Add 1 for blank char + self._num_classes = num_classes + 1 + + self.lstm_layer = nn.LSTM( + input_size=feat_in, + hidden_size=lstm_hidden_size, + num_layers=num_layers, + batch_first=True, + bidirectional=bidirectional, + ) + lstm_hidden_size = 2 * lstm_hidden_size if bidirectional else lstm_hidden_size + self.linear_layer = torch.nn.Linear(in_features=lstm_hidden_size, out_features=self._num_classes) + + @typecheck() + def forward(self, encoder_output): + output = encoder_output.transpose(1, 2) + output, _ = self.lstm_layer(output) + output = self.linear_layer(output) + return torch.nn.functional.log_softmax(output, dim=-1) + + def input_example(self, max_batch=1, max_dim=256): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + input_example = torch.randn(max_batch, self._feat_in, max_dim).to(next(self.parameters()).device) + return tuple([input_example]) + + @property + def vocabulary(self): + return self.__vocabulary + + @property + def num_classes_with_blank(self): + return self._num_classes diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/msdd_diarizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/msdd_diarizer.py new file mode 100644 index 0000000..949960e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/msdd_diarizer.py @@ -0,0 +1,442 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from nemo.core.classes.common import typecheck +from nemo.core.classes.exportable import Exportable +from nemo.core.classes.module import NeuralModule +from nemo.core.neural_types import EncodedRepresentation, LengthsType, NeuralType, SpectrogramType +from nemo.core.neural_types.elements import ProbsType + +__all__ = ['MSDD_module'] + + +class ConvLayer(nn.Module): + def __init__(self, in_channels=1, out_channels=1, kernel_size=(3, 1), stride=(1, 1)): + super(ConvLayer, self).__init__() + self.cnn = nn.Sequential( + nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride), + nn.ReLU(), + nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.99), + ) + + def forward(self, feature): + feature = self.cnn(feature) + return feature + + +class MSDD_module(NeuralModule, Exportable): + """ + Multi-scale Diarization Decoder (MSDD) for overlap-aware diarization and improved diarization accuracy from clustering diarizer. + Based on the paper: Taejin Park et. al, "Multi-scale Speaker Diarization with Dynamic Scale Weighting", Interspeech 2022. + Arxiv version: https://arxiv.org/pdf/2203.15974.pdf + + Args: + num_spks (int): + Max number of speakers that are processed by the model. In `MSDD_module`, `num_spks=2` for pairwise inference. + hidden_size (int): + Number of hidden units in sequence models and intermediate layers. + num_lstm_layers (int): + Number of the stacked LSTM layers. + dropout_rate (float): + Dropout rate for linear layers, CNN and LSTM. + cnn_output_ch (int): + Number of channels per each CNN layer. + emb_dim (int): + Dimension of the embedding vectors. + scale_n (int): + Number of scales in multi-scale system. + clamp_max (float): + Maximum value for limiting the scale weight values. + conv_repeat (int): + Number of CNN layers after the first CNN layer. + weighting_scheme (str): + Name of the methods for estimating the scale weights. + context_vector_type (str): + If 'cos_sim', cosine similarity values are used for the input of the sequence models. + If 'elem_prod', element-wise product values are used for the input of the sequence models. + """ + + @property + def output_types(self): + """ + Return definitions of module output ports. + """ + return OrderedDict( + { + "probs": NeuralType(('B', 'T', 'C'), ProbsType()), + "scale_weights": NeuralType(('B', 'T', 'C', 'D'), ProbsType()), + } + ) + + @property + def input_types(self): + """ + Return definitions of module input ports. + """ + return OrderedDict( + { + "ms_emb_seq": NeuralType(('B', 'T', 'C', 'D'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + "ms_avg_embs": NeuralType(('B', 'C', 'D', 'C'), EncodedRepresentation()), + "targets": NeuralType(('B', 'T', 'C'), ProbsType()), + } + ) + + def init_weights(self, m): + if type(m) == nn.Linear: + torch.nn.init.xavier_uniform_(m.weight) + m.bias.data.fill_(0.01) + elif type(m) in [nn.GRU, nn.LSTM, nn.RNN]: + for name, param in m.named_parameters(): + if 'weight_ih' in name: + torch.nn.init.xavier_uniform_(param.data) + elif 'weight_hh' in name: + torch.nn.init.orthogonal_(param.data) + elif 'bias' in name: + param.data.fill_(0.01) + + def __init__( + self, + num_spks: int = 2, + hidden_size: int = 256, + num_lstm_layers: int = 2, + dropout_rate: float = 0.5, + cnn_output_ch: int = 16, + emb_dim: int = 192, + scale_n: int = 5, + clamp_max: float = 1.0, + conv_repeat: int = 1, + weighting_scheme: str = 'conv_scale_weight', + context_vector_type: str = 'cos_sim', + ): + super().__init__() + self._speaker_model = None + self.batch_size: int = 1 + self.length: int = 50 + self.emb_dim: int = emb_dim + self.num_spks: int = num_spks + self.scale_n: int = scale_n + self.cnn_output_ch: int = cnn_output_ch + self.conv_repeat: int = conv_repeat + self.chan: int = 2 + self.eps: float = 1e-6 + self.num_lstm_layers: int = num_lstm_layers + self.weighting_scheme: str = weighting_scheme + self.context_vector_type: bool = context_vector_type + + self.softmax = torch.nn.Softmax(dim=2) + self.cos_dist = torch.nn.CosineSimilarity(dim=3, eps=self.eps) + self.lstm = nn.LSTM( + hidden_size, + hidden_size, + num_layers=self.num_lstm_layers, + batch_first=True, + bidirectional=True, + dropout=dropout_rate, + ) + + if self.weighting_scheme == 'conv_scale_weight': + self.conv = nn.ModuleList( + [ + ConvLayer( + in_channels=1, + out_channels=cnn_output_ch, + kernel_size=(self.scale_n + self.scale_n * num_spks, 1), + stride=(1, 1), + ) + ] + ) + for conv_idx in range(1, conv_repeat + 1): + self.conv.append( + ConvLayer( + in_channels=1, out_channels=cnn_output_ch, kernel_size=(self.cnn_output_ch, 1), stride=(1, 1) + ) + ) + self.conv_bn = nn.ModuleList() + for conv_idx in range(self.conv_repeat + 1): + self.conv_bn.append(nn.BatchNorm2d(self.emb_dim, affine=False)) + self.conv_to_linear = nn.Linear(emb_dim * cnn_output_ch, hidden_size) + self.linear_to_weights = nn.Linear(hidden_size, self.scale_n) + + elif self.weighting_scheme == 'attn_scale_weight': + self.W_a = nn.Linear(emb_dim, emb_dim, bias=False) + nn.init.eye_(self.W_a.weight) + else: + raise ValueError(f"No such weighting scheme as {self.weighting_scheme}") + + self.hidden_to_spks = nn.Linear(2 * hidden_size, self.num_spks) + if self.context_vector_type == "cos_sim": + self.dist_to_emb = nn.Linear(self.scale_n * self.num_spks, hidden_size) + self.dist_to_emb.apply(self.init_weights) + elif self.context_vector_type == "elem_prod": + self.product_to_emb = nn.Linear(self.emb_dim * self.num_spks, hidden_size) + else: + raise ValueError(f"No such context vector type as {self.context_vector_type}") + + self.dropout = nn.Dropout(dropout_rate) + self.hidden_to_spks.apply(self.init_weights) + self.lstm.apply(self.init_weights) + self.clamp_max = clamp_max + + def core_model(self, ms_emb_seq, length, ms_avg_embs, targets): + """ + Core model that accepts multi-scale cosine similarity values and estimates per-speaker binary label. + + Args: + ms_emb_seq (Tensor): + Multiscale input embedding sequence + Shape: (batch_size, length, scale_n, emb_dim) + length (Tensor): + The actual length of embedding sequences without zero padding + Shape: (batch_size,) + ms_avg_embs (Tensor): + Cluster-average speaker embedding vectors. + Shape: (batch_size, scale_n, self.emb_dim, max_spks) + targets (Tensor): + Ground-truth labels for the finest segment. + Shape: (batch_size, feats_len, max_spks) + + Returns: + preds (Tensor): + Predicted binary speaker label for each speaker. + Shape: (batch_size, feats_len, max_spks) + scale_weights (Tensor): + Multiscale weights per each base-scale segment. + Shape: (batch_size, length, scale_n, max_spks) + + """ + self.batch_size = ms_emb_seq.shape[0] + self.length = ms_emb_seq.shape[1] + self.emb_dim = ms_emb_seq.shape[-1] + + _ms_emb_seq = ms_emb_seq.unsqueeze(4).expand(-1, -1, -1, -1, self.num_spks) + ms_emb_seq_single = ms_emb_seq + ms_avg_embs = ms_avg_embs.unsqueeze(1).expand(-1, self.length, -1, -1, -1) + + ms_avg_embs_perm = ms_avg_embs.permute(0, 1, 2, 4, 3).reshape(self.batch_size, self.length, -1, self.emb_dim) + + if self.weighting_scheme == "conv_scale_weight": + scale_weights = self.conv_scale_weights(ms_avg_embs_perm, ms_emb_seq_single) + elif self.weighting_scheme == "attn_scale_weight": + scale_weights = self.attention_scale_weights(ms_avg_embs_perm, ms_emb_seq_single) + else: + raise ValueError(f"No such weighting scheme as {self.weighting_scheme}") + scale_weights = scale_weights.to(ms_emb_seq.device) + + if self.context_vector_type == "cos_sim": + context_emb = self.cosine_similarity(scale_weights, ms_avg_embs, _ms_emb_seq) + elif self.context_vector_type == "elem_prod": + context_emb = self.element_wise_product(scale_weights, ms_avg_embs, _ms_emb_seq) + else: + raise ValueError(f"No such context vector type as {self.context_vector_type}") + + context_emb = self.dropout(F.relu(context_emb)) + lstm_output = self.lstm(context_emb) + lstm_hidden_out = self.dropout(F.relu(lstm_output[0])) + spk_preds = self.hidden_to_spks(lstm_hidden_out) + preds = nn.Sigmoid()(spk_preds) + return preds, scale_weights + + def element_wise_product(self, scale_weights, ms_avg_embs, ms_emb_seq): + """ + Calculate element wise product values among cluster-average embedding vectors and input embedding vector sequences. + This function is selected by assigning `self.context_vector_type = "elem_prod"`. `elem_prod` method usually takes more + time to converge compared to `cos_sim` method. + + Args: + scale_weights (Tensor): + Multiscale weight vector. + Shape: (batch_size, feats_len, scale_n, max_spks) + ms_avg_embs_perm (Tensor): + Tensor containing cluster-average speaker embeddings for each scale. + Shape: (batch_size, length, scale_n, emb_dim) + ms_emb_seq (Tensor): + Tensor containing multi-scale speaker embedding sequences. `ms_emb_seq` is a single channel input from the + given audio stream input. + Shape: (batch_size, length, num_spks, emb_dim) + + Returns: + context_emb (Tensor): + Output of `dist_to_emb` linear layer containing context for speaker label estimation. + """ + scale_weight_flatten = scale_weights.reshape(self.batch_size * self.length, self.num_spks, self.scale_n) + ms_avg_embs_flatten = ms_avg_embs.reshape( + self.batch_size * self.length, self.scale_n, self.emb_dim, self.num_spks + ) + ms_emb_seq_flatten = ms_emb_seq.reshape(-1, self.scale_n, self.emb_dim) + ms_emb_seq_flatten_rep = ms_emb_seq_flatten.unsqueeze(3).reshape(-1, self.scale_n, self.emb_dim, self.num_spks) + elemwise_product = ms_avg_embs_flatten * ms_emb_seq_flatten_rep + context_vectors = torch.bmm( + scale_weight_flatten.reshape(self.batch_size * self.num_spks * self.length, 1, self.scale_n), + elemwise_product.reshape(self.batch_size * self.num_spks * self.length, self.scale_n, self.emb_dim), + ) + context_vectors = context_vectors.reshape(self.batch_size, self.length, self.emb_dim * self.num_spks) + context_emb = self.product_to_emb(context_vectors) + return context_emb + + def cosine_similarity(self, scale_weights, ms_avg_embs, _ms_emb_seq): + """ + Calculate cosine similarity values among cluster-average embedding vectors and input embedding vector sequences. + This function is selected by assigning self.context_vector_type = "cos_sim". + + Args: + scale_weights (Tensor): + Multiscale weight vector. + Shape: (batch_size, feats_len, scale_n, max_spks) + ms_avg_embs_perm (Tensor): + Tensor containing cluster-average speaker embeddings for each scale. + Shape: (batch_size, length, scale_n, emb_dim) + _ms_emb_seq (Tensor): + Tensor containing multi-scale speaker embedding sequences. `ms_emb_seq` is a single channel input from the + given audio stream input. + Shape: (batch_size, length, num_spks, emb_dim) + + Returns: + context_emb (Tensor): + Output of `dist_to_emb` linear layer containing context for speaker label estimation. + """ + cos_dist_seq = self.cos_dist(_ms_emb_seq, ms_avg_embs) + context_vectors = torch.mul(scale_weights, cos_dist_seq) + context_vectors = context_vectors.view(self.batch_size, self.length, -1) + context_emb = self.dist_to_emb(context_vectors) + return context_emb + + def attention_scale_weights(self, ms_avg_embs_perm, ms_emb_seq): + """ + Use weighted inner product for calculating each scale weight. W_a matrix has (emb_dim * emb_dim) learnable parameters + and W_a matrix is initialized with an identity matrix. Compared to "conv_scale_weight" method, this method shows more evenly + distributed scale weights. + + Args: + ms_avg_embs_perm (Tensor): + Tensor containing cluster-average speaker embeddings for each scale. + Shape: (batch_size, length, scale_n, emb_dim) + ms_emb_seq (Tensor): + Tensor containing multi-scale speaker embedding sequences. `ms_emb_seq` is input from the + given audio stream input. + Shape: (batch_size, length, num_spks, emb_dim) + + Returns: + scale_weights (Tensor): + Weight vectors that determine the weight of each scale. + Shape: (batch_size, length, num_spks, emb_dim) + """ + self.W_a(ms_emb_seq.flatten(0, 1)) + mat_a = self.W_a(ms_emb_seq.flatten(0, 1)) + mat_b = ms_avg_embs_perm.flatten(0, 1).permute(0, 2, 1) + + weighted_corr = torch.matmul(mat_a, mat_b).reshape(-1, self.scale_n, self.scale_n, self.num_spks) + scale_weights = torch.sigmoid(torch.diagonal(weighted_corr, dim1=1, dim2=2)) + scale_weights = scale_weights.reshape(self.batch_size, self.length, self.scale_n, self.num_spks) + scale_weights = self.softmax(scale_weights) + return scale_weights + + def conv_scale_weights(self, ms_avg_embs_perm, ms_emb_seq_single): + """ + Use multiple Convnet layers to estimate the scale weights based on the cluster-average embedding and + input embedding sequence. + + Args: + ms_avg_embs_perm (Tensor): + Tensor containing cluster-average speaker embeddings for each scale. + Shape: (batch_size, length, scale_n, emb_dim) + ms_emb_seq_single (Tensor): + Tensor containing multi-scale speaker embedding sequences. ms_emb_seq_single is input from the + given audio stream input. + Shape: (batch_size, length, num_spks, emb_dim) + + Returns: + scale_weights (Tensor): + Weight vectors that determine the weight of each scale. + Shape: (batch_size, length, num_spks, emb_dim) + """ + ms_cnn_input_seq = torch.cat([ms_avg_embs_perm, ms_emb_seq_single], dim=2) + ms_cnn_input_seq = ms_cnn_input_seq.unsqueeze(2).flatten(0, 1) + + conv_out = self.conv_forward( + ms_cnn_input_seq, conv_module=self.conv[0], bn_module=self.conv_bn[0], first_layer=True + ) + for conv_idx in range(1, self.conv_repeat + 1): + conv_out = self.conv_forward( + conv_input=conv_out, + conv_module=self.conv[conv_idx], + bn_module=self.conv_bn[conv_idx], + first_layer=False, + ) + + lin_input_seq = conv_out.view(self.batch_size, self.length, self.cnn_output_ch * self.emb_dim) + hidden_seq = self.conv_to_linear(lin_input_seq) + hidden_seq = self.dropout(F.leaky_relu(hidden_seq)) + scale_weights = self.softmax(self.linear_to_weights(hidden_seq)) + scale_weights = scale_weights.unsqueeze(3).expand(-1, -1, -1, self.num_spks) + return scale_weights + + def conv_forward(self, conv_input, conv_module, bn_module, first_layer=False): + """ + A module for convolutional neural networks with 1-D filters. As a unit layer batch normalization, non-linear layer and dropout + modules are included. + + Note: + If `first_layer=True`, the input shape is set for processing embedding input. + If `first_layer=False`, then the input shape is set for processing the output from another `conv_forward` module. + + Args: + conv_input (Tensor): + Reshaped tensor containing cluster-average embeddings and multi-scale embedding sequences. + Shape: (batch_size*length, 1, scale_n*(num_spks+1), emb_dim) + conv_module (ConvLayer): + ConvLayer instance containing torch.nn.modules.conv modules. + bn_module (torch.nn.modules.batchnorm.BatchNorm2d): + Predefined Batchnorm module. + first_layer (bool): + Boolean for switching between the first layer and the others. + Default: `False` + + Returns: + conv_out (Tensor): + Convnet output that can be fed to another ConvLayer module or linear layer. + Shape: (batch_size*length, 1, cnn_output_ch, emb_dim) + """ + conv_out = conv_module(conv_input) + conv_out = conv_out.permute(0, 2, 1, 3) if not first_layer else conv_out + conv_out = conv_out.reshape(self.batch_size, self.length, self.cnn_output_ch, self.emb_dim) + conv_out = conv_out.unsqueeze(2).flatten(0, 1) + conv_out = bn_module(conv_out.permute(0, 3, 2, 1)).permute(0, 3, 2, 1) + conv_out = self.dropout(F.leaky_relu(conv_out)) + return conv_out + + @typecheck() + def forward(self, ms_emb_seq, length, ms_avg_embs, targets): + preds, scale_weights = self.core_model(ms_emb_seq, length, ms_avg_embs, targets) + return preds, scale_weights + + def input_example(self): + """ + Generate input examples for tracing etc. + + Returns (tuple): + A tuple of input examples. + """ + device = next(self.parameters()).device + lens = torch.full(size=(input_example.shape[0],), fill_value=123, device=device) + input_example = torch.randn(1, lens, self.scale_n, self.emb_dim, device=device) + avg_embs = torch.randn(1, self.scale_n, self.emb_dim, self.num_spks, device=device) + targets = torch.randn(1, lens, self.num_spks).round().float() + return tuple([input_example, lens, avg_embs, targets]) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/rnn_encoder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/rnn_encoder.py new file mode 100644 index 0000000..0ebb89f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/rnn_encoder.py @@ -0,0 +1,178 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections import OrderedDict + +import torch +import torch.distributed +import torch.nn as nn + +from nemo.collections.asr.parts.submodules.subsampling import ConvSubsampling, StackingSubsampling +from nemo.core.classes.common import typecheck +from nemo.core.classes.exportable import Exportable +from nemo.core.classes.module import NeuralModule +from nemo.core.neural_types import AcousticEncodedRepresentation, LengthsType, NeuralType, SpectrogramType + +__all__ = ['RNNEncoder'] + + +class RNNEncoder(NeuralModule, Exportable): + """ + The RNN-based encoder for ASR models. + Followed the architecture suggested in the following paper: + 'STREAMING END-TO-END SPEECH RECOGNITION FOR MOBILE DEVICES' by Yanzhang He et al. + https://arxiv.org/pdf/1811.06621.pdf + + + Args: + feat_in (int): the size of feature channels + n_layers (int): number of layers of RNN + d_model (int): the hidden size of the model + proj_size (int): the size of the output projection after each RNN layer + rnn_type (str): the type of the RNN layers, choices=['lstm, 'gru', 'rnn'] + bidirectional (float): specifies whether RNN layers should be bidirectional or not + Defaults to True. + feat_out (int): the size of the output features + Defaults to -1 (means feat_out is d_model) + subsampling (str): the method of subsampling, choices=['stacking, 'vggnet', 'striding'] + Defaults to stacking. + subsampling_factor (int): the subsampling factor + Defaults to 4. + subsampling_conv_channels (int): the size of the convolutions in the subsampling module for vggnet and striding + Defaults to -1 which would set it to d_model. + dropout (float): the dropout rate used between all layers + Defaults to 0.2. + """ + + def input_example(self): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + input_example = torch.randn(16, self._feat_in, 256).to(next(self.parameters()).device) + input_example_length = torch.randint(0, 256, (16,)).to(next(self.parameters()).device) + return tuple([input_example, input_example_length]) + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return OrderedDict( + { + "audio_signal": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + ) + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return OrderedDict( + { + "outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + } + ) + + def __init__( + self, + feat_in: int, + n_layers: int, + d_model: int, + proj_size: int = -1, + rnn_type: str = 'lstm', + bidirectional: bool = True, + subsampling: str = 'striding', + subsampling_factor: int = 4, + subsampling_conv_channels: int = -1, + dropout: float = 0.2, + ): + super().__init__() + + self.d_model = d_model + self._feat_in = feat_in + + if subsampling_conv_channels == -1: + subsampling_conv_channels = proj_size + if subsampling and subsampling_factor > 1: + if subsampling in ['stacking', 'stacking_norm']: + self.pre_encode = StackingSubsampling( + subsampling_factor=subsampling_factor, + feat_in=feat_in, + feat_out=proj_size, + norm=True if 'norm' in subsampling else False, + ) + else: + self.pre_encode = ConvSubsampling( + subsampling=subsampling, + subsampling_factor=subsampling_factor, + feat_in=feat_in, + feat_out=proj_size, + conv_channels=subsampling_conv_channels, + activation=nn.ReLU(), + ) + else: + self.pre_encode = nn.Linear(feat_in, proj_size) + + self._feat_out = proj_size + + self.layers = nn.ModuleList() + + SUPPORTED_RNN = {"lstm": nn.LSTM, "gru": nn.GRU, "rnn": nn.RNN} + if rnn_type not in SUPPORTED_RNN: + raise ValueError(f"rnn_type can be one from the following:{SUPPORTED_RNN.keys()}") + else: + rnn_module = SUPPORTED_RNN[rnn_type] + + for i in range(n_layers): + rnn_proj_size = proj_size // 2 if bidirectional else proj_size + if rnn_type == "lstm": + layer = rnn_module( + input_size=self._feat_out, + hidden_size=d_model, + num_layers=1, + batch_first=True, + bidirectional=bidirectional, + proj_size=rnn_proj_size, + ) + self.layers.append(layer) + self.layers.append(nn.LayerNorm(proj_size)) + self.layers.append(nn.Dropout(p=dropout)) + self._feat_out = proj_size + + @typecheck() + def forward(self, audio_signal, length=None): + max_audio_length: int = audio_signal.size(-1) + + if length is None: + length = audio_signal.new_full( + audio_signal.size(0), max_audio_length, dtype=torch.int32, device=self.seq_range.device + ) + + audio_signal = torch.transpose(audio_signal, 1, 2) + + if isinstance(self.pre_encode, nn.Linear): + audio_signal = self.pre_encode(audio_signal) + else: + audio_signal, length = self.pre_encode(audio_signal, length) + + for lth, layer in enumerate(self.layers): + audio_signal = layer(audio_signal) + if isinstance(audio_signal, tuple): + audio_signal, _ = audio_signal + + audio_signal = torch.transpose(audio_signal, 1, 2) + return audio_signal, length diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/rnnt.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/rnnt.py new file mode 100644 index 0000000..5a7457f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/rnnt.py @@ -0,0 +1,2233 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright 2017 Johns Hopkins University (Shinji Watanabe) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.asr.modules import rnnt_abstract +from nemo.collections.asr.parts.submodules import stateless_net +from nemo.collections.asr.parts.utils import adapter_utils, rnnt_utils +from nemo.collections.common.parts import rnn +from nemo.core.classes import adapter_mixins, typecheck +from nemo.core.classes.exportable import Exportable +from nemo.core.classes.mixins import AdapterModuleMixin +from nemo.core.neural_types import ( + AcousticEncodedRepresentation, + ElementType, + EmbeddedTextType, + LabelsType, + LengthsType, + LogprobsType, + LossType, + NeuralType, + SpectrogramType, +) +from nemo.utils import logging + + +class StatelessTransducerDecoder(rnnt_abstract.AbstractRNNTDecoder, Exportable): + """A Stateless Neural Network Transducer Decoder / Prediction Network. + An RNN-T Decoder/Prediction stateless network that simply takes concatenation of embeddings of the history tokens as the output. + + Args: + prednet: A dict-like object which contains the following key-value pairs. + pred_hidden: int specifying the hidden dimension of the prediction net. + + dropout: float, set to 0.0 by default. Optional dropout applied at the end of the final LSTM RNN layer. + + vocab_size: int, specifying the vocabulary size of the embedding layer of the Prediction network, + excluding the RNNT blank token. + + context_size: int, specifying the size of the history context used for this decoder. + + normalization_mode: Can be either None, 'layer'. By default, is set to None. + Defines the type of normalization applied to the RNN layer. + + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "targets": NeuralType(('B', 'T'), LabelsType()), + "target_length": NeuralType(tuple('B'), LengthsType()), + "states": [NeuralType(('B', 'T'), LabelsType(), optional=True)], + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return { + "outputs": NeuralType(('B', 'D', 'T'), EmbeddedTextType()), + "prednet_lengths": NeuralType(tuple('B'), LengthsType()), + "states": [NeuralType(('B', 'T'), LabelsType(), optional=True)], + } + + def input_example(self, max_batch=1, max_dim=1): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + length = max_dim + targets = torch.full(fill_value=self.blank_idx, size=(max_batch, length), dtype=torch.int32).to( + next(self.parameters()).device + ) + target_length = torch.randint(0, length, size=(max_batch,), dtype=torch.int32).to( + next(self.parameters()).device + ) + states = tuple(self.initialize_state(targets.float())) + return (targets, target_length, states) + + def _prepare_for_export(self, **kwargs): + self._rnnt_export = True + super()._prepare_for_export(**kwargs) + + def __init__( + self, + prednet: Dict[str, Any], + vocab_size: int, + context_size: int = 1, + normalization_mode: Optional[str] = None, + ): + # Required arguments + self.pred_hidden = prednet['pred_hidden'] + self.blank_idx = vocab_size + self.context_size = context_size + + # Initialize the model (blank token increases vocab size by 1) + super().__init__(vocab_size=vocab_size, blank_idx=self.blank_idx, blank_as_pad=True) + + # Optional arguments + dropout = prednet.get('dropout', 0.0) + + self.prediction = self._predict_modules( + **{ + "context_size": context_size, + "vocab_size": vocab_size, + "emb_dim": self.pred_hidden, + "blank_idx": self.blank_idx, + "normalization_mode": normalization_mode, + "dropout": dropout, + } + ) + self._rnnt_export = False + + @typecheck() + def forward(self, targets, target_length, states=None): + # y: (B, U) + y = rnn.label_collate(targets) + + # state maintenance is unnecessary during training forward call + # to get state, use .predict() method. + if self._rnnt_export: + add_sos = False + else: + add_sos = True + + g, state = self.predict(y, state=states, add_sos=add_sos) # (B, U, D) + g = g.transpose(1, 2) # (B, D, U) + + return g, target_length, state + + def predict( + self, + y: Optional[torch.Tensor] = None, + state: Optional[torch.Tensor] = None, + add_sos: bool = True, + batch_size: Optional[int] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """ + Stateful prediction of scores and state for a tokenset. + + Here: + B - batch size + U - label length + C - context size for stateless decoder + D - total embedding size + + Args: + y: Optional torch tensor of shape [B, U] of dtype long which will be passed to the Embedding. + If None, creates a zero tensor of shape [B, 1, D] which mimics output of pad-token on Embedding. + + state: An optional one-element list of one tensor. The tensor is used to store previous context labels. + The tensor uses type long and is of shape [B, C]. + + add_sos: bool flag, whether a zero vector describing a "start of signal" token should be + prepended to the above "y" tensor. When set, output size is (B, U + 1, D). + + batch_size: An optional int, specifying the batch size of the `y` tensor. + Can be infered if `y` and `state` is None. But if both are None, then batch_size cannot be None. + + Returns: + A tuple (g, state) such that - + + If add_sos is False: + + g: + (B, U, D) + + state: + [(B, C)] storing the history context including the new words in y. + + If add_sos is True: + + g: + (B, U + 1, D) + + state: + [(B, C)] storing the history context including the new words in y. + + """ + # Get device and dtype of current module + _p = next(self.parameters()) + device = _p.device + dtype = _p.dtype + + # If y is not None, it is of shape [B, U] with dtype long. + if y is not None: + if y.device != device: + y = y.to(device) + + y, state = self.prediction(y, state) + + else: + # Y is not provided, assume zero tensor with shape [B, 1, D] is required + # Emulates output of embedding of pad token. + if batch_size is None: + B = 1 if state is None else state[0].size(1) + else: + B = batch_size + + y = torch.zeros((B, 1, self.pred_hidden), device=device, dtype=dtype) + + # Prepend blank "start of sequence" symbol (zero tensor) + if add_sos: + B, U, D = y.shape + start = torch.zeros((B, 1, D), device=y.device, dtype=y.dtype) + y = torch.cat([start, y], dim=1).contiguous() # (B, U + 1, D) + else: + start = None # makes del call later easier + + del start + return y, state + + def _predict_modules(self, **kwargs): + """ + Prepare the trainable parameters of the Prediction Network. + + Args: + vocab_size: Vocab size (excluding the blank token). + pred_n_hidden: Hidden size of the RNNs. + norm: Type of normalization to perform in RNN. + dropout: Whether to apply dropout to RNN. + """ + + net = stateless_net.StatelessNet(**kwargs) + return net + + def score_hypothesis( + self, hypothesis: rnnt_utils.Hypothesis, cache: Dict[Tuple[int], Any] + ) -> Tuple[torch.Tensor, List[torch.Tensor], torch.Tensor]: + """ + Similar to the predict() method, instead this method scores a Hypothesis during beam search. + Hypothesis is a dataclass representing one hypothesis in a Beam Search. + + Args: + hypothesis: Refer to rnnt_utils.Hypothesis. + cache: Dict which contains a cache to avoid duplicate computations. + + Returns: + Returns a tuple (y, states, lm_token) such that: + y is a torch.Tensor of shape [1, 1, H] representing the score of the last token in the Hypothesis. + state is a list of RNN states, each of shape [L, 1, H]. + lm_token is the final integer token of the hypothesis. + """ + if hypothesis.dec_state is not None: + device = hypothesis.dec_state[0].device + else: + _p = next(self.parameters()) + device = _p.device + + # parse "blank" tokens in hypothesis + if len(hypothesis.y_sequence) > 0 and hypothesis.y_sequence[-1] == self.blank_idx: + blank_state = True + else: + blank_state = False + + # Convert last token of hypothesis to torch.Tensor + target = torch.full([1, 1], fill_value=hypothesis.y_sequence[-1], device=device, dtype=torch.long) + lm_token = target[:, -1] # [1] + + # Convert current hypothesis into a tuple to preserve in cache + sequence = tuple(hypothesis.y_sequence) + + if sequence in cache: + y, new_state = cache[sequence] + else: + # Obtain score for target token and new states + if blank_state: + y, new_state = self.predict(None, state=None, add_sos=False, batch_size=1) # [1, 1, H] + + else: + y, new_state = self.predict( + target, state=hypothesis.dec_state, add_sos=False, batch_size=1 + ) # [1, 1, H] + + y = y[:, -1:, :] # Extract just last state : [1, 1, H] + cache[sequence] = (y, new_state) + + return y, new_state, lm_token + + def initialize_state(self, y: torch.Tensor) -> List[torch.Tensor]: + batch = y.size(0) + # state contains context_size - 1 elements for each utterance in batch, + # consistent with the state returned from StatelessNet.forward + state = [torch.ones([batch, self.context_size - 1], dtype=torch.long, device=y.device) * self.blank_idx] + return state + + def batch_initialize_states(self, batch_states: List[torch.Tensor], decoder_states: List[List[torch.Tensor]]): + """ + Create batch of decoder states. + + Args: + batch_states (list): batch of decoder states + ([(B, H)]) + + decoder_states (list of list): list of decoder states + [B x ([(1, C)]] + + Returns: + batch_states (tuple): batch of decoder states + ([(B, C)]) + """ + new_state = torch.stack([s[0] for s in decoder_states]) + + return [new_state] + + def batch_select_state(self, batch_states: List[torch.Tensor], idx: int) -> List[List[torch.Tensor]]: + """Get decoder state from batch of states, for given id. + + Args: + batch_states (list): batch of decoder states + [(B, C)] + + idx (int): index to extract state from batch of states + + Returns: + (tuple): decoder states for given id + [(C)] + """ + if batch_states is not None: + states = batch_states[0][idx] + states = ( + states.long() + ) # beam search code assumes the batch_states tensor is always of float type, so need conversion + return [states] + else: + return None + + def batch_concat_states(self, batch_states: List[List[torch.Tensor]]) -> List[torch.Tensor]: + """Concatenate a batch of decoder state to a packed state. + + Args: + batch_states (list): batch of decoder states + B x ([(C)] + + Returns: + (tuple): decoder states + [(B x C)] + """ + state_list = [] + batch_list = [] + for sample_id in range(len(batch_states)): + tensor = torch.stack(batch_states[sample_id]) # [1, H] + batch_list.append(tensor) + + state_tensor = torch.cat(batch_list, 0) # [B, H] + state_list.append(state_tensor) + + return state_list + + @classmethod + def batch_replace_states_mask( + cls, src_states: list[torch.Tensor], dst_states: list[torch.Tensor], mask: torch.Tensor, + ): + """Replace states in dst_states with states from src_states using the mask""" + # same as `dst_states[0][mask] = src_states[0][mask]`, but non-blocking + torch.where(mask.unsqueeze(-1), src_states[0], dst_states[0], out=dst_states[0]) + + @classmethod + def batch_replace_states_all( + cls, src_states: list[torch.Tensor], dst_states: list[torch.Tensor], + ): + """Replace states in dst_states with states from src_states""" + dst_states[0].copy_(src_states[0]) + + def batch_split_states(self, batch_states: list[torch.Tensor]) -> list[list[torch.Tensor]]: + """ + Split states into a list of states. + Useful for splitting the final state for converting results of the decoding algorithm to Hypothesis class. + """ + return [sub_state.split(1, dim=0) for sub_state in batch_states] + + def batch_copy_states( + self, + old_states: List[torch.Tensor], + new_states: List[torch.Tensor], + ids: List[int], + value: Optional[float] = None, + ) -> List[torch.Tensor]: + """Copy states from new state to old state at certain indices. + + Args: + old_states: packed decoder states + single element list of (B x C) + + new_states: packed decoder states + single element list of (B x C) + + ids (list): List of indices to copy states at. + + value (optional float): If a value should be copied instead of a state slice, a float should be provided + + Returns: + batch of decoder states with partial copy at ids (or a specific value). + (B x C) + """ + + if value is None: + old_states[0][ids, :] = new_states[0][ids, :] + + return old_states + + def mask_select_states( + self, states: Optional[List[torch.Tensor]], mask: torch.Tensor + ) -> Optional[List[torch.Tensor]]: + """ + Return states by mask selection + Args: + states: states for the batch + mask: boolean mask for selecting states; batch dimension should be the same as for states + + Returns: + states filtered by mask + """ + if states is None: + return None + return [states[0][mask]] + + def batch_score_hypothesis( + self, hypotheses: List[rnnt_utils.Hypothesis], cache: Dict[Tuple[int], Any], batch_states: List[torch.Tensor] + ) -> Tuple[torch.Tensor, List[torch.Tensor], torch.Tensor]: + """ + Used for batched beam search algorithms. Similar to score_hypothesis method. + + Args: + hypothesis: List of Hypotheses. Refer to rnnt_utils.Hypothesis. + cache: Dict which contains a cache to avoid duplicate computations. + batch_states: List of torch.Tensor which represent the states of the RNN for this batch. + Each state is of shape [L, B, H] + + Returns: + Returns a tuple (b_y, b_states, lm_tokens) such that: + b_y is a torch.Tensor of shape [B, 1, H] representing the scores of the last tokens in the Hypotheses. + b_state is a list of list of RNN states, each of shape [L, B, H]. + Represented as B x List[states]. + lm_token is a list of the final integer tokens of the hypotheses in the batch. + """ + final_batch = len(hypotheses) + if final_batch == 0: + raise ValueError("No hypotheses was provided for the batch!") + + _p = next(self.parameters()) + device = _p.device + dtype = _p.dtype + + tokens = [] + process = [] + done = [None for _ in range(final_batch)] + + # For each hypothesis, cache the last token of the sequence and the current states + for i, hyp in enumerate(hypotheses): + sequence = tuple(hyp.y_sequence) + + if sequence in cache: + done[i] = cache[sequence] + else: + tokens.append(hyp.y_sequence[-1]) + process.append((sequence, hyp.dec_state)) + + if process: + batch = len(process) + + # convert list of tokens to torch.Tensor, then reshape. + tokens = torch.tensor(tokens, device=device, dtype=torch.long).view(batch, -1) + dec_states = self.initialize_state(tokens) # [B, C] + dec_states = self.batch_initialize_states(dec_states, [d_state for seq, d_state in process]) + + y, dec_states = self.predict( + tokens, state=dec_states, add_sos=False, batch_size=batch + ) # [B, 1, H], List([L, 1, H]) + + dec_states = tuple(state.to(dtype=dtype) for state in dec_states) + + # Update done states and cache shared by entire batch. + j = 0 + for i in range(final_batch): + if done[i] is None: + # Select sample's state from the batch state list + new_state = self.batch_select_state(dec_states, j) + + # Cache [1, H] scores of the current y_j, and its corresponding state + done[i] = (y[j], new_state) + cache[process[j][0]] = (y[j], new_state) + + j += 1 + + # Set the incoming batch states with the new states obtained from `done`. + batch_states = self.batch_initialize_states(batch_states, [d_state for y_j, d_state in done]) + + # Create batch of all output scores + # List[1, 1, H] -> [B, 1, H] + batch_y = torch.stack([y_j for y_j, d_state in done]) + + # Extract the last tokens from all hypotheses and convert to a tensor + lm_tokens = torch.tensor([h.y_sequence[-1] for h in hypotheses], device=device, dtype=torch.long).view( + final_batch + ) + + return batch_y, batch_states, lm_tokens + + +class RNNTDecoder(rnnt_abstract.AbstractRNNTDecoder, Exportable, AdapterModuleMixin): + """A Recurrent Neural Network Transducer Decoder / Prediction Network (RNN-T Prediction Network). + An RNN-T Decoder/Prediction network, comprised of a stateful LSTM model. + + Args: + prednet: A dict-like object which contains the following key-value pairs. + + pred_hidden: + int specifying the hidden dimension of the prediction net. + + pred_rnn_layers: + int specifying the number of rnn layers. + + Optionally, it may also contain the following: + + forget_gate_bias: + float, set by default to 1.0, which constructs a forget gate + initialized to 1.0. + Reference: + [An Empirical Exploration of Recurrent Network Architectures](http://proceedings.mlr.press/v37/jozefowicz15.pdf) + + t_max: + int value, set to None by default. If an int is specified, performs Chrono Initialization + of the LSTM network, based on the maximum number of timesteps `t_max` expected during the course + of training. + Reference: + [Can recurrent neural networks warp time?](https://openreview.net/forum?id=SJcKhk-Ab) + + weights_init_scale: + Float scale of the weights after initialization. Setting to lower than one + sometimes helps reduce variance between runs. + + hidden_hidden_bias_scale: + Float scale for the hidden-to-hidden bias scale. Set to 0.0 for + the default behaviour. + + dropout: + float, set to 0.0 by default. Optional dropout applied at the end of the final LSTM RNN layer. + + vocab_size: int, specifying the vocabulary size of the embedding layer of the Prediction network, + excluding the RNNT blank token. + + normalization_mode: Can be either None, 'batch' or 'layer'. By default, is set to None. + Defines the type of normalization applied to the RNN layer. + + random_state_sampling: bool, set to False by default. When set, provides normal-distribution + sampled state tensors instead of zero tensors during training. + Reference: + [Recognizing long-form speech using streaming end-to-end models](https://arxiv.org/abs/1910.11455) + + blank_as_pad: bool, set to True by default. When set, will add a token to the Embedding layer of this + prediction network, and will treat this token as a pad token. In essence, the RNNT pad token will + be treated as a pad token, and the embedding layer will return a zero tensor for this token. + + It is set by default as it enables various batch optimizations required for batched beam search. + Therefore, it is not recommended to disable this flag. + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "targets": NeuralType(('B', 'T'), LabelsType()), + "target_length": NeuralType(tuple('B'), LengthsType()), + "states": [NeuralType(('D', 'B', 'D'), ElementType(), optional=True)], # must always be last + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return { + "outputs": NeuralType(('B', 'D', 'T'), EmbeddedTextType()), + "prednet_lengths": NeuralType(tuple('B'), LengthsType()), + "states": [NeuralType((('D', 'B', 'D')), ElementType(), optional=True)], # must always be last + } + + def input_example(self, max_batch=1, max_dim=1): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + length = max_dim + targets = torch.full(fill_value=self.blank_idx, size=(max_batch, length), dtype=torch.int32).to( + next(self.parameters()).device + ) + target_length = torch.randint(0, length, size=(max_batch,), dtype=torch.int32).to( + next(self.parameters()).device + ) + states = tuple(self.initialize_state(targets.float())) + return (targets, target_length, states) + + def _prepare_for_export(self, **kwargs): + self._rnnt_export = True + super()._prepare_for_export(**kwargs) + + def __init__( + self, + prednet: Dict[str, Any], + vocab_size: int, + normalization_mode: Optional[str] = None, + random_state_sampling: bool = False, + blank_as_pad: bool = True, + ): + # Required arguments + self.pred_hidden = prednet['pred_hidden'] + self.pred_rnn_layers = prednet["pred_rnn_layers"] + self.blank_idx = vocab_size + + # Initialize the model (blank token increases vocab size by 1) + super().__init__(vocab_size=vocab_size, blank_idx=self.blank_idx, blank_as_pad=blank_as_pad) + + # Optional arguments + forget_gate_bias = prednet.get('forget_gate_bias', 1.0) + t_max = prednet.get('t_max', None) + weights_init_scale = prednet.get('weights_init_scale', 1.0) + hidden_hidden_bias_scale = prednet.get('hidden_hidden_bias_scale', 0.0) + dropout = prednet.get('dropout', 0.0) + self.random_state_sampling = random_state_sampling + + self.prediction = self._predict_modules( + vocab_size=vocab_size, # add 1 for blank symbol + pred_n_hidden=self.pred_hidden, + pred_rnn_layers=self.pred_rnn_layers, + forget_gate_bias=forget_gate_bias, + t_max=t_max, + norm=normalization_mode, + weights_init_scale=weights_init_scale, + hidden_hidden_bias_scale=hidden_hidden_bias_scale, + dropout=dropout, + rnn_hidden_size=prednet.get("rnn_hidden_size", -1), + ) + self._rnnt_export = False + + @typecheck() + def forward(self, targets, target_length, states=None): + # y: (B, U) + y = rnn.label_collate(targets) + + # state maintenance is unnecessary during training forward call + # to get state, use .predict() method. + if self._rnnt_export: + add_sos = False + else: + add_sos = True + + g, states = self.predict(y, state=states, add_sos=add_sos) # (B, U, D) + g = g.transpose(1, 2) # (B, D, U) + + return g, target_length, states + + def predict( + self, + y: Optional[torch.Tensor] = None, + state: Optional[List[torch.Tensor]] = None, + add_sos: bool = True, + batch_size: Optional[int] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """ + Stateful prediction of scores and state for a (possibly null) tokenset. + This method takes various cases into consideration : + - No token, no state - used for priming the RNN + - No token, state provided - used for blank token scoring + - Given token, states - used for scores + new states + + Here: + B - batch size + U - label length + H - Hidden dimension size of RNN + L - Number of RNN layers + + Args: + y: Optional torch tensor of shape [B, U] of dtype long which will be passed to the Embedding. + If None, creates a zero tensor of shape [B, 1, H] which mimics output of pad-token on EmbeddiNg. + + state: An optional list of states for the RNN. Eg: For LSTM, it is the state list length is 2. + Each state must be a tensor of shape [L, B, H]. + If None, and during training mode and `random_state_sampling` is set, will sample a + normal distribution tensor of the above shape. Otherwise, None will be passed to the RNN. + + add_sos: bool flag, whether a zero vector describing a "start of signal" token should be + prepended to the above "y" tensor. When set, output size is (B, U + 1, H). + + batch_size: An optional int, specifying the batch size of the `y` tensor. + Can be infered if `y` and `state` is None. But if both are None, then batch_size cannot be None. + + Returns: + A tuple (g, hid) such that - + + If add_sos is False: + + g: + (B, U, H) + + hid: + (h, c) where h is the final sequence hidden state and c is the final cell state: + + h (tensor), shape (L, B, H) + + c (tensor), shape (L, B, H) + + If add_sos is True: + g: + (B, U + 1, H) + + hid: + (h, c) where h is the final sequence hidden state and c is the final cell state: + + h (tensor), shape (L, B, H) + + c (tensor), shape (L, B, H) + + """ + # Get device and dtype of current module + _p = next(self.parameters()) + device = _p.device + dtype = _p.dtype + + # If y is not None, it is of shape [B, U] with dtype long. + if y is not None: + if y.device != device: + y = y.to(device) + + # (B, U) -> (B, U, H) + y = self.prediction["embed"](y) + else: + # Y is not provided, assume zero tensor with shape [B, 1, H] is required + # Emulates output of embedding of pad token. + if batch_size is None: + B = 1 if state is None else state[0].size(1) + else: + B = batch_size + + y = torch.zeros((B, 1, self.pred_hidden), device=device, dtype=dtype) + + # Prepend blank "start of sequence" symbol (zero tensor) + if add_sos: + B, U, H = y.shape + start = torch.zeros((B, 1, H), device=y.device, dtype=y.dtype) + y = torch.cat([start, y], dim=1).contiguous() # (B, U + 1, H) + else: + start = None # makes del call later easier + + # If in training mode, and random_state_sampling is set, + # initialize state to random normal distribution tensor. + if state is None: + if self.random_state_sampling and self.training: + state = self.initialize_state(y) + + # Forward step through RNN + y = y.transpose(0, 1) # (U + 1, B, H) + g, hid = self.prediction["dec_rnn"](y, state) + g = g.transpose(0, 1) # (B, U + 1, H) + + del y, start, state + + # Adapter module forward step + if self.is_adapter_available(): + g = self.forward_enabled_adapters(g) + + return g, hid + + def _predict_modules( + self, + vocab_size, + pred_n_hidden, + pred_rnn_layers, + forget_gate_bias, + t_max, + norm, + weights_init_scale, + hidden_hidden_bias_scale, + dropout, + rnn_hidden_size, + ): + """ + Prepare the trainable parameters of the Prediction Network. + + Args: + vocab_size: Vocab size (excluding the blank token). + pred_n_hidden: Hidden size of the RNNs. + pred_rnn_layers: Number of RNN layers. + forget_gate_bias: Whether to perform unit forget gate bias. + t_max: Whether to perform Chrono LSTM init. + norm: Type of normalization to perform in RNN. + weights_init_scale: Float scale of the weights after initialization. Setting to lower than one + sometimes helps reduce variance between runs. + hidden_hidden_bias_scale: Float scale for the hidden-to-hidden bias scale. Set to 0.0 for + the default behaviour. + dropout: Whether to apply dropout to RNN. + rnn_hidden_size: the hidden size of the RNN, if not specified, pred_n_hidden would be used + """ + if self.blank_as_pad: + embed = torch.nn.Embedding(vocab_size + 1, pred_n_hidden, padding_idx=self.blank_idx) + else: + embed = torch.nn.Embedding(vocab_size, pred_n_hidden) + + layers = torch.nn.ModuleDict( + { + "embed": embed, + "dec_rnn": rnn.rnn( + input_size=pred_n_hidden, + hidden_size=rnn_hidden_size if rnn_hidden_size > 0 else pred_n_hidden, + num_layers=pred_rnn_layers, + norm=norm, + forget_gate_bias=forget_gate_bias, + t_max=t_max, + dropout=dropout, + weights_init_scale=weights_init_scale, + hidden_hidden_bias_scale=hidden_hidden_bias_scale, + proj_size=pred_n_hidden if pred_n_hidden < rnn_hidden_size else 0, + ), + } + ) + return layers + + def initialize_state(self, y: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Initialize the state of the LSTM layers, with same dtype and device as input `y`. + LSTM accepts a tuple of 2 tensors as a state. + + Args: + y: A torch.Tensor whose device the generated states will be placed on. + + Returns: + Tuple of 2 tensors, each of shape [L, B, H], where + + L = Number of RNN layers + + B = Batch size + + H = Hidden size of RNN. + """ + batch = y.size(0) + if self.random_state_sampling and self.training: + state = ( + torch.randn(self.pred_rnn_layers, batch, self.pred_hidden, dtype=y.dtype, device=y.device), + torch.randn(self.pred_rnn_layers, batch, self.pred_hidden, dtype=y.dtype, device=y.device), + ) + + else: + state = ( + torch.zeros(self.pred_rnn_layers, batch, self.pred_hidden, dtype=y.dtype, device=y.device), + torch.zeros(self.pred_rnn_layers, batch, self.pred_hidden, dtype=y.dtype, device=y.device), + ) + return state + + def score_hypothesis( + self, hypothesis: rnnt_utils.Hypothesis, cache: Dict[Tuple[int], Any] + ) -> Tuple[torch.Tensor, List[torch.Tensor], torch.Tensor]: + """ + Similar to the predict() method, instead this method scores a Hypothesis during beam search. + Hypothesis is a dataclass representing one hypothesis in a Beam Search. + + Args: + hypothesis: Refer to rnnt_utils.Hypothesis. + cache: Dict which contains a cache to avoid duplicate computations. + + Returns: + Returns a tuple (y, states, lm_token) such that: + y is a torch.Tensor of shape [1, 1, H] representing the score of the last token in the Hypothesis. + state is a list of RNN states, each of shape [L, 1, H]. + lm_token is the final integer token of the hypothesis. + """ + if hypothesis.dec_state is not None: + device = hypothesis.dec_state[0].device + else: + _p = next(self.parameters()) + device = _p.device + + # parse "blank" tokens in hypothesis + if len(hypothesis.y_sequence) > 0 and hypothesis.y_sequence[-1] == self.blank_idx: + blank_state = True + else: + blank_state = False + + # Convert last token of hypothesis to torch.Tensor + target = torch.full([1, 1], fill_value=hypothesis.y_sequence[-1], device=device, dtype=torch.long) + lm_token = target[:, -1] # [1] + + # Convert current hypothesis into a tuple to preserve in cache + sequence = tuple(hypothesis.y_sequence) + + if sequence in cache: + y, new_state = cache[sequence] + else: + # Obtain score for target token and new states + if blank_state: + y, new_state = self.predict(None, state=None, add_sos=False, batch_size=1) # [1, 1, H] + + else: + y, new_state = self.predict( + target, state=hypothesis.dec_state, add_sos=False, batch_size=1 + ) # [1, 1, H] + + y = y[:, -1:, :] # Extract just last state : [1, 1, H] + cache[sequence] = (y, new_state) + + return y, new_state, lm_token + + def batch_score_hypothesis( + self, hypotheses: List[rnnt_utils.Hypothesis], cache: Dict[Tuple[int], Any], batch_states: List[torch.Tensor] + ) -> Tuple[torch.Tensor, List[torch.Tensor], torch.Tensor]: + """ + Used for batched beam search algorithms. Similar to score_hypothesis method. + + Args: + hypothesis: List of Hypotheses. Refer to rnnt_utils.Hypothesis. + cache: Dict which contains a cache to avoid duplicate computations. + batch_states: List of torch.Tensor which represent the states of the RNN for this batch. + Each state is of shape [L, B, H] + + Returns: + Returns a tuple (b_y, b_states, lm_tokens) such that: + b_y is a torch.Tensor of shape [B, 1, H] representing the scores of the last tokens in the Hypotheses. + b_state is a list of list of RNN states, each of shape [L, B, H]. + Represented as B x List[states]. + lm_token is a list of the final integer tokens of the hypotheses in the batch. + """ + final_batch = len(hypotheses) + + if final_batch == 0: + raise ValueError("No hypotheses was provided for the batch!") + + _p = next(self.parameters()) + device = _p.device + dtype = _p.dtype + + tokens = [] + process = [] + done = [None for _ in range(final_batch)] + + # For each hypothesis, cache the last token of the sequence and the current states + for i, hyp in enumerate(hypotheses): + sequence = tuple(hyp.y_sequence) + + if sequence in cache: + done[i] = cache[sequence] + else: + tokens.append(hyp.y_sequence[-1]) + process.append((sequence, hyp.dec_state)) + + if process: + batch = len(process) + + # convert list of tokens to torch.Tensor, then reshape. + tokens = torch.tensor(tokens, device=device, dtype=torch.long).view(batch, -1) + dec_states = self.initialize_state(tokens.to(dtype=dtype)) # [L, B, H] + dec_states = self.batch_initialize_states(dec_states, [d_state for seq, d_state in process]) + + y, dec_states = self.predict( + tokens, state=dec_states, add_sos=False, batch_size=batch + ) # [B, 1, H], List([L, 1, H]) + + dec_states = tuple(state.to(dtype=dtype) for state in dec_states) + + # Update done states and cache shared by entire batch. + j = 0 + for i in range(final_batch): + if done[i] is None: + # Select sample's state from the batch state list + new_state = self.batch_select_state(dec_states, j) + + # Cache [1, H] scores of the current y_j, and its corresponding state + done[i] = (y[j], new_state) + cache[process[j][0]] = (y[j], new_state) + + j += 1 + + # Set the incoming batch states with the new states obtained from `done`. + batch_states = self.batch_initialize_states(batch_states, [d_state for y_j, d_state in done]) + + # Create batch of all output scores + # List[1, 1, H] -> [B, 1, H] + batch_y = torch.stack([y_j for y_j, d_state in done]) + + # Extract the last tokens from all hypotheses and convert to a tensor + lm_tokens = torch.tensor([h.y_sequence[-1] for h in hypotheses], device=device, dtype=torch.long).view( + final_batch + ) + + return batch_y, batch_states, lm_tokens + + def batch_initialize_states(self, batch_states: List[torch.Tensor], decoder_states: List[List[torch.Tensor]]): + """ + Create batch of decoder states. + + Args: + batch_states (list): batch of decoder states + ([L x (B, H)], [L x (B, H)]) + + decoder_states (list of list): list of decoder states + [B x ([L x (1, H)], [L x (1, H)])] + + Returns: + batch_states (tuple): batch of decoder states + ([L x (B, H)], [L x (B, H)]) + """ + # LSTM has 2 states + new_states = [[] for _ in range(len(decoder_states[0]))] + for layer in range(self.pred_rnn_layers): + for state_id in range(len(decoder_states[0])): + # batch_states[state_id][layer] = torch.stack([s[state_id][layer] for s in decoder_states]) + new_state_for_layer = torch.stack([s[state_id][layer] for s in decoder_states]) + new_states[state_id].append(new_state_for_layer) + + for state_id in range(len(decoder_states[0])): + new_states[state_id] = torch.stack([state for state in new_states[state_id]]) + + return new_states + + def batch_select_state(self, batch_states: List[torch.Tensor], idx: int) -> List[List[torch.Tensor]]: + """Get decoder state from batch of states, for given id. + + Args: + batch_states (list): batch of decoder states + ([L x (B, H)], [L x (B, H)]) + + idx (int): index to extract state from batch of states + + Returns: + (tuple): decoder states for given id + ([L x (1, H)], [L x (1, H)]) + """ + if batch_states is not None: + state_list = [] + for state_id in range(len(batch_states)): + states = [batch_states[state_id][layer][idx] for layer in range(self.pred_rnn_layers)] + state_list.append(states) + + return state_list + else: + return None + + def batch_concat_states(self, batch_states: List[List[torch.Tensor]]) -> List[torch.Tensor]: + """Concatenate a batch of decoder state to a packed state. + + Args: + batch_states (list): batch of decoder states + B x ([L x (H)], [L x (H)]) + + Returns: + (tuple): decoder states + (L x B x H, L x B x H) + """ + state_list = [] + + for state_id in range(len(batch_states[0])): + batch_list = [] + for sample_id in range(len(batch_states)): + tensor = torch.stack(batch_states[sample_id][state_id]) # [L, H] + tensor = tensor.unsqueeze(0) # [1, L, H] + batch_list.append(tensor) + + state_tensor = torch.cat(batch_list, 0) # [B, L, H] + state_tensor = state_tensor.transpose(1, 0) # [L, B, H] + state_list.append(state_tensor) + + return state_list + + @classmethod + def batch_replace_states_mask( + cls, + src_states: Tuple[torch.Tensor, torch.Tensor], + dst_states: Tuple[torch.Tensor, torch.Tensor], + mask: torch.Tensor, + ): + """Replace states in dst_states with states from src_states using the mask""" + # same as `dst_states[i][mask] = src_states[i][mask]`, but non-blocking + # we need to cast, since LSTM is calculated in fp16 even if autocast to bfloat16 is enabled + dtype = dst_states[0].dtype + torch.where(mask.unsqueeze(0).unsqueeze(-1), src_states[0].to(dtype), dst_states[0], out=dst_states[0]) + torch.where(mask.unsqueeze(0).unsqueeze(-1), src_states[1].to(dtype), dst_states[1], out=dst_states[1]) + + @classmethod + def batch_replace_states_all( + cls, src_states: Tuple[torch.Tensor, torch.Tensor], dst_states: Tuple[torch.Tensor, torch.Tensor], + ): + """Replace states in dst_states with states from src_states""" + dst_states[0].copy_(src_states[0]) + dst_states[1].copy_(src_states[1]) + + def batch_split_states( + self, batch_states: Tuple[torch.Tensor, torch.Tensor] + ) -> list[Tuple[torch.Tensor, torch.Tensor]]: + """ + Split states into a list of states. + Useful for splitting the final state for converting results of the decoding algorithm to Hypothesis class. + """ + return list(zip(batch_states[0].split(1, dim=1), batch_states[1].split(1, dim=1))) + + def batch_copy_states( + self, + old_states: List[torch.Tensor], + new_states: List[torch.Tensor], + ids: List[int], + value: Optional[float] = None, + ) -> List[torch.Tensor]: + """Copy states from new state to old state at certain indices. + + Args: + old_states(list): packed decoder states + (L x B x H, L x B x H) + + new_states: packed decoder states + (L x B x H, L x B x H) + + ids (list): List of indices to copy states at. + + value (optional float): If a value should be copied instead of a state slice, a float should be provided + + Returns: + batch of decoder states with partial copy at ids (or a specific value). + (L x B x H, L x B x H) + """ + for state_id in range(len(old_states)): + if value is None: + old_states[state_id][:, ids, :] = new_states[state_id][:, ids, :] + else: + old_states[state_id][:, ids, :] *= 0.0 + old_states[state_id][:, ids, :] += value + + return old_states + + def mask_select_states( + self, states: Tuple[torch.Tensor, torch.Tensor], mask: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Return states by mask selection + Args: + states: states for the batch + mask: boolean mask for selecting states; batch dimension should be the same as for states + + Returns: + states filtered by mask + """ + # LSTM in PyTorch returns a tuple of 2 tensors as a state + return states[0][:, mask], states[1][:, mask] + + # Adapter method overrides + def add_adapter(self, name: str, cfg: DictConfig): + # Update the config with correct input dim + cfg = self._update_adapter_cfg_input_dim(cfg) + # Add the adapter + super().add_adapter(name=name, cfg=cfg) + + def _update_adapter_cfg_input_dim(self, cfg: DictConfig): + cfg = adapter_utils.update_adapter_cfg_input_dim(self, cfg, module_dim=self.pred_hidden) + return cfg + + +class RNNTJoint(rnnt_abstract.AbstractRNNTJoint, Exportable, AdapterModuleMixin): + """A Recurrent Neural Network Transducer Joint Network (RNN-T Joint Network). + An RNN-T Joint network, comprised of a feedforward model. + + Args: + jointnet: A dict-like object which contains the following key-value pairs. + encoder_hidden: int specifying the hidden dimension of the encoder net. + pred_hidden: int specifying the hidden dimension of the prediction net. + joint_hidden: int specifying the hidden dimension of the joint net + activation: Activation function used in the joint step. Can be one of + ['relu', 'tanh', 'sigmoid']. + + Optionally, it may also contain the following: + dropout: float, set to 0.0 by default. Optional dropout applied at the end of the joint net. + + num_classes: int, specifying the vocabulary size that the joint network must predict, + excluding the RNNT blank token. + + vocabulary: Optional list of strings/tokens that comprise the vocabulary of the joint network. + Unused and kept only for easy access for character based encoding RNNT models. + + log_softmax: Optional bool, set to None by default. If set as None, will compute the log_softmax() + based on the value provided. + + preserve_memory: Optional bool, set to False by default. If the model crashes due to the memory + intensive joint step, one might try this flag to empty the tensor cache in pytorch. + + Warning: This will make the forward-backward pass much slower than normal. + It also might not fix the OOM if the GPU simply does not have enough memory to compute the joint. + + fuse_loss_wer: Optional bool, set to False by default. + + Fuses the joint forward, loss forward and + wer forward steps. In doing so, it trades of speed for memory conservation by creating sub-batches + of the provided batch of inputs, and performs Joint forward, loss forward and wer forward (optional), + all on sub-batches, then collates results to be exactly equal to results from the entire batch. + + When this flag is set, prior to calling forward, the fields `loss` and `wer` (either one) *must* + be set using the `RNNTJoint.set_loss()` or `RNNTJoint.set_wer()` methods. + + Further, when this flag is set, the following argument `fused_batch_size` *must* be provided + as a non negative integer. This value refers to the size of the sub-batch. + + When the flag is set, the input and output signature of `forward()` of this method changes. + Input - in addition to `encoder_outputs` (mandatory argument), the following arguments can be provided. + + - decoder_outputs (optional). Required if loss computation is required. + + - encoder_lengths (required) + + - transcripts (optional). Required for wer calculation. + + - transcript_lengths (optional). Required for wer calculation. + + - compute_wer (bool, default false). Whether to compute WER or not for the fused batch. + + Output - instead of the usual `joint` log prob tensor, the following results can be returned. + + - loss (optional). Returned if decoder_outputs, transcripts and transript_lengths are not None. + + - wer_numerator + wer_denominator (optional). Returned if transcripts, transcripts_lengths are provided + and compute_wer is set. + + fused_batch_size: Optional int, required if `fuse_loss_wer` flag is set. Determines the size of the + sub-batches. Should be any value below the actual batch size per GPU. + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "encoder_outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "decoder_outputs": NeuralType(('B', 'D', 'T'), EmbeddedTextType()), + "encoder_lengths": NeuralType(tuple('B'), LengthsType(), optional=True), + "transcripts": NeuralType(('B', 'T'), LabelsType(), optional=True), + "transcript_lengths": NeuralType(tuple('B'), LengthsType(), optional=True), + "compute_wer": NeuralType(optional=True), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + if not self._fuse_loss_wer: + return { + "outputs": NeuralType(('B', 'T', 'T', 'D'), LogprobsType()), + } + + else: + return { + "loss": NeuralType(elements_type=LossType(), optional=True), + "wer": NeuralType(elements_type=ElementType(), optional=True), + "wer_numer": NeuralType(elements_type=ElementType(), optional=True), + "wer_denom": NeuralType(elements_type=ElementType(), optional=True), + } + + def _prepare_for_export(self, **kwargs): + self._fuse_loss_wer = False + self.log_softmax = False + super()._prepare_for_export(**kwargs) + + def input_example(self, max_batch=1, max_dim=8192): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + B, T, U = max_batch, max_dim, max_batch + encoder_outputs = torch.randn(B, self.encoder_hidden, T).to(next(self.parameters()).device) + decoder_outputs = torch.randn(B, self.pred_hidden, U).to(next(self.parameters()).device) + return (encoder_outputs, decoder_outputs) + + @property + def disabled_deployment_input_names(self): + """Implement this method to return a set of input names disabled for export""" + return set(["encoder_lengths", "transcripts", "transcript_lengths", "compute_wer"]) + + def __init__( + self, + jointnet: Dict[str, Any], + num_classes: int, + num_extra_outputs: int = 0, + vocabulary: Optional[List] = None, + log_softmax: Optional[bool] = None, + preserve_memory: bool = False, + fuse_loss_wer: bool = False, + fused_batch_size: Optional[int] = None, + experimental_fuse_loss_wer: Any = None, + ): + super().__init__() + + self.vocabulary = vocabulary + + self._vocab_size = num_classes + self._num_extra_outputs = num_extra_outputs + self._num_classes = num_classes + 1 + num_extra_outputs # 1 is for blank + + if experimental_fuse_loss_wer is not None: + # Override fuse_loss_wer from deprecated argument + fuse_loss_wer = experimental_fuse_loss_wer + + self._fuse_loss_wer = fuse_loss_wer + self._fused_batch_size = fused_batch_size + + if fuse_loss_wer and (fused_batch_size is None): + raise ValueError("If `fuse_loss_wer` is set, then `fused_batch_size` cannot be None!") + + self._loss = None + self._wer = None + + # Log softmax should be applied explicitly only for CPU + self.log_softmax = log_softmax + self.preserve_memory = preserve_memory + + if preserve_memory: + logging.warning( + "`preserve_memory` was set for the Joint Model. Please be aware this will severely impact " + "the forward-backward step time. It also might not solve OOM issues if the GPU simply " + "does not have enough memory to compute the joint." + ) + + # Required arguments + self.encoder_hidden = jointnet['encoder_hidden'] + self.pred_hidden = jointnet['pred_hidden'] + self.joint_hidden = jointnet['joint_hidden'] + self.activation = jointnet['activation'] + + # Optional arguments + dropout = jointnet.get('dropout', 0.0) + + self.pred, self.enc, self.joint_net = self._joint_net_modules( + num_classes=self._num_classes, # add 1 for blank symbol + pred_n_hidden=self.pred_hidden, + enc_n_hidden=self.encoder_hidden, + joint_n_hidden=self.joint_hidden, + activation=self.activation, + dropout=dropout, + ) + + # Flag needed for RNNT export support + self._rnnt_export = False + + # to change, requires running ``model.temperature = T`` explicitly + self.temperature = 1.0 + + @typecheck() + def forward( + self, + encoder_outputs: torch.Tensor, + decoder_outputs: Optional[torch.Tensor], + encoder_lengths: Optional[torch.Tensor] = None, + transcripts: Optional[torch.Tensor] = None, + transcript_lengths: Optional[torch.Tensor] = None, + compute_wer: bool = False, + ) -> Union[torch.Tensor, List[Optional[torch.Tensor]]]: + # encoder = (B, D, T) + # decoder = (B, D, U) if passed, else None + encoder_outputs = encoder_outputs.transpose(1, 2) # (B, T, D) + + if decoder_outputs is not None: + decoder_outputs = decoder_outputs.transpose(1, 2) # (B, U, D) + + if not self._fuse_loss_wer: + if decoder_outputs is None: + raise ValueError( + "decoder_outputs passed is None, and `fuse_loss_wer` is not set. " + "decoder_outputs can only be None for fused step!" + ) + + out = self.joint(encoder_outputs, decoder_outputs) # [B, T, U, V + 1] + return out + + else: + # At least the loss module must be supplied during fused joint + if self._loss is None or self._wer is None: + raise ValueError("`fuse_loss_wer` flag is set, but `loss` and `wer` modules were not provided! ") + + # If fused joint step is required, fused batch size is required as well + if self._fused_batch_size is None: + raise ValueError("If `fuse_loss_wer` is set, then `fused_batch_size` cannot be None!") + + # When using fused joint step, both encoder and transcript lengths must be provided + if (encoder_lengths is None) or (transcript_lengths is None): + raise ValueError( + "`fuse_loss_wer` is set, therefore encoder and target lengths " "must be provided as well!" + ) + + losses = [] + wers, wer_nums, wer_denoms = [], [], [] + target_lengths = [] + batch_size = int(encoder_outputs.size(0)) # actual batch size + + # Iterate over batch using fused_batch_size steps + for batch_idx in range(0, batch_size, self._fused_batch_size): + begin = batch_idx + end = min(begin + self._fused_batch_size, batch_size) + + # Extract the sub batch inputs + # sub_enc = encoder_outputs[begin:end, ...] + # sub_transcripts = transcripts[begin:end, ...] + sub_enc = encoder_outputs.narrow(dim=0, start=begin, length=int(end - begin)) + sub_transcripts = transcripts.narrow(dim=0, start=begin, length=int(end - begin)) + + sub_enc_lens = encoder_lengths[begin:end] + sub_transcript_lens = transcript_lengths[begin:end] + + # Sub transcripts does not need the full padding of the entire batch + # Therefore reduce the decoder time steps to match + max_sub_enc_length = sub_enc_lens.max() + max_sub_transcript_length = sub_transcript_lens.max() + + if decoder_outputs is not None: + # Reduce encoder length to preserve computation + # Encoder: [sub-batch, T, D] -> [sub-batch, T', D]; T' < T + if sub_enc.shape[1] != max_sub_enc_length: + sub_enc = sub_enc.narrow(dim=1, start=0, length=int(max_sub_enc_length)) + + # sub_dec = decoder_outputs[begin:end, ...] # [sub-batch, U, D] + sub_dec = decoder_outputs.narrow(dim=0, start=begin, length=int(end - begin)) # [sub-batch, U, D] + + # Reduce decoder length to preserve computation + # Decoder: [sub-batch, U, D] -> [sub-batch, U', D]; U' < U + if sub_dec.shape[1] != max_sub_transcript_length + 1: + sub_dec = sub_dec.narrow(dim=1, start=0, length=int(max_sub_transcript_length + 1)) + + # Perform joint => [sub-batch, T', U', V + 1] + sub_joint = self.joint(sub_enc, sub_dec) + + del sub_dec + + # Reduce transcript length to correct alignment + # Transcript: [sub-batch, L] -> [sub-batch, L']; L' <= L + if sub_transcripts.shape[1] != max_sub_transcript_length: + sub_transcripts = sub_transcripts.narrow(dim=1, start=0, length=int(max_sub_transcript_length)) + + # Compute sub batch loss + # preserve loss reduction type + loss_reduction = self.loss.reduction + + # override loss reduction to sum + self.loss.reduction = None + + # compute and preserve loss + loss_batch = self.loss( + log_probs=sub_joint, + targets=sub_transcripts, + input_lengths=sub_enc_lens, + target_lengths=sub_transcript_lens, + ) + losses.append(loss_batch) + target_lengths.append(sub_transcript_lens) + + # reset loss reduction type + self.loss.reduction = loss_reduction + + else: + losses = None + + # Update WER for sub batch + if compute_wer: + sub_enc = sub_enc.transpose(1, 2) # [B, T, D] -> [B, D, T] + sub_enc = sub_enc.detach() + sub_transcripts = sub_transcripts.detach() + + # Update WER on each process without syncing + self.wer.update( + predictions=sub_enc, + predictions_lengths=sub_enc_lens, + targets=sub_transcripts, + targets_lengths=sub_transcript_lens, + ) + # Sync and all_reduce on all processes, compute global WER + wer, wer_num, wer_denom = self.wer.compute() + self.wer.reset() + + wers.append(wer) + wer_nums.append(wer_num) + wer_denoms.append(wer_denom) + + del sub_enc, sub_transcripts, sub_enc_lens, sub_transcript_lens + + # Reduce over sub batches + if losses is not None: + losses = self.loss.reduce(losses, target_lengths) + + # Collect sub batch wer results + if compute_wer: + wer = sum(wers) / len(wers) + wer_num = sum(wer_nums) + wer_denom = sum(wer_denoms) + else: + wer = None + wer_num = None + wer_denom = None + + return losses, wer, wer_num, wer_denom + + def project_encoder(self, encoder_output: torch.Tensor) -> torch.Tensor: + """ + Project the encoder output to the joint hidden dimension. + + Args: + encoder_output: A torch.Tensor of shape [B, T, D] + + Returns: + A torch.Tensor of shape [B, T, H] + """ + return self.enc(encoder_output) + + def project_prednet(self, prednet_output: torch.Tensor) -> torch.Tensor: + """ + Project the Prediction Network (Decoder) output to the joint hidden dimension. + + Args: + prednet_output: A torch.Tensor of shape [B, U, D] + + Returns: + A torch.Tensor of shape [B, U, H] + """ + return self.pred(prednet_output) + + def joint_after_projection(self, f: torch.Tensor, g: torch.Tensor) -> torch.Tensor: + """ + Compute the joint step of the network after projection. + + Here, + B = Batch size + T = Acoustic model timesteps + U = Target sequence length + H1, H2 = Hidden dimensions of the Encoder / Decoder respectively + H = Hidden dimension of the Joint hidden step. + V = Vocabulary size of the Decoder (excluding the RNNT blank token). + + NOTE: + The implementation of this model is slightly modified from the original paper. + The original paper proposes the following steps : + (enc, dec) -> Expand + Concat + Sum [B, T, U, H1+H2] -> Forward through joint hidden [B, T, U, H] -- *1 + *1 -> Forward through joint final [B, T, U, V + 1]. + + We instead split the joint hidden into joint_hidden_enc and joint_hidden_dec and act as follows: + enc -> Forward through joint_hidden_enc -> Expand [B, T, 1, H] -- *1 + dec -> Forward through joint_hidden_dec -> Expand [B, 1, U, H] -- *2 + (*1, *2) -> Sum [B, T, U, H] -> Forward through joint final [B, T, U, V + 1]. + + Args: + f: Output of the Encoder model. A torch.Tensor of shape [B, T, H1] + g: Output of the Decoder model. A torch.Tensor of shape [B, U, H2] + + Returns: + Logits / log softmaxed tensor of shape (B, T, U, V + 1). + """ + f = f.unsqueeze(dim=2) # (B, T, 1, H) + g = g.unsqueeze(dim=1) # (B, 1, U, H) + inp = f + g # [B, T, U, H] + + del f, g + + # Forward adapter modules on joint hidden + if self.is_adapter_available(): + inp = self.forward_enabled_adapters(inp) + + res = self.joint_net(inp) # [B, T, U, V + 1] + + del inp + + if self.preserve_memory: + torch.cuda.empty_cache() + + # If log_softmax is automatic + if self.log_softmax is None: + if not res.is_cuda: # Use log softmax only if on CPU + if self.temperature != 1.0: + res = (res / self.temperature).log_softmax(dim=-1) + else: + res = res.log_softmax(dim=-1) + else: + if self.log_softmax: + if self.temperature != 1.0: + res = (res / self.temperature).log_softmax(dim=-1) + else: + res = res.log_softmax(dim=-1) + + return res + + def _joint_net_modules(self, num_classes, pred_n_hidden, enc_n_hidden, joint_n_hidden, activation, dropout): + """ + Prepare the trainable modules of the Joint Network + + Args: + num_classes: Number of output classes (vocab size) excluding the RNNT blank token. + pred_n_hidden: Hidden size of the prediction network. + enc_n_hidden: Hidden size of the encoder network. + joint_n_hidden: Hidden size of the joint network. + activation: Activation of the joint. Can be one of [relu, tanh, sigmoid] + dropout: Dropout value to apply to joint. + """ + pred = torch.nn.Linear(pred_n_hidden, joint_n_hidden) + enc = torch.nn.Linear(enc_n_hidden, joint_n_hidden) + + if activation not in ['relu', 'sigmoid', 'tanh']: + raise ValueError("Unsupported activation for joint step - please pass one of " "[relu, sigmoid, tanh]") + + activation = activation.lower() + + if activation == 'relu': + activation = torch.nn.ReLU(inplace=True) + elif activation == 'sigmoid': + activation = torch.nn.Sigmoid() + elif activation == 'tanh': + activation = torch.nn.Tanh() + + layers = ( + [activation] + + ([torch.nn.Dropout(p=dropout)] if dropout else []) + + [torch.nn.Linear(joint_n_hidden, num_classes)] + ) + return pred, enc, torch.nn.Sequential(*layers) + + # Adapter method overrides + def add_adapter(self, name: str, cfg: DictConfig): + # Update the config with correct input dim + cfg = self._update_adapter_cfg_input_dim(cfg) + # Add the adapter + super().add_adapter(name=name, cfg=cfg) + + def _update_adapter_cfg_input_dim(self, cfg: DictConfig): + cfg = adapter_utils.update_adapter_cfg_input_dim(self, cfg, module_dim=self.joint_hidden) + return cfg + + @property + def num_classes_with_blank(self): + return self._num_classes + + @property + def num_extra_outputs(self): + return self._num_extra_outputs + + @property + def loss(self): + return self._loss + + def set_loss(self, loss): + if not self._fuse_loss_wer: + raise ValueError("Attempting to set loss module even though `fuse_loss_wer` is not set!") + + self._loss = loss + + @property + def wer(self): + return self._wer + + def set_wer(self, wer): + if not self._fuse_loss_wer: + raise ValueError("Attempting to set WER module even though `fuse_loss_wer` is not set!") + + self._wer = wer + + @property + def fuse_loss_wer(self): + return self._fuse_loss_wer + + def set_fuse_loss_wer(self, fuse_loss_wer, loss=None, metric=None): + self._fuse_loss_wer = fuse_loss_wer + + self._loss = loss + self._wer = metric + + @property + def fused_batch_size(self): + return self._fused_batch_size + + def set_fused_batch_size(self, fused_batch_size): + self._fused_batch_size = fused_batch_size + + +class RNNTDecoderJoint(torch.nn.Module, Exportable): + """ + Utility class to export Decoder+Joint as a single module + """ + + def __init__(self, decoder, joint): + super().__init__() + self.decoder = decoder + self.joint = joint + + @property + def input_types(self): + state_type = NeuralType(('D', 'B', 'D'), ElementType()) + mytypes = { + 'encoder_outputs': NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "targets": NeuralType(('B', 'T'), LabelsType()), + "target_length": NeuralType(tuple('B'), LengthsType()), + 'input_states_1': state_type, + 'input_states_2': state_type, + } + + return mytypes + + def input_example(self, max_batch=1, max_dim=1): + decoder_example = self.decoder.input_example(max_batch=max_batch, max_dim=max_dim) + state1, state2 = decoder_example[-1] + return tuple([self.joint.input_example()[0]]) + decoder_example[:2] + (state1, state2) + + @property + def output_types(self): + return { + "outputs": NeuralType(('B', 'T', 'T', 'D'), LogprobsType()), + "prednet_lengths": NeuralType(tuple('B'), LengthsType()), + "output_states_1": NeuralType((('D', 'B', 'D')), ElementType()), + "output_states_2": NeuralType((('D', 'B', 'D')), ElementType()), + } + + def forward(self, encoder_outputs, targets, target_length, input_states_1, input_states_2): + decoder_outputs = self.decoder(targets, target_length, (input_states_1, input_states_2)) + decoder_output = decoder_outputs[0] + decoder_length = decoder_outputs[1] + input_states_1, input_states_2 = decoder_outputs[2][0], decoder_outputs[2][1] + joint_output = self.joint(encoder_outputs, decoder_output) + return (joint_output, decoder_length, input_states_1, input_states_2) + + +class RNNTDecoderJointSSL(torch.nn.Module): + def __init__(self, decoder, joint): + super().__init__() + self.decoder = decoder + self.joint = joint + + @property + def needs_labels(self): + return True + + @property + def input_types(self): + return { + "encoder_output": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "targets": NeuralType(('B', 'T'), LabelsType()), + "target_lengths": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + return {"log_probs": NeuralType(('B', 'T', 'D'), SpectrogramType())} + + def forward(self, encoder_output, targets, target_lengths): + + decoder, target_length, states = self.decoder(targets=targets, target_length=target_lengths) + log_probs = self.joint(encoder_outputs=encoder_output, decoder_outputs=decoder) + + return log_probs + + +class SampledRNNTJoint(RNNTJoint): + """A Sampled Recurrent Neural Network Transducer Joint Network (RNN-T Joint Network). + An RNN-T Joint network, comprised of a feedforward model, where the vocab size will be sampled instead + of computing the full vocabulary joint. + + Args: + jointnet: A dict-like object which contains the following key-value pairs. + encoder_hidden: int specifying the hidden dimension of the encoder net. + pred_hidden: int specifying the hidden dimension of the prediction net. + joint_hidden: int specifying the hidden dimension of the joint net + activation: Activation function used in the joint step. Can be one of + ['relu', 'tanh', 'sigmoid']. + + Optionally, it may also contain the following: + dropout: float, set to 0.0 by default. Optional dropout applied at the end of the joint net. + + num_classes: int, specifying the vocabulary size that the joint network must predict, + excluding the RNNT blank token. + + n_samples: int, specifies the number of tokens to sample from the vocabulary space, + excluding the RNNT blank token. If a given value is larger than the entire vocabulary size, + then the full vocabulary will be used. + + vocabulary: Optional list of strings/tokens that comprise the vocabulary of the joint network. + Unused and kept only for easy access for character based encoding RNNT models. + + log_softmax: Optional bool, set to None by default. If set as None, will compute the log_softmax() + based on the value provided. + + preserve_memory: Optional bool, set to False by default. If the model crashes due to the memory + intensive joint step, one might try this flag to empty the tensor cache in pytorch. + + Warning: This will make the forward-backward pass much slower than normal. + It also might not fix the OOM if the GPU simply does not have enough memory to compute the joint. + + fuse_loss_wer: Optional bool, set to False by default. + + Fuses the joint forward, loss forward and + wer forward steps. In doing so, it trades of speed for memory conservation by creating sub-batches + of the provided batch of inputs, and performs Joint forward, loss forward and wer forward (optional), + all on sub-batches, then collates results to be exactly equal to results from the entire batch. + + When this flag is set, prior to calling forward, the fields `loss` and `wer` (either one) *must* + be set using the `RNNTJoint.set_loss()` or `RNNTJoint.set_wer()` methods. + + Further, when this flag is set, the following argument `fused_batch_size` *must* be provided + as a non negative integer. This value refers to the size of the sub-batch. + + When the flag is set, the input and output signature of `forward()` of this method changes. + Input - in addition to `encoder_outputs` (mandatory argument), the following arguments can be provided. + + - decoder_outputs (optional). Required if loss computation is required. + + - encoder_lengths (required) + + - transcripts (optional). Required for wer calculation. + + - transcript_lengths (optional). Required for wer calculation. + + - compute_wer (bool, default false). Whether to compute WER or not for the fused batch. + + Output - instead of the usual `joint` log prob tensor, the following results can be returned. + + - loss (optional). Returned if decoder_outputs, transcripts and transript_lengths are not None. + + - wer_numerator + wer_denominator (optional). Returned if transcripts, transcripts_lengths are provided + and compute_wer is set. + + fused_batch_size: Optional int, required if `fuse_loss_wer` flag is set. Determines the size of the + sub-batches. Should be any value below the actual batch size per GPU. + """ + + def __init__( + self, + jointnet: Dict[str, Any], + num_classes: int, + n_samples: int, + vocabulary: Optional[List] = None, + log_softmax: Optional[bool] = None, + preserve_memory: bool = False, + fuse_loss_wer: bool = False, + fused_batch_size: Optional[int] = None, + ): + super().__init__( + jointnet=jointnet, + num_classes=num_classes, + vocabulary=vocabulary, + log_softmax=log_softmax, + preserve_memory=preserve_memory, + fuse_loss_wer=fuse_loss_wer, + fused_batch_size=fused_batch_size, + ) + self.n_samples = n_samples + self.register_buffer('blank_id', torch.tensor([self.num_classes_with_blank - 1]), persistent=False) + + @typecheck() + def forward( + self, + encoder_outputs: torch.Tensor, + decoder_outputs: Optional[torch.Tensor], + encoder_lengths: Optional[torch.Tensor] = None, + transcripts: Optional[torch.Tensor] = None, + transcript_lengths: Optional[torch.Tensor] = None, + compute_wer: bool = False, + ) -> Union[torch.Tensor, List[Optional[torch.Tensor]]]: + # If in inference mode, revert to basic RNNT Joint behaviour. + # Sampled RNNT is only used for training. + if not torch.is_grad_enabled() or torch.is_inference_mode_enabled(): + # Simply call full tensor joint + return super().forward( + encoder_outputs=encoder_outputs, + decoder_outputs=decoder_outputs, + encoder_lengths=encoder_lengths, + transcripts=transcripts, + transcript_lengths=transcript_lengths, + compute_wer=compute_wer, + ) + + if transcripts is None or transcript_lengths is None: + logging.warning( + "Sampled RNNT Joint currently only works with `fuse_loss_wer` set to True, " + "and when `fused_batch_size` is a positive integer." + ) + raise ValueError( + "Sampled RNNT loss only works when the transcripts are provided during training." + "Please ensure that you correctly pass the `transcripts` and `transcript_lengths`." + ) + + # encoder = (B, D, T) + # decoder = (B, D, U) if passed, else None + encoder_outputs = encoder_outputs.transpose(1, 2) # (B, T, D) + + if decoder_outputs is not None: + decoder_outputs = decoder_outputs.transpose(1, 2) # (B, U, D) + + # At least the loss module must be supplied during fused joint + if self._loss is None or self._wer is None: + raise ValueError("`fuse_loss_wer` flag is set, but `loss` and `wer` modules were not provided! ") + + # If fused joint step is required, fused batch size is required as well + if self._fused_batch_size is None: + raise ValueError("If `fuse_loss_wer` is set, then `fused_batch_size` cannot be None!") + + # When using fused joint step, both encoder and transcript lengths must be provided + if (encoder_lengths is None) or (transcript_lengths is None): + raise ValueError( + "`fuse_loss_wer` is set, therefore encoder and target lengths " "must be provided as well!" + ) + + losses = [] + wers, wer_nums, wer_denoms = [], [], [] + target_lengths = [] + batch_size = int(encoder_outputs.size(0)) # actual batch size + + # Iterate over batch using fused_batch_size steps + for batch_idx in range(0, batch_size, self._fused_batch_size): + begin = batch_idx + end = min(begin + self._fused_batch_size, batch_size) + + # Extract the sub batch inputs + # sub_enc = encoder_outputs[begin:end, ...] + # sub_transcripts = transcripts[begin:end, ...] + sub_enc = encoder_outputs.narrow(dim=0, start=begin, length=int(end - begin)) + sub_transcripts = transcripts.narrow(dim=0, start=begin, length=int(end - begin)) + + sub_enc_lens = encoder_lengths[begin:end] + sub_transcript_lens = transcript_lengths[begin:end] + + # Sub transcripts does not need the full padding of the entire batch + # Therefore reduce the decoder time steps to match + max_sub_enc_length = sub_enc_lens.max() + max_sub_transcript_length = sub_transcript_lens.max() + + if decoder_outputs is not None: + # Reduce encoder length to preserve computation + # Encoder: [sub-batch, T, D] -> [sub-batch, T', D]; T' < T + if sub_enc.shape[1] != max_sub_enc_length: + sub_enc = sub_enc.narrow(dim=1, start=0, length=int(max_sub_enc_length)) + + # sub_dec = decoder_outputs[begin:end, ...] # [sub-batch, U, D] + sub_dec = decoder_outputs.narrow(dim=0, start=begin, length=int(end - begin)) # [sub-batch, U, D] + + # Reduce decoder length to preserve computation + # Decoder: [sub-batch, U, D] -> [sub-batch, U', D]; U' < U + if sub_dec.shape[1] != max_sub_transcript_length + 1: + sub_dec = sub_dec.narrow(dim=1, start=0, length=int(max_sub_transcript_length + 1)) + + # Reduce transcript length to correct alignment + # Transcript: [sub-batch, L] -> [sub-batch, L']; L' <= L + if sub_transcripts.shape[1] != max_sub_transcript_length: + sub_transcripts = sub_transcripts.narrow(dim=1, start=0, length=int(max_sub_transcript_length)) + + # Perform sampled joint => [sub-batch, T', U', {V' < V} + 1}] + sub_joint, sub_transcripts_remapped = self.sampled_joint( + sub_enc, sub_dec, transcript=sub_transcripts, transcript_lengths=sub_transcript_lens + ) + + del sub_dec + + # Compute sub batch loss + # preserve loss reduction type + loss_reduction = self.loss.reduction + + # override loss reduction to sum + self.loss.reduction = None + + # override blank idx in order to map to new vocabulary space + # in the new vocabulary space, we set the mapping of the RNNT Blank from index V+1 to 0 + # So the loss here needs to be updated accordingly. + # TODO: See if we can have some formal API for rnnt loss to update inner blank index. + cached_blank_id = self.loss._loss.blank + self.loss._loss.blank = 0 + + # compute and preserve loss + loss_batch = self.loss( + log_probs=sub_joint, + targets=sub_transcripts_remapped, # Note: We have to use remapped transcripts here ! + input_lengths=sub_enc_lens, + target_lengths=sub_transcript_lens, # Note: Even after remap, the transcript lengths remain intact. + ) + losses.append(loss_batch) + target_lengths.append(sub_transcript_lens) + + # reset loss reduction type and blank id + self.loss.reduction = loss_reduction + self.loss._loss.blank = cached_blank_id + + else: + losses = None + + # Update WER for sub batch + if compute_wer: + sub_enc = sub_enc.transpose(1, 2) # [B, T, D] -> [B, D, T] + sub_enc = sub_enc.detach() + sub_transcripts = sub_transcripts.detach() + + # Update WER on each process without syncing + self.wer.update( + predictions=sub_enc, + predictions_lengths=sub_enc_lens, + targets=sub_transcripts, + targets_lengths=sub_transcript_lens, + ) + + # Sync and all_reduce on all processes, compute global WER + wer, wer_num, wer_denom = self.wer.compute() + self.wer.reset() + + wers.append(wer) + wer_nums.append(wer_num) + wer_denoms.append(wer_denom) + + del sub_enc, sub_transcripts, sub_enc_lens, sub_transcript_lens + + # Reduce over sub batches + if losses is not None: + losses = self.loss.reduce(losses, target_lengths) + + # Collect sub batch wer results + if compute_wer: + wer = sum(wers) / len(wers) + wer_num = sum(wer_nums) + wer_denom = sum(wer_denoms) + else: + wer = None + wer_num = None + wer_denom = None + + return losses, wer, wer_num, wer_denom + + def sampled_joint( + self, f: torch.Tensor, g: torch.Tensor, transcript: torch.Tensor, transcript_lengths: torch.Tensor, + ) -> torch.Tensor: + """ + Compute the sampled joint step of the network. + + # Reference + - [Memory-Efficient Training of RNN-Transducer with Sampled Softmax](https://arxiv.org/abs/2203.16868) + + Here, + B = Batch size + T = Acoustic model timesteps + U = Target sequence length + H1, H2 = Hidden dimensions of the Encoder / Decoder respectively + H = Hidden dimension of the Joint hidden step. + V = Vocabulary size of the Decoder (excluding the RNNT blank token). + S = Sample size of vocabulary. + + NOTE: + The implementation of this joint model is slightly modified from the original paper. + The original paper proposes the following steps : + (enc, dec) -> Expand + Concat + Sum [B, T, U, H1+H2] -> Forward through joint hidden [B, T, U, H] -- *1 + *1 -> Forward through joint final [B, T, U, V + 1]. + + We instead split the joint hidden into joint_hidden_enc and joint_hidden_dec and act as follows: + enc -> Forward through joint_hidden_enc -> Expand [B, T, 1, H] -- *1 + dec -> Forward through joint_hidden_dec -> Expand [B, 1, U, H] -- *2 + (*1, *2) -> Sum [B, T, U, H] -> Sample Vocab V_Pos (for target tokens) and V_Neg -> + (V_Neg is sampled not uniformly by as a rand permutation of all vocab tokens, then eliminate + all Intersection(V_Pos, V_Neg) common tokens to avoid duplication of loss) -> + Concat new Vocab V_Sampled = Union(V_Pos, V_Neg) + -> Forward partially through the joint final to create [B, T, U, V_Sampled] + + Args: + f: Output of the Encoder model. A torch.Tensor of shape [B, T, H1] + g: Output of the Decoder model. A torch.Tensor of shape [B, U, H2] + transcript: Batch of transcripts. A torch.Tensor of shape [B, U] + transcript_lengths: Batch of lengths of the transcripts. A torch.Tensor of shape [B] + + Returns: + Logits / log softmaxed tensor of shape (B, T, U, V + 1). + """ + # If under inference mode, ignore sampled joint and compute full joint. + if self.training is False or torch.is_grad_enabled() is False or torch.is_inference_mode_enabled(): + # Simply call full tensor joint + return super().joint(f=f, g=g) + + # Compute sampled softmax + # f = [B, T, H1] + f = self.enc(f) + f.unsqueeze_(dim=2) # (B, T, 1, H) + + # g = [B, U, H2] + g = self.pred(g) + g.unsqueeze_(dim=1) # (B, 1, U, H) + + inp = f + g # [B, T, U, H] + + del f, g + + # Forward adapter modules on joint hidden + if self.is_adapter_available(): + inp = self.forward_enabled_adapters(inp) + + # Do partial forward of joint net (skipping the final linear) + for module in self.joint_net[:-1]: + inp = module(inp) # [B, T, U, H] + + # Begin compute of sampled RNNT joint + with torch.no_grad(): + # gather true labels + transcript_vocab_ids = torch.unique(transcript) + + # augment with blank token id + transcript_vocab_ids = torch.cat([self.blank_id, transcript_vocab_ids]) + + # Remap the transcript label ids to new positions of label ids (in the transcript_vocab_ids) + # This is necessary cause the RNNT loss doesnt care about the value, only the position of the ids + # of the transcript tokens. We can skip this step for noise samples cause those are only used for softmax + # estimation, not for computing actual label. + # From `https://stackoverflow.com/a/68969697` - bucketize algo. + t_ids = torch.arange(transcript_vocab_ids.size(0), device='cpu') + mapping = {k: v for k, v in zip(transcript_vocab_ids.to('cpu'), t_ids)} + + # From `https://stackoverflow.com/questions/13572448`. + palette, key = zip(*mapping.items()) + + t_device = transcript.device + key = torch.tensor(key, device=t_device) + palette = torch.tensor(palette, device=t_device) + + # This step maps old token id to new token id in broadcasted manner. + # For example, if original transcript tokens were [2, 1, 4, 5, 4, 1] + # But after computing the unique token set of above we get + # transcript_vocab_ids = [1, 2, 4, 5] # note: pytorch returns sorted unique values thankfully + # Then we get the index map of the new vocab ids as: + # {0: 1, 1: 2, 2: 4, 3: 5} + # Now we need to map the original transcript tokens to new vocab id space + # So we construct the inverted map as follow : + # {1: 0, 2: 1, 4: 2, 5: 3} + # Then remap the original transcript tokens to new token ids + # new_transcript = [1, 0, 2, 3, 2, 0] + index = torch.bucketize(transcript.ravel(), palette) + transcript = key[index].reshape(transcript.shape) + transcript = transcript.to(t_device) + + # Extract out partial weight tensor and bias tensor of just the V_Pos vocabulary from the full joint. + true_weights = self.joint_net[-1].weight[transcript_vocab_ids, :] + true_bias = self.joint_net[-1].bias[transcript_vocab_ids] + + # Compute the transcript joint scores (only of vocab V_Pos) + transcript_scores = torch.matmul(inp, true_weights.transpose(0, 1)) + true_bias + + # Construct acceptance criteria in vocab space, reject all tokens in Intersection(V_Pos, V_Neg) + with torch.no_grad(): + # Instead of uniform sample, first we create arange V (ignoring blank), then randomly shuffle + # this range of ids, then subset `n_samples` amount of vocab tokens out of the permuted tensor. + # This is good because it guarentees that no token will ever be repeated in V_Neg; + # which dramatically complicates loss calculation. + # Further more, with this strategy, given a `n_samples` > V + 1; we are guarenteed to get the + # V_Samples = V (i.e., full vocabulary will be used in such a case). + # Useful to debug cases where you expect sampled vocab to get exact same training curve as + # full vocab. + sample_ids = torch.randperm(n=self.num_classes_with_blank - 1, device=transcript_scores.device)[ + : self.n_samples + ] + + # We need to compute the intersection(V_Pos, V_Neg), then eliminate the intersection arguments + # from inside V_Neg. + + # First, compute the pairwise commonality to find index inside `sample_ids` which match the token id + # inside transcript_vocab_ids. + # Note: It is important to ignore the hardcoded RNNT Blank token injected at id 0 of the transcript + # vocab ids, otherwise the blank may occur twice, once for RNNT blank and once as negative sample, + # doubling the gradient of the RNNT blank token. + reject_samples = torch.where(transcript_vocab_ids[1:, None] == sample_ids[None, :]) + + # Let accept samples be a set of ids which is a subset of sample_ids + # such that intersection(V_Pos, accept_samples) is a null set. + accept_samples = sample_ids.clone() + + # In order to construct such an accept_samples tensor, first we construct a bool map + # and fill all the indices where there is a match inside of sample_ids. + # reject_samples is a tuple (transcript_vocab_position, sample_position) which gives a + # many to many map between N values of transript and M values of sample_ids. + # We dont care about transcript side matches, only the ids inside of sample_ids that matched. + sample_mask = torch.ones_like(accept_samples, dtype=torch.bool) + sample_mask[reject_samples[1]] = False + + # Finally, compute the subset of tokens by selecting only those sample_ids which had no matches + accept_samples = accept_samples[sample_mask] + + # Extract out partial weight tensor and bias tensor of just the V_Neg vocabulary from the full joint. + sample_weights = self.joint_net[-1].weight[accept_samples, :] + sample_bias = self.joint_net[-1].bias[accept_samples] + + # Compute the noise joint scores (only of vocab V_Neg) to be used for softmax + # The quality of this sample determines the quality of the softmax gradient. + # We use naive algo broadcasted over batch, but it is more efficient than sample level computation. + # One can increase `n_samples` for better estimation of rejection samples and its gradient. + noise_scores = torch.matmul(inp, sample_weights.transpose(0, 1)) + sample_bias + + # Finally, construct the sampled joint as the V_Sampled = Union(V_Pos, V_Neg) + # Here, we simply concatenate the two tensors to construct the joint with V_Sampled vocab + # because before we have properly asserted that Intersection(V_Pos, V_Neg) is a null set. + res = torch.cat([transcript_scores, noise_scores], dim=-1) + + del inp + + if self.preserve_memory: + torch.cuda.empty_cache() + + # If log_softmax is automatic + if self.log_softmax is None: + if not res.is_cuda: # Use log softmax only if on CPU + res = res.log_softmax(dim=-1) + else: + if self.log_softmax: + res = res.log_softmax(dim=-1) + + return res, transcript + + +# Add the adapter compatible modules to the registry +for cls in [RNNTDecoder, RNNTJoint, SampledRNNTJoint]: + if adapter_mixins.get_registered_adapter(cls) is None: + adapter_mixins.register_adapter(cls, cls) # base class is adapter compatible itself diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/rnnt_abstract.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/rnnt_abstract.py new file mode 100644 index 0000000..d3d9b7c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/rnnt_abstract.py @@ -0,0 +1,351 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Optional, Tuple + +import torch + +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis +from nemo.core import NeuralModule + + +class AbstractRNNTJoint(NeuralModule, ABC): + """ + An abstract RNNT Joint framework, which can possibly integrate with GreedyRNNTInfer and BeamRNNTInfer classes. + Represents the abstract RNNT Joint network, which accepts the acoustic model and prediction network + embeddings in order to compute the joint of the two prior to decoding the output sequence. + """ + + @abstractmethod + def joint_after_projection(self, f: torch.Tensor, g: torch.Tensor) -> Any: + """ + Compute the joint step of the network after the projection step. + Args: + f: Output of the Encoder model after projection. A torch.Tensor of shape [B, T, H] + g: Output of the Decoder model (Prediction Network) after projection. A torch.Tensor of shape [B, U, H] + + Returns: + Logits / log softmaxed tensor of shape (B, T, U, V + 1). + Arbitrary return type, preferably torch.Tensor, but not limited to (e.g., see HatJoint) + """ + raise NotImplementedError() + + @abstractmethod + def project_encoder(self, encoder_output: torch.Tensor) -> torch.Tensor: + """ + Project the encoder output to the joint hidden dimension. + + Args: + encoder_output: A torch.Tensor of shape [B, T, D] + + Returns: + A torch.Tensor of shape [B, T, H] + """ + raise NotImplementedError() + + @abstractmethod + def project_prednet(self, prednet_output: torch.Tensor) -> torch.Tensor: + """ + Project the Prediction Network (Decoder) output to the joint hidden dimension. + + Args: + prednet_output: A torch.Tensor of shape [B, U, D] + + Returns: + A torch.Tensor of shape [B, U, H] + """ + raise NotImplementedError() + + def joint(self, f: torch.Tensor, g: torch.Tensor) -> torch.Tensor: + """ + Compute the joint step of the network. + + Here, + B = Batch size + T = Acoustic model timesteps + U = Target sequence length + H1, H2 = Hidden dimensions of the Encoder / Decoder respectively + H = Hidden dimension of the Joint hidden step. + V = Vocabulary size of the Decoder (excluding the RNNT blank token). + + NOTE: + The implementation of this model is slightly modified from the original paper. + The original paper proposes the following steps : + (enc, dec) -> Expand + Concat + Sum [B, T, U, H1+H2] -> Forward through joint hidden [B, T, U, H] -- *1 + *1 -> Forward through joint final [B, T, U, V + 1]. + + We instead split the joint hidden into joint_hidden_enc and joint_hidden_dec and act as follows: + enc -> Forward through joint_hidden_enc -> Expand [B, T, 1, H] -- *1 + dec -> Forward through joint_hidden_dec -> Expand [B, 1, U, H] -- *2 + (*1, *2) -> Sum [B, T, U, H] -> Forward through joint final [B, T, U, V + 1]. + + Args: + f: Output of the Encoder model. A torch.Tensor of shape [B, T, H1] + g: Output of the Decoder model. A torch.Tensor of shape [B, U, H2] + + Returns: + Logits / log softmaxed tensor of shape (B, T, U, V + 1). + """ + return self.joint_after_projection(self.project_encoder(f), self.project_prednet(g)) + + @property + def num_classes_with_blank(self): + raise NotImplementedError() + + @property + def num_extra_outputs(self): + raise NotImplementedError() + + +class AbstractRNNTDecoder(NeuralModule, ABC): + """ + An abstract RNNT Decoder framework, which can possibly integrate with GreedyRNNTInfer and BeamRNNTInfer classes. + Represents the abstract RNNT Prediction/Decoder stateful network, which performs autoregressive decoding + in order to construct the output sequence. + + Args: + vocab_size: Size of the vocabulary, excluding the RNNT blank token. + blank_idx: Index of the blank token. Can be 0 or size(vocabulary). + blank_as_pad: Bool flag, whether to allocate an additional token in the Embedding layer + of this module in order to treat all RNNT `blank` tokens as pad tokens, thereby letting + the Embedding layer batch tokens more efficiently. + + It is mandatory to use this for certain Beam RNNT Infer methods - such as TSD, ALSD. + It is also more efficient to use greedy batch decoding with this flag. + """ + + def __init__(self, vocab_size, blank_idx, blank_as_pad): + super().__init__() + + self.vocab_size = vocab_size + self.blank_idx = blank_idx # first or last index of vocabulary + self.blank_as_pad = blank_as_pad + + if blank_idx not in [0, vocab_size]: + raise ValueError("`blank_idx` must be either 0 or the final token of the vocabulary") + + @abstractmethod + def predict( + self, + y: Optional[torch.Tensor] = None, + state: Optional[torch.Tensor] = None, + add_sos: bool = False, + batch_size: Optional[int] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """ + Stateful prediction of scores and state for a (possibly null) tokenset. + This method takes various cases into consideration : + - No token, no state - used for priming the RNN + - No token, state provided - used for blank token scoring + - Given token, states - used for scores + new states + + Here: + B - batch size + U - label length + H - Hidden dimension size of RNN + L - Number of RNN layers + + Args: + y: Optional torch tensor of shape [B, U] of dtype long which will be passed to the Embedding. + If None, creates a zero tensor of shape [B, 1, H] which mimics output of pad-token on Embedding. + + state: An optional list of states for the RNN. Eg: For LSTM, it is the state list length is 2. + Each state must be a tensor of shape [L, B, H]. + If None, and during training mode and `random_state_sampling` is set, will sample a + normal distribution tensor of the above shape. Otherwise, None will be passed to the RNN. + + add_sos: bool flag, whether a zero vector describing a "start of signal" token should be + prepended to the above "y" tensor. When set, output size is (B, U + 1, H). + + batch_size: An optional int, specifying the batch size of the `y` tensor. + Can be infered if `y` and `state` is None. But if both are None, then batch_size cannot be None. + + Returns: + A tuple (g, hid) such that - + + If add_sos is False: + g: (B, U, H) + hid: (h, c) where h is the final sequence hidden state and c is the final cell state: + h (tensor), shape (L, B, H) + c (tensor), shape (L, B, H) + + If add_sos is True: + g: (B, U + 1, H) + hid: (h, c) where h is the final sequence hidden state and c is the final cell state: + h (tensor), shape (L, B, H) + c (tensor), shape (L, B, H) + + """ + raise NotImplementedError() + + @abstractmethod + def initialize_state(self, y: torch.Tensor) -> List[torch.Tensor]: + """ + Initialize the state of the RNN layers, with same dtype and device as input `y`. + + Args: + y: A torch.Tensor whose device the generated states will be placed on. + + Returns: + List of torch.Tensor, each of shape [L, B, H], where + L = Number of RNN layers + B = Batch size + H = Hidden size of RNN. + """ + raise NotImplementedError() + + @abstractmethod + def score_hypothesis( + self, hypothesis: Hypothesis, cache: Dict[Tuple[int], Any] + ) -> Tuple[torch.Tensor, List[torch.Tensor], torch.Tensor]: + """ + Similar to the predict() method, instead this method scores a Hypothesis during beam search. + Hypothesis is a dataclass representing one hypothesis in a Beam Search. + + Args: + hypothesis: Refer to rnnt_utils.Hypothesis. + cache: Dict which contains a cache to avoid duplicate computations. + + Returns: + Returns a tuple (y, states, lm_token) such that: + y is a torch.Tensor of shape [1, 1, H] representing the score of the last token in the Hypothesis. + state is a list of RNN states, each of shape [L, 1, H]. + lm_token is the final integer token of the hypothesis. + """ + raise NotImplementedError() + + def batch_score_hypothesis( + self, hypotheses: List[Hypothesis], cache: Dict[Tuple[int], Any], batch_states: List[torch.Tensor] + ) -> Tuple[torch.Tensor, List[torch.Tensor], torch.Tensor]: + """ + Used for batched beam search algorithms. Similar to score_hypothesis method. + + Args: + hypothesis: List of Hypotheses. Refer to rnnt_utils.Hypothesis. + cache: Dict which contains a cache to avoid duplicate computations. + batch_states: List of torch.Tensor which represent the states of the RNN for this batch. + Each state is of shape [L, B, H] + + Returns: + Returns a tuple (b_y, b_states, lm_tokens) such that: + b_y is a torch.Tensor of shape [B, 1, H] representing the scores of the last tokens in the Hypotheses. + b_state is a list of list of RNN states, each of shape [L, B, H]. + Represented as B x List[states]. + lm_token is a list of the final integer tokens of the hypotheses in the batch. + """ + raise NotImplementedError() + + def batch_initialize_states(self, batch_states: List[torch.Tensor], decoder_states: List[List[torch.Tensor]]): + """ + Create batch of decoder states. + + Args: + batch_states (list): batch of decoder states + ([L x (B, H)], [L x (B, H)]) + + decoder_states (list of list): list of decoder states + [B x ([L x (1, H)], [L x (1, H)])] + + Returns: + batch_states (tuple): batch of decoder states + ([L x (B, H)], [L x (B, H)]) + """ + raise NotImplementedError() + + def batch_select_state(self, batch_states: List[torch.Tensor], idx: int) -> List[List[torch.Tensor]]: + """Get decoder state from batch of states, for given id. + + Args: + batch_states (list): batch of decoder states + ([L x (B, H)], [L x (B, H)]) + + idx (int): index to extract state from batch of states + + Returns: + (tuple): decoder states for given id + ([L x (1, H)], [L x (1, H)]) + """ + raise NotImplementedError() + + @classmethod + def batch_replace_states_mask( + cls, src_states: list[torch.Tensor], dst_states: list[torch.Tensor], mask: torch.Tensor, + ): + """Replace states in dst_states with states from src_states using the mask, in a way that does not synchronize with the CPU""" + raise NotImplementedError() + + @classmethod + def batch_replace_states_all( + cls, src_states: list[torch.Tensor], dst_states: list[torch.Tensor], + ): + """Replace states in dst_states with states from src_states""" + raise NotImplementedError() + + def batch_split_states(self, batch_states: list[torch.Tensor]) -> list[list[torch.Tensor]]: + """ + Split states into a list of states. + Useful for splitting the final state for converting results of the decoding algorithm to Hypothesis class. + """ + raise NotImplementedError() + + def batch_concat_states(self, batch_states: List[List[torch.Tensor]]) -> List[torch.Tensor]: + """Concatenate a batch of decoder state to a packed state. + + Args: + batch_states (list): batch of decoder states + B x ([L x (H)], [L x (H)]) + + Returns: + (tuple): decoder states + (L x B x H, L x B x H) + """ + raise NotImplementedError() + + def batch_copy_states( + self, + old_states: List[torch.Tensor], + new_states: List[torch.Tensor], + ids: List[int], + value: Optional[float] = None, + ) -> List[torch.Tensor]: + """Copy states from new state to old state at certain indices. + + Args: + old_states(list): packed decoder states + (L x B x H, L x B x H) + + new_states: packed decoder states + (L x B x H, L x B x H) + + ids (list): List of indices to copy states at. + + value (optional float): If a value should be copied instead of a state slice, a float should be provided + + Returns: + batch of decoder states with partial copy at ids (or a specific value). + (L x B x H, L x B x H) + """ + raise NotImplementedError() + + def mask_select_states(self, states: Any, mask: torch.Tensor) -> Any: + """ + Return states by mask selection + Args: + states: states for the batch (preferably a list of tensors, but not limited to) + mask: boolean mask for selecting states; batch dimension should be the same as for states + + Returns: + states filtered by mask (same type as `states`) + """ + raise NotImplementedError() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/squeezeformer_encoder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/squeezeformer_encoder.py new file mode 100644 index 0000000..ce0d498 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/squeezeformer_encoder.py @@ -0,0 +1,456 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections import OrderedDict +from typing import List, Optional, Set + +import torch +import torch.distributed +import torch.nn as nn +from omegaconf import DictConfig + +from nemo.collections.asr.parts.submodules.multi_head_attention import PositionalEncoding, RelPositionalEncoding +from nemo.collections.asr.parts.submodules.squeezeformer_modules import SqueezeformerLayer +from nemo.collections.asr.parts.submodules.subsampling import ConvSubsampling, StackingSubsampling, TimeReductionModule +from nemo.collections.asr.parts.utils import adapter_utils +from nemo.core.classes.common import typecheck +from nemo.core.classes.exportable import Exportable +from nemo.core.classes.mixins import AccessMixin, adapter_mixins +from nemo.core.classes.module import NeuralModule +from nemo.core.neural_types import AcousticEncodedRepresentation, LengthsType, NeuralType, SpectrogramType + +__all__ = ['SqueezeformerEncoder'] + + +class SqueezeformerEncoder(NeuralModule, Exportable, AccessMixin): + """ + The encoder for ASR model of Squeezeformer. + Based on this paper: + 'Squeezeformer: An Efficient Transformer for Automatic Speech Recognition' by Sehoon Kim et al. + https://arxiv.org/abs/2206.00888 + + Args: + feat_in (int): the size of feature channels + n_layers (int): number of layers of ConformerBlock + d_model (int): the hidden size of the model + feat_out (int): the size of the output features + Defaults to -1 (means feat_out is d_model) + subsampling (str): the method of subsampling, choices=['vggnet', 'striding', 'dw_striding'] + Defaults to dw_striding. + subsampling_factor (int): the subsampling factor which should be power of 2 + Defaults to 4. + subsampling_conv_channels (int): the size of the convolutions in the subsampling module + Defaults to -1 which would set it to d_model. + ff_expansion_factor (int): the expansion factor in feed forward layers + Defaults to 4. + self_attention_model (str): type of the attention layer and positional encoding + 'rel_pos': relative positional embedding and Transformer-XL + 'abs_pos': absolute positional embedding and Transformer + default is rel_pos. + pos_emb_max_len (int): the maximum length of positional embeddings + Defaulst to 5000 + n_heads (int): number of heads in multi-headed attention layers + Defaults to 4. + xscaling (bool): enables scaling the inputs to the multi-headed attention layers by sqrt(d_model) + Defaults to True. + untie_biases (bool): whether to not share (untie) the bias weights between layers of Transformer-XL + Defaults to True. + conv_kernel_size (int): the size of the convolutions in the convolutional modules + Defaults to 31. + conv_norm_type (str): the type of the normalization in the convolutional modules + Defaults to 'batch_norm'. + dropout (float): the dropout rate used in all layers except the attention layers + Defaults to 0.1. + dropout_emb (float): the dropout rate used for the positional embeddings + Defaults to 0.1. + dropout_att (float): the dropout rate used for the attention layer + Defaults to 0.0. + adaptive_scale (bool): Whether to scale the inputs to each component by affine `scale` and `bias` layer. + Or use a fixed scale=1 and bias=0. + time_reduce_idx (int): Optional integer index of a layer where a time reduction operation will occur. + All operations beyond this point will only occur at the reduced resolution. + time_recovery_idx (int): Optional integer index of a layer where the time recovery operation will occur. + All operations beyond this point will occur at the original resolution (resolution after + primary downsampling). If no value is provided, assumed to be the last layer. + """ + + def input_example(self, max_batch=1, max_dim=256): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + dev = next(self.parameters()).device + input_example = torch.randn(max_batch, self._feat_in, max_dim).to(dev) + input_example_length = torch.randint(1, max_dim, (max_batch,)).to(dev) + return tuple([input_example, input_example_length]) + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return OrderedDict( + { + "audio_signal": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + ) + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return OrderedDict( + { + "outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + } + ) + + def __init__( + self, + feat_in: int, + n_layers: int, + d_model: int, + feat_out: int = -1, + subsampling: str = 'dw_striding', + subsampling_factor: int = 4, + subsampling_conv_channels: int = -1, + ff_expansion_factor: int = 4, + self_attention_model: str = 'rel_pos', + n_heads: int = 4, + att_context_size: Optional[List[int]] = None, + xscaling: bool = True, + untie_biases: bool = True, + pos_emb_max_len: int = 5000, + conv_kernel_size: int = 31, + conv_norm_type: str = 'batch_norm', + dropout: float = 0.1, + dropout_emb: float = 0.1, + dropout_att: float = 0.0, + adaptive_scale: bool = True, + time_reduce_idx: Optional[int] = None, + time_recovery_idx: Optional[int] = None, + ): + super().__init__() + + d_ff = d_model * ff_expansion_factor + self.d_model = d_model + self._feat_in = feat_in + if att_context_size: + self.att_context_size = att_context_size + else: + self.att_context_size = [-1, -1] + + if xscaling: + self.xscale = math.sqrt(d_model) + else: + self.xscale = None + self.adaptive_scale = adaptive_scale + + self.time_reduce_idx = time_reduce_idx + if time_reduce_idx is not None: + if time_recovery_idx is None: + self.time_recovery_idx = n_layers - 1 # recover at last layer + else: + self.time_recovery_idx = time_recovery_idx # recover at given layer + + if self.time_reduce_idx is not None: + if self.time_reduce_idx < 0 or self.time_recovery_idx >= n_layers: + raise ValueError(f"Time reduce index must lie between [0, {n_layers})") + if self.time_recovery_idx < 0 or self.time_recovery_idx >= n_layers: + raise ValueError(f"Time recovery index must lie between [0, {n_layers})") + + if subsampling_conv_channels == -1: + subsampling_conv_channels = d_model + if subsampling and subsampling_factor > 1: + if subsampling == 'stacking': + self.pre_encode = StackingSubsampling( + subsampling_factor=subsampling_factor, feat_in=feat_in, feat_out=d_model + ) + else: + self.pre_encode = ConvSubsampling( + subsampling=subsampling, + subsampling_factor=subsampling_factor, + feat_in=feat_in, + feat_out=d_model, + conv_channels=subsampling_conv_channels, + activation=nn.ReLU(), + ) + # For Squeezeformer, initialize the parameters as required. + self.pre_encode.reset_parameters() + else: + self.pre_encode = nn.Linear(feat_in, d_model) + + self._feat_out = d_model + + if not untie_biases and self_attention_model == "rel_pos": + d_head = d_model // n_heads + pos_bias_u = nn.Parameter(torch.Tensor(n_heads, d_head)) + pos_bias_v = nn.Parameter(torch.Tensor(n_heads, d_head)) + nn.init.zeros_(pos_bias_u) + nn.init.zeros_(pos_bias_v) + else: + pos_bias_u = None + pos_bias_v = None + + self.pos_emb_max_len = pos_emb_max_len + if self_attention_model == "rel_pos": + self.pos_enc = RelPositionalEncoding( + d_model=d_model, + dropout_rate=dropout, + max_len=pos_emb_max_len, + xscale=self.xscale, + dropout_rate_emb=dropout_emb, + ) + elif self_attention_model == "abs_pos": + pos_bias_u = None + pos_bias_v = None + self.pos_enc = PositionalEncoding( + d_model=d_model, dropout_rate=dropout, max_len=pos_emb_max_len, xscale=self.xscale + ) + else: + raise ValueError(f"Not valid self_attention_model: '{self_attention_model}'!") + + self.layers = nn.ModuleList() + for i in range(n_layers): + layer = SqueezeformerLayer( + d_model=d_model, + d_ff=d_ff, + self_attention_model=self_attention_model, + n_heads=n_heads, + conv_kernel_size=conv_kernel_size, + conv_norm_type=conv_norm_type, + dropout=dropout, + dropout_att=dropout_att, + pos_bias_u=pos_bias_u, + pos_bias_v=pos_bias_v, + adaptive_scale=adaptive_scale, + ) + self.layers.append(layer) + + # Time Reduction and Recovery layer setup + self.time_reduce_layer = None + self.time_recovery_layer = None + self.time_reduce_pos_enc = None + # Add time reduction layer + if self.time_reduce_idx is not None: + self.time_reduce_layer = TimeReductionModule(d_model, d_model, kernel_size=5, stride=2) + self.time_recovery_layer = nn.Linear(d_model, d_model) + + # Chose same type of positional encoding as the originally determined above + if self_attention_model == "rel_pos": + self.time_reduce_pos_enc = RelPositionalEncoding( + d_model=d_model, dropout_rate=0.0, max_len=pos_emb_max_len, xscale=None, dropout_rate_emb=0.0, + ) + else: + self.time_reduce_pos_enc = PositionalEncoding( + d_model=d_model, dropout_rate=0.0, max_len=pos_emb_max_len, xscale=None, dropout_rate_emb=0.0 + ) + + self.pre_ln = nn.LayerNorm(d_model) + + if feat_out > 0 and feat_out != self._feat_out: + self.out_proj = nn.Linear(self._feat_out, feat_out) + self._feat_out = feat_out + else: + self.out_proj = None + self._feat_out = d_model + self.set_max_audio_length(self.pos_emb_max_len) + self.use_pad_mask = True + + # will be set in self.forward() if defined in AccessMixin config + self.interctc_capture_at_layers = None + + def set_max_audio_length(self, max_audio_length): + """ Sets maximum input length. + Pre-calculates internal seq_range mask. + """ + self.max_audio_length = max_audio_length + device = next(self.parameters()).device + seq_range = torch.arange(0, self.max_audio_length, device=device) + if hasattr(self, 'seq_range'): + self.seq_range = seq_range + else: + self.register_buffer('seq_range', seq_range, persistent=False) + self.pos_enc.extend_pe(max_audio_length, device) + + if self.time_reduce_pos_enc is not None: + self.time_reduce_pos_enc.extend_pe(max_audio_length, device) + + @typecheck() + def forward(self, audio_signal, length=None): + self.update_max_seq_length(seq_length=audio_signal.size(2), device=audio_signal.device) + return self.forward_for_export(audio_signal=audio_signal, length=length) + + @typecheck() + def forward_for_export(self, audio_signal, length): + max_audio_length: int = audio_signal.size(-1) + + if max_audio_length > self.max_audio_length: + self.set_max_audio_length(max_audio_length) + + if length is None: + length = audio_signal.new_full( + audio_signal.size(0), max_audio_length, dtype=torch.int32, device=self.seq_range.device + ) + + audio_signal = torch.transpose(audio_signal, 1, 2) + + if isinstance(self.pre_encode, nn.Linear): + audio_signal = self.pre_encode(audio_signal) + else: + audio_signal, length = self.pre_encode(audio_signal, length) + + audio_signal, pos_emb = self.pos_enc(audio_signal) + # adjust size + max_audio_length = audio_signal.size(1) + # Create the self-attention and padding masks + + pad_mask = self.make_pad_mask(max_audio_length, length) + att_mask = pad_mask.unsqueeze(1).repeat([1, max_audio_length, 1]) + att_mask = torch.logical_and(att_mask, att_mask.transpose(1, 2)) + if self.att_context_size[0] >= 0: + att_mask = att_mask.triu(diagonal=-self.att_context_size[0]) + if self.att_context_size[1] >= 0: + att_mask = att_mask.tril(diagonal=self.att_context_size[1]) + att_mask = ~att_mask + + if self.use_pad_mask: + pad_mask = ~pad_mask + else: + pad_mask = None + + # Create cache of activations for the time reduction step + # Note: NeMo codebase allows only a single time reduction step to occur + recovery_activation_cache = [] + + audio_signal = self.pre_ln(audio_signal) + for lth, layer in enumerate(self.layers): + # Perform time reduction + if self.time_reduce_layer is not None and lth == self.time_reduce_idx: + # Perform time reduction + recovery_activation_cache.append((audio_signal, att_mask, pad_mask, pos_emb)) + audio_signal, att_mask, pad_mask = self.time_reduce_layer( + x=audio_signal, att_mask=att_mask, pad_mask=pad_mask + ) + # Only update PE, not the original audio_signal + _, pos_emb = self.time_reduce_pos_enc(audio_signal) + + # Perform time recovery + if self.time_recovery_layer is not None and lth == self.time_recovery_idx: + recovery_audio_signal, att_mask, pad_mask, pos_emb = recovery_activation_cache.pop(0) + # repeat interleaved values for 2x seq length + audio_signal = torch.repeat_interleave(audio_signal, repeats=2, dim=1) + + B, T, D = recovery_audio_signal.size() + audio_signal = audio_signal[:, :T, :] # Slice off the exact T timesteps as original cache value + audio_signal = self.time_recovery_layer(audio_signal) # learn non linear mapping + audio_signal = recovery_audio_signal + audio_signal # learn just the residual + + audio_signal = layer(x=audio_signal, att_mask=att_mask, pos_emb=pos_emb, pad_mask=pad_mask) + + # saving tensors if required for interctc loss + if self.is_access_enabled(getattr(self, "model_guid", None)): + if self.interctc_capture_at_layers is None: + self.interctc_capture_at_layers = self.access_cfg.get('interctc', {}).get('capture_layers', []) + if lth in self.interctc_capture_at_layers: + lth_audio_signal = audio_signal + if self.out_proj is not None: + lth_audio_signal = self.out_proj(audio_signal) + # shape is the same as the shape of audio_signal output, i.e. [B, D, T] + self.register_accessible_tensor( + name=f'interctc/layer_output_{lth}', tensor=torch.transpose(lth_audio_signal, 1, 2) + ) + self.register_accessible_tensor(name=f'interctc/layer_length_{lth}', tensor=length) + + if self.out_proj is not None: + audio_signal = self.out_proj(audio_signal) + + audio_signal = torch.transpose(audio_signal, 1, 2) + return audio_signal, length + + def update_max_seq_length(self, seq_length: int, device): + # Find global max audio length across all nodes + if torch.distributed.is_initialized(): + global_max_len = torch.tensor([seq_length], dtype=torch.float32, device=device) + + # Update across all ranks in the distributed system + torch.distributed.all_reduce(global_max_len, op=torch.distributed.ReduceOp.MAX) + + seq_length = global_max_len.int().item() + + if seq_length > self.max_audio_length: + self.set_max_audio_length(seq_length) + + def make_pad_mask(self, max_audio_length, seq_lens): + """Make masking for padding.""" + mask = self.seq_range[:max_audio_length].expand(seq_lens.size(0), -1) < seq_lens.unsqueeze(-1) + return mask + + def enable_pad_mask(self, on=True): + # On inference, user may chose to disable pad mask + mask = self.use_pad_mask + self.use_pad_mask = on + return mask + + +class SqueezeformerEncoderAdapter(SqueezeformerEncoder, adapter_mixins.AdapterModuleMixin): + + # Higher level forwarding + def add_adapter(self, name: str, cfg: dict): + cfg = self._update_adapter_cfg_input_dim(cfg) + for conformer_layer in self.layers: # type: adapter_mixins.AdapterModuleMixin + conformer_layer.add_adapter(name, cfg) + + def is_adapter_available(self) -> bool: + return any([conformer_layer.is_adapter_available() for conformer_layer in self.layers]) + + def set_enabled_adapters(self, name: Optional[str] = None, enabled: bool = True): + for conformer_layer in self.layers: # type: adapter_mixins.AdapterModuleMixin + conformer_layer.set_enabled_adapters(name=name, enabled=enabled) + + def get_enabled_adapters(self) -> List[str]: + names = set([]) + for conformer_layer in self.layers: # type: adapter_mixins.AdapterModuleMixin + names.update(conformer_layer.get_enabled_adapters()) + + names = sorted(list(names)) + return names + + def _update_adapter_cfg_input_dim(self, cfg: DictConfig): + cfg = adapter_utils.update_adapter_cfg_input_dim(self, cfg, module_dim=self.d_model) + return cfg + + def get_accepted_adapter_types(self,) -> Set[type]: + types = super().get_accepted_adapter_types() + + if len(types) == 0: + self.set_accepted_adapter_types( + [ + adapter_utils.LINEAR_ADAPTER_CLASSPATH, + adapter_utils.MHA_ADAPTER_CLASSPATH, + adapter_utils.RELMHA_ADAPTER_CLASSPATH, + ] + ) + types = self.get_accepted_adapter_types() + return types + + +""" +Register any additional information +""" +if adapter_mixins.get_registered_adapter(SqueezeformerEncoder) is None: + adapter_mixins.register_adapter(base_class=SqueezeformerEncoder, adapter_class=SqueezeformerEncoderAdapter) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/__init__.py new file mode 100644 index 0000000..dc392de --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/__init__.py @@ -0,0 +1,33 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.modules.transformer.bridge_encoders import BridgeEncoder +from nemo.collections.asr.modules.transformer.perceiver_encoders import PerceiverEncoder +from nemo.collections.asr.modules.transformer.transformer_bottleneck import ( + NeMoTransformerBottleneckConfig, + NeMoTransformerBottleneckDecoderConfig, + NeMoTransformerBottleneckEncoderConfig, + TransformerBottleneckEncoderNM, +) +from nemo.collections.asr.modules.transformer.transformer_decoders import TransformerDecoder +from nemo.collections.asr.modules.transformer.transformer_encoders import TransformerEncoder +from nemo.collections.asr.modules.transformer.transformer_generators import ( + BeamSearchSequenceGenerator, + BeamSearchSequenceGeneratorWithLanguageModel, + EnsembleBeamSearchSequenceGenerator, + GreedySequenceGenerator, + TopKSequenceGenerator, +) +from nemo.collections.asr.modules.transformer.transformer_modules import AttentionBridge, TransformerEmbedding +from nemo.collections.asr.modules.transformer.transformer_utils import get_nemo_transformer diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/bridge_encoders.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/bridge_encoders.py new file mode 100644 index 0000000..5c72d27 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/bridge_encoders.py @@ -0,0 +1,141 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + +from nemo.collections.asr.modules.transformer.transformer_encoders import TransformerEncoder +from nemo.collections.asr.modules.transformer.transformer_modules import AttentionBridge + +__all__ = ["BridgeEncoder"] + + +class BridgeEncoder(torch.nn.Module): + def __init__( + self, + num_layers: int, + hidden_size: int, + inner_size: int, + mask_future: bool = False, + num_attention_heads: int = 1, + attn_score_dropout: float = 0.0, + attn_layer_dropout: float = 0.0, + ffn_dropout: float = 0.0, + hidden_act: str = "relu", + pre_ln: bool = False, + pre_ln_final_layer_norm: bool = True, + hidden_steps: int = 32, + hidden_init_method: str = "default", + hidden_blocks: int = 0, + ): + super().__init__() + + self._hidden_steps = hidden_steps + self._hidden_init_method = hidden_init_method + self._hidden_blocks = hidden_blocks + + if self._hidden_init_method == "default": + self._hidden_init_method = "enc_shared" + + if self.hidden_init_method not in self.supported_init_methods: + raise ValueError( + "Unknown hidden_init_method = {hidden_init_method}, supported methods are {supported_init_methods}".format( + hidden_init_method=self.hidden_init_method, supported_init_methods=self.supported_init_methods, + ) + ) + + # attention bridge + self.att_bridge = AttentionBridge(hidden_size=hidden_size, k=hidden_steps, bridge_size=inner_size,) + + if self.hidden_init_method == "enc": + self.init_hidden_enc = TransformerEncoder( + num_layers=num_layers, + hidden_size=hidden_size, + inner_size=inner_size, + mask_future=mask_future, + num_attention_heads=num_attention_heads, + attn_score_dropout=attn_score_dropout, + attn_layer_dropout=attn_layer_dropout, + ffn_dropout=ffn_dropout, + hidden_act=hidden_act, + pre_ln=pre_ln, + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + ) + + # self attention + self.hidden_enc = TransformerEncoder( + num_layers=num_layers, + hidden_size=hidden_size, + inner_size=inner_size, + mask_future=mask_future, + num_attention_heads=num_attention_heads, + attn_score_dropout=attn_score_dropout, + attn_layer_dropout=attn_layer_dropout, + ffn_dropout=ffn_dropout, + hidden_act=hidden_act, + pre_ln=pre_ln, + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + ) + + @property + def supported_init_methods(self): + return ["enc_shared", "identity", "enc"] + + @property + def hidden_steps(self): + return self._hidden_steps + + @property + def hidden_blocks(self): + return self._hidden_blocks + + @property + def hidden_init_method(self): + return self._hidden_init_method + + def forward(self, encoder_states, encoder_mask): + """ + Args: + encoder_states: output of the encoder (B x L_enc x H) + encoder_mask: encoder inputs mask (B x L_enc) + """ + # self-attention over input + if self.hidden_init_method == "enc_shared": + residual = encoder_states + hidden_states = self.hidden_enc(encoder_states=encoder_states, encoder_mask=encoder_mask) + # residual connection + hidden_states += residual + elif self.hidden_init_method == "identity": + hidden_states = encoder_states + elif self.hidden_init_method == "enc": + residual = encoder_states + hidden_states = self.init_hidden_enc(encoder_states=encoder_states, encoder_mask=encoder_mask) + # residual connection + hidden_states += residual + + # project encoder states to a fixed steps hidden using k attention heads + hidden_states = self.att_bridge(hidden=hidden_states, hidden_mask=encoder_mask) + + # all hidden values are active + hidden_mask = torch.ones( + encoder_states.shape[0], self._hidden_steps, dtype=encoder_mask.dtype, device=encoder_mask.device + ) + + # apply self-attention over fixed-size hidden_states + for block in range(self._hidden_blocks): + residual = hidden_states + hidden_states = self.hidden_enc(encoder_states=hidden_states, encoder_mask=hidden_mask) + # residual connection + hidden_states += residual + + return hidden_states, hidden_mask diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/decoder_module.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/decoder_module.py new file mode 100644 index 0000000..d1cb8ac --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/decoder_module.py @@ -0,0 +1,59 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC +from typing import Any, Dict, Optional + +from nemo.core.classes import NeuralModule +from nemo.core.neural_types import ChannelType, EncodedRepresentation, MaskType, NeuralType + +__all__ = ['DecoderModule'] + + +class DecoderModule(NeuralModule, ABC): + """ Base class for decoder neural module to be used in NLP models. """ + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + return { + "input_ids": NeuralType(('B', 'T'), ChannelType()), + "decoder_mask": NeuralType(('B', 'T'), MaskType(), optional=True), + "encoder_embeddings": NeuralType(('B', 'T', 'D'), ChannelType(), optional=True), + "encoder_mask": NeuralType(('B', 'T'), MaskType(), optional=True), + "decoder_mems": NeuralType(('B', 'D', 'T', 'D'), EncodedRepresentation(), optional=True), + } + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return {"last_hidden_states": NeuralType(('B', 'T', 'D'), ChannelType())} + + @property + def hidden_size(self) -> Optional[int]: + raise NotImplementedError + + @property + def vocab_size(self) -> Optional[int]: + raise NotImplementedError + + @property + def embedding(self) -> Optional[Any]: + raise NotImplementedError + + @property + def decoder(self) -> Optional[Any]: + raise NotImplementedError + + @property + def max_sequence_length(self) -> Optional[int]: + raise NotImplementedError diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/encoder_module.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/encoder_module.py new file mode 100644 index 0000000..bd3912e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/encoder_module.py @@ -0,0 +1,40 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC +from typing import Dict, Optional + +from nemo.core.classes import NeuralModule +from nemo.core.neural_types import ChannelType, MaskType, NeuralType + +__all__ = ['EncoderModule'] + + +class EncoderModule(NeuralModule, ABC): + """ Base class for encoder neural module to be used in NLP models. """ + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + return { + "input_ids": NeuralType(('B', 'T'), ChannelType()), + "encoder_mask": NeuralType(('B', 'T'), MaskType()), + } + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return {"last_hidden_states": NeuralType(('B', 'T', 'D'), ChannelType())} + + @property + def hidden_size(self) -> Optional[int]: + raise NotImplementedError diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/perceiver_encoders.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/perceiver_encoders.py new file mode 100644 index 0000000..e836e20 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/perceiver_encoders.py @@ -0,0 +1,174 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy + +import torch + +from nemo.collections.asr.modules.transformer.transformer_decoders import TransformerDecoder +from nemo.collections.asr.modules.transformer.transformer_encoders import TransformerEncoder +from nemo.collections.asr.modules.transformer.transformer_modules import AttentionBridge + +__all__ = ["PerceiverEncoder"] + + +class PerceiverEncoder(torch.nn.Module): + def __init__( + self, + num_layers: int, + hidden_size: int, + inner_size: int, + mask_future: bool = False, + num_attention_heads: int = 1, + attn_score_dropout: float = 0.0, + attn_layer_dropout: float = 0.0, + ffn_dropout: float = 0.0, + hidden_act: str = "relu", + pre_ln: bool = False, + pre_ln_final_layer_norm: bool = True, + hidden_steps: int = 32, + hidden_init_method: str = "default", + hidden_blocks: int = 2, + ): + super().__init__() + + self._hidden_steps = hidden_steps + self._hidden_init_method = hidden_init_method + self._hidden_blocks = hidden_blocks + + if self._hidden_init_method == "default": + self._hidden_init_method = "params" + + if self.hidden_init_method not in self.supported_init_methods: + raise ValueError( + "Unknown hidden_init_method = {hidden_init_method}, supported methods are {supported_init_methods}".format( + hidden_init_method=self.hidden_init_method, supported_init_methods=self.supported_init_methods, + ) + ) + + diagonal = 0 if mask_future else None + + if self.hidden_init_method == "params": + # learnable initial hidden values + self.init_hidden = torch.nn.Parameter(torch.nn.init.xavier_normal_(torch.empty(hidden_steps, hidden_size))) + self.init_cross_att = TransformerDecoder( + num_layers=1, + hidden_size=hidden_size, + inner_size=inner_size, + num_attention_heads=num_attention_heads, + attn_score_dropout=attn_score_dropout, + attn_layer_dropout=attn_layer_dropout, + ffn_dropout=ffn_dropout, + hidden_act=hidden_act, + pre_ln=pre_ln, + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + ) + self.init_cross_att.diagonal = diagonal + elif self.hidden_init_method == "bridge": + # initialize latent with attention bridge + self.att_bridge = AttentionBridge(hidden_size=hidden_size, k=hidden_steps, bridge_size=inner_size,) + + # cross-attention encoder + layer = TransformerDecoder( + num_layers=1, + hidden_size=hidden_size, + inner_size=inner_size, + num_attention_heads=num_attention_heads, + attn_score_dropout=attn_score_dropout, + attn_layer_dropout=attn_layer_dropout, + ffn_dropout=ffn_dropout, + hidden_act=hidden_act, + pre_ln=pre_ln, + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + ) + layer.diagonal = diagonal + self.cross_att_layers = torch.nn.ModuleList([copy.deepcopy(layer) for _ in range(hidden_blocks)]) + + # self-attention encoder + layer = TransformerEncoder( + num_layers=num_layers, + hidden_size=hidden_size, + inner_size=inner_size, + mask_future=mask_future, + num_attention_heads=num_attention_heads, + attn_score_dropout=attn_score_dropout, + attn_layer_dropout=attn_layer_dropout, + ffn_dropout=ffn_dropout, + hidden_act=hidden_act, + pre_ln=pre_ln, + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + ) + self.self_att_layers = torch.nn.ModuleList([copy.deepcopy(layer) for _ in range(hidden_blocks)]) + + @property + def supported_init_methods(self): + return ["params", "bridge"] + + @property + def hidden_steps(self): + return self._hidden_steps + + @property + def hidden_blocks(self): + return self._hidden_blocks + + @property + def hidden_init_method(self): + return self._hidden_init_method + + def forward(self, encoder_states, encoder_mask): + """ + Args: + encoder_states: output of the encoder (B x L_enc x H) + encoder_mask: encoder inputs mask (B x L_enc) + """ + # all hidden values are active + hidden_mask = torch.ones( + encoder_states.shape[0], self._hidden_steps, dtype=encoder_mask.dtype, device=encoder_mask.device + ) + + # initialize hidden state + if self._hidden_init_method == "params": + # initialize latent with learned parameters + hidden_states = self.init_hidden.unsqueeze(0).expand(encoder_states.shape[0], -1, -1) + hidden_states = self.init_cross_att( + decoder_states=hidden_states, + decoder_mask=hidden_mask, + encoder_states=encoder_states, + encoder_mask=encoder_mask, + ) + elif self._hidden_init_method == "bridge": + # initialize latent with attention bridge + hidden_states = self.att_bridge(hidden=encoder_states, hidden_mask=encoder_mask,) + + # apply block (cross-attention, self-attention) multiple times + # for block in range(self._hidden_blocks): + for self_att, cross_att in zip(self.self_att_layers, self.cross_att_layers): + residual = hidden_states + + # cross attention of hidden over encoder states + hidden_states = cross_att( + decoder_states=hidden_states, + decoder_mask=hidden_mask, + encoder_states=encoder_states, + encoder_mask=encoder_mask, + ) + + # self-attention over hidden + hidden_states = self_att(encoder_states=hidden_states, encoder_mask=hidden_mask,) + + # residual connection + hidden_states += residual + + return hidden_states, hidden_mask diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/reduction_encoders.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/reduction_encoders.py new file mode 100644 index 0000000..0c3355b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/reduction_encoders.py @@ -0,0 +1,148 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy + +import torch + +from nemo.collections.asr.modules.transformer.transformer_encoders import TransformerEncoder + +__all__ = ["PoolingEncoder"] + + +class PoolingEncoder(torch.nn.Module): + + _SUPPORTED_ARCH = ["max", "avg"] + + def __init__( + self, + num_layers: int, + hidden_size: int, + inner_size: int, + mask_future: bool = False, + num_attention_heads: int = 1, + attn_score_dropout: float = 0.0, + attn_layer_dropout: float = 0.0, + ffn_dropout: float = 0.0, + hidden_act: str = "relu", + pre_ln: bool = False, + pre_ln_final_layer_norm: bool = True, + hidden_steps: int = 4, + hidden_init_method: str = "default", + hidden_blocks: int = 2, + pooling_type: str = "max", + ): + super().__init__() + + # minimal steps to allow reduction + self._hidden_steps = hidden_steps + self._hidden_init_method = hidden_init_method + self._hidden_blocks = hidden_blocks + self._pooling_type = pooling_type + + if self._hidden_steps < 2: + raise ValueError("Expected hidden_steps >= 2 but received hidden_steps = {self._hidden_steps}") + + if self.hidden_init_method not in self.supported_init_methods: + raise ValueError( + "Unknown hidden_init_method = {hidden_init_method}, supported methods are {supported_init_methods}".format( + hidden_init_method=self.hidden_init_method, supported_init_methods=self.supported_init_methods, + ) + ) + + if self._pooling_type not in self.supported_arch: + raise ValueError(f"Unknown pooling_type = {pooling_type}. Available values = {self.supported_arch}") + + # self-attention encoder + layer = TransformerEncoder( + num_layers=num_layers, + hidden_size=hidden_size, + inner_size=inner_size, + mask_future=mask_future, + num_attention_heads=num_attention_heads, + attn_score_dropout=attn_score_dropout, + attn_layer_dropout=attn_layer_dropout, + ffn_dropout=ffn_dropout, + hidden_act=hidden_act, + pre_ln=pre_ln, + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + ) + self.self_att_layers = torch.nn.ModuleList([copy.deepcopy(layer) for _ in range(hidden_blocks)]) + + self.pooling = self._build_pooling_module() + + def _build_pooling_module(self): + """ + Returns pooling module. + Allows to override for child classes. + """ + if self._pooling_type == "max": + pooling = torch.nn.MaxPool1d(kernel_size=2, stride=2) + elif self._pooling_type == "avg": + pooling = torch.nn.AvgPool1d(kernel_size=2, stride=2) + + return pooling + + @property + def supported_arch(self): + return self._SUPPORTED_ARCH + + @property + def supported_init_methods(self): + return ["default"] + + @property + def hidden_steps(self): + return self._hidden_steps + + @property + def hidden_blocks(self): + return self._hidden_blocks + + @property + def hidden_init_method(self): + return self._hidden_init_method + + def forward(self, encoder_states, encoder_mask): + """ + Args: + encoder_states: output of the encoder (B x L_enc x H) + encoder_mask: encoder inputs mask (B x L_enc) + """ + # initialize hidden state + hidden_mask = encoder_mask + hidden_states = encoder_states + + # apply block (self-attention, max-pool) multiple times + for self_att in self.self_att_layers: + residual = hidden_states + + # self-attention over hidden + hidden_states = self_att(encoder_states=hidden_states, encoder_mask=hidden_mask) + + hidden_states += residual + + # max pool reduction if possible + if hidden_states.shape[1] >= self.hidden_steps: + # max pool hidden states + hidden_states = hidden_states.permute(0, 2, 1) + hidden_states = self.pooling(hidden_states) + hidden_states = hidden_states.permute(0, 2, 1) + + # max pool mask + hidden_mask = ( + self.pooling(hidden_mask.unsqueeze(0).type_as(hidden_states)).squeeze(0).type_as(hidden_mask) + ) + + return hidden_states, hidden_mask diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/text_generation.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/text_generation.py new file mode 100644 index 0000000..a261e92 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/text_generation.py @@ -0,0 +1,101 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys +from typing import List, Tuple, Union + +from torch import Tensor + +if sys.version_info >= (3, 8): + from typing import TypedDict +else: + from typing_extensions import TypedDict + + +class LengthParam(TypedDict): + max_length: int # The maximum length of the sequence to be generated. + min_length: int # The minimum length of the sequence to be generated. + + +class SamplingParam(TypedDict): + use_greedy: bool # Whether or not to use sampling ; use greedy decoding otherwise + temperature: float # sampling temperature + top_k: int # The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: float # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + repetition_penalty: float # The parameter for repetition penalty. 1.0 means no penalty. + add_BOS: bool # add the bos token at the begining of the prompt + all_probs: bool # whether return the log prob for all the tokens in vocab + compute_logprob: bool # a flag used to compute logprob of all the input text, a very special case of running inference, default False + + +class OutputType(TypedDict): + sentences: List[str] # output sentences + tokens: List[List[str]] # output sentences borken into tokens + logprob: List[List[float]] # log prob of generated tokens + full_logprob: List[List[float]] # log prob of all the tokens in the vocab + token_ids: List[List[int]] # output sentence token ids + offsets: List[List[int]] # list of tokens start positions in text + + +class TextGeneration: + """ + Interface for all text generation models. + """ + + def generate( + self, + inputs: Union[List[str], Tuple[Tensor, Tensor], List[dict]], + length_params: LengthParam, + sampling_params: SamplingParam = None, + ) -> OutputType: + """ + Public method to generate text. + + Args: + inputs (Union[List[str], Tensor, List[dict]]): + Can be one of the 3 types: + 1. List of strings. Each element of the list provides input prompt. The model will apply tokenizer on it. + E.g [‘sentence’, ‘sentence2’ … ] + 2. Tuple of Pytorch Tensors (context_tokens, context_lengths). The `context_tokens` has shape (batch_size, seq_length), it's the batched sequences of tokens used as a prompst for the generation or as model inputs to the encoder. + The generative model will skip the tokenization and padding step. The `context_lengths` has shape (batch_size,), it indicates the length of the context tokens for each of the input sequences. + E.g. ( torch.tensor([[23,5234,23,35,…], [223,323,23,23232,232,...] …]), torch.tensor([20, 30, …])) + 3. List of python dict objects. Used for prompt/p-tuning inputs where a set of key-value pairs are converted into input token embeddings for the model. + E.g. [{"prompt-tag": "sentiment", "sentence": "this is a good movie"}, + {"prompt-tag": "qa", "context": "some context text", "question": "a simple question"} ... ] + where 'prompt-tag' is used to identify the type of NLP task to solve. + length_params (LengthParam): + a dictionary type which controls the sampling length. + max_length: int, The maximum length of the sequence to be generated. + min_length: int, The minimum length of the sequence to be generated. + If None, max_length is set to 30, and min_length is set to None + sampling_params (SamplingParam): + a dictionary type which contains the parameters for text sampling. It has the following keys + use_greedy: bool, Whether or not to use sampling ; use greedy decoding otherwise + top_k: int, The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p: float, If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + repetition_penalty: float, The parameter for repetition penalty. 1.0 means no penalty. + add_BOS: bool, Whether add the bos token at the begining of the prompt + all_probs: bool # whether return the log prob for all the tokens in vocab + compute_logprob: bool # a flag used to compute logprob of all the input text, a very special case of running inference, default False + Default None, If it is None, use_greedy will be "True". + Returns: + OutputType: It generates the output in a dictionary type. It has the following keys: + sentences: List[str], output sentences + tokens: List[List[str]], output sentences borken into tokens + logprob: List[List[float]], log prob of generated tokens + full_logprob: List[List[float]], log prob of all the tokens in the vocab + token_ids: List[List[int]], output sentence token ids + offsets: List[List[int]] # list of tokens start positions in text + """ + raise NotImplementedError("please implement this method") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer.py new file mode 100644 index 0000000..718448a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer.py @@ -0,0 +1,276 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Dict, Optional + +import torch +from omegaconf.omegaconf import MISSING + +from nemo.collections.asr.modules.transformer.decoder_module import DecoderModule +from nemo.collections.asr.modules.transformer.encoder_module import EncoderModule +from nemo.collections.asr.modules.transformer.transformer_decoders import TransformerDecoder +from nemo.collections.asr.modules.transformer.transformer_encoders import TransformerEncoder +from nemo.collections.asr.modules.transformer.transformer_modules import TransformerEmbedding +from nemo.core.classes.common import typecheck +from nemo.core.classes.exportable import Exportable +from nemo.core.neural_types import ChannelType, NeuralType + + +@dataclass +class NeMoTransformerConfig: + # must be configured by the user + hidden_size: int = MISSING + num_layers: int = MISSING + inner_size: int = MISSING + num_attention_heads: int = MISSING + + # embedding + max_sequence_length: int = 512 + num_token_types: int = 2 + embedding_dropout: float = 0.0 + learn_positional_encodings: bool = False + + # transformer + ffn_dropout: float = 0.0 + attn_score_dropout: float = 0.0 + attn_layer_dropout: float = 0.0 + hidden_act: str = 'relu' + pre_ln: bool = False + pre_ln_final_layer_norm: bool = True + + # named model arguments + library: str = 'nemo' + model_name: Optional[str] = None + pretrained: bool = False + + +@dataclass +class NeMoTransformerEncoderConfig(NeMoTransformerConfig): + mask_future: bool = False + + +@dataclass +class NeMoTransformerDecoderConfig(NeMoTransformerConfig): + r2l: bool = False + + +class TransformerEncoderNM(EncoderModule, Exportable): + def __init__( + self, + vocab_size: int, + hidden_size: int, + num_layers: int, + inner_size: int, + num_attention_heads: int, + max_sequence_length: int = 512, + num_token_types: int = 2, + embedding_dropout: float = 0.0, + learn_positional_encodings: bool = False, + ffn_dropout: float = 0.0, + attn_score_dropout: float = 0.0, + attn_layer_dropout: float = 0.0, + hidden_act: str = 'relu', + mask_future: bool = False, + pre_ln: bool = False, + pre_ln_final_layer_norm: bool = True, + ): + super().__init__() + + self._vocab_size = vocab_size + self._hidden_size = hidden_size + self._max_sequence_length = max_sequence_length + + self._embedding = TransformerEmbedding( + vocab_size=self._vocab_size, + hidden_size=self._hidden_size, + max_sequence_length=max_sequence_length, + num_token_types=num_token_types, + embedding_dropout=embedding_dropout, + learn_positional_encodings=learn_positional_encodings, + ) + + self._encoder = TransformerEncoder( + hidden_size=self._hidden_size, + num_layers=num_layers, + inner_size=inner_size, + num_attention_heads=num_attention_heads, + ffn_dropout=ffn_dropout, + attn_score_dropout=attn_score_dropout, + attn_layer_dropout=attn_layer_dropout, + hidden_act=hidden_act, + mask_future=mask_future, + pre_ln=pre_ln, + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + ) + + @typecheck() + def forward(self, input_ids, encoder_mask): + embeddings = self._embedding(input_ids=input_ids) + encoder_hidden_states = self._encoder(encoder_states=embeddings, encoder_mask=encoder_mask) + return encoder_hidden_states + + @property + def hidden_size(self): + return self._hidden_size + + @property + def vocab_size(self): + return self._vocab_size + + @property + def max_sequence_length(self): + return self._max_sequence_length + + @property + def embedding(self): + return self._embedding + + @property + def encoder(self): + return self._encoder + + def input_example(self, max_batch=1, max_dim=256): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + sample = next(self.parameters()) + sz = (max_batch, max_dim) + input_ids = torch.randint(low=0, high=2048, size=sz, device=sample.device) + encoder_mask = torch.randint(low=0, high=1, size=sz, device=sample.device) + return tuple([input_ids, encoder_mask]) + + +class TransformerDecoderNM(DecoderModule, Exportable): + def __init__( + self, + vocab_size: int, + hidden_size: int, + num_layers: int, + inner_size: int, + num_attention_heads: int, + max_sequence_length: int = 512, + num_token_types: int = 2, + embedding_dropout: float = 0.0, + learn_positional_encodings: bool = False, + ffn_dropout: float = 0.0, + attn_score_dropout: float = 0.0, + attn_layer_dropout: float = 0.0, + hidden_act: str = 'relu', + pre_ln: bool = False, + pre_ln_final_layer_norm: bool = True, + ): + super().__init__() + + self._vocab_size = vocab_size + self._hidden_size = hidden_size + self._max_sequence_length = max_sequence_length + self.num_states = num_layers + 1 + self.return_mems = False + if pre_ln_final_layer_norm: + self.num_states += 1 + + self._embedding = TransformerEmbedding( + vocab_size=self.vocab_size, + hidden_size=self.hidden_size, + max_sequence_length=max_sequence_length, + num_token_types=num_token_types, + embedding_dropout=embedding_dropout, + learn_positional_encodings=learn_positional_encodings, + ) + + self._decoder = TransformerDecoder( + hidden_size=self.hidden_size, + num_layers=num_layers, + inner_size=inner_size, + num_attention_heads=num_attention_heads, + ffn_dropout=ffn_dropout, + attn_score_dropout=attn_score_dropout, + attn_layer_dropout=attn_layer_dropout, + hidden_act=hidden_act, + pre_ln=pre_ln, + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + ) + + @typecheck() + def forward( + self, input_ids, decoder_mask, encoder_embeddings, encoder_mask, decoder_mems=None, + ): + start_pos = 0 + if decoder_mems is not None: + start_pos = input_ids.shape[1] - 1 + input_ids = input_ids[:, -1:] + decoder_mask = decoder_mask[:, -1:] + decoder_mems = torch.transpose(decoder_mems, 0, 1) + decoder_embeddings = self._embedding(input_ids=input_ids, start_pos=start_pos) + decoder_hidden_states = self._decoder( + decoder_states=decoder_embeddings, + decoder_mask=decoder_mask, + encoder_states=encoder_embeddings, + encoder_mask=encoder_mask, + decoder_mems_list=decoder_mems, + return_mems=self.return_mems, + return_mems_as_list=False, + ) + if self.return_mems: + decoder_hidden_states = torch.transpose(decoder_hidden_states, 0, 1) + return decoder_hidden_states + + @property + def hidden_size(self): + return self._hidden_size + + @property + def vocab_size(self): + return self._vocab_size + + @property + def max_sequence_length(self): + return self._max_sequence_length + + @property + def embedding(self): + return self._embedding + + @property + def decoder(self): + return self._decoder + + def input_example(self, max_batch=1, max_dim=256): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + sample = next(self.parameters()) + sz = (max_batch, max_dim) + input_ids = torch.randint(low=0, high=2048, size=sz, device=sample.device) + encoder_mask = torch.randint(low=0, high=1, size=sz, device=sample.device) + mem_size = [max_batch, self.num_states, max_dim - 1, self._hidden_size] + decoder_mems = torch.rand(mem_size, device=sample.device) + return tuple([input_ids, encoder_mask, self._embedding(input_ids), encoder_mask, decoder_mems]) + + def _prepare_for_export(self, **kwargs): + self._decoder.diagonal = None + self.return_mems = True + super()._prepare_for_export(**kwargs) + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + if self.return_mems: + return {"last_hidden_states": NeuralType(('B', 'D', 'T', 'D'), ChannelType())} + else: + return {"last_hidden_states": NeuralType(('B', 'T', 'D'), ChannelType())} diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_bottleneck.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_bottleneck.py new file mode 100644 index 0000000..c463b4d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_bottleneck.py @@ -0,0 +1,336 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Dict, Optional + +from nemo.collections.asr.modules.transformer.bridge_encoders import BridgeEncoder +from nemo.collections.asr.modules.transformer.perceiver_encoders import PerceiverEncoder +from nemo.collections.asr.modules.transformer.reduction_encoders import PoolingEncoder +from nemo.collections.asr.modules.transformer.transformer import ( + NeMoTransformerConfig, + TransformerDecoderNM, + TransformerEncoderNM, +) +from nemo.core.classes.common import typecheck +from nemo.core.neural_types import MaskType, NeuralType +from nemo.core.neural_types.elements import BoolType + +__all__ = [ + "NeMoTransformerBottleneckConfig", + "NeMoTransformerBottleneckEncoderConfig", + "NeMoTransformerBottleneckDecoderConfig", + "TransformerBottleneckEncoderNM", +] + + +@dataclass +class NeMoTransformerBottleneckConfig(NeMoTransformerConfig): + # architecture details (default is no bottleneck) + arch: str = '' + hidden_steps: int = -1 + hidden_blocks: int = 1 + hidden_init_method: str = "params" + + +@dataclass +class NeMoTransformerBottleneckEncoderConfig(NeMoTransformerBottleneckConfig): + mask_future: bool = False + # change return_mask to False to return hidden states only (default for non-bottleneck encoder) + return_mask: bool = True + + +@dataclass +class NeMoTransformerBottleneckDecoderConfig(NeMoTransformerBottleneckConfig): + r2l: bool = False + + +class TransformerBottleneckEncoderNM(TransformerEncoderNM): + + _SUPPORTED_ARCH = ["seq2seq", "bridge", "perceiver", "max_pool", "avg_pool"] + + def __init__( + self, + vocab_size: int, + hidden_size: int, + num_layers: int, + inner_size: int, + num_attention_heads: int, + max_sequence_length: int = 512, + num_token_types: int = 2, + embedding_dropout: float = 0.0, + learn_positional_encodings: bool = False, + ffn_dropout: float = 0.0, + attn_score_dropout: float = 0.0, + attn_layer_dropout: float = 0.0, + hidden_act: str = 'relu', + mask_future: bool = False, + pre_ln: bool = False, + pre_ln_final_layer_norm: bool = True, + arch: str = '', + hidden_steps: int = -1, + hidden_blocks: int = 1, + hidden_init_method: str = "default", + # default whether forward() method returns hidden or (hidden, mask) + return_mask=True, + ): + super().__init__( + vocab_size=vocab_size, + hidden_size=hidden_size, + num_layers=num_layers, + inner_size=inner_size, + num_attention_heads=num_attention_heads, + max_sequence_length=max_sequence_length, + num_token_types=num_token_types, + embedding_dropout=embedding_dropout, + learn_positional_encodings=learn_positional_encodings, + ffn_dropout=ffn_dropout, + attn_score_dropout=attn_score_dropout, + attn_layer_dropout=attn_layer_dropout, + hidden_act=hidden_act, + mask_future=mask_future, + pre_ln=pre_ln, + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + ) + + self._arch = arch + self._return_mask = return_mask + + # replace encoder + self._encoder = self._build_encoder( + arch=arch, + hidden_steps=hidden_steps, + hidden_blocks=hidden_blocks, + hidden_init_method=hidden_init_method, + hidden_size=hidden_size, + num_layers=num_layers, + inner_size=inner_size, + num_attention_heads=num_attention_heads, + ffn_dropout=ffn_dropout, + attn_score_dropout=attn_score_dropout, + attn_layer_dropout=attn_layer_dropout, + hidden_act=hidden_act, + mask_future=mask_future, + pre_ln=pre_ln, + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + ) + + def _build_encoder(self, arch, **kwargs): + """ + Returns a decoder based on architecture arch and kwargs + """ + # default non-bottleneck transformer encoder + if (not arch) or (arch == "seq2seq"): + encoder = self.encoder + elif arch == "bridge": + encoder = BridgeEncoder( + num_layers=kwargs["num_layers"], + hidden_size=kwargs["hidden_size"], + inner_size=kwargs["inner_size"], + num_attention_heads=kwargs["num_attention_heads"], + attn_score_dropout=kwargs["attn_score_dropout"], + attn_layer_dropout=kwargs["attn_layer_dropout"], + ffn_dropout=kwargs["ffn_dropout"], + hidden_act=kwargs["hidden_act"], + mask_future=kwargs["mask_future"], + pre_ln=kwargs["pre_ln"], + pre_ln_final_layer_norm=kwargs["pre_ln_final_layer_norm"], + hidden_steps=kwargs["hidden_steps"], + hidden_blocks=kwargs["hidden_blocks"], + hidden_init_method=kwargs["hidden_init_method"], + ) + elif arch == "perceiver": + encoder = PerceiverEncoder( + num_layers=kwargs["num_layers"], + hidden_size=kwargs["hidden_size"], + inner_size=kwargs["inner_size"], + num_attention_heads=kwargs["num_attention_heads"], + attn_score_dropout=kwargs["attn_score_dropout"], + attn_layer_dropout=kwargs["attn_layer_dropout"], + ffn_dropout=kwargs["ffn_dropout"], + hidden_act=kwargs["hidden_act"], + mask_future=kwargs["mask_future"], + pre_ln=kwargs["pre_ln"], + pre_ln_final_layer_norm=kwargs["pre_ln_final_layer_norm"], + hidden_steps=kwargs["hidden_steps"], + hidden_blocks=kwargs["hidden_blocks"], + hidden_init_method=kwargs["hidden_init_method"], + ) + elif arch == "max_pool": + encoder = PoolingEncoder( + num_layers=kwargs["num_layers"], + hidden_size=kwargs["hidden_size"], + inner_size=kwargs["inner_size"], + num_attention_heads=kwargs["num_attention_heads"], + attn_score_dropout=kwargs["attn_score_dropout"], + attn_layer_dropout=kwargs["attn_layer_dropout"], + ffn_dropout=kwargs["ffn_dropout"], + hidden_act=kwargs["hidden_act"], + mask_future=kwargs["mask_future"], + pre_ln=kwargs["pre_ln"], + pre_ln_final_layer_norm=kwargs["pre_ln_final_layer_norm"], + hidden_steps=kwargs["hidden_steps"], + hidden_blocks=kwargs["hidden_blocks"], + hidden_init_method=kwargs["hidden_init_method"], + pooling_type="max", + ) + elif arch == "avg_pool": + encoder = PoolingEncoder( + num_layers=kwargs["num_layers"], + hidden_size=kwargs["hidden_size"], + inner_size=kwargs["inner_size"], + num_attention_heads=kwargs["num_attention_heads"], + attn_score_dropout=kwargs["attn_score_dropout"], + attn_layer_dropout=kwargs["attn_layer_dropout"], + ffn_dropout=kwargs["ffn_dropout"], + hidden_act=kwargs["hidden_act"], + mask_future=kwargs["mask_future"], + pre_ln=kwargs["pre_ln"], + pre_ln_final_layer_norm=kwargs["pre_ln_final_layer_norm"], + hidden_steps=kwargs["hidden_steps"], + hidden_blocks=kwargs["hidden_blocks"], + hidden_init_method=kwargs["hidden_init_method"], + pooling_type="avg", + ) + else: + raise ValueError(f"Unknown arch = {self.arch}, supported arch = {self.supported_arch}") + + return encoder + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + input_types = super().input_types + input_types.update( + {"return_mask": NeuralType((), BoolType(), True),} + ) + + return input_types + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + output_types = super().output_types + output_types.update( + {"hidden_mask": NeuralType(('B', 'T'), MaskType(), True),} + ) + return output_types + + @property + def supported_arch(self): + return self._SUPPORTED_ARCH + + @property + def arch(self): + return self._arch + + @typecheck() + def forward(self, input_ids, encoder_mask, return_mask=None): + if return_mask is None: + return_mask = self._return_mask + + embeddings = self._embedding(input_ids=input_ids) + + if (not self.arch) or (self.arch == "seq2seq"): + encoder_hidden_states = self._encoder(encoder_states=embeddings, encoder_mask=encoder_mask) + encoder_hidden_mask = encoder_mask + else: + encoder_hidden_states, encoder_hidden_mask = self._encoder( + encoder_states=embeddings, encoder_mask=encoder_mask, + ) + + if return_mask: + return encoder_hidden_states, encoder_hidden_mask + else: + return encoder_hidden_states + + +class TransformerBottleneckDecoderNM(TransformerDecoderNM): + _SUPPORTED_ARCH = ["seq2seq"] + + def __init__( + self, + vocab_size: int, + hidden_size: int, + num_layers: int, + inner_size: int, + num_attention_heads: int, + max_sequence_length: int = 512, + num_token_types: int = 2, + embedding_dropout: float = 0.0, + learn_positional_encodings: bool = False, + ffn_dropout: float = 0.0, + attn_score_dropout: float = 0.0, + attn_layer_dropout: float = 0.0, + hidden_act: str = 'relu', + pre_ln: bool = False, + pre_ln_final_layer_norm: bool = True, + arch='', + ): + super().__init__( + vocab_size=vocab_size, + hidden_size=hidden_size, + num_layers=num_layers, + inner_size=inner_size, + num_attention_heads=num_attention_heads, + max_sequence_length=max_sequence_length, + num_token_types=num_token_types, + embedding_dropout=embedding_dropout, + learn_positional_encodings=learn_positional_encodings, + ffn_dropout=ffn_dropout, + attn_score_dropout=attn_score_dropout, + attn_layer_dropout=attn_layer_dropout, + hidden_act=hidden_act, + pre_ln=pre_ln, + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + ) + + self._arch = arch + + # replace decoder + self._decoder = self._build_decoder( + arch=arch, + hidden_size=hidden_size, + num_layers=num_layers, + inner_size=inner_size, + num_attention_heads=num_attention_heads, + max_sequence_length=max_sequence_length, + num_token_types=num_token_types, + embedding_dropout=embedding_dropout, + learn_positional_encodings=learn_positional_encodings, + ffn_dropout=ffn_dropout, + attn_score_dropout=attn_score_dropout, + attn_layer_dropout=attn_layer_dropout, + hidden_act=hidden_act, + pre_ln=pre_ln, + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + ) + + def _build_decoder(self, arch, **kwargs): + """ + Returns a decoder based on architecture arch and kwargs + """ + # usual non-bottleneck transformer decoder + if (not arch) or (arch == "seq2seq"): + decoder = self.decoder + else: + raise ValueError(f"Unknown arch = {self.arch}, supported arch = {self.supported_arch}") + + return decoder + + @property + def supported_arch(self): + return self._SUPPORTED_ARCH + + @property + def arch(self): + return self._arch diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_decoders.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_decoders.py new file mode 100644 index 0000000..a5b2c29 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_decoders.py @@ -0,0 +1,221 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy + +import torch +import torch.nn as nn + +from nemo.collections.asr.modules.transformer.transformer_modules import MultiHeadAttention, PositionWiseFF +from nemo.collections.common.parts import form_attention_mask + +__all__ = ["TransformerDecoder"] + + +class TransformerDecoderBlock(nn.Module): + """ + Building block of Transformer decoder. + + Args: + hidden_size: size of the embeddings in the model, also known as d_model + inner_size: number of neurons in the intermediate part of feed-forward + net, usually is (4-8 x hidden_size) in the papers + num_attention_heads: number of heads in multi-head attention + attn_score_dropout: probability of dropout applied to attention scores + attn_layer_dropout: probability of dropout applied to the output of the + attention layers, but before layer normalization + ffn_dropout: probability of dropout applied to FFN output + hidden_act: activation function used between two linear layers in FFN + """ + + def __init__( + self, + hidden_size: int, + inner_size: int, + num_attention_heads: int = 1, + attn_score_dropout: float = 0.0, + attn_layer_dropout: float = 0.0, + ffn_dropout: float = 0.0, + hidden_act: str = "relu", + pre_ln: bool = False, + ): + super().__init__() + self.pre_ln = pre_ln + self.layer_norm_1 = nn.LayerNorm(hidden_size, eps=1e-5) + self.first_sub_layer = MultiHeadAttention( + hidden_size, num_attention_heads, attn_score_dropout, attn_layer_dropout + ) + self.layer_norm_2 = nn.LayerNorm(hidden_size, eps=1e-5) + self.second_sub_layer = MultiHeadAttention( + hidden_size, num_attention_heads, attn_score_dropout, attn_layer_dropout + ) + self.layer_norm_3 = nn.LayerNorm(hidden_size, eps=1e-5) + self.third_sub_layer = PositionWiseFF(hidden_size, inner_size, ffn_dropout, hidden_act) + + def forward_preln(self, decoder_query, decoder_mask, decoder_keys, encoder_states, encoder_mask): + """ + Pre-LayerNorm block + Order of operations: LN -> Self-Attn -> Residual -> LN -> Cross-Attn -> Residual -> LN -> FFN + """ + residual = decoder_query + decoder_query = self.layer_norm_1(decoder_query) + decoder_keys = self.layer_norm_1(decoder_keys) + self_attn_output = self.first_sub_layer(decoder_query, decoder_keys, decoder_keys, decoder_mask) + self_attn_output += residual + + residual = self_attn_output + self_attn_output = self.layer_norm_2(self_attn_output) + enc_dec_attn_output = self.second_sub_layer(self_attn_output, encoder_states, encoder_states, encoder_mask) + enc_dec_attn_output += residual + + residual = enc_dec_attn_output + enc_dec_attn_output = self.layer_norm_3(enc_dec_attn_output) + output_states = self.third_sub_layer(enc_dec_attn_output) + output_states += residual + + return output_states + + def forward_postln(self, decoder_query, decoder_mask, decoder_keys, encoder_states, encoder_mask): + """ + Post-LayerNorm block + Order of operations: Self-Attn -> Residual -> LN -> Cross-Attn -> Residual -> LN -> FFN -> Residual -> LN + """ + self_attn_output = self.first_sub_layer(decoder_query, decoder_keys, decoder_keys, decoder_mask) + self_attn_output += decoder_query + self_attn_output = self.layer_norm_1(self_attn_output) + + enc_dec_attn_output = self.second_sub_layer(self_attn_output, encoder_states, encoder_states, encoder_mask) + enc_dec_attn_output += self_attn_output + enc_dec_attn_output = self.layer_norm_2(enc_dec_attn_output) + + output_states = self.third_sub_layer(enc_dec_attn_output) + output_states += enc_dec_attn_output + return self.layer_norm_3(output_states) + + def forward(self, decoder_query, decoder_mask, decoder_keys, encoder_states, encoder_mask): + if self.pre_ln: + return self.forward_preln(decoder_query, decoder_mask, decoder_keys, encoder_states, encoder_mask) + else: + return self.forward_postln(decoder_query, decoder_mask, decoder_keys, encoder_states, encoder_mask) + + +class TransformerDecoder(nn.Module): + def __init__( + self, + num_layers: int, + hidden_size: int, + inner_size: int, + num_attention_heads: int = 1, + attn_score_dropout: float = 0.0, + attn_layer_dropout: float = 0.0, + ffn_dropout: float = 0.0, + hidden_act: str = "relu", + pre_ln: bool = False, + pre_ln_final_layer_norm: bool = True, + ): + super().__init__() + + if pre_ln and pre_ln_final_layer_norm: + self.final_layer_norm = nn.LayerNorm(hidden_size, eps=1e-5) + else: + self.final_layer_norm = None + + layer = TransformerDecoderBlock( + hidden_size, + inner_size, + num_attention_heads, + attn_score_dropout, + attn_layer_dropout, + ffn_dropout, + hidden_act, + pre_ln, + ) + self.layers = nn.ModuleList([copy.deepcopy(layer) for _ in range(num_layers)]) + self.diagonal = 0 + + def _get_memory_states(self, decoder_states, decoder_mems_list=None, i=0): + if decoder_mems_list is not None: + inp1 = torch.transpose(decoder_mems_list[i], 1, 2) # Putting seq_len to last dim to handle export cases + inp2 = torch.transpose(decoder_states, 1, 2) + memory_states = torch.cat((inp1, inp2), dim=2) + memory_states = torch.transpose(memory_states, 1, 2) # Transposing back + else: + memory_states = decoder_states + return memory_states + + def forward( + self, + decoder_states, + decoder_mask, + encoder_states, + encoder_mask, + decoder_mems_list=None, + return_mems=False, + return_mems_as_list=True, + ): + """ + Args: + decoder_states: output of the embedding layer (B x L_dec x H) + decoder_mask: decoder inputs mask (B x L_dec) + encoder_states: output of the encoder (B x L_enc x H) + encoder_mask: encoder inputs mask (B x L_enc) + decoder_mems_list: list of the cached decoder hidden states + for fast autoregressive generation which will be used instead + of decoder_states as keys and values if not None + return_mems: bool, whether to return outputs of all decoder layers + or the last layer only + return_mems_as_list: bool, when True, mems returned are as a list; otherwise mems are Tensor + """ + decoder_attn_mask = form_attention_mask(decoder_mask, diagonal=self.diagonal) + encoder_attn_mask = form_attention_mask(encoder_mask) + memory_states = self._get_memory_states(decoder_states, decoder_mems_list, 0) + if return_mems: + if return_mems_as_list: + cached_mems_list = [memory_states] + else: + cached_mems_list = memory_states.unsqueeze(0) + + for i, layer in enumerate(self.layers): + decoder_states = layer(decoder_states, decoder_attn_mask, memory_states, encoder_states, encoder_attn_mask) + memory_states = self._get_memory_states(decoder_states, decoder_mems_list, i + 1) + if return_mems: + if return_mems_as_list: + cached_mems_list.append(memory_states) + else: + cached_mems_list = torch.cat((cached_mems_list, memory_states.unsqueeze(0)), dim=0) + + if self.final_layer_norm is not None: + decoder_states = self.final_layer_norm(decoder_states) + memory_states = self._get_memory_states(decoder_states, decoder_mems_list, i + 2) + if return_mems: + if return_mems_as_list: + cached_mems_list.append(memory_states) + else: + cached_mems_list = torch.cat((cached_mems_list, memory_states.unsqueeze(0)), dim=0) + + if return_mems: + return cached_mems_list + else: + return memory_states + + def input_example(self, max_batch=1, max_dim=256): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + sample = next(self.parameters()) + input_ids = torch.randint(low=0, high=2048, size=(max_batch, max_dim, 1024), device=sample.device) + encoder_mask = torch.randint(low=0, high=1, size=(max_batch, max_dim), device=sample.device) + return tuple([input_ids, encoder_mask, input_ids, encoder_mask]) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_encoders.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_encoders.py new file mode 100644 index 0000000..544d561 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_encoders.py @@ -0,0 +1,174 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy + +import torch +import torch.nn as nn + +from nemo.collections.asr.modules.transformer.transformer_modules import MultiHeadAttention, PositionWiseFF +from nemo.collections.common.parts import form_attention_mask + +__all__ = ["TransformerEncoder"] + + +class TransformerEncoderBlock(nn.Module): + """ + Building block of Transformer encoder. + + Args: + hidden_size: size of the embeddings in the model, also known as d_model + inner_size: number of neurons in the intermediate part of feed-forward + net, usually is (4-8 x hidden_size) in the papers + num_attention_heads: number of heads in multi-head attention + attn_score_dropout: probability of dropout applied to attention scores + attn_layer_dropout: probability of dropout applied to the output of the + attention layers, but before layer normalization + ffn_dropout: probability of dropout applied to FFN output + hidden_act: activation function used between two linear layers in FFN + """ + + def __init__( + self, + hidden_size: int, + inner_size: int, + num_attention_heads: int = 1, + attn_score_dropout: float = 0.0, + attn_layer_dropout: float = 0.0, + ffn_dropout: float = 0.0, + hidden_act: str = "relu", + pre_ln: bool = False, + ): + super().__init__() + self.pre_ln = pre_ln + self.layer_norm_1 = nn.LayerNorm(hidden_size, eps=1e-5) + self.first_sub_layer = MultiHeadAttention( + hidden_size, num_attention_heads, attn_score_dropout, attn_layer_dropout + ) + self.layer_norm_2 = nn.LayerNorm(hidden_size, eps=1e-5) + self.second_sub_layer = PositionWiseFF(hidden_size, inner_size, ffn_dropout, hidden_act) + + def forward_preln(self, encoder_query, encoder_mask, encoder_keys): + """ + Pre-LayerNorm block + Order of operations: LN -> Self-Attn -> Residual -> LN -> Cross-Attn -> Residual -> LN -> FFN + """ + residual = encoder_query + encoder_query = self.layer_norm_1(encoder_query) + encoder_keys = self.layer_norm_1(encoder_keys) + self_attn_output = self.first_sub_layer(encoder_query, encoder_keys, encoder_keys, encoder_mask) + self_attn_output += residual + + residual = self_attn_output + self_attn_output = self.layer_norm_2(self_attn_output) + output_states = self.second_sub_layer(self_attn_output) + output_states += residual + + return output_states + + def forward_postln(self, encoder_query, encoder_mask, encoder_keys): + """ + Post-LayerNorm block + Order of operations: Self-Attn -> Residual -> LN -> Cross-Attn -> Residual -> LN -> FFN -> Residual -> LN + """ + self_attn_output = self.first_sub_layer(encoder_query, encoder_keys, encoder_keys, encoder_mask) + self_attn_output += encoder_query + self_attn_output = self.layer_norm_1(self_attn_output) + + output_states = self.second_sub_layer(self_attn_output) + output_states += self_attn_output + output_states = self.layer_norm_2(output_states) + + return output_states + + def forward(self, encoder_query, encoder_mask, encoder_keys): + if self.pre_ln: + return self.forward_preln(encoder_query, encoder_mask, encoder_keys) + else: + return self.forward_postln(encoder_query, encoder_mask, encoder_keys) + + +class TransformerEncoder(nn.Module): + def __init__( + self, + num_layers: int, + hidden_size: int, + inner_size: int, + mask_future: bool = False, + num_attention_heads: int = 1, + attn_score_dropout: float = 0.0, + attn_layer_dropout: float = 0.0, + ffn_dropout: float = 0.0, + hidden_act: str = "relu", + pre_ln: bool = False, + pre_ln_final_layer_norm: bool = True, + ): + super().__init__() + + if pre_ln and pre_ln_final_layer_norm: + self.final_layer_norm = nn.LayerNorm(hidden_size, eps=1e-5) + else: + self.final_layer_norm = None + + layer = TransformerEncoderBlock( + hidden_size, + inner_size, + num_attention_heads, + attn_score_dropout, + attn_layer_dropout, + ffn_dropout, + hidden_act, + pre_ln, + ) + self.layers = nn.ModuleList([copy.deepcopy(layer) for _ in range(num_layers)]) + self.diag = 0 if mask_future else None + + def _get_memory_states(self, encoder_states, encoder_mems_list=None, i=0): + if encoder_mems_list is not None: + memory_states = torch.cat((encoder_mems_list[i], encoder_states), dim=1) + else: + memory_states = encoder_states + return memory_states + + def forward(self, encoder_states, encoder_mask, encoder_mems_list=None, return_mems=False): + """ + Args: + encoder_states: output of the embedding_layer (B x L_enc x H) + encoder_mask: encoder inputs mask (B x L_enc) + encoder_mems_list: list of the cached encoder hidden states + for fast autoregressive generation which will be used instead + of encoder_states as keys and values if not None + return_mems: bool, whether to return outputs of all encoder layers + or the last layer only + """ + + encoder_attn_mask = form_attention_mask(encoder_mask, self.diag) + + memory_states = self._get_memory_states(encoder_states, encoder_mems_list, 0) + cached_mems_list = [memory_states] + + for i, layer in enumerate(self.layers): + encoder_states = layer(encoder_states, encoder_attn_mask, memory_states) + memory_states = self._get_memory_states(encoder_states, encoder_mems_list, i + 1) + cached_mems_list.append(memory_states) + + if self.final_layer_norm is not None: + encoder_states = self.final_layer_norm(encoder_states) + memory_states = self._get_memory_states(encoder_states, encoder_mems_list, i + 1) + cached_mems_list.append(memory_states) + + if return_mems: + return cached_mems_list + else: + return cached_mems_list[-1] diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_generators.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_generators.py new file mode 100644 index 0000000..4061f54 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_generators.py @@ -0,0 +1,916 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from contextlib import contextmanager + +import torch + +from nemo.collections.common.parts import NEG_INF, mask_padded_tokens + +__all__ = [ + "GreedySequenceGenerator", + "TopKSequenceGenerator", + "BeamSearchSequenceGenerator", + "BeamSearchSequenceGeneratorWithLanguageModel", + "EnsembleBeamSearchSequenceGenerator", +] + + +class GreedySequenceGenerator: + """ + Greedy sequence generator based on the decoder followed by log_softmax. + + Args: + embedding: nn.Module, transforms input_ids into vector embeddings + decoder: nn.Module, takes embeddings and produces hidden_states + log_softmax: nn.Module, takes hidden_states and produces log_probs + which correspond to probability distribution of tokens (ids) + pad: index of padding token in the vocabulary + bos: index of beginning of sequence token in the vocabulary + eos: index of end of sequence token in the vocabulary + max_sequence_length: maximum allowed length for generated sequences + max_delta_length: in case of encoder-decoder generation (e.g. NMT), + forbids generated sequences to be longer than the length of + source sequences plus max_delta_length + batch_size: size of the batch of generated sequences if neither + source nor target starting sequences are provided + """ + + def __init__( + self, + embedding, + decoder, + log_softmax, + pad=0, + bos=1, + eos=2, + max_sequence_length=512, + max_delta_length=20, + batch_size=1, + ): + super().__init__() + self.embedding = embedding + self.decoder = decoder + self.log_softmax = log_softmax + self.pad, self.bos, self.eos = pad, bos, eos + self.max_seq_length = max_sequence_length + self.max_delta_len = max_delta_length + self.batch_size = batch_size + + def _one_step_forward( + self, + decoder_input_ids=None, + encoder_hidden_states=None, + encoder_input_mask=None, + decoder_mems_list=None, + pos=0, + ): + """ + One step of autoregressive output generation. + + Args: + decoder_input_ids: starting sequence of tokens to generate from; + if None, generation will start from a batch of tokens + encoder_hidden_states: output of the encoder for conditional + sequence generation; if None, generator will use unconditional + mode (e.g., language modeling) + encoder_input_mask: input mask used in the encoder + decoder_mems_list: list of size num_layers with cached activations + of sequence (x[1], ..., x[k-1]) for fast generation of x[k] + pos: starting position in positional encoding + """ + + decoder_hidden_states = self.embedding.forward(decoder_input_ids, start_pos=pos) + decoder_input_mask = mask_padded_tokens(decoder_input_ids, self.pad).float() + + if encoder_hidden_states is not None: + decoder_mems_list = self.decoder.forward( + decoder_hidden_states, + decoder_input_mask, + encoder_hidden_states, + encoder_input_mask, + decoder_mems_list, + return_mems=True, + ) + else: + decoder_mems_list = self.decoder.forward( + decoder_hidden_states, decoder_input_mask, decoder_mems_list, return_mems=True + ) + log_probs = self.log_softmax.forward(hidden_states=decoder_mems_list[-1][:, -1:]) + return log_probs, decoder_mems_list + + def _prepare_for_search(self, decoder_input_ids=None, encoder_hidden_states=None): + """ + Helper function which defines starting sequence to begin generating + with and maximum allowed number of tokens to be generated. + """ + + decoder_parameter = next(self.decoder.parameters()) + batch_size = self.batch_size + + # for encoder-decoder generation, maximum length of generated sequence + # is min(max_sequence_length, src_len + max_delta_length) + if encoder_hidden_states is not None: + batch_size, src_len, _ = encoder_hidden_states.size() + if self.max_delta_len >= 0: + max_seq_length = min(self.max_seq_length, src_len + self.max_delta_len) + else: + max_seq_length = self.max_seq_length + else: + max_seq_length = self.max_seq_length + + # if no input is provided, start with the batch of tokens + if decoder_input_ids is not None: + tgt = decoder_input_ids + batch_size, tgt_len = decoder_input_ids.size() + else: + tgt = torch.zeros(batch_size, 1).long().fill_(self.bos).to(decoder_parameter.device) + tgt_len = 1 + max_generation_length = max_seq_length - tgt_len + + return tgt, batch_size, max_generation_length + + def _forward( + self, decoder_input_ids=None, encoder_hidden_states=None, encoder_input_mask=None, return_beam_scores=False + ): + assert not return_beam_scores + tgt, batch_size, max_generation_length = self._prepare_for_search(decoder_input_ids, encoder_hidden_states) + + # pad profile tracks sequences ending with token to replace + # everything after with token + decoder_parameter = next(self.decoder.parameters()) + pad_profile = torch.zeros(batch_size, 1).long().to(decoder_parameter.device) + + decoder_mems_list = None + for i in range(max_generation_length): + + log_probs, decoder_mems_list = self._one_step_forward( + tgt[:, -1:], encoder_hidden_states, encoder_input_mask, decoder_mems_list, i + ) + + next_tokens = torch.argmax(log_probs[:, -1], dim=-1, keepdim=True) + next_tokens = self.pad * pad_profile + next_tokens * (1 - pad_profile) + pad_profile = torch.max(pad_profile, (next_tokens == self.eos).long()) + tgt = torch.cat((tgt, next_tokens), dim=-1) + + # abort generation if all sequences end with + if pad_profile.sum() == batch_size: + break + + return tgt + + def __call__( + self, decoder_input_ids=None, encoder_hidden_states=None, encoder_input_mask=None, return_beam_scores=False + ): + with self.as_frozen(): + results = self._forward( + decoder_input_ids, encoder_hidden_states, encoder_input_mask, return_beam_scores=return_beam_scores + ) + if not return_beam_scores: + return results + else: + prefixes, scores, tgt = results + prefixes = prefixes.view(-1, self.beam_size, tgt.size(1)).split(1, dim=0) + scores = scores.view(-1, self.beam_size).split(1, dim=0) + prefixes = [x.squeeze(0) for x in prefixes] # each item is [beam, seq_len] + scores = [x.squeeze(0) for x in scores] # each item is [beam,] + return prefixes, scores, tgt + + def freeze(self) -> None: + """Freeze weights of embedding, decoder, and classification layers to prevent memory leak. + """ + for param in self.embedding.parameters(): + param.requires_grad = False + self.embedding.eval() + for param in self.decoder.parameters(): + param.requires_grad = False + self.decoder.eval() + for param in self.log_softmax.parameters(): + param.requires_grad = False + self.log_softmax.eval() + + def unfreeze(self) -> None: + """Unfreeze weights of embedding, decoder, and classification layers. + """ + for param in self.embedding.parameters(): + param.requires_grad = True + self.embedding.train() + for param in self.decoder.parameters(): + param.requires_grad = True + self.decoder.train() + for param in self.log_softmax.parameters(): + param.requires_grad = True + self.log_softmax.train() + + @contextmanager + def as_frozen(self): + """ + Context manager which temporarily freezes embedding, decoder, and log_softmax modules, + yields control and finally unfreezes the modules. + """ + self.freeze() + + try: + yield + finally: + self.unfreeze() + + +class TopKSequenceGenerator(GreedySequenceGenerator): + """ + Top-k sequence generator based on the decoder followed by log_softmax. + + Args: + *all args of GreedySequenceGenerator class + beam_size: size of the beam (parameter k in top-k) + temperature: temperature of top-k sampling, all logits are divided + by temperature before rescaling. High temperature leads to + uniform distribution, low leads to delta-like distribution. + Kwargs: + all remaining parameters of GreedySequenceGenerator class + """ + + def __init__(self, embedding, decoder, log_softmax, beam_size=1, temperature=1.0, **kwargs): + super().__init__(embedding, decoder, log_softmax, **kwargs) + self.beam_size = beam_size + self.temp = temperature + + # @torch.no_grad() + def _one_step_forward( + self, + decoder_input_ids=None, + encoder_hidden_states=None, + encoder_input_mask=None, + decoder_mems_list=None, + pos=0, + ): + log_probs, decoder_mems_list = super()._one_step_forward( + decoder_input_ids, encoder_hidden_states, encoder_input_mask, decoder_mems_list, pos + ) + + batch_size, seq_len, vocab_size = log_probs.size() + scores, indices = torch.topk(log_probs, self.beam_size, dim=-1) + + rescaled_logexp = torch.zeros_like(log_probs).scatter(-1, indices, scores.div(self.temp).exp()) + probs = rescaled_logexp / rescaled_logexp.norm(1, -1, keepdim=True) + + # We randomly sample next tokens from rescaled probability distribution + # over top-k candidates and return a binary tensor which indicates + # candidates that have been selected. We call this object + # `pseudo_log_probs` as genuine log_probs should have -infs instead of + # 0s and 0s instead of 1s. + ids = torch.multinomial(probs.view(-1, vocab_size), 1).view(-1, seq_len, 1) + pseudo_log_probs = torch.zeros_like(log_probs).scatter(-1, ids, 1.0) + + return pseudo_log_probs, decoder_mems_list + + +class BeamSearchSequenceGenerator(GreedySequenceGenerator): + def __init__(self, embedding, decoder, log_softmax, beam_size=1, len_pen=0, **kwargs): + """ + Beam Search sequence generator based on the decoder followed by + log_softmax. + + Args: + *all args of GreedySequenceGenerator class + beam_size: size of the beam + len_pen: length penalty parameter + Kwargs: + all remaining parameters of GreedySequenceGenerator class + """ + + super().__init__(embedding, decoder, log_softmax, **kwargs) + self.beam_size = beam_size + self.len_pen = len_pen + + @staticmethod + def compute_len_penalty(lengths, alpha): + """Returns length penalty according to https://arxiv.org/pdf/1609.08144.pdf""" + return ((5 + lengths) / 6).pow(alpha) + + def _forward( + self, decoder_input_ids=None, encoder_hidden_states=None, encoder_input_mask=None, return_beam_scores=False + ): + tgt, batch_size, max_generation_length = self._prepare_for_search(decoder_input_ids, encoder_hidden_states) + + # generate initial buffer of beam_size prefixes-hypotheses + log_probs, decoder_mems_list = self._one_step_forward(tgt, encoder_hidden_states, encoder_input_mask, None, 0) + scores, prefixes = torch.topk(log_probs.permute(0, 2, 1), self.beam_size, dim=1) + scores, prefixes = scores.view(-1, 1), prefixes.view(-1, 1) + + # repeat init target prefixes and cached memory states beam_size times + prefixes = torch.cat((tgt.repeat(1, self.beam_size).view(-1, tgt.shape[1]), prefixes), dim=1) + for j in range(len(decoder_mems_list)): + decoder_mems_list[j] = decoder_mems_list[j].repeat(self.beam_size, 1, 1) + + # repeat source sequence beam_size times for beam search + if encoder_hidden_states is not None: + _, src_length, hidden_size = encoder_hidden_states.size() + encoder_input_mask = encoder_input_mask.repeat(1, self.beam_size).view(-1, src_length) + encoder_hidden_states = encoder_hidden_states.repeat(1, self.beam_size, 1).view( + -1, src_length, hidden_size + ) + else: + hidden_size = decoder_mems_list[0].size(2) + + # pad_profile tracks finished hypotheses to generate only tokens + # if or has been generated + pad_profile = torch.zeros_like(scores).long() + + # prefixes_len tracks lengths of generated hypotheses to perform + # length penalty correction + prefixes_len = torch.zeros_like(scores).fill_(prefixes.size(1) + 1) + + tgt_len = tgt.size(-1) + for i in range(tgt_len, max_generation_length + tgt_len): + + # mask all finished hypotheses to exclude them from beam + pad_mask = pad_profile.repeat(1, self.beam_size) + + # generate and score candidates for prefixes continuation + log_probs, decoder_mems_list = self._one_step_forward( + prefixes[:, -1:], encoder_hidden_states, encoder_input_mask, decoder_mems_list, i + ) + scores_i, prefixes_i = torch.topk(log_probs[:, -1, :], self.beam_size, dim=-1) + + # for all prefixes ending with or replace generated + # continuations with + prefixes_i = self.pad * pad_mask + prefixes_i * (1 - pad_mask) + + # force all hypotheses but one generated from already finished + # hypotheses to have extremely low score, so they will not be + # considered during beam re-ranking + pad_mask[:, 1:] = pad_mask[:, 1:] * NEG_INF + scores = scores + scores_i * (1 - pad_mask).to(scores.dtype) + + # choose top-k hypotheses with length penalty applied + len_penalties = self.compute_len_penalty(prefixes_len, self.len_pen) + scores = scores / len_penalties + scores, indices_i = torch.topk(scores.view(-1, self.beam_size ** 2), self.beam_size, dim=1) + scores = scores.view(-1, 1) * len_penalties + + # select prefixes which correspond to the chosen hypotheses + prefixes = prefixes.unsqueeze(1).repeat(1, self.beam_size, 1) + prefixes = torch.cat((prefixes, prefixes_i.unsqueeze(2)), dim=2) + prefixes = prefixes.view(batch_size, self.beam_size ** 2, -1) + p_len = prefixes.size(2) + prefixes_ids = indices_i.unsqueeze(2).repeat(1, 1, p_len) + prefixes = prefixes.gather(1, prefixes_ids).view(-1, p_len) + + # reshuffle cached decoder memory states to restore the order + # of hypotheses broken after top-k selection + mems_ids = indices_i.unsqueeze(2).unsqueeze(3).repeat(1, 1, p_len - 1, hidden_size) // self.beam_size + for j in range(len(decoder_mems_list)): + decoder_mems_list[j] = ( + decoder_mems_list[j] + .view(-1, self.beam_size, p_len - 1, hidden_size) + .gather(1, mems_ids) + .view(-1, p_len - 1, hidden_size) + ) + + # update prefixes_len and pad_profile + not_eos_pad = prefixes.ne(self.eos) & prefixes.ne(self.pad) + prefixes_len = 1 + not_eos_pad.sum(dim=1, keepdim=True).to(scores.dtype) + pad_profile = (~not_eos_pad[:, -1:]).long() + + # if all hypotheses end with or , interrupt search + if pad_profile.sum() == batch_size * self.beam_size: + break + + # select best performing hypotheses in each element of the batch + len_penalties = self.compute_len_penalty(prefixes_len, self.len_pen) + scores = scores / len_penalties + best_guesses = ( + torch.argmax(scores.view(-1, self.beam_size), dim=1, keepdim=True).repeat(1, prefixes.size(1)).unsqueeze(1) + ) + tgt = prefixes.view(batch_size, self.beam_size, -1).gather(1, best_guesses).squeeze(1) + + if return_beam_scores: + return prefixes, scores * len_penalties, tgt + else: + return tgt + + +class EnsembleBeamSearchSequenceGenerator: + def __init__( + self, + encoders, + embeddings, + decoders, + log_softmaxes, + beam_size=1, + len_pen=0, + pad=0, + bos=1, + eos=2, + max_sequence_length=512, + max_delta_length=20, + batch_size=1, + language_model=None, + fusion_coef=None, + ): + """ + Ensemble Beam Search sequence generator based on the decoder followed by + log_softmax. Averages the probabilities of different models. + NOTE: All models must have been trained with the same BPE tokenizers. + + Args: + encoders: A list of encoders + embeddings: A list of decoder embedding layers + decoders: A list of decoders + log_softmaxes: A list of decoder output layers + beam_size: Beam size + len_pen: Length penalty to adjust logprob scores to favor longer sequences + pad: pad id + bos: beginning of sequence id + eos: end of sequence id + max_sequence_length: maximum sequence length + max_delta_length: maximum length difference between input and output + batch_size: batch size if not inferrable from input sequence + """ + self.encoders = encoders + self.embeddings = embeddings + self.decoders = decoders + self.log_softmaxes = log_softmaxes + self.beam_size = beam_size + self.len_pen = len_pen + self.pad, self.bos, self.eos = pad, bos, eos + self.max_seq_length = max_sequence_length + self.max_delta_len = max_delta_length + self.batch_size = batch_size + assert len(embeddings) == len(decoders) == len(log_softmaxes) == len(encoders) + self.num_models = len(encoders) + self.language_model = language_model + self.fusion_coef = fusion_coef + + @staticmethod + def compute_len_penalty(lengths, alpha): + """Returns length penalty according to https://arxiv.org/pdf/1609.08144.pdf""" + return ((5 + lengths) / 6).pow(alpha) + + def _one_step_forward_lm(self, decoder_input_ids=None, lm_mems_list=None, pos=0): + input_mask = mask_padded_tokens(decoder_input_ids, self.pad).float() + lm_hidden_states = self.language_model.encoder.embedding.forward(decoder_input_ids, start_pos=pos) + lm_mems_list = self.language_model.encoder.encoder.forward( + lm_hidden_states, input_mask, lm_mems_list, return_mems=True, + ) + lm_log_probs = self.language_model.log_softmax.forward(hidden_states=lm_mems_list[-1][:, -1:]) + return lm_log_probs, lm_mems_list + + def _one_step_forward( + self, + ensemble_index, + decoder_input_ids=None, + encoder_hidden_states=None, + encoder_input_mask=None, + decoder_mems_list=None, + pos=0, + ): + """ + One step of autoregressive output generation for one particular model. + + Args: + decoder_input_ids: starting sequence of tokens to generate from; + if None, generation will start from a batch of tokens + encoder_hidden_states: output of the encoder for conditional + sequence generation; if None, generator will use unconditional + mode (e.g., language modeling) + encoder_input_mask: input mask used in the encoder + decoder_mems_list: list of size num_layers with cached activations + of sequence (x[1], ..., x[k-1]) for fast generation of x[k] + pos: starting position in positional encoding + """ + + decoder_hidden_states = self.embeddings[ensemble_index].forward(decoder_input_ids, start_pos=pos) + decoder_input_mask = mask_padded_tokens(decoder_input_ids, self.pad).float() + + if encoder_hidden_states is not None: + decoder_mems_list = self.decoders[ensemble_index].forward( + decoder_hidden_states, + decoder_input_mask, + encoder_hidden_states, + encoder_input_mask, + decoder_mems_list, + return_mems=True, + ) + else: + decoder_mems_list = self.decoders[ensemble_index].forward( + decoder_hidden_states, decoder_input_mask, decoder_mems_list, return_mems=True + ) + log_probs = self.log_softmaxes[ensemble_index].forward(hidden_states=decoder_mems_list[-1][:, -1:]) + return log_probs, decoder_mems_list + + def _prepare_for_search(self, decoder_input_ids=None, encoder_hidden_states=None): + """ + Helper function which defines starting sequence to begin generating + with and maximum allowed number of tokens to be generated. + """ + + decoder_parameter = next(self.decoders[0].parameters()) + batch_size = self.batch_size + + # for encoder-decoder generation, maximum length of generated sequence + # is min(max_sequence_length, src_len + max_delta_length) + if encoder_hidden_states is not None: + batch_size, src_len, _ = encoder_hidden_states.size() + if self.max_delta_len >= 0: + max_seq_length = min(self.max_seq_length, src_len + self.max_delta_len) + else: + max_seq_length = self.max_seq_length + else: + max_seq_length = self.max_seq_length + + # if no input is provided, start with the batch of tokens + if decoder_input_ids is not None: + tgt = decoder_input_ids + batch_size, tgt_len = decoder_input_ids.size() + else: + tgt = torch.zeros(batch_size, 1).long().fill_(self.bos).to(decoder_parameter.device) + tgt_len = 1 + max_generation_length = max_seq_length - tgt_len + + return tgt, batch_size, max_generation_length + + def _get_encoder_hidden_states(self, src_ids, encoder_input_mask, ensemble_index): + return self.encoders[ensemble_index](input_ids=src_ids, encoder_mask=encoder_input_mask) + + def _average_probs(self, probs_list): + probs_list = torch.stack(probs_list) + return torch.log(torch.exp(probs_list).mean(0)) + # probs = torch.stack(probs_list) # Ens x B x T x V + # return torch.log(probs.sum(0) / probs.sum(-1).sum(0).unsqueeze(-1)) + + def _forward(self, src_ids, encoder_input_mask, decoder_input_ids=None, return_beam_scores=False): + encoder_hidden_states = [ + self._get_encoder_hidden_states(src_ids, encoder_input_mask, i) for i in range(self.num_models) + ] + tgt, batch_size, max_generation_length = self._prepare_for_search(decoder_input_ids, encoder_hidden_states[0]) + + # generate initial buffer of beam_size prefixes-hypotheses + outputs = [ + self._one_step_forward(i, tgt, encoder_hidden_states[i], encoder_input_mask, None, 0) + for i in range(self.num_models) + ] + nmt_log_probs = self._average_probs([x[0] for x in outputs]) + decoder_mems_lists = [x[1] for x in outputs] + + if self.language_model is not None: + lm_log_probs, lm_mems_list = self._one_step_forward_lm(tgt, None, 0) + log_probs = nmt_log_probs + self.fusion_coef * lm_log_probs + else: + log_probs = nmt_log_probs + scores, prefixes = torch.topk(log_probs.permute(0, 2, 1), self.beam_size, dim=1) + scores, prefixes = scores.view(-1, 1), prefixes.view(-1, 1) + + # repeat init target prefixes and cached memory states beam_size times + prefixes = torch.cat((tgt.repeat(1, self.beam_size).view(-1, 1), prefixes), dim=1) + for i in range(self.num_models): + for j in range(len(decoder_mems_lists[i])): + decoder_mems_lists[i][j] = decoder_mems_lists[i][j].repeat(self.beam_size, 1, 1) + + if self.language_model is not None: + for j in range(len(lm_mems_list)): + lm_mems_list[j] = lm_mems_list[j].repeat(self.beam_size, 1, 1) + lm_hidden_size = lm_mems_list[0].size(2) + + encoder_input_mask = encoder_input_mask.repeat(1, self.beam_size).view(-1, encoder_input_mask.size(1)) + for i in range(self.num_models): + _, src_length, hidden_size = encoder_hidden_states[i].size() + encoder_hidden_states[i] = ( + encoder_hidden_states[i].repeat(1, self.beam_size, 1).view(-1, src_length, hidden_size) + ) + + # pad_profile tracks finished hypotheses to generate only tokens + # if or has been generated + pad_profile = torch.zeros_like(scores).long() + + # prefixes_len tracks lengths of generated hypotheses to perform + # length penalty correction + prefixes_len = torch.zeros_like(scores).fill_(prefixes.size(1) + 1) + + for i in range(max_generation_length): + + # mask all finished hypotheses to exclude them from beam + pad_mask = pad_profile.repeat(1, self.beam_size) + + # generate and score candidates for prefixes continuation + outputs = [ + self._one_step_forward( + model_num, + prefixes[:, -1:], + encoder_hidden_states[model_num], + encoder_input_mask, + decoder_mems_lists[model_num], + i + 1, + ) + for model_num in range(self.num_models) + ] + nmt_log_probs = self._average_probs([x[0] for x in outputs]) + decoder_mems_lists = [x[1] for x in outputs] + + if self.language_model is not None: + lm_log_probs, lm_mems_list = self._one_step_forward_lm(prefixes[:, -1:], lm_mems_list, i + 1) + log_probs = nmt_log_probs + self.fusion_coef * lm_log_probs + else: + log_probs = nmt_log_probs + scores_i, prefixes_i = torch.topk(log_probs[:, -1, :], self.beam_size, dim=-1) + + # for all prefixes ending with or replace generated + # continuations with + prefixes_i = self.pad * pad_mask + prefixes_i * (1 - pad_mask) + + # force all hypotheses but one generated from already finished + # hypotheses to have extremely low score, so they will not be + # considered during beam re-ranking + pad_mask[:, 1:] = pad_mask[:, 1:] * NEG_INF + scores = scores + scores_i * (1 - pad_mask).to(scores.dtype) + + # choose top-k hypotheses with length penalty applied + len_penalties = self.compute_len_penalty(prefixes_len, self.len_pen) + scores = scores / len_penalties + scores, indices_i = torch.topk(scores.view(-1, self.beam_size ** 2), self.beam_size, dim=1) + scores = scores.view(-1, 1) * len_penalties + + # select prefixes which correspond to the chosen hypotheses + prefixes = prefixes.unsqueeze(1).repeat(1, self.beam_size, 1) + prefixes = torch.cat((prefixes, prefixes_i.unsqueeze(2)), dim=2) + prefixes = prefixes.view(batch_size, self.beam_size ** 2, -1) + p_len = prefixes.size(2) + prefixes_ids = indices_i.unsqueeze(2).repeat(1, 1, p_len) + prefixes = prefixes.gather(1, prefixes_ids).view(-1, p_len) + + # reshuffle cached decoder memory states to restore the order + # of hypotheses broken after top-k selection + for model_num in range(self.num_models): + hidden_size = decoder_mems_lists[model_num][0].size(2) + mems_ids = indices_i.unsqueeze(2).unsqueeze(3).repeat(1, 1, p_len - 1, hidden_size) // self.beam_size + for j in range(len(decoder_mems_lists[model_num])): + decoder_mems_lists[model_num][j] = ( + decoder_mems_lists[model_num][j] + .view(-1, self.beam_size, p_len - 1, hidden_size) + .gather(1, mems_ids) + .view(-1, p_len - 1, hidden_size) + ) + if self.language_model is not None: + lm_mems_ids = ( + indices_i.unsqueeze(2).unsqueeze(3).repeat(1, 1, p_len - 1, lm_hidden_size) // self.beam_size + ) + for j in range(len(lm_mems_list)): + lm_mems_list[j] = ( + lm_mems_list[j] + .view(-1, self.beam_size, p_len - 1, lm_hidden_size) + .gather(1, lm_mems_ids) + .view(-1, p_len - 1, lm_hidden_size) + ) + + # update prefixes_len and pad_profile + not_eos_pad = prefixes.ne(self.eos) & prefixes.ne(self.pad) + prefixes_len = 1 + not_eos_pad.sum(dim=1, keepdim=True).to(scores.dtype) + pad_profile = (~not_eos_pad[:, -1:]).long() + + # if all hypotheses end with or , interrupt search + if pad_profile.sum() == batch_size * self.beam_size: + break + + # select best performing hypotheses in each element of the batch + len_penalties = self.compute_len_penalty(prefixes_len, self.len_pen) + scores = scores / len_penalties + best_guesses = ( + torch.argmax(scores.view(-1, self.beam_size), dim=1, keepdim=True).repeat(1, prefixes.size(1)).unsqueeze(1) + ) + tgt = prefixes.view(batch_size, self.beam_size, -1).gather(1, best_guesses).squeeze(1) + + if return_beam_scores: + return prefixes, scores * len_penalties, tgt + else: + return tgt + + def __call__(self, src_ids, encoder_input_mask, decoder_input_ids=None, return_beam_scores=False): + with self.as_frozen(): + return self._forward(src_ids, encoder_input_mask, decoder_input_ids, return_beam_scores) + + def freeze(self) -> None: + """Freeze weights of embedding, decoder, and classification layers to prevent memory leak. + """ + for model_num in range(self.num_models): + for param in self.embeddings[model_num].parameters(): + param.requires_grad = False + self.embeddings[model_num].eval() + for param in self.decoders[model_num].parameters(): + param.requires_grad = False + self.decoders[model_num].eval() + for param in self.log_softmaxes[model_num].parameters(): + param.requires_grad = False + self.log_softmaxes[model_num].eval() + for param in self.encoders[model_num].parameters(): + param.requires_grad = False + self.encoders[model_num].eval() + + def unfreeze(self) -> None: + """Unfreeze weights of embedding, decoder, and classification layers. + """ + for model_num in range(self.num_models): + for param in self.embeddings[model_num].parameters(): + param.requires_grad = True + self.embeddings[model_num].train() + for param in self.decoders[model_num].parameters(): + param.requires_grad = True + self.decoders[model_num].train() + for param in self.log_softmaxes[model_num].parameters(): + param.requires_grad = True + self.log_softmaxes[model_num].train() + for param in self.encoders[model_num].parameters(): + param.requires_grad = True + self.encoders[model_num].train() + + @contextmanager + def as_frozen(self): + """ + Context manager which temporarily freezes embedding, decoder, and log_softmax modules, + yields control and finally unfreezes the modules. + """ + self.freeze() + + try: + yield + finally: + self.unfreeze() + + +class BeamSearchSequenceGeneratorWithLanguageModel(GreedySequenceGenerator): + def __init__( + self, embedding, decoder, log_softmax, language_model, beam_size=1, len_pen=0, fusion_coef=0.0, **kwargs + ): + """ + Beam Search sequence generator based on the decoder followed by log_softmax + with external language model fusion. + Args: + *all args of BeamSearchSequenceGenerator class + language_model: nemo TransformerLMModel + fusion_coef: coefficient before language model score, the resulting score is + score = log P_NMT(y|x) + fusion_coef * log P_LM(y) + Kwargs: + all remaining parameters of GreedySequenceGenerator class + """ + + super().__init__(embedding, decoder, log_softmax, **kwargs) + self.language_model = language_model + self.beam_size = beam_size + self.len_pen = len_pen + self.fusion_coef = fusion_coef + + def _one_step_forward( + self, + decoder_input_ids=None, + encoder_hidden_states=None, + encoder_input_mask=None, + decoder_mems_list=None, + lm_mems_list=None, + pos=0, + ): + + nmt_log_probs, decoder_mems_list = super()._one_step_forward( + decoder_input_ids, encoder_hidden_states, encoder_input_mask, decoder_mems_list, pos, + ) + input_mask = mask_padded_tokens(decoder_input_ids, self.pad).float() + lm_hidden_states = self.language_model.encoder.embedding.forward(decoder_input_ids, start_pos=pos) + + lm_mems_list = self.language_model.encoder.encoder.forward( + lm_hidden_states, input_mask, lm_mems_list, return_mems=True, + ) + lm_log_probs = self.language_model.log_softmax.forward(hidden_states=lm_mems_list[-1][:, -1:]) + + log_probs = nmt_log_probs + self.fusion_coef * lm_log_probs + + return log_probs, decoder_mems_list, lm_mems_list + + @staticmethod + def compute_len_penalty(lengths, alpha): + """Returns length penalty according to https://arxiv.org/pdf/1609.08144.pdf""" + return ((5 + lengths) / 6).pow(alpha) + + def _forward( + self, decoder_input_ids=None, encoder_hidden_states=None, encoder_input_mask=None, return_beam_scores=False + ): + + tgt, batch_size, max_generation_length = self._prepare_for_search(decoder_input_ids, encoder_hidden_states) + + # generate initial buffer of beam_size prefixes-hypotheses + log_probs, decoder_mems_list, lm_mems_list = self._one_step_forward( + tgt, encoder_hidden_states, encoder_input_mask, None, None, 0 + ) + scores, prefixes = torch.topk(log_probs.permute(0, 2, 1), self.beam_size, dim=1) + scores, prefixes = scores.view(-1, 1), prefixes.view(-1, 1) + + # repeat init target prefixes and cached memory states beam_size times + prefixes = torch.cat((tgt.repeat(1, self.beam_size).view(-1, 1), prefixes), dim=1) + for j in range(len(decoder_mems_list)): + decoder_mems_list[j] = decoder_mems_list[j].repeat(self.beam_size, 1, 1) + for j in range(len(lm_mems_list)): + lm_mems_list[j] = lm_mems_list[j].repeat(self.beam_size, 1, 1) + + # repeat source sequence beam_size times for beam search + if encoder_hidden_states is not None: + _, src_length, hidden_size = encoder_hidden_states.size() + encoder_input_mask = encoder_input_mask.repeat(1, self.beam_size).view(-1, src_length) + encoder_hidden_states = encoder_hidden_states.repeat(1, self.beam_size, 1).view( + -1, src_length, hidden_size + ) + else: + hidden_size = decoder_mems_list[0].size(2) + lm_hidden_size = lm_mems_list[0].size(2) + + # pad_profile tracks finished hypotheses to generate only tokens + # if or has been generated + pad_profile = torch.zeros_like(scores).long() + + # prefixes_len tracks lengths of generated hypotheses to perform + # length penalty correction + prefixes_len = torch.zeros_like(scores).fill_(prefixes.size(1) + 1) + + for i in range(max_generation_length): + + # mask all finished hypotheses to exclude them from beam + pad_mask = pad_profile.repeat(1, self.beam_size) + + # generate and score candidates for prefixes continuation + log_probs, decoder_mems_list, lm_mems_list = self._one_step_forward( + prefixes[:, -1:], encoder_hidden_states, encoder_input_mask, decoder_mems_list, lm_mems_list, i + 1 + ) + scores_i, prefixes_i = torch.topk(log_probs[:, -1, :], self.beam_size, dim=-1) + + # for all prefixes ending with or replace generated + # continuations with + prefixes_i = self.pad * pad_mask + prefixes_i * (1 - pad_mask) + + # force all hypotheses but one generated from already finished + # hypotheses to have extremely low score, so they will not be + # considered during beam re-ranking + pad_mask[:, 1:] = pad_mask[:, 1:] * NEG_INF + scores = scores + scores_i * (1 - pad_mask).to(scores.dtype) + + # choose top-k hypotheses with length penalty applied + len_penalties = self.compute_len_penalty(prefixes_len, self.len_pen) + scores = scores / len_penalties + scores, indices_i = torch.topk(scores.view(-1, self.beam_size ** 2), self.beam_size, dim=1) + scores = scores.view(-1, 1) * len_penalties + + # select prefixes which correspond to the chosen hypotheses + prefixes = prefixes.unsqueeze(1).repeat(1, self.beam_size, 1) + prefixes = torch.cat((prefixes, prefixes_i.unsqueeze(2)), dim=2) + prefixes = prefixes.view(batch_size, self.beam_size ** 2, -1) + p_len = prefixes.size(2) + prefixes_ids = indices_i.unsqueeze(2).repeat(1, 1, p_len) + prefixes = prefixes.gather(1, prefixes_ids).view(-1, p_len) + + # reshuffle cached decoder memory states to restore the order + # of hypotheses broken after top-k selection + mems_ids = indices_i.unsqueeze(2).unsqueeze(3).repeat(1, 1, p_len - 1, hidden_size) // self.beam_size + for j in range(len(decoder_mems_list)): + decoder_mems_list[j] = ( + decoder_mems_list[j] + .view(-1, self.beam_size, p_len - 1, hidden_size) + .gather(1, mems_ids) + .view(-1, p_len - 1, hidden_size) + ) + lm_mems_ids = indices_i.unsqueeze(2).unsqueeze(3).repeat(1, 1, p_len - 1, lm_hidden_size) // self.beam_size + for j in range(len(lm_mems_list)): + lm_mems_list[j] = ( + lm_mems_list[j] + .view(-1, self.beam_size, p_len - 1, lm_hidden_size) + .gather(1, lm_mems_ids) + .view(-1, p_len - 1, lm_hidden_size) + ) + + # update prefixes_len and pad_profile + not_eos_pad = prefixes.ne(self.eos) & prefixes.ne(self.pad) + prefixes_len = 1 + not_eos_pad.sum(dim=1, keepdim=True).to(scores.dtype) + pad_profile = (~not_eos_pad[:, -1:]).long() + + # if all hypotheses end with or , interrupt search + if pad_profile.sum() == batch_size * self.beam_size: + break + + # select best performing hypotheses in each element of the batch + len_penalties = self.compute_len_penalty(prefixes_len, self.len_pen) + scores = scores / len_penalties + best_guesses = ( + torch.argmax(scores.view(-1, self.beam_size), dim=1, keepdim=True).repeat(1, prefixes.size(1)).unsqueeze(1) + ) + tgt = prefixes.view(batch_size, self.beam_size, -1).gather(1, best_guesses).squeeze(1) + + if return_beam_scores: + return prefixes, scores * len_penalties, tgt + else: + return tgt diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_modules.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_modules.py new file mode 100644 index 0000000..25fb781 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_modules.py @@ -0,0 +1,295 @@ +# Copyright 2018 The Google AI Language Team Authors and +# The HuggingFace Inc. team. +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +import numpy as np +import torch +from torch import nn +from torch.nn.functional import gelu + +from nemo.collections.common.parts import form_attention_mask +from nemo.utils import logging + +__all__ = ["TransformerEmbedding", "AttentionBridge"] + + +class FixedPositionalEncoding(nn.Module): + """ + Fixed positional encoding (embedding layer) from sine and cosine functions + of different frequencies according to https://arxiv.org/abs/1706.03762 + + Args: + hidden_size: size of the embeddings in the model, also known as d_model + max_sequence_length: maximum allowed length of the input sequence + """ + + def __init__(self, hidden_size, max_sequence_length=512): + super().__init__() + + self._hidden_size = hidden_size + self._max_sequence_length = max_sequence_length + self._build_pos_enc(hidden_size=self._hidden_size, max_sequence_length=self._max_sequence_length) + + def _build_pos_enc(self, hidden_size, max_sequence_length, device=None): + """ + Builds/replaces pre-computed positional encoding. + """ + pos_enc = torch.zeros(max_sequence_length, hidden_size, device=device) + position = torch.arange(0.0, max_sequence_length).unsqueeze(1) + coef = -math.log(10000.0) / hidden_size + div_term = torch.exp(coef * torch.arange(0.0, hidden_size, 2)) + pos_enc[:, 0::2] = torch.sin(position * div_term) + pos_enc[:, 1::2] = torch.cos(position * div_term) + pos_enc.div_(math.sqrt(hidden_size)) + self.register_buffer('pos_enc', pos_enc) + + def forward(self, position_ids): + max_pos_id = position_ids.max() + # update positional encoding if needed + if max_pos_id >= self._max_sequence_length: + logging.warning( + f'Max position id {max_pos_id} is greater than max sequence length {self._max_sequence_length}. Expanding position embeddings just for this batch. This is not expected to work very well. Consider chunking your input into smaller sequences.' + ) + self._build_pos_enc( + hidden_size=self._hidden_size, max_sequence_length=max_pos_id + 1, device=position_ids.device, + ) + + embeddings = torch.embedding(self.pos_enc, position_ids) + + # Revert expansion of position embeddings since this wall checkpoint size mismatches. + if max_pos_id >= self._max_sequence_length: + self._build_pos_enc( + hidden_size=self._hidden_size, + max_sequence_length=self._max_sequence_length, + device=position_ids.device, + ) + return embeddings + + +class TransformerEmbedding(nn.Module): + """ + Embedding from token and position embeddings. + Optionally add token_type embedding (e.g. type of the sentence in BERT). + + Args: + vocab_size: size of the vocabulary + hidden_size: size of the embeddings in the model, also known as d_model + max_sequence_length: maximum allowed length of the input sequence + num_token_types: number of different token types + (e.g. tokens of sentence A and tokens of sentence B in BERT) + embedding_dropout: probability of dropout applied to embeddings + learn_positional_encodings: whether to learn positional encodings or + use fixed (sine-cosine) ones + """ + + def __init__( + self, + vocab_size, + hidden_size, + max_sequence_length=512, + num_token_types=2, + embedding_dropout=0.0, + learn_positional_encodings=False, + ): + super().__init__() + + self.max_sequence_length = max_sequence_length + self.learn_positional_encodings = learn_positional_encodings + self.token_embedding = nn.Embedding(vocab_size, hidden_size, padding_idx=0) + if learn_positional_encodings: + self.position_embedding = nn.Embedding(max_sequence_length, hidden_size) + else: + self.position_embedding = FixedPositionalEncoding(hidden_size, max_sequence_length) + if num_token_types > 0: + self.token_type_embedding = nn.Embedding(num_token_types, hidden_size) + self.layer_norm = nn.LayerNorm(hidden_size, eps=1e-5) + self.dropout = nn.Dropout(embedding_dropout) + + def forward(self, input_ids, token_type_ids=None, start_pos=0): + seq_length = input_ids.size(1) + # we fail here only with parametric positional embedding. FixedPositionalEncoding automatically extends. + if self.learn_positional_encodings and (seq_length > self.max_sequence_length): + raise ValueError( + f"Input sequence is longer than maximum allowed sequence length for positional encoding. " + f"Got {seq_length} and {self.max_sequence_length}" + ) + position_ids = torch.arange( + start=start_pos, end=start_pos + seq_length, dtype=torch.long, device=input_ids.device + ) + position_ids = position_ids.unsqueeze(0).repeat(input_ids.size(0), 1) + + token_embeddings = self.token_embedding(input_ids) + position_embeddings = self.position_embedding(position_ids) + embeddings = token_embeddings + position_embeddings + + if token_type_ids is not None: + token_type_embeddings = self.token_type_embedding(token_type_ids) + embeddings = embeddings + token_type_embeddings + + embeddings = self.layer_norm(embeddings) + embeddings = self.dropout(embeddings) + + return embeddings + + +class MultiHeadAttention(nn.Module): + """ + Multi-head scaled dot-product attention layer. + + Args: + hidden_size: size of the embeddings in the model, also known as d_model + num_attention_heads: number of heads in multi-head attention + attn_score_dropout: probability of dropout applied to attention scores + attn_layer_dropout: probability of dropout applied to the output of the + whole layer, but before layer normalization + """ + + def __init__(self, hidden_size, num_attention_heads, attn_score_dropout=0.0, attn_layer_dropout=0.0): + super().__init__() + if hidden_size % num_attention_heads != 0: + raise ValueError( + "The hidden size (%d) is not a multiple of the number " + "of attention heads (%d)" % (hidden_size, num_attention_heads) + ) + self.hidden_size = hidden_size + self.num_attention_heads = num_attention_heads + self.attn_head_size = int(hidden_size / num_attention_heads) + self.attn_scale = math.sqrt(math.sqrt(self.attn_head_size)) + + self.query_net = nn.Linear(hidden_size, hidden_size) + self.key_net = nn.Linear(hidden_size, hidden_size) + self.value_net = nn.Linear(hidden_size, hidden_size) + self.out_projection = nn.Linear(hidden_size, hidden_size) + + self.attn_dropout = nn.Dropout(attn_score_dropout) + self.layer_dropout = nn.Dropout(attn_layer_dropout) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attn_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward(self, queries, keys, values, attention_mask): + + # attention_mask is needed to hide the tokens which correspond to [PAD] + # in the case of BERT, or to hide the future tokens in the case of + # vanilla language modeling and translation + query = self.query_net(queries) + key = self.key_net(keys) + value = self.value_net(values) + query = self.transpose_for_scores(query) / self.attn_scale + key = self.transpose_for_scores(key) / self.attn_scale + value = self.transpose_for_scores(value) + + # for numerical stability we pre-divide query and key by sqrt(sqrt(d)) + attention_scores = torch.matmul(query, key.transpose(-1, -2)) + if attention_mask is not None: + attention_scores = attention_scores + attention_mask.to(attention_scores.dtype) + attention_probs = torch.softmax(attention_scores, dim=-1) + attention_probs = self.attn_dropout(attention_probs) + + context = torch.matmul(attention_probs, value) + context = context.permute(0, 2, 1, 3).contiguous() + new_context_shape = context.size()[:-2] + (self.hidden_size,) + context = context.view(*new_context_shape) + + # output projection + output_states = self.out_projection(context) + output_states = self.layer_dropout(output_states) + return output_states + + +class PositionWiseFF(nn.Module): + """ + Position-wise feed-forward network of Transformer block. + + Args: + hidden_size: size of the embeddings in the model, also known as d_model + inner_size: number of neurons in the intermediate part of feed-forward + net, usually is (4-8 x hidden_size) in the papers + ffn_dropout: probability of dropout applied to net output + hidden_act: activation function used between two linear layers + """ + + def __init__(self, hidden_size, inner_size, ffn_dropout=0.0, hidden_act="relu"): + super().__init__() + self.dense_in = nn.Linear(hidden_size, inner_size) + self.dense_out = nn.Linear(inner_size, hidden_size) + self.layer_dropout = nn.Dropout(ffn_dropout) + ACT2FN = {"gelu": gelu, "relu": torch.relu} + self.act_fn = ACT2FN[hidden_act] + + def forward(self, hidden_states): + output_states = self.dense_in(hidden_states) + output_states = self.act_fn(output_states) + output_states = self.dense_out(output_states) + output_states = self.layer_dropout(output_states) + return output_states + + +class AttentionBridge(torch.nn.Module): + """ + A multi-head attention bridge to project a variable-size hidden states + to k hidden states (per attention head). + + Code is based on the paper https://arxiv.org/pdf/1703.03130.pdf + """ + + def __init__(self, hidden_size, k, bridge_size): + """ + hidden_size - size of input hidden state + k - number of attention heads + bridge_size - size of internal feed forward weights (i.e., attention head size) + """ + super().__init__() + + self.hidden_size = hidden_size + self.k = k + self.bridge_size = bridge_size + + self.attn_scale = np.sqrt(np.sqrt(self.bridge_size)) + + # build model + + self.W1 = torch.nn.Linear(hidden_size, bridge_size, bias=False) + self.W2 = torch.nn.Linear(bridge_size, k, bias=False) + self.act = torch.nn.ReLU() + + def forward(self, hidden, hidden_mask=None, return_ortho_loss=False): + """ + Project hidden [B x N x H] to fixed-size [B x k x H] + + return_ortho_loss - if True returns loss term to encourage + orthogonal attention vectors + """ + + attention_scores = self.W2(self.act(self.W1(hidden) / self.attn_scale) / self.attn_scale).transpose(-1, -2) + + attention_mask = form_attention_mask(hidden_mask) + if attention_mask is not None: + attention_mask.squeeze_(1) + attention_scores = attention_scores + attention_mask.to(attention_scores.dtype) + + A = torch.softmax(attention_scores, dim=-1) + M = A @ hidden + + if return_ortho_loss: + ortho_loss = ((A @ A.transpose(-1, -2)) - torch.eye(self.k).type_as(A)).pow(2).sum() + + return M, ortho_loss + else: + return M diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_utils.py new file mode 100644 index 0000000..da9ffb8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/transformer/transformer_utils.py @@ -0,0 +1,134 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import Optional, Union + +from omegaconf.dictconfig import DictConfig + +from nemo.collections.asr.modules.transformer.transformer import TransformerDecoderNM, TransformerEncoderNM +from nemo.collections.asr.modules.transformer.transformer_bottleneck import TransformerBottleneckEncoderNM + +__all__ = ['get_nemo_transformer'] + + +def get_nemo_transformer( + model_name: Optional[str] = None, + pretrained: bool = False, + config_dict: Optional[Union[dict, DictConfig]] = None, + encoder: bool = True, + pre_ln_final_layer_norm: bool = True, +) -> Union[TransformerEncoderNM, TransformerDecoderNM]: + """Returns NeMo transformer. + The following configurations are mandatory: + vocab_size: int + hidden_size: int + num_layers: int + inner_size: int + and must be specified if using config_dict. + + Args: + model_name (Optional[str]): model name to download from NGC + pretrained: (bool): False will instantiate the named model architecture with random weights. + config_dict (Optional[dict], optional): model configuration parameters. Defaults to None. + config_file (Optional[str], optional): path to json file containing model configuration. Defaults to None. + checkpoint_file (Optional[str], optional): load weights from path to local checkpoint. Defaults to None. + encoder (bool, optional): True will use EncoderTransformerNM, False will use DecoderTransformerNM. Defaults to True. + """ + if model_name is not None: + raise ValueError(f'NeMo transformers cannot be loaded from NGC yet. model_name should be None') + + if pretrained: + raise ValueError(f'NeMo transformers cannot be loaded from NGC yet. pretrained should be False') + + cfg = None + + if not pretrained: + assert ( + config_dict.get('vocab_size') is not None + and config_dict.get('hidden_size') is not None + and config_dict.get('num_layers') is not None + and config_dict.get('inner_size') is not None + ), f'Using config_dict: {config_dict}. vocab_size, hidden_size, num_layers, and inner_size must are mandatory arguments' + + cfg = config_dict + + if encoder: + # if arch exists in cfg we return TransformerBottleneckEncoderNM + arch = cfg.get('arch', '') + if not arch: + model = TransformerEncoderNM( + vocab_size=cfg.get('vocab_size'), + hidden_size=cfg.get('hidden_size'), + num_layers=cfg.get('num_layers'), + inner_size=cfg.get('inner_size'), + max_sequence_length=cfg.get('max_sequence_length', 512), + embedding_dropout=cfg.get('embedding_dropout', 0.0), + learn_positional_encodings=cfg.get('learn_positional_encodings', False), + num_attention_heads=cfg.get('num_attention_heads'), + ffn_dropout=cfg.get('ffn_dropout', 0.0), + attn_score_dropout=cfg.get('attn_score_dropout', 0.0), + attn_layer_dropout=cfg.get('attn_layer_dropout', 0.0), + hidden_act=cfg.get('hidden_act', 'relu'), + mask_future=cfg.get('mask_future', True), + pre_ln=cfg.get('pre_ln', False), + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + num_token_types=cfg.get('num_token_types', 2), + ) + elif arch in TransformerBottleneckEncoderNM._SUPPORTED_ARCH: + model = TransformerBottleneckEncoderNM( + vocab_size=cfg.get('vocab_size'), + hidden_size=cfg.get('hidden_size'), + num_layers=cfg.get('num_layers'), + inner_size=cfg.get('inner_size'), + max_sequence_length=cfg.get('max_sequence_length', 512), + embedding_dropout=cfg.get('embedding_dropout', 0.0), + learn_positional_encodings=cfg.get('learn_positional_encodings', False), + num_attention_heads=cfg.get('num_attention_heads'), + ffn_dropout=cfg.get('ffn_dropout', 0.0), + attn_score_dropout=cfg.get('attn_score_dropout', 0.0), + attn_layer_dropout=cfg.get('attn_layer_dropout', 0.0), + hidden_act=cfg.get('hidden_act', 'relu'), + mask_future=cfg.get('mask_future', False), + pre_ln=cfg.get('pre_ln', False), + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + num_token_types=cfg.get('num_token_types', 2), + arch=cfg.get('arch', 'full'), + hidden_steps=cfg.get('hidden_steps', -1), + hidden_blocks=cfg.get('hidden_blocks', 1), + hidden_init_method=cfg.get('hidden_init_method', 'default'), + return_mask=cfg.get('return_mask', True), + ) + else: + raise ValueError(f"Unknown arch = {arch}") + else: + model = TransformerDecoderNM( + vocab_size=cfg.get('vocab_size'), + hidden_size=cfg.get('hidden_size'), + num_layers=cfg.get('num_layers'), + inner_size=cfg.get('inner_size'), + max_sequence_length=cfg.get('max_sequence_length', 512), + embedding_dropout=cfg.get('embedding_dropout', 0.0), + learn_positional_encodings=cfg.get('learn_positional_encodings', False), + num_attention_heads=cfg.get('num_attention_heads'), + ffn_dropout=cfg.get('ffn_dropout', 0.0), + attn_score_dropout=cfg.get('attn_score_dropout', 0.0), + attn_layer_dropout=cfg.get('attn_layer_dropout', 0.0), + hidden_act=cfg.get('hidden_act', 'relu'), + pre_ln=cfg.get('pre_ln', False), + pre_ln_final_layer_norm=pre_ln_final_layer_norm, + num_token_types=cfg.get('num_token_types', 2), + ) + + return model diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/wav2vec_modules.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/wav2vec_modules.py new file mode 100644 index 0000000..d1f5b09 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/modules/wav2vec_modules.py @@ -0,0 +1,359 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import random +from typing import Dict, List, Tuple + +import torch +from omegaconf import DictConfig +from omegaconf.dictconfig import DictConfig +from torch import nn +from torch.nn import functional as F + +from nemo.collections.common.parts import form_attention_mask, transformer_weights_init +from nemo.collections.nlp.modules.common.transformer import TransformerEncoder +from nemo.core.classes.module import NeuralModule +from nemo.core.neural_types import AcousticEncodedRepresentation, AudioSignal, LengthsType, NeuralType, SpectrogramType + + +class TransposeLast(torch.nn.Module): + """ + Transposes last dimension. Useful for adding to a sequential block. + """ + + def forward(self, x): + return x.transpose(-2, -1) + + +class SamePad(torch.nn.Module): + def __init__(self, kernel_size): + super().__init__() + self.remove = kernel_size % 2 == 0 + + def forward(self, x): + if self.remove: + x = x[:, :, :-1] + return x + + +class ConvFeatureEncoder(NeuralModule): + """ + Encoder used to isolate features in raw audio for Wav2Vec style training. + Treated as preprocessor module in NeMo ASR training. Defaults values are + for base model found in Baeski et al (https://arxiv.org/abs/2006.11477), + save for use of layer normalization as default schema. (Chosen for stability.) + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + input_signal: + 0: AxisType(BatchTag) + 1: AxisType(TimeTag) + input_signal_length: + 0: AxisType(BatchTag) + Note: length is in number of samples, not seconds + """ + return { + "input_signal": NeuralType(('B', 'T'), AudioSignal(freq=self._sample_rate)), + "length": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + For compatibility, processed features are treated as Spectrogram types + processed_signal: + 0: AxisType(BatchTag) + 1: AxisType(ChannelTag) + 2: AxisType(ProcessedTimeTag) + processed_signal_length: + 0: AxisType(BatchTag) + """ + return { + "processed_signal": NeuralType(('B', 'C', 'T'), SpectrogramType()), + "processed_signal_length": NeuralType(tuple('B'), LengthsType()), + } + + def __init__( + self, + conv_layers: List[Dict[str, int]], + extractor_mode: str = "layer_norm", + conv_bias: bool = False, + feature_grad_mult=1.0, + normalize_audio=True, + embedding_dim=768, + ): + super().__init__() + + self.grad_mult = feature_grad_mult + self.normalize_input = normalize_audio + + def block( + n_in, n_out, k, stride, is_layer_norm=False, is_group_norm=False, conv_bias=False, + ): + def make_conv(): + conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias) + nn.init.kaiming_normal_(conv.weight) + return conv + + assert (is_layer_norm and is_group_norm) is False, "layer norm and group norm are exclusive" + + if is_layer_norm: + return nn.Sequential( + make_conv(), + nn.Sequential(TransposeLast(), nn.LayerNorm(dim, elementwise_affine=True), TransposeLast()), + nn.GELU(), + ) + elif is_group_norm: + return nn.Sequential(make_conv(), nn.GroupNorm(dim, dim, affine=True), nn.GELU(),) + else: + return nn.Sequential(make_conv(), nn.GELU()) + + in_d = 1 + self.layer_cfg = conv_layers + self.conv_layers = nn.ModuleList() + self.mode = extractor_mode + for i, cl in enumerate(conv_layers): + assert len(cl) == 3, "invalid conv definition: " + str(cl) + dim, k, stride = cl["emb_dim"], cl["kernel_size"], cl["stride"] + + self.conv_layers.append( + block( + in_d, + dim, + k, + stride, + is_layer_norm=self.mode == "layer_norm", + is_group_norm=self.mode == "group_norm" and i == 0, # applied to first layer only + conv_bias=conv_bias, + ) + ) + in_d = dim + + # Model Layers + final_conv_dim = self.layer_cfg[-1]["emb_dim"] # Select last conv output layer dimension + self.post_extract_proj = ( # To project feature encodings to transformer + nn.Linear(final_conv_dim, embedding_dim) if final_conv_dim != embedding_dim else None + ) + self.layer_norm = nn.LayerNorm(embedding_dim) + + def apply_layers(self, x): + for conv in self.conv_layers: + x = conv(x) + return x + + def normalize(self, source, lengths): + with torch.no_grad(): # Normalizes audio source + for i in range(lengths.size(0)): + orig = source[i, : lengths[i]] + norm = F.layer_norm(orig, orig.shape) + source[i, : lengths[i]] = norm + return source + + def forward(self, input_signal, length): + if self.normalize_input: + input_signal = self.normalize(input_signal, length) + + # BxT -> BxCxT + processed_signal = input_signal.unsqueeze(1) + + # Applies grad mult scaling + if self.grad_mult > 0: + processed_signal = self.apply_layers(processed_signal) + if self.grad_mult != 1.0: + processed_signal = GradMultiply.apply(processed_signal, self.grad_mult) + else: + with torch.no_grad(): # 0 indicates frozen feature encoder + processed_signal = self.apply_layers(processed_signal) + + processed_signal = processed_signal.transpose(1, 2) # B,T,C + # Project to embedding + if self.post_extract_proj is not None: + processed_signal = self.post_extract_proj(processed_signal) + + # Adding normalization for output + if self.mode == "layer_norm": + processed_signal = self.layer_norm(processed_signal) + + processed_signal = processed_signal.transpose(1, 2) # B,C,T + + # Feature lengths will have been changed through convolutions + processed_signal_length = self.get_lengths(audio_lengths=length) + + return processed_signal, processed_signal_length + + def get_lengths(self, audio_lengths): + # converts audio lengths to timestep lengths + for conv in self.layer_cfg: + kernel = conv["kernel_size"] + stride = conv["stride"] + audio_lengths = ( + torch.div(audio_lengths - kernel, stride, rounding_mode='floor') + 1 + ) # from pytorch documentation + return audio_lengths + + +class Wav2VecTransformerEncoder(TransformerEncoder): + """ + Encoder module following Transformer encoder paradigm + as described in Vaswani et al. (https://arxiv.org/abs/1706.03762). Used for Wav2Vec + style encoding of context vectors as described by in Baeski et al (https://arxiv.org/abs/2006.11477). + Takes convolutional encodings of all time steps and adds to features before applying series + of self-attention layers. + + Example configs may be found at: https://github.com/NVIDIA/NeMo/tree/main/examples/asr/conf/wav2vec + + Args: + layer_drop: Floating point value specifying proportion of module for layer dropout (See Fan et al. https://arxiv.org/pdf/1909.11556.pdf). + If non-zero, each layer will draw from uniform probability to determine if applied in current forward call. + Occurs only during training step + pos_embed: Config specifying parameters for contextual embedding convolutions. Module configures convolutional padding + to maintain number of time steps + Must contain following: + embedding_dim: Depth/number of channels of each time step from feature encoding + conv_pos: Kernel size for convolution + conv_pos_groups: Number of groups for convolution + transformer: Config for transformer encoder. Uses self-attention layers found in: nemo.collections.nlp.modules.common.transformer + Must contain followign: + num_layers: Number of attention layers + hidden_size: Expected input depth (embedding size between model layers) + inner_size: Depth of embeddings within feed-forward sections of encoder layers + num_attention_heads: Number of attention heads + attn_score_dropout: Probability of dropout applied to attention scores + attn_layer_dropout: Probability of dropout applied to the output of the attention layers (prior to normalization) + ffn_dropout: Probability of dropout applied to feed-forward modules + hidden_act: Activation function for hidden layers + """ + + def __init__(self, pos_embed: DictConfig, transformer: DictConfig, layer_drop: float = 0.0): + super().__init__(**transformer) # see nlp.collections + + # positional convolutional embeddings + emb_dim = pos_embed.embedding_dim + self.pos_conv = nn.Conv1d( + emb_dim, + emb_dim, + kernel_size=pos_embed.conv_pos, + padding=pos_embed.conv_pos // 2, # Padding size preserves time step length + groups=pos_embed.conv_pos_groups, + ) + + self.layer_drop = layer_drop + + self.dropout = transformer.attn_layer_dropout # He initialization + std = math.sqrt((4 * (1.0 - self.dropout)) / (pos_embed.conv_pos * pos_embed.embedding_dim)) + nn.init.normal_(self.pos_conv.weight, mean=0, std=std) + nn.init.constant_(self.pos_conv.bias, 0) + + self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2) + self.pos_conv = nn.Sequential(self.pos_conv, SamePad(pos_embed.conv_pos), nn.GELU()) + + self.layer_norm = nn.LayerNorm(emb_dim) + self.apply(lambda x: transformer_weights_init(x, xavier=False)) + + @property + def input_types(self): + """Returns definitions of module output ports. + We treat features as SpectrogramType for Nemo compatibility + audio_signal: + 0: AxisType(BatchTag) + 1: AxisType(ChannelTag) + 2: AxisType(ProcessedTimeTag) + length: + 0: AxisType(BatchTag) + """ + return { + "audio_signal": NeuralType(('B', 'C', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + We're using SpectrogramType for now to keep things Nemo safe + processed_signal: + 0: AxisType(BatchTag) + 1: AxisType(ChannelTag) + 2: AxisType(ProcessedTimeTag) + processed_length: + 0: AxisType(BatchTag) + """ + return { + "processed_signal": NeuralType(('B', 'C', 'T'), AcousticEncodedRepresentation()), + "processed_length": NeuralType(tuple('B'), LengthsType()), + } + + def forward(self, audio_signal, length): + + # Padding mask needed for transformer + padding_mask = self.create_padding_mask(length) + + # Applying padding before convolution + for idx, len in enumerate(length): + audio_signal[idx, :, len:] = 0.0 + + signal_conv = self.pos_conv(audio_signal) # B, C, T + audio_signal = audio_signal + signal_conv + + audio_signal = audio_signal.transpose(1, 2) # B, C, T -> B, T, C + audio_signal = self.layer_norm(audio_signal) + + context_emb = self.apply_transformer(audio_signal, padding_mask=padding_mask) + + context_emb = context_emb.transpose(1, 2) # B, T, C -> B, C, T + + return context_emb, length # Returning length for NeMo compatibility + + def apply_transformer(self, x, padding_mask=None): + encoder_attn_mask = form_attention_mask(padding_mask) + if ( + self.layer_drop and self.training + ): # Stochastic layer drop as in: Huang et al. https://arxiv.org/pdf/1603.09382.pdf + for _, layer in enumerate(self.layers): + p = random.random() + if p > self.layer_drop: + x = layer(x, encoder_attn_mask, x) + else: + for _, layer in enumerate(self.layers): + x = layer(x, encoder_attn_mask, x) + return x + + def create_padding_mask(self, length): + # Broadcast to vectorize creating the padding mask + max_len = max(length) + padding_mask = torch.arange(max_len, device=length.device) + + # Switch to binary for transformer, 1 for valid tokens, 0 for padding + padding_mask = (padding_mask.expand(len(length), max_len) < length.unsqueeze(1)).type(torch.uint8) + + return padding_mask + + +class GradMultiply(torch.autograd.Function): + @staticmethod + def forward(ctx, x, scale): + ctx.scale = scale + res = x.new(x) + return res + + @staticmethod + def backward(ctx, grad): + return grad * ctx.scale, None diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/__init__.py new file mode 100644 index 0000000..9e32500 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/context_biasing/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/context_biasing/__init__.py new file mode 100644 index 0000000..506dc70 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/context_biasing/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.parts.context_biasing.context_biasing_utils import ( + compute_fscore, + merge_alignment_with_ws_hyps, +) +from nemo.collections.asr.parts.context_biasing.context_graph_ctc import ContextGraphCTC +from nemo.collections.asr.parts.context_biasing.ctc_based_word_spotter import run_word_spotter diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/context_biasing/context_biasing_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/context_biasing/context_biasing_utils.py new file mode 100644 index 0000000..6b36269 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/context_biasing/context_biasing_utils.py @@ -0,0 +1,267 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import os +from typing import List, Union + +import numpy as np +import texterrors + +from nemo.collections.asr.parts.context_biasing.ctc_based_word_spotter import WSHyp +from nemo.collections.asr.parts.utils import rnnt_utils +from nemo.collections.asr.parts.utils.manifest_utils import read_manifest +from nemo.utils import logging + + +def merge_alignment_with_ws_hyps( + candidate: Union[np.ndarray, rnnt_utils.Hypothesis], + asr_model, + cb_results: List[WSHyp], + decoder_type: str = "ctc", + intersection_threshold: float = 30.0, + blank_idx: int = 0, + print_stats: bool = False, + bow: str = "▁", +) -> tuple[str, str]: + """ + Merge context biasing predictions with ctc/rnnt word-level alignment. + Words from alignment will be replaced by spotted words if intersection between them is greater than threshold. + + Args: + candidate: argmax predictions per frame (for ctc) or rnnt hypothesis (for rnnt) + asr_model: ctc or hybrid transducer-ctc model + cb_results: list of context biasing predictions (spotted words) + decoder_type: ctc or rnnt + intersection_threshold: threshold for intersection between spotted word and word from alignment (in percentage) + blank_idx: blank index for ctc/rnnt decoding + print_stats: if True, print word alignment and spotted words + bow: symbol for begin of word (bow) in BPE tokenizer + Returns: + boosted_text: final text with context biasing predictions + """ + + # step 1: get token-level alignment [frame, token] + if decoder_type == "ctc": + alignment_tokens = [] + prev_token = None + for idx, token in enumerate(candidate): + if token != blank_idx: + if token == prev_token: + alignment_tokens[-1] = [idx, asr_model.tokenizer.ids_to_tokens([int(token)])[0]] + else: + alignment_tokens.append([idx, asr_model.tokenizer.ids_to_tokens([int(token)])[0]]) + prev_token = token + + elif decoder_type == "rnnt": + alignment_tokens = [] + if not isinstance(candidate.y_sequence, list): + candidate.y_sequence = candidate.y_sequence.tolist() + tokens = asr_model.tokenizer.ids_to_tokens(candidate.y_sequence) + for idx, token in enumerate(tokens): + # bow symbol may be predicted separately from token + if token == bow: + if idx + 1 < len(tokens) and not tokens[idx + 1].startswith(bow): + tokens[idx + 1] = bow + tokens[idx + 1] + continue + alignment_tokens.append([candidate.timestep[idx].item(), token]) + else: + raise ValueError(f"decoder_type {decoder_type} is not supported") + + if not alignment_tokens: + # ctc/rnnt decoding results are empty, return context biasing results only + return " ".join([ws_hyp.word for ws_hyp in cb_results]), "" + + # step 2: get word-level alignment [word, start_frame, end_frame] + word_alignment = [] + word = "" + l, r, = None, None + for item in alignment_tokens: + if not word: + word = item[1][1:] + l = r = item[0] + else: + if item[1].startswith(bow): + word_alignment.append((word, l, r)) + word = item[1][1:] + l = r = item[0] + else: + word += item[1] + r = item[0] + word_alignment.append((word, l, r)) + initial_text_transcript = " ".join([item[0] for item in word_alignment]) + if print_stats: + logging.info(f"Word alignment: {word_alignment}") + + # step 3: merge spotted words with word alignment + for ws_hyp in cb_results: + # extend ws_hyp start frame in case of rnnt (rnnt tends to predict labels one frame earlier sometimes) + if ws_hyp.start_frame > 0 and decoder_type == "rnnt": + ws_hyp.start_frame -= 1 + new_word_alignment = [] + already_inserted = False + # get interval of spotted word + ws_interval = set(range(ws_hyp.start_frame, ws_hyp.end_frame + 1)) + for item in word_alignment: + # get interval if word from alignment + li, ri = item[1], item[2] + item_interval = set(range(li, ri + 1)) + if ws_hyp.start_frame < li: + # spotted word starts before first word from alignment + if not already_inserted: + new_word_alignment.append((ws_hyp.word, ws_hyp.start_frame, ws_hyp.end_frame)) + already_inserted = True + # compute intersection between spotted word and word from alignment in percentage + intersection_part = 100 / len(item_interval) * len(ws_interval & item_interval) + if intersection_part <= intersection_threshold: + new_word_alignment.append(item) + elif not already_inserted: + # word from alignment will be replaced by spotted word + new_word_alignment.append((ws_hyp.word, ws_hyp.start_frame, ws_hyp.end_frame)) + already_inserted = True + # insert last spotted word if not yet + if not already_inserted: + new_word_alignment.append((ws_hyp.word, ws_hyp.start_frame, ws_hyp.end_frame)) + word_alignment = new_word_alignment + if print_stats: + logging.info(f"Spotted word: {ws_hyp.word} [{ws_hyp.start_frame}, {ws_hyp.end_frame}]") + + boosted_text_list = [item[0] for item in new_word_alignment] + boosted_text = " ".join(boosted_text_list) + + return boosted_text, initial_text_transcript + + +def compute_fscore( + recognition_results_manifest: str, key_words_list: List, eps: str = "" +) -> tuple[float, float, float]: + """ + Compute fscore for list of context biasing words/phrases. + The idea is to get a word-level alignment for ground truth text and prediction results from manifest file. + Then compute f-score for each word/phrase from key_words_list according to obtained word alignment. + + Args: + recognition_results_manifest: path to nemo manifest file with recognition results in pred_text field. + key_words_list: list of context biasing words/phrases. + return_scores: if True, return precision, recall and fscore (not only print). + eps: epsilon symbol for alignment ('' in case of texterrors aligner). + Returns: + Returns tuple of precision, recall and fscore. + """ + + assert key_words_list, "key_words_list is empty" + + # get data from manifest + assert os.path.isfile(recognition_results_manifest), f"manifest file {recognition_results_manifest} doesn't exist" + data = read_manifest(recognition_results_manifest) + assert len(data) > 0, "manifest file is empty" + assert data[0].get('text', None), "manifest file should contain text field" + assert data[0].get('pred_text', None), "manifest file should contain pred_text field" + + # compute max number of words in one context biasing phrase + max_ngram_order = max([len(item.split()) for item in key_words_list]) + key_words_stat = {} # a word here can be single word or phareses + for word in key_words_list: + key_words_stat[word] = [0, 0, 0] # [true positive (tp), groud truth (gt), false positive (fp)] + + for item in data: + # get alignment by texterrors + ref = item['text'].split() + hyp = item['pred_text'].split() + texterrors_ali = texterrors.align_texts(ref, hyp, False) + ali = [] + for i in range(len(texterrors_ali[0])): + ali.append((texterrors_ali[0][i], texterrors_ali[1][i])) + + # 1-grams + for idx in range(len(ali)): + word_ref = ali[idx][0] + word_hyp = ali[idx][1] + if word_ref in key_words_stat: + key_words_stat[word_ref][1] += 1 # add to gt + if word_ref == word_hyp: + key_words_stat[word_ref][0] += 1 # add to tp + elif word_hyp in key_words_stat: + key_words_stat[word_hyp][2] += 1 # add to fp + + # 2-grams and higher (takes into account epsilons in alignment) + for ngram_order in range(2, max_ngram_order + 1): + # for reference phrase + idx = 0 + item_ref = [] + while idx < len(ali): + if item_ref: + item_ref = [item_ref[1]] + idx = item_ref[0][1] + 1 # idex of second non eps word + 1 + while len(item_ref) != ngram_order and idx < len(ali): + word = ali[idx][0] + idx += 1 + if word == eps: + continue + else: + item_ref.append((word, idx - 1)) + if len(item_ref) == ngram_order: + phrase_ref = " ".join([item[0] for item in item_ref]) + phrase_hyp = " ".join([ali[item[1]][1] for item in item_ref]) + if phrase_ref in key_words_stat: + key_words_stat[phrase_ref][1] += 1 # add to gt + if phrase_ref == phrase_hyp: + key_words_stat[phrase_ref][0] += 1 # add to tp + # in case of false positive hypothesis phrase + idx = 0 + item_hyp = [] + while idx < len(ali): + if item_hyp: + item_hyp = [item_hyp[1]] + idx = item_hyp[0][1] + 1 # idex of first non eps word in previous ngram + 1 + while len(item_hyp) != ngram_order and idx < len(ali): + word = ali[idx][1] + idx += 1 + if word == eps: + continue + else: + item_hyp.append((word, idx - 1)) + if len(item_hyp) == ngram_order: + phrase_hyp = " ".join([item[0] for item in item_hyp]) + phrase_ref = " ".join([ali[item[1]][0] for item in item_hyp]) + if phrase_hyp in key_words_stat and phrase_hyp != phrase_ref: + key_words_stat[phrase_hyp][2] += 1 # add to fp + + tp = sum([key_words_stat[x][0] for x in key_words_stat]) + gt = sum([key_words_stat[x][1] for x in key_words_stat]) + fp = sum([key_words_stat[x][2] for x in key_words_stat]) + + precision = tp / (tp + fp + 1e-8) + recall = tp / (gt + 1e-8) + fscore = 2 * (precision * recall) / (precision + recall + 1e-8) + + logging.info("=" * 60) + logging.info("Per words statistic (word: correct/totall | false positive):\n") + max_len = max([len(x) for x in key_words_stat if key_words_stat[x][1] > 0 or key_words_stat[x][2] > 0]) + for word in key_words_list: + if key_words_stat[word][1] > 0 or key_words_stat[word][2] > 0: + false_positive = "" + if key_words_stat[word][2] > 0: + false_positive = key_words_stat[word][2] + logging.info( + f"{word:>{max_len}}: {key_words_stat[word][0]:3}/{key_words_stat[word][1]:<3} |{false_positive:>3}" + ) + logging.info("=" * 60) + logging.info("=" * 60) + logging.info(f"Precision: {precision:.4f} ({tp}/{tp + fp}) fp:{fp}") + logging.info(f"Recall: {recall:.4f} ({tp}/{gt})") + logging.info(f"Fscore: {fscore:.4f}") + logging.info("=" * 60) + + return (precision, recall, fscore) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/context_biasing/context_graph_ctc.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/context_biasing/context_graph_ctc.py new file mode 100644 index 0000000..bcfcdf2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/context_biasing/context_graph_ctc.py @@ -0,0 +1,242 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright 2023 Xiaomi Corp. (authors: Wei Kang) +# +# See ../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# The script was obtained and modified from Icefall repo: +# https://github.com/k2-fsa/icefall/blob/11d816d174076ec9485ab8b1d36af2592514e348/icefall/context_graph.py + +from collections import deque +from typing import Dict, List, Optional + + +try: + import graphviz + + _GRAPHVIZ_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + _GRAPHVIZ_AVAILABLE = False + + +class ContextState: + """The state in ContextGraph""" + + def __init__( + self, index: int, is_end: bool = False, word: Optional[str] = None, + ): + """Create a ContextState. + Args: + index: + The node index, only for visualization now. A node is in [0, graph.num_nodes). + The index of the root node is always 0. + is_end: + True if current node is the end of a context biasing word. + word: + The word of coresponding transcription (not None only for end states). + """ + self.index = index + self.is_end = is_end + self.word = word + # dict of next token transitions to next states (key: token, value: next state) + self.next = {} + # the best token on current state (needed for state pruning during word spotter work) + self.best_token = None + + +class ContextGraphCTC: + """ + Context-biasing graph (based on prefix tree) according to the CTC transition topology (with blank nodes). + A ContextGraph contains some words / phrases that we expect to boost their recognition accuracy. + """ + + def __init__(self, blank_id: int = 1024): + """ + Initialize the ContextGraphCTC based on given blank_id. + + Args: + blank_id: the id of blank token in ASR model + """ + + self.num_nodes = 0 + self.root = ContextState(index=self.num_nodes, is_end=False) + self.blank_token = blank_id + + def add_to_graph(self, word_items: List[tuple[str, List[List[tuple[str, int]]]]]): + """ + Adding nodes to the context graph based on given word_items. + + Args: + word_items: a list of word items, each word item is a tuple of (word, tokenizations) + word: the word to be inserted into the context graph + tokenizations: a list of BPE word tokenizations + (each word can have several tokenizations to improve the recognition accuracy) + + """ + # process context biasing words with tokenizations + for word_item in word_items: + for tokens in word_item[1]: + prev_node = self.root + prev_token = None + for i, token in enumerate(tokens): + if token not in prev_node.next: + self.num_nodes += 1 + is_end = i == len(tokens) - 1 + word = word_item[0] if is_end else None + node = ContextState(index=self.num_nodes, is_end=is_end, word=word) + node.next[token] = node + prev_node.next[token] = node + + # add blank node: + if prev_node is not self.root: + if self.blank_token in prev_node.next: + # blank node already exists + prev_node.next[self.blank_token].next[token] = node + else: + # create new blank node + self.num_nodes += 1 + blank_node = ContextState(index=self.num_nodes, is_end=False) + blank_node.next[self.blank_token] = blank_node + blank_node.next[token] = node + prev_node.next[self.blank_token] = blank_node + + # in case of two consecutive equal tokens + if token == prev_token: + # if token already in prev_node.next[balnk_token].next + if self.blank_token in prev_node.next and token in prev_node.next[self.blank_token].next: + prev_node = prev_node.next[self.blank_token].next[token] + prev_token = token + continue + # create new token + self.num_nodes += 1 + is_end = i == len(tokens) - 1 + word = word_item[0] if is_end else None + node = ContextState(index=self.num_nodes, is_end=is_end, word=word) + # add blank + if self.blank_token in prev_node.next: + prev_node.next[self.blank_token].next[token] = node + node.next[token] = node + else: + # create new blank node + self.num_nodes += 1 + blank_node = ContextState(index=self.num_nodes, is_end=False) + blank_node.next[self.blank_token] = blank_node + blank_node.next[token] = node + prev_node.next[self.blank_token] = blank_node + # rewrite previous node + if prev_node.index != prev_node.next[token].index: + prev_node = prev_node.next[token] + else: + prev_node = prev_node.next[self.blank_token].next[token] + prev_token = token + + def draw(self, title: Optional[str] = None, symbol_table: Optional[Dict[int, str]] = None,) -> "graphviz.Digraph": + """Visualize a ContextGraph via graphviz. + + Render ContextGraph as an image via graphviz, and return the Digraph object + + Note: + You need to install graphviz to use this function: + pip install graphviz + + Args: + title: + Title to be displayed in image, e.g. 'A simple FSA example' + symbol_table: + Map the token ids to symbols. + Returns: + A Diagraph from grahpviz. + """ + if _GRAPHVIZ_AVAILABLE is False: + raise ImportError("graphviz is not installed") + + graph_attr = { + "rankdir": "LR", + "size": "8.5,11", + "center": "1", + "orientation": "Portrait", + "ranksep": "0.30", + "nodesep": "0.25", + } + if title is not None: + graph_attr["label"] = title + + default_edge_attr = { + "fontsize": "12", + } + + default_node_attr = { + "shape": "circle", + "style": "bold", + "fontsize": "12", + } + + final_state_attr = { + "shape": "doublecircle", + "style": "bold", + "fontsize": "12", + } + + dot = graphviz.Digraph(name="Context Graph", graph_attr=graph_attr) + + seen = set() + queue = deque() + queue.append(self.root) + # root id is always 0 + dot.node("0", label="0", **default_node_attr) + seen.add(0) + printed_arcs = set() + + while len(queue): + current_node = queue.popleft() + for token, node in current_node.next.items(): + if node.index not in seen: + label = f"{node.index}" + if node.is_end: + dot.node(str(node.index), label=label, **final_state_attr) + else: + dot.node(str(node.index), label=label, **default_node_attr) + seen.add(node.index) + label = str(token) if symbol_table is None else symbol_table[token] + if node.index != current_node.index: + output, input, arc = str(current_node.index), str(node.index), f"{label}" + if (output, input, arc) not in printed_arcs: + if arc == self.blank_token: + dot.edge(output, input, label=self.blank_token, color="blue", **default_edge_attr) + else: + dot.edge(output, input, label=arc) + queue.append(node) + else: + output, input, arc = str(current_node.index), str(current_node.index), f"{label}" + if (output, input, arc) not in printed_arcs: + if arc == self.blank_token: + dot.edge(output, input, label=self.blank_token, color="blue", **default_edge_attr) + else: + dot.edge(output, input, label=arc, color="green") + printed_arcs.add((output, input, arc)) + + return dot diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/context_biasing/ctc_based_word_spotter.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/context_biasing/ctc_based_word_spotter.py new file mode 100644 index 0000000..ac6e19b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/context_biasing/ctc_based_word_spotter.py @@ -0,0 +1,365 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import List, Optional + +import numpy as np + +from nemo.collections.asr.parts.context_biasing.context_graph_ctc import ContextGraphCTC, ContextState + + +@dataclass +class Token: + """ + Dataclass of alignment tracking according to the Token Passing Algoritm (TPA). + + Args: + state: state of Context-Biasing graph + score: accumulated token score in log space + start_frame: index of acoustic frame from which the token was created + alive: token status (alive or dead) + """ + + state: ContextState + score: float = 0.0 + start_frame: Optional[int] = None + alive: bool = True + + +@dataclass +class WSHyp: + """ + Hypothesis of Word Spotter prediction + + Args: + word: spotted word + score: accumulative score of best token + start_frame: index of acoustic frame from which the best token was created + end_frame: index of acoustic frame from which the final state of ContextGraph was reached + """ + + word: str + score: float + start_frame: int + end_frame: int + + +def beam_pruning(next_tokens: List[Token], beam_threshold: float) -> List[Token]: + """ + Prun all tokens whose score is worse than best_token.score - beam_threshold + + Args: + next_tokens: list of input tokens + beam_threshold: beam threshold + + Returns: + list of pruned tokens + """ + if not next_tokens: + return [] + best_token = next_tokens[np.argmax([token.score for token in next_tokens])] + next_tokens = [token for token in next_tokens if token.score > best_token.score - beam_threshold] + return next_tokens + + +def state_pruning(next_tokens: List[Token]) -> List[Token]: + """ + If there are several tokens on the same state, then leave only the best of them according to score + + Args: + next_tokens: list of input tokens + + Returns: + list of pruned tokens + """ + if not next_tokens: + return [] + # traverse all tokens and check each graph state for the best token + for token in next_tokens: + if not token.state.best_token: + token.state.best_token = token + else: + if token.score <= token.state.best_token.score: + token.alive = False + else: + token.state.best_token.alive = False + token.state.best_token = token + # save only alive tokens + next_tokens_pruned = [token for token in next_tokens if token.alive] + # clean all best_tokens in context_graph + for token in next_tokens: + token.state.best_token = None + return next_tokens_pruned + + +def find_best_hyps(spotted_words: List[WSHyp], intersection_threshold: int = 10) -> List[WSHyp]: + """ + Some spotted hypotheses may have overlap. + If hypotheses intersection is greater than intersection_threshold, + then the function leaves only the best hypothesis according to the score. + + Args: + spotted_words: list of spotter hypotheses WSHyp + intersection_threshold: minimal intersection threshold (in percentages) + + Returns: + list of best hyps without intersection + """ + + hyp_intervals_dict = {} + for hyp in spotted_words: + hyp_interval = set(range(hyp.start_frame, hyp.end_frame + 1)) + h_interval_name = f"{hyp.start_frame}_{hyp.end_frame}" + insert_new_hyp = True + + # check hyp intersection with all the elements in hyp_intervals_dict + for h_interval_key in hyp_intervals_dict: + # get left and right interval values + l, r = int(h_interval_key.split("_")[0]), int(h_interval_key.split("_")[1]) + current_dict_interval = set(range(l, r + 1)) + intersection_part = 100 / len(current_dict_interval) * len(hyp_interval & current_dict_interval) + # in case of intersection: + if intersection_part >= intersection_threshold: + if hyp.score > hyp_intervals_dict[h_interval_key].score: + hyp_intervals_dict.pop(h_interval_key) + insert_new_hyp = True + break + else: + insert_new_hyp = False + if insert_new_hyp: + hyp_intervals_dict[h_interval_name] = hyp + + best_hyp_list = [hyp_intervals_dict[h_interval_key] for h_interval_key in hyp_intervals_dict] + + return best_hyp_list + + +def get_ctc_word_alignment( + logprob: np.ndarray, asr_model, token_weight: float = 1.0, blank_idx: int = 0 +) -> List[tuple]: + """ + Get word level alignment (with start and end frames) based on argmax ctc predictions. + The word score is a sum of non-blank token logprobs with additional token_weight. + token_weight is used to prevent false accepts during filtering word spotting hypotheses. + + Args: + logprob: ctc logprobs + asr_model: asr model (ctc or hybrid transducer-ctc) + token_weight: additional token weight for word-level ctc alignment + + Returns: + list of word level alignment where each element is tuple (word, left_frame, rigth_frame, word_score) + """ + + alignment_ctc = np.argmax(logprob, axis=1) + + # get token level alignment + token_alignment = [] + prev_idx = None + for i, idx in enumerate(alignment_ctc): + token_logprob = 0 + if idx != blank_idx: + token = asr_model.tokenizer.ids_to_tokens([int(idx)])[0] + if idx == prev_idx: + prev_repited_token = token_alignment.pop() + token_logprob += prev_repited_token[2] + token_logprob += logprob[i, idx].item() + token_alignment.append((token, i, token_logprob)) + prev_idx = idx + + # get word level alignment + begin_of_word = "▁" + word_alignment = [] + word = "" + l, r, score = None, None, None + for item in token_alignment: + if not word: + if word.startswith(begin_of_word): + word = item[0][1:] + else: + word = item[0][:] + l = item[1] + r = item[1] + score = item[2] + token_weight + else: + if item[0].startswith(begin_of_word): + word_alignment.append((word, l, r, score)) + word = item[0][1:] + l = item[1] + r = item[1] + score = item[2] + token_weight + else: + word += item[0] + r = item[1] + score += item[2] + token_weight + if word: + word_alignment.append((word, l, r, score)) + + if len(word_alignment) == 1 and not word_alignment[0][0]: + word_alignment = [] + + return word_alignment + + +def filter_wb_hyps(best_hyp_list: List[WSHyp], word_alignment: List[tuple]) -> List[WSHyp]: + """ + Compare scores of spotted words with overlapping words from ctc alignment. + If score of spotted word is less than overalapping words from ctc alignment, + the spotted word will removed as false positive. + A spotted word may overlap with several words from ctc alignment ("gpu" -> "g p u"). + Here we use overall_spot_score variable to accumulate scores of several words. + + Args: + best_hyp_list: list of spotted hypotheses WSHyp + word_alignment: world level ctc alignment with word scores + + Returns: + filtered best_hyp_list + """ + + if not word_alignment: + return best_hyp_list + + best_hyp_list_filtered = [] + current_word_in_ali = 0 + for hyp in best_hyp_list: + overall_spot_score = 0 + hyp_intersects = False + hyp_interval = set(range(hyp.start_frame, hyp.end_frame + 1)) + # check if spotted word overlaps with words from ctc alignment + for i in range(current_word_in_ali, len(word_alignment)): + word_stats = word_alignment[i] + word_interval = set(range(word_stats[1], word_stats[2] + 1)) + intersection_part = 100 / len(word_interval) * len(hyp_interval & word_interval) + if intersection_part: + if not hyp_intersects: + overall_spot_score = word_stats[3] + else: + overall_spot_score += intersection_part / 100 * word_stats[3] + hyp_intersects = True + elif hyp_intersects: + # add hyp to the best list + if hyp.score >= overall_spot_score: + best_hyp_list_filtered.append(hyp) + current_word_in_ali = i + hyp_intersects = False + break + # if hyp has not yet been added (end of sentence case) + if hyp_intersects and hyp.score >= overall_spot_score: + best_hyp_list_filtered.append(hyp) + + return best_hyp_list_filtered + + +def run_word_spotter( + logprobs: np.ndarray, + context_graph: ContextGraphCTC, + asr_model, + blank_idx: int = 0, + beam_threshold: float = 5.0, + cb_weight: float = 3.0, + ctc_ali_token_weight: float = 0.5, + keyword_threshold: float = -5.0, + blank_threshold: float = 0.8, + non_blank_threshold: float = 0.001, +): + """ + CTC-based Word Spotter for recognition of words from context biasing graph (paper link) + The algorithm is based on the Token Passing Algorithm (TPA) and uses run, beam and state prunings. + Blank and non-blank thresholds are used for preliminary hypotheses pruning. + The algorithm is implemented in log semiring. + + Args: + logprobs: CTC logprobs for one file [Time, Vocab+blank] + context_graph: Context-Biasing graph + blank_idx: blank index in ASR model + asr_model: ASR model (ctc or hybrid-transducer-ctc) + beam_threshold: threshold for beam pruning + cb_weight: context biasing weight + ctc_ali_token_weight: additional token weight for word-level ctc alignment + keyword_threshold: auxiliary weight for pruning final hypotheses + blank_threshold: blank threshold (probability) for preliminary hypotheses pruning + non_blank_threshold: non-blank threshold (probability) for preliminary hypotheses pruning + + Returns: + final list of spotted hypotheses WSHyp + """ + + start_state = context_graph.root + active_tokens = [] + next_tokens = [] + spotted_words = [] + + # move threshold probabilities to log space + blank_threshold = np.log(blank_threshold) + non_blank_threshold = np.log(non_blank_threshold) + + for frame in range(logprobs.shape[0]): + # add an empty token (located in the graph root) at each new frame to start new word spotting + active_tokens.append(Token(start_state, start_frame=frame)) + best_score = None + for token in active_tokens: + # skip token by the blank_threshold if empty token + if token.state is context_graph.root and logprobs[frame][blank_idx] > blank_threshold: + continue + for transition_state in token.state.next: + # skip non-blank token by the non_blank_threshold if empty token + if token.state is context_graph.root and logprobs[frame][int(transition_state)] < non_blank_threshold: + continue + # running beam pruning (start) - skips current token by score before Token class creations + if transition_state != blank_idx: + # add cb_weight only for non-blank tokens + current_score = token.score + logprobs[frame][int(transition_state)].item() + cb_weight + else: + current_score = token.score + logprobs[frame][int(transition_state)].item() + if not best_score: + best_score = current_score + else: + if current_score < best_score - beam_threshold: + continue + elif current_score > best_score: + best_score = current_score + # running beam pruning (end) + + new_token = Token(token.state.next[transition_state], current_score, token.start_frame) + # add a word as spotted if token reached the end of word state in context graph: + if new_token.state.is_end and new_token.score > keyword_threshold: + word = new_token.state.word + spotted_words.append( + WSHyp(word=word, score=new_token.score, start_frame=new_token.start_frame, end_frame=frame) + ) + # check case when the current state is the last in the branch (only one self-loop transition) + if len(new_token.state.next) == 1: + if current_score is best_score: + best_score = None + continue + next_tokens.append(new_token) + # state and beam prunings: + next_tokens = beam_pruning(next_tokens, beam_threshold) + next_tokens = state_pruning(next_tokens) + + active_tokens = next_tokens + next_tokens = [] + + # find best hyps for spotted keywords (in case of hyps overlapping): + best_hyp_list = find_best_hyps(spotted_words) + + # filter hyps according to word-level ctc alignment to avoid a high false accept rate + ctc_word_alignment = get_ctc_word_alignment( + logprobs, asr_model, token_weight=ctc_ali_token_weight, blank_idx=blank_idx + ) + best_hyp_list = filter_wb_hyps(best_hyp_list, ctc_word_alignment) + + return best_hyp_list diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/features.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/features.py new file mode 100644 index 0000000..06665c4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/features.py @@ -0,0 +1,39 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright (c) 2018 Ryan Leary +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# This file contains code artifacts adapted from https://github.com/ryanleary/patter + +""" +ALIAS FILE for backward compatibility +""" +from nemo.collections.asr.parts.preprocessing.features import * diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/__init__.py new file mode 100644 index 0000000..2db92b2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/classes.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/classes.py new file mode 100644 index 0000000..d4c498f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/classes.py @@ -0,0 +1,170 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC +from dataclasses import dataclass, field +from typing import Any, Optional, Tuple + +import torch +from omegaconf import DictConfig + +from nemo.utils import logging + + +@dataclass +class GraphIntersectDenseConfig: + """Graph dense intersection config. + """ + + search_beam: float = 20.0 + output_beam: float = 10.0 + min_active_states: int = 30 + max_active_states: int = 10000 + + +@dataclass +class GraphModuleConfig: + """Config for graph modules. + Typically used with graph losses and decoders. + """ + + topo_type: str = "default" + topo_with_self_loops: bool = True + token_lm: Optional[Any] = None + intersect_pruned: bool = False + intersect_conf: GraphIntersectDenseConfig = field(default_factory=lambda: GraphIntersectDenseConfig()) + boost_coeff: float = 0.0 + predictor_window_size: int = 0 + predictor_step_size: int = 1 + + +class ASRK2Mixin(ABC): + """k2 Mixin class that simplifies the construction of various models with k2-based losses. + + It does the following: + - Sets up the graph loss and decoder (methods _init_k2 and update_k2_modules). + - Registers external graphs, if needed. + - Augments forward(...) with optional graph decoding to get accurate predictions. + """ + + def _init_k2(self): + """ + k2-related initialization implementation. + + This method is expected to run after the __init__ which sets self._cfg + self._cfg is expected to have the attribute graph_module_cfg + """ + if not hasattr(self, "_cfg"): + raise ValueError("self._cfg must be set before calling _init_k2().") + if not hasattr(self._cfg, "graph_module_cfg") or self._cfg.graph_module_cfg is None: + raise ValueError("self._cfg.graph_module_cfg must be set and cannot be None.") + self.graph_module_cfg = self._cfg.graph_module_cfg + + # register token_lm for MAPLoss + criterion_type = self.graph_module_cfg.get("criterion_type", "ml") + self.use_graph_lm = criterion_type == "map" + if self.use_graph_lm: + token_lm_path = self.graph_module_cfg.backend_cfg.get("token_lm", None) + if token_lm_path is None: + raise ValueError( + f"graph_module_cfg.backend_cfg.token_lm is empty. It must be set for criterion_type == `{criterion_type}`" + ) + token_lm_path = self.register_artifact('graph_module_cfg.backend_cfg.token_lm', token_lm_path) + self.graph_module_cfg.backend_cfg["token_lm"] = token_lm_path + + self.update_k2_modules(self.graph_module_cfg) + + def update_k2_modules(self, input_cfg: DictConfig): + """ + Helper function to initialize or update k2 loss and transcribe_decoder. + + Args: + input_cfg: DictConfig to take new parameters from. Schema is expected as in + nemo.collections.asr.models.configs.k2_sequence_models_config.GraphModuleConfig + """ + del self.loss + if hasattr(self, "transcribe_decoder"): + del self.transcribe_decoder + + if hasattr(self, "joint"): + # RNNT + num_classes = self.joint.num_classes_with_blank - 1 + else: + # CTC, MMI, ... + num_classes = self.decoder.num_classes_with_blank - 1 + remove_consecutive = input_cfg.backend_cfg.get("topo_with_self_loops", True) and input_cfg.backend_cfg.get( + "topo_type", "default" + ) not in ["forced_blank", "identity",] + self._wer.remove_consecutive = remove_consecutive + + from nemo.collections.asr.losses.lattice_losses import LatticeLoss + + self.loss = LatticeLoss( + num_classes=num_classes, + reduction=self._cfg.get("ctc_reduction", "mean_batch"), + backend="k2", + criterion_type=input_cfg.get("criterion_type", "ml"), + loss_type=input_cfg.get("loss_type", "ctc"), + split_batch_size=input_cfg.get("split_batch_size", 0), + graph_module_cfg=input_cfg.backend_cfg, + ) + + criterion_type = self.loss.criterion_type + self.use_graph_lm = criterion_type == "map" + transcribe_training = input_cfg.get("transcribe_training", False) + if transcribe_training and criterion_type == "ml": + logging.warning( + f"""You do not need to use transcribe_training=`{transcribe_training}` + with criterion_type=`{criterion_type}`. transcribe_training will be set to False.""" + ) + transcribe_training = False + self.transcribe_training = transcribe_training + if self.use_graph_lm: + from nemo.collections.asr.modules.graph_decoder import ViterbiDecoderWithGraph + + self.transcribe_decoder = ViterbiDecoderWithGraph( + num_classes=num_classes, + backend="k2", + dec_type="token_lm", + return_type="1best", + return_ilabels=True, + output_aligned=True, + split_batch_size=input_cfg.get("split_batch_size", 0), + graph_module_cfg=input_cfg.backend_cfg, + ) + + def _forward_k2_post_processing( + self, log_probs: torch.Tensor, encoded_length: torch.Tensor, greedy_predictions: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + k2-related post-processing parf of .forward() + + Args: + log_probs: The log probabilities tensor of shape [B, T, D]. + encoded_length: The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. + greedy_predictions: The greedy token predictions of the model of shape [B, T] + + Returns: + A tuple of 3 elements - + 1) The log probabilities tensor of shape [B, T, D]. + 2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. + 3) The greedy token predictions of the model of shape [B, T] (via argmax) + """ + # greedy_predictions from .forward() are incorrect for criterion_type=`map` + # getting correct greedy_predictions, if needed + if self.use_graph_lm and (not self.training or self.transcribe_training): + greedy_predictions, encoded_length, _ = self.transcribe_decoder.forward( + log_probs=log_probs, log_probs_length=encoded_length + ) + return log_probs, encoded_length, greedy_predictions diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/grad_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/grad_utils.py new file mode 100644 index 0000000..6278fb9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/grad_utils.py @@ -0,0 +1,93 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + +from nemo.collections.asr.parts.k2.utils import make_non_pad_mask + + +class GradExpNormalize(torch.autograd.Function): + """Function for fast gradient normalization. + Typical use case is normalization for mle loss. + """ + + @staticmethod + def forward( + ctx, log_probs: torch.Tensor, input_lengths: torch.Tensor, reduction: str = "mean", + ): + mask = make_non_pad_mask(input_lengths, log_probs.shape[1]) + probs = log_probs.exp() + norm_probs = torch.zeros_like(log_probs) + norm_probs[mask] += probs[mask] + if reduction == "mean": + norm_probs /= norm_probs.shape[0] + ctx.save_for_backward(norm_probs) + return log_probs + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + return grad_output - grad_output.sum(-1).unsqueeze(-1) * ctx.saved_tensors[0], None, None + + +class GradInsert(torch.autograd.Function): + """Function to attach a pre-computed gradient to a tensor. + Typical use case is gradient computation before calling loss.backward(). + """ + + @staticmethod + def forward( + ctx, input_tensor: torch.Tensor, output_tensor: torch.Tensor, grad: torch.Tensor, mask: torch.Tensor, + ): + assert input_tensor.requires_grad + assert not output_tensor.requires_grad and not grad.requires_grad + + ctx.save_for_backward(grad, mask) + return output_tensor + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + saved_grad, mask = ctx.saved_tensors + # TODO (alaptev): make it work for grad_output with arbitrary shape + padded_grad_output = torch.zeros(saved_grad.shape[0], dtype=grad_output.dtype, device=grad_output.device) + padded_grad_output[mask] = grad_output + return (padded_grad_output * saved_grad.T).T, None, None, None + + +class PartialGrad(torch.nn.Module): + """Module for partial gradient computation. + Useful when computing loss on batch splits to save memory. + """ + + def __init__(self, func: torch.nn.Module): + super().__init__() + self.func = func + + def forward( + self, + input_tensor: torch.Tensor, + targets: torch.Tensor, + input_lengths: torch.Tensor, + target_lengths: torch.Tensor, + ): + # break the gradient chain + loc_tensor = input_tensor.detach() + loc_tensor.requires_grad_(True) + + new_tensor, mask = self.func(loc_tensor, targets, input_lengths, target_lengths) + loc_new_tensor = new_tensor.detach() + + new_tensor.sum().backward() + grad = loc_tensor.grad + + return GradInsert.apply(input_tensor, loc_new_tensor, grad, mask), mask diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/graph_compilers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/graph_compilers.py new file mode 100644 index 0000000..8b82dcf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/graph_compilers.py @@ -0,0 +1,191 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright (c) 2020, Xiaomi CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional, Tuple + +import torch + +from nemo.collections.asr.parts.k2.utils import add_self_loops, compose_with_self_loops, intersect_with_self_loops + +from nemo.core.utils.k2_guard import k2 # import k2 from guard module + + +class CtcTopologyCompiler(object): + """Default graph compiler. + It applies its topology to the input token sequence to compile the supervision graph. + + Based on https://github.com/k2-fsa/snowfall/blob/master/snowfall/training/ctc_graph.py + """ + + def __init__( + self, + num_classes: int, + blank: int, + topo_type: str = "default", + topo_with_self_loops: bool = True, + device: torch.device = torch.device("cpu"), + ): + self.topo_type = topo_type + self.device = device + from nemo.collections.asr.parts.k2.topologies import build_topo + + self.base_graph = k2.arc_sort(build_topo(topo_type, list(range(num_classes)), blank, topo_with_self_loops)).to( + self.device + ) + self.ctc_topo_inv = k2.arc_sort(self.base_graph.invert()) + + def to(self, device: torch.device): + self.ctc_topo_inv = self.ctc_topo_inv.to(device) + if self.base_graph is not None: + self.base_graph = self.base_graph.to(device) + self.device = device + + def compile(self, targets: torch.Tensor, target_lengths: torch.Tensor) -> 'k2.Fsa': + token_ids_list = [t[:l].tolist() for t, l in zip(targets, target_lengths)] + label_graph = k2.linear_fsa(token_ids_list).to(self.device) + label_graph.aux_labels = label_graph.labels.clone() + supervision_graphs = compose_with_self_loops(self.base_graph, label_graph) + supervision_graphs = k2.arc_sort(supervision_graphs).to(self.device) + + # make sure the gradient is not accumulated + supervision_graphs.requires_grad_(False) + return supervision_graphs + + +class CtcNumGraphCompiler(CtcTopologyCompiler): + """Graph compiler with auxiliary graph to compose with the topology. + The supervision graph contains the auxiliary graph information. + """ + + def __init__( + self, + num_classes: int, + blank: int, + topo_type: str = "default", + topo_with_self_loops: bool = True, + device: torch.device = torch.device("cpu"), + aux_graph: Optional['k2.Fsa'] = None, + ): + super().__init__(num_classes, blank, topo_type, topo_with_self_loops, device) + if aux_graph is None: + self.decoding_graph = k2.create_fsa_vec([self.ctc_topo_inv.invert()]).to(self.device) + else: + self.base_graph = intersect_with_self_loops(self.ctc_topo_inv, aux_graph).invert_() + self.base_graph = k2.arc_sort(self.base_graph).to(self.device) + + def compile( + self, targets: torch.Tensor, target_lengths: torch.Tensor, aux_graph: Optional['k2.Fsa'] = None, + ) -> 'k2.Fsa': + if aux_graph is None and self.base_graph is None: + raise ValueError( + f"At least one of aux_graph and self.base_graph must be set: {aux_graph}, {self.base_graph}" + ) + elif aux_graph is not None: + self.base_graph = intersect_with_self_loops(self.ctc_topo_inv, aux_graph).invert() + self.base_graph = k2.arc_sort(self.base_graph).to(self.device) + return super().compile(targets, target_lengths) + + +class MmiGraphCompiler(CtcNumGraphCompiler): + """Graph compiler for MMI loss. + The decoding graph is a composition of the auxiliary graph and the topology. + It is returned along with the supervision graph on every compile() call. + """ + + def __init__( + self, + num_classes: int, + blank: int, + topo_type: str = "default", + topo_with_self_loops: bool = True, + device: torch.device = torch.device("cpu"), + aux_graph: Optional['k2.Fsa'] = None, + ): + super().__init__(num_classes, blank, topo_type, topo_with_self_loops, device, aux_graph) + if aux_graph is None: + self.decoding_graph = k2.create_fsa_vec([self.ctc_topo_inv.invert()]).to(self.device) + else: + self.decoding_graph = k2.create_fsa_vec([self.base_graph.detach()]).to(self.device) + + def to(self, device: torch.device): + if self.decoding_graph is not None: + self.decoding_graph = self.decoding_graph.to(device) + super().to(device) + + def compile( + self, targets: torch.Tensor, target_lengths: torch.Tensor, aux_graph: Optional['k2.Fsa'] = None, + ) -> Tuple['k2.Fsa', 'k2.Fsa']: + supervision_graphs = super().compile(targets, target_lengths, aux_graph) + if aux_graph is None and self.decoding_graph is None: + raise ValueError( + f"At least one of aux_graph and self.decoding_graph must be set: {aux_graph}, {self.decoding_graph}" + ) + elif aux_graph is not None: + self.decoding_graph = k2.create_fsa_vec([self.base_graph.detach()]).to(self.device) + return supervision_graphs, self.decoding_graph + + +class RnntTopologyCompiler(CtcTopologyCompiler): + """Default graph compiler for RNNT loss. + Each supervision graph is composed with the corresponding RNNT emission adapter. + + If max_adapter_length is provided, the maximum adapter length is limited. + + Note: + The actual number of classes is `num_classes` + 1 with as the class 0. + + Warning: + It is currently not recommended to use topologies other than "minimal". + """ + + def __init__( + self, + num_classes: int, + blank: int, + topo_type: str = "minimal", + topo_with_self_loops: bool = True, + device: torch.device = torch.device("cpu"), + max_adapter_length: int = 0, + ): + if topo_type == "compact": + raise NotImplementedError(f"This compiler does not support topo_type==`compact`.") + super().__init__(num_classes, blank, topo_type, topo_with_self_loops, device) + from nemo.collections.asr.parts.k2.topologies import RnntEmissionAdapterBuilder + + self.max_adapter_length = max_adapter_length + self._builder = RnntEmissionAdapterBuilder(list(range(num_classes)), blank, num_classes) + + def compile(self, targets: torch.Tensor, target_lengths: torch.Tensor) -> 'k2.Fsa': + supervision_graphs = add_self_loops(super().compile(targets, target_lengths), self._builder.eps_num, "input") + + adapters = self._builder( + torch.where(target_lengths > self.max_adapter_length, self.max_adapter_length, target_lengths) + if self.max_adapter_length > 0 and self.max_adapter_length < target_lengths.max() + else target_lengths + ).to(device=self.device) + return k2.intersect(adapters, supervision_graphs, treat_epsilons_specially=False) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/graph_decoders.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/graph_decoders.py new file mode 100644 index 0000000..3321858 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/graph_decoders.py @@ -0,0 +1,338 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import abstractmethod +from typing import List, Optional, Tuple, Union + +import torch +from omegaconf import DictConfig + +from nemo.collections.asr.parts.k2.classes import GraphIntersectDenseConfig +from nemo.collections.asr.parts.k2.loss_mixins import CtcK2Mixin, RnntK2Mixin +from nemo.collections.asr.parts.k2.utils import invert_permutation, load_graph +from nemo.utils import logging + + +class BaseDecoder(object): + """Base graph decoder with topology for decoding graph. + Typically uses the same parameters as for the corresponding loss function. + + Can do decoding and forced alignment. + + cfg takes precedence over all optional parameters + We keep explicit parameter setting to be able to create an instance without the need of a config. + """ + + @abstractmethod + def __init__( + self, + num_classes: int, + blank: int, + cfg: Optional[DictConfig] = None, + intersect_pruned: bool = False, + intersect_conf: GraphIntersectDenseConfig = GraphIntersectDenseConfig(), + topo_type: str = "default", + topo_with_self_loops: bool = True, + device: torch.device = torch.device("cpu"), + ): + + if cfg is not None: + intersect_pruned = cfg.get("intersect_pruned", intersect_pruned) + intersect_conf = cfg.get("intersect_conf", intersect_conf) + topo_type = cfg.get("topo_type", topo_type) + topo_with_self_loops = cfg.get("topo_with_self_loops", topo_with_self_loops) + + self.num_classes = num_classes + self.blank = blank + self.intersect_pruned = intersect_pruned + self.device = device + self.topo_type = topo_type + self.topo_with_self_loops = topo_with_self_loops + self.pad_fsavec = self.topo_type == "ctc_compact" + self.intersect_conf = intersect_conf + self.graph_compiler = None # expected to be initialized in child classes + self.base_graph = None # expected to be initialized in child classes + self.decoding_graph = None + + def to(self, device: torch.device): + if self.graph_compiler.device != device: + self.graph_compiler.to(device) + if self.base_graph.device != device: + self.base_graph = self.base_graph.to(device) + if self.decoding_graph is not None and self.decoding_graph.device != device: + self.decoding_graph = self.decoding_graph.to(device) + self.device = device + + def update_graph(self, graph: 'k2.Fsa'): + raise NotImplementedError + + def _decode_impl( + self, + log_probs: torch.Tensor, + supervisions: torch.Tensor, + return_lattices: bool = False, + return_ilabels: bool = False, + output_aligned: bool = True, + ) -> Union['k2.Fsa', Tuple[List[torch.Tensor], List[torch.Tensor]]]: + if self.decoding_graph is None: + self.decoding_graph = self.base_graph + + if log_probs.device != self.device: + self.to(log_probs.device) + emissions_graphs = self._prepare_emissions_graphs(log_probs, supervisions) + + if self.intersect_pruned: + lats = k2.intersect_dense_pruned( + a_fsas=self.decoding_graph, + b_fsas=emissions_graphs, + search_beam=self.intersect_conf.search_beam, + output_beam=self.intersect_conf.output_beam, + min_active_states=self.intersect_conf.min_active_states, + max_active_states=self.intersect_conf.max_active_states, + ) + else: + indices = torch.zeros(emissions_graphs.dim0(), dtype=torch.int32, device=self.device) + dec_graphs = ( + k2.index_fsa(self.decoding_graph, indices) + if self.decoding_graph.shape[0] == 1 + else self.decoding_graph + ) + lats = k2.intersect_dense(dec_graphs, emissions_graphs, self.intersect_conf.output_beam) + self.decoding_graph = None + + order = supervisions[:, 0] + if return_lattices: + lats = k2.index_fsa(lats, invert_permutation(order).to(device=log_probs.device)) + if self.blank != 0: + # change only ilabels + # suppose self.blank == self.num_classes - 1 + lats.labels = torch.where(lats.labels == 0, self.blank, lats.labels - 1) + return lats + else: + shortest_path_fsas = k2.index_fsa( + k2.shortest_path(lats, True), invert_permutation(order).to(device=log_probs.device), + ) + return self._extract_labels_and_probabilities(shortest_path_fsas, return_ilabels, output_aligned) + + def decode( + self, + log_probs: torch.Tensor, + log_probs_length: torch.Tensor, + return_lattices: bool = False, + return_ilabels: bool = False, + output_aligned: bool = True, + ) -> Union['k2.Fsa', Tuple[List[torch.Tensor], List[torch.Tensor]]]: + log_probs, supervisions, _, _ = self._prepare_log_probs_and_targets(log_probs, log_probs_length, None, None) + return self._decode_impl( + log_probs, + supervisions, + return_lattices=return_lattices, + return_ilabels=return_ilabels, + output_aligned=output_aligned, + ) + + def align( + self, + log_probs: torch.Tensor, + log_probs_length: torch.Tensor, + targets: torch.Tensor, + target_lengths: torch.Tensor, + return_lattices: bool = False, + return_ilabels: bool = False, + output_aligned: bool = True, + ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + log_probs, supervisions, targets, target_lengths = self._prepare_log_probs_and_targets( + log_probs, log_probs_length, targets, target_lengths + ) + order = supervisions[:, 0].to(dtype=torch.long) + self.decoding_graph = self.graph_compiler.compile(targets[order], target_lengths[order]) + return self._decode_impl( + log_probs, + supervisions, + return_lattices=return_lattices, + return_ilabels=return_ilabels, + output_aligned=output_aligned, + ) + + +class CtcDecoder(BaseDecoder, CtcK2Mixin): + """Regular CTC graph decoder with custom topologies. + Available topologies: + - `default`, with or without self-loops + - `compact`, with or without self-loops + - `shared_blank`, with or without self-loops + - `minimal`, without self-loops + + Can do decoding and forced alignment. + """ + + def __init__( + self, + num_classes: int, + blank: int, + cfg: Optional[DictConfig] = None, + intersect_pruned: bool = False, + intersect_conf: GraphIntersectDenseConfig = GraphIntersectDenseConfig(), + topo_type: str = "default", + topo_with_self_loops: bool = True, + device: torch.device = torch.device("cpu"), + ): + super().__init__( + num_classes, blank, cfg, intersect_pruned, intersect_conf, topo_type, topo_with_self_loops, device + ) + from nemo.collections.asr.parts.k2.graph_compilers import CtcTopologyCompiler + + self.graph_compiler = CtcTopologyCompiler( + self.num_classes, self.blank, self.topo_type, self.topo_with_self_loops, self.device + ) + self.base_graph = k2.create_fsa_vec([self.graph_compiler.ctc_topo_inv.invert()]).to(self.device) + + +class RnntAligner(BaseDecoder, RnntK2Mixin): + """RNNT graph decoder with the `minimal` topology. + If predictor_window_size is not provided, this decoder works as a Viterbi over regular RNNT lattice. + With predictor_window_size provided, it applies uniform pruning when compiling Emission FSAs + to reduce memory and compute consumption. + + Can only do forced alignment. + """ + + def __init__( + self, + num_classes: int, + blank: int, + cfg: Optional[DictConfig] = None, + intersect_pruned: bool = False, + intersect_conf: GraphIntersectDenseConfig = GraphIntersectDenseConfig(), + topo_type: str = "default", + topo_with_self_loops: bool = True, + predictor_window_size: int = 0, + predictor_step_size: int = 1, + device: torch.device = torch.device("cpu"), + ): + if cfg is not None: + topo_type = cfg.get("topo_type", topo_type) + predictor_window_size = cfg.get("predictor_window_size", predictor_window_size) + predictor_step_size = cfg.get("predictor_step_size", predictor_step_size) + if topo_type != "minimal": + raise NotImplementedError(f"Only topo_type=`minimal` is supported at the moment.") + super().__init__( + num_classes, blank, cfg, intersect_pruned, intersect_conf, topo_type, topo_with_self_loops, device + ) + self.predictor_window_size = predictor_window_size + self.predictor_step_size = predictor_step_size + from nemo.collections.asr.parts.k2.graph_compilers import RnntTopologyCompiler + + self.graph_compiler = RnntTopologyCompiler( + self.num_classes, + self.blank, + self.topo_type, + self.topo_with_self_loops, + self.device, + max_adapter_length=self.predictor_window_size, + ) + self.base_graph = self.graph_compiler.base_graph + + def decode( + self, + log_probs: torch.Tensor, + log_probs_length: torch.Tensor, + return_lattices: bool = False, + return_ilabels: bool = False, + output_aligned: bool = True, + ) -> Union['k2.Fsa', Tuple[List[torch.Tensor], List[torch.Tensor]]]: + raise NotImplementedError("RNNT decoding is not implemented. Only .align(...) method is supported.") + + def align( + self, + log_probs: torch.Tensor, + log_probs_length: torch.Tensor, + targets: torch.Tensor, + target_lengths: torch.Tensor, + return_lattices: bool = False, + return_ilabels: bool = False, + output_aligned: bool = True, + ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + assert self.predictor_window_size == 0 or log_probs.size(2) <= self.predictor_window_size + 1 + + return super().align( + log_probs, + log_probs_length, + targets, + target_lengths, + return_lattices=return_lattices, + return_ilabels=return_ilabels, + output_aligned=output_aligned, + ) + + +class TokenLMDecoder(BaseDecoder): + """Graph decoder with token_lm-based decoding graph. + Available topologies: + - `default`, with or without self-loops + - `compact`, with or without self-loops + - `shared_blank`, with or without self-loops + - `minimal`, without self-loops + + Can do decoding and forced alignment. + + cfg takes precedence over all optional parameters + We keep explicit parameter setting to be able to create an instance without the need of a config. + """ + + def __init__( + self, + num_classes: int, + blank: int, + cfg: Optional[DictConfig] = None, + token_lm: Optional[Union['k2.Fsa', str]] = None, + intersect_pruned: bool = False, + intersect_conf: GraphIntersectDenseConfig = GraphIntersectDenseConfig(), + topo_type: str = "default", + topo_with_self_loops: bool = True, + device: torch.device = torch.device("cpu"), + ): + super().__init__( + num_classes, blank, cfg, intersect_pruned, intersect_conf, topo_type, topo_with_self_loops, device + ) + if cfg is not None: + token_lm = cfg.get("token_lm", token_lm) + if token_lm is not None: + self.token_lm = load_graph(token_lm) if isinstance(token_lm, str) else token_lm + if self.token_lm is not None: + self.update_graph(self.token_lm) + else: + logging.warning( + f"""token_lm was set to None. Use this for debug + purposes only or call .update_graph(token_lm) before using.""" + ) + else: + logging.warning( + f"""token_lm was set to None. Use this for debug + purposes only or call .update_graph(token_lm) before using.""" + ) + self.token_lm = None + + def update_graph(self, graph: 'k2.Fsa'): + self.token_lm = graph + token_lm = self.token_lm.clone() + if hasattr(token_lm, "aux_labels"): + delattr(token_lm, "aux_labels") + labels = token_lm.labels + if labels.max() != self.num_classes - 1: + raise ValueError(f"token_lm is not compatible with the num_classes: {labels.unique()}, {self.num_classes}") + self.graph_compiler = CtcNumGraphCompiler( + self.num_classes, self.blank, self.topo_type, self.topo_with_self_loops, self.device, token_lm + ) + self.base_graph = k2.create_fsa_vec([self.graph_compiler.base_graph]).to(self.device) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/graph_transducer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/graph_transducer.py new file mode 100644 index 0000000..5de8064 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/graph_transducer.py @@ -0,0 +1,483 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import abc +from contextlib import nullcontext +from typing import ContextManager +import torch +import torch.nn.functional as F + +from nemo.core.classes.loss import Loss +from nemo.core.utils.k2_guard import k2 + + +def force_float32_context() -> ContextManager: + """Get context manager to force float32 precision in autocast mode.""" + if torch.is_autocast_enabled(): + return torch.cuda.amp.autocast(dtype=torch.float32) + return nullcontext() + + +class GraphTransducerLossBase(Loss): + """ + Base class for graph transducer losses. + Implementation of the approach described in "Powerful and Extensible WFST Framework for RNN-Transducer Losses" + https://ieeexplore.ieee.org/document/10096679 + + Compose-Transducer: compose the unit (target text) and temporal schemas (graphs) into lattice. + Subclass should implement `get_unit_schema` and `get_temporal_schema` methods. + Grid-Transducer: construct the RNN-T lattice (grid) directly in code. + Subclass should implement `get_grid` method. + """ + + def __init__( + self, use_grid_implementation: bool, connect_composed=False, double_scores=False, cast_to_float32=False + ): + """ + + Args: + use_grid_implementation: Whether to use the grid implementation (Grid-Transducer). + connect_composed: Connect graph after composing unit and temporal schemas (only for Compose-Transducer). + `connect` operation is slow, it is useful for visualization, but not necessary for loss computation. + double_scores: Use calculation of loss in double precision (float64) in the lattice. + Does not significantly affect memory usage since the lattice is ~V/2 times smaller + than the joint tensor. + cast_to_float32: Force cast joint tensor to float32 before log-softmax calculation. + """ + super().__init__() + self.use_grid_implementation = use_grid_implementation + self.connect_composed = connect_composed + self.double_scores = double_scores + self.cast_to_float32 = cast_to_float32 + + @abc.abstractmethod + def get_unit_schema(self, units_tensor: torch.Tensor, vocab_size: int) -> "k2.Fsa": + """ + Get unit schema (target text) graph for Compose-Transducer. + + Args: + units_tensor: tensor with target text + vocab_size: number of labels (including blank). Needed to construct additional eps-arcs (in some cases). + + Returns: + unit schema graph (k2.Fsa). + Labels: :: (k2.Fsa: labels, aux_labels, unit_positions) + """ + pass + + @abc.abstractmethod + def get_temporal_schema(self, num_frames: int, vocab_size: int, device: torch.device) -> "k2.Fsa": + """ + Get temporal schema graph for Compose-Transducer. + + Args: + num_frames: length of the sequence (in frames) + vocab_size: number of labels (including blank) + device: device for tensor to construct + + Returns: + temporal schema graph (k2.Fsa). + Labels: :. is a unit from vocab + special units (e.g., additional eps). + """ + pass + + @abc.abstractmethod + def get_grid(self, units_tensor: torch.Tensor, num_frames: int, vocab_size: int) -> "k2.Fsa": + """ + Construct the transducer lattice (grid) directly for Grid-Transducer. + + Args: + units_tensor: tensor with target text + num_frames: length of the sequence (in frames) + vocab_size: number of labels (including blank) + + Returns: + transducer lattice (k2.Fsa). + Labels: :: (k2.Fsa: labels, aux_labels, unit_positions) + """ + pass + + def get_composed_lattice(self, units_tensor: torch.Tensor, num_frames: int, vocab_size: int) -> "k2.Fsa": + """ + Get composed lattice (unit and temporal schemas) for Compose-Transducer. Useful for visualization. + Should be equivalent to the lattice from `get_grid` method. + + Args: + units_tensor: tensor with target text + num_frames: length of the sequence (in frames) + vocab_size: vocab size (including blank) + + Returns: + composed lattice (k2.Fsa) from unit and temporal schemas + """ + fsa_text = self.get_unit_schema(units_tensor, vocab_size) + fsa_temporal = self.get_temporal_schema(num_frames, vocab_size, units_tensor.device) + composed = k2.compose(fsa_text, fsa_temporal, treat_epsilons_specially=False) + if self.connect_composed: + composed = k2.connect(composed) + return composed + + def get_graphs_batched( + self, logits_lengths: torch.Tensor, targets: torch.Tensor, target_lengths: torch.Tensor, vocab_size: int + ) -> "k2.Fsa": + """ + Get batched lattice (grid or composed) for the batch of sequences. + + Args: + logits_lengths: tensor with lengths of logits + targets: tensor with target units + target_lengths: tensor with lengths of targets + vocab_size: vocab size (including blank) + + Returns: + batched lattice - FsaVec (k2.Fsa) + """ + batch_size = logits_lengths.shape[0] + with torch.no_grad(): + if self.use_grid_implementation: + return k2.create_fsa_vec( + [ + self.get_grid( + units_tensor=targets[i, : target_lengths[i].item()], + num_frames=logits_lengths[i].item(), + vocab_size=vocab_size, + ) + for i in range(batch_size) + ] + ) + + # composed version + text_fsas = [ + self.get_unit_schema(units_tensor=targets[i, : target_lengths[i].item()], vocab_size=vocab_size,) + for i in range(batch_size) + ] + temporal_fsas = [ + self.get_temporal_schema( + num_frames=logits_lengths[i].item(), vocab_size=vocab_size, device=targets.device + ) + for i in range(batch_size) + ] + target_fsas_vec = k2.compose( + k2.create_fsa_vec(text_fsas), k2.create_fsa_vec(temporal_fsas), treat_epsilons_specially=False + ) + if self.connect_composed: + k2.connect(target_fsas_vec) + return target_fsas_vec + + def get_logits_indices(self, target_fsas_vec: k2.Fsa, logits_shape: torch.Size) -> torch.Tensor: + """ + Get indices of flatten logits for each arc in the lattices. + + Args: + target_fsas_vec: batch of target FSAs with lattices + logits_shape: shape of the logits tensor + + Returns: + 1d tensor with indices + """ + # logits_shape: B x Time x Text+1 x Labels + batch_size = logits_shape[0] + device = target_fsas_vec.device + scores_to_batch_i = torch.repeat_interleave( + torch.arange(batch_size, device=device, dtype=torch.int64), + torch.tensor( + [target_fsas_vec.arcs.index(0, i)[0].values().shape[0] for i in range(batch_size)], device=device, + ), + ) + indices = ( + scores_to_batch_i * logits_shape[1] * logits_shape[2] * logits_shape[3] # Batch + + target_fsas_vec.aux_labels.to(torch.int64) * logits_shape[2] * logits_shape[3] # Time indices + + target_fsas_vec.unit_positions.to(torch.int64) * logits_shape[3] # Units (text) indices + + target_fsas_vec.labels.to(torch.int64) # Labels + ) + return indices + + +class GraphRnntLoss(GraphTransducerLossBase): + """ + RNN-T loss implementation based on WFST according + to "Powerful and Extensible WFST Framework for RNN-Transducer Losses" + https://ieeexplore.ieee.org/document/10096679 + """ + + def __init__( + self, + blank: int, + use_grid_implementation=True, + connect_composed=False, + double_scores=False, + cast_to_float32=False, + ): + """ + Init method + + Args: + blank: blank label index + use_grid_implementation: Whether to use the grid implementation (Grid-Transducer). + connect_composed: Connect graph after composing unit and temporal schemas (only for Compose-Transducer). + `connect` operation is slow, it is useful for visualization, but not necessary for loss computation. + double_scores: Use calculation of loss in double precision (float64) in the lattice. + Does not significantly affect memory usage since the lattice is ~V/2 times smaller than the joint tensor. + cast_to_float32: Force cast joint tensor to float32 before log-softmax calculation. + """ + super().__init__( + use_grid_implementation=use_grid_implementation, + connect_composed=connect_composed, + double_scores=double_scores, + cast_to_float32=cast_to_float32, + ) + self.blank = blank + + def get_unit_schema(self, units_tensor: torch.Tensor, vocab_size: int) -> "k2.Fsa": + """ + Get unit schema (target text) graph for RNN-T loss (Compose-Transducer). + Forward arcs represent text labels. + + Example graph: text [1, 2], blank=0. + + graph:: + + 0:0:0 0:0:1 0:0:2 + +-------+ +-------+ +-------+ + v | v | v | + +-----------+ 1:1:0 +-----------+ 2:2:1 +-----------+ -1:-1:-1 #===# + | 0 | -------> | 1 | -------> | 2 | ---------> H 3 H + +-----------+ +-----------+ +-----------+ #===# + + Args: + units_tensor: 1d tensor with text units + vocab_size: number of total labels (vocab size including blank) + + Returns: + unit schema graph (k2.Fsa). + Labels: :: (k2.Fsa: labels, aux_labels, unit_positions) + """ + + blank_id = self.blank + device = units_tensor.device + text_len = units_tensor.shape[0] + + # arcs + # text_len + 1 states, in every state - self-loops (blank) and forward (text label / last forward -1) + arcs = torch.zeros(((text_len + 1) * 2, 4), dtype=torch.int32, device=device) + text_indices = torch.arange(0, text_len + 1, dtype=torch.int32, device=device) + # blank labels + arcs[::2, 0] = text_indices # from state + arcs[::2, 1] = text_indices # to state + arcs[::2, 2] = blank_id + + # text labels + arcs[1::2, 0] = text_indices # from state + arcs[1::2, 1] = text_indices + 1 # to state + arcs[1:-1:2, 2] = units_tensor # labels: text + + arcs[-1, 2] = -1 # last transition to final state, ilabel=-1 (special for k2) + olabels = arcs[:, 2].detach().clone() # same as ilabels + + fsa_text = k2.Fsa(arcs, olabels) + fsa_text.unit_positions = text_indices.expand(2, -1).transpose(0, 1).flatten() + fsa_text.unit_positions[-1] = -1 # last transition to final state + return fsa_text + + def get_temporal_schema(self, num_frames: int, vocab_size: int, device: torch.device) -> "k2.Fsa": + """ + Get temporal schema graph for RNN-T loss (Compose-Transducer). + Forward arc - blank, self-loops - all labels excluding blank + + Example graph: blank=0, num_frames=3, vocab_size=3. + Labels: :. is a unit from vocab. + + graph:: + + 1:0 1:1 1:2 + +-----+ +-----+ +-----+ + v | v | v | + +---------+ 0:0 +---------+ 0:1 +---------+ 0:2 +---+ -1:-1 #===# + | 0 | -----> | 1 | -----> | 2 | -----> | 3 | -------> H 4 H + +---------+ +---------+ +---------+ +---+ #===# + ^ 2:0 | ^ 2:1 | ^ 2:2 | + +-----+ +-----+ +-----+ + + Args: + num_frames: length of the sequence (in frames) + vocab_size: number of labels (including blank) + device: device for tensor to construct + + Returns: + temporal schema graph (k2.Fsa). + Labels: :. is a unit from vocab. + """ + blank_id = self.blank + + fsa_temporal_arcs = torch.zeros((num_frames * vocab_size + 1, 4), dtype=torch.int32, device=device) + sequence_states = torch.arange(0, num_frames, dtype=torch.int32, device=device) + # for every state - vocab_size arcs, [0, 1, ..., vocab_size-1, 0, 1, ..., vocab_size-1, ...] + start_states = sequence_states.expand(vocab_size, num_frames).transpose(0, 1).flatten() + # first: make all arcs - self-loops + fsa_temporal_arcs[:-1, 0] = start_states # from + fsa_temporal_arcs[:-1, 1] = start_states # to + fsa_temporal_arcs[:-1, 2] = ( + torch.arange(0, vocab_size, dtype=torch.int32, device=device).expand(num_frames, vocab_size).flatten() + ) + + # blank-arcs: forward + fsa_temporal_arcs[blank_id:-1:vocab_size, 1] = sequence_states + 1 # blanks + + # transition to last final state + fsa_temporal_arcs[-1, :3] = torch.tensor((num_frames, num_frames + 1, -1), dtype=torch.int32, device=device) + + # output symbols: position in the sequence, same as start states for arcs + olabels = fsa_temporal_arcs[:, 0].detach().clone() + olabels[-1] = -1 # last arc to final state + + fsa_temporal = k2.Fsa(fsa_temporal_arcs, olabels) + fsa_temporal = k2.arc_sort(fsa_temporal) # need for compose + return fsa_temporal + + @staticmethod + def relabel_states(states: torch.Tensor, n: int, m: int) -> torch.Tensor: + """ + Relabel states to be in topological order: by diagonals + + Args: + states: tensor with states + n: number of rows + m: number of columns + + Returns: + tensor with relabeled states (same shape as `states`) + """ + i = states % n + j = torch.div(states, n, rounding_mode='floor') # states // n, torch.div to avoid pytorch warnings + min_mn = min(m, n) + max_mn = max(m, n) + diag = i + j + anti_diag = m + n - 1 - diag + max_idx = n * m - 1 + cur_diag_idx = i if m > n else m - j - 1 + states = ( + diag.lt(min_mn) * ((diag * (diag + 1) >> 1) + i) + + torch.logical_and(diag.ge(min_mn), diag.lt(max_mn)) + * ((min_mn * (min_mn + 1) >> 1) + (diag - min_mn) * min_mn + cur_diag_idx) + + diag.ge(max_mn) * (max_idx - (anti_diag * (anti_diag + 1) >> 1) + m - j) + ) + return states + + def get_grid(self, units_tensor: torch.Tensor, num_frames: int, vocab_size: int) -> "k2.Fsa": + """ + Construct the RNN-T lattice directly (Grid-Transducer). + + Args: + units_tensor: 1d tensor with text units + num_frames: length of the sequence (number of frames) + vocab_size: number of total labels (vocab size including blank) + + Returns: + transducer lattice (k2.Fsa). + Labels: :: (k2.Fsa: labels, aux_labels, unit_positions) + """ + blank_id = self.blank + text_length = units_tensor.shape[0] + device = units_tensor.device + num_grid_states = num_frames * (text_length + 1) + num_forward_arcs = (num_frames - 1) * (text_length + 1) + num_text_arcs = text_length * num_frames + arcs = torch.zeros((num_forward_arcs + num_text_arcs + 2, 4), dtype=torch.int32, device=device) + # blank transitions + # i, i+, 0 , i / , i % + from_states = torch.arange(num_forward_arcs, device=device) + to_states = from_states + (text_length + 1) + arcs[:num_forward_arcs, 0] = from_states + arcs[:num_forward_arcs, 1] = to_states + arcs[:num_forward_arcs, 2] = blank_id + + # text arcs + from_states = ( + torch.arange(num_grid_states, dtype=torch.int32, device=device) + .reshape(num_frames, text_length + 1)[:, :-1] + .flatten() + ) + to_states = from_states + 1 + ilabels = units_tensor.expand(num_frames, -1).flatten() + arcs[num_forward_arcs:-2, 0] = from_states + arcs[num_forward_arcs:-2, 1] = to_states + arcs[num_forward_arcs:-2, 2] = ilabels + + # last 2 states + arcs[-2, :3] = torch.tensor((num_grid_states - 1, num_grid_states, blank_id), dtype=torch.int32, device=device) + arcs[-1, :3] = torch.tensor((num_grid_states, num_grid_states + 1, -1), dtype=torch.int32, device=device) + + # sequence indices, time indices + olabels = torch.div(arcs[:, 0], (text_length + 1), rounding_mode="floor") # arcs[:, 0] // (text_length + 1) + unit_positions = arcs[:, 0] % (text_length + 1) + # last state: final + olabels[-1] = -1 + unit_positions[-1] = -1 + + # relabel + # instead of using top sort (extremely expensive) k2.top_sort(rnnt_graph) + arcs[:-2, 0] = self.relabel_states(arcs[:-2, 0], text_length + 1, num_frames) + arcs[:-3, 1] = self.relabel_states(arcs[:-3, 1], text_length + 1, num_frames) + + # sort by start state - required in k2 + # TODO: maybe it is more optimal to avoid sort, construct arcs in ascending order + _, indices = torch.sort(arcs[:, 0], dim=0) + sorted_arcs = arcs[indices] + olabels = olabels[indices] + unit_positions = unit_positions[indices] + + rnnt_graph = k2.Fsa(sorted_arcs, olabels) + rnnt_graph.unit_positions = unit_positions + return rnnt_graph + + def forward( + self, acts: torch.Tensor, labels: torch.Tensor, act_lens: torch.Tensor, label_lens: torch.Tensor, + ) -> torch.Tensor: + """ + Compute forward method for RNN-T. + + Args: + acts: activations (joint tensor). NB: raw logits, not after log-softmax + labels: target labels + act_lens: lengths of activations + label_lens: length of labels sequences + + Returns: + batch of RNN-T scores (loss) + """ + # argument names are consistent with NeMo, see RNNTLoss.forward: + # self._loss(acts=log_probs, labels=targets, act_lens=input_lengths, label_lens=target_lengths) + logits, targets, logits_lengths, target_lengths = acts, labels, act_lens, label_lens + + # logits: B x Time x Text+1 x C + vocab_size = logits.shape[-1] + target_fsas_vec = self.get_graphs_batched(logits_lengths, targets, target_lengths, vocab_size) + + cast_context = force_float32_context() if self.cast_to_float32 else nullcontext() + with cast_context: + log_probs = F.log_softmax(logits, dim=-1) + with torch.no_grad(): + indices = self.get_logits_indices(target_fsas_vec, logits.shape) + # transition to the last state + # use 0 index (for valid index_select) and manually assign score after index_select for this case + indices[target_fsas_vec.labels == -1] = 0 + + # NB: do not assign scores -> modify, k2 will not update all scores correctly (modify -> assign) + scores = log_probs.flatten().index_select(-1, indices) + # fix weights for the arcs to the last state + scores[target_fsas_vec.labels == -1] = 0 + + target_fsas_vec.scores = scores + scores = -1 * target_fsas_vec.get_tot_scores(use_double_scores=self.double_scores, log_semiring=True) + return scores diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/loss_mixins.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/loss_mixins.py new file mode 100644 index 0000000..ad8286e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/loss_mixins.py @@ -0,0 +1,233 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC +from typing import List, Optional, Tuple + +import torch + +from nemo.collections.asr.parts.k2.grad_utils import GradExpNormalize +from nemo.collections.asr.parts.k2.utils import ( + create_supervision, + get_arc_weights, + get_uniform_rnnt_prune_ranges, + make_non_pad_mask, + make_non_pad_mask_3d, + prep_padded_densefsavec, +) +from nemo.core.utils.k2_guard import k2 # import k2 from guard module + + +class CtcK2Mixin(ABC): + """k2 Mixin class that simplifies the construction of various k2-based CTC-like losses. + + It does the following: + - Prepares and adapts the input tensors (method _prepare_log_probs_and_targets). + - Creates Emissions graphs (method _prepare_emissions_graphs). + - Extracts the labels and probabilities of the best lattice path (method _extract_labels_and_probabilities). + """ + + def _prepare_log_probs_and_targets( + self, + log_probs: torch.Tensor, + input_lengths: torch.Tensor, + targets: Optional[torch.Tensor] = None, + target_lengths: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: + """Creates k2-style supervisions and shifts targets by one if the number is not zero. + """ + assert log_probs.size(-1) == self.num_classes + supervisions = create_supervision(input_lengths) + # shift targets to make output epsilon ID zero + return ( + log_probs, + supervisions, + torch.where(targets < self.blank, targets + 1, targets) if targets is not None else None, + target_lengths, + ) + + def _prepare_emissions_graphs(self, log_probs: torch.Tensor, supervisions: torch.Tensor) -> 'k2.DenseFsaVec': + """Creates DenseFsaVec, padding it with frames if the topology is `compact`. + In particular, every second frame of the DenseFsaVec is the frame. + + frame is a frame with log-probability zero and every other log-probability is -inf. + """ + return ( + prep_padded_densefsavec(log_probs, supervisions) + if self.pad_fsavec + else k2.DenseFsaVec(log_probs, supervisions) + ) + + def _maybe_normalize_gradients(self, log_probs: torch.Tensor, input_lengths: torch.Tensor) -> torch.Tensor: + """PyTorch is doing the log-softmax normalization as part of the CTC computation. + More: https://github.com/k2-fsa/k2/issues/575 + """ + return GradExpNormalize.apply(log_probs, input_lengths, "mean" if self.reduction != "sum" else "none") + + def _extract_labels_and_probabilities( + self, shortest_path_fsas: 'k2.Fsa', return_ilabels: bool = False, output_aligned: bool = True + ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + """Extracts the labels and probabilities of the best lattice path, + dropping arcs and restoring the targets shift, if needed. + """ + shortest_paths = [] + probs = [] + # direct iterating does not work as expected + for i in range(shortest_path_fsas.shape[0]): + shortest_path_fsa = shortest_path_fsas[i] + # suppose that artificial input epsilon numbers >= self.num_classes + non_eps_mask = (shortest_path_fsa.labels != -1) & (shortest_path_fsa.labels < self.num_classes) + if return_ilabels: + labels = shortest_path_fsa.labels[non_eps_mask] + else: + labels = shortest_path_fsa.aux_labels[non_eps_mask] + if self.blank != 0: + # suppose output epsilon number == 0 + # since the input epsilons were removed, we treat all remaining epsilons as blanks + labels[labels == 0] = self.blank + labels[(labels > 0) & (labels < self.blank)] -= 1 + labels = labels.to(dtype=torch.long) + if not return_ilabels and not output_aligned: + labels = labels[labels != self.blank] + shortest_paths.append(labels) + probs.append(get_arc_weights(shortest_path_fsa)[non_eps_mask].exp().to(device=shortest_path_fsas.device)) + return shortest_paths, probs + + +class RnntK2Mixin(CtcK2Mixin): + """k2 Mixin class that simplifies the construction of various k2-based RNNT-like losses. Inherits CtcK2Mixin. + + It does the following: + - Prepares and adapts the input tensors. + - Creates Emissions graphs. + - Extracts the labels and probabilities of the best lattice path (method _extract_labels_and_probabilities). + """ + + def _prepare_log_probs_and_targets( + self, + log_probs: torch.Tensor, + input_lengths: torch.Tensor, + targets: torch.Tensor, + target_lengths: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Before calling super()._prepare_log_probs_and_targets, this method reshapes the log_probs tensor + from (B, T, U+1, D) to (B, T', D) where T' = T*(U+1), shifts paddings along T and U towards the end of T', + and recomputes input_lengths. + + It also calculates indices on which steps should be applied to the log_probs tensor to emulate + arcs shift of the Emissions graph for the pruned RNNT variant. + """ + assert len(log_probs.size()) == 4 # B T U D + B, T, U, D = log_probs.size() + TU = T * U + + # save step indices if, as we assume, decoder output pruning has been applied + if self.predictor_window_size > 0 and self.predictor_window_size < target_lengths.max(): + window_size_with_blank = self.predictor_window_size + 1 + ranges_begin = get_uniform_rnnt_prune_ranges( + input_lengths, target_lengths, window_size_with_blank, self.predictor_step_size, T, True + ) + step_sizes = ranges_begin[:, 1:] - ranges_begin[:, :-1] + raw_step_indices = torch.where(step_sizes > 0) + if self.predictor_step_size > 1: + raw_step_indices = torch.repeat_interleave( + torch.stack(raw_step_indices).T, step_sizes[raw_step_indices], dim=0 + ).T + raw_step_indices = (raw_step_indices[0], raw_step_indices[1]) + unique, count = torch.unique(raw_step_indices[0], return_counts=True) + shift_mask = raw_step_indices[0].unsqueeze(0).repeat(len(unique), 1) == unique.unsqueeze(-1) + step_indices = ( + raw_step_indices[0], + ( + torch.arange(ranges_begin.size(1)).unsqueeze(0).repeat(ranges_begin.size(0), 1) + * window_size_with_blank + )[(raw_step_indices[0], raw_step_indices[1] + 1)] + + torch.cumsum(shift_mask, 1)[shift_mask] + - 1, + ) + max_count = count.max() + max_count_vec = torch.full((B,), max_count) + max_count_vec[unique] -= count + pad_indices_row = torch.repeat_interleave(torch.arange(B), max_count_vec) + pad_unique = torch.unique(pad_indices_row) + pad_shift_mask = pad_indices_row.unsqueeze(0).repeat(len(pad_unique), 1) == pad_unique.unsqueeze(-1) + pad_indices = ( + pad_indices_row, + T * window_size_with_blank + max_count - torch.cumsum(pad_shift_mask, 1)[pad_shift_mask], + ) + self.__step_indices = ( + torch.cat((step_indices[0], pad_indices[0])), + torch.cat((step_indices[1], pad_indices[1])), + ) + self.__supervisions_add = max_count - max_count_vec + else: + self.__step_indices = None + self.__supervisions_add = None + + # reshape 4D log_probs to 3D with respect to target_lengths + non_pad_mask_true = make_non_pad_mask_3d(input_lengths, target_lengths + 1, T, U).flatten(1) + input_lengths = non_pad_mask_true.sum(1) + non_pad_mask_fake = make_non_pad_mask(input_lengths, TU).flatten() + non_pad_mask_true = non_pad_mask_true.flatten() + rearranged_indices = torch.arange(TU * B, device=log_probs.device) + rearranged_indices_buffer = rearranged_indices.clone() + rearranged_indices[non_pad_mask_fake] = rearranged_indices_buffer[non_pad_mask_true] + rearranged_indices[~non_pad_mask_fake] = rearranged_indices_buffer[~non_pad_mask_true] + log_probs = log_probs.reshape(-1, D)[rearranged_indices].view(B, -1, D) + + return super()._prepare_log_probs_and_targets(log_probs, input_lengths, targets, target_lengths) + + def _prepare_emissions_graphs(self, log_probs: torch.Tensor, supervisions: torch.Tensor) -> 'k2.DenseFsaVec': + """Overrides super()._prepare_emissions_graphs. + Creates DenseFsaVec, adding outputs to the end of the D dimension. + + If pruning is used, this method also pads the DenseFsaVec with frames + according to the steps, calculated before. + + frame is a frame with log-probability zero and every other log-probability is -inf. + """ + if self.__step_indices is None or self.__supervisions_add is None: + log_probs_eps = torch.cat( + (log_probs, torch.zeros((log_probs.size(0), log_probs.size(1), 1), device=log_probs.device)), dim=2 + ) + else: + mask = torch.zeros( + (log_probs.size(0), log_probs.size(1) + int(len(self.__step_indices[0]) / log_probs.size(0))), + dtype=torch.bool, + ) + mask[self.__step_indices] = True + log_probs_eps = torch.zeros((mask.size(0), mask.size(1), log_probs.size(2) + 1), device=log_probs.device) + log_probs_eps[mask] = torch.tensor( + [torch.finfo(torch.float32).min] * log_probs.size(2) + [0], device=log_probs.device + ) + log_probs_eps[~mask] = torch.cat( + (log_probs, torch.zeros((log_probs.size(0), log_probs.size(1), 1), device=log_probs.device)), dim=2 + ).view(-1, log_probs.size(-1) + 1) + input_lengths = supervisions[:, -1] + self.__supervisions_add[supervisions[:, 0].to(dtype=torch.long)] + if not torch.all(input_lengths[:-1] - input_lengths[1:] >= 0): + # have to reorder supervisions inplace + order = torch.argsort(input_lengths, descending=True) + # the second column is assumed to be zero + supervisions[:, 0] = supervisions[order, 0] + supervisions[:, -1] = input_lengths[order] + else: + supervisions[:, -1] = input_lengths + self.__step_indices = None + self.__supervisions_add = None + return k2.DenseFsaVec(log_probs_eps, supervisions) + + def _maybe_normalize_gradients(self, log_probs: torch.Tensor, input_lengths: torch.Tensor) -> torch.Tensor: + """Not required for RNNT. + """ + return log_probs diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/map_loss.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/map_loss.py new file mode 100644 index 0000000..c261a4f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/map_loss.py @@ -0,0 +1,320 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright (c) 2020, Xiaomi CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import abstractmethod +from typing import Any, Optional, Tuple, Union + +import torch +from omegaconf import DictConfig + +from nemo.collections.asr.parts.k2.classes import GraphIntersectDenseConfig +from nemo.collections.asr.parts.k2.loss_mixins import CtcK2Mixin +from nemo.collections.asr.parts.k2.ml_loss import MLLoss +from nemo.collections.asr.parts.k2.utils import ( + create_sparse_wrapped, + get_tot_objf_and_finite_mask, + invert_permutation, + load_graph, +) +from nemo.core.utils.k2_guard import k2 # import k2 from guard module +from nemo.utils import logging + + +class MAPLoss(MLLoss): + """ + Maximum a Posteriori Probability criterion. + It implements Lattice-Free Maximum Mutual Information (LF-MMI) and LF-boosted-MMI (LF-bMMI) losses. + + Based on https://github.com/k2-fsa/snowfall/blob/master/snowfall/objectives/mmi.py + + cfg takes precedence over all optional parameters + We keep explicit parameter setting to be able to create an instance without the need of a config. + """ + + @abstractmethod + def __init__( + self, + num_classes: int, + blank: int, + reduction: str, + cfg: Optional[DictConfig] = None, + topo_type: str = "default", + topo_with_self_loops: bool = True, + token_lm: Optional[Union['k2.Fsa', str]] = None, + intersect_pruned: bool = False, + intersect_conf: GraphIntersectDenseConfig = GraphIntersectDenseConfig(), + boost_coeff: float = 0.0, + ): + super().__init__( + num_classes=num_classes, + blank=blank, + reduction=reduction, + cfg=cfg, + topo_type=topo_type, + topo_with_self_loops=topo_with_self_loops, + ) + if cfg is not None: + token_lm = cfg.get("token_lm", token_lm) + intersect_pruned = cfg.get("intersect_pruned", intersect_pruned) + intersect_conf = cfg.get("intersect_conf", intersect_conf) + boost_coeff = cfg.get("boost_coeff", boost_coeff) + self.boost_coeff = boost_coeff + self._intersect_calc_scores_impl = ( + self._intersect_calc_scores_impl_pruned if intersect_pruned else self._intersect_calc_scores_impl_exact_opt + ) + self.intersect_conf = intersect_conf + self.graph_compiler = None # expected to be initialized in .update_graph(...) + if token_lm is None: + logging.warning( + f"""token_lm is empty. + Trainable token_lm is not supported yet. + Please call .update_graph(token_lm) before using.""" + ) + else: + self.lm_graph = load_graph(token_lm) if isinstance(token_lm, str) else token_lm + if self.lm_graph is None: + raise ValueError(f"""lm_graph is empty.""") + else: + self.update_graph(self.lm_graph) + + @abstractmethod + def update_graph(self, graph: 'k2.Fsa'): + # expected to be set in child classes + raise NotImplementedError + + def _intersect_calc_scores_impl_exact_opt( + self, dense_fsa_vec: 'k2.DenseFsaVec', num_graphs: 'k2.Fsa', den_graph: 'k2.Fsa', return_lats: bool = True, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional['k2.Fsa'], Optional['k2.Fsa']]: + """Inner intersection method. + Does joint (simultaneous) exact intersection of dense_fsa_vec against num_graphs and den_graph. + + Optiolally returns the numerator and the denominator lattices. + """ + device = dense_fsa_vec.device + assert device == num_graphs.device and device == den_graph.device + + num_fsas = num_graphs.shape[0] + assert dense_fsa_vec.dim0() == num_fsas + + den_graph = den_graph.clone() + num_graphs = num_graphs.clone() + + num_den_graphs = k2.cat([num_graphs, den_graph]) + + # NOTE: The a_to_b_map in k2.intersect_dense must be sorted + # so the following reorders num_den_graphs. + + # [0, 1, 2, ... ] + num_graphs_indexes = torch.arange(num_fsas, dtype=torch.int32) + + # [num_fsas, num_fsas, num_fsas, ... ] + den_graph_indexes = torch.tensor([num_fsas] * num_fsas, dtype=torch.int32) + + # [0, num_fsas, 1, num_fsas, 2, num_fsas, ... ] + num_den_graphs_indexes = torch.stack([num_graphs_indexes, den_graph_indexes]).t().reshape(-1).to(device) + + num_den_reordered_graphs = k2.index_fsa(num_den_graphs, num_den_graphs_indexes) + + # [[0, 1, 2, ...]] + a_to_b_map = torch.arange(num_fsas, dtype=torch.int32).reshape(1, -1) + + # [[0, 1, 2, ...]] -> [0, 0, 1, 1, 2, 2, ... ] + a_to_b_map = a_to_b_map.repeat(2, 1).t().reshape(-1).to(device) + + num_den_lats = k2.intersect_dense( + a_fsas=num_den_reordered_graphs, + b_fsas=dense_fsa_vec, + output_beam=self.intersect_conf.output_beam, + a_to_b_map=a_to_b_map, + seqframe_idx_name="seqframe_idx" if return_lats else None, + ) + + num_den_tot_scores = num_den_lats.get_tot_scores(log_semiring=True, use_double_scores=False) + num_tot_scores = num_den_tot_scores[::2] + den_tot_scores = num_den_tot_scores[1::2] + + if return_lats: + lat_slice = torch.arange(num_fsas, dtype=torch.int32).to(device) * 2 + return ( + num_tot_scores, + den_tot_scores, + k2.index_fsa(num_den_lats, lat_slice), + k2.index_fsa(num_den_lats, lat_slice + 1), + ) + else: + return num_tot_scores, den_tot_scores, None, None + + def _intersect_calc_scores_impl_pruned( + self, dense_fsa_vec: 'k2.DenseFsaVec', num_graphs: 'k2.Fsa', den_graph: 'k2.Fsa', return_lats: bool = True, + ) -> Tuple[torch.Tensor, torch.Tensor, Optional['k2.Fsa'], Optional['k2.Fsa']]: + """Inner intersection method. + Does exact intersection of dense_fsa_vec against num_graphs and pruned intersection against den_graph. + + Optiolally returns the numerator and the denominator lattices. + """ + device = dense_fsa_vec.device + assert device == num_graphs.device and device == den_graph.device + + num_fsas = num_graphs.shape[0] + assert dense_fsa_vec.dim0() == num_fsas + + num_lats = k2.intersect_dense( + a_fsas=num_graphs, + b_fsas=dense_fsa_vec, + output_beam=self.intersect_conf.output_beam, + seqframe_idx_name="seqframe_idx" if return_lats else None, + ) + den_lats = k2.intersect_dense_pruned( + a_fsas=den_graph, + b_fsas=dense_fsa_vec, + search_beam=self.intersect_conf.search_beam, + output_beam=self.intersect_conf.output_beam, + min_active_states=self.intersect_conf.min_active_states, + max_active_states=self.intersect_conf.max_active_states, + seqframe_idx_name="seqframe_idx" if return_lats else None, + ) + + num_tot_scores = num_lats.get_tot_scores(log_semiring=True, use_double_scores=False) + den_tot_scores = den_lats.get_tot_scores(log_semiring=True, use_double_scores=False) + + if return_lats: + return num_tot_scores, den_tot_scores, num_lats, den_lats + else: + return num_tot_scores, den_tot_scores, None, None + + def _intersect_calc_scores( + self, emissions_graphs: 'k2.DenseFsaVec', supervision_graphs: Any, supervisions: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Intersects emissions_graphs with supervision_graphs and calculates lattice scores. + This version implicitly assumes supervision_graphs to be a pair of the numerator and the denominator FSAs. + + It can also calculate accuracy between the numerator and the denominator lattices to use it as additional loss. + + Can be overridden. + """ + boosted = self.boost_coeff != 0.0 + num_tot_scores, den_tot_scores, num_lats, den_lats = self._intersect_calc_scores_impl( + emissions_graphs, supervision_graphs[0], supervision_graphs[1], boosted + ) + + inverted_batch_order = invert_permutation(supervisions[:, 0].to(dtype=torch.long)) + self.__batch_order = None + tot_scores = (num_tot_scores - den_tot_scores)[inverted_batch_order] + mmi_tot_scores, mmi_valid_mask = get_tot_objf_and_finite_mask(tot_scores, self.reduction) + + if boosted: + assert num_lats is not None and den_lats is not None + + size = ( + emissions_graphs.dim0(), + emissions_graphs.scores.shape[0], + emissions_graphs.scores.shape[1] - 1, + ) + row_ids = emissions_graphs.emissions_graphs.shape().row_ids(1) + num_sparse = create_sparse_wrapped( + indices=[k2.index_select(row_ids, num_lats.seqframe_idx), num_lats.seqframe_idx, num_lats.phones,], + values=num_lats.get_arc_post(False, True).exp(), + size=size, + min_col_index=0, + ) + del num_lats + den_sparse = create_sparse_wrapped( + indices=[k2.index_select(row_ids, den_lats.seqframe_idx), den_lats.seqframe_idx, den_lats.phones,], + values=den_lats.get_arc_post(False, True).exp(), + size=size, + min_col_index=0, + ) + del den_lats + + acc_loss = torch.sparse.sum((num_sparse - den_sparse).coalesce().abs(), (1, 2)).to_dense() + del num_sparse, den_sparse + + acc_tot_scores, acc_valid_mask = get_tot_objf_and_finite_mask(acc_loss, self.reduction) + valid_mask = mmi_valid_mask & acc_valid_mask + total_loss = ( + (self.boost_coeff * acc_tot_scores[inverted_batch_order][valid_mask] - mmi_tot_scores[valid_mask]) + if self.reduction == "none" + else self.boost_coeff * acc_tot_scores - mmi_tot_scores + ) + else: + valid_mask = mmi_valid_mask + total_loss = -mmi_tot_scores[valid_mask] if self.reduction == "none" else -mmi_tot_scores + return total_loss, valid_mask + + +class CtcMmiLoss(MAPLoss, CtcK2Mixin): + """MMI loss with custom CTC topologies. + Available topologies: + - `default`, with or without self-loops + - `compact`, with or without self-loops + - `shared_blank`, with or without self-loops + - `minimal`, without self-loops + + cfg takes precedence over all optional parameters + We keep explicit parameter setting to be able to create an instance without the need of a config. + """ + + def __init__( + self, + num_classes: int, + blank: int, + reduction: str, + cfg: Optional[DictConfig] = None, + topo_type: str = "default", + topo_with_self_loops: bool = True, + token_lm: Optional[Union['k2.Fsa', str]] = None, + intersect_pruned: bool = False, + intersect_conf: GraphIntersectDenseConfig = GraphIntersectDenseConfig(), + boost_coeff: float = 0.0, + ): + super().__init__( + num_classes=num_classes, + blank=blank, + reduction=reduction, + cfg=cfg, + topo_type=topo_type, + topo_with_self_loops=topo_with_self_loops, + token_lm=token_lm, + intersect_pruned=intersect_pruned, + intersect_conf=intersect_conf, + boost_coeff=boost_coeff, + ) + + def update_graph(self, graph: 'k2.Fsa'): + self.lm_graph = graph + lm_graph = self.lm_graph.clone() + if hasattr(lm_graph, "aux_labels"): + delattr(lm_graph, "aux_labels") + labels = lm_graph.labels + if labels.max() != self.num_classes - 1: + raise ValueError(f"lm_graph is not compatible with the num_classes: {labels.unique()}, {self.num_classes}") + from nemo.collections.asr.parts.k2.graph_compilers import MmiGraphCompiler as compiler + + self.graph_compiler = compiler( + self.num_classes, self.blank, self.topo_type, self.topo_with_self_loops, aux_graph=lm_graph + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/ml_loss.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/ml_loss.py new file mode 100644 index 0000000..ef916ee --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/ml_loss.py @@ -0,0 +1,220 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright (c) 2020, Xiaomi CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import abstractmethod +from typing import Any, Optional, Tuple + +import torch +from omegaconf import DictConfig + +from nemo.collections.asr.parts.k2.graph_compilers import CtcTopologyCompiler, RnntTopologyCompiler +from nemo.collections.asr.parts.k2.loss_mixins import CtcK2Mixin, RnntK2Mixin +from nemo.collections.asr.parts.k2.utils import get_tot_objf_and_finite_mask, invert_permutation +from nemo.core.utils.k2_guard import k2 # import k2 from guard module + + +class MLLoss(torch.nn.Module): + """ + Maximum Likelihood criterion. + It implements Connectionist Temporal Classification (CTC) loss, + but can be extended to support other loss functions (ASG, HMM, RNNT, ...). + + Based on https://github.com/k2-fsa/snowfall/blob/master/snowfall/objectives/ctc.py + + cfg takes precedence over all optional parameters + We keep explicit parameter setting to be able to create an instance without the need of a config. + """ + + @abstractmethod + def __init__( + self, + num_classes: int, + blank: int, + reduction: str, + cfg: Optional[DictConfig] = None, + topo_type: str = "default", + topo_with_self_loops: bool = True, + ): + super().__init__() + if cfg is not None: + topo_type = cfg.get("topo_type", topo_type) + topo_with_self_loops = cfg.get("topo_with_self_loops", topo_with_self_loops) + self.blank = blank + self.num_classes = num_classes + self.reduction = reduction + self.topo_type = topo_type + self.topo_with_self_loops = topo_with_self_loops + self.pad_fsavec = topo_type == "compact" + self.graph_compiler = None # expected to be initialized in child classes + + def _prepare_graphs_for_intersection( + self, + log_probs: torch.Tensor, + targets: torch.Tensor, + input_lengths: torch.Tensor, + target_lengths: torch.Tensor, + ) -> Tuple['k2.DenseFsaVec', Any, torch.Tensor]: + """Converts input tensors to FST graphs: + log_probs to supervision_graphs (DenseFsaVec) + targets to supervision_graphs + Can be overridden. + """ + log_probs, supervisions, targets, target_lengths = self._prepare_log_probs_and_targets( + log_probs, input_lengths, targets, target_lengths + ) + log_probs = self._maybe_normalize_gradients(log_probs, supervisions[:, -1].to(dtype=torch.long)) + emissions_graphs = self._prepare_emissions_graphs(log_probs, supervisions) + del log_probs + + if emissions_graphs.device != self.graph_compiler.device: + self.graph_compiler.to(emissions_graphs.device) + order = supervisions[:, 0].to(dtype=torch.long) + supervision_graphs = self.graph_compiler.compile(targets[order], target_lengths[order]) + + return emissions_graphs, supervision_graphs, supervisions + + def _intersect_calc_scores( + self, emissions_graphs: 'k2.DenseFsaVec', supervision_graphs: Any, supervisions: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Intersects emissions_graphs with supervision_graphs and calculates lattice scores. + Can be overridden. + """ + lats = k2.intersect_dense(supervision_graphs, emissions_graphs, torch.finfo(torch.float32).max / 10) + del emissions_graphs + + num_tot_scores = lats.get_tot_scores(log_semiring=True, use_double_scores=False) + del lats + tot_scores = num_tot_scores[invert_permutation(supervisions[:, 0].to(dtype=torch.long))] + tot_scores, valid_mask = get_tot_objf_and_finite_mask(tot_scores, self.reduction) + return -tot_scores[valid_mask] if self.reduction == "none" else -tot_scores, valid_mask + + def forward( + self, + log_probs: torch.Tensor, + targets: torch.Tensor, + input_lengths: torch.Tensor, + target_lengths: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + assert self.graph_compiler is not None + + emissions_graphs, supervision_graphs, supervisions = self._prepare_graphs_for_intersection( + log_probs, targets, input_lengths, target_lengths + ) + scores, mask = self._intersect_calc_scores(emissions_graphs, supervision_graphs, supervisions) + return scores, mask + + +class CtcLoss(MLLoss, CtcK2Mixin): + """Regular CTC loss with custom topologies. + Available topologies: + - `default`, with or without self-loops + - `compact`, with or without self-loops + - `shared_blank`, with or without self-loops + - `minimal`, without self-loops + cfg takes precedence over all optional parameters + We keep explicit parameter setting to be able to create an instance without the need of a config. + """ + + def __init__( + self, + num_classes: int, + blank: int, + reduction: str, + cfg: Optional[DictConfig] = None, + topo_type: str = "default", + topo_with_self_loops: bool = True, + ): + super().__init__( + num_classes=num_classes, + blank=blank, + reduction=reduction, + cfg=cfg, + topo_type=topo_type, + topo_with_self_loops=topo_with_self_loops, + ) + self.graph_compiler = CtcTopologyCompiler( + self.num_classes, self.blank, self.topo_type, self.topo_with_self_loops + ) + + +class RnntLoss(MLLoss, RnntK2Mixin): + """RNNT loss with the `minimal` topology. + If predictor_window_size is not provided, this loss works as regular RNNT. + With predictor_window_size provided, it applies uniform pruning when compiling Emission FSAs + to reduce memory and compute consumption. + cfg takes precedence over all optional parameters + We keep explicit parameter setting to be able to create an instance without the need of a config. + """ + + def __init__( + self, + num_classes: int, + blank: int, + reduction: str, + cfg: Optional[DictConfig] = None, + topo_type: str = "minimal", + topo_with_self_loops: bool = True, + predictor_window_size: int = 0, + predictor_step_size: int = 1, + ): + super().__init__( + num_classes=num_classes, + blank=blank, + reduction=reduction, + cfg=cfg, + topo_type=topo_type, + topo_with_self_loops=topo_with_self_loops, + ) + if cfg is not None: + topo_type = cfg.get("topo_type", topo_type) + predictor_window_size = cfg.get("predictor_window_size", predictor_window_size) + predictor_step_size = cfg.get("predictor_step_size", predictor_step_size) + if topo_type != "minimal": + raise NotImplementedError(f"Only topo_type=`minimal` is supported at the moment.") + self.predictor_window_size = predictor_window_size + self.predictor_step_size = predictor_step_size + self.graph_compiler = RnntTopologyCompiler( + self.num_classes, + self.blank, + self.topo_type, + self.topo_with_self_loops, + max_adapter_length=self.predictor_window_size, + ) + + def forward( + self, + log_probs: torch.Tensor, + targets: torch.Tensor, + input_lengths: torch.Tensor, + target_lengths: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + assert self.predictor_window_size == 0 or log_probs.size(2) <= self.predictor_window_size + 1 + + return super().forward( + log_probs=log_probs, targets=targets, input_lengths=input_lengths, target_lengths=target_lengths + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/topologies.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/topologies.py new file mode 100644 index 0000000..a3b6fcf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/topologies.py @@ -0,0 +1,211 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from functools import lru_cache +from typing import List, Optional, Union + +import torch + +from nemo.core.utils.k2_guard import k2 # import k2 from guard module + + +def build_topo(name: str, tokens: List[int], blank_num: int, with_self_loops: bool = True) -> 'k2.Fsa': + """Helper function to build a topology. + It allows to build topologies with a non-zero blank ID. + Args: + name: + The topology name. Choices: default, compact, shared_blank, minimal + tokens: + A list of tokens, e.g., phones, characters, etc. + blank_num: + Blank number. Must be in tokens + with_self_loops: + Whether to add token-to-epsilon self-loops to a topology + Returns: + Returns a topology FST. + """ + if name == "default": + ans = build_default_topo(tokens, with_self_loops) + elif name == "compact": + ans = build_compact_topo(tokens, with_self_loops) + elif name == "shared_blank": + ans = build_shared_blank_topo(tokens, with_self_loops) + elif name == "minimal": + ans = build_minimal_topo(tokens) + else: + raise ValueError(f"Unknown topo name: {name}") + if blank_num != 0: + labels = ans.labels + blank_mask = labels == 0 + labels[(labels != -1) & (labels <= blank_num)] -= 1 + labels[blank_mask] = blank_num + ans.labels = labels # force update ans.labels property to notify FSA about modifications, required by k2 + ans = k2.arc_sort(ans) + return ans + + +def build_default_topo(tokens: List[int], with_self_loops: bool = True) -> 'k2.Fsa': + """Build the default CTC topology. + Zero is assumed to be the ID of the blank symbol. + """ + assert -1 not in tokens, "We assume -1 is ID of the final transition" + assert 0 in tokens, "We assume 0 is the ID of the blank symbol" + + num_states = len(tokens) + final_state = num_states + arcs = "" if with_self_loops else f"0 0 0 0 0.0\n" + for i in range(num_states): + for j in range(num_states): + if i == j: + if with_self_loops: + arcs += f"{i} {i} {tokens[i]} 0 0.0\n" + else: + arcs += f"{i} {j} {tokens[j]} {tokens[j]} 0.0\n" + arcs += f"{i} {final_state} -1 -1 0.0\n" + arcs += f"{final_state}" + ans = k2.Fsa.from_str(arcs, num_aux_labels=1) + ans = k2.arc_sort(ans) + return ans + + +def build_compact_topo(tokens: List[int], with_self_loops: bool = True) -> 'k2.Fsa': + """Build the compact CTC topology. + Zero is assumed to be the ID of the blank symbol. + See https://arxiv.org/abs/2110.03098 + """ + assert -1 not in tokens, "We assume -1 is ID of the final transition" + assert 0 in tokens, "We assume 0 is the ID of the blank symbol" + + eps_num = tokens[-1] + 1 + selfloops_shift = int(with_self_loops) + num_states = len(tokens) + selfloops_shift + final_state = num_states + arcs = "" + for i in range(selfloops_shift, num_states): + arcs += f"0 {i} {tokens[i - selfloops_shift]} {tokens[i - selfloops_shift]} 0.0\n" + arcs += f"0 {final_state} -1 -1 0.0\n" + for i in range(1, num_states): + arcs += f"{i} 0 {eps_num} 0 0.0\n" + if with_self_loops: + arcs += f"{i} {i} {tokens[i - selfloops_shift]} 0 0.0\n" + arcs += f"{final_state}" + ans = k2.Fsa.from_str(arcs, num_aux_labels=1) + ans = k2.arc_sort(ans) + return ans + + +def build_shared_blank_topo(tokens: List[int], with_self_loops: bool = True) -> 'k2.Fsa': + """Build the shared blank CTC topology. + Zero is assumed to be the ID of the blank symbol. + See https://github.com/k2-fsa/k2/issues/746#issuecomment-856421616 + """ + assert -1 not in tokens, "We assume -1 is ID of the final transition" + assert 0 in tokens, "We assume 0 is the ID of the blank symbol" + + tokens = tokens.copy() + tokens.remove(0) + num_tokens = len(tokens) + start = 0 + final = num_tokens + 1 + arcs = [] + arcs.append([start, start, 0, 0, 0]) + arcs.append([start, final, -1, -1, 0]) + arcs.append([final]) + for i, p in enumerate(tokens): + i += 1 + arcs.append([start, start, p, p, 0]) + arcs.append([start, i, p, p, 0]) + arcs.append([i, start, p, 0, 0]) + if with_self_loops: + arcs.append([i, i, p, 0, 0]) + arcs = sorted(arcs, key=lambda arc: arc[0]) + arcs = [[str(i) for i in arc] for arc in arcs] + arcs = [" ".join(arc) for arc in arcs] + arcs = "\n".join(arcs) + ans = k2.Fsa.from_str(arcs, num_aux_labels=1) + ans = k2.arc_sort(ans) + return ans + + +def build_minimal_topo(tokens: List[int]) -> 'k2.Fsa': + """Build the minimal topology. + Zero is assumed to be the ID of the blank symbol. + See https://arxiv.org/abs/2110.03098 + """ + assert -1 not in tokens, "We assume -1 is ID of the final transition" + assert 0 in tokens, "We assume 0 is the ID of the blank symbol" + + num_tokens = len(tokens) + final_state = 1 + arcs = "" + for i in range(num_tokens): + arcs += f"0 0 {tokens[i]} {tokens[i]} 0.0\n" + arcs += f"0 {final_state} -1 -1 0.0\n" + arcs += f"{final_state}" + ans = k2.Fsa.from_str(arcs, num_aux_labels=1) + ans = k2.arc_sort(ans) + return ans + + +class RnntEmissionAdapterBuilder(object): + """Builder class for RNNT Emission Adapters. + + An Emission Adapter is an FSA used to emulate desired temporal Emissions FSA properties of a trivial Emissions FSA. + Temporal properties are emulated by -arcs with zero log-weight. + These additional arcs do not contribute to the lattice scores and can be easily removed from the best path. + + k2 does not have Emissions FSAs. Instead, it has DenseFsaVec, which is not a real FSA. + Thus, Emission Adapters should be composed with Supervision FSAs. + IMPOTRANT: -outputs are expected to be present in the DenseFsaVec. + + These RNNT adapters do only the re-routing (emulate hopping over U dimension). + Redundant non- are not removed by these adapters. + + At initialization, the builder expects a list of tokens, number and number. + When called, the builder returns adapters according to the provided text lengths. + """ + + def __init__(self, tokens: List[int], blank_num: int, eps_num: Optional[int] = None): + assert -1 not in tokens, "We assume -1 is ID of the final transition" + assert blank_num in tokens, "The blank ID must be in tokens" + assert eps_num is None or eps_num not in tokens, "The epsion ID must not be in tokens" + + self.tokens = tokens + self.blank_num = blank_num + self.eps_num = self.tokens[-1] + 1 if eps_num is None else eps_num + + def __call__(self, adapter_lengths: Union[torch.Tensor, List[int]]) -> 'k2.Fsa': + # if you don't make adapter_lengths a list beforehand, + # "i" will be implicitly converted to int, and this will always be considered a cache miss + return k2.create_fsa_vec([self._build_single_adapter(i) for i in adapter_lengths.tolist()]) + + @lru_cache(maxsize=1024) + def _build_single_adapter(self, adapter_length: int) -> 'k2.Fsa': + assert adapter_length >= 1, "`adapter_length` cannot be less than one" + + first_eps_state = adapter_length + 1 + final_state = adapter_length * 2 + 1 + arcs = "" + for i in range(adapter_length): + for j in range(len(self.tokens)): + if j != self.blank_num: + arcs += f"{i} {i + 1} {self.tokens[j]} 0.0\n" + arcs += f"{i} {first_eps_state} {self.blank_num} 0.0\n" + arcs += f"{adapter_length} {first_eps_state} {self.blank_num} 0.0\n" + for i in range(first_eps_state, final_state): + arcs += f"{i} {i + 1 if i < final_state - 1 else 0} {self.eps_num} 0.0\n" + arcs += f"{i} {final_state} -1 0.0\n" + arcs += f"{final_state}" + + return k2.arc_sort(k2.Fsa.from_str(arcs, acceptor=True)) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/utils.py new file mode 100644 index 0000000..f55620a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/utils.py @@ -0,0 +1,326 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright (c) 2020, Xiaomi CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import struct +from pickle import UnpicklingError +from typing import List, Optional, Tuple, Union + +import torch + +from nemo.core.utils.k2_guard import k2 # import k2 from guard module +from nemo.utils import logging + + +def create_supervision(input_lengths: torch.Tensor) -> torch.Tensor: + """Creates a special supervisions tensor from input lengths. + These supervisions are required for some k2 methods. + """ + supervisions = torch.stack( + (torch.tensor(range(input_lengths.shape[0])), torch.zeros(input_lengths.shape[0]), input_lengths.cpu(),), 1, + ).to(dtype=torch.int32) + # the duration column has to be sorted in decreasing order + return supervisions[torch.argsort(supervisions[:, -1], descending=True)] + + +def invert_permutation(indices: torch.Tensor) -> torch.Tensor: + """Produces a tensor of reverse permutation for a given indices. + + Based on https://github.com/k2-fsa/snowfall/blob/master/snowfall/common.py + """ + ans = torch.zeros(indices.shape, device=indices.device, dtype=indices.dtype) + ans[indices.to(dtype=torch.long)] = torch.arange(0, indices.shape[0], device=indices.device, dtype=indices.dtype) + return ans + + +def make_non_pad_mask(input_lengths: torch.Tensor, seq_len: int): + """Converts input_lengths to a non-padding mask. The mask is 2D. + """ + batch_size = input_lengths.shape[0] + seq_range = torch.arange(0, seq_len, device=input_lengths.device) + seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, seq_len) + seq_length_expand = input_lengths.clone().detach().to(seq_range_expand.device).unsqueeze(-1) + mask = seq_range_expand < seq_length_expand + return mask + + +def make_non_pad_mask_3d( + lengths_x: torch.Tensor, lengths_y: torch.Tensor, max_length_x: int, max_length_y: int +) -> torch.Tensor: + """Converts two orthogonal input_lengths to a non-padding mask. The mask is 3D. + """ + assert lengths_x.size() == lengths_y.size() + return make_non_pad_mask(lengths_x, max_length_x).unsqueeze(2) & make_non_pad_mask( + lengths_y, max_length_y + ).unsqueeze(1) + + +def ragged_to_tensor_2axes_simple(rt: k2.RaggedTensor) -> Optional[torch.Tensor]: + """Converts k2.RaggedTensor to torch.Tensor if the RaggedTensor is shallow (has two axes). + """ + rt_list = rt.tolist() + result_list = [] + for e in rt_list: + if len(e) == 0: + result_list.append(0) + elif len(e) == 1: + result_list.append(e[0]) + else: + return None + return torch.tensor(result_list, dtype=torch.int32) + + +def load_graph(graph_path: str) -> 'k2.Fsa': + """Fsa graph loading helper function. Loads graphs stored in different formats. + """ + if os.path.exists(graph_path): + errors = [] + try: + graph_dict = torch.load(graph_path, map_location="cpu") + graph = k2.Fsa.from_dict(graph_dict) + return graph + except UnpicklingError as e: + errors.append(e) + with open(graph_path, "rt", encoding="utf-8") as f: + graph_txt = f.read() + # order from the most frequent case to the least + for func, acceptor in [(k2.Fsa.from_openfst, False), (k2.Fsa.from_str, True), (k2.Fsa.from_str, False)]: + try: + graph = func(graph_txt, acceptor=acceptor) + return graph + except (TypeError, ValueError, RuntimeError) as e: + errors.append(e) + raise Exception(errors) + else: + logging.warning(f"""No such file: '{graph_path}'""") + return None + + +def intersect_with_self_loops(base_graph: 'k2.Fsa', aux_graph: 'k2.Fsa') -> 'k2.Fsa': + """Intersection helper function. + """ + assert hasattr(base_graph, "aux_labels") + assert not hasattr(aux_graph, "aux_labels") + aux_graph_with_self_loops = k2.arc_sort(k2.add_epsilon_self_loops(aux_graph)).to(base_graph.device) + result = k2.intersect(k2.arc_sort(base_graph), aux_graph_with_self_loops, treat_epsilons_specially=False) + setattr(result, "phones", result.labels) + return result + + +def compose_with_self_loops(base_graph: 'k2.Fsa', aux_graph: 'k2.Fsa') -> 'k2.Fsa': + """Composition helper function. + """ + aux_graph_with_self_loops = k2.arc_sort(k2.add_epsilon_self_loops(aux_graph)).to(base_graph.device) + return k2.compose(base_graph, aux_graph_with_self_loops, treat_epsilons_specially=False, inner_labels="phones") + + +def create_sparse_wrapped( + indices: List[torch.Tensor], + values: torch.Tensor, + size: Optional[Union[Tuple[int, int], Tuple[int, int, int]]] = None, + min_col_index: Optional[int] = None, +) -> torch.Tensor: + """Wraps up k2.create_sparse to create 2- or 3-dimensional sparse tensors. + """ + assert size is None or len(indices) == len(size) + + if len(indices) == 2: + return k2.create_sparse( + rows=indices[0], cols=indices[1], values=values, size=size, min_col_index=min_col_index, + ) + elif len(indices) == 3: + assert indices[0].ndim == indices[1].ndim == indices[2].ndim == 1 + assert indices[0].numel() == indices[1].numel() == indices[2].numel() == values.numel() + + if min_col_index is not None: + assert isinstance(min_col_index, int) + kept_indices = indices[-1] >= min_col_index + indices = [i[kept_indices] for i in indices] + values = values[kept_indices] + if size is not None: + return torch.sparse_coo_tensor( + torch.stack(indices), values, size=size, device=values.device, requires_grad=values.requires_grad, + ) + else: + return torch.sparse_coo_tensor( + torch.stack(indices), values, device=values.device, requires_grad=values.requires_grad, + ) + else: + raise ValueError(f"len(indices) = {len(indices)}") + + +def prep_padded_densefsavec(log_softmax: torch.Tensor, supervisions: torch.Tensor) -> 'k2.DenseFsaVec': + """Performs special epsilon-padding required for composition with some of the topologies. + """ + log_softmax_eps = torch.cat( + [ + log_softmax, + torch.full((log_softmax.shape[0], log_softmax.shape[1], 1), -float("inf"), device=log_softmax.device,), + ], + axis=-1, + ) + log_softmax_padded = torch.zeros( + (log_softmax_eps.shape[0], log_softmax_eps.shape[1] * 2, log_softmax_eps.shape[2],), device=log_softmax.device, + ) + log_softmax_padded[:, ::2] = log_softmax_eps + supervisions_padded = supervisions.clone() + supervisions_padded[:, 2] *= 2 + dense_log_softmax_padded = k2.DenseFsaVec(log_softmax_padded, supervisions_padded) + return dense_log_softmax_padded + + +def shift_labels_inpl(lattices: List['k2.Fsa'], shift: int): + """Shifts lattice labels and aux_labels by a given number. + This is an in-place operation, if the lattice is on GPU. + """ + for lattice in lattices: + mask = lattice.labels > 0 + lattice.labels[mask] += shift + if hasattr(lattice, "aux_labels"): + mask = lattice.aux_labels > 0 + lattice.aux_labels[mask] += shift + return reset_properties_fsa(lattices) + + +def reset_properties_fsa(graph: 'k2.Fsa'): + """Resets properties of a graph. + In-place (does not create a new graph) if the graph is on GPU. + Use this every time you alter a graph in-place. + See https://github.com/k2-fsa/k2/issues/978 for more information.""" + graph.__dict__["_properties"] = None + # CPU graphs need to be sorted e.g. for intersection + if graph.device == torch.device("cpu"): + graph = k2.arc_sort(graph) + return graph + + +def add_self_loops(graph: 'k2.Fsa', label: int = 0, mode: str = "auto"): + """Adds self-loops with given label to a graph. + Supported modes are ``input``, ``output``, and ``auto``, + Where ``input`` leaves aux_labels zeroes, if present, ``output`` leaves labels zeroes""" + assert mode in ("input", "output", "auto"), "Supported modes are ``input``, ``output``, and ``auto``: {mode}" + assert mode != "output" or hasattr(graph, "aux_labels"), "Graph must have aux_labels for mode ``output``" + new_graph, arc_map = k2.add_epsilon_self_loops(graph, ret_arc_map=True) + + if mode != "output": + new_graph.labels[arc_map == -1] = label + if mode != "input" and hasattr(graph, "aux_labels"): + new_graph.aux_labels[arc_map == -1] = label + return reset_properties_fsa(new_graph) + + +def get_arc_weights(graph: 'k2.Fsa') -> torch.Tensor: + """Returns 1d torch.Tensor with arc weights of a given graph. + """ + if len(graph.shape) > 2: + raise NotImplementedError("FsaVec is not supported at the moment.") + weights_int = graph.arcs.values()[:, -1].tolist() + weights_float = struct.unpack('%sf' % len(weights_int), struct.pack('%si' % len(weights_int), *weights_int)) + return torch.Tensor(weights_float) + + +def get_tot_objf_and_finite_mask(tot_scores: torch.Tensor, reduction: str) -> Tuple[torch.Tensor, torch.Tensor]: + """Figures out the total score(log-prob) over all successful supervision segments + (i.e. those for which the total score wasn't -infinity). + Args: + tot_scores: a Torch tensor of shape (num_segments,) containing total scores + from forward-backward + reduction: a reduction type ('mean', 'sum' or 'none') + Returns: + Returns a tuple of 2 scalar tensors: (tot_score, finite_mask) + where finite_mask is a tensor containing successful segment mask. + + Based on get_tot_objf_and_num_frames + from https://github.com/k2-fsa/snowfall/blob/master/snowfall/objectives/common.py + """ + finite_mask = ~torch.isnan(tot_scores) & torch.ne(tot_scores, -float("inf")) + if reduction == "mean": + tot_scores = tot_scores[finite_mask].mean() + elif reduction == "sum": + tot_scores = tot_scores[finite_mask].sum() + return tot_scores, finite_mask + + +def get_uniform_rnnt_prune_ranges( + encoded_lengths: torch.Tensor, + target_lengths: torch.Tensor, + window_size_with_blank: int, + step: int = 1, + max_seq_len: Optional[int] = None, + begin_only: bool = False, +) -> torch.Tensor: + """Creates the pruning ranges for the Encoder and Predictor of RNNT. + The ranges are similar to https://k2-fsa.github.io/k2/python_api/api.html#k2.get_rnnt_prune_ranges + but they are constructed under the assumption of the uniform distribution token activations across time frames + and without any posterior knowledge. + """ + assert window_size_with_blank > 1 + assert step >= 1 + assert window_size_with_blank > step + assert len(encoded_lengths) == len(target_lengths) + ranges_begin = torch.zeros( + ( + len(encoded_lengths), + encoded_lengths.max() if max_seq_len is None else max(max_seq_len, encoded_lengths.max()), + ), + dtype=torch.long, + ) + for i in (target_lengths >= window_size_with_blank).nonzero(as_tuple=True)[0]: + encoded_len = encoded_lengths[i] + ranges_begin_raw = torch.arange(int((target_lengths[i] - window_size_with_blank) / step + 2)) * step + ranges_begin_raw[-1] = target_lengths[i] - window_size_with_blank + 1 + ranges_begin[i, :encoded_len] = torch.nn.functional.interpolate( + ranges_begin_raw.reshape(1, 1, -1).to(dtype=torch.float), encoded_len, mode="nearest-exact" + ).to(dtype=torch.long) + ranges_begin[i, encoded_len:] = ranges_begin[i, encoded_len - 1] + return ( + ranges_begin + if begin_only + else ranges_begin.unsqueeze(-1).repeat(1, 1, window_size_with_blank) + torch.arange(window_size_with_blank) + ) + + +def apply_rnnt_prune_ranges( + encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor, ranges: torch.Tensor +) -> Tuple[torch.Tensor, torch.Tensor]: + """Prepares pruned encoder and decoder outputs according to the prune ranges. + Based on k2.do_rnnt_pruning(...) + """ + B, T, window_size_with_blank = ranges.size() + D1 = encoder_outputs.size(-1) + _, U, D2 = decoder_outputs.size() + assert B == encoder_outputs.size(0) + assert T == encoder_outputs.size(1) + assert B == decoder_outputs.size(0) + encoder_outputs_pruned = encoder_outputs.unsqueeze(2).expand((B, T, window_size_with_blank, D1)) + decoder_outputs_pruned = torch.gather( + decoder_outputs.unsqueeze(1).expand((B, T, U, D2)), + dim=2, + index=ranges.reshape((B, T, window_size_with_blank, 1)).expand((B, T, window_size_with_blank, D2)), + ) + return encoder_outputs_pruned, decoder_outputs_pruned diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/w_transducer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/w_transducer.py new file mode 100644 index 0000000..b38a6c5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/k2/w_transducer.py @@ -0,0 +1,340 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from contextlib import nullcontext +from typing import Union + +import torch +import torch.nn.functional as F + +from nemo.collections.asr.parts.k2.graph_transducer import GraphRnntLoss, force_float32_context +from nemo.core.utils.k2_guard import k2 +from nemo.utils.enum import PrettyStrEnum + + +class GraphWTransducerLoss(GraphRnntLoss): + """ + W-Transducer loss: RNN-T loss modification for training RNN-T model for the case + when some text at the beginning/end of the utterance is missing. + The resulting model behaves like the RNN-T model (no modification for decoding is required). + For details see "Powerful and Extensible WFST Framework for RNN-Transducer Losses" paper + https://ieeexplore.ieee.org/document/10096679 + """ + + class LastBlankMode(PrettyStrEnum): + ALLOW_IGNORE = "allow_ignore" + FORCE_FINAL = "force_final" + + def __init__( + self, + blank: int, + eps_weight: float = 0.0, + last_blank_mode: Union[LastBlankMode, str] = LastBlankMode.FORCE_FINAL, + use_grid_implementation=True, + connect_composed=False, + double_scores=False, + cast_to_float32=False, + ): + """ + Init method + + Args: + blank: blank label index + eps_weight: weight of epsilon transitions, 0 means no penalty (default) + last_blank_mode: allow to skip last blank in the prediction (default) or force it + use_grid_implementation: Whether to use the grid implementation (Grid-Transducer). + connect_composed: Connect graph after composing unit and temporal schemas + (only for Compose-Transducer). `connect` operation is slow, it is useful for visualization, + but not necessary for loss computation. + double_scores: Use calculation of loss in double precision (float64) in the lattice. + Does not significantly affect memory usage since the lattice is ~V/2 times smaller than the joint tensor. + cast_to_float32: Force cast joint tensor to float32 before log-softmax calculation. + """ + super().__init__( + blank=blank, + use_grid_implementation=use_grid_implementation, + connect_composed=connect_composed, + double_scores=double_scores, + cast_to_float32=cast_to_float32, + ) + self.eps_weight = eps_weight + self.last_blank_mode = self.LastBlankMode(last_blank_mode) + + def get_unit_schema(self, units_tensor: torch.Tensor, vocab_size: int) -> "k2.Fsa": + """ + Get unit schema (target text) graph for W-Transducer loss (Compose-Transducer). + Forward arcs represent text labels. + + Example graph: text [1, 2], blank=0. Eps ids: 3, 4. + + graph:: + + 3:3:0 0:0:1 0:0:2 + +-------+ +-------+ +-------+ + v | v | v | + +-----------+ 1:1:0 +-----------+ 2:2:1 +-----------+ -1:-1:-1 #===# + | 0 | -------> | 1 | -------> | 2 | ---------> H 3 H + +-----------+ +-----------+ +-----------+ #===# + ^ 0:0:0 | ^ 4:4:2 | + +-------+ +-------+ + + Args: + units_tensor: 1d tensor with text units + vocab_size: number of total labels (vocab size including blank) + + Returns: + unit schema graph (k2.Fsa). + Labels: :: (k2.Fsa: labels, aux_labels, unit_positions) + """ + + blank_id = self.blank + start_eps_id = vocab_size + end_eps_id = vocab_size + 1 + device = units_tensor.device + text_len = units_tensor.shape[0] + + # arcs: scr, dest, label, score + arcs = torch.zeros(((text_len + 1) * 2 + 2, 4), dtype=torch.int32, device=device) + text_indices = torch.arange(0, text_len + 1, dtype=torch.int32, device=device) + # eps + arcs[0, 2] = start_eps_id + # blank labels + arcs[1:-1:2, 0] = text_indices # from state + arcs[1:-1:2, 1] = text_indices # to state + arcs[1:-1:2, 2] = blank_id + + # text labels + arcs[2:-1:2, 0] = text_indices # from state + arcs[2:-1:2, 1] = text_indices + 1 # to state + arcs[2:-2:2, 2] = units_tensor # labels: text + + arcs[-1] = arcs[-2] + arcs[-2, 1] = text_len + arcs[-2, 2] = end_eps_id + arcs[-1, 2] = -1 # last transition to final state, ilabel=-1 (special for k2) + olabels = arcs[:, 2].detach().clone() # same as ilabels + + fsa_text = k2.Fsa(arcs, olabels) + fsa_text.unit_positions = torch.zeros_like(olabels) + fsa_text.unit_positions[1:-1] = text_indices.expand(2, -1).transpose(0, 1).flatten() + fsa_text.unit_positions[-1] = -1 + return fsa_text + + def get_temporal_schema(self, num_frames: int, vocab_size: int, device: torch.device) -> "k2.Fsa": + """ + Get temporal schema graph for W-Transducer loss (Compose-Transducer). + + Example graph: blank=0, num_frames=3, vocab_size=3, last_blank_mode="force_final". + Labels: :. is a unit from vocab + special eps ids `vocab_size`, `vocab_size+1`. + + graph for force_final:: + + 4:0 + +--------------------------------------------+ + | 4:1 | + | +--------------------+ | + 1:0 | 1:1 | 1:2 | | + +-----+ | +-----+ | +-----+ | | + v | | v | | v | v v + +--------------+ 0:0 +------------+ 0:1 +------------+ 0:2 +---+ -1:-1 #===# + | 0 | ----> | 1 | -----> | 2 | -----> | 3 | -------> H 4 H + +--------------+ +------------+ +------------+ +---+ #===# + ^ 2:0 | | | ^ 2:1 | ^ ^ 2:2 | ^ + +-----+ | | +-----+ | +-----+ | + | | 3:0 | | + | +------------------+ 3:0 | + +-------------------------------------------+ + + + Args: + num_frames: length of the sequence (in frames) + vocab_size: number of labels (including blank) + device: device for tensor to construct + + Returns: + temporal schema graph (k2.Fsa). + Labels: :. is a unit from vocab + special units (e.g., additional eps). + """ + blank_id = self.blank + start_eps_id = vocab_size + end_eps_id = vocab_size + 1 + num_eps = 2 + + num_sequence_arcs = num_frames * vocab_size + (num_frames - 1) * num_eps + 1 + fsa_temporal_arcs = torch.zeros((num_sequence_arcs, 4), dtype=torch.int32, device=device) + sequence_states = torch.arange(0, num_frames, dtype=torch.int32, device=device) + sequence_states_next = sequence_states + 1 + # for every state - vocab_size+1 arcs, [0, 1, ..., vocab_size-1, eps, 0, 1, ..., vocab_size-1, eps, ...] + start_states = sequence_states.expand(vocab_size + num_eps, num_frames).transpose(0, 1).flatten() + + # self-loops - all, make forward arcs later + fsa_temporal_arcs[:num_sequence_arcs, 0] = start_states[:-1] # from + fsa_temporal_arcs[:num_sequence_arcs, 1] = start_states[:-1] # to + fsa_temporal_arcs[:num_sequence_arcs, 2] = ( + torch.arange(0, vocab_size + num_eps, dtype=torch.int32, device=device) + .expand(num_frames, vocab_size + num_eps) + .flatten()[:-1] + ) + # forward arcs + fsa_temporal_arcs[blank_id : num_sequence_arcs : vocab_size + num_eps, 1] = sequence_states_next # blanks + # eps arcs + fsa_temporal_arcs[start_eps_id : num_sequence_arcs : vocab_size + num_eps, 0] = 0 + fsa_temporal_arcs[start_eps_id : num_sequence_arcs : vocab_size + num_eps, 1] = sequence_states + 1 + fsa_temporal_arcs[end_eps_id : num_sequence_arcs : vocab_size + num_eps, 0] = sequence_states[:-1] + fsa_temporal_arcs[end_eps_id : num_sequence_arcs : vocab_size + num_eps, 1] = ( + num_frames - 1 if self.last_blank_mode == self.LastBlankMode.FORCE_FINAL else num_frames + ) + + # transition to last final state + fsa_temporal_arcs[-1, :3] = torch.tensor((num_frames, num_frames + 1, -1), dtype=torch.int32, device=device) + + # need to sort arcs + _, indices = torch.sort(fsa_temporal_arcs[:, 0], dim=0) + fsa_temporal_arcs = fsa_temporal_arcs[indices] + + # output symbols: position in the sequence, same as start states for arcs + olabels = fsa_temporal_arcs[:, 0].detach().clone() + olabels[-1] = -1 # transition to the last final state + + fsa_temporal = k2.Fsa(fsa_temporal_arcs, olabels) + fsa_temporal = k2.arc_sort(fsa_temporal) # need for compose + return fsa_temporal + + def get_grid(self, units_tensor: torch.Tensor, num_frames: int, vocab_size: int) -> "k2.Fsa": + """ + Construct W-Transducer lattice directly (Grid-Transducer). + + Args: + units_tensor: 1d tensor with text units + num_frames: length of the sequence (number of frames) + vocab_size: number of total labels (vocab size including blank) + + Returns: + transducer lattice (k2.Fsa). + Labels: :: (k2.Fsa: labels, aux_labels, unit_positions) + """ + blank_id = self.blank + eps_id = vocab_size # beyond vocabulary + text_length = units_tensor.shape[0] + device = units_tensor.device + num_grid_states = num_frames * (text_length + 1) + num_forward_arcs_base = (num_frames - 1) * (text_length + 1) + num_forward_arcs_additional = (num_frames - 1) * 2 + num_forward_arcs = num_forward_arcs_base + num_forward_arcs_additional + num_text_arcs = text_length * num_frames + arcs = torch.zeros((num_forward_arcs + num_text_arcs + 2, 4), dtype=torch.int32, device=device) + # blank transitions + # i, i+, 0 , i / , i % + from_states = torch.arange(num_forward_arcs_base, device=device) + to_states = from_states + (text_length + 1) + arcs[:num_forward_arcs_base, 0] = from_states + arcs[:num_forward_arcs_base, 1] = to_states + arcs[:num_forward_arcs_base, 2] = blank_id + + from_states = torch.cat( + [ + torch.arange(num_frames - 1, device=device) * (text_length + 1), + text_length + torch.arange(num_frames - 1, device=device) * (text_length + 1), + ] + ) + to_states = from_states + (text_length + 1) + arcs[num_forward_arcs_base : num_forward_arcs_base + (num_frames - 1) * 2, 0] = from_states + arcs[num_forward_arcs_base : num_forward_arcs_base + (num_frames - 1) * 2, 1] = to_states + arcs[num_forward_arcs_base : num_forward_arcs_base + (num_frames - 1), 2] = eps_id + arcs[num_forward_arcs_base + (num_frames - 1) : num_forward_arcs_base + (num_frames - 1) * 2, 2] = eps_id + 1 + + arcs[num_forward_arcs_base : num_forward_arcs_base + (num_frames - 1), 0] = 0 + arcs[num_forward_arcs_base + (num_frames - 1) : num_forward_arcs_base + (num_frames - 1) * 2, 1] = ( + num_grid_states - 1 + ) # if other mode - fix later + # last eps ark - after relabel + + # text arcs + from_states = ( + torch.arange(num_grid_states, dtype=torch.int32, device=device) + .reshape(num_frames, text_length + 1)[:, :-1] + .flatten() + ) + to_states = from_states + 1 + ilabels = units_tensor.expand(num_frames, -1).flatten() + arcs[num_forward_arcs:-2, 0] = from_states + arcs[num_forward_arcs:-2, 1] = to_states + arcs[num_forward_arcs:-2, 2] = ilabels + + # last 2 states + arcs[-2, :3] = torch.tensor((num_grid_states - 1, num_grid_states, blank_id), dtype=torch.int32, device=device) + arcs[-1, :3] = torch.tensor((num_grid_states, num_grid_states + 1, -1), dtype=torch.int32, device=device) + + # sequence indices, time indices + olabels = torch.div(arcs[:, 0], (text_length + 1), rounding_mode="floor") # arcs[:, 0] // (text_length + 1) + unit_positions = arcs[:, 0] % (text_length + 1) + # last state: final + olabels[-1] = -1 + unit_positions[-1] = -1 + + # relabel + # instead of using top sort (extremely expensive) k2.top_sort(rnnt_graph) + arcs[:-2, 0] = self.relabel_states(arcs[:-2, 0], text_length + 1, num_frames) + arcs[:-3, 1] = self.relabel_states(arcs[:-3, 1], text_length + 1, num_frames) + + if self.last_blank_mode == self.LastBlankMode.ALLOW_IGNORE: + arcs[ + num_forward_arcs_base + (num_frames - 1) : num_forward_arcs_base + (num_frames - 1) * 2, 1 + ] = num_grid_states + + # sort by start state - required in k2 + # TODO: maybe it is more optimal to avoid sort, construct arcs in ascending order + _, indices = torch.sort(arcs[:, 0], dim=0) + arcs = arcs[indices] + olabels = olabels[indices] + unit_positions = unit_positions[indices] + + rnnt_graph = k2.Fsa(arcs, olabels) + rnnt_graph.unit_positions = unit_positions + return rnnt_graph + + def forward( + self, acts: torch.Tensor, labels: torch.Tensor, act_lens: torch.Tensor, label_lens: torch.Tensor, + ): + """ + Forward method is similar to RNN-T Graph-Transducer forward method, + but we need to assign eps weight to eps-transitions. + """ + # argument names are consistent with NeMo, see RNNTLoss.forward: + # self._loss(acts=log_probs, labels=targets, act_lens=input_lengths, label_lens=target_lengths) + logits, targets, logits_lengths, target_lengths = acts, labels, act_lens, label_lens + + # logits: B x Time x Text+1 x C + vocab_size = logits.shape[-1] + target_fsas_vec = self.get_graphs_batched(logits_lengths, targets, target_lengths, vocab_size) + + cast_context = force_float32_context() if self.cast_to_float32 else nullcontext() + with cast_context: + log_probs = F.log_softmax(logits, dim=-1) + with torch.no_grad(): + indices = self.get_logits_indices(target_fsas_vec, logits.shape) + # transition to the last state + eps-transitions + # use 0 index (for valid index_select) and manually assign score after index_select for this case + indices[target_fsas_vec.labels == -1] = 0 + indices[target_fsas_vec.labels >= vocab_size] = 0 # eps + + # NB: do not assign scores -> modify, k2 will not update all scores correctly (modify -> assign) + scores = log_probs.flatten().index_select(-1, indices) + # fix weights for the arcs to the last state + eps-transitions + scores[target_fsas_vec.labels == -1] = 0 + scores[target_fsas_vec.labels >= vocab_size] = self.eps_weight # eps + + target_fsas_vec.scores = scores + scores = -1 * target_fsas_vec.get_tot_scores(use_double_scores=self.double_scores, log_semiring=True) + return scores diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/__init__.py new file mode 100644 index 0000000..02378bd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/__init__.py @@ -0,0 +1,28 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.parts.mixins.asr_adapter_mixins import ASRAdapterModelMixin +from nemo.collections.asr.parts.mixins.interctc_mixin import InterCTCMixin +from nemo.collections.asr.parts.mixins.mixins import ( + ASRAdapterModelMixin, + ASRBPEMixin, + ASRModuleMixin, + DiarizationMixin, +) +from nemo.collections.asr.parts.mixins.transcription import ( + ASRTranscriptionMixin, + TranscribeConfig, + TranscriptionMixin, + TranscriptionReturnType, +) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/asr_adapter_mixins.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/asr_adapter_mixins.py new file mode 100644 index 0000000..f452acd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/asr_adapter_mixins.py @@ -0,0 +1,295 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Optional, Tuple + +from omegaconf import DictConfig, open_dict + +from nemo.core.classes.mixins.adapter_mixins import AdapterModelPTMixin, AdapterModuleMixin +from nemo.utils import logging, logging_mode + + +class ASRAdapterModelMixin(AdapterModelPTMixin): + """ ASR Adapter Mixin that can augment any Encoder module with Adapter module support. + + This mixin class should be used only with a top level ModelPT subclass, that includes an `encoder` submodule. + This mixin class adds several utility methods which are propagated to the `encoder`. + + An Adapter module is any Pytorch nn.Module that possess a few properties : + + - It's input and output dimension are the same, while the hidden dimension need not be the same. + - The final layer of the Adapter module is zero-initialized, so that the residual connection to the adapter + yields the original output. + + This mixin adds the following instance variables to the class this inherits it: + + - `adapter_layer`: A torch.nn.ModuleDict(), whose keys are the names of the adapter (globally unique), + and values are the Adapter nn.Module(). + - `adapter_cfg`: A OmegaConf DictConfig object that holds the config of the adapters that are initialized. + - `adapter_global_cfg_key`: A str representing a key in the model config that can be provided by the user. + The value resolves to `global_cfg`, and can be overridden via `model.cfg.adapters.global_cfg.*`. + + **Note**: This module **is** responsible for maintaining its config. At the ModelPT level, it will access and + write Adapter config information to `self.cfg.adapters`. + """ + + def setup_adapters(self): + """ + Utility method that is called in the ASR ModelPT-implementation constructor, so as to restore any + adapters that were previously added. + + This method should be called just once at constructor time. + """ + supports_adapters = False + + # At least the encoder must extend AdapterModuleMixin + if hasattr(self, 'encoder') and isinstance(self.encoder, AdapterModuleMixin): + supports_adapters |= True + + if hasattr(self, 'decoder') and isinstance(self.decoder, AdapterModuleMixin): + supports_adapters |= True + + if hasattr(self, 'joint') and isinstance(self.joint, AdapterModuleMixin): + supports_adapters |= True + + # If adapters are supported, setup the adapter config + any modules (pre-existing adapter modules) + if supports_adapters: + super().setup_adapters() + + def add_adapter(self, name: str, cfg: DictConfig): + """ + Add an Adapter module to this model. + + Args: + name: A globally unique name for the adapter. Will be used to access, enable and disable adapters. + cfg: A DictConfig that contains at the bare minimum `__target__` to instantiate a new Adapter module. + """ + # setup the config for adapters + super().add_adapter(name=name, cfg=cfg) + + # Resolve module name and adapter name + module_name, _ = self.resolve_adapter_module_name_(name) + + # Use + as a splitter, in order to share one name across multiple modules + if '+' in module_name: + module_names = module_name.split('+') + else: + module_names = [module_name] + + # Update the model.cfg with information about the new adapter from cfg + with open_dict(self.cfg): + for module_name in module_names: + # Check if encoder adapters should be added + if module_name in ('', 'encoder'): + # Dispatch the call to the encoder. + self.encoder.add_adapter(name=name, cfg=cfg) + + # Check if decoder adapters should be added + if module_name == 'decoder': + # Dispatch call to the decoder. + self.decoder.add_adapter(name=name, cfg=cfg) + + # Check if joint adapters should be added; + # Note: We need additional check if joint even exists in model (for CTC models) + if hasattr(self, 'joint') and module_name == 'joint': + # Dispatch call to the joint. + self.joint.add_adapter(name=name, cfg=cfg) + + def is_adapter_available(self) -> bool: + """ + Checks if any Adapter module has been instantiated. + + Returns: + bool, determining if any Adapter module has been instantiated. Returns true even if the adapters are + enabled or disabled, false only if no adapters exist. + """ + config_contains_adapter = super().is_adapter_available() + + # Forward the method call to the individual modules + if hasattr(self, 'encoder') and isinstance(self.encoder, AdapterModuleMixin): + config_contains_adapter |= self.encoder.is_adapter_available() + + if hasattr(self, 'decoder') and isinstance(self.decoder, AdapterModuleMixin): + config_contains_adapter |= self.decoder.is_adapter_available() + + if hasattr(self, 'joint') and isinstance(self.joint, AdapterModuleMixin): + config_contains_adapter |= self.joint.is_adapter_available() + + return config_contains_adapter + + def set_enabled_adapters(self, name: Optional[str] = None, enabled: bool = True): + """ + Updated the internal adapter config, determining if an adapter (or all adapters) are either + enabled or disabled. + + A common user pattern would be to disable all adapters (either after adding them, or restoring a model + with pre-existing adapters) and then simply enable one of the adapters. + + .. code:: + + model.set_enabled_adapters(enabled=False) + model.set_enabled_adapters(name=, enabled=True) + + Args: + name: Optional str. If a str name is given, the config will be updated to the value of `enabled`. + If no name is given, then all adapters will be enabled/disabled. + enabled: Bool, determines if the adapter(s) will be enabled/disabled. + """ + super().set_enabled_adapters(name=name, enabled=enabled) + + # Resolve the module name and adapter name + if name is not None: + module_name, _ = self.resolve_adapter_module_name_(name) + else: + module_name = None + + # Use + as a splitter, in order to share one name across multiple modules + if module_name is not None and '+' in module_name: + module_names = module_name.split('+') + else: + module_names = [module_name] + + for module_name in module_names: + # Check if encoder adapters should be used + # Dispatch the call to the encoder. + if name is None or module_name in ('', 'encoder'): + if self.encoder.is_adapter_available(): + self.encoder.set_enabled_adapters(name=name, enabled=enabled) + + # Dispatch the call to the decoder. + if name is None or module_name == 'decoder': + if self.decoder.is_adapter_available(): + self.decoder.set_enabled_adapters(name=name, enabled=enabled) + + # Dispatch the call to the joint. + # Note: We need additional check for joint, since it may not exist (CTC models). + if name is None or module_name == 'joint': + if hasattr(self, 'joint') and self.joint.is_adapter_available(): + self.joint.set_enabled_adapters(name=name, enabled=enabled) + + def get_enabled_adapters(self) -> List[str]: + """ + Returns a list of all enabled adapters. + + Returns: + A list of str names of each enabled adapter(s). + """ + enabled_adapters = super().get_enabled_adapters() + + # Check if encoder adapters should be used or are enabled + if hasattr(self, 'encoder') and isinstance(self.encoder, AdapterModuleMixin): + enabled_adapters.extend(self.encoder.get_enabled_adapters()) + + if hasattr(self, 'decoder') and isinstance(self.decoder, AdapterModuleMixin): + enabled_adapters.extend(self.decoder.get_enabled_adapters()) + + if hasattr(self, 'joint') and isinstance(self.joint, AdapterModuleMixin): + enabled_adapters.extend(self.joint.get_enabled_adapters()) + + enabled_adapters = list(sorted(list(set(enabled_adapters)))) + + return enabled_adapters + + def check_valid_model_with_adapter_support_(self): + """ + Utility method to test if the subclass of this mixin is an appropriate subclass of ModelPT itself. + """ + # Obtain the global adapter config if possible, otherwise use sensible defaults. + global_cfg = self._get_global_cfg() + + # Test whether the encoder supports adapters + use_encoder_adapter = global_cfg.get('check_encoder_adapter', True) + if use_encoder_adapter: + if not hasattr(self, 'encoder'): + logging.warning( + "Cannot add adapter to this object as it does not have an `encoder` sub-module!", + mode=logging_mode.ONCE, + ) + + if hasattr(self, 'encoder') and not isinstance(self.encoder, AdapterModuleMixin): + logging.warning( + f'{self.encoder.__class__.__name__} does not implement `AdapterModuleMixin`', + mode=logging_mode.ONCE, + ) + + # Test whether the decoder supports adapters + use_decoder_adapter = global_cfg.get('check_decoder_adapter', True) + if use_decoder_adapter: + if not hasattr(self, 'decoder'): + logging.warning( + "Cannot add adapter to this object as it does not have an `decoder` sub-module!", + mode=logging_mode.ONCE, + ) + + if hasattr(self, 'decoder') and not isinstance(self.decoder, AdapterModuleMixin): + logging.warning( + f'{self.decoder.__class__.__name__} does not implement `AdapterModuleMixin`', + mode=logging_mode.ONCE, + ) + + # Test whether the joint supports adapters + use_joint_adapter = global_cfg.get('check_joint_adapter', True) + if use_joint_adapter: + # Joint is only for RNNT models, skip assertion that it must always exist. + if hasattr(self, 'joint') and not isinstance(self.joint, AdapterModuleMixin): + logging.warning( + f'{self.joint.__class__.__name__} does not implement `AdapterModuleMixin`', mode=logging_mode.ONCE + ) + + def resolve_adapter_module_name_(self, name: str) -> Tuple[str, str]: + """ + Utility method to resolve a given global/module adapter name to its components. + Always returns a tuple representing (module_name, adapter_name). ":" is used as the + delimiter for denoting the module name vs the adapter name. + + Will attempt to also resolve a given adapter_name alone back to (module_name, adapter_name) + if the metadata config exists for access. + + Args: + name: A global adapter, or a module adapter name (with structure module_name:adapter_name). + + Returns: + A tuple representing (module_name, adapter_name). If a global adapter is provided, + module_name is set to ''. + """ + module_name, adapter_name = super().resolve_adapter_module_name_(name) + + # Use + as a splitter, in order to share one name across multiple modules + if '+' in module_name: + module_names = module_name.split('+') + else: + module_names = [module_name] + + # resolve name and module only for valid modules + valid_module_names = self.adapter_module_names + + for mod_name in module_names: + if mod_name not in valid_module_names: + raise ValueError(f"Provided module name `{mod_name}` is not in valid list : {valid_module_names}") + + return (module_name, adapter_name) + + def _get_global_cfg(self): + """ + Utility method, to either extract or construct the global config inside adapters config. + """ + global_config = DictConfig({}) + if 'adapters' in self.cfg and self.adapter_global_cfg_key in self.cfg.adapters: + global_config = self.adapter_cfg[self.adapter_global_cfg_key] + return global_config + + @property + def adapter_module_names(self) -> List[str]: + valid_module_names = ['', 'encoder', 'decoder', 'joint'] + return valid_module_names diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/interctc_mixin.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/interctc_mixin.py new file mode 100644 index 0000000..3e1e978 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/interctc_mixin.py @@ -0,0 +1,294 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Dict, List, Optional, Tuple + +import torch + +from nemo.core.classes.mixins import AccessMixin +from nemo.utils import logging + + +class InterCTCMixin: + """Adds utilities for computing interCTC loss from https://arxiv.org/abs/2102.03216. + + To use, make sure encoder accesses ``interctc['capture_layers']`` + property in the AccessMixin and registers ``interctc/layer_output_X`` and + ``interctc/layer_length_X`` for all layers that we want to get loss from. + Additionally, specify the following config parameters to set up loss:: + + interctc: + # can use different values + loss_weights: [0.3] + apply_at_layers: [8] + + Then call + + * ``self.setup_interctc(ctc_decoder_name, ctc_loss_name, ctc_wer_name)`` + in the init method + * ``self.add_interctc_losses`` after computing regular loss. + * ``self.finalize_interctc_metrics(metrics, outputs, prefix="val_")`` + in the `multi_validation_epoch_end` method. + * ``self.finalize_interctc_metrics(metrics, outputs, prefix="test_")`` + in the `multi_test_epoch_end` method. + """ + + def _process_config_values(self, loss_weights: List[float], apply_at_layers: List[int]): + self.set_interctc_param('intermediate_loss_weights', loss_weights) + self.set_interctc_param('apply_at_layers', apply_at_layers) + self.set_interctc_param('main_loss_weight', 1.0 - sum(loss_weights)) + if self.get_interctc_param('main_loss_weight') <= 0.0: + raise ValueError( + "Make sure that sum of intermediate loss weights is < 1.0. " + "Note that we don't do any normalization and assign " + "remaining weight to the regular model loss. " + "E.g., if interctc.loss_weights = [0.1, 0.3], regular " + "loss will have weight of 0.6" + ) + self.set_interctc_param('enabled', len(loss_weights) > 0) + + if len(apply_at_layers) != len(loss_weights): + raise ValueError('Length of interctc.apply_at_layers has to match interctc.loss_weights') + + # setting up config for AccessMixin that will be checked in encoders to + # log the layers we need + AccessMixin.update_access_cfg( + {'interctc': {'capture_layers': apply_at_layers}}, guid=getattr(self, "model_guid", None) + ) + if hasattr(self, "propagate_model_guid"): + self.propagate_model_guid() + else: + logging.warning( + f"Not able to propagate model_guid to the submodules. Make sure to call self.propagate_model_guid() in ModelPT class." + ) + + def setup_interctc(self, decoder_name, loss_name, wer_name): + """Sets up all interctc-specific parameters and checks config consistency. + + Caller has to specify names of attributes to perform CTC-specific WER, + decoder and loss computation. They will be looked up in the class + state with ``getattr``. + + The reason we get the names and look up object later is because those + objects might change without re-calling the setup of this class. So + we always want to look up the most up-to-date object instead of + "caching" it here. + """ + # registering all parameters in a dictionary to avoid conflicts with + # main class's names + self._interctc_params = {} + interctc_config = self.cfg.get("interctc") + if interctc_config is not None: + # if interctc is in the config, we want to check that it indeed defines + # the required keys and nothing else - that's automatically done by + # matching with keyword arguments in self._process_config_values + self._process_config_values(**interctc_config) + self._interctc_params['decoder_name'] = decoder_name + self._interctc_params['loss_name'] = loss_name + self._interctc_params['wer_name'] = wer_name + else: + self.set_interctc_param('enabled', False) + + def get_interctc_param(self, param_name): + """Either directly get parameter from ``self._interctc_params`` or + call getattr with the corresponding name. + """ + if param_name in ['decoder', 'loss', 'wer']: + return getattr(self, self._interctc_params[param_name + "_name"]) + return self._interctc_params[param_name] + + def set_interctc_param(self, param_name, param_value): + """Setting the parameter to the ``self._interctc_params`` dictionary. + + Raises an error if trying to set decoder, loss or wer as those should + always come from the main class. + """ + if param_name in ['decoder', 'loss', 'wer']: + raise ValueError( + 'Cannot set "decoder", "loss" or "wer" as parameters. ' + 'They are always looked up in the main class state.' + ) + self._interctc_params[param_name] = param_value + + def _verify_setup_was_called(self): + """Can be used to verify if setup_interctc was called.""" + if not hasattr(self, '_interctc_params'): + raise RuntimeError( + 'self.setup_interctc(ctc_decoder_name, ctc_loss_name, ctc_wer_name) has to be ' + 'called before InterCTC loss can be used!' + ) + + def is_interctc_enabled(self) -> bool: + """Returns whether interCTC loss is enabled.""" + self._verify_setup_was_called() + return self.get_interctc_param('enabled') + + def set_interctc_enabled(self, enabled: bool): + """Can be used to enable/disable InterCTC manually.""" + self._verify_setup_was_called() + if enabled: # checking if proper config parameters were specified + if len(self.get_interctc_param('intermediate_loss_weights')) == 0: + raise RuntimeError( + 'InterCTC cannot be enabled since interctc.loss_weights was not specified in the config.' + ) + if len(self.get_interctc_param('apply_at_layers')) != len( + self.get_interctc_param('intermediate_loss_weights') + ): + raise RuntimeError( + 'InterCTC cannot be enabled, since length of "loss_weights" does not match "apply_at_layers".' + ) + self.set_interctc_param('enabled', enabled) + + def finalize_interctc_metrics(self, metrics: Dict, outputs: List[Dict], prefix: str): + """Finalizes InterCTC WER and loss metrics for logging purposes. + + Should be called inside ``multi_validation_epoch_end`` (with ``prefix="val_"``) or + ``multi_test_epoch_end`` (with ``prefix="test_"``). + + Note that ``metrics`` dictionary is going to be updated in-place. + """ + if self.is_interctc_enabled(): + for layer_idx in self.get_interctc_param('apply_at_layers'): + # assuming that if the first batch logged the metrics, then all batches did + if f"{prefix}inter_ctc_loss_l{layer_idx}" in outputs[0]: + loss = torch.stack([x[f"{prefix}inter_ctc_loss_l{layer_idx}"] for x in outputs]).mean() + metrics["log"][f"{prefix}inter_ctc_loss_l{layer_idx}"] = loss + + if f"{prefix}inter_wer_num_l{layer_idx}" in outputs[0]: + wer_num = torch.stack([x[f"{prefix}inter_wer_num_l{layer_idx}"] for x in outputs]).sum() + wer_denom = torch.stack([x[f"{prefix}inter_wer_denom_l{layer_idx}"] for x in outputs]).sum() + metrics["log"][f"{prefix}inter_wer_l{layer_idx}"] = wer_num / wer_denom + + if f"{prefix}final_loss" in outputs[0]: + metrics["log"][f"{prefix}final_loss"] = torch.stack([x[f"{prefix}final_loss"] for x in outputs]).mean() + + def get_captured_interctc_tensors(self) -> List[Tuple[torch.Tensor, torch.Tensor]]: + """Returns a list of captured tensors from encoder: tuples of (output, length). + + Will additionally apply ``ctc_decoder`` to the outputs. + """ + if not self.is_interctc_enabled(): + return [] + + # note that we have a loop here, because tensors can be defined from + # submodules of encoder (e.g., that's the case in Jasper) + total_registry = {} + for module_registry in AccessMixin.get_module_registry(self.encoder).values(): + for key in module_registry: + if key.startswith("interctc/") and key in total_registry: + raise RuntimeError(f"layer {key} has been logged multiple times!") + total_registry.update(module_registry) + # if intermediate_loss_weights was set, the encoder has to register + # interctc/layer_output_X and interctc/layer_length_X tensors. + # We need to apply decoder to each of them and compute CTC loss. + captured_tensors = [] + for layer_idx in self.get_interctc_param('apply_at_layers'): + try: + layer_outputs = total_registry[f"interctc/layer_output_{layer_idx}"] + layer_lengths = total_registry[f"interctc/layer_length_{layer_idx}"] + except KeyError: + raise RuntimeError( + f"Intermediate layer {layer_idx} was not captured! " + "Check if length of model.encoder.captured_layer_outputs matches " + "length of model.intermediate_loss_weights properties." + ) + if len(layer_outputs) > 1 or len(layer_lengths) > 1: + raise RuntimeError( + "Make sure encoder.forward is called exactly one time before interCTC loss is computed." + ) + captured_tensors.append( + (self.get_interctc_param('decoder')(encoder_output=layer_outputs[0]), layer_lengths[0]) + ) + return captured_tensors + + def add_interctc_losses( + self, + loss_value: torch.Tensor, + transcript: torch.Tensor, + transcript_len: torch.Tensor, + compute_wer: bool, + compute_loss: bool = True, + log_wer_num_denom: bool = False, + log_prefix: str = "", + ) -> Tuple[Optional[torch.Tensor], Dict]: + """Adding interCTC losses if required. + + Will also register loss/wer metrics in the returned dictionary. + + Args: + loss_value (torch.Tensor): regular loss tensor (will add interCTC loss to it). + transcript (torch.Tensor): current utterance transcript. + transcript_len (torch.Tensor): current utterance transcript length. + compute_wer (bool): whether to compute WER for the current utterance. + Should typically be True for validation/test and only True for + training if current batch WER should be logged. + compute_loss (bool): whether to compute loss for the current utterance. + Should always be True in training and almost always True in + validation, unless all other losses are disabled as well. + Defaults to True. + log_wer_num_denom (bool): if True, will additionally log WER num/denom + in the returned metrics dictionary. Should always be True for + validation/test to allow correct metrics aggregation. Should + always be False for training. Defaults to False. + log_prefix (str): prefix added to all log values. Should be ``""`` for + training and ``"val_"`` for validation. Defaults to "". + + Returns: + tuple[Optional[torch.Tensor], Dict]: tuple of new loss tensor and dictionary with logged metrics. + """ + if not self.is_interctc_enabled() or not AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): + return loss_value, {} + metrics = {} + if compute_loss: + metrics[f"{log_prefix}final_loss"] = loss_value + else: + loss_value = None + captured_tensors = self.get_captured_interctc_tensors() + + if compute_loss: + loss_value *= self.get_interctc_param('main_loss_weight') + + for layer_idx, intermediate_result, loss_weight in zip( + self.get_interctc_param('apply_at_layers'), + captured_tensors, + self.get_interctc_param('intermediate_loss_weights'), + ): + if compute_loss: + inter_loss_value = self.get_interctc_param('loss')( + log_probs=intermediate_result[0], + targets=transcript, + target_lengths=transcript_len, + input_lengths=intermediate_result[1], + ) + metrics[f"{log_prefix}inter_ctc_loss_l{layer_idx}"] = inter_loss_value.detach() + loss_value += inter_loss_value * loss_weight + if compute_wer: + self.get_interctc_param('wer').update( + predictions=intermediate_result[0], + targets=transcript, + targets_lengths=transcript_len, + predictions_lengths=intermediate_result[1], + ) + wer, wer_num, wer_denom = self.get_interctc_param('wer').compute() + self.get_interctc_param('wer').reset() + metrics.update({f'{log_prefix}inter_wer_l{layer_idx}': wer}) + if log_wer_num_denom: + metrics.update( + { + f'{log_prefix}inter_wer_num_l{layer_idx}': wer_num, + f'{log_prefix}inter_wer_denom_l{layer_idx}': wer_denom, + } + ) + + # return total loss and dictionary of metrics + return loss_value, metrics diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/mixins.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/mixins.py new file mode 100644 index 0000000..1ec4066 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/mixins.py @@ -0,0 +1,859 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import shutil +import tarfile +from abc import ABC, abstractmethod +from typing import List + +import torch +from omegaconf import DictConfig, OmegaConf, open_dict + +import nemo.collections.asr.models as asr_models +from nemo.collections.asr.parts.mixins.asr_adapter_mixins import ASRAdapterModelMixin +from nemo.collections.asr.parts.mixins.streaming import StreamingEncoder +from nemo.collections.asr.parts.utils import asr_module_utils +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis +from nemo.collections.common import tokenizers +from nemo.utils import app_state, logging + + +class ASRBPEMixin(ABC): + """ ASR BPE Mixin class that sets up a Tokenizer via a config + + This mixin class adds the method `_setup_tokenizer(...)`, which can be used by ASR models + which depend on subword tokenization. + + The setup_tokenizer method adds the following parameters to the class - + - tokenizer_cfg: The resolved config supplied to the tokenizer (with `dir` and `type` arguments). + - tokenizer_dir: The directory path to the tokenizer vocabulary + additional metadata. + - tokenizer_type: The type of the tokenizer. Currently supports `bpe` and `wpe`, as well as `agg`. + - vocab_path: Resolved path to the vocabulary text file. + + In addition to these variables, the method will also instantiate and preserve a tokenizer + (subclass of TokenizerSpec) if successful, and assign it to self.tokenizer. + + The mixin also supports aggregate tokenizers, which consist of ordinary, monolingual tokenizers. + If a conversion between a monolongual and an aggregate tokenizer (or vice versa) is detected, + all registered artifacts will be cleaned up. + """ + + # this will be used in configs and nemo artifacts + AGGREGATE_TOKENIZERS_DICT_PREFIX = 'langs' + + def _setup_tokenizer(self, tokenizer_cfg: DictConfig): + tokenizer_type = tokenizer_cfg.get('type') + if tokenizer_type is None: + raise ValueError("`tokenizer.type` cannot be None") + elif tokenizer_type.lower() == 'agg': + self._setup_aggregate_tokenizer(tokenizer_cfg) + else: + self._setup_monolingual_tokenizer(tokenizer_cfg) + + def _setup_monolingual_tokenizer(self, tokenizer_cfg: DictConfig): + # Prevent tokenizer parallelism (unless user has explicitly set it) + if 'TOKENIZERS_PARALLELISM' not in os.environ: + os.environ['TOKENIZERS_PARALLELISM'] = 'false' + + self.tokenizer_cfg = OmegaConf.to_container(tokenizer_cfg, resolve=True) # type: dict + self.tokenizer_dir = self.tokenizer_cfg.pop('dir') # Remove tokenizer directory + self.tokenizer_type = self.tokenizer_cfg.pop('type').lower() # Remove tokenizer_type + + self.hf_tokenizer_kwargs = self.tokenizer_cfg.pop("hf_kwargs", {}) # Remove HF tokenizer kwargs + + # just in case the previous tokenizer was an aggregate + self._cleanup_aggregate_config_and_artifacts_if_needed() + + # Preserve config + if hasattr(self, 'cfg') and 'tokenizer' in self.cfg: + self.cfg.tokenizer.dir = self.tokenizer_dir + self.cfg.tokenizer.type = self.tokenizer_type + + if 'hf_kwargs' in tokenizer_cfg: + with open_dict(self.cfg.tokenizer): + self.cfg.tokenizer.hf_kwargs = tokenizer_cfg.get('hf_kwargs') + + if self.tokenizer_type not in ['bpe', 'wpe']: + raise ValueError( + "`tokenizer.type` must be either `bpe` for SentencePiece tokenizer or " + "`wpe` for BERT based tokenizer" + ) + + if self.tokenizer_type == 'bpe': + # This is a BPE Tokenizer + if 'model_path' in self.tokenizer_cfg: + model_path = self.tokenizer_cfg.get('model_path') + else: + model_path = os.path.join(self.tokenizer_dir, 'tokenizer.model') + model_path = self.register_artifact('tokenizer.model_path', model_path) + self.model_path = model_path + + if 'special_tokens' in self.tokenizer_cfg: + special_tokens = self.tokenizer_cfg['special_tokens'] + + if special_tokens is not None: + raise ValueError("`special_tokens` are no longer supported for SentencePiece based tokenizers.") + + # Update special tokens + self.tokenizer = tokenizers.SentencePieceTokenizer(model_path=model_path) + + if 'vocab_path' in self.tokenizer_cfg: + vocab_path = self.tokenizer_cfg.get('vocab_path') + else: + vocab_path = os.path.join(self.tokenizer_dir, 'vocab.txt') + vocab_path = self.register_artifact('tokenizer.vocab_path', vocab_path) + self.vocab_path = vocab_path + + try: + if 'spe_tokenizer_vocab' in self.tokenizer_cfg: + spe_vocab_path = self.tokenizer_cfg.get('spe_tokenizer_vocab') + else: + spe_vocab_path = os.path.join(self.tokenizer_dir, 'tokenizer.vocab') + spe_vocab_path = self.register_artifact('tokenizer.spe_tokenizer_vocab', spe_vocab_path) + self.spe_vocab_path = spe_vocab_path + except FileNotFoundError: + # fallback case for older checkpoints that did not preserve the tokenizer.vocab + self.spe_vocab_path = None + + vocabulary = {} + for i in range(self.tokenizer.vocab_size): + piece = self.tokenizer.ids_to_tokens([i]) + piece = piece[0] + vocabulary[piece] = i + 1 + + # wrapper method to get vocabulary conveniently + def get_vocab(): + return vocabulary + + # attach utility values to the tokenizer wrapper + self.tokenizer.tokenizer.vocab_size = len(vocabulary) + self.tokenizer.tokenizer.get_vocab = get_vocab + self.tokenizer.tokenizer.all_special_tokens = self.tokenizer.special_token_to_id + + else: + # This is a WPE Tokenizer + # If path from previous registration exists, remove it + if 'vocab_path' in self.tokenizer_cfg: + vocab_path = self.tokenizer_cfg.get('vocab_path') + else: + vocab_path = os.path.join(self.tokenizer_dir, 'vocab.txt') + vocab_path = self.register_artifact('tokenizer.vocab_path', vocab_path) + self.vocab_path = vocab_path + + # If path from previous registration exists, remove it + if 'vocab_path' in self.tokenizer_cfg: + self.tokenizer_cfg.pop('vocab_path') + + self.tokenizer = tokenizers.AutoTokenizer( + pretrained_model_name='bert-base-cased', + vocab_file=self.vocab_path, + mask_token=self.hf_tokenizer_kwargs.get('mask_token', None), + bos_token=self.hf_tokenizer_kwargs.get('bos_token', None), + eos_token=self.hf_tokenizer_kwargs.get('eos_token', None), + pad_token=self.hf_tokenizer_kwargs.get('pad_token', None), + sep_token=self.hf_tokenizer_kwargs.get('sep_token', None), + cls_token=self.hf_tokenizer_kwargs.get('cls_token', None), + unk_token=self.hf_tokenizer_kwargs.get('unk_token', None), + use_fast=self.hf_tokenizer_kwargs.get('use_fast', False), + ) + + logging.info( + "Tokenizer {} initialized with {} tokens".format( + self.tokenizer.__class__.__name__, self.tokenizer.vocab_size + ) + ) + + def _setup_aggregate_tokenizer(self, tokenizer_cfg: DictConfig): + # Prevent tokenizer parallelism (unless user has explicitly set it) + if 'TOKENIZERS_PARALLELISM' not in os.environ: + os.environ['TOKENIZERS_PARALLELISM'] = 'false' + + self.tokenizer_cfg = OmegaConf.to_container(tokenizer_cfg, resolve=True) # type: dict + + # the aggregate tokenizer does not have one tokenizer_dir but multiple ones + self.tokenizer_dir = None + + self.tokenizer_cfg.pop('dir', None) # Remove tokenizer directory, if any + # Remove tokenizer_type -- obviously if we are here, the type is 'agg' + self.tokenizer_type = self.tokenizer_cfg.pop('type').lower() + + # the aggregate tokenizer should not have these + self.hf_tokenizer_kwargs = {} + self.tokenizer_cfg.pop("hf_kwargs", {}) # Remove HF tokenizer kwargs, if any + + logging.info('_setup_tokenizer: detected an aggregate tokenizer') + # need to de-register any monolingual config items if they exist + self._cleanup_monolingual_and_aggregate_config_and_artifacts_if_needed() + + # overwrite tokenizer type + if hasattr(self, 'cfg') and 'tokenizer' in self.cfg: + self.cfg.tokenizer.type = self.tokenizer_type + + tokenizers_dict = {} + # init each of the monolingual tokenizers found in the config and assemble into AggregateTokenizer + for lang, tokenizer_config in self.tokenizer_cfg[self.AGGREGATE_TOKENIZERS_DICT_PREFIX].items(): + (tokenizer, model_path, vocab_path, spe_vocab_path,) = self._make_tokenizer(tokenizer_config, lang) + + tokenizers_dict[lang] = tokenizer + if hasattr(self, 'cfg'): + with open_dict(self.cfg.tokenizer): + self.cfg.tokenizer[self.AGGREGATE_TOKENIZERS_DICT_PREFIX][lang]['dir'] = self.tokenizer_cfg[ + self.AGGREGATE_TOKENIZERS_DICT_PREFIX + ][lang]['dir'] + self.cfg.tokenizer[self.AGGREGATE_TOKENIZERS_DICT_PREFIX][lang]['type'] = self.tokenizer_cfg[ + self.AGGREGATE_TOKENIZERS_DICT_PREFIX + ][lang]['type'] + + if "custom_tokenizer" in tokenizer_cfg: + # Class which implements this is usually a ModelPT, has access to Serializable mixin by extension + self.tokenizer = self.from_config_dict( + {"_target_": tokenizer_cfg["custom_tokenizer"]["_target_"], "tokenizers": tokenizers_dict} + ) + else: + self.tokenizer = tokenizers.AggregateTokenizer(tokenizers_dict) + + def _make_tokenizer(self, tokenizer_cfg: DictConfig, lang=None): + + tokenizer_type = tokenizer_cfg.get('type').lower() + tokenizer_dir = tokenizer_cfg.get('dir') + + if tokenizer_type not in ['bpe', 'wpe']: + raise ValueError( + '`tokenizer.type` must be either `bpe` for SentencePiece tokenizer or' '`wpe` for BERT based tokenizer' + ) + + # defaults + model_path = None + vocab_path = None + spe_vocab_path = None + + if tokenizer_type == 'bpe': + # This is a BPE Tokenizer + if 'model_path' in tokenizer_cfg: + model_path = tokenizer_cfg.get('model_path') + else: + model_path = os.path.join(tokenizer_dir, 'tokenizer.model') + + model_path = self.register_artifact( + 'tokenizer.' + self.AGGREGATE_TOKENIZERS_DICT_PREFIX + '.' + lang + '.model_path', model_path + ) + + if 'special_tokens' in tokenizer_cfg: + special_tokens = tokenizer_cfg['special_tokens'] + if special_tokens is not None: + raise ValueError('`special_tokens` are no longer supported for SentencePiece based tokenizers.') + + # Update special tokens + tokenizer = tokenizers.SentencePieceTokenizer(model_path=model_path) + + if 'vocab_path' in tokenizer_cfg: + vocab_path = tokenizer_cfg.get('vocab_path') + else: + vocab_path = os.path.join(tokenizer_dir, 'vocab.txt') + + vocab_path = self.register_artifact( + 'tokenizer.' + self.AGGREGATE_TOKENIZERS_DICT_PREFIX + '.' + lang + '.vocab_path', vocab_path + ) + + try: + if 'spe_tokenizer_vocab' in tokenizer_cfg: + spe_vocab_path = tokenizer_cfg.get('spe_tokenizer_vocab') + else: + spe_vocab_path = os.path.join(tokenizer_dir, 'tokenizer.vocab') + + spe_vocab_path = self.register_artifact( + 'tokenizer.' + self.AGGREGATE_TOKENIZERS_DICT_PREFIX + '.' + lang + '.spe_tokenizer_vocab', + spe_vocab_path, + ) + + except FileNotFoundError: + # fallback case for older checkpoints that did not preserve the tokenizer.vocab + spe_vocab_path = None + + vocabulary = {} + for i in range(tokenizer.vocab_size): + piece = tokenizer.ids_to_tokens([i]) + piece = piece[0] + vocabulary[piece] = i + 1 + + # wrapper method to get vocabulary conveniently + def get_vocab(): + return vocabulary + + # attach utility values to the tokenizer wrapper + tokenizer.tokenizer.vocab_size = len(vocabulary) + tokenizer.tokenizer.get_vocab = get_vocab + tokenizer.tokenizer.all_special_tokens = tokenizer.special_token_to_id + + else: + # This is a WPE Tokenizer + # If path from previous registration exists, remove it + if 'vocab_path' in tokenizer_cfg: + vocab_path = tokenizer_cfg.get('vocab_path') + else: + vocab_path = os.path.join(tokenizer_dir, 'vocab.txt') + + vocab_path = self.register_artifact( + 'tokenizer.' + self.AGGREGATE_TOKENIZERS_DICT_PREFIX + '.' + lang + '.vocab_path', vocab_path + ) + + # If path from previous registration exists, remove it + if 'vocab_path' in tokenizer_cfg: + tokenizer_cfg.pop('vocab_path') + + hf_tokenizer_kwargs = tokenizer_cfg.get('hf_kwargs', {}) + tokenizer = tokenizers.AutoTokenizer( + pretrained_model_name=hf_tokenizer_kwargs.get('pretrained_model_name', 'bert-base-cased'), + vocab_file=vocab_path, + mask_token=hf_tokenizer_kwargs.get('mask_token', None), + bos_token=hf_tokenizer_kwargs.get('bos_token', None), + eos_token=hf_tokenizer_kwargs.get('eos_token', None), + pad_token=hf_tokenizer_kwargs.get('pad_token', None), + sep_token=hf_tokenizer_kwargs.get('sep_token', None), + cls_token=hf_tokenizer_kwargs.get('cls_token', None), + unk_token=hf_tokenizer_kwargs.get('unk_token', None), + use_fast=hf_tokenizer_kwargs.get('use_fast', False), + ) + + logging.info( + 'Tokenizer {} initialized with {} tokens'.format(tokenizer.__class__.__name__, tokenizer.vocab_size) + ) + + return tokenizer, model_path, vocab_path, spe_vocab_path + + def _cleanup_monolingual_and_aggregate_config_and_artifacts_if_needed(self): + """ + Clean ups any monolingual and some aggregate config items and artifacts. + We need to do this when we switch from a monolingual tokenizer to an aggregate one + or go between aggregate tokenizers which could have a different number of languages + """ + if hasattr(self, 'cfg'): + with open_dict(self.cfg.tokenizer): + self.cfg.tokenizer.pop('dir', None) + self.cfg.tokenizer.pop('model_path', None) + self.cfg.tokenizer.pop('vocab_path', None) + self.cfg.tokenizer.pop('spe_tokenizer_vocab', None) + self.cfg.tokenizer.pop('hf_kwargs', None) + + # need to de-register any monolingual artifacts if they exist + if hasattr(self, 'artifacts'): + self.artifacts.pop('tokenizer.model_path', None) + self.artifacts.pop('tokenizer.vocab_path', None) + self.artifacts.pop('tokenizer.spe_tokenizer_vocab', None) + + # just in case we are replacing one aggregate tokenizer with another one, we better + # clean up the old aggregate artifacts as well + for akey in list(self.artifacts.keys()): + if akey.startswith('tokenizer.' + self.AGGREGATE_TOKENIZERS_DICT_PREFIX + '.'): + self.artifacts.pop(akey) + + def _cleanup_aggregate_config_and_artifacts_if_needed(self): + """ + Clean ups any aggregate config items and artifacts. + We need to do this when we switch from an aggregate tokenizer to a monolingual one + """ + if hasattr(self, 'cfg'): + with open_dict(self.cfg.tokenizer): + self.cfg.tokenizer.pop(self.AGGREGATE_TOKENIZERS_DICT_PREFIX, None) + + # clean up the old aggregate artifacts as well + if hasattr(self, 'artifacts'): + for akey in list(self.artifacts.keys()): + if akey.startswith('tokenizer.' + self.AGGREGATE_TOKENIZERS_DICT_PREFIX + '.'): + self.artifacts.pop(akey) + + def save_tokenizers(self, directory: str): + """ + Save the model tokenizer(s) to the specified directory. + + Args: + directory: The directory to save the tokenizer(s) to. + """ + if not hasattr(self, 'cfg'): + raise RuntimeError( + "The model has not been initialized with a tokenizer yet. Please call the model's " + "__init__ and _setup_tokenizer methods first." + ) + + if self.tokenizer_type == 'agg': + for lang in self.tokenizer.langs: + subconfig = self.cfg.tokenizer.langs.get(lang) + new_dir = os.path.join(directory, lang) + self._extract_tokenizer_from_config(subconfig, new_dir) + else: + self._extract_tokenizer_from_config(self.cfg.tokenizer, directory) + + def _extract_tokenizer_from_config(self, tokenizer_cfg: DictConfig, dir: str): + """ + Extracts the tokenizer from the config and write the objects to dir. + The file may be from a local path (new model init) or from a .nemo file (restored model). + If its from a newly initialized model, the file is copied to dir. + If its from a restored model, the file is extracted from the .nemo file and copied to dir. + + Args: + tokenizer_cfg: The tokenizer config to extract the tokenizer from. + dir: The directory to write the tokenizer objects to. + """ + if not os.path.exists(dir): + os.makedirs(dir, exist_ok=True) + + nemo_file_objects = [] + + for k, v in tokenizer_cfg.items(): + # Check if the value is a filepath (new model init) or has `nemo:` in it (restored model) + if isinstance(v, str) and os.path.exists(v): + # local file from first instantiation + loc = shutil.copy2(v, dir) + logging.info(f"Saved {k} at {loc}") + + if isinstance(v, str) and v.startswith('nemo:'): + nemo_object_name = v[5:] + nemo_file_objects.append(nemo_object_name) + + if len(nemo_file_objects) > 0: + logging.debug(f"Copying the following nemo file objects to {dir}: {nemo_file_objects}") + + if not hasattr(self, 'model_guid'): + raise ValueError( + "The model does not have a model_guid attribute. " + "Please ensure that the model has been restored from a .nemo file." + ) + + appstate = app_state.AppState() + restore_path = appstate.get_model_metadata_from_guid(self.model_guid).restoration_path + if restore_path is None: + raise ValueError( + "The model has not been restored from a .nemo file. Cannot extract the tokenizer " + "as the nemo file cannot be located." + ) + + # Read the nemo file without fully extracting all contents + # we start with an assumption of uncompressed tar, + # which should be true for versions 1.7.0 and above + tar_header = "r:" + try: + tar_test = tarfile.open(restore_path, tar_header) + tar_test.close() + except tarfile.ReadError: + # can be older checkpoint => try compressed tar + tar_header = "r:gz" + tar = tarfile.open(restore_path, tar_header) + + for nemo_object_name in nemo_file_objects: + members = [x for x in tar.getmembers() if nemo_object_name in x.name] + for member in members: + tar.extract(member, dir) + + new_name = member.name.split("_")[1:] + if len(new_name) > 1: + new_name = "_".join(new_name) + else: + new_name = new_name[0] + os.rename(os.path.join(dir, member.name), os.path.join(dir, new_name)) + + logging.info(f"Saved {nemo_object_name} at {os.path.join(dir, new_name)}") + + +class ASRModuleMixin(ASRAdapterModelMixin): + """ + ASRModuleMixin is a mixin class added to ASR models in order to add methods that are specific + to a particular instantiation of a module inside of an ASRModel. + + Each method should first check that the module is present within the subclass, and support additional + functionality if the corresponding module is present. + """ + + def change_conv_asr_se_context_window(self, context_window: int, update_config: bool = True): + """ + Update the context window of the SqueezeExcitation module if the provided model contains an + `encoder` which is an instance of `ConvASREncoder`. + + Args: + context_window: An integer representing the number of input timeframes that will be used + to compute the context. Each timeframe corresponds to a single window stride of the + STFT features. + + Say the window_stride = 0.01s, then a context window of 128 represents 128 * 0.01 s + of context to compute the Squeeze step. + update_config: Whether to update the config or not with the new context window. + """ + asr_module_utils.change_conv_asr_se_context_window( + self, context_window=context_window, update_config=update_config + ) + + def change_attention_model( + self, self_attention_model: str = None, att_context_size: List[int] = None, update_config: bool = True + ): + """ + Update the self_attention_model if function is available in encoder. + + Args: + self_attention_model (str): type of the attention layer and positional encoding + + 'rel_pos': + relative positional embedding and Transformer-XL + + 'rel_pos_local_attn': + relative positional embedding and Transformer-XL with local attention using + overlapping windows. Attention context is determined by att_context_size parameter. + + 'abs_pos': + absolute positional embedding and Transformer + + If None is provided, the self_attention_model isn't changed. Defauts to None. + att_context_size (List[int]): List of 2 ints corresponding to left and right attention context sizes, + or None to keep as it is. Defauts to None. + update_config (bool): Whether to update the config or not with the new attention model. + Defaults to True. + """ + if self_attention_model is None and att_context_size is None: + return + + if not hasattr(self, 'encoder'): + logging.info( + "Could not change the self_attention_model in encoder " + "since the model provided does not contain an `encoder` module in its config." + ) + return + + if not hasattr(self.encoder, "change_attention_model"): + logging.info("Model encoder doesn't have a change_attention_model method ") + return + + self.encoder.change_attention_model(self_attention_model, att_context_size, update_config, self.device) + if update_config: + with open_dict(self.cfg): + self.cfg.encoder.self_attention_model = self_attention_model + self.cfg.encoder.att_context_size = att_context_size + + def change_subsampling_conv_chunking_factor( + self, subsampling_conv_chunking_factor: int, update_config: bool = True + ): + """ + Update the conv_chunking_factor (int) if function is available in encoder. + Default is 1 (auto) + Set it to -1 (disabled) or to a specific value (power of 2) if you OOM in the conv subsampling layers + + Args: + conv_chunking_factor (int) + """ + + if not hasattr(self, 'encoder'): + logging.info( + "Could not call the change_subsampling_conv_chunking_factor method in encoder " + "since the model provided does not contain an `encoder` module in its config." + ) + return + + if not hasattr(self.encoder, "change_subsampling_conv_chunking_factor"): + logging.info("Model encoder doesn't have a change_subsampling_conv_chunking_factor method ") + return + + self.encoder.change_subsampling_conv_chunking_factor(subsampling_conv_chunking_factor) + if update_config: + with open_dict(self.cfg): + self.cfg.encoder.subsampling_conv_chunking_factor = subsampling_conv_chunking_factor + + def conformer_stream_step( + self, + processed_signal: torch.Tensor, + processed_signal_length: torch.Tensor = None, + cache_last_channel: torch.Tensor = None, + cache_last_time: torch.Tensor = None, + cache_last_channel_len: torch.Tensor = None, + keep_all_outputs: bool = True, + previous_hypotheses: List[Hypothesis] = None, + previous_pred_out: torch.Tensor = None, + drop_extra_pre_encoded: int = None, + return_transcription: bool = True, + return_log_probs: bool = False, + ): + """ + It simulates a forward step with caching for streaming purposes. + It supports the ASR models where their encoder supports streaming like Conformer. + Args: + processed_signal: the input audio signals + processed_signal_length: the length of the audios + cache_last_channel: the cache tensor for last channel layers like MHA + cache_last_channel_len: engths for cache_last_channel + cache_last_time: the cache tensor for last time layers like convolutions + keep_all_outputs: if set to True, would not drop the extra outputs specified by encoder.streaming_cfg.valid_out_len + previous_hypotheses: the hypotheses from the previous step for RNNT models + previous_pred_out: the predicted outputs from the previous step for CTC models + drop_extra_pre_encoded: number of steps to drop from the beginning of the outputs after the downsampling module. This can be used if extra paddings are added on the left side of the input. + return_transcription: whether to decode and return the transcriptions. It can not get disabled for Transducer models. + return_log_probs: whether to return the log probs, only valid for ctc model + + Returns: + greedy_predictions: the greedy predictions from the decoder + all_hyp_or_transcribed_texts: the decoder hypotheses for Transducer models and the transcriptions for CTC models + cache_last_channel_next: the updated tensor cache for last channel layers to be used for next streaming step + cache_last_time_next: the updated tensor cache for last time layers to be used for next streaming step + cache_last_channel_next_len: the updated lengths for cache_last_channel + best_hyp: the best hypotheses for the Transducer models + log_probs: the logits tensor of current streaming chunk, only returned when return_log_probs=True + encoded_len: the length of the output log_probs + history chunk log_probs, only returned when return_log_probs=True + """ + if not isinstance(self, asr_models.EncDecRNNTModel) and not isinstance(self, asr_models.EncDecCTCModel): + raise NotImplementedError(f"stream_step does not support {type(self)}!") + + if not isinstance(self.encoder, StreamingEncoder): + raise NotImplementedError(f"Encoder of this model does not support streaming!") + + if isinstance(self, asr_models.EncDecRNNTModel) and return_transcription is False: + logging.info( + "return_transcription can not be False for Transducer models as decoder returns the transcriptions too." + ) + + if not isinstance(self, asr_models.EncDecCTCModel) and return_log_probs is True: + logging.info("return_log_probs can only be True for CTC models.") + + ( + encoded, + encoded_len, + cache_last_channel_next, + cache_last_time_next, + cache_last_channel_next_len, + ) = self.encoder.cache_aware_stream_step( + processed_signal=processed_signal, + processed_signal_length=processed_signal_length, + cache_last_channel=cache_last_channel, + cache_last_time=cache_last_time, + cache_last_channel_len=cache_last_channel_len, + keep_all_outputs=keep_all_outputs, + drop_extra_pre_encoded=drop_extra_pre_encoded, + ) + + if isinstance(self, asr_models.EncDecCTCModel) or ( + isinstance(self, asr_models.EncDecHybridRNNTCTCModel) and self.cur_decoder == "ctc" + ): + if hasattr(self, "ctc_decoder"): + decoding = self.ctc_decoding + decoder = self.ctc_decoder + else: + decoding = self.decoding + decoder = self.decoder + + log_probs = decoder(encoder_output=encoded) + predictions_tensor = log_probs.argmax(dim=-1, keepdim=False) + + # Concatenate the previous predictions with the current one to have the full predictions. + # We drop the extra predictions for each sample by using the lengths returned by the encoder (encoded_len) + # Then create a list of the predictions for the batch. The predictions can have different lengths because of the paddings. + greedy_predictions = [] + if return_transcription: + all_hyp_or_transcribed_texts = [] + else: + all_hyp_or_transcribed_texts = None + for preds_idx, preds in enumerate(predictions_tensor): + if encoded_len is None: + preds_cur = predictions_tensor[preds_idx] + else: + preds_cur = predictions_tensor[preds_idx, : encoded_len[preds_idx]] + if previous_pred_out is not None: + greedy_predictions_concat = torch.cat((previous_pred_out[preds_idx], preds_cur), dim=-1) + encoded_len[preds_idx] += len(previous_pred_out[preds_idx]) + else: + greedy_predictions_concat = preds_cur + greedy_predictions.append(greedy_predictions_concat) + + # TODO: make decoding more efficient by avoiding the decoding process from the beginning + if return_transcription: + decoded_out = decoding.ctc_decoder_predictions_tensor( + decoder_outputs=greedy_predictions_concat.unsqueeze(0), + decoder_lengths=encoded_len[preds_idx : preds_idx + 1], + return_hypotheses=False, + ) + all_hyp_or_transcribed_texts.append(decoded_out[0][0]) + best_hyp = None + else: + best_hyp, all_hyp_or_transcribed_texts = self.decoding.rnnt_decoder_predictions_tensor( + encoder_output=encoded, + encoded_lengths=encoded_len, + return_hypotheses=True, + partial_hypotheses=previous_hypotheses, + ) + greedy_predictions = [hyp.y_sequence for hyp in best_hyp] + + if all_hyp_or_transcribed_texts is None: + all_hyp_or_transcribed_texts = best_hyp + + result = [ + greedy_predictions, + all_hyp_or_transcribed_texts, + cache_last_channel_next, + cache_last_time_next, + cache_last_channel_next_len, + best_hyp, + ] + if return_log_probs: + result.append(log_probs) + result.append(encoded_len) + + return tuple(result) + + @torch.no_grad() + def transcribe_simulate_cache_aware_streaming( + self, + paths2audio_files: List[str], + batch_size: int = 4, + logprobs: bool = False, + return_hypotheses: bool = False, + online_normalization: bool = False, + ): + """ + Args: + paths2audio_files: (a list) of paths to audio files. + batch_size: (int) batch size to use during inference. + Bigger will result in better throughput performance but would use more memory. + logprobs: (bool) pass True to get log probabilities instead of transcripts. + return_hypotheses: (bool) Either return hypotheses or text + With hypotheses can do some postprocessing like getting timestamp or rescoring + online_normalization: (bool) Perform normalization on the run per chunk. + Returns: + A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files + """ + if paths2audio_files is None or len(paths2audio_files) == 0: + return {} + + if return_hypotheses and logprobs: + raise ValueError( + "Either `return_hypotheses` or `logprobs` can be True at any given time." + "Returned hypotheses will contain the logprobs." + ) + + if not isinstance(self, asr_models.EncDecCTCModel): + raise NotImplementedError(f"simulate streaming does not support {type(self)}!") + + if not isinstance(self.encoder, StreamingEncoder): + raise NotImplementedError(f"Encoder of this model does not support streaming!") + + data_loader = self._setup_streaming_transcribe_dataloader(paths2audio_files, batch_size, online_normalization) + + total_log_probs = [] + total_texts = [] + + for streaming_buffer in data_loader: + streaming_buffer_iter = iter(streaming_buffer) + batch_size = len(streaming_buffer.streams_length) + cache_last_channel, cache_last_time, cache_last_channel_len = self.encoder.get_initial_cache_state( + batch_size=batch_size + ) + previous_hypotheses = None + pred_out_stream = None + encoded_len = None + transcribed_texts = None + batch_log_probs = [] + + for step_num, (chunk_audio, chunk_lengths) in enumerate(streaming_buffer_iter): + drop_extra_pre_encoded = self.encoder.streaming_cfg.drop_extra_pre_encoded if step_num != 0 else 0 + with torch.inference_mode(): + result = self.conformer_stream_step( + processed_signal=chunk_audio, + processed_signal_length=chunk_lengths, + cache_last_channel=cache_last_channel, + cache_last_time=cache_last_time, + cache_last_channel_len=cache_last_channel_len, + keep_all_outputs=streaming_buffer.is_buffer_empty(), + previous_hypotheses=previous_hypotheses, + previous_pred_out=pred_out_stream, + drop_extra_pre_encoded=drop_extra_pre_encoded, + return_transcription=True, + return_log_probs=logprobs or return_hypotheses, + ) + if logprobs or return_hypotheses: + ( + pred_out_stream, + transcribed_texts, + cache_last_channel, + cache_last_time, + cache_last_channel_len, + previous_hypotheses, + cur_chunk_log_probs, + encoded_len, + ) = result + batch_log_probs.append(cur_chunk_log_probs.cpu()) + else: + ( + pred_out_stream, + transcribed_texts, + cache_last_channel, + cache_last_time, + cache_last_channel_len, + previous_hypotheses, + ) = result + + if logprobs or return_hypotheses: + # concatenate chunk log probs on T dim + batch_log_probs = torch.cat(batch_log_probs, axis=1) + for log_probs, log_prob_len in zip(batch_log_probs, encoded_len): + total_log_probs.append(log_probs[0:log_prob_len]) + + if transcribed_texts is None: + total_texts += [''] * batch_size + else: + total_texts += transcribed_texts + + if logprobs: + return total_log_probs + + if not return_hypotheses: + return total_texts + + hyps = [] + for log_probs, text in zip(total_log_probs, total_texts): + hyps.append(Hypothesis(y_sequence=log_probs, text=text, score=0.0, dec_state=None)) + return hyps + + def _setup_streaming_transcribe_dataloader( + self, paths2audio_files: List[str], batch_size: int, online_normalization=False + ): + """ + Setup function for a temporary data loader which wraps the provided audio file. + + Args: + paths2audio_files: (a list) of paths to audio files. + batch_size: (int) batch size to use during inference. + Bigger will result in better throughput performance but would use more memory. + online_normalization: whether to do online normalization + Returns: + a new batch streaming buffer + """ + from nemo.collections.asr.parts.utils.streaming_utils import CacheAwareStreamingAudioBuffer + + streaming_buffer = CacheAwareStreamingAudioBuffer(model=self, online_normalization=online_normalization) + for sample_idx, sample in enumerate(paths2audio_files): + processed_signal, processed_signal_length, stream_id = streaming_buffer.append_audio_file( + sample, stream_id=-1 + ) + logging.info(f'Added this sample to the buffer: {sample}') + if (sample_idx + 1) % batch_size == 0 or sample_idx == len(paths2audio_files) - 1: + logging.info(f"Starting to stream samples {sample_idx - len(streaming_buffer) + 1} to {sample_idx}...") + yield streaming_buffer + streaming_buffer.reset_buffer() + + +class DiarizationMixin(ABC): + @abstractmethod + def diarize(self, paths2audio_files: List[str], batch_size: int = 1) -> List[str]: + """ + Takes paths to audio files and returns speaker labels + Args: + paths2audio_files: paths to audio fragment to be transcribed + + Returns: + Speaker labels + """ + pass diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/streaming.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/streaming.py new file mode 100644 index 0000000..d6fd0b9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/streaming.py @@ -0,0 +1,75 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC, abstractmethod + +import torch + + +class StreamingEncoder(ABC): + @abstractmethod + def setup_streaming_params( + self, max_look_ahead: int = 10000, + ): + """ + This function sets the needed values and parameters to perform streaming. The configuration (CacheAwareStreamingConfig) need to be stored in self.streaming_cfg. + The streaming configuration is needed to simulate streaming inference. It would set the following + """ + pass + + @abstractmethod + def get_initial_cache_state(self, batch_size, dtype, device, max_dim): + pass + + @staticmethod + def to_numpy(tensor): + if tensor is None: + return None + return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy() + + def cache_aware_stream_step( + self, + processed_signal, + processed_signal_length=None, + cache_last_channel=None, + cache_last_time=None, + cache_last_channel_len=None, + keep_all_outputs=True, + drop_extra_pre_encoded=None, + ): + if self.streaming_cfg is None: + self.setup_streaming_params() + if drop_extra_pre_encoded is not None: + prev_drop_extra_pre_encoded = self.streaming_cfg.drop_extra_pre_encoded + self.streaming_cfg.drop_extra_pre_encoded = drop_extra_pre_encoded + else: + prev_drop_extra_pre_encoded = None + + if processed_signal_length is None: + processed_signal_length = processed_signal.new_full(processed_signal.size(0), processed_signal.size(-1)) + + encoder_output = self( + audio_signal=processed_signal, + length=processed_signal_length, + cache_last_channel=cache_last_channel, + cache_last_time=cache_last_time, + cache_last_channel_len=cache_last_channel_len, + ) + + encoder_output = self.streaming_post_process(encoder_output, keep_all_outputs=keep_all_outputs) + + if prev_drop_extra_pre_encoded is not None: + self.streaming_cfg.drop_extra_pre_encoded = prev_drop_extra_pre_encoded + + return encoder_output diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/transcription.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/transcription.py new file mode 100644 index 0000000..5a71679 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/mixins/transcription.py @@ -0,0 +1,788 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import tempfile +from abc import ABC, abstractmethod +from collections.abc import Iterable +from dataclasses import dataclass +from functools import partial +from typing import Any, Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +from omegaconf import DictConfig +from torch.utils.data import DataLoader, Dataset +from tqdm import tqdm + +from nemo.collections.asr.parts.preprocessing.perturb import process_augmentations +from nemo.collections.asr.parts.preprocessing.segment import AudioSegment +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.utils import logging, logging_mode + +TranscriptionReturnType = Union[List[str], List['Hypothesis'], Tuple[List[str]], Tuple[List['Hypothesis']]] +GenericTranscriptionType = Union[List[Any], List[List[Any]], Tuple[Any], Tuple[List[Any]], Dict[str, List[Any]]] + + +@dataclass +class InternalTranscribeConfig: + # Internal values + device: Optional[torch.device] = None + dtype: Optional[torch.dtype] = None + training_mode: bool = False + logging_level: Optional[Any] = None + + # Preprocessor values + dither_value: float = 0.0 + pad_to_value: int = 0 + + # Scratch space + temp_dir: Optional[str] = None + + +@dataclass +class TranscribeConfig: + batch_size: int = 4 + return_hypotheses: bool = False + num_workers: Optional[int] = None + channel_selector: ChannelSelectorType = None + augmentor: Optional[DictConfig] = None + verbose: bool = True + + # Utility + partial_hypothesis: Optional[List[Any]] = None + + _internal: Optional[InternalTranscribeConfig] = None + + +def move_to_device(batch, device): + """ + Recursively move all tensors in `batch` to `device`. + """ + if isinstance(batch, torch.Tensor): + return batch.to(device) + elif isinstance(batch, (list, tuple)): + return [move_to_device(x, device) for x in batch] + elif isinstance(batch, dict): + return {k: move_to_device(v, device) for k, v in batch.items()} + else: + raise TypeError(f"Unsupported type: {type(batch)}") + + +def get_value_from_transcription_config(trcfg, key, default): + """ + Utility function to get a value from the transcription config. + If the value is not present in the transcription config, the default value is returned. + + Args: + trcfg: A dataclass that represents the transcription config. + key: The name of the arg to retrieve. + default: The default value to return if the key is not present in the transcription config. + + Returns: + The value of the key in the transcription config or the default value. + """ + if hasattr(trcfg, key): + return getattr(trcfg, key) + else: + logging.debug( + f"Using default value of {default} for {key} because it is not present in the transcription config {trcfg}." + ) + return default + + +class TranscriptionTensorDataset(Dataset): + def __init__(self, config: Dict[str, Any]): + super().__init__() + self.audio_tensors = config['audio_tensors'] + self.channel_selector = config['channel_selector'] + self.augmentor_cfg = config.get('augmentor', None) + self.sample_rate = config['sample_rate'] + + if self.augmentor_cfg is not None: + self.augmentor = process_augmentations(self.augmentor_cfg, global_rank=0, world_size=1) + else: + self.augmentor = None + + self.length = len(self.audio_tensors) + + def __getitem__(self, index): + if index >= self.length: + raise IndexError(f"Index {index} out of range for dataset of size {self.length}") + + return self.get_item(index) + + def __len__(self): + return self.length + + def get_item(self, index): + samples = self.audio_tensors[index] + + if self.augmentor is not None: + logging.warning( + "Audio Augmentations are being applied during inference by moving the tensor onto CPU. " + "This is highly inefficient and therefore not recommended.", + mode=logging_mode.ONCE, + ) + + original_dtype = samples.dtype + samples = samples.to(device='cpu', dtype=torch.float32).numpy() + segment = AudioSegment( + samples, self.sample_rate, target_sr=self.sample_rate, channel_selector=self.channel_selector + ) + samples = self.augmentor.perturb(segment) + samples = torch.tensor(samples.samples, dtype=original_dtype) + + # Calculate seq length + seq_len = torch.tensor(samples.shape[0], dtype=torch.long) + + # Dummy text tokens + text_tokens = torch.tensor([0], dtype=torch.long) + text_tokens_len = torch.tensor(1, dtype=torch.long) + + return (samples, seq_len, text_tokens, text_tokens_len) + + +class TranscriptionMixin(ABC): + """ + An abstract class for transcribe-able models. + + Creates a template function `transcribe()` that provides an interface to perform transcription of audio tensors or + filepaths. + + The following abstract classes must be implemented by the subclass: + + - `_transcribe_input_manifest_processing()`: + Process the provided input arguments (filepaths only) and return a + config dict for the dataloader. The data loader is should generally operate on NeMo manifests. + + - `_setup_transcribe_dataloader()`: + Setup the dataloader for transcription. Receives the output from + `_transcribe_input_manifest_processing()`. + + - `_transcribe_forward()`: + Implements the model's custom forward pass to return outputs that are processed by + `_transcribe_output_processing()`. + + - `_transcribe_output_processing()`: + Implements the post processing of the model's outputs to return the results to + the user. The result can be a list of objects, list of list of objects, tuple of objects, tuple of list of + objects, or a dict of list of objects. + + """ + + @torch.no_grad() + def transcribe( + self, + audio: Union[str, List[str], np.ndarray], + batch_size: int = 4, + return_hypotheses: bool = False, + num_workers: int = 0, + channel_selector: Optional[ChannelSelectorType] = None, + augmentor: DictConfig = None, + verbose: bool = True, + override_config: Optional[TranscribeConfig] = None, + **config_kwargs, + ) -> GenericTranscriptionType: + """ + Template function that defines the execution strategy for transcribing audio. + + Args: + audio: (a single or list) of paths to audio files or a np.ndarray audio array. + Recommended length per file is between 5 and 25 seconds. + But it is possible to pass a few hours long file if enough GPU memory is available. + batch_size: (int) batch size to use during inference. + Bigger will result in better throughput performance but would use more memory. + return_hypotheses: (bool) Either return hypotheses or text + With hypotheses can do some postprocessing like getting timestamp or rescoring + num_workers: (int) number of workers for DataLoader + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from + multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set + to `None`. Defaults to `None`. Uses zero-based indexing. + augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied. + verbose: (bool) whether to display tqdm progress bar + override_config: (Optional[TranscribeConfig]) override transcription config pre-defined by the user. + **Note**: All other arguments in the function will be ignored if override_config is passed. + You should call this argument as `model.transcribe(audio, override_config=TranscribeConfig(...))`. + **config_kwargs: (Optional[Dict]) additional arguments to override the default TranscribeConfig. + Note: If override_config is passed, these arguments will be ignored. + + Returns: + Output is defined by the subclass implementation of `TranscriptionMixin._transcribe_output_processing()`. + It can be: + + - List[str/Hypothesis] + + - List[List[str/Hypothesis]] + + - Tuple[str/Hypothesis] + + - Tuple[List[str/Hypothesis]] + + - Dict[str, List[str/Hypothesis]] + """ + + if override_config is None: + transcribe_cfg = TranscribeConfig( + batch_size=batch_size, + return_hypotheses=return_hypotheses, + num_workers=num_workers, + channel_selector=channel_selector, + augmentor=augmentor, + verbose=verbose, + **config_kwargs, + ) + else: + if not hasattr(override_config, '_internal'): + raise ValueError( + "`transcribe_cfg must have an `_internal` argument, which must be of an object of type " + "InternalTranscribeConfig or its subclass." + ) + + if override_config._internal is None: + override_config._internal = InternalTranscribeConfig() + + transcribe_cfg = override_config + + # Add new internal config + if transcribe_cfg._internal is None: + transcribe_cfg._internal = InternalTranscribeConfig() + else: + # Check if internal config is valid + if not isinstance(transcribe_cfg._internal, InternalTranscribeConfig): + raise ValueError( + "`transcribe_cfg._internal` must be of an object of type InternalTranscribeConfig or " + "its subclass" + ) + + # Hold the results here + results = None # type: GenericTranscriptionType + + try: + generator = self.transcribe_generator(audio, override_config=transcribe_cfg) + + for processed_outputs in generator: + # Store results + if isinstance(processed_outputs, list): + # Create a results of the same type as each element in processed_outputs + if results is None: + results = [] + + # if list of inner list of results, copy structure + if isinstance(processed_outputs[0], list): + for _ in processed_outputs: + results.append([]) + + # If nested list structure + if isinstance(processed_outputs[0], list): + for i, processed_output in enumerate(processed_outputs): + results[i].extend(processed_output) + else: + # If flat list structure + results.extend(processed_outputs) + + elif isinstance(processed_outputs, dict): + # Create a results of the same type as each element in processed_outputs + if results is None: + results = processed_outputs + else: + for k, v in processed_outputs.items(): + results[k].extend(v) + + elif isinstance(processed_outputs, tuple): + # Create a results of the same type as each element in processed_outputs + if results is None: + results = tuple([[] for _ in processed_outputs]) + + # If nested list structure + if isinstance(processed_outputs[0], list): + for i, processed_output in enumerate(processed_outputs): + results[i].extend(processed_output) + else: + # If flat list structure + if len(processed_outputs) != len(results): + raise RuntimeError( + f"The number of elements in the result ({len(results)}) does not " + f"match the results of the current batch ({len(processed_outputs)})." + ) + + for i, processed_output in enumerate(processed_outputs): + results[i].append(processed_output) + + else: + raise NotImplementedError( + "Given output result for transcription is not supported. " + "Please return a list of results, list of list of results, " + "a dict of list of results, or " + "a tuple of list of results." + ) + except StopIteration: + pass + + return results + + def transcribe_generator(self, audio, override_config: Optional[TranscribeConfig]): + """ + A generator version of `transcribe` function. + """ + + if override_config is None: + override_config = TranscribeConfig() + + if not hasattr(override_config, '_internal'): + raise ValueError( + "`transcribe_cfg must have an `_internal` argument, which must be of an object of type " + "InternalTranscribeConfig or its subclass." + ) + + # Add new internal config + if override_config._internal is None: + override_config._internal = InternalTranscribeConfig() + else: + # Check if internal config is valid + if not isinstance(override_config._internal, InternalTranscribeConfig): + raise ValueError( + "`transcribe_cfg._internal` must be of an object of type InternalTranscribeConfig or " + "its subclass" + ) + + transcribe_cfg = override_config + + try: + # Initialize and assert the transcription environment + self._transcribe_on_begin(audio, transcribe_cfg) + + # Work in tmp directory - will store manifest file there + with tempfile.TemporaryDirectory() as tmpdir: + transcribe_cfg._internal.temp_dir = tmpdir + + dataloader = self._transcribe_input_processing(audio, transcribe_cfg) + + if hasattr(transcribe_cfg, 'verbose'): + verbose = transcribe_cfg.verbose + else: + verbose = True + + for test_batch in tqdm(dataloader, desc="Transcribing", disable=not verbose): + # Move batch to device + test_batch = move_to_device(test_batch, transcribe_cfg._internal.device) + + # Run forward pass + model_outputs = self._transcribe_forward(test_batch, transcribe_cfg) + processed_outputs = self._transcribe_output_processing(model_outputs, transcribe_cfg) + + # clear up memory + del test_batch, model_outputs + + # Yield results if generator + yield processed_outputs + + del processed_outputs + + finally: + # set mode back to its original value + self._transcribe_on_end(transcribe_cfg) + + """ + Transcribe Execution Flow + """ + + def _transcribe_on_begin(self, audio, trcfg: TranscribeConfig): + """ + Internal function to setup the model for transcription. Perform all setup and pre-checks here. + + Args: + audio: Of type `GenericTranscriptionType` + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + """ + if audio is None: + return {} + + if isinstance(audio, (str, np.ndarray, torch.Tensor)): + audio = [audio] + + if isinstance(audio, list) and len(audio) == 0: + return {} + + _params = next(self.parameters()) + if trcfg._internal.device is None: + trcfg._internal.device = _params.device + + if trcfg._internal.dtype is None: + trcfg._internal.dtype = _params.dtype + + # Set num_workers + num_workers = get_value_from_transcription_config(trcfg, 'num_workers', default=0) + + if num_workers is None: + _batch_size = get_value_from_transcription_config(trcfg, 'batch_size', default=4) + num_workers = min(_batch_size, os.cpu_count() - 1) + + # Assign num_workers if available as key in trcfg + if hasattr(trcfg, 'num_workers'): + trcfg.num_workers = num_workers + + # Model's mode and device + trcfg._internal.training_mode = self.training + + # Switch model to evaluation mode + if hasattr(self, 'preprocessor'): + if hasattr(self.preprocessor, 'featurizer') and hasattr(self.preprocessor.featurizer, 'dither'): + trcfg._internal.dither_value = self.preprocessor.featurizer.dither + self.preprocessor.featurizer.dither = 0.0 + + if hasattr(self.preprocessor, 'featurizer') and hasattr(self.preprocessor.featurizer, 'pad_to'): + trcfg._internal.pad_to_value = self.preprocessor.featurizer.pad_to + self.preprocessor.featurizer.pad_to = 0 + + # Switch model to evaluation mode + self.eval() + + # Disable logging + trcfg._internal.logging_level = logging.get_verbosity() + logging.set_verbosity(logging.WARNING) + + def _transcribe_input_processing(self, audio, trcfg: TranscribeConfig): + """ + Internal function to process the input audio data and return a DataLoader. This function is called by + `transcribe()` and `transcribe_generator()` to setup the input data for transcription. + + Args: + audio: Of type `GenericTranscriptionType` + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + + Returns: + A DataLoader object that is used to iterate over the input audio data. + """ + if isinstance(audio, (list, tuple)): + if len(audio) == 0: + raise ValueError("Input `audio` is empty") + else: + audio = [audio] + + # Check if audio is a list of strings (filepaths or manifests) + if isinstance(audio[0], str): + audio_files = list(audio) + + tmp_dir = trcfg._internal.temp_dir + ds_config = self._transcribe_input_manifest_processing(audio_files, tmp_dir, trcfg) + + temp_dataloader = self._setup_transcribe_dataloader(ds_config) + return temp_dataloader + + # Check if audio is a list of numpy or torch tensors + elif isinstance(audio[0], (np.ndarray, torch.Tensor)): + audio_tensors = list(audio) + + # Convert numpy tensors to torch tensors + if any([isinstance(_tensor, np.ndarray) for _tensor in audio_tensors]): + audio_tensors = [ + torch.as_tensor(audio_tensor) if isinstance(audio_tensor, np.ndarray) else audio_tensor + for audio_tensor in audio_tensors + ] + + tmp_dir = trcfg._internal.temp_dir + ds_config = self._transcribe_input_tensor_processing(audio_tensors, tmp_dir, trcfg) + + temp_dataloader = self._setup_transcribe_tensor_dataloader(ds_config, trcfg) + return temp_dataloader + + else: + raise ValueError( + f"Input `audio` is of type {type(audio[0])}. " + "Only `str` (path to audio file), `np.ndarray`, and `torch.Tensor` " + "are supported as input." + ) + + def _transcribe_input_tensor_processing( + self, audio_tensors: List[Union[np.ndarray, torch.Tensor]], temp_dir: str, trcfg: TranscribeConfig + ): + """ + Internal function to process the input audio tensors and return a config dict for the dataloader. + + Args: + audio_tensors: A list of numpy or torch tensors. The user must ensure that they satisfy the correct + sample rate and channel format. + temp_dir: A temporary directory to store intermediate files. + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + + Returns: + A config dict that is used to setup the dataloader for transcription. + """ + # Check if sample rate is set + sample_rate = None + if hasattr(self, 'cfg') and 'sample_rate' in self.cfg: + sample_rate = self.cfg.sample_rate + elif hasattr(self, 'sample_rate'): + sample_rate = self.sample_rate + + if sample_rate is None: + raise RuntimeError( + "Provided `audio` data contains numpy or torch tensors, however the class " + "does not have `sample_rate` attribute. Please set `sample_rate` attribute to the model explicitly." + ) + + ds_config = { + 'audio_tensors': audio_tensors, + 'batch_size': get_value_from_transcription_config(trcfg, 'batch_size', 4), + 'temp_dir': temp_dir, + 'num_workers': get_value_from_transcription_config(trcfg, 'num_workers', 0), + 'channel_selector': get_value_from_transcription_config(trcfg, 'channel_selector', None), + 'sample_rate': sample_rate, + } + + augmentor = get_value_from_transcription_config(trcfg, 'augmentor', None) + if augmentor: + ds_config['augmentor'] = augmentor + + return ds_config + + @abstractmethod + def _transcribe_input_manifest_processing( + self, audio_files: List[str], temp_dir: str, trcfg: TranscribeConfig + ) -> Dict[str, Any]: + """ + Internal function to process the input audio filepaths and return a config dict for the dataloader. + + Args: + audio_files: A list of string filepaths for audio files, or a single string filepath for a manifest file. + temp_dir: A temporary directory to store intermediate files. + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + + Returns: + A config dict that is used to setup the dataloader for transcription. + """ + pass + + @abstractmethod + def _setup_transcribe_dataloader(self, config: Dict) -> DataLoader: + """ + Internal function to setup the dataloader for transcription. This function is called by + `transcribe()` and `transcribe_generator()` to setup the input data for transcription. + + Args: + config: A config dict that is used to setup the dataloader for transcription. It can be generated either + by `_transcribe_input_manifest_processing()` or `_transcribe_input_tensor_processing()`. + + Returns: + A DataLoader object that is used to iterate over the input audio data. + """ + pass + + @abstractmethod + def _transcribe_forward(self, batch: Any, trcfg: TranscribeConfig): + """ + Internal function to perform the model's custom forward pass to return outputs that are processed by + `_transcribe_output_processing()`. + This function is called by `transcribe()` and `transcribe_generator()` to perform the model's forward pass. + + Args: + batch: A batch of input data from the data loader that is used to perform the model's forward pass. + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + + Returns: + The model's outputs that are processed by `_transcribe_output_processing()`. + """ + pass + + @abstractmethod + def _transcribe_output_processing(self, outputs, trcfg: TranscribeConfig) -> GenericTranscriptionType: + """ + Internal function to process the model's outputs to return the results to the user. This function is called by + `transcribe()` and `transcribe_generator()` to process the model's outputs. + + Args: + outputs: The model's outputs that are processed by `_transcribe_forward()`. + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + + Returns: + The output can be a list of + objects, list of list of objects, tuple of objects, tuple of list of objects, or a dict of list of objects. + Its type is defined in `TranscriptionReturnType`. + """ + pass + + def _transcribe_on_end(self, trcfg: TranscribeConfig): + """ + Internal function to teardown the model after transcription. Perform all teardown and post-checks here. + + Args: + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + """ + # set mode back to its original value + self.train(mode=trcfg._internal.training_mode) + + if hasattr(self, 'preprocessor'): + if hasattr(self.preprocessor, 'featurizer') and hasattr(self.preprocessor.featurizer, 'dither'): + self.preprocessor.featurizer.dither = trcfg._internal.dither_value + + if hasattr(self.preprocessor, 'featurizer') and hasattr(self.preprocessor.featurizer, 'pad_to'): + self.preprocessor.featurizer.pad_to = trcfg._internal.pad_to_value + + if trcfg._internal.logging_level is not None: + logging.set_verbosity(trcfg._internal.logging_level) + + def _setup_transcribe_tensor_dataloader(self, config: Dict, trcfg: TranscribeConfig) -> DataLoader: + """ + Internal function to setup the dataloader for transcription. This function is called by + `transcribe()` and `transcribe_generator()` to setup the input data for transcription. + + Args: + config: A config dict that is used to setup the dataloader for transcription. It can be generated either + by `_transcribe_input_manifest_processing()` or `_transcribe_input_tensor_processing()`. + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + + Returns: + A DataLoader object that is used to iterate over the input audio data. + """ + dataset = TranscriptionTensorDataset(config) + + # Import collate function here to avoid circular imports + from nemo.collections.asr.data.audio_to_text import _speech_collate_fn + + # Calculate pad id + if hasattr(self, 'tokenizer') and hasattr(self.tokenizer, 'pad_id'): + pad_id = self.tokenizer.pad_id + elif hasattr(self, 'transcribe_pad_id'): + logging.info("Pad id is explicitly set to `model.transcribe_pad_id` = {}".format(self.transcribe_pad_id)) + pad_id = self.transcribe_pad_id + else: + logging.info( + "Pad id is being set to 0 because it could not be resolved from the tokenizer. " + "This can happen for various reasons, especially for character based models. " + "If pad id is incorrect, please provide the pad id explicitly by setting " + "`model.transcribe_pad_id`." + ) + pad_id = 0 + + return DataLoader( + dataset=dataset, + shuffle=False, + batch_size=config['batch_size'], + num_workers=config['num_workers'], + pin_memory=False, + drop_last=False, + collate_fn=partial(_speech_collate_fn, pad_id=pad_id), + ) + + +class ASRTranscriptionMixin(TranscriptionMixin): + """ + An abstract class for ASR models that can transcribe audio. This class is a subclass of `TranscriptionMixin` that + implements the default implementation of common abstract methods among the speech recognition model classes. + + The following abstract classes must be implemented by the subclass: + + - _transcribe_forward(): + Implements the model's custom forward pass to return outputs that are processed by + `_transcribe_output_processing()`. + + - _transcribe_output_processing(): + Implements the post processing of the model's outputs to return the results to + the user. The result can be a list of objects, list of list of objects, tuple of objects, tuple of list of + """ + + def _transcribe_input_manifest_processing( + self, audio_files: List[str], temp_dir: str, trcfg: TranscribeConfig + ) -> Dict[str, Any]: + """ + Internal function to process the input audio filepaths and return a config dict for the dataloader. + Specializes to ASR models which can have Encoder-Decoder-Joint architectures. + + Args: + audio_files: A list of string filepaths for audio files. + temp_dir: A temporary directory to store intermediate files. + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + + Returns: + A config dict that is used to setup the dataloader for transcription. + """ + with open(os.path.join(temp_dir, 'manifest.json'), 'w', encoding='utf-8') as fp: + for audio_file in audio_files: + if isinstance(audio_file, str): + entry = {'audio_filepath': audio_file, 'duration': 100000, 'text': ''} + fp.write(json.dumps(entry) + '\n') + elif isinstance(audio_file, dict): + fp.write(json.dumps(audio_file) + '\n') + else: + raise ValueError( + f"Input `audio` is of type {type(audio_file)}. " + "Only `str` (path to audio file) or `dict` are supported as input." + ) + + ds_config = { + 'paths2audio_files': audio_files, + 'batch_size': get_value_from_transcription_config(trcfg, 'batch_size', 4), + 'temp_dir': temp_dir, + 'num_workers': get_value_from_transcription_config(trcfg, 'num_workers', 0), + 'channel_selector': get_value_from_transcription_config(trcfg, 'channel_selector', None), + 'text_field': get_value_from_transcription_config(trcfg, 'text_field', 'text'), + 'lang_field': get_value_from_transcription_config(trcfg, 'lang_field', 'lang'), + } + + augmentor = get_value_from_transcription_config(trcfg, 'augmentor', None) + if augmentor: + ds_config['augmentor'] = augmentor + + return ds_config + + def _transcribe_on_begin(self, audio, trcfg: TranscribeConfig): + """ + Internal function to setup the model for transcription. Perform all setup and pre-checks here. + + Args: + audio: Of type `GenericTranscriptionType` + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + """ + super()._transcribe_on_begin(audio, trcfg) + + # Freeze the encoder and decoder modules + if hasattr(self, 'encoder'): + self.encoder.freeze() + + if hasattr(self, 'decoder'): + self.decoder.freeze() + + if hasattr(self, 'joint'): + self.joint.freeze() + + def _transcribe_on_end(self, trcfg: TranscribeConfig): + """ + Internal function to teardown the model after transcription. Perform all teardown and post-checks here. + + Args: + trcfg: The transcription config dataclass. Subclasses can change this to a different dataclass if needed. + """ + super()._transcribe_on_end(trcfg) + + # Unfreeze the encoder and decoder modules + if hasattr(self, 'encoder'): + self.encoder.unfreeze() + + if hasattr(self, 'decoder'): + self.decoder.unfreeze() + + if hasattr(self, 'joint'): + self.joint.unfreeze() + + @classmethod + def get_transcribe_config(cls) -> TranscribeConfig: + """ + Utility method that returns the default config for transcribe() function. + + Returns: + A dataclass + """ + return TranscribeConfig() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/__init__.py new file mode 100644 index 0000000..77a23cf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.parts.numba.rnnt_loss.rnnt_pytorch import RNNTLossNumba diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/__init__.py new file mode 100644 index 0000000..055d7ae --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.parts.numba.rnnt_loss.rnnt import rnnt_loss_cpu, rnnt_loss_gpu +from nemo.collections.asr.parts.numba.rnnt_loss.rnnt_pytorch import ( + MultiblankRNNTLossNumba, + RNNTLossNumba, + TDTLossNumba, +) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/rnnt.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/rnnt.py new file mode 100644 index 0000000..046aea4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/rnnt.py @@ -0,0 +1,483 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright 2018-2019, Mingkun Huang +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import multiprocessing + +import torch +from numba import cuda + +from nemo.collections.asr.parts.numba.rnnt_loss.utils import global_constants, rnnt_helper +from nemo.collections.asr.parts.numba.rnnt_loss.utils.cpu_utils import cpu_rnnt +from nemo.collections.asr.parts.numba.rnnt_loss.utils.cuda_utils import gpu_rnnt + + +def rnnt_loss_cpu( + acts: torch.Tensor, + labels: torch.Tensor, + input_lengths: torch.Tensor, + label_lengths: torch.Tensor, + costs: torch.Tensor, + grads: torch.Tensor, + blank_label: int, + fastemit_lambda: float, + clamp: float, + num_threads: int, +): + """ + Wrapper method for accessing CPU RNNT loss. + + CPU implementation ported from [HawkAaron/warp-transducer](https://github.com/HawkAaron/warp-transducer). + + Args: + acts: Activation tensor of shape [B, T, U, V+1]. + labels: Ground truth labels of shape [B, U]. + input_lengths: Lengths of the acoustic sequence as a vector of ints [B]. + label_lengths: Lengths of the target sequence as a vector of ints [B]. + costs: Zero vector of length [B] in which costs will be set. + grads: Zero tensor of shape [B, T, U, V+1] where the gradient will be set. + blank_label: Index of the blank token in the vocabulary. + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + num_threads: Number of threads for OpenMP. + """ + # aliases + log_probs = acts + flat_labels = labels + + minibatch_size = log_probs.shape[0] + maxT = log_probs.shape[1] + maxU = log_probs.shape[2] + alphabet_size = log_probs.shape[3] + + if num_threads < 0: + num_threads = multiprocessing.cpu_count() + + num_threads = max(1, num_threads) # have to use at least 1 thread + + gpu_size, status = rnnt_helper.get_workspace_size(maxT, maxU, minibatch_size, gpu=False) + if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS: + raise RuntimeError("Invalid parameter passed when calculating working space memory") + + cpu_workspace = torch.zeros(gpu_size, device=log_probs.device, dtype=log_probs.dtype, requires_grad=False) + + ### VIEW TENSORS AS VECTORS FOR POINTER INDEXING ### + log_probs, acts_shape = rnnt_helper.flatten_tensor(log_probs) + flat_labels, labels_shape = rnnt_helper.flatten_tensor(flat_labels) + + wrapper = cpu_rnnt.CPURNNT( + minibatch=minibatch_size, + maxT=maxT, + maxU=maxU, + alphabet_size=alphabet_size, + workspace=cpu_workspace, + blank=blank_label, + fastemit_lambda=fastemit_lambda, + clamp=clamp, + num_threads=num_threads, + batch_first=True, + ) + + if grads is None: + status = wrapper.score_forward( + log_probs=log_probs.data, + costs=costs, + flat_labels=flat_labels.data, + label_lengths=label_lengths.data, + input_lengths=input_lengths.data, + ) + + if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS: + raise RuntimeError("Could not calculate forward scores") + + else: + ### FLATTEN GRAD TENSOR ### + grads, grads_shape = rnnt_helper.flatten_tensor(grads) + + status = wrapper.cost_and_grad( + log_probs=log_probs.data, + grads=grads.data, + costs=costs, + flat_labels=flat_labels.data, + label_lengths=label_lengths.data, + input_lengths=input_lengths.data, + ) + + if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS: + raise RuntimeError("Could not calculate forward scores") + + del cpu_workspace, wrapper + return True + + +def rnnt_loss_gpu( + acts: torch.Tensor, + labels: torch.Tensor, + input_lengths: torch.Tensor, + label_lengths: torch.Tensor, + costs: torch.Tensor, + grads: torch.Tensor, + blank_label: int, + fastemit_lambda: float, + clamp: float, + num_threads: int, +): + """ + Wrapper method for accessing GPU RNNT loss. + + CUDA implementation ported from [HawkAaron/warp-transducer](https://github.com/HawkAaron/warp-transducer). + + Args: + acts: Activation tensor of shape [B, T, U, V+1]. + labels: Ground truth labels of shape [B, U]. + input_lengths: Lengths of the acoustic sequence as a vector of ints [B]. + label_lengths: Lengths of the target sequence as a vector of ints [B]. + costs: Zero vector of length [B] in which costs will be set. + grads: Zero tensor of shape [B, T, U, V+1] where the gradient will be set. + blank_label: Index of the blank token in the vocabulary. + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + num_threads: Number of threads for OpenMP. + """ + minibatch_size = acts.shape[0] + maxT = acts.shape[1] + maxU = acts.shape[2] + alphabet_size = acts.shape[3] + + if hasattr(cuda, 'external_stream'): + stream = cuda.external_stream(torch.cuda.current_stream(acts.device).cuda_stream) + else: + stream = cuda.default_stream() + + if num_threads < 0: + num_threads = multiprocessing.cpu_count() + + num_threads = max(1, num_threads) # have to use at least 1 thread + + gpu_size, status = rnnt_helper.get_workspace_size(maxT, maxU, minibatch_size, gpu=True) + if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS: + raise RuntimeError("Invalid parameter passed when calculating working space memory") + + # Select GPU index + cuda.select_device(acts.device.index) + gpu_workspace = torch.zeros(gpu_size, device=acts.device, dtype=torch.float32, requires_grad=False) + + ### VIEW TENSORS AS VECTORS FOR POINTER INDEXING ### + acts, acts_shape = rnnt_helper.flatten_tensor(acts) + + wrapper = gpu_rnnt.GPURNNT( + minibatch=minibatch_size, + maxT=maxT, + maxU=maxU, + alphabet_size=alphabet_size, + workspace=gpu_workspace, + blank=blank_label, + fastemit_lambda=fastemit_lambda, + clamp=clamp, + num_threads=num_threads, + stream=stream, + ) + + if grads is None: + status = wrapper.score_forward( + acts=acts.data, + costs=costs.data, + pad_labels=labels.data, + label_lengths=label_lengths.data, + input_lengths=input_lengths.data, + ) + + if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS: + raise RuntimeError("Could not calculate forward scores") + + else: + ### FLATTEN GRAD TENSOR ### + grads, grads_shape = rnnt_helper.flatten_tensor(grads) + + status = wrapper.cost_and_grad( + acts=acts.data, + grads=grads.data, + costs=costs.data, + pad_labels=labels.data, + label_lengths=label_lengths.data, + input_lengths=input_lengths.data, + ) + + if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS: + raise RuntimeError("Could not calculate forward scores") + + del gpu_workspace, wrapper + return True + + +def tdt_loss_gpu( + label_acts: torch.Tensor, + duration_acts: torch.Tensor, + labels: torch.Tensor, + input_lengths: torch.Tensor, + label_lengths: torch.Tensor, + costs: torch.Tensor, + label_grads: torch.Tensor, + duration_grads: torch.Tensor, + blank_label: int, + durations: list, + fastemit_lambda: float, + clamp: float, + num_threads: int, + sigma: float, + omega: float, +): + """ + Wrapper method for accessing GPU TDT loss (https://arxiv.org/abs/2304.06795). + + CUDA implementation ported from [HawkAaron/warp-transducer](https://github.com/HawkAaron/warp-transducer). + + Args: + label_acts: Activation tensor of shape [B, T, U, V], where V includes the blank symbol. + duration_acts: Activation tensor of shape [B, T, U, D], where D is the number of durations. + labels: Ground truth labels of shape [B, U]. + input_lengths: Lengths of the acoustic sequence as a vector of ints [B]. + label_lengths: Lengths of the target sequence as a vector of ints [B]. + costs: Zero vector of length [B] in which costs will be set. + label_grads: Zero tensor of shape [B, T, U, V] where the gradient to label_acts will be set. + duration_grads: Zero tensor of shape [B, T, U, D] where the gradient to duration_acts will be set. + blank_label: Index of the standard blank token in the vocabulary. + durations: A list of supported durations for TDT. Must include 0 and 1. + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + num_threads: Number of threads for OpenMP. + sigma: logit-undernormalization weight used in the multi-blank model. Refer to + the multi-blank paper https://arxiv.org/abs/2304.06795 for detailed explanations. + omega: weight for regular RNN-T loss + """ + minibatch_size = label_acts.shape[0] + maxT = label_acts.shape[1] + maxU = label_acts.shape[2] + alphabet_size = label_acts.shape[3] + + if hasattr(cuda, 'external_stream'): + stream = cuda.external_stream(torch.cuda.current_stream(label_acts.device).cuda_stream) + else: + stream = cuda.default_stream() + + if num_threads < 0: + num_threads = multiprocessing.cpu_count() + + num_threads = max(1, num_threads) # have to use at least 1 thread + + gpu_size, status = rnnt_helper.get_workspace_size(maxT, maxU, minibatch_size, gpu=True) + + if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS: + raise RuntimeError("Invalid parameter passed when calculating working space memory") + + # Select GPU index + cuda.select_device(label_acts.device.index) + gpu_workspace = torch.zeros(gpu_size, device=label_acts.device, dtype=label_acts.dtype, requires_grad=False) + + tdt_workspace = torch.zeros(len(durations), device=label_acts.device, dtype=torch.long, requires_grad=False) + + for i in range(0, len(durations)): + tdt_workspace[i] = durations[i] + + ### VIEW TENSORS AS VECTORS FOR POINTER INDEXING ### + label_acts, label_acts_shape = rnnt_helper.flatten_tensor(label_acts) + duration_acts, duration_acts_shape = rnnt_helper.flatten_tensor(duration_acts) + + wrapper = gpu_rnnt.GPUTDT( + minibatch=minibatch_size, + maxT=maxT, + maxU=maxU, + alphabet_size=alphabet_size, + workspace=gpu_workspace, + tdt_workspace=tdt_workspace, + num_durations=len(durations), + blank=blank_label, + fastemit_lambda=fastemit_lambda, + clamp=clamp, + num_threads=num_threads, + stream=stream, + sigma=sigma, + omega=omega, + ) + + if label_grads is None: + status = wrapper.score_forward( + label_acts=label_acts.data, + duration_acts=duration_acts.data, + costs=costs.data, + pad_labels=labels.data, + label_lengths=label_lengths.data, + input_lengths=input_lengths.data, + ) + + if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS: + raise RuntimeError("Could not calculate forward scores") + + else: + ### FLATTEN GRAD TENSOR ### + label_grads, label_grads_shape = rnnt_helper.flatten_tensor(label_grads) + duration_grads, duration_grads_shape = rnnt_helper.flatten_tensor(duration_grads) + + status = wrapper.cost_and_grad( + label_acts=label_acts.data, + duration_acts=duration_acts.data, + label_grads=label_grads.data, + duration_grads=duration_grads.data, + costs=costs.data, + pad_labels=labels.data, + label_lengths=label_lengths.data, + input_lengths=input_lengths.data, + ) + + if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS: + raise RuntimeError("Could not calculate forward scores") + + del gpu_workspace, tdt_workspace, wrapper + return True + + +def multiblank_rnnt_loss_gpu( + acts: torch.Tensor, + labels: torch.Tensor, + input_lengths: torch.Tensor, + label_lengths: torch.Tensor, + costs: torch.Tensor, + grads: torch.Tensor, + blank_label: int, + big_blank_durations: list, + fastemit_lambda: float, + clamp: float, + num_threads: int, + sigma: float, +): + """ + Wrapper method for accessing GPU Multi-blank RNNT loss (https://arxiv.org/pdf/2211.03541.pdf). + + CUDA implementation ported from [HawkAaron/warp-transducer](https://github.com/HawkAaron/warp-transducer). + + Args: + acts: Activation tensor of shape [B, T, U, V + num_big_blanks + 1]. + labels: Ground truth labels of shape [B, U]. + input_lengths: Lengths of the acoustic sequence as a vector of ints [B]. + label_lengths: Lengths of the target sequence as a vector of ints [B]. + costs: Zero vector of length [B] in which costs will be set. + grads: Zero tensor of shape [B, T, U, V + num_big_blanks + 1] where the gradient will be set. + blank_label: Index of the standard blank token in the vocabulary. + big_blank_durations: A list of supported durations for big blank symbols + in the model, e.g. [2, 4, 8]. Note we only include durations for ``big + blanks'' here and it should not include 1 for the standard blank. + Those big blanks have vocabulary indices after the standard blank index. + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + num_threads: Number of threads for OpenMP. + sigma: logit-undernormalization weight used in the multi-blank model. Refer to + the multi-blank paper https://arxiv.org/pdf/2211.03541 for detailed explanations. + """ + minibatch_size = acts.shape[0] + maxT = acts.shape[1] + maxU = acts.shape[2] + alphabet_size = acts.shape[3] + + if hasattr(cuda, 'external_stream'): + stream = cuda.external_stream(torch.cuda.current_stream(acts.device).cuda_stream) + else: + stream = cuda.default_stream() + + if num_threads < 0: + num_threads = multiprocessing.cpu_count() + + num_threads = max(1, num_threads) # have to use at least 1 thread + + gpu_size, status = rnnt_helper.get_workspace_size(maxT, maxU, minibatch_size, gpu=True) + + if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS: + raise RuntimeError("Invalid parameter passed when calculating working space memory") + + # Select GPU index + cuda.select_device(acts.device.index) + gpu_workspace = torch.zeros(gpu_size, device=acts.device, dtype=acts.dtype, requires_grad=False) + + big_blank_workspace = torch.zeros( + len(big_blank_durations), device=acts.device, dtype=torch.long, requires_grad=False + ) + + for i in range(0, len(big_blank_durations)): + big_blank_workspace[i] = big_blank_durations[i] + + ### VIEW TENSORS AS VECTORS FOR POINTER INDEXING ### + acts, acts_shape = rnnt_helper.flatten_tensor(acts) + + wrapper = gpu_rnnt.MultiblankGPURNNT( + minibatch=minibatch_size, + maxT=maxT, + maxU=maxU, + alphabet_size=alphabet_size, + workspace=gpu_workspace, + big_blank_workspace=big_blank_workspace, + num_big_blanks=len(big_blank_durations), + blank=blank_label, + fastemit_lambda=fastemit_lambda, + clamp=clamp, + num_threads=num_threads, + stream=stream, + sigma=sigma, + ) + + if grads is None: + status = wrapper.score_forward( + acts=acts.data, + costs=costs.data, + pad_labels=labels.data, + label_lengths=label_lengths.data, + input_lengths=input_lengths.data, + ) + + if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS: + raise RuntimeError("Could not calculate forward scores") + + else: + ### FLATTEN GRAD TENSOR ### + grads, grads_shape = rnnt_helper.flatten_tensor(grads) + + status = wrapper.cost_and_grad( + acts=acts.data, + grads=grads.data, + costs=costs.data, + pad_labels=labels.data, + label_lengths=label_lengths.data, + input_lengths=input_lengths.data, + ) + + if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS: + raise RuntimeError("Could not calculate forward scores") + + del gpu_workspace, big_blank_workspace, wrapper + return True diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_numpy.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_numpy.py new file mode 100644 index 0000000..5850897 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_numpy.py @@ -0,0 +1,369 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright 2018-2019, Mingkun Huang +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy as np +import torch +from torch.autograd import Function, Variable +from torch.nn import Module + + +def check_type(var, t, name): + if var.dtype is not t: + raise TypeError("{} must be {}".format(name, t)) + + +def check_contiguous(var, name): + if not var.is_contiguous(): + raise ValueError("{} must be contiguous".format(name)) + + +def check_dim(var, dim, name): + if len(var.shape) != dim: + raise ValueError("{} must be {}D".format(name, dim)) + + +def certify_inputs(log_probs, labels, lengths, label_lengths): + # check_type(log_probs, torch.float32, "log_probs") + check_type(labels, torch.int64, "labels") + check_type(label_lengths, torch.int64, "label_lengths") + check_type(lengths, torch.int64, "lengths") + check_contiguous(log_probs, "log_probs") + check_contiguous(labels, "labels") + check_contiguous(label_lengths, "label_lengths") + check_contiguous(lengths, "lengths") + + if lengths.shape[0] != log_probs.shape[0]: + raise ValueError( + f"Must have a length per example. " + f"Given lengths dim: {lengths.shape[0]}, " + f"Log probs dim : {log_probs.shape[0]}" + ) + if label_lengths.shape[0] != log_probs.shape[0]: + raise ValueError( + "Must have a label length per example. " + f"Given label lengths dim : {label_lengths.shape[0]}, " + f"Log probs dim : {log_probs.shape[0]}" + ) + + check_dim(log_probs, 4, "log_probs") + check_dim(labels, 2, "labels") + check_dim(lengths, 1, "lenghts") + check_dim(label_lengths, 1, "label_lenghts") + max_T = torch.max(lengths) + max_U = torch.max(label_lengths) + T, U = log_probs.shape[1:3] + if T != max_T: + raise ValueError(f"Input length mismatch! Given T: {T}, Expected max T from input lengths: {max_T}") + if U != max_U + 1: + raise ValueError(f"Output length mismatch! Given U: {U}, Expected max U from target lengths: {max_U} + 1") + + +def _assert_no_grad(tensor): + assert not tensor.requires_grad, ( + "gradients only computed for log_probs - please " "mark other tensors as not requiring gradients" + ) + + +class LogSoftmaxGradModification(Function): + @staticmethod + def forward(ctx, acts, clamp): + if clamp < 0: + raise ValueError("`clamp` must be 0.0 or positive float.") + + res = acts.new(acts) + ctx.clamp = clamp + return res + + @staticmethod + def backward(ctx, grad_output): + grad_output = torch.clamp(grad_output, -ctx.clamp, ctx.clamp) + return ( + grad_output, + None, + ) + + +def forward_pass(log_probs, labels, blank): + """ + Computes probability of the forward variable alpha. + + Args: + log_probs: Tensor of shape [T, U, V+1] + labels: Labels of shape [B, U] + blank: Index of the blank token. + + Returns: + A tuple of the forward variable probabilities - alpha of shape [T, U] + and the log likelihood of this forward step. + """ + T, U, _ = log_probs.shape + alphas = np.zeros((T, U), dtype='f') + + for t in range(1, T): + alphas[t, 0] = alphas[t - 1, 0] + log_probs[t - 1, 0, blank] + + for u in range(1, U): + alphas[0, u] = alphas[0, u - 1] + log_probs[0, u - 1, labels[u - 1]] + for t in range(1, T): + for u in range(1, U): + no_emit = alphas[t - 1, u] + log_probs[t - 1, u, blank] + emit = alphas[t, u - 1] + log_probs[t, u - 1, labels[u - 1]] + alphas[t, u] = np.logaddexp(emit, no_emit) + + loglike = alphas[T - 1, U - 1] + log_probs[T - 1, U - 1, blank] + return alphas, loglike + + +def backward_pass(log_probs, labels, blank): + """ + Computes probability of the backward variable beta. + + Args: + log_probs: Tensor of shape [T, U, V+1] + labels: Labels of shape [B, U] + blank: Index of the blank token. + + Returns: + A tuple of the backward variable probabilities - beta of shape [T, U] + and the log likelihood of this backward step. + """ + T, U, _ = log_probs.shape + betas = np.zeros((T, U), dtype='f') + betas[T - 1, U - 1] = log_probs[T - 1, U - 1, blank] + + for t in reversed(range(T - 1)): + betas[t, U - 1] = betas[t + 1, U - 1] + log_probs[t, U - 1, blank] + + for u in reversed(range(U - 1)): + betas[T - 1, u] = betas[T - 1, u + 1] + log_probs[T - 1, u, labels[u]] + + for t in reversed(range(T - 1)): + for u in reversed(range(U - 1)): + no_emit = betas[t + 1, u] + log_probs[t, u, blank] + emit = betas[t, u + 1] + log_probs[t, u, labels[u]] + betas[t, u] = np.logaddexp(emit, no_emit) + + return betas, betas[0, 0] + + +def compute_gradient(log_probs, alphas, betas, labels, blank, fastemit_lambda): + """ + Computes the gradients of the log_probs with respect to the log probability of this step occuring. + + Args: + Args: + log_probs: Tensor of shape [T, U, V+1] + alphas: Tensor of shape [T, U] which represents the forward variable. + betas: Tensor of shape [T, U] which represents the backward variable. + labels: Labels of shape [B, U] + blank: Index of the blank token. + + Returns: + Gradients of shape [T, U, V+1] with respect to the forward log probability + """ + T, U, _ = log_probs.shape + grads = np.full(log_probs.shape, -float("inf")) + log_like = betas[0, 0] # == alphas[T - 1, U - 1] + betas[T - 1, U - 1] + + # // grad to last blank transition + grads[T - 1, U - 1, blank] = alphas[T - 1, U - 1] + grads[: T - 1, :, blank] = alphas[: T - 1, :] + betas[1:, :] + + # // grad to label transition + for u, l in enumerate(labels): + grads[:, u, l] = alphas[:, u] + betas[:, u + 1] + + grads = -np.exp(grads + log_probs - log_like) + + if fastemit_lambda > 0.0: + for u, l in enumerate(labels): + grads[:, u, l] = (1.0 + fastemit_lambda) * grads[:, u, l] + + return grads + + +def fastemit_regularization(log_probs, labels, alphas, betas, blank, fastemit_lambda): + """ + Describes the computation of FastEmit regularization from the paper - + [FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization](https://arxiv.org/abs/2010.11148) + + Args: + log_probs: Tensor of shape [T, U, V+1] + labels: Unused. Labels of shape [B, U] + alphas: Tensor of shape [T, U] which represents the forward variable. + betas: Unused. Tensor of shape [T, U] which represents the backward variable. + blank: Index of the blank token. + fastemit_lambda: Float scaling factor for FastEmit regularization. + + Returns: + The regularized negative log likelihood - lambda * P˜(At, u|x) + """ + # General calculation of the fastemit regularization alignments + T, U, _ = log_probs.shape + # alignment = np.zeros((T, U), dtype='float32') + # + # for t in range(0, T): + # alignment[t, U - 1] = alphas[t, U - 1] + betas[t, U - 1] + # + # for t in range(0, T): + # for u in range(0, U - 1): + # emit = alphas[t, u] + log_probs[t, u, labels[u]] + betas[t, u + 1] + # alignment[t, u] = emit + # reg = fastemit_lambda * (alignment[T - 1, U - 1]) + + # The above is equivalent to below, without need of computing above + # reg = fastemit_lambda * (alphas[T - 1, U - 1] + betas[T - 1, U - 1]) + + # The above is also equivalent to below, without need of computing the betas alignment matrix + reg = fastemit_lambda * (alphas[T - 1, U - 1] + log_probs[T - 1, U - 1, blank]) + return -reg + + +def transduce(log_probs, labels, blank=0, fastemit_lambda=0.0): + """ + Args: + log_probs: 3D array with shape + [input len, output len + 1, vocab size] + labels: 1D array with shape [output time steps] + blank: Index of the blank token. + fastemit_lambda: Float scaling factor for FastEmit regularization. + + Returns: + float: The negative log-likelihood + 3D array: Gradients with respect to the + unnormalized input actications + 2d arrays: Alphas matrix (TxU) + 2d array: Betas matrix (TxU) + """ + alphas, ll_forward = forward_pass(log_probs, labels, blank) + betas, ll_backward = backward_pass(log_probs, labels, blank) + grads = compute_gradient(log_probs, alphas, betas, labels, blank, fastemit_lambda) + return -ll_forward, grads, alphas, betas + + +def transduce_batch(log_probs, labels, flen, glen, blank=0, fastemit_lambda=0.0): + """ + Compute the transducer loss of the batch. + + Args: + log_probs: [B, T, U, V+1]. Activation matrix normalized with log-softmax. + labels: [B, U+1] - ground truth labels with padded as blank token in the beginning. + flen: Length vector of the acoustic sequence. + glen: Length vector of the target sequence. + blank: Id of the blank token. + fastemit_lambda: Float scaling factor for FastEmit regularization. + + Returns: + Batch of transducer forward log probabilities (loss) and the gradients of the activation matrix. + """ + grads = np.zeros_like(log_probs) + costs = [] + for b in range(log_probs.shape[0]): + t = int(flen[b]) + u = int(glen[b]) + 1 + + ll, g, alphas, betas = transduce(log_probs[b, :t, :u, :], labels[b, : u - 1], blank, fastemit_lambda) + grads[b, :t, :u, :] = g + + reg = fastemit_regularization( + log_probs[b, :t, :u, :], labels[b, : u - 1], alphas, betas, blank, fastemit_lambda + ) + ll += reg + costs.append(ll) + return costs, grads + + +class _RNNT(Function): + @staticmethod + def forward(ctx, acts, labels, act_lens, label_lens, blank, fastemit_lambda): + costs, grads = transduce_batch( + acts.detach().cpu().numpy(), + labels.cpu().numpy(), + act_lens.cpu().numpy(), + label_lens.cpu().numpy(), + blank, + fastemit_lambda, + ) + + costs = torch.FloatTensor([sum(costs)]) + grads = torch.Tensor(grads).to(acts) + + ctx.grads = grads + return costs + + @staticmethod + def backward(ctx, grad_output): + grad_output = grad_output.view(-1, 1, 1, 1).to(ctx.grads) + return ctx.grads.mul(grad_output), None, None, None, None, None + + +class RNNTLoss(Module): + """ + Parameters: + `blank_label` (int): default 0 - label index of blank token + fastemit_lambda: Float scaling factor for FastEmit regularization. + """ + + def __init__(self, blank: int = 0, fastemit_lambda: float = 0.0, clamp: float = -1.0): + super(RNNTLoss, self).__init__() + self.blank = blank + self.fastemit_lambda = fastemit_lambda + self.clamp = float(clamp) if clamp > 0 else 0.0 + self.rnnt = _RNNT.apply + + def forward(self, acts, labels, act_lens, label_lens): + assert len(labels.size()) == 2 + _assert_no_grad(labels) + _assert_no_grad(act_lens) + _assert_no_grad(label_lens) + certify_inputs(acts, labels, act_lens, label_lens) + + # CPU Patch for fp16 - force cast to fp32 + if not acts.is_cuda and acts.dtype == torch.float16: + acts = acts.float() + + if self.clamp > 0.0: + acts = LogSoftmaxGradModification.apply(acts, self.clamp) + + acts = torch.nn.functional.log_softmax(acts, -1) + + return self.rnnt(acts, labels, act_lens, label_lens, self.blank, self.fastemit_lambda) + + +if __name__ == '__main__': + loss = RNNTLoss(fastemit_lambda=0.01) + + torch.manual_seed(0) + + acts = torch.randn(1, 2, 5, 3) + labels = torch.tensor([[0, 2, 1, 2]], dtype=torch.int64) + act_lens = torch.tensor([2], dtype=torch.int64) + label_lens = torch.tensor([len(labels[0])], dtype=torch.int64) + + loss_val = loss(acts, labels, act_lens, label_lens) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py new file mode 100644 index 0000000..5960d5a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py @@ -0,0 +1,632 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright 2018-2019, Mingkun Huang +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +from torch.autograd import Function +from torch.nn import Module + +from nemo.collections.asr.parts.numba.rnnt_loss import rnnt +from nemo.collections.asr.parts.numba.rnnt_loss.utils.cpu_utils import cpu_rnnt + +__all__ = ['rnnt_loss', 'RNNTLossNumba', 'MultiblankRNNTLossNumba', 'TDTLossNumba'] + + +class _RNNTNumba(Function): + @staticmethod + def forward(ctx, acts, labels, act_lens, label_lens, blank, reduction, fastemit_lambda, clamp): + """ + log_probs: Tensor of (batch x seqLength x labelLength x outputDim) containing output from network + labels: 2 dimensional Tensor containing all the targets of the batch with zero padded + act_lens: Tensor of size (batch) containing size of each output sequence from the network + label_lens: Tensor of (batch) containing label length of each example + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + """ + is_cuda = acts.is_cuda + + certify_inputs(acts, labels, act_lens, label_lens) + if clamp < 0: + raise ValueError("`clamp` must be 0.0 or positive float value.") + + loss_func = rnnt.rnnt_loss_gpu if is_cuda else rnnt.rnnt_loss_cpu + grads = torch.zeros_like(acts) if acts.requires_grad else None + minibatch_size = acts.size(0) + costs = torch.zeros(minibatch_size, device=acts.device, dtype=torch.float32) + + loss_func( + acts, + labels=labels, + input_lengths=act_lens, + label_lengths=label_lens, + costs=costs, + grads=grads, + blank_label=blank, + fastemit_lambda=fastemit_lambda, + clamp=clamp, + num_threads=0, + ) + + if reduction in ['sum', 'mean']: + costs = costs.sum().unsqueeze_(-1) + if reduction == 'mean': + costs /= minibatch_size + + if grads is not None: + grads /= minibatch_size + + ctx.grads = grads + + return costs + + @staticmethod + def backward(ctx, grad_output): + if grad_output is not None and ctx.grads is not None: + grad_output = grad_output.view(-1, 1, 1, 1).to(ctx.grads) + return ctx.grads.mul_(grad_output), None, None, None, None, None, None, None + + +class _TDTNumba(Function): + """ + Numba class for Token-and-Duration Transducer (TDT) loss (https://arxiv.org/abs/2304.06795) + """ + + @staticmethod + def forward( + ctx, + label_acts, + duration_acts, + labels, + act_lens, + label_lens, + blank, + durations, + reduction, + fastemit_lambda, + clamp, + sigma, + omega, + ): + """ + log_probs: Tensor of (batch x seqLength x labelLength x outputDim) containing output from network + labels: 2 dimensional Tensor containing all the targets of the batch with zero padded + act_lens: Tensor of size (batch) containing size of each output sequence from the network + label_lens: Tensor of (batch) containing label length of each example + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + durations: list of durations for TDT model, must include 0 and 1, e.g. + [0, 1, 2, 3, 4]. + sigma: hyper-parameter for logit under-normalization method for training + TDT models. Recommended value 0.05. + omega: probability for sampling the standard RNN-T loss. + Refer to https://arxiv.org/abs/2304.06795 for detailed explanations for + the above parameters; + """ + is_cuda = label_acts.is_cuda + + certify_inputs(label_acts, labels, act_lens, label_lens) + if clamp < 0: + raise ValueError("`clamp` must be 0.0 or positive float value.") + + if is_cuda: + loss_func = rnnt.tdt_loss_gpu + else: + raise ValueError("TDT is not yet implemented for non CUDA computation.") + + label_grads = torch.zeros_like(label_acts) if label_acts.requires_grad else None + duration_grads = torch.zeros_like(duration_acts) if duration_acts.requires_grad else None + minibatch_size = label_acts.size(0) + costs = torch.zeros(minibatch_size, device=label_acts.device, dtype=label_acts.dtype) + + loss_func( + label_acts, + duration_acts, + labels=labels, + input_lengths=act_lens, + label_lengths=label_lens, + costs=costs, + label_grads=label_grads, + duration_grads=duration_grads, + blank_label=blank, + durations=durations, + fastemit_lambda=fastemit_lambda, + clamp=clamp, + sigma=sigma, + omega=omega, + num_threads=0, + ) + + if reduction in ['sum', 'mean']: + costs = costs.sum().unsqueeze_(-1) + if reduction == 'mean': + costs /= minibatch_size + + if label_grads is not None: + label_grads /= minibatch_size + duration_grads /= minibatch_size + + ctx.label_grads = label_grads + ctx.duration_grads = duration_grads + + return costs + + @staticmethod + def backward(ctx, grad_output): + if grad_output is not None and ctx.label_grads is not None: + grad_output = grad_output.view(-1, 1, 1, 1).to(ctx.label_grads) + return ( + ctx.label_grads.mul_(grad_output), + ctx.duration_grads.mul_(grad_output), + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + ) + + +class _MultiblankRNNTNumba(Function): + """ + Numba class for multi-blank transducer loss (https://arxiv.org/pdf/2211.03541.pdf) + """ + + @staticmethod + def forward( + ctx, acts, labels, act_lens, label_lens, blank, big_blank_durations, reduction, fastemit_lambda, clamp, sigma + ): + """ + big_blank_durations: list of durations for multi-blank transducer, e.g. + [2, 4, 8]. + sigma: hyper-parameter for logit under-normalization method for training + multi-blank transducers. Recommended value 0.05. + Refer to https://arxiv.org/pdf/2211.03541 for detailed explanations for + the above parameters; + For other parameters for this class, refer to comment for class _RNNTNumba + """ + is_cuda = acts.is_cuda + + certify_inputs(acts, labels, act_lens, label_lens) + if clamp < 0: + raise ValueError("`clamp` must be 0.0 or positive float value.") + + if is_cuda: + loss_func = rnnt.multiblank_rnnt_loss_gpu + else: + raise NotImplementedError() + + grads = torch.zeros_like(acts) if acts.requires_grad else None + minibatch_size = acts.size(0) + costs = torch.zeros(minibatch_size, device=acts.device, dtype=acts.dtype) + + loss_func( + acts, + labels=labels, + input_lengths=act_lens, + label_lengths=label_lens, + costs=costs, + grads=grads, + blank_label=blank, + big_blank_durations=big_blank_durations, + fastemit_lambda=fastemit_lambda, + clamp=clamp, + sigma=sigma, + num_threads=0, + ) + + if reduction in ['sum', 'mean']: + costs = costs.sum().unsqueeze_(-1) + if reduction == 'mean': + costs /= minibatch_size + + if grads is not None: + grads /= minibatch_size + + ctx.grads = grads + + return costs + + @staticmethod + def backward(ctx, grad_output): + if grad_output is not None and ctx.grads is not None: + grad_output = grad_output.view(-1, 1, 1, 1).to(ctx.grads) + return ctx.grads.mul_(grad_output), None, None, None, None, None, None, None, None, None, None + + +def rnnt_loss( + acts, labels, act_lens, label_lens, blank=0, reduction='mean', fastemit_lambda: float = 0.0, clamp: float = 0.0 +): + """RNN Transducer Loss (functional form) + Args: + acts: Tensor of (batch x seqLength x labelLength x outputDim) containing output from network + labels: 2 dimensional Tensor containing all the targets of the batch with zero padded + act_lens: Tensor of size (batch) containing size of each output sequence from the network + label_lens: Tensor of (batch) containing label length of each example + blank (int, optional): blank label. Default: 0. + reduction (string, optional): Specifies the reduction to apply to the output: + 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, + 'mean': the output losses will be divided by the target lengths and + then the mean over the batch is taken. Default: 'mean' + """ + if not acts.is_cuda: + # Since CPU requires log_softmax to be computed explicitly, we need to perform grad clipping + # *after* we have obtained the gradients of loss(logsoftmax()). + # This is highly wasteful since it requires a copy of the entire joint tensor which is expensive. + # CUDA version is much more efficient since it performs an inplace logsoftmax, and therefore + # can inplace clamp the gradient. + if clamp > 0.0: + acts = cpu_rnnt.LogSoftmaxGradModification.apply(acts, clamp) + + # NOTE manually done log_softmax for CPU version, + # log_softmax is computed within GPU version. + acts = torch.nn.functional.log_softmax(acts, -1) + + return _RNNTNumba.apply(acts, labels, act_lens, label_lens, blank, reduction, fastemit_lambda, clamp) + + +def multiblank_rnnt_loss( + acts, + labels, + act_lens, + label_lens, + blank, + big_blank_durations=[], + reduction='mean', + fastemit_lambda: float = 0.0, + clamp: float = 0.0, +): + """ + Multi-blank RNN Transducer (https://arxiv.org/pdf/2211.03541.pdf) Loss (functional form) + Args: + acts: Tensor of (batch x seqLength x labelLength x outputDim) containing output from network + labels: 2 dimensional Tensor containing all the targets of the batch with zero padded + act_lens: Tensor of size (batch) containing size of each output sequence from the network + label_lens: Tensor of (batch) containing label length of each example + blank (int): standard blank label. + big_blank_durations: list of durations for multi-blank transducer, e.g. + [2, 4, 8]. + sigma: hyper-parameter for logit under-normalization method for training + multi-blank transducers. Recommended value 0.05. + Refer to https://arxiv.org/pdf/2211.03541 for detailed explanations for + the last two params. + reduction (string, optional): Specifies the reduction to apply to the output: + 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, + 'mean': the output losses will be divided by the target lengths and + then the mean over the batch is taken. Default: 'mean' + """ + if not acts.is_cuda: + # Since CPU requires log_softmax to be computed explicitly, we need to perform grad clipping + # *after* we have obtained the gradients of loss(logsoftmax()). + # This is highly wasteful since it requires a copy of the entire joint tensor which is expensive. + # CUDA version is much more efficient since it performs an inplace logsoftmax, and therefore + # can inplace clamp the gradient. + if clamp > 0.0: + acts = cpu_rnnt.LogSoftmaxGradModification.apply(acts, clamp) + + # NOTE manually done log_softmax for CPU version, + # log_softmax is computed within GPU version. + acts = torch.nn.functional.log_softmax(acts, -1) + + return _MultiblankRNNTNumba.apply( + acts, labels, act_lens, label_lens, blank, big_blank_durations, reduction, fastemit_lambda, clamp + ) + + +def tdt_loss( + acts, + labels, + act_lens, + label_lens, + blank, + durations=[], + reduction='mean', + fastemit_lambda: float = 0.0, + clamp: float = 0.0, +): + """ + TDT RNN Transducer (https://arxiv.org/abs/2304.06795) Loss (functional form) + Args: + acts: Tensor of (batch x seqLength x labelLength x outputDim) containing output from network + labels: 2 dimensional Tensor containing all the targets of the batch with zero padded + act_lens: Tensor of size (batch) containing size of each output sequence from the network + label_lens: Tensor of (batch) containing label length of each example + blank (int): standard blank label. + durations: list of durations for TDT model, e.g. + [0,1,2,3,4]. + sigma: hyper-parameter for logit under-normalization method for training + multi-blank transducers. Recommended value 0.05. + Refer to https://arxiv.org/abs/2304.06795 for detailed explanations for + the last two params. + reduction (string, optional): Specifies the reduction to apply to the output: + 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, + 'mean': the output losses will be divided by the target lengths and + then the mean over the batch is taken. Default: 'mean' + """ + if not acts.is_cuda: + # Since CPU requires log_softmax to be computed explicitly, we need to perform grad clipping + # *after* we have obtained the gradients of loss(logsoftmax()). + # This is highly wasteful since it requires a copy of the entire joint tensor which is expensive. + # CUDA version is much more efficient since it performs an inplace logsoftmax, and therefore + # can inplace clamp the gradient. + if clamp > 0.0: + acts = cpu_rnnt.LogSoftmaxGradModification.apply(acts, clamp) + + # NOTE manually done log_softmax for CPU version, + # log_softmax is computed within GPU version. + acts = torch.nn.functional.log_softmax(acts, -1) + + return _TDTNumba.apply(acts, labels, act_lens, label_lens, blank, durations, reduction, fastemit_lambda, clamp) + + +class RNNTLossNumba(Module): + """ + Parameters: + blank (int, optional): blank label. Default: 0. + reduction (string, optional): Specifies the reduction to apply to the output: + 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, + 'mean': the output losses will be divided by the target lengths and + then the mean over the batch is taken. Default: 'mean' + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + """ + + def __init__(self, blank=0, reduction='mean', fastemit_lambda: float = 0.0, clamp: float = -1): + super(RNNTLossNumba, self).__init__() + self.blank = blank + self.fastemit_lambda = fastemit_lambda + self.clamp = float(clamp) if clamp > 0 else 0.0 + self.reduction = reduction + self.loss = _RNNTNumba.apply + + def forward(self, acts, labels, act_lens, label_lens): + """ + log_probs: Tensor of (batch x seqLength x labelLength x outputDim) containing output from network + labels: 2 dimensional Tensor containing all the targets of the batch with zero padded + act_lens: Tensor of size (batch) containing size of each output sequence from the network + label_lens: Tensor of (batch) containing label length of each example + """ + if not acts.is_cuda: + # Force FP32 until log_softmax() is implemented for fp16 on CPU + if acts.dtype == torch.float16: + acts = acts.float() + + # Since CPU requires log_softmax to be computed explicitly, we need to perform grad clipping + # *after* we have obtained the gradients of loss(logsoftmax()). + # This is highly wasteful since it requires a copy of the entire joint tensor which is expensive. + # CUDA version is much more efficient since it performs an inplace logsoftmax, and therefore + # can inplace clamp the gradient. + if self.clamp > 0.0: + acts = cpu_rnnt.LogSoftmaxGradModification.apply(acts, self.clamp) + + # NOTE manually done log_softmax for CPU version, + # log_softmax is computed within GPU version. + acts = torch.nn.functional.log_softmax(acts, -1) + + return self.loss( + acts, labels, act_lens, label_lens, self.blank, self.reduction, self.fastemit_lambda, self.clamp + ) + + +class MultiblankRNNTLossNumba(Module): + """ + Parameters: + blank (int): standard blank label. + big_blank_durations: list of durations for multi-blank transducer, e.g. + [2, 4, 8]. + sigma: hyper-parameter for logit under-normalization method for training + multi-blank transducers. Recommended value 0.05. + Refer to https://arxiv.org/pdf/2211.03541 for detailed explanations for + the above parameters; + reduction (string, optional): Specifies the reduction to apply to the output: + 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, + 'mean': the output losses will be divided by the target lengths and + then the mean over the batch is taken. Default: 'mean' + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + """ + + def __init__( + self, + blank, + big_blank_durations, + reduction='mean', + fastemit_lambda: float = 0.0, + clamp: float = -1, + sigma: float = 0.0, + ): + super(MultiblankRNNTLossNumba, self).__init__() + self.blank = blank + self.big_blank_durations = big_blank_durations + self.fastemit_lambda = fastemit_lambda + self.clamp = float(clamp) if clamp > 0 else 0.0 + self.reduction = reduction + self.loss = _MultiblankRNNTNumba.apply + self.sigma = sigma + + def forward(self, acts, labels, act_lens, label_lens): + """ + log_probs: Tensor of (batch x seqLength x labelLength x outputDim) containing output from network + labels: 2 dimensional Tensor containing all the targets of the batch with zero padded + act_lens: Tensor of size (batch) containing size of each output sequence from the network + label_lens: Tensor of (batch) containing label length of each example + """ + if not acts.is_cuda: + # Since CPU requires log_softmax to be computed explicitly, we need to perform grad clipping + # *after* we have obtained the gradients of loss(logsoftmax()). + # This is highly wasteful since it requires a copy of the entire joint tensor which is expensive. + # CUDA version is much more efficient since it performs an inplace logsoftmax, and therefore + # can inplace clamp the gradient. + if self.clamp > 0.0: + acts = cpu_rnnt.LogSoftmaxGradModification.apply(acts, self.clamp) + + # NOTE manually done log_softmax for CPU version, + # log_softmax is computed within GPU version. + acts = torch.nn.functional.log_softmax(acts, -1) + + return self.loss( + acts, + labels, + act_lens, + label_lens, + self.blank, + self.big_blank_durations, + self.reduction, + self.fastemit_lambda, + self.clamp, + self.sigma, + ) + + +class TDTLossNumba(Module): + """ + Parameters: + blank (int): standard blank label. + durations: list of durations for TDT model, e.g. + [0, 1, 2, 3, 4]. + sigma: hyper-parameter for logit under-normalization method for training + TDT. Recommended value 0.05. + omega: hyper-parameter for RNN-T loss for loss combination. + Refer to https://arxiv.org/abs/2304.06795 for detailed explanations for + the above parameters; + + reduction (string, optional): Specifies the reduction to apply to the output: + 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, + 'mean': the output losses will be divided by the target lengths and + then the mean over the batch is taken. Default: 'mean' + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + """ + + def __init__( + self, + blank, + durations=None, + reduction='mean', + fastemit_lambda: float = 0.0, + clamp: float = -1, + sigma: float = 0.0, + omega: float = 0.0, + ): + super(TDTLossNumba, self).__init__() + self.blank = blank + self.durations = durations if durations is not None else [] + self.fastemit_lambda = fastemit_lambda + self.clamp = float(clamp) if clamp > 0 else 0.0 + self.reduction = reduction + self.loss = _TDTNumba.apply + self.sigma = sigma + self.omega = omega + + def forward(self, acts, labels, act_lens, label_lens): + """ + log_probs: Tensor of (batch x seqLength x labelLength x outputDim) containing output from network + labels: 2 dimensional Tensor containing all the targets of the batch with zero padded + act_lens: Tensor of size (batch) containing size of each output sequence from the network + label_lens: Tensor of (batch) containing label length of each example + """ + + # TODO(hainan): in the future, we could further optimize this so that we don't need to + # make contiguous copies of the acts tensor. + label_acts, duration_acts = torch.split( + acts, [acts.shape[-1] - len(self.durations), len(self.durations)], dim=-1 + ) + label_acts = label_acts.contiguous() + duration_acts = torch.nn.functional.log_softmax(duration_acts, dim=-1).contiguous() + + return self.loss( + label_acts, + duration_acts, + labels, + act_lens, + label_lens, + self.blank, + self.durations, + self.reduction, + self.fastemit_lambda, + self.clamp, + self.sigma, + self.omega, + ) + + +def check_type(var, t, name): + if var.dtype is not t: + raise TypeError("{} must be {}".format(name, t)) + + +def check_contiguous(var, name): + if not var.is_contiguous(): + raise ValueError("{} must be contiguous".format(name)) + + +def check_dim(var, dim, name): + if len(var.shape) != dim: + raise ValueError("{} must be {}D".format(name, dim)) + + +def certify_inputs(log_probs, labels, lengths, label_lengths): + # check_type(log_probs, torch.float32, "log_probs") + check_type(labels, torch.int64, "labels") + check_type(label_lengths, torch.int64, "label_lengths") + check_type(lengths, torch.int64, "lengths") + check_contiguous(log_probs, "log_probs") + check_contiguous(labels, "labels") + check_contiguous(label_lengths, "label_lengths") + check_contiguous(lengths, "lengths") + + if lengths.shape[0] != log_probs.shape[0]: + raise ValueError( + f"Must have a length per example. " + f"Given lengths dim: {lengths.shape[0]}, " + f"Log probs dim : {log_probs.shape[0]}" + ) + if label_lengths.shape[0] != log_probs.shape[0]: + raise ValueError( + "Must have a label length per example. " + f"Given label lengths dim : {label_lengths.shape[0]}, " + f"Log probs dim : {log_probs.shape[0]}" + ) + + check_dim(log_probs, 4, "log_probs") + check_dim(labels, 2, "labels") + check_dim(lengths, 1, "lenghts") + check_dim(label_lengths, 1, "label_lenghts") + max_T = torch.max(lengths) + max_U = torch.max(label_lengths) + T, U = log_probs.shape[1:3] + if T != max_T: + raise ValueError(f"Input length mismatch! Given T: {T}, Expected max T from input lengths: {max_T}") + if U != max_U + 1: + raise ValueError(f"Output length mismatch! Given U: {U}, Expected max U from target lengths: {max_U} + 1") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/__init__.py new file mode 100644 index 0000000..bc443be --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cpu_utils/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cpu_utils/__init__.py new file mode 100644 index 0000000..1b4bbd4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cpu_utils/__init__.py @@ -0,0 +1,27 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright 2018-2019, Mingkun Huang +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cpu_utils/cpu_rnnt.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cpu_utils/cpu_rnnt.py new file mode 100644 index 0000000..bcc1865 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cpu_utils/cpu_rnnt.py @@ -0,0 +1,422 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright 2018-2019, Mingkun Huang +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import multiprocessing +from typing import Optional + +import numba +import torch +from torch.autograd import Function + +from nemo.collections.asr.parts.numba.rnnt_loss.utils import global_constants + + +def log_sum_exp(a: torch.Tensor, b: torch.Tensor): + """ + Logsumexp with safety checks for infs. + """ + if torch.isinf(a): + return b + + if torch.isinf(b): + return a + + if a > b: + return math.log1p(math.exp(b - a)) + a + else: + return math.log1p(math.exp(a - b)) + b + + +class CpuRNNT_index: + def __init__(self, U: int, maxU: int, minibatch: int, alphabet_size: int, batch_first: bool): + """ + A placeholder Index computation class that emits the resolved index in a flattened tensor, + mimicing pointer indexing in CUDA kernels on the CPU. + + Args: + U: Length of the current target sample (without padding). + maxU: Max Length of the padded target samples. + minibatch: Minibatch index + alphabet_size: Size of the vocabulary including RNNT blank - V+1. + batch_first: Bool flag determining if batch index is first or third. + """ + super(CpuRNNT_index, self).__init__() + self.U = U + self.maxU = maxU + self.minibatch = minibatch + self.alphabet_size = alphabet_size + self.batch_first = batch_first + + def __call__(self, t: int, u: int, v: Optional[int] = None): + # if indexing all the values of the vocabulary, then only t, u are provided + if v is None: + return t * self.U + u + else: + # otherwise, t, u, v are provided to index particular value in the vocabulary. + if self.batch_first: + return (t * self.maxU + u) * self.alphabet_size + v + else: + return (t * self.maxU + u) * self.minibatch * self.alphabet_size + v + + +class CpuRNNT_metadata: + def __init__( + self, + T: int, + U: int, + workspace: torch.Tensor, + bytes_used: int, + blank: int, + labels: torch.Tensor, + log_probs: torch.Tensor, + idx: CpuRNNT_index, + ): + """ + Metadata for CPU based RNNT loss calculation. Holds the working space memory. + + Args: + T: Length of the acoustic sequence (without padding). + U: Length of the target sequence (without padding). + workspace: Working space memory for the CPU. + bytes_used: Number of bytes currently used for indexing the working space memory. Generally 0. + blank: Index of the blank token in the vocabulary. + labels: Ground truth padded labels matrix of shape [B, U] + log_probs: Log probs / activation matrix of flattented shape [B, T, U, V+1] + idx: + """ + super(CpuRNNT_metadata, self).__init__() + + self.alphas = workspace[bytes_used : bytes_used + T * U] + bytes_used += T * U + + self.betas = workspace[bytes_used : bytes_used + T * U] + bytes_used += T * U + + self.log_probs2 = workspace[bytes_used : bytes_used + T * U * 2] # // only store blank & label + bytes_used += T * U * 2 + + self.bytes_used = bytes_used + + self.setup_probs(T, U, labels, blank, log_probs, idx) + + def setup_probs( + self, T: int, U: int, labels: torch.Tensor, blank: int, log_probs: torch.Tensor, idx: CpuRNNT_index + ): + # initialize the log probs memory for blank and label token. + for t in range(T): + for u in range(U): + offset = (t * U + u) * 2 # mult with 2 is for selecting either blank or label token. Odd idx is blank. + self.log_probs2[offset] = log_probs[idx(t, u, blank)] + # // labels do not have first blank + if u < U - 1: + self.log_probs2[offset + 1] = log_probs[idx(t, u, labels[u])] + + +class LogSoftmaxGradModification(Function): + @staticmethod + def forward(ctx, acts, clamp): + if clamp < 0: + raise ValueError("`clamp` must be 0.0 or positive float.") + + # This is needed for correctness (inplace is problematic), + # but it wastes a log of memory. + res = acts.new(acts) + ctx.clamp = clamp + return res + + @staticmethod + def backward(ctx, grad_output): + # Clamp the gradients of loss(logsoftmax(...)) + # CPU computes logsoftmax explicitly, so we need to override t + grad_output = torch.clamp(grad_output, -ctx.clamp, ctx.clamp) + return ( + grad_output, + None, + ) + + +class CPURNNT: + def __init__( + self, + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + workspace: torch.Tensor, + blank: int, + fastemit_lambda: float, + clamp: float, + num_threads: int, + batch_first: bool, + ): + """ + Helper class to compute the Transducer Loss on CPU. + + Args: + minibatch: Size of the minibatch b. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank). + workspace: An allocated chunk of memory that will be sliced off and reshaped into required + blocks used as working memory. + blank: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab. + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + num_threads: Number of OMP threads to launch. + batch_first: Bool that decides if batch dimension is first or third. + """ + self.minibatch_ = minibatch + self.maxT_ = maxT + self.maxU_ = maxU + self.alphabet_size_ = alphabet_size + self.workspace = workspace # a flat vector of floatX numbers that represents allocated memory slices + self.blank_ = blank + self.fastemit_lambda_ = fastemit_lambda + self.clamp_ = abs(clamp) + self.num_threads_ = num_threads + self.batch_first = batch_first + + _torch_num_threads = torch.get_num_threads() + if num_threads > 0: + numba.set_num_threads(min(multiprocessing.cpu_count(), num_threads)) + self.num_threads_ = numba.get_num_threads() + else: + self.num_threads_ = numba.get_num_threads() + torch.set_num_threads(_torch_num_threads) + + def cost_and_grad_kernel( + self, + log_probs: torch.Tensor, + grad: torch.Tensor, + labels: torch.Tensor, + mb: int, + T: int, + U: int, + bytes_used: int, + ): + idx = CpuRNNT_index(U, self.maxU_, self.minibatch_, self.alphabet_size_, self.batch_first) + rnntm = CpuRNNT_metadata(T, U, self.workspace, bytes_used, self.blank_, labels, log_probs, idx) + + if self.batch_first: + # zero grads + grad *= 0.0 + + llForward = self.compute_alphas(rnntm.log_probs2, T, U, rnntm.alphas) + llBackward = self.compute_betas_and_grads( + grad, rnntm.log_probs2, T, U, rnntm.alphas, rnntm.betas, labels, llForward + ) + + # Scale llForward by FastEmit lambda + llForward += llForward * self.fastemit_lambda_ + llBackward += llBackward * self.fastemit_lambda_ + + diff = (llForward - llBackward).abs() + if diff > 0.1: + print(f"WARNING: Forward backward likelihood mismatch : {diff}") + + return -llForward + + def compute_alphas(self, log_probs: torch.Tensor, T: int, U: int, alphas: torch.Tensor): + """ + Compute the probability of the forward variable alpha. + + Args: + log_probs: Flattened tensor [B, T, U, V+1] + T: Length of the acoustic sequence T (not padded). + U: Length of the target sequence U (not padded). + alphas: Working space memory for alpha of shape [B, T, U]. + + Returns: + Loglikelihood of the forward variable alpha. + """ + idx = CpuRNNT_index(U, self.maxU_, self.minibatch_, self.alphabet_size_, self.batch_first) + + alphas[0] = 0 + for t in range(T): + for u in range(U): + if u == 0 and t > 0: + alphas[idx(t, 0)] = alphas[idx(t - 1, 0)] + log_probs[idx(t - 1, 0) * 2] + + if t == 0 and u > 0: + alphas[idx(0, u)] = alphas[idx(0, u - 1)] + log_probs[idx(0, u - 1) * 2 + 1] + + if t > 0 and u > 0: + no_emit = alphas[idx(t - 1, u)] + log_probs[idx(t - 1, u) * 2] + emit = alphas[idx(t, u - 1)] + log_probs[idx(t, u - 1) * 2 + 1] + alphas[idx(t, u)] = log_sum_exp(emit, no_emit) + + loglike = alphas[idx(T - 1, U - 1)] + log_probs[idx(T - 1, U - 1) * 2] + return loglike + + def compute_betas_and_grads( + self, + grad: torch.Tensor, + log_probs: torch.Tensor, + T: int, + U: int, + alphas: torch.Tensor, + betas: torch.Tensor, + labels: torch.Tensor, + logll: torch.Tensor, + ): + """ + Compute backward variable beta as well as gradients of the activation matrix wrt loglikelihood + of forward variable. + + Args: + grad: Working space memory of flattened shape [B, T, U, V+1] + log_probs: Activatio tensor of flattented shape [B, T, U, V+1] + T: Length of the acoustic sequence T (not padded). + U: Length of the target sequence U (not padded). + alphas: Working space memory for alpha of shape [B, T, U]. + betas: Working space memory for alpha of shape [B, T, U]. + labels: Ground truth label of shape [B, U] + logll: Loglikelihood of the forward variable. + + Returns: + Loglikelihood of the forward variable and inplace updates the grad tensor. + """ + # Patch for CPU + fp16 + if log_probs.dtype == torch.float16 and not log_probs.is_cuda: + log_probs = log_probs.float() + + idx = CpuRNNT_index(U, self.maxU_, self.minibatch_, self.alphabet_size_, self.batch_first) + betas[idx(T - 1, U - 1)] = log_probs[idx(T - 1, U - 1) * 2] + + for t in range(T - 1, -1, -1): + for u in range(U - 1, -1, -1): + if (u == U - 1) and (t < T - 1): + betas[idx(t, U - 1)] = betas[idx(t + 1, U - 1)] + log_probs[idx(t, U - 1) * 2] + + if (t == T - 1) and (u < U - 1): + betas[idx(T - 1, u)] = betas[idx(T - 1, u + 1)] + log_probs[idx(T - 1, u) * 2 + 1] + + if (t < T - 1) and (u < U - 1): + no_emit = betas[idx(t + 1, u)] + log_probs[idx(t, u) * 2] + emit = betas[idx(t, u + 1)] + log_probs[idx(t, u) * 2 + 1] + betas[idx(t, u)] = log_sum_exp(emit, no_emit) + + loglike = betas[0] + # // Gradients w.r.t. log probabilities + for t in range(T): + for u in range(U): + if t < T - 1: + g = alphas[idx(t, u)] + betas[idx(t + 1, u)] + grad[idx(t, u, self.blank_)] = -torch.exp(log_probs[idx(t, u) * 2] + g - loglike) + + if u < U - 1: + g = alphas[idx(t, u)] + betas[idx(t, u + 1)] + grad[idx(t, u, labels[u])] = -torch.exp( + math.log1p(self.fastemit_lambda_) + log_probs[idx(t, u) * 2 + 1] + g - loglike + ) + + # // gradient to the last blank transition + grad[idx(T - 1, U - 1, self.blank_)] = -torch.exp( + log_probs[idx(T - 1, U - 1) * 2] + alphas[idx(T - 1, U - 1)] - loglike + ) + + return loglike + + def cost_and_grad( + self, + log_probs: torch.Tensor, + grads: torch.Tensor, + costs: torch.Tensor, + flat_labels: torch.Tensor, + label_lengths: torch.Tensor, + input_lengths: torch.Tensor, + ) -> global_constants.RNNTStatus: + # // per minibatch memory + per_minibatch_bytes = 0 + + # // alphas & betas + per_minibatch_bytes += self.maxT_ * self.maxU_ * 2 + + # // blank & label log probability cache + per_minibatch_bytes += self.maxT_ * self.maxU_ * 2 + + for mb in range(self.minibatch_): + T = input_lengths[mb] # // Length of utterance (time) + U = label_lengths[mb] + 1 # // Number of labels in transcription + batch_size = self.alphabet_size_ + if self.batch_first: + batch_size = self.maxT_ * self.maxU_ * self.alphabet_size_ + + costs[mb] = self.cost_and_grad_kernel( + log_probs[(mb * batch_size) :], + grads[(mb * batch_size) :], + flat_labels[(mb * (self.maxU_ - 1)) :], + mb, + T, + U, + mb * per_minibatch_bytes, + ) + + return global_constants.RNNTStatus.RNNT_STATUS_SUCCESS + + def score_forward( + self, + log_probs: torch.Tensor, + costs: torch.Tensor, + flat_labels: torch.Tensor, + label_lengths: torch.Tensor, + input_lengths: torch.Tensor, + ): + # // per minibatch memory + per_minibatch_bytes = 0 + + # // alphas & betas + per_minibatch_bytes += self.maxT_ * self.maxU_ * 2 + + # // blank & label log probability cache + per_minibatch_bytes += self.maxT_ * self.maxU_ * 2 + + for mb in range(self.minibatch_): + T = input_lengths[mb] # // Length of utterance (time) + U = label_lengths[mb] + 1 # // Number of labels in transcription + batch_size = self.alphabet_size_ + if self.batch_first: + batch_size = self.maxT_ * self.maxU_ * self.alphabet_size_ + + idx = CpuRNNT_index(U, self.maxU_, self.minibatch_, self.alphabet_size_, self.batch_first) + rnntm = CpuRNNT_metadata( + T, + U, + self.workspace, + mb * per_minibatch_bytes, + self.blank_, + flat_labels[(mb * (self.maxU_ - 1)) :], + log_probs[(mb * batch_size) :], + idx, + ) + + costs[mb] = -self.compute_alphas(rnntm.log_probs2, T, U, rnntm.alphas) + + return global_constants.RNNTStatus.RNNT_STATUS_SUCCESS diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/__init__.py new file mode 100644 index 0000000..1b4bbd4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/__init__.py @@ -0,0 +1,27 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright 2018-2019, Mingkun Huang +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py new file mode 100644 index 0000000..87d6ee1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py @@ -0,0 +1,807 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright 2018-2019, Mingkun Huang +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import multiprocessing +import random +from typing import Optional, Tuple + +import numba +import torch +from numba import cuda + +from nemo.collections.asr.parts.numba.rnnt_loss.utils import global_constants, rnnt_helper +from nemo.collections.asr.parts.numba.rnnt_loss.utils.cuda_utils import gpu_rnnt_kernel, reduce + + +class GPURNNT: + def __init__( + self, + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + workspace, + blank: int, + fastemit_lambda: float, + clamp: float, + num_threads: int, + stream, + ): + """ + Helper class to launch the CUDA Kernels to compute the Transducer Loss. + + Args: + minibatch: Int representing the batch size. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank). + workspace: An allocated chunk of memory that will be sliced off and reshaped into required + blocks used as working memory. + blank: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab. + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + num_threads: Number of OMP threads to launch. + stream: Numba Cuda Stream. + """ + self.minibatch_ = minibatch + self.maxT_ = maxT + self.maxU_ = maxU + self.alphabet_size_ = alphabet_size + self.gpu_workspace = cuda.as_cuda_array( + workspace + ) # a flat vector of floatX numbers that represents allocated memory slices + self.blank_ = blank + self.fastemit_lambda_ = fastemit_lambda + self.clamp_ = abs(clamp) + self.num_threads_ = num_threads + self.stream_ = stream # type: cuda.cudadrv.driver.Stream + + _torch_num_threads = torch.get_num_threads() + if num_threads > 0: + numba.set_num_threads(min(multiprocessing.cpu_count(), num_threads)) + self.num_threads_ = numba.get_num_threads() + else: + self.num_threads_ = numba.get_num_threads() + torch.set_num_threads(_torch_num_threads) + + def log_softmax(self, acts: torch.Tensor, denom: torch.Tensor): + """ + Computes the log softmax denominator of the input activation tensor + and stores the result in denom. + + Args: + acts: Activation tensor of shape [B, T, U, V+1]. The input must be represented as a flat tensor + of shape [B * T * U * (V+1)] to allow pointer indexing. + denom: A zero tensor of same shape as acts. + + Updates: + This kernel inplace updates the `denom` tensor + """ + # // trans_acts + pred_acts -> log_softmax denominator + reduce.reduce_max( + acts, + denom, + rows=self.alphabet_size_, + cols=self.minibatch_ * self.maxT_ * self.maxU_, + minus=False, + stream=self.stream_, + ) + + reduce.reduce_exp( + acts, + denom, + rows=self.alphabet_size_, + cols=self.minibatch_ * self.maxT_ * self.maxU_, + minus=True, + stream=self.stream_, + ) + + def compute_cost_and_score( + self, + acts: torch.Tensor, + grads: Optional[torch.Tensor], + costs: torch.Tensor, + labels: torch.Tensor, + label_lengths: torch.Tensor, + input_lengths: torch.Tensor, + ) -> global_constants.RNNTStatus: + """ + Compute both the loss and the gradients. + + Args: + acts: A flattened tensor of shape [B, T, U, V+1] representing the activation matrix. + grad: A flattented zero tensor of same shape as acts. + costs: A zero vector of length B which will be updated inplace with the log probability costs. + flat_labels: A flattened matrix of labels of shape [B, U] + label_lengths: A vector of length B that contains the original lengths of the acoustic sequence. + input_lengths: A vector of length B that contains the original lengths of the target sequence. + + Updates: + This will launch kernels that will update inline the following variables: + - grads: Gradients of the activation matrix wrt the costs vector. + - costs: Negative log likelihood of the forward variable. + + Returns: + An enum that either represents a successful RNNT operation or failure. + """ + training = grads is not None + + if training: + grads *= 0.0 # zero grads + + used_offset, (denom, alphas, betas, llForward, llBackward) = self._prepare_workspace() + + ######## START EXECUTION ######## + self.log_softmax(acts, denom) + + # Compute alphas + gpu_rnnt_kernel.compute_alphas_kernel[self.minibatch_, self.maxU_, self.stream_, 0]( + acts, + denom, + alphas, + llForward, + input_lengths, + label_lengths, + labels, + self.minibatch_, + self.maxT_, + self.maxU_, + self.alphabet_size_, + self.blank_, + ) + + if training: + # Compute betas + gpu_rnnt_kernel.compute_betas_kernel[self.minibatch_, self.maxU_, self.stream_, 0]( + acts, + denom, + betas, + llBackward, + input_lengths, + label_lengths, + labels, + self.minibatch_, + self.maxT_, + self.maxU_, + self.alphabet_size_, + self.blank_, + ) + + # Compute gradient + grad_blocks_per_grid = self.minibatch_ * self.maxT_ * self.maxU_ + grad_threads_per_block = gpu_rnnt_kernel.GPU_RNNT_THREAD_SIZE + gpu_rnnt_kernel.compute_grad_kernel[grad_blocks_per_grid, grad_threads_per_block, self.stream_, 0]( + grads, + acts, + denom, + alphas, + betas, + llForward, + input_lengths, + label_lengths, + labels, + self.minibatch_, + self.maxT_, + self.maxU_, + self.alphabet_size_, + self.blank_, + self.fastemit_lambda_, + self.clamp_, + ) + + # // cost copy, negate (for log likelihood) and update with additional regularizers + # This needs to be done via CUDA, because we used temporary memory llForward + # passed to alpha, which was updated with log likelihoods. + # But copying this data into a pytorch pointer is more difficult (numba api is one way) + # Therefore launch a pointwise CUDA kernel to update the costs inplace from data of llForward + # Then negate to compute the loglikelihood. + threadsperblock = min(costs.shape[0], 32) + blockspergrid = (costs.shape[0] + (threadsperblock - 1)) // threadsperblock + rnnt_helper.compute_costs_data[blockspergrid, threadsperblock, self.stream_, 0]( + llForward, costs, self.fastemit_lambda_ + ) + self.stream_.synchronize() + + return global_constants.RNNTStatus.RNNT_STATUS_SUCCESS + + def cost_and_grad( + self, + acts: torch.Tensor, + grads: torch.Tensor, + costs: torch.Tensor, + pad_labels: torch.Tensor, + label_lengths: torch.Tensor, + input_lengths: torch.Tensor, + ): + if ( + acts is None + or grads is None + or costs is None + or pad_labels is None + or label_lengths is None + or input_lengths is None + ): + return global_constants.RNNTStatus.RNNT_STATUS_INVALID_VALUE + + return self.compute_cost_and_score(acts, grads, costs, pad_labels, label_lengths, input_lengths) + + def score_forward( + self, + acts: torch.Tensor, + costs: torch.Tensor, + pad_labels: torch.Tensor, + label_lengths: torch.Tensor, + input_lengths: torch.Tensor, + ): + if acts is None or costs is None or pad_labels is None or label_lengths is None or input_lengths is None: + return global_constants.RNNTStatus.RNNT_STATUS_INVALID_VALUE + + return self.compute_cost_and_score(acts, None, costs, pad_labels, label_lengths, input_lengths) + + def _prepare_workspace(self) -> Tuple[int, Tuple[torch.Tensor, ...]]: + """ + Helper method that uses the workspace and constructs slices of it that can be used. + + Returns: + An int, representing the offset of the used workspace (practically, the slice of the workspace consumed) + A tuple of tensors representing the shared workspace. + """ + used_offset = 0 + + # // denom + denom = self.gpu_workspace[used_offset : used_offset + self.maxT_ * self.maxU_ * self.minibatch_] + used_offset += self.maxT_ * self.maxU_ * self.minibatch_ + + # // alphas & betas + alphas = self.gpu_workspace[used_offset : used_offset + self.maxT_ * self.maxU_ * self.minibatch_] + used_offset += self.maxT_ * self.maxU_ * self.minibatch_ + betas = self.gpu_workspace[used_offset : used_offset + self.maxT_ * self.maxU_ * self.minibatch_] + used_offset += self.maxT_ * self.maxU_ * self.minibatch_ + + # // logllh + llForward = self.gpu_workspace[used_offset : used_offset + self.minibatch_] + used_offset += self.minibatch_ + llBackward = self.gpu_workspace[used_offset : used_offset + self.minibatch_] + used_offset += self.minibatch_ + + return used_offset, (denom, alphas, betas, llForward, llBackward) + + +class MultiblankGPURNNT(GPURNNT): + def __init__( + self, + sigma: float, + num_big_blanks: int, + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + workspace, + big_blank_workspace, + blank: int, + fastemit_lambda: float, + clamp: float, + num_threads: int, + stream, + ): + """ + Helper class to launch the CUDA Kernels to compute Multi-blank Transducer Loss (https://arxiv.org/pdf/2211.03541). + + Args: + sigma: Hyper-parameter related to the logit-normalization method in training multi-blank transducers. + num_big_blanks: Number of big blank symbols the model has. This should not include the standard blank symbol. + minibatch: Int representing the batch size. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V + 1 + num-big-blanks + workspace: An allocated chunk of memory that will be sliced off and reshaped into required + blocks used as working memory. + big_blank_workspace: An allocated chunk of memory that will be sliced off and reshaped into required + blocks used as working memory specifically for the multi-blank related computations. + blank: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab. + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + num_threads: Number of OMP threads to launch. + stream: Numba Cuda Stream. + """ + super().__init__( + minibatch, maxT, maxU, alphabet_size, workspace, blank, fastemit_lambda, clamp, num_threads, stream + ) + self.big_blank_workspace = cuda.as_cuda_array( + big_blank_workspace + ) # a flat vector of integer numbers that represents allocated memory slices + + self.num_big_blanks = num_big_blanks + self.sigma = sigma + + def compute_cost_and_score( + self, + acts: torch.Tensor, + grads: Optional[torch.Tensor], + costs: torch.Tensor, + labels: torch.Tensor, + label_lengths: torch.Tensor, + input_lengths: torch.Tensor, + ) -> global_constants.RNNTStatus: + """ + Compute both the loss and the gradients. + + Args: + acts: A flattened tensor of shape [B, T, U, V+1] representing the activation matrix. + grad: A flattented zero tensor of same shape as acts. + costs: A zero vector of length B which will be updated inplace with the log probability costs. + flat_labels: A flattened matrix of labels of shape [B, U] + label_lengths: A vector of length B that contains the original lengths of the acoustic sequence. + input_lengths: A vector of length B that contains the original lengths of the target sequence. + + Updates: + This will launch kernels that will update inline the following variables: + - grads: Gradients of the activation matrix wrt the costs vector. + - costs: Negative log likelihood of the forward variable. + + Returns: + An enum that either represents a successful RNNT operation or failure. + """ + training = grads is not None + + if training: + grads *= 0.0 # zero grads + + _, (denom, alphas, betas, llForward, llBackward, bigblank_durations) = self._prepare_workspace() + + ######## START EXECUTION ######## + self.log_softmax(acts, denom) + + # Compute alphas + gpu_rnnt_kernel.compute_multiblank_alphas_kernel[self.minibatch_, self.maxU_, self.stream_, 0]( + acts, + denom, + self.sigma, + alphas, + llForward, + input_lengths, + label_lengths, + labels, + self.minibatch_, + self.maxT_, + self.maxU_, + self.alphabet_size_, + self.blank_, + bigblank_durations, + self.num_big_blanks, + ) + + if training: + # Compute betas + gpu_rnnt_kernel.compute_multiblank_betas_kernel[self.minibatch_, self.maxU_, self.stream_, 0]( + acts, + denom, + self.sigma, + betas, + llBackward, + input_lengths, + label_lengths, + labels, + self.minibatch_, + self.maxT_, + self.maxU_, + self.alphabet_size_, + self.blank_, + bigblank_durations, + self.num_big_blanks, + ) + + # Compute gradient + grad_blocks_per_grid = self.minibatch_ * self.maxT_ * self.maxU_ + grad_threads_per_block = gpu_rnnt_kernel.GPU_RNNT_THREAD_SIZE + gpu_rnnt_kernel.compute_multiblank_grad_kernel[ + grad_blocks_per_grid, grad_threads_per_block, self.stream_, 0 + ]( + grads, + acts, + denom, + self.sigma, + alphas, + betas, + llForward, + input_lengths, + label_lengths, + labels, + self.minibatch_, + self.maxT_, + self.maxU_, + self.alphabet_size_, + self.blank_, + bigblank_durations, + self.num_big_blanks, + self.fastemit_lambda_, + self.clamp_, + ) + + # // cost copy, negate (for log likelihood) and update with additional regularizers + # This needs to be done via CUDA, because we used temporary memory llForward + # passed to alpha, which was updated with log likelihoods. + # But copying this data into a pytorch pointer is more difficult (numba api is one way) + # Therefore launch a pointwise CUDA kernel to update the costs inplace from data of llForward + # Then negate to compute the loglikelihood. + threadsperblock = min(costs.shape[0], 32) + blockspergrid = (costs.shape[0] + (threadsperblock - 1)) // threadsperblock + rnnt_helper.compute_costs_data[blockspergrid, threadsperblock, self.stream_, 0]( + llForward, costs, self.fastemit_lambda_ + ) + self.stream_.synchronize() + + return global_constants.RNNTStatus.RNNT_STATUS_SUCCESS + + def cost_and_grad( + self, + acts: torch.Tensor, + grads: torch.Tensor, + costs: torch.Tensor, + pad_labels: torch.Tensor, + label_lengths: torch.Tensor, + input_lengths: torch.Tensor, + ): + if ( + acts is None + or grads is None + or costs is None + or pad_labels is None + or label_lengths is None + or input_lengths is None + ): + return global_constants.RNNTStatus.RNNT_STATUS_INVALID_VALUE + + return self.compute_cost_and_score(acts, grads, costs, pad_labels, label_lengths, input_lengths) + + def score_forward( + self, + acts: torch.Tensor, + costs: torch.Tensor, + pad_labels: torch.Tensor, + label_lengths: torch.Tensor, + input_lengths: torch.Tensor, + ): + if acts is None or costs is None or pad_labels is None or label_lengths is None or input_lengths is None: + return global_constants.RNNTStatus.RNNT_STATUS_INVALID_VALUE + + return self.compute_cost_and_score(acts, None, costs, pad_labels, label_lengths, input_lengths) + + def _prepare_workspace(self) -> (int, Tuple[torch.Tensor]): + """ + Helper method that uses the workspace and constructs slices of it that can be used. + + Returns: + An int, representing the offset of the used workspace (practically, the slice of the workspace consumed) + A tuple of tensors representing the shared workspace. + """ + used_offset, (denom, alphas, betas, llForward, llBackward) = super()._prepare_workspace() + + bigblank_durations = self.big_blank_workspace[: self.num_big_blanks] + + return used_offset, (denom, alphas, betas, llForward, llBackward, bigblank_durations) + + +class GPUTDT(GPURNNT): + def __init__( + self, + sigma: float, + omega: float, + num_durations: int, + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + workspace, + tdt_workspace, + blank: int, + fastemit_lambda: float, + clamp: float, + num_threads: int, + stream, + ): + """ + Helper class to launch the CUDA Kernels to compute TDT Loss (https://arxiv.org/pdf/2211.03541). + + Args: + sigma: Hyper-parameter related to the logit-normalization method in training tdt transducers. + omega: Hyper-parameter related to the sampled training. + num_durations: Number of durations the model supports. + minibatch: Int representing the batch size. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V + 1 + num-big-blanks + workspace: An allocated chunk of memory that will be sliced off and reshaped into required + blocks used as working memory. + tdt_workspace: An allocated chunk of memory that will be sliced off and reshaped into required + blocks used as working memory specifically for the tdt related computations. + blank: Index of the blank token in the vocabulary. Must be the last token in the vocab. + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + num_threads: Number of OMP threads to launch. + stream: Numba Cuda Stream. + """ + super().__init__( + minibatch, maxT, maxU, alphabet_size, workspace, blank, fastemit_lambda, clamp, num_threads, stream + ) + self.tdt_workspace = cuda.as_cuda_array( + tdt_workspace + ) # a flat vector of integer numbers that represents allocated memory slices + + self.num_durations = num_durations + self.sigma = sigma + self.omega = omega + + def compute_cost_and_score( + self, + label_acts: torch.Tensor, + duration_acts: torch.Tensor, + label_grads: Optional[torch.Tensor], + duration_grads: Optional[torch.Tensor], + costs: torch.Tensor, + labels: torch.Tensor, + label_lengths: torch.Tensor, + input_lengths: torch.Tensor, + ) -> global_constants.RNNTStatus: + """ + Compute both the loss and the gradients. + + Args: + label_acts: A flattened tensor of shape [B, T, U, V] representing the activation matrix for tokens. + duration_acts: A flattened tensor of shape [B, T, U, D] representing the activation matrix for durations. + label_grad: A flattented zero tensor of same shape as label_acts. + duration_grad: A flattented zero tensor of same shape as duration_acts. + costs: A zero vector of length B which will be updated inplace with the log probability costs. + flat_labels: A flattened matrix of labels of shape [B, U] + label_lengths: A vector of length B that contains the original lengths of the acoustic sequence. + input_lengths: A vector of length B that contains the original lengths of the target sequence. + + Updates: + This will launch kernels that will update inline the following variables: + - *_grads: Gradients of the activation matrix wrt the costs vector. + - costs: Negative log likelihood of the forward variable. + + Returns: + An enum that either represents a successful RNNT operation or failure. + """ + training = label_grads is not None + + if training: + label_grads *= 0.0 # zero grads + duration_grads *= 0.0 # zero grads + + _, (denom, alphas, betas, llForward, llBackward, durations) = self._prepare_workspace() + + ######## START EXECUTION ######## + self.log_softmax(label_acts, denom) + + r = random.uniform(0, 1) + if r < self.omega: + # Compute alphas + gpu_rnnt_kernel.compute_alphas_kernel[self.minibatch_, self.maxU_, self.stream_, 0]( + label_acts, + denom, + alphas, + llForward, + input_lengths, + label_lengths, + labels, + self.minibatch_, + self.maxT_, + self.maxU_, + self.alphabet_size_, + self.blank_, + ) + else: + # Compute alphas + gpu_rnnt_kernel.compute_tdt_alphas_kernel[self.minibatch_, self.maxU_, self.stream_, 0]( + label_acts, + duration_acts, + denom, + self.sigma, + alphas, + llForward, + input_lengths, + label_lengths, + labels, + self.minibatch_, + self.maxT_, + self.maxU_, + self.alphabet_size_, + self.blank_, + durations, + self.num_durations, + ) + + if training: + # Compute betas + if r < self.omega: + gpu_rnnt_kernel.compute_betas_kernel[self.minibatch_, self.maxU_, self.stream_, 0]( + label_acts, + denom, + betas, + llBackward, + input_lengths, + label_lengths, + labels, + self.minibatch_, + self.maxT_, + self.maxU_, + self.alphabet_size_, + self.blank_, + ) + + # Compute gradient + grad_blocks_per_grid = self.minibatch_ * self.maxT_ * self.maxU_ + grad_threads_per_block = gpu_rnnt_kernel.GPU_RNNT_THREAD_SIZE + gpu_rnnt_kernel.compute_grad_kernel[grad_blocks_per_grid, grad_threads_per_block, self.stream_, 0]( + label_grads, + label_acts, + denom, + alphas, + betas, + llForward, + input_lengths, + label_lengths, + labels, + self.minibatch_, + self.maxT_, + self.maxU_, + self.alphabet_size_, + self.blank_, + self.fastemit_lambda_, + self.clamp_, + ) + else: + gpu_rnnt_kernel.compute_tdt_betas_kernel[self.minibatch_, self.maxU_, self.stream_, 0]( + label_acts, + duration_acts, + denom, + self.sigma, + betas, + llBackward, + input_lengths, + label_lengths, + labels, + self.minibatch_, + self.maxT_, + self.maxU_, + self.alphabet_size_, + self.blank_, + durations, + self.num_durations, + ) + + # Compute gradient + grad_blocks_per_grid = self.minibatch_ * self.maxT_ * self.maxU_ + grad_threads_per_block = gpu_rnnt_kernel.GPU_RNNT_THREAD_SIZE + gpu_rnnt_kernel.compute_tdt_grad_kernel[grad_blocks_per_grid, grad_threads_per_block, self.stream_, 0]( + label_grads, + duration_grads, + label_acts, + duration_acts, + denom, + self.sigma, + alphas, + betas, + llForward, + input_lengths, + label_lengths, + labels, + self.minibatch_, + self.maxT_, + self.maxU_, + self.alphabet_size_, + self.blank_, + durations, + self.num_durations, + self.fastemit_lambda_, + self.clamp_, + ) + + # // cost copy, negate (for log likelihood) and update with additional regularizers + # This needs to be done via CUDA, because we used temporary memory llForward + # passed to alpha, which was updated with log likelihoods. + # But copying this data into a pytorch pointer is more difficult (numba api is one way) + # Therefore launch a pointwise CUDA kernel to update the costs inplace from data of llForward + # Then negate to compute the loglikelihood. + threadsperblock = min(costs.shape[0], 32) + blockspergrid = (costs.shape[0] + (threadsperblock - 1)) // threadsperblock + rnnt_helper.compute_costs_data[blockspergrid, threadsperblock, self.stream_, 0]( + llForward, costs, self.fastemit_lambda_ + ) + self.stream_.synchronize() + + return global_constants.RNNTStatus.RNNT_STATUS_SUCCESS + + def cost_and_grad( + self, + label_acts: torch.Tensor, + duration_acts: torch.Tensor, + label_grads: torch.Tensor, + duration_grads: torch.Tensor, + costs: torch.Tensor, + pad_labels: torch.Tensor, + label_lengths: torch.Tensor, + input_lengths: torch.Tensor, + ): + if ( + duration_acts is None + or label_acts is None + or label_grads is None + or duration_grads is None + or costs is None + or pad_labels is None + or label_lengths is None + or input_lengths is None + ): + return global_constants.RNNTStatus.RNNT_STATUS_INVALID_VALUE + + return self.compute_cost_and_score( + label_acts, duration_acts, label_grads, duration_grads, costs, pad_labels, label_lengths, input_lengths + ) + + def score_forward( + self, + label_acts: torch.Tensor, + duration_acts: torch.Tensor, + costs: torch.Tensor, + pad_labels: torch.Tensor, + label_lengths: torch.Tensor, + input_lengths: torch.Tensor, + ): + if ( + label_acts is None + or duration_acts is None + or costs is None + or pad_labels is None + or label_lengths is None + or input_lengths is None + ): + return global_constants.RNNTStatus.RNNT_STATUS_INVALID_VALUE + + return self.compute_cost_and_score( + label_acts, duration_acts, None, None, costs, pad_labels, label_lengths, input_lengths + ) + + def _prepare_workspace(self) -> (int, Tuple[torch.Tensor]): + """ + Helper method that uses the workspace and constructs slices of it that can be used. + + Returns: + An int, representing the offset of the used workspace (practically, the slice of the workspace consumed) + A tuple of tensors representing the shared workspace. + """ + used_offset, (denom, alphas, betas, llForward, llBackward) = super()._prepare_workspace() + + durations = self.tdt_workspace[: self.num_durations] + + return used_offset, (denom, alphas, betas, llForward, llBackward, durations) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py new file mode 100644 index 0000000..4153af0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py @@ -0,0 +1,1408 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright 2018-2019, Mingkun Huang +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +import torch +from numba import cuda + +from nemo.collections.asr.parts.numba.rnnt_loss.utils import rnnt_helper + +GPU_RNNT_THREAD_SIZE = 256 + +INF = 10000.0 + + +@cuda.jit(device=True, inline=True) +def logp( + denom: torch.Tensor, acts: torch.Tensor, maxT: int, maxU: int, alphabet_size: int, mb: int, t: int, u: int, v: int +): + """ + Compute the sum of log probability from the activation tensor and its denominator. + + Args: + denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor + across entire vocabulary. + acts: Tensor of shape [B, T, U, V+1] flattened. Represents the logprobs activation tensor. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank). + mb: Batch indexer. + t: Acoustic sequence timestep indexer. + u: Target sequence timestep indexer. + v: Vocabulary token indexer. + + Returns: + The sum of logprobs[mb, t, u, v] + denom[mb, t, u] + """ + col = (mb * maxT + t) * maxU + u + return denom[col] + acts[col * alphabet_size + v] + + +@cuda.jit(device=True, inline=True) +def logp_duration(acts: torch.Tensor, maxT: int, maxU: int, num_durations: int, mb: int, t: int, u: int, v: int): + col = (mb * maxT + t) * maxU + u + return acts[col * num_durations + v] + + +@cuda.jit() +def compute_alphas_kernel( + acts: torch.Tensor, + denom: torch.Tensor, + alphas: torch.Tensor, + llForward: torch.Tensor, + xlen: torch.Tensor, + ylen: torch.Tensor, + mlabels: torch.Tensor, # [B] + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + blank_: int, +): + """ + Compute alpha (forward variable) probabilities over the transduction step. + + Args: + acts: Tensor of shape [B, T, U, V+1] flattened. Represents the logprobs activation tensor. + denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor + across entire vocabulary. + alphas: Zero tensor of shape [B, T, U]. Will be updated inside the kernel with the forward variable + probabilities. + llForward: Zero tensor of shape [B]. Represents the log-likelihood of the forward pass. + Returned as the forward pass loss that is reduced by the optimizer. + xlen: Vector of length B which contains the actual acoustic sequence lengths in the padded + activation tensor. + ylen: Vector of length B which contains the actual target sequence lengths in the padded + activation tensor. + mlabels: Matrix of shape [B, U+1] (+1 here is due to token - usually the RNNT blank). + The matrix contains the padded target transcription that must be predicted. + minibatch: Int representing the batch size. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank). + blank_: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab. + + Updates: + Kernel inplace updates the following inputs: + - alphas: forward variable scores. + - llForward: log-likelihood of forward variable. + """ + # // launch B blocks, each block has U threads + b = cuda.blockIdx.x # // batch id + u = cuda.threadIdx.x # label id, u + T = xlen[b] # select AM length of current sample + U = ylen[b] + 1 # select target length of current sample, +1 for the blank token + + labels: torch.Tensor = mlabels[b] # mb label start point, equivalent to mlabels + b * (maxU - 1) + offset = b * maxT * maxU # pointer indexing offset + + # alphas += offset # pointer offset, ignored since we explicitly add offset + + # Initilize alpha[b, t=0, u=0] for all b in B + if u == 0: + alphas[offset] = 0 + + # sync until all alphas are initialized + cuda.syncthreads() + + # Ordinary alpha calculations, broadcast across B=b and U=u + # Look up forward variable calculation from rnnt_numpy.forward_pass() + for n in range(1, T + U - 1): + t = n - u + + if u == 0: + # for t in range(1, T) step to initialize alphas[b, t, 0] + if t > 0 and t < T: + alphas[offset + t * maxU + u] = alphas[offset + (t - 1) * maxU + u] + logp( + denom, acts, maxT, maxU, alphabet_size, b, t - 1, 0, blank_ + ) + elif u < U: + # for u in range(1, U) step to initialize alphas[b, 0, u] + if t == 0: + alphas[offset + u] = alphas[offset + u - 1] + logp( + denom, acts, maxT, maxU, alphabet_size, b, 0, u - 1, labels[u - 1] + ) + + # for t in range(1, T) for u in range(1, U) step to compute alphas[b, t, u] + elif t > 0 and t < T: + no_emit = alphas[offset + (t - 1) * maxU + u] + logp( + denom, acts, maxT, maxU, alphabet_size, b, t - 1, u, blank_ + ) + emit = alphas[offset + t * maxU + u - 1] + logp( + denom, acts, maxT, maxU, alphabet_size, b, t, u - 1, labels[u - 1] + ) + + alphas[offset + t * maxU + u] = rnnt_helper.log_sum_exp(emit, no_emit) + + # sync across all B=b and U=u + cuda.syncthreads() + + # After final sync, alphas[b, T-1, U - 1] + logprobs[b, T-1, U-1, blank] + denom[b, T-1, U-1] gives + # log-likelihood of forward pass. + if u == 0: + loglike = alphas[offset + (T - 1) * maxU + U - 1] + logp( + denom, acts, maxT, maxU, alphabet_size, b, T - 1, U - 1, blank_ + ) + llForward[b] = loglike + + +@cuda.jit() +def compute_betas_kernel( + acts: torch.Tensor, + denom: torch.Tensor, + betas: torch.Tensor, + llBackward: torch.Tensor, + xlen: torch.Tensor, + ylen: torch.Tensor, + mlabels: torch.Tensor, # [B, U] + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + blank_: int, +): + """ + Compute beta (backward variable) probabilities over the transduction step. + + Args: + acts: Tensor of shape [B, T, U, V+1] flattened. Represents the logprobs activation tensor. + denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor + across entire vocabulary. + betas: Zero tensor of shape [B, T, U]. Will be updated inside the kernel with the backward variable + probabilities. + llBackward: Zero tensor of shape [B]. Represents the log-likelihood of the backward pass. + Returned as the backward pass loss that is reduced by the optimizer. + xlen: Vector of length B which contains the actual acoustic sequence lengths in the padded + activation tensor. + ylen: Vector of length B which contains the actual target sequence lengths in the padded + activation tensor. + mlabels: Matrix of shape [B, U+1] (+1 here is due to token - usually the RNNT blank). + The matrix contains the padded target transcription that must be predicted. + minibatch: Int representing the batch size. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank). + blank_: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab. + + Updates: + Kernel inplace updates the following inputs: + - betas: backward variable scores. + - llBackward: log-likelihood of backward variable. + """ + # // launch B blocks, each block has U threads + b = cuda.blockIdx.x # // batch id + u = cuda.threadIdx.x # label id, u + T = xlen[b] # select AM length of current sample + U = ylen[b] + 1 # select target length of current sample, +1 for the blank token + + labels: torch.Tensor = mlabels[b] # mb label start point, equivalent to mlabels + b * (maxU - 1) + offset = b * maxT * maxU # pointer indexing offset + + # betas += offset # pointer offset, ignored since we explicitly add offset + + # Initilize beta[b, t=T-1, u=U-1] for all b in B with log_probs[b, t=T-1, u=U-1, blank] + if u == 0: + betas[offset + (T - 1) * maxU + U - 1] = logp(denom, acts, maxT, maxU, alphabet_size, b, T - 1, U - 1, blank_) + + # sync until all betas are initialized + cuda.syncthreads() + + # Ordinary beta calculations, broadcast across B=b and U=u + # Look up backward variable calculation from rnnt_numpy.backward_pass() + for n in range(T + U - 2, -1, -1): + t = n - u + + if u == (U - 1): + # for t in reversed(range(T - 1)) step to initialize betas[b, t, U-1] + if t >= 0 and t < (T - 1): + betas[offset + t * maxU + U - 1] = betas[offset + (t + 1) * maxU + U - 1] + logp( + denom, acts, maxT, maxU, alphabet_size, b, t, U - 1, blank_ + ) + elif u < U: + if t == T - 1: + # for u in reversed(range(U - 1)) step to initialize betas[b, T-1, u] + betas[offset + (T - 1) * maxU + u] = betas[offset + (T - 1) * maxU + u + 1] + logp( + denom, acts, maxT, maxU, alphabet_size, b, T - 1, u, labels[u] + ) + elif (t >= 0) and (t < T - 1): + # for t in reversed(range(T - 1)) for u in reversed(range(U - 1)) step to compute betas[b, t, u] + no_emit = betas[offset + (t + 1) * maxU + u] + logp( + denom, acts, maxT, maxU, alphabet_size, b, t, u, blank_ + ) + emit = betas[offset + t * maxU + u + 1] + logp( + denom, acts, maxT, maxU, alphabet_size, b, t, u, labels[u] + ) + betas[offset + t * maxU + u] = rnnt_helper.log_sum_exp(emit, no_emit) + + # sync across all B=b and U=u + cuda.syncthreads() + + # After final sync, betas[b, 0, 0] gives + # log-likelihood of backward pass. + if u == 0: + llBackward[b] = betas[offset] + + +@cuda.jit() +def compute_grad_kernel( + grads: torch.Tensor, + acts: torch.Tensor, + denom: torch.Tensor, + alphas: torch.Tensor, + betas: torch.Tensor, + logll: torch.Tensor, + xlen: torch.Tensor, + ylen: torch.Tensor, + mlabels: torch.Tensor, # [B, U] + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + blank_: int, + fastemit_lambda: float, + clamp: float, +): + """ + Compute gradients over the transduction step. + + Args: + grads: Zero Tensor of shape [B, T, U, V+1]. Is updated by this kernel to contain the gradients + of this batch of samples. + acts: Tensor of shape [B, T, U, V+1] flattened. Represents the logprobs activation tensor. + denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor + across entire vocabulary. + alphas: Alpha variable, contains forward probabilities. A tensor of shape [B, T, U]. + betas: Beta varoable, contains backward probabilities. A tensor of shape [B, T, U]. + logll: Log-likelihood of the forward variable, represented as a vector of shape [B]. + Represents the log-likelihood of the forward pass. + xlen: Vector of length B which contains the actual acoustic sequence lengths in the padded + activation tensor. + ylen: Vector of length B which contains the actual target sequence lengths in the padded + activation tensor. + mlabels: Matrix of shape [B, U+1] (+1 here is due to token - usually the RNNT blank). + The matrix contains the padded target transcription that must be predicted. + minibatch: Int representing the batch size. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank). + blank_: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab. + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + + Updates: + Kernel inplace updates the following inputs: + - grads: Gradients with respect to the log likelihood (logll). + """ + # Kernel call: + # blocks_per_grid = minibatch (b) * maxT (t) * maxU (u) + # threads_per_block = constant buffer size of parallel threads (v :: Constant) + tid = cuda.threadIdx.x # represents v, taking steps of some constant size + idx = tid # index of v < V+1; in steps of constant buffer size + col = cuda.blockIdx.x # represents a fused index of b * t * u + + # Decompose original indices from fused `col` + u = col % maxU # (b * t * u) % u = u + bt = (col - u) // maxU # (b * t * u - u) // U = b * t + t = bt % maxT # (b * t) % t = t + mb = (bt - t) // maxT # (b * t - t) // T = b + + # constants + T = xlen[mb] # select AM length of current sample + U = ylen[mb] + 1 # select target length of current sample, +1 for the blank token + labels: torch.Tensor = mlabels[mb] # labels = mlabels + mb * (maxU - 1); + + # Buffered gradient calculations, broadcast across B=b, T=t and U=u, looped over V with some constant stride. + # Look up gradient calculation from rnnt_numpy.compute_gradient() + if t < T and u < U: + # For cuda kernels, maximum number of threads per block is limited to some value. + # However, it may be the case that vocabulary size is larger than this limit + # To work around this, an arbitrary thread buffer size is chosen such that, + # 1) each element within the thread pool operates independently of the other + # 2) An inner while loop moves the index of each buffer element by the size of the buffer itself, + # such that all elements of the vocabulary size are covered in (V + 1 // thread_buffer) number of steps. + # As such, each thread will perform the while loop at least (V + 1 // thread_buffer) number of times + while idx < alphabet_size: + # remember, `col` represents the tri-index [b, t, u] + # therefore; logpk = denom[b, t, u] + acts[b, t, u, v] + logpk = denom[col] + acts[col * alphabet_size + idx] + # initialize the grad of the sample acts[b, t, u, v] + grad = math.exp(alphas[col] + betas[col] + logpk - logll[mb]) + + # If FastEmit regularization is enabled, calculate the gradeint of probability of predicting the next label + # at the current timestep. + # The formula for this is Equation 9 in https://arxiv.org/abs/2010.11148, multiplied by the log probability + # of the current step (t, u), normalized by the total log likelihood. + # Once the gradient has been calculated, scale it by `fastemit_lambda`, as in Equation 10. + if fastemit_lambda > 0.0 and u < U - 1: + fastemit_grad = fastemit_lambda * math.exp( + alphas[col] # alphas(t, u) + + (denom[col] + acts[col * alphabet_size + labels[u]]) # y_hat(t, u) + + betas[col + 1] # betas(t, u+1) + + logpk # log Pr(k|t, u) + - logll[mb] # total log likelihood for normalization + ) + else: + fastemit_grad = 0.0 + + # Update the gradient of act[b, t, u, v] with the gradient from FastEmit regularization + grad = grad + fastemit_grad + + # // grad to last blank transition + # grad[b, T-1, U-1, v=blank] -= exp(alphas[b, t, u) + logpk - logll[b]) + if (idx == blank_) and (t == T - 1) and (u == U - 1): + grad -= math.exp(alphas[col] + logpk - logll[mb]) + + # grad of blank across t < T; + # grad[b, t 0.0: + g = grads[col * alphabet_size + idx] + g = min(g, clamp) + g = max(g, -clamp) + grads[col * alphabet_size + idx] = g + + # update internal index through the thread_buffer; + # until idx < V + 1, such that entire vocabulary has been updated. + idx += GPU_RNNT_THREAD_SIZE + + +@cuda.jit() +def compute_multiblank_alphas_kernel( + acts: torch.Tensor, + denom: torch.Tensor, + sigma: float, + alphas: torch.Tensor, + llForward: torch.Tensor, + xlen: torch.Tensor, + ylen: torch.Tensor, + mlabels: torch.Tensor, + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + blank_: int, + big_blank_duration: torch.Tensor, + num_big_blanks: int, +): + """ + Compute alpha (forward variable) probabilities for multi-blank transducuer loss (https://arxiv.org/pdf/2211.03541). + + Args: + acts: Tensor of shape [B, T, U, V + 1 + num_big_blanks] flattened. Represents the logprobs activation tensor. + denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor + across entire vocabulary. + sigma: Hyper-parameter for logit-undernormalization technique for training multi-blank transducers. + alphas: Zero tensor of shape [B, T, U]. Will be updated inside the kernel with the forward variable + probabilities. + llForward: Zero tensor of shape [B]. Represents the log-likelihood of the forward pass. + Returned as the forward pass loss that is reduced by the optimizer. + xlen: Vector of length B which contains the actual acoustic sequence lengths in the padded + activation tensor. + ylen: Vector of length B which contains the actual target sequence lengths in the padded + activation tensor. + mlabels: Matrix of shape [B, U+1] (+1 here is due to token - usually the RNNT blank). + The matrix contains the padded target transcription that must be predicted. + minibatch: Int representing the batch size. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank). + blank_: Index of the RNNT standard blank token in the vocabulary. + big_blank_durations: Vector of supported big blank durations of the model. + num_big_blanks: Number of big blanks of the model. + + Updates: + Kernel inplace updates the following inputs: + - alphas: forward variable scores. + - llForward: log-likelihood of forward variable. + """ + # // launch B blocks, each block has U threads + b = cuda.blockIdx.x # // batch id + u = cuda.threadIdx.x # label id, u + T = xlen[b] # select AM length of current sample + U = ylen[b] + 1 # select target length of current sample, +1 for the blank token + + labels: torch.Tensor = mlabels[b] # mb label start point, equivalent to mlabels + b * (maxU - 1) + offset = b * maxT * maxU # pointer indexing offset + + # Initilize alpha[b, t=0, u=0] for all b in B + if u == 0: + alphas[offset] = 0 + + # sync until all alphas are initialized + cuda.syncthreads() + + # Ordinary alpha calculations, broadcast across B=b and U=u + # Look up forward variable calculation from rnnt_numpy.forward_pass() + # Note: because of the logit under-normalization, everytime logp() is called, + # it is always followed by a `-sigma` term. + for n in range(1, T + U - 1): + t = n - u + + if u == 0: + # for t in range(1, T) step to initialize alphas[b, t, 0] + if t > 0 and t < T: + alphas[offset + t * maxU + u] = ( + alphas[offset + (t - 1) * maxU + u] + + logp(denom, acts, maxT, maxU, alphabet_size, b, t - 1, 0, blank_) + - sigma + ) + + # Now add the weights for big blanks. + for i in range(num_big_blanks): + if t >= big_blank_duration[i]: + alphas[offset + t * maxU + u] = rnnt_helper.log_sum_exp( + alphas[offset + t * maxU + u], + alphas[offset + (t - big_blank_duration[i]) * maxU + u] + + logp( + denom, acts, maxT, maxU, alphabet_size, b, t - big_blank_duration[i], 0, blank_ - 1 - i + ) + - sigma, + ) + + elif u < U: + # for u in range(1, U) step to initialize alphas[b, 0, u] + if t == 0: + alphas[offset + u] = ( + alphas[offset + u - 1] + + logp(denom, acts, maxT, maxU, alphabet_size, b, 0, u - 1, labels[u - 1]) + - sigma + ) + + # for t in range(1, T) for u in range(1, U) step to compute alphas[b, t, u] + elif t > 0 and t < T: + no_emit = ( + alphas[offset + (t - 1) * maxU + u] + + logp(denom, acts, maxT, maxU, alphabet_size, b, t - 1, u, blank_) + - sigma + ) + emit = ( + alphas[offset + t * maxU + u - 1] + + logp(denom, acts, maxT, maxU, alphabet_size, b, t, u - 1, labels[u - 1]) + - sigma + ) + + alphas[offset + t * maxU + u] = rnnt_helper.log_sum_exp(emit, no_emit) + + # Now add the weights for big blanks. + for i in range(num_big_blanks): + if t >= big_blank_duration[i]: + # big-blank weight here is + # alpha(t - duration, u) * p(big-blank | t - duration, u) / exp(sigma), in log domain + # do this all all big-blanks if the above condition is met + big_blank_no_emit = ( + alphas[offset + (t - big_blank_duration[i]) * maxU + u] + + logp( + denom, acts, maxT, maxU, alphabet_size, b, t - big_blank_duration[i], u, blank_ - 1 - i + ) + - sigma + ) + alphas[offset + t * maxU + u] = rnnt_helper.log_sum_exp( + alphas[offset + t * maxU + u], big_blank_no_emit + ) + + # sync across all B=b and U=u + cuda.syncthreads() + + # After final sync, alphas[b, T-1, U - 1] + logprobs[b, T-1, U-1, blank] + denom[b, T-1, U-1] gives + # log-likelihood of forward pass. + if u == 0: + loglike = ( + alphas[offset + (T - 1) * maxU + U - 1] + + logp(denom, acts, maxT, maxU, alphabet_size, b, T - 1, U - 1, blank_) + - sigma + ) + + # Now add the weights for big blanks for the final weight computation. + for i in range(num_big_blanks): + if T >= big_blank_duration[i]: + big_blank_loglike = ( + alphas[offset + (T - big_blank_duration[i]) * maxU + U - 1] + + logp(denom, acts, maxT, maxU, alphabet_size, b, T - big_blank_duration[i], U - 1, blank_ - 1 - i) + - sigma + ) + loglike = rnnt_helper.log_sum_exp(loglike, big_blank_loglike) + + llForward[b] = loglike + + +@cuda.jit() +def compute_multiblank_betas_kernel( + acts: torch.Tensor, + denom: torch.Tensor, + sigma: float, + betas: torch.Tensor, + llBackward: torch.Tensor, + xlen: torch.Tensor, + ylen: torch.Tensor, + mlabels: torch.Tensor, # [B, U] + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + blank_: int, + big_blank_duration: torch.Tensor, + num_big_blanks: int, +): + """ + Compute beta (backward variable) probabilities for multi-blank transducer loss (https://arxiv.org/pdf/2211.03541). + + Args: + acts: Tensor of shape [B, T, U, V + 1 + num-big-blanks] flattened. Represents the logprobs activation tensor. + denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor + across entire vocabulary. + sigma: Hyper-parameter for logit-undernormalization technique for training multi-blank transducers. + betas: Zero tensor of shape [B, T, U]. Will be updated inside the kernel with the backward variable + probabilities. + llBackward: Zero tensor of shape [B]. Represents the log-likelihood of the backward pass. + Returned as the backward pass loss that is reduced by the optimizer. + xlen: Vector of length B which contains the actual acoustic sequence lengths in the padded + activation tensor. + ylen: Vector of length B which contains the actual target sequence lengths in the padded + activation tensor. + mlabels: Matrix of shape [B, U+1] (+1 here is due to token - usually the RNNT blank). + The matrix contains the padded target transcription that must be predicted. + minibatch: Int representing the batch size. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank). + blank_: Index of the RNNT standard blank token in the vocabulary. + big_blank_durations: Vector of supported big blank durations of the model. + num_big_blanks: Number of big blanks of the model. + + Updates: + Kernel inplace updates the following inputs: + - betas: backward variable scores. + - llBackward: log-likelihood of backward variable. + """ + # // launch B blocks, each block has U threads + b = cuda.blockIdx.x # // batch id + u = cuda.threadIdx.x # label id, u + T = xlen[b] # select AM length of current sample + U = ylen[b] + 1 # select target length of current sample, +1 for the blank token + + labels: torch.Tensor = mlabels[b] # mb label start point, equivalent to mlabels + b * (maxU - 1) + offset = b * maxT * maxU # pointer indexing offset + + # Note: just like the alphas, because of the logit under-normalization, everytime + # logp() is called, it is always followed by a `-sigma` term. + + # Initilize beta[b, t=T-1, u=U-1] for all b in B with log_probs[b, t=T-1, u=U-1, blank] + if u == 0: + betas[offset + (T - 1) * maxU + U - 1] = ( + logp(denom, acts, maxT, maxU, alphabet_size, b, T - 1, U - 1, blank_) - sigma + ) + + # sync until all betas are initialized + cuda.syncthreads() + + # Ordinary beta calculations, broadcast across B=b and U=u + # Look up backward variable calculation from rnnt_numpy.backward_pass() + for n in range(T + U - 2, -1, -1): + t = n - u + + if u == (U - 1): + # for t in reversed(range(T - 1)) step to initialize betas[b, t, U-1] + if t >= 0 and t < (T - 1): + # beta[t, U - 1] = beta[t + 1, U - 1] * p(blank | t, U - 1) / exp(sigma) + # this part is the same as regular RNN-T. + betas[offset + t * maxU + U - 1] = ( + betas[offset + (t + 1) * maxU + U - 1] + + logp(denom, acts, maxT, maxU, alphabet_size, b, t, U - 1, blank_) + - sigma + ) + + # now add the weights from big blanks + for i in range(num_big_blanks): + if t + big_blank_duration[i] < T: + # adding to beta[t, U - 1] of weight (in log domain), + # beta[t + duration, U - 1] * p(big-blank | t, U - 1) / exp(sigma) + betas[offset + t * maxU + U - 1] = rnnt_helper.log_sum_exp( + betas[offset + t * maxU + U - 1], + betas[offset + (t + big_blank_duration[i]) * maxU + U - 1] + + logp(denom, acts, maxT, maxU, alphabet_size, b, t, U - 1, blank_ - 1 - i) + - sigma, + ) + elif t + big_blank_duration[i] == T and big_blank_duration[i] != 1: + # adding to beta[T - duration, U - 1] of weight (in log domain), + # p(big-blank | T - duration, U - 1) / exp(sigma) + betas[offset + t * maxU + U - 1] = rnnt_helper.log_sum_exp( + betas[offset + t * maxU + U - 1], + logp(denom, acts, maxT, maxU, alphabet_size, b, t, U - 1, blank_ - 1 - i) - sigma, + ) + + elif u < U: + if t == T - 1: + # for u in reversed(range(U - 1)) step to initialize betas[b, T-1, u] + betas[offset + (T - 1) * maxU + u] = ( + betas[offset + (T - 1) * maxU + u + 1] + + logp(denom, acts, maxT, maxU, alphabet_size, b, T - 1, u, labels[u]) + - sigma + ) + elif (t >= 0) and (t < T - 1): + # for t in reversed(range(T - 1)) for u in reversed(range(U - 1)) step to compute betas[b, t, u] + no_emit = ( + betas[offset + (t + 1) * maxU + u] + + logp(denom, acts, maxT, maxU, alphabet_size, b, t, u, blank_) + - sigma + ) + emit = ( + betas[offset + t * maxU + u + 1] + + logp(denom, acts, maxT, maxU, alphabet_size, b, t, u, labels[u]) + - sigma + ) + betas[offset + t * maxU + u] = rnnt_helper.log_sum_exp(emit, no_emit) + + # now add the weights from big blanks + for i in range(num_big_blanks): + if t < T - big_blank_duration[i]: + # added weight for the big-blank, + # beta[t + duration, u] * p(big-blank | t, u) / exp(sigma) + big_blank_no_emit = ( + betas[offset + (t + big_blank_duration[i]) * maxU + u] + + logp(denom, acts, maxT, maxU, alphabet_size, b, t, u, blank_ - 1 - i) + - sigma + ) + betas[offset + t * maxU + u] = rnnt_helper.log_sum_exp( + betas[offset + t * maxU + u], big_blank_no_emit + ) + + # sync across all B=b and U=u + cuda.syncthreads() + + # After final sync, betas[b, 0, 0] gives + # log-likelihood of backward pass. + if u == 0: + llBackward[b] = betas[offset] + + +@cuda.jit() +def compute_multiblank_grad_kernel( + grads: torch.Tensor, + acts: torch.Tensor, + denom: torch.Tensor, + sigma: float, + alphas: torch.Tensor, + betas: torch.Tensor, + logll: torch.Tensor, + xlen: torch.Tensor, + ylen: torch.Tensor, + mlabels: torch.Tensor, # [B, U] + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + blank_: int, + big_blank_duration: torch.Tensor, + num_big_blanks: int, + fastemit_lambda: float, + clamp: float, +): + """ + Compute gradients for multi-blank transducer loss (https://arxiv.org/pdf/2211.03541). + + Args: + grads: Zero Tensor of shape [B, T, U, V + 1 + num_big_blanks]. Is updated by this kernel to contain the gradients + of this batch of samples. + acts: Tensor of shape [B, T, U, V + 1 + num_big_blanks] flattened. Represents the logprobs activation tensor. + denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor + across entire vocabulary. + sigma: Hyper-parameter for logit-undernormalization technique for training multi-blank transducers. + alphas: Alpha variable, contains forward probabilities. A tensor of shape [B, T, U]. + betas: Beta varoable, contains backward probabilities. A tensor of shape [B, T, U]. + logll: Log-likelihood of the forward variable, represented as a vector of shape [B]. + Represents the log-likelihood of the forward pass. + xlen: Vector of length B which contains the actual acoustic sequence lengths in the padded + activation tensor. + ylen: Vector of length B which contains the actual target sequence lengths in the padded + activation tensor. + mlabels: Matrix of shape [B, U+1] (+1 here is due to token - usually the RNNT blank). + The matrix contains the padded target transcription that must be predicted. + minibatch: Int representing the batch size. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank). + blank_: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab. + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + big_blank_durations: Vector of supported big blank durations of the model. + num_big_blanks: Number of big blanks of the model. + + Updates: + Kernel inplace updates the following inputs: + - grads: Gradients with respect to the log likelihood (logll). + """ + # Kernel call: + # blocks_per_grid = minibatch (b) * maxT (t) * maxU (u) + # threads_per_block = constant buffer size of parallel threads (v :: Constant) + tid = cuda.threadIdx.x # represents v, taking steps of some constant size + idx = tid # index of v < V+1; in steps of constant buffer size + col = cuda.blockIdx.x # represents a fused index of b * t * u + + # Decompose original indices from fused `col` + u = col % maxU # (b * t * u) % u = u + bt = (col - u) // maxU # (b * t * u - u) // U = b * t + t = bt % maxT # (b * t) % t = t + mb = (bt - t) // maxT # (b * t - t) // T = b + + # constants + T = xlen[mb] # select AM length of current sample + U = ylen[mb] + 1 # select target length of current sample, +1 for the blank token + labels: torch.Tensor = mlabels[mb] # labels = mlabels + mb * (maxU - 1); + + # Buffered gradient calculations, broadcast across B=b, T=t and U=u, looped over V with some constant stride. + # Look up gradient calculation from rnnt_numpy.compute_gradient() + if t < T and u < U: + # For cuda kernels, maximum number of threads per block is limited to some value. + # However, it may be the case that vocabulary size is larger than this limit + # To work around this, an arbitrary thread buffer size is chosen such that, + # 1) each element within the thread pool operates independently of the other + # 2) An inner while loop moves the index of each buffer element by the size of the buffer itself, + # such that all elements of the vocabulary size are covered in (V + 1 // thread_buffer) number of steps. + # As such, each thread will perform the while loop at least (V + 1 // thread_buffer) number of times + while idx < alphabet_size: + # remember, `col` represents the tri-index [b, t, u] + # therefore; logpk = denom[b, t, u] + acts[b, t, u, v] + logpk = denom[col] + acts[col * alphabet_size + idx] + # initialize the grad of the sample acts[b, t, u, v] + grad = math.exp(alphas[col] + betas[col] + logpk - logll[mb]) + + # In all of the following computation, whenever logpk is used, we + # need to subtract sigma based on our derivation of the gradient of + # the logit under-normalization method. + + # If FastEmit regularization is enabled, calculate the gradeint of probability of predicting the next label + # at the current timestep. + # The formula for this is Equation 9 in https://arxiv.org/abs/2010.11148, multiplied by the log probability + # of the current step (t, u), normalized by the total log likelihood. + # Once the gradient has been calculated, scale it by `fastemit_lambda`, as in Equation 10. + if fastemit_lambda > 0.0 and u < U - 1: + fastemit_grad = fastemit_lambda * math.exp( + alphas[col] # alphas(t, u) + + (denom[col] + acts[col * alphabet_size + labels[u]]) + + betas[col + 1] # betas(t, u+1) + + logpk # log Pr(k|t, u) + - sigma + - logll[mb] # total log likelihood for normalization + ) + else: + fastemit_grad = 0.0 + + # Update the gradient of act[b, t, u, v] with the gradient from FastEmit regularization + grad = grad + fastemit_grad + + # grad to last blank transition + # grad[b, T-1, U-1, v=blank] -= exp(alphas[b, t, u) + logpk - sigma - logll[b]) + if (idx == blank_) and (t == T - 1) and (u == U - 1): + grad -= math.exp(alphas[col] + logpk - sigma - logll[mb]) + else: + # this is one difference of the multi-blank gradient from standard RNN-T + # gradient -- basically, wherever the blank_ symbol is addressed in the + # original code, we need to do similar things to big blanks, and we need + # to change the if conditions to match the duration of the big-blank. + # grad[b, T-duration, U-1, v=big-blank] -= exp(alphas[b, t, u) + logpk - sigma - logll[b]) + for i in range(num_big_blanks): + if (idx == blank_ - 1 - i) and (t == T - big_blank_duration[i]) and (u == U - 1): + grad -= math.exp(alphas[col] + logpk - sigma - logll[mb]) + + # grad of blank across t < T; + # grad[b, t 0.0: + g = grads[col * alphabet_size + idx] + g = min(g, clamp) + g = max(g, -clamp) + grads[col * alphabet_size + idx] = g + + # update internal index through the thread_buffer; + # until idx < V + 1, such that entire vocabulary has been updated. + idx += GPU_RNNT_THREAD_SIZE + + +@cuda.jit() +def compute_tdt_alphas_kernel( + acts: torch.Tensor, + duration_acts: torch.Tensor, + denom: torch.Tensor, + sigma: float, + alphas: torch.Tensor, + llForward: torch.Tensor, + xlen: torch.Tensor, + ylen: torch.Tensor, + mlabels: torch.Tensor, # [B] + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + blank_: int, + durations: torch.Tensor, + num_durations: int, +): + """ + Compute alpha (forward variable) probabilities over the transduction step. + + Args: + acts: Tensor of shape [B, T, U, V] flattened. Represents the logprobs activation tensor for tokens. + duration_acts: Tensor of shape [B, T, U, D] flattened. Represents the logprobs activation tensor for duration. + denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor for tokens. + + alphas: Zero tensor of shape [B, T, U]. Will be updated inside the kernel with the forward variable + probabilities. + llForward: Zero tensor of shape [B]. Represents the log-likelihood of the forward pass. + Returned as the forward pass loss that is reduced by the optimizer. + xlen: Vector of length B which contains the actual acoustic sequence lengths in the padded + activation tensor. + ylen: Vector of length B which contains the actual target sequence lengths in the padded + activation tensor. + mlabels: Matrix of shape [B, U+1] (+1 here is due to token - usually the RNNT blank). + The matrix contains the padded target transcription that must be predicted. + minibatch: Int representing the batch size. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank). + blank_: Index of the TDT blank token in the vocabulary. Must be the last token in the vocab. + + Updates: + Kernel inplace updates the following inputs: + - alphas: forward variable scores. + - llForward: log-likelihood of forward variable. + """ + # // launch B blocks, each block has U threads + b = cuda.blockIdx.x # // batch id + u = cuda.threadIdx.x # label id, u + T = xlen[b] # select AM length of current sample + U = ylen[b] + 1 # select target length of current sample, +1 for the blank token + + labels: torch.Tensor = mlabels[b] # mb label start point, equivalent to mlabels + b * (maxU - 1) + offset = b * maxT * maxU # pointer indexing offset + + # alphas += offset # pointer offset, ignored since we explicitly add offset + + # Initilize alpha[b, t=0, u=0] for all b in B + if u == 0: + alphas[offset] = 0 + + # sync until all alphas are initialized + cuda.syncthreads() + + # Ordinary alpha calculations, broadcast across B=b and U=u + # Look up forward variable calculation from rnnt_numpy.forward_pass() + for n in range(1, T + U - 1): + t = n - u + + if u == 0: + # when u == 0, we only consider blank emissions. + if t > 0 and t < T: + alphas[offset + t * maxU + u] = -INF + + for i in range(1, num_durations): # skip 0 since blank emission has to advance by at least one + if t >= durations[i]: + alphas[offset + t * maxU + u] = rnnt_helper.log_sum_exp( + alphas[offset + t * maxU + u], # the current alpha value + alphas[offset + (t - durations[i]) * maxU + u] # alpha(t - duration, u) + + logp( + denom, acts, maxT, maxU, alphabet_size, b, t - durations[i], u, blank_ + ) # logp of blank emission + - sigma # logit under-normalization + + logp_duration( + duration_acts, maxT, maxU, num_durations, b, t - durations[i], u, i + ), # logp of duration + ) + else: + break # since durations are in ascending order, when we encounter a duration that is too large, then + # there is no need to check larger durations after that. + + elif u < U: + # when t == 0, we only consider the non-blank emission. + if t == 0: + alphas[offset + u] = ( + alphas[offset + u - 1] # alpha(t, u - 1) + + logp( + denom, acts, maxT, maxU, alphabet_size, b, t, u - 1, labels[u - 1] + ) # logp of token emission + - sigma # logit under-normalization + + logp_duration( + duration_acts, maxT, maxU, num_durations, b, t, u - 1, 0 + ) # t = 0, so it must be duration = 0. Therefore the last argument passed to logp_duration() is 0. + ) + + # now we have t != 0 and u != 0, and we need to consider both non-blank and blank emissions. + elif t > 0 and t < T: + no_emit = -INF # no_emit stores the score for all blank emissions. + for i in range(1, num_durations): + if t >= durations[i]: + no_emit = rnnt_helper.log_sum_exp( + no_emit, # current score + alphas[offset + (t - durations[i]) * maxU + u] # alpha(t - duration, u) + + logp( + denom, acts, maxT, maxU, alphabet_size, b, t - durations[i], u, blank_ + ) # logp of blank emission + - sigma # logit under-normalization + + logp_duration( + duration_acts, maxT, maxU, num_durations, b, t - durations[i], u, i + ), # logp of duration + ) + else: + break # we can exit the loop early here, same as the case for u == 0 above. + + emit = -INF # emit stores the score for non-blank emissions. + for i in range(0, num_durations): + if t >= durations[i]: + emit = rnnt_helper.log_sum_exp( + emit, # current score + alphas[offset + (t - durations[i]) * maxU + u - 1] # alpha(t - duration, u - 1) + + logp( + denom, acts, maxT, maxU, alphabet_size, b, t - durations[i], u - 1, labels[u - 1] + ) # logp of non-blank emission + - sigma # logit under-normalization + + logp_duration( + duration_acts, maxT, maxU, num_durations, b, t - durations[i], u - 1, i + ), # logp of duration + ) + else: + break # we can exit the loop early here, same as the case for u == 0 above. + + # combining blank and non-blank emissions. + alphas[offset + t * maxU + u] = rnnt_helper.log_sum_exp(emit, no_emit) + + # sync across all B=b and U=u + cuda.syncthreads() + + # After final sync, the forward log-likelihood can be computed as the summataion of + # alpha(T - duration, U - 1) + logp(blank, duration | t - duration, U - 1), over different durations. + if u == 0: + # first we consider duration = 1 + loglike = ( + alphas[offset + (T - 1) * maxU + U - 1] + + logp(denom, acts, maxT, maxU, alphabet_size, b, T - 1, U - 1, blank_) + - sigma + + logp_duration(duration_acts, maxT, maxU, num_durations, b, T - 1, U - 1, 1) + ) + + # then we add the scores for duration > 1, if such durations are possible given the audio lengths. + for i in range(2, num_durations): + if T >= durations[i]: + big_blank_loglike = ( + alphas[offset + (T - durations[i]) * maxU + U - 1] + + logp(denom, acts, maxT, maxU, alphabet_size, b, T - durations[i], U - 1, blank_) + - sigma + + logp_duration(duration_acts, maxT, maxU, num_durations, b, T - durations[i], U - 1, i) + ) + loglike = rnnt_helper.log_sum_exp(loglike, big_blank_loglike) + else: + break + + llForward[b] = loglike + + +@cuda.jit() +def compute_tdt_betas_kernel( + acts: torch.Tensor, + duration_acts: torch.Tensor, + denom: torch.Tensor, + sigma: float, + betas: torch.Tensor, + llBackward: torch.Tensor, + xlen: torch.Tensor, + ylen: torch.Tensor, + mlabels: torch.Tensor, # [B, U] + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + blank_: int, + durations: torch.Tensor, + num_durations: int, +): + """ + Compute beta (backward variable) probabilities over the transduction step. + + Args: + acts: Tensor of shape [B, T, U, V] flattened. Represents the logprobs activation tensor for tokens. + duration_acts: Tensor of shape [B, T, U, D] flattened. Represents the logprobs activation tensor for duations. + denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor + across entire vocabulary. + betas: Zero tensor of shape [B, T, U]. Will be updated inside the kernel with the backward variable + probabilities. + llBackward: Zero tensor of shape [B]. Represents the log-likelihood of the backward pass. + Returned as the backward pass loss that is reduced by the optimizer. + xlen: Vector of length B which contains the actual acoustic sequence lengths in the padded + activation tensor. + ylen: Vector of length B which contains the actual target sequence lengths in the padded + activation tensor. + mlabels: Matrix of shape [B, U+1] (+1 here is due to token - usually the RNNT blank). + The matrix contains the padded target transcription that must be predicted. + minibatch: Int representing the batch size. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank). + blank_: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab. + + Updates: + Kernel inplace updates the following inputs: + - betas: backward variable scores. + - llBackward: log-likelihood of backward variable. + """ + # // launch B blocks, each block has U threads + b = cuda.blockIdx.x # // batch id + u = cuda.threadIdx.x # label id, u + T = xlen[b] # select AM length of current sample + U = ylen[b] + 1 # select target length of current sample, +1 for the blank token + + labels: torch.Tensor = mlabels[b] # mb label start point, equivalent to mlabels + b * (maxU - 1) + offset = b * maxT * maxU # pointer indexing offset + + # betas += offset # pointer offset, ignored since we explicitly add offset + + # Initilize beta[b, t=T-1, u=U-1] for all b in B with log_probs[b, t=T-1, u=U-1, blank] + if u == 0: + betas[offset + (T - 1) * maxU + U - 1] = ( + logp(denom, acts, maxT, maxU, alphabet_size, b, T - 1, U - 1, blank_) + - sigma + + logp_duration(duration_acts, maxT, maxU, num_durations, b, T - 1, U - 1, 1) + ) + + # sync until all betas are initialized + cuda.syncthreads() + + # Ordinary beta calculations, broadcast across B=b and U=u + # Look up backward variable calculation from rnnt_numpy.backward_pass() + for n in range(T + U - 2, -1, -1): + t = n - u + + if u == U - 1: + # u == U - 1, we only consider blank emissions. + if t >= 0 and t + 1 < T: + betas[offset + t * maxU + U - 1] = -INF + for i in range(1, num_durations): + # although similar, the computation for beta's is slightly more complex for boundary cases. + # the following two cases correspond to whether t is exactly certain duration away from T. + # and they have slightly different update rules. + + if t + durations[i] < T: + betas[offset + t * maxU + U - 1] = rnnt_helper.log_sum_exp( + betas[offset + t * maxU + U - 1], + betas[ + offset + (t + durations[i]) * maxU + U - 1 + ] # beta[t, U - 1] depends on the value beta[t + duration, U - 1] here. + + logp(denom, acts, maxT, maxU, alphabet_size, b, t, U - 1, blank_) # log prob of blank + + logp_duration( + duration_acts, maxT, maxU, num_durations, b, t, U - 1, i + ) # log prob of duration (durations[i]) + - sigma, # for logit undernormalization + ) + elif t + durations[i] == T: + betas[offset + t * maxU + U - 1] = rnnt_helper.log_sum_exp( + betas[offset + t * maxU + U - 1], + # here we have one fewer term than the "if" block above. This could be seen as having "0" here since + # beta[t + duration, U - 1] isn't defined because t + duration is out of bound. + logp(denom, acts, maxT, maxU, alphabet_size, b, t, U - 1, blank_) # log prob of blank + + logp_duration( + duration_acts, maxT, maxU, num_durations, b, t, U - 1, i + ) # log prob of duration (durations[i]) + - sigma, # for logit undernormalization. Basically every time sigma shows up is because of logit undernormalization. + ) + + elif u < U - 1: + if t == T - 1: + # t == T - 1, so we only consider non-blank with duration 0. (Note, we can't have blank emissions with duration = 0) + betas[offset + (T - 1) * maxU + u] = ( + betas[offset + (T - 1) * maxU + u + 1] + + logp(denom, acts, maxT, maxU, alphabet_size, b, T - 1, u, labels[u]) # non-blank log prob + + logp_duration(duration_acts, maxT, maxU, num_durations, b, T - 1, u, 0) # log prob of duration 0 + - sigma + ) + + elif t >= 0 and t < T - 1: + # now we need to consider both blank andnon-blanks. Similar to alphas, we first compute them separately with no_emit and emit. + no_emit = -INF + for i in range(1, num_durations): + if t + durations[i] < T: + no_emit = rnnt_helper.log_sum_exp( + no_emit, + betas[offset + (t + durations[i]) * maxU + u] + + logp(denom, acts, maxT, maxU, alphabet_size, b, t, u, blank_) + + logp_duration(duration_acts, maxT, maxU, num_durations, b, t, u, i) + - sigma, + ) + + emit = -INF + for i in range(0, num_durations): + if t + durations[i] < T: + emit = rnnt_helper.log_sum_exp( + emit, + betas[offset + (t + durations[i]) * maxU + u + 1] + + logp(denom, acts, maxT, maxU, alphabet_size, b, t, u, labels[u]) + + logp_duration(duration_acts, maxT, maxU, num_durations, b, t, u, i) + - sigma, + ) + + # combining all blank emissions and all non-blank emissions. + betas[offset + t * maxU + u] = rnnt_helper.log_sum_exp(emit, no_emit) + + # sync across all B=b and U=u + cuda.syncthreads() + + # After final sync, betas[b, 0, 0] gives log-likelihood of backward pass, same with conventional Transducers. + if u == 0: + llBackward[b] = betas[offset] + + +@cuda.jit() +def compute_tdt_grad_kernel( + label_grads: torch.Tensor, + duration_grads: torch.Tensor, + acts: torch.Tensor, + duration_acts: torch.Tensor, + denom: torch.Tensor, + sigma: float, + alphas: torch.Tensor, + betas: torch.Tensor, + logll: torch.Tensor, + xlen: torch.Tensor, + ylen: torch.Tensor, + mlabels: torch.Tensor, # [B, U] + minibatch: int, + maxT: int, + maxU: int, + alphabet_size: int, + blank_: int, + durations: torch.Tensor, + num_durations: int, + fastemit_lambda: float, + clamp: float, +): + """ + Compute gradients over the transduction step. + + Args: + grads: Zero Tensor of shape [B, T, U, V] to store gradients for tokens. + duration_grads: Zero Tensor of shape [B, T, U, D] to store gradients for durations. + + acts: Tensor of shape [B, T, U, V] flattened. Represents the logprobs activation tensor for tokens. + duration_acts: Tensor of shape [B, T, U, D] flattened. Represents the logprobs activation tensor for durations. + denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor + across entire vocabulary. + alphas: Alpha variable, contains forward probabilities. A tensor of shape [B, T, U]. + betas: Beta varoable, contains backward probabilities. A tensor of shape [B, T, U]. + logll: Log-likelihood of the forward variable, represented as a vector of shape [B]. + Represents the log-likelihood of the forward pass. + xlen: Vector of length B which contains the actual acoustic sequence lengths in the padded + activation tensor. + ylen: Vector of length B which contains the actual target sequence lengths in the padded + activation tensor. + mlabels: Matrix of shape [B, U+1] (+1 here is due to token - usually the RNNT blank). + The matrix contains the padded target transcription that must be predicted. + minibatch: Int representing the batch size. + maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor. + maxU: The maximum possible target sequence length. Represents U in the logprobs tensor. + alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank). + blank_: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab. + fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to + FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization. + clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp]. + + Updates: + Kernel inplace updates the following inputs: + - grads: Gradients with respect to the log likelihood (logll). + """ + # Kernel call: + # blocks_per_grid = minibatch (b) * maxT (t) * maxU (u) + # threads_per_block = constant buffer size of parallel threads (v :: Constant) + tid = cuda.threadIdx.x # represents v, taking steps of some constant size + idx = tid # index of v < V+1; in steps of constant buffer size + col = cuda.blockIdx.x # represents a fused index of b * t * u + + # Decompose original indices from fused `col` + u = col % maxU # (b * t * u) % u = u + bt = (col - u) // maxU # (b * t * u - u) // U = b * t + t = bt % maxT # (b * t) % t = t + mb = (bt - t) // maxT # (b * t - t) // T = b + + # constants + T = xlen[mb] # select AM length of current sample + U = ylen[mb] + 1 # select target length of current sample, +1 for the blank token + labels: torch.Tensor = mlabels[mb] # labels = mlabels + mb * (maxU - 1); + + # Buffered gradient calculations, broadcast across B=b, T=t and U=u, looped over V with some constant stride. + # Look up gradient calculation from rnnt_numpy.compute_gradient() + + if t < T and u < U: + logpk_blank = ( + denom[col] + acts[col * alphabet_size + blank_] - sigma + ) # whenever sigma is used, it is for logit under-normalization. + + if idx < num_durations: + grad = 0.0 + if t + durations[idx] < T and u < U - 1: # for label + logpk_label = denom[col] + acts[col * alphabet_size + labels[u]] - sigma + grad -= math.exp(alphas[col] + betas[col + 1 + durations[idx] * maxU] + logpk_label - logll[mb]) + + if t + durations[idx] < T and idx > 0: # for blank in the middle + grad -= math.exp(alphas[col] + betas[col + durations[idx] * maxU] + logpk_blank - logll[mb]) + + if t + durations[idx] == T and idx >= 1 and u == U - 1: # for blank as the last symbol + grad -= math.exp(alphas[col] + logpk_blank - logll[mb]) + + grad = grad * math.exp(duration_acts[col * num_durations + idx]) + duration_grads[col * num_durations + idx] = grad + + # For cuda kernels, maximum number of threads per block is limited to some value. + # However, it may be the case that vocabulary size is larger than this limit + # To work around this, an arbitrary thread buffer size is chosen such that, + # 1) each element within the thread pool operates independently of the other + # 2) An inner while loop moves the index of each buffer element by the size of the buffer itself, + # such that all elements of the vocabulary size are covered in (V + 1 // thread_buffer) number of steps. + # As such, each thread will perform the while loop at least (V + 1 // thread_buffer) number of times + while idx < alphabet_size: + # remember, `col` represents the tri-index [b, t, u] + # therefore; logpk = denom[b, t, u] + acts[b, t, u, v] + logpk = denom[col] + acts[col * alphabet_size + idx] + # initialize the grad of the sample acts[b, t, u, v] + grad = math.exp(alphas[col] + betas[col] + logpk - logll[mb]) + + # If FastEmit regularization is enabled, calculate the gradeint of probability of predicting the next label + # at the current timestep. + # The formula for this is Equation 9 in https://arxiv.org/abs/2010.11148, multiplied by the log probability + # of the current step (t, u), normalized by the total log likelihood. + # Once the gradient has been calculated, scale it by `fastemit_lambda`, as in Equation 10. + if fastemit_lambda > 0.0 and u < U - 1: + fastemit_grad = 0.0 + + for i in range(0, num_durations): + if t + durations[i] < T: + fastemit_grad += fastemit_lambda * math.exp( + alphas[col] # alphas(t, u) + + (denom[col] + acts[col * alphabet_size + labels[u]]) # log prob of token emission + + duration_acts[col * num_durations + i] # duration log-prob + + betas[col + 1 + durations[i] * maxU] # betas(t, u+1) + + logpk # log Pr(k|t, u) + - sigma # for logit under-normalization + - logll[mb] # total log likelihood for normalization + ) + else: + fastemit_grad = 0.0 + + # Update the gradient of act[b, t, u, v] with the gradient from FastEmit regularization + grad = grad + fastemit_grad + + # grad to last blank transition + # grad[b, T-1, U-1, v=blank] -= exp(alphas[b, t, u] + logpk - sigma - logll[b] + logp(duration) for all possible non-zero durations. + if idx == blank_ and u == U - 1: + for i in range(1, num_durations): + if t == T - durations[i]: + grad -= math.exp( + alphas[col] + logpk - sigma - logll[mb] + duration_acts[col * num_durations + i] + ) + + # grad of blank across t < T; + # grad[b, t 0.0: + g = label_grads[col * alphabet_size + idx] + g = min(g, clamp) + g = max(g, -clamp) + label_grads[col * alphabet_size + idx] = g + + # update internal index through the thread_buffer; + # until idx < V + 1, such that entire vocabulary has been updated. + idx += GPU_RNNT_THREAD_SIZE diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/reduce.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/reduce.py new file mode 100644 index 0000000..3e7fe2c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/reduce.py @@ -0,0 +1,362 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright 2018-2019, Mingkun Huang +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import enum +import math + +import torch +from numba import cuda + +from nemo.collections.asr.parts.numba.rnnt_loss.utils import global_constants, rnnt_helper + +warp_size = global_constants.warp_size() +dtype = global_constants.dtype() + +CTA_REDUCE_SIZE = 128 + + +class I_Op(enum.Enum): + """ + Represents an operation that is performed on the input tensor + """ + + EXPONENTIAL = 0 + IDENTITY = 1 + + +class R_Op(enum.Enum): + """ + Represents a reduction operation performed on the input tensor + """ + + ADD = 0 + MAXIMUM = 1 + + +@cuda.jit(device=True) +def CTAReduce(tid: int, x, storage, count: int, R_opid: int): + """ + CUDA Warp reduction kernel. + + It is a device kernel to be called by other kernels. + + The data will be read from the right segement recursively, and reduced (ROP) onto the left half. + Operation continues while warp size is larger than a given offset. + Beyond this offset, warp reduction is performed via `shfl_down_sync`, which halves the reduction + space and sums the two halves at each call. + + Note: + Efficient warp occurs at input shapes of 2 ^ K. + + References: + - Warp Primitives [https://developer.nvidia.com/blog/using-cuda-warp-level-primitives/] + + Args: + tid: CUDA thread index + x: activation. Single float. + storage: shared memory of size CTA_REDUCE_SIZE used for reduction in parallel threads. + count: equivalent to num_rows, which is equivalent to alphabet_size (V+1) + R_opid: Operator ID for reduction. See R_Op for more information. + """ + storage[tid] = x + + cuda.syncthreads() + + # Fold the data in half with each pass + offset = CTA_REDUCE_SIZE // 2 + while offset >= warp_size: + if (tid + offset) < count and tid < offset: + # Read from the right half and store to the left half. + if R_opid == 0: + x = rnnt_helper.add(x, storage[offset + tid]) + else: + x = rnnt_helper.maximum(x, storage[offset + tid]) + + storage[tid] = x + + cuda.syncthreads() + offset = offset // 2 + + offset = warp_size // 2 + while offset > 0: + # warp reduction and sync + shuff = cuda.shfl_down_sync(0xFFFFFFFF, x, offset) + + if (tid + offset < count) and (tid < offset): + if R_opid == 0: + x = rnnt_helper.add(x, shuff) + else: + x = rnnt_helper.maximum(x, shuff) + + offset = offset // 2 + + return x + + +@cuda.jit() +def _reduce_rows(I_opid: int, R_opid: int, acts, output, num_rows: int): + """ + CUDA Warp reduction kernel which reduces via the R_Op.Maximum + + Reduces the input data such that I_Op = Identity and R_op = Maximum. + The result is stored in the blockIdx, and is stored as an identity op. + + Note: + Efficient warp occurs at input shapes of 2 ^ K. + + References: + - Warp Primitives [https://developer.nvidia.com/blog/using-cuda-warp-level-primitives/] + + Args: + I_opid: Operator ID for input. See I_Op for more information. For this kernel, + the Identity op is chosen in general, and therefore the input is reduced in place + without scaling. + R_opid: Operator ID for reduction. See R_Op for more information. + For this kernel, generally Maximum op is chosen. It reduces the kernel via max. + acts: Flatened activation matrix of shape [B * T * U * (V+1)]. + output: Flatened output matrix of shape [B * T * U * (V+1)]. Data will be overwritten. + num_rows: Vocabulary size (including blank token) - V+1. + """ + tid = cuda.threadIdx.x + idx = tid + col = cuda.blockIdx.x + + # allocate shared thread memory + storage = cuda.shared.array(shape=(CTA_REDUCE_SIZE,), dtype=acts.dtype) + + max = output[col] + + # // Each block works on a column + if idx < num_rows: + curr = acts[col * num_rows + idx] - max + if I_opid == 0: + curr = rnnt_helper.exponential(curr) + else: + curr = rnnt_helper.identity(curr) + + idx += CTA_REDUCE_SIZE + + while idx < num_rows: + activation_ = acts[col * num_rows + idx] - max + + if I_opid == 0 and R_opid == 0: + curr = rnnt_helper.add(curr, rnnt_helper.exponential(activation_)) + elif I_opid == 0 and R_opid == 1: + curr = rnnt_helper.maximum(curr, rnnt_helper.exponential(activation_)) + elif I_opid == 1 and R_opid == 0: + curr = rnnt_helper.add(curr, rnnt_helper.identity(activation_)) + else: + curr = rnnt_helper.maximum(curr, rnnt_helper.identity(activation_)) + + idx += CTA_REDUCE_SIZE + + # // Sum thread-totals over the CTA. + curr = CTAReduce(tid, curr, storage, num_rows, R_opid) + + # // Store result in out (inplace, I_op: identity) + if tid == 0: + output[col] = curr + + +@cuda.jit() +def _reduce_minus(I_opid: int, R_opid: int, acts, output, num_rows: int): + """ + CUDA Warp reduction kernel which reduces via the R_Op.Add + + Reduces the input data such that I_Op = Exponential and R_op = Add. + The result is stored in the blockIdx, and is stored as an exp op. + + Note: + Efficient warp occurs at input shapes of 2 ^ K. + + References: + - Warp Primitives [https://developer.nvidia.com/blog/using-cuda-warp-level-primitives/] + + Args: + I_opid: Operator ID for input. See I_Op for more information. For this kernel, + the Exponential op is chosen in general, and therefore the input is reduced in place + with scaling. + R_opid: Operator ID for reduction. See R_Op for more information. + For this kernel, generally Add op is chosen. It reduces the kernel via summation. + acts: Flatened activation matrix of shape [B * T * U * (V+1)]. + output: Flatened output matrix of shape [B * T * U * (V+1)]. Data will be overwritten. + num_rows: Vocabulary size (including blank token) - V+1. + """ + tid = cuda.threadIdx.x + idx = tid + col = cuda.blockIdx.x + + # allocate shared thread memory + storage = cuda.shared.array(shape=(CTA_REDUCE_SIZE,), dtype=acts.dtype) + + max = output[col] + + # // Each block works on a column + if idx < num_rows: + curr = acts[col * num_rows + idx] - max + if I_opid == 0: + curr = rnnt_helper.exponential(curr) + else: + curr = rnnt_helper.identity(curr) + + idx += CTA_REDUCE_SIZE + + while idx < num_rows: + activation_ = acts[col * num_rows + idx] - max + + if I_opid == 0 and R_opid == 0: + curr = rnnt_helper.add(curr, rnnt_helper.exponential(activation_)) + elif I_opid == 0 and R_opid == 1: + curr = rnnt_helper.maximum(curr, rnnt_helper.exponential(activation_)) + elif I_opid == 1 and R_opid == 0: + curr = rnnt_helper.add(curr, rnnt_helper.identity(activation_)) + else: + curr = rnnt_helper.maximum(curr, rnnt_helper.identity(activation_)) + + idx += CTA_REDUCE_SIZE + + # // Sum thread-totals over the CTA. + curr = CTAReduce(tid, curr, storage, num_rows, R_opid) + + # // Store result in out (inplace, I_op: exponential) + if tid == 0: + output[col] = -max - math.log(curr) + + +def ReduceHelper( + I_opid: int, + R_opid: int, + acts: torch.Tensor, + output: torch.Tensor, + num_rows: int, + num_cols: int, + minus: bool, + stream, +): + """ + CUDA Warp reduction kernel helper which reduces via the R_Op.Add and writes + the result to `output` according to I_op id. + + The result is stored in the blockIdx. + + Note: + Efficient warp occurs at input shapes of 2 ^ K. + + References: + - Warp Primitives [https://developer.nvidia.com/blog/using-cuda-warp-level-primitives/] + + Args: + I_opid: Operator ID for input. See I_Op for more information. + R_opid: Operator ID for reduction. See R_Op for more information. + acts: Flatened activation matrix of shape [B * T * U * (V+1)]. + output: Flatened output matrix of shape [B * T * U * (V+1)]. Data will be overwritten. + num_rows: Vocabulary size (including blank token) - V+1. + Represents the number of threads per block. + num_cols: Flattened shape of activation matrix, without vocabulary dimension (B * T * U). + Represents number of blocks per grid. + minus: Bool flag whether to add or subtract as reduction. + If minus is set; calls _reduce_minus, else calls _reduce_rows kernel. + stream: CUDA Stream. + """ + if minus: + grid_size = num_cols + # call kernel + _reduce_minus[grid_size, CTA_REDUCE_SIZE, stream, 0](I_opid, R_opid, acts, output, num_rows) + + else: + grid_size = num_cols + # call kernel + _reduce_rows[grid_size, CTA_REDUCE_SIZE, stream, 0](I_opid, R_opid, acts, output, num_rows) + + return True + + +def reduce_exp(acts: torch.Tensor, denom, rows: int, cols: int, minus: bool, stream): + """ + Helper method to call the Warp Reduction Kernel to perform `exp` reduction. + + Note: + Efficient warp occurs at input shapes of 2 ^ K. + + References: + - Warp Primitives [https://developer.nvidia.com/blog/using-cuda-warp-level-primitives/] + + Args: + acts: Flatened activation matrix of shape [B * T * U * (V+1)]. + output: Flatened output matrix of shape [B * T * U * (V+1)]. Data will be overwritten. + rows: Vocabulary size (including blank token) - V+1. + Represents the number of threads per block. + cols: Flattened shape of activation matrix, without vocabulary dimension (B * T * U). + Represents number of blocks per grid. + minus: Bool flag whether to add or subtract as reduction. + If minus is set; calls _reduce_minus, else calls _reduce_rows kernel. + stream: CUDA Stream. + """ + return ReduceHelper( + I_opid=I_Op.EXPONENTIAL.value, + R_opid=R_Op.ADD.value, + acts=acts, + output=denom, + num_rows=rows, + num_cols=cols, + minus=minus, + stream=stream, + ) + + +def reduce_max(acts: torch.Tensor, denom, rows: int, cols: int, minus: bool, stream): + """ + Helper method to call the Warp Reduction Kernel to perform `max` reduction. + + Note: + Efficient warp occurs at input shapes of 2 ^ K. + + References: + - Warp Primitives [https://developer.nvidia.com/blog/using-cuda-warp-level-primitives/] + + Args: + acts: Flatened activation matrix of shape [B * T * U * (V+1)]. + output: Flatened output matrix of shape [B * T * U * (V+1)]. Data will be overwritten. + rows: Vocabulary size (including blank token) - V+1. + Represents the number of threads per block. + cols: Flattened shape of activation matrix, without vocabulary dimension (B * T * U). + Represents number of blocks per grid. + minus: Bool flag whether to add or subtract as reduction. + If minus is set; calls _reduce_minus, else calls _reduce_rows kernel. + stream: CUDA Stream. + """ + return ReduceHelper( + I_opid=I_Op.IDENTITY.value, + R_opid=R_Op.MAXIMUM.value, + acts=acts, + output=denom, + num_rows=rows, + num_cols=cols, + minus=minus, + stream=stream, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/global_constants.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/global_constants.py new file mode 100644 index 0000000..cc30475 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/global_constants.py @@ -0,0 +1,68 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright 2018-2019, Mingkun Huang +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import enum + +import numpy as np +from numba import float32 + +# Internal globals +_THREADS_PER_BLOCK = 32 +_WARP_SIZE = 32 +_DTYPE = float32 + +# Constants +FP32_INF = np.inf +FP32_NEG_INF = -np.inf +THRESHOLD = 1e-1 + +""" +Getters +""" + + +def threads_per_block(): + global _THREADS_PER_BLOCK + return _THREADS_PER_BLOCK + + +def warp_size(): + global _WARP_SIZE + return _WARP_SIZE + + +def dtype(): + global _DTYPE + return _DTYPE + + +# RNNT STATUS +class RNNTStatus(enum.Enum): + RNNT_STATUS_SUCCESS = 0 + RNNT_STATUS_INVALID_VALUE = 1 diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/rnnt_helper.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/rnnt_helper.py new file mode 100644 index 0000000..6ca7cd2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/rnnt_loss/utils/rnnt_helper.py @@ -0,0 +1,148 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright 2018-2019, Mingkun Huang +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import math +from typing import Optional, Tuple + +import numba +import torch +from numba import cuda + +from nemo.collections.asr.parts.numba.rnnt_loss.utils import global_constants + +threshold = global_constants.THRESHOLD + + +@cuda.jit(device=True, inline=True) +def log_sum_exp(a: float, b: float): + if a == global_constants.FP32_NEG_INF: + return b + + if b == global_constants.FP32_NEG_INF: + return a + + if a > b: + return math.log1p(math.exp(b - a)) + a + else: + return math.log1p(math.exp(a - b)) + b + + +@cuda.jit(device=True, inline=True) +def div_up(x: int, y: int): + return (x + y - 1) // y + + +@cuda.jit(device=True) +def maximum(x, y): + if x < y: + return y + else: + return x + + +@cuda.jit(device=True) +def add(x, y): + return x + y + + +@cuda.jit(device=True) +def identity(x): + return x + + +@cuda.jit(device=True) +def negate(x): + return -x + + +@cuda.jit(device=True) +def exponential(x): + return math.exp(x) + + +@cuda.jit(device=True) +def log_plus(p1: float, p2: float): + if p1 == global_constants.FP32_NEG_INF: + return p2 + + if p2 == global_constants.FP32_NEG_INF: + return p1 + + result = math.log1p(math.exp(-math.fabs(p1 - p2))) + maximum(p1, p2) + return result + + +@cuda.jit(device=True, inline=True) +def copy_data_1d(source: torch.Tensor, dest: torch.Tensor, idx: int): + dest[idx] = source[idx] + + +@cuda.jit() +def compute_costs_data(source: torch.Tensor, dest: torch.Tensor, fastemit_lambda: float): + block = cuda.blockIdx.x + tid = cuda.threadIdx.x + idx = block * cuda.blockDim.x + tid + length = source.shape[0] + + if idx < length: + copy_data_1d(source, dest, idx) + dest[idx] *= -1.0 + dest[idx] *= numba.float32(1.0 + fastemit_lambda) + + +def get_workspace_size( + maxT: int, maxU: int, minibatch: int, gpu: bool +) -> Tuple[Optional[int], global_constants.RNNTStatus]: + + if minibatch <= 0 or maxT <= 0 or maxU <= 0: + return (None, global_constants.RNNTStatus.RNNT_STATUS_INVALID_VALUE) + + # per minibatch memory + per_minibatch_size = 0 + + # alphas & betas + per_minibatch_size += maxT * maxU * 2 + + if not gpu: + # // blank & label log probability cache + per_minibatch_size += maxT * maxU * 2 + else: + # // softmax denominator + per_minibatch_size += maxT * maxU + # // forward - backward loglikelihood + per_minibatch_size += 2 + + size = per_minibatch_size * minibatch + return (size, global_constants.RNNTStatus.RNNT_STATUS_SUCCESS) + + +def flatten_tensor(x: torch.Tensor): + original_shape = x.shape + x = x.view([-1]) + return x, original_shape diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/spec_augment/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/spec_augment/__init__.py new file mode 100644 index 0000000..17a22fc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/spec_augment/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.parts.numba.spec_augment.spec_aug_numba import ( + SpecAugmentNumba, + spec_augment_launch_heuristics, +) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/spec_augment/spec_aug_numba.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/spec_augment/spec_aug_numba.py new file mode 100644 index 0000000..fcf5d5c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/numba/spec_augment/spec_aug_numba.py @@ -0,0 +1,305 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn as nn +from numba import cuda + +from nemo.core.classes import Typing, typecheck +from nemo.core.neural_types import LengthsType, NeuralType, SpectrogramType +from nemo.utils import logging + +MAX_THREAD_BUFFER = 512 + + +@cuda.jit() +def spec_augment_kernel( + x: torch.Tensor, + x_len: torch.Tensor, + freq_starts: torch.Tensor, + freq_widths: torch.Tensor, + time_starts: torch.Tensor, + time_widths: torch.Tensor, + mask_value: float, +): + """ + Numba CUDA kernel to perform SpecAugment in-place on the GPU. + Parallelize over freq and time axis, parallel threads over batch. + Sequential over masks (adaptive in time). + + Args: + x: Pytorch tensor of shape [B, F, T] with the acoustic features. + x_len: Pytorch tensor of shape [B] with the lengths of the padded sequence. + freq_starts: Pytorch tensor of shape [B, M_f] with the start indices of freq masks. + freq_widths: Pytorch tensor of shape [B, M_f] with the width of freq masks. + time_starts: Pytorch tensor of shape [B, M_t] with the start indices of time masks. + time_widths: Pytorch tensor of shape [B, M_t] with the width of time masks. + mask_value: Float value that will be used as mask value. + """ + f = cuda.blockIdx.x # indexes the Freq dim + t = cuda.blockIdx.y # indexes the Time dim + tid = cuda.threadIdx.x # index of the current mask + threads_per_block = cuda.blockDim.x + + # Compute the number of masks over freq axis + len_f = freq_starts.shape[1] + # For all samples in the batch, apply the freq mask + for bidx in range(0, x.shape[0], threads_per_block): + # Resolve the index of the batch (case where more masks than MAX_THREAD_BUFFER) + bm_idx = bidx + tid + + # Access mask only if valid sample id in batch + if bm_idx < x.shape[0]: + # For `len_f` number of freq masks that must be applied + for fidx in range(0, len_f): + # Access the start index and width of this freq mask + f_start = freq_starts[bm_idx, fidx] + f_width = freq_widths[bm_idx, fidx] + + # If block idx `f` >= start and < (start + width) of this freq mask + if f >= f_start and f < (f_start + f_width): + x[bm_idx, f, t] = mask_value + + # Compute the number of masks over time axis + len_t = time_starts.shape[1] + # For all samples in the batch, apply the time mask + for b_idx in range(0, x.shape[0], threads_per_block): + # Resolve the index of the batch (case where more masks than MAX_THREAD_BUFFER) + bm_idx = b_idx + tid + + # Access mask only if valid sample id in batch + if bm_idx < x.shape[0]: + # For `len_t` number of freq masks that must be applied + for tidx in range(0, len_t): + # Access the start index and width of this time mask + t_start = time_starts[bm_idx, tidx] + t_width = time_widths[bm_idx, tidx] + + # If block idx `t` >= start and < (start + width) of this time mask + if t >= t_start and t < (t_start + t_width): + # Current block idx `t` < current seq length x_len[b] + # This ensure that we mask only upto the length of that sample + # Everything after that index is padded value so unnecessary to mask + if t < x_len[bm_idx]: + x[bm_idx, f, t] = mask_value + + +def spec_augment_launch_heuristics(x: torch.Tensor, length: torch.Tensor): + """ + Heuristics to determins whether pytorch implementation or numba implementation is selected. + Assumes numba cuda is supported. + + Args: + x: Torch tensor of shape [B, F, T] + length: Optional, Torch of tensor of shape [B] - containing lengths of the tensor. + + Returns: + True if numba kernel should be selected, else False + """ + if not x.is_cuda: + return False + + if length is None: + return False + + if x.shape[0] < 8: + return False + + return True + + +def launch_spec_augment_kernel( + x: torch.Tensor, + x_len: torch.Tensor, + freq_starts: torch.Tensor, + freq_lengths: torch.Tensor, + time_starts: torch.Tensor, + time_lengths: torch.Tensor, + freq_masks: int, + time_masks: int, + mask_value: float, +): + """ + Helper method to launch the SpecAugment kernel + + Args: + x: Pytorch tensor of shape [B, F, T] with the acoustic features. + x_len: Pytorch tensor of shape [B] with the lengths of the padded sequence. + freq_starts: Pytorch tensor of shape [B, M_f] with the start indices of freq masks. + freq_widths: Pytorch tensor of shape [B, M_f] with the width of freq masks. + time_starts: Pytorch tensor of shape [B, M_t] with the start indices of time masks. + time_widths: Pytorch tensor of shape [B, M_t] with the width of time masks. + freq_masks: Int value that determines the number of time masks. + time_masks: Int value that determines the number of freq masks. + mask_value: Float value that will be used as mask value. + + Returns: + The spec augmented tensor 'x' + """ + # Setup CUDA stream + sh = x.shape + stream = cuda.external_stream(torch.cuda.current_stream(x.device).cuda_stream) + + if time_masks > 0 or freq_masks > 0: + # Parallelize over freq and time axis, parallel threads over batch + # Sequential over masks (adaptive in time). + blocks_per_grid = tuple([sh[1], sh[2]]) + # threads_per_block = min(MAX_THREAD_BUFFER, max(freq_masks, time_masks)) + threads_per_block = min(MAX_THREAD_BUFFER, x.shape[0]) + + # Numba does not support fp16, force cast to fp32 temporarily at the expense of memory + original_dtype = x.dtype + cast_x = False + if x.dtype == torch.float16: + x = x.float() + cast_x = True + + # Launch CUDA kernel + spec_augment_kernel[blocks_per_grid, threads_per_block, stream, 0]( + x, x_len, freq_starts, freq_lengths, time_starts, time_lengths, mask_value + ) + torch.cuda.synchronize() + + # Recast back to original dtype if earlier cast was performed + if cast_x: + x = x.to(dtype=original_dtype) + + return x + + +class SpecAugmentNumba(nn.Module, Typing): + """ + Zeroes out(cuts) random continuous horisontal or + vertical segments of the spectrogram as described in + SpecAugment (https://arxiv.org/abs/1904.08779). + + Utilizes a Numba CUDA kernel to perform inplace edit of the input without loops. + Parallelize over freq and time axis, parallel threads over batch. + Sequential over masks (adaptive in time). + + Args: + freq_masks - how many frequency segments should be cut + time_masks - how many time segments should be cut + freq_width - maximum number of frequencies to be cut in one segment + time_width - maximum number of time steps to be cut in one segment. + Can be a positive integer or a float value in the range [0, 1]. + If positive integer value, defines maximum number of time steps + to be cut in one segment. + If a float value, defines maximum percentage of timesteps that + are cut adaptively. + rng: Ignored. + """ + + @property + def input_types(self): + """Returns definitions of module input types + """ + return { + "input_spec": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Returns definitions of module output types + """ + return {"augmented_spec": NeuralType(('B', 'D', 'T'), SpectrogramType())} + + def __init__( + self, freq_masks=0, time_masks=0, freq_width=10, time_width=0.1, rng=None, mask_value=0.0, + ): + super().__init__() + # Message to mention that numba specaugment kernel will be available + # if input device is CUDA and lengths are provided + logging.debug("Numba SpecAugment kernel is available") + + self.freq_masks = freq_masks + self.time_masks = time_masks + + self.freq_width = freq_width + self.time_width = time_width + + self.mask_value = mask_value + + # Unused + self.rng = rng + if self.rng is not None: + logging.warning("`rng` was supplied to SpecAugmentNumba, but it is not used.") + + if isinstance(time_width, int): + self.adaptive_temporal_width = False + else: + if time_width > 1.0 or time_width < 0.0: + raise ValueError('If `time_width` is a float value, must be in range [0, 1]') + + self.adaptive_temporal_width = True + + @typecheck() + @torch.no_grad() + def forward(self, input_spec, length): + sh = input_spec.shape + bs = sh[0] + + # Construct the freq and time masks as well as start positions + if self.freq_masks > 0: + freq_starts = torch.randint( + 0, sh[1] - self.freq_width + 1, size=[bs, self.freq_masks], device=input_spec.device + ) + freq_lengths = torch.randint(0, self.freq_width + 1, size=[bs, self.freq_masks], device=input_spec.device) + else: + freq_starts = torch.zeros([bs, 1], dtype=torch.int64, device=input_spec.device) + freq_lengths = torch.zeros([bs, 1], dtype=torch.int64, device=input_spec.device) + + if self.time_masks > 0: + if self.adaptive_temporal_width: + time_width = (length * self.time_width).int().clamp(min=1) + else: + time_width = ( + torch.tensor(self.time_width, dtype=torch.int32, device=input_spec.device) + .unsqueeze(0) + .repeat(sh[0]) + ) + + time_starts = [] + time_lengths = [] + for idx in range(sh[0]): + time_starts.append( + torch.randint( + 0, max(1, length[idx] - time_width[idx]), size=[1, self.time_masks], device=input_spec.device + ) + ) + time_lengths.append( + torch.randint(0, time_width[idx] + 1, size=[1, self.time_masks], device=input_spec.device) + ) + + time_starts = torch.cat(time_starts, 0) + time_lengths = torch.cat(time_lengths, 0) + + else: + time_starts = torch.zeros([bs, 1], dtype=torch.int64, device=input_spec.device) + time_lengths = torch.zeros([bs, 1], dtype=torch.int64, device=input_spec.device) + + x = launch_spec_augment_kernel( + input_spec, + length, + freq_starts=freq_starts, + freq_lengths=freq_lengths, + time_starts=time_starts, + time_lengths=time_lengths, + freq_masks=self.freq_masks, + time_masks=self.time_masks, + mask_value=self.mask_value, + ) + + return x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/__init__.py new file mode 100644 index 0000000..a0785c5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/__init__.py @@ -0,0 +1,36 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.parts.preprocessing.feature_loader import ExternalFeatureLoader +from nemo.collections.asr.parts.preprocessing.features import FeaturizerFactory, FilterbankFeatures, WaveformFeaturizer +from nemo.collections.asr.parts.preprocessing.perturb import ( + AudioAugmentor, + AugmentationDataset, + GainPerturbation, + ImpulsePerturbation, + NoisePerturbation, + NoisePerturbationWithNormalization, + Perturbation, + RirAndNoisePerturbation, + ShiftPerturbation, + SilencePerturbation, + SpeedPerturbation, + TimeStretchPerturbation, + TranscodePerturbation, + WhiteNoisePerturbation, + perturbation_types, + process_augmentations, + register_perturbation, +) +from nemo.collections.asr.parts.preprocessing.segment import AudioSegment diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/feature_loader.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/feature_loader.py new file mode 100644 index 0000000..8c629cf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/feature_loader.py @@ -0,0 +1,73 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Optional + +import numpy as np +import torch + + +class ExternalFeatureLoader(object): + """Feature loader that load external features store in certain format. + Currently support pickle, npy and npz format. + """ + + def __init__( + self, augmentor: Optional["nemo.collections.asr.parts.perturb.FeatureAugmentor"] = None, + ): + """ + Feature loader + """ + self.augmentor = augmentor + + def load_feature_from_file(self, file_path: str): + """Load samples from file_path and convert it to be of type float32 + file_path (str) is the path of the file that stores feature/sample. + """ + + if file_path.endswith(".pt") or file_path.endswith(".pth"): + samples = torch.load(file_path, map_location="cpu").float().numpy() + return samples + else: + # load pickle/npy/npz file + samples = np.load(file_path, allow_pickle=True) + return self._convert_samples_to_float32(samples) + # TODO load other type of files such as kaldi io ark + + @staticmethod + def _convert_samples_to_float32(samples: np.ndarray) -> np.ndarray: + """Convert sample type to float32. + Integers will be scaled to [-1, 1] in float32. + """ + float32_samples = samples.astype('float32') + if samples.dtype in np.sctypes['int']: + bits = np.iinfo(samples.dtype).bits + float32_samples *= 1.0 / 2 ** (bits - 1) + elif samples.dtype in np.sctypes['float']: + pass + else: + raise TypeError("Unsupported sample type: %s." % samples.dtype) + return float32_samples + + def process(self, file_path: str) -> torch.Tensor: + features = self.load_feature_from_file(file_path) + features = self.process_segment(features) + return features + + def process_segment(self, feature_segment): + if self.augmentor: + # augmentor for external features. Here possible augmentor for external embedding feature is Diaconis Augmentation and might be implemented later + self.augmentor.perturb(feature_segment) + return torch.tensor(feature_segment, dtype=torch.float) + + return torch.tensor(feature_segment, dtype=torch.float) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/features.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/features.py new file mode 100644 index 0000000..67813f3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/features.py @@ -0,0 +1,655 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright (c) 2018 Ryan Leary +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# This file contains code artifacts adapted from https://github.com/ryanleary/patter +import math +import random +from typing import Optional, Tuple, Union + +import librosa +import numpy as np +import torch +import torch.nn as nn + +from nemo.collections.asr.parts.preprocessing.perturb import AudioAugmentor +from nemo.collections.asr.parts.preprocessing.segment import AudioSegment +from nemo.utils import logging + +try: + import torchaudio + + HAVE_TORCHAUDIO = True +except ModuleNotFoundError: + HAVE_TORCHAUDIO = False + + +CONSTANT = 1e-5 + + +def normalize_batch(x, seq_len, normalize_type): + x_mean = None + x_std = None + if normalize_type == "per_feature": + x_mean = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype, device=x.device) + x_std = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype, device=x.device) + for i in range(x.shape[0]): + if x[i, :, : seq_len[i]].shape[1] == 1: + raise ValueError( + "normalize_batch with `per_feature` normalize_type received a tensor of length 1. This will result " + "in torch.std() returning nan. Make sure your audio length has enough samples for a single " + "feature (ex. at least `hop_length` for Mel Spectrograms)." + ) + x_mean[i, :] = x[i, :, : seq_len[i]].mean(dim=1) + x_std[i, :] = x[i, :, : seq_len[i]].std(dim=1) + # make sure x_std is not zero + x_std += CONSTANT + return (x - x_mean.unsqueeze(2)) / x_std.unsqueeze(2), x_mean, x_std + elif normalize_type == "all_features": + x_mean = torch.zeros(seq_len.shape, dtype=x.dtype, device=x.device) + x_std = torch.zeros(seq_len.shape, dtype=x.dtype, device=x.device) + for i in range(x.shape[0]): + x_mean[i] = x[i, :, : seq_len[i].item()].mean() + x_std[i] = x[i, :, : seq_len[i].item()].std() + # make sure x_std is not zero + x_std += CONSTANT + return (x - x_mean.view(-1, 1, 1)) / x_std.view(-1, 1, 1), x_mean, x_std + elif "fixed_mean" in normalize_type and "fixed_std" in normalize_type: + x_mean = torch.tensor(normalize_type["fixed_mean"], device=x.device) + x_std = torch.tensor(normalize_type["fixed_std"], device=x.device) + return ( + (x - x_mean.view(x.shape[0], x.shape[1]).unsqueeze(2)) / x_std.view(x.shape[0], x.shape[1]).unsqueeze(2), + x_mean, + x_std, + ) + else: + return x, x_mean, x_std + + +def clean_spectrogram_batch(spectrogram: torch.Tensor, spectrogram_len: torch.Tensor, fill_value=0.0) -> torch.Tensor: + """ + Fill spectrogram values outside the length with `fill_value` + + Args: + spectrogram: Tensor with shape [B, C, L] containing batched spectrograms + spectrogram_len: Tensor with shape [B] containing the sequence length of each batch element + fill_value: value to fill with, 0.0 by default + + Returns: + cleaned spectrogram, tensor with shape equal to `spectrogram` + """ + device = spectrogram.device + batch_size, _, max_len = spectrogram.shape + mask = torch.arange(max_len, device=device)[None, :] >= spectrogram_len[:, None] + mask = mask.unsqueeze(1).expand_as(spectrogram) + return spectrogram.masked_fill(mask, fill_value) + + +def splice_frames(x, frame_splicing): + """ Stacks frames together across feature dim + + input is batch_size, feature_dim, num_frames + output is batch_size, feature_dim*frame_splicing, num_frames + + """ + seq = [x] + for n in range(1, frame_splicing): + seq.append(torch.cat([x[:, :, :n], x[:, :, n:]], dim=2)) + return torch.cat(seq, dim=1) + + +@torch.jit.script_if_tracing +def make_seq_mask_like( + lengths: torch.Tensor, like: torch.Tensor, time_dim: int = -1, valid_ones: bool = True +) -> torch.Tensor: + """ + + Args: + lengths: Tensor with shape [B] containing the sequence length of each batch element + like: The mask will contain the same number of dimensions as this Tensor, and will have the same max + length in the time dimension of this Tensor. + time_dim: Time dimension of the `shape_tensor` and the resulting mask. Zero-based. + valid_ones: If True, valid tokens will contain value `1` and padding will be `0`. Else, invert. + + Returns: + A :class:`torch.Tensor` containing 1's and 0's for valid and invalid tokens, respectively, if `valid_ones`, else + vice-versa. Mask will have the same number of dimensions as `like`. Batch and time dimensions will match + the `like`. All other dimensions will be singletons. E.g., if `like.shape == [3, 4, 5]` and + `time_dim == -1', mask will have shape `[3, 1, 5]`. + """ + # Mask with shape [B, T] + mask = torch.arange(like.shape[time_dim], device=like.device).repeat(lengths.shape[0], 1).lt(lengths.view(-1, 1)) + # [B, T] -> [B, *, T] where * is any number of singleton dimensions to expand to like tensor + for _ in range(like.dim() - mask.dim()): + mask = mask.unsqueeze(1) + # If needed, transpose time dim + if time_dim != -1 and time_dim != mask.dim() - 1: + mask = mask.transpose(-1, time_dim) + # Maybe invert the padded vs. valid token values + if not valid_ones: + mask = ~mask + return mask + + +class WaveformFeaturizer(object): + def __init__(self, sample_rate=16000, int_values=False, augmentor=None): + self.augmentor = augmentor if augmentor is not None else AudioAugmentor() + self.sample_rate = sample_rate + self.int_values = int_values + + def max_augmentation_length(self, length): + return self.augmentor.max_augmentation_length(length) + + def process( + self, + file_path, + offset=0, + duration=0, + trim=False, + trim_ref=np.max, + trim_top_db=60, + trim_frame_length=2048, + trim_hop_length=512, + orig_sr=None, + channel_selector=None, + normalize_db=None, + ): + audio = AudioSegment.from_file( + file_path, + target_sr=self.sample_rate, + int_values=self.int_values, + offset=offset, + duration=duration, + trim=trim, + trim_ref=trim_ref, + trim_top_db=trim_top_db, + trim_frame_length=trim_frame_length, + trim_hop_length=trim_hop_length, + orig_sr=orig_sr, + channel_selector=channel_selector, + normalize_db=normalize_db, + ) + return self.process_segment(audio) + + def process_segment(self, audio_segment): + self.augmentor.perturb(audio_segment) + return torch.tensor(audio_segment.samples, dtype=torch.float) + + @classmethod + def from_config(cls, input_config, perturbation_configs=None): + if perturbation_configs is not None: + aa = AudioAugmentor.from_config(perturbation_configs) + else: + aa = None + + sample_rate = input_config.get("sample_rate", 16000) + int_values = input_config.get("int_values", False) + + return cls(sample_rate=sample_rate, int_values=int_values, augmentor=aa) + + +class FeaturizerFactory(object): + def __init__(self): + pass + + @classmethod + def from_config(cls, input_cfg, perturbation_configs=None): + return WaveformFeaturizer.from_config(input_cfg, perturbation_configs=perturbation_configs) + + +class FilterbankFeatures(nn.Module): + """Featurizer that converts wavs to Mel Spectrograms. + See AudioToMelSpectrogramPreprocessor for args. + """ + + def __init__( + self, + sample_rate=16000, + n_window_size=320, + n_window_stride=160, + window="hann", + normalize="per_feature", + n_fft=None, + preemph=0.97, + nfilt=64, + lowfreq=0, + highfreq=None, + log=True, + log_zero_guard_type="add", + log_zero_guard_value=2 ** -24, + dither=CONSTANT, + pad_to=16, + max_duration=16.7, + frame_splicing=1, + exact_pad=False, + pad_value=0, + mag_power=2.0, + use_grads=False, + rng=None, + nb_augmentation_prob=0.0, + nb_max_freq=4000, + mel_norm="slaney", + stft_exact_pad=False, # Deprecated arguments; kept for config compatibility + stft_conv=False, # Deprecated arguments; kept for config compatibility + ): + super().__init__() + if stft_conv or stft_exact_pad: + logging.warning( + "Using torch_stft is deprecated and has been removed. The values have been forcibly set to False " + "for FilterbankFeatures and AudioToMelSpectrogramPreprocessor. Please set exact_pad to True " + "as needed." + ) + if exact_pad and n_window_stride % 2 == 1: + raise NotImplementedError( + f"{self} received exact_pad == True, but hop_size was odd. If audio_length % hop_size == 0. Then the " + "returned spectrogram would not be of length audio_length // hop_size. Please use an even hop_size." + ) + self.log_zero_guard_value = log_zero_guard_value + if ( + n_window_size is None + or n_window_stride is None + or not isinstance(n_window_size, int) + or not isinstance(n_window_stride, int) + or n_window_size <= 0 + or n_window_stride <= 0 + ): + raise ValueError( + f"{self} got an invalid value for either n_window_size or " + f"n_window_stride. Both must be positive ints." + ) + logging.info(f"PADDING: {pad_to}") + + self.win_length = n_window_size + self.hop_length = n_window_stride + self.n_fft = n_fft or 2 ** math.ceil(math.log2(self.win_length)) + self.stft_pad_amount = (self.n_fft - self.hop_length) // 2 if exact_pad else None + + if exact_pad: + logging.info("STFT using exact pad") + torch_windows = { + 'hann': torch.hann_window, + 'hamming': torch.hamming_window, + 'blackman': torch.blackman_window, + 'bartlett': torch.bartlett_window, + 'none': None, + } + window_fn = torch_windows.get(window, None) + window_tensor = window_fn(self.win_length, periodic=False) if window_fn else None + self.register_buffer("window", window_tensor) + self.stft = lambda x: torch.stft( + x, + n_fft=self.n_fft, + hop_length=self.hop_length, + win_length=self.win_length, + center=False if exact_pad else True, + window=self.window.to(dtype=torch.float), + return_complex=True, + ) + + self.normalize = normalize + self.log = log + self.dither = dither + self.frame_splicing = frame_splicing + self.nfilt = nfilt + self.preemph = preemph + self.pad_to = pad_to + highfreq = highfreq or sample_rate / 2 + + filterbanks = torch.tensor( + librosa.filters.mel( + sr=sample_rate, n_fft=self.n_fft, n_mels=nfilt, fmin=lowfreq, fmax=highfreq, norm=mel_norm + ), + dtype=torch.float, + ).unsqueeze(0) + self.register_buffer("fb", filterbanks) + + # Calculate maximum sequence length + max_length = self.get_seq_len(torch.tensor(max_duration * sample_rate, dtype=torch.float)) + max_pad = pad_to - (max_length % pad_to) if pad_to > 0 else 0 + self.max_length = max_length + max_pad + self.pad_value = pad_value + self.mag_power = mag_power + + # We want to avoid taking the log of zero + # There are two options: either adding or clamping to a small value + if log_zero_guard_type not in ["add", "clamp"]: + raise ValueError( + f"{self} received {log_zero_guard_type} for the " + f"log_zero_guard_type parameter. It must be either 'add' or " + f"'clamp'." + ) + + self.use_grads = use_grads + if not use_grads: + self.forward = torch.no_grad()(self.forward) + self._rng = random.Random() if rng is None else rng + self.nb_augmentation_prob = nb_augmentation_prob + if self.nb_augmentation_prob > 0.0: + if nb_max_freq >= sample_rate / 2: + self.nb_augmentation_prob = 0.0 + else: + self._nb_max_fft_bin = int((nb_max_freq / sample_rate) * n_fft) + + # log_zero_guard_value is the the small we want to use, we support + # an actual number, or "tiny", or "eps" + self.log_zero_guard_type = log_zero_guard_type + logging.debug(f"sr: {sample_rate}") + logging.debug(f"n_fft: {self.n_fft}") + logging.debug(f"win_length: {self.win_length}") + logging.debug(f"hop_length: {self.hop_length}") + logging.debug(f"n_mels: {nfilt}") + logging.debug(f"fmin: {lowfreq}") + logging.debug(f"fmax: {highfreq}") + logging.debug(f"using grads: {use_grads}") + logging.debug(f"nb_augmentation_prob: {nb_augmentation_prob}") + + def log_zero_guard_value_fn(self, x): + if isinstance(self.log_zero_guard_value, str): + if self.log_zero_guard_value == "tiny": + return torch.finfo(x.dtype).tiny + elif self.log_zero_guard_value == "eps": + return torch.finfo(x.dtype).eps + else: + raise ValueError( + f"{self} received {self.log_zero_guard_value} for the " + f"log_zero_guard_type parameter. It must be either a " + f"number, 'tiny', or 'eps'" + ) + else: + return self.log_zero_guard_value + + def get_seq_len(self, seq_len): + # Assuming that center is True is stft_pad_amount = 0 + pad_amount = self.stft_pad_amount * 2 if self.stft_pad_amount is not None else self.n_fft // 2 * 2 + seq_len = torch.floor_divide((seq_len + pad_amount - self.n_fft), self.hop_length) + 1 + return seq_len.to(dtype=torch.long) + + @property + def filter_banks(self): + return self.fb + + def forward(self, x, seq_len, linear_spec=False): + seq_len = self.get_seq_len(seq_len) + + if self.stft_pad_amount is not None: + x = torch.nn.functional.pad( + x.unsqueeze(1), (self.stft_pad_amount, self.stft_pad_amount), "reflect" + ).squeeze(1) + + # dither (only in training mode for eval determinism) + if self.training and self.dither > 0: + x += self.dither * torch.randn_like(x) + + # do preemphasis + if self.preemph is not None: + x = torch.cat((x[:, 0].unsqueeze(1), x[:, 1:] - self.preemph * x[:, :-1]), dim=1) + + # disable autocast to get full range of stft values + with torch.cuda.amp.autocast(enabled=False): + x = self.stft(x) + + # torch stft returns complex tensor (of shape [B,N,T]); so convert to magnitude + # guard is needed for sqrt if grads are passed through + guard = 0 if not self.use_grads else CONSTANT + x = torch.view_as_real(x) + x = torch.sqrt(x.pow(2).sum(-1) + guard) + + if self.training and self.nb_augmentation_prob > 0.0: + for idx in range(x.shape[0]): + if self._rng.random() < self.nb_augmentation_prob: + x[idx, self._nb_max_fft_bin :, :] = 0.0 + + # get power spectrum + if self.mag_power != 1.0: + x = x.pow(self.mag_power) + + # return plain spectrogram if required + if linear_spec: + return x, seq_len + + # dot with filterbank energies + x = torch.matmul(self.fb.to(x.dtype), x) + # log features if required + if self.log: + if self.log_zero_guard_type == "add": + x = torch.log(x + self.log_zero_guard_value_fn(x)) + elif self.log_zero_guard_type == "clamp": + x = torch.log(torch.clamp(x, min=self.log_zero_guard_value_fn(x))) + else: + raise ValueError("log_zero_guard_type was not understood") + + # frame splicing if required + if self.frame_splicing > 1: + x = splice_frames(x, self.frame_splicing) + + # normalize if required + if self.normalize: + x, _, _ = normalize_batch(x, seq_len, normalize_type=self.normalize) + + # mask to zero any values beyond seq_len in batch, pad to multiple of `pad_to` (for efficiency) + max_len = x.size(-1) + mask = torch.arange(max_len).to(x.device) + mask = mask.repeat(x.size(0), 1) >= seq_len.unsqueeze(1) + x = x.masked_fill(mask.unsqueeze(1).type(torch.bool).to(device=x.device), self.pad_value) + del mask + pad_to = self.pad_to + if pad_to == "max": + x = nn.functional.pad(x, (0, self.max_length - x.size(-1)), value=self.pad_value) + elif pad_to > 0: + pad_amt = x.size(-1) % pad_to + if pad_amt != 0: + x = nn.functional.pad(x, (0, pad_to - pad_amt), value=self.pad_value) + return x, seq_len + + +class FilterbankFeaturesTA(nn.Module): + """ + Exportable, `torchaudio`-based implementation of Mel Spectrogram extraction. + + See `AudioToMelSpectrogramPreprocessor` for args. + + """ + + def __init__( + self, + sample_rate: int = 16000, + n_window_size: int = 320, + n_window_stride: int = 160, + normalize: Optional[str] = "per_feature", + nfilt: int = 64, + n_fft: Optional[int] = None, + preemph: float = 0.97, + lowfreq: float = 0, + highfreq: Optional[float] = None, + log: bool = True, + log_zero_guard_type: str = "add", + log_zero_guard_value: Union[float, str] = 2 ** -24, + dither: float = 1e-5, + window: str = "hann", + pad_to: int = 0, + pad_value: float = 0.0, + mel_norm="slaney", + # Seems like no one uses these options anymore. Don't convolute the code by supporting thm. + use_grads: bool = False, # Deprecated arguments; kept for config compatibility + max_duration: float = 16.7, # Deprecated arguments; kept for config compatibility + frame_splicing: int = 1, # Deprecated arguments; kept for config compatibility + exact_pad: bool = False, # Deprecated arguments; kept for config compatibility + nb_augmentation_prob: float = 0.0, # Deprecated arguments; kept for config compatibility + nb_max_freq: int = 4000, # Deprecated arguments; kept for config compatibility + mag_power: float = 2.0, # Deprecated arguments; kept for config compatibility + rng: Optional[random.Random] = None, # Deprecated arguments; kept for config compatibility + stft_exact_pad: bool = False, # Deprecated arguments; kept for config compatibility + stft_conv: bool = False, # Deprecated arguments; kept for config compatibility + ): + super().__init__() + if not HAVE_TORCHAUDIO: + raise ValueError(f"Need to install torchaudio to instantiate a {self.__class__.__name__}") + + # Make sure log zero guard is supported, if given as a string + supported_log_zero_guard_strings = {"eps", "tiny"} + if isinstance(log_zero_guard_value, str) and log_zero_guard_value not in supported_log_zero_guard_strings: + raise ValueError( + f"Log zero guard value must either be a float or a member of {supported_log_zero_guard_strings}" + ) + + # Copied from `AudioPreprocessor` due to the ad-hoc structuring of the Mel Spec extractor class + self.torch_windows = { + 'hann': torch.hann_window, + 'hamming': torch.hamming_window, + 'blackman': torch.blackman_window, + 'bartlett': torch.bartlett_window, + 'ones': torch.ones, + None: torch.ones, + } + + # Ensure we can look up the window function + if window not in self.torch_windows: + raise ValueError(f"Got window value '{window}' but expected a member of {self.torch_windows.keys()}") + + self.win_length = n_window_size + self.hop_length = n_window_stride + self._sample_rate = sample_rate + self._normalize_strategy = normalize + self._use_log = log + self._preemphasis_value = preemph + self.log_zero_guard_type = log_zero_guard_type + self.log_zero_guard_value: Union[str, float] = log_zero_guard_value + self.dither = dither + self.pad_to = pad_to + self.pad_value = pad_value + self.n_fft = n_fft + self._mel_spec_extractor: torchaudio.transforms.MelSpectrogram = torchaudio.transforms.MelSpectrogram( + sample_rate=self._sample_rate, + win_length=self.win_length, + hop_length=self.hop_length, + n_mels=nfilt, + window_fn=self.torch_windows[window], + mel_scale="slaney", + norm=mel_norm, + n_fft=n_fft, + f_max=highfreq, + f_min=lowfreq, + wkwargs={"periodic": False}, + ) + + @property + def filter_banks(self): + """ Matches the analogous class """ + return self._mel_spec_extractor.mel_scale.fb + + def _resolve_log_zero_guard_value(self, dtype: torch.dtype) -> float: + if isinstance(self.log_zero_guard_value, float): + return self.log_zero_guard_value + return getattr(torch.finfo(dtype), self.log_zero_guard_value) + + def _apply_dithering(self, signals: torch.Tensor) -> torch.Tensor: + if self.training and self.dither > 0.0: + noise = torch.randn_like(signals) * self.dither + signals = signals + noise + return signals + + def _apply_preemphasis(self, signals: torch.Tensor) -> torch.Tensor: + if self._preemphasis_value is not None: + padded = torch.nn.functional.pad(signals, (1, 0)) + signals = signals - self._preemphasis_value * padded[:, :-1] + return signals + + def _compute_output_lengths(self, input_lengths: torch.Tensor) -> torch.Tensor: + out_lengths = input_lengths.div(self.hop_length, rounding_mode="floor").add(1).long() + return out_lengths + + def _apply_pad_to(self, features: torch.Tensor) -> torch.Tensor: + # Only apply during training; else need to capture dynamic shape for exported models + if not self.training or self.pad_to == 0 or features.shape[-1] % self.pad_to == 0: + return features + pad_length = self.pad_to - (features.shape[-1] % self.pad_to) + return torch.nn.functional.pad(features, pad=(0, pad_length), value=self.pad_value) + + def _apply_log(self, features: torch.Tensor) -> torch.Tensor: + if self._use_log: + zero_guard = self._resolve_log_zero_guard_value(features.dtype) + if self.log_zero_guard_type == "add": + features = features + zero_guard + elif self.log_zero_guard_type == "clamp": + features = features.clamp(min=zero_guard) + else: + raise ValueError(f"Unsupported log zero guard type: '{self.log_zero_guard_type}'") + features = features.log() + return features + + def _extract_spectrograms(self, signals: torch.Tensor) -> torch.Tensor: + # Complex FFT needs to be done in single precision + with torch.cuda.amp.autocast(enabled=False): + features = self._mel_spec_extractor(waveform=signals) + return features + + def _apply_normalization(self, features: torch.Tensor, lengths: torch.Tensor, eps: float = 1e-5) -> torch.Tensor: + # For consistency, this function always does a masked fill even if not normalizing. + mask: torch.Tensor = make_seq_mask_like(lengths=lengths, like=features, time_dim=-1, valid_ones=False) + features = features.masked_fill(mask, 0.0) + # Maybe don't normalize + if self._normalize_strategy is None: + return features + # Use the log zero guard for the sqrt zero guard + guard_value = self._resolve_log_zero_guard_value(features.dtype) + if self._normalize_strategy == "per_feature" or self._normalize_strategy == "all_features": + # 'all_features' reduces over each sample; 'per_feature' reduces over each channel + reduce_dim = 2 + if self._normalize_strategy == "all_features": + reduce_dim = [1, 2] + # [B, D, T] -> [B, D, 1] or [B, 1, 1] + means = features.sum(dim=reduce_dim, keepdim=True).div(lengths.view(-1, 1, 1)) + stds = ( + features.sub(means) + .masked_fill(mask, 0.0) + .pow(2.0) + .sum(dim=reduce_dim, keepdim=True) # [B, D, T] -> [B, D, 1] or [B, 1, 1] + .div(lengths.view(-1, 1, 1) - 1) # assume biased estimator + .clamp(min=guard_value) # avoid sqrt(0) + .sqrt() + ) + features = (features - means) / (stds + eps) + else: + # Deprecating constant std/mean + raise ValueError(f"Unsupported norm type: '{self._normalize_strategy}") + features = features.masked_fill(mask, 0.0) + return features + + def forward(self, input_signal: torch.Tensor, length: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + feature_lengths = self._compute_output_lengths(input_lengths=length) + signals = self._apply_dithering(signals=input_signal) + signals = self._apply_preemphasis(signals=signals) + features = self._extract_spectrograms(signals=signals) + features = self._apply_log(features=features) + features = self._apply_normalization(features=features, lengths=feature_lengths) + features = self._apply_pad_to(features=features) + return features, feature_lengths diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/perturb.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/perturb.py new file mode 100644 index 0000000..2108da0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/perturb.py @@ -0,0 +1,1334 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright (c) 2018 Ryan Leary +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# This file contains code artifacts adapted from https://github.com/ryanleary/patter +import copy +import inspect +import io +import os +import random +import subprocess +from tempfile import NamedTemporaryFile +from typing import Any, List, Optional, Union + +import librosa +import numpy as np +import soundfile as sf +from scipy import signal + +from nemo.collections.asr.parts.preprocessing.segment import AudioSegment +from nemo.collections.common.parts.preprocessing import collections, parsers +from nemo.core.classes import IterableDataset +from nemo.utils import logging + +# TODO @blisc: Perhaps refactor instead of import guarding +HAVE_OMEGACONG_WEBDATASET = True +try: + import webdataset as wds + from omegaconf import DictConfig, OmegaConf +except ModuleNotFoundError: + from nemo.utils.exceptions import LightningNotInstalledException + + HAVE_OMEGACONG_WEBDATASET = False + + +try: + from nemo.collections.asr.parts.utils import numba_utils + + HAVE_NUMBA = True +except (ImportError, ModuleNotFoundError): + HAVE_NUMBA = False + + +def read_one_audiosegment(manifest, target_sr, tarred_audio=False, audio_dataset=None): + if tarred_audio: + if audio_dataset is None: + raise TypeError("Expected augmentation dataset but got None") + audio_file, file_id, manifest_entry = next(audio_dataset) + + offset = 0 if manifest_entry.offset is None else manifest_entry.offset + duration = 0 if manifest_entry.duration is None else manifest_entry.duration + + else: + audio_record = random.sample(manifest.data, 1)[0] + audio_file = audio_record.audio_file + offset = 0 if audio_record.offset is None else audio_record.offset + duration = 0 if audio_record.duration is None else audio_record.duration + + return AudioSegment.from_file(audio_file, target_sr=target_sr, offset=offset, duration=duration) + + +class Perturbation(object): + def max_augmentation_length(self, length): + return length + + def perturb(self, data): + raise NotImplementedError + + +class SpeedPerturbation(Perturbation): + """ + Performs Speed Augmentation by re-sampling the data to a different sampling rate, + which does not preserve pitch. + + Note: This is a very slow operation for online augmentation. If space allows, + it is preferable to pre-compute and save the files to augment the dataset. + + Args: + sr: Original sampling rate. + resample_type: Type of resampling operation that will be performed. + For better speed using `resampy`'s fast resampling method, use `resample_type='kaiser_fast'`. + For high-quality resampling, set `resample_type='kaiser_best'`. + To use `scipy.signal.resample`, set `resample_type='fft'` or `resample_type='scipy'` + min_speed_rate: Minimum sampling rate modifier. + max_speed_rate: Maximum sampling rate modifier. + num_rates: Number of discrete rates to allow. Can be a positive or negative + integer. + If a positive integer greater than 0 is provided, the range of + speed rates will be discretized into `num_rates` values. + If a negative integer or 0 is provided, the full range of speed rates + will be sampled uniformly. + Note: If a positive integer is provided and the resultant discretized + range of rates contains the value '1.0', then those samples with rate=1.0, + will not be augmented at all and simply skipped. This is to unnecessary + augmentation and increase computation time. Effective augmentation chance + in such a case is = `prob * (num_rates - 1 / num_rates) * 100`% chance + where `prob` is the global probability of a sample being augmented. + rng: Random seed. Default is None + """ + + def __init__(self, sr, resample_type, min_speed_rate=0.9, max_speed_rate=1.1, num_rates=5, rng=None): + + min_rate = min(min_speed_rate, max_speed_rate) + if min_rate < 0.0: + raise ValueError("Minimum sampling rate modifier must be > 0.") + + if resample_type not in ('kaiser_best', 'kaiser_fast', 'fft', 'scipy'): + raise ValueError("Supported `resample_type` values are ('kaiser_best', 'kaiser_fast', 'fft', 'scipy')") + + self._sr = sr + self._min_rate = min_speed_rate + self._max_rate = max_speed_rate + self._num_rates = num_rates + if num_rates > 0: + self._rates = np.linspace(self._min_rate, self._max_rate, self._num_rates, endpoint=True) + self._res_type = resample_type + random.seed(rng) if rng else None + + def max_augmentation_length(self, length): + return length * self._max_rate + + def perturb(self, data): + # Select speed rate either from choice or random sample + if self._num_rates < 0: + speed_rate = random.uniform(self._min_rate, self._max_rate) + else: + speed_rate = random.choice(self._rates) + + # Skip perturbation in case of identity speed rate + if speed_rate == 1.0: + return + + new_sr = int(self._sr * speed_rate) + data._samples = librosa.core.resample( + data._samples, orig_sr=self._sr, target_sr=new_sr, res_type=self._res_type + ) + + +class TimeStretchPerturbation(Perturbation): + """ + Time-stretch an audio series by a fixed rate while preserving pitch, based on [1, 2]. + + Note: + This is a simplified implementation, intended primarily for reference and pedagogical purposes. + It makes no attempt to handle transients, and is likely to produce audible artifacts. + + Reference + [1] [Ellis, D. P. W. “A phase vocoder in Matlab.” Columbia University, 2002.] + (http://www.ee.columbia.edu/~dpwe/resources/matlab/pvoc/) + [2] [librosa.effects.time_stretch] + (https://librosa.github.io/librosa/generated/librosa.effects.time_stretch.html) + + Args: + min_speed_rate: Minimum sampling rate modifier. + max_speed_rate: Maximum sampling rate modifier. + num_rates: Number of discrete rates to allow. Can be a positive or negative + integer. + If a positive integer greater than 0 is provided, the range of + speed rates will be discretized into `num_rates` values. + If a negative integer or 0 is provided, the full range of speed rates + will be sampled uniformly. + Note: If a positive integer is provided and the resultant discretized + range of rates contains the value '1.0', then those samples with rate=1.0, + will not be augmented at all and simply skipped. This is to avoid unnecessary + augmentation and increase computation time. Effective augmentation chance + in such a case is = `prob * (num_rates - 1 / num_rates) * 100`% chance + where `prob` is the global probability of a sample being augmented. + n_fft: Number of fft filters to be computed. + rng: Random seed. Default is None + """ + + def __init__(self, min_speed_rate=0.9, max_speed_rate=1.1, num_rates=5, n_fft=512, rng=None): + + min_rate = min(min_speed_rate, max_speed_rate) + if min_rate < 0.0: + raise ValueError("Minimum sampling rate modifier must be > 0.") + + self._min_rate = min_speed_rate + self._max_rate = max_speed_rate + self._num_rates = num_rates + if num_rates > 0: + self._rates = np.linspace(self._min_rate, self._max_rate, self._num_rates, endpoint=True) + random.seed(rng) if rng else None + + # Pre-compute constants + self._n_fft = int(n_fft) + self._hop_length = int(n_fft // 2) + + # Pre-allocate buffers + self._phi_advance_fast = np.linspace(0, np.pi * self._hop_length, self._hop_length + 1) + self._scale_buffer_fast = np.empty(self._hop_length + 1, dtype=np.float32) + + self._phi_advance_slow = np.linspace(0, np.pi * self._n_fft, self._n_fft + 1) + self._scale_buffer_slow = np.empty(self._n_fft + 1, dtype=np.float32) + + def max_augmentation_length(self, length): + return length * self._max_rate + + def perturb(self, data): + # Select speed rate either from choice or random sample + if self._num_rates < 0: + speed_rate = random.uniform(self._min_rate, self._max_rate) + else: + speed_rate = random.choice(self._rates) + + # Skip perturbation in case of identity speed rate + if speed_rate == 1.0: + return + + # Increase `n_fft` based on task (speed up or slow down audio) + # This greatly reduces upper bound of maximum time taken + # to compute slowed down audio segments. + if speed_rate >= 1.0: # Speed up audio + fft_multiplier = 1 + phi_advance = self._phi_advance_fast + scale_buffer = self._scale_buffer_fast + + else: # Slow down audio + fft_multiplier = 2 + phi_advance = self._phi_advance_slow + scale_buffer = self._scale_buffer_slow + + n_fft = int(self._n_fft * fft_multiplier) + hop_length = int(self._hop_length * fft_multiplier) + + # Perform short-term Fourier transform (STFT) + stft = librosa.core.stft(data._samples, n_fft=n_fft, hop_length=hop_length) + + # Stretch by phase vocoding + if HAVE_NUMBA: + stft_stretch = numba_utils.phase_vocoder(stft, speed_rate, phi_advance, scale_buffer) + + else: + stft_stretch = librosa.core.phase_vocoder(stft, speed_rate, hop_length) + + # Predict the length of y_stretch + len_stretch = int(round(len(data._samples) / speed_rate)) + + # Invert the STFT + y_stretch = librosa.core.istft( + stft_stretch, dtype=data._samples.dtype, hop_length=hop_length, length=len_stretch + ) + + data._samples = y_stretch + + +class SilencePerturbation(Perturbation): + """ + Applies random silence at the start and/or end of the audio. + + Args: + min_start_silence_secs (float): Min start silence level in secs + max_start_silence_secs (float): Max start silence level in secs + min_end_silence_secs (float): Min end silence level in secs + max_end_silence_secs (float): Max end silence level in secs + rng (int): Random seed. Default is None + value: (float): value representing silence to be added to audio array. + """ + + def __init__( + self, + min_start_silence_secs: float = 0, + max_start_silence_secs: float = 0, + min_end_silence_secs: float = 0, + max_end_silence_secs: float = 0, + rng: int = None, + value: float = 0, + ): + self._min_start_silence_secs = min_start_silence_secs + self._max_start_silence_secs = max_start_silence_secs + self._min_end_silence_secs = min_end_silence_secs + self._max_end_silence_secs = max_end_silence_secs + + random.seed(rng) if rng else None + self._value = value + + def perturb(self, data): + start_silence_len = random.uniform(self._min_start_silence_secs, self._max_start_silence_secs) + end_silence_len = random.uniform(self._min_end_silence_secs, self._max_end_silence_secs) + start = np.full((int(start_silence_len * data.sample_rate),), self._value) + end = np.full((int(end_silence_len * data.sample_rate),), self._value) + + data._samples = np.concatenate([start, data._samples, end]) + + +class GainPerturbation(Perturbation): + """ + Applies random gain to the audio. + + Args: + min_gain_dbfs (float): Min gain level in dB + max_gain_dbfs (float): Max gain level in dB + rng (int): Random seed. Default is None + """ + + def __init__(self, min_gain_dbfs=-10, max_gain_dbfs=10, rng=None): + self._min_gain_dbfs = min_gain_dbfs + self._max_gain_dbfs = max_gain_dbfs + random.seed(rng) if rng else None + + def perturb(self, data): + gain = random.uniform(self._min_gain_dbfs, self._max_gain_dbfs) + data._samples = data._samples * (10.0 ** (gain / 20.0)) + + +class ImpulsePerturbation(Perturbation): + """ + Convolves audio with a Room Impulse Response. + + Args: + manifest_path (list): Manifest file for RIRs + audio_tar_filepaths (list): Tar files, if RIR audio files are tarred + shuffle_n (int): Shuffle parameter for shuffling buffered files from the tar files + normalize_impulse (bool): Normalize impulse response to zero mean and amplitude 1 + shift_impulse (bool): Shift impulse response to adjust for delay at the beginning + rng (int): Random seed. Default is None + """ + + def __init__( + self, + manifest_path=None, + audio_tar_filepaths=None, + shuffle_n=128, + normalize_impulse=False, + shift_impulse=False, + rng=None, + ): + self._manifest = collections.ASRAudioText(manifest_path, parser=parsers.make_parser([]), index_by_file_id=True) + self._audiodataset = None + self._tarred_audio = False + self._normalize_impulse = normalize_impulse + self._shift_impulse = shift_impulse + self._data_iterator = None + + if audio_tar_filepaths: + self._tarred_audio = True + self._audiodataset = AugmentationDataset(manifest_path, audio_tar_filepaths, shuffle_n) + self._data_iterator = iter(self._audiodataset) + + self._rng = rng + random.seed(self._rng) if rng else None + + def perturb(self, data): + impulse = read_one_audiosegment( + self._manifest, data.sample_rate, tarred_audio=self._tarred_audio, audio_dataset=self._data_iterator, + ) + + # normalize if necessary + if self._normalize_impulse: + # normalize the impulse response to zero mean and amplitude 1 + impulse_norm = impulse.samples - np.mean(impulse.samples) + impulse_norm /= max(abs(impulse_norm)) + else: + impulse_norm = impulse.samples + + # len of input data samples + len_data = len(data._samples) + + # convolve with the full impulse response + data._samples = signal.fftconvolve(data._samples, impulse_norm, "full") + + # compensate the dominant path propagation delay + if self._shift_impulse: + # Find the peak of the IR and shift the output to the left + max_ind = np.argmax(np.abs(impulse_norm)) + data._samples = data._samples[max_ind:] + + # trim to match the input data length + data._samples = data._samples[:len_data] + + # normalize data samples to [-1,1] after rir convolution to avoid nans with fp16 training + data._samples = data._samples / max(abs(data._samples)) + + +class ShiftPerturbation(Perturbation): + """ + Perturbs audio by shifting the audio in time by a random amount between min_shift_ms and max_shift_ms. + The final length of the audio is kept unaltered by padding the audio with zeros. + + + Args: + min_shift_ms (float): Minimum time in milliseconds by which audio will be shifted + max_shift_ms (float): Maximum time in milliseconds by which audio will be shifted + rng (int): Random seed. Default is None + """ + + def __init__(self, min_shift_ms=-5.0, max_shift_ms=5.0, rng=None): + self._min_shift_ms = min_shift_ms + self._max_shift_ms = max_shift_ms + random.seed(rng) if rng else None + + def perturb(self, data): + shift_ms = random.uniform(self._min_shift_ms, self._max_shift_ms) + if abs(shift_ms) / 1000 > data.duration: + # TODO: do something smarter than just ignore this condition + return + shift_samples = int(shift_ms * data.sample_rate // 1000) + # logging.debug("shift: %s", shift_samples) + if shift_samples < 0: + data._samples[-shift_samples:] = data._samples[:shift_samples] + data._samples[:-shift_samples] = 0 + elif shift_samples > 0: + data._samples[:-shift_samples] = data._samples[shift_samples:] + data._samples[-shift_samples:] = 0 + + +class NoisePerturbation(Perturbation): + """ + Perturbation that adds noise to input audio. + + Args: + manifest_path (str): Manifest file with paths to noise files + min_snr_db (float): Minimum SNR of audio after noise is added + max_snr_db (float): Maximum SNR of audio after noise is added + max_gain_db (float): Maximum gain that can be applied on the noise sample + audio_tar_filepaths (list) : Tar files, if noise audio files are tarred + shuffle_n (int): Shuffle parameter for shuffling buffered files from the tar files + orig_sr (int): Original sampling rate of the noise files + rng (int): Random seed. Default is None + """ + + def __init__( + self, + manifest_path=None, + min_snr_db=10, + max_snr_db=50, + max_gain_db=300.0, + rng=None, + audio_tar_filepaths=None, + shuffle_n=100, + orig_sr=16000, + ): + self._manifest = collections.ASRAudioText(manifest_path, parser=parsers.make_parser([]), index_by_file_id=True) + self._audiodataset = None + self._tarred_audio = False + self._orig_sr = orig_sr + self._data_iterator = None + + if audio_tar_filepaths: + self._tarred_audio = True + self._audiodataset = AugmentationDataset(manifest_path, audio_tar_filepaths, shuffle_n) + self._data_iterator = iter(self._audiodataset) + + random.seed(rng) if rng else None + self._rng = rng + + self._min_snr_db = min_snr_db + self._max_snr_db = max_snr_db + self._max_gain_db = max_gain_db + + @property + def orig_sr(self): + return self._orig_sr + + def get_one_noise_sample(self, target_sr): + return read_one_audiosegment( + self._manifest, target_sr, tarred_audio=self._tarred_audio, audio_dataset=self._data_iterator + ) + + def perturb(self, data, ref_mic=0): + """ + Args: + data (AudioSegment): audio data + ref_mic (int): reference mic index for scaling multi-channel audios + """ + noise = read_one_audiosegment( + self._manifest, data.sample_rate, tarred_audio=self._tarred_audio, audio_dataset=self._data_iterator, + ) + self.perturb_with_input_noise(data, noise, ref_mic=ref_mic) + + def perturb_with_input_noise(self, data, noise, data_rms=None, ref_mic=0): + """ + Args: + data (AudioSegment): audio data + noise (AudioSegment): noise data + data_rms (Union[float, List[float]): rms_db for data input + ref_mic (int): reference mic index for scaling multi-channel audios + """ + if data.num_channels != noise.num_channels: + raise ValueError( + f"Found mismatched channels for data ({data.num_channels}) and noise ({noise.num_channels})." + ) + + if not (0 <= ref_mic < data.num_channels): + raise ValueError( + f" reference mic ID must be an integer in [0, {data.num_channels}), got {ref_mic} instead." + ) + + snr_db = random.uniform(self._min_snr_db, self._max_snr_db) + if data_rms is None: + data_rms = data.rms_db + + if data.num_channels > 1: + noise_gain_db = data_rms[ref_mic] - noise.rms_db[ref_mic] - snr_db + else: + noise_gain_db = data_rms - noise.rms_db - snr_db + noise_gain_db = min(noise_gain_db, self._max_gain_db) + + # calculate noise segment to use + start_time = random.uniform(0.0, noise.duration - data.duration) + if noise.duration > (start_time + data.duration): + noise.subsegment(start_time=start_time, end_time=start_time + data.duration) + + # adjust gain for snr purposes and superimpose + noise.gain_db(noise_gain_db) + + if noise._samples.shape[0] < data._samples.shape[0]: + noise_idx = random.randint(0, data._samples.shape[0] - noise._samples.shape[0]) + data._samples[noise_idx : noise_idx + noise._samples.shape[0]] += noise._samples + + else: + data._samples += noise._samples + + def perturb_with_foreground_noise(self, data, noise, data_rms=None, max_noise_dur=2, max_additions=1, ref_mic=0): + """ + Args: + data (AudioSegment): audio data + noise (AudioSegment): noise data + data_rms (Union[float, List[float]): rms_db for data input + max_noise_dur: (float): max noise duration + max_additions (int): number of times for adding noise + ref_mic (int): reference mic index for scaling multi-channel audios + """ + if data.num_channels != noise.num_channels: + raise ValueError( + f"Found mismatched channels for data ({data.num_channels}) and noise ({noise.num_channels})." + ) + + if not (0 <= ref_mic < data.num_channels): + raise ValueError( + f" reference mic ID must be an integer in [0, {data.num_channels}), got {ref_mic} instead." + ) + + snr_db = random.uniform(self._min_snr_db, self._max_snr_db) + if not data_rms: + data_rms = data.rms_db + + if data.num_channels > 1: + noise_gain_db = data_rms[ref_mic] - noise.rms_db[ref_mic] - snr_db + else: + noise_gain_db = data_rms - noise.rms_db - snr_db + noise_gain_db = min(noise_gain_db, self._max_gain_db) + + n_additions = random.randint(1, max_additions) + + for i in range(n_additions): + noise_dur = random.uniform(0.0, max_noise_dur) + start_time = random.uniform(0.0, noise.duration) + start_sample = int(round(start_time * noise.sample_rate)) + end_sample = int(round(min(noise.duration, (start_time + noise_dur)) * noise.sample_rate)) + noise_samples = np.copy(noise._samples[start_sample:end_sample]) + # adjust gain for snr purposes and superimpose + noise_samples *= 10.0 ** (noise_gain_db / 20.0) + + if noise_samples.shape[0] > data._samples.shape[0]: + noise_samples = noise_samples[0 : data._samples.shape[0]] + + noise_idx = random.randint(0, data._samples.shape[0] - noise_samples.shape[0]) + data._samples[noise_idx : noise_idx + noise_samples.shape[0]] += noise_samples + + +class NoisePerturbationWithNormalization(Perturbation): + """ + Perturbation that adds noise to input audio, with normalisation to specific decibel level. + Also tiles shorter noise samples up to their corresponding clean audio length. + + Args: + manifest_path (str or list): Manifest file with paths to noise files, can be list if using multiple noise sources + min_snr_db (float): Minimum SNR of audio after noise is added + max_snr_db (float): Maximum SNR of audio after noise is added + snr_samples (list): A discrete list of SNRs DBs to sample from when mixing, will be used instead of [min_snr_db,max_snr_db] + norm_to_db (float): Will normalise clean, noise, and mixed samples to this DB + audio_tar_filepaths (str or list) : Tar files, if noise audio files are tarred, can be list for multiple sources + shuffle_n (int): Shuffle parameter for shuffling buffered files from the tar files + orig_sr (int): Original sampling rate of the noise files + rng (int): Random seed. Default is None + shard_strategy (str): if you're using tarred audio and wish to scatter instead of replicate, set this to 'scatter' + epsilon (float): minimum value for RMS DB normalisation to avoid divide by zero + """ + + def __init__( + self, + manifest_path=None, + min_snr_db=10, + max_snr_db=50, + snr_samples=None, + norm_to_db=None, + rng=None, + audio_tar_filepaths=None, + shuffle_n=128, + orig_sr=16000, + global_rank=0, + world_size=1, + shard_strategy='replicate', + epsilon=0.01, + ): + # import here to avoid circular import error + from nemo.collections.asr.data.audio_to_text import RandomizedChainDataset + + self._manifest = collections.ASRAudioText(manifest_path, parser=parsers.make_parser([]), index_by_file_id=True) + self._audiodataset = None + self._tarred_audio = False + self._orig_sr = orig_sr + self._data_iterator = None + + random.seed(rng) if rng else None + self._rng = rng + + if audio_tar_filepaths: + self._tarred_audio = True + if isinstance(manifest_path, str): + manifest_path = [manifest_path] + if isinstance(audio_tar_filepaths, str): + audio_tar_filepaths = [audio_tar_filepaths] + datasets = [] + for tarred_audio_filepath, manifest_filepath in zip(audio_tar_filepaths, manifest_path): + dataset = AugmentationDataset( + manifest_filepath, + tarred_audio_filepath, + shuffle_n, + rank=global_rank, + world_size=world_size, + shard_strategy=shard_strategy, + ) + datasets.append(dataset) + self._audiodataset = RandomizedChainDataset( + datasets, rnd_seed=(rng if rng else random.randint(0, 30000)) + global_rank + ) + if len(self._audiodataset) == 0: + raise RuntimeError( + "NoisePerturbationWithNormalization detected a zero length RandomizedChainDataset, should never happen" + ) + self._data_iterator = iter(self._audiodataset) + + self._min_snr_db = min_snr_db + self._max_snr_db = max_snr_db + self._norm_to_db = norm_to_db + self._snr_samples = snr_samples if isinstance(snr_samples, list) and len(snr_samples) > 0 else None + self._epsilon = epsilon + + @property + def orig_sr(self): + return self._orig_sr + + def read_one_audiosegment(self, target_sr): + if self._tarred_audio: + if self._data_iterator is None: + raise TypeError("Expected valid iterator but got None") + try: + audio_file, file_id, manifest_entry = next(self._data_iterator) + except StopIteration: + self._data_iterator = iter(self._audiodataset) + audio_file, file_id, manifest_entry = next(self._data_iterator) + + offset = 0 if manifest_entry.offset is None else manifest_entry.offset + duration = 0 if manifest_entry.duration is None else manifest_entry.duration + + else: + audio_record = random.sample(self._manifest.data, 1)[0] + audio_file = audio_record.audio_file + offset = 0 if audio_record.offset is None else audio_record.offset + duration = 0 if audio_record.duration is None else audio_record.duration + + return AudioSegment.from_file(audio_file, target_sr=target_sr, offset=offset, duration=duration) + + def perturb(self, data, ref_mic=0): + """ + Args: + data (AudioSegment): audio data + ref_mic (int): reference mic index for scaling multi-channel audios + """ + + noise = self.read_one_audiosegment(data.sample_rate) + + # noise samples need to be at least 1 second long to avoid strange oddities + # in the RMS SNR mixing, so we have a fail-safe here to ensure at least 1 sec duration + while noise.duration < 1: + noise = self.read_one_audiosegment(data.sample_rate) + + self.perturb_with_input_noise(data, noise, ref_mic=ref_mic, norm_to_db=self._norm_to_db) + + def snr_mixer(self, clean, noise, snr, norm_to_db=-25.0): + """ + Mixes the clean audio with the noise + Args: + clean (numpy array): the clean audio data + noise (numpy array): the noise audio data + snr (float): the SNR value for the mixing + norm_to_db (float): the DB value to normalise to before mixing + """ + clean = self.norm_audio_to_db(clean, norm_to_db) + noise = self.norm_audio_to_db(noise, norm_to_db) + + # Set the noise level for a given SNR + # note that if your noise doesn't overlap with your audio then your target SNR + # may not be achievable. Consider using an rms-threshold in the future + noisescalar = 10 ** (-snr / 20.0) + noisenewlevel = noise * noisescalar + noisyspeech = clean + noisenewlevel + + return clean, noisenewlevel, noisyspeech + + def norm_audio_to_db(self, x, norm_to_db): + """ + Normalises audio signal to particular db, with some epsilon in-case of divide by zero + Args: + x (numpy array): input audio signal + norm_to_db (float): the db to normalise to + """ + rms = (x ** 2).mean(axis=0) ** 0.5 + rms = np.where(np.isclose(rms, 0), self._epsilon, rms) + scalar = 10 ** (norm_to_db / 20.0) / rms + return x * scalar + + def concatenate_noise_sample(self, clean, noise, fs, silence_length=0.25): + """ + Tiles the noise array to match the clean audio array, with small silence between the joins + Args: + clean (numpy array): clean audio data + noise (numpy array): noise audio data + fs (int): sample rate used by both clean and noise audio data + silence_length (float): the amount of silence (in secs) to insert before tiling + """ + while len(noise) < len(clean): + if noise.ndim > 1: + zeros = np.zeros((int(fs * silence_length), noise.shape[-1])) + else: + zeros = np.zeros((int(fs * silence_length),)) + noiseconcat = np.append(noise, zeros, axis=0) + noise = np.append(noiseconcat, noise, axis=0) + + return noise + + def perturb_with_input_noise(self, data, noise, data_rms=None, ref_mic=0, norm_to_db=-25.0): + """ + Args: + data (AudioSegment): audio data + noise (AudioSegment): noise data + data_rms (Union[float, List[float]): rms_db for data input + ref_mic (int): reference mic index for scaling multi-channel audio, if set to None then + each channel will be scaled independently + norm_to_db (float): will normalise all audio to this DB + """ + if data.num_channels != noise.num_channels: + raise ValueError( + f"Found mismatched channels for data ({data.num_channels}) and noise ({noise.num_channels})." + ) + + if not (0 <= ref_mic < data.num_channels): + raise ValueError( + f" reference mic ID must be an integer in [0, {data.num_channels}), got {ref_mic} instead." + ) + + if self._snr_samples: + snr_db = random.sample(self._snr_samples, 1)[0] + else: + snr_db = random.uniform(self._min_snr_db, self._max_snr_db) + if data_rms is None: + data_rms = data.rms_db[ref_mic] if isinstance(data.rms_db, (list, np.ndarray)) else data.rms_db + + if norm_to_db is None: + norm_to_db = data_rms + + data_norm = data._samples + noise_norm = noise._samples + + if len(data_norm) == 0: + return + + if len(noise_norm) < len(data_norm): + noise_norm = self.concatenate_noise_sample(data_norm, noise_norm, data.sample_rate) + noise_norm = noise_norm[0 : len(data_norm)] + + _, _, noisy_snr = self.snr_mixer(clean=data_norm, noise=noise_norm, snr=snr_db, norm_to_db=norm_to_db) + + data._samples = noisy_snr + + +class WhiteNoisePerturbation(Perturbation): + """ + Perturbation that adds white noise to an audio file in the training dataset. + + Args: + min_level (int): Minimum level in dB at which white noise should be added + max_level (int): Maximum level in dB at which white noise should be added + rng (int): Random seed. Default is None + """ + + def __init__(self, min_level=-90, max_level=-46, rng=None): + self.min_level = int(min_level) + self.max_level = int(max_level) + np.random.seed(rng) if rng else None + + def perturb(self, data): + noise_level_db = np.random.randint(self.min_level, self.max_level, dtype='int32') + noise_signal = np.random.randn(data._samples.shape[0]) * (10.0 ** (noise_level_db / 20.0)) + data._samples += noise_signal + + +class RirAndNoisePerturbation(Perturbation): + """ + RIR augmentation with additive foreground and background noise. + In this implementation audio data is augmented by first convolving the audio with a Room Impulse Response + and then adding foreground noise and background noise at various SNRs. RIR, foreground and background noises + should either be supplied with a manifest file or as tarred audio files (faster). + + Different sets of noise audio files based on the original sampling rate of the noise. This is useful while + training a mixed sample rate model. For example, when training a mixed model with 8 kHz and 16 kHz audio with a + target sampling rate of 16 kHz, one would want to augment 8 kHz data with 8 kHz noise rather than 16 kHz noise. + + Args: + rir_manifest_path: Manifest file for RIRs + rir_tar_filepaths: Tar files, if RIR audio files are tarred + rir_prob: Probability of applying a RIR + noise_manifest_paths: Foreground noise manifest path + min_snr_db: Min SNR for foreground noise + max_snr_db: Max SNR for background noise, + noise_tar_filepaths: Tar files, if noise files are tarred + apply_noise_rir: Whether to convolve foreground noise with a a random RIR + orig_sample_rate: Original sampling rate of foreground noise audio + max_additions: Max number of times foreground noise is added to an utterance, + max_duration: Max duration of foreground noise + bg_noise_manifest_paths: Background noise manifest path + bg_min_snr_db: Min SNR for background noise + bg_max_snr_db: Max SNR for background noise + bg_noise_tar_filepaths: Tar files, if noise files are tarred + bg_orig_sample_rate: Original sampling rate of background noise audio + rng: Random seed. Default is None + + """ + + def __init__( + self, + rir_manifest_path=None, + rir_prob=0.5, + noise_manifest_paths=None, + noise_prob=1.0, + min_snr_db=0, + max_snr_db=50, + rir_tar_filepaths=None, + rir_shuffle_n=100, + noise_tar_filepaths=None, + apply_noise_rir=False, + orig_sample_rate=None, + max_additions=5, + max_duration=2.0, + bg_noise_manifest_paths=None, + bg_noise_prob=1.0, + bg_min_snr_db=10, + bg_max_snr_db=50, + bg_noise_tar_filepaths=None, + bg_orig_sample_rate=None, + rng=None, + ): + + self._rir_prob = rir_prob + self._noise_prob = noise_prob + self._bg_noise_prob = bg_noise_prob + random.seed(rng) if rng else None + self._rir_perturber = ImpulsePerturbation( + manifest_path=rir_manifest_path, + audio_tar_filepaths=rir_tar_filepaths, + shuffle_n=rir_shuffle_n, + shift_impulse=True, + ) + self._fg_noise_perturbers = None + self._bg_noise_perturbers = None + if noise_manifest_paths: + self._fg_noise_perturbers = {} + for i in range(len(noise_manifest_paths)): + if orig_sample_rate is None: + orig_sr = 16000 + else: + orig_sr = orig_sample_rate[i] + self._fg_noise_perturbers[orig_sr] = NoisePerturbation( + manifest_path=noise_manifest_paths[i], + min_snr_db=min_snr_db[i], + max_snr_db=max_snr_db[i], + audio_tar_filepaths=noise_tar_filepaths[i], + orig_sr=orig_sr, + ) + self._max_additions = max_additions + self._max_duration = max_duration + if bg_noise_manifest_paths: + self._bg_noise_perturbers = {} + for i in range(len(bg_noise_manifest_paths)): + if bg_orig_sample_rate is None: + orig_sr = 16000 + else: + orig_sr = bg_orig_sample_rate[i] + self._bg_noise_perturbers[orig_sr] = NoisePerturbation( + manifest_path=bg_noise_manifest_paths[i], + min_snr_db=bg_min_snr_db[i], + max_snr_db=bg_max_snr_db[i], + audio_tar_filepaths=bg_noise_tar_filepaths[i], + orig_sr=orig_sr, + ) + + self._apply_noise_rir = apply_noise_rir + + def perturb(self, data): + prob = random.uniform(0.0, 1.0) + + if prob < self._rir_prob: + self._rir_perturber.perturb(data) + + data_rms = data.rms_db + + if self._fg_noise_perturbers is not None and random.uniform(0.0, 1.0) < self._noise_prob: + orig_sr = data.orig_sr + if orig_sr not in self._fg_noise_perturbers: + orig_sr = max(self._fg_noise_perturbers.keys()) + fg_perturber = self._fg_noise_perturbers[orig_sr] + noise = fg_perturber.get_one_noise_sample(data.sample_rate) + if self._apply_noise_rir: + self._rir_perturber.perturb(noise) + fg_perturber.perturb_with_foreground_noise( + data, noise, data_rms=data_rms, max_noise_dur=self._max_duration, max_additions=self._max_additions + ) + + if self._bg_noise_perturbers is not None and random.uniform(0.0, 1.0) < self._bg_noise_prob: + orig_sr = data.orig_sr + if orig_sr not in self._bg_noise_perturbers: + orig_sr = max(self._bg_noise_perturbers.keys()) + bg_perturber = self._bg_noise_perturbers[orig_sr] + + noise = bg_perturber.get_one_noise_sample(data.sample_rate) + bg_perturber.perturb_with_input_noise(data, noise, data_rms=data_rms) + + +class TranscodePerturbation(Perturbation): + """ + Audio codec augmentation. This implementation uses sox to transcode audio with low rate audio codecs, + so users need to make sure that the installed sox version supports the codecs used here (G711 and amr-nb). + + Args: + codecs (List[str]):A list of codecs to be trancoded to. Default is None. + rng (int): Random seed. Default is None. + """ + + def __init__(self, codecs=None, rng=None): + random.seed(rng) if rng else None + self._codecs = codecs if codecs is not None else ["g711", "amr-nb", "ogg"] + self.att_factor = 0.8 # to avoid saturation while writing to wav + if codecs is not None: + for codec in codecs: + if codec not in ["g711", "amr-nb", "ogg"]: + raise ValueError( + f"TranscodePerturbation with {codec} isnot supported. Only {codecs} are supported" + ) + + def perturb(self, data): + max_level = np.max(np.abs(data._samples)) + if max_level > 0.8: + norm_factor = self.att_factor / max_level + norm_samples = norm_factor * data._samples + else: + norm_samples = data._samples + orig_f = NamedTemporaryFile(suffix=".wav") + sf.write(orig_f.name, norm_samples.transpose(), 16000) + + codec_ind = random.randint(0, len(self._codecs) - 1) + if self._codecs[codec_ind] == "amr-nb": + transcoded_f = NamedTemporaryFile(suffix="_amr.wav") + rates = list(range(0, 4)) + rate = rates[random.randint(0, len(rates) - 1)] + _ = subprocess.check_output( + f"sox {orig_f.name} -V0 -C {rate} -t amr-nb - | sox -t amr-nb - -V0 -b 16 -r 16000 {transcoded_f.name}", + shell=True, + ) + elif self._codecs[codec_ind] == "ogg": + transcoded_f = NamedTemporaryFile(suffix="_ogg.wav") + rates = list(range(-1, 8)) + rate = rates[random.randint(0, len(rates) - 1)] + _ = subprocess.check_output( + f"sox {orig_f.name} -V0 -C {rate} -t ogg - | sox -t ogg - -V0 -b 16 -r 16000 {transcoded_f.name}", + shell=True, + ) + elif self._codecs[codec_ind] == "g711": + transcoded_f = NamedTemporaryFile(suffix="_g711.wav") + _ = subprocess.check_output( + f"sox {orig_f.name} -V0 -r 8000 -c 1 -e a-law {transcoded_f.name} lowpass 3400 highpass 300", + shell=True, + ) + + new_data = AudioSegment.from_file(transcoded_f.name, target_sr=16000) + data._samples = new_data._samples[0 : data._samples.shape[0]] + return + + +class RandomSegmentPerturbation(Perturbation): + """ + Returns a random segment from input of duration "duration_sec". + If duration_sec > input audio length, pad_to_duration determines the outcome. + + RandomSegmentPerturbation is intended for self-supervised learning. + Not for supervised, as extracting corresponding text is not facilitated. + + + Args: + duration_sec (float): duration of the segment to be extracted + pad_to_duration (bool): zero pad if length of input audio < duration_sec + rng: Random seed. Default is None + """ + + def __init__(self, duration_sec=32.0, pad_to_duration=False, rng=None): + if duration_sec <= 0: + raise ValueError("duration_sec should be > 0") + + self._duration_sec = duration_sec + self._pad_to_duration = pad_to_duration + random.seed(rng) if rng else None + + def perturb(self, data): + if self._duration_sec > data.duration: + if not self._pad_to_duration: + raise ValueError(f"audio length < {self._duration_sec} sec and pad_to_duration is set to False") + start_time = 0.0 + pad_size = self._duration_sec * data.sample_rate - data.num_samples + data.pad(pad_size=pad_size) + else: + start_time = random.uniform(0.0, data.duration - self._duration_sec) + + end_time = start_time + self._duration_sec + data.subsegment(start_time=start_time, end_time=end_time) + + +perturbation_types = { + "speed": SpeedPerturbation, + "time_stretch": TimeStretchPerturbation, + "gain": GainPerturbation, + "silence": SilencePerturbation, + "impulse": ImpulsePerturbation, + "shift": ShiftPerturbation, + "noise": NoisePerturbation, + "noise_norm": NoisePerturbationWithNormalization, + "white_noise": WhiteNoisePerturbation, + "rir_noise_aug": RirAndNoisePerturbation, + "transcode_aug": TranscodePerturbation, + "random_segment": RandomSegmentPerturbation, +} + + +def register_perturbation(name: str, perturbation: Perturbation): + if name in perturbation_types.keys(): + raise KeyError( + f"Perturbation with the name {name} exists. " f"Type of perturbation : {perturbation_types[name]}." + ) + + perturbation_types[name] = perturbation + + +class AudioAugmentor(object): + def __init__(self, perturbations=None, rng=None): + random.seed(rng) if rng else None + self._pipeline = perturbations if perturbations is not None else [] + + def perturb(self, segment): + for (prob, p) in self._pipeline: + if random.random() < prob: + p.perturb(segment) + return + + def max_augmentation_length(self, length): + newlen = length + for (prob, p) in self._pipeline: + newlen = p.max_augmentation_length(newlen) + return newlen + + @classmethod + def from_config(cls, config): + ptbs = [] + for p in config: + if p['aug_type'] not in perturbation_types: + logging.warning("%s perturbation not known. Skipping.", p['aug_type']) + continue + perturbation = perturbation_types[p['aug_type']] + ptbs.append((p['prob'], perturbation(**p['cfg']))) + return cls(perturbations=ptbs) + + +def process_augmentations(augmenter, global_rank=0, world_size=1) -> Optional[AudioAugmentor]: + """Process list of online data augmentations. + Accepts either an AudioAugmentor object with pre-defined augmentations, + or a dictionary that points to augmentations that have been defined. + If a dictionary is passed, must follow the below structure: + Dict[str, Dict[str, Any]]: Which refers to a dictionary of string + names for augmentations, defined in `asr/parts/perturb.py`. + The inner dictionary may contain key-value arguments of the specific + augmentation, along with an essential key `prob`. `prob` declares the + probability of the augmentation being applied, and must be a float + value in the range [0, 1]. + # Example in YAML config file + Augmentations are generally applied only during training, so we can add + these augmentations to our yaml config file, and modify the behaviour + for training and evaluation. + ```yaml + AudioToSpeechLabelDataLayer: + ... # Parameters shared between train and evaluation time + train: + augmentor: + shift: + prob: 0.5 + min_shift_ms: -5.0 + max_shift_ms: 5.0 + white_noise: + prob: 1.0 + min_level: -90 + max_level: -46 + ... + eval: + ... + ``` + Then in the training script, + ```python + import copy + from ruamel.yaml import YAML + yaml = YAML(typ="safe") + with open(model_config) as f: + params = yaml.load(f) + # Train Config for Data Loader + train_dl_params = copy.deepcopy(params["AudioToTextDataLayer"]) + train_dl_params.update(params["AudioToTextDataLayer"]["train"]) + del train_dl_params["train"] + del train_dl_params["eval"] + data_layer_train = nemo_asr.AudioToTextDataLayer( + ..., + **train_dl_params, + ) + # Evaluation Config for Data Loader + eval_dl_params = copy.deepcopy(params["AudioToTextDataLayer"]) + eval_dl_params.update(params["AudioToTextDataLayer"]["eval"]) + del eval_dl_params["train"] + del eval_dl_params["eval"] + data_layer_eval = nemo_asr.AudioToTextDataLayer( + ..., + **eval_dl_params, + ) + ``` + # Registering your own Augmentations + To register custom augmentations to obtain the above convenience of + the declaring the augmentations in YAML, you can put additional keys in + `perturbation_types` dictionary as follows. + ```python + from nemo.collections.asr.parts import perturb + # Define your own perturbation here + class CustomPerturbation(perturb.Perturbation): + ... + perturb.register_perturbation(name_of_perturbation, CustomPerturbation) + ``` + Args: + augmenter: AudioAugmentor object or + dictionary of str -> kwargs (dict) which is parsed and used + to initialize an AudioAugmentor. + Note: It is crucial that each individual augmentation has + a keyword `prob`, that defines a float probability in the + the range [0, 1] of this augmentation being applied. + If this keyword is not present, then the augmentation is + disabled and a warning is logged. + Returns: AudioAugmentor object + """ + if augmenter is None: + return None + + if isinstance(augmenter, AudioAugmentor): + return augmenter + + augmenter_types = {dict} + if HAVE_OMEGACONG_WEBDATASET: + augmenter_types = {dict, DictConfig} + if not type(augmenter) in augmenter_types: + raise ValueError("Cannot parse augmenter. Must be a dict or an AudioAugmentor object ") + + if HAVE_OMEGACONG_WEBDATASET and isinstance(augmenter, DictConfig): + augmenter = OmegaConf.to_container(augmenter, resolve=True) + + augmenter = copy.deepcopy(augmenter) + + augmentations = [] + for augment_name, augment_kwargs in augmenter.items(): + prob = augment_kwargs.get('prob', None) + + if prob is None: + raise KeyError( + f'Augmentation "{augment_name}" will not be applied as ' + f'keyword argument "prob" was not defined for this augmentation.' + ) + + else: + _ = augment_kwargs.pop('prob') + + if prob < 0.0 or prob > 1.0: + raise ValueError("`prob` must be a float value between 0 and 1.") + + try: + augmentation_class = perturbation_types[augment_name] + if 'global_rank' in inspect.signature(augmentation_class).parameters: + augment_kwargs['global_rank'] = global_rank + if 'world_size' in inspect.signature(augmentation_class).parameters: + augment_kwargs['world_size'] = world_size + augmentation = augmentation_class(**augment_kwargs) + augmentations.append([prob, augmentation]) + except KeyError: + raise KeyError(f"Invalid perturbation name. Allowed values : {perturbation_types.keys()}") + + augmenter = AudioAugmentor(perturbations=augmentations) + return augmenter + + +class AugmentationDataset(IterableDataset): + """ + A class that loads tarred audio files and cycles over the files in the dataset. + Accepts a single comma-separated JSON manifest file (in the same style as for the AudioToCharDataset/AudioToBPEDataset), + as well as the path(s) to the tarball(s) containing the wav files. Each line of the manifest should + contain the information for one audio file, including at least the transcript and name of the audio + file within the tarball. + Valid formats for the audio_tar_filepaths argument include: + (1) a single string that can be brace-expanded, e.g. 'path/to/audio.tar' or 'path/to/audio_{1..100}.tar.gz', or + (2) a list of file paths that will not be brace-expanded, e.g. ['audio_1.tar', 'audio_2.tar', ...]. + Note: For brace expansion in (1), there may be cases where `{x..y}` syntax cannot be used due to shell interference. + This occurs most commonly inside SLURM scripts. Therefore we provide a few equivalent replacements. + Supported opening braces - { <=> (, [, < and the special tag _OP_. + Supported closing braces - } <=> ), ], > and the special tag _CL_. + For SLURM based tasks, we suggest the use of the special tags for ease of use. + See the WebDataset documentation for more information about accepted data and input formats. + """ + + def __init__( + self, + manifest_path: str, + tar_filepaths: Union[str, List[str]], + shuffle_n: int = 128, + rank: int = 0, + world_size: int = 1, + shard_strategy: str = "replicate", + ): + # import here to avoid circular import error + from nemo.collections.asr.data.audio_to_text import expand_sharded_filepaths + + self._manifest = collections.ASRAudioText(manifest_path, parser=parsers.make_parser([]), index_by_file_id=True) + + tar_filepaths = expand_sharded_filepaths( + tar_filepaths, shard_strategy=shard_strategy, world_size=world_size, global_rank=rank + ) + + if not HAVE_OMEGACONG_WEBDATASET: + raise LightningNotInstalledException(self) + self.audio_dataset = wds.DataPipeline( + wds.SimpleShardList(urls=tar_filepaths), + wds.shuffle(shuffle_n), + wds.tarfile_to_samples(), + wds.rename(audio='wav;ogg;flac', key='__key__'), + wds.to_tuple('audio', 'key'), + self._loop_offsets, + ) + + def __len__(self): + return len(self._manifest) + + def _loop_offsets(self, iterator): + """This function is used to iterate through utterances with different offsets for each file. + """ + + class TarredAudioLoopOffsets: + def __init__(self, collection): + self.iterator = iterator + self.collection = collection + self.current_fn = None + self.current_bytes = None + self.offset_id = 0 + + def __iter__(self): + return self + + def __next__(self): + if self.current_fn is None: + self.current_bytes, self.current_fn = next(self.iterator) + self.offset_id = 0 + else: + offset_list = self.collection.mapping[self.current_fn] + if len(offset_list) == self.offset_id + 1: + self.current_bytes, self.current_fn = next(self.iterator) + self.offset_id = 0 + else: + self.offset_id += 1 + + return self.current_bytes, self.current_fn, self.offset_id + + return TarredAudioLoopOffsets(self._manifest) + + def __iter__(self): + audio_iter = iter(self.audio_dataset) + + while True: + try: + audio_bytes, audio_filename, offset_id = next(audio_iter) + file_id, _ = os.path.splitext(os.path.basename(audio_filename)) + manifest_idx = self._manifest.mapping[file_id][offset_id] + manifest_entry = self._manifest[manifest_idx] + + # Convert audio bytes to IO stream for processing (for SoundFile to read) + audio_file = io.BytesIO(audio_bytes) + yield audio_file, file_id, manifest_entry + except StopIteration: + audio_iter = iter(self.audio_dataset) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/segment.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/segment.py new file mode 100644 index 0000000..be78ac7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/preprocessing/segment.py @@ -0,0 +1,542 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright (c) 2018 Ryan Leary +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# This file contains code artifacts adapted from https://github.com/ryanleary/patter + +import math +import os +import random +from typing import Optional + +import librosa +import numpy as np +import soundfile as sf + +from nemo.collections.asr.parts.utils.audio_utils import select_channels +from nemo.utils import logging + +# TODO @blisc: Perhaps refactor instead of import guarding +HAVE_PYDUB = True +try: + from pydub import AudioSegment as Audio + from pydub.exceptions import CouldntDecodeError +except ModuleNotFoundError: + HAVE_PYDUB = False + + +available_formats = sf.available_formats() +sf_supported_formats = ["." + i.lower() for i in available_formats.keys()] + + +class AudioSegment(object): + """Audio segment abstraction. + :param samples: Audio samples [num_samples x num_channels]. + :type samples: ndarray.float32 + :param sample_rate: Audio sample rate. + :type sample_rate: int + :raises TypeError: If the sample data type is not float or int. + """ + + def __init__( + self, + samples, + sample_rate, + target_sr=None, + trim=False, + trim_ref=np.max, + trim_top_db=60, + trim_frame_length=2048, + trim_hop_length=512, + orig_sr=None, + channel_selector=None, + normalize_db: Optional[float] = None, + ref_channel: Optional[int] = None, + ): + """Create audio segment from samples. + Samples are convert float32 internally, with int scaled to [-1, 1]. + """ + samples = self._convert_samples_to_float32(samples) + + # Check if channel selector is necessary + if samples.ndim == 1 and channel_selector not in [None, 0, 'average']: + raise ValueError( + 'Input signal is one-dimensional, channel selector (%s) cannot not be used.', str(channel_selector) + ) + elif samples.ndim == 2: + samples = select_channels(samples, channel_selector) + elif samples.ndim >= 3: + raise NotImplementedError( + 'Signals with more than two dimensions (sample, channel) are currently not supported.' + ) + + if target_sr is not None and target_sr != sample_rate: + # resample along the temporal dimension (axis=0) will be in librosa 0.10.0 (#1561) + samples = samples.transpose() + samples = librosa.core.resample(samples, orig_sr=sample_rate, target_sr=target_sr) + samples = samples.transpose() + sample_rate = target_sr + if trim: + # librosa is using channels-first layout (num_channels, num_samples), which is transpose of AudioSegment's layout + samples = samples.transpose() + samples, _ = librosa.effects.trim( + samples, top_db=trim_top_db, ref=trim_ref, frame_length=trim_frame_length, hop_length=trim_hop_length + ) + samples = samples.transpose() + self._samples = samples + self._sample_rate = sample_rate + self._orig_sr = orig_sr if orig_sr is not None else sample_rate + self._ref_channel = ref_channel + self._normalize_db = normalize_db + + if normalize_db is not None: + self.normalize_db(normalize_db, ref_channel) + + def __eq__(self, other): + """Return whether two objects are equal.""" + if type(other) is not type(self): + return False + if self._sample_rate != other._sample_rate: + return False + if self._samples.shape != other._samples.shape: + return False + if np.any(self.samples != other._samples): + return False + return True + + def __ne__(self, other): + """Return whether two objects are unequal.""" + return not self.__eq__(other) + + def __str__(self): + """Return human-readable representation of segment.""" + if self.num_channels == 1: + return "%s: num_samples=%d, sample_rate=%d, duration=%.2fsec, rms=%.2fdB" % ( + type(self), + self.num_samples, + self.sample_rate, + self.duration, + self.rms_db, + ) + else: + rms_db_str = ', '.join([f'{rms:.2f}dB' for rms in self.rms_db]) + return "%s: num_samples=%d, sample_rate=%d, duration=%.2fsec, num_channels=%d, rms=[%s]" % ( + type(self), + self.num_samples, + self.sample_rate, + self.duration, + self.num_channels, + rms_db_str, + ) + + @staticmethod + def _convert_samples_to_float32(samples): + """Convert sample type to float32. + Audio sample type is usually integer or float-point. + Integers will be scaled to [-1, 1] in float32. + """ + float32_samples = samples.astype('float32') + if samples.dtype in np.sctypes['int']: + bits = np.iinfo(samples.dtype).bits + float32_samples *= 1.0 / 2 ** (bits - 1) + elif samples.dtype in np.sctypes['float']: + pass + else: + raise TypeError("Unsupported sample type: %s." % samples.dtype) + return float32_samples + + @classmethod + def from_file( + cls, + audio_file, + target_sr=None, + int_values=False, + offset=0, + duration=0, + trim=False, + trim_ref=np.max, + trim_top_db=60, + trim_frame_length=2048, + trim_hop_length=512, + orig_sr=None, + channel_selector=None, + normalize_db=None, + ref_channel=None, + ): + """ + Load a file supported by librosa and return as an AudioSegment. + :param audio_file: path of file to load. + Alternatively, a list of paths of single-channel files can be provided + to form a multichannel signal. + :param target_sr: the desired sample rate + :param int_values: if true, load samples as 32-bit integers + :param offset: offset in seconds when loading audio + :param duration: duration in seconds when loading audio + :param trim: if true, trim leading and trailing silence from an audio signal + :param trim_ref: the reference amplitude. By default, it uses `np.max` and compares to the peak amplitude in + the signal + :param trim_top_db: the threshold (in decibels) below reference to consider as silence + :param trim_frame_length: the number of samples per analysis frame + :param trim_hop_length: the number of samples between analysis frames + :param orig_sr: the original sample rate + :param channel selector: string denoting the downmix mode, an integer denoting the channel to be selected, or an iterable + of integers denoting a subset of channels. Channel selector is using zero-based indexing. + If set to `None`, the original signal will be used. + :param normalize_db (Optional[float]): if not None, normalize the audio signal to a target RMS value + :param ref_channel (Optional[int]): channel to use as reference for normalizing multi-channel audio, set None to use max RMS across channels + :return: AudioSegment instance + """ + samples = None + if isinstance(audio_file, list): + return cls.from_file_list( + audio_file_list=audio_file, + target_sr=target_sr, + int_values=int_values, + offset=offset, + duration=duration, + trim=trim, + trim_ref=trim_ref, + trim_top_db=trim_top_db, + trim_frame_length=trim_frame_length, + trim_hop_length=trim_hop_length, + orig_sr=orig_sr, + channel_selector=channel_selector, + normalize_db=normalize_db, + ref_channel=ref_channel, + ) + + if not isinstance(audio_file, str) or os.path.splitext(audio_file)[-1] in sf_supported_formats: + try: + with sf.SoundFile(audio_file, 'r') as f: + dtype = 'int32' if int_values else 'float32' + sample_rate = f.samplerate + if offset is not None and offset > 0: + f.seek(int(offset * sample_rate)) + if duration is not None and duration > 0: + samples = f.read(int(duration * sample_rate), dtype=dtype) + else: + samples = f.read(dtype=dtype) + except RuntimeError as e: + logging.error( + f"Loading {audio_file} via SoundFile raised RuntimeError: `{e}`. " + f"NeMo will fallback to loading via pydub." + ) + + if hasattr(audio_file, "seek"): + audio_file.seek(0) + + if HAVE_PYDUB and samples is None: + try: + samples = Audio.from_file(audio_file) + sample_rate = samples.frame_rate + num_channels = samples.channels + if offset > 0: + # pydub does things in milliseconds + seconds = offset * 1000 + samples = samples[int(seconds) :] + if duration > 0: + seconds = duration * 1000 + samples = samples[: int(seconds)] + samples = np.array(samples.get_array_of_samples()) + # For multi-channel signals, channels are stacked in a one-dimensional vector + if num_channels > 1: + samples = np.reshape(samples, (-1, num_channels)) + except CouldntDecodeError as err: + logging.error(f"Loading {audio_file} via pydub raised CouldntDecodeError: `{err}`.") + + if samples is None: + libs = "soundfile, and pydub" if HAVE_PYDUB else "soundfile" + raise Exception(f"Your audio file {audio_file} could not be decoded. We tried using {libs}.") + + return cls( + samples, + sample_rate, + target_sr=target_sr, + trim=trim, + trim_ref=trim_ref, + trim_top_db=trim_top_db, + trim_frame_length=trim_frame_length, + trim_hop_length=trim_hop_length, + orig_sr=orig_sr, + channel_selector=channel_selector, + normalize_db=normalize_db, + ref_channel=ref_channel, + ) + + @classmethod + def from_file_list( + cls, + audio_file_list, + target_sr=None, + int_values=False, + offset=0, + duration=0, + trim=False, + channel_selector=None, + *args, + **kwargs, + ): + """ + Function wrapper for `from_file` method. Load a list of files from `audio_file_list`. + The length of each audio file is unified with the duration item in the input manifest file. + See `from_file` method for arguments. + + If a list of files is provided, load samples from individual single-channel files and + concatenate them along the channel dimension. + """ + if isinstance(channel_selector, int): + # Shortcut when selecting a single channel + if channel_selector >= len(audio_file_list): + raise RuntimeError( + f'Channel cannot be selected: channel_selector={channel_selector}, num_audio_files={len(audio_file_list)}' + ) + # Select only a single file + audio_file_list = [audio_file_list[channel_selector]] + # Reset the channel selector since we applied it here + channel_selector = None + + samples = None + + for a_file in audio_file_list: + # Load audio from the current file + a_segment = cls.from_file( + a_file, + target_sr=target_sr, + int_values=int_values, + offset=offset, + duration=duration, + channel_selector=None, + trim=False, # Do not apply trim to individual files, it will be applied to the concatenated signal + *args, + **kwargs, + ) + + # Only single-channel individual files are supported for now + if a_segment.num_channels != 1: + raise RuntimeError( + f'Expecting a single-channel audio signal, but loaded {a_segment.num_channels} channels from file {a_file}' + ) + + if target_sr is None: + # All files need to be loaded with the same sample rate + target_sr = a_segment.sample_rate + + # Concatenate samples + a_samples = a_segment.samples[:, None] + + if samples is None: + samples = a_samples + else: + # Check the dimensions match + if len(a_samples) != len(samples): + raise RuntimeError( + f'Loaded samples need to have identical length: {a_samples.shape} != {samples.shape}' + ) + + # Concatenate along channel dimension + samples = np.concatenate([samples, a_samples], axis=1) + + # Final setup for class initialization + samples = np.squeeze(samples) + sample_rate = target_sr + + return cls( + samples, sample_rate, target_sr=target_sr, trim=trim, channel_selector=channel_selector, *args, **kwargs, + ) + + @classmethod + def segment_from_file( + cls, + audio_file, + target_sr=None, + n_segments=0, + trim=False, + orig_sr=None, + channel_selector=None, + offset=None, + dtype='float32', + ): + """Grabs n_segments number of samples from audio_file. + If offset is not provided, n_segments are selected randomly. + If offset is provided, it is used to calculate the starting sample. + + Note that audio_file can be either the file path, or a file-like object. + + :param audio_file: path to a file or a file-like object + :param target_sr: sample rate for the output samples + :param n_segments: desired number of samples + :param trim: if true, trim leading and trailing silence from an audio signal + :param orig_sr: the original sample rate + :param channel selector: select a subset of channels. If set to `None`, the original signal will be used. + :param offset: fixed offset in seconds + :param dtype: data type to load audio as. + :return: numpy array of samples + """ + is_segmented = False + try: + with sf.SoundFile(audio_file, 'r') as f: + sample_rate = f.samplerate + if target_sr is not None: + n_segments_at_original_sr = math.ceil(n_segments * sample_rate / target_sr) + else: + n_segments_at_original_sr = n_segments + + if 0 < n_segments_at_original_sr < len(f): + max_audio_start = len(f) - n_segments_at_original_sr + if offset is None: + audio_start = random.randint(0, max_audio_start) + else: + audio_start = math.floor(offset * sample_rate) + if audio_start > max_audio_start: + raise RuntimeError( + f'Provided audio start ({audio_start}) is larger than the maximum possible ({max_audio_start})' + ) + f.seek(audio_start) + samples = f.read(n_segments_at_original_sr, dtype=dtype) + is_segmented = True + elif n_segments_at_original_sr > len(f): + logging.warning( + f"Number of segments ({n_segments_at_original_sr}) is greater than the length ({len(f)}) of the audio file {audio_file}. This may lead to shape mismatch errors." + ) + samples = f.read(dtype=dtype) + else: + samples = f.read(dtype=dtype) + except RuntimeError as e: + logging.error(f"Loading {audio_file} via SoundFile raised RuntimeError: `{e}`.") + raise e + + features = cls( + samples, sample_rate, target_sr=target_sr, trim=trim, orig_sr=orig_sr, channel_selector=channel_selector + ) + + if is_segmented: + features._samples = features._samples[:n_segments] + + return features + + @property + def samples(self): + return self._samples.copy() + + @property + def sample_rate(self): + return self._sample_rate + + @property + def num_channels(self): + if self._samples.ndim == 1: + return 1 + else: + return self._samples.shape[-1] + + @property + def num_samples(self): + return self._samples.shape[0] + + @property + def duration(self): + return self.num_samples / float(self._sample_rate) + + @property + def rms_db(self): + """Return per-channel RMS value. + """ + mean_square = np.mean(self._samples ** 2, axis=0) + return 10 * np.log10(mean_square) + + @property + def orig_sr(self): + return self._orig_sr + + def gain_db(self, gain): + self._samples *= 10.0 ** (gain / 20.0) + + def normalize_db(self, target_db=-20, ref_channel=None): + """Normalize the signal to a target RMS value in decibels. + For multi-channel audio, the RMS value is determined by the reference channel (if not None), + otherwise it will be the maximum RMS across all channels. + """ + rms_db = self.rms_db + if self.num_channels > 1: + rms_db = max(rms_db) if ref_channel is None else rms_db[ref_channel] + gain = target_db - rms_db + self.gain_db(gain) + + def pad(self, pad_size, symmetric=False): + """Add zero padding to the sample. The pad size is given in number + of samples. + If symmetric=True, `pad_size` will be added to both sides. If false, + `pad_size` + zeros will be added only to the end. + """ + samples_ndim = self._samples.ndim + if samples_ndim == 1: + pad_width = pad_size if symmetric else (0, pad_size) + elif samples_ndim == 2: + # pad samples, keep channels + pad_width = ((pad_size, pad_size), (0, 0)) if symmetric else ((0, pad_size), (0, 0)) + else: + raise NotImplementedError( + f"Padding not implemented for signals with more that 2 dimensions. Current samples dimension: {samples_ndim}." + ) + # apply padding + self._samples = np.pad(self._samples, pad_width, mode='constant',) + + def subsegment(self, start_time=None, end_time=None): + """Cut the AudioSegment between given boundaries. + Note that this is an in-place transformation. + :param start_time: Beginning of subsegment in seconds. + :type start_time: float + :param end_time: End of subsegment in seconds. + :type end_time: float + :raise ValueError: If start_time or end_time is incorrectly set, + e.g. out of bounds in time. + """ + start_time = 0.0 if start_time is None else start_time + end_time = self.duration if end_time is None else end_time + if start_time < 0.0: + start_time = self.duration + start_time + if end_time < 0.0: + end_time = self.duration + end_time + if start_time < 0.0: + raise ValueError("The slice start position (%f s) is out of bounds." % start_time) + if end_time < 0.0: + raise ValueError("The slice end position (%f s) is out of bounds." % end_time) + if start_time > end_time: + raise ValueError( + "The slice start position (%f s) is later than the end position (%f s)." % (start_time, end_time) + ) + if end_time > self.duration: + raise ValueError("The slice end position (%f s) is out of bounds (> %f s)" % (end_time, self.duration)) + start_sample = int(round(start_time * self._sample_rate)) + end_sample = int(round(end_time * self._sample_rate)) + self._samples = self._samples[start_sample:end_sample] diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/__init__.py new file mode 100644 index 0000000..bc443be --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/adapters/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/adapters/__init__.py new file mode 100644 index 0000000..6aa05d0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/adapters/__init__.py @@ -0,0 +1,26 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.parts.submodules.adapters.multi_head_attention_adapter_module import ( + MHAResidualAddAdapterStrategy, + MHAResidualAddAdapterStrategyConfig, + MultiHeadAttentionAdapter, + MultiHeadAttentionAdapterConfig, + PositionalEncodingAdapter, + PositionalEncodingAdapterConfig, + RelPositionalEncodingAdapter, + RelPositionalEncodingAdapterConfig, + RelPositionMultiHeadAttentionAdapter, + RelPositionMultiHeadAttentionAdapterConfig, +) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/adapters/multi_head_attention_adapter_module.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/adapters/multi_head_attention_adapter_module.py new file mode 100644 index 0000000..3df5109 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/adapters/multi_head_attention_adapter_module.py @@ -0,0 +1,392 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass, field +from typing import Any, Optional + +import torch +from torch import nn as nn + +from nemo.collections.asr.parts.submodules import multi_head_attention as mha +from nemo.collections.common.parts import adapter_modules +from nemo.core.classes.mixins import adapter_mixin_strategies + + +class MHAResidualAddAdapterStrategy(adapter_mixin_strategies.ResidualAddAdapterStrategy): + """ + An implementation of residual addition of an adapter module with its input for the MHA Adapters. + """ + + def forward(self, input: torch.Tensor, adapter: torch.nn.Module, *, module: 'AdapterModuleMixin'): + """ + A basic strategy, comprising of a residual connection over the input, after forward pass by + the underlying adapter. Additional work is done to pack and unpack the dictionary of inputs and outputs. + + Note: The `value` tensor is added to the output of the attention adapter as the residual connection. + + Args: + input: A dictionary of multiple input arguments for the adapter module. + + `query`, `key`, `value`: Original output tensor of the module, or the output of the + previous adapter (if more than one adapters are enabled). + + `mask`: Attention mask. + + `pos_emb`: Optional positional embedding for relative encoding. + + adapter: The adapter module that is currently required to perform the forward pass. + module: The calling module, in its entirety. It is a module that implements `AdapterModuleMixin`, + therefore the strategy can access all other adapters in this module via `module.adapter_layer`. + + Returns: + The result tensor, after one of the active adapters has finished its forward passes. + """ + out = self.compute_output(input, adapter, module=module) + + # If not in training mode, or probability of stochastic depth is 0, skip step. + p = self.stochastic_depth + if not module.training or p == 0.0: + pass + else: + out = self.apply_stochastic_depth(out, input['value'], adapter, module=module) + + # Return the residual connection output = input + adapter(input) + result = input['value'] + out + + # If l2_lambda is activated, register the loss value + self.compute_auxiliary_losses(result, input['value'], adapter, module=module) + + return result + + def compute_output( + self, input: torch.Tensor, adapter: torch.nn.Module, *, module: 'AdapterModuleMixin' + ) -> torch.Tensor: + """ + Compute the output of a single adapter to some input. + + Args: + input: Original output tensor of the module, or the output of the previous adapter (if more than + one adapters are enabled). + adapter: The adapter module that is currently required to perform the forward pass. + module: The calling module, in its entirety. It is a module that implements `AdapterModuleMixin`, + therefore the strategy can access all other adapters in this module via `module.adapter_layer`. + + Returns: + The result tensor, after one of the active adapters has finished its forward passes. + """ + if isinstance(input, (list, tuple)): + out = adapter(*input) + elif isinstance(input, dict): + out = adapter(**input) + else: + out = adapter(input) + return out + + +@dataclass +class MHAResidualAddAdapterStrategyConfig(adapter_mixin_strategies.ResidualAddAdapterStrategyConfig): + _target_: str = "{0}.{1}".format( + MHAResidualAddAdapterStrategy.__module__, MHAResidualAddAdapterStrategy.__name__ + ) # mandatory field + + +class MultiHeadAttentionAdapter(mha.MultiHeadAttention, adapter_modules.AdapterModuleUtil): + """Multi-Head Attention layer of Transformer. + + Args: + n_head (int): number of heads + n_feat (int): size of the features + dropout_rate (float): dropout rate + proj_dim (int, optional): Optional integer value for projection before computing attention. + If None, then there is no projection (equivalent to proj_dim = n_feat). + If > 0, then will project the n_feat to proj_dim before calculating attention. + If <0, then will equal n_head, so that each head has a projected dimension of 1. + adapter_strategy: By default, MHAResidualAddAdapterStrategyConfig. An adapter composition function object. + """ + + def __init__( + self, + n_head: int, + n_feat: int, + dropout_rate: float, + proj_dim: Optional[int] = None, + adapter_strategy: MHAResidualAddAdapterStrategy = None, + ): + super().__init__(n_head=n_head, n_feat=n_feat, dropout_rate=dropout_rate, max_cache_len=0) + + self.pre_norm = nn.LayerNorm(n_feat) + + # Set the projection dim to number of heads automatically + if proj_dim is not None and proj_dim < 1: + proj_dim = n_head + + self.proj_dim = proj_dim + + # Recompute weights for projection dim + if self.proj_dim is not None: + if self.proj_dim % n_head != 0: + raise ValueError(f"proj_dim ({proj_dim}) is not divisible by n_head ({n_head})") + + self.d_k = self.proj_dim // n_head + self.s_d_k = math.sqrt(self.d_k) + self.linear_q = nn.Linear(n_feat, self.proj_dim) + self.linear_k = nn.Linear(n_feat, self.proj_dim) + self.linear_v = nn.Linear(n_feat, self.proj_dim) + self.linear_out = nn.Linear(self.proj_dim, n_feat) + + # Setup adapter strategy + self.setup_adapter_strategy(adapter_strategy) + + # reset parameters for Q to be identity operation + self.reset_parameters() + + def forward(self, query, key, value, mask, pos_emb=None, cache=None): + """Compute 'Scaled Dot Product Attention'. + Args: + query (torch.Tensor): (batch, time1, size) + key (torch.Tensor): (batch, time2, size) + value(torch.Tensor): (batch, time2, size) + mask (torch.Tensor): (batch, time1, time2) + cache (torch.Tensor) : (batch, time_cache, size) + + returns: + output (torch.Tensor): transformed `value` (batch, time1, d_model) weighted by the query dot key attention + cache (torch.Tensor) : (batch, time_cache_next, size) + """ + # Need to perform duplicate computations as at this point the tensors have been + # separated by the adapter forward + query = self.pre_norm(query) + key = self.pre_norm(key) + value = self.pre_norm(value) + + return super().forward(query, key, value, mask, pos_emb, cache=cache) + + def reset_parameters(self): + with torch.no_grad(): + nn.init.zeros_(self.linear_out.weight) + nn.init.zeros_(self.linear_out.bias) + + def get_default_strategy_config(self) -> 'dataclass': + return MHAResidualAddAdapterStrategyConfig() + + +@dataclass +class MultiHeadAttentionAdapterConfig: + n_head: int + n_feat: int + dropout_rate: float = 0.0 + proj_dim: Optional[int] = None + adapter_strategy: Optional[Any] = field(default_factory=lambda: MHAResidualAddAdapterStrategyConfig()) + _target_: str = "{0}.{1}".format(MultiHeadAttentionAdapter.__module__, MultiHeadAttentionAdapter.__name__) + + +class RelPositionMultiHeadAttentionAdapter(mha.RelPositionMultiHeadAttention, adapter_modules.AdapterModuleUtil): + """Multi-Head Attention layer of Transformer-XL with support of relative positional encoding. + Paper: https://arxiv.org/abs/1901.02860 + + Args: + n_head (int): number of heads + n_feat (int): size of the features + dropout_rate (float): dropout rate + proj_dim (int, optional): Optional integer value for projection before computing attention. + If None, then there is no projection (equivalent to proj_dim = n_feat). + If > 0, then will project the n_feat to proj_dim before calculating attention. + If <0, then will equal n_head, so that each head has a projected dimension of 1. + adapter_strategy: By default, MHAResidualAddAdapterStrategyConfig. An adapter composition function object. + """ + + def __init__( + self, + n_head: int, + n_feat: int, + dropout_rate: float, + proj_dim: Optional[int] = None, + adapter_strategy: MHAResidualAddAdapterStrategyConfig = None, + ): + super().__init__( + n_head=n_head, n_feat=n_feat, dropout_rate=dropout_rate, pos_bias_u=None, pos_bias_v=None, max_cache_len=0 + ) + + self.pre_norm = nn.LayerNorm(n_feat) + + # Set the projection dim to number of heads automatically + if proj_dim is not None and proj_dim < 1: + proj_dim = n_head + + self.proj_dim = proj_dim + + # Recompute weights for projection dim + if self.proj_dim is not None: + if self.proj_dim % n_head != 0: + raise ValueError(f"proj_dim ({proj_dim}) is not divisible by n_head ({n_head})") + + self.d_k = self.proj_dim // n_head + self.s_d_k = math.sqrt(self.d_k) + self.linear_q = nn.Linear(n_feat, self.proj_dim) + self.linear_k = nn.Linear(n_feat, self.proj_dim) + self.linear_v = nn.Linear(n_feat, self.proj_dim) + self.linear_out = nn.Linear(self.proj_dim, n_feat) + self.linear_pos = nn.Linear(n_feat, self.proj_dim, bias=False) + self.pos_bias_u = nn.Parameter(torch.FloatTensor(self.h, self.d_k)) + self.pos_bias_v = nn.Parameter(torch.FloatTensor(self.h, self.d_k)) + + # Setup adapter strategy + self.setup_adapter_strategy(adapter_strategy) + + # reset parameters for Q to be identity operation + self.reset_parameters() + + def forward(self, query, key, value, mask, pos_emb, cache=None): + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + Args: + query (torch.Tensor): (batch, time1, size) + key (torch.Tensor): (batch, time2, size) + value(torch.Tensor): (batch, time2, size) + mask (torch.Tensor): (batch, time1, time2) + pos_emb (torch.Tensor) : (batch, time1, size) + cache (torch.Tensor) : (batch, time_cache, size) + Returns: + output (torch.Tensor): transformed `value` (batch, time1, d_model) weighted by the query dot key attention + cache_next (torch.Tensor) : (batch, time_cache_next, size) + """ + # Need to perform duplicate computations as at this point the tensors have been + # separated by the adapter forward + query = self.pre_norm(query) + key = self.pre_norm(key) + value = self.pre_norm(value) + + return super().forward(query, key, value, mask, pos_emb, cache=cache) + + def reset_parameters(self): + with torch.no_grad(): + nn.init.zeros_(self.linear_out.weight) + nn.init.zeros_(self.linear_out.bias) + + # NOTE: This exact procedure apparently highly important. + # Above operation is safe to do as self.linear_out.weight *= 0.0 (similar for bias) + # However: + # DO NOT REPLACE BELOW WITH self.pos_bias_u *= 0.0 OR self.pos_bias_v *= 0.0 + # For some reason at init sometimes it will cause the value of the tensor to become NaN + # All operations to compute matrix_ac and matrix_bd will then fail. + nn.init.zeros_(self.pos_bias_u) + nn.init.zeros_(self.pos_bias_v) + + def get_default_strategy_config(self) -> 'dataclass': + return MHAResidualAddAdapterStrategyConfig() + + +@dataclass +class RelPositionMultiHeadAttentionAdapterConfig: + n_head: int + n_feat: int + dropout_rate: float = 0.0 + proj_dim: Optional[int] = None + adapter_strategy: Optional[Any] = field(default_factory=lambda: MHAResidualAddAdapterStrategyConfig()) + _target_: str = "{0}.{1}".format( + RelPositionMultiHeadAttentionAdapter.__module__, RelPositionMultiHeadAttentionAdapter.__name__ + ) + + +class PositionalEncodingAdapter(mha.PositionalEncoding, adapter_modules.AdapterModuleUtil): + + """ + Absolute positional embedding adapter. + + .. note:: + + Absolute positional embedding value is added to the input tensor *without residual connection* ! + Therefore, the input is changed, if you only require the positional embedding, drop the returned `x` ! + + Args: + d_model (int): The input dimension of x. + max_len (int): The max sequence length. + xscale (float): The input scaling factor. Defaults to 1.0. + adapter_strategy (AbstractAdapterStrategy): By default, ReturnResultAdapterStrategyConfig. + An adapter composition function object. + NOTE: Since this is a positional encoding, it will not add a residual ! + """ + + def __init__( + self, + d_model: int, + max_len: int = 5000, + xscale=1.0, + adapter_strategy: adapter_mixin_strategies.ReturnResultAdapterStrategyConfig = None, + ): + + super().__init__( + d_model=d_model, dropout_rate=0.0, max_len=max_len, xscale=xscale, dropout_rate_emb=0.0, + ) + + # Setup adapter strategy + self.setup_adapter_strategy(adapter_strategy) + + def get_default_strategy_config(self) -> 'dataclass': + return adapter_mixin_strategies.ReturnResultAdapterStrategyConfig() + + +@dataclass +class PositionalEncodingAdapterConfig: + d_model: int + max_len: int = 5000 + xscale: float = 1.0 + adapter_strategy: Optional[Any] = field( + default_factory=lambda: adapter_mixin_strategies.ResidualAddAdapterStrategyConfig() + ) + _target_: str = "{0}.{1}".format(PositionalEncodingAdapter.__module__, PositionalEncodingAdapter.__name__) + + +class RelPositionalEncodingAdapter(mha.RelPositionalEncoding, adapter_modules.AdapterModuleUtil): + """ + Relative positional encoding for TransformerXL's layers + See : Appendix B in https://arxiv.org/abs/1901.02860 + + .. note:: + + Relative positional embedding value is **not** added to the input tensor ! + Therefore, the input should be updated changed, if you only require the positional embedding, drop the returned `x` ! + + Args: + d_model (int): embedding dim + max_len (int): maximum input length + xscale (bool): whether to scale the input by sqrt(d_model) + adapter_strategy: By default, ReturnResultAdapterStrategyConfig. An adapter composition function object. + """ + + def __init__( + self, + d_model: int, + max_len: int = 5000, + xscale=1.0, + adapter_strategy: adapter_mixin_strategies.ReturnResultAdapterStrategyConfig = None, + ): + super().__init__(d_model=d_model, dropout_rate=0.0, max_len=max_len, xscale=xscale, dropout_rate_emb=0.0) + + # Setup adapter strategy + self.setup_adapter_strategy(adapter_strategy) + + def get_default_strategy_config(self) -> 'dataclass': + return adapter_mixin_strategies.ReturnResultAdapterStrategyConfig() + + +@dataclass +class RelPositionalEncodingAdapterConfig: + d_model: int + max_len: int = 5000 + xscale: float = 1.0 + adapter_strategy: Optional[Any] = field( + default_factory=lambda: adapter_mixin_strategies.ResidualAddAdapterStrategyConfig() + ) + _target_: str = "{0}.{1}".format(RelPositionalEncodingAdapter.__module__, RelPositionalEncodingAdapter.__name__) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/batchnorm.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/batchnorm.py new file mode 100644 index 0000000..66a69f7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/batchnorm.py @@ -0,0 +1,103 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from functools import reduce +from typing import List + +import torch +import torch.nn as nn + + +class FusedBatchNorm1d(nn.Module): + """ + Fused BatchNorm to use in Conformer to improve accuracy in finetuning with TTS scenario + Drop-in replacement for BatchNorm1d with simple affine projection + """ + + def __init__(self, num_features: int): + """ + Args: + num_features: number of channels, see original BatchNorm1d documentation + """ + super().__init__() + self.num_features = num_features + self.weight = nn.Parameter(torch.ones(num_features)) + self.bias = nn.Parameter(torch.zeros(num_features)) + + @classmethod + def from_batchnorm(cls, bn: nn.BatchNorm1d) -> FusedBatchNorm1d: + """ + Construct FusedBatchNorm1d module from BatchNorm1d + Args: + bn: original BatchNorm module + + Returns: + FusedBatchNorm1d module with initialized params; in eval mode result is equivalent to original BatchNorm + """ + assert isinstance(bn, nn.BatchNorm1d) + fused_bn = FusedBatchNorm1d(bn.num_features) + # init projection params from original batch norm + # so, for inference mode output is the same + std = torch.sqrt(bn.running_var.data + bn.eps) + fused_bn.weight.data = bn.weight.data / std + fused_bn.bias.data = bn.bias.data - bn.running_mean.data * fused_bn.weight.data + return fused_bn + + def forward(self, x: torch.Tensor): + if x.dim() == 3: + return x * self.weight.unsqueeze(-1) + self.bias.unsqueeze(-1) + assert x.dim() == 2 + return x * self.weight + self.bias + + +def _get_module_by_name(module: nn.Module, full_layer_name: str) -> nn.Module: + names = full_layer_name.split(sep='.') + return reduce(getattr, names, module) + + +def replace_bn_with_fused_bn(module: nn.Module, full_layer_name: str): + """ + Replace BatchNorm1d named `full_layer_name` in nn.Module with FusedBatchNorm1d + Args: + module: nn.Module instance, modified inplace + full_layer_name: name of BatchNorm1d submodule in module to replace + """ + bn = _get_module_by_name(module, full_layer_name) + assert isinstance(bn, nn.BatchNorm1d) + fused_bn = FusedBatchNorm1d.from_batchnorm(bn) + try: + parent_name, norm_name = full_layer_name.rsplit(".", maxsplit=1) + setattr(_get_module_by_name(module, parent_name), norm_name, fused_bn) + except ValueError: + norm_name = full_layer_name + setattr(module, norm_name, fused_bn) + + +def replace_bn_with_fused_bn_all(model: nn.Module) -> List[str]: + """ + Replace BatchNorm1d with FusedBatchNorm1d in model + Args: + model: nn.Module instance, modified inplace + + Returns: + list of replaced module names + """ + replaced_module_names = [] + for name, module in model.named_modules(): + if isinstance(module, nn.BatchNorm1d): + replace_bn_with_fused_bn(model, name) + replaced_module_names.append(name) + return replaced_module_names diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/causal_convs.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/causal_convs.py new file mode 100644 index 0000000..32f08a8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/causal_convs.py @@ -0,0 +1,150 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Union + +import torch +import torch.nn.functional as F +from torch import nn + +__all__ = ['CausalConv2D', 'CausalConv1D'] + + +class CausalConv2D(nn.Conv2d): + """ + A causal version of nn.Conv2d where each location in the 2D matrix would have no access to locations on its right or down + All arguments are the same as nn.Conv2d except padding which should be set as None + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + padding: Union[str, int] = 0, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = 'zeros', + device=None, + dtype=None, + ) -> None: + if padding is not None: + raise ValueError("Argument padding should be set to None for CausalConv2D.") + self._left_padding = kernel_size - 1 + self._right_padding = stride - 1 + + padding = 0 + super(CausalConv2D, self).__init__( + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode, + device, + dtype, + ) + + def forward( + self, x, + ): + x = F.pad(x, pad=(self._left_padding, self._right_padding, self._left_padding, self._right_padding)) + x = super().forward(x) + return x + + +class CausalConv1D(nn.Conv1d): + """ + A causal version of nn.Conv1d where each step would have limited access to locations on its right or left + All arguments are the same as nn.Conv1d except padding. + + If padding is set None, then paddings are set automatically to make it a causal convolution where each location would not see any steps on its right. + + If padding is set as a list (size of 2), then padding[0] would be used as left padding and padding[1] as right padding. + It would make it possible to control the number of steps to be accessible on the right and left. + This mode is not supported when stride > 1. padding[0]+padding[1] should be equal to (kernel_size - 1). + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + padding: Union[str, int] = 0, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = 'zeros', + device=None, + dtype=None, + ) -> None: + self.cache_drop_size = None + if padding is None: + self._left_padding = kernel_size - 1 + self._right_padding = stride - 1 + else: + if stride != 1 and padding != kernel_size - 1: + raise ValueError("No striding allowed for non-symmetric convolutions!") + if isinstance(padding, int): + self._left_padding = padding + self._right_padding = padding + elif isinstance(padding, list) and len(padding) == 2 and padding[0] + padding[1] == kernel_size - 1: + self._left_padding = padding[0] + self._right_padding = padding[1] + else: + raise ValueError(f"Invalid padding param: {padding}!") + + self._max_cache_len = self._left_padding + + super(CausalConv1D, self).__init__( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=0, + dilation=dilation, + groups=groups, + bias=bias, + padding_mode=padding_mode, + device=device, + dtype=dtype, + ) + + def update_cache(self, x, cache=None): + if cache is None: + new_x = F.pad(x, pad=(self._left_padding, self._right_padding)) + next_cache = cache + else: + new_x = F.pad(x, pad=(0, self._right_padding)) + new_x = torch.cat([cache, new_x], dim=-1) + if self.cache_drop_size > 0: + next_cache = new_x[:, :, : -self.cache_drop_size] + else: + next_cache = new_x + next_cache = next_cache[:, :, -cache.size(-1) :] + return new_x, next_cache + + def forward(self, x, cache=None): + x, cache = self.update_cache(x, cache=cache) + x = super().forward(x) + if cache is None: + return x + else: + return x, cache diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/classifier.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/classifier.py new file mode 100644 index 0000000..7d9e425 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/classifier.py @@ -0,0 +1,85 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, Optional + +import torch +from torch import nn as nn + +from nemo.collections.common.parts import transformer_weights_init +from nemo.core.classes import Exportable, NeuralModule +from nemo.core.neural_types import ChannelType, NeuralType + +__all__ = ['Classifier'] + + +class Classifier(NeuralModule, Exportable): + """ + A baseclass for modules to perform various classification tasks. + """ + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + """ + Returns definitions of module input ports. + We implement it here since all NLP classifiers have the same inputs + """ + return {"hidden_states": NeuralType(('B', 'T', 'D'), ChannelType())} + + def __init__(self, hidden_size: int, dropout: float = 0.0,) -> None: + """ + Initializes the Classifier base module. + Args: + hidden_size: the size of the hidden dimension + dropout: dropout to apply to the input hidden states + """ + super().__init__() + self._hidden_size = hidden_size + self.dropout = nn.Dropout(dropout) + + def post_init(self, use_transformer_init: bool): + """ + Common post-processing to be called at the end of concrete Classifiers init methods + Args: + use_transformer_init : whether or not to apply transformer_weights_init + """ + if use_transformer_init: + self.apply(lambda module: transformer_weights_init(module, xavier=False)) + + def input_example(self, max_batch=1, max_dim=256): + """ + Generates input examples for tracing etc. + Returns: + A tuple of input examples. + """ + sample = next(self.parameters()) + example = torch.randn(max_batch, max_dim, self._hidden_size).to(sample.device).to(sample.dtype) + return tuple([example]) + + def save_to(self, save_path: str): + """ + Saves the module to the specified path. + Args: + save_path: Path to where to save the module. + """ + pass + + @classmethod + def restore_from(cls, restore_path: str): + """ + Restores the module from the specified path. + Args: + restore_path: Path to restore the module from. + """ + pass diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/conformer_modules.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/conformer_modules.py new file mode 100644 index 0000000..aed6cc1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/conformer_modules.py @@ -0,0 +1,413 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import torch +from torch import nn as nn +from torch.nn import LayerNorm + +from nemo.collections.asr.parts.submodules.batchnorm import FusedBatchNorm1d +from nemo.collections.asr.parts.submodules.causal_convs import CausalConv1D +from nemo.collections.asr.parts.submodules.multi_head_attention import ( + MultiHeadAttention, + RelPositionMultiHeadAttention, + RelPositionMultiHeadAttentionLongformer, +) +from nemo.collections.asr.parts.utils.activations import Swish +from nemo.collections.common.parts import adapter_modules +from nemo.collections.common.parts.utils import activation_registry +from nemo.core.classes.mixins import AccessMixin +from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin + +__all__ = ['ConformerConvolution', 'ConformerFeedForward', 'ConformerLayer'] + + +class ConformerLayer(torch.nn.Module, AdapterModuleMixin, AccessMixin): + """A single block of the Conformer encoder. + + Args: + d_model (int): input dimension of MultiheadAttentionMechanism and PositionwiseFeedForward + d_ff (int): hidden dimension of PositionwiseFeedForward + self_attention_model (str): type of the attention layer and positional encoding + 'rel_pos': relative positional embedding and Transformer-XL + 'rel_pos_local_attn': relative positional embedding and Transformer-XL with local attention using + overlapping chunks. Attention context is determined by att_context_size parameter. + 'abs_pos': absolute positional embedding and Transformer + Default is rel_pos. + global_tokens (int): number of tokens to be used for global attention. + Only relevant if self_attention_model is 'rel_pos_local_attn'. + Defaults to 0. + global_tokens_spacing (int): how far apart the global tokens are + Defaults to 1. + global_attn_separate (bool): whether the q, k, v layers used for global tokens should be separate. + Defaults to False. + n_heads (int): number of heads for multi-head attention + conv_kernel_size (int): kernel size for depthwise convolution in convolution module + dropout (float): dropout probabilities for linear layers + dropout_att (float): dropout probabilities for attention distributions + """ + + def __init__( + self, + d_model, + d_ff, + self_attention_model='rel_pos', + global_tokens=0, + global_tokens_spacing=1, + global_attn_separate=False, + n_heads=4, + conv_kernel_size=31, + conv_norm_type='batch_norm', + conv_context_size=None, + dropout=0.1, + dropout_att=0.1, + pos_bias_u=None, + pos_bias_v=None, + att_context_size=[-1, -1], + ): + super(ConformerLayer, self).__init__() + + self.self_attention_model = self_attention_model + self.n_heads = n_heads + self.fc_factor = 0.5 + + # first feed forward module + self.norm_feed_forward1 = LayerNorm(d_model) + self.feed_forward1 = ConformerFeedForward(d_model=d_model, d_ff=d_ff, dropout=dropout) + + # convolution module + self.norm_conv = LayerNorm(d_model) + self.conv = ConformerConvolution( + d_model=d_model, + kernel_size=conv_kernel_size, + norm_type=conv_norm_type, + conv_context_size=conv_context_size, + ) + + # multi-headed self-attention module + self.norm_self_att = LayerNorm(d_model) + MHA_max_cache_len = att_context_size[0] + + if self_attention_model == 'rel_pos': + self.self_attn = RelPositionMultiHeadAttention( + n_head=n_heads, + n_feat=d_model, + dropout_rate=dropout_att, + pos_bias_u=pos_bias_u, + pos_bias_v=pos_bias_v, + max_cache_len=MHA_max_cache_len, + ) + elif self_attention_model == 'rel_pos_local_attn': + self.self_attn = RelPositionMultiHeadAttentionLongformer( + n_head=n_heads, + n_feat=d_model, + dropout_rate=dropout_att, + pos_bias_u=pos_bias_u, + pos_bias_v=pos_bias_v, + max_cache_len=MHA_max_cache_len, + att_context_size=att_context_size, + global_tokens=global_tokens, + global_tokens_spacing=global_tokens_spacing, + global_attn_separate=global_attn_separate, + ) + elif self_attention_model == 'abs_pos': + self.self_attn = MultiHeadAttention( + n_head=n_heads, n_feat=d_model, dropout_rate=dropout_att, max_cache_len=MHA_max_cache_len + ) + else: + raise ValueError( + f"'{self_attention_model}' is not not a valid value for 'self_attention_model', " + f"valid values can be from ['rel_pos', 'rel_pos_local_attn', 'abs_pos']" + ) + + # second feed forward module + self.norm_feed_forward2 = LayerNorm(d_model) + self.feed_forward2 = ConformerFeedForward(d_model=d_model, d_ff=d_ff, dropout=dropout) + + self.dropout = nn.Dropout(dropout) + self.norm_out = LayerNorm(d_model) + + def forward(self, x, att_mask=None, pos_emb=None, pad_mask=None, cache_last_channel=None, cache_last_time=None): + """ + Args: + x (torch.Tensor): input signals (B, T, d_model) + att_mask (torch.Tensor): attention masks(B, T, T) + pos_emb (torch.Tensor): (L, 1, d_model) + pad_mask (torch.tensor): padding mask + cache_last_channel (torch.tensor) : cache for MHA layers (B, T_cache, d_model) + cache_last_time (torch.tensor) : cache for convolutional layers (B, d_model, T_cache) + Returns: + x (torch.Tensor): (B, T, d_model) + cache_last_channel (torch.tensor) : next cache for MHA layers (B, T_cache, d_model) + cache_last_time (torch.tensor) : next cache for convolutional layers (B, d_model, T_cache) + """ + residual = x + x = self.norm_feed_forward1(x) + x = self.feed_forward1(x) + residual = residual + self.dropout(x) * self.fc_factor + + x = self.norm_self_att(residual) + if self.self_attention_model == 'rel_pos': + x = self.self_attn(query=x, key=x, value=x, mask=att_mask, pos_emb=pos_emb, cache=cache_last_channel) + elif self.self_attention_model == 'rel_pos_local_attn': + x = self.self_attn(query=x, key=x, value=x, pad_mask=pad_mask, pos_emb=pos_emb, cache=cache_last_channel) + elif self.self_attention_model == 'abs_pos': + x = self.self_attn(query=x, key=x, value=x, mask=att_mask, cache=cache_last_channel) + else: + x = None + + if x is not None and cache_last_channel is not None: + (x, cache_last_channel) = x + + residual = residual + self.dropout(x) + + if self.is_adapter_available(): + # Call the MHA adapters + pack_ip = { + 'x': residual, + 'loc': 'mha', + 'att_mask': att_mask, + 'pos_emb': pos_emb, + } + pack_ip = self.forward_enabled_adapters(pack_ip) + residual = pack_ip['x'] + + x = self.norm_conv(residual) + x = self.conv(x, pad_mask=pad_mask, cache=cache_last_time) + if cache_last_time is not None: + (x, cache_last_time) = x + residual = residual + self.dropout(x) + + x = self.norm_feed_forward2(residual) + x = self.feed_forward2(x) + residual = residual + self.dropout(x) * self.fc_factor + + x = self.norm_out(residual) + + if self.is_adapter_available(): + # Call the adapters + pack_ip = { + 'x': x, + 'loc': 'post', + } + pack_ip = self.forward_enabled_adapters(pack_ip) + x = pack_ip['x'] + + if self.is_access_enabled(getattr(self, "model_guid", None)) and self.access_cfg.get( + 'save_encoder_tensors', False + ): + self.register_accessible_tensor(name='encoder', tensor=x) + if cache_last_channel is None: + return x + else: + return x, cache_last_channel, cache_last_time + + def forward_single_enabled_adapter_( + self, + input: dict, + adapter_module: torch.nn.Module, + *, + adapter_name: str, + adapter_strategy: 'nemo.core.classes.mixins.adapter_mixin_strategies.AbstractAdapterStrategy', + ): + """ + Perform the forward step of a single adapter module on some input data. + + **Note**: Subclasses can override this method to accommodate more complicate adapter forward steps. + + Args: + input: Dictionary of packed tensors. The dict should contain at least + `x`: output tensor + `loc`: Semantic location in module where this adapter was called + `att_mask`: Optional, Attention mask + `pos_emb`: Optional, Positional Embedding for Relative Positional Encoding. + The output tensor of the calling module is the input to the first adapter, whose output + is then chained to the next adapter until all adapters are consumed. + adapter_module: The adapter module that is currently required to perform the forward pass. + adapter_name: The resolved name of the adapter that is undergoing the current forward pass. + adapter_strategy: A subclass of `AbstractAdapterStrategy`, that determines how the + output of the adapter should be merged with the input, or if it should be merged at all. + + Returns: + The result tensor, after the current active adapter has finished its forward pass. + """ + # (input: torch.Tensor, adapter: torch.nn.Module, *, module: 'AdapterModuleMixin') + x = input['x'] + loc = input['loc'] + att_mask = input.get('att_mask', None) + pos_emb = input.get('pos_emb', None) + + if isinstance(adapter_module, adapter_modules.LinearAdapter) and loc == 'post': + output = adapter_strategy(x, adapter_module, module=self) + + elif isinstance(adapter_module, MultiHeadAttention) and loc == 'mha': + if self.self_attention_model == 'rel_pos': + x = dict(query=x, key=x, value=x, mask=att_mask, pos_emb=pos_emb) + output = adapter_strategy(x, adapter_module, module=self) + + elif self.self_attention_model == 'abs_pos': + x = dict(query=x, key=x, value=x, mask=att_mask) + output = adapter_strategy(x, adapter_module, module=self) + + else: + raise ValueError(f"Unsupported value of self_attention_model , provided {self.self_attention_model}!") + + else: + # No adapter compatible, skip + output = x + + input['x'] = output + + return input + + +class ConformerConvolution(nn.Module): + """The convolution module for the Conformer model. + Args: + d_model (int): hidden dimension + kernel_size (int): kernel size for depthwise convolution + pointwise_activation (str): name of the activation function to be used for the pointwise conv. + Note that Conformer uses a special key `glu_` which is treated as the original default from + the paper. + """ + + def __init__( + self, d_model, kernel_size, norm_type='batch_norm', conv_context_size=None, pointwise_activation='glu_' + ): + super(ConformerConvolution, self).__init__() + assert (kernel_size - 1) % 2 == 0 + self.d_model = d_model + self.kernel_size = kernel_size + self.norm_type = norm_type + + if conv_context_size is None: + conv_context_size = (kernel_size - 1) // 2 + + if pointwise_activation in activation_registry: + self.pointwise_activation = activation_registry[pointwise_activation]() + dw_conv_input_dim = d_model * 2 + + if hasattr(self.pointwise_activation, 'inplace'): + self.pointwise_activation.inplace = True + else: + self.pointwise_activation = pointwise_activation + dw_conv_input_dim = d_model + + self.pointwise_conv1 = nn.Conv1d( + in_channels=d_model, out_channels=d_model * 2, kernel_size=1, stride=1, padding=0, bias=True + ) + + self.depthwise_conv = CausalConv1D( + in_channels=dw_conv_input_dim, + out_channels=dw_conv_input_dim, + kernel_size=kernel_size, + stride=1, + padding=conv_context_size, + groups=dw_conv_input_dim, + bias=True, + ) + + if norm_type == 'batch_norm': + self.batch_norm = nn.BatchNorm1d(dw_conv_input_dim) + elif norm_type == 'instance_norm': + self.batch_norm = nn.InstanceNorm1d(dw_conv_input_dim) + elif norm_type == 'layer_norm': + self.batch_norm = nn.LayerNorm(dw_conv_input_dim) + elif norm_type == 'fused_batch_norm': + self.batch_norm = FusedBatchNorm1d(dw_conv_input_dim) + elif norm_type.startswith('group_norm'): + num_groups = int(norm_type.replace("group_norm", "")) + self.batch_norm = nn.GroupNorm(num_groups=num_groups, num_channels=d_model) + else: + raise ValueError(f"conv_norm_type={norm_type} is not valid!") + + self.activation = Swish() + self.pointwise_conv2 = nn.Conv1d( + in_channels=dw_conv_input_dim, out_channels=d_model, kernel_size=1, stride=1, padding=0, bias=True + ) + + def forward(self, x, pad_mask=None, cache=None): + x = x.transpose(1, 2) + x = self.pointwise_conv1(x) + + # Compute the activation function or use GLU for original Conformer + if self.pointwise_activation == 'glu_': + x = nn.functional.glu(x, dim=1) + else: + x = self.pointwise_activation(x) + + if pad_mask is not None: + x = x.float().masked_fill(pad_mask.unsqueeze(1), 0.0) + + x = self.depthwise_conv(x, cache=cache) + if cache is not None: + x, cache = x + + if self.norm_type == "layer_norm": + x = x.transpose(1, 2) + x = self.batch_norm(x) + x = x.transpose(1, 2) + else: + x = self.batch_norm(x) + + x = self.activation(x) + x = self.pointwise_conv2(x) + x = x.transpose(1, 2) + if cache is None: + return x + else: + return x, cache + + def reset_parameters_conv(self): + pw1_max = pw2_max = self.d_model ** -0.5 + dw_max = self.kernel_size ** -0.5 + + with torch.no_grad(): + nn.init.uniform_(self.pointwise_conv1.weight, -pw1_max, pw1_max) + nn.init.uniform_(self.pointwise_conv1.bias, -pw1_max, pw1_max) + nn.init.uniform_(self.pointwise_conv2.weight, -pw2_max, pw2_max) + nn.init.uniform_(self.pointwise_conv2.bias, -pw2_max, pw2_max) + nn.init.uniform_(self.depthwise_conv.weight, -dw_max, dw_max) + nn.init.uniform_(self.depthwise_conv.bias, -dw_max, dw_max) + + +class ConformerFeedForward(nn.Module): + """ + feed-forward module of Conformer model. + """ + + def __init__(self, d_model, d_ff, dropout, activation=Swish()): + super(ConformerFeedForward, self).__init__() + self.d_model = d_model + self.d_ff = d_ff + self.linear1 = nn.Linear(d_model, d_ff) + self.activation = activation + self.dropout = nn.Dropout(p=dropout) + self.linear2 = nn.Linear(d_ff, d_model) + + def forward(self, x): + x = self.linear1(x) + x = self.activation(x) + x = self.dropout(x) + x = self.linear2(x) + return x + + def reset_parameters_ff(self): + ffn1_max = self.d_model ** -0.5 + ffn2_max = self.d_ff ** -0.5 + with torch.no_grad(): + nn.init.uniform_(self.linear1.weight, -ffn1_max, ffn1_max) + nn.init.uniform_(self.linear1.bias, -ffn1_max, ffn1_max) + nn.init.uniform_(self.linear2.weight, -ffn2_max, ffn2_max) + nn.init.uniform_(self.linear2.bias, -ffn2_max, ffn2_max) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/ctc_beam_decoding.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/ctc_beam_decoding.py new file mode 100644 index 0000000..5ed504f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/ctc_beam_decoding.py @@ -0,0 +1,606 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import os +from dataclasses import dataclass, field +from typing import List, Optional, Tuple, Union + +import torch + +from nemo.collections.asr.parts.utils import rnnt_utils +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.core.classes import Typing, typecheck +from nemo.core.neural_types import HypothesisType, LengthsType, LogprobsType, NeuralType +from nemo.utils import logging + +DEFAULT_TOKEN_OFFSET = 100 + + +def pack_hypotheses( + hypotheses: List[rnnt_utils.NBestHypotheses], logitlen: torch.Tensor, +) -> List[rnnt_utils.NBestHypotheses]: + + if logitlen is not None: + if hasattr(logitlen, 'cpu'): + logitlen_cpu = logitlen.to('cpu') + else: + logitlen_cpu = logitlen + + for idx, hyp in enumerate(hypotheses): # type: rnnt_utils.NBestHypotheses + for candidate_idx, cand in enumerate(hyp.n_best_hypotheses): + cand.y_sequence = torch.tensor(cand.y_sequence, dtype=torch.long) + + if logitlen is not None: + cand.length = logitlen_cpu[idx] + + if cand.dec_state is not None: + cand.dec_state = _states_to_device(cand.dec_state) + + return hypotheses + + +def _states_to_device(dec_state, device='cpu'): + if torch.is_tensor(dec_state): + dec_state = dec_state.to(device) + + elif isinstance(dec_state, (list, tuple)): + dec_state = tuple(_states_to_device(dec_i, device) for dec_i in dec_state) + + return dec_state + + +class AbstractBeamCTCInfer(Typing): + """A beam CTC decoder. + + Provides a common abstraction for sample level beam decoding. + + Args: + blank_id: int, index of the blank token. Can be 0 or len(vocabulary). + beam_size: int, size of the beam used in the underlying beam search engine. + + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "decoder_output": NeuralType(('B', 'T', 'D'), LogprobsType()), + "decoder_lengths": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"predictions": [NeuralType(elements_type=HypothesisType())]} + + def __init__(self, blank_id: int, beam_size: int): + self.blank_id = blank_id + + if beam_size < 1: + raise ValueError("Beam search size cannot be less than 1!") + + self.beam_size = beam_size + + # Variables set by corresponding setter methods + self.vocab = None + self.decoding_type = None + self.tokenizer = None + + # Utility maps for vocabulary + self.vocab_index_map = None + self.index_vocab_map = None + + # Internal variable, used to prevent double reduction of consecutive tokens (ctc collapse) + self.override_fold_consecutive_value = None + + def set_vocabulary(self, vocab: List[str]): + """ + Set the vocabulary of the decoding framework. + + Args: + vocab: List of str. Each token corresponds to its location in the vocabulary emitted by the model. + Note that this vocabulary must NOT contain the "BLANK" token. + """ + self.vocab = vocab + self.vocab_index_map = {v: i for i, v in enumerate(vocab)} + self.index_vocab_map = {i: v for i, v in enumerate(vocab)} + + def set_decoding_type(self, decoding_type: str): + """ + Sets the decoding type of the framework. Can support either char or subword models. + + Args: + decoding_type: Str corresponding to decoding type. Only supports "char" and "subword". + """ + decoding_type = decoding_type.lower() + supported_types = ['char', 'subword'] + + if decoding_type not in supported_types: + raise ValueError( + f"Unsupported decoding type. Supported types = {supported_types}.\n" f"Given = {decoding_type}" + ) + + self.decoding_type = decoding_type + + def set_tokenizer(self, tokenizer: TokenizerSpec): + """ + Set the tokenizer of the decoding framework. + + Args: + tokenizer: NeMo tokenizer object, which inherits from TokenizerSpec. + """ + self.tokenizer = tokenizer + + @typecheck() + def forward( + self, decoder_output: torch.Tensor, decoder_lengths: torch.Tensor, + ) -> Tuple[List[Union[rnnt_utils.Hypothesis, rnnt_utils.NBestHypotheses]]]: + """Returns a list of hypotheses given an input batch of the encoder hidden embedding. + Output token is generated auto-repressively. + + Args: + decoder_output: A tensor of size (batch, timesteps, features) or (batch, timesteps) (each timestep is a label). + decoder_lengths: list of int representing the length of each sequence + output sequence. + + Returns: + packed list containing batch number of sentences (Hypotheses). + """ + raise NotImplementedError() + + def __call__(self, *args, **kwargs): + return self.forward(*args, **kwargs) + + +class BeamCTCInfer(AbstractBeamCTCInfer): + """A greedy CTC decoder. + + Provides a common abstraction for sample level and batch level greedy decoding. + + Args: + blank_index: int index of the blank token. Can be 0 or len(vocabulary). + preserve_alignments: Bool flag which preserves the history of logprobs generated during + decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `logprobs` in it. Here, `logprobs` is a torch.Tensors. + compute_timestamps: A bool flag, which determines whether to compute the character/subword, or + word based timestamp mapping the output log-probabilities to discrite intervals of timestamps. + The timestamps will be available in the returned Hypothesis.timestep as a dictionary. + + """ + + def __init__( + self, + blank_id: int, + beam_size: int, + search_type: str = "default", + return_best_hypothesis: bool = True, + preserve_alignments: bool = False, + compute_timestamps: bool = False, + beam_alpha: float = 1.0, + beam_beta: float = 0.0, + kenlm_path: str = None, + flashlight_cfg: Optional['FlashlightConfig'] = None, + pyctcdecode_cfg: Optional['PyCTCDecodeConfig'] = None, + ): + super().__init__(blank_id=blank_id, beam_size=beam_size) + + self.search_type = search_type + self.return_best_hypothesis = return_best_hypothesis + self.preserve_alignments = preserve_alignments + self.compute_timestamps = compute_timestamps + + if self.compute_timestamps: + raise ValueError(f"Currently this flag is not supported for beam search algorithms.") + + self.vocab = None # This must be set by specific method by user before calling forward() ! + + if search_type == "default" or search_type == "nemo": + self.search_algorithm = self.default_beam_search + elif search_type == "pyctcdecode": + self.search_algorithm = self._pyctcdecode_beam_search + elif search_type == "flashlight": + self.search_algorithm = self.flashlight_beam_search + else: + raise NotImplementedError( + f"The search type ({search_type}) supplied is not supported!\n" + f"Please use one of : (default, nemo, pyctcdecode)" + ) + + # Log the beam search algorithm + logging.info(f"Beam search algorithm: {search_type}") + + self.beam_alpha = beam_alpha + self.beam_beta = beam_beta + + # Default beam search args + self.kenlm_path = kenlm_path + + # PyCTCDecode params + if pyctcdecode_cfg is None: + pyctcdecode_cfg = PyCTCDecodeConfig() + self.pyctcdecode_cfg = pyctcdecode_cfg # type: PyCTCDecodeConfig + + if flashlight_cfg is None: + flashlight_cfg = FlashlightConfig() + self.flashlight_cfg = flashlight_cfg + + # Default beam search scorer functions + self.default_beam_scorer = None + self.pyctcdecode_beam_scorer = None + self.flashlight_beam_scorer = None + self.token_offset = 0 + + @typecheck() + def forward( + self, decoder_output: torch.Tensor, decoder_lengths: torch.Tensor, + ) -> Tuple[List[Union[rnnt_utils.Hypothesis, rnnt_utils.NBestHypotheses]]]: + """Returns a list of hypotheses given an input batch of the encoder hidden embedding. + Output token is generated auto-repressively. + + Args: + decoder_output: A tensor of size (batch, timesteps, features). + decoder_lengths: list of int representing the length of each sequence + output sequence. + + Returns: + packed list containing batch number of sentences (Hypotheses). + """ + if self.vocab is None: + raise RuntimeError("Please set the vocabulary with `set_vocabulary()` before calling this function.") + + if self.decoding_type is None: + raise ValueError("Please set the decoding type with `set_decoding_type()` before calling this function.") + + with torch.no_grad(), torch.inference_mode(): + # Process each sequence independently + prediction_tensor = decoder_output + + if prediction_tensor.ndim != 3: + raise ValueError( + f"`decoder_output` must be a tensor of shape [B, T, V] (log probs, float). " + f"Provided shape = {prediction_tensor.shape}" + ) + + # determine type of input - logprobs or labels + out_len = decoder_lengths if decoder_lengths is not None else None + hypotheses = self.search_algorithm(prediction_tensor, out_len) + + # Pack results into Hypotheses + packed_result = pack_hypotheses(hypotheses, decoder_lengths) + + # Pack the result + if self.return_best_hypothesis and isinstance(packed_result[0], rnnt_utils.NBestHypotheses): + packed_result = [res.n_best_hypotheses[0] for res in packed_result] # type: Hypothesis + + return (packed_result,) + + @torch.no_grad() + def default_beam_search( + self, x: torch.Tensor, out_len: torch.Tensor + ) -> List[Union[rnnt_utils.Hypothesis, rnnt_utils.NBestHypotheses]]: + """ + Open Seq2Seq Beam Search Algorithm (DeepSpeed) + + Args: + x: Tensor of shape [B, T, V+1], where B is the batch size, T is the maximum sequence length, + and V is the vocabulary size. The tensor contains log-probabilities. + out_len: Tensor of shape [B], contains lengths of each sequence in the batch. + + Returns: + A list of NBestHypotheses objects, one for each sequence in the batch. + """ + if self.compute_timestamps: + raise ValueError( + f"Beam Search with strategy `{self.search_type}` does not support time stamp calculation!" + ) + + if self.default_beam_scorer is None: + # Check for filepath + if self.kenlm_path is None or not os.path.exists(self.kenlm_path): + raise FileNotFoundError( + f"KenLM binary file not found at : {self.kenlm_path}. " + f"Please set a valid path in the decoding config." + ) + + # perform token offset for subword models + if self.decoding_type == 'subword': + vocab = [chr(idx + self.token_offset) for idx in range(len(self.vocab))] + else: + # char models + vocab = self.vocab + + # Must import at runtime to avoid circular dependency due to module level import. + from nemo.collections.asr.modules.beam_search_decoder import BeamSearchDecoderWithLM + + self.default_beam_scorer = BeamSearchDecoderWithLM( + vocab=vocab, + lm_path=self.kenlm_path, + beam_width=self.beam_size, + alpha=self.beam_alpha, + beta=self.beam_beta, + num_cpus=max(1, os.cpu_count()), + input_tensor=False, + ) + + x = x.to('cpu') + + with typecheck.disable_checks(): + data = [x[sample_id, : out_len[sample_id], :].softmax(dim=-1) for sample_id in range(len(x))] + beams_batch = self.default_beam_scorer.forward(log_probs=data, log_probs_length=None) + + # For each sample in the batch + nbest_hypotheses = [] + for beams_idx, beams in enumerate(beams_batch): + # For each beam candidate / hypothesis in each sample + hypotheses = [] + for candidate_idx, candidate in enumerate(beams): + hypothesis = rnnt_utils.Hypothesis( + score=0.0, y_sequence=[], dec_state=None, timestep=[], last_token=None + ) + + # For subword encoding, NeMo will double encode the subword (multiple tokens) into a + # singular unicode id. In doing so, we preserve the semantic of the unicode token, and + # compress the size of the final KenLM ARPA / Binary file. + # In order to do double encoding, we shift the subword by some token offset. + # This step is ignored for character based models. + if self.decoding_type == 'subword': + pred_token_ids = [ord(c) - self.token_offset for c in candidate[1]] + else: + # Char models + pred_token_ids = [self.vocab_index_map[c] for c in candidate[1]] + + # We preserve the token ids and the score for this hypothesis + hypothesis.y_sequence = pred_token_ids + hypothesis.score = candidate[0] + + # If alignment must be preserved, we preserve a view of the output logprobs. + # Note this view is shared amongst all beams within the sample, be sure to clone it if you + # require specific processing for each sample in the beam. + # This is done to preserve memory. + if self.preserve_alignments: + hypothesis.alignments = x[beams_idx][: out_len[beams_idx]] + + hypotheses.append(hypothesis) + + # Wrap the result in NBestHypothesis. + hypotheses = rnnt_utils.NBestHypotheses(hypotheses) + nbest_hypotheses.append(hypotheses) + + return nbest_hypotheses + + @torch.no_grad() + def _pyctcdecode_beam_search( + self, x: torch.Tensor, out_len: torch.Tensor + ) -> List[Union[rnnt_utils.Hypothesis, rnnt_utils.NBestHypotheses]]: + """ + PyCTCDecode Beam Search Algorithm. Should support Char and Subword models. + + Args: + x: Tensor of shape [B, T, V+1], where B is the batch size, T is the maximum sequence length, + and V is the vocabulary size. The tensor contains log-probabilities. + out_len: Tensor of shape [B], contains lengths of each sequence in the batch. + + Returns: + A list of NBestHypotheses objects, one for each sequence in the batch. + """ + if self.compute_timestamps: + raise ValueError( + f"Beam Search with strategy `{self.search_type}` does not support time stamp calculation!" + ) + + try: + import pyctcdecode + except (ImportError, ModuleNotFoundError): + raise ImportError( + f"Could not load `pyctcdecode` library. Please install it from pip using :\n" + f"pip install --upgrade pyctcdecode" + ) + + if self.pyctcdecode_beam_scorer is None: + self.pyctcdecode_beam_scorer = pyctcdecode.build_ctcdecoder( + labels=self.vocab, kenlm_model_path=self.kenlm_path, alpha=self.beam_alpha, beta=self.beam_beta + ) # type: pyctcdecode.BeamSearchDecoderCTC + + x = x.to('cpu').numpy() + + with typecheck.disable_checks(): + beams_batch = [] + for sample_id in range(len(x)): + logprobs = x[sample_id, : out_len[sample_id], :] + result = self.pyctcdecode_beam_scorer.decode_beams( + logprobs, + beam_width=self.beam_size, + beam_prune_logp=self.pyctcdecode_cfg.beam_prune_logp, + token_min_logp=self.pyctcdecode_cfg.token_min_logp, + prune_history=self.pyctcdecode_cfg.prune_history, + hotwords=self.pyctcdecode_cfg.hotwords, + hotword_weight=self.pyctcdecode_cfg.hotword_weight, + lm_start_state=None, + ) # Output format: text, last_lm_state, text_frames, logit_score, lm_score + beams_batch.append(result) + + nbest_hypotheses = [] + for beams_idx, beams in enumerate(beams_batch): + hypotheses = [] + for candidate_idx, candidate in enumerate(beams): + # Candidate = (text, last_lm_state, text_frames, logit_score, lm_score) + hypothesis = rnnt_utils.Hypothesis( + score=0.0, y_sequence=[], dec_state=None, timestep=[], last_token=None + ) + + # TODO: Requires token ids to be returned rather than text. + if self.decoding_type == 'subword': + if self.tokenizer is None: + raise ValueError("Tokenizer must be provided for subword decoding. Use set_tokenizer().") + + pred_token_ids = self.tokenizer.text_to_ids(candidate[0]) + else: + if self.vocab is None: + raise ValueError("Vocab must be provided for character decoding. Use set_vocab().") + + chars = list(candidate[0]) + pred_token_ids = [self.vocab_index_map[c] for c in chars] + + hypothesis.y_sequence = pred_token_ids + hypothesis.text = candidate[0] # text + hypothesis.score = candidate[4] # score + + # Inject word level timestamps + hypothesis.timestep = candidate[2] # text_frames + + if self.preserve_alignments: + hypothesis.alignments = torch.from_numpy(x[beams_idx][: out_len[beams_idx]]) + + hypotheses.append(hypothesis) + + hypotheses = rnnt_utils.NBestHypotheses(hypotheses) + nbest_hypotheses.append(hypotheses) + + return nbest_hypotheses + + @torch.no_grad() + def flashlight_beam_search( + self, x: torch.Tensor, out_len: torch.Tensor + ) -> List[Union[rnnt_utils.Hypothesis, rnnt_utils.NBestHypotheses]]: + """ + Flashlight Beam Search Algorithm. Should support Char and Subword models. + + Args: + x: Tensor of shape [B, T, V+1], where B is the batch size, T is the maximum sequence length, + and V is the vocabulary size. The tensor contains log-probabilities. + out_len: Tensor of shape [B], contains lengths of each sequence in the batch. + + Returns: + A list of NBestHypotheses objects, one for each sequence in the batch. + """ + if self.compute_timestamps: + raise ValueError( + f"Beam Search with strategy `{self.search_type}` does not support time stamp calculation!" + ) + + if self.flashlight_beam_scorer is None: + # Check for filepath + if self.kenlm_path is None or not os.path.exists(self.kenlm_path): + raise FileNotFoundError( + f"KenLM binary file not found at : {self.kenlm_path}. " + f"Please set a valid path in the decoding config." + ) + + # perform token offset for subword models + # if self.decoding_type == 'subword': + # vocab = [chr(idx + self.token_offset) for idx in range(len(self.vocab))] + # else: + # # char models + # vocab = self.vocab + + # Must import at runtime to avoid circular dependency due to module level import. + from nemo.collections.asr.modules.flashlight_decoder import FlashLightKenLMBeamSearchDecoder + + self.flashlight_beam_scorer = FlashLightKenLMBeamSearchDecoder( + lm_path=self.kenlm_path, + vocabulary=self.vocab, + tokenizer=self.tokenizer, + lexicon_path=self.flashlight_cfg.lexicon_path, + boost_path=self.flashlight_cfg.boost_path, + beam_size=self.beam_size, + beam_size_token=self.flashlight_cfg.beam_size_token, + beam_threshold=self.flashlight_cfg.beam_threshold, + lm_weight=self.beam_alpha, + word_score=self.beam_beta, + unk_weight=self.flashlight_cfg.unk_weight, + sil_weight=self.flashlight_cfg.sil_weight, + ) + + x = x.to('cpu') + + with typecheck.disable_checks(): + beams_batch = self.flashlight_beam_scorer.forward(log_probs=x) + + # For each sample in the batch + nbest_hypotheses = [] + for beams_idx, beams in enumerate(beams_batch): + # For each beam candidate / hypothesis in each sample + hypotheses = [] + for candidate_idx, candidate in enumerate(beams): + hypothesis = rnnt_utils.Hypothesis( + score=0.0, y_sequence=[], dec_state=None, timestep=[], last_token=None + ) + + # We preserve the token ids and the score for this hypothesis + hypothesis.y_sequence = candidate['tokens'].tolist() + hypothesis.score = candidate['score'] + + # If alignment must be preserved, we preserve a view of the output logprobs. + # Note this view is shared amongst all beams within the sample, be sure to clone it if you + # require specific processing for each sample in the beam. + # This is done to preserve memory. + if self.preserve_alignments: + hypothesis.alignments = x[beams_idx][: out_len[beams_idx]] + + hypotheses.append(hypothesis) + + # Wrap the result in NBestHypothesis. + hypotheses = rnnt_utils.NBestHypotheses(hypotheses) + nbest_hypotheses.append(hypotheses) + + return nbest_hypotheses + + def set_decoding_type(self, decoding_type: str): + super().set_decoding_type(decoding_type) + + # Please check train_kenlm.py in scripts/asr_language_modeling/ to find out why we need + # TOKEN_OFFSET for BPE-based models + if self.decoding_type == 'subword': + self.token_offset = DEFAULT_TOKEN_OFFSET + + +@dataclass +class PyCTCDecodeConfig: + # These arguments cannot be imported from pyctcdecode (optional dependency) + # Therefore we copy the values explicitly + # Taken from pyctcdecode.constant + beam_prune_logp: float = -10.0 + token_min_logp: float = -5.0 + prune_history: bool = False + hotwords: Optional[List[str]] = None + hotword_weight: float = 10.0 + + +@dataclass +class FlashlightConfig: + lexicon_path: Optional[str] = None + boost_path: Optional[str] = None + beam_size_token: int = 16 + beam_threshold: float = 20.0 + unk_weight: float = -math.inf + sil_weight: float = 0.0 + + +@dataclass +class BeamCTCInferConfig: + beam_size: int + search_type: str = 'default' + preserve_alignments: bool = False + compute_timestamps: bool = False + return_best_hypothesis: bool = True + + beam_alpha: float = 1.0 + beam_beta: float = 0.0 + kenlm_path: Optional[str] = None + + flashlight_cfg: Optional[FlashlightConfig] = field(default_factory=lambda: FlashlightConfig()) + pyctcdecode_cfg: Optional[PyCTCDecodeConfig] = field(default_factory=lambda: PyCTCDecodeConfig()) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/ctc_decoding.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/ctc_decoding.py new file mode 100644 index 0000000..d331a6c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/ctc_decoding.py @@ -0,0 +1,1313 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re +from abc import abstractmethod +from dataclasses import dataclass, field, is_dataclass +from typing import Callable, Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.asr.parts.submodules import ctc_beam_decoding, ctc_greedy_decoding +from nemo.collections.asr.parts.utils.asr_confidence_utils import ConfidenceConfig, ConfidenceMixin +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis, NBestHypotheses +from nemo.collections.common.tokenizers.aggregate_tokenizer import DummyTokenizer +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.utils import logging, logging_mode + + +def move_dimension_to_the_front(tensor, dim_index): + all_dims = list(range(tensor.ndim)) + return tensor.permute(*([dim_index] + all_dims[:dim_index] + all_dims[dim_index + 1 :])) + + +class AbstractCTCDecoding(ConfidenceMixin): + """ + Used for performing CTC auto-regressive / non-auto-regressive decoding of the logprobs. + + Args: + decoding_cfg: A dict-like object which contains the following key-value pairs. + strategy: + str value which represents the type of decoding that can occur. + Possible values are : + + greedy (for greedy decoding). + + beam (for DeepSpeed KenLM based decoding). + + compute_timestamps: + A bool flag, which determines whether to compute the character/subword, or + word based timestamp mapping the output log-probabilities to discrite intervals of timestamps. + The timestamps will be available in the returned Hypothesis.timestep as a dictionary. + + ctc_timestamp_type: + A str value, which represents the types of timestamps that should be calculated. + Can take the following values - "char" for character/subword time stamps, "word" for word level + time stamps and "all" (default), for both character level and word level time stamps. + + word_seperator: + Str token representing the seperator between words. + + preserve_alignments: + Bool flag which preserves the history of logprobs generated during + decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `logprobs` in it. Here, `logprobs` is a torch.Tensors. + + confidence_cfg: + A dict-like object which contains the following key-value pairs related to confidence + scores. In order to obtain hypotheses with confidence scores, please utilize + `ctc_decoder_predictions_tensor` function with the `preserve_frame_confidence` flag set to True. + + preserve_frame_confidence: + Bool flag which preserves the history of per-frame confidence scores + generated during decoding. When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `frame_confidence` is a List of floats. + + preserve_token_confidence: + Bool flag which preserves the history of per-token confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `token_confidence` in it. Here, `token_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized tokens. + + preserve_word_confidence: + Bool flag which preserves the history of per-word confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `word_confidence` in it. Here, `word_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized words. + + exclude_blank: + Bool flag indicating that blank token confidence scores are to be excluded + from the `token_confidence`. + + aggregation: + Which aggregation type to use for collapsing per-token confidence into per-word confidence. + Valid options are `mean`, `min`, `max`, `prod`. + + method_cfg: + A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: + The method name (str). + Supported values: + + 'max_prob' for using the maximum token probability as a confidence. + + 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: + Which type of entropy to use (str). + Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: + Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: + A mapping of the entropy value to the interval [0,1]. + Supported values: + + - 'lin' for using the linear mapping. + + - 'exp' for using exponential mapping with linear shift. + + batch_dim_index: + Index of the batch dimension of ``targets`` and ``predictions`` parameters of + ``ctc_decoder_predictions_tensor`` methods. Can be either 0 or 1. + + The config may further contain the following sub-dictionaries: + + "greedy": + preserve_alignments: Same as above, overrides above value. + compute_timestamps: Same as above, overrides above value. + preserve_frame_confidence: Same as above, overrides above value. + confidence_method_cfg: Same as above, overrides confidence_cfg.method_cfg. + + "beam": + beam_size: + int, defining the beam size for beam search. Must be >= 1. + If beam_size == 1, will perform cached greedy search. This might be slightly different + results compared to the greedy search above. + + return_best_hypothesis: + optional bool, whether to return just the best hypothesis or all of the + hypotheses after beam search has concluded. This flag is set by default. + + beam_alpha: + float, the strength of the Language model on the final score of a token. + final_score = acoustic_score + beam_alpha * lm_score + beam_beta * seq_length. + + beam_beta: + float, the strength of the sequence length penalty on the final score of a token. + final_score = acoustic_score + beam_alpha * lm_score + beam_beta * seq_length. + + kenlm_path: + str, path to a KenLM ARPA or .binary file (depending on the strategy chosen). + If the path is invalid (file is not found at path), will raise a deferred error at the moment + of calculation of beam search, so that users may update / change the decoding strategy + to point to the correct file. + + blank_id + The id of the RNNT blank token. + """ + + def __init__(self, decoding_cfg, blank_id: int): + super().__init__() + + # Convert dataclas to config + if is_dataclass(decoding_cfg): + decoding_cfg = OmegaConf.structured(decoding_cfg) + + if not isinstance(decoding_cfg, DictConfig): + decoding_cfg = OmegaConf.create(decoding_cfg) + + OmegaConf.set_struct(decoding_cfg, False) + + # update minimal config + minimal_cfg = ['greedy'] + for item in minimal_cfg: + if item not in decoding_cfg: + decoding_cfg[item] = OmegaConf.create({}) + + self.cfg = decoding_cfg + self.blank_id = blank_id + self.preserve_alignments = self.cfg.get('preserve_alignments', None) + self.compute_timestamps = self.cfg.get('compute_timestamps', None) + self.batch_dim_index = self.cfg.get('batch_dim_index', 0) + self.word_seperator = self.cfg.get('word_seperator', ' ') + + possible_strategies = ['greedy', 'beam', 'pyctcdecode', 'flashlight'] + if self.cfg.strategy not in possible_strategies: + raise ValueError(f"Decoding strategy must be one of {possible_strategies}. Given {self.cfg.strategy}") + + # Update preserve alignments + if self.preserve_alignments is None: + if self.cfg.strategy in ['greedy']: + self.preserve_alignments = self.cfg.greedy.get('preserve_alignments', False) + else: + self.preserve_alignments = self.cfg.beam.get('preserve_alignments', False) + + # Update compute timestamps + if self.compute_timestamps is None: + if self.cfg.strategy in ['greedy']: + self.compute_timestamps = self.cfg.greedy.get('compute_timestamps', False) + elif self.cfg.strategy in ['beam']: + self.compute_timestamps = self.cfg.beam.get('compute_timestamps', False) + + # initialize confidence-related fields + self._init_confidence(self.cfg.get('confidence_cfg', None)) + + # Confidence estimation is not implemented for strategies other than `greedy` + if ( + not self.preserve_frame_confidence + and self.cfg.strategy != 'greedy' + and self.cfg.beam.get('preserve_frame_confidence', False) + ): + raise NotImplementedError(f"Confidence calculation is not supported for strategy `{self.cfg.strategy}`") + + # we need timestamps to extract non-blank per-frame confidence + if self.compute_timestamps is not None: + self.compute_timestamps |= self.preserve_frame_confidence + + if self.cfg.strategy == 'greedy': + + self.decoding = ctc_greedy_decoding.GreedyCTCInfer( + blank_id=self.blank_id, + preserve_alignments=self.preserve_alignments, + compute_timestamps=self.compute_timestamps, + preserve_frame_confidence=self.preserve_frame_confidence, + confidence_method_cfg=self.confidence_method_cfg, + ) + + elif self.cfg.strategy == 'beam': + + self.decoding = ctc_beam_decoding.BeamCTCInfer( + blank_id=blank_id, + beam_size=self.cfg.beam.get('beam_size', 1), + search_type='default', + return_best_hypothesis=self.cfg.beam.get('return_best_hypothesis', True), + preserve_alignments=self.preserve_alignments, + compute_timestamps=self.compute_timestamps, + beam_alpha=self.cfg.beam.get('beam_alpha', 1.0), + beam_beta=self.cfg.beam.get('beam_beta', 0.0), + kenlm_path=self.cfg.beam.get('kenlm_path', None), + ) + + self.decoding.override_fold_consecutive_value = False + + elif self.cfg.strategy == 'pyctcdecode': + + self.decoding = ctc_beam_decoding.BeamCTCInfer( + blank_id=blank_id, + beam_size=self.cfg.beam.get('beam_size', 1), + search_type='pyctcdecode', + return_best_hypothesis=self.cfg.beam.get('return_best_hypothesis', True), + preserve_alignments=self.preserve_alignments, + compute_timestamps=self.compute_timestamps, + beam_alpha=self.cfg.beam.get('beam_alpha', 1.0), + beam_beta=self.cfg.beam.get('beam_beta', 0.0), + kenlm_path=self.cfg.beam.get('kenlm_path', None), + pyctcdecode_cfg=self.cfg.beam.get('pyctcdecode_cfg', None), + ) + + self.decoding.override_fold_consecutive_value = False + + elif self.cfg.strategy == 'flashlight': + + self.decoding = ctc_beam_decoding.BeamCTCInfer( + blank_id=blank_id, + beam_size=self.cfg.beam.get('beam_size', 1), + search_type='flashlight', + return_best_hypothesis=self.cfg.beam.get('return_best_hypothesis', True), + preserve_alignments=self.preserve_alignments, + compute_timestamps=self.compute_timestamps, + beam_alpha=self.cfg.beam.get('beam_alpha', 1.0), + beam_beta=self.cfg.beam.get('beam_beta', 0.0), + kenlm_path=self.cfg.beam.get('kenlm_path', None), + flashlight_cfg=self.cfg.beam.get('flashlight_cfg', None), + ) + + self.decoding.override_fold_consecutive_value = False + + else: + raise ValueError( + f"Incorrect decoding strategy supplied. Must be one of {possible_strategies}\n" + f"but was provided {self.cfg.strategy}" + ) + + def ctc_decoder_predictions_tensor( + self, + decoder_outputs: torch.Tensor, + decoder_lengths: torch.Tensor = None, + fold_consecutive: bool = True, + return_hypotheses: bool = False, + ) -> Tuple[List[str], Optional[List[List[str]]], Optional[Union[Hypothesis, NBestHypotheses]]]: + """ + Decodes a sequence of labels to words + + Args: + decoder_outputs: An integer torch.Tensor of shape [Batch, Time, {Vocabulary}] (if ``batch_index_dim == 0``) or [Time, Batch] + (if ``batch_index_dim == 1``) of integer indices that correspond to the index of some character in the + label set. + decoder_lengths: Optional tensor of length `Batch` which contains the integer lengths + of the sequence in the padded `predictions` tensor. + fold_consecutive: Bool, determine whether to perform "ctc collapse", folding consecutive tokens + into a single token. + return_hypotheses: Bool flag whether to return just the decoding predictions of the model + or a Hypothesis object that holds information such as the decoded `text`, + the `alignment` of emited by the CTC Model, and the `length` of the sequence (if available). + May also contain the log-probabilities of the decoder (if this method is called via + transcribe()) + + Returns: + Either a list of str which represent the CTC decoded strings per sample, + or a list of Hypothesis objects containing additional information. + """ + + if isinstance(decoder_outputs, torch.Tensor): + decoder_outputs = move_dimension_to_the_front(decoder_outputs, self.batch_dim_index) + + if ( + hasattr(self.decoding, 'override_fold_consecutive_value') + and self.decoding.override_fold_consecutive_value is not None + ): + logging.info( + f"Beam search requires that consecutive ctc tokens are not folded. \n" + f"Overriding provided value of `fold_consecutive` = {fold_consecutive} to " + f"{self.decoding.override_fold_consecutive_value}", + mode=logging_mode.ONCE, + ) + fold_consecutive = self.decoding.override_fold_consecutive_value + + with torch.inference_mode(): + # Resolve the forward step of the decoding strategy + hypotheses_list = self.decoding( + decoder_output=decoder_outputs, decoder_lengths=decoder_lengths + ) # type: List[List[Hypothesis]] + + # extract the hypotheses + hypotheses_list = hypotheses_list[0] # type: List[Hypothesis] + + if isinstance(hypotheses_list[0], NBestHypotheses): + hypotheses = [] + all_hypotheses = [] + + for nbest_hyp in hypotheses_list: # type: NBestHypotheses + n_hyps = nbest_hyp.n_best_hypotheses # Extract all hypotheses for this sample + decoded_hyps = self.decode_hypothesis( + n_hyps, fold_consecutive + ) # type: List[Union[Hypothesis, NBestHypotheses]] + + # If computing timestamps + if self.compute_timestamps is True: + timestamp_type = self.cfg.get('ctc_timestamp_type', 'all') + for hyp_idx in range(len(decoded_hyps)): + decoded_hyps[hyp_idx] = self.compute_ctc_timestamps(decoded_hyps[hyp_idx], timestamp_type) + + hypotheses.append(decoded_hyps[0]) # best hypothesis + all_hypotheses.append(decoded_hyps) + + if return_hypotheses: + return hypotheses, all_hypotheses + + best_hyp_text = [h.text for h in hypotheses] + all_hyp_text = [h.text for hh in all_hypotheses for h in hh] + return best_hyp_text, all_hyp_text + + else: + hypotheses = self.decode_hypothesis( + hypotheses_list, fold_consecutive + ) # type: List[Union[Hypothesis, NBestHypotheses]] + + # If computing timestamps + if self.compute_timestamps is True: + # greedy decoding, can get high-level confidence scores + if return_hypotheses and (self.preserve_word_confidence or self.preserve_token_confidence): + hypotheses = self.compute_confidence(hypotheses) + else: + # remove unused token_repetitions from Hypothesis.text + for hyp in hypotheses: + hyp.text = hyp.text[:2] + timestamp_type = self.cfg.get('ctc_timestamp_type', 'all') + for hyp_idx in range(len(hypotheses)): + hypotheses[hyp_idx] = self.compute_ctc_timestamps(hypotheses[hyp_idx], timestamp_type) + + if return_hypotheses: + return hypotheses, None + + best_hyp_text = [h.text for h in hypotheses] + return best_hyp_text, None + + def decode_hypothesis( + self, hypotheses_list: List[Hypothesis], fold_consecutive: bool + ) -> List[Union[Hypothesis, NBestHypotheses]]: + """ + Decode a list of hypotheses into a list of strings. + + Args: + hypotheses_list: List of Hypothesis. + fold_consecutive: Whether to collapse the ctc blank tokens or not. + + Returns: + A list of strings. + """ + for ind in range(len(hypotheses_list)): + # Extract the integer encoded hypothesis + hyp = hypotheses_list[ind] + prediction = hyp.y_sequence + predictions_len = hyp.length if hyp.length > 0 else None + + if fold_consecutive: + if type(prediction) != list: + prediction = prediction.numpy().tolist() + + if predictions_len is not None: + prediction = prediction[:predictions_len] + + # CTC decoding procedure + decoded_prediction = [] + token_lengths = [] # preserve token lengths + token_repetitions = [] # preserve number of repetitions per token + + previous = self.blank_id + last_length = 0 + last_repetition = 1 + + for pidx, p in enumerate(prediction): + if (p != previous or previous == self.blank_id) and p != self.blank_id: + decoded_prediction.append(p) + + token_lengths.append(pidx - last_length) + last_length = pidx + token_repetitions.append(last_repetition) + last_repetition = 1 + + if p == previous and previous != self.blank_id: + last_repetition += 1 + + previous = p + + if len(token_repetitions) > 0: + token_repetitions = token_repetitions[1:] + [last_repetition] + + else: + if predictions_len is not None: + prediction = prediction[:predictions_len] + decoded_prediction = prediction[prediction != self.blank_id].tolist() + token_lengths = [1] * len(decoded_prediction) # preserve number of repetitions per token + token_repetitions = [1] * len(decoded_prediction) # preserve number of repetitions per token + + # De-tokenize the integer tokens; if not computing timestamps + if self.compute_timestamps is True: + # keep the original predictions, wrap with the number of repetitions per token + # this is done so that `ctc_decoder_predictions_tensor()` can process this hypothesis + # in order to compute exact time stamps. + hypothesis = (decoded_prediction, token_lengths, token_repetitions) + else: + hypothesis = self.decode_tokens_to_str(decoded_prediction) + + # TODO: remove + # collapse leading spaces before . , ? for PC models + hypothesis = re.sub(r'(\s+)([\.\,\?])', r'\2', hypothesis) + + # Preserve this wrapped hypothesis or decoded text tokens. + hypotheses_list[ind].text = hypothesis + + return hypotheses_list + + def compute_confidence(self, hypotheses_list: List[Hypothesis]) -> List[Hypothesis]: + """ + Computes high-level (per-token and/or per-word) confidence scores for a list of hypotheses. + Assumes that `frame_confidence` is present in the hypotheses. + + Args: + hypotheses_list: List of Hypothesis. + + Returns: + A list of hypotheses with high-level confidence scores. + """ + for hyp in hypotheses_list: + if not isinstance(hyp.text, tuple) or len(hyp.text) != 3: + # the method must have been called in the wrong place + raise ValueError( + """Wrong format of the `text` attribute of a hypothesis.\n + Expected: (decoded_prediction, token_repetitions)\n + The method invocation is expected between .decode_hypothesis() and .compute_ctc_timestamps()""" + ) + token_repetitions = hyp.text[2] + hyp.text = hyp.text[:2] + token_confidence = [] + if self.exclude_blank_from_confidence: + non_blank_frame_confidence = hyp.non_blank_frame_confidence + i = 0 + for tr in token_repetitions: + # token repetition can be zero + j = i + tr + token_confidence.append(self._aggregate_confidence(non_blank_frame_confidence[i:j])) + i = j + else: + # tokens are considered to belong to the last non-blank token, if any. + token_lengths = hyp.text[1] + if len(token_lengths) > 0: + ts = token_lengths[0] + for tl in token_lengths[1:] + [len(hyp.frame_confidence)]: + token_confidence.append(self._aggregate_confidence(hyp.frame_confidence[ts : ts + tl])) + ts += tl + hyp.token_confidence = token_confidence + if self.preserve_word_confidence: + for hyp in hypotheses_list: + hyp.word_confidence = self._aggregate_token_confidence(hyp) + return hypotheses_list + + @abstractmethod + def decode_tokens_to_str(self, tokens: List[int]) -> str: + """ + Implemented by subclass in order to decoder a token id list into a string. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded string. + """ + raise NotImplementedError() + + @abstractmethod + def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]: + """ + Implemented by subclass in order to decode a token id list into a token list. + A token list is the string representation of each token id. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded tokens. + """ + raise NotImplementedError() + + def compute_ctc_timestamps(self, hypothesis: Hypothesis, timestamp_type: str = "all"): + """ + Method to compute time stamps at char/subword, and word level given some hypothesis. + Requires the input hypothesis to contain a `text` field that is the tuple. The tuple contains - + the ctc collapsed integer ids, and the number of repetitions of each token. + + Args: + hypothesis: A Hypothesis object, with a wrapped `text` field. + The `text` field must contain a tuple with two values - + The ctc collapsed integer ids + A list of integers that represents the number of repetitions per token. + timestamp_type: A str value that represents the type of time stamp calculated. + Can be one of "char", "word" or "all" + + Returns: + A Hypothesis object with a modified `timestep` value, which is now a dictionary containing + the time stamp information. + """ + assert timestamp_type in ['char', 'word', 'all'] + + # Unpack the temporary storage, and set the decoded predictions + decoded_prediction, token_lengths = hypothesis.text + hypothesis.text = decoded_prediction + + # Retrieve offsets + char_offsets = word_offsets = None + char_offsets = self._compute_offsets(hypothesis, token_lengths, self.blank_id) + + # Assert number of offsets and hypothesis tokens are 1:1 match. + if len(char_offsets) != len(hypothesis.text): + raise ValueError( + f"`char_offsets`: {char_offsets} and `processed_tokens`: {hypothesis.text}" + " have to be of the same length, but are: " + f"`len(offsets)`: {len(char_offsets)} and `len(processed_tokens)`:" + f" {len(hypothesis.text)}" + ) + + # Correctly process the token ids to chars/subwords. + for i, char in enumerate(hypothesis.text): + char_offsets[i]["char"] = self.decode_tokens_to_str([char]) + + # detect char vs subword models + lens = [len(list(v["char"])) > 1 for v in char_offsets] + if any(lens): + text_type = 'subword' + else: + text_type = 'char' + + # retrieve word offsets from character offsets + word_offsets = None + if timestamp_type in ['word', 'all']: + if text_type == 'char': + word_offsets = self._get_word_offsets_chars(char_offsets, word_delimiter_char=self.word_seperator) + else: + word_offsets = self._get_word_offsets_subwords_sentencepiece( + char_offsets, + hypothesis, + decode_ids_to_tokens=self.decode_ids_to_tokens, + decode_tokens_to_str=self.decode_tokens_to_str, + ) + + # attach results + if len(hypothesis.timestep) > 0: + timestep_info = hypothesis.timestep + else: + timestep_info = [] + + # Setup defaults + hypothesis.timestep = {"timestep": timestep_info} + + # Add char / subword time stamps + if char_offsets is not None and timestamp_type in ['char', 'all']: + hypothesis.timestep['char'] = char_offsets + + # Add word time stamps + if word_offsets is not None and timestamp_type in ['word', 'all']: + hypothesis.timestep['word'] = word_offsets + + # Convert the token indices to text + hypothesis.text = self.decode_tokens_to_str(hypothesis.text) + + return hypothesis + + @staticmethod + def _compute_offsets( + hypothesis: Hypothesis, token_lengths: List[int], ctc_token: int + ) -> List[Dict[str, Union[str, int]]]: + """ + Utility method that calculates the indidual time indices where a token starts and ends. + + Args: + hypothesis: A Hypothesis object that contains `text` field that holds the character / subword token + emitted at every time step after ctc collapse. + token_lengths: A list of ints representing the lengths of each emitted token. + ctc_token: The integer of the ctc blank token used during ctc collapse. + + Returns: + + """ + start_index = 0 + + # If the exact timestep information is available, utilize the 1st non-ctc blank token timestep + # as the start index. + if hypothesis.timestep is not None and len(hypothesis.timestep) > 0: + start_index = max(0, hypothesis.timestep[0] - 1) + + # Construct the start and end indices brackets + end_indices = np.asarray(token_lengths).cumsum() + start_indices = np.concatenate(([start_index], end_indices[:-1])) + + # Merge the results per token into a list of dictionaries + offsets = [ + {"char": t, "start_offset": s, "end_offset": e} + for t, s, e in zip(hypothesis.text, start_indices, end_indices) + ] + + # Filter out CTC token + offsets = list(filter(lambda offsets: offsets["char"] != ctc_token, offsets)) + return offsets + + @staticmethod + def _get_word_offsets_chars( + offsets: Dict[str, Union[str, float]], word_delimiter_char: str = " " + ) -> Dict[str, Union[str, float]]: + """ + Utility method which constructs word time stamps out of character time stamps. + + References: + This code is a port of the Hugging Face code for word time stamp construction. + + Args: + offsets: A list of dictionaries, each containing "char", "start_offset" and "end_offset". + word_delimiter_char: Character token that represents the word delimiter. By default, " ". + + Returns: + A list of dictionaries containing the word offsets. Each item contains "word", "start_offset" and + "end_offset". + """ + word_offsets = [] + + last_state = "SPACE" + word = "" + start_offset = 0 + end_offset = 0 + for i, offset in enumerate(offsets): + char = offset["char"] + state = "SPACE" if char == word_delimiter_char else "WORD" + + if state == last_state: + # If we are in the same state as before, we simply repeat what we've done before + end_offset = offset["end_offset"] + word += char + else: + # Switching state + if state == "SPACE": + # Finishing a word + word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) + else: + # Starting a new word + start_offset = offset["start_offset"] + end_offset = offset["end_offset"] + word = char + + last_state = state + if last_state == "WORD": + word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) + + return word_offsets + + @staticmethod + def _get_word_offsets_subwords_sentencepiece( + offsets: Dict[str, Union[str, float]], + hypothesis: Hypothesis, + decode_ids_to_tokens: Callable[[List[int]], str], + decode_tokens_to_str: Callable[[List[int]], str], + ) -> Dict[str, Union[str, float]]: + """ + Utility method which constructs word time stamps out of sub-word time stamps. + + **Note**: Only supports Sentencepiece based tokenizers ! + + Args: + offsets: A list of dictionaries, each containing "char", "start_offset" and "end_offset". + hypothesis: Hypothesis object that contains `text` field, where each token is a sub-word id + after ctc collapse. + decode_ids_to_tokens: A Callable function that accepts a list of integers and maps it to a sub-word. + decode_tokens_to_str: A Callable function that accepts a list of integers and maps it to text / str. + + Returns: + A list of dictionaries containing the word offsets. Each item contains "word", "start_offset" and + "end_offset". + """ + word_offsets = [] + built_token = [] + previous_token_index = 0 + # For every collapsed sub-word token + for i, char in enumerate(hypothesis.text): + # Compute the sub-word text representation, and the decoded text (stripped of sub-word markers). + token = decode_ids_to_tokens([char])[0] + token_text = decode_tokens_to_str([char]) + + # It is a sub-word token, or contains an identifier at the beginning such as _ or ## that was stripped + # after forcing partial text conversion of the token. + if token != token_text: + # If there are any partially or fully built sub-word token ids, construct to text. + # Note: This is "old" subword, that occurs *after* current sub-word has started. + if len(built_token) > 0: + word_offsets.append( + { + "word": decode_tokens_to_str(built_token), + "start_offset": offsets[previous_token_index]["start_offset"], + "end_offset": offsets[i]["start_offset"], + } + ) + + # Prepare list of new sub-word ids + built_token.clear() + built_token.append(char) + previous_token_index = i + else: + # If the token does not contain any sub-word start mark, then the sub-word has not completed yet + # Append to current sub-word list. + built_token.append(char) + + # Inject the start offset of the first token to word offsets + # This is because we always skip the delay the injection of the first sub-word due to the loop + # condition and check whether built token is ready or not. + # Therefore without this forced injection, the start_offset appears as off by 1. + if len(word_offsets) == 0: + # alaptev: sometimes word_offsets can be empty + if len(built_token) > 0: + word_offsets.append( + { + "word": decode_tokens_to_str(built_token), + "start_offset": offsets[0]["start_offset"], + "end_offset": offsets[-1]["end_offset"], + } + ) + built_token.clear() + else: + word_offsets[0]["start_offset"] = offsets[0]["start_offset"] + + # If there are any remaining tokens left, inject them all into the final word offset. + # Note: The start offset of this token is the start time of the first token inside build_token. + # Note: The end offset of this token is the end time of the last token inside build_token + if len(built_token) > 0: + word_offsets.append( + { + "word": decode_tokens_to_str(built_token), + "start_offset": offsets[-(len(built_token))]["start_offset"], + "end_offset": offsets[-1]["end_offset"], + } + ) + built_token.clear() + + return word_offsets + + @property + def preserve_alignments(self): + return self._preserve_alignments + + @preserve_alignments.setter + def preserve_alignments(self, value): + self._preserve_alignments = value + + if hasattr(self, 'decoding'): + self.decoding.preserve_alignments = value + + @property + def compute_timestamps(self): + return self._compute_timestamps + + @compute_timestamps.setter + def compute_timestamps(self, value): + self._compute_timestamps = value + + if hasattr(self, 'decoding'): + self.decoding.compute_timestamps = value + + @property + def preserve_frame_confidence(self): + return self._preserve_frame_confidence + + @preserve_frame_confidence.setter + def preserve_frame_confidence(self, value): + self._preserve_frame_confidence = value + + if hasattr(self, 'decoding'): + self.decoding.preserve_frame_confidence = value + + +class CTCDecoding(AbstractCTCDecoding): + """ + Used for performing CTC auto-regressive / non-auto-regressive decoding of the logprobs for character + based models. + + Args: + decoding_cfg: A dict-like object which contains the following key-value pairs. + + strategy: + str value which represents the type of decoding that can occur. + Possible values are : + + - greedy (for greedy decoding). + + - beam (for DeepSpeed KenLM based decoding). + + compute_timestamps: + A bool flag, which determines whether to compute the character/subword, or + word based timestamp mapping the output log-probabilities to discrite intervals of timestamps. + The timestamps will be available in the returned Hypothesis.timestep as a dictionary. + + ctc_timestamp_type: + A str value, which represents the types of timestamps that should be calculated. + Can take the following values - "char" for character/subword time stamps, "word" for word level + time stamps and "all" (default), for both character level and word level time stamps. + + word_seperator: + Str token representing the seperator between words. + + preserve_alignments: + Bool flag which preserves the history of logprobs generated during + decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `logprobs` in it. Here, `logprobs` is a torch.Tensors. + + confidence_cfg: + A dict-like object which contains the following key-value pairs related to confidence + scores. In order to obtain hypotheses with confidence scores, please utilize + `ctc_decoder_predictions_tensor` function with the `preserve_frame_confidence` flag set to True. + + preserve_frame_confidence: + Bool flag which preserves the history of per-frame confidence scores + generated during decoding. When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `frame_confidence` is a List of floats. + + preserve_token_confidence: + Bool flag which preserves the history of per-token confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `token_confidence` in it. Here, `token_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized tokens. + + preserve_word_confidence: + Bool flag which preserves the history of per-word confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `word_confidence` in it. Here, `word_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized words. + + exclude_blank: + Bool flag indicating that blank token confidence scores are to be excluded + from the `token_confidence`. + aggregation: + Which aggregation type to use for collapsing per-token confidence into per-word confidence. + Valid options are `mean`, `min`, `max`, `prod`. + + method_cfg: + A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: + The method name (str). + Supported values: + + - 'max_prob' for using the maximum token probability as a confidence. + + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: + Which type of entropy to use (str). + Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: + Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: + A mapping of the entropy value to the interval [0,1]. + Supported values: + + - 'lin' for using the linear mapping. + + - 'exp' for using exponential mapping with linear shift. + + batch_dim_index: + Index of the batch dimension of ``targets`` and ``predictions`` parameters of + ``ctc_decoder_predictions_tensor`` methods. Can be either 0 or 1. + + The config may further contain the following sub-dictionaries: + + "greedy": + preserve_alignments: Same as above, overrides above value. + compute_timestamps: Same as above, overrides above value. + preserve_frame_confidence: Same as above, overrides above value. + confidence_method_cfg: Same as above, overrides confidence_cfg.method_cfg. + + "beam": + beam_size: + int, defining the beam size for beam search. Must be >= 1. + If beam_size == 1, will perform cached greedy search. This might be slightly different + results compared to the greedy search above. + + return_best_hypothesis: + optional bool, whether to return just the best hypothesis or all of the + hypotheses after beam search has concluded. This flag is set by default. + + beam_alpha: + float, the strength of the Language model on the final score of a token. + final_score = acoustic_score + beam_alpha * lm_score + beam_beta * seq_length. + + beam_beta: + float, the strength of the sequence length penalty on the final score of a token. + final_score = acoustic_score + beam_alpha * lm_score + beam_beta * seq_length. + + kenlm_path: + str, path to a KenLM ARPA or .binary file (depending on the strategy chosen). + If the path is invalid (file is not found at path), will raise a deferred error at the moment + of calculation of beam search, so that users may update / change the decoding strategy + to point to the correct file. + + blank_id: The id of the RNNT blank token. + """ + + def __init__( + self, decoding_cfg, vocabulary, + ): + blank_id = len(vocabulary) + self.vocabulary = vocabulary + self.labels_map = dict([(i, vocabulary[i]) for i in range(len(vocabulary))]) + + super().__init__(decoding_cfg=decoding_cfg, blank_id=blank_id) + + # Finalize Beam Search Decoding framework + if isinstance(self.decoding, ctc_beam_decoding.AbstractBeamCTCInfer): + self.decoding.set_vocabulary(self.vocabulary) + self.decoding.set_decoding_type('char') + + def _aggregate_token_confidence(self, hypothesis: Hypothesis) -> List[float]: + """ + Implemented by subclass in order to aggregate token confidence to a word-level confidence. + + Args: + hypothesis: Hypothesis + + Returns: + A list of word-level confidence scores. + """ + return self._aggregate_token_confidence_chars( + self.decode_tokens_to_str(hypothesis.text[0]).split(), hypothesis.token_confidence + ) + + def decode_tokens_to_str(self, tokens: List[int]) -> str: + """ + Implemented by subclass in order to decoder a token list into a string. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded string. + """ + hypothesis = ''.join(self.decode_ids_to_tokens(tokens)) + return hypothesis + + def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]: + """ + Implemented by subclass in order to decode a token id list into a token list. + A token list is the string representation of each token id. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded tokens. + """ + token_list = [self.labels_map[c] for c in tokens if c != self.blank_id] + return token_list + + +class CTCBPEDecoding(AbstractCTCDecoding): + """ + Used for performing CTC auto-regressive / non-auto-regressive decoding of the logprobs for subword based + models. + + Args: + decoding_cfg: A dict-like object which contains the following key-value pairs. + + strategy: + str value which represents the type of decoding that can occur. + Possible values are : + + - greedy (for greedy decoding). + + - beam (for DeepSpeed KenLM based decoding). + + compute_timestamps: + A bool flag, which determines whether to compute the character/subword, or + word based timestamp mapping the output log-probabilities to discrite intervals of timestamps. + The timestamps will be available in the returned Hypothesis.timestep as a dictionary. + + ctc_timestamp_type: + A str value, which represents the types of timestamps that should be calculated. + Can take the following values - "char" for character/subword time stamps, "word" for word level + time stamps and "all" (default), for both character level and word level time stamps. + + word_seperator: + Str token representing the seperator between words. + + preserve_alignments: + Bool flag which preserves the history of logprobs generated during + decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `logprobs` in it. Here, `logprobs` is a torch.Tensors. + + confidence_cfg: + A dict-like object which contains the following key-value pairs related to confidence + scores. In order to obtain hypotheses with confidence scores, please utilize + `ctc_decoder_predictions_tensor` function with the `preserve_frame_confidence` flag set to True. + + preserve_frame_confidence: + Bool flag which preserves the history of per-frame confidence scores + generated during decoding. When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `frame_confidence` is a List of floats. + + preserve_token_confidence: + Bool flag which preserves the history of per-token confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `token_confidence` in it. Here, `token_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized tokens. + + preserve_word_confidence: + Bool flag which preserves the history of per-word confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `word_confidence` in it. Here, `word_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized words. + + exclude_blank: + Bool flag indicating that blank token confidence scores are to be excluded + from the `token_confidence`. + + aggregation: + Which aggregation type to use for collapsing per-token confidence into per-word confidence. + Valid options are `mean`, `min`, `max`, `prod`. + + method_cfg: + A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: + The method name (str). + Supported values: + + - 'max_prob' for using the maximum token probability as a confidence. + + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: + Which type of entropy to use (str). + Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: + Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: + A mapping of the entropy value to the interval [0,1]. + Supported values: + + - 'lin' for using the linear mapping. + + - 'exp' for using exponential mapping with linear shift. + + batch_dim_index: + Index of the batch dimension of ``targets`` and ``predictions`` parameters of + ``ctc_decoder_predictions_tensor`` methods. Can be either 0 or 1. + + The config may further contain the following sub-dictionaries: + + "greedy": + preserve_alignments: Same as above, overrides above value. + compute_timestamps: Same as above, overrides above value. + preserve_frame_confidence: Same as above, overrides above value. + confidence_method_cfg: Same as above, overrides confidence_cfg.method_cfg. + + "beam": + beam_size: + int, defining the beam size for beam search. Must be >= 1. + If beam_size == 1, will perform cached greedy search. This might be slightly different + results compared to the greedy search above. + + return_best_hypothesis: + optional bool, whether to return just the best hypothesis or all of the + hypotheses after beam search has concluded. This flag is set by default. + + beam_alpha: + float, the strength of the Language model on the final score of a token. + final_score = acoustic_score + beam_alpha * lm_score + beam_beta * seq_length. + + beam_beta: + float, the strength of the sequence length penalty on the final score of a token. + final_score = acoustic_score + beam_alpha * lm_score + beam_beta * seq_length. + + kenlm_path: + str, path to a KenLM ARPA or .binary file (depending on the strategy chosen). + If the path is invalid (file is not found at path), will raise a deferred error at the moment + of calculation of beam search, so that users may update / change the decoding strategy + to point to the correct file. + + tokenizer: NeMo tokenizer object, which inherits from TokenizerSpec. + """ + + def __init__(self, decoding_cfg, tokenizer: TokenizerSpec): + blank_id = tokenizer.tokenizer.vocab_size + self.tokenizer = tokenizer + + super().__init__(decoding_cfg=decoding_cfg, blank_id=blank_id) + + # Finalize Beam Search Decoding framework + if isinstance(self.decoding, ctc_beam_decoding.AbstractBeamCTCInfer): + if hasattr(self.tokenizer.tokenizer, 'get_vocab'): + vocab_dict = self.tokenizer.tokenizer.get_vocab() + if isinstance(self.tokenizer.tokenizer, DummyTokenizer): # AggregateTokenizer.DummyTokenizer + vocab = vocab_dict + else: + vocab = list(vocab_dict.keys()) + self.decoding.set_vocabulary(vocab) + self.decoding.set_tokenizer(tokenizer) + else: + logging.warning("Could not resolve the vocabulary of the tokenizer !") + + self.decoding.set_decoding_type('subword') + + def _aggregate_token_confidence(self, hypothesis: Hypothesis) -> List[float]: + """ + Implemented by subclass in order to aggregate token confidence to a word-level confidence. + + **Note**: Only supports Sentencepiece based tokenizers! + + Args: + hypothesis: Hypothesis + + Returns: + A list of word-level confidence scores. + """ + return self._aggregate_token_confidence_subwords_sentencepiece( + self.decode_tokens_to_str(hypothesis.text[0]).split(), hypothesis.token_confidence, hypothesis.text[0] + ) + + def decode_tokens_to_str(self, tokens: List[int]) -> str: + """ + Implemented by subclass in order to decoder a token list into a string. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded string. + """ + hypothesis = self.tokenizer.ids_to_text(tokens) + return hypothesis + + def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]: + """ + Implemented by subclass in order to decode a token id list into a token list. + A token list is the string representation of each token id. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded tokens. + """ + token_list = self.tokenizer.ids_to_tokens(tokens) + return token_list + + +@dataclass +class CTCDecodingConfig: + strategy: str = "greedy" + + # preserve decoding alignments + preserve_alignments: Optional[bool] = None + + # compute ctc time stamps + compute_timestamps: Optional[bool] = None + + # token representing word seperator + word_seperator: str = " " + + # type of timestamps to calculate + ctc_timestamp_type: str = "all" # can be char, word or all for both + + # batch dimension + batch_dim_index: int = 0 + + # greedy decoding config + greedy: ctc_greedy_decoding.GreedyCTCInferConfig = field( + default_factory=lambda: ctc_greedy_decoding.GreedyCTCInferConfig() + ) + + # beam decoding config + beam: ctc_beam_decoding.BeamCTCInferConfig = field( + default_factory=lambda: ctc_beam_decoding.BeamCTCInferConfig(beam_size=4) + ) + + # confidence config + confidence_cfg: ConfidenceConfig = field(default_factory=lambda: ConfidenceConfig()) + + # can be used to change temperature for decoding + temperature: float = 1.0 + + +@dataclass +class CTCBPEDecodingConfig(CTCDecodingConfig): + pass diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/ctc_greedy_decoding.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/ctc_greedy_decoding.py new file mode 100644 index 0000000..ab4b4c4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/ctc_greedy_decoding.py @@ -0,0 +1,282 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field +from typing import List, Optional + +import torch +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.asr.parts.utils import rnnt_utils +from nemo.collections.asr.parts.utils.asr_confidence_utils import ConfidenceMethodConfig, ConfidenceMethodMixin +from nemo.core.classes import Typing, typecheck +from nemo.core.neural_types import HypothesisType, LengthsType, LogprobsType, NeuralType +from nemo.utils import logging + + +def pack_hypotheses(hypotheses: List[rnnt_utils.Hypothesis], logitlen: torch.Tensor,) -> List[rnnt_utils.Hypothesis]: + + if logitlen is not None: + if hasattr(logitlen, 'cpu'): + logitlen_cpu = logitlen.to('cpu') + else: + logitlen_cpu = logitlen + + for idx, hyp in enumerate(hypotheses): # type: rnnt_utils.Hypothesis + hyp.y_sequence = torch.tensor(hyp.y_sequence, dtype=torch.long) + + if logitlen is not None: + hyp.length = logitlen_cpu[idx] + + if hyp.dec_state is not None: + hyp.dec_state = _states_to_device(hyp.dec_state) + + return hypotheses + + +def _states_to_device(dec_state, device='cpu'): + if torch.is_tensor(dec_state): + dec_state = dec_state.to(device) + + elif isinstance(dec_state, (list, tuple)): + dec_state = tuple(_states_to_device(dec_i, device) for dec_i in dec_state) + + return dec_state + + +class GreedyCTCInfer(Typing, ConfidenceMethodMixin): + """A greedy CTC decoder. + + Provides a common abstraction for sample level and batch level greedy decoding. + + Args: + blank_index: int index of the blank token. Can be 0 or len(vocabulary). + preserve_alignments: Bool flag which preserves the history of logprobs generated during + decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `logprobs` in it. Here, `logprobs` is a torch.Tensors. + compute_timestamps: A bool flag, which determines whether to compute the character/subword, or + word based timestamp mapping the output log-probabilities to discrite intervals of timestamps. + The timestamps will be available in the returned Hypothesis.timestep as a dictionary. + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores + generated during decoding. When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `frame_confidence` is a List of floats. + confidence_method_cfg: A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: The method name (str). + Supported values: + - 'max_prob' for using the maximum token probability as a confidence. + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: Which type of entropy to use (str). Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: A mapping of the entropy value to the interval [0,1]. + Supported values: + - 'lin' for using the linear mapping. + - 'exp' for using exponential mapping with linear shift. + + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + # Input can be of dimention - + # ('B', 'T', 'D') [Log probs] or ('B', 'T') [Labels] + + return { + "decoder_output": NeuralType(None, LogprobsType()), + "decoder_lengths": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"predictions": [NeuralType(elements_type=HypothesisType())]} + + def __init__( + self, + blank_id: int, + preserve_alignments: bool = False, + compute_timestamps: bool = False, + preserve_frame_confidence: bool = False, + confidence_method_cfg: Optional[DictConfig] = None, + ): + super().__init__() + + self.blank_id = blank_id + self.preserve_alignments = preserve_alignments + # we need timestamps to extract non-blank per-frame confidence + self.compute_timestamps = compute_timestamps | preserve_frame_confidence + self.preserve_frame_confidence = preserve_frame_confidence + + # set confidence calculation method + self._init_confidence_method(confidence_method_cfg) + + @typecheck() + def forward( + self, decoder_output: torch.Tensor, decoder_lengths: torch.Tensor, + ): + """Returns a list of hypotheses given an input batch of the encoder hidden embedding. + Output token is generated auto-repressively. + + Args: + decoder_output: A tensor of size (batch, timesteps, features) or (batch, timesteps) (each timestep is a label). + decoder_lengths: list of int representing the length of each sequence + output sequence. + + Returns: + packed list containing batch number of sentences (Hypotheses). + """ + with torch.inference_mode(): + hypotheses = [] + # Process each sequence independently + + if decoder_output.is_cuda: + # This two-liner is around twenty times faster than: + # `prediction_cpu_tensor = decoder_output.cpu()` + # cpu() does not use pinned memory, meaning that a slow pageable + # copy must be done instead. + prediction_cpu_tensor = torch.empty( + decoder_output.shape, dtype=decoder_output.dtype, device=torch.device("cpu"), pin_memory=True + ) + prediction_cpu_tensor.copy_(decoder_output, non_blocking=True) + else: + prediction_cpu_tensor = decoder_output + + if decoder_lengths is not None and isinstance(decoder_lengths, torch.Tensor): + # Before this change, self._greedy_decode_labels would copy + # each scalar from GPU to CPU one at a time, in the line: + # prediction = prediction[:out_len] + # Doing one GPU to CPU copy ahead of time amortizes that overhead. + decoder_lengths = decoder_lengths.cpu() + + if prediction_cpu_tensor.ndim < 2 or prediction_cpu_tensor.ndim > 3: + raise ValueError( + f"`decoder_output` must be a tensor of shape [B, T] (labels, int) or " + f"[B, T, V] (log probs, float). Provided shape = {prediction_cpu_tensor.shape}" + ) + + # determine type of input - logprobs or labels + if prediction_cpu_tensor.ndim == 2: # labels + greedy_decode = self._greedy_decode_labels + else: + greedy_decode = self._greedy_decode_logprobs + + for ind in range(prediction_cpu_tensor.shape[0]): + out_len = decoder_lengths[ind] if decoder_lengths is not None else None + hypothesis = greedy_decode(prediction_cpu_tensor[ind], out_len) + hypotheses.append(hypothesis) + + # Pack results into Hypotheses + packed_result = pack_hypotheses(hypotheses, decoder_lengths) + + return (packed_result,) + + @torch.no_grad() + def _greedy_decode_logprobs(self, x: torch.Tensor, out_len: torch.Tensor): + # x: [T, D] + # out_len: [seq_len] + + # Initialize blank state and empty label set in Hypothesis + hypothesis = rnnt_utils.Hypothesis(score=0.0, y_sequence=[], dec_state=None, timestep=[], last_token=None) + prediction = x.cpu() + + if out_len is not None: + prediction = prediction[:out_len] + + prediction_logprobs, prediction_labels = prediction.max(dim=-1) + + non_blank_ids = prediction_labels != self.blank_id + hypothesis.y_sequence = prediction_labels.tolist() + hypothesis.score = (prediction_logprobs[non_blank_ids]).sum() + + if self.preserve_alignments: + # Preserve the logprobs, as well as labels after argmax + hypothesis.alignments = (prediction.clone(), prediction_labels.clone()) + + if self.compute_timestamps: + hypothesis.timestep = torch.nonzero(non_blank_ids, as_tuple=False)[:, 0].tolist() + + if self.preserve_frame_confidence: + hypothesis.frame_confidence = self._get_confidence(prediction) + + return hypothesis + + @torch.no_grad() + def _greedy_decode_labels(self, x: torch.Tensor, out_len: torch.Tensor): + # x: [T] + # out_len: [seq_len] + + # Initialize blank state and empty label set in Hypothesis + hypothesis = rnnt_utils.Hypothesis(score=0.0, y_sequence=[], dec_state=None, timestep=[], last_token=None) + prediction_labels = x.cpu() + + if out_len is not None: + prediction_labels = prediction_labels[:out_len] + + non_blank_ids = prediction_labels != self.blank_id + hypothesis.y_sequence = prediction_labels.tolist() + hypothesis.score = -1.0 + + if self.preserve_alignments: + raise ValueError("Requested for alignments, but predictions provided were labels, not log probabilities.") + + if self.compute_timestamps: + hypothesis.timestep = torch.nonzero(non_blank_ids, as_tuple=False)[:, 0].tolist() + + if self.preserve_frame_confidence: + raise ValueError( + "Requested for per-frame confidence, but predictions provided were labels, not log probabilities." + ) + + return hypothesis + + def __call__(self, *args, **kwargs): + return self.forward(*args, **kwargs) + + +@dataclass +class GreedyCTCInferConfig: + preserve_alignments: bool = False + compute_timestamps: bool = False + preserve_frame_confidence: bool = False + confidence_method_cfg: Optional[ConfidenceMethodConfig] = field(default_factory=lambda: ConfidenceMethodConfig()) + + def __post_init__(self): + # OmegaConf.structured ensures that post_init check is always executed + self.confidence_method_cfg = OmegaConf.structured( + self.confidence_method_cfg + if isinstance(self.confidence_method_cfg, ConfidenceMethodConfig) + else ConfidenceMethodConfig(**self.confidence_method_cfg) + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/cuda_graph_rnnt_greedy_decoding.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/cuda_graph_rnnt_greedy_decoding.py new file mode 100644 index 0000000..3887374 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/cuda_graph_rnnt_greedy_decoding.py @@ -0,0 +1,358 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy as np +import torch + +try: + from cuda import cudart + + HAVE_CUDA_PYTHON = True +except ImportError: + HAVE_CUDA_PYTHON = False +from typing import List, Optional + +from nemo.collections.asr.parts.utils import rnnt_utils +from nemo.core.utils.cuda_python_utils import ( + check_cuda_python_cuda_graphs_conditional_nodes_supported, + cu_call, + run_nvrtc, + with_conditional_node, +) + +_CUDA_PROGRAM_NAME = b"while_loop_conditional.cu" + + +def create_outer_for_loop_kernel(): + """ + Creates a kernel that evaluates whether or not to enter the for loop body. + Effectively substitutes for `for time_idx in range(trip_count)` + such that that for loop can run on a GPU. + """ + kernel_string = r"""\ + typedef __device_builtin__ unsigned long long cudaGraphConditionalHandle; + + extern "C" __device__ __cudart_builtin__ void cudaGraphSetConditional(cudaGraphConditionalHandle handle, unsigned int value); + + extern "C" __global__ + void for_loop_conditional(cudaGraphConditionalHandle handle, const long *time_idx, const long *trip_count) + { + cudaGraphSetConditional(handle, *time_idx < *trip_count); + } + """ + return run_nvrtc(kernel_string, b"for_loop_conditional", _CUDA_PROGRAM_NAME) + + +def create_inner_while_loop_kernel(): + """ + Evaluates whether or not to keep evaluating the inner while loop body. + Continue until all elements of the batch output blank or the while loop + has run max_symbols times. + """ + kernel_string = r"""\ + typedef __device_builtin__ unsigned long long cudaGraphConditionalHandle; + + extern "C" __device__ __cudart_builtin__ void cudaGraphSetConditional(cudaGraphConditionalHandle handle, unsigned int value); + + extern "C" __global__ + void while_loop_conditional(cudaGraphConditionalHandle handle, const bool *not_blank, const long *symbols_added, const long *max_symbols) + { + cudaGraphSetConditional(handle, *not_blank && *symbols_added < *max_symbols); + } + """ + return run_nvrtc(kernel_string, b"while_loop_conditional", _CUDA_PROGRAM_NAME) + + +class RNNTGreedyDecodeCudaGraph: + def __init__(self, max_symbols: int, caller): + if HAVE_CUDA_PYTHON: + check_cuda_python_cuda_graphs_conditional_nodes_supported() + else: + raise ValueError("Cannot instantiate RNNTGreedyDecodeCudaGraph without `pip install cuda-python`") + + assert max_symbols is not None + + self.max_symbols = max_symbols + + # These are cuda torch.Tensors which will be lazily allocated the first time _reinitialize() is called. + # We don't do it here because we don't know which cuda device we are using yet. + self.symbols_added_t = None + self.max_symbols_t = None + self.not_all_blank_t = None + self.time_idx_t = None + self.max_out_len_t = None + + self.encoder_output = None + self.encoder_output_length = None + self.f = None + # We also lazily initialize a variable holding the current device + self.device = None + + # Reasonable default maximum time. 375 frames * (80ms / frame) = 30 seconds + # 80ms is the frame size of recent fastconformer models + # This does not affect correctness. + self.max_time = 375 + self.batch_size = 0 + + self.scores_cpu = None + self.labels_cpu = None + self.graph = None + + self.first_call = True + + self.caller = caller + + def _reinitialize(self, max_time, batch_size, encoder_output, encoder_output_length): + if self.first_call: + # We need to call the original _greedy_decode_blank_as_pad + # implementation at least once beforehand in order to make + # sure that pytorch is "initialized". Pytorch may be + # uninitialized if this code runs before any other pytorch + # operation in this process. Pytorch often lazily + # initializes things like a cudnnHandle_t via + # cudnnCreate(), which can involve synchronizing with the + # host. Such actions are not stream capturable to a graph. + with torch.cuda.stream(torch.cuda.Stream(self.device)): + self.caller._greedy_decode_blank_as_pad_loop_frames( + encoder_output, encoder_output_length, encoder_output.device + ) + + self.device = encoder_output.device + + self.symbols_added_t = torch.tensor(0, dtype=torch.int64, device=encoder_output.device) + self.max_symbols_t = torch.tensor(self.max_symbols, dtype=torch.int64, device=encoder_output.device) + self.not_all_blank_t = torch.tensor(True, dtype=torch.bool, device=encoder_output.device) + + self.time_idx_t = torch.tensor(0, dtype=torch.int64, device=encoder_output.device) + self.max_out_len_t = torch.tensor(0, dtype=torch.int64, device=encoder_output.device) + + self.first_call = False + + self.max_time = max(self.max_time, max_time) + self.batch_size = max(self.batch_size, batch_size) + + self.encoder_output = torch.zeros( + (self.batch_size, self.max_time, encoder_output.shape[-1]), + dtype=encoder_output.dtype, + device=encoder_output.device, + ) + self.encoder_output_length = torch.zeros( + (self.batch_size,), dtype=encoder_output_length.dtype, device=encoder_output_length.device + ) + + self.zero_t = torch.tensor(0.0, dtype=encoder_output.dtype, device=encoder_output.device) + self.blank_index_t = torch.tensor(self.caller._blank_index, dtype=torch.long, device=encoder_output.device) + + self.scores_cpu = torch.zeros( + (self.batch_size, self.max_time, self.max_symbols), + dtype=encoder_output.dtype, + device="cpu", + pin_memory=True, + ) + self.labels_cpu = torch.zeros( + (self.batch_size, self.max_time, self.max_symbols), dtype=torch.int64, device="cpu", pin_memory=True + ) + + self.graph = None + + self.graph = torch.cuda.CUDAGraph() + + # Always create a new stream, because the per-thread default stream disallows stream capture to a graph. + stream_for_graph = torch.cuda.Stream(self.device) + with torch.cuda.stream(stream_for_graph), torch.inference_mode(), torch.cuda.graph( + self.graph, stream=stream_for_graph + ): + # This is failing... + self.f = torch.zeros( + (self.batch_size, 1, self.encoder_output.shape[-1]), + dtype=encoder_output.dtype, + device=encoder_output.device, + ) + hidden = self.caller.decoder.initialize_state(self.f) + self.last_label = torch.full( + [self.batch_size], fill_value=self.caller._SOS, dtype=torch.long, device=encoder_output.device + ) + self.blank_mask = torch.full( + [self.batch_size], fill_value=0, dtype=torch.bool, device=encoder_output.device + ) + self.seq_idx_t = torch.zeros([1], dtype=torch.int64, device=encoder_output.device) + + self.scores = torch.zeros( + (self.max_time * self.max_symbols, self.batch_size), + dtype=encoder_output.dtype, + device=encoder_output.device, + ) + self.labels = torch.full( + (self.max_time * self.max_symbols, self.batch_size), + fill_value=self.caller._blank_index, + dtype=torch.int64, + device=encoder_output.device, + ) + # Get max sequence length + self.max_out_len_t = self.encoder_output_length.max() + + capture_status, _, graph, _, _ = cu_call( + cudart.cudaStreamGetCaptureInfo(torch.cuda.current_stream(device=self.device).cuda_stream) + ) + assert capture_status == cudart.cudaStreamCaptureStatus.cudaStreamCaptureStatusActive + + (for_loop_conditional_handle,) = cu_call(cudart.cudaGraphConditionalHandleCreate(graph, 0, 0)) + for_loop_kernel = create_outer_for_loop_kernel() + time_idx_ptr = np.array([self.time_idx_t.data_ptr()], dtype=np.uint64) + max_out_len_ptr = np.array([self.max_out_len_t.data_ptr()], dtype=np.uint64) + for_loop_args = np.array( + [for_loop_conditional_handle.getPtr(), time_idx_ptr.ctypes.data, max_out_len_ptr.ctypes.data], + dtype=np.uint64, + ) + + with with_conditional_node(for_loop_kernel, for_loop_args, for_loop_conditional_handle, self.device): + torch.index_select(self.encoder_output, 1, self.time_idx_t.unsqueeze(0), out=self.f) + + self.not_all_blank_t.fill_(True) + self.symbols_added_t.fill_(0) + + torch.ge(self.time_idx_t, self.encoder_output_length, out=self.blank_mask) + + while_loop_kernel = create_inner_while_loop_kernel() + (while_loop_conditional_handle,) = cu_call(cudart.cudaGraphConditionalHandleCreate(graph, 0, 0)) + not_blank_ptr = np.array([self.not_all_blank_t.data_ptr()], dtype=np.uint64) + symbols_added_ptr = np.array([self.symbols_added_t.data_ptr()], dtype=np.uint64) + max_symbols_ptr = np.array([self.max_symbols_t.data_ptr()], dtype=np.uint64) + while_loop_args = np.array( + [ + while_loop_conditional_handle.getPtr(), + not_blank_ptr.ctypes.data, + symbols_added_ptr.ctypes.data, + max_symbols_ptr.ctypes.data, + ], + dtype=np.uint64, + ) + with with_conditional_node( + while_loop_kernel, while_loop_args, while_loop_conditional_handle, self.device + ): + g, hidden_prime = self.caller._pred_step( + self.last_label.unsqueeze(1), hidden, batch_size=self.batch_size + ) + logp = self.caller._joint_step(self.f, g, log_normalize=None)[:, 0, 0, :] + + v, k = logp.max(1) + torch.where(self.blank_mask, self.zero_t, v, out=v) + torch.where(self.blank_mask, self.blank_index_t, k, out=k) + # Commented out code unnecessarily causes D2H copy, which is synchronous. See pytorch issue #105641 + # self.scores[self.seq_idx_t, :] = v + # self.labels[self.seq_idx_t, :] = k + self.scores.index_copy_(0, self.seq_idx_t, v.unsqueeze(0)) + self.labels.index_copy_(0, self.seq_idx_t, k.unsqueeze(0)) + + self.blank_mask.logical_or_(k == self.caller._blank_index) + + not_blank_mask = ~self.blank_mask + + self.caller.decoder.batch_replace_states_mask( + src_states=hidden_prime, dst_states=hidden, mask=not_blank_mask + ) + torch.where(self.blank_mask, self.last_label, k, out=self.last_label) + + torch.any(not_blank_mask, 0, out=self.not_all_blank_t) + self.symbols_added_t += 1 + self.seq_idx_t += 1 + + self.time_idx_t += 1 + self.seq_idx_t += self.max_symbols_t - self.symbols_added_t + + self.scores_cpu.copy_( + self.scores.transpose(0, 1).contiguous().reshape((self.batch_size, self.max_time, self.max_symbols)), + non_blocking=True, + ) + self.labels_cpu.copy_( + self.labels.transpose(0, 1).contiguous().reshape((self.batch_size, self.max_time, self.max_symbols)), + non_blocking=True, + ) + + self.last_label.fill_(self.caller._SOS) + self.time_idx_t.fill_(0) + + def __call__( + self, + x: torch.Tensor, + out_len: torch.Tensor, + device: torch.device, + partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None, + ): + if partial_hypotheses is not None: + raise NotImplementedError("`partial_hypotheses` support is not available with cuda graphs (but could be)") + + if self.caller.preserve_alignments: + raise NotImplementedError("`preserve_alignments` support is not available with cuda graphs (but could be)") + + if self.caller.preserve_frame_confidence: + raise NotImplementedError( + "`preserve_frame_confidence` support is not available with cuda graphs (but could be)" + ) + + batch_size = x.shape[0] + # We could use out_len.max() here instead of x.shape[1], in + # case for some reason the user passes in a larger buffer than + # required, since we know that `out_len.max() <= x.shape[1]`. + max_time = x.shape[1] + + if torch.is_autocast_enabled(): + x = x.to(torch.get_autocast_gpu_dtype()) + + if max_time > self.max_time or batch_size > self.batch_size or self.device != x.device: + # In the first two cases, we need to recreate the cuda + # graph to handle larger tensor sizes. In the third case, + # we need to recreate the graph, as well as all tensors, + # because the computation is now happening on a different + # GPU. Therefore, in the third case, we unconditionally + # set self.first_call to True to make sure that all + # possibly blocking initializers are initialized properly + # again on the new device. + if self.device != x.device: + self.first_call = True + self._reinitialize(max_time, batch_size, x, out_len) + + self.encoder_output[: x.shape[0], : x.shape[1], ...].copy_(x) + self.encoder_output_length[: out_len.shape[0]].copy_(out_len) + self.graph.replay() + torch.cuda.current_stream(device=self.device).synchronize() + + self.scores_cpu[self.labels_cpu == self.caller._blank_index] = 0.0 + total_scores = self.scores_cpu.sum(dtype=torch.float32, axis=(1, 2)) + + tokens_per_timestep = (self.labels_cpu != self.caller._blank_index).sum(axis=-1) + timesteps_packed = torch.repeat_interleave( + torch.arange(self.max_time).repeat(self.batch_size), tokens_per_timestep.flatten() + ) + timestep_segments = tokens_per_timestep.sum(axis=-1) + + valid_labels_mask = self.labels_cpu != self.caller._blank_index + labels_segments = valid_labels_mask.sum(axis=(1, 2)) + labels_packed = self.labels_cpu[valid_labels_mask] + + hypotheses = [ + rnnt_utils.Hypothesis(score=0.0, y_sequence=[], timestep=[], dec_state=None) for _ in range(batch_size) + ] + + timestep_start = 0 + labels_start = 0 + for i in range(batch_size): + hypotheses[i].timestep = timesteps_packed[timestep_start : timestep_start + timestep_segments[i]].tolist() + timestep_start += timestep_segments[i] + hypotheses[i].score = float(total_scores[i]) + hypotheses[i].y_sequence = labels_packed[labels_start : labels_start + labels_segments[i]].tolist() + labels_start += labels_segments[i] + + return hypotheses diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/jasper.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/jasper.py new file mode 100644 index 0000000..e53f629 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/jasper.py @@ -0,0 +1,1178 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import Callable, Iterable, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +from torch.nn.init import _calculate_correct_fan +from torch.nn.modules.utils import _single + +from nemo.collections.common.parts.utils import activation_registry +from nemo.core.classes.mixins import AccessMixin +from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin +from nemo.utils import logging + +try: + from pytorch_quantization import calib + from pytorch_quantization import nn as quant_nn + from pytorch_quantization import quant_modules + from pytorch_quantization.tensor_quant import QuantDescriptor + + PYTORCH_QUANTIZATION_AVAILABLE = True +except ImportError: + PYTORCH_QUANTIZATION_AVAILABLE = False + +jasper_activations = activation_registry + + +def tds_uniform_(tensor, mode='fan_in'): + """ + Uniform Initialization from the paper [Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions](https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2460.pdf) + Normalized to - + + .. math:: + \\text{bound} = \\text{2} \\times \\sqrt{\\frac{1}{\\text{fan\\_mode}}} + + Args: + tensor: an n-dimensional `torch.Tensor` + mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'`` + preserves the magnitude of the variance of the weights in the + forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the + backwards pass. + """ + fan = _calculate_correct_fan(tensor, mode) + gain = 2.0 # sqrt(4.0) = 2 + std = gain / math.sqrt(fan) # sqrt(4.0 / fan_in) + bound = std # Calculate uniform bounds from standard deviation + with torch.no_grad(): + return tensor.uniform_(-bound, bound) + + +def tds_normal_(tensor, mode='fan_in'): + """ + Normal Initialization from the paper [Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions](https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2460.pdf) + Normalized to - + + .. math:: + \\text{bound} = \\text{2} \\times \\sqrt{\\frac{1}{\\text{fan\\_mode}}} + + Args: + tensor: an n-dimensional `torch.Tensor` + mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'`` + preserves the magnitude of the variance of the weights in the + forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the + backwards pass. + """ + fan = _calculate_correct_fan(tensor, mode) + gain = 2.0 + std = gain / math.sqrt(fan) # sqrt(4.0 / fan_in) + bound = std # Calculate uniform bounds from standard deviation + with torch.no_grad(): + return tensor.normal_(0.0, bound) + + +def init_weights(m, mode: Optional[str] = 'xavier_uniform'): + if isinstance(m, MaskedConv1d): + init_weights(m.conv, mode) + if isinstance(m, (nn.Conv1d, nn.Linear)): + if mode is not None: + if mode == 'xavier_uniform': + nn.init.xavier_uniform_(m.weight, gain=1.0) + elif mode == 'xavier_normal': + nn.init.xavier_normal_(m.weight, gain=1.0) + elif mode == 'kaiming_uniform': + nn.init.kaiming_uniform_(m.weight, nonlinearity="relu") + elif mode == 'kaiming_normal': + nn.init.kaiming_normal_(m.weight, nonlinearity="relu") + elif mode == 'tds_uniform': + tds_uniform_(m.weight) + elif mode == 'tds_normal': + tds_normal_(m.weight) + else: + raise ValueError("Unknown Initialization mode: {0}".format(mode)) + elif isinstance(m, nn.BatchNorm1d): + if m.track_running_stats: + m.running_mean.zero_() + m.running_var.fill_(1) + m.num_batches_tracked.zero_() + if m.affine: + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + + +def compute_new_kernel_size(kernel_size, kernel_width): + new_kernel_size = max(int(kernel_size * kernel_width), 1) + # If kernel is even shape, round up to make it odd + if new_kernel_size % 2 == 0: + new_kernel_size += 1 + return new_kernel_size + + +def get_same_padding(kernel_size, stride, dilation) -> int: + if stride > 1 and dilation > 1: + raise ValueError("Only stride OR dilation may be greater than 1") + return (dilation * (kernel_size - 1)) // 2 + + +def get_asymtric_padding(kernel_size, stride, dilation, future_context): + if stride > 1 and dilation > 1: + raise ValueError("Only stride OR dilation may be greater than 1") + + left_context = kernel_size - 1 - future_context + right_context = future_context + + symmetric_padding = get_same_padding(kernel_size, stride, dilation) + + if kernel_size <= future_context: + # kernel size is smaller than future context, equivalent to using entire context of kernel + # simply return symmetric padding for this scenario + logging.warning( + f"Future context window is larger than the kernel size!\n" + f"Left context = {left_context} | Right context = greater than {right_context} | " + f"Kernel size = {kernel_size}\n" + f"Switching to symmetric padding (left context = right context = {symmetric_padding})" + ) + return symmetric_padding + + if left_context < symmetric_padding: + logging.warning( + f"Future context window is larger than half the kernel size!\n" + f"Conv layer therefore uses more future information than past to compute its output!\n" + f"Left context = {left_context} | Right context = {right_context} | " + f"Kernel size = {kernel_size}" + ) + + if dilation > 1: + left_context = dilation * kernel_size - 1 - dilation * future_context + right_context = dilation * future_context + return (left_context, right_context) + + return (left_context, right_context) + + +@torch.jit.script +def _se_pool_step_script_infer(x: torch.Tensor, context_window: int, mask: torch.Tensor): + """ + Calculates the masked average over padded limited context segment during inference mode. + + Args: + x: Input tensor. Shape = [B, C, T] + context_window: Integer context window, must be 0 or greater. + mask: Mask tensor, 1 represents value index, 0 represents padded index. Shape = [B, 1, T]. + + Returns: + A tensor reduced via masked average pool over some limited context. Shape = [B, C, 1] + """ + timesteps = x.shape[-1] + if timesteps < context_window: + y = torch.sum(x, dim=-1, keepdim=True) / mask.sum(dim=-1, keepdim=True).to(x.dtype) + else: + # << During inference prefer to use entire context >> + # x = x[:, :, :context_window] # [B, C, context_window] + # mask = mask[:, :, :context_window] # [B, 1, context_window] + # + # mask = mask.sum(dim=-1, keepdim=True).to(x.dtype) # [B, C, 1] + # y = x.sum(dim=-1, keepdim=True) # [B, 1, 1] + # y = y / (mask + 1e-8) # [B, C, 1] + y = torch.sum(x, dim=-1, keepdim=True) / mask.sum(dim=-1, keepdim=True).to(x.dtype) + + return y + + +@torch.jit.script +def _se_pool_step_script_train(x: torch.Tensor, context_window: int, mask: torch.Tensor): + """ + Calculates the masked average over padded limited context segment during training mode. + Randomly slices a segment of length `context_window` from signal+padded input tensor across all channels and + uses it for computing masked limited context. + + Args: + x: Input tensor. Shape = [B, C, T] + context_window: Integer context window, must be 0 or greater. + mask: Mask tensor, 1 represents value index, 0 represents padded index. Shape = [B, 1, T]. + + Returns: + A tensor reduced via masked average pool over some limited context. Shape = [B, C, 1] + """ + timesteps = x.shape[-1] + if timesteps < context_window: + y = torch.sum(x, dim=-1, keepdim=True) / mask.sum(dim=-1, keepdim=True).to(x.dtype) + else: + start_idx = torch.randint(0, timesteps - context_window, size=[1], dtype=torch.int32)[0] + x = x[:, :, start_idx : (start_idx + context_window)] # [B, C, context_window] + mask = mask[:, :, start_idx : (start_idx + context_window)] # [B, 1, context_window] + + mask = mask.sum(dim=-1, keepdim=True).to(x.dtype) # [B, C, 1] + y = x.sum(dim=-1, keepdim=True) # [B, 1, 1] + y = y / (mask + 1e-8) # [B, C, 1] + + return y + + +@torch.jit.script +def _masked_conv_init_lens(lens: torch.Tensor, current_maxlen: int, original_maxlen: torch.Tensor): + if current_maxlen > original_maxlen: + new_lens = torch.arange(current_maxlen) + new_max_lens = torch.tensor(current_maxlen) + else: + new_lens = lens + new_max_lens = original_maxlen + return new_lens, new_max_lens + + +class MaskedConv1d(nn.Module): + __constants__ = ["use_conv_mask", "real_out_channels", "heads"] + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + heads=-1, + bias=False, + use_mask=True, + quantize=False, + ): + super(MaskedConv1d, self).__init__() + + if not (heads == -1 or groups == in_channels): + raise ValueError("Only use heads for depthwise convolutions") + + self.real_out_channels = out_channels + if heads != -1: + in_channels = heads + out_channels = heads + groups = heads + + # preserve original padding + self._padding = padding + + # if padding is a tuple/list, it is considered as asymmetric padding + if type(padding) in (tuple, list): + self.pad_layer = nn.ConstantPad1d(padding, value=0.0) + # reset padding for conv since pad_layer will handle this + padding = 0 + else: + self.pad_layer = None + + if PYTORCH_QUANTIZATION_AVAILABLE and quantize: + self.conv = quant_nn.QuantConv1d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias, + ) + elif not PYTORCH_QUANTIZATION_AVAILABLE and quantize: + raise ImportError( + "pytorch-quantization is not installed. Install from " + "https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization." + ) + else: + self.conv = nn.Conv1d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias, + ) + self.use_mask = use_mask + self.heads = heads + + # Calculations for "same" padding cache + self.same_padding = (self.conv.stride[0] == 1) and ( + 2 * self.conv.padding[0] == self.conv.dilation[0] * (self.conv.kernel_size[0] - 1) + ) + if self.pad_layer is None: + self.same_padding_asymmetric = False + else: + self.same_padding_asymmetric = (self.conv.stride[0] == 1) and ( + sum(self._padding) == self.conv.dilation[0] * (self.conv.kernel_size[0] - 1) + ) + + # `self.lens` caches consecutive integers from 0 to `self.max_len` that are used to compute the mask for a + # batch. Recomputed to bigger size as needed. Stored on a device of the latest batch lens. + if self.use_mask: + self.max_len = torch.tensor(0) + self.lens = torch.tensor(0) + + def get_seq_len(self, lens): + if self.same_padding or self.same_padding_asymmetric: + return lens + + if self.pad_layer is None: + return ( + torch.div( + lens + 2 * self.conv.padding[0] - self.conv.dilation[0] * (self.conv.kernel_size[0] - 1) - 1, + self.conv.stride[0], + rounding_mode='trunc', + ) + + 1 + ) + else: + return ( + torch.div( + lens + sum(self._padding) - self.conv.dilation[0] * (self.conv.kernel_size[0] - 1) - 1, + self.conv.stride[0], + rounding_mode='trunc', + ) + + 1 + ) + + def forward(self, x, lens): + if self.use_mask: + # Generally will be called by ConvASREncoder, but kept as single gpu backup. + if x.size(2) > self.max_len: + self.update_masked_length(x.size(2), device=lens.device) + x = self.mask_input(x, lens) + + # Update lengths + lens = self.get_seq_len(lens) + + # asymmtric pad if necessary + if self.pad_layer is not None: + x = self.pad_layer(x) + + sh = x.shape + if self.heads != -1: + x = x.view(-1, self.heads, sh[-1]) + + out = self.conv(x) + + if self.heads != -1: + out = out.view(sh[0], self.real_out_channels, -1) + + return out, lens + + def update_masked_length(self, max_len, seq_range=None, device=None): + if seq_range is None: + self.lens, self.max_len = _masked_conv_init_lens(self.lens, max_len, self.max_len) + self.lens = self.lens.to(device) + else: + self.lens = seq_range + self.max_len = torch.tensor(max_len) + + def mask_input(self, x, lens): + max_len = x.size(2) + mask = self.lens[:max_len].unsqueeze(0).to(lens.device) < lens.unsqueeze(1) + x = x * mask.unsqueeze(1).to(device=x.device) + return x + + +class GroupShuffle(nn.Module): + def __init__(self, groups, channels): + super(GroupShuffle, self).__init__() + + self.groups = groups + self.channels_per_group = channels // groups + + def forward(self, x): + sh = x.shape + + x = x.view(-1, self.groups, self.channels_per_group, sh[-1]) + + x = torch.transpose(x, 1, 2).contiguous() + + x = x.view(-1, self.groups * self.channels_per_group, sh[-1]) + + return x + + +class SqueezeExcite(nn.Module): + def __init__( + self, + channels: int, + reduction_ratio: int, + context_window: int = -1, + interpolation_mode: str = 'nearest', + activation: Optional[Callable] = None, + quantize: bool = False, + ): + """ + Squeeze-and-Excitation sub-module. + + Args: + channels: Input number of channels. + reduction_ratio: Reduction ratio for "squeeze" layer. + context_window: Integer number of timesteps that the context + should be computed over, using stride 1 average pooling. + If value < 1, then global context is computed. + interpolation_mode: Interpolation mode of timestep dimension. + Used only if context window is > 1. + The modes available for resizing are: `nearest`, `linear` (3D-only), + `bilinear`, `area` + activation: Intermediate activation function used. Must be a + callable activation function. + """ + super(SqueezeExcite, self).__init__() + self.interpolation_mode = interpolation_mode + self._quantize = quantize + + self.pool = None # prepare a placeholder which will be updated + + if activation is None: + activation = nn.ReLU(inplace=True) + + if PYTORCH_QUANTIZATION_AVAILABLE and quantize: + self.fc = nn.Sequential( + quant_nn.QuantLinear(channels, channels // reduction_ratio, bias=False), + activation, + quant_nn.QuantLinear(channels // reduction_ratio, channels, bias=False), + ) + elif not PYTORCH_QUANTIZATION_AVAILABLE and quantize: + raise ImportError( + "pytorch-quantization is not installed. Install from " + "https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization." + ) + else: + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction_ratio, bias=False), + activation, + nn.Linear(channels // reduction_ratio, channels, bias=False), + ) + self.gap = nn.AdaptiveAvgPool1d(1) + + # Set default context window + self.change_context_window(context_window=context_window) + + # Set default max sequence length + self.set_max_len(16) + + def forward(self, x, lengths): + return self.forward_for_export(x, lengths) + + def forward_for_export(self, x, lengths): + # The use of negative indices on the transpose allow for expanded SqueezeExcite + max_len = x.shape[-1] + if max_len > self.max_len: + self.set_max_len(max_len) + dtype = x.dtype + # Computes in float32 to avoid instabilities during training with AMP. + with torch.cuda.amp.autocast(enabled=False): + # Create sample mask - 1 represents value, 0 represents pad + mask = self.make_pad_mask(lengths, max_audio_length=max_len, device=x.device) + mask = ~mask # 0 represents value, 1 represents pad + x = x.float() # For stable AMP, SE must be computed at fp32. + x.masked_fill_(mask, 0.0) # mask padded values explicitly to 0 + y = self._se_pool_step(x, mask) # [B, C, 1] + y = y.transpose(1, -1) # [B, 1, C] + y = self.fc(y) # [B, 1, C] + y = y.transpose(1, -1) # [B, C, 1] + + # Note: Keep for future, in case we improve WER from doing so. + # if self.context_window >= 0: + # y = F.interpolate(y, size=x.shape[-1], mode=self.interpolation_mode) + + y = torch.sigmoid(y) + y = x * y + return y, lengths + + def _se_pool_step(self, x, mask): + # Negate mask back to represent 1 for signal and 0 for padded timestep. + mask = ~mask + + if self.context_window < 0: + # [B, C, 1] - Masked Average over value + padding. + y = torch.sum(x, dim=-1, keepdim=True) / mask.sum(dim=-1, keepdim=True).type(x.dtype) + else: + # [B, C, 1] - Masked Average over value + padding with limited context. + # During training randomly subsegments a context_window chunk of timesteps. + # During inference selects only the first context_window chunk of timesteps. + if self.training: + y = _se_pool_step_script_train(x, self.context_window, mask) + else: + y = _se_pool_step_script_infer(x, self.context_window, mask) + return y + + def set_max_len(self, max_len, seq_range=None): + """ Sets maximum input length. + Pre-calculates internal seq_range mask. + """ + self.max_len = max_len + if seq_range is None: + device = next(self.parameters()).device + seq_range = torch.arange(0, self.max_len, device=device) + if hasattr(self, 'seq_range'): + self.seq_range = seq_range + else: + self.register_buffer('seq_range', seq_range, persistent=False) + + def make_pad_mask(self, seq_lens, max_audio_length, device=None): + """Make masking for padding.""" + if device and self.seq_range.device != device: + self.seq_range = self.seq_range.to(device) + if self.seq_range.device != seq_lens.device: + seq_lens = seq_lens.to(self.seq_range.device) + + mask = self.seq_range[:max_audio_length].expand(seq_lens.size(0), -1) < seq_lens.unsqueeze(-1) # [B, T]; bool + mask = mask.unsqueeze(1) # [B, 1, T] + + return mask + + def change_context_window(self, context_window: int): + """ + Update the context window of the SqueezeExcitation module, in-place if possible. + + Will update the pooling layer to either nn.AdaptiveAvgPool1d() (for global SE) or nn.AvgPool1d() + (for limited context SE). + + If only the context window is changing but still a limited SE context block - then + the earlier instance of nn.AvgPool1d() will be updated. + + Args: + context_window: An integer representing the number of input timeframes that will be used + to compute the context. Each timeframe corresponds to a single window stride of the + STFT features. + + Say the window_stride = 0.01s, then a context window of 128 represents 128 * 0.01 s + of context to compute the Squeeze step. + """ + if hasattr(self, 'context_window'): + logging.info(f"Changing Squeeze-Excitation context window from {self.context_window} to {context_window}") + + self.context_window = context_window + + +class JasperBlock(nn.Module, AdapterModuleMixin, AccessMixin): + """ + Constructs a single "Jasper" block. With modified parameters, also constructs other blocks for models + such as `QuartzNet` and `Citrinet`. + + - For `Jasper` : `separable` flag should be False + - For `QuartzNet` : `separable` flag should be True + - For `Citrinet` : `separable` flag and `se` flag should be True + + Note that above are general distinctions, each model has intricate differences that expand over + multiple such blocks. + + For further information about the differences between models which use JasperBlock, please review + the configs for ASR models found in the ASR examples directory. + + Args: + inplanes: Number of input channels. + planes: Number of output channels. + repeat: Number of repeated sub-blocks (R) for this block. + kernel_size: Convolution kernel size across all repeated sub-blocks. + kernel_size_factor: Floating point scale value that is multiplied with kernel size, + then rounded down to nearest odd integer to compose the kernel size. Defaults to 1.0. + stride: Stride of the convolutional layers. + dilation: Integer which defined dilation factor of kernel. Note that when dilation > 1, stride must + be equal to 1. + padding: String representing type of padding. Currently only supports "same" padding, + which symmetrically pads the input tensor with zeros. + dropout: Floating point value, determins percentage of output that is zeroed out. + activation: String representing activation functions. Valid activation functions are : + {"hardtanh": nn.Hardtanh, "relu": nn.ReLU, "selu": nn.SELU, "swish": Swish}. + Defaults to "relu". + residual: Bool that determined whether a residual branch should be added or not. + All residual branches are constructed using a pointwise convolution kernel, that may or may not + perform strided convolution depending on the parameter `residual_mode`. + groups: Number of groups for Grouped Convolutions. Defaults to 1. + separable: Bool flag that describes whether Time-Channel depthwise separable convolution should be + constructed, or ordinary convolution should be constructed. + heads: Number of "heads" for the masked convolution. Defaults to -1, which disables it. + normalization: String that represents type of normalization performed. Can be one of + "batch", "group", "instance" or "layer" to compute BatchNorm1D, GroupNorm1D, InstanceNorm or + LayerNorm (which are special cases of GroupNorm1D). + norm_groups: Number of groups used for GroupNorm (if `normalization` == "group"). + residual_mode: String argument which describes whether the residual branch should be simply + added ("add") or should first stride, then add ("stride_add"). Required when performing stride on + parallel branch as well as utilizing residual add. + residual_panes: Number of residual panes, used for Jasper-DR models. Please refer to the paper. + conv_mask: Bool flag which determines whether to utilize masked convolutions or not. In general, + it should be set to True. + se: Bool flag that determines whether Squeeze-and-Excitation layer should be used. + se_reduction_ratio: Integer value, which determines to what extend the hidden dimension of the SE + intermediate step should be reduced. Larger values reduce number of parameters, but also limit + the effectiveness of SE layers. + se_context_window: Integer value determining the number of timesteps that should be utilized in order + to compute the averaged context window. Defaults to -1, which means it uses global context - such + that all timesteps are averaged. If any positive integer is used, it will utilize limited context + window of that size. + se_interpolation_mode: String used for interpolation mode of timestep dimension for SE blocks. + Used only if context window is > 1. + The modes available for resizing are: `nearest`, `linear` (3D-only), + `bilinear`, `area`. + stride_last: Bool flag that determines whether all repeated blocks should stride at once, + (stride of S^R when this flag is False) or just the last repeated block should stride + (stride of S when this flag is True). + future_context: Int value that determins how many "right" / "future" context frames will be utilized + when calculating the output of the conv kernel. All calculations are done for odd kernel sizes only. + + By default, this is -1, which is recomputed as the symmetric padding case. + + When future_context >= 0, will compute the asymmetric padding as follows : + (left context, right context) = [K - 1 - future_context, future_context] + + Determining an exact formula to limit future context is dependent on global layout of the model. + As such, we provide both "local" and "global" guidelines below. + + Local context limit (should always be enforced) + - future context should be <= half the kernel size for any given layer + - future context > kernel size defaults to symmetric kernel + - future context of layer = number of future frames * width of each frame (dependent on stride) + + Global context limit (should be carefully considered) + - future context should be layed out in an ever reducing pattern. Initial layers should restrict + future context less than later layers, since shallow depth (and reduced stride) means each frame uses + less amounts of future context. + - Beyond a certain point, future context should remain static for a given stride level. This is + the upper bound of the amount of future context that can be provided to the model on a global scale. + - future context is calculated (roughly) as - (2 ^ stride) * (K // 2) number of future frames. + This resultant value should be bound to some global maximum number of future seconds of audio (in ms). + + Note: In the special case where K < future_context, it is assumed that the kernel is too small to limit + its future context, so symmetric padding is used instead. + + Note: There is no explicit limitation on the amount of future context used, as long as + K > future_context constraint is maintained. This might lead to cases where future_context is + more than half the actual kernel size K! In such cases, the conv layer is utilizing more of the future + context than its current and past context to compute the output. While this is possible to do, + it is not recommended and the layer will raise a warning to notify the user of such cases. + It is advised to simply use symmetric padding for such cases. + + Example: + Say we have a model that performs 8x stride and receives spectrogram frames with stride of 0.01s. + Say we wish to upper bound future context to 80 ms. + + Layer ID, Kernel Size, Stride, Future Context, Global Context + 0, K=5, S=1, FC=8, GC= 2 * (2^0) = 2 * 0.01 ms (special case, K < FC so use symmetric pad) + 1, K=7, S=1, FC=3, GC= 3 * (2^0) = 3 * 0.01 ms (note that symmetric pad here uses 3 FC frames!) + 2, K=11, S=2, FC=4, GC= 4 * (2^1) = 8 * 0.01 ms (note that symmetric pad here uses 5 FC frames!) + 3, K=15, S=1, FC=4, GC= 4 * (2^1) = 8 * 0.01 ms (note that symmetric pad here uses 7 FC frames!) + 4, K=21, S=2, FC=2, GC= 2 * (2^2) = 8 * 0.01 ms (note that symmetric pad here uses 10 FC frames!) + 5, K=25, S=2, FC=1, GC= 1 * (2^3) = 8 * 0.01 ms (note that symmetric pad here uses 14 FC frames!) + 6, K=29, S=1, FC=1, GC= 1 * (2^3) = 8 * 0.01 ms ... + quantize: Bool flag whether to quantize the Convolutional blocks. + layer_idx (int, optional): can be specified to allow layer output capture for InterCTC loss. Defaults to -1. + """ + + __constants__ = ["conv_mask", "separable", "residual_mode", "res", "mconv"] + + def __init__( + self, + inplanes, + planes, + repeat=3, + kernel_size=11, + kernel_size_factor=1, + stride=1, + dilation=1, + padding='same', + dropout=0.2, + activation=None, + residual=True, + groups=1, + separable=False, + heads=-1, + normalization="batch", + norm_groups=1, + residual_mode='add', + residual_panes=[], + conv_mask=False, + se=False, + se_reduction_ratio=16, + se_context_window=-1, + se_interpolation_mode='nearest', + stride_last=False, + future_context: int = -1, + quantize=False, + layer_idx: int = -1, # only used for capturing tensors for interctc loss + ): + super(JasperBlock, self).__init__() + + if padding != "same": + raise ValueError("currently only 'same' padding is supported") + + kernel_size_factor = float(kernel_size_factor) + if isinstance(kernel_size, Iterable): + kernel_size = [compute_new_kernel_size(k, kernel_size_factor) for k in kernel_size] + else: + kernel_size = [compute_new_kernel_size(kernel_size, kernel_size_factor)] + + if future_context < 0: + padding_val = get_same_padding(kernel_size[0], stride[0], dilation[0]) + else: + padding_val = get_asymtric_padding(kernel_size[0], stride[0], dilation[0], future_context) + + self.inplanes = inplanes + self.planes = planes + self.conv_mask = conv_mask + self.separable = separable + self.residual_mode = residual_mode + self.se = se + self.quantize = quantize + self.layer_idx = layer_idx + # will be set in self.forward() if defined in AccessMixin config + self.interctc_should_capture = None + + inplanes_loop = inplanes + conv = nn.ModuleList() + + for _ in range(repeat - 1): + # Stride last means only the last convolution in block will have stride + if stride_last: + stride_val = [1] + else: + stride_val = stride + + conv.extend( + self._get_conv_bn_layer( + inplanes_loop, + planes, + kernel_size=kernel_size, + stride=stride_val, + dilation=dilation, + padding=padding_val, + groups=groups, + heads=heads, + separable=separable, + normalization=normalization, + norm_groups=norm_groups, + quantize=quantize, + ) + ) + + conv.extend(self._get_act_dropout_layer(drop_prob=dropout, activation=activation)) + + inplanes_loop = planes + + conv.extend( + self._get_conv_bn_layer( + inplanes_loop, + planes, + kernel_size=kernel_size, + stride=stride, + dilation=dilation, + padding=padding_val, + groups=groups, + heads=heads, + separable=separable, + normalization=normalization, + norm_groups=norm_groups, + quantize=quantize, + ) + ) + + if se: + conv.append( + SqueezeExcite( + planes, + reduction_ratio=se_reduction_ratio, + context_window=se_context_window, + interpolation_mode=se_interpolation_mode, + activation=activation, + quantize=quantize, + ) + ) + + self.mconv = conv + + res_panes = residual_panes.copy() + self.dense_residual = residual + + if residual: + res_list = nn.ModuleList() + + if residual_mode == 'stride_add': + stride_val = stride + else: + stride_val = [1] + + if len(residual_panes) == 0: + res_panes = [inplanes] + self.dense_residual = False + for ip in res_panes: + res = nn.ModuleList( + self._get_conv_bn_layer( + ip, + planes, + kernel_size=1, + normalization=normalization, + norm_groups=norm_groups, + stride=stride_val, + quantize=quantize, + ) + ) + + res_list.append(res) + + self.res = res_list + if PYTORCH_QUANTIZATION_AVAILABLE and self.quantize: + self.residual_quantizer = quant_nn.TensorQuantizer(quant_nn.QuantConv2d.default_quant_desc_input) + elif not PYTORCH_QUANTIZATION_AVAILABLE and quantize: + raise ImportError( + "pytorch-quantization is not installed. Install from " + "https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization." + ) + else: + self.res = None + + self.mout = nn.Sequential(*self._get_act_dropout_layer(drop_prob=dropout, activation=activation)) + + def _get_conv( + self, + in_channels, + out_channels, + kernel_size=11, + stride=1, + dilation=1, + padding=0, + bias=False, + groups=1, + heads=-1, + separable=False, + quantize=False, + ): + use_mask = self.conv_mask + if use_mask: + return MaskedConv1d( + in_channels, + out_channels, + kernel_size, + stride=stride, + dilation=dilation, + padding=padding, + bias=bias, + groups=groups, + heads=heads, + use_mask=use_mask, + quantize=quantize, + ) + else: + if PYTORCH_QUANTIZATION_AVAILABLE and quantize: + return quant_nn.QuantConv1d( + in_channels, + out_channels, + kernel_size, + stride=stride, + dilation=dilation, + padding=padding, + bias=bias, + groups=groups, + ) + elif not PYTORCH_QUANTIZATION_AVAILABLE and quantize: + raise ImportError( + "pytorch-quantization is not installed. Install from " + "https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization." + ) + else: + return nn.Conv1d( + in_channels, + out_channels, + kernel_size, + stride=stride, + dilation=dilation, + padding=padding, + bias=bias, + groups=groups, + ) + + def _get_conv_bn_layer( + self, + in_channels, + out_channels, + kernel_size=11, + stride=1, + dilation=1, + padding=0, + bias=False, + groups=1, + heads=-1, + separable=False, + normalization="batch", + norm_groups=1, + quantize=False, + ): + if norm_groups == -1: + norm_groups = out_channels + + if separable: + layers = [ + self._get_conv( + in_channels, + in_channels, + kernel_size, + stride=stride, + dilation=dilation, + padding=padding, + bias=bias, + groups=in_channels, + heads=heads, + quantize=quantize, + ), + self._get_conv( + in_channels, + out_channels, + kernel_size=1, + stride=1, + dilation=1, + padding=0, + bias=bias, + groups=groups, + quantize=quantize, + ), + ] + else: + layers = [ + self._get_conv( + in_channels, + out_channels, + kernel_size, + stride=stride, + dilation=dilation, + padding=padding, + bias=bias, + groups=groups, + quantize=quantize, + ) + ] + + if normalization == "group": + layers.append(nn.GroupNorm(num_groups=norm_groups, num_channels=out_channels)) + elif normalization == "instance": + layers.append(nn.GroupNorm(num_groups=out_channels, num_channels=out_channels)) + elif normalization == "layer": + layers.append(nn.GroupNorm(num_groups=1, num_channels=out_channels)) + elif normalization == "batch": + layers.append(nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.1)) + else: + raise ValueError( + f"Normalization method ({normalization}) does not match" f" one of [batch, layer, group, instance]." + ) + + if groups > 1: + layers.append(GroupShuffle(groups, out_channels)) + return layers + + def _get_act_dropout_layer(self, drop_prob=0.2, activation=None): + if activation is None: + activation = nn.Hardtanh(min_val=0.0, max_val=20.0) + layers = [activation, nn.Dropout(p=drop_prob)] + return layers + + def forward(self, input_: Tuple[List[Tensor], Optional[Tensor]]) -> Tuple[List[Tensor], Optional[Tensor]]: + """ + Forward pass of the module. + + Args: + input_: The input is a tuple of two values - the preprocessed audio signal as well as the lengths + of the audio signal. The audio signal is padded to the shape [B, D, T] and the lengths are + a torch vector of length B. + + Returns: + The output of the block after processing the input through `repeat` number of sub-blocks, + as well as the lengths of the encoded audio after padding/striding. + """ + lens_orig = None + xs = input_[0] + if len(input_) == 2: + xs, lens_orig = input_ + + # compute forward convolutions + out = xs[-1] + + lens = lens_orig + for i, l in enumerate(self.mconv): + # if we're doing masked convolutions, we need to pass in and + # possibly update the sequence lengths + # if (i % 4) == 0 and self.conv_mask: + if isinstance(l, (MaskedConv1d, SqueezeExcite)): + out, lens = l(out, lens) + else: + out = l(out) + + # compute the residuals + if self.res is not None: + for i, layer in enumerate(self.res): + res_out = xs[i] + for j, res_layer in enumerate(layer): + if isinstance(res_layer, MaskedConv1d): + res_out, _ = res_layer(res_out, lens_orig) + else: + res_out = res_layer(res_out) + + if self.residual_mode == 'add' or self.residual_mode == 'stride_add': + if PYTORCH_QUANTIZATION_AVAILABLE and self.quantize: + out = self.residual_quantizer(out) + res_out + elif not PYTORCH_QUANTIZATION_AVAILABLE and self.quantize: + raise ImportError( + "pytorch-quantization is not installed. Install from " + "https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization." + ) + else: + out = out + res_out + else: + out = torch.max(out, res_out) + + # compute the output + out = self.mout(out) + + # Support ASR Adapters + if self.is_adapter_available(): + # Check for all available and enabled adapters + adapter_names = self.get_enabled_adapters() + + if len(adapter_names) > 0: + out = out.transpose(1, 2) # (B, T, C) + + # Call the adapters + out = self.forward_enabled_adapters(out) + + out = out.transpose(1, 2) # (B, C, T) + + if self.is_access_enabled(getattr(self, "model_guid", None)): + # for adapters + if self.access_cfg.get('save_encoder_tensors', False): + self.register_accessible_tensor(name='encoder', tensor=out) + # for interctc - even though in some cases it's the same, we + # want to register separate key to be able to modify it later + # during interctc processing, if required + if self.interctc_should_capture is None: + capture_layers = self.access_cfg.get('interctc', {}).get('capture_layers', []) + self.interctc_should_capture = self.layer_idx in capture_layers + if self.interctc_should_capture: + # shape is the same as the shape of audio_signal output, i.e. [B, D, T] + self.register_accessible_tensor(name=f'interctc/layer_output_{self.layer_idx}', tensor=out) + self.register_accessible_tensor(name=f'interctc/layer_length_{self.layer_idx}', tensor=lens) + + if self.res is not None and self.dense_residual: + return xs + [out], lens + + return [out], lens + + +class ParallelBlock(nn.Module): + """ + Computational module that computes several `blocks` independently from each other and aggregates the outputs. + It expects audio inputs to be passed together with lengths, just like Jasper blocks, and all outputs to have + the same dimensions but it does not impose any additional requirements on the structure of the blocks themselves. + + Args: + blocks: List of Jasper blocks that will be computed concurently. It is expected that they accept the same + input and return outputs with the same number of channels. + aggregation_mode: an optional string, indicating how the outputs will be aggregated. Supported values are + ['sum', 'dropout']. "sum" value forces outputs to be summed together. "dropout" value enables tower + dropout training with different blocks being dropped out during training. + block_dropout_prob: a probability of dropping any individual block during training with "dropout" aggregation + mode. Acts as a regularization technique. + residual_mode: an optional string indicating how residuals will be applied. Supported values are + ['sum', 'conv']. In 'sum' mode input features are summed together with the output. This will fail if the + number of channels in the input is different from the number of channels in an output tensor. In 'conv' mode + inputs are passed through pointwise convolution to make input channel dimension match output channel + dimension. In this mode `in_filters` and `out_filters` params are required. + in_filters: number of filters (channels) in the input tensor of each block. + out_filters: number of filters (channels) in the output tensor of each block. + """ + + def __init__( + self, + blocks, + aggregation_mode: str = "sum", + block_dropout_prob: int = 0.0, + residual_mode: str = "sum", + in_filters: int = None, + out_filters: int = None, + ): + super().__init__() + self.blocks = nn.ModuleList(blocks) + + self.supported_aggregations = ["sum", "dropout"] + if aggregation_mode not in self.supported_aggregations: + raise ValueError( + f"Got non-supported aggregation mode: {aggregation_mode}. Supported values are {self.supported_aggregations}." + ) + self.aggregation_mode = aggregation_mode + + if aggregation_mode == "dropout": + self.weights = nn.Parameter(torch.ones(len(blocks)), requires_grad=False) + self.dropout = nn.Dropout(block_dropout_prob) + + self.supported_residuals = ["sum", "conv"] + if residual_mode not in self.supported_residuals: + raise ValueError( + f"Got non-supported residual mode: {residual_mode}. Supported values are {self.supported_residuals}." + ) + self.residual_mode = residual_mode + + if residual_mode == "conv": + if in_filters is None or out_filters is None: + raise ValueError("in_filters and out_filters have to be specified when using 'conv' residual mode.") + self.res_conv = MaskedConv1d(in_filters, out_filters, kernel_size=1, bias=False, use_mask=True) + + def get_dropout_mask(self): + weights = self.dropout(self.weights) + while torch.sum(weights) == 0 and self.dropout.p < 1.0: + weights = self.dropout(self.weights) + return weights + + def forward(self, x: Tuple[List[Tensor], Optional[Tensor]]): + """ + Forward pass computing aggregated output. + + Args: + x: tuple of padded signal and lengths the signal. The shape of the signal is [B, D, T]. The lengths are + 1D torch tensor of length B. + + Returns: + torch tensor after passing input throught each block and aggregating these outputs according to the + aggregation mode. + """ + if len(self.blocks) == 1: + return self.blocks[0](x) + + result = None + max_mask = None + + scaling_weights = None + if self.aggregation_mode == "dropout": + scaling_weights = self.get_dropout_mask() + + for i, block in enumerate(self.blocks): + output, mask = block(x) + + weighted_output = output[-1] + if self.aggregation_mode == "dropout": + weighted_output = scaling_weights[i] * output[-1] + + if result is None: + result = weighted_output + else: + result = result + weighted_output + + if max_mask is None: + max_mask = mask + else: + max_mask = torch.max(torch.stack([mask, max_mask]), dim=0)[0] + input_feat = x[0][-1] + lens = x[1] + if self.residual_mode == "sum": + result = result + input_feat + elif self.residual_mode == "conv": + result = result + self.res_conv(input_feat, lens)[0] + return [result], max_mask diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/multi_head_attention.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/multi_head_attention.py new file mode 100644 index 0000000..6a866a6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/multi_head_attention.py @@ -0,0 +1,1026 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Copyright 2017 Johns Hopkins University (Shinji Watanabe) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +Part of this code is adopted from https://github.com/espnet/espnet +""" + +import math +from functools import lru_cache +from typing import List, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from nemo.utils import avoid_float16_autocast_context + +__all__ = [ + 'RelPositionMultiHeadAttention', + 'RelPositionalEncoding', + 'PositionalEncoding', +] + + +class MultiHeadAttention(nn.Module): + """Multi-Head Attention layer of Transformer. + Args: + n_head (int): number of heads + n_feat (int): size of the features + dropout_rate (float): dropout rate + """ + + def __init__(self, n_head, n_feat, dropout_rate, max_cache_len=0): + """Construct an MultiHeadedAttention object.""" + super(MultiHeadAttention, self).__init__() + self.cache_drop_size = None + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.s_d_k = math.sqrt(self.d_k) + self.h = n_head + self.linear_q = nn.Linear(n_feat, n_feat) + self.linear_k = nn.Linear(n_feat, n_feat) + self.linear_v = nn.Linear(n_feat, n_feat) + self.linear_out = nn.Linear(n_feat, n_feat) + self.dropout = nn.Dropout(p=dropout_rate) + + self._max_cache_len = max_cache_len + + def forward_qkv(self, query, key, value): + """Transforms query, key and value. + Args: + query (torch.Tensor): (batch, time1, size) + key (torch.Tensor): (batch, time2, size) + value (torch.Tensor): (batch, time2, size) + returns: + q (torch.Tensor): (batch, head, time1, size) + k (torch.Tensor): (batch, head, time2, size) + v (torch.Tensor): (batch, head, time2, size) + """ + n_batch = query.size(0) + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) + k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) + v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) + q = q.transpose(1, 2) + k = k.transpose(1, 2) + v = v.transpose(1, 2) + + return q, k, v + + def forward_attention(self, value, scores, mask): + """Compute attention context vector. + Args: + value (torch.Tensor): (batch, time2, size) + scores(torch.Tensor): (batch, time1, time2) + mask(torch.Tensor): (batch, time1, time2) + returns: + value (torch.Tensor): transformed `value` (batch, time2, d_model) weighted by the attention scores + """ + n_batch = value.size(0) + if mask is not None: + mask = mask.unsqueeze(1) # (batch, 1, time1, time2) + scores = scores.masked_fill(mask, -10000.0) + attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0) # (batch, head, time1, time2) + else: + attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = x.transpose(1, 2).reshape(n_batch, -1, self.h * self.d_k) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward(self, query, key, value, mask, pos_emb=None, cache=None): + """Compute 'Scaled Dot Product Attention'. + Args: + query (torch.Tensor): (batch, time1, size) + key (torch.Tensor): (batch, time2, size) + value(torch.Tensor): (batch, time2, size) + mask (torch.Tensor): (batch, time1, time2) + cache (torch.Tensor) : (batch, time_cache, size) + + returns: + output (torch.Tensor): transformed `value` (batch, time1, d_model) weighted by the query dot key attention + cache (torch.Tensor) : (batch, time_cache_next, size) + """ + key, value, query, cache = self.update_cache(key=key, value=value, query=query, cache=cache) + + if torch.is_autocast_enabled(): + query, key, value = query.to(torch.float32), key.to(torch.float32), value.to(torch.float32) + + # temporary until we solve this more gracefully + with avoid_float16_autocast_context(): + q, k, v = self.forward_qkv(query, key, value) + scores = torch.matmul(q, k.transpose(-2, -1)) / self.s_d_k + out = self.forward_attention(v, scores, mask) + if cache is None: + return out + else: + return out, cache + + def update_cache(self, key, value, query, cache): + if cache is not None: + key = value = torch.cat([cache, key], dim=1) + q_keep_size = query.shape[1] - self.cache_drop_size + cache = torch.cat([cache[:, q_keep_size:, :], query[:, :q_keep_size, :]], dim=1) + return key, value, query, cache + + +class RelPositionMultiHeadAttention(MultiHeadAttention): + """Multi-Head Attention layer of Transformer-XL with support of relative positional encoding. + Paper: https://arxiv.org/abs/1901.02860 + Args: + n_head (int): number of heads + n_feat (int): size of the features + dropout_rate (float): dropout rate + """ + + def __init__(self, n_head, n_feat, dropout_rate, pos_bias_u, pos_bias_v, max_cache_len=0): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head=n_head, n_feat=n_feat, dropout_rate=dropout_rate, max_cache_len=max_cache_len) + # linear transformation for positional encoding + self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) + # these two learnable biases are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + if pos_bias_u is None or pos_bias_v is None: + self.pos_bias_u = nn.Parameter(torch.FloatTensor(self.h, self.d_k)) + self.pos_bias_v = nn.Parameter(torch.FloatTensor(self.h, self.d_k)) + # nn.init.normal_(self.pos_bias_u, 0.0, 0.02) + # nn.init.normal_(self.pos_bias_v, 0.0, 0.02) + nn.init.zeros_(self.pos_bias_u) + nn.init.zeros_(self.pos_bias_v) + else: + self.pos_bias_u = pos_bias_u + self.pos_bias_v = pos_bias_v + + def rel_shift(self, x): + """Compute relative positional encoding. + Args: + x (torch.Tensor): (batch, nheads, time, 2*time-1) + """ + b, h, qlen, pos_len = x.size() # (b, h, t1, t2) + # need to add a column of zeros on the left side of last dimension to perform the relative shifting + x = torch.nn.functional.pad(x, pad=(1, 0)) # (b, h, t1, t2+1) + x = x.view(b, h, -1, qlen) # (b, h, t2+1, t1) + # need to drop the first row + x = x[:, :, 1:].view(b, h, qlen, pos_len) # (b, h, t1, t2) + return x + + def forward(self, query, key, value, mask, pos_emb, cache=None): + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + Args: + query (torch.Tensor): (batch, time1, size) + key (torch.Tensor): (batch, time2, size) + value(torch.Tensor): (batch, time2, size) + mask (torch.Tensor): (batch, time1, time2) + pos_emb (torch.Tensor) : (batch, time1, size) + cache (torch.Tensor) : (batch, time_cache, size) + + Returns: + output (torch.Tensor): transformed `value` (batch, time1, d_model) weighted by the query dot key attention + cache (torch.Tensor) : (batch, time_cache_next, size) + """ + key, value, query, cache = self.update_cache(key=key, value=value, query=query, cache=cache) + + if torch.is_autocast_enabled(): + query, key, value = query.to(torch.float32), key.to(torch.float32), value.to(torch.float32) + + # temporary until we solve this more gracefully + with avoid_float16_autocast_context(): + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, time1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, time2) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + matrix_bd = self.rel_shift(matrix_bd) + # drops extra elements in the matrix_bd to match the matrix_ac's size + matrix_bd = matrix_bd[:, :, :, : matrix_ac.size(-1)] + + scores = (matrix_ac + matrix_bd) / self.s_d_k # (batch, head, time1, time2) + + out = self.forward_attention(v, scores, mask) + + if cache is None: + return out + else: + return out, cache + + +class RelPositionMultiHeadAttentionLongformer(RelPositionMultiHeadAttention): + """Multi-Head Attention layer of Transformer-XL with sliding window local+global attention from Longformer. + Partially adapted from allenai (https://github.com/allenai/longformer/blob/master/longformer/sliding_chunks.py) + and huggingface (https://github.com/huggingface/transformers/blob/main/src/transformers/models/longformer/modeling_longformer.py) + Paper: https://arxiv.org/abs/1901.02860 (Transformer-XL), + https://arxiv.org/abs/2004.05150 (Longformer) + Args: + n_head (int): number of heads + n_feat (int): size of the features + dropout_rate (float): dropout rate + pos_bias_u (Tensor): the positional bias matrix U + pos_bias_v (Tensor): the positional bias matrix V + att_context_size (List[int]): List of 2 ints corresponding to left and right attention context sizes. + max_cache_len (int): the maximum size of cache + global_tokens (int): number of tokens to be used for global attention + global_tokens_spacing (int): how far apart the global tokens are + global_attn_separate (bool): whether the q, k, v layers used for global tokens should be separate + """ + + def __init__( + self, + n_head, + n_feat, + dropout_rate, + pos_bias_u, + pos_bias_v, + att_context_size, + max_cache_len=0, + global_tokens=0, + global_tokens_spacing=1, + global_attn_separate=False, + ): + """Construct an RelPositionMultiHeadAttentionLongformer object.""" + super().__init__( + n_head=n_head, + n_feat=n_feat, + dropout_rate=dropout_rate, + pos_bias_u=pos_bias_u, + pos_bias_v=pos_bias_v, + max_cache_len=max_cache_len, + ) + self.att_context_size = att_context_size + self.global_tokens = global_tokens + self.global_tokens_spacing = global_tokens_spacing + self.global_attn_separate = global_attn_separate + + if self.global_attn_separate: + self.global_q = nn.Linear(n_feat, n_feat) + self.global_k = nn.Linear(n_feat, n_feat) + self.global_v = nn.Linear(n_feat, n_feat) + + def forward(self, query, key, value, pad_mask, pos_emb, cache=None): + """Compute Scaled Dot Product Local Attention with rel. positional encoding. using overlapping chunks + Args: + query (torch.Tensor): (batch, time, size) + key (torch.Tensor): (batch, time, size) + value(torch.Tensor): (batch, time, size) + pad_mask (torch.Tensor): (batch, time) + pos_emb (torch.Tensor) : (batch, 2w + 1, size) + cache (torch.Tensor) : (batch, time_cache, size) + Returns: + output (torch.Tensor): transformed `value` (batch, time1, d_model) weighted by the query dot key attention + cache (torch.Tensor) : (batch, time_cache_next, size) + """ + + key, value, query, cache = self.update_cache(key=key, value=value, query=query, cache=cache) + + if torch.is_autocast_enabled(): + query, key, value = query.to(torch.float32), key.to(torch.float32), value.to(torch.float32) + + # temporary until we solve this more gracefully + with avoid_float16_autocast_context(): + q, k, v = self.forward_qkv(query, key, value) + n_batch, _, T, _ = q.size() + + w = max(self.att_context_size[0], self.att_context_size[1]) + if w <= 0: + raise ValueError("When using local attention, context size must be set > 0") + pad_len = (2 * w - T % (2 * w)) % (2 * w) # pad time to 2w + q = F.pad(q, (0, 0, 0, pad_len)) # (batch, head, time, size) + k = F.pad(k, (0, 0, 0, pad_len)) # (batch, head, time, size) + v = F.pad(v, (0, 0, 0, pad_len)) # (batch, head, time, size) + mask = F.pad(pad_mask, (0, pad_len), value=1.0) + + q_with_bias_u = q + self.pos_bias_u.unsqueeze(1) # (batch, head, time, size) + q_with_bias_v = q + self.pos_bias_v.unsqueeze(1) # (batch, head, time, size) + + diagonal_matrix_ac = self.sliding_chunks_matmul_qk( + q_with_bias_u, k, w, padding_value=0.0 + ) # (batch, head, time, 2w + 1) + + # add relative positional embedding + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k).transpose(1, 2) + # (batch, head, 2w, size) + diagonal_matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + # (batch, head, time, 2w + 1) + + start_pos = w - self.att_context_size[0] + end_pos = w + self.att_context_size[1] + + diagonal_matrix_ac[:, :, :, : self.att_context_size[0]] += diagonal_matrix_bd[ + :, :, :, : self.att_context_size[0] + ] + diagonal_matrix_ac[:, :, :, -(self.att_context_size[1] + 1) :] += diagonal_matrix_bd[ + :, :, :, self.att_context_size[0] : + ] + scores = diagonal_matrix_ac / self.s_d_k + # (batch, head, time, 2w + 1) + + # mask invalid positions + scores[:, :, :, :start_pos] = -10000.0 + scores[:, :, :, end_pos + 1 :] = -10000.0 + + # This implementation is fast and takes very little memory because num_heads x hidden_size = 1 + # from (bsz x seq_len) to (bsz x num_heads x seqlen x hidden_size) + mask = mask.unsqueeze(dim=1).unsqueeze(dim=-1) + # cast to float/half then replace 1's with -inf + float_mask = mask.type_as(scores).masked_fill(mask, -10000.0) + ones = float_mask.new_ones(size=float_mask.size()) # tensor of ones + # diagonal mask with zeros everywhere and -inf inplace of padding + d_mask = self.sliding_chunks_matmul_qk(ones, float_mask, w, padding_value=0.0) + # (batch, head, time, 2w + 1) + + scores += d_mask + + if self.global_tokens > 0: + + # create q, k, v for global attn + if self.global_attn_separate: + global_q = self.global_q(query).view(n_batch, -1, self.h, self.d_k) + global_k = self.global_k(key).view(n_batch, -1, self.h, self.d_k) + global_v = self.global_v(value).view(n_batch, -1, self.h, self.d_k) + global_q = global_q.transpose(1, 2) + global_k = global_k.transpose(1, 2) + global_v = global_v.transpose(1, 2) + global_q = F.pad(global_q, (0, 0, 0, pad_len)) # (batch, head, time, size) + global_k = F.pad(global_k, (0, 0, 0, pad_len)) # (batch, head, time, size) + global_v = F.pad(global_v, (0, 0, 0, pad_len)) # (batch, head, time, size) + else: + global_q, global_k, global_v = q, k, v + + global_q /= self.s_d_k + + # assign which tokens are global + is_index_global_attn = torch.zeros_like(pad_mask) + is_index_global_attn[ + :, : self.global_tokens * self.global_tokens_spacing : self.global_tokens_spacing + ] = 1.0 + + # compute global attn indices + ( + max_num_global_attn_indices, + is_index_global_attn_nonzero, + is_local_index_global_attn_nonzero, + is_local_index_no_global_attn_nonzero, + ) = self._get_global_attn_indices(is_index_global_attn=is_index_global_attn) + + # calculate global attn probs with global keys + # (batch, time, head, max_num_global_attn_indices) + global_key_attn = self._compute_global_key_attn( + query=global_q.transpose(1, 2), + key=global_k.transpose(1, 2), + max_num_global_attn_indices=max_num_global_attn_indices, + is_index_global_attn_nonzero=is_index_global_attn_nonzero, + is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero, + is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero, + ).transpose(1, 2) + + # concat to local_attn_probs + # (batch, time, head, max_num_global_attn_indices + 2*w) + scores = torch.cat((global_key_attn, scores), dim=-1) + + # free memory + del global_key_attn + + attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0) + p_attn = self.dropout(attn) + # (batch, head, time, 2w + 1) + + if self.global_tokens > 0: + # compute sum of global and local attn + out = self._compute_attn_output_with_global_indices( + value=v, + attn_probs=p_attn, + max_num_global_attn_indices=max_num_global_attn_indices, + is_index_global_attn_nonzero=is_index_global_attn_nonzero, + is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero, + w=w, + ) + else: + # compute local attn only + out = self.sliding_chunks_matmul_pv(p_attn, v, w) + + out = out.reshape(n_batch, -1, self.h * self.d_k)[:, :T] + + if self.global_tokens > 0: + out_global_to_all = self._compute_out_global_to_all( + query=global_q, + key=global_k, + value=global_v, + max_num_global_attn_indices=max_num_global_attn_indices, + is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero, + is_index_global_attn_nonzero=is_index_global_attn_nonzero, + is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero, + is_index_masked=mask, + ) + + # overwrite values with global attention + out[is_index_global_attn_nonzero] = out_global_to_all + + ret = self.linear_out(out) + + if cache is None: + return ret + else: + return ret, cache + + def _get_global_attn_indices(self, is_index_global_attn: torch.Tensor) -> Tuple: + """ + Compute global attention indices. + + Args: + is_index_global_attn (torch.Tensor): (batch, time) A boolean tensor indicating if an index is a global attention index. + + Returns: + max_num_global_attn_indices (int): Maximum number of global attention indices in the batch. + is_index_global_attn_nonzero (tuple): Indices of global attention (non-zero elements). + is_local_index_global_attn_nonzero (tuple): Indices of non-padding values within global attention indices. + is_local_index_no_global_attn_nonzero (tuple): Indices of padding values within global attention indices. + """ + # Calculate the number of global attention indices in the batch + num_global_attn_indices = is_index_global_attn.long().sum(dim=1) + + # Find the maximum number of global attention indices in the batch + max_num_global_attn_indices = num_global_attn_indices.max() + + # Get the indices of global attention (non-zero elements) + is_index_global_attn_nonzero = is_index_global_attn.nonzero(as_tuple=True) + + # Create a helper tensor to find the local indices of global attention + is_local_index_global_attn = torch.arange( + max_num_global_attn_indices, device=is_index_global_attn.device + ) < num_global_attn_indices.unsqueeze(dim=-1) + + # Find the non-padding values within global attention indices + is_local_index_global_attn_nonzero = is_local_index_global_attn.nonzero(as_tuple=True) + + # Find the padding values within global attention indices + is_local_index_no_global_attn_nonzero = (is_local_index_global_attn == 0).nonzero(as_tuple=True) + + return ( + max_num_global_attn_indices, + is_index_global_attn_nonzero, + is_local_index_global_attn_nonzero, + is_local_index_no_global_attn_nonzero, + ) + + def _compute_global_key_attn( + self, + key: torch.Tensor, + query: torch.Tensor, + max_num_global_attn_indices: int, + is_index_global_attn_nonzero: tuple, + is_local_index_global_attn_nonzero: tuple, + is_local_index_no_global_attn_nonzero: tuple, + ) -> torch.Tensor: + """ + Compute the attention probabilities using only global key vectors. + + Args: + key (torch.Tensor): (batch, time, head, head_dim) The key vectors. + query (torch.Tensor): (batch, time, head, head_dim) The query vectors. + max_num_global_attn_indices (int): Maximum number of global attention indices in the batch. + is_index_global_attn_nonzero (tuple): Indices of global attention (non-zero elements). + is_local_index_global_attn_nonzero (tuple): Non-padding values within global attention indices. + is_local_index_no_global_attn_nonzero (tuple): Padding values within global attention indices. + + Returns: + attn_probs_from_global_key (torch.Tensor): (batch, time, head, max_num_global_attn_indices) The computed attention probabilities using only global key vectors. + """ + batch_size = key.shape[0] + + # create only global key vectors + key_only_global = key.new_zeros(batch_size, max_num_global_attn_indices, self.h, self.d_k) + + key_only_global[is_local_index_global_attn_nonzero] = key[is_index_global_attn_nonzero] + + # (batch_size, seq_len, head, max_num_global_attn_indices) + attn_probs_from_global_key = torch.einsum("blhd,bshd->blhs", (query, key_only_global)) + + # need to transpose since ONNX export only supports consecutive indexing: https://pytorch.org/docs/stable/onnx.html#writes-sets + attn_probs_from_global_key = attn_probs_from_global_key.transpose(1, 3) + attn_probs_from_global_key[ + is_local_index_no_global_attn_nonzero[0], is_local_index_no_global_attn_nonzero[1], :, : + ] = torch.finfo(attn_probs_from_global_key.dtype).min + attn_probs_from_global_key = attn_probs_from_global_key.transpose(1, 3) + + return attn_probs_from_global_key + + def _compute_attn_output_with_global_indices( + self, + value: torch.Tensor, + attn_probs: torch.Tensor, + max_num_global_attn_indices: int, + is_index_global_attn_nonzero: tuple, + is_local_index_global_attn_nonzero: tuple, + w: int, + ) -> torch.Tensor: + """ + Compute the attention output with global indices. + + Args: + value (torch.Tensor): (batch, head, time, head_dim) The value vectors for global attention. + attn_probs (torch.Tensor): (batch, time, head, 2w) The attention probabilities. + max_num_global_attn_indices (int): Maximum number of global attention indices in the batch. + is_index_global_attn_nonzero (tuple): Indices of global attention (non-zero elements). + is_local_index_global_attn_nonzero (tuple): Non-padding values within global attention indices. + w (int): Local context size + Returns: + torch.Tensor: (batch, time, head x head_dim) The attention output of all tokens attending to global. + """ + batch_size, time = attn_probs.shape[0], attn_probs.shape[2] + + value = value.transpose(1, 2) + + # get value vectors for global only + value_vectors_only_global = value.new_zeros(batch_size, max_num_global_attn_indices, self.h, self.d_k) + value_vectors_only_global[is_local_index_global_attn_nonzero] = value[is_index_global_attn_nonzero] + + # cut local attn probs to global only + attn_probs_only_global = attn_probs.narrow(-1, 0, max_num_global_attn_indices) + # compute attn output only global + attn_output_only_global = torch.matmul( + attn_probs_only_global.clone(), value_vectors_only_global.transpose(1, 2).clone() + ).transpose(1, 2) + + # reshape attn probs + attn_probs_without_global = attn_probs.narrow( + -1, max_num_global_attn_indices, attn_probs.size(-1) - max_num_global_attn_indices + ).contiguous() + + # compute attn output with global + attn_output_without_global = self.sliding_chunks_matmul_pv(attn_probs_without_global, value.transpose(1, 2), w) + + return attn_output_only_global + attn_output_without_global + + def _compute_out_global_to_all( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + max_num_global_attn_indices: int, + is_local_index_global_attn_nonzero: tuple, + is_index_global_attn_nonzero: tuple, + is_local_index_no_global_attn_nonzero: tuple, + is_index_masked: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Compute the attention output of global tokens attending to all. + + Args: + query (torch.Tensor): (batch, head, time, head_dim) The queries for global attention. + key (torch.Tensor): (batch, head, time, head_dim) The keys for global attention. + value (torch.Tensor): (batch, head, time, head_dim) The values for global attention. + max_num_global_attn_indices (int): Maximum number of global attention indices in the batch. + is_local_index_global_attn_nonzero (tuple): Non-padding values within global attention indices. + is_index_global_attn_nonzero (tuple): Indices of global attention (non-zero elements). + is_local_index_no_global_attn_nonzero (tuple): Padding values within global attention indices. + is_index_masked (torch.Tensor): (batch, time) A boolean tensor indicating if an index is masked. + + Returns: + global_attn_output (torch.Tensor): (batch, max_num_global_attn_indices, head x head_dim) + The attention output of global tokens attending to all. + """ + + batch_size = key.shape[0] + seq_len = key.shape[2] + + global_k = key.reshape(batch_size * self.h, -1, self.d_k) + global_v = value.reshape(batch_size * self.h, -1, self.d_k) + + global_q = query.transpose(1, 2) + global_q_from_global = global_q.new_zeros(batch_size, max_num_global_attn_indices, self.h, self.d_k) + global_q_from_global[is_local_index_global_attn_nonzero] = global_q[is_index_global_attn_nonzero] + global_q_from_global = global_q_from_global.transpose(0, 1).reshape(batch_size * self.h, -1, self.d_k) + + # compute attn scores + global_attn_scores = torch.bmm(global_q_from_global, global_k.transpose(1, 2)) + global_attn_scores = global_attn_scores.view(batch_size, self.h, max_num_global_attn_indices, seq_len) + + # need to transpose since ONNX export only supports consecutive indexing: https://pytorch.org/docs/stable/onnx.html#writes-sets + global_attn_scores = global_attn_scores.transpose(1, 2) + global_attn_scores[ + is_local_index_no_global_attn_nonzero[0], is_local_index_no_global_attn_nonzero[1], :, : + ] = torch.finfo(global_attn_scores.dtype).min + global_attn_scores = global_attn_scores.transpose(1, 2) + + global_attn_scores = global_attn_scores.masked_fill( + is_index_masked.transpose(2, 3), torch.finfo(global_attn_scores.dtype).min, + ) + + global_attn_scores = global_attn_scores.view(batch_size * self.h, max_num_global_attn_indices, seq_len) + + # compute global attn probs + global_attn_probs_float = nn.functional.softmax(global_attn_scores, dim=-1, dtype=torch.float32) + + global_attn_probs = self.dropout(global_attn_probs_float) + + # global attn output + global_attn_output = torch.bmm(global_attn_probs, global_v) + global_attn_output = global_attn_output.view(batch_size, self.h, max_num_global_attn_indices, self.d_k) + + global_attn_output = global_attn_output[ + is_local_index_global_attn_nonzero[0], :, is_local_index_global_attn_nonzero[1] + ] + + global_attn_output = global_attn_output.reshape(global_attn_output.shape[0], -1) + + return global_attn_output + + # Longformer implementation for overlap case + # + def _skew(self, x: torch.Tensor, direction: List[int], padding_value: float) -> torch.Tensor: + """Convert diagonals into columns (or columns into diagonals depending on `direction` + + Args: + x (torch.Tensor): (batch x head, chunk_count, 2w, 2w) + direction (List[int]): padding directions + padding_value (float): value to pad with + + Returns: + output (torch.Tensor): (batch x head, chunk_count, 2w, 2w + 1) + + """ + x_padded = F.pad(x, direction, value=padding_value) + x_padded = x_padded.view(*x_padded.size()[:-2], x_padded.size(-1), x_padded.size(-2)) + return x_padded + + def _skew2(self, x: torch.Tensor, padding_value: float) -> torch.Tensor: + """Shift every row 1 step to right converting columns into diagonals + + Args: + x (torch.Tensor): (batch x head, chunks_count + 1, w, 2w + 1) + padding_value (float): value to pad with + + Returns: + output (torch.Tensor): (batch x head, chunks_count + 1, w, 3w) + """ + # X = B x C x M x L + B, C, M, L = x.size() + x = F.pad(x, (0, M + 1), value=padding_value) # B x C x M x (L+M+1) + x = x.view(B, C, -1) # B x C x ML+MM+M + x = x[:, :, :-M] # B x C x ML+MM + x = x.view(B, C, M, M + L) # B x C, M x L+M + x = x[:, :, :, :-1] + return x + + def _chunk_overlap(self, x: torch.Tensor, w: int) -> torch.Tensor: + """Convert into overlapping chunks. + + Args: + x (torch.Tensor): # (batch x head, time, size) + w (int): Chunk overlap size + + Returns: + output (torch.Tensor): # (batch x head, chunk_count, 2w, size) + """ + + # non-overlapping chunks of size = 2w + x = x.view(x.size(0), x.size(1) // (w * 2), w * 2, x.size(2)) + + # use `as_strided` to make the chunks overlap with an overlap size = w + chunk_size = list(x.size()) + chunk_size[1] = chunk_size[1] * 2 - 1 + + chunk_stride = list(x.stride()) + chunk_stride[1] = chunk_stride[1] // 2 + return x.as_strided(size=chunk_size, stride=chunk_stride) + + @lru_cache() + def _get_invalid_locations_mask(self, w: int, device: str): + + diagonals_list = [] + for j in range(-w, 1): + diagonal_mask = torch.zeros(w, device='cpu', dtype=torch.uint8) + diagonal_mask[:-j] = 1 + diagonals_list.append(diagonal_mask) + + mask = torch.stack(diagonals_list, dim=-1) + mask = mask[None, None, :, :] + + ending_mask = mask.flip(dims=(2, 3)).bool().to(device) + return mask.bool().to(device), ending_mask + + def mask_invalid_locations( + self, input_tensor: torch.Tensor, w: int, + ): + """ + Mask locations invalid for the sliding window attention + + Args: + input_tensor (torch.Tensor): # (batch x head, time, size) + w (int): Chunk overlap size + """ + beginning_mask, ending_mask = self._get_invalid_locations_mask(w, input_tensor.device) + seq_len = input_tensor.size(2) + beginning_input = input_tensor[:, :, :w, : w + 1] + beginning_mask = beginning_mask[:, :, :seq_len].expand(beginning_input.size()) + beginning_input.masked_fill_(beginning_mask, -float('inf')) + + ending_input = input_tensor[:, :, -w:, -(w + 1) :] + ending_mask = ending_mask[:, :, -seq_len:].expand(ending_input.size()) + ending_input.masked_fill_(ending_mask, -float('inf')) + + def sliding_chunks_matmul_qk(self, q: torch.Tensor, k: torch.Tensor, w: int, padding_value: float) -> torch.Tensor: + """Matrix multiplication of query x key tensors using with a sliding window attention pattern. + This implementation splits the input into overlapping chunks of size 2w + with an overlap of size w + + Args: + q (torch.Tensor): (batch, head, time, size) + k (torch.Tensor): (batch, head, time, size) + w (int): Chunk overlap size + padding_value (float): Value to pad with + + Returns: + output (torch.Tensor): (batch, head, time, 2w + 1) + """ + bsz, num_heads, seqlen, head_dim = q.size() + assert seqlen % (w * 2) == 0 + assert q.size() == k.size() + + chunks_count = seqlen // w - 1 + + # group bsz and num_heads dimensions into one, then chunk seqlen into chunks of size w * 2 + q = q.reshape(bsz * num_heads, seqlen, head_dim) + k = k.reshape(bsz * num_heads, seqlen, head_dim) + + chunk_q = self._chunk_overlap(q, w) # (batch x head, chunk_count, 2w, size) + chunk_k = self._chunk_overlap(k, w) # (batch x head, chunk_count, 2w, size) + + # matrix multipication + # bcxd: bsz*num_heads x chunks x 2w x head_dim + # bcyd: bsz*num_heads x chunks x 2w x head_dim + # bcxy: bsz*num_heads x chunks x 2w x 2w + chunk_attn = torch.einsum('bcxd,bcyd->bcxy', (chunk_q, chunk_k)) # multiply + # (batch x head, chunk_count, 2w, 2w) + + # convert diagonals into columns + diagonal_chunk_attn = self._skew(chunk_attn, direction=(0, 0, 0, 1), padding_value=padding_value) + # (batch x head, chunk_count, 2w, 2w + 1) + + # allocate space for the overall attention matrix where the chunks are combined. The last dimension + # has (w * 2 + 1) columns. The first (w) columns are the w lower triangles (attention from a word to + # w previous words). The following column is attention score from each word to itself, then + # followed by w columns for the upper triangle. + + diagonal_attn = diagonal_chunk_attn.new_empty((bsz * num_heads, chunks_count + 1, w, w * 2 + 1)) + # (batch x head, chunk_count + 1, w, 2w + 1) + + # copy parts from diagonal_chunk_attn into the compined matrix of attentions + # - copying the main diagonal and the upper triangle + diagonal_attn[:, :-1, :, w:] = diagonal_chunk_attn[:, :, :w, : w + 1] + diagonal_attn[:, -1, :, w:] = diagonal_chunk_attn[:, -1, w:, : w + 1] + # - copying the lower triangle + diagonal_attn[:, 1:, :, :w] = diagonal_chunk_attn[:, :, -(w + 1) : -1, w + 1 :] + diagonal_attn[:, 0, 1:w, 1:w] = diagonal_chunk_attn[:, 0, : w - 1, 1 - w :] + + # separate bsz and num_heads dimensions again + diagonal_attn = diagonal_attn.view(bsz, num_heads, seqlen, 2 * w + 1) + # (batch, head, time, 2w + 1) + + self.mask_invalid_locations(diagonal_attn, w) + + return diagonal_attn + + def sliding_chunks_matmul_pv(self, prob: torch.Tensor, v: torch.Tensor, w: int): + """Same as sliding_chunks_matmul_qk but for prob and value tensors. + + Args: + prob (torch.Tensor): (batch, head, time, size) + v (torch.Tensor): (batch, head, time, size) + w (int): Chunk overlap size + + Returns: + output (torch.Tensor): (batch, time, head, size) + """ + bsz, num_heads, seqlen, head_dim = v.size() + chunks_count = seqlen // w - 1 + # group bsz and num_heads dimensions into one, then chunk seqlen into chunks of size 2w + chunk_prob = prob.reshape(bsz * num_heads, seqlen // w, w, 2 * w + 1) + # (batch x head, chunks_count + 1, w, 2w + 1) + + # group bsz and num_heads dimensions into one + v = v.reshape(bsz * num_heads, seqlen, head_dim) + # (batch x head, time, size) + + # pad seqlen with w at the beginning of the sequence and another w at the end + padded_v = F.pad(v, (0, 0, w, w), value=-1) + # (batch x head, time + 2w, size) + + # chunk padded_v into chunks of size 3w and an overlap of size w + chunk_v_size = (bsz * num_heads, chunks_count + 1, 3 * w, head_dim) + chunk_v_stride = padded_v.stride() + chunk_v_stride = chunk_v_stride[0], w * chunk_v_stride[1], chunk_v_stride[1], chunk_v_stride[2] + chunk_v = padded_v.as_strided(size=chunk_v_size, stride=chunk_v_stride) + # (batch x head, chunks_count + 1, 3w, size) + + skewed_prob = self._skew2(chunk_prob, padding_value=0) + # (batch x head, chunks_count + 1, w, 3w) + + context = torch.einsum('bcwd,bcdh->bcwh', (skewed_prob, chunk_v)) + # (batch x head, chunks_count + 1, w, size) + + return context.view(bsz, num_heads, seqlen, head_dim).transpose(1, 2) + + +class PositionalEncoding(torch.nn.Module): + """Fixed sinusoidal positional encoding. + Args: + d_model (int): embedding dim + dropout_rate (float): dropout rate + max_len (int): maximum input length + xscale (bool): whether to scale the input by sqrt(d_model) + dropout_rate_emb (float): dropout rate for the positional embeddings + """ + + def __init__(self, d_model, dropout_rate, max_len=5000, xscale=None, dropout_rate_emb=0.0): + """Construct an PositionalEncoding object.""" + super(PositionalEncoding, self).__init__() + self.d_model = d_model + self.xscale = xscale + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.max_len = max_len + if dropout_rate_emb > 0: + self.dropout_emb = nn.Dropout(dropout_rate_emb) + else: + self.dropout_emb = None + + def create_pe(self, positions): + pos_length = positions.size(0) + pe = torch.zeros(pos_length, self.d_model, device=positions.device) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32, device=positions.device) + * -(math.log(10000.0) / self.d_model) + ) + pe[:, 0::2] = torch.sin(positions * div_term) + pe[:, 1::2] = torch.cos(positions * div_term) + pe = pe.unsqueeze(0) + if hasattr(self, 'pe'): + self.pe = pe + else: + self.register_buffer('pe', pe, persistent=False) + + def extend_pe(self, length, device): + """Reset and extend the positional encodings if needed.""" + if hasattr(self, 'pe') and self.pe.size(1) >= length: + return + positions = torch.arange(0, length, dtype=torch.float32, device=device).unsqueeze(1) + self.create_pe(positions=positions) + + def forward(self, x: torch.Tensor, cache_len=0): + """Adds positional encoding. + Args: + x (torch.Tensor): Input. Its shape is (batch, time, feature_size) + cache_len (int): the size of the cache which is used to shift positions + Returns: + x+pos_emb (torch.Tensor): Its shape is (batch, time, feature_size) + pos_emb (torch.Tensor): Its shape is (1, time, feature_size) + """ + input_len = x.size(1) + cache_len + if self.xscale: + x = x * self.xscale + pos_emb = self.pe[:, :input_len] + if self.dropout_emb: + pos_emb = self.dropout_emb(pos_emb) + x = x + pos_emb + return self.dropout(x), pos_emb + + +class RelPositionalEncoding(PositionalEncoding): + """Relative positional encoding for TransformerXL's layers + See : Appendix B in https://arxiv.org/abs/1901.02860 + Args: + d_model (int): embedding dim + dropout_rate (float): dropout rate + max_len (int): maximum input length + xscale (bool): whether to scale the input by sqrt(d_model) + dropout_rate_emb (float): dropout rate for the positional embeddings + """ + + def extend_pe(self, length, device): + """Reset and extend the positional encodings if needed.""" + needed_size = 2 * length - 1 + if hasattr(self, 'pe') and self.pe.size(1) >= needed_size: + return + # positions would be from negative numbers to positive + # positive positions would be used for left positions and negative for right positions + positions = torch.arange(length - 1, -length, -1, dtype=torch.float32, device=device).unsqueeze(1) + self.create_pe(positions=positions) + + def forward(self, x, cache_len=0): + """Compute positional encoding. + Args: + x (torch.Tensor): Input. Its shape is (batch, time, feature_size) + cache_len (int): the size of the cache which is used to shift positions + Returns: + x (torch.Tensor): Its shape is (batch, time, feature_size) + pos_emb (torch.Tensor): Its shape is (1, time, feature_size) + """ + + if self.xscale: + x = x * self.xscale + + # center_pos would be the index of position 0 + # negative positions would be used for right and positive for left tokens + # for input of length L, 2*L-1 positions are needed, positions from (L-1) to -(L-1) + input_len = x.size(1) + cache_len + center_pos = self.pe.size(1) // 2 + 1 + start_pos = center_pos - input_len + end_pos = center_pos + input_len - 1 + pos_emb = self.pe[:, start_pos:end_pos] + if self.dropout_emb: + pos_emb = self.dropout_emb(pos_emb) + return self.dropout(x), pos_emb + + +class LocalAttRelPositionalEncoding(PositionalEncoding): + """Relative positional encoding for sliding window attention or chunked attention. + See above for relative positional encoding based on Transformer-XL paper + Args: + left_chunk_size (int): number of frames to in past chunks + chunk size (int): number of frames (max frames if using multimode) in current chunk + d_model (int): embedding dim + dropout_rate (float): dropout rate + max_len (int): maximum input length + xscale (bool): whether to scale the input by sqrt(d_model) + dropout_rate_emb (float): dropout rate for the positional embeddings + """ + + def __init__(self, att_context_size, **kwargs): + super(LocalAttRelPositionalEncoding, self).__init__(**kwargs) + self.left_context = att_context_size[0] + self.right_context = att_context_size[1] + + def extend_pe(self, length, device): + """Reset and extend the positional encodings only at the beginning""" + if hasattr(self, 'pe'): + return + + positions = torch.arange( + self.left_context, -self.right_context - 1, -1, dtype=torch.float32, device=device + ).unsqueeze(1) + self.create_pe(positions=positions) + + def forward(self, x, cache_len=0): + """Compute positional encoding. + Args: + x (torch.Tensor): Input. Its shape is (batch, time, feature_size) + Returns: + x (torch.Tensor): Its shape is (batch, time, feature_size) + pos_emb (torch.Tensor): Its shape is (1, time, feature_size) + """ + + if self.xscale: + x = x * self.xscale + + end_pos = self.left_context + self.right_context + 1 + pos_emb = self.pe[:, :end_pos] + if self.dropout_emb: + pos_emb = self.dropout_emb(pos_emb) + return self.dropout(x), pos_emb diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/multichannel_modules.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/multichannel_modules.py new file mode 100644 index 0000000..04ab998 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/multichannel_modules.py @@ -0,0 +1,780 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import random +from typing import Callable, Optional + +import torch + +from nemo.collections.asr.parts.submodules.multi_head_attention import MultiHeadAttention +from nemo.core.classes import NeuralModule, typecheck +from nemo.core.neural_types import AudioSignal, FloatType, NeuralType, SpectrogramType +from nemo.utils import logging + +try: + import torchaudio + + HAVE_TORCHAUDIO = True +except ModuleNotFoundError: + HAVE_TORCHAUDIO = False + + +class ChannelAugment(NeuralModule): + """Randomly permute and selects a subset of channels. + + Args: + permute_channels (bool): Apply a random permutation of channels. + num_channels_min (int): Minimum number of channels to select. + num_channels_max (int): Max number of channels to select. + rng: Optional, random generator. + seed: Optional, seed for the generator. + """ + + def __init__( + self, + permute_channels: bool = True, + num_channels_min: int = 1, + num_channels_max: Optional[int] = None, + rng: Optional[Callable] = None, + seed: Optional[int] = None, + ): + super().__init__() + + self._rng = random.Random(seed) if rng is None else rng + self.permute_channels = permute_channels + self.num_channels_min = num_channels_min + self.num_channels_max = num_channels_max + + if num_channels_max is not None and num_channels_min > num_channels_max: + raise ValueError( + f'Min number of channels {num_channels_min} cannot be greater than max number of channels {num_channels_max}' + ) + + logging.debug('Initialized %s with', self.__class__.__name__) + logging.debug('\tpermute_channels: %s', self.permute_channels) + logging.debug('\tnum_channels_min: %s', self.num_channels_min) + logging.debug('\tnum_channels_max: %s', self.num_channels_max) + + @property + def input_types(self): + """Returns definitions of module input types + """ + return { + 'input': NeuralType(('B', 'C', 'T'), AudioSignal()), + } + + @property + def output_types(self): + """Returns definitions of module output types + """ + return { + 'output': NeuralType(('B', 'C', 'T'), AudioSignal()), + } + + @typecheck() + @torch.no_grad() + def forward(self, input: torch.Tensor) -> torch.Tensor: + # Expecting (B, C, T) + assert input.ndim == 3, f'Expecting input with shape (B, C, T)' + num_channels_in = input.size(1) + + if num_channels_in < self.num_channels_min: + raise RuntimeError( + f'Number of input channels ({num_channels_in}) is smaller than the min number of output channels ({self.num_channels_min})' + ) + + num_channels_max = num_channels_in if self.num_channels_max is None else self.num_channels_max + num_channels_out = self._rng.randint(self.num_channels_min, num_channels_max) + + channels = list(range(num_channels_in)) + + if self.permute_channels: + self._rng.shuffle(channels) + + channels = channels[:num_channels_out] + + return input[:, channels, :] + + +class TransformAverageConcatenate(NeuralModule): + """Apply transform-average-concatenate across channels. + We're using a version from [2]. + + Args: + in_features: Number of input features + out_features: Number of output features + + References: + [1] Luo et al, End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation, 2019 + [2] Yoshioka et al, VarArray: Array-Geometry-Agnostic Continuous Speech Separation, 2022 + """ + + def __init__(self, in_features: int, out_features: Optional[int] = None): + super().__init__() + + if out_features is None: + out_features = in_features + + # Parametrize with the total number of features (needs to be divisible by two due to stacking) + if out_features % 2 != 0: + raise ValueError(f'Number of output features should be divisible by two, currently set to {out_features}') + + self.transform_channel = torch.nn.Sequential( + torch.nn.Linear(in_features, out_features // 2, bias=False), torch.nn.ReLU() + ) + self.transform_average = torch.nn.Sequential( + torch.nn.Linear(in_features, out_features // 2, bias=False), torch.nn.ReLU() + ) + + logging.debug('Initialized %s with', self.__class__.__name__) + logging.debug('\tin_features: %d', in_features) + logging.debug('\tout_features: %d', out_features) + + @property + def input_types(self): + """Returns definitions of module input types + """ + return { + 'input': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + } + + @property + def output_types(self): + """Returns definitions of module output types + """ + return { + 'output': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + } + + @typecheck() + def forward(self, input: torch.Tensor) -> torch.Tensor: + """ + Args: + input: shape (B, M, in_features, T) + + Returns: + Output tensor with shape shape (B, M, out_features, T) + """ + B, M, F, T = input.shape + + # (B, M, F, T) -> (B, T, M, F) + input = input.permute(0, 3, 1, 2) + + # transform and average across channels + average = self.transform_average(input) + average = torch.mean(average, dim=-2, keepdim=True) + # view with the number of channels expanded to M + average = average.expand(-1, -1, M, -1) + + # transform each channel + transform = self.transform_channel(input) + + # concatenate along feature dimension + output = torch.cat([transform, average], dim=-1) + + # Return to the original layout + # (B, T, M, F) -> (B, M, F, T) + output = output.permute(0, 2, 3, 1) + + return output + + +class TransformAttendConcatenate(NeuralModule): + """Apply transform-attend-concatenate across channels. + The output is a concatenation of transformed channel and MHA + over channels. + + Args: + in_features: Number of input features + out_features: Number of output features + n_head: Number of heads for the MHA module + dropout_rate: Dropout rate for the MHA module + + References: + - Jukić et al, Flexible multichannel speech enhancement for noise-robust frontend, 2023 + """ + + def __init__(self, in_features: int, out_features: Optional[int] = None, n_head: int = 4, dropout_rate: float = 0): + super().__init__() + + if out_features is None: + out_features = in_features + + # Parametrize with the total number of features (needs to be divisible by two due to stacking) + if out_features % 2 != 0: + raise ValueError(f'Number of output features should be divisible by two, currently set to {out_features}') + + self.transform_channel = torch.nn.Sequential( + torch.nn.Linear(in_features, out_features // 2, bias=False), torch.nn.ReLU() + ) + self.transform_attend = torch.nn.Sequential( + torch.nn.Linear(in_features, out_features // 2, bias=False), torch.nn.ReLU() + ) + self.attention = MultiHeadAttention(n_head=n_head, n_feat=out_features // 2, dropout_rate=dropout_rate) + + logging.debug('Initialized %s with', self.__class__.__name__) + logging.debug('\tin_features: %d', in_features) + logging.debug('\tout_features: %d', out_features) + logging.debug('\tn_head: %d', n_head) + logging.debug('\tdropout_rate: %f', dropout_rate) + + @property + def input_types(self): + """Returns definitions of module input types + """ + return { + 'input': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + } + + @property + def output_types(self): + """Returns definitions of module output types + """ + return { + 'output': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + } + + @typecheck() + def forward(self, input: torch.Tensor) -> torch.Tensor: + """ + Args: + input: shape (B, M, in_features, T) + + Returns: + Output tensor with shape shape (B, M, out_features, T) + """ + B, M, F, T = input.shape + + # (B, M, F, T) -> (B, T, M, F) + input = input.permute(0, 3, 1, 2) + input = input.reshape(B * T, M, F) + + # transform each channel + transform = self.transform_channel(input) + + # attend + attend = self.transform_attend(input) + # attention across channels + attend = self.attention(query=attend, key=attend, value=attend, mask=None) + + # concatenate along feature dimension + output = torch.cat([transform, attend], dim=-1) + + # return to the original layout + output = output.view(B, T, M, -1) + + # (B, T, M, num_features) -> (B, M, num_features, T) + output = output.permute(0, 2, 3, 1) + + return output + + +class ChannelAveragePool(NeuralModule): + """Apply average pooling across channels. + """ + + def __init__(self): + super().__init__() + logging.debug('Initialized %s', self.__class__.__name__) + + @property + def input_types(self): + """Returns definitions of module input types + """ + return { + 'input': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + } + + @property + def output_types(self): + """Returns definitions of module output types + """ + return { + 'output': NeuralType(('B', 'D', 'T'), SpectrogramType()), + } + + @typecheck() + def forward(self, input: torch.Tensor) -> torch.Tensor: + """ + Args: + input: shape (B, M, F, T) + + Returns: + Output tensor with shape shape (B, F, T) + """ + return torch.mean(input, dim=-3) + + +class ChannelAttentionPool(NeuralModule): + """Use attention pooling to aggregate information across channels. + First apply MHA across channels and then apply averaging. + + Args: + in_features: Number of input features + out_features: Number of output features + n_head: Number of heads for the MHA module + dropout_rate: Dropout rate for the MHA module + + References: + - Wang et al, Neural speech separation using sparially distributed microphones, 2020 + - Jukić et al, Flexible multichannel speech enhancement for noise-robust frontend, 2023 + """ + + def __init__(self, in_features: int, n_head: int = 1, dropout_rate: float = 0): + super().__init__() + self.in_features = in_features + self.attention = MultiHeadAttention(n_head=n_head, n_feat=in_features, dropout_rate=dropout_rate) + + logging.debug('Initialized %s with', self.__class__.__name__) + logging.debug('\tin_features: %d', in_features) + logging.debug('\tnum_heads: %d', n_head) + logging.debug('\tdropout_rate: %d', dropout_rate) + + @property + def input_types(self): + """Returns definitions of module input types + """ + return { + 'input': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + } + + @property + def output_types(self): + """Returns definitions of module output types + """ + return { + 'output': NeuralType(('B', 'D', 'T'), SpectrogramType()), + } + + @typecheck() + def forward(self, input: torch.Tensor) -> torch.Tensor: + """ + Args: + input: shape (B, M, F, T) + + Returns: + Output tensor with shape shape (B, F, T) + """ + B, M, F, T = input.shape + + # (B, M, F, T) -> (B, T, M, F) + input = input.permute(0, 3, 1, 2) + input = input.reshape(B * T, M, F) + + # attention across channels + output = self.attention(query=input, key=input, value=input, mask=None) + + # return to the original layout + output = output.view(B, T, M, -1) + + # (B, T, M, num_features) -> (B, M, out_features, T) + output = output.permute(0, 2, 3, 1) + + # average across channels + output = torch.mean(output, axis=-3) + + return output + + +class ParametricMultichannelWienerFilter(NeuralModule): + """Parametric multichannel Wiener filter, with an adjustable + tradeoff between noise reduction and speech distortion. + It supports automatic reference channel selection based + on the estimated output SNR. + + Args: + beta: Parameter of the parameteric filter, tradeoff between noise reduction + and speech distortion (0: MVDR, 1: MWF). + rank: Rank assumption for the speech covariance matrix. + postfilter: Optional postfilter. If None, no postfilter is applied. + ref_channel: Optional, reference channel. If None, it will be estimated automatically. + ref_hard: If true, estimate a hard (one-hot) reference. If false, a soft reference. + ref_hard_use_grad: If true, use straight-through gradient when using the hard reference + ref_subband_weighting: If true, use subband weighting when estimating reference channel + num_subbands: Optional, used to determine the parameter size for reference estimation + diag_reg: Optional, diagonal regularization for the multichannel filter + eps: Small regularization constant to avoid division by zero + + References: + - Souden et al, On Optimal Frequency-Domain Multichannel Linear Filtering for Noise Reduction, 2010 + """ + + def __init__( + self, + beta: float = 1.0, + rank: str = 'one', + postfilter: Optional[str] = None, + ref_channel: Optional[int] = None, + ref_hard: bool = True, + ref_hard_use_grad: bool = True, + ref_subband_weighting: bool = False, + num_subbands: Optional[int] = None, + diag_reg: Optional[float] = 1e-6, + eps: float = 1e-8, + ): + if not HAVE_TORCHAUDIO: + logging.error('Could not import torchaudio. Some features might not work.') + + raise ModuleNotFoundError( + f"torchaudio is not installed but is necessary to instantiate a {self.__class__.__name__}" + ) + + super().__init__() + + # Parametric filter + # 0=MVDR, 1=MWF + self.beta = beta + + # Rank + # Assumed rank for the signal covariance matrix (psd_s) + self.rank = rank + + if self.rank == 'full' and self.beta == 0: + raise ValueError(f'Rank {self.rank} is not compatible with beta {self.beta}.') + + # Postfilter, applied on the output of the multichannel filter + if postfilter not in [None, 'ban']: + raise ValueError(f'Postfilter {postfilter} is not supported.') + self.postfilter = postfilter + + # Regularization + if diag_reg is not None and diag_reg < 0: + raise ValueError(f'Diagonal regularization {diag_reg} must be positive.') + self.diag_reg = diag_reg + + if eps <= 0: + raise ValueError(f'Epsilon {eps} must be positive.') + self.eps = eps + + # PSD estimator + self.psd = torchaudio.transforms.PSD() + + # Reference channel + self.ref_channel = ref_channel + if self.ref_channel == 'max_snr': + self.ref_estimator = ReferenceChannelEstimatorSNR( + hard=ref_hard, + hard_use_grad=ref_hard_use_grad, + subband_weighting=ref_subband_weighting, + num_subbands=num_subbands, + eps=eps, + ) + else: + self.ref_estimator = None + # Flag to determine if the filter is MISO or MIMO + self.is_mimo = self.ref_channel is None + + logging.debug('Initialized %s', self.__class__.__name__) + logging.debug('\tbeta: %f', self.beta) + logging.debug('\trank: %s', self.rank) + logging.debug('\tpostfilter: %s', self.postfilter) + logging.debug('\tdiag_reg: %g', self.diag_reg) + logging.debug('\teps: %g', self.eps) + logging.debug('\tref_channel: %s', self.ref_channel) + logging.debug('\tis_mimo: %s', self.is_mimo) + + @staticmethod + def trace(x: torch.Tensor, keepdim: bool = False) -> torch.Tensor: + """Calculate trace of matrix slices over the last + two dimensions in the input tensor. + + Args: + x: tensor, shape (..., C, C) + + Returns: + Trace for each (C, C) matrix. shape (...) + """ + trace = torch.diagonal(x, dim1=-2, dim2=-1).sum(-1) + if keepdim: + trace = trace.unsqueeze(-1).unsqueeze(-1) + return trace + + def apply_diag_reg(self, psd: torch.Tensor) -> torch.Tensor: + """Apply diagonal regularization on psd. + + Args: + psd: tensor, shape (..., C, C) + + Returns: + Tensor, same shape as input. + """ + # Regularization: diag_reg * trace(psd) + eps + diag_reg = self.diag_reg * self.trace(psd).real + self.eps + + # Apply regularization + psd = psd + torch.diag_embed(diag_reg.unsqueeze(-1) * torch.ones(psd.shape[-1], device=psd.device)) + + return psd + + def apply_filter(self, input: torch.Tensor, filter: torch.Tensor) -> torch.Tensor: + """Apply the MIMO filter on the input. + + Args: + input: batch with C input channels, shape (B, C, F, T) + filter: batch of C-input, M-output filters, shape (B, F, C, M) + + Returns: + M-channel filter output, shape (B, M, F, T) + """ + if not filter.is_complex(): + raise TypeError(f'Expecting complex-valued filter, found {filter.dtype}') + + if not input.is_complex(): + raise TypeError(f'Expecting complex-valued input, found {input.dtype}') + + if filter.ndim != 4 or filter.size(-2) != input.size(-3) or filter.size(-3) != input.size(-2): + raise ValueError(f'Filter shape {filter.shape}, not compatible with input shape {input.shape}') + + output = torch.einsum('bfcm,bcft->bmft', filter.conj(), input) + + return output + + def apply_ban(self, input: torch.Tensor, filter: torch.Tensor, psd_n: torch.Tensor) -> torch.Tensor: + """Apply blind analytic normalization postfilter. Note that this normalization has been + derived for the GEV beamformer in [1]. More specifically, the BAN postfilter aims to scale GEV + to satisfy the distortionless constraint and the final analytical expression is derived using + an assumption on the norm of the transfer function. + However, this may still be useful in some instances. + + Args: + input: batch with M output channels (B, M, F, T) + filter: batch of C-input, M-output filters, shape (B, F, C, M) + psd_n: batch of noise PSDs, shape (B, F, C, C) + + Returns: + Filtere input, shape (B, M, F, T) + + References: + - Warsitz and Haeb-Umbach, Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition, 2007 + """ + # number of input channel, used to normalize the numerator + num_inputs = filter.size(-2) + numerator = torch.einsum('bfcm,bfci,bfij,bfjm->bmf', filter.conj(), psd_n, psd_n, filter) + numerator = torch.sqrt(numerator.abs() / num_inputs) + + denominator = torch.einsum('bfcm,bfci,bfim->bmf', filter.conj(), psd_n, filter) + denominator = denominator.abs() + + # Scalar filter per output channel, frequency and batch + # shape (B, M, F) + ban = numerator / (denominator + self.eps) + + input = ban[..., None] * input + + return input + + @property + def input_types(self): + """Returns definitions of module input types + """ + return { + 'input': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + 'mask_s': NeuralType(('B', 'D', 'T'), FloatType()), + 'mask_n': NeuralType(('B', 'D', 'T'), FloatType()), + } + + @property + def output_types(self): + """Returns definitions of module output types + """ + return { + 'output': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()), + } + + @typecheck() + def forward(self, input: torch.Tensor, mask_s: torch.Tensor, mask_n: torch.Tensor) -> torch.Tensor: + """Return processed signal. + The output has either one channel (M=1) if a ref_channel is selected, + or the same number of channels as the input (M=C) if ref_channel is None. + + Args: + input: Input signal, complex tensor with shape (B, C, F, T) + mask_s: Mask for the desired signal, shape (B, F, T) + mask_n: Mask for the undesired noise, shape (B, F, T) + + Returns: + Processed signal, shape (B, M, F, T) + """ + iodtype = input.dtype + + with torch.cuda.amp.autocast(enabled=False): + # Convert to double + input = input.cdouble() + mask_s = mask_s.double() + mask_n = mask_n.double() + + # Calculate signal statistics + psd_s = self.psd(input, mask_s) + psd_n = self.psd(input, mask_n) + + if self.rank == 'one': + # Calculate filter W using (18) in [1] + # Diagonal regularization + if self.diag_reg: + psd_n = self.apply_diag_reg(psd_n) + + # MIMO filter + # (B, F, C, C) + W = torch.linalg.solve(psd_n, psd_s) + lam = self.trace(W, keepdim=True).real + W = W / (self.beta + lam + self.eps) + elif self.rank == 'full': + # Calculate filter W using (15) in [1] + psd_sn = psd_s + self.beta * psd_n + + if self.diag_reg: + psd_sn = self.apply_diag_reg(psd_sn) + + # MIMO filter + # (B, F, C, C) + W = torch.linalg.solve(psd_sn, psd_s) + else: + raise RuntimeError(f'Unexpected rank {self.rank}') + + if torch.jit.isinstance(self.ref_channel, int): + # Fixed ref channel + # (B, F, C, 1) + W = W[..., self.ref_channel].unsqueeze(-1) + elif self.ref_estimator is not None: + # Estimate ref channel tensor (one-hot or soft across C) + # (B, C) + ref_channel_tensor = self.ref_estimator(W=W, psd_s=psd_s, psd_n=psd_n).to(W.dtype) + # Weighting across channels + # (B, F, C, 1) + W = torch.sum(W * ref_channel_tensor[:, None, None, :], dim=-1, keepdim=True) + + output = self.apply_filter(input=input, filter=W) + + # Optional: postfilter + if self.postfilter == 'ban': + output = self.apply_ban(input=output, filter=W, psd_n=psd_n) + + return output.to(iodtype) + + +class ReferenceChannelEstimatorSNR(NeuralModule): + """Estimate a reference channel by selecting the reference + that maximizes the output SNR. It returns one-hot encoded + vector or a soft reference. + + A straight-through estimator is used for gradient when using + hard reference. + + Args: + hard: If true, use hard estimate of ref channel. + If false, use a soft estimate across channels. + hard_use_grad: Use straight-through estimator for + the gradient. + subband_weighting: If true, use subband weighting when + adding across subband SNRs. If false, use average + across subbands. + + References: + Boeddeker et al, Front-End Processing for the CHiME-5 Dinner Party Scenario, 2018 + """ + + def __init__( + self, + hard: bool = True, + hard_use_grad: bool = True, + subband_weighting: bool = False, + num_subbands: Optional[int] = None, + eps: float = 1e-8, + ): + super().__init__() + + self.hard = hard + self.hard_use_grad = hard_use_grad + self.subband_weighting = subband_weighting + self.eps = eps + + if subband_weighting and num_subbands is None: + raise ValueError(f'Number of subbands must be provided when using subband_weighting={subband_weighting}.') + # Subband weighting + self.weight_s = torch.nn.Parameter(torch.ones(num_subbands)) if subband_weighting else None + self.weight_n = torch.nn.Parameter(torch.ones(num_subbands)) if subband_weighting else None + + logging.debug('Initialized %s', self.__class__.__name__) + logging.debug('\thard: %d', self.hard) + logging.debug('\thard_use_grad: %d', self.hard_use_grad) + logging.debug('\tsubband_weighting: %d', self.subband_weighting) + logging.debug('\tnum_subbands: %s', num_subbands) + logging.debug('\teps: %e', self.eps) + + @property + def input_types(self): + """Returns definitions of module input types + """ + return { + 'W': NeuralType(('B', 'D', 'C', 'C'), SpectrogramType()), + 'psd_s': NeuralType(('B', 'D', 'C', 'C'), SpectrogramType()), + 'psd_n': NeuralType(('B', 'D', 'C', 'C'), SpectrogramType()), + } + + @property + def output_types(self): + """Returns definitions of module output types + """ + return { + 'output': NeuralType(('B', 'C'), FloatType()), + } + + @typecheck() + def forward(self, W: torch.Tensor, psd_s: torch.Tensor, psd_n: torch.Tensor) -> torch.Tensor: + """ + Args: + W: Multichannel input multichannel output filter, shape (B, F, C, M), where + C is the number of input channels and M is the number of output channels + psd_s: Covariance for the signal, shape (B, F, C, C) + psd_n: Covariance for the noise, shape (B, F, C, C) + + Returns: + One-hot or soft reference channel, shape (B, M) + """ + if self.subband_weighting: + # (B, F, M) + pow_s = torch.einsum('...jm,...jk,...km->...m', W.conj(), psd_s, W).abs() + pow_n = torch.einsum('...jm,...jk,...km->...m', W.conj(), psd_n, W).abs() + + # Subband-weighting + # (B, F, M) -> (B, M) + pow_s = torch.sum(pow_s * self.weight_s.softmax(dim=0).unsqueeze(1), dim=-2) + pow_n = torch.sum(pow_n * self.weight_n.softmax(dim=0).unsqueeze(1), dim=-2) + else: + # Sum across f as well + # (B, F, C, M), (B, F, C, C), (B, F, C, M) -> (B, M) + pow_s = torch.einsum('...fjm,...fjk,...fkm->...m', W.conj(), psd_s, W).abs() + pow_n = torch.einsum('...fjm,...fjk,...fkm->...m', W.conj(), psd_n, W).abs() + + # Estimated SNR per channel (B, C) + snr = pow_s / (pow_n + self.eps) + snr = 10 * torch.log10(snr + self.eps) + + # Soft reference + ref_soft = snr.softmax(dim=-1) + + if self.hard: + _, idx = ref_soft.max(dim=-1, keepdim=True) + ref_hard = torch.zeros_like(snr).scatter(-1, idx, 1.0) + if self.hard_use_grad: + # Straight-through for gradient + # Propagate ref_soft gradient, as if thresholding is identity + ref = ref_hard - ref_soft.detach() + ref_soft + else: + # No gradient + ref = ref_hard + else: + ref = ref_soft + + return ref diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/multitask_beam_decoding.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/multitask_beam_decoding.py new file mode 100644 index 0000000..c6dc28a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/multitask_beam_decoding.py @@ -0,0 +1,221 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import List, Optional + +import torch + +from nemo.collections.asr.modules.transformer import BeamSearchSequenceGenerator +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis, NBestHypotheses +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.core import Typing, typecheck +from nemo.core.neural_types import ChannelType, HypothesisType, LabelsType, MaskType, NeuralType +from nemo.utils import logging + + +def pack_hypotheses( + hypotheses: List[Hypothesis], beam_hypotheses: torch.Tensor, scores: List[Optional[float]] +) -> List[Hypothesis]: + + for idx, hyp in enumerate(hypotheses): # type: Hypothesis + if scores[idx] is not None: + hyp.score = scores[idx] + + hypi = beam_hypotheses[idx] + if torch.is_tensor(hypi): + hyp.y_sequence = hypi.long() + else: + hyp.y_sequence = torch.tensor(hypi, dtype=torch.long) + + if hyp.dec_state is not None: + hyp.dec_state = _states_to_device(hyp.dec_state) + + return hypotheses + + +def _states_to_device(dec_state, device='cpu'): + if torch.is_tensor(dec_state): + dec_state = dec_state.to(device) + + elif isinstance(dec_state, (list, tuple)): + dec_state = tuple(_states_to_device(dec_i, device) for dec_i in dec_state) + + return dec_state + + +class AEDBeamInfer(ABC): + def __init__( + self, + transformer_decoder: torch.nn.Module, + log_softmax_module: torch.nn.Module, + tokenizer: TokenizerSpec, + search_type: str = 'default', + return_best_hypothesis: bool = True, + preserve_alignments: bool = False, + ): + super().__init__() + + self.transformer_decoder = transformer_decoder + self.log_softmax_module = log_softmax_module + self.tokenizer = tokenizer + + self.search_type = search_type + self.return_best_hypothesis = return_best_hypothesis + self.preserve_alignments = preserve_alignments + + def __call__(self, *args, **kwargs): + return self.forward(*args, **kwargs) + + @abstractmethod + def forward( + self, + encoder_hidden_states: torch.Tensor, + encoder_input_mask: torch.Tensor, + decoder_input_ids: Optional[torch.Tensor] = None, + partial_hypotheses: Optional[List[Hypothesis]] = None, + ): + raise NotImplementedError() + + def set_decoding_type(self, decoding_type: str): + self.decoding_type = decoding_type + + +class TransformerAEDBeamInfer(AEDBeamInfer, Typing): + """A beam decoder engine for AED Transformer models. + + Provides a common abstraction for batch level beam decoding. + + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + # Input can be of dimention - + # ('B', 'T', 'D') [Log probs] or ('B', 'T') [Labels] + + return { + "encoder_hidden_states": NeuralType(tuple(('B', 'T', 'D')), ChannelType()), + "encoder_input_mask": NeuralType(tuple(('B', 'T')), MaskType()), + "decoder_input_ids": NeuralType(('B', 'T'), LabelsType()), + "partial_hypotheses": NeuralType(optional=True), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"predictions": [NeuralType(elements_type=HypothesisType())]} + + def __init__( + self, + transformer_decoder: torch.nn.Module, + log_softmax_module: torch.nn.Module, + tokenizer: TokenizerSpec, + search_type: str = 'default', + beam_size: int = 1, + length_penalty: float = 0.0, + max_generation_delta: int = 50, + return_best_hypothesis: bool = True, + preserve_alignments: bool = False, + ): + super().__init__( + transformer_decoder=transformer_decoder, + log_softmax_module=log_softmax_module, + tokenizer=tokenizer, + search_type=search_type, + return_best_hypothesis=return_best_hypothesis, + preserve_alignments=preserve_alignments, + ) + self.beam_size = beam_size + self.beam_search = BeamSearchSequenceGenerator( + embedding=transformer_decoder.embedding, + decoder=transformer_decoder.decoder, + log_softmax=log_softmax_module, + max_sequence_length=transformer_decoder.max_sequence_length, + beam_size=beam_size, + bos=tokenizer.bos_id, + pad=tokenizer.pad_id, + eos=tokenizer.eos_id, + len_pen=length_penalty, + max_delta_length=max_generation_delta, + ) + + self.preserve_alignments = preserve_alignments + if self.preserve_alignments: + logging.info( + "Preservation of alignments was requested but {} does not implement it.".format( + self.__class__.__name__ + ) + ) + + @typecheck() + def forward( + self, + encoder_hidden_states: torch.Tensor, + encoder_input_mask: torch.Tensor, + decoder_input_ids: Optional[torch.Tensor] = None, + partial_hypotheses: Optional[List[Hypothesis]] = None, + ): + """Returns a list of hypotheses given an input batch of the encoder hidden embedding. + Output token is generated auto-repressively. + + Args: + decoder_output: A tensor of size (batch, timesteps, features) or (batch, timesteps) (each timestep is a label). + decoder_lengths: list of int representing the length of each sequence + output sequence. + + Returns: + packed list containing batch number of sentences (Hypotheses). + """ + with torch.inference_mode(): + topk_hypotheses, beam_scores, best_hypo = self.beam_search( + encoder_hidden_states=encoder_hidden_states, + encoder_input_mask=encoder_input_mask, + decoder_input_ids=decoder_input_ids, + return_beam_scores=True, + ) + + if not self.return_best_hypothesis: + topk_hypotheses = [x.detach().cpu() for x in topk_hypotheses] # each item is [beam, seq_len] + beam_scores = [x.detach().cpu() for x in beam_scores] # each item is [beam,] + packed_result = [] + for i in range(len(topk_hypotheses)): + hypotheses = [Hypothesis(score=0.0, y_sequence=[], timestep=[]) for _ in range(self.beam_size)] + # Pack results into Hypotheses + packed_result.append( + NBestHypotheses(pack_hypotheses(hypotheses, topk_hypotheses[i], beam_scores[i])) + ) + else: + beam_scores = [None for _ in range(len(best_hypo))] + best_hypo = best_hypo.detach().cpu() + hypotheses = [ + Hypothesis(score=0.0, y_sequence=[], timestep=[]) for _ in range(encoder_hidden_states.shape[0]) + ] + # Pack results into Hypotheses + packed_result = pack_hypotheses(hypotheses, best_hypo, beam_scores) + + return (packed_result,) + + +@dataclass +class AEDBeamInferConfig: + beam_size: int = 1 + search_type: str = 'default' + len_pen: float = 1.0 + max_generation_delta: int = -1 # -1 means up to the max length of the decoder + return_best_hypothesis: bool = True + preserve_alignments: bool = False diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/multitask_decoding.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/multitask_decoding.py new file mode 100644 index 0000000..c336ae7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/multitask_decoding.py @@ -0,0 +1,487 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re +from abc import ABC, abstractmethod +from dataclasses import dataclass, field, is_dataclass +from typing import List, Optional, Tuple, Union + +import torch +from omegaconf import OmegaConf + +from nemo.collections.asr.parts.submodules.multitask_beam_decoding import ( + AEDBeamInfer, + AEDBeamInferConfig, + TransformerAEDBeamInfer, +) +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis, NBestHypotheses +from nemo.collections.common.tokenizers.aggregate_tokenizer import AggregateTokenizer +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.utils import logging + + +class AbstractMultiTaskDecoding(ABC): + """ + Used for performing AED auto-regressive decoding of the Multi task model given the encoder state. + + Args: + decoding_cfg: A dict-like object which contains the following key-value pairs. + strategy: str value which represents the type of decoding that can occur. + Possible values are : + - greedy, greedy_batch (for greedy decoding). + - beam, tsd, alsd (for beam search decoding). + + compute_langs: a bool flag, which allows to compute language id (LID) information per token, + word, and the entire sample (most likely language id). The LIDS will be available + in the returned Hypothesis object as a dictionary + + compute_hypothesis_token_set: A bool flag, which determines whether to compute a list of decoded + tokens as well as the decoded string. Default is False in order to avoid double decoding + unless required. + + preserve_alignments: Bool flag which preserves the history of logprobs generated during + decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `alignments` in it. Here, `alignments` is a List of List of + Tuple(Tensor (of length V + 1), Tensor(scalar, label after argmax)). + + In order to obtain this hypothesis, please utilize `rnnt_decoder_predictions_tensor` function + with the `return_hypotheses` flag set to True. + + The config may further contain the following sub-dictionaries: + "greedy": + max_symbols: int, describing the maximum number of target tokens to decode per + timestep during greedy decoding. Setting to larger values allows longer sentences + to be decoded, at the cost of increased execution time. + preserve_frame_confidence: Same as above, overrides above value. + confidence_method_cfg: Same as above, overrides confidence_cfg.method_cfg. + + "beam": + beam_size: int, defining the beam size for beam search. Must be >= 1. + If beam_size == 1, will perform cached greedy search. This might be slightly different + results compared to the greedy search above. + + length_penalty: float, length penalty for beam search decoding. Must be >= 0.0. + + max_generation_delta: int,in case of encoder-decoder generation (e.g. NMT), + forbids generated sequences to be longer than the length of source sequences plus max_generation_delta + + return_best_hypothesis: optional bool, whether to return just the best hypothesis or all of the + hypotheses after beam search has concluded. This flag is set by default. + + + transformer_decoder: Transformer decoder module. + log_softmax_module: Log Softmax projection module to the vocab size. + tokenizer: Aggregate Tokenizer. + """ + + def __init__( + self, + decoding_cfg, + transformer_decoder: torch.nn.Module, + log_softmax_module: torch.nn.Module, + tokenizer: TokenizerSpec, + ): + super().__init__() + + # Convert dataclass to config object + if is_dataclass(decoding_cfg): + decoding_cfg = OmegaConf.structured(decoding_cfg) + + self.cfg = decoding_cfg + + self.preserve_alignments = self.cfg.get('preserve_alignments', None) + self.compute_langs = self.cfg.get('compute_langs', False) + self.compute_hypothesis_token_set = self.cfg.get('compute_hypothesis_token_set', False) + + possible_strategies = ['greedy', 'greedy_batch', 'beam'] + if self.cfg.strategy not in possible_strategies: + raise ValueError(f"Decoding strategy must be one of {possible_strategies}") + + # Update preserve alignments + if self.preserve_alignments is None: + if self.cfg.strategy in ['greedy', 'greedy_batch']: + self.preserve_alignments = self.cfg.greedy.get('preserve_alignments', False) + + elif self.cfg.strategy in ['beam']: + self.preserve_alignments = self.cfg.beam.get('preserve_alignments', False) + + if self.cfg.strategy == 'greedy' or self.cfg.strategy == 'greedy_batch': + + # self.decoding = None + raise NotImplementedError("Greedy decoding is not implemented yet.") + + elif self.cfg.strategy == 'beam': + + self.decoding = TransformerAEDBeamInfer( + transformer_decoder=transformer_decoder, + log_softmax_module=log_softmax_module, + tokenizer=tokenizer, + search_type=self.cfg.beam.get('search_type', 'default'), + beam_size=self.cfg.beam.beam_size, + length_penalty=self.cfg.beam.get('length_penalty', 0.0), + max_generation_delta=self.cfg.beam.get('max_generation_delta', 50), + return_best_hypothesis=self.cfg.beam.get('return_best_hypothesis', True), + preserve_alignments=self.preserve_alignments, + ) + + else: + + raise ValueError( + f"Incorrect decoding strategy provided. Must be one of {possible_strategies}\n" + f"but was provided {self.cfg.strategy}" + ) + + def decode_predictions_tensor( + self, + encoder_hidden_states: torch.Tensor, + encoder_input_mask: torch.Tensor, + decoder_input_ids: Optional[torch.Tensor] = None, + return_hypotheses: bool = False, + partial_hypotheses: Optional[List[Hypothesis]] = None, + ) -> Tuple[List[str], Optional[List[List[str]]], Optional[Union[Hypothesis, NBestHypotheses]]]: + """ + Decode an encoder output by autoregressive decoding of the Decoder+Joint networks. + + Args: + encoder_output: torch.Tensor of shape [B, D, T]. + encoded_lengths: torch.Tensor containing lengths of the padded encoder outputs. Shape [B]. + return_hypotheses: bool. If set to True it will return list of Hypothesis or NBestHypotheses + + Returns: + If `return_best_hypothesis` is set: + A tuple (hypotheses, None): + hypotheses - list of Hypothesis (best hypothesis per sample). + Look at rnnt_utils.Hypothesis for more information. + + If `return_best_hypothesis` is not set: + A tuple(hypotheses, all_hypotheses) + hypotheses - list of Hypothesis (best hypothesis per sample). + Look at rnnt_utils.Hypothesis for more information. + all_hypotheses - list of NBestHypotheses. Each NBestHypotheses further contains a sorted + list of all the hypotheses of the model per sample. + Look at rnnt_utils.NBestHypotheses for more information. + """ + # Compute hypotheses + with torch.inference_mode(): + hypotheses_list = self.decoding( + encoder_hidden_states=encoder_hidden_states, + encoder_input_mask=encoder_input_mask, + decoder_input_ids=decoder_input_ids, + partial_hypotheses=partial_hypotheses, + ) # type: [List[Hypothesis]] + + # extract the hypotheses + hypotheses_list = hypotheses_list[0] # type: List[Hypothesis] + + prediction_list = hypotheses_list + + if isinstance(prediction_list[0], NBestHypotheses): + hypotheses = [] + all_hypotheses = [] + + for nbest_hyp in prediction_list: # type: NBestHypotheses + n_hyps = nbest_hyp.n_best_hypotheses # Extract all hypotheses for this sample + decoded_hyps = self.decode_hypothesis(n_hyps) + + hypotheses.append(decoded_hyps[0]) # best hypothesis + all_hypotheses.append(decoded_hyps) + + if return_hypotheses: + return hypotheses, all_hypotheses + + best_hyp_text = [h.text for h in hypotheses] + all_hyp_text = [h.text for hh in all_hypotheses for h in hh] + return best_hyp_text, all_hyp_text + + else: + hypotheses = self.decode_hypothesis(prediction_list) + + if return_hypotheses: + return hypotheses, None + + best_hyp_text = [h.text for h in hypotheses] + return best_hyp_text, None + + def decode_hypothesis(self, hypotheses_list: List[Hypothesis]) -> List[Union[Hypothesis, NBestHypotheses]]: + """ + Decode a list of hypotheses into a list of strings. + + Args: + hypotheses_list: List of Hypothesis. + + Returns: + A list of strings. + """ + for ind in range(len(hypotheses_list)): + # Extract the integer encoded hypothesis + prediction = hypotheses_list[ind].y_sequence + + if type(prediction) != list: + prediction = prediction.tolist() + + hypothesis = self.decode_tokens_to_str(prediction) + + if self.compute_hypothesis_token_set: + hypotheses_list[ind].tokens = self.decode_ids_to_tokens(prediction) + + # De-tokenize the integer tokens + hypotheses_list[ind].text = hypothesis + + return hypotheses_list + + @abstractmethod + def decode_tokens_to_str(self, tokens: List[int]) -> str: + """ + Implemented by subclass in order to decoder a token id list into a string. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded string. + """ + raise NotImplementedError() + + @abstractmethod + def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]: + """ + Implemented by subclass in order to decode a token id list into a token list. + A token list is the string representation of each token id. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded tokens. + """ + raise NotImplementedError() + + @abstractmethod + def decode_tokens_to_lang(self, tokens: List[int]) -> str: + """ + Implemented by subclass in order to + compute the most likely language ID (LID) string given the tokens. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded LID string. + """ + raise NotImplementedError() + + @abstractmethod + def decode_ids_to_langs(self, tokens: List[int]) -> List[str]: + """ + Implemented by subclass in order to + decode a token id list into language ID (LID) list. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded LIDS. + """ + raise NotImplementedError() + + def strip_special_tokens(self, text: str): + """ + assuming all special tokens are of format + Note that if any label/pred is of format , it will be stripped + """ + assert isinstance(text, str), f"Expected str, got {type(text)}" + text = re.sub(r'<[^>]+>', '', text) + # strip spaces at the beginning and end; + # this is training data artifact, will be fixed in future (@kpuvvada) + return text.strip() + + +class MultiTaskDecoding(AbstractMultiTaskDecoding): + """ + Used for performing AED auto-regressive decoding of the Multi task model given the encoder state. + + Args: + decoding_cfg: A dict-like object which contains the following key-value pairs. + strategy: str value which represents the type of decoding that can occur. + Possible values are : + - greedy, greedy_batch (for greedy decoding). + - beam, tsd, alsd (for beam search decoding). + + compute_langs: a bool flag, which allows to compute language id (LID) information per token, + word, and the entire sample (most likely language id). The LIDS will be available + in the returned Hypothesis object as a dictionary + + compute_hypothesis_token_set: A bool flag, which determines whether to compute a list of decoded + tokens as well as the decoded string. Default is False in order to avoid double decoding + unless required. + + preserve_alignments: Bool flag which preserves the history of logprobs generated during + decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `alignments` in it. Here, `alignments` is a List of List of + Tuple(Tensor (of length V + 1), Tensor(scalar, label after argmax)). + + In order to obtain this hypothesis, please utilize `rnnt_decoder_predictions_tensor` function + with the `return_hypotheses` flag set to True. + + The config may further contain the following sub-dictionaries: + "greedy": + max_symbols: int, describing the maximum number of target tokens to decode per + timestep during greedy decoding. Setting to larger values allows longer sentences + to be decoded, at the cost of increased execution time. + preserve_frame_confidence: Same as above, overrides above value. + confidence_method_cfg: Same as above, overrides confidence_cfg.method_cfg. + + "beam": + beam_size: int, defining the beam size for beam search. Must be >= 1. + If beam_size == 1, will perform cached greedy search. This might be slightly different + results compared to the greedy search above. + + length_penalty: float, length penalty for beam search decoding. Must be >= 0.0. + + max_generation_delta: int, maximum number of additional target tokens to generate + + return_best_hypothesis: optional bool, whether to return just the best hypothesis or all of the + hypotheses after beam search has concluded. This flag is set by default. + + + transformer_decoder: Transformer decoder module. + log_softmax_module: Log Softmax projection module to the vocab size. + tokenizer: TokenizerSpec. + """ + + def __init__( + self, + decoding_cfg, + transformer_decoder: torch.nn.Module, + log_softmax_module: torch.nn.Module, + tokenizer: TokenizerSpec, + ): + self.tokenizer = tokenizer + + super().__init__( + decoding_cfg=decoding_cfg, + transformer_decoder=transformer_decoder, + log_softmax_module=log_softmax_module, + tokenizer=tokenizer, + ) + + if isinstance(self.decoding, AEDBeamInfer): + self.decoding.set_decoding_type('subword') + + def decode_tokens_to_str(self, tokens: List[int]) -> str: + """ + Implemented by subclass in order to decoder a token list into a string. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded string. + """ + hypothesis = self.tokenizer.ids_to_text(tokens) + return hypothesis + + def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]: + """ + Implemented by subclass in order to decode a token id list into a token list. + A token list is the string representation of each token id. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded tokens. + """ + token_list = self.tokenizer.ids_to_tokens(tokens) + return token_list + + def decode_tokens_to_lang(self, tokens: List[int]) -> str: + """ + Compute the most likely language ID (LID) string given the tokens. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded LID string. + """ + lang = self.tokenizer.ids_to_lang(tokens) + return lang + + def decode_ids_to_langs(self, tokens: List[int]) -> List[str]: + """ + Decode a token id list into language ID (LID) list. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded LIDS. + """ + lang_list = self.tokenizer.ids_to_text_and_langs(tokens) + return lang_list + + def decode_hypothesis(self, hypotheses_list: List[Hypothesis]) -> List[Union[Hypothesis, NBestHypotheses]]: + """ + Decode a list of hypotheses into a list of strings. + Overrides the super() method optionally adding lang information + + Args: + hypotheses_list: List of Hypothesis. + + Returns: + A list of strings. + """ + hypotheses = super().decode_hypothesis(hypotheses_list) + if self.compute_langs: + if isinstance(self.tokenizer, AggregateTokenizer): + for ind in range(len(hypotheses_list)): + # Extract the integer encoded hypothesis + prediction = hypotheses_list[ind].y_sequence + + if type(prediction) != list: + prediction = prediction.tolist() + + hypotheses[ind].langs = self.decode_tokens_to_lang(prediction) + hypotheses[ind].langs_chars = self.decode_ids_to_langs(prediction) + else: + logging.warning( + "Ignoring request for lang output in hypotheses since the model does not use an aggregate tokenizer" + ) + + return hypotheses + + +@dataclass +class MultiTaskDecodingConfig: + strategy: str = "beam" + + compute_hypothesis_token_set: bool = False + + # preserve decoding alignments + preserve_alignments: Optional[bool] = None + + # compute language IDs + compute_langs: bool = False + + # greedy decoding config + # greedy: rnnt_greedy_decoding.GreedyBatchedRNNTInferConfig = field( + # default_factory=rnnt_greedy_decoding.GreedyBatchedRNNTInferConfig + # ) + + # beam decoding config + beam: AEDBeamInferConfig = field(default_factory=lambda: AEDBeamInferConfig(beam_size=1)) + + # can be used to change temperature for decoding + temperature: float = 1.0 diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/rnnt_beam_decoding.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/rnnt_beam_decoding.py new file mode 100644 index 0000000..ef3a0cd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/rnnt_beam_decoding.py @@ -0,0 +1,1505 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright 2017 Johns Hopkins University (Shinji Watanabe) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +from tqdm import tqdm + +from nemo.collections.asr.modules import rnnt_abstract +from nemo.collections.asr.parts.utils.rnnt_utils import ( + HATJointOutput, + Hypothesis, + NBestHypotheses, + is_prefix, + select_k_expansions, +) +from nemo.core.classes import Typing, typecheck +from nemo.core.neural_types import AcousticEncodedRepresentation, HypothesisType, LengthsType, NeuralType +from nemo.utils import logging + +try: + import kenlm + + KENLM_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + KENLM_AVAILABLE = False + + +def pack_hypotheses(hypotheses: List[Hypothesis]) -> List[Hypothesis]: + for idx, hyp in enumerate(hypotheses): # type: rnnt_utils.Hypothesis + hyp.y_sequence = torch.tensor(hyp.y_sequence, dtype=torch.long) + + if hyp.dec_state is not None: + hyp.dec_state = _states_to_device(hyp.dec_state) + + # Remove -1 from timestep + if hyp.timestep is not None and len(hyp.timestep) > 0 and hyp.timestep[0] == -1: + hyp.timestep = hyp.timestep[1:] + + return hypotheses + + +def _states_to_device(dec_state, device='cpu'): + if torch.is_tensor(dec_state): + dec_state = dec_state.to(device) + + elif isinstance(dec_state, (list, tuple)): + dec_state = tuple(_states_to_device(dec_i, device) for dec_i in dec_state) + + return dec_state + + +class BeamRNNTInfer(Typing): + """ + Beam Search implementation ported from ESPNet implementation - + https://github.com/espnet/espnet/blob/master/espnet/nets/beam_search_transducer.py + + Sequence level beam decoding or batched-beam decoding, performed auto-repressively + depending on the search type chosen. + + Args: + decoder_model: rnnt_utils.AbstractRNNTDecoder implementation. + joint_model: rnnt_utils.AbstractRNNTJoint implementation. + + beam_size: number of beams for beam search. Must be a positive integer >= 1. + If beam size is 1, defaults to stateful greedy search. + This greedy search might result in slightly different results than + the greedy results obtained by GreedyRNNTInfer due to implementation differences. + + For accurate greedy results, please use GreedyRNNTInfer or GreedyBatchedRNNTInfer. + + search_type: str representing the type of beam search to perform. + Must be one of ['beam', 'tsd', 'alsd']. 'nsc' is currently not supported. + + Algoritm used: + + `beam` - basic beam search strategy. Larger beams generally result in better decoding, + however the time required for the search also grows steadily. + + `tsd` - time synchronous decoding. Please refer to the paper: + [Alignment-Length Synchronous Decoding for RNN Transducer](https://ieeexplore.ieee.org/document/9053040) + for details on the algorithm implemented. + + Time synchronous decoding (TSD) execution time grows by the factor T * max_symmetric_expansions. + For longer sequences, T is greater, and can therefore take a long time for beams to obtain + good results. This also requires greater memory to execute. + + `alsd` - alignment-length synchronous decoding. Please refer to the paper: + [Alignment-Length Synchronous Decoding for RNN Transducer](https://ieeexplore.ieee.org/document/9053040) + for details on the algorithm implemented. + + Alignment-length synchronous decoding (ALSD) execution time is faster than TSD, with growth + factor of T + U_max, where U_max is the maximum target length expected during execution. + + Generally, T + U_max < T * max_symmetric_expansions. However, ALSD beams are non-unique, + therefore it is required to use larger beam sizes to achieve the same (or close to the same) + decoding accuracy as TSD. + + For a given decoding accuracy, it is possible to attain faster decoding via ALSD than TSD. + + `maes` = modified adaptive expansion searcn. Please refer to the paper: + [Accelerating RNN Transducer Inference via Adaptive Expansion Search](https://ieeexplore.ieee.org/document/9250505) + + Modified Adaptive Synchronous Decoding (mAES) execution time is adaptive w.r.t the + number of expansions (for tokens) required per timestep. The number of expansions can usually + be constrained to 1 or 2, and in most cases 2 is sufficient. + + This beam search technique can possibly obtain superior WER while sacrificing some evaluation time. + + score_norm: bool, whether to normalize the scores of the log probabilities. + + return_best_hypothesis: bool, decides whether to return a single hypothesis (the best out of N), + or return all N hypothesis (sorted with best score first). The container class changes based + this flag - + When set to True (default), returns a single Hypothesis. + When set to False, returns a NBestHypotheses container, which contains a list of Hypothesis. + + # The following arguments are specific to the chosen `search_type` + + tsd_max_sym_exp_per_step: Used for `search_type=tsd`. The maximum symmetric expansions allowed + per timestep during beam search. Larger values should be used to attempt decoding of longer + sequences, but this in turn increases execution time and memory usage. + + alsd_max_target_len: Used for `search_type=alsd`. The maximum expected target sequence length + during beam search. Larger values allow decoding of longer sequences at the expense of + execution time and memory. + + # The following two flags are placeholders and unused until `nsc` implementation is stabilized. + nsc_max_timesteps_expansion: Unused int. + + nsc_prefix_alpha: Unused int. + + # mAES flags + maes_num_steps: Number of adaptive steps to take. From the paper, 2 steps is generally sufficient. int > 1. + + maes_prefix_alpha: Maximum prefix length in prefix search. Must be an integer, and is advised to keep this as 1 + in order to reduce expensive beam search cost later. int >= 0. + + maes_expansion_beta: Maximum number of prefix expansions allowed, in addition to the beam size. + Effectively, the number of hypothesis = beam_size + maes_expansion_beta. Must be an int >= 0, + and affects the speed of inference since large values will perform large beam search in the next step. + + maes_expansion_gamma: Float pruning threshold used in the prune-by-value step when computing the expansions. + The default (2.3) is selected from the paper. It performs a comparison (max_log_prob - gamma <= log_prob[v]) + where v is all vocabulary indices in the Vocab set and max_log_prob is the "most" likely token to be + predicted. Gamma therefore provides a margin of additional tokens which can be potential candidates for + expansion apart from the "most likely" candidate. + Lower values will reduce the number of expansions (by increasing pruning-by-value, thereby improving speed + but hurting accuracy). Higher values will increase the number of expansions (by reducing pruning-by-value, + thereby reducing speed but potentially improving accuracy). This is a hyper parameter to be experimentally + tuned on a validation set. + + softmax_temperature: Scales the logits of the joint prior to computing log_softmax. + + preserve_alignments: Bool flag which preserves the history of alignments generated during + beam decoding (sample). When set to true, the Hypothesis will contain + the non-null value for `alignments` in it. Here, `alignments` is a List of List of Tensor (of length V + 1). + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more targets from a vocabulary. + U is the number of target tokens for the current timestep Ti. + + NOTE: `preserve_alignments` is an invalid argument for any `search_type` + other than basic beam search. + + ngram_lm_model: str + The path to the N-gram LM + ngram_lm_alpha: float + Alpha weight of N-gram LM + tokens_type: str + Tokenization type ['subword', 'char'] + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "encoder_output": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + "partial_hypotheses": [NeuralType(elements_type=HypothesisType(), optional=True)], # must always be last + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"predictions": [NeuralType(elements_type=HypothesisType())]} + + def __init__( + self, + decoder_model: rnnt_abstract.AbstractRNNTDecoder, + joint_model: rnnt_abstract.AbstractRNNTJoint, + beam_size: int, + search_type: str = 'default', + score_norm: bool = True, + return_best_hypothesis: bool = True, + tsd_max_sym_exp_per_step: Optional[int] = 50, + alsd_max_target_len: Union[int, float] = 1.0, + nsc_max_timesteps_expansion: int = 1, + nsc_prefix_alpha: int = 1, + maes_num_steps: int = 2, + maes_prefix_alpha: int = 1, + maes_expansion_gamma: float = 2.3, + maes_expansion_beta: int = 2, + language_model: Optional[Dict[str, Any]] = None, + softmax_temperature: float = 1.0, + preserve_alignments: bool = False, + ngram_lm_model: Optional[str] = None, + ngram_lm_alpha: float = 0.0, + hat_subtract_ilm: bool = False, + hat_ilm_weight: float = 0.0, + ): + self.decoder = decoder_model + self.joint = joint_model + + self.blank = decoder_model.blank_idx + self.vocab_size = decoder_model.vocab_size + self.search_type = search_type + self.return_best_hypothesis = return_best_hypothesis + + if beam_size < 1: + raise ValueError("Beam search size cannot be less than 1!") + + self.beam_size = beam_size + self.score_norm = score_norm + self.max_candidates = beam_size + + if self.beam_size == 1: + logging.info("Beam size of 1 was used, switching to sample level `greedy_search`") + self.search_algorithm = self.greedy_search + elif search_type == "default": + self.search_algorithm = self.default_beam_search + elif search_type == "tsd": + self.search_algorithm = self.time_sync_decoding + elif search_type == "alsd": + self.search_algorithm = self.align_length_sync_decoding + elif search_type == "nsc": + raise NotImplementedError("`nsc` (Constrained Beam Search) has not been implemented.") + # self.search_algorithm = self.nsc_beam_search + elif search_type == "maes": + self.search_algorithm = self.modified_adaptive_expansion_search + else: + raise NotImplementedError( + f"The search type ({search_type}) supplied is not supported!\n" + f"Please use one of : (default, tsd, alsd, nsc)" + ) + + if tsd_max_sym_exp_per_step is None: + tsd_max_sym_exp_per_step = -1 + + if search_type in ['tsd', 'alsd', 'nsc'] and not self.decoder.blank_as_pad: + raise ValueError( + f"Search type was chosen as '{search_type}', however the decoder module provided " + f"does not support the `blank` token as a pad value. {search_type} requires " + f"the blank token as pad value support in order to perform batched beam search." + f"Please chose one of the other beam search methods, or re-train your model " + f"with this support." + ) + + self.tsd_max_symmetric_expansion_per_step = tsd_max_sym_exp_per_step + self.alsd_max_target_length = alsd_max_target_len + self.nsc_max_timesteps_expansion = nsc_max_timesteps_expansion + self.nsc_prefix_alpha = int(nsc_prefix_alpha) + self.maes_prefix_alpha = int(maes_prefix_alpha) + self.maes_num_steps = int(maes_num_steps) + self.maes_expansion_gamma = float(maes_expansion_gamma) + self.maes_expansion_beta = int(maes_expansion_beta) + + if self.search_type == 'maes' and self.maes_prefix_alpha < 0: + raise ValueError("`maes_prefix_alpha` must be a positive integer.") + + if self.search_type == 'maes' and self.vocab_size < beam_size + maes_expansion_beta: + raise ValueError( + f"beam_size ({beam_size}) + expansion_beta ({maes_expansion_beta}) " + f"should be smaller or equal to vocabulary size ({self.vocab_size})." + ) + + if search_type == 'maes': + self.max_candidates += maes_expansion_beta + + if self.search_type == 'maes' and self.maes_num_steps < 2: + raise ValueError("`maes_num_steps` must be greater than 1.") + + if softmax_temperature != 1.0 and language_model is not None: + logging.warning( + "Softmax temperature is not supported with LM decoding." "Setting softmax-temperature value to 1.0." + ) + + self.softmax_temperature = 1.0 + else: + self.softmax_temperature = softmax_temperature + self.language_model = language_model + self.preserve_alignments = preserve_alignments + + self.token_offset = 0 + + if ngram_lm_model: + if KENLM_AVAILABLE: + self.ngram_lm = kenlm.Model(ngram_lm_model) + self.ngram_lm_alpha = ngram_lm_alpha + else: + raise ImportError( + "KenLM package (https://github.com/kpu/kenlm) is not installed. " "Use ngram_lm_model=None." + ) + else: + self.ngram_lm = None + + if hat_subtract_ilm: + assert hasattr(self.joint, "return_hat_ilm") + assert search_type == "maes" + self.hat_subtract_ilm = hat_subtract_ilm + self.hat_ilm_weight = hat_ilm_weight + + @typecheck() + def __call__( + self, + encoder_output: torch.Tensor, + encoded_lengths: torch.Tensor, + partial_hypotheses: Optional[List[Hypothesis]] = None, + ) -> Union[Hypothesis, NBestHypotheses]: + """Perform general beam search. + + Args: + encoder_output: Encoded speech features (B, D_enc, T_max) + encoded_lengths: Lengths of the encoder outputs + + Returns: + Either a list containing a single Hypothesis (when `return_best_hypothesis=True`, + otherwise a list containing a single NBestHypotheses, which itself contains a list of + Hypothesis. This list is sorted such that the best hypothesis is the first element. + """ + # Preserve decoder and joint training state + decoder_training_state = self.decoder.training + joint_training_state = self.joint.training + + # setup hat outputs mode + return_hat_ilm_default = False + if self.hat_subtract_ilm: + assert hasattr(self.joint, "return_hat_ilm") + return_hat_ilm_default = self.joint.return_hat_ilm + self.joint.return_hat_ilm = self.hat_subtract_ilm + + with torch.no_grad(): + # Apply optional preprocessing + encoder_output = encoder_output.transpose(1, 2) # (B, T, D) + + self.decoder.eval() + self.joint.eval() + + hypotheses = [] + with tqdm( + range(encoder_output.size(0)), + desc='Beam search progress:', + total=encoder_output.size(0), + unit='sample', + ) as idx_gen: + + # Freeze the decoder and joint to prevent recording of gradients + # during the beam loop. + with self.decoder.as_frozen(), self.joint.as_frozen(): + + _p = next(self.joint.parameters()) + dtype = _p.dtype + + # Decode every sample in the batch independently. + for batch_idx in idx_gen: + inseq = encoder_output[batch_idx : batch_idx + 1, : encoded_lengths[batch_idx], :] # [1, T, D] + logitlen = encoded_lengths[batch_idx] + + if inseq.dtype != dtype: + inseq = inseq.to(dtype=dtype) + + # Extract partial hypothesis if exists + partial_hypothesis = partial_hypotheses[batch_idx] if partial_hypotheses is not None else None + + # Execute the specific search strategy + nbest_hyps = self.search_algorithm( + inseq, logitlen, partial_hypotheses=partial_hypothesis + ) # sorted list of hypothesis + + # Prepare the list of hypotheses + nbest_hyps = pack_hypotheses(nbest_hyps) + + # Pack the result + if self.return_best_hypothesis: + best_hypothesis = nbest_hyps[0] # type: Hypothesis + else: + best_hypothesis = NBestHypotheses(nbest_hyps) # type: NBestHypotheses + hypotheses.append(best_hypothesis) + + self.decoder.train(decoder_training_state) + self.joint.train(joint_training_state) + if self.hat_subtract_ilm: + self.joint.return_hat_ilm = return_hat_ilm_default + + return (hypotheses,) + + def sort_nbest(self, hyps: List[Hypothesis]) -> List[Hypothesis]: + """Sort hypotheses by score or score given sequence length. + + Args: + hyps: list of hypotheses + + Return: + hyps: sorted list of hypotheses + """ + if self.score_norm: + return sorted(hyps, key=lambda x: x.score / len(x.y_sequence), reverse=True) + else: + return sorted(hyps, key=lambda x: x.score, reverse=True) + + def greedy_search( + self, h: torch.Tensor, encoded_lengths: torch.Tensor, partial_hypotheses: Optional[Hypothesis] = None + ) -> List[Hypothesis]: + """Greedy search implementation for transducer. + Generic case when beam size = 1. Results might differ slightly due to implementation details + as compared to `GreedyRNNTInfer` and `GreedyBatchRNNTInfer`. + + Args: + h: Encoded speech features (1, T_max, D_enc) + + Returns: + hyp: 1-best decoding results + """ + if self.preserve_alignments: + # Alignments is a 2-dimensional dangling list representing T x U + alignments = [[]] + else: + alignments = None + + # Initialize zero state vectors + dec_state = self.decoder.initialize_state(h) + + # Construct initial hypothesis + hyp = Hypothesis( + score=0.0, y_sequence=[self.blank], dec_state=dec_state, timestep=[-1], length=encoded_lengths + ) + + if partial_hypotheses is not None: + if len(partial_hypotheses.y_sequence) > 0: + hyp.y_sequence = [int(partial_hypotheses.y_sequence[-1].cpu().numpy())] + hyp.dec_state = partial_hypotheses.dec_state + hyp.dec_state = _states_to_device(hyp.dec_state, h.device) + + cache = {} + + # Initialize state and first token + y, state, _ = self.decoder.score_hypothesis(hyp, cache) + + for i in range(int(encoded_lengths)): + hi = h[:, i : i + 1, :] # [1, 1, D] + + not_blank = True + symbols_added = 0 + + # TODO: Figure out how to remove this hard coding afterwords + while not_blank and (symbols_added < 5): + ytu = torch.log_softmax(self.joint.joint(hi, y) / self.softmax_temperature, dim=-1) # [1, 1, 1, V + 1] + ytu = ytu[0, 0, 0, :] # [V + 1] + + # max() requires float + if ytu.dtype != torch.float32: + ytu = ytu.float() + + logp, pred = torch.max(ytu, dim=-1) # [1, 1] + pred = pred.item() + + if self.preserve_alignments: + # insert logprobs into last timestep + alignments[-1].append((ytu.to('cpu'), torch.tensor(pred, dtype=torch.int32))) + + if pred == self.blank: + not_blank = False + + if self.preserve_alignments: + # convert Ti-th logits into a torch array + alignments.append([]) # blank buffer for next timestep + else: + # Update state and current sequence + hyp.y_sequence.append(int(pred)) + hyp.score += float(logp) + hyp.dec_state = state + hyp.timestep.append(i) + + # Compute next state and token + y, state, _ = self.decoder.score_hypothesis(hyp, cache) + symbols_added += 1 + + # Remove trailing empty list of alignments + if self.preserve_alignments: + if len(alignments[-1]) == 0: + del alignments[-1] + + # attach alignments to hypothesis + hyp.alignments = alignments + + # Remove the original input label if partial hypothesis was provided + if partial_hypotheses is not None: + hyp.y_sequence = hyp.y_sequence[1:] + + return [hyp] + + def default_beam_search( + self, h: torch.Tensor, encoded_lengths: torch.Tensor, partial_hypotheses: Optional[Hypothesis] = None + ) -> List[Hypothesis]: + """Beam search implementation. + + Args: + x: Encoded speech features (1, T_max, D_enc) + + Returns: + nbest_hyps: N-best decoding results + """ + # Initialize states + beam = min(self.beam_size, self.vocab_size) + beam_k = min(beam, (self.vocab_size - 1)) + blank_tensor = torch.tensor([self.blank], device=h.device, dtype=torch.long) + + # Precompute some constants for blank position + ids = list(range(self.vocab_size + 1)) + ids.remove(self.blank) + + # Used when blank token is first vs last token + if self.blank == 0: + index_incr = 1 + else: + index_incr = 0 + + # Initialize zero vector states + dec_state = self.decoder.initialize_state(h) + + # Initialize first hypothesis for the beam (blank) + kept_hyps = [Hypothesis(score=0.0, y_sequence=[self.blank], dec_state=dec_state, timestep=[-1], length=0)] + cache = {} + + if partial_hypotheses is not None: + if len(partial_hypotheses.y_sequence) > 0: + kept_hyps[0].y_sequence = [int(partial_hypotheses.y_sequence[-1].cpu().numpy())] + kept_hyps[0].dec_state = partial_hypotheses.dec_state + kept_hyps[0].dec_state = _states_to_device(kept_hyps[0].dec_state, h.device) + + if self.preserve_alignments: + kept_hyps[0].alignments = [[]] + + for i in range(int(encoded_lengths)): + hi = h[:, i : i + 1, :] # [1, 1, D] + hyps = kept_hyps + kept_hyps = [] + + while True: + max_hyp = max(hyps, key=lambda x: x.score) + hyps.remove(max_hyp) + + # update decoder state and get next score + y, state, lm_tokens = self.decoder.score_hypothesis(max_hyp, cache) # [1, 1, D] + + # get next token + ytu = torch.log_softmax(self.joint.joint(hi, y) / self.softmax_temperature, dim=-1) # [1, 1, 1, V + 1] + ytu = ytu[0, 0, 0, :] # [V + 1] + + # preserve alignments + if self.preserve_alignments: + logprobs = ytu.cpu().clone() + + # remove blank token before top k + top_k = ytu[ids].topk(beam_k, dim=-1) + + # Two possible steps - blank token or non-blank token predicted + ytu = ( + torch.cat((top_k[0], ytu[self.blank].unsqueeze(0))), + torch.cat((top_k[1] + index_incr, blank_tensor)), + ) + + # for each possible step + for logp, k in zip(*ytu): + # construct hypothesis for step + new_hyp = Hypothesis( + score=(max_hyp.score + float(logp)), + y_sequence=max_hyp.y_sequence[:], + dec_state=max_hyp.dec_state, + lm_state=max_hyp.lm_state, + timestep=max_hyp.timestep[:], + length=encoded_lengths, + ) + + if self.preserve_alignments: + new_hyp.alignments = copy.deepcopy(max_hyp.alignments) + + # if current token is blank, dont update sequence, just store the current hypothesis + if k == self.blank: + kept_hyps.append(new_hyp) + else: + # if non-blank token was predicted, update state and sequence and then search more hypothesis + new_hyp.dec_state = state + new_hyp.y_sequence.append(int(k)) + new_hyp.timestep.append(i) + + hyps.append(new_hyp) + + # Determine whether the alignment should be blank or token + if self.preserve_alignments: + if k == self.blank: + new_hyp.alignments[-1].append( + (logprobs.clone(), torch.tensor(self.blank, dtype=torch.int32)) + ) + else: + new_hyp.alignments[-1].append( + (logprobs.clone(), torch.tensor(new_hyp.y_sequence[-1], dtype=torch.int32)) + ) + + # keep those hypothesis that have scores greater than next search generation + hyps_max = float(max(hyps, key=lambda x: x.score).score) + kept_most_prob = sorted([hyp for hyp in kept_hyps if hyp.score > hyps_max], key=lambda x: x.score,) + + # If enough hypothesis have scores greater than next search generation, + # stop beam search. + if len(kept_most_prob) >= beam: + if self.preserve_alignments: + # convert Ti-th logits into a torch array + for kept_h in kept_most_prob: + kept_h.alignments.append([]) # blank buffer for next timestep + + kept_hyps = kept_most_prob + break + + # Remove trailing empty list of alignments + if self.preserve_alignments: + for h in kept_hyps: + if len(h.alignments[-1]) == 0: + del h.alignments[-1] + + # Remove the original input label if partial hypothesis was provided + if partial_hypotheses is not None: + for hyp in kept_hyps: + if hyp.y_sequence[0] == partial_hypotheses.y_sequence[-1] and len(hyp.y_sequence) > 1: + hyp.y_sequence = hyp.y_sequence[1:] + + return self.sort_nbest(kept_hyps) + + def time_sync_decoding( + self, h: torch.Tensor, encoded_lengths: torch.Tensor, partial_hypotheses: Optional[Hypothesis] = None + ) -> List[Hypothesis]: + """Time synchronous beam search implementation. + Based on https://ieeexplore.ieee.org/document/9053040 + + Args: + h: Encoded speech features (1, T_max, D_enc) + + Returns: + nbest_hyps: N-best decoding results + """ + if partial_hypotheses is not None: + raise NotImplementedError("`partial_hypotheses` support is not supported") + + # Precompute some constants for blank position + ids = list(range(self.vocab_size + 1)) + ids.remove(self.blank) + + # Used when blank token is first vs last token + if self.blank == 0: + index_incr = 1 + else: + index_incr = 0 + + # prepare the batched beam states + beam = min(self.beam_size, self.vocab_size) + beam_state = self.decoder.initialize_state( + torch.zeros(beam, device=h.device, dtype=h.dtype) + ) # [L, B, H], [L, B, H] (for LSTMs) + + # Initialize first hypothesis for the beam (blank) + B = [ + Hypothesis( + y_sequence=[self.blank], + score=0.0, + dec_state=self.decoder.batch_select_state(beam_state, 0), + timestep=[-1], + length=0, + ) + ] + cache = {} + + # Initialize alignments + if self.preserve_alignments: + for hyp in B: + hyp.alignments = [[]] + + for i in range(int(encoded_lengths)): + hi = h[:, i : i + 1, :] + + # Update caches + A = [] + C = B + + h_enc = hi + + # For a limited number of symmetric expansions per timestep "i" + for v in range(self.tsd_max_symmetric_expansion_per_step): + D = [] + + # Decode a batch of beam states and scores + beam_y, beam_state, beam_lm_tokens = self.decoder.batch_score_hypothesis(C, cache, beam_state) + + # Extract the log probabilities and the predicted tokens + beam_logp = torch.log_softmax( + self.joint.joint(h_enc, beam_y) / self.softmax_temperature, dim=-1 + ) # [B, 1, 1, V + 1] + beam_logp = beam_logp[:, 0, 0, :] # [B, V + 1] + beam_topk = beam_logp[:, ids].topk(beam, dim=-1) + + seq_A = [h.y_sequence for h in A] + + for j, hyp in enumerate(C): + # create a new hypothesis in A + if hyp.y_sequence not in seq_A: + # If the sequence is not in seq_A, add it as the blank token + # In this step, we dont add a token but simply update score + _temp_hyp = Hypothesis( + score=(hyp.score + float(beam_logp[j, self.blank])), + y_sequence=hyp.y_sequence[:], + dec_state=hyp.dec_state, + lm_state=hyp.lm_state, + timestep=hyp.timestep[:], + length=encoded_lengths, + ) + + # Preserve the blank token alignment + if self.preserve_alignments: + _temp_hyp.alignments = copy.deepcopy(hyp.alignments) + _temp_hyp.alignments[-1].append( + (beam_logp[j].clone(), torch.tensor(self.blank, dtype=torch.int32)), + ) + + A.append(_temp_hyp) + else: + # merge the existing blank hypothesis score with current score. + dict_pos = seq_A.index(hyp.y_sequence) + + A[dict_pos].score = np.logaddexp( + A[dict_pos].score, (hyp.score + float(beam_logp[j, self.blank])) + ) + + if v < self.tsd_max_symmetric_expansion_per_step: + for j, hyp in enumerate(C): + # for each current hypothesis j + # extract the top token score and top token id for the jth hypothesis + for logp, k in zip(beam_topk[0][j], beam_topk[1][j] + index_incr): + # create new hypothesis and store in D + # Note: This loop does *not* include the blank token! + new_hyp = Hypothesis( + score=(hyp.score + float(logp)), + y_sequence=(hyp.y_sequence + [int(k)]), + dec_state=self.decoder.batch_select_state(beam_state, j), + lm_state=hyp.lm_state, + timestep=hyp.timestep[:] + [i], + length=encoded_lengths, + ) + + # Preserve token alignment + if self.preserve_alignments: + new_hyp.alignments = copy.deepcopy(hyp.alignments) + new_hyp.alignments[-1].append( + (beam_topk[0].clone().cpu(), torch.tensor(k, dtype=torch.int32)), + ) + + D.append(new_hyp) + + # Prune beam + C = sorted(D, key=lambda x: x.score, reverse=True)[:beam] + + if self.preserve_alignments: + # convert Ti-th logits into a torch array + for C_i in C: + # Check if the last token emitted at last timestep was a blank + # If so, move to next timestep + logp, label = C_i.alignments[-1][-1] # The last alignment of this step + if int(label) == self.blank: + C_i.alignments.append([]) # blank buffer for next timestep + + # Prune beam + B = sorted(A, key=lambda x: x.score, reverse=True)[:beam] + + if self.preserve_alignments: + # convert Ti-th logits into a torch array + for B_i in B: + # Check if the last token emitted at last timestep was a blank + # If so, move to next timestep + logp, label = B_i.alignments[-1][-1] # The last alignment of this step + if int(label) == self.blank: + B_i.alignments.append([]) # blank buffer for next timestep + + # Remove trailing empty list of alignments + if self.preserve_alignments: + for h in B: + if len(h.alignments[-1]) == 0: + del h.alignments[-1] + + return self.sort_nbest(B) + + def align_length_sync_decoding( + self, h: torch.Tensor, encoded_lengths: torch.Tensor, partial_hypotheses: Optional[Hypothesis] = None + ) -> List[Hypothesis]: + """Alignment-length synchronous beam search implementation. + Based on https://ieeexplore.ieee.org/document/9053040 + + Args: + h: Encoded speech features (1, T_max, D_enc) + + Returns: + nbest_hyps: N-best decoding results + """ + # delay this import here instead of at the beginning to avoid circular imports. + from nemo.collections.asr.modules.rnnt import RNNTDecoder, StatelessTransducerDecoder + + if partial_hypotheses is not None: + raise NotImplementedError("`partial_hypotheses` support is not supported") + + # Precompute some constants for blank position + ids = list(range(self.vocab_size + 1)) + ids.remove(self.blank) + + # Used when blank token is first vs last token + if self.blank == 0: + index_incr = 1 + else: + index_incr = 0 + + # prepare the batched beam states + beam = min(self.beam_size, self.vocab_size) + + h = h[0] # [T, D] + h_length = int(encoded_lengths) + beam_state = self.decoder.initialize_state( + torch.zeros(beam, device=h.device, dtype=h.dtype) + ) # [L, B, H], [L, B, H] for LSTMS + + # compute u_max as either a specific static limit, + # or a multiple of current `h_length` dynamically. + if type(self.alsd_max_target_length) == float: + u_max = int(self.alsd_max_target_length * h_length) + else: + u_max = int(self.alsd_max_target_length) + + # Initialize first hypothesis for the beam (blank) + B = [ + Hypothesis( + y_sequence=[self.blank], + score=0.0, + dec_state=self.decoder.batch_select_state(beam_state, 0), + timestep=[-1], + length=0, + ) + ] + + # Initialize alignments + if self.preserve_alignments: + B[0].alignments = [[]] + + final = [] + cache = {} + + # ALSD runs for T + U_max steps + for i in range(h_length + u_max): + # Update caches + A = [] + B_ = [] + h_states = [] + + # preserve the list of batch indices which are added into the list + # and those which are removed from the list + # This is necessary to perform state updates in the correct batch indices later + batch_ids = list(range(len(B))) # initialize as a list of all batch ids + batch_removal_ids = [] # update with sample ids which are removed + + for bid, hyp in enumerate(B): + u = len(hyp.y_sequence) - 1 + t = i - u + + if t > (h_length - 1): + batch_removal_ids.append(bid) + continue + + B_.append(hyp) + h_states.append((t, h[t])) + + if B_: + # Compute the subset of batch ids which were *not* removed from the list above + sub_batch_ids = None + if len(B_) != beam: + sub_batch_ids = batch_ids + for id in batch_removal_ids: + # sub_batch_ids contains list of ids *that were not removed* + sub_batch_ids.remove(id) + + # extract the states of the sub batch only. + if isinstance(self.decoder, RNNTDecoder): + # LSTM decoder, state is [layer x batch x hidden] + beam_state_ = [ + beam_state[state_id][:, sub_batch_ids, :] for state_id in range(len(beam_state)) + ] + elif isinstance(self.decoder, StatelessTransducerDecoder): + # stateless decoder, state is [batch x hidden] + beam_state_ = [beam_state[state_id][sub_batch_ids, :] for state_id in range(len(beam_state))] + else: + raise NotImplementedError("Unknown decoder type.") + + else: + # If entire batch was used (none were removed), simply take all the states + beam_state_ = beam_state + + # Decode a batch/sub-batch of beam states and scores + beam_y, beam_state_, beam_lm_tokens = self.decoder.batch_score_hypothesis(B_, cache, beam_state_) + + # If only a subset of batch ids were updated (some were removed) + if sub_batch_ids is not None: + # For each state in the RNN (2 for LSTM) + for state_id in range(len(beam_state)): + # Update the current batch states with the sub-batch states (in the correct indices) + # These indices are specified by sub_batch_ids, the ids of samples which were updated. + if isinstance(self.decoder, RNNTDecoder): + # LSTM decoder, state is [layer x batch x hidden] + beam_state[state_id][:, sub_batch_ids, :] = beam_state_[state_id][...] + elif isinstance(self.decoder, StatelessTransducerDecoder): + # stateless decoder, state is [batch x hidden] + beam_state[state_id][sub_batch_ids, :] = beam_state_[state_id][...] + else: + raise NotImplementedError("Unknown decoder type.") + else: + # If entire batch was updated, simply update all the states + beam_state = beam_state_ + + # h_states = list of [t, h[t]] + # so h[1] here is a h[t] of shape [D] + # Simply stack all of the h[t] within the sub_batch/batch (T <= beam) + h_enc = torch.stack([h[1] for h in h_states]) # [T=beam, D] + h_enc = h_enc.unsqueeze(1) # [B=beam, T=1, D]; batch over the beams + + # Extract the log probabilities and the predicted tokens + beam_logp = torch.log_softmax( + self.joint.joint(h_enc, beam_y) / self.softmax_temperature, dim=-1 + ) # [B=beam, 1, 1, V + 1] + beam_logp = beam_logp[:, 0, 0, :] # [B=beam, V + 1] + beam_topk = beam_logp[:, ids].topk(beam, dim=-1) + + for j, hyp in enumerate(B_): + # For all updated samples in the batch, add it as the blank token + # In this step, we dont add a token but simply update score + new_hyp = Hypothesis( + score=(hyp.score + float(beam_logp[j, self.blank])), + y_sequence=hyp.y_sequence[:], + dec_state=hyp.dec_state, + lm_state=hyp.lm_state, + timestep=hyp.timestep[:], + length=i, + ) + + if self.preserve_alignments: + new_hyp.alignments = copy.deepcopy(hyp.alignments) + + # Add the alignment of blank at this step + new_hyp.alignments[-1].append( + (beam_logp[j].clone().cpu(), torch.tensor(self.blank, dtype=torch.int32)) + ) + + # Add blank prediction to A + A.append(new_hyp) + + # If the prediction "timestep" t has reached the length of the input sequence + # we can add it to the "finished" hypothesis list. + if h_states[j][0] == (h_length - 1): + final.append(new_hyp) + + # Here, we carefully select the indices of the states that we want to preserve + # for the next token (non-blank) update. + if sub_batch_ids is not None: + h_states_idx = sub_batch_ids[j] + else: + h_states_idx = j + + # for each current hypothesis j + # extract the top token score and top token id for the jth hypothesis + for logp, k in zip(beam_topk[0][j], beam_topk[1][j] + index_incr): + # create new hypothesis and store in A + # Note: This loop does *not* include the blank token! + new_hyp = Hypothesis( + score=(hyp.score + float(logp)), + y_sequence=(hyp.y_sequence[:] + [int(k)]), + dec_state=self.decoder.batch_select_state(beam_state, h_states_idx), + lm_state=hyp.lm_state, + timestep=hyp.timestep[:] + [i], + length=i, + ) + + if self.preserve_alignments: + new_hyp.alignments = copy.deepcopy(hyp.alignments) + + # Add the alignment of Uj for this beam candidate at this step + new_hyp.alignments[-1].append( + (beam_logp[j].clone().cpu(), torch.tensor(new_hyp.y_sequence[-1], dtype=torch.int32)) + ) + + A.append(new_hyp) + + # Prune and recombine same hypothesis + # This may cause next beam to be smaller than max beam size + # Therefore larger beam sizes may be required for better decoding. + B = sorted(A, key=lambda x: x.score, reverse=True)[:beam] + B = self.recombine_hypotheses(B) + + if self.preserve_alignments: + # convert Ti-th logits into a torch array + for B_i in B: + # Check if the last token emitted at last timestep was a blank + # If so, move to next timestep + logp, label = B_i.alignments[-1][-1] # The last alignment of this step + if int(label) == self.blank: + B_i.alignments.append([]) # blank buffer for next timestep + + # If B_ is empty list, then we may be able to early exit + elif len(batch_ids) == len(batch_removal_ids): + # break early + break + + if final: + # Remove trailing empty list of alignments + if self.preserve_alignments: + for h in final: + if len(h.alignments[-1]) == 0: + del h.alignments[-1] + + return self.sort_nbest(final) + else: + # Remove trailing empty list of alignments + if self.preserve_alignments: + for h in B: + if len(h.alignments[-1]) == 0: + del h.alignments[-1] + + return B + + def modified_adaptive_expansion_search( + self, h: torch.Tensor, encoded_lengths: torch.Tensor, partial_hypotheses: Optional[Hypothesis] = None + ) -> List[Hypothesis]: + """ + Based on/modified from https://ieeexplore.ieee.org/document/9250505 + + Args: + h: Encoded speech features (1, T_max, D_enc) + + Returns: + nbest_hyps: N-best decoding results + """ + if partial_hypotheses is not None: + raise NotImplementedError("`partial_hypotheses` support is not supported") + + h = h[0] # [T, D] + + # prepare the batched beam states + beam = min(self.beam_size, self.vocab_size) + beam_state = self.decoder.initialize_state( + torch.zeros(beam, device=h.device, dtype=h.dtype) + ) # [L, B, H], [L, B, H] for LSTMS + + # Initialize first hypothesis for the beam (blank) + init_tokens = [ + Hypothesis( + y_sequence=[self.blank], + score=0.0, + dec_state=self.decoder.batch_select_state(beam_state, 0), + timestep=[-1], + length=0, + ) + ] + + cache = {} + + # Initialize alignment buffer + if self.preserve_alignments: + for hyp in init_tokens: + hyp.alignments = [[]] + + # Decode a batch of beam states and scores + beam_dec_out, beam_state, beam_lm_tokens = self.decoder.batch_score_hypothesis(init_tokens, cache, beam_state) + state = self.decoder.batch_select_state(beam_state, 0) + + # Setup ngram LM: + if self.ngram_lm: + init_lm_state = kenlm.State() + self.ngram_lm.BeginSentenceWrite(init_lm_state) + + # TODO: Setup LM + if self.language_model is not None: + # beam_lm_states, beam_lm_scores = self.lm.buff_predict( + # None, beam_lm_tokens, 1 + # ) + # lm_state = select_lm_state( + # beam_lm_states, 0, self.lm_layers, self.is_wordlm + # ) + # lm_scores = beam_lm_scores[0] + raise NotImplementedError() + else: + lm_state = None + lm_scores = None + + # Initialize first hypothesis for the beam (blank) for kept hypotheses + kept_hyps = [ + Hypothesis( + y_sequence=[self.blank], + score=0.0, + dec_state=state, + dec_out=[beam_dec_out[0]], + lm_state=lm_state, + lm_scores=lm_scores, + timestep=[-1], + length=0, + ) + ] + if self.ngram_lm: + kept_hyps[0].ngram_lm_state = init_lm_state + + # Initialize alignment buffer + if self.preserve_alignments: + for hyp in kept_hyps: + hyp.alignments = [[]] + + for t in range(encoded_lengths): + enc_out_t = h[t : t + 1].unsqueeze(0) # [1, 1, D] + + # Perform prefix search to obtain hypothesis + hyps = self.prefix_search( + sorted(kept_hyps, key=lambda x: len(x.y_sequence), reverse=True), + enc_out_t, + prefix_alpha=self.maes_prefix_alpha, + ) # type: List[Hypothesis] + kept_hyps = [] + + # Prepare output tensor + beam_enc_out = enc_out_t + + # List that contains the blank token emisions + list_b = [] + duplication_check = [hyp.y_sequence for hyp in hyps] + + # Repeat for number of mAES steps + for n in range(self.maes_num_steps): + # Pack the decoder logits for all current hypothesis + beam_dec_out = torch.stack([h.dec_out[-1] for h in hyps]) # [H, 1, D] + + # Extract the log probabilities + ytm, ilm_ytm = self.resolve_joint_output(beam_enc_out, beam_dec_out) + beam_logp, beam_idx = ytm.topk(self.max_candidates, dim=-1) + + beam_logp = beam_logp[:, 0, 0, :] # [B, V + 1] + beam_idx = beam_idx[:, 0, 0, :] # [B, max_candidates] + + # Compute k expansions for all the current hypotheses + k_expansions = select_k_expansions( + hyps, beam_idx, beam_logp, self.maes_expansion_gamma, self.maes_expansion_beta + ) + + # List that contains the hypothesis after prefix expansion + list_exp = [] + for i, hyp in enumerate(hyps): # For all hypothesis + for k, new_score in k_expansions[i]: # for all expansion within these hypothesis + new_hyp = Hypothesis( + y_sequence=hyp.y_sequence[:], + score=new_score, + dec_out=hyp.dec_out[:], + dec_state=hyp.dec_state, + lm_state=hyp.lm_state, + lm_scores=hyp.lm_scores, + timestep=hyp.timestep[:], + length=t, + ) + if self.ngram_lm: + new_hyp.ngram_lm_state = hyp.ngram_lm_state + + # If the expansion was for blank + if k == self.blank: + list_b.append(new_hyp) + else: + # If the expansion was a token + # new_hyp.y_sequence.append(int(k)) + if (new_hyp.y_sequence + [int(k)]) not in duplication_check: + new_hyp.y_sequence.append(int(k)) + new_hyp.timestep.append(t) + + # Setup ngram LM: + if self.ngram_lm: + lm_score, new_hyp.ngram_lm_state = self.compute_ngram_score( + hyp.ngram_lm_state, int(k) + ) + if self.hat_subtract_ilm: + new_hyp.score += self.ngram_lm_alpha * lm_score - float( + self.hat_ilm_weight * ilm_ytm[i, 0, 0, k] + ) + else: + new_hyp.score += self.ngram_lm_alpha * lm_score + + # TODO: Setup LM + if self.language_model is not None: + # new_hyp.score += self.lm_weight * float( + # hyp.lm_scores[k] + # ) + pass + + list_exp.append(new_hyp) + + # Preserve alignments + if self.preserve_alignments: + new_hyp.alignments = copy.deepcopy(hyp.alignments) + + if k == self.blank: + new_hyp.alignments[-1].append( + (beam_logp[i].clone().cpu(), torch.tensor(self.blank, dtype=torch.int32)), + ) + else: + new_hyp.alignments[-1].append( + ( + beam_logp[i].clone().cpu(), + torch.tensor(new_hyp.y_sequence[-1], dtype=torch.int32), + ), + ) + + # If there were no token expansions in any of the hypotheses, + # Early exit + if not list_exp: + kept_hyps = sorted(list_b, key=lambda x: x.score, reverse=True)[:beam] + + # Update aligments with next step + if self.preserve_alignments: + # convert Ti-th logits into a torch array + for h_i in kept_hyps: + # Check if the last token emitted at last timestep was a blank + # If so, move to next timestep + logp, label = h_i.alignments[-1][-1] # The last alignment of this step + if int(label) == self.blank: + h_i.alignments.append([]) # blank buffer for next timestep + + # Early exit + break + + else: + # Initialize the beam states for the hypotheses in the expannsion list + beam_state = self.decoder.batch_initialize_states( + beam_state, + [hyp.dec_state for hyp in list_exp], + # [hyp.y_sequence for hyp in list_exp], # + ) + + # Decode a batch of beam states and scores + beam_dec_out, beam_state, beam_lm_tokens = self.decoder.batch_score_hypothesis( + list_exp, + cache, + beam_state, + # self.language_model is not None, + ) + + # TODO: Setup LM + if self.language_model is not None: + # beam_lm_states = create_lm_batch_states( + # [hyp.lm_state for hyp in list_exp], + # self.lm_layers, + # self.is_wordlm, + # ) + # beam_lm_states, beam_lm_scores = self.lm.buff_predict( + # beam_lm_states, beam_lm_tokens, len(list_exp) + # ) + pass + + # If this isnt the last mAES step + if n < (self.maes_num_steps - 1): + # For all expanded hypothesis + for i, hyp in enumerate(list_exp): + # Preserve the decoder logits for the current beam + hyp.dec_out.append(beam_dec_out[i]) + hyp.dec_state = self.decoder.batch_select_state(beam_state, i) + + # TODO: Setup LM + if self.language_model is not None: + # hyp.lm_state = select_lm_state( + # beam_lm_states, i, self.lm_layers, self.is_wordlm + # ) + # hyp.lm_scores = beam_lm_scores[i] + pass + + # Copy the expanded hypothesis + hyps = list_exp[:] + + # Update aligments with next step + if self.preserve_alignments: + # convert Ti-th logits into a torch array + for h_i in hyps: + # Check if the last token emitted at last timestep was a blank + # If so, move to next timestep + logp, label = h_i.alignments[-1][-1] # The last alignment of this step + if int(label) == self.blank: + h_i.alignments.append([]) # blank buffer for next timestep + + else: + # Extract the log probabilities + beam_logp, _ = self.resolve_joint_output(beam_enc_out, beam_dec_out) + beam_logp = beam_logp[:, 0, 0, :] + + # For all expansions, add the score for the blank label + for i, hyp in enumerate(list_exp): + hyp.score += float(beam_logp[i, self.blank]) + + # Preserve the decoder's output and state + hyp.dec_out.append(beam_dec_out[i]) + hyp.dec_state = self.decoder.batch_select_state(beam_state, i) + + # TODO: Setup LM + if self.language_model is not None: + # hyp.lm_state = select_lm_state( + # beam_lm_states, i, self.lm_layers, self.is_wordlm + # ) + # hyp.lm_scores = beam_lm_scores[i] + pass + + # Finally, update the kept hypothesis of sorted top Beam candidates + kept_hyps = sorted(list_b + list_exp, key=lambda x: x.score, reverse=True)[:beam] + + # Update aligments with next step + if self.preserve_alignments: + # convert Ti-th logits into a torch array + for h_i in kept_hyps: + # Check if the last token emitted at last timestep was a blank + # If so, move to next timestep + logp, label = h_i.alignments[-1][-1] # The last alignment of this step + if int(label) == self.blank: + h_i.alignments.append([]) # blank buffer for next timestep + + # Remove trailing empty list of alignments + if self.preserve_alignments: + for h in kept_hyps: + if len(h.alignments[-1]) == 0: + del h.alignments[-1] + + # Sort the hypothesis with best scores + return self.sort_nbest(kept_hyps) + + def recombine_hypotheses(self, hypotheses: List[Hypothesis]) -> List[Hypothesis]: + """Recombine hypotheses with equivalent output sequence. + + Args: + hypotheses (list): list of hypotheses + + Returns: + final (list): list of recombined hypotheses + """ + final = [] + + for hyp in hypotheses: + seq_final = [f.y_sequence for f in final if f.y_sequence] + + if hyp.y_sequence in seq_final: + seq_pos = seq_final.index(hyp.y_sequence) + + final[seq_pos].score = np.logaddexp(final[seq_pos].score, hyp.score) + else: + final.append(hyp) + + return hypotheses + + def resolve_joint_output(self, enc_out: torch.Tensor, dec_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Resolve output types for RNNT and HAT joint models + """ + + joint_output = self.joint.joint(enc_out, dec_out) + if torch.is_tensor(joint_output): + ytm = torch.log_softmax(joint_output / self.softmax_temperature, dim=-1) + ilm_ytm = None + elif self.hat_subtract_ilm and isinstance(joint_output, HATJointOutput): + ytm, ilm_ytm = joint_output.hat_logprobs, joint_output.ilm_logprobs + else: + raise TypeError( + f"Joint output ({type(joint_output)}) must be torch.Tensor or HATJointOutput in case of HAT joint" + ) + + return ytm, ilm_ytm + + def prefix_search( + self, hypotheses: List[Hypothesis], enc_out: torch.Tensor, prefix_alpha: int + ) -> List[Hypothesis]: + """ + Prefix search for NSC and mAES strategies. + Based on https://arxiv.org/pdf/1211.3711.pdf + """ + + for j, hyp_j in enumerate(hypotheses[:-1]): + for hyp_i in hypotheses[(j + 1) :]: + curr_id = len(hyp_j.y_sequence) + pref_id = len(hyp_i.y_sequence) + + if is_prefix(hyp_j.y_sequence, hyp_i.y_sequence) and (curr_id - pref_id) <= prefix_alpha: + logp, ilm_logp = self.resolve_joint_output(enc_out, hyp_i.dec_out[-1]) + logp = logp[0, 0, 0, :] + curr_score = hyp_i.score + float(logp[hyp_j.y_sequence[pref_id]]) + # Setup ngram LM: + if self.ngram_lm: + lm_score, next_state = self.compute_ngram_score( + hyp_i.ngram_lm_state, int(hyp_j.y_sequence[pref_id]) + ) + if self.hat_subtract_ilm: + curr_score += self.ngram_lm_alpha * lm_score - self.hat_ilm_weight * float( + ilm_logp[0, 0, hyp_j.y_sequence[pref_id]] + ) + else: + curr_score += self.ngram_lm_alpha * lm_score + + for k in range(pref_id, (curr_id - 1)): + logp, ilm_logp = self.resolve_joint_output(enc_out, hyp_j.dec_out[k]) + logp = logp[0, 0, 0, :] + curr_score += float(logp[hyp_j.y_sequence[k + 1]]) + # Setup ngram LM: + if self.ngram_lm: + lm_score, next_state = self.compute_ngram_score(next_state, int(hyp_j.y_sequence[k + 1])) + if self.hat_subtract_ilm: + curr_score += self.ngram_lm_alpha * lm_score - self.hat_ilm_weight * float( + ilm_logp[0, 0, hyp_j.y_sequence[k + 1]] + ) + else: + curr_score += self.ngram_lm_alpha * lm_score + + hyp_j.score = np.logaddexp(hyp_j.score, curr_score) + + return hypotheses + + def compute_ngram_score(self, current_lm_state: "kenlm.State", label: int) -> Tuple[float, "kenlm.State"]: + """ + Score computation for kenlm ngram language model. + """ + + if self.token_offset: + label = chr(label + self.token_offset) + else: + label = str(label) + next_state = kenlm.State() + lm_score = self.ngram_lm.BaseScore(current_lm_state, label, next_state) + lm_score *= 1.0 / np.log10(np.e) + + return lm_score, next_state + + def set_decoding_type(self, decoding_type: str): + + # Please check train_kenlm.py in scripts/asr_language_modeling/ to find out why we need + # TOKEN_OFFSET for BPE-based models + if decoding_type == 'subword': + from nemo.collections.asr.parts.submodules.ctc_beam_decoding import DEFAULT_TOKEN_OFFSET + + self.token_offset = DEFAULT_TOKEN_OFFSET + + +@dataclass +class BeamRNNTInferConfig: + beam_size: int + search_type: str = 'default' + score_norm: bool = True + return_best_hypothesis: bool = True + tsd_max_sym_exp_per_step: Optional[int] = 50 + alsd_max_target_len: float = 1.0 + nsc_max_timesteps_expansion: int = 1 + nsc_prefix_alpha: int = 1 + maes_num_steps: int = 2 + maes_prefix_alpha: int = 1 + maes_expansion_gamma: float = 2.3 + maes_expansion_beta: int = 2 + language_model: Optional[Dict[str, Any]] = None + softmax_temperature: float = 1.0 + preserve_alignments: bool = False + ngram_lm_model: Optional[str] = None + ngram_lm_alpha: Optional[float] = 0.0 + hat_subtract_ilm: bool = False + hat_ilm_weight: float = 0.0 diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/rnnt_decoding.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/rnnt_decoding.py new file mode 100644 index 0000000..7a260f3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/rnnt_decoding.py @@ -0,0 +1,1554 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import re +from abc import abstractmethod +from dataclasses import dataclass, field, is_dataclass +from typing import Callable, Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +from omegaconf import OmegaConf + +from nemo.collections.asr.parts.submodules import rnnt_beam_decoding, rnnt_greedy_decoding +from nemo.collections.asr.parts.utils.asr_confidence_utils import ConfidenceConfig, ConfidenceMixin +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis, NBestHypotheses +from nemo.collections.common.tokenizers.aggregate_tokenizer import AggregateTokenizer +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.utils import logging + + +class AbstractRNNTDecoding(ConfidenceMixin): + """ + Used for performing RNN-T auto-regressive decoding of the Decoder+Joint network given the encoder state. + + Args: + decoding_cfg: A dict-like object which contains the following key-value pairs. + strategy: str value which represents the type of decoding that can occur. + Possible values are : + - greedy, greedy_batch (for greedy decoding). + - beam, tsd, alsd (for beam search decoding). + + compute_hypothesis_token_set: A bool flag, which determines whether to compute a list of decoded + tokens as well as the decoded string. Default is False in order to avoid double decoding + unless required. + + preserve_alignments: Bool flag which preserves the history of logprobs generated during + decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `alignments` in it. Here, `alignments` is a List of List of + Tuple(Tensor (of length V + 1), Tensor(scalar, label after argmax)). + + In order to obtain this hypothesis, please utilize `rnnt_decoder_predictions_tensor` function + with the `return_hypotheses` flag set to True. + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more targets from a vocabulary. + U is the number of target tokens for the current timestep Ti. + + compute_timestamps: A bool flag, which determines whether to compute the character/subword, or + word based timestamp mapping the output log-probabilities to discrete intervals of timestamps. + The timestamps will be available in the returned Hypothesis.timestep as a dictionary. + + rnnt_timestamp_type: A str value, which represents the types of timestamps that should be calculated. + Can take the following values - "char" for character/subword time stamps, "word" for word level + time stamps and "all" (default), for both character level and word level time stamps. + + word_seperator: Str token representing the seperator between words. + + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores + generated during decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `alignments` is a List of List of ints. + + confidence_cfg: A dict-like object which contains the following key-value pairs related to confidence + scores. In order to obtain hypotheses with confidence scores, please utilize + `rnnt_decoder_predictions_tensor` function with the `preserve_frame_confidence` flag set to True. + + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores + generated during decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `alignments` is a List of List of floats. + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more confidence scores. + U is the number of target tokens for the current timestep Ti. + preserve_token_confidence: Bool flag which preserves the history of per-token confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `token_confidence` in it. Here, `token_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized tokens. + preserve_word_confidence: Bool flag which preserves the history of per-word confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `word_confidence` in it. Here, `word_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized words. + exclude_blank: Bool flag indicating that blank token confidence scores are to be excluded + from the `token_confidence`. + aggregation: Which aggregation type to use for collapsing per-token confidence into per-word confidence. + Valid options are `mean`, `min`, `max`, `prod`. + method_cfg: A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: The method name (str). + Supported values: + - 'max_prob' for using the maximum token probability as a confidence. + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: Which type of entropy to use (str). + Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: A mapping of the entropy value to the interval [0,1]. + Supported values: + - 'lin' for using the linear mapping. + - 'exp' for using exponential mapping with linear shift. + + The config may further contain the following sub-dictionaries: + "greedy": + max_symbols: int, describing the maximum number of target tokens to decode per + timestep during greedy decoding. Setting to larger values allows longer sentences + to be decoded, at the cost of increased execution time. + preserve_frame_confidence: Same as above, overrides above value. + confidence_method_cfg: Same as above, overrides confidence_cfg.method_cfg. + + "beam": + beam_size: int, defining the beam size for beam search. Must be >= 1. + If beam_size == 1, will perform cached greedy search. This might be slightly different + results compared to the greedy search above. + + score_norm: optional bool, whether to normalize the returned beam score in the hypotheses. + Set to True by default. + + return_best_hypothesis: optional bool, whether to return just the best hypothesis or all of the + hypotheses after beam search has concluded. This flag is set by default. + + tsd_max_sym_exp: optional int, determines number of symmetric expansions of the target symbols + per timestep of the acoustic model. Larger values will allow longer sentences to be decoded, + at increased cost to execution time. + + alsd_max_target_len: optional int or float, determines the potential maximum target sequence length. + If an integer is provided, it can decode sequences of that particular maximum length. + If a float is provided, it can decode sequences of int(alsd_max_target_len * seq_len), + where seq_len is the length of the acoustic model output (T). + + NOTE: + If a float is provided, it can be greater than 1! + By default, a float of 2.0 is used so that a target sequence can be at most twice + as long as the acoustic model output length T. + + maes_num_steps: Number of adaptive steps to take. From the paper, 2 steps is generally sufficient, + and can be reduced to 1 to improve decoding speed while sacrificing some accuracy. int > 0. + + maes_prefix_alpha: Maximum prefix length in prefix search. Must be an integer, and is advised to keep this as 1 + in order to reduce expensive beam search cost later. int >= 0. + + maes_expansion_beta: Maximum number of prefix expansions allowed, in addition to the beam size. + Effectively, the number of hypothesis = beam_size + maes_expansion_beta. Must be an int >= 0, + and affects the speed of inference since large values will perform large beam search in the next step. + + maes_expansion_gamma: Float pruning threshold used in the prune-by-value step when computing the expansions. + The default (2.3) is selected from the paper. It performs a comparison (max_log_prob - gamma <= log_prob[v]) + where v is all vocabulary indices in the Vocab set and max_log_prob is the "most" likely token to be + predicted. Gamma therefore provides a margin of additional tokens which can be potential candidates for + expansion apart from the "most likely" candidate. + Lower values will reduce the number of expansions (by increasing pruning-by-value, thereby improving speed + but hurting accuracy). Higher values will increase the number of expansions (by reducing pruning-by-value, + thereby reducing speed but potentially improving accuracy). This is a hyper parameter to be experimentally + tuned on a validation set. + + softmax_temperature: Scales the logits of the joint prior to computing log_softmax. + + decoder: The Decoder/Prediction network module. + joint: The Joint network module. + blank_id: The id of the RNNT blank token. + """ + + def __init__(self, decoding_cfg, decoder, joint, blank_id: int): + super(AbstractRNNTDecoding, self).__init__() + + # Convert dataclass to config object + if is_dataclass(decoding_cfg): + decoding_cfg = OmegaConf.structured(decoding_cfg) + + self.cfg = decoding_cfg + self.blank_id = blank_id + self.num_extra_outputs = joint.num_extra_outputs + self.big_blank_durations = self.cfg.get("big_blank_durations", None) + self.durations = self.cfg.get("durations", None) + self.compute_hypothesis_token_set = self.cfg.get("compute_hypothesis_token_set", False) + self.compute_langs = decoding_cfg.get('compute_langs', False) + self.preserve_alignments = self.cfg.get('preserve_alignments', None) + self.joint_fused_batch_size = self.cfg.get('fused_batch_size', None) + self.compute_timestamps = self.cfg.get('compute_timestamps', None) + self.word_seperator = self.cfg.get('word_seperator', ' ') + + if self.durations is not None and self.durations != []: # this means it's a TDT model. + if blank_id == 0: + raise ValueError("blank_id must equal len(non_blank_vocabs) for TDT models") + if self.big_blank_durations is not None and self.big_blank_durations != []: + raise ValueError("duration and big_blank_durations can't both be not None") + if self.cfg.strategy not in ['greedy', 'greedy_batch']: + raise ValueError("currently only greedy and greedy_batch inference is supported for TDT models") + + if ( + self.big_blank_durations is not None and self.big_blank_durations != [] + ): # this means it's a multi-blank model. + if blank_id == 0: + raise ValueError("blank_id must equal len(vocabs) for multi-blank RNN-T models") + if self.cfg.strategy not in ['greedy', 'greedy_batch']: + raise ValueError( + "currently only greedy and greedy_batch inference is supported for multi-blank models" + ) + + possible_strategies = ['greedy', 'greedy_batch', 'beam', 'tsd', 'alsd', 'maes'] + if self.cfg.strategy not in possible_strategies: + raise ValueError(f"Decoding strategy must be one of {possible_strategies}") + + # Update preserve alignments + if self.preserve_alignments is None: + if self.cfg.strategy in ['greedy', 'greedy_batch']: + self.preserve_alignments = self.cfg.greedy.get('preserve_alignments', False) + + elif self.cfg.strategy in ['beam', 'tsd', 'alsd', 'maes']: + self.preserve_alignments = self.cfg.beam.get('preserve_alignments', False) + + # Update compute timestamps + if self.compute_timestamps is None: + if self.cfg.strategy in ['greedy', 'greedy_batch']: + self.compute_timestamps = self.cfg.greedy.get('compute_timestamps', False) + + elif self.cfg.strategy in ['beam', 'tsd', 'alsd', 'maes']: + self.compute_timestamps = self.cfg.beam.get('compute_timestamps', False) + + # Test if alignments are being preserved for RNNT + if self.compute_timestamps is True and self.preserve_alignments is False: + raise ValueError("If `compute_timesteps` flag is set, then `preserve_alignments` flag must also be set.") + + # initialize confidence-related fields + self._init_confidence(self.cfg.get('confidence_cfg', None)) + + # Confidence estimation is not implemented for these strategies + if ( + not self.preserve_frame_confidence + and self.cfg.strategy in ['beam', 'tsd', 'alsd', 'maes'] + and self.cfg.beam.get('preserve_frame_confidence', False) + ): + raise NotImplementedError(f"Confidence calculation is not supported for strategy `{self.cfg.strategy}`") + + if self.cfg.strategy == 'greedy': + if self.big_blank_durations is None or self.big_blank_durations == []: + if self.durations is None or self.durations == []: + self.decoding = rnnt_greedy_decoding.GreedyRNNTInfer( + decoder_model=decoder, + joint_model=joint, + blank_index=self.blank_id, + max_symbols_per_step=( + self.cfg.greedy.get('max_symbols', None) + or self.cfg.greedy.get('max_symbols_per_step', None) + ), + preserve_alignments=self.preserve_alignments, + preserve_frame_confidence=self.preserve_frame_confidence, + confidence_method_cfg=self.confidence_method_cfg, + ) + else: + self.decoding = rnnt_greedy_decoding.GreedyTDTInfer( + decoder_model=decoder, + joint_model=joint, + blank_index=self.blank_id, + durations=self.durations, + max_symbols_per_step=( + self.cfg.greedy.get('max_symbols', None) + or self.cfg.greedy.get('max_symbols_per_step', None) + ), + preserve_alignments=self.preserve_alignments, + preserve_frame_confidence=self.preserve_frame_confidence, + confidence_method_cfg=self.confidence_method_cfg, + ) + else: + self.decoding = rnnt_greedy_decoding.GreedyMultiblankRNNTInfer( + decoder_model=decoder, + joint_model=joint, + blank_index=self.blank_id, + big_blank_durations=self.big_blank_durations, + max_symbols_per_step=( + self.cfg.greedy.get('max_symbols', None) or self.cfg.greedy.get('max_symbols_per_step', None) + ), + preserve_alignments=self.preserve_alignments, + preserve_frame_confidence=self.preserve_frame_confidence, + confidence_method_cfg=self.confidence_method_cfg, + ) + + elif self.cfg.strategy == 'greedy_batch': + if self.big_blank_durations is None or self.big_blank_durations == []: + if self.durations is None or self.durations == []: + self.decoding = rnnt_greedy_decoding.GreedyBatchedRNNTInfer( + decoder_model=decoder, + joint_model=joint, + blank_index=self.blank_id, + max_symbols_per_step=( + self.cfg.greedy.get('max_symbols', None) + or self.cfg.greedy.get('max_symbols_per_step', None) + ), + preserve_alignments=self.preserve_alignments, + preserve_frame_confidence=self.preserve_frame_confidence, + confidence_method_cfg=self.confidence_method_cfg, + loop_labels=self.cfg.greedy.get('loop_labels', True), + use_cuda_graph_decoder=self.cfg.greedy.get('use_cuda_graph_decoder', False), + ) + else: + self.decoding = rnnt_greedy_decoding.GreedyBatchedTDTInfer( + decoder_model=decoder, + joint_model=joint, + blank_index=self.blank_id, + durations=self.durations, + max_symbols_per_step=( + self.cfg.greedy.get('max_symbols', None) + or self.cfg.greedy.get('max_symbols_per_step', None) + ), + preserve_alignments=self.preserve_alignments, + preserve_frame_confidence=self.preserve_frame_confidence, + confidence_method_cfg=self.confidence_method_cfg, + use_cuda_graph_decoder=self.cfg.greedy.get('use_cuda_graph_decoder', False), + ) + + else: + self.decoding = rnnt_greedy_decoding.GreedyBatchedMultiblankRNNTInfer( + decoder_model=decoder, + joint_model=joint, + blank_index=self.blank_id, + big_blank_durations=self.big_blank_durations, + max_symbols_per_step=( + self.cfg.greedy.get('max_symbols', None) or self.cfg.greedy.get('max_symbols_per_step', None) + ), + preserve_alignments=self.preserve_alignments, + preserve_frame_confidence=self.preserve_frame_confidence, + confidence_method_cfg=self.confidence_method_cfg, + ) + + elif self.cfg.strategy == 'beam': + + self.decoding = rnnt_beam_decoding.BeamRNNTInfer( + decoder_model=decoder, + joint_model=joint, + beam_size=self.cfg.beam.beam_size, + return_best_hypothesis=decoding_cfg.beam.get('return_best_hypothesis', True), + search_type='default', + score_norm=self.cfg.beam.get('score_norm', True), + softmax_temperature=self.cfg.beam.get('softmax_temperature', 1.0), + preserve_alignments=self.preserve_alignments, + ) + + elif self.cfg.strategy == 'tsd': + + self.decoding = rnnt_beam_decoding.BeamRNNTInfer( + decoder_model=decoder, + joint_model=joint, + beam_size=self.cfg.beam.beam_size, + return_best_hypothesis=decoding_cfg.beam.get('return_best_hypothesis', True), + search_type='tsd', + score_norm=self.cfg.beam.get('score_norm', True), + tsd_max_sym_exp_per_step=self.cfg.beam.get('tsd_max_sym_exp', 10), + softmax_temperature=self.cfg.beam.get('softmax_temperature', 1.0), + preserve_alignments=self.preserve_alignments, + ) + + elif self.cfg.strategy == 'alsd': + + self.decoding = rnnt_beam_decoding.BeamRNNTInfer( + decoder_model=decoder, + joint_model=joint, + beam_size=self.cfg.beam.beam_size, + return_best_hypothesis=decoding_cfg.beam.get('return_best_hypothesis', True), + search_type='alsd', + score_norm=self.cfg.beam.get('score_norm', True), + alsd_max_target_len=self.cfg.beam.get('alsd_max_target_len', 2), + softmax_temperature=self.cfg.beam.get('softmax_temperature', 1.0), + preserve_alignments=self.preserve_alignments, + ) + + elif self.cfg.strategy == 'maes': + + self.decoding = rnnt_beam_decoding.BeamRNNTInfer( + decoder_model=decoder, + joint_model=joint, + beam_size=self.cfg.beam.beam_size, + return_best_hypothesis=decoding_cfg.beam.get('return_best_hypothesis', True), + search_type='maes', + score_norm=self.cfg.beam.get('score_norm', True), + maes_num_steps=self.cfg.beam.get('maes_num_steps', 2), + maes_prefix_alpha=self.cfg.beam.get('maes_prefix_alpha', 1), + maes_expansion_gamma=self.cfg.beam.get('maes_expansion_gamma', 2.3), + maes_expansion_beta=self.cfg.beam.get('maes_expansion_beta', 2.0), + softmax_temperature=self.cfg.beam.get('softmax_temperature', 1.0), + preserve_alignments=self.preserve_alignments, + ngram_lm_model=self.cfg.beam.get('ngram_lm_model', None), + ngram_lm_alpha=self.cfg.beam.get('ngram_lm_alpha', 0.0), + hat_subtract_ilm=self.cfg.beam.get('hat_subtract_ilm', False), + hat_ilm_weight=self.cfg.beam.get('hat_ilm_weight', 0.0), + ) + + else: + + raise ValueError( + f"Incorrect decoding strategy supplied. Must be one of {possible_strategies}\n" + f"but was provided {self.cfg.strategy}" + ) + + # Update the joint fused batch size or disable it entirely if needed. + self.update_joint_fused_batch_size() + + def rnnt_decoder_predictions_tensor( + self, + encoder_output: torch.Tensor, + encoded_lengths: torch.Tensor, + return_hypotheses: bool = False, + partial_hypotheses: Optional[List[Hypothesis]] = None, + ) -> Tuple[List[str], Optional[List[List[str]]], Optional[Union[Hypothesis, NBestHypotheses]]]: + """ + Decode an encoder output by autoregressive decoding of the Decoder+Joint networks. + + Args: + encoder_output: torch.Tensor of shape [B, D, T]. + encoded_lengths: torch.Tensor containing lengths of the padded encoder outputs. Shape [B]. + return_hypotheses: bool. If set to True it will return list of Hypothesis or NBestHypotheses + + Returns: + If `return_best_hypothesis` is set: + A tuple (hypotheses, None): + hypotheses - list of Hypothesis (best hypothesis per sample). + Look at rnnt_utils.Hypothesis for more information. + + If `return_best_hypothesis` is not set: + A tuple(hypotheses, all_hypotheses) + hypotheses - list of Hypothesis (best hypothesis per sample). + Look at rnnt_utils.Hypothesis for more information. + all_hypotheses - list of NBestHypotheses. Each NBestHypotheses further contains a sorted + list of all the hypotheses of the model per sample. + Look at rnnt_utils.NBestHypotheses for more information. + """ + # Compute hypotheses + with torch.inference_mode(): + hypotheses_list = self.decoding( + encoder_output=encoder_output, encoded_lengths=encoded_lengths, partial_hypotheses=partial_hypotheses + ) # type: [List[Hypothesis]] + + # extract the hypotheses + hypotheses_list = hypotheses_list[0] # type: List[Hypothesis] + + prediction_list = hypotheses_list + + if isinstance(prediction_list[0], NBestHypotheses): + hypotheses = [] + all_hypotheses = [] + + for nbest_hyp in prediction_list: # type: NBestHypotheses + n_hyps = nbest_hyp.n_best_hypotheses # Extract all hypotheses for this sample + decoded_hyps = self.decode_hypothesis(n_hyps) # type: List[str] + + # If computing timestamps + if self.compute_timestamps is True: + timestamp_type = self.cfg.get('rnnt_timestamp_type', 'all') + for hyp_idx in range(len(decoded_hyps)): + decoded_hyps[hyp_idx] = self.compute_rnnt_timestamps(decoded_hyps[hyp_idx], timestamp_type) + + hypotheses.append(decoded_hyps[0]) # best hypothesis + all_hypotheses.append(decoded_hyps) + + if return_hypotheses: + return hypotheses, all_hypotheses + + best_hyp_text = [h.text for h in hypotheses] + all_hyp_text = [h.text for hh in all_hypotheses for h in hh] + return best_hyp_text, all_hyp_text + + else: + hypotheses = self.decode_hypothesis(prediction_list) # type: List[str] + + # If computing timestamps + if self.compute_timestamps is True: + timestamp_type = self.cfg.get('rnnt_timestamp_type', 'all') + for hyp_idx in range(len(hypotheses)): + hypotheses[hyp_idx] = self.compute_rnnt_timestamps(hypotheses[hyp_idx], timestamp_type) + + if return_hypotheses: + # greedy decoding, can get high-level confidence scores + if self.preserve_frame_confidence and ( + self.preserve_word_confidence or self.preserve_token_confidence + ): + hypotheses = self.compute_confidence(hypotheses) + return hypotheses, None + + best_hyp_text = [h.text for h in hypotheses] + return best_hyp_text, None + + def decode_hypothesis(self, hypotheses_list: List[Hypothesis]) -> List[Union[Hypothesis, NBestHypotheses]]: + """ + Decode a list of hypotheses into a list of strings. + + Args: + hypotheses_list: List of Hypothesis. + + Returns: + A list of strings. + """ + for ind in range(len(hypotheses_list)): + # Extract the integer encoded hypothesis + prediction = hypotheses_list[ind].y_sequence + + if type(prediction) != list: + prediction = prediction.tolist() + + # RNN-T sample level is already preprocessed by implicit RNNT decoding + # Simply remove any blank and possibly big blank tokens + if self.big_blank_durations is not None and self.big_blank_durations != []: # multi-blank RNNT + num_extra_outputs = len(self.big_blank_durations) + prediction = [p for p in prediction if p < self.blank_id - num_extra_outputs] + elif self.durations is not None and self.durations != []: # TDT model. + prediction = [p for p in prediction if p < self.blank_id] + else: # standard RNN-T + prediction = [p for p in prediction if p != self.blank_id] + + # De-tokenize the integer tokens; if not computing timestamps + if self.compute_timestamps is True: + # keep the original predictions, wrap with the number of repetitions per token and alignments + # this is done so that `rnnt_decoder_predictions_tensor()` can process this hypothesis + # in order to compute exact time stamps. + alignments = copy.deepcopy(hypotheses_list[ind].alignments) + token_repetitions = [1] * len(alignments) # preserve number of repetitions per token + hypothesis = (prediction, alignments, token_repetitions) + else: + hypothesis = self.decode_tokens_to_str(prediction) + + # TODO: remove + # collapse leading spaces before . , ? for PC models + hypothesis = re.sub(r'(\s+)([\.\,\?])', r'\2', hypothesis) + + if self.compute_hypothesis_token_set: + hypotheses_list[ind].tokens = self.decode_ids_to_tokens(prediction) + + # De-tokenize the integer tokens + hypotheses_list[ind].text = hypothesis + + return hypotheses_list + + def compute_confidence(self, hypotheses_list: List[Hypothesis]) -> List[Hypothesis]: + """ + Computes high-level (per-token and/or per-word) confidence scores for a list of hypotheses. + Assumes that `frame_confidence` is present in the hypotheses. + + Args: + hypotheses_list: List of Hypothesis. + + Returns: + A list of hypotheses with high-level confidence scores. + """ + if self.exclude_blank_from_confidence: + for hyp in hypotheses_list: + hyp.token_confidence = hyp.non_blank_frame_confidence + else: + for hyp in hypotheses_list: + offset = 0 + token_confidence = [] + if len(hyp.timestep) > 0: + for ts, te in zip(hyp.timestep, hyp.timestep[1:] + [len(hyp.frame_confidence)]): + if ts != te: + # tokens are considered to belong to the last non-blank token, if any. + token_confidence.append( + self._aggregate_confidence( + [hyp.frame_confidence[ts][offset]] + + [fc[0] for fc in hyp.frame_confidence[ts + 1 : te]] + ) + ) + offset = 0 + else: + token_confidence.append(hyp.frame_confidence[ts][offset]) + offset += 1 + hyp.token_confidence = token_confidence + if self.preserve_word_confidence: + for hyp in hypotheses_list: + hyp.word_confidence = self._aggregate_token_confidence(hyp) + return hypotheses_list + + @abstractmethod + def decode_tokens_to_str(self, tokens: List[int]) -> str: + """ + Implemented by subclass in order to decoder a token id list into a string. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded string. + """ + raise NotImplementedError() + + @abstractmethod + def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]: + """ + Implemented by subclass in order to decode a token id list into a token list. + A token list is the string representation of each token id. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded tokens. + """ + raise NotImplementedError() + + @abstractmethod + def decode_tokens_to_lang(self, tokens: List[int]) -> str: + """ + Implemented by subclass in order to + compute the most likely language ID (LID) string given the tokens. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded LID string. + """ + raise NotImplementedError() + + @abstractmethod + def decode_ids_to_langs(self, tokens: List[int]) -> List[str]: + """ + Implemented by subclass in order to + decode a token id list into language ID (LID) list. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded LIDS. + """ + raise NotImplementedError() + + def update_joint_fused_batch_size(self): + if self.joint_fused_batch_size is None: + # do nothing and let the Joint itself handle setting up of the fused batch + return + + if not hasattr(self.decoding.joint, 'set_fused_batch_size'): + logging.warning( + "The joint module does not have `set_fused_batch_size(int)` as a setter function.\n" + "Ignoring update of joint fused batch size." + ) + return + + if not hasattr(self.decoding.joint, 'set_fuse_loss_wer'): + logging.warning( + "The joint module does not have `set_fuse_loss_wer(bool, RNNTLoss, RNNTWER)` " + "as a setter function.\n" + "Ignoring update of joint fused batch size." + ) + return + + if self.joint_fused_batch_size > 0: + self.decoding.joint.set_fused_batch_size(self.joint_fused_batch_size) + else: + logging.info("Joint fused batch size <= 0; Will temporarily disable fused batch step in the Joint.") + self.decoding.joint.set_fuse_loss_wer(False) + + def compute_rnnt_timestamps(self, hypothesis: Hypothesis, timestamp_type: str = "all"): + assert timestamp_type in ['char', 'word', 'all'] + + # Unpack the temporary storage + decoded_prediction, alignments, token_repetitions = hypothesis.text + + # Retrieve offsets + char_offsets = word_offsets = None + char_offsets = self._compute_offsets(hypothesis, token_repetitions, self.blank_id) + + # finally, set the flattened decoded predictions to text field for later text decoding + hypothesis.text = decoded_prediction + + # Assert number of offsets and hypothesis tokens are 1:1 match. + num_flattened_tokens = 0 + for t in range(len(char_offsets)): + # Subtract one here for the extra RNNT BLANK token emitted to designate "End of timestep" + num_flattened_tokens += len(char_offsets[t]['char']) - 1 + + if num_flattened_tokens != len(hypothesis.text): + raise ValueError( + f"`char_offsets`: {char_offsets} and `processed_tokens`: {hypothesis.text}" + " have to be of the same length, but are: " + f"`len(offsets)`: {len(char_offsets)} and `len(processed_tokens)`:" + f" {len(hypothesis.text)}" + ) + + encoded_char_offsets = copy.deepcopy(char_offsets) + + # Correctly process the token ids to chars/subwords. + for i, offsets in enumerate(char_offsets): + decoded_chars = [] + for char in offsets['char'][:-1]: # ignore the RNNT Blank token at end of every timestep with -1 subset + decoded_chars.append(self.decode_tokens_to_str([int(char)])) + char_offsets[i]["char"] = decoded_chars + + # detect char vs subword models + lens = [] + for v in char_offsets: + tokens = v["char"] + # each token may be either 1 unicode token or multiple unicode token + # for character based models, only 1 token is used + # for subword, more than one token can be used. + # Computing max, then summing up total lens is a test to check for char vs subword + # For char models, len(lens) == sum(lens) + # but this is violated for subword models. + max_len = max(len(c) for c in tokens) + lens.append(max_len) + + # array of one or more chars implies subword based model with multiple char emitted per TxU step (via subword) + if sum(lens) > len(lens): + text_type = 'subword' + else: + # full array of ones implies character based model with 1 char emitted per TxU step + text_type = 'char' + + # retrieve word offsets from character offsets + word_offsets = None + if timestamp_type in ['word', 'all']: + if text_type == 'char': + word_offsets = self._get_word_offsets_chars(char_offsets, word_delimiter_char=self.word_seperator) + else: + # utilize the copy of char offsets with the correct integer ids for tokens + # so as to avoid tokenize -> detokenize -> compare -> merge steps. + word_offsets = self._get_word_offsets_subwords_sentencepiece( + encoded_char_offsets, + hypothesis, + decode_ids_to_tokens=self.decode_ids_to_tokens, + decode_tokens_to_str=self.decode_tokens_to_str, + ) + + # attach results + if len(hypothesis.timestep) > 0: + timestep_info = hypothesis.timestep + else: + timestep_info = [] + + # Setup defaults + hypothesis.timestep = {"timestep": timestep_info} + + # Add char / subword time stamps + if char_offsets is not None and timestamp_type in ['char', 'all']: + hypothesis.timestep['char'] = char_offsets + + # Add word time stamps + if word_offsets is not None and timestamp_type in ['word', 'all']: + hypothesis.timestep['word'] = word_offsets + + # Convert the flattened token indices to text + hypothesis.text = self.decode_tokens_to_str(hypothesis.text) + + return hypothesis + + @staticmethod + def _compute_offsets( + hypothesis: Hypothesis, token_repetitions: List[int], rnnt_token: int + ) -> List[Dict[str, Union[str, int]]]: + """ + Utility method that calculates the indidual time indices where a token starts and ends. + + Args: + hypothesis: A Hypothesis object that contains `text` field that holds the character / subword token + emitted at every time step after rnnt collapse. + token_repetitions: A list of ints representing the number of repetitions of each emitted token. + rnnt_token: The integer of the rnnt blank token used during rnnt collapse. + + Returns: + + """ + start_index = 0 + + # If the exact timestep information is available, utilize the 1st non-rnnt blank token timestep + # as the start index. + if hypothesis.timestep is not None and len(hypothesis.timestep) > 0: + start_index = max(0, hypothesis.timestep[0] - 1) + + # Construct the start and end indices brackets + end_indices = np.asarray(token_repetitions).cumsum() + start_indices = np.concatenate(([start_index], end_indices[:-1])) + + # Process the TxU dangling alignment tensor, containing pairs of (logits, label) + alignment_labels = [al_logits_labels for al_logits_labels in hypothesis.text[1]] + for t in range(len(alignment_labels)): + for u in range(len(alignment_labels[t])): + alignment_labels[t][u] = alignment_labels[t][u][1] # pick label from (logit, label) tuple + + # Merge the results per token into a list of dictionaries + offsets = [ + {"char": a, "start_offset": s, "end_offset": e} + for a, s, e in zip(alignment_labels, start_indices, end_indices) + ] + + # Filter out RNNT token (blank at [t][0] position). This is because blank can only occur at end of a + # time step for RNNT, so if 0th token is blank, then that timestep is skipped. + offsets = list(filter(lambda offsets: offsets["char"][0] != rnnt_token, offsets)) + return offsets + + @staticmethod + def _get_word_offsets_chars( + offsets: Dict[str, Union[str, float]], word_delimiter_char: str = " " + ) -> Dict[str, Union[str, float]]: + """ + Utility method which constructs word time stamps out of character time stamps. + + References: + This code is a port of the Hugging Face code for word time stamp construction. + + Args: + offsets: A list of dictionaries, each containing "char", "start_offset" and "end_offset". + word_delimiter_char: Character token that represents the word delimiter. By default, " ". + + Returns: + A list of dictionaries containing the word offsets. Each item contains "word", "start_offset" and + "end_offset". + """ + word_offsets = [] + + last_state = "SPACE" + word = "" + start_offset = 0 + end_offset = 0 + for i, offset in enumerate(offsets): + chars = offset["char"] + for char in chars: + state = "SPACE" if char == word_delimiter_char else "WORD" + + if state == last_state: + # If we are in the same state as before, we simply repeat what we've done before + end_offset = offset["end_offset"] + word += char + else: + # Switching state + if state == "SPACE": + # Finishing a word + word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) + else: + # Starting a new word + start_offset = offset["start_offset"] + end_offset = offset["end_offset"] + word = char + + last_state = state + + if last_state == "WORD": + word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) + + return word_offsets + + @staticmethod + def _get_word_offsets_subwords_sentencepiece( + offsets: Dict[str, Union[str, float]], + hypothesis: Hypothesis, + decode_ids_to_tokens: Callable[[List[int]], str], + decode_tokens_to_str: Callable[[List[int]], str], + ) -> Dict[str, Union[str, float]]: + """ + Utility method which constructs word time stamps out of sub-word time stamps. + + **Note**: Only supports Sentencepiece based tokenizers ! + + Args: + offsets: A list of dictionaries, each containing "char", "start_offset" and "end_offset". + hypothesis: Hypothesis object that contains `text` field, where each token is a sub-word id + after rnnt collapse. + decode_ids_to_tokens: A Callable function that accepts a list of integers and maps it to a sub-word. + decode_tokens_to_str: A Callable function that accepts a list of integers and maps it to text / str. + + Returns: + A list of dictionaries containing the word offsets. Each item contains "word", "start_offset" and + "end_offset". + """ + word_offsets = [] + built_token = [] + previous_token_index = 0 + # For every offset token + for i, offset in enumerate(offsets): + # For every subword token in offset token list (ignoring the RNNT Blank token at the end) + for char in offset['char'][:-1]: + char = int(char) + + # Compute the sub-word text representation, and the decoded text (stripped of sub-word markers). + token = decode_ids_to_tokens([char])[0] + token_text = decode_tokens_to_str([char]) + + # It is a sub-word token, or contains an identifier at the beginning such as _ or ## that was stripped + # after forcing partial text conversion of the token. + if token != token_text: + # If there are any partially or fully built sub-word token ids, construct to text. + # Note: This is "old" subword, that occurs *after* current sub-word has started. + if built_token: + word_offsets.append( + { + "word": decode_tokens_to_str(built_token), + "start_offset": offsets[previous_token_index]["start_offset"], + "end_offset": offsets[i]["start_offset"], + } + ) + + # Prepare list of new sub-word ids + built_token.clear() + built_token.append(char) + previous_token_index = i + else: + # If the token does not contain any sub-word start mark, then the sub-word has not completed yet + # Append to current sub-word list. + built_token.append(char) + + # Inject the start offset of the first token to word offsets + # This is because we always skip the delay the injection of the first sub-word due to the loop + # condition and check whether built token is ready or not. + # Therefore without this forced injection, the start_offset appears as off by 1. + # This should only be done when these arrays contain more than one element. + if offsets and word_offsets: + word_offsets[0]["start_offset"] = offsets[0]["start_offset"] + + # If there are any remaining tokens left, inject them all into the final word offset. + # The start offset of this token is the start time of the next token to process. + # The end offset of this token is the end time of the last token from offsets. + # Note that built_token is a flat list; but offsets contains a nested list which + # may have different dimensionality. + # As such, we can't rely on the length of the list of built_token to index offsets. + if built_token: + # start from the previous token index as this hasn't been committed to word_offsets yet + # if we still have content in built_token + start_offset = offsets[previous_token_index]["start_offset"] + word_offsets.append( + { + "word": decode_tokens_to_str(built_token), + "start_offset": start_offset, + "end_offset": offsets[-1]["end_offset"], + } + ) + built_token.clear() + + return word_offsets + + +class RNNTDecoding(AbstractRNNTDecoding): + """ + Used for performing RNN-T auto-regressive decoding of the Decoder+Joint network given the encoder state. + + Args: + decoding_cfg: A dict-like object which contains the following key-value pairs. + + strategy: + str value which represents the type of decoding that can occur. + Possible values are : + + - greedy, greedy_batch (for greedy decoding). + + - beam, tsd, alsd (for beam search decoding). + + compute_hypothesis_token_set: A bool flag, which determines whether to compute a list of decoded + tokens as well as the decoded string. Default is False in order to avoid double decoding + unless required. + + preserve_alignments: Bool flag which preserves the history of logprobs generated during + decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `logprobs` in it. Here, `alignments` is a List of List of + Tuple(Tensor (of length V + 1), Tensor(scalar, label after argmax)). + + In order to obtain this hypothesis, please utilize `rnnt_decoder_predictions_tensor` function + with the `return_hypotheses` flag set to True. + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more targets from a vocabulary. + U is the number of target tokens for the current timestep Ti. + + confidence_cfg: A dict-like object which contains the following key-value pairs related to confidence + scores. In order to obtain hypotheses with confidence scores, please utilize + `rnnt_decoder_predictions_tensor` function with the `preserve_frame_confidence` flag set to True. + + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores + generated during decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `alignments` is a List of List of floats. + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more confidence scores. + U is the number of target tokens for the current timestep Ti. + preserve_token_confidence: Bool flag which preserves the history of per-token confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `token_confidence` in it. Here, `token_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized tokens. + preserve_word_confidence: Bool flag which preserves the history of per-word confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `word_confidence` in it. Here, `word_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized words. + exclude_blank: Bool flag indicating that blank token confidence scores are to be excluded + from the `token_confidence`. + aggregation: Which aggregation type to use for collapsing per-token confidence into per-word confidence. + Valid options are `mean`, `min`, `max`, `prod`. + method_cfg: A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: + The method name (str). + Supported values: + + - 'max_prob' for using the maximum token probability as a confidence. + + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: + Which type of entropy to use (str). + Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: + Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: + A mapping of the entropy value to the interval [0,1]. + Supported values: + + - 'lin' for using the linear mapping. + + - 'exp' for using exponential mapping with linear shift. + + The config may further contain the following sub-dictionaries: + + "greedy": + max_symbols: int, describing the maximum number of target tokens to decode per + timestep during greedy decoding. Setting to larger values allows longer sentences + to be decoded, at the cost of increased execution time. + + preserve_frame_confidence: Same as above, overrides above value. + + confidence_method_cfg: Same as above, overrides confidence_cfg.method_cfg. + + "beam": + beam_size: int, defining the beam size for beam search. Must be >= 1. + If beam_size == 1, will perform cached greedy search. This might be slightly different + results compared to the greedy search above. + + score_norm: optional bool, whether to normalize the returned beam score in the hypotheses. + Set to True by default. + + return_best_hypothesis: optional bool, whether to return just the best hypothesis or all of the + hypotheses after beam search has concluded. This flag is set by default. + + tsd_max_sym_exp: optional int, determines number of symmetric expansions of the target symbols + per timestep of the acoustic model. Larger values will allow longer sentences to be decoded, + at increased cost to execution time. + + alsd_max_target_len: optional int or float, determines the potential maximum target sequence length. + If an integer is provided, it can decode sequences of that particular maximum length. + If a float is provided, it can decode sequences of int(alsd_max_target_len * seq_len), + where seq_len is the length of the acoustic model output (T). + + NOTE: + If a float is provided, it can be greater than 1! + By default, a float of 2.0 is used so that a target sequence can be at most twice + as long as the acoustic model output length T. + + maes_num_steps: Number of adaptive steps to take. From the paper, 2 steps is generally sufficient, + and can be reduced to 1 to improve decoding speed while sacrificing some accuracy. int > 0. + + maes_prefix_alpha: Maximum prefix length in prefix search. Must be an integer, and is advised to keep this as 1 + in order to reduce expensive beam search cost later. int >= 0. + + maes_expansion_beta: Maximum number of prefix expansions allowed, in addition to the beam size. + Effectively, the number of hypothesis = beam_size + maes_expansion_beta. Must be an int >= 0, + and affects the speed of inference since large values will perform large beam search in the next step. + + maes_expansion_gamma: Float pruning threshold used in the prune-by-value step when computing the expansions. + The default (2.3) is selected from the paper. It performs a comparison (max_log_prob - gamma <= log_prob[v]) + where v is all vocabulary indices in the Vocab set and max_log_prob is the "most" likely token to be + predicted. Gamma therefore provides a margin of additional tokens which can be potential candidates for + expansion apart from the "most likely" candidate. + Lower values will reduce the number of expansions (by increasing pruning-by-value, thereby improving speed + but hurting accuracy). Higher values will increase the number of expansions (by reducing pruning-by-value, + thereby reducing speed but potentially improving accuracy). This is a hyper parameter to be experimentally + tuned on a validation set. + + softmax_temperature: Scales the logits of the joint prior to computing log_softmax. + + decoder: The Decoder/Prediction network module. + joint: The Joint network module. + vocabulary: The vocabulary (excluding the RNNT blank token) which will be used for decoding. + """ + + def __init__( + self, decoding_cfg, decoder, joint, vocabulary, + ): + # we need to ensure blank is the last token in the vocab for the case of RNNT and Multi-blank RNNT. + blank_id = len(vocabulary) + joint.num_extra_outputs + + if hasattr(decoding_cfg, 'model_type') and decoding_cfg.model_type == 'tdt': + blank_id = len(vocabulary) + + self.labels_map = dict([(i, vocabulary[i]) for i in range(len(vocabulary))]) + + super(RNNTDecoding, self).__init__( + decoding_cfg=decoding_cfg, decoder=decoder, joint=joint, blank_id=blank_id, + ) + + if isinstance(self.decoding, rnnt_beam_decoding.BeamRNNTInfer): + self.decoding.set_decoding_type('char') + + def _aggregate_token_confidence(self, hypothesis: Hypothesis) -> List[float]: + """ + Implemented by subclass in order to aggregate token confidence to a word-level confidence. + + Args: + hypothesis: Hypothesis + + Returns: + A list of word-level confidence scores. + """ + return self._aggregate_token_confidence_chars(hypothesis.words, hypothesis.token_confidence) + + def decode_tokens_to_str(self, tokens: List[int]) -> str: + """ + Implemented by subclass in order to decoder a token list into a string. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded string. + """ + hypothesis = ''.join(self.decode_ids_to_tokens(tokens)) + return hypothesis + + def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]: + """ + Implemented by subclass in order to decode a token id list into a token list. + A token list is the string representation of each token id. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded tokens. + """ + token_list = [self.labels_map[c] for c in tokens if c < self.blank_id - self.num_extra_outputs] + return token_list + + def decode_tokens_to_lang(self, tokens: List[int]) -> str: + """ + Compute the most likely language ID (LID) string given the tokens. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded LID string. + """ + lang = self.tokenizer.ids_to_lang(tokens) + return lang + + def decode_ids_to_langs(self, tokens: List[int]) -> List[str]: + """ + Decode a token id list into language ID (LID) list. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded LIDS. + """ + lang_list = self.tokenizer.ids_to_text_and_langs(tokens) + return lang_list + + +class RNNTBPEDecoding(AbstractRNNTDecoding): + """ + Used for performing RNN-T auto-regressive decoding of the Decoder+Joint network given the encoder state. + + Args: + decoding_cfg: A dict-like object which contains the following key-value pairs. + + strategy: + str value which represents the type of decoding that can occur. + Possible values are : + + - greedy, greedy_batch (for greedy decoding). + + - beam, tsd, alsd (for beam search decoding). + + compute_hypothesis_token_set: A bool flag, which determines whether to compute a list of decoded + tokens as well as the decoded string. Default is False in order to avoid double decoding + unless required. + + preserve_alignments: Bool flag which preserves the history of logprobs generated during + decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `alignments` in it. Here, `alignments` is a List of List of + Tuple(Tensor (of length V + 1), Tensor(scalar, label after argmax)). + + In order to obtain this hypothesis, please utilize `rnnt_decoder_predictions_tensor` function + with the `return_hypotheses` flag set to True. + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more targets from a vocabulary. + U is the number of target tokens for the current timestep Ti. + + compute_timestamps: A bool flag, which determines whether to compute the character/subword, or + word based timestamp mapping the output log-probabilities to discrete intervals of timestamps. + The timestamps will be available in the returned Hypothesis.timestep as a dictionary. + + compute_langs: a bool flag, which allows to compute language id (LID) information per token, + word, and the entire sample (most likely language id). The LIDS will be available + in the returned Hypothesis object as a dictionary + + rnnt_timestamp_type: A str value, which represents the types of timestamps that should be calculated. + Can take the following values - "char" for character/subword time stamps, "word" for word level + time stamps and "all" (default), for both character level and word level time stamps. + + word_seperator: Str token representing the seperator between words. + + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores + generated during decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `alignments` is a List of List of ints. + + confidence_cfg: A dict-like object which contains the following key-value pairs related to confidence + scores. In order to obtain hypotheses with confidence scores, please utilize + `rnnt_decoder_predictions_tensor` function with the `preserve_frame_confidence` flag set to True. + + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores + generated during decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `alignments` is a List of List of floats. + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more confidence scores. + U is the number of target tokens for the current timestep Ti. + preserve_token_confidence: Bool flag which preserves the history of per-token confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `token_confidence` in it. Here, `token_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized tokens. + preserve_word_confidence: Bool flag which preserves the history of per-word confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `word_confidence` in it. Here, `word_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized words. + exclude_blank: Bool flag indicating that blank token confidence scores are to be excluded + from the `token_confidence`. + aggregation: Which aggregation type to use for collapsing per-token confidence into per-word confidence. + Valid options are `mean`, `min`, `max`, `prod`. + method_cfg: A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: + The method name (str). + Supported values: + + - 'max_prob' for using the maximum token probability as a confidence. + + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: Which type of entropy to use (str). + Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: A mapping of the entropy value to the interval [0,1]. + Supported values: + + - 'lin' for using the linear mapping. + + - 'exp' for using exponential mapping with linear shift. + + The config may further contain the following sub-dictionaries: + + "greedy": + max_symbols: int, describing the maximum number of target tokens to decode per + timestep during greedy decoding. Setting to larger values allows longer sentences + to be decoded, at the cost of increased execution time. + + preserve_frame_confidence: Same as above, overrides above value. + + confidence_method_cfg: Same as above, overrides confidence_cfg.method_cfg. + + "beam": + beam_size: int, defining the beam size for beam search. Must be >= 1. + If beam_size == 1, will perform cached greedy search. This might be slightly different + results compared to the greedy search above. + + score_norm: optional bool, whether to normalize the returned beam score in the hypotheses. + Set to True by default. + + return_best_hypothesis: optional bool, whether to return just the best hypothesis or all of the + hypotheses after beam search has concluded. + + tsd_max_sym_exp: optional int, determines number of symmetric expansions of the target symbols + per timestep of the acoustic model. Larger values will allow longer sentences to be decoded, + at increased cost to execution time. + + alsd_max_target_len: optional int or float, determines the potential maximum target sequence length. + If an integer is provided, it can decode sequences of that particular maximum length. + If a float is provided, it can decode sequences of int(alsd_max_target_len * seq_len), + where seq_len is the length of the acoustic model output (T). + + NOTE: + If a float is provided, it can be greater than 1! + By default, a float of 2.0 is used so that a target sequence can be at most twice + as long as the acoustic model output length T. + + maes_num_steps: Number of adaptive steps to take. From the paper, 2 steps is generally sufficient, + and can be reduced to 1 to improve decoding speed while sacrificing some accuracy. int > 0. + + maes_prefix_alpha: Maximum prefix length in prefix search. Must be an integer, and is advised to keep this as 1 + in order to reduce expensive beam search cost later. int >= 0. + + maes_expansion_beta: Maximum number of prefix expansions allowed, in addition to the beam size. + Effectively, the number of hypothesis = beam_size + maes_expansion_beta. Must be an int >= 0, + and affects the speed of inference since large values will perform large beam search in the next step. + + maes_expansion_gamma: Float pruning threshold used in the prune-by-value step when computing the expansions. + The default (2.3) is selected from the paper. It performs a comparison (max_log_prob - gamma <= log_prob[v]) + where v is all vocabulary indices in the Vocab set and max_log_prob is the "most" likely token to be + predicted. Gamma therefore provides a margin of additional tokens which can be potential candidates for + expansion apart from the "most likely" candidate. + Lower values will reduce the number of expansions (by increasing pruning-by-value, thereby improving speed + but hurting accuracy). Higher values will increase the number of expansions (by reducing pruning-by-value, + thereby reducing speed but potentially improving accuracy). This is a hyper parameter to be experimentally + tuned on a validation set. + + softmax_temperature: Scales the logits of the joint prior to computing log_softmax. + + decoder: The Decoder/Prediction network module. + joint: The Joint network module. + tokenizer: The tokenizer which will be used for decoding. + """ + + def __init__(self, decoding_cfg, decoder, joint, tokenizer: TokenizerSpec): + blank_id = tokenizer.tokenizer.vocab_size # RNNT or TDT models. + + # multi-blank RNNTs + if hasattr(decoding_cfg, 'model_type') and decoding_cfg.model_type == 'multiblank': + blank_id = tokenizer.tokenizer.vocab_size + joint.num_extra_outputs + + self.tokenizer = tokenizer + + super(RNNTBPEDecoding, self).__init__( + decoding_cfg=decoding_cfg, decoder=decoder, joint=joint, blank_id=blank_id + ) + + if isinstance(self.decoding, rnnt_beam_decoding.BeamRNNTInfer): + self.decoding.set_decoding_type('subword') + + def _aggregate_token_confidence(self, hypothesis: Hypothesis) -> List[float]: + """ + Implemented by subclass in order to reduce token confidence to a word-level confidence. + + **Note**: Only supports Sentencepiece based tokenizers! + + Args: + hypothesis: Hypothesis + + Returns: + A list of word-level confidence scores. + """ + return self._aggregate_token_confidence_subwords_sentencepiece( + hypothesis.words, hypothesis.token_confidence, hypothesis.y_sequence + ) + + def decode_tokens_to_str(self, tokens: List[int]) -> str: + """ + Implemented by subclass in order to decoder a token list into a string. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded string. + """ + hypothesis = self.tokenizer.ids_to_text(tokens) + return hypothesis + + def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]: + """ + Implemented by subclass in order to decode a token id list into a token list. + A token list is the string representation of each token id. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded tokens. + """ + token_list = self.tokenizer.ids_to_tokens(tokens) + return token_list + + def decode_tokens_to_lang(self, tokens: List[int]) -> str: + """ + Compute the most likely language ID (LID) string given the tokens. + + Args: + tokens: List of int representing the token ids. + + Returns: + A decoded LID string. + """ + lang = self.tokenizer.ids_to_lang(tokens) + return lang + + def decode_ids_to_langs(self, tokens: List[int]) -> List[str]: + """ + Decode a token id list into language ID (LID) list. + + Args: + tokens: List of int representing the token ids. + + Returns: + A list of decoded LIDS. + """ + lang_list = self.tokenizer.ids_to_text_and_langs(tokens) + return lang_list + + def decode_hypothesis(self, hypotheses_list: List[Hypothesis]) -> List[Union[Hypothesis, NBestHypotheses]]: + """ + Decode a list of hypotheses into a list of strings. + Overrides the super() method optionally adding lang information + + Args: + hypotheses_list: List of Hypothesis. + + Returns: + A list of strings. + """ + hypotheses = super().decode_hypothesis(hypotheses_list) + if self.compute_langs: + if isinstance(self.tokenizer, AggregateTokenizer): + for ind in range(len(hypotheses_list)): + # Extract the integer encoded hypothesis + prediction = hypotheses_list[ind].y_sequence + + if type(prediction) != list: + prediction = prediction.tolist() + + # RNN-T sample level is already preprocessed by implicit RNNT decoding + # Simply remove any blank tokens + prediction = [p for p in prediction if p != self.blank_id] + + hypotheses[ind].langs = self.decode_tokens_to_lang(prediction) + hypotheses[ind].langs_chars = self.decode_ids_to_langs(prediction) + else: + logging.warning( + "Ignoring request for lang output in hypotheses since the model does not use an aggregate tokenizer" + ) + + return hypotheses + + +@dataclass +class RNNTDecodingConfig: + model_type: str = "rnnt" # one of "rnnt", "multiblank" or "tdt" + strategy: str = "greedy_batch" + + compute_hypothesis_token_set: bool = False + + # preserve decoding alignments + preserve_alignments: Optional[bool] = None + + # confidence config + confidence_cfg: ConfidenceConfig = field(default_factory=lambda: ConfidenceConfig()) + + # RNNT Joint fused batch size + fused_batch_size: Optional[int] = None + + # compute RNNT time stamps + compute_timestamps: Optional[bool] = None + + # compute language IDs + compute_langs: bool = False + + # token representing word seperator + word_seperator: str = " " + + # type of timestamps to calculate + rnnt_timestamp_type: str = "all" # can be char, word or all for both + + # greedy decoding config + greedy: rnnt_greedy_decoding.GreedyBatchedRNNTInferConfig = field( + default_factory=rnnt_greedy_decoding.GreedyBatchedRNNTInferConfig + ) + + # beam decoding config + beam: rnnt_beam_decoding.BeamRNNTInferConfig = field( + default_factory=lambda: rnnt_beam_decoding.BeamRNNTInferConfig(beam_size=4) + ) + + # can be used to change temperature for decoding + temperature: float = 1.0 + + # config for TDT decoding. + durations: Optional[List[int]] = field(default_factory=list) + + # config for multiblank decoding. + big_blank_durations: Optional[List[int]] = field(default_factory=list) + + +@dataclass +class RNNTBPEDecodingConfig(RNNTDecodingConfig): + pass diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/rnnt_greedy_decoding.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/rnnt_greedy_decoding.py new file mode 100644 index 0000000..464dc46 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/rnnt_greedy_decoding.py @@ -0,0 +1,2744 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright 2017 Johns Hopkins University (Shinji Watanabe) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.asr.modules import rnnt_abstract +from nemo.collections.asr.parts.submodules.rnnt_loop_labels_computer import GreedyBatchedRNNTLoopLabelsComputer +from nemo.collections.asr.parts.submodules.tdt_loop_labels_computer import GreedyBatchedTDTLoopLabelsComputer +from nemo.collections.asr.parts.utils import rnnt_utils +from nemo.collections.asr.parts.utils.asr_confidence_utils import ConfidenceMethodConfig, ConfidenceMethodMixin +from nemo.collections.common.parts.rnn import label_collate +from nemo.core.classes import Typing, typecheck +from nemo.core.neural_types import AcousticEncodedRepresentation, HypothesisType, LengthsType, NeuralType +from nemo.utils import logging + + +def pack_hypotheses(hypotheses: List[rnnt_utils.Hypothesis], logitlen: torch.Tensor,) -> List[rnnt_utils.Hypothesis]: + + if hasattr(logitlen, 'cpu'): + logitlen_cpu = logitlen.to('cpu') + else: + logitlen_cpu = logitlen + + for idx, hyp in enumerate(hypotheses): # type: rnnt_utils.Hypothesis + hyp.y_sequence = ( + hyp.y_sequence.to(torch.long) + if isinstance(hyp.y_sequence, torch.Tensor) + else torch.tensor(hyp.y_sequence, dtype=torch.long) + ) + hyp.length = logitlen_cpu[idx] + + if hyp.dec_state is not None: + hyp.dec_state = _states_to_device(hyp.dec_state) + + return hypotheses + + +def _states_to_device(dec_state, device='cpu'): + if torch.is_tensor(dec_state): + dec_state = dec_state.to(device) + + elif isinstance(dec_state, (list, tuple)): + dec_state = tuple(_states_to_device(dec_i, device) for dec_i in dec_state) + + return dec_state + + +class _GreedyRNNTInfer(Typing, ConfidenceMethodMixin): + """A greedy transducer decoder. + + Provides a common abstraction for sample level and batch level greedy decoding. + + Args: + decoder_model: rnnt_utils.AbstractRNNTDecoder implementation. + joint_model: rnnt_utils.AbstractRNNTJoint implementation. + blank_index: int index of the blank token. Can be 0 or len(vocabulary). + max_symbols_per_step: Optional int. The maximum number of symbols that can be added + to a sequence in a single time step; if set to None then there is + no limit. + preserve_alignments: Bool flag which preserves the history of alignments generated during + greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `alignments` in it. Here, `alignments` is a List of List of + Tuple(Tensor (of length V + 1), Tensor(scalar, label after argmax)). + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more targets from a vocabulary. + U is the number of target tokens for the current timestep Ti. + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores generated + during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `frame_confidence` is a List of List of floats. + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more confidence scores. + U is the number of target tokens for the current timestep Ti. + confidence_method_cfg: A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: The method name (str). + Supported values: + - 'max_prob' for using the maximum token probability as a confidence. + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: Which type of entropy to use (str). Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: A mapping of the entropy value to the interval [0,1]. + Supported values: + - 'lin' for using the linear mapping. + - 'exp' for using exponential mapping with linear shift. + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "encoder_output": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + "partial_hypotheses": [NeuralType(elements_type=HypothesisType(), optional=True)], # must always be last + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"predictions": [NeuralType(elements_type=HypothesisType())]} + + def __init__( + self, + decoder_model: rnnt_abstract.AbstractRNNTDecoder, + joint_model: rnnt_abstract.AbstractRNNTJoint, + blank_index: int, + max_symbols_per_step: Optional[int] = None, + preserve_alignments: bool = False, + preserve_frame_confidence: bool = False, + confidence_method_cfg: Optional[DictConfig] = None, + ): + super().__init__() + self.decoder = decoder_model + self.joint = joint_model + + self._blank_index = blank_index + self._SOS = blank_index # Start of single index + + if max_symbols_per_step is not None and max_symbols_per_step <= 0: + raise ValueError(f"Expected max_symbols_per_step > 0 (or None), got {max_symbols_per_step}") + self.max_symbols = max_symbols_per_step + self.preserve_alignments = preserve_alignments + self.preserve_frame_confidence = preserve_frame_confidence + + # set confidence calculation method + self._init_confidence_method(confidence_method_cfg) + + def __call__(self, *args, **kwargs): + return self.forward(*args, **kwargs) + + @torch.no_grad() + def _pred_step( + self, + label: Union[torch.Tensor, int], + hidden: Optional[torch.Tensor], + add_sos: bool = False, + batch_size: Optional[int] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Common prediction step based on the AbstractRNNTDecoder implementation. + + Args: + label: (int/torch.Tensor): Label or "Start-of-Signal" token. + hidden: (Optional torch.Tensor): RNN State vector + add_sos (bool): Whether to add a zero vector at the begging as "start of sentence" token. + batch_size: Batch size of the output tensor. + + Returns: + g: (B, U, H) if add_sos is false, else (B, U + 1, H) + hid: (h, c) where h is the final sequence hidden state and c is + the final cell state: + h (tensor), shape (L, B, H) + c (tensor), shape (L, B, H) + """ + if isinstance(label, torch.Tensor): + # label: [batch, 1] + if label.dtype != torch.long: + label = label.long() + + else: + # Label is an integer + if label == self._SOS: + return self.decoder.predict(None, hidden, add_sos=add_sos, batch_size=batch_size) + + label = label_collate([[label]]) + + # output: [B, 1, K] + return self.decoder.predict(label, hidden, add_sos=add_sos, batch_size=batch_size) + + def _joint_step(self, enc, pred, log_normalize: Optional[bool] = None): + """ + Common joint step based on AbstractRNNTJoint implementation. + + Args: + enc: Output of the Encoder model. A torch.Tensor of shape [B, 1, H1] + pred: Output of the Decoder model. A torch.Tensor of shape [B, 1, H2] + log_normalize: Whether to log normalize or not. None will log normalize only for CPU. + + Returns: + logits of shape (B, T=1, U=1, V + 1) + """ + with torch.no_grad(): + logits = self.joint.joint(enc, pred) + + if log_normalize is None: + if not logits.is_cuda: # Use log softmax only if on CPU + logits = logits.log_softmax(dim=len(logits.shape) - 1) + else: + if log_normalize: + logits = logits.log_softmax(dim=len(logits.shape) - 1) + + return logits + + def _joint_step_after_projection(self, enc, pred, log_normalize: Optional[bool] = None) -> torch.Tensor: + """ + Common joint step based on AbstractRNNTJoint implementation. + + Args: + enc: Output of the Encoder model after projection. A torch.Tensor of shape [B, 1, H] + pred: Output of the Decoder model after projection. A torch.Tensor of shape [B, 1, H] + log_normalize: Whether to log normalize or not. None will log normalize only for CPU. + + Returns: + logits of shape (B, T=1, U=1, V + 1) + """ + with torch.no_grad(): + logits = self.joint.joint_after_projection(enc, pred) + + if log_normalize is None: + if not logits.is_cuda: # Use log softmax only if on CPU + logits = logits.log_softmax(dim=len(logits.shape) - 1) + else: + if log_normalize: + logits = logits.log_softmax(dim=len(logits.shape) - 1) + + return logits + + +class GreedyRNNTInfer(_GreedyRNNTInfer): + """A greedy transducer decoder. + + Sequence level greedy decoding, performed auto-regressively. + + Args: + decoder_model: rnnt_utils.AbstractRNNTDecoder implementation. + joint_model: rnnt_utils.AbstractRNNTJoint implementation. + blank_index: int index of the blank token. Can be 0 or len(vocabulary). + max_symbols_per_step: Optional int. The maximum number of symbols that can be added + to a sequence in a single time step; if set to None then there is + no limit. + preserve_alignments: Bool flag which preserves the history of alignments generated during + greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `alignments` in it. Here, `alignments` is a List of List of + Tuple(Tensor (of length V + 1), Tensor(scalar, label after argmax)). + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more targets from a vocabulary. + U is the number of target tokens for the current timestep Ti. + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores generated + during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `frame_confidence` is a List of List of floats. + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more confidence scores. + U is the number of target tokens for the current timestep Ti. + confidence_method_cfg: A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: The method name (str). + Supported values: + - 'max_prob' for using the maximum token probability as a confidence. + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: Which type of entropy to use (str). Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: A mapping of the entropy value to the interval [0,1]. + Supported values: + - 'lin' for using the linear mapping. + - 'exp' for using exponential mapping with linear shift. + """ + + def __init__( + self, + decoder_model: rnnt_abstract.AbstractRNNTDecoder, + joint_model: rnnt_abstract.AbstractRNNTJoint, + blank_index: int, + max_symbols_per_step: Optional[int] = None, + preserve_alignments: bool = False, + preserve_frame_confidence: bool = False, + confidence_method_cfg: Optional[DictConfig] = None, + ): + super().__init__( + decoder_model=decoder_model, + joint_model=joint_model, + blank_index=blank_index, + max_symbols_per_step=max_symbols_per_step, + preserve_alignments=preserve_alignments, + preserve_frame_confidence=preserve_frame_confidence, + confidence_method_cfg=confidence_method_cfg, + ) + + @typecheck() + def forward( + self, + encoder_output: torch.Tensor, + encoded_lengths: torch.Tensor, + partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None, + ): + """Returns a list of hypotheses given an input batch of the encoder hidden embedding. + Output token is generated auto-regressively. + + Args: + encoder_output: A tensor of size (batch, features, timesteps). + encoded_lengths: list of int representing the length of each sequence + output sequence. + + Returns: + packed list containing batch number of sentences (Hypotheses). + """ + # Preserve decoder and joint training state + decoder_training_state = self.decoder.training + joint_training_state = self.joint.training + + with torch.inference_mode(): + # Apply optional preprocessing + encoder_output = encoder_output.transpose(1, 2) # (B, T, D) + + self.decoder.eval() + self.joint.eval() + + hypotheses = [] + # Process each sequence independently + with self.decoder.as_frozen(), self.joint.as_frozen(): + for batch_idx in range(encoder_output.size(0)): + inseq = encoder_output[batch_idx, :, :].unsqueeze(1) # [T, 1, D] + logitlen = encoded_lengths[batch_idx] + + partial_hypothesis = partial_hypotheses[batch_idx] if partial_hypotheses is not None else None + hypothesis = self._greedy_decode(inseq, logitlen, partial_hypotheses=partial_hypothesis) + hypotheses.append(hypothesis) + + # Pack results into Hypotheses + packed_result = pack_hypotheses(hypotheses, encoded_lengths) + + self.decoder.train(decoder_training_state) + self.joint.train(joint_training_state) + + return (packed_result,) + + @torch.no_grad() + def _greedy_decode( + self, x: torch.Tensor, out_len: torch.Tensor, partial_hypotheses: Optional[rnnt_utils.Hypothesis] = None + ): + # x: [T, 1, D] + # out_len: [seq_len] + + # Initialize blank state and empty label set in Hypothesis + hypothesis = rnnt_utils.Hypothesis(score=0.0, y_sequence=[], dec_state=None, timestep=[], last_token=None) + + if partial_hypotheses is not None: + hypothesis.last_token = partial_hypotheses.last_token + hypothesis.y_sequence = ( + partial_hypotheses.y_sequence.cpu().tolist() + if isinstance(partial_hypotheses.y_sequence, torch.Tensor) + else partial_hypotheses.y_sequence + ) + if partial_hypotheses.dec_state is not None: + hypothesis.dec_state = self.decoder.batch_concat_states([partial_hypotheses.dec_state]) + hypothesis.dec_state = _states_to_device(hypothesis.dec_state, x.device) + + if self.preserve_alignments: + # Alignments is a 2-dimensional dangling list representing T x U + hypothesis.alignments = [[]] + + if self.preserve_frame_confidence: + hypothesis.frame_confidence = [[]] + + # For timestep t in X_t + for time_idx in range(out_len): + # Extract encoder embedding at timestep t + # f = x[time_idx, :, :].unsqueeze(0) # [1, 1, D] + f = x.narrow(dim=0, start=time_idx, length=1) + + # Setup exit flags and counter + not_blank = True + symbols_added = 0 + # While blank is not predicted, or we dont run out of max symbols per timestep + while not_blank and (self.max_symbols is None or symbols_added < self.max_symbols): + # In the first timestep, we initialize the network with RNNT Blank + # In later timesteps, we provide previous predicted label as input. + if hypothesis.last_token is None and hypothesis.dec_state is None: + last_label = self._SOS + else: + last_label = label_collate([[hypothesis.last_token]]) + + # Perform prediction network and joint network steps. + g, hidden_prime = self._pred_step(last_label, hypothesis.dec_state) + # If preserving per-frame confidence, log_normalize must be true + logp = self._joint_step(f, g, log_normalize=True if self.preserve_frame_confidence else None)[ + 0, 0, 0, : + ] + + del g + + # torch.max(0) op doesnt exist for FP 16. + if logp.dtype != torch.float32: + logp = logp.float() + + # get index k, of max prob + v, k = logp.max(0) + k = k.item() # K is the label at timestep t_s in inner loop, s >= 0. + + if self.preserve_alignments: + # insert logprobs into last timestep + hypothesis.alignments[-1].append((logp.to('cpu'), torch.tensor(k, dtype=torch.int32))) + + if self.preserve_frame_confidence: + # insert confidence into last timestep + hypothesis.frame_confidence[-1].append(self._get_confidence(logp)) + + del logp + + # If blank token is predicted, exit inner loop, move onto next timestep t + if k == self._blank_index: + not_blank = False + else: + # Append token to label set, update RNN state. + hypothesis.y_sequence.append(k) + hypothesis.score += float(v) + hypothesis.timestep.append(time_idx) + hypothesis.dec_state = hidden_prime + hypothesis.last_token = k + + # Increment token counter. + symbols_added += 1 + + if self.preserve_alignments: + # convert Ti-th logits into a torch array + hypothesis.alignments.append([]) # blank buffer for next timestep + + if self.preserve_frame_confidence: + hypothesis.frame_confidence.append([]) # blank buffer for next timestep + + # Remove trailing empty list of Alignments + if self.preserve_alignments: + if len(hypothesis.alignments[-1]) == 0: + del hypothesis.alignments[-1] + + # Remove trailing empty list of per-frame confidence + if self.preserve_frame_confidence: + if len(hypothesis.frame_confidence[-1]) == 0: + del hypothesis.frame_confidence[-1] + + # Unpack the hidden states + hypothesis.dec_state = self.decoder.batch_select_state(hypothesis.dec_state, 0) + + return hypothesis + + +class GreedyBatchedRNNTInfer(_GreedyRNNTInfer): + """A batch level greedy transducer decoder. + + Batch level greedy decoding, performed auto-regressively. + + Args: + decoder_model: rnnt_utils.AbstractRNNTDecoder implementation. + joint_model: rnnt_utils.AbstractRNNTJoint implementation. + blank_index: int index of the blank token. Can be 0 or len(vocabulary). + max_symbols_per_step: Optional int. The maximum number of symbols that can be added + to a sequence in a single time step; if set to None then there is + no limit. + preserve_alignments: Bool flag which preserves the history of alignments generated during + greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `alignments` in it. Here, `alignments` is a List of List of + Tuple(Tensor (of length V + 1), Tensor(scalar, label after argmax)). + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more targets from a vocabulary. + U is the number of target tokens for the current timestep Ti. + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores generated + during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `frame_confidence` is a List of List of floats. + + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more confidence scores. + U is the number of target tokens for the current timestep Ti. + confidence_method_cfg: A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: The method name (str). + Supported values: + - 'max_prob' for using the maximum token probability as a confidence. + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: Which type of entropy to use (str). Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: A mapping of the entropy value to the interval [0,1]. + Supported values: + - 'lin' for using the linear mapping. + - 'exp' for using exponential mapping with linear shift. + loop_labels: Switching between decoding algorithms. Both algorithms produce equivalent results. + loop_labels=True (default) algorithm is faster (especially for large batches) but can use a bit more memory + (negligible overhead compared to the amount of memory used by the encoder). + loop_labels=False is an implementation of a traditional decoding algorithm, which iterates over + frames (encoder output vectors), and in the inner loop, decodes labels for the current frame one by one, + stopping when is found. + loop_labels=True iterates over labels, on each step finding the next non-blank label + (evaluating Joint multiple times in inner loop); It uses a minimal possible amount of calls + to prediction network (with maximum possible batch size), + which makes it especially useful for scaling the prediction network. + """ + + def __init__( + self, + decoder_model: rnnt_abstract.AbstractRNNTDecoder, + joint_model: rnnt_abstract.AbstractRNNTJoint, + blank_index: int, + max_symbols_per_step: Optional[int] = None, + preserve_alignments: bool = False, + preserve_frame_confidence: bool = False, + confidence_method_cfg: Optional[DictConfig] = None, + loop_labels: bool = True, + use_cuda_graph_decoder: bool = False, + ): + super().__init__( + decoder_model=decoder_model, + joint_model=joint_model, + blank_index=blank_index, + max_symbols_per_step=max_symbols_per_step, + preserve_alignments=preserve_alignments, + preserve_frame_confidence=preserve_frame_confidence, + confidence_method_cfg=confidence_method_cfg, + ) + + self.use_cuda_graph_decoder = use_cuda_graph_decoder + + # Depending on availability of `blank_as_pad` support + # switch between more efficient batch decoding technique + self._decoding_computer = None + if self.decoder.blank_as_pad: + if loop_labels: + # default (faster) algo: loop over labels + self._greedy_decode = self._greedy_decode_blank_as_pad_loop_labels + self._decoding_computer = GreedyBatchedRNNTLoopLabelsComputer( + decoder=self.decoder, + joint=self.joint, + blank_index=self._blank_index, + max_symbols_per_step=self.max_symbols, + preserve_alignments=preserve_alignments, + preserve_frame_confidence=preserve_frame_confidence, + confidence_method_cfg=confidence_method_cfg, + allow_cuda_graphs=use_cuda_graph_decoder, + ) + elif use_cuda_graph_decoder: + from nemo.collections.asr.parts.submodules.cuda_graph_rnnt_greedy_decoding import ( + RNNTGreedyDecodeCudaGraph, + ) + + self._greedy_decode = RNNTGreedyDecodeCudaGraph(max_symbols_per_step, self) + else: + # previous algo: loop over frames + self._greedy_decode = self._greedy_decode_blank_as_pad_loop_frames + else: + self._greedy_decode = self._greedy_decode_masked + + @typecheck() + def forward( + self, + encoder_output: torch.Tensor, + encoded_lengths: torch.Tensor, + partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None, + ): + """Returns a list of hypotheses given an input batch of the encoder hidden embedding. + Output token is generated auto-regressively. + + Args: + encoder_output: A tensor of size (batch, features, timesteps). + encoded_lengths: list of int representing the length of each sequence + output sequence. + + Returns: + packed list containing batch number of sentences (Hypotheses). + """ + # Preserve decoder and joint training state + decoder_training_state = self.decoder.training + joint_training_state = self.joint.training + + with torch.inference_mode(): + # Apply optional preprocessing + encoder_output = encoder_output.transpose(1, 2) # (B, T, D) + logitlen = encoded_lengths + + self.decoder.eval() + self.joint.eval() + + with self.decoder.as_frozen(), self.joint.as_frozen(): + inseq = encoder_output # [B, T, D] + + hypotheses = self._greedy_decode( + inseq, logitlen, device=inseq.device, partial_hypotheses=partial_hypotheses + ) + + # Pack the hypotheses results + packed_result = pack_hypotheses(hypotheses, logitlen) + + self.decoder.train(decoder_training_state) + self.joint.train(joint_training_state) + + return (packed_result,) + + @torch.inference_mode() + def _greedy_decode_blank_as_pad_loop_labels( + self, + x: torch.Tensor, + out_len: torch.Tensor, + device: torch.device, + partial_hypotheses: Optional[list[rnnt_utils.Hypothesis]] = None, + ) -> list[rnnt_utils.Hypothesis]: + """ + Optimized batched greedy decoding. + The main idea: search for next labels for the whole batch (evaluating Joint) + and thus always evaluate prediction network with maximum possible batch size + """ + if partial_hypotheses is not None: + raise NotImplementedError("`partial_hypotheses` support is not implemented") + + batched_hyps, alignments, last_decoder_state = self._decoding_computer(x=x, out_len=out_len) + hyps = rnnt_utils.batched_hyps_to_hypotheses(batched_hyps, alignments, batch_size=x.shape[0]) + for hyp, state in zip(hyps, self.decoder.batch_split_states(last_decoder_state)): + hyp.dec_state = state + return hyps + + def _greedy_decode_blank_as_pad_loop_frames( + self, + x: torch.Tensor, + out_len: torch.Tensor, + device: torch.device, + partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None, + ): + if partial_hypotheses is not None: + raise NotImplementedError("`partial_hypotheses` support is not supported") + + with torch.inference_mode(): + # x: [B, T, D] + # out_len: [B] + # device: torch.device + + # Initialize list of Hypothesis + batchsize = x.shape[0] + hypotheses = [ + rnnt_utils.Hypothesis(score=0.0, y_sequence=[], timestep=[], dec_state=None) for _ in range(batchsize) + ] + + # Initialize Hidden state matrix (shared by entire batch) + hidden = None + + # If alignments need to be preserved, register a dangling list to hold the values + if self.preserve_alignments: + # alignments is a 3-dimensional dangling list representing B x T x U + for hyp in hypotheses: + hyp.alignments = [[]] + + # If confidence scores need to be preserved, register a dangling list to hold the values + if self.preserve_frame_confidence: + # frame_confidence is a 3-dimensional dangling list representing B x T x U + for hyp in hypotheses: + hyp.frame_confidence = [[]] + + # Last Label buffer + Last Label without blank buffer + # batch level equivalent of the last_label + last_label = torch.full([batchsize, 1], fill_value=self._blank_index, dtype=torch.long, device=device) + + # Mask buffers + blank_mask = torch.full([batchsize], fill_value=0, dtype=torch.bool, device=device) + blank_mask_prev = None + + # Get max sequence length + max_out_len = out_len.max() + for time_idx in range(max_out_len): + f = x.narrow(dim=1, start=time_idx, length=1) # [B, 1, D] + + # Prepare t timestamp batch variables + not_blank = True + symbols_added = 0 + + # Reset blank mask + blank_mask.mul_(False) + + # Update blank mask with time mask + # Batch: [B, T, D], but Bi may have seq len < max(seq_lens_in_batch) + # Forcibly mask with "blank" tokens, for all sample where current time step T > seq_len + blank_mask = time_idx >= out_len + blank_mask_prev = blank_mask.clone() + + # Start inner loop + while not_blank and (self.max_symbols is None or symbols_added < self.max_symbols): + # Batch prediction and joint network steps + # If very first prediction step, submit SOS tag (blank) to pred_step. + # This feeds a zero tensor as input to AbstractRNNTDecoder to prime the state + if time_idx == 0 and symbols_added == 0 and hidden is None: + g, hidden_prime = self._pred_step(self._SOS, hidden, batch_size=batchsize) + else: + # Perform batch step prediction of decoder, getting new states and scores ("g") + g, hidden_prime = self._pred_step(last_label, hidden, batch_size=batchsize) + + # Batched joint step - Output = [B, V + 1] + # If preserving per-frame confidence, log_normalize must be true + logp = self._joint_step(f, g, log_normalize=True if self.preserve_frame_confidence else None)[ + :, 0, 0, : + ] + + if logp.dtype != torch.float32: + logp = logp.float() + + # Get index k, of max prob for batch + v, k = logp.max(1) + del g + + # Update blank mask with current predicted blanks + # This is accumulating blanks over all time steps T and all target steps min(max_symbols, U) + k_is_blank = k == self._blank_index + blank_mask.bitwise_or_(k_is_blank) + + del k_is_blank + + # If preserving alignments, check if sequence length of sample has been reached + # before adding alignment + if self.preserve_alignments: + # Insert logprobs into last timestep per sample + logp_vals = logp.to('cpu') + logp_ids = logp_vals.max(1)[1] + for batch_idx, is_blank in enumerate(blank_mask): + # we only want to update non-blanks and first-time blanks, + # otherwise alignments will contain duplicate predictions + if time_idx < out_len[batch_idx] and (not blank_mask_prev[batch_idx] or not is_blank): + hypotheses[batch_idx].alignments[-1].append( + (logp_vals[batch_idx], logp_ids[batch_idx]) + ) + del logp_vals + + # If preserving per-frame confidence, check if sequence length of sample has been reached + # before adding confidence scores + if self.preserve_frame_confidence: + # Insert probabilities into last timestep per sample + confidence = self._get_confidence(logp) + for batch_idx, is_blank in enumerate(blank_mask): + if time_idx < out_len[batch_idx] and (not blank_mask_prev[batch_idx] or not is_blank): + hypotheses[batch_idx].frame_confidence[-1].append(confidence[batch_idx]) + del logp + + blank_mask_prev.bitwise_or_(blank_mask) + + # If all samples predict / have predicted prior blanks, exit loop early + # This is equivalent to if single sample predicted k + if blank_mask.all(): + not_blank = False + else: + # Collect batch indices where blanks occurred now/past + blank_indices = (blank_mask == 1).nonzero(as_tuple=False) + + # Recover prior state for all samples which predicted blank now/past + if hidden is not None: + # LSTM has 2 states + hidden_prime = self.decoder.batch_copy_states(hidden_prime, hidden, blank_indices) + + elif len(blank_indices) > 0 and hidden is None: + # Reset state if there were some blank and other non-blank predictions in batch + # Original state is filled with zeros so we just multiply + # LSTM has 2 states + hidden_prime = self.decoder.batch_copy_states(hidden_prime, None, blank_indices, value=0.0) + + # Recover prior predicted label for all samples which predicted blank now/past + k[blank_indices] = last_label[blank_indices, 0] + + # Update new label and hidden state for next iteration + last_label = k.clone().view(-1, 1) + hidden = hidden_prime + + # Update predicted labels, accounting for time mask + # If blank was predicted even once, now or in the past, + # Force the current predicted label to also be blank + # This ensures that blanks propogate across all timesteps + # once they have occured (normally stopping condition of sample level loop). + for kidx, ki in enumerate(k): + if blank_mask[kidx] == 0: + hypotheses[kidx].y_sequence.append(ki) + hypotheses[kidx].timestep.append(time_idx) + hypotheses[kidx].score += float(v[kidx]) + symbols_added += 1 + + # If preserving alignments, convert the current Uj alignments into a torch.Tensor + # Then preserve U at current timestep Ti + # Finally, forward the timestep history to Ti+1 for that sample + # All of this should only be done iff the current time index <= sample-level AM length. + # Otherwise ignore and move to next sample / next timestep. + if self.preserve_alignments: + + # convert Ti-th logits into a torch array + for batch_idx in range(batchsize): + + # this checks if current timestep <= sample-level AM length + # If current timestep > sample-level AM length, no alignments will be added + # Therefore the list of Uj alignments is empty here. + if len(hypotheses[batch_idx].alignments[-1]) > 0: + hypotheses[batch_idx].alignments.append([]) # blank buffer for next timestep + + # Do the same if preserving per-frame confidence + if self.preserve_frame_confidence: + + for batch_idx in range(batchsize): + if len(hypotheses[batch_idx].frame_confidence[-1]) > 0: + hypotheses[batch_idx].frame_confidence.append([]) # blank buffer for next timestep + + # Remove trailing empty list of alignments at T_{am-len} x Uj + if self.preserve_alignments: + for batch_idx in range(batchsize): + if len(hypotheses[batch_idx].alignments[-1]) == 0: + del hypotheses[batch_idx].alignments[-1] + + # Remove trailing empty list of confidence scores at T_{am-len} x Uj + if self.preserve_frame_confidence: + for batch_idx in range(batchsize): + if len(hypotheses[batch_idx].frame_confidence[-1]) == 0: + del hypotheses[batch_idx].frame_confidence[-1] + + # Preserve states + for batch_idx in range(batchsize): + hypotheses[batch_idx].dec_state = self.decoder.batch_select_state(hidden, batch_idx) + + return hypotheses + + def _greedy_decode_masked( + self, + x: torch.Tensor, + out_len: torch.Tensor, + device: torch.device, + partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None, + ): + if partial_hypotheses is not None: + raise NotImplementedError("`partial_hypotheses` support is not supported") + + # x: [B, T, D] + # out_len: [B] + # device: torch.device + + # Initialize state + batchsize = x.shape[0] + hypotheses = [ + rnnt_utils.Hypothesis(score=0.0, y_sequence=[], timestep=[], dec_state=None) for _ in range(batchsize) + ] + + # Initialize Hidden state matrix (shared by entire batch) + hidden = None + + # If alignments need to be preserved, register a danling list to hold the values + if self.preserve_alignments: + # alignments is a 3-dimensional dangling list representing B x T x U + for hyp in hypotheses: + hyp.alignments = [[]] + else: + alignments = None + + # If confidence scores need to be preserved, register a danling list to hold the values + if self.preserve_frame_confidence: + # frame_confidence is a 3-dimensional dangling list representing B x T x U + for hyp in hypotheses: + hyp.frame_confidence = [[]] + + # Last Label buffer + Last Label without blank buffer + # batch level equivalent of the last_label + last_label = torch.full([batchsize, 1], fill_value=self._blank_index, dtype=torch.long, device=device) + last_label_without_blank = last_label.clone() + + # Mask buffers + blank_mask = torch.full([batchsize], fill_value=0, dtype=torch.bool, device=device) + blank_mask_prev = None + + # Get max sequence length + max_out_len = out_len.max() + + with torch.inference_mode(): + for time_idx in range(max_out_len): + f = x.narrow(dim=1, start=time_idx, length=1) # [B, 1, D] + + # Prepare t timestamp batch variables + not_blank = True + symbols_added = 0 + + # Reset blank mask + blank_mask.mul_(False) + + # Update blank mask with time mask + # Batch: [B, T, D], but Bi may have seq len < max(seq_lens_in_batch) + # Forcibly mask with "blank" tokens, for all sample where current time step T > seq_len + blank_mask = time_idx >= out_len + blank_mask_prev = blank_mask.clone() + + # Start inner loop + while not_blank and (self.max_symbols is None or symbols_added < self.max_symbols): + # Batch prediction and joint network steps + # If very first prediction step, submit SOS tag (blank) to pred_step. + # This feeds a zero tensor as input to AbstractRNNTDecoder to prime the state + if time_idx == 0 and symbols_added == 0 and hidden is None: + g, hidden_prime = self._pred_step(self._SOS, hidden, batch_size=batchsize) + else: + # Set a dummy label for the blank value + # This value will be overwritten by "blank" again the last label update below + # This is done as vocabulary of prediction network does not contain "blank" token of RNNT + last_label_without_blank_mask = last_label == self._blank_index + last_label_without_blank[last_label_without_blank_mask] = 0 # temp change of label + last_label_without_blank[~last_label_without_blank_mask] = last_label[ + ~last_label_without_blank_mask + ] + + # Perform batch step prediction of decoder, getting new states and scores ("g") + g, hidden_prime = self._pred_step(last_label_without_blank, hidden, batch_size=batchsize) + + # Batched joint step - Output = [B, V + 1] + # If preserving per-frame confidence, log_normalize must be true + logp = self._joint_step(f, g, log_normalize=True if self.preserve_frame_confidence else None)[ + :, 0, 0, : + ] + + if logp.dtype != torch.float32: + logp = logp.float() + + # Get index k, of max prob for batch + v, k = logp.max(1) + del g + + # Update blank mask with current predicted blanks + # This is accumulating blanks over all time steps T and all target steps min(max_symbols, U) + k_is_blank = k == self._blank_index + blank_mask.bitwise_or_(k_is_blank) + + # If preserving alignments, check if sequence length of sample has been reached + # before adding alignment + if self.preserve_alignments: + # Insert logprobs into last timestep per sample + logp_vals = logp.to('cpu') + logp_ids = logp_vals.max(1)[1] + for batch_idx, is_blank in enumerate(blank_mask): + # we only want to update non-blanks and first-time blanks, + # otherwise alignments will contain duplicate predictions + if time_idx < out_len[batch_idx] and (not blank_mask_prev[batch_idx] or not is_blank): + hypotheses[batch_idx].alignments[-1].append( + (logp_vals[batch_idx], logp_ids[batch_idx]) + ) + + del logp_vals + + # If preserving per-frame confidence, check if sequence length of sample has been reached + # before adding confidence scores + if self.preserve_frame_confidence: + # Insert probabilities into last timestep per sample + confidence = self._get_confidence(logp) + for batch_idx, is_blank in enumerate(blank_mask): + if time_idx < out_len[batch_idx] and (not blank_mask_prev[batch_idx] or not is_blank): + hypotheses[batch_idx].frame_confidence[-1].append(confidence[batch_idx]) + del logp + + blank_mask_prev.bitwise_or_(blank_mask) + + # If all samples predict / have predicted prior blanks, exit loop early + # This is equivalent to if single sample predicted k + if blank_mask.all(): + not_blank = False + else: + # Collect batch indices where blanks occurred now/past + blank_indices = (blank_mask == 1).nonzero(as_tuple=False) + + # Recover prior state for all samples which predicted blank now/past + if hidden is not None: + # LSTM has 2 states + hidden_prime = self.decoder.batch_copy_states(hidden_prime, hidden, blank_indices) + + elif len(blank_indices) > 0 and hidden is None: + # Reset state if there were some blank and other non-blank predictions in batch + # Original state is filled with zeros so we just multiply + # LSTM has 2 states + hidden_prime = self.decoder.batch_copy_states(hidden_prime, None, blank_indices, value=0.0) + + # Recover prior predicted label for all samples which predicted blank now/past + k[blank_indices] = last_label[blank_indices, 0] + + # Update new label and hidden state for next iteration + last_label = k.view(-1, 1) + hidden = hidden_prime + + # Update predicted labels, accounting for time mask + # If blank was predicted even once, now or in the past, + # Force the current predicted label to also be blank + # This ensures that blanks propogate across all timesteps + # once they have occured (normally stopping condition of sample level loop). + for kidx, ki in enumerate(k): + if blank_mask[kidx] == 0: + hypotheses[kidx].y_sequence.append(ki) + hypotheses[kidx].timestep.append(time_idx) + hypotheses[kidx].score += float(v[kidx]) + + symbols_added += 1 + + # If preserving alignments, convert the current Uj alignments into a torch.Tensor + # Then preserve U at current timestep Ti + # Finally, forward the timestep history to Ti+1 for that sample + # All of this should only be done iff the current time index <= sample-level AM length. + # Otherwise ignore and move to next sample / next timestep. + if self.preserve_alignments: + + # convert Ti-th logits into a torch array + for batch_idx in range(batchsize): + + # this checks if current timestep <= sample-level AM length + # If current timestep > sample-level AM length, no alignments will be added + # Therefore the list of Uj alignments is empty here. + if len(hypotheses[batch_idx].alignments[-1]) > 0: + hypotheses[batch_idx].alignments.append([]) # blank buffer for next timestep + + # Do the same if preserving per-frame confidence + if self.preserve_frame_confidence: + + for batch_idx in range(batchsize): + if len(hypotheses[batch_idx].frame_confidence[-1]) > 0: + hypotheses[batch_idx].frame_confidence.append([]) # blank buffer for next timestep + + # Remove trailing empty list of alignments at T_{am-len} x Uj + if self.preserve_alignments: + for batch_idx in range(batchsize): + if len(hypotheses[batch_idx].alignments[-1]) == 0: + del hypotheses[batch_idx].alignments[-1] + + # Remove trailing empty list of confidence scores at T_{am-len} x Uj + if self.preserve_frame_confidence: + for batch_idx in range(batchsize): + if len(hypotheses[batch_idx].frame_confidence[-1]) == 0: + del hypotheses[batch_idx].frame_confidence[-1] + + # Preserve states + for batch_idx in range(batchsize): + hypotheses[batch_idx].dec_state = self.decoder.batch_select_state(hidden, batch_idx) + + return hypotheses + + +class ExportedModelGreedyBatchedRNNTInfer: + def __init__(self, encoder_model: str, decoder_joint_model: str, max_symbols_per_step: Optional[int] = None): + self.encoder_model_path = encoder_model + self.decoder_joint_model_path = decoder_joint_model + self.max_symbols_per_step = max_symbols_per_step + + # Will be populated at runtime + self._blank_index = None + + def __call__(self, audio_signal: torch.Tensor, length: torch.Tensor): + """Returns a list of hypotheses given an input batch of the encoder hidden embedding. + Output token is generated auto-regressively. + + Args: + encoder_output: A tensor of size (batch, features, timesteps). + encoded_lengths: list of int representing the length of each sequence + output sequence. + + Returns: + packed list containing batch number of sentences (Hypotheses). + """ + with torch.no_grad(): + # Apply optional preprocessing + encoder_output, encoded_lengths = self.run_encoder(audio_signal=audio_signal, length=length) + + if torch.is_tensor(encoder_output): + encoder_output = encoder_output.transpose(1, 2) + else: + encoder_output = encoder_output.transpose([0, 2, 1]) # (B, T, D) + logitlen = encoded_lengths + + inseq = encoder_output # [B, T, D] + hypotheses, timestamps = self._greedy_decode(inseq, logitlen) + + # Pack the hypotheses results + packed_result = [rnnt_utils.Hypothesis(score=-1.0, y_sequence=[]) for _ in range(len(hypotheses))] + for i in range(len(packed_result)): + packed_result[i].y_sequence = torch.tensor(hypotheses[i], dtype=torch.long) + packed_result[i].length = timestamps[i] + + del hypotheses + + return packed_result + + def _greedy_decode(self, x, out_len): + # x: [B, T, D] + # out_len: [B] + + # Initialize state + batchsize = x.shape[0] + hidden = self._get_initial_states(batchsize) + target_lengths = torch.ones(batchsize, dtype=torch.int32) + + # Output string buffer + label = [[] for _ in range(batchsize)] + timesteps = [[] for _ in range(batchsize)] + + # Last Label buffer + Last Label without blank buffer + # batch level equivalent of the last_label + last_label = torch.full([batchsize, 1], fill_value=self._blank_index, dtype=torch.long).numpy() + if torch.is_tensor(x): + last_label = torch.from_numpy(last_label).to(self.device) + + # Mask buffers + blank_mask = torch.full([batchsize], fill_value=0, dtype=torch.bool).numpy() + + # Get max sequence length + max_out_len = out_len.max() + for time_idx in range(max_out_len): + f = x[:, time_idx : time_idx + 1, :] # [B, 1, D] + + if torch.is_tensor(f): + f = f.transpose(1, 2) + else: + f = f.transpose([0, 2, 1]) + + # Prepare t timestamp batch variables + not_blank = True + symbols_added = 0 + + # Reset blank mask + blank_mask *= False + + # Update blank mask with time mask + # Batch: [B, T, D], but Bi may have seq len < max(seq_lens_in_batch) + # Forcibly mask with "blank" tokens, for all sample where current time step T > seq_len + blank_mask = time_idx >= out_len + # Start inner loop + while not_blank and (self.max_symbols_per_step is None or symbols_added < self.max_symbols_per_step): + + # Batch prediction and joint network steps + # If very first prediction step, submit SOS tag (blank) to pred_step. + # This feeds a zero tensor as input to AbstractRNNTDecoder to prime the state + if time_idx == 0 and symbols_added == 0: + g = torch.tensor([self._blank_index] * batchsize, dtype=torch.int32).view(-1, 1) + else: + if torch.is_tensor(last_label): + g = last_label.type(torch.int32) + else: + g = last_label.astype(np.int32) + + # Batched joint step - Output = [B, V + 1] + joint_out, hidden_prime = self.run_decoder_joint(f, g, target_lengths, *hidden) + logp, pred_lengths = joint_out + logp = logp[:, 0, 0, :] + + # Get index k, of max prob for batch + if torch.is_tensor(logp): + v, k = logp.max(1) + else: + k = np.argmax(logp, axis=1).astype(np.int32) + + # Update blank mask with current predicted blanks + # This is accumulating blanks over all time steps T and all target steps min(max_symbols, U) + k_is_blank = k == self._blank_index + blank_mask |= k_is_blank + + del k_is_blank + del logp + + # If all samples predict / have predicted prior blanks, exit loop early + # This is equivalent to if single sample predicted k + if blank_mask.all(): + not_blank = False + + else: + # Collect batch indices where blanks occurred now/past + if torch.is_tensor(blank_mask): + blank_indices = (blank_mask == 1).nonzero(as_tuple=False) + else: + blank_indices = blank_mask.astype(np.int32).nonzero() + + if type(blank_indices) in (list, tuple): + blank_indices = blank_indices[0] + + # Recover prior state for all samples which predicted blank now/past + if hidden is not None: + # LSTM has 2 states + for state_id in range(len(hidden)): + hidden_prime[state_id][:, blank_indices, :] = hidden[state_id][:, blank_indices, :] + + elif len(blank_indices) > 0 and hidden is None: + # Reset state if there were some blank and other non-blank predictions in batch + # Original state is filled with zeros so we just multiply + # LSTM has 2 states + for state_id in range(len(hidden_prime)): + hidden_prime[state_id][:, blank_indices, :] *= 0.0 + + # Recover prior predicted label for all samples which predicted blank now/past + k[blank_indices] = last_label[blank_indices, 0] + + # Update new label and hidden state for next iteration + if torch.is_tensor(k): + last_label = k.clone().reshape(-1, 1) + else: + last_label = k.copy().reshape(-1, 1) + hidden = hidden_prime + + # Update predicted labels, accounting for time mask + # If blank was predicted even once, now or in the past, + # Force the current predicted label to also be blank + # This ensures that blanks propogate across all timesteps + # once they have occured (normally stopping condition of sample level loop). + for kidx, ki in enumerate(k): + if blank_mask[kidx] == 0: + label[kidx].append(ki) + timesteps[kidx].append(time_idx) + + symbols_added += 1 + + return label, timesteps + + def _setup_blank_index(self): + raise NotImplementedError() + + def run_encoder(self, audio_signal, length): + raise NotImplementedError() + + def run_decoder_joint(self, enc_logits, targets, target_length, *states): + raise NotImplementedError() + + def _get_initial_states(self, batchsize): + raise NotImplementedError() + + +class ONNXGreedyBatchedRNNTInfer(ExportedModelGreedyBatchedRNNTInfer): + def __init__(self, encoder_model: str, decoder_joint_model: str, max_symbols_per_step: Optional[int] = 10): + super().__init__( + encoder_model=encoder_model, + decoder_joint_model=decoder_joint_model, + max_symbols_per_step=max_symbols_per_step, + ) + + try: + import onnx + import onnxruntime + except (ModuleNotFoundError, ImportError): + raise ImportError(f"`onnx` or `onnxruntime` could not be imported, please install the libraries.\n") + + if torch.cuda.is_available(): + # Try to use onnxruntime-gpu + providers = ['TensorrtExecutionProvider', 'CUDAExecutionProvider'] + else: + # Fall back to CPU and onnxruntime-cpu + providers = ['CPUExecutionProvider'] + + onnx_session_opt = onnxruntime.SessionOptions() + onnx_session_opt.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + + onnx_model = onnx.load(self.encoder_model_path) + onnx.checker.check_model(onnx_model, full_check=True) + self.encoder_model = onnx_model + self.encoder = onnxruntime.InferenceSession( + onnx_model.SerializeToString(), providers=providers, provider_options=onnx_session_opt + ) + + onnx_model = onnx.load(self.decoder_joint_model_path) + onnx.checker.check_model(onnx_model, full_check=True) + self.decoder_joint_model = onnx_model + self.decoder_joint = onnxruntime.InferenceSession( + onnx_model.SerializeToString(), providers=providers, provider_options=onnx_session_opt + ) + + logging.info("Successfully loaded encoder, decoder and joint onnx models !") + + # Will be populated at runtime + self._blank_index = None + self.max_symbols_per_step = max_symbols_per_step + + self._setup_encoder_input_output_keys() + self._setup_decoder_joint_input_output_keys() + self._setup_blank_index() + + def _setup_encoder_input_output_keys(self): + self.encoder_inputs = list(self.encoder_model.graph.input) + self.encoder_outputs = list(self.encoder_model.graph.output) + + def _setup_decoder_joint_input_output_keys(self): + self.decoder_joint_inputs = list(self.decoder_joint_model.graph.input) + self.decoder_joint_outputs = list(self.decoder_joint_model.graph.output) + + def _setup_blank_index(self): + # ASSUME: Single input with no time length information + dynamic_dim = 257 + shapes = self.encoder_inputs[0].type.tensor_type.shape.dim + ip_shape = [] + for shape in shapes: + if hasattr(shape, 'dim_param') and 'dynamic' in shape.dim_param: + ip_shape.append(dynamic_dim) # replace dynamic axes with constant + else: + ip_shape.append(int(shape.dim_value)) + + enc_logits, encoded_length = self.run_encoder( + audio_signal=torch.randn(*ip_shape), length=torch.randint(0, 1, size=(dynamic_dim,)) + ) + + # prepare states + states = self._get_initial_states(batchsize=dynamic_dim) + + # run decoder 1 step + joint_out, states = self.run_decoder_joint(enc_logits, None, None, *states) + log_probs, lengths = joint_out + + self._blank_index = log_probs.shape[-1] - 1 # last token of vocab size is blank token + logging.info( + f"Enc-Dec-Joint step was evaluated, blank token id = {self._blank_index}; vocab size = {log_probs.shape[-1]}" + ) + + def run_encoder(self, audio_signal, length): + if hasattr(audio_signal, 'cpu'): + audio_signal = audio_signal.cpu().numpy() + + if hasattr(length, 'cpu'): + length = length.cpu().numpy() + + ip = { + self.encoder_inputs[0].name: audio_signal, + self.encoder_inputs[1].name: length, + } + enc_out = self.encoder.run(None, ip) + enc_out, encoded_length = enc_out # ASSUME: single output + return enc_out, encoded_length + + def run_decoder_joint(self, enc_logits, targets, target_length, *states): + # ASSUME: Decoder is RNN Transducer + if targets is None: + targets = torch.zeros(enc_logits.shape[0], 1, dtype=torch.int32) + target_length = torch.ones(enc_logits.shape[0], dtype=torch.int32) + + if hasattr(targets, 'cpu'): + targets = targets.cpu().numpy() + + if hasattr(target_length, 'cpu'): + target_length = target_length.cpu().numpy() + + ip = { + self.decoder_joint_inputs[0].name: enc_logits, + self.decoder_joint_inputs[1].name: targets, + self.decoder_joint_inputs[2].name: target_length, + } + + num_states = 0 + if states is not None and len(states) > 0: + num_states = len(states) + for idx, state in enumerate(states): + if hasattr(state, 'cpu'): + state = state.cpu().numpy() + + ip[self.decoder_joint_inputs[len(ip)].name] = state + + dec_out = self.decoder_joint.run(None, ip) + + # unpack dec output + if num_states > 0: + new_states = dec_out[-num_states:] + dec_out = dec_out[:-num_states] + else: + new_states = None + + return dec_out, new_states + + def _get_initial_states(self, batchsize): + # ASSUME: LSTM STATES of shape (layers, batchsize, dim) + input_state_nodes = [ip for ip in self.decoder_joint_inputs if 'state' in ip.name] + num_states = len(input_state_nodes) + if num_states == 0: + return + + input_states = [] + for state_id in range(num_states): + node = input_state_nodes[state_id] + ip_shape = [] + for shape_idx, shape in enumerate(node.type.tensor_type.shape.dim): + if hasattr(shape, 'dim_param') and 'dynamic' in shape.dim_param: + ip_shape.append(batchsize) # replace dynamic axes with constant + else: + ip_shape.append(int(shape.dim_value)) + + input_states.append(torch.zeros(*ip_shape)) + + return input_states + + +class TorchscriptGreedyBatchedRNNTInfer(ExportedModelGreedyBatchedRNNTInfer): + def __init__( + self, + encoder_model: str, + decoder_joint_model: str, + cfg: DictConfig, + device: str, + max_symbols_per_step: Optional[int] = 10, + ): + super().__init__( + encoder_model=encoder_model, + decoder_joint_model=decoder_joint_model, + max_symbols_per_step=max_symbols_per_step, + ) + + self.cfg = cfg + self.device = device + + self.encoder = torch.jit.load(self.encoder_model_path, map_location=self.device) + self.decoder_joint = torch.jit.load(self.decoder_joint_model_path, map_location=self.device) + + logging.info("Successfully loaded encoder, decoder and joint torchscript models !") + + # Will be populated at runtime + self._blank_index = None + self.max_symbols_per_step = max_symbols_per_step + + self._setup_encoder_input_keys() + self._setup_decoder_joint_input_keys() + self._setup_blank_index() + + def _setup_encoder_input_keys(self): + arguments = self.encoder.forward.schema.arguments[1:] + self.encoder_inputs = [arg for arg in arguments] + + def _setup_decoder_joint_input_keys(self): + arguments = self.decoder_joint.forward.schema.arguments[1:] + self.decoder_joint_inputs = [arg for arg in arguments] + + def _setup_blank_index(self): + self._blank_index = len(self.cfg.joint.vocabulary) + + logging.info(f"Blank token id = {self._blank_index}; vocab size = {len(self.cfg.joint.vocabulary) + 1}") + + def run_encoder(self, audio_signal, length): + enc_out = self.encoder(audio_signal, length) + enc_out, encoded_length = enc_out # ASSUME: single output + return enc_out, encoded_length + + def run_decoder_joint(self, enc_logits, targets, target_length, *states): + # ASSUME: Decoder is RNN Transducer + if targets is None: + targets = torch.zeros(enc_logits.shape[0], 1, dtype=torch.int32, device=enc_logits.device) + target_length = torch.ones(enc_logits.shape[0], dtype=torch.int32, device=enc_logits.device) + + num_states = 0 + if states is not None and len(states) > 0: + num_states = len(states) + + dec_out = self.decoder_joint(enc_logits, targets, target_length, *states) + + # unpack dec output + if num_states > 0: + new_states = dec_out[-num_states:] + dec_out = dec_out[:-num_states] + else: + new_states = None + + return dec_out, new_states + + def _get_initial_states(self, batchsize): + # ASSUME: LSTM STATES of shape (layers, batchsize, dim) + input_state_nodes = [ip for ip in self.decoder_joint_inputs if 'state' in ip.name] + num_states = len(input_state_nodes) + if num_states == 0: + return + + input_states = [] + for state_id in range(num_states): + # Hardcode shape size for LSTM (1 is for num layers in LSTM, which is flattened for export) + ip_shape = [1, batchsize, self.cfg.model_defaults.pred_hidden] + input_states.append(torch.zeros(*ip_shape, device=self.device)) + + return input_states + + +class GreedyMultiblankRNNTInfer(GreedyRNNTInfer): + """A greedy transducer decoder for multi-blank RNN-T. + + Sequence level greedy decoding, performed auto-regressively. + + Args: + decoder_model: rnnt_utils.AbstractRNNTDecoder implementation. + joint_model: rnnt_utils.AbstractRNNTJoint implementation. + blank_index: int index of the blank token. Must be len(vocabulary) for multi-blank RNNTs. + big_blank_durations: a list containing durations for big blanks the model supports. + max_symbols_per_step: Optional int. The maximum number of symbols that can be added + to a sequence in a single time step; if set to None then there is + no limit. + preserve_alignments: Bool flag which preserves the history of alignments generated during + greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `alignments` in it. Here, `alignments` is a List of List of + Tuple(Tensor (of length V + 1 + num-big-blanks), Tensor(scalar, label after argmax)). + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more targets from a vocabulary. + U is the number of target tokens for the current timestep Ti. + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores generated + during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `frame_confidence` is a List of List of floats. + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more confidence scores. + U is the number of target tokens for the current timestep Ti. + confidence_method_cfg: A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: The method name (str). + Supported values: + - 'max_prob' for using the maximum token probability as a confidence. + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: Which type of entropy to use (str). Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: A mapping of the entropy value to the interval [0,1]. + Supported values: + - 'lin' for using the linear mapping. + - 'exp' for using exponential mapping with linear shift. + """ + + def __init__( + self, + decoder_model: rnnt_abstract.AbstractRNNTDecoder, + joint_model: rnnt_abstract.AbstractRNNTJoint, + blank_index: int, + big_blank_durations: list, + max_symbols_per_step: Optional[int] = None, + preserve_alignments: bool = False, + preserve_frame_confidence: bool = False, + confidence_method_cfg: Optional[DictConfig] = None, + ): + super().__init__( + decoder_model=decoder_model, + joint_model=joint_model, + blank_index=blank_index, + max_symbols_per_step=max_symbols_per_step, + preserve_alignments=preserve_alignments, + preserve_frame_confidence=preserve_frame_confidence, + confidence_method_cfg=confidence_method_cfg, + ) + self.big_blank_durations = big_blank_durations + self._SOS = blank_index - len(big_blank_durations) + + @torch.no_grad() + def _greedy_decode( + self, x: torch.Tensor, out_len: torch.Tensor, partial_hypotheses: Optional[rnnt_utils.Hypothesis] = None + ): + # x: [T, 1, D] + # out_len: [seq_len] + + # Initialize blank state and empty label set in Hypothesis + hypothesis = rnnt_utils.Hypothesis(score=0.0, y_sequence=[], dec_state=None, timestep=[], last_token=None) + + if partial_hypotheses is not None: + hypothesis.last_token = partial_hypotheses.last_token + hypothesis.y_sequence = ( + partial_hypotheses.y_sequence.cpu().tolist() + if isinstance(partial_hypotheses.y_sequence, torch.Tensor) + else partial_hypotheses.y_sequence + ) + if partial_hypotheses.dec_state is not None: + hypothesis.dec_state = self.decoder.batch_concat_states([partial_hypotheses.dec_state]) + hypothesis.dec_state = _states_to_device(hypothesis.dec_state, x.device) + + if self.preserve_alignments: + # Alignments is a 2-dimensional dangling list representing T x U + hypothesis.alignments = [[]] + + if self.preserve_frame_confidence: + hypothesis.frame_confidence = [[]] + + # if this variable is > 1, it means the last emission was a big-blank and we need to skip frames. + big_blank_duration = 1 + + # For timestep t in X_t + for time_idx in range(out_len): + if big_blank_duration > 1: + # skip frames until big_blank_duration == 1. + big_blank_duration -= 1 + continue + # Extract encoder embedding at timestep t + # f = x[time_idx, :, :].unsqueeze(0) # [1, 1, D] + f = x.narrow(dim=0, start=time_idx, length=1) + + # Setup exit flags and counter + not_blank = True + symbols_added = 0 + + # While blank is not predicted, or we dont run out of max symbols per timestep + while not_blank and (self.max_symbols is None or symbols_added < self.max_symbols): + # In the first timestep, we initialize the network with RNNT Blank + # In later timesteps, we provide previous predicted label as input. + if hypothesis.last_token is None and hypothesis.dec_state is None: + last_label = self._SOS + else: + last_label = label_collate([[hypothesis.last_token]]) + + # Perform prediction network and joint network steps. + g, hidden_prime = self._pred_step(last_label, hypothesis.dec_state) + # If preserving per-frame confidence, log_normalize must be true + logp = self._joint_step(f, g, log_normalize=True if self.preserve_frame_confidence else None)[ + 0, 0, 0, : + ] + + del g + + # torch.max(0) op doesnt exist for FP 16. + if logp.dtype != torch.float32: + logp = logp.float() + + # get index k, of max prob + v, k = logp.max(0) + k = k.item() # K is the label at timestep t_s in inner loop, s >= 0. + + # Note, we have non-blanks in the vocab first, followed by big blanks, and standard blank at last. + # here we check if it's a big blank and if yes, set the duration variable. + if k >= self._blank_index - len(self.big_blank_durations) and k < self._blank_index: + big_blank_duration = self.big_blank_durations[self._blank_index - k - 1] + + if self.preserve_alignments: + # insert logprobs into last timestep + hypothesis.alignments[-1].append((logp.to('cpu'), torch.tensor(k, dtype=torch.int32))) + + if self.preserve_frame_confidence: + # insert confidence into last timestep + hypothesis.frame_confidence[-1].append(self._get_confidence(logp)) + + del logp + + # If any type of blank token is predicted, exit inner loop, move onto next timestep t + if k >= self._blank_index - len(self.big_blank_durations): + not_blank = False + else: + # Append token to label set, update RNN state. + hypothesis.y_sequence.append(k) + hypothesis.score += float(v) + hypothesis.timestep.append(time_idx) + hypothesis.dec_state = hidden_prime + hypothesis.last_token = k + + # Increment token counter. + symbols_added += 1 + + if self.preserve_alignments: + # convert Ti-th logits into a torch array + hypothesis.alignments.append([]) # blank buffer for next timestep + + if self.preserve_frame_confidence: + hypothesis.frame_confidence.append([]) # blank buffer for next timestep + + # Remove trailing empty list of Alignments + if self.preserve_alignments: + if len(hypothesis.alignments[-1]) == 0: + del hypothesis.alignments[-1] + + # Remove trailing empty list of per-frame confidence + if self.preserve_frame_confidence: + if len(hypothesis.frame_confidence[-1]) == 0: + del hypothesis.frame_confidence[-1] + + # Unpack the hidden states + hypothesis.dec_state = self.decoder.batch_select_state(hypothesis.dec_state, 0) + + return hypothesis + + +class GreedyBatchedMultiblankRNNTInfer(GreedyBatchedRNNTInfer): + """A batch level greedy transducer decoder. + Batch level greedy decoding, performed auto-regressively. + Args: + decoder_model: rnnt_utils.AbstractRNNTDecoder implementation. + joint_model: rnnt_utils.AbstractRNNTJoint implementation. + blank_index: int index of the blank token. Must be len(vocabulary) for multi-blank RNNTs. + big_blank_durations: a list containing durations for big blanks the model supports. + max_symbols_per_step: Optional int. The maximum number of symbols that can be added + to a sequence in a single time step; if set to None then there is + no limit. + preserve_alignments: Bool flag which preserves the history of alignments generated during + greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `alignments` in it. Here, `alignments` is a List of List of + Tuple(Tensor (of length V + 1 + num-big-blanks), Tensor(scalar, label after argmax)). + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more targets from a vocabulary. + U is the number of target tokens for the current timestep Ti. + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores generated + during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `frame_confidence` is a List of List of floats. + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more confidence scores. + U is the number of target tokens for the current timestep Ti. + confidence_method_cfg: A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: The method name (str). + Supported values: + - 'max_prob' for using the maximum token probability as a confidence. + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: Which type of entropy to use (str). Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: A mapping of the entropy value to the interval [0,1]. + Supported values: + - 'lin' for using the linear mapping. + - 'exp' for using exponential mapping with linear shift. + """ + + def __init__( + self, + decoder_model: rnnt_abstract.AbstractRNNTDecoder, + joint_model: rnnt_abstract.AbstractRNNTJoint, + blank_index: int, + big_blank_durations: List[int], + max_symbols_per_step: Optional[int] = None, + preserve_alignments: bool = False, + preserve_frame_confidence: bool = False, + confidence_method_cfg: Optional[DictConfig] = None, + ): + super().__init__( + decoder_model=decoder_model, + joint_model=joint_model, + blank_index=blank_index, + max_symbols_per_step=max_symbols_per_step, + preserve_alignments=preserve_alignments, + preserve_frame_confidence=preserve_frame_confidence, + confidence_method_cfg=confidence_method_cfg, + ) + self.big_blank_durations = big_blank_durations + + # Depending on availability of `blank_as_pad` support + # switch between more efficient batch decoding technique + if self.decoder.blank_as_pad: + self._greedy_decode = self._greedy_decode_blank_as_pad + else: + self._greedy_decode = self._greedy_decode_masked + self._SOS = blank_index - len(big_blank_durations) + + def _greedy_decode_blank_as_pad( + self, + x: torch.Tensor, + out_len: torch.Tensor, + device: torch.device, + partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None, + ): + if partial_hypotheses is not None: + raise NotImplementedError("`partial_hypotheses` support is not supported") + + with torch.inference_mode(): + # x: [B, T, D] + # out_len: [B] + # device: torch.device + + # Initialize list of Hypothesis + batchsize = x.shape[0] + hypotheses = [ + rnnt_utils.Hypothesis(score=0.0, y_sequence=[], timestep=[], dec_state=None) for _ in range(batchsize) + ] + + # Initialize Hidden state matrix (shared by entire batch) + hidden = None + + # If alignments need to be preserved, register a danling list to hold the values + if self.preserve_alignments: + # alignments is a 3-dimensional dangling list representing B x T x U + for hyp in hypotheses: + hyp.alignments = [[]] + + # If confidence scores need to be preserved, register a danling list to hold the values + if self.preserve_frame_confidence: + # frame_confidence is a 3-dimensional dangling list representing B x T x U + for hyp in hypotheses: + hyp.frame_confidence = [[]] + + # Last Label buffer + Last Label without blank buffer + # batch level equivalent of the last_label + last_label = torch.full([batchsize, 1], fill_value=self._SOS, dtype=torch.long, device=device) + + # this mask is true for if the emission is *any type* of blank. + blank_mask = torch.full([batchsize], fill_value=0, dtype=torch.bool, device=device) + + # Get max sequence length + max_out_len = out_len.max() + + # We have a mask for each big blank. A mask is "true" means: the previous emission is exactly the big-blank + # with the corresponding duration, or has larger duration. E.g., for big_blank_mask for duration 2, it will + # be set true if the previous emission was a big blank with duration 4, or 3 or 2; but false if prevoius + # emission was a standard blank (with duration = 1). + big_blank_masks = [torch.full([batchsize], fill_value=0, dtype=torch.bool, device=device)] * len( + self.big_blank_durations + ) + + # if this variable > 1, it means the previous emission was big-blank and we need to skip frames. + big_blank_duration = 1 + + for time_idx in range(max_out_len): + if big_blank_duration > 1: + # skip frames until big_blank_duration == 1 + big_blank_duration -= 1 + continue + f = x.narrow(dim=1, start=time_idx, length=1) # [B, 1, D] + + # Prepare t timestamp batch variables + not_blank = True + symbols_added = 0 + + # Reset all blank masks + blank_mask.mul_(False) + for i in range(len(big_blank_masks)): + big_blank_masks[i].mul_(False) + + # Update blank mask with time mask + # Batch: [B, T, D], but Bi may have seq len < max(seq_lens_in_batch) + # Forcibly mask with "blank" tokens, for all sample where current time step T > seq_len + blank_mask = time_idx >= out_len + for i in range(len(big_blank_masks)): + big_blank_masks[i] = time_idx >= out_len + + # Start inner loop + while not_blank and (self.max_symbols is None or symbols_added < self.max_symbols): + # Batch prediction and joint network steps + # If very first prediction step, submit SOS tag (blank) to pred_step. + # This feeds a zero tensor as input to AbstractRNNTDecoder to prime the state + if time_idx == 0 and symbols_added == 0 and hidden is None: + g, hidden_prime = self._pred_step(self._SOS, hidden, batch_size=batchsize) + else: + # Perform batch step prediction of decoder, getting new states and scores ("g") + g, hidden_prime = self._pred_step(last_label, hidden, batch_size=batchsize) + + # Batched joint step - Output = [B, V + 1 + num-big-blanks] + # If preserving per-frame confidence, log_normalize must be true + logp = self._joint_step(f, g, log_normalize=True if self.preserve_frame_confidence else None)[ + :, 0, 0, : + ] + + if logp.dtype != torch.float32: + logp = logp.float() + + # Get index k, of max prob for batch + v, k = logp.max(1) + del g + + # Update blank mask with current predicted blanks + # This is accumulating blanks over all time steps T and all target steps min(max_symbols, U) + k_is_blank = k >= self._blank_index - len(self.big_blank_durations) + blank_mask.bitwise_or_(k_is_blank) + + for i in range(len(big_blank_masks)): + # using <= since as we mentioned before, the mask doesn't store exact matches. + # instead, it is True when the predicted blank's duration is >= the duration that the + # mask corresponds to. + k_is_big_blank = k <= self._blank_index - 1 - i + + # need to do a bitwise_and since it could also be a non-blank. + k_is_big_blank.bitwise_and_(k_is_blank) + big_blank_masks[i].bitwise_or_(k_is_big_blank) + + del k_is_blank + + # If preserving alignments, check if sequence length of sample has been reached + # before adding alignment + if self.preserve_alignments: + # Insert logprobs into last timestep per sample + logp_vals = logp.to('cpu') + logp_ids = logp_vals.max(1)[1] + for batch_idx in range(batchsize): + if time_idx < out_len[batch_idx]: + hypotheses[batch_idx].alignments[-1].append( + (logp_vals[batch_idx], logp_ids[batch_idx]) + ) + del logp_vals + + # If preserving per-frame confidence, check if sequence length of sample has been reached + # before adding confidence scores + if self.preserve_frame_confidence: + # Insert probabilities into last timestep per sample + confidence = self._get_confidence(logp) + for batch_idx in range(batchsize): + if time_idx < out_len[batch_idx]: + hypotheses[batch_idx].frame_confidence[-1].append(confidence[batch_idx]) + del logp + + # If all samples predict / have predicted prior blanks, exit loop early + # This is equivalent to if single sample predicted k + if blank_mask.all(): + not_blank = False + else: + # Collect batch indices where blanks occurred now/past + blank_indices = (blank_mask == 1).nonzero(as_tuple=False) + + # Recover prior state for all samples which predicted blank now/past + if hidden is not None: + # LSTM has 2 states + hidden_prime = self.decoder.batch_copy_states(hidden_prime, hidden, blank_indices) + + elif len(blank_indices) > 0 and hidden is None: + # Reset state if there were some blank and other non-blank predictions in batch + # Original state is filled with zeros so we just multiply + # LSTM has 2 states + hidden_prime = self.decoder.batch_copy_states(hidden_prime, None, blank_indices, value=0.0) + + # Recover prior predicted label for all samples which predicted blank now/past + k[blank_indices] = last_label[blank_indices, 0] + + # Update new label and hidden state for next iteration + last_label = k.clone().view(-1, 1) + hidden = hidden_prime + + # Update predicted labels, accounting for time mask + # If blank was predicted even once, now or in the past, + # Force the current predicted label to also be blank + # This ensures that blanks propogate across all timesteps + # once they have occured (normally stopping condition of sample level loop). + for kidx, ki in enumerate(k): + if blank_mask[kidx] == 0: + hypotheses[kidx].y_sequence.append(ki) + hypotheses[kidx].timestep.append(time_idx) + hypotheses[kidx].score += float(v[kidx]) + + symbols_added += 1 + + for i in range(len(big_blank_masks) + 1): + # The task here is find the shortest blank duration of all batches. + # so we start from the shortest blank duration and go up, + # and stop once we found the duration whose corresponding mask isn't all True. + if i == len(big_blank_masks) or not big_blank_masks[i].all(): + big_blank_duration = self.big_blank_durations[i - 1] if i > 0 else 1 + break + + # If preserving alignments, convert the current Uj alignments into a torch.Tensor + # Then preserve U at current timestep Ti + # Finally, forward the timestep history to Ti+1 for that sample + # All of this should only be done iff the current time index <= sample-level AM length. + # Otherwise ignore and move to next sample / next timestep. + if self.preserve_alignments: + + # convert Ti-th logits into a torch array + for batch_idx in range(batchsize): + + # this checks if current timestep <= sample-level AM length + # If current timestep > sample-level AM length, no alignments will be added + # Therefore the list of Uj alignments is empty here. + if len(hypotheses[batch_idx].alignments[-1]) > 0: + hypotheses[batch_idx].alignments.append([]) # blank buffer for next timestep + + # Do the same if preserving per-frame confidence + if self.preserve_frame_confidence: + + for batch_idx in range(batchsize): + if len(hypotheses[batch_idx].frame_confidence[-1]) > 0: + hypotheses[batch_idx].frame_confidence.append([]) # blank buffer for next timestep + + # Remove trailing empty list of alignments at T_{am-len} x Uj + if self.preserve_alignments: + for batch_idx in range(batchsize): + if len(hypotheses[batch_idx].alignments[-1]) == 0: + del hypotheses[batch_idx].alignments[-1] + + # Remove trailing empty list of confidence scores at T_{am-len} x Uj + if self.preserve_frame_confidence: + for batch_idx in range(batchsize): + if len(hypotheses[batch_idx].frame_confidence[-1]) == 0: + del hypotheses[batch_idx].frame_confidence[-1] + + # Preserve states + for batch_idx in range(batchsize): + hypotheses[batch_idx].dec_state = self.decoder.batch_select_state(hidden, batch_idx) + + return hypotheses + + def _greedy_decode_masked( + self, + x: torch.Tensor, + out_len: torch.Tensor, + device: torch.device, + partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None, + ): + if partial_hypotheses is not None: + raise NotImplementedError("`partial_hypotheses` support is not supported") + + if self.big_blank_durations != [1] * len(self.big_blank_durations): + raise NotImplementedError( + "Efficient frame-skipping version for multi-blank masked decoding is not supported." + ) + + # x: [B, T, D] + # out_len: [B] + # device: torch.device + + # Initialize state + batchsize = x.shape[0] + hypotheses = [ + rnnt_utils.Hypothesis(score=0.0, y_sequence=[], timestep=[], dec_state=None) for _ in range(batchsize) + ] + + # Initialize Hidden state matrix (shared by entire batch) + hidden = None + + # If alignments need to be preserved, register a danling list to hold the values + if self.preserve_alignments: + # alignments is a 3-dimensional dangling list representing B x T x U + for hyp in hypotheses: + hyp.alignments = [[]] + else: + hyp.alignments = None + + # If confidence scores need to be preserved, register a danling list to hold the values + if self.preserve_frame_confidence: + # frame_confidence is a 3-dimensional dangling list representing B x T x U + for hyp in hypotheses: + hyp.frame_confidence = [[]] + + # Last Label buffer + Last Label without blank buffer + # batch level equivalent of the last_label + last_label = torch.full([batchsize, 1], fill_value=self._blank_index, dtype=torch.long, device=device) + last_label_without_blank = last_label.clone() + + # Mask buffers + blank_mask = torch.full([batchsize], fill_value=0, dtype=torch.bool, device=device) + + # Get max sequence length + max_out_len = out_len.max() + + with torch.inference_mode(): + for time_idx in range(max_out_len): + f = x.narrow(dim=1, start=time_idx, length=1) # [B, 1, D] + + # Prepare t timestamp batch variables + not_blank = True + symbols_added = 0 + + # Reset blank mask + blank_mask.mul_(False) + + # Update blank mask with time mask + # Batch: [B, T, D], but Bi may have seq len < max(seq_lens_in_batch) + # Forcibly mask with "blank" tokens, for all sample where current time step T > seq_len + blank_mask = time_idx >= out_len + + # Start inner loop + while not_blank and (self.max_symbols is None or symbols_added < self.max_symbols): + # Batch prediction and joint network steps + # If very first prediction step, submit SOS tag (blank) to pred_step. + # This feeds a zero tensor as input to AbstractRNNTDecoder to prime the state + if time_idx == 0 and symbols_added == 0 and hidden is None: + g, hidden_prime = self._pred_step(self._SOS, hidden, batch_size=batchsize) + else: + # Set a dummy label for the blank value + # This value will be overwritten by "blank" again the last label update below + # This is done as vocabulary of prediction network does not contain "blank" token of RNNT + last_label_without_blank_mask = last_label >= self._blank_index + last_label_without_blank[last_label_without_blank_mask] = 0 # temp change of label + last_label_without_blank[~last_label_without_blank_mask] = last_label[ + ~last_label_without_blank_mask + ] + + # Perform batch step prediction of decoder, getting new states and scores ("g") + g, hidden_prime = self._pred_step(last_label_without_blank, hidden, batch_size=batchsize) + + # Batched joint step - Output = [B, V + 1 + num-big-blanks] + # If preserving per-frame confidence, log_normalize must be true + logp = self._joint_step(f, g, log_normalize=True if self.preserve_frame_confidence else None)[ + :, 0, 0, : + ] + + if logp.dtype != torch.float32: + logp = logp.float() + + # Get index k, of max prob for batch + v, k = logp.max(1) + del g + + # Update blank mask with current predicted blanks + # This is accumulating blanks over all time steps T and all target steps min(max_symbols, U) + k_is_blank = k == self._blank_index + blank_mask.bitwise_or_(k_is_blank) + + # If preserving alignments, check if sequence length of sample has been reached + # before adding alignment + if self.preserve_alignments: + # Insert logprobs into last timestep per sample + logp_vals = logp.to('cpu') + logp_ids = logp_vals.max(1)[1] + for batch_idx in range(batchsize): + if time_idx < out_len[batch_idx]: + hypotheses[batch_idx].alignments[-1].append( + (logp_vals[batch_idx], logp_ids[batch_idx]) + ) + del logp_vals + + # If preserving per-frame confidence, check if sequence length of sample has been reached + # before adding confidence scores + if self.preserve_frame_confidence: + # Insert probabilities into last timestep per sample + confidence = self._get_confidence(logp) + for batch_idx in range(batchsize): + if time_idx < out_len[batch_idx]: + hypotheses[batch_idx].frame_confidence[-1].append(confidence[batch_idx]) + del logp + + # If all samples predict / have predicted prior blanks, exit loop early + # This is equivalent to if single sample predicted k + if blank_mask.all(): + not_blank = False + else: + # Collect batch indices where blanks occurred now/past + blank_indices = (blank_mask == 1).nonzero(as_tuple=False) + + # Recover prior state for all samples which predicted blank now/past + if hidden is not None: + # LSTM has 2 states + hidden_prime = self.decoder.batch_copy_states(hidden_prime, hidden, blank_indices) + + elif len(blank_indices) > 0 and hidden is None: + # Reset state if there were some blank and other non-blank predictions in batch + # Original state is filled with zeros so we just multiply + # LSTM has 2 states + hidden_prime = self.decoder.batch_copy_states(hidden_prime, None, blank_indices, value=0.0) + + # Recover prior predicted label for all samples which predicted blank now/past + k[blank_indices] = last_label[blank_indices, 0] + + # Update new label and hidden state for next iteration + last_label = k.view(-1, 1) + hidden = hidden_prime + + # Update predicted labels, accounting for time mask + # If blank was predicted even once, now or in the past, + # Force the current predicted label to also be blank + # This ensures that blanks propogate across all timesteps + # once they have occured (normally stopping condition of sample level loop). + for kidx, ki in enumerate(k): + if blank_mask[kidx] == 0: + hypotheses[kidx].y_sequence.append(ki) + hypotheses[kidx].timestep.append(time_idx) + hypotheses[kidx].score += float(v[kidx]) + + symbols_added += 1 + + # If preserving alignments, convert the current Uj alignments into a torch.Tensor + # Then preserve U at current timestep Ti + # Finally, forward the timestep history to Ti+1 for that sample + # All of this should only be done iff the current time index <= sample-level AM length. + # Otherwise ignore and move to next sample / next timestep. + if self.preserve_alignments: + + # convert Ti-th logits into a torch array + for batch_idx in range(batchsize): + + # this checks if current timestep <= sample-level AM length + # If current timestep > sample-level AM length, no alignments will be added + # Therefore the list of Uj alignments is empty here. + if len(hypotheses[batch_idx].alignments[-1]) > 0: + hypotheses[batch_idx].alignments.append([]) # blank buffer for next timestep + + # Do the same if preserving per-frame confidence + if self.preserve_frame_confidence: + + for batch_idx in range(batchsize): + if len(hypotheses[batch_idx].frame_confidence[-1]) > 0: + hypotheses[batch_idx].frame_confidence.append([]) # blank buffer for next timestep + + # Remove trailing empty list of alignments at T_{am-len} x Uj + if self.preserve_alignments: + for batch_idx in range(batchsize): + if len(hypotheses[batch_idx].alignments[-1]) == 0: + del hypotheses[batch_idx].alignments[-1] + + # Remove trailing empty list of confidence scores at T_{am-len} x Uj + if self.preserve_frame_confidence: + for batch_idx in range(batchsize): + if len(hypotheses[batch_idx].frame_confidence[-1]) == 0: + del hypotheses[batch_idx].frame_confidence[-1] + + # Preserve states + for batch_idx in range(batchsize): + hypotheses[batch_idx].dec_state = self.decoder.batch_select_state(hidden, batch_idx) + + return hypotheses + + +@dataclass +class GreedyRNNTInferConfig: + max_symbols_per_step: Optional[int] = 10 + preserve_alignments: bool = False + preserve_frame_confidence: bool = False + confidence_method_cfg: Optional[ConfidenceMethodConfig] = field(default_factory=lambda: ConfidenceMethodConfig()) + + def __post_init__(self): + # OmegaConf.structured ensures that post_init check is always executed + self.confidence_method_cfg = OmegaConf.structured( + self.confidence_method_cfg + if isinstance(self.confidence_method_cfg, ConfidenceMethodConfig) + else ConfidenceMethodConfig(**self.confidence_method_cfg) + ) + + +@dataclass +class GreedyBatchedRNNTInferConfig: + max_symbols_per_step: Optional[int] = 10 + preserve_alignments: bool = False + preserve_frame_confidence: bool = False + confidence_method_cfg: Optional[ConfidenceMethodConfig] = field(default_factory=lambda: ConfidenceMethodConfig()) + loop_labels: bool = True + use_cuda_graph_decoder: bool = False + + def __post_init__(self): + # OmegaConf.structured ensures that post_init check is always executed + self.confidence_method_cfg = OmegaConf.structured( + self.confidence_method_cfg + if isinstance(self.confidence_method_cfg, ConfidenceMethodConfig) + else ConfidenceMethodConfig(**self.confidence_method_cfg) + ) + + +class GreedyTDTInfer(_GreedyRNNTInfer): + """A greedy TDT decoder. + + Sequence level greedy decoding, performed auto-regressively. + + Args: + decoder_model: rnnt_utils.AbstractRNNTDecoder implementation. + joint_model: rnnt_utils.AbstractRNNTJoint implementation. + blank_index: int index of the blank token. Must be len(vocabulary) for TDT models. + durations: a list containing durations for TDT. + max_symbols_per_step: Optional int. The maximum number of symbols that can be added + to a sequence in a single time step; if set to None then there is + no limit. + preserve_alignments: Bool flag which preserves the history of alignments generated during + greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `alignments` in it. Here, `alignments` is a List of List of + Tuple(Tensor (of length V + 1 + num-big-blanks), Tensor(scalar, label after argmax)). + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more targets from a vocabulary. + U is the number of target tokens for the current timestep Ti. + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores generated + during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `frame_confidence` is a List of List of floats. + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more confidence scores. + U is the number of target tokens for the current timestep Ti. + confidence_method_cfg: A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: The method name (str). + Supported values: + - 'max_prob' for using the maximum token probability as a confidence. + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: Which type of entropy to use (str). Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: A mapping of the entropy value to the interval [0,1]. + Supported values: + - 'lin' for using the linear mapping. + - 'exp' for using exponential mapping with linear shift. + """ + + def __init__( + self, + decoder_model: rnnt_abstract.AbstractRNNTDecoder, + joint_model: rnnt_abstract.AbstractRNNTJoint, + blank_index: int, + durations: list, + max_symbols_per_step: Optional[int] = None, + preserve_alignments: bool = False, + preserve_frame_confidence: bool = False, + confidence_method_cfg: Optional[DictConfig] = None, + ): + super().__init__( + decoder_model=decoder_model, + joint_model=joint_model, + blank_index=blank_index, + max_symbols_per_step=max_symbols_per_step, + preserve_alignments=preserve_alignments, + preserve_frame_confidence=preserve_frame_confidence, + confidence_method_cfg=confidence_method_cfg, + ) + self.durations = durations + + @typecheck() + def forward( + self, + encoder_output: torch.Tensor, + encoded_lengths: torch.Tensor, + partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None, + ): + """Returns a list of hypotheses given an input batch of the encoder hidden embedding. + Output token is generated auto-regressively. + Args: + encoder_output: A tensor of size (batch, features, timesteps). + encoded_lengths: list of int representing the length of each sequence + output sequence. + Returns: + packed list containing batch number of sentences (Hypotheses). + """ + # Preserve decoder and joint training state + decoder_training_state = self.decoder.training + joint_training_state = self.joint.training + + with torch.inference_mode(): + # Apply optional preprocessing + encoder_output = encoder_output.transpose(1, 2) # (B, T, D) + + self.decoder.eval() + self.joint.eval() + + hypotheses = [] + # Process each sequence independently + with self.decoder.as_frozen(), self.joint.as_frozen(): + for batch_idx in range(encoder_output.size(0)): + inseq = encoder_output[batch_idx, :, :].unsqueeze(1) # [T, 1, D] + logitlen = encoded_lengths[batch_idx] + + partial_hypothesis = partial_hypotheses[batch_idx] if partial_hypotheses is not None else None + hypothesis = self._greedy_decode(inseq, logitlen, partial_hypotheses=partial_hypothesis) + hypotheses.append(hypothesis) + + # Pack results into Hypotheses + packed_result = pack_hypotheses(hypotheses, encoded_lengths) + + self.decoder.train(decoder_training_state) + self.joint.train(joint_training_state) + + return (packed_result,) + + @torch.no_grad() + def _greedy_decode( + self, x: torch.Tensor, out_len: torch.Tensor, partial_hypotheses: Optional[rnnt_utils.Hypothesis] = None + ): + # x: [T, 1, D] + # out_len: [seq_len] + + # Initialize blank state and empty label set in Hypothesis + hypothesis = rnnt_utils.Hypothesis(score=0.0, y_sequence=[], dec_state=None, timestep=[], last_token=None) + + if partial_hypotheses is not None: + hypothesis.last_token = partial_hypotheses.last_token + hypothesis.y_sequence = ( + partial_hypotheses.y_sequence.cpu().tolist() + if isinstance(partial_hypotheses.y_sequence, torch.Tensor) + else partial_hypotheses.y_sequence + ) + if partial_hypotheses.dec_state is not None: + hypothesis.dec_state = self.decoder.batch_concat_states([partial_hypotheses.dec_state]) + hypothesis.dec_state = _states_to_device(hypothesis.dec_state, x.device) + + if self.preserve_alignments: + # Alignments is a 2-dimensional dangling list representing T x U + hypothesis.alignments = [[]] + + if self.preserve_frame_confidence: + hypothesis.frame_confidence = [[]] + + time_idx = 0 + while time_idx < out_len: + # Extract encoder embedding at timestep t + # f = x[time_idx, :, :].unsqueeze(0) # [1, 1, D] + f = x.narrow(dim=0, start=time_idx, length=1) + + # Setup exit flags and counter + not_blank = True + symbols_added = 0 + + need_loop = True + # While blank is not predicted, or we dont run out of max symbols per timestep + while need_loop and (self.max_symbols is None or symbols_added < self.max_symbols): + # In the first timestep, we initialize the network with RNNT Blank + # In later timesteps, we provide previous predicted label as input. + if hypothesis.last_token is None and hypothesis.dec_state is None: + last_label = self._SOS + else: + last_label = label_collate([[hypothesis.last_token]]) + + # Perform prediction network and joint network steps. + g, hidden_prime = self._pred_step(last_label, hypothesis.dec_state) + # If preserving per-frame confidence, log_normalize must be true + logits = self._joint_step(f, g, log_normalize=False) + logp = logits[0, 0, 0, : -len(self.durations)] + if self.preserve_frame_confidence: + logp = torch.log_softmax(logp, -1) + + duration_logp = torch.log_softmax(logits[0, 0, 0, -len(self.durations) :], dim=-1) + del g + + # torch.max(0) op doesnt exist for FP 16. + if logp.dtype != torch.float32: + logp = logp.float() + + # get index k, of max prob + v, k = logp.max(0) + k = k.item() # K is the label at timestep t_s in inner loop, s >= 0. + + d_v, d_k = duration_logp.max(0) + d_k = d_k.item() + + skip = self.durations[d_k] + + if self.preserve_alignments: + # insert logprobs into last timestep + hypothesis.alignments[-1].append((logp.to('cpu'), torch.tensor(k, dtype=torch.int32))) + + if self.preserve_frame_confidence: + # insert confidence into last timestep + hypothesis.frame_confidence[-1].append(self._get_confidence(logp)) + + del logp + + # If blank token is predicted, exit inner loop, move onto next timestep t + if k == self._blank_index: + not_blank = False + else: + # Append token to label set, update RNN state. + hypothesis.y_sequence.append(k) + hypothesis.score += float(v) + hypothesis.timestep.append(time_idx) + hypothesis.dec_state = hidden_prime + hypothesis.last_token = k + + # Increment token counter. + symbols_added += 1 + time_idx += skip + need_loop = skip == 0 + + # this rarely happens, but we manually increment the `skip` number + # if blank is emitted and duration=0 is predicted. This prevents possible + # infinite loops. + if skip == 0: + skip = 1 + + if self.preserve_alignments: + # convert Ti-th logits into a torch array + hypothesis.alignments.append([]) # blank buffer for next timestep + + if self.preserve_frame_confidence: + hypothesis.frame_confidence.append([]) # blank buffer for next timestep + + if symbols_added == self.max_symbols: + time_idx += 1 + + # Remove trailing empty list of Alignments + if self.preserve_alignments: + if len(hypothesis.alignments[-1]) == 0: + del hypothesis.alignments[-1] + + # Remove trailing empty list of per-frame confidence + if self.preserve_frame_confidence: + if len(hypothesis.frame_confidence[-1]) == 0: + del hypothesis.frame_confidence[-1] + + # Unpack the hidden states + hypothesis.dec_state = self.decoder.batch_select_state(hypothesis.dec_state, 0) + + return hypothesis + + +class GreedyBatchedTDTInfer(_GreedyRNNTInfer): + """A batch level greedy TDT decoder. + Batch level greedy decoding, performed auto-regressively. + Args: + decoder_model: rnnt_utils.AbstractRNNTDecoder implementation. + joint_model: rnnt_utils.AbstractRNNTJoint implementation. + blank_index: int index of the blank token. Must be len(vocabulary) for TDT models. + durations: a list containing durations. + max_symbols_per_step: Optional int. The maximum number of symbols that can be added + to a sequence in a single time step; if set to None then there is + no limit. + preserve_alignments: Bool flag which preserves the history of alignments generated during + greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `alignments` in it. Here, `alignments` is a List of List of + Tuple(Tensor (of length V + 1 + num-big-blanks), Tensor(scalar, label after argmax)). + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more targets from a vocabulary. + U is the number of target tokens for the current timestep Ti. + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores generated + during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `frame_confidence` is a List of List of floats. + The length of the list corresponds to the Acoustic Length (T). + Each value in the list (Ti) is a torch.Tensor (U), representing 1 or more confidence scores. + U is the number of target tokens for the current timestep Ti. + confidence_method_cfg: A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: The method name (str). + Supported values: + - 'max_prob' for using the maximum token probability as a confidence. + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: Which type of entropy to use (str). Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: A mapping of the entropy value to the interval [0,1]. + Supported values: + - 'lin' for using the linear mapping. + - 'exp' for using exponential mapping with linear shift. + """ + + def __init__( + self, + decoder_model: rnnt_abstract.AbstractRNNTDecoder, + joint_model: rnnt_abstract.AbstractRNNTJoint, + blank_index: int, + durations: List[int], + max_symbols_per_step: Optional[int] = None, + preserve_alignments: bool = False, + preserve_frame_confidence: bool = False, + confidence_method_cfg: Optional[DictConfig] = None, + use_cuda_graph_decoder: bool = False, + ): + super().__init__( + decoder_model=decoder_model, + joint_model=joint_model, + blank_index=blank_index, + max_symbols_per_step=max_symbols_per_step, + preserve_alignments=preserve_alignments, + preserve_frame_confidence=preserve_frame_confidence, + confidence_method_cfg=confidence_method_cfg, + ) + self.durations = durations + + # Depending on availability of `blank_as_pad` support + # switch between more efficient batch decoding technique + self._decoding_computer = None + if self.decoder.blank_as_pad: + # batched "loop frames" is not implemented for TDT + self._decoding_computer = GreedyBatchedTDTLoopLabelsComputer( + decoder=self.decoder, + joint=self.joint, + blank_index=self._blank_index, + durations=self.durations, + max_symbols_per_step=self.max_symbols, + preserve_alignments=preserve_alignments, + preserve_frame_confidence=preserve_frame_confidence, + confidence_method_cfg=confidence_method_cfg, + allow_cuda_graphs=use_cuda_graph_decoder, + ) + self._greedy_decode = self._greedy_decode_blank_as_pad_loop_labels + else: + self._greedy_decode = self._greedy_decode_masked + + @typecheck() + def forward( + self, + encoder_output: torch.Tensor, + encoded_lengths: torch.Tensor, + partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None, + ): + """Returns a list of hypotheses given an input batch of the encoder hidden embedding. + Output token is generated auto-regressively. + Args: + encoder_output: A tensor of size (batch, features, timesteps). + encoded_lengths: list of int representing the length of each sequence + output sequence. + Returns: + packed list containing batch number of sentences (Hypotheses). + """ + # Preserve decoder and joint training state + decoder_training_state = self.decoder.training + joint_training_state = self.joint.training + + with torch.inference_mode(): + # Apply optional preprocessing + encoder_output = encoder_output.transpose(1, 2) # (B, T, D) + logitlen = encoded_lengths + + self.decoder.eval() + self.joint.eval() + + with self.decoder.as_frozen(), self.joint.as_frozen(): + inseq = encoder_output # [B, T, D] + hypotheses = self._greedy_decode( + inseq, logitlen, device=inseq.device, partial_hypotheses=partial_hypotheses + ) + + # Pack the hypotheses results + packed_result = pack_hypotheses(hypotheses, logitlen) + + self.decoder.train(decoder_training_state) + self.joint.train(joint_training_state) + + return (packed_result,) + + def _greedy_decode_masked( + self, + x: torch.Tensor, + out_len: torch.Tensor, + device: torch.device, + partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None, + ): + raise NotImplementedError("masked greedy-batched decode is not supported for TDT models.") + + @torch.inference_mode() + def _greedy_decode_blank_as_pad_loop_labels( + self, + x: torch.Tensor, + out_len: torch.Tensor, + device: torch.device, + partial_hypotheses: Optional[list[rnnt_utils.Hypothesis]] = None, + ) -> list[rnnt_utils.Hypothesis]: + """ + Optimized batched greedy decoding. + The main idea: search for next labels for the whole batch (evaluating Joint) + and thus always evaluate prediction network with maximum possible batch size + """ + if partial_hypotheses is not None: + raise NotImplementedError("`partial_hypotheses` support is not implemented") + + batched_hyps, alignments, last_decoder_state = self._decoding_computer(x=x, out_len=out_len) + hyps = rnnt_utils.batched_hyps_to_hypotheses(batched_hyps, alignments, batch_size=x.shape[0]) + for hyp, state in zip(hyps, self.decoder.batch_split_states(last_decoder_state)): + hyp.dec_state = state + return hyps diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/rnnt_loop_labels_computer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/rnnt_loop_labels_computer.py new file mode 100644 index 0000000..92cb8a3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/rnnt_loop_labels_computer.py @@ -0,0 +1,727 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any, Optional, Tuple + +import numpy as np +import torch +import torch.nn.functional as F +from omegaconf import DictConfig + +from nemo.collections.asr.parts.utils import rnnt_utils +from nemo.collections.asr.parts.utils.asr_confidence_utils import ConfidenceMethodMixin +from nemo.core.utils.cuda_python_utils import ( + check_cuda_python_cuda_graphs_conditional_nodes_supported, + cu_call, + run_nvrtc, + with_conditional_node, +) +from nemo.utils import logging + +try: + from cuda import cudart + + HAVE_CUDA_PYTHON = True +except ImportError: + HAVE_CUDA_PYTHON = False + + +class LoopLabelsState: + """ + State for Loop Labels algorithm. Used only with CUDA graphs. + In initialization phase it is possible to assign values (tensors) to the state. + For algorithm code the storage should be reused (prefer copy data instead of assigning tensors). + """ + + max_time: int # maximum length of internal storage for time dimension + batch_size: int # (maximum) length of internal storage for batch dimension + device: torch.device # device to store preallocated tensors + + encoder_output_projected: torch.Tensor # projected output from the encoder for decoding algorithm + encoder_output_length: torch.Tensor # length of the (projected) output from the encoder + + labels: torch.Tensor # storage for current labels + scores: torch.Tensor # storage for current scores + + batch_indices: torch.Tensor # indices of elements in batch (constant, range [0, batch_size-1]) + + time_indices: torch.Tensor # current time indices for each element in batch + safe_time_indices: torch.Tensor # current time indices, but guaranteed to be < encoder_output_length + time_indices_current_labels: torch.Tensor # time indices for found labels (corresponding to `labels` field) + last_timesteps: torch.Tensor # indices of the last timesteps for each element (encoder_output_length - 1) + + active_mask: torch.Tensor # mask for active hypotheses (the decoding is finished for the utterance if it is False) + advance_mask: torch.Tensor # mask for "advancing" hypotheses (blank is found for the element on the current step) + blank_mask: torch.Tensor # if the element is blank + # if the element was active on the previous step: to identify the end of decoding and store final hidden state + active_mask_prev: torch.Tensor + became_inactive_mask: torch.Tensor # mask for elements that became inactive (end of decoding) + + active_mask_any: torch.Tensor # 0-dim bool tensor, condition for outer loop ('any element is still active') + advance_mask_any: torch.Tensor # 0-dim bool tensor, condition for inner loop ('should advance any index') + + last_decoder_state: Any # last state from the decoder, needed for the output + decoder_state: Any # current decoder state + decoder_output: torch.Tensor # output from the decoder (projected) + + batched_hyps: rnnt_utils.BatchedHyps # batched hypotheses - decoding result + alignments: Optional[rnnt_utils.BatchedAlignments] = None # batched alignments + + def __init__( + self, + batch_size: int, + max_time: int, + encoder_dim: int, + max_symbols: int, + device: torch.device, + float_dtype: torch.dtype, + logits_dim: int, + preserve_alignments=False, + preserve_frame_confidence=False, + ): + """ + + Args: + batch_size: batch size for encoder output storage + max_time: maximum time for encoder output storage + encoder_dim: last dimension for encoder output storage (projected encoder output) + max_symbols: max symbols per step (to avoid infinite looping and pre-allocate storage) + device: device to store tensors + float_dtype: default float dtype for tensors (should match projected encoder output) + logits_dim: output dimension for Joint + preserve_alignments: if alignments are needed + preserve_frame_confidence: if frame confidence is needed + """ + self.device = device + self.float_dtype = float_dtype + self.batch_size = batch_size + self.max_time = max_time + + self.encoder_output_projected = torch.zeros( + (self.batch_size, self.max_time, encoder_dim), dtype=float_dtype, device=self.device, + ) + self.encoder_output_length = torch.zeros((self.batch_size,), dtype=torch.long, device=self.device) + + self.labels = torch.zeros([self.batch_size], dtype=torch.long, device=self.device) + self.scores = torch.zeros([self.batch_size], dtype=float_dtype, device=self.device) + + # indices of elements in batch (constant) + self.batch_indices = torch.arange(self.batch_size, dtype=torch.long, device=self.device) + + self.time_indices = torch.zeros_like(self.batch_indices) + self.safe_time_indices = torch.zeros_like(self.batch_indices) + self.time_indices_current_labels = torch.zeros_like(self.time_indices) + self.last_timesteps = torch.zeros_like(self.time_indices) + + self.active_mask = torch.zeros([self.batch_size], dtype=torch.bool, device=self.device) + self.advance_mask = torch.zeros_like(self.active_mask) + self.blank_mask = torch.zeros_like(self.active_mask) + self.active_mask_prev = torch.zeros_like(self.active_mask) + self.became_inactive_mask = torch.zeros_like(self.active_mask) + + self.active_mask_any = torch.tensor(True, device=self.device, dtype=torch.bool) + self.advance_mask_any = torch.tensor(True, device=self.device, dtype=torch.bool) + + self.batched_hyps = rnnt_utils.BatchedHyps( + batch_size=self.batch_size, + init_length=self.max_time * max_symbols, + device=self.device, + float_dtype=float_dtype, + ) + if preserve_alignments or preserve_frame_confidence: + self.alignments = rnnt_utils.BatchedAlignments( + batch_size=batch_size, + logits_dim=logits_dim, + init_length=max_time * (max_symbols + 1), + device=self.device, + float_dtype=self.float_dtype, + store_alignments=preserve_alignments, + store_frame_confidence=preserve_frame_confidence, + ) + else: + self.alignments = None + + def need_reinit(self, encoder_output_projected: torch.Tensor) -> bool: + """Check if need to reinit state: larger batch_size/max_time, or new device""" + return ( + self.batch_size < encoder_output_projected.shape[0] + or self.max_time < encoder_output_projected.shape[1] + or self.device.index != encoder_output_projected.device.index + ) + + +class GreedyBatchedRNNTLoopLabelsComputer(ConfidenceMethodMixin): + """ + Label Looping algorithm implementation: optimized batched greedy decoding. Callable. + Iterates over labels, on each step finding the next non-blank label + (evaluating Joint multiple times in inner loop); It uses a minimal possible amount of calls + to prediction network (with maximum possible batch size), + which makes it especially useful for scaling the prediction network. + During decoding all active hypotheses ("texts") have the same lengths. + """ + + INITIAL_MAX_TIME = 375 # initial max time, used to init state for Cuda graphs + CUDA_PROGRAM_NAME = b"while_loop_labels_conditional_rnnt.cu" + + def __init__( + self, + decoder, + joint, + blank_index: int, + max_symbols_per_step: Optional[int] = None, + preserve_alignments=False, + preserve_frame_confidence=False, + confidence_method_cfg: Optional[DictConfig] = None, + allow_cuda_graphs: bool = True, + ): + """ + Init method. + Args: + decoder: Prediction network from RNN-T + joint: Joint module from RNN-T + blank_index: index of blank symbol + max_symbols_per_step: max symbols to emit on each step (to avoid infinite looping) + preserve_alignments: if alignments are needed + preserve_frame_confidence: if frame confidence is needed + confidence_method_cfg: config for the confidence + """ + super().__init__() + self.decoder = decoder + self.joint = joint + self._blank_index = blank_index + self.max_symbols = max_symbols_per_step + self.preserve_alignments = preserve_alignments + self.preserve_frame_confidence = preserve_frame_confidence + self._SOS = self._blank_index + self._init_confidence_method(confidence_method_cfg=confidence_method_cfg) + assert self._SOS == self._blank_index # "blank as pad" algorithm only + + self.use_cuda_graphs = allow_cuda_graphs + + if self.use_cuda_graphs and self.max_symbols is None: + logging.warning("Max symbols is None, which is not allowed with Cuda graphs.") + self.use_cuda_graphs = False + + if self.use_cuda_graphs: + try: + check_cuda_python_cuda_graphs_conditional_nodes_supported() + except ImportError as e: + logging.warning(f"No conditional node support. Cuda graphs will be disabled,\n{e.msg}") + self.use_cuda_graphs = False + + self.state: Optional[LoopLabelsState] = None + + def loop_labels_torch( + self, encoder_output: torch.Tensor, encoder_output_length: torch.Tensor, + ) -> Tuple[rnnt_utils.BatchedHyps, Optional[rnnt_utils.BatchedAlignments], Any]: + """ + Pure PyTorch implementation + + Args: + encoder_output: output from the encoder + encoder_output_length: lengths of the utterances in `encoder_output` + """ + batch_size, max_time, _unused = encoder_output.shape + device = encoder_output.device + + # do not recalculate joint projection, project only once + encoder_output_projected = self.joint.project_encoder(encoder_output) + + # init output structures: BatchedHyps (for results), BatchedAlignments + last decoder state + # init empty batched hypotheses + batched_hyps = rnnt_utils.BatchedHyps( + batch_size=batch_size, + init_length=max_time * self.max_symbols if self.max_symbols is not None else max_time, + device=device, + float_dtype=encoder_output_projected.dtype, + ) + # sample state, will be replaced further when the decoding for hypothesis is done + last_decoder_state = self.decoder.initialize_state(encoder_output_projected) + # init alignments if necessary + use_alignments = self.preserve_alignments or self.preserve_frame_confidence + # always use alignments variable - for torch.jit adaptation, but keep it as minimal as possible + alignments = rnnt_utils.BatchedAlignments( + batch_size=batch_size, + logits_dim=self.joint.num_classes_with_blank, + init_length=max_time * 2 if use_alignments else 1, # blank for each timestep + text tokens + device=device, + float_dtype=encoder_output_projected.dtype, + store_alignments=self.preserve_alignments, + store_frame_confidence=self.preserve_frame_confidence, + ) + + # initial state, needed for torch.jit to compile (cannot handle None) + state = self.decoder.initialize_state(encoder_output_projected) + # indices of elements in batch (constant) + batch_indices = torch.arange(batch_size, dtype=torch.long, device=device) + # last found labels - initially () symbol + labels = torch.full_like(batch_indices, fill_value=self._SOS) + + # time indices + time_indices = torch.zeros_like(batch_indices) + safe_time_indices = torch.zeros_like(time_indices) # time indices, guaranteed to be < out_len + time_indices_current_labels = torch.zeros_like(time_indices) + last_timesteps = encoder_output_length - 1 + + # masks for utterances in batch + active_mask: torch.Tensor = encoder_output_length > 0 + advance_mask = torch.empty_like(active_mask) + + # for storing the last state we need to know what elements became "inactive" on this step + active_mask_prev = torch.empty_like(active_mask) + became_inactive_mask = torch.empty_like(active_mask) + + # loop while there are active utterances + while active_mask.any(): + active_mask_prev.copy_(active_mask, non_blocking=True) + # stage 1: get decoder (prediction network) output + decoder_output, state, *_ = self.decoder.predict( + labels.unsqueeze(1), state, add_sos=False, batch_size=batch_size + ) + decoder_output = self.joint.project_prednet(decoder_output) # do not recalculate joint projection + + # stage 2: get joint output, iteratively seeking for non-blank labels + # blank label in `labels` tensor means "end of hypothesis" (for this index) + logits = ( + self.joint.joint_after_projection( + encoder_output_projected[batch_indices, safe_time_indices].unsqueeze(1), decoder_output, + ) + .squeeze(1) + .squeeze(1) + ) + scores, labels = logits.max(-1) + + # search for non-blank labels using joint, advancing time indices for blank labels + # checking max_symbols is not needed, since we already forced advancing time indices for such cases + blank_mask = labels == self._blank_index + time_indices_current_labels.copy_(time_indices, non_blocking=True) + if use_alignments: + alignments.add_results_masked_( + active_mask=active_mask, + time_indices=time_indices_current_labels, + logits=logits if self.preserve_alignments else None, + labels=labels if self.preserve_alignments else None, + confidence=self._get_confidence_tensor(F.log_softmax(logits, dim=-1)) + if self.preserve_frame_confidence + else None, + ) + + # advance_mask is a mask for current batch for searching non-blank labels; + # each element is True if non-blank symbol is not yet found AND we can increase the time index + time_indices += blank_mask + torch.minimum(time_indices, last_timesteps, out=safe_time_indices) + torch.less(time_indices, encoder_output_length, out=active_mask) + torch.logical_and(active_mask, blank_mask, out=advance_mask) + + # inner loop: find next non-blank labels (if exist) + while advance_mask.any(): + # same as: time_indices_current_labels[advance_mask] = time_indices[advance_mask], but non-blocking + # store current time indices to use further for storing the results + torch.where(advance_mask, time_indices, time_indices_current_labels, out=time_indices_current_labels) + logits = ( + self.joint.joint_after_projection( + encoder_output_projected[batch_indices, safe_time_indices].unsqueeze(1), decoder_output, + ) + .squeeze(1) + .squeeze(1) + ) + # get labels (greedy) and scores from current logits, replace labels/scores with new + # labels[advance_mask] are blank, and we are looking for non-blank labels + more_scores, more_labels = logits.max(-1) + # same as: labels[advance_mask] = more_labels[advance_mask], but non-blocking + torch.where(advance_mask, more_labels, labels, out=labels) + # same as: scores[advance_mask] = more_scores[advance_mask], but non-blocking + torch.where(advance_mask, more_scores, scores, out=scores) + + if use_alignments: + alignments.add_results_masked_( + active_mask=advance_mask, + time_indices=time_indices_current_labels, + logits=logits if self.preserve_alignments else None, + labels=more_labels if self.preserve_alignments else None, + confidence=self._get_confidence_tensor(F.log_softmax(logits, dim=-1)) + if self.preserve_frame_confidence + else None, + ) + + blank_mask = labels == self._blank_index + time_indices += blank_mask + torch.minimum(time_indices, last_timesteps, out=safe_time_indices) + torch.less(time_indices, encoder_output_length, out=active_mask) + torch.logical_and(active_mask, blank_mask, out=advance_mask) + + # stage 3: filter labels and state, store hypotheses + # select states for hyps that became inactive (is it necessary?) + # this seems to be redundant, but used in the `loop_frames` output + torch.ne(active_mask, active_mask_prev, out=became_inactive_mask) + self.decoder.batch_replace_states_mask( + src_states=state, dst_states=last_decoder_state, mask=became_inactive_mask, + ) + + # store hypotheses + if self.max_symbols is not None: + # pre-allocated memory, no need for checks + batched_hyps.add_results_masked_no_checks_( + active_mask, labels, time_indices_current_labels, scores, + ) + else: + # auto-adjusted storage + batched_hyps.add_results_masked_( + active_mask, labels, time_indices_current_labels, scores, + ) + + # stage 4: to avoid looping, go to next frame after max_symbols emission + if self.max_symbols is not None: + # if labels are non-blank (not end-of-utterance), check that last observed timestep with label: + # if it is equal to the current time index, and number of observations is >= max_symbols, force blank + force_blank_mask = torch.logical_and( + active_mask, + torch.logical_and( + torch.logical_and( + labels != self._blank_index, batched_hyps.last_timestep_lasts >= self.max_symbols, + ), + batched_hyps.last_timestep == time_indices, + ), + ) + time_indices += force_blank_mask # emit blank => advance time indices + # update safe_time_indices, non-blocking + torch.minimum(time_indices, last_timesteps, out=safe_time_indices) + # same as: active_mask = time_indices < encoder_output_length + torch.less(time_indices, encoder_output_length, out=active_mask) + if use_alignments: + return batched_hyps, alignments, last_decoder_state + return batched_hyps, None, last_decoder_state + + def loop_labels_cuda_graphs( + self, encoder_output: torch.Tensor, encoder_output_length: torch.Tensor, + ) -> Tuple[rnnt_utils.BatchedHyps, Optional[rnnt_utils.BatchedAlignments], Any]: + """ + Implementation with CUDA graphs. + + Args: + encoder_output: output from the encoder + encoder_output_length: lengths of the utterances in `encoder_output` + """ + # do not recalculate joint projection, project only once + encoder_output = self.joint.project_encoder(encoder_output) + current_batch_size = encoder_output.shape[0] + current_max_time = encoder_output.shape[1] + + if torch.is_autocast_enabled(): + encoder_output = encoder_output.to(torch.get_autocast_gpu_dtype()) + + # init or reinit graph + if self.state is None or self.state.need_reinit(encoder_output): + self._graph_reinitialize(encoder_output, encoder_output_length) + + # copy (projected) encoder output and lenghts + self.state.encoder_output_projected[:current_batch_size, :current_max_time, ...].copy_(encoder_output) + self.state.encoder_output_length[: encoder_output_length.shape[0]].copy_(encoder_output_length) + # set length to zero for elements outside the current batch + self.state.encoder_output_length[current_batch_size:].fill_(0) + self.graph.replay() + + # example manual loop (can be used instead of graph.replay()) + # self._before_outer_loop() + # while self.state.active_mask_any.item(): + # self._before_inner_loop_get_decoder_output() + # self._before_inner_loop_get_joint_output() + # while self.state.advance_mask_any.item(): + # self._inner_loop_code() + # self._after_inner_loop() + + return ( + self.state.batched_hyps, + self.state.alignments, + self.state.last_decoder_state, + ) + + @classmethod + def _create_outer_while_loop_kernel(cls): + """ + Creates a kernel that evaluates whether to enter the outer loop body (not all hypotheses are decoded). + Condition: while(active_mask_any). + """ + kernel_string = r"""\ + typedef __device_builtin__ unsigned long long cudaGraphConditionalHandle; + + extern "C" __device__ __cudart_builtin__ void cudaGraphSetConditional(cudaGraphConditionalHandle handle, unsigned int value); + + extern "C" __global__ + void outer_loop_labels_conditional(cudaGraphConditionalHandle handle, const bool *active_mask_any) + { + cudaGraphSetConditional(handle, *active_mask_any); + } + """ + return run_nvrtc(kernel_string, b"outer_loop_labels_conditional", cls.CUDA_PROGRAM_NAME) + + @classmethod + def _create_inner_while_loop_kernel(cls): + """ + Creates a kernel that evaluates whether to enter the inner loop body (not all non-blank labels found). + Condition: while(advance_mask_any). + """ + kernel_string = r"""\ + typedef __device_builtin__ unsigned long long cudaGraphConditionalHandle; + + extern "C" __device__ __cudart_builtin__ void cudaGraphSetConditional(cudaGraphConditionalHandle handle, unsigned int value); + + extern "C" __global__ + void inner_find_non_blank_conditional(cudaGraphConditionalHandle handle, const bool *advance_mask_any) + { + cudaGraphSetConditional(handle, *advance_mask_any); + } + """ + return run_nvrtc(kernel_string, b"inner_find_non_blank_conditional", cls.CUDA_PROGRAM_NAME) + + def _graph_reinitialize( + self, encoder_output_projected: torch.Tensor, encoder_output_length: torch.Tensor, + ): + batch_size, max_time, encoder_dim = encoder_output_projected.shape + + self.state = LoopLabelsState( + batch_size=batch_size, + max_time=max(max_time, self.INITIAL_MAX_TIME), + encoder_dim=encoder_dim, + max_symbols=self.max_symbols, + device=encoder_output_projected.device, + float_dtype=encoder_output_projected.dtype, + logits_dim=self.joint.num_classes_with_blank, + preserve_alignments=self.preserve_alignments, + preserve_frame_confidence=self.preserve_frame_confidence, + ) + + self.state.last_decoder_state = self.decoder.initialize_state(encoder_output_projected) + self.state.decoder_state = self.decoder.initialize_state(encoder_output_projected) + decoder_output, *_ = self.decoder.predict( + self.state.labels.unsqueeze(1), self.state.decoder_state, add_sos=False, batch_size=self.state.batch_size + ) + # to avoid recalculation of joint projection, store decoder output in state + self.state.decoder_output = self.joint.project_prednet(decoder_output) + + # Always create a new stream, because the per-thread default stream disallows stream capture to a graph. + stream_for_graph = torch.cuda.Stream(self.state.device) + self.graph = torch.cuda.CUDAGraph() + with torch.cuda.stream(stream_for_graph), torch.inference_mode(), torch.cuda.graph( + self.graph, stream=stream_for_graph + ): + self._before_outer_loop() + + capture_status, _, graph, _, _ = cu_call( + cudart.cudaStreamGetCaptureInfo(torch.cuda.current_stream(device=self.state.device).cuda_stream) + ) + assert capture_status == cudart.cudaStreamCaptureStatus.cudaStreamCaptureStatusActive + + # capture: while self.active_mask_any: + (outer_loop_conditional_handle,) = cu_call(cudart.cudaGraphConditionalHandleCreate(graph, 0, 0)) + outer_loop_kernel = self._create_outer_while_loop_kernel() + active_mask_any_ptr = np.array([self.state.active_mask_any.data_ptr()], dtype=np.uint64) + outer_loop_args = np.array( + [outer_loop_conditional_handle.getPtr(), active_mask_any_ptr.ctypes.data], dtype=np.uint64, + ) + # loop while there are active utterances + with with_conditional_node( + outer_loop_kernel, outer_loop_args, outer_loop_conditional_handle, device=self.state.device + ): + self._before_inner_loop_get_decoder_output() + self._before_inner_loop_get_joint_output() + # capture: while self.advance_mask_any.item(): + inner_while_loop_kernel = self._create_inner_while_loop_kernel() + (inner_loop_conditional_handle,) = cu_call(cudart.cudaGraphConditionalHandleCreate(graph, 0, 0)) + advance_mask_any_ptr = np.array([self.state.advance_mask_any.data_ptr()], dtype=np.uint64) + inner_loop_args = np.array( + [inner_loop_conditional_handle.getPtr(), advance_mask_any_ptr.ctypes.data,], dtype=np.uint64, + ) + with with_conditional_node( + inner_while_loop_kernel, inner_loop_args, inner_loop_conditional_handle, device=self.state.device + ): + self._inner_loop_code() + self._after_inner_loop() + + def _before_outer_loop(self): + """Clear state and compute initial active mask""" + self.state.batched_hyps.clear_() + if self.state.alignments is not None: + self.state.alignments.clear_() + + # initial state + self.decoder.batch_replace_states_all( + src_states=self.decoder.initialize_state(self.state.encoder_output_projected), + dst_states=self.state.decoder_state, + ) + # last found labels - initially () symbol + self.state.labels.fill_(self._SOS) + self.state.scores.fill_(0.0) + + # time indices + self.state.time_indices.fill_(0) + self.state.safe_time_indices.fill_(0) # safe time indices: guaranteed to be < encoder_output_length + self.state.time_indices_current_labels.fill_(0) + torch.sub(self.state.encoder_output_length, 1, out=self.state.last_timesteps) + + # masks for utterances in batch + # same as: active_mask = self.encoder_output_length > 0 + torch.greater(self.state.encoder_output_length, 0, out=self.state.active_mask) + + # for storing the last state we need to know what elements became "inactive" on this step + # same as: self.active_mask_any = active_mask.any() + torch.any(self.state.active_mask, out=self.state.active_mask_any) + + def _before_inner_loop_get_decoder_output(self): + """Get decoder output""" + # stage 1: get decoder (prediction network) output + decoder_output, new_state, *_ = self.decoder.predict( + self.state.labels.unsqueeze(1), self.state.decoder_state, add_sos=False, batch_size=self.state.batch_size + ) + self.decoder.batch_replace_states_all(src_states=new_state, dst_states=self.state.decoder_state) + decoder_output_projected = self.joint.project_prednet(decoder_output) # do not recalculate joint projection + self.state.decoder_output.copy_(decoder_output_projected) + + def _before_inner_loop_get_joint_output(self): + """Get Joint output after decoder output, prepare inner loop to search for all next non-blank labels""" + # stage 2: get joint output, iteratively seeking for non-blank labels + # blank label in `labels` tensor means "end of hypothesis" (for this index) + self.state.active_mask_prev.copy_(self.state.active_mask, non_blocking=True) + logits = ( + self.joint.joint_after_projection( + self.state.encoder_output_projected[self.state.batch_indices, self.state.safe_time_indices].unsqueeze( + 1 + ), + self.state.decoder_output, + ) + .squeeze(1) + .squeeze(1) + ) + # same as: scores, labels = logits.max(-1) + torch.max(logits, dim=-1, out=(self.state.scores, self.state.labels)) + + # search for non-blank labels using joint, advancing time indices for blank labels + # checking max_symbols is not needed, since we already forced advancing time indices for such cases + torch.eq(self.state.labels, self._blank_index, out=self.state.blank_mask) + # blank_mask = self.labels == self._blank_index + self.state.time_indices_current_labels.copy_(self.state.time_indices, non_blocking=True) + if self.state.alignments is not None: + self.state.alignments.add_results_masked_no_checks_( + active_mask=self.state.active_mask, + time_indices=self.state.time_indices_current_labels, + logits=logits if self.preserve_alignments else None, + labels=self.state.labels if self.preserve_alignments else None, + confidence=self._get_confidence_tensor(F.log_softmax(logits, dim=-1)) + if self.preserve_frame_confidence + else None, + ) + + # advance_mask is a mask for current batch for searching non-blank labels; + # each element is True if non-blank symbol is not yet found AND we can increase the time index + self.state.time_indices.add_(self.state.blank_mask) + torch.minimum(self.state.time_indices, self.state.last_timesteps, out=self.state.safe_time_indices) + torch.less(self.state.time_indices, self.state.encoder_output_length, out=self.state.active_mask) + torch.logical_and(self.state.active_mask, self.state.blank_mask, out=self.state.advance_mask) + + # inner loop: find next non-blank labels (if exist) + # same as: self.advance_mask_any = advance_mask.any() + torch.any(self.state.advance_mask, out=self.state.advance_mask_any) + + def _inner_loop_code(self): + """Find next non-blank labels - one iteration""" + # same as: time_indices_current_labels[advance_mask] = time_indices[advance_mask], but non-blocking + # store current time indices to use further for storing the results + torch.where( + self.state.advance_mask, + self.state.time_indices, + self.state.time_indices_current_labels, + out=self.state.time_indices_current_labels, + ) + logits = ( + self.joint.joint_after_projection( + self.state.encoder_output_projected[self.state.batch_indices, self.state.safe_time_indices].unsqueeze( + 1 + ), + self.state.decoder_output, + ) + .squeeze(1) + .squeeze(1) + ) + # get labels (greedy) and scores from current logits, replace labels/scores with new + # labels[advance_mask] are blank, and we are looking for non-blank labels + more_scores, more_labels = logits.max(-1) + # same as: labels[advance_mask] = more_labels[advance_mask], but non-blocking + torch.where(self.state.advance_mask, more_labels, self.state.labels, out=self.state.labels) + # same as: scores[advance_mask] = more_scores[advance_mask], but non-blocking + torch.where(self.state.advance_mask, more_scores, self.state.scores, out=self.state.scores) + + if self.state.alignments is not None: + self.state.alignments.add_results_masked_no_checks_( + active_mask=self.state.advance_mask, + time_indices=self.state.time_indices_current_labels, + logits=logits if self.preserve_alignments else None, + labels=more_labels if self.preserve_alignments else None, + confidence=self._get_confidence_tensor(F.log_softmax(logits, dim=-1)) + if self.preserve_frame_confidence + else None, + ) + + # blank_mask = self.labels == self._blank_index + torch.eq(self.state.labels, self._blank_index, out=self.state.blank_mask) + # self.time_indices += self.blank_mask + self.state.time_indices.add_(self.state.blank_mask) + + torch.minimum(self.state.time_indices, self.state.last_timesteps, out=self.state.safe_time_indices) + torch.less(self.state.time_indices, self.state.encoder_output_length, out=self.state.active_mask) + torch.logical_and(self.state.active_mask, self.state.blank_mask, out=self.state.advance_mask) + torch.any(self.state.advance_mask, out=self.state.advance_mask_any) + + def _after_inner_loop(self): + """Store hypotheses, state for finished hypotheses, avoid looping""" + # stage 3: filter labels and state, store hypotheses + # select states for hyps that became inactive (is it necessary?) + # this seems to be redundant, but used in the `loop_frames` output + torch.ne(self.state.active_mask, self.state.active_mask_prev, out=self.state.became_inactive_mask) + self.decoder.batch_replace_states_mask( + src_states=self.state.decoder_state, + dst_states=self.state.last_decoder_state, + mask=self.state.became_inactive_mask, + ) + + self.state.batched_hyps.add_results_masked_no_checks_( + self.state.active_mask, self.state.labels, self.state.time_indices_current_labels, self.state.scores, + ) + + # stage 4: to avoid looping, go to next frame after max_symbols emission + # if labels are non-blank (not end-of-utterance), check that last observed timestep with label: + # if it is equal to the current time index, and number of observations is >= max_symbols, force blank + force_blank_mask = torch.logical_and( + self.state.active_mask, + torch.logical_and( + torch.logical_and( + self.state.labels != self._blank_index, + self.state.batched_hyps.last_timestep_lasts >= self.max_symbols, + ), + self.state.batched_hyps.last_timestep == self.state.time_indices, + ), + ) + self.state.time_indices.add_(force_blank_mask) # emit blank => advance time indices + # update safe_time_indices, non-blocking + torch.minimum(self.state.time_indices, self.state.last_timesteps, out=self.state.safe_time_indices) + # same as: active_mask = time_indices < encoder_output_length + torch.less(self.state.time_indices, self.state.encoder_output_length, out=self.state.active_mask) + torch.any(self.state.active_mask, out=self.state.active_mask_any) + + def __call__( + self, x: torch.Tensor, out_len: torch.Tensor, + ) -> Tuple[rnnt_utils.BatchedHyps, Optional[rnnt_utils.BatchedAlignments], Any]: + if self.use_cuda_graphs and x.device.type == "cuda": + return self.loop_labels_cuda_graphs(encoder_output=x, encoder_output_length=out_len) + + return self.loop_labels_torch(encoder_output=x, encoder_output_length=out_len) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/spectr_augment.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/spectr_augment.py new file mode 100644 index 0000000..9b379ce --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/spectr_augment.py @@ -0,0 +1,163 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import random + +import numpy as np +import torch +import torch.nn as nn + +from nemo.core.classes import Typing, typecheck +from nemo.core.neural_types import LengthsType, NeuralType, SpectrogramType + + +class SpecAugment(nn.Module, Typing): + """ + Zeroes out(cuts) random continuous horisontal or + vertical segments of the spectrogram as described in + SpecAugment (https://arxiv.org/abs/1904.08779). + + params: + freq_masks - how many frequency segments should be cut + time_masks - how many time segments should be cut + freq_width - maximum number of frequencies to be cut in one segment + time_width - maximum number of time steps to be cut in one segment. + Can be a positive integer or a float value in the range [0, 1]. + If positive integer value, defines maximum number of time steps + to be cut in one segment. + If a float value, defines maximum percentage of timesteps that + are cut adaptively. + """ + + @property + def input_types(self): + """Returns definitions of module input types + """ + return { + "input_spec": NeuralType(('B', 'D', 'T'), SpectrogramType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + + @property + def output_types(self): + """Returns definitions of module output types + """ + return {"augmented_spec": NeuralType(('B', 'D', 'T'), SpectrogramType())} + + def __init__( + self, freq_masks=0, time_masks=0, freq_width=10, time_width=10, rng=None, mask_value=0.0, + ): + super().__init__() + + self._rng = random.Random() if rng is None else rng + + self.freq_masks = freq_masks + self.time_masks = time_masks + + self.freq_width = freq_width + self.time_width = time_width + + self.mask_value = mask_value + + if isinstance(time_width, int): + self.adaptive_temporal_width = False + else: + if time_width > 1.0 or time_width < 0.0: + raise ValueError("If `time_width` is a float value, must be in range [0, 1]") + + self.adaptive_temporal_width = True + + @typecheck() + @torch.no_grad() + def forward(self, input_spec, length): + batch_size, num_freq_bins, _ = input_spec.shape + # Move lengths to CPU before repeated indexing + lengths_cpu = length.cpu().numpy() + # Generate a numpy boolean mask. `True` elements represent where the input spec will be augmented. + fill_mask: np.array = np.full(shape=input_spec.shape, fill_value=False) + freq_start_upper_bound = num_freq_bins - self.freq_width + # Choose different mask ranges for each element of the batch + for idx in range(batch_size): + # Set freq masking + for _ in range(self.freq_masks): + start = self._rng.randint(0, freq_start_upper_bound) + width = self._rng.randint(0, self.freq_width) + fill_mask[idx, start : start + width, :] = True + + # Derive time width, sometimes based percentage of input length. + if self.adaptive_temporal_width: + time_max_width = max(1, int(lengths_cpu[idx] * self.time_width)) + else: + time_max_width = self.time_width + time_start_upper_bound = max(1, lengths_cpu[idx] - time_max_width) + + # Set time masking + for _ in range(self.time_masks): + start = self._rng.randint(0, time_start_upper_bound) + width = self._rng.randint(0, time_max_width) + fill_mask[idx, :, start : start + width] = True + # Bring the mask to device and fill spec + fill_mask = torch.from_numpy(fill_mask).to(input_spec.device) + masked_spec = input_spec.masked_fill(mask=fill_mask, value=self.mask_value) + return masked_spec + + +class SpecCutout(nn.Module, Typing): + """ + Zeroes out(cuts) random rectangles in the spectrogram + as described in (https://arxiv.org/abs/1708.04552). + + params: + rect_masks - how many rectangular masks should be cut + rect_freq - maximum size of cut rectangles along the frequency dimension + rect_time - maximum size of cut rectangles along the time dimension + """ + + @property + def input_types(self): + """Returns definitions of module input types + """ + return {"input_spec": NeuralType(('B', 'D', 'T'), SpectrogramType())} + + @property + def output_types(self): + """Returns definitions of module output types + """ + return {"augmented_spec": NeuralType(('B', 'D', 'T'), SpectrogramType())} + + def __init__(self, rect_masks=0, rect_time=5, rect_freq=20, rng=None): + super(SpecCutout, self).__init__() + + self._rng = random.Random() if rng is None else rng + + self.rect_masks = rect_masks + self.rect_time = rect_time + self.rect_freq = rect_freq + + @typecheck() + @torch.no_grad() + def forward(self, input_spec): + sh = input_spec.shape + + for idx in range(sh[0]): + for i in range(self.rect_masks): + rect_x = self._rng.randint(0, sh[1] - self.rect_freq) + rect_y = self._rng.randint(0, sh[2] - self.rect_time) + + w_x = self._rng.randint(0, self.rect_freq) + w_y = self._rng.randint(0, self.rect_time) + + input_spec[idx, rect_x : rect_x + w_x, rect_y : rect_y + w_y] = 0.0 + + return input_spec diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/squeezeformer_modules.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/squeezeformer_modules.py new file mode 100644 index 0000000..ff2cf7c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/squeezeformer_modules.py @@ -0,0 +1,262 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import torch +from torch import nn as nn +from torch.nn import LayerNorm + +from nemo.collections.asr.parts.submodules.conformer_modules import ConformerConvolution, ConformerFeedForward +from nemo.collections.asr.parts.submodules.multi_head_attention import ( + MultiHeadAttention, + RelPositionMultiHeadAttention, +) +from nemo.collections.common.parts import adapter_modules +from nemo.core.classes.mixins import AccessMixin +from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin + +__all__ = ['SqueezeformerLayer', 'ConformerFeedForward', 'SqueezeformerLayer'] + + +class ScaleBiasLayer(torch.nn.Module): + """ + Computes an affine transformation y = x * scale + bias, either learned via adaptive weights, or fixed. + Efficient alternative to LayerNorm where we can avoid computing the mean and variance of the input, and + just rescale the output of the previous layer. + + Args: + d_model (int): input dimension of layer. + adaptive_scale (bool): whether to learn the affine transformation parameters or not. If set to False, + the scale is fixed to 1 and bias to 0, effectively performing a No-Op on the input. + This is done for export compatibility. + """ + + def __init__(self, d_model: int, adaptive_scale: bool): + super().__init__() + self.adaptive_scale = adaptive_scale + if adaptive_scale: + self.scale = nn.Parameter(torch.ones(d_model)) + self.bias = nn.Parameter(torch.zeros(d_model)) + else: + self.register_buffer('scale', torch.ones(d_model), persistent=True) + self.register_buffer('bias', torch.zeros(d_model), persistent=True) + + def forward(self, x): + scale = self.scale.view(1, 1, -1) + bias = self.bias.view(1, 1, -1) + return x * scale + bias + + +class SqueezeformerLayer(torch.nn.Module, AdapterModuleMixin, AccessMixin): + """A single block of the Squeezeformer encoder. + + Args: + d_model (int): input dimension of MultiheadAttentionMechanism and PositionwiseFeedForward + d_ff (int): hidden dimension of PositionwiseFeedForward + n_heads (int): number of heads for multi-head attention + conv_kernel_size (int): kernel size for depthwise convolution in convolution module + dropout (float): dropout probabilities for linear layers + dropout_att (float): dropout probabilities for attention distributions + adaptive_scale (bool): Whether to scale the inputs to each component by affine `scale` and `bias` layer. + Or use a fixed scale=1 and bias=0. + """ + + def __init__( + self, + d_model, + d_ff, + self_attention_model='rel_pos', + n_heads=4, + conv_kernel_size=31, + conv_norm_type='batch_norm', + dropout=0.1, + dropout_att=0.1, + pos_bias_u=None, + pos_bias_v=None, + adaptive_scale: bool = True, + ): + super().__init__() + + self.self_attention_model = self_attention_model + self.n_heads = n_heads + self.fc_factor = 1.0 + self.adaptive_scale = adaptive_scale + + # first feed forward module + self.norm_feed_forward1 = LayerNorm(d_model) + self.feed_forward1 = ConformerFeedForward(d_model=d_model, d_ff=d_ff, dropout=dropout) + self.feed_forward1_scale = ScaleBiasLayer(d_model=d_model, adaptive_scale=adaptive_scale) + + # convolution module + self.norm_conv = LayerNorm(d_model) + self.conv = ConformerConvolution( + d_model=d_model, kernel_size=conv_kernel_size, norm_type=conv_norm_type, pointwise_activation='swish' + ) + self.conv_scale = ScaleBiasLayer(d_model=d_model, adaptive_scale=adaptive_scale) + + # multi-headed self-attention module + self.norm_self_att = LayerNorm(d_model) + if self_attention_model == 'rel_pos': + self.self_attn = RelPositionMultiHeadAttention( + n_head=n_heads, n_feat=d_model, dropout_rate=dropout_att, pos_bias_u=pos_bias_u, pos_bias_v=pos_bias_v + ) + elif self_attention_model == 'abs_pos': + self.self_attn = MultiHeadAttention(n_head=n_heads, n_feat=d_model, dropout_rate=dropout_att) + else: + raise ValueError( + f"'{self_attention_model}' is not not a valid value for 'self_attention_model', " + f"valid values can be from ['rel_pos', 'abs_pos']" + ) + self.self_attn_scale = ScaleBiasLayer(d_model=d_model, adaptive_scale=adaptive_scale) + + # second feed forward module + self.norm_feed_forward2 = LayerNorm(d_model) + self.feed_forward2 = ConformerFeedForward(d_model=d_model, d_ff=d_ff, dropout=dropout) + self.feed_forward2_scale = ScaleBiasLayer(d_model=d_model, adaptive_scale=adaptive_scale) + + self.dropout = nn.Dropout(dropout) + # self.norm_out = LayerNorm(d_model) + + # initialize parameters properly + self.reset_parameters() + + def forward(self, x, att_mask=None, pos_emb=None, pad_mask=None): + """ + Args: + x (torch.Tensor): input signals (B, T, d_model) + att_mask (torch.Tensor): attention masks(B, T, T) + pos_emb (torch.Tensor): (L, 1, d_model) + pad_mask (torch.tensor): padding mask + Returns: + x (torch.Tensor): (B, T, d_model) + """ + residual = x + + x = self.self_attn_scale(x) + if self.self_attention_model == 'rel_pos': + x = self.self_attn(query=x, key=x, value=x, mask=att_mask, pos_emb=pos_emb) + elif self.self_attention_model == 'abs_pos': + x = self.self_attn(query=x, key=x, value=x, mask=att_mask) + else: + x = None + x = residual + self.dropout(x) + x = self.norm_self_att(x) + residual = x + + if self.is_adapter_available(): + # Call the MHA adapters + pack_ip = { + 'x': residual, + 'loc': 'mha', + 'att_mask': att_mask, + 'pos_emb': pos_emb, + } + pack_ip = self.forward_enabled_adapters(pack_ip) + x = pack_ip['x'] + + x = self.feed_forward1_scale(x) + x = self.feed_forward1(x) + x = residual + self.dropout(x) * self.fc_factor + x = self.norm_feed_forward1(x) + residual = x + + x = self.conv_scale(x) + x = self.conv(x, pad_mask) + x = residual + self.dropout(x) + x = self.norm_conv(x) + residual = x + + x = self.feed_forward2_scale(x) + x = self.feed_forward2(x) + x = residual + self.dropout(x) * self.fc_factor + x = self.norm_feed_forward2(x) + + if self.is_adapter_available(): + # Call the adapters + pack_ip = { + 'x': x, + 'loc': 'post', + } + pack_ip = self.forward_enabled_adapters(pack_ip) + x = pack_ip['x'] + + if self.is_access_enabled(getattr(self, "model_guid", None)) and self.access_cfg.get( + 'save_encoder_tensors', False + ): + self.register_accessible_tensor(name='encoder', tensor=x) + + return x + + def forward_single_enabled_adapter_( + self, + input: dict, + adapter_module: torch.nn.Module, + *, + adapter_name: str, + adapter_strategy: 'nemo.core.classes.mixins.adapter_mixin_strategies.AbstractAdapterStrategy', + ): + """ + Perform the forward step of a single adapter module on some input data. + + **Note**: Subclasses can override this method to accommodate more complicate adapter forward steps. + + Args: + input: Dictionary of packed tensors. The dict should contain at least + `x`: output tensor + `loc`: Semantic location in module where this adapter was called + `att_mask`: Optional, Attention mask + `pos_emb`: Optional, Positional Embedding for Relative Positional Encoding. + The output tensor of the calling module is the input to the first adapter, whose output + is then chained to the next adapter until all adapters are consumed. + adapter_module: The adapter module that is currently required to perform the forward pass. + adapter_name: The resolved name of the adapter that is undergoing the current forward pass. + adapter_strategy: A subclass of `AbstractAdapterStrategy`, that determines how the + output of the adapter should be merged with the input, or if it should be merged at all. + + Returns: + The result tensor, after the current active adapter has finished its forward pass. + """ + # (input: torch.Tensor, adapter: torch.nn.Module, *, module: 'AdapterModuleMixin') + x = input['x'] + loc = input['loc'] + att_mask = input.get('att_mask', None) + pos_emb = input.get('pos_emb', None) + + if isinstance(adapter_module, adapter_modules.LinearAdapter) and loc == 'post': + output = adapter_strategy(x, adapter_module, module=self) + + elif isinstance(adapter_module, MultiHeadAttention) and loc == 'mha': + if self.self_attention_model == 'rel_pos': + x = dict(query=x, key=x, value=x, mask=att_mask, pos_emb=pos_emb) + output = adapter_strategy(x, adapter_module, module=self) + + elif self.self_attention_model == 'abs_pos': + x = dict(query=x, key=x, value=x, mask=att_mask) + output = adapter_strategy(x, adapter_module, module=self) + + else: + raise ValueError(f"Unsupported value of self_attention_model , provided {self.self_attention_model}!") + + else: + # No adapter compatible, skip + output = x + + input['x'] = output + + return input + + def reset_parameters(self): + # Used for Squeezeformer initialization only + self.feed_forward1.reset_parameters_ff() + self.feed_forward2.reset_parameters_ff() + self.conv.reset_parameters_conv() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/ssl_quantizers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/ssl_quantizers.py new file mode 100644 index 0000000..944589e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/ssl_quantizers.py @@ -0,0 +1,200 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +from torch import nn + +from nemo.collections.asr.parts.submodules.jasper import jasper_activations +from nemo.core import NeuralModule +from nemo.core.neural_types import EncodedRepresentation, LossType, NeuralType + + +class GumbelVectorQuantizer(NeuralModule): + def __init__( + self, + dim, + num_vars, + temp, + groups, + combine_groups, + vq_dim, + time_first, + activation="gelu", + weight_proj_depth=1, + weight_proj_factor=1, + ): + """Vector quantization using gumbel softmax + + Args: + dim: input dimension (channels) + num_vars: number of quantized vectors per group + temp: temperature for training. this should be a tuple of 3 elements: (start, stop, decay factor) + groups: number of groups for vector quantization + combine_groups: whether to use the vectors for all groups + vq_dim: dimensionality of the resulting quantized vector + time_first: if true, expect input in BxTxC format, otherwise in BxCxT + activation: what activation to use (should be a module). this is only used if weight_proj_depth is > 1 + weight_proj_depth: number of layers (with activation in between) to project input before computing logits + weight_proj_factor: this is used only if weight_proj_depth is > 1. scales the inner dimensionality of + projections by this factor + """ + super().__init__() + + self.groups = groups + self.combine_groups = combine_groups + self.input_dim = dim + self.num_vars = num_vars + self.time_first = time_first + + assert vq_dim % groups == 0, f"dim {vq_dim} must be divisible by groups {groups} for concatenation" + + var_dim = vq_dim // groups + num_groups = groups if not combine_groups else 1 + + self.vars = nn.Parameter(torch.FloatTensor(1, num_groups * num_vars, var_dim)) + nn.init.uniform_(self.vars) + + if weight_proj_depth > 1: + activation = jasper_activations["gelu"] + + def block(input_dim, output_dim): + return nn.Sequential(nn.Linear(input_dim, output_dim), activation) + + inner_dim = self.input_dim * weight_proj_factor + self.weight_proj = nn.Sequential( + *[block(self.input_dim if i == 0 else inner_dim, inner_dim) for i in range(weight_proj_depth - 1)], + nn.Linear(inner_dim, groups * num_vars), + ) + else: + self.weight_proj = nn.Linear(self.input_dim, groups * num_vars) + nn.init.normal_(self.weight_proj.weight, mean=0, std=1) + nn.init.zeros_(self.weight_proj.bias) + + assert len(temp) == 3, "Quantize temperature should be a tuple of 3 elements: (start, stop, decay factor)" + + self.max_temp, self.min_temp, self.temp_decay = temp + self.curr_temp = self.max_temp + self.codebook_indices = None + + def set_num_updates(self, num_updates): + self.curr_temp = max(self.max_temp * self.temp_decay ** num_updates, self.min_temp) + + def get_codebook_indices(self): + if self.codebook_indices is None: + from itertools import product + + p = [range(self.num_vars)] * self.groups + inds = list(product(*p)) + self.codebook_indices = torch.tensor(inds, dtype=torch.long, device=self.vars.device).flatten() + + if not self.combine_groups: + self.codebook_indices = self.codebook_indices.view(self.num_vars ** self.groups, -1) + for b in range(1, self.groups): + self.codebook_indices[:, b] += self.num_vars * b + self.codebook_indices = self.codebook_indices.flatten() + return self.codebook_indices + + def sample_from_codebook(self, b, n): + indices = self.get_codebook_indices() + indices = indices.view(-1, self.groups) + cb_size = indices.size(0) + assert n < cb_size, f"sample size {n} is greater than size of codebook {cb_size}" + sample_idx = torch.randint(low=0, high=cb_size, size=(b * n,)) + indices = indices[sample_idx] + + z = self.vars.squeeze(0).index_select(0, indices.flatten()).view(b, n, -1) + return z + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + if self.time_first: + return {"x": NeuralType(('B', 'T', 'D'), EncodedRepresentation())} + return {"x": NeuralType(('B', 'D', 'T'), EncodedRepresentation())} + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + if self.time_first: + return { + "x": NeuralType(('B', 'T', 'D'), EncodedRepresentation()), + "quantize_prob_ppl": NeuralType(elements_type=LossType()), + } + return { + "x": NeuralType(('B', 'D', 'T'), EncodedRepresentation()), + "quantize_prob_ppl": NeuralType(elements_type=LossType()), + } + + def forward(self, x, return_ids=False): + + if not self.time_first: + x = x.transpose(1, 2) + + bsz, tsz, fsz = x.shape + x = x.reshape(-1, fsz) + x = self.weight_proj(x) + x = x.view(bsz * tsz * self.groups, -1) + + _, k = x.max(-1) + hard_x = x.new_zeros(*x.shape).scatter_(-1, k.view(-1, 1), 1.0).view(bsz * tsz, self.groups, -1) + + # Calculate quantize prob perplexity + num_vars = self.num_vars * self.groups + avg_probs = torch.softmax(x.view(bsz * tsz, self.groups, -1).float(), dim=-1).mean(dim=0) + quantize_prob_ppl = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-7), dim=-1)).sum() + quantize_prob_ppl = (num_vars - quantize_prob_ppl) / num_vars + + if self.training: + x = F.gumbel_softmax(x.float(), tau=self.curr_temp, hard=True).type_as(x) + else: + x = hard_x + + x = x.view(bsz * tsz, -1) + + vars = self.vars + if self.combine_groups: + vars = vars.repeat(1, self.groups, 1) + + x = x.unsqueeze(-1) * vars + x = x.view(bsz * tsz, self.groups, self.num_vars, -1) + x = x.sum(-2) + x = x.view(bsz, tsz, -1) + + cur_codebook_temp = self.curr_temp + + if not self.time_first: + x = x.transpose(1, 2) # BTC -> BCT + + if return_ids: + hard_x_max = hard_x.argmax(-1).reshape(bsz, tsz, -1) + # BxTxG + + # create single id from multiple group ids + target_ids = hard_x.new_zeros(bsz, tsz).long() + + for i in range(self.groups): + target_ids *= self.num_vars + target_ids += hard_x_max[:, :, i] + + return x, quantize_prob_ppl, cur_codebook_temp, target_ids + else: + return x, quantize_prob_ppl, cur_codebook_temp diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/stateless_net.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/stateless_net.py new file mode 100644 index 0000000..7581fdc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/stateless_net.py @@ -0,0 +1,125 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch + + +class StatelessNet(torch.nn.Module): + """ + Helper class used in transducer models with stateless decoders. This stateless + simply outputs embedding or concatenated embeddings for the input label[s], + depending on the configured context size. + + Args: + context_size: history context size for the stateless decoder network. Could be any positive integer. We recommend setting this as 2. + vocab_size: total vocabulary size. + emb_dim: total embedding size of the stateless net output. + blank_idx: index for the blank symbol for the transducer model. + normalization_mode: normalization run on the output embeddings. Could be either 'layer' or None. We recommend using 'layer' to stabilize training. + dropout: dropout rate on the embedding outputs. + """ + + def __init__(self, context_size, vocab_size, emb_dim, blank_idx, normalization_mode, dropout): + super().__init__() + assert context_size > 0 + self.context_size = context_size + self.vocab_size = vocab_size + self.emb_dim = emb_dim + self.dropout = torch.nn.Dropout(dropout) + self.norm = torch.nn.Identity() + if normalization_mode == 'layer': + self.norm = torch.nn.LayerNorm(emb_dim, elementwise_affine=False) + + embeds = [] + for i in range(self.context_size): + # We use different embedding matrices for different context positions. + # In this list, a smaller index means more recent history word. + # We assign more dimensions for the most recent word in the history. + # The detailed method is, we first allocate half the embedding-size + # to the most recent history word, and then allocate the remaining + # dimensions evenly among all history contexts. E.g. if total embedding + # size is 200, and context_size is 2, then we allocate 150 dimensions + # to the last word, and 50 dimensions to the second-to-last word. + if i != 0: + embed_size = emb_dim // 2 // self.context_size + else: + embed_size = emb_dim - (emb_dim // 2 // self.context_size) * (self.context_size - 1) + + embed = torch.nn.Embedding(vocab_size + 1, embed_size, padding_idx=blank_idx) + embeds.append(embed) + + self.embeds = torch.nn.ModuleList(embeds) + self.blank_idx = blank_idx + + def forward( + self, y: Optional[torch.Tensor] = None, state: Optional[List[torch.Tensor]] = None, + ): + """ + Although this is a *stateless* net, we use the "state" parameter to + pass in the previous labels, unlike LSTMs where state would represent + hidden activations of the network. + + Args: + y: a Integer tensor of shape B x U. + state: a list of 1 tensor in order to be consistent with the stateful + decoder interface, and the element is a tensor of shape [B x context-length]. + + Returns: + The return dimension of this function's output is B x U x D, with D being the total embedding dim. + """ + outs = [] + + [B, U] = y.shape + appended_y = y + if state != None: + appended_y = torch.concat([state[0], y], axis=1) + context_size = appended_y.shape[1] + + if context_size < self.context_size: + # This is the case at the beginning of an utterance where we have + # seen less words than context_size. In this case, we need to pad + # it to the right length. + padded_state = torch.ones([B, self.context_size], dtype=torch.long, device=y.device) * self.blank_idx + padded_state[:, self.context_size - context_size :] = appended_y + elif context_size == self.context_size + 1: + padded_state = appended_y[:, 1:] + # This is the case where the previous state already has reached context_size. + # We need to truncate the history by omitting the 0'th token. + else: + # Context has just the right size. Copy directly. + padded_state = appended_y + + for i in range(self.context_size): + out = self.embeds[i](padded_state[:, self.context_size - 1 - i : self.context_size - i]) + outs.append(out) + else: + for i in range(self.context_size): + out = self.embeds[i](y) + + if i != 0: + out[:, i:, :] = out[ + :, :-i, : + ].clone() # needs clone() here or it might complain about src and dst mem location have overlaps. + out[:, :i, :] *= 0.0 + outs.append(out) + + out = self.dropout(torch.concat(outs, axis=-1)) + out = self.norm(out) + + state = None + if y is not None: + state = [appended_y[:, appended_y.shape[1] - self.context_size + 1 :]] + return out, state diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/subsampling.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/subsampling.py new file mode 100644 index 0000000..068cd36 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/subsampling.py @@ -0,0 +1,693 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +import torch +import torch.nn as nn +from torch.nn import LayerNorm + +from nemo.collections.asr.parts.submodules.causal_convs import CausalConv1D, CausalConv2D +from nemo.utils import logging + + +class StackingSubsampling(torch.nn.Module): + """Stacking subsampling which simply stacks consecutive frames to reduce the sampling rate + Args: + subsampling_factor (int): The subsampling factor + feat_in (int): size of the input features + feat_out (int): size of the output features + norm (bool): whether to use an MLP layer after the stacking along with normalization. default is False. + """ + + def __init__(self, subsampling_factor, feat_in, feat_out, norm=False): + super(StackingSubsampling, self).__init__() + self.subsampling_factor = subsampling_factor + self.proj_out = torch.nn.Linear(subsampling_factor * feat_in, feat_out) + if norm: + self.pre_norm = LayerNorm(feat_in) + else: + self.pre_norm = None + + def get_sampling_frames(self): + return self.subsampling_factor + + def get_streaming_cache_size(self): + return 0 + + def forward(self, x, lengths): + b, t, h = x.size() + pad_size = (self.subsampling_factor - (t % self.subsampling_factor)) % self.subsampling_factor + x = torch.nn.functional.pad(x, (0, 0, 0, pad_size)) + if self.pre_norm is not None: + x = self.pre_norm(x) + _, t, _ = x.size() + x = torch.reshape(x, (b, t // self.subsampling_factor, h * self.subsampling_factor)) + x = self.proj_out(x) + lengths = torch.div(lengths + pad_size, self.subsampling_factor, rounding_mode='floor') + return x, lengths + + +class ConvSubsampling(torch.nn.Module): + """Convolutional subsampling which supports VGGNet and striding approach introduced in: + VGGNet Subsampling: Transformer-transducer: end-to-end speech recognition with self-attention (https://arxiv.org/pdf/1910.12977.pdf) + Striding Subsampling: "Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition" by Linhao Dong et al. (https://ieeexplore.ieee.org/document/8462506) + Args: + subsampling (str): The subsampling technique from {"vggnet", "striding", "dw-striding"} + subsampling_factor (int): The subsampling factor which should be a power of 2 + subsampling_conv_chunking_factor (int): Input chunking factor which can be -1 (no chunking) + 1 (auto) or a power of 2. Default is 1 + feat_in (int): size of the input features + feat_out (int): size of the output features + conv_channels (int): Number of channels for the convolution layers. + activation (Module): activation function, default is nn.ReLU() + """ + + def __init__( + self, + subsampling, + subsampling_factor, + feat_in, + feat_out, + conv_channels, + subsampling_conv_chunking_factor=1, + activation=nn.ReLU(), + is_causal=False, + ): + super(ConvSubsampling, self).__init__() + self._subsampling = subsampling + self._conv_channels = conv_channels + self._feat_in = feat_in + self._feat_out = feat_out + + if subsampling_factor % 2 != 0: + raise ValueError("Sampling factor should be a multiply of 2!") + self._sampling_num = int(math.log(subsampling_factor, 2)) + self.subsampling_factor = subsampling_factor + self.is_causal = is_causal + + if ( + subsampling_conv_chunking_factor != -1 + and subsampling_conv_chunking_factor != 1 + and subsampling_conv_chunking_factor % 2 != 0 + ): + raise ValueError("subsampling_conv_chunking_factor should be -1, 1, or a power of 2") + self.subsampling_conv_chunking_factor = subsampling_conv_chunking_factor + + in_channels = 1 + layers = [] + + if subsampling == 'vggnet': + self._stride = 2 + self._kernel_size = 2 + self._ceil_mode = True + + self._left_padding = 0 + self._right_padding = 0 + + for i in range(self._sampling_num): + layers.append( + torch.nn.Conv2d( + in_channels=in_channels, out_channels=conv_channels, kernel_size=3, stride=1, padding=1 + ) + ) + layers.append(activation) + layers.append( + torch.nn.Conv2d( + in_channels=conv_channels, out_channels=conv_channels, kernel_size=3, stride=1, padding=1 + ) + ) + layers.append(activation) + layers.append( + torch.nn.MaxPool2d( + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + ceil_mode=self._ceil_mode, + ) + ) + in_channels = conv_channels + + elif subsampling == 'dw_striding': + self._stride = 2 + self._kernel_size = 3 + self._ceil_mode = False + + if self.is_causal: + self._left_padding = self._kernel_size - 1 + self._right_padding = self._stride - 1 + self._max_cache_len = subsampling_factor + 1 + else: + self._left_padding = (self._kernel_size - 1) // 2 + self._right_padding = (self._kernel_size - 1) // 2 + self._max_cache_len = 0 + + # Layer 1 + if self.is_causal: + layers.append( + CausalConv2D( + in_channels=in_channels, + out_channels=conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=None, + ) + ) + else: + layers.append( + torch.nn.Conv2d( + in_channels=in_channels, + out_channels=conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + ) + ) + in_channels = conv_channels + layers.append(activation) + + for i in range(self._sampling_num - 1): + if self.is_causal: + layers.append( + CausalConv2D( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=None, + groups=in_channels, + ) + ) + else: + layers.append( + torch.nn.Conv2d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + groups=in_channels, + ) + ) + + layers.append( + torch.nn.Conv2d( + in_channels=in_channels, + out_channels=conv_channels, + kernel_size=1, + stride=1, + padding=0, + groups=1, + ) + ) + layers.append(activation) + in_channels = conv_channels + + elif subsampling == 'striding': + self._stride = 2 + self._kernel_size = 3 + self._ceil_mode = False + + if self.is_causal: + self._left_padding = self._kernel_size - 1 + self._right_padding = self._stride - 1 + self._max_cache_len = subsampling_factor + 1 + else: + self._left_padding = (self._kernel_size - 1) // 2 + self._right_padding = (self._kernel_size - 1) // 2 + self._max_cache_len = 0 + + for i in range(self._sampling_num): + if self.is_causal: + layers.append( + CausalConv2D( + in_channels=in_channels, + out_channels=conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=None, + ) + ) + else: + layers.append( + torch.nn.Conv2d( + in_channels=in_channels, + out_channels=conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + ) + ) + layers.append(activation) + in_channels = conv_channels + + elif subsampling == 'striding_conv1d': + + in_channels = feat_in + + self._stride = 2 + self._kernel_size = 5 + self._ceil_mode = False + + if self.is_causal: + self._left_padding = self._kernel_size - 1 + self._right_padding = self._stride - 1 + self._max_cache_len = subsampling_factor + 1 + else: + self._left_padding = (self._kernel_size - 1) // 2 + self._right_padding = (self._kernel_size - 1) // 2 + self._max_cache_len = 0 + + for i in range(self._sampling_num): + if self.is_causal: + layers.append( + CausalConv1D( + in_channels=in_channels, + out_channels=feat_out if self._sampling_num == i + 1 else conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=None, + ) + ) + else: + layers.append( + torch.nn.Conv1d( + in_channels=in_channels, + out_channels=feat_out if self._sampling_num == i + 1 else conv_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + ) + ) + layers.append(activation) + in_channels = conv_channels + + elif subsampling == 'dw_striding_conv1d': + + in_channels = feat_in + + self._stride = 2 + self._kernel_size = 5 + self._ceil_mode = False + + self._left_padding = (self._kernel_size - 1) // 2 + self._right_padding = (self._kernel_size - 1) // 2 + + # Layer 1 + layers.extend( + [ + torch.nn.Conv1d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + groups=in_channels, + ), + torch.nn.Conv1d( + in_channels=in_channels, + out_channels=feat_out if self._sampling_num == 1 else conv_channels, + kernel_size=1, + stride=1, + padding=0, + groups=1, + ), + ] + ) + in_channels = conv_channels + layers.append(activation) + + for i in range(self._sampling_num - 1): + layers.extend( + [ + torch.nn.Conv1d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=self._kernel_size, + stride=self._stride, + padding=self._left_padding, + groups=in_channels, + ), + torch.nn.Conv1d( + in_channels=in_channels, + out_channels=feat_out if self._sampling_num == i + 2 else conv_channels, + kernel_size=1, + stride=1, + padding=0, + groups=1, + ), + ] + ) + layers.append(activation) + in_channels = conv_channels + + else: + raise ValueError(f"Not valid sub-sampling: {subsampling}!") + + if subsampling in ["vggnet", "dw_striding", "striding"]: + + in_length = torch.tensor(feat_in, dtype=torch.float) + out_length = calc_length( + lengths=in_length, + all_paddings=self._left_padding + self._right_padding, + kernel_size=self._kernel_size, + stride=self._stride, + ceil_mode=self._ceil_mode, + repeat_num=self._sampling_num, + ) + self.out = torch.nn.Linear(conv_channels * int(out_length), feat_out) + self.conv2d_subsampling = True + elif subsampling in ["striding_conv1d", "dw_striding_conv1d"]: + self.out = None + self.conv2d_subsampling = False + else: + raise ValueError(f"Not valid sub-sampling: {subsampling}!") + + self.conv = torch.nn.Sequential(*layers) + + def get_sampling_frames(self): + return [1, self.subsampling_factor] + + def get_streaming_cache_size(self): + return [0, self.subsampling_factor + 1] + + def forward(self, x, lengths): + lengths = calc_length( + lengths, + all_paddings=self._left_padding + self._right_padding, + kernel_size=self._kernel_size, + stride=self._stride, + ceil_mode=self._ceil_mode, + repeat_num=self._sampling_num, + ) + + # Unsqueeze Channel Axis + if self.conv2d_subsampling: + x = x.unsqueeze(1) + # Transpose to Channel First mode + else: + x = x.transpose(1, 2) + + # split inputs if chunking_factor is set + if self.subsampling_conv_chunking_factor != -1 and self.conv2d_subsampling: + if self.subsampling_conv_chunking_factor == 1: + # if subsampling_conv_chunking_factor is 1, we split only if needed + # avoiding a bug / feature limiting indexing of tensors to 2**31 + # see https://github.com/pytorch/pytorch/issues/80020 + x_ceil = 2 ** 31 / self._conv_channels * self._stride * self._stride + if torch.numel(x) > x_ceil: + need_to_split = True + else: + need_to_split = False + else: + # if subsampling_conv_chunking_factor > 1 we always split + need_to_split = True + + if need_to_split: + x, success = self.conv_split_by_batch(x) + if not success: # if unable to split by batch, try by channel + if self._subsampling == 'dw_striding': + x = self.conv_split_by_channel(x) + else: + x = self.conv(x) # try anyway + else: + x = self.conv(x) + else: + x = self.conv(x) + + # Flatten Channel and Frequency Axes + if self.conv2d_subsampling: + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).reshape(b, t, -1)) + # Transpose to Channel Last mode + else: + x = x.transpose(1, 2) + + return x, lengths + + def reset_parameters(self): + # initialize weights + if self._subsampling == 'dw_striding': + with torch.no_grad(): + # init conv + scale = 1.0 / self._kernel_size + dw_max = (self._kernel_size ** 2) ** -0.5 + pw_max = self._conv_channels ** -0.5 + + torch.nn.init.uniform_(self.conv[0].weight, -scale, scale) + torch.nn.init.uniform_(self.conv[0].bias, -scale, scale) + + for idx in range(2, len(self.conv), 3): + torch.nn.init.uniform_(self.conv[idx].weight, -dw_max, dw_max) + torch.nn.init.uniform_(self.conv[idx].bias, -dw_max, dw_max) + torch.nn.init.uniform_(self.conv[idx + 1].weight, -pw_max, pw_max) + torch.nn.init.uniform_(self.conv[idx + 1].bias, -pw_max, pw_max) + + # init fc (80 * 64 = 5120 from https://github.com/kssteven418/Squeezeformer/blob/13c97d6cf92f2844d2cb3142b4c5bfa9ad1a8951/src/models/conformer_encoder.py#L487 + fc_scale = (self._feat_out * self._feat_in / self._sampling_num) ** -0.5 + torch.nn.init.uniform_(self.out.weight, -fc_scale, fc_scale) + torch.nn.init.uniform_(self.out.bias, -fc_scale, fc_scale) + + def conv_split_by_batch(self, x): + """ Tries to split input by batch, run conv and concat results """ + b, _, _, _ = x.size() + if b == 1: # can't split if batch size is 1 + return x, False + + if self.subsampling_conv_chunking_factor > 1: + cf = self.subsampling_conv_chunking_factor + logging.debug(f'using manually set chunking factor: {cf}') + else: + # avoiding a bug / feature limiting indexing of tensors to 2**31 + # see https://github.com/pytorch/pytorch/issues/80020 + x_ceil = 2 ** 31 / self._conv_channels * self._stride * self._stride + p = math.ceil(math.log(torch.numel(x) / x_ceil, 2)) + cf = 2 ** p + logging.debug(f'using auto set chunking factor: {cf}') + + new_batch_size = b // cf + if new_batch_size == 0: # input is too big + return x, False + + logging.debug(f'conv subsampling: using split batch size {new_batch_size}') + return torch.cat([self.conv(chunk) for chunk in torch.split(x, new_batch_size, 0)]), True + + def conv_split_by_channel(self, x): + """ For dw convs, tries to split input by time, run conv and concat results """ + x = self.conv[0](x) # full conv2D + x = self.conv[1](x) # activation + + for i in range(self._sampling_num - 1): + _, c, t, _ = x.size() + + if self.subsampling_conv_chunking_factor > 1: + cf = self.subsampling_conv_chunking_factor + logging.debug(f'using manually set chunking factor: {cf}') + else: + # avoiding a bug / feature limiting indexing of tensors to 2**31 + # see https://github.com/pytorch/pytorch/issues/80020 + p = math.ceil(math.log(torch.numel(x) / 2 ** 31, 2)) + cf = 2 ** p + logging.debug(f'using auto set chunking factor: {cf}') + + new_c = int(c // cf) + if new_c == 0: + logging.warning(f'chunking factor {cf} is too high; splitting down to one channel.') + new_c = 1 + + new_t = int(t // cf) + if new_t == 0: + logging.warning(f'chunking factor {cf} is too high; splitting down to one timestep.') + new_t = 1 + + logging.debug(f'conv dw subsampling: using split C size {new_c} and split T size {new_t}') + x = self.channel_chunked_conv(self.conv[i * 3 + 2], new_c, x) # conv2D, depthwise + + # splitting pointwise convs by time + x = torch.cat([self.conv[i * 3 + 3](chunk) for chunk in torch.split(x, new_t, 2)], 2) # conv2D, pointwise + x = self.conv[i * 3 + 4](x) # activation + return x + + def channel_chunked_conv(self, conv, chunk_size, x): + """ Performs channel chunked convolution""" + + ind = 0 + out_chunks = [] + for chunk in torch.split(x, chunk_size, 1): + step = chunk.size()[1] + + if self.is_causal: + chunk = nn.functional.pad( + chunk, pad=(self._kernel_size - 1, self._stride - 1, self._kernel_size - 1, self._stride - 1) + ) + ch_out = nn.functional.conv2d( + chunk, + conv.weight[ind : ind + step, :, :, :], + bias=conv.bias[ind : ind + step], + stride=self._stride, + padding=0, + groups=step, + ) + else: + ch_out = nn.functional.conv2d( + chunk, + conv.weight[ind : ind + step, :, :, :], + bias=conv.bias[ind : ind + step], + stride=self._stride, + padding=self._left_padding, + groups=step, + ) + out_chunks.append(ch_out) + ind += step + + return torch.cat(out_chunks, 1) + + def change_subsampling_conv_chunking_factor(self, subsampling_conv_chunking_factor: int): + if ( + subsampling_conv_chunking_factor != -1 + and subsampling_conv_chunking_factor != 1 + and subsampling_conv_chunking_factor % 2 != 0 + ): + raise ValueError("subsampling_conv_chunking_factor should be -1, 1, or a power of 2") + self.subsampling_conv_chunking_factor = subsampling_conv_chunking_factor + + +def calc_length(lengths, all_paddings, kernel_size, stride, ceil_mode, repeat_num=1): + """ Calculates the output length of a Tensor passed through a convolution or max pooling layer""" + add_pad: float = all_paddings - kernel_size + one: float = 1.0 + for i in range(repeat_num): + lengths = torch.div(lengths.to(dtype=torch.float) + add_pad, stride) + one + if ceil_mode: + lengths = torch.ceil(lengths) + else: + lengths = torch.floor(lengths) + return lengths.to(dtype=torch.int) + + +class TimeReductionModule(nn.Module): + """ + Squeezeformer Time Reduction procedure. Downsamples the audio by `stride` in the time dimension. + + Args: + d_model (int): input dimension of MultiheadAttentionMechanism and PositionwiseFeedForward + out_dim (int): Output dimension of the module. + kernel_size (int): Conv kernel size for depthwise convolution in convolution module + stride (int): Downsampling factor in time dimension. + """ + + def __init__(self, d_model: int, out_dim: int, kernel_size: int = 5, stride: int = 2): + super().__init__() + + self.d_model = d_model + self.out_dim = out_dim + self.kernel_size = kernel_size + self.stride = stride + self.padding = max(0, self.kernel_size - self.stride) + + self.dw_conv = nn.Conv1d( + in_channels=d_model, + out_channels=d_model, + kernel_size=kernel_size, + stride=stride, + padding=self.padding, + groups=d_model, + ) + + self.pw_conv = nn.Conv1d( + in_channels=d_model, out_channels=out_dim, kernel_size=1, stride=1, padding=0, groups=1, + ) + + self.reset_parameters() + + def forward(self, x, att_mask=None, pad_mask=None): + x = x.transpose(1, 2) # [B, C, T] + if pad_mask is not None: + x = x.float().masked_fill(pad_mask.unsqueeze(1), 0.0) + + x = self.dw_conv(x) + x = self.pw_conv(x) + + x = x.transpose(1, 2) # [B, T, C] + + B, T, D = x.size() + if att_mask is not None and pad_mask is not None: + att_mask = att_mask[:, :: self.stride, :: self.stride] + pad_mask = pad_mask[:, :: self.stride] + L = pad_mask.size(-1) + x = torch.nn.functional.pad(x, (0, 0, 0, L - T)) + + return x, att_mask, pad_mask + + def reset_parameters(self): + dw_max = self.kernel_size ** -0.5 + pw_max = self.d_model ** -0.5 + + with torch.no_grad(): + torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max) + torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max) + torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max) + torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max) + + +class SubsamplingReductionModule(nn.Module): + """Downsamples the audio signal in time dimension.""" + + def __init__(self, reduction: str, d_model: int, reduction_factor: int = 2): + super().__init__() + + assert reduction in ['pooling', 'striding'] + + self.reduction = reduction + self.d_model = d_model + self._sampling_num = int(math.log(reduction_factor, 2)) + + if reduction == 'pooling': + self.reduction_enc = nn.MaxPool1d(kernel_size=reduction_factor) + self.padding = 0 + self.kernel_size = self.reduction_enc.kernel_size + self.stride = self.reduction_enc.stride + elif reduction == 'striding': + self.reduction_enc = ConvSubsampling( + subsampling='striding', + subsampling_factor=reduction_factor, + feat_in=d_model, + feat_out=d_model, + conv_channels=d_model, + activation=nn.ReLU(), + is_causal=False, + ) + + def forward(self, x, lengths): + """Shapes: + - x: [B, T, C] + - lengths: [B] + """ + + if self.reduction == 'striding': + x, lengths = self.reduction_enc(x=x, lengths=lengths) + else: + x = torch.transpose(x, 1, 2) # [B, C, T] + lengths = calc_length( + lengths=lengths, + all_paddings=self.padding, + kernel_size=self.kernel_size, + stride=self.stride, + ceil_mode=False, + repeat_num=self._sampling_num, + ) + x = self.reduction_enc(x) + x = torch.transpose(x, 1, 2) # [B, T, C] + + return x, lengths diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/tdnn_attention.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/tdnn_attention.py new file mode 100644 index 0000000..14f27ef --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/tdnn_attention.py @@ -0,0 +1,324 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from typing import List + +import torch +from numpy import inf +from torch import nn as nn +from torch.nn import functional as F + +from nemo.collections.asr.parts.submodules.jasper import get_same_padding, init_weights + + +class StatsPoolLayer(nn.Module): + """Statistics and time average pooling (TAP) layer + + This computes mean and, optionally, standard deviation statistics across the time dimension. + + Args: + feat_in: Input features with shape [B, D, T] + pool_mode: Type of pool mode. Supported modes are 'xvector' (mean and standard deviation) and 'tap' (time + average pooling, i.e., mean) + eps: Epsilon, minimum value before taking the square root, when using 'xvector' mode. + biased: Whether to use the biased estimator for the standard deviation when using 'xvector' mode. The default + for torch.Tensor.std() is True. + + Returns: + Pooled statistics with shape [B, D]. + + Raises: + ValueError if an unsupported pooling mode is specified. + """ + + def __init__(self, feat_in: int, pool_mode: str = 'xvector', eps: float = 1e-10, biased: bool = True): + super().__init__() + supported_modes = {"xvector", "tap"} + if pool_mode not in supported_modes: + raise ValueError(f"Pool mode must be one of {supported_modes}; got '{pool_mode}'") + self.pool_mode = pool_mode + self.feat_in = feat_in + self.eps = eps + self.biased = biased + if self.pool_mode == 'xvector': + # Mean + std + self.feat_in *= 2 + + def forward(self, encoder_output, length=None): + if length is None: + mean = encoder_output.mean(dim=-1) # Time Axis + if self.pool_mode == 'xvector': + std = encoder_output.std(dim=-1) + pooled = torch.cat([mean, std], dim=-1) + else: + pooled = mean + else: + mask = make_seq_mask_like(like=encoder_output, lengths=length, valid_ones=False) + encoder_output = encoder_output.masked_fill(mask, 0.0) + # [B, D, T] -> [B, D] + means = encoder_output.mean(dim=-1) + # Re-scale to get padded means + means = means * (encoder_output.shape[-1] / length).unsqueeze(-1) + if self.pool_mode == "xvector": + stds = ( + encoder_output.sub(means.unsqueeze(-1)) + .masked_fill(mask, 0.0) + .pow(2.0) + .sum(-1) # [B, D, T] -> [B, D] + .div(length.view(-1, 1).sub(1 if self.biased else 0)) + .clamp(min=self.eps) + .sqrt() + ) + pooled = torch.cat((means, stds), dim=-1) + else: + pooled = means + return pooled + + +@torch.jit.script_if_tracing +def make_seq_mask_like( + like: torch.Tensor, lengths: torch.Tensor, valid_ones: bool = True, time_dim: int = -1 +) -> torch.Tensor: + mask = torch.arange(like.shape[time_dim], device=like.device).repeat(lengths.shape[0], 1).lt(lengths.unsqueeze(-1)) + # Match number of dims in `like` tensor + for _ in range(like.dim() - mask.dim()): + mask = mask.unsqueeze(1) + # If time dim != -1, transpose to proper dim. + if time_dim != -1: + mask = mask.transpose(time_dim, -1) + if not valid_ones: + mask = ~mask + return mask + + +def lens_to_mask(lens: List[int], max_len: int, device: str = None): + """ + outputs masking labels for list of lengths of audio features, with max length of any + mask as max_len + input: + lens: list of lens + max_len: max length of any audio feature + output: + mask: masked labels + num_values: sum of mask values for each feature (useful for computing statistics later) + """ + lens_mat = torch.arange(max_len).to(device) + mask = lens_mat[:max_len].unsqueeze(0) < lens.unsqueeze(1) + mask = mask.unsqueeze(1) + num_values = torch.sum(mask, dim=2, keepdim=True) + return mask, num_values + + +def get_statistics_with_mask(x: torch.Tensor, m: torch.Tensor, dim: int = 2, eps: float = 1e-10): + """ + compute mean and standard deviation of input(x) provided with its masking labels (m) + input: + x: feature input + m: averaged mask labels + output: + mean: mean of input features + std: stadard deviation of input features + """ + mean = torch.sum((m * x), dim=dim) + std = torch.sqrt((m * (x - mean.unsqueeze(dim)).pow(2)).sum(dim).clamp(eps)) + return mean, std + + +class TDNNModule(nn.Module): + """ + Time Delayed Neural Module (TDNN) - 1D + input: + inp_filters: input filter channels for conv layer + out_filters: output filter channels for conv layer + kernel_size: kernel weight size for conv layer + dilation: dilation for conv layer + stride: stride for conv layer + padding: padding for conv layer (default None: chooses padding value such that input and output feature shape matches) + output: + tdnn layer output + """ + + def __init__( + self, + inp_filters: int, + out_filters: int, + kernel_size: int = 1, + dilation: int = 1, + stride: int = 1, + padding: int = None, + ): + super().__init__() + if padding is None: + padding = get_same_padding(kernel_size, stride=stride, dilation=dilation) + + self.conv_layer = nn.Conv1d( + in_channels=inp_filters, + out_channels=out_filters, + kernel_size=kernel_size, + dilation=dilation, + padding=padding, + ) + + self.activation = nn.ReLU() + self.bn = nn.BatchNorm1d(out_filters) + + def forward(self, x, length=None): + x = self.conv_layer(x) + x = self.activation(x) + return self.bn(x) + + +class MaskedSEModule(nn.Module): + """ + Squeeze and Excite module implementation with conv1d layers + input: + inp_filters: input filter channel size + se_filters: intermediate squeeze and excite channel output and input size + out_filters: output filter channel size + kernel_size: kernel_size for both conv1d layers + dilation: dilation size for both conv1d layers + + output: + squeeze and excite layer output + """ + + def __init__(self, inp_filters: int, se_filters: int, out_filters: int, kernel_size: int = 1, dilation: int = 1): + super().__init__() + self.se_layer = nn.Sequential( + nn.Conv1d(inp_filters, se_filters, kernel_size=kernel_size, dilation=dilation,), + nn.ReLU(), + nn.BatchNorm1d(se_filters), + nn.Conv1d(se_filters, out_filters, kernel_size=kernel_size, dilation=dilation,), + nn.Sigmoid(), + ) + + def forward(self, input, length=None): + if length is None: + x = torch.mean(input, dim=2, keep_dim=True) + else: + max_len = input.size(2) + mask, num_values = lens_to_mask(length, max_len=max_len, device=input.device) + x = torch.sum((input * mask), dim=2, keepdim=True) / (num_values) + + out = self.se_layer(x) + return out * input + + +class TDNNSEModule(nn.Module): + """ + Modified building SE_TDNN group module block from ECAPA implementation for faster training and inference + Reference: ECAPA-TDNN Embeddings for Speaker Diarization (https://arxiv.org/pdf/2104.01466.pdf) + inputs: + inp_filters: input filter channel size + out_filters: output filter channel size + group_scale: scale value to group wider conv channels (deafult:8) + se_channels: squeeze and excite output channel size (deafult: 1024/8= 128) + kernel_size: kernel_size for group conv1d layers (default: 1) + dilation: dilation size for group conv1d layers (default: 1) + """ + + def __init__( + self, + inp_filters: int, + out_filters: int, + group_scale: int = 8, + se_channels: int = 128, + kernel_size: int = 1, + dilation: int = 1, + init_mode: str = 'xavier_uniform', + ): + super().__init__() + self.out_filters = out_filters + padding_val = get_same_padding(kernel_size=kernel_size, dilation=dilation, stride=1) + + group_conv = nn.Conv1d( + out_filters, + out_filters, + kernel_size=kernel_size, + dilation=dilation, + padding=padding_val, + groups=group_scale, + ) + self.group_tdnn_block = nn.Sequential( + TDNNModule(inp_filters, out_filters, kernel_size=1, dilation=1), + group_conv, + nn.ReLU(), + nn.BatchNorm1d(out_filters), + TDNNModule(out_filters, out_filters, kernel_size=1, dilation=1), + ) + + self.se_layer = MaskedSEModule(out_filters, se_channels, out_filters) + + self.apply(lambda x: init_weights(x, mode=init_mode)) + + def forward(self, input, length=None): + x = self.group_tdnn_block(input) + x = self.se_layer(x, length) + return x + input + + +class AttentivePoolLayer(nn.Module): + """ + Attention pooling layer for pooling speaker embeddings + Reference: ECAPA-TDNN Embeddings for Speaker Diarization (https://arxiv.org/pdf/2104.01466.pdf) + inputs: + inp_filters: input feature channel length from encoder + attention_channels: intermediate attention channel size + kernel_size: kernel_size for TDNN and attention conv1d layers (default: 1) + dilation: dilation size for TDNN and attention conv1d layers (default: 1) + """ + + def __init__( + self, + inp_filters: int, + attention_channels: int = 128, + kernel_size: int = 1, + dilation: int = 1, + eps: float = 1e-10, + ): + super().__init__() + + self.feat_in = 2 * inp_filters + + self.attention_layer = nn.Sequential( + TDNNModule(inp_filters * 3, attention_channels, kernel_size=kernel_size, dilation=dilation), + nn.Tanh(), + nn.Conv1d( + in_channels=attention_channels, out_channels=inp_filters, kernel_size=kernel_size, dilation=dilation, + ), + ) + self.eps = eps + + def forward(self, x, length=None): + max_len = x.size(2) + + if length is None: + length = torch.ones(x.shape[0], device=x.device) + + mask, num_values = lens_to_mask(length, max_len=max_len, device=x.device) + + # encoder statistics + mean, std = get_statistics_with_mask(x, mask / num_values) + mean = mean.unsqueeze(2).repeat(1, 1, max_len) + std = std.unsqueeze(2).repeat(1, 1, max_len) + attn = torch.cat([x, mean, std], dim=1) + + # attention statistics + attn = self.attention_layer(attn) # attention pass + attn = attn.masked_fill(mask == 0, -inf) + alpha = F.softmax(attn, dim=2) # attention values, α + mu, sg = get_statistics_with_mask(x, alpha) # µ and ∑ + + # gather + return torch.cat((mu, sg), dim=1).unsqueeze(2) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/tdt_loop_labels_computer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/tdt_loop_labels_computer.py new file mode 100644 index 0000000..c289ce0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/tdt_loop_labels_computer.py @@ -0,0 +1,767 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import Any, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from omegaconf import DictConfig, ListConfig + +from nemo.collections.asr.parts.utils import rnnt_utils +from nemo.collections.asr.parts.utils.asr_confidence_utils import ConfidenceMethodMixin +from nemo.core.utils.cuda_python_utils import ( + check_cuda_python_cuda_graphs_conditional_nodes_supported, + cu_call, + run_nvrtc, + with_conditional_node, +) +from nemo.utils import logging + +try: + from cuda import cudart + + HAVE_CUDA_PYTHON = True +except ImportError: + HAVE_CUDA_PYTHON = False + + +class LoopLabelsState: + """ + State for Loop Labels algorithm. Used only with CUDA graphs. + In initialization phase it is possible to assign values (tensors) to the state. + For algorithm code the storage should be reused (prefer copy data instead of assigning tensors). + """ + + max_time: int # maximum length of internal storage for time dimension + batch_size: int # (maximum) length of internal storage for batch dimension + device: torch.device # device to store preallocated tensors + + all_durations: torch.Tensor + + encoder_output_projected: torch.Tensor # projected output from the encoder for decoding algorithm + encoder_output_length: torch.Tensor # length of the (projected) output from the encoder + + labels: torch.Tensor # storage for current labels + scores: torch.Tensor # storage for current scores + + batch_indices: torch.Tensor # indices of elements in batch (constant, range [0, batch_size-1]) + + time_indices: torch.Tensor # current time indices for each element in batch + safe_time_indices: torch.Tensor # current time indices, but guaranteed to be < encoder_output_length + time_indices_current_labels: torch.Tensor # time indices for found labels (corresponding to `labels` field) + last_timesteps: torch.Tensor # indices of the last timesteps for each element (encoder_output_length - 1) + + active_mask: torch.Tensor # mask for active hypotheses (the decoding is finished for the utterance if it is False) + advance_mask: torch.Tensor # mask for "advancing" hypotheses (blank is found for the element on the current step) + blank_mask: torch.Tensor # if the element is blank + # if the element was active on the previous step: to identify the end of decoding and store final hidden state + active_mask_prev: torch.Tensor + became_inactive_mask: torch.Tensor # mask for elements that became inactive (end of decoding) + + active_mask_any: torch.Tensor # 0-dim bool tensor, condition for outer loop ('any element is still active') + advance_mask_any: torch.Tensor # 0-dim bool tensor, condition for inner loop ('should advance any index') + + last_decoder_state: Any # last state from the decoder, needed for the output + decoder_state: Any # current decoder state + decoder_output: torch.Tensor # output from the decoder (projected) + + batched_hyps: rnnt_utils.BatchedHyps # batched hypotheses - decoding result + alignments: Optional[rnnt_utils.BatchedAlignments] = None # batched alignments + + def __init__( + self, + batch_size: int, + max_time: int, + encoder_dim: int, + max_symbols: int, + device: torch.device, + float_dtype: torch.dtype, + logits_dim: int, + preserve_alignments=False, + preserve_frame_confidence=False, + ): + """ + + Args: + batch_size: batch size for encoder output storage + max_time: maximum time for encoder output storage + encoder_dim: last dimension for encoder output storage (projected encoder output) + max_symbols: max symbols per step (to avoid infinite looping and pre-allocate storage) + device: device to store tensors + float_dtype: default float dtype for tensors (should match projected encoder output) + logits_dim: output dimension for Joint + preserve_alignments: if alignments are needed + preserve_frame_confidence: if frame confidence is needed + """ + self.device = device + self.float_dtype = float_dtype + self.batch_size = batch_size + self.max_time = max_time + + self.encoder_output_projected = torch.zeros( + (self.batch_size, self.max_time, encoder_dim), dtype=float_dtype, device=self.device, + ) + self.encoder_output_length = torch.zeros((self.batch_size,), dtype=torch.long, device=self.device) + + self.labels = torch.zeros([self.batch_size], dtype=torch.long, device=self.device) + self.scores = torch.zeros([self.batch_size], dtype=float_dtype, device=self.device) + + # indices of elements in batch (constant) + self.batch_indices = torch.arange(self.batch_size, dtype=torch.long, device=self.device) + + self.time_indices = torch.zeros_like(self.batch_indices) + self.safe_time_indices = torch.zeros_like(self.batch_indices) + self.time_indices_current_labels = torch.zeros_like(self.time_indices) + self.last_timesteps = torch.zeros_like(self.time_indices) + + self.active_mask = torch.zeros([self.batch_size], dtype=torch.bool, device=self.device) + self.advance_mask = torch.zeros_like(self.active_mask) + self.blank_mask = torch.zeros_like(self.active_mask) + self.active_mask_prev = torch.zeros_like(self.active_mask) + self.became_inactive_mask = torch.zeros_like(self.active_mask) + + self.active_mask_any = torch.tensor(True, device=self.device, dtype=torch.bool) + self.advance_mask_any = torch.tensor(True, device=self.device, dtype=torch.bool) + + self.batched_hyps = rnnt_utils.BatchedHyps( + batch_size=self.batch_size, + init_length=self.max_time * max_symbols, + device=self.device, + float_dtype=float_dtype, + ) + if preserve_alignments or preserve_frame_confidence: + self.alignments = rnnt_utils.BatchedAlignments( + batch_size=batch_size, + logits_dim=logits_dim, + init_length=max_time * (max_symbols + 1), + device=self.device, + float_dtype=self.float_dtype, + store_alignments=preserve_alignments, + store_frame_confidence=preserve_frame_confidence, + ) + else: + self.alignments = None + + def need_reinit(self, encoder_output_projected: torch.Tensor) -> bool: + """Check if need to reinit state: larger batch_size/max_time, or new device""" + return ( + self.batch_size < encoder_output_projected.shape[0] + or self.max_time < encoder_output_projected.shape[1] + or self.device.index != encoder_output_projected.device.index + ) + + +class GreedyBatchedTDTLoopLabelsComputer(ConfidenceMethodMixin): + """ + Label Looping algorithm implementation: optimized batched greedy decoding. Callable. + Iterates over labels, on each step finding the next non-blank label + (evaluating Joint multiple times in inner loop); It uses a minimal possible amount of calls + to prediction network (with maximum possible batch size), + which makes it especially useful for scaling the prediction network. + During decoding all active hypotheses ("texts") have the same lengths. + """ + + INITIAL_MAX_TIME = 375 # initial max time, used to init state for Cuda graphs + CUDA_PROGRAM_NAME = b"while_loop_labels_conditional_tdt.cu" + + def __init__( + self, + decoder, + joint, + blank_index: int, + durations: Union[list[int], ListConfig[int]], + max_symbols_per_step: Optional[int] = None, + preserve_alignments=False, + preserve_frame_confidence=False, + confidence_method_cfg: Optional[DictConfig] = None, + allow_cuda_graphs: bool = True, + ): + """ + Init method. + Args: + decoder: Prediction network from RNN-T + joint: Joint module from RNN-T + blank_index: index of blank symbol + durations: list of TDT durations, e.g., [0, 1, 2, 4, 8] + max_symbols_per_step: max symbols to emit on each step (to avoid infinite looping) + preserve_alignments: if alignments are needed + preserve_frame_confidence: if frame confidence is needed + confidence_method_cfg: config for the confidence + """ + super().__init__() + self.decoder = decoder + self.joint = joint + # keep durations on CPU to avoid side effects in multi-gpu environment + self.durations = torch.tensor(list(durations), device="cpu").to(torch.long) + self._blank_index = blank_index + self.max_symbols = max_symbols_per_step + self.preserve_alignments = preserve_alignments + self.preserve_frame_confidence = preserve_frame_confidence + self._SOS = self._blank_index + self._init_confidence_method(confidence_method_cfg=confidence_method_cfg) + assert self._SOS == self._blank_index # "blank as pad" algorithm only + + self.use_cuda_graphs = allow_cuda_graphs + + if self.use_cuda_graphs and self.max_symbols is None: + logging.warning("Max symbols is None, which is not allowed with Cuda graphs.") + self.use_cuda_graphs = False + + if self.use_cuda_graphs: + try: + check_cuda_python_cuda_graphs_conditional_nodes_supported() + except ImportError as e: + logging.warning(f"No conditional node support. Cuda graphs will be disabled,\n{e.msg}") + self.use_cuda_graphs = False + + self.state: Optional[LoopLabelsState] = None + + def loop_labels_torch( + self, encoder_output: torch.Tensor, encoder_output_length: torch.Tensor, + ) -> Tuple[rnnt_utils.BatchedHyps, Optional[rnnt_utils.BatchedAlignments], Any]: + """ + Pure PyTorch implementation + + Args: + encoder_output: output from the encoder + encoder_output_length: lengths of the utterances in `encoder_output` + """ + batch_size, max_time, _unused = encoder_output.shape + device = encoder_output.device + + # do not recalculate joint projection, project only once + encoder_output_projected = self.joint.project_encoder(encoder_output) + + # init output structures: BatchedHyps (for results), BatchedAlignments + last decoder state + # init empty batched hypotheses + batched_hyps = rnnt_utils.BatchedHyps( + batch_size=batch_size, + init_length=max_time * self.max_symbols if self.max_symbols is not None else max_time, + device=device, + float_dtype=encoder_output_projected.dtype, + ) + # sample state, will be replaced further when the decoding for hypothesis is done + last_decoder_state = self.decoder.initialize_state(encoder_output_projected) + # init alignments if necessary + use_alignments = self.preserve_alignments or self.preserve_frame_confidence + # always use alignments variable - for torch.jit adaptation, but keep it as minimal as possible + alignments = rnnt_utils.BatchedAlignments( + batch_size=batch_size, + logits_dim=self.joint.num_classes_with_blank, + init_length=max_time * 2 if use_alignments else 1, # blank for each timestep + text tokens + device=device, + float_dtype=encoder_output_projected.dtype, + store_alignments=self.preserve_alignments, + store_frame_confidence=self.preserve_frame_confidence, + ) + + # durations + all_durations = self.durations.to(device, non_blocking=True) + num_durations = all_durations.shape[0] + + # initial state, needed for torch.jit to compile (cannot handle None) + state = self.decoder.initialize_state(encoder_output_projected) + # indices of elements in batch (constant) + batch_indices = torch.arange(batch_size, dtype=torch.long, device=device) + # last found labels - initially () symbol + labels = torch.full_like(batch_indices, fill_value=self._SOS) + + # time indices + time_indices = torch.zeros_like(batch_indices) + safe_time_indices = torch.zeros_like(time_indices) # time indices, guaranteed to be < out_len + time_indices_current_labels = torch.zeros_like(time_indices) + last_timesteps = encoder_output_length - 1 + + # masks for utterances in batch + active_mask: torch.Tensor = encoder_output_length > 0 + advance_mask = torch.empty_like(active_mask) + + # for storing the last state we need to know what elements became "inactive" on this step + active_mask_prev = torch.empty_like(active_mask) + became_inactive_mask = torch.empty_like(active_mask) + + # loop while there are active utterances + while active_mask.any(): + active_mask_prev.copy_(active_mask, non_blocking=True) + # stage 1: get decoder (prediction network) output + decoder_output, state, *_ = self.decoder.predict( + labels.unsqueeze(1), state, add_sos=False, batch_size=batch_size + ) + decoder_output = self.joint.project_prednet(decoder_output) # do not recalculate joint projection + + # stage 2: get joint output, iteratively seeking for non-blank labels + # blank label in `labels` tensor means "end of hypothesis" (for this index) + logits = ( + self.joint.joint_after_projection( + encoder_output_projected[batch_indices, safe_time_indices].unsqueeze(1), decoder_output, + ) + .squeeze(1) + .squeeze(1) + ) + scores, labels = logits[:, :-num_durations].max(dim=-1) + jump_durations_indices = logits[:, -num_durations:].argmax(dim=-1) + durations = all_durations[jump_durations_indices] + + # search for non-blank labels using joint, advancing time indices for blank labels + # checking max_symbols is not needed, since we already forced advancing time indices for such cases + blank_mask = labels == self._blank_index + # for blank labels force duration >= 1 + durations.masked_fill_(torch.logical_and(durations == 0, blank_mask), 1) + time_indices_current_labels.copy_(time_indices, non_blocking=True) + if use_alignments: + alignments.add_results_masked_( + active_mask=active_mask, + time_indices=time_indices_current_labels, + logits=logits if self.preserve_alignments else None, + labels=labels if self.preserve_alignments else None, + confidence=self._get_confidence_tensor(F.log_softmax(logits[:, :-num_durations], dim=-1)) + if self.preserve_frame_confidence + else None, + ) + + # advance_mask is a mask for current batch for searching non-blank labels; + # each element is True if non-blank symbol is not yet found AND we can increase the time index + time_indices += durations + torch.minimum(time_indices, last_timesteps, out=safe_time_indices) + torch.less(time_indices, encoder_output_length, out=active_mask) + torch.logical_and(active_mask, blank_mask, out=advance_mask) + + # inner loop: find next non-blank labels (if exist) + while advance_mask.any(): + # same as: time_indices_current_labels[advance_mask] = time_indices[advance_mask], but non-blocking + # store current time indices to use further for storing the results + torch.where(advance_mask, time_indices, time_indices_current_labels, out=time_indices_current_labels) + logits = ( + self.joint.joint_after_projection( + encoder_output_projected[batch_indices, safe_time_indices].unsqueeze(1), decoder_output, + ) + .squeeze(1) + .squeeze(1) + ) + # get labels (greedy) and scores from current logits, replace labels/scores with new + # labels[advance_mask] are blank, and we are looking for non-blank labels + more_scores, more_labels = logits[:, :-num_durations].max(dim=-1) + # same as: labels[advance_mask] = more_labels[advance_mask], but non-blocking + torch.where(advance_mask, more_labels, labels, out=labels) + # same as: scores[advance_mask] = more_scores[advance_mask], but non-blocking + torch.where(advance_mask, more_scores, scores, out=scores) + jump_durations_indices = logits[:, -num_durations:].argmax(dim=-1) + durations = all_durations[jump_durations_indices] + + if use_alignments: + alignments.add_results_masked_( + active_mask=advance_mask, + time_indices=time_indices_current_labels, + logits=logits if self.preserve_alignments else None, + labels=more_labels if self.preserve_alignments else None, + confidence=self._get_confidence_tensor(F.log_softmax(logits[:, :-num_durations], dim=-1)) + if self.preserve_frame_confidence + else None, + ) + + blank_mask = labels == self._blank_index + # for blank labels force duration >= 1 + durations.masked_fill_(torch.logical_and(durations == 0, blank_mask), 1) + # same as time_indices[advance_mask] += durations[advance_mask], but non-blocking + torch.where(advance_mask, time_indices + durations, time_indices, out=time_indices) + torch.minimum(time_indices, last_timesteps, out=safe_time_indices) + torch.less(time_indices, encoder_output_length, out=active_mask) + torch.logical_and(active_mask, blank_mask, out=advance_mask) + + # stage 3: filter labels and state, store hypotheses + # select states for hyps that became inactive (is it necessary?) + # this seems to be redundant, but used in the `loop_frames` output + torch.ne(active_mask, active_mask_prev, out=became_inactive_mask) + self.decoder.batch_replace_states_mask( + src_states=state, dst_states=last_decoder_state, mask=became_inactive_mask, + ) + + # store hypotheses + if self.max_symbols is not None: + # pre-allocated memory, no need for checks + batched_hyps.add_results_masked_no_checks_( + active_mask, labels, time_indices_current_labels, scores, + ) + else: + # auto-adjusted storage + batched_hyps.add_results_masked_( + active_mask, labels, time_indices_current_labels, scores, + ) + + # stage 4: to avoid looping, go to next frame after max_symbols emission + if self.max_symbols is not None: + # if labels are non-blank (not end-of-utterance), check that last observed timestep with label: + # if it is equal to the current time index, and number of observations is >= max_symbols, force blank + force_blank_mask = torch.logical_and( + active_mask, + torch.logical_and( + torch.logical_and( + labels != self._blank_index, batched_hyps.last_timestep_lasts >= self.max_symbols, + ), + batched_hyps.last_timestep == time_indices, + ), + ) + time_indices += force_blank_mask # emit blank => advance time indices + # update safe_time_indices, non-blocking + torch.minimum(time_indices, last_timesteps, out=safe_time_indices) + # same as: active_mask = time_indices < encoder_output_length + torch.less(time_indices, encoder_output_length, out=active_mask) + if use_alignments: + return batched_hyps, alignments, last_decoder_state + return batched_hyps, None, last_decoder_state + + def loop_labels_cuda_graphs( + self, encoder_output: torch.Tensor, encoder_output_length: torch.Tensor, + ) -> Tuple[rnnt_utils.BatchedHyps, Optional[rnnt_utils.BatchedAlignments], Any]: + """ + Implementation with CUDA graphs. + + Args: + encoder_output: output from the encoder + encoder_output_length: lengths of the utterances in `encoder_output` + """ + # do not recalculate joint projection, project only once + encoder_output = self.joint.project_encoder(encoder_output) + current_batch_size = encoder_output.shape[0] + current_max_time = encoder_output.shape[1] + + if torch.is_autocast_enabled(): + encoder_output = encoder_output.to(torch.get_autocast_gpu_dtype()) + + # init or reinit graph + if self.state is None or self.state.need_reinit(encoder_output): + self._graph_reinitialize(encoder_output, encoder_output_length) + + # copy (projected) encoder output and lenghts + self.state.encoder_output_projected[:current_batch_size, :current_max_time, ...].copy_(encoder_output) + self.state.encoder_output_length[: encoder_output_length.shape[0]].copy_(encoder_output_length) + # set length to zero for elements outside the current batch + self.state.encoder_output_length[current_batch_size:].fill_(0) + self.graph.replay() + + # example manual loop (can be used instead of graph.replay()) + # self._before_outer_loop() + # while self.state.active_mask_any.item(): + # self._before_inner_loop_get_decoder_output() + # self._before_inner_loop_get_joint_output() + # while self.state.advance_mask_any.item(): + # self._inner_loop_code() + # self._after_inner_loop() + + return ( + self.state.batched_hyps, + self.state.alignments, + self.state.last_decoder_state, + ) + + @classmethod + def _create_outer_while_loop_kernel(cls): + """ + Creates a kernel that evaluates whether to enter the outer loop body (not all hypotheses are decoded). + Condition: while(active_mask_any). + """ + kernel_string = r"""\ + typedef __device_builtin__ unsigned long long cudaGraphConditionalHandle; + + extern "C" __device__ __cudart_builtin__ void cudaGraphSetConditional(cudaGraphConditionalHandle handle, unsigned int value); + + extern "C" __global__ + void outer_loop_labels_conditional(cudaGraphConditionalHandle handle, const bool *active_mask_any) + { + cudaGraphSetConditional(handle, *active_mask_any); + } + """ + return run_nvrtc(kernel_string, b"outer_loop_labels_conditional", cls.CUDA_PROGRAM_NAME) + + @classmethod + def _create_inner_while_loop_kernel(cls): + """ + Creates a kernel that evaluates whether to enter the inner loop body (not all non-blank labels found). + Condition: while(advance_mask_any). + """ + kernel_string = r"""\ + typedef __device_builtin__ unsigned long long cudaGraphConditionalHandle; + + extern "C" __device__ __cudart_builtin__ void cudaGraphSetConditional(cudaGraphConditionalHandle handle, unsigned int value); + + extern "C" __global__ + void inner_find_non_blank_conditional(cudaGraphConditionalHandle handle, const bool *advance_mask_any) + { + cudaGraphSetConditional(handle, *advance_mask_any); + } + """ + return run_nvrtc(kernel_string, b"inner_find_non_blank_conditional", cls.CUDA_PROGRAM_NAME) + + def _graph_reinitialize( + self, encoder_output_projected: torch.Tensor, encoder_output_length: torch.Tensor, + ): + batch_size, max_time, encoder_dim = encoder_output_projected.shape + + self.state = LoopLabelsState( + batch_size=batch_size, + max_time=max(max_time, self.INITIAL_MAX_TIME), + encoder_dim=encoder_dim, + max_symbols=self.max_symbols, + device=encoder_output_projected.device, + float_dtype=encoder_output_projected.dtype, + logits_dim=self.joint.num_classes_with_blank, + preserve_alignments=self.preserve_alignments, + preserve_frame_confidence=self.preserve_frame_confidence, + ) + self.state.all_durations = self.durations.to(self.state.device) + + self.state.last_decoder_state = self.decoder.initialize_state(encoder_output_projected) + self.state.decoder_state = self.decoder.initialize_state(encoder_output_projected) + decoder_output, *_ = self.decoder.predict( + self.state.labels.unsqueeze(1), self.state.decoder_state, add_sos=False, batch_size=self.state.batch_size + ) + # to avoid recalculation of joint projection, store decoder output in state + self.state.decoder_output = self.joint.project_prednet(decoder_output) + + # Always create a new stream, because the per-thread default stream disallows stream capture to a graph. + stream_for_graph = torch.cuda.Stream(self.state.device) + self.graph = torch.cuda.CUDAGraph() + with torch.cuda.stream(stream_for_graph), torch.inference_mode(), torch.cuda.graph( + self.graph, stream=stream_for_graph + ): + self._before_outer_loop() + + capture_status, _, graph, _, _ = cu_call( + cudart.cudaStreamGetCaptureInfo(torch.cuda.current_stream(device=self.state.device).cuda_stream) + ) + assert capture_status == cudart.cudaStreamCaptureStatus.cudaStreamCaptureStatusActive + + (outer_loop_conditional_handle,) = cu_call(cudart.cudaGraphConditionalHandleCreate(graph, 0, 0)) + outer_loop_kernel = self._create_outer_while_loop_kernel() + active_mask_any_ptr = np.array([self.state.active_mask_any.data_ptr()], dtype=np.uint64) + outer_loop_args = np.array( + [outer_loop_conditional_handle.getPtr(), active_mask_any_ptr.ctypes.data], dtype=np.uint64, + ) + + # loop while there are active utterances + # while self.active_mask_any: + with with_conditional_node( + outer_loop_kernel, outer_loop_args, outer_loop_conditional_handle, device=self.state.device + ): + self._before_inner_loop_get_decoder_output() + self._before_inner_loop_get_joint_output() + inner_while_loop_kernel = self._create_inner_while_loop_kernel() + (inner_loop_conditional_handle,) = cu_call(cudart.cudaGraphConditionalHandleCreate(graph, 0, 0)) + advance_mask_any_ptr = np.array([self.state.advance_mask_any.data_ptr()], dtype=np.uint64) + inner_loop_args = np.array( + [inner_loop_conditional_handle.getPtr(), advance_mask_any_ptr.ctypes.data,], dtype=np.uint64, + ) + # while self.advance_mask_any.item(): + + with with_conditional_node( + inner_while_loop_kernel, inner_loop_args, inner_loop_conditional_handle, device=self.state.device + ): + self._inner_loop_code() + self._after_inner_loop() + + def _before_outer_loop(self): + """Clear state and compute initial active mask""" + self.state.batched_hyps.clear_() + if self.state.alignments is not None: + self.state.alignments.clear_() + + # initial state + self.decoder.batch_replace_states_all( + src_states=self.decoder.initialize_state(self.state.encoder_output_projected), + dst_states=self.state.decoder_state, + ) + # last found labels - initially () symbol + self.state.labels.fill_(self._SOS) + self.state.scores.fill_(0.0) + + # time indices + self.state.time_indices.fill_(0) + self.state.safe_time_indices.fill_(0) # safe time indices: guaranteed to be < encoder_output_length + self.state.time_indices_current_labels.fill_(0) + torch.sub(self.state.encoder_output_length, 1, out=self.state.last_timesteps) + + # masks for utterances in batch + # same as: active_mask = self.encoder_output_length > 0 + torch.greater(self.state.encoder_output_length, 0, out=self.state.active_mask) + + # for storing the last state we need to know what elements became "inactive" on this step + # same as: self.active_mask_any = active_mask.any() + torch.any(self.state.active_mask, out=self.state.active_mask_any) + + def _before_inner_loop_get_decoder_output(self): + """Get decoder output""" + # stage 1: get decoder (prediction network) output + decoder_output, new_state, *_ = self.decoder.predict( + self.state.labels.unsqueeze(1), self.state.decoder_state, add_sos=False, batch_size=self.state.batch_size + ) + self.decoder.batch_replace_states_all(src_states=new_state, dst_states=self.state.decoder_state) + decoder_output_projected = self.joint.project_prednet(decoder_output) # do not recalculate joint projection + self.state.decoder_output.copy_(decoder_output_projected) + + def _before_inner_loop_get_joint_output(self): + """Get Joint output after decoder output, prepare inner loop to search for all next non-blank labels""" + # stage 2: get joint output, iteratively seeking for non-blank labels + # blank label in `labels` tensor means "end of hypothesis" (for this index) + self.state.active_mask_prev.copy_(self.state.active_mask, non_blocking=True) + logits = ( + self.joint.joint_after_projection( + self.state.encoder_output_projected[self.state.batch_indices, self.state.safe_time_indices].unsqueeze( + 1 + ), + self.state.decoder_output, + ) + .squeeze(1) + .squeeze(1) + ) + # same as: scores, labels = logits[:, : -self.state.all_durations.shape[0]].max(-1) + torch.max(logits[:, : -self.state.all_durations.shape[0]], dim=-1, out=(self.state.scores, self.state.labels)) + jump_durations_indices = logits[:, -self.state.all_durations.shape[0] :].argmax(dim=-1) + durations = self.state.all_durations[jump_durations_indices] + + # search for non-blank labels using joint, advancing time indices for blank labels + # checking max_symbols is not needed, since we already forced advancing time indices for such cases + torch.eq(self.state.labels, self._blank_index, out=self.state.blank_mask) + # blank_mask = self.labels == self._blank_index + self.state.time_indices_current_labels.copy_(self.state.time_indices, non_blocking=True) + # for blank labels force duration >= 1 + durations.masked_fill_(torch.logical_and(durations == 0, self.state.blank_mask), 1) + if self.state.alignments is not None: + self.state.alignments.add_results_masked_no_checks_( + active_mask=self.state.active_mask, + time_indices=self.state.time_indices_current_labels, + logits=logits if self.preserve_alignments else None, + labels=self.state.labels if self.preserve_alignments else None, + confidence=self._get_confidence_tensor( + F.log_softmax(logits[:, : -self.state.all_durations.shape[0]], dim=-1) + ) + if self.preserve_frame_confidence + else None, + ) + + # advance_mask is a mask for current batch for searching non-blank labels; + # each element is True if non-blank symbol is not yet found AND we can increase the time index + self.state.time_indices.add_(durations) + torch.minimum(self.state.time_indices, self.state.last_timesteps, out=self.state.safe_time_indices) + torch.less(self.state.time_indices, self.state.encoder_output_length, out=self.state.active_mask) + torch.logical_and(self.state.active_mask, self.state.blank_mask, out=self.state.advance_mask) + + # inner loop: find next non-blank labels (if exist) + # same as: self.advance_mask_any = advance_mask.any() + torch.any(self.state.advance_mask, out=self.state.advance_mask_any) + + def _inner_loop_code(self): + """Find next non-blank labels - one iteration""" + # same as: time_indices_current_labels[advance_mask] = time_indices[advance_mask], but non-blocking + # store current time indices to use further for storing the results + torch.where( + self.state.advance_mask, + self.state.time_indices, + self.state.time_indices_current_labels, + out=self.state.time_indices_current_labels, + ) + logits = ( + self.joint.joint_after_projection( + self.state.encoder_output_projected[self.state.batch_indices, self.state.safe_time_indices].unsqueeze( + 1 + ), + self.state.decoder_output, + ) + .squeeze(1) + .squeeze(1) + ) + # get labels (greedy) and scores from current logits, replace labels/scores with new + # labels[advance_mask] are blank, and we are looking for non-blank labels + more_scores, more_labels = logits[:, : -self.state.all_durations.shape[0]].max(-1) + jump_durations_indices = logits[:, -self.state.all_durations.shape[0] :].argmax(dim=-1) + durations = self.state.all_durations[jump_durations_indices] + # same as: labels[advance_mask] = more_labels[advance_mask], but non-blocking + torch.where(self.state.advance_mask, more_labels, self.state.labels, out=self.state.labels) + # same as: scores[advance_mask] = more_scores[advance_mask], but non-blocking + torch.where(self.state.advance_mask, more_scores, self.state.scores, out=self.state.scores) + + if self.state.alignments is not None: + self.state.alignments.add_results_masked_no_checks_( + active_mask=self.state.advance_mask, + time_indices=self.state.time_indices_current_labels, + logits=logits if self.preserve_alignments else None, + labels=more_labels if self.preserve_alignments else None, + confidence=self._get_confidence_tensor( + F.log_softmax(logits[:, : -self.state.all_durations.shape[0]], dim=-1) + ) + if self.preserve_frame_confidence + else None, + ) + + # blank_mask = self.labels == self._blank_index + torch.eq(self.state.labels, self._blank_index, out=self.state.blank_mask) + # for blank labels force duration >= 1 + durations.masked_fill_(torch.logical_and(durations == 0, self.state.blank_mask), 1) + # self.time_indices += self.blank_mask + torch.where( + self.state.advance_mask, + self.state.time_indices + durations, + self.state.time_indices, + out=self.state.time_indices, + ) + + torch.minimum(self.state.time_indices, self.state.last_timesteps, out=self.state.safe_time_indices) + torch.less(self.state.time_indices, self.state.encoder_output_length, out=self.state.active_mask) + torch.logical_and(self.state.active_mask, self.state.blank_mask, out=self.state.advance_mask) + torch.any(self.state.advance_mask, out=self.state.advance_mask_any) + + def _after_inner_loop(self): + """Store hypotheses, state for finished hypotheses, avoid looping""" + # stage 3: filter labels and state, store hypotheses + # select states for hyps that became inactive (is it necessary?) + # this seems to be redundant, but used in the `loop_frames` output + torch.ne(self.state.active_mask, self.state.active_mask_prev, out=self.state.became_inactive_mask) + self.decoder.batch_replace_states_mask( + src_states=self.state.decoder_state, + dst_states=self.state.last_decoder_state, + mask=self.state.became_inactive_mask, + ) + + self.state.batched_hyps.add_results_masked_no_checks_( + self.state.active_mask, self.state.labels, self.state.time_indices_current_labels, self.state.scores, + ) + + # stage 4: to avoid looping, go to next frame after max_symbols emission + # if labels are non-blank (not end-of-utterance), check that last observed timestep with label: + # if it is equal to the current time index, and number of observations is >= max_symbols, force blank + force_blank_mask = torch.logical_and( + self.state.active_mask, + torch.logical_and( + torch.logical_and( + self.state.labels != self._blank_index, + self.state.batched_hyps.last_timestep_lasts >= self.max_symbols, + ), + self.state.batched_hyps.last_timestep == self.state.time_indices, + ), + ) + self.state.time_indices.add_(force_blank_mask) # emit blank => advance time indices + # update safe_time_indices, non-blocking + torch.minimum(self.state.time_indices, self.state.last_timesteps, out=self.state.safe_time_indices) + # same as: active_mask = time_indices < encoder_output_length + torch.less(self.state.time_indices, self.state.encoder_output_length, out=self.state.active_mask) + torch.any(self.state.active_mask, out=self.state.active_mask_any) + + def __call__( + self, x: torch.Tensor, out_len: torch.Tensor, + ) -> Tuple[rnnt_utils.BatchedHyps, Optional[rnnt_utils.BatchedAlignments], Any]: + if self.use_cuda_graphs and x.device.type == "cuda": + return self.loop_labels_cuda_graphs(encoder_output=x, encoder_output_length=out_len) + + return self.loop_labels_torch(encoder_output=x, encoder_output_length=out_len) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/token_classifier.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/token_classifier.py new file mode 100644 index 0000000..4061d19 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/submodules/token_classifier.py @@ -0,0 +1,164 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Dict, Optional + +from torch import nn as nn + +from nemo.collections.asr.parts.submodules.classifier import Classifier +from nemo.collections.common.parts import MultiLayerPerceptron +from nemo.core.classes import typecheck +from nemo.core.neural_types import LogitsType, LogprobsType, NeuralType + +__all__ = ['BertPretrainingTokenClassifier', 'TokenClassifier'] + +ACT2FN = {"gelu": nn.functional.gelu, "relu": nn.functional.relu} + + +@dataclass +class TokenClassifierConfig: + num_layers: int = 1 + activation: str = 'relu' + log_softmax: bool = True + dropout: float = 0.0 + use_transformer_init: bool = True + + +class TokenClassifier(Classifier): + """ + A module to perform token level classification tasks such as Named entity recognition. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """ + Returns definitions of module output ports. + """ + if not self.log_softmax: + return {"logits": NeuralType(('B', 'T', 'C'), LogitsType())} + else: + return {"log_probs": NeuralType(('B', 'T', 'C'), LogprobsType())} + + def __init__( + self, + hidden_size: int, + num_classes: int, + num_layers: int = 1, + activation: str = 'relu', + log_softmax: bool = True, + dropout: float = 0.0, + use_transformer_init: bool = True, + ) -> None: + + """ + Initializes the Token Classifier module. + + Args: + hidden_size: the size of the hidden dimension + num_classes: number of classes + num_layers: number of fully connected layers in the multilayer perceptron (MLP) + activation: activation to usee between fully connected layers in the MLP + log_softmax: whether to apply softmax to the output of the MLP + dropout: dropout to apply to the input hidden states + use_transformer_init: whether to initialize the weights of the classifier head with the same approach used in Transformer + """ + super().__init__(hidden_size=hidden_size, dropout=dropout) + self.log_softmax = log_softmax + self.mlp = MultiLayerPerceptron( + hidden_size, num_classes, num_layers=num_layers, activation=activation, log_softmax=log_softmax + ) + self.post_init(use_transformer_init=use_transformer_init) + + @typecheck() + def forward(self, hidden_states): + """ + Performs the forward step of the module. + Args: + hidden_states: batch of hidden states (for example, from the BERT encoder module) + [BATCH_SIZE x SEQ_LENGTH x HIDDEN_SIZE] + Returns: logits value for each class [BATCH_SIZE x SEQ_LENGTH x NUM_CLASSES] + """ + hidden_states = self.dropout(hidden_states) + logits = self.mlp(hidden_states) + return logits + + +class BertPretrainingTokenClassifier(Classifier): + """ + A module to perform token level classification tasks for Bert pretraining. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """ + Returns definitions of module output ports. + """ + if not self.log_softmax: + return {"logits": NeuralType(('B', 'T', 'C'), LogitsType())} + else: + return {"log_probs": NeuralType(('B', 'T', 'C'), LogprobsType())} + + def __init__( + self, + hidden_size: int, + num_classes: int, + num_layers: int = 1, + activation: str = 'relu', + log_softmax: bool = True, + dropout: float = 0.0, + use_transformer_init: bool = True, + ) -> None: + + """ + Initializes the Token Classifier module. + + Args: + hidden_size: the size of the hidden dimension + num_classes: number of classes + num_layers: number of fully connected layers in the multilayer perceptron (MLP) + activation: activation to usee between fully connected layers in the MLP + log_softmax: whether to apply softmax to the output of the MLP + dropout: dropout to apply to the input hidden states + use_transformer_init: whether to initialize the weights of the classifier head with the same approach used in Transformer + """ + super().__init__(hidden_size=hidden_size, dropout=dropout) + + self.log_softmax = log_softmax + + if activation not in ACT2FN: + raise ValueError(f'activation "{activation}" not found') + self.dense = nn.Linear(hidden_size, hidden_size) + self.act = ACT2FN[activation] + self.norm = nn.LayerNorm(hidden_size, eps=1e-12) + self.mlp = MultiLayerPerceptron( + hidden_size, num_classes, num_layers=num_layers, activation=activation, log_softmax=log_softmax + ) + self.post_init(use_transformer_init=use_transformer_init) + + @typecheck() + def forward(self, hidden_states): + """ + Performs the forward step of the module. + Args: + hidden_states: batch of hidden states (for example, from the BERT encoder module) + [BATCH_SIZE x SEQ_LENGTH x HIDDEN_SIZE] + Returns: logits value for each class [BATCH_SIZE x SEQ_LENGTH x NUM_CLASSES] + """ + hidden_states = self.dropout(hidden_states) + hidden_states = self.dense(hidden_states) + hidden_states = self.act(hidden_states) + transform = self.norm(hidden_states) + logits = self.mlp(transform) + return logits diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/__init__.py new file mode 100644 index 0000000..f1b3db0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.asr.parts.utils.rnnt_utils import BatchedAlignments, BatchedHyps, Hypothesis, NBestHypotheses diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/activations.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/activations.py new file mode 100644 index 0000000..c4ba911 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/activations.py @@ -0,0 +1,50 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn as nn + +__all__ = ['Swish', 'Snake'] + + +@torch.jit.script +def snake(x: torch.Tensor, alpha: torch.Tensor, eps: float = 1e-9) -> torch.Tensor: + """ + equation for snake activation function: x + (alpha + eps)^-1 * sin(alpha * x)^2 + """ + shape = x.shape + x = x.reshape(shape[0], shape[1], -1) + x = x + (alpha + eps).reciprocal() * torch.sin(alpha * x).pow(2) + x = x.reshape(shape) + return x + + +class Snake(nn.Module): + """ + Snake activation function introduced in 'https://arxiv.org/abs/2006.08195' + """ + + def __init__(self, channels: int): + super().__init__() + self.alpha = nn.Parameter(torch.ones(1, channels, 1)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return snake(x, self.alpha) + + +class Swish(nn.SiLU): + """ + Swish activation function introduced in 'https://arxiv.org/abs/1710.05941' + Mathematically identical to SiLU. See note in nn.SiLU for references. + """ diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/adapter_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/adapter_utils.py new file mode 100644 index 0000000..5b74a29 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/adapter_utils.py @@ -0,0 +1,83 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import is_dataclass + +import torch +from omegaconf import DictConfig, OmegaConf + +from nemo.utils import logging + +# Constants +LINEAR_ADAPTER_CLASSPATH = "nemo.collections.common.parts.adapter_modules.LinearAdapter" +MHA_ADAPTER_CLASSPATH = ( + "nemo.collections.asr.parts.submodules.adapters.multi_head_attention_adapter_module.MultiHeadAttentionAdapter" +) +RELMHA_ADAPTER_CLASSPATH = "nemo.collections.asr.parts.submodules.adapters.multi_head_attention_adapter_module.RelPositionMultiHeadAttentionAdapter" +POS_ENCODING_ADAPTER_CLASSPATH = ( + "nemo.collections.asr.parts.submodules.adapters.multi_head_attention_adapter_module.PositionalEncodingAdapter" +) +REL_POS_ENCODING_ADAPTER_CLASSPATH = ( + "nemo.collections.asr.parts.submodules.adapters.multi_head_attention_adapter_module.RelPositionalEncodingAdapter" +) + + +def convert_adapter_cfg_to_dict_config(cfg: DictConfig): + # Convert to DictConfig from dict or Dataclass + if is_dataclass(cfg): + cfg = OmegaConf.structured(cfg) + + if not isinstance(cfg, DictConfig): + cfg = DictConfig(cfg) + + return cfg + + +def update_adapter_cfg_input_dim(module: torch.nn.Module, cfg: DictConfig, *, module_dim: int): + """ + Update the input dimension of the provided adapter config with some default value. + + Args: + module: The module that implements AdapterModuleMixin. + cfg: A DictConfig or a Dataclass representing the adapter config. + module_dim: A default module dimension, used if cfg has an incorrect input dimension. + + Returns: + A DictConfig representing the adapter's config. + """ + cfg = convert_adapter_cfg_to_dict_config(cfg) + + input_dim_valid_keys = ['in_features', 'n_feat'] + input_key = None + + for key in input_dim_valid_keys: + if key in cfg: + input_key = key + break + + if input_key is None: + raise ValueError( + f"Failed to infer the input dimension of the Adapter cfg. \nExpected one of : {input_dim_valid_keys}.\n" + f"Provided config : \n" + f"{OmegaConf.to_yaml(cfg)}" + ) + + input_dim = cfg[input_key] + + if input_dim != module_dim: + logging.info(f"Updating {module.__class__.__name__} Adapter input dim from {input_dim} to {module_dim}") + input_dim = module_dim + + cfg[input_key] = input_dim + return cfg diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/asr_batching.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/asr_batching.py new file mode 100644 index 0000000..dcbebdc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/asr_batching.py @@ -0,0 +1,237 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import Iterator, List, Optional, Union + +import numpy as np +import torch +from torch.utils.data.distributed import DistributedSampler + +from nemo.collections.asr.data.audio_to_text import AudioToBPEDataset, AudioToCharDataset +from nemo.collections.asr.models.asr_model import ASRModel +from nemo.utils import logging + + +class SemiSortBatchSampler(DistributedSampler): + def __init__( + self, + global_rank: int, + world_size: int, + durations: List[int], + batch_size: int, + batch_shuffle: bool = True, + drop_last: bool = False, + randomization_factor: Optional[float] = None, + seed: int = 42, + ) -> None: + """ + Semi Sorted Batching, as proposed in _SSB ("Speed up training with variable + length inputs by efficient batching strategies.", Zhenhao Ge et al. (2021).). + + The Semi Sorted Batch Sampler (SSB) samples the indices by their duration + with the addition of pseudo noise that is sampled from the uniform + distribution \mathbb{U}\left[ -delta * r, delta * r \right], where delta is + defined as the difference between the maximum and minimum duration and r is + the randomization factor that controls the strength of the noise (when r = 0, + there will be a strong sorting). The heuristic value of the r according to + the experiments from paper is 0.2. + + The torch calls the set_epoch method from the distributed data loader sampler + at the end of each epoch to shuffle the samples according to the seed and + epoch number. So the SSB is passed to the dataloader as a sampler with the + dataloader's batch size options and the batch_sampler option set to None to + disable automatical batching. In this case, the sampler has become an iterator + that returns a list of batch indices. + + Args: + global_rank: Rank among all GPUs. + world_size: The number of GPUs used. + durations: Sample durations parsed from `dataset.manifest_processor`. + batch_size: Micro batch size or batch size per singe gpu. + batch_shuffle: Batch sort before each epoch. + drop_last: Drop the last batch if the number of samples is less than batch + size. Defaults to False. + randomization_factor: The strength of noise that will be added to the sample + duration. If no value is passed, the value 0.2 will be used. + seed: Seed for batch shuffleling. Defaults to 42. + + Raises: + ValueError: Wrong randomization factor value. + RuntimeError: Unexpected behavior. + + .. SSB_: + https://www.isca-speech.org/archive/pdfs/interspeech_2021/ge21_interspeech.pdf + """ + if randomization_factor is None: + randomization_factor = 0.1 + logging.info("Randomization factor not found in config, default value 0.1 will be set.") + else: + logging.info(f"A randomization factor {randomization_factor} will be used.") + + if randomization_factor < 0.0: + raise ValueError(f'Randomization factor must be non-negative but found {randomization_factor}.') + + self.rank: List = global_rank + self.num_replicas: int = world_size + + self.durations: np.array = np.array(durations, dtype=np.float32) + + self.shuffle: bool = batch_shuffle + self.micro_batch_size: int = batch_size + self.drop_last: bool = drop_last + self.epoch: int = 0 + self.seed: int = seed + self.randomization_factor: float = randomization_factor + + self.local_num_batches: int = self._calculate_local_num_batches() + + logging.info(f"Semi Sorted Batch Sampler will be used") + + def _calculate_local_num_batches(self) -> int: + init_num_samples = len(self.durations) + + # delete batches with a non-integer number of samples + if self.drop_last: + init_num_samples -= init_num_samples % self.micro_batch_size + + # calculate the number of batches according to the counted number of samples + global_num_batches = math.ceil(init_num_samples / self.micro_batch_size) + + # add extra batches to make it divisible by world size (num replicas) + num_batches_pad = (self.num_replicas - global_num_batches % self.num_replicas) % self.num_replicas + global_num_batches += num_batches_pad + + # calculate the number of batches per rank + local_num_batches = global_num_batches // self.num_replicas + + return local_num_batches + + def _make_batches(self) -> List[np.array]: + max_duration: float = np.max(self.durations) + min_duration: float = np.min(self.durations) + bound: float = (max_duration - min_duration) * self.randomization_factor / 2 + + # generate pseudo noise + noise: np.array = np.random.uniform(low=-bound, high=bound, size=len(self.durations)) + + # sort indices accroding to pseudo noise + sorted_indices: np.array = np.argsort(self.durations + noise) + + # delete batches with a non-integer number of samples + tail = 0 + if self.drop_last: + tail: int = len(sorted_indices) % self.micro_batch_size + exclude = np.random.choice(len(sorted_indices), tail, replace=False) + sorted_indices = np.delete(sorted_indices, exclude) + logging.warning(f"Drop last is set to True, so {len(exclude)} samples will be dropped.") + + global_num_batches: int = math.ceil(len(sorted_indices) / self.micro_batch_size) + + # if the global_num_batches is zero than return empty list + if global_num_batches == 0: + logging.warning( + f"The number of all batches is {global_num_batches}, than dataloader will " + "be empty. To avoid this try to decrease batch size or world size or set " + "drop_last to False." + ) + return [] + + # add extra batches to make it divisible by world size (num replicas) + pad_batches_num: int = (self.num_replicas - global_num_batches % self.num_replicas) % self.num_replicas + if global_num_batches < self.num_replicas: + logging.warning( + f"The number of all batches is {global_num_batches}, which is less than the " + f"world size of {self.num_replicas}. SSB Sampler will add {pad_batches_num} " + "batches. To avoid this try to decrease batch size or world size." + ) + + if pad_batches_num != 0: + # randomly select batch indeces to pad and concatenate them + batch_indeces_pad: np.array = np.random.randint( + low=0, high=len(sorted_indices), size=pad_batches_num * self.micro_batch_size, + ) + sorted_indices: np.array = np.concatenate( + (sorted_indices, sorted_indices[batch_indeces_pad]), axis=0, + ) + + # local indeces are selected by world size and local rank + local_indices: np.array = sorted_indices[self.rank :: self.num_replicas] + + # split local batches + size_mask = range(self.micro_batch_size, len(local_indices), self.micro_batch_size) + local_batches = np.split(local_indices, size_mask, axis=0) + + if len(local_batches) != self.local_num_batches: + raise RuntimeError( + f'Number of calculated indices {len(local_batches)} is not equal to calculated ' + f'number of local batches {self.local_num_batches}.' + ) + + return local_batches + + def __iter__(self) -> Iterator[List[int]]: + local_batches = self._make_batches() + + if self.shuffle: + g = torch.Generator() + g.manual_seed(self.seed + self.epoch + 1) + indices = torch.randperm(self.local_num_batches, generator=g) + else: + indices = torch.arange(0, self.local_num_batches) + + for _, index in enumerate(indices): + yield local_batches[index] + + def __len__(self) -> int: + return self.local_num_batches + + +def get_semi_sorted_batch_sampler( + model: ASRModel, dataset: Union[AudioToCharDataset, AudioToBPEDataset], config: dict +) -> SemiSortBatchSampler: + """ + Instantiates a Semi Sorted (Batch) Sampler. + + Args: + model: ASR Model. + dataset: Dataset which allow iterate over all object and parse durations. + config: Train, Vaidation or Test dataset config. + + Raises: + ValueError: Wrong dataset type. + + Returns: + SemiSortBatchSampler: Semi Sorted Batch Sampler class. + """ + if not (isinstance(dataset, AudioToCharDataset) or isinstance(dataset, AudioToBPEDataset)): + raise ValueError( + "Only AudioToCharDataset or AudioToBPEDataset supported with semi sorted batching, " + f"but found {type(dataset)}." + ) + + durations = [sample.duration for sample in dataset.manifest_processor.collection.data] + + sampler = SemiSortBatchSampler( + global_rank=model.global_rank, + world_size=model.world_size, + durations=durations, + batch_size=config['batch_size'], + batch_shuffle=config.get('shuffle', True), + drop_last=config.get('drop_last', False), + randomization_factor=config.get('randomization_factor', None), + seed=config.get('semi_sort_sampler_seed', 42), + ) + + return sampler diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/asr_confidence_benchmarking_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/asr_confidence_benchmarking_utils.py new file mode 100644 index 0000000..8b15bc2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/asr_confidence_benchmarking_utils.py @@ -0,0 +1,183 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import contextlib +import copy +import os +from pathlib import Path +from typing import List, Optional, Tuple, Union + +import numpy as np +import texterrors +import torch +from omegaconf import open_dict + +from nemo.collections.asr.models import ASRModel, EncDecRNNTModel +from nemo.collections.asr.parts.utils.confidence_metrics import ( + auc_nt, + auc_pr, + auc_roc, + auc_yc, + ece, + nce, + save_confidence_hist, + save_custom_confidence_curve, + save_nt_curve, + save_pr_curve, + save_roc_curve, +) +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis + + +def get_correct_marks(r: Union[List[int], List[str]], h: Union[List[int], List[str]]) -> List[bool]: + """Get correct marks by aligning the reference text with a hypothesis. + + This method considers only insertions and substitutions as incorrect marks. + """ + return [ + a == b + for a, b in zip(*(texterrors.align_texts([str(rr) for rr in r], [str(hh) for hh in h], False)[:-1])) + if b != "" + ] + + +def get_token_targets_with_confidence(hyp: Hypothesis) -> List[Tuple[str, float]]: + return [(y, c) for y, c in zip(hyp.y_sequence, hyp.token_confidence)] + + +def get_word_targets_with_confidence(hyp: Hypothesis) -> List[Tuple[str, float]]: + return [(y, c) for y, c in zip(hyp.words, hyp.word_confidence)] + + +def run_confidence_benchmark( + model: ASRModel, + target_level: str, + filepaths: List[str], + reference_texts: List[str], + batch_size: int = 8, + num_workers: int = 4, + plot_dir: Optional[Union[str, Path]] = None, + autocast: Optional = None, +): + """Run benchmark and plot histograms and curves, if plot_dir is provided. + + Returns: + Dictionary with benchmark results of the following scheme: + `level: (auc_roc, auc_pr, auc_nt, nce, ece, auc_yc, std_yc, max_yc)` with `level` being 'token' or 'word'. + """ + draw_plot = plot_dir is not None + if isinstance(plot_dir, str): + plot_dir = Path(plot_dir) + is_rnnt = isinstance(model, EncDecRNNTModel) + + # setup autocast if necessary + if autocast is None: + + @contextlib.contextmanager + def autocast(): + yield + + # transcribe audio + with autocast(): + with torch.no_grad(): + transcriptions = model.transcribe( + audio=filepaths, batch_size=batch_size, return_hypotheses=True, num_workers=num_workers + ) + if is_rnnt: + transcriptions = transcriptions[0] + + levels = [] + if target_level != "word": + levels.append("token") + if target_level != "token": + levels.append("word") + results = {} + for level in levels: + if level == "token": + targets_with_confidence = [get_token_targets_with_confidence(tran) for tran in transcriptions] + correct_marks = [ + get_correct_marks(model.tokenizer.text_to_ids(r), model.tokenizer.text_to_ids(h.text)) + for r, h in zip(reference_texts, transcriptions) + ] + else: # "word" + targets_with_confidence = [get_word_targets_with_confidence(tran) for tran in transcriptions] + correct_marks = [get_correct_marks(r.split(), h.words) for r, h in zip(reference_texts, transcriptions)] + + y_true, y_score = np.array( + [[f, p[1]] for cm, twc in zip(correct_marks, targets_with_confidence) for f, p in zip(cm, twc)] + ).T + # output scheme: yc.mean(), yc.max(), yc.std() or yc.mean(), yc.max(), yc.std(), (thresholds, yc) + result_yc = auc_yc(y_true, y_score, return_std_maximum=True, return_curve=draw_plot) + # output scheme: ece or ece, (thresholds, ece_curve) + results_ece = ece(y_true, y_score, return_curve=draw_plot) + results[level] = [ + auc_roc(y_true, y_score), + auc_pr(y_true, y_score), + auc_nt(y_true, y_score), + nce(y_true, y_score), + results_ece if isinstance(results_ece, float) else results_ece[0], + ] + list(result_yc[:3]) + + if draw_plot: + os.makedirs(plot_dir, exist_ok=True) + + mask_correct = y_true == 1 + y_score_correct = y_score[mask_correct] + y_score_incorrect = y_score[~mask_correct] + # histogram of the correct distribution + save_confidence_hist(y_score_correct, plot_dir, level + "_" + "hist_correct") + # histogram of the incorrect distribution + save_confidence_hist(y_score_incorrect, plot_dir, level + "_" + "hist_incorrect") + # AUC-ROC curve + save_roc_curve(y_true, y_score, plot_dir, level + "_" + "roc") + # AUC-PR curve + save_pr_curve(y_true, y_score, plot_dir, level + "_" + "pr") + # AUC-NT curve + save_nt_curve(y_true, y_score, plot_dir, level + "_" + "nt") + # AUC-YC curve + yc_thresholds, yc_values = result_yc[-1] + save_custom_confidence_curve( + yc_thresholds, + yc_values, + plot_dir, + level + "_" + "yc", + "Threshold", + "True positive rate − False Positive Rate", + ) + # ECE curve + ece_thresholds, ece_values = results_ece[-1] + ece_values /= max(ece_values) + save_custom_confidence_curve( + ece_thresholds, ece_values, plot_dir, level + "_" + "ece", "Threshold", "|Accuracy − Confidence score|" + ) + + return results + + +def apply_confidence_parameters(decoding_cfg, hp): + """Apply parameters from a parameter grid to a decoding config. + + Returns: + Updated decoding config. + """ + new_decoding_cfg = copy.deepcopy(decoding_cfg) + confidence_cfg_fields = ("aggregation", "exclude_blank") + confidence_method_cfg_fields = ("name", "alpha", "entropy_type", "entropy_norm") + with open_dict(new_decoding_cfg): + for p, v in hp.items(): + if p in confidence_cfg_fields: + new_decoding_cfg.confidence_cfg[p] = v + elif p in confidence_method_cfg_fields: + new_decoding_cfg.confidence_cfg.method_cfg[p] = v + return new_decoding_cfg diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/asr_confidence_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/asr_confidence_utils.py new file mode 100644 index 0000000..27ced56 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/asr_confidence_utils.py @@ -0,0 +1,470 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from abc import ABC, abstractmethod +from dataclasses import dataclass, field +from functools import partial +from typing import List, Optional + +import torch +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis +from nemo.utils import logging + + +class ConfidenceMethodConstants: + NAMES = ("max_prob", "entropy") + ENTROPY_TYPES = ("gibbs", "tsallis", "renyi") + ENTROPY_NORMS = ("lin", "exp") + + @classmethod + def print(cls): + return ( + cls.__name__ + + ": " + + str({"NAMES": cls.NAMES, "ENTROPY_TYPES": cls.ENTROPY_TYPES, "ENTROPY_NORMS": cls.ENTROPY_NORMS}) + ) + + +class ConfidenceConstants: + AGGREGATIONS = ("mean", "min", "max", "prod") + + @classmethod + def print(cls): + return cls.__name__ + ": " + str({"AGGREGATIONS": cls.AGGREGATIONS}) + + +@dataclass +class ConfidenceMethodConfig: + """A Config which contains the method name and settings to compute per-frame confidence scores. + + Args: + name: The method name (str). + Supported values: + - 'max_prob' for using the maximum token probability as a confidence. + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: Which type of entropy to use (str). + Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: A mapping of the entropy value to the interval [0,1]. + Supported values: + - 'lin' for using the linear mapping. + - 'exp' for using exponential mapping with linear shift. + """ + + name: str = "entropy" + entropy_type: str = "tsallis" + alpha: float = 0.33 + entropy_norm: str = "exp" + temperature: str = "DEPRECATED" + + def __post_init__(self): + if self.temperature != "DEPRECATED": + # self.temperature has type str + self.alpha = float(self.temperature) + self.temperature = "DEPRECATED" + if self.name not in ConfidenceMethodConstants.NAMES: + raise ValueError( + f"`name` must be one of the following: " + f"{'`' + '`, `'.join(ConfidenceMethodConstants.NAMES) + '`'}. Provided: `{self.name}`" + ) + if self.entropy_type not in ConfidenceMethodConstants.ENTROPY_TYPES: + raise ValueError( + f"`entropy_type` must be one of the following: " + f"{'`' + '`, `'.join(ConfidenceMethodConstants.ENTROPY_TYPES) + '`'}. Provided: `{self.entropy_type}`" + ) + if self.alpha <= 0.0: + raise ValueError(f"`alpha` must be > 0. Provided: {self.alpha}") + if self.entropy_norm not in ConfidenceMethodConstants.ENTROPY_NORMS: + raise ValueError( + f"`entropy_norm` must be one of the following: " + f"{'`' + '`, `'.join(ConfidenceMethodConstants.ENTROPY_NORMS) + '`'}. Provided: `{self.entropy_norm}`" + ) + + +@dataclass +class ConfidenceConfig: + """A config which contains the following key-value pairs related to confidence scores. + + Args: + preserve_frame_confidence: Bool flag which preserves the history of per-frame confidence scores + generated during decoding. When set to true, the Hypothesis will contain + the non-null value for `frame_confidence` in it. Here, `frame_confidence` is a List of floats. + preserve_token_confidence: Bool flag which preserves the history of per-token confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `token_confidence` in it. Here, `token_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized tokens. + preserve_word_confidence: Bool flag which preserves the history of per-word confidence scores + generated during greedy decoding (sample / batched). When set to true, the Hypothesis will contain + the non-null value for `word_confidence` in it. Here, `word_confidence` is a List of floats. + + The length of the list corresponds to the number of recognized words. + exclude_blank: Bool flag indicating that blank token confidence scores are to be excluded + from the `token_confidence`. + aggregation: Which aggregation type to use for collapsing per-token confidence into per-word confidence. + Valid options are `mean`, `min`, `max`, `prod`. + method_cfg: A dict-like object which contains the method name and settings to compute per-frame + confidence scores. + + name: The method name (str). + Supported values: + - 'max_prob' for using the maximum token probability as a confidence. + - 'entropy' for using a normalized entropy of a log-likelihood vector. + + entropy_type: Which type of entropy to use (str). Used if confidence_method_cfg.name is set to `entropy`. + Supported values: + - 'gibbs' for the (standard) Gibbs entropy. If the alpha (α) is provided, + the formula is the following: H_α = -sum_i((p^α_i)*log(p^α_i)). + Note that for this entropy, the alpha should comply the following inequality: + (log(V)+2-sqrt(log^2(V)+4))/(2*log(V)) <= α <= (1+log(V-1))/log(V-1) + where V is the model vocabulary size. + - 'tsallis' for the Tsallis entropy with the Boltzmann constant one. + Tsallis entropy formula is the following: H_α = 1/(α-1)*(1-sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/Tsallis_entropy + - 'renyi' for the Rényi entropy. + Rényi entropy formula is the following: H_α = 1/(1-α)*log_2(sum_i(p^α_i)), + where α is a parameter. When α == 1, it works like the Gibbs entropy. + More: https://en.wikipedia.org/wiki/R%C3%A9nyi_entropy + + alpha: Power scale for logsoftmax (α for entropies). Here we restrict it to be > 0. + When the alpha equals one, scaling is not applied to 'max_prob', + and any entropy type behaves like the Shannon entropy: H = -sum_i(p_i*log(p_i)) + + entropy_norm: A mapping of the entropy value to the interval [0,1]. + Supported values: + - 'lin' for using the linear mapping. + - 'exp' for using exponential mapping with linear shift. + """ + + preserve_frame_confidence: bool = False + preserve_token_confidence: bool = False + preserve_word_confidence: bool = False + exclude_blank: bool = True + aggregation: str = "min" + method_cfg: ConfidenceMethodConfig = field(default_factory=lambda: ConfidenceMethodConfig()) + + def __post_init__(self): + # OmegaConf.structured ensures that post_init check is always executed + self.method_cfg = OmegaConf.structured( + self.method_cfg + if isinstance(self.method_cfg, ConfidenceMethodConfig) + else ConfidenceMethodConfig(**self.method_cfg) + ) + if self.aggregation not in ConfidenceConstants.AGGREGATIONS: + raise ValueError( + f"`aggregation` has to be one of the following: " + f"{'`' + '`, `'.join(ConfidenceConstants.AGGREGATIONS) + '`'}. Provided: `{self.aggregation}`" + ) + + +def get_confidence_measure_bank(): + """Generate a dictionary with confidence measure functionals. + + Supported confidence measures: + max_prob: normalized maximum probability + entropy_gibbs_lin: Gibbs entropy with linear normalization + entropy_gibbs_exp: Gibbs entropy with exponential normalization + entropy_tsallis_lin: Tsallis entropy with linear normalization + entropy_tsallis_exp: Tsallis entropy with exponential normalization + entropy_renyi_lin: Rényi entropy with linear normalization + entropy_renyi_exp: Rényi entropy with exponential normalization + + Returns: + dictionary with lambda functions. + """ + # helper functions + # Gibbs entropy is implemented without alpha + neg_entropy_gibbs = lambda x: (x.exp() * x).sum(-1) + neg_entropy_alpha = lambda x, t: (x * t).exp().sum(-1) + neg_entropy_alpha_gibbs = lambda x, t: ((x * t).exp() * x).sum(-1) + # too big for a lambda + def entropy_tsallis_exp(x, v, t): + exp_neg_max_ent = math.exp((1 - math.pow(v, 1 - t)) / (1 - t)) + return (((1 - neg_entropy_alpha(x, t)) / (1 - t)).exp() - exp_neg_max_ent) / (1 - exp_neg_max_ent) + + def entropy_gibbs_exp(x, v, t): + exp_neg_max_ent = math.pow(v, -t * math.pow(v, 1 - t)) + return ((neg_entropy_alpha_gibbs(x, t) * t).exp() - exp_neg_max_ent) / (1 - exp_neg_max_ent) + + # use Gibbs entropies for Tsallis and Rényi with t == 1.0 + entropy_gibbs_lin_baseline = lambda x, v: 1 + neg_entropy_gibbs(x) / math.log(v) + entropy_gibbs_exp_baseline = lambda x, v: (neg_entropy_gibbs(x).exp() * v - 1) / (v - 1) + # fill the measure bank + confidence_measure_bank = {} + # Maximum probability measure is implemented without alpha + confidence_measure_bank["max_prob"] = ( + lambda x, v, t: (x.max(dim=-1)[0].exp() * v - 1) / (v - 1) + if t == 1.0 + else ((x.max(dim=-1)[0] * t).exp() * math.pow(v, t) - 1) / (math.pow(v, t) - 1) + ) + confidence_measure_bank["entropy_gibbs_lin"] = ( + lambda x, v, t: entropy_gibbs_lin_baseline(x, v) + if t == 1.0 + else 1 + neg_entropy_alpha_gibbs(x, t) / math.log(v) / math.pow(v, 1 - t) + ) + confidence_measure_bank["entropy_gibbs_exp"] = ( + lambda x, v, t: entropy_gibbs_exp_baseline(x, v) if t == 1.0 else entropy_gibbs_exp(x, v, t) + ) + confidence_measure_bank["entropy_tsallis_lin"] = ( + lambda x, v, t: entropy_gibbs_lin_baseline(x, v) + if t == 1.0 + else 1 + (1 - neg_entropy_alpha(x, t)) / (math.pow(v, 1 - t) - 1) + ) + confidence_measure_bank["entropy_tsallis_exp"] = ( + lambda x, v, t: entropy_gibbs_exp_baseline(x, v) if t == 1.0 else entropy_tsallis_exp(x, v, t) + ) + confidence_measure_bank["entropy_renyi_lin"] = ( + lambda x, v, t: entropy_gibbs_lin_baseline(x, v) + if t == 1.0 + else 1 + neg_entropy_alpha(x, t).log2() / (t - 1) / math.log(v, 2) + ) + confidence_measure_bank["entropy_renyi_exp"] = ( + lambda x, v, t: entropy_gibbs_exp_baseline(x, v) + if t == 1.0 + else (neg_entropy_alpha(x, t).pow(1 / (t - 1)) * v - 1) / (v - 1) + ) + return confidence_measure_bank + + +def get_confidence_aggregation_bank(): + """Generate a dictionary with confidence aggregation functions. + + Supported confidence aggregation functions: + min: minimum + max: maximum + mean: arithmetic mean + prod: product + + Returns: + dictionary with functions. + """ + confidence_aggregation_bank = {"mean": lambda x: sum(x) / len(x), "min": min, "max": max} + # python 3.7 and earlier do not have math.prod + if hasattr(math, "prod"): + confidence_aggregation_bank["prod"] = math.prod + else: + import operator + from functools import reduce + + confidence_aggregation_bank["prod"] = lambda x: reduce(operator.mul, x, 1) + return confidence_aggregation_bank + + +class ConfidenceMethodMixin(ABC): + """Confidence Method Mixin class. + + It initializes per-frame confidence method. + """ + + def _init_confidence_method(self, confidence_method_cfg: Optional[DictConfig] = None): + """Initialize per-frame confidence method from config. + """ + # OmegaConf.structured ensures that post_init check is always executed + confidence_method_cfg = OmegaConf.structured( + ConfidenceMethodConfig() + if confidence_method_cfg is None + else ConfidenceMethodConfig(**confidence_method_cfg) + ) + + # set confidence calculation method + # we suppose that self.blank_id == len(vocabulary) + self.num_tokens = (self.blank_id if hasattr(self, "blank_id") else self._blank_index) + 1 + self.alpha = confidence_method_cfg.alpha + + # init confidence measure bank + self.confidence_measure_bank = get_confidence_measure_bank() + + measure = None + # construct measure_name + measure_name = "" + if confidence_method_cfg.name == "max_prob": + measure_name = "max_prob" + elif confidence_method_cfg.name == "entropy": + measure_name = '_'.join( + [confidence_method_cfg.name, confidence_method_cfg.entropy_type, confidence_method_cfg.entropy_norm] + ) + else: + raise ValueError(f"Unsupported `confidence_method_cfg.name`: `{confidence_method_cfg.name}`") + if measure_name not in self.confidence_measure_bank: + raise ValueError(f"Unsupported measure setup: `{measure_name}`") + measure = partial(self.confidence_measure_bank[measure_name], v=self.num_tokens, t=self.alpha) + + self._confidence_measure = measure + + def _get_confidence(self, x: torch.Tensor) -> list[float]: + """Compute confidence, return list of confidence items for each item in batch""" + return self._get_confidence_tensor(x).tolist() + + def _get_confidence_tensor(self, x: torch.Tensor) -> torch.Tensor: + """Compute confidence, return tensor""" + return self._confidence_measure(torch.nan_to_num(x)) + + +class ConfidenceMixin(ABC): + """Confidence Mixin class. + + It is responsible for confidence estimation method initialization and high-level confidence score calculation. + """ + + def _init_confidence(self, confidence_cfg: Optional[DictConfig] = None): + """Initialize confidence-related fields and confidence aggregation function from config. + """ + # OmegaConf.structured ensures that post_init check is always executed + confidence_cfg = OmegaConf.structured( + ConfidenceConfig() if confidence_cfg is None else ConfidenceConfig(**confidence_cfg) + ) + self.confidence_method_cfg = confidence_cfg.method_cfg + + # extract the config + self.preserve_word_confidence = confidence_cfg.get('preserve_word_confidence', False) + # set preserve_frame_confidence and preserve_token_confidence to True + # if preserve_word_confidence is True + self.preserve_token_confidence = ( + confidence_cfg.get('preserve_token_confidence', False) | self.preserve_word_confidence + ) + # set preserve_frame_confidence to True if preserve_token_confidence is True + self.preserve_frame_confidence = ( + confidence_cfg.get('preserve_frame_confidence', False) | self.preserve_token_confidence + ) + self.exclude_blank_from_confidence = confidence_cfg.get('exclude_blank', True) + self.word_confidence_aggregation = confidence_cfg.get('aggregation', "min") + + # define aggregation functions + self.confidence_aggregation_bank = get_confidence_aggregation_bank() + self._aggregate_confidence = self.confidence_aggregation_bank[self.word_confidence_aggregation] + + # Update preserve frame confidence + if self.preserve_frame_confidence is False: + if self.cfg.strategy in ['greedy', 'greedy_batch']: + self.preserve_frame_confidence = self.cfg.greedy.get('preserve_frame_confidence', False) + # OmegaConf.structured ensures that post_init check is always executed + confidence_method_cfg = OmegaConf.structured(self.cfg.greedy).get('confidence_method_cfg', None) + self.confidence_method_cfg = ( + OmegaConf.structured(ConfidenceMethodConfig()) + if confidence_method_cfg is None + else OmegaConf.structured(ConfidenceMethodConfig(**confidence_method_cfg)) + ) + + @abstractmethod + def compute_confidence(self, hypotheses_list: List[Hypothesis]) -> List[Hypothesis]: + """Computes high-level (per-token and/or per-word) confidence scores for a list of hypotheses. + Assumes that `frame_confidence` is present in the hypotheses. + + Args: + hypotheses_list: List of Hypothesis. + + Returns: + A list of hypotheses with high-level confidence scores. + """ + raise NotImplementedError() + + @abstractmethod + def _aggregate_token_confidence(self, hypothesis: Hypothesis) -> List[float]: + """Implemented by subclass in order to aggregate token confidence to a word-level confidence. + + Args: + hypothesis: Hypothesis + + Returns: + A list of word-level confidence scores. + """ + raise NotImplementedError() + + def _aggregate_token_confidence_chars(self, words: List[str], token_confidence: List[float]) -> List[float]: + """Implementation of token confidence aggregation for character-based models. + + Args: + words: List of words of a hypothesis. + token_confidence: List of token-level confidence scores of a hypothesis. + + Returns: + A list of word-level confidence scores. + """ + word_confidence = [] + i = 0 + for word in words: + word_len = len(word) + word_confidence.append(self._aggregate_confidence(token_confidence[i : i + word_len])) + # we assume that there is exactly one space token between words and exclude it from word confidence + i += word_len + 1 + return word_confidence + + def _aggregate_token_confidence_subwords_sentencepiece( + self, words: List[str], token_confidence: List[float], token_ids: List[int] + ) -> List[float]: + """Implementation of token confidence aggregation for subword-based models. + + **Note**: Only supports Sentencepiece based tokenizers ! + + Args: + words: List of words of a hypothesis. + token_confidence: List of token-level confidence scores of a hypothesis. + token_ids: List of token ids of a hypothesis. + + Returns: + A list of word-level confidence scores. + """ + word_confidence = [] + # run only if there are final words + if len(words) > 0: + j = 0 + prev_unk = False + prev_underline = False + for i, token_id in enumerate(token_ids): + token = self.decode_ids_to_tokens([int(token_id)])[0] + token_text = self.decode_tokens_to_str([int(token_id)]) + # treat `` as a separate word regardless of the next token + # to match the result of `tokenizer.ids_to_text` + if (token != token_text or prev_unk) and i > j: + # do not add confidence for `▁` if the current token starts with `▁` + # to match the result of `tokenizer.ids_to_text` + if not prev_underline: + word_confidence.append(self._aggregate_confidence(token_confidence[j:i])) + j = i + prev_unk = token == '' + prev_underline = token == '▁' + if not prev_underline: + word_confidence.append(self._aggregate_confidence(token_confidence[j : len(token_ids)])) + if len(words) != len(word_confidence): + raise RuntimeError( + f"""Something went wrong with word-level confidence aggregation.\n + Please check these values for debugging:\n + len(words): {len(words)},\n + len(word_confidence): {len(word_confidence)},\n + recognized text: `{' '.join(words)}`""" + ) + return word_confidence diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/asr_module_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/asr_module_utils.py new file mode 100644 index 0000000..e077d79 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/asr_module_utils.py @@ -0,0 +1,82 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional + +from omegaconf import DictConfig, open_dict + +from nemo.collections.asr.modules import conv_asr +from nemo.collections.asr.parts.submodules import jasper +from nemo.utils import logging + + +def change_conv_asr_se_context_window(model: 'ASRModel', context_window: int, update_config: bool = True): + """ + Update the context window of the SqueezeExcitation module if the provided model contains an + `encoder` which is an instance of `ConvASREncoder`. + + Args: + model: A subclass of `ASRModel`, itself a subclass of `ModelPT`. + context_window: An integer representing the number of input timeframes that will be used + to compute the context. Each timeframe corresponds to a single window stride of the + STFT features. + + Say the window_stride = 0.01s, then a context window of 128 represents 128 * 0.01 s + of context to compute the Squeeze step. + update_config: Whether to update the config or not with the new context window. + """ + if update_config and not hasattr(model.cfg, 'encoder'): + logging.info( + "Could not change the context window in SqueezeExcite module " + "since the model provided does not contain an `encoder` module in its config." + ) + return + + if not isinstance(model.encoder, conv_asr.ConvASREncoder): + logging.info( + f"Could not change the context window in SqueezeExcite module " + f"since the `encoder` module is not an instance of `ConvASREncoder`.\n" + f"Provided encoder class = {model.encoder.__class__.__name__}" + ) + return + + enc_cfg = model.cfg.encoder if update_config else None + + if enc_cfg is not None: + with open_dict(enc_cfg): + _update_se_context_window(model, context_window, cfg=enc_cfg) + else: + _update_se_context_window(model, context_window) + + # Update model config + if update_config: + model.cfg.encoder = enc_cfg + + +def _update_se_context_window(model: 'ASRModel', context_window: int, cfg: Optional[DictConfig] = None): + jasper_block_counter = -1 + for name, m in model.named_modules(): + if type(m) == jasper.JasperBlock: + jasper_block_counter += 1 + + if type(m) == jasper.MaskedConv1d: + if m.conv.stride[0] > 1 and 'mconv' in name: + context_window = context_window // m.conv.stride[0] + + if type(m) == jasper.SqueezeExcite: + m.change_context_window(context_window=context_window) + + # update config + if cfg is not None: + cfg.jasper[jasper_block_counter].se_context_size = context_window diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/audio_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/audio_utils.py new file mode 100644 index 0000000..8188dbe --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/audio_utils.py @@ -0,0 +1,604 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import Iterable, Optional, Union + +import librosa +import numpy as np +import numpy.typing as npt +import scipy +import soundfile as sf +import torch +from scipy.spatial.distance import pdist, squareform + +from nemo.utils import logging + +SOUND_VELOCITY = 343.0 # m/s +ChannelSelectorType = Union[int, Iterable[int], str] + + +def get_samples(audio_file: str, target_sr: int = 16000, dtype: str = 'float32'): + """ + Read the samples from the given audio_file path. If not specified, the input audio file is automatically + resampled to 16kHz. + + Args: + audio_file (str): + Path to the input audio file + target_sr (int): + Targeted sampling rate + Returns: + samples (numpy.ndarray): + Time-series sample data from the given audio file + """ + with sf.SoundFile(audio_file, 'r') as f: + samples = f.read(dtype=dtype) + if f.samplerate != target_sr: + samples = librosa.core.resample(samples, orig_sr=f.samplerate, target_sr=target_sr) + samples = samples.transpose() + return samples + + +def select_channels(signal: npt.NDArray, channel_selector: Optional[ChannelSelectorType] = None) -> npt.NDArray: + """ + Convert a multi-channel signal to a single-channel signal by averaging over channels or selecting a single channel, + or pass-through multi-channel signal when channel_selector is `None`. + + Args: + signal: numpy array with shape (..., num_channels) + channel selector: string denoting the downmix mode, an integer denoting the channel to be selected, or an iterable + of integers denoting a subset of channels. Channel selector is using zero-based indexing. + If set to `None`, the original signal will be returned. Uses zero-based indexing. + + Returns: + numpy array + """ + if signal.ndim == 1: + # For one-dimensional input, return the input signal. + if channel_selector not in [None, 0, 'average']: + raise ValueError( + 'Input signal is one-dimensional, channel selector (%s) cannot not be used.', str(channel_selector) + ) + return signal + + num_channels = signal.shape[-1] + num_samples = signal.size // num_channels # handle multi-dimensional signals + + if num_channels >= num_samples: + logging.warning( + 'Number of channels (%d) is greater or equal than number of samples (%d). Check for possible transposition.', + num_channels, + num_samples, + ) + + # Samples are arranged as (num_channels, ...) + if channel_selector is None: + # keep the original multi-channel signal + pass + elif channel_selector == 'average': + # default behavior: downmix by averaging across channels + signal = np.mean(signal, axis=-1) + elif isinstance(channel_selector, int): + # select a single channel + if channel_selector >= num_channels: + raise ValueError(f'Cannot select channel {channel_selector} from a signal with {num_channels} channels.') + signal = signal[..., channel_selector] + elif isinstance(channel_selector, Iterable): + # select multiple channels + if max(channel_selector) >= num_channels: + raise ValueError( + f'Cannot select channel subset {channel_selector} from a signal with {num_channels} channels.' + ) + signal = signal[..., channel_selector] + # squeeze the channel dimension if a single-channel is selected + # this is done to have the same shape as when using integer indexing + if len(channel_selector) == 1: + signal = np.squeeze(signal, axis=-1) + else: + raise ValueError(f'Unexpected value for channel_selector ({channel_selector})') + + return signal + + +def sinc_unnormalized(x: float) -> float: + """Unnormalized sinc. + + Args: + x: input value + + Returns: + Calculates sin(x)/x + """ + return np.sinc(x / np.pi) + + +def theoretical_coherence( + mic_positions: npt.NDArray, + sample_rate: float, + field: str = 'spherical', + fft_length: int = 512, + sound_velocity: float = SOUND_VELOCITY, +) -> npt.NDArray: + """Calculate a theoretical coherence matrix for given mic positions and field type. + + Args: + mic_positions: 3D Cartesian coordinates of microphone positions, shape (num_mics, 3) + field: string denoting the type of the soundfield + sample_rate: sampling rate of the input signal in Hz + fft_length: length of the fft in samples + sound_velocity: speed of sound in m/s + + Returns: + Calculated coherence with shape (num_subbands, num_mics, num_mics) + """ + assert mic_positions.shape[1] == 3, "Expecting 3D microphone positions" + num_mics = mic_positions.shape[0] + + if num_mics < 2: + raise ValueError(f'Expecting at least 2 microphones, received {num_mics}') + + num_subbands = fft_length // 2 + 1 + angular_freq = 2 * np.pi * sample_rate * np.arange(0, num_subbands) / fft_length + desired_coherence = np.zeros((num_subbands, num_mics, num_mics)) + + mic_distance = squareform(pdist(mic_positions)) + + for p in range(num_mics): + desired_coherence[:, p, p] = 1.0 + for q in range(p + 1, num_mics): + dist_pq = mic_distance[p, q] + if field == 'spherical': + desired_coherence[:, p, q] = sinc_unnormalized(angular_freq * dist_pq / sound_velocity) + else: + raise ValueError(f'Unknown noise field {field}.') + # symmetry + desired_coherence[:, q, p] = desired_coherence[:, p, q] + + return desired_coherence + + +def estimated_coherence(S: npt.NDArray, eps: float = 1e-16) -> npt.NDArray: + """Estimate complex-valued coherence for the input STFT-domain signal. + + Args: + S: STFT of the signal with shape (num_subbands, num_frames, num_channels) + eps: small regularization constant + + Returns: + Estimated coherence with shape (num_subbands, num_channels, num_channels) + """ + if S.ndim != 3: + raise RuntimeError('Expecting the input STFT to be a 3D array') + + num_subbands, num_frames, num_channels = S.shape + + if num_channels < 2: + raise ValueError('Expecting at least 2 microphones') + + psd = np.mean(np.abs(S) ** 2, axis=1) + estimated_coherence = np.zeros((num_subbands, num_channels, num_channels), dtype=complex) + + for p in range(num_channels): + estimated_coherence[:, p, p] = 1.0 + for q in range(p + 1, num_channels): + cross_psd = np.mean(S[:, :, p] * np.conjugate(S[:, :, q]), axis=1) + estimated_coherence[:, p, q] = cross_psd / np.sqrt(psd[:, p] * psd[:, q] + eps) + # symmetry + estimated_coherence[:, q, p] = np.conjugate(estimated_coherence[:, p, q]) + + return estimated_coherence + + +def generate_approximate_noise_field( + mic_positions: npt.NDArray, + noise_signal: npt.NDArray, + sample_rate: float, + field: str = 'spherical', + fft_length: int = 512, + method: str = 'cholesky', + sound_velocity: float = SOUND_VELOCITY, +): + """ + Args: + mic_positions: 3D microphone positions, shape (num_mics, 3) + noise_signal: signal used to generate the approximate noise field, shape (num_samples, num_mics). + Different channels need to be independent. + sample_rate: sampling rate of the input signal + field: string denoting the type of the soundfield + fft_length: length of the fft in samples + method: coherence decomposition method + sound_velocity: speed of sound in m/s + + Returns: + Signal with coherence approximately matching the desired coherence, shape (num_samples, num_channels) + + References: + E.A.P. Habets, I. Cohen and S. Gannot, 'Generating nonstationary multisensor + signals under a spatial coherence constraint', Journal of the Acoustical Society + of America, Vol. 124, Issue 5, pp. 2911-2917, Nov. 2008. + """ + assert fft_length % 2 == 0 + num_mics = mic_positions.shape[0] + + if num_mics < 2: + raise ValueError('Expecting at least 2 microphones') + + desired_coherence = theoretical_coherence( + mic_positions=mic_positions, + field=field, + sample_rate=sample_rate, + fft_length=fft_length, + sound_velocity=sound_velocity, + ) + + return transform_to_match_coherence(signal=noise_signal, desired_coherence=desired_coherence, method=method) + + +def transform_to_match_coherence( + signal: npt.NDArray, + desired_coherence: npt.NDArray, + method: str = 'cholesky', + ref_channel: int = 0, + corrcoef_threshold: float = 0.2, +) -> npt.NDArray: + """Transform the input multichannel signal to match the desired coherence. + + Note: It's assumed that channels are independent. + + Args: + signal: independent noise signals with shape (num_samples, num_channels) + desired_coherence: desired coherence with shape (num_subbands, num_channels, num_channels) + method: decomposition method used to construct the transformation matrix + ref_channel: reference channel for power normalization of the input signal + corrcoef_threshold: used to detect input signals with high correlation between channels + + Returns: + Signal with coherence approximately matching the desired coherence, shape (num_samples, num_channels) + + References: + E.A.P. Habets, I. Cohen and S. Gannot, 'Generating nonstationary multisensor + signals under a spatial coherence constraint', Journal of the Acoustical Society + of America, Vol. 124, Issue 5, pp. 2911-2917, Nov. 2008. + """ + num_channels = signal.shape[1] + num_subbands = desired_coherence.shape[0] + assert desired_coherence.shape[1] == num_channels + assert desired_coherence.shape[2] == num_channels + + fft_length = 2 * (num_subbands - 1) + + # remove DC component + signal = signal - np.mean(signal, axis=0) + + # channels needs to have equal power, so normalize with the ref mic + signal_power = np.mean(np.abs(signal) ** 2, axis=0) + signal = signal * np.sqrt(signal_power[ref_channel]) / np.sqrt(signal_power) + + # input channels should be uncorrelated + # here, we just check for high correlation coefficients between channels to detect ill-constructed inputs + corrcoef_matrix = np.corrcoef(signal.transpose()) + # mask the diagonal elements + np.fill_diagonal(corrcoef_matrix, 0.0) + if np.any(np.abs(corrcoef_matrix) > corrcoef_threshold): + raise RuntimeError( + f'Input channels are correlated above the threshold {corrcoef_threshold}. Max abs off-diagonal element of the coefficient matrix: {np.abs(corrcoef_matrix).max()}.' + ) + + # analysis transform + S = librosa.stft(signal.transpose(), n_fft=fft_length) + # (channel, subband, frame) -> (subband, frame, channel) + S = S.transpose(1, 2, 0) + + # generate output signal for each subband + X = np.zeros_like(S) + + # factorize the desired coherence (skip the DC component) + if method == 'cholesky': + L = np.linalg.cholesky(desired_coherence[1:]) + A = L.swapaxes(1, 2) + elif method == 'evd': + w, V = np.linalg.eig(desired_coherence[1:]) + # scale eigenvectors + A = np.sqrt(w)[:, None, :] * V + # prepare transform matrix + A = A.swapaxes(1, 2) + else: + raise ValueError(f'Unknown method {method}') + + # transform vectors at each time step: + # x_t = A^T * s_t + # or in matrix notation: X = S * A + X[1:, ...] = np.matmul(S[1:, ...], A) + + # synthesis transform + # transpose X from (subband, frame, channel) to (channel, subband, frame) + x = librosa.istft(X.transpose(2, 0, 1), length=len(signal)) + # (channel, sample) -> (sample, channel) + x = x.transpose() + + return x + + +def rms(x: np.ndarray) -> float: + """Calculate RMS value for the input signal. + + Args: + x: input signal + + Returns: + RMS of the input signal. + """ + return np.sqrt(np.mean(np.abs(x) ** 2)) + + +def mag2db(mag: float, eps: Optional[float] = 1e-16) -> float: + """Convert magnitude ratio from linear scale to dB. + + Args: + mag: linear magnitude value + eps: small regularization constant + + Returns: + Value in dB. + """ + return 20 * np.log10(mag + eps) + + +def db2mag(db: float) -> float: + """Convert value in dB to linear magnitude ratio. + + Args: + db: magnitude ratio in dB + + Returns: + Magnitude ratio in linear scale. + """ + return 10 ** (db / 20) + + +def pow2db(power: float, eps: Optional[float] = 1e-16) -> float: + """Convert power ratio from linear scale to dB. + + Args: + power: power ratio in linear scale + eps: small regularization constant + + Returns: + Power in dB. + """ + return 10 * np.log10(power + eps) + + +def get_segment_start(signal: np.ndarray, segment: np.ndarray) -> int: + """Get starting point of `segment` in `signal`. + We assume that `segment` is a sub-segment of `signal`. + For example, `signal` may be a 10 second audio signal, + and `segment` could be the signal between 2 seconds and + 5 seconds. This function will then return the index of + the sample where `segment` starts (at 2 seconds). + + Args: + signal: numpy array with shape (num_samples,) + segment: numpy array with shape (num_samples,) + + Returns: + Index of the start of `segment` in `signal`. + """ + if len(signal) <= len(segment): + raise ValueError( + f'segment must be shorter than signal: len(segment) = {len(segment)}, len(signal) = {len(signal)}' + ) + cc = scipy.signal.correlate(signal, segment, mode='valid') + return np.argmax(cc) + + +def calculate_sdr_numpy( + estimate: np.ndarray, + target: np.ndarray, + scale_invariant: bool = False, + convolution_invariant: bool = False, + convolution_filter_length: Optional[int] = None, + remove_mean: bool = True, + sdr_max: Optional[float] = None, + eps: float = 1e-8, +) -> float: + """Calculate signal-to-distortion ratio. + + SDR = 10 * log10( ||t||_2^2 / (||e-t||_2^2 + alpha * ||t||^2) + + where + alpha = 10^(-sdr_max/10) + + Optionally, apply scale-invariant scaling to target signal. + + Args: + estimate: estimated signal + target: target signal + + Returns: + SDR in dB. + """ + if scale_invariant and convolution_invariant: + raise ValueError('Arguments scale_invariant and convolution_invariant cannot be used simultaneously.') + + if remove_mean: + estimate = estimate - np.mean(estimate) + target = target - np.mean(target) + + if scale_invariant or (convolution_invariant and convolution_filter_length == 1): + target = scale_invariant_target_numpy(estimate=estimate, target=target, eps=eps) + elif convolution_invariant: + target = convolution_invariant_target_numpy( + estimate=estimate, target=target, filter_length=convolution_filter_length, eps=eps + ) + + target_pow = np.mean(np.abs(target) ** 2) + distortion_pow = np.mean(np.abs(estimate - target) ** 2) + + if sdr_max is not None: + distortion_pow = distortion_pow + 10 ** (-sdr_max / 10) * target_pow + + sdr = 10 * np.log10(target_pow / (distortion_pow + eps) + eps) + return sdr + + +def wrap_to_pi(x: torch.Tensor) -> torch.Tensor: + """Wrap angle in radians to [-pi, pi] + + Args: + x: angle in radians + + Returns: + Angle in radians wrapped to [-pi, pi] + """ + pi = torch.tensor(math.pi, device=x.device) + return torch.remainder(x + pi, 2 * pi) - pi + + +def convmtx_numpy(x: np.ndarray, filter_length: int, delay: int = 0, n_steps: Optional[int] = None) -> np.ndarray: + """Construct a causal convolutional matrix from x delayed by `delay` samples. + + Args: + x: input signal, shape (N,) + filter_length: length of the filter in samples + delay: delay the signal by a number of samples + n_steps: total number of time steps (rows) for the output matrix + + Returns: + Convolutional matrix, shape (n_steps, filter_length) + """ + if x.ndim != 1: + raise ValueError(f'Expecting one-dimensional signal. Received signal with shape {x.shape}') + + if n_steps is None: + # Keep the same length as the input signal + n_steps = len(x) + + # pad as necessary + x_pad = np.hstack([np.zeros(delay), x]) + if (pad_len := n_steps - len(x_pad)) > 0: + x_pad = np.hstack([x_pad, np.zeros(pad_len)]) + else: + x_pad = x_pad[:n_steps] + + return scipy.linalg.toeplitz(x_pad, np.hstack([x_pad[0], np.zeros(filter_length - 1)])) + + +def convmtx_mc_numpy(x: np.ndarray, filter_length: int, delay: int = 0, n_steps: Optional[int] = None) -> np.ndarray: + """Construct a causal multi-channel convolutional matrix from `x` delayed by `delay` samples. + + Args: + x: input signal, shape (N, M) + filter_length: length of the filter in samples + delay: delay the signal by a number of samples + n_steps: total number of time steps (rows) for the output matrix + + Returns: + Multi-channel convolutional matrix, shape (n_steps, M * filter_length) + """ + if x.ndim != 2: + raise ValueError(f'Expecting two-dimensional signal. Received signal with shape {x.shape}') + + mc_mtx = [] + + for m in range(x.shape[1]): + mc_mtx.append(convmtx_numpy(x[:, m], filter_length=filter_length, delay=delay, n_steps=n_steps)) + + return np.hstack(mc_mtx) + + +def scale_invariant_target_numpy(estimate: np.ndarray, target: np.ndarray, eps: float = 1e-8) -> np.ndarray: + """Calculate convolution-invariant target for a given estimated signal. + + Calculate scaled target obtained by solving + + min_scale || scale * target - estimate ||^2 + + Args: + estimate: one-dimensional estimated signal, shape (T,) + target: one-dimensional target signal, shape (T,) + eps: regularization constans + + Returns: + Scaled target signal, shape (T,) + """ + assert target.ndim == estimate.ndim == 1, f'Only one-dimensional inputs supported' + + estimate_dot_target = np.mean(estimate * target) + target_pow = np.mean(np.abs(target) ** 2) + scale = estimate_dot_target / (target_pow + eps) + return scale * target + + +def convolution_invariant_target_numpy( + estimate: np.ndarray, target: np.ndarray, filter_length, diag_reg: float = 1e-6, eps: float = 1e-8 +) -> np.ndarray: + """Calculate convolution-invariant target for a given estimated signal. + + Calculate target filtered with a linear f obtained by solving + + min_filter || conv(filter, target) - estimate ||^2 + + Args: + estimate: one-dimensional estimated signal + target: one-dimensional target signal + filter_length: length of the (convolutive) filter + diag_reg: multiplicative factor for relative diagonal loading + eps: absolute diagonal loading + """ + assert target.ndim == estimate.ndim == 1, f'Only one-dimensional inputs supported' + + n_fft = 2 ** math.ceil(math.log2(len(target) + len(estimate) - 1)) + + T = np.fft.rfft(target, n=n_fft) + E = np.fft.rfft(estimate, n=n_fft) + + # target autocorrelation + tt_corr = np.fft.irfft(np.abs(T) ** 2, n=n_fft) + # target-estimate crosscorrelation + te_corr = np.fft.irfft(T.conj() * E, n=n_fft) + + # Use only filter_length + tt_corr = tt_corr[:filter_length] + te_corr = te_corr[:filter_length] + + if diag_reg is not None: + tt_corr[0] += diag_reg * tt_corr[0] + eps + + # Construct the Toeplitz system matrix + TT = scipy.linalg.toeplitz(tt_corr) + + # Solve the linear system for the optimal filter + filt = np.linalg.solve(TT, te_corr) + + # Calculate filtered target + T_filt = T * np.fft.rfft(filt, n=n_fft) + target_filt = np.fft.irfft(T_filt, n=n_fft) + + return target_filt[: len(target)] + + +def toeplitz(x: torch.Tensor) -> torch.Tensor: + """Create Toeplitz matrix for one-dimensional signals along the last dimension. + + Args: + x: tensor with shape (..., T) + + Returns: + Tensor with shape (..., T, T) + """ + length = x.size(-1) + x = torch.cat([x[..., 1:].flip(dims=(-1,)), x], dim=-1) + return x.unfold(-1, length, 1).flip(dims=(-1,)) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/confidence_metrics.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/confidence_metrics.py new file mode 100644 index 0000000..7d793c9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/confidence_metrics.py @@ -0,0 +1,266 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import os +from pathlib import Path +from typing import List, Optional, Tuple, Union + +import matplotlib.pyplot as plt +import numpy as np +from sklearn.metrics import ( + PrecisionRecallDisplay, + RocCurveDisplay, + average_precision_score, + log_loss, + precision_recall_curve, + roc_auc_score, + roc_curve, +) + + +def auc_roc(y_true: Union[List[int], np.ndarray], y_score: Union[List[float], np.ndarray]) -> float: + """Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores. + + Note: If only one class is present in y_true, 0.5 is returned. + """ + y_true = np.array(y_true) + y_score = np.array(y_score) + assert len(y_true) == len(y_score) + assert np.all(y_true >= 0) and np.all(y_true <= 1) + if np.all(y_true == 0) or np.all(y_true == 1): + return 0.5 + return roc_auc_score(y_true, y_score) + + +def auc_pr(y_true: Union[List[int], np.ndarray], y_score: Union[List[float], np.ndarray]) -> float: + """Compute Area Under the Precision-Recall Curve (PR AUC) from prediction scores. + + Note: If only regatives are present in y_true, 0.0 is returned. + """ + y_true = np.array(y_true) + y_score = np.array(y_score) + assert len(y_true) == len(y_score) + assert np.all(y_true >= 0) and np.all(y_true <= 1) + if np.all(y_true == 0): + return 0.0 + return average_precision_score(y_true, y_score) + + +def auc_nt(y_true: Union[List[int], np.ndarray], y_score: Union[List[float], np.ndarray]) -> float: + """Compute Area Under the Negative Predictive Value vs. True Negative Rate Curve (NT AUC) from prediction scores. + + This metric can be thought of as a PR AUC in which errors are treated as positives. + + Note: If only positives are present in y_true, 0.0 is returned. + """ + y_true = np.array(y_true) + y_score = np.array(y_score) + assert len(y_true) == len(y_score) + assert np.all(y_true >= 0) and np.all(y_true <= 1) + if np.all(y_true == 1): + return 0.0 + return average_precision_score(1 - y_true, 1 - y_score) + + +def nce(y_true: Union[List[int], np.ndarray], y_score: Union[List[float], np.ndarray]) -> float: + """Compute Normalized Cross Entropy (NCE) from prediction scores. Also known as the Normalized Mutual Information. + + NCE measures how close the correct prediction scores are to one and the incorrect prediction scores are to zero. + Negative NCE values indicate that the classifier performs worse than the setting all prediction scores + as the proportion of correct predictions. + + Note: If only one class is present in y_true, 0.5 is returned. + """ + y_true = np.array(y_true) + y_score = np.array(y_score) + assert len(y_true) == len(y_score) + assert np.all(y_true >= 0) and np.all(y_true <= 1) + if np.all(y_true == 0) or np.all(y_true == 1): + return -math.inf + p = y_true.mean() + eps = 1e-15 + Hp = -(math.log(p + eps) * p + math.log(1 - p + eps) * (1 - p)) + return (Hp - log_loss(y_true, y_score)) / Hp + + +def ece( + y_true: Union[List[int], np.ndarray], + y_score: Union[List[float], np.ndarray], + n_bins: int = 100, + return_curve: bool = False, +) -> Union[float, Tuple[float, Tuple[List[int], List[float]]]]: + """Compute Expected Calibration Error (ECE) from prediction scores. + + ECE measures how close the correct prediction scores are to one and the incorrect prediction scores are to zero. + ECE ranges from zero to one with the best value zero (the lower the value, the better). + """ + y_true = np.array(y_true) + y_score = np.array(y_score) + assert len(y_true) == len(y_score) + assert np.all(y_true >= 0) and np.all(y_true <= 1) + py = np.array([1 - y_score, y_score]).T + acc, conf = np.zeros(n_bins), np.zeros(n_bins) + Bm = np.zeros(n_bins) + ece_curve = [] + thresholds = [] + for m in range(n_bins): + a, b = m / n_bins, (m + 1) / n_bins + threshold = (a + b) / 2 + thresholds.append(threshold) + py_index = (py.T[1] >= threshold).astype(int) + py_value = py[np.arange(len(py_index)), py_index] + bin_range = ((py_value > a) & (py_value <= b)).nonzero()[0] + Bm[m] = len(bin_range) + if Bm[m] > 0: + acc[m] = (py_index[bin_range] == y_true[bin_range]).sum() / Bm[m] + conf[m] = py_value[bin_range].sum() / Bm[m] + ece_curve.append(Bm[m] * np.abs(acc[m] - conf[m])) + ece = sum(ece_curve) / sum(Bm) + if return_curve: + return ece, (thresholds, ece_curve) + else: + return ece + + +def auc_yc( + y_true: Union[List[int], np.ndarray], + y_score: Union[List[float], np.ndarray], + n_bins: int = 100, + return_std_maximum: bool = False, + return_curve: bool = False, +) -> Union[ + float, + Tuple[float, Tuple[List[int], List[float]]], + Tuple[float, float, float], + Tuple[float, float, float, Tuple[List[int], List[float]]], +]: + """Compute Area Under the Youden's Curve (YC AUC) from prediction scores. + + YC AUC represents the rate of the effective threshold range. + + If return_std_maximum is set to True, std and maximum values of the Youden's Curve are returned with the AUC. + + Note: If only one class is present in y_true, zeroes are returned for every entity. + """ + y_true = np.array(y_true) + y_score = np.array(y_score) + thresholds = np.linspace(0, 1, n_bins + 1) + assert len(y_true) == len(y_score) + assert np.all(y_true >= 0) and np.all(y_true <= 1) + if np.all(y_true == 0) or np.all(y_true == 1): + if return_std_maximum and return_curve: + return 0.0, 0.0, 0.0, (thresholds, np.zeros(len(thresholds))) + elif return_std_maximum: + return 0.0, 0.0, 0.0 + elif return_curve: + return 0.0, (thresholds, np.zeros(len(thresholds))) + else: + return 0.0 + mask_correct = y_true == 1 + count_correct = max(len(mask_correct.nonzero()[0]), 1) + count_incorrect = max(len(y_true) - count_correct, 1) + y_score_correct = y_score[mask_correct] + y_score_incorrect = y_score[~mask_correct] + yc = [] + for threshold in thresholds: + tnr = len((y_score_incorrect < threshold).nonzero()[0]) / count_incorrect + fnr = len((y_score_correct < threshold).nonzero()[0]) / count_correct + yc.append(abs(tnr - fnr)) + yc = np.array(yc) + if return_std_maximum and return_curve: + return yc.mean(), yc.std(), yc.max(), (thresholds, yc) + elif return_std_maximum: + return yc.mean(), yc.std(), yc.max() + elif return_curve: + return yc.mean(), (thresholds, yc) + else: + return yc.mean() + + +def save_confidence_hist(y_score: Union[List[float], np.ndarray], plot_dir: Union[str, Path], name: str = "hist"): + os.makedirs(plot_dir, exist_ok=True) + plt.hist(np.array(y_score), 50, range=(0, 1)) + plt.title(name) + plt.xlabel("Confidence score") + plt.ylabel("Count") + plt.savefig(Path(plot_dir) / Path(name + ".png"), dpi=300) + plt.clf() + + +def save_roc_curve( + y_true: Union[List[int], np.ndarray], + y_score: Union[List[float], np.ndarray], + plot_dir: Union[str, Path], + name: str = "roc", +): + assert len(y_true) == len(y_score) + os.makedirs(plot_dir, exist_ok=True) + fpr, tpr, _ = roc_curve(1 - np.array(y_true), 1 - np.array(y_score)) + RocCurveDisplay(fpr=fpr, tpr=tpr).plot() + plt.title(name) + plt.savefig(Path(plot_dir) / Path(name + ".png"), dpi=300) + plt.clf() + + +def save_pr_curve( + y_true: Union[List[int], np.ndarray], + y_score: Union[List[float], np.ndarray], + plot_dir: Union[str, Path], + name: str = "pr", +): + assert len(y_true) == len(y_score) + os.makedirs(plot_dir, exist_ok=True) + precision, recall, _ = precision_recall_curve(np.array(y_true), np.array(y_score)) + PrecisionRecallDisplay(precision=precision, recall=recall).plot() + plt.title(name) + plt.savefig(Path(plot_dir) / Path(name + ".png"), dpi=300) + plt.clf() + + +def save_nt_curve( + y_true: Union[List[int], np.ndarray], + y_score: Union[List[float], np.ndarray], + plot_dir: Union[str, Path], + name: str = "nt", +): + assert len(y_true) == len(y_score) + os.makedirs(plot_dir, exist_ok=True) + precision, recall, _ = precision_recall_curve(1 - np.array(y_true), 1 - np.array(y_score)) + PrecisionRecallDisplay(precision=precision, recall=recall).plot() + plt.title(name) + plt.savefig(Path(plot_dir) / Path(name + ".png"), dpi=300) + plt.clf() + + +def save_custom_confidence_curve( + thresholds: Union[List[float], np.ndarray], + values: Union[List[float], np.ndarray], + plot_dir: Union[str, Path], + name: str = "my_awesome_curve", + xlabel: Optional[str] = None, + ylabel: Optional[str] = None, +): + assert len(thresholds) == len(values) + os.makedirs(plot_dir, exist_ok=True) + plt.plot(thresholds, values) + plt.xlim([0, 1]) + plt.ylim([0, 1]) + plt.title(name) + if xlabel is not None: + plt.xlabel(xlabel) + if ylabel is not None: + plt.ylabel(ylabel) + plt.savefig(Path(plot_dir) / Path(name + ".png"), dpi=300) + plt.clf() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/data_simulation_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/data_simulation_utils.py new file mode 100644 index 0000000..66b21c2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/data_simulation_utils.py @@ -0,0 +1,1142 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import os +import shutil +from collections import defaultdict +from typing import IO, Dict, List, Optional, Tuple + +import numpy as np +import torch +from scipy.stats import beta, gamma +from tqdm import tqdm + +from nemo.collections.asr.parts.preprocessing.perturb import AudioAugmentor +from nemo.collections.asr.parts.preprocessing.segment import AudioSegment +from nemo.collections.asr.parts.utils.manifest_utils import ( + get_ctm_line, + read_manifest, + write_ctm, + write_manifest, + write_text, +) +from nemo.collections.asr.parts.utils.speaker_utils import labels_to_rttmfile +from nemo.utils import logging + + +def get_cleaned_base_path(output_dir: str, overwrite_output: bool = True) -> str: + """ + Delete output directory if it exists or throw warning. + + Args: + output_dir (str): Path to output directory + overwrite_output (bool): If True, delete output directory if it exists + + Returns: + basepath (str): Path to base-path directory for writing output files + """ + if os.path.isdir(output_dir) and os.listdir(output_dir): + if overwrite_output: + if os.path.exists(output_dir): + shutil.rmtree(output_dir) + os.mkdir(output_dir) + else: + raise Exception("Output directory is nonempty and overwrite_output = false") + elif not os.path.isdir(output_dir): + os.makedirs(output_dir) + + # only add root if paths are relative + if not os.path.isabs(output_dir): + ROOT = os.getcwd() + basepath = os.path.join(ROOT, output_dir) + else: + basepath = output_dir + return basepath + + +def binary_search_alignments( + inds: List[int], max_audio_read_sec: float, min_alignment_count: int, alignments: List[float], +) -> int: + """ + Binary search to find the index of the alignment that satisfies the maximum audio read duration, + `max_audio_read_sec`. This is used to avoid reading the short audio files. + NOTE: `offset_max` should be at least 1 to avoid feeding max=0 to random sampling function. + + Args: + inds (list): List of indices to search from + max_audio_read_sec (float): Maximum audio read duration + min_alignment_count (int): Minimum number of alignments to read + audio_manifest (dict): Dictionary containing the audio file's alignments + + Returns: + offset_max (int) Index of the alignment that satisfies the maximum audio read duration + """ + # Start from the left end (at index 0) and -1 * min_alignment_count for the right end + left, right = 0, len(inds) - 1 - min_alignment_count + while left < right: + mid = left + (right - left) // 2 + dur_left = alignments[-1 * min_alignment_count] - alignments[inds[mid]] + if dur_left < max_audio_read_sec: + right = mid - 1 + elif dur_left > max_audio_read_sec: + left = mid + 1 + else: + break + mid_out = left + (right - left) // 2 + # If mid_out is on the boundary, move it to the left. + if alignments[-1 * min_alignment_count] - alignments[inds[mid_out]] < max_audio_read_sec: + mid_out -= 1 + offset_max = max(mid_out, 1) + return offset_max + + +def get_subset_of_audio_manifest( + audio_manifest: dict, offset_index: int, max_audio_read_sec: float, min_alignment_count: int, +) -> dict: + """ + Get a subset of `audio_manifest` for faster audio-file reading. + + Args: + audio_manifest (dict): Audio manifest dictionary. + keys: 'offset', 'duration', 'alignments', 'words' + offset_index (int): Index of the offset. + max_audio_read_sec (float): Maximum audio read duration. + min_alignment_count (int): Minimum number of alignments to read. + + Returns: + audio_manifest (dict): Subset of `audio_manifest` is returned for `words` and `alignments` keys. + """ + alignment_array = np.array(audio_manifest['alignments']) + alignment_array_pr = np.array(alignment_array[offset_index:]) - alignment_array[offset_index] + subset_alignments = alignment_array_pr[alignment_array_pr < max_audio_read_sec] + if len(subset_alignments) < min_alignment_count: + # Cases where the word next to the offset is longer than the max_audio_read_sec. + logging.warning( + f"subset_alignments of {audio_manifest['audio_filepath']} \n" + f"has subset alignment length:{len(subset_alignments)} at offset_index:{offset_index}, " + f"word:{audio_manifest['words'][offset_index:offset_index+min_alignment_count]}, " + f"alignments:{alignment_array_pr[:min_alignment_count]} which is longer than _max_audio_read_sec:{[0, max_audio_read_sec]}." + " Truncating the alignements." + ) + # Attach the `_max_audio_read_sec` to the `subset_alignments` to truncate the alignment timestamp. + subset_alignments = np.concatenate([subset_alignments, np.array([max_audio_read_sec])]) + audio_manifest['offset'], audio_manifest['duration'] = ( + alignment_array[offset_index], + subset_alignments[-1] - subset_alignments[0], + ) + audio_manifest['alignments'] = subset_alignments.tolist() + audio_manifest['words'] = audio_manifest['words'][offset_index : offset_index + len(subset_alignments)] + return audio_manifest + + +def read_audio_from_buffer( + audio_manifest: dict, + buffer_dict: dict, + offset_index: int, + device: torch.device, + max_audio_read_sec: float = 2.5, + min_alignment_count: int = 2, + read_subset: bool = True, +) -> Tuple[torch.Tensor, int, dict]: + """ + Read from the provided file path while maintaining a hash-table that saves loading time. + Also, this function only reads a subset of the audio file if `read_subset` is True for faster audio-file reading. + + Args: + audio_manifest (dict): Audio manifest dictionary. + keys: 'audio_filepath', 'duration', 'alignments', 'words' + buffer_dict (dict): Hash-table that saves loaded audio files. + offset_index (int): Index of the offset for the audio file. + device (torch.device): Device to load the audio file. + max_audio_read_sec (float): Maximum audio read duration. + min_alignment_count (int): Minimum number of alignments to read. + read_subset (bool): If True, read a subset of the audio file. + To control the length of the audio file, use data_simulator.session_params.max_audio_read_sec. + Note that using large value (greater than 3~4 sec) for `max_audio_read_sec` will slow down the generation process. + If False, read the entire audio file. + + Returns: + audio_file (torch.Tensor): Time-series audio data in a tensor. + sr (int): Sample rate of the audio file. + audio_manifest (dict): (modified) audio manifest dictionary. + """ + audio_file_id = f"{audio_manifest['audio_filepath']}#{offset_index}" + if audio_file_id in buffer_dict: + audio_file, sr, audio_manifest = buffer_dict[audio_file_id] + else: + if read_subset: + audio_manifest = get_subset_of_audio_manifest( + audio_manifest=audio_manifest, + offset_index=offset_index, + max_audio_read_sec=max_audio_read_sec, + min_alignment_count=min_alignment_count, + ) + segment = AudioSegment.from_file( + audio_file=audio_manifest['audio_filepath'], + offset=audio_manifest['offset'], + duration=audio_manifest['duration'], + ) + else: + segment = AudioSegment.from_file(audio_file=audio_manifest['audio_filepath']) + audio_file, sr = torch.from_numpy(segment.samples).to(device), segment.sample_rate + if read_subset and segment.duration < (audio_manifest['alignments'][-1] - audio_manifest['alignments'][0]): + audio_manifest['alignments'][-1] = min(segment.duration, audio_manifest['alignments'][-1]) + if audio_file.ndim > 1: + audio_file = torch.mean(audio_file, 1, False).to(device) + buffer_dict[audio_file_id] = (audio_file, sr, audio_manifest) + return audio_file, sr, audio_manifest + + +def perturb_audio( + audio: torch.Tensor, sr: int, augmentor: Optional[AudioAugmentor] = None, device: Optional[torch.device] = None +) -> torch.Tensor: + + """ + Perturb the audio (segment or session) using audio augmentor. + + Args: + audio (torch.Tensor): Time-series signal of the segment + sr (int): Sample rate of the original audio file + augmentor (AudioAugmentor): Audio augmentor to use + device (torch.device): Device to load the audio file + + Returns: + audio (torch.Tensor): Perturbed audio (time-series signal) of the segment + """ + if augmentor is None: + return audio + device = device if device is not None else torch.device('cpu') + if isinstance(audio, torch.Tensor): + audio = audio.cpu().numpy() + audio_segment = AudioSegment(audio, sample_rate=sr) + augmentor.perturb(audio_segment) + audio_segment = torch.from_numpy(audio_segment.samples).to(device) + return audio_segment + + +def normalize_audio(array: torch.Tensor) -> torch.Tensor: + """ + Normalize the audio signal to avoid clipping. + + Args: + array (torch.Tensor): Time-series audio data in a tensor. + + Returns: + (torch.Tensor): Normalized audio signal. + """ + return array / (1.0 * torch.max(torch.abs(array))) + + +def get_power_of_audio_file(audio_file: str, end_audio_file: int, running_len_samples: int, device: torch.device): + """ + Calculate the power of the audio signal. + + Args: + audio_file (torch.Tensor): Time-series audio data in a tensor. + end_audio_file (int): End index of the audio file. + running_len_samples (int): Running length of the audio file. + device (torch.device): Device to use. + + Returns: + (float): Power of the audio signal. + """ + return torch.mean(audio_file[: end_audio_file - running_len_samples] ** 2).to(device) + + +def get_scaled_audio_signal( + audio_file: torch.Tensor, + end_audio_file: int, + running_len_samples: int, + desired_avg_power_noise: float, + device: torch.device, +): + """ + Scale the audio signal to the desired average power. + + Args: + audio_file (torch.Tensor): Time-series audio data in a tensor. + end_audio_file (int): End index of the audio file. + running_len_samples (int): Running length of the audio file. + desired_avg_power_noise (float): Desired average power of the audio file. + device (torch.device): Device to use. + + Returns: + scaled_audio_file (torch.Tensor): Scaled audio signal. + """ + pow_audio_file = get_power_of_audio_file( + audio_file=audio_file, end_audio_file=end_audio_file, running_len_samples=running_len_samples, device=device + ) + scaled_audio_file = audio_file[: end_audio_file - running_len_samples] * torch.sqrt( + desired_avg_power_noise / pow_audio_file + ).to(device) + return scaled_audio_file + + +def get_desired_avg_power_noise( + power_array: float, snr_min: float, snr_max: float, background_noise_snr: float, +): + """ + Calculate the desired average power of the noise. + + Args: + power_array (float): Power of the audio signal. + snr_min (float): Minimum SNR. + snr_max (float): Maximum SNR. + background_noise_snr (float): SNR of the background noise. + + Returns: + desired_avg_power_noise (float): Desired average power of the noise. + """ + if (snr_min is not None) and (snr_max is not None) and (snr_min <= snr_max): + desired_snr = np.random.uniform(snr_min, snr_max) + else: + desired_snr = background_noise_snr + ratio = 10 ** (desired_snr / 20) + desired_avg_power_noise = power_array / ratio + return desired_avg_power_noise, desired_snr + + +def get_background_noise( + len_array: int, + power_array: float, + noise_samples: list, + audio_read_buffer_dict: dict, + snr_min: float, + snr_max: float, + background_noise_snr: float, + seed: int, + device: torch.device, +): + """ + Augment with background noise (inserting ambient background noise up to the desired SNR for the full clip). + + Args: + len_array (int): Length of background noise required. + power_array (float): Power of the audio signal. + noise_samples (list): List of noise samples. + audio_read_buffer_dict (dict): Dictionary containing audio read buffer. + snr_min (float): Minimum SNR. + snr_max (float): Maximum SNR. + background_noise_snr (float): SNR of the background noise. + seed (int): Seed for random number generator. + device (torch.device): Device to use. + + Returns: + bg_array (tensor): Tensor containing background noise. + desired_snr (float): Desired SNR for adding background noise. + """ + np.random.seed(seed) + bg_array = torch.zeros(len_array).to(device) + desired_avg_power_noise, desired_snr = get_desired_avg_power_noise( + power_array=power_array, snr_min=snr_min, snr_max=snr_max, background_noise_snr=background_noise_snr + ) + running_len_samples = 0 + + while running_len_samples < len_array: # build background audio stream (the same length as the full file) + file_id = np.random.randint(len(noise_samples)) + audio_file, sr, audio_manifest = read_audio_from_buffer( + audio_manifest=noise_samples[file_id], + buffer_dict=audio_read_buffer_dict, + offset_index=0, + device=device, + read_subset=False, + ) + if running_len_samples + len(audio_file) < len_array: + end_audio_file = running_len_samples + len(audio_file) + else: + end_audio_file = len_array + scaled_audio_file = get_scaled_audio_signal( + audio_file=audio_file, + end_audio_file=end_audio_file, + running_len_samples=running_len_samples, + desired_avg_power_noise=desired_avg_power_noise, + device=device, + ) + + bg_array[running_len_samples:end_audio_file] = scaled_audio_file + running_len_samples = end_audio_file + + return bg_array, desired_snr + + +def get_random_offset_index( + audio_manifest: dict, + audio_read_buffer_dict: dict, + offset_min: int = 0, + max_audio_read_sec: float = 2.5, + min_alignment_count: int = 2, +) -> int: + """ + Get an index for randomly accessing the silence in alignment timestamps. + + Args: + audio_manifest (dict): Audio manifest dictionary. + keys: 'audio_filepath', 'duration', 'alignments', 'words' + audio_read_buffer_dict (dict): Dictionary containing audio read buffer. + offset_min (int): Minimum offset index. (Default: 0) + max_audio_read_sec (float): Maximum audio read duration in seconds. (Default: 2.5) + min_alignment_count (int): Minimum number of alignment timestamps. (Default: 2) + + Returns: + (int): Random offset index smaller than `offset_count`. + """ + if len(audio_manifest['alignments']) <= min_alignment_count: + raise ValueError( + f"Audio file {audio_manifest['audio_filepath']} has less than {min_alignment_count} alignment timestamps." + ) + index_file_id = f"{audio_manifest['audio_filepath']}#index" + + # Avoid multiple indexings of the same audio file by using a hash-table. + if index_file_id in audio_read_buffer_dict: + (sil_inds, offset_max) = audio_read_buffer_dict[index_file_id] + else: + # Find all silence indices + sil_inds = np.where((np.array(audio_manifest['words']) == '') == True)[0] + if audio_manifest['alignments'][-1] - audio_manifest['alignments'][0] < max_audio_read_sec: + # The total duration is already short, therefore skip range search. + offset_max = 1 + else: + # Find the range that satisfies `max_audio_read_sec` duration. + offset_max = binary_search_alignments( + inds=sil_inds, + max_audio_read_sec=max_audio_read_sec, + min_alignment_count=min_alignment_count, + alignments=audio_manifest['alignments'], + ) + + audio_read_buffer_dict[index_file_id] = (sil_inds, offset_max) + + # If the audio file is shorter than the max_audio_read_sec, then we don't need to read a subset of the audio file. + if ( + len(sil_inds) <= min_alignment_count + or (audio_manifest['alignments'][-1] - audio_manifest['alignments'][0]) < max_audio_read_sec + ): + return offset_min + else: + offset_index = np.random.randint(offset_min, offset_max) + return sil_inds[offset_index] + + +def get_speaker_ids(sess_idx: int, speaker_samples: dict, permutated_speaker_inds: list) -> List[str]: + """ + Randomly select speaker IDs from the loaded manifest file. + + Args: + sess_idx (int): Session index in integer. + speaker_samples (dict): Dictionary mapping speaker ID to their list of samples. + permutated_speaker_inds (list): List of permutated speaker indices. + + Returns: + speaker_ids (list): List of speaker IDs + """ + all_speaker_ids = list(speaker_samples.keys()) + idx_list = permutated_speaker_inds[sess_idx, :] + speaker_ids = [all_speaker_ids[i] for i in idx_list] + return speaker_ids + + +def build_speaker_samples_map(manifest: dict) -> dict: + """ + Build a dictionary for mapping speaker ID to their list of samples + + Returns: + speaker_samples (Dict[list]): + Dictionary mapping speaker ID to their list of samples + """ + speaker_samples = defaultdict(list) + logging.info("Building speaker to samples map...") + for sample in tqdm(manifest, total=len(manifest)): + speaker_id = sample['speaker_id'] + speaker_samples[speaker_id].append(sample) + return speaker_samples + + +def read_noise_manifest(add_bg: bool, background_manifest: str): + """ + Read the noise manifest file and sample the noise manifest. + + Args: + add_bg (bool): Whether to add background noise. + background_manifest (str): Path to the background noise manifest file. + + Returns: + noise_manifest (list): List of the entire noise source samples. + """ + noise_manifest = [] + if add_bg is True: + if background_manifest is not None: + background_manifest_list = background_manifest + if isinstance(background_manifest_list, str): + background_manifest_list = [background_manifest_list] + for background_manifest in background_manifest_list: + if os.path.exists(background_manifest): + noise_manifest += read_manifest(background_manifest) + else: + raise FileNotFoundError(f"Noise manifest file: {background_manifest} file not found.") + else: + raise FileNotFoundError( + f"Noise manifest file is {background_manifest}. Please provide a valid noise manifest file/list if add_bg=True." + ) + return noise_manifest + + +def get_speaker_samples(speaker_ids: List[str], speaker_samples: dict) -> Dict[str, list]: + """ + Get a list of the samples for each of the specified speakers. + + Args: + speaker_ids (list): LibriSpeech speaker IDs for each speaker in the current session. + speaker_samples (dict): Dictionary mapping speaker ID to their list of samples. + + Returns: + speaker_wav_align_map (dict): Dictionary containing speaker IDs and their corresponding wav filepath and alignments. + """ + speaker_wav_align_map = defaultdict(list) + for sid in speaker_ids: + speaker_wav_align_map[sid] = speaker_samples[sid] + return speaker_wav_align_map + + +def add_silence_to_alignments(audio_manifest: dict): + """ + Add silence to the beginning of the alignments and words. + + Args: + audio_manifest (dict): Audio manifest dictionary. + keys: 'audio_filepath', 'duration', 'alignments', 'words' + + Returns: + audio_manifest (dict): Audio manifest dictionary with silence added to the beginning. + """ + if type(audio_manifest['words'][0]) == str and len(audio_manifest['words'][0]) > 0: + audio_manifest['words'].insert(0, "") + audio_manifest['alignments'].insert(0, 0.0) + return audio_manifest + + +def load_speaker_sample( + speaker_wav_align_map: List[dict], speaker_ids: List[str], speaker_turn: int, min_alignment_count: int, +) -> str: + """ + Load a sample for the selected speaker ID. + The first alignment and word must be silence that determines the start of the alignments. + + Args: + speaker_wav_align_map (dict): Dictionary containing speaker IDs and their corresponding wav filepath and alignments. + speaker_ids (list): LibriSpeech speaker IDs for each speaker in the current session. + speaker_turn (int): Current speaker turn. + output_precision (int): Precision of the output alignments in integer. + min_alignment_count (int): Minimum number of alignments in the audio file. + + Returns: + audio_manifest (dict): Audio manifest dictionary containing the wav filepath, words and alignments. + """ + speaker_id = speaker_ids[speaker_turn] + file_id = np.random.randint(0, max(len(speaker_wav_align_map[str(speaker_id)]) - 1, 1)) + audio_manifest = speaker_wav_align_map[str(speaker_id)][file_id] + + # Check if the alignment file has at least 2 words. + if len(audio_manifest['alignments']) < min_alignment_count: + raise ValueError( + f"Alignment file {audio_manifest['audio_filepath']} has an inappropriate length of {len(audio_manifest['alignments'])} < 2." + ) + + # Check whether the first word is silence and insert a silence token if the first token is not silence. + if audio_manifest['words'][0] != "": + audio_manifest = add_silence_to_alignments(audio_manifest) + + audio_manifest = copy.deepcopy(audio_manifest) + return audio_manifest + + +def get_split_points_in_alignments( + words: List[str], + alignments: List[float], + split_buffer: float, + sr: int, + sentence_audio_len: int, + new_start: float = 0, +): + """ + Collect split points in the alignment based on silence. + Silence is defined as a blank symbol between two words that is longer than 2 * split_buffer. + + Args: + words (List[str]): List of words in the sentence. + alignments (List[float]): List of alignment timestamps in the sentence. + split_buffer (float): Buffer length in seconds. + sr (int): Sample rate of the audio. + sentence_audio_len (int): Length of the sentence audio in samples. + new_start (float): Start of the sentence audio in seconds. + + Returns: + splits (List[List[int]]): List of integer split points in the sentence audio. + """ + splits = [] + for i in range(len(words)): + if words[i] == "" and i != 0 and i != len(words) - 1: + silence_length = alignments[i] - alignments[i - 1] + if silence_length > 2 * split_buffer: # split utterance on silence + new_end = alignments[i - 1] + split_buffer + splits.append( + [int(new_start * sr), int(new_end * sr),] + ) + new_start = alignments[i] - split_buffer + # The last split point should be added + splits.append([int(new_start * sr), sentence_audio_len]) + return splits + + +def per_speaker_normalize( + sentence_audio: torch.Tensor, splits: List[List[int]], speaker_turn: int, volume: List[float], device: torch.device +) -> torch.Tensor: + """ + Normalize time-series audio signal per speaker. + + Args: + sentence_audio (torch.Tensor): Time-series audio signal. + splits (List[List[int]]): List of integer split points in the sentence audio. + speaker_turn (int): Speaker ID of the current speaker. + volume (List[float]): List of volume levels for each speaker. + device (torch.device): Device to use for computations. + + Returns: + sentence_audio (torch.Tensor): Normalized time-series audio signal. + """ + split_length = torch.tensor(0).to(device).double() + split_sum = torch.tensor(0).to(device).double() + for split in splits: + split_length += len(sentence_audio[split[0] : split[1]]) + split_sum += torch.sum(sentence_audio[split[0] : split[1]] ** 2) + average_rms = torch.sqrt(split_sum * 1.0 / split_length) + sentence_audio = sentence_audio / (1.0 * average_rms) * volume[speaker_turn] + return sentence_audio + + +class DataAnnotator(object): + """ + Class containing the functions that create RTTM, CTM, JSON files. + + Arguments in config: + + data_simulator: + session_config: + num_speakers (int): Number of unique speakers per multispeaker audio session + session_params: + split_buffer (float): Split RTTM labels if greater than twice this amount of silence (to avoid long gaps between + utterances as being labelled as speech) + outputs: + output_dir (str): Output directory for audio sessions and corresponding label files + output_filename (str): Output filename for the wav and RTTM files + overwrite_output (bool): If true, delete the output directory if it exists + output_precision (int): Number of decimal places in output files + """ + + def __init__(self, cfg): + """ + Args: + cfg: OmegaConf configuration loaded from yaml file. + """ + self._params = cfg + self._files = {} + self._init_file_write() + self._init_filelist_lists() + + def _init_file_write(self): + """ + Initialize file writing arguments + """ + self._file_base_str = "synthetic" + self._file_types = ["wav", "rttm", "json", "ctm", "txt", "meta"] + self._annotation_types = ["rttm", "json", "ctm"] + + def _init_filelist_lists(self): + """ + Initialize lists to store the filelists for each file type + """ + self.annote_lists = {} + for file_type in self._file_types: + self.annote_lists[f"{file_type}_list"] = [] + + def init_annotation_lists(self): + """ + Initialize lists to store the annotations for each file type + """ + for file_type in self._file_types: + self.annote_lists[file_type] = [] + + def create_new_rttm_entry( + self, words: List[str], alignments: List[float], start: int, end: int, speaker_id: int + ) -> List[str]: + + """ + Create new RTTM entries (to write to output rttm file) + + Args: + words (list): List of words in the current audio file. + alignments (list): List of alignments (timestamps) for the current audio file. + start (int): Current start of the audio file being inserted. + end (int): End of the audio file being inserted. + speaker_id (int): LibriSpeech speaker ID for the current entry. + + Returns: + rttm_list (list): List of rttm entries + """ + rttm_list = [] + new_start = start + # look for split locations + for i in range(len(words)): + if words[i] == "" and i != 0 and i != len(words) - 1: + silence_length = alignments[i] - alignments[i - 1] + if ( + silence_length > 2 * self._params.data_simulator.session_params.split_buffer + ): # split utterance on silence + new_end = start + alignments[i - 1] + self._params.data_simulator.session_params.split_buffer + t_stt = round(float(new_start), self._params.data_simulator.outputs.output_precision) + t_end = round(float(new_end), self._params.data_simulator.outputs.output_precision) + rttm_list.append(f"{t_stt} {t_end} {speaker_id}") + new_start = start + alignments[i] - self._params.data_simulator.session_params.split_buffer + + t_stt = round(float(new_start), self._params.data_simulator.outputs.output_precision) + t_end = round(float(end), self._params.data_simulator.outputs.output_precision) + rttm_list.append(f"{t_stt} {t_end} {speaker_id}") + return rttm_list + + def create_new_json_entry( + self, + text: List[str], + wav_filename: str, + start: float, + length: float, + speaker_id: int, + rttm_filepath: str, + ctm_filepath: str, + ) -> dict: + """ + Create new JSON entries (to write to output json file). + + Args: + text (list): string of text for the current entry. + wav_filename (str): Filename of the wav file. + start (float): Start time of the current entry. + length (float): Length of the current entry. + speaker_id (int): speaker ID for the current entry. + rttm_filepath (str): Path to the RTTM file. + ctm_filepath (str): Path to the CTM file. + + Returns: + meta (dict): JSON entry dictionary. + """ + start = round(float(start), self._params.data_simulator.outputs.output_precision) + length = round(float(length), self._params.data_simulator.outputs.output_precision) + meta = { + "audio_filepath": wav_filename, + "offset": start, + "duration": length, + "label": speaker_id, + "text": text, + "num_speakers": self._params.data_simulator.session_config.num_speakers, + "rttm_filepath": rttm_filepath, + "ctm_filepath": ctm_filepath, + "uem_filepath": None, + } + return meta + + def create_new_ctm_entry( + self, words: List[str], alignments: List[float], session_name: str, speaker_id: int, start: int + ) -> List[str]: + """ + Create new CTM entry (to write to output ctm file) + + Args: + words (list): List of words in the current audio file. + alignments (list): List of alignments (timestamps) for the current audio file. + session_name (str): Current session name. + speaker_id (int): LibriSpeech speaker ID for the current entry. + start (int): Current start of the audio file being inserted. + + Returns: + arr (list): List of ctm entries + """ + arr = [] + start = float(round(start, self._params.data_simulator.outputs.output_precision)) + for i in range(len(words)): + word = words[i] + if ( + word != "" + ): # note that using the current alignments the first word is always empty, so there is no error from indexing the array with i-1 + prev_align = 0 if i == 0 else alignments[i - 1] + align1 = round(float(prev_align + start), self._params.data_simulator.outputs.output_precision) + align2 = round(float(alignments[i] - prev_align), self._params.data_simulator.outputs.output_precision) + text = get_ctm_line( + source=session_name, + channel=1, + start_time=align1, + duration=align2, + token=word, + conf=None, + type_of_token='lex', + speaker=speaker_id, + ) + arr.append((align1, text)) + return arr + + def add_to_filename_lists(self, basepath: str, filename: str): + """ + Add the current filename to the list of filenames for each file type. + + Args: + basepath (str): Basepath for output files. + filename (str): Base filename for all output files. + """ + full_base_filepath = os.path.join(basepath, filename) + for file_type in self._file_types: + self.annote_lists[f"{file_type}_list"].append(f"{full_base_filepath}.{file_type}") + + def write_filelist_files(self, basepath): + """ + Write all filelist files. + + Args: + basepath (str): Basepath for output files. + """ + for file_type in self._file_types: + with open(f"{basepath}/{self._file_base_str}_{file_type}.list", "w") as list_file: + list_file.write("\n".join(self.annote_lists[f"{file_type}_list"])) + list_file.close() + + def write_annotation_files(self, basepath: str, filename: str, meta_data: dict): + """ + Write all annotation files: RTTM, JSON, CTM, TXT, and META. + + Args: + basepath (str): Basepath for output files. + filename (str): Base filename for all output files. + meta_data (dict): Metadata for the current session. + rttm_list (list): List of RTTM entries. + json_list (list): List of JSON entries. + ctm_list (list): List of CTM entries. + """ + labels_to_rttmfile(self.annote_lists['rttm'], filename, self._params.data_simulator.outputs.output_dir) + write_manifest(os.path.join(basepath, filename + '.json'), self.annote_lists['json']) + write_ctm(os.path.join(basepath, filename + '.ctm'), self.annote_lists['ctm']) + write_text(os.path.join(basepath, filename + '.txt'), self.annote_lists['ctm']) + write_manifest(os.path.join(basepath, filename + '.meta'), [meta_data]) + + +class SpeechSampler(object): + """ + Class for sampling speech samples for Multispeaker Audio Session Simulator + + Args: + cfg: OmegaConf configuration loaded from yaml file. + + Variables for sampling speech: + self.running_speech_len_samples (int): Running total of speech samples in the current audio session. + self.running_silence_len_samples (int): Running total of silence samples in the current audio session. + self.running_overlap_len_samples (int): Running total of overlap samples in the current audio session. + + self.sess_silence_mean (int) : Targeted mean number of silence samples in the current audio session. + self.per_silence_min_len (int): Minimum number of silence samples in the silence segment. + self.per_silence_max_len (int): Maximum number of silence samples in the silence segment. + + self.sess_overlap_mean (int): Targeted mean number of overlap samples in the current audio session. + self.per_overlap_min_len (int): Minimum number of overlap samples in the overlap segment. + self.per_overlap_max_len (int): Maximum number of overlap samples in the overlap segment. + + data_simulator: + session_params: + mean_silence (float): Mean proportion of silence to speaking time in the audio session. Should be in range [0, 1). + mean_silence_var (float): Variance for mean silence in all audio sessions. + This value should be 0 <= mean_silence_var < mean_silence * (1 - mean_silence). + per_silence_var (float): Variance for each silence in an audio session, set large values (e.g., 20) for de-correlation. + per_silence_min (float): Minimum duration for each silence, default to 0. + per_silence_max (float): Maximum duration for each silence, default to -1 for no maximum. + + mean_overlap (float): Mean proportion of overlap in the overall non-silence duration. Should be in range [0, 1) and + recommend [0, 0.15] range for accurate results. + mean_overlap_var (float): Variance for mean overlap in all audio sessions. + This value should be 0 <= mean_overlap_var < mean_overlap * (1 - mean_overlap). + per_overlap_var (float): Variance for per overlap in each session, set large values to de-correlate silence lengths + with the latest speech segment lengths + per_overlap_min (float): Minimum per overlap duration in seconds + per_overlap_max (float): Maximum per overlap duration in seconds, set -1 for no maximum + """ + + def __init__(self, cfg): + """ + Args: + cfg: OmegaConf configuration loaded from yaml file. + """ + self._params = cfg + + self.running_speech_len_samples = 0 + self.running_silence_len_samples = 0 + self.running_overlap_len_samples = 0 + + self.sess_silence_mean = None + self.per_silence_min_len = 0 + self.per_silence_max_len = 0 + + self.sess_overlap_mean = None + self.per_overlap_min_len = 0 + self.per_overlap_max_len = 0 + + self.mean_overlap = float(self._params.data_simulator.session_params.mean_overlap) + self.mean_overlap_var = float(self._params.data_simulator.session_params.mean_overlap_var) + + self.mean_silence = float(self._params.data_simulator.session_params.mean_silence) + self.mean_silence_var = float(self._params.data_simulator.session_params.mean_silence_var) + + self.per_silence_var = float(self._params.data_simulator.session_params.per_silence_var) + self.per_overlap_var = float(self._params.data_simulator.session_params.per_overlap_var) + + self.num_noise_files = int(self._params.data_simulator.background_noise.num_noise_files) + + def _mean_var_to_a_and_b(self, mean: float, var: float) -> Tuple[float, float]: + """ + Convert mean and variance to a and b parameters for beta distribution. + + Args: + mean (float): Mean of the beta distribution. + var (float): Variance of the beta distribution. + + Returns: + Tuple[float, float]: a and b parameters for beta distribution. + """ + a = mean ** 2 * (1 - mean) / var - mean + b = mean * (1 - mean) ** 2 / var - (1 - mean) + return a, b + + def _init_silence_params(self): + """ + Initialize parameters for silence insertion in the current session. + """ + self.running_speech_len_samples = 0 + self.running_silence_len_samples = 0 + + self.per_silence_min_len = int( + max(0, self._params.data_simulator.session_params.per_silence_min) * self._params.data_simulator.sr + ) + if self._params.data_simulator.session_params.per_silence_max > 0: + self.per_silence_max_len = int( + self._params.data_simulator.session_params.per_silence_max * self._params.data_simulator.sr + ) + else: + self.per_silence_max_len = int( + self._params.data_simulator.session_config.session_length * self._params.data_simulator.sr + ) + + def _init_overlap_params(self): + """ + Initialize parameters for overlap insertion in the current session. + """ + self.running_overlap_len_samples = 0 + + self.per_overlap_min_len = int( + max(0, self._params.data_simulator.session_params.per_overlap_min) * self._params.data_simulator.sr + ) + if self._params.data_simulator.session_params.per_overlap_max > 0: + self.per_overlap_max_len = int( + self._params.data_simulator.session_params.per_overlap_max * self._params.data_simulator.sr + ) + else: + self.per_overlap_max_len = int( + self._params.data_simulator.session_config.session_length * self._params.data_simulator.sr + ) + + def silence_vs_overlap_selector(self, running_len_samples: int, non_silence_len_samples: int) -> bool: + """ + Compare the current silence ratio to the current overlap ratio. Switch to either silence or overlap mode according + to the amount of the gap between current ratio and session mean in config. + + Args: + running_len_samples (int): Length of the current session in samples. + non_silence_len_samples (int): Length of the signal that is not silence in samples. + + Returns: + add_overlap (bool): True if the current silence ratio is less than the current overlap ratio, False otherwise. + """ + if running_len_samples > 0: + self.current_silence_ratio = (running_len_samples - self.running_speech_len_samples) / running_len_samples + self.current_overlap_ratio = self.running_overlap_len_samples / non_silence_len_samples + else: + self.current_silence_ratio, self.current_overlap_ratio = 0, 0 + + # self.silence_discrepancy = max(0, self.sess_silence_mean - self.current_silence_ratio) + # self.overlap_discrepancy = max(0, self.sess_overlap_mean - self.current_overlap_ratio) + # threshold = self.silence_discrepancy / (self.overlap_discrepancy + self.silence_discrepancy + 1e-10) + # add_overlap = np.random.rand() > threshold + self.silence_discrepancy = self.current_silence_ratio - self.sess_silence_mean + self.overlap_discrepancy = self.current_overlap_ratio - self.sess_overlap_mean + add_overlap = bool(self.overlap_discrepancy < self.silence_discrepancy) + return add_overlap + + def get_session_silence_mean(self): + """ + Get the target mean silence for current session using re-parameterized Beta distribution. + The following constraints are applied to make a > 0 and b > 0: + + 0 < mean_silence < 1 + 0 < mean_silence_var < mean_silence * (1 - mean_silence) + + Args: + silence_mean (float): + Target mean silence for the current session + """ + self._init_silence_params() + mean, var = self.mean_silence, self.mean_silence_var + if var > 0: + a, b = self._mean_var_to_a_and_b(mean, var) + if a < 0 or b < 0: + raise ValueError( + f"Beta(a, b), a = {a:.3f} and b = {b:.3f} should be both greater than 0. " + f"Invalid `mean_silence_var` value {var} for sampling from Beta distribution. " + f"`mean_silence_var` should be less than `mean_silence * (1 - mean_silence)`. " + f"Please check `mean_silence_var` and try again." + ) + self.sess_silence_mean = beta(a, b).rvs() + else: + self.sess_silence_mean = mean + return self.sess_silence_mean + + def get_session_overlap_mean(self): + """ + Get the target mean overlap for current session using re-parameterized Beta distribution. + The following constraints are applied to make a > 0 and b > 0: + + 0 < mean_overlap < 1 + 0 < mean_overlap_var < mean_overlap * (1 - mean_overlap) + + Returns: + overlap_mean (float): + Target mean overlap for the current session + """ + self._init_overlap_params() + mean, var = self.mean_overlap, self.mean_overlap_var + if var > 0: + a, b = self._mean_var_to_a_and_b(mean, var) + if a < 0 or b < 0: + raise ValueError( + f"Beta(a, b), a = {a:.3f} and b = {b:.3f} should be both greater than 0. " + f"Invalid `mean_overlap_var` value {var} for sampling from Beta distribution. " + f"`mean_overlap_var` should be less than `mean_overlap * (1 - mean_overlap)`. " + f"Please check `mean_overlap_var` and try again." + ) + self.sess_overlap_mean = beta(a, b).rvs() + else: + self.sess_overlap_mean = mean + return self.sess_overlap_mean + + def sample_from_silence_model(self, running_len_samples: int) -> int: + """ + Sample from the silence model to determine the amount of silence to add between sentences. + Gamma distribution is employed for modeling the highly skewed distribution of silence length distribution. + When we add silence between sentences, we want to ensure that the proportion of silence meets the `sess_silence_mean`. + Thus, [Session Silence Mean] = [Total Running Silence Time] / [Total Running Session Time] equation holds. We employ the following + formula to determine the amount of silence to add, which is `silence_mean`: + + self.sess_silence_mean = (silence_mean + self.running_silence_len_samples) / (silence_mean + running_len_samples) + + The above equation is setting `silence_mean` to yield the desired silence ratio `self.sess_silence_mean`. + We use the above `silence_mean` value to sample silence-length for each silence occurrence. + + Args: + running_len_samples (int): + Running length of the session (in terms of number of samples). + session_len_samples (int): + Targeted total session length (in terms of number of samples). + + Returns: + silence_amount (int): Amount of silence to add between sentences (in terms of number of samples). + """ + silence_mean = ((self.sess_silence_mean * running_len_samples) - self.running_silence_len_samples) / ( + 1 - self.sess_silence_mean + ) + silence_mean = max(self.per_silence_min_len, min(silence_mean, self.per_silence_max_len)) + if silence_mean > 0: + self.per_silence_var = self._params.data_simulator.session_params.per_silence_var + silence_amount = ( + int( + gamma( + a=(silence_mean ** 2) / self.per_silence_var, scale=self.per_silence_var / silence_mean + ).rvs() + ) + if self.per_silence_var > 0 + else int(silence_mean) + ) + silence_amount = max(self.per_silence_min_len, min(silence_amount, self.per_silence_max_len)) + else: + silence_amount = 0 + return silence_amount + + def sample_from_overlap_model(self, non_silence_len_samples: int): + """ + Sample from the overlap model to determine the amount of overlap between segments. + Gamma distribution is employed for modeling the highly skewed distribution of overlap length distribution. + When we add an overlap occurrence, we want to meet the desired overlap ratio defined by `self.sess_overlap_mean`. + Thus, [Session Overlap Mean] = [Total Running Overlap Speech Time] / [Total Running Non-Silence Speech Time]. + Let `overlap_mean` be the desired overlap amount, then the mean and variance of the gamma distribution is given by: + + self.sess_overlap_mean = (overlap_mean + self.running_overlap_len_samples) / (non_silence_len_samples - overlap_mean) + + The above equation is setting `overlap_mean` to yield the desired overlap ratio `self.sess_overlap_mean`. + We use the above `overlap_mean` value to sample overlap-length for each overlap occurrence. + + Args: + non_silence_len_samples (int): + The total amount of non-silence (speech) region regardless of overlap status + + Returns: + desired_overlap_amount (int): + Amount of overlap between segments (in terms of number of samples). + """ + overlap_mean = ((self.sess_overlap_mean * non_silence_len_samples) - self.running_overlap_len_samples) / ( + 1 + self.sess_overlap_mean + ) + overlap_mean = max(self.per_overlap_min_len, min(max(0, overlap_mean), self.per_overlap_max_len)) + + if overlap_mean > 0: + desired_overlap_amount = ( + int(gamma(a=overlap_mean ** 2 / self.per_overlap_var, scale=self.per_overlap_var / overlap_mean).rvs()) + if self.per_overlap_var > 0 + else int(overlap_mean) + ) + desired_overlap_amount = max( + self.per_overlap_min_len, min(desired_overlap_amount, self.per_overlap_max_len) + ) + else: + desired_overlap_amount = 0 + return desired_overlap_amount + + def sample_noise_manifest(self, noise_manifest: dict) -> list: + """ + Sample noise manifest to a specified count `num_noise_files` for the current simulated audio session. + + Args: + noise_manifest (list): + List of noise source samples to be sampled from. + + Returns: + sampled_noise_manifest (list): + List of noise samples to be used for the current session. + """ + num_noise_files = min(len(noise_manifest), self.num_noise_files) + sampled_noise_manifest = [] + if num_noise_files > 0: + selected_noise_ids = np.random.choice(range(len(noise_manifest)), num_noise_files, replace=False) + for k in selected_noise_ids: + sampled_noise_manifest.append(noise_manifest[k]) + return sampled_noise_manifest diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/decoder_timestamps_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/decoder_timestamps_utils.py new file mode 100644 index 0000000..8ed143d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/decoder_timestamps_utils.py @@ -0,0 +1,788 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import math +from typing import Dict, List, Tuple, Type, Union + +import numpy as np +import torch +from omegaconf import OmegaConf + +import nemo.collections.asr as nemo_asr +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.models import EncDecCTCModel, EncDecCTCModelBPE +from nemo.collections.asr.parts.submodules.ctc_decoding import ( + CTCBPEDecoding, + CTCBPEDecodingConfig, + CTCDecoding, + CTCDecodingConfig, +) +from nemo.collections.asr.parts.utils.audio_utils import get_samples +from nemo.collections.asr.parts.utils.speaker_utils import audio_rttm_map, get_uniqname_from_filepath +from nemo.collections.asr.parts.utils.streaming_utils import AudioFeatureIterator, FrameBatchASR +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.utils import logging + +__all__ = ['ASRDecoderTimeStamps'] + +try: + from pyctcdecode import build_ctcdecoder + + PYCTCDECODE = True +except ImportError: + PYCTCDECODE = False + + +def if_none_get_default(param, default_value): + return (param, default_value)[param is None] + + +class WERBPE_TS(WER): + """ + This is WERBPE_TS class that is modified for generating word_timestamps with logits. + The functions in WER class is modified to save the word_timestamps whenever BPE token + is being saved into a list. + This class is designed to support ASR models based on CTC and BPE. + Please refer to the definition of WERBPE class for more information. + """ + + def __init__( + self, + tokenizer: TokenizerSpec, + batch_dim_index=0, + use_cer=False, + ctc_decode=None, + log_prediction=True, + dist_sync_on_step=False, + ): + if ctc_decode is not None: + logging.warning(f'`ctc_decode` was set to {ctc_decode}. Note that this is ignored.') + + decoding_cfg = CTCBPEDecodingConfig(batch_dim_index=batch_dim_index) + decoding = CTCBPEDecoding(decoding_cfg, tokenizer=tokenizer) + super().__init__(decoding, use_cer, log_prediction, dist_sync_on_step) + + def ctc_decoder_predictions_tensor_with_ts( + self, time_stride, predictions: torch.Tensor, predictions_len: torch.Tensor = None + ) -> List[str]: + hypotheses, timestamps, word_timestamps = [], [], [] + # '⁇' string should be removed since it causes error during string split. + unk = '⁇' + prediction_cpu_tensor = predictions.long().cpu() + # iterate over batch + self.time_stride = time_stride + for ind in range(prediction_cpu_tensor.shape[self.decoding.batch_dim_index]): + prediction = prediction_cpu_tensor[ind].detach().numpy().tolist() + if predictions_len is not None: + prediction = prediction[: predictions_len[ind]] + # CTC decoding procedure + decoded_prediction, char_ts, timestamp_list = [], [], [] + previous = self.decoding.blank_id + for pdx, p in enumerate(prediction): + if (p != previous or previous == self.decoding.blank_id) and p != self.decoding.blank_id: + decoded_prediction.append(p) + char_ts.append(round(pdx * self.time_stride, 2)) + timestamp_list.append(round(pdx * self.time_stride, 2)) + + previous = p + + hypothesis = self.decode_tokens_to_str_with_ts(decoded_prediction) + hypothesis = hypothesis.replace(unk, '') + word_ts, word_seq = self.get_ts_from_decoded_prediction(decoded_prediction, hypothesis, char_ts) + + hypotheses.append(" ".join(word_seq)) + timestamps.append(timestamp_list) + word_timestamps.append(word_ts) + return hypotheses, timestamps, word_timestamps + + def decode_tokens_to_str_with_ts(self, tokens: List[int]) -> str: + hypothesis = self.decoding.tokenizer.ids_to_text(tokens) + return hypothesis + + def decode_ids_to_tokens_with_ts(self, tokens: List[int]) -> List[str]: + token_list = self.decoding.tokenizer.ids_to_tokens(tokens) + return token_list + + def get_ts_from_decoded_prediction( + self, decoded_prediction: List[str], hypothesis: str, char_ts: List[str] + ) -> Tuple[List[List[float]], List[str]]: + decoded_char_list = self.decoding.tokenizer.ids_to_tokens(decoded_prediction) + stt_idx, end_idx = 0, len(decoded_char_list) - 1 + stt_ch_idx, end_ch_idx = 0, 0 + space = '▁' + word_ts, word_seq = [], [] + word_open_flag = False + for idx, ch in enumerate(decoded_char_list): + + # If the symbol is space and not an end of the utterance, move on + if idx != end_idx and (space == ch and space in decoded_char_list[idx + 1]): + continue + + # If the word does not containg space (the start of the word token), keep counting + if (idx == stt_idx or space == decoded_char_list[idx - 1] or (space in ch and len(ch) > 1)) and ( + ch != space + ): + _stt = char_ts[idx] + stt_ch_idx = idx + word_open_flag = True + + # If this char has `word_open_flag=True` and meets any of one of the following condition: + # (1) last word (2) unknown word (3) start symbol in the following word, + # close the `word_open_flag` and add the word to the `word_seq` list. + close_cond = idx == end_idx or ch in [''] or space in decoded_char_list[idx + 1] + if (word_open_flag and ch != space) and close_cond: + _end = round(char_ts[idx] + self.time_stride, 2) + end_ch_idx = idx + word_open_flag = False + word_ts.append([_stt, _end]) + stitched_word = ''.join(decoded_char_list[stt_ch_idx : end_ch_idx + 1]).replace(space, '') + word_seq.append(stitched_word) + + assert len(word_ts) == len(hypothesis.split()), "Text hypothesis does not match word timestamps." + return word_ts, word_seq + + +class WER_TS(WER): + """ + This is WER class that is modified for generating timestamps with logits. + The functions in WER class is modified to save the timestamps whenever character + is being saved into a list. + This class is designed to support ASR models based on CTC and Character-level tokens. + Please refer to the definition of WER class for more information. + """ + + def __init__( + self, + vocabulary, + batch_dim_index=0, + use_cer=False, + ctc_decode=None, + log_prediction=True, + dist_sync_on_step=False, + ): + if ctc_decode is not None: + logging.warning(f'`ctc_decode` was set to {ctc_decode}. Note that this is ignored.') + + decoding_cfg = CTCDecodingConfig(batch_dim_index=batch_dim_index) + decoding = CTCDecoding(decoding_cfg, vocabulary=vocabulary) + super().__init__(decoding, use_cer, log_prediction, dist_sync_on_step) + + def decode_tokens_to_str_with_ts(self, tokens: List[int], timestamps: List[int]) -> str: + """ + Take frame-level tokens and timestamp list and collect the timestamps for + start and end of each word. + """ + token_list, timestamp_list = self.decode_ids_to_tokens_with_ts(tokens, timestamps) + hypothesis = ''.join(self.decoding.decode_ids_to_tokens(tokens)) + return hypothesis, timestamp_list + + def decode_ids_to_tokens_with_ts(self, tokens: List[int], timestamps: List[int]) -> List[str]: + token_list, timestamp_list = [], [] + for i, c in enumerate(tokens): + if c != self.decoding.blank_id: + token_list.append(self.decoding.labels_map[c]) + timestamp_list.append(timestamps[i]) + return token_list, timestamp_list + + def ctc_decoder_predictions_tensor_with_ts( + self, predictions: torch.Tensor, predictions_len: torch.Tensor = None, + ) -> List[str]: + """ + A shortened version of the original function ctc_decoder_predictions_tensor(). + Replaced decode_tokens_to_str() function with decode_tokens_to_str_with_ts(). + """ + hypotheses, timestamps = [], [] + prediction_cpu_tensor = predictions.long().cpu() + for ind in range(prediction_cpu_tensor.shape[self.decoding.batch_dim_index]): + prediction = prediction_cpu_tensor[ind].detach().numpy().tolist() + if predictions_len is not None: + prediction = prediction[: predictions_len[ind]] + + # CTC decoding procedure with timestamps + decoded_prediction, decoded_timing_list = [], [] + previous = self.decoding.blank_id + for pdx, p in enumerate(prediction): + if (p != previous or previous == self.decoding.blank_id) and p != self.decoding.blank_id: + decoded_prediction.append(p) + decoded_timing_list.append(pdx) + previous = p + + text, timestamp_list = self.decode_tokens_to_str_with_ts(decoded_prediction, decoded_timing_list) + hypotheses.append(text) + timestamps.append(timestamp_list) + + return hypotheses, timestamps + + +def get_wer_feat_logit(audio_file_path, asr, frame_len, tokens_per_chunk, delay, model_stride_in_secs): + """ + Create a preprocessor to convert audio samples into raw features, + Normalization will be done per buffer in frame_bufferer. + """ + asr.reset() + asr.read_audio_file_and_return(audio_file_path, delay, model_stride_in_secs) + hyp, tokens, log_prob = asr.transcribe_with_ts(tokens_per_chunk, delay) + return hyp, tokens, log_prob + + +class FrameBatchASRLogits(FrameBatchASR): + """ + A class for streaming frame-based ASR. + Inherits from FrameBatchASR and adds new capability of returning the logit output. + Please refer to FrameBatchASR for more detailed information. + """ + + def __init__( + self, + asr_model: Type[EncDecCTCModelBPE], + frame_len: float = 1.6, + total_buffer: float = 4.0, + batch_size: int = 4, + ): + super().__init__(asr_model, frame_len, total_buffer, batch_size) + self.all_logprobs = [] + + def clear_buffer(self): + self.all_logprobs = [] + self.all_preds = [] + + def read_audio_file_and_return(self, audio_filepath: str, delay: float, model_stride_in_secs: float): + samples = get_samples(audio_filepath) + samples = np.pad(samples, (0, int(delay * model_stride_in_secs * self.asr_model._cfg.sample_rate))) + frame_reader = AudioFeatureIterator(samples, self.frame_len, self.raw_preprocessor, self.asr_model.device) + self.set_frame_reader(frame_reader) + + @torch.no_grad() + def _get_batch_preds(self, keep_logits): + device = self.asr_model.device + for batch in iter(self.data_loader): + feat_signal, feat_signal_len = batch + feat_signal, feat_signal_len = feat_signal.to(device), feat_signal_len.to(device) + log_probs, encoded_len, predictions = self.asr_model( + processed_signal=feat_signal, processed_signal_length=feat_signal_len + ) + preds = torch.unbind(predictions) + for pred in preds: + self.all_preds.append(pred.cpu().numpy()) + # Always keep logits in FrameBatchASRLogits + _ = keep_logits + log_probs_tup = torch.unbind(log_probs) + for log_prob in log_probs_tup: + self.all_logprobs.append(log_prob) + del log_probs, log_probs_tup + del encoded_len + del predictions + + def transcribe_with_ts( + self, tokens_per_chunk: int, delay: int, + ): + self.infer_logits() + self.unmerged = [] + self.part_logprobs = [] + for idx, pred in enumerate(self.all_preds): + decoded = pred.tolist() + _stt, _end = len(decoded) - 1 - delay, len(decoded) - 1 - delay + tokens_per_chunk + self.unmerged += decoded[len(decoded) - 1 - delay : len(decoded) - 1 - delay + tokens_per_chunk] + self.part_logprobs.append(self.all_logprobs[idx][_stt:_end, :]) + self.unmerged_logprobs = torch.cat(self.part_logprobs, 0) + assert ( + len(self.unmerged) == self.unmerged_logprobs.shape[0] + ), "Unmerged decoded result and log prob lengths are different." + return self.greedy_merge(self.unmerged), self.unmerged, self.unmerged_logprobs + + +class ASRDecoderTimeStamps: + """ + A class designed for extracting word timestamps while the ASR decoding process. + This class contains a few setups for a slew of NeMo ASR models such as QuartzNet, CitriNet and ConformerCTC models. + """ + + def __init__(self, cfg_diarizer): + self.manifest_filepath = cfg_diarizer.manifest_filepath + self.params = cfg_diarizer.asr.parameters + self.ctc_decoder_params = cfg_diarizer.asr.ctc_decoder_parameters + self.ASR_model_name = cfg_diarizer.asr.model_path + self.nonspeech_threshold = self.params.asr_based_vad_threshold + self.root_path = None + self.run_ASR = None + self.encdec_class = None + self.AUDIO_RTTM_MAP = audio_rttm_map(self.manifest_filepath) + self.audio_file_list = [value['audio_filepath'] for _, value in self.AUDIO_RTTM_MAP.items()] + + def set_asr_model(self): + """ + Initialize the parameters for the given ASR model. + Currently, the following NGC models are supported: + + stt_en_quartznet15x5, + stt_en_citrinet*, + stt_en_conformer_ctc* + + To assign a proper decoding function for generating timestamp output, + the name of .nemo file should include the architecture name such as: + 'quartznet', 'conformer', and 'citrinet'. + + decoder_delay_in_sec is the amount of delay that is compensated during the word timestamp extraction. + word_ts_anchor_offset is the reference point for a word and used for matching the word with diarization labels. + Each ASR model has a different optimal decoder delay and word timestamp anchor offset. + To obtain an optimized diarization result with ASR, decoder_delay_in_sec and word_ts_anchor_offset + need to be searched on a development set. + """ + if 'quartznet' in self.ASR_model_name.lower(): + self.run_ASR = self.run_ASR_QuartzNet_CTC + self.encdec_class = EncDecCTCModel + self.decoder_delay_in_sec = if_none_get_default(self.params['decoder_delay_in_sec'], 0.04) + self.word_ts_anchor_offset = if_none_get_default(self.params['word_ts_anchor_offset'], 0.12) + self.asr_batch_size = if_none_get_default(self.params['asr_batch_size'], 4) + self.model_stride_in_secs = 0.02 + + elif 'conformer' in self.ASR_model_name.lower(): + self.run_ASR = self.run_ASR_BPE_CTC + self.encdec_class = EncDecCTCModelBPE + self.decoder_delay_in_sec = if_none_get_default(self.params['decoder_delay_in_sec'], 0.08) + self.word_ts_anchor_offset = if_none_get_default(self.params['word_ts_anchor_offset'], 0.12) + self.asr_batch_size = if_none_get_default(self.params['asr_batch_size'], 16) + self.model_stride_in_secs = 0.04 + # Conformer requires buffered inference and the parameters for buffered processing. + self.chunk_len_in_sec = 5 + self.total_buffer_in_secs = 25 + + elif 'citrinet' in self.ASR_model_name.lower(): + self.run_ASR = self.run_ASR_CitriNet_CTC + self.encdec_class = EncDecCTCModelBPE + self.decoder_delay_in_sec = if_none_get_default(self.params['decoder_delay_in_sec'], 0.16) + self.word_ts_anchor_offset = if_none_get_default(self.params['word_ts_anchor_offset'], 0.2) + self.asr_batch_size = if_none_get_default(self.params['asr_batch_size'], 4) + self.model_stride_in_secs = 0.08 + + else: + raise ValueError(f"Cannot find the ASR model class for: {self.params['self.ASR_model_name']}") + + if self.ASR_model_name.endswith('.nemo'): + asr_model = self.encdec_class.restore_from(restore_path=self.ASR_model_name) + else: + asr_model = self.encdec_class.from_pretrained(model_name=self.ASR_model_name, strict=False) + + if self.ctc_decoder_params['pretrained_language_model']: + if not PYCTCDECODE: + raise ImportError( + 'LM for beam search decoding is provided but pyctcdecode is not installed. Install pyctcdecode using PyPI: pip install pyctcdecode' + ) + self.beam_search_decoder = self.load_LM_for_CTC_decoder(asr_model) + else: + self.beam_search_decoder = None + + asr_model.eval() + return asr_model + + def load_LM_for_CTC_decoder(self, asr_model: Type[Union[EncDecCTCModel, EncDecCTCModelBPE]]): + """ + Load a language model for CTC decoder (pyctcdecode). + Note that only EncDecCTCModel and EncDecCTCModelBPE models can use pyctcdecode. + """ + kenlm_model = self.ctc_decoder_params['pretrained_language_model'] + logging.info(f"Loading language model : {self.ctc_decoder_params['pretrained_language_model']}") + + if 'EncDecCTCModelBPE' in str(type(asr_model)): + vocab = asr_model.tokenizer.tokenizer.get_vocab() + labels = list(vocab.keys()) + labels[0] = "" + elif 'EncDecCTCModel' in str(type(asr_model)): + labels = asr_model.decoder.vocabulary + else: + raise ValueError(f"Cannot find a vocabulary or tokenizer for: {self.params['self.ASR_model_name']}") + + decoder = build_ctcdecoder( + labels, kenlm_model, alpha=self.ctc_decoder_params['alpha'], beta=self.ctc_decoder_params['beta'] + ) + return decoder + + def run_ASR_QuartzNet_CTC(self, asr_model: Type[EncDecCTCModel]) -> Tuple[Dict, Dict]: + """ + Launch QuartzNet ASR model and collect logit, timestamps and text output. + + Args: + asr_model (class): + The loaded NeMo ASR model. + + Returns: + words_dict (dict): + Dictionary containing the sequence of words from hypothesis. + word_ts_dict (dict): + Dictionary containing the time-stamps of words. + """ + words_dict, word_ts_dict = {}, {} + + wer_ts = WER_TS( + vocabulary=asr_model.decoder.vocabulary, + batch_dim_index=0, + use_cer=asr_model._cfg.get('use_cer', False), + ctc_decode=True, + dist_sync_on_step=True, + log_prediction=asr_model._cfg.get("log_prediction", False), + ) + + with torch.cuda.amp.autocast(): + transcript_hyps_list = asr_model.transcribe( + self.audio_file_list, batch_size=self.asr_batch_size, return_hypotheses=True + ) # type: List[nemo_asr.parts.Hypothesis] + transcript_logits_list = [hyp.alignments for hyp in transcript_hyps_list] + for idx, logit_np in enumerate(transcript_logits_list): + logit_np = logit_np.cpu().numpy() + uniq_id = get_uniqname_from_filepath(self.audio_file_list[idx]) + if self.beam_search_decoder: + logging.info( + f"Running beam-search decoder on {uniq_id} with LM {self.ctc_decoder_params['pretrained_language_model']}" + ) + hyp_words, word_ts = self.run_pyctcdecode(logit_np) + else: + log_prob = torch.from_numpy(logit_np) + logits_len = torch.from_numpy(np.array([log_prob.shape[0]])) + greedy_predictions = log_prob.argmax(dim=-1, keepdim=False).unsqueeze(0) + text, char_ts = wer_ts.ctc_decoder_predictions_tensor_with_ts( + greedy_predictions, predictions_len=logits_len + ) + trans, char_ts_in_feature_frame_idx = self.clean_trans_and_TS(text[0], char_ts[0]) + spaces_in_sec, hyp_words = self._get_spaces( + trans, char_ts_in_feature_frame_idx, self.model_stride_in_secs + ) + word_ts = self.get_word_ts_from_spaces( + char_ts_in_feature_frame_idx, spaces_in_sec, end_stamp=logit_np.shape[0] + ) + word_ts = self.align_decoder_delay(word_ts, self.decoder_delay_in_sec) + assert len(hyp_words) == len(word_ts), "Words and word timestamp list length does not match." + words_dict[uniq_id] = hyp_words + word_ts_dict[uniq_id] = word_ts + + return words_dict, word_ts_dict + + @staticmethod + def clean_trans_and_TS(trans: str, char_ts: List[str]) -> Tuple[str, List[str]]: + """ + Remove the spaces in the beginning and the end. + The char_ts need to be changed and synced accordingly. + + Args: + trans (list): + List containing the character output (str). + char_ts (list): + List containing the timestamps (int) for each character. + + Returns: + trans (list): + List containing the cleaned character output. + char_ts (list): + List containing the cleaned timestamps for each character. + """ + assert (len(trans) > 0) and (len(char_ts) > 0) + assert len(trans) == len(char_ts) + + trans = trans.lstrip() + diff_L = len(char_ts) - len(trans) + char_ts = char_ts[diff_L:] + + trans = trans.rstrip() + diff_R = len(char_ts) - len(trans) + if diff_R > 0: + char_ts = char_ts[: -1 * diff_R] + return trans, char_ts + + def _get_spaces(self, trans: str, char_ts: List[str], time_stride: float) -> Tuple[float, List[str]]: + """ + Collect the space symbols with a list of words. + + Args: + trans (list): + List containing the character output (str). + char_ts (list): + List containing the timestamps of the characters. + time_stride (float): + The size of stride of the model in second. + + Returns: + spaces_in_sec (list): + List containing the ranges of spaces + word_list (list): + List containing the words from ASR inference. + """ + blank = ' ' + spaces_in_sec, word_list = [], [] + stt_idx = 0 + assert (len(trans) > 0) and (len(char_ts) > 0), "Transcript and char_ts length should not be 0." + assert len(trans) == len(char_ts), "Transcript and timestamp lengths do not match." + + # If there is a blank, update the time stamps of the space and the word. + for k, s in enumerate(trans): + if s == blank: + spaces_in_sec.append( + [round(char_ts[k] * time_stride, 2), round((char_ts[k + 1] - 1) * time_stride, 2)] + ) + word_list.append(trans[stt_idx:k]) + stt_idx = k + 1 + + # Add the last word + if len(trans) > stt_idx and trans[stt_idx] != blank: + word_list.append(trans[stt_idx:]) + return spaces_in_sec, word_list + + def run_ASR_CitriNet_CTC(self, asr_model: Type[EncDecCTCModelBPE]) -> Tuple[Dict, Dict]: + """ + Launch CitriNet ASR model and collect logit, timestamps and text output. + + Args: + asr_model (class): + The loaded NeMo ASR model. + + Returns: + words_dict (dict): + Dictionary containing the sequence of words from hypothesis. + word_ts_dict (dict): + Dictionary containing the timestamps of hypothesis words. + """ + words_dict, word_ts_dict = {}, {} + + werbpe_ts = WERBPE_TS( + tokenizer=asr_model.tokenizer, + batch_dim_index=0, + use_cer=asr_model._cfg.get('use_cer', False), + ctc_decode=True, + dist_sync_on_step=True, + log_prediction=asr_model._cfg.get("log_prediction", False), + ) + + with torch.cuda.amp.autocast(): + transcript_hyps_list = asr_model.transcribe( + self.audio_file_list, batch_size=self.asr_batch_size, return_hypotheses=True + ) # type: List[nemo_asr.parts.Hypothesis] + transcript_logits_list = [hyp.alignments for hyp in transcript_hyps_list] + for idx, logit_np in enumerate(transcript_logits_list): + log_prob = logit_np.cpu().numpy() + uniq_id = get_uniqname_from_filepath(self.audio_file_list[idx]) + if self.beam_search_decoder: + logging.info( + f"Running beam-search decoder with LM {self.ctc_decoder_params['pretrained_language_model']}" + ) + hyp_words, word_ts = self.run_pyctcdecode(logit_np) + else: + log_prob = torch.from_numpy(logit_np) + greedy_predictions = log_prob.argmax(dim=-1, keepdim=False).unsqueeze(0) + logits_len = torch.from_numpy(np.array([log_prob.shape[0]])) + text, char_ts, word_ts = werbpe_ts.ctc_decoder_predictions_tensor_with_ts( + self.model_stride_in_secs, greedy_predictions, predictions_len=logits_len + ) + hyp_words, word_ts = text[0].split(), word_ts[0] + word_ts = self.align_decoder_delay(word_ts, self.decoder_delay_in_sec) + assert len(hyp_words) == len(word_ts), "Words and word timestamp list length does not match." + words_dict[uniq_id] = hyp_words + word_ts_dict[uniq_id] = word_ts + + return words_dict, word_ts_dict + + def set_buffered_infer_params(self, asr_model: Type[EncDecCTCModelBPE]) -> Tuple[float, float, float]: + """ + Prepare the parameters for the buffered inference. + """ + cfg = copy.deepcopy(asr_model._cfg) + OmegaConf.set_struct(cfg.preprocessor, False) + + # some changes for streaming scenario + cfg.preprocessor.dither = 0.0 + cfg.preprocessor.pad_to = 0 + cfg.preprocessor.normalize = "None" + + preprocessor = nemo_asr.models.EncDecCTCModelBPE.from_config_dict(cfg.preprocessor) + preprocessor.to(asr_model.device) + + # Disable config overwriting + OmegaConf.set_struct(cfg.preprocessor, True) + + onset_delay = ( + math.ceil(((self.total_buffer_in_secs - self.chunk_len_in_sec) / 2) / self.model_stride_in_secs) + 1 + ) + mid_delay = math.ceil( + (self.chunk_len_in_sec + (self.total_buffer_in_secs - self.chunk_len_in_sec) / 2) + / self.model_stride_in_secs + ) + tokens_per_chunk = math.ceil(self.chunk_len_in_sec / self.model_stride_in_secs) + + return onset_delay, mid_delay, tokens_per_chunk + + def run_ASR_BPE_CTC(self, asr_model: Type[EncDecCTCModelBPE]) -> Tuple[Dict, Dict]: + """ + Launch CTC-BPE based ASR model and collect logit, timestamps and text output. + + Args: + asr_model (class): + The loaded NeMo ASR model. + + Returns: + words_dict (dict): + Dictionary containing the sequence of words from hypothesis. + word_ts_dict (dict): + Dictionary containing the time-stamps of words. + """ + torch.manual_seed(0) + torch.set_grad_enabled(False) + words_dict, word_ts_dict = {}, {} + + werbpe_ts = WERBPE_TS( + tokenizer=asr_model.tokenizer, + batch_dim_index=0, + use_cer=asr_model._cfg.get('use_cer', False), + ctc_decode=True, + dist_sync_on_step=True, + log_prediction=asr_model._cfg.get("log_prediction", False), + ) + + frame_asr = FrameBatchASRLogits( + asr_model=asr_model, + frame_len=self.chunk_len_in_sec, + total_buffer=self.total_buffer_in_secs, + batch_size=self.asr_batch_size, + ) + + onset_delay, mid_delay, tokens_per_chunk = self.set_buffered_infer_params(asr_model) + onset_delay_in_sec = round(onset_delay * self.model_stride_in_secs, 2) + + with torch.cuda.amp.autocast(): + logging.info(f"Running ASR model {self.ASR_model_name}") + + for idx, audio_file_path in enumerate(self.audio_file_list): + uniq_id = get_uniqname_from_filepath(audio_file_path) + logging.info(f"[{idx+1}/{len(self.audio_file_list)}] FrameBatchASR: {audio_file_path}") + frame_asr.clear_buffer() + + hyp, greedy_predictions_list, log_prob = get_wer_feat_logit( + audio_file_path, + frame_asr, + self.chunk_len_in_sec, + tokens_per_chunk, + mid_delay, + self.model_stride_in_secs, + ) + if self.beam_search_decoder: + logging.info( + f"Running beam-search decoder with LM {self.ctc_decoder_params['pretrained_language_model']}" + ) + log_prob = log_prob.unsqueeze(0).cpu().numpy()[0] + hyp_words, word_ts = self.run_pyctcdecode(log_prob, onset_delay_in_sec=onset_delay_in_sec) + else: + logits_len = torch.from_numpy(np.array([len(greedy_predictions_list)])) + greedy_predictions_list = greedy_predictions_list[onset_delay:] + greedy_predictions = torch.from_numpy(np.array(greedy_predictions_list)).unsqueeze(0) + text, char_ts, word_ts = werbpe_ts.ctc_decoder_predictions_tensor_with_ts( + self.model_stride_in_secs, greedy_predictions, predictions_len=logits_len + ) + hyp_words, word_ts = text[0].split(), word_ts[0] + + word_ts = self.align_decoder_delay(word_ts, self.decoder_delay_in_sec) + assert len(hyp_words) == len(word_ts), "Words and word timestamp list length does not match." + words_dict[uniq_id] = hyp_words + word_ts_dict[uniq_id] = word_ts + + return words_dict, word_ts_dict + + def get_word_ts_from_spaces(self, char_ts: List[float], spaces_in_sec: List[float], end_stamp: float) -> List[str]: + """ + Take word timestamps from the spaces from the decoded prediction. + + Args: + char_ts (list): + List containing the timestamp for each character. + spaces_in_sec (list): + List containing the start and the end time of each space token. + end_stamp (float): + The end time of the session in sec. + + Returns: + word_timestamps (list): + List containing the timestamps for the resulting words. + """ + end_stamp = min(end_stamp, (char_ts[-1] + 2)) + start_stamp_in_sec = round(char_ts[0] * self.model_stride_in_secs, 2) + end_stamp_in_sec = round(end_stamp * self.model_stride_in_secs, 2) + + # In case of one word output with no space information. + if len(spaces_in_sec) == 0: + word_timestamps = [[start_stamp_in_sec, end_stamp_in_sec]] + elif len(spaces_in_sec) > 0: + # word_timetamps_middle should be an empty list if len(spaces_in_sec) == 1. + word_timetamps_middle = [ + [round(spaces_in_sec[k][1], 2), round(spaces_in_sec[k + 1][0], 2),] + for k in range(len(spaces_in_sec) - 1) + ] + word_timestamps = ( + [[start_stamp_in_sec, round(spaces_in_sec[0][0], 2)]] + + word_timetamps_middle + + [[round(spaces_in_sec[-1][1], 2), end_stamp_in_sec]] + ) + return word_timestamps + + def run_pyctcdecode( + self, logprob: np.ndarray, onset_delay_in_sec: float = 0, beam_width: int = 32 + ) -> Tuple[List[str], List[str]]: + """ + Launch pyctcdecode with the loaded pretrained language model. + + Args: + logprob (np.ndarray): + The log probability from the ASR model inference in numpy array format. + onset_delay_in_sec (float): + The amount of delay that needs to be compensated for the timestamp outputs froM pyctcdecode. + beam_width (int): + The beam width parameter for beam search decodring. + Returns: + hyp_words (list): + List containing the words in the hypothesis. + word_ts (list): + List containing the word timestamps from the decoder. + """ + beams = self.beam_search_decoder.decode_beams(logprob, beam_width=self.ctc_decoder_params['beam_width']) + word_ts_beam, words_beam = [], [] + for idx, (word, _) in enumerate(beams[0][2]): + ts = self.get_word_ts_from_wordframes(idx, beams[0][2], self.model_stride_in_secs, onset_delay_in_sec) + word_ts_beam.append(ts) + words_beam.append(word) + hyp_words, word_ts = words_beam, word_ts_beam + return hyp_words, word_ts + + @staticmethod + def get_word_ts_from_wordframes( + idx, word_frames: List[List[float]], frame_duration: float, onset_delay: float, word_block_delay: float = 2.25 + ): + """ + Extract word timestamps from word frames generated from pyctcdecode. + """ + offset = -1 * word_block_delay * frame_duration - onset_delay + frame_begin = word_frames[idx][1][0] + if frame_begin == -1: + frame_begin = word_frames[idx - 1][1][1] if idx != 0 else 0 + frame_end = word_frames[idx][1][1] + return [ + round(max(frame_begin * frame_duration + offset, 0), 2), + round(max(frame_end * frame_duration + offset, 0), 2), + ] + + @staticmethod + def align_decoder_delay(word_ts, decoder_delay_in_sec: float): + """ + Subtract decoder_delay_in_sec from the word timestamp output. + """ + for k in range(len(word_ts)): + word_ts[k] = [ + round(word_ts[k][0] - decoder_delay_in_sec, 2), + round(word_ts[k][1] - decoder_delay_in_sec, 2), + ] + return word_ts diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/diarization_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/diarization_utils.py new file mode 100644 index 0000000..f0b951e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/diarization_utils.py @@ -0,0 +1,1306 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import csv +import json +import os +from collections import OrderedDict as od +from datetime import datetime +from typing import Dict, List, Tuple + +import numpy as np + +from nemo.collections.asr.metrics.der import concat_perm_word_error_rate +from nemo.collections.asr.metrics.wer import word_error_rate +from nemo.collections.asr.models import ClusteringDiarizer +from nemo.collections.asr.parts.utils.speaker_utils import ( + audio_rttm_map, + get_uniqname_from_filepath, + labels_to_rttmfile, + rttm_to_labels, + write_rttm2manifest, +) +from nemo.utils import logging + +try: + import arpa + + ARPA = True +except ImportError: + ARPA = False + +__all__ = ['OfflineDiarWithASR'] + + +def dump_json_to_file(file_path: str, session_trans_dict: dict): + """ + Write a json file from the session_trans_dict dictionary. + + Args: + file_path (str): + Target filepath where json file is saved + session_trans_dict (dict): + Dictionary containing transcript, speaker labels and timestamps + """ + with open(file_path, "w") as outfile: + json.dump(session_trans_dict, outfile, indent=4) + + +def write_txt(w_path: str, val: str): + """ + Write a text file from the string input. + + Args: + w_path (str): + Target path for saving a file + val (str): + String variable to be written + """ + with open(w_path, "w") as output: + output.write(val + '\n') + + +def convert_ctm_to_text(ctm_file_path: str) -> Tuple[List[str], str]: + """ + Convert ctm file into a list containing transcription (space seperated string) per each speaker. + + Args: + ctm_file_path (str): + Filepath to the reference CTM files. + + Returns: + spk_reference (list): + List containing the reference transcripts for each speaker. + + Example: + >>> spk_reference = ["hi how are you well that's nice", "i'm good yeah how is your sister"] + + mix_reference (str): + Reference transcript from CTM file. This transcript has word sequence in temporal order. + + Example: + >>> mix_reference = "hi how are you i'm good well that's nice yeah how is your sister" + """ + mix_reference, per_spk_ref_trans_dict = [], {} + ctm_content = open(ctm_file_path).readlines() + for ctm_line in ctm_content: + ctm_split = ctm_line.split() + spk = ctm_split[1] + if spk not in per_spk_ref_trans_dict: + per_spk_ref_trans_dict[spk] = [] + per_spk_ref_trans_dict[spk].append(ctm_split[4]) + mix_reference.append(ctm_split[4]) + spk_reference = [" ".join(word_list) for word_list in per_spk_ref_trans_dict.values()] + mix_reference = " ".join(mix_reference) + return spk_reference, mix_reference + + +def convert_word_dict_seq_to_text(word_dict_seq_list: List[Dict[str, float]]) -> Tuple[List[str], str]: + """ + Convert word_dict_seq_list into a list containing transcription (space seperated string) per each speaker. + + Args: + word_dict_seq_list (list): + List containing words and corresponding word timestamps in dictionary format. + + Example: + >>> word_dict_seq_list = \ + >>> [{'word': 'right', 'start_time': 0.0, 'end_time': 0.04, 'speaker': 'speaker_0'}, + {'word': 'and', 'start_time': 0.64, 'end_time': 0.68, 'speaker': 'speaker_1'}, + ...], + + Returns: + spk_hypothesis (list): + Dictionary containing the hypothesis transcript for each speaker. A list containing the sequence + of words is assigned for each speaker. + + Example: + >>> spk_hypothesis= ["hi how are you well that's nice", "i'm good yeah how is your sister"] + + mix_hypothesis (str): + Hypothesis transcript from ASR output. This transcript has word sequence in temporal order. + + Example: + >>> mix_hypothesis = "hi how are you i'm good well that's nice yeah how is your sister" + """ + mix_hypothesis, per_spk_hyp_trans_dict = [], {} + for word_dict in word_dict_seq_list: + spk = word_dict['speaker'] + if spk not in per_spk_hyp_trans_dict: + per_spk_hyp_trans_dict[spk] = [] + per_spk_hyp_trans_dict[spk].append(word_dict['word']) + mix_hypothesis.append(word_dict['word']) + + # Create a list containing string formatted transcript + spk_hypothesis = [" ".join(word_list) for word_list in per_spk_hyp_trans_dict.values()] + mix_hypothesis = " ".join(mix_hypothesis) + return spk_hypothesis, mix_hypothesis + + +def convert_word_dict_seq_to_ctm( + word_dict_seq_list: List[Dict[str, float]], uniq_id: str = 'null', decimals: int = 3 +) -> Tuple[List[str], str]: + """ + Convert word_dict_seq_list into a list containing transcription in CTM format. + + Args: + word_dict_seq_list (list): + List containing words and corresponding word timestamps in dictionary format. + + Example: + >>> word_dict_seq_list = \ + >>> [{'word': 'right', 'start_time': 0.0, 'end_time': 0.34, 'speaker': 'speaker_0'}, + {'word': 'and', 'start_time': 0.64, 'end_time': 0.81, 'speaker': 'speaker_1'}, + ...], + + Returns: + ctm_lines_list (list): + List containing the hypothesis transcript in CTM format. + + Example: + >>> ctm_lines_list= ["my_audio_01 speaker_0 0.0 0.34 right 0", + my_audio_01 speaker_0 0.64 0.81 and 0", + + + """ + ctm_lines = [] + confidence = 0 + for word_dict in word_dict_seq_list: + spk = word_dict['speaker'] + stt = word_dict['start_time'] + dur = round(word_dict['end_time'] - word_dict['start_time'], decimals) + word = word_dict['word'] + ctm_line_str = f"{uniq_id} {spk} {stt} {dur} {word} {confidence}" + ctm_lines.append(ctm_line_str) + return ctm_lines + + +def get_total_result_dict( + der_results: Dict[str, Dict[str, float]], wer_results: Dict[str, Dict[str, float]], csv_columns: List[str], +): + """ + Merge WER results and DER results into a single dictionary variable. + + Args: + der_results (dict): + Dictionary containing FA, MISS, CER and DER values for both aggregated amount and + each session. + wer_results (dict): + Dictionary containing session-by-session WER and cpWER. `wer_results` only + exists when CTM files are provided. + + Returns: + total_result_dict (dict): + Dictionary containing both DER and WER results. This dictionary contains unique-IDs of + each session and `total` key that includes average (cp)WER and DER/CER/Miss/FA values. + """ + total_result_dict = {} + for uniq_id in der_results.keys(): + if uniq_id == 'total': + continue + total_result_dict[uniq_id] = {x: "-" for x in csv_columns} + total_result_dict[uniq_id]["uniq_id"] = uniq_id + if uniq_id in der_results: + total_result_dict[uniq_id].update(der_results[uniq_id]) + if uniq_id in wer_results: + total_result_dict[uniq_id].update(wer_results[uniq_id]) + total_result_jsons = list(total_result_dict.values()) + return total_result_jsons + + +def get_audacity_label(word: str, stt_sec: float, end_sec: float, speaker: str) -> str: + """ + Get a string formatted line for Audacity label. + + Args: + word (str): + A decoded word + stt_sec (float): + Start timestamp of the word + end_sec (float): + End timestamp of the word + + Returns: + speaker (str): + Speaker label in string type + """ + spk = speaker.split('_')[-1] + return f'{stt_sec}\t{end_sec}\t[{spk}] {word}' + + +def get_num_of_spk_from_labels(labels: List[str]) -> int: + """ + Count the number of speakers in a segment label list. + Args: + labels (list): + List containing segment start and end timestamp and speaker labels. + + Example: + >>> labels = ["15.25 21.82 speaker_0", "21.18 29.51 speaker_1", ... ] + + Returns: + n_spk (int): + The number of speakers in the list `labels` + + """ + spk_set = [x.split(' ')[-1].strip() for x in labels] + return len(set(spk_set)) + + +class OfflineDiarWithASR: + """ + A class designed for performing ASR and diarization together. + + Attributes: + cfg_diarizer (OmegaConf): + Hydra config for diarizer key + params (OmegaConf): + Parameters config in diarizer.asr + ctc_decoder_params (OmegaConf) + Hydra config for beam search decoder + realigning_lm_params (OmegaConf): + Hydra config for realigning language model + manifest_filepath (str): + Path to the input manifest path + nonspeech_threshold (float): + Threshold for VAD logits that are used for creating speech segments + fix_word_ts_with_VAD (bool): + Choose whether to fix word timestamps by using VAD results + root_path (str): + Path to the folder where diarization results are saved + vad_threshold_for_word_ts (float): + Threshold used for compensating word timestamps with VAD output + max_word_ts_length_in_sec (float): + Maximum limit for the duration of each word timestamp + word_ts_anchor_offset (float): + Offset for word timestamps from ASR decoders + run_ASR: + Placeholder variable for an ASR launcher function + realigning_lm: + Placeholder variable for a loaded ARPA Language model + ctm_exists (bool): + Boolean that indicates whether all files have the corresponding reference CTM file + frame_VAD (dict): + Dictionary containing frame-level VAD logits + AUDIO_RTTM_MAP: + Dictionary containing the input manifest information + color_palette (dict): + Dictionary containing the ANSI color escape codes for each speaker label (speaker index) + """ + + def __init__(self, cfg_diarizer): + self.cfg_diarizer = cfg_diarizer + self.params = cfg_diarizer.asr.parameters + self.ctc_decoder_params = cfg_diarizer.asr.ctc_decoder_parameters + self.realigning_lm_params = cfg_diarizer.asr.realigning_lm_parameters + self.manifest_filepath = cfg_diarizer.manifest_filepath + self.nonspeech_threshold = self.params.asr_based_vad_threshold + self.fix_word_ts_with_VAD = self.params.fix_word_ts_with_VAD + self.root_path = cfg_diarizer.out_dir + + self.vad_threshold_for_word_ts = 0.7 + self.max_word_ts_length_in_sec = 0.6 + self.word_ts_anchor_offset = 0.0 + self.run_ASR = None + self.realigning_lm = None + self.ctm_exists = False + self.frame_VAD = {} + + self.make_file_lists() + + self.color_palette = self.get_color_palette() + self.csv_columns = self.get_csv_columns() + + @staticmethod + def get_color_palette() -> Dict[str, str]: + return { + 'speaker_0': '\033[1;32m', + 'speaker_1': '\033[1;34m', + 'speaker_2': '\033[1;30m', + 'speaker_3': '\033[1;31m', + 'speaker_4': '\033[1;35m', + 'speaker_5': '\033[1;36m', + 'speaker_6': '\033[1;37m', + 'speaker_7': '\033[1;30m', + 'speaker_8': '\033[1;33m', + 'speaker_9': '\033[0;34m', + 'white': '\033[0;37m', + } + + @staticmethod + def get_csv_columns() -> List[str]: + return [ + 'uniq_id', + 'DER', + 'CER', + 'FA', + 'MISS', + 'est_n_spk', + 'ref_n_spk', + 'cpWER', + 'WER', + 'mapping', + ] + + def make_file_lists(self): + """ + Create lists containing the filepaths of audio clips and CTM files. + """ + self.AUDIO_RTTM_MAP = audio_rttm_map(self.manifest_filepath) + self.audio_file_list = [value['audio_filepath'] for _, value in self.AUDIO_RTTM_MAP.items()] + + self.ctm_file_list = [] + for k, audio_file_path in enumerate(self.audio_file_list): + uniq_id = get_uniqname_from_filepath(audio_file_path) + if ( + 'ctm_filepath' in self.AUDIO_RTTM_MAP[uniq_id] + and self.AUDIO_RTTM_MAP[uniq_id]['ctm_filepath'] is not None + and uniq_id in self.AUDIO_RTTM_MAP[uniq_id]['ctm_filepath'] + ): + self.ctm_file_list.append(self.AUDIO_RTTM_MAP[uniq_id]['ctm_filepath']) + + # check if all unique IDs have CTM files + if len(self.audio_file_list) == len(self.ctm_file_list): + self.ctm_exists = True + + def _load_realigning_LM(self): + """ + Load ARPA language model for realigning speaker labels for words. + """ + self.N_range = ( + self.realigning_lm_params['min_number_of_words'], + self.realigning_lm_params['max_number_of_words'], + ) + self.stt_end_tokens = ['', ''] + logging.info(f"Loading LM for realigning: {self.realigning_lm_params['arpa_language_model']}") + return arpa.loadf(self.realigning_lm_params['arpa_language_model'])[0] + + def _init_session_trans_dict(self, uniq_id: str, n_spk: int): + """ + Initialize json (in dictionary variable) formats for session level result and Gecko style json. + + Returns: + (dict): Session level result dictionary variable + """ + return od( + { + 'status': 'initialized', + 'session_id': uniq_id, + 'transcription': '', + 'speaker_count': n_spk, + 'words': [], + 'sentences': [], + } + ) + + def _init_session_gecko_dict(self): + """ + Initialize a dictionary format for Gecko style json. + + Returns: + (dict): + Gecko style json dictionary. + """ + return od({'schemaVersion': 2.0, 'monologues': []}) + + def _save_VAD_labels_list(self, word_ts_dict: Dict[str, Dict[str, List[float]]]): + """ + Take the non_speech labels from logit output. The logit output is obtained from + `run_ASR` function. + + Args: + word_ts_dict (dict): + Dictionary containing word timestamps. + """ + self.VAD_RTTM_MAP = {} + for idx, (uniq_id, word_timestamps) in enumerate(word_ts_dict.items()): + speech_labels_float = self.get_speech_labels_from_decoded_prediction( + word_timestamps, self.nonspeech_threshold + ) + speech_labels = self.get_str_speech_labels(speech_labels_float) + output_path = os.path.join(self.root_path, 'pred_rttms') + if not os.path.exists(output_path): + os.makedirs(output_path) + filename = labels_to_rttmfile(speech_labels, uniq_id, output_path) + self.VAD_RTTM_MAP[uniq_id] = {'audio_filepath': self.audio_file_list[idx], 'rttm_filepath': filename} + + @staticmethod + def get_speech_labels_from_decoded_prediction( + input_word_ts: List[float], nonspeech_threshold: float, + ) -> List[float]: + """ + Extract speech labels from the ASR output (decoded predictions) + + Args: + input_word_ts (list): + List containing word timestamps. + + Returns: + word_ts (list): + The ranges of the speech segments, which are merged ranges of input_word_ts. + """ + speech_labels = [] + word_ts = copy.deepcopy(input_word_ts) + if word_ts == []: + return speech_labels + else: + count = len(word_ts) - 1 + while count > 0: + if len(word_ts) > 1: + if word_ts[count][0] - word_ts[count - 1][1] <= nonspeech_threshold: + trangeB = word_ts.pop(count) + trangeA = word_ts.pop(count - 1) + word_ts.insert(count - 1, [trangeA[0], trangeB[1]]) + count -= 1 + return word_ts + + def run_diarization(self, diar_model_config, word_timestamps) -> Dict[str, List[str]]: + """ + Launch the diarization process using the given VAD timestamp (oracle_manifest). + + Args: + diar_model_config (OmegaConf): + Hydra configurations for speaker diarization + word_and_timestamps (list): + List containing words and word timestamps + + Returns: + diar_hyp (dict): + A dictionary containing rttm results which are indexed by a unique ID. + score Tuple[pyannote object, dict]: + A tuple containing pyannote metric instance and mapping dictionary between + speakers in hypotheses and speakers in reference RTTM files. + """ + + if diar_model_config.diarizer.asr.parameters.asr_based_vad: + self._save_VAD_labels_list(word_timestamps) + oracle_manifest = os.path.join(self.root_path, 'asr_vad_manifest.json') + oracle_manifest = write_rttm2manifest(self.VAD_RTTM_MAP, oracle_manifest) + diar_model_config.diarizer.vad.model_path = None + diar_model_config.diarizer.vad.external_vad_manifest = oracle_manifest + + diar_model = ClusteringDiarizer(cfg=diar_model_config) + score = diar_model.diarize() + if diar_model_config.diarizer.vad.model_path is not None and not diar_model_config.diarizer.oracle_vad: + self._get_frame_level_VAD( + vad_processing_dir=diar_model.vad_pred_dir, + smoothing_type=diar_model_config.diarizer.vad.parameters.smoothing, + ) + + diar_hyp = {} + for k, audio_file_path in enumerate(self.audio_file_list): + uniq_id = get_uniqname_from_filepath(audio_file_path) + pred_rttm = os.path.join(self.root_path, 'pred_rttms', uniq_id + '.rttm') + diar_hyp[uniq_id] = rttm_to_labels(pred_rttm) + return diar_hyp, score + + def _get_frame_level_VAD(self, vad_processing_dir, smoothing_type=False): + """ + Read frame-level VAD outputs. + + Args: + vad_processing_dir (str): + Path to the directory where the VAD results are saved. + smoothing_type (bool or str): [False, median, mean] + type of smoothing applied softmax logits to smooth the predictions. + """ + if isinstance(smoothing_type, bool) and not smoothing_type: + ext_type = 'frame' + else: + ext_type = smoothing_type + + for uniq_id in self.AUDIO_RTTM_MAP: + frame_vad = os.path.join(vad_processing_dir, uniq_id + '.' + ext_type) + frame_vad_float_list = [] + with open(frame_vad, 'r') as fp: + for line in fp.readlines(): + frame_vad_float_list.append(float(line.strip())) + self.frame_VAD[uniq_id] = frame_vad_float_list + + @staticmethod + def gather_eval_results( + diar_score, + audio_rttm_map_dict: Dict[str, Dict[str, str]], + trans_info_dict: Dict[str, Dict[str, float]], + root_path: str, + decimals: int = 4, + ) -> Dict[str, Dict[str, float]]: + """ + Gather diarization evaluation results from pyannote DiarizationErrorRate metric object. + + Args: + metric (DiarizationErrorRate metric): + DiarizationErrorRate metric pyannote object + trans_info_dict (dict): + Dictionary containing word timestamps, speaker labels and words from all sessions. + Each session is indexed by unique ID as a key. + mapping_dict (dict): + Dictionary containing speaker mapping labels for each audio file with key as unique name + decimals (int): + The number of rounding decimals for DER value + + Returns: + der_results (dict): + Dictionary containing scores for each audio file along with aggregated results + """ + metric, mapping_dict, _ = diar_score + results = metric.results_ + der_results = {} + count_correct_spk_counting = 0 + for result in results: + key, score = result + if 'hyp_rttm_filepath' in audio_rttm_map_dict[key]: + pred_rttm = audio_rttm_map_dict[key]['hyp_rttm_filepath'] + else: + pred_rttm = os.path.join(root_path, 'pred_rttms', key + '.rttm') + pred_labels = rttm_to_labels(pred_rttm) + + ref_rttm = audio_rttm_map_dict[key]['rttm_filepath'] + ref_labels = rttm_to_labels(ref_rttm) + ref_n_spk = get_num_of_spk_from_labels(ref_labels) + est_n_spk = get_num_of_spk_from_labels(pred_labels) + + _DER, _CER, _FA, _MISS = ( + (score['confusion'] + score['false alarm'] + score['missed detection']) / score['total'], + score['confusion'] / score['total'], + score['false alarm'] / score['total'], + score['missed detection'] / score['total'], + ) + + der_results[key] = { + "DER": round(_DER, decimals), + "CER": round(_CER, decimals), + "FA": round(_FA, decimals), + "MISS": round(_MISS, decimals), + "est_n_spk": est_n_spk, + "ref_n_spk": ref_n_spk, + "mapping": mapping_dict[key], + } + count_correct_spk_counting += int(est_n_spk == ref_n_spk) + + DER, CER, FA, MISS = ( + abs(metric), + metric['confusion'] / metric['total'], + metric['false alarm'] / metric['total'], + metric['missed detection'] / metric['total'], + ) + der_results["total"] = { + "DER": DER, + "CER": CER, + "FA": FA, + "MISS": MISS, + "spk_counting_acc": count_correct_spk_counting / len(metric.results_), + } + + return der_results + + def _get_the_closest_silence_start( + self, vad_index_word_end: float, vad_frames: np.ndarray, offset: int = 10 + ) -> float: + """ + Find the closest silence frame from the given starting position. + + Args: + vad_index_word_end (float): + The timestamp of the end of the current word. + vad_frames (numpy.array): + The numpy array containing frame-level VAD probability. + params (dict): + Contains the parameters for diarization and ASR decoding. + + Returns: + cursor (float): + A timestamp of the earliest start of a silence region from + the given time point, vad_index_word_end. + """ + + cursor = vad_index_word_end + offset + limit = int(100 * self.max_word_ts_length_in_sec + vad_index_word_end) + while cursor < len(vad_frames): + if vad_frames[cursor] < self.vad_threshold_for_word_ts: + break + else: + cursor += 1 + if cursor > limit: + break + cursor = min(len(vad_frames) - 1, cursor) + cursor = round(cursor / 100.0, 2) + return cursor + + def _compensate_word_ts_list( + self, audio_file_list: List[str], word_ts_dict: Dict[str, List[float]], + ) -> Dict[str, List[List[float]]]: + """ + Compensate the word timestamps based on the VAD output. + The length of each word is capped by self.max_word_ts_length_in_sec. + + Args: + audio_file_list (list): + List containing audio file paths. + word_ts_dict (dict): + Dictionary containing timestamps of words. + + Returns: + enhanced_word_ts_dict (dict): + Dictionary containing the enhanced word timestamp values indexed by unique-IDs. + """ + enhanced_word_ts_dict = {} + for idx, (uniq_id, word_ts_seq_list) in enumerate(word_ts_dict.items()): + N = len(word_ts_seq_list) + enhanced_word_ts_buffer = [] + for k, word_ts in enumerate(word_ts_seq_list): + if k < N - 1: + word_len = round(word_ts[1] - word_ts[0], 2) + len_to_next_word = round(word_ts_seq_list[k + 1][0] - word_ts[0] - 0.01, 2) + if uniq_id in self.frame_VAD: + vad_index_word_end = int(100 * word_ts[1]) + closest_sil_stt = self._get_the_closest_silence_start( + vad_index_word_end, self.frame_VAD[uniq_id] + ) + vad_est_len = round(closest_sil_stt - word_ts[0], 2) + else: + vad_est_len = len_to_next_word + min_candidate = min(vad_est_len, len_to_next_word) + fixed_word_len = max(min(self.max_word_ts_length_in_sec, min_candidate), word_len) + enhanced_word_ts_buffer.append([word_ts[0], word_ts[0] + fixed_word_len]) + else: + enhanced_word_ts_buffer.append([word_ts[0], word_ts[1]]) + + enhanced_word_ts_dict[uniq_id] = enhanced_word_ts_buffer + return enhanced_word_ts_dict + + def get_transcript_with_speaker_labels( + self, diar_hyp: Dict[str, List[str]], word_hyp: Dict[str, List[str]], word_ts_hyp: Dict[str, List[float]] + ) -> Dict[str, Dict[str, float]]: + """ + Match the diarization result with the ASR output. + The words and the timestamps for the corresponding words are matched in a for loop. + + Args: + diar_hyp (dict): + Dictionary of the Diarization output labels in str. Indexed by unique IDs. + + Example: + >>> diar_hyp['my_audio_01'] = ['0.0 4.375 speaker_1', '4.375 5.125 speaker_0', ...] + + word_hyp (dict): + Dictionary of words from ASR inference. Indexed by unique IDs. + + Example: + >>> word_hyp['my_audio_01'] = ['hi', 'how', 'are', ...] + + word_ts_hyp (dict): + Dictionary containing the start time and the end time of each word. + Indexed by unique IDs. + + Example: + >>> word_ts_hyp['my_audio_01'] = [[0.0, 0.04], [0.64, 0.68], [0.84, 0.88], ...] + + Returns: + trans_info_dict (dict): + Dictionary containing word timestamps, speaker labels and words from all sessions. + Each session is indexed by a unique ID. + """ + trans_info_dict = {} + if self.fix_word_ts_with_VAD: + if self.frame_VAD == {}: + logging.warning( + f"VAD timestamps are not provided. Fixing word timestamps without VAD. Please check the hydra configurations." + ) + word_ts_refined = self._compensate_word_ts_list(self.audio_file_list, word_ts_hyp) + else: + word_ts_refined = word_ts_hyp + + if self.realigning_lm_params['arpa_language_model']: + if not ARPA: + raise ImportError( + 'LM for realigning is provided but arpa is not installed. Install arpa using PyPI: pip install arpa' + ) + else: + self.realigning_lm = self._load_realigning_LM() + + word_dict_seq_list = [] + for k, audio_file_path in enumerate(self.audio_file_list): + uniq_id = get_uniqname_from_filepath(audio_file_path) + words, diar_labels = word_hyp[uniq_id], diar_hyp[uniq_id] + word_ts, word_rfnd_ts = word_ts_hyp[uniq_id], word_ts_refined[uniq_id] + + # Assign speaker labels to words + word_dict_seq_list = self.get_word_level_json_list( + words=words, word_ts=word_ts, word_rfnd_ts=word_rfnd_ts, diar_labels=diar_labels + ) + if self.realigning_lm: + word_dict_seq_list = self.realign_words_with_lm(word_dict_seq_list) + + # Create a transscript information json dictionary from the output variables + trans_info_dict[uniq_id] = self._make_json_output(uniq_id, diar_labels, word_dict_seq_list) + logging.info(f"Diarization with ASR output files are saved in: {self.root_path}/pred_rttms") + return trans_info_dict + + def get_word_level_json_list( + self, + words: List[str], + diar_labels: List[str], + word_ts: List[List[float]], + word_rfnd_ts: List[List[float]] = None, + decimals: int = 2, + ) -> Dict[str, Dict[str, str]]: + """ + Assign speaker labels to each word and save the hypothesis words and speaker labels to + a dictionary variable for future use. + + Args: + uniq_id (str): + A unique ID (key) that identifies each input audio file. + diar_labels (list): + List containing the Diarization output labels in str. Indexed by unique IDs. + + Example: + >>> diar_labels = ['0.0 4.375 speaker_1', '4.375 5.125 speaker_0', ...] + + words (list): + Dictionary of words from ASR inference. Indexed by unique IDs. + + Example: + >>> words = ['hi', 'how', 'are', ...] + + word_ts (list): + Dictionary containing the start time and the end time of each word. + Indexed by unique IDs. + + Example: + >>> word_ts = [[0.0, 0.04], [0.64, 0.68], [0.84, 0.88], ...] + + word_ts_refined (list): + Dictionary containing the refined (end point fixed) word timestamps based on hypothesis + word timestamps. Indexed by unique IDs. + + Example: + >>> word_rfnd_ts = [[0.0, 0.60], [0.64, 0.80], [0.84, 0.92], ...] + + Returns: + word_dict_seq_list (list): + List containing word by word dictionary containing word, timestamps and speaker labels. + + Example: + >>> [{'word': 'right', 'start_time': 0.0, 'end_time': 0.04, 'speaker': 'speaker_0'}, + {'word': 'and', 'start_time': 0.64, 'end_time': 0.68, 'speaker': 'speaker_1'}, + {'word': 'i', 'start_time': 0.84, 'end_time': 0.88, 'speaker': 'speaker_1'}, + ...] + """ + if word_rfnd_ts is None: + word_rfnd_ts = word_ts + start_point, end_point, speaker = diar_labels[0].split() + word_pos, turn_idx = 0, 0 + word_dict_seq_list = [] + for word_idx, (word, word_ts_stt_end, refined_word_ts_stt_end) in enumerate(zip(words, word_ts, word_rfnd_ts)): + word_pos = self._get_word_timestamp_anchor(word_ts_stt_end) + if word_pos > float(end_point): + turn_idx += 1 + turn_idx = min(turn_idx, len(diar_labels) - 1) + start_point, end_point, speaker = diar_labels[turn_idx].split() + stt_sec = round(refined_word_ts_stt_end[0], decimals) + end_sec = round(refined_word_ts_stt_end[1], decimals) + word_dict_seq_list.append({'word': word, 'start_time': stt_sec, 'end_time': end_sec, 'speaker': speaker}) + return word_dict_seq_list + + def _make_json_output( + self, uniq_id: str, diar_labels: List[str], word_dict_seq_list: List[Dict[str, float]], + ) -> Dict[str, Dict[str, str]]: + """ + Generate json output files and transcripts from the ASR and diarization results. + + Args: + uniq_id (str): + A unique ID (key) that identifies each input audio file. + diar_labels (list): + List containing the diarization hypothesis timestamps + + Example: + >>> diar_hyp['my_audio_01'] = ['0.0 4.375 speaker_1', '4.375 5.125 speaker_0', ...] + + word_dict_seq_list (list): + List containing words and corresponding word timestamps in dictionary format. + + Example: + >>> [{'word': 'right', 'start_time': 0.0, 'end_time': 0.04, 'speaker': 'speaker_0'}, + {'word': 'and', 'start_time': 0.64, 'end_time': 0.68, 'speaker': 'speaker_1'}, + {'word': 'i', 'start_time': 0.84, 'end_time': 0.88, 'speaker': 'speaker_1'}, + ...] + + Returns: + session_result_dict (dict): + A dictionary containing overall results of diarization and ASR inference. + `session_result_dict` has following keys: `status`, `session_id`, `transcription`, `speaker_count`, + `words`, `sentences`. + + Example: + >>> session_trans_dict = \ + { + 'status': 'Success', + 'session_id': 'my_audio_01', + 'transcription': 'right and i really think ...', + 'speaker_count': 2, + 'words': [{'word': 'right', 'start_time': 0.0, 'end_time': 0.04, 'speaker': 'speaker_0'}, + {'word': 'and', 'start_time': 0.64, 'end_time': 0.68, 'speaker': 'speaker_1'}, + {'word': 'i', 'start_time': 0.84, 'end_time': 0.88, 'speaker': 'speaker_1'}, + ... + ] + 'sentences': [{'sentence': 'right', 'start_time': 0.0, 'end_time': 0.04, 'speaker': 'speaker_0'}, + {'sentence': 'and i really think ...', + 'start_time': 0.92, 'end_time': 4.12, 'speaker': 'speaker_0'}, + ... + ] + } + """ + word_seq_list, audacity_label_words = [], [] + start_point, end_point, speaker = diar_labels[0].split() + prev_speaker = speaker + + sentences, terms_list = [], [] + sentence = {'speaker': speaker, 'start_time': start_point, 'end_time': end_point, 'text': ''} + + n_spk = get_num_of_spk_from_labels(diar_labels) + logging.info(f"Creating results for Session: {uniq_id} n_spk: {n_spk} ") + session_trans_dict = self._init_session_trans_dict(uniq_id=uniq_id, n_spk=n_spk) + gecko_dict = self._init_session_gecko_dict() + + for k, word_dict in enumerate(word_dict_seq_list): + word, speaker = word_dict['word'], word_dict['speaker'] + word_seq_list.append(word) + start_point, end_point = word_dict['start_time'], word_dict['end_time'] + if speaker != prev_speaker: + if len(terms_list) != 0: + gecko_dict['monologues'].append( + {'speaker': {'name': None, 'id': prev_speaker}, 'terms': terms_list} + ) + terms_list = [] + + # remove trailing space in text + sentence['text'] = sentence['text'].strip() + + # store last sentence + sentences.append(sentence) + + # start construction of a new sentence + sentence = {'speaker': speaker, 'start_time': start_point, 'end_time': end_point, 'text': ''} + else: + # correct the ending time + sentence['end_time'] = end_point + + stt_sec, end_sec = start_point, end_point + terms_list.append({'start': stt_sec, 'end': end_sec, 'text': word, 'type': 'WORD'}) + + # add current word to sentence + sentence['text'] += word.strip() + ' ' + + audacity_label_words.append(get_audacity_label(word, stt_sec, end_sec, speaker)) + prev_speaker = speaker + + session_trans_dict['words'] = word_dict_seq_list + + # note that we need to add the very last sentence. + sentence['text'] = sentence['text'].strip() + sentences.append(sentence) + gecko_dict['monologues'].append({'speaker': {'name': None, 'id': speaker}, 'terms': terms_list}) + + # Speaker independent transcription + session_trans_dict['transcription'] = ' '.join(word_seq_list) + # add sentences to transcription information dict + session_trans_dict['sentences'] = sentences + self._write_and_log(uniq_id, session_trans_dict, audacity_label_words, gecko_dict, sentences) + return session_trans_dict + + def _get_realignment_ranges(self, k: int, word_seq_len: int) -> Tuple[int, int]: + """ + Calculate word ranges for realignment operation. + N1, N2 are calculated to not exceed the start and end of the input word sequence. + + Args: + k (int): + Index of the current word + word_seq_len (int): + Length of the sentence + + Returns: + N1 (int): + Start index of the word sequence + N2 (int): + End index of the word sequence + """ + if k < self.N_range[1]: + N1 = max(k, self.N_range[0]) + N2 = min(word_seq_len - k, self.N_range[1]) + elif k > (word_seq_len - self.N_range[1]): + N1 = min(k, self.N_range[1]) + N2 = max(word_seq_len - k, self.N_range[0]) + else: + N1, N2 = self.N_range[1], self.N_range[1] + return N1, N2 + + def _get_word_timestamp_anchor(self, word_ts_stt_end: List[float]) -> float: + """ + Determine a reference point to match a word with the diarization results. + word_ts_anchor_pos determines the position of a word in relation to the given diarization labels: + - 'start' uses the beginning of the word + - 'end' uses the end of the word + - 'mid' uses the mean of start and end of the word + + word_ts_anchor_offset determines how much offset we want to add to the anchor position. + It is recommended to use the default value. + + Args: + word_ts_stt_end (list): + List containing start and end of the decoded word. + + Returns: + word_pos (float): + Floating point number that indicates temporal location of the word. + """ + if self.params['word_ts_anchor_pos'] == 'start': + word_pos = word_ts_stt_end[0] + elif self.params['word_ts_anchor_pos'] == 'end': + word_pos = word_ts_stt_end[1] + elif self.params['word_ts_anchor_pos'] == 'mid': + word_pos = (word_ts_stt_end[0] + word_ts_stt_end[1]) / 2 + else: + logging.info( + f"word_ts_anchor_pos: {self.params['word_ts_anchor']} is not a supported option. Using the default 'start' option." + ) + word_pos = word_ts_stt_end[0] + + word_pos = word_pos + self.word_ts_anchor_offset + return word_pos + + def realign_words_with_lm(self, word_dict_seq_list: List[Dict[str, float]]) -> List[Dict[str, float]]: + """ + Realign the mapping between speaker labels and words using a language model. + The realigning process calculates the probability of the certain range around the words, + especially at the boundary between two hypothetical sentences spoken by different speakers. + + Example: + k-th word: "but" + + hyp_former: + since i think like tuesday but he's coming back to albuquerque + hyp_latter: + since i think like tuesday but he's coming back to albuquerque + + The joint probabilities of words in the sentence are computed for these two hypotheses. In addition, + logprob_diff_threshold parameter is used for reducing the false positive realigning. + + Args: + word_dict_seq_list (list): + List containing words and corresponding word timestamps in dictionary format. + + Returns: + realigned_list (list): + List of dictionaries containing words, word timestamps and speaker labels. + """ + word_seq_len = len(word_dict_seq_list) + hyp_w_dict_list, spk_list = [], [] + for k, line_dict in enumerate(word_dict_seq_list): + word, spk_label = line_dict['word'], line_dict['speaker'] + hyp_w_dict_list.append(word) + spk_list.append(spk_label) + + realigned_list = [] + org_spk_list = copy.deepcopy(spk_list) + for k, line_dict in enumerate(word_dict_seq_list): + if self.N_range[0] < k < (word_seq_len - self.N_range[0]) and ( + spk_list[k] != org_spk_list[k + 1] or spk_list[k] != org_spk_list[k - 1] + ): + N1, N2 = self._get_realignment_ranges(k, word_seq_len) + hyp_former = self.realigning_lm.log_s( + ' '.join(hyp_w_dict_list[k - N1 : k] + self.stt_end_tokens + hyp_w_dict_list[k : k + N2]) + ) + hyp_latter = self.realigning_lm.log_s( + ' '.join(hyp_w_dict_list[k - N1 : k + 1] + self.stt_end_tokens + hyp_w_dict_list[k + 1 : k + N2]) + ) + log_p = [hyp_former, hyp_latter] + p_order = np.argsort(log_p)[::-1] + if log_p[p_order[0]] > log_p[p_order[1]] + self.realigning_lm_params['logprob_diff_threshold']: + if p_order[0] == 0: + spk_list[k] = org_spk_list[k + 1] + line_dict['speaker'] = spk_list[k] + realigned_list.append(line_dict) + return realigned_list + + @staticmethod + def evaluate( + audio_file_list: List[str], + hyp_trans_info_dict: Dict[str, Dict[str, float]], + hyp_ctm_file_list: List[str] = None, + ref_ctm_file_list: List[str] = None, + ) -> Dict[str, Dict[str, float]]: + """ + Evaluate the result transcripts based on the provided CTM file. WER and cpWER are calculated to assess + the performance of ASR system and diarization at the same time. + + Args: + audio_file_list (list): + List containing file path to the input audio files. + hyp_trans_info_dict (dict): + Dictionary containing the hypothesis transcriptions for all sessions. + hyp_ctm_file_list (list): + List containing file paths of the hypothesis transcriptions in CTM format for all sessions. + ref_ctm_file_list (list): + List containing file paths of the reference transcriptions in CTM format for all sessions. + + Note: Either `hyp_trans_info_dict` or `hyp_ctm_file_list` should be provided. + + Returns: + wer_results (dict): + Session-by-session results including DER, miss rate, false alarm rate, WER and cpWER + """ + wer_results = {} + + if ref_ctm_file_list is not None: + spk_hypotheses, spk_references = [], [] + mix_hypotheses, mix_references = [], [] + WER_values, uniq_id_list = [], [] + + for k, (audio_file_path, ctm_file_path) in enumerate(zip(audio_file_list, ref_ctm_file_list)): + uniq_id = get_uniqname_from_filepath(audio_file_path) + uniq_id_list.append(uniq_id) + if uniq_id != get_uniqname_from_filepath(ctm_file_path): + raise ValueError("audio_file_list has mismatch in uniq_id with ctm_file_path") + + # Either hypothesis CTM file or hyp_trans_info_dict should be provided + if hyp_ctm_file_list is not None: + if uniq_id == get_uniqname_from_filepath(hyp_ctm_file_list[k]): + spk_hypothesis, mix_hypothesis = convert_ctm_to_text(hyp_ctm_file_list[k]) + else: + raise ValueError("Hypothesis CTM files are provided but uniq_id is mismatched") + elif hyp_trans_info_dict is not None and uniq_id in hyp_trans_info_dict: + spk_hypothesis, mix_hypothesis = convert_word_dict_seq_to_text( + hyp_trans_info_dict[uniq_id]['words'] + ) + else: + raise ValueError("Hypothesis information is not provided in the correct format.") + + spk_reference, mix_reference = convert_ctm_to_text(ctm_file_path) + + spk_hypotheses.append(spk_hypothesis) + spk_references.append(spk_reference) + mix_hypotheses.append(mix_hypothesis) + mix_references.append(mix_reference) + + # Calculate session by session WER value + WER_values.append(word_error_rate([mix_hypothesis], [mix_reference])) + + cpWER_values, hyps_spk, refs_spk = concat_perm_word_error_rate(spk_hypotheses, spk_references) + + # Take an average of cpWER and regular WER value on all sessions + wer_results['total'] = {} + wer_results['total']['average_cpWER'] = word_error_rate(hypotheses=hyps_spk, references=refs_spk) + wer_results['total']['average_WER'] = word_error_rate(hypotheses=mix_hypotheses, references=mix_references) + + for (uniq_id, cpWER, WER) in zip(uniq_id_list, cpWER_values, WER_values): + # Save session-level cpWER and WER values + wer_results[uniq_id] = {} + wer_results[uniq_id]['cpWER'] = cpWER + wer_results[uniq_id]['WER'] = WER + + return wer_results + + @staticmethod + def get_str_speech_labels(speech_labels_float: List[List[float]]) -> List[str]: + """ + Convert floating point speech labels list to a list containing string values. + + Args: + speech_labels_float (list): + List containing start and end timestamps of the speech segments in floating point type + speech_labels (list): + List containing start and end timestamps of the speech segments in string format + """ + speech_labels = [] + for start, end in speech_labels_float: + speech_labels.append("{:.3f} {:.3f} speech".format(start, end)) + return speech_labels + + @staticmethod + def write_session_level_result_in_csv( + der_results: Dict[str, Dict[str, float]], + wer_results: Dict[str, Dict[str, float]], + root_path: str, + csv_columns: List[str], + csv_file_name: str = "ctm_eval.csv", + ): + """ + This function is for development use when a CTM file is provided. + Saves the session-level diarization and ASR result into a csv file. + + Args: + wer_results (dict): + Dictionary containing session-by-session results of ASR and diarization in terms of + WER and cpWER. + """ + target_path = f"{root_path}/pred_rttms" + os.makedirs(target_path, exist_ok=True) + logging.info(f"Writing {target_path}/{csv_file_name}") + total_result_jsons = get_total_result_dict(der_results, wer_results, csv_columns) + try: + with open(f"{target_path}/{csv_file_name}", 'w') as csvfile: + writer = csv.DictWriter(csvfile, fieldnames=csv_columns) + writer.writeheader() + for data in total_result_jsons: + writer.writerow(data) + except IOError: + logging.info("I/O error has occurred while writing a csv file.") + + def _break_lines(self, string_out: str, max_chars_in_line: int = 90) -> str: + """ + Break the lines in the transcript. + + Args: + string_out (str): + Input transcript with speaker labels + max_chars_in_line (int): + Maximum characters in each line + + Returns: + return_string_out (str): + String variable containing line breaking + """ + color_str_len = len('\033[1;00m') if self.params['colored_text'] else 0 + split_string_out = string_out.split('\n') + return_string_out = [] + for org_chunk in split_string_out: + buffer = [] + if len(org_chunk) - color_str_len > max_chars_in_line: + color_str = org_chunk[:color_str_len] if color_str_len > 0 else '' + for i in range(color_str_len, len(org_chunk), max_chars_in_line): + trans_str = org_chunk[i : i + max_chars_in_line] + if len(trans_str.strip()) > 0: + c_trans_str = color_str + trans_str + buffer.append(c_trans_str) + return_string_out.extend(buffer) + else: + return_string_out.append(org_chunk) + return_string_out = '\n'.join(return_string_out) + return return_string_out + + def _write_and_log( + self, + uniq_id: str, + session_trans_dict: Dict[str, Dict[str, float]], + audacity_label_words: List[str], + gecko_dict: Dict[str, Dict[str, float]], + sentences: List[Dict[str, float]], + ): + """ + Write output files and display logging messages. + + Args: + uniq_id (str): + A unique ID (key) that identifies each input audio file + session_trans_dict (dict): + Dictionary containing the transcription output for a session + audacity_label_words (list): + List containing word and word timestamp information in Audacity label format + gecko_dict (dict): + Dictionary formatted to be opened in Gecko software + sentences (list): + List containing sentence dictionary + """ + # print the sentences in the .txt output + string_out = self.print_sentences(sentences) + if self.params['break_lines']: + string_out = self._break_lines(string_out) + + session_trans_dict["status"] = "success" + ctm_lines_list = convert_word_dict_seq_to_ctm(session_trans_dict['words']) + + dump_json_to_file(f'{self.root_path}/pred_rttms/{uniq_id}.json', session_trans_dict) + dump_json_to_file(f'{self.root_path}/pred_rttms/{uniq_id}_gecko.json', gecko_dict) + write_txt(f'{self.root_path}/pred_rttms/{uniq_id}.ctm', '\n'.join(ctm_lines_list)) + write_txt(f'{self.root_path}/pred_rttms/{uniq_id}.txt', string_out.strip()) + write_txt(f'{self.root_path}/pred_rttms/{uniq_id}.w.label', '\n'.join(audacity_label_words)) + + @staticmethod + def print_errors(der_results: Dict[str, Dict[str, float]], wer_results: Dict[str, Dict[str, float]]): + """ + Print a slew of error metrics for ASR and Diarization. + + Args: + der_results (dict): + Dictionary containing FA, MISS, CER and DER values for both aggregated amount and + each session. + wer_results (dict): + Dictionary containing session-by-session WER and cpWER. `wer_results` only + exists when CTM files are provided. + """ + DER_info = f"\nDER : {der_results['total']['DER']:.4f} \ + \nFA : {der_results['total']['FA']:.4f} \ + \nMISS : {der_results['total']['MISS']:.4f} \ + \nCER : {der_results['total']['CER']:.4f} \ + \nSpk. counting acc. : {der_results['total']['spk_counting_acc']:.4f}" + if wer_results is not None and len(wer_results) > 0: + logging.info( + DER_info + + f"\ncpWER : {wer_results['total']['average_cpWER']:.4f} \ + \nWER : {wer_results['total']['average_WER']:.4f}" + ) + else: + logging.info(DER_info) + + def print_sentences(self, sentences: List[Dict[str, float]]): + """ + Print a transcript with speaker labels and timestamps. + + Args: + sentences (list): + List containing sentence-level dictionaries. + + Returns: + string_out (str): + String variable containing transcript and the corresponding speaker label. + """ + # init output + string_out = '' + + for sentence in sentences: + # extract info + speaker = sentence['speaker'] + start_point = sentence['start_time'] + end_point = sentence['end_time'] + text = sentence['text'] + + if self.params['colored_text']: + color = self.color_palette.get(speaker, '\033[0;37m') + else: + color = '' + + # cast timestamp to the correct format + datetime_offset = 16 * 3600 + if float(start_point) > 3600: + time_str = '%H:%M:%S.%f' + else: + time_str = '%M:%S.%f' + start_point, end_point = max(float(start_point), 0), max(float(end_point), 0) + start_point_str = datetime.fromtimestamp(start_point - datetime_offset).strftime(time_str)[:-4] + end_point_str = datetime.fromtimestamp(end_point - datetime_offset).strftime(time_str)[:-4] + + if self.params['print_time']: + time_str = f'[{start_point_str} - {end_point_str}] ' + else: + time_str = '' + + # string out concatenation + string_out += f'{color}{time_str}{speaker}: {text}\n' + + return string_out diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/eval_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/eval_utils.py new file mode 100644 index 0000000..5584a50 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/eval_utils.py @@ -0,0 +1,324 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import re +from typing import Optional, Tuple, Union + +from torchmetrics.text import SacreBLEUScore +from torchmetrics.text.rouge import ROUGEScore + +from nemo.collections.asr.metrics.wer import word_error_rate_detail +from nemo.utils import logging +from nemo.utils.nemo_logging import LogMode + +TEXT_METRICS_MAPPING = { + 'bleu': SacreBLEUScore, + 'rouge': ROUGEScore, +} + +from omegaconf import DictConfig + + +def flatten_dict_config(config: DictConfig, parent_key='', sep='.', join='\n') -> str: + """ + Flatten a DictConfig object into a string of parameter names and their values. + + Args: + config (DictConfig): The input DictConfig object. + parent_key (str): The parent key for nested configurations. + sep (str): Separator between keys. + + Returns: + str: Flattened string of parameter names and their values. + """ + items = [] + for k, v in config.items(): + new_key = f"{parent_key}{sep}{k}" if parent_key else k + if isinstance(v, DictConfig): + items.extend(flatten_dict_config(v, new_key, sep=sep, join=join).split(join)) + else: + items.append(f"{new_key}={v}") + return join.join(items) + + +def get_hydra_override_from_config(config: Optional[DictConfig] = None, exclude_keys: Optional[list] = None) -> str: + """ + Flatten a DictConfig object into a string of hydra overrides for commandline, for example: + >>> config = OmegaConf.create({"foo": {"bar": 1, "baz": 2}}) + >>> get_hydra_override_from_config(config) + "++foo.bar=1 ++foo.baz=2" + """ + if not config: + return "" + join = '\n' + overrides = flatten_dict_config(config, join=join).split(join) + if exclude_keys: + overrides = [x for x in overrides if not any([y == x.split("=")[0] for y in exclude_keys])] + param_str = " ".join([f"++{x}" for x in overrides]) + return param_str + + +def strip_spaces_before_punctuations(text: str) -> str: + """ + Remove spaces before punctuations, e.g. "hello , world" -> "hello, world" + """ + result = re.sub(r'(\w)\s+([.,;!?])', r'\1\2', text) + return result + + +def remove_punctuations(text: str, punctuations: Optional[Union[list, str]] = None) -> str: + """ + Remove punctuations from a string + """ + if not punctuations: + punctuations = [char for char in '!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~'] + + for punctuation in punctuations: + text = text.replace(punctuation, '') + return text + + +def clean_label(_str: str, num_to_words: bool = True, langid="en") -> str: + """ + Remove unauthorized characters in a string, lower it and remove unneeded spaces + """ + replace_with_space = [char for char in '/?*\",.:=?_{|}~¨«·»¡¿„…‧‹›≪≫!:;ː→'] + replace_with_blank = [char for char in '`¨´‘’“”`ʻ‘’“"‘”'] + replace_with_apos = [char for char in '‘’ʻ‘’‘'] + _str = _str.strip() + _str = _str.lower() + for i in replace_with_blank: + _str = _str.replace(i, "") + for i in replace_with_space: + _str = _str.replace(i, " ") + for i in replace_with_apos: + _str = _str.replace(i, "'") + if num_to_words: + if langid == "en": + _str = convert_num_to_words(_str, langid="en") + else: + logging.warning( + "Currently support basic num_to_words in English only. Please use Text Normalization to convert other languages! Skipping!", + mode=LogMode.ONCE, + ) + + ret = " ".join(_str.split()) + return ret + + +def convert_num_to_words(_str: str, langid: str = "en") -> str: + """ + Convert digits to corresponding words. Note this is a naive approach and could be replaced with text normalization. + """ + if langid == "en": + num_to_words = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] + _str = _str.strip() + words = _str.split() + out_str = "" + num_word = [] + for word in words: + if word.isdigit(): + num = int(word) + while num: + digit = num % 10 + digit_word = num_to_words[digit] + num_word.append(digit_word) + num = int(num / 10) + if not (num): + num_str = "" + num_word = num_word[::-1] + for ele in num_word: + num_str += ele + " " + out_str += num_str + " " + num_word.clear() + else: + out_str += word + " " + out_str = out_str.strip() + else: + logging.warning( + "Currently support basic num_to_words in English only. Please use Text Normalization to convert other languages!", + mode=LogMode.ONCE, + ) + return out_str + + +def cal_write_wer( + pred_manifest: str = None, + gt_text_attr_name: str = "text", + pred_text_attr_name: str = "pred_text", + clean_groundtruth_text: bool = False, + langid: str = 'en', + use_cer: bool = False, + output_filename: str = None, + ignore_capitalization: bool = False, + ignore_punctuation: bool = False, + punctuations: Optional[list] = None, + strip_punc_space: bool = False, +) -> Tuple[str, dict, str]: + """ + Calculate wer, inserion, deletion and substitution rate based on groundtruth text and pred_text_attr_name (pred_text) + We use WER in function name as a convention, but Error Rate (ER) currently support Word Error Rate (WER) and Character Error Rate (CER) + """ + samples = [] + hyps = [] + refs = [] + eval_metric = "cer" if use_cer else "wer" + + with open(pred_manifest, 'r') as fp: + for line in fp: + sample = json.loads(line) + + if gt_text_attr_name not in sample: + if "text" in sample: + gt_text_attr_name = "text" + else: + logging.info( + f"ground-truth text attribute {gt_text_attr_name} is not present in manifest! Cannot calculate WER. Returning!" + ) + return None, None, eval_metric + + hyp = sample[pred_text_attr_name].strip() + ref = sample[gt_text_attr_name].strip() + + if clean_groundtruth_text: + ref = clean_label(ref, langid=langid) + + if ignore_punctuation: + ref = remove_punctuations(ref, punctuations=punctuations) + hyp = remove_punctuations(hyp, punctuations=punctuations) + elif strip_punc_space: + ref = strip_spaces_before_punctuations(ref) + hyp = strip_spaces_before_punctuations(hyp) + + if ignore_capitalization: + ref = ref.lower() + hyp = hyp.lower() + + wer, tokens, ins_rate, del_rate, sub_rate = word_error_rate_detail( + hypotheses=[hyp], references=[ref], use_cer=use_cer + ) + sample[eval_metric] = wer # evaluatin metric, could be word error rate of character error rate + sample['tokens'] = tokens # number of word/characters/tokens + sample['ins_rate'] = ins_rate # insertion error rate + sample['del_rate'] = del_rate # deletion error rate + sample['sub_rate'] = sub_rate # substitution error rate + + samples.append(sample) + hyps.append(hyp) + refs.append(ref) + + total_wer, total_tokens, total_ins_rate, total_del_rate, total_sub_rate = word_error_rate_detail( + hypotheses=hyps, references=refs, use_cer=use_cer + ) + + if not output_filename: + output_manifest_w_wer = pred_manifest + else: + output_manifest_w_wer = output_filename + + with open(output_manifest_w_wer, 'w') as fout: + for sample in samples: + json.dump(sample, fout) + fout.write('\n') + fout.flush() + + total_res = { + "samples": len(samples), + "tokens": total_tokens, + eval_metric: total_wer, + "ins_rate": total_ins_rate, + "del_rate": total_del_rate, + "sub_rate": total_sub_rate, + } + return output_manifest_w_wer, total_res, eval_metric + + +def cal_write_text_metric( + pred_manifest: str = None, + gt_text_attr_name: str = "text", + pred_text_attr_name: str = "pred_text", + output_filename: str = None, + ignore_capitalization: bool = False, + ignore_punctuation: bool = False, + punctuations: Optional[list] = None, + metric: str = 'bleu', + metric_args: Optional[dict] = None, + strip_punc_space: bool = False, +): + samples = [] + hyps = [] + refs = [] + + if metric not in TEXT_METRICS_MAPPING: + raise ValueError(f"metric {metric} is not supported! Please choose from {TEXT_METRICS_MAPPING.keys()}") + + metric_calculator = TEXT_METRICS_MAPPING[metric](**metric_args) if metric_args else TEXT_METRICS_MAPPING[metric]() + with open(pred_manifest, 'r') as fp: + for line in fp: + sample = json.loads(line) + + if gt_text_attr_name not in sample: + if "text" in sample: + gt_text_attr_name = "text" + else: + logging.info( + f"ground-truth text attribute {gt_text_attr_name} is not present in manifest! Cannot calculate {metric}. Returning!" + ) + return None, None, metric + + hyp = sample[pred_text_attr_name].strip() + ref = sample[gt_text_attr_name].strip() + + if ignore_punctuation: + ref = remove_punctuations(ref, punctuations=punctuations) + hyp = remove_punctuations(hyp, punctuations=punctuations) + elif strip_punc_space: + ref = strip_spaces_before_punctuations(ref) + hyp = strip_spaces_before_punctuations(hyp) + + if ignore_capitalization: + ref = ref.lower() + hyp = hyp.lower() + + if metric == 'bleu': + score = metric_calculator([hyp], [[ref]]).item() + else: + score = metric_calculator(hyp, ref).item() + sample[metric] = score # evaluatin metric, could be word error rate of character error rate + + samples.append(sample) + hyps.append(hyp) + refs.append(ref) + + if metric == 'bleu': + refs = [[ref] for ref in refs] + total_score = metric_calculator(hyps, refs).item() + + if not output_filename: + output_manifest_w_wer = pred_manifest + else: + output_manifest_w_wer = output_filename + + with open(output_manifest_w_wer, 'w') as fout: + for sample in samples: + json.dump(sample, fout) + fout.write('\n') + fout.flush() + + total_res = { + "samples": len(samples), + metric: total_score, + } + return output_manifest_w_wer, total_res, metric diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/longform_clustering.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/longform_clustering.py new file mode 100644 index 0000000..171c074 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/longform_clustering.py @@ -0,0 +1,422 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, List, Tuple +import torch +from tqdm import tqdm +from nemo.collections.asr.parts.utils.offline_clustering import ( + SpeakerClustering, + get_scale_interpolated_embs, + getCosAffinityMatrix, + split_input_data, +) +from nemo.collections.asr.parts.utils.online_clustering import get_merge_quantity, run_reducer + + +class LongFormSpeakerClustering(torch.nn.Module): + def __init__(self, cuda: bool = False): + """ + Initializes a speaker clustering class tailored for long-form audio, leveraging methods from the `SpeakerClustering` class. + The clustering algorithm for long-form content is executed via the `forward_infer` function (not shown here). Input embedding + vectors are divided into chunks, each of size `embeddings_per_chunk`. Within every chunk, the clustering algorithm aims + to identify `chunk_cluster_count` distinct clusters. The resulting clustering labels are then expanded to match the original + length of the input embeddings. + + NOTE: torch.jit.script currently does not support inherited methods with a `super()` call. + + Args: + cuda (bool): + Flag indicating whether CUDA is available for computation. + """ + super().__init__() + self.speaker_clustering = SpeakerClustering(cuda=cuda) + self.embeddings_in_scales: List[torch.Tensor] = [torch.tensor([0])] + self.timestamps_in_scales: List[torch.Tensor] = [torch.tensor([0])] + self.cuda = cuda + self.device = torch.device("cuda") if self.cuda else torch.device("cpu") + + def check_input(self, embeddings_per_chunk: int, chunk_cluster_count: int, max_num_speakers: int) -> None: + """ + Checks the validity of the input parameters. + + Args: + embeddings_per_chunk (int): + The size of the windows in which the algorithm aims to identify `chunk_cluster_count` clusters. + chunk_cluster_count (int): + The target number of clusters to identify within each window. + max_num_speakers (int): + The maximum number of speakers to be detected in the audio. + """ + if chunk_cluster_count is None or embeddings_per_chunk is None: + raise ValueError( + f"chunk_cluster_count ({chunk_cluster_count}) and embeddings_per_chunk ({embeddings_per_chunk}) should be set." + ) + elif ( + all(v is not None for v in [chunk_cluster_count, embeddings_per_chunk]) + and chunk_cluster_count >= embeddings_per_chunk + ): + raise ValueError( + f"chunk_cluster_count ({chunk_cluster_count}) should be smaller than embeddings_per_chunk ({embeddings_per_chunk})." + ) + + if chunk_cluster_count <= max_num_speakers: + raise ValueError( + f"chunk_cluster_count ({chunk_cluster_count}) should be larger than max_num_speakers ({max_num_speakers})." + ) + + def unpack_labels( + self, + Y_aggr: torch.Tensor, + window_range_list: List[List[int]], + absolute_merge_mapping: List[List[torch.Tensor]], + org_len: int, + ) -> torch.LongTensor: + """ + Unpack the labels from the aggregated labels to the original labels. + + Args: + Y_aggr (Tensor): + Aggregated label vector from the merged segments. + window_range_list (List[List[int]]): + List of window ranges for each of the merged segments. + absolute_merge_mapping (List[List[torch.Tensor]]): + List of absolute mappings for each of the merged segments. Each list element contains two tensors: + - The first tensor represents the absolute index of the bypassed segment (segments that remain unchanged). + - The second tensor represents the absolute index of the merged segment (segments that have had their indexes changed). + org_len (int): + Original length of the labels. In most cases, this is a fairly large number (on the order of 10^5). + + Returns: + Y_unpack (Tensor): + Unpacked labels derived from the aggregated labels. + """ + Y_unpack = torch.zeros((org_len,)).long().to(Y_aggr.device) + for (win_rng, abs_mapping) in zip(window_range_list, absolute_merge_mapping): + inferred_merged_embs = Y_aggr[win_rng[0] : win_rng[1]] + if len(abs_mapping[1]) > 0: + Y_unpack[abs_mapping[1]] = inferred_merged_embs[-1].clone() # Merged + if len(abs_mapping[0]) > 0: + Y_unpack[abs_mapping[0]] = inferred_merged_embs[:-1].clone() # Bypass + else: + if len(abs_mapping[0]) > 0: + Y_unpack[abs_mapping[0]] = inferred_merged_embs.clone() + return Y_unpack + + def split_embs_to_windows( + self, index: int, emb: torch.Tensor, embeddings_per_chunk: int, + ) -> Tuple[torch.Tensor, int]: + """ + Splits the embedding tensor into smaller window-sized tensors based on a given index. + + Args: + index (int): The index of the desired window. This determines the starting point + of the window using the formula: + start = embeddings_per_chunk * index + emb (Tensor): The embedding tensor which needs to be split. + embeddings_per_chunk (int): + The size of the windows in which the algorithm aims to identify `chunk_cluster_count` clusters. + + Returns: + emb_part (Tensor): + The window-sized tensor, which is a portion of the `emb`. + offset_index (int): + The starting position of the window in the `emb` tensor. + """ + if embeddings_per_chunk * (index + 1) > emb.shape[0]: + emb_part = emb[-1 * embeddings_per_chunk :] + offset_index = emb.shape[0] - embeddings_per_chunk + else: + emb_part = emb[embeddings_per_chunk * index : embeddings_per_chunk * (index + 1)] + offset_index = embeddings_per_chunk * index + return emb_part, offset_index + + def forward(self, param_dict: Dict[str, torch.Tensor]) -> torch.LongTensor: + """ + A function wrapper designed for performing inference using an exported script format. + + Note: + A dictionary is used to facilitate inference with the exported jit model in the Triton server. + This is done using an easy-to-understand naming convention. + See https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#special-conventions-for-pytorch-backend + + Args: + param_dict (dict): + Dictionary containing the arguments for speaker clustering. + See `forward_infer` function for the argument information. + + Returns: + (LongTensor): Speaker labels for the segments in the given input embeddings. + """ + embeddings_in_scales = param_dict['embeddings'] + timestamps_in_scales = param_dict['timestamps'] + multiscale_segment_counts = param_dict['multiscale_segment_counts'] + multiscale_weights = param_dict['multiscale_weights'] + oracle_num_speakers = int(param_dict['oracle_num_speakers'].item()) + max_num_speakers = int(param_dict['max_num_speakers'].item()) + enhanced_count_thres = int(param_dict['enhanced_count_thres'].item()) + sparse_search_volume = int(param_dict['sparse_search_volume'].item()) + max_rp_threshold = float(param_dict['max_rp_threshold'].item()) + fixed_thres = float(param_dict['fixed_thres'].item()) + return self.forward_infer( + embeddings_in_scales=embeddings_in_scales, + timestamps_in_scales=timestamps_in_scales, + multiscale_segment_counts=multiscale_segment_counts, + multiscale_weights=multiscale_weights, + oracle_num_speakers=oracle_num_speakers, + max_rp_threshold=max_rp_threshold, + max_num_speakers=max_num_speakers, + enhanced_count_thres=enhanced_count_thres, + sparse_search_volume=sparse_search_volume, + fixed_thres=fixed_thres, + ) + + def get_div_ceil_count(self, numer: int, denomin: int) -> int: + """ + Calculates the ceiling of the division of two integers. + + Args: + numer (int): Numerator, the number of segments or clusters, for example. + denomin (int): Denominator, the number of speakers or clusters, for example. + + Returns: + (int): The ceiling of the division of the two integers (number of chunks). + """ + return int(torch.ceil(torch.tensor(numer / denomin)).item()) + + def long_forward_infer( + self, + embeddings_in_scales: torch.Tensor, + timestamps_in_scales: torch.Tensor, + multiscale_segment_counts: torch.LongTensor, + multiscale_weights: torch.Tensor, + oracle_num_speakers: int, + max_rp_threshold: float, + max_num_speakers: int, + sparse_search_volume: int, + fixed_thres: float, + chunk_cluster_count: int, + embeddings_per_chunk: int, + ) -> torch.LongTensor: + """ + This is forward function for long-form speaker clustering. + Please refer to `SpeakerClustering` class for the original argument information. + + In the `LongFormSpeakerClustering` process: + Step-1: Input embeddings are divided into smaller windows of size `embeddings_per_chunk`. + Step-2: Each window undergoes overclustering, resulting in `chunk_cluster_count` fine-grained clusters. + Step-3: These fine-grained clusters are merged to form the aggregated clustering labels `Y_aggr`. + Step-4: The `unpack_labels` function is then employed to map the aggregated labels `Y_aggr` back to the + original labels for all `org_len` input embeddings: `Y_unpack`. + + Args: + embeddings_in_scales (Tensor): + List containing concatenated Torch tensor embeddings across multiple scales. + The length of the list is equal to the number of scales. + Each tensor has dimensions of (Number of base segments) x (Embedding Dimension). + timestamps_in_scales (Tensor): + List containing concatenated Torch tensor timestamps across multiple scales. + The length of the list is equal to the number of scales. + Each tensor has dimensions of (Total number of segments across all scales) x 2. + Example: + >>> timestamps_in_scales[0] = \ + torch.Tensor([[0.4, 1.4], [0.9, 1.9], [1.4, 2.4], ... [121.2, 122.2]]) + multiscale_segment_counts (LongTensor): + A Torch tensor containing the number of segments for each scale. + The tensor has dimensions of (Number of scales). + Example: + >>> multiscale_segment_counts = torch.LongTensor([31, 52, 84, 105, 120]) + multiscale_weights (Tensor): + Multi-scale weights used when merging affinity scores. + Example: + >>> multiscale_weights = torch.tensor([1.4, 1.3, 1.2, 1.1, 1.0]) + oracle_num_speakers (int): + The number of speakers in a session as given by the reference transcript. + max_num_speakers (int): + The upper bound for the number of speakers in each session. + max_rp_threshold (float): + Limits the range of parameter search. + The clustering performance can vary based on this range. + The default value is 0.15. + enhanced_count_thres (int): + For shorter audio recordings, the clustering algorithm might not accumulate enough speaker profiles for each cluster. + Thus, the function `getEnhancedSpeakerCount` uses anchor embeddings (dummy representations) to mitigate the effects of cluster sparsity. + A value of 80 is recommended for `enhanced_count_thres`. + sparse_search_volume (int): + The number of p_values considered during NME analysis. + The default is 30. Lower values speed up the NME-analysis but might lead to poorer parameter estimations. Values below 20 are not recommended. + fixed_thres (float): + If a `fixed_thres` value is provided, the NME-analysis process will be skipped. + This value should be optimized on a development set for best results. + By default, it is set to -1.0, and the function performs NME-analysis to estimate the threshold. + kmeans_random_trials (int): + The number of random trials for initializing k-means clustering. More trials can result in more stable clustering. The default is 1. + chunk_cluster_count (int): + The target number of clusters to identify within each chunk. + embeddings_per_chunk (int): + The size of the chunks in which the algorithm aims to identify `chunk_cluster_count` clusters. + + Returns: + Y_unpack (LongTensor): + Speaker labels for the segments in the provided input embeddings. + """ + self.check_input(embeddings_per_chunk, chunk_cluster_count, max_num_speakers) + + self.embeddings_in_scales, self.timestamps_in_scales = split_input_data( + embeddings_in_scales, timestamps_in_scales, multiscale_segment_counts + ) + emb, _ = get_scale_interpolated_embs( + multiscale_weights, self.embeddings_in_scales, self.timestamps_in_scales, self.device + ) + offset_index: int = 0 + window_offset: int = 0 + total_emb: List[torch.Tensor] = [] + window_range_list: List[List[int]] = [] + absolute_merge_mapping: List[List[torch.Tensor]] = [] + total_window_count = self.get_div_ceil_count(numer=emb.shape[0], denomin=embeddings_per_chunk) + + if not torch.jit.is_scripting(): + pbar = tqdm(range(total_window_count), desc="Clustering Sub-Windows", leave=True, unit="window") + else: + pbar = range(total_window_count) + + for win_index in pbar: + # Step-1: Split the embeddings into smaller chunks + emb_part, offset_index = self.split_embs_to_windows( + index=win_index, emb=emb, embeddings_per_chunk=embeddings_per_chunk + ) + + # Step-2: Perform overclustering on the chunks to identify `chunk_cluster_count` clusters + if emb_part.shape[0] == 1: + Y_part = torch.zeros((1,), dtype=torch.int64) + else: + mat = getCosAffinityMatrix(emb_part) + overcluster_count = min(chunk_cluster_count, mat.shape[0]) + Y_part = self.speaker_clustering.forward_unit_infer( + mat=mat, + oracle_num_speakers=overcluster_count, + max_rp_threshold=max_rp_threshold, + max_num_speakers=chunk_cluster_count, + sparse_search_volume=sparse_search_volume, + ) + + # Step-3: Merge the clusters to form the aggregated clustering labels `Y_aggr` + num_to_be_merged = int(min(embeddings_per_chunk, emb_part.shape[0]) - chunk_cluster_count) + min_count_per_cluster = self.get_div_ceil_count( + numer=chunk_cluster_count, denomin=len(torch.unique(Y_part)) + ) + + # We want only one embedding vector for each cluster, so we calculate the number of embedding vectors to be removed + class_target_vol = get_merge_quantity( + num_to_be_removed=num_to_be_merged, + pre_clus_labels=Y_part, + min_count_per_cluster=min_count_per_cluster, + ) + if not torch.jit.is_scripting(): + pbar.update(1) + + # `class_target_vol` is a list of cluster-indices from overclustering + for spk_idx, merge_quantity in enumerate(list(class_target_vol)): + merged_embs, merged_clus_labels, index_mapping = run_reducer( + pre_embs=emb_part, target_spk_idx=spk_idx, merge_quantity=merge_quantity, pre_clus_labels=Y_part, + ) + total_emb.append(merged_embs) + absolute_index_mapping = [x + offset_index for x in index_mapping] + absolute_merge_mapping.append(absolute_index_mapping) + window_range_list.append([window_offset, window_offset + merged_embs.shape[0]]) + window_offset += merged_embs.shape[0] + + if not torch.jit.is_scripting(): + pbar.close() + + # Concatenate the reduced embeddings then perform high-level clustering + reduced_embs = torch.cat(total_emb) + reduced_mat = getCosAffinityMatrix(reduced_embs) + + # Step-4: Map the aggregated labels `Y_aggr` back to the original labels for all `org_len` input embeddings: `Y_unpack` + Y_aggr = self.speaker_clustering.forward_unit_infer( + mat=reduced_mat, + oracle_num_speakers=oracle_num_speakers, + max_rp_threshold=max_rp_threshold, + max_num_speakers=max_num_speakers, + sparse_search_volume=sparse_search_volume, + fixed_thres=fixed_thres, + ) + if reduced_embs.shape[0] != Y_aggr.shape[0]: + raise ValueError( + f"The number of embeddings ({reduced_embs.shape[0]}) and the number of clustered labels ({Y_aggr.shape[0]}) do not match." + ) + + # Reassign the labels to the original embeddings + Y_unpack = self.unpack_labels( + Y_aggr=Y_aggr, + window_range_list=window_range_list, + absolute_merge_mapping=absolute_merge_mapping, + org_len=emb.shape[0], + ) + if Y_unpack.shape[0] != emb.shape[0]: + raise ValueError( + f"The number of raw input embeddings ({emb.shape[0]}) and the number of clustered labels ({Y_unpack.shape[0]}) do not match." + ) + return Y_unpack + + def forward_infer( + self, + embeddings_in_scales: torch.Tensor, + timestamps_in_scales: torch.Tensor, + multiscale_segment_counts: torch.LongTensor, + multiscale_weights: torch.Tensor, + oracle_num_speakers: int = -1, + max_rp_threshold: float = 0.15, + max_num_speakers: int = 8, + enhanced_count_thres: int = 80, + sparse_search_volume: int = 30, + fixed_thres: float = -1.0, + chunk_cluster_count: int = 50, + embeddings_per_chunk: int = 10000, + ) -> torch.LongTensor: + """ + This function is a wrapper designed for toggling between long-form and short-form speaker clustering. + The details of short-form clustering is in `SpeakerClustering` class. + NOTE: `torch.jit.script` currently does not support `**kwargs` in the function signature therefore, + we need to use a wrapper function to handle the arguments. + """ + if embeddings_per_chunk is not None and torch.max(multiscale_segment_counts) > embeddings_per_chunk: + return self.long_forward_infer( + embeddings_in_scales=embeddings_in_scales, + timestamps_in_scales=timestamps_in_scales, + multiscale_segment_counts=multiscale_segment_counts, + multiscale_weights=multiscale_weights, + oracle_num_speakers=oracle_num_speakers, + max_rp_threshold=max_rp_threshold, + max_num_speakers=max_num_speakers, + sparse_search_volume=sparse_search_volume, + fixed_thres=fixed_thres, + chunk_cluster_count=chunk_cluster_count, + embeddings_per_chunk=embeddings_per_chunk, + ) + else: + cluster_labels = self.speaker_clustering.forward_infer( + embeddings_in_scales=embeddings_in_scales, + timestamps_in_scales=timestamps_in_scales, + multiscale_segment_counts=multiscale_segment_counts, + multiscale_weights=multiscale_weights, + oracle_num_speakers=oracle_num_speakers, + max_rp_threshold=max_rp_threshold, + max_num_speakers=max_num_speakers, + enhanced_count_thres=enhanced_count_thres, + sparse_search_volume=sparse_search_volume, + fixed_thres=fixed_thres, + ) + self.timestamps_in_scales = self.speaker_clustering.timestamps_in_scales + return cluster_labels diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/manifest_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/manifest_utils.py new file mode 100644 index 0000000..e9f9104 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/manifest_utils.py @@ -0,0 +1,545 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +from collections import Counter +from collections import OrderedDict as od +from pathlib import Path +from typing import Dict, List, Union + +import librosa +import numpy as np + +from nemo.collections.asr.parts.utils.speaker_utils import ( + audio_rttm_map, + get_subsegments, + get_uniqname_from_filepath, + rttm_to_labels, + segments_manifest_to_subsegments_manifest, + write_rttm2manifest, +) +from nemo.utils import logging +from nemo.utils.data_utils import DataStoreObject + + +def get_rounded_str_float(num: float, output_precision: int, min_precision=1, max_precision=3) -> str: + """ + Get a string of a float number with rounded precision. + + Args: + num (float): float number to round + output_precision (int): precision of the output floating point number + min_precision (int, optional): Minimum precision of the output floating point number. Defaults to 1. + max_precision (int, optional): Maximum precision of the output floating point number. Defaults to 3. + + Returns: + (str): Return a string of a float number with rounded precision. + """ + output_precision = min(max_precision, max(min_precision, output_precision)) + return f"{num:.{output_precision}f}" + + +def get_ctm_line( + source: str, + channel: int, + start_time: float, + duration: float, + token: str, + conf: float, + type_of_token: str, + speaker: str, + NA_token: str = 'NA', + UNK: str = 'unknown', + default_channel: str = '1', + output_precision: int = 2, +) -> str: + """ + Get a line in Conversation Time Mark (CTM) format. Following CTM format appeared in `Rich Transcription Meeting Eval Plan: RT09` document. + + CTM Format: + + + Reference: + https://web.archive.org/web/20170119114252/http://www.itl.nist.gov/iad/mig/tests/rt/2009/docs/rt09-meeting-eval-plan-v2.pdf + + Args: + source (str): is name of the source file, session name or utterance ID + channel (int): is channel number defaults to 1 + start_time (float): is the begin time of the word, which we refer to as `start_time` in NeMo. + duration (float): is duration of the word + token (str): Token or word for the current entry + conf (float): is a floating point number between 0 (no confidence) and 1 (certainty). A value of “NA” is used (in CTM format data) + when no confidence is computed and in the reference data. + type_of_token (str): is the token type. The legal values of are “lex”, “frag”, “fp”, “un-lex”, “for-lex”, “non-lex”, “misc”, or “noscore” + speaker (str): is a string identifier for the speaker who uttered the token. This should be “null” for non-speech tokens and “unknown” when + the speaker has not been determined. + NA_token (str, optional): A token for . Defaults to ''. + output_precision (int, optional): The precision of the output floating point number. Defaults to 3. + + Returns: + str: Return a line in CTM format filled with the given information. + """ + VALID_TOKEN_TYPES = ["lex", "frag", "fp", "un-lex", "for-lex", "non-lex", "misc", "noscore"] + + if type(start_time) == str and start_time.replace('.', '', 1).isdigit(): + start_time = float(start_time) + elif type(start_time) != float: + raise ValueError(f"`start_time` must be a float or str containing float, but got {type(start_time)}") + + if type(duration) == str and duration.replace('.', '', 1).isdigit(): + duration = float(duration) + elif type(duration) != float: + raise ValueError(f"`duration` must be a float or str containing float, but got {type(duration)}") + + if type(conf) == str and conf.replace('.', '', 1).isdigit(): + conf = float(conf) + elif conf is None: + conf = NA_token + elif type(conf) != float: + raise ValueError(f"`conf` must be a float or str containing float, but got {type(conf)}") + + if channel is not None and type(channel) != int: + channel = str(channel) + if conf is not None and type(conf) == float and not (0 <= conf <= 1): + raise ValueError(f"`conf` must be between 0 and 1, but got {conf}") + if type_of_token is not None and type(type_of_token) != str: + raise ValueError(f"`type` must be a string, but got {type(type_of_token)} type {type_of_token}") + if type_of_token is not None and type_of_token not in VALID_TOKEN_TYPES: + raise ValueError(f"`type` must be one of {VALID_TOKEN_TYPES}, but got {type_of_token} type {type_of_token}") + if speaker is not None and type(speaker) != str: + raise ValueError(f"`speaker` must be a string, but got {type(speaker)}") + + channel = default_channel if channel is None else channel + conf = NA_token if conf is None else conf + speaker = NA_token if speaker is None else speaker + type_of_token = UNK if type_of_token is None else type_of_token + start_time = get_rounded_str_float(start_time, output_precision) + duration = get_rounded_str_float(duration, output_precision) + conf = get_rounded_str_float(conf, output_precision) if conf != NA_token else conf + return f"{source} {channel} {start_time} {duration} {token} {conf} {type_of_token} {speaker}\n" + + +def rreplace(s: str, old: str, new: str) -> str: + """ + Replace end of string. + + Args: + s (str): string to operate on + old (str): ending of string to replace + new (str): replacement for ending of string + Returns: + new.join(li) (string): new string with end replaced + """ + li = s.rsplit(old, 1) + return new.join(li) + + +def get_uniq_id_with_period(path: str) -> str: + """ + Get uniq_id from path string with period in it. + + Args: + path (str): path to audio file + Returns: + uniq_id (str): unique speaker ID + """ + split_path = os.path.basename(path).split('.')[:-1] + uniq_id = '.'.join(split_path) if len(split_path) > 1 else split_path[0] + return uniq_id + + +def get_subsegment_dict(subsegments_manifest_file: str, window: float, shift: float, deci: int) -> Dict[str, dict]: + """ + Get subsegment dictionary from manifest file. + + Args: + subsegments_manifest_file (str): Path to subsegment manifest file + window (float): Window length for segmentation + shift (float): Shift length for segmentation + deci (int): Rounding number of decimal places + Returns: + _subsegment_dict (dict): Subsegment dictionary + """ + _subsegment_dict = {} + with open(subsegments_manifest_file, 'r') as subsegments_manifest: + segments = subsegments_manifest.readlines() + for segment in segments: + segment = segment.strip() + dic = json.loads(segment) + audio, offset, duration, label = dic['audio_filepath'], dic['offset'], dic['duration'], dic['label'] + subsegments = get_subsegments(offset=offset, window=window, shift=shift, duration=duration) + if dic['uniq_id'] is not None: + uniq_id = dic['uniq_id'] + else: + uniq_id = get_uniq_id_with_period(audio) + if uniq_id not in _subsegment_dict: + _subsegment_dict[uniq_id] = {'ts': [], 'json_dic': []} + for subsegment in subsegments: + start, dur = subsegment + _subsegment_dict[uniq_id]['ts'].append([round(start, deci), round(start + dur, deci)]) + _subsegment_dict[uniq_id]['json_dic'].append(dic) + return _subsegment_dict + + +def get_input_manifest_dict(input_manifest_path: str) -> Dict[str, dict]: + """ + Get dictionary from manifest file. + + Args: + input_manifest_path (str): Path to manifest file + Returns: + input_manifest_dict (dict): Dictionary from manifest file + """ + input_manifest_dict = {} + with open(input_manifest_path, 'r') as input_manifest_fp: + json_lines = input_manifest_fp.readlines() + for json_line in json_lines: + dic = json.loads(json_line) + dic["text"] = "-" + uniq_id = get_uniqname_from_filepath(dic["audio_filepath"]) + input_manifest_dict[uniq_id] = dic + return input_manifest_dict + + +def write_truncated_subsegments( + input_manifest_dict: Dict[str, dict], + _subsegment_dict: Dict[str, dict], + output_manifest_path: str, + step_count: int, + deci: int, +): + """ + Write subsegments to manifest filepath. + + Args: + input_manifest_dict (dict): Input manifest dictionary + _subsegment_dict (dict): Input subsegment dictionary + output_manifest_path (str): Path to output manifest file + step_count (int): Number of the unit segments you want to create per utterance + deci (int): Rounding number of decimal places + """ + with open(output_manifest_path, 'w') as output_manifest_fp: + for uniq_id, subseg_dict in _subsegment_dict.items(): + subseg_array = np.array(subseg_dict['ts']) + subseg_array_idx = np.argsort(subseg_array, axis=0) + chunked_set_count = subseg_array_idx.shape[0] // step_count + + for idx in range(chunked_set_count - 1): + chunk_index_stt = subseg_array_idx[:, 0][idx * step_count] + chunk_index_end = subseg_array_idx[:, 1][(idx + 1) * step_count] + offset_sec = subseg_array[chunk_index_stt, 0] + end_sec = subseg_array[chunk_index_end, 1] + dur = round(end_sec - offset_sec, deci) + meta = input_manifest_dict[uniq_id] + meta['offset'] = offset_sec + meta['duration'] = dur + json.dump(meta, output_manifest_fp) + output_manifest_fp.write("\n") + + +def write_file(name: str, lines: List[dict], idx: int): + """ + Write json lines to file. + + Args: + name (str): Output file path + lines (list): List of json lines + idx (int): Indices to dump to the file + """ + with open(name, 'w') as fout: + for i in idx: + dic = lines[i] + json.dump(dic, fout) + fout.write('\n') + + +def read_file(pathlist: str) -> List[str]: + """ + Read list of lines from target file. + + Args: + pathlist (str): Input file path + Returns: + sorted(pathlist) (list): List of lines + """ + with open(pathlist, 'r') as f: + pathlist = f.readlines() + return sorted(pathlist) + + +def get_dict_from_wavlist(pathlist: List[str]) -> Dict[str, str]: + """ + Read dictionaries from list of lines + + Args: + pathlist (list): List of file paths + Returns: + path_dict (dict): Dictionary containing dictionaries read from files + """ + path_dict = od() + pathlist = sorted(pathlist) + for line_path in pathlist: + uniq_id = os.path.basename(line_path).split('.')[0] + path_dict[uniq_id] = line_path + return path_dict + + +def get_dict_from_list(data_pathlist: List[str], uniqids: List[str]) -> Dict[str, str]: + """ + Create dictionaries from list of lines + + Args: + data_pathlist (list): List of file paths + uniqids (list): List of file IDs + Returns: + path_dict (dict): Dictionary containing file paths + """ + path_dict = {} + for line_path in data_pathlist: + uniq_id = os.path.basename(line_path).split('.')[0] + if uniq_id in uniqids: + path_dict[uniq_id] = line_path + else: + raise ValueError(f'uniq id {uniq_id} is not in wav filelist') + return path_dict + + +def get_path_dict(data_path: str, uniqids: List[str], len_wavs: int = None) -> Dict[str, str]: + """ + Create dictionary from list of lines (using the get_dict_from_list function) + + Args: + data_path (str): Path to file containing list of files + uniqids (list): List of file IDs + len_wavs (int): Length of file list + Returns: + data_pathdict (dict): Dictionary containing file paths + """ + if data_path is not None: + data_pathlist = read_file(data_path) + if len_wavs is not None: + assert len(data_pathlist) == len_wavs + data_pathdict = get_dict_from_list(data_pathlist, uniqids) + elif len_wavs is not None: + data_pathdict = {uniq_id: None for uniq_id in uniqids} + return data_pathdict + + +def create_segment_manifest( + input_manifest_path: str, output_manifest_path: str, window: float, shift: float, step_count: int, deci: int +): + """ + Create segmented manifest file from base manifest file + + Args: + input_manifest_path (str): Path to input manifest file + output_manifest_path (str): Path to output manifest file + window (float): Window length for segmentation + shift (float): Shift length for segmentation + step_count (int): Number of the unit segments you want to create per utterance + deci (int): Rounding number of decimal places + """ + if '.json' not in input_manifest_path: + raise ValueError("input_manifest_path file should be .json file format") + if output_manifest_path and '.json' not in output_manifest_path: + raise ValueError("output_manifest_path file should be .json file format") + elif not output_manifest_path: + output_manifest_path = rreplace(input_manifest_path, '.json', f'_{step_count}seg.json') + + input_manifest_dict = get_input_manifest_dict(input_manifest_path) + segment_manifest_path = rreplace(input_manifest_path, '.json', '_seg.json') + subsegment_manifest_path = rreplace(input_manifest_path, '.json', '_subseg.json') + min_subsegment_duration = 0.05 + step_count = int(step_count) + + AUDIO_RTTM_MAP = audio_rttm_map(input_manifest_path) + segments_manifest_file = write_rttm2manifest(AUDIO_RTTM_MAP, segment_manifest_path, deci) + subsegments_manifest_file = subsegment_manifest_path + segments_manifest_to_subsegments_manifest( + segments_manifest_file, subsegments_manifest_file, window, shift, min_subsegment_duration, + ) + subsegments_dict = get_subsegment_dict(subsegments_manifest_file, window, shift, deci) + write_truncated_subsegments(input_manifest_dict, subsegments_dict, output_manifest_path, step_count, deci) + os.remove(segment_manifest_path) + os.remove(subsegment_manifest_path) + + +def create_manifest( + wav_path: str, + manifest_filepath: str, + text_path: str = None, + rttm_path: str = None, + uem_path: str = None, + ctm_path: str = None, + add_duration: bool = False, +): + """ + Create base manifest file + + Args: + wav_path (str): Path to list of wav files + manifest_filepath (str): Path to output manifest file + text_path (str): Path to list of text files + rttm_path (str): Path to list of rttm files + uem_path (str): Path to list of uem files + ctm_path (str): Path to list of ctm files + add_duration (bool): Whether to add durations to the manifest file + """ + if os.path.exists(manifest_filepath): + os.remove(manifest_filepath) + wav_pathlist = read_file(wav_path) + wav_pathdict = get_dict_from_wavlist(wav_pathlist) + len_wavs = len(wav_pathlist) + uniqids = sorted(wav_pathdict.keys()) + + text_pathdict = get_path_dict(text_path, uniqids, len_wavs) + rttm_pathdict = get_path_dict(rttm_path, uniqids, len_wavs) + uem_pathdict = get_path_dict(uem_path, uniqids, len_wavs) + ctm_pathdict = get_path_dict(ctm_path, uniqids, len_wavs) + + lines = [] + for uid in uniqids: + wav, text, rttm, uem, ctm = ( + wav_pathdict[uid], + text_pathdict[uid], + rttm_pathdict[uid], + uem_pathdict[uid], + ctm_pathdict[uid], + ) + + audio_line = wav.strip() + if rttm is not None: + rttm = rttm.strip() + labels = rttm_to_labels(rttm) + num_speakers = Counter([l.split()[-1] for l in labels]).keys().__len__() + else: + num_speakers = None + + if uem is not None: + uem = uem.strip() + + if text is not None: + with open(text.strip()) as f: + text = f.readlines()[0].strip() + else: + text = "-" + + if ctm is not None: + ctm = ctm.strip() + + duration = None + if add_duration: + y, sr = librosa.load(audio_line, sr=None) + duration = librosa.get_duration(y=y, sr=sr) + meta = [ + { + "audio_filepath": audio_line, + "offset": 0, + "duration": duration, + "label": "infer", + "text": text, + "num_speakers": num_speakers, + "rttm_filepath": rttm, + "uem_filepath": uem, + "ctm_filepath": ctm, + } + ] + lines.extend(meta) + + write_file(manifest_filepath, lines, range(len(lines))) + + +def read_manifest(manifest: Union[Path, str]) -> List[dict]: + """ + Read manifest file + + Args: + manifest (str or Path): Path to manifest file + Returns: + data (list): List of JSON items + """ + manifest = DataStoreObject(str(manifest)) + + data = [] + try: + f = open(manifest.get(), 'r', encoding='utf-8') + except: + raise Exception(f"Manifest file could not be opened: {manifest}") + + errors = [] + for line in f.readlines(): + line = line.strip() + if not line: + continue + try: + item = json.loads(line) + except json.JSONDecodeError: + errors.append(line) + continue + data.append(item) + f.close() + if errors: + logging.error(f"{len(errors)} Errors encountered while reading manifest file: {manifest}") + for error in errors: + logging.error(f"-- Failed to parse line: `{error}`") + raise RuntimeError(f"Errors encountered while reading manifest file: {manifest}") + return data + + +def write_manifest(output_path: Union[Path, str], target_manifest: List[dict], ensure_ascii: bool = True): + """ + Write to manifest file + + Args: + output_path (str or Path): Path to output manifest file + target_manifest (list): List of manifest file entries + ensure_ascii (bool): default is True, meaning the output is guaranteed to have all incoming non-ASCII characters escaped. If ensure_ascii is false, these characters will be output as-is. + """ + with open(output_path, "w", encoding="utf-8") as outfile: + for tgt in target_manifest: + json.dump(tgt, outfile, ensure_ascii=ensure_ascii) + outfile.write('\n') + + +def write_ctm(output_path: str, target_ctm: Dict[str, dict]): + """ + Write ctm entries from diarization session to a .ctm file. + + Args: + output_path (str): target file path + target_ctm (dict): list of ctm entries + """ + target_ctm.sort(key=lambda y: y[0]) + with open(output_path, "w") as outfile: + for pair in target_ctm: + tgt = pair[1] + outfile.write(tgt) + + +def write_text(output_path: str, target_ctm: Dict[str, dict]): + """ + Write text from diarization session to a .txt file + + Args: + output_path (str): target file path + target_ctm (dict): list of ctm entries + """ + target_ctm.sort(key=lambda y: y[0]) + with open(output_path, "w") as outfile: + for pair in target_ctm: + tgt = pair[1] + word = tgt.split(' ')[4] + outfile.write(word + ' ') + outfile.write('\n') diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/numba_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/numba_utils.py new file mode 100644 index 0000000..867ecf5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/numba_utils.py @@ -0,0 +1,88 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np +from numba import jit + + +def phase_vocoder(D: np.ndarray, rate: float, phi_advance: np.ndarray, scale_buffer: np.ndarray): + """ + Optimized implementation of phase vocoder from Librosa. + Reference implementation: + - https://librosa.github.io/librosa/generated/librosa.core.phase_vocoder.html + Args: + D: Complex spectograms of shape [d, t, complex=2]. + rate: Speed rate, must be float greater than 0. + phi_advance: Precomputed phase advance buffer array of length [n_fft + 1] + scale_buffer: Precomputed numpy buffer array of length [n_fft + 1] + Returns: + Complex64 ndarray of shape [d, t / rate, complex=2] + """ + time_steps = np.arange(0, D.shape[1], rate, dtype=np.float64) + + # Create an empty output array + d_stretch = np.zeros((D.shape[0], len(time_steps)), D.dtype, order='F') + + # Phase accumulator; initialize to the first sample + phase_acc = np.angle(D[:, 0]) + + # Pad 0 columns to simplify boundary logic + D = np.pad(D, [(0, 0), (0, 2)], mode='constant') + + d_stretch = _phase_vocoder_kernel(D, time_steps, phi_advance, d_stretch, phase_acc, scale_buffer) + + return d_stretch + + +@jit(nopython=True, nogil=True) +def _phase_vocoder_kernel(D, time_steps, phi_advance, d_stretch, phase_acc, scale_buffer): + """ + Numba optimized kernel to compute the phase vocoder step. + Args: + D: Complex spectograms of shape [d, t, complex=2]. + rate: Speed rate, must be float greater than 0. + time_steps: Numpy ndarray of linearly spaced time steps, shape = [t] + phi_advance: Precomputed phase advance buffer array of length [n_fft + 1] + d_stretch: Output complex matrix of shape [d, t / rate, complex=2] + phase_acc: Phase accumulator initialized to first sample of shape [d, complex=2] + scale_buffer: Precomputed numpy buffer array of length [n_fft + 1] + Returns: + Complex64 ndarray of shape [d, t / rate, complex=2] + """ + two_pi = 2.0 * np.pi + + for (t, step) in enumerate(time_steps): + columns = D[:, int(step) : int(step + 2)] + columns_0 = columns[:, 0] + columns_1 = columns[:, 1] + + # Weighting for linear magnitude interpolation + alpha = np.mod(step, 1.0) + mag = (1.0 - alpha) * np.abs(columns_0) + alpha * np.abs(columns_1) + + # Store to output array + d_stretch[:, t] = mag * np.exp(1.0j * phase_acc) + + # Compute phase advance + dphase = np.angle(columns_1) - np.angle(columns_0) - phi_advance + + # Wrap to -pi:pi range + scale = dphase / two_pi + np.round(scale, 0, scale_buffer) + + dphase = dphase - two_pi * scale_buffer + + # Accumulate phase + phase_acc += phi_advance + dphase + + return d_stretch diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/offline_clustering.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/offline_clustering.py new file mode 100644 index 0000000..3f6c90d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/offline_clustering.py @@ -0,0 +1,1387 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright (c) 2007-2020 The scikit-learn developers. + +# BSD 3-Clause License + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +# NME-SC clustering is based on the implementation from the paper +# https://arxiv.org/pdf/2003.02405.pdf and the implementation from +# https://github.com/tango4j/Auto-Tuning-Spectral-Clustering. + +from typing import Dict, List, Tuple + +import torch +from torch.linalg import eigh, eigvalsh + + +def cos_similarity(emb_a: torch.Tensor, emb_b: torch.Tensor, eps=torch.tensor(3.5e-4)) -> torch.Tensor: + """ + Calculate cosine similarities of the given two set of tensors. The output is an N by N + matrix where N is the number of feature vectors. + + Args: + a (Tensor): + Matrix containing speaker representation vectors. (N x embedding_dim) + b (Tensor): + Matrix containing speaker representation vectors. (N x embedding_dim) + + Returns: + res (Tensor): + N by N matrix containing the cosine similarities of the values. + """ + # If number of embedding count is 1, it creates nan values + if emb_a.shape[0] == 1 or emb_b.shape[0] == 1: + raise ValueError(f"Number of feature vectors should be greater than 1 but got {emb_a.shape} and {emb_b.shape}") + a_norm = emb_a / (torch.norm(emb_a, dim=1).unsqueeze(1) + eps) + b_norm = emb_b / (torch.norm(emb_b, dim=1).unsqueeze(1) + eps) + res = torch.mm(a_norm, b_norm.transpose(0, 1)) + res.fill_diagonal_(1) + return res + + +def ScalerMinMax(X: torch.Tensor) -> torch.Tensor: + """ + Min-max scale the input affinity matrix X, which will lead to a dynamic range of [0, 1]. + + Args: + X (Tensor): + Matrix containing cosine similarity values among embedding vectors (N x N) + + Returns: + v_norm (Tensor): + Min-max normalized value of X. + """ + v_min, v_max = X.min(), X.max() + v_norm = (X - v_min) / (v_max - v_min) + return v_norm + + +def getEuclideanDistance( + specEmbA: torch.Tensor, specEmbB: torch.Tensor, device: torch.device = torch.device('cpu') +) -> torch.Tensor: + """ + Calculate Euclidean distances from the given feature tensors. + + Args: + specEmbA (Tensor): + Matrix containing spectral embedding vectors from eigenvalue decomposition (N x embedding_dim). + specEmbB (Tensor): + Matrix containing spectral embedding vectors from eigenvalue decomposition (N x embedding_dim). + + Returns: + dis (Tensor): + Euclidean distance values of the two sets of spectral embedding vectors. + """ + specEmbA, specEmbB = specEmbA.to(device), specEmbB.to(device) + A, B = specEmbA.unsqueeze(dim=1), specEmbB.unsqueeze(dim=0) + dis = (A - B) ** 2.0 + dis = dis.sum(dim=-1).squeeze() + return dis + + +def kmeans_plusplus_torch( + X: torch.Tensor, + n_clusters: int, + random_state: int, + n_local_trials: int = 30, + device: torch.device = torch.device('cpu'), +): + """ + Choose initial centroids for initializing k-means algorithm. The performance of + k-means algorithm can vary significantly by the initial centroids. To alleviate + this problem, k-means++ algorithm chooses initial centroids based on the probability + proportional to the distance from the formally chosen centroids. The centroids + selected by k-means++ algorithm improve the chance of getting more accurate and + stable clustering results. The overall implementation of k-means++ algorithm is + inspired by the numpy based k-means++ implementation in: + https://github.com/scikit-learn/scikit-learn + + Originally, the implementation of the k-means++ algorithm in scikit-learn is based + on the following research article: + Arthur, David, and Sergei Vassilvitskii. k-means++: The advantages of careful + seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete + algorithms, Society for Industrial and Applied Mathematics (2007) + + Args: + X (Tensor): + Matrix containing cosine similarity values among embedding vectors (N x N) + n_clusters (int): + Maximum number of speakers for estimating number of speakers. + Shows stable performance under 20. + random_state (int): + Seed variable for setting up a random state. + n_local_trials (int): + Number of trials for creating initial values of the center points. + device (torch.device) + Torch device variable. + + Returns: + centers (Tensor): + The coordinates for center points that are used for initializing k-means algorithm. + indices (Tensor): + The indices of the best candidate center points. + """ + torch.manual_seed(random_state) + X = X.to(device) + n_samples, n_features = X.shape + + centers = torch.zeros(n_clusters, n_features, dtype=X.dtype) + center_id = torch.randint(0, n_samples, (1,)).long() + indices = torch.full([n_clusters,], -1, dtype=torch.int) + + centers[0] = X[center_id].squeeze(0) + indices[0] = center_id.squeeze(0) + + centers = centers.to(device) + closest_dist_diff = centers[0, None].repeat(1, X.shape[0]).view(X.shape[0], -1) - X + closest_dist_sq = closest_dist_diff.pow(2).sum(dim=1).unsqueeze(dim=0) + current_pot = closest_dist_sq.sum() + + for c in range(1, n_clusters): + rand_vals = torch.rand(n_local_trials) * current_pot.item() + + if len(closest_dist_sq.shape) > 1: + torch_cumsum = torch.cumsum(closest_dist_sq, dim=1)[0] + else: + torch_cumsum = torch.cumsum(closest_dist_sq, dim=0) + + candidate_ids = torch.searchsorted(torch_cumsum, rand_vals.to(device)) + + N_ci = candidate_ids.shape[0] + distance_diff = X[candidate_ids].repeat(1, X.shape[0]).view(X.shape[0] * N_ci, -1) - X.repeat(N_ci, 1) + distance = distance_diff.pow(2).sum(dim=1).view(N_ci, -1) + distance_to_candidates = torch.minimum(closest_dist_sq, distance) + candidates_pot = distance_to_candidates.sum(dim=1) + + best_candidate = torch.argmin(candidates_pot) + current_pot = candidates_pot[best_candidate] + closest_dist_sq = distance_to_candidates[best_candidate] + best_candidate = candidate_ids[best_candidate] + + centers[c] = X[best_candidate] + indices[c] = best_candidate + return centers, indices + + +def kmeans_torch( + X: torch.Tensor, + num_clusters: int, + threshold: float = 1e-4, + iter_limit: int = 15, + random_state: int = 0, + device: torch.device = torch.device('cpu'), +) -> torch.Tensor: + """ + Run k-means algorithm on the given set of spectral embeddings in X. The threshold + and iter_limit variables are set to show the best performance on speaker diarization + tasks. The overall implementation of k-means algorithm is inspired by the k-means + algorithm implemented in https://github.com/scikit-learn/scikit-learn. + + References: + Arthur, David, and Sergei Vassilvitskii. k-means++: The advantages of careful + seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete + algorithms, Society for Industrial and Applied Mathematics (2007). + + Args: + X (Tensor): + Cosine similarity matrix calculated from speaker embeddings + num_clusters (int): + The estimated number of speakers. + threshold (float): + This threshold limits the change of center values. If the square of + the center shift values are bigger than this threshold, the iteration stops. + iter_limit (int): + The maximum number of iterations that is allowed by the k-means algorithm. + device (torch.device): + Torch device variable + + Returns: + selected_cluster_indices (Tensor): + The assigned cluster labels from the k-means clustering. + """ + # Convert tensor type to float + X = X.float().to(device) + input_size = X.shape[0] + + # Initialize the cluster centers with kmeans_plusplus algorithm. + plusplus_init_states = kmeans_plusplus_torch(X, n_clusters=num_clusters, random_state=random_state, device=device) + centers = plusplus_init_states[0] + + selected_cluster_indices = torch.zeros(input_size).long() + + for iter_count in range(iter_limit): + euc_dist = getEuclideanDistance(X, centers, device=device) + + if len(euc_dist.shape) <= 1: + break + else: + selected_cluster_indices = torch.argmin(euc_dist, dim=1) + + center_inits = centers.clone() + + for index in range(num_clusters): + selected_cluster = torch.nonzero(selected_cluster_indices == index).squeeze().to(device) + chosen_indices = torch.index_select(X, 0, selected_cluster) + + if chosen_indices.shape[0] == 0: + chosen_indices = X[torch.randint(len(X), (1,))] + + centers[index] = chosen_indices.mean(dim=0) + + # Calculate the delta from center_inits to centers + center_delta_pow = torch.pow((centers - center_inits), 2) + center_shift_pow = torch.pow(torch.sum(torch.sqrt(torch.sum(center_delta_pow, dim=1))), 2) + + # If the cluster centers are not changing significantly, stop the loop. + if center_shift_pow < threshold: + break + + return selected_cluster_indices + + +def getTheLargestComponent(affinity_mat: torch.Tensor, seg_index: int, device: torch.device) -> torch.Tensor: + """ + Find the largest affinity_mat connected components for each given node. + This is for checking whether the affinity_mat is fully connected. + + Args: + affinity_mat (Tensor): + A square matrix (tensor) containing normalized cosine distance values + seg_index (int): + The segment index that is targeted to be explored. + + Returns: + connected_nodes (Tensor): + A tensor containing booleans that indicate whether the node is connected. + """ + num_of_segments = affinity_mat.shape[0] + + connected_nodes = torch.zeros(num_of_segments, dtype=torch.bool).to(device) + nodes_to_explore = torch.zeros(num_of_segments, dtype=torch.bool).to(device) + + nodes_to_explore[seg_index] = True + nodes_to_explore = nodes_to_explore.to(device) + for k in range(num_of_segments): + last_num_component = connected_nodes.sum() + torch.logical_or(connected_nodes, nodes_to_explore, out=connected_nodes) + if last_num_component >= connected_nodes.sum(): + break + + indices = (nodes_to_explore == torch.tensor(True)).nonzero().t().squeeze() + if len(indices.size()) == 0: + indices = indices.unsqueeze(0) + for i in indices: + neighbors = affinity_mat[i].to(device) + torch.logical_or(nodes_to_explore, neighbors.squeeze(0), out=nodes_to_explore) + return connected_nodes + + +def isGraphFullyConnected(affinity_mat: torch.Tensor, device: torch.device) -> torch.Tensor: + """ + Check whether the given affinity matrix is a fully connected graph. + """ + return getTheLargestComponent(affinity_mat, 0, device).sum() == affinity_mat.shape[0] + + +def getKneighborsConnections(affinity_mat: torch.Tensor, p_value: int, mask_method: str = 'binary') -> torch.Tensor: + """ + Binarize top-p values for each row from the given affinity matrix. + + Args: + affinity_mat (Tensor): + A square matrix (tensor) containing normalized cosine similarity values + p_value (int): + The number of top values that are selected from each row. + mask_method (str): + The method that is used to manipulate the affinity matrix. The default method is 'binary'. + + Returns: + binarized_affinity_mat (Tensor): + A binarized affinity matrix based on the given mask method. + """ + dim = affinity_mat.shape + binarized_affinity_mat = torch.zeros_like(affinity_mat).half() + sorted_matrix = torch.argsort(affinity_mat, dim=1, descending=True)[:, :p_value] + binarized_affinity_mat[sorted_matrix.T, torch.arange(affinity_mat.shape[0])] = ( + torch.ones(1).to(affinity_mat.device).half() + ) + indices_row = sorted_matrix[:, :p_value].flatten() + indices_col = torch.arange(dim[1]).repeat(p_value, 1).T.flatten() + if mask_method == 'binary' or mask_method is None: + binarized_affinity_mat[indices_row, indices_col] = ( + torch.ones(indices_row.shape[0]).to(affinity_mat.device).half() + ) + elif mask_method == 'drop': + binarized_affinity_mat[indices_row, indices_col] = affinity_mat[indices_row, indices_col].half() + elif mask_method == 'sigmoid': + binarized_affinity_mat[indices_row, indices_col] = torch.sigmoid(affinity_mat[indices_row, indices_col]).half() + else: + raise ValueError(f'Unknown mask method: {mask_method}') + return binarized_affinity_mat + + +def getAffinityGraphMat(affinity_mat_raw: torch.Tensor, p_value: int) -> torch.Tensor: + """ + Calculate a binarized graph matrix and + symmetrize the binarized graph matrix. + """ + X = affinity_mat_raw if p_value <= 0 else getKneighborsConnections(affinity_mat_raw, p_value) + symm_affinity_mat = 0.5 * (X + X.T) + return symm_affinity_mat + + +def getMinimumConnection( + mat: torch.Tensor, max_N: torch.Tensor, n_list: torch.Tensor, device: torch.device +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Generate connections until fully connect all the nodes in the graph. + If the graph is not fully connected, it might generate inaccurate results. + """ + p_value = torch.tensor(1) + affinity_mat = getAffinityGraphMat(mat, p_value) + for i, p_value in enumerate(n_list): + fully_connected = isGraphFullyConnected(affinity_mat, device) + affinity_mat = getAffinityGraphMat(mat, p_value) + if fully_connected or p_value > max_N: + break + + return affinity_mat, p_value + + +def getRepeatedList(mapping_argmat: torch.Tensor, score_mat_size: torch.Tensor) -> torch.Tensor: + """ + Count the numbers in the mapping dictionary and create lists that contain + repeated indices that will be used for creating a repeated affinity matrix. + This repeated matrix is then used for fusing multiple affinity values. + """ + repeat_list = torch.zeros(score_mat_size, dtype=torch.int32).to(mapping_argmat.device) + idxs, counts = torch.unique(mapping_argmat, return_counts=True) + repeat_list[idxs] = counts.int().to(mapping_argmat.device) + return repeat_list + + +def get_argmin_mat(timestamps_in_scales: List[torch.Tensor]) -> List[torch.Tensor]: + """ + Calculate the mapping between the base scale and other scales. A segment from a longer scale is + repeatedly mapped to a segment from a shorter scale or the base scale. + + Args: + timestamps_in_scales (list): + List containing timestamp tensors for each scale. + Each tensor has dimensions of (Number of base segments) x 2. + + Returns: + session_scale_mapping_list (list): + List containing argmin arrays indexed by scale index. + """ + scale_list = list(range(len(timestamps_in_scales))) + segment_anchor_list = [] + for scale_idx in scale_list: + time_stamps_float = timestamps_in_scales[scale_idx] + segment_anchor_list.append(torch.mean(time_stamps_float, dim=1)) + base_scale_idx = max(scale_list) + base_scale_anchor = segment_anchor_list[base_scale_idx] + session_scale_mapping_list = [] + for scale_idx in scale_list: + curr_scale_anchor = segment_anchor_list[scale_idx] + curr_mat = torch.tile(curr_scale_anchor, (base_scale_anchor.shape[0], 1)) + base_mat = torch.tile(base_scale_anchor, (curr_scale_anchor.shape[0], 1)).t() + argmin_mat = torch.argmin(torch.abs(curr_mat - base_mat), dim=1) + session_scale_mapping_list.append(argmin_mat) + return session_scale_mapping_list + + +def getCosAffinityMatrix(emb: torch.Tensor) -> torch.Tensor: + """ + Calculate cosine similarity values among speaker embeddings then min-max normalize + the affinity matrix. + + Args: + emb (Tensor): + Matrix containing embedding vectors. emb variable should be float(FP32) type to make the data-type + compatible with torch.mm operation for both CPU and GPU(CUDA). + dimension: (Number of embedding vectors) x (embedding dimension) + + Returns: + sim_d (Tensor): + Matrix containing cosine similarity values among the given embedding vectors. + dimension: (Number of embedding vectors) x (Number of embedding vectors) + """ + if emb.shape[0] == 1: + sim_d = torch.tensor([[1]]).to(emb.device) + else: + emb = emb.float() + sim_d = cos_similarity(emb, emb) + sim_d = ScalerMinMax(sim_d) + return sim_d + + +def get_scale_interpolated_embs( + multiscale_weights: torch.Tensor, + embeddings_in_scales: List[torch.Tensor], + timestamps_in_scales: List[torch.Tensor], + device: torch.device = torch.device('cpu'), +) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """ + Generate a scale-interpolated single embedding vector by calculating the weighted sum + of the multiple embedding vectors from different scales. The output is a set of embedding + vectors corresponding to the base-scale segments. + + Args: + multiscale_weights (Tensor): + Tensor containing Multiscale weights + Dimensions: (Number of scales) x 1 + embeddings_in_scales (list): + List containing split embedding tensors by each scale + timestamps_in_scales (list): + List containing split timestamps tensors by each scale + device (torch.device): + Torch device variable + + Returns: + context_emb (Tensor): + A set of scale-interpolated embedding vectors. + Dimensions: (Number of base-scale segments) x (Dimensions of embedding vector) + session_scale_mapping_list (list): + List containing argmin arrays indexed by scale index. + """ + rep_mat_list = [] + multiscale_weights = multiscale_weights.to(device) + session_scale_mapping_list = get_argmin_mat(timestamps_in_scales) + scale_list = list(range(len(timestamps_in_scales))) + for scale_idx in scale_list: + mapping_argmat = session_scale_mapping_list[scale_idx] + emb_t = embeddings_in_scales[scale_idx].to(device) + mapping_argmat = mapping_argmat.to(device) + repeat_list = getRepeatedList(mapping_argmat, torch.tensor(emb_t.shape[0])).to(device) + rep_emb_t = torch.repeat_interleave(emb_t, repeats=repeat_list, dim=0) + rep_mat_list.append(rep_emb_t) + stacked_scale_embs = torch.stack(rep_mat_list) + context_emb = torch.matmul(stacked_scale_embs.permute(2, 1, 0), multiscale_weights.t()).squeeze().t() + if len(context_emb.shape) < 2: + context_emb = context_emb.unsqueeze(0) + context_emb = context_emb.to(device) + return context_emb, session_scale_mapping_list + + +def getMultiScaleCosAffinityMatrix( + multiscale_weights: torch.Tensor, + embeddings_in_scales: List[torch.Tensor], + timestamps_in_scales: List[torch.Tensor], + device: torch.device = torch.device('cpu'), +) -> torch.Tensor: + """ + Calculate cosine similarity values among speaker embeddings for each scale then + apply multiscale weights to calculate the fused similarity matrix. + NOTE: Due to CUDA memory limit, the embedding vectors in embeddings_in_scales are stored in `cpu` device. + + Args: + multiscale_weights (Tensor): + Tensor containing multiscale weights + Dimensions: (Number of scales) x 1 + embeddings_in_scales (list): + List containing split embedding tensors by each scale + timestamps_in_scales (list): + List containing split timestamps tensors by each scale + device (torch.device): + Torch device variable + + Returns: + fused_sim_d (Tensor): + An affinity matrix that is obtained by calculating the weighted sum of + the multiple affinity matrices from the different scales. + """ + multiscale_weights = torch.squeeze(multiscale_weights, dim=0).to(device) + session_scale_mapping_list = get_argmin_mat(timestamps_in_scales) + scale_list = list(range(len(timestamps_in_scales))) + fused_sim_d = torch.zeros(len(timestamps_in_scales[-1]), len(timestamps_in_scales[-1])).to(device) + for scale_idx in scale_list: + mapping_argmat = session_scale_mapping_list[scale_idx] + emb_t = embeddings_in_scales[scale_idx].half().to(device) + score_mat_torch = getCosAffinityMatrix(emb_t) + repeat_list = getRepeatedList(mapping_argmat, torch.tensor(score_mat_torch.shape[0])).to(device) + repeated_tensor_0 = torch.repeat_interleave(score_mat_torch, repeats=repeat_list, dim=0).to(device) + repeated_tensor_1 = torch.repeat_interleave(repeated_tensor_0, repeats=repeat_list, dim=1).to(device) + fused_sim_d += multiscale_weights[scale_idx] * repeated_tensor_1 + return fused_sim_d + + +def getLaplacian(X: torch.Tensor) -> torch.Tensor: + """ + Calculate a laplacian matrix from an affinity matrix X. + """ + X.fill_diagonal_(0) + D = torch.sum(torch.abs(X), dim=1) + D = torch.diag_embed(D) + L = D - X + return L + + +def eigDecompose(laplacian: torch.Tensor, cuda: bool, device: torch.device) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Calculate eigenvalues and eigenvectors from the Laplacian matrix. + """ + if cuda: + if device is None: + device = torch.cuda.current_device() + laplacian = laplacian.float().to(device) + else: + laplacian = laplacian.float().to(torch.device('cpu')) + lambdas, diffusion_map = eigh(laplacian) + return lambdas, diffusion_map + + +def eigValueSh(laplacian: torch.Tensor, cuda: bool, device: torch.device) -> torch.Tensor: + """ + Calculate only eigenvalues from the Laplacian matrix. + """ + if cuda: + if device is None: + device = torch.cuda.current_device() + laplacian = laplacian.float().to(device) + else: + laplacian = laplacian.float().to(torch.device('cpu')) + lambdas = eigvalsh(laplacian) + return lambdas + + +def getLamdaGaplist(lambdas: torch.Tensor) -> torch.Tensor: + """ + Calculate the gaps between lambda values. + """ + if torch.is_complex(lambdas): + lambdas = torch.real(lambdas) + return lambdas[1:] - lambdas[:-1] + + +def addAnchorEmb(emb: torch.Tensor, anchor_sample_n: int, anchor_spk_n: int, sigma: float) -> torch.Tensor: + """ + Add randomly generated synthetic embeddings to make eigenanalysis more stable. + We refer to these embeddings as anchor embeddings. + + emb (Tensor): + The input embedding from the embedding extractor. + anchor_sample_n (int): + Number of embedding samples per speaker. + anchor_sample_n = 10 is recommended. + anchor_spk_n (int): + Number of speakers for synthetic embedding. + anchor_spk_n = 3 is recommended. + sigma (int): + The amplitude of synthetic noise for each embedding vector. + If the sigma value is too small, under-counting could happen. + If the sigma value is too large, over-counting could happen. + sigma = 50 is recommended. + """ + emb_dim = emb.shape[1] + std_org = torch.std(emb, dim=0) + sigma = torch.tensor(sigma).to(emb.device) + new_emb_list = [] + for _ in range(anchor_spk_n): + emb_m = torch.tile(torch.randn(1, emb_dim), (anchor_sample_n, 1)).to(emb.device) + emb_noise = torch.randn(anchor_sample_n, emb_dim).T.to(emb.device) + emb_noise = torch.matmul( + torch.diag(std_org), emb_noise / torch.max(torch.abs(emb_noise), dim=0)[0].unsqueeze(0) + ).T + emb_gen = emb_m + sigma * emb_noise + new_emb_list.append(emb_gen) + + new_emb_list.append(emb) + new_emb_np = torch.vstack(new_emb_list) + return new_emb_np + + +def getEnhancedSpeakerCount( + emb: torch.Tensor, + random_test_count: int = 5, + anchor_spk_n: int = 3, + anchor_sample_n: int = 10, + sigma: float = 50, + cuda: bool = False, +) -> torch.Tensor: + """ + Calculate the number of speakers using NME analysis with anchor embeddings. Add dummy speaker + embedding vectors and run speaker counting multiple times to enhance the speaker counting accuracy + for the short audio samples. + + Args: + emb (Tensor): + The input embedding from the embedding extractor. + cuda (bool): + Use cuda for the operations if cuda==True. + random_test_count (int): + Number of trials of the enhanced counting with randomness. + The higher the count, the more accurate the enhanced counting is. + anchor_spk_n (int): + Number of speakers for synthetic embedding. + anchor_spk_n = 3 is recommended. + anchor_sample_n (int): + Number of embedding samples per speaker. + anchor_sample_n = 10 is recommended. + sigma (float): + The amplitude of synthetic noise for each embedding vector. + If the sigma value is too small, under-counting could happen. + If the sigma value is too large, over-counting could happen. + sigma = 50 is recommended. + + Returns: + comp_est_num_of_spk (Tensor): + The estimated number of speakers. `anchor_spk_n` is subtracted from the estimated + number of speakers to factor out the dummy speaker embedding vectors. + """ + est_num_of_spk_list: List[int] = [] + for seed in range(random_test_count): + torch.manual_seed(seed) + emb_aug = addAnchorEmb(emb, anchor_sample_n, anchor_spk_n, sigma) + mat = getCosAffinityMatrix(emb_aug) + nmesc = NMESC( + mat, + max_num_speakers=emb.shape[0], + max_rp_threshold=0.15, + sparse_search=True, + sparse_search_volume=10, + fixed_thres=-1.0, + nme_mat_size=300, + cuda=cuda, + ) + est_num_of_spk, _ = nmesc.forward() + est_num_of_spk_list.append(est_num_of_spk.item()) + comp_est_num_of_spk = torch.tensor(max(torch.mode(torch.tensor(est_num_of_spk_list))[0].item() - anchor_spk_n, 1)) + return comp_est_num_of_spk + + +def split_input_data( + embeddings_in_scales: torch.Tensor, + timestamps_in_scales: torch.Tensor, + multiscale_segment_counts: torch.LongTensor, +) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + """ + Split multiscale embeddings and multiscale timestamps and put split scale-wise data into python lists. + This formatting function is needed to make the input type as `torch.Tensor`. + + Args: + embeddings_in_scales (Tensor): + Concatenated Torch tensor containing embeddings in multiple scales + timestamps_in_scales (Tensor): + Concatenated Torch tensor containing timestamps in multiple scales + multiscale_segment_counts (LongTensor): + Concatenated Torch LongTensor containing number of segments per each scale + + Returns: + embeddings_in_scales (list): + List containing split embedding tensors by each scale + timestamps_in_scales (list): + List containing split timestamps tensors by each scale + """ + if len(embeddings_in_scales.shape) != 2: + raise ValueError( + f"embeddings_in_scales Tensor should have 2 dimensions, but got {len(embeddings_in_scales.shape)}." + ) + elif len(timestamps_in_scales.shape) != 2: + raise ValueError( + f"timestamps_in_scales Tensor should have 2 dimensions, but got {len(timestamps_in_scales.shape)}." + ) + elif not (torch.sum(multiscale_segment_counts) == embeddings_in_scales.shape[0] == timestamps_in_scales.shape[0]): + raise ValueError( + f"multiscale_segment_counts, embeddings_in_scales, and timestamps_in_scales should have the same length, \ + but got {multiscale_segment_counts.shape[0]}, {embeddings_in_scales.shape[0]}, and {timestamps_in_scales.shape[0]} respectively." + ) + split_index: List[int] = multiscale_segment_counts.tolist() + embeddings_in_scales = torch.split(embeddings_in_scales, split_index, dim=0) + timestamps_in_scales = torch.split(timestamps_in_scales, split_index, dim=0) + embeddings_in_scales, timestamps_in_scales = list(embeddings_in_scales), list(timestamps_in_scales) + return embeddings_in_scales, timestamps_in_scales + + +def estimateNumofSpeakers( + affinity_mat: torch.Tensor, max_num_speakers: int, cuda: bool = False +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Estimate the number of speakers using eigendecomposition on the Laplacian Matrix. + + Args: + affinity_mat (Tensor): + N by N affinity matrix + max_num_speakers (int): + Maximum number of clusters to consider for each session + cuda (bool): + If cuda available eigendecomposition is computed on GPUs. + + Returns: + num_of_spk (Tensor): + The estimated number of speakers + lambdas (Tensor): + The lambda values from eigendecomposition + lambda_gap (Tensor): + The gap between the lambda values from eigendecomposition + """ + laplacian = getLaplacian(affinity_mat) + lambdas = eigValueSh(laplacian, cuda=cuda, device=affinity_mat.device) + lambdas = torch.sort(lambdas)[0] + lambda_gap = getLamdaGaplist(lambdas) + num_of_spk = torch.argmax(lambda_gap[: min(max_num_speakers, lambda_gap.shape[0])]) + 1 + return num_of_spk, lambdas, lambda_gap + + +class SpectralClustering: + """ + Perform spectral clustering by calculating spectral embeddings then run k-means clustering + algorithm on the spectral embeddings. + """ + + def __init__( + self, + n_clusters: int = 8, + random_state: int = 0, + n_random_trials: int = 1, + cuda: bool = False, + device: torch.device = torch.device('cpu'), + ): + """ + Initialize the variables needed for spectral clustering and k-means++. + + Args: + n_clusters (int): + Number of the estimated (or oracle) number of speakers + random_state (int): + Random seed that determines a random state of k-means initialization. + n_random_trials (int): + Number of trials with different random seeds for k-means initialization. + k-means++ algorithm is executed for multiple times then the final result + is obtained by taking a majority vote. + cuda (bool): + if cuda=True, spectral clustering is done on GPU. + device (torch.device): + Torch device variable + """ + self.n_clusters = n_clusters + self.random_state = random_state + self.n_random_trials = max(n_random_trials, 1) + self.cuda = cuda + self.device = device + + def forward(self, X) -> torch.Tensor: + """ + Call self.clusterSpectralEmbeddings() function to predict cluster labels. + + Args: + X (Tensor): + Affinity matrix input + + Returns: + labels (Tensor): + Clustering label output + """ + if X.shape[0] != X.shape[1]: + raise ValueError("The affinity matrix is not a square matrix.") + labels = self.clusterSpectralEmbeddings(X, cuda=self.cuda, device=self.device) + return labels + + def clusterSpectralEmbeddings( + self, affinity: torch.Tensor, cuda: bool = False, device: torch.device = torch.device('cpu') + ) -> torch.Tensor: + """ + Perform k-means clustering on spectral embeddings. To alleviate the effect of randomness, + k-means clustering is performed for (self.n_random_trials) times then the final labels are obtained + by taking a majority vote. If speed is the major concern, self.n_random_trials should be set to 1. + n_random_trials=30 is recommended to see an improved result. + + Args: + affinity (Tensor): + Affinity matrix input + cuda (torch.bool): + Use cuda for spectral clustering if cuda=True + device (torch.device): + Torch device variable + + Returns: + labels (Tensor): + clustering label output + + """ + spectral_emb = self.getSpectralEmbeddings(affinity, n_spks=self.n_clusters, cuda=cuda) + labels_set = [] + + for random_state_seed in range(self.random_state, self.random_state + self.n_random_trials): + _labels = kmeans_torch( + X=spectral_emb, num_clusters=self.n_clusters, random_state=random_state_seed, device=device + ) + labels_set.append(_labels) + stacked_labels = torch.stack(labels_set) + label_index = torch.mode(torch.mode(stacked_labels, 0)[1])[0] + labels = stacked_labels[label_index] + return labels + + def getSpectralEmbeddings(self, affinity_mat: torch.Tensor, n_spks: int = 8, cuda: bool = False) -> torch.Tensor: + """ + Calculate eigenvalues and eigenvectors to extract spectral embeddings. + + Args: + affinity (Tensor): + Affinity matrix input + cuda (torch.bool): + Use cuda for spectral clustering if cuda=True + device (torch.device): + Torch device variable + + Returns: + labels (Tensor): + clustering label output + """ + laplacian = getLaplacian(affinity_mat) + _, diffusion_map_ = eigDecompose(laplacian, cuda=cuda, device=affinity_mat.device) + diffusion_map = diffusion_map_[:, :n_spks] + inv_idx = torch.arange(diffusion_map.size(1) - 1, -1, -1).long() + embedding = diffusion_map.T[inv_idx, :] + return embedding[:n_spks].T + + +class NMESC: + """ + Normalized Maximum Eigengap based Spectral Clustering (NME-SC) + uses Eigengap analysis to get an estimated p-value for + affinity binarization and an estimated number of speakers. + + p_value (also referred to as p_neighbors) is for taking + top p number of affinity values and convert those to 1 while + convert the rest of values to 0. + + p_value can be also tuned on a development set without performing + NME-analysis. Fixing p_value brings about significantly faster clustering + speed, but the performance is limited to the development set. + + References: + Tae Jin Park et al., Auto-Tuning Spectral Clustering for Speaker Diarization + Using Normalized Maximum Eigengap, IEEE Signal Processing Letters 27 (2019), + https://arxiv.org/abs/2003.02405 + + Args: + Please refer to def __init__(). + + Methods: + NMEanalysis(): + Performs NME-analysis to estimate p_value and the number of speakers + subsampleAffinityMat(nme_mat_size): + Subsamples the number of speakers to reduce the computational load + getPvalueList(): + Generates a list containing p-values that need to be examined. + getEigRatio(p_neighbors): + Calculates g_p, which is a ratio between p_neighbors and the maximum eigengap + getLamdaGaplist(lambdas): + Calculates lambda gap values from an array contains lambda values + estimateNumofSpeakers(affinity_mat): + Estimates the number of speakers using lambda gap list + """ + + def __init__( + self, + mat: torch.Tensor, + max_num_speakers: int = 10, + max_rp_threshold: float = 0.15, + sparse_search: bool = True, + sparse_search_volume: int = 30, + nme_mat_size: int = 512, + use_subsampling_for_nme: bool = True, + fixed_thres: float = -1.0, + maj_vote_spk_count: bool = False, + parallelism: bool = True, + cuda: bool = False, + device: torch.device = torch.device('cpu'), + ): + """ + Args: + mat (Tensor): + Cosine similarity matrix calculated from the provided speaker embeddings. + max_num_speakers (int): + Maximum number of speakers for estimating number of speakers. + Shows stable performance under 20. + max_rp_threshold (float): + Limits the range of parameter search. + Clustering performance can vary depending on this range. + Default is 0.25. + sparse_search (bool): + To increase the speed of parameter estimation, sparse_search=True + limits the number of p_values we search. + sparse_search_volume (int): + Number of p_values we search during NME analysis. + Default is 30. The lower the value, the faster NME-analysis becomes. + However, a value lower than 20 might cause a poor parameter estimation. + nme_mat_size (int): + Targeted size of matrix for NME analysis. + use_subsampling_for_nme (bool): + Use subsampling to reduce the calculational complexity. + Default is True. + fixed_thres (float or None): + A fixed threshold which can be used instead of estimating the + threshold with NME analysis. If fixed_thres is float, + it skips the NME analysis part. + maj_vote_spk_count (bool): + If True, take a majority vote on all p-values in the given range to estimate the number of speakers. + The majority voting may contribute to surpress overcounting of the speakers and improve speaker + counting accuracy. + parallelism (bool): + If True, turn on parallelism based on torch.jit.script library. + cuda (bool): + Use cuda for Eigen decomposition if cuda=True. + device (torch.device): + Torch device variable + """ + self.max_num_speakers: int = max_num_speakers + self.max_rp_threshold: float = max_rp_threshold + self.use_subsampling_for_nme: bool = use_subsampling_for_nme + self.nme_mat_size: int = nme_mat_size + self.sparse_search: bool = sparse_search + self.sparse_search_volume: int = sparse_search_volume + self.min_p_value = torch.tensor(2) + self.fixed_thres: float = fixed_thres + self.eps = 1e-10 + self.max_N = torch.tensor(0) + self.mat: torch.Tensor = mat + self.p_value_list: torch.Tensor = self.min_p_value.unsqueeze(0) + self.cuda: bool = cuda + self.device: torch.device = device + self.maj_vote_spk_count: bool = maj_vote_spk_count + self.parallelism: bool = parallelism + + def forward(self) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Subsample the input matrix to reduce the computational load. + + Returns: + est_num_of_spk (Tensor): + Estimated number of speakers from NMESC approach + p_hat_value (Tensor): + Estimated p-value (determines how many neighboring values to be selected) + """ + if self.use_subsampling_for_nme: + subsample_ratio = self.subsampleAffinityMat(self.nme_mat_size) + else: + subsample_ratio = torch.tensor(1) + + # Scans p_values and find a p_value that generates the smallest g_p value. + results: List[torch.Tensor] = [] + est_spk_n_dict: Dict[int, torch.Tensor] = {} + self.p_value_list = self.getPvalueList() + p_volume = self.p_value_list.shape[0] + eig_ratio_list = torch.zeros(p_volume,) + est_num_of_spk_list = torch.zeros(p_volume,) + + if self.parallelism: + futures: List[torch.jit.Future[torch.Tensor]] = [] + for p_idx, p_value in enumerate(self.p_value_list): + futures.append(torch.jit.fork(self.getEigRatio, p_value)) + for future in futures: + results.append(torch.jit.wait(future)) + + else: + for p_idx, p_value in enumerate(self.p_value_list): + results.append(self.getEigRatio(p_value)) + + # Retrieve the eigen analysis results + for p_idx, p_value in enumerate(self.p_value_list): + output = results[p_idx] + g_p, est_num_of_spk = output[0], output[1].int() + eig_ratio_list[p_idx] = g_p + est_spk_n_dict[p_value.item()] = est_num_of_spk + est_num_of_spk_list[p_idx] = est_num_of_spk + + index_nn = torch.argmin(eig_ratio_list) + rp_p_value = self.p_value_list[index_nn] + affinity_mat = getAffinityGraphMat(self.mat, rp_p_value) + + # Checks whether the affinity graph is fully connected. + # If not, it adds a minimum number of connections to make it fully connected. + if not isGraphFullyConnected(affinity_mat, device=self.device): + affinity_mat, rp_p_value = getMinimumConnection( + self.mat, self.max_N, self.p_value_list, device=self.device + ) + + p_hat_value = (subsample_ratio * rp_p_value).type(torch.int) + if self.maj_vote_spk_count: + est_num_of_spk = torch.mode(torch.tensor(est_num_of_spk_list))[0] + else: + est_num_of_spk = est_spk_n_dict[rp_p_value.item()] + return est_num_of_spk, p_hat_value + + def subsampleAffinityMat(self, nme_mat_size: int) -> torch.Tensor: + """ + Perform subsampling of affinity matrix. + This subsampling is for calculational complexity, not for performance. + The smaller nme_mat_size is, + - the bigger the chance of missing a speaker. + - the faster p-value estimation speed (based on eigen decomposition). + + The recommended nme_mat_size is 250~750. + However, if there are speakers who speak for very short period of time in the recording, + this subsampling might make the system miss underrepresented speakers. + Use this variable with caution. + + Args: + nme_mat_size (int): + The targeted matrix size + + Returns: + subsample_ratio (float): + The ratio between nme_mat_size and the original matrix size + """ + subsample_ratio = torch.max(torch.tensor(1), torch.tensor(self.mat.shape[0] / nme_mat_size)).type(torch.int) + self.mat = self.mat[:: subsample_ratio.item(), :: subsample_ratio.item()] + return subsample_ratio + + def getEigRatio(self, p_neighbors: int) -> torch.Tensor: + """ + For a given p_neighbors value, calculate g_p, which is a ratio between p_neighbors and the + maximum eigengap values. + References: + Tae Jin Park et al., Auto-Tuning Spectral Clustering for Speaker Diarization Using + Normalized Maximum Eigengap, IEEE Signal Processing Letters 27 (2019), + https://arxiv.org/abs/2003.02405 + + Args: + p_neighbors (int): + Determines how many binary graph connections we want to keep for each row. + + Returns: + est_num_of_spk (int): + Estimated number of speakers + g_p (float): + The ratio between p_neighbors value and the maximum eigen gap value. + """ + affinity_mat = getAffinityGraphMat(self.mat, p_neighbors) + est_num_of_spk, lambdas, lambda_gap_list = estimateNumofSpeakers( + affinity_mat, self.max_num_speakers, self.cuda + ) + arg_sorted_idx = torch.argsort(lambda_gap_list[: self.max_num_speakers], descending=True) + max_key = arg_sorted_idx[0] + max_eig_gap = lambda_gap_list[max_key] / (torch.max(lambdas).item() + self.eps) + g_p = (p_neighbors / self.mat.shape[0]) / (max_eig_gap + self.eps) + return torch.stack([g_p, est_num_of_spk]) + + def getPvalueList(self) -> torch.Tensor: + """ + Generates a p-value (p_neighbour) list for searching. p_value_list must include 2 (min_p_value) + since at least one neighboring segment should be selected other than itself. + + If fixed_thres value is specified, then only one p-value is specified. + If fixed_thres is not provided, multiple p-values are searched. + If sparse_search is True: + - Limit the number of p-values to be searched to sparse_search_volume. + - N should be at least 2 to include a number greater than 1. + If sparse_search is False: + - Scan all the p_values from 1 to max_N + - If sparse_search is False, NMESC analysis could take more time compared to sparse_search = True. + + Returns: + p_value_list (Tensor): + Tensor containing the p_values to be searched. + """ + if self.fixed_thres is not None and self.fixed_thres > 0.0: + self.max_N = torch.max( + torch.floor(torch.tensor(self.mat.shape[0] * self.fixed_thres)).type(torch.int), self.min_p_value + ) + p_value_list = self.max_N.unsqueeze(0).int() + else: + self.max_N = torch.max( + torch.floor(torch.tensor(self.mat.shape[0] * self.max_rp_threshold)).type(torch.int), self.min_p_value + ) + if self.sparse_search: + search_volume = torch.min(self.max_N, torch.tensor(self.sparse_search_volume).type(torch.int)) + # search at least two values + N = torch.max(search_volume, torch.tensor(2)) + # avoid repeating values by limiting the step size + steps = min(self.max_N, N) + p_value_list = torch.linspace(start=1, end=self.max_N, steps=steps).type(torch.int) + else: + p_value_list = torch.arange(1, self.max_N + 1) + if p_value_list.shape[0] == 0: + raise ValueError("p_value_list should not be empty.") + return p_value_list + + +class SpeakerClustering(torch.nn.Module): + def __init__( + self, + min_samples_for_nmesc: int = 6, + nme_mat_size: int = 512, + sparse_search: bool = True, + maj_vote_spk_count: bool = False, + parallelism: bool = False, + cuda: bool = False, + ): + """ + Clustering method for speaker diarization based on cosine similarity. + NME-SC part is converted to torch.tensor based operations in NeMo 1.9. + + Args: + min_samples_for_nmesc (int): + The minimum number of samples required for NME clustering. This avoids + zero p_neighbour_lists. If the input has fewer segments than min_samples, + it is directed to the enhanced speaker counting mode. + nme_mat_size (int): + The targeted matrix size for NME analysis. + sparse_search (bool): + Toggle sparse search mode. If True, limit the size of p_value_list to sparse_search_volume. + maj_vote_spk_count (bool): + If True, take a majority vote on all p-values in the given range to estimate the number of speakers. + The majority voting may contribute to surpress overcounting of the speakers and improve speaker + counting accuracy. + parallelism (bool): + Use dynamic parallelism feature in torch.jit compiler to accelerate the p-value search. + cuda (bool): + Boolean variable for toggling cuda availability. + """ + super().__init__() + self.min_samples_for_nmesc: int = min_samples_for_nmesc + self.nme_mat_size: int = nme_mat_size + self.sparse_search: bool = sparse_search + self.parallelism: bool = parallelism + self.cuda: bool = cuda + self.maj_vote_spk_count: bool = maj_vote_spk_count + self.embeddings_in_scales: List[torch.Tensor] = [torch.Tensor(0)] + self.timestamps_in_scales: List[torch.Tensor] = [torch.Tensor(0)] + self.device = torch.device("cuda") if self.cuda else torch.device("cpu") + + def forward_unit_infer( + self, + mat: torch.Tensor, + oracle_num_speakers: int = -1, + max_num_speakers: int = 8, + max_rp_threshold: float = 0.15, + sparse_search_volume: int = 30, + est_num_of_spk_enhanced: torch.Tensor = torch.tensor(-1), + fixed_thres: float = -1.0, + kmeans_random_trials: int = 1, + ) -> torch.LongTensor: + """ + This function takes a cosine similarity matrix `mat` and returns the speaker labels for the segments + in the given input embeddings. + + Args: + mat (Tensor): + Cosine similarity matrix (affinity matrix) calculated from the provided speaker embeddings. + oracle_num_speakers (int): + The number of speakers in a session, as specified by the reference transcript. + Can be used as `chunk_cluster_count` in long-form clustering mode. + max_num_speakers (int): + The upper bound for the number of speakers in each session. + max_rp_threshold (float): + Limits the range of parameter search. + The clustering performance can vary based on this range. + The default value is 0.15. + sparse_search_volume (int): + The number of p_values considered during NME analysis. + The default is 30. Lower values speed up the NME-analysis but might lead to poorer parameter estimations. Values below 20 are not recommended. + est_num_of_spk_enhanced (int): + The number of speakers estimated from enhanced speaker counting. + If the value is -1, the enhanced speaker counting is skipped. + fixed_thres (float): + If a `fixed_thres` value is provided, the NME-analysis process will be skipped. + This value should be optimized on a development set for best results. + By default, it is set to -1.0, and the function performs NME-analysis to estimate the threshold. + kmeans_random_trials (int): + The number of random trials for initializing k-means clustering. More trials can result in more stable clustering. The default is 1. + + Returns: + Y (LongTensor): + Speaker labels (clustering output) in integer format for the segments in the given input embeddings. + """ + nmesc = NMESC( + mat, + max_num_speakers=max_num_speakers, + max_rp_threshold=max_rp_threshold, + sparse_search=self.sparse_search, + sparse_search_volume=sparse_search_volume, + fixed_thres=fixed_thres, + nme_mat_size=self.nme_mat_size, + maj_vote_spk_count=self.maj_vote_spk_count, + parallelism=self.parallelism, + cuda=self.cuda, + device=self.device, + ) + # If there are less than `min_samples_for_nmesc` segments, est_num_of_spk is 1. + if mat.shape[0] > self.min_samples_for_nmesc: + est_num_of_spk, p_hat_value = nmesc.forward() + affinity_mat = getAffinityGraphMat(mat, p_hat_value) + else: + nmesc.fixed_thres = max_rp_threshold + est_num_of_spk, p_hat_value = nmesc.forward() + affinity_mat = mat + + # `n_clusters` is number of speakers estimated from spectral clustering. + if oracle_num_speakers > 0: + n_clusters = int(oracle_num_speakers) + elif est_num_of_spk_enhanced > 0: + n_clusters = int(est_num_of_spk_enhanced.item()) + else: + n_clusters = int(est_num_of_spk.item()) + + spectral_model = SpectralClustering( + n_clusters=n_clusters, n_random_trials=kmeans_random_trials, cuda=self.cuda, device=self.device + ) + Y = spectral_model.forward(affinity_mat) + return Y + + def forward(self, param_dict: Dict[str, torch.Tensor]) -> torch.LongTensor: + """ + A function wrapper designed for inference in exported script format. + + Note: + Dict is used to allow easy inference of the exported jit model in Triton server using easy to understand + naming convention. + See https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#special-conventions-for-pytorch-backend + + Args: + param_dict (dict): + Dictionary containing the arguments for speaker clustering. + See `forward_infer` function for the argument information. + + Returns: + (LongTensor): Speaker labels for the segments in the given input embeddings. + """ + embeddings_in_scales = param_dict['embeddings'] + timestamps_in_scales = param_dict['timestamps'] + multiscale_segment_counts = param_dict['multiscale_segment_counts'] + multiscale_weights = param_dict['multiscale_weights'] + oracle_num_speakers = int(param_dict['oracle_num_speakers'].item()) + max_num_speakers = int(param_dict['max_num_speakers'].item()) + enhanced_count_thres = int(param_dict['enhanced_count_thres'].item()) + sparse_search_volume = int(param_dict['sparse_search_volume'].item()) + max_rp_threshold = float(param_dict['max_rp_threshold'].item()) + fixed_thres = float(param_dict['fixed_thres'].item()) + return self.forward_infer( + embeddings_in_scales=embeddings_in_scales, + timestamps_in_scales=timestamps_in_scales, + multiscale_segment_counts=multiscale_segment_counts, + multiscale_weights=multiscale_weights, + oracle_num_speakers=oracle_num_speakers, + max_rp_threshold=max_rp_threshold, + max_num_speakers=max_num_speakers, + enhanced_count_thres=enhanced_count_thres, + sparse_search_volume=sparse_search_volume, + fixed_thres=fixed_thres, + ) + + def forward_infer( + self, + embeddings_in_scales: torch.Tensor, + timestamps_in_scales: torch.Tensor, + multiscale_segment_counts: torch.LongTensor, + multiscale_weights: torch.Tensor, + oracle_num_speakers: int = -1, + max_num_speakers: int = 8, + max_rp_threshold: float = 0.15, + enhanced_count_thres: int = 40, + sparse_search_volume: int = 30, + fixed_thres: float = -1.0, + kmeans_random_trials: int = 1, + ) -> torch.LongTensor: + """ + Calculate the affinity matrix using timestamps and speaker embeddings, run NME analysis to estimate the best + p-value, and perform spectral clustering based on the estimated p-value and the calculated affinity matrix. + + Caution: + For compatibility with libtorch, python boolean `False` has been replaced with `torch.LongTensor(-1)`. + + Args: + embeddings_in_scales (Tensor): + List containing concatenated Torch tensor embeddings across multiple scales. + The length of the list is equal to the number of scales. + Each tensor has dimensions of (Number of base segments) x (Embedding Dimension). + timestamps_in_scales (Tensor): + List containing concatenated Torch tensor timestamps across multiple scales. + The length of the list is equal to the number of scales. + Each tensor has dimensions of (Total number of segments across all scales) x 2. + Example: + >>> timestamps_in_scales[0] = \ + torch.Tensor([[0.4, 1.4], [0.9, 1.9], [1.4, 2.4], ... [121.2, 122.2]]) + multiscale_segment_counts (LongTensor): + A Torch tensor containing the number of segments for each scale. + The tensor has dimensions of (Number of scales). + Example: + >>> multiscale_segment_counts = torch.LongTensor([31, 52, 84, 105, 120]) + multiscale_weights (Tensor): + Multi-scale weights used when merging affinity scores. + Example: + >>> multiscale_weights = torch.tensor([1.4, 1.3, 1.2, 1.1, 1.0]) + oracle_num_speakers (int): + The number of speakers in a session as given by the reference transcript. + max_num_speakers (int): + The upper bound for the number of speakers in each session. + max_rp_threshold (float): + Limits the range of parameter search. + The clustering performance can vary based on this range. + The default value is 0.15. + enhanced_count_thres (int): + For shorter audio recordings, the clustering algorithm might not accumulate enough speaker profiles for each cluster. + Thus, the function `getEnhancedSpeakerCount` uses anchor embeddings (dummy representations) to mitigate the effects of cluster sparsity. + A value of 80 is recommended for `enhanced_count_thres`. + sparse_search_volume (int): + The number of p_values considered during NME analysis. + The default is 30. Lower values speed up the NME-analysis but might lead to poorer parameter estimations. Values below 20 are not recommended. + fixed_thres (float): + If a `fixed_thres` value is provided, the NME-analysis process will be skipped. + This value should be optimized on a development set for best results. + By default, it is set to -1.0, and the function performs NME-analysis to estimate the threshold. + kmeans_random_trials (int): + The number of random trials for initializing k-means clustering. More trials can result in more stable clustering. The default is 1. + + Returns: + (LongTensor): Speaker labels for the segments in the provided input embeddings. + """ + self.embeddings_in_scales, self.timestamps_in_scales = split_input_data( + embeddings_in_scales, timestamps_in_scales, multiscale_segment_counts + ) + # Last slot is the base scale embeddings + emb = self.embeddings_in_scales[-1] + + # Cases for extreamly short sessions + if emb.shape[0] == 1: + return torch.zeros((1,), dtype=torch.int64) + elif emb.shape[0] <= max(enhanced_count_thres, self.min_samples_for_nmesc) and oracle_num_speakers < 0: + est_num_of_spk_enhanced = getEnhancedSpeakerCount(emb=emb, cuda=self.cuda) + else: + est_num_of_spk_enhanced = torch.tensor(-1) + + if oracle_num_speakers > 0: + max_num_speakers = oracle_num_speakers + + mat = getMultiScaleCosAffinityMatrix( + multiscale_weights=multiscale_weights, + embeddings_in_scales=self.embeddings_in_scales, + timestamps_in_scales=self.timestamps_in_scales, + device=self.device, + ) + + return self.forward_unit_infer( + mat=mat, + oracle_num_speakers=oracle_num_speakers, + max_rp_threshold=max_rp_threshold, + max_num_speakers=max_num_speakers, + sparse_search_volume=sparse_search_volume, + est_num_of_spk_enhanced=est_num_of_spk_enhanced, + kmeans_random_trials=kmeans_random_trials, + fixed_thres=fixed_thres, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/online_clustering.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/online_clustering.py new file mode 100644 index 0000000..23ebe6c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/online_clustering.py @@ -0,0 +1,1195 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright (c) 2007-2020 The scikit-learn developers. + +# BSD 3-Clause License + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +# NME-SC clustering is based on the implementation from the paper +# https://arxiv.org/pdf/2003.02405.pdf and the implementation from +# https://github.com/tango4j/Auto-Tuning-Spectral-Clustering. + +from typing import List, Tuple +import torch + +from nemo.collections.asr.parts.utils.offline_clustering import ( + NMESC, + SpeakerClustering, + SpectralClustering, + get_scale_interpolated_embs, + getAffinityGraphMat, + getCosAffinityMatrix, + split_input_data, +) +from nemo.collections.asr.parts.utils.optimization_utils import linear_sum_assignment + + +def get_lsa_speaker_mapping( + U_set: torch.Tensor, cmm_P: torch.Tensor, cmm_Q: torch.Tensor, PandQ: torch.Tensor +) -> torch.Tensor: + """ + Find a mapping that minimizes the matching cost between the label P and Q. + One-hot encodding is employed to represent sequence and calculate the cost. + + Args: + U_set (list): + Whole set of the estimated speakers + cmm_P (Tensor): + Length-matched old sequence + cmm_Q (Tensor): + Length-matched new sequence + PandQ (Tensor): + Tensor containing the indices of the speakers that are in both old and new sequences + + Returns: + mapping_array (np.array): + Mapped labels that minimizes the cost + """ + all_spks_labels = [[x] for x in range(len(U_set))] + common_inds: List[int] = [int(x.item()) for x in PandQ] + + # Create tensors for one-hot encoding + enc_P = torch.zeros((len(cmm_P), len(all_spks_labels))).to(cmm_P.device) + enc_Q = torch.zeros((len(cmm_Q), len(all_spks_labels))).to(cmm_Q.device) + + # Create one-hot encoding + enc_P[torch.arange(len(cmm_P)), cmm_P] = 1 + enc_Q[torch.arange(len(cmm_Q)), cmm_Q] = 1 + + # Cost matrix from one-hot encoding vectors + cost = -1 * torch.matmul(enc_P.T, enc_Q).T.to(PandQ.device) + _, col_ind = linear_sum_assignment(cost) + + # If number of are speakers in each vector is not the same + mapping_array = torch.arange(0, len(U_set)).to(PandQ.device) + for x in range(col_ind.shape[0]): + if x not in common_inds: + mapping_array[x] = x + else: + mapping_array[x] = col_ind[x] + return mapping_array + + +def get_minimal_indices(Y_new: torch.Tensor) -> torch.Tensor: + """ + Force the unique indices of the labels to use the lowest numbers. + + Example: + >>> Y_new = [3, 3, 3, 4, 4, 5] + >>> get_minimal_indices(Y_new) + Return: + [0, 0, 0, 1, 1, 2] + + Args: + Y_new (Tensor): + Tensor containing cluster labels + + Returns: + (Tensor): Newly mapped cluster labels that has minimized indicies + """ + device = Y_new.device + Y_new_enlisted = torch.unique(Y_new).sort()[0].to(torch.long).to(device) + sequence = torch.arange(torch.max(Y_new_enlisted) + 1).to(device) + sequence[Y_new_enlisted] = torch.arange(len(Y_new_enlisted)).to(device) + return sequence[Y_new] + + +@torch.jit.script +def stitch_cluster_labels(Y_old: torch.Tensor, Y_new: torch.Tensor) -> torch.Tensor: + """ + Run Hungarian (linear sum assignment) algorithm to find the best permutation mapping between + the cumulated labels in history and the new clustering output labels. + + Args: + Y_old (Tensor): + Cumulated diarization labels. This will be concatenated with history embedding speaker label + then compared with the predicted label Y_new. + Y_new (Tensor): + Contains predicted labels for reduced history embeddings concatenated with the predicted label. + Permutation is not matched yet. + + Returns: + mapping_array[Y] (Tensor): + An output numpy array where the input Y_new is mapped with mapping_array. + """ + Y_new = get_minimal_indices(Y_new) + if len(Y_old) == 0: + matched_output = Y_new + else: + P_raw, Q_raw = Y_old.to(Y_new.device), Y_new + U_set = torch.unique(torch.cat([P_raw, Q_raw])) + PQ = torch.cat([P_raw, Q_raw]) + a_cat_b, counts = torch.unique(PQ, return_counts=True) + # Get a union set of old P and new Q labels + PandQ = a_cat_b[torch.where(counts.gt(1))[0]] + min_len = min(P_raw.shape[0], Q_raw.shape[0]) + P, Q = P_raw[:min_len], Q_raw[:min_len] + + if len(U_set) == 1: + # When two speaker vectors are exactly the same: No need to encode. + mapping_array = torch.tensor([0, 0]).to(Y_new.device) + else: + # Run Hungarian algorithm if there are more than one speaker in universal set U. + mapping_array = get_lsa_speaker_mapping(U_set=U_set, cmm_P=P, cmm_Q=Q, PandQ=PandQ) + matched_output = mapping_array[Y_new] + matched_output = get_minimal_indices(matched_output) + return matched_output + + +def calculate_removable_counts(removable_counts_mat: torch.Tensor, remain_count: int, num_clus: int) -> torch.Tensor: + """ + Calculate removable counts based on the arguments and calculate how many counts should be + removed from the each cluster. This function has `O(N)` (N = num_clus) time complexity to + return the desired `removable_counts_mat`. + + Example: + + The original input to `get_merge_quantity` function: + >>> pre_clus_labels = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2] + >>> num_to_be_removed = 3 + >>> min_count_per_cluster = 2 + + Histogram: (`min_count_per_cluster`=2 is removed) + 0 |***** + 1 |*** + 2 |* + + Inputs: + >>> removable_counts_mat = [5, 3, 1] + >>> remain_count = 6 + >>> num_clus = 3 + + Interim results: + >>> diff_counts + [1, 2, 2] + >>> gradual_counts + [3, 4, 2] + >>> cumsum_counts + [3, 7, 9] + + Return: + >>> removable_counts_mat + [2, 1, 0] + + Args: + removable_counts_mat (Tensor): + Tensor containing how many vectors could be removed from each cluster + remain_count (int): + Integer value that indicates the number of vectors removed from the total set + num_clus (int): + Number of clusters in the given label sequence (cardinality of a label set) + + Returns: + removable_counts_mat (Tensor): + Tensor containing the number of vectors should be removed from each cluster + """ + device = removable_counts_mat.device + zero_padded_counts = torch.cat( + [torch.tensor([0]).to(device), removable_counts_mat.sort()[0], torch.tensor([0]).to(device)], dim=0 + ) + removable_count_args = removable_counts_mat.sort(descending=True)[1] + + # Calculate the size difference between clusters + diff_counts = (zero_padded_counts[1:] - zero_padded_counts[:-1])[:num_clus] + gradual_counts = torch.arange(num_clus, 0, -1).to(device) * diff_counts + cumsum_counts = torch.cumsum(gradual_counts, dim=0) + remain_count_rem = remain_count + + # Find how many remaining counts we can use + ind: int = 0 + for ind, num in enumerate(cumsum_counts): + if remain_count < num: + break + + # Subtract the common values step by step + if ind > 0: + for knd in range(ind): + removable_counts_mat[removable_count_args[: num_clus - knd]] -= diff_counts[knd] + remain_count_rem -= int(diff_counts[knd].item()) * (num_clus - knd) + assert remain_count >= 0, "remain_count should never be negative." + + # Add remaining values + num_labels = remain_count_rem // (num_clus - ind) + rem_labels = remain_count_rem % (num_clus - ind) + removable_counts_mat[removable_count_args[: (num_clus - ind)]] -= num_labels + removable_counts_mat[removable_count_args[:rem_labels]] -= 1 + return removable_counts_mat.int() + + +def get_merge_quantity( + num_to_be_removed: int, pre_clus_labels: torch.Tensor, min_count_per_cluster: int, +) -> torch.Tensor: + """ + Determine which embeddings we need to reduce or merge in history buffer. + We want to merge or remove the embedding in the bigger cluster first. + At the same time, we keep the minimum number of embedding per cluster + with the variable named min_count_per_cluster. + + Constraint: + - Each cluster should keep the number of vectors over `min_count_per_cluster`. + - In total, `num_to_be_removed` of vectors should be removed from the total buffer. + - While merging embeddings, minimize the gap between quantities between clusters. + + Example: + >>> num_to_be_removed = 3 + >>> pre_clus_labels = [0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2] + >>> min_count_per_cluster = 2 + >>> get_merge_quantity(num_to_be_removed, pre_clus_labels, min_count_per_cluster) + Return: + torch.tensor([2, 1, 0]) + >>> # Sum should be equal to `num_to_be_removed` which is 3 + + Args: + num_to_be_removed: (int) + the quantity of the newly obtained embedding from the new stream of input. + pre_clus_labels: (Tensor) + the speaker labels of (the history_embedding_buffer_emb) + (the new embeddings to be added) + min_count_per_cluster: (int) + Minimum vector quantity for each cluster + + Returns: + removable_counts_mat: (Tensor) + Tensor containing the number of vectors should be removed from each cluster + """ + if num_to_be_removed > pre_clus_labels.shape[0] - 1: + raise ValueError(f"num_to_be_removed: {num_to_be_removed} should be less than pre_clus_labels length - 1") + remain_count = pre_clus_labels.shape[0] - num_to_be_removed + spk_freq_count = torch.bincount(pre_clus_labels) + num_clus = len(torch.unique(pre_clus_labels)) + if remain_count < min_count_per_cluster * num_clus: + raise ValueError(f"The remaining embedding vectors should be more than { min_count_per_cluster * num_clus }") + + # Minimum vector counts should be excluded from the removable amount + min_seg_count = torch.tensor([min_count_per_cluster] * len(spk_freq_count)).to(pre_clus_labels.device) + min_seg_count_mat = torch.stack((min_seg_count, spk_freq_count)).min(0)[0] + + # Exclude minimum quantities from the removable count matrix + remain_count -= int(torch.sum(min_seg_count_mat)) + removable_counts_mat = spk_freq_count - min_seg_count_mat + + # Calculate removable counts from `remain_count` variable + removable_counts_mat = calculate_removable_counts(removable_counts_mat, remain_count, num_clus) + if int(removable_counts_mat.sum()) != num_to_be_removed: + raise ValueError("Sum of `removable_counts_mat` is not equal to `num_to_be_removed` variable.") + if not torch.all(removable_counts_mat >= 0) or not torch.all(spk_freq_count - min_seg_count_mat >= 0): + raise ValueError( + f"Every value in `removable_counts_mat` should be always non-negative value but got {removable_counts_mat}" + ) + return removable_counts_mat + + +def merge_vectors( + selected_inds: torch.Tensor, emb_ndx: torch.Tensor, pre_cluster_labels: torch.Tensor +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Merge feature (embedding) vectors estimated to be the same cluster label. + + Args: + selected_inds (Tensor): + Selected indices for merging + emb_ndx (Tensor): + Feature (embedding) vectors + Dimension: (original vector counts) x (feature dimension) + pre_cluster_labels (Tensor): + Original cluster labels before merging + + Returns: + merged_vecs (Tensor): + Merged feature vectors that are concatenated + Dimension: (merged vector counts) x (feature dimension) + merged_clus_labels (Tensor): + Cluster labels for the merged feature vectors + Dimension: (merged vector counts) + """ + if emb_ndx.shape[0] != pre_cluster_labels.shape[0]: + raise ValueError("pre_cluster_labels and emb_ndx have mismatch in dimension") + avg_emb = torch.mean(emb_ndx[selected_inds, :], dim=0) + merged_clus_labels = pre_cluster_labels[selected_inds] + selected_inds_list: List[int] = selected_inds.tolist() + bypass_inds_list: List[int] = [] + for k in range(emb_ndx.shape[0]): + if k not in selected_inds_list: + bypass_inds_list.append(k) + bypass_inds = torch.tensor(bypass_inds_list) + selected_inds = torch.tensor(selected_inds_list) + if bypass_inds.shape[0] == 0: + merged_vecs = avg_emb.unsqueeze(0) + merged_clus_labels = merged_clus_labels.unsqueeze(0) + else: + merged_vecs = torch.vstack((emb_ndx[bypass_inds], avg_emb)) + merged_clus_labels = torch.hstack((pre_cluster_labels[bypass_inds], merged_clus_labels[0])) + return merged_vecs, merged_clus_labels + + +def get_closest_embeddings(affinity_mat: torch.Tensor, n_closest: int) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Get the indices of the embedding vectors we want to merge. + + Example: + >>> n_closest = 2 + >>> affinity_mat = [[1.0, 0.2, 0.8], + [0.2, 1.0, 0.4], + [0.8, 0.4, 1.0]] + >>> affinity_mat.sum(0) + [2.0, 1.6, 2.2] + + # The closest two embedding vectors are at index 0 and 2. + + Args: + affinity_mat: (Tensor) + Symmetric affinity matrix of the given embedding vector set. + n_closest (int): + The amount of vector counts that are expected to be removed from the set + Example: + Input: 10 vectors in a set + n_closest = 5 + (5+1) vectors are merged into 1 vector + Output: 5 vectors in a set + + Returns: + idx_aff_sum (torch.Tensor): + Indices of the closest `n_closest` embedding vectors + rest_inds (torch.Tensor): + Indices of the complementary set of the indices in `idx_aff_sum` + """ + comb_limit = int(affinity_mat.shape[0] - 1) + if n_closest > comb_limit: + raise ValueError(f"Got n_closest of {n_closest}: {n_closest} is bigger than comb_limit {comb_limit}") + + # Take summed values over one axis + sum_cmat = affinity_mat.sum(0) + + # `n_closest + 1` will become 1 embedding vector after merging + idx_aff_sum = torch.argsort(sum_cmat, descending=True)[: (n_closest + 1)] + rest_inds = torch.argsort(sum_cmat, descending=True)[(n_closest + 1) :] + return idx_aff_sum, rest_inds + + +def run_reducer( + pre_embs: torch.Tensor, target_spk_idx: int, merge_quantity: int, pre_clus_labels: torch.Tensor, +): + """ + Reduce the number of embedding vectors by merging the closest embedding vectors. + - This merging algorithm is based on the assumption that the closest embeddings + are the most redundant embedding vectors. + - The closest embedding vectors are chosen by selecting the highest top-N sum of + each column in a given affinity matrix. + - If merge_quantity is N, we choose (N+1) vectors into 1 embedding vector. + Thus, we reduce N embeddings in the original embedding vector set. + + Example: + >>> merge_quantity = 1 # We merge 1+1 = 2 embedding vectors + >>> affinity_mat = [[1.0, 0.2, 0.8], + [0.2, 1.0, 0.4], + [0.8, 0.4, 1.0]] + >>> affinity_mat.sum(0) + [2.0, 1.6, 2.2] + + The first and the third embedding vectors are merged into one embedding vector. + >>> index_mapping # (bypassed indices, merged indices) + ([1], [0, 2]) + + Args: + pre_embs (Tensor): + Potential Embedding vectors to be merged + affinity_mat (Tensor): + The affinity matrix of the `pre_embs` + target_spk_idx (int): + The targeted speaker index for merging + merge_quantity (int): + The count of embeddings to be reduced + pre_clus_labels (list) + The original cluster (speaker) index + + Returns: + merged_embs (torch.Tensor): + The merged embedding vectors. + merged_clus_labels (torch.Tensor): + The cluster (speaker) indices for the merged embedding vectors. + index_mapping (Tuple[torch.Tensor, torch.Tensor]): + A tuple containing the indices of the original embeddings that were not merged (`bypassed indices`) + and the indices of the new merged embeddings (`merged indices`). + """ + if pre_embs.shape[0] != pre_clus_labels.shape[0]: + raise ValueError("Dimension mismatch between `pre_embs` and `pre_clus_labels`.") + + target_emb_index = torch.where(pre_clus_labels == target_spk_idx)[0] + org_size = target_emb_index.shape[0] + if merge_quantity > 0: + if merge_quantity > (target_emb_index.shape[0] - 1): + raise ValueError( + f"merge_quantity {merge_quantity} should not be larger than target_emb_index length: {target_emb_index.shape[0]-1}" + ) + total_affinity_mat = getCosAffinityMatrix(pre_embs) + + # Get the lower triangle of the affinity_mat array + affinity_mat = total_affinity_mat[:, target_emb_index][target_emb_index, :] + if affinity_mat.shape[0] != target_emb_index.shape[0]: + raise ValueError( + "Dimension mismatch between targeted speaker affinity `affinity_mat` and targeted speaker index `target_emb_index`." + ) + # Get the indices of the closest embedding vectors + selected_inds, rest_inds = get_closest_embeddings(affinity_mat, merge_quantity) + spk_cluster_labels, selected_embs = pre_clus_labels[target_emb_index], pre_embs[target_emb_index] + + # Note that we need to return the indices of speaker-specific indices from `target_emb_index`. + index_mapping = (target_emb_index[rest_inds.sort()[0]], target_emb_index[selected_inds]) + + # Merge the embeddings targeted by the 2-dim indices `index_2d` + merged_embs, merged_clus_labels = merge_vectors(selected_inds, selected_embs, spk_cluster_labels) + + if (org_size - merge_quantity) != merged_embs.shape[0]: + raise ValueError( + f"Reducer output {merged_embs.shape[0]} is not matched to the target quantity {org_size - merge_quantity}." + ) + + else: + merged_embs = pre_embs[target_emb_index] + merged_clus_labels = pre_clus_labels[target_emb_index] + index_mapping = (target_emb_index, torch.arange(0)) + return merged_embs, merged_clus_labels, index_mapping + + +def get_first_arg_index(mat: torch.Tensor, label: int) -> int: + """ + Get the index of the first element are specified by `index` variable. + + Args: + mat (Tensor): + Source matrix filled with indices + label (int): + Label which we want to find the first occuring index + + Returns: + (int): The first index of the given label + """ + return int(torch.where(mat == label)[0][0]) + + +class OnlineSpeakerClustering(torch.nn.Module): + """ + Online clustering method for speaker diarization based on cosine similarity. + + Regular Clustering Attributes: + + max_num_speakers (int): + The upper bound for the number of speakers in each session + max_rp_threshold (float): + Limits the range of parameter search. + Clustering performance can vary depending on this range. + Default is 0.15. + enhanced_count_thres (int): + For the short audio recordings, clustering algorithm cannot + accumulate enough amount of speaker profile for each cluster. + Thus, function `getEnhancedSpeakerCount` employs anchor embeddings + (dummy representations) to mitigate the effect of cluster sparsity. + enhanced_count_thres = 40 is recommended. + sparse_search_volume (int): + Number of p_values we search during NME analysis. + Default is 30. The lower the value, the faster NME-analysis becomes. + Lower than 20 might cause a poor parameter estimation. + fixed_thres (float): + A fixed threshold for finding p-closest neighbors in affinity matrix for clustering. + If fixed_thres value is provided, NME-analysis process will be skipped. + This value should be optimized on a development set to obtain a quality result. + Default is None and performs NME-analysis to estimate the threshold. + min_samples_for_nmesc (int): + The minimum number of samples required for NME clustering. This avoids + zero p_neighbour_lists. If the input has fewer segments than min_samples, + it is directed to the enhanced speaker counting mode. + sparse_search (bool): + Toggle sparse search mode. If True, limit the size of p_value_list to sparse_search_volume. + cuda (bool): + Use cuda for Eigen decomposition if cuda=True. + + Additional Online Processing Attributes: + + history_buffer_size (int): + - This is a buffer where diarization history is saved in the form of averaged speaker embedding vector. + - The values in [50, 200] range is recommended while the system requires bigger buffer size for + sessions with larger number of speakers. + current_buffer_size (int): + - This is a buffer which process the most recent speaker embedding vector inputs. + current-buffer is first-in-first-out (FIFO) queue where the embeddings accepted earlier + get to merged and saved to history buffer. + - In general, [50, 200] range is recommended and the performance can be sensitive on this buffer size. + min_spk_counting_buffer_size (int): + Integer number for speaker counting buffer. Number of speakers are estimated through a small buffer + and the number is obtained by taking majority vote. + min_frame_per_spk (int): + Below this number, the system considers the whole input segments as a single speaker. + p_update_freq (int): + Frequency (interval) of updating p_value for NMESC algorithm. + p_value_skip_frame_thres (int): + After `frame_index` passes this number, `p_value` estimation is skipped for inference speed + p_value_queue_size (int): + `p_value` buffer for major voting + use_temporal_label_major_vote (bool): + Boolean that determines whether to use temporal majorvoting for the final speaker labels + temporal_label_major_vote_buffer_size (int): + Buffer size for major-voting the + num_spk_stat (list): + List of number of speakers for major voting. Number of speakers are estimated through + majority voting of `self.num_spk_stat` list. + p_value_hist (list): + List of p_values for major voting. + To save the computation time, p_value is estimated every `p_update_freq` frames and + saved to `self.p_value_hist`. + + Attributes for counters and buffers in streaming system: + + is_online (bool): + - If self.is_online is False: + FIFO queue does not push out any speaker embedding vector + - If self.is_online is True: + FIFO queue starts push out speaker embedding vectors and saving them into + history buffer. + max_embed_count (int): + The maximum number of segments the streaming system has ever seen. + This value keeps increasing as the system processes more and more segments. + memory_margin (int): + The margin that is added to keep the segmentation data in the streaming system + minimum_segments_per_buffer (int): + Maximum number of embedding vectors kept in history buffer per speaker. + Example: + history_buffer_size (history_n) = 100 + max_num_speakers = 4 + minimum_segments_per_buffer = 25 + history_buffer_seg_end (int): + Index that indicates the boundary between history embedding sets and current processing buffer + when history embedding vectors and current input embedding vectors are concatenated into a + single matrix. + + Attributes for history buffer: + + history_embedding_buffer_emb (Tensor) + Tensor containing speaker embedding vectors for saving the history of the previous + speaker profile in the given audio session + history_embedding_buffer_label (Tensor) + Speaker label (cluster label) for embedding vectors saved in the history buffer + Y_fullhist (Tensor) + Tensor containing the speaker label hypothesis from start to current frame + """ + + def __init__( + self, + max_num_speakers: int = 8, + max_rp_threshold: float = 0.15, + enhanced_count_thres: float = 40, + fixed_thres: float = -1.0, + sparse_search_volume: int = 10, + history_buffer_size: int = 150, + current_buffer_size: int = 150, + min_spk_counting_buffer_size: int = 3, + min_frame_per_spk: int = 15, + p_update_freq: int = 5, + p_value_skip_frame_thres: int = 50, + p_value_queue_size: int = 3, + use_temporal_label_major_vote: bool = False, + temporal_label_major_vote_buffer_size: int = 11, + cuda: bool = False, + ): + super().__init__() + self.max_num_speakers = max_num_speakers + self.max_rp_threshold = max_rp_threshold + self.enhanced_count_thres = enhanced_count_thres + self.sparse_search_volume = sparse_search_volume + self.fixed_thres = fixed_thres + self.history_n = history_buffer_size + self.current_n = current_buffer_size + self.min_spk_counting_buffer_size = min_spk_counting_buffer_size + self.min_frame_per_spk = min_frame_per_spk + self.p_update_freq = p_update_freq + self.p_value_skip_frame_thres = p_value_skip_frame_thres + self.p_value_queue_size = p_value_queue_size + self.use_temporal_label_major_vote = use_temporal_label_major_vote + self.temporal_label_major_vote_buffer_size = temporal_label_major_vote_buffer_size + self.cuda = cuda + self.num_spk_stat: List[torch.Tensor] = [torch.tensor(1)] + self.p_value_hist: List[torch.Tensor] = [torch.tensor(2)] + + # Initialize the counters and buffers in streaming system + self.is_online = False + self.max_embed_count = 0 + self.memory_margin = 0 + self.minimum_segments_per_buffer = int(self.history_n / self.max_num_speakers) + self.history_buffer_seg_end = 0 + + # Initialize the streaming buffer tensors + self.history_embedding_buffer_emb = torch.tensor([]) + self.history_embedding_buffer_label = torch.tensor([]) + self.Y_fullhist = torch.tensor([]) + + def onlineNMEanalysis(self, mat_in: torch.Tensor, frame_index: int) -> Tuple[int, int]: + """ + To save the running time, the p-value is only estimated in the beginning of the session. + After switching to online mode, the system uses the most common estimated p-value. + Estimating p-value requires a plenty of computational resource. The less frequent estimation of + p-value can speed up the clustering algorithm by a huge margin. + + Args: + mat_in (Tensor): + Tensor containing the affinity matrix for the current segments + frame_index (int): + Unique index for each segment and embedding vector + + Returns: + est_num_of_spk: (int) + The estimated number of speakers. + p_hat_value: (int) + The estimated p-value from NMESC method. + """ + nmesc = NMESC( + mat_in, + max_num_speakers=self.max_num_speakers, + max_rp_threshold=self.max_rp_threshold, + sparse_search=True, + maj_vote_spk_count=False, + sparse_search_volume=self.sparse_search_volume, + fixed_thres=self.fixed_thres, + nme_mat_size=256, + parallelism=False, + device=mat_in.device, + cuda=self.cuda, + ) + if len(self.p_value_hist) == 0 or ( + frame_index < self.p_value_skip_frame_thres and frame_index % self.p_update_freq == 0 + ): + est_num_of_spk, p_hat_value = nmesc.forward() + self.p_value_hist.append(p_hat_value) + if len(self.p_value_hist) > self.p_value_queue_size: + self.p_value_hist.pop(0) + p_hat_int_list: List[int] = [int(p) for p in self.p_value_hist] + p_hat_value = torch.mode(torch.tensor(p_hat_int_list))[0].item() + output = nmesc.getEigRatio(p_hat_value) + g_p, est_num_of_spk = output[0], output[1].int() + return est_num_of_spk, p_hat_value + + def speaker_counter_buffer(self, est_num_of_spk: int) -> torch.Tensor: + """ + Use a queue to avoid unstable speaker counting results. + + Args: + est_num_of_spk (int): + Estimated number of speakers + + Returns: + est_num_of_spk (torch.Tensor): + Estimated number of speakers from the speaker counting buffer. + """ + est_num_of_spk = torch.tensor(est_num_of_spk) + self.num_spk_stat.append(est_num_of_spk) + if len(self.num_spk_stat) > self.min_spk_counting_buffer_size: + self.num_spk_stat.pop(0) + num_spk_stat_tensor = torch.tensor([int(s) for s in self.num_spk_stat]) + num_spks_bincount = torch.bincount(num_spk_stat_tensor) + est_num_of_spk = torch.argmax(num_spks_bincount) + return est_num_of_spk + + def limit_frames_per_speaker(self, frame_index: int, est_num_of_spk: int) -> int: + """ + Limit the estimated number of speakers in proportion to the number of speakers. + + Args: + frame_index (int): + Unique index for each segment and embedding vector + est_num_of_spk (int): + Estimated number of speakers + + Returns: + (int) Estimated number of speakers capped by `self.min_frame_per_spk` + """ + return min(est_num_of_spk, int(1 + frame_index // self.min_frame_per_spk)) + + def online_spk_num_estimation(self, mat_in: torch.Tensor, frame_index: int) -> Tuple[int, torch.Tensor]: + """ + Online version of speaker estimation involves speaker counting buffer and application of per-speaker + frame count limit. + + Args: + mat_in (Tensor): + Raw affinity matrix containing similarity values of each pair of segments + frame_index (int) + Unique frame index of online processing pipeline + + Returns: + est_num_of_spk (int): + Estimated number of speakers + affinity_mat (Tensor): + Affinity matrix after applying the affinity threshold with `p_hat_value` + """ + est_num_of_spk, p_hat_value = self.onlineNMEanalysis(mat_in, frame_index) + affinity_mat = getAffinityGraphMat(mat_in, p_hat_value) + raw_est_num_of_spk = self.speaker_counter_buffer(est_num_of_spk) + est_num_of_spk = self.limit_frames_per_speaker(frame_index, raw_est_num_of_spk.item()) + return est_num_of_spk, affinity_mat + + def prepare_embedding_update( + self, emb_in: torch.Tensor, segment_indexes_matrix: torch.Tensor + ) -> Tuple[bool, int, torch.Tensor, torch.Tensor]: + """ + This function performs the following tasks: + 1. Decide whether to extract more embeddings or not (by setting `is_update`) + (Only if we need update): + 2. Calculate how many embeddings should be updated (set `new_emb_n` variable) + 3. Update history embedding vectors and save it to `pre_embs`. + + We only save the index and clustering label of each embedding. + + - Case-1: The very first step + This else statement is for the very first diarization loop. + This is the very first reduction frame. + + - Case-2: Number of embedding vectors is increased, therefore we need to update. + Since there are new embeddings, we push the same amount (new_emb_n) + of old embeddings to the history buffer. + We should also update self.history_buffer_seg_end which is a pointer. + update to history emb: emb_in[emb_idx_stt:emb_idx_end] + update to history label: self.Y_fullhist[label_stt:_end] + + - Case-3: Number of embedding vectors is decreased + If the number of embeddings is decreased compared to the last trial, + then skip embedding merging. + + Variables: + hist_curr_boundary (int): + The current boundary of between history buffer and current buffer. + This is the new history-current buffer boundary while self.history_buffer_seg_end is the old one. + Thus, the new set of embedding vectors are collected from + `label_stt=self.hist_buffer_seg_end` to `label_end=hist_curr_boundary`. + total_segments_processed_count (int): + The number of segments that are processed so far in integer format. + + Args: + emb_in (Tensor): + Tensor containing embedding vectors + Dimensions: (number of embedding vectors) x (embedding dimension) + segment_indexes_matrix (Tensor): + Tensor containing unique segment (embedding vector) index + + Returns: + is_update (bool): + Boolean indicates whether to update speaker embedding vectors. + new_emb_n (int): + The amount of embedding vectors that are exceeding FIFO queue size. + new_emb_n is also an amount of embedding vectors that needs to be merged in history buffer. + pre_embs (Tensor): + Embedding vector matrix before merging. + The subset of `pre_embs` embedding vectors will be merged. + Dimensions: (number of embedding vectors) x (embedding dimension) + pre_clus_labels (Tensor): + A set of clustering labels for each embedding vector in `pre_embs`. + """ + total_segments_processed_count = int(segment_indexes_matrix[-1] + 1) + hist_curr_boundary = int(total_segments_processed_count - self.current_n) + new_emb_n: int = 0 + pre_embs: torch.Tensor = torch.empty(0) + pre_clus_labels: torch.Tensor = torch.empty(0) + is_update = True + + if total_segments_processed_count > self.max_embed_count: + # Case-1: The very first step + if len(self.history_embedding_buffer_emb) == 0: + new_emb_n = total_segments_processed_count - (self.current_n + self.history_n) + hist_curr_boundary_emb_idx = get_first_arg_index(segment_indexes_matrix, hist_curr_boundary) + pre_embs = emb_in[:hist_curr_boundary_emb_idx] + pre_clus_labels = self.Y_fullhist[:hist_curr_boundary] + + # Case-2: Number of embedding vectors is increased, need to update history and its label + else: + # Calculate the number of new embedding vectors: `new_emb_n` + label_stt, label_end = self.history_buffer_seg_end, hist_curr_boundary + new_emb_n = label_end - label_stt + # Add embedding vectors to `pre_embs` so that we can merge it with reducer function. + emb_idx_stt = int(get_first_arg_index(segment_indexes_matrix, label_stt)) + emb_idx_end = int(get_first_arg_index(segment_indexes_matrix, label_end)) + pre_embs = torch.vstack((self.history_embedding_buffer_emb, emb_in[emb_idx_stt:emb_idx_end])) + # Update labels for `pre_embs` + pre_clus_labels = torch.hstack( + (self.history_embedding_buffer_label, self.Y_fullhist[label_stt:label_end]) + ) + + if new_emb_n > self.current_n: + raise ValueError( + "new_emb_n should be less than or equal to current buffer size (self.current_n)." + f" Getting too many segments: {new_emb_n} for the given current buffer size {self.current_n}." + " Please either (1) increase buffer size or (2) use longer segment lengths to get less number of segments." + ) + elif new_emb_n <= 0: + raise ValueError("Segment counting error. `new_emb_n` should be a positve integer number.") + if pre_embs.shape[0] != pre_clus_labels.shape[0]: + raise ValueError( + "`pre_embs` and `pre_clus_labels` should have the same length, " + f"but got {pre_embs.shape[0]} and {pre_clus_labels.shape[0]} respectively." + ) + + # Case-3: Number of embedding vectors is not increased. + else: + # There will be no embedding update, so new_emb_n is 0, pre_embs and pre_clus_labels are empty. + is_update = False + + # Update the history buffer index for the next step + self.history_buffer_seg_end = hist_curr_boundary + self.max_embed_count = max(total_segments_processed_count, self.max_embed_count) + return is_update, new_emb_n, pre_embs, pre_clus_labels + + def make_constant_length_emb(self, emb_in: torch.Tensor, base_segment_indexes: torch.Tensor) -> torch.Tensor: + """ + This function deals with edge cases when the number of segments decreases and the number of embedding falls + short for the labels. + + - ASR decoder occasionally returns less number of words compared to the previous frame. + - In this case, we obtain fewer embedding vectors for the short period of time. To match the pre-defined + length, the last embedding vector is repeated to fill the voidness. + - The repeated embedding will be soon replaced by the actual embeddings once the system takes new frames. + + Args: + emb_in (Tensor): + If self.is_online is False: + `pre_embs` contains only current speaker embedding inputs, which is FIFO queue + If self.is_online is True: + `pre_embs` contains history buffer and FIFO queue + base_segment_indexes (Tensor): + Tensor containing unique segment (embedding vector) index + + Returns: + emb_curr (Tensor): + Length preserved speaker embedding vectors + """ + curr_clustered_segments = torch.where(base_segment_indexes >= self.history_buffer_seg_end)[0] + + # Check if the current buffer result is falling short compared to `self.current_n`. + if emb_in[curr_clustered_segments].shape[0] < self.current_n: + delta_count = self.current_n - emb_in[curr_clustered_segments].shape[0] + fill_in_emb = torch.tile(emb_in[curr_clustered_segments][-1], (delta_count, 1)) + emb_curr = torch.vstack((emb_in[curr_clustered_segments], fill_in_emb)) + else: + emb_curr = emb_in[curr_clustered_segments] + return emb_curr + + def update_speaker_history_buffer( + self, emb_in: torch.Tensor, base_segment_indexes: torch.Tensor + ) -> Tuple[torch.Tensor, bool]: + """ + Merge the given embedding vectors based on the calculate affinity matrix. + if `is_update` is True, update the history buffer . + + Args: + emb_in (Tensor): + If self.is_online is False: + `emb` contains only current speaker embedding inputs, which is FIFO queue + If self.is_online is True: + `emb` contains history buffer and FIFO queue + base_segment_indexes (Tensor): + Tensor containing unique segment (embedding vector) index + + Returns: + history_embedding_buffer_emb (Tensor): + Matrix containing merged embedding vectors of the previous frames. + This matrix is referred to as "history buffer" in this class. + is_update (bool): + Boolean indicates whether to update speaker + + Example: + + at the frame index where `is_online` turns to True: + + |------hist-buffer------|-----FIFO-queue-----| + + self.history_n = 20 + self.current_n = 10 + + Step (1) + |-----------------------|ABCDEF--------------| + + If we get two more segments, "NN" as in the description: + history buffer = 20 + current buffer = 12 + + Step (2) + |-----------------------|ABCDEF--------------XY| + |---------emb_in-------| + + The newly accepted embeddings go through a FIFO queue (first come, first merge) + history buffer = 22 + current buffer = 10 + + Step (3) + |-----------------------AB|CDEF--------------XY| + |---------pre_embs--------| + + After merging (reducing) the embedding set gets back to the original size: + history buffer = 20 + current buffer = 10 + + Step (4) + |======================|CDEF--------------XY| + |-----hist_emb_buff----| + + After clustering, `self.Y_fullhist` is updated as: + + |0000000000011111111111|11110000110010010011| + + The dimension of `self.Y_fullhist` is (`history_n + current_n`) x 1 + + self.history_buffer_seg_end (int): + The total number of segments that have been merged from the beginning of the session. + (=`hist_curr_boundary`) + """ + is_update, new_emb_n, pre_embs, pre_clus_labels = self.prepare_embedding_update(emb_in, base_segment_indexes) + + # Update the history/current_buffer boundary cursor + total_emb, total_cluster_labels = [], [] + + if is_update: + # Calculate how many embedding vectors should be reduced per speaker + class_target_vol = get_merge_quantity( + num_to_be_removed=new_emb_n, + pre_clus_labels=pre_clus_labels, + min_count_per_cluster=self.minimum_segments_per_buffer, + ) + + # Merge the segments in the history buffer + for spk_idx, sub_cluster_num in enumerate(list(class_target_vol)): + merged_embs, merged_clus_labels, _ = run_reducer( + pre_embs=pre_embs, + target_spk_idx=spk_idx, + merge_quantity=sub_cluster_num.item(), + pre_clus_labels=pre_clus_labels, + ) + total_emb.append(merged_embs) + total_cluster_labels.append(merged_clus_labels) + + # Update the speaker history buffer + self.history_embedding_buffer_emb = torch.vstack(total_emb) + self.history_embedding_buffer_label = torch.hstack(total_cluster_labels) + if self.history_embedding_buffer_emb.shape[0] != self.history_n: + raise ValueError("History embedding size is not maintained correctly.") + if len(self.history_embedding_buffer_label) != self.history_n: + raise ValueError("History label size is not maintained correctly.") + + else: + total_emb.append(self.history_embedding_buffer_emb) + total_cluster_labels.append(self.history_embedding_buffer_label) + + # `emb_curr` is the incumbent set of embeddings which is the the latest. + emb_curr = self.make_constant_length_emb(emb_in, base_segment_indexes) + total_emb.append(emb_curr) + + # Before perform clustering, we attach the current_n number of estimated speaker labels + # from the previous clustering result. + total_cluster_labels.append(self.Y_fullhist[-self.current_n :]) + + history_and_current_emb = torch.vstack(total_emb) + history_and_current_labels = torch.hstack(total_cluster_labels) + if history_and_current_emb.shape[0] != len(history_and_current_labels): + raise ValueError("`history_and_current_emb` has a mismatch in length with `history_and_current_labels`.") + return history_and_current_emb, is_update + + def get_reduced_mat(self, emb_in: torch.Tensor, base_segment_indexes: torch.Tensor) -> Tuple[torch.Tensor, bool]: + """ + Choose whether we want to add embeddings to the memory or not. + The processing buffer has size of (self.current_n + self.history_n). + + Case-1: If margin_seg_n > 0, this means we have more embedding vectors than we can hold in the processing buffer. + - `is_online` should be `True` + - reduce the number of embedding vectors by merging the closest ones. + call `update_speaker_history_buffer` function + + Case-2: If margin_seg_n <= 0, this means that we can accept more embedding vectors and yet to fill the processing buffer. + - `is_online` should be `False` + - Replace `merged_emb` variable with the raw input `emb_in`. + - `add_new` is `True`, since we are adding more embedding vectors to `merged_emb` variable. + + Args: + emb_in (Tensor): + If self.is_online is False: + `emb` contains only current speaker embedding inputs + base_segment_indexes (Tensor): + Tensor containing unique segment (embedding vector) index + + Returns: + merged_emb (Tensor): + Matrix containing merged embedding vectors of the previous frames. + This matrix is referred to as "history buffer" in this class. + If self.is_online is False: + `merged_emb` contains only current speaker embedding inputs + If self.is_online is True: + `merged_emb` is a concatenated matrix with history embedding and current embedding inputs + add_new (bool): + Boolean that indicates whether there is a new set of segments. Depending on the VAD timestamps, + the number of subsegments can be ocassionally decreased. If `add_new=True`, then it adds the newly + acquired cluster labels. + """ + margin_seg_n = emb_in.shape[0] - (self.current_n + self.history_n) + if len(self.Y_fullhist) == 0 and margin_seg_n > 0: + raise ValueError( + "The number of incoming embedding vectors is larger than the total processing buffer size." + "Please either (1) increase the history and current buffer size (2) or use longer segment lengths to reduce number of segments." + ) + if margin_seg_n > 0: + self.is_online = True + merged_emb, add_new = self.update_speaker_history_buffer( + emb_in=emb_in, base_segment_indexes=base_segment_indexes + ) + else: + self.is_online = False + merged_emb = emb_in + add_new = True + return merged_emb, add_new + + def match_labels(self, Y_merged: torch.Tensor, add_new: bool) -> torch.Tensor: + """ + This function matches the newly generated clustering label sequence with the existing speaker labels in the history buffer. + `self.history_buffer_seg_end` is an integer index that tells to which point is history embedding contains from `self.Y_fullhist`. + + If embedding reducing is done correctly, we should discard (0, self.history_n) amount and take + (self.history_n, len(Y_merged)) from the new clustering output `Y_merged`. + + Args: + Y_merged (Tensor): + The newly generated clustering label sequence that may have different permutations with the existing + speaker labels in the history buffer. + add_new (bool): + This variable indicates whether there is a new set of segments. Depending on the VAD timestamps, + the number of subsegments can be occasionally decreased. If `add_new=True`, then it adds the newly + acquired cluster labels. + + Returns: + Y_out (Tensor): + Permutation-matched speaker labels based on history buffer + """ + if self.is_online: + # Online clustering mode with history buffer + Y_old = torch.hstack((self.history_embedding_buffer_label, self.Y_fullhist[self.history_buffer_seg_end :])) + + # Stitch the old history and new cluster labels + Y_matched = stitch_cluster_labels(Y_old=Y_old, Y_new=Y_merged).to(Y_merged.device) + if add_new: + if Y_matched[self.history_n :].shape[0] != self.current_n: + raise ValueError("Update point sync is not correct.") + # Concatenate the newly generated speaker labels + Y_out = torch.hstack((self.Y_fullhist[: self.history_buffer_seg_end], Y_matched[self.history_n :])) + self.Y_fullhist = Y_out + else: + # Do not update cumulative labels since there are no new segments. + Y_out = self.Y_fullhist + else: + # If no memory is used, offline clustering is applied. + Y_out = stitch_cluster_labels(Y_old=self.Y_fullhist, Y_new=Y_merged).to(Y_merged.device) + self.Y_fullhist = Y_out + return Y_out + + def forward( + self, + curr_emb, + base_segment_indexes, + max_num_speakers: int, + max_rp_threshold: float, + enhanced_count_thres: int, + sparse_search_volume: int, + frame_index: int, + cuda: bool = False, + ) -> torch.Tensor: + """ + Wrapper function for torch.jit.script compatibility. + NOTE: jit scripted classes only contain the methods which are included in the computation graph in the forward pass. + """ + Y = self.forward_infer( + curr_emb=curr_emb, + base_segment_indexes=base_segment_indexes, + max_num_speakers=max_num_speakers, + max_rp_threshold=max_rp_threshold, + enhanced_count_thres=enhanced_count_thres, + sparse_search_volume=sparse_search_volume, + frame_index=frame_index, + cuda=cuda, + ) + return Y + + def forward_infer( + self, + curr_emb: torch.Tensor, + base_segment_indexes: torch.Tensor, + max_num_speakers: int = 4, + max_rp_threshold: float = 0.15, + enhanced_count_thres: int = 40, + sparse_search_volume: int = 10, + fixed_thres: float = -1.0, + frame_index: int = 0, + cuda: bool = False, + ) -> torch.Tensor: + """ + Perform speaker clustering in online mode. Embedding vector set `emb` is expected to be containing + history embeddings to count the number of speakers. + + Args: + curr_emb (Tensor): + Current embedding vector input. + base_segment_indexes (Tensor): + Tensor containing unique segment (embedding vector) index + max_num_speakers (int): + Maximum number of speakers to be detected during online diarization session + max_rp_threshold (float): + Limits the range of parameter search. + Clustering performance can vary depending on this range. + Default is 0.25. + max_rp_threshold (float): + Limits the range of parameter search. + Clustering performance can vary depending on this range. + Default is 0.15. + frame_index (int): + Unique index for each segment (also each embedding vector) + cuda (bool): + Boolean that determines whether cuda is used or not + device (torch.device): + `torch.device` variable + + Returns: + Y (Tensor): + Speaker labels for history embeddings and current embedding inputs + """ + self.max_num_speakers = max_num_speakers + self.max_rp_threshold = max_rp_threshold + self.enhanced_count_thres = enhanced_count_thres + self.sparse_search_volume = sparse_search_volume + self.fixed_thres = fixed_thres + + # Merge the closest embeddings and reduce the size of the embedding count. + if cuda and (curr_emb.device == torch.device("cpu") or base_segment_indexes.device == torch.device("cpu")): + raise ValueError(f"CUDA is enabled but the input {curr_emb} or {base_segment_indexes} is not on the GPU.") + + merged_embs, add_new = self.get_reduced_mat(emb_in=curr_emb, base_segment_indexes=base_segment_indexes,) + # Perform clustering on the embedding matrix containing history and current FIFO buffer merged_embeddings + if merged_embs.shape[0] == 1: + Y = torch.zeros((1,), dtype=torch.int32) + else: + mat = getCosAffinityMatrix(merged_embs) + est_num_of_spk, affinity_mat = self.online_spk_num_estimation(mat, frame_index) + spectral_model = SpectralClustering(n_clusters=est_num_of_spk, cuda=cuda, device=merged_embs.device) + Y = spectral_model.forward(affinity_mat).to(merged_embs.device) + # Match the permutation of the newly obtained speaker labels and the previous labels + merged_clus_labels = self.match_labels(Y_merged=Y, add_new=add_new) + return merged_clus_labels diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/optimization_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/optimization_utils.py new file mode 100644 index 0000000..f947007 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/optimization_utils.py @@ -0,0 +1,343 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# The original code of Linear Sum Assignment solver is +# from: https://github.com/scipy/scipy/blob/v0.18.1/scipy/optimize/_hungarian.py +# The following is the full text of the license: + +# Hungarian algorithm (Kuhn-Munkres) for solving the linear sum assignment +# problem. Taken from scikit-learn. Based on original code by Brian Clapper, +# adapted to NumPy by Gael Varoquaux. +# Further improvements by Ben Root, Vlad Niculae and Lars Buitinck. +# Copyright (c) 2008 Brian M. Clapper , Gael Varoquaux +# Author: Brian M. Clapper, Gael Varoquaux +# License: 3-clause BSD + +import torch + + +@torch.jit.script +def unravel_index(index: int, shape: torch.Tensor): + """ + Unravel the index input to fit the given shape. + This function is needed for torch.jit.script compatibility. + + Args: + index (int): The index to unravel. + shape (Tesnor): The shape to unravel the index to. + + Returns: + Tensor: The unraveled index. + """ + out = [] + shape = torch.flip(shape, dims=(0,)) + for dim in shape: + out.append(index % dim) + index = index // dim + out = torch.tensor([int(x.item()) for x in out]) + return torch.flip(out, dims=(0,)) + + +@torch.jit.script +class LinearSumAssignmentSolver(object): + """ + A Solver class for the linear sum assignment (LSA) problem. + Designed for torch.jit.script compatibility in NeMo. + + The LSA problem is also referred to as bipartite matching problem. An LSA problem is described + by a matrix `cost_mat`, where each cost_mat[i,j] is the cost of matching vertex i of the first partite + set (e.g. a "worker") and vertex j of the second set (e.g. a "job"). + + Thus, the goal of LSA-solver is to find a complete assignment of column element to row element with + the minimal cost. Note that the solution may not be unique and there could be multiple solutions that + yield the same minimal cost. + + LSA problem solver is needed for the following tasks in NeMo: + - Permutation Invariant Loss (PIL) for diarization model training + - Label permutation matching for online speaker diarzation + - Concatenated minimum-permutation Word Error Rate (cp-WER) calculation + + This implementation is based on the LAP solver from scipy: + https://github.com/scipy/scipy/blob/v0.18.1/scipy/optimize/_hungarian.py + The scipy implementation comes with the following license: + + Copyright (c) 2008 Brian M. Clapper , Gael Varoquaux + Author: Brian M. Clapper, Gael Varoquaux + License: 3-clause BSD + + References + 1. http://csclab.murraystate.edu/bob.pilgrim/445/munkres.html + 2. https://en.wikipedia.org/wiki/Hungarian_algorithm + 3. https://github.com/scipy/scipy/blob/v0.18.1/scipy/optimize/_hungarian.py + + + Attributes: + cost_mat (Tensor): 2D matrix containing cost matrix. Number of columns must be larger than number of rows. + row_uncovered (Tensor): 1D matrix containing boolean values indicating whether a row is covered. + col_uncovered (Tensor): 1D matrix containing boolean values indicating whether a column is covered. + zero_row (Tensor): 1D matrix containing the row index of the last zero found. + zero_col (Tensor): 1D matrix containing the column index of the last zero found. + path (Tensor): 2D matrix containing the path taken through the matrix. + marked (Tensor): 2D matrix containing the marked zeros. + """ + + def __init__(self, cost_matrix: torch.Tensor): + # The main cost matrix + self.cost_mat = cost_matrix + row_len, col_len = self.cost_mat.shape + + # Initialize the solver state + self.zero_row = torch.tensor(0, dtype=torch.long).to(cost_matrix.device) + self.zero_col = torch.tensor(0, dtype=torch.long).to(cost_matrix.device) + + # Initialize the covered matrices + self.row_uncovered = torch.ones(row_len, dtype=torch.bool).to(cost_matrix.device) + self.col_uncovered = torch.ones(col_len, dtype=torch.bool).to(cost_matrix.device) + + # Initialize the path matrix and the mark matrix + self.path = torch.zeros((row_len + col_len, 2), dtype=torch.long).to(cost_matrix.device) + self.marked = torch.zeros((row_len, col_len), dtype=torch.long).to(cost_matrix.device) + + def _reset_uncovered_mat(self): + """ + Clear all covered matrix cells and assign `True` to all uncovered elements. + """ + self.row_uncovered[:] = True + self.col_uncovered[:] = True + + def _step1(self): + """ + Step 1 + + Goal: Subtract the smallest element of each row from its elements. + - All elements of the matrix are now non-negative. + - Therefore, an assignment of total cost 0 is the minimum cost assignment. + - This operation leads to at least one zero in each row. + + Procedure: + - For each row of the matrix, find the smallest element and subtract it from every element in its row. + - Go to Step 2. + """ + self.cost_mat -= torch.min(self.cost_mat, dim=1)[0].unsqueeze(1) + return 2 + + def _step2(self): + """ + Step 2 + + Goal: Make sure assignment with cost sum 0 is feasible. + + Procedure: + - Find a zero in the resulting cost matrix. + - If there are no marked zeros in its row or column, mark the zero. + - Repeat for each element in the matrix. + - Go to step 3. + """ + ind_out = torch.where(self.cost_mat == 0) + ind, val = list(ind_out[0]), list(ind_out[1]) + for i, j in zip(ind, val): + if self.col_uncovered[j] and self.row_uncovered[i]: + self.marked[i, j] = 1 + self.col_uncovered[j] = False + self.row_uncovered[i] = False + + self._reset_uncovered_mat() + return 3 + + def _step3(self) -> int: + """ + Step 3 + + Goal: All zeros in the matrix must be covered by marking with the least numbers of rows and columns. + + Procedure: + - Cover each column containing a marked zero. + - If n columns are covered, the marked zeros describe a complete set of unique assignments. + In this case, Go to Step 0 (Done state) + - Otherwise, Go to Step 4. + """ + marked = self.marked == 1 + self.col_uncovered[torch.any(marked, dim=0)] = False + if marked.sum() < self.cost_mat.shape[0]: + return 4 # Go to step 4 + else: + return 0 # Go to step 0 (Done state) + + def _step4(self, bypass: bool = False) -> int: + """ + Step 4 + + Goal: Cover all columns containing a marked zero. + + Procedure: + - Find a non-covered zero and put a prime mark on it. + - If there is no marked zero in the row containing this primed zero, Go to Step 5. + - Otherwise, cover this row and uncover the column containing the marked zero. + - Continue in this manner until there are no uncovered zeros left. + - Save the smallest uncovered value. + - Go to Step 6. + """ + # We convert to int as numpy operations are faster on int + cost_mat = (self.cost_mat == 0).int() + covered_cost_mat = cost_mat * self.row_uncovered.unsqueeze(1) + covered_cost_mat *= self.col_uncovered.long() + row_len, col_len = self.cost_mat.shape + if not bypass: + while True: + urv = unravel_index(torch.argmax(covered_cost_mat).item(), torch.tensor([col_len, row_len])) + row, col = int(urv[0].item()), int(urv[1].item()) + if covered_cost_mat[row, col] == 0: + return 6 + else: + self.marked[row, col] = 2 # Find the first marked element in the row + mark_col = torch.argmax((self.marked[row] == 1).int()) + if self.marked[row, mark_col] != 1: # No marked element in the row + self.zero_row = torch.tensor(row) + self.zero_col = torch.tensor(col) + return 5 + else: + col = mark_col + self.row_uncovered[row] = False + self.col_uncovered[col] = True + covered_cost_mat[:, col] = cost_mat[:, col] * self.row_uncovered + covered_cost_mat[row] = 0 + return 0 + + def _step5(self) -> int: + """ + Step 5 + + Goal: Construct a series of alternating primed and marked zeros as follows. + + Procedure: + - Let Z0 represent the uncovered primed zero found in Step 4. + - Let Z1 denote the marked zero in the column of Z0 (if any). + - Let Z2 denote the primed zero in the row of Z1 (there will always be one). + - Continue until the series terminates at a primed zero that has no marked zero in its column. + - Unmark each marked zero of the series. + - Mark each primed zero of the series. + - Erase all primes and uncover every line in the matrix. + - Return to Step 3 + """ + count = torch.tensor(0) + path = self.path + path[count, 0] = self.zero_row.long() + path[count, 1] = self.zero_col.long() + + while True: # Unmark each marked zero of the series + # Find the first marked element in the col defined by the path (= `val`) + row = torch.argmax((self.marked[:, path[count, 1]] == 1).int()) + + if self.marked[row, path[count, 1]] != 1: + # Could not find one + break + else: + count += 1 + path[count, 0] = row + path[count, 1] = path[count - 1, 1] + + # Find the first prime element in the row defined by the first path step + col = int(torch.argmax((self.marked[path[count, 0]] == 2).int())) + if self.marked[row, col] != 2: + col = -1 + count += 1 + path[count, 0] = path[count - 1, 0] + path[count, 1] = col + + # Convert paths + for i in range(int(count.item()) + 1): + if self.marked[path[i, 0], path[i, 1]] == 1: + self.marked[path[i, 0], path[i, 1]] = 0 + else: + self.marked[path[i, 0], path[i, 1]] = 1 + + self._reset_uncovered_mat() + + # Remove all prime markings in marked matrix + self.marked[self.marked == 2] = 0 + return 3 + + def _step6(self) -> int: + """ + Step 6 + + Goal: Prepare for another iteration by modifying the cost matrix. + + Procedure: + - Add the value found in Step 4 to every element of each covered row. + - Subtract it from every element of each uncovered column. + - Return to Step 4 without altering any marks, primes, or covered lines. + """ + if torch.any(self.row_uncovered) and torch.any(self.col_uncovered): + row_minval = torch.min(self.cost_mat[self.row_uncovered], dim=0)[0] + minval = torch.min(row_minval[self.col_uncovered]) + self.cost_mat[~self.row_uncovered] += minval + self.cost_mat[:, self.col_uncovered] -= minval + return 4 + + +@torch.jit.script +def linear_sum_assignment(cost_matrix: torch.Tensor, max_size: int = 100): + """ + Launch the linear sum assignment algorithm on a cost matrix. + + Args: + cost_matrix (Tensor): The cost matrix of shape (N, M) where M should be larger than N. + + Returns: + row_index (Tensor): The row indices of the optimal assignments. + col_index (Tensor): The column indices of the optimal assignments. + """ + cost_matrix = cost_matrix.clone().detach() + + if len(cost_matrix.shape) != 2: + raise ValueError(f"2-d tensor is expected but got a {cost_matrix.shape} tensor") + if max(cost_matrix.shape) > max_size: + raise ValueError( + f"Cost matrix size {cost_matrix.shape} is too large. The maximum supported size is {max_size}x{max_size}." + ) + + # The algorithm expects more columns than rows in the cost matrix. + if cost_matrix.shape[1] < cost_matrix.shape[0]: + cost_matrix = cost_matrix.T + transposed = True + else: + transposed = False + + lap_solver = LinearSumAssignmentSolver(cost_matrix) + f_int: int = 0 if 0 in cost_matrix.shape else 1 + + # while step is not Done (step 0): + # NOTE: torch.jit.scipt does not support getattr with string argument. + # Do not use getattr(lap_solver, f"_step{f_int}")() + while f_int != 0: + if f_int == 1: + f_int = lap_solver._step1() + elif f_int == 2: + f_int = lap_solver._step2() + elif f_int == 3: + f_int = lap_solver._step3() + elif f_int == 4: + f_int = lap_solver._step4() + elif f_int == 5: + f_int = lap_solver._step5() + elif f_int == 6: + f_int = lap_solver._step6() + + if transposed: + marked = lap_solver.marked.T + else: + marked = lap_solver.marked + row_index, col_index = torch.where(marked == 1) + return row_index, col_index diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/regularization_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/regularization_utils.py new file mode 100644 index 0000000..871b488 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/regularization_utils.py @@ -0,0 +1,64 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List + + +def compute_stochastic_depth_drop_probs( + num_layers: int, + stochastic_depth_drop_prob: float = 0.0, + stochastic_depth_mode: str = "linear", + stochastic_depth_start_layer: int = 1, +) -> List[float]: + """Computes drop probabilities for stochastic depth regularization technique. + The first layer is never dropped and the starting layer needs to be greater + or equal to 1. + + Args: + num_layers (int): number of layers in the network. + stochastic_depth_drop_prob (float): if non-zero, will randomly drop + layers during training. The higher this value, the more often layers + are dropped. Defaults to 0.0. + stochastic_depth_mode (str): can be either "linear" or "uniform". If + set to "uniform", all layers have the same probability of drop. If + set to "linear", the drop probability grows linearly from 0 for the + first layer to the desired value for the final layer. Defaults to + "linear". + stochastic_depth_start_layer (int): starting layer for stochastic depth. + All layers before this will never be dropped. Note that drop + probability will be adjusted accordingly if mode is "linear" when + start layer is > 1. Defaults to 1. + Returns: + List[float]: list of drop probabilities for all layers + """ + if not (0 <= stochastic_depth_drop_prob < 1.0): + raise ValueError("stochastic_depth_drop_prob has to be in [0, 1).") + if not (1 <= stochastic_depth_start_layer <= num_layers): + raise ValueError("stochastic_depth_start_layer has to be in [1, num layers].") + + # Layers before `stochastic_depth_start_layer` are never dropped + layer_drop_probs = [0.0] * stochastic_depth_start_layer + + # Layers starting with `stochastic_depth_start_layer` may be dropped + if (L := num_layers - stochastic_depth_start_layer) > 0: + if stochastic_depth_mode == "linear": + # we start with 1/L * drop_prob and and end with the desired drop probability. + layer_drop_probs += [l / L * stochastic_depth_drop_prob for l in range(1, L + 1)] + elif stochastic_depth_mode == "uniform": + layer_drop_probs += [stochastic_depth_drop_prob] * L + else: + raise ValueError( + f'stochastic_depth_mode has to be one of ["linear", "uniform"]. Current value: {stochastic_depth_mode}' + ) + return layer_drop_probs diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/rnnt_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/rnnt_utils.py new file mode 100644 index 0000000..1cd2d2d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/rnnt_utils.py @@ -0,0 +1,621 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright 2017 Johns Hopkins University (Shinji Watanabe) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch + + +@dataclass +class Hypothesis: + """Hypothesis class for beam search algorithms. + + score: A float score obtained from an AbstractRNNTDecoder module's score_hypothesis method. + + y_sequence: Either a sequence of integer ids pointing to some vocabulary, or a packed torch.Tensor + behaving in the same manner. dtype must be torch.Long in the latter case. + + dec_state: A list (or list of list) of LSTM-RNN decoder states. Can be None. + + text: (Optional) A decoded string after processing via CTC / RNN-T decoding (removing the CTC/RNNT + `blank` tokens, and optionally merging word-pieces). Should be used as decoded string for + Word Error Rate calculation. + + timestep: (Optional) A list of integer indices representing at which index in the decoding + process did the token appear. Should be of same length as the number of non-blank tokens. + + alignments: (Optional) Represents the CTC / RNNT token alignments as integer tokens along an axis of + time T (for CTC) or Time x Target (TxU). + For CTC, represented as a single list of integer indices. + For RNNT, represented as a dangling list of list of integer indices. + Outer list represents Time dimension (T), inner list represents Target dimension (U). + The set of valid indices **includes** the CTC / RNNT blank token in order to represent alignments. + + frame_confidence: (Optional) Represents the CTC / RNNT per-frame confidence scores as token probabilities + along an axis of time T (for CTC) or Time x Target (TxU). + For CTC, represented as a single list of float indices. + For RNNT, represented as a dangling list of list of float indices. + Outer list represents Time dimension (T), inner list represents Target dimension (U). + + token_confidence: (Optional) Represents the CTC / RNNT per-token confidence scores as token probabilities + along an axis of Target U. + Represented as a single list of float indices. + + word_confidence: (Optional) Represents the CTC / RNNT per-word confidence scores as token probabilities + along an axis of Target U. + Represented as a single list of float indices. + + length: Represents the length of the sequence (the original length without padding), otherwise + defaults to 0. + + y: (Unused) A list of torch.Tensors representing the list of hypotheses. + + lm_state: (Unused) A dictionary state cache used by an external Language Model. + + lm_scores: (Unused) Score of the external Language Model. + + ngram_lm_state: (Optional) State of the external n-gram Language Model. + + tokens: (Optional) A list of decoded tokens (can be characters or word-pieces. + + last_token (Optional): A token or batch of tokens which was predicted in the last step. + """ + + score: float + y_sequence: Union[List[int], torch.Tensor] + text: Optional[str] = None + dec_out: Optional[List[torch.Tensor]] = None + dec_state: Optional[Union[List[List[torch.Tensor]], List[torch.Tensor]]] = None + timestep: Union[List[int], torch.Tensor] = field(default_factory=list) + alignments: Optional[Union[List[int], List[List[int]]]] = None + frame_confidence: Optional[Union[List[float], List[List[float]]]] = None + token_confidence: Optional[List[float]] = None + word_confidence: Optional[List[float]] = None + length: Union[int, torch.Tensor] = 0 + y: List[torch.tensor] = None + lm_state: Optional[Union[Dict[str, Any], List[Any]]] = None + lm_scores: Optional[torch.Tensor] = None + ngram_lm_state: Optional[Union[Dict[str, Any], List[Any]]] = None + tokens: Optional[Union[List[int], torch.Tensor]] = None + last_token: Optional[torch.Tensor] = None + + @property + def non_blank_frame_confidence(self) -> List[float]: + """Get per-frame confidence for non-blank tokens according to self.timestep + + Returns: + List with confidence scores. The length of the list is the same as `timestep`. + """ + non_blank_frame_confidence = [] + # self.timestep can be a dict for RNNT + timestep = self.timestep['timestep'] if isinstance(self.timestep, dict) else self.timestep + if len(self.timestep) != 0 and self.frame_confidence is not None: + if any(isinstance(i, list) for i in self.frame_confidence): # rnnt + t_prev = -1 + offset = 0 + for t in timestep: + if t != t_prev: + t_prev = t + offset = 0 + else: + offset += 1 + non_blank_frame_confidence.append(self.frame_confidence[t][offset]) + else: # ctc + non_blank_frame_confidence = [self.frame_confidence[t] for t in timestep] + return non_blank_frame_confidence + + @property + def words(self) -> List[str]: + """Get words from self.text + + Returns: + List with words (str). + """ + return [] if self.text is None else self.text.split() + + +@dataclass +class NBestHypotheses: + """List of N best hypotheses""" + + n_best_hypotheses: Optional[List[Hypothesis]] + + +@dataclass +class HATJointOutput: + """HATJoint outputs for beam search decoding + + hat_logprobs: standard HATJoint outputs as for RNNTJoint + + ilm_logprobs: internal language model probabilities (for ILM subtraction) + """ + + hat_logprobs: Optional[torch.Tensor] = None + ilm_logprobs: Optional[torch.Tensor] = None + + +def is_prefix(x: List[int], pref: List[int]) -> bool: + """ + Obtained from https://github.com/espnet/espnet. + + Check if pref is a prefix of x. + + Args: + x: Label ID sequence. + pref: Prefix label ID sequence. + + Returns: + : Whether pref is a prefix of x. + """ + if len(pref) >= len(x): + return False + + for i in range(len(pref)): + if pref[i] != x[i]: + return False + + return True + + +def select_k_expansions( + hyps: List[Hypothesis], topk_idxs: torch.Tensor, topk_logps: torch.Tensor, gamma: float, beta: int, +) -> List[Tuple[int, Hypothesis]]: + """ + Obtained from https://github.com/espnet/espnet + + Return K hypotheses candidates for expansion from a list of hypothesis. + K candidates are selected according to the extended hypotheses probabilities + and a prune-by-value method. Where K is equal to beam_size + beta. + + Args: + hyps: Hypotheses. + topk_idxs: Indices of candidates hypothesis. Shape = [B, num_candidates] + topk_logps: Log-probabilities for hypotheses expansions. Shape = [B, V + 1] + gamma: Allowed logp difference for prune-by-value method. + beta: Number of additional candidates to store. + + Return: + k_expansions: Best K expansion hypotheses candidates. + """ + k_expansions = [] + + for i, hyp in enumerate(hyps): + hyp_i = [(int(k), hyp.score + float(v)) for k, v in zip(topk_idxs[i], topk_logps[i])] + k_best_exp_val = max(hyp_i, key=lambda x: x[1]) + + k_best_exp_idx = k_best_exp_val[0] + k_best_exp = k_best_exp_val[1] + + expansions = sorted(filter(lambda x: (k_best_exp - gamma) <= x[1], hyp_i), key=lambda x: x[1],) + + if len(expansions) > 0: + k_expansions.append(expansions) + else: + k_expansions.append([(k_best_exp_idx, k_best_exp)]) + + return k_expansions + + +class BatchedHyps: + """Class to store batched hypotheses (labels, time_indices, scores) for efficient RNNT decoding""" + + def __init__( + self, + batch_size: int, + init_length: int, + device: Optional[torch.device] = None, + float_dtype: Optional[torch.dtype] = None, + ): + """ + + Args: + batch_size: batch size for hypotheses + init_length: initial estimate for the length of hypotheses (if the real length is higher, tensors will be reallocated) + device: device for storing hypotheses + float_dtype: float type for scores + """ + if init_length <= 0: + raise ValueError(f"init_length must be > 0, got {init_length}") + if batch_size <= 0: + raise ValueError(f"batch_size must be > 0, got {batch_size}") + self._max_length = init_length + + # batch of current lengths of hypotheses and correspoinding timesteps + self.current_lengths = torch.zeros(batch_size, device=device, dtype=torch.long) + # tensor for storing transcripts + self.transcript = torch.zeros((batch_size, self._max_length), device=device, dtype=torch.long) + # tensor for storing timesteps corresponding to transcripts + self.timesteps = torch.zeros((batch_size, self._max_length), device=device, dtype=torch.long) + # accumulated scores for hypotheses + self.scores = torch.zeros(batch_size, device=device, dtype=float_dtype) + + # tracking last timestep of each hyp to avoid infinite looping (when max symbols per frame is restricted) + # last observed timestep (with label) for each hypothesis + self.last_timestep = torch.full((batch_size,), -1, device=device, dtype=torch.long) + # number of labels for the last timestep + self.last_timestep_lasts = torch.zeros(batch_size, device=device, dtype=torch.long) + self._batch_indices = torch.arange(batch_size, device=device) + self._ones_batch = torch.ones_like(self._batch_indices) + + def clear_(self): + self.current_lengths.fill_(0) + self.transcript.fill_(0) + self.timesteps.fill_(0) + self.scores.fill_(0.0) + self.last_timestep.fill_(-1) + self.last_timestep_lasts.fill_(0) + + def _allocate_more(self): + """ + Allocate 2x space for tensors, similar to common C++ std::vector implementations + to maintain O(1) insertion time complexity + """ + self.transcript = torch.cat((self.transcript, torch.zeros_like(self.transcript)), dim=-1) + self.timesteps = torch.cat((self.timesteps, torch.zeros_like(self.timesteps)), dim=-1) + self._max_length *= 2 + + def add_results_( + self, active_indices: torch.Tensor, labels: torch.Tensor, time_indices: torch.Tensor, scores: torch.Tensor + ): + """ + Add results (inplace) from a decoding step to the batched hypotheses. + We assume that all tensors have the same first dimension, and labels are non-blanks. + Args: + active_indices: tensor with indices of active hypotheses (indices should be within the original batch_size) + labels: non-blank labels to add + time_indices: tensor of time index for each label + scores: label scores + """ + if active_indices.shape[0] == 0: + return # nothing to add + # if needed - increase storage + if self.current_lengths.max().item() >= self._max_length: + self._allocate_more() + + self.add_results_no_checks_( + active_indices=active_indices, labels=labels, time_indices=time_indices, scores=scores + ) + + def add_results_no_checks_( + self, active_indices: torch.Tensor, labels: torch.Tensor, time_indices: torch.Tensor, scores: torch.Tensor + ): + """ + Add results (inplace) from a decoding step to the batched hypotheses without checks. + We assume that all tensors have the same first dimension, and labels are non-blanks. + Useful if all the memory is pre-allocated, especially with cuda graphs + (otherwise prefer a more safe `add_results_`) + Args: + active_indices: tensor with indices of active hypotheses (indices should be within the original batch_size) + labels: non-blank labels to add + time_indices: tensor of time index for each label + scores: label scores + """ + # accumulate scores + self.scores[active_indices] += scores + + # store transcript and timesteps + active_lengths = self.current_lengths[active_indices] + self.transcript[active_indices, active_lengths] = labels + self.timesteps[active_indices, active_lengths] = time_indices + # store last observed timestep + number of observation for the current timestep + self.last_timestep_lasts[active_indices] = torch.where( + self.last_timestep[active_indices] == time_indices, self.last_timestep_lasts[active_indices] + 1, 1 + ) + self.last_timestep[active_indices] = time_indices + # increase lengths + self.current_lengths[active_indices] += 1 + + def add_results_masked_( + self, active_mask: torch.Tensor, labels: torch.Tensor, time_indices: torch.Tensor, scores: torch.Tensor + ): + """ + Add results (inplace) from a decoding step to the batched hypotheses. + We assume that all tensors have the same first dimension, and labels are non-blanks. + Args: + active_mask: tensor with mask for active hypotheses (of batch_size) + labels: non-blank labels to add + time_indices: tensor of time index for each label + scores: label scores + """ + if (self.current_lengths + active_mask).max() >= self._max_length: + self._allocate_more() + self.add_results_masked_no_checks_( + active_mask=active_mask, labels=labels, time_indices=time_indices, scores=scores + ) + + def add_results_masked_no_checks_( + self, active_mask: torch.Tensor, labels: torch.Tensor, time_indices: torch.Tensor, scores: torch.Tensor + ): + """ + Add results (inplace) from a decoding step to the batched hypotheses without checks. + We assume that all tensors have the same first dimension, and labels are non-blanks. + Useful if all the memory is pre-allocated, especially with cuda graphs + (otherwise prefer a more safe `add_results_`) + Args: + active_mask: tensor with mask for active hypotheses (of batch_size) + labels: non-blank labels to add + time_indices: tensor of time index for each label + scores: label scores + """ + # accumulate scores + # same as self.scores[active_mask] += scores[active_mask], but non-blocking + torch.where(active_mask, self.scores + scores, self.scores, out=self.scores) + + # store transcript and timesteps + self.transcript[self._batch_indices, self.current_lengths] = labels + self.timesteps[self._batch_indices, self.current_lengths] = time_indices + # store last observed timestep + number of observation for the current timestep + # if last_timestep == time_indices, increase; else set to 1 + torch.where( + torch.logical_and(active_mask, self.last_timestep == time_indices), + self.last_timestep_lasts + 1, + self.last_timestep_lasts, + out=self.last_timestep_lasts, + ) + torch.where( + torch.logical_and(active_mask, self.last_timestep != time_indices), + self._ones_batch, + self.last_timestep_lasts, + out=self.last_timestep_lasts, + ) + # same as: self.last_timestep[active_mask] = time_indices[active_mask], but non-blocking + torch.where(active_mask, time_indices, self.last_timestep, out=self.last_timestep) + # increase lengths + self.current_lengths += active_mask + + +class BatchedAlignments: + """ + Class to store batched alignments (logits, labels, frame_confidence). + Size is different from hypotheses, since blank outputs are preserved + """ + + def __init__( + self, + batch_size: int, + logits_dim: int, + init_length: int, + device: Optional[torch.device] = None, + float_dtype: Optional[torch.dtype] = None, + store_alignments: bool = True, + store_frame_confidence: bool = False, + ): + """ + + Args: + batch_size: batch size for hypotheses + logits_dim: dimension for logits + init_length: initial estimate for the lengths of flatten alignments + device: device for storing data + float_dtype: expected logits/confidence data type + store_alignments: if alignments should be stored + store_frame_confidence: if frame confidence should be stored + """ + if init_length <= 0: + raise ValueError(f"init_length must be > 0, got {init_length}") + if batch_size <= 0: + raise ValueError(f"batch_size must be > 0, got {batch_size}") + self.with_frame_confidence = store_frame_confidence + self.with_alignments = store_alignments + self._max_length = init_length + + # tensor to store observed timesteps (for alignments / confidence scores) + self.timesteps = torch.zeros((batch_size, self._max_length), device=device, dtype=torch.long) + # current lengths of the utterances (alignments) + self.current_lengths = torch.zeros(batch_size, device=device, dtype=torch.long) + + # empty tensors instead of None to make torch.jit.script happy + self.logits = torch.zeros(0, device=device, dtype=float_dtype) + self.labels = torch.zeros(0, device=device, dtype=torch.long) + if self.with_alignments: + # logits and labels; labels can contain , different from BatchedHyps + self.logits = torch.zeros((batch_size, self._max_length, logits_dim), device=device, dtype=float_dtype) + self.labels = torch.zeros((batch_size, self._max_length), device=device, dtype=torch.long) + + # empty tensor instead of None to make torch.jit.script happy + self.frame_confidence = torch.zeros(0, device=device, dtype=float_dtype) + if self.with_frame_confidence: + # tensor to store frame confidence + self.frame_confidence = torch.zeros((batch_size, self._max_length), device=device, dtype=float_dtype) + self._batch_indices = torch.arange(batch_size, device=device) + + def clear_(self): + self.current_lengths.fill_(0) + self.timesteps.fill_(0) + self.logits.fill_(0.0) + self.labels.fill_(0) + self.frame_confidence.fill_(0) + + def _allocate_more(self): + """ + Allocate 2x space for tensors, similar to common C++ std::vector implementations + to maintain O(1) insertion time complexity + """ + self.timesteps = torch.cat((self.timesteps, torch.zeros_like(self.timesteps)), dim=-1) + if self.with_alignments: + self.logits = torch.cat((self.logits, torch.zeros_like(self.logits)), dim=1) + self.labels = torch.cat((self.labels, torch.zeros_like(self.labels)), dim=-1) + if self.with_frame_confidence: + self.frame_confidence = torch.cat((self.frame_confidence, torch.zeros_like(self.frame_confidence)), dim=-1) + self._max_length *= 2 + + def add_results_( + self, + active_indices: torch.Tensor, + time_indices: torch.Tensor, + logits: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + confidence: Optional[torch.Tensor] = None, + ): + """ + Add results (inplace) from a decoding step to the batched hypotheses. + All tensors must use the same fixed batch dimension. + Args: + active_mask: tensor with mask for active hypotheses (of batch_size) + logits: tensor with raw network outputs + labels: tensor with decoded labels (can contain blank) + time_indices: tensor of time index for each label + confidence: optional tensor with confidence for each item in batch + """ + # we assume that all tensors have the same first dimension + if active_indices.shape[0] == 0: + return # nothing to add + + # if needed - increase storage + if self.current_lengths.max().item() >= self._max_length: + self._allocate_more() + + active_lengths = self.current_lengths[active_indices] + # store timesteps - same for alignments / confidence + self.timesteps[active_indices, active_lengths] = time_indices + + if self.with_alignments and logits is not None and labels is not None: + self.logits[active_indices, active_lengths] = logits + self.labels[active_indices, active_lengths] = labels + + if self.with_frame_confidence and confidence is not None: + self.frame_confidence[active_indices, active_lengths] = confidence + # increase lengths + self.current_lengths[active_indices] += 1 + + def add_results_masked_( + self, + active_mask: torch.Tensor, + time_indices: torch.Tensor, + logits: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + confidence: Optional[torch.Tensor] = None, + ): + """ + Add results (inplace) from a decoding step to the batched hypotheses. + All tensors must use the same fixed batch dimension. + Args: + active_mask: tensor with indices of active hypotheses (indices should be within the original batch_size) + time_indices: tensor of time index for each label + logits: tensor with raw network outputs + labels: tensor with decoded labels (can contain blank) + confidence: optional tensor with confidence for each item in batch + """ + if (self.current_lengths + active_mask).max() >= self._max_length: + self._allocate_more() + self.add_results_masked_no_checks_( + active_mask=active_mask, time_indices=time_indices, logits=logits, labels=labels, confidence=confidence + ) + + def add_results_masked_no_checks_( + self, + active_mask: torch.Tensor, + time_indices: torch.Tensor, + logits: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + confidence: Optional[torch.Tensor] = None, + ): + """ + Add results (inplace) from a decoding step to the batched hypotheses. + All tensors must use the same fixed batch dimension. + Useful if all the memory is pre-allocated, especially with cuda graphs + (otherwise prefer a more safe `add_results_masked_`) + Args: + active_mask: tensor with indices of active hypotheses (indices should be within the original batch_size) + time_indices: tensor of time index for each label + logits: tensor with raw network outputs + labels: tensor with decoded labels (can contain blank) + confidence: optional tensor with confidence for each item in batch + """ + # store timesteps - same for alignments / confidence + self.timesteps[self._batch_indices, self.current_lengths] = time_indices + + if self.with_alignments and logits is not None and labels is not None: + self.timesteps[self._batch_indices, self.current_lengths] = time_indices + self.logits[self._batch_indices, self.current_lengths] = logits + self.labels[self._batch_indices, self.current_lengths] = labels + + if self.with_frame_confidence and confidence is not None: + self.frame_confidence[self._batch_indices, self.current_lengths] = confidence + # increase lengths + self.current_lengths += active_mask + + +def batched_hyps_to_hypotheses( + batched_hyps: BatchedHyps, alignments: Optional[BatchedAlignments] = None, batch_size=None +) -> List[Hypothesis]: + """ + Convert batched hypotheses to a list of Hypothesis objects. + Keep this function separate to allow for jit compilation for BatchedHyps class (see tests) + + Args: + batched_hyps: BatchedHyps object + alignments: BatchedAlignments object, optional; must correspond to BatchedHyps if present + batch_size: Batch Size to retrieve hypotheses. When working with CUDA graphs the batch size for all tensors + is constant, thus we need here the real batch size to return only necessary hypotheses + + Returns: + list of Hypothesis objects + """ + assert batch_size is None or batch_size <= batched_hyps.scores.shape[0] + num_hyps = batched_hyps.scores.shape[0] if batch_size is None else batch_size + hypotheses = [ + Hypothesis( + score=batched_hyps.scores[i].item(), + y_sequence=batched_hyps.transcript[i, : batched_hyps.current_lengths[i]], + timestep=batched_hyps.timesteps[i, : batched_hyps.current_lengths[i]], + alignments=None, + dec_state=None, + ) + for i in range(num_hyps) + ] + if alignments is not None: + # move all data to cpu to avoid overhead with moving data by chunks + alignment_lengths = alignments.current_lengths.cpu().tolist() + if alignments.with_alignments: + alignment_logits = alignments.logits.cpu() + alignment_labels = alignments.labels.cpu() + if alignments.with_frame_confidence: + frame_confidence = alignments.frame_confidence.cpu() + + # for each hypothesis - aggregate alignment using unique_consecutive for time indices (~itertools.groupby) + for i in range(len(hypotheses)): + hypotheses[i].alignments = [] + if alignments.with_frame_confidence: + hypotheses[i].frame_confidence = [] + _, grouped_counts = torch.unique_consecutive( + alignments.timesteps[i, : alignment_lengths[i]], return_counts=True + ) + start = 0 + for timestep_cnt in grouped_counts.tolist(): + if alignments.with_alignments: + hypotheses[i].alignments.append( + [(alignment_logits[i, start + j], alignment_labels[i, start + j]) for j in range(timestep_cnt)] + ) + if alignments.with_frame_confidence: + hypotheses[i].frame_confidence.append( + [frame_confidence[i, start + j] for j in range(timestep_cnt)] + ) + start += timestep_cnt + return hypotheses diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/slu_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/slu_utils.py new file mode 100644 index 0000000..47b2881 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/slu_utils.py @@ -0,0 +1,205 @@ +# ! /usr/bin/python +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from dataclasses import dataclass +from typing import List, Optional + +import torch +from omegaconf import DictConfig + +from nemo.collections.asr.modules.transformer import ( + BeamSearchSequenceGenerator, + GreedySequenceGenerator, + TopKSequenceGenerator, +) +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.core.classes.module import NeuralModule + + +@dataclass +class SequenceGeneratorConfig: + type: str = "greedy" # choices=[greedy, topk, beam] + max_sequence_length: int = 512 + max_delta_length: int = -1 + temperature: float = 1.0 # for top-k sampling + beam_size: int = 1 # K for top-k sampling, N for beam search + len_pen: float = 0.0 # for beam-search + + +class SequenceGenerator: + """ + Wrapper class for sequence generators for NeMo transformers. + """ + + TYPE_GREEDY = "greedy" + TYPE_TOPK = "topk" + TYPE_BEAM = "beam" + SEARCHER_TYPES = [TYPE_GREEDY, TYPE_TOPK, TYPE_BEAM] + + def __init__( + self, + cfg: DictConfig, + embedding: NeuralModule, + decoder: NeuralModule, + log_softmax: NeuralModule, + tokenizer: TokenizerSpec, + ) -> None: + super().__init__() + + self._type = cfg.get("type", "greedy") + self.tokenizer = tokenizer + self.pad_id = getattr(tokenizer, "pad_id", 0) + self.eos_id = getattr(tokenizer, "eos_id", -1) + self.bos_id = getattr(tokenizer, "bos_id", -1) + common_args = { + "pad": self.pad_id, + "bos": self.bos_id, + "eos": self.eos_id, + "max_sequence_length": cfg.get("max_sequence_length", 512), + "max_delta_length": cfg.get("max_delta_length", -1), + "batch_size": cfg.get("batch_size", 1), + } + if self._type == self.TYPE_GREEDY: + self.generator = GreedySequenceGenerator(embedding, decoder, log_softmax, **common_args) + elif self._type == self.TYPE_TOPK: + beam_size = cfg.get("beam_size", 1) + temperature = cfg.get("temperature", 1.0) + self.generator = TopKSequenceGenerator( + embedding, decoder, log_softmax, beam_size, temperature, **common_args + ) + elif self._type == self.TYPE_BEAM: + beam_size = cfg.get("beam_size", 1) + len_pen = cfg.get("len_pen", 0.0) + self.generator = BeamSearchSequenceGenerator( + embedding, decoder, log_softmax, beam_size, len_pen, **common_args + ) + else: + raise ValueError( + f"Sequence Generator only supports one of {self.SEARCH_TYPES}, but got {self._type} instead." + ) + + def __call__( + self, + encoder_states: torch.Tensor, + encoder_input_mask: torch.Tensor = None, + return_beam_scores: bool = False, + pad_max_len: Optional[int] = None, + return_length: bool = False, + ): + """ + Generate sequence tokens given the input encoder states and masks. + Params: + - encoder_states: a torch Tensor of shape BxTxD + - encoder_input_mask: a binary tensor of shape BxTxD + - return_beam_scores: whether to return beam scores + - pad_max_len: optional int, set it to pad all sequence to the same length + - return_length: whether to return the lengths for generated sequences (shape B) + Returns: + - generated tokens tensor of shape BxT + """ + predictions = self.generator( + encoder_hidden_states=encoder_states, + encoder_input_mask=encoder_input_mask, + return_beam_scores=return_beam_scores, + ) + + if pad_max_len: + predictions = pad_sequence(predictions, pad_max_len, self.pad_id) + + if return_length: + return predictions, self.get_seq_length(predictions) + + return predictions + + def get_seq_length(self, seq: torch.Tensor) -> torch.Tensor: + """ + Get sequence length. + Params: + - seq: batched sequence tensor of shape BxTxD + Returns: + - tensor of shape B, where each element is the length of the sequence + """ + lengths = seq.size(1) * torch.ones(seq.size(0), device=seq.device).long() + pos = (seq == self.eos_id).long().nonzero() + seq_lengths = torch.scatter(lengths, dim=0, index=pos[:, 0], src=pos[:, 1]) + return seq_lengths + + def decode_semantics_from_tokens(self, seq_tokens: torch.Tensor) -> List[str]: + """ + Decode tokens into strings + Rarams: + - seq_tokens: integer tensor of shape BxT + Returns: + - list of strings + """ + semantics_list = [] + # Drop sequence tokens to CPU + seq_tokens = seq_tokens.detach().long().cpu() + seq_lengths = self.get_seq_length(seq_tokens) + # iterate over batch + for ind in range(seq_tokens.shape[0]): + tokens = seq_tokens[ind].numpy().tolist() + length = seq_lengths[ind].long().cpu().item() + tokens = tokens[:length] + text = "".join(self.tokenizer.tokenizer.decode_ids(tokens)) + semantics_list.append(text) + return semantics_list + + +def get_seq_length(seq: torch.Tensor, eos_id: int) -> torch.Tensor: + """ + Get sequence length. + Params: + - seq: batched sequence tensor of shape BxTxD + - eos_id: integer representing the end of sentence + Returns: + - tensor of shape B, where each element is the length of the sequence + """ + lengths = seq.size(1) * torch.ones(seq.size(0), device=seq.device).long() + pos = (seq == eos_id).long().nonzero() + seq_lengths = torch.scatter(lengths, dim=0, index=pos[:, 0], src=pos[:, 1]) + return seq_lengths + + +def pad_sequence(seq: torch.Tensor, max_len: int, pad_token: int = 0) -> torch.Tensor: + """ + Params: + - seq: integer token sequences of shape BxT + - max_len: integer for max sequence length + - pad_token: integer token for padding + Returns: + - padded sequence of shape B x max_len + """ + batch = seq.size(0) + curr_len = seq.size(1) + if curr_len >= max_len: + return seq + + padding = torch.zeros(batch, max_len - curr_len, dtype=seq.dtype, device=seq.device).fill_(pad_token) + return torch.cat([seq, padding], dim=1) + + +def get_seq_mask(seq: torch.Tensor, seq_lens: torch.Tensor) -> torch.Tensor: + """ + Get the sequence mask based on the actual length of each sequence + Params: + - seq: tensor of shape [BxLxD] + - seq_len: tensor of shape [B] + Returns: + - binary mask of shape [BxL] + """ + mask = torch.arange(seq.size(1))[None, :].to(seq.device) < seq_lens[:, None] + return mask.to(seq.device, dtype=bool) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/speaker_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/speaker_utils.py new file mode 100644 index 0000000..5d3a0bf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/speaker_utils.py @@ -0,0 +1,1721 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc +import json +import math +import os +import shutil +from copy import deepcopy +from typing import Dict, List, Tuple, Union + +import numpy as np +import omegaconf +import soundfile as sf +import torch +from pyannote.core import Annotation, Segment +from tqdm import tqdm + +from nemo.collections.asr.data.audio_to_label import repeat_signal +from nemo.collections.asr.parts.utils.longform_clustering import LongFormSpeakerClustering +from nemo.collections.asr.parts.utils.offline_clustering import SpeakerClustering, get_argmin_mat, split_input_data +from nemo.utils import logging + +""" +This file contains all the utility functions required for speaker embeddings part in diarization scripts +""" + + +def get_uniqname_from_filepath(filepath): + """ + Return base name from provided filepath + """ + if type(filepath) is str: + uniq_id = os.path.splitext(os.path.basename(filepath))[0] + return uniq_id + else: + raise TypeError("input must be filepath string") + + +def get_uniq_id_from_manifest_line(line: str) -> str: + """ + Retrieve `uniq_id` from the `audio_filepath` in a manifest line. + """ + dic = json.loads(line.strip()) + uniq_id = get_uniqname_from_filepath(dic['audio_filepath']) + return uniq_id + + +def get_uniq_id_with_dur(meta, decimals=3): + """ + Return basename with offset and end time labels + """ + # bare_uniq_id = get_uniqname_from_filepath(meta['audio_filepath']) + bare_uniq_id = get_uniqname_from_filepath(meta['rttm_filepath']) + if meta['offset'] is None and meta['duration'] is None: + return bare_uniq_id + if meta['offset']: + offset = str(int(round(meta['offset'], decimals) * pow(10, decimals))) + else: + offset = 0 + if meta['duration']: + endtime = str(int(round(meta['offset'] + meta['duration'], decimals) * pow(10, decimals))) + else: + endtime = 'NULL' + uniq_id = f"{bare_uniq_id}_{offset}_{endtime}" + return uniq_id + + +def audio_rttm_map(manifest, attach_dur=False): + """ + This function creates AUDIO_RTTM_MAP which is used by all diarization components to extract embeddings, + cluster and unify time stamps + Args: manifest file that contains keys audio_filepath, rttm_filepath if exists, text, num_speakers if known and uem_filepath if exists + + returns: + AUDIO_RTTM_MAP (dict) : A dictionary with keys of uniq id, which is being used to map audio files and corresponding rttm files + """ + + AUDIO_RTTM_MAP = {} + with open(manifest, 'r') as inp_file: + lines = inp_file.readlines() + logging.info("Number of files to diarize: {}".format(len(lines))) + for line in lines: + line = line.strip() + dic = json.loads(line) + + meta = { + 'audio_filepath': dic['audio_filepath'], + 'rttm_filepath': dic.get('rttm_filepath', None), + 'offset': dic.get('offset', None), + 'duration': dic.get('duration', None), + 'text': dic.get('text', None), + 'num_speakers': dic.get('num_speakers', None), + 'uem_filepath': dic.get('uem_filepath', None), + 'ctm_filepath': dic.get('ctm_filepath', None), + } + if attach_dur: + uniqname = get_uniq_id_with_dur(meta) + else: + uniqname = get_uniqname_from_filepath(filepath=meta['audio_filepath']) + + if uniqname not in AUDIO_RTTM_MAP: + AUDIO_RTTM_MAP[uniqname] = meta + else: + raise KeyError( + "file {} is already part of AUDIO_RTTM_MAP, it might be duplicated, Note: file basename must be unique".format( + meta['audio_filepath'] + ) + ) + + return AUDIO_RTTM_MAP + + +def parse_scale_configs(window_lengths_in_sec, shift_lengths_in_sec, multiscale_weights): + """ + Check whether multiscale parameters are provided correctly. window_lengths_in_sec, shift_lengfhs_in_sec and + multiscale_weights should be all provided in omegaconf.listconfig.ListConfig type. In addition, the scales + should be provided in descending order, from the longest scale to the base scale (the shortest). + + Example: + Single-scale setting: + parameters.window_length_in_sec=1.5 + parameters.shift_length_in_sec=0.75 + parameters.multiscale_weights=null + + Multiscale setting (base scale - window_length 0.5 s and shift_length 0.25): + parameters.window_length_in_sec=[1.5,1.0,0.5] + parameters.shift_length_in_sec=[0.75,0.5,0.25] + parameters.multiscale_weights=[1,1,1] + + In addition, you can also specify session-by-session multiscale weight. In this case, each dictionary key + points to different weights. + """ + check_float_config = [isinstance(var, float) for var in (window_lengths_in_sec, shift_lengths_in_sec)] + check_list_config = [ + isinstance(var, (omegaconf.listconfig.ListConfig, list, tuple)) + for var in (window_lengths_in_sec, shift_lengths_in_sec, multiscale_weights) + ] + if all(check_list_config) or all(check_float_config): + + # If bare floating numbers are provided, convert them to list format. + if all(check_float_config): + window_lengths, shift_lengths, multiscale_weights = ( + [window_lengths_in_sec], + [shift_lengths_in_sec], + [1.0], + ) + else: + window_lengths, shift_lengths, multiscale_weights = ( + window_lengths_in_sec, + shift_lengths_in_sec, + multiscale_weights, + ) + + length_check = ( + len(set([len(window_lengths), len(shift_lengths), len(multiscale_weights)])) == 1 + and len(multiscale_weights) > 0 + ) + scale_order_check = ( + list(window_lengths) == sorted(window_lengths)[::-1] and list(shift_lengths) == sorted(shift_lengths)[::-1] + ) + + # Check whether window lengths are longer than shift lengths + if len(window_lengths) > 1: + shift_length_check = all([w > s for w, s in zip(window_lengths, shift_lengths)]) + else: + shift_length_check = window_lengths[0] > shift_lengths[0] + + multiscale_args_dict = {'use_single_scale_clustering': False} + if all([length_check, scale_order_check, shift_length_check]): + if len(window_lengths) > 1: + multiscale_args_dict['scale_dict'] = { + k: (w, s) for k, (w, s) in enumerate(zip(window_lengths, shift_lengths)) + } + else: + multiscale_args_dict['scale_dict'] = {0: (window_lengths[0], shift_lengths[0])} + multiscale_args_dict['multiscale_weights'] = multiscale_weights + return multiscale_args_dict + else: + raise ValueError('Multiscale parameters are not properly setup.') + + elif any(check_list_config): + raise ValueError( + 'You must provide a list config for all three parameters: window, shift and multiscale weights.' + ) + else: + return None + + +def get_embs_and_timestamps(multiscale_embeddings_and_timestamps, multiscale_args_dict): + """ + The embeddings and timestamps in multiscale_embeddings_and_timestamps dictionary are + indexed by scale index. This function rearranges the extracted speaker embedding and + timestamps by unique ID to make the further processing more convenient. + + Args: + multiscale_embeddings_and_timestamps (dict): + Dictionary of embeddings and timestamps for each scale. + multiscale_args_dict (dict): + Dictionary of scale information: window, shift and multiscale weights. + + Returns: + embs_and_timestamps (dict) + A dictionary containing embeddings and timestamps of each scale, indexed by unique ID. + """ + embs_and_timestamps = {uniq_id: {} for uniq_id in multiscale_embeddings_and_timestamps[0][0].keys()} + if multiscale_args_dict['use_single_scale_clustering']: + _multiscale_args_dict = deepcopy(multiscale_args_dict) + _multiscale_args_dict['scale_dict'] = {0: multiscale_args_dict['scale_dict'][0]} + _multiscale_args_dict['multiscale_weights'] = multiscale_args_dict['multiscale_weights'][:1] + else: + _multiscale_args_dict = multiscale_args_dict + + embeddings, timestamps = multiscale_embeddings_and_timestamps[0] + for uniq_id in embeddings.keys(): + embeddings_list, time_stamps_list, segment_index_list = [], [], [] + for scale_idx in sorted(_multiscale_args_dict['scale_dict'].keys()): + embeddings, timestamps = multiscale_embeddings_and_timestamps[scale_idx] + if len(embeddings[uniq_id]) != len(timestamps[uniq_id]): + raise ValueError("Mismatch of counts between embedding vectors and timestamps") + time_stamps_tensor = torch.tensor(timestamps[uniq_id]) + embeddings_list.append(embeddings[uniq_id]) + segment_index_list.append(embeddings[uniq_id].shape[0]) + time_stamps_list.append(time_stamps_tensor) + + embs_and_timestamps[uniq_id]['multiscale_weights'] = ( + torch.tensor(_multiscale_args_dict['multiscale_weights']).unsqueeze(0).float() + ) + embs_and_timestamps[uniq_id]['embeddings'] = torch.cat(embeddings_list, dim=0) + embs_and_timestamps[uniq_id]['timestamps'] = torch.cat(time_stamps_list, dim=0) + embs_and_timestamps[uniq_id]['multiscale_segment_counts'] = torch.tensor(segment_index_list) + + return embs_and_timestamps + + +def get_timestamps(multiscale_timestamps, multiscale_args_dict): + """ + The timestamps in `multiscale_timestamps` dictionary are indexed by scale index. + This function rearranges the extracted speaker embedding and timestamps by unique ID to make the further processing more convenient. + + Args: + multiscale_timestamps (dict): + Dictionary of timestamps for each scale. + multiscale_args_dict (dict): + Dictionary of scale information: window, shift and multiscale weights. + + Returns: + timestamps_dict (dict) + A dictionary containing embeddings and timestamps of each scale, indexed by unique ID. + """ + timestamps_dict = {uniq_id: {'scale_dict': {}} for uniq_id in multiscale_timestamps[0].keys()} + for scale_idx in sorted(multiscale_args_dict['scale_dict'].keys()): + time_stamps = multiscale_timestamps[scale_idx] + for uniq_id in time_stamps.keys(): + timestamps_dict[uniq_id]['scale_dict'][scale_idx] = { + 'time_stamps': time_stamps[uniq_id], + } + + return timestamps_dict + + +def get_contiguous_stamps(stamps): + """ + Return contiguous time stamps + """ + lines = deepcopy(stamps) + contiguous_stamps = [] + for i in range(len(lines) - 1): + start, end, speaker = lines[i].split() + next_start, next_end, next_speaker = lines[i + 1].split() + if float(end) > float(next_start): + avg = str((float(next_start) + float(end)) / 2.0) + lines[i + 1] = ' '.join([avg, next_end, next_speaker]) + contiguous_stamps.append(start + " " + avg + " " + speaker) + else: + contiguous_stamps.append(start + " " + end + " " + speaker) + start, end, speaker = lines[-1].split() + contiguous_stamps.append(start + " " + end + " " + speaker) + return contiguous_stamps + + +def merge_stamps(lines): + """ + Merge time stamps of the same speaker. + """ + stamps = deepcopy(lines) + overlap_stamps = [] + for i in range(len(stamps) - 1): + start, end, speaker = stamps[i].split() + next_start, next_end, next_speaker = stamps[i + 1].split() + if float(end) == float(next_start) and speaker == next_speaker: + stamps[i + 1] = ' '.join([start, next_end, next_speaker]) + else: + overlap_stamps.append(start + " " + end + " " + speaker) + + start, end, speaker = stamps[-1].split() + overlap_stamps.append(start + " " + end + " " + speaker) + + return overlap_stamps + + +def labels_to_pyannote_object(labels, uniq_name=''): + """ + Convert the given labels to pyannote object to calculate DER and for visualization + """ + annotation = Annotation(uri=uniq_name) + for label in labels: + start, end, speaker = label.strip().split() + start, end = float(start), float(end) + annotation[Segment(start, end)] = speaker + + return annotation + + +def labels_to_rttmfile(labels, uniq_id, out_rttm_dir): + """ + Write rttm file with uniq_id name in out_rttm_dir with timestamps in labels + """ + filename = os.path.join(out_rttm_dir, uniq_id + '.rttm') + with open(filename, 'w') as f: + for line in labels: + line = line.strip() + start, end, speaker = line.split() + duration = float(end) - float(start) + start = float(start) + log = 'SPEAKER {} 1 {:.3f} {:.3f} {} \n'.format(uniq_id, start, duration, speaker) + f.write(log) + + return filename + + +def string_to_float(x, round_digits): + """ + Convert string to float then round the number. + """ + return round(float(x), round_digits) + + +def convert_rttm_line(rttm_line, round_digits=3): + """ + Convert a line in RTTM file to speaker label, start and end timestamps. + + Args: + rttm_line (str): + A line in RTTM formatted file containing offset and duration of each segment. + round_digits (int): + Number of digits to be rounded. + + Returns: + start (float) + Start timestamp in floating point number. + end (float): + End timestamp in floating point number. + speaker (str): + speaker string in RTTM lines. + """ + rttm = rttm_line.strip().split() + start = string_to_float(rttm[3], round_digits) + end = string_to_float(rttm[4], round_digits) + string_to_float(rttm[3], round_digits) + speaker = rttm[7] + return start, end, speaker + + +def rttm_to_labels(rttm_filename): + """ + Prepare time stamps label list from rttm file + """ + labels = [] + with open(rttm_filename, 'r') as f: + for line in f.readlines(): + start, end, speaker = convert_rttm_line(line, round_digits=3) + labels.append('{} {} {}'.format(start, end, speaker)) + return labels + + +def write_cluster_labels(base_scale_idx, lines_cluster_labels, out_rttm_dir): + """ + Write cluster labels that are generated from clustering into a file. + Args: + base_scale_idx (int): The base scale index which is the highest scale index. + lines_cluster_labels (list): The start and end time-stamps of each segment with the predicted cluster label. + out_rttm_dir (str): The path where output rttm files are saved. + """ + out_label_name = os.path.join( + out_rttm_dir, '../speaker_outputs', f'subsegments_scale{base_scale_idx}_cluster.label' + ) + with open(out_label_name, 'w') as f: + for clus_label_line in lines_cluster_labels: + f.write(clus_label_line) + + +def generate_cluster_labels(segment_ranges: List[str], cluster_labels: List[int]): + """ + Generate cluster (speaker labels) from the segment_range list and cluster label list. + + Args: + segment_ranges (list): + List containing intervals (start and end timestapms, ranges) of each segment + cluster_labels (list): + List containing a cluster label sequence + + Returns: + diar_hyp (list): + List containing merged speaker-turn-level timestamps and labels in string format + Example: + >>> diar_hyp = ['0.0 4.375 speaker_1', '4.375 5.125 speaker_0', ...] + + lines (list) + List containing raw segment-level timestamps and labels in raw digits + >>> diar_hyp = ['0.0 0.25 speaker_1', '0.25 0.5 speaker_1', ..., '4.125 4.375 speaker_1'] + """ + lines = [] + for idx, label in enumerate(cluster_labels): + tag = 'speaker_' + str(label) + stt, end = segment_ranges[idx] + lines.append(f"{stt} {end} {tag}") + cont_lines = get_contiguous_stamps(lines) + diar_hyp = merge_stamps(cont_lines) + return diar_hyp, lines + + +def perform_clustering( + embs_and_timestamps, AUDIO_RTTM_MAP, out_rttm_dir, clustering_params, device, verbose: bool = True +): + """ + Performs spectral clustering on embeddings with time stamps generated from VAD output + + Args: + embs_and_timestamps (dict): This dictionary contains the following items indexed by unique IDs. + 'embeddings' : Tensor containing embeddings. Dimensions:(# of embs) x (emb. dimension) + 'timestamps' : Tensor containing ime stamps list for each audio recording + 'multiscale_segment_counts' : Tensor containing the number of segments for each scale + AUDIO_RTTM_MAP (dict): AUDIO_RTTM_MAP for mapping unique id with audio file path and rttm path + out_rttm_dir (str): Path to write predicted rttms + clustering_params (dict): clustering parameters provided through config that contains max_num_speakers (int), + oracle_num_speakers (bool), max_rp_threshold(float), sparse_search_volume(int) and enhance_count_threshold (int) + use_torch_script (bool): Boolean that determines whether to use torch.jit.script for speaker clustering + device (torch.device): Device we are running on ('cpu', 'cuda'). + verbose (bool): Enable TQDM progress bar. + + Returns: + all_reference (list[uniq_name,Annotation]): reference annotations for score calculation + all_hypothesis (list[uniq_name,Annotation]): hypothesis annotations for score calculation + + """ + all_hypothesis = [] + all_reference = [] + no_references = False + lines_cluster_labels = [] + + cuda = True + if device.type != 'cuda': + logging.warning("cuda=False, using CPU for eigen decomposition. This might slow down the clustering process.") + cuda = False + + speaker_clustering = LongFormSpeakerClustering(cuda=cuda) + + if clustering_params.get('export_script_module', False): + speaker_clustering = torch.jit.script(speaker_clustering) + torch.jit.save(speaker_clustering, 'speaker_clustering_script.pt') + + for uniq_id, audio_rttm_values in tqdm(AUDIO_RTTM_MAP.items(), desc='clustering', leave=True, disable=not verbose): + uniq_embs_and_timestamps = embs_and_timestamps[uniq_id] + + if clustering_params.oracle_num_speakers: + num_speakers = audio_rttm_values.get('num_speakers', None) + if num_speakers is None: + raise ValueError("Provided option as oracle num of speakers but num_speakers in manifest is null") + else: + num_speakers = -1 + + base_scale_idx = uniq_embs_and_timestamps['multiscale_segment_counts'].shape[0] - 1 + + cluster_labels = speaker_clustering.forward_infer( + embeddings_in_scales=uniq_embs_and_timestamps['embeddings'], + timestamps_in_scales=uniq_embs_and_timestamps['timestamps'], + multiscale_segment_counts=uniq_embs_and_timestamps['multiscale_segment_counts'], + multiscale_weights=uniq_embs_and_timestamps['multiscale_weights'], + oracle_num_speakers=int(num_speakers), + max_num_speakers=int(clustering_params.max_num_speakers), + max_rp_threshold=float(clustering_params.max_rp_threshold), + sparse_search_volume=int(clustering_params.sparse_search_volume), + chunk_cluster_count=clustering_params.get('chunk_cluster_count', None), + embeddings_per_chunk=clustering_params.get('embeddings_per_chunk', None), + ) + + del uniq_embs_and_timestamps + if cuda: + torch.cuda.empty_cache() + else: + gc.collect() + timestamps = speaker_clustering.timestamps_in_scales[base_scale_idx] + + cluster_labels = cluster_labels.cpu().numpy() + if len(cluster_labels) != timestamps.shape[0]: + raise ValueError("Mismatch of length between cluster_labels and timestamps.") + + labels, lines = generate_cluster_labels(timestamps, cluster_labels) + + if out_rttm_dir: + labels_to_rttmfile(labels, uniq_id, out_rttm_dir) + lines_cluster_labels.extend([f'{uniq_id} {seg_line}\n' for seg_line in lines]) + hypothesis = labels_to_pyannote_object(labels, uniq_name=uniq_id) + all_hypothesis.append([uniq_id, hypothesis]) + + rttm_file = audio_rttm_values.get('rttm_filepath', None) + if rttm_file is not None and os.path.exists(rttm_file) and not no_references: + ref_labels = rttm_to_labels(rttm_file) + reference = labels_to_pyannote_object(ref_labels, uniq_name=uniq_id) + all_reference.append([uniq_id, reference]) + else: + no_references = True + all_reference = [] + + if out_rttm_dir: + write_cluster_labels(base_scale_idx, lines_cluster_labels, out_rttm_dir) + + return all_reference, all_hypothesis + + +def get_vad_out_from_rttm_line(rttm_line): + """ + Extract VAD timestamp from the given RTTM lines. + """ + vad_out = rttm_line.strip().split() + if len(vad_out) > 3: + start, dur, _ = float(vad_out[3]), float(vad_out[4]), vad_out[7] + else: + start, dur, _ = float(vad_out[0]), float(vad_out[1]), vad_out[2] + return start, dur + + +def get_offset_and_duration(AUDIO_RTTM_MAP, uniq_id, decimals=5): + """ + Extract offset and duration information from AUDIO_RTTM_MAP dictionary. + If duration information is not specified, a duration value is extracted from the audio file directly. + + Args: + AUDIO_RTTM_MAP (dict): + Dictionary containing RTTM file information, which is indexed by unique file id. + uniq_id (str): + Unique file id + Returns: + offset (float): + The offset value that determines the beginning of the audio stream. + duration (float): + The length of audio stream that is expected to be used. + """ + audio_path = AUDIO_RTTM_MAP[uniq_id]['audio_filepath'] + if AUDIO_RTTM_MAP[uniq_id].get('duration', None): + duration = round(AUDIO_RTTM_MAP[uniq_id]['duration'], decimals) + offset = round(AUDIO_RTTM_MAP[uniq_id]['offset'], decimals) + else: + sound = sf.SoundFile(audio_path) + duration = sound.frames / sound.samplerate + offset = 0.0 + return offset, duration + + +def write_overlap_segments(outfile, AUDIO_RTTM_MAP, uniq_id, overlap_range_list, decimals=5): + """ + Write the json dictionary into the specified manifest file. + + Args: + outfile: + File pointer that indicates output file path. + AUDIO_RTTM_MAP (dict): + Dictionary containing the input manifest information + uniq_id (str): + Unique file id + overlap_range_list (list): + List containing overlapping ranges between target and source. + decimals (int): + Number of decimals to round the offset and duration values. + """ + audio_path = AUDIO_RTTM_MAP[uniq_id]['audio_filepath'] + for (stt, end) in overlap_range_list: + meta = { + "audio_filepath": audio_path, + "offset": round(stt, decimals), + "duration": round(end - stt, decimals), + "label": 'UNK', + "uniq_id": uniq_id, + } + json.dump(meta, outfile) + outfile.write("\n") + + +def read_rttm_lines(rttm_file_path): + """ + Read rttm files and return the rttm information lines. + + Args: + rttm_file_path (str): + An absolute path to an RTTM file + + Returns: + lines (list): + List containing the strings from the RTTM file. + """ + if rttm_file_path and os.path.exists(rttm_file_path): + with open(rttm_file_path, 'r') as f: + lines = f.readlines() + else: + raise FileNotFoundError( + "Requested to construct manifest from rttm with oracle VAD option or from NeMo VAD but received filename as {}".format( + rttm_file_path + ) + ) + return lines + + +def validate_vad_manifest(AUDIO_RTTM_MAP, vad_manifest): + """ + This function will check the valid speech segments in the manifest file which is either + generated from NeMo voice activity detection(VAD) or oracle VAD. + If an audio file does not contain any valid speech segments, we ignore the audio file + (indexed by uniq_id) for the rest of the processing steps. + """ + vad_uniq_ids = set() + with open(vad_manifest, 'r') as vad_file: + for line in vad_file: + line = line.strip() + dic = json.loads(line) + if dic['duration'] > 0: + vad_uniq_ids.add(dic['uniq_id']) + + provided_uniq_ids = set(AUDIO_RTTM_MAP.keys()) + silence_ids = provided_uniq_ids - vad_uniq_ids + for uniq_id in silence_ids: + del AUDIO_RTTM_MAP[uniq_id] + logging.warning(f"{uniq_id} is ignored since the file does not contain any speech signal to be processed.") + + if len(AUDIO_RTTM_MAP) == 0: + raise ValueError("All files present in manifest contains silence, aborting next steps") + + +def is_overlap(rangeA: List[float], rangeB: List[float]) -> bool: + """ + Check whether two ranges have overlap. + + Args: + rangeA (list, tuple): + List or tuple containing start and end value in float. + rangeB (list, tuple): + List or tuple containing start and end value in float. + Returns: + (bool): + Boolean that indicates whether the input ranges have overlap. + """ + start1, end1 = rangeA[0], rangeA[1] + start2, end2 = rangeB[0], rangeB[1] + return end1 > start2 and end2 > start1 + + +def get_overlap_range(rangeA: List[float], rangeB: List[float]): + """ + Calculate the overlapping range between rangeA and rangeB. + + Args: + rangeA (list, tuple): + List or tuple containing start and end value in float. + rangeB (list, tuple): + List or tuple containing start and end value in float. + + Returns: + (list): + List containing the overlapping range between rangeA and rangeB. + """ + assert is_overlap(rangeA, rangeB), f"There is no overlap between rangeA:{rangeA} and rangeB:{rangeB}" + return [max(rangeA[0], rangeB[0]), min(rangeA[1], rangeB[1])] + + +def merge_int_intervals(intervals_in: List[List[int]]) -> List[List[int]]: + """ + Interval merging algorithm which has `O(N*logN)` time complexity. (N is number of intervals) + Merge the range pairs if there is overlap exists between the given ranges. + This algorithm needs a sorted range list in terms of the start time. + Note that neighboring numbers lead to a merged range. + + Example: + input: [(1, 10), (11, 20)] + output: [(1, 20)] + + Refer to the original code at https://stackoverflow.com/a/59378428 + + Args: + intervals_in (list): + List containing ranges. + Example: + >>> intervals_in + [(102, 103), (104, 109), (107, 120)] + + Returns: + merged_list (list): + List containing the combined ranges. + Example: + >>> merged_list + [(102, 120)] + """ + num_intervals = len(intervals_in) + if num_intervals == 0: + return [] + elif num_intervals == 1: + return intervals_in + else: + merged_list: List[List[int]] = [] + stt2: int = 0 + end2: int = 0 + + intervals_in = [[int(x[0]), int(x[1])] for x in intervals_in] + interval_tensor: torch.Tensor = torch.tensor(intervals_in) + _sorted, _ = torch.sort(interval_tensor, dim=0) + _sorted_int: List[List[int]] = [[int(x[0]), int(x[1])] for x in _sorted.cpu()] + intervals: List[List[int]] = _sorted_int + + start, end = intervals[0][0], intervals[0][1] + for i in range(1, num_intervals): + stt2, end2 = intervals[i][0], intervals[i][1] + if end >= stt2: + end = max(end2, end) + else: + start, end = int(start), int(end) + merged_list.append([start, end]) + start = stt2 + end = max(end2, end) + + start, end = int(start), int(end) + merged_list.append([start, end]) + return merged_list + + +def fl2int(x: float, decimals: int = 3) -> int: + """ + Convert floating point number to integer. + """ + return torch.round(torch.tensor([x * (10 ** decimals)]), decimals=0).int().item() + + +def int2fl(x: int, decimals: int = 3) -> float: + """ + Convert integer to floating point number. + """ + return torch.round(torch.tensor([x / (10 ** decimals)]), decimals=decimals).item() + + +def merge_float_intervals(ranges: List[List[float]], decimals: int = 5, margin: int = 2) -> List[List[float]]: + """ + Combine overlaps with floating point numbers. Since neighboring integers are considered as continuous range, + we need to add margin to the starting range before merging then subtract margin from the result range. + + Args: + ranges (list): + List containing ranges. + Example: [(10.2, 10.83), (10.42, 10.91), (10.45, 12.09)] + decimals (int): + Number of rounding decimals + margin (int): + margin for determining overlap of the two ranges when ranges are converted to integer ranges. + Default is margin=2 which follows the python index convention. + + Examples: + If margin is 0: + [(1, 10), (10, 20)] -> [(1, 20)] + [(1, 10), (11, 20)] -> [(1, 20)] + If margin is 1: + [(1, 10), (10, 20)] -> [(1, 20)] + [(1, 10), (11, 20)] -> [(1, 10), (11, 20)] + If margin is 2: + [(1, 10), (10, 20)] -> [(1, 10), (10, 20)] + [(1, 10), (11, 20)] -> [(1, 10), (11, 20)] + + Returns: + merged_list (list): + List containing the combined ranges. + Example: [(10.2, 12.09)] + """ + ranges_int: List[List[int]] = [] + merged_ranges_int: List[List[int]] = [] + for x in ranges: + stt, end = int(fl2int(x[0], decimals) + margin), int(fl2int(x[1], decimals)) + if stt < end: + ranges_int.append([stt, end]) + merged_ranges_int = merge_int_intervals(ranges_int) + merged_ranges_float: List[List[float]] = [] + merged_ranges_float = [[int2fl(x[0] - margin, decimals), int2fl(x[1], decimals)] for x in merged_ranges_int] + return merged_ranges_float + + +def get_sub_range_list(target_range: List[float], source_range_list: List[List[float]]) -> List[List[float]]: + """ + Get the ranges that has overlaps with the target range from the source_range_list. + + Example: + source range: + |===--======---=====---====--| + target range: + |--------================----| + out_range: + |--------===---=====---==----| + + Args: + target_range (list): + A range (a start and end value pair) that defines the target range we want to select. + target_range = [(start, end)] + source_range_list (list): + List containing the subranges that need to be selected. + source_range = [(start0, end0), (start1, end1), ...] + Returns: + out_range (list): + List containing the overlap between target_range and + source_range_list. + """ + if len(target_range) == 0: + return [] + else: + out_range: List[List[float]] = [] + for s_range in source_range_list: + if is_overlap(s_range, target_range): + ovl_range = get_overlap_range(s_range, target_range) + out_range.append(ovl_range) + return out_range + + +def write_rttm2manifest( + AUDIO_RTTM_MAP: str, manifest_file: str, include_uniq_id: bool = False, decimals: int = 5 +) -> str: + """ + Write manifest file based on rttm files (or vad table out files). This manifest file would be used by + speaker diarizer to compute embeddings and cluster them. This function takes care of overlapping VAD timestamps + and trimmed with the given offset and duration value. + + Args: + AUDIO_RTTM_MAP (dict): + Dictionary containing keys to unique names, that contains audio filepath and rttm_filepath as its contents, + these are used to extract oracle vad timestamps. + manifest (str): + The path to the output manifest file. + + Returns: + manifest (str): + The path to the output manifest file. + """ + with open(manifest_file, 'w') as outfile: + for uniq_id in AUDIO_RTTM_MAP: + rttm_file_path = AUDIO_RTTM_MAP[uniq_id]['rttm_filepath'] + rttm_lines = read_rttm_lines(rttm_file_path) + offset, duration = get_offset_and_duration(AUDIO_RTTM_MAP, uniq_id, decimals) + vad_start_end_list_raw = [] + for line in rttm_lines: + start, dur = get_vad_out_from_rttm_line(line) + vad_start_end_list_raw.append([start, start + dur]) + vad_start_end_list = merge_float_intervals(vad_start_end_list_raw, decimals) + if len(vad_start_end_list) == 0: + logging.warning(f"File ID: {uniq_id}: The VAD label is not containing any speech segments.") + elif duration <= 0: + logging.warning(f"File ID: {uniq_id}: The audio file has negative or zero duration.") + else: + overlap_range_list = get_sub_range_list( + source_range_list=vad_start_end_list, target_range=[offset, offset + duration] + ) + write_overlap_segments(outfile, AUDIO_RTTM_MAP, uniq_id, overlap_range_list, decimals) + return manifest_file + + +def segments_manifest_to_subsegments_manifest( + segments_manifest_file: str, + subsegments_manifest_file: str = None, + window: float = 1.5, + shift: float = 0.75, + min_subsegment_duration: float = 0.05, + include_uniq_id: bool = False, +): + """ + Generate subsegments manifest from segments manifest file + Args: + segments_manifest file (str): path to segments manifest file, typically from VAD output + subsegments_manifest_file (str): path to output subsegments manifest file (default (None) : writes to current working directory) + window (float): window length for segments to subsegments length + shift (float): hop length for subsegments shift + min_subsegments_duration (float): exclude subsegments smaller than this duration value + + Returns: + returns path to subsegment manifest file + """ + if subsegments_manifest_file is None: + pwd = os.getcwd() + subsegments_manifest_file = os.path.join(pwd, 'subsegments.json') + + with open(segments_manifest_file, 'r') as segments_manifest, open( + subsegments_manifest_file, 'w' + ) as subsegments_manifest: + segments = segments_manifest.readlines() + for segment in segments: + segment = segment.strip() + dic = json.loads(segment) + audio, offset, duration, label = dic['audio_filepath'], dic['offset'], dic['duration'], dic['label'] + subsegments = get_subsegments(offset=offset, window=window, shift=shift, duration=duration) + if include_uniq_id and 'uniq_id' in dic: + uniq_id = dic['uniq_id'] + else: + uniq_id = None + for subsegment in subsegments: + start, dur = subsegment + if dur > min_subsegment_duration: + meta = { + "audio_filepath": audio, + "offset": start, + "duration": dur, + "label": label, + "uniq_id": uniq_id, + } + + json.dump(meta, subsegments_manifest) + subsegments_manifest.write("\n") + + return subsegments_manifest_file + + +def get_subsegments(offset: float, window: float, shift: float, duration: float) -> List[List[float]]: + """ + Return subsegments from a segment of audio file + Args: + offset (float): start time of audio segment + window (float): window length for segments to subsegments length + shift (float): hop length for subsegments shift + duration (float): duration of segment + Returns: + subsegments (List[tuple[float, float]]): subsegments generated for the segments as list of tuple of start and duration of each subsegment + """ + subsegments: List[List[float]] = [] + start = offset + slice_end = start + duration + base = math.ceil((duration - window) / shift) + slices = 1 if base < 0 else base + 1 + for slice_id in range(slices): + end = start + window + if end > slice_end: + end = slice_end + subsegments.append([start, end - start]) + start = offset + (slice_id + 1) * shift + return subsegments + + +def get_target_sig(sig, start_sec: float, end_sec: float, slice_length: int, sample_rate: int,) -> torch.Tensor: + """ + Extract time-series signal from the given audio buffer based on the start and end + timestamps. + + Args: + start_sec (float): + Start of the targeted segments in second + end_sec (float): + Start of the targeted segments in second + slice_length (int): + Length of the entire audio segment that the samples are extracted from + sample_rate (int): + Sampling rate of the time-series audio signal + + Returns: + (Tensor) Trimmed ime-series audio signal samples + """ + start_idx = int(start_sec * sample_rate) + end_idx = min(int(end_sec * sample_rate), int(slice_length + start_idx)) + return sig[start_idx:end_idx] + + +def check_ranges(range_tensor): + """ + Check whether the range list has any faulty timestamp order. + + Args: + range_tensor (list): + List containing the start and end time of the segments. + Example: + >>> range_tensor = [[0.5, 3.12], [3.51, 7.26], ... ] + """ + for k in range(range_tensor.shape[0]): + range_tup = range_tensor[k] + if range_tup[1] < range_tup[0]: + raise ValueError("Range start time should be preceding the end time but we got: {range_tup}") + return True + + +def tensor_to_list(range_tensor: torch.Tensor) -> List[List[float]]: + """ + For online segmentation. Force the list elements to be float type. + """ + return [[float(range_tensor[k][0]), float(range_tensor[k][1])] for k in range(range_tensor.shape[0])] + + +def get_speech_labels_for_update( + frame_start: float, + buffer_end: float, + vad_timestamps: torch.Tensor, + cumulative_speech_labels: torch.Tensor, + cursor_for_old_segments: float, +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Bring the new speech labels from the current buffer. Followingly: + + 1. Concatenate the old speech labels from self.cumulative_speech_labels for the overlapped region. + - This goes to new_speech_labels. + 2. Update the new 1 sec of speech label (speech_label_for_new_segments) to self.cumulative_speech_labels. + 3. Return the speech label from cursor_for_old_segments to buffer end. + + Args: + frame_start (float): + Start of the middle audio chunk in the audio buffer + buffer_end (float): + End of the audio buffer + vad_timestamps (Tensor): + Tensor containing VAD intervals (start and end timestamps) + cumulative_speech_labels (torch.Tensor): + Cumulative speech/non-speech timestamps (equivalent to VAD timestamps) + cursor_for_old_segments (float): + Floating point number that indicates the point where new segments should replace + the old segments + + Returns: + speech_label_for_new_segments (Tensor): + The intervals (start and end) timestamps where the new incoming speech segments should + be collected from + cumulative_speech_labels (Tensor): + Cumulative speech/non-speech timestamps (equivalent to VAD timestamps) with newly added + speech/non-speech timestamps from the `vad_timestamps` input + """ + update_overlap_range: List[float] = [] + if cursor_for_old_segments < frame_start: + update_overlap_range = [float(cursor_for_old_segments), float(frame_start)] + + # Get VAD timestamps that are in (frame_start, buffer_end) range + vad_timestamps = tensor_to_list(vad_timestamps) + cumulative_speech_labels = tensor_to_list(cumulative_speech_labels) + new_incoming_speech_labels = get_sub_range_list( + target_range=[float(frame_start), float(buffer_end)], source_range_list=vad_timestamps + ) + + # Update the speech label by including overlapping region with the previous output + update_overlap_speech_labels = get_sub_range_list( + target_range=update_overlap_range, source_range_list=cumulative_speech_labels + ) + + # Speech segments for embedding extractions + speech_label_for_new_segments = merge_float_intervals( + update_overlap_speech_labels + new_incoming_speech_labels, margin=0 + ) + + # Keep cumulative VAD labels for the future use + cumulative_speech_labels = merge_float_intervals(cumulative_speech_labels + new_incoming_speech_labels, margin=0) + + # Convert the lists back to type torch.Tensor + speech_label_for_new_segments = torch.tensor(speech_label_for_new_segments) + cumulative_speech_labels = torch.tensor(cumulative_speech_labels) + + return speech_label_for_new_segments, cumulative_speech_labels + + +def get_new_cursor_for_update(frame_start: float, segment_range_ts: List[List[float]],) -> Tuple[float, int]: + """ + Function for updating a cursor online speaker diarization. + Remove the old segments that overlap with the new frame (self.frame_start) + cursor_for_old_segments is set to the onset of the t_range popped lastly. + + + Args: + frame_start (float): + Start of streaming pipeline frame + segment_range_ts (float): + Interval (start and end timestamps) of the targeted segments + + Returns: + cursor_for_old_segments (float): + Floating point number that indicates the point where new segments should replace + the old segments + cursor_index (int): + The index of the first newly accepted segments + """ + cursor_for_old_segments = frame_start + cursor_index: int = len(segment_range_ts) + count = 0 + while True and len(segment_range_ts) > 0: + t_range = segment_range_ts[-1 * (count + 1)] + if frame_start <= t_range[1]: + count += 1 + cursor_for_old_segments = t_range[0] + else: + break + cursor_index = len(segment_range_ts) - count + return cursor_for_old_segments, cursor_index + + +def get_online_segments_from_slices( + sig: torch.Tensor, + buffer_start: float, + buffer_end: float, + subsegments: List[List[float]], + ind_offset: int, + window: float, + sample_rate: int, +) -> Tuple[int, List[torch.Tensor], List[List[float]], List[int]]: + """ + Create short speech segments from slices for online processing purpose. + + Args: + sig (Tensor): + Tensor containing the raw time-series signal + buffer_start (float): + Start point of the time-series signal buffer + buffer_end (float): + End point of the time-series signal buffer + subsegments (list): + List containing the interval information (start and duration) of each segment + ind_offset (int): + Offset for index that compensates the point of the current position in the streaming session + window (float): + Window length in second + shift (float): + Shift length in second + + Returns: + sigs_list (list): + list of sliced input signal + audio_lengths (list): + list of audio sample lengths + """ + sig_rangel_list: List[List[float]] = [] + sig_indexes: List[int] = [] + sigs_list: List[torch.Tensor] = [] + slice_length: int = int(window * sample_rate) + end_sec: float = 0.0 + for subseg in subsegments: + start_sec, dur = subseg[0], subseg[1] + + if start_sec > buffer_end: + continue + ind_offset += 1 + + buffer_len = buffer_end - buffer_start + end_sec = float(start_sec + dur) + + if end_sec > buffer_len: + end_sec = float(min(end_sec, buffer_len)) + + signal = get_target_sig(sig, start_sec, end_sec, slice_length, sample_rate) + + if len(signal) == 0: + raise ValueError("len(signal) is zero. Signal length should not be zero.") + if len(signal) < slice_length: + signal = repeat_signal(signal, len(signal), slice_length) + + start_abs_sec = buffer_start + start_sec + end_abs_sec = buffer_start + end_sec + + sigs_list.append(signal) + sig_rangel_list.append([start_abs_sec, end_abs_sec]) + sig_indexes.append(ind_offset) + + if not len(sigs_list) == len(sig_rangel_list) == len(sig_indexes): + raise ValueError("Signal information lists have a mismatch.") + + return ind_offset, sigs_list, sig_rangel_list, sig_indexes + + +def get_online_subsegments_from_buffer( + buffer_start: float, + buffer_end: float, + sample_rate: int, + speech_labels_for_update: torch.Tensor, + audio_buffer: torch.Tensor, + segment_indexes: List[int], + window: float, + shift: float, +) -> Tuple[List[torch.Tensor], List[List[float]], List[int]]: + """ + Generate subsegments for online processing from the given segment information. + This function extracts subsegments (embedding vector level) time-series from the + raw time-series buffer based on the segment interval (start and end timestamps) information. + + Args: + buffer_start (float): + Start point of the time-series signal buffer + buffer_end (float): + End point of the time-series signal buffer + sample_rate (int): + Sampling rate of the audio input + speech_labels_for_update (Tensor): + Tensor containing intervals (start and end timestamps) of the speech segments + audio_buffer (Tensor): + Tensor containing the raw time-series signal + segment_indexes (list): + List containing the unique indices of segments + window (float): + Window length in second + shift (float): + Shift length in second + + Returns: + sigs_list (list): + List containing the tensors of the old and the newly added time-series signals + sig_rangel_list (list): + List containing the old and the newly added intervals (timestamps) of the speech segments + sig_indexes (list): + List containing the old and the newly added unique indices of segments + """ + sigs_list: List[torch.Tensor] = [] + sig_rangel_list: List[List[float]] = [] + sig_indexes: List[int] = [] + if len(segment_indexes) > 0: + ind_offset = segment_indexes[-1] + else: + ind_offset = -1 + + for idx, range_spl in enumerate(speech_labels_for_update): + range_offs = [float(range_spl[0].item() - buffer_start), float(range_spl[1].item() - buffer_start)] + range_t = [max(0, range_offs[0]), range_offs[1]] + + subsegments = get_subsegments( + offset=range_t[0], window=window, shift=shift, duration=(range_t[1] - range_t[0]), + ) + ind_offset, sigs, ranges, inds = get_online_segments_from_slices( + sig=audio_buffer, + buffer_start=buffer_start, + buffer_end=buffer_end, + subsegments=subsegments, + window=window, + ind_offset=ind_offset, + sample_rate=sample_rate, + ) + + sigs_list.extend(sigs) + sig_rangel_list.extend(ranges) + sig_indexes.extend(inds) + + assert len(sigs_list) == len(sig_rangel_list) == len(sig_indexes) + return sigs_list, sig_rangel_list, sig_indexes + + +def get_scale_mapping_argmat(uniq_embs_and_timestamps: Dict[str, dict]) -> Dict[int, torch.Tensor]: + """ + Calculate cosine similarity values among speaker embeddings for each scale then + apply multiscale weights to calculate the fused similarity matrix. + + Args: + uniq_embs_and_timestamps: (dict) + The dictionary containing embeddings, timestamps and multiscale weights. + If uniq_embs_and_timestamps contains only one scale, single scale diarization + is performed. + + Returns: + scale_mapping_argmat (dict) + Dictionary containing scale mapping information matrix for each scale. + """ + scale_mapping_argmat = {} + embeddings_in_scales, timestamps_in_scales = split_input_data( + embeddings_in_scales=uniq_embs_and_timestamps['embeddings'], + timestamps_in_scales=uniq_embs_and_timestamps['timestamps'], + multiscale_segment_counts=uniq_embs_and_timestamps['multiscale_segment_counts'], + ) + session_scale_mapping_list = get_argmin_mat(timestamps_in_scales) + for scale_idx in range(len(session_scale_mapping_list)): + mapping_argmat = session_scale_mapping_list[scale_idx] + scale_mapping_argmat[scale_idx] = mapping_argmat + return scale_mapping_argmat + + +def get_overlap_stamps(cont_stamps: List[str], ovl_spk_idx: List[str]): + """ + Generate timestamps that include overlap speech. Overlap-including timestamps are created based on the segments that are + created for clustering diarizer. Overlap speech is assigned to the existing speech segments in `cont_stamps`. + + Args: + cont_stamps (list): + Non-overlapping (single speaker per segment) diarization output in string format. + Each line contains the start and end time of segments and corresponding speaker labels. + ovl_spk_idx (list): + List containing segment index of the estimated overlapped speech. The start and end of segments are based on the + single-speaker (i.e., non-overlap-aware) RTTM generation. + Returns: + total_ovl_cont_list (list): + Rendered diarization output in string format. Each line contains the start and end time of segments and + corresponding speaker labels. This format is identical to `cont_stamps`. + """ + ovl_spk_cont_list = [[] for _ in range(len(ovl_spk_idx))] + for spk_idx in range(len(ovl_spk_idx)): + for idx, cont_a_line in enumerate(cont_stamps): + start, end, speaker = cont_a_line.split() + if idx in ovl_spk_idx[spk_idx]: + ovl_spk_cont_list[spk_idx].append(f"{start} {end} speaker_{spk_idx}") + total_ovl_cont_list = [] + for ovl_cont_list in ovl_spk_cont_list: + if len(ovl_cont_list) > 0: + total_ovl_cont_list.extend(merge_stamps(ovl_cont_list)) + return total_ovl_cont_list + + +def get_adaptive_threshold(estimated_num_of_spks: int, min_threshold: float, overlap_infer_spk_limit: int): + """ + This function controls the magnitude of the sigmoid threshold based on the estimated number of speakers. As the number of + speakers becomes larger, diarization error rate is very sensitive on overlap speech detection. This function linearly increases + the threshold in proportion to the estimated number of speakers so more confident overlap speech results are reflected when + the number of estimated speakers are relatively high. + + Args: + estimated_num_of_spks (int): + Estimated number of speakers from the clustering result. + min_threshold (float): + Sigmoid threshold value from the config file. This threshold value is minimum threshold value when `estimated_num_of_spks=2` + overlap_infer_spk_limit (int): + If the `estimated_num_of_spks` is less then `overlap_infer_spk_limit`, overlap speech estimation is skipped. + + Returns: + adaptive_threshold (float): + Threshold value that is scaled based on the `estimated_num_of_spks`. + """ + adaptive_threshold = min_threshold - (estimated_num_of_spks - 2) * (min_threshold - 1) / ( + overlap_infer_spk_limit - 2 + ) + return adaptive_threshold + + +def generate_speaker_timestamps( + clus_labels: List[Union[float, int]], msdd_preds: List[torch.Tensor], **params +) -> Tuple[List[str], List[str]]: + ''' + Generate speaker timestamps from the segmentation information. If `use_clus_as_main=True`, use clustering result for main speaker + labels and use timestamps from the predicted sigmoid values. In this function, the main speaker labels in `maj_labels` exist for + every subsegment steps while overlap speaker labels in `ovl_labels` only exist for segments where overlap-speech is occuring. + + Args: + clus_labels (list): + List containing integer-valued speaker clustering results. + msdd_preds (list): + List containing tensors of the predicted sigmoid values. + Each tensor has shape of: (Session length, estimated number of speakers). + params: + Parameters for generating RTTM output and evaluation. Parameters include: + infer_overlap (bool): If False, overlap-speech will not be detected. + use_clus_as_main (bool): Add overlap-speech detection from MSDD to clustering results. If False, only MSDD output + is used for constructing output RTTM files. + overlap_infer_spk_limit (int): Above this limit, overlap-speech detection is bypassed. + use_adaptive_thres (bool): Boolean that determines whehther to use adaptive_threshold depending on the estimated + number of speakers. + max_overlap_spks (int): Maximum number of overlap speakers detected. Default is 2. + threshold (float): Sigmoid threshold for MSDD output. + + Returns: + maj_labels (list): + List containing string-formated single-speaker speech segment timestamps and corresponding speaker labels. + Example: [..., '551.685 552.77 speaker_1', '552.99 554.43 speaker_0', '554.97 558.19 speaker_0', ...] + ovl_labels (list): + List containing string-formated additional overlapping speech segment timestamps and corresponding speaker labels. + Note that `ovl_labels` includes only overlapping speech that is not included in `maj_labels`. + Example: [..., '152.495 152.745 speaker_1', '372.71 373.085 speaker_0', '554.97 555.885 speaker_1', ...] + ''' + msdd_preds.squeeze(0) + estimated_num_of_spks = msdd_preds.shape[-1] + overlap_speaker_list = [[] for _ in range(estimated_num_of_spks)] + infer_overlap = estimated_num_of_spks < int(params['overlap_infer_spk_limit']) + main_speaker_lines = [] + if params['use_adaptive_thres']: + threshold = get_adaptive_threshold( + estimated_num_of_spks, params['threshold'], params['overlap_infer_spk_limit'] + ) + else: + threshold = params['threshold'] + for seg_idx, cluster_label in enumerate(clus_labels): + msdd_preds.squeeze(0) + spk_for_seg = (msdd_preds[0, seg_idx] > threshold).int().cpu().numpy().tolist() + sm_for_seg = msdd_preds[0, seg_idx].cpu().numpy() + + if params['use_clus_as_main']: + main_spk_idx = int(cluster_label[2]) + else: + main_spk_idx = np.argsort(msdd_preds[0, seg_idx].cpu().numpy())[::-1][0] + + if sum(spk_for_seg) > 1 and infer_overlap: + idx_arr = np.argsort(sm_for_seg)[::-1] + for ovl_spk_idx in idx_arr[: params['max_overlap_spks']].tolist(): + if ovl_spk_idx != int(main_spk_idx): + overlap_speaker_list[ovl_spk_idx].append(seg_idx) + main_speaker_lines.append(f"{cluster_label[0]} {cluster_label[1]} speaker_{main_spk_idx}") + cont_stamps = get_contiguous_stamps(main_speaker_lines) + maj_labels = merge_stamps(cont_stamps) + ovl_labels = get_overlap_stamps(cont_stamps, overlap_speaker_list) + return maj_labels, ovl_labels + + +def get_uniq_id_list_from_manifest(manifest_file: str): + """Retrieve `uniq_id` values from the given manifest_file and save the IDs to a list. + """ + uniq_id_list = [] + with open(manifest_file, 'r', encoding='utf-8') as manifest: + for i, line in enumerate(manifest.readlines()): + line = line.strip() + dic = json.loads(line) + uniq_id = get_uniqname_from_filepath(dic['audio_filepath']) + uniq_id_list.append(uniq_id) + return uniq_id_list + + +def get_id_tup_dict(uniq_id_list: List[str], test_data_collection, preds_list: List[torch.Tensor]): + """ + Create session-level dictionary containing data needed to construct RTTM diarization output. + + Args: + uniq_id_list (list): + List containing the `uniq_id` values. + test_data_collection (collections.DiarizationLabelEntity): + Class instance that is containing session information such as targeted speaker indices, audio filepath and RTTM filepath. + preds_list (list): + List containing tensors of predicted sigmoid values. + + Returns: + session_dict (dict): + Dictionary containing session-level target speakers data and predicted simoid values in tensor format. + """ + session_dict = {x: [] for x in uniq_id_list} + for idx, line in enumerate(test_data_collection): + uniq_id = get_uniqname_from_filepath(line.audio_file) + session_dict[uniq_id].append([line.target_spks, preds_list[idx]]) + return session_dict + + +def prepare_split_data(manifest_filepath, _out_dir, multiscale_args_dict, global_rank): + """ + This function is needed for preparing diarization training data for multiscale diarization decoder (MSDD). + Prepare multiscale timestamp data for training. Oracle VAD timestamps from RTTM files are used as VAD timestamps. + In this function, timestamps for embedding extraction are extracted without extracting the embedding vectors. + + Args: + manifest_filepath (str): + Input manifest file for creating audio-to-RTTM mapping. + _out_dir (str): + Output directory where timestamp json files are saved. + + Returns: + multiscale_args_dict (dict): + - Dictionary containing two types of arguments: multi-scale weights and subsegment timestamps for each data sample. + - Each data sample has two keys: `multiscale_weights` and `scale_dict`. + - `multiscale_weights` key contains a list containing multiscale weights. + - `scale_dict` is indexed by integer keys which are scale index. + - Each data sample is indexed by using the following naming convention: `__` + Example: `fe_03_00106_mixed_626310_642300` + """ + speaker_dir = os.path.join(_out_dir, 'speaker_outputs') + + # Only if this is for the first run of modelPT instance, remove temp folders. + if global_rank == 0: + if os.path.exists(speaker_dir): + shutil.rmtree(speaker_dir) + os.makedirs(speaker_dir) + split_audio_rttm_map = audio_rttm_map(manifest_filepath, attach_dur=True) + + # Speech Activity Detection part + _speaker_manifest_path = os.path.join(speaker_dir, f'oracle_vad_manifest.json') + logging.info(f"Extracting oracle VAD timestamps and saving at {speaker_dir}") + if not os.path.exists(_speaker_manifest_path): + write_rttm2manifest(split_audio_rttm_map, _speaker_manifest_path, include_uniq_id=True) + + multiscale_timestamps_by_scale = {} + + # Segmentation + for scale_idx, (window, shift) in multiscale_args_dict['scale_dict'].items(): + subsegments_manifest_path = os.path.join(speaker_dir, f'subsegments_scale{scale_idx}.json') + if not os.path.exists(subsegments_manifest_path): + # Sub-segmentation for the current scale (scale_idx) + segments_manifest_to_subsegments_manifest( + segments_manifest_file=_speaker_manifest_path, + subsegments_manifest_file=subsegments_manifest_path, + window=window, + shift=shift, + include_uniq_id=True, + ) + logging.info( + f"Subsegmentation for timestamp extracted for: scale-{scale_idx} at {subsegments_manifest_path}" + ) + multiscale_timestamps = extract_timestamps(subsegments_manifest_path) + multiscale_timestamps_by_scale[scale_idx] = multiscale_timestamps + + multiscale_timestamps_dict = get_timestamps(multiscale_timestamps_by_scale, multiscale_args_dict) + return multiscale_timestamps_dict + + +def extract_timestamps(manifest_file: str): + """ + This method extracts timestamps from segments passed through manifest_file. + + Args: + manifest_file (str): + Manifest file containing segmentation information. + Returns: + time_stamps (dict): + Dictionary containing lists of timestamps. + """ + logging.info(f"Extracting timestamps from {manifest_file} for multiscale subsegmentation.") + time_stamps = {} + with open(manifest_file, 'r', encoding='utf-8') as manifest: + for i, line in enumerate(manifest.readlines()): + line = line.strip() + dic = json.loads(line) + uniq_name = dic['uniq_id'] + if uniq_name not in time_stamps: + time_stamps[uniq_name] = [] + start = dic['offset'] + end = start + dic['duration'] + time_stamps[uniq_name].append([start, end]) + return time_stamps + + +def make_rttm_with_overlap( + manifest_file_path: str, + clus_label_dict: Dict[str, List[Union[float, int]]], + msdd_preds: List[torch.Tensor], + **params, +): + """ + Create RTTM files that include detected overlap speech. Note that the effect of overlap detection is only + notable when RTTM files are evaluated with `ignore_overlap=False` option. + + Args: + manifest_file_path (str): + Path to the input manifest file. + clus_label_dict (dict): + Dictionary containing subsegment timestamps in float type and cluster labels in integer type. + Indexed by `uniq_id` string. + msdd_preds (list): + List containing tensors of the predicted sigmoid values. + Each tensor has shape of: (Session length, estimated number of speakers). + params: + Parameters for generating RTTM output and evaluation. Parameters include: + infer_overlap (bool): If False, overlap-speech will not be detected. + See docstrings of `generate_speaker_timestamps` function for other variables in `params`. + + Returns: + all_hypothesis (list): + List containing Pyannote's `Annotation` objects that are created from hypothesis RTTM outputs. + all_reference + List containing Pyannote's `Annotation` objects that are created from ground-truth RTTM outputs + """ + AUDIO_RTTM_MAP = audio_rttm_map(manifest_file_path) + manifest_file_lengths_list = [] + all_hypothesis, all_reference = [], [] + no_references = False + with open(manifest_file_path, 'r', encoding='utf-8') as manifest: + for i, line in enumerate(manifest.readlines()): + uniq_id = get_uniq_id_from_manifest_line(line) + manifest_dic = AUDIO_RTTM_MAP[uniq_id] + clus_labels = clus_label_dict[uniq_id] + manifest_file_lengths_list.append(len(clus_labels)) + maj_labels, ovl_labels = generate_speaker_timestamps(clus_labels, msdd_preds[i], **params) + if params['infer_overlap']: + hyp_labels = maj_labels + ovl_labels + else: + hyp_labels = maj_labels + hypothesis = labels_to_pyannote_object(hyp_labels, uniq_name=uniq_id) + if params['out_rttm_dir']: + hyp_labels = sorted(hyp_labels, key=lambda x: float(x.split()[0])) + labels_to_rttmfile(hyp_labels, uniq_id, params['out_rttm_dir']) + all_hypothesis.append([uniq_id, hypothesis]) + rttm_file = manifest_dic.get('rttm_filepath', None) + if rttm_file is not None and os.path.exists(rttm_file) and not no_references: + ref_labels = rttm_to_labels(rttm_file) + reference = labels_to_pyannote_object(ref_labels, uniq_name=uniq_id) + all_reference.append([uniq_id, reference]) + else: + no_references = True + all_reference = [] + return all_reference, all_hypothesis + + +def embedding_normalize(embs, use_std=False, eps=1e-10): + """ + Mean and l2 length normalize the input speaker embeddings + + Args: + embs: embeddings of shape (Batch,emb_size) + Returns: + embs: normalized embeddings of shape (Batch,emb_size) + """ + embs = embs - embs.mean(axis=0) + if use_std: + embs = embs / (embs.std(axis=0) + eps) + embs_l2_norm = np.expand_dims(np.linalg.norm(embs, ord=2, axis=-1), axis=1) + embs = embs / embs_l2_norm + + return embs + + +class OnlineSegmentor: + """ + Online Segmentor for online (streaming) diarizer. + - The class instances created by this class takes time-series signal from the audio buffer and + creates subsegments for embedding extraction. + - Since online segmentation is based on a short audio buffer, the methods in this class extracts + a few subsegments from the given intervals for the raw time-series signal. + + Attributes: + frame_start (float): + Start of the middle chunk + buffer_start (float): + Start of the entire buffer + buffer_end (float): + End of the entire buffer + sample_rate (int): + Sampling rate of the input time-series signal + cumulative_speech_labels (Tensor): + Torch tensor matrix containing culmulative VAD (speech activity) timestamps + """ + + def __init__(self, sample_rate: int): + self.frame_start: float = 0.0 + self.buffer_start: float = 0.0 + self.buffer_end: float = 0.0 + self.sample_rate: int = sample_rate + self.cumulative_speech_labels: torch.Tensor = torch.tensor([]) + + def run_online_segmentation( + self, + audio_buffer: torch.Tensor, + vad_timestamps: torch.Tensor, + segment_raw_audio: List[torch.Tensor], + segment_range_ts: List[List[float]], + segment_indexes: List[int], + window: float, + shift: float, + ): + """ + Remove the old segments that overlap with the new frame (self.frame_start) + cursor_for_old_segments is pointing at the onset of the t_range popped most recently. + + Frame is in the middle of the buffer. + + |___Buffer___[___________]____________| + |____________[ Frame ]____________| + + | <- buffer start + |____________| <- frame start + + + Args: + audio_buffer (Tensor): + Tensor containing raw time-series signal + vad_timestamps (Tensor): + Tensor containing VAD intervals (start and end timestamps) + segment_raw_audio (list): + List containing the previously added tensors of the raw time-series signal segments + segment_range_ts (list): + List containing the previously added intervals (start and end timestamps) of each segment + segment_indexes (list): + List containing the previously added global integer indicies of the segments from + start to current cursor + window (float): + Window length in second + shift (float): + Shift length in second + + Returns: + segment_raw_audio (list): + List containing the newly added tensors of the raw time-series signal + segment_range_ts (list): + List containing the newly added interval (start and end timestamps) of each segment + segment_indexes (list): + List containing the newly added global integer indicies of the segments from + start to current cursor + """ + if self.buffer_start >= 0: + # Check if this is the very first step + if len(segment_raw_audio) == 0 and vad_timestamps.shape[0] > 0: + vad_timestamps[0][0] = max(vad_timestamps[0][0], 0.0) + speech_labels_for_update = vad_timestamps + self.cumulative_speech_labels = speech_labels_for_update + else: + # Calculate a cursor for the update point + cursor_for_old_segments, cursor_index = get_new_cursor_for_update(self.frame_start, segment_range_ts) + + segment_range_ts = segment_range_ts[:cursor_index] + segment_raw_audio = segment_raw_audio[:cursor_index] + segment_indexes = segment_indexes[:cursor_index] + + if not len(segment_raw_audio) == len(segment_range_ts) == len(segment_indexes): + raise ValueError("Scale-wise segment information has a mismatch in length.") + + speech_labels_for_update, self.cumulative_speech_labels = get_speech_labels_for_update( + self.frame_start, + self.buffer_end, + self.cumulative_speech_labels, + vad_timestamps, + cursor_for_old_segments, + ) + + # Collect the timeseries signal from the buffer + sigs_list, sig_rangel_list, sig_indexes = get_online_subsegments_from_buffer( + buffer_start=self.buffer_start, + buffer_end=self.buffer_end, + sample_rate=self.sample_rate, + speech_labels_for_update=speech_labels_for_update, + audio_buffer=audio_buffer, + segment_indexes=segment_indexes, + window=window, + shift=shift, + ) + + segment_raw_audio.extend(sigs_list) + segment_range_ts.extend(sig_rangel_list) + segment_indexes.extend(sig_indexes) + + if not len(segment_raw_audio) == len(segment_range_ts) == len(segment_indexes): + raise ValueError("Segment information has a mismatch in length.") + return segment_raw_audio, segment_range_ts, segment_indexes diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/streaming_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/streaming_utils.py new file mode 100644 index 0000000..71c945b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/streaming_utils.py @@ -0,0 +1,1741 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import os +from typing import Optional + +import numpy as np +import torch +from omegaconf import OmegaConf +from torch.utils.data import DataLoader + +from nemo.collections.asr.data.audio_to_text_lhotse_prompted import canary_prompt +from nemo.collections.asr.models.ctc_bpe_models import EncDecCTCModelBPE +from nemo.collections.asr.parts.mixins.streaming import StreamingEncoder +from nemo.collections.asr.parts.preprocessing.features import normalize_batch +from nemo.collections.asr.parts.utils.audio_utils import get_samples +from nemo.core.classes import IterableDataset +from nemo.core.neural_types import LengthsType, MelSpectrogramType, NeuralType + +# Minimum number of tokens required to assign a LCS merge step, otherwise ignore and +# select all i-1 and ith buffer tokens to merge. +MIN_MERGE_SUBSEQUENCE_LEN = 1 + + +def print_alignment(alignment): + """ + Print an alignment matrix of the shape (m + 1, n + 1) + + Args: + alignment: An integer alignment matrix of shape (m + 1, n + 1) + """ + m = len(alignment) + if m > 0: + n = len(alignment[0]) + for i in range(m): + for j in range(n): + if j == 0: + print(f"{i:4d} |", end=" ") + print(f"{alignment[i][j]}", end=" ") + print() + + +def write_lcs_alignment_to_pickle(alignment, filepath, extras=None): + """ + Writes out the LCS alignment to a file, along with any extras provided. + + Args: + alignment: An alignment matrix of shape [m + 1, n + 1] + filepath: str filepath + extras: Optional dictionary of items to preserve. + """ + if extras is None: + extras = {} + + extras['alignment'] = alignment + torch.save(extras, filepath) + + +def longest_common_subsequence_merge(X, Y, filepath=None): + """ + Longest Common Subsequence merge algorithm for aligning two consecutive buffers. + + Base alignment construction algorithm is Longest Common Subsequence (reffered to as LCS hear after) + + LCS Merge algorithm looks at two chunks i-1 and i, determins the aligned overlap at the + end of i-1 and beginning of ith chunk, and then clips the subsegment of the ith chunk. + + Assumption is that the two chunks are consecutive chunks, and there exists at least small overlap acoustically. + + It is a sub-word token merge algorithm, operating on the abstract notion of integer ids representing the subword ids. + It is independent of text or character encoding. + + Since the algorithm is merge based, and depends on consecutive buffers, the very first buffer is processes using + the "middle tokens" algorithm. + + It requires a delay of some number of tokens such that: + lcs_delay = math.floor(((total_buffer_in_secs - chunk_len_in_sec)) / model_stride_in_secs) + + Total cost of the model is O(m_{i-1} * n_{i}) where (m, n) represents the number of subword ids of the buffer. + + Args: + X: The subset of the previous chunk i-1, sliced such X = X[-(lcs_delay * max_steps_per_timestep):] + Therefore there can be at most lcs_delay * max_steps_per_timestep symbols for X, preserving computation. + Y: The entire current chunk i. + filepath: Optional filepath to save the LCS alignment matrix for later introspection. + + Returns: + A tuple containing - + - i: Start index of alignment along the i-1 chunk. + - j: Start index of alignment along the ith chunk. + - slice_len: number of tokens to slice off from the ith chunk. + The LCS alignment matrix itself (shape m + 1, n + 1) + """ + # LCSuff is the table with zero + # value initially in each cell + m = len(X) + n = len(Y) + LCSuff = [[0 for k in range(n + 1)] for l in range(m + 1)] + + # To store the length of + # longest common substring + result = 0 + result_idx = [0, 0, 0] # Contains (i, j, slice_len) + + # Following steps to build + # LCSuff[m+1][n+1] in bottom up fashion + for i in range(m + 1): + for j in range(n + 1): + if i == 0 or j == 0: + LCSuff[i][j] = 0 + elif X[i - 1] == Y[j - 1]: + LCSuff[i][j] = LCSuff[i - 1][j - 1] + 1 + + if result <= LCSuff[i][j]: + result = LCSuff[i][j] # max(result, LCSuff[i][j]) + result_idx = [i, j, result] + + else: + LCSuff[i][j] = 0 + + # Check if perfect alignment was found or not + # Perfect alignment is found if : + # Longest common subsequence extends to the final row of of the old buffer + # This means that there exists a diagonal LCS backtracking to the beginning of the new buffer + i, j = result_idx[0:2] + is_complete_merge = i == m + + # Perfect alignment was found, slice eagerly + if is_complete_merge: + length = result_idx[-1] + + # In case the LCS was incomplete - missing a few tokens at the beginning + # Perform backtrack to find the origin point of the slice (j) and how many tokens should be sliced + while length >= 0 and i > 0 and j > 0: + # Alignment exists at the required diagonal + if LCSuff[i - 1][j - 1] > 0: + length -= 1 + i, j = i - 1, j - 1 + + else: + # End of longest alignment + i, j, length = i - 1, j - 1, length - 1 + break + + else: + # Expand hypothesis to catch partial mismatch + + # There are 3 steps for partial mismatch in alignment + # 1) Backward search for leftmost LCS + # 2) Greedy expansion of leftmost LCS to the right + # 3) Backtrack final leftmost expanded LCS to find origin point of slice + + # (1) Backward search for Leftmost LCS + # This is required for cases where multiple common subsequences exist + # We only need to select the leftmost one - since that corresponds + # to the last potential subsequence that matched with the new buffer. + # If we just chose the LCS (and not the leftmost LCS), then we can potentially + # slice off major sections of text which are repeated between two overlapping buffers. + + # backward linear search for leftmost j with longest subsequence + max_j = 0 + max_j_idx = n + + i_partial = m # Starting index of i for partial merge + j_partial = -1 # Index holder of j for partial merge + j_skip = 0 # Number of tokens that were skipped along the diagonal + slice_count = 0 # Number of tokens that should be sliced + + # Select leftmost LCS + for i_idx in range(m, -1, -1): # start from last timestep of old buffer + for j_idx in range(0, n + 1): # start from first token from new buffer + # Select the longest LCSuff, while minimizing the index of j (token index for new buffer) + if LCSuff[i_idx][j_idx] > max_j and j_idx <= max_j_idx: + max_j = LCSuff[i_idx][j_idx] + max_j_idx = j_idx + + # Update the starting indices of the partial merge + i_partial = i_idx + j_partial = j_idx + + # EARLY EXIT (if max subsequence length <= MIN merge length) + # Important case where there is long silence + # The end of one buffer will have many blank tokens, the beginning of new buffer may have many blank tokens + # As such, LCS will potentially be from the region of actual tokens. + # This can be detected as the max length of the suffix in LCS + # If this max length of the leftmost suffix is less than some margin, avoid slicing all together. + if max_j <= MIN_MERGE_SUBSEQUENCE_LEN: + # If the number of partiial tokens to be deleted are less than the minimum, + # dont delete any tokens at all. + + i = i_partial + j = 0 + result_idx[-1] = 0 + + else: + # Some valid long partial alignment was found + # (2) Expand this alignment along the diagonal *downwards* towards the end of the old buffer + # such that i_partial = m + 1. + # This is a common case where due to LSTM state or reduced buffer size, the alignment breaks + # in the middle but there are common subsequences between old and new buffers towards the end + # We can expand the current leftmost LCS in a diagonal manner downwards to include such potential + # merge regions. + + # Expand current partial subsequence with co-located tokens + i_temp = i_partial + 1 # diagonal next i + j_temp = j_partial + 1 # diagonal next j + + j_exp = 0 # number of tokens to expand along the diagonal + j_skip = 0 # how many diagonals didnt have the token. Incremented by 1 for every row i + + for i_idx in range(i_temp, m + 1): # walk from i_partial + 1 => m + 1 + j_any_skip = 0 # If the diagonal element at this location is not found, set to 1 + # j_any_skip expands the search space one place to the right + # This allows 1 diagonal misalignment per timestep i (and expands the search for the next timestep) + + # walk along the diagonal corresponding to i_idx, plus allowing diagonal skips to occur + # diagonal elements may not be aligned due to ASR model predicting + # incorrect token in between correct tokens + for j_idx in range(j_temp, j_temp + j_skip + 1): + if j_idx < n + 1: + if LCSuff[i_idx][j_idx] == 0: + j_any_skip = 1 + else: + j_exp = 1 + j_skip + j_any_skip + + # If the diagonal element existed, dont expand the search space, + # otherwise expand the search space 1 token to the right + j_skip += j_any_skip + + # Move one step to the right for the next diagonal j corresponding to i + j_temp += 1 + + # reset j_skip, augment j_partial with expansions + j_skip = 0 + j_partial += j_exp + + # (3) Given new leftmost j_partial with expansions, backtrack the partial alignments + # counting how many diagonal skips occured to compute slice length + # as well as starting point of slice. + + # Partial backward trace to find start of slice + while i_partial > 0 and j_partial > 0: + if LCSuff[i_partial][j_partial] == 0: + # diagonal skip occured, move j to left 1 extra time + j_partial -= 1 + j_skip += 1 + + if j_partial > 0: + # If there are more steps to be taken to the left, slice off the current j + # Then loop for next (i, j) diagonal to the upper left + slice_count += 1 + i_partial -= 1 + j_partial -= 1 + + # Recompute total slice length as slice count along diagonal + # plus the number of diagonal skips + i = max(0, i_partial) + j = max(0, j_partial) + result_idx[-1] = slice_count + j_skip + + # Set the value of i and j + result_idx[0] = i + result_idx[1] = j + + if filepath is not None: + extras = { + "is_complete_merge": is_complete_merge, + "X": X, + "Y": Y, + "slice_idx": result_idx, + } + write_lcs_alignment_to_pickle(LCSuff, filepath=filepath, extras=extras) + print("Wrote alignemnt to :", filepath) + + return result_idx, LCSuff + + +def lcs_alignment_merge_buffer(buffer, data, delay, model, max_steps_per_timestep: int = 5, filepath: str = None): + """ + Merges the new text from the current frame with the previous text contained in the buffer. + + The alignment is based on a Longest Common Subsequence algorithm, with some additional heuristics leveraging + the notion that the chunk size is >= the context window. In case this assumptio is violated, the results of the merge + will be incorrect (or at least obtain worse WER overall). + """ + # If delay timesteps is 0, that means no future context was used. Simply concatenate the buffer with new data. + if delay < 1: + buffer += data + return buffer + + # If buffer is empty, simply concatenate the buffer and data. + if len(buffer) == 0: + buffer += data + return buffer + + # Prepare a subset of the buffer that will be LCS Merged with new data + search_size = int(delay * max_steps_per_timestep) + buffer_slice = buffer[-search_size:] + + # Perform LCS Merge + lcs_idx, lcs_alignment = longest_common_subsequence_merge(buffer_slice, data, filepath=filepath) + + # Slice off new data + # i, j, slice_len = lcs_idx + slice_idx = lcs_idx[1] + lcs_idx[-1] # slice = j + slice_len + data = data[slice_idx:] + + # Concat data to buffer + buffer += data + return buffer + + +def inplace_buffer_merge(buffer, data, timesteps, model): + """ + Merges the new text from the current frame with the previous text contained in the buffer. + + The alignment is based on a Longest Common Subsequence algorithm, with some additional heuristics leveraging + the notion that the chunk size is >= the context window. In case this assumptio is violated, the results of the merge + will be incorrect (or at least obtain worse WER overall). + """ + # If delay timesteps is 0, that means no future context was used. Simply concatenate the buffer with new data. + if timesteps < 1: + buffer += data + return buffer + + # If buffer is empty, simply concatenate the buffer and data. + if len(buffer) == 0: + buffer += data + return buffer + + # Concat data to buffer + buffer += data + return buffer + + +class StreamingFeatureBufferer: + """ + Class to append each feature frame to a buffer and return an array of buffers. + This class is designed to perform a real-life streaming decoding where only a single chunk + is provided at each step of a streaming pipeline. + """ + + def __init__(self, asr_model, chunk_size, buffer_size): + ''' + Args: + asr_model: + Reference to the asr model instance for which the feature needs to be created + chunk_size (float): + Duration of the new chunk of audio + buffer_size (float): + Size of the total audio in seconds maintained in the buffer + ''' + + self.NORM_CONSTANT = 1e-5 + if hasattr(asr_model.preprocessor, 'log') and asr_model.preprocessor.log: + self.ZERO_LEVEL_SPEC_DB_VAL = -16.635 # Log-Melspectrogram value for zero signal + else: + self.ZERO_LEVEL_SPEC_DB_VAL = 0.0 + self.asr_model = asr_model + self.sr = asr_model.cfg.sample_rate + self.model_normalize_type = asr_model.cfg.preprocessor.normalize + self.chunk_size = chunk_size + timestep_duration = asr_model.cfg.preprocessor.window_stride + + self.n_chunk_look_back = int(timestep_duration * self.sr) + self.n_chunk_samples = int(chunk_size * self.sr) + self.buffer_size = buffer_size + total_buffer_len = int(buffer_size / timestep_duration) + self.n_feat = asr_model.cfg.preprocessor.features + self.sample_buffer = torch.zeros(int(self.buffer_size * self.sr)) + self.buffer = torch.ones([self.n_feat, total_buffer_len], dtype=torch.float32) * self.ZERO_LEVEL_SPEC_DB_VAL + self.feature_chunk_len = int(chunk_size / timestep_duration) + self.feature_buffer_len = total_buffer_len + + self.reset() + cfg = copy.deepcopy(asr_model.cfg) + OmegaConf.set_struct(cfg.preprocessor, False) + + cfg.preprocessor.dither = 0.0 + cfg.preprocessor.pad_to = 0 + cfg.preprocessor.normalize = "None" + self.raw_preprocessor = EncDecCTCModelBPE.from_config_dict(cfg.preprocessor) + self.raw_preprocessor.to(asr_model.device) + + def reset(self): + ''' + Reset frame_history and decoder's state + ''' + self.buffer = torch.ones(self.buffer.shape, dtype=torch.float32) * self.ZERO_LEVEL_SPEC_DB_VAL + self.frame_buffers = [] + self.sample_buffer = torch.zeros(int(self.buffer_size * self.sr)) + self.feature_buffer = ( + torch.ones([self.n_feat, self.feature_buffer_len], dtype=torch.float32) * self.ZERO_LEVEL_SPEC_DB_VAL + ) + + def _add_chunk_to_buffer(self, chunk): + """ + Add time-series audio signal to `sample_buffer` + + Args: + chunk (Tensor): + Tensor filled with time-series audio signal + """ + self.sample_buffer[: -self.n_chunk_samples] = self.sample_buffer[self.n_chunk_samples :].clone() + self.sample_buffer[-self.n_chunk_samples :] = chunk.clone() + + def _update_feature_buffer(self, feat_chunk): + """ + Add an extracted feature to `feature_buffer` + """ + self.feature_buffer[:, : -self.feature_chunk_len] = self.feature_buffer[:, self.feature_chunk_len :].clone() + self.feature_buffer[:, -self.feature_chunk_len :] = feat_chunk.clone() + + def get_raw_feature_buffer(self): + return self.feature_buffer + + def get_normalized_feature_buffer(self): + normalized_buffer, _, _ = normalize_batch( + x=self.feature_buffer.unsqueeze(0), + seq_len=torch.tensor([len(self.feature_buffer)]), + normalize_type=self.model_normalize_type, + ) + return normalized_buffer.squeeze(0) + + def _convert_buffer_to_features(self): + """ + Extract features from the time-series audio buffer `sample_buffer`. + """ + # samples for conversion to features. + # Add look_back to have context for the first feature + samples = self.sample_buffer[: -(self.n_chunk_samples + self.n_chunk_look_back)] + device = self.asr_model.device + audio_signal = samples.unsqueeze_(0).to(device) + audio_signal_len = torch.Tensor([samples.shape[1]]).to(device) + features, features_len = self.raw_preprocessor(input_signal=audio_signal, length=audio_signal_len,) + features = features.squeeze() + self._update_feature_buffer(features[:, -self.feature_chunk_len :]) + + def update_feature_buffer(self, chunk): + """ + Update time-series signal `chunk` to the buffer then generate features out of the + signal in the audio buffer. + + Args: + chunk (Tensor): + Tensor filled with time-series audio signal + """ + if len(chunk) > self.n_chunk_samples: + raise ValueError(f"chunk should be of length {self.n_chunk_samples} or less") + if len(chunk) < self.n_chunk_samples: + temp_chunk = torch.zeros(self.n_chunk_samples, dtype=torch.float32) + temp_chunk[: chunk.shape[0]] = chunk + chunk = temp_chunk + self._add_chunk_to_buffer(chunk) + self._convert_buffer_to_features() + + +class AudioFeatureIterator(IterableDataset): + def __init__(self, samples, frame_len, preprocessor, device, pad_to_frame_len=True): + self._samples = samples + self._frame_len = frame_len + self._start = 0 + self.output = True + self.count = 0 + self.pad_to_frame_len = pad_to_frame_len + timestep_duration = preprocessor._cfg['window_stride'] + self._feature_frame_len = frame_len / timestep_duration + audio_signal = torch.from_numpy(self._samples).unsqueeze_(0).to(device) + audio_signal_len = torch.Tensor([self._samples.shape[0]]).to(device) + self._features, self._features_len = preprocessor(input_signal=audio_signal, length=audio_signal_len,) + self._features = self._features.squeeze() + + def __iter__(self): + return self + + def __next__(self): + if not self.output: + raise StopIteration + last = int(self._start + self._feature_frame_len) + if last <= self._features_len[0]: + frame = self._features[:, self._start : last].cpu() + self._start = last + else: + if not self.pad_to_frame_len: + frame = self._features[:, self._start : self._features_len[0]].cpu() + else: + frame = np.zeros([self._features.shape[0], int(self._feature_frame_len)], dtype='float32') + segment = self._features[:, self._start : self._features_len[0]].cpu() + frame[:, : segment.shape[1]] = segment + self.output = False + self.count += 1 + return frame + + +def speech_collate_fn(batch): + """collate batch of audio sig, audio len, tokens, tokens len + Args: + batch (Optional[FloatTensor], Optional[LongTensor], LongTensor, + LongTensor): A tuple of tuples of signal, signal lengths, + encoded tokens, and encoded tokens length. This collate func + assumes the signals are 1d torch tensors (i.e. mono audio). + """ + _, audio_lengths = zip(*batch) + max_audio_len = 0 + has_audio = audio_lengths[0] is not None + if has_audio: + max_audio_len = max(audio_lengths).item() + + audio_signal = [] + for sig, sig_len in batch: + if has_audio: + sig_len = sig_len.item() + if sig_len < max_audio_len: + pad = (0, max_audio_len - sig_len) + sig = torch.nn.functional.pad(sig, pad) + audio_signal.append(sig) + + if has_audio: + audio_signal = torch.stack(audio_signal) + audio_lengths = torch.stack(audio_lengths) + else: + audio_signal, audio_lengths = None, None + + return audio_signal, audio_lengths + + +# simple data layer to pass buffered frames of audio samples +class AudioBuffersDataLayer(IterableDataset): + @property + def output_types(self): + return { + "processed_signal": NeuralType(('B', 'D', 'T'), MelSpectrogramType()), + "processed_length": NeuralType(tuple('B'), LengthsType()), + } + + def __init__(self): + super().__init__() + + def __iter__(self): + return self + + def __next__(self): + if self._buf_count == len(self.signal): + raise StopIteration + self._buf_count += 1 + return ( + torch.as_tensor(self.signal[self._buf_count - 1], dtype=torch.float32), + torch.as_tensor(self.signal[self._buf_count - 1].shape[1], dtype=torch.int64), + ) + + def set_signal(self, signals): + self.signal = signals + self.signal_shape = self.signal[0].shape + self._buf_count = 0 + + def __len__(self): + return 1 + + +class FeatureFrameBufferer: + """ + Class to append each feature frame to a buffer and return + an array of buffers. + """ + + def __init__(self, asr_model, frame_len=1.6, batch_size=4, total_buffer=4.0, pad_to_buffer_len=True): + ''' + Args: + frame_len: frame's duration, seconds + frame_overlap: duration of overlaps before and after current frame, seconds + offset: number of symbols to drop for smooth streaming + ''' + if hasattr(asr_model.preprocessor, 'log') and asr_model.preprocessor.log: + self.ZERO_LEVEL_SPEC_DB_VAL = -16.635 # Log-Melspectrogram value for zero signal + else: + self.ZERO_LEVEL_SPEC_DB_VAL = 0.0 + self.asr_model = asr_model + self.sr = asr_model._cfg.sample_rate + self.frame_len = frame_len + timestep_duration = asr_model._cfg.preprocessor.window_stride + self.n_frame_len = int(frame_len / timestep_duration) + + total_buffer_len = int(total_buffer / timestep_duration) + self.n_feat = asr_model._cfg.preprocessor.features + self.buffer = np.ones([self.n_feat, total_buffer_len], dtype=np.float32) * self.ZERO_LEVEL_SPEC_DB_VAL + self.pad_to_buffer_len = pad_to_buffer_len + self.batch_size = batch_size + + self.signal_end = False + self.frame_reader = None + self.feature_buffer_len = total_buffer_len + + self.feature_buffer = ( + np.ones([self.n_feat, self.feature_buffer_len], dtype=np.float32) * self.ZERO_LEVEL_SPEC_DB_VAL + ) + self.frame_buffers = [] + self.buffered_features_size = 0 + self.reset() + self.buffered_len = 0 + + def reset(self): + ''' + Reset frame_history and decoder's state + ''' + self.buffer = np.ones(shape=self.buffer.shape, dtype=np.float32) * self.ZERO_LEVEL_SPEC_DB_VAL + self.prev_char = '' + self.unmerged = [] + self.frame_buffers = [] + self.buffered_len = 0 + self.feature_buffer = ( + np.ones([self.n_feat, self.feature_buffer_len], dtype=np.float32) * self.ZERO_LEVEL_SPEC_DB_VAL + ) + + def get_batch_frames(self): + if self.signal_end: + return [] + batch_frames = [] + for frame in self.frame_reader: + batch_frames.append(np.copy(frame)) + if len(batch_frames) == self.batch_size: + return batch_frames + self.signal_end = True + + return batch_frames + + def get_frame_buffers(self, frames): + # Build buffers for each frame + self.frame_buffers = [] + for frame in frames: + curr_frame_len = frame.shape[1] + self.buffered_len += curr_frame_len + if curr_frame_len < self.feature_buffer_len and not self.pad_to_buffer_len: + self.frame_buffers.append(np.copy(frame)) + continue + self.buffer[:, :-curr_frame_len] = self.buffer[:, curr_frame_len:] + self.buffer[:, -self.n_frame_len :] = frame + self.frame_buffers.append(np.copy(self.buffer)) + return self.frame_buffers + + def set_frame_reader(self, frame_reader): + self.frame_reader = frame_reader + self.signal_end = False + + def _update_feature_buffer(self, feat_frame): + curr_frame_len = feat_frame.shape[1] + if curr_frame_len < self.feature_buffer_len and not self.pad_to_buffer_len: + self.feature_buffer = np.copy(feat_frame) # assume that only the last frame is less than the buffer length + else: + self.feature_buffer[:, : -feat_frame.shape[1]] = self.feature_buffer[:, feat_frame.shape[1] :] + self.feature_buffer[:, -feat_frame.shape[1] :] = feat_frame + self.buffered_features_size += feat_frame.shape[1] + + def get_norm_consts_per_frame(self, batch_frames): + norm_consts = [] + for i, frame in enumerate(batch_frames): + self._update_feature_buffer(frame) + mean_from_buffer = np.mean(self.feature_buffer, axis=1) + stdev_from_buffer = np.std(self.feature_buffer, axis=1) + norm_consts.append((mean_from_buffer.reshape(self.n_feat, 1), stdev_from_buffer.reshape(self.n_feat, 1))) + return norm_consts + + def normalize_frame_buffers(self, frame_buffers, norm_consts): + CONSTANT = 1e-5 + for i, frame_buffer in enumerate(frame_buffers): + frame_buffers[i] = (frame_buffer - norm_consts[i][0]) / (norm_consts[i][1] + CONSTANT) + + def get_buffers_batch(self): + batch_frames = self.get_batch_frames() + + while len(batch_frames) > 0: + + frame_buffers = self.get_frame_buffers(batch_frames) + norm_consts = self.get_norm_consts_per_frame(batch_frames) + if len(frame_buffers) == 0: + continue + self.normalize_frame_buffers(frame_buffers, norm_consts) + return frame_buffers + return [] + + +# class for streaming frame-based ASR +# 1) use reset() method to reset FrameASR's state +# 2) call transcribe(frame) to do ASR on +# contiguous signal's frames +class FrameBatchASR: + """ + class for streaming frame-based ASR use reset() method to reset FrameASR's + state call transcribe(frame) to do ASR on contiguous signal's frames + """ + + def __init__( + self, asr_model, frame_len=1.6, total_buffer=4.0, batch_size=4, pad_to_buffer_len=True, + ): + ''' + Args: + frame_len: frame's duration, seconds + frame_overlap: duration of overlaps before and after current frame, seconds + offset: number of symbols to drop for smooth streaming + ''' + self.frame_bufferer = FeatureFrameBufferer( + asr_model=asr_model, + frame_len=frame_len, + batch_size=batch_size, + total_buffer=total_buffer, + pad_to_buffer_len=pad_to_buffer_len, + ) + + self.asr_model = asr_model + self.decoder = getattr(asr_model, "decoder", None) + + self.batch_size = batch_size + self.all_logits = [] + self.all_preds = [] + + self.unmerged = [] + + if self.decoder is None: + self.blank_id = len(asr_model.tokenizer.vocabulary) + elif hasattr(asr_model.decoder, "vocabulary"): + self.blank_id = len(asr_model.decoder.vocabulary) + else: + self.blank_id = len(asr_model.joint.vocabulary) + self.tokenizer = asr_model.tokenizer + self.toks_unmerged = [] + self.frame_buffers = [] + self.reset() + cfg = copy.deepcopy(asr_model._cfg) + self.cfg = cfg + self.frame_len = frame_len + OmegaConf.set_struct(cfg.preprocessor, False) + + # some changes for streaming scenario + cfg.preprocessor.dither = 0.0 + cfg.preprocessor.pad_to = 0 + cfg.preprocessor.normalize = "None" + self.raw_preprocessor = EncDecCTCModelBPE.from_config_dict(cfg.preprocessor) + self.raw_preprocessor.to(asr_model.device) + self.preprocessor = self.raw_preprocessor + + def reset(self): + """ + Reset frame_history and decoder's state + """ + self.prev_char = '' + self.unmerged = [] + self.data_layer = AudioBuffersDataLayer() + self.data_loader = DataLoader(self.data_layer, batch_size=self.batch_size, collate_fn=speech_collate_fn) + self.all_logits = [] + self.all_preds = [] + self.toks_unmerged = [] + self.frame_buffers = [] + self.frame_bufferer.reset() + + def read_audio_file(self, audio_filepath: str, delay, model_stride_in_secs): + samples = get_samples(audio_filepath) + samples = np.pad(samples, (0, int(delay * model_stride_in_secs * self.asr_model._cfg.sample_rate))) + frame_reader = AudioFeatureIterator(samples, self.frame_len, self.raw_preprocessor, self.asr_model.device) + self.set_frame_reader(frame_reader) + + def set_frame_reader(self, frame_reader): + self.frame_bufferer.set_frame_reader(frame_reader) + + @torch.no_grad() + def infer_logits(self, keep_logits=False): + frame_buffers = self.frame_bufferer.get_buffers_batch() + + while len(frame_buffers) > 0: + self.frame_buffers += frame_buffers[:] + self.data_layer.set_signal(frame_buffers[:]) + self._get_batch_preds(keep_logits) + frame_buffers = self.frame_bufferer.get_buffers_batch() + + @torch.no_grad() + def _get_batch_preds(self, keep_logits=False): + device = self.asr_model.device + for batch in iter(self.data_loader): + + feat_signal, feat_signal_len = batch + feat_signal, feat_signal_len = feat_signal.to(device), feat_signal_len.to(device) + forward_outs = self.asr_model(processed_signal=feat_signal, processed_signal_length=feat_signal_len) + + if len(forward_outs) == 2: # hybrid ctc rnnt model + encoded, encoded_len = forward_outs + log_probs = self.asr_model.ctc_decoder(encoder_output=encoded) + predictions = log_probs.argmax(dim=-1, keepdim=False) + else: + log_probs, encoded_len, predictions = forward_outs + + preds = torch.unbind(predictions) + for pred in preds: + self.all_preds.append(pred.cpu().numpy()) + if keep_logits: + log_probs = torch.unbind(log_probs) + for log_prob in log_probs: + self.all_logits.append(log_prob.cpu()) + else: + del log_probs + del encoded_len + del predictions + + def transcribe(self, tokens_per_chunk: int, delay: int, keep_logits: bool = False): + self.infer_logits(keep_logits) + self.unmerged = [] + for pred in self.all_preds: + decoded = pred.tolist() + self.unmerged += decoded[len(decoded) - 1 - delay : len(decoded) - 1 - delay + tokens_per_chunk] + hypothesis = self.greedy_merge(self.unmerged) + if not keep_logits: + return hypothesis + + all_logits = [] + for log_prob in self.all_logits: + T = log_prob.shape[0] + log_prob = log_prob[T - 1 - delay : T - 1 - delay + tokens_per_chunk, :] + all_logits.append(log_prob) + all_logits = torch.concat(all_logits, 0) + return hypothesis, all_logits + + def greedy_merge(self, preds): + decoded_prediction = [] + previous = self.blank_id + for p in preds: + if (p != previous or previous == self.blank_id) and p != self.blank_id: + decoded_prediction.append(p) + previous = p + hypothesis = self.tokenizer.ids_to_text(decoded_prediction) + return hypothesis + + +class BatchedFeatureFrameBufferer(FeatureFrameBufferer): + """ + Batched variant of FeatureFrameBufferer where batch dimension is the independent audio samples. + """ + + def __init__(self, asr_model, frame_len=1.6, batch_size=4, total_buffer=4.0): + ''' + Args: + frame_len: frame's duration, seconds + frame_overlap: duration of overlaps before and after current frame, seconds + offset: number of symbols to drop for smooth streaming + ''' + super().__init__(asr_model, frame_len=frame_len, batch_size=batch_size, total_buffer=total_buffer) + + # OVERRIDES OF BASE CLASS + timestep_duration = asr_model._cfg.preprocessor.window_stride + total_buffer_len = int(total_buffer / timestep_duration) + self.buffer = ( + np.ones([batch_size, self.n_feat, total_buffer_len], dtype=np.float32) * self.ZERO_LEVEL_SPEC_DB_VAL + ) + + # Preserve list of buffers and indices, one for every sample + self.all_frame_reader = [None for _ in range(self.batch_size)] + self.signal_end = [False for _ in range(self.batch_size)] + self.signal_end_index = [None for _ in range(self.batch_size)] + self.buffer_number = 0 # preserve number of buffers returned since reset. + + self.reset() + del self.buffered_len + del self.buffered_features_size + + def reset(self): + ''' + Reset frame_history and decoder's state + ''' + super().reset() + self.feature_buffer = ( + np.ones([self.batch_size, self.n_feat, self.feature_buffer_len], dtype=np.float32) + * self.ZERO_LEVEL_SPEC_DB_VAL + ) + self.all_frame_reader = [None for _ in range(self.batch_size)] + self.signal_end = [False for _ in range(self.batch_size)] + self.signal_end_index = [None for _ in range(self.batch_size)] + self.buffer_number = 0 + + def get_batch_frames(self): + # Exit if all buffers of all samples have been processed + if all(self.signal_end): + return [] + + # Otherwise sequentially process frames of each sample one by one. + batch_frames = [] + for idx, frame_reader in enumerate(self.all_frame_reader): + try: + frame = next(frame_reader) + frame = np.copy(frame) + + batch_frames.append(frame) + except StopIteration: + # If this sample has finished all of its buffers + # Set its signal_end flag, and assign it the id of which buffer index + # did it finish the sample (if not previously set) + # This will let the alignment module know which sample in the batch finished + # at which index. + batch_frames.append(None) + self.signal_end[idx] = True + + if self.signal_end_index[idx] is None: + self.signal_end_index[idx] = self.buffer_number + + self.buffer_number += 1 + return batch_frames + + def get_frame_buffers(self, frames): + # Build buffers for each frame + self.frame_buffers = [] + # Loop over all buffers of all samples + for idx in range(self.batch_size): + frame = frames[idx] + # If the sample has a buffer, then process it as usual + if frame is not None: + self.buffer[idx, :, : -self.n_frame_len] = self.buffer[idx, :, self.n_frame_len :] + self.buffer[idx, :, -self.n_frame_len :] = frame + # self.buffered_len += frame.shape[1] + # WRAP the buffer at index idx into a outer list + self.frame_buffers.append([np.copy(self.buffer[idx])]) + else: + # If the buffer does not exist, the sample has finished processing + # set the entire buffer for that sample to 0 + self.buffer[idx, :, :] *= 0.0 + self.frame_buffers.append([np.copy(self.buffer[idx])]) + + return self.frame_buffers + + def set_frame_reader(self, frame_reader, idx): + self.all_frame_reader[idx] = frame_reader + self.signal_end[idx] = False + self.signal_end_index[idx] = None + + def _update_feature_buffer(self, feat_frame, idx): + # Update the feature buffer for given sample, or reset if the sample has finished processing + if feat_frame is not None: + self.feature_buffer[idx, :, : -feat_frame.shape[1]] = self.feature_buffer[idx, :, feat_frame.shape[1] :] + self.feature_buffer[idx, :, -feat_frame.shape[1] :] = feat_frame + # self.buffered_features_size += feat_frame.shape[1] + else: + self.feature_buffer[idx, :, :] *= 0.0 + + def get_norm_consts_per_frame(self, batch_frames): + for idx, frame in enumerate(batch_frames): + self._update_feature_buffer(frame, idx) + + mean_from_buffer = np.mean(self.feature_buffer, axis=2, keepdims=True) # [B, self.n_feat, 1] + stdev_from_buffer = np.std(self.feature_buffer, axis=2, keepdims=True) # [B, self.n_feat, 1] + + return (mean_from_buffer, stdev_from_buffer) + + def normalize_frame_buffers(self, frame_buffers, norm_consts): + CONSTANT = 1e-8 + for i in range(len(frame_buffers)): + frame_buffers[i] = (frame_buffers[i] - norm_consts[0][i]) / (norm_consts[1][i] + CONSTANT) + + def get_buffers_batch(self): + batch_frames = self.get_batch_frames() + + while len(batch_frames) > 0: + # while there exists at least one sample that has not been processed yet + frame_buffers = self.get_frame_buffers(batch_frames) + norm_consts = self.get_norm_consts_per_frame(batch_frames) + + self.normalize_frame_buffers(frame_buffers, norm_consts) + return frame_buffers + return [] + + +class BatchedFrameASRRNNT(FrameBatchASR): + """ + Batched implementation of FrameBatchASR for RNNT models, where the batch dimension is independent audio samples. + """ + + def __init__( + self, + asr_model, + frame_len=1.6, + total_buffer=4.0, + batch_size=32, + max_steps_per_timestep: int = 5, + stateful_decoding: bool = False, + ): + ''' + Args: + asr_model: An RNNT model. + frame_len: frame's duration, seconds. + total_buffer: duration of total audio chunk size, in seconds. + batch_size: Number of independent audio samples to process at each step. + max_steps_per_timestep: Maximum number of tokens (u) to process per acoustic timestep (t). + stateful_decoding: Boolean whether to enable stateful decoding for preservation of state across buffers. + ''' + super().__init__(asr_model, frame_len=frame_len, total_buffer=total_buffer, batch_size=batch_size) + + # OVERRIDES OF THE BASE CLASS + self.max_steps_per_timestep = max_steps_per_timestep + self.stateful_decoding = stateful_decoding + + self.all_alignments = [[] for _ in range(self.batch_size)] + self.all_preds = [[] for _ in range(self.batch_size)] + self.all_timestamps = [[] for _ in range(self.batch_size)] + self.previous_hypotheses = None + self.batch_index_map = { + idx: idx for idx in range(self.batch_size) + } # pointer from global batch id : local sub-batch id + + try: + self.eos_id = self.asr_model.tokenizer.eos_id + except Exception: + self.eos_id = -1 + + print("Performing Stateful decoding :", self.stateful_decoding) + + # OVERRIDES + self.frame_bufferer = BatchedFeatureFrameBufferer( + asr_model=asr_model, frame_len=frame_len, batch_size=batch_size, total_buffer=total_buffer + ) + + self.reset() + + def reset(self): + """ + Reset frame_history and decoder's state + """ + super().reset() + + self.all_alignments = [[] for _ in range(self.batch_size)] + self.all_preds = [[] for _ in range(self.batch_size)] + self.all_timestamps = [[] for _ in range(self.batch_size)] + self.previous_hypotheses = None + self.batch_index_map = {idx: idx for idx in range(self.batch_size)} + + self.data_layer = [AudioBuffersDataLayer() for _ in range(self.batch_size)] + self.data_loader = [ + DataLoader(self.data_layer[idx], batch_size=1, collate_fn=speech_collate_fn) + for idx in range(self.batch_size) + ] + + def read_audio_file(self, audio_filepath: list, delay, model_stride_in_secs): + assert len(audio_filepath) == self.batch_size + + # Read in a batch of audio files, one by one + for idx in range(self.batch_size): + samples = get_samples(audio_filepath[idx]) + samples = np.pad(samples, (0, int(delay * model_stride_in_secs * self.asr_model._cfg.sample_rate))) + frame_reader = AudioFeatureIterator(samples, self.frame_len, self.raw_preprocessor, self.asr_model.device) + self.set_frame_reader(frame_reader, idx) + + def set_frame_reader(self, frame_reader, idx): + self.frame_bufferer.set_frame_reader(frame_reader, idx) + + @torch.no_grad() + def infer_logits(self): + frame_buffers = self.frame_bufferer.get_buffers_batch() + + while len(frame_buffers) > 0: + # While at least 1 sample has a buffer left to process + self.frame_buffers += frame_buffers[:] + + for idx, buffer in enumerate(frame_buffers): + self.data_layer[idx].set_signal(buffer[:]) + + self._get_batch_preds() + frame_buffers = self.frame_bufferer.get_buffers_batch() + + @torch.no_grad() + def _get_batch_preds(self): + """ + Perform dynamic batch size decoding of frame buffers of all samples. + + Steps: + - Load all data loaders of every sample + - For all samples, determine if signal has finished. + - If so, skip calculation of mel-specs. + - If not, compute mel spec and length + - Perform Encoder forward over this sub-batch of samples. Maintain the indices of samples that were processed. + - If performing stateful decoding, prior to decoder forward, remove the states of samples that were not processed. + - Perform Decoder + Joint forward for samples that were processed. + - For all output RNNT alignment matrix of the joint do: + - If signal has ended previously (this was last buffer of padding), skip alignment + - Otherwise, recalculate global index of this sample from the sub-batch index, and preserve alignment. + - Same for preds + - Update indices of sub-batch with global index map. + - Redo steps until all samples were processed (sub-batch size == 0). + """ + device = self.asr_model.device + + data_iters = [iter(data_loader) for data_loader in self.data_loader] + + feat_signals = [] + feat_signal_lens = [] + + new_batch_keys = [] + # while not all(self.frame_bufferer.signal_end): + for idx in range(self.batch_size): + if self.frame_bufferer.signal_end[idx]: + continue + + batch = next(data_iters[idx]) + feat_signal, feat_signal_len = batch + feat_signal, feat_signal_len = feat_signal.to(device), feat_signal_len.to(device) + + feat_signals.append(feat_signal) + feat_signal_lens.append(feat_signal_len) + + # preserve batch indeices + new_batch_keys.append(idx) + + if len(feat_signals) == 0: + return + + feat_signal = torch.cat(feat_signals, 0) + feat_signal_len = torch.cat(feat_signal_lens, 0) + + del feat_signals, feat_signal_lens + + encoded, encoded_len = self.asr_model(processed_signal=feat_signal, processed_signal_length=feat_signal_len) + + # filter out partial hypotheses from older batch subset + if self.stateful_decoding and self.previous_hypotheses is not None: + new_prev_hypothesis = [] + for new_batch_idx, global_index_key in enumerate(new_batch_keys): + old_pos = self.batch_index_map[global_index_key] + new_prev_hypothesis.append(self.previous_hypotheses[old_pos]) + self.previous_hypotheses = new_prev_hypothesis + + best_hyp, _ = self.asr_model.decoding.rnnt_decoder_predictions_tensor( + encoded, encoded_len, return_hypotheses=True, partial_hypotheses=self.previous_hypotheses + ) + + if self.stateful_decoding: + # preserve last state from hypothesis of new batch indices + self.previous_hypotheses = best_hyp + + for idx, hyp in enumerate(best_hyp): + global_index_key = new_batch_keys[idx] # get index of this sample in the global batch + + has_signal_ended = self.frame_bufferer.signal_end[global_index_key] + if not has_signal_ended: + self.all_alignments[global_index_key].append(hyp.alignments) + + preds = [hyp.y_sequence for hyp in best_hyp] + for idx, pred in enumerate(preds): + global_index_key = new_batch_keys[idx] # get index of this sample in the global batch + + has_signal_ended = self.frame_bufferer.signal_end[global_index_key] + if not has_signal_ended: + self.all_preds[global_index_key].append(pred.cpu().numpy()) + + timestamps = [hyp.timestep for hyp in best_hyp] + for idx, timestep in enumerate(timestamps): + global_index_key = new_batch_keys[idx] # get index of this sample in the global batch + + has_signal_ended = self.frame_bufferer.signal_end[global_index_key] + if not has_signal_ended: + self.all_timestamps[global_index_key].append(timestep) + + if self.stateful_decoding: + # State resetting is being done on sub-batch only, global index information is not being updated + reset_states = self.asr_model.decoder.initialize_state(encoded) + + for idx, pred in enumerate(preds): + if len(pred) > 0 and pred[-1] == self.eos_id: + # reset states : + self.previous_hypotheses[idx].y_sequence = self.previous_hypotheses[idx].y_sequence[:-1] + self.previous_hypotheses[idx].dec_state = self.asr_model.decoder.batch_select_state( + reset_states, idx + ) + + # Position map update + if len(new_batch_keys) != len(self.batch_index_map): + for new_batch_idx, global_index_key in enumerate(new_batch_keys): + self.batch_index_map[global_index_key] = new_batch_idx # let index point from global pos -> local pos + + del encoded, encoded_len + del best_hyp, pred + + def transcribe( + self, tokens_per_chunk: int, delay: int, + ): + """ + Performs "middle token" alignment prediction using the buffered audio chunk. + """ + self.infer_logits() + + self.unmerged = [[] for _ in range(self.batch_size)] + for idx, alignments in enumerate(self.all_alignments): + + signal_end_idx = self.frame_bufferer.signal_end_index[idx] + if signal_end_idx is None: + raise ValueError("Signal did not end") + + for a_idx, alignment in enumerate(alignments): + if delay == len(alignment): # chunk size = buffer size + offset = 0 + else: # all other cases + offset = 1 + + alignment = alignment[ + len(alignment) - offset - delay : len(alignment) - offset - delay + tokens_per_chunk + ] + + ids, toks = self._alignment_decoder(alignment, self.asr_model.tokenizer, self.blank_id) + + if len(ids) > 0 and a_idx < signal_end_idx: + self.unmerged[idx] = inplace_buffer_merge(self.unmerged[idx], ids, delay, model=self.asr_model,) + + output = [] + for idx in range(self.batch_size): + output.append(self.greedy_merge(self.unmerged[idx])) + return output + + def _alignment_decoder(self, alignments, tokenizer, blank_id): + s = [] + ids = [] + + for t in range(len(alignments)): + for u in range(len(alignments[t])): + _, token_id = alignments[t][u] # (logprob, token_id) + token_id = int(token_id) + if token_id != blank_id: + token = tokenizer.ids_to_tokens([token_id])[0] + s.append(token) + ids.append(token_id) + + else: + # blank token + pass + + return ids, s + + def greedy_merge(self, preds): + decoded_prediction = [p for p in preds] + hypothesis = self.asr_model.tokenizer.ids_to_text(decoded_prediction) + return hypothesis + + +class LongestCommonSubsequenceBatchedFrameASRRNNT(BatchedFrameASRRNNT): + """ + Implements a token alignment algorithm for text alignment instead of middle token alignment. + + For more detail, read the docstring of longest_common_subsequence_merge(). + """ + + def __init__( + self, + asr_model, + frame_len=1.6, + total_buffer=4.0, + batch_size=4, + max_steps_per_timestep: int = 5, + stateful_decoding: bool = False, + alignment_basepath: str = None, + ): + ''' + Args: + asr_model: An RNNT model. + frame_len: frame's duration, seconds. + total_buffer: duration of total audio chunk size, in seconds. + batch_size: Number of independent audio samples to process at each step. + max_steps_per_timestep: Maximum number of tokens (u) to process per acoustic timestep (t). + stateful_decoding: Boolean whether to enable stateful decoding for preservation of state across buffers. + alignment_basepath: Str path to a directory where alignments from LCS will be preserved for later analysis. + ''' + super().__init__(asr_model, frame_len, total_buffer, batch_size, max_steps_per_timestep, stateful_decoding) + self.sample_offset = 0 + self.lcs_delay = -1 + + self.alignment_basepath = alignment_basepath + + def transcribe( + self, tokens_per_chunk: int, delay: int, + ): + if self.lcs_delay < 0: + raise ValueError( + "Please set LCS Delay valus as `(buffer_duration - chunk_duration) / model_stride_in_secs`" + ) + + self.infer_logits() + + self.unmerged = [[] for _ in range(self.batch_size)] + for idx, alignments in enumerate(self.all_alignments): + + signal_end_idx = self.frame_bufferer.signal_end_index[idx] + if signal_end_idx is None: + raise ValueError("Signal did not end") + + for a_idx, alignment in enumerate(alignments): + + # Middle token first chunk + if a_idx == 0: + # len(alignment) - 1 - delay + tokens_per_chunk + alignment = alignment[len(alignment) - 1 - delay :] + ids, toks = self._alignment_decoder(alignment, self.asr_model.tokenizer, self.blank_id) + + if len(ids) > 0: + self.unmerged[idx] = inplace_buffer_merge( + self.unmerged[idx], ids, delay, model=self.asr_model, + ) + + else: + ids, toks = self._alignment_decoder(alignment, self.asr_model.tokenizer, self.blank_id) + if len(ids) > 0 and a_idx < signal_end_idx: + + if self.alignment_basepath is not None: + basepath = self.alignment_basepath + sample_offset = self.sample_offset + idx + alignment_offset = a_idx + path = os.path.join(basepath, str(sample_offset)) + + os.makedirs(path, exist_ok=True) + path = os.path.join(path, "alignment_" + str(alignment_offset) + '.pt') + + filepath = path + else: + filepath = None + + self.unmerged[idx] = lcs_alignment_merge_buffer( + self.unmerged[idx], + ids, + self.lcs_delay, + model=self.asr_model, + max_steps_per_timestep=self.max_steps_per_timestep, + filepath=filepath, + ) + + output = [] + for idx in range(self.batch_size): + output.append(self.greedy_merge(self.unmerged[idx])) + return output + + +class CacheAwareStreamingAudioBuffer: + """ + A buffer to be used for cache-aware streaming. It can load a single or multiple audio files/processed signals, split them in chunks and return one on one. + It can be used to simulate streaming audio or audios. + """ + + def __init__(self, model, online_normalization=None, pad_and_drop_preencoded=False): + ''' + Args: + model: An ASR model. + online_normalization (bool): whether to perform online normalization per chunk or normalize the whole audio before chunking + pad_and_drop_preencoded (bool): if true pad first audio chunk and always drop preencoded + ''' + self.model = model + self.buffer = None + self.buffer_idx = 0 + self.streams_length = None + self.step = 0 + self.pad_and_drop_preencoded = pad_and_drop_preencoded + + self.online_normalization = online_normalization + if not isinstance(model.encoder, StreamingEncoder): + raise ValueError( + "The model's encoder is not inherited from StreamingEncoder, and likely not to support streaming!" + ) + if model.encoder.streaming_cfg is None: + model.encoder.setup_streaming_params() + self.streaming_cfg = model.encoder.streaming_cfg + + self.input_features = model.encoder._feat_in + + self.preprocessor = self.extract_preprocessor() + + if hasattr(model.encoder, "pre_encode") and hasattr(model.encoder.pre_encode, "get_sampling_frames"): + self.sampling_frames = model.encoder.pre_encode.get_sampling_frames() + else: + self.sampling_frames = None + + def __iter__(self): + while True: + if self.buffer_idx >= self.buffer.size(-1): + return + + if self.buffer_idx == 0 and isinstance(self.streaming_cfg.chunk_size, list): + if self.pad_and_drop_preencoded: + chunk_size = self.streaming_cfg.chunk_size[1] + else: + chunk_size = self.streaming_cfg.chunk_size[0] + else: + chunk_size = ( + self.streaming_cfg.chunk_size[1] + if isinstance(self.streaming_cfg.chunk_size, list) + else self.streaming_cfg.chunk_size + ) + + if self.buffer_idx == 0 and isinstance(self.streaming_cfg.shift_size, list): + if self.pad_and_drop_preencoded: + shift_size = self.streaming_cfg.shift_size[1] + else: + shift_size = self.streaming_cfg.shift_size[0] + else: + shift_size = ( + self.streaming_cfg.shift_size[1] + if isinstance(self.streaming_cfg.shift_size, list) + else self.streaming_cfg.shift_size + ) + + audio_chunk = self.buffer[:, :, self.buffer_idx : self.buffer_idx + chunk_size] + + if self.sampling_frames is not None: + # checking to make sure the audio chunk has enough frames to produce at least one output after downsampling + if self.buffer_idx == 0 and isinstance(self.sampling_frames, list): + cur_sampling_frames = self.sampling_frames[0] + else: + cur_sampling_frames = ( + self.sampling_frames[1] if isinstance(self.sampling_frames, list) else self.sampling_frames + ) + if audio_chunk.size(-1) < cur_sampling_frames: + return + + # Adding the cache needed for the pre-encoder part of the model to the chunk + # if there is not enough frames to be used as the pre-encoding cache, zeros would be added + zeros_pads = None + if self.buffer_idx == 0 and isinstance(self.streaming_cfg.pre_encode_cache_size, list): + if self.pad_and_drop_preencoded: + cache_pre_encode_num_frames = self.streaming_cfg.pre_encode_cache_size[1] + else: + cache_pre_encode_num_frames = self.streaming_cfg.pre_encode_cache_size[0] + cache_pre_encode = torch.zeros( + (audio_chunk.size(0), self.input_features, cache_pre_encode_num_frames), + device=audio_chunk.device, + dtype=audio_chunk.dtype, + ) + else: + if isinstance(self.streaming_cfg.pre_encode_cache_size, list): + pre_encode_cache_size = self.streaming_cfg.pre_encode_cache_size[1] + else: + pre_encode_cache_size = self.streaming_cfg.pre_encode_cache_size + + start_pre_encode_cache = self.buffer_idx - pre_encode_cache_size + if start_pre_encode_cache < 0: + start_pre_encode_cache = 0 + cache_pre_encode = self.buffer[:, :, start_pre_encode_cache : self.buffer_idx] + if cache_pre_encode.size(-1) < pre_encode_cache_size: + zeros_pads = torch.zeros( + ( + audio_chunk.size(0), + audio_chunk.size(-2), + pre_encode_cache_size - cache_pre_encode.size(-1), + ), + device=audio_chunk.device, + dtype=audio_chunk.dtype, + ) + + added_len = cache_pre_encode.size(-1) + audio_chunk = torch.cat((cache_pre_encode, audio_chunk), dim=-1) + + if self.online_normalization: + audio_chunk, x_mean, x_std = normalize_batch( + x=audio_chunk, + seq_len=torch.tensor([audio_chunk.size(-1)] * audio_chunk.size(0)), + normalize_type=self.model_normalize_type, + ) + + if zeros_pads is not None: + # TODO: check here when zero_pads is not None and added_len is already non-zero + audio_chunk = torch.cat((zeros_pads, audio_chunk), dim=-1) + added_len += zeros_pads.size(-1) + + max_chunk_lengths = self.streams_length - self.buffer_idx + max_chunk_lengths = max_chunk_lengths + added_len + chunk_lengths = torch.clamp(max_chunk_lengths, min=0, max=audio_chunk.size(-1)) + + self.buffer_idx += shift_size + self.step += 1 + yield audio_chunk, chunk_lengths + + def is_buffer_empty(self): + if self.buffer_idx >= self.buffer.size(-1): + return True + else: + return False + + def __len__(self): + return len(self.buffer) + + def reset_buffer(self): + self.buffer = None + self.buffer_idx = 0 + self.streams_length = None + self.step = 0 + + def reset_buffer_pointer(self): + self.buffer_idx = 0 + self.step = 0 + + def extract_preprocessor(self): + cfg = copy.deepcopy(self.model._cfg) + self.model_normalize_type = cfg.preprocessor.normalize + OmegaConf.set_struct(cfg.preprocessor, False) + cfg.preprocessor.dither = 0.0 + cfg.preprocessor.pad_to = 0 + if self.online_normalization: + cfg.preprocessor.normalize = "None" + + preprocessor = self.model.from_config_dict(cfg.preprocessor) + return preprocessor.to(self.get_model_device()) + + def append_audio_file(self, audio_filepath, stream_id=-1): + audio = get_samples(audio_filepath) + processed_signal, processed_signal_length, stream_id = self.append_audio(audio, stream_id) + return processed_signal, processed_signal_length, stream_id + + def append_audio(self, audio, stream_id=-1): + processed_signal, processed_signal_length = self.preprocess_audio(audio) + processed_signal, processed_signal_length, stream_id = self.append_processed_signal( + processed_signal, stream_id + ) + return processed_signal, processed_signal_length, stream_id + + def append_processed_signal(self, processed_signal, stream_id=-1): + processed_signal_length = torch.tensor(processed_signal.size(-1), device=processed_signal.device) + if stream_id >= 0 and (self.streams_length is not None and stream_id >= len(self.streams_length)): + raise ValueError("Not valid stream_id!") + if self.buffer is None: + if stream_id >= 0: + raise ValueError("stream_id can not be specified when there is no stream.") + self.buffer = processed_signal + self.streams_length = torch.tensor([processed_signal_length], device=processed_signal.device) + else: + if self.buffer.size(1) != processed_signal.size(1): + raise ValueError("Buffer and the processed signal have different dimensions!") + if stream_id < 0: + self.buffer = torch.nn.functional.pad(self.buffer, pad=(0, 0, 0, 0, 0, 1)) + self.streams_length = torch.cat( + (self.streams_length, torch.tensor([0], device=self.streams_length.device)), dim=-1 + ) + stream_id = len(self.streams_length) - 1 + needed_len = self.streams_length[stream_id] + processed_signal_length + if needed_len > self.buffer.size(-1): + self.buffer = torch.nn.functional.pad(self.buffer, pad=(0, needed_len - self.buffer.size(-1))) + + self.buffer[ + stream_id, :, self.streams_length[stream_id] : self.streams_length[stream_id] + processed_signal_length + ] = processed_signal + self.streams_length[stream_id] = self.streams_length[stream_id] + processed_signal.size(-1) + + if self.online_normalization: + processed_signal, x_mean, x_std = normalize_batch( + x=processed_signal, + seq_len=torch.tensor([processed_signal_length]), + normalize_type=self.model_normalize_type, + ) + return processed_signal, processed_signal_length, stream_id + + def get_model_device(self): + return self.model.device + + def preprocess_audio(self, audio, device=None): + if device is None: + device = self.get_model_device() + audio_signal = torch.from_numpy(audio).unsqueeze_(0).to(device) + audio_signal_len = torch.Tensor([audio.shape[0]]).to(device) + processed_signal, processed_signal_length = self.preprocessor( + input_signal=audio_signal, length=audio_signal_len + ) + return processed_signal, processed_signal_length + + def get_all_audios(self): + processed_signal = self.buffer + if self.online_normalization: + processed_signal, x_mean, x_std = normalize_batch( + x=processed_signal, + seq_len=torch.tensor(self.streams_length), + normalize_type=self.model_normalize_type, + ) + return processed_signal, self.streams_length + + +class FrameBatchMultiTaskAED(FrameBatchASR): + def __init__(self, asr_model, frame_len=4, total_buffer=4, batch_size=4): + super().__init__(asr_model, frame_len, total_buffer, batch_size, pad_to_buffer_len=False) + + def get_input_tokens(self, sample: dict): + if self.asr_model.prompt_format == "canary": + missing_keys = [k for k in ("source_lang", "target_lang", "taskname", "pnc") if k not in sample] + if missing_keys: + raise RuntimeError( + f"We found sample that is missing the following keys: {missing_keys}" + f"Please ensure that every utterance in the input manifests contains these keys. Sample: {sample}" + ) + tokens = canary_prompt( + tokenizer=self.asr_model.tokenizer, + text=None, + language=None, + source_language=sample['source_lang'], + target_language=sample['target_lang'], + taskname=sample['taskname'], + pnc=sample['pnc'], + ) + else: + raise ValueError(f"Unknown prompt format: {self.asr_model.prompt_format}") + return torch.tensor(tokens, dtype=torch.long, device=self.asr_model.device).unsqueeze(0) # [1, T] + + def read_audio_file(self, audio_filepath: str, delay, model_stride_in_secs, meta_data): + self.input_tokens = self.get_input_tokens(meta_data) + samples = get_samples(audio_filepath) + samples = np.pad(samples, (0, int(delay * model_stride_in_secs * self.asr_model._cfg.sample_rate))) + frame_reader = AudioFeatureIterator( + samples, self.frame_len, self.raw_preprocessor, self.asr_model.device, pad_to_frame_len=False + ) + self.set_frame_reader(frame_reader) + + @torch.no_grad() + def _get_batch_preds(self, keep_logits=False): + device = self.asr_model.device + for batch in iter(self.data_loader): + feat_signal, feat_signal_len = batch + feat_signal, feat_signal_len = feat_signal.to(device), feat_signal_len.to(device) + tokens = self.input_tokens.to(device).repeat(feat_signal.size(0), 1) + tokens_len = torch.tensor([tokens.size(1)] * tokens.size(0), device=device).long() + + batch_input = (feat_signal, feat_signal_len, None, None, tokens, tokens_len) + predictions = self.asr_model.predict_step(batch_input, has_processed_signal=True) + self.all_preds.extend(predictions) + del predictions + + def transcribe( + self, tokens_per_chunk: Optional[int] = None, delay: Optional[int] = None, keep_logits: bool = False + ): + """ + unsued params are for keeping the same signature as the parent class + """ + self.infer_logits(keep_logits) + + hypothesis = " ".join(self.all_preds) + if not keep_logits: + return hypothesis + + print("keep_logits=True is not supported for MultiTaskAEDFrameBatchInfer. Returning empty logits.") + return hypothesis, [] + + +class FrameBatchChunkedRNNT(FrameBatchASR): + def __init__(self, asr_model, frame_len=4, total_buffer=4, batch_size=4): + super().__init__(asr_model, frame_len, total_buffer, batch_size, pad_to_buffer_len=False) + + def read_audio_file(self, audio_filepath: str, delay, model_stride_in_secs): + samples = get_samples(audio_filepath) + samples = np.pad(samples, (0, int(delay * model_stride_in_secs * self.asr_model._cfg.sample_rate))) + frame_reader = AudioFeatureIterator( + samples, self.frame_len, self.raw_preprocessor, self.asr_model.device, pad_to_frame_len=False + ) + self.set_frame_reader(frame_reader) + + @torch.no_grad() + def _get_batch_preds(self, keep_logits=False): + device = self.asr_model.device + for batch in iter(self.data_loader): + feat_signal, feat_signal_len = batch + feat_signal, feat_signal_len = feat_signal.to(device), feat_signal_len.to(device) + + encoded, encoded_len = self.asr_model( + processed_signal=feat_signal, processed_signal_length=feat_signal_len + ) + + best_hyp_text, all_hyp_text = self.asr_model.decoding.rnnt_decoder_predictions_tensor( + encoder_output=encoded, encoded_lengths=encoded_len, return_hypotheses=False + ) + self.all_preds.extend(best_hyp_text) + del best_hyp_text + del all_hyp_text + del encoded + del encoded_len + + def transcribe( + self, tokens_per_chunk: Optional[int] = None, delay: Optional[int] = None, keep_logits: bool = False + ): + """ + unsued params are for keeping the same signature as the parent class + """ + self.infer_logits(keep_logits) + + hypothesis = " ".join(self.all_preds) + if not keep_logits: + return hypothesis + + print("keep_logits=True is not supported for FrameBatchChunkedRNNT. Returning empty logits.") + return hypothesis, [] + + +class FrameBatchChunkedCTC(FrameBatchASR): + def __init__(self, asr_model, frame_len=4, total_buffer=4, batch_size=4): + super().__init__(asr_model, frame_len, total_buffer, batch_size, pad_to_buffer_len=False) + + def read_audio_file(self, audio_filepath: str, delay, model_stride_in_secs): + samples = get_samples(audio_filepath) + samples = np.pad(samples, (0, int(delay * model_stride_in_secs * self.asr_model._cfg.sample_rate))) + frame_reader = AudioFeatureIterator( + samples, self.frame_len, self.raw_preprocessor, self.asr_model.device, pad_to_frame_len=False + ) + self.set_frame_reader(frame_reader) + + @torch.no_grad() + def _get_batch_preds(self, keep_logits=False): + device = self.asr_model.device + for batch in iter(self.data_loader): + feat_signal, feat_signal_len = batch + feat_signal, feat_signal_len = feat_signal.to(device), feat_signal_len.to(device) + + results = self.asr_model(processed_signal=feat_signal, processed_signal_length=feat_signal_len) + if len(results) == 2: # hybrid model + encoded, encoded_len = results + log_probs = self.asr_model.ctc_decoder(encoder_output=encoded) + transcribed_texts, _ = self.asr_model.ctc_decoding.ctc_decoder_predictions_tensor( + decoder_outputs=log_probs, decoder_lengths=encoded_len, return_hypotheses=False, + ) + else: + log_probs, encoded_len, predictions = results + transcribed_texts, _ = self.asr_model.decoding.ctc_decoder_predictions_tensor( + decoder_outputs=log_probs, decoder_lengths=encoded_len, return_hypotheses=False, + ) + + self.all_preds.extend(transcribed_texts) + del log_probs + del encoded_len + del predictions + + def transcribe( + self, tokens_per_chunk: Optional[int] = None, delay: Optional[int] = None, keep_logits: bool = False + ): + """ + unsued params are for keeping the same signature as the parent class + """ + self.infer_logits(keep_logits) + + hypothesis = " ".join(self.all_preds) + if not keep_logits: + return hypothesis + + print("keep_logits=True is not supported for FrameBatchChunkedCTC. Returning empty logits.") + return hypothesis, [] diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/transcribe_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/transcribe_utils.py new file mode 100644 index 0000000..8465406 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/transcribe_utils.py @@ -0,0 +1,697 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import glob +import json +import os +import re +from dataclasses import dataclass +from pathlib import Path +from typing import List, Optional, Tuple, Union + +import torch +from omegaconf import DictConfig +from tqdm.auto import tqdm + +import nemo.collections.asr as nemo_asr +from nemo.collections.asr.metrics.wer import word_error_rate +from nemo.collections.asr.models import ASRModel, EncDecHybridRNNTCTCModel, EncDecMultiTaskModel +from nemo.collections.asr.parts.utils import manifest_utils, rnnt_utils +from nemo.collections.asr.parts.utils.streaming_utils import FrameBatchASR, FrameBatchMultiTaskAED +from nemo.collections.common.metrics.punct_er import OccurancePunctuationErrorRate +from nemo.collections.common.parts.preprocessing.manifest import get_full_path +from nemo.utils import logging, model_utils + + +def get_buffered_pred_feat_rnnt( + asr: FrameBatchASR, + tokens_per_chunk: int, + delay: int, + model_stride_in_secs: int, + batch_size: int, + manifest: str = None, + filepaths: List[list] = None, +) -> List[rnnt_utils.Hypothesis]: + """ + Moved from examples/asr/asr_chunked_inference/rnnt/speech_to_text_buffered_infer_rnnt.py + Write all information presented in input manifest to output manifest and removed WER calculation. + """ + hyps = [] + refs = [] + + if filepaths and manifest: + raise ValueError("Please select either filepaths or manifest") + if filepaths is None and manifest is None: + raise ValueError("Either filepaths or manifest shoud not be None") + + if manifest: + filepaths = [] + with open(manifest, "r", encoding='utf_8') as mfst_f: + print("Parsing manifest files...") + for l in mfst_f: + row = json.loads(l.strip()) + audio_file = get_full_path(audio_file=row['audio_filepath'], manifest_file=manifest) + filepaths.append(audio_file) + if 'text' in row: + refs.append(row['text']) + + with torch.inference_mode(): + with torch.cuda.amp.autocast(): + batch = [] + asr.sample_offset = 0 + for idx in tqdm(range(len(filepaths)), desc='Sample:', total=len(filepaths)): + batch.append((filepaths[idx])) + + if len(batch) == batch_size: + audio_files = [sample for sample in batch] + + asr.reset() + asr.read_audio_file(audio_files, delay, model_stride_in_secs) + hyp_list = asr.transcribe(tokens_per_chunk, delay) + hyps.extend(hyp_list) + + batch.clear() + asr.sample_offset += batch_size + + if len(batch) > 0: + asr.batch_size = len(batch) + asr.frame_bufferer.batch_size = len(batch) + asr.reset() + + audio_files = [sample for sample in batch] + asr.read_audio_file(audio_files, delay, model_stride_in_secs) + hyp_list = asr.transcribe(tokens_per_chunk, delay) + hyps.extend(hyp_list) + + batch.clear() + asr.sample_offset += len(batch) + + if os.environ.get('DEBUG', '0') in ('1', 'y', 't'): + if len(refs) == 0: + print("ground-truth text does not present!") + for hyp in hyps: + print("hyp:", hyp) + else: + for hyp, ref in zip(hyps, refs): + print("hyp:", hyp) + print("ref:", ref) + + wrapped_hyps = wrap_transcription(hyps) + return wrapped_hyps + + +def get_buffered_pred_feat( + asr: FrameBatchASR, + frame_len: float, + tokens_per_chunk: int, + delay: int, + preprocessor_cfg: DictConfig, + model_stride_in_secs: int, + device: Union[List[int], int], + manifest: str = None, + filepaths: List[list] = None, +) -> List[rnnt_utils.Hypothesis]: + """ + Moved from examples/asr/asr_chunked_inference/ctc/speech_to_text_buffered_infer_ctc.py + Write all information presented in input manifest to output manifest and removed WER calculation. + """ + # Create a preprocessor to convert audio samples into raw features, + # Normalization will be done per buffer in frame_bufferer + # Do not normalize whatever the model's preprocessor setting is + preprocessor_cfg.normalize = "None" + preprocessor = nemo_asr.models.EncDecCTCModelBPE.from_config_dict(preprocessor_cfg) + preprocessor.to(device) + hyps = [] + refs = [] + + if filepaths and manifest: + raise ValueError("Please select either filepaths or manifest") + if filepaths is None and manifest is None: + raise ValueError("Either filepaths or manifest shoud not be None") + + if filepaths: + for l in tqdm(filepaths, desc="Sample:"): + asr.reset() + asr.read_audio_file(l, delay, model_stride_in_secs) + hyp = asr.transcribe(tokens_per_chunk, delay) + hyps.append(hyp) + else: + with open(manifest, "r", encoding='utf_8') as mfst_f: + for l in tqdm(mfst_f, desc="Sample:"): + asr.reset() + row = json.loads(l.strip()) + if 'text' in row: + refs.append(row['text']) + audio_file = get_full_path(audio_file=row['audio_filepath'], manifest_file=manifest) + # do not support partial audio + asr.read_audio_file(audio_file, delay, model_stride_in_secs) + hyp = asr.transcribe(tokens_per_chunk, delay) + hyps.append(hyp) + + if os.environ.get('DEBUG', '0') in ('1', 'y', 't'): + if len(refs) == 0: + print("ground-truth text does not present!") + for hyp in hyps: + print("hyp:", hyp) + else: + for hyp, ref in zip(hyps, refs): + print("hyp:", hyp) + print("ref:", ref) + + wrapped_hyps = wrap_transcription(hyps) + return wrapped_hyps + + +def get_buffered_pred_feat_multitaskAED( + asr: FrameBatchMultiTaskAED, + preprocessor_cfg: DictConfig, + model_stride_in_secs: int, + device: Union[List[int], int], + manifest: str = None, + filepaths: List[list] = None, + delay: float = 0.0, +) -> List[rnnt_utils.Hypothesis]: + # Create a preprocessor to convert audio samples into raw features, + # Normalization will be done per buffer in frame_bufferer + # Do not normalize whatever the model's preprocessor setting is + preprocessor_cfg.normalize = "None" + preprocessor = EncDecMultiTaskModel.from_config_dict(preprocessor_cfg) + preprocessor.to(device) + hyps = [] + refs = [] + + if filepaths and manifest: + raise ValueError("Please select either filepaths or manifest") + if filepaths is None and manifest is None: + raise ValueError("Either filepaths or manifest shoud not be None") + + if filepaths: + logging.info( + "Deteced audio files as input, default to English ASR with Punctuation and Capitalization output. Please use manifest input for other options." + ) + for audio_file in tqdm(filepaths, desc="Transcribing:", total=len(filepaths), ncols=80): + meta = { + 'audio_filepath': audio_file, + 'duration': 100000, + 'source_lang': 'en', + 'taskname': 'asr', + 'target_lang': 'en', + 'pnc': 'yes', + 'answer': 'nothing', + } + asr.reset() + asr.read_audio_file(audio_file, delay, model_stride_in_secs, meta_data=meta) + hyp = asr.transcribe() + hyps.append(hyp) + else: + with open(manifest, "r", encoding='utf_8') as fin: + lines = list(fin.readlines()) + for line in tqdm(lines, desc="Transcribing:", total=len(lines), ncols=80): + asr.reset() + sample = json.loads(line.strip()) + if 'text' in sample: + refs.append(sample['text']) + audio_file = get_full_path(audio_file=sample['audio_filepath'], manifest_file=manifest) + # do not support partial audio + asr.read_audio_file(audio_file, delay, model_stride_in_secs, meta_data=sample) + hyp = asr.transcribe() + hyps.append(hyp) + + wrapped_hyps = wrap_transcription(hyps) + return wrapped_hyps + + +def wrap_transcription(hyps: List[str]) -> List[rnnt_utils.Hypothesis]: + """ Wrap transcription to the expected format in func write_transcription """ + wrapped_hyps = [] + for hyp in hyps: + hypothesis = rnnt_utils.Hypothesis(score=0.0, y_sequence=[], text=hyp) + wrapped_hyps.append(hypothesis) + return wrapped_hyps + + +def setup_model(cfg: DictConfig, map_location: torch.device) -> Tuple[ASRModel, str]: + """ Setup model from cfg and return model and model name for next step """ + if cfg.model_path is not None and cfg.model_path != "None": + # restore model from .nemo file path + model_cfg = ASRModel.restore_from(restore_path=cfg.model_path, return_config=True) + classpath = model_cfg.target # original class path + imported_class = model_utils.import_class_by_path(classpath) # type: ASRModel + logging.info(f"Restoring model : {imported_class.__name__}") + asr_model = imported_class.restore_from( + restore_path=cfg.model_path, map_location=map_location, + ) # type: ASRModel + model_name = os.path.splitext(os.path.basename(cfg.model_path))[0] + else: + # restore model by name + asr_model = ASRModel.from_pretrained( + model_name=cfg.pretrained_name, map_location=map_location, + ) # type: ASRModel + model_name = cfg.pretrained_name + + if hasattr(cfg, "model_change") and hasattr(asr_model, "change_attention_model"): + asr_model.change_attention_model( + self_attention_model=cfg.model_change.conformer.get("self_attention_model", None), + att_context_size=cfg.model_change.conformer.get("att_context_size", None), + ) + + return asr_model, model_name + + +def prepare_audio_data(cfg: DictConfig) -> Tuple[List[str], bool]: + """ Prepare audio data and decide whether it's partial_audio condition. """ + # this part may need refactor alongsides with refactor of transcribe + partial_audio = False + + if cfg.audio_dir is not None and not cfg.append_pred: + filepaths = list(glob.glob(os.path.join(cfg.audio_dir, f"**/*.{cfg.audio_type}"), recursive=True)) + else: + # get filenames from manifest + filepaths = [] + if os.stat(cfg.dataset_manifest).st_size == 0: + logging.error(f"The input dataset_manifest {cfg.dataset_manifest} is empty. Exiting!") + return None + + all_entries_have_offset_and_duration = True + for item in read_and_maybe_sort_manifest(cfg.dataset_manifest, try_sort=cfg.presort_manifest): + if not ("offset" in item and "duration" in item): + all_entries_have_offset_and_duration = False + audio_key = cfg.get('audio_key', 'audio_filepath') + audio_file = get_full_path(audio_file=item[audio_key], manifest_file=cfg.dataset_manifest) + filepaths.append(audio_file) + partial_audio = all_entries_have_offset_and_duration + logging.info(f"\nTranscribing {len(filepaths)} files...\n") + + return filepaths, partial_audio + + +def read_and_maybe_sort_manifest(path: str, try_sort: bool = False) -> List[dict]: + """Sorts the manifest if duration key is available for every utterance.""" + items = manifest_utils.read_manifest(path) + if try_sort and all("duration" in item and item["duration"] is not None for item in items): + items = sorted(items, reverse=True, key=lambda item: item["duration"]) + return items + + +def restore_transcription_order(manifest_path: str, transcriptions: list) -> list: + with open(manifest_path) as f: + items = [(idx, json.loads(l)) for idx, l in enumerate(f)] + if not all("duration" in item[1] and item[1]["duration"] is not None for item in items): + return transcriptions + new2old = [item[0] for item in sorted(items, reverse=True, key=lambda it: it[1]["duration"])] + del items # free up some memory + is_list = isinstance(transcriptions[0], list) + if is_list: + transcriptions = list(zip(*transcriptions)) + reordered = [None] * len(transcriptions) + for new, old in enumerate(new2old): + reordered[old] = transcriptions[new] + if is_list: + reordered = tuple(map(list, zip(*reordered))) + return reordered + + +def compute_output_filename(cfg: DictConfig, model_name: str) -> DictConfig: + """ Compute filename of output manifest and update cfg""" + if cfg.output_filename is None: + # create default output filename + if cfg.audio_dir is not None: + cfg.output_filename = os.path.dirname(os.path.join(cfg.audio_dir, '.')) + '.json' + elif cfg.pred_name_postfix is not None: + cfg.output_filename = cfg.dataset_manifest.replace('.json', f'_{cfg.pred_name_postfix}.json') + else: + cfg.output_filename = cfg.dataset_manifest.replace('.json', f'_{model_name}.json') + return cfg + + +def normalize_timestamp_output(timestamps: dict): + """ + Normalize the dictionary of timestamp values to JSON serializable values. + Expects the following keys to exist - + "start_offset": int-like object that represents the starting index of the token + in the full audio after downsampling. + "end_offset": int-like object that represents the ending index of the token + in the full audio after downsampling. + + Args: + timestamps: Nested dict. + + Returns: + Normalized `timestamps` dictionary (in-place normalized) + """ + for val_idx in range(len(timestamps)): + timestamps[val_idx]['start_offset'] = int(timestamps[val_idx]['start_offset']) + timestamps[val_idx]['end_offset'] = int(timestamps[val_idx]['end_offset']) + return timestamps + + +def write_transcription( + transcriptions: Union[List[rnnt_utils.Hypothesis], List[List[rnnt_utils.Hypothesis]], List[str]], + cfg: DictConfig, + model_name: str, + filepaths: List[str] = None, + compute_langs: bool = False, + compute_timestamps: bool = False, +) -> Tuple[str, str]: + """ Write generated transcription to output file. """ + if cfg.append_pred: + logging.info(f'Transcripts will be written in "{cfg.output_filename}" file') + if cfg.pred_name_postfix is not None: + pred_by_model_name = cfg.pred_name_postfix + else: + pred_by_model_name = model_name + pred_text_attr_name = 'pred_text_' + pred_by_model_name + else: + pred_text_attr_name = 'pred_text' + + return_hypotheses = True + if isinstance(transcriptions[0], str): # List[str]: + best_hyps = transcriptions + return_hypotheses = False + elif isinstance(transcriptions[0], rnnt_utils.Hypothesis): # List[rnnt_utils.Hypothesis] + best_hyps = transcriptions + assert cfg.decoding.beam.return_best_hypothesis, "Works only with return_best_hypothesis=true" + elif isinstance(transcriptions[0], list) and isinstance( + transcriptions[0][0], rnnt_utils.Hypothesis + ): # List[List[rnnt_utils.Hypothesis]] NBestHypothesis + best_hyps, beams = [], [] + for hyps in transcriptions: + best_hyps.append(hyps[0]) + if not cfg.decoding.beam.return_best_hypothesis: + beam = [] + for hyp in hyps: + score = hyp.score.numpy().item() if isinstance(hyp.score, torch.Tensor) else hyp.score + beam.append((hyp.text, score)) + beams.append(beam) + else: + raise TypeError + + # create output dir if not exists + Path(cfg.output_filename).parent.mkdir(parents=True, exist_ok=True) + with open(cfg.output_filename, 'w', encoding='utf-8', newline='\n') as f: + if cfg.audio_dir is not None: + for idx, transcription in enumerate(best_hyps): # type: rnnt_utils.Hypothesis or str + if not return_hypotheses: # transcription is str + item = {'audio_filepath': filepaths[idx], pred_text_attr_name: transcription} + else: # transcription is Hypothesis + item = {'audio_filepath': filepaths[idx], pred_text_attr_name: transcription.text} + + if compute_timestamps: + timestamps = transcription.timestep + if timestamps is not None and isinstance(timestamps, dict): + timestamps.pop( + 'timestep', None + ) # Pytorch tensor calculating index of each token, not needed. + for key in timestamps.keys(): + values = normalize_timestamp_output(timestamps[key]) + item[f'timestamps_{key}'] = values + + if compute_langs: + item['pred_lang'] = transcription.langs + item['pred_lang_chars'] = transcription.langs_chars + if not cfg.decoding.beam.return_best_hypothesis: + item['beams'] = beams[idx] + f.write(json.dumps(item) + "\n") + else: + with open(cfg.dataset_manifest, 'r', encoding='utf-8') as fr: + for idx, line in enumerate(fr): + item = json.loads(line) + if not return_hypotheses: # transcription is str + item[pred_text_attr_name] = best_hyps[idx] + else: # transcription is Hypothesis + item[pred_text_attr_name] = best_hyps[idx].text + + if compute_timestamps: + timestamps = best_hyps[idx].timestep + if timestamps is not None and isinstance(timestamps, dict): + timestamps.pop( + 'timestep', None + ) # Pytorch tensor calculating index of each token, not needed. + for key in timestamps.keys(): + values = normalize_timestamp_output(timestamps[key]) + item[f'timestamps_{key}'] = values + + if compute_langs: + item['pred_lang'] = best_hyps[idx].langs + item['pred_lang_chars'] = best_hyps[idx].langs_chars + + if not cfg.decoding.beam.return_best_hypothesis: + item['beams'] = beams[idx] + f.write(json.dumps(item) + "\n") + + return cfg.output_filename, pred_text_attr_name + + +def transcribe_partial_audio( + asr_model, + path2manifest: str = None, + batch_size: int = 4, + logprobs: bool = False, + return_hypotheses: bool = False, + num_workers: int = 0, + channel_selector: Optional[int] = None, + augmentor: DictConfig = None, + decoder_type: Optional[str] = None, +) -> List[str]: + """ + See description of this function in trancribe() in nemo/collections/asr/models/ctc_models.py and nemo/collections/asr/models/rnnt_models.py + """ + + if return_hypotheses and logprobs: + raise ValueError( + "Either `return_hypotheses` or `logprobs` can be True at any given time." + "Returned hypotheses will contain the logprobs." + ) + if num_workers is None: + num_workers = min(batch_size, os.cpu_count() - 1) + + # We will store transcriptions here + hypotheses = [] + # Model's mode and device + mode = asr_model.training + device = next(asr_model.parameters()).device + dither_value = asr_model.preprocessor.featurizer.dither + pad_to_value = asr_model.preprocessor.featurizer.pad_to + + if decoder_type is not None: # Hybrid model + decode_function = ( + asr_model.decoding.rnnt_decoder_predictions_tensor + if decoder_type == 'rnnt' + else asr_model.ctc_decoding.ctc_decoder_predictions_tensor + ) + elif hasattr(asr_model, 'joint'): # RNNT model + decode_function = asr_model.decoding.rnnt_decoder_predictions_tensor + else: # CTC model + decode_function = asr_model.decoding.ctc_decoder_predictions_tensor + + try: + asr_model.preprocessor.featurizer.dither = 0.0 + asr_model.preprocessor.featurizer.pad_to = 0 + # Switch model to evaluation mode + asr_model.eval() + # Freeze the encoder and decoder modules + asr_model.encoder.freeze() + asr_model.decoder.freeze() + logging_level = logging.get_verbosity() + logging.set_verbosity(logging.WARNING) + + config = { + 'manifest_filepath': path2manifest, + 'batch_size': batch_size, + 'num_workers': num_workers, + 'channel_selector': channel_selector, + } + if augmentor: + config['augmentor'] = augmentor + + temporary_datalayer = asr_model._setup_transcribe_dataloader(config) + for test_batch in tqdm(temporary_datalayer, desc="Transcribing"): + outputs = asr_model.forward( + input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device) + ) + logits, logits_len = outputs[0], outputs[1] + + if isinstance(asr_model, EncDecHybridRNNTCTCModel) and decoder_type == "ctc": + logits = asr_model.ctc_decoder(encoder_output=logits) + + logits = logits.cpu() + + if logprobs: + logits = logits.numpy() + # dump log probs per file + for idx in range(logits.shape[0]): + lg = logits[idx][: logits_len[idx]] + hypotheses.append(lg) + else: + current_hypotheses, _ = decode_function(logits, logits_len, return_hypotheses=return_hypotheses,) + + if return_hypotheses: + # dump log probs per file + for idx in range(logits.shape[0]): + current_hypotheses[idx].y_sequence = logits[idx][: logits_len[idx]] + if current_hypotheses[idx].alignments is None: + current_hypotheses[idx].alignments = current_hypotheses[idx].y_sequence + + hypotheses += current_hypotheses + + del logits + del test_batch + + finally: + # set mode back to its original value + asr_model.train(mode=mode) + asr_model.preprocessor.featurizer.dither = dither_value + asr_model.preprocessor.featurizer.pad_to = pad_to_value + if mode is True: + asr_model.encoder.unfreeze() + asr_model.decoder.unfreeze() + logging.set_verbosity(logging_level) + return hypotheses + + +def compute_metrics_per_sample( + manifest_path: str, + reference_field: str = "text", + hypothesis_field: str = "pred_text", + metrics: List[str] = ["wer"], + punctuation_marks: List[str] = [".", ",", "?"], + output_manifest_path: str = None, +) -> dict: + + ''' + Computes metrics per sample for given manifest + + Args: + manifest_path: str, Required - path to dataset JSON manifest file (in NeMo format) + reference_field: str, Optional - name of field in .json manifest with the reference text ("text" by default). + hypothesis_field: str, Optional - name of field in .json manifest with the hypothesis text ("pred_text" by default). + metrics: list[str], Optional - list of metrics to be computed (currently supported "wer", "cer", "punct_er") + punctuation_marks: list[str], Optional - list of punctuation marks for computing punctuation error rate ([".", ",", "?"] by default). + output_manifest_path: str, Optional - path where .json manifest with calculated metrics will be saved. + + Returns: + samples: dict - Dict of samples with calculated metrics + ''' + + supported_metrics = ["wer", "cer", "punct_er"] + + if len(metrics) == 0: + raise AssertionError( + f"'metrics' list is empty. \ + Select the metrics from the supported: {supported_metrics}." + ) + + for metric in metrics: + if metric not in supported_metrics: + raise AssertionError( + f"'{metric}' metric is not supported. \ + Currently supported metrics are {supported_metrics}." + ) + + if "punct_er" in metrics: + if len(punctuation_marks) == 0: + raise AssertionError("punctuation_marks list can't be empty when 'punct_er' metric is enabled.") + else: + oper_obj = OccurancePunctuationErrorRate(punctuation_marks=punctuation_marks) + + use_wer = "wer" in metrics + use_cer = "cer" in metrics + use_punct_er = "punct_er" in metrics + + with open(manifest_path, 'r') as manifest: + lines = manifest.readlines() + samples = [json.loads(line) for line in lines] + samples_with_metrics = [] + + logging.info(f"Computing {', '.join(metrics)} per sample") + + for sample in tqdm(samples): + reference = sample[reference_field] + hypothesis = sample[hypothesis_field] + + if use_wer: + sample_wer = word_error_rate(hypotheses=[hypothesis], references=[reference], use_cer=False) + sample["wer"] = round(100 * sample_wer, 2) + + if use_cer: + sample_cer = word_error_rate(hypotheses=[hypothesis], references=[reference], use_cer=True) + sample["cer"] = round(100 * sample_cer, 2) + + if use_punct_er: + operation_amounts, substitution_amounts, punctuation_rates = oper_obj.compute( + reference=reference, hypothesis=hypothesis + ) + sample["punct_correct_rate"] = round(100 * punctuation_rates.correct_rate, 2) + sample["punct_deletions_rate"] = round(100 * punctuation_rates.deletions_rate, 2) + sample["punct_insertions_rate"] = round(100 * punctuation_rates.insertions_rate, 2) + sample["punct_substitutions_rate"] = round(100 * punctuation_rates.substitutions_rate, 2) + sample["punct_error_rate"] = round(100 * punctuation_rates.punct_er, 2) + + samples_with_metrics.append(sample) + + if output_manifest_path is not None: + with open(output_manifest_path, 'w') as output: + for sample in samples_with_metrics: + line = json.dumps(sample) + output.writelines(f'{line}\n') + logging.info(f'Output manifest saved: {output_manifest_path}') + + return samples_with_metrics + + +class PunctuationCapitalization: + def __init__(self, punctuation_marks: str): + """ + Class for text processing with punctuation and capitalization. Can be used with class TextProcessingConfig. + + Args: + punctuation_marks (str): String with punctuation marks to process. + Example: punctuation_marks = '.,?' + """ + if punctuation_marks: + self.regex_punctuation = re.compile(fr"([{''.join(punctuation_marks)}])") + self.regex_extra_space = re.compile('\s{2,}') + else: + self.regex_punctuation = None + + def separate_punctuation(self, lines: List[str]) -> List[str]: + if self.regex_punctuation is not None: + return [ + self.regex_extra_space.sub(' ', self.regex_punctuation.sub(r' \1 ', line)).strip() for line in lines + ] + else: + return lines + + def do_lowercase(self, lines: List[str]) -> List[str]: + return [line.lower() for line in lines] + + def rm_punctuation(self, lines: List[str]) -> List[str]: + if self.regex_punctuation is not None: + return [self.regex_extra_space.sub(' ', self.regex_punctuation.sub(' ', line)).strip() for line in lines] + else: + return lines + + +@dataclass +class TextProcessingConfig: + # Punctuation marks to process. Example: ".,?" + punctuation_marks: str = "" + + # Whether to apply lower case conversion on the training text. + do_lowercase: bool = False + + # Whether to remove punctuation marks from text. + rm_punctuation: bool = False + + # Whether to separate punctuation with the previouse word by space. + separate_punctuation: bool = True diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/vad_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/vad_utils.py new file mode 100644 index 0000000..68dfaf3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/asr/parts/utils/vad_utils.py @@ -0,0 +1,1718 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import glob +import json +import math +import multiprocessing +import os +import shutil +from itertools import repeat +from math import ceil, floor +from pathlib import Path +from typing import Dict, List, Optional, Tuple, Union + +import IPython.display as ipd +import librosa +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import torch +from omegaconf import DictConfig +from pyannote.core import Annotation, Segment +from pyannote.metrics import detection +from sklearn.metrics import roc_auc_score +from sklearn.model_selection import ParameterGrid +from tqdm import tqdm + +from nemo.collections.asr.models import EncDecClassificationModel, EncDecFrameClassificationModel +from nemo.collections.common.parts.preprocessing.manifest import get_full_path +from nemo.utils import logging + +try: + from torch.cuda.amp import autocast +except ImportError: + from contextlib import contextmanager + + @contextmanager + def autocast(enabled=None): + yield + + +""" +This file contains all the utility functions required for voice activity detection. +""" + + +def prepare_manifest(config: dict) -> str: + """ + Perform VAD on long audio snippet might cause CUDA out of memory issue. + Automatically split manifest entry by split_duration to avoid the potential memory issue. + """ + if 'prepared_manifest_vad_input' in config and config['prepared_manifest_vad_input']: + manifest_vad_input = config['prepared_manifest_vad_input'] + else: + default_path = "manifest_vad_input.json" + manifest_vad_input = os.path.join(config["out_dir"], default_path) if "out_dir" in config else default_path + + # input_list is a list of variable ['audio_filepath': i, "offset": xxx, "duration": xxx]) + if type(config['input']) == str: + input_list = [] + with open(config['input'], 'r', encoding='utf-8') as manifest: + for line in manifest.readlines(): + input_list.append(json.loads(line.strip())) + elif type(config['input']) == list: + input_list = config['input'] + else: + raise ValueError( + "The input for manifest preparation would either be a string of the filepath to manifest or a list of {'audio_filepath': i, 'offset': 0, 'duration': null} " + ) + + args_func = { + 'label': 'infer', + 'split_duration': config['split_duration'], + 'window_length_in_sec': config['window_length_in_sec'], + 'manifest_dir': Path(config['input']).parent if type(config['input']) == str else '', + } + + if config.get('num_workers') is not None and config['num_workers'] > 1: + with multiprocessing.Pool(processes=config['num_workers']) as p: + inputs = zip(input_list, repeat(args_func)) + results = list( + tqdm( + p.imap(write_vad_infer_manifest_star, inputs), + total=len(input_list), + desc='splitting manifest', + leave=True, + ) + ) + else: + results = [ + write_vad_infer_manifest(input_el, args_func) + for input_el in tqdm(input_list, desc='splitting manifest', leave=True) + ] + + if os.path.exists(manifest_vad_input): + logging.info("The prepared manifest file exists. Overwriting!") + os.remove(manifest_vad_input) + + with open(manifest_vad_input, 'a', encoding='utf-8') as fout: + for res in results: + for r in res: + json.dump(r, fout) + fout.write('\n') + fout.flush() + return manifest_vad_input + + +def write_vad_infer_manifest_star(args): + """ + A workaround for tqdm with starmap of multiprocessing + """ + return write_vad_infer_manifest(*args) + + +def write_vad_infer_manifest(file: dict, args_func: dict) -> list: + """ + Used by prepare_manifest. + Given a list of files, split them with maximum split_duration and write them to the manifest. + Args: + files (dict) : file to be processed + args_func: + label (str): label for audio snippet.y + split_duration (float): max duration of each audio clip (each line in json) + window_length_in_sec (float) : length of window for generating the frame. Used for taking care of joint. + Returns: + res (list) : list of generated metadata line of json for file + """ + res = [] + label = args_func['label'] + split_duration = args_func['split_duration'] + window_length_in_sec = args_func['window_length_in_sec'] + filepath = file['audio_filepath'] + in_duration = file.get('duration', None) + in_offset = file.get('offset', 0) + + # if filepath is not found, try to find it in the dir of manifest + if not Path(filepath).is_file(): + new_filepath = Path(args_func['manifest_dir']) / filepath + if new_filepath.is_file(): + filepath = new_filepath.absolute().as_posix() + + try: + sr = 16000 + x, _sr = librosa.load(filepath, sr=sr, offset=in_offset, duration=in_duration) + duration = librosa.get_duration(y=x, sr=sr) + left = duration + current_offset = in_offset + + status = 'single' + while left > 0: + if left <= split_duration: + if status == 'single': + write_duration = left + current_offset = 0 + else: + status = 'end' + write_duration = left + window_length_in_sec + current_offset -= window_length_in_sec + offset_inc = left + left = 0 + else: + if status == 'start' or status == 'next': + status = 'next' + else: + status = 'start' + + if status == 'start': + write_duration = split_duration + offset_inc = split_duration + else: + write_duration = split_duration + window_length_in_sec + current_offset -= window_length_in_sec + offset_inc = split_duration + window_length_in_sec + + left -= split_duration + + metadata = { + 'audio_filepath': filepath, + 'duration': write_duration, + 'label': label, + 'text': '_', + 'offset': current_offset, + } + res.append(metadata) + + current_offset += offset_inc + + except Exception as e: + err_file = "error.log" + with open(err_file, 'w', encoding='utf-8') as fout: + fout.write(filepath + ":" + str(e)) + return res + + +def get_vad_stream_status(data: list) -> list: + """ + Generate a list of status for each snippet in manifest. A snippet should be in single, start, next or end status. + Used for concatenating to full audio file. + Args: + data (list): list of filepath of audio snippet + Returns: + status (list): list of status of each snippet. + """ + if len(data) == 1: + return ['single'] + + status = [None] * len(data) + for i in range(len(data)): + if i == 0: + status[i] = 'start' if data[i] == data[i + 1] else 'single' + elif i == len(data) - 1: + status[i] = 'end' if data[i] == data[i - 1] else 'single' + else: + if data[i] != data[i - 1] and data[i] == data[i + 1]: + status[i] = 'start' + elif data[i] == data[i - 1] and data[i] == data[i + 1]: + status[i] = 'next' + elif data[i] == data[i - 1] and data[i] != data[i + 1]: + status[i] = 'end' + else: + status[i] = 'single' + return status + + +def load_tensor_from_file(filepath: str) -> Tuple[torch.Tensor, str]: + """ + Load torch.Tensor and the name from file + """ + frame = [] + with open(filepath, "r", encoding='utf-8') as f: + for line in f.readlines(): + frame.append(float(line)) + + name = Path(filepath).stem + return torch.tensor(frame), name + + +def generate_overlap_vad_seq( + frame_pred_dir: str, + smoothing_method: str, + overlap: float, + window_length_in_sec: float, + shift_length_in_sec: float, + num_workers: int, + out_dir: str = None, +) -> str: + """ + Generate predictions with overlapping input windows/segments. Then a smoothing filter is applied to decide the label for a frame spanned by multiple windows. + Two common smoothing filters are supported: majority vote (median) and average (mean). + This function uses multiprocessing to speed up. + Args: + frame_pred_dir (str): Directory of frame prediction file to be processed. + smoothing_method (str): median or mean smoothing filter. + overlap (float): amounts of overlap of adjacent windows. + window_length_in_sec (float): length of window for generating the frame. + shift_length_in_sec (float): amount of shift of window for generating the frame. + out_dir (str): directory of generated predictions. + num_workers(float): number of process for multiprocessing + Returns: + overlap_out_dir(str): directory of the generated predictions. + """ + + frame_filepathlist = glob.glob(frame_pred_dir + "/*.frame") + if out_dir: + overlap_out_dir = out_dir + else: + overlap_out_dir = os.path.join( + frame_pred_dir, "overlap_smoothing_output" + "_" + smoothing_method + "_" + str(overlap) + ) + + if not os.path.exists(overlap_out_dir): + os.mkdir(overlap_out_dir) + + per_args = { + "overlap": overlap, + "window_length_in_sec": window_length_in_sec, + "shift_length_in_sec": shift_length_in_sec, + "out_dir": overlap_out_dir, + "smoothing_method": smoothing_method, + } + if num_workers is not None and num_workers > 1: + with multiprocessing.Pool(processes=num_workers) as p: + inputs = zip(frame_filepathlist, repeat(per_args)) + results = list( + tqdm( + p.imap(generate_overlap_vad_seq_per_file_star, inputs), + total=len(frame_filepathlist), + desc='generating preds', + leave=True, + ) + ) + + else: + for frame_filepath in tqdm(frame_filepathlist, desc='generating preds', leave=False): + generate_overlap_vad_seq_per_file(frame_filepath, per_args) + + return overlap_out_dir + + +def generate_overlap_vad_seq_per_file_star(args): + """ + A workaround for tqdm with starmap of multiprocessing + """ + return generate_overlap_vad_seq_per_file(*args) + + +@torch.jit.script +def generate_overlap_vad_seq_per_tensor( + frame: torch.Tensor, per_args: Dict[str, float], smoothing_method: str +) -> torch.Tensor: + """ + Use generated frame prediction (generated by shifting window of shift_length_in_sec (10ms)) to generate prediction with overlapping input window/segments + See description in generate_overlap_vad_seq. + Use this for single instance pipeline. + """ + # This function will be refactor for vectorization but this is okay for now + + overlap = per_args['overlap'] + window_length_in_sec = per_args['window_length_in_sec'] + shift_length_in_sec = per_args['shift_length_in_sec'] + frame_len = per_args.get('frame_len', 0.01) + + shift = int(shift_length_in_sec / frame_len) # number of units of shift + seg = int((window_length_in_sec / frame_len + 1)) # number of units of each window/segment + + jump_on_target = int(seg * (1 - overlap)) # jump on target generated sequence + jump_on_frame = int(jump_on_target / shift) # jump on input frame sequence + + if jump_on_frame < 1: + raise ValueError( + f"Note we jump over frame sequence to generate overlapping input segments. \n \ + Your input makes jump_on_frame={jump_on_frame} < 1 which is invalid because it cannot jump and will stuck.\n \ + Please try different window_length_in_sec, shift_length_in_sec and overlap choices. \n \ + jump_on_target = int(seg * (1 - overlap)) \n \ + jump_on_frame = int(jump_on_frame/shift) " + ) + + target_len = int(len(frame) * shift) + + if smoothing_method == 'mean': + preds = torch.zeros(target_len) + pred_count = torch.zeros(target_len) + + for i, og_pred in enumerate(frame): + if i % jump_on_frame != 0: + continue + start = i * shift + end = start + seg + preds[start:end] = preds[start:end] + og_pred + pred_count[start:end] = pred_count[start:end] + 1 + + preds = preds / pred_count + last_non_zero_pred = preds[pred_count != 0][-1] + preds[pred_count == 0] = last_non_zero_pred + + elif smoothing_method == 'median': + preds = [torch.empty(0) for _ in range(target_len)] + for i, og_pred in enumerate(frame): + if i % jump_on_frame != 0: + continue + + start = i * shift + end = start + seg + for j in range(start, end): + if j <= target_len - 1: + preds[j] = torch.cat((preds[j], og_pred.unsqueeze(0)), 0) + + preds = torch.stack([torch.nanquantile(l, q=0.5) for l in preds]) + nan_idx = torch.isnan(preds) + last_non_nan_pred = preds[~nan_idx][-1] + preds[nan_idx] = last_non_nan_pred + + else: + raise ValueError("smoothing_method should be either mean or median") + + return preds + + +def generate_overlap_vad_seq_per_file(frame_filepath: str, per_args: dict) -> str: + """ + A wrapper for generate_overlap_vad_seq_per_tensor. + """ + + out_dir = per_args['out_dir'] + smoothing_method = per_args['smoothing_method'] + frame, name = load_tensor_from_file(frame_filepath) + + per_args_float: Dict[str, float] = {} + for i in per_args: + if type(per_args[i]) == float or type(per_args[i]) == int: + per_args_float[i] = per_args[i] + + preds = generate_overlap_vad_seq_per_tensor(frame, per_args_float, smoothing_method) + + overlap_filepath = os.path.join(out_dir, name + "." + smoothing_method) + with open(overlap_filepath, "w", encoding='utf-8') as f: + for pred in preds: + f.write(f"{pred:.4f}\n") + + return overlap_filepath + + +@torch.jit.script +def merge_overlap_segment(segments: torch.Tensor) -> torch.Tensor: + """ + Merged the given overlapped segments. + For example: + torch.Tensor([[0, 1.5], [1, 3.5]]) -> torch.Tensor([0, 3.5]) + """ + if ( + segments.shape == torch.Size([0]) + or segments.shape == torch.Size([0, 2]) + or segments.shape == torch.Size([1, 2]) + ): + return segments + + segments = segments[segments[:, 0].sort()[1]] + merge_boundary = segments[:-1, 1] >= segments[1:, 0] + head_padded = torch.nn.functional.pad(merge_boundary, [1, 0], mode='constant', value=0.0) + head = segments[~head_padded, 0] + tail_padded = torch.nn.functional.pad(merge_boundary, [0, 1], mode='constant', value=0.0) + tail = segments[~tail_padded, 1] + merged = torch.stack((head, tail), dim=1) + return merged + + +@torch.jit.script +def filter_short_segments(segments: torch.Tensor, threshold: float) -> torch.Tensor: + """ + Remove segments which duration is smaller than a threshold. + For example, + torch.Tensor([[0, 1.5], [1, 3.5], [4, 7]]) and threshold = 2.0 + -> + torch.Tensor([[1, 3.5], [4, 7]]) + """ + return segments[segments[:, 1] - segments[:, 0] >= threshold] + + +def percentile(data: torch.Tensor, perc: int) -> float: + """ + Calculate percentile given data + """ + size = len(data) + return float(sorted(data)[int(math.ceil((size * perc) / 100)) - 1]) + + +def cal_vad_onset_offset( + scale: str, onset: float, offset: float, sequence: torch.Tensor = None +) -> Tuple[float, float]: + """ + Calculate onset and offset threshold given different scale. + """ + if scale == "absolute": + mini = 0 + maxi = 1 + elif scale == "relative": + mini = min(sequence) + maxi = max(sequence) + elif scale == "percentile": + mini = percentile(sequence, 1) + maxi = percentile(sequence, 99) + + onset = mini + onset * (maxi - mini) + offset = mini + offset * (maxi - mini) + return float(onset), float(offset) + + +@torch.jit.script +def binarization(sequence: torch.Tensor, per_args: Dict[str, float]) -> torch.Tensor: + """ + Binarize predictions to speech and non-speech + + Reference + Paper: Gregory Gelly and Jean-Luc Gauvain. "Minimum Word Error Training of RNN-based Voice Activity Detection", InterSpeech 2015. + Implementation: https://github.com/pyannote/pyannote-audio/blob/master/pyannote/audio/utils/signal.py + + Args: + sequence (torch.Tensor) : A tensor of frame level predictions. + per_args: + onset (float): onset threshold for detecting the beginning and end of a speech + offset (float): offset threshold for detecting the end of a speech. + pad_onset (float): adding durations before each speech segment + pad_offset (float): adding durations after each speech segment; + frame_length_in_sec (float): length of frame. + + Returns: + speech_segments(torch.Tensor): A tensor of speech segment in torch.Tensor([[start1, end1], [start2, end2]]) format. + """ + frame_length_in_sec = per_args.get('frame_length_in_sec', 0.01) + + onset = per_args.get('onset', 0.5) + offset = per_args.get('offset', 0.5) + pad_onset = per_args.get('pad_onset', 0.0) + pad_offset = per_args.get('pad_offset', 0.0) + + speech = False + start = 0.0 + i = 0 + + speech_segments = torch.empty(0) + + for i in range(0, len(sequence)): + # Current frame is speech + if speech: + # Switch from speech to non-speech + if sequence[i] < offset: + if i * frame_length_in_sec + pad_offset > max(0, start - pad_onset): + new_seg = torch.tensor( + [max(0, start - pad_onset), i * frame_length_in_sec + pad_offset] + ).unsqueeze(0) + speech_segments = torch.cat((speech_segments, new_seg), 0) + + start = i * frame_length_in_sec + speech = False + + # Current frame is non-speech + else: + # Switch from non-speech to speech + if sequence[i] > onset: + start = i * frame_length_in_sec + speech = True + + # if it's speech at the end, add final segment + if speech: + new_seg = torch.tensor([max(0, start - pad_onset), i * frame_length_in_sec + pad_offset]).unsqueeze(0) + speech_segments = torch.cat((speech_segments, new_seg), 0) + + # Merge the overlapped speech segments due to padding + speech_segments = merge_overlap_segment(speech_segments) # not sorted + return speech_segments + + +@torch.jit.script +def remove_segments(original_segments: torch.Tensor, to_be_removed_segments: torch.Tensor) -> torch.Tensor: + """ + Remove speech segments list in to_be_removed_segments from original_segments. + For example, + remove torch.Tensor([[start2, end2],[start4, end4]]) from torch.Tensor([[start1, end1],[start2, end2],[start3, end3], [start4, end4]]), + -> + torch.Tensor([[start1, end1],[start3, end3]]) + """ + for y in to_be_removed_segments: + original_segments = original_segments[original_segments.eq(y).all(dim=1).logical_not()] + return original_segments + + +@torch.jit.script +def get_gap_segments(segments: torch.Tensor) -> torch.Tensor: + """ + Get the gap segments. + For example, + torch.Tensor([[start1, end1], [start2, end2], [start3, end3]]) -> torch.Tensor([[end1, start2], [end2, start3]]) + """ + segments = segments[segments[:, 0].sort()[1]] + return torch.column_stack((segments[:-1, 1], segments[1:, 0])) + + +@torch.jit.script +def filtering(speech_segments: torch.Tensor, per_args: Dict[str, float]) -> torch.Tensor: + + """ + Filter out short non_speech and speech segments. + + Reference + Paper: Gregory Gelly and Jean-Luc Gauvain. "Minimum Word Error Training of RNN-based Voice Activity Detection", InterSpeech 2015. + Implementation: https://github.com/pyannote/pyannote-audio/blob/master/pyannote/audio/utils/signal.py + Args: + speech_segments (torch.Tensor): A tensor of speech segment in torch.Tensor([[start1, end1], [start2, end2]]) format. + per_args: + min_duration_on (float): threshold for small non_speech deletion + min_duration_off (float): threshold for short speech segment deletion + filter_speech_first (float): Whether to perform short speech segment deletion first. Use 1.0 to represent True. + + Returns: + speech_segments(torch.Tensor): A tensor of filtered speech segment in torch.Tensor([[start1, end1], [start2, end2]]) format. + """ + if speech_segments.shape == torch.Size([0]): + return speech_segments + + min_duration_on = per_args.get('min_duration_on', 0.0) + min_duration_off = per_args.get('min_duration_off', 0.0) + filter_speech_first = per_args.get('filter_speech_first', 1.0) + + if filter_speech_first == 1.0: + # Filter out the shorter speech segments + if min_duration_on > 0.0: + speech_segments = filter_short_segments(speech_segments, min_duration_on) + # Filter out the shorter non-speech segments and return to be as speech segments + if min_duration_off > 0.0: + # Find non-speech segments + non_speech_segments = get_gap_segments(speech_segments) + # Find shorter non-speech segments + short_non_speech_segments = remove_segments( + non_speech_segments, filter_short_segments(non_speech_segments, min_duration_off) + ) + # Return shorter non-speech segments to be as speech segments + speech_segments = torch.cat((speech_segments, short_non_speech_segments), 0) + + # Merge the overlapped speech segments + speech_segments = merge_overlap_segment(speech_segments) + else: + if min_duration_off > 0.0: + # Find non-speech segments + non_speech_segments = get_gap_segments(speech_segments) + # Find shorter non-speech segments + short_non_speech_segments = remove_segments( + non_speech_segments, filter_short_segments(non_speech_segments, min_duration_off) + ) + + speech_segments = torch.cat((speech_segments, short_non_speech_segments), 0) + + # Merge the overlapped speech segments + speech_segments = merge_overlap_segment(speech_segments) + if min_duration_on > 0.0: + speech_segments = filter_short_segments(speech_segments, min_duration_on) + + return speech_segments + + +def prepare_gen_segment_table(sequence: torch.Tensor, per_args: dict) -> Tuple[str, dict]: + """ + Preparing for generating segment table. + """ + out_dir = per_args.get('out_dir', None) + + # calculate onset offset based on scale selection + per_args['onset'], per_args['offset'] = cal_vad_onset_offset( + per_args.get('scale', 'absolute'), per_args['onset'], per_args['offset'], sequence + ) + + # cast 'filter_speech_first' for torch.jit.script + if 'filter_speech_first' in per_args: + if per_args['filter_speech_first']: + per_args['filter_speech_first'] = 1.0 + else: + per_args['filter_speech_first'] = 0.0 + + per_args_float: Dict[str, float] = {} + for i in per_args: + if type(per_args[i]) == float or type(per_args[i]) == int: + per_args_float[i] = per_args[i] + + return out_dir, per_args_float + + +@torch.jit.script +def generate_vad_segment_table_per_tensor(sequence: torch.Tensor, per_args: Dict[str, float]) -> torch.Tensor: + """ + See description in generate_overlap_vad_seq. + Use this for single instance pipeline. + """ + UNIT_FRAME_LEN = 0.01 + + speech_segments = binarization(sequence, per_args) + speech_segments = filtering(speech_segments, per_args) + + if speech_segments.shape == torch.Size([0]): + return speech_segments + + speech_segments, _ = torch.sort(speech_segments, 0) + + dur = speech_segments[:, 1:2] - speech_segments[:, 0:1] + UNIT_FRAME_LEN + speech_segments = torch.column_stack((speech_segments, dur)) + + return speech_segments + + +def generate_vad_segment_table_per_file(pred_filepath: str, per_args: dict) -> str: + """ + A wrapper for generate_vad_segment_table_per_tensor + """ + sequence, name = load_tensor_from_file(pred_filepath) + out_dir, per_args_float = prepare_gen_segment_table(sequence, per_args) + + preds = generate_vad_segment_table_per_tensor(sequence, per_args_float) + ext = ".rttm" if per_args.get("use_rttm", False) else ".txt" + save_name = name + ext + save_path = os.path.join(out_dir, save_name) + + if preds.shape[0] == 0: + with open(save_path, "w", encoding='utf-8') as fp: + if per_args.get("use_rttm", False): + fp.write(f"SPEAKER 1 0 0 speech \n") + else: + fp.write(f"0 0 speech\n") + else: + with open(save_path, "w", encoding='utf-8') as fp: + for i in preds: + if per_args.get("use_rttm", False): + fp.write(f"SPEAKER {name} 1 {i[0]:.4f} {i[2]:.4f} speech \n") + else: + fp.write(f"{i[0]:.4f} {i[2]:.4f} speech\n") + + return save_path + + +def generate_vad_segment_table( + vad_pred_dir: str, + postprocessing_params: dict, + frame_length_in_sec: float, + num_workers: int, + out_dir: str = None, + use_rttm: bool = False, +) -> str: + """ + Convert frame level prediction to speech segment in start and end times format. + And save to csv file in rttm-like format + 0, 10, speech + 17,18, speech + Args: + vad_pred_dir (str): directory of prediction files to be processed. + postprocessing_params (dict): dictionary of thresholds for prediction score. See details in binarization and filtering. + frame_length_in_sec (float): frame length. + out_dir (str): output dir of generated table/csv file. + num_workers(float): number of process for multiprocessing + Returns: + out_dir(str): directory of the generated table. + """ + + suffixes = ("frame", "mean", "median") + vad_pred_filepath_list = [os.path.join(vad_pred_dir, x) for x in os.listdir(vad_pred_dir) if x.endswith(suffixes)] + + if not out_dir: + out_dir_name = "seg_output" + for key in postprocessing_params: + out_dir_name = out_dir_name + "-" + str(key) + str(postprocessing_params[key]) + + out_dir = os.path.join(vad_pred_dir, out_dir_name) + + if not os.path.exists(out_dir): + os.mkdir(out_dir) + + per_args = { + "frame_length_in_sec": frame_length_in_sec, + "out_dir": out_dir, + "use_rttm": use_rttm, + } + per_args = {**per_args, **postprocessing_params} + num_workers = None + if num_workers is not None and num_workers > 1: + with multiprocessing.Pool(num_workers) as p: + inputs = zip(vad_pred_filepath_list, repeat(per_args)) + list( + tqdm( + p.imap(generate_vad_segment_table_per_file_star, inputs), + total=len(vad_pred_filepath_list), + desc='creating speech segments', + leave=True, + ) + ) + else: + for vad_pred_filepath in tqdm(vad_pred_filepath_list, desc='creating speech segments', leave=True): + generate_vad_segment_table_per_file(vad_pred_filepath, per_args) + + return out_dir + + +def generate_vad_segment_table_per_file_star(args): + """ + A workaround for tqdm with starmap of multiprocessing + """ + return generate_vad_segment_table_per_file(*args) + + +def vad_construct_pyannote_object_per_file( + vad_table_filepath: str, groundtruth_RTTM_file: str +) -> Tuple[Annotation, Annotation]: + """ + Construct a Pyannote object for evaluation. + Args: + vad_table_filepath(str) : path of vad rttm-like table. + groundtruth_RTTM_file(str): path of groundtruth rttm file. + Returns: + reference(pyannote.Annotation): groundtruth + hypothesis(pyannote.Annotation): prediction + """ + + pred = pd.read_csv(vad_table_filepath, sep=" ", header=None) + label = pd.read_csv(groundtruth_RTTM_file, sep=" ", delimiter=None, header=None) + label = label.rename(columns={3: "start", 4: "dur", 7: "speaker"}) + + # construct reference + reference = Annotation() + for index, row in label.iterrows(): + reference[Segment(row['start'], row['start'] + row['dur'])] = row['speaker'] + + # construct hypothsis + hypothesis = Annotation() + for index, row in pred.iterrows(): + hypothesis[Segment(float(row[0]), float(row[0]) + float(row[1]))] = 'Speech' + return reference, hypothesis + + +def get_parameter_grid(params: dict) -> list: + """ + Get the parameter grid given a dictionary of parameters. + """ + has_filter_speech_first = False + if 'filter_speech_first' in params: + filter_speech_first = params['filter_speech_first'] + has_filter_speech_first = True + params.pop("filter_speech_first") + + params_grid = list(ParameterGrid(params)) + + if has_filter_speech_first: + for i in params_grid: + i['filter_speech_first'] = filter_speech_first + return params_grid + + +def vad_tune_threshold_on_dev( + params: dict, + vad_pred: str, + groundtruth_RTTM: str, + result_file: str = "res", + vad_pred_method: str = "frame", + focus_metric: str = "DetER", + frame_length_in_sec: float = 0.01, + num_workers: int = 20, +) -> Tuple[dict, dict]: + """ + Tune thresholds on dev set. Return best thresholds which gives the lowest detection error rate (DetER) in thresholds. + Args: + params (dict): dictionary of parameters to be tuned on. + vad_pred_method (str): suffix of prediction file. Use to locate file. Should be either in "frame", "mean" or "median". + groundtruth_RTTM_dir (str): directory of ground-truth rttm files or a file contains the paths of them. + focus_metric (str): metrics we care most when tuning threshold. Should be either in "DetER", "FA", "MISS" + frame_length_in_sec (float): frame length. + num_workers (int): number of workers. + Returns: + best_threshold (float): threshold that gives lowest DetER. + """ + min_score = 100 + all_perf = {} + try: + check_if_param_valid(params) + except: + raise ValueError("Please check if the parameters are valid") + + paired_filenames, groundtruth_RTTM_dict, vad_pred_dict = pred_rttm_map(vad_pred, groundtruth_RTTM, vad_pred_method) + metric = detection.DetectionErrorRate() + params_grid = get_parameter_grid(params) + + for param in params_grid: + for i in param: + if type(param[i]) == np.float64 or type(param[i]) == np.int64: + param[i] = float(param[i]) + try: + # Generate speech segments by performing binarization on the VAD prediction according to param. + # Filter speech segments according to param and write the result to rttm-like table. + vad_table_dir = generate_vad_segment_table( + vad_pred, param, frame_length_in_sec=frame_length_in_sec, num_workers=num_workers + ) + # add reference and hypothesis to metrics + for filename in paired_filenames: + groundtruth_RTTM_file = groundtruth_RTTM_dict[filename] + vad_table_filepath = os.path.join(vad_table_dir, filename + ".txt") + reference, hypothesis = vad_construct_pyannote_object_per_file( + vad_table_filepath, groundtruth_RTTM_file + ) + metric(reference, hypothesis) # accumulation + + # delete tmp table files + shutil.rmtree(vad_table_dir, ignore_errors=True) + + report = metric.report(display=False) + DetER = report.iloc[[-1]][('detection error rate', '%')].item() + FA = report.iloc[[-1]][('false alarm', '%')].item() + MISS = report.iloc[[-1]][('miss', '%')].item() + + assert ( + focus_metric == "DetER" or focus_metric == "FA" or focus_metric == "MISS" + ), "Metric we care most should be only in 'DetER', 'FA' or 'MISS'!" + all_perf[str(param)] = {'DetER (%)': DetER, 'FA (%)': FA, 'MISS (%)': MISS} + logging.info(f"parameter {param}, {all_perf[str(param)] }") + + score = all_perf[str(param)][focus_metric + ' (%)'] + + del report + metric.reset() # reset internal accumulator + + # save results for analysis + with open(result_file + ".txt", "a", encoding='utf-8') as fp: + fp.write(f"{param}, {all_perf[str(param)] }\n") + + if score < min_score: + best_threshold = param + optimal_scores = all_perf[str(param)] + min_score = score + print("Current best", best_threshold, optimal_scores) + + except RuntimeError as e: + print(f"Pass {param}, with error {e}") + except pd.errors.EmptyDataError as e1: + print(f"Pass {param}, with error {e1}") + + return best_threshold, optimal_scores + + +def check_if_param_valid(params: dict) -> bool: + """ + Check if the parameters are valid. + """ + for i in params: + if i == "filter_speech_first": + if not type(params["filter_speech_first"]) == bool: + raise ValueError("Invalid inputs! filter_speech_first should be either True or False!") + elif i == "pad_onset": + continue + elif i == "pad_offset": + continue + else: + for j in params[i]: + if not j >= 0: + raise ValueError( + "Invalid inputs! All float parameters except pad_onset and pad_offset should be larger than 0!" + ) + + if not (all(i <= 1 for i in params['onset']) and all(i <= 1 for i in params['offset'])): + raise ValueError("Invalid inputs! The onset and offset thresholds should be in range [0, 1]!") + + return True + + +def pred_rttm_map(vad_pred: str, groundtruth_RTTM: str, vad_pred_method: str = "frame") -> Tuple[set, dict, dict]: + """ + Find paired files in vad_pred and groundtruth_RTTM + """ + groundtruth_RTTM_dict = {} + if os.path.isfile(groundtruth_RTTM): + with open(groundtruth_RTTM, "r", encoding='utf-8') as fp: + groundtruth_RTTM_files = fp.read().splitlines() + elif os.path.isdir(groundtruth_RTTM): + groundtruth_RTTM_files = glob.glob(os.path.join(groundtruth_RTTM, "*.rttm")) + else: + raise ValueError( + "groundtruth_RTTM should either be a directory contains rttm files or a file contains paths to them!" + ) + for f in groundtruth_RTTM_files: + filename = os.path.basename(f).rsplit(".", 1)[0] + groundtruth_RTTM_dict[filename] = f + + vad_pred_dict = {} + if os.path.isfile(vad_pred): + with open(vad_pred, "r", encoding='utf-8') as fp: + vad_pred_files = fp.read().splitlines() + elif os.path.isdir(vad_pred): + vad_pred_files = glob.glob(os.path.join(vad_pred, "*." + vad_pred_method)) + else: + raise ValueError( + "vad_pred should either be a directory containing vad pred files or a file contains paths to them!" + ) + for f in vad_pred_files: + filename = os.path.basename(f).rsplit(".", 1)[0] + vad_pred_dict[filename] = f + + paired_filenames = groundtruth_RTTM_dict.keys() & vad_pred_dict.keys() + return paired_filenames, groundtruth_RTTM_dict, vad_pred_dict + + +def plot( + path2audio_file: str, + path2_vad_pred: Optional[str] = None, + path2groundtruth_rttm: Optional[str] = None, + groundtruth_labels: Optional[str] = None, + sample_rate: int = 16000, + offset: float = 0, + duration: float = None, + threshold: float = None, + per_args: dict = None, + unit_frame_len: float = 0.01, + label_repeat: int = 1, + xticks_step: int = 5, +) -> ipd.Audio: + """ + Plot Audio and/or VAD output and/or groundtruth labels for visualization + Args: + path2audio_file (str): path to audio file. + path2_vad_pred (str): path to vad prediction file, + path2groundtruth_rttm(str): path to groundtruth RTTM file. + ground_truth_labels(str): a list of groundtruth label. + sample_rate (int): sample rate of audio file. + offset (float): offset in seconds. + duration (float): duration in seconds. + threshold (float): threshold for prediction score (from 0 to 1). + per_args(dict): a dict that stores the thresholds for postprocessing. + unit_frame_len (float): unit frame length in seconds for VAD predictions. + label_repeat (int): repeat the label for this number of times to match different frame lengths in preds and labels. + xticks_step (int): step size for xticks. + """ + plt.figure(figsize=[20, 2]) + + audio, sample_rate = librosa.load( + path=path2audio_file, sr=sample_rate, mono=True, offset=offset, duration=duration + ) + dur = librosa.get_duration(y=audio, sr=sample_rate) + + time = np.arange(offset, offset + dur, unit_frame_len) + len_pred = int(dur / unit_frame_len) + 1 + + frame_snippet = None + if path2_vad_pred: + frame, _ = load_tensor_from_file(path2_vad_pred) + frame_snippet = frame[int(offset / unit_frame_len) : int((offset + dur) / unit_frame_len)] + len_pred = len(frame_snippet) + + ax1 = plt.subplot() + ax1.plot(np.arange(audio.size) / sample_rate, audio, 'gray') + ax1.set_xlim([0, int(dur) + 1]) + ax1.tick_params(axis='y', labelcolor='b') + ax1.set_ylabel('Signal') + ax1.set_ylim([-1, 1]) + ax2 = ax1.twinx() + + if threshold and per_args: + raise ValueError("threshold and per_args cannot be used at same time!") + if not threshold and not per_args: + raise ValueError("One and only one of threshold and per_args must have been used!") + + if threshold and frame_snippet is not None: + pred_snippet = np.where(frame_snippet >= threshold, 1, 0) + elif per_args and frame_snippet is not None: + _, per_args_float = prepare_gen_segment_table( + frame, per_args + ) # take whole frame here for calculating onset and offset + speech_segments = generate_vad_segment_table_per_tensor(frame, per_args_float) + pred = gen_pred_from_speech_segments(speech_segments, frame) + pred_snippet = pred[int(offset / unit_frame_len) : int((offset + dur) / unit_frame_len)] + else: + pred_snippet = None + + if path2groundtruth_rttm and path2groundtruth_rttm.endswith('.rttm'): + label = extract_labels(path2groundtruth_rttm, time) + elif groundtruth_labels: + label = [float(x) for x in groundtruth_labels] + if label_repeat > 1: + label = np.repeat(label, label_repeat) + label = label[int(offset / unit_frame_len) : int((offset + dur) / unit_frame_len)] + else: + label = None + + if label is not None: + ax2.plot(np.arange(len_pred) * unit_frame_len, label, 'r', label='label') + if pred_snippet is not None: + ax2.plot(np.arange(len_pred) * unit_frame_len, pred_snippet, 'b', label='pred') + if frame_snippet is not None: + ax2.plot(np.arange(len_pred) * unit_frame_len, frame_snippet, 'g--', label='speech prob') + + ax2.tick_params(axis='y', labelcolor='r') + ax2.legend(loc='lower right', shadow=True) + ax2.set_ylabel('Preds and Probas') + ax2.set_ylim([-0.1, 1.1]) + ax2.set_xticks(np.arange(0, int(dur) + 1, xticks_step)) + return ipd.Audio(audio, rate=sample_rate) + + +def gen_pred_from_speech_segments( + speech_segments: torch.Tensor, prob: float, shift_length_in_sec: float = 0.01 +) -> np.array: + """ + Generate prediction arrays like 000111000... from speech segments {[0,1][2,4]} + """ + pred = np.zeros(prob.shape) + speech_segments = [list(i) for i in speech_segments] + speech_segments.sort(key=lambda x: x[0]) + + for seg in speech_segments: + start = int(seg[0] / shift_length_in_sec) + end = int(seg[1] / shift_length_in_sec) + pred[start:end] = 1 + return pred + + +def extract_labels(path2ground_truth_label: str, time: list) -> list: + """ + Extract ground-truth label for given time period. + path2ground_truth_label (str): path of groundtruth RTTM file + time (list) : a list of array representing time period. + """ + + data = pd.read_csv(path2ground_truth_label, sep="\s+", delimiter=None, header=None) + data = data.rename(columns={3: "start", 4: "dur", 7: "speaker"}) + labels = [] + for pos in time: + line = data[(data["start"] <= pos) & (data["start"] + data["dur"] > pos)] + if len(line) >= 1: + labels.append(1) + else: + labels.append(0) + return labels + + +def generate_vad_frame_pred( + vad_model, + window_length_in_sec: float, + shift_length_in_sec: float, + manifest_vad_input: str, + out_dir: str, + use_feat: bool = False, +) -> str: + """ + Generate VAD frame level prediction and write to out_dir + """ + time_unit = int(window_length_in_sec / shift_length_in_sec) + trunc = int(time_unit / 2) + trunc_l = time_unit - trunc + all_len = 0 + + data = [] + with open(manifest_vad_input, 'r', encoding='utf-8') as f: + for line in f: + file = json.loads(line)['audio_filepath'].split("/")[-1] + data.append(file.split(".wav")[0]) + logging.info(f"Inference on {len(data)} audio files/json lines!") + + status = get_vad_stream_status(data) + for i, test_batch in enumerate(tqdm(vad_model.test_dataloader(), total=len(vad_model.test_dataloader()))): + test_batch = [x.to(vad_model.device) for x in test_batch] + with autocast(): + if use_feat: + log_probs = vad_model(processed_signal=test_batch[0], processed_signal_length=test_batch[1]) + else: + log_probs = vad_model(input_signal=test_batch[0], input_signal_length=test_batch[1]) + probs = torch.softmax(log_probs, dim=-1) + if len(probs.shape) == 3 and probs.shape[0] == 1: + # squeeze the batch dimension, since batch size is 1 for frame-VAD + probs = probs.squeeze(0) # [1,T,C] -> [T,C] + pred = probs[:, 1] + + if window_length_in_sec == 0: + to_save = pred + elif status[i] == 'start': + to_save = pred[:-trunc] + elif status[i] == 'next': + to_save = pred[trunc:-trunc_l] + elif status[i] == 'end': + to_save = pred[trunc_l:] + else: + to_save = pred + + to_save = to_save.cpu().tolist() + all_len += len(to_save) + outpath = os.path.join(out_dir, data[i] + ".frame") + with open(outpath, "a", encoding='utf-8') as fout: + for f in range(len(to_save)): + fout.write('{0:0.4f}\n'.format(to_save[f])) + + del test_batch + if status[i] == 'end' or status[i] == 'single': + logging.debug(f"Overall length of prediction of {data[i]} is {all_len}!") + all_len = 0 + return out_dir + + +def init_vad_model(model_path: str): + """ + Initiate VAD model with model path + """ + if model_path.endswith('.nemo'): + logging.info(f"Using local VAD model from {model_path}") + vad_model = EncDecClassificationModel.restore_from(restore_path=model_path) + elif model_path.endswith('.ckpt'): + vad_model = EncDecClassificationModel.load_from_checkpoint(checkpoint_path=model_path) + else: + logging.info(f"Using NGC cloud VAD model {model_path}") + vad_model = EncDecClassificationModel.from_pretrained(model_name=model_path) + return vad_model + + +def init_frame_vad_model(model_path: str): + """ + Initiate VAD model with model path + """ + if model_path.endswith('.nemo'): + logging.info(f"Using local VAD model from {model_path}") + vad_model = EncDecFrameClassificationModel.restore_from(restore_path=model_path) + elif model_path.endswith('.ckpt'): + vad_model = EncDecFrameClassificationModel.load_from_checkpoint(checkpoint_path=model_path) + else: + logging.info(f"Using NGC cloud VAD model {model_path}") + vad_model = EncDecFrameClassificationModel.from_pretrained(model_name=model_path) + return vad_model + + +def stitch_segmented_asr_output( + segmented_output_manifest: str, + speech_segments_tensor_dir: str = "speech_segments", + stitched_output_manifest: str = "asr_stitched_output_manifest.json", +) -> str: + """ + Stitch the prediction of speech segments. + """ + if not os.path.exists(speech_segments_tensor_dir): + os.mkdir(speech_segments_tensor_dir) + + segmented_output = [] + with open(segmented_output_manifest, 'r', encoding='utf-8') as f: + for line in f: + file = json.loads(line) + segmented_output.append(file) + + with open(stitched_output_manifest, 'w', encoding='utf-8') as fout: + speech_segments = torch.Tensor() + all_pred_text = "" + if len(segmented_output) > 1: + for i in range(1, len(segmented_output)): + start, end = ( + segmented_output[i - 1]['offset'], + segmented_output[i - 1]['offset'] + segmented_output[i - 1]['duration'], + ) + new_seg = torch.tensor([start, end]).unsqueeze(0) + speech_segments = torch.cat((speech_segments, new_seg), 0) + pred_text = segmented_output[i - 1]['pred_text'] + all_pred_text += pred_text + name = segmented_output[i - 1]['audio_filepath'].split("/")[-1].rsplit(".", 1)[0] + + if segmented_output[i - 1]['audio_filepath'] != segmented_output[i]['audio_filepath']: + + speech_segments_tensor_path = os.path.join(speech_segments_tensor_dir, name + '.pt') + torch.save(speech_segments, speech_segments_tensor_path) + meta = { + 'audio_filepath': segmented_output[i - 1]['audio_filepath'], + 'speech_segments_filepath': speech_segments_tensor_path, + 'pred_text': all_pred_text, + } + + json.dump(meta, fout) + fout.write('\n') + fout.flush() + speech_segments = torch.Tensor() + all_pred_text = "" + else: + all_pred_text += " " + else: + i = -1 + + start, end = segmented_output[i]['offset'], segmented_output[i]['offset'] + segmented_output[i]['duration'] + new_seg = torch.tensor([start, end]).unsqueeze(0) + speech_segments = torch.cat((speech_segments, new_seg), 0) + pred_text = segmented_output[i]['pred_text'] + all_pred_text += pred_text + name = segmented_output[i]['audio_filepath'].split("/")[-1].rsplit(".", 1)[0] + speech_segments_tensor_path = os.path.join(speech_segments_tensor_dir, name + '.pt') + torch.save(speech_segments, speech_segments_tensor_path) + + meta = { + 'audio_filepath': segmented_output[i]['audio_filepath'], + 'speech_segments_filepath': speech_segments_tensor_path, + 'pred_text': all_pred_text, + } + json.dump(meta, fout) + fout.write('\n') + fout.flush() + + logging.info( + f"Finish stitch segmented ASR output to {stitched_output_manifest}, the speech segments info has been stored in directory {speech_segments_tensor_dir}" + ) + return stitched_output_manifest + + +def construct_manifest_eval( + input_manifest: str, stitched_output_manifest: str, aligned_vad_asr_output_manifest: str = "vad_asr_out.json" +) -> str: + + """ + Generate aligned manifest for evaluation. + Because some pure noise samples might not appear in stitched_output_manifest. + """ + stitched_output = dict() + with open(stitched_output_manifest, 'r', encoding='utf-8') as f: + for line in f: + file = json.loads(line) + stitched_output[file["audio_filepath"]] = file + + out = [] + with open(input_manifest, 'r', encoding='utf-8') as f: + for line in f: + file = json.loads(line) + sample = file["audio_filepath"] + if sample in stitched_output: + file["pred_text"] = stitched_output[sample]["pred_text"] + file["speech_segments_filepath"] = stitched_output[sample]["speech_segments_filepath"] + else: + file["pred_text"] = "" + file["speech_segments_filepath"] = "" + + out.append(file) + + with open(aligned_vad_asr_output_manifest, 'w', encoding='utf-8') as fout: + for i in out: + json.dump(i, fout) + fout.write('\n') + fout.flush() + + return aligned_vad_asr_output_manifest + + +def load_rttm_file(filepath: str) -> pd.DataFrame: + """ + Load rttm file and extract speech segments + """ + if not Path(filepath).exists(): + raise ValueError(f"File not found: {filepath}") + data = pd.read_csv(filepath, sep="\s+", delimiter=None, header=None) + data = data.rename(columns={3: "start", 4: "dur", 7: "speaker"}) + + data['start'] = data['start'].astype(float) + data['dur'] = data['dur'].astype(float) + data['end'] = data['start'] + data['dur'] + + data = data.sort_values(by=['start']) + data['segment'] = list(zip(data['start'], data['end'])) + + return data + + +def merge_intervals(intervals: List[List[float]]) -> List[List[float]]: + """ + Merge speech segments into non-overlapping segments + """ + intervals.sort(key=lambda x: x[0]) + merged = [] + for interval in intervals: + # if the list of merged intervals is empty or if the current + # interval does not overlap with the previous, simply append it. + if not merged or merged[-1][1] < interval[0]: + merged.append(interval) + else: + # otherwise, there is overlap, so we merge the current and previous + # intervals. + merged[-1][1] = max(merged[-1][1], interval[1]) + return merged + + +def load_speech_segments_from_rttm(rttm_file: str) -> List[List[float]]: + """ + load speech segments from rttm file, where each segment is represented + as [start, end] interval + """ + speech_segments = list(load_rttm_file(rttm_file)['segment']) + speech_segments = [list(x) for x in speech_segments] + speech_segments = merge_intervals(speech_segments) + return speech_segments + + +def load_speech_overlap_segments_from_rttm(rttm_file: str) -> Tuple[List[List[float]], List[List[float]]]: + """ + Load speech segments from RTTM file, merge and extract possible overlaps + + Args: + rttm_file (str): Path to RTTM file + + Returns: + merged (List[List[float]]): merged speech intervals without overlaps + overlaps (List[List[float]]): intervals with overlap speech + """ + speech_segments = list(load_rttm_file(rttm_file)['segment']) + speech_segments = [list(x) for x in speech_segments] + speech_segments.sort(key=lambda x: x[0]) # sort by start time + merged = [] + overlaps = [] + for interval in speech_segments: + # if the list of merged intervals is empty or if the current + # interval does not overlap with the previous, simply append it. + if not merged or merged[-1][1] < interval[0]: + merged.append(interval) + else: + # otherwise, there is overlap, so we merge the current and previous + # intervals. + overlaps.append([interval[0], min(merged[-1][1], interval[1])]) + merged[-1][1] = max(merged[-1][1], interval[1]) + return merged, overlaps + + +def get_nonspeech_segments( + speech_segments: List[List[float]], max_duration: Optional[float] = None +) -> List[List[float]]: + """ + Get non-speech segments from given speech segments and maximum duration + + Args: + speech_segments (List[List[float]]): speech segment intervals loaded by load_speech_segments() + max_duration (Optional[float]): maximum duration of the audio, used to calculate the last silence segment + + Returns: + nonspeech_segments (List[List[float]]): intervals of non-speech segments + """ + nonspeech_segments = [] + start = 0.0 + for sp_seg in speech_segments: + end = sp_seg[0] + nonspeech_segments.append([start, end]) + start = sp_seg[1] + + if max_duration is not None and start < max_duration: + nonspeech_segments.append([start, max_duration]) + + return nonspeech_segments + + +def get_frame_labels( + segments: List[List[float]], frame_length: float, offset: float, duration: float, as_str: bool = True +) -> str: + """ + Generate frame-level binary labels for audio, '0' for non-speech and '1' for speech + + Args: + segments (List[List[float]]): speech segments loaded by load_speech_segments_from_rttm + frame_length (float): frame length in seconds, e.g. 0.01 for 10ms frames + offset (float): Offset of the audio clip + duration (float): duration of the audio clip + """ + labels = [] + n_frames = int(np.ceil(duration / frame_length)) + sid = 0 + for i in range(n_frames): + t = offset + i * frame_length + while sid < len(segments) - 1 and segments[sid][1] < t: + sid += 1 + if segments[sid][1] != 0 and segments[sid][0] <= t <= segments[sid][1]: + labels.append(1) + else: + labels.append(0) + if as_str: + return ' '.join([str(x) for x in labels]) + return [float(x) for x in labels] + + +def plot_sample_from_rttm( + audio_file: str, + rttm_file: str, + max_duration: Optional[float] = None, + save_path: str = "", + show: bool = True, + offset: float = 0.0, + unit_frame_len: float = 0.01, +): + """ + Plot audio signal and frame-level labels from RTTM file + """ + plt.figure(figsize=[20, 2]) + + audio, sample_rate = librosa.load(path=audio_file, sr=16000, mono=True, offset=offset, duration=max_duration) + dur = librosa.get_duration(y=audio, sr=sample_rate) + + segments = load_speech_segments_from_rttm(rttm_file) + labels = get_frame_labels(segments, unit_frame_len, offset, dur) + labels = [float(x) for x in labels.split()] + + length = len(labels) + ax1 = plt.subplot() + ax1.set_title(audio_file) + ax1.plot(np.arange(audio.size) / sample_rate, audio, 'gray') + ax1.set_xlim([0, int(dur) + 1]) + ax1.tick_params(axis='y', labelcolor='b') + ax1.set_ylabel('Signal') + ax1.set_ylim([-1, 1]) + ax2 = ax1.twinx() + + ax2.plot(np.arange(length) * unit_frame_len, labels, 'r', label='label') + ax2.tick_params(axis='y', labelcolor='r') + ax2.legend(loc='lower right', shadow=True) + ax2.set_ylabel('Labels') + ax2.set_ylim([-0.1, 1.1]) + if show: + plt.show() + if save_path: + plt.savefig(save_path) + return ipd.Audio(audio, rate=16000) + + +def align_labels_to_frames(probs, labels, threshold=0.2): + """ + Aligns labels to frames when the frame length (e.g., 10ms) is different from the label length (e.g., 20ms). + The threshold 0.2 is not important, since the actual ratio will always be close to an integer unless using frame/label + lengths that are not multiples of each other (e.g., 15ms frame length and 20ms label length), which is not valid. + The value 0.2 here is just for easier unit testing. + Args: + probs (List[float]): list of probabilities + labels (List[int]): list of labels + threshold (float): threshold for rounding ratio to integer + Returns: + labels (List[int]): list of labels aligned to frames + """ + frames_len = len(probs) + labels_len = len(labels) + probs = torch.tensor(probs).float() + labels = torch.tensor(labels).long() + + if frames_len < labels_len: + # pad labels with zeros until labels_len is a multiple of frames_len + ratio = labels_len / frames_len + res = labels_len % frames_len + if ( + ceil(ratio) - ratio < threshold + ): # e.g., ratio = 2.9, ceil(ratio) = 3, then we pad labels to make it a multiple of 3 + # pad labels with zeros until labels_max_len is a multiple of logits_max_len + labels = labels.tolist() + if len(labels) % ceil(ratio) != 0: + labels += [0] * (ceil(ratio) - len(labels) % ceil(ratio)) + labels = torch.tensor(labels).long() + labels = labels.view(-1, ceil(ratio)).amax(1) + return align_labels_to_frames(probs.tolist(), labels.long().tolist()) + # otherwise, truncate additional labels until labels_max_len is a multiple of logits_max_len + if res > 0: + labels = labels[:-res] + labels = labels.view(-1, floor(ratio)).amax(1) + return labels.long().tolist() + elif frames_len > labels_len: + # repeat labels until labels_len is a multiple of frames_len + ratio = frames_len / labels_len + res = frames_len % labels_len + if ceil(ratio) - ratio < threshold: + # e.g., ratio is 1.83, ceil(ratio) = 2, then we repeat labels to make it a multiple of 2, and discard the redundant labels + labels = labels.repeat_interleave(ceil(ratio), dim=0).long().tolist() + labels = labels[:frames_len] + else: + # e.g., ratio is 2.02, floor(ratio) = 2, then we repeat labels to make it a multiple of 2 and add additional labels + labels = labels.repeat_interleave(floor(ratio), dim=0).long().tolist() + if res > 0: + labels += labels[-res:] + return labels + else: + return labels.long().tolist() + + +def read_rttm_as_pyannote_object(rttm_file: str, speaker_override: Optional[str] = None) -> Annotation: + """ + Read rttm file and construct a Pyannote object. + Args: + rttm_file(str) : path of rttm file. + speaker_override(str) : if not None, all speakers will be replaced by this value. + Returns: + annotation(pyannote.Annotation): annotation object + """ + annotation = Annotation() + data = pd.read_csv(rttm_file, sep="\s+", delimiter=None, header=None) + data = data.rename(columns={3: "start", 4: "dur", 7: "speaker"}) + for index, row in data.iterrows(): + if speaker_override is not None: + annotation[Segment(row['start'], row['start'] + row['dur'])] = speaker_override + else: + annotation[Segment(row['start'], row['start'] + row['dur'])] = row['speaker'] + return annotation + + +def convert_labels_to_speech_segments(labels: List[float], frame_length_in_sec: float = 0.01): + """ + Convert a list of labels to a list of speech segments. + Args: + labels (List[float]): list of labels + frame_length_in_sec (float): frame length in seconds + Returns: + segments (List[Tuple[float, float]]): list of speech segments + """ + segments = [] + start = -1 + for i, label in enumerate(labels): + if label == 1: + if start == -1: + start = i * frame_length_in_sec + else: + if start > -1: + segments.append([start, (i - 1) * frame_length_in_sec]) + start = -1 + if start != -1: + segments.append([start, (len(labels) - 1) * frame_length_in_sec]) + return segments + + +def frame_vad_construct_pyannote_object_per_file( + prediction: Union[str, List[float]], groundtruth: Union[str, List[float]], frame_length_in_sec: float = 0.01 +) -> Tuple[Annotation, Annotation]: + """ + Construct a Pyannote object for evaluation. + Args: + prediction (str) : path of VAD predictions stored as RTTM or CSV-like txt. + groundtruth (str): path of groundtruth rttm file. + frame_length_in_sec(float): frame length in seconds + Returns: + reference(pyannote.Annotation): groundtruth + hypothesis(pyannote.Annotation): prediction + """ + + hypothesis = Annotation() + if isinstance(groundtruth, str) and prediction.endswith('.rttm'): + hypothesis = read_rttm_as_pyannote_object(prediction, speaker_override='speech') + elif isinstance(groundtruth, str) and prediction.endswith('.txt'): + pred = pd.read_csv(prediction, sep=" ", header=None) + for index, row in pred.iterrows(): + hypothesis[Segment(float(row[0]), float(row[0]) + float(row[1]))] = 'speech' + elif isinstance(groundtruth, list): + segments = convert_labels_to_speech_segments(prediction, frame_length_in_sec) + for segment in segments: + hypothesis[Segment(segment[0], segment[1])] = 'speech' + else: + raise ValueError('prediction must be a path to rttm file or a list of frame labels.') + + reference = Annotation() + if isinstance(groundtruth, str) and groundtruth.endswith('.rttm'): + reference = read_rttm_as_pyannote_object(groundtruth, speaker_override='speech') + elif isinstance(groundtruth, list): + segments = convert_labels_to_speech_segments(groundtruth, frame_length_in_sec) + for segment in segments: + reference[Segment(segment[0], segment[1])] = 'speech' + else: + raise ValueError('groundtruth must be a path to rttm file or a list of frame labels.') + return reference, hypothesis + + +def frame_vad_infer_load_manifest(cfg: DictConfig): + """ + Load manifest file and prepare label/rttm mapping + Args: + cfg: config file + Returns: + manifest_orig (List[Dict]): original manifest data + key_labels_map (Dict): mapping from unique_audio_name to its labels + key_rttm_map (Dict): mapping from unique_audio_name to its rttm file + """ + unique_audio_names = set() + key_labels_map = {} + key_rttm_map = {} + manifest_orig = [] + manifest_file = Path(cfg.dataset).absolute().as_posix() + with open(manifest_file, 'r') as fin: + for line in fin.readlines(): + entry = json.loads(line.strip()) + audio_filepath = get_full_path(audio_file=entry['audio_filepath'], manifest_file=manifest_file) + entry['audio_filepath'] = str(audio_filepath) + uniq_audio_name = Path(audio_filepath).stem + + if uniq_audio_name in unique_audio_names: + raise ValueError("Please make sure each line is with different audio_filepath! ") + else: + unique_audio_names.add(uniq_audio_name) + + manifest_orig.append(entry) + + # always prefer RTTM labels if exist + if "label" not in entry and ("rttm_filepath" in entry or "rttm_file" in entry): + rttm_key = "rttm_filepath" if "rttm_filepath" in entry else "rttm_file" + segments = load_speech_segments_from_rttm(entry[rttm_key]) + label_str = get_frame_labels( + segments=segments, + frame_length=cfg.vad.parameters.shift_length_in_sec, + duration=entry['duration'], + offset=entry['offset'], + ) + key_rttm_map[uniq_audio_name] = entry[rttm_key] + key_labels_map[uniq_audio_name] = [float(x) for x in label_str.split()] + elif entry.get("label", None) is not None: + key_labels_map[uniq_audio_name] = [float(x) for x in entry["label"].split()] + elif cfg.evaluate: + raise ValueError("Must have either `label` or `rttm_filepath` in manifest when evaluate=True") + + return manifest_orig, key_labels_map, key_rttm_map + + +def frame_vad_eval_detection_error( + pred_dir: str, key_labels_map: dict, key_rttm_map: dict, key_pred_rttm_map: dict, frame_length_in_sec: float +): + """ + Perform evaluation on frame-VAD results + Args: + pred_dir: directory of frame-VAD prediction files with in `.frame` format + key_labels_map: dictionary of mapping each to its labels + key_rttm_map: dictionary of mapping each to its GROUNDTRUTH rttm file + key_pred_rttm_map: dictionary of mapping each to its PREDICTED rttm file + frame_length_in_sec: frame length in seconds, e.g. 0.02s + Returns: + auroc: AUROC score in 0~100% + report: Pyannote detection.DetectionErrorRate() report + """ + all_probs = [] + all_labels = [] + metric = detection.DetectionErrorRate() + key_probs_map = {} + predictions_list = list(Path(pred_dir).glob("*.frame")) + for frame_pred in tqdm(predictions_list, desc="Evaluating VAD results", total=len(predictions_list)): + pred_probs = [] + with frame_pred.open("r") as fin: + for line in fin.readlines(): + line = line.strip() + if not line: + continue + pred_probs.append(float(line)) + key = frame_pred.stem + key_probs_map[key] = pred_probs + key_labels_map[key] = align_labels_to_frames(probs=pred_probs, labels=key_labels_map[key]) + all_probs.extend(key_probs_map[key]) + all_labels.extend(key_labels_map[key]) + + if key in key_rttm_map: + groundtruth = key_rttm_map[key] + else: + groundtruth = key_labels_map[key] + + reference, hypothesis = frame_vad_construct_pyannote_object_per_file( + prediction=key_pred_rttm_map[key], groundtruth=groundtruth, frame_length_in_sec=frame_length_in_sec, + ) + metric(reference, hypothesis) + + auroc = roc_auc_score(y_true=all_labels, y_score=all_probs) + report = metric.report(display=False) + return auroc, report diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/__init__.py new file mode 100644 index 0000000..9c20362 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/__init__.py @@ -0,0 +1,26 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import nemo.collections.common.callbacks +from nemo.collections.common import data, losses, parts, tokenizers +from nemo.package_info import __version__ + +# Set collection version equal to NeMo version. +__version = __version__ + +# Authorship. +__author__ = "NVIDIA Corporation" + +# Set collection name. +__description__ = "Common collection" diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/callbacks/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/callbacks/__init__.py new file mode 100644 index 0000000..0cf495d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/callbacks/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.common.callbacks.callbacks import LogEpochTimeCallback +from nemo.collections.common.callbacks.ema import EMA diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/callbacks/callbacks.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/callbacks/callbacks.py new file mode 100644 index 0000000..1a6c011 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/callbacks/callbacks.py @@ -0,0 +1,96 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import time + +from pytorch_lightning.callbacks import Callback +from pytorch_lightning.utilities import rank_zero_only + +# from sacrebleu import corpus_bleu + + +class LogEpochTimeCallback(Callback): + """Simple callback that logs how long each epoch takes, in seconds, to a pytorch lightning log + """ + + @rank_zero_only + def on_train_epoch_start(self, trainer, pl_module): + self.epoch_start = time.time() + + @rank_zero_only + def on_train_epoch_end(self, trainer, pl_module): + curr_time = time.time() + duration = curr_time - self.epoch_start + trainer.logger.log_metrics({"epoch_time": duration}, step=trainer.global_step) + + +# class MachineTranslationLogEvalCallback(Callback): +# def _on_eval_end(self, trainer, pl_module, mode): +# counts = np.array(self._non_pad_tokens) +# eval_loss = np.sum(np.array(self._losses) * counts) / np.sum(counts) +# sacre_bleu = corpus_bleu(self._translations, [self._ground_truths], tokenize="13a") +# print(f"{mode} results for process with global rank {pl_module.global_rank}".upper()) +# for i in range(pl_module.num_examples[mode]): +# print('\u0332'.join(f"EXAMPLE {i}:")) # Underline output +# sent_id = np.random.randint(len(self._translations)) +# print(f"Ground truth: {self._ground_truths[sent_id]}\n") +# print(f"Translation: {self._translations[sent_id]}\n") +# print() +# print("-" * 50) +# print(f"loss: {eval_loss:.3f}") +# print(f"SacreBLEU: {sacre_bleu}") +# print("-" * 50) + +# @rank_zero_only +# def on_test_end(self, trainer, pl_module): +# self._on_eval_end(trainer, pl_module, "test") + +# @rank_zero_only +# def on_validation_end(self, trainer, pl_module): +# self._on_eval_end(trainer, pl_module, "val") + +# @rank_zero_only +# def on_sanity_check_end(self, trainer, pl_module): +# self._on_eval_end(trainer, pl_module, "val") + +# def _on_eval_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx, mode): +# self._translations.extend(outputs['translations']) +# self._ground_truths.extend(outputs['ground_truths']) +# self._non_pad_tokens.append(outputs['num_non_pad_tokens']) +# self._losses.append(outputs[f'{mode}_loss']) + +# @rank_zero_only +# def on_test_batch_end(self, trainer, pl_module, batch, outputs, batch_idx, dataloader_idx): +# self._on_eval_batch_end(trainer, pl_module, batch, outputs, batch_idx, dataloader_idx, 'test') + +# @rank_zero_only +# def on_validation_batch_end(self, trainer, pl_module, batch, outputs, batch_idx, dataloader_idx): +# self._on_eval_batch_end(trainer, pl_module, batch, outputs, batch_idx, dataloader_idx, 'val') + +# def _on_eval_start(self, trainer, pl_module): +# self._translations = [] +# self._ground_truths = [] +# self._losses = [] +# self._non_pad_tokens = [] + +# @rank_zero_only +# def on_test_start(self, trainer, pl_module): +# self._on_eval_start(trainer, pl_module) + +# @rank_zero_only +# def on_validation_start(self, trainer, pl_module): +# self._on_eval_start(trainer, pl_module) + +# @rank_zero_only +# def on_sanity_check_start(self, trainer, pl_module): +# self._on_eval_start(trainer, pl_module) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/callbacks/ema.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/callbacks/ema.py new file mode 100644 index 0000000..2f295bf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/callbacks/ema.py @@ -0,0 +1,350 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import contextlib +import copy +import os +import threading +from typing import Any, Dict, Iterable + +import pytorch_lightning as pl +import torch +from pytorch_lightning import Callback +from pytorch_lightning.utilities.exceptions import MisconfigurationException +from pytorch_lightning.utilities.rank_zero import rank_zero_info + + +class EMA(Callback): + """ + Implements Exponential Moving Averaging (EMA). + + When training a model, this callback will maintain moving averages of the trained parameters. + When evaluating, we use the moving averages copy of the trained parameters. + When saving, we save an additional set of parameters with the prefix `ema`. + + Args: + decay: The exponential decay used when calculating the moving average. Has to be between 0-1. + validate_original_weights: Validate the original weights, as apposed to the EMA weights. + every_n_steps: Apply EMA every N steps. + cpu_offload: Offload weights to CPU. + """ + + def __init__( + self, decay: float, validate_original_weights: bool = False, every_n_steps: int = 1, cpu_offload: bool = False, + ): + if not (0 <= decay <= 1): + raise MisconfigurationException("EMA decay value must be between 0 and 1") + self.decay = decay + self.validate_original_weights = validate_original_weights + self.every_n_steps = every_n_steps + self.cpu_offload = cpu_offload + + def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: + device = pl_module.device if not self.cpu_offload else torch.device('cpu') + trainer.optimizers = [ + EMAOptimizer( + optim, + device=device, + decay=self.decay, + every_n_steps=self.every_n_steps, + current_step=trainer.global_step, + ) + for optim in trainer.optimizers + if not isinstance(optim, EMAOptimizer) + ] + + def on_validation_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: + if self._should_validate_ema_weights(trainer): + self.swap_model_weights(trainer) + + def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: + if self._should_validate_ema_weights(trainer): + self.swap_model_weights(trainer) + + def on_test_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: + if self._should_validate_ema_weights(trainer): + self.swap_model_weights(trainer) + + def on_test_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: + if self._should_validate_ema_weights(trainer): + self.swap_model_weights(trainer) + + def _should_validate_ema_weights(self, trainer: "pl.Trainer") -> bool: + return not self.validate_original_weights and self._ema_initialized(trainer) + + def _ema_initialized(self, trainer: "pl.Trainer") -> bool: + return any(isinstance(optimizer, EMAOptimizer) for optimizer in trainer.optimizers) + + def swap_model_weights(self, trainer: "pl.Trainer", saving_ema_model: bool = False): + for optimizer in trainer.optimizers: + assert isinstance(optimizer, EMAOptimizer) + optimizer.switch_main_parameter_weights(saving_ema_model) + + @contextlib.contextmanager + def save_ema_model(self, trainer: "pl.Trainer"): + """ + Saves an EMA copy of the model + EMA optimizer states for resume. + """ + self.swap_model_weights(trainer, saving_ema_model=True) + try: + yield + finally: + self.swap_model_weights(trainer, saving_ema_model=False) + + @contextlib.contextmanager + def save_original_optimizer_state(self, trainer: "pl.Trainer"): + for optimizer in trainer.optimizers: + assert isinstance(optimizer, EMAOptimizer) + optimizer.save_original_optimizer_state = True + try: + yield + finally: + for optimizer in trainer.optimizers: + optimizer.save_original_optimizer_state = False + + def on_load_checkpoint( + self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: Dict[str, Any] + ) -> None: + checkpoint_callback = trainer.checkpoint_callback + + # use the connector as NeMo calls the connector directly in the exp_manager when restoring. + connector = trainer._checkpoint_connector + # Replace connector._ckpt_path with below to avoid calling into lightning's protected API + ckpt_path = trainer.ckpt_path + + if ckpt_path and checkpoint_callback is not None and 'NeMo' in type(checkpoint_callback).__name__: + ext = checkpoint_callback.FILE_EXTENSION + if ckpt_path.endswith(f'-EMA{ext}'): + rank_zero_info( + "loading EMA based weights. " + "The callback will treat the loaded EMA weights as the main weights" + " and create a new EMA copy when training." + ) + return + ema_path = ckpt_path.replace(ext, f'-EMA{ext}') + if os.path.exists(ema_path): + ema_state_dict = torch.load(ema_path, map_location=torch.device('cpu')) + + checkpoint['optimizer_states'] = ema_state_dict['optimizer_states'] + del ema_state_dict + rank_zero_info("EMA state has been restored.") + else: + raise MisconfigurationException( + "Unable to find the associated EMA weights when re-loading, " + f"training will start with new EMA weights. Expected them to be at: {ema_path}", + ) + + +@torch.no_grad() +def ema_update(ema_model_tuple, current_model_tuple, decay): + torch._foreach_mul_(ema_model_tuple, decay) + torch._foreach_add_( + ema_model_tuple, current_model_tuple, alpha=(1.0 - decay), + ) + + +def run_ema_update_cpu(ema_model_tuple, current_model_tuple, decay, pre_sync_stream=None): + if pre_sync_stream is not None: + pre_sync_stream.synchronize() + + ema_update(ema_model_tuple, current_model_tuple, decay) + + +class EMAOptimizer(torch.optim.Optimizer): + r""" + EMAOptimizer is a wrapper for torch.optim.Optimizer that computes + Exponential Moving Average of parameters registered in the optimizer. + + EMA parameters are automatically updated after every step of the optimizer + with the following formula: + + ema_weight = decay * ema_weight + (1 - decay) * training_weight + + To access EMA parameters, use ``swap_ema_weights()`` context manager to + perform a temporary in-place swap of regular parameters with EMA + parameters. + + Notes: + - EMAOptimizer is not compatible with APEX AMP O2. + + Args: + optimizer (torch.optim.Optimizer): optimizer to wrap + device (torch.device): device for EMA parameters + decay (float): decay factor + + Returns: + returns an instance of torch.optim.Optimizer that computes EMA of + parameters + + Example: + model = Model().to(device) + opt = torch.optim.Adam(model.parameters()) + + opt = EMAOptimizer(opt, device, 0.9999) + + for epoch in range(epochs): + training_loop(model, opt) + + regular_eval_accuracy = evaluate(model) + + with opt.swap_ema_weights(): + ema_eval_accuracy = evaluate(model) + """ + + def __init__( + self, + optimizer: torch.optim.Optimizer, + device: torch.device, + decay: float = 0.9999, + every_n_steps: int = 1, + current_step: int = 0, + ): + self.optimizer = optimizer + self.decay = decay + self.device = device + self.current_step = current_step + self.every_n_steps = every_n_steps + self.save_original_optimizer_state = False + + self.first_iteration = True + self.rebuild_ema_params = True + self.stream = None + self.thread = None + + self.ema_params = () + self.in_saving_ema_model_context = False + + def all_parameters(self) -> Iterable[torch.Tensor]: + return (param for group in self.param_groups for param in group['params']) + + def step(self, closure=None, grad_scaler=None, **kwargs): + self.join() + + if self.first_iteration: + if any(p.is_cuda for p in self.all_parameters()): + self.stream = torch.cuda.Stream() + + self.first_iteration = False + + if self.rebuild_ema_params: + opt_params = list(self.all_parameters()) + + self.ema_params += tuple( + copy.deepcopy(param.data.detach()).to(self.device) for param in opt_params[len(self.ema_params) :] + ) + self.rebuild_ema_params = False + + if getattr(self.optimizer, "_step_supports_amp_scaling", False) and grad_scaler is not None: + loss = self.optimizer.step(closure=closure, grad_scaler=grad_scaler) + else: + loss = self.optimizer.step(closure) + + if self._should_update_at_step(): + self.update() + self.current_step += 1 + return loss + + def _should_update_at_step(self) -> bool: + return self.current_step % self.every_n_steps == 0 + + @torch.no_grad() + def update(self): + if self.stream is not None: + self.stream.wait_stream(torch.cuda.current_stream()) + + with torch.cuda.stream(self.stream): + current_model_state = tuple( + param.data.to(self.device, non_blocking=True) for param in self.all_parameters() + ) + + if self.device.type == 'cuda': + ema_update(self.ema_params, current_model_state, self.decay) + + if self.device.type == 'cpu': + self.thread = threading.Thread( + target=run_ema_update_cpu, args=(self.ema_params, current_model_state, self.decay, self.stream,), + ) + self.thread.start() + + def swap_tensors(self, tensor1, tensor2): + tmp = torch.empty_like(tensor1) + tmp.copy_(tensor1) + tensor1.copy_(tensor2) + tensor2.copy_(tmp) + + def switch_main_parameter_weights(self, saving_ema_model: bool = False): + self.join() + self.in_saving_ema_model_context = saving_ema_model + for param, ema_param in zip(self.all_parameters(), self.ema_params): + self.swap_tensors(param.data, ema_param) + + @contextlib.contextmanager + def swap_ema_weights(self, enabled: bool = True): + r""" + A context manager to in-place swap regular parameters with EMA + parameters. + It swaps back to the original regular parameters on context manager + exit. + + Args: + enabled (bool): whether the swap should be performed + """ + + if enabled: + self.switch_main_parameter_weights() + try: + yield + finally: + if enabled: + self.switch_main_parameter_weights() + + def __getattr__(self, name): + return getattr(self.optimizer, name) + + def join(self): + if self.stream is not None: + self.stream.synchronize() + + if self.thread is not None: + self.thread.join() + + def state_dict(self): + self.join() + + if self.save_original_optimizer_state: + return self.optimizer.state_dict() + + # if we are in the context of saving an EMA model, the EMA weights are in the modules' actual weights + ema_params = self.ema_params if not self.in_saving_ema_model_context else list(self.all_parameters()) + state_dict = { + 'opt': self.optimizer.state_dict(), + 'ema': ema_params, + 'current_step': self.current_step, + 'decay': self.decay, + 'every_n_steps': self.every_n_steps, + } + return state_dict + + def load_state_dict(self, state_dict): + self.join() + + self.optimizer.load_state_dict(state_dict['opt']) + self.ema_params = tuple(param.to(self.device) for param in copy.deepcopy(state_dict['ema'])) + self.current_step = state_dict['current_step'] + self.decay = state_dict['decay'] + self.every_n_steps = state_dict['every_n_steps'] + self.rebuild_ema_params = False + + def add_param_group(self, param_group): + self.optimizer.add_param_group(param_group) + self.rebuild_ema_params = True diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/__init__.py new file mode 100644 index 0000000..ecc67ef --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.common.data.dataset import CodeSwitchedDataset, ConcatDataset, ConcatMapDataset diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/dataset.py new file mode 100644 index 0000000..c2c29b5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/dataset.py @@ -0,0 +1,662 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import io +import logging +from typing import Any, List, Optional, Tuple, Union + +import numpy as np +import torch +import torch.utils.data as pt_data +from torch.utils.data import Dataset, IterableDataset + +__all__ = ['ConcatDataset', 'ConcatMapDataset', 'CodeSwitchedDataset'] + + +class ConcatDataset(IterableDataset): + """ + A dataset that accepts as argument multiple datasets and then samples from them based on the specified + sampling technique. + + Args: + datasets (list): A list of datasets to sample from. + shuffle (bool): Whether to shuffle individual datasets. Only works with non-iterable datasets. + Defaults to True. + sampling_technique (str): Sampling technique to choose which dataset to draw a sample from. + Defaults to 'temperature'. Currently supports 'temperature', 'random' and 'round-robin'. + sampling_temperature (int): Temperature value for sampling. Only used when sampling_technique = 'temperature'. + Defaults to 5. + sampling_scale: Gives you the ability to upsample / downsample the dataset. Defaults to 1. + sampling_probabilities (list): Probability values for sampling. Only used when sampling_technique = 'random'. + seed: Optional value to seed the numpy RNG. + global_rank (int): Worker rank, used for partitioning map style datasets. Defaults to 0. + world_size (int): Total number of processes, used for partitioning map style datasets. Defaults to 1. + """ + + def __init__( + self, + datasets: List[Any], + shuffle: bool = True, + sampling_technique: str = 'temperature', + sampling_temperature: int = 5, + sampling_scale: int = 1, + sampling_probabilities: List[float] = None, + seed: Optional[int] = None, + global_rank: int = 0, + world_size: int = 1, + ): + super().__init__() + + supported_sampling_techniques = ['temperature', 'random', 'round-robin'] + self.datasets = datasets + self.iterables = [None] * len(datasets) + self.shuffle = shuffle + self.global_rank = global_rank + self.world_size = world_size + self.sampling_kwargs = {} + self.sampling_scale = sampling_scale + + if sampling_technique == 'temperature': + self.index_generator = ConcatDataset.temperature_generator + self.sampling_kwargs['temperature'] = sampling_temperature + self.sampling_kwargs['seed'] = seed + elif sampling_technique == 'random': + self.index_generator = ConcatDataset.random_generator + self.sampling_kwargs['p'] = sampling_probabilities + self.sampling_kwargs['seed'] = seed + elif sampling_technique == 'round-robin': + self.index_generator = ConcatDataset.round_robin_generator + else: + raise ValueError(f"Currently we only support sampling techniques in {supported_sampling_techniques}.") + self.length = 0 + + if isinstance(datasets[0], IterableDataset): + self.kind = 'iterable' + else: + self.kind = 'map' + + for idx, dataset in enumerate(datasets): + isiterable = isinstance(dataset, IterableDataset) + if (isiterable and not self.kind == 'iterable') or (not isiterable and self.kind == 'iterable'): + raise ValueError("All datasets in ConcatDataset must be of the same kind (Iterable or Map).") + + if self.kind == 'map': + self.length += len(dataset) // world_size + else: + self.length += len(dataset) + + if self.sampling_scale != 1: + self.length = int(self.length * self.sampling_scale) + logging.info(f'applying {sampling_scale} sampling scale, concat ds len: {self.length}') + + def get_iterable(self, dataset): + if isinstance(dataset, IterableDataset): + return dataset.__iter__() + else: + indices = np.arange(len(dataset)) + if self.shuffle: + np.random.shuffle(indices) + return iter(indices) + + def __iter__(self): + worker_info = pt_data.get_worker_info() + if worker_info is None: + max_elements = self.length + wid = 0 + wnum = 1 + else: + wid = worker_info.id + wnum = worker_info.num_workers + max_elements = len(range(wid, self.length, wnum)) + + if self.kind == 'map': + for idx in range(len(self.datasets)): + start_idx = (len(self.datasets[idx]) // self.world_size) * self.global_rank + end_idx = start_idx + (len(self.datasets[idx]) // self.world_size) + if self.global_rank == self.world_size - 1: + end_idx = len(self.datasets[idx]) + indices = range(start_idx + wid, end_idx, wnum) + self.datasets[idx] = pt_data.Subset(self.datasets[idx], indices) + + for idx, dataset in enumerate(self.datasets): + iterable = self.get_iterable(dataset) + self.iterables[idx] = iterable + + n = 0 + ind_gen = self.index_generator(self.datasets, **self.sampling_kwargs) + while n < max_elements: + n += 1 + try: + ind = next(ind_gen) + except StopIteration: + return + try: + val = next(self.iterables[ind]) + if self.kind == 'map': + val = self.datasets[ind][val] + yield val + except StopIteration: + self.iterables[ind] = self.get_iterable(self.datasets[ind]) + n -= 1 + + def __len__(self): + return self.length + + @staticmethod + def temperature_generator(datasets, **kwargs): + temp = kwargs.get('temperature') + if not temp: + raise ValueError("Temperature generator expects a 'temperature' keyword argument.") + + seed = kwargs.get('seed', None) + np_rng = np.random.RandomState(seed) + lengths = [] + num = len(datasets) + for dataset in datasets: + lengths.append(len(dataset)) + + p = np.array(lengths) / np.sum(lengths) + p = np.power(p, 1 / temp) + p = p / np.sum(p) + + while True: + ind = np_rng.choice(np.arange(num), p=p) + yield ind + + @staticmethod + def round_robin_generator(datasets, **kwargs): + num = len(datasets) + while True: + for i in range(num): + yield i + + @staticmethod + def random_generator(datasets, **kwargs): + p = kwargs.get('p') + if not p: + raise ValueError("Random generator expects a 'p' keyowrd argument for sampling probabilities.") + + seed = kwargs.get('seed', None) + np_rng = np.random.RandomState(seed) + num = len(datasets) + if len(p) != num: + raise ValueError("Length of probabilities list must be equal to the number of datasets.") + + while True: + ind = np_rng.choice(np.arange(num), p=p) + yield ind + + +class ConcatMapDataset(Dataset): + """ + A dataset that accepts as argument multiple datasets and then samples from them based on the specified + sampling technique. + + Args: + datasets (list): A list of datasets to sample from. + sampling_technique (str): Sampling technique to choose which dataset to draw a sample from. + Defaults to 'temperature'. Currently supports 'temperature', 'random' and 'round-robin'. + sampling_temperature (int): Temperature value for sampling. Only used when sampling_technique = 'temperature'. + Defaults to 5. + sampling_probabilities (list): Probability values for sampling. Only used when sampling_technique = 'random'. + seed: Optional value to seed the numpy RNG. + """ + + def __init__( + self, + datasets: List[Any], + sampling_technique: str = 'temperature', + sampling_temperature: int = 5, + sampling_probabilities: Optional[List[float]] = None, + seed: Optional[int] = None, + ): + super().__init__() + self.datasets = datasets + self.lengths = [len(x) for x in self.datasets] + self.sampling_technique = sampling_technique + self.sampling_temperature = sampling_temperature + self.sampling_probabilities = sampling_probabilities + self.np_rng = np.random.RandomState(seed) + + # Build a list of size `len(self)`. Each tuple contains (dataset_id, dataset_index) + self.indices: List[Tuple[int, int]] = [] + # Current position as we consume indices from each data set + dataset_positions = [0] * len(self.datasets) + # Random permutation of each dataset. Will be regenerated when exhausted. + shuffled_indices = [self.np_rng.permutation(len(x)) for x in self.datasets] + # Build the list of randomly-chosen datasets spanning the entire length, adhering to sampling technique + if self.sampling_technique == "round-robin": + # To exhaust longest dataset, need to draw `num_datasets * max_dataset_len` samples + total_length = max(self.lengths) * len(self.lengths) + # For round robin, iterate through each dataset + dataset_ids = np.arange(total_length) % len(self.datasets) + for dataset_id in dataset_ids: + position = dataset_positions[dataset_id] + index = shuffled_indices[dataset_id][position] + self.indices.append((dataset_id, index)) + dataset_positions[dataset_id] += 1 + if dataset_positions[dataset_id] == len(shuffled_indices[dataset_id]): + dataset_positions[dataset_id] = 0 + shuffled_indices[dataset_id] = self.np_rng.permutation(len(self.datasets[dataset_id])) + else: + # Resolve probabilities of drawing from each data set + if self.sampling_technique == "random": + if sampling_probabilities is None or len(sampling_probabilities) != len(self.datasets): + raise ValueError( + f"Need {len(self.datasets)} probabilities; got " + f"{len(sampling_probabilities) if sampling_probabilities is not None else 'None'}" + ) + p = np.array(self.sampling_probabilities) + elif self.sampling_technique == "temperature": + p = np.array([len(x) for x in self.datasets]) + p = np.power(p, 1 / self.sampling_temperature) + else: + raise ValueError(f"Couldn't interpret sampling technique: {sampling_technique}") + # Normalize probabilities + p = p / np.sum(p) + # Will randomly choose from datasets + choices = np.arange(len(self.datasets)) + # Keep going until largest dataset is exhausted. + exhausted_datasets = set() + while len(exhausted_datasets) < len(self.datasets): + # Randomly choose a dataset for each position in accordance with p + dataset_id = self.np_rng.choice(a=choices, p=p) + dataset = self.datasets[dataset_id] + # Pick next index from dataset + position = dataset_positions[dataset_id] + index = shuffled_indices[dataset_id][position] + self.indices.append((dataset_id, index)) + # Maybe reset this dataset's permutation + dataset_positions[dataset_id] += 1 + if dataset_positions[dataset_id] >= len(dataset): + shuffled_indices[dataset_id] = self.np_rng.permutation(len(dataset)) + dataset_positions[dataset_id] = 0 + exhausted_datasets.add(dataset_id) + + def __len__(self): + return len(self.indices) + + def __getitem__(self, idx): + dataset_id, dataset_index = self.indices[idx] + return self.datasets[dataset_id][dataset_index] + + +class CodeSwitchedDataset(IterableDataset): + """ + A dataset that accepts as argument multiple sub-datasets (usually from different languages, but that's not required) and then + samples from them in order to create synthetic code-switched samples of up to N different sub-datasets + + Args: + datasets (list): A list of datasets + lang_probs (list): A list of probabilities (which must sum to 1) corresponding to the sampling probability for each dataset + shuffle (bool): Whether to shuffle individual datasets. Only works with non-iterable datasets. + Defaults to True. + min_duration (int): the minimum duration (secs) of each synthetic code-switched sample. Will draw randomly until this is hit. + Defaults to 4 + max_duration (int): the maximum duration (secs) of each synthetic code-switched sample. + Defaults to 20 + min_monolingual (float): this percentage of the dataset will be original monolingual samples + Defaults to 0.3 - means 30% + db_norm (float): will normalise the composite CS sample to this DB level + Defaults to -25.0 + pause_start (int): inserts silence equal to this value (msecs) at the start of each CS sample + Defaults to 0 + pause_join (int): inserts silence equal to this value (msecs) between all language changes in the CS sample + Defaults to 0 + pause_end (int): terminates all CS samples with silence equal to this value (msecs) + Defaults to 0 + sampling_scales (list or float): gives you the ability to upsample/downsample each individual dataset + seed: Optional value to seed the numpy RNG. + global_rank (int): Worker rank, used for partitioning map style datasets. Defaults to 0. + world_size (int): Total number of processes, used for partitioning map style datasets. Defaults to 1. + pure_random (bool): If true, then always draw random sample from lang_probs. If false, you only draw from those datasets + which you haven't sampled from yet for the composite sample + force_monochannel (bool): If true, then all output audio will be mono-channel + infinity_mode (bool): If true, then the dataset iterable will generate an infinite amount of samples + sample_rate (int): the sample rate of all audio being sent to this Dataset + augmentor (AudioAugmentor): The any perturbations you wish to have applied on the CS samples + """ + + def __init__( + self, + datasets: List[Any], + lang_probs: Optional[List[float]] = None, + shuffle: bool = True, + min_duration: int = 4, + max_duration: int = 20, + min_monolingual: float = 0.3, + db_norm: float = -25.0, + pause_start: int = 0, + pause_join: int = 0, + pause_end: int = 0, + sampling_scales: Optional[Union[float, List[float]]] = None, + seed: Optional[int] = None, + global_rank: int = 0, + world_size: int = 1, + pure_random: bool = False, + force_monochannel: bool = True, + infinity_mode: bool = False, + sample_rate: int = 16000, + augmentor: Optional['AudioAugmentor'] = None, + ): + super().__init__() + + if len(datasets) == 0: + raise ValueError("CodeSwitchedDataset must receive a non-zero length datasets dict object") + + self.datasets = datasets + self.langs = list(range(len(datasets))) + self.langs_set = set(self.langs) + self.lang_iterables = {k: None for k in self.langs} + self.lang_kind = {k: None for k in self.langs} + self.shuffle = shuffle + self.min_duration = min_duration + self.max_duration = max_duration + self.min_monolingual = min_monolingual + self.db_norm = db_norm + self.pause_start = pause_start + self.pause_join = pause_join + self.pause_end = pause_end + self.pure_random = pure_random + self.force_monochannel = force_monochannel + self.infinity_mode = infinity_mode + self.global_rank = global_rank + self.world_size = world_size + self.augmentor = augmentor + self.sample_rate = sample_rate + self.length = 0 + if lang_probs is None: + self.prob_dict = {l: 1.0 / len(self.langs) for l in self.langs} + else: + assert len(self.langs) == len( + lang_probs + ), "Size mismatch between languages and respective probs in CodeSwitchedDataset" + self.prob_dict = {l: lang_probs[l] for l in self.langs} + self.lang_probs = np.array(list(self.prob_dict.values())) + if sampling_scales is not None and not isinstance(sampling_scales, list): + self.sampling_scales = {k: sampling_scales for k in self.langs} + elif ( + sampling_scales is not None + and isinstance(sampling_scales, list) + and len(sampling_scales) == len(self.langs) + ): + self.sampling_scales = {k: v for k, v in zip(self.langs, sampling_scales)} + else: + self.sampling_scales = {k: 1 for k in self.langs} + + for lang, dataset in enumerate(self.datasets): + isiterable = isinstance(dataset, IterableDataset) + + if isiterable: + self.lang_kind[lang] = 'iterable' + self.length += int(len(dataset) * self.sampling_scales[lang]) + else: + self.lang_kind[lang] = 'map' + self.length += int((len(dataset) // world_size) * self.sampling_scales[lang]) + + if seed is not None: + np.random.seed(seed) + + # set this to ensure compatibility with models searching for the collate_fn + # since this class stores datasets as a dict, not list + # self.collate_fn = self.datasets[self.langs[0]].collate_fn + if hasattr(self.datasets[self.langs[0]], 'collate_fn'): + self.collate_fn = self.datasets[self.langs[0]].collate_fn + elif ( + hasattr(self.datasets[self.langs[0]], 'datasets') + and isinstance(self.datasets[self.langs[0]].datasets, list) + and len(self.datasets[self.langs[0]].datasets) > 0 + and hasattr(self.datasets[self.langs[0]].datasets[0], 'collate_fn') + ): + # support datasets that are lists of entries + self.collate_fn = self.datasets[self.langs[0]].datasets[0].collate_fn + elif ( + hasattr(self.datasets[self.langs[0]], 'datasets') + and isinstance(self.datasets[self.langs[0]].datasets, list) + and len(self.datasets[self.langs[0]].datasets) > 0 + and hasattr(self.datasets[self.langs[0]].datasets[0], 'datasets') + and isinstance(self.datasets[self.langs[0]].datasets[0].datasets, list) + and len(self.datasets[self.langs[0]].datasets[0].datasets) > 0 + and hasattr(self.datasets[self.langs[0]].datasets[0].datasets[0], 'collate_fn') + ): + # support datasets that are lists of lists + self.collate_fn = self.datasets[self.langs[0]].datasets[0].datasets[0].collate_fn + else: + raise RuntimeError("CodeSwitchedDataset could not locate a valid dataset collate_fn to bind to") + + # this method returns an iterator object for a given language ID + # it correctly handles whether the underlying dataset is IterableDataset or mappable + def get_iterable_by_lang(self, lang): + dataset = self.datasets[lang] + + if isinstance(dataset, IterableDataset): + return dataset.__iter__() + else: + indices = np.arange(len(dataset)) + if self.shuffle: + np.random.shuffle(indices) + return iter(indices) + + # this method is the main function which builds and returns a composite, synthetic code-switched + # utterance on the fly. It automatically works with all of the class-based variables stored to create + # the synthetic utterance + def build_single_CS_sample(self): + # get_sample_from_language returns a LongTensor for the transcripts so we create a LongTensor to hold + # all returned transcripts + comp_text = torch.LongTensor([]) + created_sample_duration_sec = 0 + created_sample_langs = [] + created_sample_audios = [] + + # if min_monolingual fires, it means we will just return a single, original monolingual utterance + # from one of our languages based on that language's probability + pure_mono = np.random.rand() <= self.min_monolingual + + # we continue to add to the composite utterance until we hit the min_duration + while created_sample_duration_sec < self.min_duration: + # we sample from only those languages which haven't already been sampled for this particular + # synthetic utterance, unless pure_random=True, in which case, you just sample with replacement + # every time + if (self.pure_random and not pure_mono) or ( + len(set(created_sample_langs)) == 0 or len(set(created_sample_langs)) == len(self.langs) + ): + lang_id = np.random.choice(self.langs, p=self.lang_probs) + # elif pure_mono: + # use this approach if you want synthetic utterances which are all monolingual + # lang_id = created_sample_langs[0] + else: + # this code is for when we need to sample from only those languages which haven't been sampled + # yet for this utterance + p = np.array(list(map(self.prob_dict.get, list(self.langs_set - set(created_sample_langs))))) + p = p / p.sum() + lang_id = np.random.choice(list(self.langs_set - set(created_sample_langs)), p=p) + + audio, audio_len, labels, labels_len, *_ = self.get_sample_from_language(lang_id) + + # in case you get an audio which is all silence we keep sampling + if audio.count_nonzero().item() == 0: + continue + + sample_duration = len(audio) / self.sample_rate + if (created_sample_duration_sec + sample_duration) > self.max_duration: + continue + + if comp_text.device != labels.device: + comp_text = comp_text.to(labels.device) + + if audio.ndim > 1 and self.force_monochannel: + audio = audio.mean(dim=-1) + + created_sample_duration_sec += sample_duration + created_sample_langs.append(lang_id) + # need to use numpy instead of torch here because we need numpy's trim_zeros function + created_sample_audios.append(audio.cpu().numpy()) + comp_text = torch.cat([comp_text, labels], dim=0) + + # we want a real, non-synth pure_mono sample so we break soon as we have one + if pure_mono: + break + + # check that all samples have the same number of channels + sample_channels = list(set([s.ndim for s in created_sample_audios])) + if len(sample_channels) > 1: + raise RuntimeError( + "Mixture of audios with different number of channels in CodeSwitchedDataset. All sources must be same number of channels." + ) + + multichannel = sample_channels[0] > 1 + + # we start with pause_start amount of silence (zero array) which needs the correct shape for multi/mono channel + if multichannel: + comp_audio = np.zeros( + shape=(int(self.pause_start * self.sample_rate / 1000.0), created_sample_audios[0].shape[-1]), + dtype=created_sample_audios[0].dtype, + ) + else: + comp_audio = np.zeros( + shape=(int(self.pause_start * self.sample_rate / 1000.0),), dtype=created_sample_audios[0].dtype + ) + + # iterate over all mono-lingual samples to build the final composite + for idx, wav in enumerate(created_sample_audios): + if not multichannel: + # this function only works if mono-channel + wav = np.trim_zeros(wav) + + # normalise to provided DB level + wav_norm = wav * (10.0 ** (self.db_norm / 20.0) / np.maximum(0.01, (wav ** 2).mean(axis=0) ** 0.5)) + + # this part appends the normed waveform to the existing waveform, and inserts pause_join amount of silence + # if necessary, otherwise just a straight append + if idx < len(created_sample_audios) - 1: + if multichannel: + wav_norm = np.append( + wav_norm, + np.zeros( + shape=( + int(self.pause_join * self.sample_rate / 1000.0), + created_sample_audios[0].shape[-1], + ), + dtype=comp_audio.dtype, + ), + axis=0, + ) + else: + wav_norm = np.append( + wav_norm, + np.zeros(shape=(int(self.pause_join * self.sample_rate / 1000.0),), dtype=comp_audio.dtype), + axis=0, + ) + + # this is the penultimate composite wavform, just need to add pause_end silence + comp_audio = np.append(comp_audio, wav_norm, axis=0) + + # here we add the pause_end amount of silence, in correct channel shape + if multichannel: + comp_audio = np.append( + comp_audio, + np.zeros( + shape=(int(self.pause_end * self.sample_rate / 1000.0), created_sample_audios[0].shape[-1]), + dtype=comp_audio.dtype, + ), + axis=0, + ) + else: + comp_audio = np.append( + comp_audio, + np.zeros(shape=(int(self.pause_end * self.sample_rate / 1000.0),), dtype=comp_audio.dtype), + axis=0, + ) + + # we only want augmentation to happen on the final, synthetic utterance, and not on any of the individual + # languages, which is why we set augmentor=None when building the individual language datasets in audio_to_text_dataset.get_code_switched_dataset + # here we now apply augmentation to the final, synthetic utterance only + # all of this logic here happens in-memory, nothing is written to disk + if self.augmentor is not None: + # import here to avoid circular import error + # import here because otherwise CI test-nlp-imports fails since soundfile is only in requirements_asr and not in requirements_common + import soundfile as sf + + from nemo.collections.asr.parts.preprocessing import AudioSegment + + mb = io.BytesIO() + sf.write(mb, comp_audio, self.sample_rate, format='WAV') + mb.seek(0) + comp_audio_as = AudioSegment.from_file(mb, target_sr=self.sample_rate) + self.augmentor.perturb(comp_audio_as) + comp_audio = comp_audio_as.samples + + return ( + torch.tensor(comp_audio, dtype=audio.dtype, device=audio.device), + torch.tensor(len(comp_audio), device=audio_len.device).long(), + comp_text, + torch.tensor(len(comp_text), device=labels_len.device).long(), + ) + + # this is a helper method which prepares all of the iterator objects for all languages + # based on whether that language's underlying dataset is a map or an IterableDataset + def prep_underlying_datasets(self): + worker_info = pt_data.get_worker_info() + if worker_info is None: + max_elements = self.length + wid = 0 + wnum = 1 + else: + wid = worker_info.id + wnum = worker_info.num_workers + max_elements = len(range(wid, self.length, wnum)) + + for lang in self.langs: + if self.lang_kind[lang] == 'map': + start_idx = (len(self.datasets[lang]) // self.world_size) * self.global_rank + end_idx = start_idx + (len(self.datasets[lang]) // self.world_size) + if self.global_rank == self.world_size - 1: + end_idx = len(self.datasets[lang]) + indices = range(start_idx + wid, end_idx, wnum) + self.datasets[lang] = pt_data.Subset(self.datasets[lang], indices) + + self.lang_iterables[lang] = self.get_iterable_by_lang(lang) + + return max_elements + + # returns a sample (audio and transcript) from any underlying language stored by the class on instantiation + # the sample returned is a tensor for the audio and a tensor of ints for the transcript + # this method automatically handles StopIteration errors for the underyling language and rebuilds + # the iterator if necessary + def get_sample_from_language(self, lang): + while True: + try: + val = next(self.lang_iterables[lang]) + if self.lang_kind[lang] == 'map': + val = self.datasets[lang][val] + return val + except StopIteration: + self.lang_iterables[lang] = self.get_iterable_by_lang(lang) + + def __iter__(self): + # we create primed iterators for all languages and return the grand total of samples for each + # underlying language as a sum + max_elements = self.prep_underlying_datasets() + + if self.infinity_mode: + while True: + yield self.build_single_CS_sample() + else: + n = 0 + while n < max_elements: + yield self.build_single_CS_sample() + n += 1 + + def __len__(self): + return self.length diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/lhotse/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/lhotse/__init__.py new file mode 100644 index 0000000..6bbe9e9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/lhotse/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.common.data.lhotse.cutset import read_cutset_from_config +from nemo.collections.common.data.lhotse.dataloader import get_lhotse_dataloader_from_config diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/lhotse/cutset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/lhotse/cutset.py new file mode 100644 index 0000000..028ea8b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/lhotse/cutset.py @@ -0,0 +1,199 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import warnings +from itertools import repeat +from pathlib import Path +from typing import Sequence, Tuple + +from lhotse import CutSet + +from nemo.collections.common.data.lhotse.nemo_adapters import LazyNeMoIterator, LazyNeMoTarredIterator + + +def read_cutset_from_config(config) -> Tuple[CutSet, bool]: + """ + Reads NeMo configuration and creates a CutSet either from Lhotse or NeMo manifests. + + Returns a tuple of ``CutSet`` and a boolean indicating whether the data is tarred (True) or not (False). + """ + # First, we'll figure out if we should read Lhotse manifest or NeMo manifest. + use_nemo_manifest = all(config[opt] is None for opt in ("cuts_path", "shar_path")) + if use_nemo_manifest: + assert ( + config.manifest_filepath is not None + ), "You must specify either: manifest_filepath, lhotse.cuts_path, or lhotse.shar_path" + is_tarred = config.tarred_audio_filepaths is not None + else: + is_tarred = config.shar_path is not None + if use_nemo_manifest: + # Read NeMo manifest -- use the right wrapper depending on tarred/non-tarred. + cuts = read_nemo_manifest(config, is_tarred) + else: + # Read Lhotse manifest (again handle both tarred(shar)/non-tarred). + cuts = read_lhotse_manifest(config, is_tarred) + return cuts, is_tarred + + +def read_lhotse_manifest(config, is_tarred: bool) -> CutSet: + if is_tarred: + # Lhotse Shar is the equivalent of NeMo's native "tarred" dataset. + # The combination of shuffle_shards, and repeat causes this to + # be an infinite manifest that is internally reshuffled on each epoch. + # The parameter ``config.shard_seed`` is used to determine shard shuffling order. Options: + # - "trng" means we'll defer setting the seed until the iteration + # is triggered, and we'll use system TRNG to get a completely random seed for each worker. + # This results in every dataloading worker using full data but in a completely different order. + # - "randomized" means we'll defer setting the seed until the iteration + # is triggered, and we'll use config.seed to get a pseudo-random seed for each worker. + # This results in every dataloading worker using full data but in a completely different order. + # Unlike "trng", this is deterministic, and if you resume training, you should change the seed + # to observe different data examples than in the previous run. + # - integer means we'll set a specific seed in every worker, and data would be duplicated across them. + # This is mostly useful for unit testing or debugging. + shard_seed = config.shard_seed + if config.cuts_path is not None: + warnings.warn("Note: lhotse.cuts_path will be ignored because lhotse.shar_path was provided.") + if isinstance(config.shar_path, (str, Path)): + logging.info(f"Initializing Lhotse Shar CutSet (tarred) from a single data source: '{config.shar_path}'") + cuts = CutSet.from_shar(in_dir=config.shar_path, shuffle_shards=True, seed=shard_seed).repeat() + else: + # Multiple datasets in Lhotse Shar format: we will dynamically multiplex them + # with probability approximately proportional to their size + logging.info( + "Initializing Lhotse Shar CutSet (tarred) from multiple data sources with a weighted multiplexer. " + "We found the following sources and weights: " + ) + cutsets = [] + weights = [] + for item in config.shar_path: + if isinstance(item, (str, Path)): + path = item + cs = CutSet.from_shar(in_dir=path, shuffle_shards=True, seed=shard_seed) + weight = len(cs) + else: + assert isinstance(item, Sequence) and len(item) == 2 and isinstance(item[1], (int, float)), ( + "Supported inputs types for config.shar_path are: " + "str | list[str] | list[tuple[str, number]] " + "where str is a path and number is a mixing weight (it may exceed 1.0). " + f"We got: '{item}'" + ) + path, weight = item + cs = CutSet.from_shar(in_dir=path, shuffle_shards=True, seed=shard_seed) + logging.info(f"- {path=} {weight=}") + cutsets.append(cs.repeat()) + weights.append(weight) + cuts = mux(*cutsets, weights=weights, max_open_streams=config.max_open_streams, seed=config.shard_seed) + else: + # Regular Lhotse manifest points to individual audio files (like native NeMo manifest). + cuts = CutSet.from_file(config.cuts_path) + return cuts + + +def read_nemo_manifest(config, is_tarred: bool) -> CutSet: + common_kwargs = { + "text_field": config.text_field, + "lang_field": config.lang_field, + } + # The option below is to allow a special case of NeMo manifest iteration as Lhotse CutSet + # without performing any I/O. NeMo manifests typically don't have sampling_rate information required by Lhotse. + # This is useful for utility scripts that iterate metadata and estimate optimal batching settings. + notar_kwargs = {"missing_sampling_rate_ok": config.missing_sampling_rate_ok} + if isinstance(config.manifest_filepath, (str, Path)): + logging.info(f"Initializing Lhotse CutSet from a single NeMo manifest (tarred): '{config.manifest_filepath}'") + if is_tarred: + cuts = CutSet( + LazyNeMoTarredIterator( + config.manifest_filepath, + tar_paths=config.tarred_audio_filepaths, + shuffle_shards=config.shuffle, + **common_kwargs, + ) + ) + else: + cuts = CutSet(LazyNeMoIterator(config.manifest_filepath, **notar_kwargs, **common_kwargs)) + else: + # Format option 1: + # Assume it's [[path1], [path2], ...] (same for tarred_audio_filepaths). + # This is the format for multiple NeMo buckets. + # Note: we set "weights" here to be proportional to the number of utterances in each data source. + # this ensures that we distribute the data from each source uniformly throughout each epoch. + # Setting equal weights would exhaust the shorter data sources closer the towards the beginning + # of an epoch (or over-sample it in the case of infinite CutSet iteration with .repeat()). + # Format option 1: + # Assume it's [[path1, weight1], [path2, weight2], ...] (while tarred_audio_filepaths remain unchanged). + # Note: this option allows to manually set the weights for multiple datasets. + logging.info( + f"Initializing Lhotse CutSet from multiple tarred NeMo manifest sources with a weighted multiplexer. " + f"We found the following sources and weights: " + ) + cutsets = [] + weights = [] + tar_paths = config.tarred_audio_filepaths if is_tarred else repeat((None,)) + # Create a stream for each dataset. + for manifest_info, (tar_path,) in zip(config.manifest_filepath, tar_paths): + # First, convert manifest_path[+tar_path] to an iterator. + manifest_path = manifest_info[0] + if is_tarred: + nemo_iter = LazyNeMoTarredIterator( + manifest_path=manifest_path, tar_paths=tar_path, shuffle_shards=config.shuffle, **common_kwargs + ) + else: + nemo_iter = LazyNeMoIterator(manifest_path, **notar_kwargs, **common_kwargs) + # Then, determine the weight or use one provided + if len(manifest_info) == 1: + weight = len(nemo_iter) + else: + assert ( + isinstance(manifest_info, Sequence) + and len(manifest_info) == 2 + and isinstance(manifest_info[1], (int, float)) + ), ( + "Supported inputs types for config.manifest_filepath are: " + "str | list[list[str]] | list[tuple[str, number]] " + "where str is a path and number is a mixing weight (it may exceed 1.0). " + f"We got: '{manifest_info}'" + ) + weight = manifest_info[1] + logging.info(f"- {manifest_path=} {weight=}") + # [optional] When we have a limit on the number of open streams, + # split the manifest to individual shards if applicable. + # This helps the multiplexing achieve closer data distribution + # to the one desired in spite of the limit. + if config.max_open_streams is not None: + for subiter in nemo_iter.to_shards(): + cutsets.append(CutSet(subiter)) + weights.append(weight) + else: + cutsets.append(CutSet(nemo_iter)) + weights.append(weight) + # Finally, we multiplex the dataset streams to mix the data. + cuts = mux(*cutsets, weights=weights, max_open_streams=config.max_open_streams, seed=config.shard_seed) + return cuts + + +def mux( + *cutsets: CutSet, weights: list[int | float], max_open_streams: int | None = None, seed: str | int = "trng" +) -> CutSet: + """ + Helper function to call the right multiplexing method flavour in lhotse. + The result is always an infinitely iterable ``CutSet``, but depending on whether ``max_open_streams`` is set, + it will select a more appropriate multiplexing strategy. + """ + if max_open_streams is not None: + cuts = CutSet.infinite_mux(*cutsets, weights=weights, seed=seed, max_open_streams=max_open_streams) + else: + cuts = CutSet.mux(*[cs.repeat() for cs in cutsets], weights=weights, seed=seed) + return cuts diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/lhotse/dataloader.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/lhotse/dataloader.py new file mode 100644 index 0000000..9eeb880 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/lhotse/dataloader.py @@ -0,0 +1,301 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import warnings +from dataclasses import dataclass +from functools import partial +from typing import Any, Optional + +import torch +from lhotse import CutSet +from lhotse.cut import Cut +from lhotse.dataset import ( + CutConcatenate, + DynamicBucketingSampler, + DynamicCutSampler, + IterableDatasetWrapper, + make_worker_init_fn, +) +from lhotse.lazy import LazyFlattener +from lhotse.utils import fastcopy +from omegaconf import DictConfig, OmegaConf + +from nemo.collections.common.data.lhotse.cutset import read_cutset_from_config + + +@dataclass +class LhotseDataLoadingConfig: + """ + Structured config used for OmegaConf schema validation. + It's also a single source of truth for reading default option values. + The options not supported anymore but present, e.g., in old configs, + will be emitted in a DeprecationWarning and ignored. + """ + + # 1. Data inputs. + # a. "Classic" NeMo input path fields. + manifest_filepath: Any = None # str | list[list[str | float]] | None = None + tarred_audio_filepaths: Any = None # str | list[list[str]] | None = None + # b. Lhotse CutSet manifest / Lhotse Shar tar dir paths. + cuts_path: str | None = None + shar_path: Any = None # str | list[str | tuple[str, float | int]] | None = None + + # 2. Batch size. + # a. Existing NeMo options. + batch_size: int | None = None + # b. Lhotse dynamic batch sizes. + batch_duration: float | None = None + quadratic_duration: float | None = None + # c. Lhotse bucketing. + use_bucketing: bool = False + num_buckets: int = 30 + num_cuts_for_bins_estimate: int = 10000 + bucket_duration_bins: list[float] | None = None + bucket_buffer_size: int = 10000 + # d. Other Lhotse sampling options. + shuffle_buffer_size: int | None = 10000 + drop_last: bool = False + shard_seed: int | str = "trng" + max_open_streams: int | None = None + + # 3. Supported existing NeMo options. + shuffle: bool = False + sample_rate: int = 16000 + min_duration: float | None = -1 + max_duration: float | None = float("inf") + seed: int | str = "randomized" # int | "randomized" | "trng"; the latter two are lazily resolved by Lhotse in dloading worker processes + num_workers: int = 0 + pin_memory: bool = False + + # 4. Optional Lhotse data augmentation. + # a. On-the-fly noise/audio mixing. + noise_path: str | None = None + noise_snr: tuple[float, float] = (10.0, 20.0) + noise_mix_prob: float = 0.5 + # b. On-the-fly 3-way speed perturbation. + perturb_speed: bool = False + # c. Cut concatenation (glue together multiple utterances into a single one) + concatenate_samples: bool = False + concatenate_gap_seconds: float = 0.1 + concatenate_duration_factor: float = 1.0 + concatenate_merge_supervisions: bool = True + db_norm: Optional[float] = -25.0 # from CodeSwitchingDataset + + # 5. Other Lhotse options. + text_field: str = "text" # key to read the transcript from + lang_field: str = "lang" # key to read the language tag from + # Enables iteration of NeMo non-tarred manifests that don't have a "sampling_rate" key without performing any I/O. + # Note that this will not allow actual dataloading; it's only for manifest iteration as Lhotse objects. + missing_sampling_rate_ok: bool = False + + +def get_lhotse_dataloader_from_config( + config: DictConfig, global_rank: int, world_size: int, dataset: torch.utils.data.Dataset +) -> torch.utils.data.DataLoader: + """ + Set up a Lhotse training dataloder. + + Expects a typical NeMo dataset configuration format, with additional fields: "use_lhotse=True" and "lhotse: ". + Some fields in the original NeMo configuration may be ignored. + + The ``dataset`` parameter should be an instance of a Lhotse-compatible PyTorch Dataset class. + It only needs to define the following method ``__getitem__(self, cuts: CutSet) -> Dict[str, torch.Tensor]``. + This dataset is not expected to hold a reference to any actual data; it may be interpreted as a function + mapping a Lhotse CutSet into a mini-batch of tensors. + + For example, see: :class:`nemo.collections.asr.data.audio_to_text_lhotse.LhotseSpeechToTextBpeDataset`, + which is constructed from just a tokenizer and essentially loads and collates audio and tokenizes the transcript. + """ + logging.info("We will be using a Lhotse DataLoader.") + + config = make_structured_with_schema_warnings(config) + + # 1. Load a manifest as a Lhotse CutSet. + cuts, is_tarred = read_cutset_from_config(config) + + # Resample as a safeguard; it's a no-op when SR is already OK + cuts = cuts.resample(config.sample_rate) + + # Duration filtering, same as native NeMo dataloaders. + cuts = cuts.filter(DurationFilter(config.min_duration, config.max_duration)) + + # Expands cuts if multiple translations are provided. + cuts = CutSet(LazyFlattener(cuts.map(_flatten_alt_text))) + + # 2. Optional augmentations. + # 2.a. Noise mixing. + if config.noise_path is not None: + noise = CutSet.from_file(config.noise_path) + cuts = cuts.mix( + cuts=noise, snr=config.noise_snr, mix_prob=config.noise_mix_prob, seed="trng", random_mix_offset=True + ) + + # 2.b. On-the-fly speed perturbation. + # mux here ensures it's uniformly distributed throughout sampling, + # and applying it here (before sampler/dataset) ensures optimal + # bucket allocation. + if config.perturb_speed: + cuts = CutSet.mux(cuts, cuts.perturb_speed(0.9), cuts.perturb_speed(1.1),) + + # 3. The sampler. + if config.use_bucketing: + # Bucketing. Some differences from NeMo's native bucketing: + # - we can tweak the number of buckets and bucket duration bins using the configuration + # - batch size is dynamic and configurable via a single param: max_duration (config: batch_duration) + # - quadratic_duration introduces a penalty to balance batch sizes for quadratic time complexity models + logging.info( + f"Creating a Lhotse DynamicBucketingSampler " + f"(max_batch_duration={config.batch_duration} max_batch_size={config.batch_size})" + ) + sampler = DynamicBucketingSampler( + cuts, + max_duration=config.batch_duration, + max_cuts=config.batch_size, + shuffle=config.shuffle, + drop_last=config.drop_last, + shuffle_buffer_size=config.shuffle_buffer_size, + quadratic_duration=config.quadratic_duration, + seed=config.seed, + num_buckets=config.num_buckets, + duration_bins=config.bucket_duration_bins, + num_cuts_for_bins_estimate=config.num_cuts_for_bins_estimate, + buffer_size=config.bucket_buffer_size, + rank=0 if is_tarred else global_rank, + world_size=1 if is_tarred else world_size, + ) + else: + # Non-bucketing sampler, similar to original NeMo dataloading without bucketing, + # but we also use batch_duration instead of batch_size here. + # Recommended for dev/test. + logging.info( + f"Creating a Lhotse DynamicCutSampler (bucketing is disabled, " + f"(max_batch_duration={config.batch_duration} max_batch_size={config.batch_size})" + ) + sampler = DynamicCutSampler( + cuts, + max_duration=config.batch_duration, + max_cuts=config.batch_size, + shuffle=config.shuffle, + drop_last=config.drop_last, + shuffle_buffer_size=config.shuffle_buffer_size, + quadratic_duration=config.quadratic_duration, + seed=config.seed, + rank=0 if is_tarred else global_rank, + world_size=1 if is_tarred else world_size, + ) + + if config.concatenate_samples: + # Cut concatenation will produce longer samples out of shorter samples + # by gluing them together from the shortest to longest not to exceed a duration + # of longest_cut * duration_factor (greedy knapsack algorithm for minimizing padding). + # Useful e.g. for simulated code-switching in multilingual setups. + # We follow concatenation by ``merge_supervisions`` which creates a single supervision + # object with texts joined by a whitespace so that "regular" dataset classes don't + # have to add a special support for multi-supervision cuts. + sampler = sampler.map( + CutConcatenate(gap=config.concatenate_gap_seconds, duration_factor=config.concatenate_duration_factor,) + ) + if config.db_norm is not None: + sampler = sampler.map(partial(_normalize_loudness, db_norm=config.db_norm)) + if config.concatenate_merge_supervisions: + sampler = sampler.map(_merge_supervisions) + + # 4. Creating dataloader. + if is_tarred: + # Wrapper here is necessary when using NeMo tarred data or Lhotse Shar data, + # because then I/O happens upon sampler iteration. Normally, the sampler resides + # in the training loop process, but when we use iterable dataset, we can move it to + # the dataloading worker process. + # We use lhotse's own worker_init_fn which leverages information such as rank, world_size, + # worker_id, etc. to set a different random seed for each (node, worker) combination. + # This together with infinite datasets removes the need to split data across nodes/workers. + dloader_kwargs = dict( + dataset=IterableDatasetWrapper(dataset=dataset, sampler=sampler), + worker_init_fn=make_worker_init_fn(rank=global_rank, world_size=world_size), + persistent_workers=config.num_workers > 0, # helps Lhotse Shar maintain shuffling state + ) + else: + # For non-tarred data, the sampler resides in the training loop process and + # reads only light-weight JSON objects; it samples mini-batches and passes + # the meta-data to Dataset, which performs the actual I/O inside its __getitem__ method. + dloader_kwargs = dict(dataset=dataset, sampler=sampler) + dloader = torch.utils.data.DataLoader( + **dloader_kwargs, batch_size=None, num_workers=config.num_workers, pin_memory=config.pin_memory, + ) + + return dloader + + +def make_structured_with_schema_warnings(config: DictConfig) -> DictConfig: + """ + Checks the schema and fills missing default option values. + Warns the user if any of the fields are not supported by the current schema + but does not raise exceptions. + """ + default = OmegaConf.structured(LhotseDataLoadingConfig) + + # Remove unsupported keys and warn about them. + supported_keys = set(OmegaConf.to_container(default).keys()) + received_keys = set(OmegaConf.to_container(config).keys()) + unsupported_keys = received_keys - supported_keys + if unsupported_keys: + warnings.warn( + f"The following configuration keys are no longer supported " f"and ignored: {','.join(unsupported_keys)}", + category=DeprecationWarning, + ) + config = OmegaConf.masked_copy(config, list(supported_keys)) + + return OmegaConf.merge(default, config) + + +# The helper callables below exist to avoid passing lambdas into lhotse CutSet map/filter methods. +# Lambdas are not serializable across processes by pickle. +# Note: lhotse offers LHOTSE_DILL_ENABLED=1 and ``lhotse.lazy.set_dill_enabled(True)`` +# to support pickling lambdas if its ever truly necessary. + + +class DurationFilter: + """Callable, returns ``True`` if a cut's duration is in range [d_min, d_max] and ``False`` otherwise.""" + + def __init__(self, d_min: float, d_max: float) -> None: + self.d_min = d_min + self.d_max = d_max + + def __call__(self, cut: Cut) -> bool: + return self.d_min <= cut.duration <= self.d_max + + +def _normalize_loudness(cuts: CutSet, db_norm: float) -> CutSet: + return cuts.normalize_loudness(target=db_norm, mix_first=False) + + +def _merge_supervisions(cuts: CutSet) -> CutSet: + return cuts.merge_supervisions() + + +def _flatten_alt_text(cut) -> list: + ans = [cut] + if cut.custom is None or cut.custom.get("alt_text") is None: + return ans + cut = cut.move_to_memory(audio_format="wav") # performs I/O once and holds audio in memory from now on + # Popping to ease eyesight on debug. + paired_text = cut.custom.pop("alt_text") + for data in paired_text.values(): + # Copy to avoid lazy dataloading issues + data = data.copy() + text_instance = cut.map_supervisions(lambda s: fastcopy(s, text=data["text"], language=data["lang"])) + text_instance.custom = {"text": data.pop("text"), "lang": data.pop("lang"), **data} + ans.append(text_instance) + return ans diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/lhotse/nemo_adapters.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/lhotse/nemo_adapters.py new file mode 100644 index 0000000..4fae72e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/data/lhotse/nemo_adapters.py @@ -0,0 +1,283 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import random +import re +import secrets +import tarfile +from io import BytesIO +from pathlib import Path +from typing import Generator, Iterable, List + +import soundfile +from cytoolz import groupby +from lhotse import AudioSource, Recording, SupervisionSegment +from lhotse.cut import Cut +from lhotse.lazy import LazyIteratorChain, LazyJsonlIterator +from lhotse.serialization import open_best +from lhotse.utils import compute_num_samples +from nemo.collections.common.parts.preprocessing.manifest import get_full_path + + +class LazyNeMoIterator: + """ + ``LazyNeMoIterator`` reads a NeMo (non-tarred) JSON manifest and converts it on the fly to an ``Iterable[Cut]``. + It's used to create a ``lhotse.CutSet``. + + Currently, it requires the following keys in NeMo manifests: + - "audio_filepath" + - "duration" + - "text" (overridable with ``text_field`` argument) + + Specially supported keys are: + - [recommended] "sampling_rate" allows us to provide a valid Lhotse ``Recording`` object without checking the audio file + - "offset" for partial recording reads + - "lang" is mapped to Lhotse superivsion's language (overridable with ``lang_field`` argument) + + Every other key found in the manifest will be attached to Lhotse Cut and accessible via ``cut.custom[key]``. + + .. caution:: We will perform some I/O (as much as required by soundfile.info) to discover the sampling rate + of the audio file. If this is not acceptable, convert the manifest to Lhotse format which contains + sampling rate info. For pure metadata iteration purposes we also provide a ``missing_sampling_rate_ok`` flag that + will create only partially valid Lhotse objects (with metadata related to sampling rate / num samples missing). + + Example:: + + >>> cuts = lhotse.CutSet(LazyNeMoIterator("nemo_manifests/train.json")) + """ + + def __init__( + self, + path: str | Path, + text_field: str = "text", + lang_field: str = "lang", + missing_sampling_rate_ok: bool = False, + ) -> None: + self.source = LazyJsonlIterator(path) + self.text_field = text_field + self.lang_field = lang_field + self.missing_sampling_rate_ok = missing_sampling_rate_ok + + @property + def path(self) -> str | Path: + return self.source.path + + def __iter__(self) -> Generator[Cut, None, None]: + for data in self.source: + audio_path = get_full_path(str(data.pop("audio_filepath")), str(self.path)) + duration = data.pop("duration") + offset = data.pop("offset", None) + recording = self._create_recording(audio_path, duration, data.pop("sampling_rate", None)) + cut = recording.to_cut() + if offset is not None: + cut = cut.truncate(offset=offset, duration=duration, preserve_id=True) + cut.id = f"{cut.id}-{round(offset * 1e2):06d}-{round(duration * 1e2):06d}" + # Note that start=0 and not start=offset because supervision's start if relative to the + # start of the cut; and cut.start is already set to offset + cut.supervisions.append( + SupervisionSegment( + id=cut.id, + recording_id=cut.recording_id, + start=0, + duration=cut.duration, + text=data.get(self.text_field), + language=data.get(self.lang_field), + ) + ) + cut.custom = data + yield cut + + def __len__(self) -> int: + return len(self.source) + + def __add__(self, other): + return LazyIteratorChain(self, other) + + def _create_recording(self, audio_path: str, duration: float, sampling_rate: int | None = None,) -> Recording: + if sampling_rate is not None: + # TODO(pzelasko): It will only work with single-channel audio in the current shape. + return Recording( + id=audio_path, + sources=[AudioSource(type="file", channels=[0], source=audio_path)], + sampling_rate=sampling_rate, + num_samples=compute_num_samples(duration, sampling_rate), + duration=duration, + channel_ids=[0], + ) + elif self.missing_sampling_rate_ok: + return Recording( + id=audio_path, + sources=[AudioSource(type="file", channels=[0], source=audio_path)], + sampling_rate=-1, + num_samples=-1, + duration=duration, + channel_ids=[0], + ) + else: + return Recording.from_file(audio_path) + + +class LazyNeMoTarredIterator: + """ + ``LazyNeMoTarredIterator`` reads a NeMo tarred JSON manifest and converts it on the fly to an ``Iterable[Cut]``. + It's used to create a ``lhotse.CutSet``. + + Currently, it requires the following keys in NeMo manifests: + - "audio_filepath" + - "duration" + - "text" (overridable with text_field argument) + - "shard_id" + + Specially supported keys are: + - "lang" is mapped to Lhotse superivsion's language (overridable with ``lang_field`` argument) + + Every other key found in the manifest will be attached to Lhotse Cut and accessible via ``cut.custom[key]``. + + Args ``manifest_path`` and ``tar_paths`` can be either a path/string to a single file, or a string in NeMo format + that indicates multiple paths (e.g. "[[data/bucket0/tarred_audio_paths.json],[data/bucket1/...]]"). + + Example of CutSet with inter-shard shuffling enabled:: + + >>> cuts = lhotse.CutSet(LazyNeMoTarredIterator( + ... manifest_path="nemo_manifests/train.json", + ... tar_paths=["nemo_manifests/audio_0.tar", ...], + ... shuffle_shards=True, + ... )) + """ + + def __init__( + self, + manifest_path: str | Path, + tar_paths: str | list, + shuffle_shards: bool = False, + text_field: str = "text", + lang_field: str = "lang", + ) -> None: + def strip_pipe(p): + if isinstance(p, str): + if p.startswith("pipe:"): + p = p[5:] + return Path(p) + return p + + self.shard_id_to_manifest: dict[int, Iterable[dict]] + self.paths = expand_sharded_filepaths(manifest_path) + if len(self.paths) == 1: + self.source = LazyJsonlIterator(self.paths[0]) + self.shard_id_to_manifest = groupby("shard_id", self.source) + else: + pattern = re.compile(r".+_(\d+)\.jsonl?(?:.gz)?") + shard_ids = [] + for p in self.paths: + m = pattern.match(p) + assert m is not None, f"Cannot determine shard_id from manifest path: {p}" + shard_ids.append(int(m.group(1))) + self.shard_id_to_manifest = {sid: LazyJsonlIterator(p) for sid, p in zip(shard_ids, self.paths)} + self.source = LazyIteratorChain(*self.shard_id_to_manifest.values()) + + tar_paths = expand_sharded_filepaths(tar_paths) + self.shard_id_to_tar_path: dict[int, str] = {int(strip_pipe(p).stem.split("_")[1]): p for p in tar_paths} + self.shuffle_shards = shuffle_shards + self.text_field = text_field + self.lang_field = lang_field + self._validate() + + def to_shards(self) -> List["LazyNeMoTarredIterator"]: + """Convert this iterator to a list of separate iterators for each shard.""" + if len(self.paths) == 1: + # Cannot do that if the JSON manifest is a single file for all shards; + # just return self. + return [self] + else: + return [ + LazyNeMoTarredIterator( + manifest_path=path, + tar_paths=tarpath, + shuffle_shards=False, + text_field=self.text_field, + lang_field=self.lang_field, + ) + for path, tarpath in zip(self.paths, self.shard_id_to_tar_path.values()) + ] + + def _validate(self) -> None: + shard_ids_tars = set(self.shard_id_to_tar_path) + shard_ids_manifest = set(self.shard_id_to_manifest) + assert shard_ids_tars == shard_ids_manifest, ( + f"Mismatch between shard IDs discovered from tar files ({len(shard_ids_tars)=}) and " + f"JSON manifest ({len(shard_ids_manifest)=}): {shard_ids_tars - shard_ids_manifest=}" + ) + + @property + def shard_ids(self) -> List[int]: + return sorted(self.shard_id_to_manifest.keys()) + + def __iter__(self) -> Generator[Cut, None, None]: + shard_ids = self.shard_ids + + if self.shuffle_shards: + # Use TRNG for 100% randomness + random.Random(secrets.randbelow(2 ** 32)).shuffle(shard_ids) + + for sid in shard_ids: + shard_manifest = self.shard_id_to_manifest[sid] + tar_path = self.shard_id_to_tar_path[sid] + with tarfile.open(fileobj=open_best(tar_path, mode="rb"), mode="r|*") as tar: + for data, tar_info in zip(shard_manifest, tar): + assert ( + data["audio_filepath"] == tar_info.name + ), f"Mismatched JSON manifest and tar file. {data['audio_filepath']=} != {tar_info.name=}" + raw_audio = tar.extractfile(tar_info).read() + # Note: Lhotse has a Recording.from_bytes() utility that we won't use here because + # the profiling indicated significant overhead in torchaudio ffmpeg integration + # that parses full audio instead of just reading the header for WAV files. + # recording = lhotse.Recording.from_bytes(raw_audio, recording_id=tar_info.path) + meta = soundfile.info(BytesIO(raw_audio)) + recording = Recording( + id=tar_info.path, + sources=[AudioSource(type="memory", channels=list(range(meta.channels)), source=raw_audio)], + sampling_rate=int(meta.samplerate), + num_samples=meta.frames, + duration=meta.duration, + ) + cut = recording.to_cut() + cut.supervisions.append( + SupervisionSegment( + id=cut.id, + recording_id=cut.recording_id, + start=0, + duration=cut.duration, + text=data.get(self.text_field), + language=data.get(self.lang_field), + ) + ) + cut.custom = _to_custom_attr_dict(data) + yield cut + + def __len__(self) -> int: + return len(self.source) + + def __add__(self, other): + return LazyIteratorChain(self, other) + + +def expand_sharded_filepaths(path: str | Path) -> list[str]: + # local import to avoid circular imports + from nemo.collections.asr.data.audio_to_text import expand_sharded_filepaths as _expand_sharded_filepaths + + return _expand_sharded_filepaths(str(path), shard_strategy="replicate", world_size=1, global_rank=0) + + +def _to_custom_attr_dict(d: dict, _excluded_fields: set[str] = {"duration", "audio_filepath"}) -> dict: + return {k: v for k, v in d.items() if k not in _excluded_fields} diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/__init__.py new file mode 100644 index 0000000..4d780d9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/__init__.py @@ -0,0 +1,21 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.common.losses.aggregator import AggregatorLoss +from nemo.collections.common.losses.bce_logits_loss import BCEWithLogitsLoss +from nemo.collections.common.losses.cross_entropy import CrossEntropyLoss, NLLLoss +from nemo.collections.common.losses.mse_loss import MSELoss +from nemo.collections.common.losses.multi_similarity_loss import MultiSimilarityLoss +from nemo.collections.common.losses.smoothed_cross_entropy import SmoothedCrossEntropyLoss, SmoothedNLLLoss +from nemo.collections.common.losses.spanning_loss import SpanningLoss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/aggregator.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/aggregator.py new file mode 100644 index 0000000..1987ddd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/aggregator.py @@ -0,0 +1,67 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List + +import torch + +from nemo.core.classes import Loss, typecheck +from nemo.core.neural_types import LossType, NeuralType + +__all__ = ['AggregatorLoss'] + + +class AggregatorLoss(Loss): + """ + Sums several losses into one. + + Args: + num_inputs: number of input losses + weights: a list of coefficient for merging losses + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + input_types = {} + for i in range(self._num_losses): + input_types["loss_" + str(i + 1)] = NeuralType(elements_type=LossType()) + + return input_types + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__(self, num_inputs: int = 2, weights: List[float] = None): + super().__init__() + self._num_losses = num_inputs + if weights is not None and len(weights) != num_inputs: + raise ValueError("Length of weights should be equal to the number of inputs (num_inputs)") + + self._weights = weights + + @typecheck() + def forward(self, **kwargs): + values = [kwargs[x] for x in sorted(kwargs.keys())] + loss = torch.zeros_like(values[0]) + for loss_idx, loss_value in enumerate(values): + if self._weights is not None: + loss = loss.add(loss_value, alpha=self._weights[loss_idx]) + else: + loss = loss.add(loss_value) + return loss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/bce_logits_loss.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/bce_logits_loss.py new file mode 100644 index 0000000..b65c419 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/bce_logits_loss.py @@ -0,0 +1,79 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List + +import torch +from torch import nn + +from nemo.core.classes import Serialization, Typing, typecheck +from nemo.core.neural_types import LabelsType, LogitsType, LossType, MaskType, NeuralType + +__all__ = ["BCEWithLogitsLoss"] + + +class BCEWithLogitsLoss(nn.BCEWithLogitsLoss, Serialization, Typing): + """ + BCEWithLogitsLoss + + https://pytorch.org/docs/1.9.1/generated/torch.nn.BCEWithLogitsLoss.html + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "logits": NeuralType(["B"] + ["ANY"] * (self._logits_dim - 1), LogitsType()), + "labels": [NeuralType(["B"] + ["ANY"] * (self._logits_dim - 2), LabelsType())], + "loss_mask": NeuralType(["B"] + ["ANY"] * (self._logits_dim - 2), MaskType(), optional=True), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__( + self, + logits_ndim: int = 2, + weight: torch.Tensor = None, + reduction: str = "mean", + pos_weight: torch.Tensor = None, + ): + """ + Args: + logits_ndim: number of dimensions (or rank) of the logits tensor + weight: list of rescaling weight given to each class + reduction: type of the reduction over the batch + pos_weight: weight given to positive samples + """ + if pos_weight is not None and not torch.is_tensor(pos_weight): + pos_weight = torch.FloatTensor(pos_weight) + + super().__init__(weight=weight, pos_weight=pos_weight, reduction=reduction) + self._logits_dim = logits_ndim + + @typecheck() + def forward(self, logits: float, labels: List[int], loss_mask: torch.Tensor = None): + """ + Args: + logits: output of the classifier + labels: ground truth labels + """ + labels = torch.stack(labels) + labels = labels.t().float() + + return super().forward(logits, labels) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/cross_entropy.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/cross_entropy.py new file mode 100644 index 0000000..753cc08 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/cross_entropy.py @@ -0,0 +1,140 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from torch import nn + +from nemo.core.classes import Serialization, Typing, typecheck +from nemo.core.neural_types import LabelsType, LogitsType, LogprobsType, LossType, MaskType, NeuralType +from nemo.utils import logging + +__all__ = ['CrossEntropyLoss', 'NLLLoss'] + + +class CrossEntropyLoss(nn.CrossEntropyLoss, Serialization, Typing): + """ + CrossEntropyLoss + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "logits": NeuralType(['B'] + ['ANY'] * (self._logits_dim - 1), LogitsType()), + "labels": NeuralType(['B'] + ['ANY'] * (self._logits_dim - 2), LabelsType()), + "loss_mask": NeuralType(['B'] + ['ANY'] * (self._logits_dim - 2), MaskType(), optional=True), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__(self, logits_ndim=2, weight=None, reduction='mean', ignore_index=-100): + """ + Args: + logits_ndim (int): number of dimensions (or rank) of the logits tensor + weight (list): list of rescaling weight given to each class + reduction (str): type of the reduction over the batch + """ + if weight is not None and not torch.is_tensor(weight): + weight = torch.FloatTensor(weight) + logging.info(f"Weighted Cross Entropy loss with weight {weight}") + super().__init__(weight=weight, reduction=reduction, ignore_index=ignore_index) + self._logits_dim = logits_ndim + + @typecheck() + def forward(self, logits, labels, loss_mask=None): + """ + Args: + logits (float): output of the classifier + labels (long): ground truth labels + loss_mask (bool/float/int): tensor to specify the masking + """ + logits_flatten = torch.flatten(logits, start_dim=0, end_dim=-2) + labels_flatten = torch.flatten(labels, start_dim=0, end_dim=-1) + + if loss_mask is not None: + if loss_mask.dtype is not torch.bool: + loss_mask = loss_mask > 0.5 + loss_mask_flatten = torch.flatten(loss_mask, start_dim=0, end_dim=-1) + logits_flatten = logits_flatten[loss_mask_flatten] + labels_flatten = labels_flatten[loss_mask_flatten] + + if len(labels_flatten) == 0: + return super().forward(logits, torch.argmax(logits, dim=-1)) + + loss = super().forward(logits_flatten, labels_flatten) + return loss + + +class NLLLoss(nn.NLLLoss, Serialization, Typing): + """ + NLLLoss + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "log_probs": NeuralType(("B", "T", "D"), LogprobsType()), + "labels": NeuralType(("B", "T"), LabelsType()), + "output_mask": NeuralType(("B", "T"), MaskType(), optional=True), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__(self, log_probs_ndim=2, weight=None, reduction='mean', ignore_index=-100): + """ + Args: + log_probs_ndim (int): number of dimensions (or rank) of the logprobs tensor + weight (list): list of rescaling weight given to each class + reduction (str): type of the reduction over the batch + ignore_index (int): mask out loss computation where labels = ignore_index + """ + if weight is not None and not torch.is_tensor(weight): + weight = torch.FloatTensor(weight) + super().__init__(weight=weight, reduction=reduction, ignore_index=ignore_index) + self._log_probs_dim = log_probs_ndim + + @typecheck() + def forward(self, log_probs, labels, loss_mask=None): + """ + Args: + log_probs (float): output log probability tensor + labels (long): ground truth labels + loss_mask (bool/float/int): tensor to specify the masking + """ + log_probs_flatten = torch.flatten(log_probs, start_dim=0, end_dim=-2) + labels_flatten = torch.flatten(labels, start_dim=0, end_dim=-1) + + if loss_mask is not None: + if loss_mask.dtype is not torch.bool: + loss_mask = loss_mask > 0.5 + loss_mask_flatten = torch.flatten(loss_mask, start_dim=0, end_dim=-1) + log_probs_flatten = log_probs_flatten[loss_mask_flatten] + labels_flatten = labels_flatten[loss_mask_flatten] + + if len(labels_flatten) == 0: + return super().forward(log_probs, torch.argmax(log_probs, dim=-1)) + + loss = super().forward(log_probs_flatten, labels_flatten) + return loss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/mse_loss.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/mse_loss.py new file mode 100644 index 0000000..802e8ca --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/mse_loss.py @@ -0,0 +1,57 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from torch import Tensor, nn + +from nemo.core.classes import Serialization, Typing, typecheck +from nemo.core.neural_types import LabelsType, LossType, NeuralType, RegressionValuesType + +__all__ = ['MSELoss'] + + +class MSELoss(nn.MSELoss, Serialization, Typing): + """ + MSELoss + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "preds": NeuralType(tuple('B'), RegressionValuesType()), + "labels": NeuralType(tuple('B'), LabelsType()), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__(self, reduction: str = 'mean'): + """ + Args: + reduction: type of the reduction over the batch + """ + super().__init__(reduction=reduction) + + @typecheck() + def forward(self, preds: Tensor, labels: Tensor) -> Tensor: + """ + Args: + preds: output of the classifier + labels: ground truth labels + """ + return super().forward(preds, labels) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/multi_similarity_loss.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/multi_similarity_loss.py new file mode 100644 index 0000000..022f6d6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/multi_similarity_loss.py @@ -0,0 +1,95 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional + +import torch + +from nemo.core.classes import Loss +from nemo.core.classes.common import typecheck +from nemo.core.neural_types import LabelsType, LogitsType, LossType, NeuralType +from nemo.utils import logging + +__all__ = ['MultiSimilarityLoss'] + + +class MultiSimilarityLoss(Loss): + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return {"logits": NeuralType(('B', 'D'), LogitsType()), "labels": NeuralType(('B'), LabelsType())} + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__( + self, + scale_pos: Optional[float] = 2.0, # Params found to work best in our experiments + scale_neg: Optional[float] = 40.0, + offset: Optional[float] = 0.5, + margin: Optional[float] = 0.1, + ): + super().__init__() + self._scale_pos = scale_pos + self._scale_neg = scale_neg + self._offset = offset + self._margin = margin + self._epsilon = 1e-5 + + @typecheck() + def forward(self, logits, labels): + cos_sim = torch.matmul(logits, torch.t(logits)) + losses = [] + + for i in range(logits.size(0)): + # mine hard pairs relative to anchor i + positive_sims = cos_sim[i][labels.eq(labels[i])] + positive_sims = positive_sims[positive_sims.lt(1 - self._epsilon)] # omit identical pairs + negative_sims = cos_sim[i][labels.ne(labels[i])] + + if len(negative_sims) == 0 or len(positive_sims) == 0: + continue + + # negatives that are more similar than the least-similar positive + hard_negatives = negative_sims[negative_sims.gt(min(positive_sims) - self._margin)] + + # positives that are less similar than the most-similar negative + hard_positives = positive_sims[positive_sims.lt(max(negative_sims) + self._margin)] + + if len(hard_negatives) == 0 or len(hard_positives) == 0: + continue + + pos_term = ( + 1.0 + / self._scale_pos + * torch.log(1 + torch.sum(torch.exp(-self._scale_pos * (hard_positives - self._offset)))) + ) + neg_term = ( + 1.0 + / self._scale_neg + * torch.log(1 + torch.sum(torch.exp(self._scale_neg * (hard_negatives - self._offset)))) + ) + losses.append(pos_term + neg_term) + + if len(losses) == 0: + loss = torch.zeros([], requires_grad=True).cuda() + logging.info(f'Encountered zero loss in multisimloss, loss = {loss}. No hard examples found in the batch') + else: + loss = torch.sum(torch.stack(losses)) / logits.size(0) + + return loss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/smoothed_cross_entropy.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/smoothed_cross_entropy.py new file mode 100644 index 0000000..265251a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/smoothed_cross_entropy.py @@ -0,0 +1,183 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional + +import torch + +from nemo.core.classes import Exportable, Loss, NeuralModule, typecheck +from nemo.core.neural_types import LabelsType, LogprobsType, LossType, MaskType, NeuralType + +__all__ = ["SmoothedCrossEntropyLoss", "SmoothedNLLLoss"] + + +class SmoothedCrossEntropyLoss(Loss): + """ + Calculates Cross-entropy loss with label smoothing for a batch of sequences. + + SmoothedCrossEntropyLoss: + 1) excludes padding tokens from loss calculation + 2) allows to use label smoothing regularization + 3) allows to calculate loss for the desired number of last tokens + 4) per_token_reduction - if False disables reduction per token + + Args: + label_smoothing (float): label smoothing regularization coefficient + predict_last_k (int): parameter which sets the number of last tokens to calculate the loss for, for example + 0: (default) calculate loss on the entire sequence (e.g., NMT) + 1: calculate loss on the last token only (e.g., LM evaluation) + Intermediate values allow to control the trade-off between eval + time (proportional to the number of batches) and eval performance + (proportional to the number of context tokens) + pad_id (int): padding id + eps (float): the small eps number to avoid division buy zero + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "log_probs": NeuralType(("B", "T", "D"), LogprobsType()), + "labels": NeuralType(("B", "T"), LabelsType()), + "output_mask": NeuralType(("B", "T"), MaskType(), optional=True), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__( + self, + pad_id: Optional[int] = None, + label_smoothing: Optional[float] = 0.0, + predict_last_k: Optional[int] = 0, + eps: float = 1e-6, + per_token_reduction: bool = True, + ): + super().__init__() + self._pad_id = pad_id + self._eps = eps + self._predict_last_k = predict_last_k + self._label_smoothing = label_smoothing + self._per_token_reduction = per_token_reduction + + @typecheck() + def forward(self, log_probs, labels, output_mask=None): + """ + Args: + log_probs: float tensor of shape batch_size x seq_len x vocab_size, values should be log probabilities + labels: int tensor of shape batch_size x seq_len + output_mask: binary tensor of shape batch_size x seq_len + eps: epsilon param to avoid divide by zero in loss calculation + """ + if output_mask is None and self._pad_id is None: + raise ValueError("Both output_mask and pad_id are None") + if output_mask is None and self._pad_id is not None: + output_mask = (labels != self._pad_id).to(log_probs.dtype) + + if output_mask.dtype is not log_probs.dtype: + output_mask = output_mask.to(log_probs.dtype) + + batch_size, seq_len, vocab_size = log_probs.size() + smoothing = vocab_size * self._label_smoothing / (vocab_size - 1) + target_log_probs = log_probs.gather(2, labels.unsqueeze(2)).squeeze(2) + + smoothing_log_probs = log_probs.mean(dim=-1) + neg_log_likelihood = (1.0 - smoothing) * target_log_probs + smoothing * smoothing_log_probs + neg_log_likelihood = neg_log_likelihood[:, -self._predict_last_k :] + output_mask = output_mask[:, -self._predict_last_k :] + + # when False avoid per token reduction + if self._per_token_reduction: + neg_log_likelihood = -torch.sum(neg_log_likelihood * output_mask) + neg_log_likelihood = neg_log_likelihood / (output_mask.sum() + self._eps) + else: + neg_log_likelihood = -(neg_log_likelihood * output_mask) + + return neg_log_likelihood + + +class SmoothedNLLLoss(NeuralModule, Exportable): + """ + Calculate negative log likelihodd for sequence input, also applies label smoothing (if set). + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "log_probs": NeuralType(("B", "T", "D"), LogprobsType()), + "labels": NeuralType(("B", "T"), LabelsType()), + "output_mask": NeuralType(("B", "T"), MaskType(), optional=True), + "lengths": NeuralType(("B"), LabelsType(), optional=True), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return {"loss": NeuralType(elements_type=LossType())} + + def __init__(self, reduction='mean', label_smoothing=0.0, eps=1e-8, **kwargs): + super().__init__() + self.reduction = reduction + self.label_smoothing = label_smoothing + self.nll_loss = torch.nn.NLLLoss(reduction='none', **kwargs) + self.eps = eps # small constant to avoid divide by zero + + @typecheck() + def forward(self, log_probs, labels, output_mask=None, lengths=None): + """ + Params: + - log_probs: BxTxC + - labels: B + - output_mask: BxT + - lengths: B + """ + + if output_mask is None and lengths is None: + output_mask = torch.ones_like(log_probs).float() + elif output_mask is None and lengths is not None: + output_mask = torch.arange(log_probs.size(1), device=log_probs.device)[None, :] < lengths[:, None] + output_mask = output_mask.float() + + log_probs = log_probs.transpose(1, 2) # BxTxC -> BxCxT + + loss = output_mask * self.nll_loss(log_probs, labels) + batch_size = loss.size(0) + if self.reduction == "mean": + loss = loss.sum() / (torch.sum(output_mask) + self.eps) + elif self.reduction == "batchmean": + loss = loss.sum() / batch_size + elif self.reduction == "batch": + loss = loss.reshape(batch_size, -1).sum(1) / (output_mask.reshape(batch_size, -1).sum(1) + self.eps) + + if self.label_smoothing == 0.0: + return loss + else: + # Regularizing Neural Networks by Penalizing Confident Output Distributions. + # https://arxiv.org/abs/1701.06548 + loss_reg = torch.mean(log_probs, dim=1) * output_mask + if self.reduction == "mean": + loss_reg = torch.sum(loss_reg) / torch.sum(output_mask) + elif self.reduction == "batchmean": + loss_reg = torch.sum(loss_reg) / labels.shape[0] + elif self.reduction == "batch": + loss_reg = loss_reg.sum(1) / output_mask.sum(1) + + return -self.label_smoothing * loss_reg + (1 - self.label_smoothing) * loss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/spanning_loss.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/spanning_loss.py new file mode 100644 index 0000000..a12dab6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/losses/spanning_loss.py @@ -0,0 +1,79 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from torch import nn + +from nemo.core.classes import Loss, typecheck +from nemo.core.neural_types import ChannelType, LogitsType, LossType, NeuralType + +__all__ = ['SpanningLoss'] + + +class SpanningLoss(Loss): + """ + implements start and end loss of a span e.g. for Question Answering. + """ + + @property + def input_types(self): + """Returns definitions of module input ports. + """ + return { + "logits": NeuralType(('B', 'T', 'D'), LogitsType()), + "start_positions": NeuralType(tuple('B'), ChannelType()), + "end_positions": NeuralType(tuple('B'), ChannelType()), + } + + @property + def output_types(self): + """Returns definitions of module output ports. + """ + return { + "loss": NeuralType(elements_type=LossType()), + "start_logits": NeuralType(('B', 'T'), LogitsType()), + "end_logits": NeuralType(('B', 'T'), LogitsType()), + } + + def __init__(self,): + super().__init__() + + @typecheck() + def forward(self, logits, start_positions, end_positions): + """ + Args: + logits: Output of question answering head, which is a token classfier. + start_positions: Ground truth start positions of the answer w.r.t. + input sequence. If question is unanswerable, this will be + pointing to start token, e.g. [CLS], of the input sequence. + end_positions: Ground truth end positions of the answer w.r.t. + input sequence. If question is unanswerable, this will be + pointing to start token, e.g. [CLS], of the input sequence. + """ + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1) + end_logits = end_logits.squeeze(-1) + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + ignored_index = start_logits.size(1) + start_positions.clamp_(0, ignored_index) + end_positions.clamp_(0, ignored_index) + + loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + return total_loss, start_logits, end_logits diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/__init__.py new file mode 100644 index 0000000..322e622 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.common.metrics.classification_accuracy import TopKClassificationAccuracy +from nemo.collections.common.metrics.global_average_loss_metric import GlobalAverageLossMetric +from nemo.collections.common.metrics.metric_string_to_torchmetric import MetricStringToTorchMetric +from nemo.collections.common.metrics.perplexity import Perplexity diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/classification_accuracy.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/classification_accuracy.py new file mode 100644 index 0000000..46eca74 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/classification_accuracy.py @@ -0,0 +1,262 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import re +import string +from collections import Counter +from typing import List, Union + +import torch +from torchmetrics import Metric + +__all__ = ['TopKClassificationAccuracy'] + + +class TopKClassificationAccuracy(Metric): + """ + This metric computes numerator and denominator for Overall Accuracy between logits and labels. + When doing distributed training/evaluation the result of res=TopKClassificationAccuracy(logits, labels) calls + will be all-reduced between all workers using SUM operations. + Here contains two numbers res=[correctly_predicted, total_samples]. Accuracy=correctly_predicted/total_samples. + + If used with PytorchLightning LightningModule, include correct_count and total_count inside validation_step results. + Then aggregate (sum) then at the end of validation epoch to correctly compute validation WER. + + Example: + def validation_step(self, batch, batch_idx): + ... + correct_count, total_count = self._accuracy(logits, labels) + self.val_outputs = {'val_loss': loss_value, 'val_correct_count': correct_count, 'val_total_count': total_count} + return self.val_outputs + + def on_validation_epoch_end(self): + ... + val_loss_mean = torch.stack([x['val_loss'] for x in self.val_outputs]).mean() + correct_counts = torch.stack([x['val_correct_counts'] for x in self.val_outputs]) + total_counts = torch.stack([x['val_total_counts'] for x in self.val_outputs]) + + topk_scores = compute_topk_accuracy(correct_counts, total_counts) + + tensorboard_log = {'val_loss': val_loss_mean} + for top_k, score in zip(self._accuracy.top_k, topk_scores): + tensorboard_log['val_epoch_top@{}'.format(top_k)] = score + + self.val_outputs.clear() # free memory + return {'log': tensorboard_log} + + Args: + top_k: Optional list of integers. Defaults to [1]. + + Returns: + res: a torch.Tensor object with two elements: [correct_count, total_count]. To correctly compute average + accuracy, compute acc=correct_count/total_count + """ + + full_state_update = True + + def __init__(self, top_k=None, dist_sync_on_step=False): + super().__init__(dist_sync_on_step=dist_sync_on_step) + + if top_k is None: + top_k = [1] + + self.top_k = top_k + self.add_state( + "correct_counts_k", default=torch.zeros(len(self.top_k)), dist_reduce_fx='sum', persistent=False + ) + self.add_state("total_counts_k", default=torch.zeros(len(self.top_k)), dist_reduce_fx='sum', persistent=False) + + @torch.no_grad() + def top_k_predicted_labels(self, logits: torch.Tensor) -> torch.Tensor: + max_k = max(self.top_k) + _, predictions = logits.topk(max_k, dim=1, largest=True, sorted=True) + return predictions + + def update(self, logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + with torch.no_grad(): + predictions = self.top_k_predicted_labels(logits) + predictions = predictions.t() + correct = predictions.eq(labels.view(1, -1)).expand_as(predictions) + + correct_counts_k = [] + total_counts_k = [] + + for k in self.top_k: + correct_k = correct[:k].reshape(-1).long().sum() + total_k = labels.shape[0] + + correct_counts_k.append(correct_k) + total_counts_k.append(total_k) + + self.correct_counts_k = torch.tensor(correct_counts_k, dtype=labels.dtype, device=labels.device) + self.total_counts_k = torch.tensor(total_counts_k, dtype=labels.dtype, device=labels.device) + + def compute(self): + """ + Computes the top-k accuracy. + + Returns: + A list of length `K`, such that k-th index corresponds to top-k accuracy + over all distributed processes. + """ + if not len(self.correct_counts_k) == len(self.top_k) == len(self.total_counts_k): + raise ValueError("length of counts must match to topk length") + + if self.top_k == [1]: + return [self.correct_counts_k.float() / self.total_counts_k] + + else: + top_k_scores = compute_topk_accuracy(self.correct_counts_k, self.total_counts_k) + + return top_k_scores + + @property + def top_k(self) -> List[int]: + return self._top_k + + @top_k.setter + def top_k(self, value: List[int]): + if value is None: + value = [1] + + if type(value) == int: + value = [value] + + if type(value) != list: + value = list(value) + + self._top_k = value + + +def compute_topk_accuracy(correct_counts_k, total_counts_k): + """ + Computes the top-k accuracy + Args: + correct_counts: Tensor of shape [K], K being the top-k parameter. + total_counts: Tensor of shape [K], and K being the top-k parameter. + Returns: + A list of length `K`, such that k-th index corresponds to top-k accuracy + over all distributed processes. + """ + top_k_scores = [] + + for ki in range(len(correct_counts_k)): + correct_count = correct_counts_k[ki].item() + total_count = total_counts_k[ki].item() + top_k_scores.append(correct_count / float(total_count)) + + return top_k_scores + + +class ExactStringPerCategoryMatchMetric(Metric): + def __init__(self, categories=[], dist_sync_on_step=False, *args, **kwargs): + super().__init__(dist_sync_on_step=dist_sync_on_step) + self.categories = set(categories) + + self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum") + self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum") + for category in categories: + self.add_state(f"{category}_total", default=torch.tensor(0), dist_reduce_fx="sum") + self.add_state(f"{category}_correct", default=torch.tensor(0), dist_reduce_fx="sum") + + def update(self, pred: str, target: str, category: str = None): + if pred == target: + self.correct += 1 + self.total += 1 + if category is None: + return + if category in self.categories: + val = getattr(self, f"{category}_total") + setattr(self, f"{category}_total", val + 1) + if pred == target: + val = getattr(self, f"{category}_correct") + setattr(self, f"{category}_correct", val + 1) + else: + logging.warn(f'{category} is not in the pre-defined list') + + def compute(self): + results = {} + results['acc'] = self.correct.float() / self.total + for category in self.categories: + results[category] = getattr(self, f"{category}_correct") / getattr(self, f"{category}_total") + for category in self.categories: + results[f"{category}_total"] = getattr(self, f"{category}_total") + return results + + +class ExactStringMatchMetric(Metric): + def __init__(self, dist_sync_on_step=False, *args, **kwargs): + super().__init__(dist_sync_on_step=dist_sync_on_step) + + self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum") + self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum") + + def update(self, pred: str, target: str): + if pred == target: + self.correct += 1 + self.total += 1 + + def compute(self): + return self.correct.float() / self.total + + +class TokenF1Score(Metric): + """Taken from the official evaluation script for v1.1 of the SQuAD dataset""" + + def __init__(self, dist_sync_on_step=False, *args, **kwargs): + super().__init__(dist_sync_on_step=dist_sync_on_step) + + self.add_state("correct", default=torch.tensor(0.0), dist_reduce_fx="sum") + self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum") + + def update(self, pred: str, target: Union[str, List[str]]): + if isinstance(target, str): + self.correct += self.f1_score(pred, target) + elif isinstance(target, list): + self.correct += max([self.f1_score(pred, tgt) for tgt in target]) + self.total += 1 + + def compute(self): + return self.correct.float() / self.total + + def f1_score(self, prediction, ground_truth): + prediction_tokens = self.normalize(prediction).split() + ground_truth_tokens = self.normalize(ground_truth).split() + common = Counter(prediction_tokens) & Counter(ground_truth_tokens) + num_same = sum(common.values()) + if num_same == 0: + return 0.0 + precision = 1.0 * num_same / len(prediction_tokens) + recall = 1.0 * num_same / len(ground_truth_tokens) + f1 = (2 * precision * recall) / (precision + recall) + return f1 + + def normalize(self, s): + """Lower text and remove punctuation, articles and extra whitespace.""" + + def remove_articles(text): + return re.sub(r"\b(a|an|the)\b", " ", text) + + def white_space_fix(text): + return " ".join(text.split()) + + def remove_punc(text): + exclude = set(string.punctuation) + return "".join(ch for ch in text if ch not in exclude) + + def lower(text): + return text.lower() + + return white_space_fix(remove_articles(remove_punc(lower(s)))) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/global_average_loss_metric.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/global_average_loss_metric.py new file mode 100644 index 0000000..3bbd4d1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/global_average_loss_metric.py @@ -0,0 +1,72 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from torchmetrics import Metric + +__all__ = ['GlobalAverageLossMetric'] + + +class GlobalAverageLossMetric(Metric): + """ + This class is for averaging loss across multiple processes if a distributed backend is used. True average is + computed not running average. It does not accumulate gradients so the averaged loss cannot be used for optimization. + If ``take_avg_loss`` is ``True``, the :meth:`update` method ``loss`` argument has to be a mean loss. If + ``take_avg_loss`` is ``False`` then the :meth:`update` method ``loss`` argument has to be a sum of losses. + + See :doc:`PyTorch Lightning Metrics` for the metric usage instruction. + + Args: + dist_sync_on_step: + Synchronize metric state across processes at each method :meth:`forward` call before returning the + value at the step + process_group: + Specify the process group on which synchronization is called. default: ``None`` (which selects the entire + world) + take_avg_loss: + If ``True`` values of :meth:`update` method ``loss`` argument has to be a mean loss. If ``False`` + values of :meth:`update` method ``loss`` argument has to be a sum of losses. default: ``True`` + """ + + full_state_update = True + + def __init__(self, dist_sync_on_step=False, process_group=None, take_avg_loss=True): + super().__init__(dist_sync_on_step=dist_sync_on_step, process_group=process_group) + self.add_state("loss_sum", torch.tensor(0.0, dtype=torch.float64), dist_reduce_fx='sum') + self.add_state("num_measurements", torch.tensor(0, dtype=torch.int64), dist_reduce_fx='sum') + self.take_avg_loss = take_avg_loss + + def update(self, loss, num_measurements): + """ + Updates :attr:`loss_sum` and :attr:`num_measurements`. + + Args: + loss: A float zero dimensional ``torch.Tensor`` which is either sum or average of losses for processed + examples. See ``take_avg_loss`` parameter of :meth:`__init__`. + num_measurements: An integer zero dimensional ``torch.Tensor`` which contains a number of loss measurements. + The sum or mean of the results of these measurements are in the ``loss`` parameter. + """ + if self.take_avg_loss: + self.loss_sum += loss.detach() * num_measurements + else: + self.loss_sum += loss.detach() + self.num_measurements += num_measurements + + def compute(self): + """ + Returns mean loss. + """ + if self.num_measurements.eq(0): + return torch.tensor(float('nan')) + return self.loss_sum / self.num_measurements diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/metric_string_to_torchmetric.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/metric_string_to_torchmetric.py new file mode 100644 index 0000000..b38047b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/metric_string_to_torchmetric.py @@ -0,0 +1,34 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from torchmetrics import Accuracy, AveragePrecision, F1Score, MatthewsCorrCoef, PearsonCorrCoef, SpearmanCorrCoef +from torchmetrics.text.rouge import ROUGEScore + +from nemo.collections.common.metrics.classification_accuracy import ExactStringMatchMetric, TokenF1Score + +__all__ = ['MetricStringToTorchMetric'] + +# Dictionary that maps a metric string name to its corresponding torchmetric class. + +MetricStringToTorchMetric = { + 'accuracy': Accuracy, + 'average_precision': AveragePrecision, + 'f1': F1Score, + 'token_f1': TokenF1Score, + 'pearson_corr_coef': PearsonCorrCoef, + 'spearman_corr_coef': SpearmanCorrCoef, + 'matthews_corr_coef': MatthewsCorrCoef, + 'exact_string_match': ExactStringMatchMetric, + 'rouge': ROUGEScore, +} diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/perplexity.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/perplexity.py new file mode 100644 index 0000000..9e1c217 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/perplexity.py @@ -0,0 +1,74 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from torch.distributions.categorical import Categorical +from torchmetrics import Metric + +__all__ = ['Perplexity'] + + +class Perplexity(Metric): + """ + This class computes mean perplexity of distributions in the last dimension of inputs. It is a wrapper around + :doc:`torch.distributions.Categorical.perplexity` method. You have to provide either + ``probs`` or ``logits`` to the :meth:`update` method. The class computes perplexities for distributions passed to + :meth:`update` method in ``probs`` or ``logits`` arguments and averages the perplexities. Reducing results between + all workers is done via SUM operations. + See `PyTorch Lightning Metrics `_ for the metric usage instructions. + + Args: + dist_sync_on_step: + Synchronize metric state across processes at each ``forward()`` + before returning the value at the step. + process_group: + Specify the process group on which synchronization is called. default: ``None`` (which selects the entire + world) + validate_args: + If ``True`` values of :meth:`update` method parameters are checked. ``logits`` has to not contain NaNs and + ``probs`` last dim has to be valid probability distribution. + """ + + full_state_update = True + + def __init__(self, dist_sync_on_step=False, process_group=None, validate_args=True): + super().__init__(dist_sync_on_step=dist_sync_on_step, process_group=process_group) + self.validate_args = validate_args + self.add_state('perplexities_sum', torch.tensor(0.0, dtype=torch.float64), dist_reduce_fx='sum') + # Total number of distributions seen since last reset + self.add_state('num_distributions', torch.tensor(0, dtype=torch.int64), dist_reduce_fx='sum') + + def update(self, probs=None, logits=None): + """ + Updates :attr:`perplexities_sum` and :attr:`num_distributions`. + Args: + probs: A ``torch.Tensor`` which innermost dimension is valid probability distribution. + logits: A ``torch.Tensor`` without NaNs. + """ + d = Categorical( + None if probs is None else probs.detach(), + None if logits is None else logits.detach(), + validate_args=self.validate_args, + ) + ppl = d.perplexity() + self.num_distributions += ppl.numel() + self.perplexities_sum += ppl.sum() + + def compute(self): + """ + Returns perplexity across all workers and resets to 0 :attr:`perplexities_sum` and :attr:`num_distributions`. + """ + if self.num_distributions.eq(0): + return None + return self.perplexities_sum / self.num_distributions diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/punct_er.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/punct_er.py new file mode 100644 index 0000000..933c158 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/metrics/punct_er.py @@ -0,0 +1,473 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re +from collections import namedtuple +from tqdm import tqdm + +from nemo.utils import logging + +try: + import pandas as pd + from tabulate import tabulate + + HAVE_TABLUATE_AND_PANDAS = True +except (ImportError, ModuleNotFoundError): + HAVE_TABLUATE_AND_PANDAS = False + + +def punctuation_error_rate( + references: list[str], hypotheses: list[str], punctuation_marks: list[str], punctuation_mask: str = "[PUNCT]", +) -> None: + + """ + Computes Punctuation Error Rate + + Args: + references (list[str]) - list of references + hypotheses (list[str]) - list of hypotheses + punctuation_marks (list[str]) - list of punctuation marks for computing metrics + punctuation_mask (str, by default "[PUNCT]") - mask token that will be applied to + given punctuation marks while edit distance calculation + + Return: + punct_er (float) - Punctuation Error Rate + """ + + dper_obj = DatasetPunctuationErrorRate( + references=references, + hypotheses=hypotheses, + punctuation_marks=punctuation_marks, + punctuation_mask=punctuation_mask, + ) + + dper_obj.compute() + + return dper_obj.punct_er + + +class OccurancePunctuationErrorRate: + """ + Class for computation puncutation-related absolute amounts of operations and thier rates + between reference and hypothesis strings: + - Absolute amounts of correct predictions, deletions, insertions + and substitutions for each given punctuation mark + - Rates of correct predictions, deletions, insertions + and substitutions for each given punctuation mark + - Overall rates of correct predictions, deletions, insertions + and substiturions between reference and hypothesis string + - Punctuation Error Rate + + Args to init: + punctuation_marks (list[str]) - list of punctuation marks for computing metrics + punctuation_mask (str, by default "[PUNCT]") - mask token that will be applied to + given punctuation marks while edit distance calculation + + How to use: + 1. Create object of OccurancePunctuationErrorRate class. + Example: + punctuation_marks = [".", ",", "!", "?"] + oper_obj = OccurancePunctuationErrorRate(punctuation_marks) + + 2. To compute punctuation metrics, pass reference and hypothesis string to the "compute" method + of created object. + Example: + reference_str = "Hi, dear! Nice to see you. What's" + hypothesis_str = "Hi dear! Nice to see you! What's?" + oper_obj.compute(reference_str, hypothesis_str) + + Output (listed in order of output): + 1. Dict of absolute operations amounts for each given punctuation mark: + Example: + {'.': {'Correct': 0, 'Deletions': 0, 'Insertions': 0, 'Substitutions': 1}, + ',': {'Correct': 0, 'Deletions': 1, 'Insertions': 0, 'Substitutions': 0}, + '!': {'Correct': 1, 'Deletions': 0, 'Insertions': 0, 'Substitutions': 0}, + '?': {'Correct': 0, 'Deletions': 0, 'Insertions': 1, 'Substitutions': 0}} + + 2. Dict of substitutions absolute amounts between given punctuation marks: + Example: + {'.': {'.': 0, ',': 0, '!': 1, '?': 0}, + ',': {'.': 0, ',': 0, '!': 0, '?': 0}, + '!': {'.': 0, ',': 0, '!': 0, '?': 0}, + '?': {'.': 0, ',': 0, '!': 0, '?': 0}} + + 3. namedtuple "PunctuationRates" of punctuation operation rates (in range from 0 to 1): + 3.1. correct_rate - overall correct rate + Example: correct_rate=0.25 + 3.2. deletions_rate - overall deletions rate + Example: deletions_rate=0.25 + 3.3. insertions_rate - overall insertions rate + Example: insertions_rate=0.25 + 3.4. substitutions_rate - overall substitutions_rate + Example: substitutions_rate=0.25 + 3.5. punct_er - Punctuation Error Rate + Example: punct_er=0.75 + 3.6. operation_rates - dict of operations rates for each given punctuation mark + Example: + operation_rates={ + '.': {'Correct': 0.0, 'Deletions': 0.0, 'Insertions': 0.0, 'Substitutions': 1.0}, + ',': {'Correct': 0.0, 'Deletions': 1.0, 'Insertions': 0.0, 'Substitutions': 0.0}, + '!': {'Correct': 1.0, 'Deletions': 0.0, 'Insertions': 0.0, 'Substitutions': 0.0}, + '?': {'Correct': 0.0, 'Deletions': 0.0, 'Insertions': 1.0, 'Substitutions': 0.0} + } + + 3.7. substitution_rates - dict of substitution rates for each given punctuation mark + Example: + substitution_rates={ + '.': {'.': 0.0, ',': 0.0, '!': 1.0, '?': 0.0}, + ',': {'.': 0.0, ',': 0.0, '!': 0.0, '?': 0.0}, + '!': {'.': 0.0, ',': 0.0, '!': 0.0, '?': 0.0}, + '?': {'.': 0.0, ',': 0.0, '!': 0.0, '?': 0.0} + } + """ + + def __init__(self, punctuation_marks: list[str], punctuation_mask: str = "[PUNCT]") -> None: + + assert len(punctuation_marks) != 0, f"List of punctuation marks is empty" + + self.punctuation_marks = punctuation_marks + self.punctuation_mask = punctuation_mask + + self.operations = ["Correct", "Deletions", "Insertions", "Substitutions"] + + def compute_rates(self, operation_amounts: dict, substitution_amounts: dict): + operation_rates = {pm: {operation: 0 for operation in self.operations} for pm in self.punctuation_marks} + substitution_rates = {pm: {pm: 0 for pm in self.punctuation_marks} for pm in self.punctuation_marks} + + for pm in self.punctuation_marks: + operations_amount_by_pm = sum(operation_amounts[pm].values()) + + if operations_amount_by_pm == 0: + continue + + operation_rates[pm] = { + operation: (operation_amounts[pm][operation] / operations_amount_by_pm) + for operation in self.operations + } + + substitution_rates[pm] = { + _pm: (substitution_amounts[pm][_pm] / operations_amount_by_pm) + for _pm in substitution_amounts[pm].keys() + } + + _operation_amounts = { + operation: {pm: operation_amounts[operation] for pm, operation_amounts in operation_amounts.items()} + for operation in self.operations + } + + overall_amounts_by_operation = { + operation: sum(_operation_amounts[operation].values()) for operation in _operation_amounts + } + overall_operations_amount = sum(overall_amounts_by_operation.values()) + + punctuation_rates = namedtuple( + 'PunctuationRates', + [ + 'correct_rate', + 'deletions_rate', + 'insertions_rate', + 'substitutions_rate', + 'punct_er', + 'operation_rates', + 'substitution_rates', + ], + ) + + if overall_operations_amount == 0: + rates = punctuation_rates(0, 0, 0, 0, 0, operation_rates, substitution_rates) + else: + correct_rate = overall_amounts_by_operation["Correct"] / overall_operations_amount + deletions_rate = overall_amounts_by_operation["Deletions"] / overall_operations_amount + insertions_rate = overall_amounts_by_operation["Insertions"] / overall_operations_amount + substitutions_rate = overall_amounts_by_operation["Substitutions"] / overall_operations_amount + punct_er = deletions_rate + insertions_rate + substitutions_rate + + rates = punctuation_rates( + correct_rate, + deletions_rate, + insertions_rate, + substitutions_rate, + punct_er, + operation_rates, + substitution_rates, + ) + + return rates + + def compute_operation_amounts(self, reference: str, hypothesis: str): + operation_amounts = {pm: {operation: 0 for operation in self.operations} for pm in self.punctuation_marks} + substitution_amounts = {pm: {pm: 0 for pm in self.punctuation_marks} for pm in self.punctuation_marks} + + def tokenize(text: str, punctuation_marks: list[str]): + punctuation_marks = "\\" + "\\".join(self.punctuation_marks) + tokens = re.findall(rf"[\w']+|[{punctuation_marks}]", text) + return tokens + + def mask_punct_tokens(tokens: list[str], punctuation_marks: list[str], punctuation_mask: str): + masked = [punctuation_mask if token in punctuation_marks else token for token in tokens] + return masked + + r_tokens = tokenize(reference, self.punctuation_marks) + h_tokens = tokenize(hypothesis, self.punctuation_marks) + + r_masked = mask_punct_tokens(r_tokens, self.punctuation_marks, self.punctuation_mask) + h_masked = mask_punct_tokens(h_tokens, self.punctuation_marks, self.punctuation_mask) + + r_punct_amount = r_masked.count(self.punctuation_mask) + h_punct_amount = h_masked.count(self.punctuation_mask) + + if r_punct_amount + h_punct_amount == 0: + return operation_amounts, substitution_amounts + + r_len = len(r_masked) + h_len = len(h_masked) + + costs = [[0 for inner in range(h_len + 1)] for outer in range(r_len + 1)] + backtrace = [[0 for inner in range(h_len + 1)] for outer in range(r_len + 1)] + + COR = 'C' + DEL, DEL_PENALTY = 'D', 1 + INS, INS_PENALTY = 'I', 1 + SUB, SUB_PENALTY = 'S', 1 + + for i in range(1, r_len + 1): + costs[i][0] = DEL_PENALTY * i + backtrace[i][0] = DEL + + for j in range(1, h_len + 1): + costs[0][j] = INS_PENALTY * j + backtrace[0][j] = INS + + for j in range(1, h_len + 1): + costs[0][j] = INS_PENALTY * j + backtrace[0][j] = INS + + for i in range(1, r_len + 1): + for j in range(1, h_len + 1): + if r_masked[i - 1] == h_masked[j - 1]: + costs[i][j] = costs[i - 1][j - 1] + backtrace[i][j] = COR + else: + substitution_cost = costs[i - 1][j - 1] + SUB_PENALTY + insertion_cost = costs[i][j - 1] + INS_PENALTY + deletion_cost = costs[i - 1][j] + DEL_PENALTY + + costs[i][j] = min(substitution_cost, insertion_cost, deletion_cost) + if costs[i][j] == substitution_cost: + backtrace[i][j] = SUB + elif costs[i][j] == insertion_cost: + backtrace[i][j] = INS + else: + backtrace[i][j] = DEL + + i = r_len + j = h_len + + while i > 0 or j > 0: + if backtrace[i][j] == COR: + if r_masked[i - 1] == self.punctuation_mask or h_masked[j - 1] == self.punctuation_mask: + r_token = r_tokens[i - 1] + h_token = h_tokens[j - 1] + + if r_token == h_token: + operation_amounts[r_token]['Correct'] += 1 + else: + operation_amounts[r_token]['Substitutions'] += 1 + substitution_amounts[r_token][h_token] += 1 + i -= 1 + j -= 1 + + elif backtrace[i][j] == SUB: + i -= 1 + j -= 1 + + elif backtrace[i][j] == INS: + j -= 1 + + elif backtrace[i][j] == DEL: + i -= 1 + + for pm in self.punctuation_marks: + num_of_correct = operation_amounts[pm]['Correct'] + + num_substitutions_of_pm = operation_amounts[pm]['Substitutions'] + num_substitutions_to_pm = sum([substitution_amounts[_pm][pm] for _pm in self.punctuation_marks]) + + num_of_deletions = r_tokens.count(pm) - (num_of_correct + num_substitutions_of_pm) + operation_amounts[pm]['Deletions'] = num_of_deletions + + num_of_insertions = h_tokens.count(pm) - (num_of_correct + num_substitutions_to_pm) + operation_amounts[pm]['Insertions'] = num_of_insertions + + return operation_amounts, substitution_amounts + + def compute(self, reference: str, hypothesis: str): + operation_amounts, substitution_amounts = self.compute_operation_amounts(reference, hypothesis) + punctuation_rates = self.compute_rates(operation_amounts, substitution_amounts) + return operation_amounts, substitution_amounts, punctuation_rates + + +class DatasetPunctuationErrorRate: + """ + Class for computation the total puncutation-related absolute amounts of operations and their rates + in pairs of reference and hypothesis strins: + - Absolute amounts of correct predictions, deletions, insertions + and substitutions for each given punctuation mark + - Rates of correct predictions, deletions, insertions + and substitutions for each given punctuation mark + - Total rates of correct predictions, deletions, insertions + and substiturions in pairs of reference and hypothesis strings + - Punctuation Error Rate + + Args to init: + references (list[str]) - list of references + hypotheses (list[str]) - list of hypotheses + punctuation_marks (list[str]) - list of punctuation marks for computing metrics + punctuation_mask (str, by default "[PUNCT]") - mask token that will be applied to + given punctuation marks while edit distance calculation + + How to use: + 1. Create object of DatasetPunctuationErrorRate class. + Example: + references = ["Hi, dear! Nice to see you. What's"] + hypotheses = ["Hi dear! Nice to see you! What's?"] + punctuation_marks = [".", ",", "!", "?"] + + dper_obj = DatasetPunctuationErrorRate(references, hypotheses, punctuation_marks) + + 2. To compute punctuation metrics, call the class method "compute()". + Example: + dper_obj.compute() + + Result: + The following atributes of class object will be updated with calculated metrics values. + The values are available with calling the atributes: + + dper_obj.operation_rates - dict, rates of correctness and errors for each punctuation mark + from `preset dper_obj.punctuation_marks` list. + + dper_obj.substitution_rates - dict, substitution rates between puncutation marks from + `preset dper_obj.punctuation_marks` list. + + dper_obj.correct_rate - float, total rate of correctness between provided pairs of + references and hypotheses. + + dper_obj.deletions_rate - float, total rate of deletions between provided pairs of + references and hypotheses. + + dper_obj.insertions_rate - float, total rate of insertions between provided pairs of + references and hypotheses. + + dper_obj.substitutions_rate - float, total rate of substitutions between provided pairs of + references and hypotheses. + + dper_obj.punct_er - float, total Punctuation Error Rate between provided pairs of + references and hypotheses. + """ + + def __init__( + self, + references: list[str], + hypotheses: list[str], + punctuation_marks: list[str], + punctuation_mask: str = "[PUNCT]", + ) -> None: + + self.references = references + self.hypotheses = hypotheses + self.punctuation_marks = punctuation_marks + self.punctuation_mask = punctuation_mask + + self.oper_obj = OccurancePunctuationErrorRate( + punctuation_marks=self.punctuation_marks, punctuation_mask=self.punctuation_mask + ) + + self.operation_amounts = [] + self.substitution_amounts = [] + self.rates = [] + + self.operation_rates = None + self.substitution_rates = None + self.correct_rate = None + self.deletions_rate = None + self.insertions_rate = None + self.substitutions_rate = None + self.punct_er = None + + def compute(self): + def sum_amounts(amounts_dicts: list[dict]): + amounts = {key: {_key: 0 for _key in amounts_dicts[0][key]} for key in amounts_dicts[0].keys()} + + for amounts_dict in amounts_dicts: + for outer_key, inner_dict in amounts_dict.items(): + for inner_key, value in inner_dict.items(): + amounts[outer_key][inner_key] += value + return amounts + + logging.info("Computing Punctuation Error Rate") + + for reference, hypothesis in tqdm(zip(self.references, self.hypotheses), total=len(self.references)): + operation_amounts, substitution_amounts, punctuation_rates = self.oper_obj.compute(reference, hypothesis) + self.operation_amounts.append(operation_amounts) + self.substitution_amounts.append(substitution_amounts) + self.rates.append(punctuation_rates) + + overall_operation_amounts = sum_amounts(self.operation_amounts) + overall_substitution_amounts = sum_amounts(self.substitution_amounts) + overall_rates = self.oper_obj.compute_rates( + operation_amounts=overall_operation_amounts, substitution_amounts=overall_substitution_amounts + ) + + self.operation_rates = overall_rates.operation_rates + self.substitution_rates = overall_rates.substitution_rates + self.correct_rate = overall_rates.correct_rate + self.deletions_rate = overall_rates.deletions_rate + self.insertions_rate = overall_rates.insertions_rate + self.substitutions_rate = overall_rates.substitutions_rate + self.punct_er = overall_rates.punct_er + + def reset(self): + self.operation_amounts = [] + self.substitution_amounts = [] + self.rates = [] + + self.operation_rates = None + self.substitution_rates = None + self.correct_rate = None + self.deletions_rate = None + self.insertions_rate = None + self.substitutions_rate = None + self.punct_er = None + + def print(self): + logging.info(f'Dataset PER ' + str(round(100 * self.punct_er, 2)) + '%') + + if HAVE_TABLUATE_AND_PANDAS: + rates_by_pm_df = pd.DataFrame(self.operation_rates) * 100 + substitution_rates_by_pm_df = pd.DataFrame(self.substitution_rates) * 100 + + logging.info( + "Rates of punctuation correctness and errors (%):\n" + + tabulate(rates_by_pm_df, headers='keys', tablefmt='psql') + ) + logging.info( + "Substitution rates between punctuation marks (%):\n" + + tabulate(substitution_rates_by_pm_df, headers='keys', tablefmt='psql') + ) + else: + logging.warning("Some of the modules (pandas or tabulate) can't be imported") + logging.info(f"Rates of punctuation correctness and errors (in range [0, 1]):\n{self.operation_rates}\n") + logging.info( + f"Substitution rates between punctuation marks (in range [0, 1]):\n{self.substitution_rates}\n" + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/__init__.py new file mode 100644 index 0000000..f1997e0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.common.parts.adapter_modules import LinearAdapter, LinearAdapterConfig +from nemo.collections.common.parts.mlm_scorer import MLMScorer +from nemo.collections.common.parts.multi_layer_perceptron import MultiLayerPerceptron +from nemo.collections.common.parts.transformer_utils import * +from nemo.collections.common.parts.utils import * diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/adapter_modules.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/adapter_modules.py new file mode 100644 index 0000000..2084147 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/adapter_modules.py @@ -0,0 +1,166 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field, is_dataclass +from typing import Any, Optional + +from hydra.utils import instantiate +from omegaconf import OmegaConf +from torch import nn as nn + +from nemo.collections.common.parts.utils import activation_registry +from nemo.core.classes.mixins import access_mixins, adapter_mixin_strategies + + +class AdapterModuleUtil(access_mixins.AccessMixin): + """ + Base class of Adapter Modules, providing common functionality to all Adapter Modules. + """ + + def setup_adapter_strategy(self, adapter_strategy: Optional[adapter_mixin_strategies.AbstractAdapterStrategy]): + """ + Setup adapter strategy of this class, enabling dynamic change in the way the adapter output is + merged with the input. + + When called successfully, will assign the variable `adapter_strategy` to the module. + + Args: + adapter_strategy: Can be a None or an implementation of AbstractAdapterStrategy. + """ + # set default adapter strategy + if adapter_strategy is None: + adapter_strategy = self.get_default_strategy_config() + + if is_dataclass(adapter_strategy): + adapter_strategy = OmegaConf.structured(adapter_strategy) + OmegaConf.set_struct(adapter_strategy, False) + + # The config must have the `_target_` field pointing to the actual adapter strategy class + # which will load that strategy dynamically to this module. + if isinstance(adapter_strategy, dict) or OmegaConf.is_config(adapter_strategy): + self.adapter_strategy = instantiate(adapter_strategy) + elif isinstance(adapter_strategy, adapter_mixin_strategies.AbstractAdapterStrategy): + self.adapter_strategy = adapter_strategy + else: + raise AttributeError(f'`adapter_strategy` provided is invalid : {adapter_strategy}') + + def get_default_strategy_config(self) -> 'dataclass': + """ + Returns a default adapter module strategy. + """ + return adapter_mixin_strategies.ResidualAddAdapterStrategyConfig() + + def adapter_unfreeze(self,): + """ + Sets the requires grad for all parameters in the adapter to True. + This method should be overridden for any custom unfreeze behavior that is required. + For example, if not all params of the adapter should be unfrozen. + """ + for param in self.parameters(): + param.requires_grad_(True) + + +class LinearAdapter(nn.Module, AdapterModuleUtil): + + """ + Simple Linear Feedforward Adapter module with LayerNorm and singe hidden layer with activation function. + Note: The adapter explicitly initializes its final layer with all zeros in order to avoid affecting the + original model when all adapters are disabled. + + Args: + in_features: Input dimension of the module. Note that for adapters, input_dim == output_dim. + dim: Hidden dimension of the feed forward network. + activation: Str name for an activation function. + norm_position: Str, can be `pre` or `post`. Defaults to `pre`. Determines whether the normalization + will occur in the first layer or the last layer. Certain architectures may prefer one over the other. + dropout: float value, whether to perform dropout on the output of the last layer of the adapter. + adapter_strategy: By default, ResidualAddAdapterStrategyConfig. An adapter composition function object. + """ + + def __init__( + self, + in_features: int, + dim: int, + activation: str = 'swish', + norm_position: str = 'pre', + dropout: float = 0.0, + adapter_strategy: adapter_mixin_strategies.ResidualAddAdapterStrategyConfig = None, + ): + super().__init__() + + activation = activation_registry[activation]() + # If the activation can be executed in place, do so. + if hasattr(activation, 'inplace'): + activation.inplace = True + + assert norm_position in ['pre', 'post'] + self.norm_position = norm_position + + if norm_position == 'pre': + self.module = nn.Sequential( + nn.LayerNorm(in_features), + nn.Linear(in_features, dim, bias=False), + activation, + nn.Linear(dim, in_features, bias=False), + ) + + elif norm_position == 'post': + self.module = nn.Sequential( + nn.Linear(in_features, dim, bias=False), + activation, + nn.Linear(dim, in_features, bias=False), + nn.LayerNorm(in_features), + ) + + if dropout > 0.0: + self.dropout = nn.Dropout(dropout) + else: + self.dropout = None + + # Setup adapter strategy + self.setup_adapter_strategy(adapter_strategy) + + # reset parameters + self.reset_parameters() + + def reset_parameters(self): + # Final layer initializations must be 0 + if self.norm_position == 'pre': + self.module[-1].weight.data *= 0 + + elif self.norm_position == 'post': + self.module[-1].weight.data *= 0 + self.module[-1].bias.data *= 0 + + def forward(self, x): + x = self.module(x) + + # Add dropout if available + if self.dropout is not None: + x = self.dropout(x) + + return x + + +@dataclass +class LinearAdapterConfig: + in_features: int + dim: int + activation: str = 'swish' + norm_position: str = 'pre' + dropout: float = 0.0 + adapter_strategy: Optional[Any] = field( + default_factory=lambda: adapter_mixin_strategies.ResidualAddAdapterStrategyConfig() + ) + _target_: str = "{0}.{1}".format(LinearAdapter.__module__, LinearAdapter.__name__) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/mlm_scorer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/mlm_scorer.py new file mode 100644 index 0000000..c38e4b2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/mlm_scorer.py @@ -0,0 +1,93 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2020 AWSLABS, AMAZON. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List + +import numpy as np +import torch +from torch.nn.functional import softmax +from transformers import AutoModelForMaskedLM, AutoTokenizer + +__all__ = ['MLMScorer'] + + +class MLMScorer: + def __init__(self, model_name: str, device: str = 'cpu'): + """ + Creates MLM scorer from https://arxiv.org/abs/1910.14659. + Args: + model_name: HuggingFace pretrained model name + device: either 'cpu' or 'cuda' + """ + self.model = AutoModelForMaskedLM.from_pretrained(model_name).to(device).eval() + self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) + self.device = device + self.MASK_LABEL = self.tokenizer.mask_token + + def score_sentences(self, sentences: List[str]): + """ + returns list of MLM scores for each sentence in list. + """ + return [self.score_sentence(sentence) for sentence in sentences] + + def score_sentence(self, sentence: str): + """ + returns MLM score for sentence. + """ + assert type(sentence) == str + + tokens = self.tokenizer.tokenize(sentence) + mask_idx = [] + token_type = [] + attn_mask = [] + ids = [] + for m_idx, _ in enumerate(tokens): + masked = self.__mask_text__(m_idx, tokens) + mask_idx.append(m_idx) + ids.append(self.tokenizer.encode(masked)) + id_len = len(ids[-1]) + token_type.append([0] * id_len) + attn_mask.append([1] * id_len) + + data = { + 'input_ids': torch.tensor(ids, device=self.device), + 'attention_mask': torch.tensor(attn_mask, device=self.device), + 'token_type_ids': torch.tensor(token_type, device=self.device), + } + + with torch.no_grad(): + outputs = self.model(**data) + logits = outputs.logits + + scores = [] + scores_log_prob = 0.0 + + for i, m_idx in enumerate(mask_idx): + preds = logits[i].squeeze(0) + probs = softmax(preds, dim=1) + token_id = self.tokenizer.convert_tokens_to_ids([tokens[m_idx]])[0] + log_prob = np.log(probs[m_idx + 1, token_id].cpu().numpy()).item() + scores.append(log_prob) + scores_log_prob += log_prob + + return scores_log_prob + + def __mask_text__(self, idx: int, tokens: List[str]): + """ + replaces string at index idx in list `tokens` with a masked token and returns the modified list. + """ + masked = tokens.copy() + masked[idx] = self.MASK_LABEL + return masked diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/multi_layer_perceptron.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/multi_layer_perceptron.py new file mode 100644 index 0000000..76c06bf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/multi_layer_perceptron.py @@ -0,0 +1,61 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + + +class MultiLayerPerceptron(torch.nn.Module): + """ + A simple MLP that can either be used independently or put on top + of pretrained models (such as BERT) and act as a classifier. + Args: + hidden_size (int): the size of each layer + num_classes (int): number of output classes + num_layers (int): number of layers + activation (str): type of activations for layers in between + log_softmax (bool): whether to add a log_softmax layer before output + """ + + def __init__( + self, + hidden_size: int, + num_classes: int, + num_layers: int = 2, + activation: str = 'relu', + log_softmax: bool = True, + ): + super().__init__() + self.layers = 0 + for _ in range(num_layers - 1): + layer = torch.nn.Linear(hidden_size, hidden_size) + setattr(self, f'layer{self.layers}', layer) + setattr(self, f'layer{self.layers + 1}', getattr(torch, activation)) + self.layers += 2 + layer = torch.nn.Linear(hidden_size, num_classes) + setattr(self, f'layer{self.layers}', layer) + self.layers += 1 + self.log_softmax = log_softmax + + @property + def last_linear_layer(self): + return getattr(self, f'layer{self.layers - 1}') + + def forward(self, hidden_states): + output_states = hidden_states[:] + for i in range(self.layers): + output_states = getattr(self, f'layer{i}')(output_states) + + if self.log_softmax: + output_states = torch.log_softmax(output_states, dim=-1) + return output_states diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/patch_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/patch_utils.py new file mode 100644 index 0000000..eb67d17 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/patch_utils.py @@ -0,0 +1,19 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from packaging import version + +# Library version globals +TORCH_VERSION = None +TORCH_VERSION_MIN = version.Version('1.7') diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/__init__.py new file mode 100644 index 0000000..bc443be --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/cleaners.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/cleaners.py new file mode 100644 index 0000000..40c8011 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/cleaners.py @@ -0,0 +1,259 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re + +import inflect +from text_unidecode import unidecode + +from nemo.utils import logging + +NUM_CHECK = re.compile(r'([$]?)(^|\s)(\S*[0-9]\S*)(?=(\s|$)((\S*)(\s|$))?)') + +TIME_CHECK = re.compile(r'([0-9]{1,2}):([0-9]{2})(am|pm)?') +CURRENCY_CHECK = re.compile(r'\$') +ORD_CHECK = re.compile(r'([0-9]+)(st|nd|rd|th)') +THREE_CHECK = re.compile(r'([0-9]{3})([.,][0-9]{1,2})?([!.?])?$') +DECIMAL_CHECK = re.compile(r'([.,][0-9]{1,2})$') + +ABBREVIATIONS_COMMON = [ + (re.compile('\\b%s\\.' % x[0]), x[1]) + for x in [ + ("ms", "miss"), + ("mrs", "misess"), + ("mr", "mister"), + ("messrs", "messeurs"), + ("dr", "doctor"), + ("drs", "doctors"), + ("st", "saint"), + ("co", "company"), + ("jr", "junior"), + ("sr", "senior"), + ("rev", "reverend"), + ("hon", "honorable"), + ("sgt", "sergeant"), + ("capt", "captain"), + ("maj", "major"), + ("col", "colonel"), + ("lt", "lieutenant"), + ("gen", "general"), + ("prof", "professor"), + ("lb", "pounds"), + ("rep", "representative"), + ("st", "street"), + ("ave", "avenue"), + ("etc", "et cetera"), + ("jan", "january"), + ("feb", "february"), + ("mar", "march"), + ("apr", "april"), + ("jun", "june"), + ("jul", "july"), + ("aug", "august"), + ("sep", "september"), + ("oct", "october"), + ("nov", "november"), + ("dec", "december"), + ] +] + +ABBREVIATIONS_EXPANDED = [ + (re.compile('\\b%s\\.' % x[0]), x[1]) + for x in [ + ("ltd", "limited"), + ("fig", "figure"), + ("figs", "figures"), + ("gent", "gentlemen"), + ("ft", "fort"), + ("esq", "esquire"), + ("prep", "preperation"), + ("bros", "brothers"), + ("ind", "independent"), + ("mme", "madame"), + ("pro", "professional"), + ("vs", "versus"), + ("inc", "include"), + ] +] + +ABBREVIATIONS_TTS_FASTPITCH = [ + (re.compile('\\b%s\\.' % x[0]), x[1]) + for x in [ + ("ms", "miss"), + ("mrs", "misess"), + ("mr", "mister"), + ("dr", "doctor"), + ("drs", "doctors"), + ("st", "saint"), + ("co", "company"), + ("jr", "junior"), + ("sr", "senior"), + ("rev", "reverend"), + ("hon", "honorable"), + ("sgt", "sergeant"), + ("capt", "captain"), + ("maj", "major"), + ("col", "colonel"), + ("lt", "lieutenant"), + ("gen", "general"), + ("prof", "professor"), + ("lb", "pounds"), + ("rep", "representative"), + ("st", "street"), + ("ave", "avenue"), + ("jan", "january"), + ("feb", "february"), + ("mar", "march"), + ("apr", "april"), + ("jun", "june"), + ("jul", "july"), + ("aug", "august"), + ("sep", "september"), + ("oct", "october"), + ("nov", "november"), + ("dec", "december"), + ("ltd", "limited"), + ("fig", "figure"), + ("figs", "figures"), + ("gent", "gentlemen"), + ("ft", "fort"), + ("esq", "esquire"), + ("prep", "preperation"), + ("bros", "brothers"), + ("ind", "independent"), + ("mme", "madame"), + ("pro", "professional"), + ("vs", "versus"), + ] +] + + +inflect = inflect.engine() + + +def clean_text(string, table, punctuation_to_replace, abbreviation_version=None): + warn_common_chars(string) + string = unidecode(string) + string = string.lower() + string = re.sub(r'\s+', " ", string) + string = clean_numbers(string) + string = clean_abbreviations(string, version=abbreviation_version) + string = clean_punctuations(string, table, punctuation_to_replace) + string = re.sub(r'\s+', " ", string).strip() + return string + + +def warn_common_chars(string): + if re.search(r'[£€]', string): + logging.warning("Your transcript contains one of '£' or '€' which we do not currently handle") + + +def clean_numbers(string): + cleaner = NumberCleaner() + string = NUM_CHECK.sub(cleaner.clean, string) + return string + + +def clean_abbreviations(string, version=None): + abbbreviations = ABBREVIATIONS_COMMON + if version == "fastpitch": + abbbreviations = ABBREVIATIONS_TTS_FASTPITCH + elif version == "expanded": + abbbreviations.extend = ABBREVIATIONS_EXPANDED + for regex, replacement in abbbreviations: + string = re.sub(regex, replacement, string) + return string + + +def clean_punctuations(string, table, punctuation_to_replace): + for punc, replacement in punctuation_to_replace.items(): + string = re.sub('\\{}'.format(punc), " {} ".format(replacement), string) + if table: + string = string.translate(table) + return string + + +class NumberCleaner: + def __init__(self): + super().__init__() + self.reset() + + def reset(self): + self.curr_num = [] + self.currency = None + + def format_final_number(self, whole_num, decimal): + if self.currency: + return_string = inflect.number_to_words(whole_num) + return_string += " dollar" if whole_num == 1 else " dollars" + if decimal: + return_string += " and " + inflect.number_to_words(decimal) + return_string += " cent" if whole_num == decimal else " cents" + self.reset() + return return_string + + self.reset() + if decimal: + whole_num += "." + decimal + return inflect.number_to_words(whole_num) + else: + # Check if there are non-numbers + def convert_to_word(match): + return " " + inflect.number_to_words(match.group(0)) + " " + + return re.sub(r'[0-9,]+', convert_to_word, whole_num) + + def clean(self, match): + ws = match.group(2) + number = match.group(3) + _proceeding_symbol = match.group(7) + + time_match = TIME_CHECK.match(number) + if time_match: + string = ws + inflect.number_to_words(time_match.group(1)) + "{}{}" + mins = int(time_match.group(2)) + min_string = "" + if mins != 0: + min_string = " " + inflect.number_to_words(time_match.group(2)) + ampm_string = "" + if time_match.group(3): + ampm_string = " " + time_match.group(3) + return string.format(min_string, ampm_string) + + ord_match = ORD_CHECK.match(number) + if ORD_CHECK.match(number): + return ws + inflect.number_to_words(ord_match.group(0)) + + if self.currency is None: + # Check if it is a currency + self.currency = match.group(1) or CURRENCY_CHECK.match(number) + + # Check to see if next symbol is a number + # If it is a number and it has 3 digits, then it is probably a + # continuation + three_match = THREE_CHECK.match(match.group(6)) + if three_match: + self.curr_num.append(number) + return " " + # Else we can output + else: + # Check for decimals + whole_num = "".join(self.curr_num) + number + decimal = None + decimal_match = DECIMAL_CHECK.search(whole_num) + if decimal_match: + decimal = decimal_match.group(1)[1:] + whole_num = whole_num[: -len(decimal) - 1] + whole_num = re.sub(r'\.', '', whole_num) + return ws + self.format_final_number(whole_num, decimal) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/collections.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/collections.py new file mode 100644 index 0000000..66def03 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/collections.py @@ -0,0 +1,1420 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import collections +import json +import os +from itertools import combinations +from typing import Any, Dict, Iterable, List, Optional, Union + +import pandas as pd + +from nemo.collections.common.parts.preprocessing import manifest, parsers +from nemo.utils import logging + + +class _Collection(collections.UserList): + """List of parsed and preprocessed data.""" + + OUTPUT_TYPE = None # Single element output type. + + +class Text(_Collection): + """Simple list of preprocessed text entries, result in list of tokens.""" + + OUTPUT_TYPE = collections.namedtuple('TextEntity', 'tokens') + + def __init__(self, texts: List[str], parser: parsers.CharParser): + """Instantiates text manifest and do the preprocessing step. + + Args: + texts: List of raw texts strings. + parser: Instance of `CharParser` to convert string to tokens. + """ + + data, output_type = [], self.OUTPUT_TYPE + for text in texts: + tokens = parser(text) + + if tokens is None: + logging.warning("Fail to parse '%s' text line.", text) + continue + + data.append(output_type(tokens)) + + super().__init__(data) + + +class FromFileText(Text): + """Another form of texts manifest with reading from file.""" + + def __init__(self, file: str, parser: parsers.CharParser): + """Instantiates text manifest and do the preprocessing step. + + Args: + file: File path to read from. + parser: Instance of `CharParser` to convert string to tokens. + """ + + texts = self.__parse_texts(file) + + super().__init__(texts, parser) + + @staticmethod + def __parse_texts(file: str) -> List[str]: + if not os.path.exists(file): + raise ValueError('Provided texts file does not exists!') + + _, ext = os.path.splitext(file) + if ext == '.csv': + texts = pd.read_csv(file)['transcript'].tolist() + elif ext == '.json': # Not really a correct json. + texts = list(item['text'] for item in manifest.item_iter(file)) + else: + with open(file, 'r') as f: + texts = f.readlines() + + return texts + + +class AudioText(_Collection): + """List of audio-transcript text correspondence with preprocessing.""" + + OUTPUT_TYPE = collections.namedtuple( + typename='AudioTextEntity', + field_names='id audio_file duration text_tokens offset text_raw speaker orig_sr lang', + ) + + def __init__( + self, + ids: List[int], + audio_files: List[str], + durations: List[float], + texts: List[str], + offsets: List[str], + speakers: List[Optional[int]], + orig_sampling_rates: List[Optional[int]], + token_labels: List[Optional[int]], + langs: List[Optional[str]], + parser: parsers.CharParser, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + max_number: Optional[int] = None, + do_sort_by_duration: bool = False, + index_by_file_id: bool = False, + ): + """Instantiates audio-text manifest with filters and preprocessing. + + Args: + ids: List of examples positions. + audio_files: List of audio files. + durations: List of float durations. + texts: List of raw text transcripts. + offsets: List of duration offsets or None. + speakers: List of optional speakers ids. + orig_sampling_rates: List of original sampling rates of audio files. + langs: List of language ids, one for eadh sample, or None. + parser: Instance of `CharParser` to convert string to tokens. + min_duration: Minimum duration to keep entry with (default: None). + max_duration: Maximum duration to keep entry with (default: None). + max_number: Maximum number of samples to collect. + do_sort_by_duration: True if sort samples list by duration. Not compatible with index_by_file_id. + index_by_file_id: If True, saves a mapping from filename base (ID) to index in data. + """ + + output_type = self.OUTPUT_TYPE + data, duration_filtered, num_filtered, total_duration = [], 0.0, 0, 0.0 + if index_by_file_id: + self.mapping = {} + + for id_, audio_file, duration, offset, text, speaker, orig_sr, token_labels, lang in zip( + ids, audio_files, durations, offsets, texts, speakers, orig_sampling_rates, token_labels, langs + ): + # Duration filters. + if min_duration is not None and duration < min_duration: + duration_filtered += duration + num_filtered += 1 + continue + + if max_duration is not None and duration > max_duration: + duration_filtered += duration + num_filtered += 1 + continue + + if token_labels is not None: + text_tokens = token_labels + else: + if text != '': + if hasattr(parser, "is_aggregate") and parser.is_aggregate and isinstance(text, str): + if lang is not None: + text_tokens = parser(text, lang) + # for future use if want to add language bypass to audio_to_text classes + # elif hasattr(parser, "lang") and parser.lang is not None: + # text_tokens = parser(text, parser.lang) + else: + raise ValueError("lang required in manifest when using aggregate tokenizers") + else: + text_tokens = parser(text) + else: + text_tokens = [] + + if text_tokens is None: + duration_filtered += duration + num_filtered += 1 + continue + + total_duration += duration + + data.append(output_type(id_, audio_file, duration, text_tokens, offset, text, speaker, orig_sr, lang)) + if index_by_file_id: + file_id, _ = os.path.splitext(os.path.basename(audio_file)) + if file_id not in self.mapping: + self.mapping[file_id] = [] + self.mapping[file_id].append(len(data) - 1) + + # Max number of entities filter. + if len(data) == max_number: + break + + if do_sort_by_duration: + if index_by_file_id: + logging.warning("Tried to sort dataset by duration, but cannot since index_by_file_id is set.") + else: + data.sort(key=lambda entity: entity.duration) + + logging.info("Dataset loaded with %d files totalling %.2f hours", len(data), total_duration / 3600) + logging.info("%d files were filtered totalling %.2f hours", num_filtered, duration_filtered / 3600) + + super().__init__(data) + + +class VideoText(_Collection): + """List of video-transcript text correspondence with preprocessing.""" + + OUTPUT_TYPE = collections.namedtuple( + typename='AudioTextEntity', + field_names='id video_file duration text_tokens offset text_raw speaker orig_sr lang', + ) + + def __init__( + self, + ids: List[int], + video_files: List[str], + durations: List[float], + texts: List[str], + offsets: List[str], + speakers: List[Optional[int]], + orig_sampling_rates: List[Optional[int]], + token_labels: List[Optional[int]], + langs: List[Optional[str]], + parser: parsers.CharParser, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + max_number: Optional[int] = None, + do_sort_by_duration: bool = False, + index_by_file_id: bool = False, + ): + """Instantiates video-text manifest with filters and preprocessing. + + Args: + ids: List of examples positions. + video_files: List of video files. + durations: List of float durations. + texts: List of raw text transcripts. + offsets: List of duration offsets or None. + speakers: List of optional speakers ids. + orig_sampling_rates: List of original sampling rates of audio files. + langs: List of language ids, one for eadh sample, or None. + parser: Instance of `CharParser` to convert string to tokens. + min_duration: Minimum duration to keep entry with (default: None). + max_duration: Maximum duration to keep entry with (default: None). + max_number: Maximum number of samples to collect. + do_sort_by_duration: True if sort samples list by duration. Not compatible with index_by_file_id. + index_by_file_id: If True, saves a mapping from filename base (ID) to index in data. + """ + + output_type = self.OUTPUT_TYPE + data, duration_filtered, num_filtered, total_duration = [], 0.0, 0, 0.0 + if index_by_file_id: + self.mapping = {} + + for id_, video_file, duration, offset, text, speaker, orig_sr, token_labels, lang in zip( + ids, video_files, durations, offsets, texts, speakers, orig_sampling_rates, token_labels, langs + ): + # Duration filters. + if min_duration is not None and duration < min_duration: + duration_filtered += duration + num_filtered += 1 + continue + + if max_duration is not None and duration > max_duration: + duration_filtered += duration + num_filtered += 1 + continue + + if token_labels is not None: + text_tokens = token_labels + else: + if text != '': + if hasattr(parser, "is_aggregate") and parser.is_aggregate and isinstance(text, str): + if lang is not None: + text_tokens = parser(text, lang) + else: + raise ValueError("lang required in manifest when using aggregate tokenizers") + else: + text_tokens = parser(text) + else: + text_tokens = [] + + if text_tokens is None: + duration_filtered += duration + num_filtered += 1 + continue + + total_duration += duration + + data.append(output_type(id_, video_file, duration, text_tokens, offset, text, speaker, orig_sr, lang)) + if index_by_file_id: + file_id, _ = os.path.splitext(os.path.basename(video_file)) + if file_id not in self.mapping: + self.mapping[file_id] = [] + self.mapping[file_id].append(len(data) - 1) + + # Max number of entities filter. + if len(data) == max_number: + break + + if do_sort_by_duration: + if index_by_file_id: + logging.warning("Tried to sort dataset by duration, but cannot since index_by_file_id is set.") + else: + data.sort(key=lambda entity: entity.duration) + + logging.info("Dataset loaded with %d files totalling %.2f hours", len(data), total_duration / 3600) + logging.info("%d files were filtered totalling %.2f hours", num_filtered, duration_filtered / 3600) + + super().__init__(data) + + +class ASRAudioText(AudioText): + """`AudioText` collector from asr structured json files.""" + + def __init__(self, manifests_files: Union[str, List[str]], *args, **kwargs): + """Parse lists of audio files, durations and transcripts texts. + + Args: + manifests_files: Either single string file or list of such - + manifests to yield items from. + *args: Args to pass to `AudioText` constructor. + **kwargs: Kwargs to pass to `AudioText` constructor. + """ + + ids, audio_files, durations, texts, offsets, = ( + [], + [], + [], + [], + [], + ) + speakers, orig_srs, token_labels, langs = [], [], [], [] + for item in manifest.item_iter(manifests_files): + ids.append(item['id']) + audio_files.append(item['audio_file']) + durations.append(item['duration']) + texts.append(item['text']) + offsets.append(item['offset']) + speakers.append(item['speaker']) + orig_srs.append(item['orig_sr']) + token_labels.append(item['token_labels']) + langs.append(item['lang']) + super().__init__( + ids, audio_files, durations, texts, offsets, speakers, orig_srs, token_labels, langs, *args, **kwargs + ) + + +class ASRVideoText(VideoText): + """`VideoText` collector from cv structured json files.""" + + def __init__(self, manifests_files: Union[str, List[str]], *args, **kwargs): + """Parse lists of video files, durations and transcripts texts. + + Args: + manifests_files: Either single string file or list of such - + manifests to yield items from. + *args: Args to pass to `VideoText` constructor. + **kwargs: Kwargs to pass to `VideoText` constructor. + """ + + ids, video_files, durations, texts, offsets, = ( + [], + [], + [], + [], + [], + ) + speakers, orig_srs, token_labels, langs = [], [], [], [] + for item in manifest.item_iter(manifests_files): + ids.append(item['id']) + video_files.append(item['video_file']) + durations.append(item['duration']) + texts.append(item['text']) + offsets.append(item['offset']) + speakers.append(item['speaker']) + orig_srs.append(item['orig_sr']) + token_labels.append(item['token_labels']) + langs.append(item['lang']) + super().__init__( + ids, video_files, durations, texts, offsets, speakers, orig_srs, token_labels, langs, *args, **kwargs + ) + + +class SpeechLabel(_Collection): + """List of audio-label correspondence with preprocessing.""" + + OUTPUT_TYPE = collections.namedtuple(typename='SpeechLabelEntity', field_names='audio_file duration label offset',) + + def __init__( + self, + audio_files: List[str], + durations: List[float], + labels: List[Union[int, str]], + offsets: List[Optional[float]], + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + max_number: Optional[int] = None, + do_sort_by_duration: bool = False, + index_by_file_id: bool = False, + ): + """Instantiates audio-label manifest with filters and preprocessing. + + Args: + audio_files: List of audio files. + durations: List of float durations. + labels: List of labels. + offsets: List of offsets or None. + min_duration: Minimum duration to keep entry with (default: None). + max_duration: Maximum duration to keep entry with (default: None). + max_number: Maximum number of samples to collect. + do_sort_by_duration: True if sort samples list by duration. + index_by_file_id: If True, saves a mapping from filename base (ID) to index in data. + """ + + if index_by_file_id: + self.mapping = {} + output_type = self.OUTPUT_TYPE + data, duration_filtered = [], 0.0 + total_duration = 0.0 + for audio_file, duration, command, offset in zip(audio_files, durations, labels, offsets): + # Duration filters. + if min_duration is not None and duration < min_duration: + duration_filtered += duration + continue + + if max_duration is not None and duration > max_duration: + duration_filtered += duration + continue + + data.append(output_type(audio_file, duration, command, offset)) + total_duration += duration + + if index_by_file_id: + file_id, _ = os.path.splitext(os.path.basename(audio_file)) + self.mapping[file_id] = len(data) - 1 + + # Max number of entities filter. + if len(data) == max_number: + break + + if do_sort_by_duration: + if index_by_file_id: + logging.warning("Tried to sort dataset by duration, but cannot since index_by_file_id is set.") + else: + data.sort(key=lambda entity: entity.duration) + + logging.info(f"Filtered duration for loading collection is {duration_filtered / 3600: .2f} hours.") + logging.info(f"Dataset loaded with {len(data)} items, total duration of {total_duration / 3600: .2f} hours.") + self.uniq_labels = sorted(set(map(lambda x: x.label, data))) + logging.info("# {} files loaded accounting to # {} labels".format(len(data), len(self.uniq_labels))) + + super().__init__(data) + + +class ASRSpeechLabel(SpeechLabel): + """`SpeechLabel` collector from structured json files.""" + + def __init__( + self, + manifests_files: Union[str, List[str]], + is_regression_task=False, + cal_labels_occurrence=False, + delimiter=None, + *args, + **kwargs, + ): + """Parse lists of audio files, durations and transcripts texts. + + Args: + manifests_files: Either single string file or list of such - + manifests to yield items from. + is_regression_task: It's a regression task. + cal_labels_occurrence: whether to calculate occurence of labels. + delimiter: separator for labels strings. + *args: Args to pass to `SpeechLabel` constructor. + **kwargs: Kwargs to pass to `SpeechLabel` constructor. + """ + audio_files, durations, labels, offsets = [], [], [], [] + all_labels = [] + for item in manifest.item_iter(manifests_files, parse_func=self.__parse_item): + audio_files.append(item['audio_file']) + durations.append(item['duration']) + if not is_regression_task: + label = item['label'] + label_list = label.split() if not delimiter else label.split(delimiter) + else: + label = float(item['label']) + label_list = [label] + + labels.append(label) + offsets.append(item['offset']) + all_labels.extend(label_list) + if cal_labels_occurrence: + self.labels_occurrence = collections.Counter(all_labels) + + super().__init__(audio_files, durations, labels, offsets, *args, **kwargs) + + def __parse_item(self, line: str, manifest_file: str) -> Dict[str, Any]: + item = json.loads(line) + + # Audio file + if 'audio_filename' in item: + item['audio_file'] = item.pop('audio_filename') + elif 'audio_filepath' in item: + item['audio_file'] = item.pop('audio_filepath') + else: + raise ValueError(f"Manifest file has invalid json line structure: {line} without proper audio file key.") + item['audio_file'] = manifest.get_full_path(audio_file=item['audio_file'], manifest_file=manifest_file) + + # Duration. + if 'duration' not in item: + raise ValueError(f"Manifest file has invalid json line structure: {line} without proper duration key.") + + # Label. + if 'command' in item: + item['label'] = item.pop('command') + elif 'target' in item: + item['label'] = item.pop('target') + elif 'label' in item: + pass + else: + raise ValueError(f"Manifest file has invalid json line structure: {line} without proper label key.") + + item = dict( + audio_file=item['audio_file'], + duration=item['duration'], + label=item['label'], + offset=item.get('offset', None), + ) + + return item + + +class FeatureSequenceLabel(_Collection): + """List of feature sequence of label correspondence with preprocessing.""" + + OUTPUT_TYPE = collections.namedtuple(typename='FeatureSequenceLabelEntity', field_names='feature_file seq_label',) + + def __init__( + self, + feature_files: List[str], + seq_labels: List[str], + max_number: Optional[int] = None, + index_by_file_id: bool = False, + ): + """Instantiates feature-SequenceLabel manifest with filters and preprocessing. + + Args: + feature_files: List of feature files. + seq_labels: List of sequences of labels. + max_number: Maximum number of samples to collect. + index_by_file_id: If True, saves a mapping from filename base (ID) to index in data. + """ + + output_type = self.OUTPUT_TYPE + data, num_filtered = ( + [], + 0.0, + ) + self.uniq_labels = set() + + if index_by_file_id: + self.mapping = {} + + for feature_file, seq_label in zip(feature_files, seq_labels): + + label_tokens, uniq_labels_in_seq = self.relative_speaker_parser(seq_label) + + data.append(output_type(feature_file, label_tokens)) + self.uniq_labels |= uniq_labels_in_seq + + if label_tokens is None: + num_filtered += 1 + continue + + if index_by_file_id: + file_id, _ = os.path.splitext(os.path.basename(feature_file)) + self.mapping[feature_file] = len(data) - 1 + + # Max number of entities filter. + if len(data) == max_number: + break + + logging.info("# {} files loaded including # {} unique labels".format(len(data), len(self.uniq_labels))) + super().__init__(data) + + def relative_speaker_parser(self, seq_label): + """Convert sequence of speaker labels to relative labels. + Convert sequence of absolute speaker to sequence of relative speaker [E A C A E E C] -> [0 1 2 1 0 0 2] + In this seq of label , if label do not appear before, assign new relative labels len(pos); else reuse previous assigned relative labels. + Args: + seq_label (str): A string of a sequence of labels. + + Return: + relative_seq_label (List) : A list of relative sequence of labels + unique_labels_in_seq (Set): A set of unique labels in the sequence + """ + seq = seq_label.split() + conversion_dict = dict() + relative_seq_label = [] + + for seg in seq: + if seg in conversion_dict: + converted = conversion_dict[seg] + else: + converted = len(conversion_dict) + conversion_dict[seg] = converted + + relative_seq_label.append(converted) + + unique_labels_in_seq = set(conversion_dict.keys()) + return relative_seq_label, unique_labels_in_seq + + +class ASRFeatureSequenceLabel(FeatureSequenceLabel): + """`FeatureSequenceLabel` collector from asr structured json files.""" + + def __init__( + self, manifests_files: Union[str, List[str]], max_number: Optional[int] = None, index_by_file_id: bool = False, + ): + + """Parse lists of feature files and sequences of labels. + + Args: + manifests_files: Either single string file or list of such - + manifests to yield items from. + max_number: Maximum number of samples to collect; pass to `FeatureSequenceLabel` constructor. + index_by_file_id: If True, saves a mapping from filename base (ID) to index in data; pass to `FeatureSequenceLabel` constructor. + """ + + feature_files, seq_labels = [], [] + for item in manifest.item_iter(manifests_files, parse_func=self._parse_item): + feature_files.append(item['feature_file']) + seq_labels.append(item['seq_label']) + + super().__init__(feature_files, seq_labels, max_number, index_by_file_id) + + def _parse_item(self, line: str, manifest_file: str) -> Dict[str, Any]: + item = json.loads(line) + + # Feature file + if 'feature_filename' in item: + item['feature_file'] = item.pop('feature_filename') + elif 'feature_filepath' in item: + item['feature_file'] = item.pop('feature_filepath') + else: + raise ValueError( + f"Manifest file has invalid json line " f"structure: {line} without proper feature file key." + ) + item['feature_file'] = os.path.expanduser(item['feature_file']) + + # Seq of Label. + if 'seq_label' in item: + item['seq_label'] = item.pop('seq_label') + else: + raise ValueError( + f"Manifest file has invalid json line " f"structure: {line} without proper seq_label key." + ) + + item = dict(feature_file=item['feature_file'], seq_label=item['seq_label'],) + + return item + + +class DiarizationLabel(_Collection): + """List of diarization audio-label correspondence with preprocessing.""" + + OUTPUT_TYPE = collections.namedtuple( + typename='DiarizationLabelEntity', + field_names='audio_file duration rttm_file offset target_spks sess_spk_dict clus_spk_digits rttm_spk_digits', + ) + + def __init__( + self, + audio_files: List[str], + durations: List[float], + rttm_files: List[str], + offsets: List[float], + target_spks_list: List[tuple], + sess_spk_dicts: List[Dict], + clus_spk_list: List[tuple], + rttm_spk_list: List[tuple], + max_number: Optional[int] = None, + do_sort_by_duration: bool = False, + index_by_file_id: bool = False, + ): + """Instantiates audio-label manifest with filters and preprocessing. + + Args: + audio_files: + List of audio file paths. + durations: + List of float durations. + rttm_files: + List of RTTM files (Groundtruth diarization annotation file). + offsets: + List of offsets or None. + target_spks (tuple): + List of tuples containing the two indices of targeted speakers for evaluation. + Example: [[(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)], [(0, 1), (1, 2), (0, 2)], ...] + sess_spk_dict (Dict): + List of Mapping dictionaries between RTTM speakers and speaker labels in the clustering result. + clus_spk_digits (tuple): + List of Tuple containing all the speaker indices from the clustering result. + Example: [(0, 1, 2, 3), (0, 1, 2), ...] + rttm_spkr_digits (tuple): + List of tuple containing all the speaker indices in the RTTM file. + Example: (0, 1, 2), (0, 1), ...] + max_number: Maximum number of samples to collect + do_sort_by_duration: True if sort samples list by duration + index_by_file_id: If True, saves a mapping from filename base (ID) to index in data. + """ + + if index_by_file_id: + self.mapping = {} + output_type = self.OUTPUT_TYPE + data, duration_filtered = [], 0.0 + + zipped_items = zip( + audio_files, durations, rttm_files, offsets, target_spks_list, sess_spk_dicts, clus_spk_list, rttm_spk_list + ) + for ( + audio_file, + duration, + rttm_file, + offset, + target_spks, + sess_spk_dict, + clus_spk_digits, + rttm_spk_digits, + ) in zipped_items: + + if duration is None: + duration = 0 + + data.append( + output_type( + audio_file, + duration, + rttm_file, + offset, + target_spks, + sess_spk_dict, + clus_spk_digits, + rttm_spk_digits, + ) + ) + + if index_by_file_id: + file_id, _ = os.path.splitext(os.path.basename(audio_file)) + self.mapping[file_id] = len(data) - 1 + + # Max number of entities filter. + if len(data) == max_number: + break + + if do_sort_by_duration: + if index_by_file_id: + logging.warning("Tried to sort dataset by duration, but cannot since index_by_file_id is set.") + else: + data.sort(key=lambda entity: entity.duration) + + logging.info( + "Filtered duration for loading collection is %f.", duration_filtered, + ) + logging.info(f"Total {len(data)} session files loaded accounting to # {len(audio_files)} audio clips") + + super().__init__(data) + + +class DiarizationSpeechLabel(DiarizationLabel): + """`DiarizationLabel` diarization data sample collector from structured json files.""" + + def __init__( + self, + manifests_files: Union[str, List[str]], + emb_dict: Dict, + clus_label_dict: Dict, + round_digit=2, + seq_eval_mode=False, + pairwise_infer=False, + *args, + **kwargs, + ): + """ + Parse lists of audio files, durations, RTTM (Diarization annotation) files. Since diarization model infers only + two speakers, speaker pairs are generated from the total number of speakers in the session. + + Args: + manifest_filepath (str): + Path to input manifest json files. + emb_dict (Dict): + Dictionary containing cluster-average embeddings and speaker mapping information. + clus_label_dict (Dict): + Segment-level speaker labels from clustering results. + round_digit (int): + Number of digits to be rounded. + seq_eval_mode (bool): + If True, F1 score will be calculated for each speaker pair during inference mode. + pairwise_infer (bool): + If True, this dataset class operates in inference mode. In inference mode, a set of speakers in the input audio + is split into multiple pairs of speakers and speaker tuples (e.g. 3 speakers: [(0,1), (1,2), (0,2)]) and then + fed into the diarization system to merge the individual results. + *args: Args to pass to `SpeechLabel` constructor. + **kwargs: Kwargs to pass to `SpeechLabel` constructor. + """ + self.round_digit = round_digit + self.emb_dict = emb_dict + self.clus_label_dict = clus_label_dict + self.seq_eval_mode = seq_eval_mode + self.pairwise_infer = pairwise_infer + audio_files, durations, rttm_files, offsets, target_spks_list, sess_spk_dicts, clus_spk_list, rttm_spk_list = ( + [], + [], + [], + [], + [], + [], + [], + [], + ) + + for item in manifest.item_iter(manifests_files, parse_func=self.__parse_item_rttm): + # Inference mode + if self.pairwise_infer: + clus_speaker_digits = sorted(list(set([x[2] for x in clus_label_dict[item['uniq_id']]]))) + if item['rttm_file']: + base_scale_index = max(self.emb_dict.keys()) + _sess_spk_dict = self.emb_dict[base_scale_index][item['uniq_id']]['mapping'] + sess_spk_dict = {int(v.split('_')[-1]): k for k, v in _sess_spk_dict.items()} + rttm_speaker_digits = [int(v.split('_')[1]) for k, v in _sess_spk_dict.items()] + if self.seq_eval_mode: + clus_speaker_digits = rttm_speaker_digits + else: + sess_spk_dict = None + rttm_speaker_digits = None + + # Training mode + else: + rttm_labels = [] + with open(item['rttm_file'], 'r') as f: + for line in f.readlines(): + start, end, speaker = self.split_rttm_line(line, decimals=3) + rttm_labels.append('{} {} {}'.format(start, end, speaker)) + speaker_set = set() + for rttm_line in rttm_labels: + spk_str = rttm_line.split()[-1] + speaker_set.add(spk_str) + speaker_list = sorted(list(speaker_set)) + sess_spk_dict = {key: val for key, val in enumerate(speaker_list)} + target_spks = tuple(sess_spk_dict.keys()) + clus_speaker_digits = target_spks + rttm_speaker_digits = target_spks + + if len(clus_speaker_digits) <= 2: + spk_comb_list = [(0, 1)] + else: + spk_comb_list = [x for x in combinations(clus_speaker_digits, 2)] + + for target_spks in spk_comb_list: + audio_files.append(item['audio_file']) + durations.append(item['duration']) + rttm_files.append(item['rttm_file']) + offsets.append(item['offset']) + target_spks_list.append(target_spks) + sess_spk_dicts.append(sess_spk_dict) + clus_spk_list.append(clus_speaker_digits) + rttm_spk_list.append(rttm_speaker_digits) + + super().__init__( + audio_files, + durations, + rttm_files, + offsets, + target_spks_list, + sess_spk_dicts, + clus_spk_list, + rttm_spk_list, + *args, + **kwargs, + ) + + def split_rttm_line(self, rttm_line: str, decimals: int = 3): + """ + Convert a line in RTTM file to speaker label, start and end timestamps. + + An example line of `rttm_line`: + SPEAKER abc_dev_0123 1 146.903 1.860 speaker543 + + The above example RTTM line contains the following information: + session name: abc_dev_0123 + segment start time: 146.903 + segment duration: 1.860 + speaker label: speaker543 + + Args: + rttm_line (str): + A line in RTTM formatted file containing offset and duration of each segment. + decimals (int): + Number of digits to be rounded. + + Returns: + start (float): + Start timestamp in floating point number. + end (float): + End timestamp in floating point number. + speaker (str): + speaker string in RTTM lines. + """ + rttm = rttm_line.strip().split() + start = round(float(rttm[3]), decimals) + end = round(float(rttm[4]), decimals) + round(float(rttm[3]), decimals) + speaker = rttm[7] + return start, end, speaker + + def __parse_item_rttm(self, line: str, manifest_file: str) -> Dict[str, Any]: + """Parse each rttm file and save it to in Dict format""" + item = json.loads(line) + if 'audio_filename' in item: + item['audio_file'] = item.pop('audio_filename') + elif 'audio_filepath' in item: + item['audio_file'] = item.pop('audio_filepath') + else: + raise ValueError( + f"Manifest file has invalid json line " f"structure: {line} without proper audio file key." + ) + item['audio_file'] = os.path.expanduser(item['audio_file']) + item['uniq_id'] = os.path.splitext(os.path.basename(item['audio_file']))[0] + if 'duration' not in item: + raise ValueError(f"Manifest file has invalid json line " f"structure: {line} without proper duration key.") + item = dict( + audio_file=item['audio_file'], + uniq_id=item['uniq_id'], + duration=item['duration'], + rttm_file=item['rttm_filepath'], + offset=item.get('offset', None), + ) + return item + + +class Audio(_Collection): + """Prepare a list of all audio items, filtered by duration. + """ + + OUTPUT_TYPE = collections.namedtuple(typename='Audio', field_names='audio_files duration offset text') + + def __init__( + self, + audio_files_list: List[Dict[str, str]], + duration_list: List[float], + offset_list: List[float], + text_list: List[str], + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + max_number: Optional[int] = None, + do_sort_by_duration: bool = False, + ): + """Instantiantes an list of audio files. + + Args: + audio_files_list: list of dictionaries with mapping from audio_key to audio_filepath + duration_list: list of durations of input files + offset_list: list of offsets + text_list: list of texts + min_duration: Minimum duration to keep entry with (default: None). + max_duration: Maximum duration to keep entry with (default: None). + max_number: Maximum number of samples to collect. + do_sort_by_duration: True if sort samples list by duration. + """ + + output_type = self.OUTPUT_TYPE + data, total_duration = [], 0.0 + num_filtered, duration_filtered = 0, 0.0 + + for audio_files, duration, offset, text in zip(audio_files_list, duration_list, offset_list, text_list): + # Duration filters + if min_duration is not None and duration < min_duration: + duration_filtered += duration + num_filtered += 1 + continue + + if max_duration is not None and duration > max_duration: + duration_filtered += duration + num_filtered += 1 + continue + + total_duration += duration + data.append(output_type(audio_files, duration, offset, text)) + + # Max number of entities filter + if len(data) == max_number: + break + + if do_sort_by_duration: + data.sort(key=lambda entity: entity.duration) + + logging.info("Dataset loaded with %d files totalling %.2f hours", len(data), total_duration / 3600) + logging.info("%d files were filtered totalling %.2f hours", num_filtered, duration_filtered / 3600) + + super().__init__(data) + + +class AudioCollection(Audio): + """List of audio files from a manifest file. + """ + + def __init__( + self, manifest_files: Union[str, List[str]], audio_to_manifest_key: Dict[str, str], *args, **kwargs, + ): + """Instantiates a list of audio files loaded from a manifest file. + + Args: + manifest_files: path to a single manifest file or a list of paths + audio_to_manifest_key: dictionary mapping audio signals to keys of the manifest + """ + # Support for comma-separated manifests + if type(manifest_files) == str: + manifest_files = manifest_files.split(',') + + for audio_key, manifest_key in audio_to_manifest_key.items(): + # Support for comma-separated keys + if type(manifest_key) == str and ',' in manifest_key: + audio_to_manifest_key[audio_key] = manifest_key.split(',') + + # Keys from manifest which contain audio + self.audio_to_manifest_key = audio_to_manifest_key + + # Initialize data + audio_files_list, duration_list, offset_list, text_list = [], [], [], [] + + # Parse manifest files + for item in manifest.item_iter(manifest_files, parse_func=self.__parse_item): + audio_files_list.append(item['audio_files']) + duration_list.append(item['duration']) + offset_list.append(item['offset']) + text_list.append(item['text']) + + super().__init__(audio_files_list, duration_list, offset_list, text_list, *args, **kwargs) + + def __parse_item(self, line: str, manifest_file: str) -> Dict[str, Any]: + """Parse a single line from a manifest file. + + Args: + line: a string representing a line from a manifest file in JSON format + manifest_file: path to the manifest file. Used to resolve relative paths. + + Returns: + Dictionary with audio_files, duration, and offset. + """ + # Local utility function + def get_audio_file(item: Dict, manifest_key: Union[str, List[str]]): + """Get item[key] if key is string, or a list + of strings by combining item[key[0]], item[key[1]], etc. + """ + # Prepare audio file(s) + if manifest_key is None: + # Support for inference, when a target key is None + audio_file = None + elif isinstance(manifest_key, str): + # Load files from a single manifest key + audio_file = item[manifest_key] + elif isinstance(manifest_key, Iterable): + # Load files from multiple manifest keys + audio_file = [] + for key in manifest_key: + item_key = item[key] + if isinstance(item_key, str): + audio_file.append(item_key) + elif isinstance(item_key, list): + audio_file += item_key + else: + raise ValueError(f'Unexpected type {type(item_key)} of item for key {key}: {item_key}') + else: + raise ValueError(f'Unexpected type {type(manifest_key)} of manifest_key: {manifest_key}') + + return audio_file + + # Convert JSON line to a dictionary + item = json.loads(line) + + # Handle all audio files + audio_files = {} + for audio_key, manifest_key in self.audio_to_manifest_key.items(): + + audio_file = get_audio_file(item, manifest_key) + + # Get full path to audio file(s) + if isinstance(audio_file, str): + # This dictionary entry points to a single file + audio_files[audio_key] = manifest.get_full_path(audio_file, manifest_file) + elif isinstance(audio_file, Iterable): + # This dictionary entry points to multiple files + # Get the files and keep the list structure for this key + audio_files[audio_key] = [manifest.get_full_path(f, manifest_file) for f in audio_file] + elif audio_file is None and audio_key.startswith('target'): + # For inference, we don't need the target + audio_files[audio_key] = None + else: + raise ValueError(f'Unexpected type {type(audio_file)} of audio_file: {audio_file}') + item['audio_files'] = audio_files + + # Handle duration + if 'duration' not in item: + raise ValueError(f'Duration not available in line: {line}. Manifest file: {manifest_file}') + + # Handle offset + if 'offset' not in item: + item['offset'] = 0.0 + + # Handle text + if 'text' not in item: + item['text'] = None + + return dict( + audio_files=item['audio_files'], duration=item['duration'], offset=item['offset'], text=item['text'] + ) + + +class FeatureLabel(_Collection): + """List of feature sequence and their label correspondence with preprocessing.""" + + OUTPUT_TYPE = collections.namedtuple(typename='FeatureLabelEntity', field_names='feature_file label duration',) + + def __init__( + self, + feature_files: List[str], + labels: List[str], + durations: List[float], + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + max_number: Optional[int] = None, + do_sort_by_duration: bool = False, + index_by_file_id: bool = False, + ): + """Instantiates feature-SequenceLabel manifest with filters and preprocessing. + + Args: + feature_files: List of feature files. + labels: List of labels. + max_number: Maximum number of samples to collect. + index_by_file_id: If True, saves a mapping from filename base (ID) to index in data. + """ + + output_type = self.OUTPUT_TYPE + data = [] + duration_filtered = 0.0 + total_duration = 0.0 + self.uniq_labels = set() + + if index_by_file_id: + self.mapping = {} + + for feature_file, label, duration in zip(feature_files, labels, durations): + # Duration filters. + if min_duration is not None and duration < min_duration: + duration_filtered += duration + continue + + if max_duration is not None and duration > max_duration: + duration_filtered += duration + continue + + data.append(output_type(feature_file, label, duration)) + self.uniq_labels |= set(label) + total_duration += duration + + if index_by_file_id: + file_id, _ = os.path.splitext(os.path.basename(feature_file)) + self.mapping[file_id] = len(data) - 1 + + # Max number of entities filter. + if len(data) == max_number: + break + + if do_sort_by_duration: + if index_by_file_id: + logging.warning("Tried to sort dataset by duration, but cannot since index_by_file_id is set.") + else: + data.sort(key=lambda entity: entity.duration) + + logging.info(f"Filtered duration for loading collection is {duration_filtered / 2600:.2f} hours.") + logging.info(f"Dataset loaded with {len(data)} items, total duration of {total_duration / 3600: .2f} hours.") + logging.info("# {} files loaded including # {} unique labels".format(len(data), len(self.uniq_labels))) + super().__init__(data) + + +class ASRFeatureLabel(FeatureLabel): + """`FeatureLabel` collector from asr structured json files.""" + + def __init__( + self, + manifests_files: Union[str, List[str]], + is_regression_task: bool = False, + cal_labels_occurrence: bool = False, + delimiter: Optional[str] = None, + *args, + **kwargs, + ): + + """Parse lists of feature files and sequences of labels. + + Args: + manifests_files: Either single string file or list of such - + manifests to yield items from. + max_number: Maximum number of samples to collect; pass to `FeatureSequenceLabel` constructor. + index_by_file_id: If True, saves a mapping from filename base (ID) to index in data; pass to `FeatureSequenceLabel` constructor. + """ + + feature_files, labels, durations = [], [], [] + all_labels = [] + for item in manifest.item_iter(manifests_files, parse_func=self._parse_item): + feature_files.append(item['feature_file']) + durations.append(item['duration']) + + if not is_regression_task: + label = item['label'] + label_list = label.split() if not delimiter else label.split(delimiter) + else: + label = float(item['label']) + label_list = [label] + + labels.append(label) + all_labels.extend(label_list) + if cal_labels_occurrence: + self.labels_occurrence = collections.Counter(all_labels) + + super().__init__(feature_files, labels, durations, *args, **kwargs) + + def _parse_item(self, line: str, manifest_file: str) -> Dict[str, Any]: + item = json.loads(line) + + # Feature file + if 'feature_filename' in item: + item['feature_file'] = item.pop('feature_filename') + elif 'feature_filepath' in item: + item['feature_file'] = item.pop('feature_filepath') + elif 'feature_file' not in item: + raise ValueError( + f"Manifest file has invalid json line " f"structure: {line} without proper 'feature_file' key." + ) + item['feature_file'] = manifest.get_full_path(audio_file=item['feature_file'], manifest_file=manifest_file) + + # Label. + if 'label' in item: + item['label'] = item.pop('label') + else: + raise ValueError(f"Manifest file has invalid json line structure: {line} without proper 'label' key.") + + item = dict(feature_file=item['feature_file'], label=item['label'], duration=item['duration']) + + return item + + +class FeatureText(_Collection): + """List of audio-transcript text correspondence with preprocessing.""" + + OUTPUT_TYPE = collections.namedtuple( + typename='FeatureTextEntity', + field_names='id feature_file rttm_file duration text_tokens offset text_raw speaker orig_sr lang', + ) + + def __init__( + self, + ids: List[int], + feature_files: List[str], + rttm_files: List[str], + durations: List[float], + texts: List[str], + offsets: List[str], + speakers: List[Optional[int]], + orig_sampling_rates: List[Optional[int]], + token_labels: List[Optional[int]], + langs: List[Optional[str]], + parser: parsers.CharParser, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + max_number: Optional[int] = None, + do_sort_by_duration: bool = False, + index_by_file_id: bool = False, + ): + """Instantiates feature-text manifest with filters and preprocessing. + + Args: + ids: List of examples positions. + feature_files: List of audio feature files. + rttm_files: List of audio rttm files. + durations: List of float durations. + texts: List of raw text transcripts. + offsets: List of duration offsets or None. + speakers: List of optional speakers ids. + orig_sampling_rates: List of original sampling rates of audio files. + langs: List of language ids, one for eadh sample, or None. + parser: Instance of `CharParser` to convert string to tokens. + min_duration: Minimum duration to keep entry with (default: None). + max_duration: Maximum duration to keep entry with (default: None). + max_number: Maximum number of samples to collect. + do_sort_by_duration: True if sort samples list by duration. Not compatible with index_by_file_id. + index_by_file_id: If True, saves a mapping from filename base (ID) to index in data. + """ + + output_type = self.OUTPUT_TYPE + data, duration_filtered, num_filtered, total_duration = [], 0.0, 0, 0.0 + if index_by_file_id: + self.mapping = {} + + for id_, feat_file, rttm_file, duration, offset, text, speaker, orig_sr, token_labels, lang in zip( + ids, + feature_files, + rttm_files, + durations, + offsets, + texts, + speakers, + orig_sampling_rates, + token_labels, + langs, + ): + # Duration filters. + if min_duration is not None and duration < min_duration: + duration_filtered += duration + num_filtered += 1 + continue + + if max_duration is not None and duration > max_duration: + duration_filtered += duration + num_filtered += 1 + continue + + if token_labels is not None: + text_tokens = token_labels + else: + if text != '': + if hasattr(parser, "is_aggregate") and parser.is_aggregate and isinstance(text, str): + if lang is not None: + text_tokens = parser(text, lang) + else: + raise ValueError("lang required in manifest when using aggregate tokenizers") + else: + text_tokens = parser(text) + else: + text_tokens = [] + + if text_tokens is None: + duration_filtered += duration + num_filtered += 1 + continue + + total_duration += duration + + data.append( + output_type(id_, feat_file, rttm_file, duration, text_tokens, offset, text, speaker, orig_sr, lang) + ) + if index_by_file_id: + file_id, _ = os.path.splitext(os.path.basename(feat_file)) + if file_id not in self.mapping: + self.mapping[file_id] = [] + self.mapping[file_id].append(len(data) - 1) + + # Max number of entities filter. + if len(data) == max_number: + break + + if do_sort_by_duration: + if index_by_file_id: + logging.warning("Tried to sort dataset by duration, but cannot since index_by_file_id is set.") + else: + data.sort(key=lambda entity: entity.duration) + + logging.info("Dataset loaded with %d files totalling %.2f hours", len(data), total_duration / 3600) + logging.info("%d files were filtered totalling %.2f hours", num_filtered, duration_filtered / 3600) + + super().__init__(data) + + +class ASRFeatureText(FeatureText): + """`FeatureText` collector from asr structured json files.""" + + def __init__(self, manifests_files: Union[str, List[str]], *args, **kwargs): + """Parse lists of audio files, durations and transcripts texts. + + Args: + manifests_files: Either single string file or list of such - + manifests to yield items from. + *args: Args to pass to `AudioText` constructor. + **kwargs: Kwargs to pass to `AudioText` constructor. + """ + + ids, feature_files, rttm_files, durations, texts, offsets, = ( + [], + [], + [], + [], + [], + [], + ) + speakers, orig_srs, token_labels, langs = [], [], [], [] + for item in manifest.item_iter(manifests_files): + ids.append(item['id']) + feature_files.append(item['feature_file']) + rttm_files.append(item['rttm_file']) + durations.append(item['duration']) + texts.append(item['text']) + offsets.append(item['offset']) + speakers.append(item['speaker']) + orig_srs.append(item['orig_sr']) + token_labels.append(item['token_labels']) + langs.append(item['lang']) + + super().__init__( + ids, + feature_files, + rttm_files, + durations, + texts, + offsets, + speakers, + orig_srs, + token_labels, + langs, + *args, + **kwargs, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/manifest.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/manifest.py new file mode 100644 index 0000000..1d49bd7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/manifest.py @@ -0,0 +1,280 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import re +from collections import defaultdict +from os.path import expanduser +from typing import Any, Callable, Dict, Iterator, List, Optional, Union + +from nemo.utils import logging +from nemo.utils.data_utils import DataStoreObject, datastore_path_to_local_path, is_datastore_path +from nemo.utils.nemo_logging import LogMode + + +class ManifestBase: + def __init__(self, *args, **kwargs): + raise ValueError( + "This class is deprecated, look at https://github.com/NVIDIA/NeMo/pull/284 for correct behaviour." + ) + + +class ManifestEN: + def __init__(self, *args, **kwargs): + raise ValueError( + "This class is deprecated, look at https://github.com/NVIDIA/NeMo/pull/284 for correct behaviour." + ) + + +def item_iter( + manifests_files: Union[str, List[str]], parse_func: Callable[[str, Optional[str]], Dict[str, Any]] = None +) -> Iterator[Dict[str, Any]]: + """Iterate through json lines of provided manifests. + + NeMo ASR pipelines often assume certain manifest files structure. In + particular, each manifest file should consist of line-per-sample files with + each line being correct json dict. Each such json dict should have a field + for audio file string, a field for duration float and a field for text + string. Offset also could be additional field and is set to None by + default. + + Args: + manifests_files: Either single string file or list of such - + manifests to yield items from. + + parse_func: A callable function which accepts as input a single line + of a manifest and optionally the manifest file itself, + and parses it, returning a dictionary mapping from str -> Any. + + Yields: + Parsed key to value item dicts. + + Raises: + ValueError: If met invalid json line structure. + """ + + if isinstance(manifests_files, str): + manifests_files = [manifests_files] + + if parse_func is None: + parse_func = __parse_item + + errors = defaultdict(list) + k = -1 + logging.debug('Manifest files: %s', str(manifests_files)) + for manifest_file in manifests_files: + logging.debug('Using manifest file: %s', str(manifest_file)) + cached_manifest_file = DataStoreObject(manifest_file).get() + logging.debug('Cached at: %s', str(cached_manifest_file)) + with open(expanduser(cached_manifest_file), 'r') as f: + for line in f: + line = line.strip() + if not line: + continue + k += 1 + try: + item = parse_func(line, manifest_file) + except json.JSONDecodeError: + errors[str(manifest_file)].append(line) + continue + item['id'] = k + + yield item + + if len(errors) > 0: + for filename, lines in errors.items(): + logging.error("=============================================") + logging.error(f"Failed to parse {len(lines)} lines from manifest file: {filename}") + for line in lines: + logging.error(f"-- Failed to parse line: `{line}`") + raise RuntimeError("Failed to parse some lines from manifest files. See logs for more details.") + + +def __parse_item(line: str, manifest_file: str) -> Dict[str, Any]: + item = json.loads(line) + + # Audio file + if 'audio_filename' in item: + item['audio_file'] = item.pop('audio_filename') + elif 'audio_filepath' in item: + item['audio_file'] = item.pop('audio_filepath') + + # Video File + if 'video_filename' in item: + item['video_file'] = item.pop('video_filename') + elif 'video_filepath' in item: + item['video_file'] = item.pop('video_filepath') + + if 'video_file' not in item and 'audio_file' not in item: + raise ValueError( + f"Manifest file {manifest_file} has invalid json line structure: {line} without proper audio/video file key." + ) + + # If the audio/video path is a relative path and does not exist, + # try to attach the parent directory of manifest to the audio path. + # Revert to the original path if the new path still doesn't exist. + # Assume that the audio path is like "wavs/xxxxxx.wav". + if 'audio_file' in item: + item['audio_file'] = get_full_path(audio_file=item['audio_file'], manifest_file=manifest_file) + if 'video_file' in item: + item['video_file'] = get_full_path(audio_file=item['video_file'], manifest_file=manifest_file) + + # Duration. + if 'duration' not in item: + raise ValueError( + f"Manifest file {manifest_file} has invalid json line structure: {line} without proper duration key." + ) + + # Text. + if 'text' in item: + pass + elif 'text_filepath' in item: + with open(item.pop('text_filepath'), 'r') as f: + item['text'] = f.read().replace('\n', '') + elif 'normalized_text' in item: + item['text'] = item['normalized_text'] + else: + item['text'] = "" + + # Optional RTTM file + if 'rttm_file' in item: + pass + elif 'rttm_filename' in item: + item['rttm_file'] = item.pop('rttm_filename') + elif 'rttm_filepath' in item: + item['rttm_file'] = item.pop('rttm_filepath') + else: + item['rttm_file'] = None + if item['rttm_file'] is not None: + item['rttm_file'] = get_full_path(audio_file=item['rttm_file'], manifest_file=manifest_file) + + # Optional audio feature file + if 'feature_file' in item: + pass + elif 'feature_filename' in item: + item['feature_file'] = item.pop('feature_filename') + elif 'feature_filepath' in item: + item['feature_file'] = item.pop('feature_filepath') + else: + item['feature_file'] = None + if item['feature_file'] is not None: + item['feature_file'] = get_full_path(audio_file=item['feature_file'], manifest_file=manifest_file) + + item = dict( + audio_file=item.get('audio_file', None), + video_file=item.get('video_file', None), + duration=item['duration'], + text=item['text'], + rttm_file=item['rttm_file'], + feature_file=item['feature_file'], + offset=item.get('offset', None), + speaker=item.get('speaker', None), + orig_sr=item.get('orig_sample_rate', None), + token_labels=item.get('token_labels', None), + lang=item.get('lang', None), + ) + return item + + +def is_tarred_dataset(audio_file: str, manifest_file: Optional[str] = None) -> bool: + if "/" in audio_file or manifest_file is None: + # audio files in a tarred dataset don't have `/` in their paths + return False + if os.path.basename(manifest_file) == "tarred_audio_manifest.json": + # the manifest file is a tarred manifest + return True + if "/sharded_manifests/" in manifest_file and re.match(r'^manifest_(\d+)\.json$', os.path.basename(manifest_file)): + # the manifest file is a sharded manifest + return True + return False + + +def get_full_path( + audio_file: Union[str, List[str]], + manifest_file: Optional[str] = None, + data_dir: Optional[str] = None, + audio_file_len_limit: int = 255, +) -> Union[str, List[str]]: + """Get full path to audio_file. + + If the audio_file is a relative path and does not exist, + try to attach the parent directory of manifest to the audio path. + Revert to the original path if the new path still doesn't exist. + Assume that the audio path is like "wavs/xxxxxx.wav". + + Args: + audio_file: path to an audio file, either absolute or assumed relative + to the manifest directory or data directory. + Alternatively, a list of paths may be provided. + manifest_file: path to a manifest file + data_dir: path to a directory containing data, use only if a manifest file is not provided + audio_file_len_limit: limit for length of audio_file when using relative paths + + Returns: + Full path to audio_file or a list of paths. + """ + if isinstance(audio_file, list): + # If input is a list, return a list of full paths + return [ + get_full_path( + audio_file=a_file, + manifest_file=manifest_file, + data_dir=data_dir, + audio_file_len_limit=audio_file_len_limit, + ) + for a_file in audio_file + ] + elif isinstance(audio_file, str): + # If input is a string, get the corresponding full path + if is_tarred_dataset(audio_file=audio_file, manifest_file=manifest_file): + logging.warning( + f"Manifest file `{manifest_file}` seems to be part of a tarred dataset, skip checking for relative paths. If this is not intended, please avoid having `/sharded_manifests/` and `tarred_audio_manifest.json` in manifest_filepath.", + mode=LogMode.ONCE, + ) + return audio_file + if ( + (len(audio_file) < audio_file_len_limit) + and not os.path.isabs(audio_file) + and not os.path.isfile(audio_file) + ): + # If audio_file is not available and the path is not absolute, the full path is assumed + # to be relative to the manifest file parent directory or data directory. + if manifest_file is None and data_dir is None: + raise ValueError(f'Use either manifest_file or data_dir to specify the data directory.') + elif manifest_file is not None and data_dir is not None: + raise ValueError( + f'Parameters manifest_file and data_dir cannot be used simultaneously. Currently manifest_file is {manifest_file} and data_dir is {data_dir}.' + ) + + # resolve the data directory + if data_dir is None: + data_dir = os.path.dirname(manifest_file) + + # assume audio_file path is relative to data_dir + audio_file_path = os.path.join(data_dir, audio_file) + + if is_datastore_path(audio_file_path): + # If audio was originally on an object store, use locally-cached path + audio_file_path = datastore_path_to_local_path(audio_file_path) + + if os.path.isfile(audio_file_path): + audio_file = os.path.abspath(audio_file_path) + else: + audio_file = expanduser(audio_file) + else: + audio_file = expanduser(audio_file) + return audio_file + else: + raise ValueError(f'Unexpected audio_file type {type(audio_file)}, audio_file {audio_file}.') diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/parsers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/parsers.py new file mode 100644 index 0000000..10a3522 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/preprocessing/parsers.py @@ -0,0 +1,252 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +A collection of simple character based parsers. These parser handle cleaning and tokenization by default. +We currently support English. +""" + +import string +from typing import List, Optional + +from nemo.collections.common.parts.preprocessing import cleaners + + +class CharParser: + """Functor for parsing raw strings into list of int tokens. + + Examples: + >>> parser = CharParser(['a', 'b', 'c']) + >>> parser('abc') + [0, 1, 2] + """ + + def __init__( + self, + labels: List[str], + *, + unk_id: int = -1, + blank_id: int = -1, + do_normalize: bool = True, + do_lowercase: bool = True, + do_tokenize: bool = True, + ): + """Creates simple mapping char parser. + + Args: + labels: List of labels to allocate indexes for. Essentially, + this is a id to str mapping. + unk_id: Index to choose for OOV words (default: -1). + blank_id: Index to filter out from final list of tokens + (default: -1). + do_normalize: True if apply normalization step before tokenizing + (default: True). + do_lowercase: True if apply lowercasing at normalizing step + (default: True). + """ + + self._labels = labels + self._unk_id = unk_id + self._blank_id = blank_id + self._do_normalize = do_normalize + self._do_lowercase = do_lowercase + self._do_tokenize = do_tokenize + + self._labels_map = {label: index for index, label in enumerate(labels)} + self._special_labels = set([label for label in labels if len(label) > 1]) + + def __call__(self, text: str) -> Optional[List[int]]: + if self._do_normalize: + text = self._normalize(text) + if text is None: + return None + + if not self._do_tokenize: + return text + + text_tokens = self._tokenize(text) + return text_tokens + + def _normalize(self, text: str) -> Optional[str]: + text = text.strip() + + if self._do_lowercase: + text = text.lower() + + return text + + def _tokenize(self, text: str) -> List[int]: + tokens = [] + # Split by word for find special labels. + for word_id, word in enumerate(text.split(' ')): + if word_id != 0: # Not first word - so we insert space before. + tokens.append(self._labels_map.get(' ', self._unk_id)) + + if word in self._special_labels: + tokens.append(self._labels_map[word]) + continue + + for char in word: + tokens.append(self._labels_map.get(char, self._unk_id)) + + # If unk_id == blank_id, OOV tokens are removed. + tokens = [token for token in tokens if token != self._blank_id] + + return tokens + + def decode(self, str_input): + r_map = {} + for k, v in self._labels_map.items(): + r_map[v] = k + r_map[len(self._labels_map)] = "" + r_map[len(self._labels_map) + 1] = "" + r_map[len(self._labels_map) + 2] = "

" + + out = [] + for i in str_input: + # Skip OOV + if i not in r_map: + continue + out.append(r_map[i.item()]) + + return "".join(out) + + +class ENCharParser(CharParser): + """Incorporates english-specific parsing logic.""" + + PUNCTUATION_TO_REPLACE = {'+': 'plus', '&': 'and', '%': 'percent'} + + def __init__(self, abbreviation_version=None, make_table=True, *args, **kwargs): + """Creates english-specific mapping char parser. + + This class overrides normalizing implementation. + + Args: + *args: Positional args to pass to `CharParser` constructor. + **kwargs: Key-value args to pass to `CharParser` constructor. + """ + + super().__init__(*args, **kwargs) + + self._table = None + if make_table: + self._table = self.__make_trans_table() + self.abbreviation_version = abbreviation_version + + def __make_trans_table(self): + punctuation = string.punctuation + + for char in self.PUNCTUATION_TO_REPLACE: + punctuation = punctuation.replace(char, '') + + for label in self._labels: + punctuation = punctuation.replace(label, '') + + table = str.maketrans(punctuation, ' ' * len(punctuation)) + + return table + + def _normalize(self, text: str) -> Optional[str]: + # noinspection PyBroadException + try: + text = cleaners.clean_text( + string=text, + table=self._table, + punctuation_to_replace=self.PUNCTUATION_TO_REPLACE, + abbreviation_version=self.abbreviation_version, + ) + except Exception: + return None + + return text + + +class RUCharParser(CharParser): + """Incorporates russian-specific parsing logic.""" + + PUNCTUATION_TO_REPLACE = {'+': 'плюс', 'ё': 'е'} + + def __init__(self, *args, **kwargs): + """Creates cyrillic-specific mapping char parser. + This class overrides normalizing implementation. + Args: + *args: Positional args to pass to `CharParser` constructor. + **kwargs: Key-value args to pass to `CharParser` constructor. + """ + + super().__init__(*args, **kwargs) + + self._table = self.__make_trans_table() + + def __make_trans_table(self): + punctuation = string.punctuation + + for char in self.PUNCTUATION_TO_REPLACE: + punctuation = punctuation.replace(char, '') + + for label in self._labels: + punctuation = punctuation.replace(label, '') + + table = str.maketrans(punctuation, ' ' * len(punctuation)) + + return table + + def _normalize(self, text: str) -> Optional[str]: + # noinspection PyBroadException + try: + text = cleaners.clean_text( + string=text, table=self._table, punctuation_to_replace=self.PUNCTUATION_TO_REPLACE, + ) + except Exception: + return None + + return text + + +NAME_TO_PARSER = {'base': CharParser, 'en': ENCharParser, 'ru': RUCharParser} + + +def make_parser(labels: Optional[List[str]] = None, name: str = 'base', **kwargs,) -> CharParser: + """Creates parser from labels, set of arguments and concise parser name. + + Args: + labels: List of labels to allocate indexes for. If set to + None then labels would be ascii table list. Essentially, this is an + id to str mapping (default: None). + name: Concise name of parser to create (default: 'base'). + (default: -1). + **kwargs: Other set of kwargs to pass to parser constructor. + + Returns: + Instance of `CharParser`. + + Raises: + ValueError: For invalid parser name. + + Examples: + >>> type(make_parser(['a', 'b', 'c'], 'en')) + ENCharParser + """ + + if name not in NAME_TO_PARSER: + raise ValueError('Invalid parser name.') + + if labels is None: + labels = list(string.printable) + + parser_type = NAME_TO_PARSER[name] + parser = parser_type(labels=labels, **kwargs) + + return parser diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/ptl_overrides.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/ptl_overrides.py new file mode 100644 index 0000000..0225ecd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/ptl_overrides.py @@ -0,0 +1,23 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from pytorch_lightning.plugins.precision import MixedPrecisionPlugin + + +class NeMoMixedPrecisionPlugin(MixedPrecisionPlugin): + def __init__(self, init_scale: float = 2 ** 32, growth_interval: int = 1000) -> None: + super().__init__(precision=16) + + self.scaler = torch.cuda.amp.GradScaler(init_scale=init_scale, growth_interval=growth_interval) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/rnn.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/rnn.py new file mode 100644 index 0000000..0b5435a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/rnn.py @@ -0,0 +1,561 @@ +# Copyright (c) 2019, Myrtle Software Limited. All rights reserved. +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch + +from nemo.utils import logging + + +def rnn( + input_size: int, + hidden_size: int, + num_layers: int, + norm: Optional[str] = None, + forget_gate_bias: Optional[float] = 1.0, + dropout: Optional[float] = 0.0, + norm_first_rnn: Optional[bool] = None, + t_max: Optional[int] = None, + weights_init_scale: float = 1.0, + hidden_hidden_bias_scale: float = 0.0, + proj_size: int = 0, +) -> torch.nn.Module: + """ + Utility function to provide unified interface to common LSTM RNN modules. + + Args: + input_size: Input dimension. + + hidden_size: Hidden dimension of the RNN. + + num_layers: Number of RNN layers. + + norm: Optional string representing type of normalization to apply to the RNN. + Supported values are None, batch and layer. + + forget_gate_bias: float, set by default to 1.0, which constructs a forget gate + initialized to 1.0. + Reference: + [An Empirical Exploration of Recurrent Network Architectures](http://proceedings.mlr.press/v37/jozefowicz15.pdf) + + dropout: Optional dropout to apply to end of multi-layered RNN. + + norm_first_rnn: Whether to normalize the first RNN layer. + + t_max: int value, set to None by default. If an int is specified, performs Chrono Initialization + of the LSTM network, based on the maximum number of timesteps `t_max` expected during the course + of training. + Reference: + [Can recurrent neural networks warp time?](https://openreview.net/forum?id=SJcKhk-Ab) + + weights_init_scale: Float scale of the weights after initialization. Setting to lower than one + sometimes helps reduce variance between runs. + + hidden_hidden_bias_scale: Float scale for the hidden-to-hidden bias scale. Set to 0.0 for + the default behaviour. + + Returns: + A RNN module + """ + if norm not in [None, "batch", "layer"]: + raise ValueError(f"unknown norm={norm}") + + if norm is None: + return LSTMDropout( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + dropout=dropout, + forget_gate_bias=forget_gate_bias, + t_max=t_max, + weights_init_scale=weights_init_scale, + hidden_hidden_bias_scale=hidden_hidden_bias_scale, + proj_size=proj_size, + ) + + if norm == "batch": + return BNRNNSum( + input_size=input_size, + hidden_size=hidden_size, + rnn_layers=num_layers, + batch_norm=True, + dropout=dropout, + forget_gate_bias=forget_gate_bias, + t_max=t_max, + norm_first_rnn=norm_first_rnn, + weights_init_scale=weights_init_scale, + hidden_hidden_bias_scale=hidden_hidden_bias_scale, + proj_size=proj_size, + ) + + if norm == "layer": + return torch.jit.script( + ln_lstm( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + dropout=dropout, + forget_gate_bias=forget_gate_bias, + t_max=t_max, + weights_init_scale=weights_init_scale, + hidden_hidden_bias_scale=hidden_hidden_bias_scale, + ) + ) + + +class OverLastDim(torch.nn.Module): + """Collapses a tensor to 2D, applies a module, and (re-)expands the tensor. + An n-dimensional tensor of shape (s_1, s_2, ..., s_n) is first collapsed to + a tensor with shape (s_1*s_2*...*s_n-1, s_n). The module is called with + this as input producing (s_1*s_2*...*s_n-1, s_n') --- note that the final + dimension can change. This is expanded to (s_1, s_2, ..., s_n-1, s_n') and + returned. + Args: + module (torch.nn.Module): Module to apply. Must accept a 2D tensor as + input and produce a 2D tensor as output, optionally changing the + size of the last dimension. + """ + + def __init__(self, module: torch.nn.Module): + super().__init__() + self.module = module + + def forward(self, x: torch.Tensor) -> torch.Tensor: + *dims, _ = x.size() + + reduced_dims = 1 + for dim in dims: + reduced_dims *= dim + + x = x.view(reduced_dims, -1) + x = self.module(x) + x = x.view(*dims, -1) + return x + + +class LSTMDropout(torch.nn.Module): + def __init__( + self, + input_size: int, + hidden_size: int, + num_layers: int, + dropout: Optional[float], + forget_gate_bias: Optional[float], + t_max: Optional[int] = None, + weights_init_scale: float = 1.0, + hidden_hidden_bias_scale: float = 0.0, + proj_size: int = 0, + ): + """Returns an LSTM with forget gate bias init to `forget_gate_bias`. + Args: + input_size: See `torch.nn.LSTM`. + hidden_size: See `torch.nn.LSTM`. + num_layers: See `torch.nn.LSTM`. + dropout: See `torch.nn.LSTM`. + + forget_gate_bias: float, set by default to 1.0, which constructs a forget gate + initialized to 1.0. + Reference: + [An Empirical Exploration of Recurrent Network Architectures](http://proceedings.mlr.press/v37/jozefowicz15.pdf) + + t_max: int value, set to None by default. If an int is specified, performs Chrono Initialization + of the LSTM network, based on the maximum number of timesteps `t_max` expected during the course + of training. + Reference: + [Can recurrent neural networks warp time?](https://openreview.net/forum?id=SJcKhk-Ab) + + weights_init_scale: Float scale of the weights after initialization. Setting to lower than one + sometimes helps reduce variance between runs. + + hidden_hidden_bias_scale: Float scale for the hidden-to-hidden bias scale. Set to 0.0 for + the default behaviour. + + Returns: + A `torch.nn.LSTM`. + """ + super(LSTMDropout, self).__init__() + + self.lstm = torch.nn.LSTM( + input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, dropout=dropout, proj_size=proj_size + ) + + if t_max is not None: + # apply chrono init + for name, v in self.lstm.named_parameters(): + if 'bias' in name: + p = getattr(self.lstm, name) + n = p.nelement() + hidden_size = n // 4 + p.data.fill_(0) + p.data[hidden_size : 2 * hidden_size] = torch.log( + torch.nn.init.uniform_(p.data[0:hidden_size], 1, t_max - 1) + ) + # forget gate biases = log(uniform(1, Tmax-1)) + p.data[0:hidden_size] = -p.data[hidden_size : 2 * hidden_size] + # input gate biases = -(forget gate biases) + + elif forget_gate_bias is not None: + for name, v in self.lstm.named_parameters(): + if "bias_ih" in name: + bias = getattr(self.lstm, name) + bias.data[hidden_size : 2 * hidden_size].fill_(forget_gate_bias) + if "bias_hh" in name: + bias = getattr(self.lstm, name) + bias.data[hidden_size : 2 * hidden_size] *= float(hidden_hidden_bias_scale) + + self.dropout = torch.nn.Dropout(dropout) if dropout else None + + for name, v in self.named_parameters(): + if 'weight' in name or 'bias' in name: + v.data *= float(weights_init_scale) + + def forward( + self, x: torch.Tensor, h: Optional[Tuple[torch.Tensor, torch.Tensor]] = None + ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + x, h = self.lstm(x, h) + + if self.dropout: + x = self.dropout(x) + + return x, h + + +class RNNLayer(torch.nn.Module): + """A single RNNLayer with optional batch norm.""" + + def __init__( + self, + input_size: int, + hidden_size: int, + rnn_type: torch.nn.Module = torch.nn.LSTM, + batch_norm: bool = True, + forget_gate_bias: Optional[float] = 1.0, + t_max: Optional[int] = None, + weights_init_scale: float = 1.0, + hidden_hidden_bias_scale: float = 0.0, + proj_size: int = 0, + ): + super().__init__() + + if batch_norm: + self.bn = OverLastDim(torch.nn.BatchNorm1d(input_size)) + + if isinstance(rnn_type, torch.nn.LSTM) and not batch_norm: + # batch_norm will apply bias, no need to add a second to LSTM + self.rnn = LSTMDropout( + input_size=input_size, + hidden_size=hidden_size, + num_layers=1, + dropout=0.0, + forget_gate_bias=forget_gate_bias, + t_max=t_max, + weights_init_scale=weights_init_scale, + hidden_hidden_bias_scale=hidden_hidden_bias_scale, + proj_size=proj_size, + ) + else: + self.rnn = rnn_type(input_size=input_size, hidden_size=hidden_size, bias=not batch_norm) + + def forward( + self, x: torch.Tensor, hx: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None + ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + if hasattr(self, 'bn'): + x = x.contiguous() + x = self.bn(x) + x, h = self.rnn(x, hx=hx) + return x, h + + def _flatten_parameters(self): + self.rnn.flatten_parameters() + + +class BNRNNSum(torch.nn.Module): + """RNN wrapper with optional batch norm. + Instantiates an RNN. If it is an LSTM it initialises the forget gate + bias =`lstm_gate_bias`. Optionally applies a batch normalisation layer to + the input with the statistics computed over all time steps. If dropout > 0 + then it is applied to all layer outputs except the last. + """ + + def __init__( + self, + input_size: int, + hidden_size: int, + rnn_type: torch.nn.Module = torch.nn.LSTM, + rnn_layers: int = 1, + batch_norm: bool = True, + dropout: Optional[float] = 0.0, + forget_gate_bias: Optional[float] = 1.0, + norm_first_rnn: bool = False, + t_max: Optional[int] = None, + weights_init_scale: float = 1.0, + hidden_hidden_bias_scale: float = 0.0, + proj_size: int = 0, + ): + super().__init__() + self.rnn_layers = rnn_layers + + self.layers = torch.nn.ModuleList() + for i in range(rnn_layers): + final_layer = (rnn_layers - 1) == i + + self.layers.append( + RNNLayer( + input_size, + hidden_size, + rnn_type=rnn_type, + batch_norm=batch_norm and (norm_first_rnn or i > 0), + forget_gate_bias=forget_gate_bias, + t_max=t_max, + weights_init_scale=weights_init_scale, + hidden_hidden_bias_scale=hidden_hidden_bias_scale, + proj_size=proj_size, + ) + ) + + if dropout is not None and dropout > 0.0 and not final_layer: + self.layers.append(torch.nn.Dropout(dropout)) + + input_size = hidden_size + + def forward( + self, x: torch.Tensor, hx: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None + ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + hx = self._parse_hidden_state(hx) + + hs = [] + cs = [] + rnn_idx = 0 + for layer in self.layers: + if isinstance(layer, torch.nn.Dropout): + x = layer(x) + else: + x, h_out = layer(x, hx=hx[rnn_idx]) + hs.append(h_out[0]) + cs.append(h_out[1]) + rnn_idx += 1 + del h_out + + h_0 = torch.stack(hs, dim=0) + c_0 = torch.stack(cs, dim=0) + return x, (h_0, c_0) + + def _parse_hidden_state( + self, hx: Optional[Tuple[torch.Tensor, torch.Tensor]] + ) -> Union[List[None], List[Tuple[torch.Tensor, torch.Tensor]]]: + """ + Dealing w. hidden state: + Typically in pytorch: (h_0, c_0) + h_0 = ``[num_layers * num_directions, batch, hidden_size]`` + c_0 = ``[num_layers * num_directions, batch, hidden_size]`` + """ + if hx is None: + return [None] * self.rnn_layers + else: + h_0, c_0 = hx + + if h_0.shape[0] != self.rnn_layers: + raise ValueError( + 'Provided initial state value `h_0` must be of shape : ' + '[num_layers * num_directions, batch, hidden_size]' + ) + + return [(h_0[i], c_0[i]) for i in range(h_0.shape[0])] + + def _flatten_parameters(self): + for layer in self.layers: + if isinstance(layer, (torch.nn.LSTM, torch.nn.GRU, torch.nn.RNN)): + layer._flatten_parameters() + + +class StackTime(torch.nn.Module): + """ + Stacks time within the feature dim, so as to behave as a downsampling operation. + """ + + def __init__(self, factor: int): + super().__init__() + self.factor = int(factor) + + def forward(self, x: List[Tuple[torch.Tensor]]) -> Tuple[torch.Tensor, torch.Tensor]: + # T, B, U + x, x_lens = x + seq = [x] + for i in range(1, self.factor): + tmp = torch.zeros_like(x) + tmp[:-i, :, :] = x[i:, :, :] + seq.append(tmp) + x_lens = torch.ceil(x_lens.float() / self.factor).int() + return torch.cat(seq, dim=2)[:: self.factor, :, :], x_lens + + +def ln_lstm( + input_size: int, + hidden_size: int, + num_layers: int, + dropout: Optional[float], + forget_gate_bias: Optional[float], + t_max: Optional[int], + weights_init_scale: Optional[float] = None, # ignored + hidden_hidden_bias_scale: Optional[float] = None, # ignored +) -> torch.nn.Module: + """Returns a ScriptModule that mimics a PyTorch native LSTM.""" + # The following are not implemented. + if dropout is not None and dropout != 0.0: + raise ValueError('`dropout` not supported with LayerNormLSTM') + + if t_max is not None: + logging.warning("LayerNormLSTM does not support chrono init via `t_max`") + + if weights_init_scale is not None: + logging.warning("`weights_init_scale` is ignored for LayerNormLSTM") + + if hidden_hidden_bias_scale is not None: + logging.warning("`hidden_hidden_bias_scale` is ignored for LayerNormLSTM") + + return StackedLSTM( + num_layers, + LSTMLayer, + first_layer_args=[LayerNormLSTMCell, input_size, hidden_size, forget_gate_bias], + other_layer_args=[LayerNormLSTMCell, hidden_size, hidden_size, forget_gate_bias], + ) + + +class LSTMLayer(torch.nn.Module): + def __init__(self, cell, *cell_args): + super(LSTMLayer, self).__init__() + self.cell = cell(*cell_args) + + def forward( + self, input: torch.Tensor, state: Tuple[torch.Tensor, torch.Tensor] + ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + inputs = input.unbind(0) + outputs = [] + for i in range(len(inputs)): + out, state = self.cell(inputs[i], state) + outputs += [out] + return torch.stack(outputs), state + + +class LayerNormLSTMCell(torch.nn.Module): + def __init__(self, input_size, hidden_size, forget_gate_bias): + super().__init__() + self.input_size = input_size + self.hidden_size = hidden_size + self.weight_ih = torch.nn.Parameter(torch.randn(4 * hidden_size, input_size)) + self.weight_hh = torch.nn.Parameter(torch.randn(4 * hidden_size, hidden_size)) + + # LayerNorm provide learnable biases + self.layernorm_i = torch.nn.LayerNorm(4 * hidden_size) + self.layernorm_h = torch.nn.LayerNorm(4 * hidden_size) + self.layernorm_c = torch.nn.LayerNorm(hidden_size) + + self.reset_parameters() + + self.layernorm_i.bias.data[hidden_size : 2 * hidden_size].fill_(0.0) + self.layernorm_h.bias.data[hidden_size : 2 * hidden_size].fill_(forget_gate_bias) + + def reset_parameters(self): + stdv = 1.0 / math.sqrt(self.hidden_size) + for weight in self.parameters(): + torch.nn.init.uniform_(weight, -stdv, stdv) + + def forward( + self, input: torch.Tensor, state: Tuple[torch.Tensor, torch.Tensor] + ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + hx, cx = state + igates = self.layernorm_i(torch.mm(input, self.weight_ih.t())) + hgates = self.layernorm_h(torch.mm(hx, self.weight_hh.t())) + gates = igates + hgates + ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1) + + ingate = torch.sigmoid(ingate) + forgetgate = torch.sigmoid(forgetgate) + cellgate = torch.tanh(cellgate) + outgate = torch.sigmoid(outgate) + + cy = self.layernorm_c((forgetgate * cx) + (ingate * cellgate)) + hy = outgate * torch.tanh(cy) + + return hy, (hy, cy) + + +def init_stacked_lstm( + num_layers: int, layer: torch.nn.Module, first_layer_args: List, other_layer_args: List +) -> torch.nn.ModuleList: + layers = [layer(*first_layer_args)] + [layer(*other_layer_args) for _ in range(num_layers - 1)] + return torch.nn.ModuleList(layers) + + +class StackedLSTM(torch.nn.Module): + def __init__(self, num_layers: int, layer: torch.nn.Module, first_layer_args: List, other_layer_args: List): + super(StackedLSTM, self).__init__() + self.layers: torch.nn.ModuleList = init_stacked_lstm(num_layers, layer, first_layer_args, other_layer_args) + + def forward( + self, input: torch.Tensor, states: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] + ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]: + if states is None: + temp_states: List[Tuple[torch.Tensor, torch.Tensor]] = [] + batch = input.size(1) + for layer in self.layers: + temp_states.append( + ( + torch.zeros(batch, layer.cell.hidden_size, dtype=input.dtype, device=input.device), + torch.zeros(batch, layer.cell.hidden_size, dtype=input.dtype, device=input.device), + ) + ) + + states = temp_states + + output_states: List[Tuple[torch.Tensor, torch.Tensor]] = [] + output = input + for i, rnn_layer in enumerate(self.layers): + state = states[i] + output, out_state = rnn_layer(output, state) + output_states.append(out_state) + i += 1 + return output, output_states + + +def label_collate(labels, device=None): + """Collates the label inputs for the rnn-t prediction network. + If `labels` is already in torch.Tensor form this is a no-op. + + Args: + labels: A torch.Tensor List of label indexes or a torch.Tensor. + device: Optional torch device to place the label on. + + Returns: + A padded torch.Tensor of shape (batch, max_seq_len). + """ + + if isinstance(labels, torch.Tensor): + return labels.type(torch.int64) + if not isinstance(labels, (list, tuple)): + raise ValueError(f"`labels` should be a list or tensor not {type(labels)}") + + batch_size = len(labels) + max_len = max(len(label) for label in labels) + + cat_labels = np.full((batch_size, max_len), fill_value=0.0, dtype=np.int32) + for e, l in enumerate(labels): + cat_labels[e, : len(l)] = l + labels = torch.tensor(cat_labels, dtype=torch.int64, device=device) + + return labels diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/transformer_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/transformer_utils.py new file mode 100644 index 0000000..a40e7ee --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/transformer_utils.py @@ -0,0 +1,79 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn as nn + +__all__ = ['NEG_INF', 'form_attention_mask', 'transformer_weights_init', 'mask_padded_tokens'] + +NEG_INF = -10000.0 + + +def form_attention_mask(input_mask, diagonal=None): + """ + Build attention mask with optional masking of future tokens we forbid + to attend to (e.g. as it is in Transformer decoder). + + Args: + input_mask: binary mask of size B x L with 1s corresponding to valid + tokens and 0s corresponding to padding tokens + diagonal: diagonal where triangular future mask starts + None -- do not mask anything + 0 -- regular translation or language modeling future masking + 1 -- query stream masking as in XLNet architecture + Returns: + attention_mask: mask of size B x 1 x L x L with 0s corresponding to + tokens we plan to attend to and -10000 otherwise + """ + + if input_mask is None: + return None + attn_shape = (1, input_mask.shape[1], input_mask.shape[1]) + attn_mask = input_mask.to(dtype=bool).unsqueeze(1) + if diagonal is not None: + future_mask = torch.tril(torch.ones(attn_shape, dtype=torch.bool, device=input_mask.device), diagonal) + attn_mask = attn_mask & future_mask + attention_mask = (1 - attn_mask.to(torch.float)) * NEG_INF + return attention_mask.unsqueeze(1) + + +def transformer_weights_init(module, std_init_range=0.02, xavier=True): + """ + Initialize different weights in Transformer model. + + Args: + module: torch.nn.Module to be initialized + std_init_range: standard deviation of normal initializer + xavier: if True, xavier initializer will be used in Linear layers + as was proposed in AIAYN paper, otherwise normal initializer + will be used (like in BERT paper) + """ + + if isinstance(module, nn.Linear): + if xavier: + nn.init.xavier_uniform_(module.weight) + else: + nn.init.normal_(module.weight, mean=0.0, std=std_init_range) + if module.bias is not None: + nn.init.constant_(module.bias, 0.0) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=std_init_range) + elif isinstance(module, nn.LayerNorm): + nn.init.constant_(module.weight, 1.0) + nn.init.constant_(module.bias, 0.0) + + +def mask_padded_tokens(tokens, pad_id): + mask = tokens != pad_id + return mask diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/utils.py new file mode 100644 index 0000000..c22c433 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/parts/utils.py @@ -0,0 +1,107 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import os +from typing import Iterable, List + +import torch.nn as nn + +__all__ = ['if_exist', '_compute_softmax', 'flatten'] + +activation_registry = { + "identity": nn.Identity, + "hardtanh": nn.Hardtanh, + "relu": nn.ReLU, + "selu": nn.SELU, + "swish": nn.SiLU, + "silu": nn.SiLU, + "gelu": nn.GELU, +} + + +def if_exist(outfold: str, files: List[str]): + """ + Returns true if all given files exist in the given folder + Args: + outfold: folder path + files: list of file names relative to outfold + """ + if not os.path.exists(outfold): + return False + for file in files: + if not os.path.exists(f'{outfold}/{file}'): + return False + return True + + +def _compute_softmax(scores): + """Compute softmax probability over raw logits.""" + if not scores: + return [] + + max_score = None + for score in scores: + if max_score is None or score > max_score: + max_score = score + + exp_scores = [] + total_sum = 0.0 + for score in scores: + x = math.exp(score - max_score) + exp_scores.append(x) + total_sum += x + + probs = [] + for score in exp_scores: + probs.append(score / total_sum) + return probs + + +def flatten_iterable(iter: Iterable) -> Iterable: + """Flatten an iterable which contains values or + iterables with values. + + Args: + iter: iterable containing values at the deepest level. + + Returns: + A flat iterable containing values. + """ + for it in iter: + if isinstance(it, str) or not isinstance(it, Iterable): + yield it + else: + yield from flatten_iterable(it) + + +def flatten(list_in: List) -> List: + """Flatten a list of (nested lists of) values into a flat list. + + Args: + list_in: list of values, possibly nested + + Returns: + A flat list of values. + """ + return list(flatten_iterable(list_in)) + + +def extend_instance(obj, mixin): + """Apply mixins to a class instance after creation""" + base_cls = obj.__class__ + base_cls_name = obj.__class__.__name__ + obj.__class__ = type( + base_cls_name, (mixin, base_cls), {} + ) # mixin needs to go first for our forward() logic to work diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/__init__.py new file mode 100644 index 0000000..7503986 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/__init__.py @@ -0,0 +1,23 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.common.tokenizers.aggregate_tokenizer import AggregateTokenizer +from nemo.collections.common.tokenizers.bytelevel_tokenizers import ByteLevelTokenizer +from nemo.collections.common.tokenizers.canary_tokenizer import CanaryTokenizer +from nemo.collections.common.tokenizers.char_tokenizer import CharTokenizer +from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer +from nemo.collections.common.tokenizers.regex_tokenizer import RegExTokenizer +from nemo.collections.common.tokenizers.sentencepiece_tokenizer import SentencePieceTokenizer +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.collections.common.tokenizers.word_tokenizer import WordTokenizer diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/aggregate_tokenizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/aggregate_tokenizer.py new file mode 100644 index 0000000..9c003c3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/aggregate_tokenizer.py @@ -0,0 +1,233 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, List, Union + +import numpy as np + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.utils import logging + +__all__ = ['AggregateTokenizer'] + + +class DummyTokenizer: + def __init__(self, vocab): + self.vocab = vocab + self.vocab_size = len(vocab) + + # minimum compatibility + # since all the monolingual tokenizers have a vocab + # additional methods could be added here + def get_vocab(self): + return self.vocab + + +class AggregateTokenizer(TokenizerSpec): + ''' + AggregateTokenizer, allowing one to combine multiple regular monolongual tokenizers into one tokenizer. + The intuition is that we can use existing tokenizers "as is", without retraining, and associate each tokenizer with a language id + during text processing (language id will be used to route the incoming text sample to the right tokenizer) + as well as a token id range for detokenization (e.g. [0..127] for tokenizer A, [128..255] for tokenizer B) so + that the orignal text could be reconstructed. Note that we assume that the incoming dict of langs / tokenizers + is ordered, e.g. the first tokenizer will be assigned a lower interval of token ids + Args: + tokenizers: dict of tokenizers, keys are lang ids, values are actual tokenizers + ''' + + def __init__(self, tokenizers: Dict): + + self.tokenizers_dict = tokenizers + self.vocabulary = [] + + # the tokenizers should produce non-overlapping, ordered token ids + # keys are language ids + self.token_id_offset = {} + + # keys are tokenizer numbers + self.token_id_offset_by_tokenizer_num = {} + offset = 0 + i = 0 + for lang, tokenizer in self.tokenizers_dict.items(): + self.token_id_offset[lang] = offset + self.token_id_offset_by_tokenizer_num[i] = offset + offset += len(tokenizer.vocab) + i += 1 + + for tokenizer in self.tokenizers_dict.values(): + self.vocabulary.extend(tokenizer.vocab) + + self.vocab_size = len(self.vocabulary) + logging.info(f'Aggregate vocab size: {self.vocab_size}') + + # for compatibility purposes only -- right now only the get_vocab method + # is supported, returning the joint vocab across all tokenizers + self.tokenizer = DummyTokenizer(self.vocabulary) + + # lookup tables to speed up token to text operations + # if there are two tokenizers, [0,1], ['en', 'es'], each with 128 tokens, the aggregate tokenizer + # token range will be [0,255]. The below method provides three look up tables: + # one, to convert the incoming token id -- e.g. 200 into its real id (200-127 = 73) + # second, to compute the tokenizer id that should process that token (1) + # third, the compute the lang id for that token ('es') + offset_token_ids_by_token_id, tokenizers_by_token_id, langs_by_token_id = self._calculate_offsets() + + self.offset_token_ids_by_token_id = offset_token_ids_by_token_id + self.tokenizers_by_token_id = tokenizers_by_token_id + self.langs_by_token_id = langs_by_token_id + + def _calculate_offsets(self): + offsets = {} + tokenizers = {} + langs = {} + cur_num = 0 + tot = len(self.tokenizers_dict) + for id in range(len(self.vocabulary)): + off_id = id - list(self.token_id_offset.values())[cur_num] + if cur_num + 1 < tot: + if id >= list(self.token_id_offset.values())[cur_num + 1]: + cur_num += 1 + off_id = id - list(self.token_id_offset.values())[cur_num] + offsets[id] = off_id + tokenizers[id] = list(self.tokenizers_dict.values())[cur_num] + langs[id] = list(self.tokenizers_dict.keys())[cur_num] + + return offsets, tokenizers, langs + + def text_to_tokens(self, text, lang_id): + tokenizer = self.tokenizers_dict[lang_id] + return tokenizer.text_to_tokens(text) + + def text_to_ids(self, text, lang_id): + tokenizer = self.tokenizers_dict[lang_id] + token_ids = tokenizer.text_to_ids(text) + token_ids[:] = [t + self.token_id_offset[lang_id] for t in token_ids] + + return token_ids + + def tokens_to_text(self, tokens, lang_id): + if isinstance(tokens, np.ndarray): + tokens = tokens.tolist() + + tokenizer = self.tokenizers_dict[lang_id] + return tokenizer.decode_pieces(tokens) + + def ids_to_text(self, ids): + if isinstance(ids, np.ndarray): + ids = ids.tolist() + + tokens = [] + for id in ids: + offset_id = self.offset_token_ids_by_token_id[id] + tokenizer = self.tokenizers_by_token_id[id] + tokens.extend(tokenizer.ids_to_tokens([offset_id])) + text = ''.join(tokens).replace('▁', ' ') + + return text + + def token_to_id(self, token, lang_id): + tokenizer = self.tokenizers_dict[lang_id] + return tokenizer.token_to_id(token) + self.token_id_offset[lang_id] + + def ids_to_tokens(self, ids): + tokens = [] + + for id in ids: + offset_id = self.offset_token_ids_by_token_id[id] + tokenizer = self.tokenizers_by_token_id[id] + token = tokenizer.ids_to_tokens([offset_id])[0] + tokens.append(token) + + return tokens + + def ids_to_text_and_langs(self, ids): + text_and_langs = [] + + for id in ids: + offset_id = self.offset_token_ids_by_token_id[id] + tokenizer = self.tokenizers_by_token_id[id] + token = tokenizer.ids_to_tokens([offset_id])[0] + text = token.replace('▁', ' ') + text = text.strip() # strip for display purposes + lang = self.langs_by_token_id[id] + text_and_langs.append({'char': text, 'lang': lang}) + + return text_and_langs + + def ids_to_words_and_langs(self, ids): + words_and_langs = [] + + word_ids = [] # tokens belonging to the current word + for id in ids: + offset_id = self.offset_token_ids_by_token_id[id] + tokenizer = self.tokenizers_by_token_id[id] + token = tokenizer.ids_to_tokens([offset_id])[0] + if token.startswith('▁'): + if len(word_ids) > 0: # if this isn't the first word + word = self.ids_to_text(word_ids) + word = word.strip() # strip for display purposes + lang = self.ids_to_lang(word_ids) + wl = {'word': word, 'lang': lang} + words_and_langs.append(wl) + word_ids = [] + word_ids.append(id) + + if len(word_ids) > 0: # the last tokens + word = self.ids_to_text(word_ids) + word = word.strip() # strip for display purposes + lang = self.ids_to_lang(word_ids) + wl = {'word': word, 'lang': lang} + words_and_langs.append(wl) + + return words_and_langs + + def ids_to_lang(self, ids): + lang_cnts = {} + + for id in ids: + lang = self.langs_by_token_id[id] + lang_cnt = lang_cnts.get(lang) + if lang_cnt is not None: + lang_cnts[lang] = lang_cnt + 1 + else: + lang_cnts[lang] = 1 + + max_lang = '' + max_lang_cnt = -1 + for lang, lang_cnt in lang_cnts.items(): + if lang_cnt > max_lang_cnt: + max_lang = lang + max_lang_cnt = lang_cnt + + return max_lang + + def tokens_to_ids(self, tokens: Union[str, List[str]], langs: Union[str, List[str]]) -> Union[int, List[int]]: + if isinstance(tokens, str): + tokens = [tokens] + if isinstance(langs, str): + langs = [langs] + + ids = [] + for i, token in enumerate(tokens): + lang_id = langs[i] + ids.append(self.token_to_id(token, lang_id)) + return ids + + @property + def vocab(self): + return self.vocabulary + + @property + def langs(self): + return list(self.tokenizers_dict.keys()) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/bytelevel_tokenizers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/bytelevel_tokenizers.py new file mode 100644 index 0000000..eb965b0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/bytelevel_tokenizers.py @@ -0,0 +1,111 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, List, Optional, Union + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec + +__all__ = ['ByteLevelProcessor', 'ByteLevelTokenizer'] + + +class ByteLevelProcessor: + """ + A very basic tokenization and detokenization class for use with byte-level + tokenization. + """ + + def detokenize(self, tokens: List[str]) -> str: + return ' '.join(tokens) + + def tokenize(self, text) -> str: + return text + + def normalize(self, text) -> str: + return text + + +class ByteLevelTokenizer(TokenizerSpec): + def __init__(self, special_tokens: Optional[Union[Dict[str, str], List[str]]] = None): + self.vocab_size = 259 + self.special_start = 256 + self.special_token_to_id = { + self.pad_id: self.pad_id, + self.bos_id: self.bos_id, + self.eos_id: self.eos_id, + } + special_tokens = {} if special_tokens is None else special_tokens + for tok in special_tokens: + self.special_start -= 1 + self.special_token_to_id[tok] = self.special_start + + self.id_to_special_token = {v: k for k, v in self.special_token_to_id.items()} + + # no distinction between tokens and ids. + def text_to_tokens(self, text): + return self.text_to_ids(text) + + def tokens_to_text(self, tokens): + return self.ids_to_text(tokens) + + def text_to_ids(self, text): + return list(text.encode('utf-8')) + + def ids_to_text(self, ids): + # remove special tokens. + ids = [x for x in ids if x < self.special_start] + return bytes(ids).decode('utf-8', errors='ignore').rstrip() + + def tokens_to_ids(self, tokens): + if isinstance(tokens, str): + tokens = [tokens] + ids = [] + for token in tokens: + ids.append(self.token_to_id(token)) + return ids + + def ids_to_tokens(self, ids): + if isinstance(ids, int): + ids = [ids] + tokens = [] + for id in ids: + tokens.append(self.id_to_token(id)) + return tokens + + def token_to_id(self, token): + if token in self.special_token_to_id: + return self.special_token_to_id[token] + else: + return token + + def id_to_token(self, id): + if id < self.special_start: + return id + else: + return self.id_to_special_token[id] + + @property + def pad_id(self): + return 256 + + @property + def bos_id(self): + return 257 + + @property + def eos_id(self): + return 258 + + @property + def unk_id(self): + return 259 # unused diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/canary_tokenizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/canary_tokenizer.py new file mode 100644 index 0000000..aed95c1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/canary_tokenizer.py @@ -0,0 +1,92 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +from functools import cached_property +from pathlib import Path +from typing import Dict, List + +from nemo.collections.common.tokenizers.aggregate_tokenizer import AggregateTokenizer +from nemo.collections.common.tokenizers.sentencepiece_tokenizer import SentencePieceTokenizer, create_spt_model + +from nemo.utils import logging + +__all__ = ['CanaryTokenizer'] + +# Default tokens for compatibility with Canary. +DEFAULT_TOKENS = ["<|nospeech|>", "", "<|endoftext|>", "<|startoftranscript|>", "<|pnc|>", "<|nopnc|>"] + + +class CanaryTokenizer(AggregateTokenizer): + """ + Thin wrapper around AggregateTokenizer to provide quick access to special tokens + """ + + def __init__(self, tokenizers: Dict): + super().__init__(tokenizers) + + # for easy access of special tokens + self.special_tokens = {} + for special in tokenizers['spl_tokens'].vocab: + # Search for special prompting tokens + if (special.startswith("<|") and special.endswith("|>")) or special == "": + self.special_tokens[special] = self.token_to_id(special, lang_id='spl_tokens') + + @cached_property + def eos_id(self) -> int: + return self.special_tokens["<|endoftext|>"] + + @cached_property + def bos_id(self) -> int: + return self.special_tokens["<|startoftranscript|>"] + + @cached_property + def nospeech_id(self) -> int: + return self.special_tokens["<|nospeech|>"] + + @cached_property + def pad_id(self) -> int: + return self.special_tokens[""] + + def spl_token_to_id(self, token): + if token_id := self.special_tokens.get(f"<|{token}|>", None): + return token_id + raise KeyError(f"Token {token} not found in tokenizer.") + + @staticmethod + def build_special_tokenizer( + tokens: List[str], model_dir: str | Path, force_rebuild: bool = False + ) -> SentencePieceTokenizer: + if force_rebuild: + logging.info("Building special tokenizer") + # Checks for artifacts of previous build. + for file in ["tokenizer.model", "tokenizer.vocab", "vocab.txt", "train_text.txt"]: + if os.path.exists(file): + os.remove(file) + tokens = DEFAULT_TOKENS + [f"<|{t}|>" for t in tokens] + output_dir = Path(model_dir) + output_dir.mkdir(exist_ok=True, parents=True) + text_path = output_dir / "train_text.txt" + train_text = "\n".join(tokens) + text_path.write_text(train_text) + model_path = output_dir / "tokenizer.model" + create_spt_model( + str(text_path), + vocab_size=len(tokens) + 2, + sample_size=-1, + do_lower_case=False, + output_dir=str(output_dir), + user_defined_symbols=tokens, + ) + spl_tokenizer = SentencePieceTokenizer(str(model_path)) + return spl_tokenizer diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/char_tokenizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/char_tokenizer.py new file mode 100644 index 0000000..2767425 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/char_tokenizer.py @@ -0,0 +1,521 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import json +import os +import warnings +from collections import Counter +from enum import Enum +from pathlib import Path +from typing import Dict, List, NewType, Optional, Union + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec + +__all__ = ['CharTokenizer'] + + +NUMBER_OF_CHARACTERS_READ_BUFFER_SIZE = 10 ** 7 + + +class SpecialTokenString(Enum): + MASK = 'mask' + BOS = 'bos' + EOS = 'eos' + PAD = 'pad' + SEP = 'sep' + CLS = 'cls' + UNK = 'unk' + + @classmethod + def has_value(cls, value): + return value in cls._value2member_map_ + + +SpecialTokenStringType = NewType('SpecialTokenString', SpecialTokenString) + + +class CharTokenizer(TokenizerSpec): + rf""" + Each character is a token. + Args: + vocab_file: path to file with vocabulary for a tokenizer. The file consists of valid Python string literals + separated by the new line character. Such literals must contain 1 character. Examples of valid Python + literals: ``'a'``, ``'\n'``, ``"'"``, ``'ж'``, ``'\u8976'``. Optionally the first line in the file can be a + JSON dictionary of special tokens. The keys of the special tokens dictionary are ``'mask_token'``, + ``'bos_token'`` and so on. Some special tokens names can be omitted in the special tokens dictionary line. + A file ``vocab_file`` has to be in ``'utf-8'`` encoding. + mask_token: mask token. The following is applicable to all special tokens. Parameter ``mask_token`` is used + for adding mask token to vocabulary or for modification of mask token present in special tokens dictionary + in the first line of file ``vocab_file``. Parameter ``mask_token`` can be either of type ``bool`` or a + ``str`` of length 1. + + If ``mask_token`` is ``bool`` it has to be ``False``. If ``mask_token`` is ``True`` an exception is raised. + If ``mask_token`` is ``False`` and ``mask_token`` is present in special tokens dictionary in vocabulary + file ``vocab_file``, then ``mask_token`` is remove from special tokens dictionary. + + If the parameter ``mask_token`` is a string, then such strings in the input sequence are interpreted as + mask tokens. + bos_token: the beginning of sequence token. See more in ``mask_token`` parameter description. + eos_token: the end of sequence token. Usually equal to sep_token. See more in ``mask_token`` parameter + description. + pad_token: token to use for padding. See more in ``mask_token`` parameter description. + sep_token: token used for separating sequences. See more in ``mask_token`` parameter description. + cls_token: class token. Usually equal to bos_token. See more in ``mask_token`` parameter description. + unk_token: token to use for unknown tokens. If the parameter ``unk_token`` is set and there is a character + in the input of ``text_to_ids`` of ``text_to_tokens`` methods which is not in the vocabulary, then + such an unknown character is tokenized into ``unk_token``. If the parameter ``unk_token`` is ``False``, + then unknown tokens are discarded. See more in ``mask_token`` parameter description. + special_token_to_prepend: special token to prepend to the output of ``text_to_ids`` of ``text_to_tokens`` + methods. This option can be used if you decide to add EOS and BOS tokens to the input on the stage of + tokenization. Possible options are: {[None] + [e.value for e in SpecialTokenString]}. + special_token_to_append: special token to append to the output of ``text_to_ids`` of ``text_to_tokens`` + methods. See more in the description of ``special_token_to_prepend`` parameter. + special_tokens_to_remove_while_decoding: which special tokens are remove before detokenization. If this + parameter equals ``'all'``, then all special tokens are removed. The parameter + ``special_tokens_to_remove_while_decoding`` can also be a list of values from this set + {set(e.value for e in SpecialTokenString)}. + """ + + def __init__( + self, + vocab_file: str, + mask_token: Optional[Union[str, bool]] = None, + bos_token: Optional[Union[str, bool]] = None, + eos_token: Optional[Union[str, bool]] = None, + pad_token: Optional[Union[str, bool]] = None, + sep_token: Optional[Union[str, bool]] = None, + cls_token: Optional[Union[str, bool]] = None, + unk_token: Optional[Union[str, bool]] = None, + special_token_to_prepend: Optional[SpecialTokenStringType] = None, + special_token_to_append: Optional[SpecialTokenStringType] = None, + special_tokens_to_remove_while_decoding: Union[List[SpecialTokenStringType], str] = 'all', + ): + vocab_file = Path(vocab_file).expanduser() + with vocab_file.open(encoding='utf-8') as f: + first_line = f.readline() + if first_line[0] == '{': + special_tokens_dict = json.loads(first_line) + self.check_special_tokens_dict_from_file(special_tokens_dict, vocab_file) + vocab_list = f.readlines() + else: + special_tokens_dict = {} + vocab_list = [first_line] + f.readlines() + special_tokens_dict = self.update_special_tokens_dict( + special_tokens_dict, mask_token, bos_token, eos_token, pad_token, sep_token, cls_token, unk_token + ) + for e in SpecialTokenString: + name = e.value + '_token' + setattr(self, name, special_tokens_dict[name] if name in special_tokens_dict else None) + for k, v in special_tokens_dict.items(): + setattr(self, k, v) + for value, name in [ + (special_token_to_prepend, 'special_token_to_prepend'), + (special_token_to_append, 'special_token_to_append'), + ]: + self.check_special_token_name(name, value, special_tokens_dict) + setattr(self, name, value + '_token' if isinstance(value, str) else value) + self.vocab = {} + count = 0 + for v in special_tokens_dict.values(): + self.vocab[v] = count + count += 1 + for i, token in enumerate(vocab_list): + token = eval(token.strip()) + self.check_token_from_file(token, vocab_file, i) + if token not in self.vocab: + self.vocab[token] = count + count += 1 + self.inv_vocab = {v: k for k, v in self.vocab.items()} + self.vocab_size = len(self.vocab) + self.check_special_tokens_to_remove_while_decoding( + special_tokens_to_remove_while_decoding, special_tokens_dict + ) + self.special_token_ids_to_remove_while_decoding = ( + self.tokens_to_ids([v for v in special_tokens_dict.values()]) + if special_tokens_to_remove_while_decoding == 'all' + else [getattr(self, e + '_id') for e in special_tokens_to_remove_while_decoding] + ) + + @classmethod + def check_special_tokens_dict_from_file(cls, special_tokens_dict, vocab_file): + for k, v in special_tokens_dict.items(): + if k[-6:] != '_token' or not SpecialTokenString.has_value(k[:-6]): + raise ValueError( + f"Unsupported key {repr(k)} in special tokens dictionary in vocabulary file {vocab_file} " + f"(first line). Supported keys are {[e.value + '_token' for e in SpecialTokenString]}." + ) + if not isinstance(v, str): + raise ValueError( + f"Values of special tokens dictionary in vocabulary file {vocab_file} (first line) has to belong " + f"to type `str`, whereas type of item '{k}' value {repr(v)} is `{type(v)}`." + ) + elif len(v) == 0: + raise ValueError( + f"Values of special tokens dictionary in vocabulary file {vocab_file} (first line) has to not " + f"empty strings, whereas value of item '{k}' is an empty string." + ) + cls.check_special_tokens_dict_for_duplicate_values( + special_tokens_dict, f"Loaded from vocabulary file {vocab_file}" + ) + + @staticmethod + def check_special_tokens_dict_for_duplicate_values(special_tokens_dict, err_msg_prefix): + if len(special_tokens_dict) != len(set(special_tokens_dict.values())): + tokens_with_equal_values = [] + duplicate_values = [] + for k, v in list(reversed(list(special_tokens_dict.items())))[:-1]: + tokens = [k] + for kk, vv in special_tokens_dict.items(): + if kk == k: + break + if v == vv: + tokens.append(kk) + if len(tokens) > 1: + duplicate_values.append(v) + tokens_with_equal_values.append(tokens) + if duplicate_values: + dup_values_msg = '. '.join( + [f"Tokens {t} have value '{v}'" for t, v in zip(tokens_with_equal_values, duplicate_values)] + ) + raise ValueError( + err_msg_prefix + f" special tokens dictionary has duplicate values. " + dup_values_msg + ) + + @classmethod + def update_special_tokens_dict( + cls, + init_special_tokens_dict: Dict[str, str], + mask_token: Optional[Union[str, bool]] = None, + bos_token: Optional[Union[str, bool]] = None, + eos_token: Optional[Union[str, bool]] = None, + pad_token: Optional[Union[str, bool]] = None, + sep_token: Optional[Union[str, bool]] = None, + cls_token: Optional[Union[str, bool]] = None, + unk_token: Optional[Union[str, bool]] = None, + ): + special_tokens_dict = init_special_tokens_dict.copy() + for value, name in zip( + [pad_token, unk_token, bos_token, eos_token, sep_token, mask_token, cls_token], + ['pad_token', 'unk_token', 'bos_token', 'eos_token', 'sep_token', 'mask_token', 'cls_token'], + ): + if value is not None: + if isinstance(value, bool): + if value: + raise ValueError( + f"If `CharTokenizer` constructor parameter `{name}` is `bool` it has to be `False`" + ) + else: + if name in special_tokens_dict: + del special_tokens_dict[name] + else: + warnings.warn( + f"Cannot remove special token `{name}` since it is not in special tokens dictionary " + f"{special_tokens_dict}." + ) + elif not isinstance(value, str): + raise ValueError( + f"`CharTokenizer` constructor parameter `{name}` has to be either `False` or belong to type " + f"`str`, whereas type of `{name}` is `{type(value)}`." + ) + else: + special_tokens_dict[name] = value + cls.check_special_tokens_dict_for_duplicate_values( + special_tokens_dict, + "After updating special tokens dictionary with tokens passed in `CharTokenizer` constructor parameters", + ) + return special_tokens_dict + + @staticmethod + def check_token_from_file(token, vocab_file, line_i): + if not isinstance(token, str) or isinstance(token, str) and len(token) != 1: + raise ValueError( + f"Each line in vocabulary have to be a Python string literal containing 1 character. " + f"Encountered {repr(token)} on line {line_i} in file {vocab_file}." + ) + + @staticmethod + def check_special_token_name(parameter_name, value, special_tokens_dict): + if value is not None: + if not SpecialTokenString.has_value(value): + raise ValueError( + f"Value {repr(value)} of parameter `{parameter_name}` is wrong. Supported values are " + f"{[e.value for e in SpecialTokenString]}." + ) + elif value + '_token' not in special_tokens_dict: + raise ValueError( + f"You should provide `{value + '_token'}` parameter to `CharTokenizer` constructor if " + f"you wish to pass token {repr(value)} in parameter `{parameter_name}`." + ) + + @staticmethod + def check_special_tokens_to_remove_while_decoding(special_tokens_to_remove_while_decoding, special_tokens_dict): + if isinstance(special_tokens_to_remove_while_decoding, list): + for i, value in enumerate(special_tokens_to_remove_while_decoding): + if not SpecialTokenString.has_value(value): + raise ValueError( + f'Wrong element with value {repr(value)} in position {i} of parameter ' + f'`special_tokens_to_remove_while_decoding` of `CharTokenizer` constructor. Supported values ' + f'are {[e.value for e in SpecialTokenString]}.' + ) + elif value + '_token' not in special_tokens_dict: + raise ValueError( + f"You should provide `{value + '_token'}` parameter to `CharTokenizer` constructor if " + f"you wish to pass token {repr(value)} in parameter `special_tokens_to_remove_while_decoding`. " + f"`{value + '_token'}` was detected in position {i} in " + f"`special_tokens_to_remove_while_decoding`." + ) + elif ( + isinstance(special_tokens_to_remove_while_decoding, str) + and special_tokens_to_remove_while_decoding != 'all' + or not isinstance(special_tokens_to_remove_while_decoding, str) + ): + raise ValueError( + f"Parameter `special_tokens_to_remove_while_decoding` of `CharTokenizer` constructor has to be " + f"equal to a string 'all' or be a list of values from set {set(e.value for e in SpecialTokenString)} " + f"whereas `special_tokens_to_remove_while_decoding={repr(special_tokens_to_remove_while_decoding)}`" + ) + + def text_to_tokens(self, text: str) -> List[str]: + token_candidates = [char for char in text] + tokens = [] + if self.special_token_to_prepend is not None: + tokens.append(getattr(self, self.special_token_to_prepend)) + for i, token in enumerate(token_candidates): + if token in self.vocab: + tokens.append(token) + elif self.unk_token is not None: + tokens.append(self.unk_token) + else: + warnings.warn( + f"Character {repr(token)} in position {i} is not present in vocabulary and no `` token was " + f"set. Character {repr(token)} is discarded." + ) + if self.special_token_to_append is not None: + tokens.append(getattr(self, self.special_token_to_append)) + return tokens + + def tokens_to_text(self, tokens: List[str]) -> str: + return self.ids_to_text(self.tokens_to_ids(tokens)) + + def text_to_ids(self, text: str) -> List[int]: + ids = [self.vocab[token] for token in self.text_to_tokens(text)] + return ids + + def ids_to_text(self, ids: List[int]) -> str: + ids_ = [id_ for id_ in ids if id_ not in self.special_token_ids_to_remove_while_decoding] + return "".join(self.ids_to_tokens(ids_)) + + def tokens_to_ids(self, tokens: List[str]) -> List[int]: + return [self.vocab[token] for token in tokens] + + def token_to_id(self, token: str) -> int: + return self.vocab[token] + + def ids_to_tokens(self, ids: List[int]) -> List[str]: + return [self.inv_vocab[id] for id in ids] + + @staticmethod + def check_special_token_id_getting(special_token, id_name): + if special_token is None: + token_param = id_name[:-3] + '_token' + raise ValueError( + f"Cannot return `{id_name}` since `{token_param}` is not set. To obtain `{id_name}` you need to pass " + f"parameter `{token_param}` to `CharTokenizer` constructor." + ) + + @property + def pad_id(self): + self.check_special_token_id_getting(self.pad_token, 'pad_id') + return self.vocab[self.pad_token] + + @property + def bos_id(self): + self.check_special_token_id_getting(self.bos_token, 'bos_id') + return self.vocab[self.bos_token] + + @property + def eos_id(self): + self.check_special_token_id_getting(self.eos_token, 'eos_id') + return self.vocab[self.eos_token] + + @property + def unk_id(self): + self.check_special_token_id_getting(self.unk_token, 'unk_id') + return self.vocab[self.unk_token] + + @property + def mask_id(self): + self.check_special_token_id_getting(self.mask_token, 'mask_id') + return self.vocab[self.mask_token] + + @property + def sep_id(self): + self.check_special_token_id_getting(self.sep_token, 'sep_id') + return self.vocab[self.sep_token] + + @property + def cls_id(self): + self.check_special_token_id_getting(self.cls_token, 'cls_id') + return self.vocab[self.cls_token] + + @staticmethod + def create_special_tokens_dict( + mask_token: Optional[str] = None, + bos_token: Optional[str] = None, + eos_token: Optional[str] = None, + pad_token: Optional[str] = None, + sep_token: Optional[str] = None, + cls_token: Optional[str] = None, + unk_token: Optional[str] = None, + ): + special_tokens_dict = {} + for value, name in zip( + [pad_token, unk_token, bos_token, eos_token, sep_token, mask_token, cls_token], + ['pad_token', 'unk_token', 'bos_token', 'eos_token', 'sep_token', 'mask_token', 'cls_token'], + ): + if value is not None: + if not isinstance(value, str): + raise ValueError( + f"The type of parameter `{name}` has to be `None` or `str`, found `{type(value)}`" + ) + elif len(value) == 0: + raise ValueError(f"If the parameter `{name}` is `str`, then its length has to be nonzero.") + elif value in special_tokens_dict.values(): + other_name = None + for k, v in special_tokens_dict.items(): + if v == value: + other_name = k + raise ValueError( + f"The value {repr(value)} of special token `{name}` is the same as the value of special token " + f"`{other_name}`." + ) + special_tokens_dict[name] = value + return special_tokens_dict + + @staticmethod + def check_characters_to_exclude_from_vocabulary(characters_to_exclude_from_vocabulary): + for i, char in enumerate(characters_to_exclude_from_vocabulary): + if not isinstance(char, str): + raise ValueError( + f"Character to exclude from vocabulary has to `str`, whereas an element in position {i} is of " + f"type `{type(char)}`." + ) + elif len(char) != 1: + raise ValueError( + f"A length of an element of `characters_to_exclude_from_vocabulary` parameter has to be 1. " + f"The length of an element in position {i} is {len(char)}." + ) + + @staticmethod + def check_text_and_text_file_name(text, text_file_name): + if text is None and text_file_name is None: + raise ValueError( + f'Exactly one of parameters `text` and `text_file_name` should be provided whereas both parameters ' + f'are `None`.' + ) + if text is not None and text_file_name is not None: + raise ValueError( + f"Exactly one of parameters `text` and `text_file_name` has to be provided, whereas both parameters " + f"are not `None`." + ) + if text is not None: + if not isinstance(text, str): + raise ValueError( + f"Parameter `text` has to be of type `str`, whereas it belongs to type `{type(text)}`." + ) + + @classmethod + def build_vocab( + cls, + save_path: Union[str, bytes, os.PathLike], + text: Optional[str] = None, + text_file_name: Optional[Union[str, bytes, os.PathLike]] = None, + characters_to_exclude: Optional[List[str]] = None, + vocab_size: int = None, + mask_token: Optional[str] = None, + bos_token: Optional[str] = None, + eos_token: Optional[str] = None, + pad_token: Optional[str] = None, + sep_token: Optional[str] = None, + cls_token: Optional[str] = None, + unk_token: Optional[str] = None, + ): + """ + Creates character vocabulary and saves it to file ``save_path``. You should provide one of parameters ``text`` + and ``text_file_name``. The format of created character vocabulary file is following: + ``` + {['mask_token': "ANY NON EMPTY STRING", ]['bos_token': "ANY NON EMPTY STRING", ] and so on} + ' ' + 'e' + ... + ``` + The first line is a JSON which contains special tokens. This special token are set using parameters + ``mas_token``, ``bos_token``, ``eos_token``, ``pad_token``, ``sep_token``, ``cls_token``, ``unk_token``. + Other lines in created vocabulary file are Python string literals containing one character each. + + Args: + save_path: path to the output text file. If ``save_path`` parent directory does not exist it will be created + text: string which characters are used for vocabulary creation. + text_file_name: path to a file which characters are used for vocabulary creation. Use this parameter if + the text in file is too large to be loaded in memory. + characters_to_exclude: a list of characters which will not be added to vocabulary. + vocab_size: vocabulary size. If this parameter is set only most frequent ``vocab_size`` characters are added + to vocabulary. + mask_token: mask token + bos_token: the beginning of sequence token + eos_token: the end of sequence token. Usually equal to sep_token. + pad_token: token to use for padding. + sep_token: token used for separating sequences. + cls_token: class token. Usually equal to bos_token. + unk_token: token to use for unknown tokens. If the parameter ``unk_token`` is set and there is a character + in the input of ``text_to_ids`` of ``text_to_tokens`` methods which is not in the vocabulary, then + such an unknown character is tokenized into ``unk_token``. If the parameter ``unk_token`` is ``False``, + then unknown tokens are discarded. + """ + special_tokens_dict = cls.create_special_tokens_dict( + mask_token, bos_token, eos_token, pad_token, sep_token, cls_token, unk_token + ) + if characters_to_exclude is None: + characters_to_exclude = [] + else: + cls.check_characters_to_exclude_from_vocabulary(characters_to_exclude) + cls.check_text_and_text_file_name(text, text_file_name) + if text is not None: + counter = Counter(text) + else: + assert text_file_name is not None + text_file_name = Path(text_file_name).expanduser() + counter = Counter() + with text_file_name.open(encoding='utf-8') as f: + while True: + segment = f.read(NUMBER_OF_CHARACTERS_READ_BUFFER_SIZE) + if not segment: + break + counter.update(segment) + for char in characters_to_exclude: + if char in counter: + del counter[char] + save_path = Path(save_path).expanduser() + save_path.parent.mkdir(exist_ok=True, parents=True) + with save_path.open('w', encoding='utf-8') as f: + f.write(json.dumps(special_tokens_dict) + '\n') + if vocab_size is None: + for c, _ in sorted(counter.items(), key=lambda x: -x[1]): + f.write(repr(c) + '\n') + else: + vocab_size -= len(special_tokens_dict) + for i, (c, _) in enumerate(sorted(counter.items(), key=lambda x: -x[1])): + if i < vocab_size: + f.write(repr(c) + '\n') + else: + break diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/chinese_tokenizers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/chinese_tokenizers.py new file mode 100644 index 0000000..a9923f0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/chinese_tokenizers.py @@ -0,0 +1,63 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# The MIT License (MIT) +# Copyright (c) 2016 The-Orizon +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + +# The detokenize function is based on : https://github.com/The-Orizon/nlputils/blob/master/detokenizer.py + +import re +from typing import List + +import jieba +import opencc +from pangu import spacing + + +class ChineseProcessor: + """ + Tokenizer, Detokenizer and Normalizer utilities for Chinese. + """ + + def __init__(self): + self.normalizer = opencc.OpenCC('t2s.json') + + def normalize(self, text: str) -> str: + return self.normalizer.convert(text) + + def detokenize(self, text: List[str]) -> str: + RE_WS_IN_FW = re.compile( + r'([\u2018\u2019\u201c\u201d\u2e80-\u312f\u3200-\u32ff\u3400-\u4dbf\u4e00-\u9fff\uf900-\ufaff\uff00-\uffef])\s+(?=[\u2018\u2019\u201c\u201d\u2e80-\u312f\u3200-\u32ff\u3400-\u4dbf\u4e00-\u9fff\uf900-\ufaff\uff00-\uffef])' + ) + + detokenize = lambda s: spacing(RE_WS_IN_FW.sub(r'\1', s)).strip() + return detokenize(' '.join(text)) + + def tokenize(self, text: str) -> str: + text = jieba.cut(text) + return ' '.join(text) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/column_coder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/column_coder.py new file mode 100644 index 0000000..bf1ab2f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/column_coder.py @@ -0,0 +1,305 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import Dict, List, Tuple + +import numpy as np +from numpy import ndarray +from sklearn.preprocessing import PowerTransformer, QuantileTransformer, RobustScaler + +from nemo.utils import logging + +__all__ = ["IntCode", "FloatCode", "CategoryCode", "ColumnCodes"] + + +class Code(object): + def compute_code(self, data_series: ndarray): + """ + @params: + data_series: an array of input data used to calculate mapping + """ + raise NotImplementedError() + + def __init__(self, col_name: str, code_len: int, start_id: int, fillall: bool = True, hasnan: bool = True): + """ + @params: + col_name: name of the column + code_len: number of tokens used to code the column. + start_id: offset for token_id. + fillall: if True, reserve space for digit number even the digit number is + not present in the data_series. Otherwise, only reserve space for the numbers + in the data_series. + hasnan: if True, reserve space for nan + """ + self.name = col_name + self.code_len = code_len + self.start_id = start_id + self.end_id = start_id + self.fillall = fillall + self.hasnan = hasnan + + def encode(self, item: str) -> List[int]: + raise NotImplementedError() + + def decode(self, ids: List[int]) -> str: + raise NotImplementedError() + + @property + def code_range(self) -> List[Tuple[int, int]]: + """ + get the vocab id range for each of the encoded tokens + @returns [(min, max), (min, max), ...] + """ + return [(self.start_id, self.end_id)] + + +class IntCode(Code): + def __init__( + self, col_name: str, code_len: int, start_id: int, fillall: bool = True, base: int = 100, hasnan: bool = True + ): + super().__init__(col_name, code_len, start_id, fillall, hasnan) + self.base = base + self.int_min: int = None + + def compute_code(self, data_series: ndarray): + significant_val = self.array_convert_to_int(data_series) + + digits_id_to_item = [{} for _ in range(self.code_len)] + digits_item_to_id = [{} for _ in range(self.code_len)] + for i in range(self.code_len): + id_to_item = digits_id_to_item[i] + item_to_id = digits_item_to_id[i] + v = (significant_val // self.base ** i) % self.base + if self.fillall: + uniq_items = range(0, self.base) + else: + uniq_items = sorted(np.unique(v).tolist()) + for k in range(len(uniq_items)): + item = str(uniq_items[k]) + item_to_id[item] = self.end_id + id_to_item[self.end_id] = item + self.end_id += 1 + self.digits_id_to_item = digits_id_to_item + self.digits_item_to_id = digits_item_to_id + self.NA_token = 'nan' + if self.hasnan: + self.end_id += 1 # add the N/A token + codes = [] + ranges = self.code_range + for i in ranges: + codes.append(i[1] - 1) + self.NA_token_id = codes + + def array_convert_to_int(self, val: ndarray): + val = val.astype(int) + self.int_min = val.min() + return val - self.int_min + + def convert_to_int(self, val: float) -> int: + return int(val) - self.int_min + + def reverse_convert_to_int(self, val: int) -> int: + return val + self.int_min + + @property + def code_range(self) -> List[Tuple[int, int]]: + """ + get the vocab id range for each of the encoded tokens + @returns [(min, max), (min, max), ...] + """ + # first largest digits + outputs = [] + c = 0 + for i in reversed(range(self.code_len)): + ids = self.digits_id_to_item[i].keys() + if c == 0: + if self.hasnan: + outputs.append((min(ids), max(ids) + 2)) # the first token contains the N/A + else: + outputs.append((min(ids), max(ids) + 1)) # non N/A + else: + outputs.append((min(ids), max(ids) + 1)) + c += 1 + return outputs + + def encode(self, item: str) -> List[int]: + if self.hasnan and item == self.NA_token: + return self.NA_token_id + elif not self.hasnan and item == self.NA_token: + raise ValueError(f"colum {self.name} cannot handle nan, please set hasnan=True") + val = float(item) + val_int = self.convert_to_int(val) + digits = [] + for i in range(self.code_len): + digit = (val_int // self.base ** i) % self.base + digits.append(str(digit)) + if (val_int // self.base ** self.code_len) != 0: + raise ValueError("not right length") + codes = [] + for i in reversed(range(self.code_len)): + digit_str = digits[i] + if digit_str in self.digits_item_to_id[i]: + codes.append(self.digits_item_to_id[i][digit_str]) + else: + # find the nearest encode id + allowed_digits = np.array([int(d) for d in self.digits_item_to_id[i].keys()]) + near_id = np.argmin(np.abs(allowed_digits - int(digit_str))) + digit_str = str(allowed_digits[near_id]) + codes.append(self.digits_item_to_id[i][digit_str]) + logging.warning('out of domain num is encounterd, use nearest code') + return codes + + def decode(self, ids: List[int]) -> str: + if self.hasnan and ids[0] == self.NA_token_id[0]: + return self.NA_token + v = 0 + for i in reversed(range(self.code_len)): + digit = int(self.digits_id_to_item[i][ids[self.code_len - i - 1]]) + v += digit * self.base ** i + v = self.reverse_convert_to_int(v) + return str(v) + + +class FloatCode(IntCode): + def __init__( + self, + col_name: str, + code_len: int, + start_id: int, + fillall: bool = True, + base: int = 100, + hasnan: bool = True, + transform: str = 'quantile', + ): + super().__init__(col_name, code_len, start_id, fillall, base, hasnan) + if transform == 'yeo-johnson': + self.scaler = PowerTransformer(standardize=True) + elif transform == 'quantile': + self.scaler = QuantileTransformer(output_distribution='uniform', n_quantiles=100) + elif transform == 'robust': + self.scaler = RobustScaler() + else: + raise ValueError('Supported data transformations are "yeo-johnson", "quantile", and "robust"') + + def convert_to_int(self, val: float) -> int: + val = np.expand_dims(np.array(val), axis=0) + values = self.scaler.transform(val[:, None])[:, 0] - self.mval + values = (values * self.base ** self.extra_digits).astype(int) + output = values[0] + return output + + def array_convert_to_int(self, val: ndarray): + values = self.scaler.fit_transform(val[:, None])[:, 0] + self.mval = values.min() + values = values - self.mval + digits = int(math.log(values.max(), self.base)) + 1 + # extra digits used for 'float' part of the number + extra_digits = self.code_len - digits + if extra_digits < 0: + raise ValueError("need large length to code the nummber") + self.extra_digits = extra_digits + values = (values * self.base ** self.extra_digits).astype(int) + return values + + def reverse_convert_to_int(self, val: int) -> float: + val = val / self.base ** self.extra_digits + val = np.expand_dims(np.array(val), axis=0) + v = self.scaler.inverse_transform(val[:, None] + self.mval)[0, 0] + return v + + def decode(self, ids: List[int]) -> str: + if self.hasnan and ids[0] == self.NA_token_id[0]: + return self.NA_token + v = 0 + for i in reversed(range(self.code_len)): + digit = int(self.digits_id_to_item[i][ids[self.code_len - i - 1]]) + v += digit * self.base ** i + v = self.reverse_convert_to_int(v) + accuracy = max(int(abs(np.log10(0.1 / self.base ** self.extra_digits))), 1) + return f"{v:.{accuracy}f}" + + +class CategoryCode(Code): + def __init__(self, col_name: str, start_id: int): + super().__init__(col_name, 1, start_id, True, False) + + def compute_code(self, data_series: ndarray): + uniq_items = np.unique(data_series).tolist() + id_to_item = {} + item_to_id = {} + for i in range(len(uniq_items)): + item = str(uniq_items[i]) + item_to_id[item] = self.end_id + id_to_item[self.end_id] = item + self.end_id += 1 + self.id_to_item = id_to_item + self.item_to_id = item_to_id + + def encode(self, item) -> List[int]: + return [self.item_to_id[item]] + + def decode(self, ids: List[int]) -> str: + return self.id_to_item[ids[0]] + + +column_map = {"int": IntCode, "float": FloatCode, "category": CategoryCode} + + +class ColumnCodes(object): + def __init__(self): + self.column_codes: Dict[str, Code] = {} + self.columns = [] + self.sizes = [] + + @property + def vocab_size(self): + return self.column_codes[self.columns[-1]].end_id + + def register(self, name: str, ccode: Code): + self.columns.append(name) + self.column_codes[name] = ccode + self.sizes.append(ccode.code_len) + + def encode(self, col: str, item: str) -> List[int]: + if col in self.column_codes: + return self.column_codes[col].encode(item) + else: + raise ValueError(f"cannot encode {col} {item}") + + def decode(self, col: str, ids: List[int]) -> str: + if col in self.column_codes: + return self.column_codes[col].decode(ids) + else: + raise ValueError("cannot decode") + + def get_range(self, column_id: int) -> List[Tuple[int, int]]: + return self.column_codes[self.columns[column_id]].code_range + + @classmethod + def get_column_codes(cls, column_configs, example_arrays): + column_codes = cls() + beg = 0 + cc = None + for config in column_configs: + col_name = config['name'] + coder = column_map[config['code_type']] + args = config.get('args', {}) + start_id = beg if cc is None else cc.end_id + args['start_id'] = start_id + args['col_name'] = col_name + cc = coder(**args) + cc.compute_code(example_arrays[col_name]) + column_codes.register(col_name, cc) + return column_codes diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/en_ja_tokenizers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/en_ja_tokenizers.py new file mode 100644 index 0000000..cf58130 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/en_ja_tokenizers.py @@ -0,0 +1,98 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import re +from typing import List + +from pangu import spacing +from sacremoses import MosesDetokenizer, MosesPunctNormalizer, MosesTokenizer + +try: + import ipadic + import MeCab + + HAVE_MECAB = True + HAVE_IPADIC = True +except (ImportError, ModuleNotFoundError): + HAVE_MECAB = False + HAVE_IPADIC = False + + +class EnJaProcessor: + """ + Tokenizer, Detokenizer and Normalizer utilities for Japanese & English + Args: + lang_id: One of ['en', 'ja']. + """ + + def __init__(self, lang_id: str): + self.lang_id = lang_id + self.moses_tokenizer = MosesTokenizer(lang=lang_id) + self.moses_detokenizer = MosesDetokenizer(lang=lang_id) + self.normalizer = MosesPunctNormalizer( + lang=lang_id, pre_replace_unicode_punct=True, post_remove_control_chars=True + ) + + def detokenize(self, tokens: List[str]) -> str: + """ + Detokenizes a list of tokens + Args: + tokens: list of strings as tokens + Returns: + detokenized Japanese or English string + """ + return self.moses_detokenizer.detokenize(tokens) + + def tokenize(self, text) -> str: + """ + Tokenizes text using Moses. Returns a string of tokens. + """ + tokens = self.moses_tokenizer.tokenize(text) + return ' '.join(tokens) + + def normalize(self, text) -> str: + # Normalization doesn't handle Japanese periods correctly; + # '。'becomes '.'. + if self.lang_id == 'en': + return self.normalizer.normalize(text) + else: + return text + + +class JaMecabProcessor: + """ + Tokenizer, Detokenizer and Normalizer utilities for Japanese MeCab & English + """ + + def __init__(self): + if not HAVE_MECAB or not HAVE_IPADIC: + raise ImportError("Please ensure that you have installed `MeCab` and `ipadic` to use JaMecabProcessor") + + self.mecab_tokenizer = MeCab.Tagger(ipadic.MECAB_ARGS + " -Owakati") + + def detokenize(self, text: List[str]) -> str: + RE_WS_IN_FW = re.compile( + r'([\u2018\u2019\u201c\u201d\u2e80-\u312f\u3200-\u32ff\u3400-\u4dbf\u4e00-\u9fff\uf900-\ufaff\uff00-\uffef])\s+(?=[\u2018\u2019\u201c\u201d\u2e80-\u312f\u3200-\u32ff\u3400-\u4dbf\u4e00-\u9fff\uf900-\ufaff\uff00-\uffef])' + ) + + detokenize = lambda s: spacing(RE_WS_IN_FW.sub(r'\1', s)).strip() + return detokenize(' '.join(text)) + + def tokenize(self, text) -> str: + """ + Tokenizes text using Moses. Returns a string of tokens. + """ + return self.mecab_tokenizer.parse(text).strip() + + def normalize(self, text) -> str: + return text diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/fairseq_tokenizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/fairseq_tokenizer.py new file mode 100644 index 0000000..aa30bac --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/fairseq_tokenizer.py @@ -0,0 +1,126 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +""" Code from +https://github.com/NVIDIA/DeepLearningExamples/blob/ +master/PyTorch/Translation/Transformer/fairseq/tokenizer.py +""" + +import re +import sys +import unicodedata +from collections import defaultdict + +__all__ = ['get_unicode_categories', 'tokenize_en'] + + +def get_unicode_categories(): + cats = defaultdict(list) + for c in map(chr, range(sys.maxunicode + 1)): + cats[unicodedata.category(c)].append(c) + return cats + + +NUMERICS = ''.join(get_unicode_categories()['No']) + + +def tokenize_en(line): + line = line.strip() + line = ' ' + line + ' ' + # remove ASCII junk + line = re.sub(r'\s+', ' ', line) + line = re.sub(r'[\x00-\x1F]', '', line) + # fix whitespaces + line = re.sub(r'\ +', ' ', line) + line = re.sub('^ ', '', line) + line = re.sub(' $', '', line) + # separate other special characters + line = re.sub(r'([^\s\.\'\`\,\-\w]|[_' + NUMERICS + '])', r' \g<1> ', line) + line = re.sub(r'(\w)\-(?=\w)', r'\g<1> @-@ ', line) + + # multidots stay together + line = re.sub(r'\.([\.]+)', r' DOTMULTI\g<1>', line) + while re.search(r'DOTMULTI\.', line): + line = re.sub(r'DOTMULTI\.([^\.])', r'DOTDOTMULTI \g<1>', line) + line = re.sub(r'DOTMULTI\.', r'DOTDOTMULTI', line) + + # separate out "," except if within numbers (5,300) + line = re.sub(r'([\D])[,]', r'\g<1> , ', line) + line = re.sub(r'[,]([\D])', r' , \g<1>', line) + + # separate "," after a number if it's the end of sentence + line = re.sub(r'(\d)[,]$', r'\g<1> ,', line) + + # split contractions right + line = re.sub(r'([\W\d])[\']([\W\d])', r'\g<1> \' \g<2>', line) + line = re.sub(r'(\W)[\']([\w\D])', r'\g<1> \' \g<2>', line) + line = re.sub(r'([\w\D])[\']([\W\d])', r'\g<1> \' \g<2>', line) + line = re.sub(r'([\w\D])[\']([\w\D])', r'\g<1> \'\g<2>', line) + # special case for "1990's" + line = re.sub(r'([\W\d])[\']([s])', r'\g<1> \'\g<2>', line) + + # apply nonbreaking prefixes + words = line.split() + line = '' + for i in range(len(words)): + word = words[i] + match = re.search(r'^(\S+)\.$', word) + if match: + pre = match.group(1) + if i == len(words) - 1: + """split last words independently as they are unlikely + to be non-breaking prefixes""" + word = pre + ' .' + else: + word = pre + ' .' + + word += ' ' + line += word + + # clean up extraneous spaces + line = re.sub(' +', ' ', line) + line = re.sub('^ ', '', line) + line = re.sub(' $', '', line) + + # .' at end of sentence is missed + line = re.sub(r'\.\' ?$', ' . \' ', line) + + # restore multi-dots + while re.search('DOTDOTMULTI', line): + line = re.sub('DOTDOTMULTI', 'DOTMULTI.', line) + + line = re.sub('DOTMULTI', '.', line) + + # escape special characters + line = re.sub(r'\&', r'&', line) + line = re.sub(r'\|', r'|', line) + line = re.sub(r'\<', r'<', line) + line = re.sub(r'\>', r'>', line) + line = re.sub(r'\'', r''', line) + line = re.sub(r'\"', r'"', line) + line = re.sub(r'\[', r'[', line) + line = re.sub(r'\]', r']', line) + + # ensure final line breaks + # if line[-1] is not '\n': + # line += '\n' + + return line diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/huggingface/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/huggingface/__init__.py new file mode 100644 index 0000000..7231c79 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/huggingface/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/huggingface/auto_tokenizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/huggingface/auto_tokenizer.py new file mode 100644 index 0000000..b264890 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/huggingface/auto_tokenizer.py @@ -0,0 +1,279 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections import OrderedDict +from typing import Optional + +from transformers import AutoTokenizer as AUTOTOKENIZER + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.utils import logging + +__all__ = [ + 'AutoTokenizer', +] + + +class AutoTokenizer(TokenizerSpec): + ''' + Wrapper of HuggingFace AutoTokenizer https://huggingface.co/transformers/model_doc/auto.html#autotokenizer. + ''' + + def __init__( + self, + pretrained_model_name: str, + vocab_file: Optional[str] = None, + merges_file: Optional[str] = None, + mask_token: Optional[str] = None, + bos_token: Optional[str] = None, + eos_token: Optional[str] = None, + pad_token: Optional[str] = None, + sep_token: Optional[str] = None, + cls_token: Optional[str] = None, + unk_token: Optional[str] = None, + use_fast: Optional[bool] = False, + trust_remote_code: Optional[bool] = False, + ): + + """ + Args: + pretrained_model_name: corresponds to HuggingFace-AutoTokenizer's 'pretrained_model_name_or_path' input argument. + For more details please refer to https://huggingface.co/transformers/_modules/transformers/tokenization_auto.html#AutoTokenizer.from_pretrained. + The list of all supported models can be found here: ALL_PRETRAINED_CONFIG_ARCHIVE_MAP + vocab_file: path to file with vocabulary which consists + of characters separated by '\n'. + mask_token: mask token + bos_token: the beginning of sequence token + eos_token: the end of sequence token. Usually equal to sep_token + pad_token: token to use for padding + sep_token: token used for separating sequences + cls_token: class token. Usually equal to bos_token + unk_token: token to use for unknown tokens + use_fast: whether to use fast HuggingFace tokenizer + """ + try: + # this logic deals with different huggingface tokenizers having different positional args + if vocab_file is None: + self.tokenizer = AUTOTOKENIZER.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name, + use_fast=use_fast, + trust_remote_code=trust_remote_code, + ) + elif merges_file is None: + self.tokenizer = AUTOTOKENIZER.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name, + vocab_file=vocab_file, + use_fast=use_fast, + trust_remote_code=trust_remote_code, + ) + else: + self.tokenizer = AUTOTOKENIZER.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name, + vocab_file=vocab_file, + merges_file=merges_file, + use_fast=use_fast, + trust_remote_code=trust_remote_code, + ) + except Exception as e: + raise ValueError( + f'Unable to instantiate HuggingFace AUTOTOKENIZER for {pretrained_model_name}. Exception: {e}' + ) + + self.original_vocab_size = len(self.tokenizer) + special_tokens_dict = {} + + # # setting special tokens, by default the default model's special tokens will be preserved + # # unless passes new values to the special tokens + if unk_token is not None: + special_tokens_dict["unk_token"] = unk_token + if mask_token is not None: + special_tokens_dict["mask_token"] = mask_token + if pad_token is not None: + special_tokens_dict["pad_token"] = pad_token + + # if the model does not have eos_token but has sep_token, + # set eos_token = sep_token, and vice versa + if sep_token is not None: + special_tokens_dict["sep_token"] = sep_token + elif self.tokenizer.sep_token is None and self.tokenizer.eos_token: + special_tokens_dict["sep_token"] = self.tokenizer.eos_token + if eos_token is not None: + special_tokens_dict["eos_token"] = eos_token + elif self.tokenizer.eos_token is None and self.tokenizer.sep_token: + special_tokens_dict["eos_token"] = self.tokenizer.sep_token + + # if the model does not have bos_token but has cls_token, + # set bos_token = cls_token, and vice versa + if bos_token is not None: + special_tokens_dict["bos_token"] = bos_token + elif self.tokenizer.bos_token is None and self.tokenizer.cls_token: + special_tokens_dict["bos_token"] = self.tokenizer.cls_token + if cls_token is not None: + special_tokens_dict["cls_token"] = cls_token + elif self.tokenizer.cls_token is None and self.tokenizer.bos_token: + special_tokens_dict["cls_token"] = self.tokenizer.bos_token + + new_tokens_in_vocab = [] + for token in [mask_token, bos_token, eos_token, pad_token, sep_token, cls_token, unk_token]: + if token is not None and token not in self.tokenizer.get_vocab(): + new_tokens_in_vocab.append(token) + + if len(new_tokens_in_vocab) > 0: + """ + Special tokens that were not previously included in the tokenizer's vocabulary file will be added to + the vocabulary and, as a result, the model should be resized, for example: + + # define your model + pretrained_model_name = 'roberta-base' + model = nemo_nlp.modules.get_lm_model(pretrained_model_name=pretrained_model_name) + + # define pretrained tokenizer + tokenizer_default = nemo_nlp.modules.get_tokenizer(tokenizer_name=pretrained_model_name) + + special_tokens = {'bos_token': '', + 'cls_token': '', + 'additional_special_tokens': ['', '']} + tokenizer_default.add_special_tokens(special_tokens_dict=special_tokens) + + # resize your model so that the embeddings for newly added tokens are updated during training/finetuning + model.resize_token_embeddings(tokenizer_default.vocab_size) + + See NLP_Tokenizers.ipynb for more details. + """ + logging.warning( + f'{new_tokens_in_vocab} \n will be added to the vocabulary.\n' + f'Please resize your model accordingly, ' + f'see NLP_Tokenizers.ipynb for more details.' + ) + self.add_special_tokens(special_tokens_dict) + self.space_sensitive = self.text_to_tokens('x y') != self.text_to_tokens('x') + self.text_to_tokens('y') + + @property + def vocab_size(self): + return len(self.tokenizer) + + def add_special_tokens(self, special_tokens_dict: dict) -> int: + """ + Adds a dictionary of special tokens (eos, pad, cls...). If special tokens are NOT in the vocabulary, they are added + to it (indexed starting from the last index of the current vocabulary). + Args: + special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes: + [``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, + ``additional_special_tokens``]. + Tokens are only added if they are not already in the vocabulary. + Returns: + Number of tokens added to the vocabulary. + """ + num_tokens_added = self.tokenizer.add_special_tokens(special_tokens_dict) + + if num_tokens_added > 0: + logging.info(f'{num_tokens_added} special tokens added, resize your model accordingly.') + for k in self.tokenizer.SPECIAL_TOKENS_ATTRIBUTES: + setattr(self, k, getattr(self.tokenizer, k, None)) + return num_tokens_added + + @property + def additional_special_tokens_ids(self): + """Returns a list of the additional special tokens (excluding bos, eos, pad, unk). Used to return sentinel tokens for e.g. T5.""" + return [self.token_to_id(token) for token in self.additional_special_tokens] + + def text_to_tokens(self, text): + tokens = self.tokenizer.tokenize(text) + return tokens + + def tokens_to_text(self, tokens): + text = self.tokenizer.convert_tokens_to_string(tokens) + return text + + def token_to_id(self, token): + return self.tokens_to_ids([token])[0] + + def tokens_to_ids(self, tokens): + ids = self.tokenizer.convert_tokens_to_ids(tokens) + return ids + + def ids_to_tokens(self, ids): + tokens = self.tokenizer.convert_ids_to_tokens(ids) + return tokens + + def text_to_ids(self, text): + tokens = self.text_to_tokens(text) + ids = self.tokens_to_ids(tokens) + return ids + + def ids_to_text(self, ids): + tokens = self.ids_to_tokens(ids) + tokens_clean = [t for t in tokens if t not in self.tokenizer.all_special_tokens] + text = self.tokens_to_text(tokens_clean) + return text + + @property + def vocab(self): + id2vocab = {v: k for k, v in self.tokenizer.vocab.items()} + return [id2vocab[i] for i in range(len(id2vocab))] + + @property + def pad_id(self): + if getattr(self, 'pad_token') is None: + return None + return self.tokens_to_ids([getattr(self, 'pad_token')])[0] + + @property + def bos_id(self): + if getattr(self, 'bos_token') is None: + return None + return self.tokens_to_ids([getattr(self, 'bos_token')])[0] + + @property + def eos_id(self): + if getattr(self, 'eos_token') is None: + return None + return self.tokens_to_ids([getattr(self, 'eos_token')])[0] + + @property + def eod(self): + """Returns EOS token id. Exact copy of the eos_id function. Required for megatron-core.""" + return self.tokens_to_ids([getattr(self, 'eos_token')])[0] + + @property + def sep_id(self): + if getattr(self, 'sep_token') is None: + return None + return self.tokens_to_ids([getattr(self, 'sep_token')])[0] + + @property + def cls_id(self): + if getattr(self, 'cls_token') is None: + return None + return self.tokens_to_ids([getattr(self, 'cls_token')])[0] + + @property + def unk_id(self): + if getattr(self, 'unk_token') is None: + return None + return self.tokens_to_ids([getattr(self, 'unk_token')])[0] + + @property + def mask_id(self): + if getattr(self, 'mask_token') is None: + return None + return self.tokens_to_ids([getattr(self, 'mask_token')])[0] + + @property + def name(self): + return type(self.tokenizer).__name__ + + def save_vocabulary(self, save_directory: str, filename_prefix: str = None): + """Saves tokenizer's vocabulary and other artifacts to the specified directory""" + return self.tokenizer.save_vocabulary(save_directory=save_directory, filename_prefix=filename_prefix) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/indic_tokenizers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/indic_tokenizers.py new file mode 100644 index 0000000..3b9192c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/indic_tokenizers.py @@ -0,0 +1,47 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List + +from sacremoses import MosesDetokenizer, MosesPunctNormalizer, MosesTokenizer + + +class IndicProcessor: + """ + Tokenizer, Detokenizer and Normalizer utilities in Indic Languages. + Currently supports: 'hi' + """ + + def __init__(self, lang_id: str): + if lang_id != 'hi': + raise NotImplementedError + self.moses_tokenizer = MosesTokenizer(lang=lang_id) + self.moses_detokenizer = MosesDetokenizer(lang=lang_id) + self.normalizer = MosesPunctNormalizer(lang=lang_id) + + def detokenize(self, tokens: List[str]) -> str: + """ + Detokenizes a list of tokens + Args: + tokens: list of strings as tokens + Returns: + detokenized string + """ + return self.moses_detokenizer.detokenize(tokens) + + def tokenize(self, text: str): + return text + + def normalize(self, text: str): + return text diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/moses_tokenizers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/moses_tokenizers.py new file mode 100644 index 0000000..27e91e6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/moses_tokenizers.py @@ -0,0 +1,47 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List + +from sacremoses import MosesDetokenizer, MosesPunctNormalizer, MosesTokenizer + + +class MosesProcessor: + """ + Tokenizer, Detokenizer and Normalizer utilities in Moses + """ + + def __init__(self, lang_id: str): + self.moses_tokenizer = MosesTokenizer(lang=lang_id) + self.moses_detokenizer = MosesDetokenizer(lang=lang_id) + self.normalizer = MosesPunctNormalizer(lang=lang_id) + + def detokenize(self, tokens: List[str]) -> str: + """ + Detokenizes a list of tokens + Args: + tokens: list of strings as tokens + Returns: + detokenized string + """ + return self.moses_detokenizer.detokenize(tokens) + + def tokenize(self, text: str): + """ + Tokenizes text using Moses -> Sentencepiece. + """ + return self.moses_tokenizer.tokenize(text, escape=False, return_str=True) + + def normalize(self, text: str): + return self.normalizer.normalize(text) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/regex_tokenizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/regex_tokenizer.py new file mode 100644 index 0000000..07e2780 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/regex_tokenizer.py @@ -0,0 +1,314 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import os +import re +from typing import Optional + +import pandas as pd + +from nemo.collections.common.tokenizers.char_tokenizer import TokenizerSpec +from nemo.utils import logging + +__all__ = ['RegExTokenizer'] + +DEFAULT_MASK_TOKEN = '' +DEFAULT_BOS_TOKEN = '^' +DEFAULT_EOS_TOKEN = '&' +DEFAULT_PAD_TOKEN = '' +DEFAULT_SEP_TOKEN = '' +DEFAULT_UNK_TOKEN = '?' + + +class RegExTokenizer(TokenizerSpec): + """ + A regular expression-based tokenizer at word boundary. + This tokenizer default to support MegaMolBART. + + """ + + def __init__( + self, + regex: Optional[str] = "", + mask_token: Optional[str] = DEFAULT_MASK_TOKEN, + bos_token: Optional[str] = DEFAULT_BOS_TOKEN, + eos_token: Optional[str] = DEFAULT_EOS_TOKEN, + pad_token: Optional[str] = DEFAULT_PAD_TOKEN, + sep_token: Optional[str] = DEFAULT_SEP_TOKEN, + unk_token: Optional[str] = DEFAULT_UNK_TOKEN, + ): + """ + Args: + regex: regular expression that defined tokenization rules + mask_token: mask token + bos_token: the beginning of sequence token + eos_token: the end of sequence token. Usually equal to sep_token + pad_token: token to use for padding + sep_token: token used for separating sequences + cls_token: class token. Usually equal to bos_token + unk_token: token to use for unknown tokens + """ + self.regex = regex + self.mask_token = mask_token + self.bos_token = bos_token + self.eos_token = eos_token + self.pad_token = pad_token + self.sep_token = sep_token + self.unk_token = unk_token + + # holds names of .model/.vocab files + self.regex_file = None + self.vocab_file = None + + # initialize with default vocab + self.vocab = { + self.pad_token: 0, # pad_token + self.unk_token: 1, # unk_token + self.bos_token: 2, # begin_token + self.eos_token: 3, # end_token + self.mask_token: 4, # mask_token + self.sep_token: 5, # sep_token + } + self._update_cache() + + # Computed attributes + self._compile_regex() + + def _update_cache(self): + # Cache data/attributes required for tokenization + self._unk_id = self.vocab.get(self.unk_token, DEFAULT_UNK_TOKEN) + self._decode_vocab = {i: t for t, i in self.vocab.items()} + + def _compile_regex(self): + regex_string = r"(" + regex_string += self.regex + r"|" + regex_string += r".)" + self._compiled_regex = re.compile(regex_string) + + @property + def vocab_size(self): + return len(self.vocab) + + def text_to_tokens(self, text): + tokens = self._compiled_regex.findall(text) + + return tokens + + def tokens_to_text(self, tokens): + tokens_list = [] + for token in tokens: + if token[0] == self.bos_token: + token = token[1:] + + # Remove end token and the following values + if self.eos_token in token: + eos_idx = token.index(self.eos_token) + token = token[:eos_idx] + + tokens_list.append(token) + + text = ["".join(tokens) for tokens in tokens_list] + return text + + def token_to_ids(self, tokens): + ids_list = [] + for token in tokens: + ids_list.append(self.vocab.get(token, self._unk_id)) + return ids_list + + def tokens_to_ids(self, token_data): + if isinstance(token_data, str): + token_data = [token_data] + + ids_list = [] + for tokens in token_data: + ids = self.token_to_ids(tokens) + ids_list.append(ids) + return ids_list + + def ids_to_tokens(self, ids_list): + if len(ids_list) and not isinstance(ids_list[0], list): + ids_list = [ids_list] + added_list = True + else: + added_list = False + + tokens_list = [] + for ids in ids_list: + tokens = [] + for token_id in ids: + token = self._decode_vocab.get(token_id) + if token is None: + raise ValueError(f"Token id {token_id} is not recognised") + tokens.append(token) + + tokens_list.append(tokens) + + if added_list: + return tokens_list[0] + else: + return tokens_list + + def text_to_ids(self, text): + tokens = self.text_to_tokens(text) + tokens = [tokens] + return self.tokens_to_ids(tokens)[0] + + def ids_to_text(self, ids): + tokens = self.ids_to_tokens(ids) + return self.tokens_to_text(tokens) + + @property + def pad_id(self): + return 0 + + @property + def unk_id(self): + return 1 + + @property + def bos_id(self): + return 2 + + @property + def eos_id(self): + return 3 + + @property + def mask_id(self): + return 4 + + @property + def sep_id(self): + return 5 + + def _get_regex_vocab_files(self, regex_file=None, vocab_file=None): + """ + Infers files or update if given. + """ + regex_file = regex_file or self.regex_file + if not regex_file: + raise ValueError(f"regex_file must be specified") + + vocab_file = vocab_file or self.vocab_file + # try to infer vocab_file from regex_file + if not vocab_file: + vocab_file = os.path.splitext(regex_file)[0] + '.vocab' + + self.regex_file = regex_file + self.vocab_file = vocab_file + + return regex_file, vocab_file + + def save_tokenizer(self, regex_file=None, vocab_file=None): + """ + Saves tokenizer's regex and vocab files + """ + regex_file, vocab_file = self._get_regex_vocab_files(regex_file=regex_file, vocab_file=vocab_file) + + logging.info(f"Saving vocabulary to file = {vocab_file}") + with open(vocab_file, 'w') as fp: + for token in self.vocab: + fp.write(f"{token[0]}\n") + + logging.info(f"Saving regex to file = {regex_file}") + with open(regex_file, 'w') as f: + f.write(self.regex) + + def load_tokenizer(self, regex_file=None, vocab_file=None): + """ + Loads tokenizer's regex and vocab files + """ + regex_file, vocab_file = self._get_regex_vocab_files(regex_file=regex_file, vocab_file=vocab_file) + + # load vocab file + # vocab_file: path to file with vocabulary which consists + # of characters separated by \n (None/"" for empty vocab) + + logging.info(f"Loading vocabulary from file = {vocab_file}") + if os.path.exists(vocab_file): + vocab = {} + with open(vocab_file, "r") as f: + for line in f: + line = line.strip() + if line: + vocab[line] = len(vocab) + self.vocab = vocab + else: + raise RuntimeError(f"Missing vocab_file = {vocab_file}") + + # load regex from a file + if os.path.exists(regex_file): + logging.info(f"Loading regex from file = {regex_file}") + self.regex = open(regex_file, encoding="utf-8").read().strip() + else: + raise RuntimeError(f"Missing regex_file = {regex_file}") + + self._update_cache() + self._compile_regex() + + return self + + def build_vocab_from_csv(self, data_csv_file, col="smiles"): + """ + Learns vocabulary from a CSV file. Can be called multiple times to update vocabulary. + """ + logging.debug(f"Building vocabulary from CSV col = {col} file = {data_csv_file}") + + # NOTE this has to be run on each CSV file + if not os.path.exists(data_csv_file): + raise ValueError(f"Data file: {data_csv_file} is missing") + + df = pd.read_csv(data_csv_file) + + vocab = self.vocab + for d in df[col]: + tokens = self.text_to_tokens(d) + logging.debug(f"Text: {d}, Tokens: {tokens}") + for token in tokens: + if token not in vocab: + vocab[token] = len(vocab) + + sorted_vocab = sorted(vocab.items(), key=lambda k_v: k_v[1]) + logging.debug(f"Vocab: {sorted_vocab}") + + self.vocab = vocab + self._update_cache() + + def build_vocab_from_text(self, data_text_file): + """ + Learns vocabulary from a text file. Can be called multiple times to update vocabulary. + """ + logging.debug(f"Building vocabulary from TEXT file = {data_text_file}") + + # NOTE this has to be run on each text file + if not os.path.exists(data_text_file): + raise ValueError(f"Data file: {data_text_file} is missing") + + vocab = self.vocab + with open(data_text_file, encoding="utf-8") as f: + for d in f.readlines(): + d = d.rstrip() + tokens = self.text_to_tokens(d) + logging.debug(f"Text: {d}, Tokens: {d}") + for token in tokens: + if token not in vocab: + vocab[token] = len(vocab) + + sorted_vocab = sorted(vocab.items(), key=lambda k_v: k_v[1]) + logging.debug(f"Vocab: {sorted_vocab}") + + self.vocab = vocab + self._update_cache() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/sentencepiece_tokenizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/sentencepiece_tokenizer.py new file mode 100644 index 0000000..bc10b67 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/sentencepiece_tokenizer.py @@ -0,0 +1,400 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from typing import Dict, List, Optional, Union + +import numpy as np +import sentencepiece + +from nemo.collections.common.parts.utils import if_exist +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.utils import logging + +__all__ = ['SentencePieceTokenizer', 'create_spt_model'] + + +class SentencePieceTokenizer(TokenizerSpec): + """ + Sentencepiecetokenizer https://github.com/google/sentencepiece. + + Args: + model_path: path to sentence piece tokenizer model. To create the model use create_spt_model() + special_tokens: either list of special tokens or dictionary of token name to token value + legacy: when set to True, the previous behavior of the SentecePiece wrapper will be restored, + including the possibility to add special tokens inside wrapper. + """ + + def __init__( + self, model_path: str, special_tokens: Optional[Union[Dict[str, str], List[str]]] = None, legacy: bool = False + ): + if not model_path or not os.path.exists(model_path): + raise ValueError(f"model_path: {model_path} is invalid") + self.tokenizer = sentencepiece.SentencePieceProcessor() + self.tokenizer.Load(model_path) + + self.original_vocab_size = self.tokenizer.get_piece_size() + self.vocab_size = self.tokenizer.get_piece_size() + self.legacy = legacy + self.special_token_to_id = {} + self.id_to_special_token = {} + if special_tokens: + if not self.legacy: + raise ValueError( + "Special tokens must be None when legacy is set to False. Provide special tokens at train time." + ) + self.add_special_tokens(special_tokens) + self.space_sensitive = self.text_to_tokens('x y') != self.text_to_tokens('x') + self.text_to_tokens('y') + + def text_to_tokens(self, text): + if self.legacy: + tokens = [] + idx = 0 + last_idx = 0 + + while 1: + indices = {} + + for token in self.special_token_to_id: + try: + indices[token] = text[idx:].index(token) + except ValueError: + continue + + if len(indices) == 0: + break + + next_token = min(indices, key=indices.get) + next_idx = idx + indices[next_token] + + tokens.extend(self.tokenizer.encode_as_pieces(text[idx:next_idx])) + tokens.append(next_token) + idx = next_idx + len(next_token) + + tokens.extend(self.tokenizer.encode_as_pieces(text[idx:])) + return tokens + + return self.tokenizer.encode_as_pieces(text) + + def text_to_ids(self, text): + if self.legacy: + ids = [] + idx = 0 + last_idx = 0 + + while 1: + indices = {} + + for token in self.special_token_to_id: + try: + indices[token] = text[idx:].index(token) + except ValueError: + continue + + if len(indices) == 0: + break + + next_token = min(indices, key=indices.get) + next_idx = idx + indices[next_token] + + ids.extend(self.tokenizer.encode_as_ids(text[idx:next_idx])) + ids.append(self.special_token_to_id[next_token]) + idx = next_idx + len(next_token) + + ids.extend(self.tokenizer.encode_as_ids(text[idx:])) + return ids + + return self.tokenizer.encode_as_ids(text) + + def tokens_to_text(self, tokens): + if isinstance(tokens, np.ndarray): + tokens = tokens.tolist() + + return self.tokenizer.decode_pieces(tokens) + + def ids_to_text(self, ids): + if isinstance(ids, np.ndarray): + ids = ids.tolist() + + if self.legacy: + text = "" + last_i = 0 + + for i, id in enumerate(ids): + if id in self.id_to_special_token: + text += self.tokenizer.decode_ids(ids[last_i:i]) + " " + text += self.id_to_special_token[id] + " " + last_i = i + 1 + + text += self.tokenizer.decode_ids(ids[last_i:]) + return text.strip() + + return self.tokenizer.decode_ids(ids) + + def token_to_id(self, token): + if self.legacy and token in self.special_token_to_id: + return self.special_token_to_id[token] + + return self.tokenizer.piece_to_id(token) + + def ids_to_tokens(self, ids): + tokens = [] + for id in ids: + if id >= self.original_vocab_size: + tokens.append(self.id_to_special_token[id]) + else: + tokens.append(self.tokenizer.id_to_piece(id)) + return tokens + + def tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]: + if isinstance(tokens, str): + tokens = [tokens] + ids = [] + for token in tokens: + ids.append(self.token_to_id(token)) + return ids + + def add_special_tokens(self, special_tokens): + if not self.legacy: + raise AttributeError("Special Token addition does not work when legacy is set to False.") + + if isinstance(special_tokens, list): + for token in special_tokens: + if ( + self.tokenizer.piece_to_id(token) == self.tokenizer.unk_id() + and token not in self.special_token_to_id + ): + self.special_token_to_id[token] = self.vocab_size + self.id_to_special_token[self.vocab_size] = token + self.vocab_size += 1 + elif isinstance(special_tokens, dict): + for token_name, token in special_tokens.items(): + setattr(self, token_name, token) + if ( + self.tokenizer.piece_to_id(token) == self.tokenizer.unk_id() + and token not in self.special_token_to_id + ): + self.special_token_to_id[token] = self.vocab_size + self.id_to_special_token[self.vocab_size] = token + self.vocab_size += 1 + + @property + def pad_id(self): + if self.legacy: + pad_id = self.tokens_to_ids([self.pad_token])[0] + else: + pad_id = self.tokenizer.pad_id() + return pad_id + + @property + def bos_id(self): + if self.legacy: + bos_id = self.tokens_to_ids([self.bos_token])[0] + else: + bos_id = self.tokenizer.bos_id() + return bos_id + + @property + def eos_id(self): + if self.legacy: + eos_id = self.tokens_to_ids([self.eos_token])[0] + else: + eos_id = self.tokenizer.eos_id() + return eos_id + + @property + def sep_id(self): + if self.legacy: + return self.tokens_to_ids([self.sep_token])[0] + else: + raise NameError("Use function token_to_id to retrieve special tokens other than unk, pad, bos, and eos.") + + @property + def cls_id(self): + if self.legacy: + return self.tokens_to_ids([self.cls_token])[0] + else: + raise NameError("Use function token_to_id to retrieve special tokens other than unk, pad, bos, and eos.") + + @property + def mask_id(self): + if self.legacy: + return self.tokens_to_ids([self.mask_token])[0] + else: + raise NameError("Use function token_to_id to retrieve special tokens other than unk, pad, bos, and eos.") + + @property + def unk_id(self): + return self.tokenizer.unk_id() + + @property + def additional_special_tokens_ids(self): + """Returns a list of the additional special tokens (excluding bos, eos, pad, unk). Used to return sentinel tokens for e.g. T5.""" + special_tokens = set( + [self.bos_token, self.eos_token, self.pad_token, self.mask_token, self.cls_token, self.sep_token] + ) + return [v for k, v in self.special_token_to_id.items() if k not in special_tokens] + + @property + def vocab(self): + main_vocab = [self.tokenizer.id_to_piece(id) for id in range(self.tokenizer.get_piece_size())] + special_tokens = [ + self.id_to_special_token[self.original_vocab_size + i] + for i in range(self.vocab_size - self.original_vocab_size) + ] + return main_vocab + special_tokens + + +def create_spt_model( + data_file: str, + vocab_size: int, + sample_size: int, + do_lower_case: bool, + tokenizer_type: str = 'unigram', + output_dir: Optional[str] = None, + character_coverage: float = 1.0, + train_extremely_large_corpus: bool = False, + max_sentencepiece_length: int = -1, + bos: bool = False, + eos: bool = False, + pad: bool = False, + control_symbols: List[str] = None, + user_defined_symbols: List[str] = None, + byte_fallback: bool = False, + split_digits: bool = False, + split_by_whitespace: bool = True, + split_by_unicode_script: bool = True, +): + """ + Creates sentence piece tokenizer model from data file. + + Args: + data_file: data file + vocab_size: vocabulary size + sample_size: maximum size of sentences the trainer loads + do_lower_case: if text should be lower cased before tokenizer model is created + character_coverage: float value between 0 and 1 (as a percentage). For languages with a vast charset, + can be < 1.0, but for all other languages, it should be set as 1.0 + output_dir: folder to save created tokenizer model. If not specified will store model at data_file/../spt folder + train_extremely_large_corpus: If training on huge datasets, pass this flag to allow SentencePiece + to build the tokenizer. + max_sentencepiece_length: Limits the maximum length of the SentencePiece subword that can be constructed. + By default, no limit is placed. + bos: when True, bos token "" is added to the vocabulary. + eos: when True, eos token "" is added to the vocabulary. + pad: when True, pad token "" is added to the vocabulary. + control_symbols: control symbols to add to tokenizer, as defined by sentencepiece. + These tokens get removed at decode time and are not encoded from the text - can only be added to the input programatically. + user_defined_symbols: user symbols to add to tokenizer, as defined by sentencepiece. + These tokens remain in the decoded text and are encoded automatically when present in the input text. + byte_fallback: If , fallback to a byte sequence of the character. + split_digits: If true, digits are split into individual tokens. + split_by_whitespace: Whether to respect white space while creating subwords. If False, will learn merges across whitespace. + split_by_unicode_script: Whether to include multiple Unicode scripts. Ex. is Arabic diacritics which are considered part of the letter (عِدَّةُ) + """ + + if not data_file or not os.path.exists(data_file): + raise ValueError(f"data_file must be valid file path, but got {data_file}") + data_dir = os.path.dirname(data_file) + vocab = [] + special_tokens = ["", "", "", ""] + if not output_dir: + output_dir = f'{data_dir}/spt' + if if_exist(output_dir, ['tokenizer.model']): + logging.info(f"tokenizer model {output_dir}/tokenizer.model already exists") + return f'{output_dir}/tokenizer.model', f'{output_dir}/vocab.txt' + logging.info(f'Processing {data_file} and store at {output_dir}') + os.makedirs(output_dir, exist_ok=True) + + cmd = ( + f"--input={data_file} --model_prefix={output_dir}/tokenizer " + f"--vocab_size={vocab_size} " + f"--shuffle_input_sentence=true --hard_vocab_limit=false " + f"--model_type={tokenizer_type} " + f"--character_coverage={character_coverage}" + ) + + pad_id = 3 + if not bos: + pad_id -= 1 + cmd += " --bos_id=-1" + + if not eos: + pad_id -= 1 + cmd += " --eos_id=-1" + + if pad: + cmd += f" --pad_id={pad_id}" + + if control_symbols: + control_string = (",").join(control_symbols) + cmd += f" --control_symbols={control_string}" + special_tokens += control_symbols + + if user_defined_symbols: + user_string = (",").join(user_defined_symbols) + cmd += f" --user_defined_symbols={user_string}" + special_tokens += user_defined_symbols + + if do_lower_case: + cmd += " --normalization_rule_name=nmt_nfkc_cf" + + if sample_size > 0: + cmd += f" --input_sentence_size={sample_size}" + + if train_extremely_large_corpus: + cmd += " --train_extremely_large_corpus=true" + + if max_sentencepiece_length >= 0: + cmd += f" --max_sentencepiece_length={max_sentencepiece_length}" + + if byte_fallback: + cmd += " --byte_fallback=true" + + if split_digits: + cmd += " --split_digits=true" + + if not split_by_whitespace: + cmd += " --split_by_whitespace=false" + + if not split_by_unicode_script: + cmd += " --split_by_unicode_script=false" + + sentencepiece.SentencePieceTrainer.Train(cmd) + + # Add BERT control symbols + tokens = [] + + with open(f"{output_dir}/tokenizer.vocab", "r") as f: + # Read tokens from each line and parse for vocab + for line in f: + piece = line.split("\t")[0] + if piece in special_tokens: + # skip special tokens + continue + token = piece[1:] if piece.startswith("▁") else f"##{piece}" + + if len(token) > 0: + tokens.append(token) + else: + tokens.append(piece[0]) + + vocab.extend(tokens) + + # Save vocabulary to output file + vocab_file = f'{output_dir}/vocab.txt' + with open(vocab_file, "w") as f: + for token in vocab: + f.write(f"{token}\n") + return f'{output_dir}/tokenizer.model', vocab_file diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/tabular_tokenizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/tabular_tokenizer.py new file mode 100644 index 0000000..5fa3683 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/tabular_tokenizer.py @@ -0,0 +1,199 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pickle +from typing import List + +import numpy + +from nemo.collections.common.tokenizers.column_coder import ColumnCodes +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec + +__all__ = ['TabularTokenizer'] + +END_OF_TEXT = '<|endoftext|>' +NEW_LINE = '\n' + + +def find_index_of(list_input, item): + output = -1 + try: + output = list_input.index(item) + except ValueError: + pass + return output + + +class TabularTokenizer(TokenizerSpec): + def __init__(self, coder, special_tokens=[END_OF_TEXT, NEW_LINE], delimiter=','): + if isinstance(coder, ColumnCodes): + self.code_column: ColumnCodes = coder + else: + with open(coder, 'rb') as handle: + self.code_column: ColumnCodes = pickle.load(handle) + self.num_columns = len(self.code_column.columns) + self.special_tokens = {} + self.special_tokens_decoder = {} + self.add_special_tokens(special_tokens) + self.delimiter = delimiter + self.eod_id = self.special_tokens[END_OF_TEXT] + self.eos_id = self.eod_id + self.bos_id = self.eos_id + + def __len__(self): + return self.vocab_size + + @property + def vocab_size(self): + return max(self.special_tokens_decoder.keys()) + 1 + + def text_to_ids(self, text): + return self.encode(text) + + def ids_to_text(self, token_ids): + return self.decode(token_ids) + + @property + def eod(self): + return self.eod_id + + @property + def eor(self): + return self.special_tokens[NEW_LINE] + + def add_special_tokens(self, special_tokens): + """ Add a list of additional tokens to the encoder. + The additional tokens are indexed starting from the last + index of the + current vocabulary in the order of the `special_tokens` list. + """ + if not special_tokens: + self.special_tokens = {} + self.special_tokens_decoder = {} + return + new = dict( + (tok, self.code_column.vocab_size + i) + for i, tok in enumerate(special_tokens) + if tok not in self.special_tokens + ) + self.special_tokens.update(new) + self.special_tokens_decoder = {v: k for k, v in self.special_tokens.items()} + + def text_to_tokens(self, text): + """ Tokenize a string. """ + tokens = [] + rows = text.split(NEW_LINE) + num_rows = len(rows) + for row_id in range(num_rows): + row = rows[row_id] + if row == '': + continue + fields = row.split(self.delimiter) + for f in fields: + splits = f.split(END_OF_TEXT) + if len(splits) == 1: + tokens.append(f.strip()) + elif len(splits) == 2: + if splits[0] != '': + tokens.append(splits[0].strip()) + tokens.append(END_OF_TEXT) + if splits[1] != '': + tokens.append(splits[1].strip()) + else: + raise ValueError("delimiter error") + if row_id != num_rows - 1: + tokens.append(NEW_LINE) + return tokens + + def tokens_to_ids(self, tokens: List[str]): + """ Converts a sequence of tokens into ids using the vocab. """ + ids = [] + cindex = 0 + if NEW_LINE in tokens: + idd = tokens.index(NEW_LINE) + cindex = (self.num_columns - idd) % self.num_columns + for token in tokens: + + if token in self.special_tokens: + ids.append(self.special_tokens[token]) + else: + index = cindex % self.num_columns + column = self.code_column.columns[index] + ids.extend(self.code_column.encode(column, token)) + cindex += 1 + return ids + + def ids_to_tokens(self, ids, skip_special_tokens=False): + """Converts a sequence of ids in Tabular tokens using the vocab.""" + tokens = [] + sizes = self.code_column.sizes + ids_size = sum(sizes) + cindex = 0 + eor_pos = find_index_of(ids, self.eor) + eod_pos = find_index_of(ids, self.eod) + if eor_pos >= 0 and eod_pos >= 0: + idd = min(eor_pos, eod_pos) + cindex = (ids_size - idd) % ids_size + elif eor_pos >= 0 and eod_pos < 0: + idd = eor_pos + cindex = (ids_size - idd) % ids_size + elif eod_pos >= 0 and eor_pos < 0: + idd = eod_pos + cindex = (ids_size - idd) % ids_size + cum_sizes = numpy.cumsum(sizes) + old_column_index = -1 + token_ids = [] + for i in ids: + if i in self.special_tokens_decoder: + if not skip_special_tokens: + tokens.append(self.special_tokens_decoder[i]) + else: + index = cindex % ids_size + column_index = numpy.where(index < cum_sizes)[0][0] + column = self.code_column.columns[column_index] + if old_column_index != column_index: + token_ids = [i] + old_column_index = column_index + else: + token_ids.append(i) + if len(token_ids) == sizes[column_index]: + tokens.append(self.code_column.decode(column, token_ids)) + cindex += 1 + return tokens + + def encode(self, text): + return self.tokens_to_ids(self.text_to_tokens(text)) + + def decode(self, token_ids): + tokens = self.ids_to_tokens(token_ids, skip_special_tokens=False) + return self.tokens_to_text(tokens) + + def tokens_to_text(self, tokens): + all_lines = [] + line = [] + for token in tokens: + if token == END_OF_TEXT or token == NEW_LINE: + if len(line) != 0: + line_text = self.delimiter.join(line) + all_lines.append(line_text) + all_lines.append(token) + line = [] + else: + line.append(token) + if len(line) != 0: + # remaining items + line_text = self.delimiter.join(line) + all_lines.append(line_text) + text = "".join(all_lines) + return text diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/__init__.py new file mode 100644 index 0000000..2db92b2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/ipa_lexicon.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/ipa_lexicon.py new file mode 100644 index 0000000..f408173 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/ipa_lexicon.py @@ -0,0 +1,223 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# fmt: off + +SUPPORTED_LOCALES = ["en-US", "de-DE", "es-ES", "it-IT", "fr-FR"] + +DEFAULT_PUNCTUATION = ( + ',', '.', '!', '?', '-', + ':', ';', '/', '"', '(', + ')', '[', ']', '{', '}', +) + +VITS_PUNCTUATION = ( + ',', '.', '!', '?', '-', + ':', ';', '"', '«', '»', + '“', '”', '¡', '¿', '—', + '…', +) + +GRAPHEME_CHARACTER_SETS = { + "en-US": ( + 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', + 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', + 'U', 'V', 'W', 'X', 'Y', 'Z' + ), + "es-ES": ( + 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', + 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', + 'U', 'V', 'W', 'X', 'Y', 'Z', 'Á', 'É', 'Í', 'Ñ', + 'Ó', 'Ú', 'Ü' + ), + # ref: https://en.wikipedia.org/wiki/German_orthography#Alphabet + "de-DE": ( + 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', + 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', + 'U', 'V', 'W', 'X', 'Y', 'Z', 'Ä', 'Ö', 'Ü', 'ẞ', + ), + "fr-FR": ( + 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', + 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', + 'U', 'V', 'W', 'X', 'Y', 'Z', 'À', 'Â', 'Ä', 'Æ', + 'Ç', 'È', 'É', 'Ê', 'Ë', 'Í', 'Î', 'Ï', 'Ñ', 'Ô', + 'Ö', 'Ù', 'Û', 'Ü', 'Ō', 'Œ', + ), + "it-IT": ( + 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', + 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', + 'U', 'V', 'W', 'X', 'Y', 'Z', 'À', 'È', 'É', 'Ì', + 'Ò', 'Ù' + ), +} + +IPA_CHARACTER_SETS = { + "en-US": ( + 'a', 'b', 'd', 'e', 'f', 'h', 'i', 'j', 'k', 'l', + 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', + 'x', 'z', 'æ', 'ð', 'ŋ', 'ɐ', 'ɑ', 'ɔ', 'ə', 'ɚ', + 'ɛ', 'ɜ', 'ɡ', 'ɪ', 'ɬ', 'ɹ', 'ɾ', 'ʃ', 'ʊ', 'ʌ', + 'ʒ', 'ʔ', 'ʲ', '̃', '̩', 'θ', 'ᵻ' + ), + "es-ES": ( + 'a', 'b', 'd', 'e', 'f', 'h', 'i', 'j', 'k', 'l', + 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'w', 'x', + 'ð', 'ŋ', 'ɛ', 'ɡ', 'ɣ', 'ɪ', 'ɲ', 'ɾ', 'ʃ', 'ʊ', + 'ʎ', 'ʒ', 'ʝ', 'β', 'θ' + ), + "de-DE": ( + '1', 'a', 'b', 'd', 'e', 'f', 'h', 'i', 'j', 'k', + 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', + 'w', 'x', 'y', 'z', 'ç', 'ø', 'ŋ', 'œ', 'ɐ', 'ɑ', + 'ɒ', 'ɔ', 'ə', 'ɛ', 'ɜ', 'ɡ', 'ɪ', 'ɹ', 'ɾ', 'ʃ', + 'ʊ', 'ʌ', 'ʒ', '̃', 'θ' + ), + "fr-FR": ( + 'a', 'b', 'd', 'e', 'f', 'h', 'i', 'j', 'k', 'l', + 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', + 'y', 'z', 'ð', 'ø', 'ŋ', 'œ', 'ɐ', 'ɑ', 'ɒ', 'ɔ', + 'ə', 'ɛ', 'ɜ', 'ɡ', 'ɪ', 'ɲ', 'ɹ', 'ʁ', 'ʃ', 'ʊ', + 'ʌ', 'ʒ', 'θ', 'ː', '̃' + ), + "it-IT": ( + 'a', 'b', 'd', 'e', 'f', 'h', 'i', 'j', 'k', 'l', + 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', + 'x', 'z', 'æ', 'ɐ', 'ɑ', 'ɔ', 'ə', 'ɚ', + 'ɜ', 'ɬ', 'ɹ', 'ʌ', 'ʔ', 'ʲ', '̃', '̩', 'ᵻ', + 'ð', 'ŋ', 'ɛ', 'ɡ', 'ɣ', 'ɪ', 'ɲ', 'ɾ', 'ʃ', + 'ʊ', 'ʎ', 'ʒ', 'ʝ', 'β', 'θ', 'd͡', 't͡', 'ø', 'ɒ', + 'ɕ', 'ɓ', 'ç', 'ɖ', 'ɘ', 'ɝ', 'ɞ', 'ɟ','ʄ','ɡ','ɠ', + 'ɢ','ʛ','ɦ','ɧ','ħ','ɥ','ʜ','ɨ','ɬ','ɫ','ɮ','ʟ', + 'ɱ','ɯ','ɰ','ɳ','ɵ','ɸ','œ','ɶ','ʘ','ɺ','ɻ','ʀ','ʁ', + 'ɽ','ʂ','ʈ','ʧ','ʉ','ʋ','ⱱ','ɤ','ʍ','χ','ʏ','ʑ','ʐ', + 'ʔ','ʡ','ʕ','ʢ','ǀ','ǁ','ǂ','ᵻ', 'ʃ','ː', + ), +} + +GRAPHEME_CHARACTER_CASES = ["upper", "lower", "mixed"] + +# fmt: on + + +def validate_locale(locale): + if locale not in SUPPORTED_LOCALES: + raise ValueError(f"Unsupported locale '{locale}'. " f"Supported locales {SUPPORTED_LOCALES}") + + +def get_grapheme_character_set(locale: str, case: str = "upper") -> str: + if locale not in GRAPHEME_CHARACTER_SETS: + raise ValueError( + f"Grapheme character set not found for locale '{locale}'. " + f"Supported locales {GRAPHEME_CHARACTER_SETS.keys()}" + ) + + charset_str_origin = ''.join(GRAPHEME_CHARACTER_SETS[locale]) + if case == "upper": + # Directly call .upper() will convert 'ß' into 'SS' according to https://bugs.python.org/issue30810. + charset_str = charset_str_origin.replace('ß', 'ẞ').upper() + elif case == "lower": + charset_str = charset_str_origin.lower() + elif case == "mixed": + charset_str = charset_str_origin.replace('ß', 'ẞ').upper() + charset_str_origin.lower() + else: + raise ValueError( + f"Grapheme character case not found: '{case}'. Supported cases are {GRAPHEME_CHARACTER_CASES}" + ) + + return charset_str + + +def get_ipa_character_set(locale): + if locale not in IPA_CHARACTER_SETS: + raise ValueError( + f"IPA character set not found for locale '{locale}'. " f"Supported locales {IPA_CHARACTER_SETS.keys()}" + ) + char_set = set(IPA_CHARACTER_SETS[locale]) + return char_set + + +def get_ipa_punctuation_list(locale): + if locale is None: + return sorted(list(DEFAULT_PUNCTUATION)) + + validate_locale(locale) + + punct_set = set(DEFAULT_PUNCTUATION) + # TODO @xueyang: verify potential mismatches with locale-specific punctuation sets used + # in nemo_text_processing.text_normalization.en.taggers.punctuation.py + if locale in ["de-DE", "es-ES", "it-IT", "fr-FR"]: + # ref: https://en.wikipedia.org/wiki/Guillemet#Uses + punct_set.update(['«', '»', '‹', '›']) + if locale == "de-DE": + # ref: https://en.wikipedia.org/wiki/German_orthography#Punctuation + punct_set.update( + [ + '„', # double low-9 quotation mark, U+201E, decimal 8222 + '“', # left double quotation mark, U+201C, decimal 8220 + '‚', # single low-9 quotation mark, U+201A, decimal 8218 + '‘', # left single quotation mark, U+2018, decimal 8216 + '‒', # figure dash, U+2012, decimal 8210 + '–', # en dash, U+2013, decimal 8211 + '—', # em dash, U+2014, decimal 8212 + ] + ) + if locale == "it-IT": + # ref: https://en.wikipedia.org/wiki/German_orthography#Punctuation + punct_set.update( + [ + '„', # double low-9 quotation mark, U+201E, decimal 8222 + '“', # left double quotation mark, U+201C, decimal 8220 + '‚', # single low-9 quotation mark, U+201A, decimal 8218 + '‘', # left single quotation mark, U+2018, decimal 8216 + '‒', # figure dash, U+2012, decimal 8210 + '–', # en dash, U+2013, decimal 8211 + '—', # em dash, U+2014, decimal 8212 + 'ʴ', + 'ʰ', + 'ʱ', + 'ʲ', + 'ʷ', + 'ˠ', + 'ˤ', + '˞↓', + '↑', + '→', + '↗', + '↘', + '”', + '’', + '-', + ] + ) + elif locale == "es-ES": + # ref: https://en.wikipedia.org/wiki/Spanish_orthography#Punctuation + punct_set.update(['¿', '¡']) + elif locale == "fr-FR": + punct_set.update( + [ + '–', # en dash, U+2013, decimal 8211 + '“', # left double quotation mark, U+201C, decimal 8220 + '”', # right double quotation mark, U+201D, decimal 8221 + '…', # horizontal ellipsis, U+2026, decimal 8230 + '̀', # combining grave accent, U+0300, decimal 768 + '́', # combining acute accent, U+0301, decimal 769 + '̂', # combining circumflex accent, U+0302, decimal 770 + '̈', # combining diaeresis, U+0308, decimal 776 + '̧', # combining cedilla, U+0327, decimal 807 + ] + ) + + punct_list = sorted(list(punct_set)) + return punct_list diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/tokenizer_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/tokenizer_utils.py new file mode 100644 index 0000000..542b181 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/tokenizer_utils.py @@ -0,0 +1,203 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import re +import unicodedata +from builtins import str as unicode +from typing import List, Tuple + +__all__ = [ + "french_text_preprocessing", + "chinese_text_preprocessing", + "english_text_preprocessing", + "any_locale_text_preprocessing", + "spanish_text_preprocessing", + "italian_text_preprocessing", + "any_locale_word_tokenize", + "english_word_tokenize", + "LATIN_CHARS_ALL", + "normalize_unicode_text", +] + +# Derived from LJSpeech +_synoglyphs = { + "'": ['’'], + '"': ['”', '“'], +} +SYNOGLYPH2ASCII = {g: asc for asc, glyphs in _synoglyphs.items() for g in glyphs} + +# Example of parsing by groups via _WORDS_RE_EN. +# Regular expression pattern groups: +# 1st group -- valid english words, +# 2nd group -- any substring starts from | to | (mustn't be nested), useful when you want to leave sequence unchanged, +# 3rd group -- punctuation marks or whitespaces. +# Text (first line) and mask of groups for every char (second line). +# config file must contain |EY1 EY1|, B, C, D, E, F, and G. + +# define char set based on https://en.wikipedia.org/wiki/List_of_Unicode_characters +LATIN_ALPHABET_BASIC = "A-Za-z" +ACCENTED_CHARS = "À-ÖØ-öø-ÿ" +LATIN_CHARS_ALL = f"{LATIN_ALPHABET_BASIC}{ACCENTED_CHARS}" +_WORDS_RE_EN = re.compile( + fr"([{LATIN_ALPHABET_BASIC}]+(?:[{LATIN_ALPHABET_BASIC}\-']*[{LATIN_ALPHABET_BASIC}]+)*)|(\|[^|]*\|)|([^{LATIN_ALPHABET_BASIC}|]+)" +) +_WORDS_RE_ANY_LOCALE = re.compile( + fr"([{LATIN_CHARS_ALL}]+(?:[{LATIN_CHARS_ALL}\-']*[{LATIN_CHARS_ALL}]+)*)|(\|[^|]*\|)|([^{LATIN_CHARS_ALL}|]+)" +) + + +def english_text_preprocessing(text, lower=True): + text = unicode(text) + text = ''.join(char for char in unicodedata.normalize('NFD', text) if unicodedata.category(char) != 'Mn') + text = ''.join(char if char not in SYNOGLYPH2ASCII else SYNOGLYPH2ASCII[char] for char in text) + + if lower: + text = text.lower() + + return text + + +def any_locale_text_preprocessing(text: str) -> str: + """ + Normalize unicode text with "NFC", and convert right single quotation mark (U+2019, decimal 8217) as an apostrophe. + + Args: + text (str): the original input sentence. + + Returns: normalized text (str). + """ + res = [] + for c in normalize_unicode_text(text): + if c in ['’']: # right single quotation mark (U+2019, decimal 8217) as an apostrophe + res.append("'") + else: + res.append(c) + + return ''.join(res) + + +def normalize_unicode_text(text: str) -> str: + """ + TODO @xueyang: Apply NFC form may be too aggressive since it would ignore some accented characters that do not exist + in predefined German alphabet (nemo.collections.common.tokenizers.text_to_speech.ipa_lexicon.IPA_CHARACTER_SETS), + such as 'é'. This is not expected. A better solution is to add an extra normalization with NFD to discard the + diacritics and consider 'é' and 'e' produce similar pronunciations. + + Note that the tokenizer needs to run `unicodedata.normalize("NFC", x)` before calling `encode` function, + especially for the characters that have diacritics, such as 'ö' in the German alphabet. 'ö' can be encoded as + b'\xc3\xb6' (one char) as well as b'o\xcc\x88' (two chars). Without the normalization of composing two chars + together and without a complete predefined set of diacritics, when the tokenizer reads the input sentence + char-by-char, it would skip the combining diaeresis b'\xcc\x88', resulting in indistinguishable pronunciations + for 'ö' and 'o'. + + Args: + text (str): the original input sentence. + + Returns: + NFC normalized sentence (str). + """ + # normalize word with NFC form + if not unicodedata.is_normalized("NFC", text): + text = unicodedata.normalize("NFC", text) + + return text + + +def _word_tokenize(words: List[Tuple[str, str, str]], is_lower: bool = False) -> List[Tuple[List[str], bool]]: + """ + Process a list of words and attach indicators showing if each word is unchangeable or not. Each word representation + can be one of valid word, any substring starting from | to | (unchangeable word), or punctuation marks including + whitespaces. This function will split unchanged strings by whitespaces and return them as `List[str]`. For example, + + .. code-block:: python + [ + ('Hello', '', ''), # valid word + ('', '', ' '), # punctuation mark + ('World', '', ''), # valid word + ('', '', ' '), # punctuation mark + ('', '|NVIDIA unchanged|', ''), # unchangeable word + ('', '', '!') # punctuation mark + ] + + will be converted into, + + .. code-block:: python + [ + (["Hello"], False), + ([" "], False), + (["World"], False), + ([" "], False), + (["NVIDIA", "unchanged"], True), + (["!"], False) + ] + + Args: + words (List[str]): a list of tuples like `(maybe_word, maybe_without_changes, maybe_punct)` where each element + corresponds to a non-overlapping match of either `_WORDS_RE_EN` or `_WORDS_RE_ANY_LOCALE`. + is_lower (bool): a flag to trigger lowercase all words. By default, it is False. + + Returns: List[Tuple[List[str], bool]], a list of tuples like `(a list of words, is_unchanged)`. + + """ + result = [] + for word in words: + maybe_word, maybe_without_changes, maybe_punct = word + + without_changes = False + if maybe_word != '': + if is_lower: + token = [maybe_word.lower()] + else: + token = [maybe_word] + elif maybe_punct != '': + token = [maybe_punct] + elif maybe_without_changes != '': + without_changes = True + token = maybe_without_changes[1:-1].split(" ") + else: + raise ValueError( + f"This is not expected. Found empty string: <{word}>. " + f"Please validate your regular expression pattern '_WORDS_RE_EN' or '_WORDS_RE_ANY_LOCALE'." + ) + + result.append((token, without_changes)) + + return result + + +def english_word_tokenize(text: str) -> List[Tuple[List[str], bool]]: + words = _WORDS_RE_EN.findall(text) + return _word_tokenize(words, is_lower=True) + + +def any_locale_word_tokenize(text: str) -> List[Tuple[List[str], bool]]: + words = _WORDS_RE_ANY_LOCALE.findall(text) + return _word_tokenize(words) + + +def spanish_text_preprocessing(text: str) -> str: + return text.lower() + + +def italian_text_preprocessing(text: str) -> str: + return text.lower() + + +def chinese_text_preprocessing(text: str) -> str: + return text + + +def french_text_preprocessing(text: str) -> str: + return text.lower() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/tokenizer_wrapper.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/tokenizer_wrapper.py new file mode 100644 index 0000000..e2d06f8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/tokenizer_wrapper.py @@ -0,0 +1,58 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.common.tokenizers import TokenizerSpec +from nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers import EnglishPhonemesTokenizer +from nemo.collections.tts.g2p.models.en_us_arpabet import EnglishG2p + +__all__ = ['TextToSpeechTokenizer'] + + +class TextToSpeechTokenizer(TokenizerSpec): + def __init__(self, phoneme_dict, heteronyms): + self.g2p = EnglishG2p(phoneme_dict=phoneme_dict, heteronyms=heteronyms) + self.tokenizer = EnglishPhonemesTokenizer( + self.g2p, stresses=True, chars=True, pad_with_space=True, add_blank_at=True + ) + self.vocab_size = len(self.tokenizer.tokens) + + def text_to_ids(self, text): + return self.tokenizer.encode(text) + + def text_to_tokens(self, text): + return self.g2p(text) + + def tokens_to_text(self, tokens): + pass + + def tokens_to_ids(self, tokens): + pass + + def ids_to_tokens(self, ids): + pass + + def ids_to_text(self, ids): + pass + + @property + def pad_id(self): + return self.tokenizer.pad + + @property + def bos_id(self): + return self.tokenizer.pad + + @property + def eos_id(self): + return self.tokenizer.pad diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/tts_tokenizers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/tts_tokenizers.py new file mode 100644 index 0000000..1aefc6f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/text_to_speech/tts_tokenizers.py @@ -0,0 +1,918 @@ +# -*- coding: utf-8 -*- +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import itertools +import string +from abc import ABC, abstractmethod +from contextlib import contextmanager +from typing import List, Optional + +from nemo.collections.common.tokenizers.text_to_speech.ipa_lexicon import ( + get_grapheme_character_set, + get_ipa_punctuation_list, + validate_locale, +) +from nemo.collections.common.tokenizers.text_to_speech.tokenizer_utils import ( + any_locale_text_preprocessing, + chinese_text_preprocessing, + english_text_preprocessing, + french_text_preprocessing, + italian_text_preprocessing, + spanish_text_preprocessing, +) +from nemo.utils import logging +from nemo.utils.decorators import experimental + + +class BaseTokenizer(ABC): + PAD, BLANK, OOV = '', '', '' + + def __init__(self, tokens, *, pad=PAD, blank=BLANK, oov=OOV, sep='', add_blank_at=None): + """Abstract class for creating an arbitrary tokenizer to convert string to list of int tokens. + Args: + tokens: List of tokens. + pad: Pad token as string. + blank: Blank token as string. + oov: OOV token as string. + sep: Separation token as string. + add_blank_at: Add blank to labels in the specified order ("last") or after tokens (any non None), + if None then no blank in labels. + """ + super().__init__() + + tokens = list(tokens) + # TODO @xueyang: in general, IDs of pad, sil, blank, and oov are preserved ahead instead of dynamically + # assigned according to the number of tokens. The downside of using dynamical assignment leads to different IDs + # for each. + self.pad, tokens = len(tokens), tokens + [pad] # Padding + + if add_blank_at is not None: + self.blank, tokens = len(tokens), tokens + [blank] # Reserved for blank from asr-model + else: + # use add_blank_at=None only for ASR where blank is added automatically, disable blank here + self.blank = None + + self.oov, tokens = len(tokens), tokens + [oov] # Out Of Vocabulary + + if add_blank_at == "last": + tokens[-1], tokens[-2] = tokens[-2], tokens[-1] + self.oov, self.blank = self.blank, self.oov + + self.tokens = tokens + self.sep = sep + + self._util_ids = {self.pad, self.blank, self.oov} + self._token2id = {l: i for i, l in enumerate(tokens)} + self._id2token = tokens + + def __call__(self, text: str) -> List[int]: + return self.encode(text) + + @abstractmethod + def encode(self, text: str) -> List[int]: + """Turns str text into int tokens.""" + pass + + def decode(self, tokens: List[int]) -> str: + """Turns ints tokens into str text.""" + return self.sep.join(self._id2token[t] for t in tokens if t not in self._util_ids) + + +class BaseCharsTokenizer(BaseTokenizer): + # fmt: off + # TODO @xueyang: unify definition of the default PUNCT_LIST and import from ipa_lexicon.py + PUNCT_LIST = ( # Derived from LJSpeech and "/" additionally + ',', '.', '!', '?', '-', + ':', ';', '/', '"', '(', + ')', '[', ']', '{', '}', + ) + # fmt: on + + def __init__( + self, + chars, + punct=True, + apostrophe=True, + add_blank_at=None, + pad_with_space=False, + non_default_punct_list=None, + text_preprocessing_func=lambda x: x, + ): + """Base class for char-based tokenizer. + Args: + chars: string that represents all possible characters. + punct: Whether to reserve grapheme for basic punctuation or not. + apostrophe: Whether to use apostrophe or not. + add_blank_at: Add blank to labels in the specified order ("last") or after tokens (any non None), + if None then no blank in labels. + pad_with_space: Whether to pad text with spaces at the beginning and at the end or not. + non_default_punct_list: List of punctuation marks which will be used instead default. + text_preprocessing_func: Text preprocessing function for correct execution of the tokenizer. + """ + + tokens = [] + self.space, tokens = len(tokens), tokens + [' '] # Space + tokens.extend(chars) + if apostrophe: + tokens.append("'") # Apostrophe for saving "don't" and "Joe's" + + if punct: + if non_default_punct_list is not None: + self.PUNCT_LIST = non_default_punct_list + tokens.extend(self.PUNCT_LIST) + + super().__init__(tokens, add_blank_at=add_blank_at) + + self.punct = punct + self.pad_with_space = pad_with_space + + self.text_preprocessing_func = text_preprocessing_func + + def encode(self, text): + """See base class.""" + cs, space, tokens = [], self.tokens[self.space], set(self.tokens) + + text = self.text_preprocessing_func(text) + for c in text: + # Add a whitespace if the current char is a whitespace while the previous char is not a whitespace. + if c == space and len(cs) > 0 and cs[-1] != space: + cs.append(c) + # Add the current char that is an alphanumeric or an apostrophe. + elif (c.isalnum() or c == "'") and c in tokens: + cs.append(c) + # Add a punctuation that has a single char. + elif (c in self.PUNCT_LIST) and self.punct: + cs.append(c) + # Warn about unknown char + elif c != space: + logging.warning(f"Text: [{text}] contains unknown char: [{c}]. Symbol will be skipped.") + + # Remove trailing spaces + if cs: + while cs[-1] == space: + cs.pop() + + if self.pad_with_space: + cs = [space] + cs + [space] + + return [self._token2id[p] for p in cs] + + +class EnglishCharsTokenizer(BaseCharsTokenizer): + def __init__( + self, + punct=True, + apostrophe=True, + add_blank_at=None, + pad_with_space=False, + non_default_punct_list=None, + text_preprocessing_func=english_text_preprocessing, + ): + """English char-based tokenizer. + Args: + punct: Whether to reserve grapheme for basic punctuation or not. + apostrophe: Whether to use apostrophe or not. + add_blank_at: Add blank to labels in the specified order ("last") or after tokens (any non None), + if None then no blank in labels. + pad_with_space: Whether to pad text with spaces at the beginning and at the end or not. + non_default_punct_list: List of punctuation marks which will be used instead default. + text_preprocessing_func: Text preprocessing function for correct execution of the tokenizer. + Basically, it replaces all non-unicode characters with unicode ones and apply lower() function. + """ + super().__init__( + chars=string.ascii_lowercase, + punct=punct, + apostrophe=apostrophe, + add_blank_at=add_blank_at, + pad_with_space=pad_with_space, + non_default_punct_list=non_default_punct_list, + text_preprocessing_func=text_preprocessing_func, + ) + + +class GermanCharsTokenizer(BaseCharsTokenizer): + + _LOCALE = "de-DE" + _PUNCT_LIST = get_ipa_punctuation_list(_LOCALE) + _CHARSET_STR = get_grapheme_character_set(locale=_LOCALE, case="mixed") + + def __init__( + self, + chars=_CHARSET_STR, + punct=True, + apostrophe=True, + add_blank_at=None, + pad_with_space=False, + non_default_punct_list=_PUNCT_LIST, + text_preprocessing_func=any_locale_text_preprocessing, + ): + """German grapheme-based tokenizer. + Args: + punct: Whether to reserve grapheme for basic punctuation or not. + apostrophe: Whether to use apostrophe or not. + add_blank_at: Add blank to labels in the specified order ("last") or after tokens (any non None), + if None then no blank in labels. + pad_with_space: Whether to pad text with spaces at the beginning and at the end or not. + non_default_punct_list: List of punctuation marks which will be used instead default. + text_preprocessing_func: Text preprocessing function for correct execution of the tokenizer. By default, it + would keep any word unchanged. + """ + super().__init__( + chars=chars, + punct=punct, + apostrophe=apostrophe, + add_blank_at=add_blank_at, + pad_with_space=pad_with_space, + non_default_punct_list=non_default_punct_list, + text_preprocessing_func=text_preprocessing_func, + ) + + +class SpanishCharsTokenizer(BaseCharsTokenizer): + + PUNCT_LIST = get_ipa_punctuation_list("es-ES") + + def __init__( + self, punct=True, apostrophe=True, add_blank_at=None, pad_with_space=False, non_default_punct_list=None, + ): + """Spanish grapheme tokenizer. + Args: + punct: Whether to reserve grapheme for basic punctuation or not. + apostrophe: Whether to use apostrophe or not. + add_blank_at: Add blank to labels in the specified order ("last") or after tokens (any non None), + if None then no blank in labels. + pad_with_space: Whether to pad text with spaces at the beginning and at the end or not. + non_default_punct_list: List of punctuation marks which will be used instead default. + """ + + es_alphabet = "abcdefghijklmnopqrstuvwxyzáéíñóúü" + super().__init__( + chars=es_alphabet, + punct=punct, + apostrophe=apostrophe, + add_blank_at=add_blank_at, + pad_with_space=pad_with_space, + non_default_punct_list=non_default_punct_list, + text_preprocessing_func=spanish_text_preprocessing, + ) + + +class FrenchCharsTokenizer(BaseCharsTokenizer): + + PUNCT_LIST = get_ipa_punctuation_list("fr-FR") + + def __init__( + self, punct=True, apostrophe=True, add_blank_at=None, pad_with_space=False, non_default_punct_list=None, + ): + """French grapheme tokenizer. + Args: + punct: Whether to reserve grapheme for basic punctuation or not. + apostrophe: Whether to use apostrophe or not. + add_blank_at: Add blank to labels in the specified order ("last") or after tokens (any non None), + if None then no blank in labels. + pad_with_space: Whether to pad text with spaces at the beginning and at the end or not. + non_default_punct_list: List of punctuation marks which will be used instead default. + """ + + fr_alphabet = get_grapheme_character_set(locale="fr-FR", case="lower") + super().__init__( + chars=fr_alphabet, + punct=punct, + apostrophe=apostrophe, + add_blank_at=add_blank_at, + pad_with_space=pad_with_space, + non_default_punct_list=non_default_punct_list, + text_preprocessing_func=french_text_preprocessing, + ) + + +class ItalianCharsTokenizer(BaseCharsTokenizer): + PUNCT_LIST = get_ipa_punctuation_list("it-IT") + + def __init__( + self, punct=True, apostrophe=True, add_blank_at=None, pad_with_space=False, non_default_punct_list=None + ): + """Italian grapheme tokenizer. + Args: + punct: Whether to reserve grapheme for basic punctuation or not. + apostrophe: Whether to use apostrophe or not. + add_blank_at: Add blank to labels in the specified order ("last") or after tokens (any non None), + if None then no blank in labels. + pad_with_space: Whether to pad text with spaces at the beginning and at the end or not. + non_default_punct_list: List of punctuation marks which will be used instead default. + """ + + it_alphabet = "abcdefghijklmnopqrstuvwxyzàèéìòùó" + super().__init__( + chars=it_alphabet, + punct=punct, + apostrophe=apostrophe, + add_blank_at=add_blank_at, + pad_with_space=pad_with_space, + non_default_punct_list=non_default_punct_list, + text_preprocessing_func=italian_text_preprocessing, + ) + + +class GermanPhonemesTokenizer(BaseCharsTokenizer): + # fmt: off + PUNCT_LIST = ( # Derived from LJSpeech and "/" additionally + ',', '.', '!', '?', '-', + ':', ';', '/', '"', '(', + ')', '[', ']', '{', '}', + ) + # fmt: on + + def __init__( + self, + punct=True, + apostrophe=True, + add_blank_at=None, + pad_with_space=False, + non_default_punct_list=None, + text_preprocessing_func=any_locale_text_preprocessing, + ): + """Deutsch phoneme-based tokenizer. + Args: + punct: Whether to reserve grapheme for basic punctuation or not. + apostrophe: Whether to use apostrophe or not. + add_blank_at: Add blank to labels in the specified order ("last") or after tokens (any non None), + if None then no blank in labels. + pad_with_space: Whether to pad text with spaces at the beginning and at the end or not. + non_default_punct_list: List of punctuation marks which will be used instead default. + text_preprocessing_func: Text preprocessing function for correct execution of the tokenizer. + Currently, it only applies lower() function. + """ + + de_ipa = "abdefhijklmnoprstuvwxyzçðøŋœɐɑɒɔəɛɜɡɪɹɾʃʊʌʒː̃" + de_suprasegmentals = "12" + super().__init__( + chars=de_ipa + de_suprasegmentals, + punct=punct, + apostrophe=apostrophe, + add_blank_at=add_blank_at, + pad_with_space=pad_with_space, + non_default_punct_list=non_default_punct_list, + text_preprocessing_func=text_preprocessing_func, + ) + + def encode(self, text): + """See base class.""" + cs, space, tokens = [], self.tokens[self.space], set(self.tokens) + + text = self.text_preprocessing_func(text) + for c in text: + # Add space if last one isn't one + if c == space and len(cs) > 0 and cs[-1] != space: + cs.append(c) + # Add next char + elif (c.isalnum() or c == "'" or c == "\u0303") and c in tokens: + cs.append(c) + # Add punct + elif (c in self.PUNCT_LIST) and self.punct: + cs.append(c) + # Warn about unknown char + elif c != space: + logging.warning(f"Text: [{text}] contains unknown char: [{c}]. Symbol will be skipped.") + + # Remove trailing spaces + while cs[-1] == space: + cs.pop() + + if self.pad_with_space: + cs = [space] + cs + [space] + + return [self._token2id[p] for p in cs] + + +class ItalianPhonemesTokenizer(BaseCharsTokenizer): + # fmt: off + PUNCT_LIST = ( + ',', '.', '!', '?', '-', + ':', ';', '/', '"', '(', + ')', '[', ']', '{', '}', + '„', '“', '”', '‘', '’', '‒', '—', '«', '»', '‹', '›', '_', + ) + # fmt: on + + def __init__( + self, + punct=True, + apostrophe=True, + add_blank_at=None, + pad_with_space=False, + non_default_punct_list=None, + text_preprocessing_func=italian_text_preprocessing, + ): + """Italian phoneme-based tokenizer. + Args: + punct: Whether to reserve grapheme for basic punctuation or not. + apostrophe: Whether to use apostrophe or not. + add_blank_at: Add blank to labels in the specified order ("last") or after tokens (any non None), + if None then no blank in labels. + pad_with_space: Whether to pad text with spaces at the beginning and at the end or not. + non_default_punct_list: List of punctuation marks which will be used instead default. + text_preprocessing_func: Text preprocessing function for correct execution of the tokenizer. + Currently, it only applies lower() function. + """ + + it_ipa = "abcdefghijklmnopqrstuvwxyzàèéìòùóæɐɑɔəɚɜɬɹʌʔᵻðŋɛɡɣɪɲɾʃʊʎʒʝβθd͡'t͡'øɒɕɓçɖɘɝɞɟʄɡɠɢʛɦɧħɥʜɨɬɫɮʟɱɯɰɳɵɸœɶʘɺɻʀʁɽʂʈʧʉʋⱱɤʍχʏʑʐʔʡʕʢǀǁǂᵻʃ'ː" + super().__init__( + chars=it_ipa, + punct=punct, + apostrophe=apostrophe, + add_blank_at=add_blank_at, + pad_with_space=pad_with_space, + non_default_punct_list=non_default_punct_list, + text_preprocessing_func=text_preprocessing_func, + ) + + def encode(self, text): + """See base class.""" + cs, space, tokens = [], self.tokens[self.space], set(self.tokens) + + text = self.text_preprocessing_func(text) + for c in text: + # Add space if last one isn't one + if c == space and len(cs) > 0 and cs[-1] != space: + cs.append(c) + # Add next char + elif (c.isalnum() or c == "'" or c == "\u0303") and c in tokens: + cs.append(c) + # Add punct + elif (c in self.PUNCT_LIST) and self.punct: + cs.append(c) + # Warn about unknown char + elif c != space: + logging.warning(f"Text: [{text}] contains unknown char: [{c}]. Symbol will be skipped.") + + # Remove trailing spaces + while cs[-1] == space: + cs.pop() + + if self.pad_with_space: + cs = [space] + cs + [space] + + return [self._token2id[p] for p in cs] + + +class EnglishPhonemesTokenizer(BaseTokenizer): + # fmt: off + PUNCT_LIST = ( # Derived from LJSpeech and "/" additionally + ',', '.', '!', '?', '-', + ':', ';', '/', '"', '(', + ')', '[', ']', '{', '}', + ) + VOWELS = ( + 'AA', 'AE', 'AH', 'AO', 'AW', + 'AY', 'EH', 'ER', 'EY', 'IH', + 'IY', 'OW', 'OY', 'UH', 'UW', + ) + CONSONANTS = ( + 'B', 'CH', 'D', 'DH', 'F', 'G', + 'HH', 'JH', 'K', 'L', 'M', 'N', + 'NG', 'P', 'R', 'S', 'SH', 'T', + 'TH', 'V', 'W', 'Y', 'Z', 'ZH', + ) + # fmt: on + + def __init__( + self, + g2p, + punct=True, + non_default_punct_list=None, + stresses=False, + chars=False, + *, + space=' ', + silence=None, + apostrophe=True, + oov=BaseTokenizer.OOV, + sep='|', # To be able to distinguish between 2/3 letters codes. + add_blank_at=None, + pad_with_space=False, + text_preprocessing_func=lambda text: english_text_preprocessing(text, lower=False), + ): + """English phoneme-based tokenizer. + Args: + g2p: Grapheme to phoneme module. + punct: Whether to reserve grapheme for basic punctuation or not. + non_default_punct_list: List of punctuation marks which will be used instead default. + stresses: Whether to use phonemes codes with stresses (0-2) or not. + chars: Whether to additionally use chars together with phonemes. It is useful if g2p module can return chars too. + space: Space token as string. + silence: Silence token as string (will be disabled if it is None). + apostrophe: Whether to use apostrophe or not. + oov: OOV token as string. + sep: Separation token as string. + add_blank_at: Add blank to labels in the specified order ("last") or after tokens (any non None), + if None then no blank in labels. + pad_with_space: Whether to pad text with spaces at the beginning and at the end or not. + text_preprocessing_func: Text preprocessing function for correct execution of the tokenizer. + Basically, it replaces all non-unicode characters with unicode ones. + Note that lower() function shouldn't be applied here, in case the text contains phonemes (it will be handled by g2p). + """ + + self.phoneme_probability = None + if hasattr(g2p, "phoneme_probability"): + self.phoneme_probability = g2p.phoneme_probability + tokens = [] + self.space, tokens = len(tokens), tokens + [space] # Space + + if silence is not None: + self.silence, tokens = len(tokens), tokens + [silence] # Silence + + tokens.extend(self.CONSONANTS) + vowels = list(self.VOWELS) + + if stresses: + vowels = [f'{p}{s}' for p, s in itertools.product(vowels, (0, 1, 2))] + tokens.extend(vowels) + + if chars or self.phoneme_probability is not None: + if not chars: + logging.warning( + "phoneme_probability was not None, characters will be enabled even though " + "chars was set to False." + ) + tokens.extend(string.ascii_lowercase) + + if apostrophe: + tokens.append("'") # Apostrophe + + if punct: + if non_default_punct_list is not None: + self.PUNCT_LIST = non_default_punct_list + tokens.extend(self.PUNCT_LIST) + + super().__init__(tokens, oov=oov, sep=sep, add_blank_at=add_blank_at) + + self.chars = chars if self.phoneme_probability is None else True + self.punct = punct + self.stresses = stresses + self.pad_with_space = pad_with_space + + self.text_preprocessing_func = text_preprocessing_func + self.g2p = g2p + + def encode(self, text): + """See base class for more information.""" + + text = self.text_preprocessing_func(text) + g2p_text = self.g2p(text) # TODO: handle infer + return self.encode_from_g2p(g2p_text, text) + + def encode_from_g2p(self, g2p_text: List[str], raw_text: Optional[str] = None): + """ + Encodes text that has already been run through G2P. + Called for encoding to tokens after text preprocessing and G2P. + + Args: + g2p_text: G2P's output, could be a mixture of phonemes and graphemes, + e.g. "see OOV" -> ['S', 'IY1', ' ', 'O', 'O', 'V'] + raw_text: original raw input + """ + ps, space, tokens = [], self.tokens[self.space], set(self.tokens) + for p in g2p_text: # noqa + # Remove stress + if p.isalnum() and len(p) == 3 and not self.stresses: + p = p[:2] + + # Add space if last one isn't one + if p == space and len(ps) > 0 and ps[-1] != space: + ps.append(p) + # Add next phoneme or char (if chars=True) + elif (p.isalnum() or p == "'") and p in tokens: + ps.append(p) + # Add punct + elif (p in self.PUNCT_LIST) and self.punct: + ps.append(p) + # Warn about unknown char/phoneme + elif p != space: + message = f"Text: [{''.join(g2p_text)}] contains unknown char/phoneme: [{p}]." + if raw_text is not None: + message += f"Original text: [{raw_text}]. Symbol will be skipped." + logging.warning(message) + + # Remove trailing spaces + if ps: + while ps[-1] == space: + ps.pop() + + if self.pad_with_space: + ps = [space] + ps + [space] + + return [self._token2id[p] for p in ps] + + @contextmanager + def set_phone_prob(self, prob): + if hasattr(self.g2p, "phoneme_probability"): + self.g2p.phoneme_probability = prob + try: + yield + finally: + if hasattr(self.g2p, "phoneme_probability"): + self.g2p.phoneme_probability = self.phoneme_probability + + +@experimental +class IPATokenizer(BaseTokenizer): + def __init__( + self, + g2p, + locale="en-US", + punct=True, + non_default_punct_list=None, + fixed_vocab=None, + *, + space=' ', + silence=None, + apostrophe=False, + oov=BaseTokenizer.OOV, + sep='|', # To be able to distinguish between symbols + add_blank_at=None, + pad_with_space=False, + ): + """General-purpose IPA-based tokenizer. + Args: + g2p: Grapheme to phoneme module, should be IpaG2p or some subclass thereof. + locale: Locale used to determine default text processing logic and punctuation. + Supports ["en-US", "de-DE", "es-ES", "fr-FR"]. Defaults to "en-US". + Specify None if implementing custom logic for a new locale. + punct: Whether to reserve grapheme for basic punctuation or not. + non_default_punct_list: List of punctuation marks which will be used instead default, if any. + fixed_vocab: List of valid grapheme/phoneme tokens for the model. + Set only if overriding the default vocab generation process (reading from G2P dict). + If set, any dataset entries that have unincluded graphemes will be filtered out, and any words whose + pronunciations have unincluded phonemes will be treated as OOV. + Please make sure that the grapheme prefixes and cases are consistent with the G2P module's settings. + Defaults to None, which means default vocab generation is used. + space: Space token as string. + silence: Silence token as string (will be disabled if it is None). + apostrophe: Whether to use apostrophe or not. + oov: OOV token as string. + sep: Separation token as string. + add_blank_at: Add blank to labels in the specified order ("last") or after tokens (any non None), + if None then no blank in labels. + pad_with_space: Whether to pad text with spaces at the beginning and at the end or not. + """ + if not hasattr(g2p, "symbols"): + logging.error( + f"Please make sure the G2P module passed into the IPATokenizer has a `symbols` attribute. " + f"This is required in order to build the tokenizer vocabulary.\n" + f"Expected e.g. IpaG2p, found {type(g2p)}" + ) + raise ValueError("G2P modules passed into the IPATokenizer must have `symbols` defined.") + + if locale is not None: + validate_locale(locale) + + self.phoneme_probability = None + if hasattr(g2p, "phoneme_probability"): + self.phoneme_probability = g2p.phoneme_probability + + if locale == "en-US": + self.text_preprocessing_func = lambda text: english_text_preprocessing(text, lower=False) + else: + self.text_preprocessing_func = any_locale_text_preprocessing + + # Build tokens list if fixed_vocab isn't set + if fixed_vocab: + tokens = {self.text_preprocessing_func(c) for c in fixed_vocab} + self.set_fixed_vocab = True # Used to check whether dataset entries need filtering + + if g2p.symbols == tokens: + logging.info( + "Did not replace G2P valid symbol set since the given set is equivalent to the existing one." + ) + self.set_fixed_vocab = False + else: + g2p.replace_symbols(tokens) + else: + tokens = set(g2p.symbols) + self.set_fixed_vocab = False + + if apostrophe: + tokens.add("'") + + if punct: + if non_default_punct_list is not None: + self.punct_list = non_default_punct_list + else: + self.punct_list = get_ipa_punctuation_list(locale) + + tokens.update(self.punct_list) + + # Sort to ensure that vocab is in the same order every time + tokens = sorted(list(tokens)) + + if space in g2p.symbols: + self.space = tokens.index(space) + else: + self.space, tokens = len(tokens), tokens + [space] + + if silence is not None: + self.silence, tokens = len(tokens), tokens + [silence] + + super().__init__(tokens, oov=oov, sep=sep, add_blank_at=add_blank_at) + + self.tokens_set = set(self.tokens) # To save some repeated work when filtering entries + + self.punct = punct + self.pad_with_space = pad_with_space + + self.g2p = g2p + + def encode(self, text: str) -> List[int]: + """See base class for more information.""" + # normalize the input text with "NFC" form. + text = self.text_preprocessing_func(text) + + # transliterate the text into phoneme sequences and/or grapheme sequences. + g2p_text = self.g2p(text) + + return self.encode_from_g2p(g2p_text, text) + + def encode_from_g2p(self, g2p_text: List[str], raw_text: Optional[str] = None) -> List[int]: + """ + Tokenize the `g2p_text` that has been already run through G2P. Each item in the `g2p_text` would be encoded as + one of the integer IDs predefined in `self._token2id`. Note that this function should be called after + `self.text_preprocessing_func` and `self.g2p` functions + + Args: + g2p_text (List[str]): a sequence of tokens from G2P's output. It could be a sequence of phonemes, a sequence + of graphemes, or a mixture of both. For example, `['ˈ', 's', 'i', ' ', '#O', '#O', '#V']`, which is the + G2P's output of the text "see OOV", where '#' is prepended to each grapheme in order to distinguish + graphemes from phonemes if there are overlaps in between. The prefix '#' can be customized in + `nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p.grapheme_prefix`. + raw_text (str): the original text after calling `self.text_preprocessing_func`. It is optional. It is only + used to deliver a warning message that some graphemes from the original text are skipped. + + Returns: a list of integer IDs that tokenize the `g2p_text`. + """ + ps, space, tokens = [], self.tokens[self.space], set(self.tokens) + for p in g2p_text: + if p == space and len(ps) > 0 and ps[-1] != space: + # Add space if last token isn't one + ps.append(p) + elif p in tokens: + # Add next phoneme or char (if chars=True) + ps.append(p) + elif (p in self.punct_list) and self.punct: + # Add punct + ps.append(p) + elif p != space: + message = f"Text: [{''.join(g2p_text)}] contains unknown char/phoneme: [{p}]." + if raw_text is not None: + message += f"Original text: [{raw_text}]. Symbol will be skipped." + logging.warning(message) + + # Remove trailing spaces + if ps: + while ps[-1] == space: + ps.pop() + + if self.pad_with_space: + ps = [space] + ps + [space] + + # Token index lookups + return [self._token2id[p] for p in ps] + + @contextmanager + def set_phone_prob(self, prob): + if hasattr(self.g2p, "phoneme_probability"): + self.g2p.phoneme_probability = prob + try: + yield + finally: + if hasattr(self.g2p, "phoneme_probability"): + self.g2p.phoneme_probability = self.phoneme_probability + + +class ChinesePhonemesTokenizer(BaseTokenizer): + # fmt: off + PUNCT_LIST = ( # Derived from LJSpeech and "/" additionally + ',', '.', '!', '?', '-', + ':', ';', '/', '"', '(', + ')', '[', ']', '{', '}', + ) + ZH_PUNCT_LIST = list("，。？！；：、‘’“”（）【】「」《》") + list(PUNCT_LIST) + + def __init__( + self, + g2p, + punct=True, + non_default_punct_list=None, + *, + space=' ', + silence=None, + apostrophe=True, + sep='|', # To be able to distinguish between 2/3 letters codes. + add_blank_at=None, + pad_with_space=False, + text_preprocessing_func=chinese_text_preprocessing, + ): + """Chinese phoneme-based tokenizer. + Note: This tokenizer for now covers Chinese phonemes/tones and English letters because our dataset contains + both Chinese and English graphemes. + Args: + g2p: Grapheme to phoneme module. + punct: Whether to reserve grapheme for basic punctuation or not. + non_default_punct_list: List of punctuation marks which will be used instead default. + space: Space token as string. + silence: Silence token as string (will be disabled if it is None). + apostrophe: Whether to use apostrophe or not. + sep: Separation token as string. + add_blank_at: Add blank to labels in the specified order ("last") or after tokens (any non None), + if None then no blank in labels. + pad_with_space: Whether to pad text with spaces at the beginning and at the end or not. + text_preprocessing_func: Text preprocessing function for correct execution of the tokenizer. + Basically, it replaces all non-unicode characters with unicode ones. + Note that lower() function shouldn't be applied here, in case the text contains phonemes (it will be handled by g2p). + """ + tokens = [] + self.space, tokens = len(tokens), tokens + [space] # Space + + if silence is not None: + self.silence, tokens = len(tokens), tokens + [silence] # Silence + + self.phoneme_list = g2p.phoneme_list + self.tone_list = g2p.tone_list + self.ascii_letter_list = g2p.ascii_letter_list + + tokens.extend(self.phoneme_list) + tokens.extend(self.tone_list) + tokens.extend(self.ascii_letter_list) + + self.text_preprocessing_func = text_preprocessing_func + + if apostrophe: + tokens.append("'") # Apostrophe + + if punct: + if non_default_punct_list is not None: + self.PUNCT_LIST = non_default_punct_list + else: + self.PUNCT_LIST = list(self.ZH_PUNCT_LIST) + tokens.extend(self.PUNCT_LIST) + + super().__init__(tokens, sep=sep, add_blank_at=add_blank_at) + + self.punct = punct + self.pad_with_space = pad_with_space + self.g2p = g2p + + def encode(self, text: str) -> List[int]: + """See base class for more information.""" + text = self.text_preprocessing_func(text) + g2p_text = self.g2p(text) + return self.encode_from_g2p(g2p_text, text) + + def encode_from_g2p(self, g2p_text: List[str], raw_text: Optional[str] = None): + """ + Encodes text that has already been run through G2Pr. + Called for encoding to tokens after text preprocessing and G2P. + + Args: + g2p_text: G2P's output, could be a mixture of Chinese phonemes and English letters. + raw_text: original raw input + """ + ps, space, tokens = [], self.tokens[self.space], set(self.tokens) + for p in g2p_text: # noqa + # Add space if last one isn't one + if p == space and len(ps) > 0 and ps[-1] != space: + ps.append(p) + # Add next phoneme or tone or ascii letter or apostrophe. + elif (p.isalnum() or p == "'" or p in self.phoneme_list + self.tone_list + self.ascii_letter_list) and p in tokens: + ps.append(p) + # Add punctuation + elif (p in self.PUNCT_LIST) and self.punct: + ps.append(p) + # Warn about unknown char/phoneme + elif p != space: + message = f"Text: [{' '.join(g2p_text)}] contains unknown char/phoneme: [{p}]." + if raw_text is not None: + message += f"Original text: [{raw_text}]. Symbol will be skipped." + logging.warning(message) + + # Remove trailing spaces + if ps: + while ps[-1] == space: + ps.pop() + + if self.pad_with_space: + ps = [space] + ps + [space] + + return [self._token2id[p] for p in ps] diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/tokenizer_spec.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/tokenizer_spec.py new file mode 100644 index 0000000..f6e905d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/tokenizer_spec.py @@ -0,0 +1,113 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC, abstractmethod +from collections import OrderedDict +from typing import List + +__all__ = ['TokenizerSpec'] + + +class TokenizerSpec(ABC): + """ + Inherit this class to implement a new tokenizer. + """ + + @abstractmethod + def text_to_tokens(self, text): + pass + + @abstractmethod + def tokens_to_text(self, tokens): + pass + + @abstractmethod + def tokens_to_ids(self, tokens): + pass + + @abstractmethod + def ids_to_tokens(self, ids): + pass + + @abstractmethod + def text_to_ids(self, text): + pass + + @abstractmethod + def ids_to_text(self, ids): + pass + + def add_special_tokens(self, special_tokens: List[str]): + raise NotImplementedError("To be implemented") + + @property + def name(self): + return type(self).__name__ + + @property + def unique_identifiers(self): + """Property required for use with megatron-core datasets.""" + return OrderedDict({"class": f"{type(self).__module__}.{type(self).__qualname__}"}) + + @property + def cls(self): + """Property alias to match MegatronTokenizer; returns cls_id if available.""" + if hasattr(self, 'cls_id'): + return self.cls_id + raise AttributeError(f"{type(self).__name__} has no attribute 'cls' or 'cls_id'") + + @property + def sep(self): + """Property alias to match MegatronTokenizer; returns sep_id if available.""" + if hasattr(self, 'sep_id'): + return self.sep_id + raise AttributeError(f"{type(self).__name__} has no attribute 'sep' or 'sep_id'") + + @property + def pad(self): + """Property alias to match MegatronTokenizer; returns pad_id if available.""" + if hasattr(self, 'pad_id'): + return self.pad_id + raise AttributeError(f"{type(self).__name__} has no attribute 'pad' or 'pad_id'") + + @property + def eod(self): + """Property alias to match MegatronTokenizer; returns eod_id if available.""" + if hasattr(self, 'eod_id'): + return self.eod_id + if hasattr(self, 'eos_id'): + # Default to end-of-sentence id if end-of-document is not defined. + return self.eos_id + raise AttributeError(f"{type(self).__name__} has no attribute 'eod', 'eod_id', 'eos', or 'eos_id'") + + @property + def bos(self): + """Property alias to match MegatronTokenizer; returns bos_id if available.""" + if hasattr(self, 'bos_id'): + return self.bos_id + raise AttributeError(f"{type(self).__name__} has no attribute 'bos' or 'bos_id'") + + @property + def eos(self): + """Property alias to match MegatronTokenizer; returns eos_id if available.""" + if hasattr(self, 'eos_id'): + return self.eos_id + raise AttributeError(f"{type(self).__name__} has no attribute 'eos' or 'eos_id'") + + @property + def mask(self): + """Property alias to match MegatronTokenizer; returns mask_id if available.""" + if hasattr(self, 'mask_id'): + return self.mask_id + raise AttributeError(f"{type(self).__name__} has no attribute 'mask' or 'mask_id'") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/word_tokenizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/word_tokenizer.py new file mode 100644 index 0000000..f3431af --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/word_tokenizer.py @@ -0,0 +1,72 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional + +from nemo.collections.common.tokenizers.char_tokenizer import CharTokenizer + +__all__ = ['WordTokenizer'] + + +class WordTokenizer(CharTokenizer): + "Tokenizes at word boundary" + + def __init__( + self, + vocab_file: str, + mask_token: Optional[str] = None, + bos_token: Optional[str] = None, + eos_token: Optional[str] = None, + pad_token: Optional[str] = None, + sep_token: Optional[str] = None, + cls_token: Optional[str] = None, + unk_token: Optional[str] = None, + ): + """ + Args: + vocab_file: path to file with vocabulary which consists + of characters separated by \n + mask_token: mask token + bos_token: the beginning of sequence token + eos_token: the end of sequence token. Usually equal to sep_token + pad_token: token to use for padding + sep_token: token used for separating sequences + cls_token: class token. Usually equal to bos_token + unk_token: token to use for unknown tokens + """ + + super().__init__( + vocab_file=vocab_file, + mask_token=mask_token, + bos_token=bos_token, + eos_token=eos_token, + pad_token=pad_token, + unk_token=unk_token, + sep_token=sep_token, + cls_token=cls_token, + ) + + def text_to_tokens(self, text): + token_candidates = text.strip().split() + tokens = [] + for token in token_candidates: + if token in self.vocab: + tokens.append(token) + else: + tokens.append(self.unk_token) + return tokens + + def ids_to_text(self, ids): + ids_ = [id_ for id_ in ids if id_ not in self.special_tokens] + return " ".join(self.ids_to_tokens(ids_)) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/youtokentome_tokenizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/youtokentome_tokenizer.py new file mode 100644 index 0000000..77375d3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/common/tokenizers/youtokentome_tokenizer.py @@ -0,0 +1,77 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from pathlib import Path + +import youtokentome as yttm + +from nemo.collections.common.tokenizers import TokenizerSpec + +__all__ = ['YouTokenToMeTokenizer'] + + +class YouTokenToMeTokenizer(TokenizerSpec): + def __init__(self, model_path, bpe_dropout=0.0, legacy=False, r2l=False): + model_path = Path(model_path).expanduser() + self.tokenizer = yttm.BPE(model=str(model_path)) + self.vocab_size = len(self.tokenizer.vocab()) + self.special_tokens = self.tokens_to_ids(["", "", "", ""]) + self.bpe_dropout = bpe_dropout + self.legacy = legacy + self.r2l = r2l + + def text_to_tokens(self, text): + return self.tokenizer.encode( + text, output_type=yttm.OutputType.SUBWORD, dropout_prob=self.bpe_dropout, reverse=self.r2l + ) + + def tokens_to_text(self, tokens): + return self.ids_to_text(self.tokens_to_ids(tokens)) + + def text_to_ids(self, text): + return self.tokenizer.encode( + text, output_type=yttm.OutputType.ID, dropout_prob=self.bpe_dropout, reverse=self.r2l + ) + + def ids_to_text(self, ids): + ids_ = [id_ for id_ in ids if id_ not in self.special_tokens] + if self.r2l: + ids_ = ids_[::-1] + return self.tokenizer.decode([ids_])[0] + + def tokens_to_ids(self, tokens): + return [self.tokenizer.subword_to_id(token) for token in tokens] + + def ids_to_tokens(self, ids): + if self.legacy: + ids_ = [id_ for id_ in ids if id_ not in self.special_tokens] + else: + ids_ = ids + return [self.tokenizer.id_to_subword(id_) for id_ in ids_] + + @property + def pad_id(self): + return self.tokenizer.subword_to_id("") + + @property + def bos_id(self): + return self.tokenizer.subword_to_id("") + + @property + def eos_id(self): + return self.tokenizer.subword_to_id("") + + @property + def unk_id(self): + return self.tokenizer.subword_to_id("") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/README.md b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/README.md new file mode 100644 index 0000000..c160ac8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/README.md @@ -0,0 +1,27 @@ +NeMo Multimodal Collections +============================ + +The NeMo Multimodal Collection supports a diverse range of multimodal models tailored for various tasks, including text-2-image generation, text-2-NeRF synthesis, multimodal language models (LLM), and foundational vision and language models. Leveraging existing modules from other NeMo collections such as LLM and Vision whenever feasible, our multimodal collections prioritize efficiency by avoiding redundant implementations and maximizing reuse of NeMo's existing modules. Here's a detailed list of the models currently supported within the multimodal collection: + +- **Foundation Vision-Language Models:** + - CLIP + +- **Foundation Text-to-Image Generation:** + - Stable Diffusion + - Imagen + +- **Customizable Text-to-Image Models:** + - SD-LoRA + - SD-ControlNet + - SD-Instruct pix2pix + +- **Multimodal Language Models:** + - NeVA + - LLAVA + +- **Text-to-NeRF Synthesis:** + - DreamFusion++ + +- **NSFW Detection Support** + +Our [documentation](https://docs.nvidia.com/nemo-framework/user-guide/latest/index.html) offers comprehensive insights into each supported model, facilitating seamless integration and utilization within your projects. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/augmentations/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/augmentations/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/augmentations/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/augmentations/augmentations.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/augmentations/augmentations.py new file mode 100644 index 0000000..d1de22f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/augmentations/augmentations.py @@ -0,0 +1,165 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This code is adapted from public repo +https://github.com/mlfoundations/open_clip/blob/28c994406e39a5babc749c76871d92f33e9c558d/src/open_clip/transform.py +by @yaoyu-33 +""" +from dataclasses import asdict, dataclass +from typing import Any, Dict, Optional, Tuple, Union + +import torch +import torch.nn as nn + +try: + import torchvision.transforms.functional as F + from torchvision.transforms import ( + CenterCrop, + Compose, + InterpolationMode, + Normalize, + RandomResizedCrop, + Resize, + ToTensor, + ) + + TORCHVISION_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + TORCHVISION_AVAILABLE = False + +from nemo.utils import logging + +OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) +OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) + + +@dataclass +class AugmentationCfg: + scale: Tuple[float, float] = (0.9, 1.0) + ratio: Optional[Tuple[float, float]] = None + color_jitter: Optional[Union[float, Tuple[float, float, float]]] = None + interpolation: Optional[str] = None + re_prob: Optional[float] = None + re_count: Optional[int] = None + use_timm: bool = False + + +class ResizeMaxSize(nn.Module): + def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0): + super().__init__() + if not isinstance(max_size, int): + raise TypeError(f"Size should be int. Got {type(max_size)}") + self.max_size = max_size + self.interpolation = interpolation + self.fn = min if fn == 'min' else min + self.fill = fill + + def forward(self, img): + if isinstance(img, torch.Tensor): + height, width = img.shape[:2] + else: + width, height = img.size + scale = self.max_size / float(max(height, width)) + if scale != 1.0: + assert TORCHVISION_AVAILABLE, "Torchvision imports failed but they are required." + new_size = tuple(round(dim * scale) for dim in (height, width)) + img = F.resize(img, new_size, self.interpolation) + pad_h = self.max_size - new_size[0] + pad_w = self.max_size - new_size[1] + img = F.pad(img, padding=[pad_w // 2, pad_h // 2, pad_w - pad_w // 2, pad_h - pad_h // 2], fill=self.fill) + return img + + +def _convert_to_rgb(image): + return image.convert('RGB') + + +def image_transform( + image_size: int, + is_train: bool, + mean: Optional[Tuple[float, ...]] = None, + std: Optional[Tuple[float, ...]] = None, + resize_longest_max: bool = False, + fill_color: int = 0, + aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, +): + assert TORCHVISION_AVAILABLE, "Torchvision imports failed but they are required." + mean = mean or OPENAI_DATASET_MEAN + if not isinstance(mean, (list, tuple)): + mean = (mean,) * 3 + + std = std or OPENAI_DATASET_STD + if not isinstance(std, (list, tuple)): + std = (std,) * 3 + + if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: + # for square size, pass size as int so that Resize() uses aspect preserving shortest edge + image_size = image_size[0] + + if isinstance(aug_cfg, dict): + aug_cfg = AugmentationCfg(**aug_cfg) + else: + aug_cfg = aug_cfg or AugmentationCfg() + normalize = Normalize(mean=mean, std=std) + if is_train: + aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None} + use_timm = aug_cfg_dict.pop('use_timm', False) + if use_timm: + from timm.data import create_transform # timm can still be optional + + if isinstance(image_size, (tuple, list)): + assert len(image_size) >= 2 + input_size = (3,) + image_size[-2:] + else: + input_size = (3, image_size, image_size) + # by default, timm aug randomly alternates bicubic & bilinear for better robustness at inference time + aug_cfg_dict.setdefault('interpolation', 'random') + aug_cfg_dict.setdefault('color_jitter', None) # disable by default + train_transform = create_transform( + input_size=input_size, + is_training=True, + hflip=0.0, + mean=mean, + std=std, + re_mode='pixel', + **aug_cfg_dict, + ) + else: + train_transform = Compose( + [ + RandomResizedCrop( + image_size, scale=aug_cfg_dict.pop('scale'), interpolation=InterpolationMode.BICUBIC, + ), + _convert_to_rgb, + ToTensor(), + normalize, + ] + ) + if aug_cfg_dict: + logging.warning( + f'Unused augmentation cfg items, specify `use_timm` to use ({list(aug_cfg_dict.keys())}).' + ) + return train_transform + else: + if resize_longest_max: + transforms = [ResizeMaxSize(image_size, fill=fill_color)] + else: + transforms = [ + Resize(image_size, interpolation=InterpolationMode.BICUBIC), + CenterCrop(image_size), + ] + transforms.extend( + [_convert_to_rgb, ToTensor(), normalize,] + ) + return Compose(transforms) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/clip_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/clip_dataset.py new file mode 100644 index 0000000..7e263e1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/clip_dataset.py @@ -0,0 +1,192 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from functools import partial +from typing import Any, List, Union + +import torch +from torch.utils.data import Dataset, default_collate + +from nemo.collections.multimodal.data.clip.augmentations.augmentations import image_transform +from nemo.collections.multimodal.data.clip.imagenet_zeroshot_data import imagenet_classnames, openai_imagenet_template +from nemo.collections.multimodal.data.common.webdataset import WebDatasetCommon +from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import MegatronPretrainingSampler +from nemo.collections.vision.data.megatron.image_folder import ImageFolder + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +def tokenize(texts: Union[str, List[str]], tokenizer: Any, context_length: int = 77) -> torch.LongTensor: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + tokenizer: + Tokenizer loaded in NeMo + context_length : int + The context length to use; all CLIP models use 77 as the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + texts_is_str = False + if isinstance(texts, str): + texts = [texts] + texts_is_str = True + + bos_id = tokenizer.bos_id + eos_id = tokenizer.eos_id + all_tokens = [[bos_id] + tokenizer.text_to_ids(text) + [eos_id] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + tokens = tokens[:context_length] # Truncate + tokens[-1] = eos_id + result[i, : len(tokens)] = torch.tensor(tokens) + + if texts_is_str: + result = result[0] + return result + + +def get_preprocess_fns(model_cfg, tokenizer=None, is_train=True): + # Define transforms + img_size = (model_cfg.vision.get("img_h"), model_cfg.vision.get("img_w")) + img_mean = model_cfg.vision.get("img_mean") + img_std = model_cfg.vision.get("img_std") + img_transform = image_transform(img_size, is_train=is_train, mean=img_mean, std=img_std,) + text_transform = lambda x: x + if tokenizer is not None: + text_transform = partial( + tokenize, tokenizer=tokenizer, context_length=model_cfg.text.get("max_position_embeddings"), + ) + return img_transform, text_transform + + +# This function maps data that are tuples to dictionary. +def tuple_to_dict(inp): + for input in inp: + out_dict = dict() + out_dict['images'] = input[0] + out_dict['captions'] = input[1] + yield out_dict + + +def transform_fn(sample, img_transform, text_transform): + image, text = sample["jpg"], sample["txt"] + return img_transform(image), text_transform(text) + + +def build_train_valid_datasets( + model_cfg, consumed_samples, tokenizer=None, +): + data_cfg = model_cfg.data + + train_img_transform, text_transform = get_preprocess_fns(model_cfg, tokenizer, is_train=True) + train_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=partial(transform_fn, img_transform=train_img_transform, text_transform=text_transform), + compose_fn=tuple_to_dict, + is_train=True, + ) + + val_data = None + if data_cfg.get("validation") is not None and data_cfg.validation.get("dataset_path"): + val_img_transform, text_transform = get_preprocess_fns(model_cfg, tokenizer, is_train=False) + val_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=0, + map_fn=partial(transform_fn, img_transform=val_img_transform, text_transform=text_transform), + compose_fn=tuple_to_dict, + is_train=False, + ) + + return train_data, val_data + + +# For zero-shot imagenet validation +def build_imagenet_validation_dataloader(model_cfg, tokenizer=None): + val_image_transform, text_transform = get_preprocess_fns(model_cfg, tokenizer, is_train=False) + data_cfg = model_cfg.data + + imagenet_val = {} + + imagenet_path = data_cfg.get("imagenet_val") + if imagenet_path is None: + return None + + image_dataset = ImageFolder(root=imagenet_path, transform=val_image_transform,) + + image_batch_sampler = MegatronPretrainingSampler( + total_samples=len(image_dataset), + consumed_samples=0, + micro_batch_size=model_cfg.micro_batch_size, + global_batch_size=model_cfg.global_batch_size, + data_parallel_rank=parallel_state.get_data_parallel_rank(), + data_parallel_size=parallel_state.get_data_parallel_world_size(), + drop_last=False, + ) + + def custom_collate(batch): + if len(batch) == 0: + return None, None + else: + return default_collate(batch) + + imagenet_val["images"] = torch.utils.data.DataLoader( + image_dataset, + batch_sampler=image_batch_sampler, + num_workers=min(data_cfg.num_workers, 2), + collate_fn=custom_collate, + pin_memory=True, + persistent_workers=True, + ) + + text_dataset = ImagenetClassnameDataset(imagenet_classnames, openai_imagenet_template, text_transform) + imagenet_val["texts"] = torch.utils.data.DataLoader( + text_dataset, + batch_size=text_dataset.num_templates, + num_workers=0, + pin_memory=True, + persistent_workers=False, + drop_last=False, + ) + return imagenet_val + + +class ImagenetClassnameDataset(Dataset): + def __init__(self, classnames, templates, text_transform): + self.num_templates = len(templates) + self.samples = [] + for classname in classnames: + texts = [template(classname) for template in templates] + self.samples.extend(text_transform(texts)) + + def __getitem__(self, index): + return self.samples[index] + + def __len__(self): + return len(self.samples) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/imagenet_zeroshot_data.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/imagenet_zeroshot_data.py new file mode 100644 index 0000000..c7387d3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/clip/imagenet_zeroshot_data.py @@ -0,0 +1,1100 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +imagenet_classnames = [ + "tench", + "goldfish", + "great white shark", + "tiger shark", + "hammerhead shark", + "electric ray", + "stingray", + "rooster", + "hen", + "ostrich", + "brambling", + "goldfinch", + "house finch", + "junco", + "indigo bunting", + "American robin", + "bulbul", + "jay", + "magpie", + "chickadee", + "American dipper", + "kite (bird of prey)", + "bald eagle", + "vulture", + "great grey owl", + "fire salamander", + "smooth newt", + "newt", + "spotted salamander", + "axolotl", + "American bullfrog", + "tree frog", + "tailed frog", + "loggerhead sea turtle", + "leatherback sea turtle", + "mud turtle", + "terrapin", + "box turtle", + "banded gecko", + "green iguana", + "Carolina anole", + "desert grassland whiptail lizard", + "agama", + "frilled-necked lizard", + "alligator lizard", + "Gila monster", + "European green lizard", + "chameleon", + "Komodo dragon", + "Nile crocodile", + "American alligator", + "triceratops", + "worm snake", + "ring-necked snake", + "eastern hog-nosed snake", + "smooth green snake", + "kingsnake", + "garter snake", + "water snake", + "vine snake", + "night snake", + "boa constrictor", + "African rock python", + "Indian cobra", + "green mamba", + "sea snake", + "Saharan horned viper", + "eastern diamondback rattlesnake", + "sidewinder rattlesnake", + "trilobite", + "harvestman", + "scorpion", + "yellow garden spider", + "barn spider", + "European garden spider", + "southern black widow", + "tarantula", + "wolf spider", + "tick", + "centipede", + "black grouse", + "ptarmigan", + "ruffed grouse", + "prairie grouse", + "peafowl", + "quail", + "partridge", + "african grey parrot", + "macaw", + "sulphur-crested cockatoo", + "lorikeet", + "coucal", + "bee eater", + "hornbill", + "hummingbird", + "jacamar", + "toucan", + "duck", + "red-breasted merganser", + "goose", + "black swan", + "tusker", + "echidna", + "platypus", + "wallaby", + "koala", + "wombat", + "jellyfish", + "sea anemone", + "brain coral", + "flatworm", + "nematode", + "conch", + "snail", + "slug", + "sea slug", + "chiton", + "chambered nautilus", + "Dungeness crab", + "rock crab", + "fiddler crab", + "red king crab", + "American lobster", + "spiny lobster", + "crayfish", + "hermit crab", + "isopod", + "white stork", + "black stork", + "spoonbill", + "flamingo", + "little blue heron", + "great egret", + "bittern bird", + "crane bird", + "limpkin", + "common gallinule", + "American coot", + "bustard", + "ruddy turnstone", + "dunlin", + "common redshank", + "dowitcher", + "oystercatcher", + "pelican", + "king penguin", + "albatross", + "grey whale", + "killer whale", + "dugong", + "sea lion", + "Chihuahua", + "Japanese Chin", + "Maltese", + "Pekingese", + "Shih Tzu", + "King Charles Spaniel", + "Papillon", + "toy terrier", + "Rhodesian Ridgeback", + "Afghan Hound", + "Basset Hound", + "Beagle", + "Bloodhound", + "Bluetick Coonhound", + "Black and Tan Coonhound", + "Treeing Walker Coonhound", + "English foxhound", + "Redbone Coonhound", + "borzoi", + "Irish Wolfhound", + "Italian Greyhound", + "Whippet", + "Ibizan Hound", + "Norwegian Elkhound", + "Otterhound", + "Saluki", + "Scottish Deerhound", + "Weimaraner", + "Staffordshire Bull Terrier", + "American Staffordshire Terrier", + "Bedlington Terrier", + "Border Terrier", + "Kerry Blue Terrier", + "Irish Terrier", + "Norfolk Terrier", + "Norwich Terrier", + "Yorkshire Terrier", + "Wire Fox Terrier", + "Lakeland Terrier", + "Sealyham Terrier", + "Airedale Terrier", + "Cairn Terrier", + "Australian Terrier", + "Dandie Dinmont Terrier", + "Boston Terrier", + "Miniature Schnauzer", + "Giant Schnauzer", + "Standard Schnauzer", + "Scottish Terrier", + "Tibetan Terrier", + "Australian Silky Terrier", + "Soft-coated Wheaten Terrier", + "West Highland White Terrier", + "Lhasa Apso", + "Flat-Coated Retriever", + "Curly-coated Retriever", + "Golden Retriever", + "Labrador Retriever", + "Chesapeake Bay Retriever", + "German Shorthaired Pointer", + "Vizsla", + "English Setter", + "Irish Setter", + "Gordon Setter", + "Brittany dog", + "Clumber Spaniel", + "English Springer Spaniel", + "Welsh Springer Spaniel", + "Cocker Spaniel", + "Sussex Spaniel", + "Irish Water Spaniel", + "Kuvasz", + "Schipperke", + "Groenendael dog", + "Malinois", + "Briard", + "Australian Kelpie", + "Komondor", + "Old English Sheepdog", + "Shetland Sheepdog", + "collie", + "Border Collie", + "Bouvier des Flandres dog", + "Rottweiler", + "German Shepherd Dog", + "Dobermann", + "Miniature Pinscher", + "Greater Swiss Mountain Dog", + "Bernese Mountain Dog", + "Appenzeller Sennenhund", + "Entlebucher Sennenhund", + "Boxer", + "Bullmastiff", + "Tibetan Mastiff", + "French Bulldog", + "Great Dane", + "St. Bernard", + "husky", + "Alaskan Malamute", + "Siberian Husky", + "Dalmatian", + "Affenpinscher", + "Basenji", + "pug", + "Leonberger", + "Newfoundland dog", + "Great Pyrenees dog", + "Samoyed", + "Pomeranian", + "Chow Chow", + "Keeshond", + "brussels griffon", + "Pembroke Welsh Corgi", + "Cardigan Welsh Corgi", + "Toy Poodle", + "Miniature Poodle", + "Standard Poodle", + "Mexican hairless dog (xoloitzcuintli)", + "grey wolf", + "Alaskan tundra wolf", + "red wolf or maned wolf", + "coyote", + "dingo", + "dhole", + "African wild dog", + "hyena", + "red fox", + "kit fox", + "Arctic fox", + "grey fox", + "tabby cat", + "tiger cat", + "Persian cat", + "Siamese cat", + "Egyptian Mau", + "cougar", + "lynx", + "leopard", + "snow leopard", + "jaguar", + "lion", + "tiger", + "cheetah", + "brown bear", + "American black bear", + "polar bear", + "sloth bear", + "mongoose", + "meerkat", + "tiger beetle", + "ladybug", + "ground beetle", + "longhorn beetle", + "leaf beetle", + "dung beetle", + "rhinoceros beetle", + "weevil", + "fly", + "bee", + "ant", + "grasshopper", + "cricket insect", + "stick insect", + "cockroach", + "praying mantis", + "cicada", + "leafhopper", + "lacewing", + "dragonfly", + "damselfly", + "red admiral butterfly", + "ringlet butterfly", + "monarch butterfly", + "small white butterfly", + "sulphur butterfly", + "gossamer-winged butterfly", + "starfish", + "sea urchin", + "sea cucumber", + "cottontail rabbit", + "hare", + "Angora rabbit", + "hamster", + "porcupine", + "fox squirrel", + "marmot", + "beaver", + "guinea pig", + "common sorrel horse", + "zebra", + "pig", + "wild boar", + "warthog", + "hippopotamus", + "ox", + "water buffalo", + "bison", + "ram (adult male sheep)", + "bighorn sheep", + "Alpine ibex", + "hartebeest", + "impala (antelope)", + "gazelle", + "arabian camel", + "llama", + "weasel", + "mink", + "European polecat", + "black-footed ferret", + "otter", + "skunk", + "badger", + "armadillo", + "three-toed sloth", + "orangutan", + "gorilla", + "chimpanzee", + "gibbon", + "siamang", + "guenon", + "patas monkey", + "baboon", + "macaque", + "langur", + "black-and-white colobus", + "proboscis monkey", + "marmoset", + "white-headed capuchin", + "howler monkey", + "titi monkey", + "Geoffroy's spider monkey", + "common squirrel monkey", + "ring-tailed lemur", + "indri", + "Asian elephant", + "African bush elephant", + "red panda", + "giant panda", + "snoek fish", + "eel", + "silver salmon", + "rock beauty fish", + "clownfish", + "sturgeon", + "gar fish", + "lionfish", + "pufferfish", + "abacus", + "abaya", + "academic gown", + "accordion", + "acoustic guitar", + "aircraft carrier", + "airliner", + "airship", + "altar", + "ambulance", + "amphibious vehicle", + "analog clock", + "apiary", + "apron", + "trash can", + "assault rifle", + "backpack", + "bakery", + "balance beam", + "balloon", + "ballpoint pen", + "Band-Aid", + "banjo", + "baluster / handrail", + "barbell", + "barber chair", + "barbershop", + "barn", + "barometer", + "barrel", + "wheelbarrow", + "baseball", + "basketball", + "bassinet", + "bassoon", + "swimming cap", + "bath towel", + "bathtub", + "station wagon", + "lighthouse", + "beaker", + "military hat (bearskin or shako)", + "beer bottle", + "beer glass", + "bell tower", + "baby bib", + "tandem bicycle", + "bikini", + "ring binder", + "binoculars", + "birdhouse", + "boathouse", + "bobsleigh", + "bolo tie", + "poke bonnet", + "bookcase", + "bookstore", + "bottle cap", + "hunting bow", + "bow tie", + "brass memorial plaque", + "bra", + "breakwater", + "breastplate", + "broom", + "bucket", + "buckle", + "bulletproof vest", + "high-speed train", + "butcher shop", + "taxicab", + "cauldron", + "candle", + "cannon", + "canoe", + "can opener", + "cardigan", + "car mirror", + "carousel", + "tool kit", + "cardboard box / carton", + "car wheel", + "automated teller machine", + "cassette", + "cassette player", + "castle", + "catamaran", + "CD player", + "cello", + "mobile phone", + "chain", + "chain-link fence", + "chain mail", + "chainsaw", + "storage chest", + "chiffonier", + "bell or wind chime", + "china cabinet", + "Christmas stocking", + "church", + "movie theater", + "cleaver", + "cliff dwelling", + "cloak", + "clogs", + "cocktail shaker", + "coffee mug", + "coffeemaker", + "spiral or coil", + "combination lock", + "computer keyboard", + "candy store", + "container ship", + "convertible", + "corkscrew", + "cornet", + "cowboy boot", + "cowboy hat", + "cradle", + "construction crane", + "crash helmet", + "crate", + "infant bed", + "Crock Pot", + "croquet ball", + "crutch", + "cuirass", + "dam", + "desk", + "desktop computer", + "rotary dial telephone", + "diaper", + "digital clock", + "digital watch", + "dining table", + "dishcloth", + "dishwasher", + "disc brake", + "dock", + "dog sled", + "dome", + "doormat", + "drilling rig", + "drum", + "drumstick", + "dumbbell", + "Dutch oven", + "electric fan", + "electric guitar", + "electric locomotive", + "entertainment center", + "envelope", + "espresso machine", + "face powder", + "feather boa", + "filing cabinet", + "fireboat", + "fire truck", + "fire screen", + "flagpole", + "flute", + "folding chair", + "football helmet", + "forklift", + "fountain", + "fountain pen", + "four-poster bed", + "freight car", + "French horn", + "frying pan", + "fur coat", + "garbage truck", + "gas mask or respirator", + "gas pump", + "goblet", + "go-kart", + "golf ball", + "golf cart", + "gondola", + "gong", + "gown", + "grand piano", + "greenhouse", + "radiator grille", + "grocery store", + "guillotine", + "hair clip", + "hair spray", + "half-track", + "hammer", + "hamper", + "hair dryer", + "hand-held computer", + "handkerchief", + "hard disk drive", + "harmonica", + "harp", + "combine harvester", + "hatchet", + "holster", + "home theater", + "honeycomb", + "hook", + "hoop skirt", + "gymnastic horizontal bar", + "horse-drawn vehicle", + "hourglass", + "iPod", + "clothes iron", + "carved pumpkin", + "jeans", + "jeep", + "T-shirt", + "jigsaw puzzle", + "rickshaw", + "joystick", + "kimono", + "knee pad", + "knot", + "lab coat", + "ladle", + "lampshade", + "laptop computer", + "lawn mower", + "lens cap", + "letter opener", + "library", + "lifeboat", + "lighter", + "limousine", + "ocean liner", + "lipstick", + "slip-on shoe", + "lotion", + "music speaker", + "loupe magnifying glass", + "sawmill", + "magnetic compass", + "messenger bag", + "mailbox", + "tights", + "one-piece bathing suit", + "manhole cover", + "maraca", + "marimba", + "mask", + "matchstick", + "maypole", + "maze", + "measuring cup", + "medicine cabinet", + "megalith", + "microphone", + "microwave oven", + "military uniform", + "milk can", + "minibus", + "miniskirt", + "minivan", + "missile", + "mitten", + "mixing bowl", + "mobile home", + "ford model t", + "modem", + "monastery", + "monitor", + "moped", + "mortar and pestle", + "graduation cap", + "mosque", + "mosquito net", + "vespa", + "mountain bike", + "tent", + "computer mouse", + "mousetrap", + "moving van", + "muzzle", + "metal nail", + "neck brace", + "necklace", + "baby pacifier", + "notebook computer", + "obelisk", + "oboe", + "ocarina", + "odometer", + "oil filter", + "pipe organ", + "oscilloscope", + "overskirt", + "bullock cart", + "oxygen mask", + "product packet / packaging", + "paddle", + "paddle wheel", + "padlock", + "paintbrush", + "pajamas", + "palace", + "pan flute", + "paper towel", + "parachute", + "parallel bars", + "park bench", + "parking meter", + "railroad car", + "patio", + "payphone", + "pedestal", + "pencil case", + "pencil sharpener", + "perfume", + "Petri dish", + "photocopier", + "plectrum", + "Pickelhaube", + "picket fence", + "pickup truck", + "pier", + "piggy bank", + "pill bottle", + "pillow", + "ping-pong ball", + "pinwheel", + "pirate ship", + "drink pitcher", + "block plane", + "planetarium", + "plastic bag", + "plate rack", + "farm plow", + "plunger", + "Polaroid camera", + "pole", + "police van", + "poncho", + "pool table", + "soda bottle", + "plant pot", + "potter's wheel", + "power drill", + "prayer rug", + "printer", + "prison", + "missile", + "projector", + "hockey puck", + "punching bag", + "purse", + "quill", + "quilt", + "race car", + "racket", + "radiator", + "radio", + "radio telescope", + "rain barrel", + "recreational vehicle", + "fishing casting reel", + "reflex camera", + "refrigerator", + "remote control", + "restaurant", + "revolver", + "rifle", + "rocking chair", + "rotisserie", + "eraser", + "rugby ball", + "ruler measuring stick", + "sneaker", + "safe", + "safety pin", + "salt shaker", + "sandal", + "sarong", + "saxophone", + "scabbard", + "weighing scale", + "school bus", + "schooner", + "scoreboard", + "CRT monitor", + "screw", + "screwdriver", + "seat belt", + "sewing machine", + "shield", + "shoe store", + "shoji screen / room divider", + "shopping basket", + "shopping cart", + "shovel", + "shower cap", + "shower curtain", + "ski", + "balaclava ski mask", + "sleeping bag", + "slide rule", + "sliding door", + "slot machine", + "snorkel", + "snowmobile", + "snowplow", + "soap dispenser", + "soccer ball", + "sock", + "solar thermal collector", + "sombrero", + "soup bowl", + "keyboard space bar", + "space heater", + "space shuttle", + "spatula", + "motorboat", + "spider web", + "spindle", + "sports car", + "spotlight", + "stage", + "steam locomotive", + "through arch bridge", + "steel drum", + "stethoscope", + "scarf", + "stone wall", + "stopwatch", + "stove", + "strainer", + "tram", + "stretcher", + "couch", + "stupa", + "submarine", + "suit", + "sundial", + "sunglasses", + "sunglasses", + "sunscreen", + "suspension bridge", + "mop", + "sweatshirt", + "swim trunks / shorts", + "swing", + "electrical switch", + "syringe", + "table lamp", + "tank", + "tape player", + "teapot", + "teddy bear", + "television", + "tennis ball", + "thatched roof", + "front curtain", + "thimble", + "threshing machine", + "throne", + "tile roof", + "toaster", + "tobacco shop", + "toilet seat", + "torch", + "totem pole", + "tow truck", + "toy store", + "tractor", + "semi-trailer truck", + "tray", + "trench coat", + "tricycle", + "trimaran", + "tripod", + "triumphal arch", + "trolleybus", + "trombone", + "hot tub", + "turnstile", + "typewriter keyboard", + "umbrella", + "unicycle", + "upright piano", + "vacuum cleaner", + "vase", + "vaulted or arched ceiling", + "velvet fabric", + "vending machine", + "vestment", + "viaduct", + "violin", + "volleyball", + "waffle iron", + "wall clock", + "wallet", + "wardrobe", + "military aircraft", + "sink", + "washing machine", + "water bottle", + "water jug", + "water tower", + "whiskey jug", + "whistle", + "hair wig", + "window screen", + "window shade", + "Windsor tie", + "wine bottle", + "airplane wing", + "wok", + "wooden spoon", + "wool", + "split-rail fence", + "shipwreck", + "sailboat", + "yurt", + "website", + "comic book", + "crossword", + "traffic or street sign", + "traffic light", + "dust jacket", + "menu", + "plate", + "guacamole", + "consomme", + "hot pot", + "trifle", + "ice cream", + "popsicle", + "baguette", + "bagel", + "pretzel", + "cheeseburger", + "hot dog", + "mashed potatoes", + "cabbage", + "broccoli", + "cauliflower", + "zucchini", + "spaghetti squash", + "acorn squash", + "butternut squash", + "cucumber", + "artichoke", + "bell pepper", + "cardoon", + "mushroom", + "Granny Smith apple", + "strawberry", + "orange", + "lemon", + "fig", + "pineapple", + "banana", + "jackfruit", + "cherimoya (custard apple)", + "pomegranate", + "hay", + "carbonara", + "chocolate syrup", + "dough", + "meatloaf", + "pizza", + "pot pie", + "burrito", + "red wine", + "espresso", + "tea cup", + "eggnog", + "mountain", + "bubble", + "cliff", + "coral reef", + "geyser", + "lakeshore", + "promontory", + "sandbar", + "beach", + "valley", + "volcano", + "baseball player", + "bridegroom", + "scuba diver", + "rapeseed", + "daisy", + "yellow lady's slipper", + "corn", + "acorn", + "rose hip", + "horse chestnut seed", + "coral fungus", + "agaric", + "gyromitra", + "stinkhorn mushroom", + "earth star fungus", + "hen of the woods mushroom", + "bolete", + "corn cob", + "toilet paper", +] + +openai_imagenet_template = [ + lambda c: f'a bad photo of a {c}.', + lambda c: f'a photo of many {c}.', + lambda c: f'a sculpture of a {c}.', + lambda c: f'a photo of the hard to see {c}.', + lambda c: f'a low resolution photo of the {c}.', + lambda c: f'a rendering of a {c}.', + lambda c: f'graffiti of a {c}.', + lambda c: f'a bad photo of the {c}.', + lambda c: f'a cropped photo of the {c}.', + lambda c: f'a tattoo of a {c}.', + lambda c: f'the embroidered {c}.', + lambda c: f'a photo of a hard to see {c}.', + lambda c: f'a bright photo of a {c}.', + lambda c: f'a photo of a clean {c}.', + lambda c: f'a photo of a dirty {c}.', + lambda c: f'a dark photo of the {c}.', + lambda c: f'a drawing of a {c}.', + lambda c: f'a photo of my {c}.', + lambda c: f'the plastic {c}.', + lambda c: f'a photo of the cool {c}.', + lambda c: f'a close-up photo of a {c}.', + lambda c: f'a black and white photo of the {c}.', + lambda c: f'a painting of the {c}.', + lambda c: f'a painting of a {c}.', + lambda c: f'a pixelated photo of the {c}.', + lambda c: f'a sculpture of the {c}.', + lambda c: f'a bright photo of the {c}.', + lambda c: f'a cropped photo of a {c}.', + lambda c: f'a plastic {c}.', + lambda c: f'a photo of the dirty {c}.', + lambda c: f'a jpeg corrupted photo of a {c}.', + lambda c: f'a blurry photo of the {c}.', + lambda c: f'a photo of the {c}.', + lambda c: f'a good photo of the {c}.', + lambda c: f'a rendering of the {c}.', + lambda c: f'a {c} in a video game.', + lambda c: f'a photo of one {c}.', + lambda c: f'a doodle of a {c}.', + lambda c: f'a close-up photo of the {c}.', + lambda c: f'a photo of a {c}.', + lambda c: f'the origami {c}.', + lambda c: f'the {c} in a video game.', + lambda c: f'a sketch of a {c}.', + lambda c: f'a doodle of the {c}.', + lambda c: f'a origami {c}.', + lambda c: f'a low resolution photo of a {c}.', + lambda c: f'the toy {c}.', + lambda c: f'a rendition of the {c}.', + lambda c: f'a photo of the clean {c}.', + lambda c: f'a photo of a large {c}.', + lambda c: f'a rendition of a {c}.', + lambda c: f'a photo of a nice {c}.', + lambda c: f'a photo of a weird {c}.', + lambda c: f'a blurry photo of a {c}.', + lambda c: f'a cartoon {c}.', + lambda c: f'art of a {c}.', + lambda c: f'a sketch of the {c}.', + lambda c: f'a embroidered {c}.', + lambda c: f'a pixelated photo of a {c}.', + lambda c: f'itap of the {c}.', + lambda c: f'a jpeg corrupted photo of the {c}.', + lambda c: f'a good photo of a {c}.', + lambda c: f'a plushie {c}.', + lambda c: f'a photo of the nice {c}.', + lambda c: f'a photo of the small {c}.', + lambda c: f'a photo of the weird {c}.', + lambda c: f'the cartoon {c}.', + lambda c: f'art of the {c}.', + lambda c: f'a drawing of the {c}.', + lambda c: f'a photo of the large {c}.', + lambda c: f'a black and white photo of a {c}.', + lambda c: f'the plushie {c}.', + lambda c: f'a dark photo of a {c}.', + lambda c: f'itap of a {c}.', + lambda c: f'graffiti of the {c}.', + lambda c: f'a toy {c}.', + lambda c: f'itap of my {c}.', + lambda c: f'a photo of a cool {c}.', + lambda c: f'a photo of a small {c}.', + lambda c: f'a tattoo of the {c}.', +] diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/data_samplers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/data_samplers.py new file mode 100644 index 0000000..ce19ff6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/data_samplers.py @@ -0,0 +1,141 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from multiprocessing import Value + +import torch + +from nemo.utils import logging + +try: + from webdataset.pytorch import IterableDataset + +except (ImportError, ModuleNotFoundError): + from nemo.core.classes import IterableDataset + + logging.warning("Webdataset import failed! We recommend use `webdataset==0.2.48`.") + + +class SharedEpoch: + def __init__(self, epoch: int = 0): + self.shared_epoch = Value('i', epoch) + + def set_value(self, epoch): + self.shared_epoch.value = epoch + + def get_value(self): + return self.shared_epoch.value + + +class WDSUrlsRandomSampler(IterableDataset): + def __init__( + self, + urls, + total_urls: int, + chunk_size: int, + consumed_samples: int, + data_parallel_rank: int, + data_parallel_size: int, + num_workers: int, + drop_last: bool, + data_sharding: bool, + ): + r"""Sampler for WebDataset Urls with data parallelism. + Args: + urls : The urls of the tar files from which to sample. + total_urls (int): Total number of urls in the dataset. + chunk_size (int): Number of objects per tar file. + consumed_samples (int): Number of samples consumed so far by the training process. + **Note samples here is not urls.** + data_parallel_rank (int): Rank of the current data parallel process. + data_parallel_size (int): Number of data parallel processes. + drop_last (bool): If True, drop the remaining urls if the number is smaller than `data_parallel_size`. + If False, pad the urls until its size is divisible by `data_parallel_size`. + data_sharding (bool): If True, use data sharding before data shuffling, i.e. only shuffle within the data parallel group. + """ + super().__init__() + self.urls = urls + self.total_urls = total_urls + self.chunk_size = chunk_size + + if consumed_samples % data_parallel_size == 0: + logging.warning("Multimodal data resuming will be approximate!") + self.consumed_urls = ( + consumed_samples // (data_parallel_size * num_workers) // chunk_size * (data_parallel_size * num_workers) + ) + self.consumed_samples = self.consumed_urls * chunk_size + + self.data_parallel_rank = data_parallel_rank + self.data_parallel_size = data_parallel_size + self.drop_last = drop_last + self.data_sharding = data_sharding + self.epoch = SharedEpoch() + + self.remaining_urls = self.total_urls % self.data_parallel_size + + def __len__(self): + if self.drop_last: + return self.total_urls // self.data_parallel_size + else: + return (self.total_urls + self.data_parallel_size - 1) // self.data_parallel_size + + def __iter__(self): + worker_id, num_workers = 0, 1 + worker_info = torch.utils.data.get_worker_info() + if worker_info is not None: + worker_id, num_workers = worker_info.id, worker_info.num_workers + + self.consumed_urls = ( + self.consumed_samples + // (self.data_parallel_size * num_workers) + // self.chunk_size + * (self.data_parallel_size * num_workers) + ) + + if self.drop_last or self.remaining_urls == 0: + active_total_urls = self.total_urls - self.remaining_urls + else: + active_total_urls = self.total_urls + self.data_parallel_size - self.remaining_urls + + self.epoch.set_value(self.consumed_urls // active_total_urls) + current_epoch_urls = self.consumed_urls % active_total_urls + + # data sharding and random sampling + if self.data_sharding: + bucket_size = active_total_urls // self.data_parallel_size + bucket_offset = current_epoch_urls // self.data_parallel_size + start_idx = self.data_parallel_rank * bucket_size + + g = torch.Generator() + g.manual_seed(self.epoch.get_value()) + random_idx = torch.randperm(bucket_size, generator=g).tolist() + idx_range = [start_idx + x for x in random_idx[bucket_offset:]] + else: + full_bucket_size = active_total_urls + full_bucket_offset = current_epoch_urls + g = torch.Generator() + g.manual_seed(self.epoch.get_value()) + idx_range_total = torch.randperm(full_bucket_size, generator=g).tolist() + idx_range_active = idx_range_total[full_bucket_offset:] + idx_range = idx_range_active[self.data_parallel_rank :: self.data_parallel_size] + + # Use additional permutation to replace out-of-range indices when drop_last is False + additional_random_idx = torch.randperm(self.total_urls, generator=g).tolist() + for n, idx in enumerate(idx_range): + self.consumed_samples += self.data_parallel_size * self.chunk_size + if worker_info is not None and n % num_workers != worker_id: + continue + if idx < self.total_urls: + yield dict(url=self.urls[idx]) + else: + yield dict(url=self.urls[additional_random_idx[idx - self.total_urls]]) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/utils.py new file mode 100644 index 0000000..31e83fe --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/utils.py @@ -0,0 +1,33 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import open_clip +import torch + + +def get_collate_fn(first_stage_key="images_moments", cond_stage_key="captions"): + def collate_fn_with_tokenize(batch): + images_moments = [s[first_stage_key] for s in batch] + cond_inputs = [s[cond_stage_key] for s in batch] + if cond_stage_key == "captions": + tokens = open_clip.tokenize(cond_inputs) + else: + tokens = torch.stack(cond_inputs) + batch = { + first_stage_key: torch.cat(images_moments), + cond_stage_key: tokens, + } + return batch + + return collate_fn_with_tokenize diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/webdataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/webdataset.py new file mode 100644 index 0000000..79d22f3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/webdataset.py @@ -0,0 +1,318 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import glob +import io +import itertools +import json +import os +import pickle +import random +import re +from typing import Callable, List, Union + +import boto3 +import torch.distributed as dist +from botocore.config import Config +from PIL import Image + +from nemo.collections.multimodal.data.common.data_samplers import SharedEpoch, WDSUrlsRandomSampler +from nemo.collections.multimodal.data.common.webdataset_s3 import WebDataset as WebDatasetS3 +from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults +from nemo.core.classes import IterableDataset as NeMoIterableDataset +from nemo.utils import logging + +try: + import webdataset as wds + from webdataset import WebDataset, warn_and_continue + from webdataset.filters import _shuffle + from webdataset.utils import pytorch_worker_info + + HAVE_WEBDATASET = True + +except (ImportError, AttributeError, ModuleNotFoundError): + + HAVE_WEBDATASET = False + + logging.warning("Webdataset import failed! We recommend use `webdataset==0.2.48`.") + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + +Image.MAX_IMAGE_PIXELS = 933120000 +_IMG_EXTENSIONS = "jpg jpeg png ppm pgm pbm pnm".split() + + +def pil_loader(key, data): + r""" + Function to load an image. + If the image is corrupt, it returns a black image. + Args: + key: Image key. + data: Image data stream. + """ + extension = re.sub(r".*[.]", "", key) + if extension.lower() not in _IMG_EXTENSIONS: + return None + + with io.BytesIO(data) as stream: + img = Image.open(stream) + img.load() + img = img.convert("RGB") + + return img + + +def get_world_size(): + r"""Get world size. How many GPUs are available in this job.""" + world_size = 1 + if dist.is_available(): + if dist.is_initialized(): + world_size = dist.get_world_size() + return world_size + + +class WebDatasetCommon(NeMoIterableDataset): + """ + A common dataset object shared by most of NeMo multimodal models. + """ + + def __init__( + self, + dataset_cfg, + map_fn: Callable, + compose_fn: Union[Callable, List[Callable]], + consumed_samples: int, + filter_fn: Callable = None, + gen_cfg=None, + decode_fn: Callable = None, + is_train=True, + ): + + super().__init__() + self.dataset_cfg = dataset_cfg + self.num_workers = dataset_cfg.num_workers + self.world_size = get_world_size() + self.webdata_cfg = dataset_cfg.webdataset + self.infinite_sampler = self.webdata_cfg.get("infinite_sampler", False) + self.gen_cfg = gen_cfg + self.consumed_samples = consumed_samples + + self.local_root_path = self.webdata_cfg.local_root_path + if is_train: + dataset_path = dataset_cfg.train.dataset_path + self.augmentations = dataset_cfg.train.get("augmentations", None) + self.filterings = dataset_cfg.train.get("filterings", None) + else: + dataset_path = dataset_cfg.validation.dataset_path + self.augmentations = dataset_cfg.validation.get("augmentations", None) + self.filterings = dataset_cfg.validation.get("filterings", None) + + # Optionally expand dataset as as a glob pattern + # This can be used to specify multiple .zip files: dataset_path="data/*.zip" + if isinstance(dataset_path, str): + glob_path = dataset_path + dataset_path = glob.glob(dataset_path) + assert len(dataset_path) > 0, f"No files found for {glob_path}" + + if "boto3" in dataset_cfg: + logging.info(f'Init boto3 using credentials file at {dataset_cfg.boto3.credentials_file}') + self.use_boto3 = True + assert dataset_cfg.boto3.credentials_file is not None + with open(dataset_cfg.boto3.credentials_file) as fin: + self.credentials = json.load(fin) + config = Config(connect_timeout=30, signature_version="s3", retries={"max_attempts": 999999}) + self.s3 = boto3.client('s3', **self.credentials, config=config) + self.bucket = dataset_cfg.boto3.bucket + self.local_root_path = "" + else: + logging.info(f'Read Webdataset locally. Data stores at {self.local_root_path}') + self.use_boto3 = False + self.s3 = None + self.bucket = None + + # wdinfo in a dict containing webdata information + self.wdinfo = dict() + if dataset_path[0].endswith(".pkl"): + for dset_info_path in dataset_path: + with open(dset_info_path, 'rb') as fp: + dset_info = pickle.load(fp) + if 'tar_files' not in self.wdinfo: + self.wdinfo['tar_files'] = dset_info['tar_files'] + self.wdinfo['total_key_count'] = dset_info['total_key_count'] + self.wdinfo['chunk_size'] = dset_info['chunk_size'] + else: + self.wdinfo['tar_files'].extend(dset_info['tar_files']) + self.wdinfo['total_key_count'] += dset_info['total_key_count'] + train_info = self.wdinfo + else: + train_info = self.wdinfo + train_info['tar_files'] = map(wds.shardlists.expand_urls, dataset_path) + train_info['tar_files'] = list(itertools.chain.from_iterable(train_info['tar_files'])) + train_info['chunk_size'] = self.webdata_cfg.get("chunk_size", 1000) + train_info['total_key_count'] = train_info['chunk_size'] * len(train_info['tar_files']) + + self.data_parallel_size = parallel_state.get_data_parallel_world_size() + chunk_size = train_info['chunk_size'] + + num_workers = dataset_cfg.get("num_workers") or 1 + self.consumed_urls = ( + consumed_samples + // (self.data_parallel_size * num_workers) + // chunk_size + * (self.data_parallel_size * num_workers) + ) + self.consumed_samples = self.consumed_urls * chunk_size + self.skip_ahead = consumed_samples - self.consumed_samples + + decode_fn = pil_loader if decode_fn is None else decode_fn + shards_train_list = train_info["tar_files"] + num_shards = len(shards_train_list) + assert num_shards > 0, "Did not find any training data." + + # Shuffle buffer: + shuffle_buffer_size = train_info["chunk_size"] + + if self.filterings is not None: + # TODO : Not a good way of estimating filtering (We expect user to give estimated portion) + # We should estimate in someway. This is anyway used only in progress bar + logging.info(f'Estimated {self.filterings.estimated_portion} will be remaining after filtering') + train_info["total_key_count"] = int(train_info["total_key_count"] * self.filterings.estimated_portion) + + # WDS Dataset Pipeline + # DetShuffle -> Decode -> Filter -> Map -> Compose + train_dataset, epoch = self._get_webdataset_and_epoch() + train_dataset = train_dataset.compose(detshuffle2(bufsize=shuffle_buffer_size, epoch=epoch)) + train_dataset = train_dataset.decode(decode_fn, handler=warn_and_continue) + + if self.filterings is not None: + if self.filterings.resolution is not None: + train_dataset = train_dataset.select(filter_fn) + + train_dataset = train_dataset.map(map_fn, handler=warn_and_continue) + if not isinstance(compose_fn, list): + compose_fn = [compose_fn] + for fn in compose_fn: + train_dataset = train_dataset.compose(fn) + train_dataset.total_images = train_info["total_key_count"] + + if train_info["total_key_count"] != train_info["chunk_size"] * len(train_info["tar_files"]): + logging.warning("Total image count is not equal to chunk_size * number of tar files.") + + if self.infinite_sampler: + rank, world_size, worker_id, num_workers = pytorch_worker_info() + nbatches = train_dataset.total_images // world_size // self.num_workers + logging.info(f'Setting nbatches={nbatches} for infinite sampler. world_size={world_size}') + train_dataset = train_dataset.with_epoch(nbatches=nbatches) + + logging.info("Total number of training shards: %d", num_shards) + logging.info("Total training key count: %d", train_dataset.total_images) + + self._dataset = train_dataset + + def _get_webdataset_and_epoch(self): + train_info = self.wdinfo + chunk_size = train_info["chunk_size"] + shards_train_list = train_info["tar_files"] + shards_train_list = [os.path.join(self.local_root_path, x) for x in shards_train_list] + epoch = 0 + + if not self.infinite_sampler: + logging.info(f'Initiating Webdataset Random Sampler..') + assert ( + self.filterings is None + ), 'Webdataset Random Sampler should not be used with filters. Switch to infinite sampler' + shards_train_list = WDSUrlsRandomSampler( + urls=shards_train_list, + total_urls=len(shards_train_list), + chunk_size=chunk_size, + consumed_samples=self.consumed_samples, + data_parallel_rank=parallel_state.get_data_parallel_rank(), + data_parallel_size=parallel_state.get_data_parallel_world_size(), + num_workers=self.dataset_cfg.get("num_workers") or 1, + drop_last=True, + data_sharding=self.dataset_cfg.train.get("data_sharding", True), + ) + epoch = shards_train_list.epoch + + if self.use_boto3: + train_dataset = WebDatasetS3( + shards_train_list, + handler=warn_and_continue, + resampled=self.infinite_sampler or False, + load_from_object_store=self.use_boto3, + s3_client=self.s3, + s3_bucket_name=self.bucket, + ) + else: + train_dataset = WebDataset( + shards_train_list, handler=warn_and_continue, resampled=self.infinite_sampler or False, + ) + + return train_dataset, epoch + + def __iter__(self): + ds_iter = self._dataset.__iter__() + while self.skip_ahead > 0 and not self.infinite_sampler: + try: + _ = next(ds_iter) + self.skip_ahead -= self.data_parallel_size * self.num_workers + except StopIteration: + self.skip_ahead = 0 + return ds_iter + + def __len__(self): + return self._dataset.total_images + + +if HAVE_WEBDATASET: + + class detshuffle2(wds.PipelineStage): + def __init__( + self, bufsize=1000, initial=100, seed=0, epoch=-1, + ): + self.bufsize = bufsize + self.initial = initial + self.seed = seed + self.epoch = epoch + + def run(self, src): + if isinstance(self.epoch, SharedEpoch): + epoch = self.epoch.get_value() + else: + # NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train) + # situation as different workers may wrap at different times (or not at all). + self.epoch += 1 + epoch = self.epoch + rng = random.Random() + # This seed to be deterministic AND the same across all nodes/workers in each epoch + if not parallel_state.is_initialized(): + seed = self.seed + epoch + else: + seed = self.seed + epoch + (100 * parallel_state.get_data_parallel_rank()) + rng.seed(seed) + return _shuffle(src, self.bufsize, self.initial, rng) + + +else: + + class detshuffle2(ApexGuardDefaults): + def __init__(self): + super().__init__() + logging.warning("Webdataset import failed! We recommend use `webdataset==0.2.48`.") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/webdataset_s3.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/webdataset_s3.py new file mode 100644 index 0000000..f90941d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/common/webdataset_s3.py @@ -0,0 +1,268 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import io +import os +import sys +from urllib.parse import urlparse + +import yaml + +from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults +from nemo.utils import logging + +try: + import webdataset.gopen as gopen_webdata + from webdataset import cache, filters, shardlists + from webdataset.compat import FluidInterface + from webdataset.handlers import reraise_exception + from webdataset.pipeline import DataPipeline + from webdataset.pytorch import IterableDataset + from webdataset.tariterators import group_by_keys, tar_file_expander + + HAVE_WEBDATASET = True + +except (ImportError, AttributeError, ModuleNotFoundError): + + HAVE_WEBDATASET = False + + logging.warning("Webdataset import failed! We recommend use `webdataset==0.2.48`.") + +# Number of attempts to read aws objects. +_NUM_OBJECT_STORE_READ_ATTEMPTS = 10 + +if HAVE_WEBDATASET: + + def gopen(url, mode="rb", bufsize=8192, **kw): + r"""Open the URL. + This uses the `gopen_schemes` dispatch table to dispatch based + on scheme. + Support for the following schemes is built-in: pipe, file, + http, https, sftp, ftps, scp. + When no scheme is given the url is treated as a file. + You can use the OPEN_VERBOSE argument to get info about + files being opened. + + This implementation is based on webdataset's gopen, + with the modification of supporting reading from s3 object_store: + https://webdataset.github.io/webdataset/api/webdataset/gopen.html#gopen + Args: + url (list[str]): the source URL + mode (str): the mode ("rb", "r") + bufsize (int): the buffer size + """ + global fallback_gopen + verbose = int(os.environ.get("GOPEN_VERBOSE", 0)) + if verbose: + print("GOPEN", url, gopen_webdata.info, file=sys.stderr) + + assert mode in ["rb", "wb"], mode + if url == "-": + if mode == "rb": + return sys.stdin.buffer + elif mode == "wb": + return sys.stdout.buffer + else: + raise ValueError(f"unknown mode {mode}") + + # If we specify 'object_store' in keyword arguments, + # then we would load from AWS. + # In this case, you also need to specify s3_client and s3_bucket_name + # in arguments. + if 'object_store' in kw and kw['object_store']: + # Load from object store + attempt = 0 + + while attempt < _NUM_OBJECT_STORE_READ_ATTEMPTS: + try: + s3_response_object = kw['s3_client'].get_object(Bucket=kw['s3_bucket_name'], Key=url) + object_content = s3_response_object['Body'].read() + + # This is a check to verify is the object is fully read. + full_read = s3_response_object['ContentLength'] == len(object_content) + if full_read: + return io.BytesIO(object_content) + else: + attempt += 1 + except Exception as e: # noqa + # If there is an exception (usually connectivity error or protocol error), read again + attempt += 1 + print(e) + print('Retrying tar file download, attempt {}'.format(attempt)) + continue + raise ConnectionError('Unable to read {} from PBSS. {} attempts tried.'.format(url, attempt)) + + # Append root path to the url if dataset is stored on local disk system + elif 'local_root_path' in kw and kw['local_root_path'] is not None: + url = os.path.join(kw['local_root_path'], url) + + # For all other gopen schemes, use the native webdataset gopen functions. + pr = urlparse(url) + if pr.scheme == "": + bufsize = int(os.environ.get("GOPEN_BUFFER", -1)) + return open(url, mode, buffering=bufsize) + if pr.scheme == "file": + bufsize = int(os.environ.get("GOPEN_BUFFER", -1)) + return open(pr.path, mode, buffering=bufsize) + handler = gopen_webdata.gopen_schemes["__default__"] + handler = gopen_webdata.gopen_schemes.get(pr.scheme, handler) + return handler(url, mode, bufsize, **kw) + + def url_opener(data, handler=reraise_exception, **kw): + r"""Given a stream of url names (packaged in `dict(url=url)`), yield opened streams. + + Args: + data: Iterator of dictionaires containing url paths. + handler: Exception handler. + """ + for sample in data: + assert isinstance(sample, dict), sample + assert "url" in sample + url = sample["url"] + try: + stream = gopen(url, **kw) + sample.update(stream=stream) + yield sample + except Exception as exn: + exn.args = exn.args + (url,) + if handler(exn): + continue + else: + break + + # Define a new tarfile_samples + def tarfile_samples( + src, + handler=reraise_exception, + load_from_object_store=False, + s3_client=None, + s3_bucket_name=None, + local_root_path=None, + ): + r""" + Given an iterator of filenames, this function opens the URL streams + and groups data by keys. + + Args: + src: Iterator of data dictionaires containing URL names. + handler: Exception handler. + load_from_object_store (bool): A boolean flag to specify whether to load from + object store. + s3_client: If loading from object store, specify S3 client. + s3_bucket_name: If loading from object store, specify S3 bucket name. + local_root_path: If loading from local (or mounted) disk system, + specify the root path of the dataset. + """ + streams = url_opener( + src, + handler=handler, + object_store=load_from_object_store, + s3_client=s3_client, + s3_bucket_name=s3_bucket_name, + local_root_path=local_root_path, + ) + files = tar_file_expander(streams, handler=handler) + samples = group_by_keys(files, handler=handler) + return samples + + tarfile_to_samples = filters.pipelinefilter(tarfile_samples) + + class WebDataset(DataPipeline, FluidInterface): + r"""Webdataset class modified to support loading from object store.""" + + def __init__( + self, + urls, + handler=reraise_exception, + resampled=False, + shardshuffle=None, + cache_size=-1, + cache_dir=None, + detshuffle=False, + nodesplitter=shardlists.single_node_only, + verbose=False, + load_from_object_store=False, + s3_client=None, + s3_bucket_name=None, + local_root_path=None, + ): + r""" + Args: + urls: An iterator containing a list of url names. + handler: Exception handler. + resampled: If true, sample shards from shard list with replacement. + shardshuffle: If true, shuffles the entire shard list. + cache_size: Size of cache. + cache_dir: Path to store cache. + detshuffle: Whether to use deterministic shuffling when shardshuffle is True. + nodesplitter: Function for splitting urls among nodes. + verbose: If True, prints logs. + load_from_object_store (bool): A boolean flag to specify whether to load from + object store. + s3_client: If loading from object store, specify S3 client. + s3_bucket_name: If loading from object store, specify S3 bucket name. + local_root_path: If loading from local (or mounted) disk system, + specify the root path of the dataset. + """ + super().__init__() + if isinstance(urls, IterableDataset): + assert not resampled + self.append(urls) + elif isinstance(urls, str) and (urls.endswith(".yaml") or urls.endswith(".yml")): + with (open(urls)) as stream: + spec = yaml.safe_load(stream) + assert "datasets" in spec + self.append(shardlists.MultiShardSample(spec)) + elif isinstance(urls, dict): + assert "datasets" in urls + self.append(shardlists.MultiShardSample(urls)) + elif resampled: + self.append(shardlists.ResampledShards(urls)) + else: + self.append(shardlists.SimpleShardList(urls)) + self.append(nodesplitter) + self.append(shardlists.split_by_worker) + if shardshuffle is True: + shardshuffle = 100 + if shardshuffle is not None: + if detshuffle: + self.append(filters.detshuffle(shardshuffle)) + else: + self.append(filters.shuffle(shardshuffle)) + if cache_dir is None or cache_size == 0: + self.append( + tarfile_to_samples( + handler=handler, + load_from_object_store=load_from_object_store, + s3_client=s3_client, + s3_bucket_name=s3_bucket_name, + local_root_path=local_root_path, + ) + ) + else: + + # We dont use cache. + assert cache_size == -1 or cache_size > 0 + self.append( + cache.cached_tarfile_to_samples( + handler=handler, verbose=verbose, cache_size=cache_size, cache_dir=cache_dir, + ) + ) + + +else: + + class WebDataset(ApexGuardDefaults): + def __init__(self): + super().__init__() + logging.warning("Webdataset import failed! We recommend use `webdataset==0.2.48`.") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/controlnet/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/controlnet/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/controlnet/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/controlnet/controlnet_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/controlnet/controlnet_dataset.py new file mode 100644 index 0000000..3bf7b76 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/controlnet/controlnet_dataset.py @@ -0,0 +1,145 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch + +from nemo.collections.multimodal.data.common.webdataset import WebDatasetCommon +from nemo.collections.multimodal.data.stable_diffusion.augmentation.augmentations import ( + construct_image_augmentations, + identical_transform, +) +from nemo.core.classes import Dataset as NeMoDataset + + +class ControlNetSyntheticDataset(NeMoDataset): + def __init__( + self, + image_H, + image_W, + fake_len=100000, + image_key='images', + txt_key='txt', + control_key='hint', + seq_len=80, + context_dim=768, + ): + super().__init__() + self.fake_len = fake_len + self.H = image_H + self.W = image_W + self.image_key = image_key + self.txt_key = txt_key + self.control_key = control_key + self.seq_len = seq_len + self.context_dim = context_dim + + def __getitem__(self, index): + item = {} + item[self.image_key] = torch.randn(self.H, self.W, 3) + item[self.txt_key] = f'This is meaningless fake text No.{index}' + item[self.control_key] = torch.randn(self.H, self.W, 3) + return item + + def __len__(self): + return self.fake_len + + +def build_train_valid_datasets( + model_cfg, consumed_samples, +): + data_cfg = model_cfg.data + + # This function maps data that are tuples to dictionary. + def tuple_to_dict(inp): + for input in inp: + out_dict = dict() + out_dict['images'] = input[0].permute(1, 2, 0) + out_dict['captions'] = input[1] + out_dict['hint'] = input[2].permute(1, 2, 0) + yield out_dict + + def transform_fn(sample): + + image, text, hint = sample["jpg"], sample["txt"], sample["png"] + # TODO : If no agumentations just return the image ? + img_transform = construct_image_augmentations(data_cfg.train.get("augmentations", None)) + text_transform = identical_transform + return img_transform(image), text_transform(text), img_transform(hint) + + if data_cfg.get('synthetic_data', False): + H, W = data_cfg.train.augmentations.center_crop_h_w.split(',') + train_data = ControlNetSyntheticDataset( + int(H), + int(W), + image_key=model_cfg.first_stage_key, + txt_key=model_cfg.cond_stage_key, + control_key=model_cfg.control_key, + context_dim=model_cfg.unet_config.context_dim, + fake_len=data_cfg.synthetic_data_length, + ) + else: + train_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=tuple_to_dict, + is_train=True, + ) + + val_data = None + if data_cfg.get("validation") is not None and data_cfg.validation.get("data_path"): + val_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=tuple_to_dict, + is_train=False, + ) + return train_data, val_data + + +def build_train_valid_precached_datasets( + model_cfg, consumed_samples, +): + data_cfg = model_cfg.data + + # This function maps data that are tuples to dictionary. + def tuple_to_dict(inp): + for input in inp: + out_dict = dict() + out_dict[model_cfg.first_stage_key] = torch.tensor(input['autoencoderkl_image']) + out_dict[model_cfg.cond_stage_key] = torch.tensor(input['clip-vit-large-patch14_text']) + yield out_dict + + def transform_fn(sample): + return sample['pickle'] + + train_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=tuple_to_dict, + is_train=True, + ) + + val_data = None + if data_cfg.get("validation") is not None and data_cfg.validation.get("data_path"): + val_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=tuple_to_dict, + is_train=False, + ) + + return train_data, val_data diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/dreambooth/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/dreambooth/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/dreambooth/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/dreambooth/dreambooth_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/dreambooth/dreambooth_dataset.py new file mode 100644 index 0000000..1c39b1a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/dreambooth/dreambooth_dataset.py @@ -0,0 +1,164 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +from pathlib import Path + +import torch +from PIL import Image +from pytorch_lightning.utilities import rank_zero_only +from torch.utils.data import Dataset +from tqdm import tqdm + +try: + from torchvision import transforms + + TORCHVISION_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + TORCHVISION_AVAILABLE = False + + +class DreamBoothDataset(Dataset): + """ + A dataset to prepare the instance and class images with the prompts for fine-tuning the model. + It pre-processes the images and the tokenizes prompts. + + :param instance_data_root: required, a directory with images files of the object + :param instance_prompt: captions with special token associated with instance images + :param with_prior_preservation: whether to regularize the model finetuning with the original inference output from the backbone + :param reg_data_root: a directory to save inference images from the backbone + :param reg_prompt: prompt used to generate regularization images + :param size: resizing images for training data pipeline + :param center_crop: whether performing center cropping on input images + :param load_cache_latents: when set to True, images will be converted to cached latents which will be directly loaded for training + :param vae: vae instance to encode imamges from pixel space to latent space + """ + + def __init__( + self, + instance_data_root, + instance_prompt, + with_prior_preservation=False, + reg_data_root=None, + reg_prompt=None, + size=512, + center_crop=True, + repeat=10000, + load_cache_latents=False, + cached_instance_data_root=None, + cached_reg_data_root=None, + vae=None, + text_encoder=None, + ): + self.size = size + self.center_crop = center_crop + + assert instance_data_root or cached_instance_data_root, "must provide instance images to start training." + self.instance_data_root = Path(instance_data_root) + self.cached_instance_data_root = cached_instance_data_root + self.cached_reg_data_root = cached_reg_data_root + + self.instance_images_path = list(Path(instance_data_root).iterdir()) + self.num_instance_images = len(self.instance_images_path) + self.instance_prompt = instance_prompt + self._length = self.num_instance_images * repeat + self.load_cache_latents = load_cache_latents + self.with_prior_preservation = with_prior_preservation + + if reg_data_root is not None: + self.reg_data_root = Path(reg_data_root) + self.reg_images_path = list(self.reg_data_root.iterdir()) + self.num_reg_images = len(self.reg_images_path) + self.reg_prompt = reg_prompt + else: + self.reg_data_root = None + + assert TORCHVISION_AVAILABLE, "Torchvision imports failed but they are required." + self.image_transforms = transforms.Compose( + [ + transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), + transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + if self.load_cache_latents: + if (self.cached_instance_data_root is None) or ( + self.with_prior_preservation and self.cached_reg_data_root is None + ): + self.cache_latents(vae, text_encoder) + + self.cached_instance_data_root = f'{self.instance_data_root}_cached' + self.cached_reg_data_root = f'{self.reg_data_root}_cached' + self.instance_images_path = list(Path(self.cached_instance_data_root).iterdir()) + self.num_instance_images = len(self.instance_images_path) + + if self.with_prior_preservation: + self.reg_images_path = list(Path(self.cached_reg_data_root).iterdir()) + self.num_reg_images = len(self.reg_images_path) + + if self.cached_instance_data_root: + self.instance_images_path = list(Path(self.cached_instance_data_root).iterdir()) + self.num_instance_images = len(self.instance_images_path) + if self.with_prior_preservation and self.cached_reg_data_root: + self.reg_images_path = list(Path(self.cached_reg_data_root).iterdir()) + self.num_reg_images = len(self.reg_images_path) + + def __len__(self): + return self._length + + def get_image(self, path): + image = Image.open(path) + if not image.mode == "RGB": + image = image.convert("RGB") + image = self.image_transforms(image) + return image + + def __getitem__(self, index): + example = {} + if self.load_cache_latents: + example["instance_images"] = torch.load(self.instance_images_path[index % self.num_instance_images]) + else: + example["instance_images"] = self.get_image(self.instance_images_path[index % self.num_instance_images]) + example["instance_prompt"] = self.instance_prompt + + if self.reg_data_root: + if self.load_cache_latents: + example["reg_images"] = torch.load(self.reg_images_path[index % self.num_reg_images]) + else: + example["reg_images"] = self.get_image(self.reg_images_path[index % self.num_reg_images]) + example["reg_prompt"] = self.reg_prompt + + return example + + @rank_zero_only + def cache_latents(self, vae, text_encoder): + os.makedirs(f'{self.instance_data_root}_cached', exist_ok=True) + self.cached_instance_data_root = f'{self.instance_data_root}_cached' + self.cached_reg_data_root = f'{self.reg_data_root}_cached' + if self.instance_data_root and (len(os.listdir(self.cached_instance_data_root)) < self.num_instance_images): + for i in tqdm(range(self.num_instance_images)): + x = torch.Tensor(self.get_image(self.instance_images_path[i % self.num_instance_images])) + x = torch.unsqueeze(x, dim=0) + params = vae.encode(x).parameters.squeeze(dim=0) + torch.save(params, f'{self.instance_data_root}_cached/instance_image_cache_{i}.pt') + + if self.with_prior_preservation: + os.makedirs(f'{self.reg_data_root}_cached', exist_ok=True) + if self.reg_data_root and (len(os.listdir(self.cached_reg_data_root)) < self.num_reg_images): + for i in tqdm(range(self.num_reg_images)): + x = torch.Tensor(self.get_image(self.reg_images_path[i % self.num_reg_images])) + x = torch.unsqueeze(x, dim=0) + params = vae.encode(x).parameters.squeeze(dim=0) + torch.save(params, f'{self.reg_data_root}_cached/reg_image_cache_{i}.pt') diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/augmentations/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/augmentations/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/augmentations/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/augmentations/augmentations.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/augmentations/augmentations.py new file mode 100644 index 0000000..23f481b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/augmentations/augmentations.py @@ -0,0 +1,76 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import List, Optional + +import torch + +from nemo.utils import logging + + +def build_resolution_filter(value=None, method='larger', image_idx=0): + """ + Filter image based on its resolution. + value: filter threshold + method: Either larger or smaller + image_idx: idx of the image in the tuple input + """ + assert method == 'larger' or method == 'smaller' + if method == 'larger': + logging.info(f'Only Selecting images with resolution >= {value}') + return lambda x: x[image_idx].size[0] >= value and x[image_idx].size[1] >= value + + logging.info(f'Only Selecting images with resolution <= {value}') + return lambda x: x[image_idx].size[0] <= value and x[image_idx].size[1] <= value + + +class PickleTransform: + """ + Convert encodings stored in the pickle file to encoding and mask. + Transform the pad and resize the embedding to match the generator config. + """ + + def __init__(self, encoding_lengths: List[int], encoding_keys: List[str], out_keys: Optional[List[str]] = None): + assert len(encoding_keys) == len(encoding_lengths) + self.encoding_lengths = encoding_lengths + self.encoding_keys = encoding_keys + self.out_keys = out_keys if out_keys is not None else encoding_keys + + def _pad_and_resize(self, arr, ntokens): + # Function for padding and resizing a numpy array + + arr = torch.tensor(arr) + embed_dim = arr.shape[1] + + arr_padded = torch.zeros(ntokens, embed_dim, device=arr.device, dtype=torch.float32) + + # If the input text is larger than num_text_tokens, clip it. + if arr.shape[0] > ntokens: + arr = arr[0:ntokens] + + mask = torch.LongTensor(ntokens).zero_() + if len(arr.shape) > 1: + mask[0 : arr.shape[0]] = 1 + + if len(arr.shape) > 1: + arr_padded[0 : arr.shape[0]] = arr + + return arr_padded, mask + + def __call__(self, data): + out_dict = dict() + for token_length, encoding_key, out_key in zip(self.encoding_lengths, self.encoding_keys, self.out_keys): + embed, mask = self._pad_and_resize(data[encoding_key]['encodings'], token_length) + out_dict[f'{out_key}_embeddings'] = embed + out_dict[f'{out_key}_mask'] = mask + return out_dict diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/augmentations/corruption.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/augmentations/corruption.py new file mode 100644 index 0000000..2d6a25b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/augmentations/corruption.py @@ -0,0 +1,39 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +try: + import torchvision.transforms.functional as torchvision_F + + TORCHVISION_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + TORCHVISION_AVAILABLE = False + + +class ImagePyramidNoCorruptions: + r""" + Only downsample image without any additional corruption. + """ + + def __init__(self, target_resolutions): + self.resolutions = target_resolutions + + def obtain_image_pyramid(self, image): + assert TORCHVISION_AVAILABLE, "Torchvision imports failed but they are required." + # Downsampling + data_dict = dict() + for res in self.resolutions: + image_downsampled = torchvision_F.resize( + image, res, interpolation=torchvision_F.InterpolationMode.BICUBIC, antialias=True + ) + data_dict[f'images_{res}'] = image_downsampled + return data_dict diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/imagen_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/imagen_dataset.py new file mode 100644 index 0000000..c3db3b3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/imagen/imagen_dataset.py @@ -0,0 +1,156 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch + +from nemo.collections.multimodal.data.common.webdataset import WebDatasetCommon +from nemo.collections.multimodal.data.imagen.augmentations.augmentations import ( + PickleTransform, + build_resolution_filter, +) +from nemo.collections.multimodal.data.imagen.augmentations.corruption import ImagePyramidNoCorruptions +from nemo.collections.multimodal.data.stable_diffusion.augmentation.augmentations import ( + construct_image_augmentations, + identical_transform, +) +from nemo.core.classes import Dataset as NeMoDataset +from nemo.utils import logging + + +class ImagenSyntheticDataset(NeMoDataset): + def __init__( + self, res, conditioning_cfg, fake_len=100000, no_embedding=False, + ): + super().__init__() + self.fake_len = fake_len + self.res = res + self.no_embedding = no_embedding + if not no_embedding: + self.out_key = conditioning_cfg.out_key if conditioning_cfg.out_key else conditioning_cfg.precached_key + self.token_length = conditioning_cfg.token_length + self.embed_dim = conditioning_cfg.embed_dim + + def __getitem__(self, index): + item = {} + if isinstance(self.res, list): + for resolution in self.res: + image_key = f'images_{resolution}' + item[image_key] = torch.randn(3, resolution, resolution) + else: + item['images'] = torch.randn(3, self.res, self.res) + + item['raw_text'] = f'fake text {index}' + if not self.no_embedding: + item[f'{self.out_key}_embeddings'] = torch.randn(self.token_length, self.embed_dim) + item[f'{self.out_key}_mask'] = torch.ones(self.token_length, dtype=torch.long) + return item + + def __len__(self): + return self.fake_len + + +def _build_functions_with_pickles(data_cfg, condition_cfg): + def tuple_to_dict(inp): + for input in inp: + out_dict = dict() + out_dict['images'] = input[0] + + # Output from pickle transform is already a dictionary + out_dict.update(input[1]) + + out_dict['raw_text'] = input[2] + yield out_dict + + def transform_fn(sample): + image, encodings, text = sample['jpg'], sample['pickle'], sample['txt'] + img_transform = construct_image_augmentations(data_cfg.train.get('augmentations'), normalize=True) + pickle_transform = PickleTransform( + encoding_keys=[condition_cfg.precached_key], + encoding_lengths=[condition_cfg.token_length], + out_keys=[condition_cfg.out_key], + ) + text_transform = identical_transform + return img_transform(image), pickle_transform(encodings), text_transform(text) + + return tuple_to_dict, transform_fn + + +def _build_functions_no_pickles(data_cfg): + def tuple_to_dict(inp): + for input in inp: + out_dict = dict() + out_dict['images'] = input[0] + out_dict['raw_text'] = input[1] + yield out_dict + + def transform_fn(sample): + image, text = sample['jpg'], sample['txt'] + img_transform = construct_image_augmentations(data_cfg.train.get('augmentations'), normalize=True) + text_transform = identical_transform + return img_transform(image), text_transform(text) + + return tuple_to_dict, transform_fn + + +def build_train_valid_datasets( + model_cfg, consumed_samples, +): + data_cfg = model_cfg.data + condition_cfg = model_cfg.conditioning + + if data_cfg.get('synthetic_data', False): + logging.info(f'Creating Synthetic Datasaet.') + train_data = ImagenSyntheticDataset( + res=data_cfg.train.get('target_resolutions', 64), + conditioning_cfg=condition_cfg, + fake_len=data_cfg.get('synthetic_data_length', 10000), + no_embedding=condition_cfg.get("online_encoding", False), + ) + return train_data, None + # This function maps data that are tuples to dictionary. + if condition_cfg.get("online_encoding", False): + tuple_to_dict, transform_fn = _build_functions_no_pickles(data_cfg) + else: + tuple_to_dict, transform_fn = _build_functions_with_pickles(data_cfg, condition_cfg) + + filter_cfg = data_cfg.train.get('filterings', None) + + # For adding corruptions and obtaining image pyramid + if model_cfg.unet_type.startswith('sr'): + assert data_cfg.train.get('target_resolutions'), 'SR model requires multiple resolution for training' + logging.info(f'Resizing input images into the follow resolutions: {data_cfg.train.target_resolutions}') + corruption_gen = ImagePyramidNoCorruptions(target_resolutions=data_cfg.train.target_resolutions) + else: + corruption_gen = None + + # This function is used for obtaining image pyramid + # in SR models for Imagen, we need to use low-res image as conditioning. + def obtain_image_pyramid(inp): + for data_dict in inp: + data_pyramid = corruption_gen.obtain_image_pyramid(data_dict['images']) + data_dict.update(data_pyramid) + yield data_dict + + compose_fn = [tuple_to_dict] + if corruption_gen: + compose_fn.append(obtain_image_pyramid) + + train_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=compose_fn, + filter_fn=build_resolution_filter(**filter_cfg.resolution, image_idx='jpg') if filter_cfg else None, + is_train=True, + ) + return train_data, None diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/instruct_pix2pix/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/instruct_pix2pix/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/instruct_pix2pix/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/instruct_pix2pix/edit_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/instruct_pix2pix/edit_dataset.py new file mode 100644 index 0000000..e1ff196 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/instruct_pix2pix/edit_dataset.py @@ -0,0 +1,137 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np +import torch +from einops import rearrange +from PIL import Image +from torch.utils.data import Dataset + +try: + import torchvision + + TORCHVISION_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + TORCHVISION_AVAILABLE = False + + +class EditDataset(Dataset): + def __init__( + self, + path: str, + split: str = "train", + splits: tuple[float, float, float] = (0.95, 0.04, 0.01), + min_resize_res: int = 256, + max_resize_res: int = 256, + crop_res: int = 256, + flip_prob: float = 0.0, + ): + assert split in ("train", "val", "test") + assert sum(splits) == 1 + self.path = path + self.min_resize_res = min_resize_res + self.max_resize_res = max_resize_res + self.crop_res = crop_res + self.flip_prob = flip_prob + + with open(Path(self.path, "seeds.json")) as f: + self.seeds = json.load(f) + + split_0, split_1 = { + "train": (0.0, splits[0]), + "val": (splits[0], splits[0] + splits[1]), + "test": (splits[0] + splits[1], 1.0), + }[split] + + idx_0 = math.floor(split_0 * len(self.seeds)) + idx_1 = math.floor(split_1 * len(self.seeds)) + self.seeds = self.seeds[idx_0:idx_1] + + def __len__(self) -> int: + return len(self.seeds) + + def __getitem__(self, i: int) -> dict[str, Any]: + name, seeds = self.seeds[i] + propt_dir = Path(self.path, name) + seed = seeds[torch.randint(0, len(seeds), ()).item()] + with open(propt_dir.joinpath("prompt.json")) as fp: + prompt = json.load(fp)["edit"] + + image_0 = Image.open(propt_dir.joinpath(f"{seed}_0.jpg")) + image_1 = Image.open(propt_dir.joinpath(f"{seed}_1.jpg")) + + resize_res = torch.randint(self.min_resize_res, self.max_resize_res + 1, ()).item() + image_0 = image_0.resize((resize_res, resize_res), Image.Resampling.LANCZOS) + image_1 = image_1.resize((resize_res, resize_res), Image.Resampling.LANCZOS) + + image_0 = rearrange(2 * torch.tensor(np.array(image_0)).float() / 255 - 1, "h w c -> c h w") + image_1 = rearrange(2 * torch.tensor(np.array(image_1)).float() / 255 - 1, "h w c -> c h w") + + assert TORCHVISION_AVAILABLE, "Torchvision imports failed but they are required." + crop = torchvision.transforms.RandomCrop(self.crop_res) + flip = torchvision.transforms.RandomHorizontalFlip(float(self.flip_prob)) + image_0, image_1 = flip(crop(torch.cat((image_0, image_1)))).chunk(2) + + return dict(edited=image_1, edit=dict(c_concat=image_0, c_crossattn=prompt)) + + +class EditDatasetEval(Dataset): + def __init__( + self, path: str, split: str = "train", splits: tuple[float, float, float] = (0.9, 0.05, 0.05), res: int = 256, + ): + assert split in ("train", "val", "test") + assert sum(splits) == 1 + self.path = path + self.res = res + + with open(Path(self.path, "seeds.json")) as f: + self.seeds = json.load(f) + + split_0, split_1 = { + "train": (0.0, splits[0]), + "val": (splits[0], splits[0] + splits[1]), + "test": (splits[0] + splits[1], 1.0), + }[split] + + idx_0 = math.floor(split_0 * len(self.seeds)) + idx_1 = math.floor(split_1 * len(self.seeds)) + self.seeds = self.seeds[idx_0:idx_1] + + def __len__(self) -> int: + return len(self.seeds) + + def __getitem__(self, i: int) -> dict[str, Any]: + name, seeds = self.seeds[i] + propt_dir = Path(self.path, name) + seed = seeds[torch.randint(0, len(seeds), ()).item()] + with open(propt_dir.joinpath("prompt.json")) as fp: + prompt = json.load(fp) + edit = prompt["edit"] + input_prompt = prompt["input"] + output_prompt = prompt["output"] + + image_0 = Image.open(propt_dir.joinpath(f"{seed}_0.jpg")) + + reize_res = torch.randint(self.res, self.res + 1, ()).item() + image_0 = image_0.resize((reize_res, reize_res), Image.Resampling.LANCZOS) + + image_0 = rearrange(2 * torch.tensor(np.array(image_0)).float() / 255 - 1, "h w c -> c h w") + + return dict(image_0=image_0, input_prompt=input_prompt, edit=edit, output_prompt=output_prompt) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/cameras.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/cameras.py new file mode 100644 index 0000000..72dbf69 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/cameras.py @@ -0,0 +1,192 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC, abstractmethod +from typing import List + +import numpy as np +import torch + + +class Camera(ABC): + """ + Abstract base class for Camera models. + """ + + def __init__(self, width: int, height: int, device: torch.device = 'cuda') -> None: + """ + Initializes the Camera instance with given dimensions and device. + + Parameters: + width: int - Width of the camera frame. + height: int - Height of the camera frame. + device: torch.device - The device where tensor computations will be performed. + """ + self.width = width + self.height = height + self.device = device + + @abstractmethod + def compute_intrinsics(self) -> None: + """ + Abstract method to compute camera intrinsics. + """ + pass + + @abstractmethod + def compute_projection_matrix(self) -> None: + """ + Abstract method to compute the projection matrix. + """ + pass + + +class OrthographicCamera(Camera): + """ + Class for Orthographic Camera models. + """ + + def compute_projection_matrix(self) -> torch.Tensor: + """ + Computes the projection matrix for an Orthographic camera. + + Returns: + torch.Tensor: The projection matrix. + """ + projection = torch.tensor( + [[2 / self.width, 0, 0, 0], [0, -2 / self.height, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]], + dtype=torch.float32, + device=self.device, + ).unsqueeze(0) + return projection + + +class PinholeCamera(Camera): + """ + Class for Pinhole Camera models. + """ + + def __init__(self, width: int, height: int, near: float, far: float, device: torch.device = 'cuda') -> None: + """ + Initializes the Pinhole Camera instance with given parameters. + + Parameters: + width: int - Width of the camera frame. + height: int - Height of the camera frame. + near: float - Near clipping plane. + far: float - Far clipping plane. + device: torch.device - The device where tensor computations will be performed. + """ + super().__init__(width, height, device) + self.near = near + self.far = far + + def compute_intrinsics(self, fovx: float, fovy: float) -> np.ndarray: + """ + Computes the intrinsic matrix for the camera based on field of views. + + Parameters: + fovx: float - Field of view in X direction. + fovy: float - Field of view in Y direction. + + Returns: + np.ndarray: The intrinsic matrix. + """ + focal_x = self.width / (2 * np.tan(np.deg2rad(fovx) / 2)) + focal_y = self.height / (2 * np.tan(np.deg2rad(fovy) / 2)) + cx, cy = self.width / 2, self.height / 2 + return np.array([focal_x, focal_y, cx, cy]) + + def compute_projection_matrix(self, focal_x: float, focal_y: float) -> torch.Tensor: + """ + Computes the projection matrix for the camera. + + Parameters: + focal_x: float - Focal length in X direction. + focal_y: float - Focal length in Y direction. + + Returns: + torch.Tensor: The projection matrix. + """ + projection = torch.tensor( + [ + [2 * focal_x / self.width, 0, 0, 0], + [0, -2 * focal_y / self.height, 0, 0], + [ + 0, + 0, + -(self.far + self.near) / (self.far - self.near), + -(2 * self.far * self.near) / (self.far - self.near), + ], + [0, 0, -1, 0], + ], + dtype=torch.float32, + device=self.device, + ).unsqueeze(0) + return projection + + +class CubeCamera(Camera): + """ + Class for Cube Camera models, which is essentially six pinhole cameras. + """ + + def __init__( + self, width: int, height: int, near: float = 0.01, far: float = 1000, device: torch.device = 'cuda' + ) -> None: + """ + Initializes the Cube Camera instance with given parameters. + + Parameters: + width: int - Width of each camera face. + height: int - Height of each camera face. + near: float - Near clipping plane. + far: float - Far clipping plane. + device: torch.device - The device where tensor computations will be performed. + """ + self.width = width + self.height = height + self.near = near + self.far = far + self.device = device + + def compute_intrinsics(self) -> List[np.ndarray]: + """ + Computes the intrinsic matrices for the six faces of the cube using a Pinhole camera model. + + Returns: + List[np.ndarray]: List of 6 intrinsic matrices, one for each face. + """ + # Similar to Pinhole but repeated six times for six faces of the cube + return [ + PinholeCamera( + width=self.width, height=self.height, near=self.near, far=self.far, device=self.device + ).compute_intrinsics(90, 90) + for _ in range(6) + ] + + def compute_projection_matrix(self) -> List[torch.Tensor]: + """ + Computes the projection matrices for the six faces of the cube using a Pinhole camera model. + + Returns: + List[torch.Tensor]: List of 6 projection matrices, one for each face. + """ + # Similar to Pinhole but repeated six times for six faces of the cube + return [ + PinholeCamera( + width=self.width, height=self.height, near=self.near, far=self.far, device=self.device + ).compute_projection_matrix(1, 1) + for _ in range(6) + ] diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/circle_poses.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/circle_poses.py new file mode 100644 index 0000000..93f1c96 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/circle_poses.py @@ -0,0 +1,228 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, Union + +import numpy as np +import torch +from torch.utils.data import Dataset + +from nemo.collections.multimodal.data.nerf.cameras import PinholeCamera +from nemo.collections.multimodal.data.nerf.utils import ( + compute_look_at_vectors, + construct_poses, + get_rays, + get_view_direction, +) + + +def circle_poses( + radius: torch.Tensor = torch.tensor([3.2]), + theta: torch.Tensor = torch.tensor([60]), + phi: torch.Tensor = torch.tensor([0]), + angle_overhead: float = 30, + angle_front: float = 60, + return_dirs: bool = False, + device: torch.device = "cuda", +) -> torch.Tensor: + """ + Generate camera poses based on a circular arrangement. + + Parameters: + radius: torch.Tensor - Radii for the camera positions. + theta: torch.Tensor - Theta angles for the camera positions. + phi: torch.Tensor - Phi angles for the camera positions. + angle_overhead: float - Angle range of the overhead view. + angle_front: float - Angle range of the front view. + return_dirs: bool - Whether to return the view directions. + device: str - The device to allocate the tensor on (e.g., 'cuda' or 'cpu'). + + Returns: + Tuple: Contains the following: + - poses (torch.Tensor): Generated poses, shape [size, 4, 4]. + - dirs (torch.Tensor, optional): View directions, if requested. + """ + # Convert degrees to radians for theta and phi + theta = theta / 180 * np.pi + phi = phi / 180 * np.pi + angle_overhead = angle_overhead / 180 * np.pi + angle_front = angle_front / 180 * np.pi + + # Calculate camera centers in Cartesian coordinates + centers = torch.stack( + [ + radius * torch.sin(theta) * torch.sin(phi), + radius * torch.cos(theta), + radius * torch.sin(theta) * torch.cos(phi), + ], + dim=-1, + ) # [B, 3] + + # Compute camera look-at matrix + forward_vector, up_vector, right_vector = compute_look_at_vectors(centers=centers, device=device) + + # Construct the 4x4 pose matrices + poses = construct_poses( + centers=centers, right_vector=right_vector, up_vector=up_vector, forward_vector=forward_vector, device=device + ) + + dirs = get_view_direction(theta, phi, angle_overhead, angle_front) if return_dirs else None + + return poses, dirs + + +class CirclePosesDataset(Dataset): + """ + A dataset class to generate circle poses. + """ + + def __init__( + self, + size: int = 100, + height: int = 256, + width: int = 256, + default_fovx: float = 20.0, + default_fovy: float = 20.0, + default_radius: float = 3.2, + default_polar: float = 90.0, + default_azimuth: float = 0.0, + angle_overhead: float = 30.0, + angle_front: float = 60.0, + near: float = 0.01, + far: float = 1000.0, + device: torch.device = 'cpu', + ) -> None: + """ + Initializes a new CirclePosesDataset instance. + + Parameters: + size (int): Number of samples in the dataset. + height (int): Height of the image. + width (int): Width of the image. + default_fovx (float): Default field of view in x-direction. + default_fovy (float): Default field of view in y-direction. + default_radius (float): Default radius of the circle. + default_polar (float): Default polar angle. + default_azimuth (float): Default azimuth angle. + angle_overhead (float): Overhead angle. + angle_front (float): Frontal angle. + near (float): Near clipping distance. + far (float): Far clipping distance. + device (torch.device): Device to generate data on. + """ + super().__init__() + self.size = size + self.height = height + self.width = width + + self.default_fovx = default_fovx + self.default_fovy = default_fovy + self.default_radius = default_radius + self.default_polar = default_polar + self.default_azimuth = default_azimuth + + self.angle_overhead = angle_overhead + self.angle_front = angle_front + self.near = near + self.far = far + + self.device = device + + # TODO(ahmadki): make camera type a parameter + self.camera = PinholeCamera( + width=self.width, height=self.height, near=self.near, far=self.far, device=self.device + ) + + def __len__(self) -> int: + """Returns the number of samples in the dataset.""" + return self.size + + def __getitem__(self, idx: int) -> Dict[str, Union[int, torch.Tensor]]: + """Get an item from the dataset. + + Args: + idx (int): Index of the item to retrieve. + + Returns: + dict: Data dictionary containing the following: + - height (int): Height of the image. + - width (int): Width of the image. + - rays_o (torch.Tensor): Ray origins, shape [height, width, 3]. + - rays_d (torch.Tensor): Ray directions, shape [height, width, 3]. + - dir (torch.Tensor): View direction, shape [3]. + - mvp (torch.Tensor): Model-view-projection matrix, shape [4, 4]. + - azimuth (torch.Tensor): Azimuth angle, shape [1]. + """ + # Initialize circle pose parameters + thetas = torch.FloatTensor([self.default_polar]).to(self.device) + phis = torch.FloatTensor([(idx / self.size) * 360]).to(self.device) + radius = torch.FloatTensor([self.default_radius]).to(self.device) + + # Generate circle poses and directions + poses, dirs = circle_poses( + radius=radius, + theta=thetas, + phi=phis, + angle_overhead=self.angle_overhead, + angle_front=self.angle_front, + return_dirs=True, + device=self.device, + ) + + # Compute camera intrinsics + intrinsics = self.camera.compute_intrinsics(fovx=self.default_fovx, fovy=self.default_fovy) + + # Compute projection matrix + projection = self.camera.compute_projection_matrix(focal_x=intrinsics[0], focal_y=intrinsics[1]) + mvp = projection @ torch.inverse(poses) # [1, 4, 4] + + # Sample rays + rays_o, rays_d = get_rays( + poses=poses, intrinsics=intrinsics, height=self.height, width=self.width, device=poses.device + ) + + # Compute azimuth delta + delta_azimuth = phis - self.default_azimuth + delta_azimuth[delta_azimuth > 180] -= 360 # range in [-180, 180] + + data = { + 'height': self.height, + 'width': self.width, + 'rays_o': rays_o, + 'rays_d': rays_d, + 'dir': dirs, + 'mvp': mvp, + 'azimuth': delta_azimuth, + } + + return data + + def collate_fn(self, batch: list) -> Dict[str, Union[int, torch.Tensor]]: + """Collate function to combine multiple data points into batches. + + Args: + batch (list): List of data dictionaries. + + Returns: + dict: Collated data. + """ + return { + 'height': self.height, + 'width': self.width, + 'rays_o': torch.cat([item['rays_o'] for item in batch], dim=0), + 'rays_d': torch.cat([item['rays_d'] for item in batch], dim=0), + 'mvp': torch.cat([item['mvp'] for item in batch], dim=0), + 'dir': torch.cat([item['dir'] for item in batch], dim=0), + 'azimuth': torch.cat([item['azimuth'] for item in batch], dim=0), + } diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/random_poses.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/random_poses.py new file mode 100644 index 0000000..7ecc562 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/random_poses.py @@ -0,0 +1,450 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import random +from typing import Any, Dict, Iterator, List, Optional, Tuple + +import numpy as np +import torch +import torch.nn.functional as F +from torch.utils.data import IterableDataset + +from nemo.collections.multimodal.data.nerf.cameras import PinholeCamera +from nemo.collections.multimodal.data.nerf.utils import ( + compute_look_at_vectors, + construct_poses, + get_rays, + get_view_direction, +) + + +def linear_normalization(x: float, lower_bound: float, upper_bound: float) -> float: + """ + Linearly normalize a value between lower_bound and upper_bound to a value between 0 and 1. + + Parameters: + x: The value to normalize. + lower_bound: The lower bound of the range of x. + upper_bound: The upper bound of the range of x. + + Returns: + The normalized value between 0 and 1. + """ + return min(1, max(0, (x - lower_bound) / (upper_bound - lower_bound))) + + +def rand_poses( + size: int, + radius_range: List[float] = [1, 1.5], + theta_range: List[float] = [0, 120], + phi_range: List[float] = [0, 360], + angle_overhead: float = 30, + angle_front: float = 60, + uniform_sphere_rate: float = 0.5, + jitter: bool = False, + jitter_center: float = 0.2, + jitter_target: float = 0.2, + jitter_up: float = 0.02, + return_dirs: bool = False, + device: torch.device = "cuda", +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """ + Generate random poses from an orbit camera. + + Args: + size (int): Number of poses to generate. + radius_range (List[float]): Min and max radii for camera [min, max]. + theta_range (List[float]): Elevation angle range in degrees [min, max]. + phi_range (List[float]): Azimuth angle range in degrees [min, max]. + angle_overhead (float): Overhead angle in degrees. + angle_front (float): Front angle in degrees. + uniform_sphere_rate (float): The probability of sampling from a uniform sphere. + jitter (bool): Whether to add noise to the poses. + jitter_center (float): Noise range for the camera center. + jitter_target (float): Noise range for the camera target. + jitter_up (float): Noise range for the camera up vector. + return_dirs (bool): Whether to return the view directions. + device (torch.device): The device on which to allocate tensors. + + Returns: + Tuple: Contains the following: + - poses (torch.Tensor): Generated poses, shape [size, 4, 4]. + - thetas (torch.Tensor): Elevation angles in degrees, shape [size]. + - phis (torch.Tensor): Azimuth angles in degrees, shape [size]. + - radius (torch.Tensor): Radii of the camera orbits, shape [size]. + - dirs (torch.Tensor, optional): View directions, if requested. + """ + + # Convert angles from degrees to radians + theta_range = np.radians(theta_range) + phi_range = np.radians(phi_range) + angle_overhead = np.radians(angle_overhead) + angle_front = np.radians(angle_front) + + # Generate radius for each pose + radius = torch.rand(size, device=device) * (radius_range[1] - radius_range[0]) + radius_range[0] + + # Generate camera center positions + if random.random() < uniform_sphere_rate: + centers, thetas, phis = sample_uniform_sphere(size=size, radius=radius, device=device) + else: + centers, thetas, phis = sample_orbit( + size=size, radius=radius, theta_range=theta_range, phi_range=phi_range, device=device + ) + + # Initialize targets to 0 (assuming 0 is a point in 3D space that cameras are looking at) + targets = torch.zeros_like(centers) + + # Apply jitter + if jitter: + centers += torch.rand_like(centers) * jitter_center - jitter_center / 2.0 + targets = torch.randn_like(centers) * jitter_target + + # Compute camera look-at matrix + forward_vector, up_vector, right_vector = compute_look_at_vectors( + centers=centers - targets, jitter_up=jitter_up if jitter else 0, device=device + ) + + # Construct the 4x4 pose matrices + poses = construct_poses( + centers=centers, right_vector=right_vector, up_vector=up_vector, forward_vector=forward_vector, device=device + ) + + # Optionally compute view directions + dirs = get_view_direction(thetas, phis, angle_overhead, angle_front) if return_dirs else None + + # Convert back to degrees for thetas and phis + thetas, phis = torch.rad2deg(thetas), torch.rad2deg(phis) + + return poses, thetas, phis, radius, dirs + + +def sample_uniform_sphere( + size: int, radius: torch.Tensor, device: torch.device +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Sample points uniformly on a sphere. + + Args: + size (int): Number of points to sample. + device (torch.device): Device to allocate tensors on. + radius (torch.Tensor): Radii for the points. + + Returns: + Tuple: Contains the following: + - centers (torch.Tensor): The Cartesian coordinates of the sampled points. + - thetas (torch.Tensor): Elevation angles in radians. + - phis (torch.Tensor): Azimuth angles in radians. + """ + # Generate unit vectors + unit_centers = F.normalize( + torch.stack( + [ + torch.randn(size, device=device), + torch.abs(torch.randn(size, device=device)), + torch.randn(size, device=device), + ], + dim=-1, + ), + p=2, + dim=1, + ) + # Generate radii and scale unit vectors + centers = unit_centers * radius.unsqueeze(-1) + # Calculate spherical coordinates + thetas = torch.acos(unit_centers[:, 1]) + phis = torch.atan2(unit_centers[:, 0], unit_centers[:, 2]) + phis[phis < 0] += 2 * np.pi + + return centers, thetas, phis + + +def sample_orbit( + size: int, radius: torch.Tensor, theta_range: np.ndarray, phi_range: np.ndarray, device: torch.device = "cuda" +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Sample points on a spherical orbit. + + Args: + size (int): Number of points to sample. + radius (torch.Tensor): Radii for the points. + theta_range (np.ndarray): Elevation angle range in radians [min, max]. + phi_range (np.ndarray): Azimuth angle range in radians [min, max]. + device (torch.device): Device to allocate tensors on. + + Returns: + Tuple: Contains the following: + - centers (torch.Tensor): The Cartesian coordinates of the sampled points. + - thetas (torch.Tensor): Elevation angles in radians. + - phis (torch.Tensor): Azimuth angles in radians. + """ + thetas = torch.rand(size, device=device) * (theta_range[1] - theta_range[0]) + theta_range[0] + phis = torch.rand(size, device=device) * (phi_range[1] - phi_range[0]) + phi_range[0] + phis[phis < 0] += 2 * np.pi + + x = radius * torch.sin(thetas) * torch.sin(phis) + y = radius * torch.cos(thetas) + z = radius * torch.sin(thetas) * torch.cos(phis) + + centers = torch.stack([x, y, z], dim=-1) + + return centers, thetas, phis + + +class RandomPosesDataset(IterableDataset): + """ + A dataset class to generate random poses. + """ + + def __init__( + self, + internal_batch_size: int = 100, + height: int = 256, + width: int = 256, + radius_range: Tuple[float, float] = [3.0, 3.5], + theta_range: Tuple[float, float] = [45.0, 105.0], + phi_range: Tuple[float, float] = [-180.0, 180.0], + fovx_range: Tuple[float, float] = [10.0, 30.0], + default_fovx: float = 20.0, + fovy_range: Tuple[float, float] = [10.0, 30.0], + default_fovy: float = 20.0, + default_radius: float = 3.2, + default_polar: float = 90.0, + default_azimuth: float = 0.0, + jitter: bool = False, + jitter_center: float = 0.2, + jitter_target: float = 0.2, + jitter_up: float = 0.02, + angle_overhead: float = 30.0, + angle_front: float = 60.0, + uniform_sphere_rate: float = 0.0, + near: float = 0.01, + far: float = 1000.0, + device: torch.device = 'cpu', + ) -> None: + """ + Initializes a new RandomPosesDataset instance. + + Parameters: + internal_batch_size (int): Number of samples to pre-generate internally. + height (int): Height of the image. + width (int): Width of the image. + radius_range (Tuple[float, float]): Range of generated radii. + theta_range (Tuple[float, float]): Range of generated theta angles. + phi_range (Tuple[float, float]): Range of generated phi angles. + fovx_range (Tuple[float, float]): Range of generated field of view in x-direction. + default_fovx (float): Default field of view in x-direction. + fovy_range (Tuple[float, float]): Range of generated field of view angles in y-direction. + default_fovy (float): Default field of view in y-direction. + default_radius (float): Default radius of the circle. + default_polar (float): Default polar angle. + default_azimuth (float): Default azimuth angle. + jitter (bool): Whether to jitter the poses. + jitter_center (float): Jittering center range. + jitter_target (float): Jittering target range. + jitter_up (float): Jittering up range. + angle_overhead (float): Overhead angle. + angle_front (float): Frontal angle. + uniform_sphere_rate (float): Rate of sampling uniformly on a sphere. + near (float): Near clipping distance. + far (float): Far clipping distance. + device (torch.device): Device to generate data on. + """ + + super().__init__() + self.height = height + self.width = width + self.internal_batch_size = internal_batch_size + + # TODO(ahmadki): expose for models other than dreamfusion + self.progressive_view = False + self.progressive_view_start_step = 0 + self.progressive_view_end_step = 500 + + self.default_fovx = default_fovx + self.default_fovy = default_fovy + self.default_radius = default_radius + self.default_polar = default_polar + self.default_azimuth = default_azimuth + self.same_fov_random = True + + self.radius_range = radius_range + self.theta_range = theta_range + self.phi_range = phi_range + self.fovx_range = fovx_range + self.fovy_range = fovy_range + + self.current_radius_range = radius_range + self.current_theta_range = theta_range + self.current_phi_range = phi_range + self.current_fovx_range = fovx_range + self.current_fovy_range = fovy_range + + self.angle_overhead = angle_overhead + self.angle_front = angle_front + self.uniform_sphere_rate = uniform_sphere_rate + self.jitter = jitter + self.jitter_center = jitter_center + self.jitter_target = jitter_target + self.jitter_up = jitter_up + + self.near = near + self.far = far + + self.device = device + + # TODO(ahmadki): make camera type a parameter + self.camera = PinholeCamera( + width=self.width, height=self.height, near=self.near, far=self.far, device=self.device + ) + + def update_step(self, epoch: int, global_step: int) -> None: + """ + Update the dataset at the beginning of each epoch. + + Parameters: + epoch (int): Current epoch. + global_step (int): Current global step. + + """ + if self.progressive_view: + self.progressive_view_update_step(global_step=global_step) + + def progressive_view_update_step(self, global_step: int) -> None: + """ + progressively relaxing view range + + Parameters: + global_step (int): Current global step. + """ + # TODO(ahmadki): support non-linear progressive_views + r = linear_normalization( + x=global_step, lower_bound=self.progressive_view_start_step, upper_bound=self.progressive_view_end_step + ) + self.current_phi_range = [ + (1 - r) * self.default_azimuth + r * self.phi_range[0], + (1 - r) * self.default_azimuth + r * self.phi_range[1], + ] + self.current_theta_range = [ + (1 - r) * self.default_polar + r * self.theta_range[0], + (1 - r) * self.default_polar + r * self.theta_range[1], + ] + self.current_radius_range = [ + (1 - r) * self.default_radius + r * self.radius_range[0], + (1 - r) * self.default_radius + r * self.radius_range[1], + ] + self.current_fovy_range = [ + (1 - r) * self.default_fovy + r * self.fovy_range[0], + (1 - r) * self.default_fovy + r * self.fovy_range[1], + ] + + def __iter__(self) -> Iterator[Dict[str, torch.Tensor]]: + """ + Returns an iterator over the dataset. + + Returns: + Iterator: An iterator over the dataset. + + """ + while True: + # Generate samples + rays_o, rays_d, dirs, mvp, delta_azimuth = self.generate_samples() + for i in range(self.internal_batch_size): + # Yield one sample at a time from the internal batch + yield { + 'height': self.height, + 'width': self.width, + 'rays_o': rays_o[i].unsqueeze(0), + 'rays_d': rays_d[i].unsqueeze(0), + 'dir': dirs[i].unsqueeze(0), + 'mvp': mvp[i].unsqueeze(0), + 'azimuth': delta_azimuth[i].unsqueeze(0), + } + + def generate_samples(self): + """ + Generate a batch of random poses. + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor], Dict[str, torch.Tensor], Dict[str, torch.Tensor], Dict[str, torch.Tensor]]: + A tuple containing: + - rays (Dict[str, torch.Tensor]): A dictionary containing the origin and direction of the rays. + - dirs (torch.Tensor): A tensor containing the directions of the rays. + - mvp (torch.Tensor): A tensor containing the model-view-projection matrix. + - azimuth (torch.Tensor): A A tensor containing the azimuth angle. + """ + # Generate random poses and directions + poses, dirs, thetas, phis, radius = rand_poses( + size=self.internal_batch_size, + radius_range=self.current_radius_range, + theta_range=self.current_theta_range, + phi_range=self.current_phi_range, + angle_overhead=self.angle_overhead, + angle_front=self.angle_front, + uniform_sphere_rate=self.uniform_sphere_rate, + jitter=self.jitter, + jitter_center=self.jitter_center, + jitter_target=self.jitter_target, + jitter_up=self.jitter_up, + return_dirs=True, + device=self.device, + ) + + # random focal + if self.same_fov_random: + fovx_random = random.random() + fovy_random = fovx_random + else: + fovx_random = random.random() + fovy_random = random.random() + fovx = fovx_random * (self.current_fovx_range[1] - self.current_fovx_range[0]) + self.current_fovx_range[0] + fovy = fovy_random * (self.current_fovy_range[1] - self.current_fovy_range[0]) + self.current_fovy_range[0] + + # Compute camera intrinsics + intrinsics = self.camera.compute_intrinsics(fovx=fovx, fovy=fovy) + + # Compute projection matrix + projection = self.camera.compute_projection_matrix(focal_x=intrinsics[0], focal_y=intrinsics[1]) + mvp = projection @ torch.inverse(poses) # [internal batch size, 4, 4] + + # Sample rays + rays_o, rays_d = get_rays( + poses=poses, intrinsics=intrinsics, height=self.height, width=self.width, device=poses.device + ) + + # Compute azimuth delta + delta_azimuth = phis - self.default_azimuth + delta_azimuth[delta_azimuth > 180] -= 360 # range in [-180, 180] + + return rays_o, rays_d, dirs, mvp, delta_azimuth + + def collate_fn(self, batch: List[Dict[str, Any]]) -> Dict[str, Any]: + """ + Collate function to bundle multiple samples into a single batch. + + Args: + batch (List[Dict]): List of samples to collate. + + Returns: + Dict: A dictionary containing the collated batch. + """ + return { + 'height': self.height, + 'width': self.width, + 'rays_o': torch.cat([item['rays_o'] for item in batch], dim=0), + 'rays_d': torch.cat([item['rays_d'] for item in batch], dim=0), + 'mvp': torch.cat([item['mvp'] for item in batch], dim=0), + 'dir': torch.cat([item['dir'] for item in batch], dim=0), + 'azimuth': torch.cat([item['azimuth'] for item in batch], dim=0), + } diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/utils.py new file mode 100644 index 0000000..306aeb5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nerf/utils.py @@ -0,0 +1,217 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, Optional + +import numpy as np +import torch +import torch.nn.functional as F + + +def get_view_direction(thetas: torch.Tensor, phis: torch.Tensor, overhead: float, front: float) -> torch.Tensor: + """ + Get the view direction based on given theta and phi values. + + Parameters: + - thetas (torch.Tensor): Array of theta values with shape [B,] + - phis (torch.Tensor): Array of phi values with shape [B,] + - overhead (float): Threshold for determining top and bottom views. + - front (float): Threshold for determining front, back and side views. + + Returns: + - torch.Tensor: Array of view directions. Values can be: + 0: front + 1: side (camera left) + 2: back + 3: side (camera right) + 4: top + 5: bottom + + Notes: + - Phi and theta values are assumed to be in radians. + """ + + num_samples = thetas.shape[0] + res = torch.zeros(num_samples, dtype=torch.long) + + # Normalize phis values to [0, 2*pi] + phis = phis % (2 * np.pi) + + # Determine direction based on phis + res[(phis < front / 2) | (phis >= 2 * np.pi - front / 2)] = 0 + res[(phis >= front / 2) & (phis < np.pi - front / 2)] = 1 + res[(phis >= np.pi - front / 2) & (phis < np.pi + front / 2)] = 2 + res[(phis >= np.pi + front / 2) & (phis < 2 * np.pi - front / 2)] = 3 + + # Override directions based on thetas for top and bottom views + res[thetas <= overhead] = 4 + res[thetas >= (np.pi - overhead)] = 5 + + return res + + +def compute_look_at_vectors(centers: torch.Tensor, jitter_up: Optional[float] = None, device: torch.device = "cuda"): + """ + Compute the look-at vectors for camera poses. + + Parameters: + centers: The centers of the cameras. + jitter_up: The noise range for the up vector of the camera. + device: Device to allocate the output tensor. + + Returns: + Tuple: Contains the following: + - forward_vector: The forward vectors of the cameras, shape [B, 3]. + - up_vector: The up vectors of the cameras, shape [B, 3]. + - right_vector: The right vectors of the cameras, shape [B, 3]. + """ + forward_vector = F.normalize(centers) + up_vector = torch.FloatTensor([0, 1, 0]).to(device).unsqueeze(0).repeat(len(centers), 1) + right_vector = F.normalize(torch.cross(forward_vector, up_vector, dim=-1)) + up_noise = torch.randn_like(up_vector) * jitter_up if jitter_up is not None else 0 + up_vector = F.normalize(torch.cross(right_vector, forward_vector, dim=-1) + up_noise) + + return forward_vector, up_vector, right_vector + + +def construct_poses( + centers: torch.Tensor, + right_vector: torch.Tensor, + up_vector: torch.Tensor, + forward_vector: torch.Tensor, + device: torch.device, +) -> torch.Tensor: + """ + Construct the 4x4 pose matrices. + + Args: + size (int): Number of pose matrices to construct. + centers (torch.Tensor): The Cartesian coordinates of the camera centers. + right_vector (torch.Tensor): The right vectors of the cameras. + up_vector (torch.Tensor): The up vectors of the cameras. + forward_vector (torch.Tensor): The forward vectors of the cameras. + device (torch.device): Device to allocate tensors on. + + Returns: + torch.Tensor: The pose matrices, shape [size, 4, 4]. + """ + poses = torch.eye(4, dtype=torch.float32, device=device).unsqueeze(0).repeat(len(centers), 1, 1) + poses[:, :3, :3] = torch.stack([right_vector, up_vector, forward_vector], dim=-1) + poses[:, :3, 3] = centers + + return poses + + +@torch.cuda.amp.autocast(enabled=False) +def get_rays( + poses: torch.Tensor, + intrinsics: torch.Tensor, + height: int, + width: int, + num_samples: Optional[int] = None, + error_map: Optional[torch.Tensor] = None, + device: torch.device = "cuda", +) -> Dict[str, torch.Tensor]: + """ + Generates rays from camera poses and intrinsics. + + Args: + poses (torch.Tensor): Camera poses, shape [B, 4, 4] (cam2world). + intrinsics (torch.Tensor): Intrinsic camera parameters [fx, fy, cx, cy]. + height (int): Height of the image. + width (int): Width of the image. + num_samples: Number of rays to sample, default is None for all rays. + error_map: Optional tensor to use for non-uniform sampling of rays. + device (torch.device): Device on which to generate the rays. + + Returns: + Dict[str, torch.Tensor]: A dictionary containing the following keys: + - 'rays_o': Origin of the rays, shape [B, N, 3] + - 'rays_d': Directions of the rays, shape [B, N, 3] + - 'inds': Indices of the rays, shape [B, N] (if N > 0) + - 'inds_coarse': Coarse indices of the rays, shape [B, N] (if error_map is not None) + """ + + batch_size = poses.shape[0] + fx, fy, cx, cy = intrinsics + + i, j = torch.meshgrid( + torch.linspace(0, width - 1, width, device=device), + torch.linspace(0, height - 1, height, device=device), + indexing='ij', + ) + i = i.t().reshape([1, height * width]).expand([batch_size, height * width]) + 0.5 + j = j.t().reshape([1, height * width]).expand([batch_size, height * width]) + 0.5 + + results = {} + + if num_samples is not None: + num_samples = min(num_samples, height * width) + + if error_map is None: + sampled_indices = torch.randint(0, height * width, size=[num_samples], device=device) + sampled_indices = sampled_indices.expand([batch_size, num_samples]) + else: + sampled_indices, sampled_indices_coarse = non_uniform_sampling( + error_map=error_map, num_samples=num_samples, height=height, width=width, device=device + ) + results['sampled_indices_coarse'] = sampled_indices_coarse + + i = torch.gather(i, -1, sampled_indices) + j = torch.gather(j, -1, sampled_indices) + results['sampled_indices'] = sampled_indices + else: + sampled_indices = torch.arange(height * width, device=device).expand([batch_size, height * width]) + + zs = torch.full_like(i, -1.0) + xs = -(i - cx) / fx * zs + ys = (j - cy) / fy * zs + directions = torch.stack((xs, ys, zs), dim=-1) + + rays_d = directions @ poses[:, :3, :3].transpose(-1, -2) + rays_o = poses[..., :3, 3].unsqueeze(-2).expand_as(rays_d) + + rays_o = rays_o.view(-1, height, width, 3) + rays_d = rays_d.view(-1, height, width, 3) + + return rays_o, rays_d + + +def non_uniform_sampling( + error_map: torch.Tensor, batch_size: int, num_samples: int, height: int, width: int, device: torch.device = "cuda" +) -> torch.Tensor: + """ + Perform non-uniform sampling based on the provided error_map. + + Parameters: + error_map: The error map for non-uniform sampling. + batch_size (int): Batch size of the generated samples. + num_samples (int): Number of samples to pick. + height (int): Height of the image. + width (int): Width of the image. + device: Device on which tensors are stored. + + Returns: + A tensor containing the sampled indices. + """ + + sampled_indices_coarse = torch.multinomial(error_map.to(device), num_samples, replacement=False) + inds_x, inds_y = sampled_indices_coarse // 128, sampled_indices_coarse % 128 + sx, sy = height / 128, width / 128 + + inds_x = (inds_x * sx + torch.rand(batch_size, num_samples, device=device) * sx).long().clamp(max=height - 1) + inds_y = (inds_y * sy + torch.rand(batch_size, num_samples, device=device) * sy).long().clamp(max=width - 1) + sampled_indices = inds_x * width + inds_y + + return sampled_indices, sampled_indices_coarse diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/neva/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/neva/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/neva/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/neva/conversation.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/neva/conversation.py new file mode 100644 index 0000000..d51a5f9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/neva/conversation.py @@ -0,0 +1,420 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import dataclasses +from enum import Enum, auto +from typing import List + +DEFAULT_PAD_TOKEN = "" +DEFAULT_BOS_TOKEN = "" +DEFAULT_EOS_TOKEN = "" +DEFAULT_UNK_TOKEN = "" +DEFAULT_IMAGE_TOKEN = "" +DEFAULT_SYSTEM_TOKEN = "" +DEFAULT_SEPARATOR_TOKEN = "" +DEFAULT_LABELS_TOKEN = "" +DEFAULT_IMAGE_PATCH_TOKEN = "" +DEFAULT_IM_START_TOKEN = "" +DEFAULT_IM_END_TOKEN = "" + + +class SeparatorStyle(Enum): + """Different separator style.""" + + SINGLE = auto() + TWO = auto() + PLAIN = auto() + LLAMA_2 = auto() + NVGPT = auto() + + +@dataclasses.dataclass +class Conversation: + """A class that keeps all conversation history.""" + + system: str + roles: List[str] + messages: List[List[str]] + offset: int + sep_style: SeparatorStyle = SeparatorStyle.SINGLE + sep: str = "###" + sep2: str = None + version: str = "Unknown" + skip_next: bool = False + + def get_prompt(self): + messages = self.messages + if len(messages) > 0 and type(messages[0][1]) is tuple: + messages = self.messages.copy() + init_role, init_msg = messages[0].copy() + init_msg = init_msg[0].replace("", "").strip() + if 'mmtag' in self.version: + messages[0] = (init_role, init_msg) + messages.insert(0, (self.roles[0], "")) + messages.insert(1, (self.roles[1], "Received.")) + else: + messages[0] = (init_role, "\n" + init_msg) + + if self.sep_style == SeparatorStyle.SINGLE: + ret = self.system + self.sep + for role, message in messages: + if message: + if type(message) is tuple: + message, _, _ = message + ret += role + ": " + message + self.sep + else: + ret += role + ":" + elif self.sep_style == SeparatorStyle.TWO: + seps = [self.sep, self.sep2] + ret = self.system + seps[0] + for i, (role, message) in enumerate(messages): + if message: + if type(message) is tuple: + message, _, _ = message + ret += role + ": " + message + seps[i % 2] + if i % 2 == 1 and i != len(messages) - 1: # Assistant end + ret += " " + else: + ret += role + ":" + elif self.sep_style == SeparatorStyle.LLAMA_2: + wrap_sys = lambda msg: f"<>\n{msg}\n<>\n\n" + wrap_inst = lambda msg: f"[INST] {msg} [/INST]" + ret = "" + + for i, (role, message) in enumerate(messages): + if i == 0: + assert message, "first message should not be none" + assert role == self.roles[0], "first message should come from user" + if message: + if type(message) is tuple: + message, _, _ = message + if i == 0: + message = wrap_sys(self.system) + message + if i % 2 == 0: + message = wrap_inst(message) + ret += self.sep + " " + message + else: + ret += " " + message + " " + self.sep2 + else: + ret += "" + ret = ret.lstrip(self.sep) + elif self.sep_style == SeparatorStyle.PLAIN: + seps = [self.sep, self.sep2] + ret = self.system + for i, (role, message) in enumerate(messages): + if message: + if type(message) is tuple: + message, _, _ = message + ret += message + seps[i % 2] + else: + ret += "" + elif self.sep_style == SeparatorStyle.NVGPT: + ret = self.sep2 + self.system + self.sep + for role, message in messages: + if message: + if type(message) is tuple: + message, _, _ = message + ret += role + '\n' + message + '\n' + self.sep + else: + ret += role + '\n' + else: + raise ValueError(f"Invalid style: {self.sep_style}") + + return ret + + def append_message(self, role, message): + self.messages.append([role, message]) + + def get_images(self, return_pil=False): + images = [] + for i, (role, msg) in enumerate(self.messages[self.offset :]): + if i % 2 == 0: + if type(msg) is tuple: + import base64 + from io import BytesIO + + from PIL import Image + + msg, image, image_process_mode = msg + if image_process_mode == "Pad": + + def expand2square(pil_img, background_color=(122, 116, 104)): + width, height = pil_img.size + if width == height: + return pil_img + elif width > height: + result = Image.new(pil_img.mode, (width, width), background_color) + result.paste(pil_img, (0, (width - height) // 2)) + return result + else: + result = Image.new(pil_img.mode, (height, height), background_color) + result.paste(pil_img, ((height - width) // 2, 0)) + return result + + image = expand2square(image) + elif image_process_mode == "Crop": + pass + elif image_process_mode == "Resize": + image = image.resize((336, 336)) + else: + raise ValueError(f"Invalid image_process_mode: {image_process_mode}") + max_hw, min_hw = max(image.size), min(image.size) + aspect_ratio = max_hw / min_hw + max_len, min_len = 800, 400 + shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw)) + longest_edge = int(shortest_edge * aspect_ratio) + W, H = image.size + if H > W: + H, W = longest_edge, shortest_edge + else: + H, W = shortest_edge, longest_edge + image = image.resize((W, H)) + if return_pil: + images.append(image) + else: + buffered = BytesIO() + image.save(buffered, format="JPEG") + img_b64_str = base64.b64encode(buffered.getvalue()).decode() + images.append(img_b64_str) + return images + + def to_gradio_chatbot(self): + ret = [] + for i, (role, msg) in enumerate(self.messages[self.offset :]): + if i % 2 == 0: + if type(msg) is tuple: + import base64 + from io import BytesIO + + msg, image, image_process_mode = msg + max_hw, min_hw = max(image.size), min(image.size) + aspect_ratio = max_hw / min_hw + max_len, min_len = 800, 400 + shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw)) + longest_edge = int(shortest_edge * aspect_ratio) + W, H = image.size + if H > W: + H, W = longest_edge, shortest_edge + else: + H, W = shortest_edge, longest_edge + image = image.resize((W, H)) + # image = image.resize((224, 224)) + buffered = BytesIO() + image.save(buffered, format="JPEG") + img_b64_str = base64.b64encode(buffered.getvalue()).decode() + img_str = f'user upload image' + msg = msg.replace('', img_str) + ret.append([msg, None]) + else: + ret[-1][-1] = msg + return ret + + def copy(self): + return Conversation( + system=self.system, + roles=self.roles, + messages=[[x, y] for x, y in self.messages], + offset=self.offset, + sep_style=self.sep_style, + sep=self.sep, + sep2=self.sep2, + version=self.version, + ) + + def dict(self): + if len(self.get_images()) > 0: + return { + "system": self.system, + "roles": self.roles, + "messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages], + "offset": self.offset, + "sep": self.sep, + "sep2": self.sep2, + } + return { + "system": self.system, + "roles": self.roles, + "messages": self.messages, + "offset": self.offset, + "sep": self.sep, + "sep2": self.sep2, + } + + +# Conversation Template for NVGPT +conv_nvgpt = Conversation( + system="""A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\n""", + roles=("User", "Assistant"), + version="nvgpt", + messages=(), + offset=0, + sep_style=SeparatorStyle.NVGPT, + sep=DEFAULT_SEPARATOR_TOKEN, + sep2=f"{DEFAULT_SYSTEM_TOKEN}System\n", +) + +conv_nv_dpo = Conversation( + system="\n", + roles=("User", "Assistant"), + version="nv_dpo", + messages=(), + offset=0, + sep_style=SeparatorStyle.NVGPT, + sep=DEFAULT_SEPARATOR_TOKEN, + sep2=f"{DEFAULT_SYSTEM_TOKEN}System\n", +) + +conv_vicuna_v0 = Conversation( + system="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("Human", "Assistant"), + messages=( + ("Human", "What are the key differences between renewable and non-renewable energy sources?"), + ( + "Assistant", + "Renewable energy sources are those that can be replenished naturally in a relatively " + "short amount of time, such as solar, wind, hydro, geothermal, and biomass. " + "Non-renewable energy sources, on the other hand, are finite and will eventually be " + "depleted, such as coal, oil, and natural gas. Here are some key differences between " + "renewable and non-renewable energy sources:\n" + "1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable " + "energy sources are finite and will eventually run out.\n" + "2. Environmental impact: Renewable energy sources have a much lower environmental impact " + "than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, " + "and other negative effects.\n" + "3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically " + "have lower operational costs than non-renewable sources.\n" + "4. Reliability: Renewable energy sources are often more reliable and can be used in more remote " + "locations than non-renewable sources.\n" + "5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different " + "situations and needs, while non-renewable sources are more rigid and inflexible.\n" + "6. Sustainability: Renewable energy sources are more sustainable over the long term, while " + "non-renewable sources are not, and their depletion can lead to economic and social instability.\n", + ), + ), + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +conv_vicuna_v1 = Conversation( + system="A chat between a curious user and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the user's questions.", + roles=("USER", "ASSISTANT"), + version="v1", + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2=DEFAULT_EOS_TOKEN, +) + +conv_llama_2 = Conversation( + system="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. + +If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.""", + roles=("USER", "ASSISTANT"), + version="llama_v2", + messages=(), + offset=0, + sep_style=SeparatorStyle.LLAMA_2, + sep=DEFAULT_BOS_TOKEN, + sep2=DEFAULT_EOS_TOKEN, +) + +conv_llava_llama_2 = Conversation( + system="You are a helpful language and vision assistant. " + "You are able to understand the visual content that the user provides, " + "and assist the user with a variety of tasks using natural language.", + roles=("USER", "ASSISTANT"), + version="llama_v2", + messages=(), + offset=0, + sep_style=SeparatorStyle.LLAMA_2, + sep=DEFAULT_BOS_TOKEN, + sep2=DEFAULT_EOS_TOKEN, +) + +conv_llava_plain = Conversation( + system="", roles=("", ""), messages=(), offset=0, sep_style=SeparatorStyle.PLAIN, sep="\n", +) + +conv_llava_v0 = Conversation( + system="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("Human", "Assistant"), + messages=(("Human", "Hi!"), ("Assistant", "Hi there! How can I help you today?")), + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +conv_llava_v0_mmtag = Conversation( + system="A chat between a curious user and an artificial intelligence assistant. " + "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language." + "The visual content will be provided with the following format: visual content.", + roles=("Human", "Assistant"), + messages=(), + offset=0, + sep_style=SeparatorStyle.SINGLE, + sep="###", + version="v0_mmtag", +) + +conv_llava_v1 = Conversation( + system="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("USER", "ASSISTANT"), + version="v1", + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2=DEFAULT_EOS_TOKEN, +) + +conv_llava_v1_mmtag = Conversation( + system="A chat between a curious user and an artificial intelligence assistant. " + "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language." + "The visual content will be provided with the following format: visual content.", + roles=("USER", "ASSISTANT"), + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2=DEFAULT_EOS_TOKEN, + version="v1_mmtag", +) + +default_conversation = conv_vicuna_v1 +conv_templates = { + "default": conv_vicuna_v0, + "v0": conv_vicuna_v0, + "v1": conv_vicuna_v1, + "vicuna_v1": conv_vicuna_v1, + "llama_2": conv_llama_2, + "plain": conv_llava_plain, + "v0_plain": conv_llava_plain, + "llava_v0": conv_llava_v0, + "v0_mmtag": conv_llava_v0_mmtag, + "llava_v1": conv_llava_v1, + "v1_mmtag": conv_llava_v1_mmtag, + "llava_llama_2": conv_llava_llama_2, + "nvgpt": conv_nvgpt, + "nv_steerlm": conv_nvgpt, + "nv_dpo": conv_nv_dpo, +} + + +if __name__ == "__main__": + print(default_conversation.get_prompt()) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/neva/neva_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/neva/neva_dataset.py new file mode 100644 index 0000000..15d755a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/neva/neva_dataset.py @@ -0,0 +1,861 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import copy +import json +import logging +import os +import re +import tarfile +from dataclasses import dataclass +from typing import Any, Dict, List, Sequence, Union + +import torch +import torch.nn.functional as F +import transformers +from einops import rearrange +from omegaconf import DictConfig +from PIL import Image +from torch.utils.data import Dataset, default_collate +from transformers import CLIPImageProcessor + +import nemo.collections.multimodal.data.neva.conversation as conversation_lib +from nemo.collections.multimodal.data.clip.augmentations.augmentations import image_transform +from nemo.collections.multimodal.data.neva.conversation import ( + DEFAULT_BOS_TOKEN, + DEFAULT_EOS_TOKEN, + DEFAULT_IM_END_TOKEN, + DEFAULT_IM_START_TOKEN, + DEFAULT_IMAGE_PATCH_TOKEN, + DEFAULT_IMAGE_TOKEN, + DEFAULT_LABELS_TOKEN, + DEFAULT_PAD_TOKEN, + DEFAULT_SEPARATOR_TOKEN, + DEFAULT_SYSTEM_TOKEN, + DEFAULT_UNK_TOKEN, +) +from nemo.collections.nlp.modules.common.megatron.utils import get_ltor_masks_and_position_ids + +MAX_NUM_IMAGES = 1 +IGNORE_INDEX = -1 + + +class TarOrFolderImageLoader: + """ + A class for loading images from a tar archive or a regular folder. + + This class provides functionality to open and read images from either a tar archive + (.tar file) or a standard directory with image files. It builds an index of images + if the source is a tar archive for efficient access. + + Attributes: + image_folder (str): The path to the tar archive or image folder. + tar_index (dict): A dictionary that maps file names to their tarfile member + objects if the image source is a tar archive. + + Methods: + __init__(self, image_folder): Initializes the loader with the specified image folder. + build_index(self): Builds an index of image file names and their corresponding + tarfile member objects for a tar archive. + open_image(self, file_name): Opens and returns an image by its file name. The image + is returned as an RGB PIL Image object. + """ + + def __init__(self, image_folder): + self.image_folder = image_folder + self.tar_index = {} + if self.image_folder.endswith('.tar'): + self.build_index() + + def build_index(self): + with tarfile.open(self.image_folder, 'r') as tar: + for member in tar.getmembers(): + self.tar_index[member.name] = member + + def open_image(self, file_name): + if self.image_folder.endswith('.tar'): + with tarfile.open(self.image_folder, 'r') as tar: + member = self.tar_index.get(file_name) + if member: + f = tar.extractfile(member) + return Image.open(f).convert('RGB') + else: + return Image.open(os.path.join(self.image_folder, file_name)).convert('RGB') + return None + + +def tokenize( + texts: Union[str, List[str]], tokenizer: Any, context_length: int, add_extra_token: int, +) -> torch.LongTensor: + """ + Returns the tokenized representation of given input string(s). If the list of tokens exceeds the context + length plus the number of extra tokens, it gets truncated. If it's smaller, it gets padded with zeros. + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize. + tokenizer : Any + A tokenizer to be used for tokenization. + context_length : int + The context length to be used for the output tensor. + add_extra_token : int + Number of extra tokens to add, should be either 0 or 1. + + Returns + ------- + torch.LongTensor + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length + add_extra_token]. + """ + assert add_extra_token == 0 or add_extra_token == 1, "`add_extra_token` should be either 0 or 1." + + texts_is_str = False + if isinstance(texts, str): + texts = [texts] + texts_is_str = True + tokens = tokenizer.text_to_ids(texts) + max_len = max([len(token) for token in tokens]) + context_length = min(max_len - add_extra_token, context_length) + # truncate and padding + result = torch.zeros(len(tokens), context_length + add_extra_token, dtype=torch.long) + + for i, token in enumerate(tokens): + if len(token) > context_length + add_extra_token: + token = token[: context_length + add_extra_token] # Truncate + result[i, : len(token)] = torch.tensor(token) + if texts_is_str: + result = result[0] + return result + + +def preprocess_multimodal(sources: dict, multimodal_cfg: dict, cur_token_len: int, use_plain: bool = False) -> Dict: + """ + Preprocesses multimodal sources based on the provided configuration. + + This function modifies the sources for multimodal data processing. It checks if the data is multimodal and + adjusts the token lengths accordingly. It also handles the start and end tokens for images and replaces + image tokens in conversations. + + Parameters: + - sources (dict): A dictionary containing the multimodal sources to be processed. + - multimodal_cfg (dict): A configuration dictionary specifying various options for multimodal processing. + It includes keys like 'is_multimodal', 'use_im_start_end', and 'sep_image_conv_front'. + - cur_token_len (int): The current length of tokens to be considered for image processing. + - use_plain (bool, optional): A boolean flag to use plain image token replacement without additional processing. + Defaults to False. + + Returns: + - dict: The processed sources dictionary after applying multimodal preprocessing steps. + """ + is_multimodal = multimodal_cfg['is_multimodal'] + image_token_len = cur_token_len + if not is_multimodal: + return sources + + if multimodal_cfg['use_im_start_end']: + replace_token = DEFAULT_IMAGE_PATCH_TOKEN * image_token_len + else: + replace_token = DEFAULT_IMAGE_PATCH_TOKEN * (image_token_len - 2) + replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN + + for source in sources: + conversation = source['conversations'] + if multimodal_cfg['sep_image_conv_front']: + assert DEFAULT_IMAGE_TOKEN in conversation[0]['value'] + conversation[0]['value'] = conversation[0]['value'].replace(DEFAULT_IMAGE_TOKEN, '').strip() + conversation[0]['value'] = ( + DEFAULT_IMAGE_TOKEN + + conversation_lib.default_conversation.sep + + conversation_lib.default_conversation.roles[0] + + ": " + + conversation[0]['value'] + ) + if use_plain: + assert DEFAULT_IMAGE_TOKEN in conversation[0]['value'] + conversation[0]['value'] = DEFAULT_IMAGE_TOKEN + for turn in conversation: + turn["value"] = turn["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token) + + return sources + + +def preprocess_llama_2(sources: dict, tokenizer, cfg,) -> Dict: + """ + Preprocesses sources for the LLaMA 2 model configuration. + + The function applies prompt templates and tokenizes the conversations according to the LLaMA 2 model specifications. + It involves special handling of tokens, masking of labels, and adjustments based on configuration settings. + + Parameters: + - sources (dict): A dictionary of sources containing conversations to be processed. + - tokenizer: The tokenizer to be used for processing the text. + - cfg: Configuration settings for preprocessing, including context length and additional tokens. + + Returns: + - Dict: A dictionary containing tokenized and labeled data suitable for the LLaMA 2 model. + This includes tokens, labels, and any special processing as defined in the configuration. + """ + conv = conversation_lib.conv_llava_llama_2.copy() + roles = {"human": conv.roles[0], "gpt": conv.roles[1]} + + # Apply prompt templates + conversations = [] + for i, source in enumerate(sources): + source = source['conversations'] + if roles[source[0]["from"]] != conv.roles[0]: + # Skip the first one if it is not from human + source = source[1:] + + conv.messages = [] + for j, sentence in enumerate(source): + role = roles[sentence["from"]] + assert role == conv.roles[j % 2], f"{i}" + conv.append_message(role, sentence["value"]) + conversations.append(conv.get_prompt()) + + add_extra_token = cfg.get("add_extra_token") + + # Tokenize conversations + tokens = tokenize( + texts=conversations, + tokenizer=tokenizer, + context_length=cfg.get("context_length"), + add_extra_token=add_extra_token, + ) + + # llama tricks + tokens[tokens == 32003] = 0 # DEFAULT_IMAGE_PATCH_TOKEN + tokens[tokens == 32006] = 1 # + tokens[tokens == 32007] = 2 # + labels = tokens.clone().detach() + + # Mask labels + sep = "[/INST] " + for conversation, target in zip(conversations, labels): + rounds = conversation.split(conv.sep2) + cur_len = 0 + for i, rou in enumerate(rounds): + + if rou == "": + break + + parts = rou.split(sep) + if len(parts) != 2: + break + parts[0] += sep + + round_len = len(tokenizer.text_to_ids(rou + conv.sep2)) + if i > 0: + round_len -= 1 # Remove extra token added by sp tokenizer + instruction_len = len(tokenizer.text_to_ids(parts[0])) - 2 + target[cur_len : cur_len + instruction_len] = IGNORE_INDEX + + cur_len += round_len + target[cur_len:] = IGNORE_INDEX + + # Check if masking working correctly + # print([x for x in zip(tokens[0].numpy().tolist(), labels[0].numpy().tolist())]) + + if add_extra_token: + tokens = tokens[:, :-1].contiguous() + labels = labels[:, 1:].contiguous() + else: + labels = torch.roll(labels, shifts=-1, dims=-1) + labels[:, -1] = IGNORE_INDEX + + return dict(tokens=tokens, labels=labels,) + + +def preprocess_v1(sources: dict, tokenizer, cfg,) -> Dict: + """ + Preprocesses sources for the Vicuna V1 model configuration. + + Similar to `preprocess_llama_2`, this function applies prompt templates and performs tokenization, but it is tailored + for the Vicuna V1 model. It includes specific handling for token translations, label masking, and tokenizer configuration. + + Parameters: + - sources (dict): A dictionary of sources containing conversations to be processed. + - tokenizer: The tokenizer to be used for processing the text. + - cfg: Configuration settings for preprocessing, which may include context length and additional tokens. + + Returns: + - Dict: A dictionary containing the processed data, including tokens and labels, formatted for the Vicuna V1 model. + """ + conv = conversation_lib.conv_vicuna_v1.copy() + roles = {"human": conv.roles[0], "gpt": conv.roles[1]} + + # Apply prompt templates + conversations = [] + for i, source in enumerate(sources): + source = source['conversations'] + if roles[source[0]["from"]] != conv.roles[0]: + # Skip the first one if it is not from human + source = source[1:] + + conv.messages = [] + for j, sentence in enumerate(source): + role = roles[sentence["from"]] + assert role == conv.roles[j % 2], f"{i}" + conv.append_message(role, sentence["value"]) + conversations.append(conv.get_prompt()) + + add_extra_token = cfg.get("add_extra_token") + # Tokenize conversations + tokens = tokenize( + texts=conversations, + tokenizer=tokenizer, + context_length=cfg.get("context_length"), + add_extra_token=add_extra_token, + ) + + # llama tricks + tokens[tokens == 32003] = 0 # DEFAULT_IMAGE_PATCH_TOKEN + tokens[tokens == 32006] = 1 # + tokens[tokens == 32007] = 2 # + labels = tokens.clone().detach() + + # Mask labels + sep = conv.sep + conv.roles[1] + ": " + for conversation, target in zip(conversations, labels): + + rounds = conversation.split(conv.sep2) + cur_len = 0 + for i, rou in enumerate(rounds): + if rou == "": + break + + parts = rou.split(sep) + if len(parts) != 2: + break + parts[0] += sep + + round_len = len(tokenizer.text_to_ids(rou + conv.sep2)) + instruction_len = len(tokenizer.text_to_ids(parts[0])) - 1 + if i > 0: + round_len -= 1 # Remove extra token added by sp tokenizer + instruction_len -= 1 + target[cur_len : cur_len + instruction_len] = IGNORE_INDEX + + cur_len += round_len + target[cur_len:] = IGNORE_INDEX + + if add_extra_token: + tokens = tokens[:, :-1].contiguous() + labels = labels[:, 1:].contiguous() + else: + labels = torch.roll(labels, shifts=-1, dims=-1) + labels[:, -1] = IGNORE_INDEX + + return dict(tokens=tokens, labels=labels,) + + +def preprocess_nvgpt(sources: dict, tokenizer, cfg,) -> Dict: + """ + Preprocess a given set of conversational sources using nvgpt conversation template + + This function processes conversations by first ensuring the conversation starts with a 'human' role, then tokenizes the conversations, applies specific token replacements, and finally masks labels for training purposes. + + Parameters: + - sources: A dictionary containing conversational data. Expected format is a dict of conversations, where each conversation is a list of messages, and each message is a dict with 'from' (role) and 'value' (message text). + - tokenizer: A tokenizer from the Hugging Face Transformers library used for tokenizing the conversations. + - cfg: Configuration settings which include 'add_extra_token' (bool) to determine if an extra token should be added to the tokenized output, and 'context_length' for specifying the tokenization context length. + + Returns: + - Dict: A dictionary containing two keys: + - 'tokens': A tensor of tokenized conversation data. + - 'labels': A tensor of labels for the conversation data, used for training models. Labels are masked based on the conversation structure. + + Note: + - The function includes specific token replacements (e.g., DEFAULT_IMAGE_PATCH_TOKEN, , ) and masking techniques for labels. + - It is designed to work with conversational data where messages alternate between a 'human' and a 'gpt' role. + - The function asserts that each message in a conversation alternates between the defined roles and skips messages not starting with the 'human' role. + """ + + """System\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\nUser\n{user input}\nAssistant\nquality:4,toxicity:0,humor:0,creativity:0,helpfulness:4,correctness:4,coherence:4,complexity:4,verbosity:4\n""" + + conv = conversation_lib.conv_nvgpt.copy() + + # Apply prompt templates + conversations = [] + for source in sources: + conv.messages = [] + conv.system = source.get('system', conv.system) + + strip_end_for_inference = False + for i, turn in enumerate(source['conversations']): + + if i % 2 == 1: + turn['from'] = conv.roles[1] + if 'label' not in turn: + turn[ + 'label' + ] = "quality:4,toxicity:0,humor:0,creativity:0,helpfulness:4,correctness:4,coherence:4,complexity:4,verbosity:4" + value = DEFAULT_LABELS_TOKEN + turn['label'] + '\n' + turn['value'] + conv.append_message(turn['from'], value) + if not turn["value"]: + strip_end_for_inference = ( + True # in inference, current turn is empty, thus end tokens need to striped. + ) + else: + turn['from'] = conv.roles[0] + conv.append_message(turn['from'], turn['value']) + context = conv.get_prompt() + if strip_end_for_inference: + context = context.rstrip("\n") + "\n" + conversations.append(context) + + add_extra_token = cfg.get("add_extra_token") + # Tokenize conversations + tokens = tokenize( + texts=conversations, + tokenizer=tokenizer, + context_length=cfg.get("context_length"), + add_extra_token=add_extra_token, + ) + + labels = tokens.clone().detach() + + # Mask targets + sep = conv.sep + conv.roles[1] + "\n" + labels_str_regexp = re.compile(f"{DEFAULT_LABELS_TOKEN}quality:.*\n") + for conversation, target in zip(conversations, labels): + rounds = conversation.split(conv.sep) + re_rounds = [conv.sep.join(rounds[:3])] # system + user + gpt + + for conv_idx in range(3, len(rounds), 2): + re_rounds.append(conv.sep.join(rounds[conv_idx : conv_idx + 2])) # user + gpt + + cur_len = 0 + for i, rou in enumerate(re_rounds): + if rou == "": + break + parts = rou.split(sep) + if len(parts) != 2: + break + + # Match the pattern + match = labels_str_regexp.search(parts[1]) + labels_str = match.group() if match else "" + + instruction_len = len(tokenizer.text_to_ids(parts[0] + sep + labels_str)) + round_len = len(tokenizer.text_to_ids(rou + conv.sep)) + target[cur_len : cur_len + instruction_len] = IGNORE_INDEX + + cur_len += round_len + target[cur_len:] = IGNORE_INDEX + + if add_extra_token: + tokens = tokens[:, :-1].contiguous() + labels = labels[:, 1:].contiguous() + else: + labels = torch.roll(labels, shifts=-1, dims=-1) + labels[:, -1] = IGNORE_INDEX + + return dict(tokens=tokens, labels=labels,) + + +def preprocess_nv_dpo(sources: dict, tokenizer, cfg,) -> Dict: + """ + Preprocess a given set of conversational sources using nvgpt conversation template + + This function processes conversations by first ensuring the conversation starts with a 'human' role, then tokenizes the conversations, applies specific token replacements, and finally masks labels for training purposes. + + Parameters: + - sources: A dictionary containing conversational data. Expected format is a dict of conversations, where each conversation is a list of messages, and each message is a dict with 'from' (role) and 'value' (message text). + - tokenizer: A tokenizer from the Hugging Face Transformers library used for tokenizing the conversations. + - cfg: Configuration settings which include 'add_extra_token' (bool) to determine if an extra token should be added to the tokenized output, and 'context_length' for specifying the tokenization context length. + + Returns: + - Dict: A dictionary containing two keys: + - 'tokens': A tensor of tokenized conversation data. + - 'labels': A tensor of labels for the conversation data, used for training models. Labels are masked based on the conversation structure. + + Note: + - The function includes specific token replacements (e.g., DEFAULT_IMAGE_PATCH_TOKEN, , ) and masking techniques for labels. + - It is designed to work with conversational data where messages alternate between a 'human' and a 'gpt' role. + - The function asserts that each message in a conversation alternates between the defined roles and skips messages not starting with the 'human' role. + """ + + """System\n\nUser\n{user input}\nAssistant\n""" + + conv = conversation_lib.conv_nv_dpo.copy() + + # Apply prompt templates + conversations = [] + for source in sources: + conv.messages = [] + conv.system = source.get('system', conv.system) + + strip_end_for_inference = False + for i, turn in enumerate(source['conversations']): + + if i % 2 == 1: + turn['from'] = conv.roles[1] + conv.append_message(turn['from'], turn['value']) + if not turn["value"]: + strip_end_for_inference = ( + True # in inference, current turn is empty, thus end tokens need to striped. + ) + else: + turn['from'] = conv.roles[0] + conv.append_message(turn['from'], turn['value']) + context = conv.get_prompt() + if strip_end_for_inference: + if context.endswith("\n"): + context = context[: -len("\n")] + "\n" + conversations.append(context) + + add_extra_token = cfg.get("add_extra_token") + # Tokenize conversations + tokens = tokenize( + texts=conversations, + tokenizer=tokenizer, + context_length=cfg.get("context_length"), + add_extra_token=add_extra_token, + ) + + labels = tokens.clone().detach() + + # Mask targets + sep = conv.sep + conv.roles[1] + "\n" + for conversation, target in zip(conversations, labels): + rounds = conversation.split(conv.sep) + re_rounds = [conv.sep.join(rounds[:3])] # system + user + gpt + + for conv_idx in range(3, len(rounds), 2): + re_rounds.append(conv.sep.join(rounds[conv_idx : conv_idx + 2])) # user + gpt + + cur_len = 0 + for i, rou in enumerate(re_rounds): + if rou == "": + break + parts = rou.split(sep) + if len(parts) != 2: + break + + instruction_len = len(tokenizer.text_to_ids(parts[0] + sep)) + round_len = len(tokenizer.text_to_ids(rou + conv.sep)) + target[cur_len : cur_len + instruction_len] = IGNORE_INDEX + + cur_len += round_len + target[cur_len:] = IGNORE_INDEX + + # Check if masking working correctly + # print([x for x in zip(tokens[0].numpy().tolist(), labels[0].numpy().tolist())]) + + if add_extra_token: + tokens = tokens[:, :-1].contiguous() + labels = labels[:, 1:].contiguous() + else: + labels = torch.roll(labels, shifts=-1, dims=-1) + labels[:, -1] = IGNORE_INDEX + + return dict(tokens=tokens, labels=labels,) + + +def preprocess_plain(sources, tokenizer, cfg,) -> Dict: + """ + Preprocesses plain text sources (no template) for tokenization and label generation. + + This function concatenates conversations with an end signal, tokenizes them, and prepares labels for training. + It handles sources with a specific structure (expecting two elements in 'conversations') and includes the + option to add an extra token as specified in the configuration. The function also applies masking to the labels. + + Parameters: + - sources: A list of source dictionaries. Each source dictionary should have a key 'conversations' + containing a list of conversation parts. + - tokenizer: The tokenizer to be used for converting text to tokens. + - cfg: Configuration dictionary which may include 'context_length' and 'add_extra_token' settings. + + Returns: + - Dict: A dictionary containing tokenized data and corresponding labels. This includes 'tokens' which are the + tokenized representations of the conversations, and 'labels' which are used for training the model. The labels + have specific indices masked with IGNORE_INDEX as per the preprocessing logic. + """ + # add end signal and concatenate together + conversations = [] + for source in sources: + source = source['conversations'] + assert len(source) == 2 + # This line is different from LLaVA repo, we inserted '\n' after . + conversation = source[0]['value'] + source[1]['value'] + '\n' + conversations.append(conversation) + # tokenize conversations + add_extra_token = cfg.get("add_extra_token") + tokens = tokenize( + texts=conversations, + tokenizer=tokenizer, + context_length=cfg.get("context_length"), + add_extra_token=add_extra_token, + ) + labels = tokens.clone().detach() + for target, source in zip(labels, sources): + source = source['conversations'] + tokenized_len = len(tokenizer.text_to_ids(source[0]['value'])) + target[:tokenized_len] = IGNORE_INDEX + + if add_extra_token: + tokens = tokens[:, :-1].contiguous() + labels = labels[:, 1:].contiguous() + else: + labels = torch.roll(labels, shifts=-1, dims=-1) + labels[:, -1] = IGNORE_INDEX + + return dict(tokens=tokens, labels=labels,) + + +class LazySupervisedDataset(Dataset): + """Dataset for supervised fine-tuning.""" + + def __init__(self, data_path: str, tokenizer, multimodal_cfg: dict): + super(LazySupervisedDataset, self).__init__() + logging.warning("Loading data...") + if data_path is not None: + logging.warning("Loading data...") + with open(data_path, "r") as file: + list_data_dict = json.load(file) + else: + list_data_dict = [] + + logging.warning("Formatting inputs...Skip in lazy mode") + self.tokenizer = tokenizer + self.list_data_dict = list_data_dict + self.multimodal_cfg = multimodal_cfg + self.conv_template = multimodal_cfg["conv_template"] + self.image_folder = multimodal_cfg['image_folder'] + self.processor = multimodal_cfg["image_processor"] + + self.image_loader = TarOrFolderImageLoader(self.image_folder) + + def __len__(self): + return len(self.list_data_dict) + + def __getitem__(self, i) -> Dict[str, torch.Tensor]: + sources = self.list_data_dict[i] + if isinstance(i, int): + sources = [sources] + assert len(sources) == 1, "Don't know why it is wrapped to a list" # FIXME + if 'image' in sources[0]: + if not isinstance(self.list_data_dict[i]['image'], list): + self.list_data_dict[i]['image'] = [self.list_data_dict[i]['image']] + + images = [] + for image_file in self.list_data_dict[i]['image']: + image = self.image_loader.open_image(image_file) + if image is None: + logging.warning(f"Image {image_file} could not be found!") + if isinstance(self.processor, CLIPImageProcessor): + # image processor from HF + if self.multimodal_cfg['image_aspect_ratio'] == 'keep': + max_hw, min_hw = max(image.size), min(image.size) + aspect_ratio = max_hw / min_hw + max_len, min_len = 448, 224 + shortest_edge = int(min(max_len / aspect_ratio, min_len)) + image = self.processor.preprocess( + image, return_tensors='pt', do_center_crop=False, size={"shortest_edge": shortest_edge} + )['pixel_values'][0] + elif self.multimodal_cfg['image_aspect_ratio'] == 'pad': + + def expand2square(pil_img, background_color): + width, height = pil_img.size + if width == height: + return pil_img + elif width > height: + result = Image.new(pil_img.mode, (width, width), background_color) + result.paste(pil_img, (0, (width - height) // 2)) + return result + else: + result = Image.new(pil_img.mode, (height, height), background_color) + result.paste(pil_img, ((height - width) // 2, 0)) + return result + + image = expand2square(image, tuple(int(x * 255) for x in self.processor.image_mean)) + image = self.processor.preprocess(image, return_tensors='pt')['pixel_values'][0] + else: + image = self.processor.preprocess(image, return_tensors='pt')['pixel_values'][0] + else: + assert ( + self.multimodal_cfg['image_aspect_ratio'] == 'square' + ), 'NeMo image transform with setting `image_aspect_ratio` to `square`.' + image = self.processor(image) + images.append(image) + images_tensors = torch.tensor([]) + if images: + images_tensors = torch.stack(images) + cur_token_len = (images_tensors[0].shape[1] // 14) * ( + images_tensors[0].shape[2] // 14 + ) # FIXME: 14 is hardcoded patch size + sources = preprocess_multimodal( + copy.deepcopy(sources), + self.multimodal_cfg, + cur_token_len, + use_plain=(self.conv_template == "plain"), + ) + else: + images_tensors = torch.tensor([]) + sources = copy.deepcopy(sources) + + if self.conv_template in ["nvgpt", "nv_steerlm"]: + data_dict = preprocess_nvgpt(sources, self.tokenizer, self.multimodal_cfg,) + elif self.conv_template == "nv_dpo": + data_dict = preprocess_nv_dpo(sources, self.tokenizer, self.multimodal_cfg,) + elif self.conv_template == "v1": + data_dict = preprocess_v1(sources, self.tokenizer, self.multimodal_cfg,) + elif self.conv_template == "llama_2": + data_dict = preprocess_llama_2(sources, self.tokenizer, self.multimodal_cfg,) + elif self.conv_template == "plain": + data_dict = preprocess_plain(sources, self.tokenizer, self.multimodal_cfg,) + else: + raise ValueError(f"Conversation template `{self.conv_template}` is not supported in Neva now.") + + if isinstance(i, int): + data_dict = dict(tokens=data_dict["tokens"][0], labels=data_dict["labels"][0]) + + # image exist in the data + if self.multimodal_cfg['is_multimodal']: + if isinstance(self.processor, CLIPImageProcessor): + crop_size = [self.processor.crop_size['height'], self.processor.crop_size['width']] + else: + crop_size = self.multimodal_cfg['crop_size'] + # image does not exist in the data, but the model is multimodal + zero_padding = torch.zeros( + (MAX_NUM_IMAGES - len(images_tensors), 3, crop_size[0], crop_size[1]), dtype=torch.float + ) + images_tensors = torch.cat((images_tensors, zero_padding), dim=0) + data_dict['image'] = images_tensors + return data_dict + + +class NevaDataset(LazySupervisedDataset): + """Dataset for supervised fine-tuning.""" + + def __init__(self, data_path: str, tokenizer, multimodal_cfg: dict): + + if data_path.endswith(".json"): + super(NevaDataset, self).__init__(data_path, tokenizer, multimodal_cfg) + + elif data_path.endswith(".jsonl"): + super(NevaDataset, self).__init__(None, tokenizer, multimodal_cfg) + logging.warning("Loading image inputs from SteerLM Dataset") + image_folder = multimodal_cfg['image_folder'] + for line in open(data_path, "r"): + record = json.loads(line) + + # This currently supports only a single image + # search for tag + + record['image'] = [] + for turn in record['conversations']: + matches = re.finditer('', DEFAULT_IMAGE_TOKEN, turn['value']) + + self.list_data_dict.append(record) + + else: + raise ValueError(f"Formatting of {data_path} is not supported in Neva.") + + +@dataclass +class DataCollatorForSupervisedDataset(object): + """Collate examples for supervised fine-tuning.""" + + model_cfg: DictConfig + tokenizer: transformers.PreTrainedTokenizer + + def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: + max_len = max(instance['tokens'].shape[0] for instance in instances) + max_len = (max_len - 1) // 4 * 4 + 4 + for instance in instances: + pad_len = max_len - instance['tokens'].shape[0] + instance['tokens'] = F.pad(instance['tokens'], (0, pad_len), 'constant', 0) + instance['labels'] = F.pad(instance['labels'], (0, pad_len), 'constant', -1) + + batch = default_collate(instances) + tokenizer = self.tokenizer + model_cfg = self.model_cfg + + tokens = batch['tokens'] + labels = batch['labels'] + media = batch.get('image') + + attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids( + data=tokens, + eod_token=tokenizer.eos_id, + eod_mask_loss=model_cfg.data.get("eod_mask_loss", False), + reset_attention_mask=False, + reset_position_ids=False, + ) + + loss_mask[labels == -1] = 0.0 + tokens[tokens == -1] = 0 + labels[labels == -1] = 0 + + if media is None: + raise NotImplementedError + else: + media = rearrange(media, "b T c h w -> b T 1 c h w") + + batch = { + 'tokens': tokens, + 'labels': labels, + 'attention_mask': attention_mask, + 'loss_mask': loss_mask, + 'position_ids': position_ids, + 'media': media, + } + return batch + + +def make_supervised_data_module(tokenizer, model_cfg) -> Dict: + """Make dataset and collator for supervised fine-tuning.""" + data_cfg = model_cfg.data + mm_cfg = model_cfg.mm_cfg + add_extra_token = 1 + if getattr(model_cfg, 'no_seqlen_plus_one_input_tokens', False): + add_extra_token = 0 + crop_size = data_cfg.get("crop_size", (224, 224)) + if mm_cfg.vision_encoder.from_hf: + image_processor = CLIPImageProcessor.from_pretrained( + mm_cfg.vision_encoder.from_pretrained, torch_dtype=torch.bfloat16 + ) + else: + # TODO(yuya): Fix this hard-code for our own CLIP + image_processor = image_transform(crop_size, is_train=False, mean=None, std=None,) + + train_dataset = NevaDataset( + tokenizer=tokenizer, + data_path=data_cfg.data_path, + multimodal_cfg=dict( + is_multimodal=data_cfg.is_multimodal, + sep_image_conv_front=data_cfg.sep_image_conv_front, + conv_template=data_cfg.get("conv_template", "nvgpt"), + crop_size=crop_size, + image_token_len=data_cfg.image_token_len, + image_folder=data_cfg.image_folder, + image_aspect_ratio=data_cfg.image_aspect_ratio, + use_im_start_end=getattr(model_cfg.mm_cfg, 'use_im_start_end', False), + image_processor=image_processor, + add_extra_token=add_extra_token, + context_length=model_cfg.encoder_seq_length, + ), + ) + + return dict(train_dataset=train_dataset, eval_dataset=train_dataset) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nsfw/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nsfw/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nsfw/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nsfw/nsfw_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nsfw/nsfw_dataset.py new file mode 100644 index 0000000..19ac9c6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/nsfw/nsfw_dataset.py @@ -0,0 +1,74 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import pathlib +from typing import Callable, List, Optional, Tuple + +import torch +from omegaconf.dictconfig import DictConfig +from PIL import Image + +from nemo.collections.multimodal.data.clip.augmentations.augmentations import image_transform + + +class DirectoryBasedDataset(torch.utils.data.Dataset): + """ + A custom dataset class for loading images from a directory structure. + This class inherits from torch.utils.data.Dataset. + """ + + def __init__(self, path: str, transform: Optional[Callable] = None): + super(DirectoryBasedDataset, self).__init__() + + self._transform = transform + self._samples = self._get_files(path, "nsfw", 1) + self._get_files(path, "safe", 0) + + def __getitem__(self, index: int) -> Tuple[torch.Tensor, int]: + if index >= len(self): + raise IndexError(f"Index {index} ot of bound {len(self)}") + + sample_path, category = self._samples[index] + + image = Image.open(sample_path) + + if self._transform is not None: + image = self._transform(image) + + return image, category + + def __len__(self) -> int: + return len(self._samples) + + def _get_files(self, path: str, subdir: str, category: int) -> List[Tuple[str, int]]: + globpath = pathlib.Path(path) / subdir + return [(x, category) for x in globpath.glob("*.*")] + + +def build_dataset(model_cfg: DictConfig, consumed_samples: int, is_train: bool): + """ + Builds and returns a DirectoryBasedDataset instance. + """ + img_fn = image_transform( + (model_cfg.vision.img_h, model_cfg.vision.img_w), + is_train=False, + mean=model_cfg.vision.image_mean, + std=model_cfg.vision.image_std, + resize_longest_max=True, + ) + + if is_train: + path = model_cfg.data.train.dataset_path + else: + path = model_cfg.data.validation.dataset_path + + return DirectoryBasedDataset(path, transform=img_fn) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/stable_diffusion/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/stable_diffusion/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/stable_diffusion/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/stable_diffusion/augmentation/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/stable_diffusion/augmentation/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/stable_diffusion/augmentation/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/stable_diffusion/augmentation/augmentations.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/stable_diffusion/augmentation/augmentations.py new file mode 100644 index 0000000..9c8e049 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/stable_diffusion/augmentation/augmentations.py @@ -0,0 +1,77 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +try: + import torchvision.transforms as transforms + + TORCHVISION_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + TORCHVISION_AVAILABLE = False +import numpy as np +import torch + + +def construct_clip_augmentations(n_px=224): + def _convert_image_to_rgb(image): + return image.convert("RGB") + + assert TORCHVISION_AVAILABLE, "Torchvision imports failed but they are required." + return transforms.Compose( + [ + transforms.Resize(n_px, interpolation=transforms.InterpolationMode.BICUBIC), + transforms.CenterCrop(n_px), + _convert_image_to_rgb, + transforms.ToTensor(), + transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), + ] + ) + + +def construct_image_augmentations(augmentation_dict, normalize=True): + train_img_transform = [] + for aug in augmentation_dict: + if aug == 'resize_smallest_side': + img_size = int(augmentation_dict[aug]) + train_img_transform.append( + transforms.Resize(img_size, interpolation=transforms.InterpolationMode.BICUBIC, antialias=True) + ) + + elif aug == 'center_crop_h_w': + img_w, img_h = augmentation_dict[aug].split(',') + img_w = int(img_w) + img_h = int(img_h) + train_img_transform.append(transforms.CenterCrop((img_w, img_h))) + + elif aug == 'random_crop_h_w': + img_w, img_h = augmentation_dict[aug].split(',') + img_w = int(img_w) + img_h = int(img_h) + train_img_transform.append(transforms.RandomCrop((img_w, img_h))) + + elif aug == 'horizontal_flip': + enabled = augmentation_dict[aug] + if enabled: + train_img_transform.append(transforms.RandomHorizontalFlip(p=0.5)) + else: + raise ValueError('Augmentation not supported') + + # Always need to convert data to tensor + train_img_transform.append(transforms.ToTensor()) + if normalize: + train_img_transform.append(transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))) + train_img_transform = transforms.Compose(train_img_transform) + return train_img_transform + + +def identical_transform(x): + return x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/stable_diffusion/stable_diffusion_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/stable_diffusion/stable_diffusion_dataset.py new file mode 100644 index 0000000..61f1f9c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/data/stable_diffusion/stable_diffusion_dataset.py @@ -0,0 +1,416 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from functools import partial + +import numpy as np +import torch +import torchvision.transforms as TT + +from nemo.collections.multimodal.data.common.webdataset import WebDatasetCommon +from nemo.collections.multimodal.data.stable_diffusion.augmentation.augmentations import ( + construct_image_augmentations, + identical_transform, +) +from nemo.core.classes import Dataset as NeMoDataset +from nemo.utils import logging + + +class SDSyntheticDataset(NeMoDataset): + def __init__( + self, image_H, image_W, fake_len=100000, image_key='images', txt_key='txt', seq_len=80, context_dim=768 + ): + super().__init__() + self.fake_len = fake_len + self.H = image_H + self.W = image_W + self.image_key = image_key + self.txt_key = txt_key + img_precached = image_key.endswith('encoded') or image_key.endswith('moments') + txt_precached = txt_key.endswith('encoded') + assert ( + img_precached == txt_precached + ), 'First and second stage keys should enable/disable precache at the same time.' + self.seq_len = seq_len + self.context_dim = context_dim + + def __getitem__(self, index): + item = {} + if self.image_key.endswith('encoded'): + item[self.image_key] = torch.randn(8, self.H // 8, self.W // 8) + item[self.txt_key] = torch.randn(self.seq_len, self.context_dim) + elif self.image_key.endswith('moments'): + item[self.image_key] = torch.randn(1, 8, self.H // 8, self.W // 8) + item[self.txt_key] = torch.randn(self.seq_len, self.context_dim) + else: + item[self.image_key] = torch.randn(self.H, self.W, 3) + item[self.txt_key] = f'This is meaningless fake text No.{index}' + + return item + + def __len__(self): + return self.fake_len + + +def build_train_valid_datasets( + model_cfg, consumed_samples, +): + data_cfg = model_cfg.data + + def build_resolution_filter(value=None, method='larger'): + assert method == 'larger' or method == 'smaller' + if method == 'larger': + logging.info(f'Only Selecting images with resolution >= {value}') + return lambda x: x['jpg'].size[0] >= value and x['jpg'].size[1] >= value + logging.info(f'Only Selecting images with resolution <= {value}') + return lambda x: x['jpg'].size[0] <= value and x['jpg'].size[1] <= value + + # This function maps data that are tuples to dictionary. + def tuple_to_dict(inp): + for input in inp: + out_dict = dict() + out_dict[model_cfg.first_stage_key] = input[0].permute(1, 2, 0) + out_dict[model_cfg.cond_stage_key] = input[1] + yield out_dict + + def transform_fn(sample): + image, text = sample["jpg"], sample["txt"] + # TODO : If no agumentations just return the image ? + img_transform = construct_image_augmentations(data_cfg.train.get("augmentations", None)) + text_transform = identical_transform + return img_transform(image), text_transform(text) + + if data_cfg.get('synthetic_data', False): + H, W = data_cfg.train.augmentations.center_crop_h_w.split(',') + train_data = SDSyntheticDataset( + int(H), + int(W), + image_key=model_cfg.first_stage_key, + txt_key=model_cfg.cond_stage_key, + context_dim=model_cfg.unet_config.context_dim, + fake_len=data_cfg.synthetic_data_length, + ) + + else: + filter_cfg = data_cfg.train.get('filterings', None) + filter_fn = build_resolution_filter(**filter_cfg.resolution) if filter_cfg else None + train_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=tuple_to_dict, + filter_fn=filter_fn, + is_train=True, + ) + + val_data = None + if data_cfg.get("validation") is not None and data_cfg.validation.get("data_path"): + if data_cfg.get('synthetic_data', False): + val_data = SDSyntheticDataset( + int(H), + int(W), + image_key=model_cfg.first_stage_key, + txt_key=model_cfg.cond_stage_key, + context_dim=model_cfg.unet_config.context_dim, + ) + else: + val_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=tuple_to_dict, + filter_fn=filter_fn, + is_train=False, + ) + + return train_data, val_data + + +def build_train_valid_precached_datasets( + model_cfg, consumed_samples, +): + data_cfg = model_cfg.data + has_stage_key = model_cfg.get('first_stage_key', False) + + # This function maps data that are tuples to dictionary. + def tuple_to_dict(inp): + for input in inp: + out_dict = dict() + if has_stage_key: + out_dict[model_cfg.first_stage_key] = torch.tensor(input['autoencoderkl_image']) + out_dict[model_cfg.cond_stage_key] = torch.tensor(input['clip-vit-large-patch14_text']) + else: + out_dict = input + yield out_dict + + def transform_fn(sample): + return sample['pickle'] + + if data_cfg.get('synthetic_data', False): + H, W = data_cfg.train.augmentations.center_crop_h_w.split(',') + train_data = SDSyntheticDataset( + int(H), + int(W), + image_key=model_cfg.first_stage_key, + txt_key=model_cfg.cond_stage_key, + context_dim=model_cfg.unet_config.context_dim, + ) + else: + train_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=tuple_to_dict, + is_train=True, + ) + + val_data = None + if data_cfg.get("validation") is not None and data_cfg.validation.get("data_path"): + if data_cfg.get('synthetic_data', False): + H, W = data_cfg.train.augmentations.center_crop_h_w.split(',') + val_data = SDSyntheticDataset( + int(H), + int(W), + image_key=model_cfg.first_stage_key, + txt_key=model_cfg.cond_stage_key, + context_dim=model_cfg.unet_config.context_dim, + ) + else: + val_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=tuple_to_dict, + is_train=False, + ) + + return train_data, val_data + + +def build_train_valid_precached_clip_datasets(model_cfg, consumed_samples): + data_cfg = model_cfg.data + + # This function maps data that are tuples to dictionary. + def tuple_to_dict(inp): + for input in inp: + out_dict = dict() + out_dict[model_cfg.first_stage_key] = input[0] + out_dict[model_cfg.cond_stage_key] = input[1] + yield out_dict + + def transform_fn(sample): + latents, text_embed = sample["pyd"]["image_embed"], sample["pyd"]['captions_embed'] + latents = torch.from_numpy(latents) + text_embed = torch.from_numpy(text_embed) + + # latents are of shape ([4, 64, 64]) + return latents, text_embed + + if data_cfg.get('synthetic_data', False): + H, W = data_cfg.train.augmentations.center_crop_h_w.split(',') + train_data = SDSyntheticDataset( + int(H), + int(W), + image_key=model_cfg.first_stage_key, + txt_key=model_cfg.cond_stage_key, + context_dim=model_cfg.unet_config.context_dim, + seq_len=77, + ) + else: + train_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=tuple_to_dict, + is_train=True, + ) + + val_data = None + if data_cfg.get("validation") is not None and data_cfg.validation.get("data_path"): + if data_cfg.get('synthetic_data', False): + H, W = data_cfg.train.augmentations.center_crop_h_w.split(',') + val_data = SDSyntheticDataset( + int(H), + int(W), + image_key=model_cfg.first_stage_key, + txt_key=model_cfg.cond_stage_key, + context_dim=model_cfg.unet_config.context_dim, + seq_len=77, + ) + else: + val_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=tuple_to_dict, + is_train=False, + ) + + return train_data, val_data + + +def build_sdxl_train_valid_datasets( + model_cfg, consumed_samples, +): + data_cfg = model_cfg.data + + def build_resolution_filter(value=None, method='larger'): + assert method == 'larger' or method == 'smaller' + if method == 'larger': + print(f'Only Selecting images with resolution >= {value}') + return lambda x: x['jpg'].size[0] >= value and x['jpg'].size[1] >= value + print(f'Only Selecting images with resolution <= {value}') + return lambda x: x['jpg'].size[0] <= value and x['jpg'].size[1] <= value + + # This function maps data that are tuples to dictionary. + def tuple_to_dict(inp): + for input in inp: + out_dict = dict() + out_dict['images'] = input[0].permute(1, 2, 0) + out_dict['captions'] = input[1] + yield out_dict + + def AddOriginalImageSizeAsTupleAndCropToSquare(inp): + for input in inp: + out_dict = dict() + out_dict['images'] = input[0] + out_dict['captions'] = input[1] + h, w = out_dict['images'].shape[1], out_dict['images'].shape[2] + out_dict['original_size_as_tuple'] = torch.tensor([h, w]) + size = min(h, w) + out_dict['target_size_as_tuple'] = torch.tensor([size, size]) + delta_h = h - size + delta_w = w - size + assert not all( + [delta_h, delta_w] + ) # we assume that the image is already resized such that the smallest size is at the desired size. Thus, eiter delta_h or delta_w must be zero + top = np.random.randint(0, delta_h + 1) + left = np.random.randint(0, delta_w + 1) + out_dict['images'] = TT.functional.crop( + out_dict['images'], top=top, left=left, height=size, width=size + ).permute(1, 2, 0) + out_dict["crop_coords_top_left"] = torch.tensor([top, left]) + yield out_dict + + def transform_fn(sample): + image, text = sample["jpg"], sample["txt"] + # TODO : If no agumentations just return the image ? + img_transform = construct_image_augmentations(data_cfg.train.get("augmentations", None)) + text_transform = identical_transform + return img_transform(image), text_transform(text) + + if 'center_crop_h_w' in data_cfg.train.get("augmentations", None): + print( + 'Training with center cropping, image size and crop coordinates will not be used as extra conditions during training' + ) + compose_fn = tuple_to_dict + else: + compose_fn = AddOriginalImageSizeAsTupleAndCropToSquare + + filter_cfg = data_cfg.train.get('filterings', None) + filter_fn = build_resolution_filter(**filter_cfg.resolution) if filter_cfg else None + train_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=compose_fn, + filter_fn=filter_fn, + is_train=True, + ) + + val_data = None + if data_cfg.get("validation") is not None and data_cfg.validation.get("data_path"): + val_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=tuple_to_dict, + filter_fn=filter_fn, + is_train=False, + ) + + return train_data, val_data + + +def build_sdxl_precached_text_train_valid_datasets( + model_cfg, consumed_samples, +): + data_cfg = model_cfg.data + + def build_resolution_filter(value=None, method='larger'): + assert method == 'larger' or method == 'smaller' + if method == 'larger': + print(f'Only Selecting images with resolution >= {value}') + return lambda x: x['jpg'].size[0] >= value and x['jpg'].size[1] >= value + print(f'Only Selecting images with resolution <= {value}') + return lambda x: x['jpg'].size[0] <= value and x['jpg'].size[1] <= value + + # This function maps data that are tuples to dictionary. + def tuple_to_dict(inp): + for input in inp: + out_dict = dict() + out_dict['images'] = input[0].permute(1, 2, 0) + out_dict['captions'] = input[1] + yield out_dict + + def AddOriginalImageSizeAsTupleAndCropToSquare(inp): + for input in inp: + out_dict = dict() + out_dict['images'] = input[0] + out_dict.update(input[1]) + out_dict['captions'] = 'fake caption' + h, w = out_dict['images'].shape[1], out_dict['images'].shape[2] + out_dict['original_size_as_tuple'] = torch.tensor([h, w]) + size = min(h, w) + out_dict['target_size_as_tuple'] = torch.tensor([size, size]) + delta_h = h - size + delta_w = w - size + assert not all( + [delta_h, delta_w] + ) # we assume that the image is already resized such that the smallest size is at the desired size. Thus, eiter delta_h or delta_w must be zero + top = np.random.randint(0, delta_h + 1) + left = np.random.randint(0, delta_w + 1) + out_dict['images'] = TT.functional.crop( + out_dict['images'], top=top, left=left, height=size, width=size + ).permute(1, 2, 0) + out_dict["crop_coords_top_left"] = torch.tensor([top, left]) + yield out_dict + + def transform_fn(sample): + image, pickle = sample["png"], sample["pickle"] + img_transform = construct_image_augmentations(data_cfg.train.get("augmentations", None)) + return img_transform(image), pickle + + filter_cfg = data_cfg.train.get('filterings', None) + filter_fn = build_resolution_filter(**filter_cfg.resolution) if filter_cfg else None + train_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=AddOriginalImageSizeAsTupleAndCropToSquare, + filter_fn=filter_fn, + is_train=True, + ) + + val_data = None + if data_cfg.get("validation") is not None and data_cfg.validation.get("data_path"): + val_data = WebDatasetCommon( + dataset_cfg=data_cfg, + consumed_samples=consumed_samples, + map_fn=transform_fn, + compose_fn=tuple_to_dict, + filter_fn=filter_fn, + is_train=False, + ) + + return train_data, val_data diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/losses/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/losses/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/losses/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/losses/clip_loss.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/losses/clip_loss.py new file mode 100644 index 0000000..694f29a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/losses/clip_loss.py @@ -0,0 +1,160 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.distributed.nn +import torch.nn as nn +from torch import distributed as dist +from torch.nn import functional as F + +from nemo.collections.nlp.modules.common.megatron.utils import average_losses_across_data_parallel_group + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +def gather_features( + image_features, text_features, local_loss=False, gather_with_grad=False, +): + """ + Gathers image and text features across multiple data parallel processes. + + This function is designed to work in a distributed environment where multiple + processes are handling different portions of data. It gathers the image and text + features from all processes to form a complete set of features across the entire dataset. + This is crucial for calculating loss in models like CLIP, especially when the model is + trained in a data parallel fashion. + + Parameters: + image_features (Tensor): A tensor containing the image features. + text_features (Tensor): A tensor containing the text features. + local_loss (bool, optional): A flag to determine whether to use local loss calculation. + Defaults to False. + gather_with_grad (bool, optional): A flag to enable gathering with gradient computation. + This is not currently working in the latest PyTorch version. + Defaults to False. + + Returns: + Tuple[Tensor, Tensor]: A tuple containing the gathered image features and text features + across all processes. + """ + data_parallel_world_size = parallel_state.get_data_parallel_world_size() + data_parallel_rank = parallel_state.get_data_parallel_rank() + data_parallel_group = parallel_state.get_data_parallel_group() + + if gather_with_grad: + # TODO (yuya): this is not working in current version of pytorch + # https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/loss.py#L48 + all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0) + all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0) + + else: + gathered_image_features = [torch.zeros_like(image_features) for _ in range(data_parallel_world_size)] + gathered_text_features = [torch.zeros_like(text_features) for _ in range(data_parallel_world_size)] + dist.all_gather(gathered_image_features, image_features, group=data_parallel_group) + dist.all_gather(gathered_text_features, text_features, group=data_parallel_group) + # TODO (yuya): check what's this + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + # https://amsword.medium.com/gradient-backpropagation-with-torch-distributed-all-gather-9f3941a381f8 + gathered_image_features[data_parallel_rank] = image_features + gathered_text_features[data_parallel_rank] = text_features + all_image_features = torch.cat(gathered_image_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + + return all_image_features, all_text_features + + +class ClipLoss(nn.Module): + """ + A custom loss module for CLIP (Contrastive Language–Image Pretraining) training. + + This module is specifically designed for calculating the loss in CLIP model training, + supporting features like local loss calculation, gradient gathering, and label caching + for efficiency in a distributed training setup. + + Parameters: + local_loss (bool, optional): If True, calculates loss locally on each data parallel process. + Defaults to False. + gather_with_grad (bool, optional): If True, gathers gradients during loss calculation. + Currently not functional in the latest PyTorch version. + Defaults to False. + cache_labels (bool, optional): If True, caches labels for reuse in subsequent iterations, + improving performance. Defaults to False. + + Attributes: + world_size (int): The size of the data parallel group (number of processes). + rank (int): The rank of the current process within the data parallel group. + + Methods: + forward(output_tensor): Computes the loss given the model's output tensor. This involves + gathering features across processes, computing logits, and + calculating the final cross-entropy loss. + """ + + def __init__( + self, local_loss=False, gather_with_grad=False, cache_labels=False, + ): + super().__init__() + self.local_loss = local_loss + self.gather_with_grad = gather_with_grad + self.cache_labels = cache_labels + + # cache state + self.prev_num_logits = 0 + self.labels = {} + + self.world_size = parallel_state.get_data_parallel_world_size() + self.rank = parallel_state.get_data_parallel_rank() + + def forward(self, output_tensor): + image_features, text_features, logit_scale = output_tensor + device = image_features.device + if self.world_size > 1: + all_image_features, all_text_features = gather_features( + image_features, text_features, self.local_loss, self.gather_with_grad + ) + + if self.local_loss: + logits_per_image = logit_scale * image_features @ all_text_features.T + logits_per_text = logit_scale * text_features @ all_image_features.T + else: + logits_per_image = logit_scale * all_image_features @ all_text_features.T + logits_per_text = logits_per_image.T + else: + logits_per_image = logit_scale * image_features @ text_features.T + logits_per_text = logit_scale * text_features @ image_features.T + + # calculated ground-truth and cache if enabled + num_logits = logits_per_image.shape[0] + if self.prev_num_logits != num_logits or device not in self.labels: + labels = torch.arange(num_logits, device=device, dtype=torch.long) + if self.world_size > 1 and self.local_loss: + labels = labels + num_logits * self.rank + if self.cache_labels: + self.labels[device] = labels + self.prev_num_logits = num_logits + else: + labels = self.labels[device] + + total_loss = (F.cross_entropy(logits_per_image, labels) + F.cross_entropy(logits_per_text, labels)) / 2 + + # TODO (yuya): this is not necessary; not necessary if global! + reduced_loss = average_losses_across_data_parallel_group([total_loss]) + return total_loss, {"loss": reduced_loss} diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/multimodal_llm/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/multimodal_llm/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/multimodal_llm/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/multimodal_llm/neva/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/multimodal_llm/neva/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/multimodal_llm/neva/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/multimodal_llm/neva/neva_model.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/multimodal_llm/neva/neva_model.py new file mode 100644 index 0000000..4556ba1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/multimodal_llm/neva/neva_model.py @@ -0,0 +1,1021 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from functools import partial +from itertools import chain +from typing import Any, Optional + +import torch +import torch.nn.functional as F +from einops import rearrange, repeat +from omegaconf.dictconfig import DictConfig +from pytorch_lightning.trainer.trainer import Trainer +from transformers import CLIPVisionModel + +from nemo.collections.common.parts.utils import extend_instance +from nemo.collections.multimodal.data.neva.conversation import DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN +from nemo.collections.multimodal.data.neva.neva_dataset import ( + DataCollatorForSupervisedDataset, + make_supervised_data_module, +) +from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import ( + CLIPVisionTransformer, + MegatronCLIPModel, +) +from nemo.collections.multimodal.parts.utils import load_nemo_model_weights +from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import MegatronPretrainingSampler +from nemo.collections.nlp.models.language_modeling.megatron.gpt_model import GPTModel +from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel, get_specs +from nemo.collections.nlp.models.nlp_model import NLPModel +from nemo.collections.nlp.modules.common.megatron.adapters.parallel_adapters import ( + AdapterName, + MultimodalProjectorAdapterConfig, +) +from nemo.collections.nlp.modules.common.megatron.utils import average_losses_across_data_parallel_group +from nemo.collections.nlp.modules.common.text_generation_utils import ( + generate, + get_computeprob_response, + get_default_length_params, + get_default_sampling_params, + megatron_neva_generate, +) +from nemo.collections.nlp.modules.common.transformer.text_generation import LengthParam, OutputType, SamplingParam +from nemo.collections.nlp.parts.mixins.multimodal_adapter_mixins import MultimodalAdapterModelMixin +from nemo.collections.nlp.parts.utils_funcs import get_last_rank +from nemo.collections.vision.data.megatron.data_samplers import MegatronVisionPretrainingRandomSampler +from nemo.core import adapter_mixins +from nemo.core.classes.common import PretrainedModelInfo +from nemo.utils import logging + +try: + import apex.transformer.pipeline_parallel.utils + + HAVE_APEX = True + +except (ImportError, ModuleNotFoundError): + + HAVE_APEX = False + +try: + from megatron.core import InferenceParams, dist_checkpointing, parallel_state + from megatron.core.models.gpt import GPTModel as MCoreGPTModel + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +class FrozenCLIPVisionTransformer(CLIPVisionTransformer): + """Frozen version of CLIPVisionTransformer""" + + def __init__(self, model_cfg, model_parallel_config, pre_process=True, post_process=True): + super().__init__( + model_cfg, model_parallel_config, pre_process=pre_process, post_process=post_process, skip_head=True, + ) + self.frozen = False + self.dtype = self.config.params_dtype + + def train(self, mode): + if self.frozen: + return self + + super().train(mode) + return self + + def forward(self, input): + assert self.training == False + hidden_states = self.backbone(input) + # Do not add header after backbone + return hidden_states + + def freeze(self) -> None: + for param in self.parameters(): + param.requires_grad = False + + self.eval() + self.frozen = True + + +class NevaWordEmbeddingMixin(torch.nn.Module, adapter_mixins.AdapterModuleMixin): + """ + A mixin class for integrating vision-based embeddings into language models. + + This class extends the functionality of a language model to include vision-based embeddings + by integrating a vision encoder. It allows the language model to process media inputs + alongside text inputs. + """ + + def init_vision( + self, + vision_encoder, + media_start_id, + media_end_id, + vision_select_layer=-1, + class_token_length=1, + use_im_start_end=False, + ): + self.vision_encoder = vision_encoder + self.from_hf = isinstance(vision_encoder, CLIPVisionModel) + self.media_start_id = media_start_id + self.media_end_id = media_end_id + self.class_token_length = class_token_length + self.use_im_start_end = use_im_start_end + self.vision_select_layer = vision_select_layer + self.media = None + self.set_accepted_adapter_types([MultimodalProjectorAdapterConfig._target_]) + + def set_media(self, media): + self.media = media + + def forward(self, input_ids, **kwargs): + media = self.media # avoid change the signature of embedding forward function + words_embeddings = super().forward(input_ids, **kwargs) + + return self.replace_media_embeddings(input_ids, words_embeddings, media) + + def encode_vision_x(self, vision_x: torch.Tensor): + """ + Compute media tokens from vision input by passing it through vision encoder and conditioning language model. + Args: + vision_x (torch.Tensor): Vision input + shape (B, T_img, F, C, H, W) + Images in the same chunk are collated along T_img, and frames are collated along F + Currently only F=1 is supported (single-frame videos) + + rearrange code based on https://github.com/dhansmair/flamingo-mini + """ + + assert vision_x.ndim == 6, "vision_x should be of shape (b, T_img, F, C, H, W)" + b, T, F = vision_x.shape[:3] + assert F == 1, "Only single frame supported" + + vision_x = rearrange(vision_x, "b T F c h w -> (b T F) c h w") + vision_x = vision_x.to(self.vision_encoder.dtype) + with torch.no_grad(): + if self.from_hf: + vision_x = self.vision_encoder(vision_x, output_hidden_states=True) + vision_x = vision_x.hidden_states[self.vision_select_layer] + else: + self.vision_encoder.backbone.transformer.return_select_layer = self.vision_select_layer + vision_x = self.vision_encoder(vision_x) + vision_x = rearrange(vision_x, "(b T F) v d -> b T F v d", b=b, T=T, F=F) + vision_x = vision_x[:, :, :, self.class_token_length :] + assert self.is_adapter_available(), "Cannot find multimodal vision adapter!" + vision_connector = self.get_adapter_module(AdapterName.MULTIMODAL_PROJECTOR_ADAPTER) + vision_x = vision_connector(vision_x) + return vision_x + + def replace_media_embeddings(self, input_ids, inputs_embeds, media): + if media is None: + return inputs_embeds + + batch_size, sequence_length, hidden_size = inputs_embeds.shape + + # calculate media features without gradients + media_features = self.encode_vision_x(media) # b T F S(eq) H(idden) + num_images_per_sample = media_features.size(1) + num_patches = media_features.size(3) + # flatten patches + media_features = media_features.view(batch_size, -1, hidden_size) + + # create an indices matrix used in torch.scatter + padded_media_indices = torch.ones( + (batch_size, num_images_per_sample), dtype=torch.long, device=input_ids.device + ) + padded_media_indices *= sequence_length + for idx, input_id in enumerate(input_ids): + media_end_positions = torch.where(input_id == self.media_end_id)[0] + if self.use_im_start_end: + # locate the first media token positions + padded_media_indices[idx, : len(media_end_positions)] = media_end_positions - num_patches + assert ( + input_id[padded_media_indices[idx, : len(media_end_positions)] - 1] == self.media_start_id + ).all() + else: + padded_media_indices[idx, : len(media_end_positions)] = media_end_positions - num_patches + 1 + assert (input_id[padded_media_indices[idx, : len(media_end_positions)]] == self.media_start_id).all() + + # use indices to create a span + padded_media_indices = padded_media_indices.unsqueeze(-1) + torch.arange( + num_patches, device=padded_media_indices.device + ).repeat(*padded_media_indices.shape, 1) + padded_media_indices = padded_media_indices.reshape(batch_size, -1) + padded_media_indices = repeat(padded_media_indices, 'b s -> b s h', h=hidden_size) + + # concat placeholder + updated_input_embeds = torch.cat( + (inputs_embeds, torch.zeros((batch_size, num_patches, hidden_size), device=inputs_embeds.device)), dim=1 + ) + updated_input_embeds = updated_input_embeds.type(media_features.dtype) + # scatter media_features + updated_input_embeds.scatter_(1, padded_media_indices, media_features) + + # chop off placeholder + updated_input_embeds = updated_input_embeds[:, :sequence_length] + + return updated_input_embeds + + +class NevaBaseModel: + """ + Base class for a multimedia model integrating vision and language models. + + This class initializes and manages components for a multimodal model that combines vision and language models. + It handles the integration of these models, loading weights, and freezing components based on configuration. + """ + + def __init__( + self, mm_cfg, media_start_id, media_end_id, mcore_gpt, **kwargs, + ): + self.mm_cfg = mm_cfg + self.media_start_id = media_start_id + self.media_end_id = media_end_id + self.mcore_gpt = mcore_gpt + self.is_dist_ckpt = False + if getattr(self, 'language_model', None) is not None: + self.embedding = self.language_model.embedding + + if mm_cfg.llm.from_pretrained is not None: + logging.info(f"Loading LLM weights from checkpoint {mm_cfg.llm.from_pretrained}") + self.load_llm_weights(mm_cfg.llm.from_pretrained) + if mm_cfg.llm.freeze: + self.freeze_llm(mm_cfg) + + # Initialize vision encoder and freeze it + if mm_cfg.vision_encoder.from_hf: + vision_encoder = CLIPVisionModel.from_pretrained( + mm_cfg.vision_encoder.from_pretrained, torch_dtype=torch.bfloat16, + ).cuda() + vision_encoder = vision_encoder.to(torch.bfloat16) + if mm_cfg.vision_encoder.freeze: + for param in vision_encoder.parameters(): + param.requires_grad = False + vision_encoder = vision_encoder.eval() + else: + vision_cfg = MegatronCLIPModel.restore_from( + mm_cfg.vision_encoder.from_pretrained, return_config=True + ).vision + vision_encoder = FrozenCLIPVisionTransformer(vision_cfg, self.config) + self.load_vision_encoder_weights(vision_encoder, mm_cfg.vision_encoder.from_pretrained) + if mm_cfg.vision_encoder.freeze: + vision_encoder.freeze() + + # Monkey patch embedding + if kwargs.get("pre_process", True): + extend_instance(self.embedding.word_embeddings, NevaWordEmbeddingMixin) + self.embedding.word_embeddings.init_vision( + vision_encoder, + media_start_id, + media_end_id, + vision_select_layer=mm_cfg.vision_encoder.get("vision_select_layer", -2), + class_token_length=mm_cfg.vision_encoder.get("class_token_length", 1), + use_im_start_end=mm_cfg.get("use_im_start_end", False), + ) + + def freeze_llm(self, mm_cfg): + raise NotImplementedError + + def _load_model_weights(self, nemo_path): + """ + Shared method to load model weights from a given nemo_path. + """ + sharded_state_dict = None + if getattr(self, "sharded_state_dict", None) is not None: + sharded_state_dict = self.sharded_state_dict(prefix="model.") + state_dict, self.is_dist_ckpt = load_nemo_model_weights(nemo_path, sharded_state_dict) + + return state_dict + + def load_vision_encoder_weights(self, vision_encoder, nemo_path): + state_dict = self._load_model_weights(nemo_path) + + new_state_dict = {} + for k, v in state_dict.items(): + if k.startswith("model.vision_encoder."): + new_k = k.replace("model.vision_encoder.", "") + new_state_dict[new_k] = v + + missing, unexpected = vision_encoder.load_state_dict(new_state_dict, strict=False) + print(f"Restored from {nemo_path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + + def load_llm_weights(self, nemo_path): + state_dict = self._load_model_weights(nemo_path) + + new_state_dict = {} + if self.is_dist_ckpt or self.mcore_gpt: + for k, v in state_dict.items(): + new_k = k + if k.startswith("model."): + new_k = k.replace("model.", "", 1) + new_state_dict[new_k] = v + self.load_state_dict(new_state_dict, strict=False) + else: + if ( + 'model.language_model.embedding.word_embeddings.weight' in state_dict + and state_dict['model.language_model.embedding.word_embeddings.weight'].shape[0] + < self.embedding.word_embeddings.num_embeddings_per_partition + ): + state_dict = self.pad_word_embeddings(state_dict) + + for k, v in state_dict.items(): + if k.startswith("model.language_model."): + new_k = k.replace("model.language_model.", "", 1) + module_key, param_key = new_k.split(".", 1) + if module_key not in new_state_dict: + new_state_dict[module_key] = {} + new_state_dict[module_key][param_key] = v + self.language_model.load_state_dict(new_state_dict, strict=False) + print(f"Restored LLM weights from {nemo_path}.") + + def pad_word_embeddings(self, state_dict): + assert ( + self.embedding.word_embeddings.num_embeddings + == self.embedding.word_embeddings.num_embeddings_per_partition + ), "Word embedding doesn't match the word embedding shape from checkpoint!" + + pad_length = ( + self.embedding.word_embeddings.num_embeddings + - state_dict['model.language_model.embedding.word_embeddings.weight'].shape[0] + ) + state_dict['model.language_model.embedding.word_embeddings.weight'] = F.pad( + state_dict['model.language_model.embedding.word_embeddings.weight'], (0, 0, 0, pad_length) + ) + + if 'model.language_model.output_layer.weight' in state_dict: + assert ( + state_dict['model.language_model.embedding.word_embeddings.weight'].shape + == state_dict['model.language_model.output_layer.weight'].shape + ) + state_dict['model.language_model.output_layer.weight'] = F.pad( + state_dict['model.language_model.output_layer.weight'], (0, 0, 0, pad_length) + ) + return state_dict + + +class MCoreNevaModel(MCoreGPTModel, NevaBaseModel): + """ + A specialized version of NevaBaseModel integrated with MCoreGPTModel (Megatron Core Version GPTModel). + + This class combines the functionalities of MCoreGPTModel and NevaBaseModel, + providing capabilities specific to the MCore GPT architecture within the multimodal framework. + """ + + def __init__( + self, mm_cfg, media_start_id, media_end_id, mcore_gpt, **kwargs, + ): + MCoreGPTModel.__init__(self, **kwargs) + NevaBaseModel.__init__(self, mm_cfg, media_start_id, media_end_id, mcore_gpt, **kwargs) + + def freeze_llm(self, mm_cfg): + for param in chain(self.embedding.parameters(), self.decoder.parameters(), self.output_layer.parameters(),): + param.requires_grad = False + self.embedding = self.embedding.eval() + self.decoder = self.decoder.eval() + self.output_layer = self.output_layer.eval() + + def forward( + self, *args, **kwargs, + ): + media = kwargs.pop('media', None) + self.embedding.word_embeddings.set_media(media) + return MCoreGPTModel.forward(self, *args, **kwargs) + + +class NevaModel(GPTModel, NevaBaseModel): + """ + A specialized version of NevaBaseModel integrated with the NeMo GPTModel. + + This class merges the functionalities of GPTModel with NevaBaseModel, catering to the standard GPT architecture + within the multimodal framework. + """ + + def __init__( + self, mm_cfg, media_start_id, media_end_id, mcore_gpt, **kwargs, + ): + GPTModel.__init__(self, **kwargs) + NevaBaseModel.__init__(self, mm_cfg, media_start_id, media_end_id, mcore_gpt, **kwargs) + + def freeze_llm(self, mm_cfg): + for param in self.language_model.parameters(): + param.requires_grad = False + + def forward( + self, *args, **kwargs, + ): + media = kwargs.pop('media', None) + self.embedding.word_embeddings.set_media(media) + return GPTModel.forward(self, *args, **kwargs) + + +class MegatronNevaModel(MultimodalAdapterModelMixin, MegatronGPTModel): + """ + Megatron Neva pretraining + """ + + def __init__(self, cfg: DictConfig, trainer: Trainer): + super().__init__(cfg, trainer) + self.init_neva_adapter() + + def init_neva_adapter(self): + self.base_keys = self._get_all_keys() + adapter_name = AdapterName.MULTIMODAL_PROJECTOR_ADAPTER + adapter_cfg = MultimodalProjectorAdapterConfig( + adapter_type=self.cfg.mm_cfg.get("mm_mlp_adapter_type", "linear"), + in_features=self.cfg.mm_cfg.vision_encoder.hidden_size, + out_features=self.cfg.hidden_size, + bias=True, + ) + for name, module in self.named_modules(): + self._check_and_add_adapter( + name, + module, + adapter_name, + adapter_cfg, + autocast_dtype=self.autocast_dtype if self.megatron_amp_O2 else None, + ) + self.adapter_keys = self._get_all_keys() - self.base_keys + if self.megatron_amp_O2: + self.adapter_keys = set(key.replace("model.module.", "model.", 1) for key in self.adapter_keys) + + def model_provider_func(self, pre_process, post_process): + """Model depends on pipeline paralellism.""" + media_start_id = self.tokenizer.token_to_id(DEFAULT_IM_START_TOKEN) + media_end_id = self.tokenizer.token_to_id(DEFAULT_IM_END_TOKEN) + + if self.mcore_gpt: + if not parallel_state.is_initialized(): + + def dummy(): + return + + if self.trainer.strategy.launcher is not None: + self.trainer.strategy.launcher.launch(dummy, trainer=self.trainer) + self.trainer.strategy.setup_environment() + + model = MCoreNevaModel( + mm_cfg=self.cfg.mm_cfg, + media_start_id=media_start_id, + media_end_id=media_end_id, + mcore_gpt=self.mcore_gpt, + config=self.transformer_config, + transformer_layer_spec=get_specs(self.spec_name), + vocab_size=self.cfg.get('override_vocab_size', self.padded_vocab_size), + max_sequence_length=self.cfg.get('encoder_seq_length', 512), + pre_process=pre_process, + post_process=post_process, + parallel_output=True, + share_embeddings_and_output_weights=self.cfg.get('share_embeddings_and_output_weights', True), + position_embedding_type=self.cfg.get('position_embedding_type', 'learned_absolute'), + rotary_percent=self.cfg.get('rotary_percentage', 1.0), + seq_len_interpolation_factor=self.cfg.get('seq_len_interpolation_factor', None), + rotary_base=self.cfg.get('rotary_base', 10000), + ) + else: + model = NevaModel( + mm_cfg=self.cfg.mm_cfg, + media_start_id=media_start_id, + media_end_id=media_end_id, + mcore_gpt=self.mcore_gpt, + config=self.model_parallel_config, + vocab_size=self.cfg.get('override_vocab_size', self.padded_vocab_size), + hidden_size=self.cfg.hidden_size, + max_position_embeddings=self.cfg.max_position_embeddings, + num_layers=self.cfg.num_layers, + num_attention_heads=self.cfg.num_attention_heads, + apply_query_key_layer_scaling=self.cfg.get('apply_query_key_layer_scaling', True), + kv_channels=self.cfg.get('kv_channels', None), + ffn_hidden_size=self.cfg.ffn_hidden_size, + num_tokentypes=0, + parallel_output=True, + pre_process=pre_process, + post_process=post_process, + init_method_std=self.cfg.get('init_method_std', 0.02), + use_scaled_init_method=self.cfg.get('use_scaled_init_method', True), + fp16_lm_cross_entropy=self.cfg.get('fp16_lm_cross_entropy', False), + hidden_dropout=self.cfg.get('hidden_dropout', 0.1), + attention_dropout=self.cfg.get('attention_dropout', 0.1), + ffn_dropout=self.cfg.get('ffn_dropout', 0.0), + precision=self.cfg.get('precision', 16), + fp32_residual_connection=self.cfg.get('fp32_residual_connection', False), + activations_checkpoint_granularity=self.cfg.get('activations_checkpoint_granularity', None), + activations_checkpoint_method=self.cfg.get('activations_checkpoint_method', None), + activations_checkpoint_num_layers=self.cfg.get('activations_checkpoint_num_layers', 1), + activations_checkpoint_layers_per_pipeline=self.cfg.get( + 'activations_checkpoint_layers_per_pipeline', None + ), + normalization=self.cfg.get('normalization', 'layernorm'), + layernorm_epsilon=self.cfg.get('layernorm_epsilon', 1e-5), + onnx_safe=self.cfg.get('onnx_safe', False), + bias=self.cfg.get('bias', True), + bias_activation_fusion=self.cfg.get('bias_activation_fusion', True), + bias_dropout_add_fusion=self.cfg.get('bias_dropout_add_fusion', True), + activation=self.cfg.get('activation', 'gelu'), + headscale=self.cfg.get('headscale', False), + transformer_block_type=self.cfg.get('transformer_block_type', 'pre_ln'), + openai_gelu=self.cfg.get('openai_gelu', False), + normalize_attention_scores=self.cfg.get('normalize_attention_scores', True), + position_embedding_type=self.cfg.get('position_embedding_type', 'learned_absolute'), + rotary_percentage=self.cfg.get('rotary_percentage', 1.0), + share_embeddings_and_output_weights=self.cfg.get('share_embeddings_and_output_weights', True), + attention_type=self.cfg.get('attention_type', 'multihead'), + masked_softmax_fusion=self.cfg.get('masked_softmax_fusion', True), + persist_layer_norm=self.cfg.get('persist_layer_norm', False), + transformer_engine=self.cfg.get('transformer_engine', False), + fp8=self.cfg.get('fp8', False), + fp8_e4m3=self.cfg.get('fp8_e4m3', False), + fp8_hybrid=self.cfg.get('fp8_hybrid', False), + fp8_margin=self.cfg.get('fp8_margin', 0), + fp8_interval=self.cfg.get('fp8_interval', 1), + fp8_amax_history_len=self.cfg.get('fp8_amax_history_len', 1), + fp8_amax_compute_algo=self.cfg.get('fp8_amax_compute_algo', 'most_recent'), + reduce_amax=self.cfg.get('reduce_amax', True), + use_emha=self.cfg.get('use_emha', False), + ub_tp_comm_overlap=self.cfg.get('ub_tp_comm_overlap', False), + use_flash_attention=self.cfg.get('use_flash_attention', False), + megatron_legacy=self.cfg.get('megatron_legacy', False), + seq_len_interpolation_factor=self.cfg.get('seq_len_interpolation_factor', None), + ) + + logging.info( + f"Neva model initialized with {sum(p.numel() for p in model.parameters() if p.requires_grad)} trainable parameters" + ) + + return model + + def setup_optimizer_param_groups(self): + """ModelPT override. Optimizer will get self._optimizer_param_groups""" + if self.cfg.mm_cfg.llm.freeze: + super().setup_optimizer_param_groups() + else: + MegatronGPTModel.setup_optimizer_param_groups(self) + + # filter out params doesn't have grad + for param_group in self._optimizer_param_groups: + params_with_grad = [param for param in param_group['params'] if param.requires_grad] + param_group['params'] = params_with_grad + + # set projection matrix and lora to two param groups with different LR + if self.use_peft: + assert len(self._optimizer_param_groups) == 1 + assert len(self.adapter_keys) == len(self._optimizer_param_groups[0]['params']) + # Mapping from parameter objects to their names + param_to_name = { + param: name + for name, param in self.model.named_parameters() + if name or name.replace("model.module.", "model.", "1") in self.adapter_keys + } + # Match the parameters and separate them into two groups + group1_params, group2_params = [], [] + for param in self._optimizer_param_groups[0]['params']: + param_name = param_to_name.get(param) + if 'mm_projector' in param_name: + group2_params.append(param) + else: + group1_params.append(param) + + base_lr = self._cfg.optim.get('lr') + mm_projector_lr_ratio = 0.1 # hard-coded ratio + # Create two new optimizer param groups + self._optimizer_param_groups = [ + {'params': group1_params, 'lr': base_lr}, + {'params': group2_params, 'lr': base_lr * mm_projector_lr_ratio}, + ] + + def forward(self, tokens, text_position_ids, attention_mask, labels, media=None): + forward_args = { + 'input_ids': tokens, + 'position_ids': text_position_ids, + 'attention_mask': attention_mask, + 'labels': labels, + 'media': media, + } + if not self.mcore_gpt: + forward_args['checkpoint_activations_all_layers'] = None + + output_tensor = self.model(**forward_args) + return output_tensor + + def fwd_bwd_step(self, dataloader_iter, forward_only, first_val_step=None): + return MegatronGPTModel.fwd_bwd_step(self, dataloader_iter, forward_only, first_val_step) + + def training_step(self, dataloader_iter): + """ + We pass the dataloader iterator function to the micro-batch scheduler. + The input batch to each micro-batch is fetched using the dataloader function + in the micro-batch fwd function. + """ + return MegatronGPTModel.training_step(self, dataloader_iter) + + def get_forward_output_and_loss_func(self, validation_step=False, tuning=False): + def loss_func(output_tensor, loss_mask): + loss_for_ub = self.loss_func(loss_mask, output_tensor) + if validation_step and not self.cfg.data.get('validation_drop_last', True): + raise NotImplementedError(f"`validation_drop_last=False` is not implemented in Neva!") + else: + reduced_loss = average_losses_across_data_parallel_group([loss_for_ub]) + return loss_for_ub, dict(avg=reduced_loss[0].unsqueeze(0)) + + def fwd_output_and_loss_func(dataloader_iter, model, checkpoint_activations_all_layers=None): + batch, _, _ = next(dataloader_iter) + if parallel_state.get_pipeline_model_parallel_world_size() == 1: + for k in batch.keys(): + if self.get_attention_mask_from_fusion: + batch[k] = batch[k].cuda(non_blocking=True) if k not in ['attention_mask'] else None + else: + batch[k] = batch[k].cuda(non_blocking=True) + else: + if parallel_state.is_pipeline_first_stage(): + # First pipeline stage needs tokens, position_ids, and attention_mask + for k in batch.keys(): + if self.get_attention_mask_from_fusion: + batch[k] = ( + batch[k].cuda(non_blocking=True) if k in ['tokens', 'position_ids', 'media'] else None + ) + else: + batch[k] = ( + batch[k].cuda(non_blocking=True) + if k in ['tokens', 'position_ids', 'attention_mask', 'media'] + else None + ) + elif parallel_state.is_pipeline_last_stage(): + # Last pipeline stage needs the labels, loss_mask, and attention_mask + for k in batch.keys(): + if self.get_attention_mask_from_fusion: + batch[k] = batch[k].cuda(non_blocking=True) if k in ['labels', 'loss_mask'] else None + else: + batch[k] = ( + batch[k].cuda(non_blocking=True) + if k in ['labels', 'loss_mask', 'attention_mask'] + else None + ) + else: + # Intermediate pipeline stage doesn't need any inputs + batch = {k: None for k in ['tokens', 'position_ids', 'attention_mask', 'labels', 'media']} + + forward_args = { + 'input_ids': batch['tokens'], + 'position_ids': batch['position_ids'], + 'attention_mask': batch['attention_mask'], + 'labels': batch['labels'], + 'media': batch.get('media', None), + } + if not self.mcore_gpt: + if self.use_loss_mask: + forward_args['loss_mask'] = batch['loss_mask'] + forward_args['checkpoint_activations_all_layers'] = checkpoint_activations_all_layers + + output_tensor = model(**forward_args) + + return output_tensor, partial(loss_func, loss_mask=batch['loss_mask']) + + return fwd_output_and_loss_func + + def get_forward_output_only_func(self): + def fwd_output_only_func(dataloader_iter, model): + batch, _, _ = next(dataloader_iter) + extra_arg = {} + ( + tokens, + attention_mask, + position_ids, + media, + set_inference_key_value_memory, + inference_max_sequence_len, + ) = batch + tokens = tokens.cuda() + attention_mask = attention_mask.cuda() + position_ids = position_ids.cuda() + attention_mask = attention_mask[0:1] + if media is not None: + media = media.cuda() + labels = None + if self.mcore_gpt: + # if first step, then clear KV cache, otherwise reuse inference_paarms + if set_inference_key_value_memory[0].item(): + self.inference_params = InferenceParams( + max_batch_size=tokens.size(0), max_sequence_length=inference_max_sequence_len[0].item() + ) + extra_arg['inference_params'] = self.inference_params + else: + extra_arg['set_inference_key_value_memory'] = set_inference_key_value_memory[0].item() + extra_arg['inference_max_sequence_len'] = inference_max_sequence_len[0].item() + + forward_args = { + 'input_ids': tokens, + 'position_ids': position_ids, + 'attention_mask': attention_mask, + 'labels': labels, + 'media': media, + } + if not self.mcore_gpt: + forward_args['checkpoint_activations_all_layers'] = None + output_tensor = model(**forward_args, **extra_arg) + + # Advance inference sequence offset. + if self.inference_params: + # if last stage, then (final) output is [b, s, h], otherwise it's [s, b, h] + if parallel_state.is_pipeline_last_stage(): + self.inference_params.sequence_len_offset += output_tensor.size(1) + else: + self.inference_params.sequence_len_offset += output_tensor.size(0) + + def id_func(output_tensor): + return output_tensor, {'logits': output_tensor} + + return output_tensor, id_func + + return fwd_output_only_func + + def validation_step(self, dataloader_iter): + return MegatronGPTModel.validation_step(self, dataloader_iter) + + def on_validation_epoch_end(self): + if not self.validation_step_outputs: + return + + if parallel_state.is_pipeline_last_stage(): + # only the last pipeline parallel stages return loss with their batch size + if self.cfg.data.get('validation_drop_last', True): + averaged_loss = torch.stack(self.validation_step_outputs).mean() + else: + # Compute the avg loss by total_loss across all samples / total number of samples + # total_loss_and_total_samples = torch.vstack(outputs).sum(axis=0) + # avg_loss = total_loss_and_total_samples[0] / total_loss_and_total_samples[1] + # averaged_loss = avg_loss.type(torch.float32).cuda() + raise NotImplementedError("`validation_drop_last=False` is not supported!") + else: + averaged_loss = torch.tensor(0.0, dtype=torch.float32).cuda() + + # we can only log on one rank if it is rank zero so we broadcast from last rank + torch.distributed.broadcast(averaged_loss, get_last_rank()) + self.log('val_loss', averaged_loss, prog_bar=True, rank_zero_only=True, batch_size=1) + self.validation_step_outputs.clear() # free memory + + return averaged_loss + + def on_validation_epoch_start(self): + pass + + def test_step(self, batch, batch_idx): + return self.validation_step(batch) + + def test_epoch_end(self, outputs): + averaged_loss = average_losses_across_data_parallel_group(outputs) + logging.info(f'test_loss: {averaged_loss[0]}') + + def loss_func(self, loss_mask, output_tensor): + losses = output_tensor.float() + loss_mask = loss_mask.view(-1).float() + # TODO: add nemo version here + loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum() # sequence level nll + return loss + + def setup(self, stage=None): + """ PTL hook that is executed after DDP spawns. + We setup datasets here as megatron datasets require DDP to instantiate. + See https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#setup for more information. + Args: + stage (str, optional): Can be 'fit', 'validate', 'test' or 'predict'. Defaults to None. + """ + num_parameters_on_device, total_num_parameters = self._get_total_params_across_model_parallel_groups_gpt_bert() + + logging.info( + f'Pipeline model parallel rank: {parallel_state.get_pipeline_model_parallel_rank()}, ' + f'Tensor model parallel rank: {parallel_state.get_tensor_model_parallel_rank()}, ' + f'Number of model parameters on device: {num_parameters_on_device:.2e}. ' + f'Total number of model parameters: {total_num_parameters:.2e}.' + ) + + resume_checkpoint_path = self.trainer.ckpt_path + if resume_checkpoint_path: + init_consumed_samples = self._extract_consumed_samples_from_ckpt(resume_checkpoint_path) + else: + init_consumed_samples = 0 + self.init_consumed_samples = init_consumed_samples + self.init_global_step = self.trainer.global_step + + rampup_batch_size = self.cfg.get('rampup_batch_size', None) + if rampup_batch_size: + start_batch_size = rampup_batch_size[0] + batch_size_increment = rampup_batch_size[1] + total_gpus_number = self.trainer.num_devices * self.trainer.num_nodes + + assert start_batch_size % (total_gpus_number) == 0, ( + 'expected' + ' start batch size ({}) to be divisible by total number of GPUs' + ' ({})'.format(start_batch_size, total_gpus_number) + ) + + micro_batch_size = self.cfg.get('micro_batch_size', 1) + tensor_model_parallel_size = self.cfg.get('tensor_model_parallel_size', 1) + pipeline_model_parallel_size = self.cfg.get('pipeline_model_parallel_size', 1) + total_data_parallel_size = total_gpus_number // (tensor_model_parallel_size * pipeline_model_parallel_size) + + assert batch_size_increment % (micro_batch_size * total_data_parallel_size) == 0, ( + 'expected' + ' batch size increment ({}) to be divisible by micro_batch_size ({}) times total data parallel size' + ' ({})'.format(batch_size_increment, micro_batch_size, total_data_parallel_size) + ) + + if stage == 'predict': + return + else: + # TODO: consider adding a ModelPT guard to check if model is being restored. + # allowing restored models to optionally setup datasets + self.build_train_valid_test_datasets() + self.setup_training_data(self.cfg.data) + self.setup_validation_data(self.cfg.data) + self.setup_test_data(self.cfg.data) + + # when using pipeline model parallel the final stage need to initialize word embeddings + if parallel_state.get_pipeline_model_parallel_world_size() > 1: + if isinstance(self.model, list): + for i, module in enumerate(self.model): + parallel_state.set_virtual_pipeline_model_parallel_rank(i) + if self.cfg.get('share_embeddings_and_output_weights', True): + module.sync_initial_word_embeddings() + parallel_state.set_virtual_pipeline_model_parallel_rank(0) + else: + if self.cfg.get('share_embeddings_and_output_weights', True): + self.model.sync_initial_word_embeddings() + + if self.cfg.get('transformer_engine', False): + self.setup_transformer_engine_tp_groups() + + def build_train_valid_test_datasets(self): + logging.info('Building Neva datasets.') + ds_dict = make_supervised_data_module(tokenizer=self.tokenizer, model_cfg=self.cfg,) + self._train_ds = ds_dict["train_dataset"] + self._validation_ds = ds_dict["eval_dataset"] + + return self._train_ds, self._validation_ds + + def build_pretraining_data_loader( + self, dataset, consumed_samples, dataset_type=None, drop_last=True, pad_samples_to_global_batch_size=False + ): + """Buld dataloader given an input dataset.""" + + logging.info(f'Building dataloader with consumed samples: {consumed_samples}') + # Megatron sampler + if hasattr(self.cfg.data, 'dataloader_type') and self.cfg.data.dataloader_type is not None: + if self.cfg.data.dataloader_type == 'single': + batch_sampler = MegatronPretrainingSampler( + total_samples=len(dataset), + consumed_samples=consumed_samples, + micro_batch_size=self.cfg.micro_batch_size, + data_parallel_rank=parallel_state.get_data_parallel_rank(), + data_parallel_size=parallel_state.get_data_parallel_world_size(), + drop_last=drop_last, + global_batch_size=self.cfg.global_batch_size, + pad_samples_to_global_batch_size=pad_samples_to_global_batch_size, + ) + elif self.cfg.data.dataloader_type == 'cyclic': + batch_sampler = MegatronVisionPretrainingRandomSampler( + dataset=dataset, + total_samples=len(dataset), + consumed_samples=consumed_samples, + micro_batch_size=self.cfg.micro_batch_size, + data_parallel_rank=parallel_state.get_data_parallel_rank(), + data_parallel_size=parallel_state.get_data_parallel_world_size(), + drop_last=self.cfg.get('drop_last', True), + data_sharding=False, + ) + else: + raise ValueError('cfg.data.dataloader_type must be "single" or "cyclic"') + else: + raise ValueError('cfg.data.dataloader_type not found. Must be "single" or "cyclic"') + + collate_func = DataCollatorForSupervisedDataset(self.cfg, self.tokenizer) + return torch.utils.data.DataLoader( + dataset, + batch_sampler=batch_sampler, + collate_fn=collate_func, + num_workers=self.cfg.data.num_workers, + pin_memory=True, + persistent_workers=True if self.cfg.data.num_workers > 0 else False, + ) + + @classmethod + def list_available_models(cls) -> Optional[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + Returns: + List of available pre-trained models. + """ + return None + + def setup_test_data(self, cfg): + pass + + def state_dict(self, destination=None, prefix='', keep_vars=False): + # Get the original state dictionary + original_state_dict = super().state_dict(destination=destination, prefix=prefix, keep_vars=keep_vars) + + keys_to_keep = list(self.adapter_keys) + # TODO(yuya): maybe not hard-code vision_encoder keys here + vision_encoder_keys = [k for k in self.base_keys if "vision_encoder" in k] + llm_keys = [k for k in self.base_keys if "vision_encoder" not in k] + if not self.cfg.mm_cfg.llm.freeze: + keys_to_keep += llm_keys + if not self.cfg.mm_cfg.vision_encoder.freeze: + keys_to_keep += vision_encoder_keys + new_state_dict = {k: original_state_dict[k] for k in keys_to_keep} + return new_state_dict + + def load_state_dict(self, state_dict, strict=False): + logging.warning('Loading state dict for MegatronNevaModel...') + missing_keys, unexpected_keys = NLPModel.load_state_dict(self, state_dict, strict=False) + + if len(missing_keys) > 0: + logging.warning('Missing keys were detected during the load. Please double check.') + if len(missing_keys) > 10: + logging.warning(f'Missing keys: {missing_keys[:10]} and {len(missing_keys) - 10} more.') + else: + logging.warning(f'Missing keys: {missing_keys}') + if len(unexpected_keys) > 0: + logging.critical('Unexpected keys were detected during the load. Please double check.') + logging.critical(f'Unexpected keys: \n{unexpected_keys}') + + def on_load_checkpoint(self, checkpoint) -> None: + pass + # if self.mcore_gpt: + # state_dict = checkpoint["state_dict"] + # self.load_state_dict(state_dict) + + def sharded_state_dict(self, prefix: str = ''): + return None + # sharded_state_dict = MegatronGPTModel.sharded_state_dict(self, prefix) + # return sharded_state_dict + + def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: Optional[int] = None) -> Any: + inference_config = self.get_inference_config() + + if inference_config is None: + return None + else: + # need to overwrite some configuration, make it immutable + image = os.path.join(inference_config['images_base_path'], batch['image'][0]) + prompt = batch['prompt'][0] + inference_config = inference_config.copy() + compute_logprob = inference_config['compute_logprob'] + if compute_logprob: + inference_config['inputs'] = prompt + inference_config['tokens_to_generate'] = 1 + inference_config['all_probs'] = True + inference_config["add_BOS"] = False + inference_config['greedy'] = True + inference_config['image_list'] = image + response = generate(self, **inference_config) + compute_prob_response = get_computeprob_response(self.tokenizer, response, prompt) + return compute_prob_response + else: + inference_config['inputs'] = prompt + inference_config['image_list'] = image + return generate(self, **inference_config) + + def generate( + self, input_prompts, inference_config, length_params: LengthParam, sampling_params: SamplingParam = None, + ) -> OutputType: + + # check whether the DDP is initialized + if not parallel_state.is_initialized(): + + def dummy(): + return + + if self.trainer.strategy.launcher is not None: + self.trainer.strategy.launcher.launch(dummy, trainer=self.trainer) + self.trainer.strategy.setup_environment() + + # set the default sampling params if it is None. + # default do greedy sampling + if sampling_params is None: + sampling_params = get_default_sampling_params() + + # set the default length params if it is None. + # default do greedy sampling + if length_params is None: + length_params = get_default_length_params() + + # Supports only one prompt at a time + result = megatron_neva_generate(self.cuda(), input_prompts, length_params, sampling_params, inference_config) + + return result diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/nerf/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/nerf/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/nerf/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/nerf/base.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/nerf/base.py new file mode 100644 index 0000000..e07d09b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/nerf/base.py @@ -0,0 +1,36 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.core.classes.common import Serialization +from nemo.core.classes.modelPT import ModelPT + + +class NerfModelBase(ModelPT, Serialization): + def __init__(self, cfg): + super().__init__(cfg=cfg) + self.save_hyperparameters() + self._cfg = cfg + + @staticmethod + def is_module_updatable(module): + return hasattr(module, 'update_step') and callable(module.update_step) + + def list_available_models(self): + pass + + def setup_training_data(self, config): + pass + + def setup_validation_data(self, config): + pass diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/nerf/dreamfusion.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/nerf/dreamfusion.py new file mode 100644 index 0000000..27877a6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/nerf/dreamfusion.py @@ -0,0 +1,325 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import random + +import cv2 +import imageio +import numpy as np +import torch + +from nemo.collections.multimodal.models.nerf.txt2nerf_base import Txt2NerfBase +from nemo.collections.multimodal.modules.nerf.loss.laplacian_smooth_loss import LaplacianSmoothLoss +from nemo.collections.multimodal.modules.nerf.loss.normal_consistency_loss import NormalConsistencyLoss +from nemo.collections.multimodal.modules.nerf.materials.materials_base import ShadingEnum +from nemo.core import optim + + +# TODO(ahmadki): split dmtet from dreamfusion +class DreamFusion(Txt2NerfBase): + def __init__(self, cfg): + super(DreamFusion, self).__init__(cfg) + + self.guidance_scale = cfg.guidance_scale + + self.iters = cfg.iters + self.latent_iter_ratio = cfg.latent_iter_ratio + self.albedo_iter_ratio = cfg.albedo_iter_ratio + self.min_ambient_ratio = cfg.min_ambient_ratio + self.textureless_ratio = cfg.textureless_ratio + + # Lambdas + self.lambda_sds = cfg.loss.lambda_sds + self.lambda_opacity = cfg.loss.lambda_opacity + self.lambda_entropy = cfg.loss.lambda_entropy + self.lambda_orientation = cfg.loss.lambda_orientation + self.lambda_2d_normal_smooth = cfg.loss.lambda_2d_normal_smooth + self.lambda_3d_normal_smooth = cfg.loss.lambda_3d_normal_smooth + self.lambda_mesh_normal = cfg.loss.lambda_mesh_normal + self.lambda_mesh_laplacian = cfg.loss.lambda_mesh_laplacian + + if self.lambda_mesh_normal > 0: + self.normal_consistency_loss_fn = NormalConsistencyLoss() + if self.lambda_mesh_laplacian > 0: + self.laplacian_smooth_loss_fn = LaplacianSmoothLoss() + + # Video + self.test_images = [] + self.test_depths = [] + + def training_step(self, batch, batch_idx): + # experiment iterations ratio + # i.e. what proportion of this experiment have we completed (in terms of iterations) so far? + exp_iter_ratio = self.global_step / self.iters + + # TODO(ahmadki): move to database + if exp_iter_ratio < self.latent_iter_ratio: + ambient_ratio = 1.0 + shading_type = ShadingEnum.NORMAL + as_latent = True + else: + if exp_iter_ratio <= self.albedo_iter_ratio: + ambient_ratio = 1.0 + shading_type = None + else: + # random shading + ambient_ratio = self.min_ambient_ratio + (1.0 - self.min_ambient_ratio) * random.random() + rand = random.random() + if rand >= (1.0 - self.textureless_ratio): + shading_type = ShadingEnum.TEXTURELESS + else: + shading_type = ShadingEnum.LAMBERTIAN + + as_latent = False + + return_normal_image = bool(self.lambda_2d_normal_smooth) + return_normal_perturb = bool(self.lambda_3d_normal_smooth) + return_vertices = bool(self.lambda_mesh_laplacian) + return_faces = bool(self.lambda_mesh_normal) or bool(self.lambda_mesh_laplacian) + return_faces_normals = bool(self.lambda_mesh_normal) + outputs = self( + rays_o=batch['rays_o'], # [B, H, W, 3] + rays_d=batch['rays_d'], # [B, H, W, 3] + mvp=batch['mvp'], # [B, 4, 4] + perturb=True, + ambient_ratio=ambient_ratio, + shading_type=shading_type, + binarize=False, + return_normal_image=return_normal_image, + return_normal_perturb=return_normal_perturb, + return_vertices=return_vertices, + return_faces=return_faces, + return_faces_normals=return_faces_normals, + ) + + if as_latent: + pred_rgb = ( + torch.cat([outputs['image'], outputs['opacity']], dim=-1).permute(0, 3, 1, 2).contiguous() + ) # [B, 4, H, W] + else: + pred_rgb = outputs['image'].permute(0, 3, 1, 2).contiguous() # [B, 3, H, W] + + # TODO(ahmadki): move into guidance + azimuth = batch['azimuth'] + text_z = [self.text_z['uncond']] * azimuth.shape[0] + for b in range(azimuth.shape[0]): + if azimuth[b] >= -90 and azimuth[b] < 90: + if azimuth[b] >= 0: + r = 1 - azimuth[b] / 90 + else: + r = 1 + azimuth[b] / 90 + start_z = self.text_z['front'] + end_z = self.text_z['side'] + else: + if azimuth[b] >= 0: + r = 1 - (azimuth[b] - 90) / 90 + else: + r = 1 + (azimuth[b] + 90) / 90 + start_z = self.text_z['side'] + end_z = self.text_z['back'] + pos_z = r * start_z + (1 - r) * end_z + text_z.append(pos_z) + text_z = torch.cat(text_z, dim=0) + + loss_dict = {} + + # SDS loss + guidance_loss = self.guidance.train_step( + text_z, pred_rgb, as_latent=as_latent, guidance_scale=self.guidance_scale + ) + loss_dict['lambda_sds'] = guidance_loss * self.lambda_sds + + # opacity loss + if self.lambda_opacity > 0 and 'opacity' in outputs: + loss_opacity = (outputs['opacity'] ** 2).mean() + loss_dict['loss_opacity'] = self.lambda_opacity * loss_opacity + + # entropy loss + if self.lambda_entropy > 0 and 'weights' in outputs: + alphas = outputs['weights'].clamp(1e-5, 1 - 1e-5) + loss_entropy = (-alphas * torch.log2(alphas) - (1 - alphas) * torch.log2(1 - alphas)).mean() + lambda_entropy = self.lambda_entropy * min(1, 2 * self.global_step / self.iters) + loss_dict['loss_entropy'] = lambda_entropy * loss_entropy + + if self.lambda_2d_normal_smooth > 0 and 'normal_image' in outputs: + pred_normal = outputs['normal_image'] + loss_smooth = (pred_normal[:, 1:, :, :] - pred_normal[:, :-1, :, :]).square().mean() + ( + pred_normal[:, :, 1:, :] - pred_normal[:, :, :-1, :] + ).square().mean() + loss_dict['loss_smooth'] = self.lambda_2d_normal_smooth * loss_smooth + + # orientation loss + if self.lambda_orientation > 0 and all(key in outputs for key in ['weights', 'normals', 'dirs']): + loss_orientation = ( + outputs['weights'].detach() * (outputs['normals'] * outputs['dirs']).sum(-1).clamp(min=0) ** 2 + ) + loss_orientation = loss_orientation.mean() + loss_dict['loss_orientation'] = self.lambda_orientation * loss_orientation + + if self.lambda_3d_normal_smooth > 0 and all(key in outputs for key in ['normals', 'normal_perturb']): + loss_normal_perturb = (outputs['normal_perturb'] - outputs['normals']).abs().mean() + loss_dict['loss_normal_smooth'] = self.lambda_3d_normal_smooth * loss_normal_perturb + + if self.lambda_mesh_normal > 0 and all(key in outputs for key in ['face_normals', 'faces']): + normal_consistency_loss = self.normal_consistency_loss_fn( + face_normals=outputs['face_normals'], t_pos_idx=outputs['faces'] + ) + loss_dict['normal_consistency_loss'] = self.lambda_mesh_normal * normal_consistency_loss + + if self.lambda_mesh_laplacian > 0 and all(key in outputs for key in ['verts', 'faces']): + laplacian_loss = self.laplacian_smooth_loss_fn(verts=outputs['verts'], faces=outputs['faces']) + loss_dict['laplacian_loss'] = self.lambda_mesh_laplacian * laplacian_loss + + loss = sum(loss_dict.values()) + + self.log_dict(loss_dict, prog_bar=False, rank_zero_only=True) + self.log('loss', loss, prog_bar=True, rank_zero_only=True) + + # TODO(ahmadki): LearningRateMonitor + lr = self._optimizer.param_groups[0]['lr'] + self.log('lr', lr, prog_bar=True, rank_zero_only=True) + + self.log('global_step', self.global_step + 1, prog_bar=True, rank_zero_only=True) + + return loss + + def validation_step(self, batch, batch_idx): + # save image + images, depths = self._shared_predict(batch) + + save_path = os.path.join(self.trainer.log_dir, 'validation') + os.makedirs(save_path, exist_ok=True) + for i, (image, depth) in enumerate(zip(images, depths)): + # Save image + cv2.imwrite( + os.path.join( + save_path, + f'{self.current_epoch:04d}_{self.global_step:04d}_{self.global_rank:04d}_{batch_idx:04d}_{i:04d}_rgb.png', + ), + cv2.cvtColor(image, cv2.COLOR_RGB2BGR), + ) + # Save depth + cv2.imwrite( + os.path.join( + save_path, + f'{self.current_epoch:04d}_{self.global_step:04d}_{self.global_rank:04d}_{batch_idx:04d}_{i:04d}_depth.png', + ), + depth, + ) + + def test_step(self, batch, batch_idx): + # save image + images, depths = self._shared_predict(batch) + self.test_images.append(images) + self.test_depths.append(depths) + + def on_test_epoch_end(self): + save_path = os.path.join(self.trainer.log_dir, 'test') + os.makedirs(save_path, exist_ok=True) + + images = np.concatenate(self.test_images, axis=0) + imageio.mimwrite( + os.path.join(os.path.join(save_path, f'{self.current_epoch:04d}_{self.global_step:04d}_rgb.mp4')), + images, + fps=25, + quality=8, + macro_block_size=1, + ) + + depths = np.concatenate(self.test_depths, axis=0) + imageio.mimwrite( + os.path.join(os.path.join(save_path, f'{self.current_epoch:04d}_{self.global_step:04d}_depth.mp4')), + depths, + fps=25, + quality=8, + macro_block_size=1, + ) + + self.test_images.clear() + self.test_depths.clear() + + def predict_step(self, batch, batch_idx): + return self._shared_predict(self, batch) + + def forward( + self, + rays_o, + rays_d, + mvp, + perturb, + ambient_ratio, + shading_type, + binarize, + return_normal_image, + return_normal_perturb, + return_vertices, + return_faces, + return_faces_normals, + ): + outputs = self.renderer( + rays_o=rays_o, + rays_d=rays_d, + mvp=mvp, + perturb=perturb, + ambient_ratio=ambient_ratio, + shading_type=shading_type, + binarize=binarize, + return_normal_image=return_normal_image, + return_normal_perturb=return_normal_perturb, + return_vertices=return_vertices, + return_faces=return_faces, + return_faces_normals=return_faces_normals, + ) + return outputs + + def _shared_predict(self, data): + outputs = self( + rays_o=data['rays_o'], # [B, H, W, 3] + rays_d=data['rays_d'], # [B, H, W, 3] + mvp=data['mvp'], + perturb=False, + ambient_ratio=data['ambient_ratio'] if 'ambient_ratio' in data else 1.0, # TODO(ahmadki): move to dataset + shading_type=data['shading_type'] if 'shading_type' in data else None, # TODO(ahmadki): move to dataset + binarize=False, + return_normal_image=False, + return_normal_perturb=False, + return_vertices=False, + return_faces=False, + return_faces_normals=False, + ) + + images_np = outputs['image'].detach().cpu().numpy() + images_np = (images_np * 255).astype(np.uint8) + + depths_np = outputs['depth'].detach().cpu().numpy() + depths_np = (depths_np - depths_np.min()) / (np.ptp(depths_np) + 1e-6) + depths_np = (depths_np * 255).astype(np.uint8) + + return images_np, depths_np + + # TODO(ahmadki): rework + def setup_optimization(self): + cfg = self._cfg.optim + optimizer_args = dict(cfg) + optimizer_args.pop('name', None) + + optimizer = optim.get_optimizer(cfg.name) + + optimizer = optimizer(params=self.parameters(), **optimizer_args) + + self._optimizer = optimizer + + def configure_optimizers(self): + self.setup_optimization() + return self._optimizer diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/nerf/txt2nerf_base.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/nerf/txt2nerf_base.py new file mode 100644 index 0000000..dbd6601 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/nerf/txt2nerf_base.py @@ -0,0 +1,93 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.multimodal.models.nerf.base import NerfModelBase + + +class Txt2NerfBase(NerfModelBase): + def __init__(self, cfg): + super().__init__(cfg) + self.prompt = cfg.prompt + self.negative_prompt = cfg.negative_prompt + self.front_prompt = cfg.front_prompt + self.side_prompt = cfg.side_prompt + self.back_prompt = cfg.back_prompt + + self.nerf_cfg = cfg.nerf + self.renderer_cfg = cfg.renderer + self.guidance_cfg = cfg.guidance + + nerf = self.from_config_dict(cfg.nerf) + material = self.from_config_dict(cfg.material) + background = self.from_config_dict(cfg.background) + self.renderer = self.build_renderer(cfg.renderer, nerf, material, background) + self.guidance = None + + def build_renderer(self, cfg, nerf, material, background): + renderer = self.from_config_dict(cfg) + renderer.nerf = nerf + renderer.material = material + renderer.background = background + return renderer + + def build_guidance(self, cfg): + self.guidance = self.from_config_dict(cfg) + self.guidance.eval() + for p in self.guidance.parameters(): + p.requires_grad = False + + def prepare_embeddings(self): + # TODO(ahmadki): add top view ? + self.text_z = { + "default": self.guidance.get_text_embeds([self.prompt]), + "uncond": self.guidance.get_text_embeds([self.negative_prompt]), + "front": self.guidance.get_text_embeds([f"{self.prompt}{self.front_prompt}"]), + "side": self.guidance.get_text_embeds([f"{self.prompt}{self.side_prompt}"]), + "back": self.guidance.get_text_embeds([f"{self.prompt}{self.back_prompt}"]), + } + + def on_fit_start(self) -> None: + self.build_guidance(self.guidance_cfg) + self.prepare_embeddings() + + def on_train_batch_start(self, batch, batch_idx, unused=0): + if self.is_module_updatable(self.guidance): + self.guidance.update_step(epoch=self.current_epoch, global_step=self.global_step) + + if self.is_module_updatable(self.renderer.nerf): + self.renderer.nerf.update_step(epoch=self.current_epoch, global_step=self.global_step) + + if self.is_module_updatable(self.renderer.material): + self.renderer.material.update_step(epoch=self.current_epoch, global_step=self.global_step) + + if self.is_module_updatable(self.renderer.background): + self.renderer.background.update_step(epoch=self.current_epoch, global_step=self.global_step) + + if self.is_module_updatable(self.renderer): + self.renderer.update_step(epoch=self.current_epoch, global_step=self.global_step) + + dataset = self.trainer.train_dataloader.dataset + if self.is_module_updatable(dataset): + dataset.update_step(epoch=self.current_epoch, global_step=self.global_step) + + def mesh(self, resolution, batch_size=128, density_thresh=None): + return self.nerf.mesh(resolution=resolution, batch_size=batch_size, density_thresh=density_thresh) + + def on_save_checkpoint(self, checkpoint): + # remove guidance from checkpoint. + # We can still laod the model without guidance checkpoints because the module is not initalized + # at __init__ time. + keys_to_remove = [key for key in checkpoint['state_dict'].keys() if key.startswith('guidance.')] + for key in keys_to_remove: + del checkpoint['state_dict'][key] diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/controlnet/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/controlnet/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/controlnet/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/controlnet/controlnet.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/controlnet/controlnet.py new file mode 100644 index 0000000..3f59eb6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/controlnet/controlnet.py @@ -0,0 +1,1023 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any + +import einops +import torch +import torch.nn as nn +from einops import rearrange, repeat +from omegaconf import DictConfig +from pytorch_lightning import Trainer +from pytorch_lightning.utilities.rank_zero import rank_zero_only +from torch._inductor import config as inductor_config + +from nemo.collections.multimodal.data.controlnet.controlnet_dataset import build_train_valid_datasets +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import LatentDiffusion +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.ddim import DDIMSampler +from nemo.collections.multimodal.modules.stable_diffusion.attention import SpatialTransformer +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel import ( + AttentionBlock, + Downsample, + ResBlock, + TimestepEmbedSequential, + UNetModel, +) +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import ( + conv_nd, + linear, + timestep_embedding, + zero_module, +) +from nemo.collections.multimodal.parts.stable_diffusion.utils import exists, log_txt_as_img +from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel +from nemo.collections.nlp.modules.common.megatron.module import Float16Module +from nemo.utils import logging + +try: + from apex import amp + from apex.transformer.enums import AttnMaskType + from apex.transformer.pipeline_parallel.utils import get_num_microbatches + + HAVE_APEX = True +except (ImportError, ModuleNotFoundError): + HAVE_APEX = False + +try: + from megatron.core import parallel_state + from megatron.core.pipeline_parallel.schedules import get_forward_backward_func + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + +try: + from torchvision.utils import make_grid + + TORCHVISION_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + TORCHVISION_AVAILABLE = False + + +class ControlledUnetModel(UNetModel): + ''' + Modified Unet class that combines the output of controlling copy and frozen copy during forward pass. + ''' + + def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, **kwargs): + ''' + :param x: latents of diffusion process + :param timesteps: diffusion step + :param context: text embedding guiding the denoising process + :param control: output from controlling copy of each corresponding layer + :param only_mid_control: whether to add the output of controlling copy from middle block only + ''' + hs = [] + with torch.no_grad(): + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) + emb = self.time_embed(t_emb) + h = x.type(emb.dtype) + for module in self.input_blocks: + h = module(h, emb, context) + hs.append(h) + h = self.middle_block(h, emb, context) + + if control is not None: + h += control.pop() + + for i, module in enumerate(self.output_blocks): + if only_mid_control or control is None: + h = torch.cat([h, hs.pop()], dim=1) + else: + h = torch.cat([h, hs.pop() + control.pop()], dim=1) + h = module(h, emb, context) + + h = h.type(x.dtype) + return self.out(h) + + +class ControlLDM(LatentDiffusion): + def __init__(self, cfg, model_parallel_config): + super().__init__(cfg=cfg, model_parallel_config=model_parallel_config) + self.control_model = ControlLDM.from_config_dict(cfg.control_stage_config) + self.control_key = cfg.control_key + self.only_mid_control = cfg.only_mid_control + self.control_scales = [1.0] * 13 + self.sd_locked = cfg.sd_locked + self.channels_last = cfg.channels_last + + if cfg.get("inductor", False): + # TorchInductor with CUDA graph can lead to OOM + inductor_config.triton.cudagraphs = cfg.get("inductor_cudagraphs", False) + torch._dynamo.config.dynamic_shapes = False + torch._dynamo.config.automatic_dynamic_shapes = False + self.control_model = torch.compile(self.control_model) + + if self.channels_last: + self.control_model = self.control_model.to(memory_format=torch.channels_last) + + @torch.no_grad() + def get_input(self, batch, k, bs=None, *args, **kwargs): + x, c = super().get_input(batch, self.first_stage_key, *args, **kwargs) + control = batch[self.control_key] + if bs is not None: + control = control[:bs] + control = control.to(torch.cuda.current_device()) + if self.channels_last: + control = control.permute(0, 3, 1, 2).to(non_blocking=True) + else: + control = einops.rearrange(control, 'b h w c -> b c h w') + control = control.to(memory_format=torch.contiguous_format).float() + return x, dict(c_crossattn=c, c_concat=control) + + def apply_model(self, x_noisy, t, cond, *args, **kwargs): + assert isinstance(cond, dict) + diffusion_model = self.model.diffusion_model + + # cond_txt = torch.cat(cond['c_crossattn'], 1) ## Has removed this first dim in the get_input function, same for below hint input + cond_txt = cond['c_crossattn'] + + if cond['c_concat'] is None: + eps = diffusion_model( + x=x_noisy, timesteps=t, context=cond_txt, control=None, only_mid_control=self.only_mid_control + ) + else: + control = self.control_model(x=x_noisy, hint=cond['c_concat'], timesteps=t, context=cond_txt) + control = [c * scale for c, scale in zip(control, self.control_scales)] + eps = diffusion_model( + x=x_noisy, timesteps=t, context=cond_txt, control=control, only_mid_control=self.only_mid_control + ) + return eps + + @torch.no_grad() + def get_unconditional_conditioning(self, N): + return self.get_learned_conditioning([""] * N) + + @torch.no_grad() + def log_images( + self, + batch, + N=4, + n_row=2, + sample=False, + ddim_steps=50, + ddim_eta=0.0, + return_keys=None, + quantize_denoised=True, + inpaint=True, + plot_denoise_rows=False, + plot_progressive_rows=True, + plot_diffusion_rows=False, + unconditional_guidance_scale=9.0, + unconditional_guidance_label=None, + use_ema_scope=True, + **kwargs, + ): + use_ddim = ddim_steps is not None + + log = dict() + batch = next(batch) + batch['images'] = batch['images'].to(torch.cuda.current_device()) + batch['hint'] = batch['hint'].to(torch.cuda.current_device()) + N = batch['images'].shape[0] + z, c = self.get_input(batch, self.first_stage_key, bs=N) + c_cat, c = c["c_concat"][:N], c["c_crossattn"][:N] + N = min(z.shape[0], N) + n_row = min(z.shape[0], n_row) + log["reconstruction"] = self.decode_first_stage(z) + log["control"] = c_cat * 2.0 - 1.0 + log["conditioning"] = log_txt_as_img((512, 512), batch[self.cond_stage_key], size=16) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') + diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') + assert TORCHVISION_AVAILABLE, "Torchvision imports failed but they are required." + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + samples, z_denoise_row = self.sample_log( + cond={"c_concat": c_cat, "c_crossattn": c}, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + ) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if unconditional_guidance_scale > 1.0: + uc_cross = self.get_unconditional_conditioning(N) + uc_cat = c_cat # torch.zeros_like(c_cat) + uc_full = {"c_concat": uc_cat, "c_crossattn": uc_cross} + samples_cfg, _ = self.sample_log( + cond={"c_concat": c_cat, "c_crossattn": c}, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc_full, + ) + x_samples_cfg = self.decode_first_stage(samples_cfg) + log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg + + return log + + @torch.no_grad() + def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): + ddim_sampler = DDIMSampler(self) + c, h, w = cond["c_concat"][0].shape + shape = (self.channels, h // 8, w // 8) + samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, **kwargs) + return samples, intermediates + + def parameters(self): + params = list(self.control_model.parameters()) + if not self.sd_locked: + params += list(self.model.diffusion_model.output_blocks.parameters()) + params += list(self.model.diffusion_model.out.parameters()) + return params + + def low_vram_shift(self, is_diffusing): + if is_diffusing: + self.model = self.model.cuda() + self.control_model = self.control_model.cuda() + self.first_stage_model = self.first_stage_model.cpu() + self.cond_stage_model = self.cond_stage_model.cpu() + else: + self.model = self.model.cpu() + self.control_model = self.control_model.cpu() + self.first_stage_model = self.first_stage_model.cuda() + self.cond_stage_model = self.cond_stage_model.cuda() + + +class ControlNet(nn.Module): + def __init__( + self, + image_size, + in_channels, + model_channels, + hint_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + use_checkpoint=False, + use_fp16=False, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, ###TODO MMY these are new + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + use_flash_attention=False, + from_pretrained_unet=None, + from_NeMo=True, + ): + super().__init__() + if use_spatial_transformer: + assert ( + context_dim is not None + ), 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' + + if context_dim is not None: + assert ( + use_spatial_transformer + ), 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + from omegaconf.listconfig import ListConfig + + if type(context_dim) == ListConfig: + context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.dims = dims + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError( + "provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult" + ) + self.num_res_blocks = num_res_blocks + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + assert all( + map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))) + ) + print( + f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " + f"This option has LESS priority than attention_resolutions {attention_resolutions}, " + f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " + f"attention will still not be set." + ) + + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.use_checkpoint = use_checkpoint + self.dtype = torch.float16 if use_fp16 else torch.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), + ) + + self.input_blocks = nn.ModuleList( + [TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1))] + ) + self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)]) + + self.input_hint_block = TimestepEmbedSequential( + conv_nd(dims, hint_channels, 16, 3, padding=1), + nn.SiLU(), + conv_nd(dims, 16, 16, 3, padding=1), + nn.SiLU(), + conv_nd(dims, 16, 32, 3, padding=1, stride=2), + nn.SiLU(), + conv_nd(dims, 32, 32, 3, padding=1), + nn.SiLU(), + conv_nd(dims, 32, 96, 3, padding=1, stride=2), + nn.SiLU(), + conv_nd(dims, 96, 96, 3, padding=1), + nn.SiLU(), + conv_nd(dims, 96, 256, 3, padding=1, stride=2), + nn.SiLU(), + zero_module(conv_nd(dims, 256, model_channels, 3, padding=1)), + ) + + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer( + ch, + num_heads, + dim_head, + depth=transformer_depth, + context_dim=context_dim, + disable_self_attn=disabled_sa, + use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, + use_flash_attention=use_flash_attention, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self.zero_convs.append(self.make_zero_conv(ch)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ) + ch = out_ch + input_block_chans.append(ch) + self.zero_convs.append(self.make_zero_conv(ch)) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer( # always uses a self-attn + ch, + num_heads, + dim_head, + depth=transformer_depth, + context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, + use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, + use_flash_attention=use_flash_attention, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self.middle_block_out = self.make_zero_conv(ch) + self._feature_size += ch + + if from_pretrained_unet is not None: + self.load_from_unet(from_pretrained_unet=from_pretrained_unet, from_NeMo=from_NeMo) + + def load_from_unet(self, from_pretrained_unet, from_NeMo=True): + if not from_NeMo: + print('loading from other source of unet is experimental! Carefully check if keys are loaded correctly.') + else: + print("Loading unet blocks from sd") + + state_dict = torch.load(from_pretrained_unet, map_location='cpu') + state_dict = state_dict['state_dict'] + model_state_dict = self.state_dict() + + re_state_dict = {} + for key_, value_ in state_dict.items(): + if key_.startswith('model.model.diffusion_model'): + re_state_dict[key_.replace('model.model.diffusion_model.', '')] = value_ + if key_.startswith('model.diffusion_model'): + re_state_dict[key_.replace('model.diffusion_model.', '')] = value_ + if key_.startswith('model.model._orig_mod.diffusion_model'): + re_state_dict[key_.replace('model.model._orig_mod.diffusion_model.', '')] = value_ + if key_.startswith('model._orig_mod.diffusion_model'): + re_state_dict[key_.replace('model._orig_mod.diffusion_model.', '')] = value_ + + expected_keys = list(model_state_dict.keys()) + loaded_keys = list(re_state_dict.keys()) + missing_keys = list(set(expected_keys) - set(loaded_keys)) + unexpected_keys = list(set(loaded_keys) - set(expected_keys)) + + if ( + 'input_blocks.1.0.in_layers.2.weight' in loaded_keys + and 'input_blocks.1.0.in_layers.1.weight' in expected_keys + ): + # GroupNormOpt fuses activation function to one layer, thus the indexing of weights are shifted for following + for key_ in missing_keys: + if key_.startswith('input_blocks') or key_.startswith('middle_block.'): + s = key_.split('.') + idx = int(s[-2]) + new_key_ = ".".join(s[:-2] + [str(int(idx + 1))] + [s[-1]]) + re_state_dict[key_] = re_state_dict[new_key_] + + loaded_keys = list(re_state_dict.keys()) + missing_keys = list(set(expected_keys) - set(loaded_keys)) + unexpected_keys = list(set(loaded_keys) - set(expected_keys)) + + self.load_state_dict(re_state_dict, strict=False) + + if len(missing_keys) > 42: + print( + 'warning: only input hint blocks and zero conv layers are randomly initialized. This message indicates some unet blocks are not loaded correctly.' + ) + print(f'There is {len(missing_keys)} total missing keys') + print("Missing:", missing_keys) + print("Unexpected:", unexpected_keys) + else: + print("sd blocks loaded successfully") + + def make_zero_conv(self, channels): + return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0))) + + def forward(self, x, hint, timesteps, context, **kwargs): + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) + emb = self.time_embed(t_emb) + + guided_hint = self.input_hint_block(hint, emb, context) + outs = [] + + h = x.type(self.dtype) + for module, zero_conv in zip(self.input_blocks, self.zero_convs): + if guided_hint is not None: + h = module(h, emb, context) + h += guided_hint + guided_hint = None + else: + h = module(h, emb, context) + outs.append(zero_conv(h, emb, context)) + + h = self.middle_block(h, emb, context) + outs.append(self.middle_block_out(h, emb, context)) + + return outs + + +class MegatronControlNet(MegatronBaseModel): + def __init__(self, cfg: DictConfig, trainer: Trainer): + if not HAVE_APEX: + raise ImportError( + "Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + + if not HAVE_MEGATRON_CORE: + raise ImportError( + "megatron-core was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + + # this prevents base constructor from initializing tokenizer + self.tokenizer = None + super().__init__(cfg, trainer=trainer) + + self._validate_trainer() + + # megatron_amp_O2 is not yet supported in diffusion models + self.megatron_amp_O2 = cfg.get('megatron_amp_O2', False) + + self.model = self.model_provider_func() + + self.conditioning_keys = [] + + if self.trainer.precision in ['bf16', 'bf16-mixed']: + self.autocast_dtype = torch.bfloat16 + elif self.trainer.precision in [32, '32', '32-true']: + self.autocast_dtype = torch.float + elif self.trainer.precision in [16, '16', '16-mixed']: + self.autocast_dtype = torch.half + else: + raise ValueError('precision must be in ["32-true", "16-mixed", "bf16-mixed"]') + + def get_module_list(self): + if isinstance(self.model, list): + return [model.module if isinstance(model, Float16Module) else model for model in self.model] + elif isinstance(self.model, Float16Module): + return [self.model.module] + else: + return [self.model] + + def model_provider_func(self, pre_process=True, post_process=True): + """Model depends on pipeline paralellism.""" + model = ControlLDM(cfg=self.cfg, model_parallel_config=self.model_parallel_config) + return model + + def forward(self, x, c, *args, **kwargs): + output_tensor = self.model(x, c, *args, **kwargs) + return output_tensor + + @rank_zero_only + @torch.no_grad() + def on_train_batch_start(self, batch, batch_idx, dataloader_idx=0): + if self.cfg.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0: + assert self.cfg.scale_factor == 1.0, 'rather not use custom rescaling and std-rescaling simultaneously' + batch[self.cfg.first_stage_key] = batch[self.cfg.first_stage_key].cuda(non_blocking=True) + self.model.on_train_batch_start(batch, batch_idx) + + def fwd_bwd_step(self, dataloader_iter, forward_only): + tensor_shape = None # Placeholder + + # handle asynchronous grad reduction + no_sync_func = None + if not forward_only and self.with_distributed_adam: + no_sync_func = partial(self._optimizer.no_sync, greedy_grad_copy=self.megatron_amp_O2,) + + # pipeline schedules will get these from self.model.config + for module in self.get_module_list(): + module.config.no_sync_func = no_sync_func + + # run forward and backwards passes for an entire global batch + # we do this inside training_step to support pipeline parallelism + fwd_bwd_function = get_forward_backward_func() + + losses_reduced_per_micro_batch = fwd_bwd_function( + forward_step_func=self.get_forward_output_and_loss_func(), + data_iterator=dataloader_iter, + model=self.model, + num_microbatches=get_num_microbatches(), + forward_only=forward_only, + seq_length=None, + micro_batch_size=self.cfg.micro_batch_size, + ) + + # losses_reduced_per_micro_batch is a list of dictionaries + # [{"loss": 0.1}, {"loss": 0.2}, ...] which are from gradient accumulation steps + # only the last stages of the pipeline return losses + loss_dict = {} + if losses_reduced_per_micro_batch: + if (not forward_only) or self.cfg.data.get('validation_drop_last', True): + # average loss across micro batches + for key in losses_reduced_per_micro_batch[0]: + loss_tensors_list = [loss_reduced[key] for loss_reduced in losses_reduced_per_micro_batch] + loss_tensor = torch.stack(loss_tensors_list) + loss_dict[key] = loss_tensor.mean() + loss_mean = loss_dict["train/loss"] + else: + raise NotImplementedError("Losses of micro batches sizes must be uniform!") + else: + if forward_only: + loss_mean = [] + else: + loss_mean = torch.tensor(0.0, device=torch.cuda.current_device()) + + return loss_mean, loss_dict + + def training_step(self, dataloader_iter): + """ + Our dataloaders produce a micro-batch and then we fetch + a number of microbatches depending on the global batch size and model parallel size + from the dataloader to produce a list of microbatches. + Batch should be a list of microbatches and those microbatches should on CPU. + Microbatches are then moved to GPU during the pipeline. + The list of microbatches is then piped through the pipeline using Apex fwd/bwd functions. + """ + # we zero grads here because we also call backward in the apex fwd/bwd functions + self._optimizer.zero_grad() + + loss_mean, loss_dict = self.fwd_bwd_step(dataloader_iter, False) + + if self.cfg.get('tensor_model_parallel_size', 1) > 1 and self.cfg.get('sequence_parallel', False): + self.allreduce_sequence_parallel_gradients() + + if self.with_distributed_adam: + # gradients are reduced internally in distributed optimizer + pass + elif self.megatron_amp_O2: + # # when using pipeline parallelism grads must be all-reduced after the pipeline (not asynchronously) + # if self.cfg.get('pipeline_model_parallel_size', 1) > 1 or self.cfg.get('sequence_parallel', False): + # # main grads are stored in the MainParamsOptimizer wrapper + # self._optimizer.allreduce_main_grads() + self._optimizer.allreduce_main_grads() + else: + # async grad allreduce is not currently implemented for O1/autocasting mixed precision training + # so we all-reduce gradients after the pipeline + self.allreduce_gradients() # @sangkug we think this is causing memory to blow up (hurts perf) + + if self.cfg.precision == [16, '16', '16-mixed']: + loss_scale = self.trainer.precision_plugin.scaler._scale + if loss_scale is not None: + self.log('loss_scale', loss_scale, batch_size=1) + + self.log_dict(loss_dict, prog_bar=False, logger=True, on_step=True, rank_zero_only=True, batch_size=1) + self.log('reduced_train_loss', loss_mean, prog_bar=True, rank_zero_only=True, batch_size=1) + lr = self._optimizer.param_groups[0]['lr'] + self.log('lr', lr, prog_bar=True, rank_zero_only=True, batch_size=1) + self.log('global_step', self.trainer.global_step + 1, prog_bar=True, rank_zero_only=True, batch_size=1) + self.log( + 'consumed_samples', + self.compute_consumed_samples(self.trainer.global_step + 1 - self.init_global_step), + prog_bar=True, + rank_zero_only=True, + batch_size=1, + ) + return loss_mean + + def backward(self, *args, **kwargs): + """ LightningModule hook to do backward. + We want this to do nothing since we run backward in the fwd/bwd functions from apex. + No need to call it here. + """ + pass + + def optimizer_zero_grad(self, *args, **kwargs): + """ LightningModule hook to zero grad. + We want this to do nothing as we are zeroing grads during the training_step. + """ + pass + + def _append_sequence_parallel_module_grads(self, module, grads): + """ Helper method for allreduce_sequence_parallel_gradients""" + + for param in module.parameters(): + sequence_parallel_param = getattr(param, 'sequence_parallel', False) + if sequence_parallel_param and param.requires_grad: + if self.megatron_amp_O2: + grad = param.main_grad + else: + grad = param.grad + grads.append(grad.data) + + def get_forward_output_and_loss_func(self): + def process_batch(batch): + """ Prepares the global batch for apex fwd/bwd functions. + Global batch is a list of micro batches. + """ + # noise_map, condition + batch[self.cfg.first_stage_key] = batch[self.cfg.first_stage_key].cuda(non_blocking=True) + if isinstance(batch[self.cfg.cond_stage_key], torch.Tensor): + # in the case of precached text embeddings, cond_stage is also a tensor + batch[self.cfg.cond_stage_key] = batch[self.cfg.cond_stage_key].cuda(non_blocking=True) + + # SD has more dedicated structure for encoding, so we enable autocasting here as well + with torch.cuda.amp.autocast( + self.autocast_dtype in (torch.half, torch.bfloat16), dtype=self.autocast_dtype, + ): + x, c = self.model.get_input(batch, self.cfg.first_stage_key) + + if not isinstance(c, dict): + return [x, c] + + if len(self.conditioning_keys) == 0: + self.conditioning_keys = list(c.keys()) + c_list = [c[key] for key in self.conditioning_keys] + return [x, *c_list] + + def fwd_output_and_loss_func(dataloader_iter, model): + batch, _, _ = next(dataloader_iter) + batch = process_batch(batch) + batch = [x.cuda(non_blocking=True) for x in batch] + if len(self.conditioning_keys) == 0: + x, c = batch + else: + x = batch[0] + c = {} + for idx, key in enumerate(self.conditioning_keys): + c[key] = batch[1 + idx] + loss, loss_dict = model(x, c) + + def dummy(output_tensor): + return loss, loss_dict + + # output_tensor, and a function to convert output_tensor to loss + loss_dict + return loss, dummy + + return fwd_output_and_loss_func + + def get_forward_output_only_func(self): + def fwd_output_only_func(batch, model): + raise NotImplementedError + + return fwd_output_only_func + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + tensor_shape = None # Placeholder + fwd_bwd_function = get_forward_backward_func() + + losses_reduced_per_micro_batch = fwd_bwd_function( + forward_step_func=self.get_forward_output_and_loss_func(), + data_iterator=dataloader_iter, + model=[self.model], + num_microbatches=get_num_microbatches(), + forward_only=True, + tensor_shape=None, # required by pipeline parallelism + dtype=self.autocast_dtype, + sequence_parallel=self.cfg.get('sequence_parallel', False), + enable_autocast=True, + ) + # only the last stages of the pipeline return losses + val_loss_dict = {} + if losses_reduced_per_micro_batch: + # average loss across micro batches + for key in losses_reduced_per_micro_batch[0]: + loss_tensors_list = [loss_reduced[key] for loss_reduced in losses_reduced_per_micro_batch] + loss_tensor = torch.stack(loss_tensors_list) + val_loss_dict[key] = loss_tensor.mean() + + self.log_dict(val_loss_dict, prog_bar=False, logger=True, on_step=False, on_epoch=True) + + def setup(self, stage=None): + """ PTL hook that is executed after DDP spawns. + We setup datasets here as megatron datasets require DDP to instantiate. + See https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#setup for more information. + Args: + stage (str, optional): Can be 'fit', 'validate', 'test' or 'predict'. Defaults to None. + """ + self.model.rng.manual_seed(self.cfg.seed + 100 * parallel_state.get_data_parallel_rank()) + + # log number of parameters + if isinstance(self.model, list): + num_parameters_on_device = sum( + [sum([p.nelement() for p in model_module.parameters()]) for model_module in self.model] + ) + else: + num_parameters_on_device = sum([p.nelement() for p in self.model.parameters()]) + + # to be summed across data parallel group + total_num_parameters = torch.tensor(num_parameters_on_device).cuda(non_blocking=True) + + torch.distributed.all_reduce(total_num_parameters, group=parallel_state.get_model_parallel_group()) + + logging.info( + f'Pipeline model parallel rank: {parallel_state.get_pipeline_model_parallel_rank()}, ' + f'Tensor model parallel rank: {parallel_state.get_tensor_model_parallel_rank()}, ' + f'Number of model parameters on device: {num_parameters_on_device:.2e}. ' + f'Total number of model parameters: {total_num_parameters:.2e}.' + ) + + resume_checkpoint_path = self.trainer.ckpt_path + if resume_checkpoint_path: + init_consumed_samples = self._extract_consumed_samples_from_ckpt(resume_checkpoint_path) + else: + init_consumed_samples = 0 + self.init_consumed_samples = init_consumed_samples + self.init_global_step = self.trainer.global_step + + # allowing restored models to optionally setup datasets + self.build_train_valid_test_datasets() + + # Batch size need to be provided for webdatset + self._num_micro_batches = get_num_microbatches() + self._micro_batch_size = self.cfg.micro_batch_size + + self.setup_training_data(self.cfg.data) + self.setup_validation_data(self.cfg.data) + self.setup_test_data(self.cfg.data) + + def build_train_valid_test_datasets(self): + logging.info('Building datasets for Stable Diffusion...') + if self.trainer.limit_val_batches > 1.0 and isinstance(self.trainer.limit_val_batches, float): + raise ValueError("limit_val_batches must be an integer or float less than or equal to 1.0.") + + if self.cfg.first_stage_key.endswith("encoded"): + self._train_ds, self._validation_ds = build_train_valid_precached_datasets( + model_cfg=self.cfg, consumed_samples=self.compute_consumed_samples(0), + ) + else: + self._train_ds, self._validation_ds = build_train_valid_datasets( + model_cfg=self.cfg, consumed_samples=self.compute_consumed_samples(0) + ) + self._test_ds = None + + if self._train_ds is not None: + logging.info(f'Length of train dataset: {len(self._train_ds)}') + if self._validation_ds is not None: + logging.info(f'Length of val dataset: {len(self._validation_ds)}') + if self._test_ds is not None: + logging.info(f'Length of test dataset: {len(self._test_ds)}') + logging.info(f'Finished building datasets for LatentDiffusion.') + return self._train_ds, self._validation_ds, self._test_ds + + def setup_training_data(self, cfg): + if hasattr(self, '_train_ds') and self._train_ds is not None: + consumed_samples = self.compute_consumed_samples(0) + logging.info( + f'Setting up train dataloader with len(len(self._train_ds)): {len(self._train_ds)} and consumed samples: {consumed_samples}' + ) + self._train_dl = torch.utils.data.DataLoader( + self._train_ds, + batch_size=self._micro_batch_size, + num_workers=cfg.num_workers, + pin_memory=True, + drop_last=True, + persistent_workers=True, + ) + + def setup_validation_data(self, cfg): + if hasattr(self, '_validation_ds') and self._validation_ds is not None: + consumed_samples = 0 + logging.info( + f'Setting up validation dataloader with len(len(self._validation_ds)): {len(self._validation_ds)} and consumed samples: {consumed_samples}' + ) + self._validation_dl = torch.utils.data.DataLoader( + self._validation_ds, + batch_size=self._micro_batch_size, + num_workers=cfg.num_workers, + pin_memory=True, + drop_last=False, + persistent_workers=True, + ) + + def setup_test_data(self, cfg): + if hasattr(self, '_test_ds') and self._test_ds is not None: + consumed_samples = 0 + logging.info( + f'Setting up test dataloader with len(len(self._test_ds)): {len(self._test_ds)} and consumed samples: {consumed_samples}' + ) + self._test_dl = torch.utils.data.DataLoader( + self._test_ds, batch_size=self._micro_batch_size, num_workers=cfg.num_workers, pin_memory=True, + ) + + def transfer_batch_to_device(self, batch: Any, device: torch.device, dataloader_idx: int) -> Any: + """ PTL hook: https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#transfer-batch-to-device + When using pipeline parallelism, we need the global batch to remain on the CPU, + since the memory overhead will be too high when using a large number of microbatches. + Microbatches are transferred from CPU to GPU inside the pipeline. + """ + return batch + + def _validate_trainer(self): + """ Certain trainer configurations can break training. + Here we try to catch them and raise an error. + """ + if self.trainer.accumulate_grad_batches > 1: + raise ValueError( + f'Gradient accumulation is done within training_step. trainer.accumulate_grad_batches must equal 1' + ) + + @classmethod + def list_available_models(cls): + return None + + def log_images(self, *args, **kwargs): + return self.model.log_images(*args, **kwargs) + + def parameters(self): + if isinstance(self.model, list): + return itertools.chain.from_iterable(module.parameters() for module in self.model) + else: + return self.model.parameters() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/controlnet/util.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/controlnet/util.py new file mode 100644 index 0000000..3d9a7d1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/controlnet/util.py @@ -0,0 +1,102 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import numpy as np +import torch +import torchvision +from PIL import Image +from pytorch_lightning import Callback +from pytorch_lightning.utilities.rank_zero import rank_zero_only + + +class ImageLogger(Callback): + def __init__( + self, + batch_frequency=2000, + max_images=4, + clamp=True, + increase_log_steps=True, + rescale=True, + disabled=False, + log_on_batch_idx=False, + log_first_step=False, + log_images_kwargs=None, + ): + super().__init__() + self.rescale = rescale + self.batch_freq = batch_frequency + self.max_images = max_images + if not increase_log_steps: + self.log_steps = [self.batch_freq] + self.clamp = clamp + self.disabled = disabled + self.log_on_batch_idx = log_on_batch_idx + self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {} + self.log_first_step = log_first_step + + @rank_zero_only + def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx): + root = os.path.join(save_dir, "image_log", split) + for k in images: + grid = torchvision.utils.make_grid(images[k], nrow=4) + if self.rescale: + grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w + grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1) + grid = grid.numpy() + grid = (grid * 255).astype(np.uint8) + filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(k, global_step, current_epoch, batch_idx) + path = os.path.join(root, filename) + os.makedirs(os.path.split(path)[0], exist_ok=True) + Image.fromarray(grid).save(path) + + def log_img(self, pl_module, batch, batch_idx, split="train"): + check_idx = batch_idx # if self.log_on_batch_idx else pl_module.global_step + if ( + self.check_frequency(check_idx) + and hasattr(pl_module, "log_images") # batch_idx % self.batch_freq == 0 + and callable(pl_module.log_images) + and self.max_images > 0 + ): + logger = type(pl_module.logger) + + is_train = pl_module.training + if is_train: + pl_module.eval() + + with torch.no_grad(): + images = pl_module.log_images(batch, split=split, **self.log_images_kwargs) + + for k in images: + N = min(images[k].shape[0], self.max_images) + images[k] = images[k][:N] + if isinstance(images[k], torch.Tensor): + images[k] = images[k].detach().cpu() + if self.clamp: + images[k] = torch.clamp(images[k], -1.0, 1.0) + + self.log_local( + pl_module.logger.save_dir, split, images, pl_module.global_step, pl_module.current_epoch, batch_idx + ) + + if is_train: + pl_module.train() + + def check_frequency(self, check_idx): + return check_idx % self.batch_freq == 0 + + def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx): + if not self.disabled: + self.log_img(pl_module, batch, batch_idx, split="train") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/dreambooth/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/dreambooth/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/dreambooth/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/dreambooth/dreambooth.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/dreambooth/dreambooth.py new file mode 100644 index 0000000..317cdf5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/dreambooth/dreambooth.py @@ -0,0 +1,663 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from functools import partial +from typing import Any, Optional + +import torch +from omegaconf import DictConfig +from pytorch_lightning import Trainer +from torch._inductor import config as inductor_config + +from nemo.collections.multimodal.data.dreambooth.dreambooth_dataset import DreamBoothDataset +from nemo.collections.multimodal.modules.stable_diffusion.attention import LinearWrapper +from nemo.collections.multimodal.modules.stable_diffusion.distributions.distributions import ( + DiagonalGaussianDistribution, +) +from nemo.collections.multimodal.modules.stable_diffusion.encoders.modules import LoraWrapper +from nemo.collections.multimodal.parts.utils import randn_like +from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import MegatronPretrainingRandomSampler +from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel +from nemo.collections.nlp.modules.common.megatron.module import Float16Module +from nemo.collections.nlp.parts.mixins.nlp_adapter_mixins import NLPAdapterModelMixin +from nemo.collections.nlp.parts.utils_funcs import get_last_rank +from nemo.core.classes.common import Serialization +from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin +from nemo.utils import logging + +try: + from apex import amp + from apex.transformer.enums import AttnMaskType + from apex.transformer.pipeline_parallel.utils import get_num_microbatches + + HAVE_APEX = True +except (ImportError, ModuleNotFoundError): + HAVE_APEX = False + +try: + from megatron.core import parallel_state + from megatron.core.pipeline_parallel.schedules import get_forward_backward_func + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +def _collate_fn(examples, with_prior_preservation=False): + if with_prior_preservation: + prompts = [[example["instance_prompt"], example["reg_prompt"]] for example in examples] + images = [example["instance_images"] for example in examples] + [example["reg_images"] for example in examples] + else: + prompts = [[example["instance_prompt"]] for example in examples] + images = [example["instance_images"] for example in examples] + + images = torch.stack(images) + images = images.to(memory_format=torch.contiguous_format).float() + + return prompts, images + + +class DreamBooth(torch.nn.Module, Serialization): + def __init__(self, cfg, model_parallel_config): + super().__init__() + self.cfg = cfg + self.config = model_parallel_config + self.with_prior_preservation = self.cfg.with_prior_preservation + self.num_reg_images = self.cfg.data.num_reg_images + self.prior_loss_weight = self.cfg.prior_loss_weight + self.num_images_per_prompt = self.cfg.data.num_images_per_prompt + + self.train_text_encoder = self.cfg.train_text_encoder + self.instantiate_text_encoder(self.cfg.cond_stage_config) + + self.inductor = self.cfg.inductor + self.inductor_cudagraphs = self.cfg.inductor_cudagraphs + + self.instantiate_vae(self.cfg.first_stage_config) + self.instantiate_unet(self.cfg.unet_config) + + self.scale_factor = self.cfg.scale_factor + self.num_timesteps = self.cfg.noise_scheduler.timesteps + self.parameterization = self.cfg.noise_scheduler.parameterization + self.get_noise_scheduler(self.cfg.noise_scheduler) + + self.model_type = None + self.rng = torch.Generator(device=torch.cuda.current_device(),) + + self.use_cached_latents = self.cfg.use_cached_latents + + if self.cfg.channels_last: + self.unet = self.unet.to(memory_format=torch.channels_last) + + def instantiate_unet(self, cfg): + self.unet = DreamBooth.from_config_dict(cfg) + self.unet.train() + if self.inductor: + # TorchInductor with CUDA graph can lead to OOM + inductor_config.triton.cudagraphs = self.inductor_cudagraphs + torch._dynamo.config.dynamic_shapes = False + torch._dynamo.config.automatic_dynamic_shapes = False + self.unet = torch.compile(self.unet) + + def instantiate_vae(self, cfg): + model = DreamBooth.from_config_dict(cfg) + self.vae = model.eval() + self.vae.train = disabled_train + for param in self.vae.parameters(): + param.requires_grad = False + + def instantiate_text_encoder(self, cfg): + model = DreamBooth.from_config_dict(cfg) + if self.train_text_encoder: + self.text_encoder = model.train() + if (not hasattr(model, 'lora_layers')) or len( + model.lora_layers + ) == 0: # if no lora, train all the parameters + for param in self.text_encoder.parameters(): + param.requires_grad = True + else: + self.text_encoder = model.eval() + self.text_encoder.train = disabled_train + for param in self.text_encoder.parameters(): + param.requires_grad = False + + def get_noise_scheduler(self, cfg): + model = DreamBooth.from_config_dict(cfg) + self.noise_scheduler = model.eval() + + def forward(self, batch): + + x, cond = batch + if self.use_cached_latents: + x = DiagonalGaussianDistribution(x) + latents = x.sample().detach() * self.scale_factor + else: + latents = self.vae.encode(x).sample().detach() + latents = latents * self.scale_factor + + noise = randn_like(latents, generator=self.rng) + t = torch.randint(0, self.num_timesteps, (latents.shape[0],), generator=self.rng, device=latents.device).long() + x_noisy = self.noise_scheduler(x_start=latents, t=t, noise=noise) + + # cond = self.text_encoder([t[0] for t in batch["prompts"]]) + # if self.with_prior_preservation: + # cond_prior = self.text_encoder([t[1] for t in batch["prompts"]]) + # cond = torch.cat([cond, cond_prior], dim=0) + + model_output = self.unet(x_noisy, t, cond) + + if self.parameterization == "x0": + target = latents + elif self.parameterization == "eps": + target = noise + else: + raise NotImplementedError() + + if self.with_prior_preservation: + model_pred, model_pred_prior = torch.chunk(model_output, 2, dim=0) + target, target_prior = torch.chunk(target, 2, dim=0) + loss = torch.nn.functional.mse_loss(model_pred.float(), target.float(), reduction="mean") + prior_loss = torch.nn.functional.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") + loss = loss + prior_loss * self.prior_loss_weight + + else: + loss = torch.nn.functional.mse_loss(target.float(), model_output.float(), reduction="mean") + return loss + + def parameters(self): + params = list(self.unet.parameters()) + if self.train_text_encoder: + # print(f"{self.__class__.__name__}: Also optimizing conditioner params!") + params = params + list(self.text_encoder.parameters()) + return params + + def set_input_tensor(self, input_tensor): + """See megatron.model.transformer.set_input_tensor()""" + pass + + +class MegatronDreamBooth(NLPAdapterModelMixin, MegatronBaseModel): + def __init__(self, cfg: DictConfig, trainer: Trainer): + if not HAVE_APEX: + raise ImportError( + "Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + if not HAVE_MEGATRON_CORE: + raise ImportError( + "megatron-core was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + + # this prevents base constructor from initializing tokenizer + self.tokenizer = None + super().__init__(cfg, trainer=trainer) + + self._validate_trainer() + + # megatron_amp_O2 is not yet supported in diffusion models + self.megatron_amp_O2 = cfg.get('megatron_amp_O2', False) + self.model = self.model_provider_func() + + if self.trainer.precision in ['bf16', 'bf16-mixed']: + self.autocast_dtype = torch.bfloat16 + elif self.trainer.precision in [32, '32', '32-true']: + self.autocast_dtype = torch.float + elif self.trainer.precision in [16, '16', '16-mixed']: + self.autocast_dtype = torch.half + else: + raise ValueError('precision must be in ["32-true", "16-mixed", "bf16-mixed"]') + + def get_module_list(self): + if isinstance(self.model, list): + return [model.module if isinstance(model, Float16Module) else model for model in self.model] + elif isinstance(self.model, Float16Module): + return [self.model.module] + else: + return [self.model] + + def model_provider_func(self, pre_process=True, post_process=True): + """Model depends on pipeline paralellism.""" + model = DreamBooth(cfg=self.cfg, model_parallel_config=self.model_parallel_config) + return model + + def forward(self, batch): + output_tensor = self.model(batch) + return output_tensor + + def fwd_bwd_step(self, dataloader_iter, forward_only): + tensor_shape = None # Placeholder + + # handle asynchronous grad reduction + no_sync_func = None + if not forward_only and self.with_distributed_adam: + no_sync_func = partial(self._optimizer.no_sync, greedy_grad_copy=self.megatron_amp_O2,) + + # pipeline schedules will get these from self.model.config + for module in self.get_module_list(): + module.config.no_sync_func = no_sync_func + + # run forward and backwards passes for an entire global batch + # we do this inside training_step to support pipeline parallelism + fwd_bwd_function = get_forward_backward_func() + + losses_reduced_per_micro_batch = fwd_bwd_function( + forward_step_func=self.get_forward_output_and_loss_func(), + data_iterator=dataloader_iter, + model=self.model, + num_microbatches=get_num_microbatches(), + forward_only=forward_only, + seq_length=None, + micro_batch_size=self.cfg.micro_batch_size, + ) + + # losses_reduced_per_micro_batch is a list of dictionaries + # [{"loss": 0.1}, {"loss": 0.2}, ...] which are from gradient accumulation steps + # only the last stages of the pipeline return losses + loss_dict = {} + if losses_reduced_per_micro_batch: + if (not forward_only) or self.cfg.data.get('validation_drop_last', True): + # average loss across micro batches + prefix = 'train' + for key in losses_reduced_per_micro_batch[0]: + loss_tensors_list = [loss_reduced[key] for loss_reduced in losses_reduced_per_micro_batch] + loss_tensor = torch.stack(loss_tensors_list) + loss_dict[f'{prefix}/{key}'] = loss_tensor.mean() + loss_mean = loss_dict["train/loss"] + else: + raise NotImplementedError("Losses of micro batches sizes must be uniform!") + else: + if forward_only: + loss_mean = [] + else: + loss_mean = torch.tensor(0.0, device=torch.cuda.current_device()) + + return loss_mean, loss_dict + + def training_step(self, dataloader_iter): + """ + Our dataloaders produce a micro-batch and then we fetch + a number of microbatches depending on the global batch size and model parallel size + from the dataloader to produce a list of microbatches. + Batch should be a list of microbatches and those microbatches should on CPU. + Microbatches are then moved to GPU during the pipeline. + The list of microbatches is then piped through the pipeline using Apex fwd/bwd functions. + """ + + # we zero grads here because we also call backward in the apex fwd/bwd functions + self._optimizer.zero_grad() + + loss_mean, loss_dict = self.fwd_bwd_step(dataloader_iter, False) + + torch.distributed.broadcast(loss_mean, get_last_rank()) + + # when using sequence parallelism, the sequence parallel layernorm grads must be all-reduced + if self.cfg.get('tensor_model_parallel_size', 1) > 1 and self.cfg.get('sequence_parallel', False): + self.allreduce_sequence_parallel_gradients() + + if self.with_distributed_adam: + # gradients are reduced internally in distributed optimizer + pass + elif self.megatron_amp_O2: + # # when using pipeline parallelism grads must be all-reduced after the pipeline (not asynchronously) + # if self.cfg.get('pipeline_model_parallel_size', 1) > 1 or self.cfg.get('sequence_parallel', False): + # # main grads are stored in the MainParamsOptimizer wrapper + # self._optimizer.allreduce_main_grads() + self._optimizer.allreduce_main_grads() + elif not self.cfg.get('ddp_overlap', True): + # async grad allreduce is not currently implemented for O1/autocasting mixed precision training + # so we all-reduce gradients after the pipeline + self.allreduce_gradients() # @sangkug we think this is causing memory to blow up (hurts perf) + + if self.cfg.precision in [16, '16', '16-mixed']: + loss_scale = self.trainer.precision_plugin.scaler._scale + if loss_scale is not None: + self.log('loss_scale', loss_scale, prog_bar=True, batch_size=1) + + self.log_dict(loss_dict, prog_bar=False, logger=True, on_step=True, rank_zero_only=True, batch_size=1) + self.log('reduced_train_loss', loss_mean, prog_bar=True, rank_zero_only=True, batch_size=1) + lr = self._optimizer.param_groups[0]['lr'] + self.log('lr', lr, prog_bar=True, rank_zero_only=True, batch_size=1) + self.log('global_step', self.trainer.global_step + 1, prog_bar=True, rank_zero_only=True, batch_size=1) + self.log( + 'consumed_samples', + self.compute_consumed_samples(self.trainer.global_step + 1 - self.init_global_step), + prog_bar=True, + rank_zero_only=True, + batch_size=1, + ) + return loss_mean + + def validation_step(self, dataloader_iter): + loss, val_loss_dict = self.fwd_bwd_step(dataloader_iter, True) + + self.log_dict(val_loss_dict, prog_bar=False, logger=True, on_step=False, on_epoch=True, batch_size=1) + + return loss + + def backward(self, *args, **kwargs): + """ LightningModule hook to do backward. + We want this to do nothing since we run backward in the fwd/bwd functions from apex. + No need to call it here. + """ + pass + + def optimizer_zero_grad(self, *args, **kwargs): + """ LightningModule hook to zero grad. + We want this to do nothing as we are zeroing grads during the training_step. + """ + pass + + def _append_sequence_parallel_module_grads(self, module, grads): + """ Helper method for allreduce_sequence_parallel_gradients""" + + for param in module.parameters(): + sequence_parallel_param = getattr(param, 'sequence_parallel', False) + if sequence_parallel_param and param.requires_grad: + if self.megatron_amp_O2: + grad = param.main_grad + else: + grad = param.grad + grads.append(grad.data) + + def get_forward_output_and_loss_func(self): + def process_batch(batch): + # noise_map, condition + prompts, images = batch + # DB has more dedicated structure for encoding, so we enable autocasting here as well + with torch.cuda.amp.autocast( + self.autocast_dtype in (torch.half, torch.bfloat16), dtype=self.autocast_dtype, + ): + images = images.cuda(non_blocking=True) + + cond = self.model.text_encoder([t[0] for t in prompts]) + if self.cfg.with_prior_preservation: + cond_prior = self.model.text_encoder([t[1] for t in prompts]) + cond = torch.cat([cond, cond_prior], dim=0) + + return images, cond + + def fwd_output_and_loss_func(dataloader_iter, model): + batch, _, _ = next(dataloader_iter) + batch = process_batch(batch) + batch = [x.cuda(non_blocking=True) for x in batch] + loss = model(batch) + + def dummy(output_tensor): + return loss, {'loss': loss} + + return loss, dummy + + return fwd_output_and_loss_func + + def get_forward_output_only_func(self): + def fwd_output_only_func(batch, model): + raise NotImplementedError + + return fwd_output_only_func + + def setup(self, stage=None): + """ PTL hook that is executed after DDP spawns. + We setup datasets here as megatron datasets require DDP to instantiate. + See https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#setup for more information. + Args: + stage (str, optional): Can be 'fit', 'validate', 'test' or 'predict'. Defaults to None. + """ + self.model.rng.manual_seed(self.cfg.seed + 100 * parallel_state.get_data_parallel_rank()) + + # log number of parameters + if isinstance(self.model, list): + num_parameters_on_device = sum( + [sum([p.nelement() for p in model_module.parameters()]) for model_module in self.model] + ) + else: + num_parameters_on_device = sum([p.nelement() for p in self.model.parameters()]) + + # to be summed across data parallel group + total_num_parameters = torch.tensor(num_parameters_on_device).cuda(non_blocking=True) + + torch.distributed.all_reduce(total_num_parameters, group=parallel_state.get_model_parallel_group()) + + logging.info( + f'Pipeline model parallel rank: {parallel_state.get_pipeline_model_parallel_rank()}, ' + f'Tensor model parallel rank: {parallel_state.get_tensor_model_parallel_rank()}, ' + f'Number of model parameters on device: {num_parameters_on_device:.2e}. ' + f'Total number of model parameters: {total_num_parameters:.2e}.' + ) + + resume_checkpoint_path = self.trainer.ckpt_path + if resume_checkpoint_path: + init_consumed_samples = self._extract_consumed_samples_from_ckpt(resume_checkpoint_path) + else: + init_consumed_samples = 0 + self.init_consumed_samples = init_consumed_samples + self.init_global_step = self.trainer.global_step + + # Batch size need to be provided for webdatset + self._num_micro_batches = get_num_microbatches() + self._micro_batch_size = self.cfg.micro_batch_size + + self.setup_training_data(self.cfg.data) + self.setup_complete = True + + def setup_training_data(self, cfg): + if self.cfg.with_prior_preservation: + if cfg.regularization_dir is None: + raise ValueError("Regularization images must be provided to train with prior preservation loss") + if cfg.regularization_prompt is None: + raise ValueError("Regularization prompts must be provided to train with prior preservation loss") + + self.train_dataset = DreamBoothDataset( + instance_data_root=cfg.instance_dir, + instance_prompt=cfg.instance_prompt, + with_prior_preservation=self.cfg.with_prior_preservation, + reg_data_root=cfg.regularization_dir if self.cfg.with_prior_preservation else None, + reg_prompt=cfg.regularization_prompt if self.cfg.with_prior_preservation else None, + size=cfg.resolution, + center_crop=cfg.center_crop, + load_cache_latents=self.model.use_cached_latents, + cached_instance_data_root=self.cfg.data.get("cached_instance_dir", None), + cached_reg_data_root=self.cfg.data.get("cached_reg_dir", None) + if self.cfg.with_prior_preservation + else None, + vae=self.model.vae, + text_encoder=self.model.text_encoder, + ) + + batch_sampler = MegatronPretrainingRandomSampler( + total_samples=len(self.train_dataset), + consumed_samples=self.compute_consumed_samples(0), + micro_batch_size=self.cfg.micro_batch_size, + global_batch_size=self.cfg.global_batch_size, + data_parallel_rank=parallel_state.get_data_parallel_rank(), + data_parallel_size=parallel_state.get_data_parallel_world_size(), + drop_last=True, + ) + + self._train_dl = torch.utils.data.DataLoader( + self.train_dataset, + batch_sampler=batch_sampler, + collate_fn=partial(_collate_fn, with_prior_preservation=self.cfg.with_prior_preservation), + num_workers=cfg.num_workers, + pin_memory=True, + persistent_workers=True, + ) + + def setup_validation_data(self, cfg): + pass + + def setup_test_data(self, cfg): + pass + + def transfer_batch_to_device(self, batch: Any, device: torch.device, dataloader_idx: int) -> Any: + """ PTL hook: https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#transfer-batch-to-device + When using pipeline parallelism, we need the global batch to remain on the CPU, + since the memory overhead will be too high when using a large number of microbatches. + Microbatches are transferred from CPU to GPU inside the pipeline. + """ + return batch + + def _validate_trainer(self): + """ Certain trainer configurations can break training. + Here we try to catch them and raise an error. + """ + if self.trainer.accumulate_grad_batches > 1: + raise ValueError( + f'Gradient accumulation is done within training_step. trainer.accumulate_grad_batches must equal 1' + ) + + @classmethod + def list_available_models(cls): + return None + + def parameters(self): + if isinstance(self.model, list): + return itertools.chain.from_iterable(module.parameters() for module in self.model) + else: + return self.model.parameters() + + @classmethod + def load_from_checkpoint( + cls, + checkpoint_path: str, + map_location: Any = None, + hparams_file: Optional[str] = None, + strict: bool = True, + **kwargs, + ): + """ + Loads ModelPT from checkpoint, with some maintenance of restoration. + For documentation, please refer to LightningModule.load_from_checkpoin() documentation. + """ + checkpoint = None + try: + cls._set_model_restore_state(is_being_restored=True) + # TODO: replace with proper PTL API + with pl_legacy_patch(): + if map_location is not None: + checkpoint = pl_load(checkpoint_path, map_location=map_location) + else: + checkpoint = pl_load(checkpoint_path, map_location=lambda storage, loc: storage) + + if hparams_file is not None: + extension = hparams_file.split(".")[-1] + if extension.lower() == "csv": + hparams = load_hparams_from_tags_csv(hparams_file) + elif extension.lower() in ("yml", "yaml"): + hparams = load_hparams_from_yaml(hparams_file) + else: + raise ValueError(".csv, .yml or .yaml is required for `hparams_file`") + + hparams["on_gpu"] = False + + # overwrite hparams by the given file + checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY] = hparams + + # for past checkpoint need to add the new key + if cls.CHECKPOINT_HYPER_PARAMS_KEY not in checkpoint: + checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY] = {} + # override the hparams with values that were passed in + cfg = checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY].get('cfg', checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY]) + # TODO: can we do this without overriding? + config_kwargs = kwargs.copy() + if 'trainer' in config_kwargs: + config_kwargs.pop('trainer') + cfg.update(config_kwargs) + + # Disable individual unet/vae weights loading otherwise the model will look for these partial ckpts and raise error + if cfg: + if cfg.get('unet_config') and cfg.get('unet_config').get('from_pretrained'): + cfg.unet_config.from_pretrained = None + if cfg.get('first_stage_config') and cfg.get('first_stage_config').get('from_pretrained'): + cfg.first_stage_config.from_pretrained = None + ## Now when we covert ckpt to nemo, let's always get rid of those _orig_mod + if cfg.get('inductor'): + cfg.inductor = False + ## Append some dummy configs that DB didn't support + if not cfg.get('channels_last'): + cfg.channels_last = True + if not cfg.get('capture_cudagraph_iters'): + cfg.capture_cudagraph_iters = -1 + + # compatibility for stable diffusion old checkpoint tweaks + first_key = list(checkpoint['state_dict'].keys())[0] + if first_key == "betas": + # insert "model." into for megatron wrapper + new_state_dict = {} + for key in checkpoint['state_dict'].keys(): + new_key = "model." + key + new_state_dict[new_key] = checkpoint['state_dict'][key] + checkpoint['state_dict'] = new_state_dict + elif ( + first_key == 'model.text_encoder.transformer.text_model.embeddings.position_ids' + or first_key == 'model.text_encoder.model.language_model.embedding.position_embeddings' + ): + # remap state keys from dreambooth when using HF clip + new_state_dict = {} + for key in checkpoint['state_dict'].keys(): + new_key = key.replace('._orig_mod', "") + new_key = new_key.replace('unet', 'model.diffusion_model') + new_key = new_key.replace('vae', 'first_stage_model') + new_key = new_key.replace('text_encoder', 'cond_stage_model') + new_key = new_key.replace('.noise_scheduler', '') + new_state_dict[new_key] = checkpoint['state_dict'][key] + checkpoint['state_dict'] = new_state_dict + + # compatibility for inductor in inference + if not cfg.get('inductor', False): + new_state_dict = {} + for key in checkpoint['state_dict'].keys(): + new_key = key.replace('._orig_mod', '', 1) + new_state_dict[new_key] = checkpoint['state_dict'][key] + checkpoint['state_dict'] = new_state_dict + + if cfg.get('megatron_amp_O2', False): + new_state_dict = {} + for key in checkpoint['state_dict'].keys(): + new_key = key.replace('model.', 'model.module.', 1) + new_state_dict[new_key] = checkpoint['state_dict'][key] + checkpoint['state_dict'] = new_state_dict + + if 'cfg' in kwargs: + model = ptl_load_state(cls, checkpoint, strict=strict, **kwargs) + else: + model = ptl_load_state(cls, checkpoint, strict=strict, cfg=cfg, **kwargs) + # cfg = checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY].cfg + + checkpoint = model + + finally: + cls._set_model_restore_state(is_being_restored=False) + return checkpoint + + def _check_and_add_adapter(self, name, module, peft_name, peft_cfg, name_key_to_mcore_mixins=None): + if isinstance(module, AdapterModuleMixin): + if isinstance(module, LinearWrapper): + peft_cfg.in_features, peft_cfg.out_features = module.in_features, module.out_features + elif isinstance(module, LoraWrapper): + peft_cfg.in_features, peft_cfg.out_features = module.in_features, module.out_features + else: + return + if model_utils.import_class_by_path(peft_cfg._target_) in module.get_accepted_adapter_types(): + module.add_adapter( + name=peft_name, + cfg=peft_cfg, + base_model_cfg=self.cfg, + model_parallel_config=self.model_parallel_config, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/dreambooth/util.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/dreambooth/util.py new file mode 100644 index 0000000..8e31120 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/dreambooth/util.py @@ -0,0 +1,167 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from functools import partial + +import numpy as np +import torch +import torch.nn as nn + +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import ( + extract_into_tensor, + make_beta_schedule, +) +from nemo.collections.multimodal.parts.stable_diffusion.utils import default, exists +from nemo.core.classes.common import Serialization + + +class DiffusionWrapper(torch.nn.Module, Serialization): + def __init__(self, diff_model_config, conditioning_key): + super().__init__() + if isinstance(diff_model_config, nn.Module): + self.diffusion_model = diff_model_config + else: + self.diffusion_model = DiffusionWrapper.from_config_dict(diff_model_config) + self.conditioning_key = conditioning_key + assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm'] + + def forward(self, x_noisy, t, cond, return_ids=False): + if isinstance(cond, dict): + # hybrid case, cond is exptected to be a dict + pass + else: + if not isinstance(cond, list): + cond = [cond] + key = 'c_concat' if self.conditioning_key == 'concat' else 'c_crossattn' + cond = {key: cond} + x_recon = self.apply_step(x_noisy, t, **cond) + return x_recon + + def apply_step(self, x, t, c_concat: list = None, c_crossattn: list = None): + if self.conditioning_key is None: + out = self.diffusion_model(x, t) + elif self.conditioning_key == 'concat': + xc = torch.cat([x] + c_concat, dim=1) + out = self.diffusion_model(xc, t) + elif self.conditioning_key == 'crossattn': + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(x, t, context=cc) + elif self.conditioning_key == 'hybrid': + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc) + elif self.conditioning_key == 'adm': + cc = c_crossattn[0] + out = self.diffusion_model(x, t, y=cc) + else: + raise NotImplementedError() + + return out + + +class sd_noise_scheduler(nn.Module): + def __init__( + self, + parameterization='eps', + v_posterior=0, + given_betas=None, + beta_schedule='linear', + timesteps=1000, + linear_start=0.00085, + linear_end=0.012, + cosine_s=8e-3, + ): + super().__init__() + self.parameterization = parameterization + self.v_posterior = v_posterior + self.register_schedule( + given_betas=given_betas, + beta_schedule=beta_schedule, + timesteps=timesteps, + linear_start=linear_start, + linear_end=linear_end, + cosine_s=cosine_s, + ) + + def register_schedule( + self, + given_betas=None, + beta_schedule="linear", + timesteps=1000, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + ): + if exists(given_betas): + betas = given_betas + else: + betas = make_beta_schedule( + beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s + ) + alphas = 1.0 - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1]) + + (timesteps,) = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1.0 - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1.0 - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1.0 / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1.0 / alphas_cumprod - 1))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = (1 - self.v_posterior) * betas * (1.0 - alphas_cumprod_prev) / ( + 1.0 - alphas_cumprod + ) + self.v_posterior * betas + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer( + 'posterior_mean_coef1', to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)) + ) + self.register_buffer( + 'posterior_mean_coef2', to_torch((1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)) + ) + + if self.parameterization == "eps": + lvlb_weights = self.betas ** 2 / ( + 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod) + ) + elif self.parameterization == "x0": + lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2.0 * 1 - torch.Tensor(alphas_cumprod)) + else: + raise NotImplementedError("mu not supported") + # TODO how to choose this term + lvlb_weights[0] = lvlb_weights[1] + self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) + assert not torch.isnan(self.lvlb_weights).all() + + def forward(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/imagen/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/imagen/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/imagen/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/imagen/imagen.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/imagen/imagen.py new file mode 100644 index 0000000..4fa6cd2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/imagen/imagen.py @@ -0,0 +1,598 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import itertools +from datetime import datetime +from functools import partial +from typing import Any + +import torch +from omegaconf import DictConfig, open_dict +from pytorch_lightning import Trainer + +from nemo.collections.multimodal.data.imagen.imagen_dataset import build_train_valid_datasets +from nemo.collections.multimodal.models.text_to_image.imagen.precond import ContinousDDPMPrecond, EDMPrecond +from nemo.collections.multimodal.modules.imagen.diffusionmodules.nets import EfficientUNetModel, UNetModel +from nemo.collections.multimodal.modules.imagen.encoder.t5encoder import T5Encoder +from nemo.collections.multimodal.modules.imagen.sampler.sampler import DDPMSampler, EDMSampler +from nemo.collections.multimodal.parts.imagen.utils import random_dropout +from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel +from nemo.collections.nlp.modules.common.megatron.module import Float16Module +from nemo.collections.nlp.parts.utils_funcs import get_last_rank +from nemo.core.classes.common import Serialization +from nemo.utils import logging + +try: + from apex import amp + from apex.transformer.pipeline_parallel.utils import get_num_microbatches + + HAVE_APEX = True +except (ImportError, ModuleNotFoundError): + HAVE_APEX = False + +try: + from megatron.core import parallel_state + from megatron.core.pipeline_parallel.schedules import get_forward_backward_func + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + HAVE_MEGATRON_CORE = False + +try: + from apex.contrib.group_norm import GroupNorm + + OPT_GROUP_NORM = True +except Exception: + print('Fused optimized group norm has not been installed.') + OPT_GROUP_NORM = False + +DUMMY_TENSOR = torch.tensor([1.0]) + + +class Imagen(torch.nn.Module, Serialization): + def __init__(self, cfg, model_parallel_config): + super().__init__() + self.cfg = cfg + self.config = model_parallel_config + # Make sure the initialization on different GPUs are the same + self.unet_type = cfg.get('unet_type', 'base') + self.noise_cond_aug = cfg.get('noise_cond_aug', False) + if self.unet_type == 'base': + logging.info('Initializing UNet.') + unet = UNetModel(**cfg.unet, text_embed_dim=cfg.conditioning.embed_dim) + elif self.unet_type == 'sr': + logging.info('Initializing Efficient-UNet.') + unet = EfficientUNetModel( + **cfg.unet, text_embed_dim=cfg.conditioning.embed_dim, noise_cond_aug=self.noise_cond_aug + ) + elif self.unet_type == 'sr-unet': + logging.info('Initializing UNet for SR model.') + unet = UNetModel(**cfg.unet, text_embed_dim=cfg.conditioning.embed_dim, noise_cond_aug=self.noise_cond_aug) + else: + raise NotImplemented(f'{self.unet_type} UNet is not implemented.') + + self.channels_last = cfg.get('channels_last', False) + if self.channels_last: + assert OPT_GROUP_NORM, 'Training in channels last format requires optmized group norm implementation.' + logging.info('Training in torch channels last format.') + unet = unet.to(memory_format=torch.channels_last) + + # Preconditioning + self.preconditioning_type = cfg.get('preconditioning_type', 'DDPM') + if self.preconditioning_type == 'DDPM': + logging.info('Preconditioned with Continous DDPM') + self.model = ContinousDDPMPrecond(unet=unet, **cfg.preconditioning, noise_cond_aug=self.noise_cond_aug) + self.sampler = DDPMSampler(unet_type=self.unet_type, denoiser=self.model.scheduler) + elif self.preconditioning_type == 'EDM': + logging.info('Preconditioned with EDM') + self.model = EDMPrecond(unet=unet, **cfg.preconditioning, noise_cond_aug=self.noise_cond_aug) + self.sampler = EDMSampler(unet_type=self.unet_type) + else: + raise NotImplemented(f'{self.preconditioning_type} preconditioning is not implemented.') + + self.rng = None + self.conditioning = cfg.conditioning + self.text_drop_rate = cfg.conditioning.drop_rate + self.model_type = None + self.image_size = cfg.unet.image_size + + def setup_rng(self): + # We need to set different rng seed for different GPUs/ different runs; + # otherwise, the noise map and time will be exactly the same. + self.rng = torch.Generator(device=torch.cuda.current_device()) + self.rng_seed = int(datetime.now().timestamp()) + self.cfg.seed + parallel_state.get_data_parallel_rank() + logging.info(f'RNG seed set as {self.rng_seed} for rank {parallel_state.get_data_parallel_rank()}') + self.rng.manual_seed(self.rng_seed) + self.model.set_rng(self.rng) + + @property + def unet(self): + return self.model.unet + + def get_text_encoder(self, encoder_path=None): + # TODO Assume using T5 for all + return T5Encoder(max_seq_len=self.conditioning.token_length, encoder_path=encoder_path) + + def forward(self, x_start, text_embed, text_mask, x_lowres=None): + if self.unet_type == 'base': + assert x_lowres[0].item() == DUMMY_TENSOR.item(), 'Base model should have no low-resolution conditioning' + x_lowres = None + else: + assert x_lowres[0].dim() not in [0, 1], 'SR model should have low-resolution conditioning' + + if self.channels_last: + x_start = x_start.to(memory_format=torch.channels_last) + if x_lowres is not None: + x_lowres = x_lowres.to(memory_format=torch.channels_last) + + # Apply random dropout to text embedding + text_embed = random_dropout(text_embed, drop_rate=self.text_drop_rate) + # UNet Forward Pass + low_res_cond = {'x_low_res': x_lowres} if x_lowres is not None else {} + # UNet Forward Pass and compute loss + loss = self.model.compute_loss( + x0=x_start, + text_embed=text_embed, + text_mask=text_mask, + time=None, # Randomly Sample + noise=None, # Randomly Sample + **low_res_cond, + ) + return loss, {'train/loss': loss} + + @torch.no_grad() + def sample_image( + self, + noise_map, + text_encoding, + text_mask, + x_low_res=None, + cond_scale=1.0, + sampling_steps=None, + thresholding_method='dynamic', + ): + return self.sampler( + self.model, noise_map, text_encoding, text_mask, x_low_res, cond_scale, sampling_steps, thresholding_method + ) + + def set_input_tensor(self, input_tensor): + """See megatron.model.transformer.set_input_tensor()""" + # only required for pipeline parallelism + pass + + +class MegatronImagen(MegatronBaseModel): + def __init__(self, cfg: DictConfig, trainer: Trainer): + if not HAVE_APEX: + raise ImportError( + "Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + + with open_dict(cfg): + cfg.hidden_size = cfg.unet.embed_dim + # this prevents base constructor from initializing tokenizer + self.tokenizer = None + super().__init__(cfg, trainer=trainer) + + self._validate_trainer() + # megatron_amp_O2 is not yet supported in diffusion models + self.megatron_amp_O2 = cfg.get('megatron_amp_O2', False) + + self.model = self.model_provider_func() + + if self.trainer.precision in ['bf16', 'bf16-mixed']: + self.autocast_dtype = torch.bfloat16 + elif self.trainer.precision in [32, '32', '32-true']: + self.autocast_dtype = torch.float + elif self.trainer.precision in [16, '16', '16-mixed']: + self.autocast_dtype = torch.half + else: + raise ValueError('precision must be in ["32-true", "16-mixed", "bf16-mixed"]') + + self.online_encoding = cfg.conditioning.get("online_encoding", False) + self.text_encoder_path = cfg.conditioning.get("encoder_path", None) + + def get_module_list(self): + if isinstance(self.model, list): + return [model.module if isinstance(model, Float16Module) else model for model in self.model] + elif isinstance(self.model, Float16Module): + return [self.model.module] + else: + return [self.model] + + def model_provider_func(self, pre_process=True, post_process=True): + """Model depends on pipeline paralellism.""" + model = Imagen(cfg=self.cfg, model_parallel_config=self.model_parallel_config) + return model + + def get_forward_output_and_loss_func(self): + def process_batch(batch): + """ Prepares the batch for megatron fwd/bwd functions. + Global batch is a list of micro batches. + """ + # Base model and SR models have slightly different batch input: + # Base model would only require images (64x64), + # while SR models (both SR256 and SR1024) require low-res image (64x64) and + # actual (cropped) image (256x256) + if self.cfg.unet_type == 'base': + x_start = batch['images'] + # Pass in DUMMY_TENSOR because megatron requires each input to be + # tensor (not None) with same batch size (first dim) + x_lowres = DUMMY_TENSOR.repeat(x_start.shape[0]) + elif self.cfg.unet_type == 'sr' or self.cfg.unet_type == 'sr-unet': + x_start = batch['images_256'] + x_lowres = batch['images_64'] + else: + raise NotImplemented(f'Unknown UNet type: {self.cfg.unet_type}') + + if self.cfg.conditioning.get("online_encoding", False): + input_text = batch["raw_text"] + # Encode the text embeddings using text encoder. + with torch.no_grad(): + text_embed, text_mask = self.text_encoder.encode(input_text) + else: + text_conditioning_key = self.cfg.conditioning.out_key + text_embed = batch[f'{text_conditioning_key}_embeddings'] + text_mask = batch[f'{text_conditioning_key}_mask'] + return [x_start, text_embed, text_mask, x_lowres] + + def fwd_output_and_loss_func(dataloader_iter, model): + batch, _, _ = next(dataloader_iter) + batch = process_batch(batch) + batch = [x.cuda(non_blocking=True) for x in batch] + loss, loss_dict = model(*batch) + + def dummy(output_tensor): + return loss, loss_dict + + # output_tensor, and a function to convert output_tensor to loss + loss_dict + return loss, dummy + + return fwd_output_and_loss_func + + def get_forward_output_only_func(self): + def fwd_output_only_func(batch, model): + raise NotImplementedError + + return fwd_output_only_func + + def build_train_valid_test_datasets(self): + logging.info('Building datasets for Imagen...') + if self.trainer.limit_val_batches > 1.0 and isinstance(self.trainer.limit_val_batches, float): + raise ValueError("limit_val_batches must be an integer or float less than or equal to 1.0.") + self._train_ds, self._validation_ds = build_train_valid_datasets( + model_cfg=self.cfg, consumed_samples=self.compute_consumed_samples(0) + ) + # We do not have test dataset + self._test_ds = None + + if self._train_ds is not None: + logging.info(f'Length of train dataset: {len(self._train_ds)}') + if self._validation_ds is not None: + logging.info(f'Length of val dataset: {len(self._validation_ds)}') + if self._test_ds is not None: + logging.info(f'Length of test dataset: {len(self._test_ds)}') + logging.info(f'Finished building datasets for LatentDiffusion.') + return self._train_ds, self._validation_ds, self._test_ds + + def setup_training_data(self, cfg): + if hasattr(self, '_train_ds') and self._train_ds is not None: + consumed_samples = self.compute_consumed_samples(0) + logging.info( + f'Setting up train dataloader with len(len(self._train_ds)): {len(self._train_ds)} and consumed samples: {consumed_samples}' + ) + self._train_dl = torch.utils.data.DataLoader( + self._train_ds, + batch_size=self._micro_batch_size, + num_workers=cfg.num_workers, + pin_memory=True, + drop_last=True, + persistent_workers=True, + ) + + def setup_validation_data(self, cfg): + if hasattr(self, '_validation_ds') and self._validation_ds is not None: + consumed_samples = 0 + logging.info( + f'Setting up validation dataloader with len(len(self._validation_ds)): {len(self._validation_ds)} and consumed samples: {consumed_samples}' + ) + self._validation_dl = torch.utils.data.DataLoader( + self._validation_ds, + batch_size=self._micro_batch_size, + num_workers=cfg.num_workers, + pin_memory=True, + drop_last=False, + persistent_workers=True, + ) + + def setup_test_data(self, cfg): + if hasattr(self, '_test_ds') and self._test_ds is not None: + consumed_samples = 0 + logging.info( + f'Setting up test dataloader with len(len(self._test_ds)): {len(self._test_ds)} and consumed samples: {consumed_samples}' + ) + self._test_dl = torch.utils.data.DataLoader( + self._test_ds, batch_size=self._micro_batch_size, num_workers=cfg.num_workers, pin_memory=True, + ) + + def fwd_bwd_step(self, dataloader_iter, forward_only): + tensor_shape = None + + # handle asynchronous grad reduction + no_sync_func = None + if not forward_only and self.with_distributed_adam: + no_sync_func = partial(self._optimizer.no_sync, greedy_grad_copy=self.megatron_amp_O2,) + + # pipeline schedules will get these from self.model.config + for module in self.get_module_list(): + module.config.no_sync_func = no_sync_func + + # run forward and backwards passes for an entire global batch + # we do this inside training_step to support pipeline parallelism + fwd_bwd_function = get_forward_backward_func() + + # TODO @akhattar: add num_micro_batches_with_partial_activation_checkpoints when ready + losses_reduced_per_micro_batch = fwd_bwd_function( + forward_step_func=self.get_forward_output_and_loss_func(), + data_iterator=dataloader_iter, + model=self.model, + num_microbatches=get_num_microbatches(), + forward_only=forward_only, + seq_length=None, + micro_batch_size=self.cfg.micro_batch_size, + ) + + # losses_reduced_per_micro_batch is a list of dictionaries + # [{"loss": 0.1}, {"loss": 0.2}, ...] which are from gradient accumulation steps + # only the last stages of the pipeline return losses + loss_dict = {} + if losses_reduced_per_micro_batch: + if (not forward_only) or self.cfg.data.get('validation_drop_last', True): + # average loss across micro batches + for key in losses_reduced_per_micro_batch[0]: + loss_tensors_list = [loss_reduced[key] for loss_reduced in losses_reduced_per_micro_batch] + loss_tensor = torch.stack(loss_tensors_list) + loss_dict[key] = loss_tensor.mean() + loss_mean = loss_dict["train/loss"] + else: + # Get the total loss since micro batches sizes are not uniform + raise NotImplementedError("Losses of micro batches sizes must be uniform!") + else: + # we're not on the last pipeline stage so no losses + if forward_only: + loss_mean = [] + else: + loss_mean = torch.tensor(0.0).cuda() + + return loss_mean, loss_dict + + def training_step(self, dataloader_iter): + """ + Our dataloaders produce a micro-batch and then we fetch + a number of microbatches depending on the global batch size and model parallel size + from the dataloader to produce a list of microbatches. + Batch should be a list of microbatches and those microbatches should on CPU. + Microbatches are then moved to GPU during the pipeline. + The list of microbatches is then piped through the pipeline using Apex fwd/bwd functions. + """ + + # we zero grads here because we also call backward in the megatron-core fwd/bwd functions + self._optimizer.zero_grad() + + loss_mean, loss_dict = self.fwd_bwd_step(dataloader_iter, False) + + torch.distributed.broadcast(loss_mean, get_last_rank()) + + # when using sequence parallelism, the sequence parallel layernorm grads must be all-reduced + if self.cfg.get('tensor_model_parallel_size', 1) > 1 and self.cfg.get('sequence_parallel', False): + self.allreduce_sequence_parallel_gradients() + + if self.with_distributed_adam: + # synchronize asynchronous grad reductions + # note: not necessary, but reduces performance degradation + # from multiple simultaneous NCCL calls + self._optimizer._finish_bucket_grad_sync() + elif self.megatron_amp_O2: + # # when using pipeline parallelism grads must be all-reduced after the pipeline (not asynchronously) + # if self.cfg.get('pipeline_model_parallel_size', 1) > 1 or self.cfg.get('sequence_parallel', False): + # # main grads are stored in the MainParamsOptimizer wrapper + # self._optimizer.allreduce_main_grads() + self._optimizer.allreduce_main_grads() + elif not self.cfg.get('ddp_overlap', True): + # async grad allreduce is not currently implemented for O1/autocasting mixed precision training + # so we all-reduce gradients after the pipeline + self.allreduce_gradients() # @sangkug we think this is causing memory to blow up (hurts perf) + + if self.cfg.precision in [16, '16', '16-mixed']: + loss_scale = self.trainer.precision_plugin.scaler._scale + if loss_scale is not None: + self.log('loss_scale', loss_scale, batch_size=1) + + self.log_dict(loss_dict, prog_bar=False, logger=True, on_step=True, rank_zero_only=True, batch_size=1) + self.log('reduced_train_loss', loss_mean, prog_bar=True, rank_zero_only=True, batch_size=1) + lr = self._optimizer.param_groups[0]['lr'] + self.log('lr', lr, prog_bar=True, rank_zero_only=True, batch_size=1) + self.log('global_step', self.trainer.global_step + 1, prog_bar=True, rank_zero_only=True, batch_size=1) + self.log( + 'consumed_samples', + self.compute_consumed_samples(self.trainer.global_step + 1 - self.init_global_step), + prog_bar=True, + rank_zero_only=True, + batch_size=1, + ) + return loss_mean + + def backward(self, *args, **kwargs): + """ LightningModule hook to do backward. + We want this to do nothing since we run backward in the fwd/bwd functions from apex. + No need to call it here. + """ + pass + + def optimizer_zero_grad(self, *args, **kwargs): + """ LightningModule hook to zero grad. + We want this to do nothing as we are zeroing grads during the training_step. + """ + pass + + def _append_sequence_parallel_module_grads(self, module, grads): + """ Helper method for allreduce_sequence_parallel_gradients""" + + for param in module.parameters(): + sequence_parallel_param = getattr(param, 'sequence_parallel', False) + if sequence_parallel_param and param.requires_grad: + if self.megatron_amp_O2: + grad = param.main_grad + else: + grad = param.grad + grads.append(grad.data) + + def validation_step(self, dataloader_iter): + """ + Our dataloaders produce a micro-batch and then we fetch + a number of microbatches depending on the global batch size and model parallel size + from the dataloader to produce a list of microbatches. + The list of microbatches is then piped through the pipeline using megatron-core fwd/bwd functions. """ + + loss, val_loss_dict = self.fwd_bwd_step(dataloader_iter, True) + + self.log_dict(val_loss_dict, prog_bar=False, logger=True, on_step=False, on_epoch=True, batch_size=1) + return loss + + def setup(self, stage=None): + """ PTL hook that is executed after DDP spawns. + We setup datasets here as megatron datasets require DDP to instantiate. + See https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#setup for more information. + Args: + stage (str, optional): Can be 'fit', 'validate', 'test' or 'predict'. Defaults to None. + """ + + # log number of parameters + if isinstance(self.model, list): + num_parameters_on_device = sum( + [sum([p.nelement() for p in model_module.parameters()]) for model_module in self.model] + ) + else: + num_parameters_on_device = sum([p.nelement() for p in self.model.parameters()]) + + # to be summed across data parallel group + total_num_parameters = torch.tensor(num_parameters_on_device).cuda(non_blocking=True) + + torch.distributed.all_reduce(total_num_parameters, group=parallel_state.get_model_parallel_group()) + + logging.info( + f'Pipeline model parallel rank: {parallel_state.get_pipeline_model_parallel_rank()}, ' + f'Tensor model parallel rank: {parallel_state.get_tensor_model_parallel_rank()}, ' + f'Number of model parameters on device: {num_parameters_on_device:.2e}. ' + f'Total number of model parameters: {total_num_parameters:.2e}.' + ) + + resume_checkpoint_path = self.trainer.ckpt_path + if resume_checkpoint_path: + init_consumed_samples = self._extract_consumed_samples_from_ckpt(resume_checkpoint_path) + else: + init_consumed_samples = 0 + self.init_consumed_samples = init_consumed_samples + self.init_global_step = self.trainer.global_step + + # allowing restored models to optionally setup datasets + self.build_train_valid_test_datasets() + + # Batch size need to be provided for webdatset + self._num_micro_batches = get_num_microbatches() + self._micro_batch_size = self.cfg.micro_batch_size + + self.setup_training_data(self.cfg.data) + self.setup_validation_data(self.cfg.data) + self.setup_test_data(self.cfg.data) + # Setup RNG seed in model + self.model.setup_rng() + + def transfer_batch_to_device(self, batch: Any, device: torch.device, dataloader_idx: int) -> Any: + """ PTL hook: https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#transfer-batch-to-device + When using pipeline parallelism, we need the global batch to remain on the CPU, + since the memory overhead will be too high when using a large number of microbatches. + Microbatches are transferred from CPU to GPU inside the pipeline. + """ + return batch + + def _validate_trainer(self): + """ Certain trainer configurations can break training. + Here we try to catch them and raise an error. + """ + if self.trainer.accumulate_grad_batches > 1: + raise ValueError( + f'Gradient accumulation is done within training_step. trainer.accumulate_grad_batches must equal 1' + ) + + @classmethod + def list_available_models(cls): + return None + + def parameters(self): + if isinstance(self.model, list): + return itertools.chain.from_iterable(module.parameters() for module in self.model) + else: + return self.model.parameters() + + def on_save_checkpoint(self, checkpoint) -> None: + if self.online_encoding: + # Removing the weights relating to Text encoder when saving the checkpoints + frozen_weights_keys = [k for k in checkpoint['state_dict'].keys() if k.startswith("text_encoder")] + for k in frozen_weights_keys: + del checkpoint['state_dict'][k] + + def on_load_checkpoint(self, checkpoint) -> None: + # make sure inductor naming is consistent with checkpoint's + inductor_enabled = self.cfg.get('inductor', False) + state_dict = checkpoint['state_dict'] + inductor_checkpoint = False + for k, v, in state_dict.items(): + if '_orig_mod' in k: + inductor_checkpoint = True + break + + if inductor_enabled and not inductor_checkpoint: + # ckpt needs to be converted to inductor-format weights (add .orig_mod) + logging.info('Add .orig_mod to all weight keys.') + new_state_dict = {} + for k, v in state_dict.items(): + idx = k.find('._orig_mod') + new_key = k[:idx] + k[idx + len('._orig_mod') :] + new_state_dict[new_key] = v + checkpoint['state_dict'] = new_state_dict + elif not inductor_enabled and inductor_checkpoint: + # ckpt needs to be converted to non-inductor-format weights (remove .orig_mod) + logging.info('Remove .orig_mod to all weight keys.') + new_state_dict = {} + for k, v in state_dict.items(): + new_key = k.replace("._orig_mod", "") + new_state_dict[new_key] = v + checkpoint['state_dict'] = new_state_dict + super().on_load_checkpoint(checkpoint) + + def on_fit_start(self) -> None: + if self.online_encoding: + # if encoding text online, set up text_encoder here (after loading checkpoints) instead of in __init__. + # This is because text encoder weights are not saved, so the encoder must be loaded after other weights + # are loaded. + logging.info( + f'Setting up pretrained text encoder: {self.text_encoder_path or "download or use cached t5-11b"}' + ) + self.text_encoder = self.model.get_text_encoder(encoder_path=self.text_encoder_path).to( + torch.cuda.current_device() + ) + self.text_encoder.eval() + for param in self.text_encoder.parameters(): + param.requires_grad = False diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/imagen/imagen_pipeline.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/imagen/imagen_pipeline.py new file mode 100644 index 0000000..43660c9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/imagen/imagen_pipeline.py @@ -0,0 +1,356 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import time +from dataclasses import dataclass, field +from typing import Callable, List, Optional, Union + +import torch +from omegaconf.omegaconf import OmegaConf +from pytorch_lightning import Trainer +from torch.cuda.amp import autocast + +from nemo.collections.multimodal.models.text_to_image.imagen.imagen import Imagen, MegatronImagen +from nemo.collections.multimodal.parts.utils import numpy_to_pil, setup_trainer_and_models_for_inference + + +@dataclass +class ImagenCustomizedModelConfig: + base_ckpt: Optional[str] = None + base_cfg: Optional[str] = None + sr256_ckpt: Optional[str] = None + sr256_cfg: Optional[str] = None + sr1024_ckpt: Optional[str] = None + sr1024_cfg: Optional[str] = None + + +@dataclass +class ImagenSamplingConfig: + step: Optional[int] = None + cfg: Optional[float] = 1 + + +@dataclass +class ImagenPipelineConfig: + model_name: Optional[str] = None + run_ema_model: Optional[bool] = True + customized_model: Optional[ImagenCustomizedModelConfig] = None + num_images_per_promt: Optional[int] = 8 + texts: Optional[List[str]] = field(default_factory=lambda: []) + output_path: Optional[str] = 'output/imagen_inference' + record_time: Optional[bool] = False + encoder_path: Optional[str] = None + target_resolution: Optional[int] = 256 + inference_precision: Optional[str] = '32' + thresholding_method: Optional[str] = 'dynamic' + samplings: Optional[List[ImagenSamplingConfig]] = field(default_factory=lambda: list()) + part: Optional[int] = 0 + + +class ImagenPipeline(Callable): + def __init__(self, models: List[Imagen], text_encoder, cfg, device): + self.models = [model.to(device) for model in models] + self.text_encoder = text_encoder.to(device) + self.cfg = cfg + self.device = device + + def _load_model(model_ckpt: str, model_cfg: str, eval_mode: bool = True, trainer: Trainer = None): + assert model_ckpt is not None, 'model ckpt cannot be None' + if model_ckpt.endswith('.nemo'): + model_cfg = MegatronImagen.restore_from(restore_path=model_ckpt, trainer=trainer, return_config=True) + model_cfg.unet.flash_attention = False + model_cfg.micro_batch_size = 1 + model_cfg.global_batch_size = 1 + model = MegatronImagen.restore_from( + restore_path=model_ckpt, override_config_path=model_cfg, trainer=trainer, + ) + elif model_ckpt.endswith('.ckpt'): + model_cfg = OmegaConf.load(model_cfg) + model_cfg.model.unet.flash_attention = False + model_cfg.model.micro_batch_size = 1 + model_cfg.model.global_batch_size = 1 + model = MegatronImagen(cfg=model_cfg.model, trainer=trainer) + checkpoint = torch.load(model_ckpt, map_location=lambda storage, loc: storage) + + # Change weight keys if training using TorchInductor + state_dict = checkpoint['state_dict'] + del_keys = [] + for k, v in state_dict.items(): + if '._orig_mod' in k: + del_keys.append(k) + if len(del_keys) != 0: + print('ckpt was saved with TorchInductor. Renaming weights..') + for k in del_keys: + new_k = k.replace("._orig_mod", "") + state_dict[new_k] = state_dict[k] + del state_dict[k] + model.load_state_dict(state_dict, strict=True) + else: + raise Exception('Invalid ckpt type. Should be either .nemo or .ckpt with cfg') + + model = model.model # We do not need Megatron Instance for inference + model.model.set_inference_mode(True) # Used for adding the least noise for EDM inference for SR model. + if eval_mode: + model.unet.cuda().eval() + return model + + @staticmethod + def _load_customized_model(cfg: ImagenPipelineConfig, trainer=None, megatron_loading=False, megatron_cfg=None): + if megatron_loading: + assert megatron_cfg + + def model_cfg_modifier(model_cfg): + model_cfg.inductor = False + model_cfg.unet.flash_attention = False + model_cfg.micro_batch_size = megatron_cfg.fid.ncaptions_per_batch + model_cfg.global_batch_size = model_cfg.micro_batch_size * megatron_cfg.fid.ntasks_per_node + + trainer, megatron_models = setup_trainer_and_models_for_inference( + MegatronImagen, cfg=megatron_cfg, model_cfg_modifier=model_cfg_modifier + ) + models = [mm.model for mm in megatron_models] + for model in models: + model.cuda().eval() + model.model.set_inference_mode(True) + return models + customized_models = cfg.customized_model + models = [] + print('Load base model.') + model = ImagenPipeline._load_model( + model_ckpt=customized_models.base_ckpt, model_cfg=customized_models.base_cfg, trainer=trainer, + ) + models.append(model) + + if cfg.target_resolution >= 256: + print('Load SR256 model.') + model = ImagenPipeline._load_model( + model_ckpt=customized_models.sr256_ckpt, model_cfg=customized_models.sr256_cfg, trainer=trainer + ) + models.append(model) + + if cfg.target_resolution >= 1024: + print('Load SR1024 model.') + model = ImagenPipeline._load_model( + model_ckpt=customized_models.sr1024_ckpt, model_cfg=customized_models.sr1024_cfg, trainer=trainer + ) + models.append(model) + return models + + @classmethod + def from_pretrained( + cls, cfg: ImagenPipelineConfig, trainer=None, device='cuda', megatron_loading=False, megatron_cfg=None + ): + target_resolution = cfg.target_resolution + assert target_resolution in [64, 256, 1024] + + # Set encoder_path which will be used when inst the model + if cfg.encoder_path is not None: + os.environ['ENCODER_PATH'] = cfg.encoder_path + + assert cfg.model_name is None, 'No predefined model for now' + assert cfg.customized_model is not None, 'Need to provide customized models for inference' + models = ImagenPipeline._load_customized_model(cfg, trainer, megatron_loading, megatron_cfg) + assert len(models) >= 1, 'Need to load at least one model' + if cfg.inference_precision == '16': + print('Running Inference in FP16.') + print('Converting all difussion models to FP16..') + for model in models: + model.half() + + print('Loading text encoder') + text_encoder = models[0].get_text_encoder(encoder_path=cfg.encoder_path) + if cfg.inference_precision == '16': + print('Converting text encoders to FP16..') + text_encoder.half() + return ImagenPipeline(models=models, text_encoder=text_encoder, cfg=cfg, device=device) + + @torch.no_grad() + def get_text_encodings(self, input_text, repeat=1): + # Repeat the inputs so that we generate multiple samples per query + if isinstance(input_text, str): + inp_text_batch = [input_text] + else: + inp_text_batch = input_text + # Encode the text embeddings using text encoder. + text_encodings, text_mask = self.text_encoder.encode(inp_text_batch, device=self.device) + if repeat != 1: + assert len(inp_text_batch) == 1, 'Repeat should only be applied if we feed single text to encoder.' + text_encodings = text_encodings.repeat(repeat, 1, 1) + text_mask = text_mask.repeat(repeat, 1) + return text_encodings, text_mask + + @torch.no_grad() + def __call__( + self, + prompts: Union[str, List[str]] = None, + inference_steps: Union[int, List[int]] = None, + classifier_free_guidance: Union[float, List[float]] = None, + num_images_per_promt: Optional[int] = 0, + thresholding_method: bool = None, + output_type: Optional[str] = 'pil', + seed: Union[int, List[int]] = 2000, + single_batch_mode: bool = False, + output_res: Optional[int] = None, + low_res_input: Optional[torch.Tensor] = None, + ): + if prompts is None: + prompts = OmegaConf.to_object(self.cfg.texts) + if num_images_per_promt == 0: + num_images_per_promt = self.cfg.num_images_per_promt + if thresholding_method is None: + thresholding_method = self.cfg.thresholding_method + device = self.device + inference_precision = self.cfg.inference_precision + assert inference_precision in ['16', '32', 'AMP'], "Inference Precision should be one of ['16', '32', 'AMP']" + print(f'Running inference in {inference_precision} mode.') + amp_enabled = inference_precision == 'AMP' + + # Based on output_res and low_res_input, determine which models to run + if output_res is not None or low_res_input is not None: + models = [] + if output_res is not None: + for model in self.models: + models.append(model) + if model.image_size == output_res: + break + else: + models = self.models + if low_res_input is not None: + print(f'Low-res input shape: {low_res_input.shape}') + low_res_dim = low_res_input.shape[-1] + num_images_per_promt = low_res_input.shape[0] + for idx, model in enumerate(models): + if model.image_size == low_res_dim: + models = models[idx + 1 :] + break + print(f'Running inference on {len(models)} models.') + else: + models = self.models + + if classifier_free_guidance is None: + cfgs = [each.cfg for each in self.cfg.samplings] + cfgs = cfgs[: len(models)] + else: + cfgs = classifier_free_guidance + if isinstance(cfgs, int) or isinstance(cfgs, float): + cfgs = [cfgs] * len(models) + + if inference_steps is None: + steps = [each.step for each in self.cfg.samplings] + steps = steps[: len(models)] + else: + steps = inference_steps + if isinstance(steps, int): + steps = [steps] * len(models) + + assert len(steps) == len(cfgs) == len(models) + + output = [] + all_res_output = [[] for _ in range(len(models))] + if single_batch_mode: + num_images_per_promt = len(prompts) + + throughputs = {'text-encoding': []} + for idx in range(len(models)): + throughputs[f'stage-{idx+1}'] = [] + for prompt in prompts: + if single_batch_mode: + text_input = prompts + else: + text_input = prompt.strip('\n') + print('Input caption: {}'.format(text_input)) + tic = time.perf_counter() + text_encodings, text_mask = self.get_text_encodings( + text_input, repeat=num_images_per_promt if not single_batch_mode else 1 + ) + throughputs['text-encoding'].append(time.perf_counter() - tic) + + # Set seed + noise_maps = [] + if isinstance(seed, int): + # Single seed for the batch + torch.random.manual_seed(seed) + # Generate noise maps + for model in models: + noise_map = torch.randn( + (num_images_per_promt, 3, model.unet.image_size, model.unet.image_size), device=device + ) + noise_map = noise_map.half() if inference_precision == '16' else noise_map + noise_maps.append(noise_map) + elif isinstance(seed, list): + assert len(seed) == num_images_per_promt + for model in models: + noise_map_batch = [] + for single_seed in seed: + torch.random.manual_seed(single_seed) + noise_map_single = torch.randn( + (1, 3, model.unet.image_size, model.unet.image_size), device=device + ) + noise_map_batch.append(noise_map_single) + noise_map_batch = torch.cat(noise_map_batch, dim=0) + noise_map_batch = noise_map_batch.half() if inference_precision == '16' else noise_map_batch + noise_maps.append(noise_map_batch) + else: + raise RuntimeError('Seed type incorrect.') + + x_low_res = low_res_input + all_res = [] + for idx, (model, noise_map, cfg, step) in enumerate(zip(models, noise_maps, cfgs, steps)): + tic = time.perf_counter() + with autocast(enabled=amp_enabled): + generated_images = model.sample_image( + noise_map=noise_map, + text_encoding=text_encodings, + text_mask=text_mask, + x_low_res=x_low_res, + cond_scale=cfg, + sampling_steps=step, + thresholding_method=thresholding_method, + ) + x_low_res = generated_images + all_res.append(generated_images) + throughputs[f'stage-{idx+1}'].append(time.perf_counter() - tic) + # recenter from [-1, 1] to [0, 1] + assert generated_images is not None + generated_images = ((generated_images + 1) / 2).clamp_(0, 1) + all_res = [((each + 1) / 2).clamp_(0, 1) for each in all_res] + output.append(generated_images) + for idx, each in enumerate(all_res): + all_res_output[idx].append(each) + if single_batch_mode: + break + + if output_type == 'torch': + return torch.cat(output, dim=0), [torch.cat(each, dim=0) for each in all_res_output] + output_new = [] + for x_samples_image in output: + # Convert to numpy + x_samples_image = x_samples_image.cpu().permute(0, 2, 3, 1).numpy() + if output_type == 'pil': + x_samples_image = numpy_to_pil(x_samples_image) + output_new.append(x_samples_image) + + all_res_output_new = [[] for each in range(len(models))] + for idx, res_output in enumerate(all_res_output): + for x_samples_image in res_output: + # Convert to numpy + x_samples_image = x_samples_image.cpu().permute(0, 2, 3, 1).numpy() + if output_type == 'pil': + x_samples_image = numpy_to_pil(x_samples_image) + all_res_output_new[idx].append(x_samples_image) + + for item in throughputs: + throughputs[item] = sum(throughputs[item]) / len(throughputs[item]) + + return output_new, all_res_output_new, throughputs diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/imagen/precond.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/imagen/precond.py new file mode 100644 index 0000000..fc3b3ed --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/imagen/precond.py @@ -0,0 +1,174 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn.functional as F + +from nemo.collections.multimodal.modules.imagen.sampler.batch_ops import batch_mul +from nemo.collections.multimodal.modules.imagen.sampler.continuous_ddpm import GaussianDiffusionContinuousTimes +from nemo.collections.multimodal.parts.utils import randn_like + + +class PrecondModel(torch.nn.Module): + def __init__(self, unet, loss_type): + super().__init__() + self.unet = unet + self.rng = None + self.inference = False + if loss_type == 'l1': + self.loss_fn = F.l1_loss + elif loss_type == 'l2': + self.loss_fn = F.mse_loss + elif loss_type == 'huber': + self.loss_fn = F.smooth_l1_loss + else: + raise NotImplementedError(f'{loss_type} loss is not supported') + + def set_inference_mode(self, value): + self.inference = value + + def forward(self, **model_kwargs): + return self.unet(**model_kwargs) + + def forward_with_cond_scale(self, *args, text_embed=None, cond_scale=1.0, **kwargs): + logits = self.forward(*args, text_embed=text_embed, **kwargs) + if cond_scale == 1.0: + return logits + null_logits = self.forward(*args, text_embed=torch.zeros_like(text_embed), **kwargs) + return null_logits + (logits - null_logits) * cond_scale + + def set_rng(self, generator): + self.rng = generator + + +class ContinousDDPMPrecond(PrecondModel): + def __init__( + self, + unet, + loss_type='l2', + pred_objective='noise', + noise_schedule='cosine', + timesteps=1000, + noise_cond_aug=False, + ): + super().__init__(unet, loss_type) + self.scheduler = GaussianDiffusionContinuousTimes(noise_schedule=noise_schedule, timesteps=timesteps) + self.pred_objective = pred_objective + assert noise_cond_aug == False, 'noise cond aug currently not supported for DDPM' + + def sample_time(self, batch_size, device=None): + return self.scheduler.sample_random_times(batch_size=batch_size, device=device) + + def get_xt(self, x0, t=None, epsilon=None): + if epsilon is None: + epsilon = randn_like(x0, generator=self.rng) + if t is None: + t = self.sample_time(batch_size=x0.shape[0], device=x0.device) + x_noisy, log_snr, alpha, sigma = self.scheduler.q_sample(x_start=x0, t=t, noise=epsilon,) + return x_noisy, t, epsilon + + def forward(self, x, time, text_embed, text_mask, **model_kwargs): + # Convert time to FP32 for calculating time embedding due to FP16 overflow + time = time.float() + time = self.scheduler.get_condition(time) + time = time.type_as(x) + + return self.unet(x=x, time=time, text_embed=text_embed, text_mask=text_mask, **model_kwargs) + + def compute_loss(self, x0, text_embed, text_mask, time=None, noise=None, **model_kwargs): + x_noisy, time, noise = self.get_xt(x0=x0, t=time, epsilon=noise) + pred = self.forward(x_noisy, time, text_embed, text_mask, **model_kwargs) + # Determine target + if self.pred_objective == 'noise': + target = noise + elif self.pred_objective == 'x_start': + target = x0 + else: + raise ValueError(f'unknown objective {self.pred_objective}') + return self.loss_fn(pred, target) + + def set_rng(self, generator): + self.scheduler.rng = generator + self.rng = generator + + +class EDMPrecond(PrecondModel): + def __init__( + self, + unet, # Underlying model. + loss_type='l2', + sigma_data=0.5, # Expected standard deviation of the training data. + p_mean=-1.2, + p_std=1.2, + noise_cond_aug=False, + ): + super().__init__(unet, loss_type) + self.sigma_data = sigma_data + self.p_mean = p_mean + self.p_std = p_std + self.noise_cond_aug = noise_cond_aug + + def forward(self, x, time, text_embed, text_mask, **model_kwargs): + bs = x.shape[0] + assert time.ndim <= 1, 'time should be in shape of either [bs] or scalar' + sigma = time + c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + c_out = sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2).sqrt() + c_in = 1 / (self.sigma_data ** 2 + sigma ** 2).sqrt() + c_noise = sigma.log() / 4 + + if c_noise.ndim < 1: + c_noise = c_noise.repeat(bs,) + + if self.noise_cond_aug: + # Applying noise conditioning augmentation + assert 'x_low_res' in model_kwargs, 'x_low_res does not exist when attemping to apply noise augmentation' + x_low_res = model_kwargs['x_low_res'] + if self.inference: + batch_size = x_low_res.shape[0] + time_low_res = torch.ones(batch_size, device=x_low_res.device) * 0.002 + x_low_res_noisy, time_low_res = self.get_xt(x0=x_low_res, t=time_low_res, epsilon=None) + else: + x_low_res_noisy, time_low_res = self.get_xt(x0=x_low_res, t=None, epsilon=None) + c_in_noise = 1 / (self.sigma_data ** 2 + time_low_res ** 2).sqrt() + c_noise_noise = time_low_res.log() / 4 + model_kwargs['x_low_res'] = batch_mul(c_in_noise, x_low_res_noisy) + model_kwargs['time_low_res'] = c_noise_noise + + F_x = self.unet(batch_mul(c_in, x), c_noise, text_embed, text_mask, **model_kwargs) + D_x = batch_mul(c_skip, x) + batch_mul(c_out, F_x) + return D_x + + def sample_time(self, batch_size, device=None): + return (torch.randn(batch_size, device=device, generator=self.rng) * self.p_std + self.p_mean).exp() + + def get_xt(self, x0, t=None, epsilon=None): + if epsilon is None: + epsilon = randn_like(x0, generator=self.rng) + assert epsilon.shape == x0.shape + if t is None: + t = self.sample_time(batch_size=x0.shape[0], device=x0.device) + sigma = t + noise = batch_mul(epsilon, sigma) + return x0 + noise, sigma + + def compute_loss(self, x0, text_embed, text_mask, time=None, noise=None, **model_kwargs): + x_noisy, time = self.get_xt(x0=x0, t=None, epsilon=noise) + pred = self.forward(x_noisy, time, text_embed, text_mask, **model_kwargs) + sigma = time + weight = ((sigma ** 2 + self.sigma_data ** 2) / (sigma * self.sigma_data) ** 2).sqrt() + target = x0 + return self.loss_fn(batch_mul(weight, target), batch_mul(weight, pred),) + + def set_rng(self, generator): + self.rng = generator diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/instruct_pix2pix/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/instruct_pix2pix/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/instruct_pix2pix/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/instruct_pix2pix/ldm/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/instruct_pix2pix/ldm/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/instruct_pix2pix/ldm/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/instruct_pix2pix/ldm/ddpm_edit.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/instruct_pix2pix/ldm/ddpm_edit.py new file mode 100644 index 0000000..9bb490f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/instruct_pix2pix/ldm/ddpm_edit.py @@ -0,0 +1,264 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +https://github.com/timothybrooks/instruct-pix2pix/blob/2afcb7e45bd350765f21a58a0c135871e9dc5a78/stable_diffusion/ldm/models/diffusion/ddpm_edit.py +""" + +import torch +from einops import rearrange + +from nemo.collections.multimodal.data.instruct_pix2pix.edit_dataset import EditDataset +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import ( + LatentDiffusion, + MegatronLatentDiffusion, +) +from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import ( + MegatronPretrainingRandomSampler, + MegatronPretrainingSampler, +) +from nemo.utils import logging + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +class LatentDiffusionEdit(LatentDiffusion): + def init_from_ckpt( + self, path, ignore_keys=list(), only_model=False, load_vae=True, load_unet=True, load_encoder=True, + ): + pl_sd = torch.load(path, map_location="cpu") + if "state_dict" in list(pl_sd.keys()): + pl_sd = pl_sd["state_dict"] + sd = {} + + first_key = list(pl_sd.keys())[0] + # State keys of model trained with TorchDynamo changed from + # "model.xxx" to "model._orig_mod.xxx" + for k, v in pl_sd.items(): + new_k = k.replace("._orig_mod", "") + # compatibility for stable diffusion old checkpoint + # remove megatron wrapper prefix + if first_key == "model.betas": + new_k = new_k.lstrip("model.") + sd[new_k] = v + keys = list(sd.keys()) + + # Our model adds additional channels to the first layer to condition on an input image. + # For the first layer, copy existing channel weights and initialize new channel weights to zero. + input_keys = [ + "model.diffusion_model.input_blocks.0.0.weight", + ] + + self_sd = self.state_dict() + for input_key in input_keys: + if input_key not in sd or input_key not in self_sd: + continue + + input_weight = self_sd[input_key] + if input_weight.size() != sd[input_key].size(): + print(f"Manual init: {input_key}") + input_weight.zero_() + input_weight[:, :4, :, :].copy_(sd[input_key]) + ignore_keys.append(input_key) + + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = ( + self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(sd, strict=False) + ) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + + @torch.no_grad() + def get_input( + self, + batch, + k, + return_first_stage_outputs=False, + force_c_encode=False, + cond_key=None, + return_original_cond=False, + bs=None, + uncond=0.05, + ): + x = batch[k] + if bs is not None: + x = x[:bs] + + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + cond_key = cond_key or self.cond_stage_key + xc = batch[cond_key] + if bs is not None: + xc["c_crossattn"] = xc["c_crossattn"][:bs] + xc["c_concat"] = xc["c_concat"][:bs] + cond = {} + + # To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%. + random = torch.rand(x.size(0), device=x.device) + prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1") + input_mask = 1 - rearrange((random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1") + + null_prompt = self.get_learned_conditioning([""]) + cond["c_crossattn"] = torch.where( + prompt_mask, null_prompt, self.get_learned_conditioning(xc["c_crossattn"]).detach() + ) + cond["c_concat"] = input_mask * self.encode_first_stage((xc["c_concat"].to(x.device))).mode().detach() + + out = [z, cond] + if return_first_stage_outputs: + xrec = self.decode_first_stage(z) + out.extend([x, xrec]) + if return_original_cond: + out.append(xc) + return out + + +class MegatronLatentDiffusionEdit(MegatronLatentDiffusion): + def model_provider_func(self, pre_process=True, post_process=True): + """Model depends on pipeline paralellism.""" + model = LatentDiffusionEdit(cfg=self.cfg, model_parallel_config=self.model_parallel_config) + return model + + def setup(self, stage=None): + """ PTL hook that is executed after DDP spawns. + We setup datasets here as megatron datasets require DDP to instantiate. + See https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#setup for more information. + Args: + stage (str, optional): Can be 'fit', 'validate', 'test' or 'predict'. Defaults to None. + """ + self.model.rng.manual_seed(self.cfg.seed + 100 * parallel_state.get_data_parallel_rank()) + + # log number of parameters + if isinstance(self.model, list): + num_parameters_on_device = sum( + [sum([p.nelement() for p in model_module.parameters()]) for model_module in self.model] + ) + else: + num_parameters_on_device = sum([p.nelement() for p in self.model.parameters()]) + + # to be summed across data parallel group + total_num_parameters = torch.tensor(num_parameters_on_device).cuda(non_blocking=True) + + torch.distributed.all_reduce(total_num_parameters, group=parallel_state.get_model_parallel_group()) + + logging.info( + f'Pipeline model parallel rank: {parallel_state.get_pipeline_model_parallel_rank()}, ' + f'Tensor model parallel rank: {parallel_state.get_tensor_model_parallel_rank()}, ' + f'Number of model parameters on device: {num_parameters_on_device:.2e}. ' + f'Total number of model parameters: {total_num_parameters:.2e}.' + ) + + resume_checkpoint_path = self.trainer.ckpt_path + if resume_checkpoint_path: + init_consumed_samples = self._extract_consumed_samples_from_ckpt(resume_checkpoint_path) + else: + init_consumed_samples = 0 + self.init_consumed_samples = init_consumed_samples + self.init_global_step = self.trainer.global_step + + self.build_train_valid_test_datasets() + self.setup_training_data(self.cfg.data) + self.setup_validation_data(self.cfg.data) + self.setup_test_data(self.cfg.data) + + def build_train_valid_test_datasets(self): + # TODO (yuya): set up splits ratio and other params + if self.cfg.data.data_path is not None: + self._train_ds = EditDataset(path=self.cfg.data.data_path, split="train", flip_prob=0.5) + self._validation_ds = EditDataset(path=self.cfg.data.data_path, split="val") + self._test_ds = EditDataset(path=self.cfg.data.data_path, split="test") + + def setup_training_data(self, cfg): + if hasattr(self, '_train_ds') and self._train_ds is not None: + consumed_samples = self.compute_consumed_samples(0) + logging.info( + f'Setting up train dataloader with len(len(self._train_ds)): {len(self._train_ds)} and consumed samples: {consumed_samples}' + ) + self._train_dl = self.build_pretraining_data_loader(self._train_ds, consumed_samples) + + def setup_validation_data(self, cfg): + if hasattr(self, '_validation_ds') and self._validation_ds is not None: + consumed_samples = 0 + logging.info( + f'Setting up validation dataloader with len(len(self._validation_ds)): {len(self._validation_ds)} and consumed samples: {consumed_samples}' + ) + drop_last = True + if not self.cfg.get('validation_drop_last', True): + logging.info(f'Drop last in validation dataset is set to False') + drop_last = False + self._validation_dl = self.build_pretraining_data_loader(self._validation_ds, consumed_samples, drop_last) + + def setup_test_data(self, cfg): + if hasattr(self, '_test_ds') and self._test_ds is not None: + consumed_samples = 0 + logging.info( + f'Setting up test dataloader with len(len(self._test_ds)): {len(self._test_ds)} and consumed samples: {consumed_samples}' + ) + drop_last = True + if not self.cfg.get('validation_drop_last', True): + logging.info(f'Drop last in validation dataset is set to False') + drop_last = False + self._test_dl = self.build_pretraining_data_loader(self._test_ds, consumed_samples, drop_last) + + def build_pretraining_data_loader(self, dataset, consumed_samples, drop_last=True): + """Build dataloader given an input dataset.""" + + if dataset is None: + return None + logging.info(f'Building dataloader with consumed samples: {consumed_samples}') + # Megatron sampler + if hasattr(self._cfg.data, 'dataloader_type') and self._cfg.data.dataloader_type is not None: + # TODO (yuya): fix this + if self._cfg.data.dataloader_type == 'single': + batch_sampler = MegatronPretrainingSampler( + total_samples=len(dataset), + consumed_samples=consumed_samples, + micro_batch_size=self._cfg.micro_batch_size, + global_batch_size=self._cfg.global_batch_size, + data_parallel_rank=parallel_state.get_data_parallel_rank(), + data_parallel_size=parallel_state.get_data_parallel_world_size(), + drop_last=drop_last, + ) + elif self._cfg.data.dataloader_type == 'cyclic': + batch_sampler = MegatronPretrainingRandomSampler( + total_samples=len(dataset), + consumed_samples=consumed_samples, + micro_batch_size=self._cfg.micro_batch_size, + global_batch_size=self._cfg.global_batch_size, + data_parallel_rank=parallel_state.get_data_parallel_rank(), + data_parallel_size=parallel_state.get_data_parallel_world_size(), + drop_last=drop_last, + ) + else: + raise Exception(f'{self._cfg.dataloader_type} dataloader type is not supported.') + else: + raise ValueError('cfg.data.dataloader_type not found. Must be "single" or "cyclic"') + + # Torch dataloader. + return torch.utils.data.DataLoader( + dataset, batch_sampler=batch_sampler, num_workers=self._cfg.data.num_workers, pin_memory=True, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/diffusion_engine.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/diffusion_engine.py new file mode 100644 index 0000000..efc1550 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/diffusion_engine.py @@ -0,0 +1,723 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC, abstractclassmethod +from contextlib import contextmanager +from typing import Any, Dict, List, Tuple, Union + +import hydra +import pytorch_lightning as pl +import torch +import torch._dynamo +import torch.nn as nn +from einops import rearrange +from omegaconf import DictConfig, ListConfig, OmegaConf +from pytorch_lightning import Trainer +from pytorch_lightning.utilities import rank_zero_only +from safetensors.torch import load_file as load_safetensors +from torch._dynamo import optimize +from torch.optim.lr_scheduler import LambdaLR + +from nemo.collections.multimodal.data.stable_diffusion.stable_diffusion_dataset import ( + build_sdxl_precached_text_train_valid_datasets, + build_sdxl_train_valid_datasets, + build_train_valid_precached_datasets, +) +from nemo.collections.multimodal.modules.stable_diffusion.attention import LinearWrapper +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.wrappers import OPENAIUNETWRAPPER +from nemo.collections.multimodal.parts.stable_diffusion.utils import ( + default, + disabled_train, + get_obj_from_str, + instantiate_from_config, + log_txt_as_img, +) +from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel +from nemo.collections.nlp.modules.common.megatron.module import Float16Module +from nemo.collections.nlp.parts.mixins.nlp_adapter_mixins import NLPAdapterModelMixin +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP, PEFTConfig +from nemo.collections.nlp.parts.utils_funcs import get_last_rank +from nemo.core.classes import ModelPT, Serialization +from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin +from nemo.utils import logging, model_utils + +try: + from apex import amp + from apex.transformer.enums import AttnMaskType + from apex.transformer.pipeline_parallel.utils import get_num_microbatches + + HAVE_APEX = True +except (ImportError, ModuleNotFoundError): + HAVE_APEX = False + +try: + from megatron.core import parallel_state + from megatron.core.pipeline_parallel.schedules import get_forward_backward_func + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + +UNCONDITIONAL_CONFIG = { + "target": "sgm.modules.GeneralConditioner", + "params": {"emb_models": []}, +} + + +class DiffusionEngine(nn.Module, Serialization): + def __init__(self, cfg, model_parallel_config): + super().__init__() + unet_config = cfg.unet_config + denoiser_config = cfg.denoiser_config + first_stage_config = cfg.first_stage_config + conditioner_config = cfg.conditioner_config + sampler_config = cfg.get('sampler_config', None) + optimizer_config = cfg.get('optimizer_config', None) + scheduler_config = cfg.get('scheduler_config', None) + loss_fn_config = cfg.get('loss_fn_config', None) + network_wrapper = cfg.get('network_wrapper', None) + compile_model = cfg.get('compile_model', False) + self.config = model_parallel_config + + self.channels_last = cfg.get('channels_last', False) + self.log_keys = cfg.get('log_keys', None) + self.input_key = cfg.get('input_key', 'images') + # Precaching + self.precache_mode = cfg.get('precache_mode') + + self.loss_fn = DiffusionEngine.from_config_dict(loss_fn_config) if loss_fn_config is not None else None + + model = DiffusionEngine.from_config_dict(unet_config) + self.model = get_obj_from_str(default(network_wrapper, OPENAIUNETWRAPPER))(model, compile_model=compile_model) + if cfg.get('inductor', False): + torch._dynamo.config.cache_size_limit = 256 + torch._dynamo.config.dynamic_shapes = False + torch._dynamo.config.automatic_dynamic_shapes = False + torch._dynamo.config.suppress_errors = True + self.model = torch.compile(self.model) + + self.denoiser = DiffusionEngine.from_config_dict(denoiser_config) + self.sampler = instantiate_from_config(sampler_config) if sampler_config is not None else None + + self.conditioner = DiffusionEngine.from_config_dict(default(conditioner_config, UNCONDITIONAL_CONFIG)) + self.scheduler_config = scheduler_config + # Precaching + self.precache_mode = cfg.get('precache_mode') + self._init_first_stage(first_stage_config) + self.model_type = None + + self.rng = torch.Generator(device=torch.cuda.current_device(),) + + self.use_ema = False # TODO use_ema need to switch to NeMo style + if self.use_ema: + self.model_ema = LitEma(self.model, decay=ema_decay_rate) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + self.scale_factor = cfg.scale_factor + self.disable_first_stage_autocast = cfg.disable_first_stage_autocast + self.no_cond_log = cfg.get('no_cond_log', False) + + if self.channels_last: + if self.first_stage_model: + self.first_stage_model = self.first_stage_model.to(memory_format=torch.channels_last) + self.model = self.model.to(memory_format=torch.channels_last) + + def _init_first_stage(self, config): + if self.precache_mode == 'both': + logging.info('Do not intialize VAE when caching image features.') + self.first_stage_model = None + return + model = DiffusionEngine.from_config_dict(config).eval() + model.train = disabled_train + for param in model.parameters(): + param.requires_grad = False + self.first_stage_model = model + + def get_input(self, batch): + # assuming unified data format, dataloader returns a dict. + # image tensors should be scaled to -1 ... 1 and in bchw format + return batch[self.input_key] + + @torch.no_grad() + def decode_first_stage(self, z): + z = 1.0 / self.scale_factor * z + with torch.autocast("cuda", enabled=not self.disable_first_stage_autocast): + out = self.first_stage_model.decode(z) + return out + + @torch.no_grad() + def encode_first_stage(self, x): + with torch.autocast("cuda", enabled=not self.disable_first_stage_autocast): + z = self.first_stage_model.encode(x) + z = self.scale_factor * z + return z + + def forward(self, x, batch): + loss = self.loss_fn(self.model, self.denoiser, self.conditioner, x, batch, rng=self.rng) + loss_mean = loss.mean() + log_prefix = 'train' if self.training else 'val' + loss_dict = {f"{log_prefix}/loss": loss_mean} + return loss_mean, loss_dict + + def shared_step(self, batch: Dict) -> Any: + x = self.get_input(batch) + x = self.encode_first_stage(x) + batch["global_step"] = self.global_step + loss, loss_dict = self(x, batch) + return loss, loss_dict + + def training_step(self, batch, batch_idx): + loss, loss_dict = self.shared_step(batch) + + self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=False) + + self.log( + "global_step", self.global_step, prog_bar=True, logger=True, on_step=True, on_epoch=False, + ) + + if self.scheduler_config is not None: + lr = self.optimizers().param_groups[0]["lr"] + self.log("lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) + + return loss + + def on_train_start(self, *args, **kwargs): + if self.sampler is None or self.loss_fn is None: + raise ValueError("Sampler and loss function need to be set for training.") + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self.model) + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.model.parameters()) + self.model_ema.copy_to(self.model) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.model.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def instantiate_optimizer_from_config(self, params, lr, cfg): + return get_obj_from_str(cfg["target"])(params, lr=lr, **cfg.get("params", dict())) + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + for embedder in self.conditioner.embedders: + if embedder.is_trainable: + params = params + list(embedder.parameters()) + opt = self.instantiate_optimizer_from_config(params, lr, self.optimizer_config) + if self.scheduler_config is not None: + scheduler = DiffusionEngine.from_config_dict(self.scheduler_config) + print("Setting up LambdaLR scheduler...") + scheduler = [ + {"scheduler": LambdaLR(opt, lr_lambda=scheduler.schedule), "interval": "step", "frequency": 1,} + ] + return [opt], scheduler + return opt + + @torch.no_grad() + def sample( + self, + cond: Dict, + uc: Union[Dict, None] = None, + batch_size: int = 16, + shape: Union[None, Tuple, List] = None, + **kwargs, + ): + randn = torch.randn(batch_size, *shape, generator=self.rng).to(self.device) + + denoiser = lambda input, sigma, c: self.denoiser(self.model, input, sigma, c, **kwargs) + samples = self.sampler(denoiser, randn, cond, uc=uc) + return samples + + @torch.no_grad() + def log_conditionings(self, batch: Dict, n: int) -> Dict: + """ + Defines heuristics to log different conditionings. + These can be lists of strings (text-to-image), tensors, ints, ... + """ + image_h, image_w = batch[self.input_key].shape[2:] + log = dict() + + for embedder in self.conditioner.embedders: + if ((self.log_keys is None) or (embedder.input_key in self.log_keys)) and not self.no_cond_log: + x = batch[embedder.input_key][:n] + if isinstance(x, torch.Tensor): + if x.dim() == 1: + # class-conditional, convert integer to string + x = [str(x[i].item()) for i in range(x.shape[0])] + xc = log_txt_as_img((image_h, image_w), x, size=image_h // 4) + elif x.dim() == 2: + # size and crop cond and the like + x = ["x".join([str(xx) for xx in x[i].tolist()]) for i in range(x.shape[0])] + xc = log_txt_as_img((image_h, image_w), x, size=image_h // 20) + else: + raise NotImplementedError() + elif isinstance(x, (List, ListConfig)): + if isinstance(x[0], str): + # strings + xc = log_txt_as_img((image_h, image_w), x, size=image_h // 20) + else: + raise NotImplementedError() + else: + raise NotImplementedError() + log[embedder.input_key] = xc + return log + + def set_input_tensor(self, input_tensor): + """See megatron.model.transformer.set_input_tensor()""" + # only required for pipeline parallelism + pass + + @torch.no_grad() + def log_images(self, batch: Dict, N: int = 8, sample: bool = True, ucg_keys: List[str] = None, **kwargs,) -> Dict: + conditioner_input_keys = [e.input_key for e in self.conditioner.embedders] + if ucg_keys: + assert all(map(lambda x: x in conditioner_input_keys, ucg_keys)), ( + "Each defined ucg key for sampling must be in the provided conditioner input keys," + f"but we have {ucg_keys} vs. {conditioner_input_keys}" + ) + else: + ucg_keys = conditioner_input_keys + log = dict() + + x = self.get_input(batch) + + c, uc = self.conditioner.get_unconditional_conditioning( + batch, force_uc_zero_embeddings=ucg_keys if len(self.conditioner.embedders) > 0 else [], + ) + + sampling_kwargs = {} + + N = min(x.shape[0], N) + x = x.to(self.device)[:N] + log["inputs"] = x + z = self.encode_first_stage(x) + log["reconstructions"] = self.decode_first_stage(z) + log.update(self.log_conditionings(batch, N)) + + for k in c: + if isinstance(c[k], torch.Tensor): + c[k], uc[k] = map(lambda y: y[k][:N].to(self.device), (c, uc)) + + if sample: + with self.ema_scope("Plotting"): + samples = self.sample(c, shape=z.shape[1:], uc=uc, batch_size=N, **sampling_kwargs) + samples = self.decode_first_stage(samples) + log["samples"] = samples + return log + + +class MegatronDiffusionEngine(NLPAdapterModelMixin, MegatronBaseModel): + """Megatron DiffusionEngine Model.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer): + if not HAVE_APEX: + raise ImportError( + "Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + if not HAVE_MEGATRON_CORE: + raise ImportError( + "megatron-core was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + + # this prevents base constructor from initializing tokenizer + self.tokenizer = None + super().__init__(cfg, trainer=trainer) + + self._validate_trainer() + + # megatron_amp_O2 is not yet supported in diffusion models + self.megatron_amp_O2 = cfg.get('megatron_amp_O2', False) + self.use_fsdp = cfg.get('fsdp', False) + + self.model = self.model_provider_func() + + self.conditioning_keys = [] + + if self.trainer.precision in ['bf16', 'bf16-mixed']: + self.autocast_dtype = torch.bfloat16 + elif self.trainer.precision in [32, '32', '32-true']: + self.autocast_dtype = torch.float + elif self.trainer.precision in [16, '16', '16-mixed']: + self.autocast_dtype = torch.half + else: + raise ValueError('precision must be in ["32-true", "16-mixed", "bf16-mixed"]') + + def get_module_list(self): + if isinstance(self.model, list): + return [model.module if isinstance(model, Float16Module) else model for model in self.model] + elif isinstance(self.model, Float16Module): + return [self.model.module] + else: + return [self.model] + + def model_provider_func(self, pre_process=True, post_process=True): + """Model depends on pipeline paralellism.""" + model = DiffusionEngine(cfg=self.cfg, model_parallel_config=self.model_parallel_config) + return model + + # def forward(self, x, c, *args, **kwargs): + # output_tensor = self.model(x, c, *args, **kwargs) + # return output_tensor + + def forward(self, dataloader_iter, batch_idx): + loss = self.training_step(dataloader_iter, batch_idx) + return loss + + @rank_zero_only + @torch.no_grad() + def on_train_batch_start(self, batch, batch_idx, dataloader_idx=0): + if self.cfg.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0: + assert self.cfg.scale_factor == 1.0, 'rather not use custom rescaling and std-rescaling simultaneously' + batch[self.cfg.first_stage_key] = batch[self.cfg.first_stage_key].cuda(non_blocking=True) + self.model.on_train_batch_start(batch, batch_idx) + + def fwd_bwd_step(self, dataloader_iter, forward_only): + tensor_shape = None # Placeholder + + # handle asynchronous grad reduction + no_sync_func = None + if not forward_only and self.with_distributed_adam: + no_sync_func = partial(self._optimizer.no_sync, greedy_grad_copy=self.megatron_amp_O2,) + + # pipeline schedules will get these from self.model.config + for module in self.get_module_list(): + module.config.no_sync_func = no_sync_func + fwd_bwd_function = get_forward_backward_func() + + losses_reduced_per_micro_batch = fwd_bwd_function( + forward_step_func=self.get_forward_output_and_loss_func(), + data_iterator=dataloader_iter, + model=self.model, + num_microbatches=get_num_microbatches(), + forward_only=forward_only, + seq_length=None, + micro_batch_size=self.cfg.micro_batch_size, + ) + + loss_dict = {} + if losses_reduced_per_micro_batch: + if (not forward_only) or self.cfg.data.get('validation_drop_last', True): + # average loss across micro batches + for key in losses_reduced_per_micro_batch[0]: + loss_tensors_list = [loss_reduced[key] for loss_reduced in losses_reduced_per_micro_batch] + loss_tensor = torch.stack(loss_tensors_list) + loss_dict[key] = loss_tensor.mean() + loss_mean = loss_dict["train/loss"] + else: + raise NotImplementedError("Losses of micro batches sizes must be uniform!") + else: + if forward_only: + loss_mean = [] + else: + loss_mean = torch.tensor(0.0, device=torch.cuda.current_device()) + + return loss_mean, loss_dict + + def training_step(self, dataloader_iter): + """ + Our dataloaders produce a micro-batch and then we fetch + a number of microbatches depending on the global batch size and model parallel size + from the dataloader to produce a list of microbatches. + Batch should be a list of microbatches and those microbatches should on CPU. + Microbatches are then moved to GPU during the pipeline. + The list of microbatches is then piped through the pipeline using Apex fwd/bwd functions. + """ + self._optimizer.zero_grad() + + loss_mean, loss_dict = self.fwd_bwd_step(dataloader_iter, False) + + torch.distributed.broadcast(loss_mean, get_last_rank()) + + # when using sequence parallelism, the sequence parallel layernorm grads must be all-reduced + if self.cfg.get('tensor_model_parallel_size', 1) > 1 and self.cfg.get('sequence_parallel', False): + self.allreduce_sequence_parallel_gradients() + + if self.use_fsdp: + pass + elif self.with_distributed_adam: + # gradients are reduced internally in distributed optimizer + pass + elif self.megatron_amp_O2: + # # when using pipeline parallelism grads must be all-reduced after the pipeline (not asynchronously) + # if self.cfg.get('pipeline_model_parallel_size', 1) > 1 or self.cfg.get('sequence_parallel', False): + # # main grads are stored in the MainParamsOptimizer wrapper + # self._optimizer.allreduce_main_grads() + self._optimizer.allreduce_main_grads() + elif not self.cfg.get('ddp_overlap', True): + # async grad allreduce is not currently implemented for O1/autocasting mixed precision training + # so we all-reduce gradients after the pipeline + self.allreduce_gradients() # @sangkug we think this is causing memory to blow up (hurts perf) + + if self.cfg.precision in [16, '16', '16-mixed']: + loss_scale = self.trainer.precision_plugin.scaler._scale + if loss_scale is not None: + self.log('loss_scale', loss_scale, batch_size=1) + + self.log_dict(loss_dict, prog_bar=False, logger=True, on_step=True, rank_zero_only=True, batch_size=1) + self.log('reduced_train_loss', loss_mean, prog_bar=True, rank_zero_only=True, batch_size=1) + lr = self._optimizer.param_groups[0]['lr'] + self.log('lr', lr, prog_bar=True, rank_zero_only=True, batch_size=1) + self.log('global_step', self.trainer.global_step + 1, prog_bar=True, rank_zero_only=True, batch_size=1) + self.log( + 'consumed_samples', + self.compute_consumed_samples(self.trainer.global_step + 1 - self.init_global_step), + prog_bar=True, + rank_zero_only=True, + batch_size=1, + ) + return loss_mean + + def backward(self, *args, **kwargs): + """ LightningModule hook to do backward. + We want this to do nothing since we run backward in the fwd/bwd functions from apex. + No need to call it here. + """ + pass + + def optimizer_zero_grad(self, *args, **kwargs): + """ LightningModule hook to zero grad. + We want this to do nothing as we are zeroing grads during the training_step. + """ + pass + + def _append_sequence_parallel_module_grads(self, module, grads): + """ Helper method for allreduce_sequence_parallel_gradients""" + + for param in module.parameters(): + sequence_parallel_param = getattr(param, 'sequence_parallel', False) + if sequence_parallel_param and param.requires_grad: + if self.megatron_amp_O2: + grad = param.main_grad + else: + grad = param.grad + grads.append(grad.data) + + def get_forward_output_and_loss_func(self): + def process_batch(batch): + """ Prepares the global batch for apex fwd/bwd functions. + Global batch is a list of micro batches. + """ + # SD has more dedicated structure for encoding, so we enable autocasting here as well + with torch.cuda.amp.autocast( + self.autocast_dtype in (torch.half, torch.bfloat16), dtype=self.autocast_dtype, + ): + if self.model.precache_mode == 'both': + x = batch[self.model.input_key].to(torch.cuda.current_device()) + if self.model.channels_last: + x = x.to(memory_format=torch.channels_last, non_blocking=True) + else: + x = x.to(memory_format=torch.contiguous_format, non_blocking=True) + else: + x = batch[self.model.input_key].to(torch.cuda.current_device()) + if self.model.channels_last: + x = x.permute(0, 3, 1, 2).to(memory_format=torch.channels_last, non_blocking=True) + else: + x = rearrange(x, "b h w c -> b c h w") + x = x.to(memory_format=torch.contiguous_format, non_blocking=True) + x = self.model.encode_first_stage(x) + + batch['global_step'] = self.trainer.global_step + + return x, batch + + def fwd_output_and_loss_func(dataloader_iter, model): + batch, _, _ = next(dataloader_iter) + x, batch = process_batch(batch) + + loss, loss_dict = model(x, batch) + + def dummy(output_tensor): + return loss, loss_dict + + # output_tensor, and a function to convert output_tensor to loss + loss_dict + return loss, dummy + + return fwd_output_and_loss_func + + def validation_step(self, dataloader_iter, batch_idx): + loss, val_loss_dict = self.fwd_bwd_step(dataloader_iter, batch_idx, True) + + self.log_dict(val_loss_dict, prog_bar=False, logger=True, on_step=False, on_epoch=True, batch_size=1) + + return loss + + def setup(self, stage=None): + """ PTL hook that is executed after DDP spawns. + We setup datasets here as megatron datasets require DDP to instantiate. + See https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#setup for more information. + Args: + stage (str, optional): Can be 'fit', 'validate', 'test' or 'predict'. Defaults to None. + """ + self.model.rng.manual_seed(self.cfg.seed + 100 * parallel_state.get_data_parallel_rank()) + + # log number of parameters + if isinstance(self.model, list): + num_parameters_on_device = sum( + [sum([p.nelement() for p in model_module.parameters()]) for model_module in self.model] + ) + else: + num_parameters_on_device = sum([p.nelement() for p in self.model.parameters()]) + + # to be summed across data parallel group + total_num_parameters = torch.tensor(num_parameters_on_device).cuda(non_blocking=True) + + torch.distributed.all_reduce(total_num_parameters, group=parallel_state.get_model_parallel_group()) + + logging.info( + f'Pipeline model parallel rank: {parallel_state.get_pipeline_model_parallel_rank()}, ' + f'Tensor model parallel rank: {parallel_state.get_tensor_model_parallel_rank()}, ' + f'Number of model parameters on device: {num_parameters_on_device:.2e}. ' + f'Total number of model parameters: {total_num_parameters:.2e}.' + ) + + resume_checkpoint_path = self.trainer.ckpt_path + if resume_checkpoint_path: + init_consumed_samples = self._extract_consumed_samples_from_ckpt(resume_checkpoint_path) + else: + init_consumed_samples = 0 + self.init_consumed_samples = init_consumed_samples + self.init_global_step = self.trainer.global_step + + # allowing restored models to optionally setup datasets + self.build_train_valid_test_datasets() + + # Batch size need to be provided for webdatset + self._num_micro_batches = get_num_microbatches() + self._micro_batch_size = self.cfg.micro_batch_size + + self.setup_training_data(self.cfg.data) + self.setup_validation_data(self.cfg.data) + self.setup_test_data(self.cfg.data) + + def build_train_valid_test_datasets(self): + logging.info('Building datasets for Stable Diffusion...') + if self.trainer.limit_val_batches > 1.0 and isinstance(self.trainer.limit_val_batches, float): + raise ValueError("limit_val_batches must be an integer or float less than or equal to 1.0.") + + if self.model.precache_mode == 'text': + logging.info('Precahing text only.') + build_dataset_cls = build_sdxl_precached_text_train_valid_datasets + elif self.model.precache_mode == 'both': + logging.info('Precaching text and image.') + build_dataset_cls = build_train_valid_precached_datasets + elif self.model.precache_mode is None: + build_dataset_cls = build_sdxl_train_valid_datasets + else: + raise ValueError("unsupported precache mode provided. Check your config file.") + self._train_ds, self._validation_ds = build_dataset_cls( + model_cfg=self.cfg, consumed_samples=self.compute_consumed_samples(0) + ) + self._test_ds = None + + if self._train_ds is not None: + logging.info(f'Length of train dataset: {len(self._train_ds)}') + if self._validation_ds is not None: + logging.info(f'Length of val dataset: {len(self._validation_ds)}') + if self._test_ds is not None: + logging.info(f'Length of test dataset: {len(self._test_ds)}') + logging.info(f'Finished building datasets for LatentDiffusion.') + return self._train_ds, self._validation_ds, self._test_ds + + def setup_training_data(self, cfg): + if hasattr(self, '_train_ds') and self._train_ds is not None: + consumed_samples = self.compute_consumed_samples(0) + logging.info( + f'Setting up train dataloader with len(len(self._train_ds)): {len(self._train_ds)} and consumed samples: {consumed_samples}' + ) + self._train_dl = torch.utils.data.DataLoader( + self._train_ds, + batch_size=self._micro_batch_size, + num_workers=cfg.num_workers, + pin_memory=True, + drop_last=True, + persistent_workers=True, + ) + + def setup_validation_data(self, cfg): + if hasattr(self, '_validation_ds') and self._validation_ds is not None: + consumed_samples = 0 + logging.info( + f'Setting up validation dataloader with len(len(self._validation_ds)): {len(self._validation_ds)} and consumed samples: {consumed_samples}' + ) + self._validation_dl = torch.utils.data.DataLoader( + self._validation_ds, + batch_size=self._micro_batch_size, + num_workers=cfg.num_workers, + pin_memory=True, + drop_last=False, + persistent_workers=True, + ) + + def setup_test_data(self, cfg): + if hasattr(self, '_test_ds') and self._test_ds is not None: + consumed_samples = 0 + logging.info( + f'Setting up test dataloader with len(len(self._test_ds)): {len(self._test_ds)} and consumed samples: {consumed_samples}' + ) + self._test_dl = torch.utils.data.DataLoader( + self._test_ds, batch_size=self._micro_batch_size, num_workers=cfg.num_workers, pin_memory=True, + ) + + def transfer_batch_to_device(self, batch: Any, device: torch.device, dataloader_idx: int) -> Any: + """ PTL hook: https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#transfer-batch-to-device + When using pipeline parallelism, we need the global batch to remain on the CPU, + since the memory overhead will be too high when using a large number of microbatches. + Microbatches are transferred from CPU to GPU inside the pipeline. + """ + return batch + + def _validate_trainer(self): + """ Certain trainer configurations can break training. + Here we try to catch them and raise an error. + """ + if self.trainer.accumulate_grad_batches > 1: + raise ValueError( + f'Gradient accumulation is done within training_step. trainer.accumulate_grad_batches must equal 1' + ) + + @classmethod + def list_available_models(cls): + return None + + def parameters(self): + if isinstance(self.model, list): + return itertools.chain.from_iterable(module.parameters() for module in self.model) + else: + return self.model.parameters() + + def _check_and_add_adapter(self, name, module, peft_name, peft_cfg, name_key_to_mcore_mixins=None): + if isinstance(module, AdapterModuleMixin): + if isinstance(module, LinearWrapper): + peft_cfg.in_features, peft_cfg.out_features = module.in_features, module.out_features + else: + return + if model_utils.import_class_by_path(peft_cfg._target_) in module.get_accepted_adapter_types(): + module.add_adapter( + name=peft_name, + cfg=peft_cfg, + base_model_cfg=self.cfg, + model_parallel_config=self.model_parallel_config, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/diffusion_model.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/diffusion_model.py new file mode 100644 index 0000000..45bd2e5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/diffusion_model.py @@ -0,0 +1,80 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import re +from abc import ABC, abstractclassmethod +from typing import Any, Optional + +import torch + +from nemo.core.classes import ModelPT +from nemo.utils import logging + + +class DiffusionModel(ModelPT, ABC): + @abstractclassmethod + def get_conditioning(self, c: Any) -> Any: + """ + Encode conditioning c. + For txt2img use-case, the input conditioning would be the plain text, + and output would be the encoded embedding for the corresponding text; + For img2img use-case, the input conditioning would be the raw image, + and output would be the corresponding image embedding + + Args: + c: conditioning + + Returns: + encoded conditioning + """ + pass + + @abstractclassmethod + def apply_model(self, x_t: torch.Tensor, t: torch.Tensor, c: Optional[torch.Tensor]) -> torch.Tensor: + """ + Apply Diffusion model. + If c is not given, the model acts as an unconditional diffusion model. + For diffusion model that applies on the pixel space, x_t should be in the pixel space; + for diffusion model that applies on the latent space, x_t is in latent space. + + Args: + x_t: noisy input x at timestamp t + t: timestamp + c: conditioning + + Returns: + Predicted result that has the same shape as x_t + """ + + def on_train_start(self) -> None: + super().on_train_start() + self.init_global_step = self.trainer.global_step + + def _extract_consumed_samples_from_ckpt(self, ckpt_path): + try: + init_consumed_samples = int(float(re.findall(r"consumed_samples\=([0-9]+.[0-9]+)", ckpt_path)[0])) + except (ValueError, TypeError, IndexError): + logging.warning("Cannot parse the checkpoint file to get the consumed samples. assume it is zero.") + init_consumed_samples = 0 + + return init_consumed_samples + + def compute_consumed_samples(self, steps_since_resume=0): + consumed_samples = ( + self.init_consumed_samples + + steps_since_resume + * self.trainer.world_size + * self.cfg.micro_batch_size + * self.trainer.accumulate_grad_batches + ) + return int(consumed_samples) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/ldm/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/ldm/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/ldm/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/ldm/autoencoder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/ldm/autoencoder.py new file mode 100644 index 0000000..6bd47a7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/ldm/autoencoder.py @@ -0,0 +1,627 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from contextlib import contextmanager + +import pytorch_lightning as pl +import torch +import torch.nn.functional as F + +try: + from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer +except ImportError: + from taming.modules.vqvae.quantize import VectorQuantizer + +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.model import Decoder, Encoder +from nemo.collections.multimodal.modules.stable_diffusion.distributions.distributions import ( + DiagonalGaussianDistribution, +) +from nemo.collections.multimodal.parts.stable_diffusion.utils import instantiate_from_config + + +class VQModel(pl.LightningModule): + def __init__( + self, + ddconfig, + lossconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + batch_resize_range=None, + scheduler_config=None, + lr_g_factor=1.0, + remap=None, + sane_index_shape=False, # tell vector quantizer to return indices as bhw + ): + super().__init__() + self.embed_dim = embed_dim + self.n_embed = n_embed + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, remap=remap, sane_index_shape=sane_index_shape) + self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + if colorize_nlabels is not None: + assert type(colorize_nlabels) == int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + self.batch_resize_range = batch_resize_range + if self.batch_resize_range is not None: + print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + self.scheduler_config = scheduler_config + self.lr_g_factor = lr_g_factor + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.parameters()) + self.model_ema.copy_to(self) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + print(f"Unexpected Keys: {unexpected}") + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self) + + def encode(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info + + def encode_to_prequant(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode(self, quant): + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + + def decode_code(self, code_b): + quant_b = self.quantize.embed_code(code_b) + dec = self.decode(quant_b) + return dec + + def forward(self, input, return_pred_indices=False): + quant, diff, (_, _, ind) = self.encode(input) + dec = self.decode(quant) + if return_pred_indices: + return dec, diff, ind + return dec, diff + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + if self.batch_resize_range is not None: + lower_size = self.batch_resize_range[0] + upper_size = self.batch_resize_range[1] + if self.global_step <= 4: + # do the first few batches with max size to avoid later oom + new_resize = upper_size + else: + new_resize = np.random.choice(np.arange(lower_size, upper_size + 16, 16)) + if new_resize != x.shape[2]: + x = F.interpolate(x, size=new_resize, mode="bicubic") + x = x.detach() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + # https://github.com/pytorch/pytorch/issues/37142 + # try not to fool the heuristics + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + + if optimizer_idx == 0: + # autoencode + aeloss, log_dict_ae = self.loss( + qloss, + x, + xrec, + optimizer_idx, + self.global_step, + last_layer=self.get_last_layer(), + split="train", + predicted_indices=ind, + ) + + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return aeloss + + if optimizer_idx == 1: + # discriminator + discloss, log_dict_disc = self.loss( + qloss, x, xrec, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train" + ) + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return discloss + + def validation_step(self, batch, batch_idx): + log_dict = self._validation_step(batch, batch_idx) + with self.ema_scope(): + log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema") + return log_dict + + def _validation_step(self, batch, batch_idx, suffix=""): + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + aeloss, log_dict_ae = self.loss( + qloss, + x, + xrec, + 0, + self.global_step, + last_layer=self.get_last_layer(), + split="val" + suffix, + predicted_indices=ind, + ) + + discloss, log_dict_disc = self.loss( + qloss, + x, + xrec, + 1, + self.global_step, + last_layer=self.get_last_layer(), + split="val" + suffix, + predicted_indices=ind, + ) + rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] + self.log( + f"val{suffix}/rec_loss", rec_loss, prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True + ) + self.log( + f"val{suffix}/aeloss", aeloss, prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True + ) + if version.parse(pl.__version__) >= version.parse('1.4.0'): + del log_dict_ae[f"val{suffix}/rec_loss"] + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr_d = self.learning_rate + lr_g = self.lr_g_factor * self.learning_rate + print("lr_d", lr_d) + print("lr_g", lr_g) + opt_ae = torch.optim.Adam( + list(self.encoder.parameters()) + + list(self.decoder.parameters()) + + list(self.quantize.parameters()) + + list(self.quant_conv.parameters()) + + list(self.post_quant_conv.parameters()), + lr=lr_g, + betas=(0.5, 0.9), + ) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), lr=lr_d, betas=(0.5, 0.9)) + + if self.scheduler_config is not None: + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + {'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1}, + {'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1}, + ] + return [opt_ae, opt_disc], scheduler + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if only_inputs: + log["inputs"] = x + return log + xrec, _ = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["inputs"] = x + log["reconstructions"] = xrec + if plot_ema: + with self.ema_scope(): + xrec_ema, _ = self(x) + if x.shape[1] > 3: + xrec_ema = self.to_rgb(xrec_ema) + log["reconstructions_ema"] = xrec_ema + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0 + return x + + +class VQModelInterface(VQModel): + def __init__(self, embed_dim, *args, **kwargs): + super().__init__(embed_dim=embed_dim, *args, **kwargs) + self.embed_dim = embed_dim + + def encode(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode(self, h, force_not_quantize=False): + # also go through quantization layer + if not force_not_quantize: + quant, emb_loss, info = self.quantize(h) + else: + quant = h + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + + +class AutoencoderKL(pl.LightningModule): + def __init__( + self, + ddconfig, + embed_dim, + lossconfig=None, # TODO make it configurable + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + from_pretrained: str = None, + ): + super().__init__() + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = torch.nn.Identity() # instantiate_from_config(lossconfig) + assert ddconfig["double_z"] + self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + if colorize_nlabels is not None: + assert type(colorize_nlabels) == int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + + if from_pretrained is not None: + if from_pretrained.endswith('safetensors'): + from safetensors.torch import load_file as load_safetensors + + state_dict = load_safetensors(from_pretrained) + else: + state_dict = torch.load(from_pretrained) + if 'state_dict' in state_dict: + state_dict = state_dict['state_dict'] + missing_key, unexpected_key, _, _ = self._load_pretrained_model(state_dict) + if len(missing_key) > 0: + print( + f'{self.__class__.__name__}: Following keys are missing during loading VAE weights, which may lead to compromised image quality for a resumed training. Please check the checkpoint you provided.' + ) + print(f'Missing:{missing_key}') + print(f'Unexpected:{unexpected_key}') + + def _state_key_mapping(self, state_dict: dict): + import re + + res_dict = {} + key_list = state_dict.keys() + key_str = " ".join(key_list) + up_block_pattern = re.compile('upsamplers') + p1 = re.compile('mid.block_[0-9]') + p2 = re.compile('decoder.up.[0-9]') + up_blocks_count = int(len(re.findall(up_block_pattern, key_str)) / 2 + 1) + for key_, val_ in state_dict.items(): + key_ = ( + key_.replace("up_blocks", "up") + .replace("down_blocks", "down") + .replace('resnets', 'block') + .replace('mid_block', 'mid') + .replace("mid.block.", "mid.block_") + .replace('mid.attentions.0.key', 'mid.attn_1.k') + .replace('mid.attentions.0.query', 'mid.attn_1.q') + .replace('mid.attentions.0.value', 'mid.attn_1.v') + .replace('mid.attentions.0.group_norm', 'mid.attn_1.norm') + .replace('mid.attentions.0.proj_attn', 'mid.attn_1.proj_out') + .replace('upsamplers.0', 'upsample') + .replace('downsamplers.0', 'downsample') + .replace('conv_shortcut', 'nin_shortcut') + .replace('conv_norm_out', 'norm_out') + ) + + mid_list = re.findall(p1, key_) + if len(mid_list) != 0: + mid_str = mid_list[0] + mid_id = int(mid_str[-1]) + 1 + key_ = key_.replace(mid_str, mid_str[:-1] + str(mid_id)) + + up_list = re.findall(p2, key_) + if len(up_list) != 0: + up_str = up_list[0] + up_id = up_blocks_count - 1 - int(up_str[-1]) + key_ = key_.replace(up_str, up_str[:-1] + str(up_id)) + res_dict[key_] = val_ + return res_dict + + def _load_pretrained_model(self, state_dict, ignore_mismatched_sizes=False): + state_dict = self._state_key_mapping(state_dict) + model_state_dict = self.state_dict() + loaded_keys = [k for k in state_dict.keys()] + expected_keys = list(model_state_dict.keys()) + original_loaded_keys = loaded_keys + missing_keys = list(set(expected_keys) - set(loaded_keys)) + unexpected_keys = list(set(loaded_keys) - set(expected_keys)) + + def _find_mismatched_keys( + state_dict, model_state_dict, loaded_keys, ignore_mismatched_sizes, + ): + mismatched_keys = [] + if ignore_mismatched_sizes: + for checkpoint_key in loaded_keys: + model_key = checkpoint_key + + if ( + model_key in model_state_dict + and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape + ): + mismatched_keys.append( + (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape) + ) + del state_dict[checkpoint_key] + return mismatched_keys + + if 'encoder.mid.attn_1.q.weight' in loaded_keys and ( + state_dict['encoder.mid.attn_1.q.weight'].shape == torch.Size([512, 512]) + ): + for key in [ + 'encoder.mid.attn_1.q.weight', + 'decoder.mid.attn_1.q.weight', + 'encoder.mid.attn_1.v.weight', + 'decoder.mid.attn_1.v.weight', + 'encoder.mid.attn_1.k.weight', + 'decoder.mid.attn_1.k.weight', + 'encoder.mid.attn_1.proj_out.weight', + 'decoder.mid.attn_1.proj_out.weight', + ]: + state_dict[key] = state_dict[key].unsqueeze(2).unsqueeze(3) + + if state_dict is not None: + # Whole checkpoint + mismatched_keys = _find_mismatched_keys( + state_dict, model_state_dict, original_loaded_keys, ignore_mismatched_sizes, + ) + error_msgs = self._load_state_dict_into_model(state_dict) + return missing_keys, unexpected_keys, mismatched_keys, error_msgs + + def _load_state_dict_into_model(self, state_dict): + # Convert old format to new format if needed from a PyTorch state_dict + # copy state_dict so _load_from_state_dict can modify it + state_dict = state_dict.copy() + error_msgs = [] + + # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants + # so we need to apply the function recursively. + def load(module: torch.nn.Module, prefix=""): + args = (state_dict, prefix, {}, True, [], [], error_msgs) + module._load_from_state_dict(*args) + + for name, child in module._modules.items(): + if child is not None: + load(child, prefix + name + ".") + + load(self) + + return error_msgs + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + self.load_state_dict(sd, strict=False) + print(f"Restored from {path}") + + def encode(self, x): + ''' + Encode input image in pixel space to latent representation. + ''' + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z): + ''' + Decode latent representation back to pixel space. + ''' + z = self.post_quant_conv(z) + dec = self.decoder(z) + return dec + + def forward(self, input, sample_posterior=True): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z) + return dec, posterior + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + + if optimizer_idx == 0: + # train encoder+decoder+logvar + aeloss, log_dict_ae = self.loss( + inputs, + reconstructions, + posterior, + optimizer_idx, + self.global_step, + last_layer=self.get_last_layer(), + split="train", + ) + self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return aeloss + + if optimizer_idx == 1: + # train the discriminator + discloss, log_dict_disc = self.loss( + inputs, + reconstructions, + posterior, + optimizer_idx, + self.global_step, + last_layer=self.get_last_layer(), + split="train", + ) + + self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return discloss + + def validation_step(self, batch, batch_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + aeloss, log_dict_ae = self.loss( + inputs, reconstructions, posterior, 0, self.global_step, last_layer=self.get_last_layer(), split="val" + ) + + discloss, log_dict_disc = self.loss( + inputs, reconstructions, posterior, 1, self.global_step, last_layer=self.get_last_layer(), split="val" + ) + + self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr = self.learning_rate + opt_ae = torch.optim.Adam( + list(self.encoder.parameters()) + + list(self.decoder.parameters()) + + list(self.quant_conv.parameters()) + + list(self.post_quant_conv.parameters()), + lr=lr, + betas=(0.5, 0.9), + ) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)) + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + @torch.no_grad() + def log_images(self, batch, only_inputs=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if not only_inputs: + xrec, posterior = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["samples"] = self.decode(torch.randn_like(posterior.sample())) + log["reconstructions"] = xrec + log["inputs"] = x + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0 + return x + + +class AutoencoderKLInferenceWrapper(AutoencoderKL): + def encode(self, x): + return super().encode(x).sample() + + +class IdentityFirstStage(torch.nn.Module): + def __init__(self, *args, vq_interface=False, **kwargs): + self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff + super().__init__() + + def encode(self, x, *args, **kwargs): + return x + + def decode(self, x, *args, **kwargs): + return x + + def quantize(self, x, *args, **kwargs): + if self.vq_interface: + return x, None, [None, None, None] + return x + + def forward(self, x, *args, **kwargs): + return x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/ldm/ddpm.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/ldm/ddpm.py new file mode 100644 index 0000000..a96c3c4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/ldm/ddpm.py @@ -0,0 +1,2340 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import itertools +import os +import time +from functools import partial +from typing import Any, Dict, List, Optional, Union + +import numpy as np +import pytorch_lightning as pl +import torch +import torch.nn as nn +from einops import rearrange, repeat +from lightning_fabric.utilities.cloud_io import _load as pl_load +from omegaconf import DictConfig, open_dict +from pytorch_lightning import Trainer +from pytorch_lightning.core.saving import _load_state as ptl_load_state +from pytorch_lightning.core.saving import load_hparams_from_tags_csv, load_hparams_from_yaml +from pytorch_lightning.utilities.migration import pl_legacy_patch +from pytorch_lightning.utilities.rank_zero import rank_zero_only +from torch._inductor import config as inductor_config +from torchvision.utils import make_grid +from tqdm import tqdm + +from nemo.collections.multimodal.data.common.utils import get_collate_fn +from nemo.collections.multimodal.data.stable_diffusion.stable_diffusion_dataset import ( + build_train_valid_datasets, + build_train_valid_precached_clip_datasets, + build_train_valid_precached_datasets, +) +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder import ( + AutoencoderKL, + IdentityFirstStage, + VQModelInterface, +) +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.ddim import DDIMSampler +from nemo.collections.multimodal.modules.stable_diffusion.attention import LinearWrapper +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import ( + extract_into_tensor, + make_beta_schedule, + noise_like, +) +from nemo.collections.multimodal.modules.stable_diffusion.distributions.distributions import ( + DiagonalGaussianDistribution, + normal_kl, +) +from nemo.collections.multimodal.modules.stable_diffusion.encoders.modules import LoraWrapper +from nemo.collections.multimodal.parts.stable_diffusion.utils import ( + count_params, + default, + exists, + isimage, + ismap, + log_txt_as_img, + mean_flat, +) +from nemo.collections.multimodal.parts.utils import randn_like +from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel +from nemo.collections.nlp.modules.common.megatron.module import Float16Module +from nemo.collections.nlp.parts.mixins.nlp_adapter_mixins import NLPAdapterModelMixin +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP, PEFTConfig +from nemo.collections.nlp.parts.utils_funcs import get_last_rank +from nemo.core.classes.common import Serialization +from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin +from nemo.utils import logging, model_utils + +try: + from apex import amp + from apex.transformer.enums import AttnMaskType + from apex.transformer.pipeline_parallel.utils import get_num_microbatches + + HAVE_APEX = True +except (ImportError, ModuleNotFoundError): + HAVE_APEX = False + +try: + from megatron.core import parallel_state + from megatron.core.pipeline_parallel.schedules import get_forward_backward_func + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + +__conditioning_keys__ = {'concat': 'c_concat', 'crossattn': 'c_crossattn', 'adm': 'y'} + + +def random_dropout(embeddings, drop_rate): + r""" + Function to perform random dropout for embeddings. + When we drop embeddings, we zero them out. + Args: + embeddings (tensor): Input embeddings + drop_rate (float): Rate of dropping the embedding. + """ + nsamples = embeddings.shape[0] + zero_flag = torch.ones(nsamples, 1, 1, device=torch.cuda.current_device()).to(embeddings.dtype) * (1 - drop_rate) + zero_flag = torch.bernoulli(zero_flag).cuda(non_blocking=True) + embeddings = embeddings * zero_flag + return embeddings + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +def uniform_on_device(r1, r2, shape, device): + return (r1 - r2) * torch.rand(*shape, device=device) + r2 + + +class DDPM(torch.nn.Module): + def __init__(self, cfg): + super().__init__() + assert cfg.parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"' + self.parameterization = cfg.parameterization + logging.info(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") + self.cond_stage_model = None + self.clip_denoised = cfg.clip_denoised + self.log_every_t = cfg.log_every_t + self.first_stage_key = cfg.first_stage_key + self.image_size = cfg.image_size # try conv? + self.channels = cfg.channels + self.channels_last = cfg.get("channels_last", False) + self.use_positional_encodings = cfg.use_positional_encodings + self.model = DiffusionWrapper(cfg.unet_config, cfg.conditioning_key, cfg.inductor, cfg.inductor_cudagraphs) + self.model_type = None + count_params(self.model, verbose=True) + + self.v_posterior = cfg.v_posterior + self.original_elbo_weight = cfg.original_elbo_weight + self.l_simple_weight = cfg.l_simple_weight + + self.register_schedule( + given_betas=cfg.given_betas, + beta_schedule=cfg.beta_schedule, + timesteps=cfg.timesteps, + linear_start=cfg.linear_start, + linear_end=cfg.linear_end, + cosine_s=cfg.cosine_s, + ) + + self.loss_type = cfg.loss_type + + self.learn_logvar = cfg.learn_logvar + self.logvar = torch.full(fill_value=cfg.logvar_init, size=(self.num_timesteps,)) + if self.learn_logvar: + self.logvar = nn.Parameter(self.logvar, requires_grad=True) + + cuda_graph_enabled = cfg.get("capture_cudagraph_iters", -1) >= 0 + if not cuda_graph_enabled: + logging.info("Use custom random generator") + self.rng = torch.Generator(device=torch.cuda.current_device(),) + else: + logging.info("Use system random generator since CUDA graph enabled") + self.rng = None + + def register_schedule( + self, + given_betas=None, + beta_schedule="linear", + timesteps=1000, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + ): + if exists(given_betas): + betas = given_betas + else: + betas = make_beta_schedule( + beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s + ) + alphas = 1.0 - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1]) + + (timesteps,) = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1.0 - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1.0 - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1.0 / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1.0 / alphas_cumprod - 1))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = (1 - self.v_posterior) * betas * (1.0 - alphas_cumprod_prev) / ( + 1.0 - alphas_cumprod + ) + self.v_posterior * betas + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer( + 'posterior_mean_coef1', to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)) + ) + self.register_buffer( + 'posterior_mean_coef2', to_torch((1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)) + ) + + if self.parameterization == "eps": + lvlb_weights = self.betas ** 2 / ( + 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod) + ) + elif self.parameterization == "x0": + lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2.0 * 1 - torch.Tensor(alphas_cumprod)) + elif self.parameterization == "v": + lvlb_weights = torch.ones_like( + self.betas ** 2 / (2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) + ) + else: + raise NotImplementedError("mu not supported") + # TODO how to choose this term + lvlb_weights[0] = lvlb_weights[1] + self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) + assert not torch.isnan(self.lvlb_weights).all() + + def init_from_ckpt( + self, path, ignore_keys=list(), only_model=False, load_vae=True, load_unet=True, load_encoder=True, + ): + pl_sd = torch.load(path, map_location="cpu") + if "state_dict" in list(pl_sd.keys()): + pl_sd = pl_sd["state_dict"] + + sd = {} + first_key = list(pl_sd.keys())[0] + # State keys of model trained with TorchDynamo changed from + # "model.xxx" to "model._orig_mod.xxx" + for k, v in pl_sd.items(): + new_k = k.replace("._orig_mod", "") + # compatibility for stable diffusion old checkpoint + # remove megatron wrapper prefix + if first_key == "model.betas": + new_k = new_k.lstrip("model.") + sd[new_k] = v + + logging.info(f"Loading {path}") + logging.info(f"It has {len(sd)} entries") + logging.info(f"Existing model has {len(self.state_dict())} entries") + + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + logging.info("Deleting ignored key {} from state_dict.".format(k)) + del sd[k] + + if not load_vae: + deleted = 0 + keys = list(sd.keys()) + for k in keys: + if k.startswith("first_stage_model"): + deleted += 1 + del sd[k] + logging.info(f"Deleted {deleted} keys from `first_stage_model` state_dict.") + + if not load_encoder: + deleted = 0 + keys = list(sd.keys()) + for k in keys: + if k.startswith("cond_stage_model"): + deleted += 1 + del sd[k] + logging.info(f"Deleted {deleted} keys from `cond_stage_model` state_dict.") + + if not load_unet: + deleted = 0 + keys = list(sd.keys()) + for k in keys: + if k.startswith("model.diffusion_model"): + deleted += 1 + del sd[k] + logging.info(f"Deleted {deleted} keys from `model.diffusion_model` state_dict.") + + missing, unexpected = ( + self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(sd, strict=False) + ) + logging.info(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + logging.info(f"Missing Keys: {missing}") + if len(unexpected) > 0: + logging.info(f"Unexpected Keys: {unexpected}") + + def q_mean_variance(self, x_start, t): + """ + Get the distribution q(x_t | x_0). + :param x_start: the [N x C x ...] tensor of noiseless inputs. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :return: A tuple (mean, variance, log_variance), all of x_start's shape. + """ + mean = extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) + log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t + - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def predict_start_from_z_and_v(self, x_t, t, v): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t + - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v + ) + + def predict_eps_from_z_and_v(self, x_t, t, v): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, clip_denoised: bool): + model_out = self.model(x, t) + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + if clip_denoised: + x_recon.clamp_(-1.0, 1.0) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_loop(self, shape, return_intermediates=False): + device = self.betas.device + b = shape[0] + img = torch.randn(shape, generator=self.rng, device=device) + intermediates = [img] + for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): + img = self.p_sample( + img, torch.full((b,), i, device=device, dtype=torch.long), clip_denoised=self.clip_denoised + ) + if i % self.log_every_t == 0 or i == self.num_timesteps - 1: + intermediates.append(img) + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, batch_size=16, return_intermediates=False): + image_size = self.image_size + channels = self.channels + return self.p_sample_loop( + (batch_size, channels, image_size, image_size), return_intermediates=return_intermediates + ) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: randn_like(x_start, generator=self.rng)) + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise + ) + + def get_v(self, x, noise, t): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise + - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x + ) + + def get_loss(self, pred, target, mean=True): + if self.loss_type == 'l1': + loss = (target - pred).abs() + if mean: + loss = loss.mean() + elif self.loss_type == 'l2': + if mean: + loss = torch.nn.functional.mse_loss(target, pred) + else: + loss = torch.nn.functional.mse_loss(target, pred, reduction='none') + else: + raise NotImplementedError("unknown loss type '{loss_type}'") + + return loss + + def p_losses(self, x_start, t, noise=None): + noise = default(noise, lambda: randn_like(x_start, generator=self.rng)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_out = self.model(x_noisy, t) + + loss_dict = {} + if self.parameterization == "eps": + target = noise + elif self.parameterization == "x0": + target = x_start + elif self.parameterization == "v": + target = self.get_v(x_start, noise, t) + else: + raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") + + loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) + + log_prefix = 'train' if self.training else 'val' + + loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) + loss_simple = loss.mean() * self.l_simple_weight + + loss_vlb = (self.lvlb_weights[t] * loss).mean() + loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) + + loss = loss_simple + self.original_elbo_weight * loss_vlb + + loss_dict.update({f'{log_prefix}/loss': loss}) + + return loss, loss_dict + + def forward(self, x, *args, **kwargs): + # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size + # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' + t = torch.randint(0, self.num_timesteps, (x.shape[0],), generator=self.rng, device=x.device).long() + return self.p_losses(x, t, *args, **kwargs) + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + if self.channels_last: + x = x.permute(0, 3, 1, 2).to(non_blocking=True) + else: + x = rearrange(x, "b h w c -> b c h w") + x = x.to(memory_format=torch.contiguous_format, non_blocking=True) + return x + + def shared_step(self, batch): + x = self.get_input(batch, self.first_stage_key) + loss, loss_dict = self(x) + return loss, loss_dict + + def _get_rows_from_list(self, samples): + n_imgs_per_row = len(samples) + denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): + log = dict() + x = self.get_input(batch, self.first_stage_key) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + x = x[:N] + log["inputs"] = x + + # get diffusion row + diffusion_row = list() + x_start = x[:n_row] + + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.long() + noise = randn_like(x_start, generator=self.rng) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + diffusion_row.append(x_noisy) + + log["diffusion_row"] = self._get_rows_from_list(diffusion_row) + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) + + log["samples"] = samples + log["denoise_row"] = self._get_rows_from_list(denoise_row) + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + +class LatentDiffusion(DDPM, Serialization): + """main class""" + + def __init__(self, cfg, model_parallel_config): + self.config = model_parallel_config + self.num_timesteps_cond = default(cfg.num_timesteps_cond, 1) + self.scale_by_std = cfg.scale_by_std + assert self.num_timesteps_cond <= cfg.timesteps + # for backwards compatibility after implementation of DiffusionWrapper + if cfg.conditioning_key is None: + conditioning_key = 'concat' if cfg.concat_mode else 'crossattn' + else: + conditioning_key = cfg.conditioning_key + if cfg.cond_stage_config == '__is_unconditional__': + conditioning_key = None + ckpt_path = cfg.ckpt_path + ignore_keys = cfg.ignore_keys + cfg.conditioning_key = conditioning_key + super().__init__(cfg=cfg) + self.precision = cfg.precision + self.concat_mode = cfg.concat_mode + self.cond_stage_trainable = cfg.cond_stage_trainable + self.cond_stage_key = cfg.cond_stage_key + + self.num_downs = 0 + if "ddconfig" in cfg.first_stage_config and "ch_mult" in cfg.first_stage_config.ddconfig: + self.num_downs = len(cfg.first_stage_config.ddconfig.ch_mult) - 1 + if not cfg.scale_by_std: + self.scale_factor = cfg.scale_factor + else: + self.register_buffer('scale_factor', torch.tensor(cfg.scale_factor)) + self.instantiate_first_stage(cfg.first_stage_config) + self.instantiate_cond_stage(cfg.cond_stage_config) + self.cond_stage_forward = cfg.cond_stage_forward + self.clip_denoised = False + self.bbox_tokenizer = None + self.text_embedding_dropout_rate = cfg.text_embedding_dropout_rate + self.fused_opt = cfg.fused_opt + + self.restarted_from_ckpt = False + if ckpt_path is not None: + load_vae = True if cfg.get("load_vae", None) is None else cfg.load_vae + load_unet = True if cfg.get("load_unet", None) is None else cfg.load_unet + load_encoder = True if cfg.get("load_encoder", None) is None else cfg.load_encoder + + self.init_from_ckpt( + ckpt_path, ignore_keys, load_vae=load_vae, load_unet=load_unet, load_encoder=load_encoder, + ) + self.restarted_from_ckpt = True + + if self.channels_last: + self.first_stage_model = self.first_stage_model.to(memory_format=torch.channels_last) + self.model = self.model.to(memory_format=torch.channels_last) + + def make_cond_schedule(self,): + self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) + ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() + self.cond_ids[: self.num_timesteps_cond] = ids + + def on_train_batch_start(self, batch, batch_idx, dataloader_idx=0): + # only for very first batch + # set rescale weight to 1./std of encodings + logging.info("### USING STD-RESCALING ###") + x = super().get_input(batch, self.first_stage_key) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + del self.scale_factor + self.register_buffer('scale_factor', 1.0 / z.flatten().std()) + logging.info(f"setting self.scale_factor to {self.scale_factor}") + logging.info("### USING STD-RESCALING ###") + + def register_schedule( + self, + given_betas=None, + beta_schedule="linear", + timesteps=1000, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + ): + super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) + + self.shorten_cond_schedule = self.num_timesteps_cond > 1 + if self.shorten_cond_schedule: + self.make_cond_schedule() + + def instantiate_first_stage(self, config): + model = LatentDiffusion.from_config_dict(config) + self.first_stage_model = model.eval() + self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + + def instantiate_cond_stage(self, config): + if not self.cond_stage_trainable: + if config == "__is_first_stage__": + logging.info("Using first stage also as cond stage.") + self.cond_stage_model = self.first_stage_model + elif config == "__is_unconditional__": + logging.info(f"Training {self.__class__.__name__} as an unconditional model.") + self.cond_stage_model = None + # self.be_unconditional = True + else: + model = LatentDiffusion.from_config_dict(config) + self.cond_stage_model = model.eval() + self.cond_stage_model.train = disabled_train + for param in self.cond_stage_model.parameters(): + param.requires_grad = False + else: + assert config != '__is_first_stage__' + assert config != '__is_unconditional__' + model = LatentDiffusion.from_config_dict(config) + self.cond_stage_model = model + + def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): + denoise_row = [] + for zd in tqdm(samples, desc=desc): + denoise_row.append(self.decode_first_stage(zd, force_not_quantize=force_no_decoder_quantization)) + n_imgs_per_row = len(denoise_row) + denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W + denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + def get_first_stage_encoding(self, encoder_posterior): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample() + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + return self.scale_factor * z + + def get_learned_conditioning(self, c): + if self.cond_stage_forward is None: + if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): + c = self.cond_stage_model.encode(c) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + else: + c = self.cond_stage_model(c) + else: + assert hasattr(self.cond_stage_model, self.cond_stage_forward) + c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) + return c + + def meshgrid(self, h, w): + y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) + x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) + + arr = torch.cat([y, x], dim=-1) + return arr + + def delta_border(self, h, w): + """ + :param h: height + :param w: width + :return: normalized distance to image border, + wtith min distance = 0 at border and max dist = 0.5 at image center + """ + lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) + arr = self.meshgrid(h, w) / lower_right_corner + dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] + dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] + edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] + return edge_dist + + def get_weighting(self, h, w, Ly, Lx, device): + weighting = self.delta_border(h, w) + weighting = torch.clip( + weighting, self.split_input_params["clip_min_weight"], self.split_input_params["clip_max_weight"], + ) + weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) + + if self.split_input_params["tie_braker"]: + L_weighting = self.delta_border(Ly, Lx) + L_weighting = torch.clip( + L_weighting, + self.split_input_params["clip_min_tie_weight"], + self.split_input_params["clip_max_tie_weight"], + ) + + L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) + weighting = weighting * L_weighting + return weighting + + def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code + """ + :param x: img of size (bs, c, h, w) + :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) + """ + bs, nc, h, w = x.shape + + # number of crops in image + Ly = (h - kernel_size[0]) // stride[0] + 1 + Lx = (w - kernel_size[1]) // stride[1] + 1 + + if uf == 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) + + weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) + + elif uf > 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict( + kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), + dilation=1, + padding=0, + stride=(stride[0] * uf, stride[1] * uf), + ) + fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) + + elif df > 1 and uf == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict( + kernel_size=(kernel_size[0] // df, kernel_size[0] // df), + dilation=1, + padding=0, + stride=(stride[0] // df, stride[1] // df), + ) + fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) + + else: + raise NotImplementedError + + return fold, unfold, normalization, weighting + + @torch.no_grad() + def get_input( + self, + batch, + k, + return_first_stage_outputs=False, + force_c_encode=False, + cond_key=None, + return_original_cond=False, + bs=None, + ): + if self.first_stage_key.endswith('encoded'): + gaussian_parameters = batch[self.first_stage_key] + encoder_posterior = DiagonalGaussianDistribution(gaussian_parameters) + elif self.first_stage_key.endswith('moments'): + # Loading distribution from disk and sampling encoded + distribution = batch[self.first_stage_key] # torch.size([3, 1, 8, 64, 64]) + distribution = torch.squeeze(distribution, dim=1) + encoder_posterior = DiagonalGaussianDistribution(distribution) + else: + # Loading images from disk and encoding them + x = super().get_input(batch, k) + if bs is not None: + x = x[:bs] + + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + + if self.model.conditioning_key is not None: + if cond_key is None: + cond_key = self.cond_stage_key + if cond_key != self.first_stage_key: + if cond_key in ['captions', 'coordinates_bbox', 'txt'] or cond_key.endswith("encoded"): + xc = batch[cond_key] + elif cond_key == 'class_label': + xc = batch + else: + xc = super().get_input(batch, cond_key) + else: + xc = x + if (not self.cond_stage_trainable or force_c_encode) and (not cond_key.endswith('encoded')): + if isinstance(xc, dict) or isinstance(xc, list): + # import pudb; pudb.set_trace() + c = self.get_learned_conditioning(xc) + else: + c = self.get_learned_conditioning(xc) + else: + c = xc + if bs is not None: + c = c[:bs] + + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + ckey = __conditioning_keys__[self.model.conditioning_key] + c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} + + if self.text_embedding_dropout_rate > 0: + assert self.text_embedding_dropout_rate < 1.0 + c = random_dropout(c, drop_rate=self.text_embedding_dropout_rate) + + else: + c = None + xc = None + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + c = {'pos_x': pos_x, 'pos_y': pos_y} + out = [z, c] + if return_first_stage_outputs: + xrec = self.decode_first_stage(z) + out.extend([x, xrec]) + if return_original_cond: + out.append(xc) + return out + + @torch.no_grad() + def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1.0 / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + logging.info("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + logging.info("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [ + self.first_stage_model.decode( + z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize + ) + for i in range(z.shape[-1]) + ] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + # same as above but without decorator + def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1.0 / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + logging.info("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + logging.info("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [ + self.first_stage_model.decode( + z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize + ) + for i in range(z.shape[-1]) + ] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + @torch.no_grad() + def encode_first_stage(self, x): + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + df = self.split_input_params["vqf"] + self.split_input_params['original_image_size'] = x.shape[-2:] + bs, nc, h, w = x.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + logging.info("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + logging.info("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) + z = unfold(x) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) + o = o * weighting + + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization + return decoded + + else: + return self.first_stage_model.encode(x) + else: + return self.first_stage_model.encode(x) + + def shared_step(self, batch, **kwargs): + x, c = self.get_input(batch, self.first_stage_key) + loss = self(x, c) + return loss + + def forward(self, x, c, *args, **kwargs): + t = torch.randint(0, self.num_timesteps, (x.shape[0],), generator=self.rng, device=x.device).long() + if self.model.conditioning_key is not None: + assert c is not None + if self.cond_stage_trainable: + c = self.get_learned_conditioning(c) + if self.shorten_cond_schedule: # TODO: drop this option + tc = self.cond_ids[t] + c = self.q_sample(x_start=c, t=tc, noise=randn_like(c.float(), generator=self.rng)) + return self.p_losses(x, c, t, *args, **kwargs) + + def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset + def rescale_bbox(bbox): + x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) + y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) + w = min(bbox[2] / crop_coordinates[2], 1 - x0) + h = min(bbox[3] / crop_coordinates[3], 1 - y0) + return x0, y0, w, h + + return [rescale_bbox(b) for b in bboxes] + + def apply_model(self, x_noisy, t, cond, return_ids=False): + + if isinstance(cond, dict): + # hybrid case, cond is exptected to be a dict + for key in cond: + if not isinstance(cond[key], list): + cond[key] = [cond[key]] + else: + if not isinstance(cond, list): + cond = [cond] + key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' + cond = {key: cond} + + if hasattr(self, "split_input_params"): + assert len(cond) == 1 # todo can only deal with one conditioning atm + assert not return_ids + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + + h, w = x_noisy.shape[-2:] + + fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) + + z = unfold(x_noisy) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] + + if ( + self.cond_stage_key in ["image", "LR_image", "segmentation", 'bbox_img'] + and self.model.conditioning_key + ): # todo check for completeness + c_key = next(iter(cond.keys())) # get key + c = next(iter(cond.values())) # get value + assert len(c) == 1 # todo extend to list with more than one elem + c = c[0] # get element + + c = unfold(c) + c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] + + elif self.cond_stage_key == 'coordinates_bbox': + assert ( + 'original_image_size' in self.split_input_params + ), 'BoudingBoxRescaling is missing original_image_size' + + # assuming padding of unfold is always 0 and its dilation is always 1 + n_patches_per_row = int((w - ks[0]) / stride[0] + 1) + full_img_h, full_img_w = self.split_input_params['original_image_size'] + # as we are operating on latents, we need the factor from the original image size to the + # spatial latent size to properly rescale the crops for regenerating the bbox annotations + num_downs = self.first_stage_model.encoder.num_resolutions - 1 + rescale_latent = 2 ** (num_downs) + + # get top left postions of patches as conforming for the bbbox tokenizer, therefore we + # need to rescale the tl patch coordinates to be in between (0,1) + tl_patch_coordinates = [ + ( + rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, + rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h, + ) + for patch_nr in range(z.shape[-1]) + ] + + # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) + patch_limits = [ + (x_tl, y_tl, rescale_latent * ks[0] / full_img_w, rescale_latent * ks[1] / full_img_h) + for x_tl, y_tl in tl_patch_coordinates + ] + # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] + + # tokenize crop coordinates for the bounding boxes of the respective patches + patch_limits_tknzd = [ + torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None] for bbox in patch_limits + ] # list of length l with tensors of shape (1, 2) + logging.info(patch_limits_tknzd[0].shape) + # cut tknzd crop position from conditioning + assert isinstance(cond, dict), 'cond must be dict to be fed into model' + cut_cond = cond['c_crossattn'][0][..., :-2] + logging.info(cut_cond.shape) + + adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) + adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') + logging.info(adapted_cond.shape) + adapted_cond = self.get_learned_conditioning(adapted_cond) + logging.info(adapted_cond.shape) + adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) + logging.info(adapted_cond.shape) + + cond_list = [{'c_crossattn': [e]} for e in adapted_cond] + + else: + cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient + + # apply model by loop over crops + output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] + assert not isinstance( + output_list[0], tuple + ) # todo cant deal with multiple model outputs check this never happens + + o = torch.stack(output_list, axis=-1) + o = o * weighting + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + x_recon = fold(o) / normalization + + else: + x_recon = self.model(x_noisy, t, **cond) + + if isinstance(x_recon, tuple) and not return_ids: + return x_recon[0] + else: + return x_recon + + def _predict_eps_from_xstart(self, x_t, t, pred_xstart): + return ( + extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart + ) / extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + + def _prior_bpd(self, x_start): + """ + Get the prior KL term for the variational lower-bound, measured in + bits-per-dim. + This term can't be optimized, as it only depends on the encoder. + :param x_start: the [N x C x ...] tensor of inputs. + :return: a batch of [N] KL values (in bits), one per batch element. + """ + batch_size = x_start.shape[0] + t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) + qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) + kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) + return mean_flat(kl_prior) / np.log(2.0) + + def p_losses(self, x_start, cond, t, noise=None): + noise = default(noise, lambda: randn_like(x_start, generator=self.rng)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_output = self.apply_model(x_noisy, t, cond) + + loss_dict = {} + prefix = 'train' if self.training else 'val' + + if self.parameterization == "x0": + target = x_start + elif self.parameterization == "eps": + target = noise + elif self.parameterization == "v": + target = self.get_v(x_start, noise, t) + else: + raise NotImplementedError() + + if (self.precision in ['bf16', 'bf16-mixed']) or (self.precision in [16, '16', '16-mixed']): + model_output = model_output.type(torch.float32) + loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) + loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) + self.logvar = self.logvar.cuda(non_blocking=True) + logvar_t = self.logvar[t].cuda(non_blocking=True) + loss = loss_simple / torch.exp(logvar_t) + logvar_t + # loss = loss_simple / torch.exp(self.logvar) + self.logvar + if self.learn_logvar: + loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) + loss_dict.update({'logvar': self.logvar.data.mean()}) + + loss = self.l_simple_weight * loss.mean() + + loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) + loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() + loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) + loss += self.original_elbo_weight * loss_vlb + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + def p_mean_variance( + self, + x, + c, + t, + clip_denoised: bool, + return_codebook_ids=False, + quantize_denoised=False, + return_x0=False, + score_corrector=None, + corrector_kwargs=None, + ): + t_in = t + model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) + + if score_corrector is not None: + assert self.parameterization == "eps" + model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) + + if return_codebook_ids: + model_out, logits = model_out + + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + else: + raise NotImplementedError() + + if clip_denoised: + x_recon.clamp_(-1.0, 1.0) + if quantize_denoised: + x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + if return_codebook_ids: + return model_mean, posterior_variance, posterior_log_variance, logits + elif return_x0: + return model_mean, posterior_variance, posterior_log_variance, x_recon + else: + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample( + self, + x, + c, + t, + clip_denoised=False, + repeat_noise=False, + return_codebook_ids=False, + quantize_denoised=False, + return_x0=False, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + ): + b, *_, device = *x.shape, x.device + outputs = self.p_mean_variance( + x=x, + c=c, + t=t, + clip_denoised=clip_denoised, + return_codebook_ids=return_codebook_ids, + quantize_denoised=quantize_denoised, + return_x0=return_x0, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + ) + if return_codebook_ids: + raise DeprecationWarning("Support dropped.") + elif return_x0: + model_mean, _, model_log_variance, x0 = outputs + else: + model_mean, _, model_log_variance = outputs + + noise = noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.0: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + + if return_codebook_ids: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) + if return_x0: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 + else: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def progressive_denoising( + self, + cond, + shape, + verbose=True, + callback=None, + quantize_denoised=False, + img_callback=None, + mask=None, + x0=None, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + batch_size=None, + x_T=None, + start_T=None, + log_every_t=None, + ): + if not log_every_t: + log_every_t = self.log_every_t + timesteps = self.num_timesteps + if batch_size is not None: + b = batch_size if batch_size is not None else shape[0] + shape = [batch_size] + list(shape) + else: + b = batch_size = shape[0] + if x_T is None: + img = torch.randn(shape, generator=self.rng, device=torch.cuda.current_device()) + else: + img = x_T + intermediates = [] + if cond is not None: + if isinstance(cond, dict): + cond = { + key: cond[key][:batch_size] + if not isinstance(cond[key], list) + else list(map(lambda x: x[:batch_size], cond[key])) + for key in cond + } + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = ( + tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', total=timesteps) + if verbose + else reversed(range(0, timesteps)) + ) + if type(temperature) == float: + temperature = [temperature] * timesteps + + for i in iterator: + ts = torch.full((b,), i, device=torch.cuda.current_device(), dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=randn_like(cond, generator=self.rng)) + + img, x0_partial = self.p_sample( + img, + cond, + ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised, + return_x0=True, + temperature=temperature[i], + noise_dropout=noise_dropout, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + ) + if mask is not None: + assert x0 is not None + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1.0 - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(x0_partial) + if callback: + callback(i) + if img_callback: + img_callback(img, i) + return img, intermediates + + @torch.no_grad() + def p_sample_loop( + self, + cond, + shape, + return_intermediates=False, + x_T=None, + verbose=True, + callback=None, + timesteps=None, + quantize_denoised=False, + mask=None, + x0=None, + img_callback=None, + start_T=None, + log_every_t=None, + ): + + if not log_every_t: + log_every_t = self.log_every_t + device = self.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, generator=self.rng, device=device) + else: + img = x_T + + intermediates = [img] + if timesteps is None: + timesteps = self.num_timesteps + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = ( + tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) + if verbose + else reversed(range(0, timesteps)) + ) + + if mask is not None: + assert x0 is not None + assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match + + for i in iterator: + ts = torch.full((b,), i, device=device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=randn_like(cond, generator=self.rng)) + + img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised) + if mask is not None: + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1.0 - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(img) + if callback: + callback(i) + if img_callback: + img_callback(img, i) + + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample( + self, + cond, + batch_size=16, + return_intermediates=False, + x_T=None, + verbose=True, + timesteps=None, + quantize_denoised=False, + mask=None, + x0=None, + shape=None, + **kwargs, + ): + if shape is None: + shape = (batch_size, self.channels, self.image_size, self.image_size) + if cond is not None: + if isinstance(cond, dict): + cond = { + key: cond[key][:batch_size] + if not isinstance(cond[key], list) + else list(map(lambda x: x[:batch_size], cond[key])) + for key in cond + } + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + return self.p_sample_loop( + cond, + shape, + return_intermediates=return_intermediates, + x_T=x_T, + verbose=verbose, + timesteps=timesteps, + quantize_denoised=quantize_denoised, + mask=mask, + x0=x0, + ) + + @torch.no_grad() + def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): + + if ddim: + ddim_sampler = DDIMSampler(self) + shape = (self.channels, self.image_size, self.image_size) + samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, **kwargs) + + else: + samples, intermediates = self.sample(cond=cond, batch_size=batch_size, return_intermediates=True, **kwargs) + + return samples, intermediates + + @torch.no_grad() + def log_images( + self, + batch, + N=8, + n_row=4, + sample=True, + ddim_steps=200, + ddim_eta=1.0, + return_keys=None, + quantize_denoised=True, + inpaint=True, + plot_denoise_rows=False, + plot_progressive_rows=True, + plot_diffusion_rows=True, + **kwargs, + ): + + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc = self.get_input( + batch, + self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=N, + ) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption"]: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) + log["conditioning"] = xc + elif self.cond_stage_key == 'class_label': + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) + log['conditioning'] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.long() + noise = randn_like(z_start, generator=self.rng) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') + diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, z_denoise_row = self.sample_log( + cond=c, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta + ) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if ( + quantize_denoised + and not isinstance(self.first_stage_model, AutoencoderKL) + and not isinstance(self.first_stage_model, IdentityFirstStage) + ): + # also display when quantizing x0 while sampling + with self.ema_scope("Plotting Quantized Denoised"): + samples, z_denoise_row = self.sample_log( + cond=c, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + quantize_denoised=True, + ) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, + # quantize_denoised=True) + x_samples = self.decode_first_stage(samples) + log["samples_x0_quantized"] = x_samples + + if inpaint: + # make a simple center square + b, h, w = z.shape[0], z.shape[2], z.shape[3] + mask = torch.ones(N, h, w) + # zeros will be filled in + mask[:, h // 4 : 3 * h // 4, w // 4 : 3 * w // 4] = 0.0 + mask = mask[:, None, ...] + with self.ema_scope("Plotting Inpaint"): + samples, _ = self.sample_log( + cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask + ) + x_samples = self.decode_first_stage(samples) + log["samples_inpainting"] = x_samples + log["mask"] = mask + + # outpaint + with self.ema_scope("Plotting Outpaint"): + samples, _ = self.sample_log( + cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask + ) + x_samples = self.decode_first_stage(samples) + log["samples_outpainting"] = x_samples + + if plot_progressive_rows: + with self.ema_scope("Plotting Progressives"): + img, progressives = self.progressive_denoising( + c, shape=(self.channels, self.image_size, self.image_size), batch_size=N + ) + prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") + log["progressive_row"] = prog_row + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def parameters(self): + params = list(self.model.parameters()) + if self.cond_stage_trainable: + logging.info(f"{self.__class__.__name__}: Also optimizing conditioner params!") + params = params + list(self.cond_stage_model.parameters()) + if self.learn_logvar: + logging.info('Diffusion model optimizing logvar') + params.append(self.logvar) + return params + + @torch.no_grad() + def to_rgb(self, x): + x = x.float() + if not hasattr(self, "colorize"): + self.colorize = torch.randn(3, x.shape[1], 1, 1, generator=self.rng).to(x) + x = nn.functional.conv2d(x, weight=self.colorize) + x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0 + return x + + def set_input_tensor(self, input_tensor): + """See megatron.model.transformer.set_input_tensor()""" + # only required for pipeline parallelism + pass + + +class MegatronLatentDiffusion(NLPAdapterModelMixin, MegatronBaseModel): + """Megatron LatentDiffusion Model.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer): + if not HAVE_APEX: + raise ImportError( + "Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + if not HAVE_MEGATRON_CORE: + raise ImportError( + "megatron-core was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + + # this prevents base constructor from initializing tokenizer + self.tokenizer = None + super().__init__(cfg, trainer=trainer) + + self._validate_trainer() + + # megatron_amp_O2 is not yet supported in diffusion models + self.megatron_amp_O2 = cfg.get('megatron_amp_O2', False) + + if self.cfg.precision in ['16', 16, 'bf16']: + self.model_parallel_config.enable_autocast = False + + self.model = self.model_provider_func() + + self.conditioning_keys = [] + + if self.model.precision in ['bf16', 'bf16-mixed']: + self.autocast_dtype = torch.bfloat16 + elif self.model.precision in [32, '32', '32-true']: + self.autocast_dtype = torch.float + elif self.model.precision in ['16-mixed', '16', 16]: + self.autocast_dtype = torch.half + else: + raise ValueError('precision must be in [32, "32", "32-true", "16-mixed", "16", 16, "bf16-mixed", "bf16"]') + + self.log_train_loss = bool(int(os.getenv("NEMO_LOG_TRAIN_LOSS", 1))) + self.loss_broadcast_src_rank = None + + def get_module_list(self): + if isinstance(self.model, list): + return [model.module if isinstance(model, Float16Module) else model for model in self.model] + elif isinstance(self.model, Float16Module): + return [self.model.module] + else: + return [self.model] + + def model_provider_func(self, pre_process=True, post_process=True): + """Model depends on pipeline paralellism.""" + model = LatentDiffusion(cfg=self.cfg, model_parallel_config=self.model_parallel_config) + return model + + def forward(self, x, c, *args, **kwargs): + output_tensor = self.model(x, c, *args, **kwargs) + return output_tensor + + @rank_zero_only + @torch.no_grad() + def on_train_batch_start(self, batch, batch_idx, dataloader_idx=0): + if self.cfg.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0: + assert self.cfg.scale_factor == 1.0, 'rather not use custom rescaling and std-rescaling simultaneously' + batch[self.cfg.first_stage_key] = batch[self.cfg.first_stage_key].cuda(non_blocking=True) + self.model.on_train_batch_start(batch, batch_idx) + + def fwd_bwd_step(self, dataloader_iter, forward_only): + tensor_shape = None # Placeholder + + # handle asynchronous grad reduction + no_sync_func = None + if not forward_only and self.with_distributed_adam: + no_sync_func = partial(self._optimizer.no_sync, greedy_grad_copy=self.megatron_amp_O2,) + + # pipeline schedules will get these from self.model.config + for module in self.get_module_list(): + module.config.no_sync_func = no_sync_func + + # run forward and backwards passes for an entire global batch + # we do this inside training_step to support pipeline parallelism + fwd_bwd_function = get_forward_backward_func() + + losses_reduced_per_micro_batch = fwd_bwd_function( + forward_step_func=self.get_forward_output_and_loss_func(), + data_iterator=dataloader_iter, + model=self.model, + num_microbatches=get_num_microbatches(), + forward_only=forward_only, + seq_length=None, + micro_batch_size=self.cfg.micro_batch_size, + ) + + # losses_reduced_per_micro_batch is a list of dictionaries + # [{"loss": 0.1}, {"loss": 0.2}, ...] which are from gradient accumulation steps + # only the last stages of the pipeline return losses + loss_dict = {} + if losses_reduced_per_micro_batch: + if (not forward_only) or self.cfg.data.get('validation_drop_last', True): + # average loss across micro batches + for key in losses_reduced_per_micro_batch[0]: + loss_tensors_list = [loss_reduced[key] for loss_reduced in losses_reduced_per_micro_batch] + loss_tensor = torch.stack(loss_tensors_list) + loss_dict[key] = loss_tensor.mean() + loss_mean = loss_dict["val/loss"] if forward_only else loss_dict["train/loss"] + else: + raise NotImplementedError("Losses of micro batches sizes must be uniform!") + else: + if forward_only: + loss_mean = [] + else: + loss_mean = torch.tensor(0.0, device=torch.cuda.current_device()) + + if self.log_train_loss: + # When using pipeline parallelism, loss is calculated only in the last pipeline stage and + # it should be casted to other pipeline stages for logging. + # we can avoid this broadcast by updating the PTL log function to accept specific ranks + if parallel_state.get_pipeline_model_parallel_world_size() > 1: + if self.loss_broadcast_src_rank is None: + self.loss_broadcast_src_rank = parallel_state.get_pipeline_model_parallel_last_rank() + torch.distributed.broadcast( + loss_mean, self.loss_broadcast_src_rank, group=parallel_state.get_pipeline_model_parallel_group(), + ) + + return loss_mean, loss_dict + + def training_step(self, batch): + """ + Notice: `training_step` used to have the following signature to support pipeline + parallelism: + + def training_step(self, dataloader_iter, batch_idx): + + However, full iteration CUDA Graph callback is not compatible with this signature + right now, due to we need to wrap the dataloader to generate static tensor outside + the CUDA Graph. This signature moves `next(dataloader)` into the CUDA Graph + capturing region, thus we disabled it. + + Our dataloaders produce a micro-batch and then we fetch + a number of microbatches depending on the global batch size and model parallel size + from the dataloader to produce a list of microbatches. + Batch should be a list of microbatches and those microbatches should on CPU. + Microbatches are then moved to GPU during the pipeline. + The list of microbatches is then piped through the pipeline using Apex fwd/bwd functions. + """ + + # we zero grads here because we also call backward in the megatron-core fwd/bwd functions + self._optimizer.zero_grad() + + dataloader_iter = iter([batch]) + loss_mean, loss_dict = self.fwd_bwd_step(dataloader_iter, False) + + # when using sequence parallelism, the sequence parallel layernorm grads must be all-reduced + if self.cfg.get('tensor_model_parallel_size', 1) > 1 and self.cfg.get('sequence_parallel', False): + self.allreduce_sequence_parallel_gradients() + + if self.with_distributed_adam: + # gradients are reduced internally in distributed optimizer + pass + elif self.megatron_amp_O2: + # # when using pipeline parallelism grads must be all-reduced after the pipeline (not asynchronously) + # if self.cfg.get('pipeline_model_parallel_size', 1) > 1 or self.cfg.get('sequence_parallel', False): + # # main grads are stored in the MainParamsOptimizer wrapper + # self._optimizer.allreduce_main_grads() + self._optimizer.allreduce_main_grads() + elif not self.cfg.get('ddp_overlap', True): + # async grad allreduce is not currently implemented for O1/autocasting mixed precision training + # so we all-reduce gradients after the pipeline + self.allreduce_gradients() # @sangkug we think this is causing memory to blow up (hurts perf) + else: + raise ValueError("Either distributed_fused_adam or megatron_amp_O2 needs to be set if ddp_overlap is set") + + # for cuda graph with pytorch lightning + # these values will be used outside the capturing range + if not hasattr(self, "loss_mean"): + self.loss_mean = torch.empty_like(loss_mean) + with torch.no_grad(): + self.loss_mean.copy_(loss_mean) + self.loss_dict = loss_dict + # this function is invoked by callback if with cuda graph, otherwise + # invoke it by ourselves + if self.cfg.get("capture_cudagraph_iters", -1) < 0: + self.non_cuda_graph_capturable() + + return loss_mean + + def non_cuda_graph_capturable(self): + # Moving CUDA metrics to CPU leads to sync, do not show on progress bar + # if CUDA graph is enabled. + show_metric = self.cfg.get("show_prog_bar_metric", True) and (self.cfg.get("capture_cudagraph_iters", -1) < 0) + + if self.log_train_loss: + self.log('reduced_train_loss', self.loss_mean, prog_bar=show_metric, rank_zero_only=True, batch_size=1) + + if self.cfg.precision in [16, '16', '16-mixed']: + loss_scale = self.trainer.precision_plugin.scaler._scale + if loss_scale is not None: + self.log('loss_scale', loss_scale, batch_size=1) + + self.log_dict( + self.loss_dict, prog_bar=show_metric, logger=True, on_step=True, rank_zero_only=True, batch_size=1 + ) + lr = self._optimizer.param_groups[0]['lr'] + self.log('lr', lr, prog_bar=show_metric, rank_zero_only=True, batch_size=1) + self.log('global_step', self.trainer.global_step + 1, prog_bar=show_metric, rank_zero_only=True, batch_size=1) + self.log( + 'consumed_samples', + self.compute_consumed_samples(self.trainer.global_step + 1 - self.init_global_step), + prog_bar=show_metric, + rank_zero_only=True, + batch_size=1, + ) + + ts = torch.tensor(int(time.time() * 1e3), dtype=torch.float64) + self.log("timestamp", ts, batch_size=1, rank_zero_only=True) + + def backward(self, *args, **kwargs): + """ LightningModule hook to do backward. + We want this to do nothing since we run backward in the fwd/bwd functions from apex. + No need to call it here. + """ + pass + + def optimizer_zero_grad(self, *args, **kwargs): + """ LightningModule hook to zero grad. + We want this to do nothing as we are zeroing grads during the training_step. + """ + pass + + def _append_sequence_parallel_module_grads(self, module, grads): + """ Helper method for allreduce_sequence_parallel_gradients""" + + for param in module.parameters(): + sequence_parallel_param = getattr(param, 'sequence_parallel', False) + if sequence_parallel_param and param.requires_grad: + if self.megatron_amp_O2: + grad = param.main_grad + else: + grad = param.grad + grads.append(grad.data) + + def get_forward_output_and_loss_func(self): + def process_batch(batch): + """ Prepares the global batch for apex fwd/bwd functions. + Global batch is a list of micro batches. + """ + # noise_map, condition + batch[self.cfg.first_stage_key] = batch[self.cfg.first_stage_key].cuda(non_blocking=True) + if isinstance(batch[self.cfg.cond_stage_key], torch.Tensor): + # in the case of precached text embeddings, cond_stage is also a tensor + batch[self.cfg.cond_stage_key] = batch[self.cfg.cond_stage_key].cuda(non_blocking=True) + + # SD has more dedicated structure for encoding, so we enable autocasting here as well + with torch.cuda.amp.autocast( + self.autocast_dtype in (torch.half, torch.bfloat16), dtype=self.autocast_dtype, + ): + x, c = self.model.get_input(batch, self.cfg.first_stage_key) + + if not isinstance(c, dict): + return [x, c] + + if len(self.conditioning_keys) == 0: + self.conditioning_keys = list(c.keys()) + c_list = [c[key] for key in self.conditioning_keys] + return [x, *c_list] + + def fwd_output_and_loss_func(dataloader_iter, model): + batch = next(dataloader_iter) + batch = process_batch(batch) + batch = [x.cuda(non_blocking=True) for x in batch] + if len(self.conditioning_keys) == 0: + x, c = batch + else: + x = batch[0] + c = {} + for idx, key in enumerate(self.conditioning_keys): + c[key] = batch[1 + idx] + loss, loss_dict = model(x, c) + + def dummy(output_tensor): + return loss, loss_dict + + # output_tensor, and a function to convert output_tensor to loss + loss_dict + return loss, dummy + + return fwd_output_and_loss_func + + def get_forward_output_only_func(self): + def fwd_output_only_func(batch, model): + raise NotImplementedError + + return fwd_output_only_func + + def validation_step(self, dataloader_iter): + loss, val_loss_dict = self.fwd_bwd_step(dataloader_iter, True) + + self.log_dict(val_loss_dict, prog_bar=False, logger=True, on_step=False, on_epoch=True, batch_size=1) + + return loss + + def setup(self, stage=None): + """ PTL hook that is executed after DDP spawns. + We setup datasets here as megatron datasets require DDP to instantiate. + See https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#setup for more information. + Args: + stage (str, optional): Can be 'fit', 'validate', 'test' or 'predict'. Defaults to None. + """ + if self.model.rng: + self.model.rng.manual_seed(self.cfg.seed + 100 * parallel_state.get_data_parallel_rank()) + + # log number of parameters + if isinstance(self.model, list): + num_parameters_on_device = sum( + [sum([p.nelement() for p in model_module.parameters()]) for model_module in self.model] + ) + else: + num_parameters_on_device = sum([p.nelement() for p in self.model.parameters()]) + + # to be summed across data parallel group + total_num_parameters = torch.tensor(num_parameters_on_device).cuda(non_blocking=True) + + torch.distributed.all_reduce(total_num_parameters, group=parallel_state.get_model_parallel_group()) + + logging.info( + f'Pipeline model parallel rank: {parallel_state.get_pipeline_model_parallel_rank()}, ' + f'Tensor model parallel rank: {parallel_state.get_tensor_model_parallel_rank()}, ' + f'Number of model parameters on device: {num_parameters_on_device:.2e}. ' + f'Total number of model parameters: {total_num_parameters:.2e}.' + ) + + resume_checkpoint_path = self.trainer.ckpt_path + if resume_checkpoint_path: + init_consumed_samples = self._extract_consumed_samples_from_ckpt(resume_checkpoint_path) + else: + init_consumed_samples = 0 + self.init_consumed_samples = init_consumed_samples + self.init_global_step = self.trainer.global_step + + # allowing restored models to optionally setup datasets + self.build_train_valid_test_datasets() + + # Batch size need to be provided for webdatset + self._num_micro_batches = get_num_microbatches() + self._micro_batch_size = self.cfg.micro_batch_size + + self.setup_training_data(self.cfg.data) + self.setup_validation_data(self.cfg.data) + self.setup_test_data(self.cfg.data) + self.setup_complete = True + + def build_train_valid_test_datasets(self): + logging.info("Building datasets for Stable Diffusion...") + if self.trainer.limit_val_batches > 1.0 and isinstance(self.trainer.limit_val_batches, float): + raise ValueError("limit_val_batches must be an integer or float less than or equal to 1.0.") + + if self.cfg.first_stage_key.endswith("encoded") or self.cfg.first_stage_key.endswith("moments"): + if self.cfg.cond_stage_key.endswith("clip_encoded"): + self._train_ds, self._validation_ds = build_train_valid_precached_clip_datasets( + model_cfg=self.cfg, consumed_samples=self.compute_consumed_samples(0), + ) + else: + self._train_ds, self._validation_ds = build_train_valid_precached_datasets( + model_cfg=self.cfg, consumed_samples=self.compute_consumed_samples(0), + ) + else: + self._train_ds, self._validation_ds = build_train_valid_datasets( + model_cfg=self.cfg, consumed_samples=self.compute_consumed_samples(0) + ) + self._test_ds = None + + if self._train_ds is not None: + logging.info(f'Length of train dataset: {len(self._train_ds)}') + if self._validation_ds is not None: + logging.info(f'Length of val dataset: {len(self._validation_ds)}') + if self._test_ds is not None: + logging.info(f'Length of test dataset: {len(self._test_ds)}') + logging.info(f'Finished building datasets for LatentDiffusion.') + return self._train_ds, self._validation_ds, self._test_ds + + def setup_training_data(self, cfg): + if hasattr(self, '_train_ds') and self._train_ds is not None: + consumed_samples = self.compute_consumed_samples(0) + logging.info( + f'Setting up train dataloader with len(len(self._train_ds)): {len(self._train_ds)} and consumed samples: {consumed_samples}' + ) + if self.cfg.cond_stage_key.endswith("clip_encoded"): + collate_fn = get_collate_fn( + first_stage_key=self.cfg.first_stage_key, cond_stage_key=self.cfg.cond_stage_key, + ) + else: + collate_fn = None + + self._train_dl = torch.utils.data.DataLoader( + self._train_ds, + batch_size=self._micro_batch_size, + num_workers=cfg.num_workers, + pin_memory=True, + drop_last=True, + persistent_workers=True, + collate_fn=collate_fn, + ) + + def setup_validation_data(self, cfg): + if hasattr(self, '_validation_ds') and self._validation_ds is not None: + consumed_samples = 0 + logging.info( + f'Setting up validation dataloader with len(len(self._validation_ds)): {len(self._validation_ds)} and consumed samples: {consumed_samples}' + ) + self._validation_dl = torch.utils.data.DataLoader( + self._validation_ds, + batch_size=self._micro_batch_size, + num_workers=cfg.num_workers, + pin_memory=True, + drop_last=False, + persistent_workers=True, + ) + + def setup_test_data(self, cfg): + if hasattr(self, '_test_ds') and self._test_ds is not None: + consumed_samples = 0 + logging.info( + f'Setting up test dataloader with len(len(self._test_ds)): {len(self._test_ds)} and consumed samples: {consumed_samples}' + ) + self._test_dl = torch.utils.data.DataLoader( + self._test_ds, batch_size=self._micro_batch_size, num_workers=cfg.num_workers, pin_memory=True, + ) + + def transfer_batch_to_device(self, batch: Any, device: torch.device, dataloader_idx: int) -> Any: + """ PTL hook: https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#transfer-batch-to-device + When using pipeline parallelism, we need the global batch to remain on the CPU, + since the memory overhead will be too high when using a large number of microbatches. + Microbatches are transferred from CPU to GPU inside the pipeline. + """ + return batch + + def _validate_trainer(self): + """ Certain trainer configurations can break training. + Here we try to catch them and raise an error. + """ + if self.trainer.accumulate_grad_batches > 1: + raise ValueError( + f'Gradient accumulation is done within training_step. trainer.accumulate_grad_batches must equal 1' + ) + + @classmethod + def list_available_models(cls): + return None + + def parameters(self): + if isinstance(self.model, list): + return itertools.chain.from_iterable(module.parameters() for module in self.model) + else: + return self.model.parameters() + + def save_to(self, save_path: str): + # Replace .nemo path in config for NeMo CLIP + cfg = self._cfg + if cfg.get('cond_stage_config').get('restore_from_path'): + with open_dict(cfg): + cfg.cond_stage_config.restore_from_path = None + cfg.cond_stage_config.cfg = self.model.cond_stage_model.cfg + self._cfg = cfg + super().save_to(save_path) + + @classmethod + def load_from_checkpoint( + cls, + checkpoint_path: str, + map_location: Any = None, + hparams_file: Optional[str] = None, + strict: bool = True, + **kwargs, + ): + """ + Loads ModelPT from checkpoint, with some maintenance of restoration. + For documentation, please refer to LightningModule.load_from_checkpoin() documentation. + """ + checkpoint = None + try: + cls._set_model_restore_state(is_being_restored=True) + # TODO: replace with proper PTL API + with pl_legacy_patch(): + if map_location is not None: + checkpoint = pl_load(checkpoint_path, map_location=map_location) + else: + checkpoint = pl_load(checkpoint_path, map_location=lambda storage, loc: storage) + + if hparams_file is not None: + extension = hparams_file.split(".")[-1] + if extension.lower() == "csv": + hparams = load_hparams_from_tags_csv(hparams_file) + elif extension.lower() in ("yml", "yaml"): + hparams = load_hparams_from_yaml(hparams_file) + else: + raise ValueError(".csv, .yml or .yaml is required for `hparams_file`") + + hparams["on_gpu"] = False + + # overwrite hparams by the given file + checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY] = hparams + + # for past checkpoint need to add the new key + if cls.CHECKPOINT_HYPER_PARAMS_KEY not in checkpoint: + checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY] = {} + # override the hparams with values that were passed in + cfg = checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY].get('cfg', checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY]) + # TODO: can we do this without overriding? + config_kwargs = kwargs.copy() + if 'trainer' in config_kwargs: + config_kwargs.pop('trainer') + cfg.update(config_kwargs) + + # Disable individual unet/vae weights loading otherwise the model will look for these partial ckpts and raise error + if cfg: + if cfg.get('unet_config') and cfg.get('unet_config').get('from_pretrained'): + cfg.unet_config.from_pretrained = None + if cfg.get('first_stage_config') and cfg.get('first_stage_config').get('from_pretrained'): + cfg.first_stage_config.from_pretrained = None + ## Now when we covert ckpt to nemo, let's always get rid of those _orig_mod + if cfg.get('inductor'): + cfg.inductor = False + ## Append some dummy configs that DB didn't support + if not cfg.get('channels_last'): + cfg.channels_last = True + if not cfg.get('capture_cudagraph_iters'): + cfg.capture_cudagraph_iters = -1 + + # compatibility for stable diffusion old checkpoint tweaks + first_key = list(checkpoint['state_dict'].keys())[0] + if first_key == "betas": + # insert "model." into for megatron wrapper + new_state_dict = {} + for key in checkpoint['state_dict'].keys(): + new_key = "model." + key + new_state_dict[new_key] = checkpoint['state_dict'][key] + checkpoint['state_dict'] = new_state_dict + elif ( + first_key == 'model.text_encoder.transformer.text_model.embeddings.position_ids' + or first_key == 'model.text_encoder.model.language_model.embedding.position_embeddings' + ): + # remap state keys from dreambooth when using HF clip + new_state_dict = {} + for key in checkpoint['state_dict'].keys(): + new_key = key.replace('._orig_mod', "") + new_key = new_key.replace('unet', 'model.diffusion_model') + new_key = new_key.replace('vae', 'first_stage_model') + new_key = new_key.replace('text_encoder', 'cond_stage_model') + new_key = new_key.replace('.noise_scheduler', '') + new_state_dict[new_key] = checkpoint['state_dict'][key] + checkpoint['state_dict'] = new_state_dict + + # compatibility for inductor in inference + if not cfg.get('inductor', False): + new_state_dict = {} + for key in checkpoint['state_dict'].keys(): + new_key = key.replace('._orig_mod', '', 1) + new_state_dict[new_key] = checkpoint['state_dict'][key] + checkpoint['state_dict'] = new_state_dict + + if cfg.get('megatron_amp_O2', False): + new_state_dict = {} + for key in checkpoint['state_dict'].keys(): + new_key = key.replace('model.', 'model.module.', 1) + new_state_dict[new_key] = checkpoint['state_dict'][key] + checkpoint['state_dict'] = new_state_dict + + if 'cfg' in kwargs: + model = ptl_load_state(cls, checkpoint, strict=strict, **kwargs) + else: + model = ptl_load_state(cls, checkpoint, strict=strict, cfg=cfg, **kwargs) + # cfg = checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY].cfg + + checkpoint = model + + finally: + cls._set_model_restore_state(is_being_restored=False) + return checkpoint + + def _check_and_add_adapter(self, name, module, peft_name, peft_cfg, name_key_to_mcore_mixins=None): + if isinstance(module, AdapterModuleMixin): + if isinstance(module, LinearWrapper): + peft_cfg.in_features, peft_cfg.out_features = module.in_features, module.out_features + elif isinstance(module, LoraWrapper): + peft_cfg.in_features, peft_cfg.out_features = module.in_features, module.out_features + else: + return + if model_utils.import_class_by_path(peft_cfg._target_) in module.get_accepted_adapter_types(): + module.add_adapter( + name=peft_name, + cfg=peft_cfg, + base_model_cfg=self.cfg, + model_parallel_config=self.model_parallel_config, + ) + + def load_adapters( + self, filepath: str, peft_cfgs: Optional[Union[PEFTConfig, List[PEFTConfig]]] = None, map_location: str = None, + ): + """ + Utility method that restores only the adapter module(s), and not the entire model itself. + This allows the sharing of adapters which are often just a fraction of the size of the full model, + enabling easier deliver. + + .. note:: + + During restoration, assumes that the model does not currently already have one or more adapter modules. + + Args: + filepath: Filepath of the .ckpt or .nemo file. + peft_cfgs: One or more PEFTConfig objects that specify the PEFT method configuration. + If none, will infer from the .nemo checkpoint + map_location: Pytorch flag, where to place the adapter(s) state dict(s). + """ + + def _modify_state_dict(state_dict): + # Modify state key for Dreambooth inference + new_state_dict = {} + for key in state_dict.keys(): + new_key = key.replace('unet', 'model.diffusion_model') + new_key = new_key.replace('vae', 'first_stage_model') + new_key = new_key.replace('text_encoder', 'cond_stage_model') + new_key = new_key.replace('.noise_scheduler', '') + new_key = new_key.replace('._orig_mod', '') + new_state_dict[new_key] = state_dict[key] + state_dict = new_state_dict + return state_dict + + # Determine device + if map_location is None: + if torch.cuda.is_available(): + map_location = 'cuda' + else: + map_location = 'cpu' + + if filepath.endswith('.nemo'): + conf, state_dict = self._get_config_and_state_dict_from_nemo(filepath, map_location) + elif filepath.endswith('.ckpt'): + state_dict = torch.load(filepath, map_location)['state_dict'] + else: + raise RuntimeError(f"{filepath} is not nemo file or ckpt file") + if not peft_cfgs: + assert filepath.endswith( + '.nemo' + ), "Inferring peft scheme is only supported for .nemo checkpoints. Please supply the `peft_cfgs` argument." + peft_cfgs = [PEFT_CONFIG_MAP[conf.peft.peft_scheme](conf)] + self.add_adapter(peft_cfgs) + state_dict = _modify_state_dict(state_dict) + assert set(state_dict.keys()) == self.adapter_keys + super().load_state_dict(state_dict, strict=False) + + +class DiffusionWrapper(pl.LightningModule, Serialization): + def __init__( + self, diff_model_config, conditioning_key, inductor: bool = False, inductor_cudagraphs: bool = False, + ): + super().__init__() + self.diffusion_model = DiffusionWrapper.from_config_dict(diff_model_config) + self.conditioning_key = conditioning_key + assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm'] + + # Fusing VAE and CLIP doesn't give benefit + if inductor: + # TorchInductor with CUDA graph can lead to OOM + torch._dynamo.config.dynamic_shapes = False + torch._dynamo.config.automatic_dynamic_shapes = False + inductor_config.triton.cudagraphs = inductor_cudagraphs + self.diffusion_model = torch.compile(self.diffusion_model) + + def forward(self, x, t, c_concat: list = None, c_crossattn: list = None): + if self.conditioning_key is None: + out = self.diffusion_model(x, t) + elif self.conditioning_key == 'concat': + xc = torch.cat([x] + c_concat, dim=1) + out = self.diffusion_model(xc, t) + elif self.conditioning_key == 'crossattn': + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(x, t, context=cc) + elif self.conditioning_key == 'hybrid': + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc) + elif self.conditioning_key == 'adm': + cc = c_crossattn[0] + out = self.diffusion_model(x, t, y=cc) + else: + raise NotImplementedError() + + return out diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/ldm_config.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/ldm_config.py new file mode 100644 index 0000000..2f2acb4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/ldm_config.py @@ -0,0 +1,144 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass, field +from typing import Any, List, Optional + +from nemo.core.config import modelPT as model_cfg + + +@dataclass +class LDMUnetConfig: + cls: Optional[str] = 'nemo.collections.multimodal.modules.diffusionmodules.openaimodel.UNetModel' + image_size: Optional[int] = 32 # unused + in_channels: Optional[int] = 4 + out_channels: Optional[int] = 4 + model_channels: Optional[int] = 320 + attention_resolutions: Optional[List[int]] = field(default_factory=lambda: [4, 2, 1]) + num_res_blocks: Optional[int] = 2 + channel_mult: Optional[List[int]] = field(default_factory=lambda: [1, 2, 4, 4]) + num_heads: Optional[int] = 8 + use_spatial_transformer: Optional[bool] = True + transformer_depth: Optional[int] = 1 + context_dim: Optional[int] = 768 + use_checkpoint: Optional[bool] = True + legacy: Optional[bool] = False + use_flash_attention: Optional[bool] = False + + +@dataclass +class SchedulerConfig: + cls: Optional[str] = 'nemo.collections.multimodal.parts.lr_scheduler.LambdaLinearScheduler' + warm_up_steps: Optional[List[int]] = field(default_factory=lambda: [10000]) + cycle_lengths: Optional[List[int]] = field( + default_factory=lambda: [10000000000000] + ) # incredibly large number to prevent corner cases + f_start: Optional[List[float]] = field(default_factory=lambda: [1.0e-6]) + f_max: Optional[List[float]] = field(default_factory=lambda: [1.0]) + f_min: Optional[List[float]] = field(default_factory=lambda: [1.0]) + + +@dataclass +class CLIPEmbedderConfig: + cls: Optional[str] = 'nemo.collections.multimodal.modules.encoders.modules.FrozenCLIPEmbedder' + version: Optional[str] = 'openai/clip-vit-large-patch14' + device: Optional[str] = 'cuda' + max_length: Optional[int] = 77 + + +@dataclass +class LDMEncoderConfig: + double_z: Optional[bool] = True + z_channels: Optional[int] = 4 + resolution: Optional[int] = 256 + in_channels: Optional[int] = 3 + out_ch: Optional[int] = 3 + ch: Optional[int] = 128 + ch_mult: Optional[List[int]] = field(default_factory=lambda: [1, 2, 4, 4]) + num_res_blocks: Optional[int] = 2 + attn_resolutions: Optional[List[int]] = field(default_factory=lambda: []) + dropout: Optional[float] = 0.0 + + +@dataclass +class LDMFirstStageConfig: # Autoencoder + cls: Optional[str] = 'nemo.collections.multimodal.models.ldm.autoencoder.AutoencoderKL' + embed_dim: Optional[int] = 4 + monitor: Optional[str] = 'val/rec_loss' + ddconfig: Optional[LDMEncoderConfig] = LDMEncoderConfig() + + +@dataclass +class DDPMDiffusionModelConfig(model_cfg.ModelConfig): + unet_config: Optional[LDMUnetConfig] = LDMUnetConfig() + timesteps: Optional[int] = 1000 + beta_schedule: Optional[str] = 'linear' + loss_type: Optional[str] = 'l2' + ckpt_path: Optional[str] = None + ignore_keys: Optional[List[str]] = field(default_factory=list) + load_only_unet: Optional[bool] = False + monitor: Optional[str] = 'val/loss' + use_ema: Optional[bool] = True + first_stage_key: Optional[str] = 'image' + image_size: Optional[int] = 256 + channels: Optional[int] = 3 + log_every_t: Optional[int] = 100 + clip_denoised: Optional[bool] = True + linear_start: Optional[float] = 1e-4 + linear_end: Optional[float] = 2e-2 + cosine_s: Optional[float] = 8e-3 + given_betas: Optional[float] = None + original_elbo_weight: Optional[float] = 0.0 + v_posterior: Optional[ + float + ] = 0.0 # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta + l_simple_weight: Optional[float] = 1.0 + conditioning_key: Optional[str] = None + parameterization: Optional[str] = 'eps' # all assuming fixed variance schedules + scheduler_config: Optional[Any] = None + use_positional_encodings: Optional[bool] = False + learn_logvar: Optional[bool] = False + logvar_init: Optional[float] = 0.0 + learning_rate: Optional[float] = 1.0e-04 + + +@dataclass +class LatentDiffusionModelConfig(DDPMDiffusionModelConfig): + # Overrite Default values + linear_start: Optional[float] = 0.00085 + linear_end: Optional[float] = 0.0120 + num_timesteps_cond: Optional[int] = 1 + log_every_t: Optional[int] = 200 + timesteps: Optional[int] = 1000 + first_stage_key: Optional[str] = 'jpg' + cond_stage_key: Optional[str] = 'txt' + image_size: Optional[int] = 64 + channels: Optional[int] = 4 + cond_stage_trainable: Optional[bool] = False + conditioning_key: Optional[str] = 'crossattn' + monitor: Optional[str] = 'val/loss_simple_ema' + scale_factor: Optional[float] = 0.18215 + use_ema: Optional[bool] = False # TODO + unet_config: Optional[LDMUnetConfig] = LDMUnetConfig() + first_stage_config: Optional[LDMFirstStageConfig] = LDMFirstStageConfig() + scheduler_config: Optional[SchedulerConfig] = SchedulerConfig() + # New attributes in additon to DDPMDiffusionModel + concat_mode: Optional[bool] = True + trainable: Optional[bool] = False + cond_stage_config: Optional[CLIPEmbedderConfig] = CLIPEmbedderConfig() + cond_stage_forward: Optional[Any] = None + scale_by_std: Optional[bool] = False + text_embedding_dropout_rate: Optional[float] = 0 + fused_opt: Optional[bool] = False + inductor: Optional[bool] = False + inductor_cudagraphs: Optional[bool] = False diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/__init__.py new file mode 100644 index 0000000..7025605 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from enum import Enum + +Sampler = Enum('Sampler', ['PLMS', 'DDIM', 'DPM', 'PARA_DDIM']) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/base_sampler.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/base_sampler.py new file mode 100644 index 0000000..08ecdcb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/base_sampler.py @@ -0,0 +1,389 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from abc import ABC, abstractmethod + +import numpy as np +import torch +from tqdm import tqdm + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers import Sampler +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import ( + make_ddim_sampling_parameters, + make_ddim_timesteps, + noise_like, +) + + +class AbstractBaseSampler(ABC): + def __init__(self, model, sampler, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + assert isinstance(sampler, Sampler), "Sampler should be of ENUM type Sampler" + self.sampler = sampler + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True): + self.ddim_timesteps = make_ddim_timesteps( + ddim_discr_method=ddim_discretize, + num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps, + verbose=verbose, + ) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, "alphas have to be defined for each timestep" + to_torch = lambda x: x.clone().detach().to(torch.float32).to(torch.cuda.current_device()) + self.register_buffer("betas", to_torch(self.model.betas)) + self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod)) + self.register_buffer("alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)) + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer( + "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())), + ) + self.register_buffer("log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu()))) + self.register_buffer("sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))) + self.register_buffer( + "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)), + ) + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev, ddim_variance = make_ddim_sampling_parameters( + alphacums=alphas_cumprod.cpu(), ddim_timesteps=self.ddim_timesteps, eta=ddim_eta, verbose=verbose, + ) + self.register_buffer("ddim_sigmas", ddim_sigmas) + self.register_buffer("ddim_alphas", ddim_alphas) + self.register_buffer("ddim_alphas_prev", ddim_alphas_prev) + self.register_buffer("ddim_variance", ddim_variance) + self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) + / (1 - self.alphas_cumprod) + * (1 - self.alphas_cumprod / self.alphas_cumprod_prev) + ) + self.register_buffer("ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps) + + @abstractmethod + def p_sampling_fn(self): + pass + + def dpm_sampling_fn(self): + pass + + def para_ddim_sampling_fn(self): + pass + + @torch.no_grad() + def sample( + self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0.0, + mask=None, + x0=None, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + parallelism=8, + tolerance=0.1, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs, + ): + self.verbose = verbose + + if conditioning is not None: + if isinstance(conditioning, dict): + ctmp = conditioning[list(conditioning.keys())[0]] + while isinstance(ctmp, list): + ctmp = ctmp[0] + cbs = ctmp.shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + if self.verbose: + print(f"Data shape for sampling is {size}, eta {eta}") + + if self.sampler is Sampler.DPM: + return self.dpm_sampling_fn( + shape=shape, + steps=S, + conditioning=conditioning, + unconditional_conditioning=unconditional_conditioning, + unconditional_guidance_scale=unconditional_guidance_scale, + x_T=x_T, + ) + + if self.sampler is Sampler.PARA_DDIM: + return self.para_ddim_sampling_fn( + cond=conditioning, + batch_size=batch_size, + per_latent_shape=shape, + x_T=x_T, + steps=S, + parallelism=parallelism, + tolerance=tolerance, + temperature=temperature, + noise_dropout=noise_dropout, + quantize_denoised=quantize_x0, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + ) + + samples, intermediates = self.sampling_fn( + conditioning, + size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, + x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + ) + return samples, intermediates + + @torch.no_grad() + def sampling_fn( + self, + cond, + shape, + x_T=None, + ddim_use_original_steps=False, + callback=None, + timesteps=None, + quantize_denoised=False, + mask=None, + x0=None, + img_callback=None, + log_every_t=100, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + ): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, generator=self.model.rng, device=device) + else: + img = x_T + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + intermediates = {"x_inter": [img], "pred_x0": [img]} + + # TODO: Is this needed + if self.sampler is Sampler.PLMS: + time_range = list(reversed(range(0, timesteps))) if ddim_use_original_steps else np.flip(timesteps) + else: + time_range = reversed(range(0, timesteps)) if ddim_use_original_steps else np.flip(timesteps) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + + if self.verbose: + print(f"Running {self.sampler.name} Sampling with {total_steps} timesteps") + iterator = tqdm(time_range, desc=f"{self.sampler.name} Sampler", total=total_steps) + else: + iterator = time_range + + old_eps = [] + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + if self.sampler is Sampler.PLMS: + ts_next = torch.full( + (b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long, + ) + else: + old_eps = None + ts_next = None + if mask is not None: + assert x0 is not None + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1.0 - mask) * img + outs = self.p_sampling_fn( + img, + cond, + ts, + index=index, + use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, + temperature=temperature, + noise_dropout=noise_dropout, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + old_eps=old_eps, + t_next=ts_next, + ) + img, pred_x0 = outs[0], outs[1] + if self.sampler is Sampler.PLMS: + e_t = outs[2] + old_eps.append(e_t) + if len(old_eps) >= 4: + old_eps.pop(0) + if callback: + callback(i) + if img_callback: + img_callback(pred_x0, i) + if index % log_every_t == 0 or index == total_steps - 1: + intermediates["x_inter"].append(img) + intermediates["pred_x0"].append(pred_x0) + return img, intermediates + + def single_ddim_denoise_step( + self, + img, + total_steps, + i, + b, + device, + step, + cond, + ddim_use_original_steps=None, + quantize_denoised=None, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + ): + + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + + outs, eps_t = self.grad_p_sampling_fn( + img, + cond, + ts, + index=index, + use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, + temperature=temperature, + noise_dropout=noise_dropout, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + old_eps=None, + t_next=None, + ) + + img, pred_x0 = outs[0], outs[1] + return img, pred_x0, eps_t + + def _get_model_output( + self, x, t, unconditional_conditioning, unconditional_guidance_scale, score_corrector, c, corrector_kwargs, + ): + if unconditional_conditioning is None or unconditional_guidance_scale == 1.0: + model_output = self.model.apply_model(x, t, c) + elif isinstance(c, dict): + ### Contolnet conditioning is dict format + model_t = self.model.apply_model(x, t, c) + model_uncond = self.model.apply_model(x, t, unconditional_conditioning) + model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + c_in = torch.cat([unconditional_conditioning, c]) + e_t_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + model_output = e_t_uncond + unconditional_guidance_scale * (model_t - e_t_uncond) + if self.model.parameterization == "v": + e_t = self.model.predict_eps_from_z_and_v(x, t, model_output) + else: + e_t = model_output + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + return e_t, model_output + + def _get_x_prev_and_pred_x0( + self, + use_original_steps, + b, + index, + device, + x, + t, + model_output, + e_t, + quantize_denoised, + repeat_noise, + temperature, + noise_dropout, + ): + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = ( + self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + ) + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device) + # current prediction for x_0 + if self.model.parameterization != "v": + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + else: + pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1.0 - a_prev - sigma_t ** 2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.0: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/ddim.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/ddim.py new file mode 100644 index 0000000..761401d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/ddim.py @@ -0,0 +1,157 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SAMPLING ONLY.""" + +import numpy as np +import torch +from tqdm import tqdm + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers import Sampler +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.base_sampler import AbstractBaseSampler +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import extract_into_tensor +from nemo.collections.multimodal.parts.utils import randn_like + + +class DDIMSampler(AbstractBaseSampler): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__(model, sampler=Sampler.DDIM, schedule="linear", **kwargs) + + @torch.no_grad() + def p_sampling_fn( + self, + x, + c, + t, + index, + repeat_noise=False, + use_original_steps=False, + quantize_denoised=False, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + old_eps=None, + t_next=None, + ): + b, *_, device = *x.shape, x.device + e_t, model_output = self._get_model_output( + x, t, unconditional_conditioning, unconditional_guidance_scale, score_corrector, c, corrector_kwargs + ) + x_prev, pred_x0 = self._get_x_prev_and_pred_x0( + use_original_steps, + b, + index, + device, + x, + t, + model_output, + e_t, + quantize_denoised, + repeat_noise, + temperature, + noise_dropout, + ) + return x_prev, pred_x0 + + def grad_p_sampling_fn( + self, + x, + c, + t, + index, + repeat_noise=False, + use_original_steps=False, + quantize_denoised=False, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + old_eps=None, + t_next=None, + ): + b, *_, device = *x.shape, x.device + e_t, model_output = self._get_model_output( + x, t, unconditional_conditioning, unconditional_guidance_scale, score_corrector, c, corrector_kwargs + ) + outs = self._get_x_prev_and_pred_x0( + use_original_steps, + b, + index, + device, + x, + t, + model_output, + e_t, + quantize_denoised, + repeat_noise, + temperature, + noise_dropout, + ) + return outs, e_t + + @torch.no_grad() + def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): + # fast, but does not allow for exact reconstruction + # t serves as an index to gather the correct alphas + if use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas + + if noise is None: + noise = randn_like(x0, generator=self.model.rng) + return ( + extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise + ) + + @torch.no_grad() + def decode( + self, + x_latent, + cond, + t_start, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + use_original_steps=False, + ): + + timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps + timesteps = timesteps[:t_start] + + time_range = np.flip(timesteps) + total_steps = timesteps.shape[0] + print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='Decoding image', total=total_steps) + x_dec = x_latent + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) + x_dec, _ = self.p_sample_ddim( + x_dec, + cond, + ts, + index=index, + use_original_steps=use_original_steps, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + ) + return x_dec diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/dpmsolver.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/dpmsolver.py new file mode 100644 index 0000000..b1b046a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/dpmsolver.py @@ -0,0 +1,493 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math + +import torch + +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import expand_dims, interpolate_fn + + +class NoiseScheduleVP: + def __init__( + self, schedule="discrete", betas=None, alphas_cumprod=None, continuous_beta_0=0.1, continuous_beta_1=20.0, + ): + """Create a wrapper class for the forward SDE.""" + + if schedule not in ["discrete", "linear", "cosine"]: + raise ValueError( + "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format( + schedule + ) + ) + + self.schedule = schedule + if schedule == "discrete": + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.total_N = len(log_alphas) + self.T = 1.0 + self.t_array = torch.linspace(0.0, 1.0, self.total_N + 1)[1:].reshape((1, -1)) + self.log_alpha_array = log_alphas.reshape((1, -1,)) + else: + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + self.cosine_s = 0.008 + self.cosine_beta_max = 999.0 + self.cosine_t_max = ( + math.atan(self.cosine_beta_max * (1.0 + self.cosine_s) / math.pi) + * 2.0 + * (1.0 + self.cosine_s) + / math.pi + - self.cosine_s + ) + self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1.0 + self.cosine_s) * math.pi / 2.0)) + self.schedule = schedule + if schedule == "cosine": + self.T = 0.9946 + else: + self.T = 1.0 + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == "discrete": + return interpolate_fn( + t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device), + ).reshape((-1)) + elif self.schedule == "linear": + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + elif self.schedule == "cosine": + + def log_alpha_fn(s): + return torch.log(torch.cos((s + self.cosine_s) / (1.0 + self.cosine_s) * math.pi / 2.0)) + + log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 + return log_alpha_t + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1.0 - torch.exp(2.0 * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1.0 - torch.exp(2.0 * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == "linear": + tmp = 2.0 * (self.beta_1 - self.beta_0) * torch.logaddexp(-2.0 * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0 ** 2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == "discrete": + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2.0 * lamb) + t = interpolate_fn( + log_alpha.reshape((-1, 1)), + torch.flip(self.log_alpha_array.to(lamb.device), [1]), + torch.flip(self.t_array.to(lamb.device), [1]), + ) + return t.reshape((-1,)) + else: + log_alpha = -0.5 * torch.logaddexp(-2.0 * lamb, torch.zeros((1,)).to(lamb)) + + def t_fn(log_alpha_t): + return ( + torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) + * 2.0 + * (1.0 + self.cosine_s) + / math.pi + - self.cosine_s + ) + + t = t_fn(log_alpha) + return t + + +def model_wrapper( + model, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + condition=None, + unconditional_condition=None, + guidance_scale=1.0, + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model.""" + + def get_model_input_time(t_continuous): + if noise_schedule.schedule == "discrete": + return (t_continuous - 1.0 / noise_schedule.total_N) * 1000.0 + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + t_input = get_model_input_time(t_continuous) + if cond is None: + output = model(x, t_input, **model_kwargs) + else: + output = model(x, t_input, cond, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = ( + noise_schedule.marginal_alpha(t_continuous), + noise_schedule.marginal_std(t_continuous), + ) + dims = x.dim() + return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) + elif model_type == "v": + alpha_t, sigma_t = ( + noise_schedule.marginal_alpha(t_continuous), + noise_schedule.marginal_std(t_continuous), + ) + dims = x.dim() + return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x + + def cond_grad_fn(x, t_input): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1.0 or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class DPMSolver: + def __init__( + self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.0, + ): + """Construct a DPM-Solver.""" + self.model = model_fn + self.noise_schedule = noise_schedule + self.predict_x0 = predict_x0 + self.thresholding = thresholding + self.max_val = max_val + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with thresholding). + """ + noise = self.noise_prediction_fn(x, t) + dims = x.dim() + alpha_t, sigma_t = ( + self.noise_schedule.marginal_alpha(t), + self.noise_schedule.marginal_std(t), + ) + x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) + if self.thresholding: + p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.predict_x0: + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling.""" + if skip_type == "logSNR": + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == "time_uniform": + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == "time_quadratic": + t_order = 2 + t = torch.linspace(t_T ** (1.0 / t_order), t_0 ** (1.0 / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError( + "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type) + ) + + def denoise_to_zero_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False): + """ + DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`. + """ + ns = self.noise_schedule + dims = x.dim() + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + log_alpha_s, log_alpha_t = ( + ns.marginal_log_mean_coeff(s), + ns.marginal_log_mean_coeff(t), + ) + sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + if self.predict_x0: + phi_1 = torch.expm1(-h) + if model_s is None: + model_s = self.model_fn(x, s) + x_t = expand_dims(sigma_t / sigma_s, dims) * x - expand_dims(alpha_t * phi_1, dims) * model_s + if return_intermediate: + return x_t, {"model_s": model_s} + else: + return x_t + else: + phi_1 = torch.expm1(h) + if model_s is None: + model_s = self.model_fn(x, s) + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + ) + if return_intermediate: + return x_t, {"model_s": model_s} + else: + return x_t + + def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"): + """ + Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`. + """ + if solver_type not in ["dpm_solver", "taylor"]: + raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) + ns = self.noise_schedule + dims = x.dim() + model_prev_1, model_prev_0 = model_prev_list + t_prev_1, t_prev_0 = t_prev_list + lambda_prev_1, lambda_prev_0, lambda_t = ( + ns.marginal_lambda(t_prev_1), + ns.marginal_lambda(t_prev_0), + ns.marginal_lambda(t), + ) + log_alpha_prev_0, log_alpha_t = ( + ns.marginal_log_mean_coeff(t_prev_0), + ns.marginal_log_mean_coeff(t), + ) + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + h_0 = lambda_prev_0 - lambda_prev_1 + h = lambda_t - lambda_prev_0 + r0 = h_0 / h + D1_0 = expand_dims(1.0 / r0, dims) * (model_prev_0 - model_prev_1) + if self.predict_x0: + if solver_type == "dpm_solver": + x_t = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * (torch.exp(-h) - 1.0), dims) * model_prev_0 + - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.0), dims) * D1_0 + ) + elif solver_type == "taylor": + x_t = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * (torch.exp(-h) - 1.0), dims) * model_prev_0 + + expand_dims(alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0), dims) * D1_0 + ) + else: + if solver_type == "dpm_solver": + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * (torch.exp(h) - 1.0), dims) * model_prev_0 + - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.0), dims) * D1_0 + ) + elif solver_type == "taylor": + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * (torch.exp(h) - 1.0), dims) * model_prev_0 + - expand_dims(sigma_t * ((torch.exp(h) - 1.0) / h - 1.0), dims) * D1_0 + ) + return x_t + + def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"): + """ + Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`. + """ + ns = self.noise_schedule + dims = x.dim() + model_prev_2, model_prev_1, model_prev_0 = model_prev_list + t_prev_2, t_prev_1, t_prev_0 = t_prev_list + lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ( + ns.marginal_lambda(t_prev_2), + ns.marginal_lambda(t_prev_1), + ns.marginal_lambda(t_prev_0), + ns.marginal_lambda(t), + ) + log_alpha_prev_0, log_alpha_t = ( + ns.marginal_log_mean_coeff(t_prev_0), + ns.marginal_log_mean_coeff(t), + ) + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + h_1 = lambda_prev_1 - lambda_prev_2 + h_0 = lambda_prev_0 - lambda_prev_1 + h = lambda_t - lambda_prev_0 + r0, r1 = h_0 / h, h_1 / h + D1_0 = expand_dims(1.0 / r0, dims) * (model_prev_0 - model_prev_1) + D1_1 = expand_dims(1.0 / r1, dims) * (model_prev_1 - model_prev_2) + D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1) + D2 = expand_dims(1.0 / (r0 + r1), dims) * (D1_0 - D1_1) + if self.predict_x0: + x_t = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * (torch.exp(-h) - 1.0), dims) * model_prev_0 + + expand_dims(alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0), dims) * D1 + - expand_dims(alpha_t * ((torch.exp(-h) - 1.0 + h) / h ** 2 - 0.5), dims) * D2 + ) + else: + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * (torch.exp(h) - 1.0), dims) * model_prev_0 + - expand_dims(sigma_t * ((torch.exp(h) - 1.0) / h - 1.0), dims) * D1 + - expand_dims(sigma_t * ((torch.exp(h) - 1.0 - h) / h ** 2 - 0.5), dims) * D2 + ) + return x_t + + def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type="dpm_solver"): + """ + Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`. + """ + if order == 1: + return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1]) + elif order == 2: + return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type) + elif order == 3: + return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type) + else: + raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order)) + + def sample( + self, + x, + steps=20, + t_start=None, + t_end=None, + order=3, + skip_type="time_uniform", + method="singlestep", + lower_order_final=True, + denoise_to_zero=False, + solver_type="dpm_solver", + atol=0.0078, + rtol=0.05, + ): + """ + Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`. + """ + t_0 = 1.0 / self.noise_schedule.total_N if t_end is None else t_end + t_T = self.noise_schedule.T if t_start is None else t_start + device = x.device + + if method == "multistep": + assert steps >= order + timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + assert timesteps.shape[0] - 1 == steps + with torch.no_grad(): + vec_t = timesteps[0].expand((x.shape[0])) + model_prev_list = [self.model_fn(x, vec_t)] + t_prev_list = [vec_t] + # Init the first `order` values by lower order multistep DPM-Solver. + for init_order in range(1, order): + vec_t = timesteps[init_order].expand(x.shape[0]) + x = self.multistep_dpm_solver_update( + x, model_prev_list, t_prev_list, vec_t, init_order, solver_type=solver_type, + ) + model_prev_list.append(self.model_fn(x, vec_t)) + t_prev_list.append(vec_t) + # Compute the remaining values by `order`-th order multistep DPM-Solver. + for step in range(order, steps + 1): + vec_t = timesteps[step].expand(x.shape[0]) + if lower_order_final and steps < 15: + step_order = min(order, steps + 1 - step) + else: + step_order = order + x = self.multistep_dpm_solver_update( + x, model_prev_list, t_prev_list, vec_t, step_order, solver_type=solver_type, + ) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = vec_t + # We do not need to evaluate the final model value. + if step < steps: + model_prev_list[-1] = self.model_fn(x, vec_t) + if denoise_to_zero: + x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) + return x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/k_diffusion.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/k_diffusion.py new file mode 100644 index 0000000..ac4f8f7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/k_diffusion.py @@ -0,0 +1,838 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math + +import torch +import torchsde +from scipy import integrate +from torch import nn +from torchdiffeq import odeint +from tqdm.auto import tqdm, trange + + +def append_zero(x): + return torch.cat([x, x.new_zeros([1])]) + + +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') + return x[(...,) + (None,) * dims_to_append] + + +def get_sigmas_karras(n, sigma_min, sigma_max, rho=7.0, device='cpu'): + """Constructs the noise schedule of Karras et al. (2022).""" + ramp = torch.linspace(0, 1, n) + min_inv_rho = sigma_min ** (1 / rho) + max_inv_rho = sigma_max ** (1 / rho) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho + return append_zero(sigmas).to(device) + + +def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'): + """Constructs an exponential noise schedule.""" + sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp() + return append_zero(sigmas) + + +def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1.0, device='cpu'): + """Constructs an polynomial in log sigma noise schedule.""" + ramp = torch.linspace(1, 0, n, device=device) ** rho + sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min)) + return append_zero(sigmas) + + +def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): + """Constructs a continuous VP noise schedule.""" + t = torch.linspace(1, eps_s, n, device=device) + sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1) + return append_zero(sigmas) + + +def to_d(x, sigma, denoised): + """Converts a denoiser output to a Karras ODE derivative.""" + return (x - denoised) / append_dims(sigma, x.ndim) + + +def get_ancestral_step(sigma_from, sigma_to, eta=1.0): + """Calculates the noise level (sigma_down) to step down to and the amount + of noise to add (sigma_up) when doing an ancestral sampling step.""" + if not eta: + return sigma_to, 0.0 + sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) + sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 + return sigma_down, sigma_up + + +def default_noise_sampler(x): + return lambda sigma, sigma_next: torch.randn_like(x) + + +class BatchedBrownianTree: + """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" + + def __init__(self, x, t0, t1, seed=None, **kwargs): + t0, t1, self.sign = self.sort(t0, t1) + w0 = kwargs.get('w0', torch.zeros_like(x)) + if seed is None: + seed = torch.randint(0, 2 ** 63 - 1, []).item() + self.batched = True + try: + assert len(seed) == x.shape[0] + w0 = w0[0] + except TypeError: + seed = [seed] + self.batched = False + self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed] + + @staticmethod + def sort(a, b): + return (a, b, 1) if a < b else (b, a, -1) + + def __call__(self, t0, t1): + t0, t1, sign = self.sort(t0, t1) + w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign) + return w if self.batched else w[0] + + +class BrownianTreeNoiseSampler: + """A noise sampler backed by a torchsde.BrownianTree. + + Args: + x (Tensor): The tensor whose shape, device and dtype to use to generate + random samples. + sigma_min (float): The low end of the valid interval. + sigma_max (float): The high end of the valid interval. + seed (int or List[int]): The random seed. If a list of seeds is + supplied instead of a single integer, then the noise sampler will + use one BrownianTree per batch item, each with its own seed. + transform (callable): A function that maps sigma to the sampler's + internal timestep. + """ + + def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x): + self.transform = transform + t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max)) + self.tree = BatchedBrownianTree(x, t0, t1, seed) + + def __call__(self, sigma, sigma_next): + t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next)) + return self.tree(t0, t1) / (t1 - t0).abs().sqrt() + + +@torch.no_grad() +def sample_euler( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + s_churn=0.0, + s_tmin=0.0, + s_tmax=float('inf'), + s_noise=1.0, +): + """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0 + eps = torch.randn_like(x) * s_noise + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + # Euler method + x = x + d * dt + return x + + +@torch.no_grad() +def sample_euler_ancestral( + model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1.0, noise_sampler=None +): + """Ancestral sampling with Euler method steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], denoised) + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +@torch.no_grad() +def sample_heun( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + s_churn=0.0, + s_tmin=0.0, + s_tmax=float('inf'), + s_noise=1.0, +): + """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0 + eps = torch.randn_like(x) * s_noise + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + if sigmas[i + 1] == 0: + # Euler method + x = x + d * dt + else: + # Heun's method + x_2 = x + d * dt + denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + d_prime = (d + d_2) / 2 + x = x + d_prime * dt + return x + + +@torch.no_grad() +def sample_dpm_2( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + s_churn=0.0, + s_tmin=0.0, + s_tmax=float('inf'), + s_noise=1.0, +): + """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0 + eps = torch.randn_like(x) * s_noise + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + dt = sigmas[i + 1] - sigma_hat + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp() + dt_1 = sigma_mid - sigma_hat + dt_2 = sigmas[i + 1] - sigma_hat + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + return x + + +@torch.no_grad() +def sample_dpm_2_ancestral( + model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1.0, noise_sampler=None +): + """Ancestral sampling with DPM-Solver second-order steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], denoised) + if sigma_down == 0: + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() + dt_1 = sigma_mid - sigmas[i] + dt_2 = sigma_down - sigmas[i] + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +def linear_multistep_coeff(order, t, i, j): + if order - 1 > i: + raise ValueError(f'Order {order} too high for step {i}') + + def fn(tau): + prod = 1.0 + for k in range(order): + if j == k: + continue + prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) + return prod + + return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] + + +@torch.no_grad() +def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigmas_cpu = sigmas.detach().cpu().numpy() + ds = [] + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + d = to_d(x, sigmas[i], denoised) + ds.append(d) + if len(ds) > order: + ds.pop(0) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + cur_order = min(i + 1, order) + coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] + x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) + return x + + +@torch.no_grad() +def log_likelihood(model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + v = torch.randint_like(x, 2) * 2 - 1 + fevals = 0 + + def ode_fn(sigma, x): + nonlocal fevals + with torch.enable_grad(): + x = x[0].detach().requires_grad_() + denoised = model(x, sigma * s_in, **extra_args) + d = to_d(x, sigma, denoised) + fevals += 1 + grad = torch.autograd.grad((d * v).sum(), x)[0] + d_ll = (v * grad).flatten(1).sum(1) + return d.detach(), d_ll + + x_min = x, x.new_zeros([x.shape[0]]) + t = x.new_tensor([sigma_min, sigma_max]) + sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method='dopri5') + latent, delta_ll = sol[0][-1], sol[1][-1] + ll_prior = torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1) + return ll_prior + delta_ll, {'fevals': fevals} + + +class PIDStepSizeController: + """A PID controller for ODE adaptive step size control.""" + + def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8): + self.h = h + self.b1 = (pcoeff + icoeff + dcoeff) / order + self.b2 = -(pcoeff + 2 * dcoeff) / order + self.b3 = dcoeff / order + self.accept_safety = accept_safety + self.eps = eps + self.errs = [] + + def limiter(self, x): + return 1 + math.atan(x - 1) + + def propose_step(self, error): + inv_error = 1 / (float(error) + self.eps) + if not self.errs: + self.errs = [inv_error, inv_error, inv_error] + self.errs[0] = inv_error + factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3 + factor = self.limiter(factor) + accept = factor >= self.accept_safety + if accept: + self.errs[2] = self.errs[1] + self.errs[1] = self.errs[0] + self.h *= factor + return accept + + +class DPMSolver(nn.Module): + """DPM-Solver. See https://arxiv.org/abs/2206.00927.""" + + def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None): + super().__init__() + self.model = model + self.extra_args = {} if extra_args is None else extra_args + self.eps_callback = eps_callback + self.info_callback = info_callback + + def t(self, sigma): + return -sigma.log() + + def sigma(self, t): + return t.neg().exp() + + def eps(self, eps_cache, key, x, t, *args, **kwargs): + if key in eps_cache: + return eps_cache[key], eps_cache + sigma = self.sigma(t) * x.new_ones([x.shape[0]]) + eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t) + if self.eps_callback is not None: + self.eps_callback() + return eps, {key: eps, **eps_cache} + + def dpm_solver_1_step(self, x, t, t_next, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + x_1 = x - self.sigma(t_next) * h.expm1() * eps + return x_1, eps_cache + + def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + s1 = t + r1 * h + u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps + eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) + x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps) + return x_2, eps_cache + + def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + s1 = t + r1 * h + s2 = t + r2 * h + u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps + eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) + u2 = ( + x + - self.sigma(s2) * (r2 * h).expm1() * eps + - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps) + ) + eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2) + x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps) + return x_3, eps_cache + + def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0.0, s_noise=1.0, noise_sampler=None): + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + if not t_end > t_start and eta: + raise ValueError('eta must be 0 for reverse sampling') + + m = math.floor(nfe / 3) + 1 + ts = torch.linspace(t_start, t_end, m + 1, device=x.device) + + if nfe % 3 == 0: + orders = [3] * (m - 2) + [2, 1] + else: + orders = [3] * (m - 1) + [nfe % 3] + + for i in range(len(orders)): + eps_cache = {} + t, t_next = ts[i], ts[i + 1] + if eta: + sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta) + t_next_ = torch.minimum(t_end, self.t(sd)) + su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5 + else: + t_next_, su = t_next, 0.0 + + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + denoised = x - self.sigma(t) * eps + if self.info_callback is not None: + self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised}) + + if orders[i] == 1: + x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache) + elif orders[i] == 2: + x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache) + else: + x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache) + + x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next)) + + return x + + def dpm_solver_adaptive( + self, + x, + t_start, + t_end, + order=3, + rtol=0.05, + atol=0.0078, + h_init=0.05, + pcoeff=0.0, + icoeff=1.0, + dcoeff=0.0, + accept_safety=0.81, + eta=0.0, + s_noise=1.0, + noise_sampler=None, + ): + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + if order not in {2, 3}: + raise ValueError('order should be 2 or 3') + forward = t_end > t_start + if not forward and eta: + raise ValueError('eta must be 0 for reverse sampling') + h_init = abs(h_init) * (1 if forward else -1) + atol = torch.tensor(atol) + rtol = torch.tensor(rtol) + s = t_start + x_prev = x + accept = True + pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety) + info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0} + + while s < t_end - 1e-5 if forward else s > t_end + 1e-5: + eps_cache = {} + t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h) + if eta: + sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta) + t_ = torch.minimum(t_end, self.t(sd)) + su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5 + else: + t_, su = t, 0.0 + + eps, eps_cache = self.eps(eps_cache, 'eps', x, s) + denoised = x - self.sigma(s) * eps + + if order == 2: + x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache) + x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache) + else: + x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache) + x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache) + delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs())) + error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5 + accept = pid.propose_step(error) + if accept: + x_prev = x_low + x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t)) + s = t + info['n_accept'] += 1 + else: + info['n_reject'] += 1 + info['nfe'] += order + info['steps'] += 1 + + if self.info_callback is not None: + self.info_callback( + { + 'x': x, + 'i': info['steps'] - 1, + 't': s, + 't_up': s, + 'denoised': denoised, + 'error': error, + 'h': pid.h, + **info, + } + ) + + return x, info + + +@torch.no_grad() +def sample_dpm_fast( + model, + x, + sigma_min, + sigma_max, + n, + extra_args=None, + callback=None, + disable=None, + eta=0.0, + s_noise=1.0, + noise_sampler=None, +): + """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927.""" + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError('sigma_min and sigma_max must not be 0') + with tqdm(total=n, disable=disable) as pbar: + dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) + if callback is not None: + dpm_solver.info_callback = lambda info: callback( + {'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info} + ) + return dpm_solver.dpm_solver_fast( + x, + dpm_solver.t(torch.tensor(sigma_max)), + dpm_solver.t(torch.tensor(sigma_min)), + n, + eta, + s_noise, + noise_sampler, + ) + + +@torch.no_grad() +def sample_dpm_adaptive( + model, + x, + sigma_min, + sigma_max, + extra_args=None, + callback=None, + disable=None, + order=3, + rtol=0.05, + atol=0.0078, + h_init=0.05, + pcoeff=0.0, + icoeff=1.0, + dcoeff=0.0, + accept_safety=0.81, + eta=0.0, + s_noise=1.0, + noise_sampler=None, + return_info=False, +): + """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927.""" + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError('sigma_min and sigma_max must not be 0') + with tqdm(disable=disable) as pbar: + dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) + if callback is not None: + dpm_solver.info_callback = lambda info: callback( + {'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info} + ) + x, info = dpm_solver.dpm_solver_adaptive( + x, + dpm_solver.t(torch.tensor(sigma_max)), + dpm_solver.t(torch.tensor(sigma_min)), + order, + rtol, + atol, + h_init, + pcoeff, + icoeff, + dcoeff, + accept_safety, + eta, + s_noise, + noise_sampler, + ) + if return_info: + return x, info + return x + + +@torch.no_grad() +def sample_dpmpp_2s_ancestral( + model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1.0, noise_sampler=None +): + """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigma_down == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++(2S) + t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) + r = 1 / 2 + h = t_next - t + s = t + r * h + x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 + # Noise addition + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +@torch.no_grad() +def sample_dpmpp_sde( + model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1.0, noise_sampler=None, r=1 / 2 +): + """DPM-Solver++ (stochastic).""" + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigmas[i + 1] - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++ + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + s = t + h * r + fac = 1 / (2 * r) + + # Step 1 + sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta) + s_ = t_fn(sd) + x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised + x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + + # Step 2 + sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta) + t_next_ = t_fn(sd) + denoised_d = (1 - fac) * denoised + fac * denoised_2 + x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d + x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su + return x + + +@torch.no_grad() +def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None): + """DPM-Solver++(2M).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + old_denoised = None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + if old_denoised is None or sigmas[i + 1] == 0: + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised + else: + h_last = t - t_fn(sigmas[i - 1]) + r = h_last / h + denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d + old_denoised = denoised + return x + + +class DiscreteSchedule(nn.Module): + """A mapping between continuous noise levels (sigmas) and a list of discrete noise + levels.""" + + def __init__(self, sigmas, quantize): + super().__init__() + self.register_buffer('sigmas', sigmas) + self.register_buffer('log_sigmas', sigmas.log()) + self.quantize = quantize + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def get_sigmas(self, n=None): + if n is None: + return append_zero(self.sigmas.flip(0)) + t_max = len(self.sigmas) - 1 + t = torch.linspace(t_max, 0, n, device=self.sigmas.device) + return append_zero(self.t_to_sigma(t)) + + def sigma_to_t(self, sigma, quantize=None): + quantize = self.quantize if quantize is None else quantize + log_sigma = sigma.log() + dists = log_sigma - self.log_sigmas[:, None] + if quantize: + return dists.abs().argmin(dim=0).view(sigma.shape) + low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2) + high_idx = low_idx + 1 + low, high = self.log_sigmas[low_idx], self.log_sigmas[high_idx] + w = (low - log_sigma) / (low - high) + w = w.clamp(0, 1) + t = (1 - w) * low_idx + w * high_idx + return t.view(sigma.shape) + + def t_to_sigma(self, t): + t = t.float() + low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac() + log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] + return log_sigma.exp() + + +class DiscreteEpsDDPMDenoiser(DiscreteSchedule): + """A wrapper for discrete schedule DDPM models that output eps (the predicted + noise).""" + + def __init__(self, model, quantize=False): + alphas_cumprod = model.alphas_cumprod + super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize) + self.inner_model = model + self.sigma_data = 1.0 + + def get_scalings(self, sigma): + c_out = -sigma + c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + return c_out, c_in + + def get_eps(self, *args, **kwargs): + return self.inner_model.apply_model(*args, **kwargs) + + def loss(self, input, noise, sigma, **kwargs): + c_out, c_in = [append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + noised_input = input + noise * append_dims(sigma, input.ndim) + eps = self.get_eps(noised_input * c_in, self.sigma_to_t(sigma), **kwargs) + return (eps - noise).pow(2).flatten(1).mean(1) + + def forward(self, input, sigma, **kwargs): + c_out, c_in = [append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs) + return input + eps * c_out diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/para_ddim.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/para_ddim.py new file mode 100644 index 0000000..f389b8e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/para_ddim.py @@ -0,0 +1,231 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any, Tuple + +import numpy as np +import torch +import torch.nn as nn +from tqdm import tqdm + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers import Sampler +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.base_sampler import AbstractBaseSampler +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import noise_like + + +class ParaDDIMSampler(AbstractBaseSampler): + """ Parallel version of DDIM sampler. Utilizes Parallel Sampling (https://arxiv.org/abs/2305.16317). + It reduces the latency of a model, but the total compute cost is increased. + + The main three parameters that affect the performance of the algorithm are: + Parallelism (int): Defines the maximal size of the window. That many diffusion steps can happen in + parallel. + Tolerance (float): Sets the maximal error tolerance defined as a ratio between drift of the trajectory + and noise. The larger the tolerance the faster the method is. The smaller the tolerance the better + quality output is achieved. + Number of GPUs (int): Number of GPUs utilizing DataParallel parallelism to compute diffusion steps in + parallel. + + Different combination of these parameters values can result in different latency-quality-compute trade-off. + For more details please refer to the Parallel Sampling paper (https://arxiv.org/abs/2305.16317). + """ + + def __init__(self, model, **kwargs): + super().__init__(model, sampler=Sampler.PARA_DDIM, **kwargs) + + @torch.no_grad() + def p_sampling_fn(self): + pass + + @torch.no_grad() + def para_ddim_sampling_fn( + self, + cond: torch.tensor, + batch_size: int, + per_latent_shape: Tuple[int, ...], + x_T: torch.tensor = None, + steps: int = 50, + parallelism: int = 8, + tolerance: float = 0.1, + temperature: float = 0.0, + noise_dropout: float = 0.0, + quantize_denoised: bool = False, + unconditional_guidance_scale: float = 1.0, + unconditional_conditioning: torch.tensor = None, + score_corrector=None, + corrector_kwargs=None, + ): + print( + f"Running {self.sampler.name} with {steps} timesteps, " + f"parallelism={parallelism}, " + f"and tolerance={tolerance}" + ) + + device = self.model.betas.device + size = (batch_size, *per_latent_shape) + x_T = torch.randn(size, generator=self.model.rng, device=device) if x_T is None else x_T + time_range = np.flip(self.ddim_timesteps).copy() # Make a copy to resolve issue with negative strides + + # Processing window of timesteps [window_start, window_end) in parallel + window_start = 0 + window_size = min(parallelism, steps) + window_end = window_size + + # Store the whole trajectory in memory; it will be iteratively improved + latents = torch.stack([x_T] * (steps + 1)) + + # Pre-computing noises to ensure noise is sampled once per diffusion step + noises = torch.zeros_like(latents) + for i in range(steps - 1, -1, -1): + gaussian_noise = torch.randn_like(x_T) + noise = (self.ddim_variance[i] ** 0.5) * gaussian_noise + noises[i] = noise.clone() + + # Store inverse of the variance to avoid division at every iteration + variance = [self.ddim_variance[i] for i in range(steps - 1, -1, -1)] + [0] + inverse_variance = 1.0 / torch.tensor(variance).to(noises.device) + latent_dim = noises[0, 0].numel() + inverse_variance_norm = inverse_variance[:, None] / latent_dim + + scaled_tolerance = tolerance ** 2 + + with tqdm(total=steps) as progress_bar: + while window_start < steps: + window_size = window_end - window_start + + # Prepare the input to the model. Model will perform window_size noise predictions in parallel + window_cond = torch.stack([cond] * window_size) + window_uncond_cond = torch.stack([unconditional_conditioning] * window_size) + window_latents = latents[window_start:window_end] + window_timesteps = torch.tensor(time_range[window_start:window_end], device=device).repeat( + 1, batch_size + ) + + # Reshape (w, b, ...) -> (w * b, ...) + latents_input = window_latents.flatten(0, 1) + timesteps_input = window_timesteps.flatten(0, 1) + cond_input = window_cond.flatten(0, 1) + uncond_cond_input = window_uncond_cond.flatten(0, 1) + + # Model call + e_t, _ = self._get_model_output( + latents_input, + timesteps_input, + uncond_cond_input, + unconditional_guidance_scale, + score_corrector, + cond_input, + corrector_kwargs, + ) + # Reshape back (w * b, ...) -> (w, b, ...) + e_t = e_t.reshape(window_size, batch_size, *per_latent_shape) + + # Perform Picard iteration + window_latents_picard_iteration = self._get_x_prev( + batch_size=batch_size, + steps=steps, + x=window_latents, + e_t=e_t, + temperature=temperature, + noise_dropout=noise_dropout, + quantize_denoised=quantize_denoised, + window_start=window_start, + window_end=window_end, + device=device, + ).reshape(window_latents.shape) + + # Calculate cumulative drift + delta = window_latents_picard_iteration - window_latents + delta_cum = torch.cumsum(delta, dim=0) + block_latents_new = latents[window_start][None,] + delta_cum + + # Calculate the error + error = torch.linalg.norm( + (block_latents_new - latents[window_start + 1 : window_end + 1]).reshape( + window_size, batch_size, -1 + ), + dim=-1, + ).pow(2) + + # Calculate error magnitude + error_magnitude = error * inverse_variance_norm[window_start + 1 : window_end + 1] + # Pad so at least one value exceeds tolerance + error_magnitude = nn.functional.pad(error_magnitude, (0, 0, 0, 1), value=1e9) + error_exceeding = torch.max(error_magnitude > scaled_tolerance, dim=1).values.int() + + # Find how many diffusion steps have error below given threshold tolerance and shift the window + ind = torch.argmax(error_exceeding).item() + new_window_start = window_start + min(1 + ind, window_size) + new_window_end = min(new_window_start + window_size, steps) + + # Update the trajectory + latents[window_start + 1 : window_end + 1] = block_latents_new + latents[window_end : new_window_end + 1] = latents[window_end][ + None, + ] + + progress_bar.update(new_window_start - window_start) + window_start = new_window_start + window_end = new_window_end + + intermediates = {"x_inter": [latents[i] for i in range(steps)]} + return latents[-1], intermediates + + def _get_x_prev( + self, + batch_size: int, + steps: int, + x: torch.tensor, + e_t: torch.tensor, + temperature: float, + noise_dropout: float, + quantize_denoised: bool, + window_start: int, + window_end: int, + device: Any, + ): + alphas = self.ddim_alphas + alphas_prev = self.ddim_alphas_prev + sqrt_one_minus_alphas = self.ddim_sqrt_one_minus_alphas + sigmas = self.ddim_sigmas + window_size = window_end - window_start + + def prepare_tensor(x): + x = torch.tensor(x, device=device).flip(dims=[0]) + x = x.unsqueeze(1).repeat(1, batch_size).reshape(window_size, batch_size, 1, 1, 1) + return x + + # Select parameters corresponding to the currently considered timesteps. Note that index_end < index_start, + # because during diffusion the time is reversed (we go from timestep step to 0) + index_start = steps - window_start + index_end = steps - window_end + a_t = prepare_tensor(alphas[index_end:index_start]) + a_prev = prepare_tensor(alphas_prev[index_end:index_start]) + sigma_t = prepare_tensor(sigmas[index_end:index_start]) + sqrt_one_minus_at = prepare_tensor(sqrt_one_minus_alphas[index_end:index_start]) + + # Current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + + # Direction pointing to x_t + dir_xt = (1.0 - a_prev - sigma_t ** 2).sqrt() * e_t + + noise = sigma_t * noise_like(x.shape, device) * temperature + if noise_dropout > 0.0: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/plms.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/plms.py new file mode 100644 index 0000000..2a721d1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/plms.py @@ -0,0 +1,105 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SAMPLING ONLY.""" + +import torch + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers import Sampler +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.base_sampler import AbstractBaseSampler + + +class PLMSSampler(AbstractBaseSampler): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__(model, sampler=Sampler.PLMS, schedule="linear", **kwargs) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=False): + if ddim_eta != 0: + raise ValueError('ddim_eta must be 0 for PLMS') + super().make_schedule(ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=False) + + @torch.no_grad() + def p_sampling_fn( + self, + x, + c, + t, + index, + repeat_noise=False, + use_original_steps=False, + quantize_denoised=False, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + old_eps=None, + t_next=None, + ): + b, *_, device = *x.shape, x.device + e_t, model_output = self._get_model_output( + x, t, unconditional_conditioning, unconditional_guidance_scale, score_corrector, c, corrector_kwargs + ) + if len(old_eps) == 0: + # Pseudo Improved Euler (2nd order) + x_prev, pred_x0 = self._get_x_prev_and_pred_x0( + use_original_steps, + b, + index, + device, + x, + t, + model_output, + e_t, + quantize_denoised, + repeat_noise, + temperature, + noise_dropout, + ) + e_t_next, model_output = self._get_model_output( + x_prev, + t_next, + unconditional_conditioning, + unconditional_guidance_scale, + score_corrector, + c, + corrector_kwargs, + ) + e_t_prime = (e_t + e_t_next) / 2 + elif len(old_eps) == 1: + # 2nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (3 * e_t - old_eps[-1]) / 2 + elif len(old_eps) == 2: + # 3nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 + elif len(old_eps) >= 3: + # 4nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 + + x_prev, pred_x0 = self._get_x_prev_and_pred_x0( + use_original_steps, + b, + index, + device, + x, + t, + model_output, + e_t_prime, + quantize_denoised, + repeat_noise, + temperature, + noise_dropout, + ) + + return x_prev, pred_x0, e_t diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/sampler_dpm.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/sampler_dpm.py new file mode 100644 index 0000000..98a1b69 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/sampler_dpm.py @@ -0,0 +1,76 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SAMPLING ONLY.""" + +import torch + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers import Sampler +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.base_sampler import AbstractBaseSampler +from .dpmsolver import DPMSolver, NoiseScheduleVP, model_wrapper + +MODEL_TYPES = {"eps": "noise", "v": "v"} + + +class DPMSolverSampler(AbstractBaseSampler): + def __init__(self, model, **kwargs): + + super().__init__(model, sampler=Sampler.DPM, **kwargs) + + def to_torch(x, model): + x_copy = x.clone() + x_detached = x_copy.detach() + x_float32 = x_detached.to(torch.float32) + x_device = x_float32.to(model.betas.device) + return x_device + + self.register_buffer("alphas_cumprod", to_torch(model.alphas_cumprod, model)) + + @torch.no_grad() + def p_sampling_fn(self): + pass + + @torch.no_grad() + def dpm_sampling_fn( + self, + shape, + steps, + conditioning=None, + unconditional_conditioning=None, + unconditional_guidance_scale=1.0, + x_T=None, + ): + + device = self.model.betas.device + if x_T is None: + img = torch.randn(shape, generator=self.model.rng, device=device) + else: + img = x_T + + ns = NoiseScheduleVP("discrete", alphas_cumprod=self.alphas_cumprod) + + model_fn = model_wrapper( + lambda x, t, c: self.model.apply_model(x, t, c), + ns, + model_type=MODEL_TYPES[self.model.parameterization], + guidance_type="classifier-free", + condition=conditioning, + unconditional_condition=unconditional_conditioning, + guidance_scale=unconditional_guidance_scale, + ) + dpm_solver = DPMSolver(model_fn, ns, predict_x0=True, thresholding=False) + x = dpm_solver.sample( + img, steps=steps, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True, + ) + + return x.to(device), None diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/vision_language_foundation/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/vision_language_foundation/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/vision_language_foundation/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/vision_language_foundation/clip/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/vision_language_foundation/clip/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/vision_language_foundation/clip/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/vision_language_foundation/clip/megatron_clip_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/vision_language_foundation/clip/megatron_clip_models.py new file mode 100644 index 0000000..fe35ae1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/vision_language_foundation/clip/megatron_clip_models.py @@ -0,0 +1,959 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import itertools +from functools import partial +from typing import Any, Optional + +import numpy as np +import torch +import torch.nn.functional as F +from omegaconf.dictconfig import DictConfig +from pytorch_lightning.accelerators import CPUAccelerator +from pytorch_lightning.trainer.trainer import Trainer +from tqdm import tqdm + +from nemo.collections.multimodal.data.clip.clip_dataset import ( + build_imagenet_validation_dataloader, + build_train_valid_datasets, +) +from nemo.collections.multimodal.losses.clip_loss import ClipLoss +from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel +from nemo.collections.nlp.modules.common.megatron.build_model import build_model +from nemo.collections.nlp.modules.common.megatron.language_model import get_language_model +from nemo.collections.nlp.modules.common.megatron.module import Float16Module, MegatronModule +from nemo.collections.nlp.modules.common.megatron.utils import ( + average_losses_across_data_parallel_group, + get_all_params_for_weight_decay_optimization, + get_params_for_weight_decay_optimization, + init_method_normal, + scaled_init_method_normal, +) +from nemo.collections.nlp.parts.utils_funcs import get_last_rank, torch_dtype_from_precision +from nemo.collections.vision.modules.vit.vit_backbone import VitBackbone +from nemo.core.classes.common import PretrainedModelInfo +from nemo.utils import logging + +try: + from apex.transformer.enums import AttnMaskType + from apex.transformer.pipeline_parallel.utils import get_num_microbatches + + HAVE_APEX = True +except (ImportError, ModuleNotFoundError): + HAVE_APEX = False + +try: + from megatron.core import parallel_state + from megatron.core.pipeline_parallel.schedules import get_forward_backward_func + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +class CLIPVisionTransformer(MegatronModule): + """Vision Transformer Model.""" + + def __init__(self, model_cfg, model_parallel_config, pre_process=True, post_process=True, skip_head=False): + super(CLIPVisionTransformer, self).__init__() + + scaled_init_method = ( + scaled_init_method_normal(model_cfg.init_method_std, model_cfg.num_layers) + if model_cfg.use_scaled_init_method + else init_method_normal(model_cfg.init_method_std) + ) + + self.config = model_parallel_config + self.hidden_size = model_cfg.hidden_size + self.global_average_pool = model_cfg.global_average_pool + self.pre_process = pre_process + self.post_process = post_process + self.skip_head = skip_head + + if model_cfg.get("class_token_length") is None or model_cfg.get("class_token_length") <= 0: + class_token = False + else: + class_token = True + self.backbone = VitBackbone( + model_cfg, + model_parallel_config, + init_method=init_method_normal(model_cfg.init_method_std), + scaled_init_method=scaled_init_method, + pre_process=self.pre_process, + post_process=self.post_process, + class_token=class_token, + single_token_output=False, + ) + + if self.post_process and not skip_head: + self.output_dim = model_cfg.output_dim + self.head = torch.nn.Linear(self.hidden_size, self.output_dim, bias=False,) + + def set_input_tensor(self, input_tensor): + """See megatron.model.transformer.set_input_tensor()""" + self.backbone.set_input_tensor(input_tensor) + + def forward(self, input): + hidden_states = self.backbone(input) + + if self.post_process and not self.skip_head: + if self.global_average_pool: + hidden_states = hidden_states.mean(dim=1) + else: + hidden_states = hidden_states[:, 0] + hidden_states = self.head(hidden_states) + # print("vision_head", hidden_states.shape) + return hidden_states + + +class CLIPTextTransformer(MegatronModule): + """Text Transformer Model.""" + + def __init__(self, model_cfg, model_parallel_config, padded_vocab_size, pre_process=True, post_process=True): + super(CLIPTextTransformer, self).__init__() + + self.config = model_parallel_config + self.pre_process = pre_process + self.post_process = post_process + self.fp16_lm_cross_entropy = model_cfg.fp16_lm_cross_entropy + self.sequence_parallel = model_cfg.sequence_parallel + self.gradient_accumulation_fusion = model_cfg.gradient_accumulation_fusion + + scaled_init_method = ( + scaled_init_method_normal(model_cfg.init_method_std, model_cfg.num_layers) + if model_cfg.use_scaled_init_method + else init_method_normal(model_cfg.init_method_std) + ) + self.language_model, self._language_model_key = get_language_model( + config=model_parallel_config, + vocab_size=padded_vocab_size, + hidden_size=model_cfg.hidden_size, + hidden_dropout=model_cfg.hidden_dropout, + attention_dropout=model_cfg.attention_dropout, + num_tokentypes=0, + max_position_embeddings=model_cfg.max_position_embeddings, + num_layers=model_cfg.num_layers, + num_attention_heads=model_cfg.num_attention_heads, + apply_query_key_layer_scaling=model_cfg.apply_query_key_layer_scaling, + kv_channels=model_cfg.kv_channels, + ffn_hidden_size=model_cfg.ffn_hidden_size, + add_pooler=False, + encoder_attn_mask_type=AttnMaskType.causal, + position_embedding_type=model_cfg.get("position_embedding_type", "learned_absolute"), + init_method=init_method_normal(model_cfg.init_method_std), + scaled_init_method=scaled_init_method, + pre_process=self.pre_process, + post_process=self.post_process, + init_method_std=model_cfg.init_method_std, + precision=model_cfg.precision, + fp32_residual_connection=model_cfg.fp32_residual_connection, + activations_checkpoint_granularity=model_cfg.activations_checkpoint_granularity, + activations_checkpoint_method=model_cfg.activations_checkpoint_method, + activations_checkpoint_num_layers=model_cfg.activations_checkpoint_num_layers, + activations_checkpoint_layers_per_pipeline=model_cfg.activations_checkpoint_layers_per_pipeline, + normalization=model_cfg.normalization, + layernorm_epsilon=model_cfg.layernorm_epsilon, + bias_activation_fusion=model_cfg.bias_activation_fusion, + bias_dropout_add_fusion=model_cfg.bias_dropout_add_fusion, + masked_softmax_fusion=model_cfg.masked_softmax_fusion, + persist_layer_norm=model_cfg.persist_layer_norm, + openai_gelu=model_cfg.openai_gelu, + onnx_safe=model_cfg.onnx_safe, + megatron_legacy=model_cfg.megatron_legacy, + transformer_engine=model_cfg.transformer_engine, + fp8=model_cfg.fp8, + fp8_e4m3=model_cfg.fp8_e4m3, + fp8_hybrid=model_cfg.fp8_hybrid, + fp8_margin=model_cfg.fp8_margin, + fp8_interval=model_cfg.fp8_interval, + fp8_amax_history_len=model_cfg.fp8_amax_history_len, + fp8_amax_compute_algo=model_cfg.fp8_amax_compute_algo, + reduce_amax=model_cfg.get('reduce_amax', True), + use_emha=model_cfg.use_emha, + activation=model_cfg.get('activation', 'gelu'), + use_flash_attention=model_cfg.get('flash_attention', False), + ) + + self.initialize_word_embeddings( + init_method=init_method_normal(model_cfg.init_method_std), + vocab_size=padded_vocab_size, + hidden_size=model_cfg.hidden_size, + ) + + # TODO (yuya): check this position id + self.position_ids = None + if self.pre_process: + self.position_ids = torch.arange(model_cfg.max_position_embeddings).expand(1, -1).cuda() + + if self.post_process: + self.output_dim = model_cfg.output_dim + self.head = torch.nn.Linear(model_cfg.hidden_size, self.output_dim, bias=False,) + + self.attn_mask = self.build_attention_mask(model_cfg.max_position_embeddings) + + def set_input_tensor(self, input_tensor): + """See megatron.model.transformer.set_input_tensor()""" + self.language_model.set_input_tensor(input_tensor) + + def build_attention_mask(self, max_position_embeddings): + # lazily create causal attention mask, with full attention between the tokens + mask = torch.empty(max_position_embeddings, max_position_embeddings, dtype=bool, device='cuda') + mask.fill_(True) + mask.triu_(1) # zero out the lower diagonal + mask = mask.reshape(1, 1, max_position_embeddings, max_position_embeddings) + return mask + + def forward( + self, input_ids, + ): + # input_ids: [b, s] + # position_ids: [b, s] + # attention_mask: [1, 1, s, s] + + hidden_states = self.language_model( + input_ids, + self.position_ids, + self.attn_mask, + token_type_ids=None, + layer_past=None, + get_key_value=False, + encoder_input=None, + set_inference_key_value_memory=False, + inference_max_sequence_len=None, + checkpoint_activations_all_layers=None, + ) + + if self.post_process: + # shape = [seq, bsz, hidden] + # take features from the eot embedding (eot_token is the highest number in each sequence) + hidden_states = hidden_states[input_ids.argmax(dim=-1), torch.arange(hidden_states.shape[1])] + return self.head(hidden_states) + + return hidden_states + + +class CLIPModel(MegatronModule): + """CLIP Model""" + + def __init__(self, model_cfg, model_parallel_config, padded_vocab_size, pre_process=True, post_process=True): + super(CLIPModel, self).__init__() + + self.config = model_parallel_config + self.pre_process = pre_process + self.post_process = post_process + self.vision_encoder = CLIPVisionTransformer( + model_cfg.vision, model_parallel_config, pre_process=self.pre_process, post_process=self.post_process, + ) + self.text_encoder = CLIPTextTransformer( + model_cfg.text, + model_parallel_config, + padded_vocab_size, + pre_process=self.pre_process, + post_process=self.post_process, + ) + + self.logit_scale = torch.nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def set_input_tensor(self, input_tensor): + """See megatron.model.transformer.set_input_tensor()""" + # TODO (yuya): fix this + pass + + def forward(self, images, captions): + image_features = self.vision_encoder(images) + text_features = self.text_encoder(captions) + + if self.post_process: + return F.normalize(image_features, dim=-1), F.normalize(text_features, dim=-1), self.logit_scale.exp() + + return image_features, text_features + + +class MegatronCLIPModel(MegatronBaseModel): + """Megatron CLIP Model.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer): + if not HAVE_APEX: + raise ImportError( + "Apex was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + if not HAVE_MEGATRON_CORE: + raise ImportError( + "megatron-core was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + + # this prevents base constructor from initializing tokenizer + self.tokenizer = None + self.imagenet_val = None + super().__init__(cfg, trainer=trainer) + + self._validate_trainer() + + self.megatron_amp_O2 = cfg.get('megatron_amp_O2', False) + + if not self.megatron_amp_O2 and self.cfg.get('virtual_pipeline_model_parallel_size', None): + raise ValueError('Virtual pipeline model parallel is only supported when using megatron_amp_O2') + + # build_model returns a list of modules which are used for interleaved pipeline parallelism + if isinstance(self.trainer.accelerator, CPUAccelerator): + self.model = build_model( + model_provider_func=self.model_provider_func, + wrap_with_ddp=False, + on_cpu=True, + virtual_pipeline_model_parallel_size=self.cfg.get('virtual_pipeline_model_parallel_size', None), + ) + else: + self.model = build_model( + model_provider_func=self.model_provider_func, + wrap_with_ddp=False, + virtual_pipeline_model_parallel_size=self.cfg.get('virtual_pipeline_model_parallel_size', None), + ) + + # if we're not using interleaved, then self.model is a module. + if self.cfg.get('virtual_pipeline_model_parallel_size', None) is None: + self.model = self.model[0] + + if self.megatron_amp_O2: + + if not self.with_distributed_adam: + # Pre-allocate the model on GPU to have master parameters allocated on the same device with matching data type + if isinstance(self.model, list): + for module in self.model: + module.cuda(torch.cuda.current_device()) + else: + self.model.cuda(torch.cuda.current_device()) + + # Model wrapper to convert both model and inputs to half precision + # TODO (yuya): check this; FP16 Module might not work; when self.model is a list? + if isinstance(self.model, list): + converted_model = [] + for module in self.model: + converted_model.append( + Float16Module(config=self.model_parallel_config, module=module, precision=cfg.precision) + ) + self.model = converted_model + else: + self.model = Float16Module( + config=self.model_parallel_config, module=self.model, precision=cfg.precision + ) + + self.autocast_dtype = torch_dtype_from_precision(self.trainer.precision) + self.enable_autocast = ( + True if (not self.megatron_amp_O2) and (self.autocast_dtype in [torch.float16, torch.bfloat16]) else False + ) + + self.transformer_engine = cfg.get('transformer_engine', False) + + # Convert the global-batch-based profile index to micro-batch index + if hasattr(self, '_nsys_profile_enabled'): + mp_size = cfg.get('tensor_model_parallel_size', 1) * cfg.get('pipeline_model_parallel_size', 1) + data_parallel_world_size = trainer.world_size // mp_size + grad_accum_steps = cfg.get('global_batch_size') // (cfg.get('micro_batch_size') * data_parallel_world_size) + self._nsys_profile_start_step *= grad_accum_steps + self._nsys_profile_end_step *= grad_accum_steps + self.get_attention_mask_from_fusion = self.cfg.get('get_attention_mask_from_fusion', True) + self.initialize_ub = self.cfg.get('ub_tp_comm_overlap', False) + + def get_module_list(self): + if isinstance(self.model, list): + return [model.module if isinstance(model, Float16Module) else model for model in self.model] + elif isinstance(self.model, Float16Module): + return [self.model.module] + else: + return [self.model] + + def model_provider_func(self, pre_process, post_process): + """Model depends on pipeline paralellism.""" + model = CLIPModel( + model_cfg=self.cfg, + model_parallel_config=self.model_parallel_config, + padded_vocab_size=self.padded_vocab_size, + pre_process=pre_process, + post_process=post_process, + ) + return model + + def setup_optimizer_param_groups(self): + """ModelPT override. Optimizer will get self._optimizer_param_groups""" + if self.cfg.get('do_layer_norm_weight_decay', False): + if isinstance(self.model, list): + self._optimizer_param_groups = get_all_params_for_weight_decay_optimization(self.model) + else: + self._optimizer_param_groups = get_all_params_for_weight_decay_optimization([self.model]) + + else: + self._optimizer_param_groups = get_params_for_weight_decay_optimization(self.model) + + def configure_optimizers(self): + + if self.with_distributed_adam: + + # Disable overlapped grad sync for layer norm grads when + # sequence parallelism is enabled + for param in self.parameters(): + if getattr(param, 'sequence_parallel', False): + param._disable_greedy_grad_copy = not self.megatron_amp_O2 + param._disable_overlap_grad_sync = True + + # Initialize parameter buckets for overlapped grad and param syncs + # Note: Params with disabled overlapping are put in the + # last param bucket + buckets = [] + if self.cfg.get('virtual_pipeline_model_parallel_size', None) is not None: + # Initialize a bucket for each virtual pipeline stage + for module in self.model: + if isinstance(module, Float16Module): + module = module.module + stage_bucket = [] + for layer in itertools.chain( + module.vision_encoder.backbone.transformer.layers, + module.text_encoder.language_model.encoder.layers, + ): + stage_bucket.extend( + p for p in layer.parameters() if not getattr(p, '_disable_overlap_grad_sync', False) + ) + buckets.append(stage_bucket) + else: + # Initialize a bucket for each Transformer layer + modules = self.model if isinstance(self.model, list) else [self.model] + for module in modules: + if isinstance(module, Float16Module): + module = module.module + for layer in itertools.chain( + module.vision_encoder.backbone.transformer.layers, + module.text_encoder.language_model.encoder.layers, + ): + buckets.append( + [p for p in layer.parameters() if not getattr(p, '_disable_overlap_grad_sync', False)] + ) + buckets.reverse() + used_params = set() + for bucket in buckets: + used_params.update(bucket) + buckets[-1].extend(p for p in self.parameters() if p not in used_params) + self.distributed_adam_buckets = buckets + + return super().configure_optimizers() + + def forward(self, image, text): + output_tensor = self.model(image, text) + return output_tensor + + def fwd_bwd_step(self, dataloader_iter, forward_only): + + # handle asynchronous grad reduction + no_sync_func = None + grad_sync_func = None + param_sync_func = None + if not forward_only and self.with_distributed_adam: + no_sync_func = partial(self._optimizer.no_sync, greedy_grad_copy=self.megatron_amp_O2,) + grad_sync_func = self.reduce_overlap_gradients + param_sync_func = self.sync_overlap_parameters + + # pipeline schedules will get these from self.model.config + for module in self.get_module_list(): + module.config.no_sync_func = no_sync_func + module.config.grad_sync_func = grad_sync_func + module.config.param_sync_func = param_sync_func + + # run forward and backwards passes for an entire global batch + # we do this inside training_step to support pipeline parallelism + fwd_bwd_function = get_forward_backward_func() + + # TODO @akhattar: add num_micro_batches_with_partial_activation_checkpoints when ready + losses_reduced_per_micro_batch = fwd_bwd_function( + forward_step_func=self.get_forward_output_and_loss_func(), + data_iterator=dataloader_iter, + model=self.model, + num_microbatches=get_num_microbatches(), + forward_only=forward_only, + seq_length=None, + micro_batch_size=self.cfg.micro_batch_size, + ) + + # only the last stages of the pipeline return losses + if losses_reduced_per_micro_batch: + if (not forward_only) or self.cfg.data.get('validation_drop_last', True): + # average loss across micro batches + loss_tensors_list = [loss_reduced['loss'] for loss_reduced in losses_reduced_per_micro_batch] + loss_tensor = torch.stack(loss_tensors_list) + loss_mean = loss_tensor.mean() + else: + # Get the total loss since micro batches sizes are not uniform + raise NotImplementedError("Losses of micro batches sizes must be uniform!") + else: + # we're not on the last pipeline stage so no losses + if forward_only: + loss_mean = [] + else: + loss_mean = torch.tensor(0.0).cuda() + + return loss_mean + + def initialize_ub_func(self): + ub_cfgs = self.cfg.get('ub_tp_comm_overlap_cfg', None) + if ub_cfgs is None: + warnings.warn( + "Couldn't find TP config. Please check the path correctness. Initializing TP comm overlap with the default config." + ) + + input_shape = [ + self.cfg.get('encoder_seq_length') * self.cfg.get('micro_batch_size'), + self.cfg.get('hidden_size'), + ] + + te_module.base.initialize_ub( + shape=input_shape, + tp_size=self.cfg.get('tensor_model_parallel_size'), + use_fp8=self.cfg.get('fp8'), + ub_cfgs=ub_cfgs, + ) + self.initialize_ub = False + + def training_step(self, dataloader_iter): + """ + Our dataloaders produce a micro-batch and then we fetch + a number of microbatches depending on the global batch size and model parallel size + from the dataloader to produce a list of microbatches. + Batch should be a list of microbatches and those microbatches should on CPU. + Microbatches are then moved to GPU during the pipeline. + The list of microbatches is then piped through the pipeline using Apex fwd/bwd functions. + """ + # Initialize userbuffer communicators. + if self.initialize_ub: + self.initialize_ub_func() + + # we zero grads here because we also call backward in the megatron-core fwd/bwd functions + self._optimizer.zero_grad() + + if self.with_distributed_adam: + # hack to enable overlapping param sync and forward compute + # note: the distributed optimizer monkey-patches each + # parameter's __getattribute__ function so that it can + # launch parameter all-gathers the first time the + # parameter is accessed after the optimizer step. However, + # PyTorch directly passes embedding parameters into a C++, + # bypassing this process. A quick-and-dirty hack is to + # manually interact with the parameter. + modules = self.model if isinstance(self.model, list) else [self.model] + for module in modules: + if isinstance(module, Float16Module): + module = module.module + module = module.text_encoder.language_model + if hasattr(module, 'embedding'): + for param in module.embedding.parameters(): + param.data_ptr() + + loss_mean = self.fwd_bwd_step(dataloader_iter, False) + + # when using sequence parallelism, the sequence parallel layernorm grads must be all-reduced + if self.cfg.get('tensor_model_parallel_size', 1) > 1 and self.cfg.get('sequence_parallel', False): + self.allreduce_sequence_parallel_gradients() + + if self.with_distributed_adam: + # synchronize asynchronous grad reductions + # note: not necessary, but reduces performance degradation + # from multiple simultaneous NCCL calls + self._optimizer._finish_bucket_grad_sync() + elif self.megatron_amp_O2: + # when using pipeline parallelism grads must be all-reduced after the pipeline (not asynchronously) + # if self.cfg.get('pipeline_model_parallel_size', 1) > 1 or self.cfg.get('sequence_parallel', False): + # # main grads are stored in the MainParamsOptimizer wrapper + self._optimizer.allreduce_main_grads() + else: + # async grad allreduce is not currently implemented for O1/autocasting mixed precision training + # so we all-reduce gradients after the pipeline + self.allreduce_gradients() # @sangkug we think this is causing memory to blow up (hurts perf) + + ## logging + # we can only log on one rank if it is rank zero so we broadcast from last rank + # we can avoid this broadcast by updating the PTL log function to accept specific ranks + torch.distributed.broadcast(loss_mean, get_last_rank()) + + if self.cfg.precision in [16, '16', '16-mixed']: + loss_scale = self.trainer.precision_plugin.scaler._scale + if loss_scale is not None: + self.log('loss_scale', loss_scale, batch_size=1) + + self.log('reduced_train_loss', loss_mean, prog_bar=True, rank_zero_only=True, batch_size=1) + lr = self._optimizer.param_groups[0]['lr'] + self.log('lr', lr, rank_zero_only=True, batch_size=1) + self.log('global_step', self.trainer.global_step + 1, prog_bar=True, rank_zero_only=True, batch_size=1) + self.log( + 'consumed_samples', + self.compute_consumed_samples(self.trainer.global_step + 1 - self.init_global_step), + prog_bar=True, + rank_zero_only=True, + batch_size=1, + ) + + return loss_mean + + def backward(self, *args, **kwargs): + """ LightningModule hook to do backward. + We want this to do nothing since we run backward in the fwd/bwd functions from apex. + No need to call it here. + """ + pass + + def optimizer_zero_grad(self, *args, **kwargs): + """ LightningModule hook to zero grad. + We want this to do nothing as we are zeroing grads during the training_step. + """ + pass + + def _append_sequence_parallel_module_grads(self, module, grads): + """ Helper method for allreduce_sequence_parallel_gradients""" + + for param in module.parameters(): + sequence_parallel_param = getattr(param, 'sequence_parallel', False) + if sequence_parallel_param and param.requires_grad: + if self.megatron_amp_O2: + grad = param.main_grad + else: + grad = param.grad + grads.append(grad.data) + + def allreduce_sequence_parallel_gradients(self): + """ All-reduce layernorm parameters across model parallel nodes when sequence parallelism is used. + Modified from megatron-lm: + https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/blob/3f91f09bb2ab32f9904b47f46f19d2fc3f518ed8/megatron/training.py#L425 + """ + + grads = [] + if isinstance(self.model, list): + for module in self.model: + self._append_sequence_parallel_module_grads(module, grads) + else: + self._append_sequence_parallel_module_grads(self.model, grads) + + coalesced = torch._utils._flatten_dense_tensors(grads) + torch.distributed.all_reduce(coalesced, group=parallel_state.get_tensor_model_parallel_group()) + for buf, synced in zip(grads, torch._utils._unflatten_dense_tensors(coalesced, grads)): + buf.copy_(synced) + + def get_forward_output_and_loss_func(self): + loss_func = ClipLoss(local_loss=self.cfg.local_loss, gather_with_grad=self.cfg.gather_with_grad,) + + def fwd_output_and_loss_func(dataloader_iter, model): + batch, _, _ = next(dataloader_iter) + if parallel_state.get_pipeline_model_parallel_world_size() == 1: + images = batch["images"].cuda(non_blocking=True) + captions = batch["captions"].cuda(non_blocking=True) + else: + # GPT3 uses only causal mask, which doesn't need attention mask + if parallel_state.is_pipeline_first_stage(): + # Fist pipeline stage needs only the tokens and position_ids + images = batch["images"].cuda(non_blocking=True) + captions = batch["captions"].cuda(non_blocking=True) + else: + # Intermediate / Last pipeline stage doesn't need any inputs + images, captions = None, None + + output_tensor = model(images, captions) + return output_tensor, loss_func + + return fwd_output_and_loss_func + + def get_forward_output_only_func(self): + def fwd_output_only_func(batch, model): + raise NotImplementedError + + return fwd_output_only_func + + def zero_shot_classifier(self): + if self.cfg.get("megatron_amp_O2", False): + text_encoder = self.model.module.text_encoder + else: + text_encoder = self.model.text_encoder + + with torch.no_grad(): + zeroshot_weights = [] + for texts in self.imagenet_val["texts"]: + texts = texts.cuda(non_blocking=True) + # TODO (yuya): distributed not working + with torch.cuda.amp.autocast( + enabled=self.autocast_dtype in (torch.half, torch.bfloat16), dtype=self.autocast_dtype, + ): + class_embeddings = text_encoder(texts) + class_embedding = F.normalize(class_embeddings, dim=-1).mean(dim=0) + class_embedding /= class_embedding.norm() + zeroshot_weights.append(class_embedding) + zeroshot_weights = torch.stack(zeroshot_weights, dim=1) + return zeroshot_weights + + def zero_shot_eval(self): + def accuracy(output, target, topk=(1,)): + pred = output.topk(max(topk), 1, True, True)[1].t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + return [float(correct[:k].reshape(-1).float().sum(0, keepdim=True).cpu().numpy()) for k in topk] + + logging.info('Starting zero-shot imagenet.') + + logging.info('Building zero-shot classifier') + classifier = self.zero_shot_classifier() + + logging.info('Using classifier') + + if self.cfg.get("megatron_amp_O2", False): + vision_encoder = self.model.module.vision_encoder + else: + vision_encoder = self.model.vision_encoder + with torch.no_grad(): + top1, top5, n = 0.0, 0.0, 0.0 + for images, target in tqdm(self.imagenet_val["images"], desc="Imagenet Zero-shot Evaluation", leave=False): + if images is None or target is None: + continue + + images = images.cuda(non_blocking=True).to(self.autocast_dtype) + target = target.cuda(non_blocking=True) + # predict + with torch.cuda.amp.autocast( + enabled=self.autocast_dtype in (torch.half, torch.bfloat16), dtype=self.autocast_dtype, + ): + image_features = vision_encoder(images) + image_features = F.normalize(image_features, dim=-1) + logits = 100.0 * image_features @ classifier + + # measure accuracy + acc1, acc5 = accuracy(logits, target, topk=(1, 5)) + top1 += acc1 + top5 += acc5 + n += images.size(0) + + logging.info('Finished zero-shot imagenet.') + top1 = top1 / n + top5 = top5 / n + return top1, top5 + + def validation_step(self, dataloader_iter): + """ + Our dataloaders produce a micro-batch and then we fetch + a number of microbatches depending on the global batch size and model parallel size + from the dataloader to produce a list of microbatches. + The list of microbatches is then piped through the pipeline using megatron-core fwd/bwd functions. """ + # Initialize userbuffer communicators. + if self.initialize_ub: + self.initialize_ub_func() + + loss = self.fwd_bwd_step(dataloader_iter, True) + self.validation_step_outputs.append(loss) + + return loss + + def on_validation_epoch_end(self): + # TODO (yuya): need fix later, check with Sean + if not self.validation_step_outputs: + return + + # Run zero shot imagenet evaluation + if self.imagenet_val is not None: + imagenet_metric = torch.zeros(2).cuda() + imagenet_metric[0], imagenet_metric[1] = self.zero_shot_eval() + imagenet_metric = average_losses_across_data_parallel_group(imagenet_metric) + self.log('imagenet_top1', imagenet_metric[0], prog_bar=True, rank_zero_only=True, batch_size=1) + self.log('imagenet_top5', imagenet_metric[1], prog_bar=True, rank_zero_only=True, batch_size=1) + + if parallel_state.is_pipeline_last_stage(): + averaged_metrics = torch.tensor( + [torch.stack(self.validation_step_outputs).mean()], dtype=torch.float32, device='cuda' + ) + else: + averaged_metrics = torch.tensor([0.0], dtype=torch.float32, device='cuda') + + # we can only log on one rank if it is rank zero so we broadcast from last rank + torch.distributed.broadcast(averaged_metrics, get_last_rank()) + averaged_loss = averaged_metrics + + self.log('global_step', self.trainer.global_step, prog_bar=True, rank_zero_only=True, batch_size=1) + self.log('val_loss', averaged_loss, prog_bar=True, rank_zero_only=True, batch_size=1) + self.validation_step_outputs.clear() # free memory + + return averaged_loss + + def test_step(self, batch, batch_idx): + return self.validation_step(batch) + + def test_epoch_end(self, outputs): + averaged_loss = average_losses_across_data_parallel_group(outputs) + logging.info(f'test_loss: {averaged_loss[0]}') + + def build_train_valid_test_datasets(self): + logging.info('Building datasets for CLIP...') + if self.trainer.limit_val_batches > 1.0 and isinstance(self.trainer.limit_val_batches, float): + raise ValueError("limit_val_batches must be an integer or float less than or equal to 1.0.") + + self._train_ds, self._validation_ds = build_train_valid_datasets( + model_cfg=self.cfg, consumed_samples=self.compute_consumed_samples(0), tokenizer=self.tokenizer, + ) + self._test_ds = None + + if self._train_ds is not None: + logging.info(f'Length of train dataset: {len(self._train_ds)}') + if self._validation_ds is not None: + logging.info(f'Length of val dataset: {len(self._validation_ds)}') + if self._test_ds is not None: + logging.info(f'Length of test dataset: {len(self._test_ds)}') + logging.info(f'Finished building datasets for CLIP.') + + return self._train_ds, self._validation_ds, self._test_ds + + def setup(self, stage=None): + """ PTL hook that is executed after DDP spawns. + We setup datasets here as megatron datasets require DDP to instantiate. + See https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#setup for more information. + Args: + stage (str, optional): Can be 'fit', 'validate', 'test' or 'predict'. Defaults to None. + """ + + # log number of parameters + if isinstance(self.model, list): + num_parameters_on_device = sum( + [sum([p.nelement() for p in model_module.parameters()]) for model_module in self.model] + ) + else: + num_parameters_on_device = sum([p.nelement() for p in self.model.parameters()]) + + # to be summed across data parallel group + total_num_parameters = torch.tensor(num_parameters_on_device).cuda() + + torch.distributed.all_reduce(total_num_parameters, group=parallel_state.get_model_parallel_group()) + + logging.info( + f'Pipeline model parallel rank: {parallel_state.get_pipeline_model_parallel_rank()}, ' + f'Tensor model parallel rank: {parallel_state.get_tensor_model_parallel_rank()}, ' + f'Number of model parameters on device: {num_parameters_on_device:.2e}. ' + f'Total number of model parameters: {total_num_parameters:.2e}.' + ) + + resume_checkpoint_path = self.trainer.ckpt_path + if resume_checkpoint_path: + init_consumed_samples = self._extract_consumed_samples_from_ckpt(resume_checkpoint_path) + else: + init_consumed_samples = 0 + self.init_consumed_samples = init_consumed_samples + self.init_global_step = self.trainer.global_step + + # allowing restored models to optionally setup datasets + self.build_train_valid_test_datasets() + + # Batch size need to be provided for webdatset + self._num_micro_batches = get_num_microbatches() + self._micro_batch_size = self.cfg.micro_batch_size + + self.setup_training_data(self.cfg.data) + self.setup_validation_data(self.cfg.data) + self.setup_test_data(self.cfg.data) + + if self.cfg.data.get("imagenet_val") is not None: + self.imagenet_val = build_imagenet_validation_dataloader(self.cfg, self.tokenizer) + + # when using pipeline model parallel the final stage need to initialize word embeddings + if parallel_state.get_pipeline_model_parallel_world_size() > 1: + if isinstance(self.model, list): + for i, module in enumerate(self.model): + parallel_state.set_virtual_pipeline_model_parallel_rank(i) + parallel_state.set_virtual_pipeline_model_parallel_rank(0) + + def setup_training_data(self, cfg): + if hasattr(self, '_train_ds') and self._train_ds is not None: + consumed_samples = self.compute_consumed_samples(0) + logging.info( + f'Setting up train dataloader with len(len(self._train_ds)): {len(self._train_ds)} and consumed samples: {consumed_samples}' + ) + self._train_dl = torch.utils.data.DataLoader( + self._train_ds, + batch_size=self._micro_batch_size, + num_workers=cfg.num_workers, + pin_memory=True, + drop_last=cfg.train.get("drop_last", True), + persistent_workers=True if cfg.num_workers > 0 else False, + ) + + def setup_validation_data(self, cfg): + if hasattr(self, '_validation_ds') and self._validation_ds is not None: + consumed_samples = 0 + logging.info( + f'Setting up validation dataloader with len(len(self._validation_ds)): {len(self._validation_ds)} and consumed samples: {consumed_samples}' + ) + self._validation_dl = torch.utils.data.DataLoader( + self._validation_ds, + batch_size=self._micro_batch_size, + num_workers=cfg.num_workers, + pin_memory=True, + drop_last=cfg.train.get("drop_last", True), + persistent_workers=True if cfg.num_workers > 0 else False, + ) + + def setup_test_data(self, cfg): + if hasattr(self, '_test_ds') and self._test_ds is not None: + consumed_samples = 0 + logging.info( + f'Setting up test dataloader with len(len(self._test_ds)): {len(self._test_ds)} and consumed samples: {consumed_samples}' + ) + self._test_dl = torch.utils.data.DataLoader( + self._test_ds, batch_size=self._micro_batch_size, num_workers=cfg.num_workers, pin_memory=True, + ) + + def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: Optional[int] = None) -> Any: + raise NotImplementedError + + def transfer_batch_to_device(self, batch: Any, device: torch.device, dataloader_idx: int) -> Any: + """ PTL hook: https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#transfer-batch-to-device + When using pipeline parallelism, we need the global batch to remain on the CPU, + since the memory overhead will be too high when using a large number of microbatches. + Microbatches are transferred from CPU to GPU inside the pipeline. + """ + return batch + + def _validate_trainer(self): + """ Certain trainer configurations can break training. + Here we try to catch them and raise an error. + """ + if self.trainer.accumulate_grad_batches > 1: + raise ValueError( + f'Gradient accumulation is done within training_step. trainer.accumulate_grad_batches must equal 1' + ) + + @classmethod + def list_available_models(cls) -> Optional[PretrainedModelInfo]: + return None + + def on_save_checkpoint(self, checkpoint) -> None: + """LightningModule hook: + https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#on-save-checkpoint + """ + if isinstance(self.model, list): + for i in range(len(self.model)): + parallel_state.set_virtual_pipeline_model_parallel_rank(i) + checkpoint[f'model{i}'] = self.model[i].module.state_dict_for_save_checkpoint() + parallel_state.set_virtual_pipeline_model_parallel_rank(0) + + def on_load_checkpoint(self, checkpoint) -> None: + """LightningModule hook: + https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#on-load-checkpoint + """ + if isinstance(self.model, list): + for i in range(len(self.model)): + parallel_state.set_virtual_pipeline_model_parallel_rank(i) + self.model[i].module.load_state_dict(checkpoint[f'model{i}'], strict=True) + parallel_state.set_virtual_pipeline_model_parallel_rank(0) + + def parameters(self): + if isinstance(self.model, list): + return itertools.chain.from_iterable(module.parameters() for module in self.model) + else: + return self.model.parameters() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/vision_language_foundation/megatron_nsfw_clip_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/vision_language_foundation/megatron_nsfw_clip_models.py new file mode 100644 index 0000000..24c2bfc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/models/vision_language_foundation/megatron_nsfw_clip_models.py @@ -0,0 +1,391 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import functools +import itertools +from typing import List, Optional, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from apex.transformer.pipeline_parallel.utils import get_num_microbatches +from megatron.core import parallel_state +from megatron.core.pipeline_parallel.schedules import get_forward_backward_func +from omegaconf.dictconfig import DictConfig +from pytorch_lightning.accelerators import CPUAccelerator +from pytorch_lightning.trainer.trainer import Trainer + +from nemo.collections.multimodal.data.clip.clip_dataset import tokenize +from nemo.collections.multimodal.data.nsfw.nsfw_dataset import build_dataset +from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import ( + CLIPTextTransformer, + CLIPVisionTransformer, +) +from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel +from nemo.collections.nlp.modules.common.megatron.build_model import build_model +from nemo.collections.nlp.modules.common.megatron.module import Float16Module, MegatronModule +from nemo.collections.nlp.parts.utils_funcs import get_last_rank, torch_dtype_from_precision +from nemo.core.classes.common import PretrainedModelInfo +from nemo.utils import logging + + +class ContentFilteringModel(MegatronModule): + """Clip based content filtering model for NSFW.""" + + def __init__(self, model_cfg: DictConfig, model_parallel_config, padded_vocab_size: int, tokenizer: Optional): + super(ContentFilteringModel, self).__init__() + self.cfg = model_cfg + self.config = model_parallel_config + self.tokenizer = tokenizer + + self.concept_list = self._load_concept_list(model_cfg.concepts) + self.concept_count = len(self.concept_list) + + self.vision_encoder = CLIPVisionTransformer( + model_cfg.vision, model_parallel_config, pre_process=True, post_process=True + ) + + if "text" in model_cfg and model_cfg.text is not None: + self.text_encoder = CLIPTextTransformer( + model_cfg.text, model_parallel_config, padded_vocab_size, pre_process=True, post_process=True + ) + else: + self.text_encoder = None + + self.mlp_similarity_model = nn.Sequential( + nn.Linear(model_cfg.output_dim * 2, model_cfg.sim_hidden_dim), + nn.ReLU(), + nn.Linear(model_cfg.sim_hidden_dim, 1), + ) + + self.nn_classifier = nn.Sequential( + nn.Linear(self.concept_count * 2 + model_cfg.output_dim, model_cfg.cls_hidden_dim), + nn.ReLU(), + nn.Linear(model_cfg.cls_hidden_dim, 1), + ) + + self.register_buffer("concepts", torch.zeros(self.concept_count, model_cfg.output_dim)) + + def initialize_concept_embeddings(self, concepts: torch.Tensor): + if self.text_encoder is None: + return + + self.concepts.copy_(concepts.detach()) + del self.text_encoder + self.text_encoder = None + + def forward(self, image: torch.Tensor, mlp_factor: float = 1.0, emb_factor: float = 1.0) -> torch.Tensor: + """Perform model forward pass for given image and factor. + While inferencing, factors should be equal to default value + """ + + with torch.no_grad(): + embedding = self.vision_encoder(image).detach() + cos_similarity = self.cosine_similarity(embedding, self.concepts) + mlp_similarity = self.mlp_similarity(embedding, self.concepts) + + features = torch.cat([cos_similarity, mlp_similarity * mlp_factor, embedding * emb_factor], dim=-1) + + return self.nn_classifier(features) + + def cosine_similarity(self, prediction: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + """Compute cosine similarity between prediction tensor and target tensor + Args: + prediction: Tensor of shape [X, H] for prediction embedding + target: Tensor of shape [Y, H] for target to compare + Returns: + Similarity matrix of shape [X, Y] and value range [-1, 1] + """ + normalized_prediction = F.normalize(prediction) + normalized_target = F.normalize(target) + + return torch.matmul(normalized_prediction, normalized_target.t()) + + def mlp_similarity(self, prediction: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + """Compute mlp based similarity between prediction tensor and target tensor + Args: + prediction: Tensor of shape [X, H] for prediction embedding + target: Tensor of shape [Y, H] for target to compare + Returns: + Similarity matrix of shape [X, Y] and value range [-1, 1] + """ + + prediction, target = torch.broadcast_tensors(prediction.unsqueeze(1), target.unsqueeze(0)) + + combined = torch.cat([prediction, target], dim=-1) + + return torch.tanh(self.mlp_similarity_model(combined).squeeze(-1)) + + def set_input_tensor(self, input_tensor: torch.Tensor): + pass + + def _load_concept_list(self, config: Union[str, List[str]]) -> List[str]: + if isinstance(config, str): + config = [config] + + result_list = [] + for concept_file in config: + with open(concept_file, "r") as f: + result_list += [x.strip() for x in f.readlines() if x.strip() != ""] + + return result_list + + +class MegatronContentFilteringModel(MegatronBaseModel): + def __init__(self, cfg: DictConfig, trainer: Trainer): + super(MegatronContentFilteringModel, self).__init__(cfg, trainer) + + self.model = build_model( + model_provider_func=self.model_provider_func, + wrap_with_ddp=False, + on_cpu=isinstance(self.trainer.accelerator, CPUAccelerator), + virtual_pipeline_model_parallel_size=None, + ) + self.model = self.model[0] + + self.megatron_amp_O2 = cfg.get("megatron_amp_O2", False) + if self.megatron_amp_O2: + if isinstance(self.model, list): + self.model = [ + Float16Module(config=self.model_parallel_config, module=x, precision=cfg.precision) + for x in self.model + ] + else: + self.model = Float16Module( + config=self.model_parallel_config, module=self.model, precision=cfg.precision + ) + + self.autocast_dtype = torch_dtype_from_precision(self.trainer.precision) + self.enable_autocast = (not self.megatron_amp_O2) and (self.autocast_dtype in [torch.float16, torch.bfloat16]) + + self.init_consumed_samples = 0 + self.mlp_factor = 1.0 + self.emb_factor = 1.0 + + self.validation_metrics = None + + def get_module_list(self): + if isinstance(self.model, Float16Module): + return [self.model.module] + else: + return [self.model] + + def model_provider_func(self, pre_process, post_process): + return ContentFilteringModel(self.cfg, self.model_parallel_config, self.padded_vocab_size, self.tokenizer) + + def forward(self, image: torch.Tensor, mlp_factor: float = 1.0, emb_factor: float = 1.0) -> torch.Tensor: + return self.model(image, mlp_factor, emb_factor) + + def get_forward_output_and_loss_func(self, with_accuracy: bool = False): + def loss_fn(prediction: torch.Tensor, target: torch.Tensor): + loss = F.binary_cross_entropy_with_logits(prediction, target) + out_dict = {"loss": loss} + + if with_accuracy: + accuracy_components = torch.stack( + [ + ((prediction > 0) & (target == 1.0)).sum(), # tp + ((prediction < 0) & (target == 0.0)).sum(), # tn + ((prediction > 0) & (target == 0.0)).sum(), # fp + ((prediction < 0) & (target == 1.0)).sum(), # fn + ] + ) + out_dict["accuracy"] = accuracy_components + + return loss, out_dict + + def forward_step(dataloader_iter, model): + images, labels = next(dataloader_iter) + + if ( + parallel_state.get_pipeline_model_parallel_world_size() == 1 + or parallel_state.is_pipeline_first_stage() + ): + images = images.cuda(non_blocking=True) + labels = labels.cuda(non_blocking=True) + else: + images, labels = None, None + + classification = model(images, mlp_factor=self.mlp_factor, emb_factor=self.emb_factor) + + return classification.squeeze(-1), functools.partial(loss_fn, target=labels.float()) + + return forward_step + + def get_forward_embedding_func(self): + def forward_step(dataloader_iter, model): + concepts = next(dataloader_iter) + concepts = tokenize(concepts, self.tokenizer, self.cfg.text.max_position_embeddings) + return (model.text_encoder(concepts.cuda(non_blocking=True)), lambda x: (0.0, {"concepts": x})) + + return forward_step + + def fwd_bwd_step(self, dataloader_iter, batch_idx: int, forward_only: bool): + fwd_bwd_function = get_forward_backward_func() + losses_reduced_per_micro_batch = fwd_bwd_function( + forward_step_func=self.get_forward_output_and_loss_func(with_accuracy=forward_only), + data_iterator=dataloader_iter, + model=self.model, + num_microbatches=get_num_microbatches(), + forward_only=forward_only, + seq_length=None, + micro_batch_size=self.cfg.micro_batch_size, + ) + + metrics = None + if losses_reduced_per_micro_batch: + loss_mean = torch.stack([l["loss"] for l in losses_reduced_per_micro_batch]).mean() + if forward_only: + metrics = torch.stack([l["accuracy"] for l in losses_reduced_per_micro_batch]).sum(dim=0) + else: + loss_mean = 0.0 + + return loss_mean, metrics + + def training_step(self, dataloader_iter, batch_idx): + self._optimizer.zero_grad() + + loss_mean, _ = self.fwd_bwd_step(dataloader_iter, batch_idx, forward_only=False) + + if self.megatron_amp_O2: + self._optimizer.allreduce_main_grads() + else: + self.allreduce_gradients() + + torch.distributed.broadcast(loss_mean, get_last_rank()) + if self.cfg.precision == 16: + loss_scale = self.trainer.precision_plugin.scaler._scale + if loss_scale is not None: + self.log("loss_scale", loss_scale, batch_size=1, prog_bar=True) + + self.log('reduced_train_loss', loss_mean, prog_bar=True, rank_zero_only=True, batch_size=1) + lr = self._optimizer.param_groups[0]['lr'] + self.log('lr', lr, rank_zero_only=True, batch_size=1, prog_bar=True) + self.log('global_step', self.trainer.global_step + 1, prog_bar=True, rank_zero_only=True, batch_size=1) + self.log( + 'consumed_samples', + self.compute_consumed_samples(self.trainer.global_step + 1 - self.init_global_step), + prog_bar=True, + rank_zero_only=True, + batch_size=1, + ) + + return loss_mean + + def validation_step(self, dataloader_iter, batch_idx): + loss, metrics = self.fwd_bwd_step(dataloader_iter, batch_idx, forward_only=True) + if self.validation_metrics is None: + self.validation_metrics = metrics + else: + self.validation_metrics += metrics + + self.validation_step_outputs.append(loss) + return loss + + def on_validation_epoch_end(self): + torch.distributed.all_reduce(self.validation_metrics, op=torch.distributed.ReduceOp.SUM) + accuracy = (self.validation_metrics[0] + self.validation_metrics[1]) / self.validation_metrics.sum() + self.validation_metrics = None + + averaged_metrics = 0 + if parallel_state.is_pipeline_last_stage(): + averaged_metrics = torch.stack(self.validation_step_outputs).mean() + torch.distributed.broadcast(averaged_metrics, get_last_rank()) + self.log("val_loss", averaged_metrics, prog_bar=True, rank_zero_only=True, batch_size=1) + self.log("accuracy", accuracy, prog_bar=True, rank_zero_only=True, batch_size=1) + + logging.info(f"Current evaluation accuracy: {accuracy}") + + return averaged_metrics + + def test_step(self, dataloader_iter, batch_idx): + return self.validation_step(dataloader_iter, batch_idx) + + def backward(self, *args, **kwargs): + pass + + def optimizer_zero_grad(self, *args, **kwargs): + pass + + def on_fit_start(self): + if self.model.text_encoder is not None: + fwd_bwd_function = get_forward_backward_func() + losses_reduced_per_micro_batch = fwd_bwd_function( + forward_step_func=self.get_forward_embedding_func(), + data_iterator=iter([self.model.concept_list]), + model=self.model, + num_microbatches=get_num_microbatches(), + forward_only=True, + seq_length=None, + micro_batch_size=self.model.concept_count, + ) + + concepts = torch.cat([x["concepts"] for x in losses_reduced_per_micro_batch], dim=0) + self.model.initialize_concept_embeddings(concepts) + self._cfg["text"] = None + + def setup(self, stage): + resume_checkpoint_path = self.trainer.ckpt_path + self.init_consumed_samples = ( + self._extract_consumed_samples_from_ckpt(resume_checkpoint_path) if resume_checkpoint_path else 0 + ) + self.setup_training_data(self.cfg) + self.setup_validation_data(self.cfg) + + def setup_training_data(self, cfg: DictConfig) -> None: + logging.info("Setting up training dataset.") + train_ds = build_dataset(cfg, self.compute_consumed_samples(0), is_train=True) + + sampler = torch.utils.data.distributed.DistributedSampler( + train_ds, num_replicas=self.trainer.world_size, rank=self.trainer.global_rank, shuffle=True + ) + + self._train_dl = torch.utils.data.DataLoader( + train_ds, + sampler=sampler, + batch_size=cfg.micro_batch_size, + num_workers=cfg.data.num_workers, + pin_memory=True, + drop_last=cfg.data.train.get("drop_last", True), + persistent_workers=True if cfg.data.num_workers > 0 else False, + ) + + def setup_validation_data(self, cfg: DictConfig) -> None: + logging.info("Setting up validation dataset.") + val_ds = build_dataset(cfg, self.compute_consumed_samples(0), is_train=False) + + sampler = torch.utils.data.distributed.DistributedSampler( + val_ds, num_replicas=self.trainer.world_size, rank=self.trainer.global_rank, shuffle=True + ) + + self._validation_dl = torch.utils.data.DataLoader( + val_ds, + sampler=sampler, + batch_size=cfg.micro_batch_size, + num_workers=cfg.data.num_workers, + pin_memory=True, + drop_last=cfg.data.validation.get("drop_last", True), + persistent_workers=True if cfg.data.num_workers > 0 else False, + ) + + def parameters(self): + return itertools.chain(self.model.mlp_similarity_model.parameters(), self.model.nn_classifier.parameters()) + + def on_load_checkpoint(self, checkpoint) -> None: + if "model.concepts" in checkpoint["state_dict"]: + self.model.text_encoder = None + + @classmethod + def list_available_models(cls) -> Optional[PretrainedModelInfo]: + return None diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/__init__.py new file mode 100644 index 0000000..aee9513 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/__init__.py @@ -0,0 +1,24 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.package_info import __version__ + +# Set collection version equal to NeMo version. +__version = __version__ + +# Authorship. +__author__ = "NVIDIA Corporation" + +# Set collection name. +__description__ = "Speech Computer Vision collection" diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/__init__.py new file mode 100644 index 0000000..aee9513 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/__init__.py @@ -0,0 +1,24 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.package_info import __version__ + +# Set collection version equal to NeMo version. +__version = __version__ + +# Authorship. +__author__ = "NVIDIA Corporation" + +# Set collection name. +__description__ = "Speech Computer Vision collection" diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/attention.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/attention.py new file mode 100644 index 0000000..de301e0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/attention.py @@ -0,0 +1,317 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Adapted from: +https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/unet.py +""" +import math + +import numpy as np +import torch +import torch.nn as nn +from torch.cuda.amp import custom_bwd, custom_fwd + + +def count_flops_attn(model, _x, y): + """ + A counter for the `thop` package to count the operations in an + attention operation. + Meant to be used like: + macs, params = thop.profile( + model, + inputs=(inputs, timestamps), + custom_ops={QKVAttention: QKVAttention.count_flops}, + ) + """ + b, c, *spatial = y[0].shape + num_spatial = int(np.prod(spatial)) + # We perform two matmuls with the same number of ops. + # The first computes the weight matrix, the second computes + # the combination of the value vectors. + matmul_ops = 2 * b * (num_spatial ** 2) * c + model.total_ops += torch.DoubleTensor([matmul_ops]) + + +# Stable attention +class StableAttentionOp(torch.autograd.Function): + # This function defines the attention weight computation in a stable way + # The idea is to scale the gradients of weight matrix by the maximum absolute value. + # In case of overflow, this will prevent weight gradients from exploding. + # In case of underflow, since we clipped the scale to 1e-4, this will prevent underflow. + + @staticmethod + def forward(ctx, q, k): + w = torch.einsum('ncq,nck->nqk', q, k / math.sqrt(k.shape[1])).softmax(dim=2) + ctx.save_for_backward(q, k, w) + return w + + @staticmethod + def backward(ctx, dw): + q, k, w = ctx.saved_tensors + + s = dw.detach().norm(float('inf'), dim=[1, 2], keepdim=True).clip(min=1e-4) + dw = dw / s + + # Due to softmax, w is fp32, making db fp32. + # Type casting is required for amp to work. + db = torch._softmax_backward_data(grad_output=dw, output=w, dim=2, input_dtype=dw.dtype).to(q.dtype) + s = s / math.sqrt(k.shape[1]) + + dq = torch.einsum('nck,nqk->ncq', k, db) * s + dk = torch.einsum('ncq,nqk->nck', q, db) * s + + return dq, dk + + +class QKVStableAttention(nn.Module): + """ + A module which performs QKV attention and splits in a different order. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + + :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.chunk(3, dim=1) + + # Reshaping q and k + # try: + # q = q.view(bs * self.n_heads, ch, length) + # k = k.view(bs * self.n_heads, ch, length) + # except Exception: + q = q.reshape(bs * self.n_heads, ch, length) + k = k.reshape(bs * self.n_heads, ch, length) + + weight = StableAttentionOp.apply(q, k) + a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) + return a.reshape(bs, -1, length), weight + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class QKVAttention(nn.Module): + """ + A module which performs QKV attention and splits in a different order. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + + :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.chunk(3, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = torch.einsum( + "bct,bcs->bts", + (q * scale).view(bs * self.n_heads, ch, length), + (k * scale).view(bs * self.n_heads, ch, length), + ) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) + return a.reshape(bs, -1, length), weight + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class StableMaskedAttentionOp(torch.autograd.Function): + # Robust attention operation in case of masked attention + @staticmethod + @custom_fwd + def forward(ctx, q, k, mask): + max_neg_value = -float('inf') + w = torch.einsum('ncq,nck->nqk', q, k / math.sqrt(k.shape[1])) + w = w.masked_fill(mask, max_neg_value) + w = w.softmax(dim=2) + + # When we use an arbitrary mask, there is a possibility that we get nans in softmax. + # In this case, use nan_to_num to make it a stable number. + w = w.nan_to_num_() + ctx.save_for_backward(q, k, w, mask) + return w + + @staticmethod + @custom_bwd + def backward(ctx, dw): + q, k, w, mask = ctx.saved_tensors + max_neg_value = -torch.finfo(q.dtype).max + s = dw.detach().norm(float('inf'), dim=[1, 2], keepdim=True).clip(min=1e-4) + dw = dw / s + db = torch._softmax_backward_data(grad_output=dw, output=w, dim=2, input_dtype=dw.dtype) + + # Masking db + db_in = db.clone().masked_fill_(mask, 0) + + s = s / math.sqrt(k.shape[1]) + dq = torch.einsum('nck,nqk->ncq', k, db_in) * s + dk = torch.einsum('ncq,nqk->nck', q, db_in) * s + + # These are dummy derivatives since mask is a constant + dmask = (max_neg_value - w) * db.clone() * s + + return dq, dk, dmask + + +class QKVMaskedAttention(nn.Module): + """ + A module which performs QKV attention. + Attention mask is accepted as input. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, q, k, v, mask): + r""" + Apply QKV attention with attention mask. + + Args: + q: an [N x d x n_seq1] of queries. + k: an [N x d x n_seq2] of keys. + v: an [N x d x n_seq2] of values. + mask: Attention mask of size N x n_seq1 x n_seq2 + + Returns: an [N x d x n_seq1] tensor after attention. + """ + + bs, width, length_q = q.shape + _, _, length_k = k.shape + + assert width % self.n_heads == 0 + ch = width // self.n_heads + + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = torch.einsum( + "bct,bcs->bts", + (q * scale).view(bs * self.n_heads, ch, length_q), + (k * scale).view(bs * self.n_heads, ch, length_k), + ) # More stable with f16 than dividing afterwards + + # Duplicate mask n_heads times + mask = mask.repeat_interleave(self.n_heads, dim=0) + assert mask.shape == weight.shape + max_neg_value = -float('inf') + weight = weight.masked_fill(~mask, max_neg_value) + + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + + # When we use an arbitrary mask, there is a possibility that we get nans in softmax. + # In this case, use nan_to_num to make it a non-nan number. + weight = weight.nan_to_num_() + a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length_k)) + # We also return weight here for attention visualization. + return a.reshape(bs, -1, length_q), weight + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class QKVStableMaskedAttention(nn.Module): + """ + A module which performs QKV attention. + Attention mask is accepted as input. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, q, k, v, mask): + r""" + Apply QKV attention with attention mask. + + Args: + q: an [N x d x n_seq1] of queries. + k: an [N x d x n_seq2] of keys. + v: an [N x d x n_seq2] of values. + mask: Attention mask of size N x n_seq1 x n_seq2 + + Returns: an [N x d x n_seq1] tensor after attention. + """ + + bs, width, length_q = q.shape + _, _, length_k = k.shape + + assert width % self.n_heads == 0 + ch = width // self.n_heads + + q = q.view(bs * self.n_heads, ch, length_q) + k = k.view(bs * self.n_heads, ch, length_k) + + # Forming attention mask + mask = mask.repeat_interleave(self.n_heads, dim=0) + + weight = StableMaskedAttentionOp.apply(q, k, ~mask) + + a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length_k)) + # We also return weight here for attention visualization. + return a.reshape(bs, -1, length_q), weight + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class SelfAttentionPooling(nn.Module): + """ + Implementation of SelfAttentionPooling + Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition + https://arxiv.org/pdf/2008.01077v1.pdf + Taken from: https://gist.github.com/pohanchi/c77f6dbfbcbc21c5215acde4f62e4362 + """ + + def __init__(self, input_dim): + super(SelfAttentionPooling, self).__init__() + self.W = nn.Linear(input_dim, 1) + + def forward(self, batch_rep): + """ + input: + batch_rep : size (N, T, H), N: batch size, T: sequence length, H: Hidden dimension + + attention_weight: + att_w : size (N, T, 1) + + return: + utter_rep: size (N, H) + """ + softmax = nn.functional.softmax + att_w = softmax(self.W(batch_rep).squeeze(-1), dim=1).unsqueeze(-1) + utter_rep = torch.sum(batch_rep * att_w, dim=1) + + return utter_rep diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/attention_alt.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/attention_alt.py new file mode 100644 index 0000000..8927226 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/attention_alt.py @@ -0,0 +1,321 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Adapted from: +https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/unet.py +""" +import math + +import numpy as np +import torch +import torch.nn as nn +from torch.cuda.amp import custom_bwd, custom_fwd + +USE_ALT = False + + +def count_flops_attn(model, _x, y): + """ + A counter for the `thop` package to count the operations in an + attention operation. + Meant to be used like: + macs, params = thop.profile( + model, + inputs=(inputs, timestamps), + custom_ops={QKVAttention: QKVAttention.count_flops}, + ) + """ + b, c, *spatial = y[0].shape + num_spatial = int(np.prod(spatial)) + # We perform two matmuls with the same number of ops. + # The first computes the weight matrix, the second computes + # the combination of the value vectors. + matmul_ops = 2 * b * (num_spatial ** 2) * c + model.total_ops += torch.DoubleTensor([matmul_ops]) + + +# Stable attention +class StableAttentionOp(torch.autograd.Function): + # This function defines the attention weight computation in a stable way + # The idea is to scale the gradients of weight matrix by the maximum absolute value. + # In case of overflow, this will prevent weight gradients from exploding. + # In case of underflow, since we clipped the scale to 1e-4, this will prevent underflow. + + @staticmethod + def forward(ctx, q, k): + w = torch.einsum('ncq,nck->nqk', q, k / math.sqrt(k.shape[1])).softmax(dim=2) + ctx.save_for_backward(q, k, w) + return w + + @staticmethod + def backward(ctx, dw): + q, k, w = ctx.saved_tensors + + s = dw.detach().norm(float('inf'), dim=[1, 2], keepdim=True).clip(min=1e-4) + dw = dw / s + + # Due to softmax, w is fp32, making db fp32. + # Type casting is required for amp to work. + db = torch._softmax_backward_data(grad_output=dw, output=w, dim=2, input_dtype=dw.dtype).to(q.dtype) + s = s / math.sqrt(k.shape[1]) + + dq = torch.einsum('nck,nqk->ncq', k, db) * s + dk = torch.einsum('ncq,nqk->nck', q, db) * s + + return dq, dk + + +class QKVStableAttention(nn.Module): + """ + A module which performs QKV attention and splits in a different order. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + + :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.chunk(3, dim=1) + + # Reshaping q and k + # try: + # q = q.view(bs * self.n_heads, ch, length) + # k = k.view(bs * self.n_heads, ch, length) + # except Exception: + q = q.reshape(bs * self.n_heads, ch, length) + k = k.reshape(bs * self.n_heads, ch, length) + + weight = StableAttentionOp.apply(q, k) + a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) + return a.reshape(bs, -1, length), weight + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class QKVAttention(nn.Module): + """ + A module which performs QKV attention and splits in a different order. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + + :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.chunk(3, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = torch.einsum( + "bct,bcs->bts", + (q * scale).view(bs * self.n_heads, ch, length), + (k * scale).view(bs * self.n_heads, ch, length), + ) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) + return a.reshape(bs, -1, length), weight + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class StableMaskedAttentionOp(torch.autograd.Function): + # Robust attention operation in case of masked attention + @staticmethod + @custom_fwd + def forward(ctx, q, k, mask): + max_neg_value = -float('inf') + w = torch.einsum('ncq,nck->nqk', q, k / math.sqrt(k.shape[1])) + w = w.masked_fill(mask, max_neg_value) + w = w.softmax(dim=2) + + # When we use an arbitrary mask, there is a possibility that we get nans in softmax. + # In this case, use nan_to_num to make it a stable number. + # w = w.nan_to_num_() + ctx.save_for_backward(q, k, w, mask) + return w + + @staticmethod + @custom_bwd + def backward(ctx, dw): + q, k, w, mask = ctx.saved_tensors + max_neg_value = -torch.finfo(q.dtype).max + s = dw.detach().norm(float('inf'), dim=[1, 2], keepdim=True).clip(min=1e-4) + dw = dw / s + db = torch._softmax_backward_data(grad_output=dw, output=w, dim=2, input_dtype=dw.dtype) + + # Masking db + db_in = db.clone().masked_fill_(mask, 0) + + s = s / math.sqrt(k.shape[1]) + dq = torch.einsum('nck,nqk->ncq', k, db_in) * s + dk = torch.einsum('ncq,nqk->nck', q, db_in) * s + + # These are dummy derivatives since mask is a constant + dmask = (max_neg_value - w) * db.clone() * s + + return dq, dk, dmask + + +class QKVMaskedAttention(nn.Module): + """ + A module which performs QKV attention. + Attention mask is accepted as input. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, q, k, v, mask): + r""" + Apply QKV attention with attention mask. + + Args: + q: an [N x d x n_seq1] of queries. + k: an [N x d x n_seq2] of keys. + v: an [N x d x n_seq2] of values. + mask: Attention mask of size N x n_seq1 x n_seq2 + + Returns: an [N x d x n_seq1] tensor after attention. + """ + + bs, width, length_q = q.shape + _, _, length_k = k.shape + + assert width % self.n_heads == 0 + ch = width // self.n_heads + + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = torch.einsum( + "bct,bcs->bts", + (q * scale).view(bs * self.n_heads, ch, length_q), + (k * scale).view(bs * self.n_heads, ch, length_k), + ) # More stable with f16 than dividing afterwards + + # Duplicate mask n_heads times + # mask = mask.repeat_interleave(self.n_heads, dim=0) + mask = mask.unsqueeze(0).repeat(self.n_heads, 1, 1, 1).transpose(0, 1).flatten(0, 1) + assert mask.shape == weight.shape + max_neg_value = -float('inf') + weight = weight.masked_fill(~mask, max_neg_value) + + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + + # When we use an arbitrary mask, there is a possibility that we get nans in softmax. + # In this case, use nan_to_num to make it a non-nan number. + # weight = weight.nan_to_num_() + a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length_k)) + # We also return weight here for attention visualization. + return a.reshape(bs, -1, length_q), weight + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class QKVStableMaskedAttention(nn.Module): + """ + A module which performs QKV attention. + Attention mask is accepted as input. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, q, k, v, mask): + r""" + Apply QKV attention with attention mask. + + Args: + q: an [N x d x n_seq1] of queries. + k: an [N x d x n_seq2] of keys. + v: an [N x d x n_seq2] of values. + mask: Attention mask of size N x n_seq1 x n_seq2 + + Returns: an [N x d x n_seq1] tensor after attention. + """ + + bs, width, length_q = q.shape + _, _, length_k = k.shape + + assert width % self.n_heads == 0 + ch = width // self.n_heads + + q = q.view(bs * self.n_heads, ch, length_q) + k = k.view(bs * self.n_heads, ch, length_k) + + # Forming attention mask + # mask = mask.repeat_interleave(self.n_heads, dim=0) + mask = mask.unsqueeze(0).repeat(self.n_heads, 1, 1, 1).transpose(0, 1).flatten(0, 1) + + weight = StableMaskedAttentionOp.apply(q, k, ~mask) + + a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length_k)) + # We also return weight here for attention visualization. + return a.reshape(bs, -1, length_q), weight + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class SelfAttentionPooling(nn.Module): + """ + Implementation of SelfAttentionPooling + Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition + https://arxiv.org/pdf/2008.01077v1.pdf + Taken from: https://gist.github.com/pohanchi/c77f6dbfbcbc21c5215acde4f62e4362 + """ + + def __init__(self, input_dim): + super(SelfAttentionPooling, self).__init__() + self.W = nn.Linear(input_dim, 1) + + def forward(self, batch_rep): + """ + input: + batch_rep : size (N, T, H), N: batch size, T: sequence length, H: Hidden dimension + + attention_weight: + att_w : size (N, T, 1) + + return: + utter_rep: size (N, H) + """ + softmax = nn.functional.softmax + att_w = softmax(self.W(batch_rep).squeeze(-1), dim=1).unsqueeze(-1) + utter_rep = torch.sum(batch_rep * att_w, dim=1) + + return utter_rep diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/blocks.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/blocks.py new file mode 100644 index 0000000..1d6b839 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/blocks.py @@ -0,0 +1,906 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Adapted from: +https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/unet.py +""" +import math +from abc import abstractmethod + +import torch as th +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from einops import rearrange + +from nemo.collections.multimodal.modules.imagen.diffusionmodules import attention_alt + +if attention_alt.USE_ALT: + from nemo.collections.multimodal.modules.imagen.diffusionmodules.attention_alt import ( + QKVAttention, + QKVMaskedAttention, + QKVStableAttention, + QKVStableMaskedAttention, + ) +else: + from nemo.collections.multimodal.modules.imagen.diffusionmodules.attention import ( + QKVAttention, + QKVMaskedAttention, + QKVStableAttention, + QKVStableMaskedAttention, + ) +from nemo.collections.multimodal.modules.imagen.diffusionmodules.layers import ( + Downsample, + Upsample, + UpsampleLearnable, + conv_nd, + linear, + normalization, + zero_module, +) + + +def check_cuda(): + if not th.cuda.is_available(): + raise RuntimeError('CUDA is not available') + cur_device = th.cuda.current_device() + dprops = th.cuda.get_device_properties(cur_device) + + is_sm75 = dprops.major == 7 and dprops.minor == 5 + is_sm8x = dprops.major == 8 and dprops.minor >= 0 + is_sm90 = dprops.major == 9 and dprops.minor >= 0 + + return is_sm8x or is_sm75 or is_sm90 + + +try: + from flash_attn import flash_attn_varlen_func, flash_attn_varlen_kvpacked_func + + flash_attn_installed = check_cuda() +except ImportError: + flash_attn_installed = False + + +class TextConditionedBlock(nn.Module): + r""" + Any module where forward() takes text embeddings as arguments. + """ + + @abstractmethod + def forward(self, x, text_emb, text_mask): + """ + Apply the module to `x` given `text_emb` text embedding and 'text_mask' text valid mask. + """ + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class ConditionalSequential(nn.Sequential, TimestepBlock, TextConditionedBlock): + r""" + A sequential module that accepts timestep embeddings, text embedding and text mask in addition to the input x. + Depending on the type of block, we either pass timestep embedding or text embeddings as inputs. + """ + + def forward(self, x, emb, text_emb, text_mask): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, TextConditionedBlock): + x = layer(x, text_emb, text_mask) + else: + x = layer(x) + return x + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + learnable_upsampling=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + + self.in_layers = nn.Sequential( + normalization(channels), nn.SiLU(), conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + if learnable_upsampling: + upsample_fn = UpsampleLearnable + else: + upsample_fn = Upsample + + if up: + self.h_upd = upsample_fn(channels, False, dims) + self.x_upd = upsample_fn(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), linear(emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels,), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module(conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + if self.use_checkpoint: + return checkpoint.checkpoint(self._forward, x, emb) + else: + return self._forward(x, emb) + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = th.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class EfficientResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + Follow Figure A.27 in Imagen Paper. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + out_channels=None, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + skip_connection_scaling=False, + ): + super().__init__() + + out_channels = out_channels or channels + + self.use_scale_shift_norm = use_scale_shift_norm + self.use_checkpoint = use_checkpoint + + self.in_layers = nn.Sequential( + normalization(channels), nn.SiLU(), conv_nd(dims, channels, out_channels, 3, padding=1) + ) + + self.emb_layers = nn.Sequential( + nn.SiLU(), nn.Linear(emb_channels, 2 * out_channels if use_scale_shift_norm else out_channels,), + ) + + self.out_layers = nn.Sequential( + normalization(out_channels), + nn.SiLU(), + zero_module(conv_nd(dims, out_channels, out_channels, 3, padding=1)), + ) + + self.shortcut = conv_nd(dims, channels, out_channels, 1) + self.shortcut_scale = 1 / math.sqrt(2) if skip_connection_scaling else 1 + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + if self.use_checkpoint: + return checkpoint.checkpoint(self._forward, x, emb) + else: + return self._forward(x, emb) + + def _forward(self, x, emb): + h = self.in_layers(x) + emb_out = self.emb_layers(emb) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = th.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + + return h + self.shortcut(x) * self.shortcut_scale + + +class Block(nn.Module): + def __init__( + self, + channels, + emb_channels, + out_channels=None, + use_scale_shift_norm=True, + num_resblocks=2, + attention_type=None, + text_embed_dim=0, + stable_attention=True, + flash_attention=False, + num_head_channels=-1, + num_heads=8, + dims=2, + use_checkpoint=False, + skip_connection_scaling=False, + ): + super().__init__() + + out_channels = out_channels or channels + + self.attention_type = attention_type + self.text_embed_dim = text_embed_dim + + blocks = [ + EfficientResBlock( + channels, + emb_channels, + out_channels=out_channels, + use_scale_shift_norm=use_scale_shift_norm, + dims=dims, + use_checkpoint=use_checkpoint, + skip_connection_scaling=skip_connection_scaling, + ) + ] + + blocks += [ + EfficientResBlock( + out_channels, + emb_channels, + out_channels=out_channels, + use_scale_shift_norm=use_scale_shift_norm, + dims=dims, + use_checkpoint=use_checkpoint, + skip_connection_scaling=skip_connection_scaling, + ) + for _ in range(num_resblocks - 1) + ] + + self.blocks = nn.ModuleList(blocks) + + # Attention blocks + # Self - Self-attention blocks + # fused - Single attention layer for fusing self and cross attention. + if self.attention_type is not None: + assert self.attention_type in ('self', 'cross', 'fused', 'stacked') + attention_kwargs = dict() + + if self.attention_type == 'self': + attention_fn = SelfAttentionBlock + elif self.attention_type == 'cross': + attention_fn = CrossAttentionBlock + attention_kwargs['context_dim'] = self.text_embed_dim + elif self.attention_type == 'stacked': + attention_fn = StackedCrossAttentionBlock + attention_kwargs['context_dim'] = self.text_embed_dim + else: + attention_fn = FusedCrossAttentionBlock + attention_kwargs['context_dim'] = self.text_embed_dim + + self.attention_layer = attention_fn( + out_channels, + num_heads=num_heads, + num_head_channels=num_head_channels, + use_checkpoint=use_checkpoint, + stable_attention=stable_attention, + flash_attention=flash_attention, + **attention_kwargs, + ) + + @abstractmethod + def forward(self, x, emb, text_embed=None, text_mask=None): + pass + + +class DBlock(Block): + def __init__( + self, + channels, + emb_channels, + out_channels=None, + use_scale_shift_norm=True, + conv_down=True, + stride=2, + num_resblocks=2, + attention_type=None, + text_embed_dim=0, + stable_attention=True, + flash_attention=False, + num_head_channels=-1, + num_heads=8, + dims=2, + use_checkpoint=False, + skip_connection_scaling=False, + ): + super().__init__( + channels, + emb_channels, + out_channels=out_channels, + use_scale_shift_norm=use_scale_shift_norm, + num_resblocks=num_resblocks, + attention_type=attention_type, + text_embed_dim=text_embed_dim, + stable_attention=stable_attention, + flash_attention=flash_attention, + num_head_channels=num_head_channels, + num_heads=num_heads, + dims=dims, + use_checkpoint=use_checkpoint, + skip_connection_scaling=skip_connection_scaling, + ) + + self.conv_down = conv_down + if self.conv_down: + # self.conv = nn.Conv2d(channels, channels, 3, stride=stride, padding=1) + self.conv = nn.Conv2d(channels, channels, 4, stride=stride, padding=1) + + def forward(self, x, emb, text_embed=None, text_mask=None): + if self.conv_down: + x = self.conv(x) + + for block in self.blocks: + x = block(x, emb) + + if self.attention_type in ('cross', 'fused', 'stacked'): + x = self.attention_layer(x, text_embed, text_mask) + elif self.attention_type == 'self': + x = self.attention_layer(x) + + return x + + +class UBlock(Block): + def __init__( + self, + channels, + emb_channels, + out_channels=None, + use_scale_shift_norm=True, + conv_up=True, + stride=2, + num_resblocks=2, + attention_type=None, + text_embed_dim=0, + stable_attention=True, + flash_attention=False, + num_head_channels=-1, + num_heads=8, + dims=2, + use_checkpoint=False, + skip_connection_scaling=False, + ): + super().__init__( + channels, + emb_channels, + out_channels=out_channels, + use_scale_shift_norm=use_scale_shift_norm, + num_resblocks=num_resblocks, + attention_type=attention_type, + text_embed_dim=text_embed_dim, + stable_attention=stable_attention, + flash_attention=flash_attention, + num_head_channels=num_head_channels, + num_heads=num_heads, + dims=dims, + use_checkpoint=use_checkpoint, + skip_connection_scaling=skip_connection_scaling, + ) + + self.conv_up = conv_up + if self.conv_up: + self.conv = nn.ConvTranspose2d(out_channels, out_channels, 4, stride, 1) + + def forward(self, x, emb, text_embed=None, text_mask=None): + for block in self.blocks: + x = block(x, emb) + + if self.attention_type in ('cross', 'fused', 'stacked'): + x = self.attention_layer(x, text_embed, text_mask) + elif self.attention_type == 'self': + x = self.attention_layer(x) + + if self.conv_up: + x = self.conv(x) + + return x + + +class FusedCrossAttentionBlock(TextConditionedBlock): + """ + An attention block that fuses self-attention and cross-attention + in a single block. + """ + + def __init__( + self, + channels, + context_dim, + num_heads=1, + num_head_channels=-1, + use_checkpoint=False, + stable_attention=True, + flash_attention=False, + ): + super().__init__() + self.channels = channels + if num_head_channels == -1: + self.num_heads = num_heads + else: + assert ( + channels % num_head_channels == 0 + ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = channels // num_head_channels + self.use_checkpoint = use_checkpoint + self.flash_attention = flash_attention + self.norm = normalization(channels) + self.norm_context = normalization(context_dim) + self.norm_self = normalization(channels) + + # For image features + self.q = conv_nd(1, channels, channels, 1) + + # For context + self.kv_context = conv_nd(1, context_dim, channels * 2, 1) + + # For spatial + self.kv_self = conv_nd(1, channels, channels * 2, 1) + + if flash_attention: + assert flash_attn_installed, "FlashAttention is not installed." + assert not stable_attention, "FlashAttention doesn't support the stable form." + + elif stable_attention: + self.attention = QKVStableMaskedAttention(self.num_heads) + else: + self.attention = QKVMaskedAttention(self.num_heads) + + self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) + + def forward(self, x, context, mask): + if self.use_checkpoint: + return checkpoint.checkpoint(self._forward, x, context, mask) + else: + return self._forward(x, context, mask) + + def _forward(self, x, context, mask): + + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + + q = self.q(self.norm(x)) + + # Key-value pairs for self-attention + kv_self = self.kv_self(self.norm_self(x)) + k_self, v_self = kv_self.chunk(2, dim=1) + k_self = k_self.contiguous() + v_self = v_self.contiguous() + + # Key-value pairs for cross-attention + context = th.permute(context, (0, 2, 1)) + context_n = self.norm_context(context) + kv_context = self.kv_context(context_n) + k_context, v_context = kv_context.chunk(2, dim=1) + k_context = k_context.contiguous() + v_context = v_context.contiguous() + + # Appending key-value pairs + k_full = th.cat([k_self, k_context], dim=2) + v_full = th.cat([v_self, v_context], dim=2) + + if self.flash_attention: + # q: b (h d) s, k_context: b (h d) s + batch_size = q.shape[0] + max_seqlen_q, max_seqlen_k = q.shape[2], q.shape[2] + k_context.shape[2] + q = rearrange(q, 'b (h d) s -> (b s) h d', h=self.num_heads) + + mask_self = th.ones((batch_size, max_seqlen_q), device=q.device, dtype=th.bool) + mask_context = mask.bool() + mask_full = th.cat([mask_self, mask_context], dim=1) + + k_full_unpadded = k_full.transpose(1, 2)[mask_full] + total_k = k_full_unpadded.shape[0] + k_full_unpadded = k_full_unpadded.view(total_k, self.num_heads, -1) + + v_full_unpadded = v_full.transpose(1, 2)[mask_full] + v_full_unpadded = v_full_unpadded.view(total_k, self.num_heads, -1) + + # (b s) t h d + kv_full_unpadded = th.stack([k_full_unpadded, v_full_unpadded], dim=1) + + cu_seqlens_q = th.arange( + 0, (batch_size + 1) * max_seqlen_q, step=max_seqlen_q, dtype=th.int32, device=q.device + ) + cu_seqlens_k = th.zeros((batch_size + 1), dtype=th.int32, device=k_full.device) + cu_seqlens_k[1:] = th.cumsum(mask.sum(dim=1), dim=0) + cu_seqlens_k += cu_seqlens_q + + out = flash_attn_varlen_kvpacked_func( + q, kv_full_unpadded, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, 0.0 + ) + h = rearrange(out, '(b s) h d -> b (h d) s', b=batch_size, h=self.num_heads) + else: + # Computing mask for self attention + mask_self = th.ones(k_self.shape[0], q.shape[2], k_self.shape[2], device=mask.device) + + # Mask for cross attention + mask_context = mask.view(mask.shape[0], 1, mask.shape[1]) + mask_context = mask_context.repeat(1, q.shape[2], 1) + + # Fused mask + mask_full = th.cat([mask_self, mask_context], dim=2) + mask_full = mask_full.to(th.bool) + + h, _ = self.attention(q, k_full, v_full, mask_full) + + h = self.proj_out(h) + return (x + h).reshape(b, c, *spatial) + + +class SelfAttentionBlock(nn.Module): + """ + An attention block that allows spatial positions to attend to each other. + + Originally ported from here, but adapted to the N-d case. + https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. + """ + + def __init__( + self, + channels, + num_heads=1, + num_head_channels=-1, + use_checkpoint=False, + stable_attention=False, + flash_attention=False, + ): + super().__init__() + self.channels = channels + if num_head_channels == -1: + self.num_heads = num_heads + else: + assert ( + channels % num_head_channels == 0 + ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = channels // num_head_channels + self.use_checkpoint = use_checkpoint + self.norm = normalization(channels) + self.qkv = conv_nd(1, channels, channels * 3, 1) + self.flash_attention = flash_attention + if flash_attention: + assert flash_attn_installed, "FlashAttention is not installed." + assert not stable_attention, "FlashAttention doesn't support the stable form." + elif stable_attention: + self.attention = QKVStableAttention(self.num_heads) + else: + self.attention = QKVAttention(self.num_heads) + + self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) + + def forward(self, x): + if self.use_checkpoint: + return checkpoint.checkpoint(self._forward, x) + else: + return self._forward(x) + + def _forward(self, x): + + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + qkv = self.qkv(self.norm(x)) + + if self.flash_attention: + # qkv shape: (b, (3 h d) s), need to reshape to (b, s, h, d) for each q, k, v + b, _, _ = qkv.shape + h = self.num_heads + q, k, v = qkv.chunk(3, dim=1) + max_seqlen_q, max_seqlen_k = q.shape[2], k.shape[2] + q = rearrange(q, 'b (h d) s -> (b s) h d', h=self.num_heads) + k = rearrange(k, 'b (h d) s -> (b s) h d', h=self.num_heads) + v = rearrange(v, 'b (h d) s -> (b s) h d', h=self.num_heads) + cu_seqlens_q = th.arange(0, (b + 1) * max_seqlen_q, step=max_seqlen_q, dtype=th.int32, device=q.device) + cu_seqlens_k = th.arange(0, (b + 1) * max_seqlen_k, step=max_seqlen_k, dtype=th.int32, device=k.device) + h = flash_attn_varlen_func(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, 0.0) + h = rearrange(h, '(b s) h d -> b (h d) s', b=b, h=self.num_heads) + else: + h, _ = self.attention(qkv) + h = self.proj_out(h) + return (x + h).reshape(b, c, *spatial) + + +######################################################################### +# These are the attention blocks as implemented by Stable Diffusion +# https://github.com/CompVis/stable-diffusion/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/ldm/modules/attention.py#L196 + + +class CrossAttentionBlock(TextConditionedBlock): + """ + An attention block that allows spatial positions to attend to context. + In our case, context is the token-wise text embeddings. + """ + + def __init__( + self, + channels, + context_dim, + num_heads=1, + num_head_channels=-1, + use_checkpoint=False, + stable_attention=True, + flash_attention=False, + ): + super().__init__() + self.channels = channels + if num_head_channels == -1: + self.num_heads = num_heads + else: + assert ( + channels % num_head_channels == 0 + ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = channels // num_head_channels + self.use_checkpoint = use_checkpoint + self.norm = normalization(channels) + self.norm_context = normalization(context_dim) + self.flash_attention = flash_attention + # For image features + self.q = conv_nd(1, channels, channels, 1) + + # For context + self.kv = conv_nd(1, context_dim, channels * 2, 1) + + if flash_attention: + assert flash_attn_installed, "FlashAttention is not installed." + assert not stable_attention, "FlashAttention doesn't support the stable form." + elif stable_attention: + self.attention = QKVStableMaskedAttention(self.num_heads) + else: + self.attention = QKVMaskedAttention(self.num_heads) + + self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) + + def forward(self, x, context, mask): + if self.use_checkpoint: + return checkpoint.checkpoint(self._forward, x, context, mask) + else: + return self._forward(x, context, mask) + + def _forward(self, x, context, mask): + + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + + q = self.q(self.norm(x)) + context = th.permute(context, (0, 2, 1)) + context_n = self.norm_context(context) + kv = self.kv(context_n) + k, v = kv.chunk(2, dim=1) + k = k.contiguous() + v = v.contiguous() + + if self.flash_attention: + batch_size = q.shape[0] + max_seqlen_q, max_seqlen_k = q.shape[2], k.shape[2] + q = rearrange(q, 'b (h d) s -> (b s) h d', h=self.num_heads) + mask = mask.to(th.bool) + k_unpadded = k.transpose(1, 2)[mask] + total_k = k_unpadded.shape[0] + k_unpadded = k_unpadded.view(total_k, self.num_heads, -1) + v_unpadded = v.transpose(1, 2)[mask] + v_unpadded = v_unpadded.view(total_k, self.num_heads, -1) + kv_unpadded = th.stack([k_unpadded, v_unpadded], dim=1) + cu_seqlens_q = th.arange( + 0, (batch_size + 1) * max_seqlen_q, step=max_seqlen_q, dtype=th.int32, device=q.device + ) + cu_seqlens_k = th.zeros((batch_size + 1), dtype=th.int32, device=q.device) + cu_seqlens_k[1:] = th.cumsum(mask.sum(dim=1), dim=0) + + out = flash_attn_varlen_kvpacked_func( + q, kv_unpadded, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, 0.0 + ) + h = rearrange(out, '(b s) h d -> b (h d) s', b=batch_size, h=self.num_heads) + else: + # Computing mask for cross attention + mask = mask.view(mask.shape[0], 1, mask.shape[1]) + mask = mask.repeat(1, q.shape[-1], 1) + mask = mask.to(th.bool) + + h, _ = self.attention(q, k, v, mask) + h = self.proj_out(h) + return (x + h).reshape(b, c, *spatial) + + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, mult=4, glu=False, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + project_in = nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) if not glu else GEGLU(dim, inner_dim) + + self.norm = normalization(dim) + self.net = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim)) + + def forward(self, x): + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + + h = self.norm(x) + + # Reshape so that the channel dim moves to last + # Linear function operates on the last dimension + h = th.permute(h, (0, 2, 1)) + + h = self.net(h) + + # Permute it back + h = th.permute(h, (0, 2, 1)) + + return (x + h).reshape(b, c, *spatial) + + +class StackedCrossAttentionBlock(TextConditionedBlock): + """ + An attention block that stacks self-attention and cross-attention layers + in a single block. + """ + + def __init__( + self, + channels, + context_dim, + num_heads=1, + num_head_channels=-1, + use_checkpoint=False, + stable_attention=True, + flash_attention=False, + ): + super().__init__() + self.proj_in = conv_nd(2, channels, channels, 1) + self.norm = normalization(channels) + self.use_checkpoint = use_checkpoint + + self.self_attention_block = SelfAttentionBlock( + channels=channels, + num_heads=num_heads, + num_head_channels=num_head_channels, + use_checkpoint=use_checkpoint, + stable_attention=stable_attention, + flash_attention=flash_attention, + ) + + self.cross_attention_block = CrossAttentionBlock( + channels=channels, + context_dim=context_dim, + num_heads=num_heads, + num_head_channels=num_head_channels, + use_checkpoint=use_checkpoint, + stable_attention=stable_attention, + flash_attention=flash_attention, + ) + + self.ff = FeedForward(dim=channels, glu=True) + self.proj_out = zero_module(conv_nd(2, channels, channels, 1)) + + def forward(self, x, context, mask): + if self.use_checkpoint: + return checkpoint.checkpoint(self._forward, x, context, mask) + else: + return self._forward(x, context, mask) + + def _forward(self, x, context, mask): + + h = self.norm(x) + h = self.proj_in(h) + + h = self.self_attention_block(h) + h = self.cross_attention_block(h, context, mask) + h = self.ff(h) + + h = self.proj_out(h) + return h + x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/embs.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/embs.py new file mode 100644 index 0000000..12ba494 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/embs.py @@ -0,0 +1,69 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math + +import torch +import torch.nn as nn +from einops import rearrange + + +class LearnedSinusoidalPosEmb(nn.Module): + """ following @crowsonkb 's lead with learned sinusoidal pos emb """ + + """ https://github.com/crowsonkb/v-diffusion-jax/blob/master/diffusion/models/danbooru_128.py#L8 """ + + def __init__(self, dim): + super().__init__() + assert (dim % 2) == 0 + half_dim = dim // 2 + self.weights = nn.Parameter(torch.randn(half_dim)) + + def forward(self, x): + x = rearrange(x, 'b -> b 1') + freqs = x * rearrange(self.weights, 'd -> 1 d') * 2 * math.pi + fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1) + fouriered = torch.cat((x, fouriered), dim=-1) + return fouriered + + +class UnLearnedSinusoidalPosEmb(nn.Module): + def __init__(self, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + super().__init__() + self.dim = dim + self.max_period = max_period + print(f'Unlearned Timestep Embedding Schedule: dim={dim}, max_period={max_period}') + + def forward(self, timesteps): + dim = self.dim + half = dim // 2 + max_period = self.max_period + dtype = timesteps.dtype + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to( + device=timesteps.device + ) + args = timesteps[:, None].float() * freqs[None] + args = args.to(dtype=dtype) + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/layers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/layers.py new file mode 100644 index 0000000..72e7025 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/layers.py @@ -0,0 +1,240 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Copyright (c) 2021 OpenAI +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +""" +Brought from: +https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/nn.py + +Various utilities for neural networks. +""" + +import math + +import torch as th +import torch.nn as nn +import torch.nn.functional as F +from apex.contrib.group_norm import GroupNorm + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return nn.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def update_ema(target_params, source_params, rate=0.99): + """ + Update target parameters to be closer to those of source parameters using + an exponential moving average. + + :param target_params: the target parameter sequence. + :param source_params: the source parameter sequence. + :param rate: the EMA rate (closer to 1 means slower). + """ + for targ, src in zip(target_params, source_params): + targ.detach().mul_(rate).add_(src, alpha=1 - rate) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def normalization(channels, act=""): + """ + Make a standard normalization layer. + + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNorm(32, channels, act=act) + + +def timestep_embedding(timesteps, dim, max_period=10000, dtype=th.float32): + """ + Create sinusoidal timestep embeddings. + + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + half = dim // 2 + freqs = th.exp(-math.log(max_period) * th.arange(start=0, end=half, dtype=th.float32) / half).to( + device=timesteps.device + ) + args = timesteps[:, None].float() * freqs[None] + args = args.to(dtype=dtype) + embedding = th.cat([th.cos(args), th.sin(args)], dim=-1) + if dim % 2: + embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + +# Native ADM nearest neighbor upsampling +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest") + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + + +class UpsampleLearnable(nn.Module): + """ + Upsampling based on ConvTranspose2d. This is needed for bfloat support. + + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + + if self.dims == 2: + self.conv = nn.ConvTranspose2d(self.channels, self.out_channels, 4, 2, 1) + elif self.dims == 3: + self.conv = nn.ConvTranspose3d( + self.channels, self.out_channels, kernel_size=(1, 4, 4), stride=(1, 2, 2), padding=(0, 1, 1) + ) + else: + raise ValueError('Upsampling support only for 2D and 3D') + + def forward(self, x): + assert x.shape[1] == self.channels + x = self.conv(x) + return x + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=1) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/nets.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/nets.py new file mode 100644 index 0000000..96b1a5d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/diffusionmodules/nets.py @@ -0,0 +1,698 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn as nn +import torch.nn.functional as F + +from nemo.collections.multimodal.modules.imagen.diffusionmodules.attention import SelfAttentionPooling +from nemo.collections.multimodal.modules.imagen.diffusionmodules.blocks import ( + ConditionalSequential, + DBlock, + FusedCrossAttentionBlock, + ResBlock, + StackedCrossAttentionBlock, + UBlock, +) +from nemo.collections.multimodal.modules.imagen.diffusionmodules.embs import ( + LearnedSinusoidalPosEmb, + UnLearnedSinusoidalPosEmb, +) +from nemo.collections.multimodal.modules.imagen.diffusionmodules.layers import Downsample +from nemo.collections.multimodal.modules.imagen.diffusionmodules.layers import UpsampleLearnable as Upsample +from nemo.collections.multimodal.modules.imagen.diffusionmodules.layers import linear, normalization, zero_module + + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding used for Imagen Base and SR model. + + :param embed_dim: Dimension of embeddings. Also used to calculate the number of channels in ResBlock. + :param image_size: Input image size. Used to calculate where to inject attention layers in UNet. + :param channels: Input channel number, defaults to 3. + :param text_embed_dim: Dimension of conditioned text embedding. Different text encoders and different model versions have different values, defaults to 512 + :param num_res_blocks: Number of ResBlock in each level of UNet, defaults to 3. + :param channel_mult: Used with embed_dim to calculate the number of channels for each level of UNet, defaults to [1, 2, 3, 4] + :param num_attn_heads: The number of heads in the attention layer, defaults to 4. + :param per_head_channels: The number of channels per attention head, defaults to 64. + :param cond_dim: Dimension of Conditioning projections, defaults to 512. + :param attention_type: Type of attention layer, defaults to 'fused'. + :param feature_pooling_type: Type of pooling, defaults to 'attention'. + :param learned_sinu_pos_emb_dim: Dimension of learned time positional embedding. 0 for unlearned timestep embeddings. Defaults to 16 + :param attention_resolutions: List of resolutions to inject attention layers. Defaults to [8, 16, 32] + :param dropout: The rate of dropout, defaults to 0. + :param use_null_token: Whether to create a learned null token for attention, defaults to False. + :param init_conv_kernel_size: Initial Conv kernel size, defaults to 3. + :param gradient_checkpointing: Whether to use gradient checkpointing, defaults to False. + :param scale_shift_norm: Whether to use scale shift norm, defaults to False. + :param stable_attention: Whether to use numerically-stable attention calculation, defaults to True. + :param flash_attention: Whether to use flash attention calculation, defaults to False. + :param resblock_updown: Whether to use ResBlock or Downsample/Upsample, defaults to False. + :param resample_with_conv: When resblock_updown=False, whether to use conv in addition to Pooling&ConvTranspose. Defaults to True. + :param low_res_cond: Whether conditioned on low-resolution input, used for SR model. Defaults to False. + :param noise_cond_aug: Whether to add noise conditioned augmentation with low-resolution input. Defaults to False. + """ + + def __init__( + self, + embed_dim, # Dimension of embeddings. Also used to calculate the number of channels in ResBlock + image_size, # Input image size. Used to calculate where to inject attention layers in UNet + channels=3, # Input channel number + text_embed_dim=512, # Dimension of conditioned text embedding. Different text encoders and different model versions have different values + num_res_blocks=3, # Number of ResBlock in each level of UNet + channel_mult=[1, 2, 3, 4], # Used with embed_dim to calculate the number of channels for each level of UNet + num_attn_heads=4, # The number of heads in the attention layer + per_head_channels=64, # The number of channels per attention head + cond_dim=512, # Dimension of Conditioning projections + attention_type='fused', # Type of attention layer + feature_pooling_type='attention', # Type of pooling + learned_sinu_pos_emb_dim=16, # Dimension of learned time positional embedding. 0 for unlearned timestep embeddings. + attention_resolutions=[8, 16, 32], # List of resolutions to inject attention layers + dropout=False, # The rate of dropout + use_null_token=False, # Whether to create a learned null token for attention + init_conv_kernel_size=3, # Initial Conv kernel size. imagen_pytorch uses 7 + gradient_checkpointing=False, # Whether to use gradient checkpointing + scale_shift_norm=True, # Whether to use scale shift norm + stable_attention=True, # Whether to use numerically-stable attention calculation + flash_attention=False, # Whether to use flash attention calculation + resblock_updown=False, # Whether to use ResBlock or Downsample/Upsample + resample_with_conv=True, # When resblock_updown=False, whether to use conv in addition to Pooling&ConvTranspose + low_res_cond=False, + noise_cond_aug=False, + ): + super().__init__() + + # Attention Class + if attention_type == 'stacked': + attention_fn = StackedCrossAttentionBlock + elif attention_type == 'fused': + attention_fn = FusedCrossAttentionBlock + else: + raise ValueError('Attention {} not defined'.format(attention_type)) + + # Time embedding for log(snr) noise from continous version + time_embed_dim = embed_dim * 4 + assert learned_sinu_pos_emb_dim >= 0 + if learned_sinu_pos_emb_dim > 0: + sinu_pos_emb = LearnedSinusoidalPosEmb(learned_sinu_pos_emb_dim) + sinu_pos_emb_input_dim = learned_sinu_pos_emb_dim + 1 + self.time_embed = nn.Sequential( + sinu_pos_emb, + nn.Linear(sinu_pos_emb_input_dim, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + else: + # Unlearned Time Embedding + sinu_pos_emb = UnLearnedSinusoidalPosEmb(embed_dim) + self.time_embed = nn.Sequential( + sinu_pos_emb, linear(embed_dim, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim) + ) + + # Pooling + assert feature_pooling_type == 'attention' or feature_pooling_type == 'mean' + self.feature_pooling_type = feature_pooling_type + if feature_pooling_type == 'attention': + self.attention_pooling = nn.Sequential( + SelfAttentionPooling(input_dim=text_embed_dim), + nn.LayerNorm(text_embed_dim), + nn.Linear(text_embed_dim, cond_dim), + ) + + # Context Projections + self.text_to_cond = linear(text_embed_dim, cond_dim) + self.to_text_non_attn_cond = nn.Sequential( + nn.LayerNorm(cond_dim), + nn.Linear(cond_dim, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + + # Register for Null Token + if use_null_token: + self.null_text_embedding = nn.Parameter(torch.randn(1, 1, cond_dim, dtype=self.text_to_cond.weight.dtype)) + self.use_null_token = use_null_token + + # Converting attention resolutions to downsampling factor + attention_ds = [] + attention_resolutions = sorted(attention_resolutions) + self.image_size = image_size + for res in attention_resolutions: + attention_ds.append(image_size // int(res)) + + self.low_res_cond = low_res_cond + # Low res noise conditioning augmentation + self.noise_cond_aug = noise_cond_aug + if self.noise_cond_aug: + assert ( + self.low_res_cond + ), 'noise conditioning augmentation should only be enabled when training with low-res cond' + if learned_sinu_pos_emb_dim > 0: + lowres_sinu_pos_emb = LearnedSinusoidalPosEmb(learned_sinu_pos_emb_dim) + lowres_sinu_pos_emb_dim = learned_sinu_pos_emb_dim + 1 + else: + lowres_sinu_pos_emb = UnLearnedSinusoidalPosEmb(embed_dim) + lowres_sinu_pos_emb_dim = embed_dim + self.lowres_time_embed = nn.Sequential( + lowres_sinu_pos_emb, + nn.Linear(lowres_sinu_pos_emb_dim, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + + # Initial Convolution + in_channels = 2 * channels if low_res_cond else channels + init_dim = embed_dim * channel_mult[0] + self.init_conv = ConditionalSequential( + nn.Conv2d(in_channels, init_dim, init_conv_kernel_size, padding=init_conv_kernel_size // 2) + ) + + if isinstance(num_res_blocks, int): + res_blocks_list = [num_res_blocks] * len(channel_mult) + else: + res_blocks_list = num_res_blocks + # UNet Init + # Downsampling Layers + # We use Conv2D for UNet + CONV_DIM = 2 + ch = init_dim + ds = 1 + self.input_blocks = nn.ModuleList([self.init_conv]) + num_input_block_channels = [ch] + for level, mult in enumerate(channel_mult): + num_res_blocks = res_blocks_list[level] + for _ in range(num_res_blocks): + out_channels = mult * embed_dim + layers = [ + ResBlock( + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=out_channels, + dims=CONV_DIM, + use_checkpoint=gradient_checkpointing, + use_scale_shift_norm=scale_shift_norm, + learnable_upsampling=True, + ) + ] + ch = out_channels + if ds in attention_ds: + layers.append( + attention_fn( + channels=ch, + num_heads=num_attn_heads, + num_head_channels=per_head_channels, + use_checkpoint=gradient_checkpointing, + stable_attention=stable_attention, + flash_attention=flash_attention, + context_dim=cond_dim, + ) + ) + self.input_blocks.append(ConditionalSequential(*layers)) + num_input_block_channels.append(ch) + is_last_level = level == len(channel_mult) - 1 + if not is_last_level: + # DownSampling + self.input_blocks.append( + ConditionalSequential( + ResBlock( + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=ch, + dims=CONV_DIM, + use_checkpoint=gradient_checkpointing, + use_scale_shift_norm=scale_shift_norm, + down=True, + learnable_upsampling=True, + ) + if resblock_updown + else Downsample(channels=ch, use_conv=resample_with_conv, dims=CONV_DIM, out_channels=ch,) + ) + ) + num_input_block_channels.append(ch) + ds *= 2 + + # Middle Layers + self.middle_block = ConditionalSequential( + # Mid Block 1 + ResBlock( + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + dims=CONV_DIM, + use_checkpoint=gradient_checkpointing, + use_scale_shift_norm=scale_shift_norm, + learnable_upsampling=True, + ), + # Attention Layer + attention_fn( + channels=ch, + num_heads=num_attn_heads, + num_head_channels=per_head_channels, + use_checkpoint=gradient_checkpointing, + stable_attention=stable_attention, + flash_attention=flash_attention, + context_dim=cond_dim, + ), + # Mid Block 2 + ResBlock( + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + dims=CONV_DIM, + use_checkpoint=gradient_checkpointing, + use_scale_shift_norm=scale_shift_norm, + learnable_upsampling=True, + ), + ) + + # Upsampling Layers + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + num_res_blocks = res_blocks_list[level] + for i in range(num_res_blocks + 1): + ich = num_input_block_channels.pop() + out_channels = embed_dim * mult + layers = [ + ResBlock( + channels=ch + ich, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=out_channels, + dims=CONV_DIM, + use_checkpoint=gradient_checkpointing, + use_scale_shift_norm=scale_shift_norm, + learnable_upsampling=True, + ) + ] + ch = out_channels + + if ds in attention_ds: + layers.append( + attention_fn( + channels=ch, + num_heads=-1, # TODO + num_head_channels=per_head_channels, + use_checkpoint=gradient_checkpointing, + stable_attention=stable_attention, + flash_attention=flash_attention, + context_dim=cond_dim, + ) + ) + is_last_block = i == num_res_blocks + if level and is_last_block: + layers.append( + ResBlock( + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=ch, + dims=CONV_DIM, + use_checkpoint=gradient_checkpointing, + use_scale_shift_norm=scale_shift_norm, + up=True, + learnable_upsampling=True, + ) + if resblock_updown + else Upsample(channels=ch, use_conv=resample_with_conv, dims=CONV_DIM, out_channels=ch) + ) + ds //= 2 + self.output_blocks.append(ConditionalSequential(*layers)) + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(nn.Conv2d(init_dim, channels, init_conv_kernel_size, padding=init_conv_kernel_size // 2)), + ) + + def forward( + self, x, time, text_embed=None, text_mask=None, x_low_res=None, time_low_res=None, + ): + if self.low_res_cond: + assert x_low_res is not None, 'x_low_res cannot be None' + else: + assert x_low_res is None, 'x_low_res cannot be presented' + if self.noise_cond_aug: + assert time_low_res is not None, 'time_low_res cannot be None when training with noise conditioning aug' + else: + assert time_low_res is None, 'time_low_res cannot be presented' + # Concatenating low resolution images + if x_low_res is not None: + if x_low_res.shape != x.shape: + # Upscale if not done in the trainer + _, _, new_height, new_width = x.shape + x_low_res = F.interpolate(x_low_res, (new_height, new_width), mode="bicubic") + x = torch.cat([x, x_low_res], dim=1) + batch_size, device = x.shape[0], x.device + + if x.dtype != time.dtype or time.dtype != text_embed.dtype: + dtype = text_embed.dtype + x = x.to(dtype=dtype) + time = time.to(dtype=dtype) + if x_low_res is not None: + x_low_res = x_low_res.to(dtype=dtype) + if time_low_res is not None: + time_low_res = time_low_res.to(dtype=dtype) + # Time Conditioning + t = self.time_embed(time) + # Add lowres time conditioning + if self.noise_cond_aug: + lowres_t = self.lowres_time_embed(time_low_res) + t += lowres_t + # Text Conditioning + text_cond = self.text_to_cond(text_embed) + + # Context Embedding + # TODO We may want to concat time token here + if self.use_null_token: + # Null Context (Helpful when text_embed is drop) + null_context = self.null_text_embedding.repeat(batch_size, 1, 1) + context_emb = torch.cat([text_cond, null_context], dim=1) + context_mask = torch.cat([text_mask, torch.ones(batch_size, 1).to(device)], dim=1) + else: + context_emb = text_cond + context_mask = text_mask + + # Add pooled text embeddings to the diffusion timestep + # TODO We may only want to calculated the pooled feature based on text token length + if self.feature_pooling_type == 'mean': + pooled_text_cond = text_cond.mean(dim=-2) + elif self.feature_pooling_type == 'attention': + pooled_text_cond = self.attention_pooling(text_embed) + text_hiddens = self.to_text_non_attn_cond(pooled_text_cond) + t += text_hiddens + + h = x + hs = [] + # UNet Forward + for module in self.input_blocks: + h = module(h, t, context_emb, context_mask) + hs.append(h) + h = self.middle_block(h, t, context_emb, context_mask) + for module in self.output_blocks: + h_prev = hs.pop() + h = torch.cat([h, h_prev], dim=1) + h = module(h, t, context_emb, context_mask) + return self.out(h) + + def forward_with_cond_scale(self, *args, text_embed=None, cond_scale=1.0, **kwargs): + logits = self.forward(*args, text_embed=text_embed, **kwargs) + if cond_scale == 1.0: + return logits + null_logits = self.forward(*args, text_embed=torch.zeros_like(text_embed), **kwargs) + return null_logits + (logits - null_logits) * cond_scale + + +class EfficientUNetModel(nn.Module): + """ + The full Efficient UNet model with attention and timestep embedding used for Imagen SR model. + + :param embed_dim: Dimension of embeddings. Also used to calculate the number of channels in ResBlock. + :param image_size: Input image size. Used to calculate where to inject attention layers in UNet. + :param channels: Input channel number, defaults to 3. + :param text_embed_dim: Dimension of conditioned text embedding. Different text encoders and different model versions have different values, defaults to 512 + :param channel_mult: Used with embed_dim to calculate the number of channels for each level of UNet, defaults to [1, 1, 2, 4, 8]. + :param num_attn_heads: The number of heads in the attention layer, defaults to 8. + :param per_head_channels: The number of channels per attention head, defaults to 64. + :param attention_type: Type of attention layer, defaults to 'fused'. + :param atnn_enabled_at: Whether to enable attention at each level, defaults to [0, 0, 0, 0, 1]. + :param feature_pooling_type: Type of pooling, defaults to 'attention'. + :param stride: Stride in ResBlock, defaults to 2. + :param num_resblocks: Used with num_res_blocks to calculate the number of residual blocks at each level of Efficient-UNet. Defaults to [1, 2, 4, 8, 8]. + :param learned_sinu_pos_emb_dim: Dimension of learned time positional embedding. 0 for unlearned timestep embeddings. Defaults to 16 + :param use_null_token: Whether to create a learned null token for attention, defaults to False. + :param init_conv_kernel_size: Initial Conv kernel size, defaults to 3. + :param gradient_checkpointing: Whether to use gradient checkpointing, defaults to False. + :param scale_shift_norm: Whether to use scale shift norm, defaults to False. + :param stable_attention: Whether to use numerically-stable attention calculation, defaults to True. + :param flash_attention: Whether to use flash attention calculation, defaults to False. + :param skip_connection_scaling: Whether to use 1/sqrt(2) scaling for ResBlock skip connection, defaults to False. + :param noise_cond_aug: Whether to add noise conditioned augmentation with low-resolution input. Defaults to False. + """ + + def __init__( + self, + embed_dim, + image_size, + channels=3, + text_embed_dim=512, # Dimension of conditioned text embedding. Different text encoders and different model versions have different values + channel_mult=[ + 1, + 1, + 2, + 4, + 8, + ], # Used with embed_dim to calculate the number of channels for each level of Efficient-UNet + num_attn_heads=8, # The number of heads in the attention layer + per_head_channels=64, # The number of channels per attention head + attention_type='fused', # Type of attention layer + atnn_enabled_at=[0, 0, 0, 0, 1], # Whether to enable attention at each level + feature_pooling_type='attention', # Type of pooling + stride=2, # Stride in ResBlock + num_resblocks=[ + 1, + 2, + 4, + 8, + 8, + ], # Used with num_res_blocks to calculate the number of residual blocks at each level of Efficient-UNet + learned_sinu_pos_emb_dim=16, # Dimension of learned time positional embedding. 0 for unlearned timestep embeddings. + use_null_token=False, # Whether to create a learned null token for attention + init_conv_kernel_size=3, # Initial Conv kernel size. imagen_pytorch uses 7 + gradient_checkpointing=False, # Whether to use gradient checkpointing + scale_shift_norm=True, # Whether to use scale shift norm + stable_attention=True, # Whether to use numerically-stable attention calculation + flash_attention=False, # Whether to use flash attention calculation + skip_connection_scaling=False, # Whether to use 1/sqrt(2) scaling for ResBlock skip connection + noise_cond_aug=False, + ): + + super().__init__() + + self.n_levels = len(channel_mult) + self.image_size = image_size + # Time embedding for log(snr) noise from continous version + time_embed_dim = embed_dim * 4 + assert learned_sinu_pos_emb_dim >= 0 + if learned_sinu_pos_emb_dim > 0: + sinu_pos_emb = LearnedSinusoidalPosEmb(learned_sinu_pos_emb_dim) + sinu_pos_emb_input_dim = learned_sinu_pos_emb_dim + 1 + self.time_embed = nn.Sequential( + sinu_pos_emb, + nn.Linear(sinu_pos_emb_input_dim, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + else: + # Unlearned Time Embedding + sinu_pos_emb = UnLearnedSinusoidalPosEmb(embed_dim) + self.time_embed = nn.Sequential( + sinu_pos_emb, linear(embed_dim, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim) + ) + + self.noise_cond_aug = noise_cond_aug + if self.noise_cond_aug: + if learned_sinu_pos_emb_dim > 0: + lowres_sinu_pos_emb = LearnedSinusoidalPosEmb(learned_sinu_pos_emb_dim) + lowres_sinu_pos_emb_dim = learned_sinu_pos_emb_dim + 1 + else: + lowres_sinu_pos_emb = UnLearnedSinusoidalPosEmb(embed_dim) + lowres_sinu_pos_emb_dim = embed_dim + self.lowres_time_embed = nn.Sequential( + lowres_sinu_pos_emb, + nn.Linear(lowres_sinu_pos_emb_dim, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + cond_dim = text_embed_dim # time_embed_dim + # Pooling + assert feature_pooling_type == 'attention' or feature_pooling_type == 'mean' + self.feature_pooling_type = feature_pooling_type + if feature_pooling_type == 'attention': + self.attention_pooling = nn.Sequential( + SelfAttentionPooling(input_dim=text_embed_dim), + nn.LayerNorm(text_embed_dim), + nn.Linear(text_embed_dim, cond_dim), + ) + + # Context Projections + self.text_to_cond = linear(text_embed_dim, cond_dim) + self.to_text_non_attn_cond = nn.Sequential( + nn.LayerNorm(cond_dim), + nn.Linear(cond_dim, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + # Register for Null Token + if use_null_token: + self.null_text_embedding = nn.Parameter(torch.randn(1, 1, cond_dim, dtype=self.text_to_cond.weight.dtype)) + self.use_null_token = use_null_token + + # Initial Convolution + # Multiply in_channels by 2 because we concatenate with low res inputs. + in_channels = channels * 2 + init_dim = embed_dim * channel_mult[0] + self.init_conv = nn.Conv2d(in_channels, init_dim, init_conv_kernel_size, padding=init_conv_kernel_size // 2) + # Efficient-UNet Init + self.DBlocks = nn.ModuleDict() + self.UBlocks = nn.ModuleDict() + ch = init_dim + for level, mult in enumerate(channel_mult): + # Different level has different num of res blocks + num_resblock = num_resblocks[level] + # Only perform upsample/downsample if it is not the last (deepest) level + is_last_level = level == len(channel_mult) - 1 + level_attention_type = attention_type if atnn_enabled_at[level] else None + + level_key = str(level) # TODO Change to more meaningful naming + self.DBlocks[level_key] = DBlock( + channels=ch, + emb_channels=time_embed_dim, + out_channels=int(mult * embed_dim), + use_scale_shift_norm=scale_shift_norm, + conv_down=not is_last_level, + stride=stride, + num_resblocks=num_resblock, + attention_type=level_attention_type, + text_embed_dim=cond_dim, + num_heads=num_attn_heads, + num_head_channels=per_head_channels, + use_checkpoint=gradient_checkpointing, + stable_attention=stable_attention, + flash_attention=flash_attention, + skip_connection_scaling=skip_connection_scaling, + ) + self.UBlocks[level_key] = UBlock( + channels=int(mult * embed_dim), + emb_channels=time_embed_dim, + out_channels=ch, + use_scale_shift_norm=scale_shift_norm, + conv_up=not is_last_level, + stride=stride, + num_resblocks=num_resblock, + attention_type=level_attention_type, + text_embed_dim=cond_dim, + num_heads=num_attn_heads, + num_head_channels=per_head_channels, + use_checkpoint=gradient_checkpointing, + stable_attention=stable_attention, + flash_attention=flash_attention, + skip_connection_scaling=skip_connection_scaling, + ) + ch = int(mult * embed_dim) + self.out = nn.Conv2d(channel_mult[0] * embed_dim, channels, 1) + + def forward( + self, x, time, text_embed, text_mask, x_low_res, time_low_res=None, + ): + if self.noise_cond_aug: + assert time_low_res is not None, 'time_low_res cannot be None when training with noise conditioning aug' + else: + assert time_low_res is None, 'time_low_res cannot be presented' + + if x.dtype != time.dtype or time.dtype != text_embed.dtype: + dtype = text_embed.dtype + x = x.to(dtype=dtype) + time = time.to(dtype=dtype) + if x_low_res is not None: + x_low_res = x_low_res.to(dtype=dtype) + if time_low_res is not None: + time_low_res = time_low_res.to(dtype=dtype) + + batch_size, device = x.shape[0], x.device + # Time Conditioning + t = self.time_embed(time) + # Text Conditioning + text_cond = self.text_to_cond(text_embed) + # Concatenating low resolution images + if x_low_res.shape != x.shape: + # Upscale if not done in the trainer + _, _, new_height, new_width = x.shape + x_low_res = F.interpolate(x_low_res, (new_height, new_width), mode="bicubic") + x = torch.cat([x, x_low_res], dim=1) + + # Add lowres time conditioning + if self.noise_cond_aug: + lowres_t = self.lowres_time_embed(time_low_res) + t += lowres_t + # Context Embedding + # TODO We may want to concat time token here + if self.use_null_token: + # Null Context (Helpful when text_embed is drop) + null_context = self.null_text_embedding.repeat(batch_size, 1, 1) + context_emb = torch.cat([text_cond, null_context], dim=1) + context_mask = torch.cat([text_mask, torch.ones(batch_size, 1).to(device)], dim=1) + else: + context_emb = text_cond + context_mask = text_mask + + # Add pooled text embeddings to the diffusion timestep + # TODO We may only want to calculated the pooled feature based on text token length + if self.feature_pooling_type == 'mean': + pooled_text_cond = text_cond.mean(dim=-2) + elif self.feature_pooling_type == 'attention': + pooled_text_cond = self.attention_pooling(text_embed) + text_hiddens = self.to_text_non_attn_cond(pooled_text_cond) + t += text_hiddens + + # UNet forward + x = self.init_conv(x) + feats = dict() + for level in range(self.n_levels): + level_key = str(level) + x = self.DBlocks[level_key](x, t, context_emb, context_mask) + # Save feats for UBlocks + if level < self.n_levels - 1: + feats[level_key] = x + for level in range(self.n_levels - 1, -1, -1): + level_key = str(level) + if level < self.n_levels - 1: + x = x + feats[level_key] + x = self.UBlocks[level_key](x, t, context_emb, context_mask) + return self.out(x) + + def forward_with_cond_scale(self, *args, text_embed=None, cond_scale=1.0, **kwargs): + logits = self.forward(*args, text_embed=text_embed, **kwargs) + if cond_scale == 1.0: + return logits + null_logits = self.forward(*args, text_embed=torch.zeros_like(text_embed), **kwargs) + return null_logits + (logits - null_logits) * cond_scale + + +if __name__ == '__main__': + model = UNetModel(embed_dim=512, image_size=64,) + + pytorch_total_params = sum(p.numel() for p in model.parameters()) + print(pytorch_total_params) + + image_batch = torch.rand(4, 3, 64, 64) + text_cond = torch.rand(4, 88, 512) + text_mask = torch.ones(4, 88) + time = torch.ones(4) + + output = model(image_batch, time, text_cond, text_mask,) + + print(output.shape) + + model_sr = EfficientUNetModel(embed_dim=128, image_size=256) + pytorch_total_params = sum(p.numel() for p in model_sr.parameters()) + print(pytorch_total_params) + output = model_sr( + torch.randn(4, 3, 256, 256), + torch.randn(4, 3, 256, 256), + torch.ones(4), + torch.randn(4, 88, 512), + torch.ones(4, 88), + ) + print(output.shape) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/encoder/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/encoder/__init__.py new file mode 100644 index 0000000..aee9513 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/encoder/__init__.py @@ -0,0 +1,24 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.package_info import __version__ + +# Set collection version equal to NeMo version. +__version = __version__ + +# Authorship. +__author__ = "NVIDIA Corporation" + +# Set collection name. +__description__ = "Speech Computer Vision collection" diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/encoder/t5encoder.json b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/encoder/t5encoder.json new file mode 100644 index 0000000..3fb4ffd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/encoder/t5encoder.json @@ -0,0 +1,51 @@ +{ + "architectures": [ + "T5WithLMHeadModel" + ], + "d_ff": 65536, + "d_kv": 128, + "d_model": 1024, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "n_positions": 512, + "num_heads": 128, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "task_specific_params": { + "summarization": { + "early_stopping": true, + "length_penalty": 2.0, + "max_length": 200, + "min_length": 30, + "no_repeat_ngram_size": 3, + "num_beams": 4, + "prefix": "summarize: " + }, + "translation_en_to_de": { + "early_stopping": true, + "max_length": 300, + "num_beams": 4, + "prefix": "translate English to German: " + }, + "translation_en_to_fr": { + "early_stopping": true, + "max_length": 300, + "num_beams": 4, + "prefix": "translate English to French: " + }, + "translation_en_to_ro": { + "early_stopping": true, + "max_length": 300, + "num_beams": 4, + "prefix": "translate English to Romanian: " + } + }, + "vocab_size": 32128 +} diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/encoder/t5encoder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/encoder/t5encoder.py new file mode 100644 index 0000000..c660bc0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/encoder/t5encoder.py @@ -0,0 +1,68 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os + +import torch +from transformers import T5Config, T5EncoderModel, T5Tokenizer + + +class T5Encoder(torch.nn.Module): + def __init__(self, max_seq_len=512, encoder_path=None): + """ + Initialize the T5 Encoder. + + :param max_seq_len: Maximum token length, defaults to 512 + :param encoder_path: Optional if loaded T5 on the disk, defaults to None + """ + super().__init__() + self.max_seq_len = max_seq_len + + self.model_seq_len = 512 + # Initializing T5 model + self.tokenizer = T5Tokenizer.from_pretrained("t5-11b", model_max_length=self.model_seq_len) + + if encoder_path is None: + self.model = T5EncoderModel.from_pretrained("t5-11b", low_cpu_mem_usage=True) + else: + print(f'Load T5 encoder from {encoder_path}') + hard_coded_encoder_weight_location = os.path.join(encoder_path, "t5xxl-encoder.bin") + hard_coded_encoder_config_location = os.path.join(os.path.dirname(__file__), "t5encoder.json") + self.model = T5EncoderModel.from_pretrained( + hard_coded_encoder_weight_location, + config=T5Config.from_json_file(hard_coded_encoder_config_location), + low_cpu_mem_usage=True, + ) + + def encode(self, text_batch, device='cuda'): + ''' + Encode a batch of text to T5 embeddings. + ''' + encoded = self.tokenizer.batch_encode_plus( + text_batch, return_tensors="pt", padding="max_length", max_length=self.model_seq_len, truncation=True + ) + # We expect all the processing is done in GPU. + input_ids = encoded.input_ids.to(device=device) + attn_mask = encoded.attention_mask.to(device=device) + + with torch.no_grad(): + output = self.model(input_ids=input_ids, attention_mask=attn_mask) + encoded_text = output.last_hidden_state.detach() + + encoded_text = encoded_text[:, 0 : self.max_seq_len] + attn_mask = attn_mask[:, 0 : self.max_seq_len] + for bnum in range(encoded_text.shape[0]): + nvalid_elem = attn_mask[bnum].sum().item() + encoded_text[bnum][nvalid_elem:] = 0 + + return encoded_text, attn_mask diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/sampler/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/sampler/__init__.py new file mode 100644 index 0000000..aee9513 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/sampler/__init__.py @@ -0,0 +1,24 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.package_info import __version__ + +# Set collection version equal to NeMo version. +__version = __version__ + +# Authorship. +__author__ = "NVIDIA Corporation" + +# Set collection name. +__description__ = "Speech Computer Vision collection" diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/sampler/batch_ops.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/sampler/batch_ops.py new file mode 100644 index 0000000..029bbf6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/sampler/batch_ops.py @@ -0,0 +1,57 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Functions for performing operations with broadcasting to the right axis +# +# Example +# input1: tensor of size (N1, N2) +# input2: tensor of size (N1, N2, N3, N4) +# batch_mul(input1, input2) = input1[:, :, None, None] * input2 +# +# If the common dimensions don't match, we raise an assertion error. + + +def common_broadcast(x, y): + ndims1 = x.ndim + ndims2 = y.ndim + + common_ndims = min(ndims1, ndims2) + for axis in range(common_ndims): + assert x.shape[axis] == y.shape[axis], 'Dimensions not equal at axis {}'.format(axis) + + if ndims1 < ndims2: + x = x.reshape(x.shape + (1,) * (ndims2 - ndims1)) + elif ndims2 < ndims1: + y = y.reshape(y.shape + (1,) * (ndims1 - ndims2)) + + return x, y + + +def batch_add(x, y): + x, y = common_broadcast(x, y) + return x + y + + +def batch_mul(x, y): + x, y = common_broadcast(x, y) + return x * y + + +def batch_sub(x, y): + x, y = common_broadcast(x, y) + return x - y + + +def batch_div(x, y): + x, y = common_broadcast(x, y) + return x / y diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/sampler/continuous_ddpm.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/sampler/continuous_ddpm.py new file mode 100644 index 0000000..2b48f28 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/sampler/continuous_ddpm.py @@ -0,0 +1,168 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math +from functools import partial, wraps + +import torch +import torch.nn as nn +from einops import repeat +from torch.special import expm1 + +from nemo.collections.multimodal.parts.utils import randn_like + + +def exists(val): + return val is not None + + +def default(val, d): + if exists(val): + return val + return d() if callable(d) else d + + +def maybe(fn): + @wraps(fn) + def inner(x): + if not exists(x): + return x + return fn(x) + + return inner + + +def log(t, eps: float = 1e-12): + return torch.log(t.clamp(min=eps)) + + +def right_pad_dims_to(x, t): + padding_dims = x.ndim - t.ndim + if padding_dims <= 0: + return t + return t.view(*t.shape, *((1,) * padding_dims)) + + +@torch.jit.script +def beta_linear_log_snr(t): + return -torch.log(expm1(1e-4 + 10 * (t ** 2))) + + +@torch.jit.script +def alpha_cosine_log_snr(t, s: float = 0.008): + return -log( + (torch.cos((t + s) / (1 + s) * math.pi * 0.5) ** -2) - 1, eps=1e-5 + ) # not sure if this accounts for beta being clipped to 0.999 in discrete version + + +def log_snr_to_alpha_sigma(log_snr): + return torch.sqrt(torch.sigmoid(log_snr)), torch.sqrt(torch.sigmoid(-log_snr)) + + +class GaussianDiffusionContinuousTimes(nn.Module): + def __init__(self, *, noise_schedule, timesteps=1000, rng=None): + super().__init__() + + if noise_schedule == "linear": + self.log_snr = beta_linear_log_snr + elif noise_schedule == "cosine": + self.log_snr = alpha_cosine_log_snr + else: + raise ValueError(f'invalid noise schedule {noise_schedule}') + + self.num_timesteps = timesteps + self.rng = rng + + def get_times(self, batch_size, noise_level, *, device): + return torch.full((batch_size,), noise_level, device=device, dtype=torch.float32) + + def sample_random_times(self, batch_size, *, device): + return torch.rand((batch_size,), device=device, generator=self.rng, dtype=torch.float32) + + def get_condition(self, times): + return maybe(self.log_snr)(times) + + def get_sampling_timesteps(self, batch, *, device): + times = torch.linspace(1.0, 0.0, self.num_timesteps + 1, device=device) + times = repeat(times, 't -> b t', b=batch) + times = torch.stack((times[:, :-1], times[:, 1:]), dim=0) + times = times.unbind(dim=-1) + return times + + def q_posterior(self, x_start, x_t, t, *, t_next=None): + t_next = default(t_next, lambda: (t - 1.0 / self.num_timesteps).clamp(min=0.0)) + + """ https://openreview.net/attachment?id=2LdBqxc1Yv&name=supplementary_material """ + log_snr = self.log_snr(t) + log_snr_next = self.log_snr(t_next) + log_snr, log_snr_next = map(partial(right_pad_dims_to, x_t), (log_snr, log_snr_next)) + + alpha, sigma = log_snr_to_alpha_sigma(log_snr) + alpha_next, sigma_next = log_snr_to_alpha_sigma(log_snr_next) + + # c - as defined near eq 33 + c = -expm1(log_snr - log_snr_next) + posterior_mean = alpha_next * (x_t * (1 - c) / alpha + c * x_start) + + # following (eq. 33) + posterior_variance = (sigma_next ** 2) * c + posterior_log_variance_clipped = log(posterior_variance, eps=1e-20) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def q_sample(self, x_start, t, noise=None): + dtype = x_start.dtype + + if isinstance(t, float): + batch = x_start.shape[0] + t = torch.full((batch,), t, device=x_start.device, dtype=dtype) + + noise = default(noise, lambda: randn_like(x_start, generator=self.rng)) + log_snr = self.log_snr(t).type(dtype) + log_snr_padded_dim = right_pad_dims_to(x_start, log_snr) + alpha, sigma = log_snr_to_alpha_sigma(log_snr_padded_dim) + + return alpha * x_start + sigma * noise, log_snr, alpha, sigma + + def q_sample_from_to(self, x_from, from_t, to_t, noise=None): + shape, device, dtype = x_from.shape, x_from.device, x_from.dtype + batch = shape[0] + + if isinstance(from_t, float): + from_t = torch.full((batch,), from_t, device=device, dtype=dtype) + + if isinstance(to_t, float): + to_t = torch.full((batch,), to_t, device=device, dtype=dtype) + + noise = default(noise, lambda: randn_like(x_from, generator=self.rng)) + + log_snr = self.log_snr(from_t) + log_snr_padded_dim = right_pad_dims_to(x_from, log_snr) + alpha, sigma = log_snr_to_alpha_sigma(log_snr_padded_dim) + + log_snr_to = self.log_snr(to_t) + log_snr_padded_dim_to = right_pad_dims_to(x_from, log_snr_to) + alpha_to, sigma_to = log_snr_to_alpha_sigma(log_snr_padded_dim_to) + + return x_from * (alpha_to / alpha) + noise * (sigma_to * alpha - sigma * alpha_to) / alpha + + def predict_start_from_v(self, x_t, t, v): + log_snr = self.log_snr(t) + log_snr = right_pad_dims_to(x_t, log_snr) + alpha, sigma = log_snr_to_alpha_sigma(log_snr) + return alpha * x_t - sigma * v + + def predict_start_from_noise(self, x_t, t, noise): + log_snr = self.log_snr(t) + log_snr = right_pad_dims_to(x_t, log_snr) + alpha, sigma = log_snr_to_alpha_sigma(log_snr) + return (x_t - sigma * noise) / alpha.clamp(min=1e-8) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/sampler/sampler.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/sampler/sampler.py new file mode 100644 index 0000000..2fd05fa --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/imagen/sampler/sampler.py @@ -0,0 +1,250 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np +import torch +from einops import rearrange +from tqdm import tqdm + +from nemo.collections.multimodal.modules.imagen.sampler.batch_ops import batch_div, batch_mul +from nemo.collections.multimodal.modules.imagen.sampler.continuous_ddpm import GaussianDiffusionContinuousTimes + + +def right_pad_dims_to(x, t): + padding_dims = x.ndim - t.ndim + if padding_dims <= 0: + return t + return t.view(*t.shape, *((1,) * padding_dims)) + + +def thresholding_x0(x0, method='dynamic', th=0.995): + if method is None: + return x0 + elif method == 'static': + return x0.clamp(-1.0, 1.0) + elif method == 'dynamic': + # torch.quantile only suppoprt either float or double dtype + # we need to manual cast it if running in FP16/AMP mode + original_dtype = x0.dtype + if original_dtype not in [torch.float, torch.double]: + x0 = x0.float() + s = torch.quantile(rearrange(x0, 'b ... -> b (...)').abs(), th, dim=-1) # From Figure A.10 (b) + s.clamp_(min=1.0) + s = right_pad_dims_to(x0, s) + x0 = x0.clamp(-s, s) / s + return x0.type(original_dtype) + else: + raise RuntimeError(f'Thresholding method: {method} not supported.') + + +def thresholding_derivative(x, t, d, thresholding_method='dynamic'): + x0 = x - batch_mul(d, t) + corrected_x0 = thresholding_x0(x0, thresholding_method) + corrected_d = batch_div(x - corrected_x0, t) + return corrected_d + + +class Sampler(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, model, model_kwargs, shape, z=None): + pass + + +class DDPMSampler(Sampler): + def __init__(self, unet_type, denoiser): + super().__init__() + self.unet_type = unet_type + self.noise_scheduler = denoiser + self.pred_objective = 'noise' + + def p_mean_variance( + self, unet, x, t, t_next, text_embeds, text_mask, x_low_res=None, cond_scale=1.0, thresholding_method='dynamic' + ): + + if self.unet_type == 'base': + pred = unet.forward_with_cond_scale( + x=x, time=t, text_embed=text_embeds, text_mask=text_mask, cond_scale=cond_scale + ) + elif self.unet_type == 'sr': + pred = unet.forward_with_cond_scale( + x=x, x_low_res=x_low_res, time=t, text_embed=text_embeds, text_mask=text_mask, cond_scale=cond_scale + ) + + if self.pred_objective == 'noise': + x_start = self.noise_scheduler.predict_start_from_noise(x, t=t, noise=pred) + elif self.pred_objective == 'x_start': + x_start = pred + elif self.pred_objective == 'v': + x_start = self.noise_scheduler.predict_start_from_v(x, t=t, v=pred) + else: + raise ValueError(f'unknown objective {self.pred_objective}') + + x_start = thresholding_x0(x_start, method=thresholding_method) + mean_and_variance = self.noise_scheduler.q_posterior(x_start=x_start, x_t=x, t=t, t_next=t_next) + return mean_and_variance, x_start + + @torch.no_grad() + def p_sample( + self, unet, x, t, t_next, text_embeds, text_mask, x_low_res=None, cond_scale=1.0, thresholding_method='dynamic' + ): + (model_mean, _, model_log_variance), x_start = self.p_mean_variance( + unet=unet, + x=x, + t=t, + t_next=t_next, + text_embeds=text_embeds, + text_mask=text_mask, + cond_scale=cond_scale, + x_low_res=x_low_res, + thresholding_method=thresholding_method, + ) + noise = torch.randn_like(x) + # no noise when t == 0 + b = x.shape[0] + is_last_sampling_timestep = ( + (t_next == 0) if isinstance(self.noise_scheduler, GaussianDiffusionContinuousTimes) else (t == 0) + ) + nonzero_mask = (1 - is_last_sampling_timestep.type_as(x)).reshape(b, *((1,) * (len(x.shape) - 1))) + pred = model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + return pred, x_start + + def forward( + self, + model, + noise_map, + text_encoding, + text_mask, + x_low_res=None, + cond_scale=1.0, + sampling_steps=None, + thresholding_method='dynamic', + ): + batch = noise_map.shape[0] + device = noise_map.device + dtype = noise_map.dtype + original_steps = self.noise_scheduler.num_timesteps + if sampling_steps: + self.noise_scheduler.num_timesteps = sampling_steps + timesteps = self.noise_scheduler.get_sampling_timesteps(batch, device=device) + img = noise_map + for times, times_next in tqdm(timesteps, total=len(timesteps)): + img, x_start = self.p_sample( + unet=model, + x=img.type(dtype), + t=times.type(dtype), + t_next=times_next.type(dtype), + text_embeds=text_encoding, + text_mask=text_mask, + cond_scale=cond_scale, + x_low_res=x_low_res.type(dtype) if x_low_res is not None else None, + thresholding_method=thresholding_method, + ) + self.noise_scheduler.num_timesteps = original_steps + return img + + +class EDMSampler(Sampler): + def __init__( + self, + unet_type, + num_steps=50, + sigma_min=0.002, + sigma_max=80, + rho=7, + S_churn=0, + S_min=0, + S_max=float('inf'), + S_noise=1, + ): + super().__init__() + self.unet_type = unet_type + self.sigma_min = sigma_min + self.sigma_max = sigma_max + self.rho = rho + self.S_churn = S_churn + self.S_min = S_min + self.S_max = S_max + self.S_noise = S_noise + self.num_steps = num_steps + + def forward( + self, + unet, + noise_map, + text_encoding, + text_mask, + x_low_res=None, + cond_scale=1.0, + sampling_steps=None, + thresholding_method='dynamic', + ): + if self.unet_type == 'base': + assert x_low_res is None + elif self.unet_type == 'sr': + assert x_low_res is not None + low_res_cond = {'x_low_res': x_low_res} if x_low_res is not None else {} + thresholding_method = 'dynamic' + sigma_min = self.sigma_min + sigma_max = self.sigma_max + print(f'Sampling with sigma in [{sigma_min}, {sigma_max}], cfg={cond_scale}') + # Time step discretization + num_steps = sampling_steps if sampling_steps else self.num_steps + step_indices = torch.arange(num_steps, device=noise_map.device) + # Table 1: Sampling - Time steps + t_steps = ( + sigma_max ** (1 / self.rho) + + step_indices / (num_steps - 1) * (sigma_min ** (1 / self.rho) - sigma_max ** (1 / self.rho)) + ) ** self.rho + t_steps = torch.cat([t_steps, torch.zeros_like(t_steps[:1])]) # t_N = 0 + + # Main sampling loop. + x_next = noise_map * t_steps[0] + for i, (t_cur, t_next) in tqdm( + enumerate(zip(t_steps[:-1], t_steps[1:])), total=len(t_steps[:-1]) + ): # 0, ..., N-1 + x_cur = x_next + + # Increase noise temporarily. + gamma = min(self.S_churn / num_steps, np.sqrt(2) - 1) if self.S_min <= t_cur <= self.S_max else 0 + t_hat = (t_cur + gamma * t_cur).to(x_cur.device) + x_hat = x_cur + (t_hat ** 2 - t_cur ** 2).sqrt() * self.S_noise * torch.randn_like(x_cur) + + # Euler step. + denoised = unet.forward_with_cond_scale( + x=x_hat.to(torch.float32), + time=t_hat.to(torch.float32), + text_embed=text_encoding, + text_mask=text_mask, + cond_scale=cond_scale, + **low_res_cond, + ) + d_cur = (x_hat - denoised) / t_hat + d_cur = thresholding_derivative(x_hat, t_hat, d_cur, thresholding_method=thresholding_method) + x_next = x_hat + (t_next - t_hat) * d_cur + + # Apply 2nd order correction. + if i < num_steps - 1: + denoised = unet.forward_with_cond_scale( + x=x_next.to(torch.float32), + time=t_next.to(torch.float32), + text_embed=text_encoding, + text_mask=text_mask, + cond_scale=cond_scale, + **low_res_cond, + ) + d_prime = (x_next - denoised) / t_next + d_prime = thresholding_derivative(x_next, t_next, d_prime, thresholding_method=thresholding_method) + x_next = x_hat + (t_next - t_hat) * (0.5 * d_cur + 0.5 * d_prime) + return x_next diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/nerf_background_base.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/nerf_background_base.py new file mode 100644 index 0000000..90b98d0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/nerf_background_base.py @@ -0,0 +1,35 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn as nn + +# TODO(ahmadki): abstract class +class NeRFBackgroundBase(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, rays_d: torch.Tensor) -> torch.Tensor: + """ + positions = [B*N, 3] + """ + raise NotImplementedError + + def forward_net(self, rays_d_encoding: torch.Tensor) -> torch.Tensor: + raise NotImplementedError + + def forward(self, rays_d: torch.Tensor) -> torch.Tensor: + rays_d_encoding = self.encode(rays_d) + features = self.forward_net(rays_d_encoding) + features = torch.sigmoid(features) + return features diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/random_background.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/random_background.py new file mode 100644 index 0000000..2b725f6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/random_background.py @@ -0,0 +1,32 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import random +from typing import Tuple + +import torch +import torch.nn as nn + + +class RandomBackground(nn.Module): + def __init__(self, base_background: Tuple, random_ratio: float) -> None: + super().__init__() + self.random_ratio = random_ratio + self.num_output_dims = len(base_background) + self.register_buffer("base_background", torch.tensor(base_background)) + + def forward(self, rays_d: torch.Tensor) -> torch.Tensor: + if random.random() < self.random_ratio: + return torch.rand(rays_d.shape[0], self.num_output_dims).to(rays_d) + else: + return self.base_background.to(rays_d).expand(rays_d.shape[0], -1) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/static_background.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/static_background.py new file mode 100644 index 0000000..a8ac33c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/static_background.py @@ -0,0 +1,27 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Tuple + +import torch +import torch.nn as nn + + +class StaticBackground(nn.Module): + def __init__(self, background: Tuple) -> None: + super().__init__() + self.register_buffer("background", torch.tensor(background)) + + def forward(self, rays_d: torch.Tensor) -> torch.Tensor: + background = self.background.to(rays_d) + return background.expand(rays_d.shape[0], -1) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/tcnn_background.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/tcnn_background.py new file mode 100644 index 0000000..8ffc623 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/tcnn_background.py @@ -0,0 +1,45 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Dict + +import numpy as np +import tinycudann as tcnn +import torch + +from nemo.collections.multimodal.modules.nerf.background.nerf_background_base import NeRFBackgroundBase + + +class TCNNBackground(NeRFBackgroundBase): + def __init__( + self, + bound: int, + encoder_num_input_dims: int, + encoder_cfg: Dict, + background_net_num_output_dims: int, + background_net_cfg: Dict, + ): + super().__init__() + self.bound = bound + if encoder_cfg.get('per_level_scale') is None: + encoder_cfg['per_level_scale'] = np.exp2(np.log2(2048 * self.bound / 16) / (16 - 1)) + self.encoder = tcnn.Encoding(n_input_dims=encoder_num_input_dims, encoding_config=dict(encoder_cfg)) + self.background_net = tcnn.Network( + self.encoder.n_output_dims, background_net_num_output_dims, network_config=dict(background_net_cfg) + ) + + def encode(self, rays_d: torch.Tensor) -> torch.Tensor: + return self.encoder(rays_d) + + def forward_net(self, rays_d_encoding: torch.Tensor) -> torch.Tensor: + return self.background_net(rays_d_encoding) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/torchngp_background.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/torchngp_background.py new file mode 100644 index 0000000..18a5c6d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/background/torchngp_background.py @@ -0,0 +1,44 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Dict + +import torch + +from nemo.collections.multimodal.modules.nerf.background.nerf_background_base import NeRFBackgroundBase +from nemo.collections.multimodal.modules.nerf.geometry.layers import MLP +from nemo.collections.multimodal.modules.nerf.utils.torch_ngp.encoding import get_encoder + + +class TorchNGPBackground(NeRFBackgroundBase): + def __init__( + self, encoder_type: str, encoder_input_dims: int, encoder_multi_res: int, num_output_dims: int, net_cfg: Dict + ): + super().__init__() + + self.encoder, self.encoder_output_dims = get_encoder( + encoder_type, input_dim=encoder_input_dims, multires=encoder_multi_res + ) + self.background_net = MLP( + num_input_dims=self.encoder_output_dims, + num_output_dims=num_output_dims, + num_hidden_dims=net_cfg.num_hidden_dims, + num_layers=net_cfg.num_layers, + bias=net_cfg.bias, + ) + + def encode(self, rays_d: torch.Tensor) -> torch.Tensor: + return self.encoder(rays_d) + + def forward_net(self, rays_d_encoding: torch.Tensor) -> torch.Tensor: + return self.background_net(rays_d_encoding) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/dmtet.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/dmtet.py new file mode 100644 index 0000000..f6bd770 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/dmtet.py @@ -0,0 +1,163 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch + + +class DeepMarchingTetrahedra: + """ + Class for Deep Marching Tetrahedra (DMTet). + + Attributes: + device (torch.device): Device to place the tensors. + triangle_table (Tensor): Lookup table for the triangles. + num_triangles_table (Tensor): Table for the number of triangles. + base_tet_edges (Tensor): The base edges for the tetrahedrons. + """ + + def __init__(self, device: torch.device) -> None: + """Initialize DMTet instance with the given device. + + Args: + device (torch.device): The device to place the tensors on. + """ + self.device = device + self.triangle_table = self._create_triangle_table() + self.num_triangles_table = self._create_num_triangles_table() + self.base_tet_edges = self._create_base_tet_edges() + + def _create_triangle_table(self) -> torch.Tensor: + """Create the lookup table for triangles. + + Returns: + Tensor: The triangle lookup table. + """ + return torch.tensor( + [ + [-1, -1, -1, -1, -1, -1], + [1, 0, 2, -1, -1, -1], + [4, 0, 3, -1, -1, -1], + [1, 4, 2, 1, 3, 4], + [3, 1, 5, -1, -1, -1], + [2, 3, 0, 2, 5, 3], + [1, 4, 0, 1, 5, 4], + [4, 2, 5, -1, -1, -1], + [4, 5, 2, -1, -1, -1], + [4, 1, 0, 4, 5, 1], + [3, 2, 0, 3, 5, 2], + [1, 3, 5, -1, -1, -1], + [4, 1, 2, 4, 3, 1], + [3, 0, 4, -1, -1, -1], + [2, 0, 1, -1, -1, -1], + [-1, -1, -1, -1, -1, -1], + ], + dtype=torch.long, + device=self.device, + ) + + def _create_num_triangles_table(self) -> torch.Tensor: + """Create the table for number of triangles. + + Returns: + Tensor: The number of triangles table. + """ + return torch.tensor([0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long, device=self.device) + + def _create_base_tet_edges(self) -> torch.Tensor: + """Create the base edges for the tetrahedrons. + + Returns: + Tensor: The base edges for tetrahedrons. + """ + return torch.tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long, device=self.device) + + def _sort_edges(self, edges_ex2: torch.Tensor) -> torch.Tensor: + """Sort the given edges. + + Args: + edges_ex2 (Tensor): The edges to be sorted. + + Returns: + Tensor: The sorted edges. + """ + with torch.no_grad(): + order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long() + order = order.unsqueeze(dim=1) + a = torch.gather(input=edges_ex2, index=order, dim=1) + b = torch.gather(input=edges_ex2, index=1 - order, dim=1) + return torch.stack([a, b], -1) + + # TODO(ahmadki): rename to forward ? return mesh ? + def __call__(self, positions: torch.Tensor, sdf_n: torch.Tensor, tet_fx4: torch.Tensor) -> tuple: + """ + Process the provided data to generate vertices and faces. + + Args: + positions (Tensor): Position tensor with shape [N, 3]. + sdf_n (Tensor): SDF tensor with shape [N]. + tet_fx4 (Tensor): Tetrahedron faces tensor with shape [F, 4]. + + Returns: + tuple: Vertices and faces tensors. + """ + with torch.no_grad(): + occ_n = sdf_n > 0 + occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4) + occ_sum = torch.sum(occ_fx4, -1) + valid_tets = (occ_sum > 0) & (occ_sum < 4) + occ_sum = occ_sum[valid_tets] + + # find all vertices + all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2) + all_edges = self._sort_edges(all_edges) + unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True) + + unique_edges = unique_edges.long() + mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1 + mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=self.device) * -1 + mapping[mask_edges] = torch.arange(mask_edges.sum(), dtype=torch.long, device=self.device) + idx_map = mapping[idx_map] # map edges to verts + + interp_v = unique_edges[mask_edges] + + edges_to_interp = positions[interp_v.reshape(-1)].reshape(-1, 2, 3) + edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1) + edges_to_interp_sdf[:, -1] *= -1 + + denominator = edges_to_interp_sdf.sum(1, keepdim=True) + edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator + verts = (edges_to_interp * edges_to_interp_sdf).sum(1) + + idx_map = idx_map.reshape(-1, 6) + v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=self.device)) + tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1) + num_triangles = self.num_triangles_table[tetindex] + + # Generate triangle indices + faces = torch.cat( + ( + torch.gather( + input=idx_map[num_triangles == 1], + dim=1, + index=self.triangle_table[tetindex[num_triangles == 1]][:, :3], + ).reshape(-1, 3), + torch.gather( + input=idx_map[num_triangles == 2], + dim=1, + index=self.triangle_table[tetindex[num_triangles == 2]][:, :6], + ).reshape(-1, 3), + ), + dim=0, + ) + + return verts, faces diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/layers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/layers.py new file mode 100644 index 0000000..294bcfc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/layers.py @@ -0,0 +1,142 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Callable, List, Type, Union + +import torch +import torch.nn as nn + +BlockBuilder = Union[Callable[[int, int, bool], nn.Module], Type[nn.Module], None] + + +class MLP(nn.Module): + """ + A Multi-Layer Perceptron (MLP) module. + + Args: + num_input_dims (int): Number of input dimensions. + num_output_dims (int): Number of output dimensions. + num_hidden_dims (int): Number of hidden dimensions. + num_layers (int): Number of layers in the MLP. + bias (bool): If True, enables the bias in Linear layers. Default is True. + block (BlockBuilder): A callable or class for constructing a block. Default is None. + """ + + def __init__( + self, + num_input_dims: int, + num_output_dims: int, + num_hidden_dims: int, + num_layers: int, + bias: bool = True, + block: BlockBuilder = None, + ): + super().__init__() + + # Initialize the network as an empty list + network = [] + + # Add input layer + network.append(nn.Linear(num_input_dims, num_hidden_dims, bias=bias)) + network.append(nn.ReLU(inplace=True)) + + # Add hidden layers + for _ in range(1, num_layers - 1): + network.extend(self.build_layer(num_hidden_dims, num_hidden_dims, bias, block)) + + # Add output layer + network.append(nn.Linear(num_hidden_dims, num_output_dims, bias=bias)) + + # Wrap layers in ModuleList for proper registration + self.net = nn.ModuleList(network) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass through the MLP. + + Args: + x (torch.Tensor): Input tensor. + + Returns: + torch.Tensor: Output tensor. + """ + for module in self.net: + x = module(x) + return x + + @staticmethod + def build_layer( + num_input_dims: int, num_output_dims: int, bias: bool = True, block_builder: BlockBuilder = None + ) -> List[nn.Module]: + """ + Build a single layer for the MLP. + + Args: + num_input_dims (int): Number of input dimensions. + num_output_dims (int): Number of output dimensions. + bias (bool): If True, enables the bias in Linear layers. Default is True. + block_builder (BlockBuilder): A callable or class for constructing a block. Default is None. + + Returns: + List[nn.Module]: A list containing the layer's modules. + """ + if block_builder is None: + return [nn.Linear(num_input_dims, num_output_dims, bias=bias), nn.ReLU(inplace=True)] + else: + return [block_builder(num_input_dims, num_output_dims, bias=bias)] + + +class ResBlock(nn.Module): + """ + A residual block module. + + Args: + num_input_dims (int): Number of input dimensions. + num_output_dims (int): Number of output dimensions. + bias (bool): If True, enables the bias in Linear layers. Default is True. + """ + + def __init__(self, num_input_dims: int, num_output_dims: int, bias: bool = True): + super().__init__() + + self.dense = nn.Linear(num_input_dims, num_output_dims, bias=bias) + self.norm = nn.LayerNorm(num_output_dims) + self.activation = nn.SiLU(inplace=True) + + if num_input_dims != num_output_dims: + self.skip = nn.Linear(num_input_dims, num_output_dims, bias=False) + else: + self.skip = None + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass through the residual block. + + Args: + x (torch.Tensor): Input tensor. + + Returns: + torch.Tensor: Output tensor. + """ + identity = x + + out = self.dense(x) + out = self.norm(out) + + if self.skip is not None: + identity = self.skip(identity) + + out += identity + out = self.activation(out) + + return out diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/nerf_base.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/nerf_base.py new file mode 100644 index 0000000..6ea7e98 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/nerf_base.py @@ -0,0 +1,362 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from enum import Enum +from typing import Optional, Tuple + +import mcubes +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import trimesh + +from nemo.collections.multimodal.modules.nerf.utils.activation import trunc_exp + + +class DensityActivationEnum(str, Enum): + EXP = "exp" + SOFTPLUS = "softplus" + + +class NormalTypeEnum(str, Enum): + AUTOGRAD = "autograd" + FORWARD_FINITE_DIFFERENCE = "forward_finite_difference" + BACKWARD_FINITE_DIFFERENCE = "backward_finite_difference" + CENTRAL_FINITE_DIFFERENCE = "central_finite_difference" + + +# TODO(ahmadki): make abstract +class NeRFBase(nn.Module): + """ + A base class for Neural Radiance Fields (NeRF) models. + + Args: + num_input_dims (int): Number of input dimensions. + bound (torch.Tensor): The bounding box tensor. + density_activation (DensityActivationEnum): Activation function for density. + blob_radius (float): Radius for the blob. + blob_density (float): Density for the blob. + normal_type (Optional[NormalTypeEnum]): Method to compute normals. + """ + + def __init__( + self, + num_input_dims: int, + bound: torch.Tensor, + density_activation: DensityActivationEnum, + blob_radius: float, + blob_density: float, + normal_type: Optional[NormalTypeEnum] = NormalTypeEnum.CENTRAL_FINITE_DIFFERENCE, + ) -> None: + super().__init__() + self.num_input_dims = num_input_dims + self.bound = bound + self.density_activation = density_activation + self.blob_radius = blob_radius + self.blob_density = blob_density + self.normal_type = normal_type + + def encode(self, positions: torch.Tensor) -> torch.Tensor: + """Encode 3D positions. To be implemented by subclasses.""" + raise NotImplementedError + + def sigma_net(self, positions_encoding: torch.Tensor) -> torch.Tensor: + """Calculate sigma (density). To be implemented by subclasses.""" + raise NotImplementedError + + def features_net(self, positions_encoding: torch.Tensor) -> torch.Tensor: + """Calculate features. To be implemented by subclasses.""" + raise NotImplementedError + + def forward( + self, positions: torch.Tensor, return_normal: bool = True + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """ + Forward pass for the NeRF model. + + Args: + positions (torch.Tensor): The positions. + return_normal (bool): Flag to indicate whether to return normals or not. + + Returns: + Tuple containing density, features, and possibly normals. + """ + + if return_normal: + if self.normal_type == NormalTypeEnum.AUTOGRAD: + with torch.enable_grad(): + positions.requires_grad_(True) + sigma, features = self.forward_density_features(positions) + normal = -torch.autograd.grad(torch.sum(sigma), positions, create_graph=True)[0] # [N, D] + elif self.normal_type in [ + NormalTypeEnum.CENTRAL_FINITE_DIFFERENCE, + NormalTypeEnum.FORWARD_FINITE_DIFFERENCE, + NormalTypeEnum.BACKWARD_FINITE_DIFFERENCE, + ]: + sigma, features = self.forward_density_features(positions) + normal = self.normal_finite_differences(positions) + else: + raise NotImplementedError("Invalid normal type.") + + normal = F.normalize(normal) + normal = torch.nan_to_num(normal) + else: + sigma, features = self.forward_density_features(positions) + normal = None + + return sigma, features, normal + + def forward_density_features(self, positions: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Calculate both density and features based on the input positions. + + This function takes into account edge cases like empty input tensors and calculates + the density and features accordingly. See GitHub issues for details: + - https://github.com/KAIR-BAIR/nerfacc/issues/207#issuecomment-1653621720 + - https://github.com/ashawkey/torch-ngp/issues/176 + + Args: + positions (torch.Tensor): Input positions tensor with shape [B*N, D]. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple containing density and features tensors. + """ + + # Handle empty positions + if positions.shape[0] == 0: + sigma = torch.zeros(0, device=positions.device) + features = torch.zeros(0, self.num_input_dims, device=positions.device) + return sigma, features + + # Encode positions + positions_encoding = self.encode(positions) + + # Compute density + density = self.forward_density(positions, positions_encoding) + + # Compute features + features = self.forward_features(positions, positions_encoding) + + return density, features + + def forward_density( + self, positions: torch.Tensor, positions_encoding: Optional[torch.Tensor] = None + ) -> torch.Tensor: + """ + Calculate the density based on the input positions and their encoding. + + Args: + positions (torch.Tensor): Input positions tensor with shape [B*N, D]. + positions_encoding (Optional[torch.Tensor]): Optional encoded positions. + Will be computed from `positions` if not provided. + + Returns: + torch.Tensor: Density tensor. + """ + + # Handle empty positions + if positions.shape[0] == 0: + sigma = torch.zeros(0, device=positions.device) + return sigma + + # Compute encoded positions if not provided + if positions_encoding is None: + positions_encoding = self.encode(positions) + + # Compute sigma using the neural network + sigma = self.sigma_net(positions_encoding) + + # Compute density using activation function + if self.density_activation == DensityActivationEnum.EXP: + density = trunc_exp(sigma + self.density_blob(positions)) + elif self.density_activation == DensityActivationEnum.SOFTPLUS: + density = F.softplus(sigma + self.density_blob(positions)) + else: + raise NotImplementedError("Invalid density activation.") + + return density + + def forward_features( + self, positions: torch.Tensor, positions_encoding: Optional[torch.Tensor] = None + ) -> torch.Tensor: + """ + Compute the features based on the input positions and their encoding. + + Args: + positions (torch.Tensor): Input positions tensor with shape [B*N, D]. + positions_encoding (Optional[torch.Tensor]): Optional encoded positions. + Will be computed from `positions` if not provided. + + Returns: + torch.Tensor: Features tensor with shape [B*N, num_features_dims]. + """ + + # Handle empty positions + if positions.shape[0] == 0: + features = torch.zeros(0, self.num_features_dims, device=positions.device) + return features + + # Compute encoded positions if not provided + if positions_encoding is None: + positions_encoding = self.encode(positions) + + # Compute features using the neural network + features = self.features_net(positions_encoding) + + # Apply the sigmoid activation function to the features + features = torch.sigmoid(features) + + return features + + @torch.no_grad() + def density_blob(self, positions: torch.Tensor) -> torch.Tensor: + """ + Compute the density blob for the given positions. + + This method computes a density blob for each position in the tensor. It is + used to add a density value based on the distance of each position from the origin. + + Args: + positions (torch.Tensor): Input positions tensor with shape [B*N, D]. + + Returns: + torch.Tensor: Density blob tensor with shape [B*N, 1]. + """ + + # Compute the squared distance for each position + d = (positions ** 2).sum(-1) + + # Compute the density blob based on the activation function + if self.density_activation == DensityActivationEnum.EXP: + g = self.blob_density * torch.exp(-d / (2 * self.blob_radius ** 2)) + elif self.density_activation == DensityActivationEnum.SOFTPLUS: + g = self.blob_density * (1 - torch.sqrt(d) / self.blob_radius) + else: + raise NotImplementedError("Invalid density activation.") + + return g + + def normal_finite_differences(self, positions: torch.Tensor, eps: float = 1e-2) -> torch.Tensor: + """ + Calculate normals using finite differences. + + Args: + positions (torch.Tensor): Input positions tensor with shape [B*N, D]. + eps (float): A small value for finite difference calculation. Default is 1e-2. + + Returns: + torch.Tensor: Calculated normals tensor [B*N, D] + """ + # Create perturbation tensor + perturb = torch.eye(self.num_input_dims).to(positions.device).float() * eps # Shape (D, D) + + # Expand dims for batched operation + positions_expanded = positions[:, None, :] # (B*N, 1, D) + perturb_expanded = perturb[None, :, :] # (1, D, D) + + # Compute perturbed points + if self.normal_type == NormalTypeEnum.FORWARD_FINITE_DIFFERENCE: + positions_perturbed = positions_expanded + perturb_expanded # (B*N, D, D) + elif self.normal_type == NormalTypeEnum.BACKWARD_FINITE_DIFFERENCE: + positions_perturbed = positions_expanded - perturb_expanded # (B*N, D, D) + elif self.normal_type == NormalTypeEnum.CENTRAL_FINITE_DIFFERENCE: + positions_perturbed_pos = positions_expanded + perturb_expanded # (B*N, D, D) + positions_perturbed_neg = positions_expanded - perturb_expanded # (B*N, D, D) + positions_perturbed = torch.cat([positions_perturbed_pos, positions_perturbed_neg], dim=1) # (B*N, 2*D, D) + + # Reshape perturbed points for batched function call + positions_perturbed_reshaped = positions_perturbed.view(-1, self.num_input_dims) # (B*N * {D or 2*D}, D) + + # Evaluate function at perturbed points + perturbed_sigma = self.forward_density(positions_perturbed_reshaped) # (B*N * {D or 2*D}, 1) + + # Reshape function values + if self.normal_type == NormalTypeEnum.CENTRAL_FINITE_DIFFERENCE: + perturbed_sigma = perturbed_sigma.view(-1, 2 * self.num_input_dims) # (B*N, 2*D) + sigma_pos, sigma_neg = torch.chunk(perturbed_sigma, 2, dim=1) # (B*N, D) each + normal = 0.5 * (sigma_pos - sigma_neg) / eps # (B*N, D) + else: + perturbed_sigma = perturbed_sigma.view(-1, self.num_input_dims) # (B*N, D) + sigma = self.forward_density(positions) # (B*N,) # TODO(ahmadki): use the value from forward ? + if self.normal_type == NormalTypeEnum.FORWARD_FINITE_DIFFERENCE: + normal = (perturbed_sigma - sigma[:, None]) / eps # (B*N, D) + else: # self.normal_type == BACKWARD_FINITE_DIFFERENCE + normal = (sigma[:, None] - perturbed_sigma) / eps # (B*N, D) + + return -normal + + # TODO(ahmadki): needs ar ework: + # 1. texture/vertices are off-axis, needs a fix. + # 2. device='cuda' is hardcoded + # 3. DMTet needs to go through a different code path ? create a base volume nerf, and a base dmtet nerf class ? + @torch.no_grad() + def mesh( + self, resolution: Optional[int] = 128, batch_size: int = 128, density_thresh: Optional[float] = None + ) -> trimesh.base.Trimesh: + """ + Generate a mesh from the nerf. + + Args: + resolution (Optional[int]): Resolution of the mesh grid. Default is 128. + batch_size (int): Batch size for the mesh generation. Default is 128. + density_thresh (Optional[float]): Density threshold for the mesh generation. Default is None, will be calculated from mean density. + + Returns: + trimesh.base.Trimesh: Mesh object. + """ + # Generate a grid of 3D points + x = np.linspace(-self.bound, self.bound, resolution) + y = np.linspace(-self.bound, self.bound, resolution) + z = np.linspace(-self.bound, self.bound, resolution) + xx, yy, zz = np.meshgrid(x, y, z) + + grid = np.stack((xx, yy, zz), axis=-1) # Shape (resolution, resolution, resolution, 3) + torch_grid = torch.tensor(grid, dtype=torch.float32).reshape(-1, 3).to(device="cuda") + + def batch_process(fn, input, batch_size): + num_points = input.shape[0] + batches = [input[i : i + batch_size] for i in range(0, num_points, batch_size)] + results = [fn(batch) for batch in batches] + results = [result.detach().cpu().numpy() for result in results] + return np.concatenate(results, axis=0) + + density = batch_process(fn=self.forward_density, input=torch_grid, batch_size=batch_size) + density = density.reshape(resolution, resolution, resolution) + + # If not provided set density_thresh based on mean density + if density_thresh is None: + density_thresh = density[density > 1e-3].mean().item() + + # Apply Marching Cubes + vertices, triangles = mcubes.marching_cubes(density, density_thresh) + + # Create a new Mesh + mesh = trimesh.Trimesh(vertices=vertices, faces=triangles) + + # Basic mesh cleaning and optimization + mesh.remove_unreferenced_vertices() + mesh.remove_infinite_values() + mesh.remove_duplicate_faces() + + # Scale vertices back to [-self.bound, self.bound] + scaled_vertices = -self.bound + (mesh.vertices / resolution) * 2 * self.bound + mesh.vertices = scaled_vertices + + # Assigning color to vertices + scaled_vertices_torch = torch.tensor(scaled_vertices, dtype=torch.float32).to(device="cuda") + color = batch_process(fn=self.forward_features, input=scaled_vertices_torch, batch_size=batch_size) + color = (color * 255).astype(np.uint8) + mesh.visual.vertex_colors = color + + return mesh diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/tcnn_nerf.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/tcnn_nerf.py new file mode 100644 index 0000000..a7db0ee --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/tcnn_nerf.py @@ -0,0 +1,121 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Dict, Optional + +import numpy as np +import tinycudann as tcnn +import torch + +from nemo.collections.multimodal.modules.nerf.geometry.nerf_base import DensityActivationEnum, NeRFBase, NormalTypeEnum + + +# Don't fuse sigma_net with features_net: +# 1. performance benefit is questionable, especially that we sometimes require only density or features +# 2. we sacrifice generality +class TCNNNerf(NeRFBase): + """ + NeRF model with TCNN encoding and MLPs for sigma and features. + + Args: + num_input_dims (int): Number of input dimensions. + bound (torch.Tensor): The bounding box tensor. + density_activation (DensityActivationEnum): Activation function for density. + blob_radius (float): Radius for the blob. + blob_density (float): Density for the blob. + normal_type (Optional[NormalTypeEnum]): Method to compute normals. + encoder_cfg (Dict): Configuration for the TCNN encoder. + sigma_net_num_output_dims (int): Number of output dimensions for the sigma network. + sigma_net_cfg (Dict): Configuration for the sigma network. + features_net_num_output_dims (int): Number of output dimensions for the features network. + features_net_cfg (Optional[Dict]): Configuration for the features network. + """ + + def __init__( + self, + num_input_dims: int, + bound: torch.Tensor, + density_activation: DensityActivationEnum, + blob_radius: float, + blob_density: float, + normal_type: Optional[NormalTypeEnum], + encoder_cfg: Dict, + sigma_net_num_output_dims: int, + sigma_net_cfg: Dict, + features_net_num_output_dims: int, + features_net_cfg: Optional[Dict], + ) -> None: + super().__init__( + num_input_dims=num_input_dims, + bound=bound, + density_activation=density_activation, + blob_radius=blob_radius, + blob_density=blob_density, + normal_type=normal_type, + ) + + # Set per_level_scale if not set + if encoder_cfg.get('per_level_scale') is None: + encoder_cfg['per_level_scale'] = np.exp2(np.log2(2048 * self.bound / 16) / (16 - 1)) + # Build the TCNN encoder + self.encoder = tcnn.Encoding(n_input_dims=num_input_dims, encoding_config=dict(encoder_cfg)) + + # Build the sigma network + assert sigma_net_num_output_dims == 1, "sigma_net_num_output_dims!=1 is not supported" + self.sigma_tcnn = tcnn.Network( + self.encoder.n_output_dims, sigma_net_num_output_dims, network_config=dict(sigma_net_cfg) + ) + + # Build the features network + self.features_tcnn = None + if features_net_cfg is not None: + self.features_tcnn = tcnn.Network( + self.encoder.n_output_dims, features_net_num_output_dims, network_config=dict(features_net_cfg) + ) + + def encode(self, positions: torch.Tensor) -> torch.Tensor: + """ + Encode the positions using the TCNN encoder. + + Args: + positions (torch.Tensor): The positions tensor. + + Returns: + torch.Tensor: The encoded positions tensor. + """ + # TODO(ahmadki): is it safe to do with FP16 ? + return self.encoder((positions + self.bound) / (2 * self.bound)) + + def sigma_net(self, positions_encoding: torch.Tensor) -> torch.Tensor: + """ + Compute the sigma using the TCNN network. + + Args: + positions_encoding (torch.Tensor): The encoded positions tensor. + + Returns: + torch.Tensor: The sigma tensor. + """ + return self.sigma_tcnn(positions_encoding).squeeze() + + def features_net(self, positions_encoding: torch.Tensor) -> torch.Tensor: + """ + Compute the features using the TCNN network. + + Args: + positions_encoding (torch.Tensor): The encoded positions tensor. + + Returns: + torch.Tensor: The features tensor. + """ + return self.features_tcnn(positions_encoding) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/torchngp_nerf.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/torchngp_nerf.py new file mode 100644 index 0000000..4b1d5e3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/geometry/torchngp_nerf.py @@ -0,0 +1,127 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Dict, Optional + +import torch + +from nemo.collections.multimodal.modules.nerf.geometry.layers import MLP +from nemo.collections.multimodal.modules.nerf.geometry.nerf_base import DensityActivationEnum, NeRFBase, NormalTypeEnum +from nemo.collections.multimodal.modules.nerf.utils.torch_ngp.encoding import get_encoder + + +# Don't fuse sigma_net with features_net: +# 1. performance benefit is questionable, especially that we sometimes require only density or features +# 2. we sacrifice generality +class TorchNGPNerf(NeRFBase): + """ + NeRF model with Torch-NGP encoding and MLPs for sigma and features. + + Args: + num_input_dims (int): Number of input dimensions. + bound (torch.Tensor): The bounding box tensor. + density_activation (DensityActivationEnum): Activation function for density. + blob_radius (float): Radius for the blob. + blob_density (float): Density for the blob. + normal_type (Optional[NormalTypeEnum]): Method to compute normals. + encoder_type (str): Type of the encoder. + encoder_max_level (int): Maximum level of the encoder. + sigma_net_num_output_dims (int): Number of output dimensions for the sigma network. + sigma_net_cfg (Dict): Configuration for the sigma network. + features_net_num_output_dims (int): Number of output dimensions for the features network. + features_net_cfg (Optional[Dict]): Configuration for the features network. + """ + + def __init__( + self, + num_input_dims: int, + bound: torch.Tensor, + density_activation: DensityActivationEnum, + blob_radius: float, + blob_density: float, + normal_type: Optional[NormalTypeEnum], + encoder_cfg: Dict, + sigma_net_num_output_dims: int, + sigma_net_cfg: Dict, + features_net_num_output_dims: int, + features_net_cfg: Optional[Dict], + ): + super().__init__( + num_input_dims=num_input_dims, + bound=bound, + density_activation=density_activation, + blob_radius=blob_radius, + blob_density=blob_density, + normal_type=normal_type, + ) + + # Build the Torch-NGP encoder + self.encoder_max_level = encoder_cfg.get('encoder_max_level', None) + self.encoder, self.encoder_output_dims = get_encoder(input_dim=num_input_dims, **encoder_cfg) + + # Build the sigma network + assert sigma_net_num_output_dims == 1, "sigma_net_num_output_dims must be equal to 1" + self.sigma_mlp = MLP( + num_input_dims=self.encoder_output_dims, + num_output_dims=sigma_net_num_output_dims, + num_hidden_dims=sigma_net_cfg.num_hidden_dims, + num_layers=sigma_net_cfg.num_layers, + bias=sigma_net_cfg.bias, + ) + + # Build the features network + self.features_mlp = None + if features_net_cfg is not None: + self.features_mlp = MLP( + num_input_dims=self.encoder_output_dims, + num_output_dims=features_net_num_output_dims, + num_hidden_dims=features_net_cfg.num_hidden_dims, + num_layers=features_net_cfg.num_layers, + bias=features_net_cfg.bias, + ) + + def encode(self, positions: torch.Tensor) -> torch.Tensor: + """ + Encode the positions. + + Args: + positions (torch.Tensor): The positions tensor. + + Returns: + torch.Tensor: The encoded positions tensor. + """ + return self.encoder(positions, bound=self.bound, max_level=self.encoder_max_level) + + def sigma_net(self, positions_encoding: torch.Tensor) -> torch.Tensor: + """ + Compute the sigma using the sigma network. + + Args: + positions_encoding (torch.Tensor): The encoded positions tensor. + + Returns: + torch.Tensor: The sigma tensor. + """ + return self.sigma_mlp(positions_encoding).squeeze() + + def features_net(self, positions_encoding: torch.Tensor) -> torch.Tensor: + """ + Compute the features using the features network. + + Args: + positions_encoding (torch.Tensor): The encoded positions tensor. + + Returns: + torch.Tensor: The features tensor. + """ + return self.features_mlp(positions_encoding) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/stablediffusion_huggingface_pipeline.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/stablediffusion_huggingface_pipeline.py new file mode 100644 index 0000000..ed5a2fd --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/stablediffusion_huggingface_pipeline.py @@ -0,0 +1,155 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import List + +import torch +import torch.nn.functional as F +from diffusers import DDIMScheduler, StableDiffusionPipeline + +from nemo.collections.multimodal.modules.nerf.guidance.txt2img_guidance_base import Txt2ImgGuidanceBase + + +class StableDiffusion(Txt2ImgGuidanceBase): + def __init__( + self, + model_key: str = "stabilityai/stable-diffusion-2-1-base", + t_range: List[float] = [0.02, 0.98], + precision: str = "16", + device: torch.device = torch.device('cuda'), + ): + """ + Initialize StableDiffusion with model_key, t_range, precision and device. + + Parameters: + model_key (str): Pre-trained model key. + t_range (List[float]): Range for timesteps. + precision (str): Model precision ("16", "bf16" or other for float32). + device (torch.device): Device for torch tensor. + """ + super().__init__() + + self.device = device + self.model_key = model_key + self.precision_t = self._get_precision_type(precision) + + # Create model + pipe = StableDiffusionPipeline.from_pretrained(model_key, torch_dtype=self.precision_t).to(self.device) + if self.precision_t in [torch.float16, torch.bfloat16]: + pipe.unet.to(memory_format=torch.channels_last) + + self.vae = pipe.vae + self.tokenizer = pipe.tokenizer + self.text_encoder = pipe.text_encoder + self.unet = pipe.unet + self.scheduler = DDIMScheduler.from_pretrained(model_key, subfolder="scheduler", torch_dtype=self.precision_t) + + del pipe + + self.num_train_timesteps = self.scheduler.config.num_train_timesteps + self.min_step = int(self.num_train_timesteps * t_range[0]) + self.max_step = int(self.num_train_timesteps * t_range[1]) + self.alphas = self.scheduler.alphas_cumprod.to(self.device) + + def _get_precision_type(self, precision: str) -> torch.dtype: + """ + Map string precision representation to torch dtype. + + Parameters: + precision (str): String representation of precision. + + Returns: + torch.dtype: Corresponding torch dtype. + """ + precision_map = {"16": torch.float16, "bf16": torch.bfloat16} + return precision_map.get(precision, torch.float32) + + @torch.no_grad() + def get_text_embeds(self, prompt: str) -> torch.Tensor: + """ + Get text embeddings from the given prompt. + + Parameters: + prompt (str): Input text. + + Returns: + torch.Tensor: Text embeddings tensor [B, 77, 1024]. + """ + inputs = self.tokenizer( + prompt, padding='max_length', max_length=self.tokenizer.model_max_length, return_tensors='pt' + ) + embeddings = self.text_encoder(inputs.input_ids.to(self.device))[0] + return embeddings + + # @torch.compile() # TODO(ahmadki) + def train_step( + self, + text_embeddings: torch.Tensor, + pred_rgb: torch.Tensor, + guidance_scale: float = 100.0, + as_latent: bool = False, + ) -> float: + """ + Train step function for StableDiffusion. + + Parameters: + text_embeddings (torch.Tensor): Embeddings tensor [B, 512]. + pred_rgb (torch.Tensor): Predicted RGB tensor [B, 3, 512, 512]. + guidance_scale (float): Guidance scaling factor. + as_latent (bool): If True, considers pred_rgb as latent. + + Returns: + float: Loss value. + """ + if as_latent: + latents = F.interpolate(pred_rgb, (64, 64), mode='bilinear', align_corners=False) * 2 - 1 + else: + pred_rgb_512 = F.interpolate(pred_rgb, (512, 512), mode='bilinear', align_corners=False) + latents = self.encode_imgs(pred_rgb_512) + + t = torch.randint(self.min_step, self.max_step + 1, (latents.shape[0],), dtype=torch.long, device=self.device) + + with torch.no_grad(): + # add noise + noise = torch.randn_like(latents) + latents_noisy = self.scheduler.add_noise(latents, noise, t) + # pred noise + latent_model_input = torch.cat([latents_noisy] * 2) + td = torch.cat([t] * 2) + noise_pred = self.unet(latent_model_input, td, encoder_hidden_states=text_embeddings).sample + + noise_pred_uncond, noise_pred_pos = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_pos - noise_pred_uncond) + + w = 1 - self.alphas[t] + grad = w[:, None, None, None] * (noise_pred - noise) + grad = torch.nan_to_num(grad) + + targets = (latents - grad).detach() + loss = 0.5 * F.mse_loss(latents.float(), targets, reduction='sum') / latents.shape[0] + return loss + + def encode_imgs(self, imgs: torch.Tensor) -> torch.Tensor: + """ + Encode images into latent representations. + + Parameters: + imgs (torch.Tensor): Image tensor [B, 3, H, W]. + + Returns: + torch.Tensor: Encoded latent tensor. + """ + imgs = 2 * imgs - 1 + posterior = self.vae.encode(imgs).latent_dist + latents = posterior.sample() * self.vae.config.scaling_factor + return latents diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/stablediffusion_nemo_pipeline.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/stablediffusion_nemo_pipeline.py new file mode 100644 index 0000000..6c2f96d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/stablediffusion_nemo_pipeline.py @@ -0,0 +1,141 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import tempfile + +import torch +import torch.nn.functional as F +from omegaconf import OmegaConf + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import LatentDiffusion +from nemo.collections.multimodal.modules.nerf.guidance.txt2img_guidance_base import Txt2ImgGuidanceBase +from nemo.collections.multimodal.modules.stable_diffusion.distributions.distributions import ( + DiagonalGaussianDistribution, +) +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector + + +class StableDiffusion(Txt2ImgGuidanceBase): + def __init__( + self, checkpoint, sampler_type="DDIM", t_range=[0.02, 0.98], precision="16", device=torch.device('cuda') + ): + super().__init__() + + self.device = device + self.checkpoint = checkpoint + self.sampler_type = sampler_type + + cfg, state_dict = self.load_config_and_state_from_nemo(checkpoint) + + cfg.precision = precision + cfg.ckpt_path = None + cfg.unet_config.from_pretrained = None + cfg.first_stage_config.from_pretrained = None + + self.model = LatentDiffusion(cfg).to(device) + + sd_state_dict = {} + # Remove Megatron wrapper and inductor + for key, value in state_dict.items(): + key = key[6:] + sd_state_dict[key] = value + self.model.load_state_dict(sd_state_dict) + self.first_stage_model = self.model.first_stage_model + self.text_encoder = self.model.cond_stage_model.encode + + self.num_train_timesteps = self.model.num_timesteps + self.min_step = int(self.num_train_timesteps * t_range[0]) + self.max_step = int(self.num_train_timesteps * t_range[1]) + self.alphas = self.model.alphas_cumprod.to(self.device) + + @torch.no_grad() + def get_text_embeds(self, prompt): + return self.text_encoder(prompt) + + @torch.autocast(device_type="cuda") + def train_step(self, text_embeddings, pred_rgb, guidance_scale=100, as_latent=False): + + if as_latent: + latents = F.interpolate(pred_rgb, (64, 64), mode='bilinear', align_corners=False) * 2 - 1 + else: + # interp to 512x512 to be fed into vae. + pred_rgb_512 = F.interpolate(pred_rgb, (512, 512), mode='bilinear', align_corners=False) + # encode image into latents with vae, requires grad! + latents = self.encode_imgs(pred_rgb_512) + + # timestep ~ U(0.02, 0.98) to avoid very high/low noise level + t = torch.randint(self.min_step, self.max_step + 1, (latents.shape[0],), dtype=torch.long, device=self.device) + + with torch.no_grad(): + noise = torch.randn_like(latents) + latents_noisy = self.model.q_sample(x_start=latents, t=t, noise=noise) + latent_model_input = torch.cat([latents_noisy] * 2) + td = torch.cat([t] * 2) + noise_pred = self.model.apply_model(latent_model_input, td, text_embeddings) + + noise_pred_uncond, noise_pred_pos = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_pos - noise_pred_uncond) + + # w(t), sigma_t^2 + w = 1 - self.alphas[t] + grad = w[:, None, None, None] * (noise_pred - noise) + grad = torch.nan_to_num(grad) + + targets = (latents - grad).detach() + loss = 0.5 * F.mse_loss(latents.float(), targets, reduction='sum') / latents.shape[0] + return loss + + def image_encoder(self, x): + h = self.first_stage_model.encoder(x) + moments = self.first_stage_model.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def encode_imgs(self, imgs): + # imgs: [B, 3, H, W] + + imgs = 2 * imgs - 1 + + posterior = self.image_encoder(imgs) + latents = ( + posterior.sample() * self.image_encoder.config.scaling_factor + ) # self.vae.config.scaling_factor==0.18215 + + return latents + + def load_config_and_state_from_nemo(self, nemo_path): + if torch.cuda.is_available(): + map_location = torch.device('cuda') + else: + map_location = torch.device('cpu') + save_restore_connector = NLPSaveRestoreConnector() + cwd = os.getcwd() + + with tempfile.TemporaryDirectory() as tmpdir: + try: + save_restore_connector._unpack_nemo_file(path2file=nemo_path, out_folder=tmpdir) + + # Change current working directory to + os.chdir(tmpdir) + config_yaml = os.path.join(tmpdir, save_restore_connector.model_config_yaml) + cfg = OmegaConf.load(config_yaml) + + model_weights = os.path.join(tmpdir, save_restore_connector.model_weights_ckpt) + state_dict = save_restore_connector._load_state_dict_from_disk( + model_weights, map_location=map_location + ) + finally: + os.chdir(cwd) + + return cfg, state_dict diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/stablediffusion_trt_pipeline.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/stablediffusion_trt_pipeline.py new file mode 100644 index 0000000..884c862 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/stablediffusion_trt_pipeline.py @@ -0,0 +1,234 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +import os +import tempfile + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from omegaconf import OmegaConf +from polygraphy import cuda +from transformers import CLIPTokenizer + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import LatentDiffusion +from nemo.collections.multimodal.modules.nerf.guidance.txt2img_guidance_base import Txt2ImgGuidanceBase +from nemo.collections.multimodal.modules.nerf.utils.trt_engine import Engine, device_view +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import ( + extract_into_tensor, + make_beta_schedule, +) +from nemo.collections.multimodal.parts.stable_diffusion.utils import default +from nemo.collections.multimodal.parts.utils import randn_like +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector + + +class LatentDiffusionWrapper(Txt2ImgGuidanceBase): + def __init__(self, plan_dir, checkpoint): + super().__init__() + with open(os.path.join(plan_dir, "conf.yaml"), "rb") as fp: + config = OmegaConf.load(fp.name) + max_batch_size = config.batch_size + + self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") + self.max_length = config.clip.max_length + self.rng = torch.Generator(device=torch.cuda.current_device(),) + + self.set_beta_schedule() + + stream = cuda.Stream() + + self.image_encoder = self.load_vae_from_checkpoint(checkpoint) + + self.text_encoder = Engine(os.path.join(plan_dir, "clip.plan")) + shape_dict = {'tokens': config.clip.tokens, 'logits': config.clip.logits} + self.text_encoder.set_engine(stream, shape_dict) + + self.unet = Engine(os.path.join(plan_dir, "unet.plan")) + shape_dict = { + 'x': config.unet.x, + 't': (max_batch_size * 2,), + 'context': config.unet.context, + 'logits': config.unet.logits, + } + self.unet.set_engine(stream, shape_dict) + + def set_beta_schedule(self): + betas = make_beta_schedule("linear", 1000, linear_start=0.00085, linear_end=0.0120, cosine_s=0.008) + alphas = 1.0 - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + betas = torch.tensor(betas) + alphas = torch.tensor(alphas) + alphas_cumprod = torch.tensor(alphas_cumprod) + to_torch = lambda x: x.clone().detach().to(torch.float32).to(torch.cuda.current_device()) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1.0 - alphas_cumprod.cpu()))) + (timesteps,) = betas.shape + self.num_timesteps = int(timesteps) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: randn_like(x_start, generator=self.rng)) + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise + ) + + def encode_imgs(self, imgs): + imgs = 2 * imgs - 1 + posterior = self.image_encoder(imgs) + latents = posterior.sample() * 0.18215 + return latents + + def clip_encode(self, text): + batch_encoding = self.tokenizer( + text, + truncation=True, + max_length=self.max_length, + return_length=True, + return_overflowing_tokens=False, + padding="max_length", + return_tensors="pt", + ) + tokens = batch_encoding["input_ids"].to("cuda", non_blocking=True) + z = self.text_encoder.infer({"tokens": device_view(tokens.type(torch.int32))})['logits'].clone() + seq_len = (z.shape[1] + 8 - 1) // 8 * 8 + z = torch.nn.functional.pad(z, (0, 0, 0, seq_len - z.shape[1]), value=0.0) + return z + + def apply_model(self, x, t, cond, return_ids=False): + self.conditioning_key = "crossattn" + if isinstance(cond, dict): + # hybrid case, cond is exptected to be a dict + pass + else: + if not isinstance(cond, list): + cond = [cond] + # key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' + key = 'c_crossattn' + cond = {key: cond} + # UNET TRT + cc = torch.cat(cond['c_crossattn'], 1) # needs to be changed I think + out = self.unet.infer( + { + "x": device_view(x.contiguous()), + "t": device_view(t.type(torch.int32).contiguous()), + "context": device_view(cc.contiguous()), + } + )['logits'].clone() + if isinstance(out, tuple) and not return_ids: + return out[0] + else: + return out + + def load_vae_from_checkpoint(self, checkpoint): + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + cfg, state_dict = self.load_config_and_state_from_nemo(checkpoint) + + if cfg.get('unet_config') and cfg.get('unet_config').get('from_pretrained'): + cfg.unet_config.from_pretrained = None + if cfg.get('first_stage_config') and cfg.get('first_stage_config').get('from_pretrained'): + cfg.first_stage_config.from_pretrained = None + + model = LatentDiffusion(cfg).to(device) + + sd_state_dict = {} + for key, value in state_dict.items(): + key = key[6:] + sd_state_dict[key] = value + model.load_state_dict(sd_state_dict) + + return model.first_stage_model.encode + + def load_config_and_state_from_nemo(self, nemo_path): + if torch.cuda.is_available(): + map_location = torch.device('cuda') + else: + map_location = torch.device('cpu') + save_restore_connector = NLPSaveRestoreConnector() + cwd = os.getcwd() + + with tempfile.TemporaryDirectory() as tmpdir: + try: + save_restore_connector._unpack_nemo_file(path2file=nemo_path, out_folder=tmpdir) + + # Change current working directory to + os.chdir(tmpdir) + config_yaml = os.path.join(tmpdir, save_restore_connector.model_config_yaml) + cfg = OmegaConf.load(config_yaml) + + model_weights = os.path.join(tmpdir, save_restore_connector.model_weights_ckpt) + state_dict = save_restore_connector._load_state_dict_from_disk( + model_weights, map_location=map_location + ) + finally: + os.chdir(cwd) + + return cfg, state_dict + + +class StableDiffusion(nn.Module): + def __init__(self, plan_dir, checkpoint, sampler_type="DDIM", t_range=[0.02, 0.98], device=torch.device('cuda')): + super().__init__() + logging.info(f'loading stable diffusion...') + + self.device = device + self.sampler_type = sampler_type + self.model = LatentDiffusionWrapper(plan_dir, checkpoint) + + self.text_encoder = self.model.clip_encode + + self.num_train_timesteps = self.model.num_timesteps + self.min_step = int(self.num_train_timesteps * t_range[0]) + self.max_step = int(self.num_train_timesteps * t_range[1]) + self.alphas = self.model.alphas_cumprod.to(self.device) # for convenience + + logging.info(f'loaded stable diffusion!') + + @torch.no_grad() + def get_text_embeds(self, prompt): + return self.text_encoder(prompt) + + def train_step(self, text_embeddings, pred_rgb, guidance_scale=100, as_latent=False): + + if as_latent: + latents = F.interpolate(pred_rgb, (64, 64), mode='bilinear', align_corners=False) * 2 - 1 + else: + # interp to 512x512 to be fed into vae. + pred_rgb_512 = F.interpolate(pred_rgb, (512, 512), mode='bilinear', align_corners=False) + # encode image into latents with vae, requires grad! + latents = self.model.encode_imgs(pred_rgb_512) + + # timestep ~ U(0.02, 0.98) to avoid very high/low noise level + t = torch.randint(self.min_step, self.max_step + 1, (latents.shape[0],), dtype=torch.long, device=self.device) + + with torch.no_grad(): + noise = torch.randn_like(latents) + latents_noisy = self.model.q_sample(x_start=latents, t=t, noise=noise) + latent_model_input = torch.cat([latents_noisy] * 2) + td = torch.cat([t] * 2) + noise_pred = self.model.apply_model(latent_model_input, td, text_embeddings) + + noise_pred_uncond, noise_pred_pos = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_pos - noise_pred_uncond) + + # w(t), sigma_t^2 + w = 1 - self.alphas[t] + grad = w[:, None, None, None] * (noise_pred - noise) + grad = torch.nan_to_num(grad) + + targets = (latents - grad).detach() + loss = 0.5 * F.mse_loss(latents.float(), targets, reduction='sum') / latents.shape[0] + return loss diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/txt2img_guidance_base.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/txt2img_guidance_base.py new file mode 100644 index 0000000..db82584 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/guidance/txt2img_guidance_base.py @@ -0,0 +1,19 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch.nn as nn + + +class Txt2ImgGuidanceBase(nn.Module): + def __init__(self): + super().__init__() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/loss/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/loss/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/loss/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/loss/laplacian_smooth_loss.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/loss/laplacian_smooth_loss.py new file mode 100644 index 0000000..93b9398 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/loss/laplacian_smooth_loss.py @@ -0,0 +1,51 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn as nn + + +class LaplacianSmoothLoss(nn.Module): + def __init__(self): + super(LaplacianSmoothLoss, self).__init__() + + @torch.cuda.amp.autocast(enabled=False) + def forward(self, verts, faces): + with torch.no_grad(): + L = self.laplacian_uniform(verts, faces.long()) + loss = L.mm(verts) + loss = loss.norm(dim=1) + loss = loss.mean() + return loss + + # TODO(ahmadki): should be moved to a separate mesh class + def laplacian_uniform(self, verts, faces): + V = verts.shape[0] + F = faces.shape[0] + + # Neighbor indices + ii = faces[:, [1, 2, 0]].flatten() + jj = faces[:, [2, 0, 1]].flatten() + adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(dim=1) + adj_values = torch.ones(adj.shape[1], device=verts.device, dtype=torch.float) + + # Diagonal indices + diag_idx = adj[0] + + # Build the sparse matrix + idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1) + values = torch.cat((-adj_values, adj_values)) + + # The coalesce operation sums the duplicate indices, resulting in the + # correct diagonal + return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/loss/normal_consistency_loss.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/loss/normal_consistency_loss.py new file mode 100644 index 0000000..ef0c31d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/loss/normal_consistency_loss.py @@ -0,0 +1,69 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn as nn + + +class NormalConsistencyLoss(nn.Module): + def __init__(self): + super(NormalConsistencyLoss, self).__init__() + + # TODO(ahmadki): is this safe to do in FP16 ? + def forward(self, face_normals, t_pos_idx): + tris_per_edge = self.compute_edge_to_face_mapping(t_pos_idx) + + # Fetch normals for both faces sharind an edge + n0 = face_normals[tris_per_edge[:, 0], :] + n1 = face_normals[tris_per_edge[:, 1], :] + + # Compute error metric based on normal difference + term = torch.clamp(torch.sum(n0 * n1, -1, keepdim=True), min=-1.0, max=1.0) + term = 1.0 - term + + return torch.mean(torch.abs(term)) + + # TODO(ahmadki): should belog to mesh class + def compute_edge_to_face_mapping(self, attr_idx): + with torch.no_grad(): + # Get unique edges + # Create all edges, packed by triangle + all_edges = torch.cat( + ( + torch.stack((attr_idx[:, 0], attr_idx[:, 1]), dim=-1), + torch.stack((attr_idx[:, 1], attr_idx[:, 2]), dim=-1), + torch.stack((attr_idx[:, 2], attr_idx[:, 0]), dim=-1), + ), + dim=-1, + ).view(-1, 2) + + # Swap edge order so min index is always first + order = (all_edges[:, 0] > all_edges[:, 1]).long().unsqueeze(dim=1) + sorted_edges = torch.cat( + (torch.gather(all_edges, 1, order), torch.gather(all_edges, 1, 1 - order)), dim=-1 + ) + + # Elliminate duplicates and return inverse mapping + unique_edges, idx_map = torch.unique(sorted_edges, dim=0, return_inverse=True) + + tris = torch.arange(attr_idx.shape[0]).repeat_interleave(3).cuda() + + tris_per_edge = torch.zeros((unique_edges.shape[0], 2), dtype=torch.int64).cuda() + + # Compute edge to face table + mask0 = order[:, 0] == 0 + mask1 = order[:, 0] == 1 + tris_per_edge[idx_map[mask0], 0] = tris[mask0] + tris_per_edge[idx_map[mask1], 1] = tris[mask1] + + return tris_per_edge diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/materials/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/materials/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/materials/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/materials/basic_shading.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/materials/basic_shading.py new file mode 100644 index 0000000..45d41b2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/materials/basic_shading.py @@ -0,0 +1,79 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Optional + +import torch + +from nemo.collections.multimodal.modules.nerf.materials.materials_base import MaterialsBase, ShadingEnum + + +class BasicShading(MaterialsBase): + """ + Material model for handling various shading types. + """ + + def __init__(self): + super(BasicShading, self).__init__() + self.specular = torch.nn.Parameter(torch.rand(3)) + self.shininess = torch.nn.Parameter(torch.rand(1)) + + def forward( + self, + albedo: torch.Tensor, + normals: torch.Tensor, + light_d: torch.Tensor, + ambient_ratio: float, + shading_type: Optional[ShadingEnum] = None, + ) -> torch.Tensor: + """ + Apply material and shading to the input RGB tensor. + + Args: + albedo (Tensor): Base albedo values. + normals (Tensor): Normal vectors at each ray intersection. + light_d (Tensor): Light direction. + ambient_ratio (float): Ratio for ambient lighting. + shading_type (ShadingEnum): The type of shading to apply + + Returns: + Tensor: The output RGB tensor after applying material and shading. + """ + if shading_type is None: + return albedo + elif shading_type == ShadingEnum.TEXTURELESS: + return torch.ones_like(albedo) * ambient_ratio + elif shading_type == ShadingEnum.NORMAL: + return (normals + 1) / 2 # Map normals from [-1, 1] to [0, 1] + elif shading_type in [ShadingEnum.LAMBERTIAN, ShadingEnum.PHONG]: + # Ambient light + ambient_light = ambient_ratio * albedo + # Dot product between light direction and normals + dot_product = torch.sum(normals * light_d, dim=1, keepdim=True) + # Lambertian term + diffuse_term = albedo * torch.clamp(dot_product, min=0) + + if shading_type == ShadingEnum.LAMBERTIAN: + return ambient_light + diffuse_term + elif shading_type == ShadingEnum.PHONG: + # Phong specular term + specular_term = ( + self.specular + * (self.shininess + 2) + * torch.pow(torch.clamp(dot_product, min=0), self.shininess) + / (2 * 3.14159) + ) + + return ambient_light + diffuse_term + specular_term + else: + raise ValueError(f"Unknown shading_type: {shading_type}") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/materials/materials_base.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/materials/materials_base.py new file mode 100644 index 0000000..be8e816 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/materials/materials_base.py @@ -0,0 +1,41 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from enum import Enum + +from torch import nn + + +class ShadingEnum(str, Enum): + TEXTURELESS = "textureless" + NORMAL = "normal" + LAMBERTIAN = "lambertian" + PHONG = "phong" + + # TODO(ahmadki): + # Oren–Nayar + # Minnaert + # Cook–Torrance + # Ward anisotropic + # Hanrahan–Krueger + # Cel shading + # Gooch shading + + +class MaterialsBase(nn.Module): + """ + Base class for materials. + """ + + def __init__(self): + super(MaterialsBase, self).__init__() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/base_renderer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/base_renderer.py new file mode 100644 index 0000000..61753bc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/base_renderer.py @@ -0,0 +1,31 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn as nn + +# TODO(ahmadki): make abstract +class BaseRenderer(nn.Module): + def __init__(self, bound, update_interval): + super().__init__() + self.bound = bound + aabb = torch.FloatTensor([-bound, -bound, -bound, bound, bound, bound]) + self.register_buffer('aabb', aabb) + self.update_interval = update_interval + + @torch.no_grad() + def update_step(self, epoch: int, global_step: int, decay: float = 0.95, **kwargs): + raise NotImplementedError + + def forward(self, rays_o, rays_d, return_normal_image=False, return_normal_perturb=False, **kwargs): + raise NotImplementedError diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/base_sdf_renderer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/base_sdf_renderer.py new file mode 100644 index 0000000..48450fc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/base_sdf_renderer.py @@ -0,0 +1,33 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch + +from nemo.collections.multimodal.modules.renderer.base_renderer import RendererBase + + +class BaseSDFRenderer(RendererBase): + def __init__(self, bound): + super().__init__(bound) + + # TODO(ahmadki): needs a rework + @torch.no_grad() + def get_vertices_and_triangles(self, resolution=None, S=128): + deform = torch.tanh(self.deform) / self.grid_size + + vertices, triangles = self.dmtet(self.verts + deform, self.sdf, self.indices) + + vertices = vertices.detach().cpu().numpy() + triangles = triangles.detach().cpu().numpy() + + return vertices, triangles diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/base_volume_renderer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/base_volume_renderer.py new file mode 100644 index 0000000..4801b0e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/base_volume_renderer.py @@ -0,0 +1,19 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from nemo.collections.multimodal.modules.renderer.base_renderer import RendererBase + + +class BaseVolumeRenderer(RendererBase): + def __init__(self, bound, update_interval): + super().__init__(bound, update_interval) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/nerfacc_volume_renderer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/nerfacc_volume_renderer.py new file mode 100644 index 0000000..3bf74b8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/nerfacc_volume_renderer.py @@ -0,0 +1,376 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import collections +from typing import Optional + +import torch +from nerfacc.estimators.occ_grid import OccGridEstimator +from nerfacc.grid import ray_aabb_intersect, traverse_grids +from nerfacc.volrend import accumulate_along_rays_, render_weight_from_density, rendering + +from nemo.collections.multimodal.modules.renderer.base_renderer import BaseRenderer + +Rays = collections.namedtuple("Rays", ("origins", "viewdirs")) + + +def namedtuple_map(fn, tup): + """Apply `fn` to each element of `tup` and cast to `tup`'s namedtuple.""" + return type(tup)(*(None if x is None else fn(x) for x in tup)) + + +def render_image_with_occgrid( + # scene + nerf: torch.nn.Module, + estimator: OccGridEstimator, + rays: Rays, + # rendering options + near_plane: float = 0.0, + far_plane: float = 1e10, + render_step_size: float = 1e-3, + render_bkgd: Optional[torch.Tensor] = None, + cone_angle: float = 0.0, + alpha_thre: float = 0.0, + # test options + test_chunk_size: int = 8192, +): + """Render the pixels of an image.""" + rays_shape = rays.origins.shape + if len(rays_shape) == 3: + height, width, _ = rays_shape + num_rays = height * width + rays = namedtuple_map(lambda r: r.reshape([num_rays] + list(r.shape[2:])), rays) + else: + num_rays, _ = rays_shape + + # TODO(ahmadki): optimize, cache result between sigma_fn and rgb_sigma_fn + def sigma_fn(t_starts, t_ends, ray_indices): + t_origins = chunk_rays.origins[ray_indices] + t_dirs = chunk_rays.viewdirs[ray_indices] + positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0 + sigmas = nerf.density(positions)['sigma'] + return sigmas + + def rgb_sigma_fn(t_starts, t_ends, ray_indices): + t_origins = chunk_rays.origins[ray_indices] + t_dirs = chunk_rays.viewdirs[ray_indices] + positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0 + sigmas, rgbs, normal = nerf( + positions=positions, view_dirs=None, light_dirs=t_dirs + ) # TODO(ahmadki): t_dirs is incorrect + return rgbs, sigmas + + results = [] + chunk = torch.iinfo(torch.int32).max if nerf.training else test_chunk_size + + for i in range(0, num_rays, chunk): + chunk_rays = namedtuple_map(lambda r: r[i : i + chunk], rays) + ray_indices, t_starts, t_ends = estimator.sampling( + chunk_rays.origins, + chunk_rays.viewdirs, + sigma_fn=sigma_fn, + near_plane=near_plane, + far_plane=far_plane, + render_step_size=render_step_size, + stratified=nerf.training, + cone_angle=cone_angle, + alpha_thre=alpha_thre, + ) + rgb, opacity, depth, extras = rendering( + t_starts, + t_ends, + ray_indices, + n_rays=chunk_rays.origins.shape[0], + rgb_sigma_fn=rgb_sigma_fn, + render_bkgd=render_bkgd, + ) + + weight = extras["weights"] + alpha = extras["alphas"] + + chunk_results = [rgb, opacity, depth, weight, alpha, len(t_starts)] + results.append(chunk_results) + + colors, opacities, depths, weights, alphas, n_rendering_samples = [ + torch.cat(r, dim=0) if isinstance(r[0], torch.Tensor) else r for r in zip(*results) + ] + + return ( + colors.view((*rays_shape[:-1], -1)), + opacities.view((*rays_shape[:-1], -1)), + depths.view((*rays_shape[:-1], -1)), + weights, + alphas, + sum(n_rendering_samples), + ) + + +@torch.no_grad() +def render_image_with_occgrid_test( + max_samples: int, + # scene + nerf: torch.nn.Module, + estimator: OccGridEstimator, + rays: Rays, + # rendering options + near_plane: float = 0.0, + far_plane: float = 1e10, + render_step_size: float = 1e-3, + render_bkgd: Optional[torch.Tensor] = None, + cone_angle: float = 0.0, + alpha_thre: float = 0.0, + early_stop_eps: float = 1e-4, +): + """Render the pixels of an image.""" + rays_shape = rays.origins.shape + if len(rays_shape) == 3: + height, width, _ = rays_shape + num_rays = height * width + rays = namedtuple_map(lambda r: r.reshape([num_rays] + list(r.shape[2:])), rays) + else: + num_rays, _ = rays_shape + + def rgb_sigma_fn(t_starts, t_ends, ray_indices): + t_origins = rays.origins[ray_indices] + t_dirs = rays.viewdirs[ray_indices] + positions = t_origins + t_dirs * (t_starts[:, None] + t_ends[:, None]) / 2.0 + sigmas, rgbs, normal = nerf( + positions=positions, view_dirs=None, light_dirs=t_dirs + ) # TODO(ahmadki): t_dirs is incorrect ? + return rgbs, sigmas + + device = rays.origins.device + opacity = torch.zeros(num_rays, 1, device=device) + depth = torch.zeros(num_rays, 1, device=device) + rgb = torch.zeros(num_rays, 3, device=device) + + ray_mask = torch.ones(num_rays, device=device).bool() + + # 1 for synthetic scenes, 4 for real scenes + min_samples = 1 if cone_angle == 0 else 4 + + iter_samples = total_samples = 0 + + rays_o = rays.origins + rays_d = rays.viewdirs + + near_planes = torch.full_like(rays_o[..., 0], fill_value=near_plane) + far_planes = torch.full_like(rays_o[..., 0], fill_value=far_plane) + + t_mins, t_maxs, hits = ray_aabb_intersect(rays_o, rays_d, estimator.aabbs) + + n_grids = estimator.binaries.size(0) + + if n_grids > 1: + t_sorted, t_indices = torch.sort(torch.cat([t_mins, t_maxs], -1), -1) + else: + t_sorted = torch.cat([t_mins, t_maxs], -1) + t_indices = torch.arange(0, n_grids * 2, device=t_mins.device, dtype=torch.int64).expand(num_rays, n_grids * 2) + + opc_thre = 1 - early_stop_eps + + while iter_samples < max_samples: + + n_alive = ray_mask.sum().item() + if n_alive == 0: + break + + # the number of samples to add on each ray + n_samples = max(min(num_rays // n_alive, 64), min_samples) + iter_samples += n_samples + + # ray marching + (intervals, samples, termination_planes) = traverse_grids( + # rays + rays_o, # [n_rays, 3] + rays_d, # [n_rays, 3] + # grids + estimator.binaries, # [m, resx, resy, resz] + estimator.aabbs, # [m, 6] + # options + near_planes, # [n_rays] + far_planes, # [n_rays] + render_step_size, + cone_angle, + n_samples, + True, + ray_mask, + # pre-compute intersections + t_sorted, # [n_rays, m*2] + t_indices, # [n_rays, m*2] + hits, # [n_rays, m] + ) + t_starts = intervals.vals[intervals.is_left] + t_ends = intervals.vals[intervals.is_right] + ray_indices = samples.ray_indices[samples.is_valid] + packed_info = samples.packed_info + + # get rgb and sigma from radiance field + rgbs, sigmas = rgb_sigma_fn(t_starts, t_ends, ray_indices) + # volume rendering using native cuda scan + weights, _, alphas = render_weight_from_density( + t_starts, + t_ends, + sigmas, + ray_indices=ray_indices, + n_rays=num_rays, + prefix_trans=1 - opacity[ray_indices].squeeze(-1), + ) + if alpha_thre > 0: + vis_mask = alphas >= alpha_thre + ray_indices, rgbs, weights, t_starts, t_ends = ( + ray_indices[vis_mask], + rgbs[vis_mask], + weights[vis_mask], + t_starts[vis_mask], + t_ends[vis_mask], + ) + + accumulate_along_rays_( + weights, values=rgbs, ray_indices=ray_indices, outputs=rgb, + ) + accumulate_along_rays_( + weights, values=None, ray_indices=ray_indices, outputs=opacity, + ) + accumulate_along_rays_( + weights, values=(t_starts + t_ends)[..., None] / 2.0, ray_indices=ray_indices, outputs=depth, + ) + # update near_planes using termination planes + near_planes = termination_planes + # update rays status + ray_mask = torch.logical_and( + # early stopping + opacity.view(-1) <= opc_thre, + # remove rays that have reached the far plane + packed_info[:, 1] == n_samples, + ) + total_samples += ray_indices.shape[0] + + if render_bkgd is not None: + rgb = rgb + render_bkgd * (1.0 - opacity) + + depth = depth / opacity.clamp_min(torch.finfo(rgbs.dtype).eps) + + return ( + rgb.view((*rays_shape[:-1], -1)), + opacity.view((*rays_shape[:-1], -1)), + depth.view((*rays_shape[:-1], -1)), + weights, + alphas, + total_samples, + ) + + +class NerfaccVolumeBaseRenderer(BaseRenderer): + def __init__( + self, + bound, + grid_resolution, + grid_levels, + render_step_size=1e-3, + near_plane=0.2, + cone_angle=0.004, + alpha_thre=1e-2, + ): + + super().__init__(bound) + + self.grid_resolution = grid_resolution + self.grid_levels = grid_levels + self.render_step_size = render_step_size + self.near_plane = near_plane + self.cone_angle = cone_angle + self.alpha_thre = alpha_thre + self.nerf = None + + self.estimator = OccGridEstimator(roi_aabb=self.aabb, resolution=self.grid_resolution, levels=self.grid_levels) + + @torch.no_grad() # TODO(ahmadki) + def update_step( + self, + epoch: int, + global_step: int, + update_interval: int = 16, + decay: float = 0.95, + occ_thre: float = 0.01, + warmup_steps: int = 256, + **kwargs + ): + def occ_eval_fn(x): + density = self.nerf.forward_density(x) + return density * self.render_step_size + + self.estimator.update_every_n_steps( + step=global_step, + occ_eval_fn=occ_eval_fn, + occ_thre=occ_thre, + ema_decay=decay, + warmup_steps=warmup_steps, + n=update_interval, + ) + + def forward(self, rays_o, rays_d, mvp, h, w, staged=False, max_ray_batch=4096, step=None, **kwargs): + return self._render(rays_o=rays_o, rays_d=rays_d, step=step, **kwargs) + + def _render( + self, + rays_o, + rays_d, + light_d=None, + ambient_ratio=1.0, + shading='albedo', + bg_color=None, + perturb=False, + T_thresh=1e-4, + binarize=False, + step=None, + **kwargs + ): + rays_o = rays_o.contiguous().view(-1, 3) + rays_d = rays_d.contiguous().view(-1, 3) + + N = rays_o.shape[0] # N = B * N, in fact + + rays = Rays(origins=rays_o, viewdirs=rays_d) + + if self.training: + rgb, acc, depth, weights, alphas, n_rendering_samples = render_image_with_occgrid( + nerf=self.nerf, + estimator=self.estimator, + rays=rays, + near_plane=self.near_plane, + render_step_size=self.render_step_size, + render_bkgd=bg_color, + cone_angle=self.cone_angle, + alpha_thre=self.alpha_thre, + ) + else: + rgb, acc, depth, weights, alphas, n_rendering_samples = render_image_with_occgrid_test( + max_samples=1024, + nerf=self.nerf, + estimator=self.estimator, + rays=rays, + near_plane=self.near_plane, + render_step_size=self.render_step_size, + render_bkgd=bg_color, + cone_angle=self.cone_angle, + alpha_thre=self.alpha_thre, + ) + + results = {} + results['weights'] = weights + results['image'] = rgb.view(1, -1, 3) + results['depth'] = depth.view(1, -1) + results['weights_sum'] = acc.view(1, -1) + + return results diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/nvdiffrast_renderer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/nvdiffrast_renderer.py new file mode 100644 index 0000000..ef8472c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/nvdiffrast_renderer.py @@ -0,0 +1,235 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math + +import numpy as np +import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F + +from nemo.collections.multimodal.modules.nerf.geometry.dmtet import DeepMarchingTetrahedra +from nemo.collections.multimodal.modules.nerf.geometry.nerf_base import DensityActivationEnum +from nemo.collections.multimodal.modules.nerf.renderers.base_renderer import BaseRenderer + + +# TODO: self.density_thresh, self.mean_density need a rework, they can be infered at run time +# and shouldn't be loaded from the checkpoint +class NVDiffRastRenderer(BaseRenderer): + def __init__(self, bound, update_interval, grid_resolution, density_thresh, quartet_file): + + super().__init__(bound, update_interval) + + self.grid_resolution = grid_resolution + self.density_thresh = density_thresh + self.quartet_file = quartet_file + + self.cascade = 1 + math.ceil(math.log2(bound)) + density_grid = torch.zeros([self.cascade, self.grid_resolution ** 3]) # [CAS, H * H * H] + density_bitfield = torch.zeros( + self.cascade * self.grid_resolution ** 3 // 8, dtype=torch.uint8 + ) # [CAS * H * H * H // 8] + self.register_buffer('density_grid', density_grid) + self.register_buffer('density_bitfield', density_bitfield) + self.mean_density = 0 + self.iter_density = 0 + + # load dmtet vertices + # TODO(ahmadki): hard coded devices + tets = np.load(quartet_file) + self.verts = -torch.tensor(tets['vertices'], dtype=torch.float32, device='cuda') * 2 # covers [-1, 1] + self.indices = torch.tensor(tets['indices'], dtype=torch.long, device='cuda') + self.tet_scale = torch.tensor([1, 1, 1], dtype=torch.float32, device='cuda') + self.dmtet = DeepMarchingTetrahedra(device='cuda') + + # vert sdf and deform + sdf = torch.nn.Parameter(torch.zeros_like(self.verts[..., 0]), requires_grad=True) + self.register_parameter('sdf', sdf) + deform = torch.nn.Parameter(torch.zeros_like(self.verts), requires_grad=True) + self.register_parameter('deform', deform) + + edges = torch.tensor( + [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long, device="cuda" + ) # six edges for each tetrahedron. + all_edges = self.indices[:, edges].reshape(-1, 2) # [M * 6, 2] + all_edges_sorted = torch.sort(all_edges, dim=1)[0] + self.all_edges = torch.unique(all_edges_sorted, dim=0) + + self.initialized = False # TODO(ahmadki): not a good approach + + self.glctx = dr.RasterizeCudaContext() + + # TODO(ahmadki): not a good approach + self.nerf = None + self.material = None + self.background = None + + # TODO(ahmkadi): doesn't look good to me !! + @torch.no_grad() + def update_step(self, epoch: int, global_step: int, decay: float = 0.95, S: int = 128, **kwargs): + pass + + @torch.no_grad() + def init_tet(self): + # TODO(ahmadki): a better approach would be to have a global nerf representation (mesh) that + # we can init the tets from. this would work with checkpoints. + + # TODO(ahmadki): a placeholder, but it works for now + self.mean_density = 300 + density_thresh = min(self.mean_density, self.density_thresh) + + if self.nerf.density_activation == DensityActivationEnum.SOFTPLUS: + density_thresh = density_thresh * 25 + + # Get initial sigma + sigma = self.nerf.forward_density(positions=self.verts) + mask = sigma > density_thresh + valid_verts = self.verts[mask] + self.tet_scale = valid_verts.abs().amax(dim=0) + 1e-1 + + # Scale vertices + self.verts = self.verts * self.tet_scale + + # get sigma using the scaled vertices + sigma = self.nerf.forward_density(positions=self.verts) + self.sdf.data += (sigma - density_thresh).clamp(-1, 1) + + def forward( + self, + rays_o, + rays_d, + mvp, + light_d=None, + ambient_ratio=1.0, + shading_type=None, + return_normal_image=False, + return_vertices=False, + return_faces=False, + return_faces_normals=False, + **kwargs + ): + if not self.initialized: + self.init_tet() + self.initialized = True + return self._render( + rays_o=rays_o, + rays_d=rays_d, + mvp=mvp, + light_d=light_d, + ambient_ratio=ambient_ratio, + shading_type=shading_type, + return_normal_image=return_normal_image, + return_vertices=return_vertices, + return_faces=return_faces, + return_faces_normals=return_faces_normals, + **kwargs + ) + + def _render( + self, + rays_o, + rays_d, + mvp, + light_d=None, + ambient_ratio=1.0, + shading_type=None, + return_normal_image=False, + return_vertices=False, + return_faces=False, + return_faces_normals=False, + **kwargs + ): + # mvp: [B, 4, 4] + B, H, W, _ = rays_o.shape + + # TODO(ahmadki): move to dataset + # random sample light_d if not provided + if light_d is None: + # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face) + light_d = rays_o + torch.randn(3, device=rays_o.device) + light_d = F.normalize(light_d) + + results = {} + + # get mesh + deform = torch.tanh(self.deform) / self.grid_resolution + + verts, faces = self.dmtet(self.verts + deform, self.sdf, self.indices) + + # get normals + i0, i1, i2 = faces[:, 0], faces[:, 1], faces[:, 2] + v0, v1, v2 = verts[i0, :], verts[i1, :], verts[i2, :] + + faces = faces.int() + + face_normals = torch.cross(v1 - v0, v2 - v0) + face_normals = F.normalize(face_normals) + + vn = torch.zeros_like(verts) + vn.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals) + vn.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals) + vn.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals) + + vn = torch.where( + torch.sum(vn * vn, -1, keepdim=True) > 1e-20, + vn, + torch.tensor([0.0, 0.0, 1.0], dtype=torch.float32, device=vn.device), + ) + + # rasterization + verts_clip = torch.bmm( + F.pad(verts, pad=(0, 1), mode='constant', value=1.0).unsqueeze(0).repeat(mvp.shape[0], 1, 1), + mvp.permute(0, 2, 1), + ).float() # [B, N, 4] + rast, _ = dr.rasterize(self.glctx, verts_clip, faces, (H, W)) + + alpha = (rast[..., 3:] > 0).float() + xyzs, _ = dr.interpolate(verts.unsqueeze(0), rast, faces) # [B, H, W, 3] + normal, _ = dr.interpolate(vn.unsqueeze(0).contiguous(), rast, faces) + normal = F.normalize(normal) + + xyzs = xyzs.view(-1, 3) + mask = (rast[..., 3:] > 0).view(-1).detach() + + # do the lighting here since we have normal from mesh now. + albedo = torch.zeros_like(xyzs, dtype=torch.float32) + if mask.any(): + masked_albedo = self.nerf.forward_features(positions=xyzs[mask]) + albedo[mask] = masked_albedo.float() + albedo = albedo.view(B, H, W, 3) + fg_color = self.material( + albedo=albedo, normals=normal, light_d=light_d, ambient_ratio=ambient_ratio, shading_type=shading_type + ) + + fg_color = dr.antialias(fg_color, rast, verts_clip, faces).clamp(0, 1) # [B, H, W, 3] + alpha = dr.antialias(alpha, rast, verts_clip, faces).clamp(0, 1) # [B, H, W, 1] + + # mix background color + bg_color = self.background(rays_d=rays_d) # [N, 3] + + depth = rast[:, :, :, [2]] # [B, H, W] + color = fg_color + (1 - alpha) * bg_color + + results['depth'] = depth + results['image'] = color + if return_normal_image: + results['normal_image'] = dr.antialias((normal + 1) / 2, rast, verts_clip, faces).clamp( + 0, 1 + ) # [B, H, W, 3] + if return_vertices: + results['vertices'] = verts + if return_faces: + results['faces'] = faces + if return_faces_normals: + results['face_normals'] = face_normals + return results diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/torchngp_volume_renderer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/torchngp_volume_renderer.py new file mode 100644 index 0000000..da66f57 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/renderers/torchngp_volume_renderer.py @@ -0,0 +1,288 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math + +import torch +import torch.nn.functional as F + +import nemo.collections.multimodal.modules.nerf.utils.torch_ngp.raymarching as raymarching +from nemo.collections.multimodal.modules.nerf.materials.materials_base import ShadingEnum +from nemo.collections.multimodal.modules.nerf.renderers.base_renderer import BaseRenderer + + +class TorchNGPVolumeRenderer(BaseRenderer): + def __init__(self, bound, update_interval, grid_resolution, density_thresh, max_steps, dt_gamma): + + super().__init__(bound, update_interval) + + self.cascade = 1 + math.ceil(math.log2(bound)) + self.grid_resolution = grid_resolution + self.density_thresh = density_thresh + self.dt_gamma = dt_gamma + self.max_steps = max_steps + + # density grid + # TODO(ahmadki): needs rework + density_grid = torch.zeros([self.cascade, self.grid_resolution ** 3]) # [CAS, H * H * H] + density_bitfield = torch.zeros( + self.cascade * self.grid_resolution ** 3 // 8, dtype=torch.uint8 + ) # [CAS * H * H * H // 8] + self.register_buffer('density_grid', density_grid) + self.register_buffer('density_bitfield', density_bitfield) + self.mean_density = 0 + self.iter_density = 0 + + # TODO(ahmadki): needs rework + self.nerf = None + self.material = None + self.background = None + + @torch.no_grad() + def update_step(self, epoch: int, global_step: int, decay: float = 0.95, S: int = 128, **kwargs): + if global_step % self.update_interval != 0: + return + + ### update density grid + tmp_grid = -torch.ones_like(self.density_grid) + + X = torch.arange(self.grid_resolution, dtype=torch.int32, device=self.aabb.device).split(S) + Y = torch.arange(self.grid_resolution, dtype=torch.int32, device=self.aabb.device).split(S) + Z = torch.arange(self.grid_resolution, dtype=torch.int32, device=self.aabb.device).split(S) + + for xs in X: + for ys in Y: + for zs in Z: + + # construct points + xx, yy, zz = torch.meshgrid(xs, ys, zs, indexing='ij') + coords = torch.cat( + [xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1 + ) # [N, 3], in [0, 128) + indices = raymarching.morton3D(coords).long() # [N] + xyzs = 2 * coords.float() / (self.grid_resolution - 1) - 1 # [N, 3] in [-1, 1] + + # cascading + for cas in range(self.cascade): + bound = min(2 ** cas, self.bound) + half_grid_resolution = bound / self.grid_resolution + # scale to current cascade's resolution + cas_xyzs = xyzs * (bound - half_grid_resolution) + # add noise in [-hgs, hgs] + cas_xyzs += (torch.rand_like(cas_xyzs) * 2 - 1) * half_grid_resolution + # query density + density = self.nerf.forward_density(cas_xyzs).reshape(-1).detach() + # assign + tmp_grid[cas, indices] = density + # ema update + valid_mask = self.density_grid >= 0 + self.density_grid[valid_mask] = torch.maximum(self.density_grid[valid_mask] * decay, tmp_grid[valid_mask]) + self.mean_density = torch.mean(self.density_grid[valid_mask]).item() + self.iter_density += 1 + + # convert to bitfield + density_thresh = min(self.mean_density, self.density_thresh) + self.density_bitfield = raymarching.packbits(self.density_grid, density_thresh, self.density_bitfield) + + def forward( + self, + rays_o, + rays_d, + light_d=None, + ambient_ratio=1.0, + shading_type=None, + return_normal_image=False, + return_normal_perturb=False, + **kwargs + ): + return self._render( + rays_o=rays_o, + rays_d=rays_d, + light_d=light_d, + ambient_ratio=ambient_ratio, + shading_type=shading_type, + return_normal_image=return_normal_image, + return_normal_perturb=return_normal_perturb, + **kwargs + ) + + # TODO(ahmadki): return_normal_image is always False ? + def _render( + self, + rays_o, + rays_d, + light_d=None, + ambient_ratio=1.0, + shading_type=None, + return_normal_image=False, + return_normal_perturb=False, + perturb=False, + T_thresh=1e-4, + binarize=False, + **kwargs + ): + # rays_o, rays_d: [B, H, W, 3] + B, H, W, _ = rays_o.shape + + # group all rays into a single batch + rays_o = rays_o.contiguous().view(-1, 3) + rays_d = rays_d.contiguous().view(-1, 3) + num_rays = rays_o.shape[0] # num_rays = B * H * W + + # pre-calculate near far + nears, fars = raymarching.near_far_from_aabb(rays_o, rays_d, self.aabb) + + # random sample light_d if not provided + # TODO(ahmadki): move to dataset + if light_d is None: + # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face) + light_d = rays_o + torch.randn(3, device=rays_o.device) + light_d = F.normalize(light_d) + + normal_image = None + normals_perturb = None + weights = None + + if self.training: + positions, dirs, ts, rays = raymarching.march_rays_train( + rays_o, + rays_d, + self.bound, + self.density_bitfield, + self.cascade, + self.grid_resolution, + nears, + fars, + perturb, + self.dt_gamma, + self.max_steps, + ) + dirs = F.normalize(dirs) + + if light_d.shape[0] > 1: + flatten_rays = raymarching.flatten_rays(rays, positions.shape[0]).long() + light_d = light_d[flatten_rays] + + return_normal = (shading_type is not None) or return_normal_image + sigmas, albedo, normals = self.nerf(positions=positions, return_normal=return_normal) + + fg_color = self.material( + albedo=albedo, normals=normals, light_d=light_d, ambient_ratio=ambient_ratio, shading_type=shading_type + ) + + weights, opacity, depth, image = raymarching.composite_rays_train( + sigmas, fg_color, ts, rays, T_thresh, binarize + ) + + if return_normal_image and normals is not None: + _, _, _, normal_image = raymarching.composite_rays_train( + sigmas.detach(), (normals + 1) / 2, ts, rays, T_thresh, binarize + ) + + if return_normal_perturb: + perturb_positions = positions + torch.randn_like(positions) * 1e-2 + normals_perturb = self.normal(positions=perturb_positions) + + else: + # allocate tensors + image = torch.zeros(num_rays, 3, device=rays_o.device) + depth = torch.zeros(num_rays, device=rays_o.device) + opacity = torch.zeros(num_rays, device=rays_o.device) + + n_alive = num_rays + rays_alive = torch.arange(n_alive, dtype=torch.int32, device=rays_o.device) + rays_t = nears.clone() + + step = 0 + + while step < self.max_steps: # hard coded max step + # count alive rays + n_alive = rays_alive.shape[0] + + # exit loop + if n_alive <= 0: + break + + # decide compact_steps + n_step = max(min(num_rays // n_alive, 8), 1) + + positions, dirs, ts = raymarching.march_rays( + n_alive, + n_step, + rays_alive, + rays_t, + rays_o, + rays_d, + self.bound, + self.density_bitfield, + self.cascade, + self.grid_resolution, + nears, + fars, + perturb if step == 0 else False, + self.dt_gamma, + self.max_steps, + ) + dirs = F.normalize(dirs) + + return_normal = shading_type not in [None, ShadingEnum.TEXTURELESS] + sigmas, albedo, normals = self.nerf(positions=positions, return_normal=return_normal) + + fg_color = self.material( + albedo=albedo, + normals=normals, + light_d=light_d, + ambient_ratio=ambient_ratio, + shading_type=shading_type, + ) + raymarching.composite_rays( + n_alive, + n_step, + rays_alive, + rays_t, + sigmas, + fg_color, + ts, + opacity, + depth, + image, + T_thresh, + binarize, + ) + + # TODO(ahmadki): add optoin to return normal_image, like in training + + rays_alive = rays_alive[rays_alive >= 0] + + step += n_step + + # mix background color + bg_color = self.background(rays_d) # [N, 3] + image = image + (1 - opacity).unsqueeze(-1) * bg_color + + results = { + "image": image.view(B, H, W, 3), + "depth": depth.view(B, H, W, 1), + "opacity": opacity.view(B, H, W, 1), + "dirs": dirs, + } + if normals is not None: + results["normals"] = normals + if weights is not None: + results["weights"] = weights + if normal_image is not None: + results["normal_image"] = normal_image.view(B, H, W, 3) + if normals_perturb is not None: + results["normal_perturb"] = normals_perturb + + return results diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/activation.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/activation.py new file mode 100644 index 0000000..1b79676 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/activation.py @@ -0,0 +1,33 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +from torch.autograd import Function +from torch.cuda.amp import custom_bwd, custom_fwd + + +class _trunc_exp(Function): + @staticmethod + @custom_fwd(cast_inputs=torch.float) + def forward(ctx, x): + ctx.save_for_backward(x) + return torch.exp(x) + + @staticmethod + @custom_bwd + def backward(ctx, g): + x = ctx.saved_tensors[0] + return g * torch.exp(x.clamp(max=15)) + + +trunc_exp = _trunc_exp.apply diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/encoding.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/encoding.py new file mode 100644 index 0000000..59c6caa --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/encoding.py @@ -0,0 +1,149 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn as nn + + +class FreqEncoder_torch(nn.Module): + def __init__( + self, + input_dim, + max_freq_log2, + N_freqs, + log_sampling=True, + include_input=True, + periodic_fns=(torch.sin, torch.cos), + ): + + super().__init__() + + self.input_dim = input_dim + self.include_input = include_input + self.periodic_fns = periodic_fns + self.N_freqs = N_freqs + + self.output_dim = 0 + if self.include_input: + self.output_dim += self.input_dim + + self.output_dim += self.input_dim * N_freqs * len(self.periodic_fns) + + if log_sampling: + self.freq_bands = 2 ** torch.linspace(0, max_freq_log2, N_freqs) + else: + self.freq_bands = torch.linspace(2 ** 0, 2 ** max_freq_log2, N_freqs) + + self.freq_bands = self.freq_bands.numpy().tolist() + + def forward(self, input, max_level=None, **kwargs): + + if max_level is None: + max_level = self.N_freqs + else: + max_level = int(max_level * self.N_freqs) + + out = [] + if self.include_input: + out.append(input) + + for i in range(max_level): + freq = self.freq_bands[i] + for p_fn in self.periodic_fns: + out.append(p_fn(input * freq)) + + # append 0 + if self.N_freqs - max_level > 0: + out.append( + torch.zeros( + input.shape[0], + (self.N_freqs - max_level) * 2 * input.shape[1], + device=input.device, + dtype=input.dtype, + ) + ) + + out = torch.cat(out, dim=-1) + + return out + + +def get_encoder( + encoder_type, + input_dim=3, + multires=6, + degree=4, + num_levels=16, + level_dim=2, + base_resolution=16, + log2_hashmap_size=19, + desired_resolution=2048, + align_corners=False, + interpolation='linear', + **kwargs +): + + if encoder_type is None: + return lambda x, **kwargs: x, input_dim + + elif encoder_type == 'frequency_torch': + encoder = FreqEncoder_torch( + input_dim=input_dim, max_freq_log2=multires - 1, N_freqs=multires, log_sampling=True + ) + + elif encoder_type == 'frequency': # CUDA implementation, faster than torch. + from nemo.collections.multimodal.modules.nerf.utils.torch_ngp.freqencoder import FreqEncoder + + encoder = FreqEncoder(input_dim=input_dim, degree=multires) + + elif encoder_type == 'sphere_harmonics': + from nemo.collections.multimodal.modules.nerf.utils.torch_ngp.shencoder import SHEncoder + + encoder = SHEncoder(input_dim=input_dim, degree=degree) + + elif encoder_type == 'hashgrid': + from nemo.collections.multimodal.modules.nerf.utils.torch_ngp.gridencoder import GridEncoder + + encoder = GridEncoder( + input_dim=input_dim, + num_levels=num_levels, + level_dim=level_dim, + base_resolution=base_resolution, + log2_hashmap_size=log2_hashmap_size, + desired_resolution=desired_resolution, + gridtype='hash', + align_corners=align_corners, + interpolation=interpolation, + ) + + elif encoder_type == 'tiledgrid': + from nemo.collections.multimodal.modules.nerf.utils.torch_ngp.gridencoder import GridEncoder + + encoder = GridEncoder( + input_dim=input_dim, + num_levels=num_levels, + level_dim=level_dim, + base_resolution=base_resolution, + log2_hashmap_size=log2_hashmap_size, + desired_resolution=desired_resolution, + gridtype='tiled', + align_corners=align_corners, + interpolation=interpolation, + ) + + else: + raise NotImplementedError( + 'Unknown encoder type, choose from [None, frequency, sphere_harmonics, hashgrid, tiledgrid]' + ) + + return encoder, encoder.output_dim diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/freqencoder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/freqencoder.py new file mode 100644 index 0000000..f426174 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/freqencoder.py @@ -0,0 +1,84 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import _freqencoder as _backend +import torch +import torch.nn as nn +from torch.autograd import Function +from torch.cuda.amp import custom_bwd, custom_fwd + + +class _freq_encoder(Function): + @staticmethod + @custom_fwd(cast_inputs=torch.float32) # force float32 for better precision + def forward(ctx, inputs, degree, output_dim): + # inputs: [B, input_dim], float + # RETURN: [B, F], float + + if not inputs.is_cuda: + inputs = inputs.cuda() + inputs = inputs.contiguous() + + B, input_dim = inputs.shape # batch size, coord dim + + outputs = torch.empty(B, output_dim, dtype=inputs.dtype, device=inputs.device) + + _backend.freq_encode_forward(inputs, B, input_dim, degree, output_dim, outputs) + + ctx.save_for_backward(inputs, outputs) + ctx.dims = [B, input_dim, degree, output_dim] + + return outputs + + @staticmethod + # @once_differentiable + @custom_bwd + def backward(ctx, grad): + # grad: [B, C * C] + + grad = grad.contiguous() + inputs, outputs = ctx.saved_tensors + B, input_dim, degree, output_dim = ctx.dims + + grad_inputs = torch.zeros_like(inputs) + _backend.freq_encode_backward(grad, outputs, B, input_dim, degree, output_dim, grad_inputs) + + return grad_inputs, None, None + + +freq_encode = _freq_encoder.apply + + +class FreqEncoder(nn.Module): + def __init__(self, input_dim=3, degree=4): + super().__init__() + + self.input_dim = input_dim + self.degree = degree + self.output_dim = input_dim + input_dim * 2 * degree + + def __repr__(self): + return f"FreqEncoder: input_dim={self.input_dim} degree={self.degree} output_dim={self.output_dim}" + + def forward(self, inputs, **kwargs): + # inputs: [..., input_dim] + # return: [..., ] + + prefix_shape = list(inputs.shape[:-1]) + inputs = inputs.reshape(-1, self.input_dim) + + outputs = freq_encode(inputs, self.degree, self.output_dim) + + outputs = outputs.reshape(prefix_shape + [self.output_dim]) + + return outputs diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/gridencoder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/gridencoder.py new file mode 100644 index 0000000..be173fb --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/gridencoder.py @@ -0,0 +1,299 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math + +import _gridencoder as _backend +import numpy as np +import torch +import torch.nn as nn +from torch.autograd import Function +from torch.cuda.amp import custom_bwd, custom_fwd + +_gridtype_to_id = { + 'hash': 0, + 'tiled': 1, +} + +_interp_to_id = { + 'linear': 0, + 'smoothstep': 1, +} + + +class _grid_encode(Function): + @staticmethod + @custom_fwd + def forward( + ctx, + inputs, + embeddings, + offsets, + per_level_scale, + base_resolution, + calc_grad_inputs=False, + gridtype=0, + align_corners=False, + interpolation=0, + max_level=None, + ): + # inputs: [B, D], float in [0, 1] + # embeddings: [sO, C], float + # offsets: [L + 1], int + # RETURN: [B, F], float + + inputs = inputs.contiguous() + + B, D = inputs.shape # batch size, coord dim + L = offsets.shape[0] - 1 # level + C = embeddings.shape[1] # embedding dim for each level + S = np.log2(per_level_scale) # resolution multiplier at each level, apply log2 for later CUDA exp2f + H = base_resolution # base resolution + + max_level = L if max_level is None else max(min(int(math.ceil(max_level * L)), L), 1) + + # manually handle autocast (only use half precision embeddings, inputs must be float for enough precision) + # if C % 2 != 0, force float, since half for atomicAdd is very slow. + if torch.is_autocast_enabled() and C % 2 == 0: + embeddings = embeddings.to(torch.half) + + # L first, optimize cache for cuda kernel, but needs an extra permute later + outputs = torch.empty(L, B, C, device=inputs.device, dtype=embeddings.dtype) + + # zero init if we only calculate partial levels + if max_level < L: + outputs.zero_() + + if calc_grad_inputs: + dy_dx = torch.empty(B, L * D * C, device=inputs.device, dtype=embeddings.dtype) + if max_level < L: + dy_dx.zero_() + else: + dy_dx = None + + _backend.grid_encode_forward( + inputs, + embeddings, + offsets, + outputs, + B, + D, + C, + L, + max_level, + S, + H, + dy_dx, + gridtype, + align_corners, + interpolation, + ) + + # permute back to [B, L * C] + outputs = outputs.permute(1, 0, 2).reshape(B, L * C) + + ctx.save_for_backward(inputs, embeddings, offsets, dy_dx) + ctx.dims = [B, D, C, L, S, H, gridtype, interpolation, max_level] + ctx.align_corners = align_corners + + return outputs + + @staticmethod + # @once_differentiable + @custom_bwd + def backward(ctx, grad): + + inputs, embeddings, offsets, dy_dx = ctx.saved_tensors + B, D, C, L, S, H, gridtype, interpolation, max_level = ctx.dims + align_corners = ctx.align_corners + + # grad: [B, L * C] --> [L, B, C] + grad = grad.view(B, L, C).permute(1, 0, 2).contiguous() + + grad_embeddings = torch.zeros_like(embeddings) + + if dy_dx is not None: + grad_inputs = torch.zeros_like(inputs, dtype=embeddings.dtype) + else: + grad_inputs = None + + _backend.grid_encode_backward( + grad, + inputs, + embeddings, + offsets, + grad_embeddings, + B, + D, + C, + L, + max_level, + S, + H, + dy_dx, + grad_inputs, + gridtype, + align_corners, + interpolation, + ) + + if dy_dx is not None: + grad_inputs = grad_inputs.to(inputs.dtype) + + return grad_inputs, grad_embeddings, None, None, None, None, None, None, None, None + + +grid_encode = _grid_encode.apply + + +class GridEncoder(nn.Module): + def __init__( + self, + input_dim=3, + num_levels=16, + level_dim=2, + per_level_scale=2, + base_resolution=16, + log2_hashmap_size=19, + desired_resolution=None, + gridtype='hash', + align_corners=False, + interpolation='linear', + ): + super().__init__() + + # the finest resolution desired at the last level, if provided, overridee per_level_scale + if desired_resolution is not None: + per_level_scale = np.exp2(np.log2(desired_resolution / base_resolution) / (num_levels - 1)) + + self.input_dim = input_dim # coord dims, 2 or 3 + self.num_levels = num_levels # num levels, each level multiply resolution by 2 + self.level_dim = level_dim # encode channels per level + self.per_level_scale = per_level_scale # multiply resolution by this scale at each level. + self.log2_hashmap_size = log2_hashmap_size + self.base_resolution = base_resolution + self.output_dim = num_levels * level_dim + self.gridtype = gridtype + self.gridtype_id = _gridtype_to_id[gridtype] # "tiled" or "hash" + self.interpolation = interpolation + self.interp_id = _interp_to_id[interpolation] # "linear" or "smoothstep" + self.align_corners = align_corners + + # allocate parameters + offsets = [] + offset = 0 + self.max_params = 2 ** log2_hashmap_size + for i in range(num_levels): + resolution = int(np.ceil(base_resolution * per_level_scale ** i)) + params_in_level = min(self.max_params, (resolution) ** input_dim) # limit max number + params_in_level = int(np.ceil(params_in_level / 8) * 8) # make divisible + offsets.append(offset) + offset += params_in_level + offsets.append(offset) + offsets = torch.from_numpy(np.array(offsets, dtype=np.int32)) + self.register_buffer('offsets', offsets) + + self.n_params = offsets[-1] * level_dim + + # parameters + self.embeddings = nn.Parameter(torch.empty(offset, level_dim)) + + self.reset_parameters() + + def reset_parameters(self): + std = 1e-4 + self.embeddings.data.uniform_(-std, std) + + def __repr__(self): + return f"GridEncoder: input_dim={self.input_dim} num_levels={self.num_levels} level_dim={self.level_dim} resolution={self.base_resolution} -> {int(round(self.base_resolution * self.per_level_scale ** (self.num_levels - 1)))} per_level_scale={self.per_level_scale:.4f} params={tuple(self.embeddings.shape)} gridtype={self.gridtype} align_corners={self.align_corners} interpolation={self.interpolation}" + + def forward(self, inputs, bound=1, max_level=None): + # inputs: [..., input_dim], normalized real world positions in [-bound, bound] + # max_level: only calculate first max_level levels (None will use all levels) + # return: [..., num_levels * level_dim] + + inputs = (inputs + bound) / (2 * bound) # map to [0, 1] + + # print('inputs', inputs.shape, inputs.dtype, inputs.min().item(), inputs.max().item()) + + prefix_shape = list(inputs.shape[:-1]) + inputs = inputs.view(-1, self.input_dim) + + outputs = grid_encode( + inputs, + self.embeddings, + self.offsets, + self.per_level_scale, + self.base_resolution, + inputs.requires_grad, + self.gridtype_id, + self.align_corners, + self.interp_id, + max_level, + ) + outputs = outputs.view(prefix_shape + [self.output_dim]) + + # print('outputs', outputs.shape, outputs.dtype, outputs.min().item(), outputs.max().item()) + + return outputs + + # always run in float precision! + @torch.cuda.amp.autocast(enabled=False) + def grad_total_variation(self, weight=1e-7, inputs=None, bound=1, B=1000000): + # inputs: [..., input_dim], float in [-b, b], location to calculate TV loss. + + D = self.input_dim + C = self.embeddings.shape[1] # embedding dim for each level + L = self.offsets.shape[0] - 1 # level + S = np.log2(self.per_level_scale) # resolution multiplier at each level, apply log2 for later CUDA exp2f + H = self.base_resolution # base resolution + + if inputs is None: + # randomized in [0, 1] + inputs = torch.rand(B, self.input_dim, device=self.embeddings.device) + else: + inputs = (inputs + bound) / (2 * bound) # map to [0, 1] + inputs = inputs.view(-1, self.input_dim) + B = inputs.shape[0] + + if self.embeddings.grad is None: + raise ValueError('grad is None, should be called after loss.backward() and before optimizer.step()!') + + _backend.grad_total_variation( + inputs, + self.embeddings, + self.embeddings.grad, + self.offsets, + weight, + B, + D, + C, + L, + S, + H, + self.gridtype_id, + self.align_corners, + ) + + @torch.cuda.amp.autocast(enabled=False) + def grad_weight_decay(self, weight=0.1): + # level-wise meaned weight decay (ref: zip-nerf) + + B = self.embeddings.shape[0] # size of embedding + C = self.embeddings.shape[1] # embedding dim for each level + L = self.offsets.shape[0] - 1 # level + + if self.embeddings.grad is None: + raise ValueError('grad is None, should be called after loss.backward() and before optimizer.step()!') + + _backend.grad_weight_decay(self.embeddings, self.embeddings.grad, self.offsets, weight, B, C, L) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/raymarching.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/raymarching.py new file mode 100644 index 0000000..2c414c5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/raymarching.py @@ -0,0 +1,561 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from torch.autograd import Function +from torch.cuda.amp import custom_bwd, custom_fwd + +# lazy building: +# `import raymarching` will not immediately build the extension, only if you actually call any functions. + +BACKEND = None + + +def get_backend(): + global BACKEND + + if BACKEND is None: + try: + import _raymarching as _backend + except ImportError: + from .backend import _backend + + BACKEND = _backend + + return BACKEND + + +# ---------------------------------------- +# utils +# ---------------------------------------- + + +class _near_far_from_aabb(Function): + @staticmethod + @custom_fwd(cast_inputs=torch.float32) + def forward(ctx, rays_o, rays_d, aabb, min_near=0.2): + ''' near_far_from_aabb, CUDA implementation + Calculate rays' intersection time (near and far) with aabb + Args: + rays_o: float, [N, 3] + rays_d: float, [N, 3] + aabb: float, [6], (xmin, ymin, zmin, xmax, ymax, zmax) + min_near: float, scalar + Returns: + nears: float, [N] + fars: float, [N] + ''' + if not rays_o.is_cuda: + rays_o = rays_o.cuda() + if not rays_d.is_cuda: + rays_d = rays_d.cuda() + + rays_o = rays_o.contiguous().view(-1, 3) + rays_d = rays_d.contiguous().view(-1, 3) + + N = rays_o.shape[0] # num rays + + nears = torch.empty(N, dtype=rays_o.dtype, device=rays_o.device) + fars = torch.empty(N, dtype=rays_o.dtype, device=rays_o.device) + + get_backend().near_far_from_aabb(rays_o, rays_d, aabb, N, min_near, nears, fars) + + return nears, fars + + +near_far_from_aabb = _near_far_from_aabb.apply + + +class _sph_from_ray(Function): + @staticmethod + @custom_fwd(cast_inputs=torch.float32) + def forward(ctx, rays_o, rays_d, radius): + ''' sph_from_ray, CUDA implementation + get spherical coordinate on the background sphere from rays. + Assume rays_o are inside the Sphere(radius). + Args: + rays_o: [N, 3] + rays_d: [N, 3] + radius: scalar, float + Return: + coords: [N, 2], in [-1, 1], theta and phi on a sphere. (further-surface) + ''' + if not rays_o.is_cuda: + rays_o = rays_o.cuda() + if not rays_d.is_cuda: + rays_d = rays_d.cuda() + + rays_o = rays_o.contiguous().view(-1, 3) + rays_d = rays_d.contiguous().view(-1, 3) + + N = rays_o.shape[0] # num rays + + coords = torch.empty(N, 2, dtype=rays_o.dtype, device=rays_o.device) + + get_backend().sph_from_ray(rays_o, rays_d, radius, N, coords) + + return coords + + +sph_from_ray = _sph_from_ray.apply + + +class _morton3D(Function): + @staticmethod + def forward(ctx, coords): + ''' morton3D, CUDA implementation + Args: + coords: [N, 3], int32, in [0, 128) (for some reason there is no uint32 tensor in torch...) + TODO: check if the coord range is valid! (current 128 is safe) + Returns: + indices: [N], int32, in [0, 128^3) + + ''' + if not coords.is_cuda: + coords = coords.cuda() + + N = coords.shape[0] + + indices = torch.empty(N, dtype=torch.int32, device=coords.device) + + get_backend().morton3D(coords.int(), N, indices) + + return indices + + +morton3D = _morton3D.apply + + +class _morton3D_invert(Function): + @staticmethod + def forward(ctx, indices): + ''' morton3D_invert, CUDA implementation + Args: + indices: [N], int32, in [0, 128^3) + Returns: + coords: [N, 3], int32, in [0, 128) + + ''' + if not indices.is_cuda: + indices = indices.cuda() + + N = indices.shape[0] + + coords = torch.empty(N, 3, dtype=torch.int32, device=indices.device) + + get_backend().morton3D_invert(indices.int(), N, coords) + + return coords + + +morton3D_invert = _morton3D_invert.apply + + +class _packbits(Function): + @staticmethod + @custom_fwd(cast_inputs=torch.float32) + def forward(ctx, grid, thresh, bitfield=None): + ''' packbits, CUDA implementation + Pack up the density grid into a bit field to accelerate ray marching. + Args: + grid: float, [C, H * H * H], assume H % 2 == 0 + thresh: float, threshold + Returns: + bitfield: uint8, [C, H * H * H / 8] + ''' + if not grid.is_cuda: + grid = grid.cuda() + grid = grid.contiguous() + + C = grid.shape[0] + H3 = grid.shape[1] + N = C * H3 // 8 + + if bitfield is None: + bitfield = torch.empty(N, dtype=torch.uint8, device=grid.device) + + get_backend().packbits(grid, N, thresh, bitfield) + + return bitfield + + +packbits = _packbits.apply + + +class _flatten_rays(Function): + @staticmethod + def forward(ctx, rays, M): + ''' flatten rays + Args: + rays: [N, 2], all rays' (point_offset, point_count), + M: scalar, int, count of points (we cannot get this info from rays unfortunately...) + Returns: + res: [M], flattened ray index. + ''' + if not rays.is_cuda: + rays = rays.cuda() + rays = rays.contiguous() + + N = rays.shape[0] + + res = torch.zeros(M, dtype=torch.int, device=rays.device) + + get_backend().flatten_rays(rays, N, M, res) + + return res + + +flatten_rays = _flatten_rays.apply + +# ---------------------------------------- +# train functions +# ---------------------------------------- + + +class _march_rays_train(Function): + @staticmethod + @custom_fwd(cast_inputs=torch.float32) + def forward( + ctx, + rays_o, + rays_d, + bound, + density_bitfield, + C, + H, + nears, + fars, + perturb=False, + dt_gamma=0, + max_steps=1024, + contract=False, + ): + ''' march rays to generate points (forward only) + Args: + rays_o/d: float, [N, 3] + bound: float, scalar + density_bitfield: uint8: [CHHH // 8] + C: int + H: int + nears/fars: float, [N] + step_counter: int32, (2), used to count the actual number of generated points. + mean_count: int32, estimated mean steps to accelerate training. (but will randomly drop rays if the actual point count exceeded this threshold.) + perturb: bool + align: int, pad output so its size is dividable by align, set to -1 to disable. + force_all_rays: bool, ignore step_counter and mean_count, always calculate all rays. Useful if rendering the whole image, instead of some rays. + dt_gamma: float, called cone_angle in instant-ngp, exponentially accelerate ray marching if > 0. (very significant effect, but generally lead to worse performance) + max_steps: int, max number of sampled points along each ray, also affect min_stepsize. + Returns: + xyzs: float, [M, 3], all generated points' coords. (all rays concated, need to use `rays` to extract points belonging to each ray) + dirs: float, [M, 3], all generated points' view dirs. + ts: float, [M, 2], all generated points' ts. + rays: int32, [N, 2], all rays' (point_offset, point_count), e.g., xyzs[rays[i, 0]:(rays[i, 0] + rays[i, 1])] --> points belonging to rays[i, 0] + ''' + + if not rays_o.is_cuda: + rays_o = rays_o.cuda() + if not rays_d.is_cuda: + rays_d = rays_d.cuda() + if not density_bitfield.is_cuda: + density_bitfield = density_bitfield.cuda() + + rays_o = rays_o.float().contiguous().view(-1, 3) + rays_d = rays_d.float().contiguous().view(-1, 3) + density_bitfield = density_bitfield.contiguous() + + N = rays_o.shape[0] # num rays + + step_counter = torch.zeros(1, dtype=torch.int32, device=rays_o.device) # point counter, ray counter + + if perturb: + noises = torch.rand(N, dtype=rays_o.dtype, device=rays_o.device) + else: + noises = torch.zeros(N, dtype=rays_o.dtype, device=rays_o.device) + + # first pass: write rays, get total number of points M to render + rays = torch.empty(N, 2, dtype=torch.int32, device=rays_o.device) # id, offset, num_steps + get_backend().march_rays_train( + rays_o, + rays_d, + density_bitfield, + bound, + contract, + dt_gamma, + max_steps, + N, + C, + H, + nears, + fars, + None, + None, + None, + rays, + step_counter, + noises, + ) + + # allocate based on M + M = step_counter.item() + # print(M, N) + # print(rays[:, 0].max()) + + xyzs = torch.zeros(M, 3, dtype=rays_o.dtype, device=rays_o.device) + dirs = torch.zeros(M, 3, dtype=rays_o.dtype, device=rays_o.device) + ts = torch.zeros(M, 2, dtype=rays_o.dtype, device=rays_o.device) + + # second pass: write outputs + get_backend().march_rays_train( + rays_o, + rays_d, + density_bitfield, + bound, + contract, + dt_gamma, + max_steps, + N, + C, + H, + nears, + fars, + xyzs, + dirs, + ts, + rays, + step_counter, + noises, + ) + + return xyzs, dirs, ts, rays + + +march_rays_train = _march_rays_train.apply + + +class _composite_rays_train(Function): + @staticmethod + @custom_fwd(cast_inputs=torch.float32) + def forward(ctx, sigmas, rgbs, ts, rays, T_thresh=1e-4, binarize=False): + ''' composite rays' rgbs, according to the ray marching formula. + Args: + rgbs: float, [M, 3] + sigmas: float, [M,] + ts: float, [M, 2] + rays: int32, [N, 3] + Returns: + weights: float, [M] + weights_sum: float, [N,], the alpha channel + depth: float, [N, ], the Depth + image: float, [N, 3], the RGB channel (after multiplying alpha!) + ''' + + sigmas = sigmas.float().contiguous() + rgbs = rgbs.float().contiguous() + + M = sigmas.shape[0] + N = rays.shape[0] + + weights = torch.zeros(M, dtype=sigmas.dtype, device=sigmas.device) # may leave unmodified, so init with 0 + weights_sum = torch.empty(N, dtype=sigmas.dtype, device=sigmas.device) + + depth = torch.empty(N, dtype=sigmas.dtype, device=sigmas.device) + image = torch.empty(N, 3, dtype=sigmas.dtype, device=sigmas.device) + + get_backend().composite_rays_train_forward( + sigmas, rgbs, ts, rays, M, N, T_thresh, binarize, weights, weights_sum, depth, image + ) + + ctx.save_for_backward(sigmas, rgbs, ts, rays, weights_sum, depth, image) + ctx.dims = [M, N, T_thresh, binarize] + + return weights, weights_sum, depth, image + + @staticmethod + @custom_bwd + def backward(ctx, grad_weights, grad_weights_sum, grad_depth, grad_image): + + grad_weights = grad_weights.contiguous() + grad_weights_sum = grad_weights_sum.contiguous() + grad_depth = grad_depth.contiguous() + grad_image = grad_image.contiguous() + + sigmas, rgbs, ts, rays, weights_sum, depth, image = ctx.saved_tensors + M, N, T_thresh, binarize = ctx.dims + + grad_sigmas = torch.zeros_like(sigmas) + grad_rgbs = torch.zeros_like(rgbs) + + get_backend().composite_rays_train_backward( + grad_weights, + grad_weights_sum, + grad_depth, + grad_image, + sigmas, + rgbs, + ts, + rays, + weights_sum, + depth, + image, + M, + N, + T_thresh, + binarize, + grad_sigmas, + grad_rgbs, + ) + + return grad_sigmas, grad_rgbs, None, None, None, None + + +composite_rays_train = _composite_rays_train.apply + +# ---------------------------------------- +# infer functions +# ---------------------------------------- + + +class _march_rays(Function): + @staticmethod + @custom_fwd(cast_inputs=torch.float32) + def forward( + ctx, + n_alive, + n_step, + rays_alive, + rays_t, + rays_o, + rays_d, + bound, + density_bitfield, + C, + H, + near, + far, + perturb=False, + dt_gamma=0, + max_steps=1024, + contract=False, + ): + ''' march rays to generate points (forward only, for inference) + Args: + n_alive: int, number of alive rays + n_step: int, how many steps we march + rays_alive: int, [N], the alive rays' IDs in N (N >= n_alive, but we only use first n_alive) + rays_t: float, [N], the alive rays' time, we only use the first n_alive. + rays_o/d: float, [N, 3] + bound: float, scalar + density_bitfield: uint8: [CHHH // 8] + C: int + H: int + nears/fars: float, [N] + align: int, pad output so its size is dividable by align, set to -1 to disable. + perturb: bool/int, int > 0 is used as the random seed. + dt_gamma: float, called cone_angle in instant-ngp, exponentially accelerate ray marching if > 0. (very significant effect, but generally lead to worse performance) + max_steps: int, max number of sampled points along each ray, also affect min_stepsize. + Returns: + xyzs: float, [n_alive * n_step, 3], all generated points' coords + dirs: float, [n_alive * n_step, 3], all generated points' view dirs. + ts: float, [n_alive * n_step, 2], all generated points' ts + ''' + + if not rays_o.is_cuda: + rays_o = rays_o.cuda() + if not rays_d.is_cuda: + rays_d = rays_d.cuda() + + rays_o = rays_o.float().contiguous().view(-1, 3) + rays_d = rays_d.float().contiguous().view(-1, 3) + + M = n_alive * n_step + + xyzs = torch.zeros(M, 3, dtype=rays_o.dtype, device=rays_o.device) + dirs = torch.zeros(M, 3, dtype=rays_o.dtype, device=rays_o.device) + ts = torch.zeros(M, 2, dtype=rays_o.dtype, device=rays_o.device) # 2 vals, one for rgb, one for depth + + if perturb: + # torch.manual_seed(perturb) # test_gui uses spp index as seed + noises = torch.rand(n_alive, dtype=rays_o.dtype, device=rays_o.device) + else: + noises = torch.zeros(n_alive, dtype=rays_o.dtype, device=rays_o.device) + + get_backend().march_rays( + n_alive, + n_step, + rays_alive, + rays_t, + rays_o, + rays_d, + bound, + contract, + dt_gamma, + max_steps, + C, + H, + density_bitfield, + near, + far, + xyzs, + dirs, + ts, + noises, + ) + + return xyzs, dirs, ts + + +march_rays = _march_rays.apply + + +class _composite_rays(Function): + @staticmethod + @custom_fwd(cast_inputs=torch.float32) # need to cast sigmas & rgbs to float + def forward( + ctx, + n_alive, + n_step, + rays_alive, + rays_t, + sigmas, + rgbs, + ts, + weights_sum, + depth, + image, + T_thresh=1e-2, + binarize=False, + ): + ''' composite rays' rgbs, according to the ray marching formula. (for inference) + Args: + n_alive: int, number of alive rays + n_step: int, how many steps we march + rays_alive: int, [n_alive], the alive rays' IDs in N (N >= n_alive) + rays_t: float, [N], the alive rays' time + sigmas: float, [n_alive * n_step,] + rgbs: float, [n_alive * n_step, 3] + ts: float, [n_alive * n_step, 2] + In-place Outputs: + weights_sum: float, [N,], the alpha channel + depth: float, [N,], the depth value + image: float, [N, 3], the RGB channel (after multiplying alpha!) + ''' + sigmas = sigmas.float().contiguous() + rgbs = rgbs.float().contiguous() + get_backend().composite_rays( + n_alive, n_step, T_thresh, binarize, rays_alive, rays_t, sigmas, rgbs, ts, weights_sum, depth, image + ) + return tuple() + + +composite_rays = _composite_rays.apply diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/shencoder.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/shencoder.py new file mode 100644 index 0000000..446b584 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/shencoder.py @@ -0,0 +1,93 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import _shencoder as _backend +import torch +import torch.nn as nn +from torch.autograd import Function +from torch.cuda.amp import custom_bwd, custom_fwd + + +class _sh_encoder(Function): + @staticmethod + @custom_fwd(cast_inputs=torch.float32) # force float32 for better precision + def forward(ctx, inputs, degree, calc_grad_inputs=False): + # inputs: [B, input_dim], float in [-1, 1] + # RETURN: [B, F], float + + inputs = inputs.contiguous() + B, input_dim = inputs.shape # batch size, coord dim + output_dim = degree ** 2 + + outputs = torch.empty(B, output_dim, dtype=inputs.dtype, device=inputs.device) + + if calc_grad_inputs: + dy_dx = torch.empty(B, input_dim * output_dim, dtype=inputs.dtype, device=inputs.device) + else: + dy_dx = None + + _backend.sh_encode_forward(inputs, outputs, B, input_dim, degree, dy_dx) + + ctx.save_for_backward(inputs, dy_dx) + ctx.dims = [B, input_dim, degree] + + return outputs + + @staticmethod + # @once_differentiable + @custom_bwd + def backward(ctx, grad): + # grad: [B, C * C] + + inputs, dy_dx = ctx.saved_tensors + + if dy_dx is not None: + grad = grad.contiguous() + B, input_dim, degree = ctx.dims + grad_inputs = torch.zeros_like(inputs) + _backend.sh_encode_backward(grad, inputs, B, input_dim, degree, dy_dx, grad_inputs) + return grad_inputs, None, None + else: + return None, None, None + + +sh_encode = _sh_encoder.apply + + +class SHEncoder(nn.Module): + def __init__(self, input_dim=3, degree=4): + super().__init__() + + self.input_dim = input_dim # coord dims, must be 3 + self.degree = degree # 0 ~ 4 + self.output_dim = degree ** 2 + + assert self.input_dim == 3, "SH encoder only support input dim == 3" + assert self.degree > 0 and self.degree <= 8, "SH encoder only supports degree in [1, 8]" + + def __repr__(self): + return f"SHEncoder: input_dim={self.input_dim} degree={self.degree}" + + def forward(self, inputs, size=1): + # inputs: [..., input_dim], normalized real world positions in [-size, size] + # return: [..., degree^2] + + inputs = inputs / size # [-1, 1] + + prefix_shape = list(inputs.shape[:-1]) + inputs = inputs.reshape(-1, self.input_dim) + + outputs = sh_encode(inputs, self.degree, inputs.requires_grad) + outputs = outputs.reshape(prefix_shape + [self.output_dim]) + + return outputs diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/trt_engine.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/trt_engine.py new file mode 100644 index 0000000..97fb1dc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/nerf/utils/trt_engine.py @@ -0,0 +1,170 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from collections import OrderedDict +from copy import copy + +import numpy as np +import tensorrt as trt +import torch +from polygraphy import cuda +from polygraphy.backend.common import bytes_from_path +from polygraphy.backend.trt import engine_from_bytes +from polygraphy.backend.trt import util as trt_util + +TRT_LOGGER = trt.Logger(trt.Logger.ERROR) + +# Map of numpy dtype -> torch dtype +numpy_to_torch_dtype_dict = { + np.uint8: torch.uint8, + np.int8: torch.int8, + np.int16: torch.int16, + np.int32: torch.int32, + np.int64: torch.int64, + np.float16: torch.float16, + np.float32: torch.float32, + np.float64: torch.float64, + np.complex64: torch.complex64, + np.complex128: torch.complex128, +} +if np.version.full_version >= "1.24.0": + numpy_to_torch_dtype_dict[np.bool_] = torch.bool +else: + numpy_to_torch_dtype_dict[np.bool] = torch.bool + +# Map of torch dtype -> numpy dtype +torch_to_numpy_dtype_dict = {value: key for (key, value) in numpy_to_torch_dtype_dict.items()} + + +def device_view(t): + return cuda.DeviceView(ptr=t.data_ptr(), shape=t.shape, dtype=torch_to_numpy_dtype_dict[t.dtype]) + + +class Engine: + def __init__( + self, engine_path, + ): + self.engine_path = engine_path + self.engine = None + self.context = None + self.buffers = OrderedDict() + self.tensors = OrderedDict() + + def __del__(self): + [buf.free() for buf in self.buffers.values() if isinstance(buf, cuda.DeviceArray)] + del self.engine + del self.context + del self.buffers + del self.tensors + + def set_engine(self, stream, shape_dict): + self.load() + self.activate() + self.stream = stream + self.allocate_buffers(shape_dict, device='cuda') + + def load(self): + print(f"Loading TensorRT engine: {self.engine_path}") + self.engine = engine_from_bytes(bytes_from_path(self.engine_path)) + + def activate(self): + self.context = self.engine.create_execution_context() + + def allocate_buffers(self, shape_dict=None, device="cuda"): + for idx in range(trt_util.get_bindings_per_profile(self.engine)): + binding = self.engine[idx] + if shape_dict and binding in shape_dict: + shape = shape_dict[binding] + else: + shape = self.engine.get_binding_shape(binding) + dtype = trt.nptype(self.engine.get_binding_dtype(binding)) + if self.engine.binding_is_input(binding): + self.context.set_binding_shape(idx, shape) + tensor = torch.empty(tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]).to(device=device) + self.tensors[binding] = tensor + self.buffers[binding] = cuda.DeviceView(ptr=tensor.data_ptr(), shape=shape, dtype=dtype) + + def infer(self, feed_dict): + stream = self.stream + start_binding, end_binding = trt_util.get_active_profile_bindings(self.context) + # shallow copy of ordered dict + device_buffers = copy(self.buffers) + for name, buf in feed_dict.items(): + assert isinstance(buf, cuda.DeviceView) + device_buffers[name] = buf + bindings = [0] * start_binding + [buf.ptr for buf in device_buffers.values()] + noerror = self.context.execute_async_v2(bindings=bindings, stream_handle=stream.ptr) + if not noerror: + raise ValueError(f"ERROR: inference failed.") + + return self.tensors + + +def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if schedule == "linear": + betas = torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 + + elif schedule == "cosine": + timesteps = torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "sqrt_linear": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + elif schedule == "sqrt": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + + +def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): + if ddim_discr_method == 'uniform': + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == 'quad': + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps)) ** 2).astype(int) + else: + raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + print(f'Selected timesteps for ddim sampler: {steps_out}') + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + if verbose: + print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') + print( + f'For the chosen value of eta, which is {eta}, ' + f'this results in the following sigma_t schedule for ddim sampler {sigmas}' + ) + return sigmas, alphas, alphas_prev + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/attention.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/attention.py new file mode 100644 index 0000000..254479d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/attention.py @@ -0,0 +1,511 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math +from inspect import isfunction + +import torch +import torch.nn.functional as F +# from apex.contrib.group_norm import GroupNorm +from torch.nn import GroupNorm +from einops import rearrange, repeat +from torch import einsum, nn +from torch._dynamo import disable + +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import checkpoint +from nemo.collections.nlp.modules.common.megatron.adapters.parallel_adapters import ( + AdapterName, + ParallelLinearAdapterConfig, +) +from nemo.core import adapter_mixins +from nemo.utils import logging + + +def check_cuda(): + if not torch.cuda.is_available(): + raise ImportError('CUDA is not available') + cur_device = torch.cuda.current_device() + dprops = torch.cuda.get_device_properties(cur_device) + + is_sm75 = dprops.major == 7 and dprops.minor == 5 + is_sm8x = dprops.major == 8 and dprops.minor >= 0 + is_sm90 = dprops.major == 9 and dprops.minor >= 0 + + return is_sm8x or is_sm75 or is_sm90 + + +try: + import torch.nn as nn + from flash_attn.modules.mha import FlashCrossAttention, FlashSelfAttention + + flash_attn_installed = check_cuda() + print("FlashAttention Installed") + + # Disable TorchDynamo on FlashAttention + FlashSelfAttention.forward = disable(FlashSelfAttention.forward) + FlashCrossAttention.forward = disable(FlashCrossAttention.forward) +except ImportError: + flash_attn_installed = False + + +def exists(val): + return val is not None + + +def uniq(arr): + return {el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + if isinstance(d, (torch.Tensor, float, int)): + return d + return d() if isfunction(d) else d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = LinearWrapper(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential(LinearWrapper(dim, inner_dim), nn.GELU()) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential(project_in, nn.Dropout(dropout), LinearWrapper(inner_dim, dim_out)) + + def forward(self, x): + return self.net(x) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def Normalize(in_channels, num_groups=32, act=""): + return GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True, act=act) + + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads=self.heads, qkv=3) + k = k.softmax(dim=-1) + context = torch.einsum('bhdn,bhen->bhde', k, v) + out = torch.einsum('bhde,bhdn->bhen', context, q) + out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) + return self.to_out(out) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = rearrange(q, 'b c h w -> b (h w) c') + k = rearrange(k, 'b c h w -> b c (h w)') + w_ = torch.einsum('bij,bjk->bik', q, k) + + w_ = w_ * (int(c) ** (-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, 'b c h w -> b c (h w)') + w_ = rearrange(w_, 'b i j -> b j i') + h_ = torch.einsum('bij,bjk->bik', v, w_) + h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) + h_ = self.proj_out(h_) + + return x + h_ + + +# b n (h d) -> (b h) n d +def rearrange_heads_outer(t: torch.Tensor, h: int) -> torch.Tensor: + b, n, ch = t.shape + return t.view(b, n, h, -1).transpose(1, 2).reshape(b * h, n, -1) + + +# (b h) n d -> b n (h d) +def rearrange_heads_inner(t: torch.Tensor, h: int) -> torch.Tensor: + b = t.shape[0] // h + n = t.shape[1] + return t.view(b, h, n, -1).transpose(1, 2).reshape(b, n, -1) + + +class LinearWrapper(nn.Linear, adapter_mixins.AdapterModuleMixin): + def __init__(self, in_features, out_features, bias=True, lora_network_alpha=None): + super().__init__(in_features, out_features, bias) + self.set_accepted_adapter_types([ParallelLinearAdapterConfig._target_]) + self.lora_network_alpha = lora_network_alpha + + def forward(self, x): + mixed_x = super().forward(x) + if self.is_adapter_available(): + lora_linear_adapter = self.get_adapter_module(AdapterName.PARALLEL_LINEAR_ADAPTER) + lora_mixed_x = lora_linear_adapter(x) + # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script. + # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning + if self.lora_network_alpha: + mixed_x = mixed_x + lora_mixed_x * (self.lora_network_alpha / lora_linear_adapter.dim) + else: + mixed_x = mixed_x + lora_mixed_x + return mixed_x + + def add_adapter(self, name, cfg, **kwargs): + self.lora_network_alpha = cfg.network_alpha + kwargs = {} + adapter_mixins.AdapterModuleMixin.add_adapter(self, name, cfg, **kwargs) + + +class CrossAttention(nn.Module): + def __init__( + self, + query_dim, + context_dim=None, + heads=8, + dim_head=64, + dropout=0.0, + use_flash_attention=False, + lora_network_alpha=None, + ): + super().__init__() + + self.inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + # make attention part be aware of self-attention/cross-attention + self.context_dim = context_dim + self.query_dim = query_dim + self.dim_head = dim_head + + self.scale = dim_head ** -0.5 + self.heads = heads + + self.to_q = LinearWrapper(query_dim, self.inner_dim, bias=False, lora_network_alpha=lora_network_alpha) + self.to_k = LinearWrapper(context_dim, self.inner_dim, bias=False, lora_network_alpha=lora_network_alpha) + self.to_v = LinearWrapper(context_dim, self.inner_dim, bias=False, lora_network_alpha=lora_network_alpha) + + self.to_out = nn.Sequential( + LinearWrapper(self.inner_dim, query_dim, lora_network_alpha=lora_network_alpha), nn.Dropout(dropout) + ) + self.use_flash_attention = use_flash_attention + + if dim_head <= 160 and (dim_head % 8) == 0 and flash_attn_installed: + if context_dim == query_dim: + self.flash_attn = FlashSelfAttention(softmax_scale=self.scale) + else: + self.flash_attn = FlashCrossAttention(softmax_scale=self.scale) + + def forward(self, x, context=None, mask=None, additional_tokens=None, n_times_crossframe_attn_in_self=0): + h = self.heads + + if additional_tokens is not None: + # get the number of masked tokens at the beginning of the output sequence + n_tokens_to_mask = additional_tokens.shape[1] + # add additional token + x = torch.cat([additional_tokens, x], dim=1) + + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + if n_times_crossframe_attn_in_self: + # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439 + assert x.shape[0] % n_times_crossframe_attn_in_self == 0 + n_cp = x.shape[0] // n_times_crossframe_attn_in_self + k = repeat(k[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp) + v = repeat(v[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp) + + out = self._attention(q, k, v, mask, additional_tokens=None) + + return self.to_out(out) + + def _attention(self, q, k, v, mask=None, additional_tokens=None): + h = self.heads + + if ( + not flash_attn_installed + or not self.use_flash_attention + or q.dtype == torch.float32 + or (self.dim_head > 160 or (self.dim_head % 8) != 0) + or mask is not None + ): + # original implementation + # b n (h d) -> (b h) n d + q = rearrange_heads_outer(q, h) + k = rearrange_heads_outer(k, h) + v = rearrange_heads_outer(v, h) + + sim = einsum('b i d, b j d -> b i j', q, k) * self.scale + + if exists(mask): + # standard stable diffusion does not run into here + mask = mask.view(mask.shape[0], -1) + b, j = mask.shape + mask = mask.unsqueeze(1).expand(b, h, j).reshape(b * h, 1, j) # b j -> (b h) () j + sim.masked_fill_(~mask, self.max_neg[sim.dtype]) + + # attention, what we cannot get enough of + attn = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', attn, v) + + # (b h) n d -> b n (h d) + out = rearrange_heads_inner(out, h) + elif self.context_dim == self.query_dim: + # self-attention + qkv = torch.stack([q, k, v], dim=2) + b, s, t, hd = qkv.shape + d = hd // h + qkv = qkv.view(b, s, t, h, d) + + out = self.flash_attn(qkv) + out = out.view(b, s, hd) + else: + # cross-attention + kv = torch.stack([k, v], dim=2) + + s_q = q.shape[1] + b, s_kv, t, hd = kv.shape + d = hd // h + + q = q.view(b, s_q, h, d) + kv = kv.view(b, s_kv, t, h, d) + + out = self.flash_attn(q, kv) + out = out.view(b, s_q, hd) + if additional_tokens is not None: + # remove additional token + out = out[:, n_tokens_to_mask:] + return out + + +class BasicTransformerBlock(nn.Module): + def __init__( + self, + dim, + n_heads, + d_head, + dropout=0.0, + context_dim=None, + gated_ff=True, + use_checkpoint=False, + use_flash_attention=False, + disable_self_attn=False, + lora_network_alpha=None, + ): + super().__init__() + self.disable_self_attn = disable_self_attn + self.attn1 = CrossAttention( + query_dim=dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + use_flash_attention=use_flash_attention, + context_dim=context_dim if self.disable_self_attn else None, + lora_network_alpha=lora_network_alpha, + ) # is a self-attention + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = CrossAttention( + query_dim=dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + use_flash_attention=use_flash_attention, + lora_network_alpha=lora_network_alpha, + ) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.use_checkpoint = use_checkpoint + + def forward(self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0): + kwargs = {"x": x} + + if context is not None: + kwargs.update({"context": context}) + if additional_tokens is not None: + kwargs.update({"additional_tokens": additional_tokens}) + + if n_times_crossframe_attn_in_self: + kwargs.update({"n_times_crossframe_attn_in_self": n_times_crossframe_attn_in_self}) + + if self.use_checkpoint: + return checkpoint(self._forward, (x, context), self.parameters(), self.use_checkpoint) + else: + return self._forward(x, context) + + def _forward(self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0): + x = ( + self.attn1( + self.norm1(x), + context=context if self.disable_self_attn else None, + additional_tokens=additional_tokens, + n_times_crossframe_attn_in_self=n_times_crossframe_attn_in_self if not self.disable_self_attn else 0, + ) + + x + ) + x = self.attn2(self.norm2(x), context=context, additional_tokens=additional_tokens) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + + def __init__( + self, + in_channels, + n_heads, + d_head, + depth=1, + dropout=0.0, + context_dim=None, + disable_self_attn=False, + use_linear=False, + use_checkpoint=False, + use_flash_attention=False, + lora_network_alpha=None, + ): + super().__init__() + logging.info( + f"constructing {self.__class__.__name__} of depth {depth} w/ {in_channels} channels and {n_heads} heads" + ) + from omegaconf import ListConfig + + if exists(context_dim) and not isinstance(context_dim, (list, ListConfig)): + context_dim = [context_dim] + if exists(context_dim) and isinstance(context_dim, list): + if depth != len(context_dim): + logging.info( + f"WARNING: {self.__class__.__name__}: Found context dims {context_dim} of depth {len(context_dim)}, " + f"which does not match the specified 'depth' of {depth}. Setting context_dim to {depth * [context_dim[0]]} now." + ) + # depth does not match context dims. + assert all( + map(lambda x: x == context_dim[0], context_dim) + ), "need homogenous context_dim to match depth automatically" + context_dim = depth * [context_dim[0]] + elif context_dim is None: + context_dim = [None] * depth + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = Normalize(in_channels) + + if not use_linear: + self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + n_heads, + d_head, + dropout=dropout, + context_dim=context_dim[d], + use_checkpoint=use_checkpoint, + use_flash_attention=use_flash_attention, + disable_self_attn=disable_self_attn, + lora_network_alpha=lora_network_alpha, + ) + for d in range(depth) + ] + ) + + if not use_linear: + self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) + else: + self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) + # self.proj_out = zero_module(nn.Linear(inner_dim, in_channels)) + # Usually inner_dim is the same as in_channels. + self.use_linear = use_linear + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = x.view(b, c, -1).transpose(1, 2) # b c h w -> b (h w) c + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + if i > 0 and len(context) == 1: + i = 0 # use same context for each block + x = block(x, context=context[i]) + if self.use_linear: + x = self.proj_out(x) + x = x.transpose(1, 2).view(b, c, h, w) # b (h w) c -> b c h w + if not self.use_linear: + x = self.proj_out(x) + return x_in + x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/denoiser.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/denoiser.py new file mode 100644 index 0000000..df1f274 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/denoiser.py @@ -0,0 +1,75 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch.nn as nn + +from nemo.collections.multimodal.parts.stable_diffusion.utils import append_dims, instantiate_from_config +from nemo.core.classes import Serialization + + +class Denoiser(nn.Module, Serialization): + def __init__(self, weighting_config, scaling_config): + super().__init__() + self.weighting = weighting_config + self.scaling = scaling_config + + def possibly_quantize_sigma(self, sigma): + return sigma + + def possibly_quantize_c_noise(self, c_noise): + return c_noise + + def w(self, sigma): + return self.weighting(sigma) + + def __call__(self, network, input, sigma, cond): + sigma = self.possibly_quantize_sigma(sigma) + sigma_shape = sigma.shape + sigma = append_dims(sigma, input.ndim) + c_skip, c_out, c_in, c_noise = self.scaling(sigma) + c_noise = self.possibly_quantize_c_noise(c_noise.reshape(sigma_shape)) + return network(input * c_in, c_noise, cond) * c_out + input * c_skip + + +class DiscreteDenoiser(Denoiser): + def __init__( + self, + weighting_config, + scaling_config, + num_idx, + discretization_config, + do_append_zero=False, + quantize_c_noise=True, + flip=True, + ): + super().__init__(weighting_config, scaling_config) + sigmas = discretization_config(num_idx, do_append_zero=do_append_zero, flip=flip) + self.register_buffer("sigmas", sigmas) + self.quantize_c_noise = quantize_c_noise + + def sigma_to_idx(self, sigma): + dists = sigma - self.sigmas[:, None] + return dists.abs().argmin(dim=0).view(sigma.shape) + + def idx_to_sigma(self, idx): + return self.sigmas[idx] + + def possibly_quantize_sigma(self, sigma): + return self.idx_to_sigma(self.sigma_to_idx(sigma)) + + def possibly_quantize_c_noise(self, c_noise): + if self.quantize_c_noise: + return self.sigma_to_idx(c_noise) + else: + return c_noise diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/denoiser_scaling.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/denoiser_scaling.py new file mode 100644 index 0000000..0ccaa09 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/denoiser_scaling.py @@ -0,0 +1,45 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + + +class EDMScaling: + def __init__(self, sigma_data=0.5): + self.sigma_data = sigma_data + + def __call__(self, sigma): + c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + c_out = sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + c_noise = 0.25 * sigma.log() + return c_skip, c_out, c_in, c_noise + + +class EpsScaling: + def __call__(self, sigma): + c_skip = torch.ones_like(sigma, device=sigma.device) + c_out = -sigma + c_in = 1 / (sigma ** 2 + 1.0) ** 0.5 + c_noise = sigma.clone() + return c_skip, c_out, c_in, c_noise + + +class VScaling: + def __call__(self, sigma): + c_skip = 1.0 / (sigma ** 2 + 1.0) + c_out = -sigma / (sigma ** 2 + 1.0) ** 0.5 + c_in = 1.0 / (sigma ** 2 + 1.0) ** 0.5 + c_noise = sigma.clone() + return c_skip, c_out, c_in, c_noise diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/denoiser_weighting.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/denoiser_weighting.py new file mode 100644 index 0000000..470f433 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/denoiser_weighting.py @@ -0,0 +1,38 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + + +class UnitWeighting: + def __call__(self, sigma): + return torch.ones_like(sigma, device=sigma.device) + + +class EDMWeighting: + def __init__(self, sigma_data=0.5): + self.sigma_data = sigma_data + + def __call__(self, sigma): + return (sigma ** 2 + self.sigma_data ** 2) / (sigma * self.sigma_data) ** 2 + + +class VWeighting(EDMWeighting): + def __init__(self): + super().__init__(sigma_data=1.0) + + +class EpsWeighting: + def __call__(self, sigma): + return sigma ** -2.0 diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/discretizer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/discretizer.py new file mode 100644 index 0000000..d348e07 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/discretizer.py @@ -0,0 +1,76 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import abstractmethod +from functools import partial + +import numpy as np +import torch + +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import make_beta_schedule +from nemo.collections.multimodal.parts.stable_diffusion.utils import append_zero + + +def generate_roughly_equally_spaced_steps(num_substeps: int, max_step: int) -> np.ndarray: + return np.linspace(max_step - 1, 0, num_substeps, endpoint=False).astype(int)[::-1] + + +class Discretization: + def __call__(self, n, do_append_zero=True, device="cpu", flip=False): + sigmas = self.get_sigmas(n, device=device) + sigmas = append_zero(sigmas) if do_append_zero else sigmas + return sigmas if not flip else torch.flip(sigmas, (0,)) + + @abstractmethod + def get_sigmas(self, n, device): + pass + + +class EDMDiscretization(Discretization): + def __init__(self, sigma_min=0.02, sigma_max=80.0, rho=7.0): + self.sigma_min = sigma_min + self.sigma_max = sigma_max + self.rho = rho + + def get_sigmas(self, n, device="cpu"): + ramp = torch.linspace(0, 1, n, device=device) + min_inv_rho = self.sigma_min ** (1 / self.rho) + max_inv_rho = self.sigma_max ** (1 / self.rho) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** self.rho + return sigmas + + +class LegacyDDPMDiscretization(Discretization): + def __init__( + self, linear_start=0.00085, linear_end=0.0120, num_timesteps=1000, + ): + super().__init__() + self.num_timesteps = num_timesteps + betas = make_beta_schedule("linear", num_timesteps, linear_start=linear_start, linear_end=linear_end) + alphas = 1.0 - betas + self.alphas_cumprod = np.cumprod(alphas, axis=0) + self.to_torch = partial(torch.tensor, dtype=torch.float32) + + def get_sigmas(self, n, device="cpu"): + if n < self.num_timesteps: + timesteps = generate_roughly_equally_spaced_steps(n, self.num_timesteps) + alphas_cumprod = self.alphas_cumprod[timesteps] + elif n == self.num_timesteps: + alphas_cumprod = self.alphas_cumprod + else: + raise ValueError + + to_torch = partial(torch.tensor, dtype=torch.float32, device=device) + sigmas = to_torch((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 + return torch.flip(sigmas, (0,)) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/guiders.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/guiders.py new file mode 100644 index 0000000..55c20ec --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/guiders.py @@ -0,0 +1,64 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from functools import partial + +import torch + +from nemo.collections.multimodal.parts.stable_diffusion.utils import default, instantiate_from_config + + +class VanillaCFG: + """ + implements parallelized CFG + """ + + def __init__(self, scale, dyn_thresh_config=None): + scale_schedule = lambda scale, sigma: scale # independent of step + self.scale_schedule = partial(scale_schedule, scale) + self.dyn_thresh = instantiate_from_config( + default( + dyn_thresh_config, {"target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"}, + ) + ) + + def __call__(self, x, sigma): + x_u, x_c = x.chunk(2) + scale_value = self.scale_schedule(sigma) + x_pred = self.dyn_thresh(x_u, x_c, scale_value) + return x_pred + + def prepare_inputs(self, x, s, c, uc): + c_out = dict() + + for k in c: + if k in ["vector", "crossattn", "concat"]: + c_out[k] = torch.cat((uc[k], c[k]), 0) + else: + assert c[k] == uc[k] + c_out[k] = c[k] + return torch.cat([x] * 2), torch.cat([s] * 2), c_out + + +class IdentityGuider: + def __call__(self, x, sigma): + return x + + def prepare_inputs(self, x, s, c, uc): + c_out = dict() + + for k in c: + c_out[k] = c[k] + + return x, s, c_out diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/loss.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/loss.py new file mode 100644 index 0000000..f6de830 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/loss.py @@ -0,0 +1,75 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Optional, Union + +import torch +import torch.nn as nn +from omegaconf import ListConfig + +from nemo.collections.multimodal.parts.stable_diffusion.utils import append_dims, instantiate_from_config +from nemo.collections.multimodal.parts.utils import randn_like + + +class StandardDiffusionLoss(nn.Module): + def __init__( + self, + sigma_sampler, + type="l2", + offset_noise_level=0.0, + batch2model_keys: Optional[Union[str, List[str], ListConfig]] = None, + ): + super().__init__() + + assert type in ["l2", "l1", "lpips"] + + self.sigma_sampler = sigma_sampler + + self.type = type + self.offset_noise_level = offset_noise_level + + if type == "lpips": + self.lpips = LPIPS().eval() + + if not batch2model_keys: + batch2model_keys = [] + + if isinstance(batch2model_keys, str): + batch2model_keys = [batch2model_keys] + + self.batch2model_keys = set(batch2model_keys) + + def __call__(self, network, denoiser, conditioner, input, batch, rng=None): + cond = conditioner(batch) + additional_model_inputs = {key: batch[key] for key in self.batch2model_keys.intersection(batch)} + + rand = torch.randint(0, self.sigma_sampler.num_idx, (input.shape[0],), generator=rng, device=rng.device).to( + self.sigma_sampler.sigmas.device + ) + sigmas = self.sigma_sampler(input.shape[0], rand=rand).to(input.device) + noise = randn_like(input, generator=rng) + if self.offset_noise_level > 0.0: + noise = noise + self.offset_noise_level * append_dims( + torch.randn(input.shape[0], device=input.device, generator=rng), input.ndim + ) + noised_input = input + noise * append_dims(sigmas, input.ndim) + model_output = denoiser(network, noised_input, sigmas, cond, **additional_model_inputs) + w = append_dims(denoiser.w(sigmas), input.ndim) + return self.get_loss(model_output, input, w) + + def get_loss(self, model_output, target, w): + if self.type == "l2": + return torch.mean((w * (model_output - target) ** 2).reshape(target.shape[0], -1), 1) + elif self.type == "l1": + return torch.mean((w * (model_output - target).abs()).reshape(target.shape[0], -1), 1) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/model.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/model.py new file mode 100644 index 0000000..7fc5c20 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/model.py @@ -0,0 +1,881 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# pytorch_diffusion + derived encoder decoder +import math + +import numpy as np +import torch +import torch.nn as nn +from apex.contrib.group_norm import GroupNorm +from einops import rearrange + +from nemo.collections.multimodal.modules.stable_diffusion.attention import LinearAttention +from nemo.collections.multimodal.parts.stable_diffusion.utils import instantiate_from_config + + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + + +def nonlinearity(x): + # swish + return torch.nn.functional.silu(x) + + +def Normalize(in_channels, num_groups=32, act=""): + return GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True, act=act) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + # TODO(yuya): Remove this cast once the issue is fixed in PyTorch + # https://github.com/pytorch/pytorch/issues/86679 + dtype = x.dtype + if dtype == torch.bfloat16: + x = x.to(torch.float32) + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if dtype == torch.bfloat16: + x = x.to(dtype) + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels, act="silu") + self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = Normalize(out_channels, act="silu") + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + else: + self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] + + h = self.norm2(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x + h + + +class LinAttnBlock(LinearAttention): + """ + to match AttnBlock usage + """ + + def __init__(self, in_channels): + super().__init__(dim=in_channels, heads=1, dim_head=in_channels) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, c, h * w) + q = q.permute(0, 2, 1) # b,hw,c + k = k.reshape(b, c, h * w) # b,c,hw + w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c) ** (-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b, c, h * w) + w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b, c, h, w) + + h_ = self.proj_out(h_) + + return x + h_ + + +def make_attn(in_channels, attn_type="vanilla"): + assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown' + print(f"making attention of type '{attn_type}' with {in_channels} in_channels") + if attn_type == "vanilla": + return AttnBlock(in_channels) + elif attn_type == "none": + return nn.Identity(in_channels) + else: + return LinAttnBlock(in_channels) + + +class Model(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + use_timestep=True, + use_linear_attn=False, + attn_type="vanilla", + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch * 4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList( + [torch.nn.Linear(self.ch, self.temb_ch), torch.nn.Linear(self.temb_ch, self.temb_ch),] + ) + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout + ) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + skip_in = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + if i_block == self.num_res_blocks: + skip_in = ch * in_ch_mult[i_level] + block.append( + ResnetBlock( + in_channels=block_in + skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) + + def forward(self, x, t=None, context=None): + # assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + z_channels, + double_z=True, + use_linear_attn=False, + attn_type="vanilla", + **ignore_kwargs, + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout + ) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout + ) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, 2 * z_channels if double_z else z_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + z_channels, + give_pre_end=False, + tanh_out=False, + use_linear_attn=False, + attn_type="vanilla", + **ignorekwargs, + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,) + tuple(ch_mult) + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + print("Working with z of shape {} = {} dimensions.".format(self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout + ) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) + + def forward(self, z): + # assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + if self.tanh_out: + h = torch.tanh(h) + return h + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList( + [ + nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock(in_channels=in_channels, out_channels=2 * in_channels, temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=2 * in_channels, out_channels=4 * in_channels, temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=4 * in_channels, out_channels=2 * in_channels, temb_channels=0, dropout=0.0), + nn.Conv2d(2 * in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True), + ] + ) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1, 2, 3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, ch_mult=(2, 2), dropout=0.0): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.res_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append( + ResnetBlock( + in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout + ) + ) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, out_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class LatentRescaler(nn.Module): + def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): + super().__init__() + # residual block, interpolate, residual block + self.factor = factor + self.conv_in = nn.Conv2d(in_channels, mid_channels, kernel_size=3, stride=1, padding=1) + self.res_block1 = nn.ModuleList( + [ + ResnetBlock(in_channels=mid_channels, out_channels=mid_channels, temb_channels=0, dropout=0.0) + for _ in range(depth) + ] + ) + self.attn = AttnBlock(mid_channels) + self.res_block2 = nn.ModuleList( + [ + ResnetBlock(in_channels=mid_channels, out_channels=mid_channels, temb_channels=0, dropout=0.0) + for _ in range(depth) + ] + ) + + self.conv_out = nn.Conv2d(mid_channels, out_channels, kernel_size=1,) + + def forward(self, x): + x = self.conv_in(x) + for block in self.res_block1: + x = block(x, None) + x = torch.nn.functional.interpolate( + x, size=(int(round(x.shape[2] * self.factor)), int(round(x.shape[3] * self.factor))) + ) + x = self.attn(x) + for block in self.res_block2: + x = block(x, None) + x = self.conv_out(x) + return x + + +class MergedRescaleEncoder(nn.Module): + def __init__( + self, + in_channels, + ch, + resolution, + out_ch, + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + ch_mult=(1, 2, 4, 8), + rescale_factor=1.0, + rescale_module_depth=1, + ): + super().__init__() + intermediate_chn = ch * ch_mult[-1] + self.encoder = Encoder( + in_channels=in_channels, + num_res_blocks=num_res_blocks, + ch=ch, + ch_mult=ch_mult, + z_channels=intermediate_chn, + double_z=False, + resolution=resolution, + attn_resolutions=attn_resolutions, + dropout=dropout, + resamp_with_conv=resamp_with_conv, + out_ch=None, + ) + self.rescaler = LatentRescaler( + factor=rescale_factor, + in_channels=intermediate_chn, + mid_channels=intermediate_chn, + out_channels=out_ch, + depth=rescale_module_depth, + ) + + def forward(self, x): + x = self.encoder(x) + x = self.rescaler(x) + return x + + +class MergedRescaleDecoder(nn.Module): + def __init__( + self, + z_channels, + out_ch, + resolution, + num_res_blocks, + attn_resolutions, + ch, + ch_mult=(1, 2, 4, 8), + dropout=0.0, + resamp_with_conv=True, + rescale_factor=1.0, + rescale_module_depth=1, + ): + super().__init__() + tmp_chn = z_channels * ch_mult[-1] + self.decoder = Decoder( + out_ch=out_ch, + z_channels=tmp_chn, + attn_resolutions=attn_resolutions, + dropout=dropout, + resamp_with_conv=resamp_with_conv, + in_channels=None, + num_res_blocks=num_res_blocks, + ch_mult=ch_mult, + resolution=resolution, + ch=ch, + ) + self.rescaler = LatentRescaler( + factor=rescale_factor, + in_channels=z_channels, + mid_channels=tmp_chn, + out_channels=tmp_chn, + depth=rescale_module_depth, + ) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Upsampler(nn.Module): + def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): + super().__init__() + assert out_size >= in_size + num_blocks = int(np.log2(out_size // in_size)) + 1 + factor_up = 1.0 + (out_size % in_size) + print( + f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}" + ) + self.rescaler = LatentRescaler( + factor=factor_up, in_channels=in_channels, mid_channels=2 * in_channels, out_channels=in_channels + ) + self.decoder = Decoder( + out_ch=out_channels, + resolution=out_size, + z_channels=in_channels, + num_res_blocks=2, + attn_resolutions=[], + in_channels=None, + ch=in_channels, + ch_mult=[ch_mult for _ in range(num_blocks)], + ) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Resize(nn.Module): + def __init__(self, in_channels=None, learned=False, mode="bilinear"): + super().__init__() + self.with_conv = learned + self.mode = mode + if self.with_conv: + print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") + raise NotImplementedError() + assert in_channels is not None + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=4, stride=2, padding=1) + + def forward(self, x, scale_factor=1.0): + if scale_factor == 1.0: + return x + else: + x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) + return x + + +class FirstStagePostProcessor(nn.Module): + def __init__( + self, + ch_mult: list, + in_channels, + pretrained_model: nn.Module = None, + reshape=False, + n_channels=None, + dropout=0.0, + pretrained_config=None, + ): + super().__init__() + if pretrained_config is None: + assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.pretrained_model = pretrained_model + else: + assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.instantiate_pretrained(pretrained_config) + + self.do_reshape = reshape + + if n_channels is None: + n_channels = self.pretrained_model.encoder.ch + + self.proj_norm = Normalize(in_channels, num_groups=in_channels // 2) + self.proj = nn.Conv2d(in_channels, n_channels, kernel_size=3, stride=1, padding=1) + + blocks = [] + downs = [] + ch_in = n_channels + for m in ch_mult: + blocks.append(ResnetBlock(in_channels=ch_in, out_channels=m * n_channels, dropout=dropout)) + ch_in = m * n_channels + downs.append(Downsample(ch_in, with_conv=False)) + + self.model = nn.ModuleList(blocks) + self.downsampler = nn.ModuleList(downs) + + def instantiate_pretrained(self, config): + model = instantiate_from_config(config) + self.pretrained_model = model.eval() + # self.pretrained_model.train = False + for param in self.pretrained_model.parameters(): + param.requires_grad = False + + @torch.no_grad() + def encode_with_pretrained(self, x): + c = self.pretrained_model.encode(x) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + return c + + def forward(self, x): + z_fs = self.encode_with_pretrained(x) + z = self.proj_norm(z_fs) + z = self.proj(z) + z = nonlinearity(z) + + for submodel, downmodel in zip(self.model, self.downsampler): + z = submodel(z, temb=None) + z = downmodel(z) + + if self.do_reshape: + z = rearrange(z, 'b c h w -> b (h w) c') + return z diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/openaimodel.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/openaimodel.py new file mode 100644 index 0000000..14560ba --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/openaimodel.py @@ -0,0 +1,1398 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math +from abc import abstractmethod +from collections.abc import Iterable +from functools import partial +from typing import Iterable + +import numpy as np +import torch +import torch as th +import torch.nn as nn +import torch.nn.functional as F + +from apex.contrib.group_norm import GroupNorm +from nemo.collections.multimodal.modules.stable_diffusion.attention import SpatialTransformer +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import ( + avg_pool_nd, + build_timestep_embedding, + checkpoint, + conv_nd, + default, + exists, + linear, + normalization, + timestep_embedding, + zero_module, +) +from nemo.utils import logging + + +def convert_module_to_dtype(module, dtype, enable_norm_layers=False): + # Convert module parameters to dtype + if isinstance(module, (torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Linear)): + module.weight.data = module.weight.data.to(dtype) + if module.bias is not None: + module.bias.data = module.bias.data.to(dtype) + + if enable_norm_layers: + if isinstance(module, (nn.LayerNorm, nn.GroupNorm, GroupNorm)): + module.weight.data = module.weight.data.to(dtype) + if module.bias is not None: + module.bias.data = module.bias.data.to(dtype) + + +def convert_module_to_fp16(module, enable_norm_layers=False): + convert_module_to_dtype(module, torch.float16, enable_norm_layers) + + +def convert_module_to_fp32(module, enable_norm_layers=False): + convert_module_to_dtype(module, torch.float32, enable_norm_layers) + + +class AttentionPool2d(nn.Module): + """ + Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py + """ + + def __init__( + self, spacial_dim: int, embed_dim: int, num_heads_channels: int, output_dim: int = None, + ): + super().__init__() + self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5) + self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) + self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) + self.num_heads = embed_dim // num_heads_channels + self.attention = QKVAttention(self.num_heads) + + def forward(self, x): + b, c, *_spatial = x.shape + x = x.reshape(b, c, -1) # NC(HW) + x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) + x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) + x = self.qkv_proj(x) + x = self.attention(x) + x = self.c_proj(x) + return x[:, :, 0] + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialTransformer): + x = layer(x, context) + else: + x = layer(x) + return x + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + + This layer performs upsampling on the given input with the option to apply a convolution operation. + The upsampling can be applied to 1D, 2D, or 3D signals, depending on the specified dimensions. + + Parameters: + channels (int): The number of channels in both the inputs and outputs. + use_conv (bool): A bool determining if a convolution is applied. + dims (int): Specifies the dimensionality of the signal. + It can be 1, 2, or 3. If set to 3, upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, third_up=False): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + self.third_up = third_up + if use_conv: + self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) + + def forward(self, x): + assert x.shape[1] == self.channels + + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + # TODO(yuya): Remove this cast once the issue is fixed in PyTorch + # https://github.com/pytorch/pytorch/issues/86679 + dtype = x.dtype + if dtype == torch.bfloat16: + x = x.to(torch.float32) + if self.dims == 3: + t_factor = 1 if not self.third_up else 2 + x = F.interpolate(x, (t_factor * x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest") + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + if dtype == torch.bfloat16: + x = x.to(dtype) + + if self.use_conv: + x = self.conv(x) + return x + + +class TransposedUpsample(nn.Module): + 'Learned 2x upsampling without padding' + + def __init__(self, channels, out_channels=None, ks=5): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + + self.up = nn.ConvTranspose2d(self.channels, self.out_channels, kernel_size=ks, stride=2) + + def forward(self, x): + return self.up(x) + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + + This layer performs downsampling on the given input and optionally applies a convolution operation. + The downsampling can be applied to 1D, 2D, or 3D signals, with specific behavior for 3D signals. + + Parameters: + channels (int): The number of channels in both the inputs and outputs. + use_conv (bool): Determines whether a convolution is applied. + True to apply convolution, False otherwise. + dims (int): Specifies the dimensionality of the signal. + It can be 1, 2, or 3. For 3D signals, downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, third_down=False): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else ((1, 2, 2) if not third_down else (2, 2, 2)) + if use_conv: + self.op = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=padding) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(TimestepBlock): + """ + A residual block that optionally changes the number of channels. + + Parameters: + channels (int): The number of input channels. + emb_channels (int): The number of timestep embedding channels. + dropout (float): The rate of dropout to apply. + out_channels (int, optional): The number of output channels. If not specified, the output channels + will be the same as the input channels. + use_conv (bool): If True and out_channels is specified, a spatial convolution is used instead of a + smaller 1x1 convolution to change the channels in the skip connection. + dims (int): Determines if the signal is 1D, 2D, or 3D. + use_checkpoint (bool): If True, gradient checkpointing is used on this module. This can save memory + at the cost of additional compute. + up (bool): If True, the block is used for upsampling. + down (bool): If True, the block is used for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + kernel_size=3, + exchange_temb_dims=False, + skip_t_emb=False, + resblock_gn_groups=32, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + self.exchange_temb_dims = exchange_temb_dims + + if isinstance(kernel_size, Iterable): + padding = [k // 2 for k in kernel_size] + else: + padding = kernel_size // 2 + + self.in_layers = nn.Sequential( + normalization(channels, act="silu", gn_groups=resblock_gn_groups), + conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.skip_t_emb = skip_t_emb + self.emb_out_channels = 2 * self.out_channels if use_scale_shift_norm else self.out_channels + if self.skip_t_emb: + logging.info(f"Skipping timestep embedding in {self.__class__.__name__}") + assert not self.use_scale_shift_norm + self.emb_layers = None + self.exchange_temb_dims = False + else: + self.emb_layers = nn.Sequential(nn.SiLU(), linear(emb_channels, self.emb_out_channels),) + self.out_layers = nn.Sequential( + normalization(self.out_channels, act="silu", gn_groups=resblock_gn_groups), + nn.Dropout(p=dropout), + zero_module(conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + Parameters: + x (Tensor): An input Tensor of shape [N x C x ...], where N is the batch size, C is the number of channels, + and '...' represents additional dimensions. + emb (Tensor): A Tensor of timestep embeddings of shape [N x emb_channels], where emb_channels is the number + of embedding channels. + + Returns: + Tensor: An output Tensor of shape [N x C x ...], representing the processed features. + """ + if self.use_checkpoint: + return checkpoint(self._forward, (x, emb), self.parameters(), self.use_checkpoint) + else: + return self._forward(x, emb) + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + + if self.skip_t_emb: + emb_out = th.zeros_like(h) + else: + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = th.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + if self.exchange_temb_dims: + emb_out = rearrange(emb_out, "b t c ... -> b c t ...") + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class AttentionBlock(nn.Module): + """ + An attention block that allows spatial positions to attend to each other. + Originally ported from here, but adapted to the N-d case. + https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. + """ + + def __init__( + self, channels, num_heads=1, num_head_channels=-1, use_checkpoint=False, use_new_attention_order=False, + ): + super().__init__() + self.channels = channels + if num_head_channels == -1: + self.num_heads = num_heads + else: + assert ( + channels % num_head_channels == 0 + ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = channels // num_head_channels + self.use_checkpoint = use_checkpoint + self.norm = normalization(channels) + self.qkv = conv_nd(1, channels, channels * 3, 1) + if use_new_attention_order: + # split qkv before split heads + self.attention = QKVAttention(self.num_heads) + else: + # split heads before split qkv + self.attention = QKVAttentionLegacy(self.num_heads) + + self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) + + def forward(self, x, **kwargs): + return checkpoint(self._forward, (x,), self.parameters(), True) + + def _forward(self, x): + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + qkv = self.qkv(self.norm(x)) + h = self.attention(qkv) + h = self.proj_out(h) + return (x + h).reshape(b, c, *spatial) + + +def count_flops_attn(model, _x, y): + """ + A counter for the `thop` package to count the operations in an + attention operation. + Meant to be used like: + macs, params = thop.profile( + model, + inputs=(inputs, timestamps), + custom_ops={QKVAttention: QKVAttention.count_flops}, + ) + """ + b, c, *spatial = y[0].shape + num_spatial = int(np.prod(spatial)) + # We perform two matmuls with the same number of ops. + # The first computes the weight matrix, the second computes + # the combination of the value vectors. + matmul_ops = 2 * b * (num_spatial ** 2) * c + model.total_ops += th.DoubleTensor([matmul_ops]) + + +class QKVAttentionLegacy(nn.Module): + """ + A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = th.einsum("bct,bcs->bts", q * scale, k * scale) # More stable with f16 than dividing afterwards + weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) + a = th.einsum("bts,bcs->bct", weight, v) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class QKVAttention(nn.Module): + """ + A module which performs QKV attention and splits in a different order. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV (Query-Key-Value) attention. + + Parameters: + qkv (Tensor): An input tensor of shape [N x (3 * H * C) x T], where N is the batch size, + H is the number of attention heads, C is the channel size, and T is the sequence length. + This tensor includes queries, keys, and values concatenated together. + + Returns: + Tensor: An output tensor of shape [N x (H * C) x T] after applying attention. This tensor + contains the processed information with the same sequence length but with modified features. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.chunk(3, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = th.einsum( + "bct,bcs->bts", + (q * scale).view(bs * self.n_heads, ch, length), + (k * scale).view(bs * self.n_heads, ch, length), + ) # More stable with f16 than dividing afterwards + weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) + a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class Timestep(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, t): + return timestep_embedding(t, self.dim) + + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + + Parameters: + in_channels (int): The number of channels in the input Tensor. + model_channels (int): The base channel count for the model. + out_channels (int): The number of channels in the output Tensor. + num_res_blocks (int): The number of residual blocks per downsample. + attention_resolutions (set/list/tuple): The downsampling rates at which attention is applied. + For example, if this includes 4, attention is used at 4x downsampling. + dropout (float): The dropout probability. + channel_mult (list/tuple): A channel multiplier for each level of the UNet. + conv_resample (bool): If True, use learned convolutions for upsampling and downsampling. + dims (int): Determines if the signal is 1D, 2D, or 3D. + num_classes (int, optional): If specified, the model becomes class-conditional with the given number of classes. + use_checkpoint (bool): If True, use gradient checkpointing to reduce memory usage. + num_heads (int): The number of attention heads in each attention layer. + num_heads_channels (int, optional): If specified, overrides num_heads and uses a fixed channel width per attention head. + num_heads_upsample (int, optional): Sets a different number of heads for upsampling. Deprecated. + use_scale_shift_norm (bool): If True, use a FiLM-like conditioning mechanism. + resblock_updown (bool): If True, use residual blocks for up/downsampling. + use_new_attention_order (bool): If True, use a different attention pattern for potentially increased efficiency. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + resblock_gn_groups=32, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + adm_in_channels=None, + offload_to_cpu=False, + transformer_depth_middle=None, + from_pretrained: str = None, + from_NeMo=False, + # It must be specified when from pretrained is not None. It indicates loading unet from NeMo trained ckpt or HF + use_flash_attention: bool = False, + unet_precision: str = "fp32", + lora_network_alpha=None, + timesteps=1000, + ): + super().__init__() + from omegaconf.listconfig import ListConfig + + if use_spatial_transformer: + assert ( + context_dim is not None + ), 'You forgot to include the dimension of your cross-attention conditioning...' + + if context_dim is not None: + assert ( + use_spatial_transformer + ), 'You forgot to use the spatial transformer for your cross-attention conditioning...' + + if type(context_dim) == ListConfig: + context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + if isinstance(transformer_depth, int): + transformer_depth = len(channel_mult) * [transformer_depth] + elif isinstance(transformer_depth, ListConfig): + transformer_depth = list(transformer_depth) + transformer_depth_middle = default(transformer_depth_middle, transformer_depth[-1]) + + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError( + "provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult" + ) + self.num_res_blocks = num_res_blocks + # self.num_res_blocks = num_res_blocks + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + assert all( + map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)),) + ) + logging.info( + f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " + f"This option has LESS priority than attention_resolutions {attention_resolutions}, " + f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " + f"attention will still not be set." + ) # todo: convert to warning + + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), + ) + + self.time_embeddings = torch.Tensor(build_timestep_embedding(model_channels, timesteps)).to('cuda') + if unet_precision == 'fp16-mixed' or unet_precision == 'fp16': + self.time_embeddings = self.time_embeddings.to(torch.float16) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + elif self.num_classes == "continuous": + logging.info("setting up linear c_adm embedding layer") + self.label_emb = nn.Linear(1, time_embed_dim) + elif self.num_classes == "timestep": + self.label_emb = nn.Sequential( + Timestep(model_channels), + nn.Sequential( + linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), + ), + ) + elif self.num_classes == "sequential": + assert adm_in_channels is not None + self.label_emb = nn.Sequential( + nn.Sequential( + linear(adm_in_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), + ) + ) + else: + raise ValueError() + self.input_blocks = nn.ModuleList( + [TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1))] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + resblock_gn_groups=resblock_gn_groups, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer( + ch, + num_heads, + dim_head, + depth=transformer_depth[level], + context_dim=context_dim, + disable_self_attn=disabled_sa, + use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, + use_flash_attention=use_flash_attention, + lora_network_alpha=lora_network_alpha, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + resblock_gn_groups=resblock_gn_groups, + ) + if resblock_updown + else Downsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + resblock_gn_groups=resblock_gn_groups, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer( + ch, + num_heads, + dim_head, + depth=transformer_depth_middle, + context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, + use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, + use_flash_attention=use_flash_attention, + lora_network_alpha=lora_network_alpha, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + resblock_gn_groups=resblock_gn_groups, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(self.num_res_blocks[level] + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + resblock_gn_groups=resblock_gn_groups, + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or i < num_attention_blocks[level]: + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads_upsample, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer( + ch, + num_heads, + dim_head, + depth=transformer_depth[level], + context_dim=context_dim, + disable_self_attn=disabled_sa, + use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, + use_flash_attention=use_flash_attention, + lora_network_alpha=lora_network_alpha, + ) + ) + if level and i == self.num_res_blocks[level]: + out_ch = ch + layers.append( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + resblock_gn_groups=resblock_gn_groups, + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch, act="silu", gn_groups=resblock_gn_groups), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + normalization(ch), + conv_nd(dims, model_channels, n_embed, 1), + # nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + if from_pretrained is not None: + if from_pretrained.endswith('safetensors'): + from safetensors.torch import load_file as load_safetensors + + state_dict = load_safetensors(from_pretrained) + else: + state_dict = torch.load(from_pretrained, map_location='cpu') + if 'state_dict' in state_dict.keys(): + state_dict = state_dict['state_dict'] + missing_key, unexpected_keys, _, _ = self._load_pretrained_model(state_dict, from_NeMo=from_NeMo) + if len(missing_key) > 0: + logging.info( + 'Following keys are missing during loading unet weights, which may lead to compromised image quality for a resumed training. Please check the checkpoint you provided.' + ) + logging.info(f"Missing keys: {missing_key}") + logging.info(f"Unexpected keys: {unexpected_keys}") + + if unet_precision == "fp16-mixed": # AMP O2 + self.convert_to_fp16() + elif unet_precision == 'fp16': + self.convert_to_fp16(enable_norm_layers=True) + + self.unet_precision = unet_precision + + def _input_blocks_mapping(self, input_dict): + res_dict = {} + for key_, value_ in input_dict.items(): + id_0 = int(key_[13]) + if "resnets" in key_: + id_1 = int(key_[23]) + target_id = 3 * id_0 + 1 + id_1 + post_fix = ( + key_[25:] + .replace('time_emb_proj', 'emb_layers.1') + .replace('norm1', 'in_layers.0') + .replace('norm2', 'out_layers.0') + .replace('conv1', 'in_layers.2') + .replace('conv2', 'out_layers.3') + .replace('conv_shortcut', 'skip_connection') + ) + res_dict["input_blocks." + str(target_id) + '.0.' + post_fix] = value_ + elif "attentions" in key_: + id_1 = int(key_[26]) + target_id = 3 * id_0 + 1 + id_1 + post_fix = key_[28:] + res_dict["input_blocks." + str(target_id) + '.1.' + post_fix] = value_ + elif "downsamplers" in key_: + post_fix = key_[35:] + target_id = 3 * (id_0 + 1) + res_dict["input_blocks." + str(target_id) + '.0.op.' + post_fix] = value_ + return res_dict + + def _mid_blocks_mapping(self, mid_dict): + res_dict = {} + for key_, value_ in mid_dict.items(): + if "resnets" in key_: + temp_key_ = ( + key_.replace('time_emb_proj', 'emb_layers.1') + .replace('norm1', 'in_layers.0') + .replace('norm2', 'out_layers.0') + .replace('conv1', 'in_layers.2') + .replace('conv2', 'out_layers.3') + .replace('conv_shortcut', 'skip_connection') + .replace('middle_block.resnets.0', 'middle_block.0') + .replace('middle_block.resnets.1', 'middle_block.2') + ) + res_dict[temp_key_] = value_ + elif "attentions" in key_: + res_dict[key_.replace('attentions.0', '1')] = value_ + return res_dict + + def _other_blocks_mapping(self, other_dict): + res_dict = {} + for key_, value_ in other_dict.items(): + tmp_key = ( + key_.replace('conv_in', 'input_blocks.0.0') + .replace('time_embedding.linear_1', 'time_embed.0') + .replace('time_embedding.linear_2', 'time_embed.2') + .replace('conv_norm_out', 'out.0') + .replace('conv_out', 'out.2') + ) + res_dict[tmp_key] = value_ + return res_dict + + def _output_blocks_mapping(self, output_dict): + res_dict = {} + for key_, value_ in output_dict.items(): + id_0 = int(key_[14]) + if "resnets" in key_: + id_1 = int(key_[24]) + target_id = 3 * id_0 + id_1 + post_fix = ( + key_[26:] + .replace('time_emb_proj', 'emb_layers.1') + .replace('norm1', 'in_layers.0') + .replace('norm2', 'out_layers.0') + .replace('conv1', 'in_layers.2') + .replace('conv2', 'out_layers.3') + .replace('conv_shortcut', 'skip_connection') + ) + res_dict["output_blocks." + str(target_id) + '.0.' + post_fix] = value_ + elif "attentions" in key_: + id_1 = int(key_[27]) + target_id = 3 * id_0 + id_1 + post_fix = key_[29:] + res_dict["output_blocks." + str(target_id) + '.1.' + post_fix] = value_ + elif "upsamplers" in key_: + post_fix = key_[34:] + target_id = 3 * (id_0 + 1) - 1 + mid_str = '.2.conv.' if target_id != 2 else '.1.conv.' + res_dict["output_blocks." + str(target_id) + mid_str + post_fix] = value_ + return res_dict + + def _sdxl_embedding_mapping(self, sdxl_dict): + res_dict = {} + for key_, value_ in sdxl_dict.items(): + new_key_ = ( + key_.replace('add_embedding.', 'label_emb.').replace('linear_1.', '0.0.').replace('linear_2.', '0.2.') + ) + res_dict[new_key_] = value_ + return res_dict + + def _state_key_mapping(self, state_dict: dict): + import re + + res_dict = {} + input_dict = {} + mid_dict = {} + output_dict = {} + other_dict = {} + sdxl_dict = {} + for key_, value_ in state_dict.items(): + if "down_blocks" in key_: + input_dict[key_.replace('down_blocks', 'input_blocks')] = value_ + elif "up_blocks" in key_: + output_dict[key_.replace('up_blocks', 'output_blocks')] = value_ + elif "mid_block" in key_: + mid_dict[key_.replace('mid_block', 'middle_block')] = value_ + elif "add_embedding" in key_: + # SDXL related mapping + sdxl_dict[key_] = value_ + else: + other_dict[key_] = value_ + + input_dict = self._input_blocks_mapping(input_dict) + output_dict = self._output_blocks_mapping(output_dict) + mid_dict = self._mid_blocks_mapping(mid_dict) + other_dict = self._other_blocks_mapping(other_dict) + sdxl_dict = self._sdxl_embedding_mapping(sdxl_dict) + # key_list = state_dict.keys() + # key_str = " ".join(key_list) + + # for key_, val_ in state_dict.items(): + # key_ = key_.replace("down_blocks", "input_blocks")\ + # .replace("up_blocks", 'output_blocks') + # res_dict[key_] = val_ + res_dict.update(input_dict) + res_dict.update(output_dict) + res_dict.update(mid_dict) + res_dict.update(other_dict) + res_dict.update(sdxl_dict) + + return res_dict + + def _load_pretrained_model(self, state_dict, ignore_mismatched_sizes=False, from_NeMo=False): + state_dict = self._strip_unet_key_prefix(state_dict) + if not from_NeMo: + state_dict = self._state_key_mapping(state_dict) + + model_state_dict = self.state_dict() + loaded_keys = [k for k in state_dict.keys()] + expected_keys = list(model_state_dict.keys()) + original_loaded_keys = loaded_keys + missing_keys = list(set(expected_keys) - set(loaded_keys)) + unexpected_keys = list(set(loaded_keys) - set(expected_keys)) + + # SDXL specific mapping + if 'output_blocks.2.2.conv.bias' in missing_keys and 'output_blocks.2.1.conv.bias' in loaded_keys: + state_dict['output_blocks.2.2.conv.bias'] = state_dict['output_blocks.2.1.conv.bias'] + state_dict['output_blocks.2.2.conv.weight'] = state_dict['output_blocks.2.1.conv.weight'] + + if 'out.1.weight' in missing_keys: + state_dict['out.1.weight'] = state_dict['out.2.weight'] + state_dict['out.1.bias'] = state_dict['out.2.bias'] + + if ( + 'input_blocks.1.0.in_layers.2.weight' in loaded_keys + and 'input_blocks.1.0.in_layers.1.weight' in expected_keys + ): + # GroupNormOpt fuses activation function to one layer, thus the indexing of weights are shifted for following + for key_ in missing_keys: + try: + s = key_.split('.') + idx = int(s[-2]) + new_key_ = ".".join(s[:-2] + [str(int(idx + 1))] + [s[-1]]) + state_dict[key_] = state_dict[new_key_] + except: + continue + + loaded_keys = list(state_dict.keys()) + missing_keys = list(set(expected_keys) - set(loaded_keys)) + unexpected_keys = list(set(loaded_keys) - set(expected_keys)) + + def _find_mismatched_keys( + state_dict, model_state_dict, loaded_keys, ignore_mismatched_sizes, + ): + mismatched_keys = [] + if ignore_mismatched_sizes: + for checkpoint_key in loaded_keys: + model_key = checkpoint_key + + if ( + model_key in model_state_dict + and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape + ): + mismatched_keys.append( + (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape) + ) + del state_dict[checkpoint_key] + return mismatched_keys + + if state_dict is not None: + # Whole checkpoint + mismatched_keys = _find_mismatched_keys( + state_dict, model_state_dict, original_loaded_keys, ignore_mismatched_sizes, + ) + error_msgs = self._load_state_dict_into_model(state_dict) + return missing_keys, unexpected_keys, mismatched_keys, error_msgs + + # TODO MMY maybe combine these cases of key prefix + def _strip_unet_key_prefix(self, state_dict): + re_state_dict = {} + for key_, value_ in state_dict.items(): + if key_.startswith('model.diffusion_model'): + re_state_dict[key_.replace('model.diffusion_model.', '')] = value_ + if key_.startswith('model.model.diffusion_model'): + re_state_dict[key_.replace('model.model.diffusion_model.', '')] = value_ + if key_.startswith('model._orig_mod.diffusion_model.'): + re_state_dict[key_.replace('model._orig_mod.diffusion_model.', '')] = value_ + if key_.startswith('model.model._orig_mod.diffusion_model.'): + re_state_dict[key_.replace('model.model._orig_mod.diffusion_model.', '')] = value_ + if key_.startswith('model.model.diffusion_model._orig_mod.'): + re_state_dict[key_.replace('model.model.diffusion_model._orig_mod.', '')] = value_ + return re_state_dict + + def _load_state_dict_into_model(self, state_dict): + # Convert old format to new format if needed from a PyTorch state_dict + # copy state_dict so _load_from_state_dict can modify it + state_dict = state_dict.copy() + error_msgs = [] + + # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants + # so we need to apply the function recursively. + def load(module: torch.nn.Module, prefix=""): + args = (state_dict, prefix, {}, True, [], [], error_msgs) + module._load_from_state_dict(*args) + + for name, child in module._modules.items(): + if child is not None: + load(child, prefix + name + ".") + + load(self) + + return error_msgs + + def convert_to_fp16(self, enable_norm_layers=False): + """ + Convert the torso of the model to float16. + """ + self.apply(lambda module: convert_module_to_fp16(module=module, enable_norm_layers=enable_norm_layers)) + + def forward(self, x, timesteps=None, context=None, y=None, **kwargs): + """ + Apply the model to an input batch. + + Parameters: + x (Tensor): An input tensor of shape [N x C x ...], where N is the batch size, C is the number of channels, + and '...' represents additional dimensions. + timesteps (Tensor): A 1-D tensor representing a batch of timesteps. + context (Tensor, optional): An optional tensor for additional conditioning, used via cross-attention. + y (Tensor, optional): An optional 1-D tensor of labels of shape [N], used if the model is class-conditional. + + Returns: + Tensor: An output tensor of shape [N x C x ...], representing the processed batch. + """ + + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + + if self.unet_precision == "fp16-mixed" or self.unet_precision == "fp16": + x = x.type(torch.float16) + if context is not None: + context = context.type(torch.float16) + + t_emb = timestep_embedding(timesteps, self.model_channels, cached_embedding=self.time_embeddings) + emb = self.time_embed(t_emb) + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x.type(emb.dtype) + for module in self.input_blocks: + h = module(h, emb, context) + hs.append(h) + h = self.middle_block(h, emb, context) + for module in self.output_blocks: + h = th.cat([h, hs.pop()], dim=1) + h = module(h, emb, context) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) + + +class EncoderUNetModel(nn.Module): + """ + The half UNet model with attention and timestep embedding. + For usage, see UNet. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + use_checkpoint=False, + use_fp16=False, + num_heads=1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + pool="adaptive", + resblock_gn_groups=32, + *args, + **kwargs, + ): + super().__init__() + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.use_checkpoint = use_checkpoint + self.dtype = th.float16 if use_fp16 else th.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), + ) + + self.input_blocks = nn.ModuleList( + [TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1))] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for _ in range(num_res_blocks): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + resblock_gn_groups=resblock_gn_groups, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=num_head_channels, + use_new_attention_order=use_new_attention_order, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + resblock_gn_groups=resblock_gn_groups, + ) + if resblock_updown + else Downsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + resblock_gn_groups=resblock_gn_groups, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=num_head_channels, + use_new_attention_order=use_new_attention_order, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + resblock_gn_groups=resblock_gn_groups, + ), + ) + self._feature_size += ch + self.pool = pool + if pool == "adaptive": + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + nn.AdaptiveAvgPool2d((1, 1)), + zero_module(conv_nd(dims, ch, out_channels, 1)), + nn.Flatten(), + ) + elif pool == "attention": + assert num_head_channels != -1 + self.out = nn.Sequential( + normalization(ch), nn.SiLU(), AttentionPool2d((image_size // ds), ch, num_head_channels, out_channels), + ) + elif pool == "spatial": + self.out = nn.Sequential( + nn.Linear(self._feature_size, 2048), nn.ReLU(), nn.Linear(2048, self.out_channels), + ) + elif pool == "spatial_v2": + self.out = nn.Sequential( + nn.Linear(self._feature_size, 2048), + normalization(2048), + nn.SiLU(), + nn.Linear(2048, self.out_channels), + ) + else: + raise NotImplementedError(f"Unexpected {pool} pooling") + + def convert_to_fp16(self): + """ + Convert the torso of the model to float16. + """ + self.input_blocks.apply(convert_module_to_fp16) + self.middle_block.apply(convert_module_to_fp16) + + def forward(self, x, timesteps): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :return: an [N x K] Tensor of outputs. + """ + emb = self.time_embed(timestep_embedding(timesteps, self.model_channels), use_fp16=self.use_fp16) + + # future support + if self.dtype == th.float32: + self.dtype == x.dtype + + results = [] + h = x.type(self.dtype) + for module in self.input_blocks: + h = module(h, emb) + if self.pool.startswith("spatial"): + results.append(h.type(x.dtype).mean(dim=(2, 3))) + h = self.middle_block(h, emb) + if self.pool.startswith("spatial"): + results.append(h.type(x.dtype).mean(dim=(2, 3))) + h = th.cat(results, axis=-1) + return self.out(h) + else: + h = h.type(x.dtype) + return self.out(h) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/sampling.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/sampling.py new file mode 100644 index 0000000..c636ffe --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/sampling.py @@ -0,0 +1,315 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" + Partially ported from https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py +""" + +from typing import Dict, Union + +import torch +from omegaconf import ListConfig, OmegaConf +from tqdm import tqdm + +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.sampling_utils import ( + get_ancestral_step, + linear_multistep_coeff, + to_d, + to_neg_log_sigma, + to_sigma, +) +from nemo.collections.multimodal.parts.stable_diffusion.utils import append_dims, default, instantiate_from_config + +DEFAULT_GUIDER = { + "target": "nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.guiders.IdentityGuider" +} + + +class BaseDiffusionSampler: + def __init__( + self, + discretization_config: Union[Dict, ListConfig, OmegaConf], + num_steps: Union[int, None] = None, + guider_config: Union[Dict, ListConfig, OmegaConf, None] = None, + verbose: bool = False, + device: str = "cuda", + ): + self.num_steps = num_steps + self.discretization = instantiate_from_config(discretization_config) + self.guider = instantiate_from_config(default(guider_config, DEFAULT_GUIDER,)) + self.verbose = verbose + self.device = device + + def prepare_sampling_loop(self, x, cond, uc=None, num_steps=None): + sigmas = self.discretization(self.num_steps if num_steps is None else num_steps, device=self.device) + uc = default(uc, cond) + + x *= torch.sqrt(1.0 + sigmas[0] ** 2.0) + num_sigmas = len(sigmas) + + s_in = x.new_ones([x.shape[0]]) + + return x, s_in, sigmas, num_sigmas, cond, uc + + def denoise(self, x, denoiser, sigma, cond, uc): + denoised = denoiser(*self.guider.prepare_inputs(x, sigma, cond, uc)) + denoised = self.guider(denoised, sigma) + return denoised + + def get_sigma_gen(self, num_sigmas): + sigma_generator = range(num_sigmas - 1) + if self.verbose: + print("#" * 30, " Sampling setting ", "#" * 30) + print(f"Sampler: {self.__class__.__name__}") + print(f"Discretization: {self.discretization.__class__.__name__}") + print(f"Guider: {self.guider.__class__.__name__}") + sigma_generator = tqdm( + sigma_generator, + total=num_sigmas, + desc=f"Sampling with {self.__class__.__name__} for {num_sigmas} steps", + ) + return sigma_generator + + +class SingleStepDiffusionSampler(BaseDiffusionSampler): + def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc, *args, **kwargs): + raise NotImplementedError + + def euler_step(self, x, d, dt): + return x + dt * d + + +class EDMSampler(SingleStepDiffusionSampler): + def __init__(self, s_churn=0.0, s_tmin=0.0, s_tmax=float("inf"), s_noise=1.0, *args, **kwargs): + super().__init__(*args, **kwargs) + + self.s_churn = s_churn + self.s_tmin = s_tmin + self.s_tmax = s_tmax + self.s_noise = s_noise + + def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, gamma=0.0): + sigma_hat = sigma * (gamma + 1.0) + if gamma > 0: + eps = torch.randn_like(x) * self.s_noise + x = x + eps * append_dims(sigma_hat ** 2 - sigma ** 2, x.ndim) ** 0.5 + + denoised = self.denoise(x, denoiser, sigma_hat, cond, uc) + d = to_d(x, sigma_hat, denoised) + dt = append_dims(next_sigma - sigma_hat, x.ndim) + + euler_step = self.euler_step(x, d, dt) + x = self.possible_correction_step(euler_step, x, d, dt, next_sigma, denoiser, cond, uc) + return x + + def __call__(self, denoiser, x, cond, uc=None, num_steps=None): + x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(x, cond, uc, num_steps) + + for i in self.get_sigma_gen(num_sigmas): + gamma = ( + min(self.s_churn / (num_sigmas - 1), 2 ** 0.5 - 1) if self.s_tmin <= sigmas[i] <= self.s_tmax else 0.0 + ) + x = self.sampler_step(s_in * sigmas[i], s_in * sigmas[i + 1], denoiser, x, cond, uc, gamma,) + + return x + + +class AncestralSampler(SingleStepDiffusionSampler): + def __init__(self, eta=1.0, s_noise=1.0, *args, **kwargs): + super().__init__(*args, **kwargs) + + self.eta = eta + self.s_noise = s_noise + self.noise_sampler = lambda x: torch.randn_like(x) + + def ancestral_euler_step(self, x, denoised, sigma, sigma_down): + d = to_d(x, sigma, denoised) + dt = append_dims(sigma_down - sigma, x.ndim) + + return self.euler_step(x, d, dt) + + def ancestral_step(self, x, sigma, next_sigma, sigma_up): + x = torch.where( + append_dims(next_sigma, x.ndim) > 0.0, + x + self.noise_sampler(x) * self.s_noise * append_dims(sigma_up, x.ndim), + x, + ) + return x + + def __call__(self, denoiser, x, cond, uc=None, num_steps=None): + x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(x, cond, uc, num_steps) + + for i in self.get_sigma_gen(num_sigmas): + x = self.sampler_step(s_in * sigmas[i], s_in * sigmas[i + 1], denoiser, x, cond, uc,) + + return x + + +class LinearMultistepSampler(BaseDiffusionSampler): + def __init__( + self, order=4, *args, **kwargs, + ): + super().__init__(*args, **kwargs) + + self.order = order + + def __call__(self, denoiser, x, cond, uc=None, num_steps=None, **kwargs): + x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(x, cond, uc, num_steps) + + ds = [] + sigmas_cpu = sigmas.detach().cpu().numpy() + for i in self.get_sigma_gen(num_sigmas): + sigma = s_in * sigmas[i] + denoised = denoiser(*self.guider.prepare_inputs(x, sigma, cond, uc), **kwargs) + denoised = self.guider(denoised, sigma) + d = to_d(x, sigma, denoised) + ds.append(d) + if len(ds) > self.order: + ds.pop(0) + cur_order = min(i + 1, self.order) + coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] + x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) + + return x + + +class EulerEDMSampler(EDMSampler): + def possible_correction_step(self, euler_step, x, d, dt, next_sigma, denoiser, cond, uc): + return euler_step + + +class HeunEDMSampler(EDMSampler): + def possible_correction_step(self, euler_step, x, d, dt, next_sigma, denoiser, cond, uc): + if torch.sum(next_sigma) < 1e-14: + # Save a network evaluation if all noise levels are 0 + return euler_step + else: + denoised = self.denoise(euler_step, denoiser, next_sigma, cond, uc) + d_new = to_d(euler_step, next_sigma, denoised) + d_prime = (d + d_new) / 2.0 + + # apply correction if noise level is not 0 + x = torch.where(append_dims(next_sigma, x.ndim) > 0.0, x + d_prime * dt, euler_step) + return x + + +class EulerAncestralSampler(AncestralSampler): + def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc): + sigma_down, sigma_up = get_ancestral_step(sigma, next_sigma, eta=self.eta) + denoised = self.denoise(x, denoiser, sigma, cond, uc) + x = self.ancestral_euler_step(x, denoised, sigma, sigma_down) + x = self.ancestral_step(x, sigma, next_sigma, sigma_up) + + return x + + +class DPMPP2SAncestralSampler(AncestralSampler): + def get_variables(self, sigma, sigma_down): + t, t_next = [to_neg_log_sigma(s) for s in (sigma, sigma_down)] + h = t_next - t + s = t + 0.5 * h + return h, s, t, t_next + + def get_mult(self, h, s, t, t_next): + mult1 = to_sigma(s) / to_sigma(t) + mult2 = (-0.5 * h).expm1() + mult3 = to_sigma(t_next) / to_sigma(t) + mult4 = (-h).expm1() + + return mult1, mult2, mult3, mult4 + + def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, **kwargs): + sigma_down, sigma_up = get_ancestral_step(sigma, next_sigma, eta=self.eta) + denoised = self.denoise(x, denoiser, sigma, cond, uc) + x_euler = self.ancestral_euler_step(x, denoised, sigma, sigma_down) + + if torch.sum(sigma_down) < 1e-14: + # Save a network evaluation if all noise levels are 0 + x = x_euler + else: + h, s, t, t_next = self.get_variables(sigma, sigma_down) + mult = [append_dims(mult, x.ndim) for mult in self.get_mult(h, s, t, t_next)] + + x2 = mult[0] * x - mult[1] * denoised + denoised2 = self.denoise(x2, denoiser, to_sigma(s), cond, uc) + x_dpmpp2s = mult[2] * x - mult[3] * denoised2 + + # apply correction if noise level is not 0 + x = torch.where(append_dims(sigma_down, x.ndim) > 0.0, x_dpmpp2s, x_euler) + + x = self.ancestral_step(x, sigma, next_sigma, sigma_up) + return x + + +class DPMPP2MSampler(BaseDiffusionSampler): + def get_variables(self, sigma, next_sigma, previous_sigma=None): + t, t_next = [to_neg_log_sigma(s) for s in (sigma, next_sigma)] + h = t_next - t + + if previous_sigma is not None: + h_last = t - to_neg_log_sigma(previous_sigma) + r = h_last / h + return h, r, t, t_next + else: + return h, None, t, t_next + + def get_mult(self, h, r, t, t_next, previous_sigma): + mult1 = to_sigma(t_next) / to_sigma(t) + mult2 = (-h).expm1() + + if previous_sigma is not None: + mult3 = 1 + 1 / (2 * r) + mult4 = 1 / (2 * r) + return mult1, mult2, mult3, mult4 + else: + return mult1, mult2 + + def sampler_step( + self, old_denoised, previous_sigma, sigma, next_sigma, denoiser, x, cond, uc=None, + ): + denoised = self.denoise(x, denoiser, sigma, cond, uc) + + h, r, t, t_next = self.get_variables(sigma, next_sigma, previous_sigma) + mult = [append_dims(mult, x.ndim) for mult in self.get_mult(h, r, t, t_next, previous_sigma)] + + x_standard = mult[0] * x - mult[1] * denoised + if old_denoised is None or torch.sum(next_sigma) < 1e-14: + # Save a network evaluation if all noise levels are 0 or on the first step + return x_standard, denoised + else: + denoised_d = mult[2] * denoised - mult[3] * old_denoised + x_advanced = mult[0] * x - mult[1] * denoised_d + + # apply correction if noise level is not 0 and not first step + x = torch.where(append_dims(next_sigma, x.ndim) > 0.0, x_advanced, x_standard) + + return x, denoised + + def __call__(self, denoiser, x, cond, uc=None, num_steps=None, **kwargs): + x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(x, cond, uc, num_steps) + + old_denoised = None + for i in self.get_sigma_gen(num_sigmas): + x, old_denoised = self.sampler_step( + old_denoised, + None if i == 0 else s_in * sigmas[i - 1], + s_in * sigmas[i], + s_in * sigmas[i + 1], + denoiser, + x, + cond, + uc=uc, + ) + + return x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/sampling_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/sampling_utils.py new file mode 100644 index 0000000..9770f4d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/sampling_utils.py @@ -0,0 +1,60 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from scipy import integrate + +from nemo.collections.multimodal.parts.stable_diffusion.utils import append_dims + + +class NoDynamicThresholding: + def __call__(self, uncond, cond, scale): + return uncond + scale * (cond - uncond) + + +def linear_multistep_coeff(order, t, i, j, epsrel=1e-4): + if order - 1 > i: + raise ValueError(f"Order {order} too high for step {i}") + + def fn(tau): + prod = 1.0 + for k in range(order): + if j == k: + continue + prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) + return prod + + return integrate.quad(fn, t[i], t[i + 1], epsrel=epsrel)[0] + + +def get_ancestral_step(sigma_from, sigma_to, eta=1.0): + if not eta: + return sigma_to, 0.0 + sigma_up = torch.minimum( + sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5, + ) + sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 + return sigma_down, sigma_up + + +def to_d(x, sigma, denoised): + return (x - denoised) / append_dims(sigma, x.ndim) + + +def to_neg_log_sigma(sigma): + return sigma.log().neg() + + +def to_sigma(neg_log_sigma): + return neg_log_sigma.neg().exp() diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/sigma_sampling.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/sigma_sampling.py new file mode 100644 index 0000000..5f54f7c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/sigma_sampling.py @@ -0,0 +1,40 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + +from nemo.collections.multimodal.parts.stable_diffusion.utils import default, instantiate_from_config + + +class EDMSampling: + def __init__(self, p_mean=-1.2, p_std=1.2): + self.p_mean = p_mean + self.p_std = p_std + + def __call__(self, n_samples, rand=None): + log_sigma = self.p_mean + self.p_std * default(rand, torch.randn((n_samples,))) + return log_sigma.exp() + + +class DiscreteSampling: + def __init__(self, discretization, num_idx, do_append_zero=False, flip=True): + self.num_idx = num_idx + self.sigmas = discretization(num_idx, do_append_zero=do_append_zero, flip=flip) + + def idx_to_sigma(self, idx): + return self.sigmas[idx] + + def __call__(self, n_samples, rand=None): + idx = default(rand, torch.randint(0, self.num_idx, (n_samples,)),) + return self.idx_to_sigma(idx) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/util.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/util.py new file mode 100644 index 0000000..c59f50a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/util.py @@ -0,0 +1,347 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +''' +adopted from +https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +and +https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +and +https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py + +thanks! +''' + +import math +from inspect import isfunction + +import numpy as np +import torch +import torch.nn as nn +# from apex.contrib.group_norm import GroupNorm +from torch.nn import GroupNorm +from einops import repeat +from torch._dynamo import disable +from torch.cuda.amp import custom_bwd, custom_fwd + + +def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if schedule == "linear": + betas = torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 + + elif schedule == "cosine": + timesteps = torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "sqrt_linear": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + elif schedule == "sqrt": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + + +def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): + if ddim_discr_method == "uniform": + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == "quad": + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps)) ** 2).astype(int) + else: + raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + print(f"Selected timesteps for ddim sampler: {steps_out}") + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + variance = (1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev) + sigmas = eta * np.sqrt(variance) + if verbose: + print(f"Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}") + print( + f"For the chosen value of eta, which is {eta}, " + f"this results in the following sigma_t schedule for ddim sampler {sigmas}" + ) + return sigmas, alphas, alphas_prev, variance + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule based on a discretized alpha_t_bar function. + + Parameters: + num_diffusion_timesteps (int): The number of beta values to produce, corresponding to the number of timesteps in the diffusion process. + alpha_bar (function): A lambda function that accepts a time value t ranging from 0 to 1 and returns the cumulative product of (1-beta) up to that point in the diffusion process. + max_beta (float): The maximum allowable value for beta. Setting this to a value lower than 1 helps in preventing singularities in the diffusion process. + + Returns: + list: A list of beta values that correspond to each timestep in the diffusion process. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations. + + Parameters: + func (function): The function to be evaluated. This should be a callable object. + inputs (sequence): The arguments to pass to `func`. This is a sequence of inputs that `func` will be called with. + params (sequence): A sequence of parameters that `func` depends on but does not explicitly take as arguments. + These are additional parameters required by `func`. + flag (bool): If set to False, disables gradient checkpointing. If True, enables gradient checkpointing which + allows for memory savings at the cost of extra compute during the backward pass. + + Returns: + The result of evaluating `func` with the given inputs and parameters, with reduced memory usage during the forward pass. + """ + + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + @custom_fwd + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + @custom_bwd + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, ctx.input_tensors + ctx.input_params, output_grads, allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +# Temporary hack to get rid of TorchDynamo issue with DDP +# TODO: remove this if https://github.com/pytorch/pytorch/issues/94574 fixed +@disable +def get_idx(end, device): + return torch.arange(start=0, end=end, dtype=torch.float32, device=device) + + +def build_timestep_embedding(dim, max_timesteps, max_period=10000): + timesteps = np.arange(start=0, stop=max_timesteps, dtype=np.float32) + half = dim // 2 + idx = np.arange(start=0, stop=half, dtype=np.float32) + freqs = np.exp(-math.log(max_period) / half * idx) + args = timesteps[:, None] * freqs[None] + embedding = np.concatenate([np.cos(args), np.sin(args)], axis=-1) + if dim % 2: + embedding = np.concatenate([embedding, np.zeros_like(embedding[:, :1])], axis=-1) + return embedding + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False, cached_embedding=None): + """ + Create sinusoidal timestep embeddings. + + Parameters: + timesteps (Tensor): A 1-D tensor of N indices, one per batch element. These indices may be fractional and + represent the timesteps for which embeddings are to be created. + dim (int): The dimension of the output embeddings. Each timestep will be represented as a vector of this dimension. + max_period (float): Controls the minimum frequency of the embeddings. Higher values result in higher frequency + components in the embedding. + + Returns: + Tensor: An [N x dim] tensor of positional embeddings, where each row corresponds to the embedding for a timestep. + """ + + if not repeat_only: + if cached_embedding is not None: + # using cached embedding and lookup in the cache + embedding = cached_embedding[timesteps, :] + else: + half = dim // 2 + idx = get_idx(half, timesteps.device) + freqs = torch.exp(-math.log(max_period) / half * idx) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = repeat(timesteps, "b -> b d", d=dim) + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def normalization(in_channels, act="", gn_groups=32): + return GroupNorm(num_groups=gn_groups, num_channels=in_channels, eps=1e-5, affine=True, act=act) + + +# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. +class SiLU(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +class GroupNorm32(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return nn.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() + + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where(torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where(torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + """ + return v[(...,) + (None,) * (dims - 1)] + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/wrappers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/wrappers.py new file mode 100644 index 0000000..0d465c1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/wrappers.py @@ -0,0 +1,42 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn as nn +from packaging import version + +OPENAIUNETWRAPPER = "nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.wrappers.OpenAIWrapper" + + +class IdentityWrapper(nn.Module): + def __init__(self, diffusion_model, compile_model: bool = False): + super().__init__() + compile = ( + torch.compile + if (version.parse(torch.__version__) >= version.parse("2.0.0")) and compile_model + else lambda x: x + ) + self.diffusion_model = compile(diffusion_model) + + def forward(self, *args, **kwargs): + return self.diffusion_model(*args, **kwargs) + + +class OpenAIWrapper(IdentityWrapper): + def forward(self, x: torch.Tensor, t: torch.Tensor, c: dict, **kwargs) -> torch.Tensor: + if c.get("concat", None): + x = torch.cat((x, c.get("concat")), dim=1) + return self.diffusion_model( + x, timesteps=t, context=c.get("crossattn", None), y=c.get("vector", None), **kwargs, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/distributions/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/distributions/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/distributions/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/distributions/distributions.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/distributions/distributions.py new file mode 100644 index 0000000..81d79ac --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/distributions/distributions.py @@ -0,0 +1,98 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np +import torch + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean, device=self.parameters.device) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape, device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.0]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var + - 1.0 + - self.logvar + + other.logvar, + dim=[1, 2, 3], + ) + + def nll(self, sample, dims=[1, 2, 3]): + if self.deterministic: + return torch.Tensor([0.0]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) for x in (logvar1, logvar2)] + + return 0.5 * ( + -1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/encoders/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/encoders/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/encoders/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/encoders/modules.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/encoders/modules.py new file mode 100644 index 0000000..446b81a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/encoders/modules.py @@ -0,0 +1,880 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import tempfile +from functools import partial +from typing import Dict, List, Optional, Tuple, Union + +import open_clip +import torch +import torch.nn as nn +from einops import rearrange, repeat +from omegaconf import ListConfig, OmegaConf +from torch.utils.checkpoint import checkpoint +from transformers import CLIPTextModel, CLIPTokenizer + +from nemo.collections.multimodal.data.clip.clip_dataset import get_preprocess_fns +from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import CLIPModel +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel import Timestep +from nemo.collections.multimodal.modules.stable_diffusion.encoders.x_transformer import ( + TransformerWrapper, # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test +) +from nemo.collections.multimodal.modules.stable_diffusion.encoders.x_transformer import Encoder +from nemo.collections.multimodal.parts.stable_diffusion.utils import ( + count_params, + disabled_train, + expand_dims_like, + instantiate_from_config, +) +from nemo.collections.nlp.modules.common.megatron.adapters.parallel_adapters import ( + AdapterName, + ParallelLinearAdapterConfig, +) +from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer +from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector +from nemo.core import adapter_mixins +from nemo.utils import logging + +try: + from megatron.core import ModelParallelConfig, parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + ModelParallelConfig = ApexGuardDefaults + + HAVE_MEGATRON_CORE = False + + +class AbstractEncoder(nn.Module): + def __init__(self, enable_lora_finetune=False, target_block=[], target_module=[]): + super().__init__() + self.TARGET_BLOCK = target_block + self.TARGET_MODULE = target_module + if enable_lora_finetune: + self.lora_layers = [] + + def encode(self, *args, **kwargs): + raise NotImplementedError + + def _enable_lora(self, lora_model): + for module_name, module in lora_model.named_modules(): + if module.__class__.__name__ in self.TARGET_BLOCK: + tmp = {} + for sub_name, sub_module in module.named_modules(): + if sub_module.__class__.__name__ in self.TARGET_MODULE: + if hasattr(sub_module, "input_size") and hasattr( + sub_module, "output_size" + ): # for megatron ParallelLinear + lora = LoraWrapper(sub_module, sub_module.input_size, sub_module.output_size) + else: # for nn.Linear + lora = LoraWrapper(sub_module, sub_module.in_features, sub_module.out_features) + self.lora_layers.append(lora) + if sub_name not in tmp.keys(): + tmp.update({sub_name: lora}) + else: + print(f"Duplicate subnames are found in module {module_name}") + for sub_name, lora_layer in tmp.items(): + lora_name = f'{sub_name}_lora' + module.add_module(lora_name, lora_layer) + + +class AbstractEmbModel(nn.Module): + def __init__(self, enable_lora_finetune=False, target_block=[], target_module=[]): + super().__init__() + self._is_trainable = None + self._ucg_rate = None + self._input_key = None + + self.TARGET_BLOCK = target_block + self.TARGET_MODULE = target_module + if enable_lora_finetune: + self.lora_layers = [] + + @property + def is_trainable(self) -> bool: + return self._is_trainable + + @property + def ucg_rate(self) -> Union[float, torch.Tensor]: + return self._ucg_rate + + @property + def input_key(self) -> str: + return self._input_key + + @is_trainable.setter + def is_trainable(self, value: bool): + self._is_trainable = value + + @ucg_rate.setter + def ucg_rate(self, value: Union[float, torch.Tensor]): + self._ucg_rate = value + + @input_key.setter + def input_key(self, value: str): + self._input_key = value + + @is_trainable.deleter + def is_trainable(self): + del self._is_trainable + + @ucg_rate.deleter + def ucg_rate(self): + del self._ucg_rate + + @input_key.deleter + def input_key(self): + del self._input_key + + def encode(self, *args, **kwargs): + raise NotImplementedError + + def _enable_lora(self, lora_model): + for module_name, module in lora_model.named_modules(): + if module.__class__.__name__ in self.TARGET_BLOCK: + tmp = {} + for sub_name, sub_module in module.named_modules(): + if sub_module.__class__.__name__ in self.TARGET_MODULE: + if hasattr(sub_module, "input_size") and hasattr( + sub_module, "output_size" + ): # for megatron ParallelLinear + lora = LoraWrapper(sub_module, sub_module.input_size, sub_module.output_size) + else: # for nn.Linear + lora = LoraWrapper(sub_module, sub_module.in_features, sub_module.out_features) + self.lora_layers.append(lora) + if sub_name not in tmp.keys(): + tmp.update({sub_name: lora}) + else: + print(f"Duplicate subnames are found in module {module_name}") + for sub_name, lora_layer in tmp.items(): + lora_name = f'{sub_name}_lora' + module.add_module(lora_name, lora_layer) + + +class GeneralConditioner(nn.Module): + OUTPUT_DIM2KEYS = {2: "vector", 3: "crossattn", 4: "concat", 5: "concat"} + KEY2CATDIM = {"vector": 1, "crossattn": 2, "concat": 1} + + def __init__(self, emb_models: List[ListConfig]): + super().__init__() + embedders = [] + + for n, embconfig in enumerate(emb_models): + embedder = embconfig['emb_model'] + assert isinstance( + embedder, AbstractEmbModel + ), f"embedder model {embedder.__class__.__name__} has to inherit from AbstractEmbModel" + embedder.is_trainable = embconfig.get("is_trainable", False) + embedder.ucg_rate = embconfig.get("ucg_rate", 0.0) + if not embedder.is_trainable: + embedder.train = disabled_train + for param in embedder.parameters(): + param.requires_grad = False + embedder.eval() + print( + f"Initialized embedder #{n}: {embedder.__class__.__name__} " + f"with {count_params(embedder, False)} params. Trainable: {embedder.is_trainable}" + ) + + if "input_key" in embconfig: + embedder.input_key = embconfig["input_key"] + elif "input_keys" in embconfig: + embedder.input_keys = embconfig["input_keys"] + else: + raise KeyError(f"need either 'input_key' or 'input_keys' for embedder {embedder.__class__.__name__}") + + embedder.legacy_ucg_val = embconfig.get("legacy_ucg_value", None) + if embedder.legacy_ucg_val is not None: + embedder.ucg_prng = np.random.RandomState() + + embedders.append(embedder) + self.embedders = nn.ModuleList(embedders) + + def possibly_get_ucg_val(self, embedder: AbstractEmbModel, batch: Dict) -> Dict: + assert embedder.legacy_ucg_val is not None + p = embedder.ucg_rate + val = embedder.legacy_ucg_val + for i in range(len(batch[embedder.input_key])): + if embedder.ucg_prng.choice(2, p=[1 - p, p]): + batch[embedder.input_key][i] = val + return batch + + def forward(self, batch: Dict, force_zero_embeddings: Optional[List] = None) -> Dict: + output = dict() + if force_zero_embeddings is None: + force_zero_embeddings = [] + for embedder in self.embedders: + embedding_context = nullcontext if embedder.is_trainable else torch.no_grad + with embedding_context(): + if hasattr(embedder, "input_key") and (embedder.input_key is not None): + if embedder.legacy_ucg_val is not None: + batch = self.possibly_get_ucg_val(embedder, batch) + emb_out = embedder(batch[embedder.input_key]) + elif hasattr(embedder, "input_keys"): + emb_out = embedder(*[batch[k] for k in embedder.input_keys]) + assert isinstance( + emb_out, (torch.Tensor, list, tuple) + ), f"encoder outputs must be tensors or a sequence, but got {type(emb_out)}" + if not isinstance(emb_out, (list, tuple)): + emb_out = [emb_out] + for emb in emb_out: + out_key = self.OUTPUT_DIM2KEYS[emb.dim()] + if embedder.ucg_rate > 0.0 and embedder.legacy_ucg_val is None: + emb = ( + expand_dims_like( + torch.bernoulli((1.0 - embedder.ucg_rate) * torch.ones(emb.shape[0], device=emb.device)), + emb, + ) + * emb + ) + if hasattr(embedder, "input_key") and embedder.input_key in force_zero_embeddings: + emb = torch.zeros_like(emb) + if out_key in output: + output[out_key] = torch.cat((output[out_key], emb), self.KEY2CATDIM[out_key]) + else: + output[out_key] = emb + return output + + def get_unconditional_conditioning(self, batch_c, batch_uc=None, force_uc_zero_embeddings=None): + if force_uc_zero_embeddings is None: + force_uc_zero_embeddings = [] + ucg_rates = list() + for embedder in self.embedders: + ucg_rates.append(embedder.ucg_rate) + embedder.ucg_rate = 0.0 + c = self(batch_c) + uc = self(batch_c if batch_uc is None else batch_uc, force_uc_zero_embeddings) + + for embedder, rate in zip(self.embedders, ucg_rates): + embedder.ucg_rate = rate + return c, uc + + +class ClassEmbedder(nn.Module): + def __init__(self, embed_dim, n_classes=1000, key='class'): + super().__init__() + self.key = key + self.embedding = nn.Embedding(n_classes, embed_dim) + + def forward(self, batch, key=None): + if key is None: + key = self.key + # this is for use in crossattn + c = batch[key][:, None] + c = self.embedding(c) + return c + + +class TransformerEmbedder(AbstractEncoder): + """Some transformer encoder layers""" + + def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): + super().__init__() + self.device = device + self.transformer = TransformerWrapper( + num_tokens=vocab_size, max_seq_len=max_seq_len, attn_layers=Encoder(dim=n_embed, depth=n_layer) + ) + + def forward(self, tokens): + tokens = tokens.to(self.device) # meh + z = self.transformer(tokens, return_embeddings=True) + return z + + def encode(self, x): + return self(x) + + +class BERTTokenizer(AbstractEncoder): + """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" + + def __init__(self, device="cuda", vq_interface=True, max_length=77): + super().__init__() + from transformers import BertTokenizerFast # TODO: add to reuquirements + + self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") + self.device = device + self.vq_interface = vq_interface + self.max_length = max_length + + def forward(self, text): + batch_encoding = self.tokenizer( + text, + truncation=True, + max_length=self.max_length, + return_length=True, + return_overflowing_tokens=False, + padding="max_length", + return_tensors="pt", + ) + tokens = batch_encoding["input_ids"].to(self.device) + return tokens + + @torch.no_grad() + def encode(self, text): + tokens = self(text) + if not self.vq_interface: + return tokens + return None, None, [None, None, tokens] + + def decode(self, text): + return text + + +class BERTEmbedder(AbstractEncoder): + """Uses the BERT tokenizr model and add some transformer encoder layers""" + + def __init__( + self, + n_embed, + n_layer, + vocab_size=30522, + max_seq_len=77, + device="cuda", + use_tokenizer=True, + embedding_dropout=0.0, + ): + super().__init__() + self.use_tknz_fn = use_tokenizer + if self.use_tknz_fn: + self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) + self.device = device + self.transformer = TransformerWrapper( + num_tokens=vocab_size, + max_seq_len=max_seq_len, + attn_layers=Encoder(dim=n_embed, depth=n_layer), + emb_dropout=embedding_dropout, + ) + + def forward(self, text): + if self.use_tknz_fn: + tokens = self.tknz_fn(text) # .to(self.device) + else: + tokens = text + z = self.transformer(tokens, return_embeddings=True) + return z + + def encode(self, text): + # output of length 77 + return self(text) + + +class SpatialRescaler(nn.Module): + def __init__(self, n_stages=1, method='bilinear', multiplier=0.5, in_channels=3, out_channels=None, bias=False): + super().__init__() + self.n_stages = n_stages + assert self.n_stages >= 0 + assert method in ['nearest', 'linear', 'bilinear', 'trilinear', 'bicubic', 'area'] + self.multiplier = multiplier + self.interpolator = partial(torch.nn.functional.interpolate, mode=method) + self.remap_output = out_channels is not None + if self.remap_output: + print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.') + self.channel_mapper = nn.Conv2d(in_channels, out_channels, 1, bias=bias) + + def forward(self, x): + for stage in range(self.n_stages): + x = self.interpolator(x, scale_factor=self.multiplier) + + if self.remap_output: + x = self.channel_mapper(x) + return x + + def encode(self, x): + return self(x) + + +class LoraWrapper(nn.Module, adapter_mixins.AdapterModuleMixin): + def __init__(self, target_module, in_features, out_features, lora_network_alpha=None): + super().__init__() + self.target_module = target_module + self.set_accepted_adapter_types([ParallelLinearAdapterConfig._target_]) + self.lora_network_alpha = lora_network_alpha + self.in_features = in_features + self.out_features = out_features + + def forward(self, x): + org_results = self.target_forward(x) + if self.is_adapter_available(): + lora_linear_adapter = self.get_adapter_module(AdapterName.PARALLEL_LINEAR_ADAPTER) + lora_mixed_x = lora_linear_adapter(x) + # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script. + # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning + mixed_x = org_results[0] if isinstance(org_results, tuple) else org_results + + if self.lora_network_alpha: + mixed_x = mixed_x + lora_mixed_x * (self.lora_network_alpha / lora_linear_adapter.dim) + else: + mixed_x = mixed_x + lora_mixed_x + + if isinstance(org_results, tuple): + org_results = (mixed_x, *org_results[1:]) + else: + org_results = mixed_x + + return org_results + + def add_adapter(self, name, cfg, **kwargs): + self.lora_network_alpha = cfg.network_alpha + kwargs = {} + adapter_mixins.AdapterModuleMixin.add_adapter(self, name, cfg, **kwargs) + self.target_forward = self.target_module.forward + self.target_module.forward = self.forward + del self.target_module + + +class FrozenCLIPEmbedder(AbstractEmbModel): + """Uses the CLIP transformer encoder for text (from Hugging Face)""" + + LAYERS = ["last", "pooled", "hidden"] + + def __init__( + self, + version="openai/clip-vit-large-patch14", + device="cuda", + max_length=77, + enable_lora_finetune=False, + layer="last", + layer_idx=None, + always_return_pooled=False, + ): + super().__init__(enable_lora_finetune, target_block=["CLIPAttention", "CLIPMLP"], target_module=["Linear"]) + self.tokenizer = CLIPTokenizer.from_pretrained(version) + self.transformer = CLIPTextModel.from_pretrained(version) + self.device = device + self.max_length = max_length + self.freeze() + if enable_lora_finetune: + self._enable_lora(self.transformer) + print(f"CLIP transformer encoder add {len(self.lora_layers)} lora layers.") + + self.layer = layer + self.layer_idx = layer_idx + self.return_pooled = always_return_pooled + if layer == "hidden": + assert layer_idx is not None + assert 0 <= abs(layer_idx) <= 12 + + def freeze(self): + self.transformer = self.transformer.eval() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer( + text, + truncation=True, + max_length=self.max_length, + return_length=True, + return_overflowing_tokens=False, + padding="max_length", + return_tensors="pt", + ) + tokens = batch_encoding["input_ids"].to(self.device, non_blocking=True) + outputs = self.transformer(input_ids=tokens, output_hidden_states=(self.layer == "hidden")) + + if self.layer == "last": + z = outputs.last_hidden_state + elif self.layer == "pooled": + z = outputs.pooler_output[:, None, :] + else: + z = outputs.hidden_states[self.layer_idx] + + # Pad the seq length to multiple of 8 + seq_len = (z.shape[1] + 8 - 1) // 8 * 8 + z = torch.nn.functional.pad(z, (0, 0, 0, seq_len - z.shape[1]), value=0.0) + if self.return_pooled: + return z, outputs.pooler_output + return z + + def encode(self, text): + return self(text) + + +class FrozenOpenCLIPEmbedder(AbstractEncoder): + """ + Uses the OpenCLIP transformer encoder for text + """ + + LAYERS = [ + # "pooled", + "last", + "penultimate", + ] + + def __init__( + self, + arch="ViT-H-14", + version="laion2b_s32b_b79k", + device="cuda", + max_length=77, + freeze=True, + layer="last", + use_fp16=False, + cache_dir=None, + ): + super().__init__() + assert layer in self.LAYERS + print(f"Downloading clip with", arch, version, cache_dir) + self.device = device + model, _, _ = open_clip.create_model_and_transforms( + arch, device=torch.device("cpu"), pretrained=version, cache_dir=cache_dir, + ) + del model.visual + self.model = model + + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + if self.layer == "last": + self.layer_idx = 0 + elif self.layer == "penultimate": + self.layer_idx = 1 + else: + raise NotImplementedError() + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + if isinstance(text, list) and isinstance(text[0], str): + tokens = open_clip.tokenize(text) + else: + # tokenizer has been invoked before + tokens = text + z = self.encode_with_transformer(tokens.to(self.device, non_blocking=True)) + return z + + def encode_with_transformer(self, text): + x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] + x = x + self.model.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.model.ln_final(x) + return x + + def text_transformer_forward(self, x: torch.Tensor, attn_mask=None): + for i, r in enumerate(self.model.transformer.resblocks): + if i == len(self.model.transformer.resblocks) - self.layer_idx: + break + if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + def encode(self, text): + return self(text) + + +class FrozenMegatronCLIPEmbedder(AbstractEmbModel): + def __init__( + self, + restore_from_path, + device="cuda", + layer="last", + freeze=True, + cfg=None, + always_return_pooled=False, + enable_lora_finetune=False, + ): + super().__init__( + enable_lora_finetune=enable_lora_finetune, + target_block=["ParallelAttention", "ParallelMLP"], + target_module=["ColumnParallelLinear", "RowParallelLinear"], + ) + if restore_from_path is not None: + cfg, state_dict = self.load_config_and_state_from_nemo(restore_from_path) + elif cfg is not None: + state_dict = None + else: + raise ValueError("Either restore_from_path or cfg should not be None") + + self.cfg = cfg + self.build_tokenizer(cfg) + self.load_model(cfg, state_dict) + self.return_pooled = always_return_pooled + + self.device = device + if freeze: + self.freeze() + self.layer = layer + if self.layer == "last": + self.layer_idx = 0 + elif self.layer == "penultimate": + self.layer_idx = 1 + else: + raise NotImplementedError() + + if enable_lora_finetune: + self._enable_lora(self.model.language_model) + print(f"Megatron CLIP encoder add {len(self.lora_layers)} lora layers.") + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + def load_config_and_state_from_nemo(self, nemo_path): + if torch.cuda.is_available(): + map_location = torch.device('cuda') + else: + map_location = torch.device('cpu') + save_restore_connector = NLPSaveRestoreConnector() + cwd = os.getcwd() + + with tempfile.TemporaryDirectory() as tmpdir: + try: + save_restore_connector._unpack_nemo_file(path2file=nemo_path, out_folder=tmpdir) + + # Change current working directory to + os.chdir(tmpdir) + config_yaml = os.path.join(tmpdir, save_restore_connector.model_config_yaml) + cfg = OmegaConf.load(config_yaml) + + model_weights = os.path.join(tmpdir, save_restore_connector.model_weights_ckpt) + state_dict = save_restore_connector._load_state_dict_from_disk( + model_weights, map_location=map_location + ) + finally: + os.chdir(cwd) + + return cfg, state_dict + + def build_tokenizer(self, cfg): + legacy = cfg.tokenizer.sentencepiece_legacy + self.tokenizer = get_nmt_tokenizer( + library=cfg.tokenizer.library, + model_name=cfg.tokenizer.type, + tokenizer_model=cfg.tokenizer.model, + vocab_file=cfg.tokenizer.vocab_file, + merges_file=cfg.tokenizer.merge_file, + delimiter=cfg.tokenizer.get('delimiter', None), + legacy=legacy, + ) + + _, self.text_transform = get_preprocess_fns(cfg, self.tokenizer, is_train=False,) + self.max_length = cfg.text.get("max_position_embeddings") + + def load_model(self, cfg, state_dict): + padded_vocab_size = self._vocab_size_with_padding( + orig_vocab_size=self.tokenizer.vocab_size, + make_vocab_size_divisible_by=cfg.get('make_vocab_size_divisible_by', 128), + tensor_model_parallel_size=cfg.get('tensor_model_parallel_size', 1), + ) + model = CLIPModel( + model_cfg=cfg, + model_parallel_config=ModelParallelConfig(), + padded_vocab_size=padded_vocab_size, + pre_process=cfg.text.pre_process, + post_process=cfg.text.post_process, + ) + + if state_dict is not None: + clip_state_dict = {} + for key, value in state_dict.items(): + key = key[6:] + clip_state_dict[key] = value + model.load_state_dict(clip_state_dict) + + del model.vision_encoder + self.model = model.text_encoder + + def _vocab_size_with_padding(self, orig_vocab_size, make_vocab_size_divisible_by, tensor_model_parallel_size): + after = orig_vocab_size + multiple = make_vocab_size_divisible_by * tensor_model_parallel_size + while (after % multiple) != 0: + after += 1 + return after + + def forward(self, text): + ''' + Get embeddings from input text + ''' + texts = self.text_transform(text) + z, z_pooled = self.encode_with_transformer(texts.to(self.device)) + # # Pad the seq length to multiple of 8 + seq_len = (z.shape[1] + 8 - 1) // 8 * 8 + z = torch.nn.functional.pad(z, (0, 0, 0, seq_len - z.shape[1]), value=0.0) + if self.return_pooled: + return z, z_pooled + return z + + def encode_with_transformer(self, text): + x = self.model.language_model.embedding.word_embeddings(text) + x += self.model.language_model.embedding.position_embeddings + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) + x = self.model.language_model.encoder.final_layernorm(x) + x = x.permute(1, 0, 2) # LND -> NLD + if self.return_pooled: + pooled = self.pool(x, text) + return x, pooled + return x, None + + def pool(self, x, text): + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ (self.model.head.weight.T) + return x + + def text_transformer_forward(self, x: torch.Tensor, attn_mask=None): + for i, r in enumerate(self.model.language_model.encoder.layers): + if i == len(self.model.language_model.encoder.layers) - self.layer_idx: + break + x = r(x, attn_mask) + return x + + def encode(self, text): + return self(text) + + +class FrozenOpenCLIPEmbedder2(AbstractEmbModel): + """ + Uses the OpenCLIP transformer encoder for text + """ + + LAYERS = ["pooled", "last", "penultimate"] + + def __init__( + self, + arch="ViT-H-14", + version="laion2b_s32b_b79k", + device="cuda", + max_length=77, + freeze=True, + layer="last", + always_return_pooled=False, + legacy=True, + ): + super().__init__() + assert layer in self.LAYERS + self.projection_dim = 1280 + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device("cpu"), pretrained=version,) + del model.visual + self.model = model + + self.device = device + self.max_length = max_length + self.return_pooled = always_return_pooled + if freeze: + self.freeze() + self.layer = layer + if self.layer == "last": + self.layer_idx = 0 + elif self.layer == "penultimate": + self.layer_idx = 1 + else: + raise NotImplementedError() + self.legacy = legacy + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + tokens = open_clip.tokenize(text) + z = self.encode_with_transformer(tokens.to(self.device)) + if not self.return_pooled and self.legacy: + return z + if self.return_pooled: + assert not self.legacy + z_layer = z[self.layer] + # # Pad the seq length to multiple of 8 + seq_len = (z_layer.shape[1] + 8 - 1) // 8 * 8 + z_layer = torch.nn.functional.pad(z_layer, (0, 0, 0, seq_len - z_layer.shape[1]), value=0.0) + return z_layer, z["pooled"] + return z[self.layer] + + def encode_with_transformer(self, text): + x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] + x = x + self.model.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) + if self.legacy: + x = x[self.layer] + x = self.model.ln_final(x) + return x + else: + # x is a dict and will stay a dict + o = x["last"] + o = self.model.ln_final(o) + pooled = self.pool(o, text) + x["pooled"] = pooled + return x + + def pool(self, x, text): + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.model.text_projection + return x + + def text_transformer_forward(self, x: torch.Tensor, attn_mask=None): + outputs = {} + for i, r in enumerate(self.model.transformer.resblocks): + if i == len(self.model.transformer.resblocks) - 1: + outputs["penultimate"] = x.permute(1, 0, 2) # LND -> NLD + if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + outputs["last"] = x.permute(1, 0, 2) # LND -> NLD + return outputs + + def encode(self, text): + return self(text) + + +class ConcatTimestepEmbedderND(AbstractEmbModel): + """embeds each dimension independently and concatenates them""" + + def __init__(self, outdim, device='cuda'): + super().__init__() + self.timestep = Timestep(outdim) + self.outdim = outdim + self.device = device + + def forward(self, x): + if x.ndim == 1: + x = x[:, None] + assert len(x.shape) == 2 + b, dims = x.shape[0], x.shape[1] + x = rearrange(x, "b d -> (b d)") + emb = self.timestep(x) + emb = rearrange(emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim) + if self.device == 'cuda': + return emb.to(torch.cuda.current_device()) + return emb + + +class PrecachedEmbModel(AbstractEmbModel): + def __init__(self, device='cuda'): + super().__init__() + self.device = device + + def forward(self, *args): + if self.device == 'cuda': + return [arg.to(torch.cuda.current_device()) for arg in args] + return list(args) + + +if __name__ == "__main__": + from ldm.util import count_params + + model = FrozenCLIPEmbedder() + count_params(model, verbose=True) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/encoders/x_transformer.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/encoders/x_transformer.py new file mode 100644 index 0000000..edbfadf --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/encoders/x_transformer.py @@ -0,0 +1,630 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""adopted from https://github.com/lucidrains/x-transformers/tree/main/x_transformers""" +from collections import namedtuple +from functools import partial +from inspect import isfunction + +import torch +import torch.nn.functional as F +from einops import rearrange, reduce, repeat +from torch import einsum, nn + +# constants + +DEFAULT_DIM_HEAD = 64 + +Intermediates = namedtuple('Intermediates', ['pre_softmax_attn', 'post_softmax_attn']) + +LayerIntermediates = namedtuple('Intermediates', ['hiddens', 'attn_intermediates']) + + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.emb = nn.Embedding(max_seq_len, dim) + self.init_() + + def init_(self): + nn.init.normal_(self.emb.weight, std=0.02) + + def forward(self, x): + n = torch.arange(x.shape[1], device=x.device) + return self.emb(n)[None, :, :] + + +class FixedPositionalEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer('inv_freq', inv_freq) + + def forward(self, x, seq_dim=1, offset=0): + t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset + sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq) + emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) + return emb[None, :, :] + + +# helpers + + +def exists(val): + return val is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def always(val): + def inner(*args, **kwargs): + return val + + return inner + + +def not_equals(val): + def inner(x): + return x != val + + return inner + + +def equals(val): + def inner(x): + return x == val + + return inner + + +def max_neg_value(tensor): + return -torch.finfo(tensor.dtype).max + + +# keyword argument helpers + + +def pick_and_pop(keys, d): + values = list(map(lambda key: d.pop(key), keys)) + return dict(zip(keys, values)) + + +def group_dict_by_key(cond, d): + return_val = [dict(), dict()] + for key in d.keys(): + match = bool(cond(key)) + ind = int(not match) + return_val[ind][key] = d[key] + return (*return_val,) + + +def string_begins_with(prefix, str): + return str.startswith(prefix) + + +def group_by_key_prefix(prefix, d): + return group_dict_by_key(partial(string_begins_with, prefix), d) + + +def groupby_prefix_and_trim(prefix, d): + kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d) + kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix) :], x[1]), tuple(kwargs_with_prefix.items()))) + return kwargs_without_prefix, kwargs + + +# classes +class Scale(nn.Module): + def __init__(self, value, fn): + super().__init__() + self.value = value + self.fn = fn + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.value, *rest) + + +class Rezero(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + self.g = nn.Parameter(torch.zeros(1)) + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.g, *rest) + + +class ScaleNorm(nn.Module): + def __init__(self, dim, eps=1e-5): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(1)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class RMSNorm(nn.Module): + def __init__(self, dim, eps=1e-8): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class Residual(nn.Module): + def forward(self, x, residual): + return x + residual + + +class GRUGating(nn.Module): + def __init__(self, dim): + super().__init__() + self.gru = nn.GRUCell(dim, dim) + + def forward(self, x, residual): + gated_output = self.gru(rearrange(x, 'b n d -> (b n) d'), rearrange(residual, 'b n d -> (b n) d')) + + return gated_output.reshape_as(x) + + +# feedforward + + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)) + + def forward(self, x): + return self.net(x) + + +# attention. +class Attention(nn.Module): + def __init__( + self, + dim, + dim_head=DEFAULT_DIM_HEAD, + heads=8, + causal=False, + mask=None, + talking_heads=False, + sparse_topk=None, + use_entmax15=False, + num_mem_kv=0, + dropout=0.0, + on_attn=False, + ): + super().__init__() + if use_entmax15: + raise NotImplementedError("Check out entmax activation instead of softmax activation!") + self.scale = dim_head ** -0.5 + self.heads = heads + self.causal = causal + self.mask = mask + + inner_dim = dim_head * heads + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_k = nn.Linear(dim, inner_dim, bias=False) + self.to_v = nn.Linear(dim, inner_dim, bias=False) + self.dropout = nn.Dropout(dropout) + + # talking heads + self.talking_heads = talking_heads + if talking_heads: + self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + + # explicit topk sparse attention + self.sparse_topk = sparse_topk + + # entmax + # self.attn_fn = entmax15 if use_entmax15 else F.softmax + self.attn_fn = F.softmax + + # add memory key / values + self.num_mem_kv = num_mem_kv + if num_mem_kv > 0: + self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + + # attention on attention + self.attn_on_attn = on_attn + self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + rel_pos=None, + sinusoidal_emb=None, + prev_attn=None, + mem=None, + ): + b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device + kv_input = default(context, x) + + q_input = x + k_input = kv_input + v_input = kv_input + + if exists(mem): + k_input = torch.cat((mem, k_input), dim=-2) + v_input = torch.cat((mem, v_input), dim=-2) + + if exists(sinusoidal_emb): + # in shortformer, the query would start at a position offset depending on the past cached memory + offset = k_input.shape[-2] - q_input.shape[-2] + q_input = q_input + sinusoidal_emb(q_input, offset=offset) + k_input = k_input + sinusoidal_emb(k_input) + + q = self.to_q(q_input) + k = self.to_k(k_input) + v = self.to_v(v_input) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)) + + input_mask = None + if any(map(exists, (mask, context_mask))): + q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool()) + k_mask = q_mask if not exists(context) else context_mask + k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool()) + q_mask = rearrange(q_mask, 'b i -> b () i ()') + k_mask = rearrange(k_mask, 'b j -> b () () j') + input_mask = q_mask * k_mask + + if self.num_mem_kv > 0: + mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v)) + k = torch.cat((mem_k, k), dim=-2) + v = torch.cat((mem_v, v), dim=-2) + if exists(input_mask): + input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True) + + dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale + mask_value = max_neg_value(dots) + + if exists(prev_attn): + dots = dots + prev_attn + + pre_softmax_attn = dots + + if talking_heads: + dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous() + + if exists(rel_pos): + dots = rel_pos(dots) + + if exists(input_mask): + dots.masked_fill_(~input_mask, mask_value) + del input_mask + + if self.causal: + i, j = dots.shape[-2:] + r = torch.arange(i, device=device) + mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j') + mask = F.pad(mask, (j - i, 0), value=False) + dots.masked_fill_(mask, mask_value) + del mask + + if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]: + top, _ = dots.topk(self.sparse_topk, dim=-1) + vk = top[..., -1].unsqueeze(-1).expand_as(dots) + mask = dots < vk + dots.masked_fill_(mask, mask_value) + del mask + + attn = self.attn_fn(dots, dim=-1) + post_softmax_attn = attn + + attn = self.dropout(attn) + + if talking_heads: + attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous() + + out = einsum('b h i j, b h j d -> b h i d', attn, v) + out = rearrange(out, 'b h n d -> b n (h d)') + + intermediates = Intermediates(pre_softmax_attn=pre_softmax_attn, post_softmax_attn=post_softmax_attn) + + return self.to_out(out), intermediates + + +class AttentionLayers(nn.Module): + def __init__( + self, + dim, + depth, + heads=8, + causal=False, + cross_attend=False, + only_cross=False, + use_scalenorm=False, + use_rmsnorm=False, + use_rezero=False, + rel_pos_num_buckets=32, + rel_pos_max_distance=128, + position_infused_attn=False, + custom_layers=None, + sandwich_coef=None, + par_ratio=None, + residual_attn=False, + cross_residual_attn=False, + macaron=False, + pre_norm=True, + gate_residual=False, + **kwargs, + ): + super().__init__() + ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs) + attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs) + + dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD) + + self.dim = dim + self.depth = depth + self.layers = nn.ModuleList([]) + + self.has_pos_emb = position_infused_attn + self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None + self.rotary_pos_emb = always(None) + + assert ( + rel_pos_num_buckets <= rel_pos_max_distance + ), 'number of relative position buckets must be less than the relative position max distance' + self.rel_pos = None + + self.pre_norm = pre_norm + + self.residual_attn = residual_attn + self.cross_residual_attn = cross_residual_attn + + norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm + norm_class = RMSNorm if use_rmsnorm else norm_class + norm_fn = partial(norm_class, dim) + + norm_fn = nn.Identity if use_rezero else norm_fn + branch_fn = Rezero if use_rezero else None + + if cross_attend and not only_cross: + default_block = ('a', 'c', 'f') + elif cross_attend and only_cross: + default_block = ('c', 'f') + else: + default_block = ('a', 'f') + + if macaron: + default_block = ('f',) + default_block + + if exists(custom_layers): + layer_types = custom_layers + elif exists(par_ratio): + par_depth = depth * len(default_block) + assert 1 < par_ratio <= par_depth, 'par ratio out of range' + default_block = tuple(filter(not_equals('f'), default_block)) + par_attn = par_depth // par_ratio + depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper + par_width = (depth_cut + depth_cut // par_attn) // par_attn + assert len(default_block) <= par_width, 'default block is too large for par_ratio' + par_block = default_block + ('f',) * (par_width - len(default_block)) + par_head = par_block * par_attn + layer_types = par_head + ('f',) * (par_depth - len(par_head)) + elif exists(sandwich_coef): + assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth' + layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef + else: + layer_types = default_block * depth + + self.layer_types = layer_types + self.num_attn_layers = len(list(filter(equals('a'), layer_types))) + + for layer_type in self.layer_types: + if layer_type == 'a': + layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs) + elif layer_type == 'c': + layer = Attention(dim, heads=heads, **attn_kwargs) + elif layer_type == 'f': + layer = FeedForward(dim, **ff_kwargs) + layer = layer if not macaron else Scale(0.5, layer) + else: + raise Exception(f'invalid layer type {layer_type}') + + if isinstance(layer, Attention) and exists(branch_fn): + layer = branch_fn(layer) + + if gate_residual: + residual_fn = GRUGating(dim) + else: + residual_fn = Residual() + + self.layers.append(nn.ModuleList([norm_fn(), layer, residual_fn])) + + def forward(self, x, context=None, mask=None, context_mask=None, mems=None, return_hiddens=False): + hiddens = [] + intermediates = [] + prev_attn = None + prev_cross_attn = None + + mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers + + for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)): + is_last = ind == (len(self.layers) - 1) + + if layer_type == 'a': + hiddens.append(x) + layer_mem = mems.pop(0) + + residual = x + + if self.pre_norm: + x = norm(x) + + if layer_type == 'a': + out, inter = block( + x, + mask=mask, + sinusoidal_emb=self.pia_pos_emb, + rel_pos=self.rel_pos, + prev_attn=prev_attn, + mem=layer_mem, + ) + elif layer_type == 'c': + out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn) + elif layer_type == 'f': + out = block(x) + + x = residual_fn(out, residual) + + if layer_type in ('a', 'c'): + intermediates.append(inter) + + if layer_type == 'a' and self.residual_attn: + prev_attn = inter.pre_softmax_attn + elif layer_type == 'c' and self.cross_residual_attn: + prev_cross_attn = inter.pre_softmax_attn + + if not self.pre_norm and not is_last: + x = norm(x) + + if return_hiddens: + intermediates = LayerIntermediates(hiddens=hiddens, attn_intermediates=intermediates) + + return x, intermediates + + return x + + +class Encoder(AttentionLayers): + def __init__(self, **kwargs): + assert 'causal' not in kwargs, 'cannot set causality on encoder' + super().__init__(causal=False, **kwargs) + + +class TransformerWrapper(nn.Module): + def __init__( + self, + *, + num_tokens, + max_seq_len, + attn_layers, + emb_dim=None, + max_mem_len=0.0, + emb_dropout=0.0, + num_memory_tokens=None, + tie_embedding=False, + use_pos_emb=True, + ): + super().__init__() + assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder' + + dim = attn_layers.dim + emb_dim = default(emb_dim, dim) + + self.max_seq_len = max_seq_len + self.max_mem_len = max_mem_len + self.num_tokens = num_tokens + + self.token_emb = nn.Embedding(num_tokens, emb_dim) + self.pos_emb = ( + AbsolutePositionalEmbedding(emb_dim, max_seq_len) + if (use_pos_emb and not attn_layers.has_pos_emb) + else always(0) + ) + self.emb_dropout = nn.Dropout(emb_dropout) + + self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity() + self.attn_layers = attn_layers + self.norm = nn.LayerNorm(dim) + + self.init_() + + self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t() + + # memory tokens (like [cls]) from Memory Transformers paper + num_memory_tokens = default(num_memory_tokens, 0) + self.num_memory_tokens = num_memory_tokens + if num_memory_tokens > 0: + self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim)) + + # let funnel encoder know number of memory tokens, if specified + if hasattr(attn_layers, 'num_memory_tokens'): + attn_layers.num_memory_tokens = num_memory_tokens + + def init_(self): + nn.init.normal_(self.token_emb.weight, std=0.02) + + def forward( + self, x, return_embeddings=False, mask=None, return_mems=False, return_attn=False, mems=None, **kwargs + ): + b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens + x = self.token_emb(x) + x += self.pos_emb(x) + x = self.emb_dropout(x) + + x = self.project_emb(x) + + if num_mem > 0: + mem = repeat(self.memory_tokens, 'n d -> b n d', b=b) + x = torch.cat((mem, x), dim=1) + + # auto-handle masking after appending memory tokens + if exists(mask): + mask = F.pad(mask, (num_mem, 0), value=True) + + x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs) + x = self.norm(x) + + mem, x = x[:, :num_mem], x[:, num_mem:] + + out = self.to_logits(x) if not return_embeddings else x + + if return_mems: + hiddens = intermediates.hiddens + new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens + new_mems = list(map(lambda t: t[..., -self.max_mem_len :, :].detach(), new_mems)) + return out, new_mems + + if return_attn: + attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates)) + return out, attn_maps + + return out diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/schedulers/ddim_scheduler.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/schedulers/ddim_scheduler.py new file mode 100644 index 0000000..7f2544e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/modules/stable_diffusion/schedulers/ddim_scheduler.py @@ -0,0 +1,407 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import ABC +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch + + +class DDIMScheduler(ABC): + """ + Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising + diffusion probabilistic models (DDPMs) with non-Markovian guidance. + + [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` + function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. + [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and + [`~SchedulerMixin.from_pretrained`] functions. + + For more details, see the original paper: https://arxiv.org/abs/2010.02502 + + Args: + num_train_timesteps (`int`): number of diffusion steps used to train the model. + beta_start (`float`): the starting `beta` value of inference. + beta_end (`float`): the final `beta` value. + beta_schedule (`str`): + the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from + `linear`, `scaled_linear`, or `squaredcos_cap_v2`. + trained_betas (`np.ndarray`, optional): + option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. + clip_sample (`bool`, default `True`): + option to clip predicted sample for numerical stability. + clip_sample_range (`float`, default `1.0`): + the maximum magnitude for sample clipping. Valid only when `clip_sample=True`. + set_alpha_to_one (`bool`, default `True`): + each diffusion step uses the value of alphas product at that step and at the previous one. For the final + step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`, + otherwise it uses the value of alpha at step 0. + steps_offset (`int`, default `0`): + an offset added to the inference steps. You can use a combination of `offset=1` and + `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in + stable diffusion. + prediction_type (`str`, default `epsilon`, optional): + prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion + process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 + https://imagen.research.google/video/paper.pdf) + thresholding (`bool`, default `False`): + whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487). + Note that the thresholding method is unsuitable for latent-space diffusion models (such as + stable-diffusion). + dynamic_thresholding_ratio (`float`, default `0.995`): + the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen + (https://arxiv.org/abs/2205.11487). Valid only when `thresholding=True`. + sample_max_value (`float`, default `1.0`): + the threshold value for dynamic thresholding. Valid only when `thresholding=True`. + timestep_spacing (`str`, default `"leading"`): + The way the timesteps should be scaled. Refer to Table 2. of [Common Diffusion Noise Schedules and Sample + Steps are Flawed](https://arxiv.org/abs/2305.08891) for more information. + rescale_betas_zero_snr (`bool`, default `False`): + whether to rescale the betas to have zero terminal SNR (proposed by https://arxiv.org/pdf/2305.08891.pdf). + This can enable the model to generate very bright and dark samples instead of limiting it to samples with + medium brightness. Loosely related to + [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506). + """ + + order = 1 + + def __init__( + self, + num_train_timesteps: int = 1000, + beta_start: float = 0.0001, + beta_end: float = 0.02, + beta_schedule: str = "linear", + trained_betas: Optional[Union[np.ndarray, List[float]]] = None, + clip_sample: bool = True, + set_alpha_to_one: bool = True, + steps_offset: int = 0, + prediction_type: str = "epsilon", + thresholding: bool = False, + dynamic_thresholding_ratio: float = 0.995, + clip_sample_range: float = 1.0, + sample_max_value: float = 1.0, + timestep_spacing: str = "leading", + rescale_betas_zero_snr: bool = False, + ): + self.num_train_timesteps = num_train_timesteps + self.beta_start = beta_start + self.beta_end = beta_end + self.beta_schedule = beta_schedule + self.trained_betas = trained_betas + self.clip_sample = clip_sample + self.steps_offset = steps_offset + self.prediction_type = prediction_type + self.thresholding = thresholding + self.dynamic_thresholding_ratio = dynamic_thresholding_ratio + self.clip_sample_range = clip_sample_range + self.sample_max_value = sample_max_value + self.timestep_spacing = timestep_spacing + + if trained_betas is not None: + self.betas = torch.tensor(trained_betas, dtype=torch.float32) + elif beta_schedule == "linear": + self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) + elif beta_schedule == "scaled_linear": + # this schedule is very specific to the latent diffusion model. + self.betas = ( + torch.linspace(beta_start ** 0.5, beta_end ** 0.5, num_train_timesteps, dtype=torch.float32) ** 2 + ) + elif beta_schedule == "squaredcos_cap_v2": + # Glide cosine schedule + self.betas = betas_for_alpha_bar(num_train_timesteps) + else: + raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") + + # Rescale for zero SNR + if rescale_betas_zero_snr: + self.betas = rescale_zero_terminal_snr(self.betas) + + self.alphas = 1.0 - self.betas + self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) + + # At every step in ddim, we are looking into the previous alphas_cumprod + # For the final step, there is no previous alphas_cumprod because we are already at 0 + # `set_alpha_to_one` decides whether we set this parameter simply to one or + # whether we use the final alpha of the "non-previous" one. + self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0] + + # standard deviation of the initial noise distribution + self.init_noise_sigma = 1.0 + + # setable values + self.num_inference_steps = None + self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64)) + + def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: + """ + Ensures interchangeability with schedulers that need to scale the denoising model input depending on the + current timestep. + + Args: + sample (`torch.FloatTensor`): input sample + timestep (`int`, optional): current timestep + + Returns: + `torch.FloatTensor`: scaled input sample + """ + return sample + + def _get_variance(self, timestep, prev_timestep): + alpha_prod_t = self.alphas_cumprod[timestep] + alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod + beta_prod_t = 1 - alpha_prod_t + beta_prod_t_prev = 1 - alpha_prod_t_prev + + variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev) + + return variance + + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample + def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: + """ + "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the + prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by + s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing + pixels from saturation at each step. We find that dynamic thresholding results in significantly better + photorealism as well as better image-text alignment, especially when using very large guidance weights." + + https://arxiv.org/abs/2205.11487 + """ + dtype = sample.dtype + batch_size, channels, height, width = sample.shape + + if dtype not in (torch.float32, torch.float64): + sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half + + # Flatten sample for doing quantile calculation along each image + sample = sample.reshape(batch_size, channels * height * width) + + abs_sample = sample.abs() # "a certain percentile absolute pixel value" + + s = torch.quantile(abs_sample, self.dynamic_thresholding_ratio, dim=1) + s = torch.clamp( + s, min=1, max=self.sample_max_value + ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] + + s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 + sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" + + sample = sample.reshape(batch_size, channels, height, width) + sample = sample.to(dtype) + + return sample + + def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): + """ + Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. + + Args: + num_inference_steps (`int`): + the number of diffusion steps used when generating samples with a pre-trained model. + """ + + if num_inference_steps > self.num_train_timesteps: + raise ValueError( + f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.train_timesteps`:" + f" {self.num_train_timesteps} as the unet model trained with this scheduler can only handle" + f" maximal {self.num_train_timesteps} timesteps." + ) + + self.num_inference_steps = num_inference_steps + + # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891 + if self.timestep_spacing == "linspace": + timesteps = ( + np.linspace(0, self.num_train_timesteps - 1, num_inference_steps).round()[::-1].copy().astype(np.int64) + ) + elif self.timestep_spacing == "leading": + step_ratio = self.num_train_timesteps // self.num_inference_steps + # creates integer timesteps by multiplying by ratio + # casting to int to avoid issues when num_inference_step is power of 3 + timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64) + timesteps += self.steps_offset + elif self.timestep_spacing == "trailing": + step_ratio = self.num_train_timesteps / self.num_inference_steps + # creates integer timesteps by multiplying by ratio + # casting to int to avoid issues when num_inference_step is power of 3 + timesteps = np.round(np.arange(self.num_train_timesteps, 0, -step_ratio)).astype(np.int64) + timesteps -= 1 + else: + raise ValueError( + f"{self.timestep_spacing} is not supported. Please make sure to choose one of 'leading' or 'trailing'." + ) + + self.timesteps = torch.from_numpy(timesteps).to(device) + + def step( + self, + model_output: torch.FloatTensor, + timestep: int, + sample: torch.FloatTensor, + eta: float = 0.0, + use_clipped_model_output: bool = False, + generator=None, + variance_noise: Optional[torch.FloatTensor] = None, + ): + """ + Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): direct output from learned diffusion model. + timestep (`int`): current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + current instance of sample being created by diffusion process. + eta (`float`): weight of noise for added noise in diffusion step. + use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped + predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when + `self.clip_sample` is `True`. If no clipping has happened, "corrected" `model_output` would + coincide with the one provided as input and `use_clipped_model_output` will have not effect. + generator: random number generator. + variance_noise (`torch.FloatTensor`): instead of generating noise for the variance using `generator`, we + can directly provide the noise for the variance itself. This is useful for methods such as + CycleDiffusion. (https://arxiv.org/abs/2210.05559) + return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class + + Returns: + [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`: + [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When + returning a tuple, the first element is the sample tensor. + + """ + if self.num_inference_steps is None: + raise ValueError( + "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" + ) + + # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf + # Ideally, read DDIM paper in-detail understanding + + # Notation ( -> + # - pred_noise_t -> e_theta(x_t, t) + # - pred_original_sample -> f_theta(x_t, t) or x_0 + # - std_dev_t -> sigma_t + # - eta -> η + # - pred_sample_direction -> "direction pointing to x_t" + # - pred_prev_sample -> "x_t-1" + + # 1. get previous step value (=t-1) + prev_timestep = timestep - self.num_train_timesteps // self.num_inference_steps + + # 2. compute alphas, betas + alpha_prod_t = self.alphas_cumprod[timestep] + alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod + + beta_prod_t = 1 - alpha_prod_t + + # 3. compute predicted original sample from predicted noise also called + # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf + if self.prediction_type == "epsilon": + pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) + pred_epsilon = model_output + elif self.prediction_type == "sample": + pred_original_sample = model_output + pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5) + elif self.prediction_type == "v_prediction": + pred_original_sample = (alpha_prod_t ** 0.5) * sample - (beta_prod_t ** 0.5) * model_output + pred_epsilon = (alpha_prod_t ** 0.5) * model_output + (beta_prod_t ** 0.5) * sample + else: + raise ValueError( + f"prediction_type given as {self.prediction_type} must be one of `epsilon`, `sample`, or" + " `v_prediction`" + ) + + # 4. Clip or threshold "predicted x_0" + if self.thresholding: + pred_original_sample = self._threshold_sample(pred_original_sample) + elif self.clip_sample: + pred_original_sample = pred_original_sample.clamp(-self.clip_sample_range, self.clip_sample_range) + + # 5. compute variance: "sigma_t(η)" -> see formula (16) + # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) + variance = self._get_variance(timestep, prev_timestep) + std_dev_t = eta * variance ** (0.5) + + if use_clipped_model_output: + # the pred_epsilon is always re-derived from the clipped x_0 in Glide + pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5) + + # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf + pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t ** 2) ** (0.5) * pred_epsilon + + # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf + prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction + + if eta > 0: + if variance_noise is not None and generator is not None: + raise ValueError( + "Cannot pass both generator and variance_noise. Please make sure that either `generator` or" + " `variance_noise` stays `None`." + ) + + if variance_noise is None: + variance_noise = randn_tensor( + model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype + ) + variance = std_dev_t * variance_noise + + prev_sample = prev_sample + variance + + return (prev_sample,) + + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise + def add_noise( + self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor, + ) -> torch.FloatTensor: + # Make sure alphas_cumprod and timestep have same device and dtype as original_samples + alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) + timesteps = timesteps.to(original_samples.device) + + sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 + sqrt_alpha_prod = sqrt_alpha_prod.flatten() + while len(sqrt_alpha_prod.shape) < len(original_samples.shape): + sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) + + sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() + while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) + + noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise + return noisy_samples + + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity + def get_velocity( + self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor + ) -> torch.FloatTensor: + # Make sure alphas_cumprod and timestep have same device and dtype as sample + alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype) + timesteps = timesteps.to(sample.device) + + sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 + sqrt_alpha_prod = sqrt_alpha_prod.flatten() + while len(sqrt_alpha_prod.shape) < len(sample.shape): + sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) + + sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() + while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape): + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) + + velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample + return velocity + + def __len__(self): + return self.num_train_timesteps diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/imagen/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/imagen/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/imagen/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/imagen/utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/imagen/utils.py new file mode 100644 index 0000000..565b1ed --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/imagen/utils.py @@ -0,0 +1,29 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch + + +def random_dropout(embeddings, drop_rate): + r""" + Function to perform random dropout for embeddings. + When we drop embeddings, we zero them out. + Args: + embeddings (tensor): Input embeddings + drop_rate (float): Rate of dropping the embedding. + """ + nsamples = embeddings.shape[0] + zero_flag = torch.ones(nsamples, 1, 1).to(embeddings.dtype) * (1 - drop_rate) + zero_flag = torch.bernoulli(zero_flag).cuda() + embeddings = embeddings * zero_flag + return embeddings diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/lr_scheduler.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/lr_scheduler.py new file mode 100644 index 0000000..620d1dc --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/lr_scheduler.py @@ -0,0 +1,112 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np + + +class LambdaWarmUpCosineScheduler: + """ + note: use with a base_lr of 1.0 + """ + + def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): + self.lr_warm_up_steps = warm_up_steps + self.lr_start = lr_start + self.lr_min = lr_min + self.lr_max = lr_max + self.lr_max_decay_steps = max_decay_steps + self.last_lr = 0.0 + self.verbosity_interval = verbosity_interval + + def schedule(self, n, **kwargs): + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: + print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") + if n < self.lr_warm_up_steps: + lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start + self.last_lr = lr + return lr + else: + t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) + t = min(t, 1.0) + lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (1 + np.cos(t * np.pi)) + self.last_lr = lr + return lr + + def __call__(self, n, **kwargs): + return self.schedule(n, **kwargs) + + +class LambdaWarmUpCosineScheduler2: + """ + supports repeated iterations, configurable via lists + note: use with a base_lr of 1.0. + """ + + def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0): + assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths) + self.lr_warm_up_steps = warm_up_steps + self.f_start = f_start + self.f_min = f_min + self.f_max = f_max + self.cycle_lengths = cycle_lengths + self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths)) + self.last_f = 0.0 + self.verbosity_interval = verbosity_interval + + def find_in_interval(self, n): + interval = 0 + for cl in self.cum_cycles[1:]: + if n <= cl: + return interval + interval += 1 + + def schedule(self, n, **kwargs): + cycle = self.find_in_interval(n) + n = n - self.cum_cycles[cycle] + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: + print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " f"current cycle {cycle}") + if n < self.lr_warm_up_steps[cycle]: + f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] + self.last_f = f + return f + else: + t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]) + t = min(t, 1.0) + f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (1 + np.cos(t * np.pi)) + self.last_f = f + return f + + def __call__(self, n, **kwargs): + return self.schedule(n, **kwargs) + + +class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): + def schedule(self, n, **kwargs): + cycle = self.find_in_interval(n) + n = n - self.cum_cycles[cycle] + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: + print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " f"current cycle {cycle}") + + if n < self.lr_warm_up_steps[cycle]: + f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] + self.last_f = f + return f + else: + f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / ( + self.cycle_lengths[cycle] + ) + self.last_f = f + return f diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/pipeline.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/pipeline.py new file mode 100644 index 0000000..b28bfc6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/pipeline.py @@ -0,0 +1,224 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import pickle +import time +from collections import defaultdict +from itertools import chain + +import torch +from PIL import Image + +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.ddim import DDIMSampler +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.para_ddim import ParaDDIMSampler +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.plms import PLMSSampler +from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.sampler_dpm import DPMSolverSampler +from nemo.collections.multimodal.parts.stable_diffusion.utils import DataParallelWrapper + + +def encode_prompt(cond_stage_model, prompts, unconditional_guidance_scale): + c = cond_stage_model.encode(prompts) + if unconditional_guidance_scale != 1.0: + uc = cond_stage_model.encode(len(prompts) * [""]) + else: + uc = None + return c, uc + + +def initialize_sampler(model, sampler_type): + if sampler_type == 'DDIM': + sampler = DDIMSampler(model) + elif sampler_type == 'PLMS': + sampler = PLMSSampler(model) + elif sampler_type == 'DPM': + sampler = DPMSolverSampler(model) + elif sampler_type == 'PARA_DDIM': + sampler = ParaDDIMSampler(model) + else: + raise ValueError(f'Sampler {sampler_type} is not supported.') + return sampler + + +def decode_images(model, samples): + images = model.decode_first_stage(samples) + + images = torch.clamp((images + 1.0) / 2.0, min=0.0, max=1.0) + + return images + + +def numpy_to_pil(images): + """ + Convert a numpy image or a batch of images to a PIL image. + """ + if images.ndim == 3: + images = images[None, ...] + images = (images * 255).round().astype("uint8") + pil_images = [Image.fromarray(image) for image in images] + + return pil_images + + +def torch_to_numpy(images): + numpy_images = [x.float().cpu().permute(0, 2, 3, 1).numpy() for x in images] + return numpy_images + + +def pad_with_zeros(cond, u_cond, batch_size): + b, *shape = cond.shape + filler = torch.zeros(batch_size - b, *shape, device=cond.device) + return torch.cat([cond, filler]), torch.cat([u_cond, filler]) + + +def pipeline(model, cfg, verbose=True, rng=None): + # setup default values for inference configs + unconditional_guidance_scale = cfg.infer.get("unconditional_guidance_scale", 7.5) + num_images_per_prompt = cfg.infer.get('num_images_per_prompt', 1) + batch_size = cfg.infer.get('batch_size', 1) + prompts = cfg.infer.get('prompts', []) + height = cfg.infer.get('height', 512) + width = cfg.infer.get('width', 512) + downsampling_factor = cfg.infer.get('down_factor', 8) + sampler_type = cfg.infer.get('sampler_type', 'DDIM') + sampler_parallelism = cfg.infer.get('sampler_parallelism', 1) + sampler_tolerance = cfg.infer.get('sampler_tolerance', 0.1) + inference_steps = cfg.infer.get('inference_steps', 50) + output_type = cfg.infer.get('output_type', 'pil') + save_to_file = cfg.infer.get('save_to_file', True) + out_path = cfg.infer.get('out_path', '') + eta = cfg.infer.get('eta', 0) + num_devices = cfg.infer.get('devices', 1) + + if sampler_parallelism > 1: + if not sampler_type.startswith('PARA'): + raise ValueError('Parallel sampler is required when parallelism > 1') + if not num_devices > 1: + print("It is recommended to run parallel sampler with multiple GPUs") + + if num_devices > 1: + print(f"Running DataParallel model with {num_devices} GPUs.") + model.model.diffusion_model = DataParallelWrapper( + model.model.diffusion_model, device_ids=list(range(num_devices)) + ) + + # get autocast_dtype + if cfg.trainer.precision in ['bf16', 'bf16-mixed']: + autocast_dtype = torch.bfloat16 + elif cfg.trainer.precision in [32, '32', '32-true']: + autocast_dtype = torch.float + elif cfg.trainer.precision in [16, '16', '16-mixed']: + autocast_dtype = torch.half + else: + raise ValueError('precision must be in [32, 16, "bf16"]') + + with torch.no_grad(), torch.cuda.amp.autocast( + enabled=autocast_dtype in (torch.half, torch.bfloat16), dtype=autocast_dtype, + ): + + in_channels = model.model.diffusion_model.in_channels + + sampler = initialize_sampler(model, sampler_type.upper()) + + output = [] + throughput = [] + + if isinstance(prompts, str): + prompts = [prompts] + + multi_prompts = [p for p in prompts for _ in range(num_images_per_prompt)] + batched_prompts = [multi_prompts[i : i + batch_size] for i in range(0, len(multi_prompts), batch_size)] + # decrease batch_size if the number of imputs is lower than bs in the config + batch_size = min(len(batched_prompts[0]), batch_size) + + for batch in batched_prompts: + tic = time.perf_counter() + tic_total = tic + cond, u_cond = encode_prompt(model.cond_stage_model, batch, unconditional_guidance_scale,) + cond, u_cond = pad_with_zeros(cond, u_cond, batch_size) + toc = time.perf_counter() + conditioning_time = toc - tic + + latent_shape = [in_channels, height // downsampling_factor, width // downsampling_factor] + latents = torch.randn( + [batch_size, in_channels, height // downsampling_factor, width // downsampling_factor], generator=rng + ).to(torch.cuda.current_device()) + assert len(cond) == len(latents), (len(cond), len(latents)) + + tic = time.perf_counter() + samples, intermediates = sampler.sample( + S=inference_steps, + conditioning=cond, + batch_size=batch_size, + shape=latent_shape, + verbose=False, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=u_cond, + eta=eta, + x_T=latents, + parallelism=sampler_parallelism, + tolerance=sampler_tolerance, + ) + toc = time.perf_counter() + sampling_time = toc - tic + + tic = time.perf_counter() + images = decode_images(model, samples) + # remove padding + images = images[: len(batch)] + toc = time.perf_counter() + decode_time = toc - tic + + toc_total = time.perf_counter() + total_time = toc_total - tic_total + output.append(images) + + throughput.append( + { + 'text-conditioning-time': conditioning_time, + 'sampling-time': sampling_time, + 'decode-time': decode_time, + 'total-time': total_time, + 'sampling-steps': inference_steps, + } + ) + + # Convert output type and save to disk + if output_type == 'torch': + output = torch.cat(output, dim=0) + else: + output = torch_to_numpy(output) + if output_type == 'pil': + output = [numpy_to_pil(x) for x in output] + + if save_to_file: + os.makedirs(out_path, exist_ok=True) + if output_type == 'pil': + prompts = chain.from_iterable(batched_prompts) + pils = chain.from_iterable(output) + counts = defaultdict(int) + for text_prompt, image in zip(prompts, pils): + idx = counts[text_prompt] + counts[text_prompt] += 1 + image.save(os.path.join(out_path, f'{text_prompt[:50]}_{idx}.png')) + else: + with open(os.path.join(out_path, 'output.pkl'), 'wb') as f: + pickle.dump(output, f) + else: + return output + + ave_metrics = {} + for key in throughput[0].keys(): + ave_metrics[f'avg-{key}'] = sum([dicts[key] for dicts in throughput]) / len(throughput) + if verbose: + print(ave_metrics) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/sdxl_helpers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/sdxl_helpers.py new file mode 100644 index 0000000..cb50494 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/sdxl_helpers.py @@ -0,0 +1,246 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import os +from typing import List, Optional, Union + +import numpy as np +import torch +from einops import rearrange +from omegaconf import ListConfig +from PIL import Image +from torch import autocast + +from nemo.collections.multimodal.parts.stable_diffusion.utils import append_dims +from nemo.collections.multimodal.parts.utils import randn_like + + +def get_unique_embedder_keys_from_conditioner(conditioner): + return list({x.input_key for x in conditioner.embedders}) + + +def perform_save_locally(save_path, samples): + os.makedirs(os.path.join(save_path), exist_ok=True) + base_count = len(os.listdir(os.path.join(save_path))) + # samples = embed_watermark(samples) + for sample in samples: + sample = sample.squeeze(0) + sample = 255.0 * rearrange(sample.cpu().numpy(), "c h w -> h w c") + Image.fromarray(sample.astype(np.uint8)).save(os.path.join(save_path, f"{base_count:09}.png")) + base_count += 1 + + +class Img2ImgDiscretizationWrapper: + """ + wraps a discretizer, and prunes the sigmas + params: + strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned) + """ + + def __init__(self, discretization, strength: float = 1.0): + self.discretization = discretization + self.strength = strength + assert 0.0 <= self.strength <= 1.0 + + def __call__(self, *args, **kwargs): + # sigmas start large first, and decrease then + sigmas = self.discretization(*args, **kwargs) + print(f"sigmas after discretization, before pruning img2img: ", sigmas) + sigmas = torch.flip(sigmas, (0,)) + sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)] + print("prune index:", max(int(self.strength * len(sigmas)), 1)) + sigmas = torch.flip(sigmas, (0,)) + print(f"sigmas after pruning: ", sigmas) + return sigmas + + +def do_sample( + model, + sampler, + value_dict, + num_samples, + H, + W, + C, + F, + force_uc_zero_embeddings: Optional[List] = None, + batch2model_input: Optional[List] = None, + return_latents=False, + filter=None, + seed=42, + device="cuda", +): + if force_uc_zero_embeddings is None: + force_uc_zero_embeddings = [] + if batch2model_input is None: + batch2model_input = [] + + rng = torch.Generator().manual_seed(seed) + + with torch.no_grad(): + with autocast(device) as precision_scope: + with model.ema_scope(): + num_samples = [num_samples] + batch, batch_uc = get_batch( + get_unique_embedder_keys_from_conditioner(model.conditioner), value_dict, num_samples, + ) + for key in batch: + if isinstance(batch[key], torch.Tensor): + print(key, batch[key].shape) + elif isinstance(batch[key], list): + print(key, [len(l) for l in batch[key]]) + else: + print(key, batch[key]) + c, uc = model.conditioner.get_unconditional_conditioning( + batch, batch_uc=batch_uc, force_uc_zero_embeddings=force_uc_zero_embeddings, + ) + + for k in c: + if not k == "crossattn": + c[k], uc[k] = map(lambda y: y[k][: math.prod(num_samples)].to(device), (c, uc)) + + additional_model_inputs = {} + for k in batch2model_input: + additional_model_inputs[k] = batch[k] + + shape = (math.prod(num_samples), C, H // F, W // F) + randn = torch.randn(shape, generator=rng).to(device) + + def denoiser(input, sigma, c): + return model.denoiser(model.model, input, sigma, c, **additional_model_inputs) + + samples_z = sampler(denoiser, randn, cond=c, uc=uc) + samples_x = model.decode_first_stage(samples_z) + samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0) + + if filter is not None: + samples = filter(samples) + + if return_latents: + return samples, samples_z + return samples + + +def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"): + # Hardcoded demo setups; might undergo some changes in the future + + batch = {} + batch_uc = {} + + for key in keys: + if key == "txt": + batch["txt"] = np.repeat([value_dict["prompt"]], repeats=math.prod(N)).reshape(N).tolist() + batch_uc["txt"] = np.repeat([value_dict["negative_prompt"]], repeats=math.prod(N)).reshape(N).tolist() + elif key == "captions": + batch["captions"] = np.repeat([value_dict["prompt"]], repeats=math.prod(N)).reshape(N).tolist() + batch_uc["captions"] = np.repeat([value_dict["negative_prompt"]], repeats=math.prod(N)).reshape(N).tolist() + elif key == "original_size_as_tuple": + batch["original_size_as_tuple"] = ( + torch.tensor([value_dict["orig_height"], value_dict["orig_width"]]).to(device).repeat(*N, 1) + ) + elif key == "crop_coords_top_left": + batch["crop_coords_top_left"] = ( + torch.tensor([value_dict["crop_coords_top"], value_dict["crop_coords_left"]]).to(device).repeat(*N, 1) + ) + elif key == "aesthetic_score": + batch["aesthetic_score"] = torch.tensor([value_dict["aesthetic_score"]]).to(device).repeat(*N, 1) + batch_uc["aesthetic_score"] = ( + torch.tensor([value_dict["negative_aesthetic_score"]]).to(device).repeat(*N, 1) + ) + + elif key == "target_size_as_tuple": + batch["target_size_as_tuple"] = ( + torch.tensor([value_dict["target_height"], value_dict["target_width"]]).to(device).repeat(*N, 1) + ) + else: + batch[key] = value_dict[key] + + for key in batch.keys(): + if key not in batch_uc and isinstance(batch[key], torch.Tensor): + batch_uc[key] = torch.clone(batch[key]) + return batch, batch_uc + + +def get_input_image_tensor(image: Image.Image, device="cuda"): + w, h = image.size + print(f"loaded input image of size ({w}, {h})") + width, height = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64 + image = image.resize((width, height)) + image_array = np.array(image.convert("RGB")) + image_array = image_array[None].transpose(0, 3, 1, 2) + image_tensor = torch.from_numpy(image_array).to(dtype=torch.float32) / 127.5 - 1.0 + return image_tensor.to(device) + + +def do_img2img( + img, + model, + sampler, + value_dict, + num_samples, + force_uc_zero_embeddings=[], + additional_kwargs={}, + offset_noise_level: float = 0.0, + return_latents=False, + skip_encode=False, + filter=None, + seed=42, + device="cuda", +): + rng = torch.Generator(device=device).manual_seed(seed) + + with torch.no_grad(): + with autocast(device) as precision_scope: + with model.ema_scope(): + batch, batch_uc = get_batch( + get_unique_embedder_keys_from_conditioner(model.conditioner), value_dict, [num_samples], + ) + c, uc = model.conditioner.get_unconditional_conditioning( + batch, batch_uc=batch_uc, force_uc_zero_embeddings=force_uc_zero_embeddings, + ) + + for k in c: + c[k], uc[k] = map(lambda y: y[k][:num_samples].to(device), (c, uc)) + + for k in additional_kwargs: + c[k] = uc[k] = additional_kwargs[k] + if skip_encode: + z = img + else: + z = model.encode_first_stage(img) + noise = randn_like(z, generator=rng) + sigmas = sampler.discretization(sampler.num_steps) + sigma = sigmas[0].to(z.device) + + if offset_noise_level > 0.0: + noise = noise + offset_noise_level * append_dims(torch.randn(z.shape[0], device=z.device), z.ndim) + noised_z = z + noise * append_dims(sigma, z.ndim) + noised_z = noised_z / torch.sqrt( + 1.0 + sigmas[0] ** 2.0 + ) # Note: hardcoded to DDPM-like scaling. need to generalize later. + + def denoiser(x, sigma, c): + return model.denoiser(model.model, x, sigma, c) + + samples_z = sampler(denoiser, noised_z, cond=c, uc=uc) + samples_x = model.decode_first_stage(samples_z) + samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0) + + if filter is not None: + samples = filter(samples) + + if return_latents: + return samples, samples_z + return samples diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/sdxl_pipeline.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/sdxl_pipeline.py new file mode 100644 index 0000000..5c0c669 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/sdxl_pipeline.py @@ -0,0 +1,250 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional + +from omegaconf import OmegaConf + +from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.sampling import ( + DPMPP2MSampler, + DPMPP2SAncestralSampler, + EulerAncestralSampler, + EulerEDMSampler, + HeunEDMSampler, + LinearMultistepSampler, +) +from nemo.collections.multimodal.parts.stable_diffusion.sdxl_helpers import ( + Img2ImgDiscretizationWrapper, + do_img2img, + do_sample, +) + + +class SamplingPipeline: + def __init__(self, model, device="cuda", use_fp16=True, is_legacy=False) -> None: + self.device = device + self.model = model + if use_fp16: + model.conditioner.half() + model.model.half() + self.vae_scale_factor = 2 ** (self.model.first_stage_model.encoder.num_resolutions - 1) + self.is_legacy = is_legacy + + def text_to_image( + self, + params, + prompt: str, + negative_prompt: str = "", + samples: int = 1, + return_latents: bool = False, + seed: int = 42, + ): + sampler = get_sampler_config(params) + value_dict = OmegaConf.to_container(params, resolve=True) + value_dict["prompt"] = prompt + value_dict["negative_prompt"] = negative_prompt + value_dict["target_width"] = params.width + value_dict["target_height"] = params.height + return do_sample( + self.model, + sampler, + value_dict, + samples, + params.height, + params.width, + self.model.model.diffusion_model.in_channels, + self.vae_scale_factor, + force_uc_zero_embeddings=["txt"] if not self.is_legacy else [], + return_latents=return_latents, + filter=None, + seed=seed, + ) + + def image_to_image( + self, + params, + image, + prompt: str, + negative_prompt: str = "", + samples: int = 1, + return_latents: bool = False, + seed: int = 42, + ): + sampler = get_sampler_config(params) + + if params.img2img_strength < 1.0: + sampler.discretization = Img2ImgDiscretizationWrapper( + sampler.discretization, strength=params.img2img_strength, + ) + height, width = image.shape[2], image.shape[3] + value_dict = OmegaConf.to_container(params, resolve=True) + value_dict["prompt"] = prompt + value_dict["negative_prompt"] = negative_prompt + value_dict["target_width"] = width + value_dict["target_height"] = height + return do_img2img( + image, + self.model, + sampler, + value_dict, + samples, + force_uc_zero_embeddings=["txt"] if not self.model.is_legacy else [], + return_latents=return_latents, + filter=None, + seed=seed, + ) + + def refiner( + self, + params, + image, + prompt: str, + negative_prompt: Optional[str] = None, + samples: int = 1, + return_latents: bool = False, + seed: int = 42, + ): + sampler = get_sampler_config(params) + if params.img2img_strength < 1.0: + sampler.discretization = Img2ImgDiscretizationWrapper( + sampler.discretization, strength=params.img2img_strength, + ) + value_dict = { + "orig_width": image.shape[3] * 8, + "orig_height": image.shape[2] * 8, + "target_width": image.shape[3] * 8, + "target_height": image.shape[2] * 8, + "prompt": prompt, + "negative_prompt": negative_prompt, + "crop_coords_top": params.crop_coords_top, + "crop_coords_left": params.crop_coords_left, + "aesthetic_score": params.aesthetic_score, + "negative_aesthetic_score": params.negative_aesthetic_score, + } + + return do_img2img( + image, + self.model, + sampler, + value_dict, + samples, + skip_encode=True, + return_latents=return_latents, + filter=None, + seed=seed, + ) + + +def get_guider_config(params): + if params.guider == "IdentityGuider": + guider_config = { + "target": "nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.guiders.IdentityGuider" + } + elif params.guider == "VanillaCFG": + scale = params.scale + + thresholder = params.thresholder + + if thresholder == "None": + dyn_thresh_config = { + "target": "nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.sampling_utils.NoDynamicThresholding" + } + else: + raise NotImplementedError + + guider_config = { + "target": "nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.guiders.VanillaCFG", + "params": {"scale": scale, "dyn_thresh_config": dyn_thresh_config}, + } + else: + raise NotImplementedError + return guider_config + + +def get_discretization_config(params): + if params.discretization == "LegacyDDPMDiscretization": + discretization_config = { + "target": "nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.discretizer.LegacyDDPMDiscretization", + } + elif params.discretization == "EDMDiscretization": + discretization_config = { + "target": "nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.discretizer.EDMDiscretization", + "params": {"sigma_min": params.sigma_min, "sigma_max": params.sigma_max, "rho": params.rho,}, + } + else: + raise ValueError(f"unknown discretization {params.discretization}") + return discretization_config + + +def get_sampler_config(params): + discretization_config = get_discretization_config(params) + guider_config = get_guider_config(params) + sampler = None + if params.sampler == "EulerEDMSampler": + return EulerEDMSampler( + num_steps=params.steps, + discretization_config=discretization_config, + guider_config=guider_config, + s_churn=params.s_churn, + s_tmin=params.s_tmin, + s_tmax=params.s_tmax, + s_noise=params.s_noise, + verbose=True, + ) + if params.sampler == "HeunEDMSampler": + return HeunEDMSampler( + num_steps=params.steps, + discretization_config=discretization_config, + guider_config=guider_config, + s_churn=params.s_churn, + s_tmin=params.s_tmin, + s_tmax=params.s_tmax, + s_noise=params.s_noise, + verbose=True, + ) + if params.sampler == "EulerAncestralSampler": + return EulerAncestralSampler( + num_steps=params.steps, + discretization_config=discretization_config, + guider_config=guider_config, + eta=params.eta, + s_noise=params.s_noise, + verbose=True, + ) + if params.sampler == "DPMPP2SAncestralSampler": + return DPMPP2SAncestralSampler( + num_steps=params.steps, + discretization_config=discretization_config, + guider_config=guider_config, + eta=params.eta, + s_noise=params.s_noise, + verbose=True, + ) + if params.sampler == "DPMPP2MSampler": + return DPMPP2MSampler( + num_steps=params.steps, + discretization_config=discretization_config, + guider_config=guider_config, + verbose=True, + ) + if params.sampler == "LinearMultistepSampler": + return LinearMultistepSampler( + num_steps=params.steps, + discretization_config=discretization_config, + guider_config=guider_config, + order=params.order, + verbose=True, + ) + + raise ValueError(f"unknown sampler {params.sampler}!") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/utils.py new file mode 100644 index 0000000..591f72a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/stable_diffusion/utils.py @@ -0,0 +1,233 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import importlib +import multiprocessing as mp +from collections import abc +from inspect import isfunction +from queue import Queue +from threading import Thread + +import numpy as np +import torch +from PIL import Image, ImageDraw + +from nemo.utils import logging + + +class DataParallelWrapper(torch.nn.DataParallel): + def __getattr__(self, name): + try: + return super().__getattr__(name) + except AttributeError: + return getattr(self.module, name) + + +def log_txt_as_img(wh, xc, size=10): + # wh a tuple of (width, height) + # xc a list of captions to plot + b = len(xc) + txts = list() + for bi in range(b): + txt = Image.new("RGB", wh, color="white") + draw = ImageDraw.Draw(txt) + nc = int(40 * (wh[0] / 256)) + lines = "\n".join(xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc)) + + try: + draw.text((0, 0), lines, fill="black") + except UnicodeEncodeError: + logging.info("Cant encode string for logging. Skipping.") + + txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 + txts.append(txt) + txts = np.stack(txts) + txts = torch.tensor(txts) + return txts + + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + + +def isimage(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def mean_flat(tensor): + """ + https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + logging.info(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.") + return total_params + + +def instantiate_from_config(config): + if not "target" in config: + if config == '__is_first_stage__': + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + logging.info(f'Getting module=<{module}>, cls=<{cls}>') + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False): + # create dummy dataset instance + + # run prefetching + if idx_to_fn: + res = func(data, worker_id=idx) + else: + res = func(data) + Q.put([idx, res]) + Q.put("Done") + + +def parallel_data_prefetch( + func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False +): + if isinstance(data, np.ndarray) and target_data_type == "list": + raise ValueError("list expected but function got ndarray.") + elif isinstance(data, abc.Iterable): + if isinstance(data, dict): + logging.info( + f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.' + ) + data = list(data.values()) + if target_data_type == "ndarray": + data = np.asarray(data) + else: + data = list(data) + else: + raise TypeError( + f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}." + ) + + if cpu_intensive: + Q = mp.Queue(1000) + proc = mp.Process + else: + Q = Queue(1000) + proc = Thread + # spawn processes + if target_data_type == "ndarray": + arguments = [[func, Q, part, i, use_worker_id] for i, part in enumerate(np.array_split(data, n_proc))] + else: + step = int(len(data) / n_proc + 1) if len(data) % n_proc != 0 else int(len(data) / n_proc) + arguments = [ + [func, Q, part, i, use_worker_id] + for i, part in enumerate([data[i : i + step] for i in range(0, len(data), step)]) + ] + processes = [] + for i in range(n_proc): + p = proc(target=_do_parallel_data_prefetch, args=arguments[i]) + processes += [p] + + # start processes + logging.info(f"Start prefetching...") + import time + + start = time.time() + gather_res = [[] for _ in range(n_proc)] + try: + for p in processes: + p.start() + + k = 0 + while k < n_proc: + # get result + res = Q.get() + if res == "Done": + k += 1 + else: + gather_res[res[0]] = res[1] + + except Exception as e: + logging.info("Exception: ", e) + for p in processes: + p.terminate() + + raise e + finally: + for p in processes: + p.join() + logging.info(f"Prefetching complete. [{time.time() - start} sec.]") + + if target_data_type == 'ndarray': + if not isinstance(gather_res[0], np.ndarray): + return np.concatenate([np.asarray(r) for r in gather_res], axis=0) + + # order outputs + return np.concatenate(gather_res, axis=0) + elif target_data_type == 'list': + out = [] + for r in gather_res: + out.extend(r) + return out + else: + return gather_res + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less") + return x[(...,) + (None,) * dims_to_append] + + +def expand_dims_like(x, y): + while x.dim() != y.dim(): + x = x.unsqueeze(-1) + return x + + +def append_zero(x): + return torch.cat([x, x.new_zeros([1])]) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/utils.py new file mode 100644 index 0000000..723e965 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/parts/utils.py @@ -0,0 +1,470 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import tempfile +from typing import Any, Callable, Tuple + +import torch +from omegaconf import DictConfig, OmegaConf, open_dict +from PIL import Image +from pytorch_lightning import Trainer +from pytorch_lightning.plugins.environments import TorchElasticEnvironment +from transformers import CLIPImageProcessor + +from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel +from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector +from nemo.collections.nlp.parts.peft_config import PEFT_CONFIG_MAP +from nemo.utils import AppState, logging +from nemo.utils.model_utils import inject_model_parallel_rank + +try: + from megatron.core import dist_checkpointing + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +def numpy_to_pil(images): + """ + Convert a numpy image or a batch of images to a PIL image. + """ + if images.ndim == 3: + images = images[None, ...] + images = (images * 255).round().astype("uint8") + pil_images = [Image.fromarray(image) for image in images] + + return pil_images + + +def randn_like(x, generator=None): + return torch.randn(x.shape, dtype=x.dtype, device=x.device, generator=generator) + + +def extend_instance(obj, mixin): + """Apply mixins to a class instance after creation""" + base_cls = obj.__class__ + base_cls_name = obj.__class__.__name__ + obj.__class__ = type( + base_cls_name, (mixin, base_cls), {} + ) # mixin needs to go first for our forward() logic to work + + +def getattr_recursive(obj, att): + """ + Return nested attribute of obj + Example: getattr_recursive(obj, 'a.b.c') is equivalent to obj.a.b.c + """ + if att == "": + return obj + i = att.find(".") + if i < 0: + return getattr(obj, att) + else: + return getattr_recursive(getattr(obj, att[:i]), att[i + 1 :]) + + +def setattr_recursive(obj, att, val): + """ + Set nested attribute of obj + Example: setattr_recursive(obj, 'a.b.c', val) is equivalent to obj.a.b.c = val + """ + if "." in att: + obj = getattr_recursive(obj, ".".join(att.split(".")[:-1])) + setattr(obj, att.split(".")[-1], val) + + +def apply_with_stopping_condition(module, apply_fn, apply_condition=None, stopping_condition=None, **other_args): + if stopping_condition(module): + return + if apply_condition(module): + apply_fn(module, **other_args) + for child in module.children(): + apply_with_stopping_condition( + child, apply_fn, apply_condition=apply_condition, stopping_condition=stopping_condition, **other_args + ) + + +def load_nemo_model_weights(nemo_path, sharded_state_dict=None): + """ + Shared method to load model weights from a given nemo_path. + """ + if torch.cuda.is_available(): + map_location = torch.device('cuda') + else: + map_location = torch.device('cpu') + + save_restore_connector = NLPSaveRestoreConnector() + cwd = os.getcwd() + app_state = AppState() + is_dist_ckpt = False + + with tempfile.TemporaryDirectory() as tmpdir: + try: + if os.path.isfile(nemo_path): + save_restore_connector._unpack_nemo_file(path2file=nemo_path, out_folder=tmpdir) + else: + tmpdir = nemo_path + os.chdir(tmpdir) + if app_state.model_parallel_size is not None and app_state.model_parallel_size > 1: + model_weights = save_restore_connector._inject_model_parallel_rank_for_ckpt( + tmpdir, save_restore_connector.model_weights_ckpt + ) + else: + model_weights = os.path.join(tmpdir, save_restore_connector.model_weights_ckpt) + + state_dict = save_restore_connector._load_state_dict_from_disk(model_weights, map_location=map_location) + + # distributed checkpointing + if state_dict is None and sharded_state_dict is not None: + is_dist_ckpt = True + checkpoint = dict(state_dict=sharded_state_dict) + tmp_model_weights_ckpt = os.path.join(tmpdir, save_restore_connector.model_weights_ckpt) + tmp_model_weights_dir = os.path.splitext(tmp_model_weights_ckpt)[0] + assert os.path.isdir(tmp_model_weights_dir), f'Expected {tmp_model_weights_dir} to be a directory.' + checkpoint = dist_checkpointing.load( + sharded_state_dict=checkpoint, checkpoint_dir=tmp_model_weights_dir, + ) + state_dict = checkpoint["state_dict"] + + finally: + os.chdir(cwd) + + return state_dict, is_dist_ckpt + + +def setup_trainer_and_models_for_inference( + model_provider: Any, cfg: DictConfig, model_cfg_modifier: Callable, +): + """ + Set up a trainer and NeMo model for inference. + + Args: + model_provider (Any): An object that provides the NeMo model. + cfg (DictConfig): The configuration dictionary, containing the + necessary settings for the trainer and the models. + model_cfg_modifier (Callable): A function that modifies the model + configuration for inference. + + Returns: + Tuple[Trainer, Any]: A tuple containing the trainer and the model. + """ + + # Check if we need to use the TorchElasticEnvironment plugin for the trainer. + plugins = [] + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + + # Use the NLPDDPStrategy for the distributed data parallel strategy. + # We don't use DDP for async grad allreduce and don't find unused parameters. + strategy = NLPDDPStrategy(no_ddp_communication_hook=True, find_unused_parameters=False,) + + # Set up the trainer with the specified plugins and strategy. + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer) + + # Create the NLPSaveRestoreConnector object for model saving and restoring. + save_restore_connector = NLPSaveRestoreConnector() + + print(f'Loading {cfg.models} models') + models = [] + for single_model_cfg in cfg.models: + if not single_model_cfg.restore_from_path: + continue + if single_model_cfg.restore_from_path.endswith(".nemo"): + # Set the model_extracted_dir attribute if the restore path is a directory. + if os.path.isdir(single_model_cfg.restore_from_path): + save_restore_connector.model_extracted_dir = single_model_cfg.restore_from_path + + # Restore the model configuration from the specified path and modify it for inference. + model_cfg = model_provider.restore_from( + restore_path=single_model_cfg.restore_from_path, + trainer=trainer, + save_restore_connector=save_restore_connector, + return_config=True, + ) + with open_dict(model_cfg): + model_cfg_modifier(model_cfg) # modify the configuration for inference + + # Restore the model from the specified path and configuration, and set it up for inference. + model = model_provider.restore_from( + restore_path=single_model_cfg.restore_from_path, + trainer=trainer, + override_config_path=model_cfg, + save_restore_connector=save_restore_connector, + strict=True, + ) + models.append(model) + + elif single_model_cfg.restore_from_path.endswith(".ckpt"): + logging.warning( + "Loading from .ckpt checkpoint for inference is experimental! It doesn't support models with model parallelism!" + ) + + model = model_provider.load_from_checkpoint( + single_model_cfg.restore_from_path, hparams_file=cfg.model.get("hparams_file"), trainer=trainer, + ) + models.append(model) + + else: + raise ValueError(f"Unrecognized checkpoint type: {single_model_cfg.restore_from_path}") + + # initialize apex DDP strategy + def dummy(): + return + + if trainer.strategy.launcher is not None: + trainer.strategy.launcher.launch(dummy, trainer=trainer) + trainer.strategy.setup_environment() + + models = [model.cuda() for model in models] # move the model to the GPU + for model in models: + model.eval().requires_grad_(False) # set the model to evaluation mode and disable gradients + + # Return the trainer and model objects. + return trainer, models + + +def setup_trainer_and_model_for_inference( + model_provider: Any, cfg: DictConfig, model_cfg_modifier: Callable, +) -> Tuple[Trainer, Any]: + """ + Set up a trainer and NeMo model for inference. + + Args: + model_provider (Any): An object that provides the NeMo model. + cfg (DictConfig): The configuration dictionary, containing the + necessary settings for the trainer and the model. + model_cfg_modifier (Callable): A function that modifies the model + configuration for inference. + + Returns: + Tuple[Trainer, Any]: A tuple containing the trainer and the model. + """ + + # Check if we need to use the TorchElasticEnvironment plugin for the trainer. + plugins = [] + plugins.append(TorchElasticEnvironment()) + + # Use the NLPDDPStrategy for the distributed data parallel strategy. + # We don't use DDP for async grad allreduce and don't find unused parameters. + strategy = NLPDDPStrategy(no_ddp_communication_hook=True, find_unused_parameters=False,) + + # Set up the trainer with the specified plugins and strategy. + trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer) + + # Create the NLPSaveRestoreConnector object for model saving and restoring. + save_restore_connector = NLPSaveRestoreConnector() + + if cfg.model.restore_from_path.endswith(".nemo") or os.path.isdir(cfg.model.restore_from_path): + # Set the model_extracted_dir attribute if the restore path is a directory. + if os.path.isdir(cfg.model.restore_from_path): + save_restore_connector.model_extracted_dir = cfg.model.restore_from_path + + # Restore the model configuration from the specified path and modify it for inference. + model_cfg = model_provider.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + save_restore_connector=save_restore_connector, + return_config=True, + ) + with open_dict(model_cfg): + model_cfg_modifier(model_cfg) # modify the configuration for inference + + # Restore the model from the specified path and configuration, and set it up for inference. + model = model_provider.restore_from( + restore_path=cfg.model.restore_from_path, + trainer=trainer, + override_config_path=model_cfg, + save_restore_connector=save_restore_connector, + strict=True, + ) + + elif cfg.model.restore_from_path.endswith(".ckpt"): + logging.warning( + "Loading from .ckpt checkpoint for inference is experimental! It doesn't support models with model parallelism!" + ) + + model = model_provider.load_from_checkpoint( + cfg.model.restore_from_path, hparams_file=cfg.model.get("hparams_file"), trainer=trainer, + ) + + else: + raise ValueError(f"Unrecognized checkpoint type: {cfg.model.restore_from_path}") + + # initialize apex DDP strategy + def dummy(): + return + + if trainer.strategy.launcher is not None: + trainer.strategy.launcher.launch(dummy, trainer=trainer) + trainer.strategy.setup_environment() + + model = model.cuda() # move the model to the GPU + model.eval().requires_grad_(False) # set the model to evaluation mode and disable gradients + + # Return the trainer and model objects. + return trainer, model + + +def create_neva_model_and_processor(cfg): + from nemo.collections.multimodal.models.neva.neva_model import MegatronNevaModel + + plugins = [] + if cfg.get('cluster_type', None) == 'BCP': + plugins.append(TorchElasticEnvironment()) + # trainer required for restoring model parallel models + trainer = Trainer(plugins=plugins, strategy=NLPDDPStrategy(), **cfg.trainer) + + if ( + cfg.tensor_model_parallel_size < 0 + or cfg.pipeline_model_parallel_size < 0 + or cfg.get('pipeline_model_parallel_split_rank', -1) < 0 + ): + model_config = MegatronNevaModel.restore_from( + restore_path=cfg.neva_model_file, trainer=trainer, return_config=True, + ) + + with open_dict(cfg): + cfg.tensor_model_parallel_size = model_config.get('tensor_model_parallel_size', 1) + cfg.pipeline_model_parallel_size = model_config.get('pipeline_model_parallel_size', 1) + cfg.pipeline_model_parallel_split_rank = model_config.get('pipeline_model_parallel_split_rank', 0) + + assert ( + cfg.trainer.devices * cfg.trainer.num_nodes + == cfg.tensor_model_parallel_size * cfg.pipeline_model_parallel_size + ), "devices * num_nodes should equal tensor_model_parallel_size * pipeline_model_parallel_size" + + if cfg.neva_model_file: + save_restore_connector = NLPSaveRestoreConnector() + if os.path.isdir(cfg.neva_model_file): + save_restore_connector.model_extracted_dir = cfg.neva_model_file + + neva_cfg = MegatronNevaModel.restore_from( + restore_path=cfg.neva_model_file, + trainer=trainer, + return_config=True, + save_restore_connector=save_restore_connector, + ) + OmegaConf.set_struct(neva_cfg, True) + with open_dict(neva_cfg): + neva_cfg.sequence_parallel = False + neva_cfg.activations_checkpoint_granularity = None + neva_cfg.activations_checkpoint_method = None + neva_cfg.precision = trainer.precision + neva_cfg.mm_cfg.llm.from_pretrained = cfg.get('base_model_file', None) + neva_cfg.apply_rope_fusion = False + # neva_cfg.mm_cfg.vision_encoder.from_pretrained = None + + model = MegatronNevaModel.restore_from( + restore_path=cfg.neva_model_file, + trainer=trainer, + override_config_path=neva_cfg, + save_restore_connector=save_restore_connector, + ) + if neva_cfg.get('peft') is not None: + peft_cfg_cls = PEFT_CONFIG_MAP[neva_cfg.peft.peft_scheme] + if peft_cfg_cls is not None: + model.load_adapters(cfg.neva_model_file, peft_cfg_cls(neva_cfg)) + + elif cfg.checkpoint_dir: + app_state = AppState() + if cfg.tensor_model_parallel_size > 1 or cfg.pipeline_model_parallel_size > 1: + app_state.model_parallel_size = cfg.tensor_model_parallel_size * cfg.pipeline_model_parallel_size + app_state.tensor_model_parallel_size = cfg.tensor_model_parallel_size + app_state.pipeline_model_parallel_size = cfg.pipeline_model_parallel_size + ( + app_state.tensor_model_parallel_rank, + app_state.pipeline_model_parallel_rank, + app_state.model_parallel_size, + app_state.data_parallel_size, + app_state.pipeline_model_parallel_split_rank, + app_state.virtual_pipeline_model_parallel_rank, + ) = fake_initialize_model_parallel( + world_size=app_state.model_parallel_size, + rank=trainer.global_rank, + tensor_model_parallel_size_=cfg.tensor_model_parallel_size, + pipeline_model_parallel_size_=cfg.pipeline_model_parallel_size, + pipeline_model_parallel_split_rank_=cfg.pipeline_model_parallel_split_rank, + ) + checkpoint_path = inject_model_parallel_rank(os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name)) + # TODO: This wont work properly (We need to set model.llm.from_pretrained model.vision.from_pretrained to nul) + model = MegatronNevaModel.load_from_checkpoint(checkpoint_path, hparams_file=cfg.hparams_file, trainer=trainer) + else: + raise ValueError("need at least a nemo file or checkpoint dir") + + model.freeze() + + # Have to turn off activations_checkpoint_method for inference + try: + model.model.language_model.encoder.activations_checkpoint_method = None + except AttributeError: + pass + try: + model.model.module.language_model.encoder.activations_checkpoint_method = None + except AttributeError: + pass + + def image_processor(maybe_image_path): + if isinstance(maybe_image_path, str): + image = Image.open(maybe_image_path).convert('RGB') + else: + image = maybe_image_path + + if neva_cfg.mm_cfg.vision_encoder.from_hf: + processor = CLIPImageProcessor.from_pretrained( + neva_cfg.mm_cfg.vision_encoder.from_pretrained, torch_dtype=torch.bfloat16 + ) + else: + processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=torch.bfloat16) + + if neva_cfg.data.image_aspect_ratio == 'keep': + max_hw, min_hw = max(image.size), min(image.size) + aspect_ratio = max_hw / min_hw + max_len, min_len = 448, 224 + shortest_edge = int(min(max_len / aspect_ratio, min_len)) + image = processor.preprocess( + image, return_tensors='pt', do_center_crop=False, size={"shortest_edge": shortest_edge} + )['pixel_values'][0] + elif neva_cfg.data.image_aspect_ratio == 'pad': + + def expand2square(pil_img, background_color): + width, height = pil_img.size + if width == height: + return pil_img + elif width > height: + result = Image.new(pil_img.mode, (width, width), background_color) + result.paste(pil_img, (0, (width - height) // 2)) + return result + else: + result = Image.new(pil_img.mode, (height, height), background_color) + result.paste(pil_img, ((height - width) // 2, 0)) + return result + + image = expand2square(image, tuple(int(x * 255) for x in processor.image_mean)) + image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0] + else: + image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0] + + if neva_cfg.precision in [16, '16', '16-mixed']: + media = image.type(torch.float16) + elif neva_cfg.precision in [32, '32', '32-true']: + media = image.type(torch.float32) + else: + media = image.type(torch.bfloat16) + + return media.unsqueeze(dim=0).unsqueeze(dim=0).unsqueeze(dim=0) + + return model, image_processor diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/__init__.py new file mode 100644 index 0000000..e13e481 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/__init__.py @@ -0,0 +1,25 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.multimodal.speech_cv import data, models, modules +from nemo.package_info import __version__ + +# Set collection version equal to NeMo version. +__version = __version__ + +# Authorship. +__author__ = "NVIDIA Corporation" + +# Set collection name. +__description__ = "Speech Computer Vision collection" diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/data/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/data/__init__.py new file mode 100644 index 0000000..9e32500 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/data/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/data/video_to_text.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/data/video_to_text.py new file mode 100644 index 0000000..a20d6e5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/data/video_to_text.py @@ -0,0 +1,866 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union + +import torch +import webdataset as wds + +from nemo.collections.asr.data.audio_to_text import cache_datastore_manifests, expand_sharded_filepaths +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.common import tokenizers +from nemo.collections.common.parts.preprocessing import collections, parsers +from nemo.collections.multimodal.speech_cv.parts.preprocessing.features import VideoFeaturizer +from nemo.core.classes import Dataset, IterableDataset +from nemo.core.neural_types import * +from nemo.utils import logging +from nemo.utils.distributed import webdataset_split_by_workers + + +def _video_speech_collate_fn(batch, pad_id): + """collate batch of video sig, video len, tokens, tokens len + Args: + batch (Optional[FloatTensor], Optional[LongTensor], LongTensor, + LongTensor): A tuple of tuples of signal, signal lengths, + encoded tokens, and encoded tokens length. This collate func + assumes the signals are 4d torch tensors (Time, Height, Width, Channels). + """ + packed_batch = list(zip(*batch)) + + if len(packed_batch) == 5: + _, video_lengths, _, tokens_lengths, sample_ids = packed_batch + elif len(packed_batch) == 4: + sample_ids = None + _, video_lengths, _, tokens_lengths = packed_batch + else: + raise ValueError("Expects 4 or 5 tensors in the batch!") + + # Max Video Len + max_video_len = 0 + has_video = video_lengths[0] is not None + if has_video: + max_video_len = max(video_lengths).item() + + # Max Token Len + max_tokens_len = max(tokens_lengths).item() + + video_signal, tokens = [], [] + for b in batch: + + if len(b) == 5: + video_sig, video_sig_len, tokens_i, tokens_i_len, _ = b + else: + video_sig, video_sig_len, tokens_i, tokens_i_len = b + + # Pad and Append Video + if has_video: + video_sig_len = video_sig_len.item() + if video_sig_len < max_video_len: + pad = (0, 0, 0, 0, 0, 0, 0, max_video_len - video_sig_len) + video_sig = torch.nn.functional.pad(video_sig, pad) + video_signal.append(video_sig) + + # Pad and Append Token + tokens_i_len = tokens_i_len.item() + if tokens_i_len < max_tokens_len: + pad = (0, max_tokens_len - tokens_i_len) + tokens_i = torch.nn.functional.pad(tokens_i, pad, value=pad_id) + tokens.append(tokens_i) + + # Stack Video + if has_video: + video_signal = torch.stack(video_signal) + video_lengths = torch.stack(video_lengths) + else: + video_signal, video_lengths = None, None + + # Stack Text + tokens = torch.stack(tokens) + tokens_lengths = torch.stack(tokens_lengths) + + # Return + if sample_ids is None: + return video_signal, video_lengths, tokens, tokens_lengths + else: + sample_ids = torch.tensor(sample_ids, dtype=torch.int32) + return video_signal, video_lengths, tokens, tokens_lengths, sample_ids + + +class _VideoTextDataset(Dataset): + """ + Dataset that loads tensors via a json file containing paths to video files, transcripts, and durations (in seconds). + Each new line is a different sample. Example below: + {"video_filepath": "/path/to/video.mp4", "text_filepath": "/path/to/video.txt", "duration": 23.147} + ... + {"video_filepath": "/path/to/video.mp4", "text": "the transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + Args: + manifest_filepath: Path to manifest json as described above. Can be comma-separated paths. + parser: Str for a language specific preprocessor or a callable. + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + max_duration: If video exceeds this length, do not include in dataset + min_duration: If video is less than this length, do not include in dataset + max_utts: Limit number of utterances + trim: whether or not to trim silence. Defaults to False + bos_id: Id of beginning of sequence symbol to append if not None + eos_id: Id of end of sequence symbol to append if not None + pad_id: Id of pad symbol. Defaults to 0 + return_sample_id (bool): whether to return the sample_id as a part of each sample + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'video_signal': NeuralType(('B', 'C', 'T', 'H', 'W'), VideoSignal()), + 'video_sig_length': NeuralType(tuple('B'), LengthsType()), + 'transcripts': NeuralType(('B', 'T'), LabelsType()), + 'transcript_length': NeuralType(tuple('B'), LengthsType()), + 'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def __init__( + self, + manifest_filepath: str, + parser: Union[str, Callable], + int_values: bool = False, + max_duration: Optional[int] = None, + min_duration: Optional[int] = None, + max_utts: int = 0, + trim: bool = False, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + ): + if type(manifest_filepath) == str: + manifest_filepath = manifest_filepath.split(",") + + # If necessary, cache manifests and audio from object store + cache_datastore_manifests(manifest_filepaths=manifest_filepath, cache_audio=True) + + self.manifest_processor = VSRManifestProcessor( + manifest_filepath=manifest_filepath, + parser=parser, + max_duration=max_duration, + min_duration=min_duration, + max_utts=max_utts, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + ) + self.video_featurizer = VideoFeaturizer() + self.trim = trim + self.return_sample_id = return_sample_id + self.channel_selector = channel_selector + + def get_manifest_sample(self, sample_id): + return self.manifest_processor.collection[sample_id] + + def __getitem__(self, index): + + # Select Sample + sample = self.manifest_processor.collection[index] + + # Offset + offset = sample.offset + if offset is None: + offset = 0 + + # Load Video + video_features = self.video_featurizer.process(sample.video_file, offset=offset, duration=sample.duration) + vf, vfl = video_features, torch.tensor(video_features.shape[0]).long() + + # Load Tokens + t, tl = self.manifest_processor.process_text_by_sample(sample=sample) + + if self.return_sample_id: + output = vf, vfl, torch.tensor(t).long(), torch.tensor(tl).long(), index + else: + output = vf, vfl, torch.tensor(t).long(), torch.tensor(tl).long() + + return output + + def __len__(self): + return len(self.manifest_processor.collection) + + def _collate_fn(self, batch): + return _video_speech_collate_fn(batch, pad_id=self.manifest_processor.pad_id) + + +class VSRManifestProcessor: + """ + Class that processes a manifest json file containing paths to video files, transcripts, and durations (in seconds). + Each new line is a different sample. Example below: + {"video_filepath": "/path/to/video.mp4", "text_filepath": "/path/to/video.txt", "duration": 23.147} + ... + {"video_filepath": "/path/to/video.mp4", "text": "the transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + Args: + manifest_filepath: Path to manifest json as described above. Can be comma-separated paths. + parser: Str for a language specific preprocessor or a callable. + max_duration: If video exceeds this length, do not include in dataset. + min_duration: If video is less than this length, do not include in dataset. + max_utts: Limit number of utterances. + bos_id: Id of beginning of sequence symbol to append if not None. + eos_id: Id of end of sequence symbol to append if not None. + pad_id: Id of pad symbol. Defaults to 0. + """ + + def __init__( + self, + manifest_filepath: str, + parser: Union[str, Callable], + max_duration: Optional[float] = None, + min_duration: Optional[float] = None, + max_utts: int = 0, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + index_by_file_id: bool = False, + ): + self.parser = parser + + self.collection = collections.ASRVideoText( + manifests_files=manifest_filepath, + parser=parser, + min_duration=min_duration, + max_duration=max_duration, + max_number=max_utts, + index_by_file_id=index_by_file_id, + ) + + self.eos_id = eos_id + self.bos_id = bos_id + self.pad_id = pad_id + + def process_text_by_id(self, index: int) -> Tuple[List[int], int]: + sample = self.collection[index] + return self.process_text_by_sample(sample) + + def process_text_by_file_id(self, file_id: str) -> Tuple[List[int], int]: + manifest_idx = self.collection.mapping[file_id][0] + sample = self.collection[manifest_idx] + return self.process_text_by_sample(sample) + + def process_text_by_sample(self, sample: collections.ASRAudioText.OUTPUT_TYPE) -> Tuple[List[int], int]: + t, tl = sample.text_tokens, len(sample.text_tokens) + + if self.bos_id is not None: + t = [self.bos_id] + t + tl += 1 + if self.eos_id is not None: + t = t + [self.eos_id] + tl += 1 + + return t, tl + + +class VideoToBPEDataset(_VideoTextDataset): + """ + Dataset that loads tensors via a json file containing paths to video + files, transcripts, and durations (in seconds). Each new line is a + different sample. Example below: + {"video_filepath": "/path/to/video.mp4", "text_filepath": + "/path/to/video.txt", "duration": 23.147} + ... + {"video_filepath": "/path/to/video.mp4", "text": "the + transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + + In practice, the dataset and manifest used for character encoding and byte pair encoding + are exactly the same. The only difference lies in how the dataset tokenizes the text in + the manifest. + + Args: + manifest_filepath: Path to manifest json as described above. Can + be comma-separated paths. + tokenizer: A subclass of the Tokenizer wrapper found in the common collection, + nemo.collections.common.tokenizers.TokenizerSpec. ASR Models support a subset of + all available tokenizers. + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + max_duration: If video exceeds this length, do not include in dataset + min_duration: If video is less than this length, do not include + in dataset + max_utts: Limit number of utterances + trim: Whether to trim silence segments + use_start_end_token: Boolean which dictates whether to add [BOS] and [EOS] + tokens to beginning and ending of speech respectively. + return_sample_id (bool): whether to return the sample_id as a part of each sample + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'video_signal': NeuralType(('B', 'C', 'T', 'H', 'W'), VideoSignal()), + 'video_sig_length': NeuralType(tuple('B'), LengthsType()), + 'transcripts': NeuralType(('B', 'T'), LabelsType()), + 'transcript_length': NeuralType(tuple('B'), LengthsType()), + 'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def __init__( + self, + manifest_filepath: str, + tokenizer: 'nemo.collections.common.tokenizers.TokenizerSpec', + int_values: bool = False, + max_duration: Optional[int] = None, + min_duration: Optional[int] = None, + max_utts: int = 0, + trim: bool = False, + use_start_end_token: bool = True, + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + ): + if use_start_end_token and hasattr(tokenizer, "bos_id") and tokenizer.bos_id > 0: + bos_id = tokenizer.bos_id + else: + bos_id = None + + if use_start_end_token and hasattr(tokenizer, "eos_id") and tokenizer.eos_id > 0: + eos_id = tokenizer.eos_id + else: + eos_id = None + + if hasattr(tokenizer, "pad_id") and tokenizer.pad_id > 0: + pad_id = tokenizer.pad_id + else: + pad_id = 0 + + class TokenizerWrapper: + def __init__(self, tokenizer): + if isinstance(tokenizer, tokenizers.aggregate_tokenizer.AggregateTokenizer): + self.is_aggregate = True + else: + self.is_aggregate = False + self._tokenizer = tokenizer + + def __call__(self, *args): + if isinstance(args[0], List) and self.is_aggregate: + t = [] + for span in args[0]: + t.extend(self._tokenizer.text_to_ids(span['str'], span['lang'])) + return t + + t = self._tokenizer.text_to_ids(*args) + return t + + super().__init__( + manifest_filepath=manifest_filepath, + parser=TokenizerWrapper(tokenizer), + int_values=int_values, + max_duration=max_duration, + min_duration=min_duration, + max_utts=max_utts, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + trim=trim, + return_sample_id=return_sample_id, + channel_selector=channel_selector, + ) + + +class VideoToCharDataset(_VideoTextDataset): + """ + Dataset that loads tensors via a json file containing paths to video + files, transcripts, and durations (in seconds). Each new line is a + different sample. Example below: + {"video_filepath": "/path/to/video.mp4", "text_filepath": + "/path/to/video.txt", "duration": 23.147} + ... + {"video_filepath": "/path/to/video.mp4", "text": "the + transcription", "offset": 301.75, "duration": 0.82, "utt": + "utterance_id", "ctm_utt": "en_4156", "side": "A"} + + Args: + manifest_filepath: Path to manifest json as described above. Can + be comma-separated paths. + labels: String containing all the possible characters to map to + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + max_duration: If video exceeds this length, do not include in dataset + min_duration: If video is less than this length, do not include + in dataset + max_utts: Limit number of utterances + blank_index: blank character index, default = -1 + unk_index: unk_character index, default = -1 + normalize: whether to normalize transcript text (default): True + bos_id: Id of beginning of sequence symbol to append if not None + eos_id: Id of end of sequence symbol to append if not None + return_sample_id (bool): whether to return the sample_id as a part of each sample + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'video_signal': NeuralType(('B', 'C', 'T', 'H', 'W'), VideoSignal()), + 'video_sig_length': NeuralType(tuple('B'), LengthsType()), + 'transcripts': NeuralType(('B', 'T'), LabelsType()), + 'transcript_length': NeuralType(tuple('B'), LengthsType()), + 'sample_id': NeuralType(tuple('B'), LengthsType(), optional=True), + } + + def __init__( + self, + manifest_filepath: str, + labels: Union[str, List[str]], + int_values: bool = False, + max_duration: Optional[float] = None, + min_duration: Optional[float] = None, + max_utts: int = 0, + blank_index: int = -1, + unk_index: int = -1, + normalize: bool = True, + trim: bool = False, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + parser: Union[str, Callable] = 'en', + return_sample_id: bool = False, + channel_selector: Optional[ChannelSelectorType] = None, + ): + self.labels = labels + + parser = parsers.make_parser( + labels=labels, name=parser, unk_id=unk_index, blank_id=blank_index, do_normalize=normalize + ) + + super().__init__( + manifest_filepath=manifest_filepath, + parser=parser, + int_values=int_values, + max_duration=max_duration, + min_duration=min_duration, + max_utts=max_utts, + trim=trim, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + return_sample_id=return_sample_id, + channel_selector=channel_selector, + ) + + +class _TarredVideoToTextDataset(IterableDataset): + """ + A similar Dataset to the VideoToCharDataset/VideoToBPEDataset, but which loads tarred video files. + + Accepts a single comma-separated JSON manifest file (in the same style as for the VideoToCharDataset/VideoToBPEDataset), + as well as the path(s) to the tarball(s) containing the mp4 files. Each line of the manifest should + contain the information for one video file, including at least the transcript and name of the audio + file within the tarball. + + Valid formats for the audio_tar_filepaths argument include: + (1) a single string that can be brace-expanded, e.g. 'path/to/audio.tar' or 'path/to/audio_{1..100}.tar.gz', or + (2) a list of file paths that will not be brace-expanded, e.g. ['audio_1.tar', 'audio_2.tar', ...]. + + Note: For brace expansion in (1), there may be cases where `{x..y}` syntax cannot be used due to shell interference. + This occurs most commonly inside SLURM scripts. Therefore we provide a few equivalent replacements. + Supported opening braces - { <=> (, [, < and the special tag _OP_. + Supported closing braces - } <=> ), ], > and the special tag _CL_. + For SLURM based tasks, we suggest the use of the special tags for ease of use. + + See the WebDataset documentation for more information about accepted data and input formats. + + If using multiple workers the number of shards should be divisible by world_size to ensure an + even split among workers. If it is not divisible, logging will give a warning but training will proceed. + In addition, if using mutiprocessing, each shard MUST HAVE THE SAME NUMBER OF ENTRIES after filtering + is applied. We currently do not check for this, but your program may hang if the shards are uneven! + + Notice that a few arguments are different from the AudioToCharDataset; for example, shuffle (bool) has been + replaced by shuffle_n (int). + + Additionally, please note that the len() of this DataLayer is assumed to be the length of the manifest + after filtering. An incorrect manifest length may lead to some DataLoader issues down the line. + + Args: + audio_tar_filepaths: Either a list of audio tarball filepaths, or a + string (can be brace-expandable). + manifest_filepath (str): Path to the manifest. + parser (callable): A callable which is used to pre-process the text output. + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + shuffle_n (int): How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + Defaults to 0. + min_duration (float): Dataset parameter. + All training files which have a duration less than min_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to 0.1. + max_duration (float): Dataset parameter. + All training files which have a duration more than max_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to None. + blank_index (int): Blank character index, defaults to -1. + unk_index (int): Unknown character index, defaults to -1. + normalize (bool): Dataset parameter. + Whether to use automatic text cleaning. + It is highly recommended to manually clean text for best results. + Defaults to True. + trim (bool): Whether to use trim silence from beginning and end + of audio signal using librosa.effects.trim(). + Defaults to False. + bos_id (id): Dataset parameter. + Beginning of string symbol id used for seq2seq models. + Defaults to None. + eos_id (id): Dataset parameter. + End of string symbol id used for seq2seq models. + Defaults to None. + pad_id (id): Token used to pad when collating samples in batches. + If this is None, pads using 0s. + Defaults to None. + shard_strategy (str): Tarred dataset shard distribution strategy chosen as a str value during ddp. + - `scatter`: The default shard strategy applied by WebDataset, where each node gets + a unique set of shards, which are permanently pre-allocated and never changed at runtime. + - `replicate`: Optional shard strategy, where each node gets all of the set of shards + available in the tarred dataset, which are permanently pre-allocated and never changed at runtime. + The benefit of replication is that it allows each node to sample data points from the entire + dataset independently of other nodes, and reduces dependence on value of `shuffle_n`. + + .. warning:: + Replicated strategy allows every node to sample the entire set of available tarfiles, + and therefore more than one node may sample the same tarfile, and even sample the same + data points! As such, there is no assured guarantee that all samples in the dataset will be + sampled at least once during 1 epoch. Scattered strategy, on the other hand, on specific + occasions (when the number of shards is not divisible with ``world_size``), will not sample + the entire dataset. For these reasons it is not advisable to use tarred datasets as validation + or test datasets. + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 0. + return_sample_id (bool): whether to return the sample_id as a part of each sample + """ + + def __init__( + self, + audio_tar_filepaths: Union[str, List[str]], + manifest_filepath: str, + parser: Callable, + int_values: bool = False, + shuffle_n: int = 0, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + trim: bool = False, + bos_id: Optional[int] = None, + eos_id: Optional[int] = None, + pad_id: int = 0, + shard_strategy: str = "scatter", + global_rank: int = 0, + world_size: int = 0, + return_sample_id: bool = False, + ): + # If necessary, cache manifests from object store + cache_datastore_manifests(manifest_filepaths=manifest_filepath) + + self.manifest_processor = VSRManifestProcessor( + manifest_filepath=manifest_filepath, + parser=parser, + max_duration=max_duration, + min_duration=min_duration, + max_utts=0, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + index_by_file_id=True, # Must set this so the manifest lines can be indexed by file ID + ) + + self.video_featurizer = VideoFeaturizer() + self.trim = trim + self.eos_id = eos_id + self.bos_id = bos_id + self.pad_id = pad_id + self.return_sample_id = return_sample_id + + audio_tar_filepaths = expand_sharded_filepaths( + audio_tar_filepaths=audio_tar_filepaths, + shard_strategy=shard_strategy, + world_size=world_size, + global_rank=global_rank, + ) + + # Put together WebDataset + self._dataset = wds.DataPipeline( + wds.SimpleShardList(urls=audio_tar_filepaths), + webdataset_split_by_workers, + wds.shuffle(shuffle_n), + wds.tarfile_to_samples(), + wds.map(wds.autodecode.Decoder([wds.torch_video])), + wds.rename(video="mp4", key='__key__'), + wds.to_tuple('video', 'key'), + self._filter, + self._loop_offsets, + wds.map(self._build_sample), + ) + + def _filter(self, iterator): + """This function is used to remove samples that have been filtered out by ASRVideoText already. + Otherwise, we would get a KeyError as _build_sample attempts to find the manifest entry for a sample + that was filtered out (e.g. for duration). + Note that if using multi-GPU training, filtering may lead to an imbalance in samples in each shard, + which may make your code hang as one process will finish before the other. + """ + + class TarredAudioFilter: + def __init__(self, collection): + self.iterator = iterator + self.collection = collection + + def __iter__(self): + return self + + def __next__(self): + while True: + try: + video_bytes, audio_filename = next(self.iterator) + except: + print("except") + continue + file_id, _ = os.path.splitext(os.path.basename(audio_filename)) + if file_id in self.collection.mapping: + return video_bytes, audio_filename + + return TarredAudioFilter(self.manifest_processor.collection) + + def _loop_offsets(self, iterator): + """This function is used to iterate through utterances with different offsets for each file. + """ + + class TarredAudioLoopOffsets: + def __init__(self, collection): + self.iterator = iterator + self.collection = collection + self.current_fn = None + self.current_video_bytes = None + self.offset_id = 0 + + def __iter__(self): + return self + + def __next__(self): + if self.current_fn is None: + self.current_video_bytes, self.current_fn = next(self.iterator) + self.offset_id = 0 + else: + offset_list = self.collection.mapping[self.current_fn] + if len(offset_list) == self.offset_id + 1: + self.current_video_bytes, self.current_fn = next(self.iterator) + self.offset_id = 0 + else: + self.offset_id += 1 + + return self.current_video_bytes, self.current_fn, self.offset_id + + return TarredAudioLoopOffsets(self.manifest_processor.collection) + + def _collate_fn(self, batch): + return _video_speech_collate_fn(batch, self.pad_id) + + def _build_sample(self, tup): + """Builds the training sample by combining the data from the WebDataset with the manifest info. + """ + video_tuple, audio_filename, offset_id = tup + + # Grab manifest entry from self.manifest_preprocessor.collection + file_id, _ = os.path.splitext(os.path.basename(audio_filename)) + manifest_idx = self.manifest_processor.collection.mapping[file_id][offset_id] + manifest_entry = self.manifest_processor.collection[manifest_idx] + + offset = manifest_entry.offset + if offset is None: + offset = 0 + + # Load Video + video_features = video_tuple[0] + + # Signal length + vf, vfl = video_features, torch.tensor(video_features.shape[0]).long() + + # Load Tokens + t, tl = manifest_entry.text_tokens, len(manifest_entry.text_tokens) + + self.manifest_processor.process_text_by_sample(sample=manifest_entry) + + if self.bos_id is not None: + t = [self.bos_id] + t + tl += 1 + if self.eos_id is not None: + t = t + [self.eos_id] + tl += 1 + + if self.return_sample_id: + return vf, vfl, torch.tensor(t).long(), torch.tensor(tl).long(), manifest_idx + else: + return vf, vfl, torch.tensor(t).long(), torch.tensor(tl).long() + + def get_manifest_sample(self, sample_id): + return self.manifest_processor.collection[sample_id] + + def __iter__(self): + return self._dataset.__iter__() + + def __len__(self): + return len(self.manifest_processor.collection) + + +class TarredVideoToBPEDataset(_TarredVideoToTextDataset): + """ + A similar Dataset to the VideoToBPEDataset, but which loads tarred audio files. + + Accepts a single comma-separated JSON manifest file (in the same style as for the VideoToBPEDataset), + as well as the path(s) to the tarball(s) containing the wav files. Each line of the manifest should + contain the information for one audio file, including at least the transcript and name of the audio + file within the tarball. + + Valid formats for the audio_tar_filepaths argument include: + (1) a single string that can be brace-expanded, e.g. 'path/to/audio.tar' or 'path/to/audio_{1..100}.tar.gz', or + (2) a list of file paths that will not be brace-expanded, e.g. ['audio_1.tar', 'audio_2.tar', ...]. + + See the WebDataset documentation for more information about accepted data and input formats. + + If using multiple workers the number of shards should be divisible by world_size to ensure an + even split among workers. If it is not divisible, logging will give a warning but training will proceed. + In addition, if using mutiprocessing, each shard MUST HAVE THE SAME NUMBER OF ENTRIES after filtering + is applied. We currently do not check for this, but your program may hang if the shards are uneven! + + Notice that a few arguments are different from the AudioToBPEDataset; for example, shuffle (bool) has been + replaced by shuffle_n (int). + + Additionally, please note that the len() of this DataLayer is assumed to be the length of the manifest + after filtering. An incorrect manifest length may lead to some DataLoader issues down the line. + + Args: + audio_tar_filepaths: Either a list of audio tarball filepaths, or a + string (can be brace-expandable). + manifest_filepath (str): Path to the manifest. + tokenizer (TokenizerSpec): Either a Word Piece Encoding tokenizer (BERT), + or a Sentence Piece Encoding tokenizer (BPE). The CTC blank + symbol is automatically added later for models using ctc. + int_values (bool): If true, load samples as 32-bit integers. Defauts to False. + shuffle_n (int): How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + Defaults to 0. + min_duration (float): Dataset parameter. + All training files which have a duration less than min_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to 0.1. + max_duration (float): Dataset parameter. + All training files which have a duration more than max_duration + are dropped. Note: Duration is read from the manifest JSON. + Defaults to None. + trim (bool): Whether to use trim silence from beginning and end + of audio signal using librosa.effects.trim(). + Defaults to False. + use_start_end_token: Boolean which dictates whether to add [BOS] and [EOS] + tokens to beginning and ending of speech respectively. + pad_id (id): Token used to pad when collating samples in batches. + If this is None, pads using 0s. + Defaults to None. + shard_strategy (str): Tarred dataset shard distribution strategy chosen as a str value during ddp. + + - `scatter`: The default shard strategy applied by WebDataset, where each node gets + a unique set of shards, which are permanently pre-allocated and never changed at runtime. + - `replicate`: Optional shard strategy, where each node gets all of the set of shards + available in the tarred dataset, which are permanently pre-allocated and never changed at runtime. + The benefit of replication is that it allows each node to sample data points from the entire + dataset independently of other nodes, and reduces dependence on value of `shuffle_n`. + + .. warning:: + + Replicated strategy allows every node to sample the entire set of available tarfiles, + and therefore more than one node may sample the same tarfile, and even sample the same + data points! As such, there is no assured guarantee that all samples in the dataset will be + sampled at least once during 1 epoch. Scattered strategy, on the other hand, on specific + occasions (when the number of shards is not divisible with ``world_size``), will not sample + the entire dataset. For these reasons it is not advisable to use tarred datasets as validation + or test datasets. + + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 0. + return_sample_id (bool): whether to return the sample_id as a part of each sample + """ + + def __init__( + self, + audio_tar_filepaths: Union[str, List[str]], + manifest_filepath: str, + tokenizer: 'nemo.collections.common.tokenizers.TokenizerSpec', + int_values: bool = False, + shuffle_n: int = 0, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, + trim: bool = False, + use_start_end_token: bool = True, + shard_strategy: str = "scatter", + global_rank: int = 0, + world_size: int = 0, + return_sample_id: bool = False, + ): + if use_start_end_token and hasattr(tokenizer, "bos_id") and tokenizer.bos_id > 0: + bos_id = tokenizer.bos_id + else: + bos_id = None + + if use_start_end_token and hasattr(tokenizer, "eos_id") and tokenizer.eos_id > 0: + eos_id = tokenizer.eos_id + else: + eos_id = None + + if hasattr(tokenizer, "pad_id") and tokenizer.pad_id > 0: + pad_id = tokenizer.pad_id + else: + pad_id = 0 + + class TokenizerWrapper: + def __init__(self, tokenizer): + if isinstance(tokenizer, tokenizers.aggregate_tokenizer.AggregateTokenizer): + self.is_aggregate = True + else: + self.is_aggregate = False + self._tokenizer = tokenizer + + def __call__(self, *args): + if isinstance(args[0], Iterable) and self.is_aggregate: + t = [] + for span in args[0]: + t.extend(self._tokenizer.text_to_ids(span['str'], span['lang'])) + return t + + t = self._tokenizer.text_to_ids(*args) + return t + + super().__init__( + audio_tar_filepaths=audio_tar_filepaths, + manifest_filepath=manifest_filepath, + parser=TokenizerWrapper(tokenizer), + int_values=int_values, + shuffle_n=shuffle_n, + min_duration=min_duration, + max_duration=max_duration, + trim=trim, + bos_id=bos_id, + eos_id=eos_id, + pad_id=pad_id, + shard_strategy=shard_strategy, + global_rank=global_rank, + world_size=world_size, + return_sample_id=return_sample_id, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/data/video_to_text_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/data/video_to_text_dataset.py new file mode 100644 index 0000000..56e1ab4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/data/video_to_text_dataset.py @@ -0,0 +1,283 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from math import isclose +from typing import Optional + +from omegaconf import DictConfig + +from nemo.collections.asr.data.audio_to_text_dataset import convert_to_config_list, get_chain_dataset +from nemo.collections.multimodal.speech_cv.data import video_to_text +from nemo.utils import logging + + +def get_video_to_text_bpe_dataset_from_config( + config, + local_rank: int, + global_rank: int, + world_size: int, + tokenizer, + preprocessor_cfg: Optional[DictConfig] = None, +): + """ + Construct Video-To-Text BPE dataset from a config. + Args: + config: BPE dataset config + local_rank: model local rank + global_rank: model global rand + world_size: world size + tokenizer: BPE tokenizer + preprocessor_cfg: preprocessor config, for DALI BPE dataset + + Returns: + constructed dataset or None if dataset config is invalid or nothing to load + """ + + is_concat = config.get('is_concat', False) + if is_concat: + if 'concat_sampling' in config and config['concat_sampling'] is None: + logging.warning(f"Concat dataset requires `concat_sampling` but it was not provided. Config: {config}") + return None + + if not 'concat_probabilities' in config: + logging.warning( + f"Concat dataset requires `concat_probabilities` list but it was not provided. Config: {config}" + ) + return None + else: + if not isclose(sum(config['concat_probabilities']), 1, abs_tol=1e-6): + logging.warning(f"`concat_probabilities` need to sum to 1. Config: {config}") + return None + + shuffle = config['shuffle'] + + # Instantiate tarred dataset loader or normal dataset loader + if config.get('is_tarred', False): + if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or ( + 'manifest_filepath' in config and config['manifest_filepath'] is None + ): + logging.warning( + "Could not load dataset as `manifest_filepath` was None or " + f"`tarred_audio_filepaths` is None. Provided config : {config}" + ) + return None + + shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 + if is_concat: + raise NotImplementedError("get_concat_tarred_dataset method not implemented") + else: + dataset = get_tarred_dataset( + config=config, tokenizer=tokenizer, shuffle_n=shuffle_n, global_rank=global_rank, world_size=world_size + ) + else: + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") + return None + if is_concat: + raise NotImplementedError("get_concat_bpe_dataset method not implemented") + else: + dataset = get_bpe_dataset(config=config, tokenizer=tokenizer) + return dataset + + +def get_video_to_text_char_dataset_from_config( + config, local_rank: int, global_rank: int, world_size: int, preprocessor_cfg: Optional[DictConfig] = None +): + """ + Construct Video-To-Text Char dataset from a config. + Args: + config: dataset config + local_rank: model local rank + global_rank: model global rand + world_size: world size + preprocessor_cfg: preprocessor config, for DALI dataset + + Returns: + constructed dataset or None if dataset config is invalid or nothing to load + """ + + is_concat = config.get('is_concat', False) + if is_concat: + if 'concat_sampling' in config and config['concat_sampling'] is None: + logging.warning(f"Concat dataset requires `concat_sampling` but it was not provided. Config: {config}") + return None + + if not 'concat_probabilities' in config: + logging.warning( + f"Concat dataset requires `concat_probabilities` list but it was not provided. Config: {config}" + ) + return None + else: + if not isclose(sum(config['concat_probabilities']), 1, abs_tol=1e-6): + logging.warning(f"`concat_probabilities` need to sum to 1. Config: {config}") + return None + + shuffle = config['shuffle'] + + # Instantiate tarred dataset loader or normal dataset loader + if config.get('is_tarred', False): + if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or ( + 'manifest_filepath' in config and config['manifest_filepath'] is None + ): + logging.warning( + "Could not load dataset as `manifest_filepath` was None or " + f"`tarred_audio_filepaths` is None. Provided config : {config}" + ) + return None + + shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0 + if is_concat: + raise Exception("get_concat_tarred_dataset method not implemented") + else: + dataset = get_tarred_dataset( + config=config, shuffle_n=shuffle_n, global_rank=global_rank, world_size=world_size, + ) + else: + if 'manifest_filepath' in config and config['manifest_filepath'] is None: + logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}") + return None + if is_concat: + raise Exception("get_concat_char_dataset method not implemented") + else: + dataset = get_char_dataset(config=config) + return dataset + + +def get_bpe_dataset(config: dict, tokenizer: 'TokenizerSpec') -> video_to_text.VideoToBPEDataset: + """ + Instantiates a Byte Pair Encoding / Word Piece Encoding based VideoToBPEDataset. + + Args: + config: Config of the VideoToBPEDataset. + tokenizer: An instance of a TokenizerSpec object. + + Returns: + An instance of VideoToBPEDataset. + """ + dataset = video_to_text.VideoToBPEDataset( + manifest_filepath=config['manifest_filepath'], + tokenizer=tokenizer, + int_values=config.get('int_values', False), + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + max_utts=config.get('max_utts', 0), + trim=config.get('trim_silence', False), + use_start_end_token=config.get('use_start_end_token', True), + return_sample_id=config.get('return_sample_id', False), + channel_selector=config.get('channel_selector', None), + ) + return dataset + + +def get_char_dataset(config: dict) -> video_to_text.VideoToCharDataset: + """ + Instantiates a Character Encoding based VideoToCharDataset. + + Args: + config: Config of the VideoToCharDataset. + + Returns: + An instance of VideoToCharDataset. + """ + if 'labels' not in config: + logging.warning(f"dataset does not have explicitly defined labels") + + dataset = video_to_text.VideoToCharDataset( + manifest_filepath=config['manifest_filepath'], + labels=config.get('labels', None), + int_values=config.get('int_values', False), + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + max_utts=config.get('max_utts', 0), + blank_index=config.get('blank_index', -1), + unk_index=config.get('unk_index', -1), + normalize=config.get('normalize_transcripts', False), + trim=config.get('trim_silence', False), + parser=config.get('parser', 'en'), + return_sample_id=config.get('return_sample_id', False), + channel_selector=config.get('channel_selector', None), + ) + return dataset + + +def get_tarred_dataset( + config: dict, shuffle_n: int, global_rank: int, world_size: int, tokenizer: Optional['TokenizerSpec'] = None, +) -> video_to_text.TarredVideoToBPEDataset: + """ + Instantiates a Word Piece/BPE Encoding based TarredVideoToBPEDataset or a char based TarredVideoToCharDataset. + + Args: + config: Config of the TarredVideoToBPEDataset or TarredVideoToCharDataset. + shuffle_n: How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + tokenizer: An instance of a TokenizerSpec object if BPE dataset is needed. + global_rank: Global rank of this device. + world_size: Global world size in the training method. + Passsing None would return a char-based dataset. + + Returns: + An instance of TarredVideoToBPEDataset or TarredVideoToCharDataset. + """ + tarred_audio_filepaths = config['tarred_audio_filepaths'] + manifest_filepaths = config['manifest_filepath'] + datasets = [] + tarred_audio_filepaths = convert_to_config_list(tarred_audio_filepaths) + manifest_filepaths = convert_to_config_list(manifest_filepaths) + + bucketing_weights = config.get('bucketing_weights', None) # For upsampling buckets + if bucketing_weights: + for idx, weight in enumerate(bucketing_weights): + if not isinstance(weight, int) or weight <= 0: + raise ValueError(f"bucket weights must be positive integers") + + if len(manifest_filepaths) != len(tarred_audio_filepaths): + raise ValueError( + f"manifest_filepaths (length={len(manifest_filepaths)}) and tarred_audio_filepaths (length={len(tarred_audio_filepaths)}) need to have the same number of buckets." + ) + + if 'labels' not in config: + logging.warning(f"dataset does not have explicitly defined labels") + + if 'max_utts' in config: + raise ValueError('"max_utts" parameter is not supported for tarred datasets') + + for dataset_idx, (tarred_audio_filepath, manifest_filepath) in enumerate( + zip(tarred_audio_filepaths, manifest_filepaths) + ): + if len(tarred_audio_filepath) == 1: + tarred_audio_filepath = tarred_audio_filepath[0] + if tokenizer is None: + raise Exception("video_to_text.TarredVideoToCharDataset class not Implemented") + else: + dataset = video_to_text.TarredVideoToBPEDataset( + audio_tar_filepaths=tarred_audio_filepath, + manifest_filepath=manifest_filepath, + tokenizer=tokenizer, + int_values=config.get('int_values', False), + shuffle_n=shuffle_n, + max_duration=config.get('max_duration', None), + min_duration=config.get('min_duration', None), + trim=config.get('trim_silence', False), + use_start_end_token=config.get('use_start_end_token', True), + shard_strategy=config.get('tarred_shard_strategy', 'scatter'), + global_rank=global_rank, + world_size=world_size, + return_sample_id=config.get('return_sample_id', False), + ) + if bucketing_weights: + [datasets.append(dataset) for _ in range(bucketing_weights[dataset_idx])] + else: + datasets.append(dataset) + + return get_chain_dataset(datasets=datasets, ds_config=config) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/__init__.py new file mode 100644 index 0000000..c34b4c1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/__init__.py @@ -0,0 +1,27 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# CTC +from nemo.collections.multimodal.speech_cv.models.visual_ctc_bpe_models import VisualEncDecCTCModelBPE +from nemo.collections.multimodal.speech_cv.models.visual_ctc_models import VisualEncDecCTCModel +from nemo.collections.multimodal.speech_cv.models.visual_hybrid_rnnt_ctc_bpe_models import ( + VisualEncDecHybridRNNTCTCBPEModel, +) + +# Hybrid CTC/RNN-T +from nemo.collections.multimodal.speech_cv.models.visual_hybrid_rnnt_ctc_models import VisualEncDecHybridRNNTCTCModel +from nemo.collections.multimodal.speech_cv.models.visual_rnnt_bpe_models import VisualEncDecRNNTBPEModel + +# RNN-T +from nemo.collections.multimodal.speech_cv.models.visual_rnnt_models import VisualEncDecRNNTModel diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_ctc_bpe_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_ctc_bpe_models.py new file mode 100644 index 0000000..c675140 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_ctc_bpe_models.py @@ -0,0 +1,315 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import os +from typing import Dict, List, Optional, Union + +import torch +from omegaconf import DictConfig, ListConfig, OmegaConf, open_dict + +from nemo.collections.asr.losses.ctc import CTCLoss +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.parts.mixins import ASRBPEMixin +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCBPEDecoding, CTCBPEDecodingConfig +from nemo.collections.multimodal.speech_cv.data import video_to_text_dataset +from nemo.collections.multimodal.speech_cv.models.visual_ctc_models import VisualEncDecCTCModel +from nemo.core.classes.common import PretrainedModelInfo +from nemo.utils import logging, model_utils + +__all__ = ['VisualEncDecCTCModelBPE'] + + +class VisualEncDecCTCModelBPE(VisualEncDecCTCModel, ASRBPEMixin): + """Encoder decoder CTC-based models with Byte Pair Encoding.""" + + def __init__(self, cfg: DictConfig, trainer=None): + # Convert to Hydra 1.0 compatible DictConfig + cfg = model_utils.convert_model_config_to_dict_config(cfg) + cfg = model_utils.maybe_update_config_version(cfg) + + if 'tokenizer' not in cfg: + raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !") + + # Setup the tokenizer + self._setup_tokenizer(cfg.tokenizer) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + # Set the new vocabulary + with open_dict(cfg): + # sidestepping the potential overlapping tokens issue in aggregate tokenizers + if self.tokenizer_type == "agg": + cfg.decoder.vocabulary = ListConfig(vocabulary) + else: + cfg.decoder.vocabulary = ListConfig(list(vocabulary.keys())) + + # Override number of classes if placeholder provided + num_classes = cfg.decoder["num_classes"] + + if num_classes < 1: + logging.info( + "\nReplacing placeholder number of classes ({}) with actual number of classes - {}".format( + num_classes, len(vocabulary) + ) + ) + cfg.decoder["num_classes"] = len(vocabulary) + + super().__init__(cfg=cfg, trainer=trainer) + + # Setup decoding objects + decoding_cfg = self.cfg.get('decoding', None) + + # In case decoding config not found, use default config + if decoding_cfg is None: + decoding_cfg = OmegaConf.structured(CTCBPEDecodingConfig) + with open_dict(self.cfg): + self.cfg.decoding = decoding_cfg + + self.decoding = CTCBPEDecoding(self.cfg.decoding, tokenizer=self.tokenizer) + + # Setup metric with decoding strategy + self._wer = WER( + decoding=self.decoding, + use_cer=self._cfg.get('use_cer', False), + dist_sync_on_step=True, + log_prediction=self._cfg.get("log_prediction", False), + ) + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + dataset = video_to_text_dataset.get_video_to_text_bpe_dataset_from_config( + config=config, + local_rank=self.local_rank, + global_rank=self.global_rank, + world_size=self.world_size, + tokenizer=self.tokenizer, + preprocessor_cfg=self.cfg.get("preprocessor", None), + ) + + if dataset is None: + return None + + shuffle = config['shuffle'] + if config.get('is_tarred', False): + shuffle = False + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + else: + collate_fn = dataset.datasets[0].collate_fn + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + prefetch_factor=config.get('prefetch_factor', 2), + ) + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided video file. + + Args: + config: A python dictionary which contains the following keys: + paths2video_files: (a list) of paths to video files. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the video manifest is temporarily + stored. + num_workers: (int) number of workers. Depends of the batch_size and machine. \ + 0 - only the main process will load batches, 1 - one worker (not main process) + + Returns: + A pytorch DataLoader for the given video file(s). + """ + + if 'manifest_filepath' in config: + manifest_filepath = config['manifest_filepath'] + batch_size = config['batch_size'] + else: + manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json') + batch_size = min(config['batch_size'], len(config['paths2video_files'])) + + dl_config = { + 'manifest_filepath': manifest_filepath, + 'batch_size': batch_size, + 'shuffle': False, + 'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)), + 'pin_memory': True, + 'channel_selector': config.get('channel_selector', None), + 'use_start_end_token': self.cfg.validation_ds.get('use_start_end_token', False), + } + + if config.get("augmentor"): + dl_config['augmentor'] = config.get("augmentor") + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer + + def change_vocabulary( + self, + new_tokenizer_dir: Union[str, DictConfig], + new_tokenizer_type: str, + decoding_cfg: Optional[DictConfig] = None, + ): + """ + Changes vocabulary of the tokenizer used during CTC decoding process. + Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_tokenizer_dir: Directory path to tokenizer or a config for a new tokenizer (if the tokenizer type is `agg`) + new_tokenizer_type: Either `agg`, `bpe` or `wpe`. `bpe` is used for SentencePiece tokenizers, + whereas `wpe` is used for `BertTokenizer`. + new_tokenizer_cfg: A config for the new tokenizer. if provided, pre-empts the dir and type + + Returns: None + + """ + if isinstance(new_tokenizer_dir, DictConfig): + if new_tokenizer_type == 'agg': + new_tokenizer_cfg = new_tokenizer_dir + else: + raise ValueError( + f'New tokenizer dir should be a string unless the tokenizer is `agg`, but this tokenizer type is: {new_tokenizer_type}' + ) + else: + new_tokenizer_cfg = None + + if new_tokenizer_cfg is not None: + tokenizer_cfg = new_tokenizer_cfg + else: + if not os.path.isdir(new_tokenizer_dir): + raise NotADirectoryError( + f'New tokenizer dir must be non-empty path to a directory. But I got: {new_tokenizer_dir}' + f"New tokenizer dir must be non-empty path to a directory. But I got: {new_tokenizer_dir}" + ) + + if new_tokenizer_type.lower() not in ('bpe', 'wpe'): + raise ValueError(f'New tokenizer type must be either `bpe` or `wpe`') + + tokenizer_cfg = OmegaConf.create({'dir': new_tokenizer_dir, 'type': new_tokenizer_type}) + + # Setup the tokenizer + self._setup_tokenizer(tokenizer_cfg) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + # Set the new vocabulary + decoder_config = copy.deepcopy(self.decoder.to_config_dict()) + # sidestepping the potential overlapping tokens issue in aggregate tokenizers + if self.tokenizer_type == "agg": + decoder_config.vocabulary = ListConfig(vocabulary) + else: + decoder_config.vocabulary = ListConfig(list(vocabulary.keys())) + + decoder_num_classes = decoder_config['num_classes'] + + # Override number of classes if placeholder provided + logging.info( + "\nReplacing old number of classes ({}) with new number of classes - {}".format( + decoder_num_classes, len(vocabulary) + ) + ) + + decoder_config['num_classes'] = len(vocabulary) + + del self.decoder + self.decoder = VisualEncDecCTCModelBPE.from_config_dict(decoder_config) + del self.loss + self.loss = CTCLoss( + num_classes=self.decoder.num_classes_with_blank - 1, + zero_infinity=True, + reduction=self._cfg.get("ctc_reduction", "mean_batch"), + ) + + if decoding_cfg is None: + # Assume same decoding config as before + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(CTCBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = CTCBPEDecoding(decoding_cfg=decoding_cfg, tokenizer=self.tokenizer) + + self._wer = WER( + decoding=self.decoding, + use_cer=self._cfg.get('use_cer', False), + log_prediction=self._cfg.get("log_prediction", False), + dist_sync_on_step=True, + ) + + # Update config + with open_dict(self.cfg.decoder): + self._cfg.decoder = decoder_config + + with open_dict(self.cfg.decoding): + self._cfg.decoding = decoding_cfg + + logging.info(f"Changed tokenizer to {self.decoder.vocabulary} vocabulary.") + + def change_decoding_strategy(self, decoding_cfg: DictConfig): + """ + Changes decoding strategy used during CTC decoding process. + + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + """ + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(CTCBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = CTCBPEDecoding(decoding_cfg=decoding_cfg, tokenizer=self.tokenizer,) + + self._wer = WER( + decoding=self.decoding, + use_cer=self._wer.use_cer, + log_prediction=self._wer.log_prediction, + dist_sync_on_step=True, + ) + + # Update config + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}") + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + return results diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_ctc_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_ctc_models.py new file mode 100644 index 0000000..a8226c3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_ctc_models.py @@ -0,0 +1,701 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import json +import os +import tempfile +from math import ceil +from typing import Dict, List, Optional, Union + +import torch +from omegaconf import DictConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer +from tqdm.auto import tqdm + +from nemo.collections.asr.data import audio_to_text_dataset +from nemo.collections.asr.losses.ctc import CTCLoss +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel +from nemo.collections.asr.parts.mixins import ASRModuleMixin, InterCTCMixin +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecoding, CTCDecodingConfig +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.multimodal.speech_cv.data import video_to_text_dataset +from nemo.core.classes.common import PretrainedModelInfo, typecheck +from nemo.core.classes.mixins import AccessMixin +from nemo.core.neural_types import LabelsType, LengthsType, LogprobsType, NeuralType, VideoSignal +from nemo.utils import logging + +__all__ = ['VisualEncDecCTCModel'] + + +class VisualEncDecCTCModel(ASRModel, ExportableEncDecModel, ASRModuleMixin, InterCTCMixin): + """Base class for encoder decoder CTC-based models.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + + # Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable + # Global_rank and local_rank is set by LightningModule in Lightning 1.2.0 + self.world_size = 1 + if trainer is not None: + self.world_size = trainer.world_size + + # Init + super().__init__(cfg=cfg, trainer=trainer) + + # Preprocessor, video transforms + self.video_preprocessor = VisualEncDecCTCModel.from_config_dict(self._cfg.video_preprocessor) + + # Augmentation, video augmentations + self.video_augmentation = VisualEncDecCTCModel.from_config_dict(self._cfg.video_augment) + + # Front-end Network, learned module that transform videos to temporal sequence + self.video_front_end = VisualEncDecCTCModel.from_config_dict(self._cfg.video_front_end) + + # Encoder Network + self.encoder = VisualEncDecCTCModel.from_config_dict(self._cfg.encoder) + + with open_dict(self._cfg): + if "feat_in" not in self._cfg.decoder or ( + not self._cfg.decoder.feat_in and hasattr(self.encoder, '_feat_out') + ): + self._cfg.decoder.feat_in = self.encoder._feat_out + if "feat_in" not in self._cfg.decoder or not self._cfg.decoder.feat_in: + raise ValueError("param feat_in of the decoder's config is not set!") + + if self.cfg.decoder.num_classes < 1 and self.cfg.decoder.vocabulary is not None: + logging.info( + "\nReplacing placeholder number of classes ({}) with actual number of classes - {}".format( + self.cfg.decoder.num_classes, len(self.cfg.decoder.vocabulary) + ) + ) + cfg.decoder["num_classes"] = len(self.cfg.decoder.vocabulary) + + # Decoder + self.decoder = VisualEncDecCTCModel.from_config_dict(self._cfg.decoder) + + # CTC Loss + self.loss = CTCLoss( + num_classes=self.decoder.num_classes_with_blank - 1, + zero_infinity=True, + reduction=self._cfg.get("ctc_reduction", "mean_batch"), + ) + + # Setup decoding objects + decoding_cfg = self.cfg.get('decoding', None) + + # In case decoding config not found, use default config + if decoding_cfg is None: + decoding_cfg = OmegaConf.structured(CTCDecodingConfig) + with open_dict(self.cfg): + self.cfg.decoding = decoding_cfg + + # Decoding + self.decoding = CTCDecoding(self.cfg.decoding, vocabulary=self.decoder.vocabulary) + + # Setup metric with decoding strategy + self.wer = WER( + decoding=self.decoding, + use_cer=self._cfg.get('use_cer', False), + dist_sync_on_step=True, + log_prediction=self._cfg.get("log_prediction", False), + ) + + # Setup optional Optimization flags + self.setup_optimization_flags() + + # setting up interCTC loss (from InterCTCMixin) + self.setup_interctc(decoder_name='decoder', loss_name='loss', wer_name='_wer') + + # Adapter modules setup (from ASRAdapterModelMixin) + self.setup_adapters() + + @torch.no_grad() + def transcribe( + self, + paths2video_files: List[str], + batch_size: int = 4, + logprobs: bool = False, + return_hypotheses: bool = False, + num_workers: int = 0, + channel_selector: Optional[ChannelSelectorType] = None, + augmentor: DictConfig = None, + ) -> List[str]: + """ + If modify this function, please remember update transcribe_partial_audio() in + nemo/collections/asr/parts/utils/trancribe_utils.py + + Uses greedy decoding to transcribe video files. Use this method for debugging and prototyping. + + Args: + paths2video_files: (a list) of paths to video files. + batch_size: (int) batch size to use during inference. + Bigger will result in better throughput performance but would use more memory. + logprobs: (bool) pass True to get log probabilities instead of transcripts. + return_hypotheses: (bool) Either return hypotheses or text + With hypotheses can do some postprocessing like getting timestamp or rescoring + num_workers: (int) number of workers for DataLoader + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. + augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied. + Returns: + A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2video_files + """ + if paths2video_files is None or len(paths2video_files) == 0: + return {} + + if return_hypotheses and logprobs: + raise ValueError( + "Either `return_hypotheses` or `logprobs` can be True at any given time." + "Returned hypotheses will contain the logprobs." + ) + + if num_workers is None: + num_workers = min(batch_size, os.cpu_count() - 1) + + # We will store transcriptions here + hypotheses = [] + all_hypotheses = [] + + # Model's mode and device + mode = self.training + device = next(self.parameters()).device + + try: + # Switch model to evaluation mode + self.eval() + # Freeze the visual front-end, encoder and decoder modules + self.video_front_end.freeze() + self.encoder.freeze() + self.decoder.freeze() + logging_level = logging.get_verbosity() + logging.set_verbosity(logging.WARNING) + # Work in tmp directory - will store manifest file there + with tempfile.TemporaryDirectory() as tmpdir: + with open(os.path.join(tmpdir, 'manifest.json'), 'w', encoding='utf-8') as fp: + for video_file in paths2video_files: + entry = {'video_filepath': video_file, 'duration': 100000, 'text': ''} + fp.write(json.dumps(entry) + '\n') + + config = { + 'paths2video_files': paths2video_files, + 'batch_size': batch_size, + 'temp_dir': tmpdir, + 'num_workers': num_workers, + 'channel_selector': channel_selector, + } + + if augmentor: + config['augmentor'] = augmentor + + temporary_datalayer = self._setup_transcribe_dataloader(config) + for test_batch in tqdm(temporary_datalayer, desc="Transcribing"): + logits, logits_len, greedy_predictions = self.forward( + input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device) + ) + if logprobs: + # dump log probs per file + for idx in range(logits.shape[0]): + lg = logits[idx][: logits_len[idx]] + hypotheses.append(lg.cpu().numpy()) + else: + current_hypotheses, all_hyp = self.decoding.ctc_decoder_predictions_tensor( + logits, decoder_lengths=logits_len, return_hypotheses=return_hypotheses, + ) + + if return_hypotheses: + # dump log probs per file + for idx in range(logits.shape[0]): + current_hypotheses[idx].y_sequence = logits[idx][: logits_len[idx]] + if current_hypotheses[idx].alignments is None: + current_hypotheses[idx].alignments = current_hypotheses[idx].y_sequence + + if all_hyp is None: + hypotheses += current_hypotheses + else: + hypotheses += all_hyp + + del greedy_predictions + del logits + del test_batch + finally: + # set mode back to its original value + self.train(mode=mode) + if mode is True: + self.video_front_end.unfreeze() + self.encoder.unfreeze() + self.decoder.unfreeze() + logging.set_verbosity(logging_level) + + return hypotheses + + def change_vocabulary(self, new_vocabulary: List[str], decoding_cfg: Optional[DictConfig] = None): + """ + Changes vocabulary used during CTC decoding process. Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on a data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + If new_vocabulary == self.decoder.vocabulary then nothing will be changed. + + Args: + + new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \ + this is target alphabet. + + Returns: None + + """ + if self.decoder.vocabulary == new_vocabulary: + logging.warning(f"Old {self.decoder.vocabulary} and new {new_vocabulary} match. Not changing anything.") + else: + if new_vocabulary is None or len(new_vocabulary) == 0: + raise ValueError(f'New vocabulary must be non-empty list of chars. But I got: {new_vocabulary}') + decoder_config = self.decoder.to_config_dict() + new_decoder_config = copy.deepcopy(decoder_config) + new_decoder_config['vocabulary'] = new_vocabulary + new_decoder_config['num_classes'] = len(new_vocabulary) + + del self.decoder + self.decoder = VisualEncDecCTCModel.from_config_dict(new_decoder_config) + del self.loss + self.loss = CTCLoss( + num_classes=self.decoder.num_classes_with_blank - 1, + zero_infinity=True, + reduction=self._cfg.get("ctc_reduction", "mean_batch"), + ) + + if decoding_cfg is None: + # Assume same decoding config as before + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(CTCDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = CTCDecoding(decoding_cfg=decoding_cfg, vocabulary=self.decoder.vocabulary) + + self.wer = WER( + decoding=self.decoding, + use_cer=self._cfg.get('use_cer', False), + dist_sync_on_step=True, + log_prediction=self._cfg.get("log_prediction", False), + ) + + # Update config + with open_dict(self.cfg.decoder): + self._cfg.decoder = new_decoder_config + + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + ds_keys = ['train_ds', 'validation_ds', 'test_ds'] + for key in ds_keys: + if key in self.cfg: + with open_dict(self.cfg[key]): + self.cfg[key]['labels'] = OmegaConf.create(new_vocabulary) + + logging.info(f"Changed decoder to output to {self.decoder.vocabulary} vocabulary.") + + def change_decoding_strategy(self, decoding_cfg: DictConfig): + """ + Changes decoding strategy used during CTC decoding process. + + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + """ + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(CTCDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = CTCDecoding(decoding_cfg=decoding_cfg, vocabulary=self.decoder.vocabulary) + + self.wer = WER( + decoding=self.decoding, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Update config + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}") + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + # Automatically inject args from model config to dataloader config + audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='sample_rate') + audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='labels') + dataset = video_to_text_dataset.get_video_to_text_char_dataset_from_config( + config=config, + local_rank=self.local_rank, + global_rank=self.global_rank, + world_size=self.world_size, + preprocessor_cfg=self._cfg.get("preprocessor", None), + ) + + if dataset is None: + return None + + shuffle = config['shuffle'] + if config.get('is_tarred', False): + shuffle = False + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + else: + collate_fn = dataset.datasets[0].collate_fn + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the training data loader via a Dict-like object. + + Args: + train_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.multimodal.speech_cv.data.video_to_text.VideoToCharDataset` + - :class:`~nemo.collections.asr.data.video_to_text.VideoToBPEDataset` + - :class:`~nemo.collections.asr.data.video_to_text.TarredVideoToBPEDataset` + """ + if 'shuffle' not in train_data_config: + train_data_config['shuffle'] = True + + # preserve config + self._update_dataset_config(dataset_name='train', config=train_data_config) + + self._train_dl = self._setup_dataloader_from_config(config=train_data_config) + + # Need to set this because if using an IterableDataset, the length of the dataloader is the total number + # of samples rather than the number of batches, and this messes up the tqdm progress bar. + # So we set the number of steps manually (to the correct number) to fix this. + if 'is_tarred' in train_data_config and train_data_config['is_tarred']: + # We also need to check if limit_train_batches is already set. + # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches, + # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0). + if self._trainer is not None and isinstance(self._trainer.limit_train_batches, float): + self._trainer.limit_train_batches = int( + self._trainer.limit_train_batches + * ceil((len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size']) + ) + elif self._trainer is None: + logging.warning( + "Model Trainer was not set before constructing the dataset, incorrect number of " + "training batches will be used. Please set the trainer and rebuild the dataset." + ) + + def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the validation data loader via a Dict-like object. + + Args: + val_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.multimodal.speech_cv.data.video_to_text.VideoToCharDataset` + - :class:`~nemo.collections.asr.data.video_to_text.VideoToBPEDataset` + - :class:`~nemo.collections.asr.data.video_to_text.TarredVideoToBPEDataset` + """ + if 'shuffle' not in val_data_config: + val_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='validation', config=val_data_config) + + self._validation_dl = self._setup_dataloader_from_config(config=val_data_config) + + def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the test data loader via a Dict-like object. + + Args: + test_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.multimodal.speech_cv.data.video_to_text.VideoToCharDataset` + - :class:`~nemo.collections.asr.data.video_to_text.VideoToBPEDataset` + - :class:`~nemo.collections.asr.data.video_to_text.TarredVideoToBPEDataset` + """ + if 'shuffle' not in test_data_config: + test_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='test', config=test_data_config) + + self._test_dl = self._setup_dataloader_from_config(config=test_data_config) + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + return { + "input_video_signal": NeuralType(('B', 'C', 'T', 'H', 'W'), VideoSignal(), optional=True), + "input_video_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + "sample_id": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return { + "outputs": NeuralType(('B', 'T', 'D'), LogprobsType()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + "greedy_predictions": NeuralType(('B', 'T'), LabelsType()), + } + + @typecheck() + def forward(self, input_video_signal=None, input_video_signal_length=None): + """ + Forward pass of the model. + + Args: + input_video_signal: Tensor that represents a batch of video signals, + of shape [B, T, H, W, C]. T here represents timesteps, H height, W width and C channels + input_video_signal_length: Vector of length B, that contains the individual lengths of the video + sequences. + + Returns: + A tuple of 3 elements - + 1) The log probabilities tensor of shape [B, T, D]. + 2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. + 3) The greedy token predictions of the model of shape [B, T] (via argmax) + """ + + # Preprocessing + processed_video_signal, processed_video_signal_length = self.video_preprocessor( + input_signal=input_video_signal, length=input_video_signal_length + ) + + # Augmentation + processed_video_signal = self.video_augmentation( + input_signal=processed_video_signal, length=processed_video_signal_length + ) + + # Front-end Networks + processed_video_signal, processed_video_signal_length = self.video_front_end( + input_signal=processed_video_signal, length=processed_video_signal_length + ) + + # Back-end Networks + encoded, encoded_len = self.encoder(audio_signal=processed_video_signal, length=processed_video_signal_length) + + log_probs = self.decoder(encoder_output=encoded) + greedy_predictions = log_probs.argmax(dim=-1, keepdim=False) + + return ( + log_probs, + encoded_len, + greedy_predictions, + ) + + # PTL-specific methods + def training_step(self, batch, batch_nb): + # Reset access registry + if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): + AccessMixin.reset_registry(self) + + if self.is_interctc_enabled(): + AccessMixin.set_access_enabled(access_enabled=True, guid=self.model_guid) + + video_signal, video_signal_len, transcript, transcript_len = batch + log_probs, encoded_len, predictions = self.forward( + input_video_signal=video_signal, input_video_signal_length=video_signal_len + ) + + if hasattr(self, '_trainer') and self._trainer is not None: + log_every_n_steps = self._trainer.log_every_n_steps + else: + log_every_n_steps = 1 + + loss_value = self.loss( + log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len + ) + + # Add auxiliary losses, if registered + loss_value = self.add_auxiliary_losses(loss_value) + # only computing WER when requested in the logs (same as done for final-layer WER below) + loss_value, tensorboard_logs = self.add_interctc_losses( + loss_value, transcript, transcript_len, compute_wer=((batch_nb + 1) % log_every_n_steps == 0) + ) + + # Reset access registry + if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): + AccessMixin.reset_registry(self) + + tensorboard_logs.update( + { + 'train_loss': loss_value, + 'learning_rate': self._optimizer.param_groups[0]['lr'], + 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), + } + ) + + if (batch_nb + 1) % log_every_n_steps == 0: + self.wer.update( + predictions=log_probs, + targets=transcript, + target_lengths=transcript_len, + predictions_lengths=encoded_len, + ) + wer, _, _ = self.wer.compute() + self.wer.reset() + tensorboard_logs.update({'training_batch_wer': wer}) + + return {'loss': loss_value, 'log': tensorboard_logs} + + def predict_step(self, batch, batch_idx, dataloader_idx=0): + video_signal, video_signal_len, transcript, transcript_len, sample_id = batch + log_probs, encoded_len, predictions = self.forward( + input_video_signal=video_signal, input_video_signal_length=video_signal_len + ) + + transcribed_texts, _ = self.wer.decoding.ctc_decoder_predictions_tensor( + decoder_outputs=log_probs, decoder_lengths=encoded_len, return_hypotheses=False, + ) + + sample_id = sample_id.cpu().detach().numpy() + return list(zip(sample_id, transcribed_texts)) + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + if self.is_interctc_enabled(): + AccessMixin.set_access_enabled(access_enabled=True, guid=self.model_guid) + + video_signal, video_signal_len, transcript, transcript_len = batch + log_probs, encoded_len, predictions = self.forward( + input_video_signal=video_signal, input_video_signal_length=video_signal_len + ) + + loss_value = self.loss( + log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len + ) + loss_value, metrics = self.add_interctc_losses( + loss_value, transcript, transcript_len, compute_wer=True, log_wer_num_denom=True, log_prefix="val_", + ) + + self.wer.update( + predictions=log_probs, targets=transcript, target_lengths=transcript_len, predictions_lengths=encoded_len + ) + wer, wer_num, wer_denom = self.wer.compute() + self.wer.reset() + metrics.update({'val_loss': loss_value, 'val_wer_num': wer_num, 'val_wer_denom': wer_denom, 'val_wer': wer}) + + self.log('global_step', torch.tensor(self.trainer.global_step, dtype=torch.float32)) + + # Reset access registry + if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): + AccessMixin.reset_registry(self) + + return metrics + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + metrics = super().multi_validation_epoch_end(outputs, dataloader_idx) + self.finalize_interctc_metrics(metrics, outputs, prefix="val_") + return metrics + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + metrics = super().multi_test_epoch_end(outputs, dataloader_idx) + self.finalize_interctc_metrics(metrics, outputs, prefix="test_") + return metrics + + def test_step(self, batch, batch_idx, dataloader_idx=0): + logs = self.validation_step(batch, batch_idx, dataloader_idx=dataloader_idx) + test_logs = {name.replace("val_", "test_"): value for name, value in logs.items()} + if type(self.trainer.test_dataloaders) == list and len(self.trainer.test_dataloaders) > 1: + self.test_step_outputs[dataloader_idx].append(test_logs) + else: + self.test_step_outputs.append(test_logs) + return test_logs + + def test_dataloader(self): + if self._test_dl is not None: + return self._test_dl + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided video file. + + Args: + config: A python dictionary which contains the following keys: + paths2video_files: (a list) of paths to video files. The files should be relatively short fragments. \ + Recommended length per file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the video manifest is temporarily + stored. + num_workers: (int) number of workers. Depends of the batch_size and machine. \ + 0 - only the main process will load batches, 1 - one worker (not main process) + + Returns: + A pytorch DataLoader for the given video file(s). + """ + if 'manifest_filepath' in config: + manifest_filepath = config['manifest_filepath'] + batch_size = config['batch_size'] + else: + manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json') + batch_size = min(config['batch_size'], len(config['paths2video_files'])) + + dl_config = { + 'manifest_filepath': manifest_filepath, + 'labels': self.decoder.vocabulary, + 'batch_size': batch_size, + 'trim_silence': False, + 'shuffle': False, + 'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)), + 'pin_memory': True, + 'channel_selector': config.get('channel_selector', None), + } + if config.get("augmentor"): + dl_config['augmentor'] = config.get("augmentor") + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + return results + + @property + def wer(self): + return self._wer + + @wer.setter + def wer(self, wer): + self._wer = wer diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_hybrid_rnnt_ctc_bpe_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_hybrid_rnnt_ctc_bpe_models.py new file mode 100644 index 0000000..106fbc4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_hybrid_rnnt_ctc_bpe_models.py @@ -0,0 +1,456 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import os +from typing import Dict, Optional, Union + +import torch +from omegaconf import DictConfig, ListConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer + +from nemo.collections.asr.losses.ctc import CTCLoss +from nemo.collections.asr.losses.rnnt import RNNTLoss +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.parts.mixins import ASRBPEMixin +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCBPEDecoding, CTCBPEDecodingConfig +from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTBPEDecoding, RNNTBPEDecodingConfig +from nemo.collections.multimodal.speech_cv.data import video_to_text_dataset +from nemo.collections.multimodal.speech_cv.models.visual_hybrid_rnnt_ctc_models import VisualEncDecHybridRNNTCTCModel +from nemo.core.classes.common import PretrainedModelInfo +from nemo.utils import logging, model_utils + + +class VisualEncDecHybridRNNTCTCBPEModel(VisualEncDecHybridRNNTCTCModel, ASRBPEMixin): + """Base class for encoder decoder RNNT-based models with auxiliary CTC decoder/loss and subword tokenization.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + # Convert to Hydra 1.0 compatible DictConfig + cfg = model_utils.convert_model_config_to_dict_config(cfg) + cfg = model_utils.maybe_update_config_version(cfg) + + # Tokenizer is necessary for this model + if 'tokenizer' not in cfg: + raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !") + + if not isinstance(cfg, DictConfig): + cfg = OmegaConf.create(cfg) + + # Setup the tokenizer + self._setup_tokenizer(cfg.tokenizer) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + # Set the new vocabulary + with open_dict(cfg): + cfg.labels = ListConfig(list(vocabulary)) + + with open_dict(cfg.decoder): + cfg.decoder.vocab_size = len(vocabulary) + + with open_dict(cfg.joint): + cfg.joint.num_classes = len(vocabulary) + cfg.joint.vocabulary = ListConfig(list(vocabulary)) + cfg.joint.jointnet.encoder_hidden = cfg.model_defaults.enc_hidden + cfg.joint.jointnet.pred_hidden = cfg.model_defaults.pred_hidden + + # setup auxiliary CTC decoder + if 'aux_ctc' not in cfg: + raise ValueError( + "The config need to have a section for the CTC decoder named as aux_ctc for Hybrid models." + ) + + with open_dict(cfg): + if self.tokenizer_type == "agg": + cfg.aux_ctc.decoder.vocabulary = ListConfig(vocabulary) + else: + cfg.aux_ctc.decoder.vocabulary = ListConfig(list(vocabulary.keys())) + + if cfg.aux_ctc.decoder["num_classes"] < 1: + logging.info( + "\nReplacing placholder number of classes ({}) with actual number of classes - {}".format( + cfg.aux_ctc.decoder["num_classes"], len(vocabulary) + ) + ) + cfg.aux_ctc.decoder["num_classes"] = len(vocabulary) + + super().__init__(cfg=cfg, trainer=trainer) + + # Setup decoding object + self.decoding = RNNTBPEDecoding( + decoding_cfg=self.cfg.decoding, decoder=self.decoder, joint=self.joint, tokenizer=self.tokenizer, + ) + + # Setup wer object + self.wer = WER( + decoding=self.decoding, + batch_dim_index=0, + use_cer=self.cfg.get('use_cer', False), + log_prediction=self.cfg.get('log_prediction', True), + dist_sync_on_step=True, + ) + + # Setup fused Joint step if flag is set + if self.joint.fuse_loss_wer: + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Setup CTC decoding + ctc_decoding_cfg = self.cfg.aux_ctc.get('decoding', None) + if ctc_decoding_cfg is None: + ctc_decoding_cfg = OmegaConf.structured(CTCBPEDecodingConfig) + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoding = ctc_decoding_cfg + self.ctc_decoding = CTCBPEDecoding(self.cfg.aux_ctc.decoding, tokenizer=self.tokenizer) + + # Setup CTC WER + self.ctc_wer = WER( + decoding=self.ctc_decoding, + use_cer=self.cfg.aux_ctc.get('use_cer', False), + dist_sync_on_step=True, + log_prediction=self.cfg.get("log_prediction", False), + ) + + # setting the RNNT decoder as the default one + self.use_rnnt_decoder = True + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + dataset = video_to_text_dataset.get_video_to_text_bpe_dataset_from_config( + config=config, + local_rank=self.local_rank, + global_rank=self.global_rank, + world_size=self.world_size, + tokenizer=self.tokenizer, + preprocessor_cfg=self.cfg.get("preprocessor", None), + ) + + if dataset is None: + return None + + shuffle = config['shuffle'] + if config.get('is_tarred', False): + shuffle = False + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + else: + collate_fn = dataset.datasets[0].collate_fn + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided video file. + + Args: + config: A python dictionary which contains the following keys: + paths2video_files: (a list) of paths to video files. The files should be relatively short fragments. \ + Recommended length per file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the video manifest is temporarily + stored. + num_workers: (int) number of workers. Depends of the batch_size and machine. \ + 0 - only the main process will load batches, 1 - one worker (not main process) + + Returns: + A pytorch DataLoader for the given video file(s). + """ + + if 'manifest_filepath' in config: + manifest_filepath = config['manifest_filepath'] + batch_size = config['batch_size'] + else: + manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json') + batch_size = min(config['batch_size'], len(config['paths2video_files'])) + + dl_config = { + 'manifest_filepath': manifest_filepath, + 'batch_size': batch_size, + 'shuffle': False, + 'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)), + 'pin_memory': True, + 'channel_selector': config.get('channel_selector', None), + 'use_start_end_token': self.cfg.validation_ds.get('use_start_end_token', False), + } + + if config.get("augmentor"): + dl_config['augmentor'] = config.get("augmentor") + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer + + def change_vocabulary( + self, + new_tokenizer_dir: Union[str, DictConfig], + new_tokenizer_type: str, + decoding_cfg: Optional[DictConfig] = None, + ctc_decoding_cfg: Optional[DictConfig] = None, + ): + """ + Changes vocabulary used during RNNT decoding process. Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_tokenizer_dir: Directory path to tokenizer or a config for a new tokenizer (if the tokenizer type is `agg`) + new_tokenizer_type: Type of tokenizer. Can be either `agg`, `bpe` or `wpe`. + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + ctc_decoding_cfg: A config for auxiliary CTC decoding, which is optional and can be used to change the decoding type. + + Returns: None + + """ + if isinstance(new_tokenizer_dir, DictConfig): + if new_tokenizer_type == 'agg': + new_tokenizer_cfg = new_tokenizer_dir + else: + raise ValueError( + f'New tokenizer dir should be a string unless the tokenizer is `agg`, but this tokenizer type is: {new_tokenizer_type}' + ) + else: + new_tokenizer_cfg = None + + if new_tokenizer_cfg is not None: + tokenizer_cfg = new_tokenizer_cfg + else: + if not os.path.isdir(new_tokenizer_dir): + raise NotADirectoryError( + f'New tokenizer dir must be non-empty path to a directory. But I got: {new_tokenizer_dir}' + ) + + if new_tokenizer_type.lower() not in ('bpe', 'wpe'): + raise ValueError(f'New tokenizer type must be either `bpe` or `wpe`') + + tokenizer_cfg = OmegaConf.create({'dir': new_tokenizer_dir, 'type': new_tokenizer_type}) + + # Setup the tokenizer + self._setup_tokenizer(tokenizer_cfg) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + joint_config = self.joint.to_config_dict() + new_joint_config = copy.deepcopy(joint_config) + if self.tokenizer_type == "agg": + new_joint_config["vocabulary"] = ListConfig(vocabulary) + else: + new_joint_config["vocabulary"] = ListConfig(list(vocabulary.keys())) + + new_joint_config['num_classes'] = len(vocabulary) + del self.joint + self.joint = VisualEncDecHybridRNNTCTCBPEModel.from_config_dict(new_joint_config) + + decoder_config = self.decoder.to_config_dict() + new_decoder_config = copy.deepcopy(decoder_config) + new_decoder_config.vocab_size = len(vocabulary) + del self.decoder + self.decoder = VisualEncDecHybridRNNTCTCBPEModel.from_config_dict(new_decoder_config) + + del self.loss + self.loss = RNNTLoss(num_classes=self.joint.num_classes_with_blank - 1) + + if decoding_cfg is None: + # Assume same decoding config as before + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(RNNTBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = RNNTBPEDecoding( + decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, tokenizer=self.tokenizer, + ) + + self.wer = WER( + decoding=self.decoding, + batch_dim_index=self.wer.batch_dim_index, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Setup fused Joint step + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Update config + with open_dict(self.cfg.joint): + self.cfg.joint = new_joint_config + + with open_dict(self.cfg.decoder): + self.cfg.decoder = new_decoder_config + + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed tokenizer of the RNNT decoder to {self.joint.vocabulary} vocabulary.") + + # set up the new tokenizer for the CTC decoder + if hasattr(self, 'ctc_decoder'): + ctc_decoder_config = copy.deepcopy(self.ctc_decoder.to_config_dict()) + # sidestepping the potential overlapping tokens issue in aggregate tokenizers + if self.tokenizer_type == "agg": + ctc_decoder_config.vocabulary = ListConfig(vocabulary) + else: + ctc_decoder_config.vocabulary = ListConfig(list(vocabulary.keys())) + + decoder_num_classes = ctc_decoder_config['num_classes'] + # Override number of classes if placeholder provided + logging.info( + "\nReplacing old number of classes ({}) with new number of classes - {}".format( + decoder_num_classes, len(vocabulary) + ) + ) + ctc_decoder_config['num_classes'] = len(vocabulary) + + del self.ctc_decoder + self.ctc_decoder = VisualEncDecHybridRNNTCTCBPEModel.from_config_dict(ctc_decoder_config) + del self.ctc_loss + self.ctc_loss = CTCLoss( + num_classes=self.ctc_decoder.num_classes_with_blank - 1, + zero_infinity=True, + reduction=self.cfg.aux_ctc.get("ctc_reduction", "mean_batch"), + ) + + if ctc_decoding_cfg is None: + # Assume same decoding config as before + ctc_decoding_cfg = self.cfg.aux_ctc.decoding + + # Assert the decoding config with all hyper parameters + ctc_decoding_cls = OmegaConf.structured(CTCBPEDecodingConfig) + ctc_decoding_cls = OmegaConf.create(OmegaConf.to_container(ctc_decoding_cls)) + ctc_decoding_cfg = OmegaConf.merge(ctc_decoding_cls, ctc_decoding_cfg) + + self.ctc_decoding = CTCBPEDecoding(decoding_cfg=ctc_decoding_cfg, tokenizer=self.tokenizer) + + self.ctc_wer = WER( + decoding=self.ctc_decoding, + use_cer=self.cfg.aux_ctc.get('use_cer', False), + log_prediction=self.cfg.get("log_prediction", False), + dist_sync_on_step=True, + ) + + # Update config + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoder = ctc_decoder_config + + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoding = ctc_decoding_cfg + + logging.info(f"Changed tokenizer of the CTC decoder to {self.ctc_decoder.vocabulary} vocabulary.") + + def change_decoding_strategy(self, decoding_cfg: DictConfig, decoder_type: str = None): + """ + Changes decoding strategy used during RNNT decoding process. + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + decoder_type: (str) Can be set to 'rnnt' or 'ctc' to switch between appropriate decoder in a + model having both RNN-T and CTC decoders. Defaults to None, in which case RNN-T decoder is + used. If set to 'ctc', it raises error if 'ctc_decoder' is not an attribute of the model. + """ + if decoder_type is None or decoder_type == 'rnnt': + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(RNNTBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = RNNTBPEDecoding( + decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, tokenizer=self.tokenizer, + ) + + self.wer = WER( + decoding=self.decoding, + batch_dim_index=self.wer.batch_dim_index, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Setup fused Joint step + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Update config + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed decoding strategy of the RNNT decoder to \n{OmegaConf.to_yaml(self.cfg.decoding)}") + elif decoder_type == 'ctc': + if not hasattr(self, 'ctc_decoding'): + raise ValueError("The model does not have the ctc_decoding module and does not support ctc decoding.") + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.aux_ctc.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(CTCBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.ctc_decoding = CTCBPEDecoding(decoding_cfg=decoding_cfg, tokenizer=self.tokenizer) + + self.ctc_wer = WER( + decoding=self.ctc_decoding, + use_cer=self.ctc_wer.use_cer, + log_prediction=self.ctc_wer.log_prediction, + dist_sync_on_step=True, + ) + + # Update config + with open_dict(self.cfg.aux_ctc.decoding): + self.cfg.aux_ctc.decoding = decoding_cfg + + self.use_rnnt_decoder = False + logging.info( + f"Changed decoding strategy of the CTC decoder to \n{OmegaConf.to_yaml(self.cfg.aux_ctc.decoding)}" + ) + else: + raise ValueError(f"decoder_type={decoder_type} is not supported. Supported values: [ctc,rnnt]") + + @classmethod + def list_available_models(cls) -> Optional[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + return results diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_hybrid_rnnt_ctc_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_hybrid_rnnt_ctc_models.py new file mode 100644 index 0000000..07dc46d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_hybrid_rnnt_ctc_models.py @@ -0,0 +1,655 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import json +import os +import tempfile +from typing import List, Optional + +import torch +from omegaconf import DictConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer +from tqdm.auto import tqdm + +from nemo.collections.asr.losses.ctc import CTCLoss +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.parts.mixins import ASRBPEMixin, InterCTCMixin +from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecoding, CTCDecodingConfig +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.multimodal.speech_cv.models.visual_rnnt_models import VisualEncDecRNNTModel +from nemo.core.classes.common import PretrainedModelInfo +from nemo.core.classes.mixins import AccessMixin +from nemo.utils import logging, model_utils + + +class VisualEncDecHybridRNNTCTCModel(VisualEncDecRNNTModel, ASRBPEMixin, InterCTCMixin): + """Base class for hybrid RNNT/CTC models.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + cfg = model_utils.convert_model_config_to_dict_config(cfg) + cfg = model_utils.maybe_update_config_version(cfg) + super().__init__(cfg=cfg, trainer=trainer) + + if 'aux_ctc' not in self.cfg: + raise ValueError( + "The config need to have a section for the CTC decoder named as aux_ctc for Hybrid models." + ) + with open_dict(self.cfg.aux_ctc): + if "feat_in" not in self.cfg.aux_ctc.decoder or ( + not self.cfg.aux_ctc.decoder.feat_in and hasattr(self.encoder, '_feat_out') + ): + self.cfg.aux_ctc.decoder.feat_in = self.encoder._feat_out + if "feat_in" not in self.cfg.aux_ctc.decoder or not self.cfg.aux_ctc.decoder.feat_in: + raise ValueError("param feat_in of the decoder's config is not set!") + + if self.cfg.aux_ctc.decoder.num_classes < 1 and self.cfg.aux_ctc.decoder.vocabulary is not None: + logging.info( + "\nReplacing placeholder number of classes ({}) with actual number of classes - {}".format( + self.cfg.aux_ctc.decoder.num_classes, len(self.cfg.aux_ctc.decoder.vocabulary) + ) + ) + self.cfg.aux_ctc.decoder["num_classes"] = len(self.cfg.aux_ctc.decoder.vocabulary) + + self.ctc_decoder = VisualEncDecHybridRNNTCTCModel.from_config_dict(self.cfg.aux_ctc.decoder) + self.ctc_loss_weight = self.cfg.aux_ctc.get("ctc_loss_weight", 0.5) + + self.ctc_loss = CTCLoss( + num_classes=self.ctc_decoder.num_classes_with_blank - 1, + zero_infinity=True, + reduction=self.cfg.aux_ctc.get("ctc_reduction", "mean_batch"), + ) + + ctc_decoding_cfg = self.cfg.aux_ctc.get('decoding', None) + if ctc_decoding_cfg is None: + ctc_decoding_cfg = OmegaConf.structured(CTCDecodingConfig) + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoding = ctc_decoding_cfg + + self.ctc_decoding = CTCDecoding(self.cfg.aux_ctc.decoding, vocabulary=self.ctc_decoder.vocabulary) + self.ctc_wer = WER( + decoding=self.ctc_decoding, + use_cer=self.cfg.aux_ctc.get('use_cer', False), + dist_sync_on_step=True, + log_prediction=self.cfg.get("log_prediction", False), + ) + + # setting the RNNT decoder as the default one + self.use_rnnt_decoder = True + + # setting up interCTC loss (from InterCTCMixin) + self.setup_interctc(decoder_name='decoder', loss_name='loss', wer_name='_wer') + + @torch.no_grad() + def transcribe( + self, + paths2video_files: List[str], + batch_size: int = 4, + return_hypotheses: bool = False, + partial_hypothesis: Optional[List['Hypothesis']] = None, + num_workers: int = 0, + channel_selector: Optional[ChannelSelectorType] = None, + ) -> (List[str], Optional[List['Hypothesis']]): + """ + Uses greedy decoding to transcribe video files. Use this method for debugging and prototyping. + + Args: + + paths2video_files: (a list) of paths to video files. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + return_hypotheses: (bool) Either return hypotheses or text + With hypotheses can do some postprocessing like getting timestamp or rescoring + num_workers: (int) number of workers for DataLoader + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + + Returns: + Returns a tuple of 2 items - + * A list of greedy transcript texts / Hypothesis + * An optional list of beam search transcript texts / Hypothesis / NBestHypothesis. + """ + if self.use_rnnt_decoder: + return super().transcribe( + paths2video_files=paths2video_files, + batch_size=batch_size, + return_hypotheses=return_hypotheses, + partial_hypothesis=partial_hypothesis, + num_workers=num_workers, + channel_selector=channel_selector, + ) + + if paths2video_files is None or len(paths2video_files) == 0: + return {} + # We will store transcriptions here + hypotheses = [] + all_hypotheses = [] + # Model's mode and device + mode = self.training + device = next(self.parameters()).device + + if num_workers is None: + num_workers = min(batch_size, os.cpu_count() - 1) + + try: + + # Switch model to evaluation mode + self.eval() + # Freeze the visual front-end, encoder and decoder modules + self.video_front_end.freeze() + self.encoder.freeze() + self.decoder.freeze() + self.joint.freeze() + if hasattr(self, 'ctc_decoder'): + self.ctc_decoder.freeze() + + logging_level = logging.get_verbosity() + logging.set_verbosity(logging.WARNING) + # Work in tmp directory - will store manifest file there + with tempfile.TemporaryDirectory() as tmpdir: + with open(os.path.join(tmpdir, 'manifest.json'), 'w', encoding='utf-8') as fp: + for video_file in paths2video_files: + entry = {'video_filepath': video_file, 'duration': 100000, 'text': ''} + fp.write(json.dumps(entry) + '\n') + + config = { + 'paths2video_files': paths2video_files, + 'batch_size': batch_size, + 'temp_dir': tmpdir, + 'num_workers': num_workers, + 'channel_selector': channel_selector, + } + + temporary_datalayer = self._setup_transcribe_dataloader(config) + for test_batch in tqdm(temporary_datalayer, desc="Transcribing"): + encoded, encoded_len = self.forward( + input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device) + ) + + logits = self.ctc_decoder(encoder_output=encoded) + best_hyp, all_hyp = self.ctc_decoding.ctc_decoder_predictions_tensor( + logits, encoded_len, return_hypotheses=return_hypotheses, + ) + if return_hypotheses: + # dump log probs per file + for idx in range(logits.shape[0]): + best_hyp[idx].y_sequence = logits[idx][: encoded_len[idx]] + if best_hyp[idx].alignments is None: + best_hyp[idx].alignments = best_hyp[idx].y_sequence + del logits + + hypotheses += best_hyp + if all_hyp is not None: + all_hypotheses += all_hyp + else: + all_hypotheses += best_hyp + + del encoded + del test_batch + finally: + # set mode back to its original value + self.train(mode=mode) + + logging.set_verbosity(logging_level) + if mode is True: + self.video_front_end.unfreeze() + self.encoder.unfreeze() + self.decoder.unfreeze() + self.joint.unfreeze() + if hasattr(self, 'ctc_decoder'): + self.ctc_decoder.unfreeze() + return hypotheses, all_hypotheses + + def change_vocabulary( + self, + new_vocabulary: List[str], + decoding_cfg: Optional[DictConfig] = None, + ctc_decoding_cfg: Optional[DictConfig] = None, + ): + """ + Changes vocabulary used during RNNT decoding process. Use this method when fine-tuning a pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \ + this is target alphabet. + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + ctc_decoding_cfg: A config for CTC decoding, which is optional and can be used to change decoding type. + + Returns: None + + """ + super().change_vocabulary(new_vocabulary=new_vocabulary, decoding_cfg=decoding_cfg) + + # set up the new tokenizer for the CTC decoder + if hasattr(self, 'ctc_decoder'): + if self.ctc_decoder.vocabulary == new_vocabulary: + logging.warning( + f"Old {self.ctc_decoder.vocabulary} and new {new_vocabulary} match. Not changing anything." + ) + else: + if new_vocabulary is None or len(new_vocabulary) == 0: + raise ValueError(f'New vocabulary must be non-empty list of chars. But I got: {new_vocabulary}') + decoder_config = self.ctc_decoder.to_config_dict() + new_decoder_config = copy.deepcopy(decoder_config) + new_decoder_config['vocabulary'] = new_vocabulary + new_decoder_config['num_classes'] = len(new_vocabulary) + + del self.ctc_decoder + self.ctc_decoder = VisualEncDecHybridRNNTCTCModel.from_config_dict(new_decoder_config) + del self.ctc_loss + self.ctc_loss = CTCLoss( + num_classes=self.ctc_decoder.num_classes_with_blank - 1, + zero_infinity=True, + reduction=self.cfg.aux_ctc.get("ctc_reduction", "mean_batch"), + ) + + if ctc_decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `ctc_decoding_cfg` passed when changing decoding strategy, using internal config") + ctc_decoding_cfg = self.cfg.aux_ctc.decoding + + # Assert the decoding config with all hyper parameters + ctc_decoding_cls = OmegaConf.structured(CTCDecodingConfig) + ctc_decoding_cls = OmegaConf.create(OmegaConf.to_container(ctc_decoding_cls)) + ctc_decoding_cfg = OmegaConf.merge(ctc_decoding_cls, ctc_decoding_cfg) + + self.ctc_decoding = CTCDecoding(decoding_cfg=ctc_decoding_cfg, vocabulary=self.ctc_decoder.vocabulary) + + self.ctc_wer = WER( + decoding=self.ctc_decoding, + use_cer=self.ctc_wer.use_cer, + log_prediction=self.ctc_wer.log_prediction, + dist_sync_on_step=True, + ) + + # Update config + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoding = ctc_decoding_cfg + + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoder = new_decoder_config + + ds_keys = ['train_ds', 'validation_ds', 'test_ds'] + for key in ds_keys: + if key in self.cfg: + with open_dict(self.cfg[key]): + self.cfg[key]['labels'] = OmegaConf.create(new_vocabulary) + + logging.info(f"Changed the tokenizer of the CTC decoder to {self.ctc_decoder.vocabulary} vocabulary.") + + def change_decoding_strategy(self, decoding_cfg: DictConfig, decoder_type: str = None): + """ + Changes decoding strategy used during RNNT decoding process. + + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + decoder_type: (str) Can be set to 'rnnt' or 'ctc' to switch between appropriate decoder in a + model having RNN-T and CTC decoders. Defaults to None, in which case RNN-T decoder is + used. If set to 'ctc', it raises error if 'ctc_decoder' is not an attribute of the model. + """ + if decoder_type is None or decoder_type == 'rnnt': + self.use_rnnt_decoder = True + return super().change_decoding_strategy(decoding_cfg=decoding_cfg) + + assert decoder_type == 'ctc' and hasattr(self, 'ctc_decoder') + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.aux_ctc.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(CTCDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.ctc_decoding = CTCDecoding(decoding_cfg=decoding_cfg, vocabulary=self.ctc_decoder.vocabulary) + + self.ctc_wer = WER( + decoding=self.ctc_decoding, + use_cer=self.ctc_wer.use_cer, + log_prediction=self.ctc_wer.log_prediction, + dist_sync_on_step=True, + ) + + # Update config + with open_dict(self.cfg.aux_ctc): + self.cfg.aux_ctc.decoding = decoding_cfg + + self.use_rnnt_decoder = False + logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.aux_ctc.decoding)}") + + # PTL-specific methods + def training_step(self, batch, batch_nb): + # Reset access registry + if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): + AccessMixin.reset_registry(self) + + if self.is_interctc_enabled(): + AccessMixin.set_access_enabled(access_enabled=True, guid=self.model_guid) + + signal, signal_len, transcript, transcript_len = batch + + # forward() only performs encoder forward + encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) + del signal + + # During training, loss must be computed, so decoder forward is necessary + decoder, target_length, states = self.decoder(targets=transcript, target_length=transcript_len) + + if hasattr(self, '_trainer') and self._trainer is not None: + log_every_n_steps = self._trainer.log_every_n_steps + sample_id = self._trainer.global_step + else: + log_every_n_steps = 1 + sample_id = batch_nb + + # If fused Joint-Loss-WER is not used + if not self.joint.fuse_loss_wer: + # Compute full joint and loss + joint = self.joint(encoder_outputs=encoded, decoder_outputs=decoder) + loss_value = self.loss( + log_probs=joint, targets=transcript, input_lengths=encoded_len, target_lengths=target_length + ) + + # Add auxiliary losses, if registered + loss_value = self.add_auxiliary_losses(loss_value) + + # Reset access registry + # if AccessMixin.is_access_enabled(): + # AccessMixin.reset_registry(self) + + tensorboard_logs = { + 'train_loss': loss_value, + 'learning_rate': self._optimizer.param_groups[0]['lr'], + 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), + } + + if (sample_id + 1) % log_every_n_steps == 0: + self.wer.update( + predictions=encoded, + predictions_lengths=encoded_len, + targets=transcript, + targets_lengths=transcript_len, + ) + _, scores, words = self.wer.compute() + self.wer.reset() + tensorboard_logs.update({'training_batch_wer': scores.float() / words}) + + else: + # If fused Joint-Loss-WER is used + if (sample_id + 1) % log_every_n_steps == 0: + compute_wer = True + else: + compute_wer = False + + # Fused joint step + loss_value, wer, _, _ = self.joint( + encoder_outputs=encoded, + decoder_outputs=decoder, + encoder_lengths=encoded_len, + transcripts=transcript, + transcript_lengths=transcript_len, + compute_wer=compute_wer, + ) + + # Add auxiliary losses, if registered + loss_value = self.add_auxiliary_losses(loss_value) + + # Reset access registry + # if AccessMixin.is_access_enabled(): + # AccessMixin.reset_registry(self) + + tensorboard_logs = { + 'train_loss': loss_value, + 'learning_rate': self._optimizer.param_groups[0]['lr'], + 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), + } + + if compute_wer: + tensorboard_logs.update({'training_batch_wer': wer}) + + if self.ctc_loss_weight > 0: + log_probs = self.ctc_decoder(encoder_output=encoded) + ctc_loss = self.ctc_loss( + log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len + ) + + # Add Interctc Losses + ctc_loss, interctc_tensorboard_logs = self.add_interctc_losses( + ctc_loss, transcript, transcript_len, compute_wer=((batch_nb + 1) % log_every_n_steps == 0) + ) + tensorboard_logs.update(interctc_tensorboard_logs) + + tensorboard_logs['train_rnnt_loss'] = loss_value + tensorboard_logs['train_ctc_loss'] = ctc_loss + loss_value = (1 - self.ctc_loss_weight) * loss_value + self.ctc_loss_weight * ctc_loss + tensorboard_logs['train_loss'] = loss_value + if (sample_id + 1) % log_every_n_steps == 0: + self.ctc_wer.update( + predictions=log_probs, + targets=transcript, + target_lengths=transcript_len, + predictions_lengths=encoded_len, + ) + ctc_wer, _, _ = self.ctc_wer.compute() + self.ctc_wer.reset() + tensorboard_logs.update({'training_batch_wer_ctc': ctc_wer}) + + # Reset access registry + if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): + AccessMixin.reset_registry(self) + + # Log items + self.log_dict(tensorboard_logs) + + # Preserve batch acoustic model T and language model U parameters if normalizing + if self._optim_normalize_joint_txu: + self._optim_normalize_txu = [encoded_len.max(), transcript_len.max()] + + return {'loss': loss_value} + + def predict_step(self, batch, batch_idx, dataloader_idx=0): + # TODO: add support for CTC decoding + signal, signal_len, transcript, transcript_len, sample_id = batch + + # forward() only performs encoder forward + encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) + del signal + + best_hyp_text, all_hyp_text = self.decoding.rnnt_decoder_predictions_tensor( + encoder_output=encoded, encoded_lengths=encoded_len, return_hypotheses=False + ) + + sample_id = sample_id.cpu().detach().numpy() + return list(zip(sample_id, best_hyp_text)) + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + if self.is_interctc_enabled(): + AccessMixin.set_access_enabled(access_enabled=True, guid=self.model_guid) + + signal, signal_len, transcript, transcript_len = batch + + # forward() only performs encoder forward + encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) + del signal + + tensorboard_logs = {} + + # If experimental fused Joint-Loss-WER is not used + if not self.joint.fuse_loss_wer: + if self.compute_eval_loss: + decoder, target_length, states = self.decoder(targets=transcript, target_length=transcript_len) + joint = self.joint(encoder_outputs=encoded, decoder_outputs=decoder) + + loss_value = self.loss( + log_probs=joint, targets=transcript, input_lengths=encoded_len, target_lengths=target_length + ) + + tensorboard_logs['val_loss'] = loss_value + + self.wer.update( + predictions=encoded, + predictions_lengths=encoded_len, + targets=transcript, + targets_lengths=transcript_len, + ) + wer, wer_num, wer_denom = self.wer.compute() + self.wer.reset() + + tensorboard_logs['val_wer_num'] = wer_num + tensorboard_logs['val_wer_denom'] = wer_denom + tensorboard_logs['val_wer'] = wer + + else: + # If experimental fused Joint-Loss-WER is used + compute_wer = True + + if self.compute_eval_loss: + decoded, target_len, states = self.decoder(targets=transcript, target_length=transcript_len) + else: + decoded = None + target_len = transcript_len + + # Fused joint step + loss_value, wer, wer_num, wer_denom = self.joint( + encoder_outputs=encoded, + decoder_outputs=decoded, + encoder_lengths=encoded_len, + transcripts=transcript, + transcript_lengths=target_len, + compute_wer=compute_wer, + ) + + if loss_value is not None: + tensorboard_logs['val_loss'] = loss_value + + tensorboard_logs['val_wer_num'] = wer_num + tensorboard_logs['val_wer_denom'] = wer_denom + tensorboard_logs['val_wer'] = wer + + log_probs = self.ctc_decoder(encoder_output=encoded) + if self.compute_eval_loss: + ctc_loss = self.ctc_loss( + log_probs=log_probs, targets=transcript, input_lengths=encoded_len, target_lengths=transcript_len + ) + + # Add interCTC losses + ctc_loss, interctc_tensorboard_logs = self.add_interctc_losses( + ctc_loss, transcript, transcript_len, compute_wer=True, log_wer_num_denom=True, log_prefix="val_", + ) + tensorboard_logs.update(interctc_tensorboard_logs) + + tensorboard_logs['val_ctc_loss'] = ctc_loss + tensorboard_logs['val_rnnt_loss'] = loss_value + loss_value = (1 - self.ctc_loss_weight) * loss_value + self.ctc_loss_weight * ctc_loss + tensorboard_logs['val_loss'] = loss_value + self.ctc_wer.update( + predictions=log_probs, targets=transcript, target_lengths=transcript_len, predictions_lengths=encoded_len, + ) + ctc_wer, ctc_wer_num, ctc_wer_denom = self.ctc_wer.compute() + self.ctc_wer.reset() + tensorboard_logs['val_wer_num_ctc'] = ctc_wer_num + tensorboard_logs['val_wer_denom_ctc'] = ctc_wer_denom + tensorboard_logs['val_wer_ctc'] = ctc_wer + + self.log('global_step', torch.tensor(self.trainer.global_step, dtype=torch.float32)) + + # Reset access registry + if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): + AccessMixin.reset_registry(self) + + return tensorboard_logs + + def test_step(self, batch, batch_idx, dataloader_idx=0): + logs = self.validation_step(batch, batch_idx, dataloader_idx=dataloader_idx) + test_logs = {name.replace("val_", "test_"): value for name, value in logs.items()} + if type(self.trainer.test_dataloaders) == list and len(self.trainer.test_dataloaders) > 1: + self.test_step_outputs[dataloader_idx].append(test_logs) + else: + self.test_step_outputs.append(test_logs) + return test_logs + + """ + def test_step(self, batch, batch_idx, dataloader_idx=0): + logs = self.validation_step(batch, batch_idx, dataloader_idx=dataloader_idx) + test_logs = { + 'test_wer_num': logs['val_wer_num'], + 'test_wer_denom': logs['val_wer_denom'], + # 'test_wer': logs['val_wer'], + } + if 'val_loss' in logs: + test_logs['test_loss'] = logs['val_loss'] + + if self.ctc_loss_weight > 0: + test_logs['test_wer_num_ctc'] = logs['val_wer_num_ctc'] + test_logs['test_wer_denom_ctc'] = logs['val_wer_denom_ctc'] + if 'val_ctc_loss' in logs: + test_logs['test_ctc_loss'] = logs['val_ctc_loss'] + if 'val_rnnt_loss' in logs: + test_logs['test_rnnt_loss'] = logs['val_rnnt_loss'] + + return test_logs + """ + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + if self.compute_eval_loss: + val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean() + val_loss_log = {'val_loss': val_loss_mean} + else: + val_loss_log = {} + wer_num = torch.stack([x['val_wer_num'] for x in outputs]).sum() + wer_denom = torch.stack([x['val_wer_denom'] for x in outputs]).sum() + tensorboard_logs = {**val_loss_log, 'val_wer': wer_num.float() / wer_denom} + if self.ctc_loss_weight > 0: + ctc_wer_num = torch.stack([x['val_wer_num_ctc'] for x in outputs]).sum() + ctc_wer_denom = torch.stack([x['val_wer_denom_ctc'] for x in outputs]).sum() + tensorboard_logs['val_wer_ctc'] = ctc_wer_num.float() / ctc_wer_denom + + metrics = {**val_loss_log, 'log': tensorboard_logs} + self.finalize_interctc_metrics(metrics, outputs, prefix="val_") + return metrics + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + if self.compute_eval_loss: + test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean() + test_loss_log = {'test_loss': test_loss_mean} + else: + test_loss_log = {} + wer_num = torch.stack([x['test_wer_num'] for x in outputs]).sum() + wer_denom = torch.stack([x['test_wer_denom'] for x in outputs]).sum() + tensorboard_logs = {**test_loss_log, 'test_wer': wer_num.float() / wer_denom} + + if self.ctc_loss_weight > 0: + ctc_wer_num = torch.stack([x['test_wer_num_ctc'] for x in outputs]).sum() + ctc_wer_denom = torch.stack([x['test_wer_denom_ctc'] for x in outputs]).sum() + tensorboard_logs['test_wer_ctc'] = ctc_wer_num.float() / ctc_wer_denom + + metrics = {**test_loss_log, 'log': tensorboard_logs} + self.finalize_interctc_metrics(metrics, outputs, prefix="test_") + return metrics + + @classmethod + def list_available_models(cls) -> Optional[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + return results diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_rnnt_bpe_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_rnnt_bpe_models.py new file mode 100644 index 0000000..eeffb90 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_rnnt_bpe_models.py @@ -0,0 +1,322 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import os +from typing import Dict, List, Optional, Union + +import torch +from omegaconf import DictConfig, ListConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer + +from nemo.collections.asr.losses.rnnt import RNNTLoss +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.parts.mixins import ASRBPEMixin +from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTBPEDecoding, RNNTBPEDecodingConfig +from nemo.collections.multimodal.speech_cv.data import video_to_text_dataset +from nemo.collections.multimodal.speech_cv.models.visual_rnnt_models import VisualEncDecRNNTModel +from nemo.core.classes.common import PretrainedModelInfo +from nemo.utils import logging, model_utils + + +class VisualEncDecRNNTBPEModel(VisualEncDecRNNTModel, ASRBPEMixin): + """Base class for encoder decoder RNNT-based models with subword tokenization.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + # Convert to Hydra 1.0 compatible DictConfig + cfg = model_utils.convert_model_config_to_dict_config(cfg) + cfg = model_utils.maybe_update_config_version(cfg) + + # Tokenizer is necessary for this model + if 'tokenizer' not in cfg: + raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !") + + if not isinstance(cfg, DictConfig): + cfg = OmegaConf.create(cfg) + + # Setup the tokenizer + self._setup_tokenizer(cfg.tokenizer) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + # Set the new vocabulary + with open_dict(cfg): + cfg.labels = ListConfig(list(vocabulary)) + + with open_dict(cfg.decoder): + cfg.decoder.vocab_size = len(vocabulary) + + with open_dict(cfg.joint): + cfg.joint.num_classes = len(vocabulary) + cfg.joint.vocabulary = ListConfig(list(vocabulary)) + cfg.joint.jointnet.encoder_hidden = cfg.model_defaults.enc_hidden + cfg.joint.jointnet.pred_hidden = cfg.model_defaults.pred_hidden + + super().__init__(cfg=cfg, trainer=trainer) + + # Setup decoding object + self.decoding = RNNTBPEDecoding( + decoding_cfg=self.cfg.decoding, decoder=self.decoder, joint=self.joint, tokenizer=self.tokenizer, + ) + + # Setup wer object + self.wer = WER( + decoding=self.decoding, + batch_dim_index=0, + use_cer=self._cfg.get('use_cer', False), + log_prediction=self._cfg.get('log_prediction', True), + dist_sync_on_step=True, + ) + + # Setup fused Joint step if flag is set + if self.joint.fuse_loss_wer: + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + def change_vocabulary( + self, + new_tokenizer_dir: Union[str, DictConfig], + new_tokenizer_type: str, + decoding_cfg: Optional[DictConfig] = None, + ): + """ + Changes vocabulary used during RNNT decoding process. Use this method when fine-tuning on from pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_tokenizer_dir: Directory path to tokenizer or a config for a new tokenizer (if the tokenizer type is `agg`) + new_tokenizer_type: Type of tokenizer. Can be either `agg`, `bpe` or `wpe`. + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + + Returns: None + + """ + if isinstance(new_tokenizer_dir, DictConfig): + if new_tokenizer_type == 'agg': + new_tokenizer_cfg = new_tokenizer_dir + else: + raise ValueError( + f'New tokenizer dir should be a string unless the tokenizer is `agg`, but this tokenizer type is: {new_tokenizer_type}' + ) + else: + new_tokenizer_cfg = None + + if new_tokenizer_cfg is not None: + tokenizer_cfg = new_tokenizer_cfg + else: + if not os.path.isdir(new_tokenizer_dir): + raise NotADirectoryError( + f'New tokenizer dir must be non-empty path to a directory. But I got: {new_tokenizer_dir}' + ) + + if new_tokenizer_type.lower() not in ('bpe', 'wpe'): + raise ValueError(f'New tokenizer type must be either `bpe` or `wpe`') + + tokenizer_cfg = OmegaConf.create({'dir': new_tokenizer_dir, 'type': new_tokenizer_type}) + + # Setup the tokenizer + self._setup_tokenizer(tokenizer_cfg) + + # Initialize a dummy vocabulary + vocabulary = self.tokenizer.tokenizer.get_vocab() + + joint_config = self.joint.to_config_dict() + new_joint_config = copy.deepcopy(joint_config) + if self.tokenizer_type == "agg": + new_joint_config["vocabulary"] = ListConfig(vocabulary) + else: + new_joint_config["vocabulary"] = ListConfig(list(vocabulary.keys())) + + new_joint_config['num_classes'] = len(vocabulary) + del self.joint + self.joint = VisualEncDecRNNTBPEModel.from_config_dict(new_joint_config) + + decoder_config = self.decoder.to_config_dict() + new_decoder_config = copy.deepcopy(decoder_config) + new_decoder_config.vocab_size = len(vocabulary) + del self.decoder + self.decoder = VisualEncDecRNNTBPEModel.from_config_dict(new_decoder_config) + + del self.loss + self.loss = RNNTLoss(num_classes=self.joint.num_classes_with_blank - 1) + + if decoding_cfg is None: + # Assume same decoding config as before + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(RNNTBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = RNNTBPEDecoding( + decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, tokenizer=self.tokenizer, + ) + + self.wer = WER( + decoding=self.decoding, + batch_dim_index=self.wer.batch_dim_index, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Setup fused Joint step + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Update config + with open_dict(self.cfg.joint): + self.cfg.joint = new_joint_config + + with open_dict(self.cfg.decoder): + self.cfg.decoder = new_decoder_config + + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed decoder to output to {self.joint.vocabulary} vocabulary.") + + def change_decoding_strategy(self, decoding_cfg: DictConfig): + """ + Changes decoding strategy used during RNNT decoding process. + + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + """ + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(RNNTBPEDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = RNNTBPEDecoding( + decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, tokenizer=self.tokenizer, + ) + + self.wer = WER( + decoding=self.decoding, + batch_dim_index=self.wer.batch_dim_index, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Setup fused Joint step + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Update config + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}") + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + dataset = video_to_text_dataset.get_video_to_text_bpe_dataset_from_config( + config=config, + local_rank=self.local_rank, + global_rank=self.global_rank, + world_size=self.world_size, + tokenizer=self.tokenizer, + preprocessor_cfg=self.cfg.get("preprocessor", None), + ) + + if dataset is None: + return None + + shuffle = config['shuffle'] + if config.get('is_tarred', False): + shuffle = False + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + else: + collate_fn = dataset.datasets[0].collate_fn + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided video file. + + Args: + config: A python dictionary which contains the following keys: + paths2video_files: (a list) of paths to video files. The files should be relatively short fragments. \ + Recommended length per file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the video manifest is temporarily + stored. + + Returns: + A pytorch DataLoader for the given video file(s). + """ + if 'manifest_filepath' in config: + manifest_filepath = config['manifest_filepath'] + batch_size = config['batch_size'] + else: + manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json') + batch_size = min(config['batch_size'], len(config['paths2video_files'])) + + dl_config = { + 'manifest_filepath': manifest_filepath, + 'batch_size': batch_size, + 'shuffle': False, + 'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)), + 'pin_memory': True, + 'channel_selector': config.get('channel_selector', None), + 'use_start_end_token': self.cfg.validation_ds.get('use_start_end_token', False), + } + + if config.get("augmentor"): + dl_config['augmentor'] = config.get("augmentor") + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + return results diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_rnnt_models.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_rnnt_models.py new file mode 100644 index 0000000..f5519b4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/models/visual_rnnt_models.py @@ -0,0 +1,939 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import json +import os +import tempfile +from math import ceil +from typing import Dict, List, Optional, Tuple, Union + +import torch +from omegaconf import DictConfig, OmegaConf, open_dict +from pytorch_lightning import Trainer +from tqdm.auto import tqdm + +from nemo.collections.asr.data import audio_to_text_dataset +from nemo.collections.asr.losses.rnnt import RNNTLoss, resolve_rnnt_default_loss_name +from nemo.collections.asr.metrics.wer import WER +from nemo.collections.asr.models.asr_model import ASRModel +from nemo.collections.asr.modules.rnnt import RNNTDecoderJoint +from nemo.collections.asr.parts.mixins import ASRModuleMixin +from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTDecoding, RNNTDecodingConfig +from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType +from nemo.collections.multimodal.speech_cv.data import video_to_text_dataset +from nemo.core.classes import Exportable +from nemo.core.classes.common import PretrainedModelInfo, typecheck +from nemo.core.classes.mixins import AccessMixin +from nemo.core.neural_types import AcousticEncodedRepresentation, LengthsType, NeuralType, VideoSignal +from nemo.utils import logging + + +class VisualEncDecRNNTModel(ASRModel, ASRModuleMixin, Exportable): + """Base class for encoder decoder RNNT-based models.""" + + def __init__(self, cfg: DictConfig, trainer: Trainer = None): + # Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable + # Global_rank and local_rank is set by LightningModule in Lightning 1.2.0 + self.world_size = 1 + if trainer is not None: + self.world_size = trainer.world_size + + super().__init__(cfg=cfg, trainer=trainer) + + # Preprocessors + self.video_preprocessor = VisualEncDecRNNTModel.from_config_dict(self._cfg.video_preprocessor) + + # Augmentations + self.video_augmentation = VisualEncDecRNNTModel.from_config_dict(self._cfg.video_augment) + + # Front-end Networks + self.video_front_end = VisualEncDecRNNTModel.from_config_dict(self._cfg.video_front_end) + + # Back-end Networks + self.encoder = VisualEncDecRNNTModel.from_config_dict(self._cfg.encoder) + + # Update config values required by components dynamically + with open_dict(self.cfg.decoder): + self.cfg.decoder.vocab_size = len(self.cfg.labels) + + with open_dict(self.cfg.joint): + self.cfg.joint.num_classes = len(self.cfg.labels) + self.cfg.joint.vocabulary = self.cfg.labels + self.cfg.joint.jointnet.encoder_hidden = self.cfg.model_defaults.enc_hidden + self.cfg.joint.jointnet.pred_hidden = self.cfg.model_defaults.pred_hidden + + self.decoder = VisualEncDecRNNTModel.from_config_dict(self.cfg.decoder) + self.joint = VisualEncDecRNNTModel.from_config_dict(self.cfg.joint) + + # Setup RNNT Loss + loss_name, loss_kwargs = self.extract_rnnt_loss_cfg(self.cfg.get("loss", None)) + + self.loss = RNNTLoss( + num_classes=self.joint.num_classes_with_blank - 1, + loss_name=loss_name, + loss_kwargs=loss_kwargs, + reduction=self.cfg.get("rnnt_reduction", "mean_batch"), + ) + + # Setup decoding objects + self.decoding = RNNTDecoding( + decoding_cfg=self.cfg.decoding, decoder=self.decoder, joint=self.joint, vocabulary=self.joint.vocabulary, + ) + # Setup WER calculation + self.wer = WER( + decoding=self.decoding, + batch_dim_index=0, + use_cer=self._cfg.get('use_cer', False), + log_prediction=self._cfg.get('log_prediction', True), + dist_sync_on_step=True, + ) + + # Whether to compute loss during evaluation + if 'compute_eval_loss' in self.cfg: + self.compute_eval_loss = self.cfg.compute_eval_loss + else: + self.compute_eval_loss = True + + # Setup fused Joint step if flag is set + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Setup optimization normalization (if provided in config) + self.setup_optim_normalization() + + # Setup optional Optimization flags + self.setup_optimization_flags() + + # Setup encoder adapters (from ASRAdapterModelMixin) + self.setup_adapters() + + def setup_optim_normalization(self): + """ + Helper method to setup normalization of certain parts of the model prior to the optimization step. + + Supported pre-optimization normalizations are as follows: + + .. code-block:: yaml + + # Variation Noise injection + model: + variational_noise: + std: 0.0 + start_step: 0 + + # Joint - Length normalization + model: + normalize_joint_txu: false + + # Encoder Network - gradient normalization + model: + normalize_encoder_norm: false + + # Decoder / Prediction Network - gradient normalization + model: + normalize_decoder_norm: false + + # Joint - gradient normalization + model: + normalize_joint_norm: false + """ + # setting up the variational noise for the decoder + if hasattr(self.cfg, 'variational_noise'): + self._optim_variational_noise_std = self.cfg['variational_noise'].get('std', 0) + self._optim_variational_noise_start = self.cfg['variational_noise'].get('start_step', 0) + else: + self._optim_variational_noise_std = 0 + self._optim_variational_noise_start = 0 + + # Setup normalized gradients for model joint by T x U scaling factor (joint length normalization) + self._optim_normalize_joint_txu = self.cfg.get('normalize_joint_txu', False) + self._optim_normalize_txu = None + + # Setup normalized encoder norm for model + self._optim_normalize_encoder_norm = self.cfg.get('normalize_encoder_norm', False) + + # Setup normalized decoder norm for model + self._optim_normalize_decoder_norm = self.cfg.get('normalize_decoder_norm', False) + + # Setup normalized joint norm for model + self._optim_normalize_joint_norm = self.cfg.get('normalize_joint_norm', False) + + def extract_rnnt_loss_cfg(self, cfg: Optional[DictConfig]): + """ + Helper method to extract the rnnt loss name, and potentially its kwargs + to be passed. + + Args: + cfg: Should contain `loss_name` as a string which is resolved to a RNNT loss name. + If the default should be used, then `default` can be used. + Optionally, one can pass additional kwargs to the loss function. The subdict + should have a keyname as follows : `{loss_name}_kwargs`. + + Note that whichever loss_name is selected, that corresponding kwargs will be + selected. For the "default" case, the "{resolved_default}_kwargs" will be used. + + Examples: + .. code-block:: yaml + + loss_name: "default" + warprnnt_numba_kwargs: + kwargs2: some_other_val + + Returns: + A tuple, the resolved loss name as well as its kwargs (if found). + """ + if cfg is None: + cfg = DictConfig({}) + + loss_name = cfg.get("loss_name", "default") + + if loss_name == "default": + loss_name = resolve_rnnt_default_loss_name() + + loss_kwargs = cfg.get(f"{loss_name}_kwargs", None) + + logging.info(f"Using RNNT Loss : {loss_name}\n" f"Loss {loss_name}_kwargs: {loss_kwargs}") + + return loss_name, loss_kwargs + + @torch.no_grad() + def transcribe( + self, + paths2video_files: List[str], + batch_size: int = 4, + return_hypotheses: bool = False, + partial_hypothesis: Optional[List['Hypothesis']] = None, + num_workers: int = 0, + channel_selector: Optional[ChannelSelectorType] = None, + augmentor: DictConfig = None, + ) -> Tuple[List[str], Optional[List['Hypothesis']]]: + """ + Uses greedy decoding to transcribe video files. Use this method for debugging and prototyping. + + Args: + + paths2video_files: (a list) of paths to video files. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + return_hypotheses: (bool) Either return hypotheses or text + With hypotheses can do some postprocessing like getting timestamp or rescoring + num_workers: (int) number of workers for DataLoader + channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. + augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied. + Returns: + Returns a tuple of 2 items - + * A list of greedy transcript texts / Hypothesis + * An optional list of beam search transcript texts / Hypothesis / NBestHypothesis. + """ + if paths2video_files is None or len(paths2video_files) == 0: + return {} + # We will store transcriptions here + hypotheses = [] + all_hypotheses = [] + # Model's mode and device + mode = self.training + device = next(self.parameters()).device + + if num_workers is None: + num_workers = min(batch_size, os.cpu_count() - 1) + + try: + + # Switch model to evaluation mode + self.eval() + # Freeze the visual front-end, encoder and decoder modules + self.video_front_end.freeze() + self.encoder.freeze() + self.decoder.freeze() + self.joint.freeze() + logging_level = logging.get_verbosity() + logging.set_verbosity(logging.WARNING) + # Work in tmp directory - will store manifest file there + with tempfile.TemporaryDirectory() as tmpdir: + with open(os.path.join(tmpdir, 'manifest.json'), 'w', encoding='utf-8') as fp: + for video_file in paths2video_files: + entry = {'video_filepath': video_file, 'duration': 100000, 'text': ''} + fp.write(json.dumps(entry) + '\n') + + config = { + 'paths2video_files': paths2video_files, + 'batch_size': batch_size, + 'temp_dir': tmpdir, + 'num_workers': num_workers, + 'channel_selector': channel_selector, + } + + if augmentor: + config['augmentor'] = augmentor + + temporary_datalayer = self._setup_transcribe_dataloader(config) + for test_batch in tqdm(temporary_datalayer, desc="Transcribing"): + encoded, encoded_len = self.forward( + input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device) + ) + best_hyp, all_hyp = self.decoding.rnnt_decoder_predictions_tensor( + encoded, + encoded_len, + return_hypotheses=return_hypotheses, + partial_hypotheses=partial_hypothesis, + ) + + hypotheses += best_hyp + if all_hyp is not None: + all_hypotheses += all_hyp + else: + all_hypotheses += best_hyp + + del encoded + del test_batch + finally: + # set mode back to its original value + self.train(mode=mode) + + logging.set_verbosity(logging_level) + if mode is True: + self.video_front_end.unfreeze() + self.encoder.unfreeze() + self.decoder.unfreeze() + self.joint.unfreeze() + return hypotheses, all_hypotheses + + def change_vocabulary(self, new_vocabulary: List[str], decoding_cfg: Optional[DictConfig] = None): + """ + Changes vocabulary used during RNNT decoding process. Use this method when fine-tuning a pre-trained model. + This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would + use it if you want to use pretrained encoder when fine-tuning on data in another language, or when you'd need + model to learn capitalization, punctuation and/or special characters. + + Args: + new_vocabulary: list with new vocabulary. Must contain at least 2 elements. Typically, \ + this is target alphabet. + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + + Returns: None + + """ + if self.joint.vocabulary == new_vocabulary: + logging.warning(f"Old {self.joint.vocabulary} and new {new_vocabulary} match. Not changing anything.") + else: + if new_vocabulary is None or len(new_vocabulary) == 0: + raise ValueError(f'New vocabulary must be non-empty list of chars. But I got: {new_vocabulary}') + + joint_config = self.joint.to_config_dict() + new_joint_config = copy.deepcopy(joint_config) + new_joint_config['vocabulary'] = new_vocabulary + new_joint_config['num_classes'] = len(new_vocabulary) + del self.joint + self.joint = VisualEncDecRNNTModel.from_config_dict(new_joint_config) + + decoder_config = self.decoder.to_config_dict() + new_decoder_config = copy.deepcopy(decoder_config) + new_decoder_config.vocab_size = len(new_vocabulary) + del self.decoder + self.decoder = VisualEncDecRNNTModel.from_config_dict(new_decoder_config) + + del self.loss + loss_name, loss_kwargs = self.extract_rnnt_loss_cfg(self.cfg.get('loss', None)) + self.loss = RNNTLoss( + num_classes=self.joint.num_classes_with_blank - 1, loss_name=loss_name, loss_kwargs=loss_kwargs + ) + + if decoding_cfg is None: + # Assume same decoding config as before + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(RNNTDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = RNNTDecoding( + decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, vocabulary=self.joint.vocabulary, + ) + + self.wer = WER( + decoding=self.decoding, + batch_dim_index=self.wer.batch_dim_index, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Setup fused Joint step + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Update config + with open_dict(self.cfg.joint): + self.cfg.joint = new_joint_config + + with open_dict(self.cfg.decoder): + self.cfg.decoder = new_decoder_config + + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + ds_keys = ['train_ds', 'validation_ds', 'test_ds'] + for key in ds_keys: + if key in self.cfg: + with open_dict(self.cfg[key]): + self.cfg[key]['labels'] = OmegaConf.create(new_vocabulary) + + logging.info(f"Changed decoder to output to {self.joint.vocabulary} vocabulary.") + + def change_decoding_strategy(self, decoding_cfg: DictConfig): + """ + Changes decoding strategy used during RNNT decoding process. + + Args: + decoding_cfg: A config for the decoder, which is optional. If the decoding type + needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here. + """ + if decoding_cfg is None: + # Assume same decoding config as before + logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config") + decoding_cfg = self.cfg.decoding + + # Assert the decoding config with all hyper parameters + decoding_cls = OmegaConf.structured(RNNTDecodingConfig) + decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls)) + decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg) + + self.decoding = RNNTDecoding( + decoding_cfg=decoding_cfg, decoder=self.decoder, joint=self.joint, vocabulary=self.joint.vocabulary, + ) + + self.wer = WER( + decoding=self.decoding, + batch_dim_index=self.wer.batch_dim_index, + use_cer=self.wer.use_cer, + log_prediction=self.wer.log_prediction, + dist_sync_on_step=True, + ) + + # Setup fused Joint step + if self.joint.fuse_loss_wer or ( + self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0 + ): + self.joint.set_loss(self.loss) + self.joint.set_wer(self.wer) + + # Update config + with open_dict(self.cfg.decoding): + self.cfg.decoding = decoding_cfg + + logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}") + + def _setup_dataloader_from_config(self, config: Optional[Dict]): + # Automatically inject args from model config to dataloader config + audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='sample_rate') + audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='labels') + dataset = video_to_text_dataset.get_video_to_text_bpe_dataset_from_config( + config=config, + local_rank=self.local_rank, + global_rank=self.global_rank, + world_size=self.world_size, + preprocessor_cfg=self._cfg.get("preprocessor", None), + ) + + if dataset is None: + return None + + shuffle = config['shuffle'] + if config.get('is_tarred', False): + shuffle = False + + if hasattr(dataset, 'collate_fn'): + collate_fn = dataset.collate_fn + else: + collate_fn = dataset.datasets[0].collate_fn + + return torch.utils.data.DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + collate_fn=collate_fn, + drop_last=config.get('drop_last', False), + shuffle=shuffle, + num_workers=config.get('num_workers', 0), + pin_memory=config.get('pin_memory', False), + ) + + def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the training data loader via a Dict-like object. + + Args: + train_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.multimodal.speech_cv.data.video_to_text.VideoToCharDataset` + - :class:`~nemo.collections.asr.data.video_to_text.VideoToBPEDataset` + - :class:`~nemo.collections.asr.data.video_to_text.TarredVideoToBPEDataset` + """ + if 'shuffle' not in train_data_config: + train_data_config['shuffle'] = True + + # preserve config + self._update_dataset_config(dataset_name='train', config=train_data_config) + + self._train_dl = self._setup_dataloader_from_config(config=train_data_config) + + # Need to set this because if using an IterableDataset, the length of the dataloader is the total number + # of samples rather than the number of batches, and this messes up the tqdm progress bar. + # So we set the number of steps manually (to the correct number) to fix this. + if 'is_tarred' in train_data_config and train_data_config['is_tarred']: + # We also need to check if limit_train_batches is already set. + # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches, + # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0). + if self._trainer is not None and isinstance(self._trainer.limit_train_batches, float): + self._trainer.limit_train_batches = int( + self._trainer.limit_train_batches + * ceil((len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size']) + ) + elif self._trainer is None: + logging.warning( + "Model Trainer was not set before constructing the dataset, incorrect number of " + "training batches will be used. Please set the trainer and rebuild the dataset." + ) + + def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the validation data loader via a Dict-like object. + + Args: + val_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.multimodal.speech_cv.data.video_to_text.VideoToCharDataset` + - :class:`~nemo.collections.asr.data.video_to_text.VideoToBPEDataset` + - :class:`~nemo.collections.asr.data.video_to_text.TarredVideoToBPEDataset` + """ + if 'shuffle' not in val_data_config: + val_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='validation', config=val_data_config) + + self._validation_dl = self._setup_dataloader_from_config(config=val_data_config) + + def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]): + """ + Sets up the test data loader via a Dict-like object. + + Args: + test_data_config: A config that contains the information regarding construction + of an ASR Training dataset. + + Supported Datasets: + - :class:`~nemo.collections.multimodal.speech_cv.data.video_to_text.VideoToCharDataset` + - :class:`~nemo.collections.asr.data.video_to_text.VideoToBPEDataset` + - :class:`~nemo.collections.asr.data.video_to_text.TarredVideoToBPEDataset` + """ + if 'shuffle' not in test_data_config: + test_data_config['shuffle'] = False + + # preserve config + self._update_dataset_config(dataset_name='test', config=test_data_config) + + self._test_dl = self._setup_dataloader_from_config(config=test_data_config) + + @property + def input_types(self) -> Optional[Dict[str, NeuralType]]: + + return { + "input_signal": NeuralType(('B', 'C', 'T', 'H', 'W'), VideoSignal(), optional=True), + "input_signal_length": NeuralType(tuple('B'), LengthsType(), optional=True), + } + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return { + "outputs": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation()), + "encoded_lengths": NeuralType(tuple('B'), LengthsType()), + } + + @typecheck() + def forward(self, input_signal=None, input_signal_length=None): + """ + Forward pass of the model. Note that for RNNT Models, the forward pass of the model is a 3 step process, + and this method only performs the first step - forward of the acoustic/visual model. + + Please refer to the `training_step` in order to see the full `forward` step for training - which + performs the forward of the acoustic model, the prediction network and then the joint network. + Finally, it computes the loss and possibly compute the detokenized text via the `decoding` step. + + Please refer to the `validation_step` in order to see the full `forward` step for inference - which + performs the forward of the acoustic model, the prediction network and then the joint network. + Finally, it computes the decoded tokens via the `decoding` step and possibly compute the batch metrics. + + Args: + input_signal: Tensor that represents a batch of video signals, + of shape [B, T, H, W, C]. T here represents timesteps, H height, W width and C channels + input_signal_length: Vector of length B, that contains the individual lengths of the video + sequences. + + Returns: + A tuple of 2 elements - + 1) The log probabilities tensor of shape [B, T, D]. + 2) The lengths of the acoustic sequence after propagation through the encoder, of shape [B]. + """ + + # Preprocessing + processed_video_signal, processed_video_signal_length = self.video_preprocessor( + input_signal=input_signal, length=input_signal_length + ) + + # Augmentation + processed_video_signal = self.video_augmentation( + input_signal=processed_video_signal, length=processed_video_signal_length + ) + + # Front-end Networks + processed_video_signal, processed_video_signal_length = self.video_front_end( + input_signal=processed_video_signal, length=processed_video_signal_length + ) + + # Back-end Networks + encoded, encoded_len = self.encoder(audio_signal=processed_video_signal, length=processed_video_signal_length) + + return encoded, encoded_len + + # PTL-specific methods + def training_step(self, batch, batch_nb): + # Reset access registry + if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): + AccessMixin.reset_registry(self) + + signal, signal_len, transcript, transcript_len = batch + + # forward() only performs encoder forward + encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) + del signal + + # During training, loss must be computed, so decoder forward is necessary + decoder, target_length, states = self.decoder(targets=transcript, target_length=transcript_len) + + if hasattr(self, '_trainer') and self._trainer is not None: + log_every_n_steps = self._trainer.log_every_n_steps + sample_id = self._trainer.global_step + else: + log_every_n_steps = 1 + sample_id = batch_nb + + # If experimental fused Joint-Loss-WER is not used + if not self.joint.fuse_loss_wer: + # Compute full joint and loss + joint = self.joint(encoder_outputs=encoded, decoder_outputs=decoder) + loss_value = self.loss( + log_probs=joint, targets=transcript, input_lengths=encoded_len, target_lengths=target_length + ) + + # Add auxiliary losses, if registered + loss_value = self.add_auxiliary_losses(loss_value) + + # Reset access registry + if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): + AccessMixin.reset_registry(self) + + tensorboard_logs = { + 'train_loss': loss_value, + 'learning_rate': self._optimizer.param_groups[0]['lr'], + 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), + } + + if (sample_id + 1) % log_every_n_steps == 0: + self.wer.update( + predictions=encoded, + predictions_lengths=encoded_len, + targets=transcript, + targets_lengths=transcript_len, + ) + _, scores, words = self.wer.compute() + self.wer.reset() + tensorboard_logs.update({'training_batch_wer': scores.float() / words}) + + else: + # If experimental fused Joint-Loss-WER is used + if (sample_id + 1) % log_every_n_steps == 0: + compute_wer = True + else: + compute_wer = False + + # Fused joint step + loss_value, wer, _, _ = self.joint( + encoder_outputs=encoded, + decoder_outputs=decoder, + encoder_lengths=encoded_len, + transcripts=transcript, + transcript_lengths=transcript_len, + compute_wer=compute_wer, + ) + + # Add auxiliary losses, if registered + loss_value = self.add_auxiliary_losses(loss_value) + + # Reset access registry + if AccessMixin.is_access_enabled(getattr(self, "model_guid", None)): + AccessMixin.reset_registry(self) + + tensorboard_logs = { + 'train_loss': loss_value, + 'learning_rate': self._optimizer.param_groups[0]['lr'], + 'global_step': torch.tensor(self.trainer.global_step, dtype=torch.float32), + } + + if compute_wer: + tensorboard_logs.update({'training_batch_wer': wer}) + + # Log items + self.log_dict(tensorboard_logs) + + # Preserve batch acoustic model T and language model U parameters if normalizing + if self._optim_normalize_joint_txu: + self._optim_normalize_txu = [encoded_len.max(), transcript_len.max()] + + return {'loss': loss_value} + + def predict_step(self, batch, batch_idx, dataloader_idx=0): + signal, signal_len, transcript, transcript_len, sample_id = batch + + # forward() only performs encoder forward + encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) + del signal + + best_hyp_text, all_hyp_text = self.decoding.rnnt_decoder_predictions_tensor( + encoder_output=encoded, encoded_lengths=encoded_len, return_hypotheses=False + ) + + sample_id = sample_id.cpu().detach().numpy() + return list(zip(sample_id, best_hyp_text)) + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + signal, signal_len, transcript, transcript_len = batch + + # forward() only performs encoder forward + encoded, encoded_len = self.forward(input_signal=signal, input_signal_length=signal_len) + del signal + + tensorboard_logs = {} + + # If experimental fused Joint-Loss-WER is not used + if not self.joint.fuse_loss_wer: + if self.compute_eval_loss: + decoder, target_length, states = self.decoder(targets=transcript, target_length=transcript_len) + joint = self.joint(encoder_outputs=encoded, decoder_outputs=decoder) + + loss_value = self.loss( + log_probs=joint, targets=transcript, input_lengths=encoded_len, target_lengths=target_length + ) + + tensorboard_logs['val_loss'] = loss_value + + self.wer.update( + predictions=encoded, + predictions_lengths=encoded_len, + targets=transcript, + targets_lengths=transcript_len, + ) + wer, wer_num, wer_denom = self.wer.compute() + self.wer.reset() + + tensorboard_logs['val_wer_num'] = wer_num + tensorboard_logs['val_wer_denom'] = wer_denom + tensorboard_logs['val_wer'] = wer + + else: + # If experimental fused Joint-Loss-WER is used + compute_wer = True + + if self.compute_eval_loss: + decoded, target_len, states = self.decoder(targets=transcript, target_length=transcript_len) + else: + decoded = None + target_len = transcript_len + + # Fused joint step + loss_value, wer, wer_num, wer_denom = self.joint( + encoder_outputs=encoded, + decoder_outputs=decoded, + encoder_lengths=encoded_len, + transcripts=transcript, + transcript_lengths=target_len, + compute_wer=compute_wer, + ) + + if loss_value is not None: + tensorboard_logs['val_loss'] = loss_value + + tensorboard_logs['val_wer_num'] = wer_num + tensorboard_logs['val_wer_denom'] = wer_denom + tensorboard_logs['val_wer'] = wer + + self.log('global_step', torch.tensor(self.trainer.global_step, dtype=torch.float32)) + + return tensorboard_logs + + def test_step(self, batch, batch_idx, dataloader_idx=0): + logs = self.validation_step(batch, batch_idx, dataloader_idx=dataloader_idx) + test_logs = { + 'test_wer_num': logs['val_wer_num'], + 'test_wer_denom': logs['val_wer_denom'], + # 'test_wer': logs['val_wer'], + } + if 'val_loss' in logs: + test_logs['test_loss'] = logs['val_loss'] + return test_logs + + def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0): + if self.compute_eval_loss: + val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean() + val_loss_log = {'val_loss': val_loss_mean} + else: + val_loss_log = {} + wer_num = torch.stack([x['val_wer_num'] for x in outputs]).sum() + wer_denom = torch.stack([x['val_wer_denom'] for x in outputs]).sum() + tensorboard_logs = {**val_loss_log, 'val_wer': wer_num.float() / wer_denom} + return {**val_loss_log, 'log': tensorboard_logs} + + def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0): + if self.compute_eval_loss: + test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean() + test_loss_log = {'test_loss': test_loss_mean} + else: + test_loss_log = {} + wer_num = torch.stack([x['test_wer_num'] for x in outputs]).sum() + wer_denom = torch.stack([x['test_wer_denom'] for x in outputs]).sum() + tensorboard_logs = {**test_loss_log, 'test_wer': wer_num.float() / wer_denom} + return {**test_loss_log, 'log': tensorboard_logs} + + def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader': + """ + Setup function for a temporary data loader which wraps the provided video file. + + Args: + config: A python dictionary which contains the following keys: + paths2video_files: (a list) of paths to video files. The files should be relatively short fragments. \ + Recommended length per file is between 5 and 25 seconds. + batch_size: (int) batch size to use during inference. \ + Bigger will result in better throughput performance but would use more memory. + temp_dir: (str) A temporary directory where the video manifest is temporarily + stored. + + Returns: + A pytorch DataLoader for the given video file(s). + """ + if 'manifest_filepath' in config: + manifest_filepath = config['manifest_filepath'] + batch_size = config['batch_size'] + else: + manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json') + batch_size = min(config['batch_size'], len(config['paths2video_files'])) + + dl_config = { + 'manifest_filepath': manifest_filepath, + 'labels': self.joint.vocabulary, + 'batch_size': batch_size, + 'trim_silence': False, + 'shuffle': False, + 'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)), + 'pin_memory': True, + } + + if config.get("augmentor"): + dl_config['augmentor'] = config.get("augmentor") + + temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config)) + return temporary_datalayer + + def on_after_backward(self): + super().on_after_backward() + if self._optim_variational_noise_std > 0 and self.global_step >= self._optim_variational_noise_start: + for param_name, param in self.decoder.named_parameters(): + if param.grad is not None: + noise = torch.normal( + mean=0.0, + std=self._optim_variational_noise_std, + size=param.size(), + device=param.device, + dtype=param.dtype, + ) + param.grad.data.add_(noise) + + if self._optim_normalize_joint_txu: + T, U = self._optim_normalize_txu + if T is not None and U is not None: + for param_name, param in self.encoder.named_parameters(): + if param.grad is not None: + param.grad.data.div_(U) + + for param_name, param in self.decoder.named_parameters(): + if param.grad is not None: + param.grad.data.div_(T) + + if self._optim_normalize_encoder_norm: + for param_name, param in self.encoder.named_parameters(): + if param.grad is not None: + norm = param.grad.norm() + param.grad.data.div_(norm) + + if self._optim_normalize_decoder_norm: + for param_name, param in self.decoder.named_parameters(): + if param.grad is not None: + norm = param.grad.norm() + param.grad.data.div_(norm) + + if self._optim_normalize_joint_norm: + for param_name, param in self.joint.named_parameters(): + if param.grad is not None: + norm = param.grad.norm() + param.grad.data.div_(norm) + + # EncDecRNNTModel is exported in 2 parts + def list_export_subnets(self): + return ['encoder', 'decoder_joint'] + + # for export + @property + def decoder_joint(self): + return RNNTDecoderJoint(self.decoder, self.joint) + + @classmethod + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + + Returns: + List of available pre-trained models. + """ + results = [] + + return results + + @property + def wer(self): + return self._wer + + @wer.setter + def wer(self, wer): + self._wer = wer diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/__init__.py new file mode 100644 index 0000000..bfc0908 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.multimodal.speech_cv.modules.linear_projection_video_front_end import ( + LinearProjectionVideoFrontEnd, +) +from nemo.collections.multimodal.speech_cv.modules.resnet_video_front_end import ResNetVideoFrontEnd +from nemo.collections.multimodal.speech_cv.modules.video_augment import VideoAugmentation +from nemo.collections.multimodal.speech_cv.modules.video_preprocessing import VideoPreprocessor diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/linear_projection_video_front_end.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/linear_projection_video_front_end.py new file mode 100644 index 0000000..45e7971 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/linear_projection_video_front_end.py @@ -0,0 +1,143 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections import OrderedDict + +import torch +from torch import nn + +from nemo.core.classes.module import NeuralModule +from nemo.core.neural_types import LengthsType, NeuralType, VideoSignal + + +class LinearProjectionVideoFrontEnd(NeuralModule): + + """ + Linear Projection Video Front-End for Lip Reading + + The spatial dimension is flattened and projected to dim_output using a Linear layer. + This is equivalent to having a convolution layer with a kernel size of the size of the image. + Circle crop can be used as pre-processing to crop the image as a circle around lips and ignore corner pixels + + Args: + in_channels: number of inputs video channels, 1 for grayscale and 3 for RGB + in_height: image height + in_width: image width + dim_output: output feature dimension for linear projection + out_channels_first: Whether outputs should have channels_first format (Batch, Dout, Time) or channels_last (Batch, Time, Dout) + circle_crop: crop the image as a circle before the Linear layer, default to False + circle_radius: the circle radius, default to 1 for full circle + + """ + + def __init__( + self, + in_channels, + in_height, + in_width, + dim_output, + out_channels_first=True, + circle_crop=False, + circle_radius=1.0, + ): + super(LinearProjectionVideoFrontEnd, self).__init__() + + self.out_channels_first = out_channels_first + self.in_height = in_height + self.in_width = in_width + self.dim_output = dim_output + self.in_channels = in_channels + self.circle_crop = circle_crop + self.circle_radius = circle_radius + self.circle_indices = self.get_circle_indices() + + if self.dim_output is not None: + if self.circle_crop: + self.linear_proj = nn.Linear(in_channels * len(self.circle_indices), dim_output) + else: + self.linear_proj = nn.Linear(in_channels * in_height * in_width, dim_output) + else: + self.linear_proj = nn.Identity() + + @property + def input_types(self): + """Returns definitions of module input ports.""" + return OrderedDict( + { + "audio_signal": NeuralType(('B', 'D', 'T', 'H', 'W'), VideoSignal()), + "length": NeuralType(tuple('B'), LengthsType()), + } + ) + + @property + def input_types_for_export(self): + """Returns definitions of module input ports.""" + return OrderedDict( + { + "output_signal": NeuralType(('B', 'D', 'T'), NeuralType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + ) + + def get_circle_indices(self): + + """ return image indices inside circle of radius circle_radius """ + + # Create linspace + linspace_height = (torch.linspace(0, 2, steps=self.in_height) - 1).abs() + linspace_width = (torch.linspace(0, 2, steps=self.in_width) - 1).abs() + + # Repeat linspace along height/width + linspace_height = linspace_height.unsqueeze(dim=-1).repeat(1, self.in_width).flatten() + linspace_width = linspace_width.repeat(self.in_height) + + # Compute norm + dist = torch.sqrt(linspace_height.square() + linspace_width.square()) + + # Get circle indices + circle_indices = torch.nonzero(dist <= self.circle_radius).squeeze(dim=-1) + + return circle_indices + + def forward(self, input_signal, length): + + # Permute (B, C, T, H, W) -> (B, T, H, W, C) + input_signal = input_signal.permute(0, 2, 3, 4, 1) + + # Circle Crop + if self.circle_crop: + + # Flatten height, width (B, T, H, W, C) -> (B, T, H*W, C) + input_signal = input_signal.flatten(start_dim=2, end_dim=-2) + + # (B, T, H*W, C) -> (B, T, N circle, C) + input_signal = input_signal[:, :, self.circle_indices] + + # Flatten circle and channels (B, T, N circle, C) -> (B, T, N) + input_signal = input_signal.flatten(start_dim=2, end_dim=-1) + + # Flatten height, width and channels (B, T, H, W, C) -> (B, T, N) + else: + input_signal = input_signal.flatten(start_dim=2, end_dim=-1) + + # Project (B, T, N) -> (B, T, Dout) + input_signal = self.linear_proj(input_signal) + + # Transpose to channels_last format (Batch, Dout, Time) -> (Batch, Time, Dout) + if self.out_channels_first: + output_signal = input_signal.transpose(1, 2) + else: + output_signal = input_signal + + return output_signal, length diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/resnet_video_front_end.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/resnet_video_front_end.py new file mode 100644 index 0000000..33c89e6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/resnet_video_front_end.py @@ -0,0 +1,83 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections import OrderedDict + +from torch import nn + +from nemo.collections.multimodal.speech_cv.parts.submodules.resnet import ResNet +from nemo.core.classes.module import NeuralModule +from nemo.core.neural_types import LengthsType, NeuralType, VideoSignal + + +class ResNetVideoFrontEnd(NeuralModule): + """ + Lip Reading / Visual Speech Recognition (VSR) ResNet Front-End Network + + Paper: + 'Audio-Visual Efficient Conformer for Robust Speech Recognition' by Burchi and Timofte + https://arxiv.org/abs/2301.01456 + + Args: + in_channels: number of inputs video channels, 1 for grayscale and 3 for RGB + model: model size in ["ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"] + dim_output: output feature dimension for linear projection after spacial average pooling + out_channels_first: Whether outputs should have channels_first format (Batch, Dout, Time) or channels_last (Batch, Time, Dout) + """ + + def __init__(self, in_channels=1, model="ResNet18", dim_output=256, out_channels_first=True): + super(ResNetVideoFrontEnd, self).__init__() + + self.front_end = nn.Sequential( + nn.Conv3d( + in_channels=in_channels, out_channels=64, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=(2, 3, 3) + ), + nn.BatchNorm3d(num_features=64), + nn.ReLU(), + nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)), + ResNet(include_stem=False, dim_output=dim_output, model=model), + ) + + self.out_channels_first = out_channels_first + + @property + def input_types(self): + """Returns definitions of module input ports.""" + return OrderedDict( + { + "audio_signal": NeuralType(('B', 'D', 'T', 'H', 'W'), VideoSignal()), + "length": NeuralType(tuple('B'), LengthsType()), + } + ) + + @property + def input_types_for_export(self): + """Returns definitions of module input ports.""" + return OrderedDict( + { + "output_signal": NeuralType(('B', 'D', 'T'), NeuralType()), + "length": NeuralType(tuple('B'), LengthsType()), + } + ) + + def forward(self, input_signal, length): + + # Front-End Network (Batch, Din, Time, Height, Width) -> (Batch, Dout, Time) + input_signal = self.front_end(input_signal) + + # Transpose to channels_last format (Batch, Dout, Time) -> (Batch, Time, Dout) + if not self.out_channels_first: + input_signal = input_signal.transpose(1, 2) + + return input_signal, length diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/video_augment.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/video_augment.py new file mode 100644 index 0000000..ba0bd55 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/video_augment.py @@ -0,0 +1,224 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import random +from collections import OrderedDict + +import torch +from torch import nn + +from nemo.core.classes.module import NeuralModule +from nemo.core.neural_types import NeuralType, VideoSignal + +try: + import torchvision + + TORCHVISION_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + TORCHVISION_AVAILABLE = False + + +class VideoAugmentation(NeuralModule): + + """ Video Augmentation for batched video input: input_signal shape (B, C, T, H, W) """ + + def __init__( + self, + random_crop, + crop_size, + horizontal_flip, + time_masking, + num_mask_second=1.0, + spatial_masking=False, + mean_frame=True, + ): + super().__init__() + + # Params + self.random_crop = random_crop + self.crop_size = crop_size + self.horizontal_flip = horizontal_flip + self.time_masking = time_masking + self.spatial_masking = spatial_masking + + self.training_augments = nn.ModuleList() + self.inference_augments = nn.ModuleList() + + # Random Crop + if self.random_crop: + if TORCHVISION_AVAILABLE: + self.training_augments.append(torchvision.transforms.RandomCrop(self.crop_size)) + self.inference_augments.append(torchvision.transforms.CenterCrop(self.crop_size)) + else: + raise Exception("RandomCrop transform requires torchvision") + + # Horizontal Flip + if self.horizontal_flip: + if TORCHVISION_AVAILABLE: + self.training_augments.append(torchvision.transforms.RandomHorizontalFlip()) + else: + raise Exception("RandomHorizontalFlip transform requires torchvision") + + # Time Masking + if self.time_masking: + self.training_augments.append(VideoFrameMasking(num_mask_second=num_mask_second, mean_frame=mean_frame)) + + # Spatial Masking + if self.spatial_masking: + self.training_augments.append(SpatialVideoMasking(mean_frame=mean_frame)) + + @property + def input_types(self): + """Returns definitions of module input ports.""" + return OrderedDict({"input_signal": NeuralType(('B', 'D', 'T', 'H', 'W'), VideoSignal()),}) + + @property + def input_types_for_export(self): + """Returns definitions of module input ports.""" + return OrderedDict({"output_signal": NeuralType(('B', 'D', 'T', 'H', 'W'), VideoSignal()),}) + + @torch.no_grad() + def forward(self, input_signal, length): + + if self.training: + augments = self.training_augments + else: + augments = self.inference_augments + + output_signal = input_signal + + for augment in augments: + if isinstance(augment, VideoFrameMasking) or isinstance(augment, SpatialVideoMasking): + output_signal = augment(output_signal, length) + else: + output_signal = augment(output_signal) + + return output_signal + + +class SpatialVideoMasking(NeuralModule): + + """ Spatial Video Mask + + Will mask videos frames in the spatial dimensions using horizontal and vertical masks + + params: + num_horizontal_masks: number of horizontal masks + num_vertical_masks: number of vertical masks + max_h: maximum width of horizontal mask + max_v: maximum width of vertical mask + mean_frame: mask using video mean instead of zeros + + """ + + def __init__(self, num_horizontal_masks=1, num_vertical_masks=1, max_h=30, max_v=30, mean_frame=True): + super().__init__() + + self.num_horizontal_masks = num_horizontal_masks + self.num_vertical_masks = num_vertical_masks + self.max_h = max_h + self.max_v = max_v + self.mean_frame = mean_frame + self.random = random.Random() + + def forward(self, input_signal, length): + + # (B, C, T, H, W) + shape = input_signal.shape + + # Batch loop + for b in range(shape[0]): + + # Mask Value + mask_value = input_signal[b, :, : length[b]].mean() if self.mean_frame else 0.0 + + # Horizontal Mask loop + for i in range(self.num_horizontal_masks): + + # Start index + x = self.random.randint(0, shape[3] - self.max_h) + + # Mask width + w = self.random.randint(0, self.max_h) + + # Apply mask + input_signal[b, :, :, x : x + w] = mask_value + + # Vertical Mask loop + for i in range(self.num_vertical_masks): + + # Start index + x = self.random.randint(0, shape[4] - self.max_v) + + # Mask width + w = self.random.randint(0, self.max_v) + + # Apply mask + input_signal[b, :, :, :, x : x + w] = mask_value + + return input_signal + + +class VideoFrameMasking(NeuralModule): + + """ Video Frame Mask: + + As explained in: + "Visual Speech Recognition for Multiple Languages in the Wild" + https://arxiv.org/abs/2202.13084 + + S6 Time Masking + We mask n consecutive frames with the mean frame of the video. + The duration tn is chosen from 0 to an upper bound nmax using a uniform distribution. + Since there is a large variance in the video lengths of the LRS2 and LRS3 datasets, we set the number of masks proportional to the sequence length. + Specifically, we use one mask per second, and for each mask, the maximum duration nmax is set to 0.4 seconds. + + """ + + def __init__(self, T_second=0.4, num_mask_second=1.0, fps=25.0, mean_frame=True): + super().__init__() + + self.T = int(T_second * fps) + self.num_mask_second = num_mask_second + self.mean_frame = mean_frame + self.fps = fps + self.random = random.Random() + + def forward(self, input_signal, length): + + # (B, C, T, H, W) + shape = input_signal.shape + + # Batch loop + for b in range(shape[0]): + + # Mask per second + mT = int(length[b] / self.fps * self.num_mask_second) + + # Mask Value + mask_value = input_signal[b, :, : length[b]].mean() if self.mean_frame else 0.0 + + # Mask loop + for i in range(mT): + + # Start left Frame + x_left = self.random.randint(0, length[b] - self.T) + + # Mask width + w = self.random.randint(0, self.T) + + # Apply mask + input_signal[b, :, x_left : x_left + w] = mask_value + + return input_signal diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/video_preprocessing.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/video_preprocessing.py new file mode 100644 index 0000000..30accea --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/modules/video_preprocessing.py @@ -0,0 +1,138 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from torch import nn + +from nemo.collections.multimodal.speech_cv.parts.submodules.permute import Permute +from nemo.core.classes import NeuralModule, typecheck + +try: + import torchvision + + TORCHVISION_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + TORCHVISION_AVAILABLE = False + + +class VideoPreprocessor(NeuralModule): + + """ Video Pre-processing + + args: + grayscale: convert images to grayscale + normalize: normalize videos + resize: resize videos + resize_size: output image size for resize + norm_mean: normalize mean + norm_std: normalize std + + """ + + def __init__(self, grayscale, normalize, resize, resize_size, norm_mean, norm_std): + super().__init__() + + # Params + self.grayscale = grayscale + self.normalize = normalize + self.resize = resize + self.resize_size = resize_size + self.norm_mean = norm_mean + self.norm_std = norm_std + + self.transforms = nn.ModuleList() + + # Convert float32 [0:255] -> [0:1] + if TORCHVISION_AVAILABLE: + self.transforms.append(torchvision.transforms.ConvertImageDtype(dtype=torch.float32)) + else: + raise Exception("ConvertImageDtype transform requires torchvision") + + # Convert Channels First + self.transforms.append(Permute(dims=(0, 4, 1, 2, 3))) # (B, T, H, W, C) -> (B, C, T, H, W) + + # Resize + if self.resize: + self.transforms.append(ResizeVideo(self.resize_size)) # (B, C, T, H, W) -> (B, C, T, H', W') + + # Grayscale + if self.grayscale: + if TORCHVISION_AVAILABLE: + self.transforms.append( + nn.Sequential( + Permute(dims=(0, 2, 1, 3, 4)), # (B, C, T, H, W) -> (B, T, C, H, W) + torchvision.transforms.Grayscale(), + Permute(dims=(0, 2, 1, 3, 4)), # (B, T, C, H, W) -> (B, C, T, H, W) + ) + ) + else: + raise Exception("Grayscale transform requires torchvision") + + # Normalize + if self.normalize: + self.transforms.append(NormalizeVideo(mean=norm_mean, std=norm_std)) + + @typecheck() + @torch.no_grad() + def forward(self, input_signal, length): + + for transform in self.transforms: + input_signal = transform(input_signal) + + return input_signal, length + + +class NormalizeVideo(NeuralModule): + def __init__(self, mean, std): + super().__init__() + + self.register_buffer( + "mean", torch.tensor(mean, dtype=torch.float32).reshape(len(mean), 1, 1, 1), persistent=False + ) + self.register_buffer( + "std", torch.tensor(std, dtype=torch.float32).reshape(len(std), 1, 1, 1), persistent=False + ) + + def forward(self, x): + + x = (x - self.mean) / self.std + + return x + + +class ResizeVideo(NeuralModule): + def __init__(self, size): + super().__init__() + + self.size = size + if TORCHVISION_AVAILABLE: + self.resize = torchvision.transforms.Resize(size=self.size) + else: + raise Exception("Resize transform requires torchvision") + + def forward(self, x): + + # (B, C, T, H, W) + if x.dim() == 5: + + B, C = x.shape[:2] + x = x.flatten(start_dim=0, end_dim=1) + x = self.resize(x) + x = x.reshape((B, C) + x.shape[1:]) + + # (C, T, H, W) + elif x.dim() == 4: + x = self.resize(x) + + return x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/preprocessing/features.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/preprocessing/features.py new file mode 100644 index 0000000..29b5268 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/preprocessing/features.py @@ -0,0 +1,62 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import tempfile + +try: + import torchvision + + TORCHVISION_AVAILABLE = True +except (ImportError, ModuleNotFoundError): + TORCHVISION_AVAILABLE = False + + +class VideoFeaturizer(object): + def __init__(self): + pass + + def process(self, video_file, offset, duration): + + # Load Video + video = self.from_file(video_file, offset=offset, duration=duration) + + return video + + def from_file(self, video_file, offset, duration): + + if not TORCHVISION_AVAILABLE: + raise Exception("Reading Video requires torchvision") + + # Load from filename + if isinstance(video_file, str): + video, audio, infos = torchvision.io.read_video( + video_file, start_pts=offset, end_pts=offset + duration, pts_unit="sec" + ) + + # Load from bytes + elif isinstance(video_file, bytes): + + # webdataset.torch_video + with tempfile.TemporaryDirectory() as dirname: + fname = os.path.join(dirname, f"file.mp4") + with open(fname, "wb") as stream: + stream.write(video_file) + video, audio, infos = torchvision.io.read_video( + fname, start_pts=offset, end_pts=offset + duration, pts_unit="sec" + ) + else: + raise Exception("Unknown video data format") + + return video diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/__init__.py new file mode 100644 index 0000000..4fc5054 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/conv2d.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/conv2d.py new file mode 100644 index 0000000..25f6e54 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/conv2d.py @@ -0,0 +1,72 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Union + +from torch import nn +from torch.nn import init +from torch.nn.common_types import _size_2_t + + +class Conv2d(nn.Conv2d): + + """ + Conv2d layer with ResNet initialization: + + Reference: "Deep Residual Learning for Image Recognition" by He et al. + https://arxiv.org/abs/1512.03385 + + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: _size_2_t, + stride: _size_2_t = 1, + padding: Union[str, _size_2_t] = 0, + dilation: _size_2_t = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = 'zeros', + device=None, + dtype=None, + weight_init: str = "default", + bias_init: str = "default", + ): + + super(Conv2d, self).__init__( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias, + padding_mode=padding_mode, + device=device, + dtype=dtype, + ) + + # Weight Init + assert weight_init in ["default", "he_normal"] + if weight_init == "he_normal": + init.kaiming_normal_(self.weight) + + # Bias Init + assert bias_init in ["default", "zeros"] + if self.bias is not None: + if bias_init == "zeros": + init.zeros_(self.bias) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/global_avg_pool2d.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/global_avg_pool2d.py new file mode 100644 index 0000000..6c248d8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/global_avg_pool2d.py @@ -0,0 +1,28 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from torch import nn + + +class GlobalAvgPool2d(nn.Module): + def __init__(self, dim=(2, 3), keepdim=False): + super(GlobalAvgPool2d, self).__init__() + self.dim = dim + self.keepdim = keepdim + + def forward(self, x): + + assert x.dim() == 4, "input signal should have 4 dims, has {}".format(x.dim()) + + return x.mean(dim=self.dim, keepdim=self.keepdim) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/permute.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/permute.py new file mode 100644 index 0000000..abd4ce3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/permute.py @@ -0,0 +1,28 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from torch import nn + + +class Permute(nn.Module): + def __init__(self, dims, make_contiguous=False): + super(Permute, self).__init__() + self.dims = dims + self.make_contiguous = make_contiguous + + def forward(self, x): + x = x.permute(self.dims) + if self.make_contiguous: + x = x.contiguous() + return x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/resnet.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/resnet.py new file mode 100644 index 0000000..c911db6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/resnet.py @@ -0,0 +1,175 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from torch import nn + +from nemo.collections.multimodal.speech_cv.parts.submodules.conv2d import Conv2d +from nemo.collections.multimodal.speech_cv.parts.submodules.global_avg_pool2d import GlobalAvgPool2d +from nemo.collections.multimodal.speech_cv.parts.submodules.resnet_block import ResNetBlock +from nemo.collections.multimodal.speech_cv.parts.submodules.resnet_bottleneck_block import ResNetBottleneckBlock + + +class ResNet(nn.Module): + + """ ResNet (ResNet18, ResNet34, ResNet50, ResNet101, ResNet152) + Models: 224 x 224 + ResNet18: 11,689,512 Params + ResNet34: 21,797,672 Params + ResNet50: 25,557,032 Params + ResNet101: 44,549,160 Params + Resnet152: 60,192,808 Params + Reference: "Deep Residual Learning for Image Recognition" by He et al. + https://arxiv.org/abs/1512.03385 + """ + + def __init__(self, dim_input=3, dim_output=1000, model="ResNet50", include_stem=True, include_head=True): + super(ResNet, self).__init__() + + assert model in ["ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"] + + if model == "ResNet18": + dim_stem = 64 + dim_blocks = [64, 128, 256, 512] + num_blocks = [2, 2, 2, 2] + bottleneck = False + elif model == "ResNet34": + dim_stem = 64 + dim_blocks = [64, 128, 256, 512] + num_blocks = [3, 4, 6, 3] + bottleneck = False + elif model == "ResNet50": + dim_stem = 64 + dim_blocks = [256, 512, 1024, 2048] + num_blocks = [3, 4, 6, 3] + bottleneck = True + elif model == "ResNet101": + dim_stem = 64 + dim_blocks = [256, 512, 1024, 2048] + num_blocks = [3, 4, 23, 3] + bottleneck = True + elif model == "ResNet152": + dim_stem = 64 + dim_blocks = [256, 512, 1024, 2048] + num_blocks = [3, 8, 36, 3] + bottleneck = True + + self.stem = ( + nn.Sequential( + Conv2d( + in_channels=dim_input, + out_channels=dim_stem, + kernel_size=(7, 7), + stride=(2, 2), + weight_init="he_normal", + bias=False, + ), + nn.BatchNorm2d(num_features=dim_stem), + nn.ReLU(), + nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)), + ) + if include_stem + else nn.Identity() + ) + + # Blocks + self.blocks = nn.ModuleList() + for stage_id in range(4): + + for block_id in range(num_blocks[stage_id]): + + # Projection Block + if block_id == 0: + if stage_id == 0: + stride = (1, 1) + bottleneck_ratio = 1 + in_features = dim_stem + else: + stride = (2, 2) + bottleneck_ratio = 2 + in_features = dim_blocks[stage_id - 1] + # Default Block + else: + stride = (1, 1) + in_features = dim_blocks[stage_id] + bottleneck_ratio = 4 + + if bottleneck: + self.blocks.append( + ResNetBottleneckBlock( + in_features=in_features, + out_features=dim_blocks[stage_id], + bottleneck_ratio=bottleneck_ratio, + kernel_size=(3, 3), + stride=stride, + ) + ) + else: + self.blocks.append( + ResNetBlock( + in_features=in_features, + out_features=dim_blocks[stage_id], + kernel_size=(3, 3), + stride=stride, + ) + ) + + # Head + self.head = ( + nn.Sequential( + GlobalAvgPool2d(), + nn.Linear(in_features=dim_blocks[-1], out_features=dim_output) + if dim_output is not None + else nn.Identity(), + ) + if include_head + else nn.Identity() + ) + + def forward(self, x): + + # Is Video + if x.dim() == 5: + + is_video = True + batch_size = x.shape[0] + video_frames = x.shape[2] + + # (B, Din, T, H, W) -> (B * T, Din, H, W) + x = x.transpose(1, 2).flatten(start_dim=0, end_dim=1) + + else: + is_video = False + + # (B, Din, H, W) -> (B, D0, H//4, W//4) + x = self.stem(x) + + # (B, D0, H//4, W//4) -> (B, D4, H//32, W//32) + for block in self.blocks: + x = block(x) + + # (B, D4, H//32, W//32) -> (B, Dout) + x = self.head(x) + + # Is Video + if is_video: + + # (B * T, Dout) -> (B, Dout, T) + if x.dim() == 2: + x = x.reshape(batch_size, video_frames, -1).transpose(1, 2) + + # (B * T, D4, H//32, W//32) -> (B, D4, T, H//32, W//32) + else: + x = x.reshape(batch_size, video_frames, x.shape[1], x.shape[2], x.shape[3]).transpose(1, 2) + + return x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/resnet_block.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/resnet_block.py new file mode 100644 index 0000000..1943631 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/resnet_block.py @@ -0,0 +1,86 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from torch import nn +from torch.nn.modules.utils import _pair + +from nemo.collections.multimodal.speech_cv.parts.submodules.conv2d import Conv2d + + +class ResNetBlock(nn.Module): + + """ ResNet Residual Block used by ResNet18 and ResNet34 networks. + References: "Deep Residual Learning for Image Recognition", He et al. + https://arxiv.org/abs/1512.03385 + """ + + def __init__(self, in_features, out_features, kernel_size, stride, weight_init="he_normal", bias_init="zeros"): + super(ResNetBlock, self).__init__() + + # Convert to pair + kernel_size = _pair(kernel_size) + + # layers + self.layers = nn.Sequential( + Conv2d( + in_channels=in_features, + out_channels=out_features, + kernel_size=kernel_size, + stride=stride, + bias=False, + weight_init=weight_init, + bias_init=bias_init, + padding=((kernel_size[0] - 1) // 2, kernel_size[1] // 2), + ), + nn.BatchNorm2d(out_features), + nn.ReLU(), + Conv2d( + in_channels=out_features, + out_channels=out_features, + kernel_size=kernel_size, + bias=False, + weight_init=weight_init, + bias_init=bias_init, + padding=((kernel_size[0] - 1) // 2, kernel_size[1] // 2), + ), + nn.BatchNorm2d(out_features), + ) + + # Residual Block + if torch.prod(torch.tensor(stride)) > 1 or in_features != out_features: + self.residual = nn.Sequential( + Conv2d( + in_channels=in_features, + out_channels=out_features, + kernel_size=1, + stride=stride, + bias=False, + weight_init=weight_init, + bias_init=bias_init, + ), + nn.BatchNorm2d(out_features), + ) + else: + self.residual = nn.Identity() + + # Joined Post Act + self.joined_post_act = nn.ReLU() + + def forward(self, x): + + # Forward Layers + x = self.joined_post_act(self.layers(x) + self.residual(x)) + + return x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/resnet_bottleneck_block.py b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/resnet_bottleneck_block.py new file mode 100644 index 0000000..50cafa5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/multimodal/speech_cv/parts/submodules/resnet_bottleneck_block.py @@ -0,0 +1,107 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from torch import nn +from torch.nn.modules.utils import _pair + +from nemo.collections.multimodal.speech_cv.parts.submodules.conv2d import Conv2d + + +class ResNetBottleneckBlock(nn.Module): + + """ ResNet Bottleneck Residual Block used by ResNet50, ResNet101 and ResNet152 networks. + References: "Deep Residual Learning for Image Recognition", He et al. + https://arxiv.org/abs/1512.03385 + """ + + def __init__( + self, + in_features, + out_features, + bottleneck_ratio, + kernel_size, + stride, + weight_init="he_normal", + bias_init="zeros", + ): + super(ResNetBottleneckBlock, self).__init__() + + # Assert + assert in_features % bottleneck_ratio == 0 + + # Convert to pair + kernel_size = _pair(kernel_size) + + # layers + self.layers = nn.Sequential( + Conv2d( + in_channels=in_features, + out_channels=in_features // bottleneck_ratio, + kernel_size=1, + bias=False, + weight_init=weight_init, + bias_init=bias_init, + ), + nn.BatchNorm2d(in_features // bottleneck_ratio), + nn.ReLU(), + Conv2d( + in_channels=in_features // bottleneck_ratio, + out_channels=in_features // bottleneck_ratio, + kernel_size=kernel_size, + stride=stride, + bias=False, + weight_init=weight_init, + bias_init=bias_init, + padding=((kernel_size[0] - 1) // 2, kernel_size[1] // 2), + ), + nn.BatchNorm2d(in_features // bottleneck_ratio), + nn.ReLU(), + Conv2d( + in_channels=in_features // bottleneck_ratio, + out_channels=out_features, + kernel_size=1, + bias=False, + weight_init=weight_init, + bias_init=bias_init, + ), + nn.BatchNorm2d(out_features), + ) + + # Joined Post Act + self.joined_post_act = nn.ReLU() + + # Residual Block + if torch.prod(torch.tensor(stride)) > 1 or in_features != out_features: + self.residual = nn.Sequential( + Conv2d( + in_channels=in_features, + out_channels=out_features, + kernel_size=1, + stride=stride, + bias=False, + weight_init=weight_init, + bias_init=bias_init, + ), + nn.BatchNorm2d(out_features), + ) + else: + self.residual = nn.Identity() + + def forward(self, x): + + # Forward Layers + x = self.joined_post_act(self.layers(x) + self.residual(x)) + + return x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/README.md b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/README.md new file mode 100644 index 0000000..fc6644d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/README.md @@ -0,0 +1,13 @@ +NeMo NLP/LLM Collection +======================== + +The NeMo NLP/LLM Collection is designed to provide comprehensive support for on-demand large language community models as well as Nvidia's top LLM offerings. By harnessing the cutting-edge Megatron Core, our LLM collection is highly optimized, empowering NeMo users to undertake foundation model training across thousands of GPUs while facilitating fine-tuning of LLMs using techniques such as SFT and PEFT. Leveraging the Transformer Engine library, our collection ensures seamless support for FP8 workloads on Hopper H100 GPUs. Additionally, we prioritize supporting TRTLLM export for the released models, which can accelerate inference by 2-3x depending on the model size. Here's a detailed list of the models currently supported within the LLM collection: + +- **Bert** +- **GPT-style models** +- **Falcon** +- **code-llama 7B** +- **Mistral** +- **Mixtral** + +Our [documentation](https://docs.nvidia.com/nemo-framework/user-guide/latest/index.html) offers comprehensive insights into each supported model, facilitating seamless integration and utilization within your projects. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/__init__.py new file mode 100644 index 0000000..f6e986d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/__init__.py @@ -0,0 +1,25 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp import data, losses, models, modules +from nemo.package_info import __version__ + +# Set collection version equal to NeMo version. +__version = __version__ + +# Authorship. +__author__ = "NVIDIA Corporation" + +# Set collection name. +__description__ = "Natural Language Processing collection" diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/__init__.py new file mode 100644 index 0000000..78ed9ee --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/__init__.py @@ -0,0 +1,45 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.data_utils import * +from nemo.collections.nlp.data.entity_linking.entity_linking_dataset import EntityLinkingDataset +from nemo.collections.nlp.data.information_retrieval.information_retrieval_dataset import ( + BertInformationRetrievalDataset, +) +from nemo.collections.nlp.data.language_modeling.l2r_lm_dataset import ( + L2RLanguageModelingDataset, + TarredL2RLanguageModelingDataset, +) +from nemo.collections.nlp.data.language_modeling.lm_bert_dataset import ( + BertPretrainingDataset, + BertPretrainingPreprocessedDataloader, +) +from nemo.collections.nlp.data.language_modeling.sentence_dataset import SentenceDataset, TarredSentenceDataset +from nemo.collections.nlp.data.machine_translation.machine_translation_dataset import ( + TarredTranslationDataset, + TranslationDataset, +) +from nemo.collections.nlp.data.question_answering_squad.qa_dataset import SquadDataset +from nemo.collections.nlp.data.text2sparql.text2sparql_dataset import Text2SparqlDataset +from nemo.collections.nlp.data.text_normalization.decoder_dataset import TextNormalizationDecoderDataset +from nemo.collections.nlp.data.text_normalization.tagger_dataset import TextNormalizationTaggerDataset +from nemo.collections.nlp.data.text_normalization.test_dataset import TextNormalizationTestDataset +from nemo.collections.nlp.data.token_classification.token_classification_dataset import ( + BertTokenClassificationDataset, + BertTokenClassificationInferDataset, +) +from nemo.collections.nlp.data.zero_shot_intent_recognition.zero_shot_intent_dataset import ( + ZeroShotIntentDataset, + ZeroShotIntentInferenceDataset, +) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/common/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/common/__init__.py new file mode 100644 index 0000000..4e2ba23 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/common/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.common.sequence_to_sequence_dataset import SequenceToSequenceDataset diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/common/sequence_to_sequence_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/common/sequence_to_sequence_dataset.py new file mode 100644 index 0000000..39f8f35 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/common/sequence_to_sequence_dataset.py @@ -0,0 +1,398 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import numpy as np +import torch + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import ( + get_indexed_dataset_, + get_samples_mapping, +) +from nemo.collections.nlp.data.language_modeling.text_memmap_dataset import TextMemMapDataset +from nemo.core.classes import Dataset +from nemo.utils import logging + +__all__ = ['SequenceToSequenceDataset', 'TextMemmapSequenceToSequenceDataset'] + + +class SequenceToSequenceDataset(Dataset): + """Sequence to Sequence Dataset in memory.""" + + def __init__( + self, + src_file_name: str, + tgt_file_name: str, + src_tokenizer: TokenizerSpec, + tgt_tokenizer: TokenizerSpec, + max_src_seq_length: int, + max_tgt_seq_length: int, + add_bos_to_input: bool = True, + add_eos_to_input: bool = True, + replace_bos_with_pad: bool = False, + ): + super().__init__() + self.src_file_name = src_file_name + self.tgt_file_name = tgt_file_name + self.src_tokenizer = src_tokenizer + self.tgt_tokenizer = tgt_tokenizer + self.max_src_seq_length = max_src_seq_length + self.max_tgt_seq_length = max_tgt_seq_length + self.add_bos_to_input = add_bos_to_input + self.add_eos_to_input = add_eos_to_input + self.replace_bos_with_pad = replace_bos_with_pad + assert self.max_src_seq_length > 0 + assert self.max_tgt_seq_length > 0 + self._check_files_exist() + self._get_examples() + + def _check_files_exist(self): + if not os.path.exists(self.src_file_name): + raise FileNotFoundError(f"Source file {self.src_file_name} not found") + if not os.path.exists(self.tgt_file_name): + raise FileNotFoundError(f"Source file {self.src_file_name} not found") + + def __len__(self): + return len(self.examples) + + def __getitem__(self, idx): + example = self.examples[idx] + text_enc = example['src'] + text_dec = example['tgt'][:-1] + labels = example['tgt'][1:] + return {'text_enc': text_enc, 'text_dec': text_dec, 'labels': labels} + + def _get_examples(self): + self.examples = [] + with open(self.src_file_name, encoding='utf8') as f_src, open(self.tgt_file_name, encoding='utf8') as f_tgt: + for i, (src, tgt) in enumerate(zip(f_src, f_tgt)): + if i % 10000 == 0 and i != 0: + logging.info(f"Read {i} lines from {self.src_file_name} & {self.tgt_file_name}") + src = self.src_tokenizer.text_to_ids(src.strip()) + if self.add_bos_to_input: + src = [self.src_tokenizer.pad_id if self.replace_bos_with_pad else self.src_tokenizer.bos_id] + src + if self.add_eos_to_input: + src = src + [self.src_tokenizer.eos_id] + + tgt = ( + [self.tgt_tokenizer.pad_id if self.replace_bos_with_pad else self.tgt_tokenizer.bos_id] + + self.tgt_tokenizer.text_to_ids(tgt.strip()) + + [self.tgt_tokenizer.eos_id] + ) + # Truncate to max sequence length. + if len(src) > self.max_src_seq_length: + src = src[-self.max_src_seq_length + 1 :] + if len(tgt) > self.max_tgt_seq_length: + tgt = tgt[-self.max_tgt_seq_length + 1 :] + self.examples.append({'src': src, 'tgt': tgt}) + + logging.info(f'Dataset Length : {len(self.examples)}') + + def collate_fn(self, batch): + text_enc = [item['text_enc'] for item in batch] + text_dec = [item['text_dec'] for item in batch] + labels = [item['labels'] for item in batch] + + if isinstance(text_enc[0], np.ndarray): + text_enc = [x.tolist() for x in text_enc] + + if isinstance(text_dec[0], np.ndarray): + text_dec = [x.tolist() for x in text_dec] + + if isinstance(labels[0], np.ndarray): + labels = [x.tolist() for x in labels] + + max_dec_input_length = max([len(item) for item in text_dec]) if text_dec else 0 + max_enc_input_length = max([len(item) for item in text_enc]) if text_enc else 0 + max_label_length = max([len(item) for item in labels]) if labels else 0 + + loss_mask = [([1] * (len(item))) + ([0] * (max_label_length - len(item))) for item in labels] + text_enc = [item + [self.src_tokenizer.pad_id] * (max_enc_input_length - len(item)) for item in text_enc] + text_dec = [item + [self.tgt_tokenizer.pad_id] * (max_dec_input_length - len(item)) for item in text_dec] + labels = [item + [self.tgt_tokenizer.pad_id] * (max_label_length - len(item)) for item in labels] + + text_enc = torch.LongTensor(text_enc) + text_dec = torch.LongTensor(text_dec) + labels = torch.LongTensor(labels) + loss_mask = torch.LongTensor(loss_mask) + + enc_mask = (text_enc != self.src_tokenizer.pad_id).long() + dec_mask = (text_dec != self.tgt_tokenizer.pad_id).long() + + return { + 'text_enc': text_enc, + 'text_dec': text_dec, + 'labels': labels, + 'loss_mask': loss_mask, + 'enc_mask': enc_mask, + 'dec_mask': dec_mask, + } + + +class IndexedSequenceToSequenceDataset(SequenceToSequenceDataset): + """Abstract class for TextMemmapSequenceToSequenceDataset and BinarizedMemmapSequenceToSequenceDataset. + This class is not meant to be used standalone and just as an abstract class for the two subclasses. + """ + + def __init__( + self, + src_file_name: str, + tgt_file_name: str, + src_tokenizer: TokenizerSpec, + tgt_tokenizer: TokenizerSpec, + max_src_seq_length: int, + max_tgt_seq_length: int, + seed: int = 1234, + add_bos_to_enc: bool = True, + add_eos_to_enc: bool = True, + max_num_samples: int = None, + prepend_id: int = None, + ): + """ + src_file_name: Path to a single source file on disk. This is either the path to a raw text file or the prefix to the processed src_file_name.bin/idx files. + src_file_name: Path to a single target file on disk. This is either the path to a raw text file or the prefix to the processed tgt_file_name.bin/idx files. + src_tokenizer: Tokenizer for the source dataset. Instance of a class that inherits TokenizerSpec (ex: SentencePiece). + tgt_tokenizer: Tokenizer for the target dataset. Instance of a class that inherits TokenizerSpec (ex: SentencePiece). + max_src_seq_length: Maximum length of the source sequences. Lines above this length will be truncated. + max_tgt_seq_length: Maximum length of the target sequences. Lines above this length will be truncated. + seed: Random seed for data shuffling. + max_num_samples: Maximum number of samples to load. This can be > dataset length if you want to oversample data. If None, all samples will be loaded. + prepend_id: If not None, prepend this id to the encoder input. + """ + super().__init__( + src_file_name=src_file_name, + tgt_file_name=tgt_file_name, + src_tokenizer=src_tokenizer, + tgt_tokenizer=tgt_tokenizer, + max_src_seq_length=max_src_seq_length, + max_tgt_seq_length=max_tgt_seq_length, + ) + self.seed = seed + self.max_num_samples = max_num_samples + self.add_bos_to_enc = add_bos_to_enc + self.add_eos_to_enc = add_eos_to_enc + self.prepend_id = prepend_id + + logging.info(f'Desired number of samples : {self.max_num_samples}') + logging.info(f'Source Dataset Length : {len(self.src_indexed_dataset)}') + logging.info(f'Target Dataset Length : {len(self.tgt_indexed_dataset)}') + + def __len__(self): + if self.max_num_samples is None: + return len(self.src_indexed_dataset) + else: + return self.max_num_samples + + def _get_sample(self, idx): + if isinstance(idx, np.int64): + idx = idx.item() + + if self.samples_mapping is not None: + assert idx < len(self.samples_mapping) + idx, _, _ = self.samples_mapping[idx] + if isinstance(idx, np.uint32): + idx = idx.item() + + assert idx < len(self.src_indexed_dataset) + src = self.src_indexed_dataset[idx] + tgt = self.tgt_indexed_dataset[idx] + + return src, tgt + + def __getitem__(self, idx): + src, tgt = self._get_sample(idx) + offset = 0 + if self.add_bos_to_enc: + offset += 1 + if self.add_eos_to_enc: + offset += 1 + if self.prepend_id is not None: + offset += 1 + + if len(src) > self.max_src_seq_length - offset: + src = src[: self.max_src_seq_length - offset] + + if self.add_bos_to_enc: + src = np.concatenate([[self.src_tokenizer.bos_id], src]) + + if self.prepend_id is not None: + src = np.concatenate([[self.prepend_id], src]) + + if self.add_eos_to_enc: + src = np.concatenate([src, [self.src_tokenizer.eos_id]]) + + if len(tgt) > self.max_tgt_seq_length - 2: + tgt = tgt[: self.max_tgt_seq_length - 2] + + text_dec = np.concatenate([[self.tgt_tokenizer.bos_id], tgt]) + labels = np.concatenate([tgt, [self.tgt_tokenizer.eos_id]]) + + return {'text_enc': src, 'text_dec': text_dec, 'labels': labels} + + def _build_samples_mapping(self): + if self.max_num_samples is not None: + # This means max src and max tgt sequence length need to be the same + if self.max_src_seq_length != self.max_tgt_seq_length: + raise ValueError( + f"max_src_seq_length ({self.max_src_seq_length}) != max_tgt_seq_length ({self.max_tgt_seq_length}). This is needed for max_samples based training for now." + ) + + self.samples_mapping = get_samples_mapping( + indexed_dataset=self.src_indexed_dataset, + data_prefix=self.src_file_name, + num_epochs=None, + max_num_samples=self.max_num_samples, + max_seq_length=self.max_src_seq_length - 2, + short_seq_prob=0, + seed=self.seed, + name=self.src_file_name.split('/')[-1], + binary_head=False, + ) + else: + self.samples_mapping = None + + +class TextMemmapSequenceToSequenceDataset(IndexedSequenceToSequenceDataset): + """Memory-mapped text sequence to sequence dataset. Operates on raw text files and tokenizes the text on-the-fly.""" + + def __init__( + self, + src_file_name: str, + tgt_file_name: str, + src_tokenizer: TokenizerSpec, + tgt_tokenizer: TokenizerSpec, + max_src_seq_length: int, + max_tgt_seq_length: int, + seed: int = 1234, + max_num_samples: int = None, + add_bos_to_enc: bool = True, + add_eos_to_enc: bool = True, + prepend_id: int = None, + ): + """ + src_file_name: Path to a single source file on disk. The file should contain one sentence per line and be raw text. + tgt_file_name: Path to a single target file on disk. The file should contain one sentence per line aligned with src_file_name and be raw text. + src_tokenizer: Tokenizer for the source dataset. Instance of a class that inherits TokenizerSpec (ex: SentencePiece). + tgt_tokenizer: Tokenizer for the target dataset. Instance of a class that inherits TokenizerSpec (ex: SentencePiece). + max_src_seq_length: Maximum length of the source sequences. Lines above this length will be truncated. + max_tgt_seq_length: Maximum length of the target sequences. Lines above this length will be truncated. + seed: Random seed for data shuffling. + max_num_samples: Maximum number of samples to load. This can be > dataset length if you want to oversample data. If None, all samples will be loaded. + add_bos_to_enc: Add BOS token to the encoder input. + add_eos_to_enc: Add EOS token to the encoder input. + prepend_id: If not None, prepend this id to the encoder input. + """ + self.seed = seed + self.max_num_samples = max_num_samples + super().__init__( + src_file_name=src_file_name, + tgt_file_name=tgt_file_name, + src_tokenizer=src_tokenizer, + tgt_tokenizer=tgt_tokenizer, + max_src_seq_length=max_src_seq_length, + max_tgt_seq_length=max_tgt_seq_length, + seed=seed, + max_num_samples=max_num_samples, + add_bos_to_enc=add_bos_to_enc, + add_eos_to_enc=add_eos_to_enc, + prepend_id=prepend_id, + ) + + def _get_examples(self): + self.src_indexed_dataset = TextMemMapDataset( + dataset_paths=[self.src_file_name], tokenizer=self.src_tokenizer, header_lines=0 + ) + self.tgt_indexed_dataset = TextMemMapDataset( + dataset_paths=[self.tgt_file_name], tokenizer=self.tgt_tokenizer, header_lines=0 + ) + + assert len(self.src_indexed_dataset) == len( + self.tgt_indexed_dataset + ), "src and tgt has different number of lines" + self._build_samples_mapping() + + +class BinarizedMemmapSequenceToSequenceDataset(IndexedSequenceToSequenceDataset): + """Memory-mapped text sequence to sequence dataset. Operates pre-tokenized binarized data files.""" + + def __init__( + self, + src_dataset_prefix: str, + tgt_dataset_prefix: str, + src_tokenizer: TokenizerSpec, + tgt_tokenizer: TokenizerSpec, + max_src_seq_length: int, + max_tgt_seq_length: int, + seed: int = 1234, + max_num_samples: int = None, + add_bos_to_enc: bool = True, + add_eos_to_enc: bool = True, + prepend_id: int = None, + ): + """ + src_dataset_prefix: Path to the *prefix* of a single source bin/idx file on disk. This necessitates the existance src_file_prefix.bin and src_file_prefix.idx. + tgt_dataset_prefix: Path to the *prefix* of a single target aligned with source bin/idx file on disk. This necessitates the existance tgt_file_prefix.bin and tgt_file_prefix.idx. + src_tokenizer: Tokenizer for the source dataset. Instance of a class that inherits TokenizerSpec (ex: SentencePiece). + tgt_tokenizer: Tokenizer for the target dataset. Instance of a class that inherits TokenizerSpec (ex: SentencePiece). + max_src_seq_length: Maximum length of the source sequences. Lines above this length will be truncated. + max_tgt_seq_length: Maximum length of the target sequences. Lines above this length will be truncated. + seed: Random seed for data shuffling. + max_num_samples: Maximum number of samples to load. This can be > dataset length if you want to oversample data. If None, all samples will be loaded. + add_bos_to_enc: Add BOS token to the encoder input. + add_eos_to_enc: Add EOS token to the encoder input. + prepend_id: If not None, prepend this id to the encoder input. + """ + self.src_dataset_prefix = src_dataset_prefix + self.tgt_dataset_prefix = tgt_dataset_prefix + self.seed = seed + self.max_num_samples = max_num_samples + super().__init__( + src_file_name=src_dataset_prefix, + tgt_file_name=tgt_dataset_prefix, + src_tokenizer=src_tokenizer, + tgt_tokenizer=tgt_tokenizer, + max_src_seq_length=max_src_seq_length, + max_tgt_seq_length=max_tgt_seq_length, + seed=seed, + max_num_samples=max_num_samples, + add_bos_to_enc=add_bos_to_enc, + add_eos_to_enc=add_eos_to_enc, + prepend_id=prepend_id, + ) + + def _check_files_exist(self): + if not os.path.exists(self.src_dataset_prefix + ".bin") or not os.path.exists( + self.src_dataset_prefix + ".idx" + ): + raise FileNotFoundError(f"{self.src_dataset_prefix}.bin or {self.src_dataset_prefix}.idx not found") + if not os.path.exists(self.tgt_dataset_prefix + ".bin") or not os.path.exists( + self.tgt_dataset_prefix + ".idx" + ): + raise FileNotFoundError(f"{self.tgt_dataset_prefix}.bin or {self.tgt_dataset_prefix}.idx not found") + + def _get_examples(self): + self.src_indexed_dataset = self._get_indexed_dataset( + self.src_dataset_prefix, data_impl='mmap', skip_warmup=True + ) + self.tgt_indexed_dataset = self._get_indexed_dataset( + self.tgt_dataset_prefix, data_impl='mmap', skip_warmup=True + ) + assert len(self.src_indexed_dataset) == len(self.tgt_indexed_dataset) + self._build_samples_mapping() + + def _get_indexed_dataset(self, data_prefix, data_impl, skip_warmup): + indexed_dataset = get_indexed_dataset_(data_prefix, data_impl, skip_warmup) + return indexed_dataset diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/data_utils/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/data_utils/__init__.py new file mode 100644 index 0000000..f57d67b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/data_utils/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.data_utils.data_preprocessing import * diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/data_utils/data_preprocessing.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/data_utils/data_preprocessing.py new file mode 100644 index 0000000..25884a8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/data_utils/data_preprocessing.py @@ -0,0 +1,623 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import csv +import json +import os +import pickle +import random +import re +import string +from collections import Counter + +import numpy as np +import torch +from tqdm.auto import tqdm + +from nemo.utils import logging +from nemo.utils.env_var_parsing import get_envint + +__all__ = [ + "DataProcessor", + "get_label_stats", + "get_multi_label_stats", + "partition_data", + "write_files", + "write_data", + "create_dataset", + "read_csv", + "get_dataset", + "partition", + "map_entities", + "get_entities", + "get_data", + "reverse_dict", + "get_intent_labels", + "get_stats", + "DATABASE_EXISTS_TMP", + "MODE_EXISTS_TMP", + "is_whitespace", + "write_vocab", + "if_exist", + "remove_punctuation_from_sentence", + "dataset_to_ids", + "get_freq_weights", + "get_freq_weights_bce_with_logits_loss", + "fill_class_weights", + "normalize_answer", + "get_labels_to_labels_id_mapping", + "get_vocab", + "find_newlines", + "load_data_indices", + "chinese_punctuation", + "check_chinese_char", + "normalize_chinese_answer", +] + +DATABASE_EXISTS_TMP = "{} dataset has already been processed and stored at {}" +MODE_EXISTS_TMP = "{} mode of {} dataset has already been processed and stored at {}" + + +class DataProcessor(object): + """Base class for data converters for sequence classification data sets.""" + + def get_train_examples(self, data_dir): + """Gets a collection of `InputExample`s for the train set.""" + raise NotImplementedError() + + def get_dev_examples(self, data_dir): + """Gets a collection of `InputExample`s for the dev set.""" + raise NotImplementedError() + + def get_labels(self): + """Gets the list of labels for this data set.""" + raise NotImplementedError() + + @classmethod + def _read_tsv(cls, input_file, quotechar=None): + """Reads a tab separated value file.""" + with open(input_file, "r", encoding="utf-8-sig") as f: + reader = csv.reader(f, delimiter="\t", quotechar=quotechar) + lines = [] + for line in reader: + # if sys.version_info[0] == 2: + # line = list(unicode(cell, 'utf-8') for cell in line) + lines.append(line) + return lines + + +chinese_punctuation = { + "——", + "‘", + "’", + "“", + "”", + "…", + "、", + "。", + "〈", + "〉", + "《", + "》", + "「", + "」", + "『", + "』", + "【", + "】", + "〔", + "〕", + "！", + "（", + "）", + "，", + "．", + "：", + "；", + "？", +} + + +def check_chinese_char(ch): + """Check if a character is in Chinese.""" + if "\u4e00" <= ch <= "\u9fff" or ch in chinese_punctuation: + return True + else: + return False + + +def normalize_chinese_answer(text): + """Remove the Chinese punctuation and separate Chinese answers to char-level""" + + def remove_punc(text): + exclude = chinese_punctuation + return "".join(ch for ch in text if ch not in exclude) + + def separate_char(text): + ch_list = [] + for ch in text: + ch_list.append(ch) + return ch_list + + return separate_char(remove_punc(text)) + + +def normalize_answer(s): + """Lower text and remove punctuation, articles and extra whitespace.""" + + def remove_articles(text): + return re.sub(r"\b(a|an|the)\b", " ", text) + + def white_space_fix(text): + return " ".join(text.split()) + + def remove_punc(text): + exclude = set(string.punctuation) + return "".join(ch for ch in text if ch not in exclude) + + def lower(text): + return text.lower() + + return white_space_fix(remove_articles(remove_punc(lower(s)))) + + +def get_label_stats(labels, outfile="stats.tsv", verbose=True): + """ + Args: + labels: list of all labels + outfile: path to the file where to save label stats + Returns: + total (int): total number of labels + label_frequencies (list of tuples): each tuple represent (label, label frequency) + max id of the labels + """ + labels = Counter(labels) + total = sum(labels.values()) + out = open(outfile, "w") + i = 0 + freq_dict = {} + label_frequencies = labels.most_common() + for k, v in label_frequencies: + out.write(f"{k}\t\t{round(v/total,5)}\t\t{v}\n") + if verbose and i < 3: + logging.info(f"label: {k}, {v} out of {total} ({(v / total)*100.0:.2f}%).") + i += 1 + freq_dict[k] = v + + return total, freq_dict, max(labels.keys()) + + +def get_multi_label_stats(labels, outfile="stats.tsv", verbose=True): + """ + Args: + labels: list of tuples containing labels for each utterance + Example: If there are 5 intents in total, then (0,1,1,1,0) represents the labels + for an individual utterance. (0,1,1,1,0) indicates that the utterance has labels + at index/line 1,2, and 3 in dict.intents. The list of tuples contain labels for + all utterances. + + outfile: path to the file where to save label stats + + Returns: + total (int): total number of labels + freq_dict (list of tuples): each tuple represents class counts in the form of (negative, positive) + """ + total = len(labels) + positive_class_total = 0 + class_count_dict = {} + + # Get the count of each label in the label dictionary, both the positive and negative classes + for label in labels: + for label_index, val in enumerate(label): + if label_index not in class_count_dict: + class_count_dict[label_index] = [0, 0] + + if val == 1: + positive_class_total += 1 + class_count_dict[label_index][1] += 1 + else: + class_count_dict[label_index][0] += 1 + + if verbose: + three_most_frequent_classes = sorted(class_count_dict, key=lambda idx: class_count_dict[idx][1], reverse=True) + + for cnt, idx in enumerate(three_most_frequent_classes): + if cnt > 2: + break + + positives = class_count_dict[idx][1] + logging.info( + f"label: {idx}, {positives} out of {positive_class_total} ({(positives / positive_class_total)*100.0:.2f}%)." + ) + + return total, class_count_dict, len(labels[0]) - 1 + + +def partition_data(intent_queries, slot_tags, split=0.1): + n = len(intent_queries) + n_dev = int(n * split) + dev_idx = set(random.sample(range(n), n_dev)) + dev_intents, dev_slots, train_intents, train_slots = [], [], [], [] + + dev_intents.append("sentence\tlabel\n") + train_intents.append("sentence\tlabel\n") + + for i, item in enumerate(intent_queries): + if i in dev_idx: + dev_intents.append(item) + dev_slots.append(slot_tags[i]) + else: + train_intents.append(item) + train_slots.append(slot_tags[i]) + return train_intents, train_slots, dev_intents, dev_slots + + +def write_files(data, outfile): + with open(outfile, "w") as f: + for item in data: + item = f"{item.strip()}\n" + f.write(item) + + +def write_data(data, slot_dict, intent_dict, outfold, mode, uncased): + intent_file = open(f"{outfold}/{mode}.tsv", "w") + intent_file.write("sentence\tlabel\n") + slot_file = open(f"{outfold}/{mode}_slots.tsv", "w") + for tokens, slots, intent in data: + text = " ".join(tokens) + if uncased: + text = text.lower() + intent_file.write(f"{text}\t{intent_dict[intent]}\n") + slots = [str(slot_dict[slot]) for slot in slots] + slot_file.write(" ".join(slots) + "\n") + intent_file.close() + slot_file.close() + + +def create_dataset(train, dev, slots, intents, uncased, outfold): + os.makedirs(outfold, exist_ok=True) + if "O" in slots: + slots.remove("O") + slots = sorted(list(slots)) + ["O"] + intents = sorted(list(intents)) + slots = write_vocab(slots, f"{outfold}/dict.slots.csv") + intents = write_vocab(intents, f"{outfold}/dict.intents.csv") + write_data(train, slots, intents, outfold, "train", uncased) + write_data(dev, slots, intents, outfold, "test", uncased) + + +def read_csv(file_path): + rows = [] + with open(file_path, "r") as csvfile: + read_csv = csv.reader(csvfile, delimiter=",") + for row in read_csv: + rows.append(row) + return rows + + +def get_dataset(files, dev_split=0.1): + # entity2value, value2entity = get_entities(files) + data, slots, intents = get_data(files) + if len(data) == 1: + train, dev = partition(data[0], split=dev_split) + else: + train, dev = data[0], data[1] + return train, dev, slots, intents + + +def partition(data, split=0.1): + n = len(data) + n_dev = int(n * split) + dev_idx = set(random.sample(range(n), n_dev)) + dev, train = [], [] + + for i, item in enumerate(data): + if i in dev_idx: + dev.append(item) + else: + train.append(item) + return train, dev + + +def map_entities(entity2value, entities): + for key in entities: + if "data" in entities[key]: + if key not in entity2value: + entity2value[key] = set([]) + + values = [] + for value in entities[key]["data"]: + values.append(value["value"]) + values.extend(value["synonyms"]) + entity2value[key] = entity2value[key] | set(values) + + return entity2value + + +def get_entities(files): + entity2value = {} + for file in files: + with open(file, "r") as json_file: + data = json.load(json_file) + entity2value = map_entities(entity2value, data["entities"]) + + value2entity = reverse_dict(entity2value) + return entity2value, value2entity + + +def get_data(files): + all_data, all_slots, all_intents = [], set(["O"]), set() + for file in files: + file_data = [] + with open(file, "r") as json_file: + data = json.load(json_file) + for intent in data["intents"]: + all_intents.add(intent) + utterances = data["intents"][intent]["utterances"] + for utterance in utterances: + tokens, slots = [], [] + for frag in utterance["data"]: + frag_tokens = frag["text"].strip().split() + tokens.extend(frag_tokens) + if "slot_name" not in frag: + slot = "O" + else: + slot = frag["slot_name"] + all_slots.add(slot) + slots.extend([slot] * len(frag_tokens)) + file_data.append((tokens, slots, intent)) + all_data.append(file_data) + return all_data, all_slots, all_intents + + +def reverse_dict(entity2value): + value2entity = {} + for entity in entity2value: + for value in entity2value[entity]: + value2entity[value] = entity + return value2entity + + +def get_intent_labels(intent_file): + labels = {} + label = 0 + with open(intent_file, "r") as f: + for line in f: + intent = line.strip() + labels[intent] = label + label += 1 + return labels + + +def get_stats(lengths): + logging.info("Some stats of the lengths of the sequences:") + lengths = np.asarray(lengths) + logging.info( + f"Min: {np.min(lengths)} | \ + Max: {np.max(lengths)} | \ + Mean: {np.mean(lengths)} | \ + Median: {np.median(lengths)}" + ) + logging.info(f"75 percentile: {np.percentile(lengths, 75):.2f}") + logging.info(f"99 percentile: {np.percentile(lengths, 99):.2f}") + + +def is_whitespace(c): + if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F: + return True + return False + + +def write_vocab(items, outfile): + vocab = {} + idx = 0 + with open(outfile, "w") as f: + for item in items: + f.write(item + "\n") + vocab[item] = idx + idx += 1 + return vocab + + +def get_labels_to_labels_id_mapping(file): + """ + Reads labels from the file and returns labels to id mapping dictionary + Args: + file: path to file + Returns: + labels to id mapping dictionary + """ + lines = open(file, "r").readlines() + lines = [line.strip() for line in lines if line.strip()] + label_ids = {lines[i]: i for i in range(len(lines))} + return label_ids + + +def if_exist(outfold, files): + if not os.path.exists(outfold): + return False + for file in files: + if not os.path.exists(f"{outfold}/{file}"): + return False + return True + + +def remove_punctuation_from_sentence(sentence): + sentence = re.sub("[" + string.punctuation + "]", "", sentence) + sentence = sentence.lower() + return sentence + + +def dataset_to_ids( + dataset, + tokenizer, + cache_ids=False, + add_bos_eos=True, + cache_data_per_node=False, + use_cache=False, + remove_trailing_newline=False, +): + """ + Reads dataset from file line by line, tokenizes each line with tokenizer, + and returns list of lists which corresponds to ids of tokenized strings. + + Args: + dataset (str): path to dataset + tokenizer: tokenizer to convert text into ids + cache_ids (bool): if True, ids are saved to disk as pickle file + with similar name (e.g., data.txt --> data.txt.pkl) + add_bos_eos (bool): whether to add and symbols (e.g., for NMT) + cache_data_per_node (bool): Cache data on local_rank 0. Use when there is not a shared-filesystem. + use_cache (bool): Use cached ids if they exist. + remove_trailing_newline (bool): Remove trailing newline character. + Returns: + ids: list of ids which correspond to tokenized strings of the dataset + """ + + cached_ids_dataset = dataset + str(".pkl") + if use_cache and os.path.isfile(cached_ids_dataset): + logging.info("Loading cached tokenized dataset ...") + ids = pickle.load(open(cached_ids_dataset, "rb")) + else: + logging.info(f"Tokenizing dataset {dataset}...") + data = open(dataset, "rb").readlines() + ids = [] + for sentence in tqdm(data, desc="Tokenizing sentence"): + text = sentence.decode("utf-8") + if remove_trailing_newline: + text = text.rstrip("\n") + sent_ids = tokenizer.text_to_ids(text) + if add_bos_eos: + sent_ids = [tokenizer.bos_id] + sent_ids + [tokenizer.eos_id] + ids.append(sent_ids) + if cache_ids and ( + not torch.distributed.is_initialized() or (cache_data_per_node and get_envint("LOCAL_RANK", 0) == 0) + ): + logging.info("Caching tokenized dataset ...") + pickle.dump(ids, open(cached_ids_dataset, "wb")) + return ids + + +def get_freq_weights(label_freq): + """ + Goal is to give more weight to the classes with less samples + so as to match the ones with the higher frequencies. We achieve this by + dividing the total frequency by the freq of each label to calculate its weight. + """ + total_size = 0 + for lf in label_freq.values(): + total_size += lf + weighted_slots = {label: (total_size / (len(label_freq) * freq)) for label, freq in label_freq.items()} + + return weighted_slots + + +def get_freq_weights_bce_with_logits_loss(label_freq): + """ + Calculate positive class weights to be passed to BCEWithLogitsLoss + https://pytorch.org/docs/1.9.1/generated/torch.nn.BCEWithLogitsLoss.html + + Args: + label_freq: dictionary of tuples where keys represents class id, and tuple represents counts of positive and negative classes, + positive classes are at index 1 and negative at index 0 + Returns: + weights: dictionary of labels with their weights + """ + weights = {} + + for label_id, class_values in label_freq.items(): + positive_class = class_values[1] + negative_class = class_values[0] + + if positive_class == 0: + weights[label_id] = 0 + + else: + weights[label_id] = float(negative_class) / float(positive_class) + + return weights + + +def fill_class_weights(weights, max_id=-1): + """ + Gets a dictionary of labels with their weights and creates a list with size of the labels filled with those weights. + Missing labels in the dictionary would get value 1. + + Args: + weights: dictionary of weights for labels, labels as keys and weights are their values + max_id: the largest label id in the dataset, default=-1 would consider the largest label in the weights dictionary as max_id + Returns: + weights_list: list of weights for labels + """ + if max_id < 0: + max_id = 0 + for l in weights.keys(): + max_id = max(max_id, l) + + all_weights = [1.0] * (max_id + 1) + for i in range(len(all_weights)): + if i in weights: + all_weights[i] = weights[i] + return all_weights + + +def get_vocab(file): + lines = open(file, "r").readlines() + lines = [line.strip() for line in lines if line.strip()] + labels = {i: lines[i] for i in range(len(lines))} + return labels + + +def find_newlines(contents): + """ + Finds all of the newline positions in a text file. + """ + start = 0 + + while True: + try: + # index and split are much faster than Python for loops + new_start = contents.index(b"\n", start) + line = ( + contents[start:new_start] + .replace(b"\xc2\x99", b" ") + .replace(b"\xc2\xa0", b" ") + .decode("utf-8", errors="ignore") + ) + + if len(line.split()) > 0: + yield start + + start = new_start + 1 + + except ValueError: + break + + +def load_data_indices(idx_file: str, data_file: str, savename: str): + """ + Loads dataset index file if it exsits + """ + data_dir = data_file[: data_file.rfind("/")] + mode = data_file[data_file.rfind("/") + 1 : data_file.rfind(".")] + idx_file = f"{data_dir}/{mode}_{savename}.pkl" + + if os.path.isfile(idx_file): + # If the sentence indices file already exists, load from it + with open(idx_file, "rb") as f: + indices = pickle.load(f) + + return indices, idx_file, data_dir + + return None, idx_file, data_dir diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/__init__.py new file mode 100644 index 0000000..a3992ef --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/__init__.py @@ -0,0 +1,22 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.dialogue.data_processor.sgd_data_processor import DialogueSGDDataProcessor +from nemo.collections.nlp.data.dialogue.dataset import ( + DialogueBERTDataset, + DialogueGPTClassificationDataset, + DialogueSGDBERTDataset, + DialogueZeroShotIntentDataset, +) +from nemo.collections.nlp.data.dialogue.sgd.schema import Schema diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/__init__.py new file mode 100644 index 0000000..2db92b2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/assistant_data_processor.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/assistant_data_processor.py new file mode 100644 index 0000000..98d2480 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/assistant_data_processor.py @@ -0,0 +1,209 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +from nemo.collections.nlp.data.dialogue.data_processor.data_processor import DialogueDataProcessor +from nemo.collections.nlp.data.dialogue.input_example.input_example import DialogueInputExample + +__all__ = ['DialogueAssistantDataProcessor'] + + +class DialogueAssistantDataProcessor(DialogueDataProcessor): + """Data Processor for Assistant dialogues.""" + + def __init__(self, data_dir: str, tokenizer: object, cfg): + """ + Constructs DialogueAssistantDataProcessor + Args: + data_dir: path to data directory + tokenizer: tokenizer object + """ + self.data_dir = data_dir + self._tokenizer = tokenizer + self.cfg = cfg + self.intents = self.open_file("dict.intents.csv") + if self.cfg.preprocess_intent_function == 'remove_domain': + self.intents = [ + DialogueAssistantDataProcessor.normalize_zero_shot_intent(intent) for intent in self.intents + ] + self.slots = self.open_file("dict.slots.csv") + ( + bio_slot_ids_to_unified_slot_ids, + unified_slots, + ) = DialogueAssistantDataProcessor.map_bio_format_slots_to_unified_slots(self.slots) + self.slots = unified_slots + + self.bio_slot_ids_to_unified_slot_ids = bio_slot_ids_to_unified_slot_ids + self.services = sorted(list(set([intent.split('_')[0] for intent in self.intents]))) + self.empty_slot_id = [str(idx) for idx, slot_name in enumerate(self.slots) if slot_name == "O"][0] + + @staticmethod + def normalize_zero_shot_intent(label): + label = label.split('.')[1] + if label == 'nomatch': + return 'no match' + else: + return label.replace('_', ' ') + + def open_file(self, filename): + """ + Reads file into a list + """ + filename = os.path.join(self.data_dir, filename) + with open(filename, "r", encoding="UTF-8") as f: + lines = [i.strip() for i in f.readlines()] + return lines + + @staticmethod + def get_continuous_slots(slot_ids, empty_slot_id, bio_slot_ids_to_unified_slot_ids): + + """ + Extract continuous spans of slot_ids + + To accomodate slots with distinct labels for B-label1 and I-label1, + slot_id = self.bio_slot_ids_to_unified_slot_ids[slot_id] is called to map them both to label1 + + Args: + Slot: list of int representing slot of each word token + For instance, 54 54 54 54 54 54 54 54 18 54 44 44 54 46 46 54 12 + Corresponds to "please set an alarm clock for my next meeting with the team at three pm next friday" + Except for the empty_slot_id (54 in this case), we hope to extract the continuous spans of tokens, + each containing a start position and an exclusive end position + E.g {18: [9, 10], 44: [11, 13], 46: [14, 16], 12: [17, 18]} + """ + slot_id_stack = [] + position_stack = [] + for i in range(len(slot_ids)): + slot_id = slot_ids[i] + + slot_id = bio_slot_ids_to_unified_slot_ids[slot_id] + + if not slot_id_stack or slot_id != slot_id_stack[-1]: + slot_id_stack.append(slot_id) + position_stack.append([]) + position_stack[-1].append(i) + + slot_id_to_start_and_exclusive_end = { + slot_id_stack[i]: [position_stack[i][0], position_stack[i][-1] + 1] + for i in range(len(position_stack)) + if slot_id_stack[i] != empty_slot_id + } + + return slot_id_to_start_and_exclusive_end + + @staticmethod + def map_bio_format_slots_to_unified_slots(slots): + """ + maps BIO format slots to unified slots (meaning that B-alarm_time and I-alarm_time both map to alarm_time) + called even slots does not contain BIO, for unified interface + in that case slots == unified_slots and bio_slot_ids_to_unified_slot_ids is an identity mapping i.e. {"0": "0", "1": "1"} + """ + bio_slot_ids_to_unified_slot_ids = {} + unified_slots = [] + unified_idx = -1 + for idx, slot in enumerate(slots): + if slot.replace('I-', '').replace('B-', '') not in unified_slots: + unified_idx += 1 + unified_slots.append(slot.replace('I-', '').replace('B-', '')) + bio_slot_ids_to_unified_slot_ids[str(idx)] = str(unified_idx) + return bio_slot_ids_to_unified_slot_ids, unified_slots + + def get_dialog_examples(self, dataset_split: str): + """ + Process raw files into DialogueInputExample + Args: + dataset_split: {train, dev, test} + For the assistant dataset, there is no explicit dev set (instead uses the test set as the dev set) + Therefore, this function creates a dev set and a new train set from the train set. + This is done by taking every 10th example and putting it into the dev set, + with all other examples going into the new train set. + """ + examples = [] + + dataset_split_print = {"train": "train", "dev": "train", "test": "test"} + + raw_examples_intent = self.open_file("{}.tsv".format(dataset_split_print[dataset_split])) + # removes header of tsv file + raw_examples_intent = raw_examples_intent[1:] + raw_examples_slots = self.open_file("{}_slots.tsv".format(dataset_split_print[dataset_split])) + + if dataset_split in ["train", "dev"]: + train_idx = [] + dev_idx = [] + for idx in range(len(raw_examples_intent)): + if idx % 10 == 0: + dev_idx.append(idx) + else: + train_idx.append(idx) + + if dataset_split == "train": + raw_examples_intent = [raw_examples_intent[idx] for idx in train_idx] + raw_examples_slots = [raw_examples_slots[idx] for idx in train_idx] + elif dataset_split == "dev": + raw_examples_intent = [raw_examples_intent[idx] for idx in dev_idx] + raw_examples_slots = [raw_examples_slots[idx] for idx in dev_idx] + + for i in range(len(raw_examples_intent)): + utterance, intent_id = raw_examples_intent[i].split('\t') + slot_ids = raw_examples_slots[i].split() + utterance_tokens = utterance.split() + intent = self.intents[int(intent_id)] + slot_id_to_start_and_exclusive_end = DialogueAssistantDataProcessor.get_continuous_slots( + slot_ids, self.empty_slot_id, self.bio_slot_ids_to_unified_slot_ids + ) + + slot_to_start_and_exclusive_end = { + self.slots[int(slot_id)]: position for slot_id, position in slot_id_to_start_and_exclusive_end.items() + } + slot_to_words = { + slot: ' '.join(utterance_tokens[position[0] : position[1]]) + for slot, position in slot_to_start_and_exclusive_end.items() + } + input_example = { + "utterance": utterance, + "labels": {"service": intent.split('_')[0], "intent": intent, "slots": slot_to_words}, + "label_positions": { + "slots": { + slot: {"start": position[0], "exclusive_end": position[1], "slot": slot,} + for slot, position in slot_to_start_and_exclusive_end.items() + } + }, + "possible_labels": { + "service": self.services, + "intent": self.intents, + "slots": { + # this dataset does not support categorical slots (i.e. only extractive slots) + # therefore use empty list for all values + slot: [] + for slot in self.slots + }, + }, + } + example = DialogueInputExample(input_example) + examples.append(example) + return examples + + def get_train_examples(self): + """Gets a collection of `InputExample`s for the train set.""" + return self.get_dialog_examples("train") + + def get_dev_examples(self): + """Gets a collection of `InputExample`s for the dev set.""" + return self.get_dialog_examples("dev") + + def get_test_examples(self): + """Gets a collection of `InputExample`s for the test set.""" + return self.get_dialog_examples("test") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/data_processor.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/data_processor.py new file mode 100644 index 0000000..2a4b21c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/data_processor.py @@ -0,0 +1,86 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import random + +from nemo.collections.nlp.data.data_utils.data_preprocessing import DataProcessor + +__all__ = ['DialogueDataProcessor'] + + +class DialogueDataProcessor(DataProcessor): + """ + Base class for Data Processing for all data sources + + Data Processor is designed to be Model-independent (but Data-dependent) so that + - Encourages experimentation with a variety of models \ + (BERT-style; GPT-style; T5-style), \ + which have different tokenization/preprocessing requirements + - Facilitates experiments with a variety of data sources, + as data is processed into a common format + + Roles + 1. Processes raw files into Dialogue Input Examples. + 2. Keeps all possibly relevant information from the raw files, which + the Dataset class can then determine which labels to use + + """ + + def __init__(self): + raise NotImplementedError() + + def get_train_examples(self): + """Gets a collection of `InputExample`s for the train set.""" + raise NotImplementedError() + + def get_dev_examples(self): + """Gets a collection of `InputExample`s for the dev set.""" + raise NotImplementedError() + + def get_test_examples(self): + """Gets a collection of `InputExample`s for the test set.""" + raise NotImplementedError() + + @staticmethod + def get_relevant_idxs(dataset_split, n_samples, dev_proportion): + """ + Obtain indexes for each dataset_split, when train and dev sets are not in separate files + + Args: + dataset_split: train, dev or test + n_samples: total number of samples + dev_proportion: value from 1 to 99 that represent proportion of data in dev set + Returns: + idxs: indices for relevant samples + """ + + if dataset_split in ["train", "dev"]: + n_dev = int(n_samples * (dev_proportion / 100)) + dev_idxs = random.sample(list(range(n_samples)), n_dev) + if dataset_split == "dev": + idxs = dev_idxs + else: + dev_idxs_set = set(dev_idxs) + train_idxs = [idx for idx in list(range(n_samples)) if idx not in dev_idxs_set] + idxs = train_idxs + + elif dataset_split == "test": + idxs = list(range(n_samples)) + + else: + raise ValueError("please select dataset split from train, dev and test") + + return idxs diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/design_data_processor.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/design_data_processor.py new file mode 100644 index 0000000..5e58919 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/design_data_processor.py @@ -0,0 +1,133 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import pandas as pd + +from nemo.collections.nlp.data.dialogue.data_processor.data_processor import DialogueDataProcessor +from nemo.collections.nlp.data.dialogue.input_example.input_example import DialogueInputExample + +__all__ = ['DialogueDesignDataProcessor'] + + +class DialogueDesignDataProcessor(DialogueDataProcessor): + """Data Processor for Design Dataset""" + + def __init__(self, data_dir: str, tokenizer: object, cfg=None): + """ + Constructs DialogueDesignDataProcessor + Args: + data_dir: path to data directory + tokenizer: tokenizer object + cfg: cfg container for dataset + """ + self.data_dir = data_dir + self._tokenizer = tokenizer + self.cfg = cfg + + def open_csv(self, filename): + """ + Reads file into a list + """ + filename = os.path.join(self.data_dir, filename) + with open(filename, "r", encoding="UTF-8") as f: + df = pd.read_csv(filename) + return df.to_dict(orient='index') + + def get_dialog_examples(self, dataset_split: str): + """ + Process raw files into DialogueInputExample + Args: + dataset_split: {train, dev, test} + Dev set contains self.cfg.dev_proportion % of samples with the rest going into the train set + Test set contains the whole dataset (Dev + Train) as this dataset is small (~100) and primarily used in a zero shot setting + """ + + examples = [] + + raw_examples = self.open_csv('mellon_design_OV.csv') + # remove disabled examples + raw_examples = [raw_examples[i] for i in range(len(raw_examples)) if raw_examples[i]['disabled'] != 'yes'] + + n_samples = len(raw_examples) + + idxs = DialogueDataProcessor.get_relevant_idxs(dataset_split, n_samples, self.cfg.dev_proportion) + + all_intents = sorted(list(set(raw_examples[i]['intent labels'] for i in range(len(raw_examples))))) + all_services = sorted(list(set(raw_examples[i]['domain'] for i in range(len(raw_examples))))) + for i in idxs: + raw_example = raw_examples[i] + utterances = [raw_example['example_{}'.format(i)] for i in range(1, 4)] + service = raw_example['domain'] + intent = raw_example['intent'] + intent_description = raw_example['intent labels'] + system_utterance = raw_example['response'] + + slot_names = [raw_example['slot{}'.format(i)] for i in range(1, 3)] + # these are possible slot values not ground truth slot values + slot_values = [raw_example['slot{}_values'.format(i)] for i in range(1, 3)] + slot_questions = [raw_example['slot{}_values'.format(i)] for i in range(1, 3)] + + for j in range(1, 3): + value = raw_example['slot{}'.format(j)] + if isinstance(value, str): + system_utterance = system_utterance.replace('slot{}'.format(j), value) + + valid_slots_ids = [i for i, slot in enumerate(slot_names) if isinstance(slot, str)] + slot_names = [slot_names[i] for i in valid_slots_ids] + slot_values = [slot_values[i] if isinstance(slot_values[i], str) else '' for i in valid_slots_ids] + slot_questions = [slot_questions[i] if isinstance(slot_questions[i], str) else '' for i in valid_slots_ids] + + for utterance in utterances: + if not isinstance(utterance, str): + continue + input_example = { + "utterance": utterance, + "system_utterance": system_utterance, + "labels": { + "service": service, + "intent": intent_description, + "slots": { + slot: '' for slot in slot_names + }, # dataset does not contain ground truth slot values + }, + "possible_labels": { + 'intent': all_intents, + "service": all_services, + "slots": {slot: slot_values[i] for i, slot in enumerate(slot_names)}, + }, + "description": { + "service": service, + "intent": intent_description, + "slots": {slot: slot_questions[i] for i, slot in enumerate(slot_names)}, + }, + } + + example = DialogueInputExample(input_example) + examples.append(example) + return examples + + def get_train_examples(self): + """Gets a collection of `InputExample`s for the train set.""" + return self.get_dialog_examples("train") + + def get_dev_examples(self): + """Gets a collection of `InputExample`s for the dev set.""" + return self.get_dialog_examples("dev") + + def get_test_examples(self): + """Gets a collection of `InputExample`s for the test set.""" + return self.get_dialog_examples("test") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/mellon_qa_data_processor.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/mellon_qa_data_processor.py new file mode 100644 index 0000000..58814a8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/mellon_qa_data_processor.py @@ -0,0 +1,101 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import pandas as pd + +from nemo.collections.nlp.data.dialogue.data_processor.data_processor import DialogueDataProcessor +from nemo.collections.nlp.data.dialogue.input_example.input_example import DialogueInputExample + +__all__ = ['DialogueMellonQADataProcessor'] + + +class DialogueMellonQADataProcessor(DialogueDataProcessor): + """Data Processor for Mellon QA dialogues. + """ + + def __init__(self, data_dir: str, tokenizer: object, cfg=None): + """ + Constructs DialogueMSMarcoDataProcessor + Args: + data_dir: path to data directory + tokenizer: tokenizer object + cfg: cfg container for dataset + """ + self.data_dir = data_dir + self._tokenizer = tokenizer + self.cfg = cfg + + def open_csv(self, filename): + """ + Reads file into a list + """ + filename = os.path.join(self.data_dir, filename) + with open(filename, "r", encoding="UTF-8") as f: + df = pd.read_csv(filename) + return df.to_dict(orient='index') + + def get_dialog_examples(self, dataset_split: str): + """ + Process raw files into DialogueInputExample + Args: + dataset_split: {train, dev, test} + For the Mellon QA dataset, there is no explicit dev set (instead uses the test set as the dev set) + Therefore, this function creates a dev set and a new train set from the train set. + Dev set contains self.cfg.dev_proportion % of samples with the rest going into the train set + Test set contains the whole dataset (Dev + Train) as this dataset is small (~100) and primarily used in a zero shot setting + """ + + examples = [] + + raw_examples = self.open_csv('mellon_qa_data.csv') + raw_examples = list(raw_examples.values()) + # filter out answers with no answer + raw_examples = [ + example + for example in raw_examples + if isinstance(example['Non Generative Question Answering '], str) + and isinstance(example['Generative Question Answering '], str) + ] + + n_samples = len(raw_examples) + idxs = DialogueDataProcessor.get_relevant_idxs(dataset_split, n_samples, self.cfg.dev_proportion) + + for i in idxs: + utterance = str(raw_examples[i]['Question']) + answer = str(raw_examples[i]['Non Generative Question Answering ']) + well_formed_answer = str(raw_examples[i]['Generative Question Answering ']) + passage = raw_examples[i]['Passage'] + input_example = { + "utterance": utterance, + "example_id": i, + "labels": {"response": answer, "fluent_response": well_formed_answer, "passage": passage,}, + } + example = DialogueInputExample(input_example) + examples.append(example) + return examples + + def get_train_examples(self): + """Gets a collection of `InputExample`s for the train set.""" + return self.get_dialog_examples("train") + + def get_dev_examples(self): + """Gets a collection of `InputExample`s for the dev set.""" + return self.get_dialog_examples("dev") + + def get_test_examples(self): + """Gets a collection of `InputExample`s for the test set.""" + return self.get_dialog_examples("test") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/ms_marco_data_processor.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/ms_marco_data_processor.py new file mode 100644 index 0000000..78f434c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/ms_marco_data_processor.py @@ -0,0 +1,129 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +from ast import literal_eval + +from nemo.collections.nlp.data.dialogue.data_processor.data_processor import DialogueDataProcessor +from nemo.collections.nlp.data.dialogue.input_example.input_example import DialogueInputExample + +__all__ = ['DialogueMSMarcoDataProcessor'] + + +class DialogueMSMarcoDataProcessor(DialogueDataProcessor): + """Data Processor for MS Marco dialogues. (https://github.com/microsoft/MSMARCO-Question-Answering) + Please agree to the Terms of Use before downloading data at + https://msmarco.blob.core.windows.net/msmarco/train_v2.1.json.gz + https://msmarco.blob.core.windows.net/msmarco/dev_v2.1.json.gz + """ + + def __init__(self, data_dir: str, tokenizer: object, cfg=None): + """ + Constructs DialogueMSMarcoDataProcessor + Args: + data_dir: path to data directory + tokenizer: tokenizer object + debug_mode: reduce number of samples to load in order to increase speed of processing + cfg: cfg container for dataset + """ + self.data_dir = data_dir + self._tokenizer = tokenizer + self.cfg = cfg + + def open_json(self, filename): + """ + Reads file into a list + """ + filename = os.path.join(self.data_dir, filename) + with open(filename, "r", encoding="UTF-8") as f: + data = json.load(f) + return data + + def get_dialog_examples(self, dataset_split: str): + """ + Process raw files into DialogueInputExample + Args: + dataset_split: {train, dev, test} + For the MS Marco dataset, there is no explicit dev set (instead uses the test set as the dev set) + Therefore, this function creates a dev set and a new train set from the train set. + Dev set contains self.cfg.dev_proportion % of samples with the rest going into the train set + """ + + examples = [] + + dataset_split_print = {"train": "train", "dev": "train", "test": "dev"} + + raw_examples = self.open_json("{}_v2.1.json".format(dataset_split_print[dataset_split])) + + n_samples = len(raw_examples['answers']) + + idxs = DialogueDataProcessor.get_relevant_idxs(dataset_split, n_samples, self.cfg.dev_proportion) + + if self.cfg.debug_mode: + idxs = idxs[:100] + + for i in idxs: + utterance = raw_examples['query'][str(i)] + # answer need not be extracted from passage + # taking the first answer as the ground truth correct answer as only <1% has multiple answers + answer = raw_examples['answers'][str(i)] + answer = answer[0] if isinstance(answer, list) else answer + + well_formed_answer = raw_examples['wellFormedAnswers'][str(i)] + well_formed_answer = ( + well_formed_answer if isinstance(well_formed_answer, list) else literal_eval(well_formed_answer) + ) + well_formed_answer = well_formed_answer[0] if well_formed_answer else None + query_type = raw_examples['query_type'][str(i)] + candidate_passages = raw_examples['passages'][str(i)] + passage = [ + candidate_passage["passage_text"] + for candidate_passage in candidate_passages + if int(candidate_passage["is_selected"]) + ] + passage = passage[0] if passage else None + + possible_passages = [candidate_passage["passage_text"] for candidate_passage in candidate_passages] + + input_example = { + "utterance": utterance, + "example_id": i, + "labels": { + "service": query_type, + "response": answer, + "fluent_response": well_formed_answer, + "passage": passage, + }, + "possible_labels": { + "service": "LOCATION,NUMERIC,PERSON,DESCRIPTION,ENTITY".split(','), + "passage": possible_passages, + }, + } + example = DialogueInputExample(input_example) + examples.append(example) + return examples + + def get_train_examples(self): + """Gets a collection of `InputExample`s for the train set.""" + return self.get_dialog_examples("train") + + def get_dev_examples(self): + """Gets a collection of `InputExample`s for the dev set.""" + return self.get_dialog_examples("dev") + + def get_test_examples(self): + """Gets a collection of `InputExample`s for the test set.""" + return self.get_dialog_examples("test") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/sgd_data_processor.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/sgd_data_processor.py new file mode 100644 index 0000000..a78e197 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/data_processor/sgd_data_processor.py @@ -0,0 +1,568 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This file contains code artifacts adapted from the original implementation: +https://github.com/google-research/google-research/blob/master/schema_guided_dst/baseline/data_utils.py +""" +import collections +import json +import os +import pickle +import re +from typing import List + +from nemo.collections.nlp.data.dialogue.data_processor.data_processor import DialogueDataProcessor +from nemo.collections.nlp.data.dialogue.input_example.input_example import DialogueInputExample +from nemo.collections.nlp.data.dialogue.sgd.schema import Schema +from nemo.utils import logging +from nemo.utils.get_rank import is_global_rank_zero + +__all__ = ['DialogueSGDDataProcessor'] + +FILE_RANGES = { + "sgd_single_domain": {"train": range(1, 44), "dev": range(1, 8), "test": range(1, 12)}, + "sgd_multi_domain": {"train": range(44, 128), "dev": range(8, 21), "test": range(12, 35)}, + "sgd_all": {"train": range(1, 128), "dev": range(1, 21), "test": range(1, 35)}, + "sgd_all_single": {"train": range(1, 128), "dev": range(1, 8), "test": range(1, 12)}, + "multiwoz": {"train": range(1, 18), "dev": range(1, 3), "test": range(1, 3)}, + "debug_sample": {"train": range(1, 2), "dev": range(1, 2), "test": range(1, 2)}, +} + + +class DialogueSGDDataProcessor(DialogueDataProcessor): + """Data Processor for SGD dialogues. + + More information at https://arxiv.org/abs/1909.05855 + + ***Downloading the dataset*** + # git clone https://github.com/google-research-datasets/dstc8-schema-guided-dialogue.git + + ***Data format*** + SGD data comes with a JSON schema file and dialogue files for each dataset split. + + In the following we will show an example for a service entry in the schema file. + * service_name + * description + * slots + * name + * description + * is_categorical + * possible values + * intents + * name + * description + * required_slots (not used) + * is_transactional (not used) + * optional_slots (not used) + * result_slots (not used) + + + In the following we will show an example for a dialogue. + * dialogue_id + * services + * turns + * frames + * actions + * act + * slot + * values + * service + * slots + * exclusive_end + * slot + * start + * state + * active_intent + * requeste_slots + * slot_values + * speaker - [USER, SYSTEM] + * utterance + + """ + + def __init__( + self, data_dir: str, dialogues_example_dir: str, tokenizer: object, cfg=None, + ): + """ + Constructs DialogueSGDDataProcessor + Args: + data_dir: path to data directory + dialogues_example_dir: path to store processed dialogue examples + tokenizer: tokenizer object + cfg: cfg container for dataset + """ + self.data_dir = data_dir + self.cfg = cfg + + self._task_name = self.cfg.task_name # e.g. "sgd_single_domain" + self._subsample = self.cfg.subsample + + all_schema_json_paths = [] + for dataset_split in ['train', 'test', 'dev']: + all_schema_json_paths.append(os.path.join(self.cfg.data_dir, dataset_split, "schema.json")) + self.schemas = Schema(all_schema_json_paths) + + self.schema_config = { + "MAX_NUM_CAT_SLOT": self.cfg.max_num_cat_slot, + "MAX_NUM_NONCAT_SLOT": self.cfg.max_num_noncat_slot, + "MAX_NUM_VALUE_PER_CAT_SLOT": self.cfg.max_value_per_cat_slot, + "MAX_NUM_INTENT": self.cfg.max_num_intent, + "NUM_TASKS": self.cfg.num_tasks, + "MAX_SEQ_LENGTH": self.cfg.max_seq_length, + } + + train_file_range = FILE_RANGES[self._task_name]["train"] + dev_file_range = FILE_RANGES[self._task_name]["dev"] + test_file_range = FILE_RANGES[self._task_name]["test"] + + self._file_ranges = { + "train": train_file_range, + "dev": dev_file_range, + "test": test_file_range, + } + + self._seen_services = { + "train": set(), + "dev": set(), + "test": set(), + } + + self._tokenizer = tokenizer + + self._dialogues_example_dir = dialogues_example_dir + + self.dial_files = {} + + # slots_relation_list.np would contain the candidate list of slots for each (service, slot) which would be + # looked into when a switch between two services happens in the dialogue and we can not find any value for a slot in the current user utterance. + # This file would get generated from the dialogues in the training set. + self.slots_relation_file = os.path.join( + dialogues_example_dir, f"{self._task_name}_train_slots_relation_list.np" + ) + for dataset in ["train", "dev", "test"]: + # Process dialogue files + dial_file = f"{self._task_name}_{dataset}_examples.json" + dial_file = os.path.join(dialogues_example_dir, dial_file) + self.dial_files[(self._task_name, dataset)] = dial_file + + dialog_paths = DialogueSGDDataProcessor.get_dialogue_files(data_dir, dataset, self._task_name) + dialogs = DialogueSGDDataProcessor.load_dialogues(dialog_paths) + for dialog in dialogs: + self._seen_services[dataset].update(set(dialog['services'])) + + if is_global_rank_zero(): + overwrite_dial_files = not self.cfg.use_cache + self.save_dialog_examples(overwrite_dial_files=overwrite_dial_files) + + def save_dialog_examples(self, overwrite_dial_files: bool): + """ + Preprocesses dialogues and saves to disk. + Args: + overwrite_dial_files: whether or not to overwrite saved file if already exists + """ + for dataset in ["train", "dev", "test"]: + dial_file = self.dial_files[(self._task_name, dataset)] + if not os.path.exists(dial_file) or overwrite_dial_files: + logging.info(f"Start generating the dialogue examples for {dataset} dataset.") + if not os.path.exists(self._dialogues_example_dir): + os.makedirs(self._dialogues_example_dir) + dial_examples, slots_relation_list = self._generate_dialog_examples( + dataset, self.schemas, self._subsample + ) + + with open(dial_file, "w", encoding="UTF-8") as f: + json.dump([i.data for i in dial_examples], f) + + if dataset == "train": + with open(self.slots_relation_file, "wb") as f: + pickle.dump(slots_relation_list, f) + logging.info(f"The slot carry-over list for train set is stored at {self.slots_relation_file}") + + logging.info(f"The dialogue examples for {dataset} dataset saved at {dial_file}") + logging.info(f"Finish generating the dialogue examples for {dataset} dataset.") + + # common interface for Data Processor + def get_train_examples(self): + """Gets a collection of `InputExample`s for the train set.""" + return self.get_dialog_examples("train") + + def get_dev_examples(self): + """Gets a collection of `InputExample`s for the dev set.""" + return self.get_dialog_examples("dev") + + def get_test_examples(self): + """Gets a collection of `InputExample`s for the test set.""" + return self.get_dialog_examples("test") + + def get_labels(self): + """Gets the list of labels for this data set.""" + raise NotImplementedError() + + def get_dialog_examples(self, dataset_split: str) -> List[object]: + """ + Loads preprocessed dialogue examples from disk. + Args: + dataset_split: dataset split + Returns: + dial_examples: list of InputExample's. + """ + if (self._task_name, dataset_split) not in self.dial_files or not os.path.exists( + self.dial_files[(self._task_name, dataset_split)] + ): + raise ValueError( + f"{dataset_split} dialogue examples were not processed for {self._task_name} task. Re-initialize SGDDataProcessor and add {dataset_split} dataset split to datasets arg." + ) + dial_file = self.dial_files[(self._task_name, dataset_split)] + logging.info(f"Loading dialogue examples from {dial_file}.") + + with open(dial_file, "rb") as f: + dial_examples = json.load(f) + dial_examples = [DialogueInputExample(i) for i in dial_examples] + if not os.path.exists(self.slots_relation_file): + raise ValueError( + f"Slots relation file {self.slots_relation_file} does not exist. It is needed for the carry-over mechanism of state tracker for switches between services." + ) + if os.path.getsize(self.slots_relation_file) > 0: + with open(self.slots_relation_file, "rb") as f: + self.schemas._slots_relation_list = pickle.load(f) + logging.info( + f"Loaded the slot relation list for value carry-over between services from {self.slots_relation_file}." + ) + + return dial_examples + + def get_seen_services(self, dataset_split: str): + """ + Returns list of seen services, i.e. both in given and training split + Args: + dataset_split: data split + Returns: + seen_services: list of seen services + """ + seen_services = self._seen_services[dataset_split] + return seen_services + + def _generate_dialog_examples(self, dataset_split: str, schemas: object, subsample: bool): + """ + Returns a list of `InputExample`s of the data splits' dialogues. + Args: + dataset_split: data split, can be "train", "dev", or "test". + schemas: schema for all services of all datasets + subsample: whether to balance postive and negative samples in the dataset + Returns: + examples: a list of `InputExample`s. + """ + logging.info(f'Creating examples and slot relation list from the dialogues started...') + dialog_paths = [ + os.path.join(self.data_dir, dataset_split, "dialogues_{:03d}.json".format(i)) + for i in self._file_ranges[dataset_split] + ] + dialogs = DialogueSGDDataProcessor.load_dialogues(dialog_paths) + + examples = [] + slot_carryover_candlist = collections.defaultdict(int) + for dialog_idx, dialog in enumerate(dialogs): + if dialog_idx % 1000 == 0: + logging.info(f'Processed {dialog_idx} dialogues.') + examples.extend( + self._create_examples_from_dialog(dialog, schemas, dataset_split, slot_carryover_candlist, subsample) + ) + + slots_relation_list = collections.defaultdict(list) + for slots_relation, relation_size in slot_carryover_candlist.items(): + if relation_size > 0: + slots_relation_list[(slots_relation[0], slots_relation[1])].append( + (slots_relation[2], slots_relation[3], relation_size) + ) + slots_relation_list[(slots_relation[2], slots_relation[3])].append( + (slots_relation[0], slots_relation[1], relation_size) + ) + + return examples, slots_relation_list + + def _create_examples_from_dialog( + self, dialog: dict, schemas: object, dataset_split: str, slot_carryover_candlist: dict, subsample: bool + ): + """ + Create examples for every turn in the dialogue. + Args: + dialog: dialogue example + schemas: schema for all services of all datasets + dataset_split: data split + slot_carryover_candlist: a dictionary to keep and count the number of carry-over cases between two slots from two different services + subsample: whether to balance postive and negative samples in the dataset + Returns: + examples: a list of `InputExample`s. + """ + dialog_id = dialog["dialogue_id"] + prev_states = {} + examples = [] + for turn_idx, turn in enumerate(dialog["turns"]): + # Generate an example for every frame in every user turn. + if turn["speaker"] == "USER": + user_utterance = turn["utterance"] + user_frames = {f["service"]: f for f in turn["frames"]} + if self.cfg.system_utterance == 'prev_turn': + if turn_idx > 0: + system_turn = dialog["turns"][turn_idx - 1] + system_utterance = system_turn["utterance"] + system_frames = {f["service"]: f for f in system_turn["frames"]} + else: + system_utterance = "" + system_frames = {} + else: # takes the system utterance of the next turn + system_turn = dialog["turns"][turn_idx + 1] + system_utterance = system_turn["utterance"] + system_frames = {f["service"]: f for f in system_turn["frames"]} + + turn_id = "{}-{}-{:02d}".format(dataset_split, dialog_id, turn_idx) + turn_examples, prev_states, slot_carryover_values = self._create_examples_from_turn( + turn_id, + system_utterance, + user_utterance, + system_frames, + user_frames, + prev_states, + schemas, + subsample, + ) + examples.extend(turn_examples) + + for value, slots_list in slot_carryover_values.items(): + if value in ["True", "False"]: + continue + if len(slots_list) > 1: + for service1, slot1 in slots_list: + for service2, slot2 in slots_list: + if service1 == service2: + continue + if service1 > service2: + service1, service2 = service2, service1 + slot1, slot2 = slot2, slot1 + slot_carryover_candlist[(service1, slot1, service2, slot2)] += 1 + return examples + + def _get_state_update(self, current_state: dict, prev_state: dict) -> dict: + """ + Updates dialogue state + Args: + current_state: slot values pairs for the current dialogue turn + prev_state: slot values pairs for the previous dialogue turns + Returns: + state_update: slot values pairs that are added/updated during the current dialogue turn + """ + state_update = dict(current_state) + for slot, values in current_state.items(): + if slot in prev_state and prev_state[slot][0] in values: + # Remove the slot from state if its value didn't change. + state_update.pop(slot) + return state_update + + @staticmethod + def convert_camelcase_to_lower(label): + """Converts camelcase to lowercase with spaces e.g. 'HelloWorld' --> 'hello world'""" + if label.lower() == "none": + return "none" + label = label.split("_")[0] + tokens = re.findall('[A-Z][^A-Z]*', label) + return ' '.join([token.lower() for token in tokens]) + + def preprocess_intent(self, intent, schemas, service): + if self.cfg.preprocess_intent_function == 'default': + return intent + elif self.cfg.preprocess_intent_function == 'lowercase': + return DialogueSGDDataProcessor.convert_camelcase_to_lower(intent) + elif self.cfg.preprocess_intent_function == 'description': + return schemas.get_service_schema(service).intent_descriptions[intent] + else: + raise ValueError( + 'Only default, lowercase and description are allowed for model.dataset.preprocess_intent_function for SGD task' + ) + + def _create_examples_from_turn( + self, + turn_id: int, + system_utterance: str, + user_utterance: str, + system_frames: dict, + user_frames: dict, + prev_states: dict, + schemas: object, + subsample: bool, + ): + """ + Creates an example for each frame in the user turn. + Args: + turn_id: turn number + system_utterance: last system utterance + user_utterance: lst user utterance + system_frames: all system utterances and slot - slot value pairs + user_frames: all user utterances and slot - slot value pairs + prev_states: slot - slot value pairs from the previous turns + schemas: schema for all services of all datasets + subsample: whether to balance postive and negative samples in the dataset + Returns: + examples: a list of `InputExample`s. + prev_states: updated dialogue state e.g. {'Restaurants_1': {'city': ['San Jose'], 'cuisine': ['American']}} + """ + system_user_utterance = system_utterance + ' ' + user_utterance + states = {} + + examples = [] + slot_carryover_values = collections.defaultdict(list) + + for service, user_frame in user_frames.items(): + + state = user_frame["state"]["slot_values"] + state_update = self._get_state_update(state, prev_states.get(service, {})) + states[service] = state + system_frame = system_frames.get(service, None) + dataset_split, dialog_id, turn_id_ = turn_id.split('-') + dialog_id_1, dialog_id_2 = dialog_id.split('_') + example_id = f"{turn_id}-{service}" + example_id_num = [ + int(dialog_id_1), + int(dialog_id_2), + int(turn_id_), + schemas.get_service_id(service), + ] + intent = user_frames[service]["state"]['active_intent'] + all_possible_slots = schemas.get_service_schema(service).slots + categorical_slots = schemas.get_service_schema(service).categorical_slots + one_example = { + "example_id": example_id, + "example_id_num": example_id_num, + "utterance": user_utterance, + "system_utterance": system_utterance, + "system_slots": {slot["slot"]: slot for slot in system_frame["slots"]} + if system_frame is not None + else None, + "system_actions": system_frame["actions"] if system_frame is not None else None, + "labels": { + "service": service, + "intent": self.preprocess_intent(intent, schemas, service), + "slots": {slot: state[slot] for slot in state_update}, + }, + "label_positions": {"slots": {slot["slot"]: slot for slot in user_frames[service]["slots"]}}, + "possible_labels": { + "service": schemas.services, + "intent": [ + self.preprocess_intent(intent, schemas, service) + for intent in schemas.get_service_schema(service).intents + ], + "slots": { + slot: schemas.get_service_schema(service).get_categorical_slot_values(slot) + if slot in categorical_slots + else [] + for slot in all_possible_slots + }, + }, + "description": { + "service": schemas.get_service_schema(service).description, + "intent": schemas.get_service_schema(service).intent_descriptions[intent], + "slots": { + slot: schemas.get_service_schema(service).slot_descriptions[slot] for slot in state_update + }, + }, + } + + examples.append(DialogueInputExample(one_example)) + + if service not in prev_states and int(turn_id_) > 0: + for slot_name, values in state_update.items(): + for value in values: + slot_carryover_values[value].append((service, slot_name)) + for prev_service, prev_slot_value_list in prev_states.items(): + if prev_service == service: + continue + if prev_service in state: + prev_slot_value_list = state[prev_service] + for prev_slot_name, prev_values in prev_slot_value_list.items(): + for prev_value in prev_values: + slot_carryover_values[prev_value].append((prev_service, prev_slot_name)) + + return examples, states, slot_carryover_values + + def _find_subword_indices( + self, + slot_values: dict, + utterance: str, + char_slot_spans: dict, + alignments: List[int], + subwords: List[str], + bias: int, + ) -> dict: + """ + Find indices for subwords corresponding to slot values. + Args: + slot_values: slot - slot value pairs + utterance: utterance + char_slot_spans: char - slot spans + alignments: alignments + subwords: subtokens mapping + bias: offset + Returns: + span_boundaries: span boundaries + """ + span_boundaries = {} + for slot, values in slot_values.items(): + # Get all values present in the utterance for the specified slot. + value_char_spans = {} + for slot_span in char_slot_spans: + if slot_span["slot"] == slot: + value = utterance[slot_span["start"] : slot_span["exclusive_end"]] + start_tok_idx = alignments[slot_span["start"]] + end_tok_idx = alignments[slot_span["exclusive_end"] - 1] + if 0 <= start_tok_idx < len(subwords): + end_tok_idx = min(end_tok_idx, len(subwords) - 1) + value_char_spans[value] = (start_tok_idx + bias, end_tok_idx + bias) + for v in values: + if v in value_char_spans: + span_boundaries[slot] = value_char_spans[v] + break + return span_boundaries + + @classmethod + def load_dialogues(cls, dialog_json_filepaths: List[str]) -> List[dict]: + """ + Obtain the list of all dialogues from specified json files. + Args: + dialog_json_filepaths: list of json files + Returns: + dialogs: the list of all dialogues + """ + dialogs = [] + for dialog_json_filepath in sorted(dialog_json_filepaths): + with open(dialog_json_filepath, 'r', encoding="UTF-8") as f: + dialogs.extend(json.load(f)) + f.close() + return dialogs + + @classmethod + def get_dialogue_files(cls, data_dir: str, dataset_split: str, task_name: str): + """ + Obtain the list of all dialogue json files + Args: + data_dir: path to the data folder + dataset_split: data split + task_name: SGD task name, see keys of the FILE_RANGES + Returns: + dialog: the list of all dialogue json files paths + """ + return [ + os.path.join(data_dir, dataset_split, 'dialogues_{:03d}.json'.format(fid)) + for fid in FILE_RANGES[task_name][dataset_split] + ] diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/__init__.py new file mode 100644 index 0000000..3352c7b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.dialogue.dataset.dialogue_bert_dataset import DialogueBERTDataset +from nemo.collections.nlp.data.dialogue.dataset.dialogue_gpt_classification_dataset import ( + DialogueGPTClassificationDataset, +) +from nemo.collections.nlp.data.dialogue.dataset.dialogue_sgd_bert_dataset import DialogueSGDBERTDataset +from nemo.collections.nlp.data.dialogue.dataset.dialogue_zero_shot_intent_dataset import DialogueZeroShotIntentDataset diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_bert_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_bert_dataset.py new file mode 100644 index 0000000..0931fe3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_bert_dataset.py @@ -0,0 +1,332 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, Optional + +import numpy as np + +from nemo.collections.nlp.data.data_utils import get_stats +from nemo.collections.nlp.data.dialogue.dataset.dialogue_dataset import DialogueDataset +from nemo.core.neural_types import ChannelType, LabelsType, MaskType, NeuralType +from nemo.utils import logging + +__all__ = ['DialogueBERTDataset', 'DialogueIntentSlotInferenceDataset'] + + +class DialogueBERTDataset(DialogueDataset): + + """ + Creates a dataset to use for the task of joint intent + and slot classification with pretrained model. + + For a dataset to use during inference without labels, see + IntentSlotDataset. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'input_ids': NeuralType(('B', 'T'), ChannelType()), + 'segment_ids': NeuralType(('B', 'T'), ChannelType()), + 'input_mask': NeuralType(('B', 'T'), MaskType()), + 'loss_mask': NeuralType(('B', 'T'), MaskType()), + 'subtokens_mask': NeuralType(('B', 'T'), MaskType()), + 'intent_labels': NeuralType(('B'), LabelsType()), + 'slot_labels': NeuralType(('B', 'T'), LabelsType()), + } + + def __init__(self, dataset_split: str, dialogues_processor: object, tokenizer, cfg): + """ + Args: + dataset_split: dataset split + dialogues_processor: Data generator for dialogues + tokenizer: tokenizer + cfg: config container for dataset + """ + self.cfg = cfg + self.all_possible_labels = dialogues_processor.intents + self.label_to_label_id = {self.all_possible_labels[i]: i for i in range(len(self.all_possible_labels))} + self.all_possible_slots = dialogues_processor.slots + self.slot_name_to_slot_id = {self.all_possible_slots[i]: i for i in range(len(self.all_possible_slots))} + self.empty_slot_name = 'O' + + self.features = dialogues_processor.get_dialog_examples(dataset_split) + self.features = self.features if self.cfg.num_samples == -1 else self.features[: self.cfg.num_samples] + + queries = [feature.data["utterance"] for feature in self.features] + if self.cfg.do_lowercase: + queries = [query.lower() for query in queries] + intents = [self.label_to_label_id[feature.data["labels"]["intent"]] for feature in self.features] + word_level_slots = [self.convert_slot_position_to_slot_ids(feature.data) for feature in self.features] + + features = DialogueBERTDataset.get_features( + queries, + self.cfg.max_seq_length, + tokenizer, + pad_label=self.cfg.pad_label, + word_level_slots=word_level_slots, + ignore_extra_tokens=self.cfg.ignore_extra_tokens, + ignore_start_end=self.cfg.ignore_start_end, + ) + + self.all_input_ids = features[0] + self.all_segment_ids = features[1] + self.all_input_mask = features[2] + self.all_loss_mask = features[3] + self.all_subtokens_mask = features[4] + self.all_slots = features[5] + self.all_intents = intents + + def convert_slot_position_to_slot_ids(self, feature): + slot_ids = [self.slot_name_to_slot_id[self.empty_slot_name] for i in range(len(feature["utterance"].split()))] + slot_name_to_positions = feature["label_positions"]["slots"] + + for slot_name in slot_name_to_positions: + slot_id = self.slot_name_to_slot_id[slot_name] + start = slot_name_to_positions[slot_name]["start"] + exclusive_end = slot_name_to_positions[slot_name]["exclusive_end"] + for to_replace_position in range(start, min(exclusive_end, len(slot_ids))): + slot_ids[to_replace_position] = slot_id + + return slot_ids + + def __len__(self): + return len(self.all_input_ids) + + def __getitem__(self, idx): + return ( + np.array(self.all_input_ids[idx]), + np.array(self.all_segment_ids[idx]), + np.array(self.all_input_mask[idx], dtype=np.longlong), + np.array(self.all_loss_mask[idx]), + np.array(self.all_subtokens_mask[idx]), + self.all_intents[idx], + np.array(self.all_slots[idx]), + ) + + @staticmethod + def truncate_and_pad( + max_seq_length, + ignore_start_end, + with_label, + pad_label, + tokenizer, + all_slots, + all_subtokens, + all_input_mask, + all_loss_mask, + all_subtokens_mask, + all_input_ids, + all_segment_ids, + ): + + too_long_count = 0 + + for i, subtokens in enumerate(all_subtokens): + if len(subtokens) > max_seq_length: + subtokens = [tokenizer.cls_token] + subtokens[-max_seq_length + 1 :] + all_input_mask[i] = [1] + all_input_mask[i][-max_seq_length + 1 :] + all_loss_mask[i] = [1 - ignore_start_end] + all_loss_mask[i][-max_seq_length + 1 :] + all_subtokens_mask[i] = [0] + all_subtokens_mask[i][-max_seq_length + 1 :] + + if with_label: + all_slots[i] = [pad_label] + all_slots[i][-max_seq_length + 1 :] + too_long_count += 1 + + all_input_ids.append([tokenizer.tokens_to_ids(t) for t in subtokens]) + + if len(subtokens) < max_seq_length: + extra = max_seq_length - len(subtokens) + all_input_ids[i] = all_input_ids[i] + [0] * extra + all_loss_mask[i] = all_loss_mask[i] + [0] * extra + all_subtokens_mask[i] = all_subtokens_mask[i] + [0] * extra + all_input_mask[i] = all_input_mask[i] + [0] * extra + + if with_label: + all_slots[i] = all_slots[i] + [pad_label] * extra + + all_segment_ids.append([0] * max_seq_length) + + logging.info(f'{too_long_count} are longer than {max_seq_length}') + return ( + all_slots, + all_subtokens, + all_input_mask, + all_loss_mask, + all_subtokens_mask, + all_input_ids, + all_segment_ids, + ) + + @staticmethod + def get_features( + queries, + max_seq_length, + tokenizer, + pad_label=128, + word_level_slots=None, + ignore_extra_tokens=False, + ignore_start_end=False, + ): + """ + Convert queries (utterance, intent label and slot labels) to BERT input format + """ + + all_subtokens = [] + all_loss_mask = [] + all_subtokens_mask = [] + all_segment_ids = [] + all_input_ids = [] + all_input_mask = [] + sent_lengths = [] + all_slots = [] + + with_label = word_level_slots is not None + + for i, query in enumerate(queries): + words = query.strip().split() + subtokens = [tokenizer.cls_token] + loss_mask = [1 - ignore_start_end] + subtokens_mask = [0] + if with_label: + slots = [pad_label] + + for j, word in enumerate(words): + word_tokens = tokenizer.text_to_tokens(word) + + # to handle emojis that could be neglected during tokenization + if len(word.strip()) > 0 and len(word_tokens) == 0: + word_tokens = [tokenizer.ids_to_tokens(tokenizer.unk_id)] + + subtokens.extend(word_tokens) + # mask all sub-word tokens except the first token in a word + # use the label for the first sub-word token as the label for the entire word to eliminate need for disambiguation + loss_mask.append(1) + loss_mask.extend([int(not ignore_extra_tokens)] * (len(word_tokens) - 1)) + + subtokens_mask.append(1) + subtokens_mask.extend([0] * (len(word_tokens) - 1)) + + if with_label: + slots.extend([word_level_slots[i][j]] * len(word_tokens)) + + subtokens.append(tokenizer.sep_token) + loss_mask.append(1 - ignore_start_end) + subtokens_mask.append(0) + sent_lengths.append(len(subtokens)) + all_subtokens.append(subtokens) + all_loss_mask.append(loss_mask) + all_subtokens_mask.append(subtokens_mask) + all_input_mask.append([1] * len(subtokens)) + if with_label: + slots.append(pad_label) + all_slots.append(slots) + max_seq_length_data = max(sent_lengths) + max_seq_length = min(max_seq_length, max_seq_length_data) if max_seq_length > 0 else max_seq_length_data + logging.info(f'Setting max length to: {max_seq_length}') + get_stats(sent_lengths) + + # truncate and pad samples + ( + all_slots, + all_subtokens, + all_input_mask, + all_loss_mask, + all_subtokens_mask, + all_input_ids, + all_segment_ids, + ) = DialogueBERTDataset.truncate_and_pad( + max_seq_length, + ignore_start_end, + with_label, + pad_label, + tokenizer, + all_slots, + all_subtokens, + all_input_mask, + all_loss_mask, + all_subtokens_mask, + all_input_ids, + all_segment_ids, + ) + + # log examples for debugging + logging.debug("*** Some Examples of Processed Data ***") + for i in range(min(len(all_input_ids), 5)): + logging.debug("i: %s" % (i)) + logging.debug("subtokens: %s" % " ".join(list(map(str, all_subtokens[i])))) + logging.debug("loss_mask: %s" % " ".join(list(map(str, all_loss_mask[i])))) + logging.debug("input_mask: %s" % " ".join(list(map(str, all_input_mask[i])))) + logging.debug("subtokens_mask: %s" % " ".join(list(map(str, all_subtokens_mask[i])))) + if with_label: + logging.debug("slots_label: %s" % " ".join(list(map(str, all_slots[i])))) + + return (all_input_ids, all_segment_ids, all_input_mask, all_loss_mask, all_subtokens_mask, all_slots) + + +class DialogueIntentSlotInferenceDataset(DialogueBERTDataset): + """ + Creates dataset to use for the task of joint intent + and slot classification with pretrained model. + This is to be used during inference only. + It uses list of queries as the input. + + Args: + queries (list): list of queries to run inference on + max_seq_length (int): max sequence length minus 2 for [CLS] and [SEP] + tokenizer (Tokenizer): such as NemoBertTokenizer + pad_label (int): pad value use for slot labels. + by default, it's the neutral label. + + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """ + Returns definitions of module output ports. + """ + return { + 'input_ids': NeuralType(('B', 'T'), ChannelType()), + 'segment_ids': NeuralType(('B', 'T'), ChannelType()), + 'input_mask': NeuralType(('B', 'T'), MaskType()), + 'loss_mask': NeuralType(('B', 'T'), MaskType()), + 'subtokens_mask': NeuralType(('B', 'T'), MaskType()), + } + + def __init__(self, queries, max_seq_length, tokenizer, do_lower_case): + if do_lower_case: + queries = [query.lower() for query in queries] + + features = DialogueBERTDataset.get_features(queries, max_seq_length, tokenizer) + + self.all_input_ids = features[0] + self.all_segment_ids = features[1] + self.all_input_mask = features[2] + self.all_loss_mask = features[3] + self.all_subtokens_mask = features[4] + + def __len__(self): + return len(self.all_input_ids) + + def __getitem__(self, idx): + return ( + np.array(self.all_input_ids[idx]), + np.array(self.all_segment_ids[idx]), + np.array(self.all_input_mask[idx], dtype=np.longlong), + np.array(self.all_loss_mask[idx]), + np.array(self.all_subtokens_mask[idx]), + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_dataset.py new file mode 100644 index 0000000..5540dd3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_dataset.py @@ -0,0 +1,37 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.core.classes import Dataset + +__all__ = ['DialogueDataset'] + + +class DialogueDataset(Dataset): + ''' + Base class for Dialogue Datasets + 1. Performs Model-dependent (but Data-independent) operations (tokenization etc) + 2. This can allow the same model preprocessing for multiple datasources + 3. Users can configurate which labels to use for modelling + (e.g. intent classification, slot filling or sequence generation etc) + ''' + + def __init__(self, dataset_split: str, dialogues_processor: object, **kwargs): + raise NotImplementedError + + def __len__(self): + raise NotImplementedError + + def __getitem__(self, idx: int): + raise NotImplementedError diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_gpt_classification_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_gpt_classification_dataset.py new file mode 100644 index 0000000..1ac04a8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_gpt_classification_dataset.py @@ -0,0 +1,311 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import random +from collections import defaultdict + +import torch + +from nemo.collections.nlp.data.dialogue.dataset.dialogue_dataset import DialogueDataset +from nemo.utils import logging + + +class DialogueGPTClassificationDataset(DialogueDataset): + ''' + Designed for classification tasks such as intent/domain classification as well as slot tagging + + Dataset Class + 1. Performs Model-dependent (but Data-independent) operations (tokenization etc) + 2. This can allow the same model preprocessing for multiple datasources + 3. Users can configurate which labels to use for modelling + (e.g. intent classification, slot filling or both together etc) + ''' + + def __init__(self, dataset_split: str, dialogues_processor: object, tokenizer, cfg): + """ Constructor + Args: + dataset_split: dataset split + dialogues_processor: Data generator for SGD dialogues + tokenizer: tokenizer + cfg: cfg container for dataset + """ + self.cfg = cfg + + if self.cfg.target_template == "with_slots" and self.cfg.eval_mode != "generation": + raise ValueError( + "slot-filling is not supported by eval_mode {}, please set model.dataset.eval_mode=generation instead".format( + self.cfg.eval_mode + ) + ) + if self.cfg.target_template != "with_slots" and self.cfg.field == "slots": + raise ValueError("please set model.dataset.target_template='with_slots' if model.dataset.field='slots'") + self.label_type = self.cfg.field + if self.cfg.target_template == "with_description": + self.label_to_description = defaultdict(str) + self.all_possible_labels = set() + self.tokenizer = tokenizer + self.tokenizer.tokenizer.padding_side = "right" + self.max_candidates = 2 + if not isinstance(dataset_split, str): + dataset_split = dataset_split[0] + self.features = dialogues_processor.get_dialog_examples(dataset_split) + for idx in range(len(self.features)): + self.preprocess_feature(idx) + if self.cfg.debug_mode: + self.features = self.features[:16] + # for few shot learning to append in the prompt + self.lm_features = self.get_lm_samples() + + def transform(self, label): + """ + Normalize labels by replacing underscore with space + + Args: + label: str + Returns: + normalized_label: str + """ + if self.cfg.task == "assistant" and self.cfg.prompt_template != "prompt_tuning": + label = label.replace('_', ' ') + return label + + def __len__(self): + return len(self.features) + + def get_n_tokens_in_sentence(self, sentence): + encodings_dict = self.tokenizer.tokenizer( + sentence, truncation=True, max_length=self.cfg.max_seq_length, padding=False, return_tensors="pt" + ) + output = torch.squeeze(encodings_dict['input_ids']) + return len(output) if len(output.size()) > 0 else 0 + + def preprocess_feature(self, idx): + ex = self.features[idx].data + label = ex["labels"][self.label_type] + candidates = ex["possible_labels"][self.label_type] + + if self.label_type in ["service", "intent"]: + label = self.transform(label) + candidates = [self.transform(candidate) for candidate in candidates] + + self.features[idx].data["labels"][self.label_type] = label + self.features[idx].data["possible_labels"][self.label_type] = candidates + if self.cfg.target_template == "with_description": + description = ex["description"][self.label_type] + self.label_to_description[label] = description + for candidate in candidates: + self.all_possible_labels.add(candidate) + self.max_candidates = max(self.max_candidates, len(candidates)) + + def default_encode(self, sentence): + encodings_dict = self.tokenizer.tokenizer( + sentence, truncation=True, max_length=self.cfg.max_seq_length, padding="max_length", return_tensors="pt" + ) + input_ids = torch.squeeze(encodings_dict['input_ids']) + attn_masks = torch.squeeze(encodings_dict['attention_mask']) + return encodings_dict, input_ids, attn_masks + + @staticmethod + def linearize_slots(slots): + """ + Serialize slots into a linear text + + Args: + slots: dict with each slot_name as key and possible slot values as value + Returns: + linear_slots: text based representation of slot names and values + """ + if not slots: + return "None" + return ", ".join( + ["{}({})".format(slot, value if isinstance(value, str) else value[0]) for slot, value in slots.items()] + ) + + def format_target(self, target, slots=None): + """ + Formats the back part of the training example, after the base_template + for instance, "restaurant" in " service: restaurant" + or "set alarm\nslots: (), ()" in \ + "\nintent: set alarm\nslots: (), ()" + """ + if self.cfg.target_template == "with_description": + return target + ' (' + self.label_to_description[target] + ')' + elif self.cfg.target_template == "default": + return target + elif self.cfg.target_template == "with_slots" and slots is not None and self.cfg.field == "intent": + return target + '\nslots: ' + DialogueGPTClassificationDataset.linearize_slots(slots) + elif self.cfg.target_template == "with_slots" and slots is not None and self.cfg.field == "slots": + return DialogueGPTClassificationDataset.linearize_slots(slots) + else: + raise ValueError("Please choose a target format from {default, with_description, with_slots}") + + def get_lm_samples(self): + max_sample_length = 0 + lm_features = [] + for idx in range(len(self.features)): + ex = self.features[idx].data + utterance = ex["utterance"] + label = ex["labels"][self.label_type] + slots = ex["labels"]["slots"] if self.cfg.target_template == "with_slots" else None + lm_feature = self.format_prompt(utterance) + ' ' + self.format_target(label, slots=slots) + feature_len = self.get_n_tokens_in_sentence(lm_feature) + max_sample_length = max(max_sample_length, feature_len) + lm_features.append(lm_feature) + logging.info("max feature length per sample with label: ".format(max_sample_length)) + logging.info( + "please adjust max seq len to at least {} * ({} + 1) = {} but not too much more for efficiency".format( + max_sample_length, self.cfg.few_shot, max_sample_length * (1 + self.cfg.few_shot) + ) + ) + return lm_features + + def format_prompt(self, utterance, few_shot=0, idx=None): + if self.cfg.prompt_template == "default": + base_template = utterance + ' ' + self.label_type + ':' + elif self.cfg.prompt_template == "i_want_to": + base_template = utterance + ' ' + 'I want to' + elif self.cfg.prompt_template == "prompt_tuning": + base_template = utterance + '\n' + self.label_type + ':' + elif self.cfg.prompt_template == "prompt_tuning_with_options": + base_template = ( + 'possible intents: ' + + ', '.join(sorted(list(self.all_possible_labels))) + + '\n\n' + + utterance + + '\n' + + self.label_type + + ':' + ) + + if few_shot > 0: + few_shot_indices = random.sample(range(len(self.features)), few_shot + 1) + few_shot_indices = [i for i in few_shot_indices if i != idx][:few_shot] + few_shot_samples = [self.lm_features[i] for i in few_shot_indices] + base_template = ( + self.tokenizer.tokenizer.pad_token.join(few_shot_samples) + + self.tokenizer.tokenizer.pad_token + + base_template + ) + return base_template + + def collate_fn(self, batch): + """ + Truncates elements to max length in batch + """ + _, _, _, _, candidate_attn_masks, _, _, _ = zip(*batch) + # determine max length in batch + batch_max_length = 0 + for candidate_attn_mask in candidate_attn_masks: + for one_attn_mask in candidate_attn_mask: + batch_max_length = max(batch_max_length, torch.sum(one_attn_mask).item()) + # padding for tp=2 situation + if batch_max_length % 2: + batch_max_length += 1 + + all_items = [] + for item in zip(*batch): + if isinstance(item[0], int): + item = [torch.tensor(i) for i in item] + item_stack = torch.stack(item) + # if item_stack is 1d, elements refers to indexes and there is no need to truncate + if len(item_stack.size()) == 1: + all_items.append(item_stack) + # otherwise, truncate last dimension to max length in batch + else: + all_items.append(item_stack[..., :batch_max_length]) + return all_items + + def __getitem__(self, idx: int): + + ''' + State how the input and output samples look like + + This template can be changed + + Training example: + e.g. service: restaurant + e.g. service: restaurant + e.g. \nintent: set alarm\nslots: (), () + + Generation example: + e.g. service: + + ''' + ex = self.features[idx].data + + utterance = ex["utterance"] + utterance_length = self.get_n_tokens_in_sentence(utterance) + + label = ex["labels"][self.label_type] + candidates = ex["possible_labels"][self.label_type] + + slots = ex["labels"]["slots"] if self.cfg.target_template == "with_slots" else None + + base_template = self.format_prompt(utterance, few_shot=self.cfg.few_shot, idx=idx) + + sentence_without_answer = base_template + + sentence = base_template + ' ' + self.format_target(label, slots=slots) + + if self.cfg.eval_mode == "binary_score": + candidate_sentences = [] + for candidate in candidates: + positive_answer = base_template + ' ' + candidate + ' Answer: ' + 'yes' + negative_answer = base_template + ' ' + candidate + ' Answer: ' + 'no' + if candidate == label: + correct_candidate = len(candidate_sentences) // 2 + candidate_sentences.append(positive_answer) + candidate_sentences.append(negative_answer) + else: + candidate_sentences.append(negative_answer) + candidate_sentences.append(positive_answer) + else: + correct_candidate = 0 + candidate_sentences = [ + base_template + ' ' + self.format_target(candidate, slots=slots) for candidate in candidates + ] + + encodings_dict, input_ids, attn_masks = self.default_encode(sentence) + + candidate_tokenized_sentences = [ + self.default_encode(candidate_sentence) for candidate_sentence in candidate_sentences + ] + + # ensure all samples have the same number of candidates for collating into tensor + while len(candidate_tokenized_sentences) < self.max_candidates: + candidate_tokenized_sentences.append(candidate_tokenized_sentences[0]) + + candidate_input_ids = torch.stack([i[1] for i in candidate_tokenized_sentences]) + candidate_attn_masks = torch.stack([i[2] for i in candidate_tokenized_sentences]) + + labels = copy.copy(torch.squeeze(encodings_dict['input_ids'])) + + training_mask_end = self.get_n_tokens_in_sentence(sentence_without_answer) + + labels.data = torch.tensor( + [-100 if i < training_mask_end else labels.data[i] for i in range(len(labels.data))] + ) + + return ( + input_ids, + attn_masks, + labels, + candidate_input_ids, + candidate_attn_masks, + training_mask_end, + utterance_length, + correct_candidate, + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_gpt_generation_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_gpt_generation_dataset.py new file mode 100644 index 0000000..7de02d7 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_gpt_generation_dataset.py @@ -0,0 +1,130 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy + +import torch + +from nemo.collections.nlp.data.dialogue.dataset.dialogue_dataset import DialogueDataset + + +class DialogueGPTGenerationDataset(DialogueDataset): + def __init__(self, dataset_split: str, dialogues_processor: object, tokenizer, cfg): + """ Constructor + Designed for free form generation tasks such as Dialogue Response Generation + + Args: + dataset_split: dataset split + dialogues_processor: dialogues processor + tokenizer: tokenizer + cfg: cfg container for dataset + """ + self.cfg = cfg + self.input_label_type = self.cfg.input_field + self.output_label_type = self.cfg.output_field + self.tokenizer = tokenizer + self.tokenizer.tokenizer.padding_side = "right" + if not isinstance(dataset_split, str): + dataset_split = dataset_split[0] + + self.features = dialogues_processor.get_dialog_examples(dataset_split) + self.features = self.remove_invalid_samples(self.features) + + if self.cfg.debug_mode: + self.features = self.features[:16] + + def remove_invalid_samples(self, features): + valid_idxs = [] + all_fields = self.input_label_type.split('+') + self.output_label_type.split('+') + for i in range(len(features)): + features[i].data["labels"]["utterance"] = features[i].data["utterance"] + all_fields_non_empty = True + for field in all_fields: + if not features[i].data["labels"][field] or not features[i].data["labels"][field].strip(): + all_fields_non_empty = False + if all_fields_non_empty: + valid_idxs.append(i) + return [features[i] for i in valid_idxs] + + def __len__(self): + return len(self.features) + + def get_n_tokens_in_sentence(self, sentence): + encodings_dict = self.tokenizer.tokenizer( + sentence, truncation=True, max_length=self.cfg.max_seq_length, padding=False, return_tensors="pt" + ) + output = torch.squeeze(encodings_dict['input_ids']) + return len(output) if len(output.size()) > 0 else 0 + + def default_encode(self, sentence): + encodings_dict = self.tokenizer.tokenizer( + sentence, truncation=True, max_length=self.cfg.max_seq_length, padding="max_length", return_tensors="pt" + ) + input_ids = torch.squeeze(encodings_dict['input_ids']) + attn_masks = torch.squeeze(encodings_dict['attention_mask']) + return encodings_dict, input_ids, attn_masks + + def format_prompt(self, ex): + ''' + Formats training prompt based on self.input_field_type + + Training example: + e.g. response: # input_label_type = response + e.g. utterance: # input_label_type = utterance + e.g. passage: utterance: # input_label_type = passage+utterance + ''' + ex["labels"]["utterance"] = ex["utterance"] + parts = self.input_label_type.split('+') + input_sentence = ' '.join([part + ': ' + ex["labels"][part] for part in parts]) + return input_sentence + + def __getitem__(self, idx: int): + + ''' + For each example, this function determines the format of input and output sequences based on user-specified conguration. + This is controlled by model.dataset.input_field and model.dataset.output_field + For instance: + If model.dataset.input_field == response and model.dataset.output_field == fluent_response: + Input = "response: " and output = "response: fluent_response: " (with loss calculated from only) + If model.dataset.input_field == utterance and model.dataset.output_field == response: + Input = "utterance: " and output = "utterance: response: " (with loss calculated from only) + If model.dataset.input_field == passage+utterance and model.dataset.output_field == response: + Input = "passage: utterance: " and output="passage: utterance: response: " (with loss calculated from only) + ''' + ex = self.features[idx].data + + input_sentence = self.format_prompt(ex) + + utterance_length = self.get_n_tokens_in_sentence(input_sentence) + + output_sentence = ex["labels"][self.output_label_type] + + base_template = input_sentence + + sentence_without_answer = base_template + ' ' + self.output_label_type + ':' + + sentence = sentence_without_answer + ' ' + output_sentence + + encodings_dict, input_ids, attn_masks = self.default_encode(sentence) + + labels = copy.copy(torch.squeeze(encodings_dict['input_ids'])) + + training_mask_end = self.get_n_tokens_in_sentence(sentence_without_answer) + + labels.data = torch.tensor( + [-100 if i < training_mask_end else labels.data[i] for i in range(len(labels.data))] + ) + + return (input_ids, attn_masks, labels, training_mask_end, utterance_length) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_nearest_neighbour_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_nearest_neighbour_dataset.py new file mode 100644 index 0000000..8618f2f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_nearest_neighbour_dataset.py @@ -0,0 +1,87 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch + +from nemo.collections.nlp.data.dialogue.dataset.dialogue_dataset import DialogueDataset + +__all__ = ['DialogueNearestNeighbourDataset'] + + +class DialogueNearestNeighbourDataset(DialogueDataset): + """ + Dataset for training a Nearest Neighbour model for zero shot intent recognition. + """ + + def __init__(self, dataset_split: str, dialogues_processor: object, tokenizer, cfg): + """ + Args: + dataset_split: dataset split + dialogues_processor: Data generator for dialogues + tokenizer: tokenizer to split text into sub-word tokens + """ + self.cfg = cfg + self.tokenizer = tokenizer + self.raw_features = dialogues_processor.get_dialog_examples(dataset_split) + self.max_n = self.find_max_n_candidates() + self.examples = self._create_examples(self.raw_features) + + def find_max_n_candidates(self): + max_n = 0 + for idx in range(len(self.raw_features)): + ex = self.raw_features[idx].data + n = len(ex["possible_labels"]["intent"]) + max_n = max(max_n, n) + return max_n + + def _create_examples(self, raw_features): + """Creates examples for the training and dev sets.""" + examples = [] + seen_utterances = set() + for idx in range(len(raw_features)): + ex = self.raw_features[idx].data + user_utterance = ex["utterance"] + if user_utterance in seen_utterances: + continue + seen_utterances.add(user_utterance) + intent = ex["labels"]["intent"] + sentences = [user_utterance] + labels = [-1] + for candidate_intent in ex["possible_labels"]["intent"]: + text_b = "{} {}".format(self.cfg.prompt_template, candidate_intent) + label = 1 if candidate_intent == intent else 0 + labels.append(label) + sentences.append(text_b) + + while self.max_n > len(labels) - 1: + labels.append(label) + sentences.append(text_b) + + encoded_input = self.tokenizer.tokenizer( + sentences, + padding='max_length', + truncation=True, + return_tensors='pt', + max_length=self.cfg.max_seq_length, + ) + examples.append((encoded_input['input_ids'], encoded_input['attention_mask'], torch.tensor(labels))) + return examples + + def __len__(self): + return len(self.examples) + + def __getitem__(self, idx: int): + return self.examples[idx] diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_s2s_generation_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_s2s_generation_dataset.py new file mode 100644 index 0000000..78fda55 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_s2s_generation_dataset.py @@ -0,0 +1,161 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + +from nemo.collections.nlp.data.dialogue.dataset.dialogue_dataset import DialogueDataset + + +class DialogueS2SGenerationDataset(DialogueDataset): + def __init__(self, dataset_split: str, dialogues_processor: object, tokenizer, cfg): + """ Constructor + Designed for free form generation tasks such as Dialogue Response Generation + + Args: + dataset_split: dataset split + dialogues_processor: dialogues processor + tokenizer: tokenizer + cfg: cfg container for dataset + """ + self.cfg = cfg + self.input_label_type = self.cfg.input_field + self.output_label_type = self.cfg.output_field + self.tokenizer = tokenizer + if not isinstance(dataset_split, str): + dataset_split = dataset_split[0] + + self.features = dialogues_processor.get_dialog_examples(dataset_split) + self.features = self.remove_invalid_samples(self.features) + + if self.cfg.debug_mode: + self.features = self.features[:16] + + @staticmethod + def format_actions(prompt_template, actions): + """ + Formats actions based on prompt_template + + Args: + prompt_template: determines whether acts, slot-names, slot-values are necessary in formatted actions + actions: list of actions, each a dict containing keys 'act', 'slot' and 'values' with their corresponding values as their attribute-values + + Returns: + formatted_actions: string representations of actions, formatted based on the fields needed. + """ + actions_str = [] + for action in actions: + act = action['act'].lower() + slot = action['slot'] + value = action['values'][0] if action['values'] else '' + + if prompt_template == 'values': + action_str = value + elif prompt_template == 'slots_values': + if value: + action_str = '{} ({})'.format(slot, value) + else: + action_str = slot + elif prompt_template == 'acts_slots_values': + if value: + action_str = '{} {} ({})'.format(act, slot, value) + elif slot: + action_str = '{} {}'.format(act, slot) + else: + action_str = act + else: + raise ValueError( + "Please set model.dataset.prompt_template to acts_slots_values, slots_values or values" + ) + actions_str.append(action_str) + return ' '.join(actions_str) + + def remove_invalid_samples(self, features): + valid_idxs = [] + for i in range(len(features)): + for field in ['utterance', 'system_utterance', 'system_actions']: + if field in features[i].data: + features[i].data["labels"][field] = features[i].data[field] + all_fields = self.input_label_type.split('+') + self.output_label_type.split('+') + all_fields_non_empty = True + for field in all_fields: + if not features[i].data["labels"][field]: + all_fields_non_empty = False + if all_fields_non_empty: + valid_idxs.append(i) + return [features[i] for i in valid_idxs] + + def __len__(self): + return len(self.features) + + def get_n_tokens_in_sentence(self, sentence): + encodings_dict = self.tokenizer.tokenizer( + sentence, truncation=True, max_length=self.cfg.max_seq_length, padding=False, return_tensors="pt" + ) + output = torch.squeeze(encodings_dict['input_ids']) + return len(output) if len(output.size()) > 0 else 0 + + def default_encode(self, sentence): + encodings_dict = self.tokenizer.tokenizer( + sentence, truncation=True, max_length=self.cfg.max_seq_length, padding="max_length", return_tensors="pt" + ) + input_ids = torch.squeeze(encodings_dict['input_ids']) + attn_masks = torch.squeeze(encodings_dict['attention_mask']) + return encodings_dict, input_ids, attn_masks + + def format_prompt(self, ex): + ''' + Formats training prompt based on self.input_field_type + + Training example: + e.g. response: # input_label_type = response + e.g. utterance: # input_label_type = utterance + e.g. passage: utterance: # input_label_type = passage+utterance + ''' + parts = self.input_label_type.split('+') + input_sentence = ' '.join([part + ': ' + ex["labels"][part] for part in parts]) + return input_sentence + + def __getitem__(self, idx: int): + + ''' + State how the input and output samples look like + + This template can be changed + + Training example: + e.g. INPUT - "response: " OUTPUT - "" # input_label_type = response, output_label_type = fluent_response + e.g. INPUT - "utterance: " OUTPUT - "" # input_label_type = utterance, output_label_type = response + e.g. INPUT - "passage: utterance: " OUTPUT - "" # input_label_type = passage+utterance, output_label_type = response + ''' + ex = self.features[idx].data + for field in ['utterance', 'system_utterance']: + if field in ex: + ex["labels"][field] = ex[field] + + if 'system_actions' in ex: + ex["labels"]['system_actions'] = DialogueS2SGenerationDataset.format_actions( + self.cfg.prompt_template, ex['system_actions'] + ) + + input_sentence = self.format_prompt(ex) + output_sentence = ex["labels"][self.output_label_type] + + _, input_ids, attn_masks = self.default_encode(input_sentence) + + _, labels, _ = self.default_encode(output_sentence) + + labels[labels == self.tokenizer.tokenizer.pad_token_id] = -100 + + return input_ids, attn_masks, labels diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_sgd_bert_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_sgd_bert_dataset.py new file mode 100644 index 0000000..fcab5e9 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_sgd_bert_dataset.py @@ -0,0 +1,425 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This file contains code artifacts adapted from the original implementation: +https://github.com/google-research/google-research/blob/master/schema_guided_dst +""" + +import os +import re +from typing import List + +import numpy as np + +from nemo.collections.nlp.data.dialogue.dataset.dialogue_dataset import DialogueDataset +from nemo.collections.nlp.data.dialogue.input_example.sgd_input_example import SGDInputExample + +__all__ = ['DialogueSGDBERTDataset'] + + +class DialogueSGDBERTDataset(DialogueDataset): + ''' + Dataset Class + 1. Performs Model-dependent (but Data-independent) operations (tokenization etc) + 2. This can allow the same model preprocessing for multiple datasources + 3. Users can configurate which labels to use for modelling + (e.g. intent classification, slot filling or both together etc) + ''' + + def __init__(self, dataset_split: str, dialogues_processor: object, tokenizer, schemas, schema_config, cfg): + """ Constructor + Args: + dataset_split: dataset split + dialogues_processor: Data generator for SGD dialogues + tokenizer: tokenizer + schemas: SGD schema for domain, intent and slots + schema_config: config dict for schemas + cfg: cfg container for dataset + """ + self.dataset_split = dataset_split + self.tokenizer = tokenizer + self.schemas = schemas + self.schema_config = schema_config + self.dialogues_processor = dialogues_processor + self.cfg = cfg + self.subsample = self.dialogues_processor._subsample + + dial_file = f"{dialogues_processor._task_name}_{dataset_split}_examples_bert.processed" + self.dial_file = os.path.join(self.cfg.data_dir, dial_file) + if self.cfg.use_cache and os.path.exists(self.dial_file): + self.load_features() + else: + self.process_features() + self.save_features() + + def load_features(self): + with open(self.dial_file, "rb") as f: + self.features = np.load(f, allow_pickle=True) + + def process_features(self): + self.features = [] + self.raw_features = self.dialogues_processor.get_dialog_examples(self.dataset_split) + for idx in range(len(self.raw_features)): + self.bert_process_one_sample(idx) + + def save_features(self): + with open(self.dial_file, "wb") as f: + np.save(f, self.features) + + def _tokenize(self, utterance: str): + """ + Tokenize the utterance + + Args: + utterance: A string containing the utterance to be tokenized. + + Returns: + bert_tokens: A list of tokens obtained by word-piece tokenization of the + utterance. + alignments: A dict mapping indices of characters corresponding to start + and end positions of words (not subwords) to corresponding indices in + bert_tokens list. + inverse_alignments: A list of size equal to bert_tokens. Each element is a + tuple containing the index of the starting and inclusive ending + character of the word corresponding to the subword. This list is used + during inference to map word-piece indices to spans in the original + utterance. + """ + # utterance = tokenization.convert_to_unicode(utterance) + + # After _naive_tokenize, spaces and punctuation marks are all retained, i.e. + # direct concatenation of all the tokens in the sequence will be the + # original string. + tokens = DialogueSGDBERTDataset._naive_tokenize(utterance) + # ['I', ' ', 'am', ' ', 'feeling', ' ', 'hungry', ' ', 'so', ' ', 'I', ' ', 'would', ' ', 'like', ' ', 'to', ' ', 'find', ' ', 'a', ' ', 'place', ' ', 'to', ' ', 'eat', '.'] + # Filter out empty tokens and obtain aligned character index for each token. + alignments = {} + char_index = 0 + bert_tokens = ( + [] + ) # ['I', 'am', 'feeling', 'hungry', 'so', 'I', 'would', 'like', 'to', 'find', 'a', 'place', 'to', 'eat', '.'] + # These lists store inverse alignments to be used during inference. + bert_tokens_start_chars = [] + bert_tokens_end_chars = [] + for token in tokens: + if token.strip(): + subwords = self.tokenizer.text_to_tokens(token) + # Store the alignment for the index of starting character and the + # inclusive ending character of the token. + alignments[char_index] = len(bert_tokens) + bert_tokens_start_chars.extend([char_index] * len(subwords)) + bert_tokens.extend(subwords) + # The inclusive ending character index corresponding to the word. + inclusive_char_end = char_index + len(token) - 1 + alignments[inclusive_char_end] = len(bert_tokens) - 1 + bert_tokens_end_chars.extend([inclusive_char_end] * len(subwords)) + char_index += len(token) + inverse_alignments = list(zip(bert_tokens_start_chars, bert_tokens_end_chars)) + return bert_tokens, alignments, inverse_alignments + + @classmethod + def _naive_tokenize(cls, s: str): + """ + Tokenizes a string, separating words, spaces and punctuations. + Args: + s: a string + Returns: + seq_tok: list of words, spaces and punctuations from the string + """ + # Spaces and punctuation marks are all retained, i.e. direct concatenation + # of all the tokens in the sequence will be the original string. + seq_tok = [tok for tok in re.split(r"([^a-zA-Z0-9])", s) if tok] + return seq_tok + + def __len__(self): + return len(self.features) + + def __getitem__(self, idx: int): + ex = self.features[idx] + + return ( + np.array(ex.example_id_num), + np.array(ex.example_id_num[-1]), # service_id + np.array(ex.utterance_ids), + np.array(ex.utterance_segment), + np.array(ex.utterance_mask, dtype=np.longlong), + np.array(ex.intent_status, dtype=np.float32), + np.array(ex.requested_slot_status, dtype=np.float32), + np.array(ex.categorical_slot_status), + np.array(ex.categorical_slot_value_status, dtype=np.float32), + np.array(ex.noncategorical_slot_status), + np.array(ex.noncategorical_slot_value_start), + np.array(ex.noncategorical_slot_value_end), + np.array(ex.start_char_idx), # noncat_alignment_start + np.array(ex.end_char_idx), # noncat_alignment_end + np.array(ex.task_mask), # noncat_alignment_end + ) + + def bert_process_one_sample(self, idx): + """ + Creates an example for each frame in the user turn. + Args: + turn_id: turn number + system_utterance: last system utterance + user_utterance: lst user utterance + system_frames: all system utterances and slot - slot value pairs + user_frames: all user utterances and slot - slot value pairs + prev_states: slot - slot value pairs from the previous turns + schemas: schema for all services of all datasets + subsample: whether to balance postive and negative samples in the dataset + Returns: + examples: a list of `InputExample`s. + prev_states: updated dialogue state e.g. {'Restaurants_1': {'city': ['San Jose'], 'cuisine': ['American']}} + """ + + ex = self.raw_features[idx].data + example_id_num = ex["example_id_num"] + example_id = ex["example_id"] + user_utterance = ex["utterance"] + system_utterance = ex["system_utterance"] + service = ex["labels"]["service"] + schemas = self.schemas + state_update = ex["labels"]["slots"] + system_slots = ex["system_slots"] + + user_tokens, user_alignments, user_inv_alignments = self._tokenize(user_utterance) + system_tokens, system_alignments, system_inv_alignments = self._tokenize(system_utterance) + system_user_utterance = system_utterance + ' ' + user_utterance + system_user_tokens, system_user_alignments, system_user_inv_alignments = self._tokenize(system_user_utterance) + examples = [] + + base_example = SGDInputExample(schema_config=self.schema_config, tokenizer=self.tokenizer) + base_example.service_schema = self.schemas.get_service_schema(service) + base_example.service_id = example_id_num[-1] + + base_example.example_id = example_id + base_example.example_id_num = example_id_num + + for model_task in range(self.schema_config["NUM_TASKS"]): + if model_task == 0: + for intent_id, intent in enumerate(schemas.get_service_schema(service).intents): + task_example = base_example.make_copy() + task_example.task_mask[model_task] = 1 + task_example.intent_id = intent_id + task_example.example_id += f"-{model_task}-{intent_id}-0" + task_example.example_id_num.extend([model_task, intent_id, 0]) + intent_description = ( + intent + " " + self.schemas.get_service_schema(service).intent_descriptions[intent] + ) + intent_tokens, intent_alignments, intent_inv_alignments = self._tokenize(intent_description) + task_example.add_utterance_features( + intent_tokens, + intent_inv_alignments, + system_user_tokens, + system_user_inv_alignments, + intent_description, + system_user_utterance, + ) + + task_example.add_intents(ex) + examples.append(task_example) + + if model_task == 1: + for slot_id, slot in enumerate(schemas.get_service_schema(service).slots): + task_example = base_example.make_copy() + task_example.task_mask[model_task] = 1 + task_example.requested_slot_id = slot_id + task_example.example_id += f"-{model_task}-{slot_id}-0" + task_example.example_id_num.extend([model_task, slot_id, 0]) + slot_description = slot + " " + self.schemas.get_service_schema(service).slot_descriptions[slot] + slot_tokens, slot_alignments, slot_inv_alignments = self._tokenize(slot_description) + task_example.add_utterance_features( + slot_tokens, + slot_inv_alignments, + user_tokens, + user_inv_alignments, + slot_description, + user_utterance, + ) + + task_example.add_requested_slots(ex) + examples.append(task_example) + + if model_task == 2: + off_slots = [] + on_slots = [] + for slot_id, slot in enumerate(schemas.get_service_schema(service).categorical_slots): + task_example = base_example.make_copy() + task_example.task_mask[model_task] = 1 + + # assert task_example.task_mask == [0, 0, 1, 0, 0, 0] + task_example.categorical_slot_id = slot_id + task_example.example_id += f"-{model_task}-{slot_id}-0" + task_example.example_id_num.extend([model_task, slot_id, 0]) + slot_description = slot + " " + schemas.get_service_schema(service).slot_descriptions[slot] + slot_tokens, slot_alignments, slot_inv_alignments = self._tokenize(slot_description) + task_example.add_utterance_features( + slot_tokens, + slot_inv_alignments, + system_user_tokens, + system_user_inv_alignments, + slot_description, + system_user_utterance, + ) + task_example.add_categorical_slots(state_update) + + if task_example.categorical_slot_status == 0: + off_slots.append(task_example) + else: + on_slots.append(task_example) + examples.append(task_example) + old_example = task_example + + for value_id, value in enumerate( + schemas.get_service_schema(service).get_categorical_slot_values(slot) + ): + if self.dataset_split != 'train' or task_example.categorical_slot_status == 1: + task_example = old_example.make_copy_of_categorical_features() + task_example.task_mask[3] = 1 + # assert task_example.task_mask == [0, 0, 0, 1, 0, 0] + task_example.categorical_slot_id = slot_id + task_example.categorical_slot_value_id = value_id + task_example.example_id = base_example.example_id + f"-3-{slot_id}-{value_id}" + task_example.example_id_num = base_example.example_id_num + [3, slot_id, value_id] + slot_description = slot + " " + value # add slot description + slot_tokens, slot_alignments, slot_inv_alignments = self._tokenize(slot_description) + task_example.add_utterance_features( + slot_tokens, + slot_inv_alignments, + system_user_tokens, + system_user_inv_alignments, + slot_description, + system_user_utterance, + ) + task_example.add_categorical_slots(state_update) + assert task_example.categorical_slot_status == old_example.categorical_slot_status + examples.append(task_example) + + if self.dataset_split == 'train' and self.subsample: + num_on_slots = len(on_slots) + examples.extend( + np.random.choice(off_slots, replace=False, size=min(max(num_on_slots, 1), len(off_slots))) + ) + else: + examples.extend(off_slots) + + if model_task == 4: # noncat slot status + off_slots = [] + on_slots = [] + for slot_id, slot in enumerate(schemas.get_service_schema(service).non_categorical_slots): + task_example = base_example.make_copy() + task_example.task_mask[model_task] = 1 + # assert task_example.task_mask == [0, 0, 0, 0, 1, 0] + task_example.noncategorical_slot_id = slot_id + task_example.example_id += f"-{model_task}-{slot_id}-0" + task_example.example_id_num.extend([model_task, slot_id, 0]) + slot_description = slot + " " + schemas.get_service_schema(service).slot_descriptions[slot] + slot_tokens, slot_alignments, slot_inv_alignments = self._tokenize(slot_description) + task_example.add_utterance_features( + slot_tokens, + slot_inv_alignments, + system_user_tokens, + system_user_inv_alignments, + slot_description, + system_user_utterance, + ) + + user_span_boundaries = self._find_subword_indices( + state_update, + user_utterance, + ex["label_positions"]["slots"], + user_alignments, + user_tokens, + 2 + len(slot_tokens) + len(system_tokens), + ) + + if system_slots is not None: + system_span_boundaries = self._find_subword_indices( + state_update, + system_utterance, + system_slots, + system_alignments, + system_tokens, + 2 + len(slot_tokens), + ) + else: + system_span_boundaries = {} + + task_example.add_noncategorical_slots(state_update, user_span_boundaries, system_span_boundaries) + if task_example.noncategorical_slot_status == 0: + off_slots.append(task_example) + else: + on_slots.append(task_example) + examples.append(task_example) + + if self.dataset_split != 'train' or task_example.noncategorical_slot_status == 1: + task_example = task_example.make_copy_of_non_categorical_features() + task_example.task_mask[5] = 1 + # assert task_example.task_mask == [0, 0, 0, 0, 0, 1] + task_example.example_id = base_example.example_id + f"-5-{slot_id}-0" + task_example.example_id_num = base_example.example_id_num + [5, slot_id, 0] + examples.append(task_example) + + if self.dataset_split == 'train' and self.subsample: + num_on_slots = len(on_slots) + examples.extend( + np.random.choice(off_slots, replace=False, size=min(max(num_on_slots, 1), len(off_slots))) + ) + else: + examples.extend(off_slots) + + for example in examples: + self.features.append(example) + + def _find_subword_indices( + self, + slot_values: dict, + utterance: str, + char_slot_spans: dict, + alignments: List[int], + subwords: List[str], + bias: int, + ) -> dict: + """ + Find indices for subwords corresponding to slot values. + Args: + slot_values: slot - slot value pairs + utterance: utterance + char_slot_spans: char - slot spans + alignments: alignments + subwords: subtokens mapping + bias: offset + Returns: + span_boundaries: span boundaries + """ + span_boundaries = {} + for slot, values in slot_values.items(): + # Get all values present in the utterance for the specified slot. + value_char_spans = {} + for key, slot_span in char_slot_spans.items(): + # print(key, slot, slot_span, char_slot_spans) + if slot_span["slot"] == slot: + value = utterance[slot_span["start"] : slot_span["exclusive_end"]] + start_tok_idx = alignments[slot_span["start"]] + end_tok_idx = alignments[slot_span["exclusive_end"] - 1] + if 0 <= start_tok_idx < len(subwords): + end_tok_idx = min(end_tok_idx, len(subwords) - 1) + value_char_spans[value] = (start_tok_idx + bias, end_tok_idx + bias) + for v in values: + if v in value_char_spans: + span_boundaries[slot] = value_char_spans[v] + break + return span_boundaries diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_zero_shot_intent_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_zero_shot_intent_dataset.py new file mode 100644 index 0000000..f2a0f58 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/dataset/dialogue_zero_shot_intent_dataset.py @@ -0,0 +1,297 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import Dict, List, Optional, Union + +import numpy as np + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.collections.nlp.data.glue_benchmark.data_processors import InputExample +from nemo.collections.nlp.data.glue_benchmark.glue_benchmark_dataset import GLUEDataset +from nemo.core.neural_types import CategoricalValuesType, ChannelType, MaskType, NeuralType +from nemo.utils import logging + +__all__ = ['DialogueZeroShotIntentDataset'] + + +class DialogueZeroShotIntentDataset(GLUEDataset): + """ + Dataset for training a NLI model for zero shot intent recognition. Similar to GLUE/MNLI + dataset, but allows the user to specify which columns in the data files contain the + premise, hypothesis, and gold label. + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'input_ids': NeuralType(('B', 'T'), ChannelType()), + 'segment_ids': NeuralType(('B', 'T'), ChannelType()), + 'input_mask': NeuralType(('B', 'T'), MaskType()), + 'labels': NeuralType(tuple('B'), CategoricalValuesType()), + } + + def __init__(self, dataset_split: str, dialogues_processor: object, tokenizer, cfg): + """ + Args: + dataset_split: dataset split + dialogues_processor: Data generator for dialogues + tokenizer: tokenizer to split text into sub-word tokens + cfg: config dict for dataset + num_classes: number of classes in the data (should be either 2 or 3, corresponding to + labels ['entailment', 'not_entailment'] or ["contradiction", "entailment", "neutral"]) + """ + self.cfg = cfg + self.tokenizer = tokenizer + if self.cfg.num_classes not in [2, 3]: + raise ValueError("num_classes must be either 2 or 3!") + self.label_list = ( + ["contradiction", "entailment", "neutral"] + if self.cfg.num_classes == 3 + else ['not_entailment', 'entailment'] + ) + token_params = { + 'bos_token': None, + 'eos_token': tokenizer.eos_token, + 'pad_token': tokenizer.pad_token, + 'cls_token': tokenizer.cls_token, + 'sep_token_extra': tokenizer.eos_token + if hasattr(tokenizer, 'name') and 'roberta' in tokenizer.name.lower() + else None, + } + + self.raw_features = dialogues_processor.get_dialog_examples(dataset_split) + self.examples = self._create_examples(self.raw_features, dataset_split) + self.features = self.convert_examples_to_features( + self.examples, + [0, 1, 2, 3], + self.cfg.max_seq_length, + tokenizer, + output_mode="classification", + **token_params, + ) + + def _create_examples(self, raw_features, dataset_split: str): + """Creates examples for the training and dev sets.""" + examples = [] + for idx in range(len(raw_features)): + ex = self.raw_features[idx].data + user_utterance = ex["utterance"] + intent = ex["labels"]["intent"] + for candidate_idx, candidate_intent in enumerate(ex["possible_labels"]["intent"]): + guid = "{}-{}-{}".format(dataset_split, idx, candidate_idx) + text_a = user_utterance + text_b = "{} {}".format(self.cfg.prompt_template, candidate_intent) + label = 1 if candidate_intent == intent else 0 + examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def convert_examples_to_features( + self, + examples: List[str], + label_list: List[int], + max_seq_length: int, + tokenizer: TokenizerSpec, + output_mode: str, + bos_token: str = None, + eos_token: str = '[SEP]', + pad_token: str = '[PAD]', + cls_token: str = '[CLS]', + sep_token_extra: str = None, + cls_token_at_end: bool = False, + cls_token_segment_id: int = 0, + pad_token_segment_id: int = 0, + pad_on_left: bool = False, + mask_padding_with_zero: bool = True, + sequence_a_segment_id: int = 0, + sequence_b_segment_id: int = 1, + ): + """ + Loads a data file into a list of `InputBatch`s. + The `cls_token_at_end` defines the location of the CLS token: + + * False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP] + * True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS] + + The `cls_token_segment_id` defines the segment id associated to the CLS token (0 for BERT, 2 for XLNet) + + The convention in BERT is: + + a. For sequence pairs: + * tokens: [CLS] is this jack ##ville ? [SEP] no it is not . [SEP] + * type_ids: 0 0 0 0 0 0 0 1 1 1 1 1 1 + b. For single sequences: + * tokens: [CLS] the dog is hairy . [SEP] + * type_ids: 0 0 0 0 0 0 0 + + Where "type_ids" are used to indicate whether this is the first + sequence or the second sequence. The embedding vectors for `type=0` + and `type=1` were learned during pre-training and are added to the + wordpiece embedding vector (and position vector). This is + not *strictly* necessarysince the [SEP] token unambiguously separates + the sequences, but it makes it easier for the model to learn + the concept of sequences. + For classification tasks, the first vector (corresponding to [CLS]) + is used as as the "sentence vector". Note that this only makes sense + because the entire model is fine-tuned. + + The convention for NMT is: + + a. For sequence pairs: + * tokens: is this jack ##ville ? no it is not . + * type_ids:0 0 0 0 0 0 0 1 1 1 1 1 1 1 + b. For single sequences: + * tokens: the dog is hairy . + * type_ids: 0 0 0 0 0 0 0 + + """ + label_map = {label: i for i, label in enumerate(label_list)} + + features = [] + for ex_index, example in enumerate(examples): + if example.label == "-": # skip examples without a consensus label (e.g. in SNLI data set) + continue + if ex_index % 10000 == 0: + logging.info("Writing example %d of %d" % (ex_index, len(examples))) + + if hasattr(tokenizer, 'text_to_tokens'): + tokens_a = tokenizer.text_to_tokens(example.text_a) + else: + tokens_a = tokenizer.tokenize(example.text_a) + + tokens_b = None + if example.text_b: + if hasattr(tokenizer, 'text_to_tokens'): + tokens_b = tokenizer.text_to_tokens(example.text_b) + else: + tokens_b = tokenizer.tokenize(example.text_b) + + special_tokens_count = 2 if eos_token else 0 + special_tokens_count += 1 if sep_token_extra else 0 + special_tokens_count += 2 if bos_token else 0 + special_tokens_count += 1 if cls_token else 0 + self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - special_tokens_count) + else: + special_tokens_count = 1 if eos_token else 0 + special_tokens_count += 1 if sep_token_extra else 0 + special_tokens_count += 1 if bos_token else 0 + if len(tokens_a) > max_seq_length - special_tokens_count: + tokens_a = tokens_a[: max_seq_length - special_tokens_count] + # Add special tokens to sequence_a + tokens = tokens_a + if bos_token: + tokens = [bos_token] + tokens + if eos_token: + tokens += [eos_token] + segment_ids = [sequence_a_segment_id] * len(tokens) + + # Add sequence separator between sequences + if tokens_b and sep_token_extra: + tokens += [sep_token_extra] + segment_ids += [sequence_a_segment_id] + + # Add special tokens to sequence_b + if tokens_b: + if bos_token: + tokens += [bos_token] + segment_ids += [sequence_b_segment_id] + tokens += tokens_b + segment_ids += [sequence_b_segment_id] * (len(tokens_b)) + if eos_token: + tokens += [eos_token] + segment_ids += [sequence_b_segment_id] + + # Add classification token - for BERT models + if cls_token: + if cls_token_at_end: + tokens += [cls_token] + segment_ids += [cls_token_segment_id] + else: + tokens = [cls_token] + tokens + segment_ids = [cls_token_segment_id] + segment_ids + if hasattr(tokenizer, 'tokens_to_ids'): + input_ids = tokenizer.tokens_to_ids(tokens) + else: + input_ids = tokenizer.convert_tokens_to_ids(tokens) + + # The mask has 1 for real tokens and 0 for padding tokens. Only real + # tokens are attended to. + input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids) + + # Zero-pad up to the sequence length. + padding_length = max_seq_length - len(input_ids) + + if hasattr(tokenizer, 'tokens_to_ids'): + pad_token_id = tokenizer.tokens_to_ids([pad_token])[0] + else: + pad_token_id = tokenizer.convert_tokens_to_ids([pad_token])[0] + + if pad_on_left: + input_ids = ([pad_token_id] * padding_length) + input_ids + input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask + segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids + else: + input_ids = input_ids + ([pad_token_id] * padding_length) + input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length) + segment_ids = segment_ids + ([pad_token_segment_id] * padding_length) + if len(input_ids) != max_seq_length: + raise ValueError("input_ids must be of length max_seq_length") + if len(input_mask) != max_seq_length: + raise ValueError("input_mask must be of length max_seq_length") + if len(segment_ids) != max_seq_length: + raise ValueError("segment_ids must be of length max_seq_length") + if output_mode == "classification": + label_id = label_map[example.label] + elif output_mode == "regression": + label_id = np.float32(example.label) + else: + raise KeyError(output_mode) + + if ex_index < 5: + logging.info("*** Example ***") + logging.info("guid: %s" % (example.guid)) + logging.info("tokens: %s" % " ".join(list(map(str, tokens)))) + logging.info("input_ids: %s" % " ".join(list(map(str, input_ids)))) + logging.info("input_mask: %s" % " ".join(list(map(str, input_mask)))) + logging.info("segment_ids: %s" % " ".join(list(map(str, segment_ids)))) + logging.info("label: %s (id = %d)" % (example.label, label_id)) + + features.append( + InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_id=label_id) + ) + + return features + + +class InputFeatures(object): + """A single set of features of data. + + Args: + input_ids: input/token ids + input_mask: masks out subword tokens + segment_ids: distinguish one sentence from the other one (if present) + label_ids: label for the current example + """ + + def __init__( + self, input_ids: List[int], input_mask: List[int], segment_ids: List[int], label_id: Union[float, int] + ): + """Initialized InputFeatures.""" + self.input_ids = input_ids + self.input_mask = input_mask + self.segment_ids = segment_ids + self.label_id = label_id diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/__init__.py new file mode 100644 index 0000000..de4cf41 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.dialogue.input_example.assistant_input_example import DialogueAssistantInputExample +from nemo.collections.nlp.data.dialogue.input_example.input_example import DialogueInputExample +from nemo.collections.nlp.data.dialogue.input_example.sgd_input_example import DialogueSGDInputExample, SGDInputExample diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/assistant_input_example.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/assistant_input_example.py new file mode 100644 index 0000000..c5574e8 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/assistant_input_example.py @@ -0,0 +1,61 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.dialogue.input_example.input_example import DialogueInputExample + + +class DialogueAssistantInputExample(DialogueInputExample): + """ + Template for DialogueAssistantInputExample + + Meant as a descriptor rather than to be instantiated + + Please instantiate using the base class 'DialogueInputExample' + + { + + "utterance": , + "labels": { + "service": , + "intent": , + "slots": { + "": [, ], + "": [], + } + }, + "label_positions":{ + "slots": { + "": { + # note for the Assistant dataset, start and end are word positions rather than char position + # these are whitespace-delimited word positions rather than tokenization-specific sub-word tokens. + "exclusive_end": 3, + "slot": "restaurant_name", + "start": 1 + }, + } + }, + "possible_labels": { + "service": [, , ...], + "intent": [, , ...], + "slots": { + # all slots for categorical variables + # empty list for extractive slots + # Assistant only support extractive slots + "": [], + "": [], + } + } + } + """ diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/design_input_example.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/design_input_example.py new file mode 100644 index 0000000..80f3152 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/design_input_example.py @@ -0,0 +1,55 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.dialogue.input_example.input_example import DialogueInputExample + + +class DialogueDesignInputExample(DialogueInputExample): + """ + Template for DialogueDesignInputExample + + Meant as a descriptor rather than to be instantiated + + Please instantiate using the base class 'DialogueInputExample' + + { + "utterance": , + "system_utterance": , + "labels": { + "service": , + "intent": , + "slots": { + : '', + : '', + }, # dataset does not contain ground truth slot values + }, + "possible_labels": { + 'intent': [, , ...], + "service": [, , ...], + "slots": { + "": [, , ...], + "": [, , ...], + } + }, + "description": { + "service": , + "intent": , + "slots": { + "": "", + "": "", + } + }, + } + """ diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/input_example.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/input_example.py new file mode 100644 index 0000000..4920c29 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/input_example.py @@ -0,0 +1,41 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +__all__ = ['DialogueInputExample'] + + +class DialogueInputExample(object): + """ + Generic Dialogue Input Example + Uses data: dict as a flexible interface to support various input types. + This ranges from classification labels, to complex nested labels such as those in SGD + + { + "utterance": , + "labels": { + "intent": , + "slots": { ... }, + } + } + """ + + def __init__(self, data: dict): + self.data = data + + def __repr__(self): + return self.data + + def __str__(self): + return self.data diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/mellon_qa_input_example.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/mellon_qa_input_example.py new file mode 100644 index 0000000..e6576d4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/mellon_qa_input_example.py @@ -0,0 +1,35 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.dialogue.input_example.input_example import DialogueInputExample + + +class MellonQAInputExample(DialogueInputExample): + """ + Template for MellonQAInputExample + + Meant as a descriptor rather than to be instantiated + + Please instantiate using the base class 'DialogueInputExample' + + { + "utterance": , + "labels": { + "example_id": , + "response": , + "fluent_response": , # written version of the response that is more fluent + "passage": , # passage which supports generating the response (answer) to the utterance (question) + } + } + """ diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/ms_marco_input_example.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/ms_marco_input_example.py new file mode 100644 index 0000000..ded84d3 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/ms_marco_input_example.py @@ -0,0 +1,42 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.dialogue.input_example.input_example import DialogueInputExample + + +class DialogueMSMarcoInputExample(DialogueInputExample): + """ + Template for DialogueMSMarcoInputExample + + Meant as a descriptor rather than to be instantiated + + Please instantiate using the base class 'DialogueInputExample' + + { + + "utterance": , + "labels": { + "service": , # this is the domain + "example_id": , + "response": , + "fluent_response": , # written version of the response that is more fluent + "passage": , # passage which supports generating the response (answer) to the utterance (question) + }, + "possible_labels": { + "service": [, , ...], + "passage": [, , ...], + } + } + """ diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/sgd_input_example.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/sgd_input_example.py new file mode 100644 index 0000000..9862a07 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/input_example/sgd_input_example.py @@ -0,0 +1,481 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This file contains code artifacts adapted from the original implementation: +https://github.com/google-research/google-research/blob/master/schema_guided_dst/baseline/data_utils.py +""" + +from typing import List + +from nemo.collections.nlp.data.dialogue.input_example.input_example import DialogueInputExample +from nemo.utils import logging + +__all__ = [ + 'SGDInputExample', + 'STR_DONTCARE', + 'STATUS_OFF', + 'STATUS_ACTIVE', + 'STATUS_DONTCARE', +] + + +class DialogueSGDInputExample(DialogueInputExample): + + """ + Template for DialogueSGDInputExample + + Meant as a descriptor rather than to be instantiated + + Please instantiate using the base class 'DialogueInputExample' + + { + "example_id": , + "example_id_num": , + "utterance": , + "system_utterance": , + "system_slots": None or { + "": { + "exclusive_end": 46, + "slot": "restaurant_name", + "start": 34 + }, + "system_actions": None or [{ + "act": "INFORM", + "canonical_values": [ + "2019-03-02" + ], + "slot": "date", + "values": [ + "March 2nd" + ] + }, ...] + "labels": { + "service": , + "intent": , + "slots": { + #only non-empty slots + #most slot values are list of length 1 + #but there are some of length 2 as both are accepted + #e.g. 1930 and 7:30 pm + "": [, ], + "": [], + } + }, + "label_positions":{ + "slots": { + "": { + "exclusive_end": 46, + "slot": "restaurant_name", + "start": 34 + }, + } + }, + "possible_labels": { + "service": [, , ...], + "intent": [, , ...], + "slots": { + #all slots including empty + "": [, , ...], + "": [, , ...], + } + }, + "description": { + "service": , + "intent": , + "slots": { + #only non-empty slots + "": , + "": , + } + } + } + + """ + + +STR_DONTCARE = "dontcare" + +# These are used to represent the status of slots (off, active, dontcare) and +# intents (off, active) in dialogue state tracking. +STATUS_OFF = 0 +STATUS_ACTIVE = 1 +STATUS_DONTCARE = 2 + + +class SGDInputExample(object): + """An example for training/inference.""" + + def __init__( + self, + schema_config: dict, + tokenizer: object, + service_schema: object = None, + example_id: str = "NONE", + example_id_num: List[int] = [], + ): + """ + Constructs an InputExample. + Args: + schema_config: configuration + tokenizer: tokenizer object + service_schema: A ServiceSchema object wrapping the schema for the service + corresponding to this example. + example_id: Unique identifier for the example, like: 'train-1_00000-00-Restaurants_1' + example_id_num: dialogue_id and turn_id combined and service id combined into a list of ints, + like: [1, 0, 0, 18] + """ + self.schema_config = schema_config + self.service_schema = service_schema + self.service_id = None + if service_schema: + self.service_id = service_schema.service_id + self.example_id = example_id + self.example_id_num = example_id_num + self._max_seq_length = schema_config["MAX_SEQ_LENGTH"] + self._tokenizer = tokenizer + if self._tokenizer is None: + raise ValueError("Must specify tokenizer") + + self.user_utterance = '' + self.system_utterance = '' + # The id of each subword in the vocabulary for BERT. + self.utterance_ids = [0] * self._max_seq_length + # Denotes the identity of the sequence. Takes values 0 (schema description) and 1 (system and user utterance). + self.utterance_segment = [0] * self._max_seq_length + # Mask which takes the value 0 for padded tokens and 1 otherwise. + self.utterance_mask = [0] * self._max_seq_length + # Start and inclusive end character indices in the original utterance + # corresponding to the tokens. This is used to obtain the character indices + # from the predicted subword indices during inference. + # NOTE: A positive value indicates the character indices in the schema description + # whereas a negative value indicates the character indices in the + # utterance. The indices are offset by 1 to prevent ambiguity in the + # 0 index, which could be in either the schema description or utterance by the + # above convention. Now the 0 index corresponds to padded tokens. + self.start_char_idx = [0] * self._max_seq_length + self.end_char_idx = [0] * self._max_seq_length + + # Id of categorical slot present in the example or 0 if not present. + self.categorical_slot_id = 0 + # Id of non categorical slot present in the example or 0 if not present. + self.noncategorical_slot_id = 0 + # The status of categorical slot in the example. + self.categorical_slot_status = STATUS_OFF + # The status of non categorical slot in the example. + self.noncategorical_slot_status = STATUS_OFF + # Masks out tasks not represented by example + self.task_mask = [0] * schema_config["NUM_TASKS"] + + # The index of the starting subword corresponding to the slot span + # for a non-categorical slot value. + self.noncategorical_slot_value_start = 0 + # The index of the ending (inclusive) subword corresponding to the slot span + # for a non-categorical slot value. + self.noncategorical_slot_value_end = 0 + + # Id of categorical slot value present in the example or 0 if not present. + self.categorical_slot_value_id = 0 + # The status of categorical slot value in the example. + self.categorical_slot_value_status = STATUS_OFF + # Id of requested slot present in the example or 0 if not present. + self.requested_slot_id = 0 + # Takes value 1 if the corresponding slot is requested, 0 otherwise. + self.requested_slot_status = STATUS_OFF + + # ID of intent present in the example. + self.intent_id = 0 + # Takes value 1 if the intent is active, 0 otherwise. + self.intent_status = STATUS_OFF + + @property + def readable_summary(self): + """Get a readable dict that summarizes the attributes of an InputExample.""" + seq_length = sum(self.utterance_mask) + utt_toks = self._tokenizer.ids_to_tokens(self.utterance_ids[:seq_length]) + utt_tok_mask_pairs = list(zip(utt_toks, self.utterance_segment[:seq_length])) + active_intent = ( + self.service_schema.get_intent_from_id(self.intent_id) if self.intent_status == STATUS_ACTIVE else "" + ) + slot_values_in_state = {} + if self.categorical_slot_status == STATUS_ACTIVE: + slot_values_in_state[ + self.service_schema.get_categorical_slot_from_id(self.categorical_slot_id) + ] = self.service_schema.get_categorical_slot_value_from_id( + self.categorical_slot_id, self.categorical_slot_value_id + ) + elif self.categorical_slot_status == STATUS_DONTCARE: + slot_values_in_state[ + self.service_schema.get_categorical_slot_from_id(self.categorical_slot_id) + ] = STR_DONTCARE + if self.noncategorical_slot_status == STATUS_ACTIVE: + slot = self.service_schema.get_non_categorical_slot_from_id(self.noncategorical_slot_id) + start_id = self.noncategorical_slot_value_start[slot] + end_id = self.noncategorical_slot_value_end[slot] + # Token list is consisted of the subwords that may start with "##". We + # remove "##" to reconstruct the original value. Note that it's not a + # strict restoration of the original string. It's primarily used for + # debugging. + # ex. ["san", "j", "##ose"] --> "san jose" + readable_value = " ".join(utt_toks[start_id : end_id + 1]).replace(" ##", "") + slot_values_in_state[slot] = readable_value + elif self.noncategorical_slot_status == STATUS_DONTCARE: + slot = self.service_schema.get_non_categorical_slot_from_id(self.noncategorical_slot_id) + slot_values_in_state[slot] = STR_DONTCARE + + summary_dict = { + "utt_tok_mask_pairs": utt_tok_mask_pairs, + "utt_len": seq_length, + "categorical_slot_id": self.categorical_slot_id, + "noncategorical_slot_id": self.noncategorical_slot_id, + "intent_id": self.intent_id, + "service_name": self.service_schema.service_name, + "active_intent": active_intent, + "slot_values_in_state": slot_values_in_state, + } + return summary_dict + + def add_utterance_features( + self, system_tokens, system_inv_alignments, user_tokens, user_inv_alignments, system_utterance, user_utterance + ): + """Add utterance related features input to InputExample. + + Note: this method modifies the system tokens and user_tokens in place to + make their total length <= the maximum input length for BERT model. + + Args: + system_tokens: a list of strings which represents schema description. + system_inv_alignments: a list of tuples which denotes the start and end + charater of the tpken that a bert token originates from in the original + schema description. + user_tokens: a list of strings which represents utterance. + user_inv_alignments: a list of tuples which denotes the start and end + charater of the token that a bert token originates from in the original + system and user utterance. + """ + # Input sequence length for utterance BERT encoder + max_utt_len = self._max_seq_length + + # Modify lengths of schema description & utterance so that length of total utt + # (including cls_token, setp_token, sep_token) is no more than max_utt_len + is_too_long = truncate_seq_pair(system_tokens, user_tokens, max_utt_len - 3) + if is_too_long: + logging.debug( + f'Utterance sequence truncated in example id - {self.example_id} from {len(system_tokens) + len(user_tokens)}.' + ) + + # Construct the tokens, segment mask and valid token mask which will be + # input to BERT, using the tokens for schema description (sequence A) and + # system and user utterance (sequence B). + utt_subword = [] + utt_seg = [] + utt_mask = [] + start_char_idx = [] + end_char_idx = [] + + utt_subword.append(self._tokenizer.cls_token) + utt_seg.append(0) + utt_mask.append(1) + start_char_idx.append(0) + end_char_idx.append(0) + + for subword_idx, subword in enumerate(system_tokens): + utt_subword.append(subword) + utt_seg.append(0) + utt_mask.append(1) + st, en = system_inv_alignments[subword_idx] + start_char_idx.append(-(st + 1)) + end_char_idx.append(-(en + 1)) + + utt_subword.append(self._tokenizer.sep_token) + utt_seg.append(0) + utt_mask.append(1) + start_char_idx.append(0) + end_char_idx.append(0) + + for subword_idx, subword in enumerate(user_tokens): + utt_subword.append(subword) + utt_seg.append(1) + utt_mask.append(1) + st, en = user_inv_alignments[subword_idx] + start_char_idx.append(st + 1) + end_char_idx.append(en + 1) + + utt_subword.append(self._tokenizer.sep_token) + utt_seg.append(1) + utt_mask.append(1) + start_char_idx.append(0) + end_char_idx.append(0) + + utterance_ids = self._tokenizer.tokens_to_ids(utt_subword) + + # Zero-pad up to the BERT input sequence length. + while len(utterance_ids) < max_utt_len: + utterance_ids.append(0) + utt_seg.append(0) + utt_mask.append(0) + start_char_idx.append(0) + end_char_idx.append(0) + self.utterance_ids = utterance_ids + self.utterance_segment = utt_seg + self.utterance_mask = utt_mask + self.start_char_idx = start_char_idx + self.end_char_idx = end_char_idx + + self.user_utterance = user_utterance + self.system_utterance = system_utterance + + def make_copy(self): + """Make a copy of the current example with utterance features.""" + new_example = SGDInputExample( + schema_config=self.schema_config, + service_schema=self.service_schema, + example_id=self.example_id, + example_id_num=self.example_id_num.copy(), + tokenizer=self._tokenizer, + ) + return new_example + + def make_copy_of_categorical_features(self): + """Make a copy of the current example with utterance and categorical features.""" + new_example = self.make_copy() + + new_example.categorical_slot_status = self.categorical_slot_status + return new_example + + def make_copy_of_non_categorical_features(self): + """Make a copy of the current example with utterance features and non categorical features.""" + new_example = self.make_copy() + new_example.noncategorical_slot_id = self.noncategorical_slot_id + new_example.noncategorical_slot_status = self.noncategorical_slot_status + new_example.utterance_ids = list(self.utterance_ids) + new_example.utterance_segment = list(self.utterance_segment) + new_example.utterance_mask = list(self.utterance_mask) + new_example.start_char_idx = list(self.start_char_idx) + new_example.end_char_idx = list(self.end_char_idx) + new_example.user_utterance = self.user_utterance + new_example.system_utterance = self.system_utterance + new_example.noncategorical_slot_status = self.noncategorical_slot_status + new_example.noncategorical_slot_value_start = self.noncategorical_slot_value_start + new_example.noncategorical_slot_value_end = self.noncategorical_slot_value_end + return new_example + + def add_categorical_slots(self, state_update: dict): + """Add features for categorical slots. + Args: + state_update: slot value pairs of the state update + """ + + categorical_slots = self.service_schema.categorical_slots + if not categorical_slots: + return + slot = categorical_slots[self.categorical_slot_id] + values = state_update.get(slot, []) + + if not values: + self.categorical_slot_status = STATUS_OFF + elif values[0] == STR_DONTCARE: + self.categorical_slot_status = STATUS_DONTCARE + else: + self.categorical_slot_status = STATUS_ACTIVE + self.categorical_slot_value_status = ( + self.categorical_slot_value_id == self.service_schema.get_categorical_slot_value_id(slot, values[0]) + ) + + def add_noncategorical_slots(self, state_update: dict, system_span_boundaries: dict, user_span_boundaries: dict): + """Add features for non-categorical slots. + Args: + state_update: slot value pairs of state update + system_span_boundaries: span boundaries of schema description + user_span_boundaries: span boundaries of utterance + """ + + noncategorical_slots = self.service_schema.non_categorical_slots + slot = noncategorical_slots[self.noncategorical_slot_id] + + values = state_update.get(slot, []) + if not values: + self.noncategorical_slot_status = STATUS_OFF + elif values[0] == STR_DONTCARE: + self.noncategorical_slot_status = STATUS_DONTCARE + else: + self.noncategorical_slot_status = STATUS_ACTIVE + # Add indices of the start and end tokens for the first encountered + # value. Spans in user utterance are prioritized over the system + # utterance. If a span is not found, the slot value is ignored. + if slot in user_span_boundaries: + start, end = user_span_boundaries[slot] + elif slot in system_span_boundaries: + start, end = system_span_boundaries[slot] + else: + # A span may not be found because the value was cropped out or because + # the value was mentioned earlier in the dialogue. Since this model + # only makes use of the last two utterances to predict state updates, + # it will fail in such cases. + logging.debug( + f'"Slot values {str(values)} not found in user or system utterance in example with id - {self.example_id}.' + ) + start = 0 + end = 0 + self.noncategorical_slot_value_start = start + self.noncategorical_slot_value_end = end + + def add_requested_slots(self, frame: dict): + """Add requested slots to InputExample + Args: + frame: frame object from which requested slots are extracted + """ + all_slots = self.service_schema.slots + slot = all_slots[self.requested_slot_id] + if slot in frame["labels"]["slots"]: + self.requested_slot_status = STATUS_ACTIVE + + def add_intents(self, frame): + """Add intents to InputExample + Args: + frame: frame object from which intents are extracted + """ + all_intents = self.service_schema.intents + intent = all_intents[self.intent_id] + if intent == frame["labels"]["intent"]: + self.intent_status = STATUS_ACTIVE + + +# Modified from run_classifier._truncate_seq_pair in the public bert model repo. +# https://github.com/google-research/bert/blob/master/run_classifier.py. +def truncate_seq_pair(tokens_a: List[int], tokens_b: List[int], max_length: int) -> bool: + """Truncate a seq pair in place so that their total length <= max_length. + Args: + tokens_a: first token sequence + tokens_b: second token sequence + max_length: truncated sequence length + Returns: + is_too_long: whether combined sequences exceed maximum sequence length + """ + is_too_long = False + # This is a simple heuristic which will always truncate the longer sequence + # one token at a time. This makes more sense than truncating an equal percent + # of tokens from each, since if one sequence is very short then each token + # that's truncated likely contains more information than a longer sequence. + while True: + total_length = len(tokens_a) + len(tokens_b) + if total_length <= max_length: + break + is_too_long = True + if len(tokens_a) > len(tokens_b): + tokens_a.pop() + else: + tokens_b.pop() + return is_too_long diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/sgd/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/sgd/__init__.py new file mode 100644 index 0000000..9bc88d0 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/sgd/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.dialogue.sgd.evaluate import evaluate, get_in_domain_services +from nemo.collections.nlp.data.dialogue.sgd.schema import Schema diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/sgd/evaluate.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/sgd/evaluate.py new file mode 100644 index 0000000..0829543 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/sgd/evaluate.py @@ -0,0 +1,294 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Evaluate predictions JSON file, w.r.t. ground truth file. +This file contains code artifacts adapted from the original implementation: +https://github.com/google-research/google-research/blob/master/schema_guided_dst/evaluate.py +""" + +import collections +import glob +import json +import os + +import numpy as np + +from nemo.collections.nlp.metrics.sgd_metrics import ( + ACTIVE_INTENT_ACCURACY, + JOINT_CAT_ACCURACY, + JOINT_GOAL_ACCURACY, + JOINT_NONCAT_ACCURACY, + NAN_VAL, + REQUESTED_SLOTS_F1, + REQUESTED_SLOTS_PRECISION, + REQUESTED_SLOTS_RECALL, + SLOT_TAGGING_F1, + SLOT_TAGGING_PRECISION, + SLOT_TAGGING_RECALL, + get_active_intent_accuracy, + get_average_and_joint_goal_accuracy, + get_requested_slots_f1, + get_slot_tagging_f1, +) +from nemo.utils import logging + +__all__ = ['get_in_domain_services'] + +ALL_SERVICES = "#ALL_SERVICES" +SEEN_SERVICES = "#SEEN_SERVICES" +UNSEEN_SERVICES = "#UNSEEN_SERVICES" + +# Name of the file containing all predictions and their corresponding frame metrics. +PER_FRAME_OUTPUT_FILENAME = "dialogues_and_metrics.json" + + +def get_service_set(schema_path: str) -> set: + """ + Get the set of all services present in a schema. + Args: + schema_path: schema file path + Returns: + service_set: set of services in file + """ + service_set = set() + with open(schema_path, encoding="UTF-8") as f: + schema = json.load(f) + for service in schema: + service_set.add(service["service_name"]) + f.close() + return service_set + + +def get_in_domain_services(schema_path: str, service_set: set) -> set: + """Get the set of common services between a schema and set of services. + Args: + schema_path: path to schema file + service_set: set of services + Returns: + joint_services: joint services between schema path file and service set + """ + joint_services = get_service_set(schema_path) & service_set + return joint_services + + +def get_dataset_as_dict(file_path_patterns) -> dict: + """Read the DSTC8/SGD json dialogue data as dictionary with dialog ID as keys. + Args: + file_path_patterns: list or directory of files + Returns: + dataset_dict: dataset dictionary with dialog ID as keys + """ + dataset_dict = {} + if isinstance(file_path_patterns, list): + list_fp = file_path_patterns + else: + list_fp = sorted(glob.glob(file_path_patterns)) + for fp in list_fp: + if PER_FRAME_OUTPUT_FILENAME in fp: + continue + logging.debug("Loading file: %s", fp) + with open(fp, encoding="UTF-8") as f: + data = json.load(f) + if isinstance(data, list): + for dial in data: + dataset_dict[dial["dialogue_id"]] = dial + elif isinstance(data, dict): + dataset_dict.update(data) + f.close() + return dataset_dict + + +def get_metrics( + dataset_ref: dict, + dataset_hyp: dict, + service_schemas: dict, + in_domain_services: set, + joint_acc_across_turn: bool, + use_fuzzy_match: bool, +): + """Calculate the DSTC8/SGD metrics. + Args: + dataset_ref: The ground truth dataset represented as a dict mapping dialogue id to the corresponding dialogue. + dataset_hyp: The predictions in the same format as `dataset_ref`. + service_schemas: A dict mapping service name to the schema for the service. + in_domain_services: The set of services which are present in the training set. + joint_acc_across_turn: Whether to compute joint accuracy across turn instead of across service. Should be set to True when conducting multiwoz style evaluation. + use_fuzzy_match: Whether to use fuzzy string matching when comparing non-categorical slot values. Should be set to False when conducting multiwoz style evaluation. + + Returns: + all_metric_aggregate: A dict mapping a metric collection name to a dict containing the values + for various metrics. Each metric collection aggregates the metrics across a specific set of frames in the dialogues. + per_frame_metric: metrics aggregated for each frame + """ + # Metrics can be aggregated in various ways, eg over all dialogues, only for + # dialogues containing unseen services or for dialogues corresponding to a + # single service. This aggregation is done through metric_collections, which + # is a dict mapping a collection name to a dict, which maps a metric to a list + # of values for that metric. Each value in this list is the value taken by + # the metric on a frame. + metric_collections = collections.defaultdict(lambda: collections.defaultdict(list)) + + # Ensure the dialogs in dataset_hyp also occur in dataset_ref. + assert set(dataset_hyp.keys()).issubset(set(dataset_ref.keys())) + logging.debug("len(dataset_hyp)=%d, len(dataset_ref)=%d", len(dataset_hyp), len(dataset_ref)) + + # Store metrics for every frame for debugging. + per_frame_metric = {} + + for dial_id, dial_hyp in dataset_hyp.items(): + dial_ref = dataset_ref[dial_id] + + if set(dial_ref["services"]) != set(dial_hyp["services"]): + raise ValueError( + "Set of services present in ground truth and predictions don't match " + "for dialogue with id {}".format(dial_id) + ) + + joint_metrics = [JOINT_GOAL_ACCURACY, JOINT_CAT_ACCURACY, JOINT_NONCAT_ACCURACY] + for turn_id, (turn_ref, turn_hyp) in enumerate(zip(dial_ref["turns"], dial_hyp["turns"])): + metric_collections_per_turn = collections.defaultdict(lambda: collections.defaultdict(lambda: 1.0)) + if turn_ref["speaker"] != turn_hyp["speaker"]: + raise ValueError("Speakers don't match in dialogue with id {}".format(dial_id)) + + # Skip system turns because metrics are only computed for user turns. + if turn_ref["speaker"] != "USER": + continue + + if turn_ref["utterance"] != turn_hyp["utterance"]: + logging.error("Ref utt: %s", turn_ref["utterance"]) + logging.error("Hyp utt: %s", turn_hyp["utterance"]) + raise ValueError("Utterances don't match for dialogue with id {}".format(dial_id)) + + hyp_frames_by_service = {frame["service"]: frame for frame in turn_hyp["frames"]} + + # Calculate metrics for each frame in each user turn. + for frame_ref in turn_ref["frames"]: + service_name = frame_ref["service"] + if service_name not in hyp_frames_by_service: + raise ValueError( + "Frame for service {} not found in dialogue with id {}".format(service_name, dial_id) + ) + service = service_schemas[service_name] + frame_hyp = hyp_frames_by_service[service_name] + + active_intent_acc = get_active_intent_accuracy(frame_ref, frame_hyp) + slot_tagging_f1_scores = get_slot_tagging_f1(frame_ref, frame_hyp, turn_ref["utterance"], service) + requested_slots_f1_scores = get_requested_slots_f1(frame_ref, frame_hyp) + goal_accuracy_dict = get_average_and_joint_goal_accuracy( + frame_ref, frame_hyp, service, use_fuzzy_match + ) + + frame_metric = { + ACTIVE_INTENT_ACCURACY: active_intent_acc, + REQUESTED_SLOTS_F1: requested_slots_f1_scores.f1, + REQUESTED_SLOTS_PRECISION: requested_slots_f1_scores.precision, + REQUESTED_SLOTS_RECALL: requested_slots_f1_scores.recall, + } + if slot_tagging_f1_scores is not None: + frame_metric[SLOT_TAGGING_F1] = slot_tagging_f1_scores.f1 + frame_metric[SLOT_TAGGING_PRECISION] = slot_tagging_f1_scores.precision + frame_metric[SLOT_TAGGING_RECALL] = slot_tagging_f1_scores.recall + frame_metric.update(goal_accuracy_dict) + + frame_id = "{:s}-{:03d}-{:s}".format(dial_id, turn_id, frame_hyp["service"]) + per_frame_metric[frame_id] = frame_metric + # Add the frame-level metric result back to dialogues. + frame_hyp["metrics"] = frame_metric + + # Get the domain name of the service. + domain_name = frame_hyp["service"].split("_")[0] + domain_keys = [ALL_SERVICES, frame_hyp["service"], domain_name] + if frame_hyp["service"] in in_domain_services: + domain_keys.append(SEEN_SERVICES) + + else: + domain_keys.append(UNSEEN_SERVICES) + for domain_key in domain_keys: + for metric_key, metric_value in frame_metric.items(): + if metric_value != NAN_VAL: + if joint_acc_across_turn and metric_key in joint_metrics: + metric_collections_per_turn[domain_key][metric_key] *= metric_value + else: + metric_collections[domain_key][metric_key].append(metric_value) + if joint_acc_across_turn: + # Conduct multiwoz style evaluation that computes joint goal accuracy + # across all the slot values of all the domains for each turn. + for domain_key in metric_collections_per_turn: + for metric_key, metric_value in metric_collections_per_turn[domain_key].items(): + metric_collections[domain_key][metric_key].append(metric_value) + + all_metric_aggregate = {} + for domain_key, domain_metric_vals in metric_collections.items(): + domain_metric_aggregate = {} + for metric_key, value_list in domain_metric_vals.items(): + if value_list: + # Metrics are macro-averaged across all frames. + domain_metric_aggregate[metric_key] = round(float(np.mean(value_list)) * 100.0, 2) + else: + domain_metric_aggregate[metric_key] = NAN_VAL + all_metric_aggregate[domain_key] = domain_metric_aggregate + return all_metric_aggregate, per_frame_metric + + +def evaluate( + prediction_dir: str, + data_dir: str, + eval_dataset: str, + in_domain_services: set, + joint_acc_across_turn: bool, + use_fuzzy_match: bool, +) -> dict: + """Calculate the DSTC8/SGD metrics for given data. + + Args: + prediction_dir: prediction location + data_dir: ground truth data location. + eval_dataset: evaluation data split + in_domain_services: The set of services which are present in the training set. + joint_acc_across_turn: Whether to compute joint goal accuracy across turn instead of across service. Should be set to True when conducting multiwoz style evaluation. + use_fuzzy_match: Whether to use fuzzy string matching when comparing non-categorical slot values. Should be set to False when conducting multiwoz style evaluation. + + Returns: + A dict mapping a metric collection name to a dict containing the values + for various metrics for all dialogues and all services + """ + + with open(os.path.join(data_dir, eval_dataset, "schema.json"), encoding="UTF-8") as f: + eval_services = {} + list_services = json.load(f) + for service in list_services: + eval_services[service["service_name"]] = service + f.close() + + dataset_ref = get_dataset_as_dict(os.path.join(data_dir, eval_dataset, "dialogues_*.json")) + dataset_hyp = get_dataset_as_dict(os.path.join(prediction_dir, "*.json")) + + # has ALLSERVICE, SEEN_SERVICES, UNSEEN_SERVICES, SERVICE, DOMAIN + all_metric_aggregate, _ = get_metrics( + dataset_ref, dataset_hyp, eval_services, in_domain_services, joint_acc_across_turn, use_fuzzy_match + ) + if SEEN_SERVICES in all_metric_aggregate: + logging.info(f'Dialog metrics for {SEEN_SERVICES} : {sorted(all_metric_aggregate[SEEN_SERVICES].items())}') + if UNSEEN_SERVICES in all_metric_aggregate: + logging.info(f'Dialog metrics for {UNSEEN_SERVICES}: {sorted(all_metric_aggregate[UNSEEN_SERVICES].items())}') + if ALL_SERVICES in all_metric_aggregate: + logging.info(f'Dialog metrics for {ALL_SERVICES} : {sorted(all_metric_aggregate[ALL_SERVICES].items())}') + + # Write the per-frame metrics values with the corrresponding dialogue frames. + with open(os.path.join(prediction_dir, PER_FRAME_OUTPUT_FILENAME), "w", encoding="UTF-8") as f: + json.dump(dataset_hyp, f, indent=2, separators=(",", ": ")) + f.close() + return all_metric_aggregate[ALL_SERVICES] diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/sgd/prediction_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/sgd/prediction_utils.py new file mode 100644 index 0000000..c9ddd2f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/sgd/prediction_utils.py @@ -0,0 +1,251 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Prediction and evaluation-related utility functions. +This file contains code artifacts adapted from the original implementation: +https://github.com/google-research/google-research/blob/master/schema_guided_dst/baseline/pred_utils.py +""" + +import json +import os +from collections import OrderedDict, defaultdict +from typing import Dict, List, Optional + +from nemo.collections.nlp.data.dialogue.input_example.sgd_input_example import ( + STATUS_ACTIVE, + STATUS_DONTCARE, + STR_DONTCARE, +) +from nemo.utils import logging + +REQ_SLOT_THRESHOLD = 0.5 + + +__all__ = ['write_predictions_to_file'] + + +def set_cat_slot(predictions_status: dict, predictions_value: dict, cat_slot_values: Dict[str, List[str]]) -> dict: + """ + Extract predicted categorical slot information + Args: + predictions_status: predicted statuses + predictions_value: predicted slot values + cat_slot_values: possible categorical slots and their potential values for this service + Returns: + out_dict: predicted slot value pairs + """ + out_dict = {} + for slot_idx, slot in enumerate(cat_slot_values): + slot_status = predictions_status[slot_idx][0]["cat_slot_status"] + if slot_status == STATUS_DONTCARE: + out_dict[slot] = STR_DONTCARE + elif slot_status == STATUS_ACTIVE: + tmp = predictions_value[slot_idx] + value_idx = max(tmp, key=lambda k: tmp[k]['cat_slot_value_status'][0].item()) + out_dict[slot] = cat_slot_values[slot][value_idx] + return out_dict + + +def set_noncat_slot( + predictions_status: dict, + predictions_value: dict, + non_cat_slots: List[str], + user_utterance: str, + sys_slots_agg: Optional[dict] = None, +) -> dict: + """ + Extract predicted non categorical slot information + Args: + predictions_status: predicted statuses + predictions_value: predicted slot values + non_cat_slots: list of possible non categorical slots for this service + user_utterance: system and user utterance + sys_slots_agg: system retrieval lookup table. Contains for each slot the most recent value seen in the history + Returns: + out_dict: predicted slot value pairs + """ + out_dict = {} + for slot_idx, slot in enumerate(non_cat_slots): + slot_status = predictions_status[slot_idx][0]["noncat_slot_status"] + if slot_status == STATUS_DONTCARE: + out_dict[slot] = STR_DONTCARE + elif slot_status == STATUS_ACTIVE: + tok_start_idx = predictions_value[slot_idx][0]["noncat_slot_start"] + tok_end_idx = predictions_value[slot_idx][0]["noncat_slot_end"] + ch_start_idx = predictions_value[slot_idx][0]["noncat_alignment_start"][tok_start_idx] + ch_end_idx = predictions_value[slot_idx][0]["noncat_alignment_end"][tok_end_idx] + if ch_start_idx > 0 and ch_end_idx > 0: + # Add span from the utterance. + out_dict[slot] = user_utterance[ch_start_idx - 1 : ch_end_idx] + elif sys_slots_agg and slot in sys_slots_agg: + # system retrieval + out_dict[slot] = sys_slots_agg[slot] + return out_dict + + +def get_predicted_dialog(dialog: dict, all_predictions: dict, schemas: object, state_tracker: str) -> dict: + """Overwrite the labels in the turn with the predictions from the model. For test set, these labels are missing from the data and hence they are added. + Args: + dialog: ground truth dialog + all_predictions: predictions + schemas: schema object of all services of all datasets + state_tracker: state tracker option, e.g. nemotracker + Returns: + dialog: dialog overwritten with prediction information + """ + dialog_id = dialog["dialogue_id"] + if state_tracker == "baseline": + sys_slots_agg = {} + else: + sys_slots_agg = defaultdict(OrderedDict) + all_slot_values = defaultdict(dict) + for turn_idx, turn in enumerate(dialog["turns"]): + if turn["speaker"] == "SYSTEM" and state_tracker == 'nemotracker': + for frame in turn["frames"]: + if frame["service"] not in sys_slots_agg: + sys_slots_agg[frame["service"]] = OrderedDict() + for action in frame["actions"]: + if action["slot"] and len(action["values"]) > 0: + sys_slots_agg[frame["service"]][action["slot"]] = action["values"][0] + if turn["speaker"] == "USER": + user_utterance = turn["utterance"] + system_utterance = dialog["turns"][turn_idx - 1]["utterance"] if turn_idx else "" + system_user_utterance = system_utterance + ' ' + user_utterance + turn_id = "{:02d}".format(turn_idx) + for frame in turn["frames"]: + + predictions = all_predictions[(dialog_id, turn_id, frame["service"])] + slot_values = all_slot_values[frame["service"]] + service_schema = schemas.get_service_schema(frame["service"]) + # Remove the slot spans and state if present. + frame.pop("slots", None) + frame.pop("state", None) + + # The baseline model doesn't predict slot spans. Only state predictions + # are added. + state = {} + + # Add prediction for active intent. No Offset is subtracted since schema has now NONE intent at index 0 + state["active_intent"] = get_predicted_intent( + predictions=predictions[0], intents=service_schema.intents + ) + # Add prediction for requested slots. + state["requested_slots"] = get_requested_slot(predictions=predictions[1], slots=service_schema.slots) + + # Add prediction for user goal (slot values). + # Categorical slots. + cat_out_dict = set_cat_slot( + predictions_status=predictions[2], + predictions_value=predictions[3], + cat_slot_values=service_schema.categorical_slot_values, + ) + for k, v in cat_out_dict.items(): + slot_values[k] = v + + # Non-categorical slots. + noncat_out_dict = set_noncat_slot( + predictions_status=predictions[4], + predictions_value=predictions[5], + non_cat_slots=service_schema.non_categorical_slots, + user_utterance=system_user_utterance, + sys_slots_agg=sys_slots_agg.get(frame["service"], None), + ) + for k, v in noncat_out_dict.items(): + slot_values[k] = v + # Create a new dict to avoid overwriting the state in previous turns + # because of use of same objects. + state["slot_values"] = {s: [v] for s, v in slot_values.items()} + frame["state"] = state + return dialog + + +def get_predicted_intent(predictions: dict, intents: List[str]) -> str: + """ + Returns intent name with maximum score + Args: + predictions: predictions + intents: list of possible intents for this service + Returns: + intent: predicted intent + """ + assert len(predictions) == len(intents) + active_intent_id = max(predictions, key=lambda k: predictions[k][0]['intent_status']) + intent = intents[active_intent_id] + return intent + + +def get_requested_slot(predictions: dict, slots: List[str]) -> List[str]: + """ + Returns list of slots which are predicted to be requested + Args: + predictions: predictions + slots: list of possible slots + Returns: + requested_slots: list of requested slots + """ + active_indices = [k for k in predictions if predictions[k][0]["req_slot_status"] > REQ_SLOT_THRESHOLD] + requested_slots = list(map(lambda k: slots[k], active_indices)) + return requested_slots + + +def write_predictions_to_file( + predictions: List[dict], + input_json_files: List[str], + output_dir: str, + schemas: object, + state_tracker: str, + eval_debug: bool, + in_domain_services: set, +): + """Save predicted dialogues as json files. + + Args: + predictions: An iterator containing model predictions. This is the output of + the predict method in the estimator. + input_json_files: A list of json paths containing the dialogues to run + inference on. + output_dir: The directory where output json files will be created. + schemas: Schemas to all services in the dst dataset + state_tracker: state tracker option + eval_debug: output evaluation debugging information + in_domain_services: in domain services + """ + logging.info(f"Writing predictions to {output_dir} started.") + + # Index all predictions. + all_predictions = defaultdict(lambda: defaultdict(lambda: defaultdict(dict))) + for idx, prediction in enumerate(predictions): + eval_dataset, dialog_id, turn_id, service_name, model_task, slot_intent_id, value_id = prediction[ + 'example_id' + ].split('-') + all_predictions[(dialog_id, turn_id, service_name)][int(model_task)][int(slot_intent_id)][ + int(value_id) + ] = prediction + logging.info(f'Predictions for {idx} examples in {eval_dataset} dataset are getting processed.') + + # Read each input file and write its predictions. + for input_file_path in input_json_files: + with open(input_file_path, encoding="UTF-8") as f: + dialogs = json.load(f) + logging.debug(f'{input_file_path} file is loaded') + pred_dialogs = [] + for d in dialogs: + pred_dialog = get_predicted_dialog(d, all_predictions, schemas, state_tracker) + pred_dialogs.append(pred_dialog) + input_file_name = os.path.basename(input_file_path) + output_file_path = os.path.join(output_dir, input_file_name) + with open(output_file_path, "w", encoding="UTF-8") as f: + json.dump(pred_dialogs, f, indent=2, separators=(",", ": "), sort_keys=True) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/sgd/schema.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/sgd/schema.py new file mode 100644 index 0000000..b12a11f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/dialogue/sgd/schema.py @@ -0,0 +1,222 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright 2019 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Wrappers for schemas of different services. +This file contains code artifacts adapted from the original implementation: +https://github.com/google-research/google-research/blob/master/schema_guided_dst/schema.py +""" + +import json +from typing import List, Optional, Union + +from nemo.utils import logging + +__all__ = ['Schema'] + + +class ServiceSchema(object): + """A wrapper for schema for a service.""" + + def __init__(self, schema_json: dict, service_id: Optional[int] = None): + """ + Constructor for ServiceSchema. + Args: + schema_json: schema json dict + service_id: service ID + """ + self._service_name = schema_json["service_name"] + self._description = schema_json["description"] + self._schema_json = schema_json + self._service_id = service_id + + # Construct the vocabulary for intents, slots, categorical slots, + # non-categorical slots and categorical slot values. + self._intents = ["NONE"] + sorted(i["name"] for i in schema_json["intents"]) + self._intent_descriptions = {i["name"]: i["description"] for i in schema_json["intents"]} + self._intent_descriptions["NONE"] = "none" + self._slots = sorted(s["name"] for s in schema_json["slots"]) + self._slots_descriptions = {s["name"]: s["description"] for s in schema_json["slots"]} + self._categorical_slots = sorted( + s["name"] for s in schema_json["slots"] if s["is_categorical"] and s["name"] in self.state_slots + ) + self._non_categorical_slots = sorted( + s["name"] for s in schema_json["slots"] if not s["is_categorical"] and s["name"] in self.state_slots + ) + slot_schemas = {s["name"]: s for s in schema_json["slots"]} + categorical_slot_values = {} + categorical_slot_value_ids = {} + categorical_slot_ids = {} + non_categorical_slot_ids = {} + for slot_id, slot in enumerate(self._categorical_slots): + slot_schema = slot_schemas[slot] + values = sorted(slot_schema["possible_values"]) + categorical_slot_values[slot] = values + value_ids = {value: idx for idx, value in enumerate(values)} + categorical_slot_value_ids[slot] = value_ids + categorical_slot_ids[slot] = slot_id + + for slot_id, slot in enumerate(self._non_categorical_slots): + non_categorical_slot_ids[slot] = slot_id + + self._categorical_slot_values = categorical_slot_values + self._categorical_slot_value_ids = categorical_slot_value_ids + + self._categorical_slot_ids = categorical_slot_ids + self._non_categorical_slot_ids = non_categorical_slot_ids + + @property + def schema_json(self) -> dict: + """Returns schema json dictionary""" + return self._schema_json + + @property + def state_slots(self) -> set: + """Set of slots which are permitted to be in the dialogue state.""" + state_slots = set() + for intent in self._schema_json["intents"]: + state_slots.update(intent["required_slots"]) + state_slots.update(intent["optional_slots"]) + return state_slots + + @property + def service_name(self): + return self._service_name + + @property + def service_id(self): + return self._service_id + + @property + def description(self): + return self._description + + @property + def slots(self): + return self._slots + + @property + def intents(self): + return self._intents + + @property + def intent_descriptions(self): + return self._intent_descriptions + + @property + def slot_descriptions(self): + return self._slots_descriptions + + @property + def categorical_slots(self): + return self._categorical_slots + + @property + def non_categorical_slots(self): + return self._non_categorical_slots + + @property + def categorical_slot_values(self): + return self._categorical_slot_values + + def get_categorical_slot_values(self, slot): + return self._categorical_slot_values[slot] + + def get_slot_from_id(self, slot_id): + return self._slots[slot_id] + + def get_intent_from_id(self, intent_id): + return self._intents[intent_id] + + def get_categorical_slot_from_id(self, slot_id): + return self._categorical_slots[slot_id] + + def get_non_categorical_slot_from_id(self, slot_id): + return self._non_categorical_slots[slot_id] + + def get_categorical_slot_value_from_id(self, slot_id, value_id): + slot = self._categorical_slots[slot_id] + return self._categorical_slot_values[slot][value_id] + + def get_categorical_slot_value_id(self, slot, value): + return self._categorical_slot_value_ids[slot][value] + + def get_categorical_slot_id(self, slot): + return self._categorical_slot_ids[slot] + + def get_non_categorical_slot_id(self, slot): + return self._non_categorical_slot_ids[slot] + + +class Schema(object): + """Wrapper for schemas for all services in a dataset.""" + + def __init__(self, schema_json_paths: Union[str, List[str]]): + """ + schema_json_paths: list of .json path to schema files of a single str with path to the json file. + """ + # Load the schema from the json file. + if isinstance(schema_json_paths, str): + with open(schema_json_paths, "r") as f: + all_schemas = json.load(f) + f.close() + else: + # load multiple schemas from the list of the json files + all_schemas = [] + completed_services = [] + for schema_json_path in schema_json_paths: + with open(schema_json_path, "r") as f: + schemas = json.load(f) + f.close() + logging.debug("Num of services in %s: %s", schema_json_path, len(schemas)) + + for service in schemas: + if service['service_name'] not in completed_services: + completed_services.append(service['service_name']) + all_schemas.append(service) + + self._services = sorted(schema["service_name"] for schema in all_schemas) + self._services_vocab = {v: k for k, v in enumerate(self._services)} + self._services_id_to_vocab = {v: k for k, v in self._services_vocab.items()} + service_schemas = {} + for schema in all_schemas: + service = schema["service_name"] + service_schemas[service] = ServiceSchema(schema, service_id=self.get_service_id(service)) + + self._service_schemas = service_schemas + self._schemas = all_schemas + self._slots_relation_list = {} + + def get_service_id(self, service: str): + return self._services_vocab[service] + + def get_service_from_id(self, service_id: int): + return self._services[service_id] + + def get_service_schema(self, service: str): + return self._service_schemas[service] + + @property + def services(self): + return self._services + + def save_to_file(self, file_path): + """ + Saves schema object to file + Args: + file_path: path to store schema object at + """ + with open(file_path, "w") as f: + json.dump(self._schemas, f, indent=2) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/entity_linking/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/entity_linking/__init__.py new file mode 100644 index 0000000..659718d --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/entity_linking/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.entity_linking.entity_linking_dataset import EntityLinkingDataset diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/entity_linking/entity_linking_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/entity_linking/entity_linking_dataset.py new file mode 100644 index 0000000..3b1d97a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/entity_linking/entity_linking_dataset.py @@ -0,0 +1,135 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import array +import pickle as pkl +from typing import Optional + +import torch + +from nemo.collections.nlp.data.data_utils.data_preprocessing import find_newlines, load_data_indices +from nemo.core.classes import Dataset +from nemo.utils import logging + +__all__ = ['EntityLinkingDataset'] + + +class EntityLinkingDataset(Dataset): + """ + Parent class for entity linking encoder training and index + datasets + + Args: + tokenizer (obj): huggingface tokenizer, + data_file (str): path to tab separated column file where data + pairs apear in the format + concept_ID\tconcept_synonym1\tconcept_synonym2\n + newline_idx_file (str): path to pickle file containing location + of data_file newline characters + max_seq_length (int): maximum length of a concept in tokens + is_index_data (bool): Whether dataset will be used for building + a nearest neighbors index + """ + + def __init__( + self, + tokenizer: object, + data_file: str, + newline_idx_file: Optional[str] = None, + max_seq_length: Optional[int] = 512, + is_index_data: bool = False, + ): + + self.tokenizer = tokenizer + + # Try and load pair indices file if already exists + newline_indices, newline_idx_file, _ = load_data_indices(newline_idx_file, data_file, "newline_indices") + + # If pair indices file doesn't exists, generate and store them + if newline_indices is None: + logging.info("Getting datafile newline indices") + + with open(data_file, "rb") as f: + contents = f.read() + newline_indices = find_newlines(contents) + newline_indices = array.array("I", newline_indices) + + # Store data file indicies to avoid generating them again + with open(newline_idx_file, "wb") as f: + pkl.dump(newline_indices, f) + + self.newline_indices = newline_indices + self.data_file = data_file + self.num_lines = len(newline_indices) + self.max_seq_length = max_seq_length + self.is_index_data = is_index_data + + logging.info(f"Loaded dataset with {self.num_lines} examples") + + def __len__(self): + return self.num_lines + + def __getitem__(self, idx): + + concept_offset = self.newline_indices[idx] + + with open(self.data_file, "r", encoding='utf-8-sig') as f: + # Find data pair within datafile using byte offset + f.seek(concept_offset) + concept = f.readline()[:-1] + concept = concept.strip().split("\t") + + if self.is_index_data: + concept_id, concept = concept + return (int(concept_id), concept) + + else: + concept_id, concept1, concept2 = concept + return (int(concept_id), concept1, concept2) + + def _collate_fn(self, batch): + """collate batch of input_ids, segment_ids, input_mask, and label + + Args: + batch: A list of tuples of format (concept_ID, concept_synonym1, concept_synonym2). + """ + if self.is_index_data: + concept_ids, concepts = zip(*batch) + concept_ids = list(concept_ids) + concepts = list(concepts) + + else: + concept_ids, concepts1, concepts2 = zip(*batch) + concept_ids = list(concept_ids) + concept_ids.extend(concept_ids) # Need to double label list to match each concept + concepts = list(concepts1) + concepts.extend(concepts2) + + batch = self.tokenizer( + concepts, + add_special_tokens=True, + padding=True, + truncation=True, + max_length=self.max_seq_length, + return_token_type_ids=True, + return_attention_mask=True, + return_length=True, + ) + + return ( + torch.LongTensor(batch["input_ids"]), + torch.LongTensor(batch["token_type_ids"]), + torch.LongTensor(batch["attention_mask"]), + torch.LongTensor(concept_ids), + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/glue_benchmark/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/glue_benchmark/__init__.py new file mode 100644 index 0000000..7534113 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/glue_benchmark/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.glue_benchmark.glue_benchmark_dataset import GLUEDataset diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/glue_benchmark/data_processors.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/glue_benchmark/data_processors.py new file mode 100644 index 0000000..3d907f2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/glue_benchmark/data_processors.py @@ -0,0 +1,445 @@ +# Copyright 2018 The Google AI Language Team Authors and +# The HuggingFace Inc. team. +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +from nemo.collections.nlp.data.data_utils.data_preprocessing import DataProcessor +from nemo.utils import logging + +__all__ = [ + 'ColaProcessor', + 'MnliProcessor', + 'MnliMismatchedProcessor', + 'MrpcProcessor', + 'Sst2Processor', + 'StsbProcessor', + 'QqpProcessor', + 'QnliProcessor', + 'RteProcessor', + 'WnliProcessor', + 'XNLIProcessor', +] + + +class MrpcProcessor(DataProcessor): + """Processor for the MRPC data set (GLUE version).""" + + def get_train_examples(self, data_dir): + """See base class.""" + logging.info(f'LOOKING AT {os.path.join(data_dir, "train.tsv")}') + return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") + + def get_examples(self, file_path): + return self._create_examples(self._read_tsv(file_path), "example") + + def get_labels(self): + """See base class.""" + return ["0", "1"] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "%s-%s" % (set_type, i) + text_a = line[3] + text_b = line[4] + label = line[0] + examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_t5_prompted_query(self, text_a, text_b): + return f"mrpc sentence1: {text_a} sentence2: {text_b}" + + def label2string(self, label): + return "equivalent" if label == "1" else "not equivalent" + + +class MnliProcessor(DataProcessor): + """Processor for the MultiNLI data set (GLUE version).""" + + def get_train_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), "dev_matched") + + def get_examples(self, file_path): + return self._create_examples(self._read_tsv(file_path), "example") + + def get_labels(self): + """See base class.""" + return ["contradiction", "entailment", "neutral"] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "%s-%s" % (set_type, line[0]) + text_a = line[8] + text_b = line[9] + label = line[-1] + examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_t5_prompted_query(self, text_a, text_b): + return f"mnli hypothesis: {text_a} premise: {text_b}" + + def label2string(self, label): + return label + + +class XNLIProcessor(DataProcessor): + """Processor for the MultiNLI data set (GLUE version).""" + + def get_examples(self, file_path): + return self._create_examples(self._read_tsv(file_path), "example") + + def get_labels(self): + """See base class.""" + return ["contradiction", "entailment", "neutral"] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "%s-%s" % (set_type, line[0]) + text_a = line[6] + text_b = line[7] + label = line[1] + examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_t5_prompted_query(self, text_a, text_b): + return f"mnli hypothesis: {text_a} premise: {text_b}" + + def label2string(self, label): + return label + + +class MnliMismatchedProcessor(MnliProcessor): + """Processor for the MultiNLI Mismatched data set (GLUE version).""" + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")), "dev_matched") + + def get_examples(self, file_path): + return self._create_examples(self._read_tsv(file_path), "example") + + +class ColaProcessor(DataProcessor): + """Processor for the CoLA data set (GLUE version).""" + + def get_train_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") + + def get_examples(self, file_path): + return self._create_examples(self._read_tsv(file_path), "example") + + def get_labels(self): + """See base class.""" + return ["0", "1"] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + guid = "%s-%s" % (set_type, i) + text_a = line[3] + label = line[1] + examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) + return examples + + def get_t5_prompted_query(self, text_a, text_b): + assert text_b is None + return f"cola sentence: {text_a}" + + def label2string(self, label): + return "acceptable" if label == "1" else "not acceptable" + + +class Sst2Processor(DataProcessor): + """Processor for the SST-2 data set (GLUE version).""" + + def get_train_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") + + def get_examples(self, file_path): + return self._create_examples(self._read_tsv(file_path), "example") + + def get_labels(self): + """See base class.""" + return ["0", "1"] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "%s-%s" % (set_type, i) + text_a = line[0] + label = line[1] + examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) + return examples + + def get_t5_prompted_query(self, text_a, text_b): + assert text_b is None + return f"sst2 sentence: {text_a}" + + def label2string(self, label): + return "positive" if label == "1" else "negative" + + +class StsbProcessor(DataProcessor): + """Processor for the STS-B data set (GLUE version).""" + + def get_train_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") + + def get_examples(self, file_path): + return self._create_examples(self._read_tsv(file_path), "example") + + def get_labels(self): + """See base class.""" + return [None] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "%s-%s" % (set_type, line[0]) + text_a = line[7] + text_b = line[8] + label = line[-1] + examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_t5_prompted_query(self, text_a, text_b): + return f"stsb sentence1: {text_a} sentence2: {text_b}" + + def label2string(self, label): + return '%.1f' % float(label) + + +class QqpProcessor(DataProcessor): + """Processor for the QQP data set (GLUE version).""" + + def get_train_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") + + def get_examples(self, file_path): + return self._create_examples(self._read_tsv(file_path), "example") + + def get_labels(self): + """See base class.""" + return ["0", "1"] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "%s-%s" % (set_type, line[0]) + try: + text_a = line[3] + text_b = line[4] + label = line[5] + except IndexError: + continue + examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_t5_prompted_query(self, text_a, text_b): + return f"qqp question1: {text_a} question2: {text_b}" + + def label2string(self, label): + return "duplicate" if label == "1" else "not_duplicate" + + +class QnliProcessor(DataProcessor): + """Processor for the QNLI data set (GLUE version).""" + + def get_train_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") + + def get_examples(self, file_path): + return self._create_examples(self._read_tsv(file_path), "example") + + def get_labels(self): + """See base class.""" + return ["entailment", "not_entailment"] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "%s-%s" % (set_type, line[0]) + text_a = line[1] + text_b = line[2] + label = line[-1] + examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_t5_prompted_query(self, text_a, text_b): + return f"qnli question: {text_a} sentence: {text_b}" + + def label2string(self, label): + return label + + +class RteProcessor(DataProcessor): + """Processor for the RTE data set (GLUE version).""" + + def get_train_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") + + def get_examples(self, file_path): + return self._create_examples(self._read_tsv(file_path), "example") + + def get_labels(self): + """See base class.""" + return ["entailment", "not_entailment"] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "%s-%s" % (set_type, line[0]) + text_a = line[1] + text_b = line[2] + label = line[-1] + examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_t5_prompted_query(self, text_a, text_b): + return f"rte sentence1: {text_a} sentence2: {text_b}" + + def label2string(self, label): + return label + + +class WnliProcessor(DataProcessor): + """Processor for the WNLI data set (GLUE version).""" + + def get_train_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") + + def get_examples(self, file_path): + return self._create_examples(self._read_tsv(file_path), "example") + + def get_labels(self): + """See base class.""" + return ["0", "1"] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "%s-%s" % (set_type, line[0]) + text_a = line[1] + text_b = line[2] + label = line[-1] + examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_t5_prompted_query(self, text_a, text_b): + raise NotImplementedError("NeMo-Megatron T5 does not support WNLI at the moment.") + + def label2string(self, label): + raise NotImplementedError("NeMo-Megatron T5 does not support WNLI at the moment.") + + +class InputExample(object): + """A single training/test example for simple sequence classification. + + Args: + guid: Unique id for the example. + text_a: The untokenized text of the first sequence. + For single sequence tasks, only this sequence must be specified. + text_b: The untokenized text of the second + sequence. Only must be specified for sequence pair tasks. + label:The label of the example. This should be + specified for train and dev examples, but not for test examples. + """ + + def __init__(self, guid: int, text_a: str, text_b: str = None, label: str = None): + """Constructs a InputExample.""" + self.guid = guid + self.text_a = text_a + self.text_b = text_b + self.label = label + + def __repr__(self): + return ( + f"InputExample(guid='{self.guid}', text_a='{self.text_a}', text_b='{self.text_b}', label='{self.label}')" + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/glue_benchmark/glue_benchmark_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/glue_benchmark/glue_benchmark_dataset.py new file mode 100644 index 0000000..ef78458 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/glue_benchmark/glue_benchmark_dataset.py @@ -0,0 +1,561 @@ +# Copyright 2018 The Google AI Language Team Authors and +# The HuggingFace Inc. team. +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Some code of this file was adapted from the HuggingFace library available at +# https://github.com/huggingface/transformers + +import os +import pickle +from typing import Dict, List, Optional, Union + +import numpy as np +import torch + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.collections.nlp.data.glue_benchmark.data_processors import ( + ColaProcessor, + MnliMismatchedProcessor, + MnliProcessor, + MrpcProcessor, + QnliProcessor, + QqpProcessor, + RteProcessor, + Sst2Processor, + StsbProcessor, + WnliProcessor, + XNLIProcessor, +) +from nemo.core.classes import Dataset +from nemo.core.neural_types import CategoricalValuesType, ChannelType, MaskType, NeuralType, RegressionValuesType +from nemo.utils import logging + +__all__ = ['GLUEDataset', 'TextToTextGLUEDataset', 'TextToTextXNLIDataset'] + +processors = { + "cola": ColaProcessor, + "mnli": MnliProcessor, + "mnli-mm": MnliMismatchedProcessor, + "mrpc": MrpcProcessor, + "sst-2": Sst2Processor, + "sts-b": StsbProcessor, + "qqp": QqpProcessor, + "qnli": QnliProcessor, + "rte": RteProcessor, + "wnli": WnliProcessor, + "xnli": XNLIProcessor, +} +output_modes = { + "cola": "classification", + "mnli": "classification", + "mnli-mm": "classification", + "mrpc": "classification", + "sst-2": "classification", + "sts-b": "regression", + "qqp": "classification", + "qnli": "classification", + "rte": "classification", + "wnli": "classification", + "xnli": "classification", +} +GLUE_TASKS_NUM_LABELS = { + "cola": 2, + "mnli": 3, + "mrpc": 2, + "sst-2": 2, + "sts-b": 1, + "qqp": 2, + "qnli": 2, + "rte": 2, + "wnli": 2, +} + + +class GLUEDataset(Dataset): + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'input_ids': NeuralType(('B', 'T'), ChannelType()), + 'segment_ids': NeuralType(('B', 'T'), ChannelType()), + 'input_mask': NeuralType(('B', 'T'), MaskType()), + "labels": NeuralType( + tuple('B'), RegressionValuesType() if self.task_name == 'sts-b' else CategoricalValuesType() + ), + } + + def __init__( + self, + file_name: str, + task_name: str, + tokenizer: TokenizerSpec, + max_seq_length: str, + use_cache: bool = True, + compute_features: bool = True, + ): + """ + Processes GLUE datasets + Args: + file_name: path to file + task_name: GLUE task name + tokenizer: such as AutoTokenizer + max_seq_length: max sequence length minus 2 for [CLS] and [SEP] + use_cache: whether to use data cache + """ + original_file_name = file_name + logging.info(f'Processing {file_name}') + data_dir, file_name = os.path.split(file_name) + file_name = file_name[:-4] + self.tokenizer = tokenizer + evaluate = False if 'train' in file_name else True + + if task_name not in processors: + raise ValueError(f'{task_name} not supported. Choose from {processors.keys()}') + + if task_name == 'mnli' and 'dev_mismatched' in file_name: + self.task_name = 'mnli-mm' + else: + self.task_name = task_name + + processor = processors[self.task_name]() + output_mode = output_modes[self.task_name] + self.label_list = processor.get_labels() + + # TODO: use a different variable to decide whether to trust the user provided filename. This is a temporary workaround for T5 GLUE and XNLI. + if not compute_features: + if not os.path.exists(original_file_name): + raise ValueError(f"Could not find file : {original_file_name}") + self.examples = processor.get_examples(original_file_name) + else: + self.examples = ( + processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir) + ) + processor_name = type(processor).__name__ + vocab_size = getattr(tokenizer, "vocab_size", 0) + if compute_features: + cached_features_file = os.path.join( + data_dir, + "cached_{}_{}_{}_{}_{}".format( + processor_name, file_name, tokenizer.name, str(max_seq_length), str(vocab_size) + ), + ) + + if use_cache and os.path.exists(cached_features_file): + logging.info(f"loading from {cached_features_file}") + with open(cached_features_file, "rb") as reader: + self.features = pickle.load(reader) + else: + token_params = { + 'bos_token': None, + 'eos_token': tokenizer.eos_token, + 'pad_token': tokenizer.pad_token, + 'cls_token': tokenizer.cls_token, + 'sep_token_extra': tokenizer.eos_token if 'roberta' in tokenizer.name.lower() else None, + } + + self.features = self.convert_examples_to_features( + self.examples, self.label_list, max_seq_length, tokenizer, output_mode, **token_params + ) + master_device = not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0 + if master_device: + logging.info(f'Saving train features into {cached_features_file}') + with open(cached_features_file, "wb") as writer: + pickle.dump(self.features, writer) + + def __len__(self): + return len(self.features) + + def __getitem__(self, idx): + feature = self.features[idx] + return ( + np.array(feature.input_ids), + np.array(feature.segment_ids), + np.array(feature.input_mask, dtype=np.longlong), + np.array(feature.label_id), + ) + + def convert_examples_to_features( + self, + examples: List[str], + label_list: List[int], + max_seq_length: int, + tokenizer: TokenizerSpec, + output_mode: str, + bos_token: str = None, + eos_token: str = '[SEP]', + pad_token: str = '[PAD]', + cls_token: str = '[CLS]', + sep_token_extra: str = None, + cls_token_at_end: bool = False, + cls_token_segment_id: int = 0, + pad_token_segment_id: int = 0, + pad_on_left: bool = False, + mask_padding_with_zero: bool = True, + sequence_a_segment_id: int = 0, + sequence_b_segment_id: int = 1, + ): + """ + Loads a data file into a list of `InputBatch`s. + The `cls_token_at_end` defines the location of the CLS token: + + * False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP] + * True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS] + + The `cls_token_segment_id` defines the segment id associated to the CLS token (0 for BERT, 2 for XLNet) + + The convention in BERT is: + + a. For sequence pairs: + * tokens: [CLS] is this jack ##ville ? [SEP] no it is not . [SEP] + * type_ids: 0 0 0 0 0 0 0 1 1 1 1 1 1 + b. For single sequences: + * tokens: [CLS] the dog is hairy . [SEP] + * type_ids: 0 0 0 0 0 0 0 + + Where "type_ids" are used to indicate whether this is the first + sequence or the second sequence. The embedding vectors for `type=0` + and `type=1` were learned during pre-training and are added to the + wordpiece embedding vector (and position vector). This is + not *strictly* necessarysince the [SEP] token unambiguously separates + the sequences, but it makes it easier for the model to learn + the concept of sequences. + For classification tasks, the first vector (corresponding to [CLS]) + is used as as the "sentence vector". Note that this only makes sense + because the entire model is fine-tuned. + + The convention for NMT is: + + a. For sequence pairs: + * tokens: is this jack ##ville ? no it is not . + * type_ids:0 0 0 0 0 0 0 1 1 1 1 1 1 1 + b. For single sequences: + * tokens: the dog is hairy . + * type_ids: 0 0 0 0 0 0 0 + + """ + label_map = {label: i for i, label in enumerate(label_list)} + + features = [] + for ex_index, example in enumerate(examples): + if example.label == "-": # skip examples without a consensus label (e.g. in SNLI data set) + continue + if ex_index % 10000 == 0: + logging.info("Writing example %d of %d" % (ex_index, len(examples))) + + tokens_a = tokenizer.text_to_tokens(example.text_a) + + tokens_b = None + if example.text_b: + tokens_b = tokenizer.text_to_tokens(example.text_b) + + special_tokens_count = 2 if eos_token else 0 + special_tokens_count += 1 if sep_token_extra else 0 + special_tokens_count += 2 if bos_token else 0 + special_tokens_count += 1 if cls_token else 0 + self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - special_tokens_count) + else: + special_tokens_count = 1 if eos_token else 0 + special_tokens_count += 1 if sep_token_extra else 0 + special_tokens_count += 1 if bos_token else 0 + if len(tokens_a) > max_seq_length - special_tokens_count: + tokens_a = tokens_a[: max_seq_length - special_tokens_count] + # Add special tokens to sequence_a + tokens = tokens_a + if bos_token: + tokens = [bos_token] + tokens + if eos_token: + tokens += [eos_token] + segment_ids = [sequence_a_segment_id] * len(tokens) + + # Add sequence separator between sequences + if tokens_b and sep_token_extra: + tokens += [sep_token_extra] + segment_ids += [sequence_a_segment_id] + + # Add special tokens to sequence_b + if tokens_b: + if bos_token: + tokens += [bos_token] + segment_ids += [sequence_b_segment_id] + tokens += tokens_b + segment_ids += [sequence_b_segment_id] * (len(tokens_b)) + if eos_token: + tokens += [eos_token] + segment_ids += [sequence_b_segment_id] + + # Add classification token - for BERT models + if cls_token: + if cls_token_at_end: + tokens += [cls_token] + segment_ids += [cls_token_segment_id] + else: + tokens = [cls_token] + tokens + segment_ids = [cls_token_segment_id] + segment_ids + input_ids = tokenizer.tokens_to_ids(tokens) + + # The mask has 1 for real tokens and 0 for padding tokens. Only real + # tokens are attended to. + input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids) + + # Zero-pad up to the sequence length. + padding_length = max_seq_length - len(input_ids) + pad_token_id = tokenizer.tokens_to_ids([pad_token])[0] + if pad_on_left: + input_ids = ([pad_token_id] * padding_length) + input_ids + input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask + segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids + else: + input_ids = input_ids + ([pad_token_id] * padding_length) + input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length) + segment_ids = segment_ids + ([pad_token_segment_id] * padding_length) + if len(input_ids) != max_seq_length: + raise ValueError("input_ids must be of length max_seq_length") + if len(input_mask) != max_seq_length: + raise ValueError("input_mask must be of length max_seq_length") + if len(segment_ids) != max_seq_length: + raise ValueError("segment_ids must be of length max_seq_length") + if output_mode == "classification": + label_id = label_map[example.label] + elif output_mode == "regression": + label_id = np.float32(example.label) + else: + raise KeyError(output_mode) + + if ex_index < 5: + logging.info("*** Example ***") + logging.info("guid: %s" % (example.guid)) + logging.info("tokens: %s" % " ".join(list(map(str, tokens)))) + logging.info("input_ids: %s" % " ".join(list(map(str, input_ids)))) + logging.info("input_mask: %s" % " ".join(list(map(str, input_mask)))) + logging.info("segment_ids: %s" % " ".join(list(map(str, segment_ids)))) + logging.info("label: %s (id = %d)" % (example.label, label_id)) + + features.append( + InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_id=label_id) + ) + return features + + def _truncate_seq_pair(self, tokens_a: str, tokens_b: str, max_length: int): + """Truncates a sequence pair in place to the maximum length. + + This will always truncate the longer sequence one token at a time. + This makes more sense than truncating an equal percent + of tokens from each, since if one sequence is very short then each token + that's truncated likely contains more information than a longer sequence. + """ + while True: + total_length = len(tokens_a) + len(tokens_b) + if total_length <= max_length: + break + if len(tokens_a) > len(tokens_b): + tokens_a.pop() + else: + tokens_b.pop() + + +class TextToTextGLUEDataset(GLUEDataset): + """GLUE Dataset in a text-to-text format.""" + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + return + + def __init__( + self, + file_name: str, + task_name: str, + tokenizer: TokenizerSpec, + max_seq_length: int, + max_seq_length_decoder: int = 128, + use_cache: bool = True, + prefix_override: str = None, + pad_to_max_length: bool = True, + ): + """ + Processes GLUE datasets + Args: + file_name: path to file + task_name: GLUE task name + tokenizer: such as AutoTokenizer + max_seq_length: max sequence length minus 2 for [CLS] and [SEP] + use_cache: whether to use data cache + prefix_override: if you want to override default prompt for this task specify this via a string. + pad_to_max_length: If true, pad to the maximum length. + """ + super().__init__(file_name, task_name, tokenizer, max_seq_length, use_cache, compute_features=False) + self.max_seq_length = max_seq_length + self.max_seq_length_decoder = max_seq_length_decoder + self.pad_to_max_length = pad_to_max_length + self.processor = processors[self.task_name]() + self.prefix_override = prefix_override + self.features = self.convert_examples_to_features() + + def __len__(self): + return len(self.examples) + + def __getitem__(self, idx): + enc_query, dec_input, labels = self.features[idx] + return {'text_enc': enc_query, 'text_dec': dec_input, 'labels': labels} + + def collate_fn(self, batch): + enc_query = [item['text_enc'] for item in batch] + dec_input = [item['text_dec'] for item in batch] + labels = [item['labels'] for item in batch] + + max_enc_query_length = max([len(item) for item in enc_query]) if enc_query else 0 + max_dec_input_length = max([len(item) for item in dec_input]) if dec_input else 0 + max_label_length = max([len(item) for item in labels]) if labels else 0 + if self.pad_to_max_length: + assert max_enc_query_length <= self.max_seq_length + assert max_dec_input_length <= self.max_seq_length_decoder + assert max_label_length <= self.max_seq_length_decoder + max_enc_query_length = self.max_seq_length + max_dec_input_length = self.max_seq_length_decoder + max_label_length = self.max_seq_length_decoder + + loss_mask = [([1] * (len(item))) + ([0] * (max_label_length - len(item))) for item in labels] + enc_query = [item + [self.tokenizer.pad_id] * (max_enc_query_length - len(item)) for item in enc_query] + dec_input = [item + [self.tokenizer.pad_id] * (max_dec_input_length - len(item)) for item in dec_input] + labels = [item + [self.tokenizer.pad_id] * (max_label_length - len(item)) for item in labels] + + enc_query = torch.LongTensor(enc_query) + dec_input = torch.LongTensor(dec_input) + labels = torch.LongTensor(labels) + loss_mask = torch.LongTensor(loss_mask) + + enc_mask = (enc_query != self.tokenizer.pad_id).long() + dec_mask = (dec_input != self.tokenizer.pad_id).long() + + return { + 'text_enc': enc_query, + 'text_dec': dec_input, + 'labels': labels, + 'loss_mask': loss_mask, + 'enc_mask': enc_mask, + 'dec_mask': dec_mask, + } + + def make_history_mask_3d(self, block): + batch, length = block.shape + arange = np.arange(length) + history_mask = (arange[None,] <= arange[:, None])[ + None, + ] + history_mask = np.repeat(history_mask, batch, 0) + return history_mask + + def convert_examples_to_features(self): + """ + Converts examples into Text-to-Text batches to be used with a model like T5. + Inputs are prefixed with a text prompt that indicates the task to perform. + """ + features = [] + for ex_index, example in enumerate(self.examples): + if ex_index % 10000 == 0: + logging.info(f"Writing example {ex_index} of {len(self.examples)}") + + text_to_text_query = self.processor.get_t5_prompted_query(example.text_a, example.text_b) + enc_query = self.tokenizer.text_to_ids(text_to_text_query) + if len(enc_query) > self.max_seq_length: + enc_query = enc_query[: self.max_seq_length] + dec_query = ( + [self.tokenizer.bos_id] + + self.tokenizer.text_to_ids(self.processor.label2string(example.label)) + + [self.tokenizer.eos_id] + ) + + dec_input = dec_query[:-1] + labels = dec_query[1:] + + features.append([enc_query, dec_input, labels]) + + return features + + +class TextToTextXNLIDataset(TextToTextGLUEDataset): + """XNLI Dataset in a text-to-text format.""" + + def __init__( + self, + file_name: str, + task_name: str, + tokenizer: TokenizerSpec, + max_seq_length: int, + max_seq_length_decoder: int = 128, + use_cache: bool = True, + prefix_override: str = None, + lang_list: List[str] = None, + pad_to_max_length: bool = True, + ): + self.lang_list = set(lang_list) + super().__init__( + file_name, + task_name, + tokenizer, + max_seq_length, + max_seq_length_decoder, + use_cache, + prefix_override, + pad_to_max_length, + ) + if len(lang_list) <= 0 or lang_list is None: + raise ValueError(f"Found an empty or None lang_list for {self.task_name}") + self.features = self.convert_xnli_examples_to_features() + + def __getitem__(self, idx): + enc_query, dec_input, labels, lang = self.features[idx] + return {'text_enc': enc_query, 'text_dec': dec_input, 'labels': labels, 'lang': lang} + + def collate_fn(self, batch): + base_batch = super().collate_fn(batch) + base_batch['lang'] = [item['lang'] for item in batch] + return base_batch + + def convert_xnli_examples_to_features(self): + """ + Converts examples into Text-to-Text batches to be used with a model like T5. + Inputs are prefixed with a text prompt that indicates the task to perform. + """ + features = self.features + lang_filtered_features = [] + for ex_index, example in enumerate(self.examples): + language = example.guid.split('-')[1] + if language in self.lang_list: + lang_filtered_features.append(features[ex_index] + [language]) + return lang_filtered_features + + def __len__(self): + return len(self.features) + + +class InputFeatures(object): + """A single set of features of data. + + Args: + input_ids: input/token ids + input_mask: masks out subword tokens + segment_ids: distinguish one sentence from the other one (if present) + label_ids: label for the current example + """ + + def __init__( + self, input_ids: List[int], input_mask: List[int], segment_ids: List[int], label_id: Union[float, int] + ): + """Initialized InputFeatures.""" + self.input_ids = input_ids + self.input_mask = input_mask + self.segment_ids = segment_ids + self.label_id = label_id diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/information_retrieval/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/information_retrieval/__init__.py new file mode 100644 index 0000000..a32196e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/information_retrieval/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.information_retrieval.information_retrieval_dataset import ( + BertInformationRetrievalDataset, +) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/information_retrieval/bert_embedding_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/information_retrieval/bert_embedding_dataset.py new file mode 100644 index 0000000..3c57b1a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/information_retrieval/bert_embedding_dataset.py @@ -0,0 +1,297 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Mapping, Optional + +import datasets +import numpy as np +import torch +from torch.utils.data import Dataset + +# hack to avoid the "not enough disk space" error in some slurm cluster +datasets.builder.has_sufficient_disk_space = lambda needed_bytes, directory='.': True +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import get_samples_mapping +from nemo.collections.nlp.data.language_modeling.text_memmap_dataset import JSONLMemMapDataset +from nemo.core.classes import Dataset +from nemo.utils import logging + +__all__ = ['BertEmbeddingDataset'] + + +class BertEmbeddingDataset(Dataset): + def __init__( + self, + file_path: str, + tokenizer: TokenizerSpec, + max_seq_length: int = 1024, + min_seq_length: int = 1, + add_bos: bool = True, + add_eos: bool = True, + max_num_samples: int = None, + seed: int = 1234, + index_mapping_dir: str = None, + virtual_tokens: int = 0, + memmap_workers: Optional[int] = None, + truncation_method: str = 'right', + special_tokens: Optional[Mapping[str, str]] = None, # special tokens, a dictory of {token_type: token} + data_type: str = 'train', # train, query or doc + num_hard_negatives: int = 4, + ): + """ + file_path: Path to a JSONL dataset with (query,pos_doc,neg_doc) triplets in jsonl format. + tokenizer: Tokenizer for the dataset. Instance of a class that inherits TokenizerSpec (ex: YTTM, SentencePiece). + max_seq_length (int): maximum sequence length for each dataset examples. Examples will either be truncated to fit this length or dropped if they cannot be truncated. + min_seq_length (int): min length of each data example in the dataset. Data examples will be dropped if they do not meet the min length requirements. + add_bos (bool): Whether to add a beginning of sentence token to each data example + add_eos (bool): Whether to add an end of sentence token to each data example + seed: Random seed for data shuffling. + max_num_samples: Maximum number of samples to load. This can be > dataset length if you want to oversample data. If None, all samples will be loaded. + index_mapping_dir: Directory to save the index mapping to. If None, will write to the same folder as the dataset. + truncation_method: Truncation from which position. Options: ['left', 'right'] + special_tokens: special tokens for the chat prompts, a dictionary of {token_type: token}. Default: {'system_turn_start': '', 'turn_start': '', 'label_start': '', 'end_of_turn': '\n', "end_of_name": "\n"} + """ + # TODO: lot of copy-paste from GPTSFDDataset, should refactor both to use a common base class (@adithyare) + self.tokenizer = tokenizer + self.file_path = file_path + self.max_seq_length = max_seq_length + self.min_seq_length = min_seq_length + self.add_bos = add_bos + self.add_eos = add_eos + self.max_num_samples = max_num_samples + self.seed = seed + self.index_mapping_dir = index_mapping_dir + self.virtual_tokens = virtual_tokens + self.truncation_method = truncation_method + if special_tokens is None: + self.special_tokens = { + "system_turn_start": "", + "turn_start": "", + "label_start": "", + "end_of_turn": "\n", + "end_of_name": "\n", + } + else: + self.special_tokens = special_tokens + self.data_type = data_type + self.num_hard_negatives = num_hard_negatives + + self.indexed_dataset = JSONLMemMapDataset( + dataset_paths=[file_path], + tokenizer=None, + header_lines=0, + index_mapping_dir=index_mapping_dir, + workers=memmap_workers, + ) + # Will be None after this call if `max_num_samples` is None + self.samples_mapping = None + self._build_samples_mapping() + + def _build_samples_mapping(self): + if self.max_num_samples is not None: + self.samples_mapping = get_samples_mapping( + indexed_dataset=self.indexed_dataset, + data_prefix=self.file_path, + num_epochs=None, + max_num_samples=self.max_num_samples, + max_seq_length=self.max_seq_length - 2, + short_seq_prob=0, + seed=self.seed, + name=self.file_path.split('/')[-1], + binary_head=False, + index_mapping_dir=self.index_mapping_dir, + ) + else: + self.samples_mapping = None + + def __len__(self): + if self.max_num_samples is None: + return len(self.indexed_dataset) + else: + assert self.samples_mapping is not None + return len(self.samples_mapping) + + def __getitem__(self, idx): + if isinstance(idx, np.int64): + idx = idx.item() + + if self.samples_mapping is not None: + assert idx < len(self.samples_mapping) + idx, _, _ = self.samples_mapping[idx] + if isinstance(idx, np.uint32): + idx = idx.item() + + assert idx < len(self.indexed_dataset) + # idx may < 0 because we pad_samples_to_global_batch_size, e.g. id = -1 + if idx < 0: + idx = len(self) + idx + auto_gen_idx = True + else: + auto_gen_idx = False + try: + example = self.indexed_dataset[idx] + if auto_gen_idx: + example['__AUTOGENERATED__'] = True + except Exception as e: + logging.error(f"Error while loading example {idx} from dataset {self.file_path}") + raise e + return self._process_example(example) + + def _process_example(self, example): + """ + Create an example by concatenating text and answer. + Truncation is carried out when needed, but it is performed only on the prompt side. + BOS, EOS, and SEP, are added if specified. + """ + + metadata = {k: v for k, v in example.items()} + if self.data_type == 'train': + q = self.tokenizer.text_to_ids("query: " + example['query'].strip()) + d = self.tokenizer.text_to_ids("passage: " + example['pos_doc'].strip()) + nd = [ + self.tokenizer.text_to_ids("passage: " + example['neg_doc'][i].strip()) + for i in range(self.num_hard_negatives) + ] + + elif self.data_type == 'query': + q = self.tokenizer.text_to_ids("query: " + example['query'].strip()) + d, nd = None, None + assert "query_id" in example, "query_id is required for query dataset" + assert "doc_id" in example, "doc_id is required for query dataset" + elif self.data_type == 'doc': + d = self.tokenizer.text_to_ids("passage: " + example['pos_doc'].strip()) + assert "doc_id" in example, "doc_id is required for doc dataset" + q, nd = None, None + else: + raise ValueError(f"Invalid data type: {self.data_type}") + + q = q if q is not None else [] + d = d if d is not None else [] + nd = nd if nd is not None else [] + + if self.virtual_tokens: + # (@adithyare) we are going to insert "pad/eos" tokens in the beginning of the text and context + # these pad/eos tokens are placeholders for virtual tokens for ptuning (if used) + q = [self.tokenizer.eos_id] * self.virtual_tokens + q # type: ignore + d = [self.tokenizer.eos_id] * self.virtual_tokens + d # type: ignore + nd = [[self.tokenizer.eos_id] * self.virtual_tokens + n for n in nd] # type: ignore + + if self.add_bos: + q = [self.tokenizer.bos_id] + q # type: ignore + d = [self.tokenizer.bos_id] + d # type: ignore + nd = [[self.tokenizer.bos_id] + n for n in nd] # type: ignore + + # TODO: (@adithyare) should probably add a warning before truncation + q = q[: self.max_seq_length - 1] + d = d[: self.max_seq_length - 1] + nd = [n[: self.max_seq_length - 1] for n in nd] + + if self.add_eos: + q = q + [self.tokenizer.eos_id] # type: ignore + d = d + [self.tokenizer.eos_id] # type: ignore + nd = [n + [self.tokenizer.eos_id] for n in nd] # type: ignore + + processed_example = { + 'query': q, + 'pos_doc': d, + 'neg_doc': nd, + 'metadata': metadata, + } + return processed_example + + def _maybe_cast_to_list(self, x): + if isinstance(x, np.ndarray): + return [item.tolist() for item in x] + return x + + def _ceil_to_nearest(self, n, m): + return (n + m - 1) // m * m + + def _collate_item(self, item, max_length, pad_id): + item = self._maybe_cast_to_list(item) + # max_length = max([len(x) for x in item]) if item else 0 + # here [0] should be tokenizer.pad_id + item = [x + [pad_id] * (max_length - len(x)) for x in item] + return item + + @torch.no_grad() + def _create_attention_mask(self, max_length): + """Create `attention_mask`. + Args: + input_ids: A 1D tensor that holds the indices of tokens. + """ + # seq_length = len(input_ids) + # `attention_mask` has the shape of [1, seq_length, seq_length] + attention_mask = torch.tril(torch.ones((max_length, max_length))).unsqueeze(0) + attention_mask = attention_mask < 0.5 + return attention_mask + + @torch.no_grad() + def _create_attention_mask2(self, max_length, item_lengh): + """Create `attention_mask`. + Args: + input_ids: A 1D tensor that holds the indices of tokens. + """ + # seq_length = len(input_ids) + # `attention_mask` has the shape of [1, seq_length, seq_length] + attention_mask = torch.zeros(max_length) + attention_mask[:item_lengh] = 1 + return attention_mask + + def collate_fn(self, batch): + input_ids = [] + metadata = [] + lengths = [] + max_length = -1 + for item in batch: + metadata.append(item['metadata']) + if self.data_type == 'train': + input_ids.append(item['query']) + lengths.append(len(item['query'])) + input_ids.append(item['pos_doc']) + lengths.append(len(item['pos_doc'])) + for nd in item['neg_doc']: + input_ids.append(nd) + lengths.append(len(nd)) + max_length = max( + max_length, len(item['query']), len(item['pos_doc']), *(len(nd) for nd in item['neg_doc']) + ) + elif self.data_type == 'query': + input_ids.append(item['query']) + lengths.append(len(item['query'])) + max_length = max(max_length, len(item['query'])) + elif self.data_type == 'doc': + input_ids.append(item['pos_doc']) + lengths.append(len(item['pos_doc'])) + max_length = max(max_length, len(item['pos_doc'])) + else: + raise ValueError(f"Invalid data type: {self.data_type}") + + max_length = min(self.max_seq_length, self._ceil_to_nearest(max_length, 16)) + assert max_length <= self.max_seq_length + + attention_mask = [self._create_attention_mask2(max_length, len) for len in lengths] + attention_mask = torch.stack(attention_mask) + position_ids = [list(range(max_length)) for _ in batch] + position_ids = torch.LongTensor(position_ids) + input_ids = torch.LongTensor(self._collate_item(input_ids, max_length=max_length, pad_id=0)) + lengths = torch.LongTensor(lengths) - 1 # subtract 1 to account for the eos token + + processed_batch = { + 'input_ids': input_ids, + 'token_type_ids': torch.zeros_like(input_ids), + 'attention_mask': attention_mask, + } + + return processed_batch diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/information_retrieval/gpt_embedding_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/information_retrieval/gpt_embedding_dataset.py new file mode 100644 index 0000000..e697d5e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/information_retrieval/gpt_embedding_dataset.py @@ -0,0 +1,281 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Mapping, Optional + +import datasets +import numpy as np +import torch + +# hack to avoid the "not enough disk space" error in some slurm cluster +datasets.builder.has_sufficient_disk_space = lambda needed_bytes, directory='.': True + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import get_samples_mapping +from nemo.collections.nlp.data.language_modeling.text_memmap_dataset import JSONLMemMapDataset +from nemo.core.classes import Dataset +from nemo.utils import logging + +__all__ = ['GPTEmbeddingDataset'] + + +class GPTEmbeddingDataset(Dataset): + def __init__( + self, + file_path: str, + tokenizer: TokenizerSpec, + max_seq_length: int = 1024, + min_seq_length: int = 1, + add_bos: bool = False, + add_eos: bool = True, + max_num_samples: int = None, + seed: int = 1234, + index_mapping_dir: str = None, + virtual_tokens: int = 0, + memmap_workers: Optional[int] = None, + truncation_method: str = 'right', + special_tokens: Optional[Mapping[str, str]] = None, # special tokens, a dictory of {token_type: token} + data_type: str = 'train', # train, query or doc + ): + """ + file_path: Path to a JSONL dataset with (query,pos_doc,neg_doc) triplets in jsonl format. + tokenizer: Tokenizer for the dataset. Instance of a class that inherits TokenizerSpec (ex: YTTM, SentencePiece). + max_seq_length (int): maximum sequence length for each dataset examples. Examples will either be truncated to fit this length or dropped if they cannot be truncated. + min_seq_length (int): min length of each data example in the dataset. Data examples will be dropped if they do not meet the min length requirements. + add_bos (bool): Whether to add a beginning of sentence token to each data example + add_eos (bool): Whether to add an end of sentence token to each data example + seed: Random seed for data shuffling. + max_num_samples: Maximum number of samples to load. This can be > dataset length if you want to oversample data. If None, all samples will be loaded. + index_mapping_dir: Directory to save the index mapping to. If None, will write to the same folder as the dataset. + truncation_method: Truncation from which position. Options: ['left', 'right'] + special_tokens: special tokens for the chat prompts, a dictionary of {token_type: token}. Default: {'system_turn_start': '', 'turn_start': '', 'label_start': '', 'end_of_turn': '\n', "end_of_name": "\n"} + """ + # TODO: lot of copy-paste from GPTSFDDataset, should refactor both to use a common base class (@adithyare) + self.tokenizer = tokenizer + self.file_path = file_path + self.max_seq_length = max_seq_length + self.min_seq_length = min_seq_length + self.add_bos = add_bos + self.add_eos = add_eos + self.max_num_samples = max_num_samples + self.seed = seed + self.index_mapping_dir = index_mapping_dir + self.virtual_tokens = virtual_tokens + self.truncation_method = truncation_method + if special_tokens is None: + self.special_tokens = { + "system_turn_start": "", + "turn_start": "", + "label_start": "", + "end_of_turn": "\n", + "end_of_name": "\n", + } + else: + self.special_tokens = special_tokens + self.data_type = data_type + + self.indexed_dataset = JSONLMemMapDataset( + dataset_paths=[file_path], + tokenizer=None, + header_lines=0, + index_mapping_dir=index_mapping_dir, + workers=memmap_workers, + ) + + # Will be None after this call if `max_num_samples` is None + self.samples_mapping = None + self._build_samples_mapping() + + def _build_samples_mapping(self): + if self.max_num_samples is not None: + self.samples_mapping = get_samples_mapping( + indexed_dataset=self.indexed_dataset, + data_prefix=self.file_path, + num_epochs=None, + max_num_samples=self.max_num_samples, + max_seq_length=self.max_seq_length - 2, + short_seq_prob=0, + seed=self.seed, + name=self.file_path.split('/')[-1], + binary_head=False, + index_mapping_dir=self.index_mapping_dir, + ) + else: + self.samples_mapping = None + + def __len__(self): + if self.max_num_samples is None: + return len(self.indexed_dataset) + else: + assert self.samples_mapping is not None + return len(self.samples_mapping) + + def __getitem__(self, idx): + if isinstance(idx, np.int64): + idx = idx.item() + + if self.samples_mapping is not None: + assert idx < len(self.samples_mapping) + idx, _, _ = self.samples_mapping[idx] + if isinstance(idx, np.uint32): + idx = idx.item() + + assert idx < len(self.indexed_dataset) + # idx may < 0 because we pad_samples_to_global_batch_size, e.g. id = -1 + if idx < 0: + idx = len(self) + idx + auto_gen_idx = True + else: + auto_gen_idx = False + try: + example = self.indexed_dataset[idx] + if auto_gen_idx: + example['__AUTOGENERATED__'] = True + except Exception as e: + logging.error(f"Error while loading example {idx} from dataset {self.file_path}") + raise e + return self._process_example(example) + + def _process_example(self, example): + """ + Create an example by concatenating text and answer. + Truncation is carried out when needed, but it is performed only on the prompt side. + BOS, EOS, and SEP, are added if specified. + """ + metadata = {k: v for k, v in example.items()} + if self.data_type == 'train': + q = self.tokenizer.text_to_ids("query: " + example['query'].strip()) + d = self.tokenizer.text_to_ids("passage: " + example['pos_doc'].strip()) + nd = self.tokenizer.text_to_ids("passage: " + example['neg_doc'].strip()) + elif self.data_type == 'query': + q = self.tokenizer.text_to_ids("query: " + example['query'].strip()) + d, nd = None, None + assert "query_id" in example, "query_id is required for query dataset" + assert "doc_id" in example, "doc_id is required for query dataset" + elif self.data_type == 'doc': + d = self.tokenizer.text_to_ids("passage: " + example['pos_doc'].strip()) + assert "doc_id" in example, "doc_id is required for doc dataset" + q, nd = None, None + else: + raise ValueError(f"Invalid data type: {self.data_type}") + + q = q if q is not None else [] + d = d if d is not None else [] + nd = nd if nd is not None else [] + + if self.virtual_tokens: + # (@adithyare) we are going to insert "pad/eos" tokens in the beginning of the text and context + # these pad/eos tokens are placeholders for virtual tokens for ptuning (if used) + q = [self.tokenizer.eos_id] * self.virtual_tokens + q # type: ignore + d = [self.tokenizer.eos_id] * self.virtual_tokens + d # type: ignore + nd = [self.tokenizer.eos_id] * self.virtual_tokens + nd # type: ignore + + if self.add_bos: + q = [self.tokenizer.bos_id] + q # type: ignore + d = [self.tokenizer.bos_id] + d # type: ignore + nd = [self.tokenizer.bos_id] + nd # type: ignore + + # TODO: (@adithyare) should probably add a warning before truncation + q = q[: self.max_seq_length - 1] + d = d[: self.max_seq_length - 1] + nd = nd[: self.max_seq_length - 1] + + if self.add_eos: + q = q + [self.tokenizer.eos_id] # type: ignore + d = d + [self.tokenizer.eos_id] # type: ignore + nd = nd + [self.tokenizer.eos_id] # type: ignore + + processed_example = { + 'query': q, + 'pos_doc': d, + 'neg_doc': nd, + 'metadata': metadata, + } + + return processed_example + + def _maybe_cast_to_list(self, x): + if isinstance(x, np.ndarray): + return [item.tolist() for item in x] + return x + + def _ceil_to_nearest(self, n, m): + return (n + m - 1) // m * m + + def _collate_item(self, item, max_length, pad_id): + item = self._maybe_cast_to_list(item) + # max_length = max([len(x) for x in item]) if item else 0 + # here [0] should be tokenizer.pad_id + item = [x + [pad_id] * (max_length - len(x)) for x in item] + return item + + @torch.no_grad() + def _create_attention_mask(self, max_length): + """Create `attention_mask`. + Args: + input_ids: A 1D tensor that holds the indices of tokens. + """ + # seq_length = len(input_ids) + # `attention_mask` has the shape of [1, seq_length, seq_length] + attention_mask = torch.tril(torch.ones((max_length, max_length))).unsqueeze(0) + attention_mask = attention_mask < 0.5 + return attention_mask + + def collate_fn(self, batch): + input_ids = [] + metadata = [] + lengths = [] + max_length = -1 + for item in batch: + metadata.append(item['metadata']) + if self.data_type == 'train': + input_ids.append(item['query']) + lengths.append(len(item['query'])) + input_ids.append(item['pos_doc']) + lengths.append(len(item['pos_doc'])) + input_ids.append(item['neg_doc']) + lengths.append(len(item['neg_doc'])) + max_length = max(max_length, len(item['query']), len(item['pos_doc']), len(item['neg_doc'])) + elif self.data_type == 'query': + input_ids.append(item['query']) + lengths.append(len(item['query'])) + max_length = max(max_length, len(item['query'])) + elif self.data_type == 'doc': + input_ids.append(item['pos_doc']) + lengths.append(len(item['pos_doc'])) + max_length = max(max_length, len(item['pos_doc'])) + else: + raise ValueError(f"Invalid data type: {self.data_type}") + + max_length = min(self.max_seq_length, self._ceil_to_nearest(max_length, 16)) + assert max_length <= self.max_seq_length + + attention_mask = [self._create_attention_mask(max_length) for _ in input_ids] + attention_mask = torch.stack(attention_mask) + position_ids = [list(range(max_length)) for _ in input_ids] + position_ids = torch.LongTensor(position_ids) + input_ids = torch.LongTensor( + self._collate_item(input_ids, max_length=max_length, pad_id=self.tokenizer.eos_id) + ) + lengths = torch.LongTensor(lengths) - 1 # subtract 1 to account for the eos token + + processed_batch = { + 'tokens': input_ids, + 'attention_mask': attention_mask, + 'loss_mask': lengths, + 'position_ids': position_ids, + 'metadata': metadata, + } + + return processed_batch diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/information_retrieval/information_retrieval_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/information_retrieval/information_retrieval_dataset.py new file mode 100644 index 0000000..349f9e4 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/information_retrieval/information_retrieval_dataset.py @@ -0,0 +1,278 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import multiprocessing as mp +import os +import pickle +import random +from typing import Optional + +import numpy as np +from torch.utils.data import Dataset + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec + +__all__ = ["BertInformationRetrievalDataset"] + + +class BaseInformationRetrievalDataset(Dataset): + """ + Base information retrieval dataset on which other datasets are built. + + Args: + tokenizer: tokenizer + max_query_length: maximum length of query in tokens + max_passage_length: maximum length of passage in tokens + """ + + def __init__( + self, tokenizer: TokenizerSpec, max_query_length: Optional[int] = 31, max_passage_length: Optional[int] = 190, + ): + self.tokenizer = tokenizer + self.max_query_length = max_query_length + self.max_passage_length = max_passage_length + + def parse_npz(self, file, max_seq_length): + """ + Function which parses passages (documents) in npz format. + After pre-processing and tokenization, the dataset will be saved + as numpy matrix, i_th entry of which corresponds to i_th passage (document) + and has the following form: + [n, token_1, ..., token_n, 0, ..., 0] + where n is the passage length (in tokens) and 0s correspond to pad tokens. + + Args: + file: str, path to file with passages (documents) + max_seq_length: maximum length of sequence in tokens + """ + cached_collection = file + ".npz" + if os.path.isfile(cached_collection): + dataset_npz = np.load(cached_collection)["data"] + else: + dataset_dict = self.tokenize_dataset(file, max_seq_length) + dataset_npz = np.zeros((len(dataset_dict), max_seq_length + 1)) + for key in dataset_dict: + dataset_npz[key][0] = len(dataset_dict[key]) + dataset_npz[key][1 : len(dataset_dict[key]) + 1] = dataset_dict[key] + np.savez(cached_collection, data=dataset_npz) + return dataset_npz + + def parse_pkl(self, file, max_seq_length): + """ + Function which parses passages (documents, queries) in pkl format. + After pre-processing and tokenization, the dataset will be saved + as pkl dict, i_th entry of which corresponds to i_th passage (document, query) + and has the following form: + {passage_id: [token_1, ..., token_n]} + where n is the passage length (in tokens). + + Args: + file: str, path to file with passages (documents) + max_seq_length: maximum length of sequence in tokens + """ + cached_collection = file + ".pkl" + if os.path.isfile(cached_collection): + dataset_dict = pickle.load(open(cached_collection, "rb")) + else: + dataset_dict = self.tokenize_dataset(file, max_seq_length) + pickle.dump(dataset_dict, open(cached_collection, "wb")) + return dataset_dict + + def tokenize_dataset(self, file, max_seq_length): + """ + Function which pre-tokenizes the dataset. + """ + lines = open(file, "r").readlines() + with mp.Pool() as pool: + dataset_dict = pool.map(self.preprocess_line, lines) + dataset_dict = {id_: tokens[:max_seq_length] for (id_, tokens) in dataset_dict} + return dataset_dict + + def preprocess_line(self, line): + """ + Parse a single entry (line) of tsv file. + """ + if "\t" not in line: + raise ValueError(f"Provided dataset does not have a form of tsv file") + id_, text = line.split("\t") + token_ids = self.tokenizer.text_to_ids(text.strip()) + return int(id_), token_ids + + def construct_input(self, token_ids1, max_seq_length, token_ids2=None): + """ + Function which constructs a valid input to BERT from tokens. + + If only one list of tokens (token_ids1) is passed, the input will be + [CLS] token_ids1 [SEP] + + if two lists of tokens are passed, the input will be + [CLS] token_ids1 [SEP] token_ids2 [SEP] + """ + + input_ids = [self.tokenizer.pad_id] * max_seq_length + bert_input = [self.tokenizer.cls_id] + token_ids1 + [self.tokenizer.sep_id] + sentence1_length = len(bert_input) + if token_ids2 is not None: + bert_input = bert_input + token_ids2 + [self.tokenizer.sep_id] + + bert_input = bert_input[:max_seq_length] + + num_nonpad_tokens = len(bert_input) + + input_ids[:num_nonpad_tokens] = bert_input + input_ids = np.array(input_ids, dtype=np.longlong) + input_mask = input_ids != self.tokenizer.pad_id + input_type_ids = np.ones_like(input_ids) + input_type_ids[:sentence1_length] = 0 + + return input_ids, input_mask, input_type_ids + + def preprocess_bert(self, query_id, psg_ids): + """ + Transforms query id (Q) and a list of passages ids (P1, ..., Pk) + into a tensor of size [k, max_length] with the following rows: + [CLS] Q_text [SEP] Pi_text [SEP], i = 1, ..., k + """ + + max_seq_length = self.max_query_length + self.max_passage_length + 3 + input_ids, input_mask, input_type_ids = [], [], [] + for psg_id in psg_ids: + inputs = self.construct_input(self.queries[query_id], max_seq_length, self._psgid2tokens(psg_id)) + input_ids.append(inputs[0]) + input_mask.append(inputs[1]) + input_type_ids.append(inputs[2]) + + input_ids = np.stack(input_ids) + input_mask = np.stack(input_mask) + input_type_ids = np.stack(input_type_ids) + + return input_ids, input_mask, input_type_ids + + def preprocess_dpr(self, query_id, psg_ids): + """ + Transforms query id (Q) and a list of passages ids (P1, ..., Pk) + into two tensors of sizes [1, max_q_length] and [k, max_p_length] + with the following rows: + 1) [CLS] Q_text [SEP] + 2) [CLS] Pi_text [SEP], i = 1, ..., k + """ + + q_input_ids, q_input_mask, q_type_ids = self.construct_input(self.queries[query_id], self.max_query_length + 2) + input_ids, input_mask, input_type_ids = [], [], [] + for psg_id in psg_ids: + inputs = self.construct_input(self._psgid2tokens(psg_id), self.max_passage_length + 2) + input_ids.append(inputs[0]) + input_mask.append(inputs[1]) + input_type_ids.append(inputs[2]) + input_ids = np.stack(input_ids) + input_mask = np.stack(input_mask) + input_type_ids = np.stack(input_type_ids) + return ( + q_input_ids[None, ...], + q_input_mask[None, ...], + q_type_ids[None, ...], + input_ids, + input_mask, + input_type_ids, + ) + + def _psgid2tokens(self, psg_id): + """ + Internal function which maps passage id to its tokens. + """ + pass + + def psgid2tokens_npz(self, psg_id): + """ + Mapping from passage id to its tokens in case of npz cache format. + """ + seq_len = self.passages[psg_id][0] + return self.passages[psg_id][1 : seq_len + 1].tolist() + + def psgid2tokens_pkl(self, psg_id): + """ + Mapping from passage id to its tokens in case of pkl cache format. + """ + return self.passages[psg_id] + + +class BertInformationRetrievalDataset(BaseInformationRetrievalDataset): + def __init__( + self, + tokenizer: TokenizerSpec, + passages: str, + queries: str, + query_to_passages: str, + max_query_length: Optional[int] = 31, + max_passage_length: Optional[int] = 190, + num_negatives: Optional[int] = 10, + preprocess_fn: Optional[str] = "preprocess_bert", + psg_cache_format: Optional[str] = "npz", + ): + """ + Dataset for training information retrieval models. + + Args: + tokenizer: tokenizer + passages: path to tsv with [psg_id, psg_text] entries + queries: path to tsv with [query_id, query_text] entries + query_to_passages: path to tsv with + [query_id, pos_psg_id, neg_psg_id_1, ..., neg_psg_id_k] entries + max_query_length: maximum length of query in tokens + max_passage_length: maximum length of passage in tokens + num_negatives: number of negative passages per positive to use for training + preprocess_fn: either preprocess_bert or preprocess_dpr + preprocess_bert: joint input: [CLS] query [SEP] passage [SEP] + preprocess_dpr: separate inputs: [CLS] query [SEP], [CLS] passage [SEP] + psg_cache_format: either pkl or npz + """ + + super().__init__(tokenizer, max_query_length, max_passage_length) + self.num_negatives = num_negatives + + self.passages = getattr(self, f"parse_{psg_cache_format}")(passages, max_passage_length) + self._psgid2tokens = getattr(self, f"psgid2tokens_{psg_cache_format}") + self.queries = self.parse_pkl(queries, max_query_length) + self.idx2psgs = self.parse_query_to_passages(query_to_passages) + self._preprocess_fn = getattr(self, preprocess_fn) + + def __getitem__(self, idx): + query_and_psgs = self.idx2psgs[idx] + query_id, psg_ids = query_and_psgs[0], query_and_psgs[1:] + inputs = self._preprocess_fn(query_id, psg_ids) + return [*inputs, query_id, np.array(psg_ids)] + + def __len__(self): + return len(self.idx2psgs) + + def parse_query_to_passages(self, file): + """ + Function which parses query to passages correspondence file. + """ + idx2psgs = {} + idx = 0 + for line in open(file, "r").readlines(): + if "\t" not in line: + raise ValueError(f"Provided dataset does not have a form of tsv file") + query_and_psgs = line.split("\t") + query_and_psgs_ids = [int(id_) for id_ in query_and_psgs] + query_and_rel_psg_ids, irrel_psgs_ids = query_and_psgs_ids[:2], query_and_psgs_ids[2:] + random.shuffle(irrel_psgs_ids) + num_samples = len(irrel_psgs_ids) // self.num_negatives + for j in range(num_samples): + left = self.num_negatives * j + right = self.num_negatives * (j + 1) + idx2psgs[idx] = query_and_rel_psg_ids + irrel_psgs_ids[left:right] + idx += 1 + return idx2psgs diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/__init__.py new file mode 100644 index 0000000..3e1782e --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/__init__.py @@ -0,0 +1,28 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from nemo.collections.nlp.data.intent_slot_classification.intent_slot_classification_dataset import ( + IntentSlotClassificationDataset, + IntentSlotInferenceDataset, +) +from nemo.collections.nlp.data.intent_slot_classification.intent_slot_classification_descriptor import ( + IntentSlotDataDesc, +) +from nemo.collections.nlp.data.intent_slot_classification.multi_label_intent_slot_classification_dataset import ( + MultiLabelIntentSlotClassificationDataset, +) +from nemo.collections.nlp.data.intent_slot_classification.multi_label_intent_slot_classification_descriptor import ( + MultiLabelIntentSlotDataDesc, +) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/intent_slot_classification_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/intent_slot_classification_dataset.py new file mode 100644 index 0000000..a73341a --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/intent_slot_classification_dataset.py @@ -0,0 +1,297 @@ +# Copyright 2018 The Google AI Language Team Authors and +# The HuggingFace Inc. team. +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, Optional + +import numpy as np + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.collections.nlp.data.data_utils import get_stats +from nemo.core.classes import Dataset +from nemo.core.neural_types import ChannelType, LabelsType, MaskType, NeuralType +from nemo.utils import logging + +__all__ = ['IntentSlotClassificationDataset', 'IntentSlotInferenceDataset'] + + +def get_features( + queries, + max_seq_length, + tokenizer, + pad_label=128, + raw_slots=None, + ignore_extra_tokens=False, + ignore_start_end=False, +): + all_subtokens = [] + all_loss_mask = [] + all_subtokens_mask = [] + all_segment_ids = [] + all_input_ids = [] + all_input_mask = [] + sent_lengths = [] + all_slots = [] + + with_label = False + if raw_slots is not None: + with_label = True + + for i, query in enumerate(queries): + words = query.strip().split() + subtokens = [tokenizer.cls_token] + loss_mask = [1 - ignore_start_end] + subtokens_mask = [0] + if with_label: + slots = [pad_label] + + for j, word in enumerate(words): + word_tokens = tokenizer.text_to_tokens(word) + + # to handle emojis that could be neglected during tokenization + if len(word.strip()) > 0 and len(word_tokens) == 0: + word_tokens = [tokenizer.ids_to_tokens(tokenizer.unk_id)] + + subtokens.extend(word_tokens) + + loss_mask.append(1) + loss_mask.extend([int(not ignore_extra_tokens)] * (len(word_tokens) - 1)) + + subtokens_mask.append(1) + subtokens_mask.extend([0] * (len(word_tokens) - 1)) + + if with_label: + slots.extend([raw_slots[i][j]] * len(word_tokens)) + + subtokens.append(tokenizer.sep_token) + loss_mask.append(1 - ignore_start_end) + subtokens_mask.append(0) + sent_lengths.append(len(subtokens)) + all_subtokens.append(subtokens) + all_loss_mask.append(loss_mask) + all_subtokens_mask.append(subtokens_mask) + all_input_mask.append([1] * len(subtokens)) + if with_label: + slots.append(pad_label) + all_slots.append(slots) + + max_seq_length_data = max(sent_lengths) + max_seq_length = min(max_seq_length, max_seq_length_data) if max_seq_length > 0 else max_seq_length_data + logging.info(f'Setting max length to: {max_seq_length}') + get_stats(sent_lengths) + too_long_count = 0 + + for i, subtokens in enumerate(all_subtokens): + if len(subtokens) > max_seq_length: + subtokens = [tokenizer.cls_token] + subtokens[-max_seq_length + 1 :] + all_input_mask[i] = [1] + all_input_mask[i][-max_seq_length + 1 :] + all_loss_mask[i] = [1 - ignore_start_end] + all_loss_mask[i][-max_seq_length + 1 :] + all_subtokens_mask[i] = [0] + all_subtokens_mask[i][-max_seq_length + 1 :] + + if with_label: + all_slots[i] = [pad_label] + all_slots[i][-max_seq_length + 1 :] + too_long_count += 1 + + all_input_ids.append([tokenizer.tokens_to_ids(t) for t in subtokens]) + + if len(subtokens) < max_seq_length: + extra = max_seq_length - len(subtokens) + all_input_ids[i] = all_input_ids[i] + [0] * extra + all_loss_mask[i] = all_loss_mask[i] + [0] * extra + all_subtokens_mask[i] = all_subtokens_mask[i] + [0] * extra + all_input_mask[i] = all_input_mask[i] + [0] * extra + + if with_label: + all_slots[i] = all_slots[i] + [pad_label] * extra + + all_segment_ids.append([0] * max_seq_length) + + logging.info(f'{too_long_count} are longer than {max_seq_length}') + + # May be useful for debugging + logging.debug("*** Some Examples of Processed Data ***") + for i in range(min(len(all_input_ids), 5)): + logging.debug("i: %s" % (i)) + logging.debug("subtokens: %s" % " ".join(list(map(str, all_subtokens[i])))) + logging.debug("loss_mask: %s" % " ".join(list(map(str, all_loss_mask[i])))) + logging.debug("input_mask: %s" % " ".join(list(map(str, all_input_mask[i])))) + logging.debug("subtokens_mask: %s" % " ".join(list(map(str, all_subtokens_mask[i])))) + if with_label: + logging.debug("slots_label: %s" % " ".join(list(map(str, all_slots[i])))) + + return (all_input_ids, all_segment_ids, all_input_mask, all_loss_mask, all_subtokens_mask, all_slots) + + +class IntentSlotClassificationDataset(Dataset): + """ + Creates dataset to use for the task of joint intent + and slot classification with pretrained model. + + Converts from raw data to an instance that can be used by + NMDataLayer. + + For dataset to use during inference without labels, see + IntentSlotDataset. + + Args: + input_file: file to sequence + label. the first line is header (sentence [tab] label) + each line should be [sentence][tab][label] + slot_file: file to slot labels, each line corresponding to slot labels for a sentence in input_file. No header. + max_seq_length: max sequence length minus 2 for [CLS] and [SEP] + tokenizer: such as NemoBertTokenizer + num_samples: number of samples you want to use for the dataset. If -1, use all dataset. Useful for testing. + pad_label: pad value use for slot labels. by default, it's the neutral label. + ignore_extra_tokens: whether to ignore extra tokens in the loss_mask. + ignore_start_end: whether to ignore bos and eos tokens in the loss_mask. + do_lower_case: convert query to lower case or not + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'input_ids': NeuralType(('B', 'T'), ChannelType()), + 'segment_ids': NeuralType(('B', 'T'), ChannelType()), + 'input_mask': NeuralType(('B', 'T'), MaskType()), + 'loss_mask': NeuralType(('B', 'T'), MaskType()), + 'subtokens_mask': NeuralType(('B', 'T'), MaskType()), + 'intent_labels': NeuralType(('B'), LabelsType()), + 'slot_labels': NeuralType(('B', 'T'), LabelsType()), + } + + def __init__( + self, + input_file: str, + slot_file: str, + max_seq_length: int, + tokenizer: TokenizerSpec, + num_samples: int = -1, + pad_label: int = 128, + ignore_extra_tokens: bool = False, + ignore_start_end: bool = False, + do_lower_case: bool = False, + ): + if num_samples == 0: + raise ValueError("num_samples has to be positive", num_samples) + + with open(slot_file, 'r') as f: + slot_lines = f.readlines() + + with open(input_file, 'r') as f: + input_lines = f.readlines()[1:] + + assert len(slot_lines) == len(input_lines) + + dataset = list(zip(slot_lines, input_lines)) + + if num_samples > 0: + dataset = dataset[:num_samples] + + raw_slots, queries, raw_intents = [], [], [] + for slot_line, input_line in dataset: + raw_slots.append([int(slot) for slot in slot_line.strip().split()]) + parts = input_line.strip().split() + raw_intents.append(int(parts[-1])) + query = ' '.join(parts[:-1]) + if do_lower_case: + query = query.lower() + queries.append(query) + + features = get_features( + queries, + max_seq_length, + tokenizer, + pad_label=pad_label, + raw_slots=raw_slots, + ignore_extra_tokens=ignore_extra_tokens, + ignore_start_end=ignore_start_end, + ) + self.all_input_ids = features[0] + self.all_segment_ids = features[1] + self.all_input_mask = features[2] + self.all_loss_mask = features[3] + self.all_subtokens_mask = features[4] + self.all_slots = features[5] + self.all_intents = raw_intents + + def __len__(self): + return len(self.all_input_ids) + + def __getitem__(self, idx): + return ( + np.array(self.all_input_ids[idx]), + np.array(self.all_segment_ids[idx]), + np.array(self.all_input_mask[idx], dtype=np.longlong), + np.array(self.all_loss_mask[idx]), + np.array(self.all_subtokens_mask[idx]), + self.all_intents[idx], + np.array(self.all_slots[idx]), + ) + + +class IntentSlotInferenceDataset(Dataset): + """ + Creates dataset to use for the task of joint intent + and slot classification with pretrained model. + This is to be used during inference only. + It uses list of queries as the input. + + Args: + queries (list): list of queries to run inference on + max_seq_length (int): max sequence length minus 2 for [CLS] and [SEP] + tokenizer (Tokenizer): such as NemoBertTokenizer + pad_label (int): pad value use for slot labels. + by default, it's the neutral label. + + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """ + Returns definitions of module output ports. + """ + return { + 'input_ids': NeuralType(('B', 'T'), ChannelType()), + 'segment_ids': NeuralType(('B', 'T'), ChannelType()), + 'input_mask': NeuralType(('B', 'T'), MaskType()), + 'loss_mask': NeuralType(('B', 'T'), MaskType()), + 'subtokens_mask': NeuralType(('B', 'T'), MaskType()), + } + + def __init__(self, queries, max_seq_length, tokenizer, do_lower_case): + if do_lower_case: + for idx, query in enumerate(queries): + queries[idx] = queries[idx].lower() + + features = get_features(queries, max_seq_length, tokenizer) + + self.all_input_ids = features[0] + self.all_segment_ids = features[1] + self.all_input_mask = features[2] + self.all_loss_mask = features[3] + self.all_subtokens_mask = features[4] + + def __len__(self): + return len(self.all_input_ids) + + def __getitem__(self, idx): + return ( + np.array(self.all_input_ids[idx]), + np.array(self.all_segment_ids[idx]), + np.array(self.all_input_mask[idx], dtype=np.longlong), + np.array(self.all_loss_mask[idx]), + np.array(self.all_subtokens_mask[idx]), + ) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/intent_slot_classification_descriptor.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/intent_slot_classification_descriptor.py new file mode 100644 index 0000000..544b5e1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/intent_slot_classification_descriptor.py @@ -0,0 +1,163 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import itertools +from typing import List + +from nemo.collections.nlp.data.data_utils.data_preprocessing import ( + fill_class_weights, + get_freq_weights, + get_label_stats, + if_exist, +) +from nemo.utils import logging + + +class IntentSlotDataDesc: + """ Convert the raw data to the standard format supported by + IntentSlotDataDesc. + + By default, the None label for slots is 'O'. + + IntentSlotDataDesc requires two files: + + input_file: file to sequence + label. + the first line is header (sentence [tab] label) + each line should be [sentence][tab][label] + + slot_file: file to slot labels, each line corresponding to + slot labels for a sentence in input_file. No header. + + To keep the mapping from label index to label consistent during + training and inferencing we require the following files: + dicts.intents.csv: each line is an intent. The first line + corresponding to the 0 intent label, the second line + corresponding to the 1 intent label, and so on. + + dicts.slots.csv: each line is a slot. The first line + corresponding to the 0 slot label, the second line + corresponding to the 1 slot label, and so on. + + Args: + data_dir: the directory of the dataset + modes: ['train', 'test', 'dev'], + none_slot_label: the label for slots that aren't identified defaulted to 'O' + pad_label: the int used for padding. If set to -1, it'll be set to the whatever the None label is. + """ + + def __init__( + self, + data_dir: str, + modes: List[str] = ['train', 'test', 'dev'], + none_slot_label: str = 'O', + pad_label: int = -1, + ): + if not if_exist(data_dir, ['dict.intents.csv', 'dict.slots.csv']): + raise FileNotFoundError( + "Make sure that your data follows the standard format " + "supported by JointIntentSlotDataset. Your data must " + "contain dict.intents.csv and dict.slots.csv." + ) + + self.data_dir = data_dir + self.intent_dict_file = self.data_dir + '/dict.intents.csv' + self.slot_dict_file = self.data_dir + '/dict.slots.csv' + + self.intents_label_ids = IntentSlotDataDesc.label2idx(self.intent_dict_file) + self.num_intents = len(self.intents_label_ids) + self.slots_label_ids = IntentSlotDataDesc.label2idx(self.slot_dict_file) + self.num_slots = len(self.slots_label_ids) + + infold = self.data_dir + for mode in modes: + if not if_exist(self.data_dir, [f'{mode}.tsv']): + logging.info(f' Stats calculation for {mode} mode' f' is skipped as {mode}.tsv was not found.') + continue + logging.info(f' Stats calculating for {mode} mode...') + slot_file = f'{self.data_dir}/{mode}_slots.tsv' + with open(slot_file, 'r') as f: + slot_lines = f.readlines() + + input_file = f'{self.data_dir}/{mode}.tsv' + with open(input_file, 'r') as f: + input_lines = f.readlines()[1:] # Skipping headers at index 0 + + if len(slot_lines) != len(input_lines): + raise ValueError( + "Make sure that the number of slot lines match the " + "number of intent lines. There should be a 1-1 " + "correspondence between every slot and intent lines." + ) + + dataset = list(zip(slot_lines, input_lines)) + + raw_slots, raw_intents = [], [] + for slot_line, input_line in dataset: + slot_list = [int(slot) for slot in slot_line.strip().split()] + raw_slots.append(slot_list) + parts = input_line.strip().split() + raw_intents.append(int(parts[-1])) + + logging.info(f'Three most popular intents in {mode} mode:') + total_intents, intent_label_freq, max_id = get_label_stats( + raw_intents, infold + f'/{mode}_intent_stats.tsv' + ) + + merged_slots = itertools.chain.from_iterable(raw_slots) + logging.info(f'Three most popular slots in {mode} mode:') + slots_total, slots_label_freq, max_id = get_label_stats(merged_slots, infold + f'/{mode}_slot_stats.tsv') + + logging.info(f'Total Number of Intents: {total_intents}') + logging.info(f'Intent Label Frequencies: {intent_label_freq}') + logging.info(f'Total Number of Slots: {slots_total}') + logging.info(f'Slots Label Frequencies: {slots_label_freq}') + + if mode == 'train': + intent_weights_dict = get_freq_weights(intent_label_freq) + logging.info(f'Intent Weights: {intent_weights_dict}') + slot_weights_dict = get_freq_weights(slots_label_freq) + logging.info(f'Slot Weights: {slot_weights_dict}') + + self.intent_weights = fill_class_weights(intent_weights_dict, self.num_intents - 1) + self.slot_weights = fill_class_weights(slot_weights_dict, self.num_slots - 1) + + if pad_label != -1: + self.pad_label = pad_label + else: + if none_slot_label not in self.slots_label_ids: + raise ValueError(f'none_slot_label {none_slot_label} not ' f'found in {self.slot_dict_file}.') + self.pad_label = self.slots_label_ids[none_slot_label] + + @staticmethod + def label2idx(file): + lines = open(file, 'r').readlines() + lines = [line.strip() for line in lines if line.strip()] + labels = {lines[i]: i for i in range(len(lines))} + return labels + + @staticmethod + def intent_slot_dicts(data_dir): + ''' + Return Intent and slot dictionaries + ''' + intent_dict_file = data_dir + '/dict.intents.csv' + slot_dict_file = data_dir + '/dict.slots.csv' + + intents_labels = open(intent_dict_file, 'r').readlines() + intents_labels = [line.strip() for line in intents_labels if line.strip()] + + slots_labels = open(slot_dict_file, 'r').readlines() + slots_labels = [line.strip() for line in slots_labels if line.strip()] + + return intents_labels, slots_labels diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/multi_label_intent_slot_classification_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/multi_label_intent_slot_classification_dataset.py new file mode 100644 index 0000000..32a72d1 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/multi_label_intent_slot_classification_dataset.py @@ -0,0 +1,121 @@ +# Copyright 2018 The Google AI Language Team Authors and +# The HuggingFace Inc. team. +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, Optional + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.collections.nlp.data.intent_slot_classification import IntentSlotClassificationDataset +from nemo.collections.nlp.data.intent_slot_classification.intent_slot_classification_dataset import get_features +from nemo.core.neural_types import ChannelType, LabelsType, MaskType, NeuralType + +__all__ = ['MultiLabelIntentSlotClassificationDataset'] + + +class MultiLabelIntentSlotClassificationDataset(IntentSlotClassificationDataset): + """ + Creates dataset to use for the task of multi-label joint intent + and slot classification with pretrained model. + + Converts from raw data to an instance that can be used by + NMDataLayer. + + Args: + input_file: file containing sentences + labels. The first line is header (sentence [tab] label) + each line should be [sentence][tab][label] where label can be multiple labels separated by a comma + slot_file: file containing slot labels, each line corresponding to slot labels for a sentence in input_file. No header. + num_intents: total number of intents in dict.intents file + max_seq_length: max sequence length minus 2 for [CLS] and [SEP] + tokenizer: such as NemoBertTokenizer + num_samples: number of samples you want to use for the dataset. If -1, use all dataset. Useful for testing. + pad_label: pad value use for slot labels. by default, it's the neutral label. + ignore_extra_tokens: whether to ignore extra tokens in the loss_mask. + ignore_start_end: whether to ignore bos and eos tokens in the loss_mask. + do_lower_case: convert query to lower case or not + """ + + @property + def output_types(self) -> Optional[Dict[str, NeuralType]]: + """Returns definitions of module output ports. + """ + return { + 'input_ids': NeuralType(('B', 'T'), ChannelType()), + 'segment_ids': NeuralType(('B', 'T'), ChannelType()), + 'input_mask': NeuralType(('B', 'T'), MaskType()), + 'loss_mask': NeuralType(('B', 'T'), MaskType()), + 'subtokens_mask': NeuralType(('B', 'T'), MaskType()), + 'intent_labels': [NeuralType(('B'), LabelsType())], + 'slot_labels': NeuralType(('B', 'T'), LabelsType()), + } + + def __init__( + self, + input_file: str, + slot_file: str, + num_intents: int, + max_seq_length: int, + tokenizer: TokenizerSpec, + num_samples: int = -1, + pad_label: int = 128, + ignore_extra_tokens: bool = False, + ignore_start_end: bool = False, + do_lower_case: bool = False, + ): + if num_samples == 0: + raise ValueError("num_samples has to be positive", num_samples) + + with open(slot_file, 'r') as f: + slot_lines = f.readlines() + + with open(input_file, 'r') as f: + input_lines = f.readlines()[1:] + + assert len(slot_lines) == len(input_lines) + + dataset = list(zip(slot_lines, input_lines)) + + if num_samples > 0: + dataset = dataset[:num_samples] + + raw_slots, queries, raw_intents = [], [], [] + for slot_line, input_line in dataset: + raw_slots.append([int(slot) for slot in slot_line.strip().split()]) + parts = input_line.strip().split("\t")[1:][0] + parts = list(map(int, parts.split(","))) + parts = [1 if label in parts else 0 for label in range(num_intents)] + raw_intents.append(tuple(parts)) + tokens = input_line.strip().split("\t")[0].split() + query = ' '.join(tokens) + if do_lower_case: + query = query.lower() + queries.append(query) + + features = get_features( + queries, + max_seq_length, + tokenizer, + pad_label=pad_label, + raw_slots=raw_slots, + ignore_extra_tokens=ignore_extra_tokens, + ignore_start_end=ignore_start_end, + ) + + self.all_input_ids = features[0] + self.all_segment_ids = features[1] + self.all_input_mask = features[2] + self.all_loss_mask = features[3] + self.all_subtokens_mask = features[4] + self.all_slots = features[5] + self.all_intents = raw_intents diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/multi_label_intent_slot_classification_descriptor.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/multi_label_intent_slot_classification_descriptor.py new file mode 100644 index 0000000..ddde1a2 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/intent_slot_classification/multi_label_intent_slot_classification_descriptor.py @@ -0,0 +1,146 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import itertools +from typing import List + +from nemo.collections.nlp.data.data_utils.data_preprocessing import ( + fill_class_weights, + get_freq_weights, + get_freq_weights_bce_with_logits_loss, + get_label_stats, + get_labels_to_labels_id_mapping, + get_multi_label_stats, + if_exist, +) +from nemo.utils import logging + + +class MultiLabelIntentSlotDataDesc: + """ Convert the raw data to the standard format supported by + MultiLabelIntentSlotDataDesc. + + By default, the None label for slots is 'O'. + + MultiLabelIntentSlotDataDesc requires two files: + + input_file: file containing sentences + labels. + the first line is header (sentence [tab] label) + each line should be [sentence][tab][label] where label is a string of comma separated values. + Example: 1 or 1,2 are both valid labels + + slot_file: file containing slot labels, each line corresponding to + slot labels for a sentence in input_file. No header. + + To keep the mapping from label index to label consistent during + training and inferencing we require the following files: + dicts.intents.csv: each line is an intent. The first line + corresponding to the 0 intent label, the second line + corresponding to the 1 intent label, and so on. + + dicts.slots.csv: each line is a slot. The first line + corresponding to the 0 slot label, the second line + corresponding to the 1 slot label, and so on. + + Args: + data_dir: the directory of the dataset + modes: ['train', 'test', 'dev'], + none_slot_label: the label for slots that aren't identified defaulted to 'O' + pad_label: the int used for padding. If set to -1, it'll be set to the whatever the None label is. + """ + + def __init__( + self, + data_dir: str, + modes: List[str] = ["train", "test", "dev"], + none_slot_label: str = "O", + pad_label: int = -1, + ): + if not if_exist(data_dir, ["dict.intents.csv", "dict.slots.csv"]): + raise FileNotFoundError( + "Make sure that your data follows the standard format " + "supported by MultiLabelIntentSlotDataset. Your data must " + "contain dict.intents.csv and dict.slots.csv." + ) + + self.data_dir = data_dir + self.intent_dict_file = self.data_dir + "/dict.intents.csv" + self.slot_dict_file = self.data_dir + "/dict.slots.csv" + + self.intents_label_ids = get_labels_to_labels_id_mapping(self.intent_dict_file) + self.num_intents = len(self.intents_label_ids) + self.slots_label_ids = get_labels_to_labels_id_mapping(self.slot_dict_file) + self.num_slots = len(self.slots_label_ids) + + infold = self.data_dir + for mode in modes: + if not if_exist(self.data_dir, [f"{mode}.tsv"]): + logging.info(f" Stats calculation for {mode} mode" f" is skipped as {mode}.tsv was not found.") + continue + logging.info(f" Stats calculating for {mode} mode...") + slot_file = f"{self.data_dir}/{mode}_slots.tsv" + with open(slot_file, "r") as f: + slot_lines = f.readlines() + + input_file = f"{self.data_dir}/{mode}.tsv" + with open(input_file, "r") as f: + input_lines = f.readlines()[1:] # Skipping headers at index 0 + + if len(slot_lines) != len(input_lines): + raise ValueError( + "Make sure that the number of slot lines match the " + "number of intent lines. There should be a 1-1 " + "correspondence between every slot and intent lines." + ) + + dataset = list(zip(slot_lines, input_lines)) + + raw_slots, raw_intents = [], [] + for slot_line, input_line in dataset: + slot_list = [int(slot) for slot in slot_line.strip().split()] + raw_slots.append(slot_list) + parts = input_line.strip().split("\t")[1:][0] + parts = list(map(int, parts.split(","))) + parts = [1 if label in parts else 0 for label in range(self.num_intents)] + raw_intents.append(tuple(parts)) + + logging.info(f"Three most popular intents in {mode} mode:") + total_intents, intent_label_freq, max_id = get_multi_label_stats( + raw_intents, infold + f"/{mode}_intent_stats.tsv" + ) + + merged_slots = itertools.chain.from_iterable(raw_slots) + logging.info(f"Three most popular slots in {mode} mode:") + slots_total, slots_label_freq, max_id = get_label_stats(merged_slots, infold + f"/{mode}_slot_stats.tsv") + + logging.info(f"Total Number of Intent Labels: {total_intents}") + logging.info(f"Intent Label Frequencies: {intent_label_freq}") + logging.info(f"Total Number of Slots: {slots_total}") + logging.info(f"Slots Label Frequencies: {slots_label_freq}") + + if mode == "train": + intent_weights_dict = get_freq_weights_bce_with_logits_loss(intent_label_freq) + logging.info(f"Intent Weights: {intent_weights_dict}") + slot_weights_dict = get_freq_weights(slots_label_freq) + logging.info(f"Slot Weights: {slot_weights_dict}") + + self.intent_weights = fill_class_weights(intent_weights_dict, self.num_intents - 1) + self.slot_weights = fill_class_weights(slot_weights_dict, self.num_slots - 1) + + if pad_label != -1: + self.pad_label = pad_label + else: + if none_slot_label not in self.slots_label_ids: + raise ValueError(f"none_slot_label {none_slot_label} not " f"found in {self.slot_dict_file}.") + self.pad_label = self.slots_label_ids[none_slot_label] diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/__init__.py new file mode 100644 index 0000000..16831be --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.language_modeling.l2r_lm_dataset import L2RLanguageModelingDataset +from nemo.collections.nlp.data.language_modeling.lm_bert_dataset import ( + BertPretrainingDataset, + BertPretrainingPreprocessedDataloader, +) +from nemo.collections.nlp.data.language_modeling.sentence_dataset import SentenceDataset, TarredSentenceDataset diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/l2r_lm_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/l2r_lm_dataset.py new file mode 100644 index 0000000..e0bdda5 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/l2r_lm_dataset.py @@ -0,0 +1,251 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import io +import json +from typing import Optional + +import braceexpand +import numpy as np +import webdataset as wds +from torch.utils.data import Dataset, IterableDataset + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.collections.nlp.data.data_utils import dataset_to_ids +from nemo.utils import logging +from nemo.utils.distributed import webdataset_split_by_workers + +__all__ = ['L2RLanguageModelingDataset', 'TarredL2RLanguageModelingDataset'] + + +class L2RLanguageModelingDataset(Dataset): + """ + Dataset for training and evaluating left-to-right language models. + + Args: + tokenizer: tokenizer, such as WordTokenizer or CharTokenizer + dataset: path to data + max_seq_length: maximum sequence length (in tokens) of input tensors + batch_step: distance (in tokens) between two successive sequences of + the text. By default, it is equal to max_seq_length which corresponds + to splitting text into disjoint segments covering full dataset + use_cache: bool value, defaults to False. Determines whether the preprocessed, + tokenized dataset should be cached into a pickle file. If true, cache is saved + at the path provided in `dataset`. + """ + + def __init__( + self, + tokenizer: TokenizerSpec, + dataset: str, + max_seq_length: Optional[int] = 512, + batch_step: Optional[int] = None, + use_cache: bool = False, + ): + self.tokenizer = tokenizer + self.max_seq_length = max_seq_length + self.batch_step = batch_step or self.max_seq_length + ids = dataset_to_ids(dataset, tokenizer, cache_ids=use_cache, add_bos_eos=False) + self.ids = np.array([j for i in ids for j in i]) + + def __len__(self): + return (len(self.ids) - self.max_seq_length) // self.batch_step + + def __getitem__(self, idx): + left = idx * self.batch_step + right = left + self.max_seq_length + src_ids = self.ids[left:right] + labels = self.ids[left + 1 : right + 1] + src_mask = (src_ids != self.tokenizer.pad_id).astype(np.float32) + return src_ids, src_mask, labels + + +class TarredL2RLanguageModelingDataset(IterableDataset): + """ + A similar Dataset to the L2RLanguageModelingDataset, but which loads tarred tokenized numpy files. + Accepts a single JSON metadata manifest file as well as the path(s) to the tarball(s) containing the wav files. + The manifest should contain information such as the number of shards, the number of tokens in the corpus, + and the number of tokens contained within each shard of the tarfile(s). + + Valid formats for the text_tar_filepaths argument include: + (1) a single string that can be brace-expanded, e.g. 'path/to/text.tar' or 'path/to/text_{1..100}.tar.gz', or + (2) a list of file paths that will not be brace-expanded, e.g. ['text_1.tar', 'text_2.tar', ...]. + + Note: For brace expansion in (1), there may be cases where `{x..y}` syntax cannot be used due to shell interference. + This occurs most commonly inside SLURM scripts. Therefore we provide a few equivalent replacements. + Supported opening braces - { <=> (, [, < and the special tag _OP_. + Supported closing braces - } <=> ), ], > and the special tag _CL_. + For SLURM based tasks, we suggest the use of the special tags for ease of use. + See the WebDataset documentation for more information about accepted data and input formats. + + If using multiple processes the number of shards should be divisible by the number of workers to ensure an + even split among workers. If it is not divisible, logging will give a warning but training will proceed. + + Additionally, please note that the len() of this DataLayer is assumed to be the number of tokens + of the text data. An incorrect manifest length may lead to some DataLoader issues down the line. + + Args: + text_tar_filepaths: Either a list of tokenized text tarball filepaths, or a + string (can be brace-expandable). + metadata_path (str): Path to the metadata manifest. + tokenizer: tokenizer, such as WordTokenizer or CharTokenizer + dataset: path to data + max_seq_length: maximum sequence length (in tokens) of input tensors + batch_step: distance (in tokens) between two successive sequences of + the text. By default, it is equal to max_seq_length which corresponds + to splitting text into disjoint segments covering full dataset + shuffle_n (int): How many samples to look ahead and load to be shuffled. + See WebDataset documentation for more details. + Defaults to 0. + shard_strategy (str): Tarred dataset shard distribution strategy chosen as a str value during ddp. + - `scatter`: The default shard strategy applied by WebDataset, where each node gets + a unique set of shards, which are permanently pre-allocated and never changed at runtime. + - `replicate`: Optional shard strategy, where each node gets all of the set of shards + available in the tarred dataset, which are permanently pre-allocated and never changed at runtime. + The benefit of replication is that it allows each node to sample data points from the entire + dataset independently of other nodes, and reduces dependence on value of `shuffle_n`. + + .. warning:: + Replicated strategy allows every node to sample the entire set of available tarfiles, + and therefore more than one node may sample the same tarfile, and even sample the same + data points! As such, there is no assured guarantee that all samples in the dataset will be + sampled at least once during 1 epoch. Scattered strategy, on the other hand, on specific + occasions (when the number of shards is not divisible with ``world_size``), will not sample + the entire dataset. For these reasons it is not advisable to use tarred datasets as validation + or test datasets. + global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. + world_size (int): Total number of processes, used for partitioning shards. Defaults to 0. + """ + + def __init__( + self, + text_tar_filepaths: str, + metadata_path: str, + tokenizer, + max_seq_length: int = 512, + batch_step: int = None, + shuffle_n: int = 1, + shard_strategy: str = "scatter", + global_rank: int = 0, + world_size: int = 0, + ): + super(TarredL2RLanguageModelingDataset, self).__init__() + + self.tokenizer = tokenizer + self.max_seq_length = max_seq_length + self.batch_step = batch_step or self.max_seq_length + + valid_shard_strategies = ['scatter', 'replicate'] + if shard_strategy not in valid_shard_strategies: + raise ValueError( + f"Invalid shard strategy of type {type(shard_strategy)} " + f"{repr(shard_strategy) if len(repr(shard_strategy)) < 100 else repr(shard_strategy)[:100] + '...'}! " + f"Allowed values are: {valid_shard_strategies}." + ) + + with open(metadata_path, 'r') as f: + metadata = json.load(f) + + self.metadata = metadata + + if isinstance(text_tar_filepaths, str): + # Replace '(', '[', '<' and '_OP_' with '{' + brace_keys_open = ['(', '[', '<', '_OP_'] + for bkey in brace_keys_open: + if bkey in text_tar_filepaths: + text_tar_filepaths = text_tar_filepaths.replace(bkey, "{") + + # Replace ')', ']', '>' and '_CL_' with '}' + brace_keys_close = [')', ']', '>', '_CL_'] + for bkey in brace_keys_close: + if bkey in text_tar_filepaths: + text_tar_filepaths = text_tar_filepaths.replace(bkey, "}") + + if isinstance(text_tar_filepaths, str): + # Brace expand + text_tar_filepaths = list(braceexpand.braceexpand(text_tar_filepaths)) + + if shard_strategy == 'scatter': + logging.info("All tarred dataset shards will be scattered evenly across all nodes.") + + if len(text_tar_filepaths) % world_size != 0: + logging.warning( + f"Number of shards in tarred dataset ({len(text_tar_filepaths)}) is not divisible " + f"by number of distributed workers ({world_size})." + ) + + begin_idx = (len(text_tar_filepaths) // world_size) * global_rank + end_idx = begin_idx + (len(text_tar_filepaths) // world_size) + text_tar_filepaths = text_tar_filepaths[begin_idx:end_idx] + logging.info( + "Partitioning tarred dataset: process (%d) taking shards [%d, %d)", global_rank, begin_idx, end_idx + ) + + elif shard_strategy == 'replicate': + logging.info("All tarred dataset shards will be replicated across all nodes.") + + else: + raise ValueError(f"Invalid shard strategy ! Allowed values are : {valid_shard_strategies}") + + self.tarpath = text_tar_filepaths + + # Put together WebDataset + self._dataset = wds.DataPipeline( + wds.SimpleShardList(text_tar_filepaths), + webdataset_split_by_workers, + wds.shuffle(shuffle_n), + wds.tarfile_to_samples(), + wds.rename(npy='npy', key='__key__'), + wds.to_tuple('npy', 'key'), + wds.map(self._build_sample), + ) + + def _build_sample(self, tup): + # Load file + npy, filepath = tup + npy = io.BytesIO(npy) + data = np.load(npy) # loads np.int64 vector + npy.close() + + # Select random contiguous subsegment + idx = np.random.randint(0, (len(data) - self.max_seq_length) // self.batch_step) + + # Slice of data chunk + left = idx * self.batch_step + right = left + self.max_seq_length + data = data[left : right + 1] + + # Create batch + src_ids = data[:-1] + labels = data[1:] + src_mask = (src_ids != self.tokenizer.pad_id).astype(np.float32) + return src_ids, src_mask, labels + + def __iter__(self): + # We need to wrap an infinite generator since the actual files + # within the tar files contains large chunks of contiguous data. + # This prevents PTL from early exiting the train loop after exhausting + # all of the files in one iteration (though the actual dataset is many + # times larger due to each file containing a large chunk of data). + dl_iter = iter(self._dataset) + while True: + try: + batch = next(dl_iter) + yield batch + except StopIteration: + dl_iter = iter(self._dataset) + continue + + def __len__(self): + return (self.metadata['num_text'] - self.max_seq_length) // self.batch_step diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/lm_bert_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/lm_bert_dataset.py new file mode 100644 index 0000000..b02d250 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/lm_bert_dataset.py @@ -0,0 +1,406 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import array +import os +import pickle +import random +from typing import Dict, List, Optional + +import h5py +import numpy as np +from torch.utils.data import DataLoader, DistributedSampler +from tqdm import tqdm + +from nemo.collections.nlp.data.data_utils.data_preprocessing import find_newlines, load_data_indices +from nemo.core.classes import Dataset + +__all__ = ['BertPretrainingDataset', 'BertPretrainingPreprocessedDataloader'] + + +def load_h5(input_file: str): + return h5py.File(input_file, "r") + + +class BertPretrainingDataset(Dataset): + """ + Dataset for bert pretraining when using data preprocessing including tokenization + """ + + def __init__( + self, + tokenizer: object, + data_file: str, + max_seq_length: Optional[int] = 128, + mask_prob: Optional[float] = 0.15, + short_seq_prob: Optional[float] = 0.1, + seq_a_ratio: Optional[float] = 0.6, + sentence_idx_file: Optional[str] = None, + ): + """ + Args: + tokenizer: tokenizer + data_file: path to data + max_seq_length: maximum sequence length of input tensors + mask_probability: proability to mask token + short_seq_prob: probability to create a sequence shorter than max_seq_length + seq_a_ratio: ratio between lengths of first and second sequence + sentence_idx_file: sentence indices file for caching + """ + self.tokenizer = tokenizer + + # Loading enormous datasets into RAM isn't always feasible -- for + # example, the pubmed corpus is 200+ GB, which doesn't fit into RAM on + # most computers. To get around this, we store the indices of newlines + # in each file so we can seek to and retrieve sentences immediately + # from main memory when needed during training. + + # Try and load sentence indices file if already exists + sentence_indices, sentence_idx_file, data_dir = load_data_indices( + sentence_idx_file, data_file, "sentence_indices" + ) + + # If sentence indices file doesn't exists, generate and store sentence indices + if sentence_indices is None: + sentence_indices = {} + filenames = [data_file] + + for filename in tqdm(filenames): + with open(filename, "rb") as f: + contents = f.read() + newline_indices = find_newlines(contents) + + if os.path.isdir(data_dir): + # Only keep the parts of the filepath that are invariant to + # the dataset's location on disk + filename = os.path.basename(filename) + + # In python, arrays are much more space-efficient than lists + sentence_indices[filename] = array.array("I", newline_indices) + + # Save sentence indices so we don't have to do this again + with open(sentence_idx_file, "wb") as f: + pickle.dump(sentence_indices, f) + + corpus_size = 0 + empty_files = [] + + # Find total number of newlines across entire corpus and remove files + # without any newlines + for filename in sentence_indices: + if len(sentence_indices[filename]) <= 1: + empty_files.append(filename) + else: + corpus_size += len(sentence_indices[filename]) + + for filename in empty_files: + del sentence_indices[filename] + + self.corpus_size = corpus_size + self.dataset = data_dir + self.filenames = list(sentence_indices.keys()) + self.mask_probability = mask_prob + self.max_seq_length = max_seq_length + self.sentence_indices = sentence_indices + self.vocab_size = self.tokenizer.vocab_size + self.short_seq_prob = short_seq_prob + self.seq_a_ratio = seq_a_ratio + + def __len__(self): + return self.corpus_size + + def __getitem__(self, idx: int, min_doc_length: Optional[int] = 16): + # Each sequence has three special tokens, as follows: + # tokenizer.cls_token tokenizer.sep_token tokenizer.eos_token + num_special_tokens = 3 + + max_num_tokens = self.max_seq_length - num_special_tokens + target_seq_length = max_num_tokens + if random.random() < self.short_seq_prob: + # TODO: maybe introduce an argument to control this. + target_seq_length = random.randint(2, max_num_tokens) + + # prefer the seq_a to be slightly longer than seq_b, 0.6 by default + target_seq_length_a = int(round(target_seq_length * self.seq_a_ratio)) + target_seq_length_b = target_seq_length - target_seq_length_a + + def get_document(filepath, offset): + # Retrieve a specific line from a file and return as a document + if os.path.isdir(self.dataset): + filepath = os.path.join(self.dataset, filepath) + + with open(filepath, "rb") as f: + f.seek(offset) + doc_text = f.readline()[:-1].decode("utf-8", errors="ignore") + document = self.tokenizer.text_to_ids(doc_text) + + return document + + def match_target_seq_length( + document: str, target_seq_length: int, filename: str, line_idx: int, sentence_indices: Dict[str, dict] + ): + # If document is shorter than target sequence length, + # append the next line or take a random line as replacement. + num_lines = len(sentence_indices[filename]) + + while len(document) < target_seq_length: + if line_idx < (num_lines - 1): + # append the next line + line_idx += 1 + else: + # current line is the last line, take a random one + line_idx = random.randrange(num_lines) + document = [] + + offset = sentence_indices[filename][line_idx] + document += get_document(filename, offset) + + return document, line_idx + + # Take sequence A from a random file and a random line + a_filename = random.choice(self.filenames) + a_line_idx = random.randrange(len(self.sentence_indices[a_filename])) + a_line_offset = self.sentence_indices[a_filename][a_line_idx] + a_document = get_document(a_filename, a_line_offset) + a_document, a_line_idx = match_target_seq_length( + a_document, target_seq_length_a, a_filename, a_line_idx, self.sentence_indices + ) + + is_last_line = a_line_idx >= (len(self.sentence_indices[a_filename]) - 1) + # About 50% of the time, B is a random sentence from the corpus + take_random_b = (random.random() < 0.5) or is_last_line + + if take_random_b: + # This should rarely go for more than one iteration for large + # corpora. However, just to be careful, we try to make sure that + # the random document is not the same as the document + # we're processing. + for _ in range(10): + b_filename = random.choice(self.filenames) + b_line_idx = random.choice(range(len(self.sentence_indices[b_filename]))) + if b_filename != a_filename: + break + else: + # Take another line from the same file + b_line_pos = self.sentence_indices[b_filename][b_line_idx] + a_line_pos = self.sentence_indices[a_filename][a_line_idx] + # TODO unclear about the following check + if abs(b_line_pos - a_line_pos) > max_num_tokens: + break + else: + pass + else: + b_filename = a_filename + b_line_idx = a_line_idx + 1 + + is_next = int(not take_random_b) + b_line_pos = self.sentence_indices[b_filename][b_line_idx] + b_document = get_document(b_filename, b_line_pos) + b_document, b_line_idx = match_target_seq_length( + b_document, target_seq_length_b, b_filename, b_line_idx, self.sentence_indices + ) + + def truncate_seq_pair(a, b, max_num_tokens): + # Truncates a pair of sequences to a maximum sequence length + while (len(a) + len(b)) > max_num_tokens: + # Truncate the longer sequence + if len(a) > len(b): + trunc_document = a + else: + trunc_document = b + + if len(trunc_document) <= 1: + raise ValueError( + "Input text corpora probably too small. " + "Failed to truncate sequence pair to " + "maximum sequence legnth." + ) + + # Randomly truncate from the front or the back + if random.random() < 0.5: + del trunc_document[0] + else: + trunc_document.pop() + + truncate_seq_pair(a_document, b_document, max_num_tokens) + + output_ids = ( + [self.tokenizer.cls_id] + a_document + [self.tokenizer.sep_id] + b_document + [self.tokenizer.eos_id] + ) + + input_ids, output_mask = self.mask_ids(output_ids) + + input_mask = np.zeros(self.max_seq_length, dtype=np.longlong) + input_mask[: len(input_ids)] = 1 + + input_type_ids = np.zeros(self.max_seq_length, dtype=np.int64) + input_type_ids[len(a_document) + 2 : len(output_ids) + 1] = 1 + + padding_length = max(0, self.max_seq_length - len(input_ids)) + if padding_length > 0: + input_ids.extend([self.tokenizer.pad_id] * padding_length) + output_ids.extend([self.tokenizer.pad_id] * padding_length) + output_mask.extend([0] * padding_length) + + # TODO: wrap the return value with () for consistent style. + return ( + np.array(input_ids), + input_type_ids, + np.array(input_mask, dtype=np.longlong), + np.array(output_ids), + np.array(output_mask, dtype=np.float32), + is_next, + ) + + def mask_ids(self, ids: List[int]): + """ + Args: + ids: list of token ids representing a chunk of text + Returns: + masked_ids: list of input tokens with some of the entries masked + according to the following protocol from the original BERT paper: + each token is masked with a probability of 15% and is replaced with + 1) the [MASK] token 80% of the time, + 2) random token 10% of the time, + 3) the same token 10% of the time. + output_mask: list of binary variables which indicate what tokens has + been masked (to calculate the loss function for these tokens only) + """ + + # Whole-word masking by default, as it gives better performance. + cand_indexes = [[ids[0]]] + for tid in ids[1:]: + token = self.tokenizer.ids_to_tokens([tid])[0] + is_suffix = token.startswith('\u2581') + if is_suffix: + # group together with its previous token to form a whole-word + cand_indexes[-1].append(tid) + else: + cand_indexes.append([tid]) + + masked_ids, output_mask = [], [] + mask_id = self.tokenizer.token_to_id("[MASK]") + + for word_ids in cand_indexes: + is_special = (word_ids[0] == self.tokenizer.cls_id) or (word_ids[0] == self.tokenizer.sep_id) + if is_special or (random.random() > self.mask_probability): + output_mask.extend([0] * len(word_ids)) + masked_ids.extend(word_ids) + else: + output_mask.extend([1] * len(word_ids)) + p = random.random() + # for 80%, replace with mask + if p < 0.8: + masked_ids.extend([mask_id] * len(word_ids)) + # for 10%, replace by a random token + elif p < 0.9: + for _ in word_ids: + # randomly select a valid word + random_word = random.randrange(self.vocab_size) + while random_word in (self.tokenizer.cls_id, self.tokenizer.sep_id): + random_word = random.randrange(self.vocab_size) + masked_ids.append(random_word) + # for 10%, use same token + else: + masked_ids.extend(word_ids) + + return masked_ids, output_mask + + +class BertPretrainingPreprocessedDataset(Dataset): + """ + Dataset for already preprocessed data. + """ + + def __init__(self, input_file: str, max_predictions_per_seq: int): + """ + Args: + input_file: data file in hdf5 format with preprocessed data in array format + max_predictions_per_seq: maximum number of masked tokens per sequence. Need to be consistent with data in input file. + """ + self.input_file = input_file + self.max_predictions_per_seq = max_predictions_per_seq + f = load_h5(input_file) + keys = [ + 'input_ids', + 'input_mask', + 'segment_ids', + 'masked_lm_positions', + 'masked_lm_ids', + 'next_sentence_labels', + ] + self.inputs = [np.asarray(f[key][:]) for key in keys] + f.close() + + def __len__(self): + 'Denotes the total number of samples' + return len(self.inputs[0]) + + def __getitem__(self, index: int): + [input_ids, input_mask, segment_ids, masked_lm_positions, masked_lm_ids, next_sentence_labels] = [ + input[index].astype(np.int64) for input in self.inputs + ] + + output_mask = np.zeros_like(input_ids) + output_ids = input_ids.copy() + + index = self.max_predictions_per_seq + padded_mask_indices = (masked_lm_positions == 0).nonzero() + if len(padded_mask_indices[0]) != 0: + index = padded_mask_indices[0][0] + + output_mask[masked_lm_positions[:index]] = 1.0 + output_ids[masked_lm_positions[:index]] = masked_lm_ids[:index] + + # input_mask = np.asarray(input_mask, dtype=np.float32) + # output_mask = np.asarray(output_mask, dtype=np.float32) + return (input_ids, segment_ids, input_mask, output_ids, output_mask, next_sentence_labels) + + +class BertPretrainingPreprocessedDataloader(DataLoader): + """ + Dataloader for already preprocessed data in hdf5 files that is already in the format expected by BERT model. + """ + + def __init__(self, data_files: List[str], max_predictions_per_seq: int, batch_size: int, seed: Optional[int] = 42): + """ + Args: + data_files: list of data files in hdf5 format with preprocessed data in array format + max_predictions_per_seq: maximum number of masked tokens per sequence. Need to be consistent with data in input file. + batch_size: batch size per gpu per forward pass + seed: seed to ensure each gpu process opens the same data file in each iteration + """ + super().__init__(None, batch_size=batch_size) + self.random = random.Random(seed) + self.data_files = data_files + self.max_predictions_per_seq = max_predictions_per_seq + + # def __len__(self): + # return sum([len(load_h5(data_file)['input_ids']) for data_file in self.data_files])//(self.batch_size) + + def __iter__(self): + self.random.shuffle(self.data_files) + for data_file in self.data_files: + train_data = BertPretrainingPreprocessedDataset( + input_file=data_file, max_predictions_per_seq=self.max_predictions_per_seq + ) + train_sampler = DistributedSampler(train_data) + # print("---") + # print(os.getpid(), train_sampler.rank, train_sampler.num_replicas, train_sampler.num_samples) + # print("---") + train_dataloader = DataLoader( + dataset=train_data, sampler=train_sampler, batch_size=self.batch_size, shuffle=False, + ) + for x in train_dataloader: + yield x diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/Makefile b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/Makefile new file mode 100644 index 0000000..1509390 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/Makefile @@ -0,0 +1,23 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +CXXFLAGS += -O3 -Wall -shared -std=c++11 -fPIC -fdiagnostics-color +CPPFLAGS += $(shell python3 -m pybind11 --includes) +LIBNAME = helpers +LIBEXT = $(shell python3-config --extension-suffix) + +default: $(LIBNAME)$(LIBEXT) + +%$(LIBEXT): %.cpp + $(CXX) $(CXXFLAGS) $(CPPFLAGS) $< -o $@ diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/__init__.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/__init__.py new file mode 100644 index 0000000..a45f68f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo.collections.nlp.data.language_modeling.megatron.bert_dataset import BertDataset +from nemo.collections.nlp.data.language_modeling.megatron.gpt_dataset import GPTDataset +from nemo.collections.nlp.data.language_modeling.megatron.gpt_prompt_learning_dataset import GPTPromptLearningDataset +from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import IndexedDataset, MMapIndexedDataset +from nemo.collections.nlp.data.language_modeling.megatron.t5_dataset import T5Dataset diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/bart_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/bart_dataset.py new file mode 100644 index 0000000..b6a046b --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/bart_dataset.py @@ -0,0 +1,205 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""BART Style dataset.""" + +import numpy as np + +from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import ( + create_masked_lm_predictions, + get_samples_mapping, +) +from nemo.collections.nlp.data.language_modeling.megatron.t5_dataset import T5Dataset + + +class BARTDataset(T5Dataset): + # account for added tokens + MAX_SEQ_LENGTH_DELTA = 2 + + def __init__( + self, + cfg, + trainer, + tokenizer, + name, + indexed_dataset, + data_prefix, + num_epochs, + max_num_samples, + max_seq_length, + seed, + masked_lm_prob=0.15, + short_seq_prob=0.1, + max_ngram_size=10, + mean_ngram_size=None, + geometric_dist=True, + permutation=False, + whole_word_masking=True, + favor_long_ngrams=False, + delete_mask_prob=0, + respect_document_boundaries=True, + documents=None, + ): + super().__init__( + cfg=cfg, + trainer=trainer, + tokenizer=tokenizer, + name=name, + indexed_dataset=indexed_dataset, + data_prefix=data_prefix, + num_epochs=num_epochs, + max_num_samples=max_num_samples, + max_seq_length=max_seq_length, + max_seq_length_dec=None, + seed=seed, + masked_lm_prob=masked_lm_prob, + short_seq_prob=short_seq_prob, + max_ngram_size=max_ngram_size, + mean_ngram_size=mean_ngram_size, + geometric_dist=geometric_dist, + permutation=permutation, + whole_word_masking=whole_word_masking, + favor_long_ngrams=favor_long_ngrams, + respect_document_boundaries=respect_document_boundaries, + documents=documents, + ) + + # Params to store. + self.delete_mask_prob = delete_mask_prob + + def _build(self): + """ + Class-specific build method to be overridden by child classes. + """ + pass + + def __getitem__(self, idx): + np_rng = np.random.RandomState(seed=(self.seed + idx)) + + sample, seq_length = self._get_sample(idx) + + # flatten sentences into one list + tokens = [token for sentence in sample for token in sentence] + + # Truncate to `target_sequence_length`. + max_num_tokens = seq_length + tokens = tokens[:max_num_tokens] + + # Masking. + max_predictions_per_seq = self.masked_lm_prob * max_num_tokens + + lm_pred = create_masked_lm_predictions( + tokens=tokens, + vocab_id_list=self.vocab_id_list, + vocab_id_to_token_dict=self.vocab_id_to_token_dict, + masked_lm_prob=self.masked_lm_prob, + cls_id=self.cls_id, + sep_id=self.sep_id, + mask_id=self.mask_id, + max_predictions_per_seq=max_predictions_per_seq, + np_rng=np_rng, + max_ngram_size=self.max_ngram_size, + whole_word_masking=self.whole_word_masking, + favor_long_ngrams=self.favor_long_ngrams, + mean_ngram_size=self.mean_ngram_size, + permutation=self.permutation, + geometric_dist=self.geometric_dist, + masking_style="t5", + tokenizer_type=self.tokenizer_type, + ) + + if self.masked_lm_prob == 0: + (output_tokens, masked_positions, masked_labels, _) = lm_pred + masked_spans = None + else: + (output_tokens, masked_positions, masked_labels, _, masked_spans) = lm_pred + + # Padding. + tokens_enc, tokens_dec_in, labels, enc_mask, dec_mask, loss_mask = self.pad_and_convert_to_numpy( + tokens=tokens, + output_tokens=output_tokens, + masked_positions=masked_positions, + masked_labels=masked_labels, + masked_spans=masked_spans, + np_rng=np_rng, + ) + + train_sample = { + 'text_enc': tokens_enc, + 'text_dec': tokens_dec_in, + 'labels': labels, + 'loss_mask': loss_mask, + 'enc_mask': enc_mask, + 'dec_mask': dec_mask, + } + + return train_sample + + def pad_and_convert_to_numpy( + self, tokens, output_tokens, masked_positions, masked_labels, masked_spans=None, np_rng=None, + ): + """Pad sequences and convert them to numpy.""" + bart_decoder_in = [self.bos_id] + tokens + bart_decoder_out = tokens + [self.eos_id] + + if masked_spans is not None: + # construct bart input by collapsing multiple into one, and delete randomly + bart_input = [] + (start_index, end_index) = (0, None) + for span in masked_spans: + end_index = span.index[0] + bart_input.extend(output_tokens[start_index:end_index]) + # delete mask with probability delete_mask_prob + if np_rng.rand() >= self.delete_mask_prob: + bart_input.append(self.mask_id) + + # the next start index is the token after the last span token + start_index = span.index[-1] + 1 + + # Add the remaining tokens to the BART input + bart_input.extend(output_tokens[start_index:]) + else: + bart_input = output_tokens + + # Some checks. + # Encoder-side padding mask. + num_tokens = len(bart_input) + padding_length = self.max_seq_length - num_tokens + assert padding_length >= 0 + assert len(masked_positions) == len(masked_labels) + + # Tokens.. + filler = [self.pad_id] * padding_length + tokens_enc = np.array(bart_input + filler, dtype=np.int64) + + # Decoder-side padding mask. + num_tokens_dec = len(bart_decoder_in) + padding_length_dec = self.max_seq_length - num_tokens_dec + assert padding_length_dec >= 0 + filler_dec = [self.pad_id] * padding_length_dec + tokens_dec_in = np.array(bart_decoder_in + filler_dec, dtype=np.int64) + + # Create attention masks + enc_mask = (tokens_enc != self.pad_id).astype(np.int64) + dec_mask = (tokens_dec_in != self.pad_id).astype(np.int64) + + # Labels mask. + labels = bart_decoder_out + ([-1] * padding_length_dec) + labels = np.array(labels, dtype=np.int64) + + # Loss mask + loss_mask = ([1] * num_tokens_dec) + ([0] * padding_length_dec) + loss_mask = np.array(loss_mask, dtype=np.int64) + + return tokens_enc, tokens_dec_in, labels, enc_mask, dec_mask, loss_mask diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/base_dataset_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/base_dataset_utils.py new file mode 100644 index 0000000..96b7f57 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/base_dataset_utils.py @@ -0,0 +1,77 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + + +def get_datasets_weights_and_num_samples(data_prefix, num_samples): + + # The data prefix should be in the format of: + # weight-1, data-prefix-1, weight-2, data-prefix-2, .. + assert len(data_prefix) % 2 == 0 + num_datasets = len(data_prefix) // 2 + weights = [0] * num_datasets + prefixes = [0] * num_datasets + for i in range(num_datasets): + weights[i] = float(data_prefix[2 * i]) + prefixes[i] = (data_prefix[2 * i + 1]).strip() + # Normalize weights + weight_sum = 0.0 + for weight in weights: + weight_sum += weight + assert weight_sum > 0.0 + weights = [weight / weight_sum for weight in weights] + + # Add 0.5% (the 1.005 factor) so in case the bleding dataset does + # not uniformly distribute the number of samples, we still have + # samples left to feed to the network. + # TODO: check data leakage between train/val/test? + datasets_train_valid_test_num_samples = [] + for weight in weights: + # Comes here when we have seperate train,test and validation datasets. + if isinstance(num_samples, int): + datasets_train_valid_test_num_samples.append(int(math.ceil(num_samples * weight * 1.005))) + else: + datasets_train_valid_test_num_samples.append([int(math.ceil(val * weight * 1.005)) for val in num_samples]) + + return prefixes, weights, datasets_train_valid_test_num_samples + + +def get_train_valid_test_split_(splits_string, size): + """ Get dataset splits from comma or '/' separated string list.""" + + splits = [] + if splits_string.find(',') != -1: + splits = [float(s) for s in splits_string.split(',')] + elif splits_string.find('/') != -1: + splits = [float(s) for s in splits_string.split('/')] + else: + splits = [float(splits_string)] + if len(splits) != 3: + raise ValueError(f"Invalid splits string: {splits_string}. Expected 3 comma separated values.") + while len(splits) < 3: + splits.append(0.0) + splits = splits[:3] + splits_sum = sum(splits) + assert splits_sum > 0.0 + splits = [split / splits_sum for split in splits] + splits_index = [0] + for index, split in enumerate(splits): + splits_index.append(splits_index[index] + int(round(split * float(size)))) + diff = splits_index[-1] - size + for index in range(1, len(splits_index)): + splits_index[index] -= diff + assert len(splits_index) == 4 + assert splits_index[-1] == size + return splits_index diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/base_prompt_learning_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/base_prompt_learning_dataset.py new file mode 100644 index 0000000..5d98546 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/base_prompt_learning_dataset.py @@ -0,0 +1,218 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + +from nemo.collections.nlp.modules.common import VirtualPromptSource +from nemo.core import Dataset +from nemo.utils import logging + +__all__ = ['BasePromptLearningDataset'] + + +class BasePromptLearningDataset(Dataset): + """ + The base dataset class for prompt-tuning or p-tuning. + TODO: (@adithyare) should be merged into GPTPromptLearningDataset + """ + + def __init__( + self, + datasets, + tokenizer, + virtual_prompt_source: VirtualPromptSource, + task_templates: dict, + pseudo_tokens, + pad_token_id: str, + max_seq_length: int, + min_seq_length: int = 1, + add_bos: bool = False, + add_eos: bool = True, + for_train: bool = True, + ): + self.tokenizer = tokenizer + self.virtual_prompt_source = virtual_prompt_source + self.task_templates = task_templates + self.pseudo_tokens = pseudo_tokens + self.pseudo_token_ids = set(self.tokenizer.tokens_to_ids(self.pseudo_tokens)) + self.pad_token_id = pad_token_id + self.max_seq_length = max_seq_length + self.min_seq_length = min_seq_length + self.add_bos = add_bos + self.add_eos = add_eos + self.for_train = for_train + self.examples = [] + + assert self.min_seq_length <= max_seq_length, "Min sequence length should be less than or equal to max" + assert self.max_seq_length > 0, "Max sequence length should be greater than 0" + + logging.info("Loading and tokenizing dataset ... ") + + # Datasets is just a list of json dicts + if isinstance(datasets[0], dict): + self.load_data(datasets) + + # Datasets are a list of file path strings to .json or .jsonl files + elif isinstance(datasets[0], str): + for path in datasets: + dataset = open(path, 'r', encoding='utf-8') + self.load_data(dataset) + else: + raise ValueError("Datasets must be a list of dicts or a list of filepath strings") + + def _insert_virtual_token_placeholders(self, input_example, virtual_token_splits): + """ Insert the correct number of pseudo tokens at the <|VIRTUAL_PROMPT_n|> markers """ + total_inserted_tokens = 0 + + for idx in range(len(virtual_token_splits)): + split_start = total_inserted_tokens + split_end = total_inserted_tokens + virtual_token_splits[idx] + pseudo_tokens_for_split = "".join(self.pseudo_tokens[split_start:split_end]) + input_example = input_example.replace(f'<|VIRTUAL_PROMPT_{idx}|>', pseudo_tokens_for_split) + total_inserted_tokens = split_end + + return input_example + + def _truncate_input(self, truncation_field, input_ids, taskname, doc, total_virtual_tokens=0): + """ Try to truncate input text to fit into the max sequence length """ + logging.info( + f"Input greater than max sequence length. Attempting to truncate: '{truncation_field}' in task: '{taskname}'" + ) + + # Truncate the text ids in this part of input to try and fit max sequence length + if truncation_field is not None and truncation_field in doc.keys(): + truncation_length = len(input_ids) - self.max_seq_length + field_text = doc[truncation_field] + field_text = self._add_leading_space(taskname, truncation_field, field_text) + + # Truncate field text + field_text_ids = self.tokenizer.text_to_ids(field_text) + truncated_text_ids = field_text_ids[: -min(truncation_length, len(field_text_ids))] + + # Replace original text ids with truncated text ids + field_start, field_end = find_subsequence_location(input_ids, field_text_ids) + input_ids = input_ids[:field_start] + truncated_text_ids + input_ids[field_end + 1 :] + else: + if not self.for_train: + # Hack alert! Slash and burn + # @TODO (@adithyare) need a more graceful truncation here, we should not skip examples in test + input_ids = ( + input_ids[:total_virtual_tokens] + + input_ids[total_virtual_tokens:][-self.max_seq_length + total_virtual_tokens :] + ) + + return input_ids + + def _add_leading_space(self, taskname, field_name, field_text): + """ Add leading space to text if there is a space before it in the template """ + prompt_template = self.task_templates[taskname]["prompt_template"] + field_text_start = prompt_template.find("{" + field_name + "}") + if field_text_start != 0 and prompt_template[field_text_start - 1] == " ": + field_text = " " + field_text + + return field_text + + def __len__(self): + return len(self.examples) + + def __getitem__(self, idx): + return self.examples[idx] + + def _input_sanity_checks( + self, + total_virtual_tokens, + virtual_token_splits, + prompt_template, + prompt_template_fields, + truncation_field, + answer_field, + doc, + answer_only_loss=None, + ): + # Sanity check amount of virtual token + assert ( + total_virtual_tokens < self.max_seq_length + ), "virtual prompt tokens should not exceed max sequence length" + + # Make sure virtual token splits add up to the total number of virtual tokens + assert ( + sum(virtual_token_splits) == total_virtual_tokens + ), "Sum of prompt token split values must equal total number of prompt tokens" + + # Make sure number of virtual prompt locations match the number of virtual prompt splits + assert prompt_template.count('<|VIRTUAL_PROMPT_') == len( + virtual_token_splits + ), "The number of '<|VIRTUAL_PROMPT_n|>' markers and the number of prompt token splits must match" + + # Check if input example has fields not present in template + keys_not_in_template = list(set(doc.keys()) - set(prompt_template_fields) - set(['taskname'])) + assert ( + len(keys_not_in_template) == 0 + ), f"Examples in your dataset contain the fields: {keys_not_in_template} that are not in the task template." + + # Check answer field + if self.for_train: + assert answer_field is not None, "An answer_field must be given" + assert answer_field in doc.keys(), f"The given answer_field '{answer_field}' is not in data json" + assert truncation_field != answer_field, "Answer field and truncation field should not match" + + answer_placeholder = "{" + answer_field + "}" + answer_placeholder_len = len(answer_placeholder) + placeholder_start = len(prompt_template) - answer_placeholder_len + assert prompt_template[placeholder_start:] == answer_placeholder, "Answer field must be at prompt end" + + def pad_taskname_ids(self, taskname_ids): + # Pad taskname_ids to be the same length for the prompt encoder + if self.virtual_prompt_source == VirtualPromptSource.PROMPT_ENCODER: + max_taskname_length = max(len(ids) for ids in taskname_ids) + taskname_ids = [ids + [self.pad_token_id] * (max_taskname_length - len(ids)) for ids in taskname_ids] + taskname_ids = torch.tensor(taskname_ids) + + # Task ids are just used for a look up embeddings for prompt-table + elif self.virtual_prompt_source == VirtualPromptSource.NO_PROMPT: + taskname_ids = torch.tensor(taskname_ids) + + return taskname_ids + + +def find_subsequence_location(sequence, subsequence): + """ Finds the start and end index of the first occurance + of a given subsequence within a larger list. Returns + the two indices corresponding to the postition of + the first and last token of the subseqeunce. + Assumes subsequence is known to be in sequence. + """ + assert len(sequence) >= len(subsequence), "subsequence too long" + + start_idx = None + next_subseq_token = subsequence[0] + next_subsequence_idx = 1 + + for seq_idx, token in enumerate(sequence): + if token == next_subseq_token: + if start_idx is None: + start_idx = seq_idx + + if next_subsequence_idx == len(subsequence): + end_idx = seq_idx + return start_idx, end_idx + else: + next_subseq_token = subsequence[next_subsequence_idx] + next_subsequence_idx += 1 + else: + start_idx = None + next_subseq_token = subsequence[0] + next_subsequence_idx = 1 + + raise ValueError("Subsequence not found in sequence") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/bert_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/bert_dataset.py new file mode 100644 index 0000000..e8aa232 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/bert_dataset.py @@ -0,0 +1,237 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""BERT Style dataset.""" + +import os +from typing import Any, Optional + +import numpy as np +import torch + +from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import ( + create_masked_lm_predictions, + create_tokens_and_tokentypes, + get_a_and_b_segments, + get_samples_mapping, + truncate_segments, +) +from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import MMapIndexedDataset + + +class BertDataset(torch.utils.data.Dataset): + def __init__( + self, + cfg: dict, + name: str, + indexed_dataset: MMapIndexedDataset, + data_prefix: str, + num_epochs: Optional[int], + max_num_samples: int, + masked_lm_prob: float, + max_seq_length: int, + short_seq_prob: float, + seed: int, + binary_head: bool, + tokenizer: Any, + ): + + # Params to store. + self.name = name + self.seed = seed + self.masked_lm_prob = masked_lm_prob + self.max_seq_length = max_seq_length + self.binary_head = binary_head + + # Dataset. + self.indexed_dataset = indexed_dataset + + # save index mappings to a configurable dir + self.index_mapping_dir = cfg.data.get('index_mapping_dir', None) + + # create index_mapping_dir on rank 0 + if torch.distributed.is_available() and torch.distributed.is_initialized(): + if torch.distributed.get_rank() == 0: + if self.index_mapping_dir is not None and not os.path.isdir(self.index_mapping_dir): + os.makedirs(self.index_mapping_dir) + torch.distributed.barrier() + + # Build the samples mapping. + self.samples_mapping = get_samples_mapping( + self.indexed_dataset, + data_prefix, + num_epochs, + max_num_samples, + self.max_seq_length - 3, # account for added tokens + short_seq_prob, + self.seed, + self.name, + self.binary_head, + index_mapping_dir=self.index_mapping_dir, + ) + + # Vocab stuff. + self.vocab_id_list = list(tokenizer.ids_to_tokens.keys()) + self.vocab_id_to_token_dict = tokenizer.ids_to_tokens + self.cls_id = tokenizer.cls_token_id + self.sep_id = tokenizer.sep_token_id + self.mask_id = tokenizer.mask_token_id + self.pad_id = tokenizer.pad_token_id + + def __len__(self): + return self.samples_mapping.shape[0] + + def __getitem__(self, idx): + start_idx, end_idx, seq_length = self.samples_mapping[idx] + sample = [self.indexed_dataset[i] for i in range(start_idx, end_idx)] + # Note that this rng state should be numpy and not python since + # python randint is inclusive whereas the numpy one is exclusive. + # We % 2**32 since numpy requres the seed to be between 0 and 2**32 - 1 + np_rng = np.random.RandomState(seed=((self.seed + idx) % 2 ** 32)) + return build_training_sample( + sample, + seq_length, + self.max_seq_length, # needed for padding + self.vocab_id_list, + self.vocab_id_to_token_dict, + self.cls_id, + self.sep_id, + self.mask_id, + self.pad_id, + self.masked_lm_prob, + np_rng, + self.binary_head, + ) + + +def build_training_sample( + sample, + target_seq_length, + max_seq_length, + vocab_id_list, + vocab_id_to_token_dict, + cls_id, + sep_id, + mask_id, + pad_id, + masked_lm_prob, + np_rng, + binary_head, + whole_word_masking=True, + skip_masking_id=None, +): + """Biuld training sample. + + Arguments: + sample: A list of sentences in which each sentence is a list token ids. + target_seq_length: Desired sequence length. + max_seq_length: Maximum length of the sequence. All values are padded to + this length. + vocab_id_list: List of vocabulary ids. Used to pick a random id. + vocab_id_to_token_dict: A dictionary from vocab ids to text tokens. + cls_id: Start of example id. + sep_id: Separator id. + mask_id: Mask token id. + pad_id: Padding token id. + masked_lm_prob: Probability to mask tokens. + np_rng: Random number genenrator. Note that this rng state should be + numpy and not python since python randint is inclusive for + the opper bound whereas the numpy one is exclusive. + whole_word_masking: Whether to mask only whole words instead of independent subwords. + skip_mask_id: ID of a token that should not be masked. #TODO: make this a list of tokens. + """ + if binary_head: + # We assume that we have at least two sentences in the sample + assert len(sample) > 1 + assert target_seq_length <= max_seq_length + + # Divide sample into two segments (A and B). + if binary_head: + tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample, np_rng) + else: + tokens_a = [] + for j in range(len(sample)): + tokens_a.extend(sample[j]) + tokens_b = [] + is_next_random = False + + # Truncate to `target_sequence_length`. + max_num_tokens = target_seq_length + truncated = truncate_segments(tokens_a, tokens_b, len(tokens_a), len(tokens_b), max_num_tokens, np_rng) + + # Build tokens and toketypes. + tokens, tokentypes = create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id) + + # Masking. + max_predictions_per_seq = masked_lm_prob * max_num_tokens + (tokens, masked_positions, masked_labels, _, _) = create_masked_lm_predictions( + tokens, + vocab_id_list, + vocab_id_to_token_dict, + masked_lm_prob, + cls_id, + sep_id, + mask_id, + max_predictions_per_seq, + np_rng, + whole_word_masking=whole_word_masking, + skip_masking_id=skip_masking_id, + ) + + # Padding. + tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np = pad_and_convert_to_numpy( + tokens, tokentypes, masked_positions, masked_labels, pad_id, max_seq_length + ) + + train_sample = { + 'text': tokens_np, + 'types': tokentypes_np, + 'labels': labels_np, + 'is_random': int(is_next_random), + 'loss_mask': loss_mask_np, + 'padding_mask': padding_mask_np, + 'truncated': int(truncated), + } + return train_sample + + +def pad_and_convert_to_numpy(tokens, tokentypes, masked_positions, masked_labels, pad_id, max_seq_length): + """Pad sequences and convert them to numpy.""" + + # Some checks. + num_tokens = len(tokens) + padding_length = max_seq_length - num_tokens + assert padding_length >= 0 + assert len(tokentypes) == num_tokens + assert len(masked_positions) == len(masked_labels) + + # Tokens and token types. + filler = [pad_id] * padding_length + tokens_np = np.array(tokens + filler, dtype=np.int64) + tokentypes_np = np.array(tokentypes + filler, dtype=np.int64) + + # Padding mask. + padding_mask_np = np.array([1] * num_tokens + [0] * padding_length, dtype=np.int64) + + # Lables and loss mask. + labels = [-1] * max_seq_length + loss_mask = [0] * max_seq_length + for i in range(len(masked_positions)): + assert masked_positions[i] < num_tokens + labels[masked_positions[i]] = masked_labels[i] + loss_mask[masked_positions[i]] = 1 + labels_np = np.array(labels, dtype=np.int64) + loss_mask_np = np.array(loss_mask, dtype=np.int64) + + return tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/blendable_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/blendable_dataset.py new file mode 100644 index 0000000..ae2b5ff --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/blendable_dataset.py @@ -0,0 +1,182 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Blendable dataset.""" + +import time + +import numpy as np +import torch + +from nemo.utils import logging +from nemo.utils.app_state import AppState + + +class BlendableDataset(torch.utils.data.Dataset): + def __init__(self, datasets, weights, size): + + self.datasets = datasets + num_datasets = len(datasets) + assert num_datasets == len(weights) + + self.size = size + + # Normalize weights. + weights = np.array(weights, dtype=np.float64) + sum_weights = np.sum(weights) + assert sum_weights > 0.0 + weights /= sum_weights + + # Build indecies. + start_time = time.time() + assert num_datasets < 255 + self.dataset_index = np.zeros(self.size, dtype=np.uint8) + self.dataset_sample_index = np.zeros(self.size, dtype=np.int64) + app_state = AppState() + try: + if app_state.local_rank == 0: + from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import compile_helper + + compile_helper() + torch.distributed.barrier() + from nemo.collections.nlp.data.language_modeling.megatron import helpers + except ImportError: + raise ImportError( + f'Could not compile megatron dataset C++ helper functions and therefore cannot import helpers python file.' + ) + + helpers.build_blending_indices( + self.dataset_index, + self.dataset_sample_index, + weights, + num_datasets, + self.size, + torch.distributed.get_rank() == 0, + ) + logging.info( + '> elapsed time for building blendable dataset indices: ' '{:.2f} (sec)'.format(time.time() - start_time) + ) + + def __len__(self): + return self.size + + def __getitem__(self, idx): + dataset_idx = self.dataset_index[idx] + sample_idx = self.dataset_sample_index[idx] + return self.datasets[dataset_idx][sample_idx] + + def create_data_mmap(self): + for dataset in self.datasets: + dataset.create_data_mmap() + + +class MemoryEfficientBlendableDataset(torch.utils.data.Dataset): + """ + A BlendableDataset implementation that uses less memory than the original implementation. + Indices are computed algorithmically instead of storing them in memory. + + To test call: MemoryEfficientBlendableDataset.test_index_blending() + """ + + def __init__(self, datasets, weights, size, weight_bins=100): + self.datasets = datasets + num_datasets = len(datasets) + assert num_datasets == len(weights) + + weight_bins = min(weight_bins, size) + + self.size = size + self.weight_bins = weight_bins + + # Normalize weights. + weights = np.array(weights, dtype=np.float64) + assert (weights > 0.0).all() + sum_weights = np.sum(weights) + assert sum_weights > 0.0 + self.weights = weights / sum_weights + + # create ds index based on weights + ds_index = [] + ds_bias = [] + for i, w in enumerate(self.weights): + n = int(w * weight_bins) + ds_index.extend([i] * n) + ds_bias.extend(range(n)) + # make sure arrays have length of weight_bins + n = weight_bins - len(ds_index) + ds_index.extend([i] * n) + ds_bias.extend(range(ds_bias[-1], ds_bias[-1] + n)) + + self.ds_index = np.array(ds_index, dtype=np.uint32) + self.ds_index_size = np.array([(self.ds_index == i).sum() for i in range(num_datasets)], dtype=np.uint32) + assert ( + self.ds_index_size > 0 + ).all(), f"Some datasets have no samples in the blendable dataset, increase weight_bins or the offending weight. ds_index_size = {self.ds_index_size}" + self.ds_bias = np.array(ds_bias, dtype=np.uint32) + + self.ds_size = np.array([len(ds) for ds in datasets], dtype=np.uint32) + + def get_ds_sample_idx(self, idx): + """Returns ds index and sample index (within the ds) for the given index in the blendable dataset.""" + + bin = idx % self.weight_bins + ds_idx = self.ds_index[bin] + sample_idx = (self.ds_bias[bin] + (idx // self.weight_bins) * self.ds_index_size[ds_idx]) % self.ds_size[ + ds_idx + ] + + return ds_idx, sample_idx + + def __len__(self): + return self.size + + def __getitem__(self, idx): + ds_idx, sample_idx = self.get_ds_sample_idx(idx) + + return self.datasets[ds_idx][sample_idx] + + @classmethod + def test_index_blending(cls): + """Visualize indices of blended dataset""" + + import matplotlib.pyplot as plt + + plt.ion() + + class DS(torch.utils.data.Dataset): + def __init__(self, size, data): + self.size = size + self.data = data + + def __len__(self): + return self.size + + def __getitem__(self, idx): + return self.data[idx] + + for weight_bins in [10, 100]: + blend_ds = MemoryEfficientBlendableDataset( + [DS(10, "a"), DS(10, "b"), DS(10, "c")], [0.5, 0.3, 0.2], 50, weight_bins=weight_bins + ) + + ds_sample_idx_list = [blend_ds.get_ds_sample_idx(i) for i in range(50)] + ds_list = list(zip(*ds_sample_idx_list))[0] + sample_list = list(zip(*ds_sample_idx_list))[1] + + plt.figure() + plt.plot(ds_list, label="ds idx") + plt.plot(sample_list, label="sample") + plt.legend() + plt.grid() + plt.title(f"weight_bins={weight_bins}") diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/data_samplers.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/data_samplers.py new file mode 100644 index 0000000..6818f99 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/data_samplers.py @@ -0,0 +1,207 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Dataloaders.""" + +import abc +from itertools import chain +from typing import Optional + +import torch + +from nemo.utils import logging + + +class BaseMegatronSampler: + def __init__( + self, + total_samples: int, + consumed_samples: int, + micro_batch_size: int, + data_parallel_rank: int, + data_parallel_size: int, + drop_last: bool = True, + global_batch_size: Optional[int] = None, + rampup_batch_size: Optional[list] = None, + pad_samples_to_global_batch_size: Optional[bool] = False, + ) -> None: + # Sanity checks. + if total_samples <= 0: + raise RuntimeError("no sample to consume: {}".format(total_samples)) + if micro_batch_size <= 0: + raise RuntimeError(f"micro_batch_size size must be greater than 0, but {micro_batch_size}") + if data_parallel_size <= 0: + raise RuntimeError(f"data parallel size must be greater than 0, but {data_parallel_size}") + if data_parallel_rank >= data_parallel_size: + raise RuntimeError( + "data_parallel_rank should be smaller than data size, but {} >= {}".format( + data_parallel_rank, data_parallel_size + ) + ) + if global_batch_size is not None and rampup_batch_size is None: + if global_batch_size % (micro_batch_size * data_parallel_size) != 0: + raise RuntimeError( + f"`global_batch_size` ({global_batch_size}) is not divisible by " + f"`micro_batch_size ({micro_batch_size}) x data_parallel_size " + f"({data_parallel_size})`" + ) + if pad_samples_to_global_batch_size and global_batch_size is None: + raise RuntimeError( + f"`pad_samples_to_global_batch_size` can be `True` only when " + f"`global_batch_size` is set to an integer value" + ) + + # Keep a copy of input params for later use. + self.total_samples = total_samples + self.consumed_samples = consumed_samples + self.micro_batch_size = micro_batch_size + self.data_parallel_rank = data_parallel_rank + self.data_parallel_size = data_parallel_size + self.micro_batch_times_data_parallel_size = self.micro_batch_size * data_parallel_size + self.drop_last = drop_last + self.global_batch_size = global_batch_size + self.pad_samples_to_global_batch_size = pad_samples_to_global_batch_size + + logging.info( + f'Instantiating MegatronPretrainingSampler with total_samples: {total_samples} and consumed_samples: {consumed_samples}' + ) + + def __len__(self): + num_available_samples: int = self.total_samples - self.consumed_samples + if self.global_batch_size is not None: + if self.drop_last: + num_global_batches = num_available_samples // self.global_batch_size + else: + num_global_batches = (num_available_samples + self.global_batch_size - 1) // self.global_batch_size + # return len of dataloader in terms of micro batches to avoid discrepancy between len of dataloader and + # num of batches fetched (as training step fetches in terms of micro batches) + return num_global_batches * (self.global_batch_size // self.micro_batch_times_data_parallel_size) + else: + return (num_available_samples - 1) // self.micro_batch_times_data_parallel_size + 1 + + @abc.abstractmethod + def __iter__(self): + ... + + +class MegatronPretrainingSampler(BaseMegatronSampler): + def get_start_end_idx(self): + start_idx = self.data_parallel_rank * self.micro_batch_size + end_idx = start_idx + self.micro_batch_size + return start_idx, end_idx + + def __iter__(self): + batch = [] + # Last batch will be dropped if drop_last is not set False + indices = range(self.consumed_samples, self.total_samples) + if (not self.drop_last) and self.pad_samples_to_global_batch_size: + pad_samples_num = -len(indices) % self.global_batch_size + pad_indices = range(-1, -pad_samples_num - 1, -1) + indices = chain(indices, pad_indices) + + for idx in indices: + batch.append(idx) + if len(batch) == self.micro_batch_times_data_parallel_size: + start_idx, end_idx = self.get_start_end_idx() + yield batch[start_idx:end_idx] + batch = [] + + # Check the last partial batch and see drop_last is set + if len(batch) > 0 and not self.drop_last: + assert ( + not self.pad_samples_to_global_batch_size + ), 'with pad_samples_to_global_batch_size all batches should be complete' + start_idx, end_idx = self.get_start_end_idx() + yield batch[start_idx:end_idx] + + +class MegatronPretrainingRandomSampler(BaseMegatronSampler): + def __init__( + self, + total_samples: int, + consumed_samples: int, + micro_batch_size: int, + data_parallel_rank: int, + data_parallel_size: int, + drop_last: bool = True, + global_batch_size: Optional[int] = None, + pad_samples_to_global_batch_size: Optional[bool] = False, + seed: int = 0, + ) -> None: + super().__init__( + total_samples=total_samples, + consumed_samples=consumed_samples, + micro_batch_size=micro_batch_size, + data_parallel_rank=data_parallel_rank, + data_parallel_size=data_parallel_size, + drop_last=drop_last, + global_batch_size=global_batch_size, + pad_samples_to_global_batch_size=pad_samples_to_global_batch_size, + ) + assert ( + not pad_samples_to_global_batch_size + ), "`MegatronPretrainingRandomSampler` does not support sample padding" + if (not drop_last) and self.micro_batch_times_data_parallel_size > 1: + raise RuntimeError( + "`MegatronPretrainingRandomSampler` does not support drop_last=False when micro_batch_size * data_parallel_size > 1. \ + please reduce your MBS and data parallelism to 1 if you want to use drop_last=False, or switch to drop_last=True to avoid this error" + ) + self.last_batch_size = self.total_samples % self.micro_batch_times_data_parallel_size + self.seed = seed + + def __len__(self): + active_total_samples = self.total_samples - (self.last_batch_size if self.drop_last else 0) + num_available_samples = active_total_samples - self.consumed_samples % active_total_samples + if self.global_batch_size is not None: + if self.drop_last: + num_global_batches = num_available_samples // self.global_batch_size + else: + num_global_batches = (num_available_samples + self.global_batch_size - 1) // self.global_batch_size + # return len of dataloader in terms of micro batches to avoid discrepancy between len of dataloader and + # num of batches fetched (as training step fetches in terms of micro batches) + return num_global_batches * (self.global_batch_size // self.micro_batch_times_data_parallel_size) + else: + if self.drop_last: + return num_available_samples // self.micro_batch_times_data_parallel_size + else: + return (num_available_samples - 1) // self.micro_batch_times_data_parallel_size + + def __iter__(self): + active_total_samples = self.total_samples - self.last_batch_size + self.epoch = self.consumed_samples // active_total_samples + current_epoch_samples = self.consumed_samples % active_total_samples + assert current_epoch_samples % self.micro_batch_times_data_parallel_size == 0 + + # data sharding and random sampling + bucket_size = (self.total_samples // self.micro_batch_times_data_parallel_size) * self.micro_batch_size + bucket_offset = current_epoch_samples // self.data_parallel_size + start_idx = self.data_parallel_rank * bucket_size + + g = torch.Generator() + g.manual_seed(self.seed + self.epoch) + random_idx = torch.randperm(bucket_size, generator=g).tolist() + idx_range = [start_idx + x for x in random_idx[bucket_offset:]] + + batch = [] + # Last batch if not complete will be dropped. + for idx in idx_range: + batch.append(idx) + if len(batch) == self.micro_batch_size: + self.consumed_samples += self.micro_batch_times_data_parallel_size + yield batch + batch = [] + + # Check the last partial batch and see drop_last is set + if len(batch) > 0 and not self.drop_last: + yield batch diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/dataset_utils.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/dataset_utils.py new file mode 100644 index 0000000..17ffc01 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/dataset_utils.py @@ -0,0 +1,1351 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright 2018 The Google AI Team Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Most of the code here has been copied from: +# https://github.com/google-research/albert/blob/master/create_pretraining_data.py +# with some modifications. + +import collections +import os +import subprocess +import time +from typing import Any + +import numpy as np +import torch +from omegaconf import OmegaConf +from omegaconf.dictconfig import DictConfig + +from nemo.collections.nlp.data.language_modeling.megatron.base_dataset_utils import ( + get_datasets_weights_and_num_samples, + get_train_valid_test_split_, +) +from nemo.collections.nlp.data.language_modeling.megatron.blendable_dataset import BlendableDataset +from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import deallocate_indexed_dataset_memory +from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import make_dataset as make_indexed_dataset +from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import make_indexed_dataset_compatibility +from nemo.collections.nlp.data.language_modeling.megatron.length_distribution_type import LengthDistribution +from nemo.collections.nlp.data.language_modeling.megatron.lm_adapted_t5_dataset import T5LMAdaptedDataset +from nemo.utils import logging +from nemo.utils.get_rank import is_global_rank_zero + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +DSET_TYPE_BERT = 'standard_bert' +DSET_TYPE_ICT = 'ict' +DSET_TYPE_T5 = 't5' +DSET_TYPE_T5_LM = 't5_prefix_lm' +DSET_TYPE_BART = 'bart' +DSET_TYPE_UL2 = 'ul2' + +DSET_TYPES = [DSET_TYPE_BERT, DSET_TYPE_ICT, DSET_TYPE_T5, DSET_TYPE_T5_LM, DSET_TYPE_BART, DSET_TYPE_UL2] + + +def compile_helper(): + """Compile helper function ar runtime. Make sure this + is invoked on a single process.""" + + path = os.path.abspath(os.path.dirname(__file__)) + ret = subprocess.run(['make', '-C', path]) + if ret.returncode != 0: + logging.error("Making C++ dataset helpers module failed, exiting.") + import sys + + sys.exit(1) + + +def get_a_and_b_segments(sample, np_rng): + """Divide sample into a and b segments.""" + + # Number of sentences in the sample. + n_sentences = len(sample) + # Make sure we always have two sentences. + assert n_sentences > 1, 'make sure each sample has at least two sentences.' + + # First part: + # `a_end` is how many sentences go into the `A`. + a_end = 1 + if n_sentences >= 3: + # Note that randin in numpy is exclusive. + a_end = np_rng.randint(1, n_sentences) + tokens_a = [] + for j in range(a_end): + tokens_a.extend(sample[j]) + + # Second part: + tokens_b = [] + for j in range(a_end, n_sentences): + tokens_b.extend(sample[j]) + + # Random next: + is_next_random = False + if np_rng.random() < 0.5: + is_next_random = True + tokens_a, tokens_b = tokens_b, tokens_a + + return tokens_a, tokens_b, is_next_random + + +def truncate_segments(tokens_a, tokens_b, len_a, len_b, max_num_tokens, np_rng): + """Truncates a pair of sequences to a maximum sequence length.""" + # print(len_a, len_b, max_num_tokens) + assert len_a > 0 + if len_a + len_b <= max_num_tokens: + return False + while len_a + len_b > max_num_tokens: + if len_a > len_b: + len_a -= 1 + tokens = tokens_a + else: + len_b -= 1 + tokens = tokens_b + if np_rng.random() < 0.5: + del tokens[0] + else: + tokens.pop() + return True + + +def create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id): + """Merge segments A and B, add [CLS] and [SEP] and build tokentypes.""" + + tokens = [] + tokentypes = [] + # [CLS]. + tokens.append(cls_id) + tokentypes.append(0) + # Segment A. + for token in tokens_a: + tokens.append(token) + tokentypes.append(0) + # [SEP]. + tokens.append(sep_id) + tokentypes.append(0) + # Segment B. + for token in tokens_b: + tokens.append(token) + tokentypes.append(1) + if tokens_b: + # [SEP]. + tokens.append(sep_id) + tokentypes.append(1) + + return tokens, tokentypes + + +MaskedLmInstance = collections.namedtuple("MaskedLmInstance", ["index", "label"]) + + +def is_start_piece(piece): + """Check if the current word piece is the starting piece. (BERT)""" + # When a word has been split into + # WordPieces, the first token does not have any marker and any subsequence + # tokens are prefixed with ##. So whenever we see the ## token, we + # append it to the previous set of word indexes. + return not piece.startswith("##") + + +def create_masked_lm_predictions( + tokens, + vocab_id_list, + vocab_id_to_token_dict, + masked_lm_prob, + cls_id, + sep_id, + mask_id, + max_predictions_per_seq, + np_rng, + max_ngram_size=3, + mean_ngram_size=None, + whole_word_masking=True, + favor_long_ngrams=False, + permutation=False, + geometric_dist=False, + masking_style="bert", + tokenizer_type="wordpiece", + skip_masking_id=None, +): + """Creates the predictions for the masked LM objective. + Note: Tokens here are vocab ids and not text tokens.""" + if not geometric_dist and mean_ngram_size is not None: + raise ValueError(f"Mean ngram size is only supported for geometric distribution.") + + cand_indexes = [] + # Note(mingdachen): We create a list for recording if the piece is + # the starting piece of current token, where 1 means true, so that + # on-the-fly whole word masking is possible. + token_boundary = [0] * len(tokens) + skip_mask_idx = None # Store the index of token that cannot be masked. + for (i, token) in enumerate(tokens): + if token == skip_masking_id: + skip_mask_idx = i + if token == cls_id or token == sep_id: + token_boundary[i] = 1 + continue + # Whole Word Masking means that if we mask all of the wordpieces + # corresponding to an original word. + # + # Note that Whole Word Masking does *not* change the training code + # at all -- we still predict each WordPiece independently, softmaxed + # over the entire vocabulary. + if whole_word_masking and len(cand_indexes) >= 1 and not is_start_piece(vocab_id_to_token_dict[token]): + cand_indexes[-1].append(i) + else: + cand_indexes.append([i]) + if is_start_piece(vocab_id_to_token_dict[token]): + token_boundary[i] = 1 + + output_tokens = list(tokens) + + masked_lm_positions = [] + masked_lm_labels = [] + + if masked_lm_prob == 0: + return (output_tokens, masked_lm_positions, masked_lm_labels, token_boundary) + + num_to_predict = min(max_predictions_per_seq, max(1, int(round(len(tokens) * masked_lm_prob)))) + if masking_style != "bert": + num_to_predict = max(1, num_to_predict) + if num_to_predict < 1: + logging.warning( + F'Number of tokens is : {len(tokens)} and mask_probability is {masked_lm_prob}. None of the tokens will be masked' + ) + + ngrams = np.arange(1, max_ngram_size + 1, dtype=np.int64) + if not geometric_dist: + # Note(mingdachen): + # By default, we set the probilities to favor shorter ngram sequences. + pvals = 1.0 / np.arange(1, max_ngram_size + 1) + pvals /= pvals.sum(keepdims=True) + if favor_long_ngrams: + pvals = pvals[::-1] + + ngram_indexes = [] + for idx in range(len(cand_indexes)): + ngram_index = {} + for n in ngrams: + # Skip this ngram if it contains the index of token that should not be masked. + # TODO: (sandeepsub) Generalize this to be a list of tokens that cannot be masked. + if skip_mask_idx is not None and skip_mask_idx >= idx and skip_mask_idx <= idx + n: + continue + ngram_index[n] = cand_indexes[idx : idx + n] + ngram_indexes.append(ngram_index) + + np_rng.shuffle(ngram_indexes) + + (masked_lms, masked_spans) = ([], []) + covered_indexes = set() + for cand_index_set in ngram_indexes: + if len(masked_lms) >= num_to_predict: + break + if not cand_index_set: + continue + # Note(mingdachen): + # Skip current piece if they are covered in lm masking or previous ngrams. + for index_set in cand_index_set[1]: + for index in index_set: + if index in covered_indexes: + continue + + if not geometric_dist: + # Not all ngrams are available because of skip_masking_id that prevents a certain ID from being masked. + available_ngrams = list(cand_index_set.keys()) + # n - 1 because pvals is 0-indexed and available ngrams are 1-indexed. + pvals_current = np.array([pvals[n - 1] for n in available_ngrams]) + n = np_rng.choice(available_ngrams, p=pvals_current / pvals_current.sum(keepdims=True),) + else: + # Sampling "n" from the geometric distribution and clipping it to + # the max_ngrams. Using p=0.2 default from the SpanBERT paper + # https://arxiv.org/pdf/1907.10529.pdf (Sec 3.1) + + # The expectation of a geometric distribution is E[X] = 1 / p + p = 1 / mean_ngram_size if mean_ngram_size is not None else 0.2 + n = min(np_rng.geometric(p), max_ngram_size) + # n may not be in the candidate index set because of skip_masking_id. + # we try to find the nearest one in the candidate index set. + if n not in cand_index_set: + n = _truncate_to_nearest(cand_index_set, n) + index_set = sum(cand_index_set[n], []) + n -= 1 + # Note(mingdachen): + # Repeatedly looking for a candidate that does not exceed the + # maximum number of predictions by trying shorter ngrams. + while len(masked_lms) + len(index_set) > num_to_predict: + if n == 0: + break + if n - 1 in cand_index_set: + index_set = sum(cand_index_set[n - 1], []) + n -= 1 + # If adding a whole-word mask would exceed the maximum number of + # predictions, then just skip this candidate. + if len(masked_lms) + len(index_set) > num_to_predict: + continue + is_any_index_covered = False + for index in index_set: + if index in covered_indexes: + is_any_index_covered = True + break + if is_any_index_covered: + continue + for index in index_set: + covered_indexes.add(index) + masked_token = None + if masking_style == "bert": + # 80% of the time, replace with [MASK] + if np_rng.random() < 0.8: + masked_token = mask_id + else: + # 10% of the time, keep original + if np_rng.random() < 0.5: + masked_token = tokens[index] + # 10% of the time, replace with random word + else: + masked_token = vocab_id_list[np_rng.randint(0, len(vocab_id_list))] + elif masking_style == "t5": + masked_token = mask_id + elif masking_style == "bart": + masked_token = mask_id + else: + raise ValueError("invalid value of masking style") + + output_tokens[index] = masked_token + masked_lms.append(MaskedLmInstance(index=index, label=tokens[index])) + + masked_spans.append(MaskedLmInstance(index=index_set, label=[tokens[index] for index in index_set])) + + assert len(masked_lms) <= num_to_predict + np_rng.shuffle(ngram_indexes) + + select_indexes = set() + if permutation: + if skip_masking_id is not None: + raise ValueError(f"permutation=True is not supported when skip_masking_id is not None.") + for cand_index_set in ngram_indexes: + if len(select_indexes) >= num_to_predict: + break + if not cand_index_set: + continue + # Note(mingdachen): + # Skip current piece if they are covered in lm masking or previous ngrams. + for index_set in cand_index_set[0]: + for index in index_set: + if index in covered_indexes or index in select_indexes: + continue + + n = np.random.choice( + ngrams[: len(cand_index_set)], + p=pvals[: len(cand_index_set)] / pvals[: len(cand_index_set)].sum(keepdims=True), + ) + index_set = sum(cand_index_set[n - 1], []) + n -= 1 + + while len(select_indexes) + len(index_set) > num_to_predict: + if n == 0: + break + index_set = sum(cand_index_set[n - 1], []) + n -= 1 + # If adding a whole-word mask would exceed the maximum number of + # predictions, then just skip this candidate. + if len(select_indexes) + len(index_set) > num_to_predict: + continue + is_any_index_covered = False + for index in index_set: + if index in covered_indexes or index in select_indexes: + is_any_index_covered = True + break + if is_any_index_covered: + continue + for index in index_set: + select_indexes.add(index) + assert len(select_indexes) <= num_to_predict + + select_indexes = sorted(select_indexes) + permute_indexes = list(select_indexes) + np_rng.shuffle(permute_indexes) + orig_token = list(output_tokens) + + for src_i, tgt_i in zip(select_indexes, permute_indexes): + output_tokens[src_i] = orig_token[tgt_i] + masked_lms.append(MaskedLmInstance(index=src_i, label=orig_token[src_i])) + + masked_lms = sorted(masked_lms, key=lambda x: x.index) + # Sort the spans by the index of the first span + masked_spans = sorted(masked_spans, key=lambda x: x.index[0]) + + for p in masked_lms: + masked_lm_positions.append(p.index) + masked_lm_labels.append(p.label) + return (output_tokens, masked_lm_positions, masked_lm_labels, token_boundary, masked_spans) + + +def _truncate_to_nearest(cand_index_set, n): + min_dist = 9999 + for key in cand_index_set: + if abs(key - n) < min_dist: + n = key + min_dist = abs(key - n) + + return n + + +def create_extreme_masked_lm_predictions( + tokens, + masked_lm_prob, + mask_id, + max_predictions_per_seq, + np_rng, + max_ngram_size=10, + min_ngram_size=2, + mean_ngram_size=5, + span_length_distribution=LengthDistribution.uniform, + skip_masking_id=None, +): + """Creates the predictions for the extreme span-masking UL2 objective. + Note: Tokens here are vocab ids and not text tokens.""" + output_tokens = list(tokens) + + masked_lm_positions = [] + masked_lm_labels = [] + + num_to_predict = int(min(max_predictions_per_seq, max(1, int(round(len(tokens) * masked_lm_prob))))) + # If the number of tokens to predict is less than the min ngram size, clam it to max predictions. + min_ngram_size = int(min(num_to_predict, min_ngram_size)) + + ngrams = np.arange(min_ngram_size, max_ngram_size + 1, dtype=np.int64) + if span_length_distribution == "uniform": + pvals = np.array([1.0 / (max_ngram_size - min_ngram_size + 1)] * (max_ngram_size - min_ngram_size + 1)) + + ngram_indexes = [] + if skip_masking_id is not None: + skip_mask_idx = None + for idx in range(len(tokens)): + if tokens[idx] == skip_masking_id: + skip_mask_idx = idx + break + else: + skip_mask_idx = None + + cand_indexes = [[i] for i in range(len(tokens))] + for idx in range(len(cand_indexes)): + ngram_index = {} + for n in ngrams: + # Skip this ngram if it contains the index of token that should not be masked. + # TODO: (sandeepsub) Generalize this to be a list of tokens that cannot be masked. + if skip_mask_idx is not None and skip_mask_idx >= idx and skip_mask_idx <= idx + n: + continue + ngram_index[n] = cand_indexes[idx : idx + n] + ngram_indexes.append(ngram_index) + + np_rng.shuffle(ngram_indexes) + + (masked_lms, masked_spans) = ([], []) + covered_indexes = set() + for cand_index_set in ngram_indexes: + if len(masked_lms) >= num_to_predict: + break + if not cand_index_set: + continue + # Note(mingdachen): + # Skip current piece if they are covered in lm masking or previous ngrams. + for index_set in cand_index_set[min_ngram_size]: + for index in index_set: + if index in covered_indexes: + continue + + if span_length_distribution == LengthDistribution.uniform: + available_ngrams = list(cand_index_set.keys()) + pvals_current = np.array([pvals[n] for n in available_ngrams]) + n = np_rng.choice(available_ngrams, p=pvals_current / pvals_current.sum(keepdims=True),) + elif span_length_distribution == LengthDistribution.geometric: + # Sampling "n" from the geometric distribution and clipping it to + # the max_ngrams. Using p=0.2 default from the SpanBERT paper + # https://arxiv.org/pdf/1907.10529.pdf (Sec 3.1) + + # The expectation of a geometric distribution is E[X] = 1 / p + p = 1 / mean_ngram_size if mean_ngram_size is not None else 0.2 + n = min(np_rng.geometric(p), max_ngram_size) + # n may not be in the candidate index set because of skip_masking_id. + # we try to find the nearest one in the candidate index set. + if n not in cand_index_set: + n = _truncate_to_nearest(cand_index_set, n) + n = int(np.clip(n, min_ngram_size, max_ngram_size)) + elif span_length_distribution == LengthDistribution.truncated_normal: + # Sampling "n" from a truncated normal distribution. + mu = mean_ngram_size if mean_ngram_size is not None else (max_ngram_size - min_ngram_size) // 2 + n = int(np.clip(np_rng.normal(loc=mu, scale=np.sqrt(mu)), min_ngram_size, max_ngram_size)) + if n not in cand_index_set: + n = _truncate_to_nearest(cand_index_set, n) + n = int(np.clip(n, min_ngram_size, max_ngram_size)) + + index_set = sum(cand_index_set[n], []) + n -= 1 + # Note(mingdachen): + # Repeatedly looking for a candidate that does not exceed the + # maximum number of predictions by trying shorter ngrams. + while len(masked_lms) + len(index_set) > num_to_predict: + if n < min_ngram_size: + break + if n in cand_index_set: + index_set = sum(cand_index_set[n], []) + n -= 1 + + # If adding a whole-word mask would exceed the maximum number of + # predictions, then just skip this candidate. + if len(masked_lms) + len(index_set) > num_to_predict: + continue + is_any_index_covered = False + for index in index_set: + if index in covered_indexes: + is_any_index_covered = True + break + if is_any_index_covered: + continue + for index in index_set: + covered_indexes.add(index) + output_tokens[index] = mask_id + masked_lms.append(MaskedLmInstance(index=index, label=tokens[index])) + + masked_spans.append(MaskedLmInstance(index=index_set, label=[tokens[index] for index in index_set])) + + assert len(masked_lms) <= num_to_predict + np_rng.shuffle(ngram_indexes) + + masked_lms = sorted(masked_lms, key=lambda x: x.index) + # Sort the spans by the index of the first span + masked_spans = sorted(masked_spans, key=lambda x: x.index[0]) + + for p in masked_lms: + masked_lm_positions.append(p.index) + masked_lm_labels.append(p.label) + return (output_tokens, masked_lm_positions, masked_lm_labels, masked_spans) + + +def pad_and_convert_to_numpy(tokens, tokentypes, masked_positions, masked_labels, pad_id, max_seq_length): + """Pad sequences and convert them to numpy.""" + + # Some checks. + num_tokens = len(tokens) + padding_length = max_seq_length - num_tokens + assert padding_length >= 0 + assert len(tokentypes) == num_tokens + assert len(masked_positions) == len(masked_labels) + + # Tokens and token types. + filler = [pad_id] * padding_length + tokens_np = np.array(tokens + filler, dtype=np.int64) + tokentypes_np = np.array(tokentypes + filler, dtype=np.int64) + + # Padding mask. + padding_mask_np = np.array([1] * num_tokens + [0] * padding_length, dtype=np.int64) + + # Lables and loss mask. + labels = [-1] * max_seq_length + loss_mask = [0] * max_seq_length + for i in range(len(masked_positions)): + assert masked_positions[i] < num_tokens + labels[masked_positions[i]] = masked_labels[i] + loss_mask[masked_positions[i]] = 1 + labels_np = np.array(labels, dtype=np.int64) + loss_mask_np = np.array(loss_mask, dtype=np.int64) + + return tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np + + +def get_dataset( + indexed_dataset, + start_index, + end_index, + cfg, + trainer, + num_samples, + masked_lm_prob, + short_seq_prob, + binary_head, + max_seq_length_dec, + dataset_type='standard_bert', + tokenizer=None, + max_ngram_size=3, + mean_ngram_size=None, + geometric_dist=True, + permutation=False, + whole_word_masking=True, + favor_long_ngrams=False, + delete_mask_prob=0, # This flag is used in BART only, and will not have effect on T5/BERT + respect_document_boundaries=True, + **kwargs, +): + + if dataset_type not in DSET_TYPES: + raise ValueError("Invalid dataset_type: ", dataset_type) + + # from nemo.collections.nlp.data.language_modeling.megatron.ict_dataset import ICTDataset + from nemo.collections.nlp.data.language_modeling.megatron.bart_dataset import BARTDataset + from nemo.collections.nlp.data.language_modeling.megatron.bert_dataset import BertDataset + from nemo.collections.nlp.data.language_modeling.megatron.length_distribution_type import LengthDistribution + from nemo.collections.nlp.data.language_modeling.megatron.t5_dataset import T5Dataset + from nemo.collections.nlp.data.language_modeling.megatron.ul2_dataset import UL2Dataset + + if dataset_type == DSET_TYPE_ICT: + raise NotImplementedError("ICT dataset is not implemented yet.") + ''' + dataset = ICTDataset( + block_dataset=indexed_dataset, + title_dataset=title_dataset, + query_in_block_prob=args.query_in_block_prob, + use_one_sent_docs=args.use_one_sent_docs, + binary_head=binary_head, + **kwargs, + ) + ''' + elif dataset_type == DSET_TYPE_T5: + assert tokenizer is not None, "Tokenizer is required for T5 dataset" + logging.info("Instatiating T5 Dataset ...") + documents = np.arange(start=start_index, stop=end_index, step=1, dtype=np.int32) + dataset = T5Dataset( + cfg=cfg, + trainer=trainer, + tokenizer=tokenizer, + indexed_dataset=indexed_dataset, + masked_lm_prob=masked_lm_prob, + max_seq_length_dec=max_seq_length_dec, + short_seq_prob=short_seq_prob, + max_ngram_size=max_ngram_size, + mean_ngram_size=mean_ngram_size, + geometric_dist=geometric_dist, + permutation=permutation, + whole_word_masking=whole_word_masking, + favor_long_ngrams=favor_long_ngrams, + documents=documents, + respect_document_boundaries=respect_document_boundaries, + **kwargs, + ) + elif dataset_type == DSET_TYPE_BERT: + logging.info("Instatiating BERT Dataset ...") + dataset = BertDataset( + cfg=cfg, + indexed_dataset=indexed_dataset, + masked_lm_prob=masked_lm_prob, + short_seq_prob=short_seq_prob, + binary_head=binary_head, + tokenizer=tokenizer, + **kwargs, + ) + elif dataset_type == DSET_TYPE_T5_LM: + documents = np.arange(start=start_index, stop=end_index, step=1, dtype=np.int32) + logging.info("Instatiating T5 Prefix-LM Dataset ...") + dataset = T5LMAdaptedDataset( + cfg=cfg, + trainer=trainer, + tokenizer=tokenizer, + documents=documents, + indexed_dataset=indexed_dataset, + num_samples=num_samples, + max_seq_length_encoder=kwargs["max_seq_length"], + max_seq_length_decoder=max_seq_length_dec, + **kwargs, + ) + elif dataset_type == DSET_TYPE_BART: + assert tokenizer is not None, "Tokenizer is required for BART dataset" + documents = np.arange(start=start_index, stop=end_index, step=1, dtype=np.int32) + logging.info("Instatiating BART Dataset ...") + dataset = BARTDataset( + cfg=cfg, + trainer=trainer, + tokenizer=tokenizer, + indexed_dataset=indexed_dataset, + masked_lm_prob=masked_lm_prob, + short_seq_prob=short_seq_prob, + max_ngram_size=max_ngram_size, + mean_ngram_size=mean_ngram_size, + geometric_dist=geometric_dist, + permutation=permutation, + whole_word_masking=whole_word_masking, + favor_long_ngrams=favor_long_ngrams, + delete_mask_prob=delete_mask_prob, + documents=documents, + respect_document_boundaries=respect_document_boundaries, + **kwargs, + ) + elif dataset_type == DSET_TYPE_UL2: + assert tokenizer is not None, "Tokenizer is required for UL2 dataset" + documents = np.arange(start=start_index, stop=end_index, step=1, dtype=np.int32) + logging.info("Instatiating UL2 Dataset ...") + extreme_ngram_span_length_distribution = cfg.data.get( + "extreme_ngram_span_length_distribution", "truncated_normal" + ) + ngram_span_length_distribution = cfg.data.get("ngram_span_length_distribution", "geometric") + if extreme_ngram_span_length_distribution == "truncated_normal": + extreme_ngram_span_length_distribution = LengthDistribution.truncated_normal + elif extreme_ngram_span_length_distribution == "uniform": + extreme_ngram_span_length_distribution = LengthDistribution.uniform + elif extreme_ngram_span_length_distribution == "geometric": + extreme_ngram_span_length_distribution = LengthDistribution.geometric + + if ngram_span_length_distribution == "truncated_normal": + ngram_span_length_distribution = LengthDistribution.truncated_normal + elif ngram_span_length_distribution == "uniform": + ngram_span_length_distribution = LengthDistribution.uniform + elif ngram_span_length_distribution == "geometric": + ngram_span_length_distribution = LengthDistribution.geometric + + dataset = UL2Dataset( + cfg=cfg, + trainer=trainer, + tokenizer=tokenizer, + indexed_dataset=indexed_dataset, + masked_lm_prob=masked_lm_prob, + max_seq_length_dec=max_seq_length_dec, + short_seq_prob=short_seq_prob, + max_ngram_size=max_ngram_size, + mean_ngram_size=mean_ngram_size, + ngram_span_length_distribution=ngram_span_length_distribution, + extreme_ngram_span_length_distribution=extreme_ngram_span_length_distribution, + permutation=permutation, + whole_word_masking=whole_word_masking, + favor_long_ngrams=favor_long_ngrams, + extreme_masked_lm_prob=cfg.data.get("extreme_masked_lm_prob", 0.5), + extreme_max_ngram_size=cfg.data.get("extreme_max_ngram_size", 128), + extreme_mean_ngram_size=cfg.data.get("extreme_mean_ngram_size", 64), + extreme_min_ngram_size=cfg.data.get("extreme_min_ngram_size", 32), + prefix_lm_pivot_mean=cfg.data.get("prefix_lm_pivot_mean", 0.25), + respect_document_boundaries=respect_document_boundaries, + documents=documents, + **kwargs, + ) + else: + raise NotImplementedError(f"Dataset type {dataset_type} not fully implemented.") + return dataset + + +def build_dataset( + cfg, + trainer, + data_prefix, + data_impl, + num_samples, + max_seq_length, + masked_lm_prob, + short_seq_prob, + seed, + skip_warmup, + binary_head, + max_seq_length_dec, + name, + dataset_type, + tokenizer, + max_ngram_size, + mean_ngram_size, + geometric_dist, + permutation, + whole_word_masking, + favor_long_ngrams, + delete_mask_prob, + respect_document_boundaries, + data_impl_kwargs, +): + def _build_dataset(current_data_prefix, current_num_samples): + indexed_dataset = get_indexed_dataset_( + current_data_prefix, data_impl, skip_warmup, data_impl_kwargs=data_impl_kwargs + ) + total_num_of_documents = indexed_dataset.sizes.shape[0] + # Print stats about the splits. + logging.info(' > dataset split:') + logging.info(' Total {} documents is : {} '.format(name, total_num_of_documents)) + if hasattr(indexed_dataset, 'get_doc_idx'): + doc_idx_ptr = indexed_dataset.get_doc_idx() + indexed_dataset.set_doc_idx(doc_idx_ptr[0:total_num_of_documents]) + + kwargs = dict( + name=name, + data_prefix=current_data_prefix, + num_epochs=None, + max_num_samples=int(current_num_samples), + max_seq_length=max_seq_length, + seed=seed, + ) + + dataset = get_dataset( + indexed_dataset, + 0, + total_num_of_documents, + cfg, + trainer, + current_num_samples, + masked_lm_prob, + short_seq_prob, + binary_head, + max_seq_length_dec, + dataset_type, + tokenizer, + max_ngram_size, + mean_ngram_size, + geometric_dist, + permutation, + whole_word_masking, + favor_long_ngrams, + delete_mask_prob, + respect_document_boundaries, + **kwargs, + ) + + # Set the original pointer so dataset remains the main dataset. + if hasattr(indexed_dataset, 'set_doc_idx'): + indexed_dataset.set_doc_idx(doc_idx_ptr) + # Checks. + assert indexed_dataset.doc_idx[0] == 0 + assert indexed_dataset.doc_idx.shape[0] == (total_num_of_documents + 1) + return dataset + + if len(data_prefix) == 1: + return _build_dataset(data_prefix[0], num_samples) + + else: + output = get_datasets_weights_and_num_samples(data_prefix, num_samples) + prefixes, weights, datasets_num_samples = output + datasets = [] + for i in range(len(prefixes)): + dataset = _build_dataset(prefixes[i], datasets_num_samples[i]) + datasets.append(dataset) + return BlendableDataset(datasets, weights, num_samples) + + +def build_train_valid_test_datasets( + cfg, + trainer, + data_prefix, + data_impl, + splits_string, + train_valid_test_num_samples, + max_seq_length, + masked_lm_prob, + short_seq_prob, + seed, + skip_warmup, + binary_head=False, + max_seq_length_dec=None, + dataset_type='standard_bert', + tokenizer=None, + max_ngram_size=3, + mean_ngram_size=None, + geometric_dist=True, + permutation=False, + whole_word_masking=True, + favor_long_ngrams=False, + delete_mask_prob=0, + respect_document_boundaries=True, + data_impl_kwargs={}, +): + # for VSC and text memmap we need to provide a tokenizer, if not given + if data_impl in ["text_mmap", "csv_mmap"]: + if "tokenizer" not in data_impl_kwargs: + if isinstance(data_impl_kwargs, DictConfig): + data_impl_kwargs = OmegaConf.to_object(data_impl_kwargs) + else: + # prevent updating the default + data_impl_kwargs = data_impl_kwargs.copy() + + data_impl_kwargs["tokenizer"] = tokenizer + + if not respect_document_boundaries and data_impl_kwargs != {}: + raise ValueError( + "respect_document_boundaries=False is not compatible with text_memmap and csv_memmap (data_impl_kwargs != {})" + ) + + if data_impl in ["mock"]: + logging.info(f'Initializing mock dataset, type {dataset_type}, for train, validate, and test') + if len(data_prefix) != 0: + # Files from this location will not be read; mock data will be generated instead. + logging.warning(f"Requested data_impl={data_impl}, so ignoring data_prefix setting: {data_prefix}") + if dataset_type == DSET_TYPE_T5: + from nemo.collections.nlp.data.language_modeling.megatron.t5_dataset import MockT5Dataset + + if tokenizer is None: + # Tokenizer is used to infer vocabulary size for mock data. + raise ValueError("Tokenizer is required for a mock T5 dataset") + train_ds = MockT5Dataset( + cfg, + tokenizer, + "train", + int(train_valid_test_num_samples[0]), + max_seq_length, + max_seq_length_dec, + seed, + ) + valid_ds = MockT5Dataset( + cfg, + tokenizer, + "valid", + int(train_valid_test_num_samples[1]), + max_seq_length, + max_seq_length_dec, + seed, + ) + test_ds = MockT5Dataset( + cfg, tokenizer, "test", int(train_valid_test_num_samples[2]), max_seq_length, max_seq_length_dec, seed, + ) + return train_ds, valid_ds, test_ds + else: + raise NotImplementedError(f"Mock dataset is not implemented for requested type: {dataset_type}") + + if isinstance(data_prefix, DictConfig): + assert ( + data_prefix.get('train') is not None + and data_prefix.get('test') is not None + and data_prefix.get('validation') is not None + ), f"Data prefix dictionary should have train, test and validation keys. data_prefix currently has only {data_prefix.keys()}" + if cfg.data.splits_string is not None: + logging.warning(cfg.data.splits_string + " ignored since data prefix is of type dictionary.") + train_ds = build_dataset( + cfg, + trainer, + data_prefix["train"], + data_impl, + int(train_valid_test_num_samples[0]), + max_seq_length, + masked_lm_prob, + short_seq_prob, + seed, + skip_warmup, + binary_head, + max_seq_length_dec, + "train", + dataset_type=dataset_type, + tokenizer=tokenizer, + max_ngram_size=max_ngram_size, + mean_ngram_size=mean_ngram_size, + geometric_dist=geometric_dist, + permutation=permutation, + whole_word_masking=whole_word_masking, + favor_long_ngrams=favor_long_ngrams, + delete_mask_prob=delete_mask_prob, + respect_document_boundaries=respect_document_boundaries, + data_impl_kwargs=data_impl_kwargs, + ) + validation_ds = build_dataset( + cfg, + trainer, + data_prefix["validation"], + data_impl, + int(train_valid_test_num_samples[1]), + max_seq_length, + masked_lm_prob, + short_seq_prob, + seed, + skip_warmup, + binary_head, + max_seq_length_dec, + "valid", + dataset_type=dataset_type, + tokenizer=tokenizer, + max_ngram_size=max_ngram_size, + mean_ngram_size=mean_ngram_size, + geometric_dist=geometric_dist, + permutation=permutation, + whole_word_masking=whole_word_masking, + favor_long_ngrams=favor_long_ngrams, + delete_mask_prob=delete_mask_prob, + respect_document_boundaries=respect_document_boundaries, + data_impl_kwargs=data_impl_kwargs, + ) + test_ds = build_dataset( + cfg, + trainer, + data_prefix["test"], + data_impl, + int(train_valid_test_num_samples[2]), + max_seq_length, + masked_lm_prob, + short_seq_prob, + seed, + skip_warmup, + binary_head, + max_seq_length_dec, + "test", + dataset_type=dataset_type, + tokenizer=tokenizer, + max_ngram_size=max_ngram_size, + mean_ngram_size=mean_ngram_size, + geometric_dist=geometric_dist, + permutation=permutation, + whole_word_masking=whole_word_masking, + favor_long_ngrams=favor_long_ngrams, + delete_mask_prob=delete_mask_prob, + respect_document_boundaries=respect_document_boundaries, + data_impl_kwargs=data_impl_kwargs, + ) + return train_ds, validation_ds, test_ds + + else: + + if len(data_prefix) == 1: + return _build_train_valid_test_datasets( + cfg, + trainer, + data_prefix[0], + data_impl, + splits_string, + train_valid_test_num_samples, + max_seq_length, + masked_lm_prob, + short_seq_prob, + seed, + skip_warmup, + binary_head, + max_seq_length_dec, + dataset_type=dataset_type, + tokenizer=tokenizer, + max_ngram_size=max_ngram_size, + mean_ngram_size=mean_ngram_size, + geometric_dist=geometric_dist, + permutation=permutation, + whole_word_masking=whole_word_masking, + favor_long_ngrams=favor_long_ngrams, + delete_mask_prob=delete_mask_prob, + respect_document_boundaries=respect_document_boundaries, + data_impl_kwargs=data_impl_kwargs, + ) + # Blending dataset. + # Parse the values. + output = get_datasets_weights_and_num_samples(data_prefix, train_valid_test_num_samples) + prefixes, weights, datasets_train_valid_test_num_samples = output + train_n, valid_n, test_n = map(sum, zip(*datasets_train_valid_test_num_samples)) + + # Build individual datasets. + train_datasets = [] + valid_datasets = [] + test_datasets = [] + for i in range(len(prefixes)): + train_ds, valid_ds, test_ds = _build_train_valid_test_datasets( + cfg, + trainer, + prefixes[i], + data_impl, + splits_string, + datasets_train_valid_test_num_samples[i], + max_seq_length, + masked_lm_prob, + short_seq_prob, + seed, + skip_warmup, + binary_head, + max_seq_length_dec, + dataset_type=dataset_type, + tokenizer=tokenizer, + max_ngram_size=max_ngram_size, + mean_ngram_size=mean_ngram_size, + geometric_dist=geometric_dist, + permutation=permutation, + whole_word_masking=whole_word_masking, + favor_long_ngrams=favor_long_ngrams, + delete_mask_prob=delete_mask_prob, + respect_document_boundaries=respect_document_boundaries, + data_impl_kwargs=data_impl_kwargs, + ) + if train_ds: + train_datasets.append(train_ds) + if valid_ds: + valid_datasets.append(valid_ds) + if test_ds: + test_datasets.append(test_ds) + + # Blend. + blending_train_dataset = None + if train_datasets: + blending_train_dataset = BlendableDataset(train_datasets, weights, train_n) + blending_valid_dataset = None + if valid_datasets: + blending_valid_dataset = BlendableDataset(valid_datasets, weights, valid_n) + blending_test_dataset = None + if test_datasets: + blending_test_dataset = BlendableDataset(test_datasets, weights, test_n) + + return (blending_train_dataset, blending_valid_dataset, blending_test_dataset) + + +def _build_train_valid_test_datasets( + cfg, + trainer, + data_prefix, + data_impl, + splits_string, + train_valid_test_num_samples, + max_seq_length, + masked_lm_prob, + short_seq_prob, + seed, + skip_warmup, + binary_head, + max_seq_length_dec, + dataset_type='standard_bert', + tokenizer=None, + max_ngram_size=3, + mean_ngram_size=None, + geometric_dist=True, + permutation=False, + whole_word_masking=True, + favor_long_ngrams=False, + delete_mask_prob=0, # This flag is used in BART only, and will not have effect on T5/BERT + respect_document_boundaries=True, + data_impl_kwargs={}, +): + + # Indexed dataset. + indexed_dataset = get_indexed_dataset_(data_prefix, data_impl, skip_warmup, data_impl_kwargs=data_impl_kwargs) + + # if dataset_type == DSET_TYPE_ICT: + # title_dataset = get_indexed_dataset_(args.titles_data_path, data_impl, skip_warmup) + + # Get start and end indices of train/valid/train into doc-idx + # Note that doc-idx is desinged to be num-docs + 1 so we can + # easily iterate over it. + total_num_of_documents = indexed_dataset.doc_idx.shape[0] - 1 + splits = get_train_valid_test_split_(splits_string, total_num_of_documents) + + # Print stats about the splits. + logging.info(' > dataset split:') + + def print_split_stats(name, index): + logging.info(' {}:'.format(name)) + logging.info( + ' document indices in [{}, {}) total of {} ' + 'documents'.format(splits[index], splits[index + 1], splits[index + 1] - splits[index]) + ) + start_index = indexed_dataset.doc_idx[splits[index]] + end_index = indexed_dataset.doc_idx[splits[index + 1]] + logging.info( + ' sentence indices in [{}, {}) total of {} ' + 'sentences'.format(start_index, end_index, end_index - start_index) + ) + + print_split_stats('train', 0) + print_split_stats('validation', 1) + print_split_stats('test', 2) + + def build_dataset(index, name): + # from nemo.collections.nlp.data.language_modeling.megatron.ict_dataset import ICTDataset + from nemo.collections.nlp.data.language_modeling.megatron.bart_dataset import BARTDataset + from nemo.collections.nlp.data.language_modeling.megatron.bert_dataset import BertDataset + from nemo.collections.nlp.data.language_modeling.megatron.length_distribution_type import LengthDistribution + from nemo.collections.nlp.data.language_modeling.megatron.t5_dataset import T5Dataset + from nemo.collections.nlp.data.language_modeling.megatron.ul2_dataset import UL2Dataset + + dataset = None + if splits[index + 1] > splits[index]: + # Get the pointer to the original doc-idx so we can set it later. + if hasattr(indexed_dataset, 'get_doc_idx'): + doc_idx_ptr = indexed_dataset.get_doc_idx() + # Slice the doc-idx + start_index = splits[index] + # Add +1 so we can index into the dataset to get the upper bound. + end_index = splits[index + 1] + 1 + # New doc_idx view. + if hasattr(indexed_dataset, 'set_doc_idx'): + indexed_dataset.set_doc_idx(doc_idx_ptr[start_index:end_index]) + # Build the dataset accordingly. + kwargs = dict( + name=name, + data_prefix=data_prefix, + num_epochs=None, + max_num_samples=int(train_valid_test_num_samples[index]), + max_seq_length=max_seq_length, + seed=seed, + ) + + dataset = get_dataset( + indexed_dataset, + splits[index], + splits[index + 1], + cfg, + trainer, + int(train_valid_test_num_samples[index]), + masked_lm_prob, + short_seq_prob, + binary_head, + max_seq_length_dec, + dataset_type, + tokenizer, + max_ngram_size, + mean_ngram_size, + geometric_dist, + permutation, + whole_word_masking, + favor_long_ngrams, + delete_mask_prob, + respect_document_boundaries, + **kwargs, + ) + + # Set the original pointer so dataset remains the main dataset. + if hasattr(indexed_dataset, 'set_doc_idx'): + indexed_dataset.set_doc_idx(doc_idx_ptr) + # Checks. + if getattr(indexed_dataset, 'doc_idx', None) is not None: + assert indexed_dataset.doc_idx[0] == 0 + assert indexed_dataset.doc_idx.shape[0] == (total_num_of_documents + 1) + + return dataset + + train_dataset = build_dataset(0, 'train') + valid_dataset = build_dataset(1, 'valid') + test_dataset = build_dataset(2, 'test') + + return (train_dataset, valid_dataset, test_dataset) + + +def get_indexed_dataset_(data_prefix, data_impl, skip_warmup, data_impl_kwargs={}): + + logging.info(' > building dataset index ...') + + start_time = time.time() + indexed_dataset = make_indexed_dataset(data_prefix, data_impl, skip_warmup, impl_kwargs=data_impl_kwargs) + if data_impl in ['text_mmap', 'csv_mmap']: + # make csv/text memmap compatible with Megatron sampling + make_indexed_dataset_compatibility(indexed_dataset) + + assert indexed_dataset.sizes.shape[0] == indexed_dataset.doc_idx[-1] + logging.info(' > finished creating indexed dataset in {:4f} ' 'seconds'.format(time.time() - start_time)) + + logging.info(' > indexed dataset stats:') + logging.info(' number of documents: {}'.format(indexed_dataset.doc_idx.shape[0] - 1)) + logging.info(' number of sentences: {}'.format(indexed_dataset.sizes.shape[0])) + + return indexed_dataset + + +def get_samples_mapping( + indexed_dataset, + data_prefix, + num_epochs, + max_num_samples, + max_seq_length, + short_seq_prob, + seed, + name, + binary_head, + index_mapping_dir: str = None, + samples_mapping: Any = None, +): + """Get a list that maps a sample index to a starting sentence index, end sentence index, and length""" + + if not num_epochs: + if not max_num_samples: + raise ValueError("Need to specify either max_num_samples " "or num_epochs") + num_epochs = np.iinfo(np.int32).max - 1 + if not max_num_samples: + max_num_samples = np.iinfo(np.int64).max - 1 + + # Filename of the index mapping + if index_mapping_dir is not None: + indexmap_filename = os.path.join(index_mapping_dir, os.path.basename(data_prefix)) + else: + indexmap_filename = data_prefix + indexmap_filename += '_{}_indexmap'.format(name) + if num_epochs != (np.iinfo(np.int32).max - 1): + indexmap_filename += '_{}ep'.format(num_epochs) + if max_num_samples != (np.iinfo(np.int64).max - 1): + indexmap_filename += '_{}mns'.format(max_num_samples) + indexmap_filename += '_{}msl'.format(max_seq_length) + indexmap_filename += '_{:0.2f}ssp'.format(short_seq_prob) + indexmap_filename += '_{}s'.format(seed) + indexmap_filename += '.npy' + + # Build the indexed mapping if not exist and not provided externally. + if samples_mapping is None and torch.distributed.get_rank() == 0 and not os.path.isfile(indexmap_filename): + # Fake index mapping if missing + if (getattr(indexed_dataset, 'doc_idx', None) is None) and (getattr(indexed_dataset, 'sizes', None) is None): + make_indexed_dataset_compatibility(indexed_dataset) + + print( + ' > WARNING: could not find index map file {}, building ' + 'the indices on rank 0 ...'.format(indexmap_filename) + ) + + # Make sure the types match the helpers input types. + assert indexed_dataset.doc_idx.dtype == np.int64 + assert indexed_dataset.sizes.dtype == np.int32 + + # Build samples mapping + verbose = torch.distributed.get_rank() == 0 + start_time = time.time() + logging.info(' > building samples index mapping for {} ...'.format(name)) + # First compile and then import. + try: + if is_global_rank_zero(): + compile_helper() + from nemo.collections.nlp.data.language_modeling.megatron import helpers + except ImportError: + raise ImportError( + f'Could not compile megatron dataset C++ helper functions and therefore cannot import helpers python file.' + ) + samples_mapping = helpers.build_mapping( + indexed_dataset.doc_idx, + indexed_dataset.sizes, + num_epochs, + max_num_samples, + max_seq_length, + short_seq_prob, + seed, + verbose, + 2 if binary_head else 1, + ) + logging.info(' > done building samples index maping') + np.save(indexmap_filename, samples_mapping, allow_pickle=True) + logging.info(' > saved the index mapping in {}'.format(indexmap_filename)) + # Make sure all the ranks have built the mapping + logging.info( + ' > elasped time to build and save samples mapping ' '(seconds): {:4f}'.format(time.time() - start_time) + ) + torch.distributed.barrier() + counts = torch.cuda.LongTensor([1]) + torch.distributed.all_reduce(counts, group=parallel_state.get_data_parallel_group(with_context_parallel=True)) + torch.distributed.all_reduce(counts, group=parallel_state.get_pipeline_model_parallel_group()) + assert counts[0].item() == ( + torch.distributed.get_world_size() + // torch.distributed.get_world_size(group=parallel_state.get_tensor_model_parallel_group()) + ) + # Load indexed dataset if not given externally. + if samples_mapping is None: + logging.info(' > loading indexed mapping from {}'.format(indexmap_filename)) + start_time = time.time() + samples_mapping = np.load(indexmap_filename, allow_pickle=True, mmap_mode='r') + logging.info(' loaded indexed file in {:3.3f} seconds'.format(time.time() - start_time)) + logging.info(' total number of samples: {}'.format(samples_mapping.shape[0])) + + # Deallocate temporary numpy arrays that were created for `get_samples_mapping()` when needed + if hasattr(indexed_dataset, 'doc_idx') and hasattr(indexed_dataset, 'sizes'): + deallocate_indexed_dataset_memory(indexed_dataset) + + return samples_mapping diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_dataset.py new file mode 100644 index 0000000..b7fec4f --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_dataset.py @@ -0,0 +1,842 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""GPT style dataset.""" + +import os +import time + +import numpy as np +import torch +from omegaconf.dictconfig import DictConfig + +from nemo.collections.nlp.data.language_modeling.megatron.base_dataset_utils import ( + get_datasets_weights_and_num_samples, + get_train_valid_test_split_, +) +from nemo.collections.nlp.data.language_modeling.megatron.blendable_dataset import BlendableDataset +from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import deallocate_indexed_dataset_memory +from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import make_dataset as make_indexed_dataset +from nemo.core import Dataset +from nemo.utils import logging + +try: + from megatron.core import parallel_state + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError): + + HAVE_MEGATRON_CORE = False + + +def build_dataset(cfg, trainer, data_prefix, data_impl, num_samples, seq_length, seed, skip_warmup, tokenizer, name): + def _build_dataset(current_data_prefix, current_num_samples): + delay_data_mmap = cfg.data.get('delay_data_mmap', False) + indexed_dataset = get_indexed_dataset_(current_data_prefix, data_impl, skip_warmup, delay_data_mmap) + total_num_of_documents = indexed_dataset.sizes.shape[0] + # Print stats about the splits. + logging.info(' > dataset split:') + logging.info(' Total {} documents is : {} '.format(name, total_num_of_documents)) + drop_last = True + if name == "valid": + drop_last = cfg.data.get("validation_drop_last", True) + dataset = GPTDataset( + cfg, + trainer, + tokenizer, + name, + current_data_prefix, + np.arange(start=0, stop=total_num_of_documents, step=1, dtype=np.int32), + indexed_dataset, + current_num_samples, + seq_length, + seed, + drop_last=drop_last, + ) + return dataset + + if len(data_prefix) == 1: + return _build_dataset(data_prefix[0], num_samples) + + else: + output = get_datasets_weights_and_num_samples(data_prefix, num_samples) + prefixes, weights, datasets_num_samples = output + datasets = [] + for i in range(len(prefixes)): + dataset = _build_dataset(prefixes[i], datasets_num_samples[i]) + datasets.append(dataset) + return BlendableDataset(datasets, weights, num_samples) + + +def build_train_valid_test_datasets( + cfg, + trainer, + data_prefix, + data_impl, + splits_string, + train_valid_test_num_samples, + seq_length, + seed, + skip_warmup, + tokenizer, +): + if data_impl in ['mock']: + logging.info('Initializing mock GPT dataset for train, validate, and test') + if data_prefix is not None and len(data_prefix) != 0: + # Mock data will be generated instead of loading files. + logging.warning(f"Requested data_impl={data_impl}, so ignoring data_prefix setting: {data_prefix}") + if tokenizer is None: + # Vocabulary size is inferred from tokenizer. + raise ValueError("Tokenizer is required for a mock GPT dataset") + train_ds = MockGPTDataset(cfg, tokenizer, "train", int(train_valid_test_num_samples[0]), seq_length, seed,) + valid_ds = MockGPTDataset(cfg, tokenizer, "valid", int(train_valid_test_num_samples[1]), seq_length, seed,) + test_ds = MockGPTDataset(cfg, tokenizer, "test", int(train_valid_test_num_samples[2]), seq_length, seed,) + return train_ds, valid_ds, test_ds + + if isinstance(data_prefix, DictConfig): + assert ( + data_prefix.get('train') is not None + and data_prefix.get('test') is not None + and data_prefix.get('validation') is not None + ), f"Data prefix dictionary should have train, test and validation keys. data_prefix currently has only {data_prefix.keys()}" + if cfg.data.splits_string is not None: + logging.warning(cfg.data.splits_string + " ignored since data prefix is of type dictionary.") + train_ds = build_dataset( + cfg, + trainer, + data_prefix["train"], + data_impl, + int(train_valid_test_num_samples[0]), + seq_length, + seed, + skip_warmup, + tokenizer, + "train", + ) + validation_ds = build_dataset( + cfg, + trainer, + data_prefix["validation"], + data_impl, + int(train_valid_test_num_samples[1]), + seq_length, + seed, + skip_warmup, + tokenizer, + "valid", + ) + test_ds = build_dataset( + cfg, + trainer, + data_prefix["test"], + data_impl, + int(train_valid_test_num_samples[2]), + seq_length, + seed, + skip_warmup, + tokenizer, + "test", + ) + return train_ds, validation_ds, test_ds + + else: + # Single dataset. + if len(data_prefix) == 1: + return _build_train_valid_test_datasets( + cfg, + trainer, + data_prefix[0], + data_impl, + splits_string, + train_valid_test_num_samples, + seq_length, + seed, + skip_warmup, + tokenizer, + ) + + # Blending dataset. + # Parse the values. + output = get_datasets_weights_and_num_samples(data_prefix, train_valid_test_num_samples) + prefixes, weights, datasets_train_valid_test_num_samples = output + + # Build individual datasets. + train_datasets = [] + valid_datasets = [] + test_datasets = [] + for i in range(len(prefixes)): + train_ds, valid_ds, test_ds = _build_train_valid_test_datasets( + cfg, + trainer, + prefixes[i], + data_impl, + splits_string, + datasets_train_valid_test_num_samples[i], + seq_length, + seed, + skip_warmup, + tokenizer, + ) + if train_ds: + train_datasets.append(train_ds) + if valid_ds: + valid_datasets.append(valid_ds) + if test_ds: + test_datasets.append(test_ds) + + train_n, valid_n, test_n = map(sum, zip(*datasets_train_valid_test_num_samples)) + + # Blend. + blending_train_dataset = None + if train_datasets: + blending_train_dataset = BlendableDataset(train_datasets, weights, train_n) + blending_valid_dataset = None + if valid_datasets: + blending_valid_dataset = BlendableDataset(valid_datasets, weights, valid_n) + blending_test_dataset = None + if test_datasets: + blending_test_dataset = BlendableDataset(test_datasets, weights, test_n) + + return (blending_train_dataset, blending_valid_dataset, blending_test_dataset) + + +def _build_train_valid_test_datasets( + cfg, + trainer, + data_prefix, + data_impl, + splits_string, + train_valid_test_num_samples, + seq_length, + seed, + skip_warmup, + tokenizer, +): + """Build train, valid, and test datasets.""" + + # Indexed dataset. + delay_data_mmap = cfg.data.get('delay_data_mmap', False) + indexed_dataset = get_indexed_dataset_(data_prefix, data_impl, skip_warmup, delay_data_mmap) + + total_num_of_documents = indexed_dataset.sizes.shape[0] + splits = get_train_valid_test_split_(splits_string, total_num_of_documents) + + # Print stats about the splits. + logging.info(' > dataset split:') + + def print_split_stats(name, index): + logging.info(' {}:'.format(name)) + logging.info( + ' document indices in [{}, {}) total of {} ' + 'documents'.format(splits[index], splits[index + 1], splits[index + 1] - splits[index]) + ) + + print_split_stats('train', 0) + print_split_stats('validation', 1) + print_split_stats('test', 2) + + def build_dataset(index, name): + dataset = None + if splits[index + 1] > splits[index]: + documents = np.arange(start=splits[index], stop=splits[index + 1], step=1, dtype=np.int32) + drop_last = True + if name == "valid": + drop_last = cfg.data.get("validation_drop_last", True) + dataset = GPTDataset( + cfg, + trainer, + tokenizer, + name, + data_prefix, + documents, + indexed_dataset, + train_valid_test_num_samples[index], + seq_length, + seed, + drop_last=drop_last, + ) + return dataset + + train_dataset = build_dataset(0, 'train') + valid_dataset = build_dataset(1, 'valid') + test_dataset = build_dataset(2, 'test') + + return (train_dataset, valid_dataset, test_dataset) + + +def get_indexed_dataset_(data_prefix, data_impl, skip_warmup, delay_data_mmap=False): + """Build indexed dataset.""" + logging.info(' > building dataset index ...') + + start_time = time.time() + indexed_dataset = make_indexed_dataset(data_prefix, data_impl, skip_warmup, delay_data_mmap=delay_data_mmap) + logging.info(' > finished creating indexed dataset in {:4f} ' 'seconds'.format(time.time() - start_time)) + logging.info(' number of documents: {}'.format(indexed_dataset.sizes.shape[0])) + + return indexed_dataset + + +class GPTDataset(Dataset): + def __init__( + self, + cfg, + trainer, + tokenizer, + name, + data_prefix, + documents, + indexed_dataset, + num_samples, + seq_length, + seed, + drop_last=True, + ): + if not HAVE_MEGATRON_CORE: + raise ImportError( + "megatron-core was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + + super().__init__() + self.name = name + self.indexed_dataset = indexed_dataset + self.drop_last = drop_last + self.seq_length = seq_length + self.get_attention_mask_from_fusion = cfg.get('get_attention_mask_from_fusion', True) + + # Checks + assert np.min(documents) >= 0 + assert np.max(documents) < indexed_dataset.sizes.shape[0] + + self.reset_position_ids = cfg.data.get('reset_position_ids', False) + self.reset_attention_mask = cfg.data.get('reset_attention_mask', False) + self.eod_mask_loss = cfg.data.get('eod_mask_loss', False) + self.create_inputs = any([self.reset_position_ids, self.reset_attention_mask, self.eod_mask_loss]) + self.cached_inputs = False + self.eos_id = tokenizer.eos_id + self.no_seqlen_plus_one_input_tokens = cfg.data.get('no_seqlen_plus_one_input_tokens', False) + self.add_extra_token = 1 + if self.no_seqlen_plus_one_input_tokens: + self.add_extra_token = 0 + self.shuffle_documents = cfg.data.get('shuffle_documents', True) + self.exchange_indices_distributed = cfg.data.get('exchange_indices_distributed', False) + + # save index mappings to a configurable dir + self.index_mapping_dir = cfg.data.get('index_mapping_dir', None) + + # create index_mapping_dir on rank 0 + if torch.distributed.is_available() and torch.distributed.is_initialized(): + if torch.distributed.get_rank() == 0: + if self.index_mapping_dir is not None and not os.path.isdir(self.index_mapping_dir): + os.makedirs(self.index_mapping_dir) + torch.distributed.barrier() + + # Build index mappings. + self.doc_idx, self.sample_idx, self.shuffle_idx = _build_index_mappings( + self.name, + data_prefix, + documents, + self.indexed_dataset.sizes, + num_samples, + seq_length, + seed, + index_mapping_dir=self.index_mapping_dir, + drop_last=drop_last, + add_extra_token=self.add_extra_token, + shuffle_documents=self.shuffle_documents, + exchange_indices_distributed=self.exchange_indices_distributed, + ) + deallocate_indexed_dataset_memory(self.indexed_dataset) + + def create_data_mmap(self): + self.indexed_dataset.create_data_mmap() + + def __len__(self): + # -1 is due to data structure used to retieve the index: + # sample i --> [sample_idx[i], sample_idx[i+1]) + return self.sample_idx.shape[0] - 1 + + def _get_text(self, idx: int) -> np.ndarray: + + # Get the shuffled index. + idx = self.shuffle_idx[idx] + # Start and end documents and offsets. + doc_index_f = self.sample_idx[idx][0] + doc_index_l = self.sample_idx[idx + 1][0] + offset_f = self.sample_idx[idx][1] + offset_l = self.sample_idx[idx + 1][1] + # If we are within the same document, just extract the chunk. + if doc_index_f == doc_index_l: + sample = self.indexed_dataset.get( + self.doc_idx[doc_index_f], offset=offset_f, length=offset_l - offset_f + self.add_extra_token + ) + else: + # Otherwise, get the rest of the initial document. + sample_list = [self.indexed_dataset.get(self.doc_idx[doc_index_f], offset=offset_f)] + # Loop over all in between documents and add the entire document. + for i in range(doc_index_f + 1, doc_index_l): + sample_list.append(self.indexed_dataset.get(self.doc_idx[i])) + # And finally add the relevant portion of last document. + sample_list.append( + self.indexed_dataset.get(self.doc_idx[doc_index_l], length=offset_l + self.add_extra_token) + ) + sample = np.concatenate(sample_list) + if len(sample) != (self.seq_length + self.add_extra_token): + logging.info( + F' > WARNING: Got sample of length: {len(sample)} for sequence length={self.seq_length+self.add_extra_token}, padding the sample to match sequence length' + ) + sample = np.array(sample, dtype=np.int64) + sample = np.pad( + sample, (0, self.seq_length + self.add_extra_token - len(sample)), mode='constant', constant_values=-1 + ) + return sample.astype(np.int64) + + def __getitem__(self, idx): + text = torch.from_numpy(self._get_text(idx)) + if self.add_extra_token: + tokens = text[:-1].contiguous() + labels = text[1:].contiguous() + else: + tokens = text + labels = torch.roll(text, shifts=-1, dims=0) + labels[-1] = -1 + if self.create_inputs or not self.cached_inputs: + attention_mask, loss_mask, position_ids = _create_ltor_masks_and_position_ids( + tokens, self.eos_id, self.reset_position_ids, self.reset_attention_mask, self.eod_mask_loss, + ) + if not self.create_inputs: + self.cached_attention_mask = attention_mask + self.cached_loss_mask = loss_mask + self.cached_position_ids = position_ids + self.cached_inputs = True + else: + attention_mask = self.cached_attention_mask + loss_mask = self.cached_loss_mask + position_ids = self.cached_position_ids + loss_mask[labels == -1] = 0.0 + tokens[tokens == -1] = 0 + labels[labels == -1] = 0 + + # Negative index comes when we pad the last batch in MegatronPretrainingBatchSampler + # We make the loss_mask zero to mask out loss from these samples + if idx < 0: + logging.debug('Got negative index. Masking loss from this sample') + loss_mask = torch.zeros_like(loss_mask) + + if self.get_attention_mask_from_fusion: + return { + 'tokens': tokens, + 'labels': labels, + 'loss_mask': loss_mask, + 'position_ids': position_ids, + } + else: + return { + 'tokens': tokens, + 'labels': labels, + 'attention_mask': attention_mask, + 'loss_mask': loss_mask, + 'position_ids': position_ids, + } + + +class MockGPTDataset(Dataset): + def __init__( + self, cfg, tokenizer, name, num_samples, seq_length, seed, + ): + if not HAVE_MEGATRON_CORE: + raise ImportError( + "Megatron core was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt." + ) + + super().__init__() + self.name = name + self.seq_length = seq_length + self.vocab_size = tokenizer.vocab_size + self.length = num_samples + self.seed = seed + self.get_attention_mask_from_fusion = cfg.get('get_attention_mask_from_fusion', True) + + self.attention_mask = torch.tril(torch.ones((self.seq_length, self.seq_length))).unsqueeze(0) + self.attention_mask = self.attention_mask < 0.5 + self.loss_mask = torch.ones(self.seq_length, dtype=torch.float) + self.position_ids = torch.arange(self.seq_length, dtype=torch.int64) + + def __len__(self): + return self.length + + def _get_text(self, idx: int) -> np.ndarray: + np_gen = np.random.default_rng(seed=(self.seed + idx)) + return np_gen.integers(self.vocab_size, size=[self.seq_length], dtype=np.int64) + + def __getitem__(self, idx): + # Generate data of the expected size and datatype (based on GPTDataset). + np_gen = np.random.default_rng(seed=(self.seed + idx)) + tokens = torch.from_numpy(np_gen.integers(self.vocab_size, size=[self.seq_length], dtype=np.int64)) + labels = torch.from_numpy(np_gen.integers(self.vocab_size, size=[self.seq_length], dtype=np.int64)) + + if self.get_attention_mask_from_fusion: + return { + 'tokens': tokens, + 'labels': labels, + 'loss_mask': self.loss_mask, + 'position_ids': self.position_ids, + } + else: + return { + 'tokens': tokens, + 'labels': labels, + 'attention_mask': self.attention_mask, + 'loss_mask': self.loss_mask, + 'position_ids': self.position_ids, + } + + +@torch.no_grad() +def _create_ltor_masks_and_position_ids( + tokens: torch.Tensor, eod_token: int, reset_position_ids: bool, reset_attention_mask: bool, eod_mask_loss: bool, +): + """Create `attention_mask`, `loss_mask`, and `position_ids`. + + This function is modified :func:`get_ltor_masks_and_position_ids` in nemo/collections/nlp/modules/common/megatron/utils.py: + `get_ltor_masks_and_position_ids` assumes a microbatch of ``tokens``, i.e. 2D tensor while + this function assumes ``tokens`` to be 1D tensor. + + Args: + tokens: A 1D tensor that holds the indices of tokens. + eod_token: + reset_position_ids: + reset_attention_mask: + eod_mask_loss + + """ + assert tokens.ndim == 1 + seq_length = tokens.numel() + # `attention_mask` has the shape of [1, seq_length, seq_length] + attention_mask = torch.tril(torch.ones((seq_length, seq_length))).unsqueeze(0) + loss_mask = torch.ones(seq_length, dtype=torch.float) + if eod_mask_loss: + loss_mask[tokens == eod_token] = 0.0 + + position_ids = torch.arange(seq_length, dtype=torch.int64) + if reset_position_ids: + position_ids = position_ids.clone() + + if reset_position_ids or reset_attention_mask: + # Find indices where EOD token is. + eod_index = position_ids[tokens[b] == eod_token] + # Detach indices from positions if going to modify positions. + if reset_position_ids: + eod_index = eod_index.clone() + prev_index = 0 + for j in range(eod_index.numel()): + i = eod_index[j] + if reset_attention_mask: + attention_mask[0, (i + 1) :, : (i + 1)] = 0 + if reset_position_ids: + position_ids[(i + 1) :] -= i + 1 - prev_index + prev_index = i + 1 + # Convert attention mask to binary. + attention_mask = attention_mask < 0.5 + return attention_mask, loss_mask, position_ids + + +def _build_index_mappings( + name, + data_prefix, + documents, + sizes, + num_samples, + seq_length, + seed, + index_mapping_dir: str = None, + drop_last: bool = True, + add_extra_token: int = 1, + shuffle_documents: bool = True, + exchange_indices_distributed: bool = False, +): + """Build doc-idx, sample-idx, and shuffle-idx. + doc-idx: is an array (ordered) of documents to be used in training. + sample-idx: is the start document index and document offset for each + training sample. + shuffle-idx: maps the sample index into a random index into sample-idx. + """ + # Number of tokens in each epoch and number of required epochs. + tokens_per_epoch = _num_tokens(documents, sizes) + num_epochs = _num_epochs(tokens_per_epoch, seq_length, num_samples, add_extra_token) + # rng state + np_rng = np.random.RandomState(seed=seed) + + # Filename of the index mappings. + if index_mapping_dir is not None: + _filename = os.path.join(index_mapping_dir, os.path.basename(data_prefix)) + else: + _filename = data_prefix + _filename += '_{}_indexmap'.format(name) + _filename += '_{}ns'.format(num_samples) + _filename += '_{}sl'.format(seq_length) + _filename += '_{}s'.format(seed) + doc_idx_filename = _filename + '_doc_idx.npy' + sample_idx_filename = _filename + '_sample_idx.npy' + shuffle_idx_filename = _filename + '_shuffle_idx.npy' + + # Build the indexed mapping if not exist. + if torch.distributed.get_rank() == 0: + using_cached_indices = True + if ( + (not os.path.isfile(doc_idx_filename)) + or (not os.path.isfile(sample_idx_filename)) + or (not os.path.isfile(shuffle_idx_filename)) + ): + using_cached_indices = False + logging.info(' > WARNING: could not find index map files, building ' 'the indices on rank 0 ...') + + # For the last epoch, decide whether include the entire epoch + # in the global shuffle or not. + + # If we need only one epoch, then separating last epoch does + # not mean anything. + if num_epochs == 1: + separate_last_epoch = False + print(' > only one epoch required, setting ' 'separate_last_epoch to False', flush=True) + + else: + # Get the number of samples for the last epoch + num_samples_from_epochs_minus_one = ( + (num_epochs - 1) * tokens_per_epoch - add_extra_token + ) // seq_length + last_epoch_num_samples = num_samples - num_samples_from_epochs_minus_one + assert last_epoch_num_samples >= 0, 'last epoch number of samples should be non-negative.' + num_samples_per_epoch = (tokens_per_epoch - add_extra_token) // seq_length + assert last_epoch_num_samples <= ( + num_samples_per_epoch + 1 + ), 'last epoch number of samples exceeded max value.' + # If we have less than 80% of the samples for the last epoch, + # seperate out the epoch and treat it differently. + # Note: the 80% number is just based on common sense and can + # be adjusted if needed. + separate_last_epoch = last_epoch_num_samples < int(0.80 * num_samples_per_epoch) + if separate_last_epoch: + string = ( + ' > last epoch number of samples ({}) is smaller ' + 'than 80% of number of samples per epoch ({}), ' + 'setting separate_last_epoch to True' + ) + else: + string = ( + ' > last epoch number of samples ({}) is larger ' + 'than 80% of number of samples per epoch ({}), ' + 'setting separate_last_epoch to False' + ) + print(string.format(last_epoch_num_samples, num_samples_per_epoch), flush=True) + + # doc-idx. + start_time = time.time() + doc_idx = _build_doc_idx(documents, num_epochs, np_rng, separate_last_epoch, shuffle_documents) + np.save(doc_idx_filename, doc_idx, allow_pickle=True) + logging.info( + ' > elasped time to build and save doc-idx mapping ' + '(seconds): {:4f}'.format(time.time() - start_time) + ) + # sample-idx. + start_time = time.time() + # Use C++ implementation for speed. + # First compile and then import. + assert doc_idx.dtype == np.int32 + assert sizes.dtype == np.int32 + try: + from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import compile_helper + + compile_helper() + from nemo.collections.nlp.data.language_modeling.megatron import helpers + except ImportError: + raise ImportError( + f'Could not compile megatron dataset C++ helper functions and therefore cannot import helpers python file.' + ) + + sample_idx = helpers.build_sample_idx( + sizes, doc_idx, seq_length, num_epochs, tokens_per_epoch, drop_last, add_extra_token + ) + # sample_idx = _build_sample_idx(sizes, doc_idx, seq_length, + # num_epochs, tokens_per_epoch, drop_last, add_extra_token) + np.save(sample_idx_filename, sample_idx, allow_pickle=True) + logging.info( + ' > elasped time to build and save sample-idx mapping ' + '(seconds): {:4f}'.format(time.time() - start_time) + ) + # shuffle-idx. + start_time = time.time() + # -1 is due to data structure used to retieve the index: + # sample i --> [sample_idx[i], sample_idx[i+1]) + if separate_last_epoch: + num_samples_ = num_samples_from_epochs_minus_one + else: + num_samples_ = sample_idx.shape[0] - 1 + shuffle_idx = _build_shuffle_idx(num_samples_, sample_idx.shape[0] - 1, np_rng) + np.save(shuffle_idx_filename, shuffle_idx, allow_pickle=True) + logging.info( + ' > elasped time to build and save shuffle-idx mapping' + ' (seconds): {:4f}'.format(time.time() - start_time) + ) + + torch.distributed.barrier() + counts = torch.cuda.LongTensor([1]) + torch.distributed.all_reduce(counts, group=parallel_state.get_data_parallel_group(with_context_parallel=True)) + torch.distributed.all_reduce(counts, group=parallel_state.get_pipeline_model_parallel_group()) + assert counts[0].item() == ( + torch.distributed.get_world_size() + // torch.distributed.get_world_size(group=parallel_state.get_tensor_model_parallel_group()) + ) + + if not exchange_indices_distributed or (torch.distributed.get_rank() == 0 and using_cached_indices): + # Load mappings. + start_time = time.time() + logging.info(' > loading doc-idx mapping from {}'.format(doc_idx_filename)) + doc_idx = np.load(doc_idx_filename, allow_pickle=True, mmap_mode='r') + logging.info(' > loading sample-idx mapping from {}'.format(sample_idx_filename)) + sample_idx = np.load(sample_idx_filename, allow_pickle=True, mmap_mode='r') + logging.info(' > loading shuffle-idx mapping from {}'.format(shuffle_idx_filename)) + shuffle_idx = np.load(shuffle_idx_filename, allow_pickle=True, mmap_mode='r') + logging.info(' loaded indexed file in {:3.3f} seconds'.format(time.time() - start_time)) + logging.info(' total number of samples: {}'.format(sample_idx.shape[0])) + logging.info(' total number of epochs: {}'.format(num_epochs)) + + if exchange_indices_distributed: + if torch.distributed.get_rank() == 0: + indices = [(doc_idx, sample_idx, shuffle_idx)] + else: + indices = [None] + torch.distributed.broadcast_object_list(indices) + doc_idx, sample_idx, shuffle_idx = indices[0] + + return doc_idx, sample_idx, shuffle_idx + + +def _num_tokens(documents, sizes): + """Total number of tokens in the dataset.""" + return np.sum(sizes[documents]) + + +def _num_epochs(tokens_per_epoch, seq_length, num_samples, add_extra_token=1): + """Based on number of samples and sequence lenght, calculate how many + epochs will be needed.""" + num_epochs = 0 + total_tokens = 0 + while True: + num_epochs += 1 + total_tokens += tokens_per_epoch + # -1 is because we need to retrieve seq_length + 1 token each time + # but the last token will overlap with the first token of the next + # sample except for the last sample. + if ((total_tokens - add_extra_token) // seq_length) >= num_samples: + return num_epochs + + +def _build_doc_idx(documents, num_epochs, np_rng, separate_last_epoch, shuffle=True): + """Build an array with length = number-of-epochs * number-of-dcuments. + Each index is mapped to a corresponding document.""" + if not separate_last_epoch or num_epochs == 1: + doc_idx = np.mgrid[0:num_epochs, 0 : len(documents)][1] + doc_idx[:] = documents + doc_idx = doc_idx.reshape(-1) + doc_idx = doc_idx.astype(np.int32) + if shuffle: + np_rng.shuffle(doc_idx) + else: + logging.info('Document shuffling disabled') + return doc_idx + + doc_idx_first = _build_doc_idx(documents, num_epochs - 1, np_rng, False, shuffle) + doc_idx_last = _build_doc_idx(documents, 1, np_rng, False, shuffle) + return np.concatenate((doc_idx_first, doc_idx_last)) + + +def _build_sample_idx(sizes, doc_idx, seq_length, num_epochs, tokens_per_epoch, drop_last=True, add_extra_token=1): + """Sample index mapping is a 2D array with sizes + [number-of-samples + 1, 2] where [..., 0] contains + the index into `doc_idx` and [..., 1] is the + starting offset in that document.""" + + # Total number of samples. For -1 see comments in `_num_epochs`. + if not drop_last: + num_samples = -(-(num_epochs * tokens_per_epoch - add_extra_token) // seq_length) + else: + num_samples = (num_epochs * tokens_per_epoch - add_extra_token) // seq_length + sample_idx = np.zeros([num_samples + 1, 2], dtype=np.int32) + + # Index into sample_idx. + sample_index = 0 + # Index into doc_idx. + doc_idx_index = 0 + # Begining offset for each document. + doc_offset = 0 + # Start with first document and no offset. + sample_idx[sample_index][0] = doc_idx_index + sample_idx[sample_index][1] = doc_offset + sample_index += 1 + while sample_index <= num_samples: + # Start with a fresh sequence. + remaining_seq_length = seq_length + add_extra_token + while remaining_seq_length != 0: + # Get the document length. + doc_id = doc_idx[doc_idx_index] + doc_length = sizes[doc_id] - doc_offset + # And add it to the current sequence. + remaining_seq_length -= doc_length + # If we have more than a full sequence, adjust offset and set + # remaining length to zero so we return from the while loop. + # Note that -1 here is for the same reason we have -1 in + # `_num_epochs` calculations. + if remaining_seq_length <= 0: + doc_offset += remaining_seq_length + doc_length - add_extra_token + remaining_seq_length = 0 + else: + # Otherwise, start from the begining of the next document. + if doc_idx_index == (len(doc_idx) - 1): + assert ( + sample_index == num_samples + ), F"sample_index={sample_index} and num_samples={num_samples} should be the same" + doc_offset = sizes[doc_idx[doc_idx_index]] - add_extra_token + break + doc_idx_index += 1 + doc_offset = 0 + # Record the sequence. + sample_idx[sample_index][0] = doc_idx_index + sample_idx[sample_index][1] = doc_offset + sample_index += 1 + + return sample_idx + + +def _build_shuffle_idx(num_samples, total_size, np_rng): + """Build the range [0, size) and shuffle.""" + print( + ' > building shuffle index with split [0, {}) and [{}, {}) ' + '...'.format(num_samples, num_samples, total_size), + flush=True, + ) + + dtype_ = np.uint32 + if total_size >= (np.iinfo(np.uint32).max - 1): + dtype_ = np.int64 + + shuffle_idx_first = np.arange(start=0, stop=num_samples, step=1, dtype=dtype_) + np_rng.shuffle(shuffle_idx_first) + if num_samples == total_size: + return shuffle_idx_first + + shuffle_idx_last = np.arange(start=num_samples, stop=total_size, step=1, dtype=dtype_) + np_rng.shuffle(shuffle_idx_last) + + return np.concatenate((shuffle_idx_first, shuffle_idx_last)) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_fim_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_fim_dataset.py new file mode 100644 index 0000000..474761c --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_fim_dataset.py @@ -0,0 +1,307 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Tuple + +import numpy as np + +from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults + +try: + from megatron.core.datasets.gpt_dataset import GPTDataset, GPTDatasetConfig + from megatron.core.datasets.indexed_dataset import IndexedDataset + from megatron.core.datasets.utils import Split + + HAVE_MEGATRON_CORE = True + +except (ImportError, ModuleNotFoundError) as e: + + GPTDataset = GPTDatasetConfig = IndexedDataset = Split = ApexGuardDefaults + + HAVE_MEGATRON_CORE = False + IMPORT_ERROR = e + + +class GPTFIMDatasetConfig(GPTDatasetConfig): + """Configuration object for Megatron Core GPT FIM datasets + + Attributes: + fim: fill in the middle parameters config + """ + + def __init__(self, fim, **kwargs): + if not HAVE_MEGATRON_CORE: + raise ImportError(IMPORT_ERROR) + + super().__init__(**kwargs) + self.fim = fim + + +class GPTFIMDataset(GPTDataset): + """The base GPT dataset + + Args: + indexed_dataset (IndexedDataset): The IndexedDataset around which to build the + MegatronDataset + + indexed_indices (np.ndarray): The set of the documents indices to expose + + num_samples (int): The number of samples to draw from the indexed dataset + + index_split (Split): The indexed_indices Split + + config (GPTFIMDatasetConfig): The GPT-specific container for all config sourced parameters + """ + + def __init__( + self, + indexed_dataset: IndexedDataset, + dataset_path: str, + indexed_indices: np.ndarray, + num_samples: int, + index_split: Split, + config: GPTFIMDatasetConfig, + ) -> None: + if not HAVE_MEGATRON_CORE: + raise ImportError(IMPORT_ERROR) + + super().__init__(indexed_dataset, dataset_path, indexed_indices, num_samples, index_split, config) + + self.indexed_dataset = indexed_dataset + + def _query_document_sample_shuffle_indices(self, idx: int) -> Tuple[np.ndarray, np.ndarray]: + """Get the text (token ids) and document ids for a given index + + Args: + idx (int): The index into the dataset + + Returns: + Tuple[np.ndarray, np.ndarray]: The text ids and document ids + """ + # Do the shuffle mapping + idx = self.shuffle_index[idx] + + # Get the beginning and end documents and offsets + doc_index_beg, doc_index_beg_offset = self.sample_index[idx] + doc_index_end, doc_index_end_offset = self.sample_index[idx + 1] + + document_ids = [] + sample_parts = [] + + # Sample spans a single document + if doc_index_beg == doc_index_end: + # Add the document id + document_ids.append(self.document_index[doc_index_beg]) + + # Add the entire sample + sample_parts.append( + self.indexed_dataset.get( + self.document_index[doc_index_beg], + offset=doc_index_beg_offset, + length=doc_index_end_offset - doc_index_beg_offset + 1, + ) + ) + + # Sample spans multiple documents + else: + for i in range(doc_index_beg, doc_index_end + 1): + # Add the document id + document_ids.append(self.document_index[i]) + + # Add the sample part + offset = 0 if i > doc_index_beg else doc_index_beg_offset + length = None if i < doc_index_end else doc_index_end_offset + 1 + sample_parts.append(self.indexed_dataset.get(self.document_index[i], offset=offset, length=length)) + + sample = np.concatenate(sample_parts) + + # get FIM params + self.fim_rate = self.config.fim.get('rate', 0.5) + self.fim_spm_rate = self.config.fim.get('spm_rate', 0.5) + self.fragment_fim_rate = self.config.fim.get('fragment_rate', 0.5) + split_sample = self.config.fim.get('split_sample', None) + self.fim_split_sample = self.config.tokenizer.tokens_to_ids(split_sample) if split_sample else None + self.no_fim_prefix = self.config.fim.get('no_prefix', None) + + # get extra tokens ids + fim_tokens = self.config.fim.extra_tokens + fim_tokens = [fim_tokens.prefix, fim_tokens.middle, fim_tokens.suffix, fim_tokens.pad, fim_tokens.eod] + fim_tokens_ids = self.config.tokenizer.tokens_to_ids(fim_tokens) + ( + self.prefix_tok_id, + self.middle_tok_id, + self.suffix_tok_id, + self.pad_tok_id, + self.eod_tok_id, + ) = fim_tokens_ids + + sample_len = sample.shape[0] + segment_breaks = np.argwhere(sample == self.eod_tok_id) + np_rng = np.random.RandomState(seed=self.config.random_seed) + + if segment_breaks.shape != (0, 1): # then there is an EOD token in this example + curr_start_position = 0 + new_samples = [] + for loc in np.nditer(segment_breaks): + # Only permute non-empty segments. + if loc - curr_start_position > 0: + # permute {prefix, suffix, middle} or {suffix, prefix, middle} + permuted = self._fim_split_and_permute_sequence(sample[curr_start_position:loc], np_rng) + new_samples += [permuted, [self.eod_tok_id]] + + curr_start_position = loc + 1 # jump over the EOD token + # Permute the segment after the last EOD + permuted = self._fim_split_and_permute_sequence(sample[curr_start_position:], np_rng) + new_samples.append(permuted) + + sample = np.concatenate(new_samples) + else: + sample = self._fim_split_and_permute_sequence(sample, np_rng) + + diff = sample.shape[0] - sample_len + if diff > 0: # too long + sample = sample[:sample_len] + elif diff < 0: # too short + sample = np.concatenate([sample, np.full((-1 * diff), self.pad_tok_id)]) + + assert sample.shape[0] == sample_len + + return ( + np.array(sample, dtype=np.int64), + np.array(document_ids, dtype=np.int64), + ) + + def _fim_permute_sequence(self, sequence, np_rng, rate): + return self._permute( + sequence, + np_rng, + rate, + self.fim_spm_rate, + self.config.tokenizer, + truncate_or_pad=False, + suffix_tok_id=self.suffix_tok_id, + prefix_tok_id=self.prefix_tok_id, + middle_tok_id=self.middle_tok_id, + pad_tok_id=self.pad_tok_id, + no_fim_prefix=self.no_fim_prefix, + ) + + def _fim_split_and_permute_sequence(self, sequence, np_rng): + """ + If self.fim_split_sample is not None, split the sequence. + Then apply FIM on the fragments, or the whole sequence if self.fim_split_sample is None. + """ + if self.fim_split_sample is None: + return self._fim_permute_sequence(sequence, np_rng, self.fim_rate) + # fim_split_sample is set: split the sample on this token and permute each fragment separately. + # Typically, if each sample is a repository, then we split again on the file level. + # Each fragment is a file, and we permute the files. + fragment_breaks = np.argwhere(sequence == self.fim_split_sample) + if fragment_breaks.shape == (0, 1): + # no split token in this sample + return self._fim_permute_sequence(sequence, np_rng, self.fim_rate) + if not np_rng.binomial(1, self.fim_rate): + # don't do FIM preproc + return sequence + # Do FIM on each fragment + curr_start_position = 0 + new_samples = [] + for loc in np.nditer(fragment_breaks): + if loc - curr_start_position > 0: + permuted = self._fim_permute_sequence( + sequence[curr_start_position:loc], np_rng, self.fragment_fim_rate + ) + new_samples += [permuted, [self.fim_split_sample]] + curr_start_position = loc + 1 # Jump over the split token + # Permute the segment after the last split token + permuted = self._fim_permute_sequence(sequence[curr_start_position:], np_rng, self.fragment_fim_rate) + new_samples.append(permuted) + + return np.concatenate(new_samples) + + def _permute( + self, + sample, + np_rng, + fim_rate, + fim_spm_rate, + tokenizer, + truncate_or_pad=True, + suffix_tok_id=None, + prefix_tok_id=None, + middle_tok_id=None, + pad_tok_id=None, + no_fim_prefix=None, + ): + """ + Take in a sample (np array w/ size (0,chunklength)) and perform a FIM transformation on it. + Maintain the same sample length (if transform creates a few extra tokens, drop them). + """ + if np_rng.binomial(1, fim_rate): # sample bernoulli dist + + contents = tokenizer.ids_to_text(sample) + + # Do not apply FIM if the sample starts with no_fim_prefix + if no_fim_prefix is not None and contents.startswith(no_fim_prefix): + return sample + + try: + # A boundary can be =0 (prefix will be empty) + # a boundary can be =len(contents) (suffix will be empty) + # The two boundaries can be equal (middle will be empty) + boundaries = list(np_rng.randint(low=0, high=len(contents) + 1, size=2)) + boundaries.sort() + except ValueError as e: + print(len(contents), contents) + print(e) + raise e + + prefix = contents[: boundaries[0]] + middle = contents[boundaries[0] : boundaries[1]] + suffix = contents[boundaries[1] :] + + prefix = np.array([*tokenizer.text_to_ids(prefix)], dtype=np.int64) + middle = np.array([*tokenizer.text_to_ids(middle)], dtype=np.int64) + suffix = np.array([*tokenizer.text_to_ids(suffix)], dtype=np.int64) + + # here we truncate each given segment to fit the same length as it was before + # A consequence is that we never reach the end of a file? + # we should rather truncate at the context-level + if truncate_or_pad: + # need to make same length as the input. Take the 3 sentinel tokens into account + new_length = suffix.shape[0] + prefix.shape[0] + middle.shape[0] + 3 + diff = new_length - sample.shape[0] + if diff > 0: # too long + if ( + suffix.shape[0] <= diff + ): # if there's no space to truncate the suffix: stop and report it. atm i should have stopped this from happening + return sample, np_rng + suffix = suffix[: suffix.shape[0] - diff] + elif diff < 0: # too short + suffix = np.concatenate([suffix, np.full((-1 * diff), pad_tok_id)]) + + if np_rng.binomial(1, fim_spm_rate): + # SPM (variant 2 from FIM paper) + new_sample = np.concatenate([[prefix_tok_id, suffix_tok_id], suffix, [middle_tok_id], prefix, middle]) + else: + # PSM + new_sample = np.concatenate( + [[prefix_tok_id], prefix, [suffix_tok_id], suffix, [middle_tok_id], middle] + ) + + else: + # don't do FIM preproc + new_sample = sample + + return new_sample diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_prompt_learning_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_prompt_learning_dataset.py new file mode 100755 index 0000000..4b1b4f6 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_prompt_learning_dataset.py @@ -0,0 +1,425 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import pickle + +import torch +from tqdm.auto import tqdm + +from nemo.collections.nlp.modules.common import VirtualPromptSource +from nemo.collections.nlp.modules.common.megatron.utils import build_position_ids +from nemo.core import Dataset +from nemo.utils import AppState, logging + +__all__ = ['GPTPromptLearningDataset'] + + +class GPTPromptLearningDataset(Dataset): + """ + The dataset class for prompt-tuning or p-tuning pretrained GPT models. + + Args: + data (list[strings], list[dicts]): (1) paths to .jsonl or .json files, (2) dict objects corresponding to each input example + tokenizer (tokenizer): Tokenizer from frozen language model + virtual_prompt_source (Enum): Either VirtualPromptSource.NO_PROMPTS or VirtualPromptSource.PROMPT_ENCODER + task_templates (dict): Dictionary containing all task template information needed to format prompts. Created in the GPTPromptLearningModel class. + pseudo_tokens (list[strings]): A list of virtual prompt token placeholders e.g [, , ...] up to max num virtual tokens + pad_token_id (int): ID of pad token from tokenizer + max_seq_length (int): maximum sequence length for each dataset examples. Examples will either be truncated to fit this length or dropped if they cannot be truncated. + min_seq_length (int): min length of each data example in the dataset. Data examples will be dropped if they do not meet the min length requirements. + add_bos (bool): Whether to add a beginning of sentence token to each data example + add_eos (bool): Whether to add an end of sentence token to each data example + for_train (bool): Whether you're creating a dataset for training or inference + tokens_to_generate (int): (inference only) Number of tokens to generate during inference + """ + + def __init__( + self, + data, + tokenizer, + virtual_prompt_source: VirtualPromptSource, + task_templates: dict, + pseudo_tokens, + pad_token_id: int, + max_seq_length: int, + min_seq_length: int = 1, + add_bos: bool = False, + add_eos: bool = True, + for_train: bool = True, + tokens_to_generate=None, + cache_data_path: str = None, # the cache file + load_cache: bool = True, # whether to load from the cache if it is available + ): + self.tokenizer = tokenizer + self.virtual_prompt_source = virtual_prompt_source + self.task_templates = task_templates + self.pseudo_tokens = pseudo_tokens + self.pseudo_token_ids = set(self.tokenizer.tokens_to_ids(self.pseudo_tokens)) + self.pad_token_id = pad_token_id + self.max_seq_length = max_seq_length + self.min_seq_length = min_seq_length + self.add_bos = add_bos + self.add_eos = add_eos + self.for_train = for_train + self.examples = [] + + if not self.for_train: + self.tokens_to_generate = tokens_to_generate + + assert self.min_seq_length <= max_seq_length, "Min sequence length should be less than or equal to max" + assert self.max_seq_length > 0, "Max sequence length should be greater than 0" + + logging.info("Loading and tokenizing dataset ... ") + + if load_cache and cache_data_path is not None and os.path.exists(cache_data_path): + # load it from the cache + logging.info(f'load the data from the cache file {cache_data_path}') + with open(cache_data_path, 'rb') as f: + self.examples = pickle.load(f) + else: + # Data is just a list of dicts already loaded from a json file or passed in directly as a dict + if isinstance(data[0], dict): + self.load_data(data) + + # Datasets are a list of file path strings to .json or .jsonl files + elif isinstance(data[0], str): + for path in data: + dataset = open(path, 'r', encoding='utf-8') + self.load_data(dataset) + else: + raise ValueError("Datasets must be a list of filepath strings or a list of data example dicts") + if cache_data_path is not None: + # the first worker save the results into the cache file + app_state = AppState() + if app_state._global_rank == 0: + with open(cache_data_path, 'wb') as f: + pickle.dump(self.examples, f) + logging.info(f'save the data to the cache file {cache_data_path}') + + def load_data(self, dataset): + """ + Loads a dataset by filling in the task templates specified in the config file + with the information from each training/inference example. Converts all input + text into token ids. Also replaces the <|VIRTUAL_PROMPT_#|> placeholders in + the task templates with the actual virtual prompt token ids. + + params: + dataset: A list of json objects or a dictionary objects each + containing the information needed for a training example + """ + skipped = 0 + + for json_line in tqdm(dataset): + + # Read example dict or load the information for a single example from .json file + if type(json_line) == dict: + doc = json_line + else: + doc = json.loads(json_line) + + taskname = doc["taskname"] + prompt_template = self.task_templates[taskname]["prompt_template"] + prompt_template_fields = self.task_templates[taskname]["prompt_template_fields"] + total_virtual_tokens = self.task_templates[taskname]["total_virtual_tokens"] + virtual_token_splits = self.task_templates[taskname]["virtual_token_splits"] + truncation_field = self.task_templates[taskname]['truncate_field'] + answer_only_loss = self.task_templates[taskname]["answer_only_loss"] + answer_field = self.task_templates[taskname]["answer_field"] + + input_example = prompt_template + + self._input_sanity_checks( + total_virtual_tokens, + virtual_token_splits, + prompt_template, + prompt_template_fields, + truncation_field, + answer_only_loss, + answer_field, + doc, + ) + + # Format the input example according to the template + input_example = self._insert_text_in_template(input_example, prompt_template_fields, doc) + input_example = self._insert_virtual_token_placeholders(input_example, virtual_token_splits) + input_ids = self.tokenizer.text_to_ids(input_example) + + # Add BOS/EOS if desired, adds EOS by default + if self.add_bos: + input_ids = [self.tokenizer.bos_id] + input_ids + if self.add_eos: + input_ids = input_ids + [self.tokenizer.eos_id] + + # Try to truncate input text to fit into the max sequence length + if len(input_ids) > self.max_seq_length: + input_ids = self._truncate_input( + truncation_field, + input_ids, + taskname, + doc, + prompt_template, + prompt_template_fields, + virtual_token_splits, + ) + + # Skip example if the final length doesn't fit length requirements even after truncation + if self.min_seq_length <= len(input_ids) <= self.max_seq_length: + if self.virtual_prompt_source == VirtualPromptSource.PROMPT_ENCODER: + taskname_id = self.tokenizer.text_to_ids(taskname) + elif self.virtual_prompt_source == VirtualPromptSource.NO_PROMPT: + taskname_id = -1 + else: + raise ValueError("Invalid virtual prompt source specified") + + # Find answer field indices if training and answer_only_loss is True + answer_start_idx = None + if answer_only_loss and self.for_train: + answer_start_idx = self._find_answer_start(taskname, input_ids, answer_field, doc) + + self.examples.append((taskname_id, input_ids, answer_start_idx)) + else: + skipped += 1 + + logging.info(f'Skipped {skipped} sentences, sequence length too short or too long even after truncation') + + def _input_sanity_checks( + self, + total_virtual_tokens, + virtual_token_splits, + prompt_template, + prompt_template_fields, + truncation_field, + answer_only_loss, + answer_field, + doc, + ): + # Sanity check amount of virtual token + assert ( + total_virtual_tokens < self.max_seq_length + ), "virtual prompt tokens should not exceed max sequence length" + + # Make sure virtual token splits add up to the total number of virtual tokens + assert ( + sum(virtual_token_splits) == total_virtual_tokens + ), "Sum of prompt token split values must equal total number of prompt tokens" + + # Make sure number of virtual prompt locations match the number of virtual prompt splits + assert prompt_template.count('<|VIRTUAL_PROMPT_') == len( + virtual_token_splits + ), "The number of '<|VIRTUAL_PROMPT_n|>' markers and the number of prompt token splits must match" + + # Check if input example has fields not present in template + keys_not_in_template = list(set(doc.keys()) - set(prompt_template_fields) - set(['taskname'])) + assert ( + len(keys_not_in_template) == 0 + ), f"Examples in your dataset contain the fields: {keys_not_in_template} that are not in the task template." + + # Answer field checks + if answer_only_loss and self.for_train: + assert answer_field is not None, "If answer_only_loss=True, an answer_field must be given" + assert ( + answer_field in doc.keys() + ), f"answer_only_loss=True but the given answer_field '{answer_field}' is not in data json" + assert truncation_field != answer_field, "Answer field and truncation field should not match" + + answer_placeholder = "{" + answer_field + "}" + answer_placeholder_len = len(answer_placeholder) + placeholder_start = len(prompt_template) - answer_placeholder_len + assert prompt_template[placeholder_start:] == answer_placeholder, "Answer field must be at prompt end" + + def _insert_text_in_template(self, input_example, prompt_template_fields, doc): + """ Format the input example according to the template """ + for field in prompt_template_fields: + if field in doc.keys(): + field_text = doc[field] + input_example = input_example.replace('{' + field + '}', field_text) + + # If some fields from the template aren't present, e.g. {answer} during inference + # just remove that field from the template, leaving the space blank + else: + input_example = input_example.replace('{' + field + '}', "") + + return input_example.strip(" ") + + def _insert_virtual_token_placeholders(self, input_example, virtual_token_splits): + """ Insert the correct number of pseudo tokens at the <|VIRTUAL_PROMPT_n|> markers """ + total_inserted_tokens = 0 + + for idx in range(len(virtual_token_splits)): + split_start = total_inserted_tokens + split_end = total_inserted_tokens + virtual_token_splits[idx] + pseudo_tokens_for_split = "".join(self.pseudo_tokens[split_start:split_end]) + input_example = input_example.replace(f'<|VIRTUAL_PROMPT_{idx}|>', pseudo_tokens_for_split) + total_inserted_tokens = split_end + + return input_example + + def _truncate_input( + self, truncation_field, input_ids, taskname, doc, prompt_template, prompt_template_fields, virtual_token_splits + ): + """ Try to truncate input text to fit into the max sequence length """ + logging.info( + f"Input greater than max sequence length. Attempting to truncate: '{truncation_field}' in task: '{taskname}'" + ) + + # Truncate the text ids in this part of input to try and fit max sequence length + if truncation_field is not None and truncation_field in doc.keys(): + truncation_length = (len(input_ids) - self.max_seq_length) + 1 + field_text = doc[truncation_field] + + # Truncate field text + field_text_ids = self.tokenizer.text_to_ids(field_text) + truncated_text_ids = field_text_ids[: -min(truncation_length, len(field_text_ids))] + truncated_field_text = self.tokenizer.ids_to_text(truncated_text_ids) + doc[truncation_field] = truncated_field_text + + # Re-insert the truncated text string into the text prompt + input_example = prompt_template + input_example = self._insert_text_in_template(input_example, prompt_template_fields, doc) + input_example = self._insert_virtual_token_placeholders(input_example, virtual_token_splits) + + # Re-tokenize the whole prompt + input_ids = self.tokenizer.text_to_ids(input_example) + + return input_ids + + def _find_answer_start(self, taskname, input_ids, answer_field, doc): + """ Find the token ids corresponding to the answer start, for loss masking purposes. + Assumes the answer is always at the end of the prompt. + """ + answer_text = doc[answer_field] + answer_text = self._add_leading_space(taskname, answer_field, answer_text) + answer_text_ids = self.tokenizer.text_to_ids(answer_text) + num_answer_text_ids = len(answer_text_ids) + + if self.add_eos: + num_answer_text_ids += 1 + + answer_start_idx = len(input_ids) - num_answer_text_ids + + return answer_start_idx + + def _add_leading_space(self, taskname, field_name, field_text): + """ Add leading space to text if there is a space before it in the template """ + prompt_template = self.task_templates[taskname]["prompt_template"] + field_text_start = prompt_template.find("{" + field_name + "}") + if field_text_start != 0 and prompt_template[field_text_start - 1] == " ": + field_text = " " + field_text + + return field_text + + def __len__(self): + return len(self.examples) + + def __getitem__(self, idx): + return self.examples[idx] + + def _ceil_to_nearest(self, n, m): + return (n + m - 1) // m * m + + def collate_fn(self, batch, tp_workers=0): + """ Prepares input_ids, labels, loss mask, attention_mask, and position ids for global batch """ + taskname_ids, input_ids, answer_starts = zip(*batch) + + # Pad taskname_ids to be the same length for the prompt encoder + if self.virtual_prompt_source == VirtualPromptSource.PROMPT_ENCODER: + max_taskname_length = max(len(ids) for ids in taskname_ids) + taskname_ids = [ids + [self.pad_token_id] * (max_taskname_length - len(ids)) for ids in taskname_ids] + taskname_ids = torch.tensor(taskname_ids) + + # Task ids are just used for a look up embeddings for prompt-table + elif self.virtual_prompt_source == VirtualPromptSource.NO_PROMPT: + taskname_ids = torch.tensor(taskname_ids) + + # Get max sequence length of batch + batch_max = max(len(ids) for ids in input_ids) + + if tp_workers > 1: + # more sure the sequence length is multiply of number of tp_workers, needed for sequence parallel. + resi_padding = (tp_workers - (batch_max - 1) % tp_workers) % tp_workers + else: + resi_padding = 0 + batch_max += resi_padding + ceil_batch_max = self._ceil_to_nearest( + batch_max, 8 + ) # @adithyare this padding does not conflict with the tp_workers padding above + # since tp_workers is always a multiple of 2. the padding to multiple of 8 is to ensure an mem-optimized softmax is used. + batch_max = ceil_batch_max + 1 + input_ids, loss_mask = self.pad_batch_and_build_loss_mask(input_ids, batch_max, answer_starts) + # Should be a label for every token in batch, label is the next token + labels = input_ids[:, 1:].contiguous() + input_ids = input_ids[:, :-1].contiguous() + batch_max -= 1 # @adithyare I *think* this negatition is done to account for the above 2 lines which removes one item from the input_ids seq. + + # Loss mask should align with labels + loss_mask = loss_mask[:, 1:].contiguous() + + # Using causal attention mask for whole input + batch_size = len(input_ids) + attention_mask = torch.tril(torch.ones((batch_size, batch_max, batch_max))).view( + batch_size, 1, batch_max, batch_max + ) + + # Convert attention mask from float to bool + attention_mask = attention_mask < 0.5 + position_ids = build_position_ids(input_ids) + + return input_ids, labels, loss_mask, position_ids, attention_mask, taskname_ids + + def pad_batch_and_build_loss_mask(self, input_ids, batch_max, answer_starts): + """ Pad input_ids in batch to max batch length while building loss mask """ + batch_loss_masks = [] + padded_input_ids = [] + for ids, answer_start_idx in zip(input_ids, answer_starts): + if answer_start_idx is not None: + # Loss mask where answer tokens are 1.0 and all other tokens are 0.0 + loss_mask = [float(idx >= answer_start_idx) for idx in range(len(ids))] + else: + # Loss mask where virtual tokens are 0.0 and all other tokens are 1.0 + loss_mask = [float(token_id not in self.pseudo_token_ids) for token_id in ids] + + # Pad to max length + input_length = len(ids) + padding_length = batch_max - input_length + pad_extend = [self.pad_token_id] * padding_length + ids = ids + pad_extend + padded_input_ids.append(ids) + + # Account for padding in loss mask + loss_mask.extend([0.0] * padding_length) + batch_loss_masks.append(torch.tensor(loss_mask, dtype=torch.float)) + + # Make into torch tensors + padded_input_ids = torch.tensor(padded_input_ids, dtype=torch.long) + batch_loss_masks = torch.stack(batch_loss_masks) + + return padded_input_ids, batch_loss_masks + + def inference_collate_fn(self, batch): + """ + Used for loading inference data. + """ + task_id_nums, input_ids, answer_starts = zip(*batch) + input_lengths = torch.cuda.LongTensor([len(inputs) for inputs in input_ids]) + task_id_nums = torch.cuda.LongTensor(task_id_nums) + batch_max = input_lengths.max().item() + batch_max += self.tokens_to_generate + + input_ids, _ = self.pad_batch_and_build_loss_mask(input_ids, batch_max, answer_starts) + input_ids = input_ids.cuda() + input_ids = torch.cuda.LongTensor(input_ids) + + return task_id_nums, (input_ids, input_lengths) diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py new file mode 100644 index 0000000..3d5d7ef --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py @@ -0,0 +1,401 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy + +import torch + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.collections.nlp.data.language_modeling.megatron.gpt_sft_dataset import GPTSFTDataset +from nemo.utils import logging + +__all__ = ['GPTSFTChatDataset', 'get_prompt_template_example'] + + +PREFIX_STR = ( + "\x00" # the prefix string used in the tokenizer to deal with the added empty token for some of the tokenizers +) + +IGNORE_INDEX = -100 +SYSTEM_TOKEN = "System" + +TYPE_INSTRUCTION = { + 'TEXT_TO_VALUE': "", + 'VALUE_TO_TEXT': '', +} + + +def _get_header_conversation_type_mask_role(source, special_tokens): + END_SIGNAL = special_tokens['end_of_turn'] + END_NAME_SIGNAL = special_tokens['end_of_name'] + + data_type = None + if 'type' in source: + data_type = source['type'] + if data_type is not None: + assert data_type in TYPE_INSTRUCTION, f"source type {data_type} not supported" + # add end signal and concatenate together + conversation = source['system'] + if data_type is not None: + if TYPE_INSTRUCTION[data_type] != '': + conversation = conversation + '\n' + TYPE_INSTRUCTION[data_type] + mask_role = source.get('mask', 'User') + header = f"{special_tokens['system_turn_start']}{SYSTEM_TOKEN}{END_NAME_SIGNAL}{conversation}{END_SIGNAL}" + conversation = _add_speaker_and_signal(header, source['conversations'], mask_role, data_type, special_tokens) + return header, conversation, data_type, mask_role + + +def get_prompt_template_example(special_tokens): + source = { + 'system': '{system message}', + 'conversations': [ + {'from': 'User', 'value': '{turn 1 user message}', 'label': None}, + {'from': 'Assistant', 'value': '{turn 1 assistant message}', 'label': '{turn 1 assistant label}'}, + {'from': 'User', 'value': '{turn 2 user message}', 'label': None}, + {'from': 'Assistant', 'value': '{turn 2 assistant message}', 'label': '{turn 2 assistant label}'}, + ], + "mask": "User", + "type": "VALUE_TO_TEXT", + } + _, conversation, _, _ = _get_header_conversation_type_mask_role(source, special_tokens) + return conversation + + +def identify_start_index_of_subsequence(subsequence, sequence): + """ find the location of the small tensor in the large tensor. + e.g. small = [1,3], large = [2,3,1,3], returns 2 + small = [3,2], large = [2,3,1,3], returns -1 + Args: + small (tensor): small tensor + large (tensor): large tensor + """ + for i in range(sequence.size(0) - subsequence.size(0) + 1): + if torch.equal(sequence[i : i + subsequence.size(0)], subsequence): + return i + return -1 + + +def _mask_targets( + target, + tokenized_lens, + speakers, + header_len, + s_ids, + tokenizer, + mask_role, + gtype, + name_end_token_ids, + special_tokens, + label_start_ids, + num_turn_start_tokens, +): + """ This function masks the tokens so the loss is computed only on the non-masked role's responses. + For 'TEXT_TO_VALUE' type, the loss is computed on the value attributes. + + Args: + target (Tensor): input ids + tokenized_lens (List[int]): array of lengths of each turns + speakers (List[str]): array of speakers of each turns + header_len (int): the system prompt length + s_ids (List[Tensor]): array of tokenized ids of each turns + tokenizer (TokenizerSpec): tokenizer object + mask_role (str): the speaker id to be masked from loss computation + gtype (str): either 'TEXT_TO_VALUE' or 'VALUE_TO_TEXT' + name_end_token_ids (int): end of name token ids + special_tokens (dict): special tokens used for the chat prompt. It has the keys: system_turn_start, turn_start, label_start, end_of_turn + label_start_ids (list): list of label start token ids, + num_turn_start_tokens (int): number of tokens of the turn_start str + """ + TURN_TOKEN = special_tokens['turn_start'] + END_NAME_SIGNAL = special_tokens['end_of_name'] + label_start_ids = torch.tensor(label_start_ids) + name_end_token_ids = torch.tensor(name_end_token_ids) + + cur_idx = header_len + tgt_len = target.shape[0] + for i, (tokenized_len, speaker, s_id) in enumerate(zip(tokenized_lens, speakers, s_ids)): + # note, sentence piece will add extra empty token in front. has to compute the diff + id1 = tokenizer.text_to_ids(PREFIX_STR) + id2 = tokenizer.text_to_ids(PREFIX_STR + TURN_TOKEN + speaker + END_NAME_SIGNAL) + skip_name_len = len(id2) - len( + id1 + ) # s_ids[:skip_name_len] is the name part of the prompt 'TURN_TOKEN + speaker + END_NAME_SIGNAL' + # get the position of the label start string in this turn + location = identify_start_index_of_subsequence(label_start_ids, s_id) + + if location >= 0: + # if it contains the label start tokens + if gtype == 'VALUE_TO_TEXT': + # handles the case that condition on labels to generate respone + # the next token after the name part of the prompt is the beginning of the label start tokens + assert skip_name_len == location + # find the first new line token after the label part, which indicates the end of the whole label string + # newline_loc = torch.where((s_id[skip_name_len:] == name_end_token_ids))[0] + newline_loc = identify_start_index_of_subsequence(name_end_token_ids, s_id[skip_name_len:]) + if newline_loc < 0: + # cannot find new line token, which means the the whole turn is just a partial label string. Mask the whole turn + target[cur_idx : cur_idx + tokenized_len] = IGNORE_INDEX + continue + # skip the label part and the new line token + more_skip_len = newline_loc + len(name_end_token_ids) + # skip the name part and the label part + skip_name_len += more_skip_len + elif gtype == 'TEXT_TO_VALUE': + # handles the case that condition on response to generate label + # skip the name part, response and the label start tokens part, the remainder is the label string without label start, e.g. 'quality:9,toxicity:8...' + skip_name_len = location + len(label_start_ids) + if cur_idx >= tgt_len: + break + elif cur_idx + tokenized_len < tgt_len: + # Check whether the mask is applied to the correct position, the first token is turn start tokens + if not torch.equal(target[cur_idx + 1 : cur_idx + tokenized_len], s_id[1:]): + logging.warning("a sentence mismatches the corresponding piece " "in the conversation") + if i == 0 and (gtype == 'VALUE_TO_TEXT' or gtype is None): + # mask the first turn completely to provide at least one turn as context for the rest + target[cur_idx : cur_idx + tokenized_len] = IGNORE_INDEX + elif speaker == mask_role and i == 1 and gtype == 'TEXT_TO_VALUE': + # leave the first turn start tag unmasked, servers severs as the end of turn signal + target[cur_idx + num_turn_start_tokens : cur_idx + tokenized_len] = IGNORE_INDEX + elif speaker == mask_role and (i > 1): + # leave the first turn start tag unmasked, which severs as the end of turn signal + target[cur_idx + num_turn_start_tokens : cur_idx + tokenized_len] = IGNORE_INDEX + elif speaker == mask_role and (i <= 1): + # mask out everything in the second turn + target[cur_idx : cur_idx + tokenized_len] = IGNORE_INDEX + else: + # mask up to name part, label part for VALUE_TO_TEXT, or name part, response and label start tokens for TEXT_TO_VALUE, or just the name part if gtype is None + target[cur_idx : cur_idx + skip_name_len] = IGNORE_INDEX + cur_idx += tokenized_len + + +def response_value_formater(label, label_start, end_signal): + if isinstance(label, str): + return label_start + label + end_signal + elif label is None: + return '' + else: + raise ValueError(f'Unknown label type {type(label)}, only str type is supported') + + +def _add_speaker_and_signal(header, source, mask_role, gtype, special_tokens): + TURN_TOKEN = special_tokens['turn_start'] + END_SIGNAL = special_tokens['end_of_turn'] + LABEL_START = special_tokens['label_start'] + END_NAME_SIGNAL = special_tokens['end_of_name'] + + """Add speaker and start/end signal on each round.""" + BEGIN_SIGNAL = "" + conversation = header + for i, sentence in enumerate(source): + sentence_from = sentence["from"] + role_token = TURN_TOKEN + if gtype is None: + sentence["value"] = ( + BEGIN_SIGNAL + role_token + sentence_from + END_NAME_SIGNAL + sentence["value"] + END_SIGNAL + ) + elif gtype == "VALUE_TO_TEXT": + sentence["value"] = ( + BEGIN_SIGNAL + + role_token + + sentence_from + + END_NAME_SIGNAL + + ( + response_value_formater(sentence['label'], LABEL_START, END_NAME_SIGNAL) + if 'label' in sentence + else '' + ) + + sentence["value"] + + END_SIGNAL + ) + elif gtype == "TEXT_TO_VALUE": + sentence["value"] = ( + BEGIN_SIGNAL + + role_token + + sentence_from + + END_NAME_SIGNAL + + sentence["value"] + + END_SIGNAL + + ( + response_value_formater(sentence['label'], LABEL_START, END_NAME_SIGNAL) + if 'label' in sentence + else '' + ) + ) + else: + raise ValueError( + f"source type {gtype} not supported, only 'VALUE_TO_TEXT' and 'TEXT_TO_VALUE' are supported" + ) + conversation += sentence["value"] + # if the last turn is not masked, add next token start token to the end, which will be included for loss calculation + if sentence_from != mask_role and i == len(source) - 1: + conversation += TURN_TOKEN + return conversation + + +def preprocess( + source: dict, + tokenizer: TokenizerSpec, + name_end_token_ids: int, + label_start_ids: list, + special_tokens: dict, + num_turn_start_tokens: int, +): + """ + Given a conversation list. This transform: + 1. Add signal '### ' at the beginning each sentence, with end signal '\n'; + 2. Concatenate conversations together; + 3. Tokenize the concatenated conversation; + 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX. + """ + header, conversation, data_type, mask_role = _get_header_conversation_type_mask_role(source, special_tokens) + # tokenize conversations + input_ids = tokenizer.text_to_ids(conversation) + target = copy.deepcopy(input_ids) + header_tokens = tokenizer.text_to_ids(header) + header_len = len(header_tokens) + + ids = [] + tokenized_lens = [] + assert torch.equal(torch.tensor(target[:header_len]), torch.tensor(header_tokens)) + for s in source['conversations']: + # hack to remove the extra empty token in front + id1 = tokenizer.text_to_ids(PREFIX_STR + s["value"]) + id2 = tokenizer.text_to_ids(PREFIX_STR) + tokenized_sentence = id1[len(id2) :] + ids.append(torch.tensor(tokenized_sentence)) + tokenized_lens.append(len(tokenized_sentence)) + speakers = [sentence["from"] for sentence in source['conversations']] + assert mask_role in speakers, "mask role not in the conversation" + target = torch.LongTensor(target) + # not going to train on the header + target[:header_len] = IGNORE_INDEX + input_ids = torch.LongTensor(input_ids) + _mask_targets( + target, + tokenized_lens, + speakers, + header_len, + ids, + tokenizer, + mask_role, + data_type, + name_end_token_ids, + special_tokens, + label_start_ids, + num_turn_start_tokens, + ) + mask = (target != IGNORE_INDEX).bool() + assert mask.sum().item() != 0, "mask is empty" + # Choose the last conversation as answer other history are context + last_ignore_index_pos = torch.nonzero(target == IGNORE_INDEX)[-1].item() + 1 + context_ids = input_ids[:last_ignore_index_pos] + answer_ids = input_ids[last_ignore_index_pos:] + return dict(input_ids=input_ids, mask=mask, context_ids=context_ids, answer_ids=answer_ids) + + +class GPTSFTChatDataset(GPTSFTDataset): + def _maybe_validate_prompt_template(self): + pass + + def _build_samples_mapping(self): + super()._build_samples_mapping() + assert hasattr(self.tokenizer, "vocab"), "tokenizer should have vocab property, not supported" + LABEL_START = self.special_tokens['label_start'] + END_NAME_SIGNAL = self.special_tokens['end_of_name'] + + id1 = self.tokenizer.text_to_ids(PREFIX_STR) + id2 = self.tokenizer.text_to_ids(PREFIX_STR + LABEL_START) + self.label_start_tokens = id2[len(id1) :] + + id1 = self.tokenizer.text_to_ids(PREFIX_STR + END_NAME_SIGNAL) + id2 = self.tokenizer.text_to_ids(PREFIX_STR) + self.name_end_token_ids = id1[len(id2) :] + + id1 = self.tokenizer.text_to_ids(PREFIX_STR + self.special_tokens['turn_start']) + id2 = self.tokenizer.text_to_ids(PREFIX_STR) + self.num_turn_start_tokens = len(id1) - len(id2) + + def _process_example(self, example): + """ + Create an example by concatenating text and answer. + Truncation is carried out when needed, but it is performed only on the prompt side. + BOS, EOS, and SEP, are added if specified. + """ + result = preprocess( + example, + self.tokenizer, + self.name_end_token_ids, + self.label_start_tokens, + self.special_tokens, + self.num_turn_start_tokens, + ) + + # store metadata in dataset, in case user may have keys required in the prediction json files + metadata = {k: v for k, v in example.items() if k not in ['conversations']} + result['metadata'] = metadata + if self.output_original_text: + result['metadata']['conversations'] = example['conversations'] + + return result + + def collate_fn(self, batch): + input_ids = [item['input_ids'][:-1].tolist() for item in batch] + labels = [item['input_ids'][1:].tolist() for item in batch] + contexts = [item['context_ids'].tolist() for item in batch] + answers = [item['answer_ids'].tolist() for item in batch] + loss_mask = [item['mask'][1:].tolist() for item in batch] + metadata = [item['metadata'] for item in batch] + + max_length = max(max([len(x) for x in input_ids]), max([len(x) for x in contexts]) + self.tokens_to_generate) + if max_length > self.max_seq_length: + # truncate the sequences if it is longer than max_seq_length + input_ids = [x[: self.max_seq_length] for x in input_ids] + labels = [x[: self.max_seq_length] for x in labels] + loss_mask = [x[: self.max_seq_length] for x in loss_mask] + contexts = [x[: self.max_seq_length] for x in contexts] + answers = [x[: self.max_seq_length] for x in answers] + + # increase max length to nearest multiple of 4 or 8 + if self.pad_to_max_length: + max_length = self.max_seq_length + else: + max_length = min(self.max_seq_length, self._ceil_to_nearest(max_length, 8)) + assert max_length <= self.max_seq_length + + attention_mask = [self._create_attention_mask(max_length) for _ in batch] + attention_mask = torch.stack(attention_mask) + position_ids = [list(range(max_length)) for _ in batch] + position_ids = torch.LongTensor(position_ids) + input_ids = torch.LongTensor( + self._collate_item(input_ids, max_length=max_length, pad_id=self.tokenizer.eos_id) + ) + labels = torch.LongTensor(self._collate_item(labels, max_length=max_length, pad_id=self.tokenizer.eos_id)) + loss_mask = torch.LongTensor(self._collate_item(loss_mask, max_length=max_length, pad_id=0)) + context_lengths = torch.LongTensor([len(x) for x in contexts]) + contexts = torch.LongTensor(self._collate_item(contexts, max_length=max_length, pad_id=self.tokenizer.eos_id)) + answers = torch.LongTensor(self._collate_item(answers, max_length=max_length, pad_id=self.tokenizer.eos_id)) + + processed_batch = { + 'tokens': input_ids, + 'labels': labels, + 'attention_mask': attention_mask, + 'loss_mask': loss_mask, + 'position_ids': position_ids, + 'contexts': contexts, + 'context_lengths': context_lengths, + 'answers': answers, + 'metadata': metadata, + } + + return processed_batch diff --git a/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_dataset.py b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_dataset.py new file mode 100644 index 0000000..501c766 --- /dev/null +++ b/NeMo-2.0.0.rc0.beta/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_dataset.py @@ -0,0 +1,634 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re +from typing import List, Mapping, Optional + +import datasets +import numpy as np +import torch + +# hack to avoid the "not enough disk space" error in some slurm cluster +datasets.builder.has_sufficient_disk_space = lambda needed_bytes, directory='.': True +from datasets import load_dataset + +from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec +from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import get_samples_mapping +from nemo.collections.nlp.data.language_modeling.text_memmap_dataset import JSONLMemMapDataset, OnlineSampleMapping +from nemo.core.classes import Dataset +from nemo.utils import logging + +__all__ = ['GPTSFTDataset'] + + +class GPTSFTDataset(Dataset): + def __init__( + self, + file_path: str, + tokenizer: TokenizerSpec, + max_seq_length: int = 1024, + min_seq_length: int = 1, + pad_seq_length_to_mult: int = 16, + add_bos: bool = False, + add_eos: bool = True, + add_sep: bool = False, + sep_id: int = None, + max_num_samples: int = None, + seed: int = 1234, + label_key: str = "answer", + answer_only_loss: bool = True, + truncation_field: str = "text", + pad_to_max_length: bool = False, # (@adithyare) allows for much faster training especially in PEFT settings. + index_mapping_dir: str = None, + prompt_template: str = None, + virtual_tokens: int = 0, + tokens_to_generate: int = 0, + memmap_workers: Optional[int] = None, + hf_dataset: bool = False, + truncation_method: str = 'right', + special_tokens: Optional[Mapping[str, str]] = None, # special tokens, a dictory of {token_type: token} + is_test: bool = False, + output_original_text: bool = False, + ): + """ + file_path: Path to a JSONL GPT supervised fine-tuning dataset. Data is formatted as multiple JSON lines with each line formatted as follows. {'input': 'John von Neumann\nVon Neumann made fundamental contributions .... Q: What did the math of artificial viscosity do?', 'output': 'smoothed the shock transition without sacrificing basic physics'} + tokenizer: Tokenizer for the dataset. Instance of a class that inherits TokenizerSpec (ex: SentencePiece). + max_seq_length (int): maximum sequence length for each dataset examples. Examples will either be truncated to fit this length or dropped if they cannot be truncated. + min_seq_length (int): min length of each data example in the dataset. Data examples will be dropped if they do not meet the min length requirements. + add_bos (bool): Whether to add a beginning of sentence token to each data example + add_eos (bool): Whether to add an end of sentence token to each data example + add_sep (bool): Whether to add a separation token to each data example (goes between prompt and answer) + tokens_to_generate (int): (inference only) Number of tokens to generate during inference + seed: Random seed for data shuffling. + max_num_samples: Maximum number of samples to load. This can be > dataset length if you want to oversample data. If None, all samples will be loaded. + seed: int = 1234, + label_key: Key to use for the label in your JSONL file + answer_only_loss: If True, will compute the loss only on the answer part of the input. If False, will compute the loss on the entire input. + truncation_field: Field to use for truncation. (Options: keys in prompt_template). Field to be used for truncation if the combined length exceeds the max sequence length. + pad_to_max_length: Whether to pad the input to the max sequence length. If False, will pad to the max length of the current batch. + index_mapping_dir: Directory to save the index mapping to. If None, will write to the same folder as the dataset. + prompt_template: Prompt template to inject via an fstring. Formatted like Q: {context_key}\n\nA: {label_key} + hf_dataset: Whether to load the json file with the HuggingFace dataset. otherwise, will load the jsonl file with the JSONLMemMapDataset. + truncation_method: Truncation from which position. Options: ['left', 'right'] + special_tokens: special tokens for the chat prompts, a dictionary of {token_type: token}. Default: {'system_turn_start': '', 'turn_start': '', 'label_start': '', 'end_of_turn': '\n', "end_of_name": "\n"} + is_test: Whether this dataset is the test split. + output_original_text (bool): if true, will keep the original text in the output alongside the tokenized ids. + """ + self.tokenizer = tokenizer + self.file_path = file_path + self.max_seq_length = max_seq_length + self.min_seq_length = min_seq_length + self.pad_seq_length_to_mult = pad_seq_length_to_mult + self.add_bos = add_bos + self.add_eos = add_eos + self.add_sep = add_sep + self.sep_id = sep_id + self.max_num_samples = max_num_samples + self.seed = seed + self.label_key = label_key + self.answer_only_loss = answer_only_loss + self.truncation_fields = truncation_field.split(',') + self.pad_to_max_length = pad_to_max_length + self.index_mapping_dir = index_mapping_dir + self.prompt_template = prompt_template + self.virtual_tokens = virtual_tokens + self.tokens_to_generate = tokens_to_generate + self.memmap_workers = memmap_workers + self.hf_dataset = hf_dataset + self.truncation_method = truncation_method + self.is_test = is_test + self.output_original_text = output_original_text + if special_tokens is None: + self.special_tokens = { + "system_turn_start": "", + "turn_start": "", + "label_start": "", + "end_of_turn": "\n", + "end_of_name": "\n", + } + else: + self.special_tokens = special_tokens + + self._load_dataset() + + # Validate prompt template + self._maybe_validate_prompt_template() + + # Will be None after this call if `max_num_samples` is None + self._build_samples_mapping() + + def _load_dataset(self): + if self.hf_dataset: + self.indexed_dataset = load_dataset( + 'json', + data_files=self.file_path, + cache_dir=self.index_mapping_dir, + num_proc=self.memmap_workers, + split='train', + ) + else: + self.indexed_dataset = JSONLMemMapDataset( + dataset_paths=[self.file_path], + tokenizer=None, + header_lines=0, + index_mapping_dir=self.index_mapping_dir, + workers=self.memmap_workers, + ) + + def _maybe_validate_prompt_template(self): + assert ( + self.prompt_template is not None + ), f'we need prompt_template to combine contexts and label {self.label_key}' + # When providing things like newlines in the prompt template via the CLI, they are escaped. This line unescapes them. + self.prompt_template = self.prompt_template.encode('utf-8').decode('unicode_escape') + self.prompt_template_keys = re.findall(r'{(.*?)}', self.prompt_template) + + label_placeholder = f'{{{self.label_key}}}' + assert ( + self.prompt_template[-len(label_placeholder) :] == label_placeholder + ), f'{label_placeholder} must be at the end of prompt_template.' + + # Legacy checkpoints has self.truncation_fields = ['context'] and self.prompt_template_keys = ['input', 'output'] + if self.prompt_template_keys[0] == 'input' and self.truncation_fields[0] == 'context': + self.truncation_fields[0] = self.prompt_template_keys[0] + + assert set(self.truncation_fields).issubset( + self.prompt_template_keys + ), f'truncation_fields {self.truncation_fields} must in {self.prompt_template_keys}' + + def _build_samples_mapping(self): + if self.max_num_samples is not None: + osm = OnlineSampleMapping(dataset_size=len(self.indexed_dataset), num_samples=self.max_num_samples) + self.samples_mapping = get_samples_mapping( + indexed_dataset=self.indexed_dataset, + data_prefix=self.file_path, + num_epochs=None, + max_num_samples=self.max_num_samples, + max_seq_length=self.max_seq_length - 2, + short_seq_prob=0, + seed=self.seed, + name=self.file_path.split('/')[-1], + binary_head=False, + index_mapping_dir=self.index_mapping_dir, + samples_mapping=osm, + ) + else: + self.samples_mapping = None + + def __len__(self): + if self.max_num_samples is None: + return len(self.indexed_dataset) + else: + return len(self.samples_mapping) + + def __getitem__(self, idx): + if isinstance(idx, np.int64): + idx = idx.item() + + if self.samples_mapping is not None: + assert idx < len(self.samples_mapping) + idx, _, _ = self.samples_mapping[idx] + if isinstance(idx, np.uint32): + idx = idx.item() + + assert idx < len(self.indexed_dataset) + # idx may < 0 because we pad_samples_to_global_batch_size, e.g. id = -1 + if idx < 0: + idx = len(self) + idx + auto_gen_idx = True + else: + auto_gen_idx = False + try: + example = self.indexed_dataset[idx] + if auto_gen_idx: + example['__AUTOGENERATED__'] = True + except Exception as e: + logging.error(f"Error while loading example {idx} from dataset {self.file_path}") + raise e + return self._process_example(example) + + def _separate_template(self, prompt_template_values: List[str]): + """ + Combine contexts and label based on prompt_template into a list of strings and a list of keys. + + Args: + prompt_template_values (List[str]): the list of context and label strings extrated from jsonl file with prompt_template_keys. + + Returns: + template_strings (List[str]): separated prompt_template with contexts/label placeholder filled with corresponding strings + template_strings_keys (List[str]): strings point to placeholder keys or